diff --git a/finegym/b_1/20250624_084232.log b/finegym/b_1/20250624_084232.log new file mode 100644 index 0000000000000000000000000000000000000000..e3ddfce9052778d1a3c3f846daf30307a727d1cf --- /dev/null +++ b/finegym/b_1/20250624_084232.log @@ -0,0 +1,3471 @@ +2025-06-24 08:42:32,290 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2025-06-24 08:42:32,498 - pyskl - INFO - Config: modality = 'b' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/b_1' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2025-06-24 08:42:32,498 - pyskl - INFO - Set random seed to 135571342, deterministic: False +2025-06-24 08:42:33,954 - pyskl - INFO - 20484 videos remain after valid thresholding +2025-06-24 08:42:37,991 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-24 08:42:37,992 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1 +2025-06-24 08:42:37,992 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2025-06-24 08:42:37,992 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2025-06-24 08:42:37,993 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1 by HardDiskBackend. +2025-06-24 08:43:15,924 - pyskl - INFO - Epoch [1][100/1281] lr: 2.500e-02, eta: 20:14:00, time: 0.379, data_time: 0.168, memory: 4082, top1_acc: 0.0775, top5_acc: 0.2494, loss_cls: 4.4720, loss: 4.4720 +2025-06-24 08:43:37,848 - pyskl - INFO - Epoch [1][200/1281] lr: 2.500e-02, eta: 15:57:21, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.0756, top5_acc: 0.3475, loss_cls: 4.4392, loss: 4.4392 +2025-06-24 08:43:59,621 - pyskl - INFO - Epoch [1][300/1281] lr: 2.500e-02, eta: 14:29:57, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.1288, top5_acc: 0.3869, loss_cls: 4.2202, loss: 4.2202 +2025-06-24 08:44:21,534 - pyskl - INFO - Epoch [1][400/1281] lr: 2.500e-02, eta: 13:47:12, time: 0.219, data_time: 0.001, memory: 4082, top1_acc: 0.1100, top5_acc: 0.4031, loss_cls: 4.2550, loss: 4.2550 +2025-06-24 08:44:43,609 - pyskl - INFO - Epoch [1][500/1281] lr: 2.500e-02, eta: 13:22:25, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.1206, top5_acc: 0.4431, loss_cls: 4.0044, loss: 4.0044 +2025-06-24 08:45:05,668 - pyskl - INFO - Epoch [1][600/1281] lr: 2.500e-02, eta: 13:05:42, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.1619, top5_acc: 0.4781, loss_cls: 3.7852, loss: 3.7852 +2025-06-24 08:45:27,314 - pyskl - INFO - Epoch [1][700/1281] lr: 2.500e-02, eta: 12:51:46, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.2087, top5_acc: 0.5425, loss_cls: 3.5211, loss: 3.5211 +2025-06-24 08:45:49,014 - pyskl - INFO - Epoch [1][800/1281] lr: 2.500e-02, eta: 12:41:27, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.2362, top5_acc: 0.6138, loss_cls: 3.3370, loss: 3.3370 +2025-06-24 08:46:10,826 - pyskl - INFO - Epoch [1][900/1281] lr: 2.500e-02, eta: 12:33:44, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.2669, top5_acc: 0.6406, loss_cls: 3.1559, loss: 3.1559 +2025-06-24 08:46:32,585 - pyskl - INFO - Epoch [1][1000/1281] lr: 2.500e-02, eta: 12:27:20, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.3144, top5_acc: 0.6813, loss_cls: 2.9708, loss: 2.9708 +2025-06-24 08:46:54,246 - pyskl - INFO - Epoch [1][1100/1281] lr: 2.500e-02, eta: 12:21:44, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.3144, top5_acc: 0.6906, loss_cls: 2.9437, loss: 2.9437 +2025-06-24 08:47:16,057 - pyskl - INFO - Epoch [1][1200/1281] lr: 2.500e-02, eta: 12:17:24, time: 0.218, data_time: 0.001, memory: 4082, top1_acc: 0.3400, top5_acc: 0.7094, loss_cls: 2.8599, loss: 2.8599 +2025-06-24 08:47:34,419 - pyskl - INFO - Saving checkpoint at 1 epochs +2025-06-24 08:48:17,427 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 08:48:17,479 - pyskl - INFO - +top1_acc 0.3090 +top5_acc 0.7075 +2025-06-24 08:48:17,479 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 08:48:17,484 - pyskl - INFO - +mean_acc 0.1655 +2025-06-24 08:48:17,651 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2025-06-24 08:48:17,651 - pyskl - INFO - Best top1_acc is 0.3090 at 1 epoch. +2025-06-24 08:48:17,654 - pyskl - INFO - Epoch(val) [1][533] top1_acc: 0.3090, top5_acc: 0.7075, mean_class_accuracy: 0.1655 +2025-06-24 08:48:57,792 - pyskl - INFO - Epoch [2][100/1281] lr: 2.500e-02, eta: 12:12:33, time: 0.401, data_time: 0.183, memory: 4082, top1_acc: 0.3481, top5_acc: 0.7488, loss_cls: 2.6659, loss: 2.6659 +2025-06-24 08:49:19,836 - pyskl - INFO - Epoch [2][200/1281] lr: 2.500e-02, eta: 12:10:02, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.4037, top5_acc: 0.7925, loss_cls: 2.5267, loss: 2.5267 +2025-06-24 08:49:41,937 - pyskl - INFO - Epoch [2][300/1281] lr: 2.500e-02, eta: 12:07:52, time: 0.221, data_time: 0.001, memory: 4082, top1_acc: 0.4269, top5_acc: 0.8150, loss_cls: 2.3842, loss: 2.3842 +2025-06-24 08:50:03,886 - pyskl - INFO - Epoch [2][400/1281] lr: 2.500e-02, eta: 12:05:40, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.4425, top5_acc: 0.8231, loss_cls: 2.3297, loss: 2.3297 +2025-06-24 08:50:25,946 - pyskl - INFO - Epoch [2][500/1281] lr: 2.499e-02, eta: 12:03:51, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.4587, top5_acc: 0.8500, loss_cls: 2.2463, loss: 2.2463 +2025-06-24 08:50:47,633 - pyskl - INFO - Epoch [2][600/1281] lr: 2.499e-02, eta: 12:01:34, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.5062, top5_acc: 0.8750, loss_cls: 2.0824, loss: 2.0824 +2025-06-24 08:51:09,387 - pyskl - INFO - Epoch [2][700/1281] lr: 2.499e-02, eta: 11:59:35, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.4856, top5_acc: 0.8662, loss_cls: 2.1354, loss: 2.1354 +2025-06-24 08:51:31,516 - pyskl - INFO - Epoch [2][800/1281] lr: 2.499e-02, eta: 11:58:20, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.4919, top5_acc: 0.8556, loss_cls: 2.1117, loss: 2.1117 +2025-06-24 08:51:53,561 - pyskl - INFO - Epoch [2][900/1281] lr: 2.499e-02, eta: 11:57:02, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5150, top5_acc: 0.8962, loss_cls: 1.9696, loss: 1.9696 +2025-06-24 08:52:15,285 - pyskl - INFO - Epoch [2][1000/1281] lr: 2.499e-02, eta: 11:55:23, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.5188, top5_acc: 0.8969, loss_cls: 1.9813, loss: 1.9813 +2025-06-24 08:52:36,844 - pyskl - INFO - Epoch [2][1100/1281] lr: 2.499e-02, eta: 11:53:37, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.5606, top5_acc: 0.9038, loss_cls: 1.8661, loss: 1.8661 +2025-06-24 08:52:58,838 - pyskl - INFO - Epoch [2][1200/1281] lr: 2.499e-02, eta: 11:52:31, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5406, top5_acc: 0.9006, loss_cls: 1.9230, loss: 1.9230 +2025-06-24 08:53:16,898 - pyskl - INFO - Saving checkpoint at 2 epochs +2025-06-24 08:54:00,061 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 08:54:00,135 - pyskl - INFO - +top1_acc 0.4744 +top5_acc 0.8495 +2025-06-24 08:54:00,135 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 08:54:00,144 - pyskl - INFO - +mean_acc 0.3156 +2025-06-24 08:54:00,149 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_1.pth was removed +2025-06-24 08:54:00,348 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2025-06-24 08:54:00,349 - pyskl - INFO - Best top1_acc is 0.4744 at 2 epoch. +2025-06-24 08:54:00,352 - pyskl - INFO - Epoch(val) [2][533] top1_acc: 0.4744, top5_acc: 0.8495, mean_class_accuracy: 0.3156 +2025-06-24 08:54:40,675 - pyskl - INFO - Epoch [3][100/1281] lr: 2.499e-02, eta: 11:51:16, time: 0.403, data_time: 0.185, memory: 4082, top1_acc: 0.5700, top5_acc: 0.9181, loss_cls: 1.7820, loss: 1.7820 +2025-06-24 08:55:02,647 - pyskl - INFO - Epoch [3][200/1281] lr: 2.499e-02, eta: 11:50:15, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5800, top5_acc: 0.9256, loss_cls: 1.7001, loss: 1.7001 +2025-06-24 08:55:24,675 - pyskl - INFO - Epoch [3][300/1281] lr: 2.499e-02, eta: 11:49:21, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5794, top5_acc: 0.9187, loss_cls: 1.7817, loss: 1.7817 +2025-06-24 08:55:46,657 - pyskl - INFO - Epoch [3][400/1281] lr: 2.499e-02, eta: 11:48:27, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6225, top5_acc: 0.9244, loss_cls: 1.6474, loss: 1.6474 +2025-06-24 08:56:08,639 - pyskl - INFO - Epoch [3][500/1281] lr: 2.498e-02, eta: 11:47:34, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5894, top5_acc: 0.9400, loss_cls: 1.6743, loss: 1.6743 +2025-06-24 08:56:30,408 - pyskl - INFO - Epoch [3][600/1281] lr: 2.498e-02, eta: 11:46:31, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.5994, top5_acc: 0.9319, loss_cls: 1.6707, loss: 1.6707 +2025-06-24 08:56:52,338 - pyskl - INFO - Epoch [3][700/1281] lr: 2.498e-02, eta: 11:45:39, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6069, top5_acc: 0.9437, loss_cls: 1.6371, loss: 1.6371 +2025-06-24 08:57:14,213 - pyskl - INFO - Epoch [3][800/1281] lr: 2.498e-02, eta: 11:44:47, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6225, top5_acc: 0.9325, loss_cls: 1.6222, loss: 1.6222 +2025-06-24 08:57:35,974 - pyskl - INFO - Epoch [3][900/1281] lr: 2.498e-02, eta: 11:43:49, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6369, top5_acc: 0.9387, loss_cls: 1.5260, loss: 1.5260 +2025-06-24 08:57:57,601 - pyskl - INFO - Epoch [3][1000/1281] lr: 2.498e-02, eta: 11:42:47, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6144, top5_acc: 0.9413, loss_cls: 1.6061, loss: 1.6061 +2025-06-24 08:58:19,325 - pyskl - INFO - Epoch [3][1100/1281] lr: 2.498e-02, eta: 11:41:52, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6281, top5_acc: 0.9563, loss_cls: 1.5082, loss: 1.5082 +2025-06-24 08:58:41,146 - pyskl - INFO - Epoch [3][1200/1281] lr: 2.498e-02, eta: 11:41:03, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6412, top5_acc: 0.9481, loss_cls: 1.4948, loss: 1.4948 +2025-06-24 08:58:59,496 - pyskl - INFO - Saving checkpoint at 3 epochs +2025-06-24 08:59:42,397 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 08:59:42,468 - pyskl - INFO - +top1_acc 0.6337 +top5_acc 0.9418 +2025-06-24 08:59:42,468 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 08:59:42,475 - pyskl - INFO - +mean_acc 0.4855 +2025-06-24 08:59:42,480 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_2.pth was removed +2025-06-24 08:59:42,668 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2025-06-24 08:59:42,668 - pyskl - INFO - Best top1_acc is 0.6337 at 3 epoch. +2025-06-24 08:59:42,671 - pyskl - INFO - Epoch(val) [3][533] top1_acc: 0.6337, top5_acc: 0.9418, mean_class_accuracy: 0.4855 +2025-06-24 09:00:22,640 - pyskl - INFO - Epoch [4][100/1281] lr: 2.497e-02, eta: 11:40:01, time: 0.400, data_time: 0.180, memory: 4082, top1_acc: 0.6469, top5_acc: 0.9494, loss_cls: 1.4387, loss: 1.4387 +2025-06-24 09:00:44,701 - pyskl - INFO - Epoch [4][200/1281] lr: 2.497e-02, eta: 11:39:27, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.6619, top5_acc: 0.9600, loss_cls: 1.4074, loss: 1.4074 +2025-06-24 09:01:06,598 - pyskl - INFO - Epoch [4][300/1281] lr: 2.497e-02, eta: 11:38:46, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6525, top5_acc: 0.9550, loss_cls: 1.4718, loss: 1.4718 +2025-06-24 09:01:28,594 - pyskl - INFO - Epoch [4][400/1281] lr: 2.497e-02, eta: 11:38:10, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6575, top5_acc: 0.9556, loss_cls: 1.3940, loss: 1.3940 +2025-06-24 09:01:50,454 - pyskl - INFO - Epoch [4][500/1281] lr: 2.497e-02, eta: 11:37:29, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6681, top5_acc: 0.9600, loss_cls: 1.3750, loss: 1.3750 +2025-06-24 09:02:11,814 - pyskl - INFO - Epoch [4][600/1281] lr: 2.497e-02, eta: 11:36:28, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.6719, top5_acc: 0.9656, loss_cls: 1.3838, loss: 1.3838 +2025-06-24 09:02:33,559 - pyskl - INFO - Epoch [4][700/1281] lr: 2.497e-02, eta: 11:35:44, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6794, top5_acc: 0.9725, loss_cls: 1.3110, loss: 1.3110 +2025-06-24 09:02:54,907 - pyskl - INFO - Epoch [4][800/1281] lr: 2.496e-02, eta: 11:34:45, time: 0.213, data_time: 0.000, memory: 4082, top1_acc: 0.6675, top5_acc: 0.9650, loss_cls: 1.3982, loss: 1.3982 +2025-06-24 09:03:16,257 - pyskl - INFO - Epoch [4][900/1281] lr: 2.496e-02, eta: 11:33:48, time: 0.213, data_time: 0.000, memory: 4082, top1_acc: 0.6819, top5_acc: 0.9606, loss_cls: 1.3404, loss: 1.3404 +2025-06-24 09:03:37,828 - pyskl - INFO - Epoch [4][1000/1281] lr: 2.496e-02, eta: 11:33:01, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6831, top5_acc: 0.9644, loss_cls: 1.3256, loss: 1.3256 +2025-06-24 09:03:59,716 - pyskl - INFO - Epoch [4][1100/1281] lr: 2.496e-02, eta: 11:32:27, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6775, top5_acc: 0.9600, loss_cls: 1.3376, loss: 1.3376 +2025-06-24 09:04:21,302 - pyskl - INFO - Epoch [4][1200/1281] lr: 2.496e-02, eta: 11:31:42, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6813, top5_acc: 0.9625, loss_cls: 1.3270, loss: 1.3270 +2025-06-24 09:04:39,162 - pyskl - INFO - Saving checkpoint at 4 epochs +2025-06-24 09:05:22,483 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:05:22,539 - pyskl - INFO - +top1_acc 0.6836 +top5_acc 0.9647 +2025-06-24 09:05:22,539 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:05:22,545 - pyskl - INFO - +mean_acc 0.5472 +2025-06-24 09:05:22,549 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_3.pth was removed +2025-06-24 09:05:22,775 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2025-06-24 09:05:22,776 - pyskl - INFO - Best top1_acc is 0.6836 at 4 epoch. +2025-06-24 09:05:22,780 - pyskl - INFO - Epoch(val) [4][533] top1_acc: 0.6836, top5_acc: 0.9647, mean_class_accuracy: 0.5472 +2025-06-24 09:06:02,791 - pyskl - INFO - Epoch [5][100/1281] lr: 2.495e-02, eta: 11:30:57, time: 0.400, data_time: 0.185, memory: 4082, top1_acc: 0.7063, top5_acc: 0.9762, loss_cls: 1.2301, loss: 1.2301 +2025-06-24 09:06:24,573 - pyskl - INFO - Epoch [5][200/1281] lr: 2.495e-02, eta: 11:30:21, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7137, top5_acc: 0.9675, loss_cls: 1.2396, loss: 1.2396 +2025-06-24 09:06:46,442 - pyskl - INFO - Epoch [5][300/1281] lr: 2.495e-02, eta: 11:29:48, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7037, top5_acc: 0.9769, loss_cls: 1.2525, loss: 1.2525 +2025-06-24 09:07:07,928 - pyskl - INFO - Epoch [5][400/1281] lr: 2.495e-02, eta: 11:29:03, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7181, top5_acc: 0.9750, loss_cls: 1.2050, loss: 1.2050 +2025-06-24 09:07:29,516 - pyskl - INFO - Epoch [5][500/1281] lr: 2.495e-02, eta: 11:28:22, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6875, top5_acc: 0.9675, loss_cls: 1.2522, loss: 1.2522 +2025-06-24 09:07:51,340 - pyskl - INFO - Epoch [5][600/1281] lr: 2.495e-02, eta: 11:27:50, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7156, top5_acc: 0.9719, loss_cls: 1.2288, loss: 1.2288 +2025-06-24 09:08:12,961 - pyskl - INFO - Epoch [5][700/1281] lr: 2.494e-02, eta: 11:27:11, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7131, top5_acc: 0.9744, loss_cls: 1.2239, loss: 1.2239 +2025-06-24 09:08:34,700 - pyskl - INFO - Epoch [5][800/1281] lr: 2.494e-02, eta: 11:26:37, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6956, top5_acc: 0.9731, loss_cls: 1.1946, loss: 1.1946 +2025-06-24 09:08:56,580 - pyskl - INFO - Epoch [5][900/1281] lr: 2.494e-02, eta: 11:26:07, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7031, top5_acc: 0.9706, loss_cls: 1.2332, loss: 1.2332 +2025-06-24 09:09:18,376 - pyskl - INFO - Epoch [5][1000/1281] lr: 2.494e-02, eta: 11:25:35, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7013, top5_acc: 0.9756, loss_cls: 1.2008, loss: 1.2008 +2025-06-24 09:09:39,845 - pyskl - INFO - Epoch [5][1100/1281] lr: 2.494e-02, eta: 11:24:54, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7344, top5_acc: 0.9706, loss_cls: 1.1585, loss: 1.1585 +2025-06-24 09:10:01,497 - pyskl - INFO - Epoch [5][1200/1281] lr: 2.493e-02, eta: 11:24:18, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7300, top5_acc: 0.9794, loss_cls: 1.1368, loss: 1.1368 +2025-06-24 09:10:19,435 - pyskl - INFO - Saving checkpoint at 5 epochs +2025-06-24 09:11:02,413 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:11:02,467 - pyskl - INFO - +top1_acc 0.7058 +top5_acc 0.9689 +2025-06-24 09:11:02,468 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:11:02,476 - pyskl - INFO - +mean_acc 0.5528 +2025-06-24 09:11:02,481 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_4.pth was removed +2025-06-24 09:11:02,662 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2025-06-24 09:11:02,662 - pyskl - INFO - Best top1_acc is 0.7058 at 5 epoch. +2025-06-24 09:11:02,665 - pyskl - INFO - Epoch(val) [5][533] top1_acc: 0.7058, top5_acc: 0.9689, mean_class_accuracy: 0.5528 +2025-06-24 09:11:42,715 - pyskl - INFO - Epoch [6][100/1281] lr: 2.493e-02, eta: 11:23:40, time: 0.400, data_time: 0.186, memory: 4082, top1_acc: 0.7425, top5_acc: 0.9806, loss_cls: 1.0997, loss: 1.0997 +2025-06-24 09:12:04,577 - pyskl - INFO - Epoch [6][200/1281] lr: 2.493e-02, eta: 11:23:11, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7550, top5_acc: 0.9781, loss_cls: 1.0613, loss: 1.0613 +2025-06-24 09:12:26,082 - pyskl - INFO - Epoch [6][300/1281] lr: 2.492e-02, eta: 11:22:33, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7406, top5_acc: 0.9825, loss_cls: 1.1061, loss: 1.1061 +2025-06-24 09:12:47,845 - pyskl - INFO - Epoch [6][400/1281] lr: 2.492e-02, eta: 11:22:02, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7156, top5_acc: 0.9700, loss_cls: 1.1735, loss: 1.1735 +2025-06-24 09:13:09,626 - pyskl - INFO - Epoch [6][500/1281] lr: 2.492e-02, eta: 11:21:32, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7362, top5_acc: 0.9825, loss_cls: 1.1185, loss: 1.1185 +2025-06-24 09:13:31,328 - pyskl - INFO - Epoch [6][600/1281] lr: 2.492e-02, eta: 11:21:00, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7394, top5_acc: 0.9738, loss_cls: 1.1057, loss: 1.1057 +2025-06-24 09:13:52,805 - pyskl - INFO - Epoch [6][700/1281] lr: 2.492e-02, eta: 11:20:22, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7488, top5_acc: 0.9831, loss_cls: 1.0801, loss: 1.0801 +2025-06-24 09:14:14,250 - pyskl - INFO - Epoch [6][800/1281] lr: 2.491e-02, eta: 11:19:45, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7575, top5_acc: 0.9831, loss_cls: 1.0538, loss: 1.0538 +2025-06-24 09:14:35,935 - pyskl - INFO - Epoch [6][900/1281] lr: 2.491e-02, eta: 11:19:13, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7419, top5_acc: 0.9781, loss_cls: 1.0851, loss: 1.0851 +2025-06-24 09:14:57,517 - pyskl - INFO - Epoch [6][1000/1281] lr: 2.491e-02, eta: 11:18:40, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7562, top5_acc: 0.9838, loss_cls: 1.0526, loss: 1.0526 +2025-06-24 09:15:19,252 - pyskl - INFO - Epoch [6][1100/1281] lr: 2.491e-02, eta: 11:18:10, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7462, top5_acc: 0.9825, loss_cls: 1.0751, loss: 1.0751 +2025-06-24 09:15:41,307 - pyskl - INFO - Epoch [6][1200/1281] lr: 2.490e-02, eta: 11:17:48, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7456, top5_acc: 0.9731, loss_cls: 1.1109, loss: 1.1109 +2025-06-24 09:15:59,960 - pyskl - INFO - Saving checkpoint at 6 epochs +2025-06-24 09:16:43,076 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:16:43,143 - pyskl - INFO - +top1_acc 0.7145 +top5_acc 0.9683 +2025-06-24 09:16:43,143 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:16:43,150 - pyskl - INFO - +mean_acc 0.6276 +2025-06-24 09:16:43,154 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_5.pth was removed +2025-06-24 09:16:43,346 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_6.pth. +2025-06-24 09:16:43,346 - pyskl - INFO - Best top1_acc is 0.7145 at 6 epoch. +2025-06-24 09:16:43,349 - pyskl - INFO - Epoch(val) [6][533] top1_acc: 0.7145, top5_acc: 0.9683, mean_class_accuracy: 0.6276 +2025-06-24 09:17:23,325 - pyskl - INFO - Epoch [7][100/1281] lr: 2.490e-02, eta: 11:17:10, time: 0.400, data_time: 0.181, memory: 4082, top1_acc: 0.7412, top5_acc: 0.9819, loss_cls: 1.0699, loss: 1.0699 +2025-06-24 09:17:45,417 - pyskl - INFO - Epoch [7][200/1281] lr: 2.490e-02, eta: 11:16:50, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7606, top5_acc: 0.9831, loss_cls: 1.0106, loss: 1.0106 +2025-06-24 09:18:07,100 - pyskl - INFO - Epoch [7][300/1281] lr: 2.489e-02, eta: 11:16:19, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7781, top5_acc: 0.9825, loss_cls: 0.9704, loss: 0.9704 +2025-06-24 09:18:28,928 - pyskl - INFO - Epoch [7][400/1281] lr: 2.489e-02, eta: 11:15:53, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7481, top5_acc: 0.9781, loss_cls: 1.0800, loss: 1.0800 +2025-06-24 09:18:50,691 - pyskl - INFO - Epoch [7][500/1281] lr: 2.489e-02, eta: 11:15:24, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7575, top5_acc: 0.9831, loss_cls: 1.0324, loss: 1.0324 +2025-06-24 09:19:12,344 - pyskl - INFO - Epoch [7][600/1281] lr: 2.489e-02, eta: 11:14:54, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7606, top5_acc: 0.9862, loss_cls: 1.0085, loss: 1.0085 +2025-06-24 09:19:34,328 - pyskl - INFO - Epoch [7][700/1281] lr: 2.488e-02, eta: 11:14:31, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7656, top5_acc: 0.9831, loss_cls: 1.0073, loss: 1.0073 +2025-06-24 09:19:56,258 - pyskl - INFO - Epoch [7][800/1281] lr: 2.488e-02, eta: 11:14:07, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7712, top5_acc: 0.9838, loss_cls: 0.9712, loss: 0.9712 +2025-06-24 09:20:18,050 - pyskl - INFO - Epoch [7][900/1281] lr: 2.488e-02, eta: 11:13:40, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7650, top5_acc: 0.9838, loss_cls: 1.0264, loss: 1.0264 +2025-06-24 09:20:39,706 - pyskl - INFO - Epoch [7][1000/1281] lr: 2.487e-02, eta: 11:13:10, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7812, top5_acc: 0.9819, loss_cls: 0.9843, loss: 0.9843 +2025-06-24 09:21:01,365 - pyskl - INFO - Epoch [7][1100/1281] lr: 2.487e-02, eta: 11:12:41, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7581, top5_acc: 0.9825, loss_cls: 1.0390, loss: 1.0390 +2025-06-24 09:21:23,152 - pyskl - INFO - Epoch [7][1200/1281] lr: 2.487e-02, eta: 11:12:14, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7631, top5_acc: 0.9850, loss_cls: 1.0219, loss: 1.0219 +2025-06-24 09:21:41,399 - pyskl - INFO - Saving checkpoint at 7 epochs +2025-06-24 09:22:23,879 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:22:23,932 - pyskl - INFO - +top1_acc 0.6940 +top5_acc 0.9605 +2025-06-24 09:22:23,932 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:22:23,939 - pyskl - INFO - +mean_acc 0.5809 +2025-06-24 09:22:23,940 - pyskl - INFO - Epoch(val) [7][533] top1_acc: 0.6940, top5_acc: 0.9605, mean_class_accuracy: 0.5809 +2025-06-24 09:23:04,018 - pyskl - INFO - Epoch [8][100/1281] lr: 2.486e-02, eta: 11:11:39, time: 0.401, data_time: 0.182, memory: 4082, top1_acc: 0.7825, top5_acc: 0.9875, loss_cls: 0.9629, loss: 0.9629 +2025-06-24 09:23:26,126 - pyskl - INFO - Epoch [8][200/1281] lr: 2.486e-02, eta: 11:11:19, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7887, top5_acc: 0.9869, loss_cls: 0.9329, loss: 0.9329 +2025-06-24 09:23:48,082 - pyskl - INFO - Epoch [8][300/1281] lr: 2.486e-02, eta: 11:10:56, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7581, top5_acc: 0.9831, loss_cls: 1.0092, loss: 1.0092 +2025-06-24 09:24:10,052 - pyskl - INFO - Epoch [8][400/1281] lr: 2.485e-02, eta: 11:10:33, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7856, top5_acc: 0.9875, loss_cls: 0.9465, loss: 0.9465 +2025-06-24 09:24:31,767 - pyskl - INFO - Epoch [8][500/1281] lr: 2.485e-02, eta: 11:10:05, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7819, top5_acc: 0.9850, loss_cls: 0.9167, loss: 0.9167 +2025-06-24 09:24:53,610 - pyskl - INFO - Epoch [8][600/1281] lr: 2.485e-02, eta: 11:09:40, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7850, top5_acc: 0.9850, loss_cls: 0.9611, loss: 0.9611 +2025-06-24 09:25:15,270 - pyskl - INFO - Epoch [8][700/1281] lr: 2.484e-02, eta: 11:09:12, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7812, top5_acc: 0.9825, loss_cls: 0.9800, loss: 0.9800 +2025-06-24 09:25:37,255 - pyskl - INFO - Epoch [8][800/1281] lr: 2.484e-02, eta: 11:08:49, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7719, top5_acc: 0.9831, loss_cls: 0.9726, loss: 0.9726 +2025-06-24 09:25:59,093 - pyskl - INFO - Epoch [8][900/1281] lr: 2.484e-02, eta: 11:08:24, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7744, top5_acc: 0.9856, loss_cls: 0.9625, loss: 0.9625 +2025-06-24 09:26:20,936 - pyskl - INFO - Epoch [8][1000/1281] lr: 2.483e-02, eta: 11:07:59, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7900, top5_acc: 0.9812, loss_cls: 0.9683, loss: 0.9683 +2025-06-24 09:26:42,569 - pyskl - INFO - Epoch [8][1100/1281] lr: 2.483e-02, eta: 11:07:31, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7925, top5_acc: 0.9856, loss_cls: 0.9283, loss: 0.9283 +2025-06-24 09:27:04,439 - pyskl - INFO - Epoch [8][1200/1281] lr: 2.483e-02, eta: 11:07:06, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7987, top5_acc: 0.9844, loss_cls: 0.9274, loss: 0.9274 +2025-06-24 09:27:22,683 - pyskl - INFO - Saving checkpoint at 8 epochs +2025-06-24 09:28:06,300 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:28:06,358 - pyskl - INFO - +top1_acc 0.7877 +top5_acc 0.9811 +2025-06-24 09:28:06,359 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:28:06,365 - pyskl - INFO - +mean_acc 0.6924 +2025-06-24 09:28:06,370 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_6.pth was removed +2025-06-24 09:28:06,563 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_8.pth. +2025-06-24 09:28:06,564 - pyskl - INFO - Best top1_acc is 0.7877 at 8 epoch. +2025-06-24 09:28:06,566 - pyskl - INFO - Epoch(val) [8][533] top1_acc: 0.7877, top5_acc: 0.9811, mean_class_accuracy: 0.6924 +2025-06-24 09:28:46,859 - pyskl - INFO - Epoch [9][100/1281] lr: 2.482e-02, eta: 11:06:35, time: 0.403, data_time: 0.188, memory: 4082, top1_acc: 0.7875, top5_acc: 0.9906, loss_cls: 0.8897, loss: 0.8897 +2025-06-24 09:29:08,900 - pyskl - INFO - Epoch [9][200/1281] lr: 2.482e-02, eta: 11:06:14, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7831, top5_acc: 0.9856, loss_cls: 0.9139, loss: 0.9139 +2025-06-24 09:29:30,636 - pyskl - INFO - Epoch [9][300/1281] lr: 2.481e-02, eta: 11:05:47, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7800, top5_acc: 0.9912, loss_cls: 0.8921, loss: 0.8921 +2025-06-24 09:29:52,259 - pyskl - INFO - Epoch [9][400/1281] lr: 2.481e-02, eta: 11:05:19, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7725, top5_acc: 0.9844, loss_cls: 0.9620, loss: 0.9620 +2025-06-24 09:30:13,896 - pyskl - INFO - Epoch [9][500/1281] lr: 2.481e-02, eta: 11:04:51, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9919, loss_cls: 0.8425, loss: 0.8425 +2025-06-24 09:30:35,491 - pyskl - INFO - Epoch [9][600/1281] lr: 2.480e-02, eta: 11:04:22, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7844, top5_acc: 0.9881, loss_cls: 0.9328, loss: 0.9328 +2025-06-24 09:30:56,959 - pyskl - INFO - Epoch [9][700/1281] lr: 2.480e-02, eta: 11:03:51, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7512, top5_acc: 0.9781, loss_cls: 1.0349, loss: 1.0349 +2025-06-24 09:31:18,776 - pyskl - INFO - Epoch [9][800/1281] lr: 2.480e-02, eta: 11:03:27, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8106, top5_acc: 0.9856, loss_cls: 0.8764, loss: 0.8764 +2025-06-24 09:31:40,280 - pyskl - INFO - Epoch [9][900/1281] lr: 2.479e-02, eta: 11:02:57, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7756, top5_acc: 0.9838, loss_cls: 0.9636, loss: 0.9636 +2025-06-24 09:32:01,948 - pyskl - INFO - Epoch [9][1000/1281] lr: 2.479e-02, eta: 11:02:30, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9906, loss_cls: 0.8733, loss: 0.8733 +2025-06-24 09:32:23,370 - pyskl - INFO - Epoch [9][1100/1281] lr: 2.479e-02, eta: 11:01:59, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7831, top5_acc: 0.9856, loss_cls: 0.9363, loss: 0.9363 +2025-06-24 09:32:45,010 - pyskl - INFO - Epoch [9][1200/1281] lr: 2.478e-02, eta: 11:01:32, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7919, top5_acc: 0.9875, loss_cls: 0.9050, loss: 0.9050 +2025-06-24 09:33:03,147 - pyskl - INFO - Saving checkpoint at 9 epochs +2025-06-24 09:33:47,251 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:33:47,307 - pyskl - INFO - +top1_acc 0.7117 +top5_acc 0.9587 +2025-06-24 09:33:47,308 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:33:47,316 - pyskl - INFO - +mean_acc 0.6408 +2025-06-24 09:33:47,317 - pyskl - INFO - Epoch(val) [9][533] top1_acc: 0.7117, top5_acc: 0.9587, mean_class_accuracy: 0.6408 +2025-06-24 09:34:27,374 - pyskl - INFO - Epoch [10][100/1281] lr: 2.477e-02, eta: 11:00:57, time: 0.401, data_time: 0.184, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9925, loss_cls: 0.8814, loss: 0.8814 +2025-06-24 09:34:49,276 - pyskl - INFO - Epoch [10][200/1281] lr: 2.477e-02, eta: 11:00:34, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9906, loss_cls: 0.7888, loss: 0.7888 +2025-06-24 09:35:11,159 - pyskl - INFO - Epoch [10][300/1281] lr: 2.477e-02, eta: 11:00:11, time: 0.219, data_time: 0.001, memory: 4082, top1_acc: 0.8144, top5_acc: 0.9844, loss_cls: 0.8686, loss: 0.8686 +2025-06-24 09:35:32,773 - pyskl - INFO - Epoch [10][400/1281] lr: 2.476e-02, eta: 10:59:43, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8069, top5_acc: 0.9912, loss_cls: 0.8641, loss: 0.8641 +2025-06-24 09:35:54,539 - pyskl - INFO - Epoch [10][500/1281] lr: 2.476e-02, eta: 10:59:18, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9881, loss_cls: 0.8825, loss: 0.8825 +2025-06-24 09:36:16,086 - pyskl - INFO - Epoch [10][600/1281] lr: 2.476e-02, eta: 10:58:50, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8013, top5_acc: 0.9888, loss_cls: 0.9134, loss: 0.9134 +2025-06-24 09:36:37,683 - pyskl - INFO - Epoch [10][700/1281] lr: 2.475e-02, eta: 10:58:23, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9862, loss_cls: 0.8857, loss: 0.8857 +2025-06-24 09:36:59,191 - pyskl - INFO - Epoch [10][800/1281] lr: 2.475e-02, eta: 10:57:54, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7781, top5_acc: 0.9862, loss_cls: 0.9145, loss: 0.9145 +2025-06-24 09:37:20,566 - pyskl - INFO - Epoch [10][900/1281] lr: 2.474e-02, eta: 10:57:24, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.8069, top5_acc: 0.9881, loss_cls: 0.8411, loss: 0.8411 +2025-06-24 09:37:42,414 - pyskl - INFO - Epoch [10][1000/1281] lr: 2.474e-02, eta: 10:57:01, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8031, top5_acc: 0.9875, loss_cls: 0.8910, loss: 0.8910 +2025-06-24 09:38:04,268 - pyskl - INFO - Epoch [10][1100/1281] lr: 2.473e-02, eta: 10:56:37, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9894, loss_cls: 0.8241, loss: 0.8241 +2025-06-24 09:38:26,045 - pyskl - INFO - Epoch [10][1200/1281] lr: 2.473e-02, eta: 10:56:13, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8087, top5_acc: 0.9881, loss_cls: 0.8683, loss: 0.8683 +2025-06-24 09:38:44,165 - pyskl - INFO - Saving checkpoint at 10 epochs +2025-06-24 09:39:27,079 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:39:27,146 - pyskl - INFO - +top1_acc 0.7025 +top5_acc 0.9693 +2025-06-24 09:39:27,146 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:39:27,154 - pyskl - INFO - +mean_acc 0.6068 +2025-06-24 09:39:27,156 - pyskl - INFO - Epoch(val) [10][533] top1_acc: 0.7025, top5_acc: 0.9693, mean_class_accuracy: 0.6068 +2025-06-24 09:40:07,662 - pyskl - INFO - Epoch [11][100/1281] lr: 2.472e-02, eta: 10:55:44, time: 0.405, data_time: 0.188, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9912, loss_cls: 0.8472, loss: 0.8472 +2025-06-24 09:40:29,499 - pyskl - INFO - Epoch [11][200/1281] lr: 2.472e-02, eta: 10:55:21, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8213, top5_acc: 0.9825, loss_cls: 0.8400, loss: 0.8400 +2025-06-24 09:40:51,309 - pyskl - INFO - Epoch [11][300/1281] lr: 2.471e-02, eta: 10:54:57, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8206, top5_acc: 0.9881, loss_cls: 0.8003, loss: 0.8003 +2025-06-24 09:41:13,158 - pyskl - INFO - Epoch [11][400/1281] lr: 2.471e-02, eta: 10:54:34, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9912, loss_cls: 0.8178, loss: 0.8178 +2025-06-24 09:41:34,817 - pyskl - INFO - Epoch [11][500/1281] lr: 2.471e-02, eta: 10:54:08, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9875, loss_cls: 0.8542, loss: 0.8542 +2025-06-24 09:41:56,566 - pyskl - INFO - Epoch [11][600/1281] lr: 2.470e-02, eta: 10:53:43, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7987, top5_acc: 0.9881, loss_cls: 0.9016, loss: 0.9016 +2025-06-24 09:42:18,341 - pyskl - INFO - Epoch [11][700/1281] lr: 2.470e-02, eta: 10:53:19, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8206, top5_acc: 0.9944, loss_cls: 0.7949, loss: 0.7949 +2025-06-24 09:42:40,180 - pyskl - INFO - Epoch [11][800/1281] lr: 2.469e-02, eta: 10:52:56, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9919, loss_cls: 0.8004, loss: 0.8004 +2025-06-24 09:43:01,743 - pyskl - INFO - Epoch [11][900/1281] lr: 2.469e-02, eta: 10:52:29, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9906, loss_cls: 0.8238, loss: 0.8238 +2025-06-24 09:43:23,433 - pyskl - INFO - Epoch [11][1000/1281] lr: 2.468e-02, eta: 10:52:03, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9856, loss_cls: 0.8833, loss: 0.8833 +2025-06-24 09:43:45,057 - pyskl - INFO - Epoch [11][1100/1281] lr: 2.468e-02, eta: 10:51:38, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8075, top5_acc: 0.9925, loss_cls: 0.8634, loss: 0.8634 +2025-06-24 09:44:06,991 - pyskl - INFO - Epoch [11][1200/1281] lr: 2.467e-02, eta: 10:51:16, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9888, loss_cls: 0.8157, loss: 0.8157 +2025-06-24 09:44:25,158 - pyskl - INFO - Saving checkpoint at 11 epochs +2025-06-24 09:45:08,777 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:45:08,829 - pyskl - INFO - +top1_acc 0.7937 +top5_acc 0.9842 +2025-06-24 09:45:08,830 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:45:08,835 - pyskl - INFO - +mean_acc 0.7025 +2025-06-24 09:45:08,839 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_8.pth was removed +2025-06-24 09:45:09,006 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_11.pth. +2025-06-24 09:45:09,006 - pyskl - INFO - Best top1_acc is 0.7937 at 11 epoch. +2025-06-24 09:45:09,009 - pyskl - INFO - Epoch(val) [11][533] top1_acc: 0.7937, top5_acc: 0.9842, mean_class_accuracy: 0.7025 +2025-06-24 09:45:48,910 - pyskl - INFO - Epoch [12][100/1281] lr: 2.467e-02, eta: 10:50:38, time: 0.399, data_time: 0.184, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9931, loss_cls: 0.8408, loss: 0.8408 +2025-06-24 09:46:10,845 - pyskl - INFO - Epoch [12][200/1281] lr: 2.466e-02, eta: 10:50:16, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9944, loss_cls: 0.7633, loss: 0.7633 +2025-06-24 09:46:32,585 - pyskl - INFO - Epoch [12][300/1281] lr: 2.466e-02, eta: 10:49:52, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8125, top5_acc: 0.9881, loss_cls: 0.7876, loss: 0.7876 +2025-06-24 09:46:54,593 - pyskl - INFO - Epoch [12][400/1281] lr: 2.465e-02, eta: 10:49:31, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8013, top5_acc: 0.9925, loss_cls: 0.8440, loss: 0.8440 +2025-06-24 09:47:16,339 - pyskl - INFO - Epoch [12][500/1281] lr: 2.465e-02, eta: 10:49:07, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9912, loss_cls: 0.8202, loss: 0.8202 +2025-06-24 09:47:38,052 - pyskl - INFO - Epoch [12][600/1281] lr: 2.464e-02, eta: 10:48:42, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9906, loss_cls: 0.8139, loss: 0.8139 +2025-06-24 09:47:59,804 - pyskl - INFO - Epoch [12][700/1281] lr: 2.464e-02, eta: 10:48:18, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8087, top5_acc: 0.9875, loss_cls: 0.8553, loss: 0.8553 +2025-06-24 09:48:21,440 - pyskl - INFO - Epoch [12][800/1281] lr: 2.463e-02, eta: 10:47:52, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9844, loss_cls: 0.8336, loss: 0.8336 +2025-06-24 09:48:42,994 - pyskl - INFO - Epoch [12][900/1281] lr: 2.463e-02, eta: 10:47:26, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9950, loss_cls: 0.8144, loss: 0.8144 +2025-06-24 09:49:04,635 - pyskl - INFO - Epoch [12][1000/1281] lr: 2.462e-02, eta: 10:47:01, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9912, loss_cls: 0.8156, loss: 0.8156 +2025-06-24 09:49:26,196 - pyskl - INFO - Epoch [12][1100/1281] lr: 2.462e-02, eta: 10:46:35, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9894, loss_cls: 0.8537, loss: 0.8537 +2025-06-24 09:49:47,809 - pyskl - INFO - Epoch [12][1200/1281] lr: 2.461e-02, eta: 10:46:09, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9919, loss_cls: 0.7849, loss: 0.7849 +2025-06-24 09:50:05,849 - pyskl - INFO - Saving checkpoint at 12 epochs +2025-06-24 09:50:49,308 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:50:49,362 - pyskl - INFO - +top1_acc 0.7784 +top5_acc 0.9779 +2025-06-24 09:50:49,362 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:50:49,369 - pyskl - INFO - +mean_acc 0.6862 +2025-06-24 09:50:49,371 - pyskl - INFO - Epoch(val) [12][533] top1_acc: 0.7784, top5_acc: 0.9779, mean_class_accuracy: 0.6862 +2025-06-24 09:51:29,394 - pyskl - INFO - Epoch [13][100/1281] lr: 2.460e-02, eta: 10:45:33, time: 0.400, data_time: 0.184, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9931, loss_cls: 0.7781, loss: 0.7781 +2025-06-24 09:51:51,318 - pyskl - INFO - Epoch [13][200/1281] lr: 2.460e-02, eta: 10:45:11, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9906, loss_cls: 0.8321, loss: 0.8321 +2025-06-24 09:52:12,930 - pyskl - INFO - Epoch [13][300/1281] lr: 2.459e-02, eta: 10:44:46, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9912, loss_cls: 0.7442, loss: 0.7442 +2025-06-24 09:52:34,580 - pyskl - INFO - Epoch [13][400/1281] lr: 2.459e-02, eta: 10:44:21, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9925, loss_cls: 0.7694, loss: 0.7694 +2025-06-24 09:52:56,241 - pyskl - INFO - Epoch [13][500/1281] lr: 2.458e-02, eta: 10:43:56, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9931, loss_cls: 0.7767, loss: 0.7767 +2025-06-24 09:53:17,853 - pyskl - INFO - Epoch [13][600/1281] lr: 2.458e-02, eta: 10:43:31, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8406, top5_acc: 0.9894, loss_cls: 0.7486, loss: 0.7486 +2025-06-24 09:53:39,559 - pyskl - INFO - Epoch [13][700/1281] lr: 2.457e-02, eta: 10:43:07, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9894, loss_cls: 0.8288, loss: 0.8288 +2025-06-24 09:54:01,173 - pyskl - INFO - Epoch [13][800/1281] lr: 2.457e-02, eta: 10:42:41, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9950, loss_cls: 0.7818, loss: 0.7818 +2025-06-24 09:54:22,857 - pyskl - INFO - Epoch [13][900/1281] lr: 2.456e-02, eta: 10:42:17, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8144, top5_acc: 0.9906, loss_cls: 0.8151, loss: 0.8151 +2025-06-24 09:54:44,282 - pyskl - INFO - Epoch [13][1000/1281] lr: 2.455e-02, eta: 10:41:50, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9906, loss_cls: 0.7655, loss: 0.7655 +2025-06-24 09:55:05,916 - pyskl - INFO - Epoch [13][1100/1281] lr: 2.455e-02, eta: 10:41:25, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9900, loss_cls: 0.7596, loss: 0.7596 +2025-06-24 09:55:27,757 - pyskl - INFO - Epoch [13][1200/1281] lr: 2.454e-02, eta: 10:41:02, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9900, loss_cls: 0.8458, loss: 0.8458 +2025-06-24 09:55:45,893 - pyskl - INFO - Saving checkpoint at 13 epochs +2025-06-24 09:56:29,146 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:56:29,202 - pyskl - INFO - +top1_acc 0.7393 +top5_acc 0.9783 +2025-06-24 09:56:29,202 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:56:29,209 - pyskl - INFO - +mean_acc 0.6651 +2025-06-24 09:56:29,211 - pyskl - INFO - Epoch(val) [13][533] top1_acc: 0.7393, top5_acc: 0.9783, mean_class_accuracy: 0.6651 +2025-06-24 09:57:09,327 - pyskl - INFO - Epoch [14][100/1281] lr: 2.453e-02, eta: 10:40:28, time: 0.401, data_time: 0.184, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9888, loss_cls: 0.7756, loss: 0.7756 +2025-06-24 09:57:31,322 - pyskl - INFO - Epoch [14][200/1281] lr: 2.453e-02, eta: 10:40:07, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9956, loss_cls: 0.7225, loss: 0.7225 +2025-06-24 09:57:52,729 - pyskl - INFO - Epoch [14][300/1281] lr: 2.452e-02, eta: 10:39:39, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9944, loss_cls: 0.7594, loss: 0.7594 +2025-06-24 09:58:14,746 - pyskl - INFO - Epoch [14][400/1281] lr: 2.452e-02, eta: 10:39:19, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9931, loss_cls: 0.7326, loss: 0.7326 +2025-06-24 09:58:36,431 - pyskl - INFO - Epoch [14][500/1281] lr: 2.451e-02, eta: 10:38:54, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9912, loss_cls: 0.7710, loss: 0.7710 +2025-06-24 09:58:58,032 - pyskl - INFO - Epoch [14][600/1281] lr: 2.451e-02, eta: 10:38:29, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9931, loss_cls: 0.7642, loss: 0.7642 +2025-06-24 09:59:19,463 - pyskl - INFO - Epoch [14][700/1281] lr: 2.450e-02, eta: 10:38:03, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9944, loss_cls: 0.7766, loss: 0.7766 +2025-06-24 09:59:41,077 - pyskl - INFO - Epoch [14][800/1281] lr: 2.449e-02, eta: 10:37:38, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9888, loss_cls: 0.7863, loss: 0.7863 +2025-06-24 10:00:02,733 - pyskl - INFO - Epoch [14][900/1281] lr: 2.449e-02, eta: 10:37:14, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9919, loss_cls: 0.7554, loss: 0.7554 +2025-06-24 10:00:24,401 - pyskl - INFO - Epoch [14][1000/1281] lr: 2.448e-02, eta: 10:36:49, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9919, loss_cls: 0.7995, loss: 0.7995 +2025-06-24 10:00:46,364 - pyskl - INFO - Epoch [14][1100/1281] lr: 2.448e-02, eta: 10:36:28, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9931, loss_cls: 0.8208, loss: 0.8208 +2025-06-24 10:01:08,537 - pyskl - INFO - Epoch [14][1200/1281] lr: 2.447e-02, eta: 10:36:09, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8344, top5_acc: 0.9919, loss_cls: 0.7679, loss: 0.7679 +2025-06-24 10:01:26,859 - pyskl - INFO - Saving checkpoint at 14 epochs +2025-06-24 10:02:10,125 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:02:10,181 - pyskl - INFO - +top1_acc 0.8001 +top5_acc 0.9873 +2025-06-24 10:02:10,181 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:02:10,188 - pyskl - INFO - +mean_acc 0.7023 +2025-06-24 10:02:10,192 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_11.pth was removed +2025-06-24 10:02:10,357 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_14.pth. +2025-06-24 10:02:10,357 - pyskl - INFO - Best top1_acc is 0.8001 at 14 epoch. +2025-06-24 10:02:10,360 - pyskl - INFO - Epoch(val) [14][533] top1_acc: 0.8001, top5_acc: 0.9873, mean_class_accuracy: 0.7023 +2025-06-24 10:02:50,382 - pyskl - INFO - Epoch [15][100/1281] lr: 2.446e-02, eta: 10:35:33, time: 0.400, data_time: 0.184, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9919, loss_cls: 0.7635, loss: 0.7635 +2025-06-24 10:03:12,263 - pyskl - INFO - Epoch [15][200/1281] lr: 2.445e-02, eta: 10:35:11, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8237, top5_acc: 0.9919, loss_cls: 0.8123, loss: 0.8123 +2025-06-24 10:03:34,327 - pyskl - INFO - Epoch [15][300/1281] lr: 2.445e-02, eta: 10:34:50, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9925, loss_cls: 0.7360, loss: 0.7360 +2025-06-24 10:03:56,259 - pyskl - INFO - Epoch [15][400/1281] lr: 2.444e-02, eta: 10:34:29, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8175, top5_acc: 0.9831, loss_cls: 0.8006, loss: 0.8006 +2025-06-24 10:04:17,965 - pyskl - INFO - Epoch [15][500/1281] lr: 2.444e-02, eta: 10:34:05, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9944, loss_cls: 0.7308, loss: 0.7308 +2025-06-24 10:04:39,909 - pyskl - INFO - Epoch [15][600/1281] lr: 2.443e-02, eta: 10:33:44, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8413, top5_acc: 0.9938, loss_cls: 0.7367, loss: 0.7367 +2025-06-24 10:05:01,471 - pyskl - INFO - Epoch [15][700/1281] lr: 2.442e-02, eta: 10:33:19, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8381, top5_acc: 0.9962, loss_cls: 0.7211, loss: 0.7211 +2025-06-24 10:05:23,333 - pyskl - INFO - Epoch [15][800/1281] lr: 2.442e-02, eta: 10:32:56, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9931, loss_cls: 0.7416, loss: 0.7416 +2025-06-24 10:05:45,177 - pyskl - INFO - Epoch [15][900/1281] lr: 2.441e-02, eta: 10:32:34, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8538, top5_acc: 0.9962, loss_cls: 0.7047, loss: 0.7047 +2025-06-24 10:06:06,846 - pyskl - INFO - Epoch [15][1000/1281] lr: 2.441e-02, eta: 10:32:10, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9919, loss_cls: 0.7755, loss: 0.7755 +2025-06-24 10:06:28,493 - pyskl - INFO - Epoch [15][1100/1281] lr: 2.440e-02, eta: 10:31:46, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9869, loss_cls: 0.8444, loss: 0.8444 +2025-06-24 10:06:50,185 - pyskl - INFO - Epoch [15][1200/1281] lr: 2.439e-02, eta: 10:31:22, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8063, top5_acc: 0.9881, loss_cls: 0.8250, loss: 0.8250 +2025-06-24 10:07:08,576 - pyskl - INFO - Saving checkpoint at 15 epochs +2025-06-24 10:07:51,694 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:07:51,750 - pyskl - INFO - +top1_acc 0.8031 +top5_acc 0.9862 +2025-06-24 10:07:51,750 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:07:51,757 - pyskl - INFO - +mean_acc 0.7208 +2025-06-24 10:07:51,761 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_14.pth was removed +2025-06-24 10:07:51,934 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_15.pth. +2025-06-24 10:07:51,934 - pyskl - INFO - Best top1_acc is 0.8031 at 15 epoch. +2025-06-24 10:07:51,937 - pyskl - INFO - Epoch(val) [15][533] top1_acc: 0.8031, top5_acc: 0.9862, mean_class_accuracy: 0.7208 +2025-06-24 10:08:31,958 - pyskl - INFO - Epoch [16][100/1281] lr: 2.438e-02, eta: 10:30:46, time: 0.400, data_time: 0.182, memory: 4082, top1_acc: 0.8750, top5_acc: 0.9956, loss_cls: 0.6219, loss: 0.6219 +2025-06-24 10:08:53,911 - pyskl - INFO - Epoch [16][200/1281] lr: 2.438e-02, eta: 10:30:24, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9938, loss_cls: 0.7396, loss: 0.7396 +2025-06-24 10:09:15,574 - pyskl - INFO - Epoch [16][300/1281] lr: 2.437e-02, eta: 10:30:00, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9900, loss_cls: 0.7236, loss: 0.7236 +2025-06-24 10:09:37,276 - pyskl - INFO - Epoch [16][400/1281] lr: 2.436e-02, eta: 10:29:37, time: 0.217, data_time: 0.001, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9900, loss_cls: 0.6828, loss: 0.6828 +2025-06-24 10:09:59,169 - pyskl - INFO - Epoch [16][500/1281] lr: 2.436e-02, eta: 10:29:15, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9919, loss_cls: 0.7313, loss: 0.7313 +2025-06-24 10:10:20,950 - pyskl - INFO - Epoch [16][600/1281] lr: 2.435e-02, eta: 10:28:52, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9938, loss_cls: 0.7849, loss: 0.7849 +2025-06-24 10:10:42,508 - pyskl - INFO - Epoch [16][700/1281] lr: 2.434e-02, eta: 10:28:27, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9925, loss_cls: 0.7270, loss: 0.7270 +2025-06-24 10:11:04,176 - pyskl - INFO - Epoch [16][800/1281] lr: 2.434e-02, eta: 10:28:03, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9875, loss_cls: 0.7770, loss: 0.7770 +2025-06-24 10:11:25,945 - pyskl - INFO - Epoch [16][900/1281] lr: 2.433e-02, eta: 10:27:40, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9894, loss_cls: 0.7715, loss: 0.7715 +2025-06-24 10:11:47,561 - pyskl - INFO - Epoch [16][1000/1281] lr: 2.432e-02, eta: 10:27:16, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9931, loss_cls: 0.7612, loss: 0.7612 +2025-06-24 10:12:09,222 - pyskl - INFO - Epoch [16][1100/1281] lr: 2.432e-02, eta: 10:26:52, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9925, loss_cls: 0.7064, loss: 0.7064 +2025-06-24 10:12:31,092 - pyskl - INFO - Epoch [16][1200/1281] lr: 2.431e-02, eta: 10:26:30, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8481, top5_acc: 0.9950, loss_cls: 0.7171, loss: 0.7171 +2025-06-24 10:12:49,965 - pyskl - INFO - Saving checkpoint at 16 epochs +2025-06-24 10:14:01,138 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:14:01,193 - pyskl - INFO - +top1_acc 0.7861 +top5_acc 0.9854 +2025-06-24 10:14:01,193 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:14:01,200 - pyskl - INFO - +mean_acc 0.7118 +2025-06-24 10:14:01,202 - pyskl - INFO - Epoch(val) [16][533] top1_acc: 0.7861, top5_acc: 0.9854, mean_class_accuracy: 0.7118 +2025-06-24 10:15:03,980 - pyskl - INFO - Epoch [17][100/1281] lr: 2.430e-02, eta: 10:29:03, time: 0.628, data_time: 0.194, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9950, loss_cls: 0.6501, loss: 0.6501 +2025-06-24 10:15:45,683 - pyskl - INFO - Epoch [17][200/1281] lr: 2.429e-02, eta: 10:31:25, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9931, loss_cls: 0.7095, loss: 0.7095 +2025-06-24 10:16:27,224 - pyskl - INFO - Epoch [17][300/1281] lr: 2.428e-02, eta: 10:33:43, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9931, loss_cls: 0.7307, loss: 0.7307 +2025-06-24 10:17:08,586 - pyskl - INFO - Epoch [17][400/1281] lr: 2.428e-02, eta: 10:35:58, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8413, top5_acc: 0.9881, loss_cls: 0.7460, loss: 0.7460 +2025-06-24 10:17:50,004 - pyskl - INFO - Epoch [17][500/1281] lr: 2.427e-02, eta: 10:38:11, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9950, loss_cls: 0.7194, loss: 0.7194 +2025-06-24 10:18:31,475 - pyskl - INFO - Epoch [17][600/1281] lr: 2.426e-02, eta: 10:40:24, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9944, loss_cls: 0.7543, loss: 0.7543 +2025-06-24 10:19:12,979 - pyskl - INFO - Epoch [17][700/1281] lr: 2.426e-02, eta: 10:42:35, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9975, loss_cls: 0.7330, loss: 0.7330 +2025-06-24 10:19:54,552 - pyskl - INFO - Epoch [17][800/1281] lr: 2.425e-02, eta: 10:44:45, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8812, top5_acc: 0.9881, loss_cls: 0.6730, loss: 0.6730 +2025-06-24 10:20:36,153 - pyskl - INFO - Epoch [17][900/1281] lr: 2.424e-02, eta: 10:46:54, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8494, top5_acc: 0.9925, loss_cls: 0.7068, loss: 0.7068 +2025-06-24 10:21:17,654 - pyskl - INFO - Epoch [17][1000/1281] lr: 2.424e-02, eta: 10:49:00, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9906, loss_cls: 0.7484, loss: 0.7484 +2025-06-24 10:21:52,066 - pyskl - INFO - Epoch [17][1100/1281] lr: 2.423e-02, eta: 10:50:09, time: 0.344, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9931, loss_cls: 0.7081, loss: 0.7081 +2025-06-24 10:22:25,519 - pyskl - INFO - Epoch [17][1200/1281] lr: 2.422e-02, eta: 10:51:09, time: 0.334, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9900, loss_cls: 0.7711, loss: 0.7711 +2025-06-24 10:22:59,638 - pyskl - INFO - Saving checkpoint at 17 epochs +2025-06-24 10:24:11,287 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:24:11,355 - pyskl - INFO - +top1_acc 0.8160 +top5_acc 0.9858 +2025-06-24 10:24:11,355 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:24:11,363 - pyskl - INFO - +mean_acc 0.7463 +2025-06-24 10:24:11,368 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_15.pth was removed +2025-06-24 10:24:11,557 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_17.pth. +2025-06-24 10:24:11,557 - pyskl - INFO - Best top1_acc is 0.8160 at 17 epoch. +2025-06-24 10:24:11,560 - pyskl - INFO - Epoch(val) [17][533] top1_acc: 0.8160, top5_acc: 0.9858, mean_class_accuracy: 0.7463 +2025-06-24 10:25:12,963 - pyskl - INFO - Epoch [18][100/1281] lr: 2.421e-02, eta: 10:53:02, time: 0.614, data_time: 0.197, memory: 4082, top1_acc: 0.8525, top5_acc: 0.9944, loss_cls: 0.6603, loss: 0.6603 +2025-06-24 10:25:54,558 - pyskl - INFO - Epoch [18][200/1281] lr: 2.420e-02, eta: 10:55:03, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8569, top5_acc: 0.9906, loss_cls: 0.6448, loss: 0.6448 +2025-06-24 10:26:36,118 - pyskl - INFO - Epoch [18][300/1281] lr: 2.419e-02, eta: 10:57:02, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9944, loss_cls: 0.6632, loss: 0.6632 +2025-06-24 10:27:17,806 - pyskl - INFO - Epoch [18][400/1281] lr: 2.419e-02, eta: 10:59:01, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9931, loss_cls: 0.7458, loss: 0.7458 +2025-06-24 10:27:59,262 - pyskl - INFO - Epoch [18][500/1281] lr: 2.418e-02, eta: 11:00:56, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8712, top5_acc: 0.9925, loss_cls: 0.6162, loss: 0.6162 +2025-06-24 10:28:40,770 - pyskl - INFO - Epoch [18][600/1281] lr: 2.417e-02, eta: 11:02:51, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9944, loss_cls: 0.6589, loss: 0.6589 +2025-06-24 10:29:22,208 - pyskl - INFO - Epoch [18][700/1281] lr: 2.417e-02, eta: 11:04:43, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9925, loss_cls: 0.7121, loss: 0.7121 +2025-06-24 10:30:03,875 - pyskl - INFO - Epoch [18][800/1281] lr: 2.416e-02, eta: 11:06:36, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9931, loss_cls: 0.7059, loss: 0.7059 +2025-06-24 10:30:45,280 - pyskl - INFO - Epoch [18][900/1281] lr: 2.415e-02, eta: 11:08:26, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8425, top5_acc: 0.9912, loss_cls: 0.7450, loss: 0.7450 +2025-06-24 10:31:26,867 - pyskl - INFO - Epoch [18][1000/1281] lr: 2.414e-02, eta: 11:10:16, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8544, top5_acc: 0.9950, loss_cls: 0.6640, loss: 0.6640 +2025-06-24 10:32:02,314 - pyskl - INFO - Epoch [18][1100/1281] lr: 2.414e-02, eta: 11:11:18, time: 0.354, data_time: 0.000, memory: 4082, top1_acc: 0.8569, top5_acc: 0.9925, loss_cls: 0.6642, loss: 0.6642 +2025-06-24 10:32:35,216 - pyskl - INFO - Epoch [18][1200/1281] lr: 2.413e-02, eta: 11:12:02, time: 0.329, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9944, loss_cls: 0.7308, loss: 0.7308 +2025-06-24 10:33:10,035 - pyskl - INFO - Saving checkpoint at 18 epochs +2025-06-24 10:34:21,982 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:34:22,051 - pyskl - INFO - +top1_acc 0.8243 +top5_acc 0.9874 +2025-06-24 10:34:22,051 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:34:22,058 - pyskl - INFO - +mean_acc 0.7497 +2025-06-24 10:34:22,062 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_17.pth was removed +2025-06-24 10:34:22,235 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_18.pth. +2025-06-24 10:34:22,235 - pyskl - INFO - Best top1_acc is 0.8243 at 18 epoch. +2025-06-24 10:34:22,238 - pyskl - INFO - Epoch(val) [18][533] top1_acc: 0.8243, top5_acc: 0.9874, mean_class_accuracy: 0.7497 +2025-06-24 10:35:23,637 - pyskl - INFO - Epoch [19][100/1281] lr: 2.411e-02, eta: 11:13:32, time: 0.614, data_time: 0.199, memory: 4082, top1_acc: 0.8556, top5_acc: 0.9950, loss_cls: 0.6487, loss: 0.6487 +2025-06-24 10:36:05,160 - pyskl - INFO - Epoch [19][200/1281] lr: 2.411e-02, eta: 11:15:16, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9925, loss_cls: 0.6978, loss: 0.6978 +2025-06-24 10:36:46,825 - pyskl - INFO - Epoch [19][300/1281] lr: 2.410e-02, eta: 11:16:59, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8738, top5_acc: 0.9925, loss_cls: 0.6256, loss: 0.6256 +2025-06-24 10:37:28,661 - pyskl - INFO - Epoch [19][400/1281] lr: 2.409e-02, eta: 11:18:43, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8456, top5_acc: 0.9919, loss_cls: 0.7035, loss: 0.7035 +2025-06-24 10:38:10,315 - pyskl - INFO - Epoch [19][500/1281] lr: 2.408e-02, eta: 11:20:24, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9931, loss_cls: 0.6992, loss: 0.6992 +2025-06-24 10:38:52,090 - pyskl - INFO - Epoch [19][600/1281] lr: 2.408e-02, eta: 11:22:05, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9912, loss_cls: 0.7467, loss: 0.7467 +2025-06-24 10:39:35,393 - pyskl - INFO - Epoch [19][700/1281] lr: 2.407e-02, eta: 11:23:56, time: 0.433, data_time: 0.000, memory: 4082, top1_acc: 0.8512, top5_acc: 0.9912, loss_cls: 0.6918, loss: 0.6918 +2025-06-24 10:40:16,901 - pyskl - INFO - Epoch [19][800/1281] lr: 2.406e-02, eta: 11:25:32, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9956, loss_cls: 0.6741, loss: 0.6741 +2025-06-24 10:40:58,448 - pyskl - INFO - Epoch [19][900/1281] lr: 2.405e-02, eta: 11:27:08, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8413, top5_acc: 0.9931, loss_cls: 0.7510, loss: 0.7510 +2025-06-24 10:41:39,782 - pyskl - INFO - Epoch [19][1000/1281] lr: 2.405e-02, eta: 11:28:41, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9950, loss_cls: 0.6889, loss: 0.6889 +2025-06-24 10:42:15,105 - pyskl - INFO - Epoch [19][1100/1281] lr: 2.404e-02, eta: 11:29:31, time: 0.353, data_time: 0.000, memory: 4082, top1_acc: 0.8719, top5_acc: 0.9938, loss_cls: 0.6481, loss: 0.6481 +2025-06-24 10:42:47,811 - pyskl - INFO - Epoch [19][1200/1281] lr: 2.403e-02, eta: 11:30:02, time: 0.327, data_time: 0.000, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9881, loss_cls: 0.7113, loss: 0.7113 +2025-06-24 10:43:22,911 - pyskl - INFO - Saving checkpoint at 19 epochs +2025-06-24 10:44:35,175 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:44:35,243 - pyskl - INFO - +top1_acc 0.8269 +top5_acc 0.9863 +2025-06-24 10:44:35,243 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:44:35,251 - pyskl - INFO - +mean_acc 0.7547 +2025-06-24 10:44:35,255 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_18.pth was removed +2025-06-24 10:44:35,469 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_19.pth. +2025-06-24 10:44:35,469 - pyskl - INFO - Best top1_acc is 0.8269 at 19 epoch. +2025-06-24 10:44:35,472 - pyskl - INFO - Epoch(val) [19][533] top1_acc: 0.8269, top5_acc: 0.9863, mean_class_accuracy: 0.7547 +2025-06-24 10:45:36,911 - pyskl - INFO - Epoch [20][100/1281] lr: 2.402e-02, eta: 11:31:13, time: 0.614, data_time: 0.199, memory: 4082, top1_acc: 0.8650, top5_acc: 0.9950, loss_cls: 0.6724, loss: 0.6724 +2025-06-24 10:46:18,442 - pyskl - INFO - Epoch [20][200/1281] lr: 2.401e-02, eta: 11:32:43, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9950, loss_cls: 0.6958, loss: 0.6958 +2025-06-24 10:47:00,042 - pyskl - INFO - Epoch [20][300/1281] lr: 2.400e-02, eta: 11:34:12, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9938, loss_cls: 0.6704, loss: 0.6704 +2025-06-24 10:47:41,575 - pyskl - INFO - Epoch [20][400/1281] lr: 2.399e-02, eta: 11:35:40, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9956, loss_cls: 0.6726, loss: 0.6726 +2025-06-24 10:48:23,200 - pyskl - INFO - Epoch [20][500/1281] lr: 2.398e-02, eta: 11:37:08, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8562, top5_acc: 0.9900, loss_cls: 0.7004, loss: 0.7004 +2025-06-24 10:49:04,740 - pyskl - INFO - Epoch [20][600/1281] lr: 2.398e-02, eta: 11:38:34, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9938, loss_cls: 0.6700, loss: 0.6700 +2025-06-24 10:49:46,241 - pyskl - INFO - Epoch [20][700/1281] lr: 2.397e-02, eta: 11:39:58, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8725, top5_acc: 0.9969, loss_cls: 0.6204, loss: 0.6204 +2025-06-24 10:50:27,922 - pyskl - INFO - Epoch [20][800/1281] lr: 2.396e-02, eta: 11:41:23, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8481, top5_acc: 0.9925, loss_cls: 0.6956, loss: 0.6956 +2025-06-24 10:51:09,541 - pyskl - INFO - Epoch [20][900/1281] lr: 2.395e-02, eta: 11:42:46, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8512, top5_acc: 0.9906, loss_cls: 0.6940, loss: 0.6940 +2025-06-24 10:51:51,220 - pyskl - INFO - Epoch [20][1000/1281] lr: 2.394e-02, eta: 11:44:09, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9975, loss_cls: 0.6721, loss: 0.6721 +2025-06-24 10:52:25,814 - pyskl - INFO - Epoch [20][1100/1281] lr: 2.393e-02, eta: 11:44:45, time: 0.346, data_time: 0.000, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9919, loss_cls: 0.6911, loss: 0.6911 +2025-06-24 10:52:58,490 - pyskl - INFO - Epoch [20][1200/1281] lr: 2.393e-02, eta: 11:45:07, time: 0.327, data_time: 0.000, memory: 4082, top1_acc: 0.8556, top5_acc: 0.9969, loss_cls: 0.6538, loss: 0.6538 +2025-06-24 10:53:33,390 - pyskl - INFO - Saving checkpoint at 20 epochs +2025-06-24 10:54:45,112 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:54:45,167 - pyskl - INFO - +top1_acc 0.8319 +top5_acc 0.9870 +2025-06-24 10:54:45,167 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:54:45,173 - pyskl - INFO - +mean_acc 0.7765 +2025-06-24 10:54:45,177 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_19.pth was removed +2025-06-24 10:54:45,352 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_20.pth. +2025-06-24 10:54:45,353 - pyskl - INFO - Best top1_acc is 0.8319 at 20 epoch. +2025-06-24 10:54:45,355 - pyskl - INFO - Epoch(val) [20][533] top1_acc: 0.8319, top5_acc: 0.9870, mean_class_accuracy: 0.7765 +2025-06-24 10:55:47,305 - pyskl - INFO - Epoch [21][100/1281] lr: 2.391e-02, eta: 11:46:04, time: 0.619, data_time: 0.196, memory: 4082, top1_acc: 0.8788, top5_acc: 0.9950, loss_cls: 0.5920, loss: 0.5920 +2025-06-24 10:56:29,150 - pyskl - INFO - Epoch [21][200/1281] lr: 2.390e-02, eta: 11:47:24, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8794, top5_acc: 0.9956, loss_cls: 0.5994, loss: 0.5994 +2025-06-24 10:57:10,661 - pyskl - INFO - Epoch [21][300/1281] lr: 2.389e-02, eta: 11:48:41, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8756, top5_acc: 0.9944, loss_cls: 0.5931, loss: 0.5931 +2025-06-24 10:57:52,193 - pyskl - INFO - Epoch [21][400/1281] lr: 2.389e-02, eta: 11:49:58, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8700, top5_acc: 0.9925, loss_cls: 0.6120, loss: 0.6120 +2025-06-24 10:58:33,809 - pyskl - INFO - Epoch [21][500/1281] lr: 2.388e-02, eta: 11:51:14, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8669, top5_acc: 0.9931, loss_cls: 0.6302, loss: 0.6302 +2025-06-24 10:59:15,322 - pyskl - INFO - Epoch [21][600/1281] lr: 2.387e-02, eta: 11:52:28, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8662, top5_acc: 0.9900, loss_cls: 0.6729, loss: 0.6729 +2025-06-24 10:59:56,802 - pyskl - INFO - Epoch [21][700/1281] lr: 2.386e-02, eta: 11:53:41, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8800, top5_acc: 0.9950, loss_cls: 0.6082, loss: 0.6082 +2025-06-24 11:00:38,335 - pyskl - INFO - Epoch [21][800/1281] lr: 2.385e-02, eta: 11:54:54, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9956, loss_cls: 0.7651, loss: 0.7651 +2025-06-24 11:01:19,803 - pyskl - INFO - Epoch [21][900/1281] lr: 2.384e-02, eta: 11:56:05, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9944, loss_cls: 0.7038, loss: 0.7038 +2025-06-24 11:02:01,323 - pyskl - INFO - Epoch [21][1000/1281] lr: 2.383e-02, eta: 11:57:16, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8700, top5_acc: 0.9925, loss_cls: 0.6220, loss: 0.6220 +2025-06-24 11:02:37,512 - pyskl - INFO - Epoch [21][1100/1281] lr: 2.383e-02, eta: 11:57:53, time: 0.362, data_time: 0.000, memory: 4082, top1_acc: 0.8538, top5_acc: 0.9956, loss_cls: 0.6877, loss: 0.6877 +2025-06-24 11:03:09,867 - pyskl - INFO - Epoch [21][1200/1281] lr: 2.382e-02, eta: 11:58:06, time: 0.324, data_time: 0.000, memory: 4082, top1_acc: 0.8612, top5_acc: 0.9956, loss_cls: 0.6794, loss: 0.6794 +2025-06-24 11:03:45,365 - pyskl - INFO - Saving checkpoint at 21 epochs +2025-06-24 11:04:56,487 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:04:56,542 - pyskl - INFO - +top1_acc 0.8200 +top5_acc 0.9852 +2025-06-24 11:04:56,542 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:04:56,549 - pyskl - INFO - +mean_acc 0.7481 +2025-06-24 11:04:56,551 - pyskl - INFO - Epoch(val) [21][533] top1_acc: 0.8200, top5_acc: 0.9852, mean_class_accuracy: 0.7481 +2025-06-24 11:05:58,034 - pyskl - INFO - Epoch [22][100/1281] lr: 2.380e-02, eta: 11:58:47, time: 0.615, data_time: 0.200, memory: 4082, top1_acc: 0.8825, top5_acc: 0.9975, loss_cls: 0.5826, loss: 0.5826 +2025-06-24 11:06:39,572 - pyskl - INFO - Epoch [22][200/1281] lr: 2.379e-02, eta: 11:59:54, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8594, top5_acc: 0.9956, loss_cls: 0.6424, loss: 0.6424 +2025-06-24 11:07:21,131 - pyskl - INFO - Epoch [22][300/1281] lr: 2.378e-02, eta: 12:01:02, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8681, top5_acc: 0.9931, loss_cls: 0.6767, loss: 0.6767 +2025-06-24 11:08:02,519 - pyskl - INFO - Epoch [22][400/1281] lr: 2.378e-02, eta: 12:02:07, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8794, top5_acc: 0.9950, loss_cls: 0.6421, loss: 0.6421 +2025-06-24 11:08:43,898 - pyskl - INFO - Epoch [22][500/1281] lr: 2.377e-02, eta: 12:03:11, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8525, top5_acc: 0.9944, loss_cls: 0.6682, loss: 0.6682 +2025-06-24 11:09:25,222 - pyskl - INFO - Epoch [22][600/1281] lr: 2.376e-02, eta: 12:04:15, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9931, loss_cls: 0.6908, loss: 0.6908 +2025-06-24 11:10:06,820 - pyskl - INFO - Epoch [22][700/1281] lr: 2.375e-02, eta: 12:05:19, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8644, top5_acc: 0.9919, loss_cls: 0.6489, loss: 0.6489 +2025-06-24 11:10:48,349 - pyskl - INFO - Epoch [22][800/1281] lr: 2.374e-02, eta: 12:06:22, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8538, top5_acc: 0.9962, loss_cls: 0.6621, loss: 0.6621 +2025-06-24 11:11:29,764 - pyskl - INFO - Epoch [22][900/1281] lr: 2.373e-02, eta: 12:07:24, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8562, top5_acc: 0.9956, loss_cls: 0.6635, loss: 0.6635 +2025-06-24 11:12:11,314 - pyskl - INFO - Epoch [22][1000/1281] lr: 2.372e-02, eta: 12:08:25, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8631, top5_acc: 0.9944, loss_cls: 0.6774, loss: 0.6774 +2025-06-24 11:12:47,529 - pyskl - INFO - Epoch [22][1100/1281] lr: 2.371e-02, eta: 12:08:55, time: 0.362, data_time: 0.000, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9950, loss_cls: 0.6966, loss: 0.6966 +2025-06-24 11:13:19,477 - pyskl - INFO - Epoch [22][1200/1281] lr: 2.370e-02, eta: 12:08:59, time: 0.319, data_time: 0.000, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9938, loss_cls: 0.6352, loss: 0.6352 +2025-06-24 11:13:55,341 - pyskl - INFO - Saving checkpoint at 22 epochs +2025-06-24 11:15:05,942 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:15:06,005 - pyskl - INFO - +top1_acc 0.8109 +top5_acc 0.9853 +2025-06-24 11:15:06,005 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:15:06,013 - pyskl - INFO - +mean_acc 0.7364 +2025-06-24 11:15:06,016 - pyskl - INFO - Epoch(val) [22][533] top1_acc: 0.8109, top5_acc: 0.9853, mean_class_accuracy: 0.7364 +2025-06-24 11:16:07,543 - pyskl - INFO - Epoch [23][100/1281] lr: 2.369e-02, eta: 12:09:28, time: 0.615, data_time: 0.199, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9962, loss_cls: 0.6887, loss: 0.6887 +2025-06-24 11:16:49,159 - pyskl - INFO - Epoch [23][200/1281] lr: 2.368e-02, eta: 12:10:27, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8631, top5_acc: 0.9944, loss_cls: 0.6227, loss: 0.6227 +2025-06-24 11:17:30,764 - pyskl - INFO - Epoch [23][300/1281] lr: 2.367e-02, eta: 12:11:26, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8806, top5_acc: 0.9944, loss_cls: 0.5861, loss: 0.5861 +2025-06-24 11:18:12,373 - pyskl - INFO - Epoch [23][400/1281] lr: 2.366e-02, eta: 12:12:24, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8881, top5_acc: 0.9981, loss_cls: 0.5801, loss: 0.5801 +2025-06-24 11:18:53,955 - pyskl - INFO - Epoch [23][500/1281] lr: 2.365e-02, eta: 12:13:21, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8681, top5_acc: 0.9938, loss_cls: 0.6333, loss: 0.6333 +2025-06-24 11:19:35,581 - pyskl - INFO - Epoch [23][600/1281] lr: 2.364e-02, eta: 12:14:17, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9956, loss_cls: 0.6426, loss: 0.6426 +2025-06-24 11:20:17,162 - pyskl - INFO - Epoch [23][700/1281] lr: 2.363e-02, eta: 12:15:13, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8869, top5_acc: 0.9969, loss_cls: 0.5765, loss: 0.5765 +2025-06-24 11:20:58,868 - pyskl - INFO - Epoch [23][800/1281] lr: 2.362e-02, eta: 12:16:09, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8681, top5_acc: 0.9956, loss_cls: 0.6239, loss: 0.6239 +2025-06-24 11:21:40,331 - pyskl - INFO - Epoch [23][900/1281] lr: 2.361e-02, eta: 12:17:02, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8594, top5_acc: 0.9931, loss_cls: 0.6534, loss: 0.6534 +2025-06-24 11:22:22,053 - pyskl - INFO - Epoch [23][1000/1281] lr: 2.360e-02, eta: 12:17:57, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8675, top5_acc: 0.9925, loss_cls: 0.6382, loss: 0.6382 +2025-06-24 11:22:58,590 - pyskl - INFO - Epoch [23][1100/1281] lr: 2.359e-02, eta: 12:18:22, time: 0.365, data_time: 0.000, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9900, loss_cls: 0.7214, loss: 0.7214 +2025-06-24 11:23:30,486 - pyskl - INFO - Epoch [23][1200/1281] lr: 2.359e-02, eta: 12:18:20, time: 0.319, data_time: 0.000, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9919, loss_cls: 0.6779, loss: 0.6779 +2025-06-24 11:24:06,449 - pyskl - INFO - Saving checkpoint at 23 epochs +2025-06-24 11:25:17,355 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:25:17,412 - pyskl - INFO - +top1_acc 0.8278 +top5_acc 0.9858 +2025-06-24 11:25:17,412 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:25:17,419 - pyskl - INFO - +mean_acc 0.7397 +2025-06-24 11:25:17,421 - pyskl - INFO - Epoch(val) [23][533] top1_acc: 0.8278, top5_acc: 0.9858, mean_class_accuracy: 0.7397 +2025-06-24 11:26:19,439 - pyskl - INFO - Epoch [24][100/1281] lr: 2.357e-02, eta: 12:18:41, time: 0.620, data_time: 0.201, memory: 4082, top1_acc: 0.8781, top5_acc: 0.9962, loss_cls: 0.5995, loss: 0.5995 +2025-06-24 11:27:01,042 - pyskl - INFO - Epoch [24][200/1281] lr: 2.356e-02, eta: 12:19:32, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8962, top5_acc: 0.9950, loss_cls: 0.5572, loss: 0.5572 +2025-06-24 11:27:42,666 - pyskl - INFO - Epoch [24][300/1281] lr: 2.355e-02, eta: 12:20:23, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8825, top5_acc: 0.9938, loss_cls: 0.5893, loss: 0.5893 +2025-06-24 11:28:24,279 - pyskl - INFO - Epoch [24][400/1281] lr: 2.354e-02, eta: 12:21:13, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8788, top5_acc: 0.9956, loss_cls: 0.6084, loss: 0.6084 +2025-06-24 11:29:05,826 - pyskl - INFO - Epoch [24][500/1281] lr: 2.353e-02, eta: 12:22:03, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9931, loss_cls: 0.6705, loss: 0.6705 +2025-06-24 11:29:47,517 - pyskl - INFO - Epoch [24][600/1281] lr: 2.352e-02, eta: 12:22:52, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8794, top5_acc: 0.9969, loss_cls: 0.5552, loss: 0.5552 +2025-06-24 11:30:29,219 - pyskl - INFO - Epoch [24][700/1281] lr: 2.351e-02, eta: 12:23:41, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8612, top5_acc: 0.9938, loss_cls: 0.6226, loss: 0.6226 +2025-06-24 11:31:10,816 - pyskl - INFO - Epoch [24][800/1281] lr: 2.350e-02, eta: 12:24:28, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8612, top5_acc: 0.9944, loss_cls: 0.6376, loss: 0.6376 +2025-06-24 11:31:52,467 - pyskl - INFO - Epoch [24][900/1281] lr: 2.349e-02, eta: 12:25:16, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8750, top5_acc: 0.9931, loss_cls: 0.6093, loss: 0.6093 +2025-06-24 11:32:34,162 - pyskl - INFO - Epoch [24][1000/1281] lr: 2.348e-02, eta: 12:26:02, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8731, top5_acc: 0.9944, loss_cls: 0.6093, loss: 0.6093 +2025-06-24 11:33:10,819 - pyskl - INFO - Epoch [24][1100/1281] lr: 2.347e-02, eta: 12:26:22, time: 0.367, data_time: 0.000, memory: 4082, top1_acc: 0.8712, top5_acc: 0.9900, loss_cls: 0.6568, loss: 0.6568 +2025-06-24 11:33:42,741 - pyskl - INFO - Epoch [24][1200/1281] lr: 2.346e-02, eta: 12:26:17, time: 0.319, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9950, loss_cls: 0.6601, loss: 0.6601 +2025-06-24 11:34:18,455 - pyskl - INFO - Saving checkpoint at 24 epochs +2025-06-24 11:35:29,603 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:35:29,669 - pyskl - INFO - +top1_acc 0.8258 +top5_acc 0.9896 +2025-06-24 11:35:29,669 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:35:29,678 - pyskl - INFO - +mean_acc 0.7551 +2025-06-24 11:35:29,681 - pyskl - INFO - Epoch(val) [24][533] top1_acc: 0.8258, top5_acc: 0.9896, mean_class_accuracy: 0.7551 +2025-06-24 11:36:30,629 - pyskl - INFO - Epoch [25][100/1281] lr: 2.344e-02, eta: 12:26:23, time: 0.609, data_time: 0.193, memory: 4082, top1_acc: 0.8669, top5_acc: 0.9969, loss_cls: 0.6137, loss: 0.6137 +2025-06-24 11:37:11,994 - pyskl - INFO - Epoch [25][200/1281] lr: 2.343e-02, eta: 12:27:06, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8794, top5_acc: 0.9912, loss_cls: 0.6034, loss: 0.6034 +2025-06-24 11:37:53,651 - pyskl - INFO - Epoch [25][300/1281] lr: 2.342e-02, eta: 12:27:50, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8781, top5_acc: 0.9969, loss_cls: 0.5871, loss: 0.5871 +2025-06-24 11:38:35,258 - pyskl - INFO - Epoch [25][400/1281] lr: 2.341e-02, eta: 12:28:33, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8725, top5_acc: 0.9969, loss_cls: 0.6190, loss: 0.6190 +2025-06-24 11:39:16,720 - pyskl - INFO - Epoch [25][500/1281] lr: 2.340e-02, eta: 12:29:15, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8456, top5_acc: 0.9894, loss_cls: 0.6985, loss: 0.6985 +2025-06-24 11:39:58,242 - pyskl - INFO - Epoch [25][600/1281] lr: 2.339e-02, eta: 12:29:57, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8525, top5_acc: 0.9919, loss_cls: 0.6666, loss: 0.6666 +2025-06-24 11:40:39,678 - pyskl - INFO - Epoch [25][700/1281] lr: 2.338e-02, eta: 12:30:38, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8675, top5_acc: 0.9938, loss_cls: 0.6225, loss: 0.6225 +2025-06-24 11:41:21,169 - pyskl - INFO - Epoch [25][800/1281] lr: 2.337e-02, eta: 12:31:18, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8825, top5_acc: 0.9975, loss_cls: 0.5771, loss: 0.5771 +2025-06-24 11:42:02,730 - pyskl - INFO - Epoch [25][900/1281] lr: 2.336e-02, eta: 12:31:59, time: 0.416, data_time: 0.001, memory: 4082, top1_acc: 0.8650, top5_acc: 0.9931, loss_cls: 0.6540, loss: 0.6540 +2025-06-24 11:42:44,259 - pyskl - INFO - Epoch [25][1000/1281] lr: 2.335e-02, eta: 12:32:38, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8612, top5_acc: 0.9969, loss_cls: 0.6360, loss: 0.6360 +2025-06-24 11:43:20,449 - pyskl - INFO - Epoch [25][1100/1281] lr: 2.334e-02, eta: 12:32:51, time: 0.362, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9938, loss_cls: 0.6487, loss: 0.6487 +2025-06-24 11:43:51,737 - pyskl - INFO - Epoch [25][1200/1281] lr: 2.333e-02, eta: 12:32:38, time: 0.313, data_time: 0.000, memory: 4082, top1_acc: 0.8744, top5_acc: 0.9938, loss_cls: 0.5948, loss: 0.5948 +2025-06-24 11:44:27,965 - pyskl - INFO - Saving checkpoint at 25 epochs +2025-06-24 11:45:37,596 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:45:37,650 - pyskl - INFO - +top1_acc 0.8102 +top5_acc 0.9874 +2025-06-24 11:45:37,651 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:45:37,657 - pyskl - INFO - +mean_acc 0.7361 +2025-06-24 11:45:37,659 - pyskl - INFO - Epoch(val) [25][533] top1_acc: 0.8102, top5_acc: 0.9874, mean_class_accuracy: 0.7361 +2025-06-24 11:46:40,053 - pyskl - INFO - Epoch [26][100/1281] lr: 2.332e-02, eta: 12:32:44, time: 0.624, data_time: 0.192, memory: 4082, top1_acc: 0.8581, top5_acc: 0.9938, loss_cls: 0.6422, loss: 0.6422 +2025-06-24 11:47:21,659 - pyskl - INFO - Epoch [26][200/1281] lr: 2.330e-02, eta: 12:33:22, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8812, top5_acc: 0.9956, loss_cls: 0.5968, loss: 0.5968 +2025-06-24 11:48:03,351 - pyskl - INFO - Epoch [26][300/1281] lr: 2.329e-02, eta: 12:34:00, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8800, top5_acc: 0.9981, loss_cls: 0.5463, loss: 0.5463 +2025-06-24 11:48:44,942 - pyskl - INFO - Epoch [26][400/1281] lr: 2.328e-02, eta: 12:34:37, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8781, top5_acc: 0.9931, loss_cls: 0.5995, loss: 0.5995 +2025-06-24 11:49:26,444 - pyskl - INFO - Epoch [26][500/1281] lr: 2.327e-02, eta: 12:35:13, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8781, top5_acc: 0.9950, loss_cls: 0.6032, loss: 0.6032 +2025-06-24 11:50:08,028 - pyskl - INFO - Epoch [26][600/1281] lr: 2.326e-02, eta: 12:35:49, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8662, top5_acc: 0.9962, loss_cls: 0.6490, loss: 0.6490 +2025-06-24 11:50:49,654 - pyskl - INFO - Epoch [26][700/1281] lr: 2.325e-02, eta: 12:36:25, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8725, top5_acc: 0.9944, loss_cls: 0.6260, loss: 0.6260 +2025-06-24 11:51:31,149 - pyskl - INFO - Epoch [26][800/1281] lr: 2.324e-02, eta: 12:37:00, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8706, top5_acc: 0.9969, loss_cls: 0.5634, loss: 0.5634 +2025-06-24 11:52:12,613 - pyskl - INFO - Epoch [26][900/1281] lr: 2.323e-02, eta: 12:37:34, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8744, top5_acc: 0.9956, loss_cls: 0.5939, loss: 0.5939 +2025-06-24 11:52:54,210 - pyskl - INFO - Epoch [26][1000/1281] lr: 2.322e-02, eta: 12:38:08, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9912, loss_cls: 0.6556, loss: 0.6556 +2025-06-24 11:53:31,886 - pyskl - INFO - Epoch [26][1100/1281] lr: 2.321e-02, eta: 12:38:23, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8762, top5_acc: 0.9944, loss_cls: 0.6326, loss: 0.6326 +2025-06-24 11:54:02,772 - pyskl - INFO - Epoch [26][1200/1281] lr: 2.320e-02, eta: 12:38:06, time: 0.309, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9919, loss_cls: 0.6556, loss: 0.6556 +2025-06-24 11:54:39,821 - pyskl - INFO - Saving checkpoint at 26 epochs +2025-06-24 11:55:49,587 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:55:49,661 - pyskl - INFO - +top1_acc 0.8274 +top5_acc 0.9870 +2025-06-24 11:55:49,661 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:55:49,671 - pyskl - INFO - +mean_acc 0.7640 +2025-06-24 11:55:49,674 - pyskl - INFO - Epoch(val) [26][533] top1_acc: 0.8274, top5_acc: 0.9870, mean_class_accuracy: 0.7640 +2025-06-24 11:56:50,865 - pyskl - INFO - Epoch [27][100/1281] lr: 2.318e-02, eta: 12:37:58, time: 0.612, data_time: 0.196, memory: 4082, top1_acc: 0.8981, top5_acc: 0.9950, loss_cls: 0.5325, loss: 0.5325 +2025-06-24 11:57:32,301 - pyskl - INFO - Epoch [27][200/1281] lr: 2.317e-02, eta: 12:38:30, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9938, loss_cls: 0.6291, loss: 0.6291 +2025-06-24 11:58:13,887 - pyskl - INFO - Epoch [27][300/1281] lr: 2.316e-02, eta: 12:39:03, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8769, top5_acc: 0.9962, loss_cls: 0.5664, loss: 0.5664 +2025-06-24 11:58:55,440 - pyskl - INFO - Epoch [27][400/1281] lr: 2.315e-02, eta: 12:39:34, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8850, top5_acc: 0.9931, loss_cls: 0.5971, loss: 0.5971 +2025-06-24 11:59:37,015 - pyskl - INFO - Epoch [27][500/1281] lr: 2.314e-02, eta: 12:40:05, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8862, top5_acc: 0.9931, loss_cls: 0.5761, loss: 0.5761 +2025-06-24 12:00:18,504 - pyskl - INFO - Epoch [27][600/1281] lr: 2.313e-02, eta: 12:40:36, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8694, top5_acc: 0.9950, loss_cls: 0.6320, loss: 0.6320 +2025-06-24 12:01:01,218 - pyskl - INFO - Epoch [27][700/1281] lr: 2.312e-02, eta: 12:41:11, time: 0.427, data_time: 0.000, memory: 4082, top1_acc: 0.8819, top5_acc: 0.9962, loss_cls: 0.5785, loss: 0.5785 +2025-06-24 12:01:45,301 - pyskl - INFO - Epoch [27][800/1281] lr: 2.311e-02, eta: 12:41:53, time: 0.441, data_time: 0.000, memory: 4082, top1_acc: 0.8844, top5_acc: 0.9944, loss_cls: 0.5504, loss: 0.5504 +2025-06-24 12:02:27,858 - pyskl - INFO - Epoch [27][900/1281] lr: 2.310e-02, eta: 12:42:27, time: 0.426, data_time: 0.000, memory: 4082, top1_acc: 0.9025, top5_acc: 0.9981, loss_cls: 0.5167, loss: 0.5167 +2025-06-24 12:03:09,417 - pyskl - INFO - Epoch [27][1000/1281] lr: 2.308e-02, eta: 12:42:56, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8869, top5_acc: 0.9950, loss_cls: 0.5635, loss: 0.5635 +2025-06-24 12:03:47,871 - pyskl - INFO - Epoch [27][1100/1281] lr: 2.307e-02, eta: 12:43:10, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8656, top5_acc: 0.9956, loss_cls: 0.6301, loss: 0.6301 +2025-06-24 12:04:16,821 - pyskl - INFO - Epoch [27][1200/1281] lr: 2.306e-02, eta: 12:42:41, time: 0.289, data_time: 0.000, memory: 4082, top1_acc: 0.8538, top5_acc: 0.9894, loss_cls: 0.7149, loss: 0.7149 +2025-06-24 12:04:54,428 - pyskl - INFO - Saving checkpoint at 27 epochs +2025-06-24 12:06:02,480 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:06:02,548 - pyskl - INFO - +top1_acc 0.8600 +top5_acc 0.9908 +2025-06-24 12:06:02,548 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:06:02,556 - pyskl - INFO - +mean_acc 0.7940 +2025-06-24 12:06:02,560 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_20.pth was removed +2025-06-24 12:06:02,760 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_27.pth. +2025-06-24 12:06:02,761 - pyskl - INFO - Best top1_acc is 0.8600 at 27 epoch. +2025-06-24 12:06:02,765 - pyskl - INFO - Epoch(val) [27][533] top1_acc: 0.8600, top5_acc: 0.9908, mean_class_accuracy: 0.7940 +2025-06-24 12:07:04,604 - pyskl - INFO - Epoch [28][100/1281] lr: 2.304e-02, eta: 12:42:31, time: 0.618, data_time: 0.200, memory: 4082, top1_acc: 0.8850, top5_acc: 0.9969, loss_cls: 0.5414, loss: 0.5414 +2025-06-24 12:07:46,106 - pyskl - INFO - Epoch [28][200/1281] lr: 2.303e-02, eta: 12:42:58, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8719, top5_acc: 0.9931, loss_cls: 0.5896, loss: 0.5896 +2025-06-24 12:08:27,744 - pyskl - INFO - Epoch [28][300/1281] lr: 2.302e-02, eta: 12:43:25, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8994, top5_acc: 0.9962, loss_cls: 0.5244, loss: 0.5244 +2025-06-24 12:09:09,353 - pyskl - INFO - Epoch [28][400/1281] lr: 2.301e-02, eta: 12:43:52, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8700, top5_acc: 0.9944, loss_cls: 0.5908, loss: 0.5908 +2025-06-24 12:09:50,941 - pyskl - INFO - Epoch [28][500/1281] lr: 2.300e-02, eta: 12:44:19, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8719, top5_acc: 0.9994, loss_cls: 0.5589, loss: 0.5589 +2025-06-24 12:10:32,322 - pyskl - INFO - Epoch [28][600/1281] lr: 2.299e-02, eta: 12:44:44, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8775, top5_acc: 0.9962, loss_cls: 0.5491, loss: 0.5491 +2025-06-24 12:11:13,800 - pyskl - INFO - Epoch [28][700/1281] lr: 2.298e-02, eta: 12:45:09, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8669, top5_acc: 0.9950, loss_cls: 0.6225, loss: 0.6225 +2025-06-24 12:11:55,376 - pyskl - INFO - Epoch [28][800/1281] lr: 2.297e-02, eta: 12:45:34, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8819, top5_acc: 0.9956, loss_cls: 0.5777, loss: 0.5777 +2025-06-24 12:12:36,943 - pyskl - INFO - Epoch [28][900/1281] lr: 2.295e-02, eta: 12:45:59, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8769, top5_acc: 0.9931, loss_cls: 0.5650, loss: 0.5650 +2025-06-24 12:13:18,594 - pyskl - INFO - Epoch [28][1000/1281] lr: 2.294e-02, eta: 12:46:24, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8838, top5_acc: 0.9938, loss_cls: 0.5514, loss: 0.5514 +2025-06-24 12:13:57,360 - pyskl - INFO - Epoch [28][1100/1281] lr: 2.293e-02, eta: 12:46:36, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.8669, top5_acc: 0.9919, loss_cls: 0.6404, loss: 0.6404 +2025-06-24 12:14:26,910 - pyskl - INFO - Epoch [28][1200/1281] lr: 2.292e-02, eta: 12:46:07, time: 0.295, data_time: 0.000, memory: 4082, top1_acc: 0.8819, top5_acc: 0.9894, loss_cls: 0.6060, loss: 0.6060 +2025-06-24 12:15:04,383 - pyskl - INFO - Saving checkpoint at 28 epochs +2025-06-24 12:16:12,904 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:16:12,964 - pyskl - INFO - +top1_acc 0.8222 +top5_acc 0.9877 +2025-06-24 12:16:12,964 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:16:12,972 - pyskl - INFO - +mean_acc 0.7782 +2025-06-24 12:16:12,974 - pyskl - INFO - Epoch(val) [28][533] top1_acc: 0.8222, top5_acc: 0.9877, mean_class_accuracy: 0.7782 +2025-06-24 12:17:14,475 - pyskl - INFO - Epoch [29][100/1281] lr: 2.290e-02, eta: 12:45:50, time: 0.615, data_time: 0.200, memory: 4082, top1_acc: 0.8756, top5_acc: 0.9962, loss_cls: 0.5851, loss: 0.5851 +2025-06-24 12:17:56,449 - pyskl - INFO - Epoch [29][200/1281] lr: 2.289e-02, eta: 12:46:15, time: 0.420, data_time: 0.000, memory: 4082, top1_acc: 0.8806, top5_acc: 0.9962, loss_cls: 0.5841, loss: 0.5841 +2025-06-24 12:18:40,502 - pyskl - INFO - Epoch [29][300/1281] lr: 2.288e-02, eta: 12:46:48, time: 0.441, data_time: 0.000, memory: 4082, top1_acc: 0.8762, top5_acc: 0.9962, loss_cls: 0.5535, loss: 0.5535 +2025-06-24 12:19:24,238 - pyskl - INFO - Epoch [29][400/1281] lr: 2.287e-02, eta: 12:47:20, time: 0.437, data_time: 0.000, memory: 4082, top1_acc: 0.8775, top5_acc: 0.9906, loss_cls: 0.6168, loss: 0.6168 +2025-06-24 12:20:07,763 - pyskl - INFO - Epoch [29][500/1281] lr: 2.285e-02, eta: 12:47:50, time: 0.435, data_time: 0.000, memory: 4082, top1_acc: 0.8744, top5_acc: 0.9925, loss_cls: 0.5657, loss: 0.5657 +2025-06-24 12:20:50,056 - pyskl - INFO - Epoch [29][600/1281] lr: 2.284e-02, eta: 12:48:15, time: 0.423, data_time: 0.000, memory: 4082, top1_acc: 0.8812, top5_acc: 0.9919, loss_cls: 0.5541, loss: 0.5541 +2025-06-24 12:21:31,476 - pyskl - INFO - Epoch [29][700/1281] lr: 2.283e-02, eta: 12:48:36, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8619, top5_acc: 0.9931, loss_cls: 0.6224, loss: 0.6224 +2025-06-24 12:22:13,044 - pyskl - INFO - Epoch [29][800/1281] lr: 2.282e-02, eta: 12:48:57, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8694, top5_acc: 0.9944, loss_cls: 0.6251, loss: 0.6251 +2025-06-24 12:22:54,509 - pyskl - INFO - Epoch [29][900/1281] lr: 2.281e-02, eta: 12:49:17, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8662, top5_acc: 0.9956, loss_cls: 0.6102, loss: 0.6102 +2025-06-24 12:23:36,042 - pyskl - INFO - Epoch [29][1000/1281] lr: 2.280e-02, eta: 12:49:37, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8794, top5_acc: 0.9956, loss_cls: 0.5701, loss: 0.5701 +2025-06-24 12:24:14,337 - pyskl - INFO - Epoch [29][1100/1281] lr: 2.279e-02, eta: 12:49:43, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8769, top5_acc: 0.9969, loss_cls: 0.5696, loss: 0.5696 +2025-06-24 12:24:43,725 - pyskl - INFO - Epoch [29][1200/1281] lr: 2.277e-02, eta: 12:49:12, time: 0.294, data_time: 0.000, memory: 4082, top1_acc: 0.8581, top5_acc: 0.9988, loss_cls: 0.6031, loss: 0.6031 +2025-06-24 12:25:21,355 - pyskl - INFO - Saving checkpoint at 29 epochs +2025-06-24 12:26:30,513 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:26:30,569 - pyskl - INFO - +top1_acc 0.8265 +top5_acc 0.9885 +2025-06-24 12:26:30,569 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:26:30,576 - pyskl - INFO - +mean_acc 0.7846 +2025-06-24 12:26:30,579 - pyskl - INFO - Epoch(val) [29][533] top1_acc: 0.8265, top5_acc: 0.9885, mean_class_accuracy: 0.7846 +2025-06-24 12:27:41,299 - pyskl - INFO - Epoch [30][100/1281] lr: 2.275e-02, eta: 12:49:28, time: 0.707, data_time: 0.197, memory: 4082, top1_acc: 0.8800, top5_acc: 0.9956, loss_cls: 0.6062, loss: 0.6062 +2025-06-24 12:28:31,459 - pyskl - INFO - Epoch [30][200/1281] lr: 2.274e-02, eta: 12:50:22, time: 0.502, data_time: 0.000, memory: 4082, top1_acc: 0.8719, top5_acc: 0.9938, loss_cls: 0.5864, loss: 0.5864 +2025-06-24 12:29:22,902 - pyskl - INFO - Epoch [30][300/1281] lr: 2.273e-02, eta: 12:51:22, time: 0.514, data_time: 0.000, memory: 4082, top1_acc: 0.8856, top5_acc: 0.9956, loss_cls: 0.5328, loss: 0.5328 +2025-06-24 12:30:14,119 - pyskl - INFO - Epoch [30][400/1281] lr: 2.272e-02, eta: 12:52:19, time: 0.512, data_time: 0.000, memory: 4082, top1_acc: 0.8794, top5_acc: 0.9988, loss_cls: 0.5581, loss: 0.5581 +2025-06-24 12:31:05,887 - pyskl - INFO - Epoch [30][500/1281] lr: 2.271e-02, eta: 12:53:19, time: 0.518, data_time: 0.000, memory: 4082, top1_acc: 0.9006, top5_acc: 0.9975, loss_cls: 0.5129, loss: 0.5129 +2025-06-24 12:31:56,913 - pyskl - INFO - Epoch [30][600/1281] lr: 2.269e-02, eta: 12:54:15, time: 0.510, data_time: 0.000, memory: 4082, top1_acc: 0.8875, top5_acc: 0.9950, loss_cls: 0.5616, loss: 0.5616 +2025-06-24 12:32:48,147 - pyskl - INFO - Epoch [30][700/1281] lr: 2.268e-02, eta: 12:55:11, time: 0.512, data_time: 0.000, memory: 4082, top1_acc: 0.8719, top5_acc: 0.9925, loss_cls: 0.6187, loss: 0.6187 +2025-06-24 12:33:40,278 - pyskl - INFO - Epoch [30][800/1281] lr: 2.267e-02, eta: 12:56:10, time: 0.521, data_time: 0.000, memory: 4082, top1_acc: 0.8831, top5_acc: 0.9962, loss_cls: 0.5696, loss: 0.5696 +2025-06-24 12:34:09,643 - pyskl - INFO - Epoch [30][900/1281] lr: 2.266e-02, eta: 12:55:36, time: 0.294, data_time: 0.000, memory: 4082, top1_acc: 0.8769, top5_acc: 0.9912, loss_cls: 0.5895, loss: 0.5895 +2025-06-24 12:35:00,763 - pyskl - INFO - Epoch [30][1000/1281] lr: 2.265e-02, eta: 12:56:31, time: 0.511, data_time: 0.000, memory: 4082, top1_acc: 0.8681, top5_acc: 0.9938, loss_cls: 0.6255, loss: 0.6255 +2025-06-24 12:35:35,810 - pyskl - INFO - Epoch [30][1100/1281] lr: 2.263e-02, eta: 12:56:20, time: 0.350, data_time: 0.001, memory: 4082, top1_acc: 0.8631, top5_acc: 0.9944, loss_cls: 0.6604, loss: 0.6604 +2025-06-24 12:36:26,666 - pyskl - INFO - Epoch [30][1200/1281] lr: 2.262e-02, eta: 12:57:12, time: 0.509, data_time: 0.000, memory: 4082, top1_acc: 0.8706, top5_acc: 0.9962, loss_cls: 0.5872, loss: 0.5872 +2025-06-24 12:37:08,791 - pyskl - INFO - Saving checkpoint at 30 epochs +2025-06-24 12:38:20,716 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:38:20,778 - pyskl - INFO - +top1_acc 0.8437 +top5_acc 0.9876 +2025-06-24 12:38:20,778 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:38:20,785 - pyskl - INFO - +mean_acc 0.7782 +2025-06-24 12:38:20,788 - pyskl - INFO - Epoch(val) [30][533] top1_acc: 0.8437, top5_acc: 0.9876, mean_class_accuracy: 0.7782 +2025-06-24 12:39:56,570 - pyskl - INFO - Epoch [31][100/1281] lr: 2.260e-02, eta: 12:59:00, time: 0.958, data_time: 0.202, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9975, loss_cls: 0.6818, loss: 0.6818 +2025-06-24 12:40:49,699 - pyskl - INFO - Epoch [31][200/1281] lr: 2.259e-02, eta: 13:00:00, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9956, loss_cls: 0.7064, loss: 0.7064 +2025-06-24 12:41:43,738 - pyskl - INFO - Epoch [31][300/1281] lr: 2.258e-02, eta: 13:01:03, time: 0.540, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9969, loss_cls: 0.7667, loss: 0.7667 +2025-06-24 12:42:36,820 - pyskl - INFO - Epoch [31][400/1281] lr: 2.256e-02, eta: 13:02:01, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9944, loss_cls: 0.6881, loss: 0.6881 +2025-06-24 12:43:08,973 - pyskl - INFO - Epoch [31][500/1281] lr: 2.255e-02, eta: 13:01:37, time: 0.322, data_time: 0.001, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9962, loss_cls: 0.6796, loss: 0.6796 +2025-06-24 12:44:00,388 - pyskl - INFO - Epoch [31][600/1281] lr: 2.254e-02, eta: 13:02:28, time: 0.514, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9969, loss_cls: 0.7064, loss: 0.7064 +2025-06-24 12:44:36,444 - pyskl - INFO - Epoch [31][700/1281] lr: 2.253e-02, eta: 13:02:18, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9956, loss_cls: 0.7351, loss: 0.7351 +2025-06-24 12:45:29,720 - pyskl - INFO - Epoch [31][800/1281] lr: 2.252e-02, eta: 13:03:16, time: 0.533, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9938, loss_cls: 0.7337, loss: 0.7337 +2025-06-24 12:46:22,925 - pyskl - INFO - Epoch [31][900/1281] lr: 2.250e-02, eta: 13:04:12, time: 0.532, data_time: 0.000, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9919, loss_cls: 0.8392, loss: 0.8392 +2025-06-24 12:47:16,439 - pyskl - INFO - Epoch [31][1000/1281] lr: 2.249e-02, eta: 13:05:09, time: 0.535, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9931, loss_cls: 0.8112, loss: 0.8112 +2025-06-24 12:48:10,241 - pyskl - INFO - Epoch [31][1100/1281] lr: 2.248e-02, eta: 13:06:07, time: 0.538, data_time: 0.001, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9944, loss_cls: 0.7345, loss: 0.7345 +2025-06-24 12:49:03,966 - pyskl - INFO - Epoch [31][1200/1281] lr: 2.247e-02, eta: 13:07:04, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9944, loss_cls: 0.7182, loss: 0.7182 +2025-06-24 12:49:48,644 - pyskl - INFO - Saving checkpoint at 31 epochs +2025-06-24 12:51:01,133 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:51:01,191 - pyskl - INFO - +top1_acc 0.8464 +top5_acc 0.9905 +2025-06-24 12:51:01,191 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:51:01,199 - pyskl - INFO - +mean_acc 0.7776 +2025-06-24 12:51:01,202 - pyskl - INFO - Epoch(val) [31][533] top1_acc: 0.8464, top5_acc: 0.9905, mean_class_accuracy: 0.7776 +2025-06-24 12:52:08,053 - pyskl - INFO - Epoch [32][100/1281] lr: 2.244e-02, eta: 13:06:49, time: 0.668, data_time: 0.193, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9931, loss_cls: 0.6431, loss: 0.6431 +2025-06-24 12:52:58,634 - pyskl - INFO - Epoch [32][200/1281] lr: 2.243e-02, eta: 13:07:33, time: 0.506, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9981, loss_cls: 0.6661, loss: 0.6661 +2025-06-24 12:53:37,798 - pyskl - INFO - Epoch [32][300/1281] lr: 2.242e-02, eta: 13:07:33, time: 0.392, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9962, loss_cls: 0.7019, loss: 0.7019 +2025-06-24 12:54:30,794 - pyskl - INFO - Epoch [32][400/1281] lr: 2.241e-02, eta: 13:08:25, time: 0.530, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9969, loss_cls: 0.6521, loss: 0.6521 +2025-06-24 12:55:24,498 - pyskl - INFO - Epoch [32][500/1281] lr: 2.239e-02, eta: 13:09:19, time: 0.537, data_time: 0.001, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9925, loss_cls: 0.6609, loss: 0.6609 +2025-06-24 12:56:18,471 - pyskl - INFO - Epoch [32][600/1281] lr: 2.238e-02, eta: 13:10:14, time: 0.540, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9950, loss_cls: 0.6209, loss: 0.6209 +2025-06-24 12:57:12,647 - pyskl - INFO - Epoch [32][700/1281] lr: 2.237e-02, eta: 13:11:09, time: 0.542, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9925, loss_cls: 0.6782, loss: 0.6782 +2025-06-24 12:58:07,082 - pyskl - INFO - Epoch [32][800/1281] lr: 2.236e-02, eta: 13:12:04, time: 0.544, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9944, loss_cls: 0.6603, loss: 0.6603 +2025-06-24 12:59:01,014 - pyskl - INFO - Epoch [32][900/1281] lr: 2.234e-02, eta: 13:12:57, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9969, loss_cls: 0.6177, loss: 0.6177 +2025-06-24 12:59:54,254 - pyskl - INFO - Epoch [32][1000/1281] lr: 2.233e-02, eta: 13:13:47, time: 0.532, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9931, loss_cls: 0.6925, loss: 0.6925 +2025-06-24 13:00:49,241 - pyskl - INFO - Epoch [32][1100/1281] lr: 2.232e-02, eta: 13:14:43, time: 0.550, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9950, loss_cls: 0.6970, loss: 0.6970 +2025-06-24 13:01:24,934 - pyskl - INFO - Epoch [32][1200/1281] lr: 2.231e-02, eta: 13:14:27, time: 0.357, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9956, loss_cls: 0.7044, loss: 0.7044 +2025-06-24 13:02:12,681 - pyskl - INFO - Saving checkpoint at 32 epochs +2025-06-24 13:03:24,135 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:03:24,192 - pyskl - INFO - +top1_acc 0.8343 +top5_acc 0.9917 +2025-06-24 13:03:24,192 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:03:24,199 - pyskl - INFO - +mean_acc 0.7775 +2025-06-24 13:03:24,201 - pyskl - INFO - Epoch(val) [32][533] top1_acc: 0.8343, top5_acc: 0.9917, mean_class_accuracy: 0.7775 +2025-06-24 13:04:55,074 - pyskl - INFO - Epoch [33][100/1281] lr: 2.228e-02, eta: 13:15:34, time: 0.909, data_time: 0.193, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9962, loss_cls: 0.5908, loss: 0.5908 +2025-06-24 13:05:49,390 - pyskl - INFO - Epoch [33][200/1281] lr: 2.227e-02, eta: 13:16:26, time: 0.543, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9981, loss_cls: 0.6222, loss: 0.6222 +2025-06-24 13:06:43,113 - pyskl - INFO - Epoch [33][300/1281] lr: 2.226e-02, eta: 13:17:15, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9956, loss_cls: 0.6454, loss: 0.6454 +2025-06-24 13:07:37,718 - pyskl - INFO - Epoch [33][400/1281] lr: 2.225e-02, eta: 13:18:07, time: 0.546, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9944, loss_cls: 0.6950, loss: 0.6950 +2025-06-24 13:08:31,812 - pyskl - INFO - Epoch [33][500/1281] lr: 2.223e-02, eta: 13:18:56, time: 0.541, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9956, loss_cls: 0.5945, loss: 0.5945 +2025-06-24 13:09:26,312 - pyskl - INFO - Epoch [33][600/1281] lr: 2.222e-02, eta: 13:19:46, time: 0.545, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9956, loss_cls: 0.6530, loss: 0.6530 +2025-06-24 13:10:19,242 - pyskl - INFO - Epoch [33][700/1281] lr: 2.221e-02, eta: 13:20:30, time: 0.529, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9962, loss_cls: 0.6162, loss: 0.6162 +2025-06-24 13:10:52,964 - pyskl - INFO - Epoch [33][800/1281] lr: 2.219e-02, eta: 13:20:05, time: 0.337, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9975, loss_cls: 0.5917, loss: 0.5917 +2025-06-24 13:11:33,293 - pyskl - INFO - Epoch [33][900/1281] lr: 2.218e-02, eta: 13:20:03, time: 0.403, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9931, loss_cls: 0.5954, loss: 0.5954 +2025-06-24 13:12:20,332 - pyskl - INFO - Epoch [33][1000/1281] lr: 2.217e-02, eta: 13:20:25, time: 0.470, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9962, loss_cls: 0.5538, loss: 0.5538 +2025-06-24 13:13:15,088 - pyskl - INFO - Epoch [33][1100/1281] lr: 2.216e-02, eta: 13:21:14, time: 0.548, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9969, loss_cls: 0.6590, loss: 0.6590 +2025-06-24 13:14:08,764 - pyskl - INFO - Epoch [33][1200/1281] lr: 2.214e-02, eta: 13:21:59, time: 0.537, data_time: 0.001, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9956, loss_cls: 0.6068, loss: 0.6068 +2025-06-24 13:14:53,077 - pyskl - INFO - Saving checkpoint at 33 epochs +2025-06-24 13:16:05,180 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:16:05,235 - pyskl - INFO - +top1_acc 0.8533 +top5_acc 0.9923 +2025-06-24 13:16:05,236 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:16:05,243 - pyskl - INFO - +mean_acc 0.7888 +2025-06-24 13:16:05,245 - pyskl - INFO - Epoch(val) [33][533] top1_acc: 0.8533, top5_acc: 0.9923, mean_class_accuracy: 0.7888 +2025-06-24 13:17:32,415 - pyskl - INFO - Epoch [34][100/1281] lr: 2.212e-02, eta: 13:22:44, time: 0.872, data_time: 0.202, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9925, loss_cls: 0.6241, loss: 0.6241 +2025-06-24 13:18:25,706 - pyskl - INFO - Epoch [34][200/1281] lr: 2.211e-02, eta: 13:23:26, time: 0.533, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9975, loss_cls: 0.6073, loss: 0.6073 +2025-06-24 13:19:17,240 - pyskl - INFO - Epoch [34][300/1281] lr: 2.209e-02, eta: 13:24:02, time: 0.515, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9956, loss_cls: 0.6147, loss: 0.6147 +2025-06-24 13:19:52,567 - pyskl - INFO - Epoch [34][400/1281] lr: 2.208e-02, eta: 13:23:40, time: 0.353, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9962, loss_cls: 0.6094, loss: 0.6094 +2025-06-24 13:20:31,186 - pyskl - INFO - Epoch [34][500/1281] lr: 2.207e-02, eta: 13:23:30, time: 0.386, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9962, loss_cls: 0.6726, loss: 0.6726 +2025-06-24 13:21:17,879 - pyskl - INFO - Epoch [34][600/1281] lr: 2.205e-02, eta: 13:23:48, time: 0.467, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9969, loss_cls: 0.6165, loss: 0.6165 +2025-06-24 13:22:09,290 - pyskl - INFO - Epoch [34][700/1281] lr: 2.204e-02, eta: 13:24:22, time: 0.514, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9956, loss_cls: 0.5973, loss: 0.5973 +2025-06-24 13:23:03,434 - pyskl - INFO - Epoch [34][800/1281] lr: 2.203e-02, eta: 13:25:05, time: 0.541, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9944, loss_cls: 0.6105, loss: 0.6105 +2025-06-24 13:23:57,347 - pyskl - INFO - Epoch [34][900/1281] lr: 2.201e-02, eta: 13:25:47, time: 0.539, data_time: 0.001, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9962, loss_cls: 0.5803, loss: 0.5803 +2025-06-24 13:24:50,188 - pyskl - INFO - Epoch [34][1000/1281] lr: 2.200e-02, eta: 13:26:25, time: 0.528, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9975, loss_cls: 0.5929, loss: 0.5929 +2025-06-24 13:25:44,350 - pyskl - INFO - Epoch [34][1100/1281] lr: 2.199e-02, eta: 13:27:07, time: 0.542, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9919, loss_cls: 0.6086, loss: 0.6086 +2025-06-24 13:26:38,245 - pyskl - INFO - Epoch [34][1200/1281] lr: 2.197e-02, eta: 13:27:47, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9969, loss_cls: 0.6019, loss: 0.6019 +2025-06-24 13:27:22,400 - pyskl - INFO - Saving checkpoint at 34 epochs +2025-06-24 13:28:34,184 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:28:34,241 - pyskl - INFO - +top1_acc 0.8305 +top5_acc 0.9874 +2025-06-24 13:28:34,241 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:28:34,248 - pyskl - INFO - +mean_acc 0.7680 +2025-06-24 13:28:34,251 - pyskl - INFO - Epoch(val) [34][533] top1_acc: 0.8305, top5_acc: 0.9874, mean_class_accuracy: 0.7680 +2025-06-24 13:29:50,125 - pyskl - INFO - Epoch [35][100/1281] lr: 2.195e-02, eta: 13:27:45, time: 0.759, data_time: 0.195, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9975, loss_cls: 0.6036, loss: 0.6036 +2025-06-24 13:30:43,613 - pyskl - INFO - Epoch [35][200/1281] lr: 2.194e-02, eta: 13:28:23, time: 0.535, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9969, loss_cls: 0.5932, loss: 0.5932 +2025-06-24 13:31:37,475 - pyskl - INFO - Epoch [35][300/1281] lr: 2.192e-02, eta: 13:29:02, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9950, loss_cls: 0.5638, loss: 0.5638 +2025-06-24 13:32:31,483 - pyskl - INFO - Epoch [35][400/1281] lr: 2.191e-02, eta: 13:29:41, time: 0.540, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9956, loss_cls: 0.5385, loss: 0.5385 +2025-06-24 13:33:25,386 - pyskl - INFO - Epoch [35][500/1281] lr: 2.190e-02, eta: 13:30:20, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9938, loss_cls: 0.5945, loss: 0.5945 +2025-06-24 13:34:20,151 - pyskl - INFO - Epoch [35][600/1281] lr: 2.188e-02, eta: 13:31:00, time: 0.548, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9981, loss_cls: 0.5363, loss: 0.5363 +2025-06-24 13:35:14,523 - pyskl - INFO - Epoch [35][700/1281] lr: 2.187e-02, eta: 13:31:39, time: 0.544, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9969, loss_cls: 0.6256, loss: 0.6256 +2025-06-24 13:36:08,304 - pyskl - INFO - Epoch [35][800/1281] lr: 2.185e-02, eta: 13:32:16, time: 0.538, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9950, loss_cls: 0.5486, loss: 0.5486 +2025-06-24 13:37:02,806 - pyskl - INFO - Epoch [35][900/1281] lr: 2.184e-02, eta: 13:32:54, time: 0.545, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9944, loss_cls: 0.5755, loss: 0.5755 +2025-06-24 13:37:50,747 - pyskl - INFO - Epoch [35][1000/1281] lr: 2.183e-02, eta: 13:33:11, time: 0.479, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9969, loss_cls: 0.5739, loss: 0.5739 +2025-06-24 13:38:33,050 - pyskl - INFO - Epoch [35][1100/1281] lr: 2.181e-02, eta: 13:33:08, time: 0.423, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9956, loss_cls: 0.5776, loss: 0.5776 +2025-06-24 13:39:04,732 - pyskl - INFO - Epoch [35][1200/1281] lr: 2.180e-02, eta: 13:32:30, time: 0.317, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9950, loss_cls: 0.5944, loss: 0.5944 +2025-06-24 13:39:45,290 - pyskl - INFO - Saving checkpoint at 35 epochs +2025-06-24 13:40:57,044 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:40:57,120 - pyskl - INFO - +top1_acc 0.8484 +top5_acc 0.9880 +2025-06-24 13:40:57,121 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:40:57,133 - pyskl - INFO - +mean_acc 0.8036 +2025-06-24 13:40:57,138 - pyskl - INFO - Epoch(val) [35][533] top1_acc: 0.8484, top5_acc: 0.9880, mean_class_accuracy: 0.8036 +2025-06-24 13:42:24,204 - pyskl - INFO - Epoch [36][100/1281] lr: 2.178e-02, eta: 13:33:00, time: 0.871, data_time: 0.204, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9919, loss_cls: 0.6076, loss: 0.6076 +2025-06-24 13:43:18,090 - pyskl - INFO - Epoch [36][200/1281] lr: 2.176e-02, eta: 13:33:34, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9938, loss_cls: 0.6151, loss: 0.6151 +2025-06-24 13:44:11,944 - pyskl - INFO - Epoch [36][300/1281] lr: 2.175e-02, eta: 13:34:09, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9969, loss_cls: 0.5487, loss: 0.5487 +2025-06-24 13:45:07,017 - pyskl - INFO - Epoch [36][400/1281] lr: 2.173e-02, eta: 13:34:46, time: 0.551, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9981, loss_cls: 0.5640, loss: 0.5640 +2025-06-24 13:46:00,596 - pyskl - INFO - Epoch [36][500/1281] lr: 2.172e-02, eta: 13:35:19, time: 0.536, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9944, loss_cls: 0.5691, loss: 0.5691 +2025-06-24 13:46:48,183 - pyskl - INFO - Epoch [36][600/1281] lr: 2.171e-02, eta: 13:35:31, time: 0.476, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9988, loss_cls: 0.5675, loss: 0.5675 +2025-06-24 13:47:30,158 - pyskl - INFO - Epoch [36][700/1281] lr: 2.169e-02, eta: 13:35:26, time: 0.420, data_time: 0.001, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9950, loss_cls: 0.6175, loss: 0.6175 +2025-06-24 13:48:02,210 - pyskl - INFO - Epoch [36][800/1281] lr: 2.168e-02, eta: 13:34:48, time: 0.321, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9975, loss_cls: 0.5445, loss: 0.5445 +2025-06-24 13:48:52,173 - pyskl - INFO - Epoch [36][900/1281] lr: 2.167e-02, eta: 13:35:08, time: 0.500, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9981, loss_cls: 0.5686, loss: 0.5686 +2025-06-24 13:49:47,223 - pyskl - INFO - Epoch [36][1000/1281] lr: 2.165e-02, eta: 13:35:44, time: 0.550, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9988, loss_cls: 0.5394, loss: 0.5394 +2025-06-24 13:50:40,342 - pyskl - INFO - Epoch [36][1100/1281] lr: 2.164e-02, eta: 13:36:13, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9975, loss_cls: 0.5550, loss: 0.5550 +2025-06-24 13:51:35,641 - pyskl - INFO - Epoch [36][1200/1281] lr: 2.162e-02, eta: 13:36:49, time: 0.553, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9969, loss_cls: 0.5514, loss: 0.5514 +2025-06-24 13:52:20,264 - pyskl - INFO - Saving checkpoint at 36 epochs +2025-06-24 13:53:31,500 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:53:31,556 - pyskl - INFO - +top1_acc 0.8667 +top5_acc 0.9903 +2025-06-24 13:53:31,557 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:53:31,563 - pyskl - INFO - +mean_acc 0.8060 +2025-06-24 13:53:31,568 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_27.pth was removed +2025-06-24 13:53:31,755 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_36.pth. +2025-06-24 13:53:31,756 - pyskl - INFO - Best top1_acc is 0.8667 at 36 epoch. +2025-06-24 13:53:31,758 - pyskl - INFO - Epoch(val) [36][533] top1_acc: 0.8667, top5_acc: 0.9903, mean_class_accuracy: 0.8060 +2025-06-24 13:55:01,182 - pyskl - INFO - Epoch [37][100/1281] lr: 2.160e-02, eta: 13:37:18, time: 0.894, data_time: 0.200, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9969, loss_cls: 0.5344, loss: 0.5344 +2025-06-24 13:55:46,113 - pyskl - INFO - Epoch [37][200/1281] lr: 2.158e-02, eta: 13:37:21, time: 0.449, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9956, loss_cls: 0.5669, loss: 0.5669 +2025-06-24 13:56:33,736 - pyskl - INFO - Epoch [37][300/1281] lr: 2.157e-02, eta: 13:37:31, time: 0.476, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9981, loss_cls: 0.5786, loss: 0.5786 +2025-06-24 13:56:59,924 - pyskl - INFO - Epoch [37][400/1281] lr: 2.156e-02, eta: 13:36:34, time: 0.262, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9962, loss_cls: 0.5548, loss: 0.5548 +2025-06-24 13:57:53,016 - pyskl - INFO - Epoch [37][500/1281] lr: 2.154e-02, eta: 13:37:01, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9969, loss_cls: 0.5369, loss: 0.5369 +2025-06-24 13:58:46,412 - pyskl - INFO - Epoch [37][600/1281] lr: 2.153e-02, eta: 13:37:29, time: 0.534, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9944, loss_cls: 0.5533, loss: 0.5533 +2025-06-24 13:59:39,850 - pyskl - INFO - Epoch [37][700/1281] lr: 2.151e-02, eta: 13:37:56, time: 0.534, data_time: 0.001, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9988, loss_cls: 0.5512, loss: 0.5512 +2025-06-24 14:00:33,317 - pyskl - INFO - Epoch [37][800/1281] lr: 2.150e-02, eta: 13:38:23, time: 0.535, data_time: 0.001, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9962, loss_cls: 0.5555, loss: 0.5555 +2025-06-24 14:01:27,234 - pyskl - INFO - Epoch [37][900/1281] lr: 2.149e-02, eta: 13:38:51, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9944, loss_cls: 0.5849, loss: 0.5849 +2025-06-24 14:02:21,819 - pyskl - INFO - Epoch [37][1000/1281] lr: 2.147e-02, eta: 13:39:21, time: 0.546, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9956, loss_cls: 0.6375, loss: 0.6375 +2025-06-24 14:03:16,163 - pyskl - INFO - Epoch [37][1100/1281] lr: 2.146e-02, eta: 13:39:50, time: 0.543, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9944, loss_cls: 0.6325, loss: 0.6325 +2025-06-24 14:04:09,219 - pyskl - INFO - Epoch [37][1200/1281] lr: 2.144e-02, eta: 13:40:15, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9938, loss_cls: 0.6352, loss: 0.6352 +2025-06-24 14:04:53,561 - pyskl - INFO - Saving checkpoint at 37 epochs +2025-06-24 14:06:01,882 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:06:01,939 - pyskl - INFO - +top1_acc 0.8518 +top5_acc 0.9917 +2025-06-24 14:06:01,939 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:06:01,946 - pyskl - INFO - +mean_acc 0.7943 +2025-06-24 14:06:01,949 - pyskl - INFO - Epoch(val) [37][533] top1_acc: 0.8518, top5_acc: 0.9917, mean_class_accuracy: 0.7943 +2025-06-24 14:07:09,805 - pyskl - INFO - Epoch [38][100/1281] lr: 2.142e-02, eta: 13:39:33, time: 0.679, data_time: 0.198, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9969, loss_cls: 0.5618, loss: 0.5618 +2025-06-24 14:08:03,393 - pyskl - INFO - Epoch [38][200/1281] lr: 2.140e-02, eta: 13:39:58, time: 0.536, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9975, loss_cls: 0.5101, loss: 0.5101 +2025-06-24 14:08:58,175 - pyskl - INFO - Epoch [38][300/1281] lr: 2.139e-02, eta: 13:40:27, time: 0.548, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9956, loss_cls: 0.5180, loss: 0.5180 +2025-06-24 14:09:52,910 - pyskl - INFO - Epoch [38][400/1281] lr: 2.137e-02, eta: 13:40:55, time: 0.547, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9938, loss_cls: 0.5692, loss: 0.5692 +2025-06-24 14:10:46,839 - pyskl - INFO - Epoch [38][500/1281] lr: 2.136e-02, eta: 13:41:21, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9938, loss_cls: 0.5635, loss: 0.5635 +2025-06-24 14:11:40,843 - pyskl - INFO - Epoch [38][600/1281] lr: 2.134e-02, eta: 13:41:46, time: 0.540, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9988, loss_cls: 0.5694, loss: 0.5694 +2025-06-24 14:12:35,003 - pyskl - INFO - Epoch [38][700/1281] lr: 2.133e-02, eta: 13:42:12, time: 0.542, data_time: 0.001, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9956, loss_cls: 0.5430, loss: 0.5430 +2025-06-24 14:13:29,117 - pyskl - INFO - Epoch [38][800/1281] lr: 2.132e-02, eta: 13:42:37, time: 0.541, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9931, loss_cls: 0.5833, loss: 0.5833 +2025-06-24 14:14:23,921 - pyskl - INFO - Epoch [38][900/1281] lr: 2.130e-02, eta: 13:43:04, time: 0.548, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9950, loss_cls: 0.5850, loss: 0.5850 +2025-06-24 14:14:52,458 - pyskl - INFO - Epoch [38][1000/1281] lr: 2.129e-02, eta: 13:42:12, time: 0.285, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9956, loss_cls: 0.5940, loss: 0.5940 +2025-06-24 14:15:40,069 - pyskl - INFO - Epoch [38][1100/1281] lr: 2.127e-02, eta: 13:42:17, time: 0.476, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9962, loss_cls: 0.6096, loss: 0.6096 +2025-06-24 14:16:22,959 - pyskl - INFO - Epoch [38][1200/1281] lr: 2.126e-02, eta: 13:42:08, time: 0.429, data_time: 0.001, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9950, loss_cls: 0.5800, loss: 0.5800 +2025-06-24 14:17:06,711 - pyskl - INFO - Saving checkpoint at 38 epochs +2025-06-24 14:18:18,744 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:18:18,799 - pyskl - INFO - +top1_acc 0.8520 +top5_acc 0.9907 +2025-06-24 14:18:18,799 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:18:18,806 - pyskl - INFO - +mean_acc 0.8119 +2025-06-24 14:18:18,808 - pyskl - INFO - Epoch(val) [38][533] top1_acc: 0.8520, top5_acc: 0.9907, mean_class_accuracy: 0.8119 +2025-06-24 14:19:46,715 - pyskl - INFO - Epoch [39][100/1281] lr: 2.123e-02, eta: 13:42:22, time: 0.879, data_time: 0.202, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9931, loss_cls: 0.5263, loss: 0.5263 +2025-06-24 14:20:40,205 - pyskl - INFO - Epoch [39][200/1281] lr: 2.122e-02, eta: 13:42:43, time: 0.535, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9956, loss_cls: 0.4948, loss: 0.4948 +2025-06-24 14:21:33,953 - pyskl - INFO - Epoch [39][300/1281] lr: 2.120e-02, eta: 13:43:05, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9962, loss_cls: 0.5701, loss: 0.5701 +2025-06-24 14:22:28,798 - pyskl - INFO - Epoch [39][400/1281] lr: 2.119e-02, eta: 13:43:30, time: 0.548, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9975, loss_cls: 0.5019, loss: 0.5019 +2025-06-24 14:23:21,420 - pyskl - INFO - Epoch [39][500/1281] lr: 2.117e-02, eta: 13:43:48, time: 0.526, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9956, loss_cls: 0.5771, loss: 0.5771 +2025-06-24 14:23:53,290 - pyskl - INFO - Epoch [39][600/1281] lr: 2.116e-02, eta: 13:43:05, time: 0.319, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9931, loss_cls: 0.5160, loss: 0.5160 +2025-06-24 14:24:38,362 - pyskl - INFO - Epoch [39][700/1281] lr: 2.114e-02, eta: 13:43:01, time: 0.451, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9938, loss_cls: 0.5362, loss: 0.5362 +2025-06-24 14:25:21,243 - pyskl - INFO - Epoch [39][800/1281] lr: 2.113e-02, eta: 13:42:51, time: 0.429, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9956, loss_cls: 0.5731, loss: 0.5731 +2025-06-24 14:26:15,084 - pyskl - INFO - Epoch [39][900/1281] lr: 2.111e-02, eta: 13:43:11, time: 0.538, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9950, loss_cls: 0.5794, loss: 0.5794 +2025-06-24 14:27:09,641 - pyskl - INFO - Epoch [39][1000/1281] lr: 2.110e-02, eta: 13:43:34, time: 0.546, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9962, loss_cls: 0.5598, loss: 0.5598 +2025-06-24 14:28:04,405 - pyskl - INFO - Epoch [39][1100/1281] lr: 2.108e-02, eta: 13:43:56, time: 0.548, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9956, loss_cls: 0.5525, loss: 0.5525 +2025-06-24 14:28:58,802 - pyskl - INFO - Epoch [39][1200/1281] lr: 2.107e-02, eta: 13:44:18, time: 0.544, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9975, loss_cls: 0.5316, loss: 0.5316 +2025-06-24 14:29:42,917 - pyskl - INFO - Saving checkpoint at 39 epochs +2025-06-24 14:30:54,409 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:30:54,467 - pyskl - INFO - +top1_acc 0.8629 +top5_acc 0.9905 +2025-06-24 14:30:54,467 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:30:54,473 - pyskl - INFO - +mean_acc 0.8105 +2025-06-24 14:30:54,475 - pyskl - INFO - Epoch(val) [39][533] top1_acc: 0.8629, top5_acc: 0.9905, mean_class_accuracy: 0.8105 +2025-06-24 14:32:19,012 - pyskl - INFO - Epoch [40][100/1281] lr: 2.104e-02, eta: 13:44:16, time: 0.845, data_time: 0.196, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9925, loss_cls: 0.5228, loss: 0.5228 +2025-06-24 14:32:47,122 - pyskl - INFO - Epoch [40][200/1281] lr: 2.103e-02, eta: 13:43:23, time: 0.281, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9975, loss_cls: 0.5571, loss: 0.5571 +2025-06-24 14:33:35,889 - pyskl - INFO - Epoch [40][300/1281] lr: 2.101e-02, eta: 13:43:27, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9994, loss_cls: 0.4904, loss: 0.4904 +2025-06-24 14:34:17,841 - pyskl - INFO - Epoch [40][400/1281] lr: 2.100e-02, eta: 13:43:12, time: 0.419, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9975, loss_cls: 0.5368, loss: 0.5368 +2025-06-24 14:35:12,809 - pyskl - INFO - Epoch [40][500/1281] lr: 2.098e-02, eta: 13:43:34, time: 0.550, data_time: 0.001, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9988, loss_cls: 0.5619, loss: 0.5619 +2025-06-24 14:36:06,489 - pyskl - INFO - Epoch [40][600/1281] lr: 2.097e-02, eta: 13:43:52, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9981, loss_cls: 0.5106, loss: 0.5106 +2025-06-24 14:37:00,687 - pyskl - INFO - Epoch [40][700/1281] lr: 2.095e-02, eta: 13:44:11, time: 0.542, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9919, loss_cls: 0.6434, loss: 0.6434 +2025-06-24 14:37:53,968 - pyskl - INFO - Epoch [40][800/1281] lr: 2.094e-02, eta: 13:44:27, time: 0.533, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9944, loss_cls: 0.6216, loss: 0.6216 +2025-06-24 14:38:47,705 - pyskl - INFO - Epoch [40][900/1281] lr: 2.092e-02, eta: 13:44:44, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9950, loss_cls: 0.5257, loss: 0.5257 +2025-06-24 14:39:42,153 - pyskl - INFO - Epoch [40][1000/1281] lr: 2.091e-02, eta: 13:45:03, time: 0.544, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9962, loss_cls: 0.5304, loss: 0.5304 +2025-06-24 14:40:36,028 - pyskl - INFO - Epoch [40][1100/1281] lr: 2.089e-02, eta: 13:45:20, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9975, loss_cls: 0.5039, loss: 0.5039 +2025-06-24 14:41:28,070 - pyskl - INFO - Epoch [40][1200/1281] lr: 2.088e-02, eta: 13:45:31, time: 0.520, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9969, loss_cls: 0.5543, loss: 0.5543 +2025-06-24 14:41:58,765 - pyskl - INFO - Saving checkpoint at 40 epochs +2025-06-24 14:43:03,392 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:43:03,448 - pyskl - INFO - +top1_acc 0.8756 +top5_acc 0.9904 +2025-06-24 14:43:03,448 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:43:03,455 - pyskl - INFO - +mean_acc 0.8238 +2025-06-24 14:43:03,459 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_36.pth was removed +2025-06-24 14:43:03,632 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_40.pth. +2025-06-24 14:43:03,632 - pyskl - INFO - Best top1_acc is 0.8756 at 40 epoch. +2025-06-24 14:43:03,635 - pyskl - INFO - Epoch(val) [40][533] top1_acc: 0.8756, top5_acc: 0.9904, mean_class_accuracy: 0.8238 +2025-06-24 14:44:30,464 - pyskl - INFO - Epoch [41][100/1281] lr: 2.085e-02, eta: 13:45:32, time: 0.868, data_time: 0.190, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9969, loss_cls: 0.5787, loss: 0.5787 +2025-06-24 14:45:23,724 - pyskl - INFO - Epoch [41][200/1281] lr: 2.083e-02, eta: 13:45:46, time: 0.533, data_time: 0.001, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9962, loss_cls: 0.5283, loss: 0.5283 +2025-06-24 14:46:17,158 - pyskl - INFO - Epoch [41][300/1281] lr: 2.082e-02, eta: 13:46:00, time: 0.534, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9950, loss_cls: 0.4898, loss: 0.4898 +2025-06-24 14:47:11,743 - pyskl - INFO - Epoch [41][400/1281] lr: 2.080e-02, eta: 13:46:18, time: 0.546, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9969, loss_cls: 0.5009, loss: 0.5009 +2025-06-24 14:48:05,124 - pyskl - INFO - Epoch [41][500/1281] lr: 2.079e-02, eta: 13:46:32, time: 0.534, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9969, loss_cls: 0.5121, loss: 0.5121 +2025-06-24 14:48:59,290 - pyskl - INFO - Epoch [41][600/1281] lr: 2.077e-02, eta: 13:46:47, time: 0.542, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9975, loss_cls: 0.5346, loss: 0.5346 +2025-06-24 14:49:53,841 - pyskl - INFO - Epoch [41][700/1281] lr: 2.076e-02, eta: 13:47:04, time: 0.546, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9981, loss_cls: 0.5276, loss: 0.5276 +2025-06-24 14:50:48,710 - pyskl - INFO - Epoch [41][800/1281] lr: 2.074e-02, eta: 13:47:21, time: 0.549, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9962, loss_cls: 0.5725, loss: 0.5725 +2025-06-24 14:51:20,749 - pyskl - INFO - Epoch [41][900/1281] lr: 2.073e-02, eta: 13:46:36, time: 0.320, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9950, loss_cls: 0.6470, loss: 0.6470 +2025-06-24 14:52:11,791 - pyskl - INFO - Epoch [41][1000/1281] lr: 2.071e-02, eta: 13:46:43, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9925, loss_cls: 0.5742, loss: 0.5742 +2025-06-24 14:52:44,594 - pyskl - INFO - Epoch [41][1100/1281] lr: 2.070e-02, eta: 13:46:00, time: 0.328, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9975, loss_cls: 0.5288, loss: 0.5288 +2025-06-24 14:53:32,640 - pyskl - INFO - Epoch [41][1200/1281] lr: 2.068e-02, eta: 13:45:58, time: 0.480, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9956, loss_cls: 0.5191, loss: 0.5191 +2025-06-24 14:54:12,502 - pyskl - INFO - Saving checkpoint at 41 epochs +2025-06-24 14:55:12,748 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:55:12,803 - pyskl - INFO - +top1_acc 0.8540 +top5_acc 0.9905 +2025-06-24 14:55:12,803 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:55:12,811 - pyskl - INFO - +mean_acc 0.8211 +2025-06-24 14:55:12,813 - pyskl - INFO - Epoch(val) [41][533] top1_acc: 0.8540, top5_acc: 0.9905, mean_class_accuracy: 0.8211 +2025-06-24 14:56:31,999 - pyskl - INFO - Epoch [42][100/1281] lr: 2.065e-02, eta: 13:45:34, time: 0.792, data_time: 0.201, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9988, loss_cls: 0.4543, loss: 0.4543 +2025-06-24 14:57:20,296 - pyskl - INFO - Epoch [42][200/1281] lr: 2.064e-02, eta: 13:45:32, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9981, loss_cls: 0.5117, loss: 0.5117 +2025-06-24 14:58:08,825 - pyskl - INFO - Epoch [42][300/1281] lr: 2.062e-02, eta: 13:45:31, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9975, loss_cls: 0.5477, loss: 0.5477 +2025-06-24 14:58:57,385 - pyskl - INFO - Epoch [42][400/1281] lr: 2.061e-02, eta: 13:45:29, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9975, loss_cls: 0.5244, loss: 0.5244 +2025-06-24 14:59:45,863 - pyskl - INFO - Epoch [42][500/1281] lr: 2.059e-02, eta: 13:45:28, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9981, loss_cls: 0.5030, loss: 0.5030 +2025-06-24 15:00:34,413 - pyskl - INFO - Epoch [42][600/1281] lr: 2.057e-02, eta: 13:45:26, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9975, loss_cls: 0.4885, loss: 0.4885 +2025-06-24 15:01:22,492 - pyskl - INFO - Epoch [42][700/1281] lr: 2.056e-02, eta: 13:45:23, time: 0.481, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9956, loss_cls: 0.5523, loss: 0.5523 +2025-06-24 15:02:08,940 - pyskl - INFO - Epoch [42][800/1281] lr: 2.054e-02, eta: 13:45:15, time: 0.464, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9975, loss_cls: 0.5138, loss: 0.5138 +2025-06-24 15:02:45,667 - pyskl - INFO - Epoch [42][900/1281] lr: 2.053e-02, eta: 13:44:42, time: 0.367, data_time: 0.001, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9969, loss_cls: 0.5188, loss: 0.5188 +2025-06-24 15:03:23,068 - pyskl - INFO - Epoch [42][1000/1281] lr: 2.051e-02, eta: 13:44:11, time: 0.374, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9950, loss_cls: 0.5678, loss: 0.5678 +2025-06-24 15:03:57,724 - pyskl - INFO - Epoch [42][1100/1281] lr: 2.050e-02, eta: 13:43:33, time: 0.347, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9975, loss_cls: 0.4997, loss: 0.4997 +2025-06-24 15:04:46,827 - pyskl - INFO - Epoch [42][1200/1281] lr: 2.048e-02, eta: 13:43:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9988, loss_cls: 0.5098, loss: 0.5098 +2025-06-24 15:05:27,254 - pyskl - INFO - Saving checkpoint at 42 epochs +2025-06-24 15:06:26,197 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:06:26,262 - pyskl - INFO - +top1_acc 0.8446 +top5_acc 0.9906 +2025-06-24 15:06:26,262 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:06:26,271 - pyskl - INFO - +mean_acc 0.7988 +2025-06-24 15:06:26,274 - pyskl - INFO - Epoch(val) [42][533] top1_acc: 0.8446, top5_acc: 0.9906, mean_class_accuracy: 0.7988 +2025-06-24 15:07:47,649 - pyskl - INFO - Epoch [43][100/1281] lr: 2.045e-02, eta: 13:43:10, time: 0.814, data_time: 0.198, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9969, loss_cls: 0.5795, loss: 0.5795 +2025-06-24 15:08:36,588 - pyskl - INFO - Epoch [43][200/1281] lr: 2.044e-02, eta: 13:43:08, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9981, loss_cls: 0.4851, loss: 0.4851 +2025-06-24 15:09:25,906 - pyskl - INFO - Epoch [43][300/1281] lr: 2.042e-02, eta: 13:43:07, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9969, loss_cls: 0.5163, loss: 0.5163 +2025-06-24 15:10:15,084 - pyskl - INFO - Epoch [43][400/1281] lr: 2.040e-02, eta: 13:43:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9956, loss_cls: 0.5233, loss: 0.5233 +2025-06-24 15:11:04,279 - pyskl - INFO - Epoch [43][500/1281] lr: 2.039e-02, eta: 13:43:04, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9969, loss_cls: 0.5230, loss: 0.5230 +2025-06-24 15:11:53,385 - pyskl - INFO - Epoch [43][600/1281] lr: 2.037e-02, eta: 13:43:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9969, loss_cls: 0.4870, loss: 0.4870 +2025-06-24 15:12:42,747 - pyskl - INFO - Epoch [43][700/1281] lr: 2.036e-02, eta: 13:43:00, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9988, loss_cls: 0.5078, loss: 0.5078 +2025-06-24 15:13:31,872 - pyskl - INFO - Epoch [43][800/1281] lr: 2.034e-02, eta: 13:42:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9956, loss_cls: 0.5513, loss: 0.5513 +2025-06-24 15:13:59,646 - pyskl - INFO - Epoch [43][900/1281] lr: 2.033e-02, eta: 13:42:01, time: 0.278, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9975, loss_cls: 0.5283, loss: 0.5283 +2025-06-24 15:14:48,070 - pyskl - INFO - Epoch [43][1000/1281] lr: 2.031e-02, eta: 13:41:57, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9975, loss_cls: 0.5192, loss: 0.5192 +2025-06-24 15:15:20,038 - pyskl - INFO - Epoch [43][1100/1281] lr: 2.029e-02, eta: 13:41:11, time: 0.320, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9962, loss_cls: 0.5206, loss: 0.5206 +2025-06-24 15:16:09,249 - pyskl - INFO - Epoch [43][1200/1281] lr: 2.028e-02, eta: 13:41:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9956, loss_cls: 0.5806, loss: 0.5806 +2025-06-24 15:16:49,503 - pyskl - INFO - Saving checkpoint at 43 epochs +2025-06-24 15:17:49,702 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:17:49,757 - pyskl - INFO - +top1_acc 0.8663 +top5_acc 0.9911 +2025-06-24 15:17:49,757 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:17:49,764 - pyskl - INFO - +mean_acc 0.7937 +2025-06-24 15:17:49,766 - pyskl - INFO - Epoch(val) [43][533] top1_acc: 0.8663, top5_acc: 0.9911, mean_class_accuracy: 0.7937 +2025-06-24 15:19:10,303 - pyskl - INFO - Epoch [44][100/1281] lr: 2.025e-02, eta: 13:40:42, time: 0.805, data_time: 0.197, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9988, loss_cls: 0.4204, loss: 0.4204 +2025-06-24 15:19:59,382 - pyskl - INFO - Epoch [44][200/1281] lr: 2.023e-02, eta: 13:40:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9962, loss_cls: 0.5297, loss: 0.5297 +2025-06-24 15:20:48,667 - pyskl - INFO - Epoch [44][300/1281] lr: 2.022e-02, eta: 13:40:35, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9981, loss_cls: 0.5096, loss: 0.5096 +2025-06-24 15:21:37,900 - pyskl - INFO - Epoch [44][400/1281] lr: 2.020e-02, eta: 13:40:32, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9956, loss_cls: 0.4986, loss: 0.4986 +2025-06-24 15:22:26,840 - pyskl - INFO - Epoch [44][500/1281] lr: 2.018e-02, eta: 13:40:27, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9969, loss_cls: 0.4784, loss: 0.4784 +2025-06-24 15:23:15,912 - pyskl - INFO - Epoch [44][600/1281] lr: 2.017e-02, eta: 13:40:23, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9962, loss_cls: 0.5056, loss: 0.5056 +2025-06-24 15:24:04,973 - pyskl - INFO - Epoch [44][700/1281] lr: 2.015e-02, eta: 13:40:19, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9981, loss_cls: 0.5379, loss: 0.5379 +2025-06-24 15:24:54,164 - pyskl - INFO - Epoch [44][800/1281] lr: 2.014e-02, eta: 13:40:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9962, loss_cls: 0.5296, loss: 0.5296 +2025-06-24 15:25:22,461 - pyskl - INFO - Epoch [44][900/1281] lr: 2.012e-02, eta: 13:39:20, time: 0.283, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9962, loss_cls: 0.5881, loss: 0.5881 +2025-06-24 15:26:10,797 - pyskl - INFO - Epoch [44][1000/1281] lr: 2.010e-02, eta: 13:39:13, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9919, loss_cls: 0.5824, loss: 0.5824 +2025-06-24 15:26:41,495 - pyskl - INFO - Epoch [44][1100/1281] lr: 2.009e-02, eta: 13:38:24, time: 0.307, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9975, loss_cls: 0.5204, loss: 0.5204 +2025-06-24 15:27:31,074 - pyskl - INFO - Epoch [44][1200/1281] lr: 2.007e-02, eta: 13:38:20, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9975, loss_cls: 0.5442, loss: 0.5442 +2025-06-24 15:28:11,936 - pyskl - INFO - Saving checkpoint at 44 epochs +2025-06-24 15:29:11,305 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:29:11,365 - pyskl - INFO - +top1_acc 0.8749 +top5_acc 0.9912 +2025-06-24 15:29:11,365 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:29:11,374 - pyskl - INFO - +mean_acc 0.8362 +2025-06-24 15:29:11,377 - pyskl - INFO - Epoch(val) [44][533] top1_acc: 0.8749, top5_acc: 0.9912, mean_class_accuracy: 0.8362 +2025-06-24 15:30:32,079 - pyskl - INFO - Epoch [45][100/1281] lr: 2.004e-02, eta: 13:37:52, time: 0.807, data_time: 0.194, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9975, loss_cls: 0.5469, loss: 0.5469 +2025-06-24 15:31:21,447 - pyskl - INFO - Epoch [45][200/1281] lr: 2.003e-02, eta: 13:37:47, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9962, loss_cls: 0.5444, loss: 0.5444 +2025-06-24 15:32:10,530 - pyskl - INFO - Epoch [45][300/1281] lr: 2.001e-02, eta: 13:37:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9944, loss_cls: 0.5528, loss: 0.5528 +2025-06-24 15:32:59,848 - pyskl - INFO - Epoch [45][400/1281] lr: 1.999e-02, eta: 13:37:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9950, loss_cls: 0.4560, loss: 0.4560 +2025-06-24 15:33:49,359 - pyskl - INFO - Epoch [45][500/1281] lr: 1.998e-02, eta: 13:37:32, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9962, loss_cls: 0.4804, loss: 0.4804 +2025-06-24 15:34:38,466 - pyskl - INFO - Epoch [45][600/1281] lr: 1.996e-02, eta: 13:37:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9956, loss_cls: 0.5891, loss: 0.5891 +2025-06-24 15:35:27,942 - pyskl - INFO - Epoch [45][700/1281] lr: 1.994e-02, eta: 13:37:21, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9962, loss_cls: 0.4930, loss: 0.4930 +2025-06-24 15:36:17,792 - pyskl - INFO - Epoch [45][800/1281] lr: 1.993e-02, eta: 13:37:17, time: 0.498, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9975, loss_cls: 0.4922, loss: 0.4922 +2025-06-24 15:36:45,943 - pyskl - INFO - Epoch [45][900/1281] lr: 1.991e-02, eta: 13:36:21, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9950, loss_cls: 0.5382, loss: 0.5382 +2025-06-24 15:37:37,013 - pyskl - INFO - Epoch [45][1000/1281] lr: 1.989e-02, eta: 13:36:20, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9975, loss_cls: 0.4785, loss: 0.4785 +2025-06-24 15:38:06,688 - pyskl - INFO - Epoch [45][1100/1281] lr: 1.988e-02, eta: 13:35:28, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9950, loss_cls: 0.4954, loss: 0.4954 +2025-06-24 15:38:56,023 - pyskl - INFO - Epoch [45][1200/1281] lr: 1.986e-02, eta: 13:35:22, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9969, loss_cls: 0.4990, loss: 0.4990 +2025-06-24 15:39:36,322 - pyskl - INFO - Saving checkpoint at 45 epochs +2025-06-24 15:40:35,757 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:40:35,813 - pyskl - INFO - +top1_acc 0.8676 +top5_acc 0.9919 +2025-06-24 15:40:35,814 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:40:35,822 - pyskl - INFO - +mean_acc 0.7991 +2025-06-24 15:40:35,824 - pyskl - INFO - Epoch(val) [45][533] top1_acc: 0.8676, top5_acc: 0.9919, mean_class_accuracy: 0.7991 +2025-06-24 15:41:56,369 - pyskl - INFO - Epoch [46][100/1281] lr: 1.983e-02, eta: 13:34:50, time: 0.805, data_time: 0.197, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9981, loss_cls: 0.4907, loss: 0.4907 +2025-06-24 15:42:45,957 - pyskl - INFO - Epoch [46][200/1281] lr: 1.981e-02, eta: 13:34:45, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9975, loss_cls: 0.4597, loss: 0.4597 +2025-06-24 15:43:34,907 - pyskl - INFO - Epoch [46][300/1281] lr: 1.980e-02, eta: 13:34:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9962, loss_cls: 0.5433, loss: 0.5433 +2025-06-24 15:44:24,324 - pyskl - INFO - Epoch [46][400/1281] lr: 1.978e-02, eta: 13:34:31, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9956, loss_cls: 0.4988, loss: 0.4988 +2025-06-24 15:45:13,529 - pyskl - INFO - Epoch [46][500/1281] lr: 1.976e-02, eta: 13:34:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9975, loss_cls: 0.4610, loss: 0.4610 +2025-06-24 15:46:02,498 - pyskl - INFO - Epoch [46][600/1281] lr: 1.975e-02, eta: 13:34:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9969, loss_cls: 0.5017, loss: 0.5017 +2025-06-24 15:46:51,661 - pyskl - INFO - Epoch [46][700/1281] lr: 1.973e-02, eta: 13:34:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9956, loss_cls: 0.4760, loss: 0.4760 +2025-06-24 15:47:41,028 - pyskl - INFO - Epoch [46][800/1281] lr: 1.971e-02, eta: 13:34:02, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9975, loss_cls: 0.5155, loss: 0.5155 +2025-06-24 15:48:10,601 - pyskl - INFO - Epoch [46][900/1281] lr: 1.970e-02, eta: 13:33:09, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9962, loss_cls: 0.5184, loss: 0.5184 +2025-06-24 15:49:01,844 - pyskl - INFO - Epoch [46][1000/1281] lr: 1.968e-02, eta: 13:33:06, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9944, loss_cls: 0.5661, loss: 0.5661 +2025-06-24 15:49:32,036 - pyskl - INFO - Epoch [46][1100/1281] lr: 1.966e-02, eta: 13:32:15, time: 0.302, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9962, loss_cls: 0.5826, loss: 0.5826 +2025-06-24 15:50:21,307 - pyskl - INFO - Epoch [46][1200/1281] lr: 1.965e-02, eta: 13:32:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9956, loss_cls: 0.5238, loss: 0.5238 +2025-06-24 15:51:01,661 - pyskl - INFO - Saving checkpoint at 46 epochs +2025-06-24 15:52:00,911 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:52:00,967 - pyskl - INFO - +top1_acc 0.8378 +top5_acc 0.9838 +2025-06-24 15:52:00,967 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:52:00,974 - pyskl - INFO - +mean_acc 0.7875 +2025-06-24 15:52:00,976 - pyskl - INFO - Epoch(val) [46][533] top1_acc: 0.8378, top5_acc: 0.9838, mean_class_accuracy: 0.7875 +2025-06-24 15:53:20,876 - pyskl - INFO - Epoch [47][100/1281] lr: 1.962e-02, eta: 13:31:32, time: 0.799, data_time: 0.191, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9956, loss_cls: 0.5271, loss: 0.5271 +2025-06-24 15:54:09,976 - pyskl - INFO - Epoch [47][200/1281] lr: 1.960e-02, eta: 13:31:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9956, loss_cls: 0.4979, loss: 0.4979 +2025-06-24 15:54:59,465 - pyskl - INFO - Epoch [47][300/1281] lr: 1.958e-02, eta: 13:31:16, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9975, loss_cls: 0.4889, loss: 0.4889 +2025-06-24 15:55:48,674 - pyskl - INFO - Epoch [47][400/1281] lr: 1.957e-02, eta: 13:31:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9988, loss_cls: 0.5034, loss: 0.5034 +2025-06-24 15:56:38,187 - pyskl - INFO - Epoch [47][500/1281] lr: 1.955e-02, eta: 13:31:00, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9962, loss_cls: 0.5391, loss: 0.5391 +2025-06-24 15:57:27,590 - pyskl - INFO - Epoch [47][600/1281] lr: 1.953e-02, eta: 13:30:52, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9950, loss_cls: 0.4556, loss: 0.4556 +2025-06-24 15:58:16,942 - pyskl - INFO - Epoch [47][700/1281] lr: 1.952e-02, eta: 13:30:43, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9962, loss_cls: 0.5130, loss: 0.5130 +2025-06-24 15:59:06,083 - pyskl - INFO - Epoch [47][800/1281] lr: 1.950e-02, eta: 13:30:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9962, loss_cls: 0.5503, loss: 0.5503 +2025-06-24 15:59:35,766 - pyskl - INFO - Epoch [47][900/1281] lr: 1.948e-02, eta: 13:29:42, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9962, loss_cls: 0.4909, loss: 0.4909 +2025-06-24 16:00:26,955 - pyskl - INFO - Epoch [47][1000/1281] lr: 1.947e-02, eta: 13:29:37, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9962, loss_cls: 0.5548, loss: 0.5548 +2025-06-24 16:00:55,220 - pyskl - INFO - Epoch [47][1100/1281] lr: 1.945e-02, eta: 13:28:42, time: 0.283, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9962, loss_cls: 0.4651, loss: 0.4651 +2025-06-24 16:01:44,180 - pyskl - INFO - Epoch [47][1200/1281] lr: 1.943e-02, eta: 13:28:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9981, loss_cls: 0.5397, loss: 0.5397 +2025-06-24 16:02:24,481 - pyskl - INFO - Saving checkpoint at 47 epochs +2025-06-24 16:03:23,451 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:03:23,516 - pyskl - INFO - +top1_acc 0.8796 +top5_acc 0.9930 +2025-06-24 16:03:23,516 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:03:23,526 - pyskl - INFO - +mean_acc 0.8340 +2025-06-24 16:03:23,532 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_40.pth was removed +2025-06-24 16:03:23,724 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_47.pth. +2025-06-24 16:03:23,725 - pyskl - INFO - Best top1_acc is 0.8796 at 47 epoch. +2025-06-24 16:03:23,727 - pyskl - INFO - Epoch(val) [47][533] top1_acc: 0.8796, top5_acc: 0.9930, mean_class_accuracy: 0.8340 +2025-06-24 16:04:44,833 - pyskl - INFO - Epoch [48][100/1281] lr: 1.940e-02, eta: 13:27:57, time: 0.811, data_time: 0.190, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9975, loss_cls: 0.4725, loss: 0.4725 +2025-06-24 16:05:33,871 - pyskl - INFO - Epoch [48][200/1281] lr: 1.938e-02, eta: 13:27:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4436, loss: 0.4436 +2025-06-24 16:06:22,896 - pyskl - INFO - Epoch [48][300/1281] lr: 1.937e-02, eta: 13:27:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9981, loss_cls: 0.4647, loss: 0.4647 +2025-06-24 16:07:12,247 - pyskl - INFO - Epoch [48][400/1281] lr: 1.935e-02, eta: 13:27:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9962, loss_cls: 0.5156, loss: 0.5156 +2025-06-24 16:08:01,368 - pyskl - INFO - Epoch [48][500/1281] lr: 1.933e-02, eta: 13:27:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9950, loss_cls: 0.5281, loss: 0.5281 +2025-06-24 16:08:50,427 - pyskl - INFO - Epoch [48][600/1281] lr: 1.932e-02, eta: 13:27:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9938, loss_cls: 0.5632, loss: 0.5632 +2025-06-24 16:09:39,587 - pyskl - INFO - Epoch [48][700/1281] lr: 1.930e-02, eta: 13:26:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9962, loss_cls: 0.5246, loss: 0.5246 +2025-06-24 16:10:28,776 - pyskl - INFO - Epoch [48][800/1281] lr: 1.928e-02, eta: 13:26:46, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9969, loss_cls: 0.4771, loss: 0.4771 +2025-06-24 16:10:59,111 - pyskl - INFO - Epoch [48][900/1281] lr: 1.926e-02, eta: 13:25:55, time: 0.303, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9981, loss_cls: 0.4847, loss: 0.4847 +2025-06-24 16:11:50,278 - pyskl - INFO - Epoch [48][1000/1281] lr: 1.925e-02, eta: 13:25:48, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9975, loss_cls: 0.4824, loss: 0.4824 +2025-06-24 16:12:17,501 - pyskl - INFO - Epoch [48][1100/1281] lr: 1.923e-02, eta: 13:24:51, time: 0.272, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9975, loss_cls: 0.5069, loss: 0.5069 +2025-06-24 16:13:06,909 - pyskl - INFO - Epoch [48][1200/1281] lr: 1.921e-02, eta: 13:24:40, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9981, loss_cls: 0.4945, loss: 0.4945 +2025-06-24 16:13:47,312 - pyskl - INFO - Saving checkpoint at 48 epochs +2025-06-24 16:14:47,189 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:14:47,246 - pyskl - INFO - +top1_acc 0.8640 +top5_acc 0.9897 +2025-06-24 16:14:47,246 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:14:47,253 - pyskl - INFO - +mean_acc 0.8160 +2025-06-24 16:14:47,255 - pyskl - INFO - Epoch(val) [48][533] top1_acc: 0.8640, top5_acc: 0.9897, mean_class_accuracy: 0.8160 +2025-06-24 16:16:06,046 - pyskl - INFO - Epoch [49][100/1281] lr: 1.918e-02, eta: 13:23:59, time: 0.788, data_time: 0.192, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9988, loss_cls: 0.4869, loss: 0.4869 +2025-06-24 16:16:54,978 - pyskl - INFO - Epoch [49][200/1281] lr: 1.916e-02, eta: 13:23:47, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3875, loss: 0.3875 +2025-06-24 16:17:44,368 - pyskl - INFO - Epoch [49][300/1281] lr: 1.915e-02, eta: 13:23:37, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9994, loss_cls: 0.4305, loss: 0.4305 +2025-06-24 16:18:33,705 - pyskl - INFO - Epoch [49][400/1281] lr: 1.913e-02, eta: 13:23:26, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9975, loss_cls: 0.5507, loss: 0.5507 +2025-06-24 16:19:22,850 - pyskl - INFO - Epoch [49][500/1281] lr: 1.911e-02, eta: 13:23:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9956, loss_cls: 0.5831, loss: 0.5831 +2025-06-24 16:20:11,765 - pyskl - INFO - Epoch [49][600/1281] lr: 1.909e-02, eta: 13:23:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9962, loss_cls: 0.4470, loss: 0.4470 +2025-06-24 16:21:01,177 - pyskl - INFO - Epoch [49][700/1281] lr: 1.908e-02, eta: 13:22:51, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9969, loss_cls: 0.5087, loss: 0.5087 +2025-06-24 16:21:50,172 - pyskl - INFO - Epoch [49][800/1281] lr: 1.906e-02, eta: 13:22:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9994, loss_cls: 0.4942, loss: 0.4942 +2025-06-24 16:22:23,109 - pyskl - INFO - Epoch [49][900/1281] lr: 1.904e-02, eta: 13:21:53, time: 0.329, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 1.0000, loss_cls: 0.5246, loss: 0.5246 +2025-06-24 16:23:14,319 - pyskl - INFO - Epoch [49][1000/1281] lr: 1.902e-02, eta: 13:21:45, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9944, loss_cls: 0.5197, loss: 0.5197 +2025-06-24 16:23:40,616 - pyskl - INFO - Epoch [49][1100/1281] lr: 1.901e-02, eta: 13:20:46, time: 0.263, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9975, loss_cls: 0.5053, loss: 0.5053 +2025-06-24 16:24:30,245 - pyskl - INFO - Epoch [49][1200/1281] lr: 1.899e-02, eta: 13:20:34, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9975, loss_cls: 0.5080, loss: 0.5080 +2025-06-24 16:25:10,420 - pyskl - INFO - Saving checkpoint at 49 epochs +2025-06-24 16:26:10,054 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:26:10,113 - pyskl - INFO - +top1_acc 0.8675 +top5_acc 0.9916 +2025-06-24 16:26:10,113 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:26:10,121 - pyskl - INFO - +mean_acc 0.8135 +2025-06-24 16:26:10,123 - pyskl - INFO - Epoch(val) [49][533] top1_acc: 0.8675, top5_acc: 0.9916, mean_class_accuracy: 0.8135 +2025-06-24 16:27:28,859 - pyskl - INFO - Epoch [50][100/1281] lr: 1.896e-02, eta: 13:19:51, time: 0.787, data_time: 0.189, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9981, loss_cls: 0.4648, loss: 0.4648 +2025-06-24 16:28:17,828 - pyskl - INFO - Epoch [50][200/1281] lr: 1.894e-02, eta: 13:19:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9962, loss_cls: 0.4624, loss: 0.4624 +2025-06-24 16:29:06,923 - pyskl - INFO - Epoch [50][300/1281] lr: 1.892e-02, eta: 13:19:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9956, loss_cls: 0.4676, loss: 0.4676 +2025-06-24 16:29:56,166 - pyskl - INFO - Epoch [50][400/1281] lr: 1.891e-02, eta: 13:19:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9969, loss_cls: 0.4845, loss: 0.4845 +2025-06-24 16:30:45,398 - pyskl - INFO - Epoch [50][500/1281] lr: 1.889e-02, eta: 13:19:00, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9988, loss_cls: 0.4725, loss: 0.4725 +2025-06-24 16:31:34,860 - pyskl - INFO - Epoch [50][600/1281] lr: 1.887e-02, eta: 13:18:48, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9969, loss_cls: 0.4969, loss: 0.4969 +2025-06-24 16:32:24,471 - pyskl - INFO - Epoch [50][700/1281] lr: 1.885e-02, eta: 13:18:36, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9988, loss_cls: 0.5252, loss: 0.5252 +2025-06-24 16:33:13,695 - pyskl - INFO - Epoch [50][800/1281] lr: 1.884e-02, eta: 13:18:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9956, loss_cls: 0.4956, loss: 0.4956 +2025-06-24 16:33:47,339 - pyskl - INFO - Epoch [50][900/1281] lr: 1.882e-02, eta: 13:17:39, time: 0.336, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9975, loss_cls: 0.4655, loss: 0.4655 +2025-06-24 16:34:38,673 - pyskl - INFO - Epoch [50][1000/1281] lr: 1.880e-02, eta: 13:17:30, time: 0.513, data_time: 0.001, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9981, loss_cls: 0.4548, loss: 0.4548 +2025-06-24 16:35:04,142 - pyskl - INFO - Epoch [50][1100/1281] lr: 1.878e-02, eta: 13:16:29, time: 0.255, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9962, loss_cls: 0.4799, loss: 0.4799 +2025-06-24 16:35:53,324 - pyskl - INFO - Epoch [50][1200/1281] lr: 1.876e-02, eta: 13:16:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9975, loss_cls: 0.4766, loss: 0.4766 +2025-06-24 16:36:33,767 - pyskl - INFO - Saving checkpoint at 50 epochs +2025-06-24 16:37:33,167 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:37:33,223 - pyskl - INFO - +top1_acc 0.8743 +top5_acc 0.9923 +2025-06-24 16:37:33,223 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:37:33,230 - pyskl - INFO - +mean_acc 0.8427 +2025-06-24 16:37:33,232 - pyskl - INFO - Epoch(val) [50][533] top1_acc: 0.8743, top5_acc: 0.9923, mean_class_accuracy: 0.8427 +2025-06-24 16:38:53,315 - pyskl - INFO - Epoch [51][100/1281] lr: 1.873e-02, eta: 13:15:33, time: 0.801, data_time: 0.190, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9975, loss_cls: 0.4459, loss: 0.4459 +2025-06-24 16:39:42,372 - pyskl - INFO - Epoch [51][200/1281] lr: 1.871e-02, eta: 13:15:19, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9994, loss_cls: 0.3715, loss: 0.3715 +2025-06-24 16:40:31,771 - pyskl - INFO - Epoch [51][300/1281] lr: 1.870e-02, eta: 13:15:06, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9969, loss_cls: 0.4586, loss: 0.4586 +2025-06-24 16:41:20,911 - pyskl - INFO - Epoch [51][400/1281] lr: 1.868e-02, eta: 13:14:52, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9962, loss_cls: 0.5128, loss: 0.5128 +2025-06-24 16:42:09,896 - pyskl - INFO - Epoch [51][500/1281] lr: 1.866e-02, eta: 13:14:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9975, loss_cls: 0.5233, loss: 0.5233 +2025-06-24 16:42:59,234 - pyskl - INFO - Epoch [51][600/1281] lr: 1.864e-02, eta: 13:14:24, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9969, loss_cls: 0.4933, loss: 0.4933 +2025-06-24 16:43:48,595 - pyskl - INFO - Epoch [51][700/1281] lr: 1.863e-02, eta: 13:14:10, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9994, loss_cls: 0.4913, loss: 0.4913 +2025-06-24 16:44:37,599 - pyskl - INFO - Epoch [51][800/1281] lr: 1.861e-02, eta: 13:13:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9956, loss_cls: 0.5305, loss: 0.5305 +2025-06-24 16:45:10,628 - pyskl - INFO - Epoch [51][900/1281] lr: 1.859e-02, eta: 13:13:09, time: 0.330, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9988, loss_cls: 0.5128, loss: 0.5128 +2025-06-24 16:46:01,850 - pyskl - INFO - Epoch [51][1000/1281] lr: 1.857e-02, eta: 13:12:59, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9981, loss_cls: 0.5086, loss: 0.5086 +2025-06-24 16:46:27,492 - pyskl - INFO - Epoch [51][1100/1281] lr: 1.855e-02, eta: 13:11:59, time: 0.256, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9950, loss_cls: 0.4917, loss: 0.4917 +2025-06-24 16:47:14,635 - pyskl - INFO - Epoch [51][1200/1281] lr: 1.854e-02, eta: 13:11:40, time: 0.471, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9975, loss_cls: 0.4489, loss: 0.4489 +2025-06-24 16:47:54,792 - pyskl - INFO - Saving checkpoint at 51 epochs +2025-06-24 16:48:54,808 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:48:54,866 - pyskl - INFO - +top1_acc 0.8900 +top5_acc 0.9940 +2025-06-24 16:48:54,867 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:48:54,876 - pyskl - INFO - +mean_acc 0.8453 +2025-06-24 16:48:54,882 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_47.pth was removed +2025-06-24 16:48:55,083 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_51.pth. +2025-06-24 16:48:55,083 - pyskl - INFO - Best top1_acc is 0.8900 at 51 epoch. +2025-06-24 16:48:55,086 - pyskl - INFO - Epoch(val) [51][533] top1_acc: 0.8900, top5_acc: 0.9940, mean_class_accuracy: 0.8453 +2025-06-24 16:50:15,079 - pyskl - INFO - Epoch [52][100/1281] lr: 1.850e-02, eta: 13:10:56, time: 0.800, data_time: 0.193, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9969, loss_cls: 0.4423, loss: 0.4423 +2025-06-24 16:51:04,248 - pyskl - INFO - Epoch [52][200/1281] lr: 1.849e-02, eta: 13:10:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9969, loss_cls: 0.4105, loss: 0.4105 +2025-06-24 16:51:53,559 - pyskl - INFO - Epoch [52][300/1281] lr: 1.847e-02, eta: 13:10:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9981, loss_cls: 0.4999, loss: 0.4999 +2025-06-24 16:52:42,783 - pyskl - INFO - Epoch [52][400/1281] lr: 1.845e-02, eta: 13:10:12, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9981, loss_cls: 0.4766, loss: 0.4766 +2025-06-24 16:53:32,318 - pyskl - INFO - Epoch [52][500/1281] lr: 1.843e-02, eta: 13:09:57, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9988, loss_cls: 0.4209, loss: 0.4209 +2025-06-24 16:54:21,823 - pyskl - INFO - Epoch [52][600/1281] lr: 1.841e-02, eta: 13:09:43, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9969, loss_cls: 0.5024, loss: 0.5024 +2025-06-24 16:55:11,075 - pyskl - INFO - Epoch [52][700/1281] lr: 1.840e-02, eta: 13:09:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9969, loss_cls: 0.4844, loss: 0.4844 +2025-06-24 16:56:00,610 - pyskl - INFO - Epoch [52][800/1281] lr: 1.838e-02, eta: 13:09:13, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9975, loss_cls: 0.4260, loss: 0.4260 +2025-06-24 16:56:35,112 - pyskl - INFO - Epoch [52][900/1281] lr: 1.836e-02, eta: 13:08:29, time: 0.345, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9956, loss_cls: 0.5283, loss: 0.5283 +2025-06-24 16:57:26,414 - pyskl - INFO - Epoch [52][1000/1281] lr: 1.834e-02, eta: 13:08:18, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9981, loss_cls: 0.3942, loss: 0.3942 +2025-06-24 16:57:51,319 - pyskl - INFO - Epoch [52][1100/1281] lr: 1.832e-02, eta: 13:07:16, time: 0.249, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9981, loss_cls: 0.4473, loss: 0.4473 +2025-06-24 16:58:37,559 - pyskl - INFO - Epoch [52][1200/1281] lr: 1.831e-02, eta: 13:06:55, time: 0.462, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9956, loss_cls: 0.4445, loss: 0.4445 +2025-06-24 16:59:18,187 - pyskl - INFO - Saving checkpoint at 52 epochs +2025-06-24 17:00:17,240 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:00:17,310 - pyskl - INFO - +top1_acc 0.8634 +top5_acc 0.9920 +2025-06-24 17:00:17,310 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:00:17,318 - pyskl - INFO - +mean_acc 0.8215 +2025-06-24 17:00:17,320 - pyskl - INFO - Epoch(val) [52][533] top1_acc: 0.8634, top5_acc: 0.9920, mean_class_accuracy: 0.8215 +2025-06-24 17:01:37,226 - pyskl - INFO - Epoch [53][100/1281] lr: 1.827e-02, eta: 13:06:09, time: 0.799, data_time: 0.188, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9956, loss_cls: 0.4792, loss: 0.4792 +2025-06-24 17:02:26,156 - pyskl - INFO - Epoch [53][200/1281] lr: 1.826e-02, eta: 13:05:53, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9981, loss_cls: 0.4832, loss: 0.4832 +2025-06-24 17:03:15,413 - pyskl - INFO - Epoch [53][300/1281] lr: 1.824e-02, eta: 13:05:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9969, loss_cls: 0.3857, loss: 0.3857 +2025-06-24 17:04:04,618 - pyskl - INFO - Epoch [53][400/1281] lr: 1.822e-02, eta: 13:05:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9975, loss_cls: 0.4690, loss: 0.4690 +2025-06-24 17:04:53,792 - pyskl - INFO - Epoch [53][500/1281] lr: 1.820e-02, eta: 13:05:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9988, loss_cls: 0.5043, loss: 0.5043 +2025-06-24 17:05:42,905 - pyskl - INFO - Epoch [53][600/1281] lr: 1.818e-02, eta: 13:04:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.4913, loss: 0.4913 +2025-06-24 17:06:32,430 - pyskl - INFO - Epoch [53][700/1281] lr: 1.816e-02, eta: 13:04:33, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4437, loss: 0.4437 +2025-06-24 17:07:22,066 - pyskl - INFO - Epoch [53][800/1281] lr: 1.815e-02, eta: 13:04:17, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9988, loss_cls: 0.4469, loss: 0.4469 +2025-06-24 17:07:58,897 - pyskl - INFO - Epoch [53][900/1281] lr: 1.813e-02, eta: 13:03:38, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9950, loss_cls: 0.5234, loss: 0.5234 +2025-06-24 17:08:50,185 - pyskl - INFO - Epoch [53][1000/1281] lr: 1.811e-02, eta: 13:03:25, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9969, loss_cls: 0.4931, loss: 0.4931 +2025-06-24 17:09:14,513 - pyskl - INFO - Epoch [53][1100/1281] lr: 1.809e-02, eta: 13:02:22, time: 0.243, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9981, loss_cls: 0.4672, loss: 0.4672 +2025-06-24 17:09:59,930 - pyskl - INFO - Epoch [53][1200/1281] lr: 1.807e-02, eta: 13:01:59, time: 0.454, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9956, loss_cls: 0.5027, loss: 0.5027 +2025-06-24 17:10:40,613 - pyskl - INFO - Saving checkpoint at 53 epochs +2025-06-24 17:11:39,921 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:11:39,977 - pyskl - INFO - +top1_acc 0.8722 +top5_acc 0.9905 +2025-06-24 17:11:39,977 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:11:39,985 - pyskl - INFO - +mean_acc 0.8146 +2025-06-24 17:11:39,987 - pyskl - INFO - Epoch(val) [53][533] top1_acc: 0.8722, top5_acc: 0.9905, mean_class_accuracy: 0.8146 +2025-06-24 17:13:01,845 - pyskl - INFO - Epoch [54][100/1281] lr: 1.804e-02, eta: 13:01:15, time: 0.819, data_time: 0.199, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9981, loss_cls: 0.4534, loss: 0.4534 +2025-06-24 17:13:51,052 - pyskl - INFO - Epoch [54][200/1281] lr: 1.802e-02, eta: 13:00:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9975, loss_cls: 0.3977, loss: 0.3977 +2025-06-24 17:14:40,213 - pyskl - INFO - Epoch [54][300/1281] lr: 1.800e-02, eta: 13:00:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9994, loss_cls: 0.3856, loss: 0.3856 +2025-06-24 17:15:29,253 - pyskl - INFO - Epoch [54][400/1281] lr: 1.798e-02, eta: 13:00:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9981, loss_cls: 0.4506, loss: 0.4506 +2025-06-24 17:16:18,524 - pyskl - INFO - Epoch [54][500/1281] lr: 1.797e-02, eta: 13:00:07, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9988, loss_cls: 0.4593, loss: 0.4593 +2025-06-24 17:17:07,759 - pyskl - INFO - Epoch [54][600/1281] lr: 1.795e-02, eta: 12:59:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9981, loss_cls: 0.4114, loss: 0.4114 +2025-06-24 17:17:57,182 - pyskl - INFO - Epoch [54][700/1281] lr: 1.793e-02, eta: 12:59:33, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9981, loss_cls: 0.4883, loss: 0.4883 +2025-06-24 17:18:46,715 - pyskl - INFO - Epoch [54][800/1281] lr: 1.791e-02, eta: 12:59:16, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9975, loss_cls: 0.5068, loss: 0.5068 +2025-06-24 17:19:23,925 - pyskl - INFO - Epoch [54][900/1281] lr: 1.789e-02, eta: 12:58:37, time: 0.372, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9944, loss_cls: 0.5187, loss: 0.5187 +2025-06-24 17:20:15,002 - pyskl - INFO - Epoch [54][1000/1281] lr: 1.787e-02, eta: 12:58:22, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9956, loss_cls: 0.4728, loss: 0.4728 +2025-06-24 17:20:39,007 - pyskl - INFO - Epoch [54][1100/1281] lr: 1.786e-02, eta: 12:57:20, time: 0.240, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9981, loss_cls: 0.4608, loss: 0.4608 +2025-06-24 17:21:23,667 - pyskl - INFO - Epoch [54][1200/1281] lr: 1.784e-02, eta: 12:56:54, time: 0.447, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9962, loss_cls: 0.4563, loss: 0.4563 +2025-06-24 17:22:04,183 - pyskl - INFO - Saving checkpoint at 54 epochs +2025-06-24 17:23:03,573 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:23:03,644 - pyskl - INFO - +top1_acc 0.8571 +top5_acc 0.9894 +2025-06-24 17:23:03,644 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:23:03,654 - pyskl - INFO - +mean_acc 0.8258 +2025-06-24 17:23:03,657 - pyskl - INFO - Epoch(val) [54][533] top1_acc: 0.8571, top5_acc: 0.9894, mean_class_accuracy: 0.8258 +2025-06-24 17:24:25,025 - pyskl - INFO - Epoch [55][100/1281] lr: 1.780e-02, eta: 12:56:08, time: 0.814, data_time: 0.196, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9981, loss_cls: 0.4471, loss: 0.4471 +2025-06-24 17:25:14,485 - pyskl - INFO - Epoch [55][200/1281] lr: 1.779e-02, eta: 12:55:51, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.4075, loss: 0.4075 +2025-06-24 17:26:03,593 - pyskl - INFO - Epoch [55][300/1281] lr: 1.777e-02, eta: 12:55:33, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9981, loss_cls: 0.4138, loss: 0.4138 +2025-06-24 17:26:52,655 - pyskl - INFO - Epoch [55][400/1281] lr: 1.775e-02, eta: 12:55:14, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9975, loss_cls: 0.4892, loss: 0.4892 +2025-06-24 17:27:41,723 - pyskl - INFO - Epoch [55][500/1281] lr: 1.773e-02, eta: 12:54:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9981, loss_cls: 0.3740, loss: 0.3740 +2025-06-24 17:28:30,536 - pyskl - INFO - Epoch [55][600/1281] lr: 1.771e-02, eta: 12:54:37, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9969, loss_cls: 0.4875, loss: 0.4875 +2025-06-24 17:29:19,677 - pyskl - INFO - Epoch [55][700/1281] lr: 1.769e-02, eta: 12:54:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9969, loss_cls: 0.4525, loss: 0.4525 +2025-06-24 17:30:08,930 - pyskl - INFO - Epoch [55][800/1281] lr: 1.767e-02, eta: 12:54:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9969, loss_cls: 0.4753, loss: 0.4753 +2025-06-24 17:30:47,970 - pyskl - INFO - Epoch [55][900/1281] lr: 1.766e-02, eta: 12:53:24, time: 0.390, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9988, loss_cls: 0.4632, loss: 0.4632 +2025-06-24 17:31:39,169 - pyskl - INFO - Epoch [55][1000/1281] lr: 1.764e-02, eta: 12:53:09, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9975, loss_cls: 0.4709, loss: 0.4709 +2025-06-24 17:32:02,639 - pyskl - INFO - Epoch [55][1100/1281] lr: 1.762e-02, eta: 12:52:06, time: 0.235, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9962, loss_cls: 0.4486, loss: 0.4486 +2025-06-24 17:32:47,706 - pyskl - INFO - Epoch [55][1200/1281] lr: 1.760e-02, eta: 12:51:40, time: 0.451, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9969, loss_cls: 0.4859, loss: 0.4859 +2025-06-24 17:33:28,286 - pyskl - INFO - Saving checkpoint at 55 epochs +2025-06-24 17:34:27,623 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:34:27,694 - pyskl - INFO - +top1_acc 0.8621 +top5_acc 0.9906 +2025-06-24 17:34:27,694 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:34:27,704 - pyskl - INFO - +mean_acc 0.8121 +2025-06-24 17:34:27,706 - pyskl - INFO - Epoch(val) [55][533] top1_acc: 0.8621, top5_acc: 0.9906, mean_class_accuracy: 0.8121 +2025-06-24 17:35:48,797 - pyskl - INFO - Epoch [56][100/1281] lr: 1.757e-02, eta: 12:50:52, time: 0.811, data_time: 0.194, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4426, loss: 0.4426 +2025-06-24 17:36:37,995 - pyskl - INFO - Epoch [56][200/1281] lr: 1.755e-02, eta: 12:50:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9975, loss_cls: 0.4422, loss: 0.4422 +2025-06-24 17:37:27,112 - pyskl - INFO - Epoch [56][300/1281] lr: 1.753e-02, eta: 12:50:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9969, loss_cls: 0.4371, loss: 0.4371 +2025-06-24 17:38:16,554 - pyskl - INFO - Epoch [56][400/1281] lr: 1.751e-02, eta: 12:49:56, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9981, loss_cls: 0.4269, loss: 0.4269 +2025-06-24 17:39:05,676 - pyskl - INFO - Epoch [56][500/1281] lr: 1.749e-02, eta: 12:49:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9969, loss_cls: 0.4017, loss: 0.4017 +2025-06-24 17:39:54,927 - pyskl - INFO - Epoch [56][600/1281] lr: 1.747e-02, eta: 12:49:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9988, loss_cls: 0.4302, loss: 0.4302 +2025-06-24 17:40:44,064 - pyskl - INFO - Epoch [56][700/1281] lr: 1.745e-02, eta: 12:48:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9981, loss_cls: 0.4388, loss: 0.4388 +2025-06-24 17:41:33,368 - pyskl - INFO - Epoch [56][800/1281] lr: 1.743e-02, eta: 12:48:39, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9956, loss_cls: 0.5378, loss: 0.5378 +2025-06-24 17:42:12,317 - pyskl - INFO - Epoch [56][900/1281] lr: 1.742e-02, eta: 12:48:02, time: 0.389, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9981, loss_cls: 0.4564, loss: 0.4564 +2025-06-24 17:43:03,650 - pyskl - INFO - Epoch [56][1000/1281] lr: 1.740e-02, eta: 12:47:46, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9938, loss_cls: 0.5514, loss: 0.5514 +2025-06-24 17:43:27,556 - pyskl - INFO - Epoch [56][1100/1281] lr: 1.738e-02, eta: 12:46:44, time: 0.239, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9981, loss_cls: 0.4558, loss: 0.4558 +2025-06-24 17:44:12,351 - pyskl - INFO - Epoch [56][1200/1281] lr: 1.736e-02, eta: 12:46:17, time: 0.448, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9962, loss_cls: 0.4223, loss: 0.4223 +2025-06-24 17:44:52,845 - pyskl - INFO - Saving checkpoint at 56 epochs +2025-06-24 17:45:52,212 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:45:52,267 - pyskl - INFO - +top1_acc 0.8747 +top5_acc 0.9917 +2025-06-24 17:45:52,268 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:45:52,275 - pyskl - INFO - +mean_acc 0.8414 +2025-06-24 17:45:52,277 - pyskl - INFO - Epoch(val) [56][533] top1_acc: 0.8747, top5_acc: 0.9917, mean_class_accuracy: 0.8414 +2025-06-24 17:47:13,848 - pyskl - INFO - Epoch [57][100/1281] lr: 1.733e-02, eta: 12:45:29, time: 0.816, data_time: 0.193, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9988, loss_cls: 0.4613, loss: 0.4613 +2025-06-24 17:48:03,282 - pyskl - INFO - Epoch [57][200/1281] lr: 1.731e-02, eta: 12:45:09, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9962, loss_cls: 0.4271, loss: 0.4271 +2025-06-24 17:48:52,474 - pyskl - INFO - Epoch [57][300/1281] lr: 1.729e-02, eta: 12:44:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9988, loss_cls: 0.3820, loss: 0.3820 +2025-06-24 17:49:41,361 - pyskl - INFO - Epoch [57][400/1281] lr: 1.727e-02, eta: 12:44:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9975, loss_cls: 0.3918, loss: 0.3918 +2025-06-24 17:50:30,504 - pyskl - INFO - Epoch [57][500/1281] lr: 1.725e-02, eta: 12:44:09, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9950, loss_cls: 0.4686, loss: 0.4686 +2025-06-24 17:51:19,564 - pyskl - INFO - Epoch [57][600/1281] lr: 1.723e-02, eta: 12:43:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9969, loss_cls: 0.4492, loss: 0.4492 +2025-06-24 17:52:09,387 - pyskl - INFO - Epoch [57][700/1281] lr: 1.721e-02, eta: 12:43:29, time: 0.498, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9975, loss_cls: 0.4385, loss: 0.4385 +2025-06-24 17:52:58,754 - pyskl - INFO - Epoch [57][800/1281] lr: 1.719e-02, eta: 12:43:09, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9981, loss_cls: 0.4560, loss: 0.4560 +2025-06-24 17:53:37,084 - pyskl - INFO - Epoch [57][900/1281] lr: 1.717e-02, eta: 12:42:31, time: 0.383, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9956, loss_cls: 0.5014, loss: 0.5014 +2025-06-24 17:54:28,441 - pyskl - INFO - Epoch [57][1000/1281] lr: 1.716e-02, eta: 12:42:14, time: 0.514, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9969, loss_cls: 0.4379, loss: 0.4379 +2025-06-24 17:54:52,454 - pyskl - INFO - Epoch [57][1100/1281] lr: 1.714e-02, eta: 12:41:13, time: 0.240, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9975, loss_cls: 0.4272, loss: 0.4272 +2025-06-24 17:55:36,736 - pyskl - INFO - Epoch [57][1200/1281] lr: 1.712e-02, eta: 12:40:44, time: 0.443, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9969, loss_cls: 0.4505, loss: 0.4505 +2025-06-24 17:56:17,380 - pyskl - INFO - Saving checkpoint at 57 epochs +2025-06-24 17:57:16,854 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:57:16,939 - pyskl - INFO - +top1_acc 0.8806 +top5_acc 0.9930 +2025-06-24 17:57:16,939 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:57:16,948 - pyskl - INFO - +mean_acc 0.8392 +2025-06-24 17:57:16,950 - pyskl - INFO - Epoch(val) [57][533] top1_acc: 0.8806, top5_acc: 0.9930, mean_class_accuracy: 0.8392 +2025-06-24 17:58:35,581 - pyskl - INFO - Epoch [58][100/1281] lr: 1.708e-02, eta: 12:39:50, time: 0.786, data_time: 0.185, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.4100, loss: 0.4100 +2025-06-24 17:59:25,032 - pyskl - INFO - Epoch [58][200/1281] lr: 1.706e-02, eta: 12:39:30, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9988, loss_cls: 0.3543, loss: 0.3543 +2025-06-24 18:00:13,939 - pyskl - INFO - Epoch [58][300/1281] lr: 1.704e-02, eta: 12:39:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9988, loss_cls: 0.3588, loss: 0.3588 +2025-06-24 18:01:02,955 - pyskl - INFO - Epoch [58][400/1281] lr: 1.703e-02, eta: 12:38:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.3603, loss: 0.3603 +2025-06-24 18:01:52,213 - pyskl - INFO - Epoch [58][500/1281] lr: 1.701e-02, eta: 12:38:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.4071, loss: 0.4071 +2025-06-24 18:02:41,090 - pyskl - INFO - Epoch [58][600/1281] lr: 1.699e-02, eta: 12:38:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9962, loss_cls: 0.4200, loss: 0.4200 +2025-06-24 18:03:30,309 - pyskl - INFO - Epoch [58][700/1281] lr: 1.697e-02, eta: 12:37:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.4557, loss: 0.4557 +2025-06-24 18:04:19,481 - pyskl - INFO - Epoch [58][800/1281] lr: 1.695e-02, eta: 12:37:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9956, loss_cls: 0.5160, loss: 0.5160 +2025-06-24 18:04:59,964 - pyskl - INFO - Epoch [58][900/1281] lr: 1.693e-02, eta: 12:36:48, time: 0.405, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9981, loss_cls: 0.4487, loss: 0.4487 +2025-06-24 18:05:51,158 - pyskl - INFO - Epoch [58][1000/1281] lr: 1.691e-02, eta: 12:36:30, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9981, loss_cls: 0.4087, loss: 0.4087 +2025-06-24 18:06:14,401 - pyskl - INFO - Epoch [58][1100/1281] lr: 1.689e-02, eta: 12:35:28, time: 0.232, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9981, loss_cls: 0.4548, loss: 0.4548 +2025-06-24 18:06:58,320 - pyskl - INFO - Epoch [58][1200/1281] lr: 1.687e-02, eta: 12:34:58, time: 0.439, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9988, loss_cls: 0.4679, loss: 0.4679 +2025-06-24 18:07:38,800 - pyskl - INFO - Saving checkpoint at 58 epochs +2025-06-24 18:08:38,240 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:08:38,316 - pyskl - INFO - +top1_acc 0.8764 +top5_acc 0.9913 +2025-06-24 18:08:38,316 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:08:38,324 - pyskl - INFO - +mean_acc 0.8410 +2025-06-24 18:08:38,326 - pyskl - INFO - Epoch(val) [58][533] top1_acc: 0.8764, top5_acc: 0.9913, mean_class_accuracy: 0.8410 +2025-06-24 18:09:59,377 - pyskl - INFO - Epoch [59][100/1281] lr: 1.684e-02, eta: 12:34:07, time: 0.810, data_time: 0.193, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9975, loss_cls: 0.4691, loss: 0.4691 +2025-06-24 18:10:48,687 - pyskl - INFO - Epoch [59][200/1281] lr: 1.682e-02, eta: 12:33:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9988, loss_cls: 0.4208, loss: 0.4208 +2025-06-24 18:11:37,896 - pyskl - INFO - Epoch [59][300/1281] lr: 1.680e-02, eta: 12:33:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9994, loss_cls: 0.3958, loss: 0.3958 +2025-06-24 18:12:26,932 - pyskl - INFO - Epoch [59][400/1281] lr: 1.678e-02, eta: 12:33:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9950, loss_cls: 0.4287, loss: 0.4287 +2025-06-24 18:13:16,246 - pyskl - INFO - Epoch [59][500/1281] lr: 1.676e-02, eta: 12:32:41, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9969, loss_cls: 0.4424, loss: 0.4424 +2025-06-24 18:14:05,086 - pyskl - INFO - Epoch [59][600/1281] lr: 1.674e-02, eta: 12:32:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9988, loss_cls: 0.4264, loss: 0.4264 +2025-06-24 18:14:54,003 - pyskl - INFO - Epoch [59][700/1281] lr: 1.672e-02, eta: 12:31:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9994, loss_cls: 0.4355, loss: 0.4355 +2025-06-24 18:15:43,194 - pyskl - INFO - Epoch [59][800/1281] lr: 1.670e-02, eta: 12:31:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9994, loss_cls: 0.4210, loss: 0.4210 +2025-06-24 18:16:21,801 - pyskl - INFO - Epoch [59][900/1281] lr: 1.668e-02, eta: 12:30:56, time: 0.386, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9981, loss_cls: 0.4123, loss: 0.4123 +2025-06-24 18:17:12,951 - pyskl - INFO - Epoch [59][1000/1281] lr: 1.667e-02, eta: 12:30:37, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9975, loss_cls: 0.4422, loss: 0.4422 +2025-06-24 18:17:36,789 - pyskl - INFO - Epoch [59][1100/1281] lr: 1.665e-02, eta: 12:29:36, time: 0.238, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9975, loss_cls: 0.4344, loss: 0.4344 +2025-06-24 18:18:22,145 - pyskl - INFO - Epoch [59][1200/1281] lr: 1.663e-02, eta: 12:29:08, time: 0.454, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9969, loss_cls: 0.4765, loss: 0.4765 +2025-06-24 18:19:02,614 - pyskl - INFO - Saving checkpoint at 59 epochs +2025-06-24 18:20:01,710 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:20:01,765 - pyskl - INFO - +top1_acc 0.8729 +top5_acc 0.9914 +2025-06-24 18:20:01,765 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:20:01,771 - pyskl - INFO - +mean_acc 0.8256 +2025-06-24 18:20:01,773 - pyskl - INFO - Epoch(val) [59][533] top1_acc: 0.8729, top5_acc: 0.9914, mean_class_accuracy: 0.8256 +2025-06-24 18:21:22,725 - pyskl - INFO - Epoch [60][100/1281] lr: 1.659e-02, eta: 12:28:15, time: 0.809, data_time: 0.192, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9988, loss_cls: 0.4200, loss: 0.4200 +2025-06-24 18:22:12,045 - pyskl - INFO - Epoch [60][200/1281] lr: 1.657e-02, eta: 12:27:53, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 1.0000, loss_cls: 0.3767, loss: 0.3767 +2025-06-24 18:23:01,099 - pyskl - INFO - Epoch [60][300/1281] lr: 1.655e-02, eta: 12:27:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9975, loss_cls: 0.3979, loss: 0.3979 +2025-06-24 18:23:50,266 - pyskl - INFO - Epoch [60][400/1281] lr: 1.653e-02, eta: 12:27:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9994, loss_cls: 0.4194, loss: 0.4194 +2025-06-24 18:24:39,873 - pyskl - INFO - Epoch [60][500/1281] lr: 1.651e-02, eta: 12:26:47, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9981, loss_cls: 0.4209, loss: 0.4209 +2025-06-24 18:25:29,230 - pyskl - INFO - Epoch [60][600/1281] lr: 1.650e-02, eta: 12:26:25, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9988, loss_cls: 0.3798, loss: 0.3798 +2025-06-24 18:26:18,564 - pyskl - INFO - Epoch [60][700/1281] lr: 1.648e-02, eta: 12:26:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9950, loss_cls: 0.4227, loss: 0.4227 +2025-06-24 18:27:07,703 - pyskl - INFO - Epoch [60][800/1281] lr: 1.646e-02, eta: 12:25:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9975, loss_cls: 0.4729, loss: 0.4729 +2025-06-24 18:27:44,766 - pyskl - INFO - Epoch [60][900/1281] lr: 1.644e-02, eta: 12:24:59, time: 0.371, data_time: 0.001, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9969, loss_cls: 0.4177, loss: 0.4177 +2025-06-24 18:28:36,034 - pyskl - INFO - Epoch [60][1000/1281] lr: 1.642e-02, eta: 12:24:39, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9988, loss_cls: 0.4167, loss: 0.4167 +2025-06-24 18:29:00,818 - pyskl - INFO - Epoch [60][1100/1281] lr: 1.640e-02, eta: 12:23:39, time: 0.248, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9975, loss_cls: 0.4256, loss: 0.4256 +2025-06-24 18:29:48,856 - pyskl - INFO - Epoch [60][1200/1281] lr: 1.638e-02, eta: 12:23:15, time: 0.480, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9975, loss_cls: 0.4824, loss: 0.4824 +2025-06-24 18:30:29,040 - pyskl - INFO - Saving checkpoint at 60 epochs +2025-06-24 18:31:29,181 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:31:29,248 - pyskl - INFO - +top1_acc 0.8789 +top5_acc 0.9945 +2025-06-24 18:31:29,248 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:31:29,256 - pyskl - INFO - +mean_acc 0.8296 +2025-06-24 18:31:29,258 - pyskl - INFO - Epoch(val) [60][533] top1_acc: 0.8789, top5_acc: 0.9945, mean_class_accuracy: 0.8296 +2025-06-24 18:32:50,008 - pyskl - INFO - Epoch [61][100/1281] lr: 1.634e-02, eta: 12:22:21, time: 0.807, data_time: 0.195, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9962, loss_cls: 0.4364, loss: 0.4364 +2025-06-24 18:33:39,200 - pyskl - INFO - Epoch [61][200/1281] lr: 1.632e-02, eta: 12:21:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9988, loss_cls: 0.4209, loss: 0.4209 +2025-06-24 18:34:28,580 - pyskl - INFO - Epoch [61][300/1281] lr: 1.630e-02, eta: 12:21:35, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9988, loss_cls: 0.3763, loss: 0.3763 +2025-06-24 18:35:18,011 - pyskl - INFO - Epoch [61][400/1281] lr: 1.629e-02, eta: 12:21:13, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9975, loss_cls: 0.4088, loss: 0.4088 +2025-06-24 18:36:07,227 - pyskl - INFO - Epoch [61][500/1281] lr: 1.627e-02, eta: 12:20:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9969, loss_cls: 0.4224, loss: 0.4224 +2025-06-24 18:36:56,738 - pyskl - INFO - Epoch [61][600/1281] lr: 1.625e-02, eta: 12:20:27, time: 0.495, data_time: 0.001, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9975, loss_cls: 0.3876, loss: 0.3876 +2025-06-24 18:37:45,985 - pyskl - INFO - Epoch [61][700/1281] lr: 1.623e-02, eta: 12:20:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9988, loss_cls: 0.4104, loss: 0.4104 +2025-06-24 18:38:35,676 - pyskl - INFO - Epoch [61][800/1281] lr: 1.621e-02, eta: 12:19:41, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9988, loss_cls: 0.4293, loss: 0.4293 +2025-06-24 18:39:07,450 - pyskl - INFO - Epoch [61][900/1281] lr: 1.619e-02, eta: 12:18:52, time: 0.318, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4425, loss: 0.4425 +2025-06-24 18:39:58,719 - pyskl - INFO - Epoch [61][1000/1281] lr: 1.617e-02, eta: 12:18:31, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.4093, loss: 0.4093 +2025-06-24 18:40:25,115 - pyskl - INFO - Epoch [61][1100/1281] lr: 1.615e-02, eta: 12:17:35, time: 0.264, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9988, loss_cls: 0.4294, loss: 0.4294 +2025-06-24 18:41:14,777 - pyskl - INFO - Epoch [61][1200/1281] lr: 1.613e-02, eta: 12:17:12, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9981, loss_cls: 0.4186, loss: 0.4186 +2025-06-24 18:41:55,438 - pyskl - INFO - Saving checkpoint at 61 epochs +2025-06-24 18:42:54,817 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:42:54,893 - pyskl - INFO - +top1_acc 0.8680 +top5_acc 0.9899 +2025-06-24 18:42:54,893 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:42:54,901 - pyskl - INFO - +mean_acc 0.8159 +2025-06-24 18:42:54,903 - pyskl - INFO - Epoch(val) [61][533] top1_acc: 0.8680, top5_acc: 0.9899, mean_class_accuracy: 0.8159 +2025-06-24 18:44:14,575 - pyskl - INFO - Epoch [62][100/1281] lr: 1.609e-02, eta: 12:16:15, time: 0.797, data_time: 0.189, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9975, loss_cls: 0.4654, loss: 0.4654 +2025-06-24 18:45:04,186 - pyskl - INFO - Epoch [62][200/1281] lr: 1.607e-02, eta: 12:15:52, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9962, loss_cls: 0.4279, loss: 0.4279 +2025-06-24 18:45:53,317 - pyskl - INFO - Epoch [62][300/1281] lr: 1.605e-02, eta: 12:15:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9975, loss_cls: 0.4027, loss: 0.4027 +2025-06-24 18:46:42,339 - pyskl - INFO - Epoch [62][400/1281] lr: 1.603e-02, eta: 12:15:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9981, loss_cls: 0.4439, loss: 0.4439 +2025-06-24 18:47:32,005 - pyskl - INFO - Epoch [62][500/1281] lr: 1.602e-02, eta: 12:14:41, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.4099, loss: 0.4099 +2025-06-24 18:48:21,057 - pyskl - INFO - Epoch [62][600/1281] lr: 1.600e-02, eta: 12:14:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3814, loss: 0.3814 +2025-06-24 18:49:10,003 - pyskl - INFO - Epoch [62][700/1281] lr: 1.598e-02, eta: 12:13:53, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9988, loss_cls: 0.4269, loss: 0.4269 +2025-06-24 18:49:59,445 - pyskl - INFO - Epoch [62][800/1281] lr: 1.596e-02, eta: 12:13:29, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9956, loss_cls: 0.4503, loss: 0.4503 +2025-06-24 18:50:30,757 - pyskl - INFO - Epoch [62][900/1281] lr: 1.594e-02, eta: 12:12:39, time: 0.313, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9981, loss_cls: 0.4103, loss: 0.4103 +2025-06-24 18:51:21,962 - pyskl - INFO - Epoch [62][1000/1281] lr: 1.592e-02, eta: 12:12:18, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9994, loss_cls: 0.4395, loss: 0.4395 +2025-06-24 18:51:48,092 - pyskl - INFO - Epoch [62][1100/1281] lr: 1.590e-02, eta: 12:11:21, time: 0.261, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9969, loss_cls: 0.4422, loss: 0.4422 +2025-06-24 18:52:37,087 - pyskl - INFO - Epoch [62][1200/1281] lr: 1.588e-02, eta: 12:10:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9962, loss_cls: 0.4393, loss: 0.4393 +2025-06-24 18:53:17,551 - pyskl - INFO - Saving checkpoint at 62 epochs +2025-06-24 18:54:17,065 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:54:17,139 - pyskl - INFO - +top1_acc 0.8860 +top5_acc 0.9920 +2025-06-24 18:54:17,139 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:54:17,154 - pyskl - INFO - +mean_acc 0.8510 +2025-06-24 18:54:17,157 - pyskl - INFO - Epoch(val) [62][533] top1_acc: 0.8860, top5_acc: 0.9920, mean_class_accuracy: 0.8510 +2025-06-24 18:55:38,069 - pyskl - INFO - Epoch [63][100/1281] lr: 1.584e-02, eta: 12:10:01, time: 0.809, data_time: 0.195, memory: 4083, top1_acc: 0.9181, top5_acc: 1.0000, loss_cls: 0.4000, loss: 0.4000 +2025-06-24 18:56:27,551 - pyskl - INFO - Epoch [63][200/1281] lr: 1.582e-02, eta: 12:09:37, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9969, loss_cls: 0.4009, loss: 0.4009 +2025-06-24 18:57:16,594 - pyskl - INFO - Epoch [63][300/1281] lr: 1.580e-02, eta: 12:09:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9981, loss_cls: 0.3685, loss: 0.3685 +2025-06-24 18:58:06,030 - pyskl - INFO - Epoch [63][400/1281] lr: 1.578e-02, eta: 12:08:49, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9975, loss_cls: 0.4118, loss: 0.4118 +2025-06-24 18:58:55,243 - pyskl - INFO - Epoch [63][500/1281] lr: 1.576e-02, eta: 12:08:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.3681, loss: 0.3681 +2025-06-24 18:59:44,527 - pyskl - INFO - Epoch [63][600/1281] lr: 1.574e-02, eta: 12:08:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.3074, loss: 0.3074 +2025-06-24 19:00:33,427 - pyskl - INFO - Epoch [63][700/1281] lr: 1.572e-02, eta: 12:07:35, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9994, loss_cls: 0.3763, loss: 0.3763 +2025-06-24 19:01:22,576 - pyskl - INFO - Epoch [63][800/1281] lr: 1.570e-02, eta: 12:07:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9994, loss_cls: 0.3771, loss: 0.3771 +2025-06-24 19:01:53,052 - pyskl - INFO - Epoch [63][900/1281] lr: 1.568e-02, eta: 12:06:19, time: 0.305, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9975, loss_cls: 0.4314, loss: 0.4314 +2025-06-24 19:02:44,138 - pyskl - INFO - Epoch [63][1000/1281] lr: 1.566e-02, eta: 12:05:57, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9962, loss_cls: 0.4909, loss: 0.4909 +2025-06-24 19:03:13,748 - pyskl - INFO - Epoch [63][1100/1281] lr: 1.564e-02, eta: 12:05:05, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9988, loss_cls: 0.3877, loss: 0.3877 +2025-06-24 19:04:03,287 - pyskl - INFO - Epoch [63][1200/1281] lr: 1.562e-02, eta: 12:04:41, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9944, loss_cls: 0.4346, loss: 0.4346 +2025-06-24 19:04:43,825 - pyskl - INFO - Saving checkpoint at 63 epochs +2025-06-24 19:05:43,591 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:05:43,661 - pyskl - INFO - +top1_acc 0.9034 +top5_acc 0.9953 +2025-06-24 19:05:43,661 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:05:43,670 - pyskl - INFO - +mean_acc 0.8616 +2025-06-24 19:05:43,675 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_51.pth was removed +2025-06-24 19:05:44,159 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_63.pth. +2025-06-24 19:05:44,160 - pyskl - INFO - Best top1_acc is 0.9034 at 63 epoch. +2025-06-24 19:05:44,163 - pyskl - INFO - Epoch(val) [63][533] top1_acc: 0.9034, top5_acc: 0.9953, mean_class_accuracy: 0.8616 +2025-06-24 19:07:05,994 - pyskl - INFO - Epoch [64][100/1281] lr: 1.559e-02, eta: 12:03:46, time: 0.818, data_time: 0.197, memory: 4083, top1_acc: 0.9369, top5_acc: 1.0000, loss_cls: 0.3317, loss: 0.3317 +2025-06-24 19:07:55,446 - pyskl - INFO - Epoch [64][200/1281] lr: 1.557e-02, eta: 12:03:21, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9981, loss_cls: 0.4077, loss: 0.4077 +2025-06-24 19:08:44,564 - pyskl - INFO - Epoch [64][300/1281] lr: 1.555e-02, eta: 12:02:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9994, loss_cls: 0.3571, loss: 0.3571 +2025-06-24 19:09:33,776 - pyskl - INFO - Epoch [64][400/1281] lr: 1.553e-02, eta: 12:02:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9981, loss_cls: 0.3473, loss: 0.3473 +2025-06-24 19:10:23,347 - pyskl - INFO - Epoch [64][500/1281] lr: 1.551e-02, eta: 12:02:06, time: 0.496, data_time: 0.001, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9981, loss_cls: 0.4070, loss: 0.4070 +2025-06-24 19:11:12,947 - pyskl - INFO - Epoch [64][600/1281] lr: 1.549e-02, eta: 12:01:41, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9981, loss_cls: 0.3704, loss: 0.3704 +2025-06-24 19:12:02,065 - pyskl - INFO - Epoch [64][700/1281] lr: 1.547e-02, eta: 12:01:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9981, loss_cls: 0.4025, loss: 0.4025 +2025-06-24 19:12:51,575 - pyskl - INFO - Epoch [64][800/1281] lr: 1.545e-02, eta: 12:00:51, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.4299, loss: 0.4299 +2025-06-24 19:13:20,358 - pyskl - INFO - Epoch [64][900/1281] lr: 1.543e-02, eta: 11:59:58, time: 0.288, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9962, loss_cls: 0.3988, loss: 0.3988 +2025-06-24 19:14:08,385 - pyskl - INFO - Epoch [64][1000/1281] lr: 1.541e-02, eta: 11:59:31, time: 0.480, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9956, loss_cls: 0.3657, loss: 0.3657 +2025-06-24 19:14:41,910 - pyskl - INFO - Epoch [64][1100/1281] lr: 1.539e-02, eta: 11:58:44, time: 0.335, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9981, loss_cls: 0.4554, loss: 0.4554 +2025-06-24 19:15:31,221 - pyskl - INFO - Epoch [64][1200/1281] lr: 1.537e-02, eta: 11:58:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9969, loss_cls: 0.4574, loss: 0.4574 +2025-06-24 19:16:11,519 - pyskl - INFO - Saving checkpoint at 64 epochs +2025-06-24 19:17:10,899 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:17:10,956 - pyskl - INFO - +top1_acc 0.8804 +top5_acc 0.9933 +2025-06-24 19:17:10,957 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:17:10,963 - pyskl - INFO - +mean_acc 0.8546 +2025-06-24 19:17:10,965 - pyskl - INFO - Epoch(val) [64][533] top1_acc: 0.8804, top5_acc: 0.9933, mean_class_accuracy: 0.8546 +2025-06-24 19:18:31,861 - pyskl - INFO - Epoch [65][100/1281] lr: 1.533e-02, eta: 11:57:22, time: 0.809, data_time: 0.192, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3494, loss: 0.3494 +2025-06-24 19:19:21,170 - pyskl - INFO - Epoch [65][200/1281] lr: 1.531e-02, eta: 11:56:56, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9994, loss_cls: 0.4057, loss: 0.4057 +2025-06-24 19:20:10,420 - pyskl - INFO - Epoch [65][300/1281] lr: 1.529e-02, eta: 11:56:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3658, loss: 0.3658 +2025-06-24 19:20:59,599 - pyskl - INFO - Epoch [65][400/1281] lr: 1.527e-02, eta: 11:56:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 1.0000, loss_cls: 0.3239, loss: 0.3239 +2025-06-24 19:21:48,799 - pyskl - INFO - Epoch [65][500/1281] lr: 1.526e-02, eta: 11:55:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9975, loss_cls: 0.3747, loss: 0.3747 +2025-06-24 19:22:37,875 - pyskl - INFO - Epoch [65][600/1281] lr: 1.524e-02, eta: 11:55:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9994, loss_cls: 0.3863, loss: 0.3863 +2025-06-24 19:23:27,394 - pyskl - INFO - Epoch [65][700/1281] lr: 1.522e-02, eta: 11:54:48, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9975, loss_cls: 0.4143, loss: 0.4143 +2025-06-24 19:24:16,373 - pyskl - INFO - Epoch [65][800/1281] lr: 1.520e-02, eta: 11:54:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9975, loss_cls: 0.4529, loss: 0.4529 +2025-06-24 19:24:48,246 - pyskl - INFO - Epoch [65][900/1281] lr: 1.518e-02, eta: 11:53:33, time: 0.319, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9981, loss_cls: 0.3744, loss: 0.3744 +2025-06-24 19:25:31,134 - pyskl - INFO - Epoch [65][1000/1281] lr: 1.516e-02, eta: 11:52:59, time: 0.429, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9981, loss_cls: 0.4212, loss: 0.4212 +2025-06-24 19:26:08,208 - pyskl - INFO - Epoch [65][1100/1281] lr: 1.514e-02, eta: 11:52:17, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9981, loss_cls: 0.3853, loss: 0.3853 +2025-06-24 19:26:57,432 - pyskl - INFO - Epoch [65][1200/1281] lr: 1.512e-02, eta: 11:51:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9994, loss_cls: 0.3954, loss: 0.3954 +2025-06-24 19:27:38,125 - pyskl - INFO - Saving checkpoint at 65 epochs +2025-06-24 19:28:37,385 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:28:37,445 - pyskl - INFO - +top1_acc 0.8984 +top5_acc 0.9942 +2025-06-24 19:28:37,445 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:28:37,452 - pyskl - INFO - +mean_acc 0.8633 +2025-06-24 19:28:37,454 - pyskl - INFO - Epoch(val) [65][533] top1_acc: 0.8984, top5_acc: 0.9942, mean_class_accuracy: 0.8633 +2025-06-24 19:29:58,203 - pyskl - INFO - Epoch [66][100/1281] lr: 1.508e-02, eta: 11:50:52, time: 0.807, data_time: 0.198, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9994, loss_cls: 0.3540, loss: 0.3540 +2025-06-24 19:30:47,507 - pyskl - INFO - Epoch [66][200/1281] lr: 1.506e-02, eta: 11:50:26, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9988, loss_cls: 0.3489, loss: 0.3489 +2025-06-24 19:31:36,718 - pyskl - INFO - Epoch [66][300/1281] lr: 1.504e-02, eta: 11:50:00, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3377, loss: 0.3377 +2025-06-24 19:32:25,875 - pyskl - INFO - Epoch [66][400/1281] lr: 1.502e-02, eta: 11:49:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9988, loss_cls: 0.3825, loss: 0.3825 +2025-06-24 19:33:15,184 - pyskl - INFO - Epoch [66][500/1281] lr: 1.500e-02, eta: 11:49:07, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 1.0000, loss_cls: 0.4298, loss: 0.4298 +2025-06-24 19:34:04,326 - pyskl - INFO - Epoch [66][600/1281] lr: 1.498e-02, eta: 11:48:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 1.0000, loss_cls: 0.4075, loss: 0.4075 +2025-06-24 19:34:53,296 - pyskl - INFO - Epoch [66][700/1281] lr: 1.496e-02, eta: 11:48:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9988, loss_cls: 0.3834, loss: 0.3834 +2025-06-24 19:35:39,140 - pyskl - INFO - Epoch [66][800/1281] lr: 1.494e-02, eta: 11:47:43, time: 0.458, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9988, loss_cls: 0.4458, loss: 0.4458 +2025-06-24 19:36:19,285 - pyskl - INFO - Epoch [66][900/1281] lr: 1.492e-02, eta: 11:47:05, time: 0.401, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9969, loss_cls: 0.4053, loss: 0.4053 +2025-06-24 19:36:53,311 - pyskl - INFO - Epoch [66][1000/1281] lr: 1.490e-02, eta: 11:46:19, time: 0.340, data_time: 0.001, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9975, loss_cls: 0.4394, loss: 0.4394 +2025-06-24 19:37:33,331 - pyskl - INFO - Epoch [66][1100/1281] lr: 1.488e-02, eta: 11:45:41, time: 0.400, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9975, loss_cls: 0.4061, loss: 0.4061 +2025-06-24 19:38:22,621 - pyskl - INFO - Epoch [66][1200/1281] lr: 1.486e-02, eta: 11:45:14, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.4053, loss: 0.4053 +2025-06-24 19:39:03,066 - pyskl - INFO - Saving checkpoint at 66 epochs +2025-06-24 19:40:02,169 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:40:02,284 - pyskl - INFO - +top1_acc 0.8751 +top5_acc 0.9918 +2025-06-24 19:40:02,284 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:40:02,293 - pyskl - INFO - +mean_acc 0.8305 +2025-06-24 19:40:02,295 - pyskl - INFO - Epoch(val) [66][533] top1_acc: 0.8751, top5_acc: 0.9918, mean_class_accuracy: 0.8305 +2025-06-24 19:41:23,660 - pyskl - INFO - Epoch [67][100/1281] lr: 1.482e-02, eta: 11:44:16, time: 0.814, data_time: 0.192, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9988, loss_cls: 0.4228, loss: 0.4228 +2025-06-24 19:42:12,777 - pyskl - INFO - Epoch [67][200/1281] lr: 1.480e-02, eta: 11:43:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 1.0000, loss_cls: 0.3530, loss: 0.3530 +2025-06-24 19:43:01,886 - pyskl - INFO - Epoch [67][300/1281] lr: 1.478e-02, eta: 11:43:22, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9994, loss_cls: 0.3476, loss: 0.3476 +2025-06-24 19:43:51,134 - pyskl - INFO - Epoch [67][400/1281] lr: 1.476e-02, eta: 11:42:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9981, loss_cls: 0.3614, loss: 0.3614 +2025-06-24 19:44:40,606 - pyskl - INFO - Epoch [67][500/1281] lr: 1.474e-02, eta: 11:42:29, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9981, loss_cls: 0.4123, loss: 0.4123 +2025-06-24 19:45:29,930 - pyskl - INFO - Epoch [67][600/1281] lr: 1.472e-02, eta: 11:42:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9975, loss_cls: 0.4219, loss: 0.4219 +2025-06-24 19:46:19,672 - pyskl - INFO - Epoch [67][700/1281] lr: 1.470e-02, eta: 11:41:36, time: 0.497, data_time: 0.001, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9975, loss_cls: 0.3795, loss: 0.3795 +2025-06-24 19:47:03,673 - pyskl - INFO - Epoch [67][800/1281] lr: 1.468e-02, eta: 11:41:02, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 1.0000, loss_cls: 0.3995, loss: 0.3995 +2025-06-24 19:47:45,945 - pyskl - INFO - Epoch [67][900/1281] lr: 1.466e-02, eta: 11:40:26, time: 0.423, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9988, loss_cls: 0.3816, loss: 0.3816 +2025-06-24 19:48:17,400 - pyskl - INFO - Epoch [67][1000/1281] lr: 1.464e-02, eta: 11:39:37, time: 0.315, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.4092, loss: 0.4092 +2025-06-24 19:48:58,457 - pyskl - INFO - Epoch [67][1100/1281] lr: 1.462e-02, eta: 11:39:00, time: 0.411, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9962, loss_cls: 0.4242, loss: 0.4242 +2025-06-24 19:49:47,355 - pyskl - INFO - Epoch [67][1200/1281] lr: 1.460e-02, eta: 11:38:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 1.0000, loss_cls: 0.4121, loss: 0.4121 +2025-06-24 19:50:27,639 - pyskl - INFO - Saving checkpoint at 67 epochs +2025-06-24 19:51:27,455 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:51:27,510 - pyskl - INFO - +top1_acc 0.8857 +top5_acc 0.9941 +2025-06-24 19:51:27,510 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:51:27,517 - pyskl - INFO - +mean_acc 0.8439 +2025-06-24 19:51:27,519 - pyskl - INFO - Epoch(val) [67][533] top1_acc: 0.8857, top5_acc: 0.9941, mean_class_accuracy: 0.8439 +2025-06-24 19:52:46,637 - pyskl - INFO - Epoch [68][100/1281] lr: 1.456e-02, eta: 11:37:31, time: 0.791, data_time: 0.184, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9994, loss_cls: 0.3461, loss: 0.3461 +2025-06-24 19:53:36,144 - pyskl - INFO - Epoch [68][200/1281] lr: 1.454e-02, eta: 11:37:04, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9969, loss_cls: 0.3445, loss: 0.3445 +2025-06-24 19:54:25,425 - pyskl - INFO - Epoch [68][300/1281] lr: 1.452e-02, eta: 11:36:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9994, loss_cls: 0.3646, loss: 0.3646 +2025-06-24 19:55:14,733 - pyskl - INFO - Epoch [68][400/1281] lr: 1.450e-02, eta: 11:36:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.4177, loss: 0.4177 +2025-06-24 19:56:03,889 - pyskl - INFO - Epoch [68][500/1281] lr: 1.448e-02, eta: 11:35:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9950, loss_cls: 0.3681, loss: 0.3681 +2025-06-24 19:56:53,484 - pyskl - INFO - Epoch [68][600/1281] lr: 1.446e-02, eta: 11:35:15, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9994, loss_cls: 0.3546, loss: 0.3546 +2025-06-24 19:57:42,794 - pyskl - INFO - Epoch [68][700/1281] lr: 1.444e-02, eta: 11:34:47, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9981, loss_cls: 0.3559, loss: 0.3559 +2025-06-24 19:58:25,818 - pyskl - INFO - Epoch [68][800/1281] lr: 1.442e-02, eta: 11:34:12, time: 0.430, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3643, loss: 0.3643 +2025-06-24 19:59:11,643 - pyskl - INFO - Epoch [68][900/1281] lr: 1.440e-02, eta: 11:33:40, time: 0.458, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9988, loss_cls: 0.3738, loss: 0.3738 +2025-06-24 19:59:39,432 - pyskl - INFO - Epoch [68][1000/1281] lr: 1.438e-02, eta: 11:32:47, time: 0.278, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9981, loss_cls: 0.4244, loss: 0.4244 +2025-06-24 20:00:21,307 - pyskl - INFO - Epoch [68][1100/1281] lr: 1.436e-02, eta: 11:32:10, time: 0.419, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9994, loss_cls: 0.3720, loss: 0.3720 +2025-06-24 20:01:10,498 - pyskl - INFO - Epoch [68][1200/1281] lr: 1.434e-02, eta: 11:31:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9988, loss_cls: 0.3907, loss: 0.3907 +2025-06-24 20:01:51,052 - pyskl - INFO - Saving checkpoint at 68 epochs +2025-06-24 20:02:50,807 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:02:50,881 - pyskl - INFO - +top1_acc 0.8791 +top5_acc 0.9916 +2025-06-24 20:02:50,881 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:02:50,889 - pyskl - INFO - +mean_acc 0.8416 +2025-06-24 20:02:50,891 - pyskl - INFO - Epoch(val) [68][533] top1_acc: 0.8791, top5_acc: 0.9916, mean_class_accuracy: 0.8416 +2025-06-24 20:04:09,379 - pyskl - INFO - Epoch [69][100/1281] lr: 1.431e-02, eta: 11:30:39, time: 0.785, data_time: 0.187, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9994, loss_cls: 0.3781, loss: 0.3781 +2025-06-24 20:04:58,477 - pyskl - INFO - Epoch [69][200/1281] lr: 1.429e-02, eta: 11:30:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9994, loss_cls: 0.3415, loss: 0.3415 +2025-06-24 20:05:47,735 - pyskl - INFO - Epoch [69][300/1281] lr: 1.427e-02, eta: 11:29:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2956, loss: 0.2956 +2025-06-24 20:06:36,752 - pyskl - INFO - Epoch [69][400/1281] lr: 1.425e-02, eta: 11:29:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4104, loss: 0.4104 +2025-06-24 20:07:25,931 - pyskl - INFO - Epoch [69][500/1281] lr: 1.423e-02, eta: 11:28:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9988, loss_cls: 0.3920, loss: 0.3920 +2025-06-24 20:08:15,503 - pyskl - INFO - Epoch [69][600/1281] lr: 1.420e-02, eta: 11:28:20, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9969, loss_cls: 0.4117, loss: 0.4117 +2025-06-24 20:09:04,964 - pyskl - INFO - Epoch [69][700/1281] lr: 1.418e-02, eta: 11:27:52, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9975, loss_cls: 0.3725, loss: 0.3725 +2025-06-24 20:09:48,111 - pyskl - INFO - Epoch [69][800/1281] lr: 1.416e-02, eta: 11:27:17, time: 0.431, data_time: 0.001, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9975, loss_cls: 0.3570, loss: 0.3570 +2025-06-24 20:10:33,283 - pyskl - INFO - Epoch [69][900/1281] lr: 1.414e-02, eta: 11:26:44, time: 0.452, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9988, loss_cls: 0.3715, loss: 0.3715 +2025-06-24 20:11:02,434 - pyskl - INFO - Epoch [69][1000/1281] lr: 1.412e-02, eta: 11:25:52, time: 0.292, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3517, loss: 0.3517 +2025-06-24 20:11:46,472 - pyskl - INFO - Epoch [69][1100/1281] lr: 1.410e-02, eta: 11:25:18, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9975, loss_cls: 0.3461, loss: 0.3461 +2025-06-24 20:12:35,611 - pyskl - INFO - Epoch [69][1200/1281] lr: 1.408e-02, eta: 11:24:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3865, loss: 0.3865 +2025-06-24 20:13:16,292 - pyskl - INFO - Saving checkpoint at 69 epochs +2025-06-24 20:14:15,783 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:14:15,852 - pyskl - INFO - +top1_acc 0.8933 +top5_acc 0.9939 +2025-06-24 20:14:15,852 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:14:15,859 - pyskl - INFO - +mean_acc 0.8548 +2025-06-24 20:14:15,861 - pyskl - INFO - Epoch(val) [69][533] top1_acc: 0.8933, top5_acc: 0.9939, mean_class_accuracy: 0.8548 +2025-06-24 20:15:35,204 - pyskl - INFO - Epoch [70][100/1281] lr: 1.405e-02, eta: 11:23:47, time: 0.793, data_time: 0.192, memory: 4083, top1_acc: 0.9381, top5_acc: 1.0000, loss_cls: 0.3518, loss: 0.3518 +2025-06-24 20:16:24,421 - pyskl - INFO - Epoch [70][200/1281] lr: 1.403e-02, eta: 11:23:19, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9975, loss_cls: 0.3435, loss: 0.3435 +2025-06-24 20:17:13,502 - pyskl - INFO - Epoch [70][300/1281] lr: 1.401e-02, eta: 11:22:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9962, loss_cls: 0.3921, loss: 0.3921 +2025-06-24 20:18:02,528 - pyskl - INFO - Epoch [70][400/1281] lr: 1.399e-02, eta: 11:22:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 1.0000, loss_cls: 0.3288, loss: 0.3288 +2025-06-24 20:18:51,862 - pyskl - INFO - Epoch [70][500/1281] lr: 1.397e-02, eta: 11:21:53, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.3127, loss: 0.3127 +2025-06-24 20:19:41,084 - pyskl - INFO - Epoch [70][600/1281] lr: 1.395e-02, eta: 11:21:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9981, loss_cls: 0.3912, loss: 0.3912 +2025-06-24 20:20:30,256 - pyskl - INFO - Epoch [70][700/1281] lr: 1.392e-02, eta: 11:20:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9962, loss_cls: 0.3799, loss: 0.3799 +2025-06-24 20:21:11,149 - pyskl - INFO - Epoch [70][800/1281] lr: 1.390e-02, eta: 11:20:18, time: 0.409, data_time: 0.001, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9981, loss_cls: 0.3843, loss: 0.3843 +2025-06-24 20:22:01,702 - pyskl - INFO - Epoch [70][900/1281] lr: 1.388e-02, eta: 11:19:51, time: 0.506, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9988, loss_cls: 0.4246, loss: 0.4246 +2025-06-24 20:22:25,895 - pyskl - INFO - Epoch [70][1000/1281] lr: 1.386e-02, eta: 11:18:53, time: 0.242, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9956, loss_cls: 0.4063, loss: 0.4063 +2025-06-24 20:23:09,106 - pyskl - INFO - Epoch [70][1100/1281] lr: 1.384e-02, eta: 11:18:18, time: 0.432, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9988, loss_cls: 0.4071, loss: 0.4071 +2025-06-24 20:23:58,401 - pyskl - INFO - Epoch [70][1200/1281] lr: 1.382e-02, eta: 11:17:49, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9988, loss_cls: 0.4413, loss: 0.4413 +2025-06-24 20:24:38,614 - pyskl - INFO - Saving checkpoint at 70 epochs +2025-06-24 20:25:38,018 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:25:38,078 - pyskl - INFO - +top1_acc 0.8973 +top5_acc 0.9951 +2025-06-24 20:25:38,078 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:25:38,085 - pyskl - INFO - +mean_acc 0.8663 +2025-06-24 20:25:38,087 - pyskl - INFO - Epoch(val) [70][533] top1_acc: 0.8973, top5_acc: 0.9951, mean_class_accuracy: 0.8663 +2025-06-24 20:26:58,922 - pyskl - INFO - Epoch [71][100/1281] lr: 1.379e-02, eta: 11:16:48, time: 0.808, data_time: 0.194, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2817, loss: 0.2817 +2025-06-24 20:27:48,216 - pyskl - INFO - Epoch [71][200/1281] lr: 1.377e-02, eta: 11:16:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.2896, loss: 0.2896 +2025-06-24 20:28:37,397 - pyskl - INFO - Epoch [71][300/1281] lr: 1.375e-02, eta: 11:15:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9981, loss_cls: 0.2882, loss: 0.2882 +2025-06-24 20:29:26,723 - pyskl - INFO - Epoch [71][400/1281] lr: 1.373e-02, eta: 11:15:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9988, loss_cls: 0.3402, loss: 0.3402 +2025-06-24 20:30:16,049 - pyskl - INFO - Epoch [71][500/1281] lr: 1.371e-02, eta: 11:14:53, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9994, loss_cls: 0.4087, loss: 0.4087 +2025-06-24 20:31:05,289 - pyskl - INFO - Epoch [71][600/1281] lr: 1.368e-02, eta: 11:14:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9988, loss_cls: 0.3625, loss: 0.3625 +2025-06-24 20:31:54,606 - pyskl - INFO - Epoch [71][700/1281] lr: 1.366e-02, eta: 11:13:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9988, loss_cls: 0.3604, loss: 0.3604 +2025-06-24 20:32:33,898 - pyskl - INFO - Epoch [71][800/1281] lr: 1.364e-02, eta: 11:13:14, time: 0.393, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 1.0000, loss_cls: 0.3506, loss: 0.3506 +2025-06-24 20:33:25,220 - pyskl - INFO - Epoch [71][900/1281] lr: 1.362e-02, eta: 11:12:48, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9994, loss_cls: 0.3587, loss: 0.3587 +2025-06-24 20:33:48,880 - pyskl - INFO - Epoch [71][1000/1281] lr: 1.360e-02, eta: 11:11:50, time: 0.237, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3554, loss: 0.3554 +2025-06-24 20:34:33,653 - pyskl - INFO - Epoch [71][1100/1281] lr: 1.358e-02, eta: 11:11:16, time: 0.448, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9981, loss_cls: 0.3434, loss: 0.3434 +2025-06-24 20:35:22,877 - pyskl - INFO - Epoch [71][1200/1281] lr: 1.356e-02, eta: 11:10:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9981, loss_cls: 0.3510, loss: 0.3510 +2025-06-24 20:36:03,273 - pyskl - INFO - Saving checkpoint at 71 epochs +2025-06-24 20:37:03,725 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:37:03,796 - pyskl - INFO - +top1_acc 0.8751 +top5_acc 0.9933 +2025-06-24 20:37:03,797 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:37:03,809 - pyskl - INFO - +mean_acc 0.8294 +2025-06-24 20:37:03,811 - pyskl - INFO - Epoch(val) [71][533] top1_acc: 0.8751, top5_acc: 0.9933, mean_class_accuracy: 0.8294 +2025-06-24 20:38:23,851 - pyskl - INFO - Epoch [72][100/1281] lr: 1.353e-02, eta: 11:09:44, time: 0.800, data_time: 0.195, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9981, loss_cls: 0.3348, loss: 0.3348 +2025-06-24 20:39:13,259 - pyskl - INFO - Epoch [72][200/1281] lr: 1.351e-02, eta: 11:09:15, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3023, loss: 0.3023 +2025-06-24 20:40:02,293 - pyskl - INFO - Epoch [72][300/1281] lr: 1.349e-02, eta: 11:08:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9975, loss_cls: 0.3242, loss: 0.3242 +2025-06-24 20:40:51,834 - pyskl - INFO - Epoch [72][400/1281] lr: 1.346e-02, eta: 11:08:16, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9956, loss_cls: 0.3019, loss: 0.3019 +2025-06-24 20:41:40,946 - pyskl - INFO - Epoch [72][500/1281] lr: 1.344e-02, eta: 11:07:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 1.0000, loss_cls: 0.3144, loss: 0.3144 +2025-06-24 20:42:30,342 - pyskl - INFO - Epoch [72][600/1281] lr: 1.342e-02, eta: 11:07:18, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9988, loss_cls: 0.4043, loss: 0.4043 +2025-06-24 20:43:19,595 - pyskl - INFO - Epoch [72][700/1281] lr: 1.340e-02, eta: 11:06:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9994, loss_cls: 0.3367, loss: 0.3367 +2025-06-24 20:43:56,833 - pyskl - INFO - Epoch [72][800/1281] lr: 1.338e-02, eta: 11:06:06, time: 0.372, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9969, loss_cls: 0.4090, loss: 0.4090 +2025-06-24 20:44:48,096 - pyskl - INFO - Epoch [72][900/1281] lr: 1.336e-02, eta: 11:05:38, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9988, loss_cls: 0.3626, loss: 0.3626 +2025-06-24 20:45:12,526 - pyskl - INFO - Epoch [72][1000/1281] lr: 1.334e-02, eta: 11:04:42, time: 0.244, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 1.0000, loss_cls: 0.3446, loss: 0.3446 +2025-06-24 20:45:59,002 - pyskl - INFO - Epoch [72][1100/1281] lr: 1.332e-02, eta: 11:04:09, time: 0.465, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9994, loss_cls: 0.3606, loss: 0.3606 +2025-06-24 20:46:48,196 - pyskl - INFO - Epoch [72][1200/1281] lr: 1.330e-02, eta: 11:03:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9975, loss_cls: 0.3836, loss: 0.3836 +2025-06-24 20:47:28,685 - pyskl - INFO - Saving checkpoint at 72 epochs +2025-06-24 20:48:28,037 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:48:28,098 - pyskl - INFO - +top1_acc 0.9039 +top5_acc 0.9958 +2025-06-24 20:48:28,098 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:48:28,106 - pyskl - INFO - +mean_acc 0.8683 +2025-06-24 20:48:28,110 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_63.pth was removed +2025-06-24 20:48:28,282 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_72.pth. +2025-06-24 20:48:28,283 - pyskl - INFO - Best top1_acc is 0.9039 at 72 epoch. +2025-06-24 20:48:28,286 - pyskl - INFO - Epoch(val) [72][533] top1_acc: 0.9039, top5_acc: 0.9958, mean_class_accuracy: 0.8683 +2025-06-24 20:49:48,780 - pyskl - INFO - Epoch [73][100/1281] lr: 1.326e-02, eta: 11:02:36, time: 0.805, data_time: 0.194, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3295, loss: 0.3295 +2025-06-24 20:50:37,853 - pyskl - INFO - Epoch [73][200/1281] lr: 1.324e-02, eta: 11:02:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2743, loss: 0.2743 +2025-06-24 20:51:27,093 - pyskl - INFO - Epoch [73][300/1281] lr: 1.322e-02, eta: 11:01:37, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9981, loss_cls: 0.3372, loss: 0.3372 +2025-06-24 20:52:16,083 - pyskl - INFO - Epoch [73][400/1281] lr: 1.320e-02, eta: 11:01:07, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9981, loss_cls: 0.2732, loss: 0.2732 +2025-06-24 20:53:05,178 - pyskl - INFO - Epoch [73][500/1281] lr: 1.318e-02, eta: 11:00:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.3430, loss: 0.3430 +2025-06-24 20:53:54,266 - pyskl - INFO - Epoch [73][600/1281] lr: 1.316e-02, eta: 11:00:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 1.0000, loss_cls: 0.3251, loss: 0.3251 +2025-06-24 20:54:43,448 - pyskl - INFO - Epoch [73][700/1281] lr: 1.314e-02, eta: 10:59:37, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9981, loss_cls: 0.3349, loss: 0.3349 +2025-06-24 20:55:17,944 - pyskl - INFO - Epoch [73][800/1281] lr: 1.312e-02, eta: 10:58:51, time: 0.345, data_time: 0.001, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9988, loss_cls: 0.3759, loss: 0.3759 +2025-06-24 20:56:09,244 - pyskl - INFO - Epoch [73][900/1281] lr: 1.310e-02, eta: 10:58:24, time: 0.513, data_time: 0.001, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9981, loss_cls: 0.3665, loss: 0.3665 +2025-06-24 20:56:34,514 - pyskl - INFO - Epoch [73][1000/1281] lr: 1.308e-02, eta: 10:57:28, time: 0.253, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9981, loss_cls: 0.3650, loss: 0.3650 +2025-06-24 20:57:23,646 - pyskl - INFO - Epoch [73][1100/1281] lr: 1.306e-02, eta: 10:56:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9969, loss_cls: 0.3738, loss: 0.3738 +2025-06-24 20:58:12,741 - pyskl - INFO - Epoch [73][1200/1281] lr: 1.304e-02, eta: 10:56:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9975, loss_cls: 0.3863, loss: 0.3863 +2025-06-24 20:58:53,461 - pyskl - INFO - Saving checkpoint at 73 epochs +2025-06-24 20:59:53,006 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:59:53,061 - pyskl - INFO - +top1_acc 0.8953 +top5_acc 0.9946 +2025-06-24 20:59:53,062 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:59:53,068 - pyskl - INFO - +mean_acc 0.8689 +2025-06-24 20:59:53,070 - pyskl - INFO - Epoch(val) [73][533] top1_acc: 0.8953, top5_acc: 0.9946, mean_class_accuracy: 0.8689 +2025-06-24 21:01:13,108 - pyskl - INFO - Epoch [74][100/1281] lr: 1.300e-02, eta: 10:55:24, time: 0.800, data_time: 0.193, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9981, loss_cls: 0.2976, loss: 0.2976 +2025-06-24 21:02:02,522 - pyskl - INFO - Epoch [74][200/1281] lr: 1.298e-02, eta: 10:54:54, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3266, loss: 0.3266 +2025-06-24 21:02:51,730 - pyskl - INFO - Epoch [74][300/1281] lr: 1.296e-02, eta: 10:54:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.3123, loss: 0.3123 +2025-06-24 21:03:41,097 - pyskl - INFO - Epoch [74][400/1281] lr: 1.294e-02, eta: 10:53:54, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9988, loss_cls: 0.3056, loss: 0.3056 +2025-06-24 21:04:30,087 - pyskl - INFO - Epoch [74][500/1281] lr: 1.292e-02, eta: 10:53:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9988, loss_cls: 0.2981, loss: 0.2981 +2025-06-24 21:05:19,081 - pyskl - INFO - Epoch [74][600/1281] lr: 1.290e-02, eta: 10:52:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.3113, loss: 0.3113 +2025-06-24 21:06:08,590 - pyskl - INFO - Epoch [74][700/1281] lr: 1.288e-02, eta: 10:52:23, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3663, loss: 0.3663 +2025-06-24 21:06:40,208 - pyskl - INFO - Epoch [74][800/1281] lr: 1.286e-02, eta: 10:51:34, time: 0.316, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9994, loss_cls: 0.3553, loss: 0.3553 +2025-06-24 21:07:31,436 - pyskl - INFO - Epoch [74][900/1281] lr: 1.284e-02, eta: 10:51:06, time: 0.512, data_time: 0.001, memory: 4083, top1_acc: 0.9225, top5_acc: 1.0000, loss_cls: 0.3984, loss: 0.3984 +2025-06-24 21:07:59,738 - pyskl - INFO - Epoch [74][1000/1281] lr: 1.282e-02, eta: 10:50:14, time: 0.283, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9994, loss_cls: 0.3416, loss: 0.3416 +2025-06-24 21:08:49,029 - pyskl - INFO - Epoch [74][1100/1281] lr: 1.280e-02, eta: 10:49:43, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9981, loss_cls: 0.3928, loss: 0.3928 +2025-06-24 21:09:38,600 - pyskl - INFO - Epoch [74][1200/1281] lr: 1.278e-02, eta: 10:49:13, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9975, loss_cls: 0.3437, loss: 0.3437 +2025-06-24 21:10:19,027 - pyskl - INFO - Saving checkpoint at 74 epochs +2025-06-24 21:11:18,117 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:11:18,185 - pyskl - INFO - +top1_acc 0.8903 +top5_acc 0.9937 +2025-06-24 21:11:18,185 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:11:18,195 - pyskl - INFO - +mean_acc 0.8461 +2025-06-24 21:11:18,197 - pyskl - INFO - Epoch(val) [74][533] top1_acc: 0.8903, top5_acc: 0.9937, mean_class_accuracy: 0.8461 +2025-06-24 21:12:36,912 - pyskl - INFO - Epoch [75][100/1281] lr: 1.274e-02, eta: 10:48:07, time: 0.787, data_time: 0.190, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9981, loss_cls: 0.3185, loss: 0.3185 +2025-06-24 21:13:25,874 - pyskl - INFO - Epoch [75][200/1281] lr: 1.272e-02, eta: 10:47:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2606, loss: 0.2606 +2025-06-24 21:14:15,197 - pyskl - INFO - Epoch [75][300/1281] lr: 1.270e-02, eta: 10:47:06, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3307, loss: 0.3307 +2025-06-24 21:15:04,385 - pyskl - INFO - Epoch [75][400/1281] lr: 1.268e-02, eta: 10:46:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9975, loss_cls: 0.3674, loss: 0.3674 +2025-06-24 21:15:53,740 - pyskl - INFO - Epoch [75][500/1281] lr: 1.266e-02, eta: 10:46:05, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9975, loss_cls: 0.3587, loss: 0.3587 +2025-06-24 21:16:43,260 - pyskl - INFO - Epoch [75][600/1281] lr: 1.264e-02, eta: 10:45:35, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9988, loss_cls: 0.3194, loss: 0.3194 +2025-06-24 21:17:32,511 - pyskl - INFO - Epoch [75][700/1281] lr: 1.262e-02, eta: 10:45:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.3205, loss: 0.3205 +2025-06-24 21:18:03,003 - pyskl - INFO - Epoch [75][800/1281] lr: 1.260e-02, eta: 10:44:14, time: 0.305, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.3290, loss: 0.3290 +2025-06-24 21:18:54,071 - pyskl - INFO - Epoch [75][900/1281] lr: 1.258e-02, eta: 10:43:45, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9975, loss_cls: 0.3729, loss: 0.3729 +2025-06-24 21:19:22,134 - pyskl - INFO - Epoch [75][1000/1281] lr: 1.256e-02, eta: 10:42:53, time: 0.281, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 1.0000, loss_cls: 0.3460, loss: 0.3460 +2025-06-24 21:20:11,964 - pyskl - INFO - Epoch [75][1100/1281] lr: 1.254e-02, eta: 10:42:23, time: 0.498, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9994, loss_cls: 0.4146, loss: 0.4146 +2025-06-24 21:21:01,238 - pyskl - INFO - Epoch [75][1200/1281] lr: 1.252e-02, eta: 10:41:52, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9994, loss_cls: 0.3725, loss: 0.3725 +2025-06-24 21:21:41,791 - pyskl - INFO - Saving checkpoint at 75 epochs +2025-06-24 21:22:41,561 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:22:41,622 - pyskl - INFO - +top1_acc 0.8910 +top5_acc 0.9945 +2025-06-24 21:22:41,622 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:22:41,637 - pyskl - INFO - +mean_acc 0.8606 +2025-06-24 21:22:41,641 - pyskl - INFO - Epoch(val) [75][533] top1_acc: 0.8910, top5_acc: 0.9945, mean_class_accuracy: 0.8606 +2025-06-24 21:24:01,875 - pyskl - INFO - Epoch [76][100/1281] lr: 1.248e-02, eta: 10:40:47, time: 0.802, data_time: 0.197, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9988, loss_cls: 0.3113, loss: 0.3113 +2025-06-24 21:24:50,740 - pyskl - INFO - Epoch [76][200/1281] lr: 1.246e-02, eta: 10:40:16, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9994, loss_cls: 0.3501, loss: 0.3501 +2025-06-24 21:25:39,788 - pyskl - INFO - Epoch [76][300/1281] lr: 1.244e-02, eta: 10:39:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 1.0000, loss_cls: 0.2936, loss: 0.2936 +2025-06-24 21:26:29,038 - pyskl - INFO - Epoch [76][400/1281] lr: 1.242e-02, eta: 10:39:14, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9988, loss_cls: 0.2941, loss: 0.2941 +2025-06-24 21:27:18,135 - pyskl - INFO - Epoch [76][500/1281] lr: 1.240e-02, eta: 10:38:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3786, loss: 0.3786 +2025-06-24 21:28:07,617 - pyskl - INFO - Epoch [76][600/1281] lr: 1.238e-02, eta: 10:38:12, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9981, loss_cls: 0.3251, loss: 0.3251 +2025-06-24 21:28:56,965 - pyskl - INFO - Epoch [76][700/1281] lr: 1.236e-02, eta: 10:37:41, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9994, loss_cls: 0.3384, loss: 0.3384 +2025-06-24 21:29:25,693 - pyskl - INFO - Epoch [76][800/1281] lr: 1.234e-02, eta: 10:36:50, time: 0.287, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9994, loss_cls: 0.3581, loss: 0.3581 +2025-06-24 21:30:16,821 - pyskl - INFO - Epoch [76][900/1281] lr: 1.232e-02, eta: 10:36:20, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3271, loss: 0.3271 +2025-06-24 21:30:46,485 - pyskl - INFO - Epoch [76][1000/1281] lr: 1.230e-02, eta: 10:35:30, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 1.0000, loss_cls: 0.3504, loss: 0.3504 +2025-06-24 21:31:35,676 - pyskl - INFO - Epoch [76][1100/1281] lr: 1.228e-02, eta: 10:34:59, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.3095, loss: 0.3095 +2025-06-24 21:32:24,894 - pyskl - INFO - Epoch [76][1200/1281] lr: 1.225e-02, eta: 10:34:28, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9994, loss_cls: 0.3671, loss: 0.3671 +2025-06-24 21:33:05,378 - pyskl - INFO - Saving checkpoint at 76 epochs +2025-06-24 21:34:05,433 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:34:05,488 - pyskl - INFO - +top1_acc 0.9045 +top5_acc 0.9959 +2025-06-24 21:34:05,488 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:34:05,495 - pyskl - INFO - +mean_acc 0.8796 +2025-06-24 21:34:05,499 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_72.pth was removed +2025-06-24 21:34:05,673 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_76.pth. +2025-06-24 21:34:05,673 - pyskl - INFO - Best top1_acc is 0.9045 at 76 epoch. +2025-06-24 21:34:05,675 - pyskl - INFO - Epoch(val) [76][533] top1_acc: 0.9045, top5_acc: 0.9959, mean_class_accuracy: 0.8796 +2025-06-24 21:35:25,961 - pyskl - INFO - Epoch [77][100/1281] lr: 1.222e-02, eta: 10:33:22, time: 0.803, data_time: 0.197, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2484, loss: 0.2484 +2025-06-24 21:36:15,136 - pyskl - INFO - Epoch [77][200/1281] lr: 1.220e-02, eta: 10:32:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2652, loss: 0.2652 +2025-06-24 21:37:04,329 - pyskl - INFO - Epoch [77][300/1281] lr: 1.218e-02, eta: 10:32:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3319, loss: 0.3319 +2025-06-24 21:37:53,213 - pyskl - INFO - Epoch [77][400/1281] lr: 1.216e-02, eta: 10:31:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9981, loss_cls: 0.2772, loss: 0.2772 +2025-06-24 21:38:42,531 - pyskl - INFO - Epoch [77][500/1281] lr: 1.214e-02, eta: 10:31:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 1.0000, loss_cls: 0.3481, loss: 0.3481 +2025-06-24 21:39:31,773 - pyskl - INFO - Epoch [77][600/1281] lr: 1.212e-02, eta: 10:30:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.3101, loss: 0.3101 +2025-06-24 21:40:21,048 - pyskl - INFO - Epoch [77][700/1281] lr: 1.210e-02, eta: 10:30:14, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9981, loss_cls: 0.3129, loss: 0.3129 +2025-06-24 21:40:49,201 - pyskl - INFO - Epoch [77][800/1281] lr: 1.207e-02, eta: 10:29:22, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9969, loss_cls: 0.4039, loss: 0.4039 +2025-06-24 21:41:38,176 - pyskl - INFO - Epoch [77][900/1281] lr: 1.205e-02, eta: 10:28:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9981, loss_cls: 0.3855, loss: 0.3855 +2025-06-24 21:42:10,628 - pyskl - INFO - Epoch [77][1000/1281] lr: 1.203e-02, eta: 10:28:03, time: 0.325, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3776, loss: 0.3776 +2025-06-24 21:42:59,884 - pyskl - INFO - Epoch [77][1100/1281] lr: 1.201e-02, eta: 10:27:31, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9994, loss_cls: 0.3446, loss: 0.3446 +2025-06-24 21:43:49,001 - pyskl - INFO - Epoch [77][1200/1281] lr: 1.199e-02, eta: 10:26:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9975, loss_cls: 0.3809, loss: 0.3809 +2025-06-24 21:44:29,576 - pyskl - INFO - Saving checkpoint at 77 epochs +2025-06-24 21:45:29,086 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:45:29,143 - pyskl - INFO - +top1_acc 0.9013 +top5_acc 0.9953 +2025-06-24 21:45:29,144 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:45:29,151 - pyskl - INFO - +mean_acc 0.8595 +2025-06-24 21:45:29,153 - pyskl - INFO - Epoch(val) [77][533] top1_acc: 0.9013, top5_acc: 0.9953, mean_class_accuracy: 0.8595 +2025-06-24 21:46:49,209 - pyskl - INFO - Epoch [78][100/1281] lr: 1.196e-02, eta: 10:25:54, time: 0.801, data_time: 0.197, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9988, loss_cls: 0.2729, loss: 0.2729 +2025-06-24 21:47:38,553 - pyskl - INFO - Epoch [78][200/1281] lr: 1.194e-02, eta: 10:25:22, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9988, loss_cls: 0.3453, loss: 0.3453 +2025-06-24 21:48:27,639 - pyskl - INFO - Epoch [78][300/1281] lr: 1.192e-02, eta: 10:24:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9988, loss_cls: 0.3354, loss: 0.3354 +2025-06-24 21:49:17,079 - pyskl - INFO - Epoch [78][400/1281] lr: 1.190e-02, eta: 10:24:19, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.2850, loss: 0.2850 +2025-06-24 21:50:06,294 - pyskl - INFO - Epoch [78][500/1281] lr: 1.187e-02, eta: 10:23:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9988, loss_cls: 0.3048, loss: 0.3048 +2025-06-24 21:50:55,441 - pyskl - INFO - Epoch [78][600/1281] lr: 1.185e-02, eta: 10:23:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.3060, loss: 0.3060 +2025-06-24 21:51:44,700 - pyskl - INFO - Epoch [78][700/1281] lr: 1.183e-02, eta: 10:22:43, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 1.0000, loss_cls: 0.2781, loss: 0.2781 +2025-06-24 21:52:15,430 - pyskl - INFO - Epoch [78][800/1281] lr: 1.181e-02, eta: 10:21:54, time: 0.307, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9988, loss_cls: 0.3375, loss: 0.3375 +2025-06-24 21:52:59,560 - pyskl - INFO - Epoch [78][900/1281] lr: 1.179e-02, eta: 10:21:17, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9981, loss_cls: 0.3646, loss: 0.3646 +2025-06-24 21:53:35,174 - pyskl - INFO - Epoch [78][1000/1281] lr: 1.177e-02, eta: 10:20:33, time: 0.356, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9988, loss_cls: 0.2756, loss: 0.2756 +2025-06-24 21:54:24,130 - pyskl - INFO - Epoch [78][1100/1281] lr: 1.175e-02, eta: 10:20:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9981, loss_cls: 0.3079, loss: 0.3079 +2025-06-24 21:55:13,325 - pyskl - INFO - Epoch [78][1200/1281] lr: 1.173e-02, eta: 10:19:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9988, loss_cls: 0.3170, loss: 0.3170 +2025-06-24 21:55:53,687 - pyskl - INFO - Saving checkpoint at 78 epochs +2025-06-24 21:56:53,570 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:56:53,628 - pyskl - INFO - +top1_acc 0.9008 +top5_acc 0.9947 +2025-06-24 21:56:53,628 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:56:53,635 - pyskl - INFO - +mean_acc 0.8704 +2025-06-24 21:56:53,637 - pyskl - INFO - Epoch(val) [78][533] top1_acc: 0.9008, top5_acc: 0.9947, mean_class_accuracy: 0.8704 +2025-06-24 21:58:14,409 - pyskl - INFO - Epoch [79][100/1281] lr: 1.169e-02, eta: 10:18:23, time: 0.808, data_time: 0.193, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.3030, loss: 0.3030 +2025-06-24 21:59:03,750 - pyskl - INFO - Epoch [79][200/1281] lr: 1.167e-02, eta: 10:17:51, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2508, loss: 0.2508 +2025-06-24 21:59:53,129 - pyskl - INFO - Epoch [79][300/1281] lr: 1.165e-02, eta: 10:17:19, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9988, loss_cls: 0.2677, loss: 0.2677 +2025-06-24 22:00:42,337 - pyskl - INFO - Epoch [79][400/1281] lr: 1.163e-02, eta: 10:16:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9988, loss_cls: 0.2610, loss: 0.2610 +2025-06-24 22:01:31,391 - pyskl - INFO - Epoch [79][500/1281] lr: 1.161e-02, eta: 10:16:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9988, loss_cls: 0.2837, loss: 0.2837 +2025-06-24 22:02:20,964 - pyskl - INFO - Epoch [79][600/1281] lr: 1.159e-02, eta: 10:15:43, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9994, loss_cls: 0.3192, loss: 0.3192 +2025-06-24 22:03:08,147 - pyskl - INFO - Epoch [79][700/1281] lr: 1.157e-02, eta: 10:15:09, time: 0.472, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.3416, loss: 0.3416 +2025-06-24 22:03:46,584 - pyskl - INFO - Epoch [79][800/1281] lr: 1.155e-02, eta: 10:14:27, time: 0.384, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9988, loss_cls: 0.3174, loss: 0.3174 +2025-06-24 22:04:22,303 - pyskl - INFO - Epoch [79][900/1281] lr: 1.153e-02, eta: 10:13:42, time: 0.357, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 1.0000, loss_cls: 0.3155, loss: 0.3155 +2025-06-24 22:05:01,546 - pyskl - INFO - Epoch [79][1000/1281] lr: 1.151e-02, eta: 10:13:01, time: 0.392, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2766, loss: 0.2766 +2025-06-24 22:05:50,831 - pyskl - INFO - Epoch [79][1100/1281] lr: 1.149e-02, eta: 10:12:29, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 1.0000, loss_cls: 0.3311, loss: 0.3311 +2025-06-24 22:06:40,566 - pyskl - INFO - Epoch [79][1200/1281] lr: 1.147e-02, eta: 10:11:57, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.3120, loss: 0.3120 +2025-06-24 22:07:20,910 - pyskl - INFO - Saving checkpoint at 79 epochs +2025-06-24 22:08:20,708 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:08:20,762 - pyskl - INFO - +top1_acc 0.8890 +top5_acc 0.9942 +2025-06-24 22:08:20,763 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:08:20,769 - pyskl - INFO - +mean_acc 0.8503 +2025-06-24 22:08:20,771 - pyskl - INFO - Epoch(val) [79][533] top1_acc: 0.8890, top5_acc: 0.9942, mean_class_accuracy: 0.8503 +2025-06-24 22:09:41,101 - pyskl - INFO - Epoch [80][100/1281] lr: 1.143e-02, eta: 10:10:50, time: 0.803, data_time: 0.192, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2693, loss: 0.2693 +2025-06-24 22:10:30,083 - pyskl - INFO - Epoch [80][200/1281] lr: 1.141e-02, eta: 10:10:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9988, loss_cls: 0.2329, loss: 0.2329 +2025-06-24 22:11:19,292 - pyskl - INFO - Epoch [80][300/1281] lr: 1.139e-02, eta: 10:09:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9988, loss_cls: 0.2734, loss: 0.2734 +2025-06-24 22:12:08,291 - pyskl - INFO - Epoch [80][400/1281] lr: 1.137e-02, eta: 10:09:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9981, loss_cls: 0.2778, loss: 0.2778 +2025-06-24 22:12:57,463 - pyskl - INFO - Epoch [80][500/1281] lr: 1.135e-02, eta: 10:08:40, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.2930, loss: 0.2930 +2025-06-24 22:13:46,638 - pyskl - INFO - Epoch [80][600/1281] lr: 1.133e-02, eta: 10:08:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9981, loss_cls: 0.2594, loss: 0.2594 +2025-06-24 22:14:31,349 - pyskl - INFO - Epoch [80][700/1281] lr: 1.131e-02, eta: 10:07:31, time: 0.447, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9981, loss_cls: 0.3466, loss: 0.3466 +2025-06-24 22:15:14,929 - pyskl - INFO - Epoch [80][800/1281] lr: 1.129e-02, eta: 10:06:53, time: 0.436, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.2994, loss: 0.2994 +2025-06-24 22:15:44,939 - pyskl - INFO - Epoch [80][900/1281] lr: 1.127e-02, eta: 10:06:04, time: 0.300, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3463, loss: 0.3463 +2025-06-24 22:16:26,999 - pyskl - INFO - Epoch [80][1000/1281] lr: 1.125e-02, eta: 10:05:25, time: 0.421, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9994, loss_cls: 0.3496, loss: 0.3496 +2025-06-24 22:17:16,188 - pyskl - INFO - Epoch [80][1100/1281] lr: 1.123e-02, eta: 10:04:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9988, loss_cls: 0.3720, loss: 0.3720 +2025-06-24 22:18:05,407 - pyskl - INFO - Epoch [80][1200/1281] lr: 1.121e-02, eta: 10:04:20, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9981, loss_cls: 0.3911, loss: 0.3911 +2025-06-24 22:18:45,782 - pyskl - INFO - Saving checkpoint at 80 epochs +2025-06-24 22:19:44,749 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:19:44,818 - pyskl - INFO - +top1_acc 0.8904 +top5_acc 0.9923 +2025-06-24 22:19:44,818 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:19:44,827 - pyskl - INFO - +mean_acc 0.8600 +2025-06-24 22:19:44,829 - pyskl - INFO - Epoch(val) [80][533] top1_acc: 0.8904, top5_acc: 0.9923, mean_class_accuracy: 0.8600 +2025-06-24 22:21:06,296 - pyskl - INFO - Epoch [81][100/1281] lr: 1.117e-02, eta: 10:03:14, time: 0.815, data_time: 0.196, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2279, loss: 0.2279 +2025-06-24 22:21:55,285 - pyskl - INFO - Epoch [81][200/1281] lr: 1.115e-02, eta: 10:02:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.2927, loss: 0.2927 +2025-06-24 22:22:44,643 - pyskl - INFO - Epoch [81][300/1281] lr: 1.113e-02, eta: 10:02:08, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9994, loss_cls: 0.3722, loss: 0.3722 +2025-06-24 22:23:33,800 - pyskl - INFO - Epoch [81][400/1281] lr: 1.111e-02, eta: 10:01:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9988, loss_cls: 0.3274, loss: 0.3274 +2025-06-24 22:24:23,050 - pyskl - INFO - Epoch [81][500/1281] lr: 1.109e-02, eta: 10:01:03, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2864, loss: 0.2864 +2025-06-24 22:25:12,430 - pyskl - INFO - Epoch [81][600/1281] lr: 1.107e-02, eta: 10:00:30, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9994, loss_cls: 0.3002, loss: 0.3002 +2025-06-24 22:25:54,246 - pyskl - INFO - Epoch [81][700/1281] lr: 1.105e-02, eta: 9:59:51, time: 0.418, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.3061, loss: 0.3061 +2025-06-24 22:26:40,878 - pyskl - INFO - Epoch [81][800/1281] lr: 1.103e-02, eta: 9:59:15, time: 0.466, data_time: 0.001, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9956, loss_cls: 0.3160, loss: 0.3160 +2025-06-24 22:27:08,187 - pyskl - INFO - Epoch [81][900/1281] lr: 1.101e-02, eta: 9:58:24, time: 0.273, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9981, loss_cls: 0.3561, loss: 0.3561 +2025-06-24 22:27:50,519 - pyskl - INFO - Epoch [81][1000/1281] lr: 1.099e-02, eta: 9:57:45, time: 0.423, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 1.0000, loss_cls: 0.2856, loss: 0.2856 +2025-06-24 22:28:39,745 - pyskl - INFO - Epoch [81][1100/1281] lr: 1.097e-02, eta: 9:57:12, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.2716, loss: 0.2716 +2025-06-24 22:29:28,974 - pyskl - INFO - Epoch [81][1200/1281] lr: 1.095e-02, eta: 9:56:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9981, loss_cls: 0.2680, loss: 0.2680 +2025-06-24 22:30:09,630 - pyskl - INFO - Saving checkpoint at 81 epochs +2025-06-24 22:31:09,388 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:31:09,461 - pyskl - INFO - +top1_acc 0.8951 +top5_acc 0.9957 +2025-06-24 22:31:09,461 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:31:09,468 - pyskl - INFO - +mean_acc 0.8680 +2025-06-24 22:31:09,470 - pyskl - INFO - Epoch(val) [81][533] top1_acc: 0.8951, top5_acc: 0.9957, mean_class_accuracy: 0.8680 +2025-06-24 22:32:29,623 - pyskl - INFO - Epoch [82][100/1281] lr: 1.091e-02, eta: 9:55:32, time: 0.801, data_time: 0.201, memory: 4083, top1_acc: 0.9437, top5_acc: 1.0000, loss_cls: 0.2954, loss: 0.2954 +2025-06-24 22:33:18,906 - pyskl - INFO - Epoch [82][200/1281] lr: 1.089e-02, eta: 9:54:59, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2280, loss: 0.2280 +2025-06-24 22:34:07,968 - pyskl - INFO - Epoch [82][300/1281] lr: 1.087e-02, eta: 9:54:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 1.0000, loss_cls: 0.2606, loss: 0.2606 +2025-06-24 22:34:57,144 - pyskl - INFO - Epoch [82][400/1281] lr: 1.085e-02, eta: 9:53:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 1.0000, loss_cls: 0.2897, loss: 0.2897 +2025-06-24 22:35:46,552 - pyskl - INFO - Epoch [82][500/1281] lr: 1.083e-02, eta: 9:53:19, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9981, loss_cls: 0.2919, loss: 0.2919 +2025-06-24 22:36:35,939 - pyskl - INFO - Epoch [82][600/1281] lr: 1.081e-02, eta: 9:52:46, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9962, loss_cls: 0.3408, loss: 0.3408 +2025-06-24 22:37:16,703 - pyskl - INFO - Epoch [82][700/1281] lr: 1.079e-02, eta: 9:52:06, time: 0.408, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3032, loss: 0.3032 +2025-06-24 22:38:06,160 - pyskl - INFO - Epoch [82][800/1281] lr: 1.077e-02, eta: 9:51:33, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9981, loss_cls: 0.2496, loss: 0.2496 +2025-06-24 22:38:31,172 - pyskl - INFO - Epoch [82][900/1281] lr: 1.075e-02, eta: 9:50:39, time: 0.250, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9981, loss_cls: 0.3115, loss: 0.3115 +2025-06-24 22:39:14,508 - pyskl - INFO - Epoch [82][1000/1281] lr: 1.073e-02, eta: 9:50:01, time: 0.433, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9994, loss_cls: 0.3245, loss: 0.3245 +2025-06-24 22:40:03,805 - pyskl - INFO - Epoch [82][1100/1281] lr: 1.071e-02, eta: 9:49:28, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2742, loss: 0.2742 +2025-06-24 22:40:53,364 - pyskl - INFO - Epoch [82][1200/1281] lr: 1.069e-02, eta: 9:48:55, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2479, loss: 0.2479 +2025-06-24 22:41:33,540 - pyskl - INFO - Saving checkpoint at 82 epochs +2025-06-24 22:42:33,001 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:42:33,058 - pyskl - INFO - +top1_acc 0.8972 +top5_acc 0.9931 +2025-06-24 22:42:33,059 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:42:33,065 - pyskl - INFO - +mean_acc 0.8691 +2025-06-24 22:42:33,067 - pyskl - INFO - Epoch(val) [82][533] top1_acc: 0.8972, top5_acc: 0.9931, mean_class_accuracy: 0.8691 +2025-06-24 22:43:54,410 - pyskl - INFO - Epoch [83][100/1281] lr: 1.065e-02, eta: 9:47:48, time: 0.813, data_time: 0.199, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2511, loss: 0.2511 +2025-06-24 22:44:43,504 - pyskl - INFO - Epoch [83][200/1281] lr: 1.063e-02, eta: 9:47:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2495, loss: 0.2495 +2025-06-24 22:45:32,729 - pyskl - INFO - Epoch [83][300/1281] lr: 1.061e-02, eta: 9:46:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 1.0000, loss_cls: 0.2795, loss: 0.2795 +2025-06-24 22:46:22,252 - pyskl - INFO - Epoch [83][400/1281] lr: 1.059e-02, eta: 9:46:08, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9981, loss_cls: 0.2644, loss: 0.2644 +2025-06-24 22:47:11,513 - pyskl - INFO - Epoch [83][500/1281] lr: 1.057e-02, eta: 9:45:35, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2788, loss: 0.2788 +2025-06-24 22:48:00,692 - pyskl - INFO - Epoch [83][600/1281] lr: 1.055e-02, eta: 9:45:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.2892, loss: 0.2892 +2025-06-24 22:48:39,756 - pyskl - INFO - Epoch [83][700/1281] lr: 1.053e-02, eta: 9:44:19, time: 0.391, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2816, loss: 0.2816 +2025-06-24 22:49:30,867 - pyskl - INFO - Epoch [83][800/1281] lr: 1.051e-02, eta: 9:43:48, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9988, loss_cls: 0.2567, loss: 0.2567 +2025-06-24 22:49:54,861 - pyskl - INFO - Epoch [83][900/1281] lr: 1.049e-02, eta: 9:42:53, time: 0.240, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 1.0000, loss_cls: 0.2947, loss: 0.2947 +2025-06-24 22:50:40,406 - pyskl - INFO - Epoch [83][1000/1281] lr: 1.047e-02, eta: 9:42:17, time: 0.455, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9975, loss_cls: 0.2629, loss: 0.2629 +2025-06-24 22:51:29,437 - pyskl - INFO - Epoch [83][1100/1281] lr: 1.045e-02, eta: 9:41:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2848, loss: 0.2848 +2025-06-24 22:52:18,656 - pyskl - INFO - Epoch [83][1200/1281] lr: 1.043e-02, eta: 9:41:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9988, loss_cls: 0.3111, loss: 0.3111 +2025-06-24 22:52:59,275 - pyskl - INFO - Saving checkpoint at 83 epochs +2025-06-24 22:53:58,667 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:53:58,722 - pyskl - INFO - +top1_acc 0.9004 +top5_acc 0.9940 +2025-06-24 22:53:58,722 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:53:58,728 - pyskl - INFO - +mean_acc 0.8597 +2025-06-24 22:53:58,730 - pyskl - INFO - Epoch(val) [83][533] top1_acc: 0.9004, top5_acc: 0.9940, mean_class_accuracy: 0.8597 +2025-06-24 22:55:20,258 - pyskl - INFO - Epoch [84][100/1281] lr: 1.040e-02, eta: 9:40:03, time: 0.815, data_time: 0.199, memory: 4083, top1_acc: 0.9494, top5_acc: 1.0000, loss_cls: 0.2871, loss: 0.2871 +2025-06-24 22:56:09,381 - pyskl - INFO - Epoch [84][200/1281] lr: 1.038e-02, eta: 9:39:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 1.0000, loss_cls: 0.2648, loss: 0.2648 +2025-06-24 22:56:58,500 - pyskl - INFO - Epoch [84][300/1281] lr: 1.036e-02, eta: 9:38:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.2543, loss: 0.2543 +2025-06-24 22:57:48,109 - pyskl - INFO - Epoch [84][400/1281] lr: 1.034e-02, eta: 9:38:22, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9975, loss_cls: 0.2681, loss: 0.2681 +2025-06-24 22:58:37,263 - pyskl - INFO - Epoch [84][500/1281] lr: 1.031e-02, eta: 9:37:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2745, loss: 0.2745 +2025-06-24 22:59:26,398 - pyskl - INFO - Epoch [84][600/1281] lr: 1.029e-02, eta: 9:37:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2870, loss: 0.2870 +2025-06-24 23:00:02,156 - pyskl - INFO - Epoch [84][700/1281] lr: 1.027e-02, eta: 9:36:29, time: 0.358, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9994, loss_cls: 0.3586, loss: 0.3586 +2025-06-24 23:00:53,331 - pyskl - INFO - Epoch [84][800/1281] lr: 1.025e-02, eta: 9:35:57, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9988, loss_cls: 0.3127, loss: 0.3127 +2025-06-24 23:01:18,602 - pyskl - INFO - Epoch [84][900/1281] lr: 1.023e-02, eta: 9:35:04, time: 0.253, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.3030, loss: 0.3030 +2025-06-24 23:02:06,651 - pyskl - INFO - Epoch [84][1000/1281] lr: 1.021e-02, eta: 9:34:30, time: 0.480, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2209, loss: 0.2209 +2025-06-24 23:02:55,976 - pyskl - INFO - Epoch [84][1100/1281] lr: 1.019e-02, eta: 9:33:56, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 1.0000, loss_cls: 0.3375, loss: 0.3375 +2025-06-24 23:03:45,149 - pyskl - INFO - Epoch [84][1200/1281] lr: 1.017e-02, eta: 9:33:22, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2604, loss: 0.2604 +2025-06-24 23:04:25,937 - pyskl - INFO - Saving checkpoint at 84 epochs +2025-06-24 23:05:25,874 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:05:25,935 - pyskl - INFO - +top1_acc 0.8961 +top5_acc 0.9942 +2025-06-24 23:05:25,936 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:05:25,942 - pyskl - INFO - +mean_acc 0.8818 +2025-06-24 23:05:25,944 - pyskl - INFO - Epoch(val) [84][533] top1_acc: 0.8961, top5_acc: 0.9942, mean_class_accuracy: 0.8818 +2025-06-24 23:06:45,301 - pyskl - INFO - Epoch [85][100/1281] lr: 1.014e-02, eta: 9:32:13, time: 0.794, data_time: 0.188, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9988, loss_cls: 0.2854, loss: 0.2854 +2025-06-24 23:07:34,448 - pyskl - INFO - Epoch [85][200/1281] lr: 1.012e-02, eta: 9:31:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2357, loss: 0.2357 +2025-06-24 23:08:23,670 - pyskl - INFO - Epoch [85][300/1281] lr: 1.010e-02, eta: 9:31:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2386, loss: 0.2386 +2025-06-24 23:09:12,679 - pyskl - INFO - Epoch [85][400/1281] lr: 1.008e-02, eta: 9:30:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2180, loss: 0.2180 +2025-06-24 23:10:02,090 - pyskl - INFO - Epoch [85][500/1281] lr: 1.006e-02, eta: 9:29:57, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2395, loss: 0.2395 +2025-06-24 23:10:51,625 - pyskl - INFO - Epoch [85][600/1281] lr: 1.004e-02, eta: 9:29:23, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 1.0000, loss_cls: 0.3047, loss: 0.3047 +2025-06-24 23:11:24,653 - pyskl - INFO - Epoch [85][700/1281] lr: 1.002e-02, eta: 9:28:36, time: 0.330, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 1.0000, loss_cls: 0.2959, loss: 0.2959 +2025-06-24 23:12:15,750 - pyskl - INFO - Epoch [85][800/1281] lr: 9.998e-03, eta: 9:28:04, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9981, loss_cls: 0.2748, loss: 0.2748 +2025-06-24 23:12:42,971 - pyskl - INFO - Epoch [85][900/1281] lr: 9.978e-03, eta: 9:27:13, time: 0.272, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9994, loss_cls: 0.2182, loss: 0.2182 +2025-06-24 23:13:32,340 - pyskl - INFO - Epoch [85][1000/1281] lr: 9.958e-03, eta: 9:26:39, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 1.0000, loss_cls: 0.2446, loss: 0.2446 +2025-06-24 23:14:21,417 - pyskl - INFO - Epoch [85][1100/1281] lr: 9.937e-03, eta: 9:26:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9988, loss_cls: 0.2437, loss: 0.2437 +2025-06-24 23:15:10,818 - pyskl - INFO - Epoch [85][1200/1281] lr: 9.917e-03, eta: 9:25:30, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9994, loss_cls: 0.2762, loss: 0.2762 +2025-06-24 23:15:51,094 - pyskl - INFO - Saving checkpoint at 85 epochs +2025-06-24 23:16:50,807 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:16:50,879 - pyskl - INFO - +top1_acc 0.8983 +top5_acc 0.9953 +2025-06-24 23:16:50,879 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:16:50,888 - pyskl - INFO - +mean_acc 0.8726 +2025-06-24 23:16:50,891 - pyskl - INFO - Epoch(val) [85][533] top1_acc: 0.8983, top5_acc: 0.9953, mean_class_accuracy: 0.8726 +2025-06-24 23:18:11,624 - pyskl - INFO - Epoch [86][100/1281] lr: 9.881e-03, eta: 9:24:22, time: 0.807, data_time: 0.196, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9975, loss_cls: 0.2351, loss: 0.2351 +2025-06-24 23:19:00,912 - pyskl - INFO - Epoch [86][200/1281] lr: 9.861e-03, eta: 9:23:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2750, loss: 0.2750 +2025-06-24 23:19:50,147 - pyskl - INFO - Epoch [86][300/1281] lr: 9.841e-03, eta: 9:23:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9988, loss_cls: 0.2502, loss: 0.2502 +2025-06-24 23:20:39,355 - pyskl - INFO - Epoch [86][400/1281] lr: 9.821e-03, eta: 9:22:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9994, loss_cls: 0.2802, loss: 0.2802 +2025-06-24 23:21:28,458 - pyskl - INFO - Epoch [86][500/1281] lr: 9.801e-03, eta: 9:22:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.2808, loss: 0.2808 +2025-06-24 23:22:17,519 - pyskl - INFO - Epoch [86][600/1281] lr: 9.781e-03, eta: 9:21:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2379, loss: 0.2379 +2025-06-24 23:22:47,178 - pyskl - INFO - Epoch [86][700/1281] lr: 9.762e-03, eta: 9:20:41, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9988, loss_cls: 0.2549, loss: 0.2549 +2025-06-24 23:23:38,396 - pyskl - INFO - Epoch [86][800/1281] lr: 9.742e-03, eta: 9:20:08, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9994, loss_cls: 0.2723, loss: 0.2723 +2025-06-24 23:24:07,797 - pyskl - INFO - Epoch [86][900/1281] lr: 9.722e-03, eta: 9:19:19, time: 0.294, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9988, loss_cls: 0.3056, loss: 0.3056 +2025-06-24 23:24:57,443 - pyskl - INFO - Epoch [86][1000/1281] lr: 9.702e-03, eta: 9:18:45, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 1.0000, loss_cls: 0.3222, loss: 0.3222 +2025-06-24 23:25:46,781 - pyskl - INFO - Epoch [86][1100/1281] lr: 9.682e-03, eta: 9:18:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2426, loss: 0.2426 +2025-06-24 23:26:36,025 - pyskl - INFO - Epoch [86][1200/1281] lr: 9.662e-03, eta: 9:17:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2629, loss: 0.2629 +2025-06-24 23:27:16,324 - pyskl - INFO - Saving checkpoint at 86 epochs +2025-06-24 23:28:15,492 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:28:15,547 - pyskl - INFO - +top1_acc 0.9049 +top5_acc 0.9946 +2025-06-24 23:28:15,547 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:28:15,554 - pyskl - INFO - +mean_acc 0.8759 +2025-06-24 23:28:15,558 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_76.pth was removed +2025-06-24 23:28:15,752 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_86.pth. +2025-06-24 23:28:15,752 - pyskl - INFO - Best top1_acc is 0.9049 at 86 epoch. +2025-06-24 23:28:15,756 - pyskl - INFO - Epoch(val) [86][533] top1_acc: 0.9049, top5_acc: 0.9946, mean_class_accuracy: 0.8759 +2025-06-24 23:29:34,837 - pyskl - INFO - Epoch [87][100/1281] lr: 9.626e-03, eta: 9:16:26, time: 0.791, data_time: 0.184, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.2729, loss: 0.2729 +2025-06-24 23:30:24,113 - pyskl - INFO - Epoch [87][200/1281] lr: 9.606e-03, eta: 9:15:52, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2301, loss: 0.2301 +2025-06-24 23:31:13,322 - pyskl - INFO - Epoch [87][300/1281] lr: 9.586e-03, eta: 9:15:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 1.0000, loss_cls: 0.2458, loss: 0.2458 +2025-06-24 23:32:02,744 - pyskl - INFO - Epoch [87][400/1281] lr: 9.566e-03, eta: 9:14:43, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2171, loss: 0.2171 +2025-06-24 23:32:52,233 - pyskl - INFO - Epoch [87][500/1281] lr: 9.546e-03, eta: 9:14:08, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9969, loss_cls: 0.3063, loss: 0.3063 +2025-06-24 23:33:41,583 - pyskl - INFO - Epoch [87][600/1281] lr: 9.527e-03, eta: 9:13:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2448, loss: 0.2448 +2025-06-24 23:34:10,627 - pyskl - INFO - Epoch [87][700/1281] lr: 9.507e-03, eta: 9:12:45, time: 0.290, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.2851, loss: 0.2851 +2025-06-24 23:35:01,661 - pyskl - INFO - Epoch [87][800/1281] lr: 9.487e-03, eta: 9:12:11, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9988, loss_cls: 0.2798, loss: 0.2798 +2025-06-24 23:35:32,975 - pyskl - INFO - Epoch [87][900/1281] lr: 9.467e-03, eta: 9:11:23, time: 0.313, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2509, loss: 0.2509 +2025-06-24 23:36:21,738 - pyskl - INFO - Epoch [87][1000/1281] lr: 9.447e-03, eta: 9:10:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2092, loss: 0.2092 +2025-06-24 23:37:10,818 - pyskl - INFO - Epoch [87][1100/1281] lr: 9.427e-03, eta: 9:10:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.2655, loss: 0.2655 +2025-06-24 23:38:00,083 - pyskl - INFO - Epoch [87][1200/1281] lr: 9.408e-03, eta: 9:09:39, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9988, loss_cls: 0.2812, loss: 0.2812 +2025-06-24 23:38:40,807 - pyskl - INFO - Saving checkpoint at 87 epochs +2025-06-24 23:39:39,773 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:39:39,840 - pyskl - INFO - +top1_acc 0.8951 +top5_acc 0.9932 +2025-06-24 23:39:39,840 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:39:39,848 - pyskl - INFO - +mean_acc 0.8579 +2025-06-24 23:39:39,850 - pyskl - INFO - Epoch(val) [87][533] top1_acc: 0.8951, top5_acc: 0.9932, mean_class_accuracy: 0.8579 +2025-06-24 23:40:58,342 - pyskl - INFO - Epoch [88][100/1281] lr: 9.372e-03, eta: 9:08:28, time: 0.785, data_time: 0.184, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9994, loss_cls: 0.2440, loss: 0.2440 +2025-06-24 23:41:47,497 - pyskl - INFO - Epoch [88][200/1281] lr: 9.352e-03, eta: 9:07:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2051, loss: 0.2051 +2025-06-24 23:42:36,745 - pyskl - INFO - Epoch [88][300/1281] lr: 9.332e-03, eta: 9:07:19, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1753, loss: 0.1753 +2025-06-24 23:43:25,599 - pyskl - INFO - Epoch [88][400/1281] lr: 9.312e-03, eta: 9:06:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1973, loss: 0.1973 +2025-06-24 23:44:14,757 - pyskl - INFO - Epoch [88][500/1281] lr: 9.293e-03, eta: 9:06:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2041, loss: 0.2041 +2025-06-24 23:45:04,166 - pyskl - INFO - Epoch [88][600/1281] lr: 9.273e-03, eta: 9:05:34, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.2089, loss: 0.2089 +2025-06-24 23:45:32,414 - pyskl - INFO - Epoch [88][700/1281] lr: 9.253e-03, eta: 9:04:44, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2380, loss: 0.2380 +2025-06-24 23:46:23,532 - pyskl - INFO - Epoch [88][800/1281] lr: 9.233e-03, eta: 9:04:11, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2383, loss: 0.2383 +2025-06-24 23:46:54,804 - pyskl - INFO - Epoch [88][900/1281] lr: 9.214e-03, eta: 9:03:23, time: 0.313, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9988, loss_cls: 0.2407, loss: 0.2407 +2025-06-24 23:47:43,960 - pyskl - INFO - Epoch [88][1000/1281] lr: 9.194e-03, eta: 9:02:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2246, loss: 0.2246 +2025-06-24 23:48:33,135 - pyskl - INFO - Epoch [88][1100/1281] lr: 9.174e-03, eta: 9:02:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9981, loss_cls: 0.2590, loss: 0.2590 +2025-06-24 23:49:22,712 - pyskl - INFO - Epoch [88][1200/1281] lr: 9.155e-03, eta: 9:01:38, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.2966, loss: 0.2966 +2025-06-24 23:50:02,861 - pyskl - INFO - Saving checkpoint at 88 epochs +2025-06-24 23:51:02,022 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:51:02,078 - pyskl - INFO - +top1_acc 0.9049 +top5_acc 0.9918 +2025-06-24 23:51:02,079 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:51:02,086 - pyskl - INFO - +mean_acc 0.8743 +2025-06-24 23:51:02,087 - pyskl - INFO - Epoch(val) [88][533] top1_acc: 0.9049, top5_acc: 0.9918, mean_class_accuracy: 0.8743 +2025-06-24 23:52:21,658 - pyskl - INFO - Epoch [89][100/1281] lr: 9.119e-03, eta: 9:00:28, time: 0.796, data_time: 0.185, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.2113, loss: 0.2113 +2025-06-24 23:53:10,748 - pyskl - INFO - Epoch [89][200/1281] lr: 9.099e-03, eta: 8:59:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2101, loss: 0.2101 +2025-06-24 23:53:59,952 - pyskl - INFO - Epoch [89][300/1281] lr: 9.080e-03, eta: 8:59:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2363, loss: 0.2363 +2025-06-24 23:54:49,579 - pyskl - INFO - Epoch [89][400/1281] lr: 9.060e-03, eta: 8:58:43, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9988, loss_cls: 0.2859, loss: 0.2859 +2025-06-24 23:55:38,960 - pyskl - INFO - Epoch [89][500/1281] lr: 9.040e-03, eta: 8:58:08, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9988, loss_cls: 0.2736, loss: 0.2736 +2025-06-24 23:56:28,196 - pyskl - INFO - Epoch [89][600/1281] lr: 9.021e-03, eta: 8:57:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9988, loss_cls: 0.2606, loss: 0.2606 +2025-06-24 23:56:57,295 - pyskl - INFO - Epoch [89][700/1281] lr: 9.001e-03, eta: 8:56:44, time: 0.291, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9988, loss_cls: 0.2697, loss: 0.2697 +2025-06-24 23:57:46,625 - pyskl - INFO - Epoch [89][800/1281] lr: 8.982e-03, eta: 8:56:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9994, loss_cls: 0.2508, loss: 0.2508 +2025-06-24 23:58:18,537 - pyskl - INFO - Epoch [89][900/1281] lr: 8.962e-03, eta: 8:55:22, time: 0.319, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2774, loss: 0.2774 +2025-06-24 23:59:08,247 - pyskl - INFO - Epoch [89][1000/1281] lr: 8.942e-03, eta: 8:54:47, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2626, loss: 0.2626 +2025-06-24 23:59:57,377 - pyskl - INFO - Epoch [89][1100/1281] lr: 8.923e-03, eta: 8:54:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 1.0000, loss_cls: 0.2719, loss: 0.2719 +2025-06-25 00:00:46,499 - pyskl - INFO - Epoch [89][1200/1281] lr: 8.903e-03, eta: 8:53:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9994, loss_cls: 0.1985, loss: 0.1985 +2025-06-25 00:01:27,257 - pyskl - INFO - Saving checkpoint at 89 epochs +2025-06-25 00:02:26,633 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:02:26,702 - pyskl - INFO - +top1_acc 0.9089 +top5_acc 0.9951 +2025-06-25 00:02:26,703 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:02:26,711 - pyskl - INFO - +mean_acc 0.8877 +2025-06-25 00:02:26,716 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_86.pth was removed +2025-06-25 00:02:26,895 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_89.pth. +2025-06-25 00:02:26,895 - pyskl - INFO - Best top1_acc is 0.9089 at 89 epoch. +2025-06-25 00:02:26,898 - pyskl - INFO - Epoch(val) [89][533] top1_acc: 0.9089, top5_acc: 0.9951, mean_class_accuracy: 0.8877 +2025-06-25 00:03:46,199 - pyskl - INFO - Epoch [90][100/1281] lr: 8.868e-03, eta: 8:52:26, time: 0.793, data_time: 0.187, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9988, loss_cls: 0.2224, loss: 0.2224 +2025-06-25 00:04:35,351 - pyskl - INFO - Epoch [90][200/1281] lr: 8.848e-03, eta: 8:51:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.2803, loss: 0.2803 +2025-06-25 00:05:24,626 - pyskl - INFO - Epoch [90][300/1281] lr: 8.829e-03, eta: 8:51:15, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9994, loss_cls: 0.1910, loss: 0.1910 +2025-06-25 00:06:14,034 - pyskl - INFO - Epoch [90][400/1281] lr: 8.809e-03, eta: 8:50:40, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2715, loss: 0.2715 +2025-06-25 00:07:03,097 - pyskl - INFO - Epoch [90][500/1281] lr: 8.790e-03, eta: 8:50:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2314, loss: 0.2314 +2025-06-25 00:07:52,498 - pyskl - INFO - Epoch [90][600/1281] lr: 8.770e-03, eta: 8:49:29, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2802, loss: 0.2802 +2025-06-25 00:08:21,060 - pyskl - INFO - Epoch [90][700/1281] lr: 8.751e-03, eta: 8:48:40, time: 0.285, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2225, loss: 0.2225 +2025-06-25 00:09:09,314 - pyskl - INFO - Epoch [90][800/1281] lr: 8.731e-03, eta: 8:48:04, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2024, loss: 0.2024 +2025-06-25 00:09:43,577 - pyskl - INFO - Epoch [90][900/1281] lr: 8.712e-03, eta: 8:47:18, time: 0.343, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9988, loss_cls: 0.2557, loss: 0.2557 +2025-06-25 00:10:32,668 - pyskl - INFO - Epoch [90][1000/1281] lr: 8.692e-03, eta: 8:46:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2189, loss: 0.2189 +2025-06-25 00:11:21,997 - pyskl - INFO - Epoch [90][1100/1281] lr: 8.673e-03, eta: 8:46:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2908, loss: 0.2908 +2025-06-25 00:12:11,233 - pyskl - INFO - Epoch [90][1200/1281] lr: 8.653e-03, eta: 8:45:32, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9988, loss_cls: 0.2723, loss: 0.2723 +2025-06-25 00:12:51,602 - pyskl - INFO - Saving checkpoint at 90 epochs +2025-06-25 00:13:50,559 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:13:50,626 - pyskl - INFO - +top1_acc 0.9101 +top5_acc 0.9948 +2025-06-25 00:13:50,626 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:13:50,634 - pyskl - INFO - +mean_acc 0.8769 +2025-06-25 00:13:50,638 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_89.pth was removed +2025-06-25 00:13:50,847 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_90.pth. +2025-06-25 00:13:50,847 - pyskl - INFO - Best top1_acc is 0.9101 at 90 epoch. +2025-06-25 00:13:50,851 - pyskl - INFO - Epoch(val) [90][533] top1_acc: 0.9101, top5_acc: 0.9948, mean_class_accuracy: 0.8769 +2025-06-25 00:15:09,608 - pyskl - INFO - Epoch [91][100/1281] lr: 8.618e-03, eta: 8:44:21, time: 0.788, data_time: 0.189, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2363, loss: 0.2363 +2025-06-25 00:15:58,607 - pyskl - INFO - Epoch [91][200/1281] lr: 8.599e-03, eta: 8:43:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2043, loss: 0.2043 +2025-06-25 00:16:48,032 - pyskl - INFO - Epoch [91][300/1281] lr: 8.579e-03, eta: 8:43:10, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2157, loss: 0.2157 +2025-06-25 00:17:37,286 - pyskl - INFO - Epoch [91][400/1281] lr: 8.560e-03, eta: 8:42:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2516, loss: 0.2516 +2025-06-25 00:18:26,544 - pyskl - INFO - Epoch [91][500/1281] lr: 8.540e-03, eta: 8:41:59, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2076, loss: 0.2076 +2025-06-25 00:19:15,795 - pyskl - INFO - Epoch [91][600/1281] lr: 8.521e-03, eta: 8:41:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2501, loss: 0.2501 +2025-06-25 00:19:47,998 - pyskl - INFO - Epoch [91][700/1281] lr: 8.502e-03, eta: 8:40:36, time: 0.322, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2397, loss: 0.2397 +2025-06-25 00:20:30,658 - pyskl - INFO - Epoch [91][800/1281] lr: 8.482e-03, eta: 8:39:56, time: 0.427, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.2610, loss: 0.2610 +2025-06-25 00:21:08,154 - pyskl - INFO - Epoch [91][900/1281] lr: 8.463e-03, eta: 8:39:13, time: 0.375, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9988, loss_cls: 0.2644, loss: 0.2644 +2025-06-25 00:21:57,332 - pyskl - INFO - Epoch [91][1000/1281] lr: 8.444e-03, eta: 8:38:37, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9994, loss_cls: 0.2178, loss: 0.2178 +2025-06-25 00:22:46,753 - pyskl - INFO - Epoch [91][1100/1281] lr: 8.424e-03, eta: 8:38:02, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2352, loss: 0.2352 +2025-06-25 00:23:36,111 - pyskl - INFO - Epoch [91][1200/1281] lr: 8.405e-03, eta: 8:37:26, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9994, loss_cls: 0.2194, loss: 0.2194 +2025-06-25 00:24:16,564 - pyskl - INFO - Saving checkpoint at 91 epochs +2025-06-25 00:25:15,092 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:25:15,150 - pyskl - INFO - +top1_acc 0.9066 +top5_acc 0.9945 +2025-06-25 00:25:15,151 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:25:15,157 - pyskl - INFO - +mean_acc 0.8769 +2025-06-25 00:25:15,159 - pyskl - INFO - Epoch(val) [91][533] top1_acc: 0.9066, top5_acc: 0.9945, mean_class_accuracy: 0.8769 +2025-06-25 00:26:33,069 - pyskl - INFO - Epoch [92][100/1281] lr: 8.370e-03, eta: 8:36:14, time: 0.779, data_time: 0.183, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9981, loss_cls: 0.2357, loss: 0.2357 +2025-06-25 00:27:22,252 - pyskl - INFO - Epoch [92][200/1281] lr: 8.351e-03, eta: 8:35:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.2012, loss: 0.2012 +2025-06-25 00:28:11,526 - pyskl - INFO - Epoch [92][300/1281] lr: 8.332e-03, eta: 8:35:03, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2137, loss: 0.2137 +2025-06-25 00:29:00,535 - pyskl - INFO - Epoch [92][400/1281] lr: 8.312e-03, eta: 8:34:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2005, loss: 0.2005 +2025-06-25 00:29:49,960 - pyskl - INFO - Epoch [92][500/1281] lr: 8.293e-03, eta: 8:33:51, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 0.9994, loss_cls: 0.1815, loss: 0.1815 +2025-06-25 00:30:38,805 - pyskl - INFO - Epoch [92][600/1281] lr: 8.274e-03, eta: 8:33:15, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.1682, loss: 0.1682 +2025-06-25 00:31:12,223 - pyskl - INFO - Epoch [92][700/1281] lr: 8.255e-03, eta: 8:32:29, time: 0.334, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1877, loss: 0.1877 +2025-06-25 00:31:52,446 - pyskl - INFO - Epoch [92][800/1281] lr: 8.235e-03, eta: 8:31:47, time: 0.402, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9994, loss_cls: 0.2201, loss: 0.2201 +2025-06-25 00:32:28,842 - pyskl - INFO - Epoch [92][900/1281] lr: 8.216e-03, eta: 8:31:03, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9994, loss_cls: 0.2505, loss: 0.2505 +2025-06-25 00:33:17,929 - pyskl - INFO - Epoch [92][1000/1281] lr: 8.197e-03, eta: 8:30:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2172, loss: 0.2172 +2025-06-25 00:34:06,957 - pyskl - INFO - Epoch [92][1100/1281] lr: 8.178e-03, eta: 8:29:51, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2171, loss: 0.2171 +2025-06-25 00:34:55,977 - pyskl - INFO - Epoch [92][1200/1281] lr: 8.159e-03, eta: 8:29:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2670, loss: 0.2670 +2025-06-25 00:35:36,407 - pyskl - INFO - Saving checkpoint at 92 epochs +2025-06-25 00:36:35,451 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:36:35,507 - pyskl - INFO - +top1_acc 0.9073 +top5_acc 0.9953 +2025-06-25 00:36:35,507 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:36:35,514 - pyskl - INFO - +mean_acc 0.8752 +2025-06-25 00:36:35,516 - pyskl - INFO - Epoch(val) [92][533] top1_acc: 0.9073, top5_acc: 0.9953, mean_class_accuracy: 0.8752 +2025-06-25 00:37:54,797 - pyskl - INFO - Epoch [93][100/1281] lr: 8.124e-03, eta: 8:28:04, time: 0.793, data_time: 0.194, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1917, loss: 0.1917 +2025-06-25 00:38:43,711 - pyskl - INFO - Epoch [93][200/1281] lr: 8.105e-03, eta: 8:27:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 0.9994, loss_cls: 0.1968, loss: 0.1968 +2025-06-25 00:39:32,921 - pyskl - INFO - Epoch [93][300/1281] lr: 8.086e-03, eta: 8:26:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.1997, loss: 0.1997 +2025-06-25 00:40:22,210 - pyskl - INFO - Epoch [93][400/1281] lr: 8.067e-03, eta: 8:26:16, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.1965, loss: 0.1965 +2025-06-25 00:41:11,309 - pyskl - INFO - Epoch [93][500/1281] lr: 8.047e-03, eta: 8:25:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.2170, loss: 0.2170 +2025-06-25 00:41:59,900 - pyskl - INFO - Epoch [93][600/1281] lr: 8.028e-03, eta: 8:25:03, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1950, loss: 0.1950 +2025-06-25 00:42:34,913 - pyskl - INFO - Epoch [93][700/1281] lr: 8.009e-03, eta: 8:24:18, time: 0.350, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1887, loss: 0.1887 +2025-06-25 00:43:13,662 - pyskl - INFO - Epoch [93][800/1281] lr: 7.990e-03, eta: 8:23:36, time: 0.387, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9994, loss_cls: 0.1917, loss: 0.1917 +2025-06-25 00:43:51,772 - pyskl - INFO - Epoch [93][900/1281] lr: 7.971e-03, eta: 8:22:53, time: 0.381, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1975, loss: 0.1975 +2025-06-25 00:44:41,159 - pyskl - INFO - Epoch [93][1000/1281] lr: 7.952e-03, eta: 8:22:17, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9994, loss_cls: 0.2233, loss: 0.2233 +2025-06-25 00:45:30,335 - pyskl - INFO - Epoch [93][1100/1281] lr: 7.933e-03, eta: 8:21:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2622, loss: 0.2622 +2025-06-25 00:46:19,655 - pyskl - INFO - Epoch [93][1200/1281] lr: 7.914e-03, eta: 8:21:05, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2199, loss: 0.2199 +2025-06-25 00:46:59,910 - pyskl - INFO - Saving checkpoint at 93 epochs +2025-06-25 00:47:58,437 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:47:58,492 - pyskl - INFO - +top1_acc 0.9150 +top5_acc 0.9950 +2025-06-25 00:47:58,492 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:47:58,498 - pyskl - INFO - +mean_acc 0.8846 +2025-06-25 00:47:58,502 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_90.pth was removed +2025-06-25 00:47:58,678 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_93.pth. +2025-06-25 00:47:58,678 - pyskl - INFO - Best top1_acc is 0.9150 at 93 epoch. +2025-06-25 00:47:58,682 - pyskl - INFO - Epoch(val) [93][533] top1_acc: 0.9150, top5_acc: 0.9950, mean_class_accuracy: 0.8846 +2025-06-25 00:49:17,220 - pyskl - INFO - Epoch [94][100/1281] lr: 7.880e-03, eta: 8:19:53, time: 0.785, data_time: 0.192, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2131, loss: 0.2131 +2025-06-25 00:50:06,389 - pyskl - INFO - Epoch [94][200/1281] lr: 7.861e-03, eta: 8:19:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1647, loss: 0.1647 +2025-06-25 00:50:55,513 - pyskl - INFO - Epoch [94][300/1281] lr: 7.842e-03, eta: 8:18:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.1902, loss: 0.1902 +2025-06-25 00:51:44,979 - pyskl - INFO - Epoch [94][400/1281] lr: 7.823e-03, eta: 8:18:04, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2143, loss: 0.2143 +2025-06-25 00:52:34,129 - pyskl - INFO - Epoch [94][500/1281] lr: 7.804e-03, eta: 8:17:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2060, loss: 0.2060 +2025-06-25 00:53:21,493 - pyskl - INFO - Epoch [94][600/1281] lr: 7.785e-03, eta: 8:16:50, time: 0.474, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9994, loss_cls: 0.2403, loss: 0.2403 +2025-06-25 00:53:56,876 - pyskl - INFO - Epoch [94][700/1281] lr: 7.766e-03, eta: 8:16:06, time: 0.354, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2056, loss: 0.2056 +2025-06-25 00:54:35,109 - pyskl - INFO - Epoch [94][800/1281] lr: 7.747e-03, eta: 8:15:23, time: 0.382, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2305, loss: 0.2305 +2025-06-25 00:55:11,993 - pyskl - INFO - Epoch [94][900/1281] lr: 7.728e-03, eta: 8:14:39, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2038, loss: 0.2038 +2025-06-25 00:56:01,147 - pyskl - INFO - Epoch [94][1000/1281] lr: 7.709e-03, eta: 8:14:03, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9994, loss_cls: 0.2335, loss: 0.2335 +2025-06-25 00:56:50,355 - pyskl - INFO - Epoch [94][1100/1281] lr: 7.690e-03, eta: 8:13:26, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9994, loss_cls: 0.2080, loss: 0.2080 +2025-06-25 00:57:39,781 - pyskl - INFO - Epoch [94][1200/1281] lr: 7.672e-03, eta: 8:12:50, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2145, loss: 0.2145 +2025-06-25 00:58:20,322 - pyskl - INFO - Saving checkpoint at 94 epochs +2025-06-25 00:59:19,352 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:59:19,420 - pyskl - INFO - +top1_acc 0.9180 +top5_acc 0.9939 +2025-06-25 00:59:19,420 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:59:19,428 - pyskl - INFO - +mean_acc 0.8950 +2025-06-25 00:59:19,432 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_93.pth was removed +2025-06-25 00:59:19,628 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_94.pth. +2025-06-25 00:59:19,629 - pyskl - INFO - Best top1_acc is 0.9180 at 94 epoch. +2025-06-25 00:59:19,631 - pyskl - INFO - Epoch(val) [94][533] top1_acc: 0.9180, top5_acc: 0.9939, mean_class_accuracy: 0.8950 +2025-06-25 01:00:39,653 - pyskl - INFO - Epoch [95][100/1281] lr: 7.637e-03, eta: 8:11:39, time: 0.800, data_time: 0.187, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9994, loss_cls: 0.1958, loss: 0.1958 +2025-06-25 01:01:28,978 - pyskl - INFO - Epoch [95][200/1281] lr: 7.619e-03, eta: 8:11:03, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.1900, loss: 0.1900 +2025-06-25 01:02:17,799 - pyskl - INFO - Epoch [95][300/1281] lr: 7.600e-03, eta: 8:10:26, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2500, loss: 0.2500 +2025-06-25 01:03:07,097 - pyskl - INFO - Epoch [95][400/1281] lr: 7.581e-03, eta: 8:09:49, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 1.0000, loss_cls: 0.2393, loss: 0.2393 +2025-06-25 01:03:56,223 - pyskl - INFO - Epoch [95][500/1281] lr: 7.562e-03, eta: 8:09:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1934, loss: 0.1934 +2025-06-25 01:04:43,299 - pyskl - INFO - Epoch [95][600/1281] lr: 7.543e-03, eta: 8:08:35, time: 0.471, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 0.9994, loss_cls: 0.1847, loss: 0.1847 +2025-06-25 01:05:20,904 - pyskl - INFO - Epoch [95][700/1281] lr: 7.525e-03, eta: 8:07:52, time: 0.376, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9994, loss_cls: 0.2081, loss: 0.2081 +2025-06-25 01:05:56,548 - pyskl - INFO - Epoch [95][800/1281] lr: 7.506e-03, eta: 8:07:07, time: 0.356, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9994, loss_cls: 0.1781, loss: 0.1781 +2025-06-25 01:06:34,510 - pyskl - INFO - Epoch [95][900/1281] lr: 7.487e-03, eta: 8:06:24, time: 0.380, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1858, loss: 0.1858 +2025-06-25 01:07:23,613 - pyskl - INFO - Epoch [95][1000/1281] lr: 7.468e-03, eta: 8:05:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2206, loss: 0.2206 +2025-06-25 01:08:12,650 - pyskl - INFO - Epoch [95][1100/1281] lr: 7.450e-03, eta: 8:05:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1962, loss: 0.1962 +2025-06-25 01:09:01,831 - pyskl - INFO - Epoch [95][1200/1281] lr: 7.431e-03, eta: 8:04:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9994, loss_cls: 0.1917, loss: 0.1917 +2025-06-25 01:09:42,366 - pyskl - INFO - Saving checkpoint at 95 epochs +2025-06-25 01:10:40,119 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:10:40,183 - pyskl - INFO - +top1_acc 0.9099 +top5_acc 0.9940 +2025-06-25 01:10:40,183 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:10:40,193 - pyskl - INFO - +mean_acc 0.8889 +2025-06-25 01:10:40,197 - pyskl - INFO - Epoch(val) [95][533] top1_acc: 0.9099, top5_acc: 0.9940, mean_class_accuracy: 0.8889 +2025-06-25 01:11:58,183 - pyskl - INFO - Epoch [96][100/1281] lr: 7.397e-03, eta: 8:03:22, time: 0.780, data_time: 0.182, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1528, loss: 0.1528 +2025-06-25 01:12:47,202 - pyskl - INFO - Epoch [96][200/1281] lr: 7.379e-03, eta: 8:02:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1590, loss: 0.1590 +2025-06-25 01:13:36,518 - pyskl - INFO - Epoch [96][300/1281] lr: 7.360e-03, eta: 8:02:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1579, loss: 0.1579 +2025-06-25 01:14:25,492 - pyskl - INFO - Epoch [96][400/1281] lr: 7.341e-03, eta: 8:01:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9994, loss_cls: 0.2131, loss: 0.2131 +2025-06-25 01:15:14,610 - pyskl - INFO - Epoch [96][500/1281] lr: 7.323e-03, eta: 8:00:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2109, loss: 0.2109 +2025-06-25 01:16:03,827 - pyskl - INFO - Epoch [96][600/1281] lr: 7.304e-03, eta: 8:00:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1762, loss: 0.1762 +2025-06-25 01:16:37,156 - pyskl - INFO - Epoch [96][700/1281] lr: 7.286e-03, eta: 7:59:32, time: 0.333, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9994, loss_cls: 0.2364, loss: 0.2364 +2025-06-25 01:17:17,414 - pyskl - INFO - Epoch [96][800/1281] lr: 7.267e-03, eta: 7:58:51, time: 0.403, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2067, loss: 0.2067 +2025-06-25 01:17:54,678 - pyskl - INFO - Epoch [96][900/1281] lr: 7.249e-03, eta: 7:58:07, time: 0.373, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9994, loss_cls: 0.2047, loss: 0.2047 +2025-06-25 01:18:43,853 - pyskl - INFO - Epoch [96][1000/1281] lr: 7.230e-03, eta: 7:57:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2127, loss: 0.2127 +2025-06-25 01:19:33,202 - pyskl - INFO - Epoch [96][1100/1281] lr: 7.211e-03, eta: 7:56:54, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2435, loss: 0.2435 +2025-06-25 01:20:22,293 - pyskl - INFO - Epoch [96][1200/1281] lr: 7.193e-03, eta: 7:56:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2580, loss: 0.2580 +2025-06-25 01:21:02,813 - pyskl - INFO - Saving checkpoint at 96 epochs +2025-06-25 01:22:00,681 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:22:00,744 - pyskl - INFO - +top1_acc 0.9089 +top5_acc 0.9939 +2025-06-25 01:22:00,744 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:22:00,751 - pyskl - INFO - +mean_acc 0.8855 +2025-06-25 01:22:00,753 - pyskl - INFO - Epoch(val) [96][533] top1_acc: 0.9089, top5_acc: 0.9939, mean_class_accuracy: 0.8855 +2025-06-25 01:23:20,325 - pyskl - INFO - Epoch [97][100/1281] lr: 7.159e-03, eta: 7:55:05, time: 0.796, data_time: 0.181, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1547, loss: 0.1547 +2025-06-25 01:24:09,409 - pyskl - INFO - Epoch [97][200/1281] lr: 7.141e-03, eta: 7:54:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2038, loss: 0.2038 +2025-06-25 01:24:58,451 - pyskl - INFO - Epoch [97][300/1281] lr: 7.123e-03, eta: 7:53:51, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1691, loss: 0.1691 +2025-06-25 01:25:47,520 - pyskl - INFO - Epoch [97][400/1281] lr: 7.104e-03, eta: 7:53:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.1806, loss: 0.1806 +2025-06-25 01:26:36,598 - pyskl - INFO - Epoch [97][500/1281] lr: 7.086e-03, eta: 7:52:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2377, loss: 0.2377 +2025-06-25 01:27:24,731 - pyskl - INFO - Epoch [97][600/1281] lr: 7.067e-03, eta: 7:52:00, time: 0.481, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.2135, loss: 0.2135 +2025-06-25 01:28:00,012 - pyskl - INFO - Epoch [97][700/1281] lr: 7.049e-03, eta: 7:51:15, time: 0.353, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1728, loss: 0.1728 +2025-06-25 01:28:38,299 - pyskl - INFO - Epoch [97][800/1281] lr: 7.030e-03, eta: 7:50:32, time: 0.383, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1760, loss: 0.1760 +2025-06-25 01:29:15,644 - pyskl - INFO - Epoch [97][900/1281] lr: 7.012e-03, eta: 7:49:49, time: 0.373, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 1.0000, loss_cls: 0.2421, loss: 0.2421 +2025-06-25 01:30:04,473 - pyskl - INFO - Epoch [97][1000/1281] lr: 6.994e-03, eta: 7:49:12, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 0.9988, loss_cls: 0.1962, loss: 0.1962 +2025-06-25 01:30:53,706 - pyskl - INFO - Epoch [97][1100/1281] lr: 6.975e-03, eta: 7:48:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9994, loss_cls: 0.1991, loss: 0.1991 +2025-06-25 01:31:42,785 - pyskl - INFO - Epoch [97][1200/1281] lr: 6.957e-03, eta: 7:47:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2036, loss: 0.2036 +2025-06-25 01:32:22,854 - pyskl - INFO - Saving checkpoint at 97 epochs +2025-06-25 01:33:21,106 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:33:21,162 - pyskl - INFO - +top1_acc 0.9155 +top5_acc 0.9946 +2025-06-25 01:33:21,162 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:33:21,169 - pyskl - INFO - +mean_acc 0.8924 +2025-06-25 01:33:21,170 - pyskl - INFO - Epoch(val) [97][533] top1_acc: 0.9155, top5_acc: 0.9946, mean_class_accuracy: 0.8924 +2025-06-25 01:34:40,282 - pyskl - INFO - Epoch [98][100/1281] lr: 6.924e-03, eta: 7:46:45, time: 0.791, data_time: 0.180, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1494, loss: 0.1494 +2025-06-25 01:35:29,240 - pyskl - INFO - Epoch [98][200/1281] lr: 6.906e-03, eta: 7:46:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9994, loss_cls: 0.1779, loss: 0.1779 +2025-06-25 01:36:18,477 - pyskl - INFO - Epoch [98][300/1281] lr: 6.887e-03, eta: 7:45:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1432, loss: 0.1432 +2025-06-25 01:37:07,686 - pyskl - INFO - Epoch [98][400/1281] lr: 6.869e-03, eta: 7:44:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1807, loss: 0.1807 +2025-06-25 01:37:56,666 - pyskl - INFO - Epoch [98][500/1281] lr: 6.851e-03, eta: 7:44:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2017, loss: 0.2017 +2025-06-25 01:38:45,113 - pyskl - INFO - Epoch [98][600/1281] lr: 6.833e-03, eta: 7:43:39, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2080, loss: 0.2080 +2025-06-25 01:39:21,832 - pyskl - INFO - Epoch [98][700/1281] lr: 6.814e-03, eta: 7:42:55, time: 0.367, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.1727, loss: 0.1727 +2025-06-25 01:39:58,666 - pyskl - INFO - Epoch [98][800/1281] lr: 6.796e-03, eta: 7:42:12, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1553, loss: 0.1553 +2025-06-25 01:40:37,220 - pyskl - INFO - Epoch [98][900/1281] lr: 6.778e-03, eta: 7:41:29, time: 0.386, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 0.9988, loss_cls: 0.1589, loss: 0.1589 +2025-06-25 01:41:26,183 - pyskl - INFO - Epoch [98][1000/1281] lr: 6.760e-03, eta: 7:40:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1541, loss: 0.1541 +2025-06-25 01:42:15,407 - pyskl - INFO - Epoch [98][1100/1281] lr: 6.742e-03, eta: 7:40:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2042, loss: 0.2042 +2025-06-25 01:43:04,423 - pyskl - INFO - Epoch [98][1200/1281] lr: 6.724e-03, eta: 7:39:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2310, loss: 0.2310 +2025-06-25 01:43:44,833 - pyskl - INFO - Saving checkpoint at 98 epochs +2025-06-25 01:44:42,658 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:44:42,712 - pyskl - INFO - +top1_acc 0.9046 +top5_acc 0.9935 +2025-06-25 01:44:42,713 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:44:42,719 - pyskl - INFO - +mean_acc 0.8808 +2025-06-25 01:44:42,721 - pyskl - INFO - Epoch(val) [98][533] top1_acc: 0.9046, top5_acc: 0.9935, mean_class_accuracy: 0.8808 +2025-06-25 01:46:02,040 - pyskl - INFO - Epoch [99][100/1281] lr: 6.691e-03, eta: 7:38:25, time: 0.793, data_time: 0.184, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1782, loss: 0.1782 +2025-06-25 01:46:51,183 - pyskl - INFO - Epoch [99][200/1281] lr: 6.673e-03, eta: 7:37:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1289, loss: 0.1289 +2025-06-25 01:47:40,289 - pyskl - INFO - Epoch [99][300/1281] lr: 6.655e-03, eta: 7:37:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1599, loss: 0.1599 +2025-06-25 01:48:29,472 - pyskl - INFO - Epoch [99][400/1281] lr: 6.637e-03, eta: 7:36:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 0.9994, loss_cls: 0.1603, loss: 0.1603 +2025-06-25 01:49:18,324 - pyskl - INFO - Epoch [99][500/1281] lr: 6.619e-03, eta: 7:35:55, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9994, loss_cls: 0.1647, loss: 0.1647 +2025-06-25 01:50:05,295 - pyskl - INFO - Epoch [99][600/1281] lr: 6.601e-03, eta: 7:35:17, time: 0.470, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.1757, loss: 0.1757 +2025-06-25 01:50:43,279 - pyskl - INFO - Epoch [99][700/1281] lr: 6.583e-03, eta: 7:34:34, time: 0.380, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1830, loss: 0.1830 +2025-06-25 01:51:18,771 - pyskl - INFO - Epoch [99][800/1281] lr: 6.565e-03, eta: 7:33:49, time: 0.355, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9994, loss_cls: 0.1863, loss: 0.1863 +2025-06-25 01:51:57,205 - pyskl - INFO - Epoch [99][900/1281] lr: 6.547e-03, eta: 7:33:06, time: 0.384, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1723, loss: 0.1723 +2025-06-25 01:52:46,415 - pyskl - INFO - Epoch [99][1000/1281] lr: 6.529e-03, eta: 7:32:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1713, loss: 0.1713 +2025-06-25 01:53:35,382 - pyskl - INFO - Epoch [99][1100/1281] lr: 6.511e-03, eta: 7:31:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2149, loss: 0.2149 +2025-06-25 01:54:24,515 - pyskl - INFO - Epoch [99][1200/1281] lr: 6.493e-03, eta: 7:31:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2122, loss: 0.2122 +2025-06-25 01:55:04,815 - pyskl - INFO - Saving checkpoint at 99 epochs +2025-06-25 01:56:03,226 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:56:03,280 - pyskl - INFO - +top1_acc 0.9130 +top5_acc 0.9952 +2025-06-25 01:56:03,281 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:56:03,287 - pyskl - INFO - +mean_acc 0.8731 +2025-06-25 01:56:03,288 - pyskl - INFO - Epoch(val) [99][533] top1_acc: 0.9130, top5_acc: 0.9952, mean_class_accuracy: 0.8731 +2025-06-25 01:57:22,400 - pyskl - INFO - Epoch [100][100/1281] lr: 6.460e-03, eta: 7:30:02, time: 0.791, data_time: 0.183, memory: 4083, top1_acc: 0.9862, top5_acc: 0.9994, loss_cls: 0.1275, loss: 0.1275 +2025-06-25 01:58:11,346 - pyskl - INFO - Epoch [100][200/1281] lr: 6.442e-03, eta: 7:29:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1486, loss: 0.1486 +2025-06-25 01:59:00,369 - pyskl - INFO - Epoch [100][300/1281] lr: 6.425e-03, eta: 7:28:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1481, loss: 0.1481 +2025-06-25 01:59:49,308 - pyskl - INFO - Epoch [100][400/1281] lr: 6.407e-03, eta: 7:28:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1135, loss: 0.1135 +2025-06-25 02:00:38,299 - pyskl - INFO - Epoch [100][500/1281] lr: 6.389e-03, eta: 7:27:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1264, loss: 0.1264 +2025-06-25 02:01:25,596 - pyskl - INFO - Epoch [100][600/1281] lr: 6.371e-03, eta: 7:26:53, time: 0.473, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1902, loss: 0.1902 +2025-06-25 02:02:01,212 - pyskl - INFO - Epoch [100][700/1281] lr: 6.353e-03, eta: 7:26:09, time: 0.356, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1575, loss: 0.1575 +2025-06-25 02:02:39,102 - pyskl - INFO - Epoch [100][800/1281] lr: 6.336e-03, eta: 7:25:25, time: 0.379, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1620, loss: 0.1620 +2025-06-25 02:03:16,304 - pyskl - INFO - Epoch [100][900/1281] lr: 6.318e-03, eta: 7:24:42, time: 0.372, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9988, loss_cls: 0.2130, loss: 0.2130 +2025-06-25 02:04:05,531 - pyskl - INFO - Epoch [100][1000/1281] lr: 6.300e-03, eta: 7:24:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9988, loss_cls: 0.2733, loss: 0.2733 +2025-06-25 02:04:54,806 - pyskl - INFO - Epoch [100][1100/1281] lr: 6.282e-03, eta: 7:23:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 0.9994, loss_cls: 0.1738, loss: 0.1738 +2025-06-25 02:05:43,776 - pyskl - INFO - Epoch [100][1200/1281] lr: 6.265e-03, eta: 7:22:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 0.9994, loss_cls: 0.2106, loss: 0.2106 +2025-06-25 02:06:24,266 - pyskl - INFO - Saving checkpoint at 100 epochs +2025-06-25 02:07:21,968 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:07:22,022 - pyskl - INFO - +top1_acc 0.9164 +top5_acc 0.9946 +2025-06-25 02:07:22,022 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:07:22,029 - pyskl - INFO - +mean_acc 0.8953 +2025-06-25 02:07:22,031 - pyskl - INFO - Epoch(val) [100][533] top1_acc: 0.9164, top5_acc: 0.9946, mean_class_accuracy: 0.8953 +2025-06-25 02:08:41,702 - pyskl - INFO - Epoch [101][100/1281] lr: 6.232e-03, eta: 7:21:37, time: 0.797, data_time: 0.183, memory: 4083, top1_acc: 0.9844, top5_acc: 0.9994, loss_cls: 0.1484, loss: 0.1484 +2025-06-25 02:09:30,849 - pyskl - INFO - Epoch [101][200/1281] lr: 6.215e-03, eta: 7:20:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1747, loss: 0.1747 +2025-06-25 02:10:19,891 - pyskl - INFO - Epoch [101][300/1281] lr: 6.197e-03, eta: 7:20:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1528, loss: 0.1528 +2025-06-25 02:11:09,300 - pyskl - INFO - Epoch [101][400/1281] lr: 6.180e-03, eta: 7:19:44, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.1123, loss: 0.1123 +2025-06-25 02:11:58,425 - pyskl - INFO - Epoch [101][500/1281] lr: 6.162e-03, eta: 7:19:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 0.9994, loss_cls: 0.0933, loss: 0.0933 +2025-06-25 02:12:47,060 - pyskl - INFO - Epoch [101][600/1281] lr: 6.144e-03, eta: 7:18:28, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1611, loss: 0.1611 +2025-06-25 02:13:22,289 - pyskl - INFO - Epoch [101][700/1281] lr: 6.127e-03, eta: 7:17:44, time: 0.352, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 0.9994, loss_cls: 0.1642, loss: 0.1642 +2025-06-25 02:14:00,522 - pyskl - INFO - Epoch [101][800/1281] lr: 6.109e-03, eta: 7:17:01, time: 0.382, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1535, loss: 0.1535 +2025-06-25 02:14:38,576 - pyskl - INFO - Epoch [101][900/1281] lr: 6.092e-03, eta: 7:16:18, time: 0.381, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1858, loss: 0.1858 +2025-06-25 02:15:27,526 - pyskl - INFO - Epoch [101][1000/1281] lr: 6.074e-03, eta: 7:15:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9994, loss_cls: 0.1691, loss: 0.1691 +2025-06-25 02:16:16,579 - pyskl - INFO - Epoch [101][1100/1281] lr: 6.057e-03, eta: 7:15:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.2090, loss: 0.2090 +2025-06-25 02:17:05,764 - pyskl - INFO - Epoch [101][1200/1281] lr: 6.039e-03, eta: 7:14:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1756, loss: 0.1756 +2025-06-25 02:17:46,375 - pyskl - INFO - Saving checkpoint at 101 epochs +2025-06-25 02:18:44,334 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:18:44,390 - pyskl - INFO - +top1_acc 0.9173 +top5_acc 0.9938 +2025-06-25 02:18:44,390 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:18:44,396 - pyskl - INFO - +mean_acc 0.8887 +2025-06-25 02:18:44,398 - pyskl - INFO - Epoch(val) [101][533] top1_acc: 0.9173, top5_acc: 0.9938, mean_class_accuracy: 0.8887 +2025-06-25 02:20:03,700 - pyskl - INFO - Epoch [102][100/1281] lr: 6.007e-03, eta: 7:13:11, time: 0.793, data_time: 0.184, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1795, loss: 0.1795 +2025-06-25 02:20:52,539 - pyskl - INFO - Epoch [102][200/1281] lr: 5.990e-03, eta: 7:12:33, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 0.9994, loss_cls: 0.1848, loss: 0.1848 +2025-06-25 02:21:41,773 - pyskl - INFO - Epoch [102][300/1281] lr: 5.972e-03, eta: 7:11:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1594, loss: 0.1594 +2025-06-25 02:22:30,929 - pyskl - INFO - Epoch [102][400/1281] lr: 5.955e-03, eta: 7:11:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1158, loss: 0.1158 +2025-06-25 02:23:19,922 - pyskl - INFO - Epoch [102][500/1281] lr: 5.938e-03, eta: 7:10:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1542, loss: 0.1542 +2025-06-25 02:24:08,393 - pyskl - INFO - Epoch [102][600/1281] lr: 5.920e-03, eta: 7:10:02, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1547, loss: 0.1547 +2025-06-25 02:24:41,337 - pyskl - INFO - Epoch [102][700/1281] lr: 5.903e-03, eta: 7:09:16, time: 0.329, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 0.9994, loss_cls: 0.1572, loss: 0.1572 +2025-06-25 02:25:21,771 - pyskl - INFO - Epoch [102][800/1281] lr: 5.886e-03, eta: 7:08:34, time: 0.404, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1382, loss: 0.1382 +2025-06-25 02:25:58,760 - pyskl - INFO - Epoch [102][900/1281] lr: 5.868e-03, eta: 7:07:51, time: 0.370, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 0.9994, loss_cls: 0.1448, loss: 0.1448 +2025-06-25 02:26:47,591 - pyskl - INFO - Epoch [102][1000/1281] lr: 5.851e-03, eta: 7:07:13, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.1939, loss: 0.1939 +2025-06-25 02:27:36,519 - pyskl - INFO - Epoch [102][1100/1281] lr: 5.834e-03, eta: 7:06:35, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1474, loss: 0.1474 +2025-06-25 02:28:25,433 - pyskl - INFO - Epoch [102][1200/1281] lr: 5.816e-03, eta: 7:05:57, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1509, loss: 0.1509 +2025-06-25 02:29:05,656 - pyskl - INFO - Saving checkpoint at 102 epochs +2025-06-25 02:30:04,127 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:30:04,182 - pyskl - INFO - +top1_acc 0.9134 +top5_acc 0.9957 +2025-06-25 02:30:04,182 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:30:04,189 - pyskl - INFO - +mean_acc 0.8909 +2025-06-25 02:30:04,191 - pyskl - INFO - Epoch(val) [102][533] top1_acc: 0.9134, top5_acc: 0.9957, mean_class_accuracy: 0.8909 +2025-06-25 02:31:24,618 - pyskl - INFO - Epoch [103][100/1281] lr: 5.785e-03, eta: 7:04:44, time: 0.804, data_time: 0.185, memory: 4083, top1_acc: 0.9731, top5_acc: 0.9994, loss_cls: 0.1620, loss: 0.1620 +2025-06-25 02:32:13,715 - pyskl - INFO - Epoch [103][200/1281] lr: 5.768e-03, eta: 7:04:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1318, loss: 0.1318 +2025-06-25 02:33:02,958 - pyskl - INFO - Epoch [103][300/1281] lr: 5.751e-03, eta: 7:03:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1260, loss: 0.1260 +2025-06-25 02:33:52,256 - pyskl - INFO - Epoch [103][400/1281] lr: 5.733e-03, eta: 7:02:50, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1443, loss: 0.1443 +2025-06-25 02:34:41,517 - pyskl - INFO - Epoch [103][500/1281] lr: 5.716e-03, eta: 7:02:12, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1449, loss: 0.1449 +2025-06-25 02:35:28,115 - pyskl - INFO - Epoch [103][600/1281] lr: 5.699e-03, eta: 7:01:33, time: 0.466, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1158, loss: 0.1158 +2025-06-25 02:36:05,599 - pyskl - INFO - Epoch [103][700/1281] lr: 5.682e-03, eta: 7:00:50, time: 0.375, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1331, loss: 0.1331 +2025-06-25 02:36:41,163 - pyskl - INFO - Epoch [103][800/1281] lr: 5.665e-03, eta: 7:00:05, time: 0.356, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1600, loss: 0.1600 +2025-06-25 02:37:19,131 - pyskl - INFO - Epoch [103][900/1281] lr: 5.648e-03, eta: 6:59:22, time: 0.380, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1714, loss: 0.1714 +2025-06-25 02:38:08,165 - pyskl - INFO - Epoch [103][1000/1281] lr: 5.631e-03, eta: 6:58:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1804, loss: 0.1804 +2025-06-25 02:38:57,213 - pyskl - INFO - Epoch [103][1100/1281] lr: 5.614e-03, eta: 6:58:06, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 0.9988, loss_cls: 0.1667, loss: 0.1667 +2025-06-25 02:39:45,954 - pyskl - INFO - Epoch [103][1200/1281] lr: 5.597e-03, eta: 6:57:28, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 0.9994, loss_cls: 0.1609, loss: 0.1609 +2025-06-25 02:40:26,561 - pyskl - INFO - Saving checkpoint at 103 epochs +2025-06-25 02:41:24,676 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:41:24,747 - pyskl - INFO - +top1_acc 0.9177 +top5_acc 0.9954 +2025-06-25 02:41:24,748 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:41:24,754 - pyskl - INFO - +mean_acc 0.8893 +2025-06-25 02:41:24,756 - pyskl - INFO - Epoch(val) [103][533] top1_acc: 0.9177, top5_acc: 0.9954, mean_class_accuracy: 0.8893 +2025-06-25 02:42:42,858 - pyskl - INFO - Epoch [104][100/1281] lr: 5.566e-03, eta: 6:56:14, time: 0.781, data_time: 0.187, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.1881, loss: 0.1881 +2025-06-25 02:43:31,955 - pyskl - INFO - Epoch [104][200/1281] lr: 5.549e-03, eta: 6:55:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1417, loss: 0.1417 +2025-06-25 02:44:20,948 - pyskl - INFO - Epoch [104][300/1281] lr: 5.532e-03, eta: 6:54:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 0.9994, loss_cls: 0.1264, loss: 0.1264 +2025-06-25 02:45:10,192 - pyskl - INFO - Epoch [104][400/1281] lr: 5.515e-03, eta: 6:54:19, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1597, loss: 0.1597 +2025-06-25 02:45:59,241 - pyskl - INFO - Epoch [104][500/1281] lr: 5.498e-03, eta: 6:53:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1450, loss: 0.1450 +2025-06-25 02:46:47,749 - pyskl - INFO - Epoch [104][600/1281] lr: 5.481e-03, eta: 6:53:03, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1429, loss: 0.1429 +2025-06-25 02:47:21,994 - pyskl - INFO - Epoch [104][700/1281] lr: 5.464e-03, eta: 6:52:18, time: 0.342, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1629, loss: 0.1629 +2025-06-25 02:48:01,326 - pyskl - INFO - Epoch [104][800/1281] lr: 5.447e-03, eta: 6:51:35, time: 0.393, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1560, loss: 0.1560 +2025-06-25 02:48:39,142 - pyskl - INFO - Epoch [104][900/1281] lr: 5.430e-03, eta: 6:50:52, time: 0.378, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1110, loss: 0.1110 +2025-06-25 02:49:28,430 - pyskl - INFO - Epoch [104][1000/1281] lr: 5.413e-03, eta: 6:50:14, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 0.9994, loss_cls: 0.1502, loss: 0.1502 +2025-06-25 02:50:17,377 - pyskl - INFO - Epoch [104][1100/1281] lr: 5.397e-03, eta: 6:49:36, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1487, loss: 0.1487 +2025-06-25 02:51:06,688 - pyskl - INFO - Epoch [104][1200/1281] lr: 5.380e-03, eta: 6:48:57, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 0.9994, loss_cls: 0.1431, loss: 0.1431 +2025-06-25 02:51:46,991 - pyskl - INFO - Saving checkpoint at 104 epochs +2025-06-25 02:52:44,538 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:52:44,593 - pyskl - INFO - +top1_acc 0.9135 +top5_acc 0.9941 +2025-06-25 02:52:44,594 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:52:44,600 - pyskl - INFO - +mean_acc 0.8799 +2025-06-25 02:52:44,602 - pyskl - INFO - Epoch(val) [104][533] top1_acc: 0.9135, top5_acc: 0.9941, mean_class_accuracy: 0.8799 +2025-06-25 02:54:04,042 - pyskl - INFO - Epoch [105][100/1281] lr: 5.349e-03, eta: 6:47:44, time: 0.794, data_time: 0.184, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1552, loss: 0.1552 +2025-06-25 02:54:52,897 - pyskl - INFO - Epoch [105][200/1281] lr: 5.333e-03, eta: 6:47:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0982, loss: 0.0982 +2025-06-25 02:55:42,193 - pyskl - INFO - Epoch [105][300/1281] lr: 5.316e-03, eta: 6:46:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0972, loss: 0.0972 +2025-06-25 02:56:31,354 - pyskl - INFO - Epoch [105][400/1281] lr: 5.299e-03, eta: 6:45:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1521, loss: 0.1521 +2025-06-25 02:57:20,604 - pyskl - INFO - Epoch [105][500/1281] lr: 5.283e-03, eta: 6:45:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1507, loss: 0.1507 +2025-06-25 02:58:08,574 - pyskl - INFO - Epoch [105][600/1281] lr: 5.266e-03, eta: 6:44:32, time: 0.480, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1245, loss: 0.1245 +2025-06-25 02:58:42,751 - pyskl - INFO - Epoch [105][700/1281] lr: 5.249e-03, eta: 6:43:47, time: 0.342, data_time: 0.001, memory: 4083, top1_acc: 0.9819, top5_acc: 0.9994, loss_cls: 0.1301, loss: 0.1301 +2025-06-25 02:59:22,105 - pyskl - INFO - Epoch [105][800/1281] lr: 5.233e-03, eta: 6:43:05, time: 0.394, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1000, loss: 0.1000 +2025-06-25 02:59:59,558 - pyskl - INFO - Epoch [105][900/1281] lr: 5.216e-03, eta: 6:42:21, time: 0.375, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1507, loss: 0.1507 +2025-06-25 03:00:48,963 - pyskl - INFO - Epoch [105][1000/1281] lr: 5.199e-03, eta: 6:41:43, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1744, loss: 0.1744 +2025-06-25 03:01:38,203 - pyskl - INFO - Epoch [105][1100/1281] lr: 5.183e-03, eta: 6:41:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 0.9994, loss_cls: 0.1248, loss: 0.1248 +2025-06-25 03:02:27,637 - pyskl - INFO - Epoch [105][1200/1281] lr: 5.166e-03, eta: 6:40:26, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1578, loss: 0.1578 +2025-06-25 03:03:08,081 - pyskl - INFO - Saving checkpoint at 105 epochs +2025-06-25 03:04:05,937 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:04:05,992 - pyskl - INFO - +top1_acc 0.9150 +top5_acc 0.9953 +2025-06-25 03:04:05,992 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:04:05,999 - pyskl - INFO - +mean_acc 0.8780 +2025-06-25 03:04:06,001 - pyskl - INFO - Epoch(val) [105][533] top1_acc: 0.9150, top5_acc: 0.9953, mean_class_accuracy: 0.8780 +2025-06-25 03:05:25,126 - pyskl - INFO - Epoch [106][100/1281] lr: 5.136e-03, eta: 6:39:13, time: 0.791, data_time: 0.182, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1065, loss: 0.1065 +2025-06-25 03:06:13,834 - pyskl - INFO - Epoch [106][200/1281] lr: 5.120e-03, eta: 6:38:34, time: 0.487, data_time: 0.001, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1267, loss: 0.1267 +2025-06-25 03:07:03,052 - pyskl - INFO - Epoch [106][300/1281] lr: 5.103e-03, eta: 6:37:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1370, loss: 0.1370 +2025-06-25 03:07:52,403 - pyskl - INFO - Epoch [106][400/1281] lr: 5.087e-03, eta: 6:37:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0999, loss: 0.0999 +2025-06-25 03:08:41,629 - pyskl - INFO - Epoch [106][500/1281] lr: 5.070e-03, eta: 6:36:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0897, loss: 0.0897 +2025-06-25 03:09:30,734 - pyskl - INFO - Epoch [106][600/1281] lr: 5.054e-03, eta: 6:36:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0955, loss: 0.0955 +2025-06-25 03:10:03,500 - pyskl - INFO - Epoch [106][700/1281] lr: 5.038e-03, eta: 6:35:15, time: 0.328, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1085, loss: 0.1085 +2025-06-25 03:10:44,454 - pyskl - INFO - Epoch [106][800/1281] lr: 5.021e-03, eta: 6:34:33, time: 0.410, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1337, loss: 0.1337 +2025-06-25 03:11:21,268 - pyskl - INFO - Epoch [106][900/1281] lr: 5.005e-03, eta: 6:33:49, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1069, loss: 0.1069 +2025-06-25 03:12:10,398 - pyskl - INFO - Epoch [106][1000/1281] lr: 4.988e-03, eta: 6:33:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1096, loss: 0.1096 +2025-06-25 03:12:59,475 - pyskl - INFO - Epoch [106][1100/1281] lr: 4.972e-03, eta: 6:32:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1522, loss: 0.1522 +2025-06-25 03:13:48,742 - pyskl - INFO - Epoch [106][1200/1281] lr: 4.956e-03, eta: 6:31:54, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1322, loss: 0.1322 +2025-06-25 03:14:28,989 - pyskl - INFO - Saving checkpoint at 106 epochs +2025-06-25 03:15:27,300 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:15:27,364 - pyskl - INFO - +top1_acc 0.9117 +top5_acc 0.9951 +2025-06-25 03:15:27,364 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:15:27,370 - pyskl - INFO - +mean_acc 0.8802 +2025-06-25 03:15:27,372 - pyskl - INFO - Epoch(val) [106][533] top1_acc: 0.9117, top5_acc: 0.9951, mean_class_accuracy: 0.8802 +2025-06-25 03:16:45,277 - pyskl - INFO - Epoch [107][100/1281] lr: 4.926e-03, eta: 6:30:39, time: 0.779, data_time: 0.180, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1388, loss: 0.1388 +2025-06-25 03:17:34,533 - pyskl - INFO - Epoch [107][200/1281] lr: 4.910e-03, eta: 6:30:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1236, loss: 0.1236 +2025-06-25 03:18:23,981 - pyskl - INFO - Epoch [107][300/1281] lr: 4.894e-03, eta: 6:29:22, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1266, loss: 0.1266 +2025-06-25 03:19:13,487 - pyskl - INFO - Epoch [107][400/1281] lr: 4.878e-03, eta: 6:28:44, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1002, loss: 0.1002 +2025-06-25 03:20:02,690 - pyskl - INFO - Epoch [107][500/1281] lr: 4.862e-03, eta: 6:28:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1018, loss: 0.1018 +2025-06-25 03:20:51,786 - pyskl - INFO - Epoch [107][600/1281] lr: 4.845e-03, eta: 6:27:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1279, loss: 0.1279 +2025-06-25 03:21:23,252 - pyskl - INFO - Epoch [107][700/1281] lr: 4.829e-03, eta: 6:26:41, time: 0.315, data_time: 0.001, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.1532, loss: 0.1532 +2025-06-25 03:22:06,713 - pyskl - INFO - Epoch [107][800/1281] lr: 4.813e-03, eta: 6:26:00, time: 0.435, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1229, loss: 0.1229 +2025-06-25 03:22:43,033 - pyskl - INFO - Epoch [107][900/1281] lr: 4.797e-03, eta: 6:25:16, time: 0.363, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1159, loss: 0.1159 +2025-06-25 03:23:32,229 - pyskl - INFO - Epoch [107][1000/1281] lr: 4.781e-03, eta: 6:24:37, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1760, loss: 0.1760 +2025-06-25 03:24:21,836 - pyskl - INFO - Epoch [107][1100/1281] lr: 4.765e-03, eta: 6:23:59, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1278, loss: 0.1278 +2025-06-25 03:25:11,457 - pyskl - INFO - Epoch [107][1200/1281] lr: 4.749e-03, eta: 6:23:20, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1162, loss: 0.1162 +2025-06-25 03:25:51,626 - pyskl - INFO - Saving checkpoint at 107 epochs +2025-06-25 03:26:49,267 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:26:49,323 - pyskl - INFO - +top1_acc 0.9304 +top5_acc 0.9954 +2025-06-25 03:26:49,323 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:26:49,330 - pyskl - INFO - +mean_acc 0.9021 +2025-06-25 03:26:49,334 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_94.pth was removed +2025-06-25 03:26:49,658 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_107.pth. +2025-06-25 03:26:49,658 - pyskl - INFO - Best top1_acc is 0.9304 at 107 epoch. +2025-06-25 03:26:49,661 - pyskl - INFO - Epoch(val) [107][533] top1_acc: 0.9304, top5_acc: 0.9954, mean_class_accuracy: 0.9021 +2025-06-25 03:28:09,106 - pyskl - INFO - Epoch [108][100/1281] lr: 4.720e-03, eta: 6:22:07, time: 0.794, data_time: 0.184, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0981, loss: 0.0981 +2025-06-25 03:28:58,371 - pyskl - INFO - Epoch [108][200/1281] lr: 4.704e-03, eta: 6:21:28, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1262, loss: 0.1262 +2025-06-25 03:29:47,594 - pyskl - INFO - Epoch [108][300/1281] lr: 4.688e-03, eta: 6:20:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0900, loss: 0.0900 +2025-06-25 03:30:36,631 - pyskl - INFO - Epoch [108][400/1281] lr: 4.672e-03, eta: 6:20:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1103, loss: 0.1103 +2025-06-25 03:31:25,748 - pyskl - INFO - Epoch [108][500/1281] lr: 4.656e-03, eta: 6:19:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 0.9994, loss_cls: 0.0905, loss: 0.0905 +2025-06-25 03:32:14,593 - pyskl - INFO - Epoch [108][600/1281] lr: 4.640e-03, eta: 6:18:53, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1015, loss: 0.1015 +2025-06-25 03:32:47,082 - pyskl - INFO - Epoch [108][700/1281] lr: 4.624e-03, eta: 6:18:07, time: 0.325, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1155, loss: 0.1155 +2025-06-25 03:33:27,831 - pyskl - INFO - Epoch [108][800/1281] lr: 4.608e-03, eta: 6:17:25, time: 0.407, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1415, loss: 0.1415 +2025-06-25 03:34:03,398 - pyskl - INFO - Epoch [108][900/1281] lr: 4.593e-03, eta: 6:16:41, time: 0.356, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1279, loss: 0.1279 +2025-06-25 03:34:52,459 - pyskl - INFO - Epoch [108][1000/1281] lr: 4.577e-03, eta: 6:16:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 0.9988, loss_cls: 0.1338, loss: 0.1338 +2025-06-25 03:35:41,855 - pyskl - INFO - Epoch [108][1100/1281] lr: 4.561e-03, eta: 6:15:24, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1531, loss: 0.1531 +2025-06-25 03:36:31,152 - pyskl - INFO - Epoch [108][1200/1281] lr: 4.545e-03, eta: 6:14:45, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1307, loss: 0.1307 +2025-06-25 03:37:12,045 - pyskl - INFO - Saving checkpoint at 108 epochs +2025-06-25 03:38:10,795 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:38:10,875 - pyskl - INFO - +top1_acc 0.9202 +top5_acc 0.9951 +2025-06-25 03:38:10,875 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:38:10,883 - pyskl - INFO - +mean_acc 0.8948 +2025-06-25 03:38:10,885 - pyskl - INFO - Epoch(val) [108][533] top1_acc: 0.9202, top5_acc: 0.9951, mean_class_accuracy: 0.8948 +2025-06-25 03:39:29,904 - pyskl - INFO - Epoch [109][100/1281] lr: 4.517e-03, eta: 6:13:31, time: 0.790, data_time: 0.189, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1237, loss: 0.1237 +2025-06-25 03:40:19,189 - pyskl - INFO - Epoch [109][200/1281] lr: 4.501e-03, eta: 6:12:52, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1096, loss: 0.1096 +2025-06-25 03:41:08,271 - pyskl - INFO - Epoch [109][300/1281] lr: 4.485e-03, eta: 6:12:13, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1435, loss: 0.1435 +2025-06-25 03:41:57,043 - pyskl - INFO - Epoch [109][400/1281] lr: 4.470e-03, eta: 6:11:34, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1118, loss: 0.1118 +2025-06-25 03:42:46,097 - pyskl - INFO - Epoch [109][500/1281] lr: 4.454e-03, eta: 6:10:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0894, loss: 0.0894 +2025-06-25 03:43:34,886 - pyskl - INFO - Epoch [109][600/1281] lr: 4.438e-03, eta: 6:10:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1191, loss: 0.1191 +2025-06-25 03:44:07,281 - pyskl - INFO - Epoch [109][700/1281] lr: 4.423e-03, eta: 6:09:31, time: 0.324, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 0.9994, loss_cls: 0.1065, loss: 0.1065 +2025-06-25 03:44:49,325 - pyskl - INFO - Epoch [109][800/1281] lr: 4.407e-03, eta: 6:08:49, time: 0.420, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 0.9994, loss_cls: 0.1132, loss: 0.1132 +2025-06-25 03:45:26,224 - pyskl - INFO - Epoch [109][900/1281] lr: 4.391e-03, eta: 6:08:05, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1263, loss: 0.1263 +2025-06-25 03:46:15,357 - pyskl - INFO - Epoch [109][1000/1281] lr: 4.376e-03, eta: 6:07:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1259, loss: 0.1259 +2025-06-25 03:47:04,538 - pyskl - INFO - Epoch [109][1100/1281] lr: 4.360e-03, eta: 6:06:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1227, loss: 0.1227 +2025-06-25 03:47:53,833 - pyskl - INFO - Epoch [109][1200/1281] lr: 4.345e-03, eta: 6:06:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1348, loss: 0.1348 +2025-06-25 03:48:34,213 - pyskl - INFO - Saving checkpoint at 109 epochs +2025-06-25 03:49:31,974 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:49:32,030 - pyskl - INFO - +top1_acc 0.9251 +top5_acc 0.9952 +2025-06-25 03:49:32,030 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:49:32,037 - pyskl - INFO - +mean_acc 0.9014 +2025-06-25 03:49:32,039 - pyskl - INFO - Epoch(val) [109][533] top1_acc: 0.9251, top5_acc: 0.9952, mean_class_accuracy: 0.9014 +2025-06-25 03:50:50,853 - pyskl - INFO - Epoch [110][100/1281] lr: 4.317e-03, eta: 6:04:54, time: 0.788, data_time: 0.185, memory: 4083, top1_acc: 0.9875, top5_acc: 0.9994, loss_cls: 0.0966, loss: 0.0966 +2025-06-25 03:51:39,778 - pyskl - INFO - Epoch [110][200/1281] lr: 4.301e-03, eta: 6:04:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0873, loss: 0.0873 +2025-06-25 03:52:29,478 - pyskl - INFO - Epoch [110][300/1281] lr: 4.286e-03, eta: 6:03:36, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0846, loss: 0.0846 +2025-06-25 03:53:18,563 - pyskl - INFO - Epoch [110][400/1281] lr: 4.271e-03, eta: 6:02:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1074, loss: 0.1074 +2025-06-25 03:54:07,675 - pyskl - INFO - Epoch [110][500/1281] lr: 4.255e-03, eta: 6:02:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 0.9994, loss_cls: 0.1040, loss: 0.1040 +2025-06-25 03:54:57,075 - pyskl - INFO - Epoch [110][600/1281] lr: 4.240e-03, eta: 6:01:39, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0899, loss: 0.0899 +2025-06-25 03:55:30,720 - pyskl - INFO - Epoch [110][700/1281] lr: 4.225e-03, eta: 6:00:54, time: 0.336, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0730, loss: 0.0730 +2025-06-25 03:56:11,350 - pyskl - INFO - Epoch [110][800/1281] lr: 4.209e-03, eta: 6:00:12, time: 0.406, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1159, loss: 0.1159 +2025-06-25 03:56:49,367 - pyskl - INFO - Epoch [110][900/1281] lr: 4.194e-03, eta: 5:59:29, time: 0.380, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 0.9994, loss_cls: 0.1119, loss: 0.1119 +2025-06-25 03:57:38,526 - pyskl - INFO - Epoch [110][1000/1281] lr: 4.179e-03, eta: 5:58:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1296, loss: 0.1296 +2025-06-25 03:58:27,743 - pyskl - INFO - Epoch [110][1100/1281] lr: 4.164e-03, eta: 5:58:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.0923, loss: 0.0923 +2025-06-25 03:59:17,260 - pyskl - INFO - Epoch [110][1200/1281] lr: 4.148e-03, eta: 5:57:32, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1300, loss: 0.1300 +2025-06-25 03:59:57,980 - pyskl - INFO - Saving checkpoint at 110 epochs +2025-06-25 04:00:55,893 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:00:55,947 - pyskl - INFO - +top1_acc 0.9203 +top5_acc 0.9962 +2025-06-25 04:00:55,947 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:00:55,954 - pyskl - INFO - +mean_acc 0.8963 +2025-06-25 04:00:55,955 - pyskl - INFO - Epoch(val) [110][533] top1_acc: 0.9203, top5_acc: 0.9962, mean_class_accuracy: 0.8963 +2025-06-25 04:02:15,335 - pyskl - INFO - Epoch [111][100/1281] lr: 4.121e-03, eta: 5:56:17, time: 0.794, data_time: 0.187, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0982, loss: 0.0982 +2025-06-25 04:03:04,674 - pyskl - INFO - Epoch [111][200/1281] lr: 4.106e-03, eta: 5:55:38, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0949, loss: 0.0949 +2025-06-25 04:03:53,718 - pyskl - INFO - Epoch [111][300/1281] lr: 4.091e-03, eta: 5:54:59, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0711, loss: 0.0711 +2025-06-25 04:04:42,579 - pyskl - INFO - Epoch [111][400/1281] lr: 4.075e-03, eta: 5:54:20, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0864, loss: 0.0864 +2025-06-25 04:05:31,752 - pyskl - INFO - Epoch [111][500/1281] lr: 4.060e-03, eta: 5:53:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0644, loss: 0.0644 +2025-06-25 04:06:20,322 - pyskl - INFO - Epoch [111][600/1281] lr: 4.045e-03, eta: 5:53:01, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0824, loss: 0.0824 +2025-06-25 04:06:54,896 - pyskl - INFO - Epoch [111][700/1281] lr: 4.030e-03, eta: 5:52:17, time: 0.346, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1007, loss: 0.1007 +2025-06-25 04:07:33,773 - pyskl - INFO - Epoch [111][800/1281] lr: 4.015e-03, eta: 5:51:34, time: 0.389, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0906, loss: 0.0906 +2025-06-25 04:08:11,022 - pyskl - INFO - Epoch [111][900/1281] lr: 4.000e-03, eta: 5:50:51, time: 0.372, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1009, loss: 0.1009 +2025-06-25 04:08:59,692 - pyskl - INFO - Epoch [111][1000/1281] lr: 3.985e-03, eta: 5:50:11, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1018, loss: 0.1018 +2025-06-25 04:09:48,928 - pyskl - INFO - Epoch [111][1100/1281] lr: 3.970e-03, eta: 5:49:32, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0930, loss: 0.0930 +2025-06-25 04:10:38,207 - pyskl - INFO - Epoch [111][1200/1281] lr: 3.955e-03, eta: 5:48:53, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1003, loss: 0.1003 +2025-06-25 04:11:18,619 - pyskl - INFO - Saving checkpoint at 111 epochs +2025-06-25 04:12:16,947 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:12:17,003 - pyskl - INFO - +top1_acc 0.9316 +top5_acc 0.9962 +2025-06-25 04:12:17,003 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:12:17,010 - pyskl - INFO - +mean_acc 0.9107 +2025-06-25 04:12:17,014 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_107.pth was removed +2025-06-25 04:12:17,188 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_111.pth. +2025-06-25 04:12:17,189 - pyskl - INFO - Best top1_acc is 0.9316 at 111 epoch. +2025-06-25 04:12:17,192 - pyskl - INFO - Epoch(val) [111][533] top1_acc: 0.9316, top5_acc: 0.9962, mean_class_accuracy: 0.9107 +2025-06-25 04:13:38,245 - pyskl - INFO - Epoch [112][100/1281] lr: 3.928e-03, eta: 5:47:39, time: 0.810, data_time: 0.191, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0760, loss: 0.0760 +2025-06-25 04:14:27,360 - pyskl - INFO - Epoch [112][200/1281] lr: 3.914e-03, eta: 5:47:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0587, loss: 0.0587 +2025-06-25 04:15:16,457 - pyskl - INFO - Epoch [112][300/1281] lr: 3.899e-03, eta: 5:46:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0615, loss: 0.0615 +2025-06-25 04:16:05,722 - pyskl - INFO - Epoch [112][400/1281] lr: 3.884e-03, eta: 5:45:41, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0826, loss: 0.0826 +2025-06-25 04:16:54,848 - pyskl - INFO - Epoch [112][500/1281] lr: 3.869e-03, eta: 5:45:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0661, loss: 0.0661 +2025-06-25 04:17:41,329 - pyskl - INFO - Epoch [112][600/1281] lr: 3.854e-03, eta: 5:44:21, time: 0.465, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0657, loss: 0.0657 +2025-06-25 04:18:20,434 - pyskl - INFO - Epoch [112][700/1281] lr: 3.840e-03, eta: 5:43:39, time: 0.391, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0627, loss: 0.0627 +2025-06-25 04:18:55,196 - pyskl - INFO - Epoch [112][800/1281] lr: 3.825e-03, eta: 5:42:54, time: 0.348, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0714, loss: 0.0714 +2025-06-25 04:19:34,680 - pyskl - INFO - Epoch [112][900/1281] lr: 3.810e-03, eta: 5:42:12, time: 0.395, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0653, loss: 0.0653 +2025-06-25 04:20:23,968 - pyskl - INFO - Epoch [112][1000/1281] lr: 3.795e-03, eta: 5:41:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0782, loss: 0.0782 +2025-06-25 04:21:13,114 - pyskl - INFO - Epoch [112][1100/1281] lr: 3.781e-03, eta: 5:40:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0898, loss: 0.0898 +2025-06-25 04:22:02,042 - pyskl - INFO - Epoch [112][1200/1281] lr: 3.766e-03, eta: 5:40:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 0.9994, loss_cls: 0.0763, loss: 0.0763 +2025-06-25 04:22:42,499 - pyskl - INFO - Saving checkpoint at 112 epochs +2025-06-25 04:23:40,574 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:23:40,630 - pyskl - INFO - +top1_acc 0.9336 +top5_acc 0.9962 +2025-06-25 04:23:40,630 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:23:40,638 - pyskl - INFO - +mean_acc 0.9120 +2025-06-25 04:23:40,642 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_111.pth was removed +2025-06-25 04:23:40,830 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_112.pth. +2025-06-25 04:23:40,830 - pyskl - INFO - Best top1_acc is 0.9336 at 112 epoch. +2025-06-25 04:23:40,833 - pyskl - INFO - Epoch(val) [112][533] top1_acc: 0.9336, top5_acc: 0.9962, mean_class_accuracy: 0.9120 +2025-06-25 04:25:00,588 - pyskl - INFO - Epoch [113][100/1281] lr: 3.740e-03, eta: 5:38:59, time: 0.798, data_time: 0.191, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0925, loss: 0.0925 +2025-06-25 04:25:49,545 - pyskl - INFO - Epoch [113][200/1281] lr: 3.725e-03, eta: 5:38:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 0.9994, loss_cls: 0.0975, loss: 0.0975 +2025-06-25 04:26:38,594 - pyskl - INFO - Epoch [113][300/1281] lr: 3.711e-03, eta: 5:37:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1013, loss: 0.1013 +2025-06-25 04:27:28,101 - pyskl - INFO - Epoch [113][400/1281] lr: 3.696e-03, eta: 5:37:01, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1198, loss: 0.1198 +2025-06-25 04:28:17,105 - pyskl - INFO - Epoch [113][500/1281] lr: 3.682e-03, eta: 5:36:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0755, loss: 0.0755 +2025-06-25 04:29:02,757 - pyskl - INFO - Epoch [113][600/1281] lr: 3.667e-03, eta: 5:35:41, time: 0.457, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0805, loss: 0.0805 +2025-06-25 04:29:43,641 - pyskl - INFO - Epoch [113][700/1281] lr: 3.653e-03, eta: 5:34:58, time: 0.409, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0740, loss: 0.0740 +2025-06-25 04:30:16,317 - pyskl - INFO - Epoch [113][800/1281] lr: 3.638e-03, eta: 5:34:14, time: 0.327, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0860, loss: 0.0860 +2025-06-25 04:30:56,013 - pyskl - INFO - Epoch [113][900/1281] lr: 3.624e-03, eta: 5:33:31, time: 0.397, data_time: 0.001, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0877, loss: 0.0877 +2025-06-25 04:31:45,162 - pyskl - INFO - Epoch [113][1000/1281] lr: 3.610e-03, eta: 5:32:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0843, loss: 0.0843 +2025-06-25 04:32:34,152 - pyskl - INFO - Epoch [113][1100/1281] lr: 3.595e-03, eta: 5:32:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1133, loss: 0.1133 +2025-06-25 04:33:23,490 - pyskl - INFO - Epoch [113][1200/1281] lr: 3.581e-03, eta: 5:31:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0792, loss: 0.0792 +2025-06-25 04:34:04,057 - pyskl - INFO - Saving checkpoint at 113 epochs +2025-06-25 04:35:02,052 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:35:02,121 - pyskl - INFO - +top1_acc 0.9251 +top5_acc 0.9959 +2025-06-25 04:35:02,121 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:35:02,128 - pyskl - INFO - +mean_acc 0.8963 +2025-06-25 04:35:02,130 - pyskl - INFO - Epoch(val) [113][533] top1_acc: 0.9251, top5_acc: 0.9959, mean_class_accuracy: 0.8963 +2025-06-25 04:36:21,578 - pyskl - INFO - Epoch [114][100/1281] lr: 3.555e-03, eta: 5:30:18, time: 0.794, data_time: 0.187, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0782, loss: 0.0782 +2025-06-25 04:37:10,338 - pyskl - INFO - Epoch [114][200/1281] lr: 3.541e-03, eta: 5:29:38, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0798, loss: 0.0798 +2025-06-25 04:37:59,538 - pyskl - INFO - Epoch [114][300/1281] lr: 3.526e-03, eta: 5:28:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0793, loss: 0.0793 +2025-06-25 04:38:48,905 - pyskl - INFO - Epoch [114][400/1281] lr: 3.512e-03, eta: 5:28:19, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0729, loss: 0.0729 +2025-06-25 04:39:38,177 - pyskl - INFO - Epoch [114][500/1281] lr: 3.498e-03, eta: 5:27:40, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0589, loss: 0.0589 +2025-06-25 04:40:24,012 - pyskl - INFO - Epoch [114][600/1281] lr: 3.484e-03, eta: 5:26:59, time: 0.458, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0614, loss: 0.0614 +2025-06-25 04:41:03,602 - pyskl - INFO - Epoch [114][700/1281] lr: 3.470e-03, eta: 5:26:16, time: 0.396, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0486, loss: 0.0486 +2025-06-25 04:41:37,690 - pyskl - INFO - Epoch [114][800/1281] lr: 3.456e-03, eta: 5:25:32, time: 0.341, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0738, loss: 0.0738 +2025-06-25 04:42:16,196 - pyskl - INFO - Epoch [114][900/1281] lr: 3.442e-03, eta: 5:24:49, time: 0.385, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0568, loss: 0.0568 +2025-06-25 04:43:05,072 - pyskl - INFO - Epoch [114][1000/1281] lr: 3.427e-03, eta: 5:24:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0737, loss: 0.0737 +2025-06-25 04:43:54,156 - pyskl - INFO - Epoch [114][1100/1281] lr: 3.413e-03, eta: 5:23:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0758, loss: 0.0758 +2025-06-25 04:44:42,982 - pyskl - INFO - Epoch [114][1200/1281] lr: 3.399e-03, eta: 5:22:50, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0817, loss: 0.0817 +2025-06-25 04:45:23,878 - pyskl - INFO - Saving checkpoint at 114 epochs +2025-06-25 04:46:22,666 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:46:22,724 - pyskl - INFO - +top1_acc 0.9310 +top5_acc 0.9966 +2025-06-25 04:46:22,724 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:46:22,732 - pyskl - INFO - +mean_acc 0.9056 +2025-06-25 04:46:22,734 - pyskl - INFO - Epoch(val) [114][533] top1_acc: 0.9310, top5_acc: 0.9966, mean_class_accuracy: 0.9056 +2025-06-25 04:47:43,013 - pyskl - INFO - Epoch [115][100/1281] lr: 3.374e-03, eta: 5:21:35, time: 0.803, data_time: 0.192, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0549, loss: 0.0549 +2025-06-25 04:48:31,989 - pyskl - INFO - Epoch [115][200/1281] lr: 3.360e-03, eta: 5:20:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0464, loss: 0.0464 +2025-06-25 04:49:21,234 - pyskl - INFO - Epoch [115][300/1281] lr: 3.346e-03, eta: 5:20:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0417, loss: 0.0417 +2025-06-25 04:50:10,279 - pyskl - INFO - Epoch [115][400/1281] lr: 3.332e-03, eta: 5:19:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0586, loss: 0.0586 +2025-06-25 04:50:59,354 - pyskl - INFO - Epoch [115][500/1281] lr: 3.318e-03, eta: 5:18:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0653, loss: 0.0653 +2025-06-25 04:51:44,993 - pyskl - INFO - Epoch [115][600/1281] lr: 3.305e-03, eta: 5:18:16, time: 0.456, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0776, loss: 0.0776 +2025-06-25 04:52:24,426 - pyskl - INFO - Epoch [115][700/1281] lr: 3.291e-03, eta: 5:17:33, time: 0.394, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0681, loss: 0.0681 +2025-06-25 04:52:58,337 - pyskl - INFO - Epoch [115][800/1281] lr: 3.277e-03, eta: 5:16:48, time: 0.339, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0417, loss: 0.0417 +2025-06-25 04:53:37,018 - pyskl - INFO - Epoch [115][900/1281] lr: 3.263e-03, eta: 5:16:05, time: 0.387, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0470, loss: 0.0470 +2025-06-25 04:54:26,165 - pyskl - INFO - Epoch [115][1000/1281] lr: 3.249e-03, eta: 5:15:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0829, loss: 0.0829 +2025-06-25 04:55:15,215 - pyskl - INFO - Epoch [115][1100/1281] lr: 3.236e-03, eta: 5:14:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0494, loss: 0.0494 +2025-06-25 04:56:04,217 - pyskl - INFO - Epoch [115][1200/1281] lr: 3.222e-03, eta: 5:14:06, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0718, loss: 0.0718 +2025-06-25 04:56:44,740 - pyskl - INFO - Saving checkpoint at 115 epochs +2025-06-25 04:57:42,678 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:57:42,746 - pyskl - INFO - +top1_acc 0.9331 +top5_acc 0.9962 +2025-06-25 04:57:42,746 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:57:42,755 - pyskl - INFO - +mean_acc 0.9080 +2025-06-25 04:57:42,757 - pyskl - INFO - Epoch(val) [115][533] top1_acc: 0.9331, top5_acc: 0.9962, mean_class_accuracy: 0.9080 +2025-06-25 04:59:00,986 - pyskl - INFO - Epoch [116][100/1281] lr: 3.197e-03, eta: 5:12:51, time: 0.782, data_time: 0.184, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0624, loss: 0.0624 +2025-06-25 04:59:50,118 - pyskl - INFO - Epoch [116][200/1281] lr: 3.184e-03, eta: 5:12:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0526, loss: 0.0526 +2025-06-25 05:00:38,965 - pyskl - INFO - Epoch [116][300/1281] lr: 3.170e-03, eta: 5:11:31, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0556, loss: 0.0556 +2025-06-25 05:01:28,200 - pyskl - INFO - Epoch [116][400/1281] lr: 3.156e-03, eta: 5:10:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0482, loss: 0.0482 +2025-06-25 05:02:17,298 - pyskl - INFO - Epoch [116][500/1281] lr: 3.143e-03, eta: 5:10:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0688, loss: 0.0688 +2025-06-25 05:03:05,694 - pyskl - INFO - Epoch [116][600/1281] lr: 3.129e-03, eta: 5:09:31, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.0888, loss: 0.0888 +2025-06-25 05:03:40,193 - pyskl - INFO - Epoch [116][700/1281] lr: 3.116e-03, eta: 5:08:47, time: 0.345, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0785, loss: 0.0785 +2025-06-25 05:04:19,353 - pyskl - INFO - Epoch [116][800/1281] lr: 3.102e-03, eta: 5:08:04, time: 0.392, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0778, loss: 0.0778 +2025-06-25 05:04:55,802 - pyskl - INFO - Epoch [116][900/1281] lr: 3.089e-03, eta: 5:07:21, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0689, loss: 0.0689 +2025-06-25 05:05:44,885 - pyskl - INFO - Epoch [116][1000/1281] lr: 3.075e-03, eta: 5:06:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0690, loss: 0.0690 +2025-06-25 05:06:34,169 - pyskl - INFO - Epoch [116][1100/1281] lr: 3.062e-03, eta: 5:06:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0619, loss: 0.0619 +2025-06-25 05:07:23,351 - pyskl - INFO - Epoch [116][1200/1281] lr: 3.049e-03, eta: 5:05:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0527, loss: 0.0527 +2025-06-25 05:08:03,547 - pyskl - INFO - Saving checkpoint at 116 epochs +2025-06-25 05:09:01,958 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:09:02,013 - pyskl - INFO - +top1_acc 0.9301 +top5_acc 0.9964 +2025-06-25 05:09:02,013 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:09:02,019 - pyskl - INFO - +mean_acc 0.9070 +2025-06-25 05:09:02,021 - pyskl - INFO - Epoch(val) [116][533] top1_acc: 0.9301, top5_acc: 0.9964, mean_class_accuracy: 0.9070 +2025-06-25 05:10:21,448 - pyskl - INFO - Epoch [117][100/1281] lr: 3.024e-03, eta: 5:04:06, time: 0.794, data_time: 0.182, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0725, loss: 0.0725 +2025-06-25 05:11:10,720 - pyskl - INFO - Epoch [117][200/1281] lr: 3.011e-03, eta: 5:03:26, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0800, loss: 0.0800 +2025-06-25 05:11:59,805 - pyskl - INFO - Epoch [117][300/1281] lr: 2.998e-03, eta: 5:02:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0577, loss: 0.0577 +2025-06-25 05:12:48,533 - pyskl - INFO - Epoch [117][400/1281] lr: 2.984e-03, eta: 5:02:06, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0578, loss: 0.0578 +2025-06-25 05:13:37,994 - pyskl - INFO - Epoch [117][500/1281] lr: 2.971e-03, eta: 5:01:27, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0473, loss: 0.0473 +2025-06-25 05:14:27,203 - pyskl - INFO - Epoch [117][600/1281] lr: 2.958e-03, eta: 5:00:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0442, loss: 0.0442 +2025-06-25 05:14:59,873 - pyskl - INFO - Epoch [117][700/1281] lr: 2.945e-03, eta: 5:00:02, time: 0.327, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0404, loss: 0.0404 +2025-06-25 05:15:40,759 - pyskl - INFO - Epoch [117][800/1281] lr: 2.932e-03, eta: 4:59:20, time: 0.409, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0530, loss: 0.0530 +2025-06-25 05:16:16,799 - pyskl - INFO - Epoch [117][900/1281] lr: 2.919e-03, eta: 4:58:36, time: 0.360, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0353, loss: 0.0353 +2025-06-25 05:17:05,966 - pyskl - INFO - Epoch [117][1000/1281] lr: 2.905e-03, eta: 4:57:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0491, loss: 0.0491 +2025-06-25 05:17:55,159 - pyskl - INFO - Epoch [117][1100/1281] lr: 2.892e-03, eta: 4:57:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0479, loss: 0.0479 +2025-06-25 05:18:44,420 - pyskl - INFO - Epoch [117][1200/1281] lr: 2.879e-03, eta: 4:56:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0814, loss: 0.0814 +2025-06-25 05:19:24,905 - pyskl - INFO - Saving checkpoint at 117 epochs +2025-06-25 05:20:23,362 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:20:23,419 - pyskl - INFO - +top1_acc 0.9364 +top5_acc 0.9965 +2025-06-25 05:20:23,419 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:20:23,426 - pyskl - INFO - +mean_acc 0.9106 +2025-06-25 05:20:23,429 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_112.pth was removed +2025-06-25 05:20:23,599 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_117.pth. +2025-06-25 05:20:23,600 - pyskl - INFO - Best top1_acc is 0.9364 at 117 epoch. +2025-06-25 05:20:23,602 - pyskl - INFO - Epoch(val) [117][533] top1_acc: 0.9364, top5_acc: 0.9965, mean_class_accuracy: 0.9106 +2025-06-25 05:21:42,202 - pyskl - INFO - Epoch [118][100/1281] lr: 2.856e-03, eta: 4:55:21, time: 0.786, data_time: 0.186, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0365, loss: 0.0365 +2025-06-25 05:22:31,535 - pyskl - INFO - Epoch [118][200/1281] lr: 2.843e-03, eta: 4:54:41, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0470, loss: 0.0470 +2025-06-25 05:23:20,560 - pyskl - INFO - Epoch [118][300/1281] lr: 2.830e-03, eta: 4:54:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0447, loss: 0.0447 +2025-06-25 05:24:09,536 - pyskl - INFO - Epoch [118][400/1281] lr: 2.817e-03, eta: 4:53:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0463, loss: 0.0463 +2025-06-25 05:24:58,855 - pyskl - INFO - Epoch [118][500/1281] lr: 2.804e-03, eta: 4:52:40, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0482, loss: 0.0482 +2025-06-25 05:25:47,909 - pyskl - INFO - Epoch [118][600/1281] lr: 2.791e-03, eta: 4:52:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0422, loss: 0.0422 +2025-06-25 05:26:20,129 - pyskl - INFO - Epoch [118][700/1281] lr: 2.778e-03, eta: 4:51:16, time: 0.322, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0627, loss: 0.0627 +2025-06-25 05:27:01,269 - pyskl - INFO - Epoch [118][800/1281] lr: 2.765e-03, eta: 4:50:33, time: 0.411, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0476, loss: 0.0476 +2025-06-25 05:27:37,257 - pyskl - INFO - Epoch [118][900/1281] lr: 2.753e-03, eta: 4:49:50, time: 0.360, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0558, loss: 0.0558 +2025-06-25 05:28:26,248 - pyskl - INFO - Epoch [118][1000/1281] lr: 2.740e-03, eta: 4:49:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0396, loss: 0.0396 +2025-06-25 05:29:15,160 - pyskl - INFO - Epoch [118][1100/1281] lr: 2.727e-03, eta: 4:48:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0465, loss: 0.0465 +2025-06-25 05:30:04,315 - pyskl - INFO - Epoch [118][1200/1281] lr: 2.714e-03, eta: 4:47:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0386, loss: 0.0386 +2025-06-25 05:30:44,541 - pyskl - INFO - Saving checkpoint at 118 epochs +2025-06-25 05:31:43,388 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:31:43,448 - pyskl - INFO - +top1_acc 0.9397 +top5_acc 0.9967 +2025-06-25 05:31:43,448 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:31:43,455 - pyskl - INFO - +mean_acc 0.9173 +2025-06-25 05:31:43,459 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_117.pth was removed +2025-06-25 05:31:43,623 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2025-06-25 05:31:43,623 - pyskl - INFO - Best top1_acc is 0.9397 at 118 epoch. +2025-06-25 05:31:43,626 - pyskl - INFO - Epoch(val) [118][533] top1_acc: 0.9397, top5_acc: 0.9967, mean_class_accuracy: 0.9173 +2025-06-25 05:33:03,805 - pyskl - INFO - Epoch [119][100/1281] lr: 2.691e-03, eta: 4:46:34, time: 0.802, data_time: 0.182, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0520, loss: 0.0520 +2025-06-25 05:33:53,142 - pyskl - INFO - Epoch [119][200/1281] lr: 2.679e-03, eta: 4:45:54, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0418, loss: 0.0418 +2025-06-25 05:34:42,294 - pyskl - INFO - Epoch [119][300/1281] lr: 2.666e-03, eta: 4:45:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0362, loss: 0.0362 +2025-06-25 05:35:31,756 - pyskl - INFO - Epoch [119][400/1281] lr: 2.653e-03, eta: 4:44:34, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0383, loss: 0.0383 +2025-06-25 05:36:20,747 - pyskl - INFO - Epoch [119][500/1281] lr: 2.641e-03, eta: 4:43:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0344, loss: 0.0344 +2025-06-25 05:37:09,354 - pyskl - INFO - Epoch [119][600/1281] lr: 2.628e-03, eta: 4:43:13, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0454, loss: 0.0454 +2025-06-25 05:37:44,033 - pyskl - INFO - Epoch [119][700/1281] lr: 2.616e-03, eta: 4:42:29, time: 0.347, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0326, loss: 0.0326 +2025-06-25 05:38:22,674 - pyskl - INFO - Epoch [119][800/1281] lr: 2.603e-03, eta: 4:41:46, time: 0.386, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0555, loss: 0.0555 +2025-06-25 05:39:00,432 - pyskl - INFO - Epoch [119][900/1281] lr: 2.591e-03, eta: 4:41:03, time: 0.378, data_time: 0.001, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0475, loss: 0.0475 +2025-06-25 05:39:49,755 - pyskl - INFO - Epoch [119][1000/1281] lr: 2.578e-03, eta: 4:40:23, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0810, loss: 0.0810 +2025-06-25 05:40:39,077 - pyskl - INFO - Epoch [119][1100/1281] lr: 2.566e-03, eta: 4:39:43, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0408, loss: 0.0408 +2025-06-25 05:41:27,917 - pyskl - INFO - Epoch [119][1200/1281] lr: 2.554e-03, eta: 4:39:03, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0478, loss: 0.0478 +2025-06-25 05:42:08,341 - pyskl - INFO - Saving checkpoint at 119 epochs +2025-06-25 05:43:06,162 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:43:06,221 - pyskl - INFO - +top1_acc 0.9299 +top5_acc 0.9974 +2025-06-25 05:43:06,221 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:43:06,228 - pyskl - INFO - +mean_acc 0.9066 +2025-06-25 05:43:06,229 - pyskl - INFO - Epoch(val) [119][533] top1_acc: 0.9299, top5_acc: 0.9974, mean_class_accuracy: 0.9066 +2025-06-25 05:44:25,639 - pyskl - INFO - Epoch [120][100/1281] lr: 2.531e-03, eta: 4:37:47, time: 0.794, data_time: 0.189, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0586, loss: 0.0586 +2025-06-25 05:45:14,756 - pyskl - INFO - Epoch [120][200/1281] lr: 2.519e-03, eta: 4:37:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0634, loss: 0.0634 +2025-06-25 05:46:04,183 - pyskl - INFO - Epoch [120][300/1281] lr: 2.507e-03, eta: 4:36:27, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0447, loss: 0.0447 +2025-06-25 05:46:53,229 - pyskl - INFO - Epoch [120][400/1281] lr: 2.494e-03, eta: 4:35:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0447, loss: 0.0447 +2025-06-25 05:47:42,294 - pyskl - INFO - Epoch [120][500/1281] lr: 2.482e-03, eta: 4:35:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0478, loss: 0.0478 +2025-06-25 05:48:30,257 - pyskl - INFO - Epoch [120][600/1281] lr: 2.470e-03, eta: 4:34:26, time: 0.480, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 0.9994, loss_cls: 0.0714, loss: 0.0714 +2025-06-25 05:49:04,073 - pyskl - INFO - Epoch [120][700/1281] lr: 2.458e-03, eta: 4:33:41, time: 0.338, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0445, loss: 0.0445 +2025-06-25 05:49:43,216 - pyskl - INFO - Epoch [120][800/1281] lr: 2.446e-03, eta: 4:32:59, time: 0.391, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0586, loss: 0.0586 +2025-06-25 05:50:20,265 - pyskl - INFO - Epoch [120][900/1281] lr: 2.433e-03, eta: 4:32:15, time: 0.370, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0599, loss: 0.0599 +2025-06-25 05:51:09,423 - pyskl - INFO - Epoch [120][1000/1281] lr: 2.421e-03, eta: 4:31:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0515, loss: 0.0515 +2025-06-25 05:51:58,473 - pyskl - INFO - Epoch [120][1100/1281] lr: 2.409e-03, eta: 4:30:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0407, loss: 0.0407 +2025-06-25 05:52:47,666 - pyskl - INFO - Epoch [120][1200/1281] lr: 2.397e-03, eta: 4:30:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0444, loss: 0.0444 +2025-06-25 05:53:28,072 - pyskl - INFO - Saving checkpoint at 120 epochs +2025-06-25 05:54:25,977 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:54:26,032 - pyskl - INFO - +top1_acc 0.9331 +top5_acc 0.9958 +2025-06-25 05:54:26,032 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:54:26,038 - pyskl - INFO - +mean_acc 0.9048 +2025-06-25 05:54:26,040 - pyskl - INFO - Epoch(val) [120][533] top1_acc: 0.9331, top5_acc: 0.9958, mean_class_accuracy: 0.9048 +2025-06-25 05:55:45,937 - pyskl - INFO - Epoch [121][100/1281] lr: 2.375e-03, eta: 4:28:59, time: 0.799, data_time: 0.185, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0572, loss: 0.0572 +2025-06-25 05:56:34,796 - pyskl - INFO - Epoch [121][200/1281] lr: 2.363e-03, eta: 4:28:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0462, loss: 0.0462 +2025-06-25 05:57:23,739 - pyskl - INFO - Epoch [121][300/1281] lr: 2.351e-03, eta: 4:27:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0386, loss: 0.0386 +2025-06-25 05:58:12,868 - pyskl - INFO - Epoch [121][400/1281] lr: 2.340e-03, eta: 4:26:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0459, loss: 0.0459 +2025-06-25 05:59:01,856 - pyskl - INFO - Epoch [121][500/1281] lr: 2.328e-03, eta: 4:26:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0433, loss: 0.0433 +2025-06-25 05:59:50,666 - pyskl - INFO - Epoch [121][600/1281] lr: 2.316e-03, eta: 4:25:37, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0282, loss: 0.0282 +2025-06-25 06:00:24,034 - pyskl - INFO - Epoch [121][700/1281] lr: 2.304e-03, eta: 4:24:52, time: 0.334, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0356, loss: 0.0356 +2025-06-25 06:01:04,419 - pyskl - INFO - Epoch [121][800/1281] lr: 2.292e-03, eta: 4:24:10, time: 0.404, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0303, loss: 0.0303 +2025-06-25 06:01:41,259 - pyskl - INFO - Epoch [121][900/1281] lr: 2.280e-03, eta: 4:23:27, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0501, loss: 0.0501 +2025-06-25 06:02:30,522 - pyskl - INFO - Epoch [121][1000/1281] lr: 2.269e-03, eta: 4:22:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0309, loss: 0.0309 +2025-06-25 06:03:19,912 - pyskl - INFO - Epoch [121][1100/1281] lr: 2.257e-03, eta: 4:22:06, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0343, loss: 0.0343 +2025-06-25 06:04:09,190 - pyskl - INFO - Epoch [121][1200/1281] lr: 2.245e-03, eta: 4:21:25, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0293, loss: 0.0293 +2025-06-25 06:04:49,307 - pyskl - INFO - Saving checkpoint at 121 epochs +2025-06-25 06:05:47,249 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:05:47,303 - pyskl - INFO - +top1_acc 0.9363 +top5_acc 0.9969 +2025-06-25 06:05:47,304 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:05:47,310 - pyskl - INFO - +mean_acc 0.9141 +2025-06-25 06:05:47,312 - pyskl - INFO - Epoch(val) [121][533] top1_acc: 0.9363, top5_acc: 0.9969, mean_class_accuracy: 0.9141 +2025-06-25 06:07:06,331 - pyskl - INFO - Epoch [122][100/1281] lr: 2.224e-03, eta: 4:20:10, time: 0.790, data_time: 0.182, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0365, loss: 0.0365 +2025-06-25 06:07:55,672 - pyskl - INFO - Epoch [122][200/1281] lr: 2.212e-03, eta: 4:19:29, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0296, loss: 0.0296 +2025-06-25 06:08:44,800 - pyskl - INFO - Epoch [122][300/1281] lr: 2.201e-03, eta: 4:18:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-25 06:09:34,090 - pyskl - INFO - Epoch [122][400/1281] lr: 2.189e-03, eta: 4:18:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0261, loss: 0.0261 +2025-06-25 06:10:23,239 - pyskl - INFO - Epoch [122][500/1281] lr: 2.178e-03, eta: 4:17:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0261, loss: 0.0261 +2025-06-25 06:11:12,507 - pyskl - INFO - Epoch [122][600/1281] lr: 2.166e-03, eta: 4:16:47, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0303, loss: 0.0303 +2025-06-25 06:11:44,483 - pyskl - INFO - Epoch [122][700/1281] lr: 2.155e-03, eta: 4:16:03, time: 0.320, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0378, loss: 0.0378 +2025-06-25 06:12:26,328 - pyskl - INFO - Epoch [122][800/1281] lr: 2.143e-03, eta: 4:15:21, time: 0.418, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0389, loss: 0.0389 +2025-06-25 06:13:01,606 - pyskl - INFO - Epoch [122][900/1281] lr: 2.132e-03, eta: 4:14:37, time: 0.353, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0442, loss: 0.0442 +2025-06-25 06:13:50,868 - pyskl - INFO - Epoch [122][1000/1281] lr: 2.120e-03, eta: 4:13:56, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0299, loss: 0.0299 +2025-06-25 06:14:40,275 - pyskl - INFO - Epoch [122][1100/1281] lr: 2.109e-03, eta: 4:13:16, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0352, loss: 0.0352 +2025-06-25 06:15:29,798 - pyskl - INFO - Epoch [122][1200/1281] lr: 2.098e-03, eta: 4:12:35, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0416, loss: 0.0416 +2025-06-25 06:16:09,974 - pyskl - INFO - Saving checkpoint at 122 epochs +2025-06-25 06:17:08,345 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:17:08,400 - pyskl - INFO - +top1_acc 0.9374 +top5_acc 0.9965 +2025-06-25 06:17:08,400 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:17:08,407 - pyskl - INFO - +mean_acc 0.9107 +2025-06-25 06:17:08,408 - pyskl - INFO - Epoch(val) [122][533] top1_acc: 0.9374, top5_acc: 0.9965, mean_class_accuracy: 0.9107 +2025-06-25 06:18:27,424 - pyskl - INFO - Epoch [123][100/1281] lr: 2.077e-03, eta: 4:11:20, time: 0.790, data_time: 0.180, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0348, loss: 0.0348 +2025-06-25 06:19:16,469 - pyskl - INFO - Epoch [123][200/1281] lr: 2.066e-03, eta: 4:10:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0338, loss: 0.0338 +2025-06-25 06:20:05,730 - pyskl - INFO - Epoch [123][300/1281] lr: 2.055e-03, eta: 4:09:59, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0272, loss: 0.0272 +2025-06-25 06:20:54,976 - pyskl - INFO - Epoch [123][400/1281] lr: 2.044e-03, eta: 4:09:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0319, loss: 0.0319 +2025-06-25 06:21:44,093 - pyskl - INFO - Epoch [123][500/1281] lr: 2.032e-03, eta: 4:08:37, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0307, loss: 0.0307 +2025-06-25 06:22:33,058 - pyskl - INFO - Epoch [123][600/1281] lr: 2.021e-03, eta: 4:07:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0279, loss: 0.0279 +2025-06-25 06:23:02,973 - pyskl - INFO - Epoch [123][700/1281] lr: 2.010e-03, eta: 4:07:12, time: 0.299, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0250, loss: 0.0250 +2025-06-25 06:23:48,084 - pyskl - INFO - Epoch [123][800/1281] lr: 1.999e-03, eta: 4:06:30, time: 0.451, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0264, loss: 0.0264 +2025-06-25 06:24:21,656 - pyskl - INFO - Epoch [123][900/1281] lr: 1.988e-03, eta: 4:05:46, time: 0.336, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 06:25:11,000 - pyskl - INFO - Epoch [123][1000/1281] lr: 1.977e-03, eta: 4:05:06, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0263, loss: 0.0263 +2025-06-25 06:25:59,868 - pyskl - INFO - Epoch [123][1100/1281] lr: 1.966e-03, eta: 4:04:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0495, loss: 0.0495 +2025-06-25 06:26:48,900 - pyskl - INFO - Epoch [123][1200/1281] lr: 1.955e-03, eta: 4:03:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0443, loss: 0.0443 +2025-06-25 06:27:29,147 - pyskl - INFO - Saving checkpoint at 123 epochs +2025-06-25 06:28:28,117 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:28:28,172 - pyskl - INFO - +top1_acc 0.9340 +top5_acc 0.9967 +2025-06-25 06:28:28,173 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:28:28,179 - pyskl - INFO - +mean_acc 0.9089 +2025-06-25 06:28:28,180 - pyskl - INFO - Epoch(val) [123][533] top1_acc: 0.9340, top5_acc: 0.9967, mean_class_accuracy: 0.9089 +2025-06-25 06:29:47,170 - pyskl - INFO - Epoch [124][100/1281] lr: 1.935e-03, eta: 4:02:29, time: 0.790, data_time: 0.187, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0293, loss: 0.0293 +2025-06-25 06:30:36,142 - pyskl - INFO - Epoch [124][200/1281] lr: 1.924e-03, eta: 4:01:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0291, loss: 0.0291 +2025-06-25 06:31:25,110 - pyskl - INFO - Epoch [124][300/1281] lr: 1.913e-03, eta: 4:01:07, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0522, loss: 0.0522 +2025-06-25 06:32:14,258 - pyskl - INFO - Epoch [124][400/1281] lr: 1.902e-03, eta: 4:00:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0364, loss: 0.0364 +2025-06-25 06:33:03,660 - pyskl - INFO - Epoch [124][500/1281] lr: 1.892e-03, eta: 3:59:46, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0315, loss: 0.0315 +2025-06-25 06:33:53,100 - pyskl - INFO - Epoch [124][600/1281] lr: 1.881e-03, eta: 3:59:05, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 06:34:22,327 - pyskl - INFO - Epoch [124][700/1281] lr: 1.870e-03, eta: 3:58:20, time: 0.292, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-25 06:35:09,464 - pyskl - INFO - Epoch [124][800/1281] lr: 1.859e-03, eta: 3:57:39, time: 0.471, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0265, loss: 0.0265 +2025-06-25 06:35:43,075 - pyskl - INFO - Epoch [124][900/1281] lr: 1.849e-03, eta: 3:56:55, time: 0.336, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0310, loss: 0.0310 +2025-06-25 06:36:31,904 - pyskl - INFO - Epoch [124][1000/1281] lr: 1.838e-03, eta: 3:56:14, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0327, loss: 0.0327 +2025-06-25 06:37:21,490 - pyskl - INFO - Epoch [124][1100/1281] lr: 1.827e-03, eta: 3:55:33, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0279, loss: 0.0279 +2025-06-25 06:38:11,053 - pyskl - INFO - Epoch [124][1200/1281] lr: 1.817e-03, eta: 3:54:53, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0283, loss: 0.0283 +2025-06-25 06:38:51,296 - pyskl - INFO - Saving checkpoint at 124 epochs +2025-06-25 06:39:49,739 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:39:49,805 - pyskl - INFO - +top1_acc 0.9372 +top5_acc 0.9972 +2025-06-25 06:39:49,806 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:39:49,812 - pyskl - INFO - +mean_acc 0.9161 +2025-06-25 06:39:49,814 - pyskl - INFO - Epoch(val) [124][533] top1_acc: 0.9372, top5_acc: 0.9972, mean_class_accuracy: 0.9161 +2025-06-25 06:41:08,545 - pyskl - INFO - Epoch [125][100/1281] lr: 1.797e-03, eta: 3:53:37, time: 0.787, data_time: 0.189, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0268, loss: 0.0268 +2025-06-25 06:41:57,881 - pyskl - INFO - Epoch [125][200/1281] lr: 1.787e-03, eta: 3:52:56, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0301, loss: 0.0301 +2025-06-25 06:42:47,073 - pyskl - INFO - Epoch [125][300/1281] lr: 1.776e-03, eta: 3:52:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0312, loss: 0.0312 +2025-06-25 06:43:36,179 - pyskl - INFO - Epoch [125][400/1281] lr: 1.766e-03, eta: 3:51:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0318, loss: 0.0318 +2025-06-25 06:44:25,502 - pyskl - INFO - Epoch [125][500/1281] lr: 1.755e-03, eta: 3:50:54, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0248, loss: 0.0248 +2025-06-25 06:45:14,822 - pyskl - INFO - Epoch [125][600/1281] lr: 1.745e-03, eta: 3:50:13, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0360, loss: 0.0360 +2025-06-25 06:45:42,797 - pyskl - INFO - Epoch [125][700/1281] lr: 1.735e-03, eta: 3:49:28, time: 0.280, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0423, loss: 0.0423 +2025-06-25 06:46:31,604 - pyskl - INFO - Epoch [125][800/1281] lr: 1.724e-03, eta: 3:48:47, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0383, loss: 0.0383 +2025-06-25 06:47:05,651 - pyskl - INFO - Epoch [125][900/1281] lr: 1.714e-03, eta: 3:48:03, time: 0.340, data_time: 0.001, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0479, loss: 0.0479 +2025-06-25 06:47:54,726 - pyskl - INFO - Epoch [125][1000/1281] lr: 1.704e-03, eta: 3:47:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0601, loss: 0.0601 +2025-06-25 06:48:44,127 - pyskl - INFO - Epoch [125][1100/1281] lr: 1.693e-03, eta: 3:46:42, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0404, loss: 0.0404 +2025-06-25 06:49:33,301 - pyskl - INFO - Epoch [125][1200/1281] lr: 1.683e-03, eta: 3:46:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0273, loss: 0.0273 +2025-06-25 06:50:13,478 - pyskl - INFO - Saving checkpoint at 125 epochs +2025-06-25 06:51:12,214 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:51:12,269 - pyskl - INFO - +top1_acc 0.9416 +top5_acc 0.9962 +2025-06-25 06:51:12,269 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:51:12,275 - pyskl - INFO - +mean_acc 0.9197 +2025-06-25 06:51:12,279 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_118.pth was removed +2025-06-25 06:51:12,458 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_125.pth. +2025-06-25 06:51:12,459 - pyskl - INFO - Best top1_acc is 0.9416 at 125 epoch. +2025-06-25 06:51:12,461 - pyskl - INFO - Epoch(val) [125][533] top1_acc: 0.9416, top5_acc: 0.9962, mean_class_accuracy: 0.9197 +2025-06-25 06:52:31,861 - pyskl - INFO - Epoch [126][100/1281] lr: 1.665e-03, eta: 3:44:45, time: 0.794, data_time: 0.189, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0309, loss: 0.0309 +2025-06-25 06:53:21,021 - pyskl - INFO - Epoch [126][200/1281] lr: 1.654e-03, eta: 3:44:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0297, loss: 0.0297 +2025-06-25 06:54:10,128 - pyskl - INFO - Epoch [126][300/1281] lr: 1.644e-03, eta: 3:43:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0330, loss: 0.0330 +2025-06-25 06:54:59,592 - pyskl - INFO - Epoch [126][400/1281] lr: 1.634e-03, eta: 3:42:42, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0323, loss: 0.0323 +2025-06-25 06:55:48,663 - pyskl - INFO - Epoch [126][500/1281] lr: 1.624e-03, eta: 3:42:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0263, loss: 0.0263 +2025-06-25 06:56:37,866 - pyskl - INFO - Epoch [126][600/1281] lr: 1.614e-03, eta: 3:41:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0251, loss: 0.0251 +2025-06-25 06:57:07,984 - pyskl - INFO - Epoch [126][700/1281] lr: 1.604e-03, eta: 3:40:36, time: 0.301, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 06:57:53,132 - pyskl - INFO - Epoch [126][800/1281] lr: 1.594e-03, eta: 3:39:54, time: 0.451, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0257, loss: 0.0257 +2025-06-25 06:58:27,595 - pyskl - INFO - Epoch [126][900/1281] lr: 1.584e-03, eta: 3:39:10, time: 0.345, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0257, loss: 0.0257 +2025-06-25 06:59:16,532 - pyskl - INFO - Epoch [126][1000/1281] lr: 1.574e-03, eta: 3:38:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 07:00:05,590 - pyskl - INFO - Epoch [126][1100/1281] lr: 1.564e-03, eta: 3:37:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0338, loss: 0.0338 +2025-06-25 07:00:54,817 - pyskl - INFO - Epoch [126][1200/1281] lr: 1.554e-03, eta: 3:37:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0324, loss: 0.0324 +2025-06-25 07:01:34,709 - pyskl - INFO - Saving checkpoint at 126 epochs +2025-06-25 07:02:33,409 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:02:33,484 - pyskl - INFO - +top1_acc 0.9401 +top5_acc 0.9971 +2025-06-25 07:02:33,484 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:02:33,493 - pyskl - INFO - +mean_acc 0.9196 +2025-06-25 07:02:33,495 - pyskl - INFO - Epoch(val) [126][533] top1_acc: 0.9401, top5_acc: 0.9971, mean_class_accuracy: 0.9196 +2025-06-25 07:03:54,649 - pyskl - INFO - Epoch [127][100/1281] lr: 1.536e-03, eta: 3:35:52, time: 0.811, data_time: 0.189, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0227, loss: 0.0227 +2025-06-25 07:04:43,690 - pyskl - INFO - Epoch [127][200/1281] lr: 1.527e-03, eta: 3:35:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-25 07:05:32,913 - pyskl - INFO - Epoch [127][300/1281] lr: 1.517e-03, eta: 3:34:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0296, loss: 0.0296 +2025-06-25 07:06:22,162 - pyskl - INFO - Epoch [127][400/1281] lr: 1.507e-03, eta: 3:33:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0307, loss: 0.0307 +2025-06-25 07:07:11,220 - pyskl - INFO - Epoch [127][500/1281] lr: 1.497e-03, eta: 3:33:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-25 07:08:00,336 - pyskl - INFO - Epoch [127][600/1281] lr: 1.488e-03, eta: 3:32:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 07:08:32,820 - pyskl - INFO - Epoch [127][700/1281] lr: 1.478e-03, eta: 3:31:43, time: 0.325, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 07:09:14,104 - pyskl - INFO - Epoch [127][800/1281] lr: 1.468e-03, eta: 3:31:00, time: 0.413, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0279, loss: 0.0279 +2025-06-25 07:09:50,231 - pyskl - INFO - Epoch [127][900/1281] lr: 1.459e-03, eta: 3:30:17, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0223, loss: 0.0223 +2025-06-25 07:10:39,446 - pyskl - INFO - Epoch [127][1000/1281] lr: 1.449e-03, eta: 3:29:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 07:11:28,482 - pyskl - INFO - Epoch [127][1100/1281] lr: 1.440e-03, eta: 3:28:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 07:12:17,436 - pyskl - INFO - Epoch [127][1200/1281] lr: 1.430e-03, eta: 3:28:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0254, loss: 0.0254 +2025-06-25 07:12:57,742 - pyskl - INFO - Saving checkpoint at 127 epochs +2025-06-25 07:13:56,666 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:13:56,734 - pyskl - INFO - +top1_acc 0.9390 +top5_acc 0.9966 +2025-06-25 07:13:56,734 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:13:56,742 - pyskl - INFO - +mean_acc 0.9184 +2025-06-25 07:13:56,743 - pyskl - INFO - Epoch(val) [127][533] top1_acc: 0.9390, top5_acc: 0.9966, mean_class_accuracy: 0.9184 +2025-06-25 07:15:17,192 - pyskl - INFO - Epoch [128][100/1281] lr: 1.413e-03, eta: 3:26:58, time: 0.804, data_time: 0.192, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 07:16:06,084 - pyskl - INFO - Epoch [128][200/1281] lr: 1.404e-03, eta: 3:26:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0256, loss: 0.0256 +2025-06-25 07:16:55,171 - pyskl - INFO - Epoch [128][300/1281] lr: 1.394e-03, eta: 3:25:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 07:17:44,443 - pyskl - INFO - Epoch [128][400/1281] lr: 1.385e-03, eta: 3:24:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-25 07:18:33,686 - pyskl - INFO - Epoch [128][500/1281] lr: 1.376e-03, eta: 3:24:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 07:19:21,207 - pyskl - INFO - Epoch [128][600/1281] lr: 1.366e-03, eta: 3:23:32, time: 0.475, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 07:19:58,318 - pyskl - INFO - Epoch [128][700/1281] lr: 1.357e-03, eta: 3:22:49, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 07:20:34,730 - pyskl - INFO - Epoch [128][800/1281] lr: 1.348e-03, eta: 3:22:06, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 07:21:13,549 - pyskl - INFO - Epoch [128][900/1281] lr: 1.339e-03, eta: 3:21:23, time: 0.388, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 07:22:02,379 - pyskl - INFO - Epoch [128][1000/1281] lr: 1.329e-03, eta: 3:20:42, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 07:22:51,442 - pyskl - INFO - Epoch [128][1100/1281] lr: 1.320e-03, eta: 3:20:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 07:23:40,593 - pyskl - INFO - Epoch [128][1200/1281] lr: 1.311e-03, eta: 3:19:19, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0293, loss: 0.0293 +2025-06-25 07:24:21,027 - pyskl - INFO - Saving checkpoint at 128 epochs +2025-06-25 07:25:19,267 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:25:19,351 - pyskl - INFO - +top1_acc 0.9364 +top5_acc 0.9967 +2025-06-25 07:25:19,358 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:25:19,368 - pyskl - INFO - +mean_acc 0.9117 +2025-06-25 07:25:19,381 - pyskl - INFO - Epoch(val) [128][533] top1_acc: 0.9364, top5_acc: 0.9967, mean_class_accuracy: 0.9117 +2025-06-25 07:26:38,574 - pyskl - INFO - Epoch [129][100/1281] lr: 1.295e-03, eta: 3:18:03, time: 0.792, data_time: 0.188, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0252, loss: 0.0252 +2025-06-25 07:27:27,287 - pyskl - INFO - Epoch [129][200/1281] lr: 1.286e-03, eta: 3:17:22, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0255, loss: 0.0255 +2025-06-25 07:28:16,188 - pyskl - INFO - Epoch [129][300/1281] lr: 1.277e-03, eta: 3:16:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 07:29:05,123 - pyskl - INFO - Epoch [129][400/1281] lr: 1.268e-03, eta: 3:16:00, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 07:29:54,136 - pyskl - INFO - Epoch [129][500/1281] lr: 1.259e-03, eta: 3:15:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 07:30:41,016 - pyskl - INFO - Epoch [129][600/1281] lr: 1.250e-03, eta: 3:14:37, time: 0.469, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 07:31:18,474 - pyskl - INFO - Epoch [129][700/1281] lr: 1.241e-03, eta: 3:13:54, time: 0.375, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 07:31:54,591 - pyskl - INFO - Epoch [129][800/1281] lr: 1.232e-03, eta: 3:13:10, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 07:32:34,105 - pyskl - INFO - Epoch [129][900/1281] lr: 1.223e-03, eta: 3:12:27, time: 0.395, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 07:33:23,390 - pyskl - INFO - Epoch [129][1000/1281] lr: 1.214e-03, eta: 3:11:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-25 07:34:12,602 - pyskl - INFO - Epoch [129][1100/1281] lr: 1.206e-03, eta: 3:11:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-25 07:35:01,952 - pyskl - INFO - Epoch [129][1200/1281] lr: 1.197e-03, eta: 3:10:24, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0262, loss: 0.0262 +2025-06-25 07:35:42,327 - pyskl - INFO - Saving checkpoint at 129 epochs +2025-06-25 07:36:41,042 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:36:41,099 - pyskl - INFO - +top1_acc 0.9397 +top5_acc 0.9975 +2025-06-25 07:36:41,099 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:36:41,105 - pyskl - INFO - +mean_acc 0.9148 +2025-06-25 07:36:41,107 - pyskl - INFO - Epoch(val) [129][533] top1_acc: 0.9397, top5_acc: 0.9975, mean_class_accuracy: 0.9148 +2025-06-25 07:38:01,466 - pyskl - INFO - Epoch [130][100/1281] lr: 1.181e-03, eta: 3:09:08, time: 0.804, data_time: 0.191, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 07:38:50,487 - pyskl - INFO - Epoch [130][200/1281] lr: 1.172e-03, eta: 3:08:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 07:39:39,643 - pyskl - INFO - Epoch [130][300/1281] lr: 1.164e-03, eta: 3:07:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 07:40:28,906 - pyskl - INFO - Epoch [130][400/1281] lr: 1.155e-03, eta: 3:07:04, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0269, loss: 0.0269 +2025-06-25 07:41:17,847 - pyskl - INFO - Epoch [130][500/1281] lr: 1.147e-03, eta: 3:06:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0219, loss: 0.0219 +2025-06-25 07:42:01,899 - pyskl - INFO - Epoch [130][600/1281] lr: 1.138e-03, eta: 3:05:41, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 07:42:44,866 - pyskl - INFO - Epoch [130][700/1281] lr: 1.130e-03, eta: 3:04:58, time: 0.430, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 07:43:15,326 - pyskl - INFO - Epoch [130][800/1281] lr: 1.121e-03, eta: 3:04:14, time: 0.305, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0219, loss: 0.0219 +2025-06-25 07:43:56,506 - pyskl - INFO - Epoch [130][900/1281] lr: 1.113e-03, eta: 3:03:32, time: 0.412, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 07:44:45,696 - pyskl - INFO - Epoch [130][1000/1281] lr: 1.104e-03, eta: 3:02:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 07:45:34,615 - pyskl - INFO - Epoch [130][1100/1281] lr: 1.096e-03, eta: 3:02:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 07:46:23,674 - pyskl - INFO - Epoch [130][1200/1281] lr: 1.088e-03, eta: 3:01:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 07:47:04,351 - pyskl - INFO - Saving checkpoint at 130 epochs +2025-06-25 07:48:01,912 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:48:01,968 - pyskl - INFO - +top1_acc 0.9398 +top5_acc 0.9971 +2025-06-25 07:48:01,968 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:48:01,976 - pyskl - INFO - +mean_acc 0.9159 +2025-06-25 07:48:01,978 - pyskl - INFO - Epoch(val) [130][533] top1_acc: 0.9398, top5_acc: 0.9971, mean_class_accuracy: 0.9159 +2025-06-25 07:49:22,259 - pyskl - INFO - Epoch [131][100/1281] lr: 1.072e-03, eta: 3:00:12, time: 0.803, data_time: 0.183, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 07:50:11,177 - pyskl - INFO - Epoch [131][200/1281] lr: 1.064e-03, eta: 2:59:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 07:51:00,495 - pyskl - INFO - Epoch [131][300/1281] lr: 1.056e-03, eta: 2:58:49, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0222, loss: 0.0222 +2025-06-25 07:51:49,614 - pyskl - INFO - Epoch [131][400/1281] lr: 1.048e-03, eta: 2:58:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 07:52:38,630 - pyskl - INFO - Epoch [131][500/1281] lr: 1.040e-03, eta: 2:57:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 07:53:23,484 - pyskl - INFO - Epoch [131][600/1281] lr: 1.031e-03, eta: 2:56:44, time: 0.449, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 07:54:06,067 - pyskl - INFO - Epoch [131][700/1281] lr: 1.023e-03, eta: 2:56:02, time: 0.426, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 07:54:37,196 - pyskl - INFO - Epoch [131][800/1281] lr: 1.015e-03, eta: 2:55:18, time: 0.311, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 07:55:19,454 - pyskl - INFO - Epoch [131][900/1281] lr: 1.007e-03, eta: 2:54:35, time: 0.423, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 07:56:08,501 - pyskl - INFO - Epoch [131][1000/1281] lr: 9.992e-04, eta: 2:53:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 07:56:57,643 - pyskl - INFO - Epoch [131][1100/1281] lr: 9.912e-04, eta: 2:53:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 07:57:46,416 - pyskl - INFO - Epoch [131][1200/1281] lr: 9.832e-04, eta: 2:52:31, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0218, loss: 0.0218 +2025-06-25 07:58:27,050 - pyskl - INFO - Saving checkpoint at 131 epochs +2025-06-25 07:59:24,895 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:59:24,951 - pyskl - INFO - +top1_acc 0.9427 +top5_acc 0.9969 +2025-06-25 07:59:24,951 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:59:24,958 - pyskl - INFO - +mean_acc 0.9203 +2025-06-25 07:59:24,962 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_125.pth was removed +2025-06-25 07:59:25,129 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_131.pth. +2025-06-25 07:59:25,129 - pyskl - INFO - Best top1_acc is 0.9427 at 131 epoch. +2025-06-25 07:59:25,132 - pyskl - INFO - Epoch(val) [131][533] top1_acc: 0.9427, top5_acc: 0.9969, mean_class_accuracy: 0.9203 +2025-06-25 08:00:44,458 - pyskl - INFO - Epoch [132][100/1281] lr: 9.689e-04, eta: 2:51:15, time: 0.793, data_time: 0.183, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 08:01:33,733 - pyskl - INFO - Epoch [132][200/1281] lr: 9.610e-04, eta: 2:50:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 08:02:23,045 - pyskl - INFO - Epoch [132][300/1281] lr: 9.532e-04, eta: 2:49:52, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 08:03:12,369 - pyskl - INFO - Epoch [132][400/1281] lr: 9.454e-04, eta: 2:49:10, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 08:04:01,636 - pyskl - INFO - Epoch [132][500/1281] lr: 9.376e-04, eta: 2:48:29, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 08:04:45,526 - pyskl - INFO - Epoch [132][600/1281] lr: 9.298e-04, eta: 2:47:47, time: 0.439, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-25 08:05:29,475 - pyskl - INFO - Epoch [132][700/1281] lr: 9.221e-04, eta: 2:47:05, time: 0.439, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 08:05:58,563 - pyskl - INFO - Epoch [132][800/1281] lr: 9.144e-04, eta: 2:46:20, time: 0.291, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 08:06:39,614 - pyskl - INFO - Epoch [132][900/1281] lr: 9.068e-04, eta: 2:45:38, time: 0.410, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 08:07:28,875 - pyskl - INFO - Epoch [132][1000/1281] lr: 8.991e-04, eta: 2:44:56, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 08:08:17,775 - pyskl - INFO - Epoch [132][1100/1281] lr: 8.915e-04, eta: 2:44:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 08:09:06,819 - pyskl - INFO - Epoch [132][1200/1281] lr: 8.840e-04, eta: 2:43:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 08:09:46,991 - pyskl - INFO - Saving checkpoint at 132 epochs +2025-06-25 08:10:45,204 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:10:45,260 - pyskl - INFO - +top1_acc 0.9426 +top5_acc 0.9975 +2025-06-25 08:10:45,261 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:10:45,268 - pyskl - INFO - +mean_acc 0.9215 +2025-06-25 08:10:45,270 - pyskl - INFO - Epoch(val) [132][533] top1_acc: 0.9426, top5_acc: 0.9975, mean_class_accuracy: 0.9215 +2025-06-25 08:12:05,029 - pyskl - INFO - Epoch [133][100/1281] lr: 8.704e-04, eta: 2:42:17, time: 0.798, data_time: 0.182, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 08:12:54,108 - pyskl - INFO - Epoch [133][200/1281] lr: 8.629e-04, eta: 2:41:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 08:13:43,026 - pyskl - INFO - Epoch [133][300/1281] lr: 8.554e-04, eta: 2:40:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 08:14:32,155 - pyskl - INFO - Epoch [133][400/1281] lr: 8.480e-04, eta: 2:40:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0140, loss: 0.0140 +2025-06-25 08:15:21,502 - pyskl - INFO - Epoch [133][500/1281] lr: 8.406e-04, eta: 2:39:31, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 08:16:05,640 - pyskl - INFO - Epoch [133][600/1281] lr: 8.333e-04, eta: 2:38:49, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 08:16:47,883 - pyskl - INFO - Epoch [133][700/1281] lr: 8.260e-04, eta: 2:38:06, time: 0.422, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 08:17:19,167 - pyskl - INFO - Epoch [133][800/1281] lr: 8.187e-04, eta: 2:37:22, time: 0.313, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 08:17:59,846 - pyskl - INFO - Epoch [133][900/1281] lr: 8.114e-04, eta: 2:36:40, time: 0.407, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 08:18:48,777 - pyskl - INFO - Epoch [133][1000/1281] lr: 8.042e-04, eta: 2:35:58, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 08:19:37,793 - pyskl - INFO - Epoch [133][1100/1281] lr: 7.970e-04, eta: 2:35:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 08:20:26,543 - pyskl - INFO - Epoch [133][1200/1281] lr: 7.898e-04, eta: 2:34:35, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 08:21:06,728 - pyskl - INFO - Saving checkpoint at 133 epochs +2025-06-25 08:22:04,596 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:22:04,663 - pyskl - INFO - +top1_acc 0.9423 +top5_acc 0.9975 +2025-06-25 08:22:04,663 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:22:04,672 - pyskl - INFO - +mean_acc 0.9203 +2025-06-25 08:22:04,674 - pyskl - INFO - Epoch(val) [133][533] top1_acc: 0.9423, top5_acc: 0.9975, mean_class_accuracy: 0.9203 +2025-06-25 08:23:23,822 - pyskl - INFO - Epoch [134][100/1281] lr: 7.769e-04, eta: 2:33:18, time: 0.791, data_time: 0.185, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 08:24:12,777 - pyskl - INFO - Epoch [134][200/1281] lr: 7.699e-04, eta: 2:32:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 08:25:02,054 - pyskl - INFO - Epoch [134][300/1281] lr: 7.628e-04, eta: 2:31:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 08:25:51,031 - pyskl - INFO - Epoch [134][400/1281] lr: 7.558e-04, eta: 2:31:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 08:26:39,888 - pyskl - INFO - Epoch [134][500/1281] lr: 7.488e-04, eta: 2:30:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 08:27:25,207 - pyskl - INFO - Epoch [134][600/1281] lr: 7.419e-04, eta: 2:29:50, time: 0.453, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 08:28:05,043 - pyskl - INFO - Epoch [134][700/1281] lr: 7.349e-04, eta: 2:29:07, time: 0.398, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 08:28:38,883 - pyskl - INFO - Epoch [134][800/1281] lr: 7.281e-04, eta: 2:28:24, time: 0.338, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 08:29:19,826 - pyskl - INFO - Epoch [134][900/1281] lr: 7.212e-04, eta: 2:27:41, time: 0.409, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 08:30:08,786 - pyskl - INFO - Epoch [134][1000/1281] lr: 7.144e-04, eta: 2:26:59, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 08:30:58,071 - pyskl - INFO - Epoch [134][1100/1281] lr: 7.076e-04, eta: 2:26:18, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 08:31:47,357 - pyskl - INFO - Epoch [134][1200/1281] lr: 7.008e-04, eta: 2:25:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 08:32:27,549 - pyskl - INFO - Saving checkpoint at 134 epochs +2025-06-25 08:33:26,055 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:33:26,111 - pyskl - INFO - +top1_acc 0.9432 +top5_acc 0.9975 +2025-06-25 08:33:26,111 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:33:26,118 - pyskl - INFO - +mean_acc 0.9214 +2025-06-25 08:33:26,122 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_131.pth was removed +2025-06-25 08:33:26,301 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_134.pth. +2025-06-25 08:33:26,302 - pyskl - INFO - Best top1_acc is 0.9432 at 134 epoch. +2025-06-25 08:33:26,305 - pyskl - INFO - Epoch(val) [134][533] top1_acc: 0.9432, top5_acc: 0.9975, mean_class_accuracy: 0.9214 +2025-06-25 08:34:45,848 - pyskl - INFO - Epoch [135][100/1281] lr: 6.887e-04, eta: 2:24:19, time: 0.795, data_time: 0.180, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 08:35:34,846 - pyskl - INFO - Epoch [135][200/1281] lr: 6.820e-04, eta: 2:23:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 08:36:23,646 - pyskl - INFO - Epoch [135][300/1281] lr: 6.753e-04, eta: 2:22:56, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 08:37:12,661 - pyskl - INFO - Epoch [135][400/1281] lr: 6.687e-04, eta: 2:22:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 08:38:01,886 - pyskl - INFO - Epoch [135][500/1281] lr: 6.622e-04, eta: 2:21:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 08:38:46,424 - pyskl - INFO - Epoch [135][600/1281] lr: 6.556e-04, eta: 2:20:50, time: 0.445, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 08:39:29,204 - pyskl - INFO - Epoch [135][700/1281] lr: 6.491e-04, eta: 2:20:08, time: 0.428, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 08:39:59,968 - pyskl - INFO - Epoch [135][800/1281] lr: 6.426e-04, eta: 2:19:24, time: 0.308, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 08:40:40,487 - pyskl - INFO - Epoch [135][900/1281] lr: 6.362e-04, eta: 2:18:41, time: 0.405, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 08:41:29,386 - pyskl - INFO - Epoch [135][1000/1281] lr: 6.297e-04, eta: 2:18:00, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 08:42:18,431 - pyskl - INFO - Epoch [135][1100/1281] lr: 6.233e-04, eta: 2:17:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 08:43:07,596 - pyskl - INFO - Epoch [135][1200/1281] lr: 6.170e-04, eta: 2:16:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 08:43:48,050 - pyskl - INFO - Saving checkpoint at 135 epochs +2025-06-25 08:44:45,701 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:44:45,779 - pyskl - INFO - +top1_acc 0.9459 +top5_acc 0.9978 +2025-06-25 08:44:45,779 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:44:45,787 - pyskl - INFO - +mean_acc 0.9260 +2025-06-25 08:44:45,792 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_134.pth was removed +2025-06-25 08:44:45,988 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_135.pth. +2025-06-25 08:44:45,988 - pyskl - INFO - Best top1_acc is 0.9459 at 135 epoch. +2025-06-25 08:44:45,991 - pyskl - INFO - Epoch(val) [135][533] top1_acc: 0.9459, top5_acc: 0.9978, mean_class_accuracy: 0.9260 +2025-06-25 08:46:05,676 - pyskl - INFO - Epoch [136][100/1281] lr: 6.056e-04, eta: 2:15:20, time: 0.797, data_time: 0.182, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 08:46:54,909 - pyskl - INFO - Epoch [136][200/1281] lr: 5.993e-04, eta: 2:14:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 08:47:44,250 - pyskl - INFO - Epoch [136][300/1281] lr: 5.931e-04, eta: 2:13:56, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0137, loss: 0.0137 +2025-06-25 08:48:33,308 - pyskl - INFO - Epoch [136][400/1281] lr: 5.868e-04, eta: 2:13:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 08:49:22,438 - pyskl - INFO - Epoch [136][500/1281] lr: 5.807e-04, eta: 2:12:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 08:50:08,966 - pyskl - INFO - Epoch [136][600/1281] lr: 5.745e-04, eta: 2:11:51, time: 0.465, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0146, loss: 0.0146 +2025-06-25 08:50:47,970 - pyskl - INFO - Epoch [136][700/1281] lr: 5.684e-04, eta: 2:11:08, time: 0.390, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 08:51:22,543 - pyskl - INFO - Epoch [136][800/1281] lr: 5.623e-04, eta: 2:10:24, time: 0.346, data_time: 0.001, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 08:52:01,697 - pyskl - INFO - Epoch [136][900/1281] lr: 5.563e-04, eta: 2:09:42, time: 0.392, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 08:52:51,003 - pyskl - INFO - Epoch [136][1000/1281] lr: 5.503e-04, eta: 2:09:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 08:53:40,403 - pyskl - INFO - Epoch [136][1100/1281] lr: 5.443e-04, eta: 2:08:18, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 08:54:29,575 - pyskl - INFO - Epoch [136][1200/1281] lr: 5.384e-04, eta: 2:07:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 08:55:10,098 - pyskl - INFO - Saving checkpoint at 136 epochs +2025-06-25 08:56:08,794 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:56:08,850 - pyskl - INFO - +top1_acc 0.9436 +top5_acc 0.9977 +2025-06-25 08:56:08,850 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:56:08,857 - pyskl - INFO - +mean_acc 0.9235 +2025-06-25 08:56:08,859 - pyskl - INFO - Epoch(val) [136][533] top1_acc: 0.9436, top5_acc: 0.9977, mean_class_accuracy: 0.9235 +2025-06-25 08:57:29,059 - pyskl - INFO - Epoch [137][100/1281] lr: 5.277e-04, eta: 2:06:20, time: 0.802, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 08:58:18,041 - pyskl - INFO - Epoch [137][200/1281] lr: 5.218e-04, eta: 2:05:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 08:59:07,238 - pyskl - INFO - Epoch [137][300/1281] lr: 5.160e-04, eta: 2:04:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0145, loss: 0.0145 +2025-06-25 08:59:56,495 - pyskl - INFO - Epoch [137][400/1281] lr: 5.102e-04, eta: 2:04:14, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 09:00:45,647 - pyskl - INFO - Epoch [137][500/1281] lr: 5.044e-04, eta: 2:03:32, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 09:01:30,867 - pyskl - INFO - Epoch [137][600/1281] lr: 4.987e-04, eta: 2:02:50, time: 0.452, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 09:02:12,717 - pyskl - INFO - Epoch [137][700/1281] lr: 4.930e-04, eta: 2:02:07, time: 0.418, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0139, loss: 0.0139 +2025-06-25 09:02:44,095 - pyskl - INFO - Epoch [137][800/1281] lr: 4.873e-04, eta: 2:01:24, time: 0.314, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 09:03:23,642 - pyskl - INFO - Epoch [137][900/1281] lr: 4.817e-04, eta: 2:00:41, time: 0.395, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 09:04:12,703 - pyskl - INFO - Epoch [137][1000/1281] lr: 4.761e-04, eta: 1:59:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 09:05:01,681 - pyskl - INFO - Epoch [137][1100/1281] lr: 4.705e-04, eta: 1:59:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 09:05:50,882 - pyskl - INFO - Epoch [137][1200/1281] lr: 4.650e-04, eta: 1:58:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 09:06:31,267 - pyskl - INFO - Saving checkpoint at 137 epochs +2025-06-25 09:07:30,033 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:07:30,103 - pyskl - INFO - +top1_acc 0.9439 +top5_acc 0.9978 +2025-06-25 09:07:30,103 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:07:30,117 - pyskl - INFO - +mean_acc 0.9223 +2025-06-25 09:07:30,119 - pyskl - INFO - Epoch(val) [137][533] top1_acc: 0.9439, top5_acc: 0.9978, mean_class_accuracy: 0.9223 +2025-06-25 09:08:50,625 - pyskl - INFO - Epoch [138][100/1281] lr: 4.550e-04, eta: 1:57:19, time: 0.805, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 09:09:39,791 - pyskl - INFO - Epoch [138][200/1281] lr: 4.496e-04, eta: 1:56:37, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0227, loss: 0.0227 +2025-06-25 09:10:28,866 - pyskl - INFO - Epoch [138][300/1281] lr: 4.442e-04, eta: 1:55:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 09:11:18,121 - pyskl - INFO - Epoch [138][400/1281] lr: 4.388e-04, eta: 1:55:13, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 09:12:07,338 - pyskl - INFO - Epoch [138][500/1281] lr: 4.334e-04, eta: 1:54:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 09:12:51,745 - pyskl - INFO - Epoch [138][600/1281] lr: 4.281e-04, eta: 1:53:49, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 09:13:34,285 - pyskl - INFO - Epoch [138][700/1281] lr: 4.228e-04, eta: 1:53:06, time: 0.425, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0142, loss: 0.0142 +2025-06-25 09:14:04,962 - pyskl - INFO - Epoch [138][800/1281] lr: 4.176e-04, eta: 1:52:23, time: 0.307, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 09:14:46,370 - pyskl - INFO - Epoch [138][900/1281] lr: 4.124e-04, eta: 1:51:40, time: 0.414, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 09:15:35,665 - pyskl - INFO - Epoch [138][1000/1281] lr: 4.072e-04, eta: 1:50:58, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 09:16:24,963 - pyskl - INFO - Epoch [138][1100/1281] lr: 4.020e-04, eta: 1:50:16, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 09:17:13,905 - pyskl - INFO - Epoch [138][1200/1281] lr: 3.969e-04, eta: 1:49:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 09:17:54,176 - pyskl - INFO - Saving checkpoint at 138 epochs +2025-06-25 09:18:52,751 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:18:52,806 - pyskl - INFO - +top1_acc 0.9465 +top5_acc 0.9977 +2025-06-25 09:18:52,807 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:18:52,813 - pyskl - INFO - +mean_acc 0.9249 +2025-06-25 09:18:52,817 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_135.pth was removed +2025-06-25 09:18:53,129 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_138.pth. +2025-06-25 09:18:53,129 - pyskl - INFO - Best top1_acc is 0.9465 at 138 epoch. +2025-06-25 09:18:53,132 - pyskl - INFO - Epoch(val) [138][533] top1_acc: 0.9465, top5_acc: 0.9977, mean_class_accuracy: 0.9249 +2025-06-25 09:20:14,677 - pyskl - INFO - Epoch [139][100/1281] lr: 3.877e-04, eta: 1:48:17, time: 0.815, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0144, loss: 0.0144 +2025-06-25 09:21:04,001 - pyskl - INFO - Epoch [139][200/1281] lr: 3.827e-04, eta: 1:47:35, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 09:21:53,043 - pyskl - INFO - Epoch [139][300/1281] lr: 3.777e-04, eta: 1:46:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0145, loss: 0.0145 +2025-06-25 09:22:41,946 - pyskl - INFO - Epoch [139][400/1281] lr: 3.727e-04, eta: 1:46:11, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 09:23:31,144 - pyskl - INFO - Epoch [139][500/1281] lr: 3.678e-04, eta: 1:45:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 09:24:12,643 - pyskl - INFO - Epoch [139][600/1281] lr: 3.628e-04, eta: 1:44:47, time: 0.415, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0142, loss: 0.0142 +2025-06-25 09:25:01,987 - pyskl - INFO - Epoch [139][700/1281] lr: 3.580e-04, eta: 1:44:05, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0141, loss: 0.0141 +2025-06-25 09:25:26,000 - pyskl - INFO - Epoch [139][800/1281] lr: 3.531e-04, eta: 1:43:21, time: 0.240, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 09:26:08,132 - pyskl - INFO - Epoch [139][900/1281] lr: 3.483e-04, eta: 1:42:38, time: 0.421, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0141, loss: 0.0141 +2025-06-25 09:26:57,752 - pyskl - INFO - Epoch [139][1000/1281] lr: 3.436e-04, eta: 1:41:56, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 09:27:47,085 - pyskl - INFO - Epoch [139][1100/1281] lr: 3.388e-04, eta: 1:41:14, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 09:28:36,620 - pyskl - INFO - Epoch [139][1200/1281] lr: 3.341e-04, eta: 1:40:32, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 09:29:17,227 - pyskl - INFO - Saving checkpoint at 139 epochs +2025-06-25 09:30:16,241 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:30:16,302 - pyskl - INFO - +top1_acc 0.9432 +top5_acc 0.9974 +2025-06-25 09:30:16,302 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:30:16,309 - pyskl - INFO - +mean_acc 0.9204 +2025-06-25 09:30:16,312 - pyskl - INFO - Epoch(val) [139][533] top1_acc: 0.9432, top5_acc: 0.9974, mean_class_accuracy: 0.9204 +2025-06-25 09:31:36,337 - pyskl - INFO - Epoch [140][100/1281] lr: 3.257e-04, eta: 1:39:15, time: 0.800, data_time: 0.188, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 09:32:24,993 - pyskl - INFO - Epoch [140][200/1281] lr: 3.210e-04, eta: 1:38:33, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0136, loss: 0.0136 +2025-06-25 09:33:14,123 - pyskl - INFO - Epoch [140][300/1281] lr: 3.165e-04, eta: 1:37:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0146, loss: 0.0146 +2025-06-25 09:34:02,936 - pyskl - INFO - Epoch [140][400/1281] lr: 3.119e-04, eta: 1:37:09, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 09:34:52,043 - pyskl - INFO - Epoch [140][500/1281] lr: 3.074e-04, eta: 1:36:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 09:35:33,539 - pyskl - INFO - Epoch [140][600/1281] lr: 3.029e-04, eta: 1:35:44, time: 0.415, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 09:36:22,789 - pyskl - INFO - Epoch [140][700/1281] lr: 2.984e-04, eta: 1:35:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0138, loss: 0.0138 +2025-06-25 09:36:47,984 - pyskl - INFO - Epoch [140][800/1281] lr: 2.940e-04, eta: 1:34:18, time: 0.252, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0146, loss: 0.0146 +2025-06-25 09:37:31,503 - pyskl - INFO - Epoch [140][900/1281] lr: 2.896e-04, eta: 1:33:36, time: 0.435, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 09:38:20,435 - pyskl - INFO - Epoch [140][1000/1281] lr: 2.853e-04, eta: 1:32:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0145, loss: 0.0145 +2025-06-25 09:39:09,653 - pyskl - INFO - Epoch [140][1100/1281] lr: 2.809e-04, eta: 1:32:12, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 09:39:59,008 - pyskl - INFO - Epoch [140][1200/1281] lr: 2.767e-04, eta: 1:31:29, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 09:40:39,073 - pyskl - INFO - Saving checkpoint at 140 epochs +2025-06-25 09:41:37,719 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:41:37,777 - pyskl - INFO - +top1_acc 0.9437 +top5_acc 0.9975 +2025-06-25 09:41:37,777 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:41:37,785 - pyskl - INFO - +mean_acc 0.9222 +2025-06-25 09:41:37,787 - pyskl - INFO - Epoch(val) [140][533] top1_acc: 0.9437, top5_acc: 0.9975, mean_class_accuracy: 0.9222 +2025-06-25 09:42:56,591 - pyskl - INFO - Epoch [141][100/1281] lr: 2.690e-04, eta: 1:30:13, time: 0.788, data_time: 0.182, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0134, loss: 0.0134 +2025-06-25 09:43:45,521 - pyskl - INFO - Epoch [141][200/1281] lr: 2.648e-04, eta: 1:29:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 09:44:34,656 - pyskl - INFO - Epoch [141][300/1281] lr: 2.606e-04, eta: 1:28:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 09:45:23,789 - pyskl - INFO - Epoch [141][400/1281] lr: 2.565e-04, eta: 1:28:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 09:46:13,035 - pyskl - INFO - Epoch [141][500/1281] lr: 2.524e-04, eta: 1:27:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 09:46:54,263 - pyskl - INFO - Epoch [141][600/1281] lr: 2.483e-04, eta: 1:26:41, time: 0.412, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 09:47:42,540 - pyskl - INFO - Epoch [141][700/1281] lr: 2.443e-04, eta: 1:25:59, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 09:48:08,127 - pyskl - INFO - Epoch [141][800/1281] lr: 2.402e-04, eta: 1:25:15, time: 0.256, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 09:48:50,150 - pyskl - INFO - Epoch [141][900/1281] lr: 2.363e-04, eta: 1:24:33, time: 0.420, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0132, loss: 0.0132 +2025-06-25 09:49:39,302 - pyskl - INFO - Epoch [141][1000/1281] lr: 2.323e-04, eta: 1:23:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0138, loss: 0.0138 +2025-06-25 09:50:28,759 - pyskl - INFO - Epoch [141][1100/1281] lr: 2.284e-04, eta: 1:23:08, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0141, loss: 0.0141 +2025-06-25 09:51:18,009 - pyskl - INFO - Epoch [141][1200/1281] lr: 2.246e-04, eta: 1:22:26, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 09:51:58,486 - pyskl - INFO - Saving checkpoint at 141 epochs +2025-06-25 09:52:57,102 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:52:57,157 - pyskl - INFO - +top1_acc 0.9441 +top5_acc 0.9979 +2025-06-25 09:52:57,157 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:52:57,164 - pyskl - INFO - +mean_acc 0.9221 +2025-06-25 09:52:57,166 - pyskl - INFO - Epoch(val) [141][533] top1_acc: 0.9441, top5_acc: 0.9979, mean_class_accuracy: 0.9221 +2025-06-25 09:54:17,505 - pyskl - INFO - Epoch [142][100/1281] lr: 2.176e-04, eta: 1:21:09, time: 0.803, data_time: 0.181, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 09:55:06,830 - pyskl - INFO - Epoch [142][200/1281] lr: 2.139e-04, eta: 1:20:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0142, loss: 0.0142 +2025-06-25 09:55:56,059 - pyskl - INFO - Epoch [142][300/1281] lr: 2.101e-04, eta: 1:19:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 09:56:45,286 - pyskl - INFO - Epoch [142][400/1281] lr: 2.064e-04, eta: 1:19:03, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 09:57:34,432 - pyskl - INFO - Epoch [142][500/1281] lr: 2.027e-04, eta: 1:18:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0140, loss: 0.0140 +2025-06-25 09:58:16,192 - pyskl - INFO - Epoch [142][600/1281] lr: 1.991e-04, eta: 1:17:38, time: 0.418, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 09:59:02,653 - pyskl - INFO - Epoch [142][700/1281] lr: 1.954e-04, eta: 1:16:55, time: 0.465, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0140, loss: 0.0140 +2025-06-25 09:59:29,746 - pyskl - INFO - Epoch [142][800/1281] lr: 1.919e-04, eta: 1:16:12, time: 0.271, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 10:00:11,610 - pyskl - INFO - Epoch [142][900/1281] lr: 1.883e-04, eta: 1:15:29, time: 0.419, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0144, loss: 0.0144 +2025-06-25 10:01:00,701 - pyskl - INFO - Epoch [142][1000/1281] lr: 1.848e-04, eta: 1:14:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 10:01:49,751 - pyskl - INFO - Epoch [142][1100/1281] lr: 1.813e-04, eta: 1:14:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 10:02:38,779 - pyskl - INFO - Epoch [142][1200/1281] lr: 1.779e-04, eta: 1:13:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 10:03:19,100 - pyskl - INFO - Saving checkpoint at 142 epochs +2025-06-25 10:04:17,078 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:04:17,134 - pyskl - INFO - +top1_acc 0.9457 +top5_acc 0.9975 +2025-06-25 10:04:17,134 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:04:17,141 - pyskl - INFO - +mean_acc 0.9232 +2025-06-25 10:04:17,143 - pyskl - INFO - Epoch(val) [142][533] top1_acc: 0.9457, top5_acc: 0.9975, mean_class_accuracy: 0.9232 +2025-06-25 10:05:36,680 - pyskl - INFO - Epoch [143][100/1281] lr: 1.717e-04, eta: 1:12:05, time: 0.795, data_time: 0.185, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 10:06:26,197 - pyskl - INFO - Epoch [143][200/1281] lr: 1.683e-04, eta: 1:11:23, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0139, loss: 0.0139 +2025-06-25 10:07:15,470 - pyskl - INFO - Epoch [143][300/1281] lr: 1.650e-04, eta: 1:10:41, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 10:08:04,551 - pyskl - INFO - Epoch [143][400/1281] lr: 1.617e-04, eta: 1:09:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 10:08:53,744 - pyskl - INFO - Epoch [143][500/1281] lr: 1.585e-04, eta: 1:09:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0146, loss: 0.0146 +2025-06-25 10:09:36,395 - pyskl - INFO - Epoch [143][600/1281] lr: 1.552e-04, eta: 1:08:34, time: 0.427, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 10:10:23,192 - pyskl - INFO - Epoch [143][700/1281] lr: 1.520e-04, eta: 1:07:51, time: 0.468, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0137, loss: 0.0137 +2025-06-25 10:10:50,545 - pyskl - INFO - Epoch [143][800/1281] lr: 1.489e-04, eta: 1:07:08, time: 0.274, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0140, loss: 0.0140 +2025-06-25 10:11:33,019 - pyskl - INFO - Epoch [143][900/1281] lr: 1.457e-04, eta: 1:06:25, time: 0.425, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0138, loss: 0.0138 +2025-06-25 10:12:22,233 - pyskl - INFO - Epoch [143][1000/1281] lr: 1.426e-04, eta: 1:05:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 10:13:11,530 - pyskl - INFO - Epoch [143][1100/1281] lr: 1.396e-04, eta: 1:05:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 10:14:00,697 - pyskl - INFO - Epoch [143][1200/1281] lr: 1.366e-04, eta: 1:04:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 10:14:41,256 - pyskl - INFO - Saving checkpoint at 143 epochs +2025-06-25 10:15:39,761 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:15:39,817 - pyskl - INFO - +top1_acc 0.9463 +top5_acc 0.9979 +2025-06-25 10:15:39,817 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:15:39,824 - pyskl - INFO - +mean_acc 0.9243 +2025-06-25 10:15:39,826 - pyskl - INFO - Epoch(val) [143][533] top1_acc: 0.9463, top5_acc: 0.9979, mean_class_accuracy: 0.9243 +2025-06-25 10:16:59,490 - pyskl - INFO - Epoch [144][100/1281] lr: 1.312e-04, eta: 1:03:01, time: 0.797, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 10:17:48,632 - pyskl - INFO - Epoch [144][200/1281] lr: 1.282e-04, eta: 1:02:19, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 10:18:37,867 - pyskl - INFO - Epoch [144][300/1281] lr: 1.253e-04, eta: 1:01:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 10:19:26,946 - pyskl - INFO - Epoch [144][400/1281] lr: 1.224e-04, eta: 1:00:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 10:20:16,015 - pyskl - INFO - Epoch [144][500/1281] lr: 1.196e-04, eta: 1:00:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0138, loss: 0.0138 +2025-06-25 10:20:57,979 - pyskl - INFO - Epoch [144][600/1281] lr: 1.168e-04, eta: 0:59:29, time: 0.420, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 10:21:45,268 - pyskl - INFO - Epoch [144][700/1281] lr: 1.140e-04, eta: 0:58:47, time: 0.473, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 10:22:11,308 - pyskl - INFO - Epoch [144][800/1281] lr: 1.113e-04, eta: 0:58:03, time: 0.260, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0144, loss: 0.0144 +2025-06-25 10:22:54,209 - pyskl - INFO - Epoch [144][900/1281] lr: 1.086e-04, eta: 0:57:21, time: 0.429, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 10:23:43,471 - pyskl - INFO - Epoch [144][1000/1281] lr: 1.059e-04, eta: 0:56:38, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 10:24:32,657 - pyskl - INFO - Epoch [144][1100/1281] lr: 1.033e-04, eta: 0:55:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 10:25:21,840 - pyskl - INFO - Epoch [144][1200/1281] lr: 1.007e-04, eta: 0:55:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0134, loss: 0.0134 +2025-06-25 10:26:02,438 - pyskl - INFO - Saving checkpoint at 144 epochs +2025-06-25 10:27:00,505 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:27:00,561 - pyskl - INFO - +top1_acc 0.9447 +top5_acc 0.9975 +2025-06-25 10:27:00,561 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:27:00,568 - pyskl - INFO - +mean_acc 0.9227 +2025-06-25 10:27:00,570 - pyskl - INFO - Epoch(val) [144][533] top1_acc: 0.9447, top5_acc: 0.9975, mean_class_accuracy: 0.9227 +2025-06-25 10:28:19,509 - pyskl - INFO - Epoch [145][100/1281] lr: 9.605e-05, eta: 0:53:56, time: 0.789, data_time: 0.180, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0144, loss: 0.0144 +2025-06-25 10:29:08,099 - pyskl - INFO - Epoch [145][200/1281] lr: 9.353e-05, eta: 0:53:14, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 10:29:57,419 - pyskl - INFO - Epoch [145][300/1281] lr: 9.106e-05, eta: 0:52:31, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0142, loss: 0.0142 +2025-06-25 10:30:46,323 - pyskl - INFO - Epoch [145][400/1281] lr: 8.861e-05, eta: 0:51:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 10:31:35,527 - pyskl - INFO - Epoch [145][500/1281] lr: 8.620e-05, eta: 0:51:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 10:32:19,529 - pyskl - INFO - Epoch [145][600/1281] lr: 8.382e-05, eta: 0:50:24, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 10:33:03,158 - pyskl - INFO - Epoch [145][700/1281] lr: 8.147e-05, eta: 0:49:41, time: 0.436, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0141, loss: 0.0141 +2025-06-25 10:33:32,589 - pyskl - INFO - Epoch [145][800/1281] lr: 7.916e-05, eta: 0:48:58, time: 0.294, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 10:34:14,568 - pyskl - INFO - Epoch [145][900/1281] lr: 7.688e-05, eta: 0:48:15, time: 0.420, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 10:35:03,600 - pyskl - INFO - Epoch [145][1000/1281] lr: 7.463e-05, eta: 0:47:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0141, loss: 0.0141 +2025-06-25 10:35:52,691 - pyskl - INFO - Epoch [145][1100/1281] lr: 7.242e-05, eta: 0:46:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 10:36:41,866 - pyskl - INFO - Epoch [145][1200/1281] lr: 7.024e-05, eta: 0:46:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0133, loss: 0.0133 +2025-06-25 10:37:22,151 - pyskl - INFO - Saving checkpoint at 145 epochs +2025-06-25 10:38:20,169 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:38:20,224 - pyskl - INFO - +top1_acc 0.9445 +top5_acc 0.9978 +2025-06-25 10:38:20,224 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:38:20,231 - pyskl - INFO - +mean_acc 0.9226 +2025-06-25 10:38:20,233 - pyskl - INFO - Epoch(val) [145][533] top1_acc: 0.9445, top5_acc: 0.9978, mean_class_accuracy: 0.9226 +2025-06-25 10:39:39,089 - pyskl - INFO - Epoch [146][100/1281] lr: 6.638e-05, eta: 0:44:51, time: 0.789, data_time: 0.180, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 10:40:28,437 - pyskl - INFO - Epoch [146][200/1281] lr: 6.429e-05, eta: 0:44:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0137, loss: 0.0137 +2025-06-25 10:41:17,546 - pyskl - INFO - Epoch [146][300/1281] lr: 6.224e-05, eta: 0:43:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 10:42:06,625 - pyskl - INFO - Epoch [146][400/1281] lr: 6.022e-05, eta: 0:42:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 10:42:55,814 - pyskl - INFO - Epoch [146][500/1281] lr: 5.823e-05, eta: 0:42:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 10:43:40,367 - pyskl - INFO - Epoch [146][600/1281] lr: 5.628e-05, eta: 0:41:18, time: 0.446, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0141, loss: 0.0141 +2025-06-25 10:44:23,639 - pyskl - INFO - Epoch [146][700/1281] lr: 5.436e-05, eta: 0:40:36, time: 0.433, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 10:44:54,080 - pyskl - INFO - Epoch [146][800/1281] lr: 5.247e-05, eta: 0:39:53, time: 0.304, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 10:45:35,643 - pyskl - INFO - Epoch [146][900/1281] lr: 5.061e-05, eta: 0:39:10, time: 0.416, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 10:46:25,301 - pyskl - INFO - Epoch [146][1000/1281] lr: 4.879e-05, eta: 0:38:27, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 10:47:14,436 - pyskl - INFO - Epoch [146][1100/1281] lr: 4.701e-05, eta: 0:37:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 10:48:03,663 - pyskl - INFO - Epoch [146][1200/1281] lr: 4.525e-05, eta: 0:37:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0134, loss: 0.0134 +2025-06-25 10:48:44,146 - pyskl - INFO - Saving checkpoint at 146 epochs +2025-06-25 10:49:42,229 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:49:42,284 - pyskl - INFO - +top1_acc 0.9434 +top5_acc 0.9980 +2025-06-25 10:49:42,285 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:49:42,292 - pyskl - INFO - +mean_acc 0.9214 +2025-06-25 10:49:42,294 - pyskl - INFO - Epoch(val) [146][533] top1_acc: 0.9434, top5_acc: 0.9980, mean_class_accuracy: 0.9214 +2025-06-25 10:51:01,205 - pyskl - INFO - Epoch [147][100/1281] lr: 4.216e-05, eta: 0:35:45, time: 0.789, data_time: 0.181, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0139, loss: 0.0139 +2025-06-25 10:51:50,105 - pyskl - INFO - Epoch [147][200/1281] lr: 4.050e-05, eta: 0:35:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 10:52:38,957 - pyskl - INFO - Epoch [147][300/1281] lr: 3.887e-05, eta: 0:34:20, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 10:53:28,221 - pyskl - INFO - Epoch [147][400/1281] lr: 3.728e-05, eta: 0:33:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 10:54:17,400 - pyskl - INFO - Epoch [147][500/1281] lr: 3.572e-05, eta: 0:32:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0136, loss: 0.0136 +2025-06-25 10:55:01,769 - pyskl - INFO - Epoch [147][600/1281] lr: 3.419e-05, eta: 0:32:12, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 10:55:42,541 - pyskl - INFO - Epoch [147][700/1281] lr: 3.270e-05, eta: 0:31:29, time: 0.408, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0138, loss: 0.0138 +2025-06-25 10:56:14,814 - pyskl - INFO - Epoch [147][800/1281] lr: 3.124e-05, eta: 0:30:46, time: 0.323, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 10:56:56,418 - pyskl - INFO - Epoch [147][900/1281] lr: 2.981e-05, eta: 0:30:04, time: 0.416, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0142, loss: 0.0142 +2025-06-25 10:57:45,785 - pyskl - INFO - Epoch [147][1000/1281] lr: 2.842e-05, eta: 0:29:21, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 10:58:35,037 - pyskl - INFO - Epoch [147][1100/1281] lr: 2.706e-05, eta: 0:28:39, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0134, loss: 0.0134 +2025-06-25 10:59:24,207 - pyskl - INFO - Epoch [147][1200/1281] lr: 2.573e-05, eta: 0:27:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0138, loss: 0.0138 +2025-06-25 11:00:04,402 - pyskl - INFO - Saving checkpoint at 147 epochs +2025-06-25 11:01:02,292 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:01:02,359 - pyskl - INFO - +top1_acc 0.9430 +top5_acc 0.9981 +2025-06-25 11:01:02,359 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:01:02,367 - pyskl - INFO - +mean_acc 0.9204 +2025-06-25 11:01:02,369 - pyskl - INFO - Epoch(val) [147][533] top1_acc: 0.9430, top5_acc: 0.9981, mean_class_accuracy: 0.9204 +2025-06-25 11:02:21,619 - pyskl - INFO - Epoch [148][100/1281] lr: 2.341e-05, eta: 0:26:39, time: 0.792, data_time: 0.182, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 11:03:10,507 - pyskl - INFO - Epoch [148][200/1281] lr: 2.218e-05, eta: 0:25:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0145, loss: 0.0145 +2025-06-25 11:04:00,101 - pyskl - INFO - Epoch [148][300/1281] lr: 2.098e-05, eta: 0:25:13, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0132, loss: 0.0132 +2025-06-25 11:04:49,770 - pyskl - INFO - Epoch [148][400/1281] lr: 1.981e-05, eta: 0:24:31, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 11:05:38,760 - pyskl - INFO - Epoch [148][500/1281] lr: 1.868e-05, eta: 0:23:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 11:06:23,095 - pyskl - INFO - Epoch [148][600/1281] lr: 1.758e-05, eta: 0:23:06, time: 0.443, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 11:07:07,519 - pyskl - INFO - Epoch [148][700/1281] lr: 1.651e-05, eta: 0:22:23, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 11:07:36,378 - pyskl - INFO - Epoch [148][800/1281] lr: 1.548e-05, eta: 0:21:40, time: 0.289, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 11:08:17,720 - pyskl - INFO - Epoch [148][900/1281] lr: 1.448e-05, eta: 0:20:57, time: 0.413, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 11:09:06,814 - pyskl - INFO - Epoch [148][1000/1281] lr: 1.351e-05, eta: 0:20:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0132, loss: 0.0132 +2025-06-25 11:09:56,120 - pyskl - INFO - Epoch [148][1100/1281] lr: 1.258e-05, eta: 0:19:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0139, loss: 0.0139 +2025-06-25 11:10:45,290 - pyskl - INFO - Epoch [148][1200/1281] lr: 1.168e-05, eta: 0:18:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0135, loss: 0.0135 +2025-06-25 11:11:25,682 - pyskl - INFO - Saving checkpoint at 148 epochs +2025-06-25 11:12:23,692 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:12:23,759 - pyskl - INFO - +top1_acc 0.9441 +top5_acc 0.9975 +2025-06-25 11:12:23,759 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:12:23,767 - pyskl - INFO - +mean_acc 0.9215 +2025-06-25 11:12:23,769 - pyskl - INFO - Epoch(val) [148][533] top1_acc: 0.9441, top5_acc: 0.9975, mean_class_accuracy: 0.9215 +2025-06-25 11:13:43,314 - pyskl - INFO - Epoch [149][100/1281] lr: 1.013e-05, eta: 0:17:32, time: 0.795, data_time: 0.183, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0145, loss: 0.0145 +2025-06-25 11:14:32,515 - pyskl - INFO - Epoch [149][200/1281] lr: 9.328e-06, eta: 0:16:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 11:15:22,056 - pyskl - INFO - Epoch [149][300/1281] lr: 8.555e-06, eta: 0:16:07, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 11:16:11,217 - pyskl - INFO - Epoch [149][400/1281] lr: 7.816e-06, eta: 0:15:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 11:17:00,743 - pyskl - INFO - Epoch [149][500/1281] lr: 7.110e-06, eta: 0:14:41, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 11:17:46,059 - pyskl - INFO - Epoch [149][600/1281] lr: 6.437e-06, eta: 0:13:58, time: 0.453, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0140, loss: 0.0140 +2025-06-25 11:18:28,003 - pyskl - INFO - Epoch [149][700/1281] lr: 5.798e-06, eta: 0:13:16, time: 0.419, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0137, loss: 0.0137 +2025-06-25 11:18:59,660 - pyskl - INFO - Epoch [149][800/1281] lr: 5.192e-06, eta: 0:12:33, time: 0.317, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 11:19:39,543 - pyskl - INFO - Epoch [149][900/1281] lr: 4.620e-06, eta: 0:11:50, time: 0.399, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 11:20:28,431 - pyskl - INFO - Epoch [149][1000/1281] lr: 4.081e-06, eta: 0:11:07, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0145, loss: 0.0145 +2025-06-25 11:21:17,817 - pyskl - INFO - Epoch [149][1100/1281] lr: 3.576e-06, eta: 0:10:25, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 11:22:07,167 - pyskl - INFO - Epoch [149][1200/1281] lr: 3.104e-06, eta: 0:09:42, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0135, loss: 0.0135 +2025-06-25 11:22:47,490 - pyskl - INFO - Saving checkpoint at 149 epochs +2025-06-25 11:23:45,638 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:23:45,706 - pyskl - INFO - +top1_acc 0.9440 +top5_acc 0.9975 +2025-06-25 11:23:45,706 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:23:45,714 - pyskl - INFO - +mean_acc 0.9228 +2025-06-25 11:23:45,717 - pyskl - INFO - Epoch(val) [149][533] top1_acc: 0.9440, top5_acc: 0.9975, mean_class_accuracy: 0.9228 +2025-06-25 11:25:03,706 - pyskl - INFO - Epoch [150][100/1281] lr: 2.334e-06, eta: 0:08:25, time: 0.780, data_time: 0.177, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0146, loss: 0.0146 +2025-06-25 11:25:52,834 - pyskl - INFO - Epoch [150][200/1281] lr: 1.956e-06, eta: 0:07:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 11:26:41,978 - pyskl - INFO - Epoch [150][300/1281] lr: 1.611e-06, eta: 0:06:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0146, loss: 0.0146 +2025-06-25 11:27:31,071 - pyskl - INFO - Epoch [150][400/1281] lr: 1.300e-06, eta: 0:06:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0135, loss: 0.0135 +2025-06-25 11:28:20,700 - pyskl - INFO - Epoch [150][500/1281] lr: 1.022e-06, eta: 0:05:34, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 11:29:08,008 - pyskl - INFO - Epoch [150][600/1281] lr: 7.771e-07, eta: 0:04:51, time: 0.473, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 11:29:46,455 - pyskl - INFO - Epoch [150][700/1281] lr: 5.659e-07, eta: 0:04:08, time: 0.384, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 11:30:21,950 - pyskl - INFO - Epoch [150][800/1281] lr: 3.881e-07, eta: 0:03:25, time: 0.355, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 11:31:00,393 - pyskl - INFO - Epoch [150][900/1281] lr: 2.438e-07, eta: 0:02:42, time: 0.384, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0145, loss: 0.0145 +2025-06-25 11:31:49,555 - pyskl - INFO - Epoch [150][1000/1281] lr: 1.329e-07, eta: 0:02:00, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 11:32:38,532 - pyskl - INFO - Epoch [150][1100/1281] lr: 5.534e-08, eta: 0:01:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0131, loss: 0.0131 +2025-06-25 11:33:27,553 - pyskl - INFO - Epoch [150][1200/1281] lr: 1.123e-08, eta: 0:00:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 11:34:07,966 - pyskl - INFO - Saving checkpoint at 150 epochs +2025-06-25 11:35:06,114 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:35:06,177 - pyskl - INFO - +top1_acc 0.9457 +top5_acc 0.9977 +2025-06-25 11:35:06,178 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:35:06,187 - pyskl - INFO - +mean_acc 0.9237 +2025-06-25 11:35:06,189 - pyskl - INFO - Epoch(val) [150][533] top1_acc: 0.9457, top5_acc: 0.9977, mean_class_accuracy: 0.9237 +2025-06-25 11:35:10,667 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-25 11:42:51,090 - pyskl - INFO - Testing results of the last checkpoint +2025-06-25 11:42:51,091 - pyskl - INFO - top1_acc: 0.9455 +2025-06-25 11:42:51,091 - pyskl - INFO - top5_acc: 0.9977 +2025-06-25 11:42:51,091 - pyskl - INFO - mean_class_accuracy: 0.9236 +2025-06-25 11:42:51,091 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/finegym/b_1/best_top1_acc_epoch_138.pth +2025-06-25 11:50:26,448 - pyskl - INFO - Testing results of the best checkpoint +2025-06-25 11:50:26,448 - pyskl - INFO - top1_acc: 0.9481 +2025-06-25 11:50:26,448 - pyskl - INFO - top5_acc: 0.9977 +2025-06-25 11:50:26,448 - pyskl - INFO - mean_class_accuracy: 0.9274 diff --git a/finegym/b_1/20250624_084232.log.json b/finegym/b_1/20250624_084232.log.json new file mode 100644 index 0000000000000000000000000000000000000000..0cdb9b0f94841ff37c96a2c36f92e4b6ce9f344d --- /dev/null +++ b/finegym/b_1/20250624_084232.log.json @@ -0,0 +1,1951 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 135571342, "config_name": "b_1.py", "work_dir": "b_1", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.16793, "top1_acc": 0.0775, "top5_acc": 0.24938, "loss_cls": 4.47196, "loss": 4.47196, "time": 0.37928} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.07562, "top5_acc": 0.3475, "loss_cls": 4.43916, "loss": 4.43916, "time": 0.21923} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.12875, "top5_acc": 0.38688, "loss_cls": 4.22021, "loss": 4.22021, "time": 0.21772} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00077, "top1_acc": 0.11, "top5_acc": 0.40312, "loss_cls": 4.25503, "loss": 4.25503, "time": 0.21912} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.025, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.12062, "top5_acc": 0.44312, "loss_cls": 4.00442, "loss": 4.00442, "time": 0.22074} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.025, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.16188, "top5_acc": 0.47812, "loss_cls": 3.78522, "loss": 3.78522, "time": 0.22057} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.025, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.20875, "top5_acc": 0.5425, "loss_cls": 3.5211, "loss": 3.5211, "time": 0.21645} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.025, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.23625, "top5_acc": 0.61375, "loss_cls": 3.33703, "loss": 3.33703, "time": 0.21699} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.025, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.26687, "top5_acc": 0.64062, "loss_cls": 3.15585, "loss": 3.15585, "time": 0.21812} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.025, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.31438, "top5_acc": 0.68125, "loss_cls": 2.97084, "loss": 2.97084, "time": 0.21758} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.025, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.31438, "top5_acc": 0.69062, "loss_cls": 2.94372, "loss": 2.94372, "time": 0.21659} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.025, "memory": 4082, "data_time": 0.00059, "top1_acc": 0.34, "top5_acc": 0.70938, "loss_cls": 2.85995, "loss": 2.85995, "time": 0.21811} +{"mode": "val", "epoch": 1, "iter": 533, "lr": 0.025, "top1_acc": 0.309, "top5_acc": 0.70755, "mean_class_accuracy": 0.16547} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.1833, "top1_acc": 0.34812, "top5_acc": 0.74875, "loss_cls": 2.66592, "loss": 2.66592, "time": 0.40133} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.40375, "top5_acc": 0.7925, "loss_cls": 2.5267, "loss": 2.5267, "time": 0.22043} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00056, "top1_acc": 0.42688, "top5_acc": 0.815, "loss_cls": 2.38416, "loss": 2.38416, "time": 0.22092} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.4425, "top5_acc": 0.82312, "loss_cls": 2.32967, "loss": 2.32967, "time": 0.21948} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.02499, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.45875, "top5_acc": 0.85, "loss_cls": 2.24632, "loss": 2.24632, "time": 0.22059} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.02499, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.50625, "top5_acc": 0.875, "loss_cls": 2.08237, "loss": 2.08237, "time": 0.21686} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.48562, "top5_acc": 0.86625, "loss_cls": 2.13535, "loss": 2.13535, "time": 0.21753} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.02499, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.49188, "top5_acc": 0.85562, "loss_cls": 2.11166, "loss": 2.11166, "time": 0.22128} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.02499, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.515, "top5_acc": 0.89625, "loss_cls": 1.96963, "loss": 1.96963, "time": 0.22044} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.02499, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.51875, "top5_acc": 0.89688, "loss_cls": 1.98128, "loss": 1.98128, "time": 0.21722} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.02499, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.56063, "top5_acc": 0.90375, "loss_cls": 1.86614, "loss": 1.86614, "time": 0.21558} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.02499, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.54062, "top5_acc": 0.90062, "loss_cls": 1.92296, "loss": 1.92296, "time": 0.21993} +{"mode": "val", "epoch": 2, "iter": 533, "lr": 0.02499, "top1_acc": 0.47436, "top5_acc": 0.84955, "mean_class_accuracy": 0.31556} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.02499, "memory": 4082, "data_time": 0.18471, "top1_acc": 0.57, "top5_acc": 0.91812, "loss_cls": 1.78199, "loss": 1.78199, "time": 0.40318} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.02499, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.58, "top5_acc": 0.92562, "loss_cls": 1.70005, "loss": 1.70005, "time": 0.21971} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.02499, "memory": 4082, "data_time": 0.00049, "top1_acc": 0.57938, "top5_acc": 0.91875, "loss_cls": 1.78174, "loss": 1.78174, "time": 0.22027} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.02499, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.6225, "top5_acc": 0.92438, "loss_cls": 1.64736, "loss": 1.64736, "time": 0.2198} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.02498, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.58938, "top5_acc": 0.94, "loss_cls": 1.67426, "loss": 1.67426, "time": 0.21982} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.02498, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.59938, "top5_acc": 0.93188, "loss_cls": 1.67067, "loss": 1.67067, "time": 0.21768} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.02498, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.60688, "top5_acc": 0.94375, "loss_cls": 1.63709, "loss": 1.63709, "time": 0.2193} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.02498, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.6225, "top5_acc": 0.9325, "loss_cls": 1.62222, "loss": 1.62222, "time": 0.21875} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.02498, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.63688, "top5_acc": 0.93875, "loss_cls": 1.52602, "loss": 1.52602, "time": 0.2176} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.02498, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.61438, "top5_acc": 0.94125, "loss_cls": 1.60608, "loss": 1.60608, "time": 0.21626} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.02498, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.62813, "top5_acc": 0.95625, "loss_cls": 1.50817, "loss": 1.50817, "time": 0.21723} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.02498, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.64125, "top5_acc": 0.94812, "loss_cls": 1.49479, "loss": 1.49479, "time": 0.2182} +{"mode": "val", "epoch": 3, "iter": 533, "lr": 0.02498, "top1_acc": 0.63373, "top5_acc": 0.94179, "mean_class_accuracy": 0.48549} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.02497, "memory": 4082, "data_time": 0.17996, "top1_acc": 0.64688, "top5_acc": 0.94938, "loss_cls": 1.43869, "loss": 1.43869, "time": 0.39964} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.02497, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.66188, "top5_acc": 0.96, "loss_cls": 1.40742, "loss": 1.40742, "time": 0.2206} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.02497, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.6525, "top5_acc": 0.955, "loss_cls": 1.47185, "loss": 1.47185, "time": 0.21896} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.02497, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.6575, "top5_acc": 0.95562, "loss_cls": 1.39398, "loss": 1.39398, "time": 0.21995} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.02497, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.66812, "top5_acc": 0.96, "loss_cls": 1.37498, "loss": 1.37498, "time": 0.21859} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.02497, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.67188, "top5_acc": 0.96562, "loss_cls": 1.38383, "loss": 1.38383, "time": 0.2136} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.02497, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.67938, "top5_acc": 0.9725, "loss_cls": 1.31097, "loss": 1.31097, "time": 0.21744} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.02496, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.6675, "top5_acc": 0.965, "loss_cls": 1.3982, "loss": 1.3982, "time": 0.21347} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.02496, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.68188, "top5_acc": 0.96062, "loss_cls": 1.34043, "loss": 1.34043, "time": 0.21349} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.02496, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.68312, "top5_acc": 0.96438, "loss_cls": 1.32559, "loss": 1.32559, "time": 0.2157} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.02496, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.6775, "top5_acc": 0.96, "loss_cls": 1.33757, "loss": 1.33757, "time": 0.21888} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.02496, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.68125, "top5_acc": 0.9625, "loss_cls": 1.32701, "loss": 1.32701, "time": 0.21585} +{"mode": "val", "epoch": 4, "iter": 533, "lr": 0.02496, "top1_acc": 0.68361, "top5_acc": 0.96468, "mean_class_accuracy": 0.5472} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.02495, "memory": 4082, "data_time": 0.18476, "top1_acc": 0.70625, "top5_acc": 0.97625, "loss_cls": 1.23015, "loss": 1.23015, "time": 0.40006} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.02495, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.71375, "top5_acc": 0.9675, "loss_cls": 1.23955, "loss": 1.23955, "time": 0.21781} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.02495, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.70375, "top5_acc": 0.97688, "loss_cls": 1.25251, "loss": 1.25251, "time": 0.21867} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.02495, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.71812, "top5_acc": 0.975, "loss_cls": 1.20502, "loss": 1.20502, "time": 0.21486} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.02495, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.6875, "top5_acc": 0.9675, "loss_cls": 1.25224, "loss": 1.25224, "time": 0.21587} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.02495, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.71562, "top5_acc": 0.97188, "loss_cls": 1.22883, "loss": 1.22883, "time": 0.21823} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.02494, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.71312, "top5_acc": 0.97438, "loss_cls": 1.22393, "loss": 1.22393, "time": 0.2162} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.02494, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.69562, "top5_acc": 0.97312, "loss_cls": 1.19462, "loss": 1.19462, "time": 0.21738} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.02494, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.70312, "top5_acc": 0.97062, "loss_cls": 1.23317, "loss": 1.23317, "time": 0.21879} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.02494, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.70125, "top5_acc": 0.97562, "loss_cls": 1.20075, "loss": 1.20075, "time": 0.21795} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.02494, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.73438, "top5_acc": 0.97062, "loss_cls": 1.15847, "loss": 1.15847, "time": 0.21469} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.02493, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.73, "top5_acc": 0.97938, "loss_cls": 1.13683, "loss": 1.13683, "time": 0.21651} +{"mode": "val", "epoch": 5, "iter": 533, "lr": 0.02493, "top1_acc": 0.70579, "top5_acc": 0.9689, "mean_class_accuracy": 0.55282} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.02493, "memory": 4082, "data_time": 0.18559, "top1_acc": 0.7425, "top5_acc": 0.98062, "loss_cls": 1.0997, "loss": 1.0997, "time": 0.40046} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.02493, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.755, "top5_acc": 0.97812, "loss_cls": 1.06128, "loss": 1.06128, "time": 0.21861} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.02492, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.74062, "top5_acc": 0.9825, "loss_cls": 1.10606, "loss": 1.10606, "time": 0.21505} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.02492, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.71562, "top5_acc": 0.97, "loss_cls": 1.17349, "loss": 1.17349, "time": 0.21762} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.02492, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.73625, "top5_acc": 0.9825, "loss_cls": 1.11852, "loss": 1.11852, "time": 0.2178} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.02492, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.73938, "top5_acc": 0.97375, "loss_cls": 1.1057, "loss": 1.1057, "time": 0.21701} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.02492, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.74875, "top5_acc": 0.98312, "loss_cls": 1.0801, "loss": 1.0801, "time": 0.21475} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.02491, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.7575, "top5_acc": 0.98312, "loss_cls": 1.05383, "loss": 1.05383, "time": 0.21445} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.02491, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.74188, "top5_acc": 0.97812, "loss_cls": 1.08514, "loss": 1.08514, "time": 0.21685} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.02491, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.75625, "top5_acc": 0.98375, "loss_cls": 1.05265, "loss": 1.05265, "time": 0.21581} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.02491, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.74625, "top5_acc": 0.9825, "loss_cls": 1.07514, "loss": 1.07514, "time": 0.21734} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.0249, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.74562, "top5_acc": 0.97312, "loss_cls": 1.11093, "loss": 1.11093, "time": 0.22054} +{"mode": "val", "epoch": 6, "iter": 533, "lr": 0.0249, "top1_acc": 0.71447, "top5_acc": 0.96831, "mean_class_accuracy": 0.62762} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0249, "memory": 4082, "data_time": 0.18068, "top1_acc": 0.74125, "top5_acc": 0.98188, "loss_cls": 1.06985, "loss": 1.06985, "time": 0.39972} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0249, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.76062, "top5_acc": 0.98312, "loss_cls": 1.01059, "loss": 1.01059, "time": 0.22091} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.02489, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.77812, "top5_acc": 0.9825, "loss_cls": 0.97037, "loss": 0.97037, "time": 0.21682} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.02489, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.74812, "top5_acc": 0.97812, "loss_cls": 1.07995, "loss": 1.07995, "time": 0.21826} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.02489, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.7575, "top5_acc": 0.98312, "loss_cls": 1.03245, "loss": 1.03245, "time": 0.21762} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.02489, "memory": 4082, "data_time": 0.0002, "top1_acc": 0.76062, "top5_acc": 0.98625, "loss_cls": 1.00848, "loss": 1.00848, "time": 0.21653} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.02488, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.76562, "top5_acc": 0.98312, "loss_cls": 1.00731, "loss": 1.00731, "time": 0.21983} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.02488, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77125, "top5_acc": 0.98375, "loss_cls": 0.97121, "loss": 0.97121, "time": 0.21929} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.02488, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.765, "top5_acc": 0.98375, "loss_cls": 1.02643, "loss": 1.02643, "time": 0.21791} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.02487, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.78125, "top5_acc": 0.98188, "loss_cls": 0.98428, "loss": 0.98428, "time": 0.21656} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.02487, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.75812, "top5_acc": 0.9825, "loss_cls": 1.03902, "loss": 1.03902, "time": 0.21658} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.02487, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.76312, "top5_acc": 0.985, "loss_cls": 1.02192, "loss": 1.02192, "time": 0.21786} +{"mode": "val", "epoch": 7, "iter": 533, "lr": 0.02487, "top1_acc": 0.69405, "top5_acc": 0.96045, "mean_class_accuracy": 0.58094} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.02486, "memory": 4082, "data_time": 0.18208, "top1_acc": 0.7825, "top5_acc": 0.9875, "loss_cls": 0.96293, "loss": 0.96293, "time": 0.40074} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.02486, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78875, "top5_acc": 0.98688, "loss_cls": 0.93287, "loss": 0.93287, "time": 0.22107} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.02486, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.75812, "top5_acc": 0.98312, "loss_cls": 1.00921, "loss": 1.00921, "time": 0.21955} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.02485, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78562, "top5_acc": 0.9875, "loss_cls": 0.94648, "loss": 0.94648, "time": 0.21968} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.02485, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.78188, "top5_acc": 0.985, "loss_cls": 0.91673, "loss": 0.91673, "time": 0.21714} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.02485, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.785, "top5_acc": 0.985, "loss_cls": 0.96112, "loss": 0.96112, "time": 0.21842} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.02484, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78125, "top5_acc": 0.9825, "loss_cls": 0.97996, "loss": 0.97996, "time": 0.21659} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.02484, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.77188, "top5_acc": 0.98312, "loss_cls": 0.97262, "loss": 0.97262, "time": 0.21983} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.02484, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.77438, "top5_acc": 0.98562, "loss_cls": 0.96255, "loss": 0.96255, "time": 0.21838} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.02483, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79, "top5_acc": 0.98125, "loss_cls": 0.96825, "loss": 0.96825, "time": 0.21842} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.02483, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.7925, "top5_acc": 0.98562, "loss_cls": 0.92828, "loss": 0.92828, "time": 0.21632} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.02483, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.79875, "top5_acc": 0.98438, "loss_cls": 0.92743, "loss": 0.92743, "time": 0.21869} +{"mode": "val", "epoch": 8, "iter": 533, "lr": 0.02482, "top1_acc": 0.7877, "top5_acc": 0.98111, "mean_class_accuracy": 0.69236} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.02482, "memory": 4082, "data_time": 0.18765, "top1_acc": 0.7875, "top5_acc": 0.99062, "loss_cls": 0.88967, "loss": 0.88967, "time": 0.40288} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.02482, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.78312, "top5_acc": 0.98562, "loss_cls": 0.91395, "loss": 0.91395, "time": 0.2204} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.02481, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.78, "top5_acc": 0.99125, "loss_cls": 0.89211, "loss": 0.89211, "time": 0.21735} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.02481, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7725, "top5_acc": 0.98438, "loss_cls": 0.96204, "loss": 0.96204, "time": 0.21622} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.02481, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81, "top5_acc": 0.99188, "loss_cls": 0.84255, "loss": 0.84255, "time": 0.21637} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.0248, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.78438, "top5_acc": 0.98812, "loss_cls": 0.93278, "loss": 0.93278, "time": 0.21594} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.0248, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.75125, "top5_acc": 0.97812, "loss_cls": 1.03493, "loss": 1.03493, "time": 0.21467} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.0248, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.81062, "top5_acc": 0.98562, "loss_cls": 0.87637, "loss": 0.87637, "time": 0.21816} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.02479, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.77562, "top5_acc": 0.98375, "loss_cls": 0.96361, "loss": 0.96361, "time": 0.21503} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.02479, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79938, "top5_acc": 0.99062, "loss_cls": 0.87334, "loss": 0.87334, "time": 0.21668} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.02479, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.78312, "top5_acc": 0.98562, "loss_cls": 0.93634, "loss": 0.93634, "time": 0.21421} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.02478, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.79188, "top5_acc": 0.9875, "loss_cls": 0.90503, "loss": 0.90503, "time": 0.21639} +{"mode": "val", "epoch": 9, "iter": 533, "lr": 0.02478, "top1_acc": 0.71165, "top5_acc": 0.95869, "mean_class_accuracy": 0.64079} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.02477, "memory": 4082, "data_time": 0.1843, "top1_acc": 0.80062, "top5_acc": 0.9925, "loss_cls": 0.88141, "loss": 0.88141, "time": 0.40053} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.02477, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82938, "top5_acc": 0.99062, "loss_cls": 0.78883, "loss": 0.78883, "time": 0.21901} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.02477, "memory": 4082, "data_time": 0.00051, "top1_acc": 0.81438, "top5_acc": 0.98438, "loss_cls": 0.86864, "loss": 0.86864, "time": 0.21882} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.02476, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.80688, "top5_acc": 0.99125, "loss_cls": 0.86411, "loss": 0.86411, "time": 0.21613} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.02476, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81188, "top5_acc": 0.98812, "loss_cls": 0.88251, "loss": 0.88251, "time": 0.21766} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.02476, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.80125, "top5_acc": 0.98875, "loss_cls": 0.91336, "loss": 0.91336, "time": 0.21545} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.02475, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81, "top5_acc": 0.98625, "loss_cls": 0.88574, "loss": 0.88574, "time": 0.21596} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.02475, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.77812, "top5_acc": 0.98625, "loss_cls": 0.9145, "loss": 0.9145, "time": 0.21507} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.02474, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.80688, "top5_acc": 0.98812, "loss_cls": 0.84112, "loss": 0.84112, "time": 0.21374} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.02474, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.80312, "top5_acc": 0.9875, "loss_cls": 0.89102, "loss": 0.89102, "time": 0.21848} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.02473, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.82438, "top5_acc": 0.98938, "loss_cls": 0.82414, "loss": 0.82414, "time": 0.21853} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.02473, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.80875, "top5_acc": 0.98812, "loss_cls": 0.86828, "loss": 0.86828, "time": 0.21776} +{"mode": "val", "epoch": 10, "iter": 533, "lr": 0.02473, "top1_acc": 0.7025, "top5_acc": 0.96925, "mean_class_accuracy": 0.60676} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.02472, "memory": 4082, "data_time": 0.18832, "top1_acc": 0.80188, "top5_acc": 0.99125, "loss_cls": 0.84719, "loss": 0.84719, "time": 0.40501} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.02472, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.82125, "top5_acc": 0.9825, "loss_cls": 0.83998, "loss": 0.83998, "time": 0.21836} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.02471, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.82062, "top5_acc": 0.98812, "loss_cls": 0.80029, "loss": 0.80029, "time": 0.21809} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.02471, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.815, "top5_acc": 0.99125, "loss_cls": 0.81777, "loss": 0.81777, "time": 0.21847} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.02471, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8, "top5_acc": 0.9875, "loss_cls": 0.85424, "loss": 0.85424, "time": 0.21658} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.0247, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79875, "top5_acc": 0.98812, "loss_cls": 0.90164, "loss": 0.90164, "time": 0.21748} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.0247, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82062, "top5_acc": 0.99438, "loss_cls": 0.79492, "loss": 0.79492, "time": 0.21774} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.02469, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.82812, "top5_acc": 0.99188, "loss_cls": 0.80043, "loss": 0.80043, "time": 0.21838} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.02469, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.815, "top5_acc": 0.99062, "loss_cls": 0.82384, "loss": 0.82384, "time": 0.21562} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.02468, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.80812, "top5_acc": 0.98562, "loss_cls": 0.88325, "loss": 0.88325, "time": 0.21689} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.02468, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.8075, "top5_acc": 0.9925, "loss_cls": 0.8634, "loss": 0.8634, "time": 0.21623} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.02467, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.81562, "top5_acc": 0.98875, "loss_cls": 0.8157, "loss": 0.8157, "time": 0.21933} +{"mode": "val", "epoch": 11, "iter": 533, "lr": 0.02467, "top1_acc": 0.79369, "top5_acc": 0.98416, "mean_class_accuracy": 0.70249} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.02467, "memory": 4082, "data_time": 0.18438, "top1_acc": 0.81, "top5_acc": 0.99312, "loss_cls": 0.84082, "loss": 0.84082, "time": 0.39897} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.02466, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.81812, "top5_acc": 0.99438, "loss_cls": 0.76327, "loss": 0.76327, "time": 0.21935} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.02466, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.8125, "top5_acc": 0.98812, "loss_cls": 0.78762, "loss": 0.78762, "time": 0.21738} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.02465, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.80125, "top5_acc": 0.9925, "loss_cls": 0.84398, "loss": 0.84398, "time": 0.22007} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.02465, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.82438, "top5_acc": 0.99125, "loss_cls": 0.8202, "loss": 0.8202, "time": 0.21745} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.02464, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82938, "top5_acc": 0.99062, "loss_cls": 0.81395, "loss": 0.81395, "time": 0.21713} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.02464, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.80875, "top5_acc": 0.9875, "loss_cls": 0.85533, "loss": 0.85533, "time": 0.21751} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.02463, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.825, "top5_acc": 0.98438, "loss_cls": 0.83363, "loss": 0.83363, "time": 0.21634} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.02463, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.81, "top5_acc": 0.995, "loss_cls": 0.81444, "loss": 0.81444, "time": 0.21554} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.02462, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.82188, "top5_acc": 0.99125, "loss_cls": 0.81561, "loss": 0.81561, "time": 0.2164} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.02462, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81688, "top5_acc": 0.98938, "loss_cls": 0.85367, "loss": 0.85367, "time": 0.2156} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.02461, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.82562, "top5_acc": 0.99188, "loss_cls": 0.78493, "loss": 0.78493, "time": 0.21613} +{"mode": "val", "epoch": 12, "iter": 533, "lr": 0.02461, "top1_acc": 0.77843, "top5_acc": 0.97794, "mean_class_accuracy": 0.68622} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.0246, "memory": 4082, "data_time": 0.18368, "top1_acc": 0.82875, "top5_acc": 0.99312, "loss_cls": 0.77805, "loss": 0.77805, "time": 0.40019} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.0246, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.815, "top5_acc": 0.99062, "loss_cls": 0.83214, "loss": 0.83214, "time": 0.21923} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.02459, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.83, "top5_acc": 0.99125, "loss_cls": 0.74416, "loss": 0.74416, "time": 0.21611} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.02459, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82562, "top5_acc": 0.9925, "loss_cls": 0.76937, "loss": 0.76937, "time": 0.21649} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.02458, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82438, "top5_acc": 0.99312, "loss_cls": 0.77669, "loss": 0.77669, "time": 0.2166} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.02458, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84062, "top5_acc": 0.98938, "loss_cls": 0.74862, "loss": 0.74862, "time": 0.21611} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.02457, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.8, "top5_acc": 0.98938, "loss_cls": 0.82878, "loss": 0.82878, "time": 0.21705} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.02457, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83688, "top5_acc": 0.995, "loss_cls": 0.78175, "loss": 0.78175, "time": 0.21613} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.02456, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.81438, "top5_acc": 0.99062, "loss_cls": 0.81506, "loss": 0.81506, "time": 0.21684} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.02455, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83188, "top5_acc": 0.99062, "loss_cls": 0.76555, "loss": 0.76555, "time": 0.21423} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.02455, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83562, "top5_acc": 0.99, "loss_cls": 0.75958, "loss": 0.75958, "time": 0.21634} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.02454, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80188, "top5_acc": 0.99, "loss_cls": 0.84581, "loss": 0.84581, "time": 0.2184} +{"mode": "val", "epoch": 13, "iter": 533, "lr": 0.02454, "top1_acc": 0.73935, "top5_acc": 0.97829, "mean_class_accuracy": 0.66507} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.02453, "memory": 4082, "data_time": 0.18425, "top1_acc": 0.83062, "top5_acc": 0.98875, "loss_cls": 0.77557, "loss": 0.77557, "time": 0.40111} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.02453, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83938, "top5_acc": 0.99562, "loss_cls": 0.72246, "loss": 0.72246, "time": 0.21994} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.02452, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.83562, "top5_acc": 0.99438, "loss_cls": 0.75938, "loss": 0.75938, "time": 0.21406} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.02452, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.84312, "top5_acc": 0.99312, "loss_cls": 0.73258, "loss": 0.73258, "time": 0.22016} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.02451, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83188, "top5_acc": 0.99125, "loss_cls": 0.771, "loss": 0.771, "time": 0.21684} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.02451, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83562, "top5_acc": 0.99312, "loss_cls": 0.76416, "loss": 0.76416, "time": 0.216} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.0245, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83, "top5_acc": 0.99438, "loss_cls": 0.77659, "loss": 0.77659, "time": 0.21429} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.02449, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81875, "top5_acc": 0.98875, "loss_cls": 0.78632, "loss": 0.78632, "time": 0.21615} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.02449, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.84438, "top5_acc": 0.99188, "loss_cls": 0.75544, "loss": 0.75544, "time": 0.21655} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.02448, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82688, "top5_acc": 0.99188, "loss_cls": 0.7995, "loss": 0.7995, "time": 0.21667} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.02448, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82188, "top5_acc": 0.99312, "loss_cls": 0.82085, "loss": 0.82085, "time": 0.21962} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.02447, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83438, "top5_acc": 0.99188, "loss_cls": 0.7679, "loss": 0.7679, "time": 0.22172} +{"mode": "val", "epoch": 14, "iter": 533, "lr": 0.02447, "top1_acc": 0.80014, "top5_acc": 0.98733, "mean_class_accuracy": 0.70229} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.02446, "memory": 4082, "data_time": 0.18381, "top1_acc": 0.83188, "top5_acc": 0.99188, "loss_cls": 0.76348, "loss": 0.76348, "time": 0.40018} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.02445, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82375, "top5_acc": 0.99188, "loss_cls": 0.81229, "loss": 0.81229, "time": 0.2188} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.02445, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.83875, "top5_acc": 0.9925, "loss_cls": 0.73601, "loss": 0.73601, "time": 0.22057} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.02444, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.8175, "top5_acc": 0.98312, "loss_cls": 0.80064, "loss": 0.80064, "time": 0.21937} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.02444, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83562, "top5_acc": 0.99438, "loss_cls": 0.7308, "loss": 0.7308, "time": 0.21705} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.02443, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84125, "top5_acc": 0.99375, "loss_cls": 0.73675, "loss": 0.73675, "time": 0.21942} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.02442, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83812, "top5_acc": 0.99625, "loss_cls": 0.72109, "loss": 0.72109, "time": 0.21562} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.02442, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.83875, "top5_acc": 0.99312, "loss_cls": 0.7416, "loss": 0.7416, "time": 0.21861} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.02441, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85375, "top5_acc": 0.99625, "loss_cls": 0.70475, "loss": 0.70475, "time": 0.21843} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.02441, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83375, "top5_acc": 0.99188, "loss_cls": 0.77549, "loss": 0.77549, "time": 0.21668} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.0244, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81188, "top5_acc": 0.98688, "loss_cls": 0.84441, "loss": 0.84441, "time": 0.21647} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.02439, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80625, "top5_acc": 0.98812, "loss_cls": 0.82499, "loss": 0.82499, "time": 0.21691} +{"mode": "val", "epoch": 15, "iter": 533, "lr": 0.02439, "top1_acc": 0.80307, "top5_acc": 0.98615, "mean_class_accuracy": 0.72078} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.02438, "memory": 4082, "data_time": 0.18208, "top1_acc": 0.875, "top5_acc": 0.99562, "loss_cls": 0.62189, "loss": 0.62189, "time": 0.40016} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.02438, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.83625, "top5_acc": 0.99375, "loss_cls": 0.73965, "loss": 0.73965, "time": 0.21951} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.02437, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.8475, "top5_acc": 0.99, "loss_cls": 0.72357, "loss": 0.72357, "time": 0.21663} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.02436, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.855, "top5_acc": 0.99, "loss_cls": 0.68284, "loss": 0.68284, "time": 0.21701} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.02436, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.83375, "top5_acc": 0.99188, "loss_cls": 0.73132, "loss": 0.73132, "time": 0.21892} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.02435, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.82438, "top5_acc": 0.99375, "loss_cls": 0.78491, "loss": 0.78491, "time": 0.2178} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.02434, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.84875, "top5_acc": 0.9925, "loss_cls": 0.72704, "loss": 0.72704, "time": 0.21557} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.02434, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.82938, "top5_acc": 0.9875, "loss_cls": 0.77702, "loss": 0.77702, "time": 0.21666} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.02433, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83188, "top5_acc": 0.98938, "loss_cls": 0.77145, "loss": 0.77145, "time": 0.21769} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.02432, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.83625, "top5_acc": 0.99312, "loss_cls": 0.76121, "loss": 0.76121, "time": 0.21615} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.02432, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84625, "top5_acc": 0.9925, "loss_cls": 0.70644, "loss": 0.70644, "time": 0.2166} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.02431, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.84812, "top5_acc": 0.995, "loss_cls": 0.71711, "loss": 0.71711, "time": 0.21869} +{"mode": "val", "epoch": 16, "iter": 533, "lr": 0.0243, "top1_acc": 0.78606, "top5_acc": 0.98545, "mean_class_accuracy": 0.7118} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.0243, "memory": 4082, "data_time": 0.19444, "top1_acc": 0.86375, "top5_acc": 0.995, "loss_cls": 0.65014, "loss": 0.65014, "time": 0.62774} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.02429, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.8475, "top5_acc": 0.99312, "loss_cls": 0.70952, "loss": 0.70952, "time": 0.417} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.02428, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84375, "top5_acc": 0.99312, "loss_cls": 0.73073, "loss": 0.73073, "time": 0.41539} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.02428, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84125, "top5_acc": 0.98812, "loss_cls": 0.74601, "loss": 0.74601, "time": 0.41361} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.02427, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.84188, "top5_acc": 0.995, "loss_cls": 0.71943, "loss": 0.71943, "time": 0.41419} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.02426, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83, "top5_acc": 0.99438, "loss_cls": 0.75425, "loss": 0.75425, "time": 0.41469} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.02426, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84188, "top5_acc": 0.9975, "loss_cls": 0.73303, "loss": 0.73303, "time": 0.41503} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.02425, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.88125, "top5_acc": 0.98812, "loss_cls": 0.67304, "loss": 0.67304, "time": 0.41572} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.02424, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.84938, "top5_acc": 0.9925, "loss_cls": 0.7068, "loss": 0.7068, "time": 0.41599} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.02424, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.84188, "top5_acc": 0.99062, "loss_cls": 0.74841, "loss": 0.74841, "time": 0.41502} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.02423, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.84375, "top5_acc": 0.99312, "loss_cls": 0.70811, "loss": 0.70811, "time": 0.34412} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.02422, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.83938, "top5_acc": 0.99, "loss_cls": 0.77108, "loss": 0.77108, "time": 0.3345} +{"mode": "val", "epoch": 17, "iter": 533, "lr": 0.02422, "top1_acc": 0.81598, "top5_acc": 0.9858, "mean_class_accuracy": 0.7463} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.02421, "memory": 4082, "data_time": 0.19726, "top1_acc": 0.8525, "top5_acc": 0.99438, "loss_cls": 0.66027, "loss": 0.66027, "time": 0.61397} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.0242, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.85688, "top5_acc": 0.99062, "loss_cls": 0.6448, "loss": 0.6448, "time": 0.41593} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.02419, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85875, "top5_acc": 0.99438, "loss_cls": 0.66316, "loss": 0.66316, "time": 0.4156} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.02419, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82812, "top5_acc": 0.99312, "loss_cls": 0.74579, "loss": 0.74579, "time": 0.41687} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.02418, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.87125, "top5_acc": 0.9925, "loss_cls": 0.61617, "loss": 0.61617, "time": 0.41455} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.02417, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.85875, "top5_acc": 0.99438, "loss_cls": 0.65887, "loss": 0.65887, "time": 0.41508} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.02417, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84312, "top5_acc": 0.9925, "loss_cls": 0.71207, "loss": 0.71207, "time": 0.41436} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.02416, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.84188, "top5_acc": 0.99312, "loss_cls": 0.70587, "loss": 0.70587, "time": 0.41666} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.02415, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.8425, "top5_acc": 0.99125, "loss_cls": 0.74497, "loss": 0.74497, "time": 0.41404} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.02414, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.85438, "top5_acc": 0.995, "loss_cls": 0.66399, "loss": 0.66399, "time": 0.41586} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.02414, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.85688, "top5_acc": 0.9925, "loss_cls": 0.66416, "loss": 0.66416, "time": 0.35448} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.02413, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.84188, "top5_acc": 0.99438, "loss_cls": 0.73076, "loss": 0.73076, "time": 0.32901} +{"mode": "val", "epoch": 18, "iter": 533, "lr": 0.02412, "top1_acc": 0.82432, "top5_acc": 0.98744, "mean_class_accuracy": 0.74969} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.02411, "memory": 4082, "data_time": 0.1985, "top1_acc": 0.85562, "top5_acc": 0.995, "loss_cls": 0.64872, "loss": 0.64872, "time": 0.61394} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.02411, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83875, "top5_acc": 0.9925, "loss_cls": 0.69783, "loss": 0.69783, "time": 0.4152} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.0241, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.87375, "top5_acc": 0.9925, "loss_cls": 0.62562, "loss": 0.62562, "time": 0.41665} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.02409, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84562, "top5_acc": 0.99188, "loss_cls": 0.70347, "loss": 0.70347, "time": 0.41836} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.02408, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.84312, "top5_acc": 0.99312, "loss_cls": 0.69916, "loss": 0.69916, "time": 0.41653} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.02408, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.82688, "top5_acc": 0.99125, "loss_cls": 0.74667, "loss": 0.74667, "time": 0.41775} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.02407, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85125, "top5_acc": 0.99125, "loss_cls": 0.69183, "loss": 0.69183, "time": 0.43302} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.02406, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84688, "top5_acc": 0.99562, "loss_cls": 0.67414, "loss": 0.67414, "time": 0.41508} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.02405, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84125, "top5_acc": 0.99312, "loss_cls": 0.75097, "loss": 0.75097, "time": 0.41546} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.02405, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.86, "top5_acc": 0.995, "loss_cls": 0.68895, "loss": 0.68895, "time": 0.41331} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.02404, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.87188, "top5_acc": 0.99375, "loss_cls": 0.64808, "loss": 0.64808, "time": 0.35325} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.02403, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.85, "top5_acc": 0.98812, "loss_cls": 0.7113, "loss": 0.7113, "time": 0.32703} +{"mode": "val", "epoch": 19, "iter": 533, "lr": 0.02402, "top1_acc": 0.8269, "top5_acc": 0.98627, "mean_class_accuracy": 0.75466} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.02402, "memory": 4082, "data_time": 0.19944, "top1_acc": 0.865, "top5_acc": 0.995, "loss_cls": 0.67236, "loss": 0.67236, "time": 0.61434} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.02401, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.86062, "top5_acc": 0.995, "loss_cls": 0.69577, "loss": 0.69577, "time": 0.41527} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.024, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.86, "top5_acc": 0.99375, "loss_cls": 0.67043, "loss": 0.67043, "time": 0.41601} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.02399, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.85188, "top5_acc": 0.99562, "loss_cls": 0.67265, "loss": 0.67265, "time": 0.41532} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.02398, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85625, "top5_acc": 0.99, "loss_cls": 0.70043, "loss": 0.70043, "time": 0.41624} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.02398, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85875, "top5_acc": 0.99375, "loss_cls": 0.66999, "loss": 0.66999, "time": 0.41539} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.02397, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.8725, "top5_acc": 0.99688, "loss_cls": 0.62042, "loss": 0.62042, "time": 0.41499} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.02396, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.84812, "top5_acc": 0.9925, "loss_cls": 0.69563, "loss": 0.69563, "time": 0.41681} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.02395, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.85125, "top5_acc": 0.99062, "loss_cls": 0.69403, "loss": 0.69403, "time": 0.41619} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.02394, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8475, "top5_acc": 0.9975, "loss_cls": 0.67211, "loss": 0.67211, "time": 0.41678} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.02393, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.855, "top5_acc": 0.99188, "loss_cls": 0.69112, "loss": 0.69112, "time": 0.34593} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.02393, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.85562, "top5_acc": 0.99688, "loss_cls": 0.65382, "loss": 0.65382, "time": 0.32674} +{"mode": "val", "epoch": 20, "iter": 533, "lr": 0.02392, "top1_acc": 0.83194, "top5_acc": 0.98697, "mean_class_accuracy": 0.77653} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.02391, "memory": 4082, "data_time": 0.19633, "top1_acc": 0.87875, "top5_acc": 0.995, "loss_cls": 0.59198, "loss": 0.59198, "time": 0.61945} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.0239, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87938, "top5_acc": 0.99562, "loss_cls": 0.59937, "loss": 0.59937, "time": 0.41842} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.02389, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.87562, "top5_acc": 0.99438, "loss_cls": 0.59308, "loss": 0.59308, "time": 0.4151} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.02389, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.87, "top5_acc": 0.9925, "loss_cls": 0.61203, "loss": 0.61203, "time": 0.4153} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.02388, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.86688, "top5_acc": 0.99312, "loss_cls": 0.63019, "loss": 0.63019, "time": 0.41616} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.02387, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.86625, "top5_acc": 0.99, "loss_cls": 0.67287, "loss": 0.67287, "time": 0.41512} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.02386, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.88, "top5_acc": 0.995, "loss_cls": 0.60817, "loss": 0.60817, "time": 0.41479} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.02385, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83, "top5_acc": 0.99562, "loss_cls": 0.76512, "loss": 0.76512, "time": 0.41531} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.02384, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.85062, "top5_acc": 0.99438, "loss_cls": 0.70384, "loss": 0.70384, "time": 0.41469} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.02383, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87, "top5_acc": 0.9925, "loss_cls": 0.622, "loss": 0.622, "time": 0.4152} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.02383, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.85375, "top5_acc": 0.99562, "loss_cls": 0.68773, "loss": 0.68773, "time": 0.36188} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.02382, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.86125, "top5_acc": 0.99562, "loss_cls": 0.6794, "loss": 0.6794, "time": 0.32353} +{"mode": "val", "epoch": 21, "iter": 533, "lr": 0.02381, "top1_acc": 0.81997, "top5_acc": 0.98521, "mean_class_accuracy": 0.74812} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.0238, "memory": 4082, "data_time": 0.19975, "top1_acc": 0.8825, "top5_acc": 0.9975, "loss_cls": 0.58258, "loss": 0.58258, "time": 0.61478} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.02379, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.85938, "top5_acc": 0.99562, "loss_cls": 0.64238, "loss": 0.64238, "time": 0.41537} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.02378, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.86812, "top5_acc": 0.99312, "loss_cls": 0.67673, "loss": 0.67673, "time": 0.41558} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.02378, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.87938, "top5_acc": 0.995, "loss_cls": 0.64209, "loss": 0.64209, "time": 0.41387} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.02377, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8525, "top5_acc": 0.99438, "loss_cls": 0.66821, "loss": 0.66821, "time": 0.41377} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.02376, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84625, "top5_acc": 0.99312, "loss_cls": 0.69081, "loss": 0.69081, "time": 0.41323} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.02375, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.86438, "top5_acc": 0.99188, "loss_cls": 0.64892, "loss": 0.64892, "time": 0.41598} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.02374, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.85375, "top5_acc": 0.99625, "loss_cls": 0.66214, "loss": 0.66214, "time": 0.41526} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.02373, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.85625, "top5_acc": 0.99562, "loss_cls": 0.66352, "loss": 0.66352, "time": 0.41414} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.02372, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.86312, "top5_acc": 0.99438, "loss_cls": 0.67741, "loss": 0.67741, "time": 0.4155} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.02371, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.85, "top5_acc": 0.995, "loss_cls": 0.69655, "loss": 0.69655, "time": 0.36214} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0237, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.86375, "top5_acc": 0.99375, "loss_cls": 0.63521, "loss": 0.63521, "time": 0.31948} +{"mode": "val", "epoch": 22, "iter": 533, "lr": 0.0237, "top1_acc": 0.81094, "top5_acc": 0.98533, "mean_class_accuracy": 0.73641} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.02369, "memory": 4082, "data_time": 0.19938, "top1_acc": 0.84375, "top5_acc": 0.99625, "loss_cls": 0.68875, "loss": 0.68875, "time": 0.61519} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.02368, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.86312, "top5_acc": 0.99438, "loss_cls": 0.62268, "loss": 0.62268, "time": 0.41616} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.02367, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.88062, "top5_acc": 0.99438, "loss_cls": 0.58608, "loss": 0.58608, "time": 0.41603} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.02366, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.88812, "top5_acc": 0.99812, "loss_cls": 0.58012, "loss": 0.58012, "time": 0.41609} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.02365, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.86812, "top5_acc": 0.99375, "loss_cls": 0.63326, "loss": 0.63326, "time": 0.4158} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.02364, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.85875, "top5_acc": 0.99562, "loss_cls": 0.64261, "loss": 0.64261, "time": 0.41626} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.02363, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.88688, "top5_acc": 0.99688, "loss_cls": 0.5765, "loss": 0.5765, "time": 0.41579} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.02362, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.86812, "top5_acc": 0.99562, "loss_cls": 0.62385, "loss": 0.62385, "time": 0.41705} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.02361, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.85938, "top5_acc": 0.99312, "loss_cls": 0.65342, "loss": 0.65342, "time": 0.41464} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.0236, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.8675, "top5_acc": 0.9925, "loss_cls": 0.63823, "loss": 0.63823, "time": 0.4172} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.02359, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83688, "top5_acc": 0.99, "loss_cls": 0.72139, "loss": 0.72139, "time": 0.36538} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.02359, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.86062, "top5_acc": 0.99188, "loss_cls": 0.67786, "loss": 0.67786, "time": 0.31892} +{"mode": "val", "epoch": 23, "iter": 533, "lr": 0.02358, "top1_acc": 0.82784, "top5_acc": 0.9858, "mean_class_accuracy": 0.73969} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.02357, "memory": 4082, "data_time": 0.20089, "top1_acc": 0.87812, "top5_acc": 0.99625, "loss_cls": 0.59952, "loss": 0.59952, "time": 0.62013} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.02356, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.89625, "top5_acc": 0.995, "loss_cls": 0.55718, "loss": 0.55718, "time": 0.41601} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.02355, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.8825, "top5_acc": 0.99375, "loss_cls": 0.58932, "loss": 0.58932, "time": 0.41625} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.02354, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87875, "top5_acc": 0.99562, "loss_cls": 0.6084, "loss": 0.6084, "time": 0.4161} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.02353, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86062, "top5_acc": 0.99312, "loss_cls": 0.67054, "loss": 0.67054, "time": 0.41545} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.02352, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.87938, "top5_acc": 0.99688, "loss_cls": 0.55522, "loss": 0.55522, "time": 0.41692} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.02351, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.86125, "top5_acc": 0.99375, "loss_cls": 0.62261, "loss": 0.62261, "time": 0.41699} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.0235, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.86125, "top5_acc": 0.99438, "loss_cls": 0.63765, "loss": 0.63765, "time": 0.41598} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.02349, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.875, "top5_acc": 0.99312, "loss_cls": 0.60932, "loss": 0.60932, "time": 0.41649} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.02348, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.87312, "top5_acc": 0.99438, "loss_cls": 0.60929, "loss": 0.60929, "time": 0.41694} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.02347, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87125, "top5_acc": 0.99, "loss_cls": 0.65682, "loss": 0.65682, "time": 0.36659} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.02346, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.85875, "top5_acc": 0.995, "loss_cls": 0.66008, "loss": 0.66008, "time": 0.31918} +{"mode": "val", "epoch": 24, "iter": 533, "lr": 0.02345, "top1_acc": 0.82584, "top5_acc": 0.98956, "mean_class_accuracy": 0.75514} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.02344, "memory": 4082, "data_time": 0.1929, "top1_acc": 0.86688, "top5_acc": 0.99688, "loss_cls": 0.61372, "loss": 0.61372, "time": 0.60943} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.02343, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.87938, "top5_acc": 0.99125, "loss_cls": 0.6034, "loss": 0.6034, "time": 0.41363} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.02342, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.87812, "top5_acc": 0.99688, "loss_cls": 0.58711, "loss": 0.58711, "time": 0.41655} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.02341, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8725, "top5_acc": 0.99688, "loss_cls": 0.61901, "loss": 0.61901, "time": 0.41607} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.0234, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.84562, "top5_acc": 0.98938, "loss_cls": 0.69853, "loss": 0.69853, "time": 0.4146} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.02339, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.8525, "top5_acc": 0.99188, "loss_cls": 0.66659, "loss": 0.66659, "time": 0.41522} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.02338, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.8675, "top5_acc": 0.99375, "loss_cls": 0.62249, "loss": 0.62249, "time": 0.41434} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.02337, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.8825, "top5_acc": 0.9975, "loss_cls": 0.57709, "loss": 0.57709, "time": 0.41492} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.02336, "memory": 4082, "data_time": 0.00063, "top1_acc": 0.865, "top5_acc": 0.99312, "loss_cls": 0.65399, "loss": 0.65399, "time": 0.41558} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.02335, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.86125, "top5_acc": 0.99688, "loss_cls": 0.63597, "loss": 0.63597, "time": 0.41529} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.02334, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85875, "top5_acc": 0.99375, "loss_cls": 0.64867, "loss": 0.64867, "time": 0.36191} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.02333, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.87438, "top5_acc": 0.99375, "loss_cls": 0.59485, "loss": 0.59485, "time": 0.31285} +{"mode": "val", "epoch": 25, "iter": 533, "lr": 0.02333, "top1_acc": 0.81023, "top5_acc": 0.98744, "mean_class_accuracy": 0.73607} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.02332, "memory": 4082, "data_time": 0.19227, "top1_acc": 0.85812, "top5_acc": 0.99375, "loss_cls": 0.64218, "loss": 0.64218, "time": 0.62389} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.0233, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.88125, "top5_acc": 0.99562, "loss_cls": 0.59679, "loss": 0.59679, "time": 0.41605} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.02329, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.88, "top5_acc": 0.99812, "loss_cls": 0.54629, "loss": 0.54629, "time": 0.41692} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.02328, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.87812, "top5_acc": 0.99312, "loss_cls": 0.59954, "loss": 0.59954, "time": 0.41589} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.02327, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87812, "top5_acc": 0.995, "loss_cls": 0.60317, "loss": 0.60317, "time": 0.41501} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.02326, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.86625, "top5_acc": 0.99625, "loss_cls": 0.64903, "loss": 0.64903, "time": 0.41584} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.02325, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8725, "top5_acc": 0.99438, "loss_cls": 0.62604, "loss": 0.62604, "time": 0.41624} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.02324, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87062, "top5_acc": 0.99688, "loss_cls": 0.56338, "loss": 0.56338, "time": 0.41494} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.02323, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87438, "top5_acc": 0.99562, "loss_cls": 0.59389, "loss": 0.59389, "time": 0.41463} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.02322, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.855, "top5_acc": 0.99125, "loss_cls": 0.65555, "loss": 0.65555, "time": 0.41597} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.02321, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.87625, "top5_acc": 0.99438, "loss_cls": 0.63265, "loss": 0.63265, "time": 0.37675} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.0232, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85875, "top5_acc": 0.99188, "loss_cls": 0.65562, "loss": 0.65562, "time": 0.30883} +{"mode": "val", "epoch": 26, "iter": 533, "lr": 0.02319, "top1_acc": 0.82737, "top5_acc": 0.98697, "mean_class_accuracy": 0.76398} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.02318, "memory": 4082, "data_time": 0.19573, "top1_acc": 0.89812, "top5_acc": 0.995, "loss_cls": 0.53247, "loss": 0.53247, "time": 0.61182} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.02317, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.86, "top5_acc": 0.99375, "loss_cls": 0.62911, "loss": 0.62911, "time": 0.41435} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.02316, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.87688, "top5_acc": 0.99625, "loss_cls": 0.56642, "loss": 0.56642, "time": 0.41585} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.02315, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.885, "top5_acc": 0.99312, "loss_cls": 0.59709, "loss": 0.59709, "time": 0.41552} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.02314, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.88625, "top5_acc": 0.99312, "loss_cls": 0.57613, "loss": 0.57613, "time": 0.41575} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.02313, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86938, "top5_acc": 0.995, "loss_cls": 0.63204, "loss": 0.63204, "time": 0.41487} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.02312, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.88188, "top5_acc": 0.99625, "loss_cls": 0.57855, "loss": 0.57855, "time": 0.42715} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.02311, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.88438, "top5_acc": 0.99438, "loss_cls": 0.55037, "loss": 0.55037, "time": 0.44082} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.0231, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.9025, "top5_acc": 0.99812, "loss_cls": 0.51669, "loss": 0.51669, "time": 0.42555} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.02308, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.88688, "top5_acc": 0.995, "loss_cls": 0.56349, "loss": 0.56349, "time": 0.41558} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.02307, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.86562, "top5_acc": 0.99562, "loss_cls": 0.63007, "loss": 0.63007, "time": 0.38455} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.02306, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.85375, "top5_acc": 0.98938, "loss_cls": 0.7149, "loss": 0.7149, "time": 0.28946} +{"mode": "val", "epoch": 27, "iter": 533, "lr": 0.02305, "top1_acc": 0.85999, "top5_acc": 0.99085, "mean_class_accuracy": 0.79397} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.02304, "memory": 4082, "data_time": 0.20005, "top1_acc": 0.885, "top5_acc": 0.99688, "loss_cls": 0.54137, "loss": 0.54137, "time": 0.61833} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.02303, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.87188, "top5_acc": 0.99312, "loss_cls": 0.58958, "loss": 0.58958, "time": 0.41501} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.02302, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.89938, "top5_acc": 0.99625, "loss_cls": 0.52439, "loss": 0.52439, "time": 0.41635} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.02301, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.87, "top5_acc": 0.99438, "loss_cls": 0.59083, "loss": 0.59083, "time": 0.41608} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.023, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.87188, "top5_acc": 0.99938, "loss_cls": 0.55892, "loss": 0.55892, "time": 0.41589} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.02299, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.8775, "top5_acc": 0.99625, "loss_cls": 0.5491, "loss": 0.5491, "time": 0.4138} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.02298, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.86688, "top5_acc": 0.995, "loss_cls": 0.62247, "loss": 0.62247, "time": 0.41477} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.02297, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.88188, "top5_acc": 0.99562, "loss_cls": 0.57771, "loss": 0.57771, "time": 0.41575} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.02295, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87688, "top5_acc": 0.99312, "loss_cls": 0.56495, "loss": 0.56495, "time": 0.41565} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.02294, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.88375, "top5_acc": 0.99375, "loss_cls": 0.55141, "loss": 0.55141, "time": 0.41649} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.02293, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86688, "top5_acc": 0.99188, "loss_cls": 0.64041, "loss": 0.64041, "time": 0.38767} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.02292, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.88188, "top5_acc": 0.98938, "loss_cls": 0.606, "loss": 0.606, "time": 0.29548} +{"mode": "val", "epoch": 28, "iter": 533, "lr": 0.02291, "top1_acc": 0.8222, "top5_acc": 0.98768, "mean_class_accuracy": 0.77819} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.0229, "memory": 4082, "data_time": 0.20024, "top1_acc": 0.87562, "top5_acc": 0.99625, "loss_cls": 0.58514, "loss": 0.58514, "time": 0.61496} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.02289, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.88062, "top5_acc": 0.99625, "loss_cls": 0.58408, "loss": 0.58408, "time": 0.41972} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.02288, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.87625, "top5_acc": 0.99625, "loss_cls": 0.5535, "loss": 0.5535, "time": 0.44051} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.02287, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8775, "top5_acc": 0.99062, "loss_cls": 0.6168, "loss": 0.6168, "time": 0.43736} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.02285, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87438, "top5_acc": 0.9925, "loss_cls": 0.56575, "loss": 0.56575, "time": 0.43523} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.02284, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.88125, "top5_acc": 0.99188, "loss_cls": 0.55413, "loss": 0.55413, "time": 0.42291} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.02283, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86188, "top5_acc": 0.99312, "loss_cls": 0.62245, "loss": 0.62245, "time": 0.41418} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.02282, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.86938, "top5_acc": 0.99438, "loss_cls": 0.62506, "loss": 0.62506, "time": 0.41568} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.02281, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.86625, "top5_acc": 0.99562, "loss_cls": 0.61023, "loss": 0.61023, "time": 0.41461} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.0228, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.87938, "top5_acc": 0.99562, "loss_cls": 0.57006, "loss": 0.57006, "time": 0.41534} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.02279, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.87688, "top5_acc": 0.99688, "loss_cls": 0.5696, "loss": 0.5696, "time": 0.38294} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.02277, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.85812, "top5_acc": 0.99875, "loss_cls": 0.60307, "loss": 0.60307, "time": 0.29386} +{"mode": "val", "epoch": 29, "iter": 533, "lr": 0.02276, "top1_acc": 0.82655, "top5_acc": 0.9885, "mean_class_accuracy": 0.7846} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.02275, "memory": 4082, "data_time": 0.19714, "top1_acc": 0.88, "top5_acc": 0.99562, "loss_cls": 0.60621, "loss": 0.60621, "time": 0.70715} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.02274, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.87188, "top5_acc": 0.99375, "loss_cls": 0.58643, "loss": 0.58643, "time": 0.50158} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.02273, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.88562, "top5_acc": 0.99562, "loss_cls": 0.53284, "loss": 0.53284, "time": 0.51444} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.02272, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87938, "top5_acc": 0.99875, "loss_cls": 0.55815, "loss": 0.55815, "time": 0.51214} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.02271, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.90062, "top5_acc": 0.9975, "loss_cls": 0.51286, "loss": 0.51286, "time": 0.51767} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.02269, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8875, "top5_acc": 0.995, "loss_cls": 0.56155, "loss": 0.56155, "time": 0.51026} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.02268, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.87188, "top5_acc": 0.9925, "loss_cls": 0.61867, "loss": 0.61867, "time": 0.51231} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.02267, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.88312, "top5_acc": 0.99625, "loss_cls": 0.56963, "loss": 0.56963, "time": 0.52132} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.02266, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.87688, "top5_acc": 0.99125, "loss_cls": 0.58949, "loss": 0.58949, "time": 0.29364} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.02265, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.86812, "top5_acc": 0.99375, "loss_cls": 0.62546, "loss": 0.62546, "time": 0.51118} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.02263, "memory": 4082, "data_time": 0.00051, "top1_acc": 0.86312, "top5_acc": 0.99438, "loss_cls": 0.66045, "loss": 0.66045, "time": 0.35047} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.02262, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87062, "top5_acc": 0.99625, "loss_cls": 0.58719, "loss": 0.58719, "time": 0.50853} +{"mode": "val", "epoch": 30, "iter": 533, "lr": 0.02261, "top1_acc": 0.84368, "top5_acc": 0.98756, "mean_class_accuracy": 0.7782} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.0226, "memory": 4083, "data_time": 0.20177, "top1_acc": 0.90125, "top5_acc": 0.9975, "loss_cls": 0.68177, "loss": 0.68177, "time": 0.95775} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.02259, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.8925, "top5_acc": 0.99562, "loss_cls": 0.70636, "loss": 0.70636, "time": 0.53128} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.02258, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8625, "top5_acc": 0.99688, "loss_cls": 0.76674, "loss": 0.76674, "time": 0.54038} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.02256, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89438, "top5_acc": 0.99438, "loss_cls": 0.68814, "loss": 0.68814, "time": 0.53081} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.02255, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.89, "top5_acc": 0.99625, "loss_cls": 0.67958, "loss": 0.67958, "time": 0.32152} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.02254, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89812, "top5_acc": 0.99688, "loss_cls": 0.70635, "loss": 0.70635, "time": 0.51415} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.02253, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88188, "top5_acc": 0.99562, "loss_cls": 0.73507, "loss": 0.73507, "time": 0.36055} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.02252, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.8775, "top5_acc": 0.99375, "loss_cls": 0.73372, "loss": 0.73372, "time": 0.53273} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0225, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.85688, "top5_acc": 0.99188, "loss_cls": 0.83917, "loss": 0.83917, "time": 0.53205} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.02249, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86438, "top5_acc": 0.99312, "loss_cls": 0.81119, "loss": 0.81119, "time": 0.53513} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.02248, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.88375, "top5_acc": 0.99438, "loss_cls": 0.73447, "loss": 0.73447, "time": 0.53803} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.02247, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88438, "top5_acc": 0.99438, "loss_cls": 0.71823, "loss": 0.71823, "time": 0.53723} +{"mode": "val", "epoch": 31, "iter": 533, "lr": 0.02246, "top1_acc": 0.84638, "top5_acc": 0.99049, "mean_class_accuracy": 0.77755} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.02244, "memory": 4083, "data_time": 0.19309, "top1_acc": 0.88938, "top5_acc": 0.99312, "loss_cls": 0.64312, "loss": 0.64312, "time": 0.66845} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.02243, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88062, "top5_acc": 0.99812, "loss_cls": 0.66609, "loss": 0.66609, "time": 0.50581} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.02242, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.875, "top5_acc": 0.99625, "loss_cls": 0.70187, "loss": 0.70187, "time": 0.39161} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.02241, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89, "top5_acc": 0.99688, "loss_cls": 0.65207, "loss": 0.65207, "time": 0.52995} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.02239, "memory": 4083, "data_time": 0.00064, "top1_acc": 0.89062, "top5_acc": 0.9925, "loss_cls": 0.66088, "loss": 0.66088, "time": 0.53703} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.02238, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89375, "top5_acc": 0.995, "loss_cls": 0.62087, "loss": 0.62087, "time": 0.53972} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.02237, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88438, "top5_acc": 0.9925, "loss_cls": 0.67817, "loss": 0.67817, "time": 0.54175} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.02236, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87812, "top5_acc": 0.99438, "loss_cls": 0.66034, "loss": 0.66034, "time": 0.54436} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.02234, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.89312, "top5_acc": 0.99688, "loss_cls": 0.6177, "loss": 0.6177, "time": 0.53932} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.02233, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87562, "top5_acc": 0.99312, "loss_cls": 0.6925, "loss": 0.6925, "time": 0.53238} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.02232, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87812, "top5_acc": 0.995, "loss_cls": 0.69699, "loss": 0.69699, "time": 0.54986} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.02231, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.8725, "top5_acc": 0.99562, "loss_cls": 0.70442, "loss": 0.70442, "time": 0.35693} +{"mode": "val", "epoch": 32, "iter": 533, "lr": 0.0223, "top1_acc": 0.83429, "top5_acc": 0.99167, "mean_class_accuracy": 0.77751} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.02228, "memory": 4083, "data_time": 0.19275, "top1_acc": 0.89312, "top5_acc": 0.99625, "loss_cls": 0.5908, "loss": 0.5908, "time": 0.90869} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.02227, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88188, "top5_acc": 0.99812, "loss_cls": 0.62215, "loss": 0.62215, "time": 0.54315} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.02226, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.875, "top5_acc": 0.99562, "loss_cls": 0.64536, "loss": 0.64536, "time": 0.5372} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.02225, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.8675, "top5_acc": 0.99438, "loss_cls": 0.69496, "loss": 0.69496, "time": 0.54604} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.02223, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.8975, "top5_acc": 0.99562, "loss_cls": 0.59455, "loss": 0.59455, "time": 0.54094} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.02222, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88, "top5_acc": 0.99562, "loss_cls": 0.65298, "loss": 0.65298, "time": 0.54499} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.02221, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88812, "top5_acc": 0.99625, "loss_cls": 0.61623, "loss": 0.61623, "time": 0.52929} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.02219, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.89, "top5_acc": 0.9975, "loss_cls": 0.5917, "loss": 0.5917, "time": 0.33721} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.02218, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8875, "top5_acc": 0.99312, "loss_cls": 0.59538, "loss": 0.59538, "time": 0.4033} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.02217, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8975, "top5_acc": 0.99625, "loss_cls": 0.55375, "loss": 0.55375, "time": 0.47036} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.02216, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.8675, "top5_acc": 0.99688, "loss_cls": 0.65895, "loss": 0.65895, "time": 0.54756} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.02214, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.88562, "top5_acc": 0.99562, "loss_cls": 0.6068, "loss": 0.6068, "time": 0.53675} +{"mode": "val", "epoch": 33, "iter": 533, "lr": 0.02213, "top1_acc": 0.8533, "top5_acc": 0.99225, "mean_class_accuracy": 0.78882} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.02212, "memory": 4083, "data_time": 0.20221, "top1_acc": 0.88125, "top5_acc": 0.9925, "loss_cls": 0.62412, "loss": 0.62412, "time": 0.87164} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.02211, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8875, "top5_acc": 0.9975, "loss_cls": 0.6073, "loss": 0.6073, "time": 0.53289} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.02209, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8875, "top5_acc": 0.99562, "loss_cls": 0.61467, "loss": 0.61467, "time": 0.51534} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.02208, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8825, "top5_acc": 0.99625, "loss_cls": 0.60944, "loss": 0.60944, "time": 0.35324} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.02207, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.86562, "top5_acc": 0.99625, "loss_cls": 0.67265, "loss": 0.67265, "time": 0.38621} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.02205, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88, "top5_acc": 0.99688, "loss_cls": 0.61649, "loss": 0.61649, "time": 0.46692} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.02204, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.88438, "top5_acc": 0.99562, "loss_cls": 0.59733, "loss": 0.59733, "time": 0.51408} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.02203, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.88562, "top5_acc": 0.99438, "loss_cls": 0.61054, "loss": 0.61054, "time": 0.54143} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.02201, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.88312, "top5_acc": 0.99625, "loss_cls": 0.58031, "loss": 0.58031, "time": 0.53913} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.022, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87938, "top5_acc": 0.9975, "loss_cls": 0.59293, "loss": 0.59293, "time": 0.52839} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.02199, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.89062, "top5_acc": 0.99188, "loss_cls": 0.60856, "loss": 0.60856, "time": 0.54161} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.02197, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88312, "top5_acc": 0.99688, "loss_cls": 0.60194, "loss": 0.60194, "time": 0.53894} +{"mode": "val", "epoch": 34, "iter": 533, "lr": 0.02196, "top1_acc": 0.83054, "top5_acc": 0.98744, "mean_class_accuracy": 0.76802} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.02195, "memory": 4083, "data_time": 0.19535, "top1_acc": 0.87562, "top5_acc": 0.9975, "loss_cls": 0.60358, "loss": 0.60358, "time": 0.75868} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.02194, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.885, "top5_acc": 0.99688, "loss_cls": 0.59317, "loss": 0.59317, "time": 0.53487} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.02192, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.905, "top5_acc": 0.995, "loss_cls": 0.56381, "loss": 0.56381, "time": 0.53861} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.02191, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89562, "top5_acc": 0.99562, "loss_cls": 0.53848, "loss": 0.53848, "time": 0.54007} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.0219, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.885, "top5_acc": 0.99375, "loss_cls": 0.5945, "loss": 0.5945, "time": 0.53903} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.02188, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90312, "top5_acc": 0.99812, "loss_cls": 0.53632, "loss": 0.53632, "time": 0.54764} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.02187, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87625, "top5_acc": 0.99688, "loss_cls": 0.62561, "loss": 0.62561, "time": 0.5437} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.02185, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89562, "top5_acc": 0.995, "loss_cls": 0.54865, "loss": 0.54865, "time": 0.53781} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.02184, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.89, "top5_acc": 0.99438, "loss_cls": 0.57549, "loss": 0.57549, "time": 0.54501} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.02183, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89375, "top5_acc": 0.99688, "loss_cls": 0.57386, "loss": 0.57386, "time": 0.47941} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.02181, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.89312, "top5_acc": 0.99562, "loss_cls": 0.57761, "loss": 0.57761, "time": 0.42302} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.0218, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88688, "top5_acc": 0.995, "loss_cls": 0.59442, "loss": 0.59442, "time": 0.31681} +{"mode": "val", "epoch": 35, "iter": 533, "lr": 0.02179, "top1_acc": 0.84837, "top5_acc": 0.98803, "mean_class_accuracy": 0.80365} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.02178, "memory": 4083, "data_time": 0.20362, "top1_acc": 0.8825, "top5_acc": 0.99188, "loss_cls": 0.60756, "loss": 0.60756, "time": 0.87057} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.02176, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88, "top5_acc": 0.99375, "loss_cls": 0.61513, "loss": 0.61513, "time": 0.53885} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.02175, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89875, "top5_acc": 0.99688, "loss_cls": 0.54867, "loss": 0.54867, "time": 0.53854} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.02173, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89438, "top5_acc": 0.99812, "loss_cls": 0.56404, "loss": 0.56404, "time": 0.55072} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.02172, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.89562, "top5_acc": 0.99438, "loss_cls": 0.56908, "loss": 0.56908, "time": 0.53578} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.02171, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.88875, "top5_acc": 0.99875, "loss_cls": 0.56747, "loss": 0.56747, "time": 0.47586} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.02169, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.88125, "top5_acc": 0.995, "loss_cls": 0.61746, "loss": 0.61746, "time": 0.41974} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.02168, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89625, "top5_acc": 0.9975, "loss_cls": 0.54454, "loss": 0.54454, "time": 0.32052} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.02167, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89125, "top5_acc": 0.99812, "loss_cls": 0.56861, "loss": 0.56861, "time": 0.49962} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.02165, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89812, "top5_acc": 0.99875, "loss_cls": 0.53942, "loss": 0.53942, "time": 0.55047} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.02164, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89125, "top5_acc": 0.9975, "loss_cls": 0.55495, "loss": 0.55495, "time": 0.53119} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.02162, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.89062, "top5_acc": 0.99688, "loss_cls": 0.55141, "loss": 0.55141, "time": 0.55298} +{"mode": "val", "epoch": 36, "iter": 533, "lr": 0.02161, "top1_acc": 0.86668, "top5_acc": 0.99026, "mean_class_accuracy": 0.80603} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.0216, "memory": 4083, "data_time": 0.19973, "top1_acc": 0.89625, "top5_acc": 0.99688, "loss_cls": 0.53436, "loss": 0.53436, "time": 0.89418} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.02158, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8875, "top5_acc": 0.99562, "loss_cls": 0.56693, "loss": 0.56693, "time": 0.44931} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.02157, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88125, "top5_acc": 0.99812, "loss_cls": 0.57855, "loss": 0.57855, "time": 0.47622} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.02156, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.89438, "top5_acc": 0.99625, "loss_cls": 0.55485, "loss": 0.55485, "time": 0.26187} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.02154, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89188, "top5_acc": 0.99688, "loss_cls": 0.53694, "loss": 0.53694, "time": 0.53091} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.02153, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89562, "top5_acc": 0.99438, "loss_cls": 0.55333, "loss": 0.55333, "time": 0.53394} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.02151, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.88875, "top5_acc": 0.99875, "loss_cls": 0.55118, "loss": 0.55118, "time": 0.53435} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0215, "memory": 4083, "data_time": 0.00065, "top1_acc": 0.88688, "top5_acc": 0.99625, "loss_cls": 0.55552, "loss": 0.55552, "time": 0.53467} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.02149, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89562, "top5_acc": 0.99438, "loss_cls": 0.58487, "loss": 0.58487, "time": 0.53915} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.02147, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.87188, "top5_acc": 0.99562, "loss_cls": 0.63746, "loss": 0.63746, "time": 0.54586} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.02146, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87688, "top5_acc": 0.99438, "loss_cls": 0.63248, "loss": 0.63248, "time": 0.54343} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.02144, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.87312, "top5_acc": 0.99375, "loss_cls": 0.63515, "loss": 0.63515, "time": 0.53056} +{"mode": "val", "epoch": 37, "iter": 533, "lr": 0.02143, "top1_acc": 0.85178, "top5_acc": 0.99167, "mean_class_accuracy": 0.79432} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.02142, "memory": 4083, "data_time": 0.19779, "top1_acc": 0.88625, "top5_acc": 0.99688, "loss_cls": 0.56179, "loss": 0.56179, "time": 0.67851} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.0214, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89, "top5_acc": 0.9975, "loss_cls": 0.51008, "loss": 0.51008, "time": 0.53585} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.02139, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90562, "top5_acc": 0.99562, "loss_cls": 0.51803, "loss": 0.51803, "time": 0.54782} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.02137, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89938, "top5_acc": 0.99375, "loss_cls": 0.56921, "loss": 0.56921, "time": 0.54734} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.02136, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8925, "top5_acc": 0.99375, "loss_cls": 0.56348, "loss": 0.56348, "time": 0.53927} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.02134, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90125, "top5_acc": 0.99875, "loss_cls": 0.56942, "loss": 0.56942, "time": 0.54005} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.02133, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.91, "top5_acc": 0.99562, "loss_cls": 0.54299, "loss": 0.54299, "time": 0.54159} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.02132, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89062, "top5_acc": 0.99312, "loss_cls": 0.58335, "loss": 0.58335, "time": 0.54111} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.0213, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89188, "top5_acc": 0.995, "loss_cls": 0.585, "loss": 0.585, "time": 0.54805} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.02129, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87938, "top5_acc": 0.99562, "loss_cls": 0.59401, "loss": 0.59401, "time": 0.28536} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.02127, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.88375, "top5_acc": 0.99625, "loss_cls": 0.6096, "loss": 0.6096, "time": 0.47611} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.02126, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.88562, "top5_acc": 0.995, "loss_cls": 0.57997, "loss": 0.57997, "time": 0.42887} +{"mode": "val", "epoch": 38, "iter": 533, "lr": 0.02125, "top1_acc": 0.85201, "top5_acc": 0.99073, "mean_class_accuracy": 0.81193} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.02123, "memory": 4083, "data_time": 0.20204, "top1_acc": 0.90375, "top5_acc": 0.99312, "loss_cls": 0.52629, "loss": 0.52629, "time": 0.87902} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.02122, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91062, "top5_acc": 0.99562, "loss_cls": 0.49485, "loss": 0.49485, "time": 0.5349} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.0212, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89125, "top5_acc": 0.99625, "loss_cls": 0.57013, "loss": 0.57013, "time": 0.53746} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.02119, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92062, "top5_acc": 0.9975, "loss_cls": 0.50185, "loss": 0.50185, "time": 0.54843} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.02117, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89, "top5_acc": 0.99562, "loss_cls": 0.57707, "loss": 0.57707, "time": 0.52621} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.02116, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.90875, "top5_acc": 0.99312, "loss_cls": 0.51598, "loss": 0.51598, "time": 0.31868} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.02114, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.90438, "top5_acc": 0.99375, "loss_cls": 0.53622, "loss": 0.53622, "time": 0.45072} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.02113, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.89125, "top5_acc": 0.99562, "loss_cls": 0.57314, "loss": 0.57314, "time": 0.42877} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.02111, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88875, "top5_acc": 0.995, "loss_cls": 0.57942, "loss": 0.57942, "time": 0.53842} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.0211, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88625, "top5_acc": 0.99625, "loss_cls": 0.5598, "loss": 0.5598, "time": 0.54555} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.02108, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89688, "top5_acc": 0.99562, "loss_cls": 0.55248, "loss": 0.55248, "time": 0.54763} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.02107, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89438, "top5_acc": 0.9975, "loss_cls": 0.53155, "loss": 0.53155, "time": 0.54398} +{"mode": "val", "epoch": 39, "iter": 533, "lr": 0.02106, "top1_acc": 0.86293, "top5_acc": 0.99049, "mean_class_accuracy": 0.81052} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.02104, "memory": 4083, "data_time": 0.19553, "top1_acc": 0.9, "top5_acc": 0.9925, "loss_cls": 0.52285, "loss": 0.52285, "time": 0.8453} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.02103, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89875, "top5_acc": 0.9975, "loss_cls": 0.55711, "loss": 0.55711, "time": 0.2811} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.02101, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90812, "top5_acc": 0.99938, "loss_cls": 0.4904, "loss": 0.4904, "time": 0.48767} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.021, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.89125, "top5_acc": 0.9975, "loss_cls": 0.53685, "loss": 0.53685, "time": 0.4195} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.02098, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.89062, "top5_acc": 0.99875, "loss_cls": 0.56186, "loss": 0.56186, "time": 0.54968} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.02097, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.90188, "top5_acc": 0.99812, "loss_cls": 0.51059, "loss": 0.51059, "time": 0.53678} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.02095, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.87438, "top5_acc": 0.99188, "loss_cls": 0.64344, "loss": 0.64344, "time": 0.54198} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.02094, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88688, "top5_acc": 0.99438, "loss_cls": 0.62158, "loss": 0.62158, "time": 0.53277} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.02092, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89688, "top5_acc": 0.995, "loss_cls": 0.52565, "loss": 0.52565, "time": 0.53737} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.02091, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91188, "top5_acc": 0.99625, "loss_cls": 0.53037, "loss": 0.53037, "time": 0.54448} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.02089, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90312, "top5_acc": 0.9975, "loss_cls": 0.50394, "loss": 0.50394, "time": 0.53874} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.02088, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88938, "top5_acc": 0.99688, "loss_cls": 0.55429, "loss": 0.55429, "time": 0.52041} +{"mode": "val", "epoch": 40, "iter": 533, "lr": 0.02086, "top1_acc": 0.8756, "top5_acc": 0.99038, "mean_class_accuracy": 0.82377} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.02085, "memory": 4083, "data_time": 0.19029, "top1_acc": 0.88375, "top5_acc": 0.99688, "loss_cls": 0.57873, "loss": 0.57873, "time": 0.86823} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.02083, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.90375, "top5_acc": 0.99625, "loss_cls": 0.52833, "loss": 0.52833, "time": 0.53259} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.02082, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9075, "top5_acc": 0.995, "loss_cls": 0.48977, "loss": 0.48977, "time": 0.53432} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.0208, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.91, "top5_acc": 0.99688, "loss_cls": 0.50085, "loss": 0.50085, "time": 0.54585} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.02079, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91688, "top5_acc": 0.99688, "loss_cls": 0.51208, "loss": 0.51208, "time": 0.5338} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.02077, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89688, "top5_acc": 0.9975, "loss_cls": 0.53456, "loss": 0.53456, "time": 0.54164} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.02076, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90062, "top5_acc": 0.99812, "loss_cls": 0.52761, "loss": 0.52761, "time": 0.54551} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.02074, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.89062, "top5_acc": 0.99625, "loss_cls": 0.57252, "loss": 0.57252, "time": 0.54868} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.02073, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.87062, "top5_acc": 0.995, "loss_cls": 0.64702, "loss": 0.64702, "time": 0.32039} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.02071, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88562, "top5_acc": 0.9925, "loss_cls": 0.57424, "loss": 0.57424, "time": 0.51042} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.0207, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.88938, "top5_acc": 0.9975, "loss_cls": 0.52881, "loss": 0.52881, "time": 0.328} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.02068, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.9025, "top5_acc": 0.99562, "loss_cls": 0.51912, "loss": 0.51912, "time": 0.48047} +{"mode": "val", "epoch": 41, "iter": 533, "lr": 0.02067, "top1_acc": 0.85401, "top5_acc": 0.99049, "mean_class_accuracy": 0.82111} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.02065, "memory": 4083, "data_time": 0.20108, "top1_acc": 0.92188, "top5_acc": 0.99875, "loss_cls": 0.45428, "loss": 0.45428, "time": 0.79181} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.02064, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90125, "top5_acc": 0.99812, "loss_cls": 0.51169, "loss": 0.51169, "time": 0.48295} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.02062, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88812, "top5_acc": 0.9975, "loss_cls": 0.54771, "loss": 0.54771, "time": 0.48529} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.02061, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90125, "top5_acc": 0.9975, "loss_cls": 0.52445, "loss": 0.52445, "time": 0.48557} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.02059, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.90688, "top5_acc": 0.99812, "loss_cls": 0.50304, "loss": 0.50304, "time": 0.48478} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.02057, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90062, "top5_acc": 0.9975, "loss_cls": 0.48852, "loss": 0.48852, "time": 0.4855} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.02056, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89, "top5_acc": 0.99562, "loss_cls": 0.55234, "loss": 0.55234, "time": 0.48075} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.02054, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91062, "top5_acc": 0.9975, "loss_cls": 0.51382, "loss": 0.51382, "time": 0.4645} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.02053, "memory": 4083, "data_time": 0.00067, "top1_acc": 0.89438, "top5_acc": 0.99688, "loss_cls": 0.5188, "loss": 0.5188, "time": 0.36724} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.02051, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88438, "top5_acc": 0.995, "loss_cls": 0.56781, "loss": 0.56781, "time": 0.37402} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.0205, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91188, "top5_acc": 0.9975, "loss_cls": 0.49971, "loss": 0.49971, "time": 0.34653} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.02048, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.89812, "top5_acc": 0.99875, "loss_cls": 0.50976, "loss": 0.50976, "time": 0.49103} +{"mode": "val", "epoch": 42, "iter": 533, "lr": 0.02047, "top1_acc": 0.84462, "top5_acc": 0.99061, "mean_class_accuracy": 0.79879} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.02045, "memory": 4083, "data_time": 0.19772, "top1_acc": 0.88375, "top5_acc": 0.99688, "loss_cls": 0.57954, "loss": 0.57954, "time": 0.81369} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.02044, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.915, "top5_acc": 0.99812, "loss_cls": 0.48512, "loss": 0.48512, "time": 0.48938} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.02042, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90562, "top5_acc": 0.99688, "loss_cls": 0.5163, "loss": 0.5163, "time": 0.49316} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.0204, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.91, "top5_acc": 0.99562, "loss_cls": 0.52325, "loss": 0.52325, "time": 0.49178} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.02039, "memory": 4083, "data_time": 0.00122, "top1_acc": 0.90188, "top5_acc": 0.99688, "loss_cls": 0.52297, "loss": 0.52297, "time": 0.49192} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.02037, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.90188, "top5_acc": 0.99688, "loss_cls": 0.48699, "loss": 0.48699, "time": 0.49107} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.02036, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90562, "top5_acc": 0.99875, "loss_cls": 0.50779, "loss": 0.50779, "time": 0.4936} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.02034, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89062, "top5_acc": 0.99562, "loss_cls": 0.55132, "loss": 0.55132, "time": 0.49125} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.02033, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.90125, "top5_acc": 0.9975, "loss_cls": 0.52831, "loss": 0.52831, "time": 0.27773} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.02031, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.9, "top5_acc": 0.9975, "loss_cls": 0.51923, "loss": 0.51923, "time": 0.48424} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.02029, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8975, "top5_acc": 0.99625, "loss_cls": 0.52061, "loss": 0.52061, "time": 0.31967} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.02028, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88938, "top5_acc": 0.99562, "loss_cls": 0.58064, "loss": 0.58064, "time": 0.49209} +{"mode": "val", "epoch": 43, "iter": 533, "lr": 0.02026, "top1_acc": 0.86633, "top5_acc": 0.99108, "mean_class_accuracy": 0.7937} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.02025, "memory": 4083, "data_time": 0.19673, "top1_acc": 0.92812, "top5_acc": 0.99875, "loss_cls": 0.42037, "loss": 0.42037, "time": 0.8053} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.02023, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9, "top5_acc": 0.99625, "loss_cls": 0.52969, "loss": 0.52969, "time": 0.49079} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.02022, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8925, "top5_acc": 0.99812, "loss_cls": 0.50964, "loss": 0.50964, "time": 0.49284} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.0202, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91, "top5_acc": 0.99562, "loss_cls": 0.49863, "loss": 0.49863, "time": 0.49232} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.02018, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90375, "top5_acc": 0.99688, "loss_cls": 0.47844, "loss": 0.47844, "time": 0.48937} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.02017, "memory": 4083, "data_time": 0.00067, "top1_acc": 0.90562, "top5_acc": 0.99625, "loss_cls": 0.50555, "loss": 0.50555, "time": 0.49074} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.02015, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89562, "top5_acc": 0.99812, "loss_cls": 0.53789, "loss": 0.53789, "time": 0.49058} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.02014, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89875, "top5_acc": 0.99625, "loss_cls": 0.52955, "loss": 0.52955, "time": 0.4919} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.02012, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88625, "top5_acc": 0.99625, "loss_cls": 0.58814, "loss": 0.58814, "time": 0.28298} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.0201, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.895, "top5_acc": 0.99188, "loss_cls": 0.58237, "loss": 0.58237, "time": 0.48335} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.02009, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90312, "top5_acc": 0.9975, "loss_cls": 0.52038, "loss": 0.52038, "time": 0.30697} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.02007, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.8925, "top5_acc": 0.9975, "loss_cls": 0.54424, "loss": 0.54424, "time": 0.49577} +{"mode": "val", "epoch": 44, "iter": 533, "lr": 0.02006, "top1_acc": 0.8749, "top5_acc": 0.9912, "mean_class_accuracy": 0.83623} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.02004, "memory": 4083, "data_time": 0.1936, "top1_acc": 0.89, "top5_acc": 0.9975, "loss_cls": 0.54686, "loss": 0.54686, "time": 0.80696} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.02003, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89125, "top5_acc": 0.99625, "loss_cls": 0.54437, "loss": 0.54437, "time": 0.49367} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.02001, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89, "top5_acc": 0.99438, "loss_cls": 0.55278, "loss": 0.55278, "time": 0.49082} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.01999, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.91812, "top5_acc": 0.995, "loss_cls": 0.45596, "loss": 0.45596, "time": 0.49316} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.01998, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90125, "top5_acc": 0.99625, "loss_cls": 0.48035, "loss": 0.48035, "time": 0.49509} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.01996, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88562, "top5_acc": 0.99562, "loss_cls": 0.58909, "loss": 0.58909, "time": 0.49106} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.01994, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90562, "top5_acc": 0.99625, "loss_cls": 0.49299, "loss": 0.49299, "time": 0.49475} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.01993, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90188, "top5_acc": 0.9975, "loss_cls": 0.49217, "loss": 0.49217, "time": 0.49849} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.01991, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90062, "top5_acc": 0.995, "loss_cls": 0.5382, "loss": 0.5382, "time": 0.2815} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.01989, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91375, "top5_acc": 0.9975, "loss_cls": 0.47849, "loss": 0.47849, "time": 0.51069} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.01988, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91, "top5_acc": 0.995, "loss_cls": 0.49544, "loss": 0.49544, "time": 0.29675} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.01986, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90688, "top5_acc": 0.99688, "loss_cls": 0.49904, "loss": 0.49904, "time": 0.49333} +{"mode": "val", "epoch": 45, "iter": 533, "lr": 0.01985, "top1_acc": 0.86762, "top5_acc": 0.9919, "mean_class_accuracy": 0.7991} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.01983, "memory": 4083, "data_time": 0.19677, "top1_acc": 0.90562, "top5_acc": 0.99812, "loss_cls": 0.49075, "loss": 0.49075, "time": 0.8054} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.01981, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91938, "top5_acc": 0.9975, "loss_cls": 0.45971, "loss": 0.45971, "time": 0.49584} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.0198, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89438, "top5_acc": 0.99625, "loss_cls": 0.54329, "loss": 0.54329, "time": 0.48951} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.01978, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90438, "top5_acc": 0.99562, "loss_cls": 0.49877, "loss": 0.49877, "time": 0.49416} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.01976, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9175, "top5_acc": 0.9975, "loss_cls": 0.46101, "loss": 0.46101, "time": 0.49204} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.01975, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90562, "top5_acc": 0.99688, "loss_cls": 0.50175, "loss": 0.50175, "time": 0.48966} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.01973, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91062, "top5_acc": 0.99562, "loss_cls": 0.47602, "loss": 0.47602, "time": 0.49163} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.01971, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89625, "top5_acc": 0.9975, "loss_cls": 0.51552, "loss": 0.51552, "time": 0.49366} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.0197, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.89625, "top5_acc": 0.99625, "loss_cls": 0.51837, "loss": 0.51837, "time": 0.29574} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.01968, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89188, "top5_acc": 0.99438, "loss_cls": 0.56608, "loss": 0.56608, "time": 0.51242} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.01966, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.895, "top5_acc": 0.99625, "loss_cls": 0.58257, "loss": 0.58257, "time": 0.30192} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.01965, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89688, "top5_acc": 0.99562, "loss_cls": 0.52384, "loss": 0.52384, "time": 0.49269} +{"mode": "val", "epoch": 46, "iter": 533, "lr": 0.01963, "top1_acc": 0.83781, "top5_acc": 0.9838, "mean_class_accuracy": 0.78748} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.01962, "memory": 4083, "data_time": 0.19139, "top1_acc": 0.89812, "top5_acc": 0.99562, "loss_cls": 0.52713, "loss": 0.52713, "time": 0.79894} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.0196, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.905, "top5_acc": 0.99562, "loss_cls": 0.49792, "loss": 0.49792, "time": 0.49097} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.01958, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.90938, "top5_acc": 0.9975, "loss_cls": 0.48888, "loss": 0.48888, "time": 0.49491} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.01957, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9, "top5_acc": 0.99875, "loss_cls": 0.50344, "loss": 0.50344, "time": 0.49207} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.01955, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90375, "top5_acc": 0.99625, "loss_cls": 0.5391, "loss": 0.5391, "time": 0.49511} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.01953, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9125, "top5_acc": 0.995, "loss_cls": 0.45556, "loss": 0.45556, "time": 0.49403} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.01952, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.905, "top5_acc": 0.99625, "loss_cls": 0.513, "loss": 0.513, "time": 0.49349} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.0195, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9025, "top5_acc": 0.99625, "loss_cls": 0.55025, "loss": 0.55025, "time": 0.49142} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.01948, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.90625, "top5_acc": 0.99625, "loss_cls": 0.49089, "loss": 0.49089, "time": 0.29682} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.01947, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89688, "top5_acc": 0.99625, "loss_cls": 0.55482, "loss": 0.55482, "time": 0.51188} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.01945, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9125, "top5_acc": 0.99625, "loss_cls": 0.46508, "loss": 0.46508, "time": 0.28263} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.01943, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.89, "top5_acc": 0.99812, "loss_cls": 0.53974, "loss": 0.53974, "time": 0.48959} +{"mode": "val", "epoch": 47, "iter": 533, "lr": 0.01942, "top1_acc": 0.87959, "top5_acc": 0.99296, "mean_class_accuracy": 0.83398} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.0194, "memory": 4083, "data_time": 0.19044, "top1_acc": 0.90938, "top5_acc": 0.9975, "loss_cls": 0.47253, "loss": 0.47253, "time": 0.81101} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.01938, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.44365, "loss": 0.44365, "time": 0.49035} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.01937, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91062, "top5_acc": 0.99812, "loss_cls": 0.46469, "loss": 0.46469, "time": 0.49026} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.01935, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9, "top5_acc": 0.99625, "loss_cls": 0.51556, "loss": 0.51556, "time": 0.49348} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.01933, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89562, "top5_acc": 0.995, "loss_cls": 0.52805, "loss": 0.52805, "time": 0.49122} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.01932, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.89312, "top5_acc": 0.99375, "loss_cls": 0.56324, "loss": 0.56324, "time": 0.49057} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.0193, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90188, "top5_acc": 0.99625, "loss_cls": 0.52456, "loss": 0.52456, "time": 0.4916} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.01928, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91188, "top5_acc": 0.99688, "loss_cls": 0.47709, "loss": 0.47709, "time": 0.49184} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.01926, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9125, "top5_acc": 0.99812, "loss_cls": 0.48475, "loss": 0.48475, "time": 0.3034} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.01925, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90688, "top5_acc": 0.9975, "loss_cls": 0.4824, "loss": 0.4824, "time": 0.51166} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.01923, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9075, "top5_acc": 0.9975, "loss_cls": 0.50687, "loss": 0.50687, "time": 0.27222} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.01921, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90375, "top5_acc": 0.99812, "loss_cls": 0.49451, "loss": 0.49451, "time": 0.49407} +{"mode": "val", "epoch": 48, "iter": 533, "lr": 0.0192, "top1_acc": 0.86398, "top5_acc": 0.98967, "mean_class_accuracy": 0.81603} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.01918, "memory": 4083, "data_time": 0.19196, "top1_acc": 0.9125, "top5_acc": 0.99875, "loss_cls": 0.48692, "loss": 0.48692, "time": 0.78786} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.01916, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.38754, "loss": 0.38754, "time": 0.48931} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.01915, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.9225, "top5_acc": 0.99938, "loss_cls": 0.43046, "loss": 0.43046, "time": 0.49389} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.01913, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89188, "top5_acc": 0.9975, "loss_cls": 0.55068, "loss": 0.55068, "time": 0.49335} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.01911, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88188, "top5_acc": 0.99562, "loss_cls": 0.58314, "loss": 0.58314, "time": 0.49144} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.01909, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.91875, "top5_acc": 0.99625, "loss_cls": 0.44696, "loss": 0.44696, "time": 0.48913} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.01908, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90062, "top5_acc": 0.99688, "loss_cls": 0.50874, "loss": 0.50874, "time": 0.49412} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.01906, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89875, "top5_acc": 0.99938, "loss_cls": 0.49418, "loss": 0.49418, "time": 0.48992} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.01904, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89812, "top5_acc": 1.0, "loss_cls": 0.52455, "loss": 0.52455, "time": 0.32938} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.01902, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89875, "top5_acc": 0.99438, "loss_cls": 0.51967, "loss": 0.51967, "time": 0.51209} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.01901, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.90312, "top5_acc": 0.9975, "loss_cls": 0.50532, "loss": 0.50532, "time": 0.26296} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.01899, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90375, "top5_acc": 0.9975, "loss_cls": 0.50801, "loss": 0.50801, "time": 0.49626} +{"mode": "val", "epoch": 49, "iter": 533, "lr": 0.01898, "top1_acc": 0.8675, "top5_acc": 0.99155, "mean_class_accuracy": 0.81355} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.01896, "memory": 4083, "data_time": 0.18928, "top1_acc": 0.91062, "top5_acc": 0.99812, "loss_cls": 0.46475, "loss": 0.46475, "time": 0.78732} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.01894, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91, "top5_acc": 0.99625, "loss_cls": 0.46242, "loss": 0.46242, "time": 0.48968} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.01892, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91062, "top5_acc": 0.99562, "loss_cls": 0.46759, "loss": 0.46759, "time": 0.49092} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.01891, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91625, "top5_acc": 0.99688, "loss_cls": 0.48448, "loss": 0.48448, "time": 0.49244} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.01889, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91312, "top5_acc": 0.99875, "loss_cls": 0.47254, "loss": 0.47254, "time": 0.49231} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.01887, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.90625, "top5_acc": 0.99688, "loss_cls": 0.49686, "loss": 0.49686, "time": 0.49459} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.01885, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.8975, "top5_acc": 0.99875, "loss_cls": 0.5252, "loss": 0.5252, "time": 0.49611} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.01884, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.90812, "top5_acc": 0.99562, "loss_cls": 0.49557, "loss": 0.49557, "time": 0.49223} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.01882, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9125, "top5_acc": 0.9975, "loss_cls": 0.46549, "loss": 0.46549, "time": 0.33644} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.0188, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.91188, "top5_acc": 0.99812, "loss_cls": 0.45484, "loss": 0.45484, "time": 0.51332} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.01878, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.90688, "top5_acc": 0.99625, "loss_cls": 0.47994, "loss": 0.47994, "time": 0.25468} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.01876, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91312, "top5_acc": 0.9975, "loss_cls": 0.47662, "loss": 0.47662, "time": 0.49182} +{"mode": "val", "epoch": 50, "iter": 533, "lr": 0.01875, "top1_acc": 0.87431, "top5_acc": 0.99225, "mean_class_accuracy": 0.84271} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.01873, "memory": 4083, "data_time": 0.19037, "top1_acc": 0.9225, "top5_acc": 0.9975, "loss_cls": 0.44593, "loss": 0.44593, "time": 0.80079} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.01871, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93375, "top5_acc": 0.99938, "loss_cls": 0.37145, "loss": 0.37145, "time": 0.49054} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.0187, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.91125, "top5_acc": 0.99688, "loss_cls": 0.45857, "loss": 0.45857, "time": 0.49398} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.01868, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.9075, "top5_acc": 0.99625, "loss_cls": 0.5128, "loss": 0.5128, "time": 0.4914} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.01866, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90438, "top5_acc": 0.9975, "loss_cls": 0.52327, "loss": 0.52327, "time": 0.48983} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.01864, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90625, "top5_acc": 0.99688, "loss_cls": 0.49327, "loss": 0.49327, "time": 0.49338} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.01863, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.8975, "top5_acc": 0.99938, "loss_cls": 0.49127, "loss": 0.49127, "time": 0.4936} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.01861, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89375, "top5_acc": 0.99562, "loss_cls": 0.53047, "loss": 0.53047, "time": 0.49002} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.01859, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9075, "top5_acc": 0.99875, "loss_cls": 0.51282, "loss": 0.51282, "time": 0.33029} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.01857, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89875, "top5_acc": 0.99812, "loss_cls": 0.50859, "loss": 0.50859, "time": 0.51221} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.01855, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90938, "top5_acc": 0.995, "loss_cls": 0.49169, "loss": 0.49169, "time": 0.2564} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.01854, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91375, "top5_acc": 0.9975, "loss_cls": 0.44893, "loss": 0.44893, "time": 0.47143} +{"mode": "val", "epoch": 51, "iter": 533, "lr": 0.01852, "top1_acc": 0.89004, "top5_acc": 0.99401, "mean_class_accuracy": 0.8453} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.0185, "memory": 4083, "data_time": 0.19258, "top1_acc": 0.9125, "top5_acc": 0.99688, "loss_cls": 0.44234, "loss": 0.44234, "time": 0.79988} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.01849, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9325, "top5_acc": 0.99688, "loss_cls": 0.41052, "loss": 0.41052, "time": 0.49168} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.01847, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90312, "top5_acc": 0.99812, "loss_cls": 0.49986, "loss": 0.49986, "time": 0.49309} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.01845, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91562, "top5_acc": 0.99812, "loss_cls": 0.47658, "loss": 0.47658, "time": 0.49224} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.01843, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92312, "top5_acc": 0.99875, "loss_cls": 0.42088, "loss": 0.42088, "time": 0.49534} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.01841, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91062, "top5_acc": 0.99688, "loss_cls": 0.50239, "loss": 0.50239, "time": 0.49502} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.0184, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.90062, "top5_acc": 0.99688, "loss_cls": 0.4844, "loss": 0.4844, "time": 0.4925} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.01838, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92062, "top5_acc": 0.9975, "loss_cls": 0.42599, "loss": 0.42599, "time": 0.49536} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.01836, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90688, "top5_acc": 0.99562, "loss_cls": 0.52828, "loss": 0.52828, "time": 0.34501} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.01834, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.92625, "top5_acc": 0.99812, "loss_cls": 0.39418, "loss": 0.39418, "time": 0.51301} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.01832, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91375, "top5_acc": 0.99812, "loss_cls": 0.44732, "loss": 0.44732, "time": 0.24904} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.01831, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91625, "top5_acc": 0.99562, "loss_cls": 0.44455, "loss": 0.44455, "time": 0.46237} +{"mode": "val", "epoch": 52, "iter": 533, "lr": 0.01829, "top1_acc": 0.8634, "top5_acc": 0.99202, "mean_class_accuracy": 0.82152} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.01827, "memory": 4083, "data_time": 0.18835, "top1_acc": 0.91312, "top5_acc": 0.99562, "loss_cls": 0.47917, "loss": 0.47917, "time": 0.79901} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.01826, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90688, "top5_acc": 0.99812, "loss_cls": 0.48319, "loss": 0.48319, "time": 0.48929} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.01824, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93688, "top5_acc": 0.99688, "loss_cls": 0.38571, "loss": 0.38571, "time": 0.49255} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.01822, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.915, "top5_acc": 0.9975, "loss_cls": 0.46897, "loss": 0.46897, "time": 0.49202} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.0182, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90375, "top5_acc": 0.99875, "loss_cls": 0.50427, "loss": 0.50427, "time": 0.49174} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.01818, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.49133, "loss": 0.49133, "time": 0.49113} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.01816, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.44367, "loss": 0.44367, "time": 0.49524} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.01815, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91188, "top5_acc": 0.99875, "loss_cls": 0.44687, "loss": 0.44687, "time": 0.49634} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.01813, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.90562, "top5_acc": 0.995, "loss_cls": 0.52337, "loss": 0.52337, "time": 0.36831} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.01811, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9075, "top5_acc": 0.99688, "loss_cls": 0.49312, "loss": 0.49312, "time": 0.51287} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.01809, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90938, "top5_acc": 0.99812, "loss_cls": 0.46716, "loss": 0.46716, "time": 0.24328} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.01807, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90625, "top5_acc": 0.99562, "loss_cls": 0.5027, "loss": 0.5027, "time": 0.45415} +{"mode": "val", "epoch": 53, "iter": 533, "lr": 0.01806, "top1_acc": 0.8722, "top5_acc": 0.99049, "mean_class_accuracy": 0.81456} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.01804, "memory": 4083, "data_time": 0.19908, "top1_acc": 0.915, "top5_acc": 0.99812, "loss_cls": 0.45344, "loss": 0.45344, "time": 0.81851} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.01802, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.92625, "top5_acc": 0.9975, "loss_cls": 0.39766, "loss": 0.39766, "time": 0.49207} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.018, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92625, "top5_acc": 0.99938, "loss_cls": 0.38563, "loss": 0.38563, "time": 0.4916} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.01798, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9075, "top5_acc": 0.99812, "loss_cls": 0.45063, "loss": 0.45063, "time": 0.49039} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.01797, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.91625, "top5_acc": 0.99875, "loss_cls": 0.45928, "loss": 0.45928, "time": 0.4927} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.01795, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9275, "top5_acc": 0.99812, "loss_cls": 0.41137, "loss": 0.41137, "time": 0.49233} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.01793, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90188, "top5_acc": 0.99812, "loss_cls": 0.48834, "loss": 0.48834, "time": 0.49422} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.01791, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90625, "top5_acc": 0.9975, "loss_cls": 0.50676, "loss": 0.50676, "time": 0.4953} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.01789, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.89812, "top5_acc": 0.99438, "loss_cls": 0.51874, "loss": 0.51874, "time": 0.37211} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.01787, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9175, "top5_acc": 0.99562, "loss_cls": 0.47278, "loss": 0.47278, "time": 0.51072} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.01786, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.915, "top5_acc": 0.99812, "loss_cls": 0.46083, "loss": 0.46083, "time": 0.24005} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.01784, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.915, "top5_acc": 0.99625, "loss_cls": 0.45635, "loss": 0.45635, "time": 0.44658} +{"mode": "val", "epoch": 54, "iter": 533, "lr": 0.01782, "top1_acc": 0.85706, "top5_acc": 0.98944, "mean_class_accuracy": 0.82579} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.0178, "memory": 4083, "data_time": 0.19596, "top1_acc": 0.92062, "top5_acc": 0.99812, "loss_cls": 0.44707, "loss": 0.44707, "time": 0.81364} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.01779, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.40752, "loss": 0.40752, "time": 0.49458} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.01777, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.92062, "top5_acc": 0.99812, "loss_cls": 0.4138, "loss": 0.4138, "time": 0.49106} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.01775, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.8975, "top5_acc": 0.9975, "loss_cls": 0.48921, "loss": 0.48921, "time": 0.49063} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.01773, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93062, "top5_acc": 0.99812, "loss_cls": 0.37397, "loss": 0.37397, "time": 0.49066} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.01771, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91062, "top5_acc": 0.99688, "loss_cls": 0.48745, "loss": 0.48745, "time": 0.48812} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.01769, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9125, "top5_acc": 0.99688, "loss_cls": 0.45253, "loss": 0.45253, "time": 0.4914} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.01767, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90875, "top5_acc": 0.99688, "loss_cls": 0.47526, "loss": 0.47526, "time": 0.49251} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.01766, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.91, "top5_acc": 0.99875, "loss_cls": 0.46324, "loss": 0.46324, "time": 0.3904} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.01764, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.915, "top5_acc": 0.9975, "loss_cls": 0.47091, "loss": 0.47091, "time": 0.51199} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.01762, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.91625, "top5_acc": 0.99625, "loss_cls": 0.44865, "loss": 0.44865, "time": 0.23468} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.0176, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9075, "top5_acc": 0.99688, "loss_cls": 0.48592, "loss": 0.48592, "time": 0.45065} +{"mode": "val", "epoch": 55, "iter": 533, "lr": 0.01758, "top1_acc": 0.86211, "top5_acc": 0.99061, "mean_class_accuracy": 0.81208} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.01757, "memory": 4083, "data_time": 0.19432, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.44264, "loss": 0.44264, "time": 0.81086} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.01755, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91062, "top5_acc": 0.9975, "loss_cls": 0.44223, "loss": 0.44223, "time": 0.49198} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.01753, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9225, "top5_acc": 0.99688, "loss_cls": 0.43711, "loss": 0.43711, "time": 0.49114} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.01751, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.92812, "top5_acc": 0.99812, "loss_cls": 0.42691, "loss": 0.42691, "time": 0.49441} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.01749, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.92312, "top5_acc": 0.99688, "loss_cls": 0.40166, "loss": 0.40166, "time": 0.49123} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.01747, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.91438, "top5_acc": 0.99875, "loss_cls": 0.43019, "loss": 0.43019, "time": 0.49249} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.01745, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91875, "top5_acc": 0.99812, "loss_cls": 0.43882, "loss": 0.43882, "time": 0.49137} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.01743, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89875, "top5_acc": 0.99562, "loss_cls": 0.53777, "loss": 0.53777, "time": 0.49303} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.01742, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.91125, "top5_acc": 0.99812, "loss_cls": 0.45643, "loss": 0.45643, "time": 0.38948} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.0174, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.89375, "top5_acc": 0.99375, "loss_cls": 0.55144, "loss": 0.55144, "time": 0.51332} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.01738, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91625, "top5_acc": 0.99812, "loss_cls": 0.45582, "loss": 0.45582, "time": 0.23905} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.01736, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92312, "top5_acc": 0.99625, "loss_cls": 0.42229, "loss": 0.42229, "time": 0.44794} +{"mode": "val", "epoch": 56, "iter": 533, "lr": 0.01734, "top1_acc": 0.87466, "top5_acc": 0.99167, "mean_class_accuracy": 0.84136} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.01733, "memory": 4083, "data_time": 0.19343, "top1_acc": 0.91312, "top5_acc": 0.99875, "loss_cls": 0.46135, "loss": 0.46135, "time": 0.81565} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.01731, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.92188, "top5_acc": 0.99625, "loss_cls": 0.42712, "loss": 0.42712, "time": 0.49433} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.01729, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93688, "top5_acc": 0.99875, "loss_cls": 0.38203, "loss": 0.38203, "time": 0.49187} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.01727, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93125, "top5_acc": 0.9975, "loss_cls": 0.39182, "loss": 0.39182, "time": 0.48884} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.01725, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.915, "top5_acc": 0.995, "loss_cls": 0.46861, "loss": 0.46861, "time": 0.49142} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.01723, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91688, "top5_acc": 0.99688, "loss_cls": 0.4492, "loss": 0.4492, "time": 0.49061} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.01721, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91938, "top5_acc": 0.9975, "loss_cls": 0.43849, "loss": 0.43849, "time": 0.49822} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.01719, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92375, "top5_acc": 0.99812, "loss_cls": 0.45602, "loss": 0.45602, "time": 0.49365} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.01717, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91125, "top5_acc": 0.99562, "loss_cls": 0.50141, "loss": 0.50141, "time": 0.38331} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.01716, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.91688, "top5_acc": 0.99688, "loss_cls": 0.43792, "loss": 0.43792, "time": 0.51356} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.01714, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.91875, "top5_acc": 0.9975, "loss_cls": 0.42721, "loss": 0.42721, "time": 0.24012} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.01712, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.91125, "top5_acc": 0.99688, "loss_cls": 0.4505, "loss": 0.4505, "time": 0.44281} +{"mode": "val", "epoch": 57, "iter": 533, "lr": 0.0171, "top1_acc": 0.88065, "top5_acc": 0.99296, "mean_class_accuracy": 0.83917} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.01708, "memory": 4083, "data_time": 0.18473, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.41002, "loss": 0.41002, "time": 0.78626} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.01706, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94562, "top5_acc": 0.99875, "loss_cls": 0.35429, "loss": 0.35429, "time": 0.49448} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.01704, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94125, "top5_acc": 0.99875, "loss_cls": 0.35882, "loss": 0.35882, "time": 0.48906} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.01703, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.36034, "loss": 0.36034, "time": 0.49016} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.01701, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.40708, "loss": 0.40708, "time": 0.49257} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.01699, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92125, "top5_acc": 0.99625, "loss_cls": 0.42003, "loss": 0.42003, "time": 0.48875} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.01697, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.4557, "loss": 0.4557, "time": 0.49219} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.01695, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89875, "top5_acc": 0.99562, "loss_cls": 0.51602, "loss": 0.51602, "time": 0.4917} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.01693, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.91625, "top5_acc": 0.99812, "loss_cls": 0.44872, "loss": 0.44872, "time": 0.40482} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.01691, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.93062, "top5_acc": 0.99812, "loss_cls": 0.40866, "loss": 0.40866, "time": 0.51193} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.01689, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.91312, "top5_acc": 0.99812, "loss_cls": 0.45475, "loss": 0.45475, "time": 0.23242} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.01687, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.90562, "top5_acc": 0.99875, "loss_cls": 0.46787, "loss": 0.46787, "time": 0.43918} +{"mode": "val", "epoch": 58, "iter": 533, "lr": 0.01686, "top1_acc": 0.87642, "top5_acc": 0.99132, "mean_class_accuracy": 0.84105} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.01684, "memory": 4083, "data_time": 0.19344, "top1_acc": 0.91, "top5_acc": 0.9975, "loss_cls": 0.46906, "loss": 0.46906, "time": 0.81043} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.01682, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9225, "top5_acc": 0.99875, "loss_cls": 0.42082, "loss": 0.42082, "time": 0.4931} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.0168, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92625, "top5_acc": 0.99938, "loss_cls": 0.39581, "loss": 0.39581, "time": 0.49208} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.01678, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91625, "top5_acc": 0.995, "loss_cls": 0.42869, "loss": 0.42869, "time": 0.49037} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.01676, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9125, "top5_acc": 0.99688, "loss_cls": 0.44241, "loss": 0.44241, "time": 0.49313} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.01674, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92438, "top5_acc": 0.99875, "loss_cls": 0.42642, "loss": 0.42642, "time": 0.48837} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.01672, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.92188, "top5_acc": 0.99938, "loss_cls": 0.43548, "loss": 0.43548, "time": 0.48911} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.0167, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91625, "top5_acc": 0.99938, "loss_cls": 0.42098, "loss": 0.42098, "time": 0.49195} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.01668, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92188, "top5_acc": 0.99812, "loss_cls": 0.41231, "loss": 0.41231, "time": 0.38608} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.01667, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9225, "top5_acc": 0.9975, "loss_cls": 0.44219, "loss": 0.44219, "time": 0.5115} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.01665, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.91875, "top5_acc": 0.9975, "loss_cls": 0.4344, "loss": 0.4344, "time": 0.23836} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.01663, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91062, "top5_acc": 0.99688, "loss_cls": 0.47646, "loss": 0.47646, "time": 0.45356} +{"mode": "val", "epoch": 59, "iter": 533, "lr": 0.01661, "top1_acc": 0.8729, "top5_acc": 0.99143, "mean_class_accuracy": 0.82559} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.01659, "memory": 4083, "data_time": 0.19216, "top1_acc": 0.92188, "top5_acc": 0.99875, "loss_cls": 0.41996, "loss": 0.41996, "time": 0.80948} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.01657, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92875, "top5_acc": 1.0, "loss_cls": 0.37666, "loss": 0.37666, "time": 0.49318} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.01655, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.92875, "top5_acc": 0.9975, "loss_cls": 0.39793, "loss": 0.39793, "time": 0.49052} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.01653, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92312, "top5_acc": 0.99938, "loss_cls": 0.41936, "loss": 0.41936, "time": 0.49166} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.01651, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.9175, "top5_acc": 0.99812, "loss_cls": 0.42085, "loss": 0.42085, "time": 0.49607} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.0165, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.92875, "top5_acc": 0.99875, "loss_cls": 0.37976, "loss": 0.37976, "time": 0.49357} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.01648, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92312, "top5_acc": 0.995, "loss_cls": 0.42269, "loss": 0.42269, "time": 0.49331} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.01646, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91062, "top5_acc": 0.9975, "loss_cls": 0.47289, "loss": 0.47289, "time": 0.49139} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.01644, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.92312, "top5_acc": 0.99688, "loss_cls": 0.41773, "loss": 0.41773, "time": 0.37063} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.01642, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9225, "top5_acc": 0.99875, "loss_cls": 0.41666, "loss": 0.41666, "time": 0.51267} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.0164, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92, "top5_acc": 0.9975, "loss_cls": 0.4256, "loss": 0.4256, "time": 0.24783} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.01638, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.90438, "top5_acc": 0.9975, "loss_cls": 0.48237, "loss": 0.48237, "time": 0.48035} +{"mode": "val", "epoch": 60, "iter": 533, "lr": 0.01636, "top1_acc": 0.87889, "top5_acc": 0.99448, "mean_class_accuracy": 0.82956} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.01634, "memory": 4083, "data_time": 0.19491, "top1_acc": 0.91625, "top5_acc": 0.99625, "loss_cls": 0.43635, "loss": 0.43635, "time": 0.80744} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.01632, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92312, "top5_acc": 0.99875, "loss_cls": 0.42089, "loss": 0.42089, "time": 0.49193} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.0163, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92562, "top5_acc": 0.99875, "loss_cls": 0.37632, "loss": 0.37632, "time": 0.49378} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.01629, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9275, "top5_acc": 0.9975, "loss_cls": 0.40884, "loss": 0.40884, "time": 0.49431} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.01627, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92688, "top5_acc": 0.99688, "loss_cls": 0.42243, "loss": 0.42243, "time": 0.49212} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.01625, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.93062, "top5_acc": 0.9975, "loss_cls": 0.38762, "loss": 0.38762, "time": 0.49512} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.01623, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91875, "top5_acc": 0.99875, "loss_cls": 0.41042, "loss": 0.41042, "time": 0.49247} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.01621, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92688, "top5_acc": 0.99875, "loss_cls": 0.42929, "loss": 0.42929, "time": 0.49687} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.01619, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.44251, "loss": 0.44251, "time": 0.31776} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.01617, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.40926, "loss": 0.40926, "time": 0.51268} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.01615, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92125, "top5_acc": 0.99875, "loss_cls": 0.42944, "loss": 0.42944, "time": 0.26395} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.01613, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9275, "top5_acc": 0.99812, "loss_cls": 0.41863, "loss": 0.41863, "time": 0.49661} +{"mode": "val", "epoch": 61, "iter": 533, "lr": 0.01611, "top1_acc": 0.86797, "top5_acc": 0.98991, "mean_class_accuracy": 0.81586} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.01609, "memory": 4083, "data_time": 0.18855, "top1_acc": 0.90688, "top5_acc": 0.9975, "loss_cls": 0.4654, "loss": 0.4654, "time": 0.79664} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.01607, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92, "top5_acc": 0.99625, "loss_cls": 0.4279, "loss": 0.4279, "time": 0.49613} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.01605, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92688, "top5_acc": 0.9975, "loss_cls": 0.40267, "loss": 0.40267, "time": 0.49128} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.01603, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.91562, "top5_acc": 0.99812, "loss_cls": 0.44385, "loss": 0.44385, "time": 0.49021} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.01602, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.40988, "loss": 0.40988, "time": 0.49667} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.016, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.38137, "loss": 0.38137, "time": 0.4905} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.01598, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9225, "top5_acc": 0.99875, "loss_cls": 0.42693, "loss": 0.42693, "time": 0.48945} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.01596, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91375, "top5_acc": 0.99562, "loss_cls": 0.45035, "loss": 0.45035, "time": 0.49441} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.01594, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9225, "top5_acc": 0.99812, "loss_cls": 0.41029, "loss": 0.41029, "time": 0.31312} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.01592, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92188, "top5_acc": 0.99938, "loss_cls": 0.43954, "loss": 0.43954, "time": 0.51205} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.0159, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92375, "top5_acc": 0.99688, "loss_cls": 0.4422, "loss": 0.4422, "time": 0.26129} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.01588, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.91812, "top5_acc": 0.99625, "loss_cls": 0.43928, "loss": 0.43928, "time": 0.48993} +{"mode": "val", "epoch": 62, "iter": 533, "lr": 0.01586, "top1_acc": 0.88605, "top5_acc": 0.99202, "mean_class_accuracy": 0.85105} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.01584, "memory": 4083, "data_time": 0.19485, "top1_acc": 0.91812, "top5_acc": 1.0, "loss_cls": 0.40003, "loss": 0.40003, "time": 0.80906} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.01582, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93062, "top5_acc": 0.99688, "loss_cls": 0.4009, "loss": 0.4009, "time": 0.49481} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.0158, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.935, "top5_acc": 0.99812, "loss_cls": 0.36855, "loss": 0.36855, "time": 0.49042} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.01578, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92, "top5_acc": 0.9975, "loss_cls": 0.41176, "loss": 0.41176, "time": 0.49435} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.01576, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.36809, "loss": 0.36809, "time": 0.49213} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.01574, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.30742, "loss": 0.30742, "time": 0.49282} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.01572, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.935, "top5_acc": 0.99938, "loss_cls": 0.37631, "loss": 0.37631, "time": 0.48899} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.0157, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9275, "top5_acc": 0.99938, "loss_cls": 0.3771, "loss": 0.3771, "time": 0.49149} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.01568, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.915, "top5_acc": 0.9975, "loss_cls": 0.43144, "loss": 0.43144, "time": 0.30475} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.01566, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90688, "top5_acc": 0.99625, "loss_cls": 0.49089, "loss": 0.49089, "time": 0.51085} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.01564, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93062, "top5_acc": 0.99875, "loss_cls": 0.38768, "loss": 0.38768, "time": 0.29609} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.01562, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92688, "top5_acc": 0.99438, "loss_cls": 0.43459, "loss": 0.43459, "time": 0.49537} +{"mode": "val", "epoch": 63, "iter": 533, "lr": 0.01561, "top1_acc": 0.90342, "top5_acc": 0.99531, "mean_class_accuracy": 0.86158} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.01559, "memory": 4083, "data_time": 0.19677, "top1_acc": 0.93688, "top5_acc": 1.0, "loss_cls": 0.33168, "loss": 0.33168, "time": 0.81825} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.01557, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.92062, "top5_acc": 0.99812, "loss_cls": 0.40773, "loss": 0.40773, "time": 0.49451} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.01555, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94062, "top5_acc": 0.99938, "loss_cls": 0.35707, "loss": 0.35707, "time": 0.49116} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.01553, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94, "top5_acc": 0.99812, "loss_cls": 0.34733, "loss": 0.34733, "time": 0.49211} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.01551, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.92812, "top5_acc": 0.99812, "loss_cls": 0.40697, "loss": 0.40697, "time": 0.4957} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.01549, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9325, "top5_acc": 0.99812, "loss_cls": 0.3704, "loss": 0.3704, "time": 0.496} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.01547, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92562, "top5_acc": 0.99812, "loss_cls": 0.40254, "loss": 0.40254, "time": 0.49111} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.01545, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.42987, "loss": 0.42987, "time": 0.49515} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.01543, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93125, "top5_acc": 0.99625, "loss_cls": 0.39877, "loss": 0.39877, "time": 0.28781} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.01541, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93812, "top5_acc": 0.99562, "loss_cls": 0.36574, "loss": 0.36574, "time": 0.48027} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.01539, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.9125, "top5_acc": 0.99812, "loss_cls": 0.45543, "loss": 0.45543, "time": 0.33525} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.01537, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.9175, "top5_acc": 0.99688, "loss_cls": 0.45737, "loss": 0.45737, "time": 0.4931} +{"mode": "val", "epoch": 64, "iter": 533, "lr": 0.01535, "top1_acc": 0.88041, "top5_acc": 0.99331, "mean_class_accuracy": 0.8546} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.01533, "memory": 4083, "data_time": 0.19237, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.34941, "loss": 0.34941, "time": 0.80891} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.01531, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92438, "top5_acc": 0.99938, "loss_cls": 0.40567, "loss": 0.40567, "time": 0.49307} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.01529, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.36584, "loss": 0.36584, "time": 0.49249} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.01527, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93938, "top5_acc": 1.0, "loss_cls": 0.32388, "loss": 0.32388, "time": 0.49178} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.01526, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92938, "top5_acc": 0.9975, "loss_cls": 0.37473, "loss": 0.37473, "time": 0.49199} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.01524, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93312, "top5_acc": 0.99938, "loss_cls": 0.38629, "loss": 0.38629, "time": 0.49076} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.01522, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92562, "top5_acc": 0.9975, "loss_cls": 0.41431, "loss": 0.41431, "time": 0.49517} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0152, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.91375, "top5_acc": 0.9975, "loss_cls": 0.45289, "loss": 0.45289, "time": 0.48977} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.01518, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93688, "top5_acc": 0.99812, "loss_cls": 0.37439, "loss": 0.37439, "time": 0.31872} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.01516, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91938, "top5_acc": 0.99812, "loss_cls": 0.42119, "loss": 0.42119, "time": 0.42889} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.01514, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93, "top5_acc": 0.99812, "loss_cls": 0.38528, "loss": 0.38528, "time": 0.37071} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.01512, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.925, "top5_acc": 0.99938, "loss_cls": 0.39543, "loss": 0.39543, "time": 0.49225} +{"mode": "val", "epoch": 65, "iter": 533, "lr": 0.0151, "top1_acc": 0.89837, "top5_acc": 0.99425, "mean_class_accuracy": 0.86335} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.01508, "memory": 4083, "data_time": 0.19777, "top1_acc": 0.92688, "top5_acc": 0.99938, "loss_cls": 0.35398, "loss": 0.35398, "time": 0.80744} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.01506, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93812, "top5_acc": 0.99875, "loss_cls": 0.34894, "loss": 0.34894, "time": 0.49304} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.01504, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.33774, "loss": 0.33774, "time": 0.49209} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.01502, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9275, "top5_acc": 0.99875, "loss_cls": 0.38246, "loss": 0.38246, "time": 0.49157} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.015, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.90875, "top5_acc": 1.0, "loss_cls": 0.42979, "loss": 0.42979, "time": 0.49305} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.01498, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.92062, "top5_acc": 1.0, "loss_cls": 0.4075, "loss": 0.4075, "time": 0.49143} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.01496, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93312, "top5_acc": 0.99875, "loss_cls": 0.38338, "loss": 0.38338, "time": 0.48968} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.01494, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91562, "top5_acc": 0.99875, "loss_cls": 0.4458, "loss": 0.4458, "time": 0.45844} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.01492, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9275, "top5_acc": 0.99688, "loss_cls": 0.40528, "loss": 0.40528, "time": 0.40144} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.0149, "memory": 4083, "data_time": 0.00076, "top1_acc": 0.91312, "top5_acc": 0.9975, "loss_cls": 0.4394, "loss": 0.4394, "time": 0.34026} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.01488, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9175, "top5_acc": 0.9975, "loss_cls": 0.40609, "loss": 0.40609, "time": 0.40019} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.01486, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.40532, "loss": 0.40532, "time": 0.4929} +{"mode": "val", "epoch": 66, "iter": 533, "lr": 0.01484, "top1_acc": 0.87513, "top5_acc": 0.99179, "mean_class_accuracy": 0.83045} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.01482, "memory": 4083, "data_time": 0.1919, "top1_acc": 0.92312, "top5_acc": 0.99875, "loss_cls": 0.42283, "loss": 0.42283, "time": 0.81359} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.0148, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93375, "top5_acc": 1.0, "loss_cls": 0.35303, "loss": 0.35303, "time": 0.49116} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.01478, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.93438, "top5_acc": 0.99938, "loss_cls": 0.34762, "loss": 0.34762, "time": 0.49107} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.01476, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93625, "top5_acc": 0.99812, "loss_cls": 0.36138, "loss": 0.36138, "time": 0.4925} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.01474, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92125, "top5_acc": 0.99812, "loss_cls": 0.41228, "loss": 0.41228, "time": 0.49471} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.01472, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.91812, "top5_acc": 0.9975, "loss_cls": 0.42195, "loss": 0.42195, "time": 0.49323} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.0147, "memory": 4083, "data_time": 0.00061, "top1_acc": 0.92938, "top5_acc": 0.9975, "loss_cls": 0.37951, "loss": 0.37951, "time": 0.49741} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.01468, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.92875, "top5_acc": 1.0, "loss_cls": 0.39953, "loss": 0.39953, "time": 0.44} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.01466, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.93125, "top5_acc": 0.99875, "loss_cls": 0.38156, "loss": 0.38156, "time": 0.42271} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.01464, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.40915, "loss": 0.40915, "time": 0.31454} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.01462, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91875, "top5_acc": 0.99625, "loss_cls": 0.42424, "loss": 0.42424, "time": 0.41056} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.0146, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.92562, "top5_acc": 1.0, "loss_cls": 0.41211, "loss": 0.41211, "time": 0.48898} +{"mode": "val", "epoch": 67, "iter": 533, "lr": 0.01458, "top1_acc": 0.88569, "top5_acc": 0.99413, "mean_class_accuracy": 0.84393} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.01456, "memory": 4083, "data_time": 0.18437, "top1_acc": 0.94188, "top5_acc": 0.99938, "loss_cls": 0.34608, "loss": 0.34608, "time": 0.79114} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.01454, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93812, "top5_acc": 0.99688, "loss_cls": 0.34448, "loss": 0.34448, "time": 0.49506} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.01452, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93375, "top5_acc": 0.99938, "loss_cls": 0.36455, "loss": 0.36455, "time": 0.49278} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.0145, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.41766, "loss": 0.41766, "time": 0.49309} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.01448, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93312, "top5_acc": 0.995, "loss_cls": 0.36815, "loss": 0.36815, "time": 0.49154} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.01446, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93688, "top5_acc": 0.99938, "loss_cls": 0.3546, "loss": 0.3546, "time": 0.49595} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.01444, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.9325, "top5_acc": 0.99812, "loss_cls": 0.3559, "loss": 0.3559, "time": 0.49308} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.01442, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.36425, "loss": 0.36425, "time": 0.43025} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.0144, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93125, "top5_acc": 0.99875, "loss_cls": 0.37378, "loss": 0.37378, "time": 0.45824} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.01438, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92375, "top5_acc": 0.99812, "loss_cls": 0.42443, "loss": 0.42443, "time": 0.27786} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.01436, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93438, "top5_acc": 0.99938, "loss_cls": 0.37199, "loss": 0.37199, "time": 0.41875} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.01434, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93375, "top5_acc": 0.99875, "loss_cls": 0.39067, "loss": 0.39067, "time": 0.49189} +{"mode": "val", "epoch": 68, "iter": 533, "lr": 0.01433, "top1_acc": 0.87912, "top5_acc": 0.99155, "mean_class_accuracy": 0.84163} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.01431, "memory": 4083, "data_time": 0.18701, "top1_acc": 0.93125, "top5_acc": 0.99938, "loss_cls": 0.37806, "loss": 0.37806, "time": 0.78481} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.01429, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.93688, "top5_acc": 0.99938, "loss_cls": 0.34154, "loss": 0.34154, "time": 0.49097} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.01427, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.29562, "loss": 0.29562, "time": 0.49258} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.01425, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.41045, "loss": 0.41045, "time": 0.49015} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.01423, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.92875, "top5_acc": 0.99875, "loss_cls": 0.39195, "loss": 0.39195, "time": 0.4918} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.0142, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.925, "top5_acc": 0.99688, "loss_cls": 0.41173, "loss": 0.41173, "time": 0.49571} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.01418, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93625, "top5_acc": 0.9975, "loss_cls": 0.37248, "loss": 0.37248, "time": 0.4946} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.01416, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.9325, "top5_acc": 0.9975, "loss_cls": 0.35704, "loss": 0.35704, "time": 0.43147} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.01414, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92688, "top5_acc": 0.99875, "loss_cls": 0.37153, "loss": 0.37153, "time": 0.45171} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.01412, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.35175, "loss": 0.35175, "time": 0.2915} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.0141, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94062, "top5_acc": 0.9975, "loss_cls": 0.3461, "loss": 0.3461, "time": 0.44037} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.01408, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.38652, "loss": 0.38652, "time": 0.49139} +{"mode": "val", "epoch": 69, "iter": 533, "lr": 0.01407, "top1_acc": 0.89332, "top5_acc": 0.9939, "mean_class_accuracy": 0.85483} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.01405, "memory": 4083, "data_time": 0.19159, "top1_acc": 0.93812, "top5_acc": 1.0, "loss_cls": 0.35181, "loss": 0.35181, "time": 0.79338} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.01403, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.935, "top5_acc": 0.9975, "loss_cls": 0.3435, "loss": 0.3435, "time": 0.49215} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.01401, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92812, "top5_acc": 0.99625, "loss_cls": 0.39213, "loss": 0.39213, "time": 0.4908} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.01399, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93625, "top5_acc": 1.0, "loss_cls": 0.32879, "loss": 0.32879, "time": 0.49026} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.01397, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.3127, "loss": 0.3127, "time": 0.49332} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.01395, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.92688, "top5_acc": 0.99812, "loss_cls": 0.39123, "loss": 0.39123, "time": 0.49222} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.01392, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.93062, "top5_acc": 0.99625, "loss_cls": 0.37994, "loss": 0.37994, "time": 0.4917} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.0139, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.92875, "top5_acc": 0.99812, "loss_cls": 0.38434, "loss": 0.38434, "time": 0.40893} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.01388, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91625, "top5_acc": 0.99875, "loss_cls": 0.42459, "loss": 0.42459, "time": 0.5055} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.01386, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.91938, "top5_acc": 0.99562, "loss_cls": 0.40629, "loss": 0.40629, "time": 0.24194} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.01384, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.92562, "top5_acc": 0.99875, "loss_cls": 0.40709, "loss": 0.40709, "time": 0.43209} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.01382, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91562, "top5_acc": 0.99875, "loss_cls": 0.4413, "loss": 0.4413, "time": 0.49296} +{"mode": "val", "epoch": 70, "iter": 533, "lr": 0.01381, "top1_acc": 0.89731, "top5_acc": 0.99507, "mean_class_accuracy": 0.86632} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.01379, "memory": 4083, "data_time": 0.19388, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.28166, "loss": 0.28166, "time": 0.8083} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.01377, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.28957, "loss": 0.28957, "time": 0.49293} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.01375, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9425, "top5_acc": 0.99812, "loss_cls": 0.28819, "loss": 0.28819, "time": 0.49179} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.01373, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93938, "top5_acc": 0.99875, "loss_cls": 0.34024, "loss": 0.34024, "time": 0.49326} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.01371, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92625, "top5_acc": 0.99938, "loss_cls": 0.40871, "loss": 0.40871, "time": 0.49325} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.01368, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9375, "top5_acc": 0.99875, "loss_cls": 0.36253, "loss": 0.36253, "time": 0.4924} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.01366, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92812, "top5_acc": 0.99875, "loss_cls": 0.36045, "loss": 0.36045, "time": 0.49315} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.01364, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93312, "top5_acc": 1.0, "loss_cls": 0.35057, "loss": 0.35057, "time": 0.39292} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.01362, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93625, "top5_acc": 0.99938, "loss_cls": 0.35866, "loss": 0.35866, "time": 0.51321} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.0136, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.35538, "loss": 0.35538, "time": 0.2366} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.01358, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94562, "top5_acc": 0.99812, "loss_cls": 0.34339, "loss": 0.34339, "time": 0.44772} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.01356, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93562, "top5_acc": 0.99812, "loss_cls": 0.35098, "loss": 0.35098, "time": 0.49222} +{"mode": "val", "epoch": 71, "iter": 533, "lr": 0.01355, "top1_acc": 0.87513, "top5_acc": 0.99331, "mean_class_accuracy": 0.82942} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.01353, "memory": 4083, "data_time": 0.19455, "top1_acc": 0.94625, "top5_acc": 0.99812, "loss_cls": 0.33479, "loss": 0.33479, "time": 0.80034} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.01351, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.30232, "loss": 0.30232, "time": 0.49406} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.01349, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.945, "top5_acc": 0.9975, "loss_cls": 0.32416, "loss": 0.32416, "time": 0.49033} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.01346, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95062, "top5_acc": 0.99562, "loss_cls": 0.3019, "loss": 0.3019, "time": 0.4954} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.01344, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94062, "top5_acc": 1.0, "loss_cls": 0.31443, "loss": 0.31443, "time": 0.49111} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.01342, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92312, "top5_acc": 0.99875, "loss_cls": 0.40426, "loss": 0.40426, "time": 0.49395} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.0134, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94062, "top5_acc": 0.99938, "loss_cls": 0.33672, "loss": 0.33672, "time": 0.4925} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.01338, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92812, "top5_acc": 0.99688, "loss_cls": 0.40902, "loss": 0.40902, "time": 0.3724} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.01336, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.93562, "top5_acc": 0.99875, "loss_cls": 0.36263, "loss": 0.36263, "time": 0.51262} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.01334, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94562, "top5_acc": 1.0, "loss_cls": 0.34463, "loss": 0.34463, "time": 0.24429} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.01332, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93188, "top5_acc": 0.99938, "loss_cls": 0.36059, "loss": 0.36059, "time": 0.46475} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.0133, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.935, "top5_acc": 0.9975, "loss_cls": 0.38364, "loss": 0.38364, "time": 0.49193} +{"mode": "val", "epoch": 72, "iter": 533, "lr": 0.01329, "top1_acc": 0.90388, "top5_acc": 0.99578, "mean_class_accuracy": 0.8683} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.01326, "memory": 4083, "data_time": 0.19387, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.32949, "loss": 0.32949, "time": 0.80488} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.01324, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.27426, "loss": 0.27426, "time": 0.49072} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.01322, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94688, "top5_acc": 0.99812, "loss_cls": 0.33724, "loss": 0.33724, "time": 0.4924} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.0132, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95375, "top5_acc": 0.99812, "loss_cls": 0.27321, "loss": 0.27321, "time": 0.48989} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.01318, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.34304, "loss": 0.34304, "time": 0.49094} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.01316, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94062, "top5_acc": 1.0, "loss_cls": 0.32513, "loss": 0.32513, "time": 0.49087} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.01314, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94, "top5_acc": 0.99812, "loss_cls": 0.33485, "loss": 0.33485, "time": 0.49182} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.01312, "memory": 4083, "data_time": 0.00073, "top1_acc": 0.9275, "top5_acc": 0.99875, "loss_cls": 0.37586, "loss": 0.37586, "time": 0.34495} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.0131, "memory": 4083, "data_time": 0.00074, "top1_acc": 0.935, "top5_acc": 0.99812, "loss_cls": 0.36654, "loss": 0.36654, "time": 0.513} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.01308, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92938, "top5_acc": 0.99812, "loss_cls": 0.36499, "loss": 0.36499, "time": 0.25269} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.01306, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92875, "top5_acc": 0.99688, "loss_cls": 0.37376, "loss": 0.37376, "time": 0.49131} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.01304, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92875, "top5_acc": 0.9975, "loss_cls": 0.38628, "loss": 0.38628, "time": 0.49094} +{"mode": "val", "epoch": 73, "iter": 533, "lr": 0.01302, "top1_acc": 0.89532, "top5_acc": 0.9946, "mean_class_accuracy": 0.86889} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.013, "memory": 4083, "data_time": 0.19301, "top1_acc": 0.95625, "top5_acc": 0.99812, "loss_cls": 0.2976, "loss": 0.2976, "time": 0.80033} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.01298, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.32657, "loss": 0.32657, "time": 0.49413} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.01296, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.31229, "loss": 0.31229, "time": 0.49207} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.01294, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9475, "top5_acc": 0.99875, "loss_cls": 0.30556, "loss": 0.30556, "time": 0.49365} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.01292, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94688, "top5_acc": 0.99875, "loss_cls": 0.29812, "loss": 0.29812, "time": 0.4899} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.0129, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.31133, "loss": 0.31133, "time": 0.48992} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.01288, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.3663, "loss": 0.3663, "time": 0.49508} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.01286, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.9325, "top5_acc": 0.99938, "loss_cls": 0.35528, "loss": 0.35528, "time": 0.31617} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.01284, "memory": 4083, "data_time": 0.00065, "top1_acc": 0.9225, "top5_acc": 1.0, "loss_cls": 0.39837, "loss": 0.39837, "time": 0.51228} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.01282, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.93625, "top5_acc": 0.99938, "loss_cls": 0.3416, "loss": 0.3416, "time": 0.283} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.0128, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92812, "top5_acc": 0.99812, "loss_cls": 0.39282, "loss": 0.39282, "time": 0.4929} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.01278, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9425, "top5_acc": 0.9975, "loss_cls": 0.34374, "loss": 0.34374, "time": 0.4957} +{"mode": "val", "epoch": 74, "iter": 533, "lr": 0.01276, "top1_acc": 0.89027, "top5_acc": 0.99366, "mean_class_accuracy": 0.84609} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.01274, "memory": 4083, "data_time": 0.18955, "top1_acc": 0.94812, "top5_acc": 0.99812, "loss_cls": 0.31852, "loss": 0.31852, "time": 0.78707} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.01272, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.26056, "loss": 0.26056, "time": 0.48962} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.0127, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.33072, "loss": 0.33072, "time": 0.49323} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.01268, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93, "top5_acc": 0.9975, "loss_cls": 0.36743, "loss": 0.36743, "time": 0.49187} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.01266, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93875, "top5_acc": 0.9975, "loss_cls": 0.35871, "loss": 0.35871, "time": 0.49354} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.01264, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93812, "top5_acc": 0.99875, "loss_cls": 0.31945, "loss": 0.31945, "time": 0.4952} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.01262, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.32052, "loss": 0.32052, "time": 0.49248} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.0126, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.329, "loss": 0.329, "time": 0.30493} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.01258, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93562, "top5_acc": 0.9975, "loss_cls": 0.37288, "loss": 0.37288, "time": 0.51067} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.01256, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.935, "top5_acc": 1.0, "loss_cls": 0.34602, "loss": 0.34602, "time": 0.28062} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.01254, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.91938, "top5_acc": 0.99938, "loss_cls": 0.41461, "loss": 0.41461, "time": 0.49829} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.01252, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93188, "top5_acc": 0.99938, "loss_cls": 0.37253, "loss": 0.37253, "time": 0.49273} +{"mode": "val", "epoch": 75, "iter": 533, "lr": 0.0125, "top1_acc": 0.89098, "top5_acc": 0.99448, "mean_class_accuracy": 0.86056} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.01248, "memory": 4083, "data_time": 0.19663, "top1_acc": 0.94938, "top5_acc": 0.99875, "loss_cls": 0.31126, "loss": 0.31126, "time": 0.80228} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.01246, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.93812, "top5_acc": 0.99938, "loss_cls": 0.35007, "loss": 0.35007, "time": 0.48863} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.01244, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.94562, "top5_acc": 1.0, "loss_cls": 0.29357, "loss": 0.29357, "time": 0.49043} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.01242, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.94562, "top5_acc": 0.99875, "loss_cls": 0.29407, "loss": 0.29407, "time": 0.49254} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.0124, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.37863, "loss": 0.37863, "time": 0.49096} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.01238, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.94625, "top5_acc": 0.99812, "loss_cls": 0.32514, "loss": 0.32514, "time": 0.4948} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.01236, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93812, "top5_acc": 0.99938, "loss_cls": 0.33841, "loss": 0.33841, "time": 0.49348} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.01234, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93625, "top5_acc": 0.99938, "loss_cls": 0.35814, "loss": 0.35814, "time": 0.28728} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.01232, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.32714, "loss": 0.32714, "time": 0.51127} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.0123, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.93438, "top5_acc": 1.0, "loss_cls": 0.35041, "loss": 0.35041, "time": 0.29661} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.01228, "memory": 4083, "data_time": 0.00074, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.30954, "loss": 0.30954, "time": 0.49191} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.01225, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.92562, "top5_acc": 0.99938, "loss_cls": 0.36709, "loss": 0.36709, "time": 0.49217} +{"mode": "val", "epoch": 76, "iter": 533, "lr": 0.01224, "top1_acc": 0.90447, "top5_acc": 0.99589, "mean_class_accuracy": 0.87962} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.01222, "memory": 4083, "data_time": 0.19651, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.24844, "loss": 0.24844, "time": 0.80276} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0122, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.26516, "loss": 0.26516, "time": 0.49174} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.01218, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.3319, "loss": 0.3319, "time": 0.49192} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.01216, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95, "top5_acc": 0.99812, "loss_cls": 0.27718, "loss": 0.27718, "time": 0.48882} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.01214, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93688, "top5_acc": 1.0, "loss_cls": 0.34814, "loss": 0.34814, "time": 0.49316} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.01212, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.31012, "loss": 0.31012, "time": 0.49242} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.0121, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94312, "top5_acc": 0.99812, "loss_cls": 0.31295, "loss": 0.31295, "time": 0.49273} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.01207, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.9225, "top5_acc": 0.99688, "loss_cls": 0.40391, "loss": 0.40391, "time": 0.28153} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.01205, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92625, "top5_acc": 0.99812, "loss_cls": 0.38552, "loss": 0.38552, "time": 0.48974} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.01203, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.37759, "loss": 0.37759, "time": 0.32452} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.01201, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93438, "top5_acc": 0.99938, "loss_cls": 0.34463, "loss": 0.34463, "time": 0.49254} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.01199, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9275, "top5_acc": 0.9975, "loss_cls": 0.3809, "loss": 0.3809, "time": 0.49117} +{"mode": "val", "epoch": 77, "iter": 533, "lr": 0.01198, "top1_acc": 0.9013, "top5_acc": 0.99531, "mean_class_accuracy": 0.85952} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.01196, "memory": 4083, "data_time": 0.19669, "top1_acc": 0.95688, "top5_acc": 0.99875, "loss_cls": 0.27292, "loss": 0.27292, "time": 0.80051} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.01194, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93938, "top5_acc": 0.99875, "loss_cls": 0.34532, "loss": 0.34532, "time": 0.49341} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.01192, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94125, "top5_acc": 0.99875, "loss_cls": 0.33538, "loss": 0.33538, "time": 0.49086} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.0119, "memory": 4083, "data_time": 0.00063, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.28495, "loss": 0.28495, "time": 0.49439} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.01187, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94438, "top5_acc": 0.99875, "loss_cls": 0.30483, "loss": 0.30483, "time": 0.49215} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.01185, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.30597, "loss": 0.30597, "time": 0.49143} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.01183, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.95, "top5_acc": 1.0, "loss_cls": 0.27812, "loss": 0.27812, "time": 0.49258} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.01181, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94125, "top5_acc": 0.99875, "loss_cls": 0.33746, "loss": 0.33746, "time": 0.3073} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.01179, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93375, "top5_acc": 0.99812, "loss_cls": 0.36456, "loss": 0.36456, "time": 0.44127} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.01177, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.95688, "top5_acc": 0.99875, "loss_cls": 0.27558, "loss": 0.27558, "time": 0.35618} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.01175, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94125, "top5_acc": 0.99812, "loss_cls": 0.30791, "loss": 0.30791, "time": 0.48955} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.01173, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.94, "top5_acc": 0.99875, "loss_cls": 0.31704, "loss": 0.31704, "time": 0.49192} +{"mode": "val", "epoch": 78, "iter": 533, "lr": 0.01172, "top1_acc": 0.90083, "top5_acc": 0.99472, "mean_class_accuracy": 0.87039} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.01169, "memory": 4083, "data_time": 0.19297, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.30299, "loss": 0.30299, "time": 0.80768} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.01167, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.2508, "loss": 0.2508, "time": 0.4934} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.01165, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95625, "top5_acc": 0.99875, "loss_cls": 0.2677, "loss": 0.2677, "time": 0.49378} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.01163, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.95438, "top5_acc": 0.99875, "loss_cls": 0.26101, "loss": 0.26101, "time": 0.49206} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.01161, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95312, "top5_acc": 0.99875, "loss_cls": 0.28374, "loss": 0.28374, "time": 0.49055} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.01159, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94688, "top5_acc": 0.99938, "loss_cls": 0.31923, "loss": 0.31923, "time": 0.49571} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.01157, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.34164, "loss": 0.34164, "time": 0.47183} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.01155, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.94688, "top5_acc": 0.99875, "loss_cls": 0.31738, "loss": 0.31738, "time": 0.38434} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.01153, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.93875, "top5_acc": 1.0, "loss_cls": 0.31553, "loss": 0.31553, "time": 0.35719} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.01151, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.27661, "loss": 0.27661, "time": 0.39243} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.01149, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93438, "top5_acc": 1.0, "loss_cls": 0.33107, "loss": 0.33107, "time": 0.49283} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.01147, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.31202, "loss": 0.31202, "time": 0.49734} +{"mode": "val", "epoch": 79, "iter": 533, "lr": 0.01145, "top1_acc": 0.88898, "top5_acc": 0.99425, "mean_class_accuracy": 0.85026} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.01143, "memory": 4083, "data_time": 0.19237, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.26926, "loss": 0.26926, "time": 0.80325} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.01141, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.96625, "top5_acc": 0.99875, "loss_cls": 0.23292, "loss": 0.23292, "time": 0.48979} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.01139, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9575, "top5_acc": 0.99875, "loss_cls": 0.2734, "loss": 0.2734, "time": 0.49208} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.01137, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.9475, "top5_acc": 0.99812, "loss_cls": 0.27783, "loss": 0.27783, "time": 0.48997} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.01135, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.29303, "loss": 0.29303, "time": 0.49172} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.01133, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95625, "top5_acc": 0.99812, "loss_cls": 0.25945, "loss": 0.25945, "time": 0.49174} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.01131, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93875, "top5_acc": 0.99812, "loss_cls": 0.34656, "loss": 0.34656, "time": 0.44711} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.01129, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.29941, "loss": 0.29941, "time": 0.43578} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.01127, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.3463, "loss": 0.3463, "time": 0.3001} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.01125, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93438, "top5_acc": 0.99938, "loss_cls": 0.34955, "loss": 0.34955, "time": 0.42058} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.01123, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92812, "top5_acc": 0.99875, "loss_cls": 0.37203, "loss": 0.37203, "time": 0.49189} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.01121, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.93, "top5_acc": 0.99812, "loss_cls": 0.39106, "loss": 0.39106, "time": 0.49219} +{"mode": "val", "epoch": 80, "iter": 533, "lr": 0.01119, "top1_acc": 0.89039, "top5_acc": 0.99225, "mean_class_accuracy": 0.86} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.01117, "memory": 4083, "data_time": 0.19579, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.22792, "loss": 0.22792, "time": 0.81461} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.01115, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.29269, "loss": 0.29269, "time": 0.48989} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.01113, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93, "top5_acc": 0.99938, "loss_cls": 0.37216, "loss": 0.37216, "time": 0.49354} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.01111, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94625, "top5_acc": 0.99875, "loss_cls": 0.32745, "loss": 0.32745, "time": 0.49156} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.01109, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.2864, "loss": 0.2864, "time": 0.49252} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.01107, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.94688, "top5_acc": 0.99938, "loss_cls": 0.30022, "loss": 0.30022, "time": 0.49378} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.01105, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.30612, "loss": 0.30612, "time": 0.41816} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.01103, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.95, "top5_acc": 0.99562, "loss_cls": 0.31596, "loss": 0.31596, "time": 0.46629} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.01101, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.93688, "top5_acc": 0.99812, "loss_cls": 0.35615, "loss": 0.35615, "time": 0.27309} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.01099, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94812, "top5_acc": 1.0, "loss_cls": 0.28559, "loss": 0.28559, "time": 0.42331} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.01097, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.27164, "loss": 0.27164, "time": 0.49226} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.01095, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95625, "top5_acc": 0.99812, "loss_cls": 0.26795, "loss": 0.26795, "time": 0.49228} +{"mode": "val", "epoch": 81, "iter": 533, "lr": 0.01093, "top1_acc": 0.89508, "top5_acc": 0.99566, "mean_class_accuracy": 0.86799} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.01091, "memory": 4083, "data_time": 0.20074, "top1_acc": 0.94375, "top5_acc": 1.0, "loss_cls": 0.29536, "loss": 0.29536, "time": 0.80148} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.01089, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.22796, "loss": 0.22796, "time": 0.49283} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.01087, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95, "top5_acc": 1.0, "loss_cls": 0.26063, "loss": 0.26063, "time": 0.4906} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.01085, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94562, "top5_acc": 1.0, "loss_cls": 0.28966, "loss": 0.28966, "time": 0.49176} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.01083, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95, "top5_acc": 0.99812, "loss_cls": 0.2919, "loss": 0.2919, "time": 0.49406} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.01081, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9375, "top5_acc": 0.99625, "loss_cls": 0.34085, "loss": 0.34085, "time": 0.49385} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.01079, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.30322, "loss": 0.30322, "time": 0.40764} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.01077, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96125, "top5_acc": 0.99812, "loss_cls": 0.24958, "loss": 0.24958, "time": 0.49456} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.01075, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94875, "top5_acc": 0.99812, "loss_cls": 0.31153, "loss": 0.31153, "time": 0.25012} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.01073, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.945, "top5_acc": 0.99938, "loss_cls": 0.32449, "loss": 0.32449, "time": 0.43334} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.01071, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.27423, "loss": 0.27423, "time": 0.49296} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.01069, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.24786, "loss": 0.24786, "time": 0.49559} +{"mode": "val", "epoch": 82, "iter": 533, "lr": 0.01067, "top1_acc": 0.8972, "top5_acc": 0.99308, "mean_class_accuracy": 0.86905} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.01065, "memory": 4083, "data_time": 0.19863, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.25107, "loss": 0.25107, "time": 0.81336} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.01063, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.24951, "loss": 0.24951, "time": 0.49094} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.01061, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94938, "top5_acc": 1.0, "loss_cls": 0.27955, "loss": 0.27955, "time": 0.49223} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.01059, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95688, "top5_acc": 0.99812, "loss_cls": 0.26445, "loss": 0.26445, "time": 0.49521} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.01057, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.27877, "loss": 0.27877, "time": 0.49261} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.01055, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.28917, "loss": 0.28917, "time": 0.49179} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.01053, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.28157, "loss": 0.28157, "time": 0.39063} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.01051, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95438, "top5_acc": 0.99875, "loss_cls": 0.25667, "loss": 0.25667, "time": 0.5111} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.01049, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9475, "top5_acc": 1.0, "loss_cls": 0.29473, "loss": 0.29473, "time": 0.23993} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.01047, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95688, "top5_acc": 0.9975, "loss_cls": 0.26295, "loss": 0.26295, "time": 0.45543} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.01045, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.2848, "loss": 0.2848, "time": 0.4903} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.01043, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94312, "top5_acc": 0.99875, "loss_cls": 0.31112, "loss": 0.31112, "time": 0.49219} +{"mode": "val", "epoch": 83, "iter": 533, "lr": 0.01042, "top1_acc": 0.90036, "top5_acc": 0.99401, "mean_class_accuracy": 0.85974} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.0104, "memory": 4083, "data_time": 0.19875, "top1_acc": 0.94938, "top5_acc": 1.0, "loss_cls": 0.28711, "loss": 0.28711, "time": 0.81522} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.01038, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95562, "top5_acc": 1.0, "loss_cls": 0.26483, "loss": 0.26483, "time": 0.49122} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.01036, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.25431, "loss": 0.25431, "time": 0.49119} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.01034, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9525, "top5_acc": 0.9975, "loss_cls": 0.26814, "loss": 0.26814, "time": 0.49605} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.01031, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.27453, "loss": 0.27453, "time": 0.49155} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.01029, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.28697, "loss": 0.28697, "time": 0.49133} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.01027, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.92938, "top5_acc": 0.99938, "loss_cls": 0.35865, "loss": 0.35865, "time": 0.35758} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.01025, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94125, "top5_acc": 0.99875, "loss_cls": 0.31268, "loss": 0.31268, "time": 0.51174} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.01023, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.30299, "loss": 0.30299, "time": 0.2527} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.01021, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.22094, "loss": 0.22094, "time": 0.48048} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.01019, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93188, "top5_acc": 1.0, "loss_cls": 0.33752, "loss": 0.33752, "time": 0.49323} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.01017, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.26041, "loss": 0.26041, "time": 0.49171} +{"mode": "val", "epoch": 84, "iter": 533, "lr": 0.01016, "top1_acc": 0.89614, "top5_acc": 0.99425, "mean_class_accuracy": 0.88183} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.01014, "memory": 4083, "data_time": 0.18818, "top1_acc": 0.95188, "top5_acc": 0.99875, "loss_cls": 0.28536, "loss": 0.28536, "time": 0.79352} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.01012, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.23571, "loss": 0.23571, "time": 0.49146} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.0101, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.23864, "loss": 0.23864, "time": 0.4922} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.01008, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.218, "loss": 0.218, "time": 0.49008} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.01006, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.23948, "loss": 0.23948, "time": 0.49411} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.01004, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94, "top5_acc": 1.0, "loss_cls": 0.30474, "loss": 0.30474, "time": 0.49532} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.01002, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9475, "top5_acc": 1.0, "loss_cls": 0.29586, "loss": 0.29586, "time": 0.3303} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.01, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95375, "top5_acc": 0.99812, "loss_cls": 0.27477, "loss": 0.27477, "time": 0.51097} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.00998, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.96125, "top5_acc": 0.99938, "loss_cls": 0.21822, "loss": 0.21822, "time": 0.27218} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.00996, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95, "top5_acc": 1.0, "loss_cls": 0.24455, "loss": 0.24455, "time": 0.49369} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.00994, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.95625, "top5_acc": 0.99875, "loss_cls": 0.24366, "loss": 0.24366, "time": 0.49076} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.00992, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95438, "top5_acc": 0.99938, "loss_cls": 0.27622, "loss": 0.27622, "time": 0.49398} +{"mode": "val", "epoch": 85, "iter": 533, "lr": 0.0099, "top1_acc": 0.89825, "top5_acc": 0.99531, "mean_class_accuracy": 0.87262} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.00988, "memory": 4083, "data_time": 0.19607, "top1_acc": 0.965, "top5_acc": 0.9975, "loss_cls": 0.23513, "loss": 0.23513, "time": 0.80721} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.00986, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.27497, "loss": 0.27497, "time": 0.49287} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.00984, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95938, "top5_acc": 0.99875, "loss_cls": 0.2502, "loss": 0.2502, "time": 0.49235} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.00982, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.945, "top5_acc": 0.99938, "loss_cls": 0.28018, "loss": 0.28018, "time": 0.49206} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.0098, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.28081, "loss": 0.28081, "time": 0.49104} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.00978, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.23795, "loss": 0.23795, "time": 0.49058} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.00976, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.955, "top5_acc": 0.99875, "loss_cls": 0.25487, "loss": 0.25487, "time": 0.2966} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.00974, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.95438, "top5_acc": 0.99938, "loss_cls": 0.2723, "loss": 0.2723, "time": 0.51217} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.00972, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.9425, "top5_acc": 0.99875, "loss_cls": 0.30564, "loss": 0.30564, "time": 0.29399} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.0097, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94, "top5_acc": 1.0, "loss_cls": 0.32219, "loss": 0.32219, "time": 0.49646} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.00968, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.24264, "loss": 0.24264, "time": 0.49337} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.00966, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.26295, "loss": 0.26295, "time": 0.49244} +{"mode": "val", "epoch": 86, "iter": 533, "lr": 0.00965, "top1_acc": 0.90494, "top5_acc": 0.9946, "mean_class_accuracy": 0.87585} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.00963, "memory": 4083, "data_time": 0.18407, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.2729, "loss": 0.2729, "time": 0.79074} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.00961, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.23011, "loss": 0.23011, "time": 0.49275} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.00959, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95188, "top5_acc": 1.0, "loss_cls": 0.24577, "loss": 0.24577, "time": 0.49208} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.00957, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.21713, "loss": 0.21713, "time": 0.49421} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.00955, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94625, "top5_acc": 0.99688, "loss_cls": 0.30627, "loss": 0.30627, "time": 0.49489} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.00953, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.24484, "loss": 0.24484, "time": 0.49349} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.00951, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.28506, "loss": 0.28506, "time": 0.29044} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.00949, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95625, "top5_acc": 0.99875, "loss_cls": 0.27979, "loss": 0.27979, "time": 0.51033} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.00947, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.25094, "loss": 0.25094, "time": 0.31313} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.00945, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.20918, "loss": 0.20918, "time": 0.48761} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.00943, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.2655, "loss": 0.2655, "time": 0.4908} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.00941, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9475, "top5_acc": 0.99875, "loss_cls": 0.28124, "loss": 0.28124, "time": 0.49264} +{"mode": "val", "epoch": 87, "iter": 533, "lr": 0.00939, "top1_acc": 0.89508, "top5_acc": 0.99319, "mean_class_accuracy": 0.85787} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.00937, "memory": 4083, "data_time": 0.1842, "top1_acc": 0.95812, "top5_acc": 0.99938, "loss_cls": 0.24401, "loss": 0.24401, "time": 0.78488} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.00935, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.20509, "loss": 0.20509, "time": 0.49153} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.00933, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.17529, "loss": 0.17529, "time": 0.49246} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.00931, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.19729, "loss": 0.19729, "time": 0.48855} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.00929, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.20413, "loss": 0.20413, "time": 0.49157} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.00927, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.20893, "loss": 0.20893, "time": 0.49407} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.00925, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.23803, "loss": 0.23803, "time": 0.28248} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.00923, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.23835, "loss": 0.23835, "time": 0.51118} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.00921, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95938, "top5_acc": 0.99875, "loss_cls": 0.24067, "loss": 0.24067, "time": 0.3127} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.00919, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.22463, "loss": 0.22463, "time": 0.49156} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.00917, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95062, "top5_acc": 0.99812, "loss_cls": 0.25905, "loss": 0.25905, "time": 0.49174} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.00915, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.29662, "loss": 0.29662, "time": 0.49576} +{"mode": "val", "epoch": 88, "iter": 533, "lr": 0.00914, "top1_acc": 0.90494, "top5_acc": 0.99179, "mean_class_accuracy": 0.87433} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.00912, "memory": 4083, "data_time": 0.18522, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.21129, "loss": 0.21129, "time": 0.79565} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0091, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.21011, "loss": 0.21011, "time": 0.49089} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.00908, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.23634, "loss": 0.23634, "time": 0.49201} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.00906, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95188, "top5_acc": 0.99875, "loss_cls": 0.28586, "loss": 0.28586, "time": 0.49629} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.00904, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.95125, "top5_acc": 0.99875, "loss_cls": 0.27359, "loss": 0.27359, "time": 0.4938} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.00902, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.95438, "top5_acc": 0.99875, "loss_cls": 0.26062, "loss": 0.26062, "time": 0.49234} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.009, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.95812, "top5_acc": 0.99875, "loss_cls": 0.26971, "loss": 0.26971, "time": 0.29099} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.00898, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95688, "top5_acc": 0.99938, "loss_cls": 0.25077, "loss": 0.25077, "time": 0.49318} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.00896, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.2774, "loss": 0.2774, "time": 0.31912} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.00894, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.26255, "loss": 0.26255, "time": 0.49708} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.00892, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.95562, "top5_acc": 1.0, "loss_cls": 0.27193, "loss": 0.27193, "time": 0.49131} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.0089, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.96875, "top5_acc": 0.99938, "loss_cls": 0.19846, "loss": 0.19846, "time": 0.49119} +{"mode": "val", "epoch": 89, "iter": 533, "lr": 0.00889, "top1_acc": 0.90893, "top5_acc": 0.99507, "mean_class_accuracy": 0.88775} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.00887, "memory": 4083, "data_time": 0.18702, "top1_acc": 0.96125, "top5_acc": 0.99875, "loss_cls": 0.22241, "loss": 0.22241, "time": 0.79295} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.00885, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.28031, "loss": 0.28031, "time": 0.49151} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.00883, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97062, "top5_acc": 0.99938, "loss_cls": 0.191, "loss": 0.191, "time": 0.49275} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.00881, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.27149, "loss": 0.27149, "time": 0.49407} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.00879, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.23135, "loss": 0.23135, "time": 0.49063} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.00877, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.28021, "loss": 0.28021, "time": 0.49399} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.00875, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.22251, "loss": 0.22251, "time": 0.2855} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.00873, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.20238, "loss": 0.20238, "time": 0.48266} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.00871, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.95688, "top5_acc": 0.99875, "loss_cls": 0.25569, "loss": 0.25569, "time": 0.34261} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.00869, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.21891, "loss": 0.21891, "time": 0.49091} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.00867, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.29077, "loss": 0.29077, "time": 0.49328} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.00865, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95562, "top5_acc": 0.99875, "loss_cls": 0.27226, "loss": 0.27226, "time": 0.49235} +{"mode": "val", "epoch": 90, "iter": 533, "lr": 0.00864, "top1_acc": 0.9101, "top5_acc": 0.99484, "mean_class_accuracy": 0.87688} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.00862, "memory": 4083, "data_time": 0.18861, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.23627, "loss": 0.23627, "time": 0.78752} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0086, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.20426, "loss": 0.20426, "time": 0.48998} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.00858, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.21573, "loss": 0.21573, "time": 0.49423} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.00856, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.25159, "loss": 0.25159, "time": 0.49252} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.00854, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.20756, "loss": 0.20756, "time": 0.49259} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.00852, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.25008, "loss": 0.25008, "time": 0.49248} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.0085, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.23966, "loss": 0.23966, "time": 0.32203} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.00848, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.261, "loss": 0.261, "time": 0.4266} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.00846, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95438, "top5_acc": 0.99875, "loss_cls": 0.26442, "loss": 0.26442, "time": 0.37494} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.00844, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.96562, "top5_acc": 0.99938, "loss_cls": 0.21784, "loss": 0.21784, "time": 0.49175} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.00842, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.23523, "loss": 0.23523, "time": 0.49423} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.0084, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96375, "top5_acc": 0.99938, "loss_cls": 0.21938, "loss": 0.21938, "time": 0.49357} +{"mode": "val", "epoch": 91, "iter": 533, "lr": 0.00839, "top1_acc": 0.90658, "top5_acc": 0.99448, "mean_class_accuracy": 0.87691} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.00837, "memory": 4083, "data_time": 0.18349, "top1_acc": 0.95875, "top5_acc": 0.99812, "loss_cls": 0.23574, "loss": 0.23574, "time": 0.77905} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.00835, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.20119, "loss": 0.20119, "time": 0.49181} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.00833, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.21371, "loss": 0.21371, "time": 0.49275} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.00831, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.20054, "loss": 0.20054, "time": 0.49008} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.00829, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97312, "top5_acc": 0.99938, "loss_cls": 0.18146, "loss": 0.18146, "time": 0.49425} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.00827, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.16817, "loss": 0.16817, "time": 0.48844} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.00825, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.18773, "loss": 0.18773, "time": 0.33417} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.00824, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.96625, "top5_acc": 0.99938, "loss_cls": 0.22008, "loss": 0.22008, "time": 0.40223} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.00822, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.95688, "top5_acc": 0.99938, "loss_cls": 0.2505, "loss": 0.2505, "time": 0.36394} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.0082, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.21722, "loss": 0.21722, "time": 0.49086} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.00818, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.21714, "loss": 0.21714, "time": 0.49027} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.00816, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.267, "loss": 0.267, "time": 0.49019} +{"mode": "val", "epoch": 92, "iter": 533, "lr": 0.00814, "top1_acc": 0.90729, "top5_acc": 0.99531, "mean_class_accuracy": 0.87517} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.00812, "memory": 4083, "data_time": 0.19406, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.19169, "loss": 0.19169, "time": 0.79275} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.0081, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97188, "top5_acc": 0.99938, "loss_cls": 0.19675, "loss": 0.19675, "time": 0.48914} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.00809, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.1997, "loss": 0.1997, "time": 0.49207} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.00807, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.19645, "loss": 0.19645, "time": 0.49291} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.00805, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.21703, "loss": 0.21703, "time": 0.49097} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.00803, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.19498, "loss": 0.19498, "time": 0.4859} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.00801, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.18868, "loss": 0.18868, "time": 0.35011} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.00799, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96875, "top5_acc": 0.99938, "loss_cls": 0.19174, "loss": 0.19174, "time": 0.3875} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.00797, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.19745, "loss": 0.19745, "time": 0.3811} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.00795, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96625, "top5_acc": 0.99938, "loss_cls": 0.2233, "loss": 0.2233, "time": 0.49385} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.00793, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.26216, "loss": 0.26216, "time": 0.49176} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.00791, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.21989, "loss": 0.21989, "time": 0.49319} +{"mode": "val", "epoch": 93, "iter": 533, "lr": 0.0079, "top1_acc": 0.91503, "top5_acc": 0.99495, "mean_class_accuracy": 0.88464} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.00788, "memory": 4083, "data_time": 0.19238, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.21314, "loss": 0.21314, "time": 0.78531} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.00786, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.16468, "loss": 0.16468, "time": 0.49168} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.00784, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.19022, "loss": 0.19022, "time": 0.49124} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.00782, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.2143, "loss": 0.2143, "time": 0.49464} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.0078, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.20596, "loss": 0.20596, "time": 0.49149} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.00778, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.96, "top5_acc": 0.99938, "loss_cls": 0.2403, "loss": 0.2403, "time": 0.47364} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.00777, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.20557, "loss": 0.20557, "time": 0.35382} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.00775, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.23048, "loss": 0.23048, "time": 0.38233} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.00773, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.20383, "loss": 0.20383, "time": 0.36883} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.00771, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96688, "top5_acc": 0.99938, "loss_cls": 0.23355, "loss": 0.23355, "time": 0.49154} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.00769, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96625, "top5_acc": 0.99938, "loss_cls": 0.20796, "loss": 0.20796, "time": 0.49207} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.00767, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.21447, "loss": 0.21447, "time": 0.49426} +{"mode": "val", "epoch": 94, "iter": 533, "lr": 0.00766, "top1_acc": 0.91797, "top5_acc": 0.9939, "mean_class_accuracy": 0.89504} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.00764, "memory": 4083, "data_time": 0.18677, "top1_acc": 0.96688, "top5_acc": 0.99938, "loss_cls": 0.19575, "loss": 0.19575, "time": 0.80014} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.00762, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.19005, "loss": 0.19005, "time": 0.49324} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.0076, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.25002, "loss": 0.25002, "time": 0.48823} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.00758, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.95562, "top5_acc": 1.0, "loss_cls": 0.23933, "loss": 0.23933, "time": 0.49296} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.00756, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.19339, "loss": 0.19339, "time": 0.49125} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.00754, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97312, "top5_acc": 0.99938, "loss_cls": 0.1847, "loss": 0.1847, "time": 0.47077} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.00752, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96875, "top5_acc": 0.99938, "loss_cls": 0.20814, "loss": 0.20814, "time": 0.37603} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.00751, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97062, "top5_acc": 0.99938, "loss_cls": 0.17806, "loss": 0.17806, "time": 0.35645} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.00749, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.18583, "loss": 0.18583, "time": 0.3796} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.00747, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.22061, "loss": 0.22061, "time": 0.49103} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.00745, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.19616, "loss": 0.19616, "time": 0.49036} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.00743, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97062, "top5_acc": 0.99938, "loss_cls": 0.1917, "loss": 0.1917, "time": 0.49179} +{"mode": "val", "epoch": 95, "iter": 533, "lr": 0.00742, "top1_acc": 0.90987, "top5_acc": 0.99401, "mean_class_accuracy": 0.88886} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.0074, "memory": 4083, "data_time": 0.18211, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.15281, "loss": 0.15281, "time": 0.7798} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.00738, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.15896, "loss": 0.15896, "time": 0.49016} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.00736, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.1579, "loss": 0.1579, "time": 0.49317} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.00734, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96562, "top5_acc": 0.99938, "loss_cls": 0.21307, "loss": 0.21307, "time": 0.48973} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.00732, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.21085, "loss": 0.21085, "time": 0.49117} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.0073, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.17621, "loss": 0.17621, "time": 0.49217} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.00729, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.95812, "top5_acc": 0.99938, "loss_cls": 0.23642, "loss": 0.23642, "time": 0.33327} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.00727, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.20673, "loss": 0.20673, "time": 0.40258} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.00725, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97062, "top5_acc": 0.99938, "loss_cls": 0.20473, "loss": 0.20473, "time": 0.37263} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.00723, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.21266, "loss": 0.21266, "time": 0.49172} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.00721, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.24347, "loss": 0.24347, "time": 0.4935} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.00719, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.25798, "loss": 0.25798, "time": 0.4909} +{"mode": "val", "epoch": 96, "iter": 533, "lr": 0.00718, "top1_acc": 0.90893, "top5_acc": 0.9939, "mean_class_accuracy": 0.88551} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.00716, "memory": 4083, "data_time": 0.18104, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.15467, "loss": 0.15467, "time": 0.79566} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.00714, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.20382, "loss": 0.20382, "time": 0.49084} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.00712, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.16905, "loss": 0.16905, "time": 0.4904} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.0071, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.18056, "loss": 0.18056, "time": 0.49068} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.00709, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.23775, "loss": 0.23775, "time": 0.49077} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.00707, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.21349, "loss": 0.21349, "time": 0.48133} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.00705, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.17285, "loss": 0.17285, "time": 0.3528} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.00703, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.17597, "loss": 0.17597, "time": 0.38286} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.00701, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.95188, "top5_acc": 1.0, "loss_cls": 0.24208, "loss": 0.24208, "time": 0.37343} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.00699, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97312, "top5_acc": 0.99875, "loss_cls": 0.19621, "loss": 0.19621, "time": 0.4883} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.00698, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96625, "top5_acc": 0.99938, "loss_cls": 0.19909, "loss": 0.19909, "time": 0.4923} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.00696, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.20361, "loss": 0.20361, "time": 0.49078} +{"mode": "val", "epoch": 97, "iter": 533, "lr": 0.00694, "top1_acc": 0.9155, "top5_acc": 0.9946, "mean_class_accuracy": 0.89238} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.00692, "memory": 4083, "data_time": 0.17957, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14943, "loss": 0.14943, "time": 0.79107} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.00691, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.96875, "top5_acc": 0.99938, "loss_cls": 0.17787, "loss": 0.17787, "time": 0.48957} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.00689, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.14319, "loss": 0.14319, "time": 0.49236} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.00687, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.18068, "loss": 0.18068, "time": 0.49207} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.00685, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.20169, "loss": 0.20169, "time": 0.4898} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.00683, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.20796, "loss": 0.20796, "time": 0.48448} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.00681, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.17267, "loss": 0.17267, "time": 0.36718} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.0068, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.15532, "loss": 0.15532, "time": 0.36833} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.00678, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97312, "top5_acc": 0.99875, "loss_cls": 0.15888, "loss": 0.15888, "time": 0.38553} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.00676, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.15405, "loss": 0.15405, "time": 0.48963} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.00674, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.20415, "loss": 0.20415, "time": 0.49222} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.00672, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.23101, "loss": 0.23101, "time": 0.49015} +{"mode": "val", "epoch": 98, "iter": 533, "lr": 0.00671, "top1_acc": 0.90459, "top5_acc": 0.99355, "mean_class_accuracy": 0.88081} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.00669, "memory": 4083, "data_time": 0.18403, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.17817, "loss": 0.17817, "time": 0.79315} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.00667, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12893, "loss": 0.12893, "time": 0.49142} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.00665, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.15988, "loss": 0.15988, "time": 0.49104} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.00664, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97562, "top5_acc": 0.99938, "loss_cls": 0.16034, "loss": 0.16034, "time": 0.49182} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.00662, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97062, "top5_acc": 0.99938, "loss_cls": 0.16472, "loss": 0.16472, "time": 0.48852} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.0066, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.17566, "loss": 0.17566, "time": 0.46971} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.00658, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.18299, "loss": 0.18299, "time": 0.37983} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.00656, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96562, "top5_acc": 0.99938, "loss_cls": 0.18625, "loss": 0.18625, "time": 0.35492} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.00655, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.1723, "loss": 0.1723, "time": 0.38432} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.00653, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.1713, "loss": 0.1713, "time": 0.49209} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.00651, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.21494, "loss": 0.21494, "time": 0.48966} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.00649, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.21216, "loss": 0.21216, "time": 0.49131} +{"mode": "val", "epoch": 99, "iter": 533, "lr": 0.00648, "top1_acc": 0.91304, "top5_acc": 0.99519, "mean_class_accuracy": 0.87306} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.00646, "memory": 4083, "data_time": 0.18254, "top1_acc": 0.98625, "top5_acc": 0.99938, "loss_cls": 0.12751, "loss": 0.12751, "time": 0.79107} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.00644, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.14858, "loss": 0.14858, "time": 0.48943} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.00642, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.14811, "loss": 0.14811, "time": 0.49024} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.00641, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.11347, "loss": 0.11347, "time": 0.48938} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.00639, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.12636, "loss": 0.12636, "time": 0.4899} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.00637, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.19018, "loss": 0.19018, "time": 0.47297} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.00635, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.15747, "loss": 0.15747, "time": 0.35614} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.00634, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.16197, "loss": 0.16197, "time": 0.37889} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.00632, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95688, "top5_acc": 0.99875, "loss_cls": 0.21299, "loss": 0.21299, "time": 0.37202} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.0063, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.95, "top5_acc": 0.99875, "loss_cls": 0.27334, "loss": 0.27334, "time": 0.49226} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.00628, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.97438, "top5_acc": 0.99938, "loss_cls": 0.17376, "loss": 0.17376, "time": 0.49274} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.00626, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.96812, "top5_acc": 0.99938, "loss_cls": 0.21058, "loss": 0.21058, "time": 0.48969} +{"mode": "val", "epoch": 100, "iter": 533, "lr": 0.00625, "top1_acc": 0.91644, "top5_acc": 0.9946, "mean_class_accuracy": 0.89526} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.00623, "memory": 4083, "data_time": 0.18323, "top1_acc": 0.98438, "top5_acc": 0.99938, "loss_cls": 0.14845, "loss": 0.14845, "time": 0.79665} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.00621, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.17473, "loss": 0.17473, "time": 0.49148} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.0062, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.15279, "loss": 0.15279, "time": 0.4904} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.00618, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.11232, "loss": 0.11232, "time": 0.49407} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.00616, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98938, "top5_acc": 0.99938, "loss_cls": 0.09328, "loss": 0.09328, "time": 0.49125} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.00614, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.16113, "loss": 0.16113, "time": 0.48634} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.00613, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.97438, "top5_acc": 0.99938, "loss_cls": 0.16416, "loss": 0.16416, "time": 0.35228} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.00611, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.15349, "loss": 0.15349, "time": 0.38232} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.00609, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.18583, "loss": 0.18583, "time": 0.38052} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.00607, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.96875, "top5_acc": 0.99938, "loss_cls": 0.16914, "loss": 0.16914, "time": 0.48949} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.00606, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.20903, "loss": 0.20903, "time": 0.49054} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.00604, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.17556, "loss": 0.17556, "time": 0.49183} +{"mode": "val", "epoch": 101, "iter": 533, "lr": 0.00602, "top1_acc": 0.91726, "top5_acc": 0.99378, "mean_class_accuracy": 0.88875} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.00601, "memory": 4083, "data_time": 0.18417, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.17953, "loss": 0.17953, "time": 0.79297} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.00599, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.96812, "top5_acc": 0.99938, "loss_cls": 0.18482, "loss": 0.18482, "time": 0.4884} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.00597, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.1594, "loss": 0.1594, "time": 0.49231} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.00596, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11583, "loss": 0.11583, "time": 0.49155} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.00594, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.15415, "loss": 0.15415, "time": 0.48994} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.00592, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.15472, "loss": 0.15472, "time": 0.4847} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.0059, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.98, "top5_acc": 0.99938, "loss_cls": 0.15722, "loss": 0.15722, "time": 0.32942} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.00589, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13818, "loss": 0.13818, "time": 0.40435} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.00587, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9775, "top5_acc": 0.99938, "loss_cls": 0.14476, "loss": 0.14476, "time": 0.36989} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.00585, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.19393, "loss": 0.19393, "time": 0.48828} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.00583, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.14745, "loss": 0.14745, "time": 0.48928} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.00582, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.15086, "loss": 0.15086, "time": 0.48913} +{"mode": "val", "epoch": 102, "iter": 533, "lr": 0.0058, "top1_acc": 0.91339, "top5_acc": 0.99566, "mean_class_accuracy": 0.89091} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.00579, "memory": 4083, "data_time": 0.18532, "top1_acc": 0.97312, "top5_acc": 0.99938, "loss_cls": 0.16198, "loss": 0.16198, "time": 0.80421} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.00577, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.13181, "loss": 0.13181, "time": 0.49096} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.00575, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12596, "loss": 0.12596, "time": 0.49243} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.00573, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.14428, "loss": 0.14428, "time": 0.49295} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.00572, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.14489, "loss": 0.14489, "time": 0.49262} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.0057, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11575, "loss": 0.11575, "time": 0.46597} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.00568, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.1331, "loss": 0.1331, "time": 0.37482} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.00566, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.15995, "loss": 0.15995, "time": 0.35564} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.00565, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.1714, "loss": 0.1714, "time": 0.37966} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.00563, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.18039, "loss": 0.18039, "time": 0.49034} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.00561, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.97812, "top5_acc": 0.99875, "loss_cls": 0.16674, "loss": 0.16674, "time": 0.49047} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.0056, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97312, "top5_acc": 0.99938, "loss_cls": 0.16091, "loss": 0.16091, "time": 0.48741} +{"mode": "val", "epoch": 103, "iter": 533, "lr": 0.00558, "top1_acc": 0.91773, "top5_acc": 0.99542, "mean_class_accuracy": 0.88926} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.00557, "memory": 4083, "data_time": 0.18702, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.18811, "loss": 0.18811, "time": 0.78097} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.00555, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.1417, "loss": 0.1417, "time": 0.49097} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.00553, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.9875, "top5_acc": 0.99938, "loss_cls": 0.12645, "loss": 0.12645, "time": 0.4899} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.00551, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.15966, "loss": 0.15966, "time": 0.49243} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.0055, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.14501, "loss": 0.14501, "time": 0.49047} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.00548, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.1429, "loss": 0.1429, "time": 0.48508} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.00546, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.16286, "loss": 0.16286, "time": 0.34245} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.00545, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.15604, "loss": 0.15604, "time": 0.39331} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.00543, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11105, "loss": 0.11105, "time": 0.37815} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.00541, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97875, "top5_acc": 0.99938, "loss_cls": 0.15018, "loss": 0.15018, "time": 0.49285} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.0054, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14871, "loss": 0.14871, "time": 0.48947} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.00538, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97812, "top5_acc": 0.99938, "loss_cls": 0.14315, "loss": 0.14315, "time": 0.49309} +{"mode": "val", "epoch": 104, "iter": 533, "lr": 0.00537, "top1_acc": 0.91351, "top5_acc": 0.99413, "mean_class_accuracy": 0.87991} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.00535, "memory": 4083, "data_time": 0.1838, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.15522, "loss": 0.15522, "time": 0.79434} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.00533, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.09825, "loss": 0.09825, "time": 0.48856} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.00532, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09725, "loss": 0.09725, "time": 0.49296} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.0053, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.15214, "loss": 0.15214, "time": 0.49159} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.00528, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.15067, "loss": 0.15067, "time": 0.4925} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.00527, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.12449, "loss": 0.12449, "time": 0.47969} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.00525, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.98188, "top5_acc": 0.99938, "loss_cls": 0.13012, "loss": 0.13012, "time": 0.34175} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.00523, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10002, "loss": 0.10002, "time": 0.39354} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.00522, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.1507, "loss": 0.1507, "time": 0.37452} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.0052, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.17437, "loss": 0.17437, "time": 0.49404} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.00518, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98188, "top5_acc": 0.99938, "loss_cls": 0.12476, "loss": 0.12476, "time": 0.4924} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.00517, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.15776, "loss": 0.15776, "time": 0.49432} +{"mode": "val", "epoch": 105, "iter": 533, "lr": 0.00515, "top1_acc": 0.91503, "top5_acc": 0.99531, "mean_class_accuracy": 0.87801} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.00514, "memory": 4083, "data_time": 0.18246, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.10652, "loss": 0.10652, "time": 0.7912} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.00512, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12668, "loss": 0.12668, "time": 0.48707} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.0051, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.13699, "loss": 0.13699, "time": 0.49217} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.00509, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.09992, "loss": 0.09992, "time": 0.4935} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.00507, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.08974, "loss": 0.08974, "time": 0.49224} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.00505, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.09554, "loss": 0.09554, "time": 0.49103} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.00504, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.10845, "loss": 0.10845, "time": 0.32766} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.00502, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13366, "loss": 0.13366, "time": 0.40955} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.005, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.10685, "loss": 0.10685, "time": 0.36812} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.00499, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10965, "loss": 0.10965, "time": 0.49131} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.00497, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.15222, "loss": 0.15222, "time": 0.49077} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.00496, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.13217, "loss": 0.13217, "time": 0.49265} +{"mode": "val", "epoch": 106, "iter": 533, "lr": 0.00494, "top1_acc": 0.91175, "top5_acc": 0.99507, "mean_class_accuracy": 0.88022} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.00493, "memory": 4083, "data_time": 0.17993, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.13882, "loss": 0.13882, "time": 0.779} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.00491, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.12361, "loss": 0.12361, "time": 0.49255} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.00489, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.12658, "loss": 0.12658, "time": 0.49447} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.00488, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.10019, "loss": 0.10019, "time": 0.49505} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.00486, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.10182, "loss": 0.10182, "time": 0.49203} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.00485, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.12792, "loss": 0.12792, "time": 0.49093} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.00483, "memory": 4083, "data_time": 0.00063, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.15324, "loss": 0.15324, "time": 0.31467} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.00481, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.12293, "loss": 0.12293, "time": 0.4346} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.0048, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11591, "loss": 0.11591, "time": 0.36319} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.00478, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.17602, "loss": 0.17602, "time": 0.49195} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.00476, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.12784, "loss": 0.12784, "time": 0.49605} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.00475, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.11622, "loss": 0.11622, "time": 0.49621} +{"mode": "val", "epoch": 107, "iter": 533, "lr": 0.00474, "top1_acc": 0.93041, "top5_acc": 0.99542, "mean_class_accuracy": 0.90213} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.00472, "memory": 4083, "data_time": 0.18389, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09808, "loss": 0.09808, "time": 0.7944} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0047, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.12615, "loss": 0.12615, "time": 0.49265} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.00469, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.09001, "loss": 0.09001, "time": 0.49222} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.00467, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.11028, "loss": 0.11028, "time": 0.49035} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.00466, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.98938, "top5_acc": 0.99938, "loss_cls": 0.09046, "loss": 0.09046, "time": 0.49118} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.00464, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.10145, "loss": 0.10145, "time": 0.48844} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.00462, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11552, "loss": 0.11552, "time": 0.32488} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.00461, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14154, "loss": 0.14154, "time": 0.40748} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.00459, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.12787, "loss": 0.12787, "time": 0.35566} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.00458, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97688, "top5_acc": 0.99875, "loss_cls": 0.13379, "loss": 0.13379, "time": 0.49061} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.00456, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.15305, "loss": 0.15305, "time": 0.49395} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.00455, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.13067, "loss": 0.13067, "time": 0.49295} +{"mode": "val", "epoch": 108, "iter": 533, "lr": 0.00453, "top1_acc": 0.9202, "top5_acc": 0.99507, "mean_class_accuracy": 0.89484} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.00452, "memory": 4083, "data_time": 0.18924, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12367, "loss": 0.12367, "time": 0.79012} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.0045, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.10964, "loss": 0.10964, "time": 0.49284} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.00449, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14348, "loss": 0.14348, "time": 0.4908} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.00447, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11179, "loss": 0.11179, "time": 0.48774} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.00445, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08936, "loss": 0.08936, "time": 0.49053} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.00444, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11912, "loss": 0.11912, "time": 0.48788} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.00442, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98562, "top5_acc": 0.99938, "loss_cls": 0.10651, "loss": 0.10651, "time": 0.32392} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.00441, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.98, "top5_acc": 0.99938, "loss_cls": 0.11322, "loss": 0.11322, "time": 0.42045} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.00439, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12629, "loss": 0.12629, "time": 0.36898} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.00438, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.12586, "loss": 0.12586, "time": 0.49132} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.00436, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.12271, "loss": 0.12271, "time": 0.49181} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.00434, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.13479, "loss": 0.13479, "time": 0.49293} +{"mode": "val", "epoch": 109, "iter": 533, "lr": 0.00433, "top1_acc": 0.92513, "top5_acc": 0.99519, "mean_class_accuracy": 0.90144} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.00432, "memory": 4083, "data_time": 0.1847, "top1_acc": 0.9875, "top5_acc": 0.99938, "loss_cls": 0.09663, "loss": 0.09663, "time": 0.78809} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.0043, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08729, "loss": 0.08729, "time": 0.48924} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.00429, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08457, "loss": 0.08457, "time": 0.497} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.00427, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.10743, "loss": 0.10743, "time": 0.49084} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.00426, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.9875, "top5_acc": 0.99938, "loss_cls": 0.10398, "loss": 0.10398, "time": 0.49109} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.00424, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08986, "loss": 0.08986, "time": 0.49401} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.00422, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.073, "loss": 0.073, "time": 0.33643} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.00421, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.11594, "loss": 0.11594, "time": 0.40631} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.00419, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98625, "top5_acc": 0.99938, "loss_cls": 0.11193, "loss": 0.11193, "time": 0.38014} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.00418, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.12959, "loss": 0.12959, "time": 0.4916} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.00416, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.09225, "loss": 0.09225, "time": 0.49217} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.00415, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.13002, "loss": 0.13002, "time": 0.49515} +{"mode": "val", "epoch": 110, "iter": 533, "lr": 0.00414, "top1_acc": 0.92031, "top5_acc": 0.99624, "mean_class_accuracy": 0.89634} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.00412, "memory": 4083, "data_time": 0.18748, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.09823, "loss": 0.09823, "time": 0.79373} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.00411, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.09489, "loss": 0.09489, "time": 0.49339} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.00409, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07108, "loss": 0.07108, "time": 0.49044} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.00408, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.08637, "loss": 0.08637, "time": 0.48859} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.00406, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06436, "loss": 0.06436, "time": 0.49171} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.00405, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.08238, "loss": 0.08238, "time": 0.48571} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.00403, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10071, "loss": 0.10071, "time": 0.34572} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.00402, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.0906, "loss": 0.0906, "time": 0.38877} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.004, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.10089, "loss": 0.10089, "time": 0.37248} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.00399, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.1018, "loss": 0.1018, "time": 0.48669} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.00397, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09304, "loss": 0.09304, "time": 0.49236} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.00396, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.10032, "loss": 0.10032, "time": 0.49278} +{"mode": "val", "epoch": 111, "iter": 533, "lr": 0.00394, "top1_acc": 0.93158, "top5_acc": 0.99624, "mean_class_accuracy": 0.91067} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.00393, "memory": 4083, "data_time": 0.19076, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07598, "loss": 0.07598, "time": 0.81044} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.00391, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0587, "loss": 0.0587, "time": 0.49115} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.0039, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.06149, "loss": 0.06149, "time": 0.49094} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.00388, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.08259, "loss": 0.08259, "time": 0.49266} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.00387, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.06614, "loss": 0.06614, "time": 0.49125} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.00385, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06569, "loss": 0.06569, "time": 0.4648} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.00384, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.0627, "loss": 0.0627, "time": 0.39102} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.00382, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07138, "loss": 0.07138, "time": 0.34763} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.00381, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06526, "loss": 0.06526, "time": 0.39482} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.0038, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.07819, "loss": 0.07819, "time": 0.49286} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.00378, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.08975, "loss": 0.08975, "time": 0.49147} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.00377, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99125, "top5_acc": 0.99938, "loss_cls": 0.07633, "loss": 0.07633, "time": 0.48927} +{"mode": "val", "epoch": 112, "iter": 533, "lr": 0.00375, "top1_acc": 0.93358, "top5_acc": 0.99624, "mean_class_accuracy": 0.91202} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.00374, "memory": 4083, "data_time": 0.19113, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.09245, "loss": 0.09245, "time": 0.7975} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.00373, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.98188, "top5_acc": 0.99938, "loss_cls": 0.09746, "loss": 0.09746, "time": 0.48955} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.00371, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10128, "loss": 0.10128, "time": 0.49048} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.0037, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.11976, "loss": 0.11976, "time": 0.49505} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.00368, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.0755, "loss": 0.0755, "time": 0.49005} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.00367, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08051, "loss": 0.08051, "time": 0.45651} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.00365, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.074, "loss": 0.074, "time": 0.40881} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.00364, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08603, "loss": 0.08603, "time": 0.32677} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.00362, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08773, "loss": 0.08773, "time": 0.39695} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.00361, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08435, "loss": 0.08435, "time": 0.49148} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0036, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.1133, "loss": 0.1133, "time": 0.48989} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.00358, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.07924, "loss": 0.07924, "time": 0.49338} +{"mode": "val", "epoch": 113, "iter": 533, "lr": 0.00357, "top1_acc": 0.92513, "top5_acc": 0.99589, "mean_class_accuracy": 0.8963} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.00355, "memory": 4083, "data_time": 0.18678, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07822, "loss": 0.07822, "time": 0.7944} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.00354, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.0798, "loss": 0.0798, "time": 0.48761} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.00353, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.07929, "loss": 0.07929, "time": 0.49199} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.00351, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07291, "loss": 0.07291, "time": 0.49366} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.0035, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.0589, "loss": 0.0589, "time": 0.4927} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.00348, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06141, "loss": 0.06141, "time": 0.45835} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.00347, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04856, "loss": 0.04856, "time": 0.39588} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.00346, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07384, "loss": 0.07384, "time": 0.34088} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.00344, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.05681, "loss": 0.05681, "time": 0.38503} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.00343, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.07374, "loss": 0.07374, "time": 0.48877} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.00341, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.0758, "loss": 0.0758, "time": 0.49084} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.0034, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08174, "loss": 0.08174, "time": 0.48824} +{"mode": "val", "epoch": 114, "iter": 533, "lr": 0.00339, "top1_acc": 0.93099, "top5_acc": 0.9966, "mean_class_accuracy": 0.90556} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.00337, "memory": 4083, "data_time": 0.1921, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05488, "loss": 0.05488, "time": 0.80274} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.00336, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04635, "loss": 0.04635, "time": 0.48976} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.00335, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.04168, "loss": 0.04168, "time": 0.49242} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.00333, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.0586, "loss": 0.0586, "time": 0.49044} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.00332, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06527, "loss": 0.06527, "time": 0.49073} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.0033, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07761, "loss": 0.07761, "time": 0.45641} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.00329, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06814, "loss": 0.06814, "time": 0.39431} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.00328, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04169, "loss": 0.04169, "time": 0.33911} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.00326, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04701, "loss": 0.04701, "time": 0.38678} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.00325, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.0829, "loss": 0.0829, "time": 0.49147} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.00324, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04944, "loss": 0.04944, "time": 0.4905} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.00322, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.0718, "loss": 0.0718, "time": 0.49001} +{"mode": "val", "epoch": 115, "iter": 533, "lr": 0.00321, "top1_acc": 0.93311, "top5_acc": 0.99624, "mean_class_accuracy": 0.90801} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.0032, "memory": 4083, "data_time": 0.18376, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06242, "loss": 0.06242, "time": 0.78222} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.00318, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05265, "loss": 0.05265, "time": 0.49133} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.00317, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05564, "loss": 0.05564, "time": 0.48845} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.00316, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.04824, "loss": 0.04824, "time": 0.49235} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.00314, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.06882, "loss": 0.06882, "time": 0.49095} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.00313, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.08875, "loss": 0.08875, "time": 0.48397} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.00312, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07854, "loss": 0.07854, "time": 0.34496} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.0031, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07779, "loss": 0.07779, "time": 0.39161} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.00309, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.0689, "loss": 0.0689, "time": 0.36448} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.00308, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.06899, "loss": 0.06899, "time": 0.49082} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.00306, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06191, "loss": 0.06191, "time": 0.49283} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.00305, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05272, "loss": 0.05272, "time": 0.49181} +{"mode": "val", "epoch": 116, "iter": 533, "lr": 0.00304, "top1_acc": 0.93006, "top5_acc": 0.99636, "mean_class_accuracy": 0.90703} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.00302, "memory": 4083, "data_time": 0.18207, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07246, "loss": 0.07246, "time": 0.79422} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.00301, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07996, "loss": 0.07996, "time": 0.49271} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.003, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05773, "loss": 0.05773, "time": 0.49085} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.00298, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.0578, "loss": 0.0578, "time": 0.48727} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.00297, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.04735, "loss": 0.04735, "time": 0.4946} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.00296, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04419, "loss": 0.04419, "time": 0.49208} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.00294, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04035, "loss": 0.04035, "time": 0.32668} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.00293, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05297, "loss": 0.05297, "time": 0.40886} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.00292, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03533, "loss": 0.03533, "time": 0.36039} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.00291, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.04908, "loss": 0.04908, "time": 0.49166} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.00289, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04788, "loss": 0.04788, "time": 0.49193} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.00288, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.0814, "loss": 0.0814, "time": 0.49258} +{"mode": "val", "epoch": 117, "iter": 533, "lr": 0.00287, "top1_acc": 0.93639, "top5_acc": 0.99648, "mean_class_accuracy": 0.9106} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.00286, "memory": 4083, "data_time": 0.18578, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.0365, "loss": 0.0365, "time": 0.78594} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.00284, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04703, "loss": 0.04703, "time": 0.49333} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.00283, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04471, "loss": 0.04471, "time": 0.49023} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.00282, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04634, "loss": 0.04634, "time": 0.48975} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.0028, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04821, "loss": 0.04821, "time": 0.4932} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.00279, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04216, "loss": 0.04216, "time": 0.49053} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.00278, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06267, "loss": 0.06267, "time": 0.32218} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.00277, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04762, "loss": 0.04762, "time": 0.41141} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.00275, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.05585, "loss": 0.05585, "time": 0.35986} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.00274, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.03961, "loss": 0.03961, "time": 0.48989} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.00273, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04651, "loss": 0.04651, "time": 0.48911} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.00271, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03857, "loss": 0.03857, "time": 0.49157} +{"mode": "val", "epoch": 118, "iter": 533, "lr": 0.0027, "top1_acc": 0.93968, "top5_acc": 0.99671, "mean_class_accuracy": 0.91727} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.00269, "memory": 4083, "data_time": 0.18234, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05198, "loss": 0.05198, "time": 0.80174} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.00268, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04177, "loss": 0.04177, "time": 0.49337} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.00267, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03616, "loss": 0.03616, "time": 0.4915} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.00265, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.0383, "loss": 0.0383, "time": 0.4946} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.00264, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03443, "loss": 0.03443, "time": 0.48992} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.00263, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04543, "loss": 0.04543, "time": 0.48606} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.00262, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03257, "loss": 0.03257, "time": 0.34677} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.0026, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.05547, "loss": 0.05547, "time": 0.38641} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.00259, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.04754, "loss": 0.04754, "time": 0.37756} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.00258, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08097, "loss": 0.08097, "time": 0.49324} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.00257, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04083, "loss": 0.04083, "time": 0.49319} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.00255, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.04782, "loss": 0.04782, "time": 0.4884} +{"mode": "val", "epoch": 119, "iter": 533, "lr": 0.00254, "top1_acc": 0.92994, "top5_acc": 0.99742, "mean_class_accuracy": 0.9066} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.00253, "memory": 4083, "data_time": 0.18923, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05864, "loss": 0.05864, "time": 0.79399} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.00252, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06337, "loss": 0.06337, "time": 0.49121} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.00251, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.0447, "loss": 0.0447, "time": 0.49427} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.00249, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.04466, "loss": 0.04466, "time": 0.49044} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.00248, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.0478, "loss": 0.0478, "time": 0.49065} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.00247, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99, "top5_acc": 0.99938, "loss_cls": 0.07139, "loss": 0.07139, "time": 0.47962} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.00246, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04445, "loss": 0.04445, "time": 0.33814} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.00245, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.05858, "loss": 0.05858, "time": 0.39143} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.00243, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.05989, "loss": 0.05989, "time": 0.37047} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.00242, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05146, "loss": 0.05146, "time": 0.49157} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00241, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04066, "loss": 0.04066, "time": 0.49051} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.0024, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04443, "loss": 0.04443, "time": 0.49192} +{"mode": "val", "epoch": 120, "iter": 533, "lr": 0.00239, "top1_acc": 0.93311, "top5_acc": 0.99578, "mean_class_accuracy": 0.90479} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00238, "memory": 4083, "data_time": 0.18459, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.05721, "loss": 0.05721, "time": 0.79893} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00236, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04617, "loss": 0.04617, "time": 0.48858} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.00235, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.03859, "loss": 0.03859, "time": 0.48942} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00234, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04593, "loss": 0.04593, "time": 0.49129} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00233, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.0433, "loss": 0.0433, "time": 0.48985} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00232, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02818, "loss": 0.02818, "time": 0.48811} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.0023, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03562, "loss": 0.03562, "time": 0.33367} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00229, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03029, "loss": 0.03029, "time": 0.40384} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.00228, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05006, "loss": 0.05006, "time": 0.36838} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00227, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03086, "loss": 0.03086, "time": 0.49262} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00226, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03433, "loss": 0.03433, "time": 0.4939} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00225, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02925, "loss": 0.02925, "time": 0.49276} +{"mode": "val", "epoch": 121, "iter": 533, "lr": 0.00224, "top1_acc": 0.93628, "top5_acc": 0.99695, "mean_class_accuracy": 0.91413} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00222, "memory": 4083, "data_time": 0.1818, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03647, "loss": 0.03647, "time": 0.79014} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00221, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02958, "loss": 0.02958, "time": 0.4934} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.0022, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02384, "loss": 0.02384, "time": 0.49128} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00219, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02611, "loss": 0.02611, "time": 0.49289} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00218, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02614, "loss": 0.02614, "time": 0.49147} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00217, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03028, "loss": 0.03028, "time": 0.49267} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00215, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.03784, "loss": 0.03784, "time": 0.31976} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00214, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03889, "loss": 0.03889, "time": 0.41845} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.00213, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04417, "loss": 0.04417, "time": 0.35276} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00212, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02993, "loss": 0.02993, "time": 0.49262} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00211, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03516, "loss": 0.03516, "time": 0.49406} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.0021, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.04157, "loss": 0.04157, "time": 0.4952} +{"mode": "val", "epoch": 122, "iter": 533, "lr": 0.00209, "top1_acc": 0.93745, "top5_acc": 0.99648, "mean_class_accuracy": 0.91073} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00208, "memory": 4083, "data_time": 0.1802, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03476, "loss": 0.03476, "time": 0.7901} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00207, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03385, "loss": 0.03385, "time": 0.49044} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00205, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02721, "loss": 0.02721, "time": 0.49259} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00204, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.0319, "loss": 0.0319, "time": 0.49247} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00203, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03071, "loss": 0.03071, "time": 0.49115} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00202, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02789, "loss": 0.02789, "time": 0.48964} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00201, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02497, "loss": 0.02497, "time": 0.29915} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.002, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02638, "loss": 0.02638, "time": 0.45111} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00199, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02123, "loss": 0.02123, "time": 0.33571} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.00198, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02626, "loss": 0.02626, "time": 0.49344} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00197, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.04951, "loss": 0.04951, "time": 0.48866} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00195, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04426, "loss": 0.04426, "time": 0.49031} +{"mode": "val", "epoch": 123, "iter": 533, "lr": 0.00195, "top1_acc": 0.93405, "top5_acc": 0.99671, "mean_class_accuracy": 0.90893} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00194, "memory": 4083, "data_time": 0.18651, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02935, "loss": 0.02935, "time": 0.78984} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00192, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.02909, "loss": 0.02909, "time": 0.48972} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00191, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05216, "loss": 0.05216, "time": 0.48964} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.0019, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0364, "loss": 0.0364, "time": 0.49149} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00189, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03147, "loss": 0.03147, "time": 0.49401} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00188, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01943, "loss": 0.01943, "time": 0.4944} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00187, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02199, "loss": 0.02199, "time": 0.29225} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00186, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02649, "loss": 0.02649, "time": 0.47136} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00185, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03097, "loss": 0.03097, "time": 0.33609} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00184, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03265, "loss": 0.03265, "time": 0.48828} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00183, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02785, "loss": 0.02785, "time": 0.49586} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.00182, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02831, "loss": 0.02831, "time": 0.49563} +{"mode": "val", "epoch": 124, "iter": 533, "lr": 0.00181, "top1_acc": 0.93721, "top5_acc": 0.99718, "mean_class_accuracy": 0.91609} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.0018, "memory": 4083, "data_time": 0.1887, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.0268, "loss": 0.0268, "time": 0.78727} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.00179, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03009, "loss": 0.03009, "time": 0.49335} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00178, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03119, "loss": 0.03119, "time": 0.49192} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00177, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03179, "loss": 0.03179, "time": 0.49103} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00176, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02477, "loss": 0.02477, "time": 0.49322} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00175, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03602, "loss": 0.03602, "time": 0.49321} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00173, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04229, "loss": 0.04229, "time": 0.27974} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00172, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03834, "loss": 0.03834, "time": 0.48807} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.00171, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.04786, "loss": 0.04786, "time": 0.34045} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.0017, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06009, "loss": 0.06009, "time": 0.49074} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00169, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04039, "loss": 0.04039, "time": 0.49399} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00168, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02732, "loss": 0.02732, "time": 0.49174} +{"mode": "val", "epoch": 125, "iter": 533, "lr": 0.00167, "top1_acc": 0.94156, "top5_acc": 0.99624, "mean_class_accuracy": 0.91969} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00166, "memory": 4083, "data_time": 0.18896, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03089, "loss": 0.03089, "time": 0.79395} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00165, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02972, "loss": 0.02972, "time": 0.49159} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00164, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03304, "loss": 0.03304, "time": 0.49107} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00163, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03231, "loss": 0.03231, "time": 0.49462} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00162, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02631, "loss": 0.02631, "time": 0.49071} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00161, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0251, "loss": 0.0251, "time": 0.492} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0016, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02162, "loss": 0.02162, "time": 0.30118} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00159, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02569, "loss": 0.02569, "time": 0.45148} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00158, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0257, "loss": 0.0257, "time": 0.34461} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00157, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.024, "loss": 0.024, "time": 0.48936} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00156, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.03385, "loss": 0.03385, "time": 0.49058} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00155, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.03237, "loss": 0.03237, "time": 0.49227} +{"mode": "val", "epoch": 126, "iter": 533, "lr": 0.00155, "top1_acc": 0.94015, "top5_acc": 0.99707, "mean_class_accuracy": 0.91962} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00154, "memory": 4083, "data_time": 0.18924, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02269, "loss": 0.02269, "time": 0.81148} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00153, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02329, "loss": 0.02329, "time": 0.49042} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00152, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02962, "loss": 0.02962, "time": 0.49221} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00151, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03068, "loss": 0.03068, "time": 0.49249} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.0015, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02154, "loss": 0.02154, "time": 0.49055} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.00149, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02168, "loss": 0.02168, "time": 0.49117} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00148, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02443, "loss": 0.02443, "time": 0.32482} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00147, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02794, "loss": 0.02794, "time": 0.41284} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00146, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02233, "loss": 0.02233, "time": 0.36126} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00145, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01823, "loss": 0.01823, "time": 0.49213} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00144, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01927, "loss": 0.01927, "time": 0.49037} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00143, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02542, "loss": 0.02542, "time": 0.48951} +{"mode": "val", "epoch": 127, "iter": 533, "lr": 0.00142, "top1_acc": 0.93897, "top5_acc": 0.9966, "mean_class_accuracy": 0.91836} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00141, "memory": 4083, "data_time": 0.19172, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02104, "loss": 0.02104, "time": 0.80443} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.0014, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02558, "loss": 0.02558, "time": 0.4889} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00139, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01945, "loss": 0.01945, "time": 0.49085} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00138, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02202, "loss": 0.02202, "time": 0.49272} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00138, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01865, "loss": 0.01865, "time": 0.49242} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00137, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01802, "loss": 0.01802, "time": 0.47522} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.00136, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01897, "loss": 0.01897, "time": 0.37109} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00135, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01958, "loss": 0.01958, "time": 0.36412} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00134, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01672, "loss": 0.01672, "time": 0.38818} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00133, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01864, "loss": 0.01864, "time": 0.48829} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00132, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0174, "loss": 0.0174, "time": 0.49062} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00131, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.0293, "loss": 0.0293, "time": 0.4915} +{"mode": "val", "epoch": 128, "iter": 533, "lr": 0.0013, "top1_acc": 0.93639, "top5_acc": 0.99671, "mean_class_accuracy": 0.91175} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.00129, "memory": 4083, "data_time": 0.18822, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02523, "loss": 0.02523, "time": 0.79187} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00129, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02548, "loss": 0.02548, "time": 0.48711} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00128, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01962, "loss": 0.01962, "time": 0.489} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00127, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01987, "loss": 0.01987, "time": 0.48935} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00126, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01987, "loss": 0.01987, "time": 0.49011} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00125, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02445, "loss": 0.02445, "time": 0.46881} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00124, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01753, "loss": 0.01753, "time": 0.37455} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00123, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01946, "loss": 0.01946, "time": 0.36118} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.00122, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02004, "loss": 0.02004, "time": 0.39513} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00121, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02212, "loss": 0.02212, "time": 0.49285} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00121, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02407, "loss": 0.02407, "time": 0.49211} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.0012, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02625, "loss": 0.02625, "time": 0.4935} +{"mode": "val", "epoch": 129, "iter": 533, "lr": 0.00119, "top1_acc": 0.93968, "top5_acc": 0.99754, "mean_class_accuracy": 0.91477} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00118, "memory": 4083, "data_time": 0.19109, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.01998, "loss": 0.01998, "time": 0.80353} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00117, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02137, "loss": 0.02137, "time": 0.49021} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00116, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01759, "loss": 0.01759, "time": 0.49155} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00116, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02694, "loss": 0.02694, "time": 0.49262} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.00115, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02187, "loss": 0.02187, "time": 0.48939} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00114, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01665, "loss": 0.01665, "time": 0.44052} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00113, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0166, "loss": 0.0166, "time": 0.42965} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00112, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02187, "loss": 0.02187, "time": 0.30461} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00111, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01856, "loss": 0.01856, "time": 0.4118} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.0011, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02164, "loss": 0.02164, "time": 0.49186} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.0011, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02432, "loss": 0.02432, "time": 0.4892} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00109, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02102, "loss": 0.02102, "time": 0.49057} +{"mode": "val", "epoch": 130, "iter": 533, "lr": 0.00108, "top1_acc": 0.9398, "top5_acc": 0.99707, "mean_class_accuracy": 0.91589} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00107, "memory": 4083, "data_time": 0.18284, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0199, "loss": 0.0199, "time": 0.80275} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.00106, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01596, "loss": 0.01596, "time": 0.48918} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00106, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02225, "loss": 0.02225, "time": 0.49316} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00105, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01592, "loss": 0.01592, "time": 0.49118} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00104, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02137, "loss": 0.02137, "time": 0.49017} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00103, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01738, "loss": 0.01738, "time": 0.44853} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00102, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01986, "loss": 0.01986, "time": 0.42581} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00102, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01598, "loss": 0.01598, "time": 0.31129} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00101, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0184, "loss": 0.0184, "time": 0.42257} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.001, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01649, "loss": 0.01649, "time": 0.49047} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.00099, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01642, "loss": 0.01642, "time": 0.49141} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00098, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02179, "loss": 0.02179, "time": 0.48772} +{"mode": "val", "epoch": 131, "iter": 533, "lr": 0.00098, "top1_acc": 0.94273, "top5_acc": 0.99695, "mean_class_accuracy": 0.92029} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.00097, "memory": 4083, "data_time": 0.18267, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01774, "loss": 0.01774, "time": 0.7932} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00096, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01945, "loss": 0.01945, "time": 0.49275} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00095, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01855, "loss": 0.01855, "time": 0.49311} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00095, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01782, "loss": 0.01782, "time": 0.49322} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00094, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01817, "loss": 0.01817, "time": 0.49267} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00093, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02111, "loss": 0.02111, "time": 0.43889} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00092, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01594, "loss": 0.01594, "time": 0.43946} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00091, "memory": 4083, "data_time": 0.00044, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01601, "loss": 0.01601, "time": 0.29089} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00091, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01518, "loss": 0.01518, "time": 0.41048} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0009, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01689, "loss": 0.01689, "time": 0.49262} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00089, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01792, "loss": 0.01792, "time": 0.48899} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00088, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01768, "loss": 0.01768, "time": 0.49043} +{"mode": "val", "epoch": 132, "iter": 533, "lr": 0.00088, "top1_acc": 0.94261, "top5_acc": 0.99754, "mean_class_accuracy": 0.92151} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.00087, "memory": 4083, "data_time": 0.18171, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01921, "loss": 0.01921, "time": 0.79752} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00086, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01876, "loss": 0.01876, "time": 0.49079} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00086, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02002, "loss": 0.02002, "time": 0.48916} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00085, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01404, "loss": 0.01404, "time": 0.49128} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00084, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01561, "loss": 0.01561, "time": 0.49347} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00083, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01719, "loss": 0.01719, "time": 0.44138} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00083, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01663, "loss": 0.01663, "time": 0.42241} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00082, "memory": 4083, "data_time": 0.0004, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01654, "loss": 0.01654, "time": 0.31284} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00081, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01471, "loss": 0.01471, "time": 0.40677} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.0008, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01528, "loss": 0.01528, "time": 0.4893} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0008, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01612, "loss": 0.01612, "time": 0.49015} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00079, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0156, "loss": 0.0156, "time": 0.4875} +{"mode": "val", "epoch": 133, "iter": 533, "lr": 0.00078, "top1_acc": 0.94226, "top5_acc": 0.99754, "mean_class_accuracy": 0.92035} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00078, "memory": 4083, "data_time": 0.18498, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01862, "loss": 0.01862, "time": 0.79143} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00077, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01747, "loss": 0.01747, "time": 0.48953} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00076, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0155, "loss": 0.0155, "time": 0.49277} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.00076, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01596, "loss": 0.01596, "time": 0.48977} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00075, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01541, "loss": 0.01541, "time": 0.48854} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00074, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01468, "loss": 0.01468, "time": 0.4532} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00073, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02308, "loss": 0.02308, "time": 0.39834} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00073, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01927, "loss": 0.01927, "time": 0.3384} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00072, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02305, "loss": 0.02305, "time": 0.40941} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00071, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0161, "loss": 0.0161, "time": 0.4896} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00071, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01679, "loss": 0.01679, "time": 0.49283} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.0007, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0161, "loss": 0.0161, "time": 0.49286} +{"mode": "val", "epoch": 134, "iter": 533, "lr": 0.0007, "top1_acc": 0.9432, "top5_acc": 0.99754, "mean_class_accuracy": 0.92139} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00069, "memory": 4083, "data_time": 0.17988, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.016, "loss": 0.016, "time": 0.79538} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00068, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0197, "loss": 0.0197, "time": 0.48995} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00068, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01626, "loss": 0.01626, "time": 0.488} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00067, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01617, "loss": 0.01617, "time": 0.49015} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00066, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01866, "loss": 0.01866, "time": 0.49223} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00066, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01711, "loss": 0.01711, "time": 0.44538} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00065, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01558, "loss": 0.01558, "time": 0.42779} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00064, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01813, "loss": 0.01813, "time": 0.30763} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.00064, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01562, "loss": 0.01562, "time": 0.40517} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00063, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0193, "loss": 0.0193, "time": 0.48899} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00062, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01593, "loss": 0.01593, "time": 0.49043} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00062, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01959, "loss": 0.01959, "time": 0.49166} +{"mode": "val", "epoch": 135, "iter": 533, "lr": 0.00061, "top1_acc": 0.9459, "top5_acc": 0.99777, "mean_class_accuracy": 0.92597} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00061, "memory": 4083, "data_time": 0.18237, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01586, "loss": 0.01586, "time": 0.79679} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.0006, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01688, "loss": 0.01688, "time": 0.49233} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00059, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01371, "loss": 0.01371, "time": 0.49339} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00059, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01562, "loss": 0.01562, "time": 0.49059} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.00058, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01615, "loss": 0.01615, "time": 0.49128} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.00057, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01457, "loss": 0.01457, "time": 0.46528} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00057, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01544, "loss": 0.01544, "time": 0.39002} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00056, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02072, "loss": 0.02072, "time": 0.34572} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00056, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0168, "loss": 0.0168, "time": 0.39152} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00055, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01478, "loss": 0.01478, "time": 0.49307} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00054, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01836, "loss": 0.01836, "time": 0.49397} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00054, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01709, "loss": 0.01709, "time": 0.49174} +{"mode": "val", "epoch": 136, "iter": 533, "lr": 0.00053, "top1_acc": 0.94355, "top5_acc": 0.99765, "mean_class_accuracy": 0.92351} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00053, "memory": 4083, "data_time": 0.18462, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0158, "loss": 0.0158, "time": 0.80193} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00052, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01486, "loss": 0.01486, "time": 0.48982} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00052, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01453, "loss": 0.01453, "time": 0.49197} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.00051, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01709, "loss": 0.01709, "time": 0.49253} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.0005, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01803, "loss": 0.01803, "time": 0.49154} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.0005, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01474, "loss": 0.01474, "time": 0.4522} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00049, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01388, "loss": 0.01388, "time": 0.41848} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00049, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01551, "loss": 0.01551, "time": 0.31379} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00048, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0159, "loss": 0.0159, "time": 0.39544} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00048, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01553, "loss": 0.01553, "time": 0.49062} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00047, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01515, "loss": 0.01515, "time": 0.48975} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00046, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01489, "loss": 0.01489, "time": 0.49202} +{"mode": "val", "epoch": 137, "iter": 533, "lr": 0.00046, "top1_acc": 0.9439, "top5_acc": 0.99777, "mean_class_accuracy": 0.92234} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00046, "memory": 4083, "data_time": 0.18611, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01569, "loss": 0.01569, "time": 0.80501} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00045, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02267, "loss": 0.02267, "time": 0.49165} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00044, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0153, "loss": 0.0153, "time": 0.49072} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00044, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01571, "loss": 0.01571, "time": 0.49256} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.00043, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01648, "loss": 0.01648, "time": 0.49216} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.00043, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01553, "loss": 0.01553, "time": 0.44406} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00042, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01417, "loss": 0.01417, "time": 0.42538} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00042, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01653, "loss": 0.01653, "time": 0.30677} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00041, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01767, "loss": 0.01767, "time": 0.41408} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00041, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.016, "loss": 0.016, "time": 0.49293} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.0004, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01606, "loss": 0.01606, "time": 0.49298} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.0004, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01625, "loss": 0.01625, "time": 0.48941} +{"mode": "val", "epoch": 138, "iter": 533, "lr": 0.00039, "top1_acc": 0.94649, "top5_acc": 0.99765, "mean_class_accuracy": 0.92489} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00039, "memory": 4083, "data_time": 0.18622, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0144, "loss": 0.0144, "time": 0.81541} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00038, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01622, "loss": 0.01622, "time": 0.49321} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00038, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01448, "loss": 0.01448, "time": 0.49042} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00037, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01623, "loss": 0.01623, "time": 0.48902} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00037, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01612, "loss": 0.01612, "time": 0.49198} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00036, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0142, "loss": 0.0142, "time": 0.41498} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00036, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01408, "loss": 0.01408, "time": 0.49342} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00035, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01521, "loss": 0.01521, "time": 0.24013} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00035, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01408, "loss": 0.01408, "time": 0.4213} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.00034, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01516, "loss": 0.01516, "time": 0.49621} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.00034, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01578, "loss": 0.01578, "time": 0.4933} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00033, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01862, "loss": 0.01862, "time": 0.49535} +{"mode": "val", "epoch": 139, "iter": 533, "lr": 0.00033, "top1_acc": 0.9432, "top5_acc": 0.99742, "mean_class_accuracy": 0.92042} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00033, "memory": 4083, "data_time": 0.18757, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0157, "loss": 0.0157, "time": 0.8002} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00032, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01363, "loss": 0.01363, "time": 0.48655} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.00032, "memory": 4083, "data_time": 0.0004, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01458, "loss": 0.01458, "time": 0.49127} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.00031, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01595, "loss": 0.01595, "time": 0.48813} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00031, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01611, "loss": 0.01611, "time": 0.49107} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.0003, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01475, "loss": 0.01475, "time": 0.41496} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.0003, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01377, "loss": 0.01377, "time": 0.49247} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00029, "memory": 4083, "data_time": 0.00042, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01456, "loss": 0.01456, "time": 0.25196} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00029, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01497, "loss": 0.01497, "time": 0.43516} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00029, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01447, "loss": 0.01447, "time": 0.48931} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00028, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0148, "loss": 0.0148, "time": 0.49219} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00028, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01508, "loss": 0.01508, "time": 0.49354} +{"mode": "val", "epoch": 140, "iter": 533, "lr": 0.00027, "top1_acc": 0.94367, "top5_acc": 0.99754, "mean_class_accuracy": 0.92217} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00027, "memory": 4083, "data_time": 0.18194, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01344, "loss": 0.01344, "time": 0.78797} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00026, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01638, "loss": 0.01638, "time": 0.4893} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00026, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01737, "loss": 0.01737, "time": 0.49134} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00026, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01532, "loss": 0.01532, "time": 0.4913} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00025, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01576, "loss": 0.01576, "time": 0.49247} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00025, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01581, "loss": 0.01581, "time": 0.41228} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00024, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01548, "loss": 0.01548, "time": 0.48275} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00024, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01426, "loss": 0.01426, "time": 0.25588} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00024, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01323, "loss": 0.01323, "time": 0.42022} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00023, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01375, "loss": 0.01375, "time": 0.4915} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00023, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01406, "loss": 0.01406, "time": 0.49458} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00022, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01546, "loss": 0.01546, "time": 0.49247} +{"mode": "val", "epoch": 141, "iter": 533, "lr": 0.00022, "top1_acc": 0.94414, "top5_acc": 0.99789, "mean_class_accuracy": 0.92205} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00022, "memory": 4083, "data_time": 0.18141, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01579, "loss": 0.01579, "time": 0.80332} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00021, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01415, "loss": 0.01415, "time": 0.49327} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00021, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01778, "loss": 0.01778, "time": 0.49226} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00021, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01515, "loss": 0.01515, "time": 0.49226} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.0002, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01398, "loss": 0.01398, "time": 0.49145} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.0002, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01431, "loss": 0.01431, "time": 0.4176} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.0002, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01397, "loss": 0.01397, "time": 0.4646} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00019, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01676, "loss": 0.01676, "time": 0.27093} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00019, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01436, "loss": 0.01436, "time": 0.41863} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00018, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01481, "loss": 0.01481, "time": 0.49088} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00018, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01597, "loss": 0.01597, "time": 0.49052} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00018, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01485, "loss": 0.01485, "time": 0.49027} +{"mode": "val", "epoch": 142, "iter": 533, "lr": 0.00018, "top1_acc": 0.94566, "top5_acc": 0.99754, "mean_class_accuracy": 0.92321} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.00017, "memory": 4083, "data_time": 0.18454, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01594, "loss": 0.01594, "time": 0.79532} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00017, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01389, "loss": 0.01389, "time": 0.49517} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00017, "memory": 4083, "data_time": 0.00042, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01637, "loss": 0.01637, "time": 0.4927} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00016, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01494, "loss": 0.01494, "time": 0.49081} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00016, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01462, "loss": 0.01462, "time": 0.49193} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00016, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01581, "loss": 0.01581, "time": 0.4265} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00015, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01367, "loss": 0.01367, "time": 0.46794} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00015, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01396, "loss": 0.01396, "time": 0.27354} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00015, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01378, "loss": 0.01378, "time": 0.42472} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00014, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01425, "loss": 0.01425, "time": 0.49213} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00014, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01528, "loss": 0.01528, "time": 0.49297} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00014, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01481, "loss": 0.01481, "time": 0.49166} +{"mode": "val", "epoch": 143, "iter": 533, "lr": 0.00013, "top1_acc": 0.94625, "top5_acc": 0.99789, "mean_class_accuracy": 0.92433} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00013, "memory": 4083, "data_time": 0.18504, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01554, "loss": 0.01554, "time": 0.79658} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00013, "memory": 4083, "data_time": 0.00051, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01425, "loss": 0.01425, "time": 0.49141} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00013, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01495, "loss": 0.01495, "time": 0.49235} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00012, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01548, "loss": 0.01548, "time": 0.49078} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00012, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01382, "loss": 0.01382, "time": 0.49068} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00012, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01513, "loss": 0.01513, "time": 0.41963} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00011, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01425, "loss": 0.01425, "time": 0.47286} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.00011, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01438, "loss": 0.01438, "time": 0.26042} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.00011, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01664, "loss": 0.01664, "time": 0.42899} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.00011, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0153, "loss": 0.0153, "time": 0.4926} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.0001, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01637, "loss": 0.01637, "time": 0.49187} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.0001, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01343, "loss": 0.01343, "time": 0.49182} +{"mode": "val", "epoch": 144, "iter": 533, "lr": 0.0001, "top1_acc": 0.94472, "top5_acc": 0.99754, "mean_class_accuracy": 0.92272} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.0001, "memory": 4083, "data_time": 0.18039, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01445, "loss": 0.01445, "time": 0.78932} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 9e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01536, "loss": 0.01536, "time": 0.48592} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 9e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0142, "loss": 0.0142, "time": 0.49319} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 9e-05, "memory": 4083, "data_time": 0.0004, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01555, "loss": 0.01555, "time": 0.48903} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 9e-05, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0162, "loss": 0.0162, "time": 0.49204} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 8e-05, "memory": 4083, "data_time": 0.0004, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01563, "loss": 0.01563, "time": 0.44001} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 8e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0141, "loss": 0.0141, "time": 0.43627} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 8e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01467, "loss": 0.01467, "time": 0.2943} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 8e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01618, "loss": 0.01618, "time": 0.41977} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 7e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01407, "loss": 0.01407, "time": 0.49032} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 7e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01618, "loss": 0.01618, "time": 0.4909} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 7e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01327, "loss": 0.01327, "time": 0.49175} +{"mode": "val", "epoch": 145, "iter": 533, "lr": 7e-05, "top1_acc": 0.94449, "top5_acc": 0.99777, "mean_class_accuracy": 0.92263} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 7e-05, "memory": 4083, "data_time": 0.18041, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01525, "loss": 0.01525, "time": 0.78851} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 6e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01372, "loss": 0.01372, "time": 0.49346} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 6e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01425, "loss": 0.01425, "time": 0.49109} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 6e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01628, "loss": 0.01628, "time": 0.49079} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 6e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01472, "loss": 0.01472, "time": 0.49187} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 6e-05, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01412, "loss": 0.01412, "time": 0.44552} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 5e-05, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0149, "loss": 0.0149, "time": 0.43271} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 5e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0161, "loss": 0.0161, "time": 0.30441} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 5e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01545, "loss": 0.01545, "time": 0.41562} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 5e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01621, "loss": 0.01621, "time": 0.49657} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 5e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01431, "loss": 0.01431, "time": 0.49134} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 5e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01341, "loss": 0.01341, "time": 0.49225} +{"mode": "val", "epoch": 146, "iter": 533, "lr": 4e-05, "top1_acc": 0.94343, "top5_acc": 0.998, "mean_class_accuracy": 0.92143} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 4e-05, "memory": 4083, "data_time": 0.18093, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01393, "loss": 0.01393, "time": 0.78905} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 4e-05, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01559, "loss": 0.01559, "time": 0.48901} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 4e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01573, "loss": 0.01573, "time": 0.4885} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 4e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01645, "loss": 0.01645, "time": 0.49263} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 4e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01356, "loss": 0.01356, "time": 0.49179} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 3e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01504, "loss": 0.01504, "time": 0.44368} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 3e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01383, "loss": 0.01383, "time": 0.40769} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 3e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01528, "loss": 0.01528, "time": 0.32273} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 3e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01425, "loss": 0.01425, "time": 0.41602} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 3e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0162, "loss": 0.0162, "time": 0.49368} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 3e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01344, "loss": 0.01344, "time": 0.49251} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 3e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01379, "loss": 0.01379, "time": 0.49169} +{"mode": "val", "epoch": 147, "iter": 533, "lr": 2e-05, "top1_acc": 0.94296, "top5_acc": 0.99812, "mean_class_accuracy": 0.9204} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 2e-05, "memory": 4083, "data_time": 0.18168, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01485, "loss": 0.01485, "time": 0.79245} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 2e-05, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01454, "loss": 0.01454, "time": 0.48887} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 2e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01316, "loss": 0.01316, "time": 0.49593} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 2e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01511, "loss": 0.01511, "time": 0.49668} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 2e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01573, "loss": 0.01573, "time": 0.48989} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 2e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01562, "loss": 0.01562, "time": 0.44334} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 2e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01577, "loss": 0.01577, "time": 0.44423} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 2e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01477, "loss": 0.01477, "time": 0.28859} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 1e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01493, "loss": 0.01493, "time": 0.4134} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 1e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01321, "loss": 0.01321, "time": 0.49094} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 1e-05, "memory": 4083, "data_time": 0.00019, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01393, "loss": 0.01393, "time": 0.49304} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01348, "loss": 0.01348, "time": 0.49171} +{"mode": "val", "epoch": 148, "iter": 533, "lr": 1e-05, "top1_acc": 0.94414, "top5_acc": 0.99754, "mean_class_accuracy": 0.9215} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 1e-05, "memory": 4083, "data_time": 0.18312, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01449, "loss": 0.01449, "time": 0.79538} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 1e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01628, "loss": 0.01628, "time": 0.49201} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 1e-05, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01899, "loss": 0.01899, "time": 0.4954} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 1e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0169, "loss": 0.0169, "time": 0.4916} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 1e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01497, "loss": 0.01497, "time": 0.49526} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 1e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01398, "loss": 0.01398, "time": 0.45316} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 1e-05, "memory": 4083, "data_time": 0.00042, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01372, "loss": 0.01372, "time": 0.4194} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 1e-05, "memory": 4083, "data_time": 0.00045, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01503, "loss": 0.01503, "time": 0.31659} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01428, "loss": 0.01428, "time": 0.39881} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01448, "loss": 0.01448, "time": 0.48887} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01489, "loss": 0.01489, "time": 0.49387} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01347, "loss": 0.01347, "time": 0.4935} +{"mode": "val", "epoch": 149, "iter": 533, "lr": 0.0, "top1_acc": 0.94402, "top5_acc": 0.99754, "mean_class_accuracy": 0.92279} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 0.0, "memory": 4083, "data_time": 0.17704, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01458, "loss": 0.01458, "time": 0.77985} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0155, "loss": 0.0155, "time": 0.49125} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 0.0, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01456, "loss": 0.01456, "time": 0.49143} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01346, "loss": 0.01346, "time": 0.49092} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 0.0, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01475, "loss": 0.01475, "time": 0.4963} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01637, "loss": 0.01637, "time": 0.47306} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 0.0, "memory": 4083, "data_time": 0.00051, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0157, "loss": 0.0157, "time": 0.38446} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 0.0, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01521, "loss": 0.01521, "time": 0.35495} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01452, "loss": 0.01452, "time": 0.38442} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01485, "loss": 0.01485, "time": 0.49161} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01306, "loss": 0.01306, "time": 0.48976} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01548, "loss": 0.01548, "time": 0.49021} +{"mode": "val", "epoch": 150, "iter": 533, "lr": 0.0, "top1_acc": 0.94566, "top5_acc": 0.99765, "mean_class_accuracy": 0.92375} diff --git a/finegym/b_1/b_1.py b/finegym/b_1/b_1.py new file mode 100644 index 0000000000000000000000000000000000000000..d71efeae5a8bb2b3684d9d56356d64774eb110c9 --- /dev/null +++ b/finegym/b_1/b_1.py @@ -0,0 +1,113 @@ +modality = 'b' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/b_1' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/finegym/b_1/best_pred.pkl b/finegym/b_1/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..a17a413d2496df2cee1afa663db826a974e1e497 --- /dev/null +++ b/finegym/b_1/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1667686a27b2ef3a7ac7d2ce0505853620eb489ad558a9c32820b2dc465f2e78 +size 5254933 diff --git a/finegym/b_1/best_top1_acc_epoch_138.pth b/finegym/b_1/best_top1_acc_epoch_138.pth new file mode 100644 index 0000000000000000000000000000000000000000..9611b4de590027052d2cb0a8277b8560f552e3e3 --- /dev/null +++ b/finegym/b_1/best_top1_acc_epoch_138.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1754379abe875e2d57bc6466ceda3a254a57f4e88260bd42c1691b520dcc6015 +size 31999601 diff --git a/finegym/b_2/20250624_084254.log b/finegym/b_2/20250624_084254.log new file mode 100644 index 0000000000000000000000000000000000000000..6e55b12ddd2ca8d7d2203f814cc9c63b0f76139e --- /dev/null +++ b/finegym/b_2/20250624_084254.log @@ -0,0 +1,3474 @@ +2025-06-24 08:42:54,192 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2025-06-24 08:42:54,390 - pyskl - INFO - Config: modality = 'b' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/b_2' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2025-06-24 08:42:54,391 - pyskl - INFO - Set random seed to 1488861688, deterministic: False +2025-06-24 08:42:55,815 - pyskl - INFO - 20484 videos remain after valid thresholding +2025-06-24 08:42:59,854 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-24 08:42:59,855 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2 +2025-06-24 08:42:59,855 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2025-06-24 08:42:59,855 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2025-06-24 08:42:59,856 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2 by HardDiskBackend. +2025-06-24 08:43:38,804 - pyskl - INFO - Epoch [1][100/1281] lr: 2.500e-02, eta: 20:46:34, time: 0.389, data_time: 0.175, memory: 4082, top1_acc: 0.0681, top5_acc: 0.2437, loss_cls: 4.5232, loss: 4.5232 +2025-06-24 08:44:00,066 - pyskl - INFO - Epoch [1][200/1281] lr: 2.500e-02, eta: 16:03:02, time: 0.213, data_time: 0.000, memory: 4082, top1_acc: 0.0938, top5_acc: 0.3544, loss_cls: 4.4434, loss: 4.4434 +2025-06-24 08:44:21,627 - pyskl - INFO - Epoch [1][300/1281] lr: 2.500e-02, eta: 14:31:29, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.0875, top5_acc: 0.3812, loss_cls: 4.2217, loss: 4.2217 +2025-06-24 08:44:43,275 - pyskl - INFO - Epoch [1][400/1281] lr: 2.500e-02, eta: 13:46:13, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.1256, top5_acc: 0.4300, loss_cls: 4.0282, loss: 4.0282 +2025-06-24 08:45:04,499 - pyskl - INFO - Epoch [1][500/1281] lr: 2.500e-02, eta: 13:16:13, time: 0.212, data_time: 0.000, memory: 4082, top1_acc: 0.1500, top5_acc: 0.4844, loss_cls: 3.8271, loss: 3.8271 +2025-06-24 08:45:25,883 - pyskl - INFO - Epoch [1][600/1281] lr: 2.500e-02, eta: 12:56:56, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.1862, top5_acc: 0.5144, loss_cls: 3.6721, loss: 3.6721 +2025-06-24 08:45:47,318 - pyskl - INFO - Epoch [1][700/1281] lr: 2.500e-02, eta: 12:43:18, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.2181, top5_acc: 0.5537, loss_cls: 3.5399, loss: 3.5399 +2025-06-24 08:46:08,673 - pyskl - INFO - Epoch [1][800/1281] lr: 2.500e-02, eta: 12:32:40, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.2612, top5_acc: 0.6131, loss_cls: 3.3254, loss: 3.3254 +2025-06-24 08:46:30,268 - pyskl - INFO - Epoch [1][900/1281] lr: 2.500e-02, eta: 12:25:10, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.2706, top5_acc: 0.6262, loss_cls: 3.2020, loss: 3.2020 +2025-06-24 08:46:51,892 - pyskl - INFO - Epoch [1][1000/1281] lr: 2.500e-02, eta: 12:19:11, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.2913, top5_acc: 0.6575, loss_cls: 3.0940, loss: 3.0940 +2025-06-24 08:47:13,536 - pyskl - INFO - Epoch [1][1100/1281] lr: 2.500e-02, eta: 12:14:17, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.3063, top5_acc: 0.6975, loss_cls: 2.9214, loss: 2.9214 +2025-06-24 08:47:35,230 - pyskl - INFO - Epoch [1][1200/1281] lr: 2.500e-02, eta: 12:10:16, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.3569, top5_acc: 0.7381, loss_cls: 2.7354, loss: 2.7354 +2025-06-24 08:47:53,473 - pyskl - INFO - Saving checkpoint at 1 epochs +2025-06-24 08:48:36,358 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 08:48:36,411 - pyskl - INFO - +top1_acc 0.3995 +top5_acc 0.7879 +2025-06-24 08:48:36,411 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 08:48:36,423 - pyskl - INFO - +mean_acc 0.2130 +2025-06-24 08:48:36,593 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2025-06-24 08:48:36,593 - pyskl - INFO - Best top1_acc is 0.3995 at 1 epoch. +2025-06-24 08:48:36,596 - pyskl - INFO - Epoch(val) [1][533] top1_acc: 0.3995, top5_acc: 0.7879, mean_class_accuracy: 0.2130 +2025-06-24 08:49:16,602 - pyskl - INFO - Epoch [2][100/1281] lr: 2.500e-02, eta: 12:06:03, time: 0.400, data_time: 0.184, memory: 4082, top1_acc: 0.3919, top5_acc: 0.7994, loss_cls: 2.5334, loss: 2.5334 +2025-06-24 08:49:38,432 - pyskl - INFO - Epoch [2][200/1281] lr: 2.500e-02, eta: 12:03:31, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.3981, top5_acc: 0.8081, loss_cls: 2.5091, loss: 2.5091 +2025-06-24 08:50:00,107 - pyskl - INFO - Epoch [2][300/1281] lr: 2.500e-02, eta: 12:00:56, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.4369, top5_acc: 0.8269, loss_cls: 2.3453, loss: 2.3453 +2025-06-24 08:50:21,768 - pyskl - INFO - Epoch [2][400/1281] lr: 2.500e-02, eta: 11:58:36, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.4612, top5_acc: 0.8331, loss_cls: 2.3006, loss: 2.3006 +2025-06-24 08:50:43,557 - pyskl - INFO - Epoch [2][500/1281] lr: 2.499e-02, eta: 11:56:42, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.4512, top5_acc: 0.8488, loss_cls: 2.2694, loss: 2.2694 +2025-06-24 08:51:05,214 - pyskl - INFO - Epoch [2][600/1281] lr: 2.499e-02, eta: 11:54:45, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.4600, top5_acc: 0.8669, loss_cls: 2.1630, loss: 2.1630 +2025-06-24 08:51:26,819 - pyskl - INFO - Epoch [2][700/1281] lr: 2.499e-02, eta: 11:52:53, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.4806, top5_acc: 0.8725, loss_cls: 2.1396, loss: 2.1396 +2025-06-24 08:51:48,781 - pyskl - INFO - Epoch [2][800/1281] lr: 2.499e-02, eta: 11:51:42, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5044, top5_acc: 0.8769, loss_cls: 2.0691, loss: 2.0691 +2025-06-24 08:52:10,462 - pyskl - INFO - Epoch [2][900/1281] lr: 2.499e-02, eta: 11:50:11, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.5094, top5_acc: 0.8812, loss_cls: 2.0220, loss: 2.0220 +2025-06-24 08:52:32,076 - pyskl - INFO - Epoch [2][1000/1281] lr: 2.499e-02, eta: 11:48:40, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.5112, top5_acc: 0.8912, loss_cls: 1.9777, loss: 1.9777 +2025-06-24 08:52:53,911 - pyskl - INFO - Epoch [2][1100/1281] lr: 2.499e-02, eta: 11:47:33, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.5275, top5_acc: 0.8869, loss_cls: 1.9321, loss: 1.9321 +2025-06-24 08:53:15,579 - pyskl - INFO - Epoch [2][1200/1281] lr: 2.499e-02, eta: 11:46:17, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.5494, top5_acc: 0.8938, loss_cls: 1.9134, loss: 1.9134 +2025-06-24 08:53:33,774 - pyskl - INFO - Saving checkpoint at 2 epochs +2025-06-24 08:54:16,866 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 08:54:16,923 - pyskl - INFO - +top1_acc 0.5394 +top5_acc 0.9010 +2025-06-24 08:54:16,923 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 08:54:16,929 - pyskl - INFO - +mean_acc 0.3727 +2025-06-24 08:54:16,935 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_1.pth was removed +2025-06-24 08:54:17,131 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2025-06-24 08:54:17,131 - pyskl - INFO - Best top1_acc is 0.5394 at 2 epoch. +2025-06-24 08:54:17,134 - pyskl - INFO - Epoch(val) [2][533] top1_acc: 0.5394, top5_acc: 0.9010, mean_class_accuracy: 0.3727 +2025-06-24 08:54:57,211 - pyskl - INFO - Epoch [3][100/1281] lr: 2.499e-02, eta: 11:45:11, time: 0.401, data_time: 0.187, memory: 4082, top1_acc: 0.5675, top5_acc: 0.9150, loss_cls: 1.8082, loss: 1.8082 +2025-06-24 08:55:18,622 - pyskl - INFO - Epoch [3][200/1281] lr: 2.499e-02, eta: 11:43:45, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.5706, top5_acc: 0.9150, loss_cls: 1.7889, loss: 1.7889 +2025-06-24 08:55:40,164 - pyskl - INFO - Epoch [3][300/1281] lr: 2.499e-02, eta: 11:42:33, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.5813, top5_acc: 0.9281, loss_cls: 1.7373, loss: 1.7373 +2025-06-24 08:56:01,911 - pyskl - INFO - Epoch [3][400/1281] lr: 2.499e-02, eta: 11:41:37, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.5875, top5_acc: 0.9294, loss_cls: 1.7159, loss: 1.7159 +2025-06-24 08:56:23,286 - pyskl - INFO - Epoch [3][500/1281] lr: 2.498e-02, eta: 11:40:21, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.5713, top5_acc: 0.9200, loss_cls: 1.7391, loss: 1.7391 +2025-06-24 08:56:44,526 - pyskl - INFO - Epoch [3][600/1281] lr: 2.498e-02, eta: 11:39:00, time: 0.212, data_time: 0.000, memory: 4082, top1_acc: 0.6044, top5_acc: 0.9256, loss_cls: 1.6852, loss: 1.6852 +2025-06-24 08:57:06,017 - pyskl - INFO - Epoch [3][700/1281] lr: 2.498e-02, eta: 11:37:57, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.5994, top5_acc: 0.9437, loss_cls: 1.6409, loss: 1.6409 +2025-06-24 08:57:27,723 - pyskl - INFO - Epoch [3][800/1281] lr: 2.498e-02, eta: 11:37:09, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6062, top5_acc: 0.9450, loss_cls: 1.6154, loss: 1.6154 +2025-06-24 08:57:49,208 - pyskl - INFO - Epoch [3][900/1281] lr: 2.498e-02, eta: 11:36:10, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.6319, top5_acc: 0.9444, loss_cls: 1.5595, loss: 1.5595 +2025-06-24 08:58:10,644 - pyskl - INFO - Epoch [3][1000/1281] lr: 2.498e-02, eta: 11:35:10, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.6425, top5_acc: 0.9519, loss_cls: 1.4973, loss: 1.4973 +2025-06-24 08:58:32,104 - pyskl - INFO - Epoch [3][1100/1281] lr: 2.498e-02, eta: 11:34:14, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.6150, top5_acc: 0.9425, loss_cls: 1.6283, loss: 1.6283 +2025-06-24 08:58:53,702 - pyskl - INFO - Epoch [3][1200/1281] lr: 2.498e-02, eta: 11:33:27, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6275, top5_acc: 0.9500, loss_cls: 1.5224, loss: 1.5224 +2025-06-24 08:59:11,785 - pyskl - INFO - Saving checkpoint at 3 epochs +2025-06-24 08:59:55,508 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 08:59:55,564 - pyskl - INFO - +top1_acc 0.5991 +top5_acc 0.9347 +2025-06-24 08:59:55,564 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 08:59:55,571 - pyskl - INFO - +mean_acc 0.4454 +2025-06-24 08:59:55,575 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_2.pth was removed +2025-06-24 08:59:55,762 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2025-06-24 08:59:55,762 - pyskl - INFO - Best top1_acc is 0.5991 at 3 epoch. +2025-06-24 08:59:55,765 - pyskl - INFO - Epoch(val) [3][533] top1_acc: 0.5991, top5_acc: 0.9347, mean_class_accuracy: 0.4454 +2025-06-24 09:00:35,736 - pyskl - INFO - Epoch [4][100/1281] lr: 2.497e-02, eta: 11:32:47, time: 0.400, data_time: 0.182, memory: 4082, top1_acc: 0.6456, top5_acc: 0.9537, loss_cls: 1.4666, loss: 1.4666 +2025-06-24 09:00:57,506 - pyskl - INFO - Epoch [4][200/1281] lr: 2.497e-02, eta: 11:32:10, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6506, top5_acc: 0.9563, loss_cls: 1.4189, loss: 1.4189 +2025-06-24 09:01:19,208 - pyskl - INFO - Epoch [4][300/1281] lr: 2.497e-02, eta: 11:31:31, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6606, top5_acc: 0.9550, loss_cls: 1.4052, loss: 1.4052 +2025-06-24 09:01:40,732 - pyskl - INFO - Epoch [4][400/1281] lr: 2.497e-02, eta: 11:30:44, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.6450, top5_acc: 0.9506, loss_cls: 1.5064, loss: 1.5064 +2025-06-24 09:02:02,799 - pyskl - INFO - Epoch [4][500/1281] lr: 2.497e-02, eta: 11:30:23, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.6231, top5_acc: 0.9544, loss_cls: 1.4858, loss: 1.4858 +2025-06-24 09:02:24,227 - pyskl - INFO - Epoch [4][600/1281] lr: 2.497e-02, eta: 11:29:34, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.6506, top5_acc: 0.9525, loss_cls: 1.4200, loss: 1.4200 +2025-06-24 09:02:45,782 - pyskl - INFO - Epoch [4][700/1281] lr: 2.497e-02, eta: 11:28:52, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6763, top5_acc: 0.9650, loss_cls: 1.3261, loss: 1.3261 +2025-06-24 09:03:07,536 - pyskl - INFO - Epoch [4][800/1281] lr: 2.496e-02, eta: 11:28:19, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6881, top5_acc: 0.9656, loss_cls: 1.3615, loss: 1.3615 +2025-06-24 09:03:29,397 - pyskl - INFO - Epoch [4][900/1281] lr: 2.496e-02, eta: 11:27:50, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6806, top5_acc: 0.9619, loss_cls: 1.3752, loss: 1.3752 +2025-06-24 09:03:51,128 - pyskl - INFO - Epoch [4][1000/1281] lr: 2.496e-02, eta: 11:27:17, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6875, top5_acc: 0.9625, loss_cls: 1.3213, loss: 1.3213 +2025-06-24 09:04:12,849 - pyskl - INFO - Epoch [4][1100/1281] lr: 2.496e-02, eta: 11:26:44, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6894, top5_acc: 0.9587, loss_cls: 1.3502, loss: 1.3502 +2025-06-24 09:04:34,442 - pyskl - INFO - Epoch [4][1200/1281] lr: 2.496e-02, eta: 11:26:06, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6900, top5_acc: 0.9594, loss_cls: 1.2917, loss: 1.2917 +2025-06-24 09:04:52,379 - pyskl - INFO - Saving checkpoint at 4 epochs +2025-06-24 09:05:36,039 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:05:36,093 - pyskl - INFO - +top1_acc 0.6840 +top5_acc 0.9596 +2025-06-24 09:05:36,094 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:05:36,101 - pyskl - INFO - +mean_acc 0.5586 +2025-06-24 09:05:36,105 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_3.pth was removed +2025-06-24 09:05:36,283 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2025-06-24 09:05:36,284 - pyskl - INFO - Best top1_acc is 0.6840 at 4 epoch. +2025-06-24 09:05:36,287 - pyskl - INFO - Epoch(val) [4][533] top1_acc: 0.6840, top5_acc: 0.9596, mean_class_accuracy: 0.5586 +2025-06-24 09:06:16,191 - pyskl - INFO - Epoch [5][100/1281] lr: 2.495e-02, eta: 11:25:29, time: 0.399, data_time: 0.182, memory: 4082, top1_acc: 0.6906, top5_acc: 0.9663, loss_cls: 1.2890, loss: 1.2890 +2025-06-24 09:06:38,145 - pyskl - INFO - Epoch [5][200/1281] lr: 2.495e-02, eta: 11:25:05, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6787, top5_acc: 0.9719, loss_cls: 1.2767, loss: 1.2767 +2025-06-24 09:06:59,679 - pyskl - INFO - Epoch [5][300/1281] lr: 2.495e-02, eta: 11:24:27, time: 0.215, data_time: 0.001, memory: 4082, top1_acc: 0.6769, top5_acc: 0.9675, loss_cls: 1.2991, loss: 1.2991 +2025-06-24 09:07:21,330 - pyskl - INFO - Epoch [5][400/1281] lr: 2.495e-02, eta: 11:23:54, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7019, top5_acc: 0.9663, loss_cls: 1.2722, loss: 1.2722 +2025-06-24 09:07:42,886 - pyskl - INFO - Epoch [5][500/1281] lr: 2.495e-02, eta: 11:23:17, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7031, top5_acc: 0.9725, loss_cls: 1.2429, loss: 1.2429 +2025-06-24 09:08:04,403 - pyskl - INFO - Epoch [5][600/1281] lr: 2.495e-02, eta: 11:22:40, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7113, top5_acc: 0.9762, loss_cls: 1.2054, loss: 1.2054 +2025-06-24 09:08:26,125 - pyskl - INFO - Epoch [5][700/1281] lr: 2.494e-02, eta: 11:22:10, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7087, top5_acc: 0.9712, loss_cls: 1.2324, loss: 1.2324 +2025-06-24 09:08:47,806 - pyskl - INFO - Epoch [5][800/1281] lr: 2.494e-02, eta: 11:21:39, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7087, top5_acc: 0.9650, loss_cls: 1.2261, loss: 1.2261 +2025-06-24 09:09:09,316 - pyskl - INFO - Epoch [5][900/1281] lr: 2.494e-02, eta: 11:21:03, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.6969, top5_acc: 0.9719, loss_cls: 1.2182, loss: 1.2182 +2025-06-24 09:09:30,656 - pyskl - INFO - Epoch [5][1000/1281] lr: 2.494e-02, eta: 11:20:23, time: 0.213, data_time: 0.000, memory: 4082, top1_acc: 0.7169, top5_acc: 0.9750, loss_cls: 1.2064, loss: 1.2064 +2025-06-24 09:09:51,993 - pyskl - INFO - Epoch [5][1100/1281] lr: 2.494e-02, eta: 11:19:42, time: 0.213, data_time: 0.000, memory: 4082, top1_acc: 0.7100, top5_acc: 0.9663, loss_cls: 1.2030, loss: 1.2030 +2025-06-24 09:10:13,311 - pyskl - INFO - Epoch [5][1200/1281] lr: 2.493e-02, eta: 11:19:02, time: 0.213, data_time: 0.000, memory: 4082, top1_acc: 0.7200, top5_acc: 0.9744, loss_cls: 1.1516, loss: 1.1516 +2025-06-24 09:10:31,367 - pyskl - INFO - Saving checkpoint at 5 epochs +2025-06-24 09:11:14,819 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:11:14,874 - pyskl - INFO - +top1_acc 0.7080 +top5_acc 0.9666 +2025-06-24 09:11:14,874 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:11:14,881 - pyskl - INFO - +mean_acc 0.5733 +2025-06-24 09:11:14,885 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_4.pth was removed +2025-06-24 09:11:15,067 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2025-06-24 09:11:15,067 - pyskl - INFO - Best top1_acc is 0.7080 at 5 epoch. +2025-06-24 09:11:15,070 - pyskl - INFO - Epoch(val) [5][533] top1_acc: 0.7080, top5_acc: 0.9666, mean_class_accuracy: 0.5733 +2025-06-24 09:11:55,537 - pyskl - INFO - Epoch [6][100/1281] lr: 2.493e-02, eta: 11:18:45, time: 0.405, data_time: 0.189, memory: 4082, top1_acc: 0.7200, top5_acc: 0.9700, loss_cls: 1.1732, loss: 1.1732 +2025-06-24 09:12:17,217 - pyskl - INFO - Epoch [6][200/1281] lr: 2.493e-02, eta: 11:18:16, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7331, top5_acc: 0.9750, loss_cls: 1.1663, loss: 1.1663 +2025-06-24 09:12:38,826 - pyskl - INFO - Epoch [6][300/1281] lr: 2.492e-02, eta: 11:17:45, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7319, top5_acc: 0.9794, loss_cls: 1.1330, loss: 1.1330 +2025-06-24 09:13:00,051 - pyskl - INFO - Epoch [6][400/1281] lr: 2.492e-02, eta: 11:17:04, time: 0.212, data_time: 0.000, memory: 4082, top1_acc: 0.7231, top5_acc: 0.9762, loss_cls: 1.1295, loss: 1.1295 +2025-06-24 09:13:21,934 - pyskl - INFO - Epoch [6][500/1281] lr: 2.492e-02, eta: 11:16:41, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7431, top5_acc: 0.9788, loss_cls: 1.1148, loss: 1.1148 +2025-06-24 09:13:43,739 - pyskl - INFO - Epoch [6][600/1281] lr: 2.492e-02, eta: 11:16:16, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7419, top5_acc: 0.9775, loss_cls: 1.1084, loss: 1.1084 +2025-06-24 09:14:05,571 - pyskl - INFO - Epoch [6][700/1281] lr: 2.492e-02, eta: 11:15:52, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7312, top5_acc: 0.9719, loss_cls: 1.1711, loss: 1.1711 +2025-06-24 09:14:27,200 - pyskl - INFO - Epoch [6][800/1281] lr: 2.491e-02, eta: 11:15:22, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7488, top5_acc: 0.9781, loss_cls: 1.0753, loss: 1.0753 +2025-06-24 09:14:48,656 - pyskl - INFO - Epoch [6][900/1281] lr: 2.491e-02, eta: 11:14:49, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7412, top5_acc: 0.9744, loss_cls: 1.0961, loss: 1.0961 +2025-06-24 09:15:10,502 - pyskl - INFO - Epoch [6][1000/1281] lr: 2.491e-02, eta: 11:14:26, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7188, top5_acc: 0.9681, loss_cls: 1.1981, loss: 1.1981 +2025-06-24 09:15:31,921 - pyskl - INFO - Epoch [6][1100/1281] lr: 2.491e-02, eta: 11:13:52, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7475, top5_acc: 0.9819, loss_cls: 1.0751, loss: 1.0751 +2025-06-24 09:15:53,582 - pyskl - INFO - Epoch [6][1200/1281] lr: 2.490e-02, eta: 11:13:24, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7450, top5_acc: 0.9769, loss_cls: 1.1060, loss: 1.1060 +2025-06-24 09:16:11,977 - pyskl - INFO - Saving checkpoint at 6 epochs +2025-06-24 09:16:55,602 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:16:55,676 - pyskl - INFO - +top1_acc 0.7098 +top5_acc 0.9668 +2025-06-24 09:16:55,676 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:16:55,685 - pyskl - INFO - +mean_acc 0.5972 +2025-06-24 09:16:55,690 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_5.pth was removed +2025-06-24 09:16:55,871 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_6.pth. +2025-06-24 09:16:55,872 - pyskl - INFO - Best top1_acc is 0.7098 at 6 epoch. +2025-06-24 09:16:55,874 - pyskl - INFO - Epoch(val) [6][533] top1_acc: 0.7098, top5_acc: 0.9668, mean_class_accuracy: 0.5972 +2025-06-24 09:17:35,979 - pyskl - INFO - Epoch [7][100/1281] lr: 2.490e-02, eta: 11:12:56, time: 0.401, data_time: 0.183, memory: 4082, top1_acc: 0.7412, top5_acc: 0.9744, loss_cls: 1.1101, loss: 1.1101 +2025-06-24 09:17:57,496 - pyskl - INFO - Epoch [7][200/1281] lr: 2.490e-02, eta: 11:12:25, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7469, top5_acc: 0.9788, loss_cls: 1.0499, loss: 1.0499 +2025-06-24 09:18:19,281 - pyskl - INFO - Epoch [7][300/1281] lr: 2.489e-02, eta: 11:12:00, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7631, top5_acc: 0.9869, loss_cls: 1.0021, loss: 1.0021 +2025-06-24 09:18:40,812 - pyskl - INFO - Epoch [7][400/1281] lr: 2.489e-02, eta: 11:11:30, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7500, top5_acc: 0.9831, loss_cls: 1.0753, loss: 1.0753 +2025-06-24 09:19:02,398 - pyskl - INFO - Epoch [7][500/1281] lr: 2.489e-02, eta: 11:11:01, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7531, top5_acc: 0.9812, loss_cls: 1.0612, loss: 1.0612 +2025-06-24 09:19:23,928 - pyskl - INFO - Epoch [7][600/1281] lr: 2.489e-02, eta: 11:10:32, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7431, top5_acc: 0.9819, loss_cls: 1.0841, loss: 1.0841 +2025-06-24 09:19:45,370 - pyskl - INFO - Epoch [7][700/1281] lr: 2.488e-02, eta: 11:10:00, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7544, top5_acc: 0.9750, loss_cls: 1.0587, loss: 1.0587 +2025-06-24 09:20:07,081 - pyskl - INFO - Epoch [7][800/1281] lr: 2.488e-02, eta: 11:09:35, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7494, top5_acc: 0.9762, loss_cls: 1.0687, loss: 1.0687 +2025-06-24 09:20:28,483 - pyskl - INFO - Epoch [7][900/1281] lr: 2.488e-02, eta: 11:09:03, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7462, top5_acc: 0.9788, loss_cls: 1.0368, loss: 1.0368 +2025-06-24 09:20:50,069 - pyskl - INFO - Epoch [7][1000/1281] lr: 2.487e-02, eta: 11:08:35, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7600, top5_acc: 0.9794, loss_cls: 1.0615, loss: 1.0615 +2025-06-24 09:21:11,774 - pyskl - INFO - Epoch [7][1100/1281] lr: 2.487e-02, eta: 11:08:09, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7519, top5_acc: 0.9812, loss_cls: 1.0599, loss: 1.0599 +2025-06-24 09:21:33,578 - pyskl - INFO - Epoch [7][1200/1281] lr: 2.487e-02, eta: 11:07:46, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7538, top5_acc: 0.9762, loss_cls: 1.0639, loss: 1.0639 +2025-06-24 09:21:51,783 - pyskl - INFO - Saving checkpoint at 7 epochs +2025-06-24 09:22:35,523 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:22:35,580 - pyskl - INFO - +top1_acc 0.7171 +top5_acc 0.9716 +2025-06-24 09:22:35,580 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:22:35,587 - pyskl - INFO - +mean_acc 0.6211 +2025-06-24 09:22:35,591 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_6.pth was removed +2025-06-24 09:22:35,775 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2025-06-24 09:22:35,776 - pyskl - INFO - Best top1_acc is 0.7171 at 7 epoch. +2025-06-24 09:22:35,778 - pyskl - INFO - Epoch(val) [7][533] top1_acc: 0.7171, top5_acc: 0.9716, mean_class_accuracy: 0.6211 +2025-06-24 09:23:16,076 - pyskl - INFO - Epoch [8][100/1281] lr: 2.486e-02, eta: 11:07:21, time: 0.403, data_time: 0.186, memory: 4082, top1_acc: 0.7837, top5_acc: 0.9862, loss_cls: 0.9567, loss: 0.9567 +2025-06-24 09:23:37,542 - pyskl - INFO - Epoch [8][200/1281] lr: 2.486e-02, eta: 11:06:51, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7675, top5_acc: 0.9819, loss_cls: 0.9809, loss: 0.9809 +2025-06-24 09:23:59,191 - pyskl - INFO - Epoch [8][300/1281] lr: 2.486e-02, eta: 11:06:25, time: 0.216, data_time: 0.001, memory: 4082, top1_acc: 0.7588, top5_acc: 0.9825, loss_cls: 1.0359, loss: 1.0359 +2025-06-24 09:24:20,746 - pyskl - INFO - Epoch [8][400/1281] lr: 2.485e-02, eta: 11:05:57, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7500, top5_acc: 0.9812, loss_cls: 1.0395, loss: 1.0395 +2025-06-24 09:24:42,504 - pyskl - INFO - Epoch [8][500/1281] lr: 2.485e-02, eta: 11:05:33, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7481, top5_acc: 0.9806, loss_cls: 1.0499, loss: 1.0499 +2025-06-24 09:25:04,041 - pyskl - INFO - Epoch [8][600/1281] lr: 2.485e-02, eta: 11:05:05, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7544, top5_acc: 0.9838, loss_cls: 1.0043, loss: 1.0043 +2025-06-24 09:25:25,258 - pyskl - INFO - Epoch [8][700/1281] lr: 2.484e-02, eta: 11:04:31, time: 0.212, data_time: 0.000, memory: 4082, top1_acc: 0.7681, top5_acc: 0.9844, loss_cls: 0.9884, loss: 0.9884 +2025-06-24 09:25:46,696 - pyskl - INFO - Epoch [8][800/1281] lr: 2.484e-02, eta: 11:04:02, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7644, top5_acc: 0.9781, loss_cls: 1.0216, loss: 1.0216 +2025-06-24 09:26:08,158 - pyskl - INFO - Epoch [8][900/1281] lr: 2.484e-02, eta: 11:03:33, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7338, top5_acc: 0.9744, loss_cls: 1.0668, loss: 1.0668 +2025-06-24 09:26:29,632 - pyskl - INFO - Epoch [8][1000/1281] lr: 2.483e-02, eta: 11:03:04, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7725, top5_acc: 0.9831, loss_cls: 0.9610, loss: 0.9610 +2025-06-24 09:26:51,318 - pyskl - INFO - Epoch [8][1100/1281] lr: 2.483e-02, eta: 11:02:40, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9850, loss_cls: 0.9393, loss: 0.9393 +2025-06-24 09:27:12,991 - pyskl - INFO - Epoch [8][1200/1281] lr: 2.483e-02, eta: 11:02:15, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7538, top5_acc: 0.9819, loss_cls: 1.0015, loss: 1.0015 +2025-06-24 09:27:30,849 - pyskl - INFO - Saving checkpoint at 8 epochs +2025-06-24 09:28:14,126 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:28:14,178 - pyskl - INFO - +top1_acc 0.7205 +top5_acc 0.9752 +2025-06-24 09:28:14,178 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:28:14,184 - pyskl - INFO - +mean_acc 0.6372 +2025-06-24 09:28:14,188 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_7.pth was removed +2025-06-24 09:28:14,358 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_8.pth. +2025-06-24 09:28:14,359 - pyskl - INFO - Best top1_acc is 0.7205 at 8 epoch. +2025-06-24 09:28:14,361 - pyskl - INFO - Epoch(val) [8][533] top1_acc: 0.7205, top5_acc: 0.9752, mean_class_accuracy: 0.6372 +2025-06-24 09:28:54,550 - pyskl - INFO - Epoch [9][100/1281] lr: 2.482e-02, eta: 11:01:47, time: 0.402, data_time: 0.186, memory: 4082, top1_acc: 0.7850, top5_acc: 0.9875, loss_cls: 0.9338, loss: 0.9338 +2025-06-24 09:29:16,217 - pyskl - INFO - Epoch [9][200/1281] lr: 2.482e-02, eta: 11:01:22, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7794, top5_acc: 0.9831, loss_cls: 0.9758, loss: 0.9758 +2025-06-24 09:29:37,769 - pyskl - INFO - Epoch [9][300/1281] lr: 2.481e-02, eta: 11:00:55, time: 0.216, data_time: 0.001, memory: 4082, top1_acc: 0.7806, top5_acc: 0.9806, loss_cls: 0.9632, loss: 0.9632 +2025-06-24 09:29:59,152 - pyskl - INFO - Epoch [9][400/1281] lr: 2.481e-02, eta: 11:00:26, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7738, top5_acc: 0.9806, loss_cls: 1.0012, loss: 1.0012 +2025-06-24 09:30:20,584 - pyskl - INFO - Epoch [9][500/1281] lr: 2.481e-02, eta: 10:59:57, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7700, top5_acc: 0.9850, loss_cls: 0.9535, loss: 0.9535 +2025-06-24 09:30:42,134 - pyskl - INFO - Epoch [9][600/1281] lr: 2.480e-02, eta: 10:59:31, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7694, top5_acc: 0.9838, loss_cls: 0.9643, loss: 0.9643 +2025-06-24 09:31:03,866 - pyskl - INFO - Epoch [9][700/1281] lr: 2.480e-02, eta: 10:59:07, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7875, top5_acc: 0.9869, loss_cls: 0.9062, loss: 0.9062 +2025-06-24 09:31:25,349 - pyskl - INFO - Epoch [9][800/1281] lr: 2.480e-02, eta: 10:58:40, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7887, top5_acc: 0.9831, loss_cls: 0.9796, loss: 0.9796 +2025-06-24 09:31:46,936 - pyskl - INFO - Epoch [9][900/1281] lr: 2.479e-02, eta: 10:58:14, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7625, top5_acc: 0.9762, loss_cls: 1.0202, loss: 1.0202 +2025-06-24 09:32:08,540 - pyskl - INFO - Epoch [9][1000/1281] lr: 2.479e-02, eta: 10:57:49, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7700, top5_acc: 0.9838, loss_cls: 0.9804, loss: 0.9804 +2025-06-24 09:32:30,658 - pyskl - INFO - Epoch [9][1100/1281] lr: 2.479e-02, eta: 10:57:32, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7825, top5_acc: 0.9812, loss_cls: 0.9392, loss: 0.9392 +2025-06-24 09:32:52,158 - pyskl - INFO - Epoch [9][1200/1281] lr: 2.478e-02, eta: 10:57:05, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7812, top5_acc: 0.9825, loss_cls: 0.9306, loss: 0.9306 +2025-06-24 09:33:10,246 - pyskl - INFO - Saving checkpoint at 9 epochs +2025-06-24 09:33:53,340 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:33:53,395 - pyskl - INFO - +top1_acc 0.7322 +top5_acc 0.9730 +2025-06-24 09:33:53,395 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:33:53,403 - pyskl - INFO - +mean_acc 0.6264 +2025-06-24 09:33:53,407 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_8.pth was removed +2025-06-24 09:33:53,586 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_9.pth. +2025-06-24 09:33:53,587 - pyskl - INFO - Best top1_acc is 0.7322 at 9 epoch. +2025-06-24 09:33:53,590 - pyskl - INFO - Epoch(val) [9][533] top1_acc: 0.7322, top5_acc: 0.9730, mean_class_accuracy: 0.6264 +2025-06-24 09:34:33,825 - pyskl - INFO - Epoch [10][100/1281] lr: 2.477e-02, eta: 10:56:37, time: 0.402, data_time: 0.188, memory: 4082, top1_acc: 0.7850, top5_acc: 0.9844, loss_cls: 0.9468, loss: 0.9468 +2025-06-24 09:34:55,361 - pyskl - INFO - Epoch [10][200/1281] lr: 2.477e-02, eta: 10:56:11, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7650, top5_acc: 0.9862, loss_cls: 0.9616, loss: 0.9616 +2025-06-24 09:35:17,022 - pyskl - INFO - Epoch [10][300/1281] lr: 2.477e-02, eta: 10:55:46, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7887, top5_acc: 0.9912, loss_cls: 0.9072, loss: 0.9072 +2025-06-24 09:35:38,423 - pyskl - INFO - Epoch [10][400/1281] lr: 2.476e-02, eta: 10:55:18, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7825, top5_acc: 0.9819, loss_cls: 0.9256, loss: 0.9256 +2025-06-24 09:36:00,226 - pyskl - INFO - Epoch [10][500/1281] lr: 2.476e-02, eta: 10:54:56, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7744, top5_acc: 0.9819, loss_cls: 0.9381, loss: 0.9381 +2025-06-24 09:36:21,644 - pyskl - INFO - Epoch [10][600/1281] lr: 2.476e-02, eta: 10:54:28, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7800, top5_acc: 0.9788, loss_cls: 0.9664, loss: 0.9664 +2025-06-24 09:36:43,017 - pyskl - INFO - Epoch [10][700/1281] lr: 2.475e-02, eta: 10:54:00, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7819, top5_acc: 0.9869, loss_cls: 0.9443, loss: 0.9443 +2025-06-24 09:37:04,502 - pyskl - INFO - Epoch [10][800/1281] lr: 2.475e-02, eta: 10:53:34, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7725, top5_acc: 0.9856, loss_cls: 0.9665, loss: 0.9665 +2025-06-24 09:37:26,257 - pyskl - INFO - Epoch [10][900/1281] lr: 2.474e-02, eta: 10:53:11, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7781, top5_acc: 0.9806, loss_cls: 0.9589, loss: 0.9589 +2025-06-24 09:37:47,831 - pyskl - INFO - Epoch [10][1000/1281] lr: 2.474e-02, eta: 10:52:46, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7837, top5_acc: 0.9888, loss_cls: 0.9278, loss: 0.9278 +2025-06-24 09:38:09,412 - pyskl - INFO - Epoch [10][1100/1281] lr: 2.473e-02, eta: 10:52:21, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7781, top5_acc: 0.9869, loss_cls: 0.9228, loss: 0.9228 +2025-06-24 09:38:31,352 - pyskl - INFO - Epoch [10][1200/1281] lr: 2.473e-02, eta: 10:52:01, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7956, top5_acc: 0.9838, loss_cls: 0.9183, loss: 0.9183 +2025-06-24 09:38:49,533 - pyskl - INFO - Saving checkpoint at 10 epochs +2025-06-24 09:39:32,456 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:39:32,511 - pyskl - INFO - +top1_acc 0.7073 +top5_acc 0.9653 +2025-06-24 09:39:32,511 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:39:32,518 - pyskl - INFO - +mean_acc 0.6302 +2025-06-24 09:39:32,520 - pyskl - INFO - Epoch(val) [10][533] top1_acc: 0.7073, top5_acc: 0.9653, mean_class_accuracy: 0.6302 +2025-06-24 09:40:12,542 - pyskl - INFO - Epoch [11][100/1281] lr: 2.472e-02, eta: 10:51:29, time: 0.400, data_time: 0.184, memory: 4082, top1_acc: 0.7819, top5_acc: 0.9831, loss_cls: 0.9141, loss: 0.9141 +2025-06-24 09:40:34,224 - pyskl - INFO - Epoch [11][200/1281] lr: 2.472e-02, eta: 10:51:06, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7762, top5_acc: 0.9881, loss_cls: 0.9217, loss: 0.9217 +2025-06-24 09:40:55,855 - pyskl - INFO - Epoch [11][300/1281] lr: 2.471e-02, eta: 10:50:41, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7925, top5_acc: 0.9919, loss_cls: 0.8724, loss: 0.8724 +2025-06-24 09:41:17,685 - pyskl - INFO - Epoch [11][400/1281] lr: 2.471e-02, eta: 10:50:20, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7869, top5_acc: 0.9869, loss_cls: 0.9278, loss: 0.9278 +2025-06-24 09:41:39,569 - pyskl - INFO - Epoch [11][500/1281] lr: 2.471e-02, eta: 10:49:59, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7700, top5_acc: 0.9831, loss_cls: 0.9673, loss: 0.9673 +2025-06-24 09:42:01,198 - pyskl - INFO - Epoch [11][600/1281] lr: 2.470e-02, eta: 10:49:35, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7906, top5_acc: 0.9856, loss_cls: 0.8905, loss: 0.8905 +2025-06-24 09:42:23,217 - pyskl - INFO - Epoch [11][700/1281] lr: 2.470e-02, eta: 10:49:16, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8163, top5_acc: 0.9856, loss_cls: 0.8371, loss: 0.8371 +2025-06-24 09:42:45,235 - pyskl - INFO - Epoch [11][800/1281] lr: 2.469e-02, eta: 10:48:57, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7806, top5_acc: 0.9850, loss_cls: 0.9076, loss: 0.9076 +2025-06-24 09:43:06,875 - pyskl - INFO - Epoch [11][900/1281] lr: 2.469e-02, eta: 10:48:33, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7844, top5_acc: 0.9875, loss_cls: 0.9062, loss: 0.9062 +2025-06-24 09:43:28,276 - pyskl - INFO - Epoch [11][1000/1281] lr: 2.468e-02, eta: 10:48:06, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7788, top5_acc: 0.9838, loss_cls: 0.9471, loss: 0.9471 +2025-06-24 09:43:49,924 - pyskl - INFO - Epoch [11][1100/1281] lr: 2.468e-02, eta: 10:47:42, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7956, top5_acc: 0.9888, loss_cls: 0.8771, loss: 0.8771 +2025-06-24 09:44:11,724 - pyskl - INFO - Epoch [11][1200/1281] lr: 2.467e-02, eta: 10:47:20, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7887, top5_acc: 0.9869, loss_cls: 0.9170, loss: 0.9170 +2025-06-24 09:44:29,894 - pyskl - INFO - Saving checkpoint at 11 epochs +2025-06-24 09:45:13,709 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:45:13,778 - pyskl - INFO - +top1_acc 0.7604 +top5_acc 0.9798 +2025-06-24 09:45:13,778 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:45:13,787 - pyskl - INFO - +mean_acc 0.6611 +2025-06-24 09:45:13,792 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_9.pth was removed +2025-06-24 09:45:13,992 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_11.pth. +2025-06-24 09:45:13,993 - pyskl - INFO - Best top1_acc is 0.7604 at 11 epoch. +2025-06-24 09:45:13,996 - pyskl - INFO - Epoch(val) [11][533] top1_acc: 0.7604, top5_acc: 0.9798, mean_class_accuracy: 0.6611 +2025-06-24 09:45:54,642 - pyskl - INFO - Epoch [12][100/1281] lr: 2.467e-02, eta: 10:46:55, time: 0.406, data_time: 0.189, memory: 4082, top1_acc: 0.7850, top5_acc: 0.9888, loss_cls: 0.8763, loss: 0.8763 +2025-06-24 09:46:16,503 - pyskl - INFO - Epoch [12][200/1281] lr: 2.466e-02, eta: 10:46:34, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9894, loss_cls: 0.8674, loss: 0.8674 +2025-06-24 09:46:38,108 - pyskl - INFO - Epoch [12][300/1281] lr: 2.466e-02, eta: 10:46:10, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7850, top5_acc: 0.9881, loss_cls: 0.9117, loss: 0.9117 +2025-06-24 09:46:59,919 - pyskl - INFO - Epoch [12][400/1281] lr: 2.465e-02, eta: 10:45:48, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7969, top5_acc: 0.9869, loss_cls: 0.8774, loss: 0.8774 +2025-06-24 09:47:21,913 - pyskl - INFO - Epoch [12][500/1281] lr: 2.465e-02, eta: 10:45:28, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8013, top5_acc: 0.9875, loss_cls: 0.8485, loss: 0.8485 +2025-06-24 09:47:43,441 - pyskl - INFO - Epoch [12][600/1281] lr: 2.464e-02, eta: 10:45:03, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8044, top5_acc: 0.9856, loss_cls: 0.8802, loss: 0.8802 +2025-06-24 09:48:05,111 - pyskl - INFO - Epoch [12][700/1281] lr: 2.464e-02, eta: 10:44:40, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7937, top5_acc: 0.9894, loss_cls: 0.9182, loss: 0.9182 +2025-06-24 09:48:26,644 - pyskl - INFO - Epoch [12][800/1281] lr: 2.463e-02, eta: 10:44:14, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9869, loss_cls: 0.8584, loss: 0.8584 +2025-06-24 09:48:47,937 - pyskl - INFO - Epoch [12][900/1281] lr: 2.463e-02, eta: 10:43:47, time: 0.213, data_time: 0.000, memory: 4082, top1_acc: 0.8025, top5_acc: 0.9894, loss_cls: 0.8528, loss: 0.8528 +2025-06-24 09:49:09,465 - pyskl - INFO - Epoch [12][1000/1281] lr: 2.462e-02, eta: 10:43:22, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7981, top5_acc: 0.9819, loss_cls: 0.9199, loss: 0.9199 +2025-06-24 09:49:31,040 - pyskl - INFO - Epoch [12][1100/1281] lr: 2.462e-02, eta: 10:42:57, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9862, loss_cls: 0.9041, loss: 0.9041 +2025-06-24 09:49:52,482 - pyskl - INFO - Epoch [12][1200/1281] lr: 2.461e-02, eta: 10:42:31, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7950, top5_acc: 0.9850, loss_cls: 0.8949, loss: 0.8949 +2025-06-24 09:50:10,397 - pyskl - INFO - Saving checkpoint at 12 epochs +2025-06-24 09:50:53,335 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:50:53,391 - pyskl - INFO - +top1_acc 0.7910 +top5_acc 0.9832 +2025-06-24 09:50:53,391 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:50:53,398 - pyskl - INFO - +mean_acc 0.6991 +2025-06-24 09:50:53,402 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_11.pth was removed +2025-06-24 09:50:53,585 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_12.pth. +2025-06-24 09:50:53,585 - pyskl - INFO - Best top1_acc is 0.7910 at 12 epoch. +2025-06-24 09:50:53,589 - pyskl - INFO - Epoch(val) [12][533] top1_acc: 0.7910, top5_acc: 0.9832, mean_class_accuracy: 0.6991 +2025-06-24 09:51:33,706 - pyskl - INFO - Epoch [13][100/1281] lr: 2.460e-02, eta: 10:41:59, time: 0.401, data_time: 0.183, memory: 4082, top1_acc: 0.8063, top5_acc: 0.9925, loss_cls: 0.8186, loss: 0.8186 +2025-06-24 09:51:55,235 - pyskl - INFO - Epoch [13][200/1281] lr: 2.460e-02, eta: 10:41:34, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8050, top5_acc: 0.9919, loss_cls: 0.8391, loss: 0.8391 +2025-06-24 09:52:16,990 - pyskl - INFO - Epoch [13][300/1281] lr: 2.459e-02, eta: 10:41:12, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7900, top5_acc: 0.9869, loss_cls: 0.8978, loss: 0.8978 +2025-06-24 09:52:38,359 - pyskl - INFO - Epoch [13][400/1281] lr: 2.459e-02, eta: 10:40:45, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7906, top5_acc: 0.9900, loss_cls: 0.8617, loss: 0.8617 +2025-06-24 09:53:00,089 - pyskl - INFO - Epoch [13][500/1281] lr: 2.458e-02, eta: 10:40:23, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9919, loss_cls: 0.8409, loss: 0.8409 +2025-06-24 09:53:21,564 - pyskl - INFO - Epoch [13][600/1281] lr: 2.458e-02, eta: 10:39:57, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8125, top5_acc: 0.9912, loss_cls: 0.7970, loss: 0.7970 +2025-06-24 09:53:43,237 - pyskl - INFO - Epoch [13][700/1281] lr: 2.457e-02, eta: 10:39:34, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9869, loss_cls: 0.8326, loss: 0.8326 +2025-06-24 09:54:04,653 - pyskl - INFO - Epoch [13][800/1281] lr: 2.457e-02, eta: 10:39:08, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.8200, top5_acc: 0.9900, loss_cls: 0.8209, loss: 0.8209 +2025-06-24 09:54:26,069 - pyskl - INFO - Epoch [13][900/1281] lr: 2.456e-02, eta: 10:38:42, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7900, top5_acc: 0.9875, loss_cls: 0.8923, loss: 0.8923 +2025-06-24 09:54:47,610 - pyskl - INFO - Epoch [13][1000/1281] lr: 2.455e-02, eta: 10:38:18, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7919, top5_acc: 0.9838, loss_cls: 0.9135, loss: 0.9135 +2025-06-24 09:55:09,021 - pyskl - INFO - Epoch [13][1100/1281] lr: 2.455e-02, eta: 10:37:52, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.8163, top5_acc: 0.9900, loss_cls: 0.8199, loss: 0.8199 +2025-06-24 09:55:30,794 - pyskl - INFO - Epoch [13][1200/1281] lr: 2.454e-02, eta: 10:37:30, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9888, loss_cls: 0.8179, loss: 0.8179 +2025-06-24 09:55:48,646 - pyskl - INFO - Saving checkpoint at 13 epochs +2025-06-24 09:56:31,838 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:56:31,899 - pyskl - INFO - +top1_acc 0.7942 +top5_acc 0.9857 +2025-06-24 09:56:31,899 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:56:31,906 - pyskl - INFO - +mean_acc 0.6852 +2025-06-24 09:56:31,910 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_12.pth was removed +2025-06-24 09:56:32,149 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_13.pth. +2025-06-24 09:56:32,150 - pyskl - INFO - Best top1_acc is 0.7942 at 13 epoch. +2025-06-24 09:56:32,152 - pyskl - INFO - Epoch(val) [13][533] top1_acc: 0.7942, top5_acc: 0.9857, mean_class_accuracy: 0.6852 +2025-06-24 09:57:12,473 - pyskl - INFO - Epoch [14][100/1281] lr: 2.453e-02, eta: 10:37:00, time: 0.403, data_time: 0.186, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9931, loss_cls: 0.8133, loss: 0.8133 +2025-06-24 09:57:34,262 - pyskl - INFO - Epoch [14][200/1281] lr: 2.453e-02, eta: 10:36:38, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9912, loss_cls: 0.7782, loss: 0.7782 +2025-06-24 09:57:55,814 - pyskl - INFO - Epoch [14][300/1281] lr: 2.452e-02, eta: 10:36:14, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7750, top5_acc: 0.9850, loss_cls: 0.9755, loss: 0.9755 +2025-06-24 09:58:17,259 - pyskl - INFO - Epoch [14][400/1281] lr: 2.452e-02, eta: 10:35:49, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7906, top5_acc: 0.9888, loss_cls: 0.9002, loss: 0.9002 +2025-06-24 09:58:38,744 - pyskl - INFO - Epoch [14][500/1281] lr: 2.451e-02, eta: 10:35:24, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9938, loss_cls: 0.7888, loss: 0.7888 +2025-06-24 09:59:00,136 - pyskl - INFO - Epoch [14][600/1281] lr: 2.451e-02, eta: 10:34:58, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7925, top5_acc: 0.9888, loss_cls: 0.8609, loss: 0.8609 +2025-06-24 09:59:21,397 - pyskl - INFO - Epoch [14][700/1281] lr: 2.450e-02, eta: 10:34:31, time: 0.213, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9894, loss_cls: 0.8156, loss: 0.8156 +2025-06-24 09:59:42,970 - pyskl - INFO - Epoch [14][800/1281] lr: 2.449e-02, eta: 10:34:07, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7800, top5_acc: 0.9856, loss_cls: 0.9355, loss: 0.9355 +2025-06-24 10:00:04,486 - pyskl - INFO - Epoch [14][900/1281] lr: 2.449e-02, eta: 10:33:43, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8194, top5_acc: 0.9888, loss_cls: 0.8149, loss: 0.8149 +2025-06-24 10:00:26,055 - pyskl - INFO - Epoch [14][1000/1281] lr: 2.448e-02, eta: 10:33:19, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8237, top5_acc: 0.9900, loss_cls: 0.7914, loss: 0.7914 +2025-06-24 10:00:47,446 - pyskl - INFO - Epoch [14][1100/1281] lr: 2.448e-02, eta: 10:32:53, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.8113, top5_acc: 0.9862, loss_cls: 0.8414, loss: 0.8414 +2025-06-24 10:01:09,344 - pyskl - INFO - Epoch [14][1200/1281] lr: 2.447e-02, eta: 10:32:33, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7906, top5_acc: 0.9856, loss_cls: 0.9067, loss: 0.9067 +2025-06-24 10:01:27,428 - pyskl - INFO - Saving checkpoint at 14 epochs +2025-06-24 10:02:10,486 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:02:10,541 - pyskl - INFO - +top1_acc 0.7844 +top5_acc 0.9826 +2025-06-24 10:02:10,541 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:02:10,548 - pyskl - INFO - +mean_acc 0.6842 +2025-06-24 10:02:10,549 - pyskl - INFO - Epoch(val) [14][533] top1_acc: 0.7844, top5_acc: 0.9826, mean_class_accuracy: 0.6842 +2025-06-24 10:02:50,690 - pyskl - INFO - Epoch [15][100/1281] lr: 2.446e-02, eta: 10:32:00, time: 0.401, data_time: 0.185, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9919, loss_cls: 0.8046, loss: 0.8046 +2025-06-24 10:03:12,523 - pyskl - INFO - Epoch [15][200/1281] lr: 2.445e-02, eta: 10:31:39, time: 0.218, data_time: 0.001, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9912, loss_cls: 0.8106, loss: 0.8106 +2025-06-24 10:03:34,028 - pyskl - INFO - Epoch [15][300/1281] lr: 2.445e-02, eta: 10:31:15, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8131, top5_acc: 0.9906, loss_cls: 0.8308, loss: 0.8308 +2025-06-24 10:03:55,585 - pyskl - INFO - Epoch [15][400/1281] lr: 2.444e-02, eta: 10:30:51, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9875, loss_cls: 0.7640, loss: 0.7640 +2025-06-24 10:04:16,861 - pyskl - INFO - Epoch [15][500/1281] lr: 2.444e-02, eta: 10:30:24, time: 0.213, data_time: 0.000, memory: 4082, top1_acc: 0.8231, top5_acc: 0.9919, loss_cls: 0.7779, loss: 0.7779 +2025-06-24 10:04:38,366 - pyskl - INFO - Epoch [15][600/1281] lr: 2.443e-02, eta: 10:30:00, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9850, loss_cls: 0.8389, loss: 0.8389 +2025-06-24 10:04:59,959 - pyskl - INFO - Epoch [15][700/1281] lr: 2.442e-02, eta: 10:29:36, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9906, loss_cls: 0.8027, loss: 0.8027 +2025-06-24 10:05:21,473 - pyskl - INFO - Epoch [15][800/1281] lr: 2.442e-02, eta: 10:29:12, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9881, loss_cls: 0.8344, loss: 0.8344 +2025-06-24 10:05:42,996 - pyskl - INFO - Epoch [15][900/1281] lr: 2.441e-02, eta: 10:28:48, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8037, top5_acc: 0.9862, loss_cls: 0.8623, loss: 0.8623 +2025-06-24 10:06:04,532 - pyskl - INFO - Epoch [15][1000/1281] lr: 2.441e-02, eta: 10:28:24, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9919, loss_cls: 0.7874, loss: 0.7874 +2025-06-24 10:06:26,000 - pyskl - INFO - Epoch [15][1100/1281] lr: 2.440e-02, eta: 10:28:00, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8163, top5_acc: 0.9888, loss_cls: 0.8220, loss: 0.8220 +2025-06-24 10:06:47,679 - pyskl - INFO - Epoch [15][1200/1281] lr: 2.439e-02, eta: 10:27:37, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8113, top5_acc: 0.9919, loss_cls: 0.8163, loss: 0.8163 +2025-06-24 10:07:05,818 - pyskl - INFO - Saving checkpoint at 15 epochs +2025-06-24 10:07:49,303 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:07:49,370 - pyskl - INFO - +top1_acc 0.7766 +top5_acc 0.9791 +2025-06-24 10:07:49,370 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:07:49,379 - pyskl - INFO - +mean_acc 0.6900 +2025-06-24 10:07:49,381 - pyskl - INFO - Epoch(val) [15][533] top1_acc: 0.7766, top5_acc: 0.9791, mean_class_accuracy: 0.6900 +2025-06-24 10:08:28,861 - pyskl - INFO - Epoch [16][100/1281] lr: 2.438e-02, eta: 10:26:59, time: 0.395, data_time: 0.178, memory: 4082, top1_acc: 0.8063, top5_acc: 0.9888, loss_cls: 0.8615, loss: 0.8615 +2025-06-24 10:08:50,474 - pyskl - INFO - Epoch [16][200/1281] lr: 2.438e-02, eta: 10:26:35, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9894, loss_cls: 0.7867, loss: 0.7867 +2025-06-24 10:09:12,073 - pyskl - INFO - Epoch [16][300/1281] lr: 2.437e-02, eta: 10:26:12, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9925, loss_cls: 0.8034, loss: 0.8034 +2025-06-24 10:09:33,707 - pyskl - INFO - Epoch [16][400/1281] lr: 2.436e-02, eta: 10:25:49, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8275, top5_acc: 0.9938, loss_cls: 0.7920, loss: 0.7920 +2025-06-24 10:09:54,952 - pyskl - INFO - Epoch [16][500/1281] lr: 2.436e-02, eta: 10:25:23, time: 0.212, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9875, loss_cls: 0.7881, loss: 0.7881 +2025-06-24 10:10:16,436 - pyskl - INFO - Epoch [16][600/1281] lr: 2.435e-02, eta: 10:24:59, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9944, loss_cls: 0.7747, loss: 0.7747 +2025-06-24 10:10:38,145 - pyskl - INFO - Epoch [16][700/1281] lr: 2.434e-02, eta: 10:24:37, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9931, loss_cls: 0.7637, loss: 0.7637 +2025-06-24 10:10:59,519 - pyskl - INFO - Epoch [16][800/1281] lr: 2.434e-02, eta: 10:24:11, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.8125, top5_acc: 0.9862, loss_cls: 0.8075, loss: 0.8075 +2025-06-24 10:11:21,248 - pyskl - INFO - Epoch [16][900/1281] lr: 2.433e-02, eta: 10:23:49, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9906, loss_cls: 0.7383, loss: 0.7383 +2025-06-24 10:11:42,770 - pyskl - INFO - Epoch [16][1000/1281] lr: 2.432e-02, eta: 10:23:26, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8231, top5_acc: 0.9912, loss_cls: 0.7649, loss: 0.7649 +2025-06-24 10:12:04,485 - pyskl - INFO - Epoch [16][1100/1281] lr: 2.432e-02, eta: 10:23:04, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9906, loss_cls: 0.8393, loss: 0.8393 +2025-06-24 10:12:26,123 - pyskl - INFO - Epoch [16][1200/1281] lr: 2.431e-02, eta: 10:22:41, time: 0.216, data_time: 0.001, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9900, loss_cls: 0.8393, loss: 0.8393 +2025-06-24 10:12:44,713 - pyskl - INFO - Saving checkpoint at 16 epochs +2025-06-24 10:13:28,882 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:13:28,968 - pyskl - INFO - +top1_acc 0.7800 +top5_acc 0.9816 +2025-06-24 10:13:28,968 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:13:28,978 - pyskl - INFO - +mean_acc 0.7087 +2025-06-24 10:13:28,980 - pyskl - INFO - Epoch(val) [16][533] top1_acc: 0.7800, top5_acc: 0.9816, mean_class_accuracy: 0.7087 +2025-06-24 10:14:10,349 - pyskl - INFO - Epoch [17][100/1281] lr: 2.430e-02, eta: 10:22:18, time: 0.414, data_time: 0.196, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9912, loss_cls: 0.7598, loss: 0.7598 +2025-06-24 10:14:32,876 - pyskl - INFO - Epoch [17][200/1281] lr: 2.429e-02, eta: 10:22:03, time: 0.225, data_time: 0.001, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9888, loss_cls: 0.7321, loss: 0.7321 +2025-06-24 10:15:09,576 - pyskl - INFO - Epoch [17][300/1281] lr: 2.428e-02, eta: 10:23:44, time: 0.367, data_time: 0.000, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9906, loss_cls: 0.7813, loss: 0.7813 +2025-06-24 10:15:51,121 - pyskl - INFO - Epoch [17][400/1281] lr: 2.428e-02, eta: 10:26:03, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8381, top5_acc: 0.9912, loss_cls: 0.7594, loss: 0.7594 +2025-06-24 10:16:32,612 - pyskl - INFO - Epoch [17][500/1281] lr: 2.427e-02, eta: 10:28:21, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9900, loss_cls: 0.7895, loss: 0.7895 +2025-06-24 10:17:14,362 - pyskl - INFO - Epoch [17][600/1281] lr: 2.426e-02, eta: 10:30:39, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9925, loss_cls: 0.7801, loss: 0.7801 +2025-06-24 10:17:56,041 - pyskl - INFO - Epoch [17][700/1281] lr: 2.426e-02, eta: 10:32:54, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9919, loss_cls: 0.7276, loss: 0.7276 +2025-06-24 10:18:37,612 - pyskl - INFO - Epoch [17][800/1281] lr: 2.425e-02, eta: 10:35:07, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8400, top5_acc: 0.9944, loss_cls: 0.7387, loss: 0.7387 +2025-06-24 10:19:18,868 - pyskl - INFO - Epoch [17][900/1281] lr: 2.424e-02, eta: 10:37:16, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9906, loss_cls: 0.8178, loss: 0.8178 +2025-06-24 10:20:00,475 - pyskl - INFO - Epoch [17][1000/1281] lr: 2.424e-02, eta: 10:39:27, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9894, loss_cls: 0.7725, loss: 0.7725 +2025-06-24 10:20:41,895 - pyskl - INFO - Epoch [17][1100/1281] lr: 2.423e-02, eta: 10:41:34, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9875, loss_cls: 0.8073, loss: 0.8073 +2025-06-24 10:21:23,447 - pyskl - INFO - Epoch [17][1200/1281] lr: 2.422e-02, eta: 10:43:40, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9925, loss_cls: 0.7782, loss: 0.7782 +2025-06-24 10:21:57,677 - pyskl - INFO - Saving checkpoint at 17 epochs +2025-06-24 10:23:09,276 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:23:09,332 - pyskl - INFO - +top1_acc 0.8026 +top5_acc 0.9857 +2025-06-24 10:23:09,332 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:23:09,339 - pyskl - INFO - +mean_acc 0.7239 +2025-06-24 10:23:09,344 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_13.pth was removed +2025-06-24 10:23:09,558 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_17.pth. +2025-06-24 10:23:09,558 - pyskl - INFO - Best top1_acc is 0.8026 at 17 epoch. +2025-06-24 10:23:09,561 - pyskl - INFO - Epoch(val) [17][533] top1_acc: 0.8026, top5_acc: 0.9857, mean_class_accuracy: 0.7239 +2025-06-24 10:24:10,550 - pyskl - INFO - Epoch [18][100/1281] lr: 2.421e-02, eta: 10:45:35, time: 0.610, data_time: 0.193, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9919, loss_cls: 0.7594, loss: 0.7594 +2025-06-24 10:24:39,943 - pyskl - INFO - Epoch [18][200/1281] lr: 2.420e-02, eta: 10:46:03, time: 0.294, data_time: 0.001, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9931, loss_cls: 0.6933, loss: 0.6933 +2025-06-24 10:25:16,289 - pyskl - INFO - Epoch [18][300/1281] lr: 2.419e-02, eta: 10:47:25, time: 0.363, data_time: 0.000, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9912, loss_cls: 0.7262, loss: 0.7262 +2025-06-24 10:25:57,826 - pyskl - INFO - Epoch [18][400/1281] lr: 2.419e-02, eta: 10:49:25, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8425, top5_acc: 0.9944, loss_cls: 0.7326, loss: 0.7326 +2025-06-24 10:26:39,210 - pyskl - INFO - Epoch [18][500/1281] lr: 2.418e-02, eta: 10:51:23, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9938, loss_cls: 0.7431, loss: 0.7431 +2025-06-24 10:27:20,749 - pyskl - INFO - Epoch [18][600/1281] lr: 2.417e-02, eta: 10:53:21, time: 0.415, data_time: 0.001, memory: 4082, top1_acc: 0.8344, top5_acc: 0.9856, loss_cls: 0.7706, loss: 0.7706 +2025-06-24 10:28:02,152 - pyskl - INFO - Epoch [18][700/1281] lr: 2.417e-02, eta: 10:55:16, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9906, loss_cls: 0.8091, loss: 0.8091 +2025-06-24 10:28:43,744 - pyskl - INFO - Epoch [18][800/1281] lr: 2.416e-02, eta: 10:57:11, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9900, loss_cls: 0.7564, loss: 0.7564 +2025-06-24 10:29:25,243 - pyskl - INFO - Epoch [18][900/1281] lr: 2.415e-02, eta: 10:59:04, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9931, loss_cls: 0.8196, loss: 0.8196 +2025-06-24 10:30:06,709 - pyskl - INFO - Epoch [18][1000/1281] lr: 2.414e-02, eta: 11:00:56, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9912, loss_cls: 0.7338, loss: 0.7338 +2025-06-24 10:30:48,209 - pyskl - INFO - Epoch [18][1100/1281] lr: 2.414e-02, eta: 11:02:46, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9906, loss_cls: 0.7063, loss: 0.7063 +2025-06-24 10:31:29,635 - pyskl - INFO - Epoch [18][1200/1281] lr: 2.413e-02, eta: 11:04:35, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8344, top5_acc: 0.9906, loss_cls: 0.7677, loss: 0.7677 +2025-06-24 10:32:03,813 - pyskl - INFO - Saving checkpoint at 18 epochs +2025-06-24 10:33:15,295 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:33:15,351 - pyskl - INFO - +top1_acc 0.7850 +top5_acc 0.9798 +2025-06-24 10:33:15,352 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:33:15,360 - pyskl - INFO - +mean_acc 0.6993 +2025-06-24 10:33:15,362 - pyskl - INFO - Epoch(val) [18][533] top1_acc: 0.7850, top5_acc: 0.9798, mean_class_accuracy: 0.6993 +2025-06-24 10:34:15,921 - pyskl - INFO - Epoch [19][100/1281] lr: 2.411e-02, eta: 11:06:03, time: 0.606, data_time: 0.193, memory: 4082, top1_acc: 0.8512, top5_acc: 0.9931, loss_cls: 0.7129, loss: 0.7129 +2025-06-24 10:34:45,668 - pyskl - INFO - Epoch [19][200/1281] lr: 2.411e-02, eta: 11:06:23, time: 0.297, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9894, loss_cls: 0.6981, loss: 0.6981 +2025-06-24 10:35:21,389 - pyskl - INFO - Epoch [19][300/1281] lr: 2.410e-02, eta: 11:07:27, time: 0.357, data_time: 0.000, memory: 4082, top1_acc: 0.8481, top5_acc: 0.9925, loss_cls: 0.6926, loss: 0.6926 +2025-06-24 10:36:02,877 - pyskl - INFO - Epoch [19][400/1281] lr: 2.409e-02, eta: 11:09:11, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9944, loss_cls: 0.7087, loss: 0.7087 +2025-06-24 10:36:44,257 - pyskl - INFO - Epoch [19][500/1281] lr: 2.408e-02, eta: 11:10:53, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9969, loss_cls: 0.7275, loss: 0.7275 +2025-06-24 10:37:25,673 - pyskl - INFO - Epoch [19][600/1281] lr: 2.408e-02, eta: 11:12:34, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9925, loss_cls: 0.7512, loss: 0.7512 +2025-06-24 10:38:07,030 - pyskl - INFO - Epoch [19][700/1281] lr: 2.407e-02, eta: 11:14:13, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9938, loss_cls: 0.7260, loss: 0.7260 +2025-06-24 10:38:48,390 - pyskl - INFO - Epoch [19][800/1281] lr: 2.406e-02, eta: 11:15:51, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9906, loss_cls: 0.7430, loss: 0.7430 +2025-06-24 10:39:29,849 - pyskl - INFO - Epoch [19][900/1281] lr: 2.405e-02, eta: 11:17:29, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8400, top5_acc: 0.9919, loss_cls: 0.7437, loss: 0.7437 +2025-06-24 10:40:11,278 - pyskl - INFO - Epoch [19][1000/1281] lr: 2.405e-02, eta: 11:19:06, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9925, loss_cls: 0.7420, loss: 0.7420 +2025-06-24 10:40:52,689 - pyskl - INFO - Epoch [19][1100/1281] lr: 2.404e-02, eta: 11:20:41, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8344, top5_acc: 0.9844, loss_cls: 0.7798, loss: 0.7798 +2025-06-24 10:41:34,142 - pyskl - INFO - Epoch [19][1200/1281] lr: 2.403e-02, eta: 11:22:15, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8531, top5_acc: 0.9919, loss_cls: 0.7080, loss: 0.7080 +2025-06-24 10:42:08,648 - pyskl - INFO - Saving checkpoint at 19 epochs +2025-06-24 10:43:20,880 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:43:20,934 - pyskl - INFO - +top1_acc 0.7902 +top5_acc 0.9857 +2025-06-24 10:43:20,934 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:43:20,941 - pyskl - INFO - +mean_acc 0.7044 +2025-06-24 10:43:20,942 - pyskl - INFO - Epoch(val) [19][533] top1_acc: 0.7902, top5_acc: 0.9857, mean_class_accuracy: 0.7044 +2025-06-24 10:44:21,404 - pyskl - INFO - Epoch [20][100/1281] lr: 2.402e-02, eta: 11:23:23, time: 0.605, data_time: 0.194, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9944, loss_cls: 0.6986, loss: 0.6986 +2025-06-24 10:44:51,358 - pyskl - INFO - Epoch [20][200/1281] lr: 2.401e-02, eta: 11:23:36, time: 0.300, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9925, loss_cls: 0.7520, loss: 0.7520 +2025-06-24 10:45:27,409 - pyskl - INFO - Epoch [20][300/1281] lr: 2.400e-02, eta: 11:24:30, time: 0.360, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9906, loss_cls: 0.7473, loss: 0.7473 +2025-06-24 10:46:08,846 - pyskl - INFO - Epoch [20][400/1281] lr: 2.399e-02, eta: 11:26:00, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9894, loss_cls: 0.7579, loss: 0.7579 +2025-06-24 10:46:50,469 - pyskl - INFO - Epoch [20][500/1281] lr: 2.398e-02, eta: 11:27:30, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8456, top5_acc: 0.9938, loss_cls: 0.7012, loss: 0.7012 +2025-06-24 10:47:32,033 - pyskl - INFO - Epoch [20][600/1281] lr: 2.398e-02, eta: 11:28:59, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9925, loss_cls: 0.7753, loss: 0.7753 +2025-06-24 10:48:13,620 - pyskl - INFO - Epoch [20][700/1281] lr: 2.397e-02, eta: 11:30:27, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8575, top5_acc: 0.9912, loss_cls: 0.6545, loss: 0.6545 +2025-06-24 10:48:55,174 - pyskl - INFO - Epoch [20][800/1281] lr: 2.396e-02, eta: 11:31:54, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9950, loss_cls: 0.7262, loss: 0.7262 +2025-06-24 10:49:36,574 - pyskl - INFO - Epoch [20][900/1281] lr: 2.395e-02, eta: 11:33:18, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8313, top5_acc: 0.9900, loss_cls: 0.7682, loss: 0.7682 +2025-06-24 10:50:18,088 - pyskl - INFO - Epoch [20][1000/1281] lr: 2.394e-02, eta: 11:34:42, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9900, loss_cls: 0.7344, loss: 0.7344 +2025-06-24 10:50:59,623 - pyskl - INFO - Epoch [20][1100/1281] lr: 2.393e-02, eta: 11:36:06, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8344, top5_acc: 0.9900, loss_cls: 0.7242, loss: 0.7242 +2025-06-24 10:51:41,143 - pyskl - INFO - Epoch [20][1200/1281] lr: 2.393e-02, eta: 11:37:28, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9919, loss_cls: 0.6968, loss: 0.6968 +2025-06-24 10:52:15,404 - pyskl - INFO - Saving checkpoint at 20 epochs +2025-06-24 10:53:26,597 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:53:26,656 - pyskl - INFO - +top1_acc 0.8194 +top5_acc 0.9887 +2025-06-24 10:53:26,656 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:53:26,666 - pyskl - INFO - +mean_acc 0.7431 +2025-06-24 10:53:26,671 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_17.pth was removed +2025-06-24 10:53:26,864 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_20.pth. +2025-06-24 10:53:26,864 - pyskl - INFO - Best top1_acc is 0.8194 at 20 epoch. +2025-06-24 10:53:26,867 - pyskl - INFO - Epoch(val) [20][533] top1_acc: 0.8194, top5_acc: 0.9887, mean_class_accuracy: 0.7431 +2025-06-24 10:54:26,424 - pyskl - INFO - Epoch [21][100/1281] lr: 2.391e-02, eta: 11:38:14, time: 0.596, data_time: 0.194, memory: 4082, top1_acc: 0.8525, top5_acc: 0.9925, loss_cls: 0.6959, loss: 0.6959 +2025-06-24 10:54:57,133 - pyskl - INFO - Epoch [21][200/1281] lr: 2.390e-02, eta: 11:38:24, time: 0.307, data_time: 0.000, memory: 4082, top1_acc: 0.8556, top5_acc: 0.9919, loss_cls: 0.6987, loss: 0.6987 +2025-06-24 10:55:31,954 - pyskl - INFO - Epoch [21][300/1281] lr: 2.389e-02, eta: 11:39:01, time: 0.348, data_time: 0.000, memory: 4082, top1_acc: 0.8525, top5_acc: 0.9906, loss_cls: 0.7090, loss: 0.7090 +2025-06-24 10:56:13,449 - pyskl - INFO - Epoch [21][400/1281] lr: 2.389e-02, eta: 11:40:20, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9931, loss_cls: 0.7487, loss: 0.7487 +2025-06-24 10:56:55,186 - pyskl - INFO - Epoch [21][500/1281] lr: 2.388e-02, eta: 11:41:39, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9975, loss_cls: 0.6702, loss: 0.6702 +2025-06-24 10:57:36,733 - pyskl - INFO - Epoch [21][600/1281] lr: 2.387e-02, eta: 11:42:56, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8194, top5_acc: 0.9944, loss_cls: 0.7627, loss: 0.7627 +2025-06-24 10:58:18,189 - pyskl - INFO - Epoch [21][700/1281] lr: 2.386e-02, eta: 11:44:11, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9956, loss_cls: 0.7064, loss: 0.7064 +2025-06-24 10:58:59,524 - pyskl - INFO - Epoch [21][800/1281] lr: 2.385e-02, eta: 11:45:25, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9931, loss_cls: 0.7158, loss: 0.7158 +2025-06-24 10:59:42,257 - pyskl - INFO - Epoch [21][900/1281] lr: 2.384e-02, eta: 11:46:47, time: 0.427, data_time: 0.000, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9906, loss_cls: 0.7072, loss: 0.7072 +2025-06-24 11:00:23,594 - pyskl - INFO - Epoch [21][1000/1281] lr: 2.383e-02, eta: 11:47:59, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9925, loss_cls: 0.7421, loss: 0.7421 +2025-06-24 11:01:05,065 - pyskl - INFO - Epoch [21][1100/1281] lr: 2.383e-02, eta: 11:49:12, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9931, loss_cls: 0.7300, loss: 0.7300 +2025-06-24 11:01:46,577 - pyskl - INFO - Epoch [21][1200/1281] lr: 2.382e-02, eta: 11:50:23, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9938, loss_cls: 0.7312, loss: 0.7312 +2025-06-24 11:02:20,909 - pyskl - INFO - Saving checkpoint at 21 epochs +2025-06-24 11:03:32,362 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:03:32,417 - pyskl - INFO - +top1_acc 0.8379 +top5_acc 0.9853 +2025-06-24 11:03:32,417 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:03:32,427 - pyskl - INFO - +mean_acc 0.7717 +2025-06-24 11:03:32,431 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_20.pth was removed +2025-06-24 11:03:32,606 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_21.pth. +2025-06-24 11:03:32,606 - pyskl - INFO - Best top1_acc is 0.8379 at 21 epoch. +2025-06-24 11:03:32,610 - pyskl - INFO - Epoch(val) [21][533] top1_acc: 0.8379, top5_acc: 0.9853, mean_class_accuracy: 0.7717 +2025-06-24 11:04:31,127 - pyskl - INFO - Epoch [22][100/1281] lr: 2.380e-02, eta: 11:50:49, time: 0.585, data_time: 0.197, memory: 4082, top1_acc: 0.8712, top5_acc: 0.9950, loss_cls: 0.6228, loss: 0.6228 +2025-06-24 11:05:03,230 - pyskl - INFO - Epoch [22][200/1281] lr: 2.379e-02, eta: 11:51:01, time: 0.321, data_time: 0.000, memory: 4082, top1_acc: 0.8538, top5_acc: 0.9925, loss_cls: 0.6726, loss: 0.6726 +2025-06-24 11:05:36,724 - pyskl - INFO - Epoch [22][300/1281] lr: 2.378e-02, eta: 11:51:22, time: 0.335, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9925, loss_cls: 0.6827, loss: 0.6827 +2025-06-24 11:06:18,455 - pyskl - INFO - Epoch [22][400/1281] lr: 2.378e-02, eta: 11:52:32, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8544, top5_acc: 0.9962, loss_cls: 0.6617, loss: 0.6617 +2025-06-24 11:07:00,047 - pyskl - INFO - Epoch [22][500/1281] lr: 2.377e-02, eta: 11:53:40, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8650, top5_acc: 0.9944, loss_cls: 0.6334, loss: 0.6334 +2025-06-24 11:07:41,457 - pyskl - INFO - Epoch [22][600/1281] lr: 2.376e-02, eta: 11:54:46, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8569, top5_acc: 0.9931, loss_cls: 0.6775, loss: 0.6775 +2025-06-24 11:08:22,977 - pyskl - INFO - Epoch [22][700/1281] lr: 2.375e-02, eta: 11:55:53, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8531, top5_acc: 0.9938, loss_cls: 0.6790, loss: 0.6790 +2025-06-24 11:09:04,372 - pyskl - INFO - Epoch [22][800/1281] lr: 2.374e-02, eta: 11:56:57, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9950, loss_cls: 0.6943, loss: 0.6943 +2025-06-24 11:09:46,020 - pyskl - INFO - Epoch [22][900/1281] lr: 2.373e-02, eta: 11:58:03, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9906, loss_cls: 0.6923, loss: 0.6923 +2025-06-24 11:10:27,516 - pyskl - INFO - Epoch [22][1000/1281] lr: 2.372e-02, eta: 11:59:06, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9944, loss_cls: 0.7533, loss: 0.7533 +2025-06-24 11:11:08,993 - pyskl - INFO - Epoch [22][1100/1281] lr: 2.371e-02, eta: 12:00:09, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9894, loss_cls: 0.7459, loss: 0.7459 +2025-06-24 11:11:50,664 - pyskl - INFO - Epoch [22][1200/1281] lr: 2.370e-02, eta: 12:01:13, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8194, top5_acc: 0.9925, loss_cls: 0.7852, loss: 0.7852 +2025-06-24 11:12:24,875 - pyskl - INFO - Saving checkpoint at 22 epochs +2025-06-24 11:13:36,123 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:13:36,200 - pyskl - INFO - +top1_acc 0.8089 +top5_acc 0.9859 +2025-06-24 11:13:36,200 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:13:36,212 - pyskl - INFO - +mean_acc 0.7425 +2025-06-24 11:13:36,216 - pyskl - INFO - Epoch(val) [22][533] top1_acc: 0.8089, top5_acc: 0.9859, mean_class_accuracy: 0.7425 +2025-06-24 11:14:33,070 - pyskl - INFO - Epoch [23][100/1281] lr: 2.369e-02, eta: 12:01:17, time: 0.568, data_time: 0.194, memory: 4082, top1_acc: 0.8656, top5_acc: 0.9994, loss_cls: 0.6107, loss: 0.6107 +2025-06-24 11:15:06,600 - pyskl - INFO - Epoch [23][200/1281] lr: 2.368e-02, eta: 12:01:32, time: 0.335, data_time: 0.000, memory: 4082, top1_acc: 0.8413, top5_acc: 0.9938, loss_cls: 0.7025, loss: 0.7025 +2025-06-24 11:15:39,434 - pyskl - INFO - Epoch [23][300/1281] lr: 2.367e-02, eta: 12:01:42, time: 0.328, data_time: 0.000, memory: 4082, top1_acc: 0.8494, top5_acc: 0.9944, loss_cls: 0.6578, loss: 0.6578 +2025-06-24 11:16:20,944 - pyskl - INFO - Epoch [23][400/1281] lr: 2.366e-02, eta: 12:02:42, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8538, top5_acc: 0.9925, loss_cls: 0.6598, loss: 0.6598 +2025-06-24 11:17:03,175 - pyskl - INFO - Epoch [23][500/1281] lr: 2.365e-02, eta: 12:03:45, time: 0.422, data_time: 0.000, memory: 4082, top1_acc: 0.8800, top5_acc: 0.9981, loss_cls: 0.5936, loss: 0.5936 +2025-06-24 11:17:45,705 - pyskl - INFO - Epoch [23][600/1281] lr: 2.364e-02, eta: 12:04:49, time: 0.425, data_time: 0.000, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9975, loss_cls: 0.6398, loss: 0.6398 +2025-06-24 11:18:27,502 - pyskl - INFO - Epoch [23][700/1281] lr: 2.363e-02, eta: 12:05:48, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9938, loss_cls: 0.7073, loss: 0.7073 +2025-06-24 11:19:09,124 - pyskl - INFO - Epoch [23][800/1281] lr: 2.362e-02, eta: 12:06:46, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9894, loss_cls: 0.7091, loss: 0.7091 +2025-06-24 11:19:50,642 - pyskl - INFO - Epoch [23][900/1281] lr: 2.361e-02, eta: 12:07:42, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9938, loss_cls: 0.6936, loss: 0.6936 +2025-06-24 11:20:32,163 - pyskl - INFO - Epoch [23][1000/1281] lr: 2.360e-02, eta: 12:08:38, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9944, loss_cls: 0.6952, loss: 0.6952 +2025-06-24 11:21:13,720 - pyskl - INFO - Epoch [23][1100/1281] lr: 2.359e-02, eta: 12:09:33, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8569, top5_acc: 0.9912, loss_cls: 0.6739, loss: 0.6739 +2025-06-24 11:21:55,332 - pyskl - INFO - Epoch [23][1200/1281] lr: 2.359e-02, eta: 12:10:28, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9919, loss_cls: 0.7530, loss: 0.7530 +2025-06-24 11:22:29,771 - pyskl - INFO - Saving checkpoint at 23 epochs +2025-06-24 11:23:41,086 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:23:41,141 - pyskl - INFO - +top1_acc 0.8237 +top5_acc 0.9856 +2025-06-24 11:23:41,141 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:23:41,149 - pyskl - INFO - +mean_acc 0.7402 +2025-06-24 11:23:41,151 - pyskl - INFO - Epoch(val) [23][533] top1_acc: 0.8237, top5_acc: 0.9856, mean_class_accuracy: 0.7402 +2025-06-24 11:24:37,515 - pyskl - INFO - Epoch [24][100/1281] lr: 2.357e-02, eta: 12:10:21, time: 0.564, data_time: 0.200, memory: 4082, top1_acc: 0.8581, top5_acc: 0.9938, loss_cls: 0.6631, loss: 0.6631 +2025-06-24 11:25:12,099 - pyskl - INFO - Epoch [24][200/1281] lr: 2.356e-02, eta: 12:10:36, time: 0.346, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9950, loss_cls: 0.6790, loss: 0.6790 +2025-06-24 11:25:44,074 - pyskl - INFO - Epoch [24][300/1281] lr: 2.355e-02, eta: 12:10:36, time: 0.320, data_time: 0.000, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9938, loss_cls: 0.6479, loss: 0.6479 +2025-06-24 11:26:25,662 - pyskl - INFO - Epoch [24][400/1281] lr: 2.354e-02, eta: 12:11:28, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9931, loss_cls: 0.7280, loss: 0.7280 +2025-06-24 11:27:07,346 - pyskl - INFO - Epoch [24][500/1281] lr: 2.353e-02, eta: 12:12:20, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8538, top5_acc: 0.9944, loss_cls: 0.6961, loss: 0.6961 +2025-06-24 11:27:49,017 - pyskl - INFO - Epoch [24][600/1281] lr: 2.352e-02, eta: 12:13:12, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8644, top5_acc: 0.9944, loss_cls: 0.6602, loss: 0.6602 +2025-06-24 11:28:30,510 - pyskl - INFO - Epoch [24][700/1281] lr: 2.351e-02, eta: 12:14:02, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8556, top5_acc: 0.9900, loss_cls: 0.6911, loss: 0.6911 +2025-06-24 11:29:11,964 - pyskl - INFO - Epoch [24][800/1281] lr: 2.350e-02, eta: 12:14:51, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9944, loss_cls: 0.6518, loss: 0.6518 +2025-06-24 11:29:53,340 - pyskl - INFO - Epoch [24][900/1281] lr: 2.349e-02, eta: 12:15:39, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8656, top5_acc: 0.9938, loss_cls: 0.6405, loss: 0.6405 +2025-06-24 11:30:35,050 - pyskl - INFO - Epoch [24][1000/1281] lr: 2.348e-02, eta: 12:16:28, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9944, loss_cls: 0.6935, loss: 0.6935 +2025-06-24 11:31:16,799 - pyskl - INFO - Epoch [24][1100/1281] lr: 2.347e-02, eta: 12:17:17, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8581, top5_acc: 0.9900, loss_cls: 0.6832, loss: 0.6832 +2025-06-24 11:31:58,472 - pyskl - INFO - Epoch [24][1200/1281] lr: 2.346e-02, eta: 12:18:05, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9881, loss_cls: 0.6733, loss: 0.6733 +2025-06-24 11:32:32,742 - pyskl - INFO - Saving checkpoint at 24 epochs +2025-06-24 11:33:44,076 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:33:44,132 - pyskl - INFO - +top1_acc 0.8148 +top5_acc 0.9866 +2025-06-24 11:33:44,132 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:33:44,140 - pyskl - INFO - +mean_acc 0.7443 +2025-06-24 11:33:44,143 - pyskl - INFO - Epoch(val) [24][533] top1_acc: 0.8148, top5_acc: 0.9866, mean_class_accuracy: 0.7443 +2025-06-24 11:34:39,208 - pyskl - INFO - Epoch [25][100/1281] lr: 2.344e-02, eta: 12:17:44, time: 0.551, data_time: 0.197, memory: 4082, top1_acc: 0.8675, top5_acc: 0.9962, loss_cls: 0.6394, loss: 0.6394 +2025-06-24 11:35:14,839 - pyskl - INFO - Epoch [25][200/1281] lr: 2.343e-02, eta: 12:17:59, time: 0.356, data_time: 0.001, memory: 4082, top1_acc: 0.8738, top5_acc: 0.9962, loss_cls: 0.5901, loss: 0.5901 +2025-06-24 11:35:46,029 - pyskl - INFO - Epoch [25][300/1281] lr: 2.342e-02, eta: 12:17:51, time: 0.312, data_time: 0.000, memory: 4082, top1_acc: 0.8650, top5_acc: 0.9956, loss_cls: 0.6193, loss: 0.6193 +2025-06-24 11:36:27,698 - pyskl - INFO - Epoch [25][400/1281] lr: 2.341e-02, eta: 12:18:37, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8538, top5_acc: 0.9919, loss_cls: 0.6621, loss: 0.6621 +2025-06-24 11:37:09,220 - pyskl - INFO - Epoch [25][500/1281] lr: 2.340e-02, eta: 12:19:21, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8662, top5_acc: 0.9931, loss_cls: 0.6488, loss: 0.6488 +2025-06-24 11:37:50,616 - pyskl - INFO - Epoch [25][600/1281] lr: 2.339e-02, eta: 12:20:05, time: 0.414, data_time: 0.001, memory: 4082, top1_acc: 0.8556, top5_acc: 0.9938, loss_cls: 0.6507, loss: 0.6507 +2025-06-24 11:38:32,301 - pyskl - INFO - Epoch [25][700/1281] lr: 2.338e-02, eta: 12:20:49, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8725, top5_acc: 0.9956, loss_cls: 0.6125, loss: 0.6125 +2025-06-24 11:39:13,856 - pyskl - INFO - Epoch [25][800/1281] lr: 2.337e-02, eta: 12:21:32, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9906, loss_cls: 0.6429, loss: 0.6429 +2025-06-24 11:39:55,409 - pyskl - INFO - Epoch [25][900/1281] lr: 2.336e-02, eta: 12:22:15, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9925, loss_cls: 0.6391, loss: 0.6391 +2025-06-24 11:40:36,829 - pyskl - INFO - Epoch [25][1000/1281] lr: 2.335e-02, eta: 12:22:56, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9925, loss_cls: 0.6579, loss: 0.6579 +2025-06-24 11:41:18,408 - pyskl - INFO - Epoch [25][1100/1281] lr: 2.334e-02, eta: 12:23:38, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9931, loss_cls: 0.7188, loss: 0.7188 +2025-06-24 11:41:59,865 - pyskl - INFO - Epoch [25][1200/1281] lr: 2.333e-02, eta: 12:24:18, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8456, top5_acc: 0.9919, loss_cls: 0.7268, loss: 0.7268 +2025-06-24 11:42:34,278 - pyskl - INFO - Saving checkpoint at 25 epochs +2025-06-24 11:43:46,346 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:43:46,401 - pyskl - INFO - +top1_acc 0.7979 +top5_acc 0.9845 +2025-06-24 11:43:46,401 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:43:46,408 - pyskl - INFO - +mean_acc 0.7238 +2025-06-24 11:43:46,410 - pyskl - INFO - Epoch(val) [25][533] top1_acc: 0.7979, top5_acc: 0.9845, mean_class_accuracy: 0.7238 +2025-06-24 11:44:42,597 - pyskl - INFO - Epoch [26][100/1281] lr: 2.332e-02, eta: 12:23:56, time: 0.562, data_time: 0.202, memory: 4082, top1_acc: 0.8725, top5_acc: 0.9956, loss_cls: 0.6530, loss: 0.6530 +2025-06-24 11:45:17,991 - pyskl - INFO - Epoch [26][200/1281] lr: 2.330e-02, eta: 12:24:05, time: 0.354, data_time: 0.000, memory: 4082, top1_acc: 0.8531, top5_acc: 0.9944, loss_cls: 0.6765, loss: 0.6765 +2025-06-24 11:45:51,427 - pyskl - INFO - Epoch [26][300/1281] lr: 2.329e-02, eta: 12:24:05, time: 0.334, data_time: 0.000, memory: 4082, top1_acc: 0.8594, top5_acc: 0.9944, loss_cls: 0.6453, loss: 0.6453 +2025-06-24 11:46:34,619 - pyskl - INFO - Epoch [26][400/1281] lr: 2.328e-02, eta: 12:24:52, time: 0.432, data_time: 0.001, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9962, loss_cls: 0.6642, loss: 0.6642 +2025-06-24 11:47:16,132 - pyskl - INFO - Epoch [26][500/1281] lr: 2.327e-02, eta: 12:25:30, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8756, top5_acc: 0.9956, loss_cls: 0.6054, loss: 0.6054 +2025-06-24 11:47:57,771 - pyskl - INFO - Epoch [26][600/1281] lr: 2.326e-02, eta: 12:26:09, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8694, top5_acc: 0.9938, loss_cls: 0.6082, loss: 0.6082 +2025-06-24 11:48:39,262 - pyskl - INFO - Epoch [26][700/1281] lr: 2.325e-02, eta: 12:26:46, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8694, top5_acc: 0.9931, loss_cls: 0.6120, loss: 0.6120 +2025-06-24 11:49:20,634 - pyskl - INFO - Epoch [26][800/1281] lr: 2.324e-02, eta: 12:27:23, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8669, top5_acc: 0.9950, loss_cls: 0.6296, loss: 0.6296 +2025-06-24 11:50:02,155 - pyskl - INFO - Epoch [26][900/1281] lr: 2.323e-02, eta: 12:27:59, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8581, top5_acc: 0.9925, loss_cls: 0.6506, loss: 0.6506 +2025-06-24 11:50:43,815 - pyskl - INFO - Epoch [26][1000/1281] lr: 2.322e-02, eta: 12:28:36, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9969, loss_cls: 0.6962, loss: 0.6962 +2025-06-24 11:51:25,329 - pyskl - INFO - Epoch [26][1100/1281] lr: 2.321e-02, eta: 12:29:11, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9962, loss_cls: 0.6594, loss: 0.6594 +2025-06-24 11:52:06,831 - pyskl - INFO - Epoch [26][1200/1281] lr: 2.320e-02, eta: 12:29:46, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8525, top5_acc: 0.9900, loss_cls: 0.6988, loss: 0.6988 +2025-06-24 11:52:41,206 - pyskl - INFO - Saving checkpoint at 26 epochs +2025-06-24 11:53:53,035 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:53:53,090 - pyskl - INFO - +top1_acc 0.8228 +top5_acc 0.9863 +2025-06-24 11:53:53,090 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:53:53,097 - pyskl - INFO - +mean_acc 0.7602 +2025-06-24 11:53:53,099 - pyskl - INFO - Epoch(val) [26][533] top1_acc: 0.8228, top5_acc: 0.9863, mean_class_accuracy: 0.7602 +2025-06-24 11:54:49,651 - pyskl - INFO - Epoch [27][100/1281] lr: 2.318e-02, eta: 12:29:20, time: 0.565, data_time: 0.199, memory: 4082, top1_acc: 0.8769, top5_acc: 0.9962, loss_cls: 0.6082, loss: 0.6082 +2025-06-24 11:55:24,389 - pyskl - INFO - Epoch [27][200/1281] lr: 2.317e-02, eta: 12:29:23, time: 0.347, data_time: 0.000, memory: 4082, top1_acc: 0.8744, top5_acc: 0.9956, loss_cls: 0.5741, loss: 0.5741 +2025-06-24 11:55:56,970 - pyskl - INFO - Epoch [27][300/1281] lr: 2.316e-02, eta: 12:29:14, time: 0.326, data_time: 0.000, memory: 4082, top1_acc: 0.8612, top5_acc: 0.9962, loss_cls: 0.6193, loss: 0.6193 +2025-06-24 11:56:38,510 - pyskl - INFO - Epoch [27][400/1281] lr: 2.315e-02, eta: 12:29:48, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8656, top5_acc: 0.9931, loss_cls: 0.6400, loss: 0.6400 +2025-06-24 11:57:20,696 - pyskl - INFO - Epoch [27][500/1281] lr: 2.314e-02, eta: 12:30:24, time: 0.422, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9925, loss_cls: 0.6463, loss: 0.6463 +2025-06-24 11:58:03,597 - pyskl - INFO - Epoch [27][600/1281] lr: 2.313e-02, eta: 12:31:03, time: 0.429, data_time: 0.000, memory: 4082, top1_acc: 0.8856, top5_acc: 0.9956, loss_cls: 0.5718, loss: 0.5718 +2025-06-24 11:58:45,074 - pyskl - INFO - Epoch [27][700/1281] lr: 2.312e-02, eta: 12:31:35, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8525, top5_acc: 0.9938, loss_cls: 0.6706, loss: 0.6706 +2025-06-24 11:59:26,661 - pyskl - INFO - Epoch [27][800/1281] lr: 2.311e-02, eta: 12:32:07, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8644, top5_acc: 0.9888, loss_cls: 0.6528, loss: 0.6528 +2025-06-24 12:00:08,026 - pyskl - INFO - Epoch [27][900/1281] lr: 2.310e-02, eta: 12:32:38, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8625, top5_acc: 0.9950, loss_cls: 0.6533, loss: 0.6533 +2025-06-24 12:00:49,728 - pyskl - INFO - Epoch [27][1000/1281] lr: 2.308e-02, eta: 12:33:09, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9900, loss_cls: 0.6787, loss: 0.6787 +2025-06-24 12:01:31,197 - pyskl - INFO - Epoch [27][1100/1281] lr: 2.307e-02, eta: 12:33:40, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8800, top5_acc: 0.9969, loss_cls: 0.5992, loss: 0.5992 +2025-06-24 12:02:12,827 - pyskl - INFO - Epoch [27][1200/1281] lr: 2.306e-02, eta: 12:34:10, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9956, loss_cls: 0.6830, loss: 0.6830 +2025-06-24 12:02:47,062 - pyskl - INFO - Saving checkpoint at 27 epochs +2025-06-24 12:03:57,807 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:03:57,871 - pyskl - INFO - +top1_acc 0.8242 +top5_acc 0.9813 +2025-06-24 12:03:57,871 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:03:57,880 - pyskl - INFO - +mean_acc 0.7548 +2025-06-24 12:03:57,883 - pyskl - INFO - Epoch(val) [27][533] top1_acc: 0.8242, top5_acc: 0.9813, mean_class_accuracy: 0.7548 +2025-06-24 12:04:52,708 - pyskl - INFO - Epoch [28][100/1281] lr: 2.304e-02, eta: 12:33:31, time: 0.548, data_time: 0.194, memory: 4082, top1_acc: 0.8612, top5_acc: 0.9950, loss_cls: 0.6404, loss: 0.6404 +2025-06-24 12:05:28,707 - pyskl - INFO - Epoch [28][200/1281] lr: 2.303e-02, eta: 12:33:36, time: 0.360, data_time: 0.000, memory: 4082, top1_acc: 0.8631, top5_acc: 0.9938, loss_cls: 0.6525, loss: 0.6525 +2025-06-24 12:05:59,912 - pyskl - INFO - Epoch [28][300/1281] lr: 2.302e-02, eta: 12:33:18, time: 0.312, data_time: 0.000, memory: 4082, top1_acc: 0.8725, top5_acc: 0.9956, loss_cls: 0.6071, loss: 0.6071 +2025-06-24 12:06:41,325 - pyskl - INFO - Epoch [28][400/1281] lr: 2.301e-02, eta: 12:33:46, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9938, loss_cls: 0.6688, loss: 0.6688 +2025-06-24 12:07:23,105 - pyskl - INFO - Epoch [28][500/1281] lr: 2.300e-02, eta: 12:34:16, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8769, top5_acc: 0.9944, loss_cls: 0.5777, loss: 0.5777 +2025-06-24 12:08:04,534 - pyskl - INFO - Epoch [28][600/1281] lr: 2.299e-02, eta: 12:34:43, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9888, loss_cls: 0.6571, loss: 0.6571 +2025-06-24 12:08:45,985 - pyskl - INFO - Epoch [28][700/1281] lr: 2.298e-02, eta: 12:35:10, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8625, top5_acc: 0.9956, loss_cls: 0.6207, loss: 0.6207 +2025-06-24 12:09:27,584 - pyskl - INFO - Epoch [28][800/1281] lr: 2.297e-02, eta: 12:35:38, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8675, top5_acc: 0.9944, loss_cls: 0.6196, loss: 0.6196 +2025-06-24 12:10:09,152 - pyskl - INFO - Epoch [28][900/1281] lr: 2.295e-02, eta: 12:36:04, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8538, top5_acc: 0.9925, loss_cls: 0.6772, loss: 0.6772 +2025-06-24 12:10:50,519 - pyskl - INFO - Epoch [28][1000/1281] lr: 2.294e-02, eta: 12:36:30, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8669, top5_acc: 0.9925, loss_cls: 0.6298, loss: 0.6298 +2025-06-24 12:11:31,946 - pyskl - INFO - Epoch [28][1100/1281] lr: 2.293e-02, eta: 12:36:56, time: 0.414, data_time: 0.001, memory: 4082, top1_acc: 0.8569, top5_acc: 0.9919, loss_cls: 0.6542, loss: 0.6542 +2025-06-24 12:12:13,528 - pyskl - INFO - Epoch [28][1200/1281] lr: 2.292e-02, eta: 12:37:21, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8594, top5_acc: 0.9950, loss_cls: 0.6666, loss: 0.6666 +2025-06-24 12:12:47,949 - pyskl - INFO - Saving checkpoint at 28 epochs +2025-06-24 12:13:59,353 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:13:59,407 - pyskl - INFO - +top1_acc 0.8439 +top5_acc 0.9876 +2025-06-24 12:13:59,407 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:13:59,414 - pyskl - INFO - +mean_acc 0.7820 +2025-06-24 12:13:59,419 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_21.pth was removed +2025-06-24 12:13:59,609 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_28.pth. +2025-06-24 12:13:59,609 - pyskl - INFO - Best top1_acc is 0.8439 at 28 epoch. +2025-06-24 12:13:59,613 - pyskl - INFO - Epoch(val) [28][533] top1_acc: 0.8439, top5_acc: 0.9876, mean_class_accuracy: 0.7820 +2025-06-24 12:14:54,699 - pyskl - INFO - Epoch [29][100/1281] lr: 2.290e-02, eta: 12:36:40, time: 0.551, data_time: 0.197, memory: 4082, top1_acc: 0.8800, top5_acc: 0.9938, loss_cls: 0.5906, loss: 0.5906 +2025-06-24 12:15:30,410 - pyskl - INFO - Epoch [29][200/1281] lr: 2.289e-02, eta: 12:36:39, time: 0.357, data_time: 0.000, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9950, loss_cls: 0.6421, loss: 0.6421 +2025-06-24 12:16:01,664 - pyskl - INFO - Epoch [29][300/1281] lr: 2.288e-02, eta: 12:36:20, time: 0.313, data_time: 0.001, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9925, loss_cls: 0.6942, loss: 0.6942 +2025-06-24 12:16:43,230 - pyskl - INFO - Epoch [29][400/1281] lr: 2.287e-02, eta: 12:36:44, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8769, top5_acc: 0.9962, loss_cls: 0.5773, loss: 0.5773 +2025-06-24 12:17:24,703 - pyskl - INFO - Epoch [29][500/1281] lr: 2.285e-02, eta: 12:37:08, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8700, top5_acc: 0.9962, loss_cls: 0.6055, loss: 0.6055 +2025-06-24 12:18:06,178 - pyskl - INFO - Epoch [29][600/1281] lr: 2.284e-02, eta: 12:37:31, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8669, top5_acc: 0.9919, loss_cls: 0.6319, loss: 0.6319 +2025-06-24 12:18:47,575 - pyskl - INFO - Epoch [29][700/1281] lr: 2.283e-02, eta: 12:37:54, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8769, top5_acc: 0.9931, loss_cls: 0.6061, loss: 0.6061 +2025-06-24 12:19:29,048 - pyskl - INFO - Epoch [29][800/1281] lr: 2.282e-02, eta: 12:38:17, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8612, top5_acc: 0.9969, loss_cls: 0.6129, loss: 0.6129 +2025-06-24 12:20:10,633 - pyskl - INFO - Epoch [29][900/1281] lr: 2.281e-02, eta: 12:38:39, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8681, top5_acc: 0.9956, loss_cls: 0.6405, loss: 0.6405 +2025-06-24 12:20:52,159 - pyskl - INFO - Epoch [29][1000/1281] lr: 2.280e-02, eta: 12:39:02, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8650, top5_acc: 0.9956, loss_cls: 0.6382, loss: 0.6382 +2025-06-24 12:21:33,559 - pyskl - INFO - Epoch [29][1100/1281] lr: 2.279e-02, eta: 12:39:23, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8688, top5_acc: 0.9938, loss_cls: 0.6401, loss: 0.6401 +2025-06-24 12:22:15,125 - pyskl - INFO - Epoch [29][1200/1281] lr: 2.277e-02, eta: 12:39:45, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8731, top5_acc: 0.9950, loss_cls: 0.6346, loss: 0.6346 +2025-06-24 12:22:49,575 - pyskl - INFO - Saving checkpoint at 29 epochs +2025-06-24 12:24:01,135 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:24:01,192 - pyskl - INFO - +top1_acc 0.8344 +top5_acc 0.9885 +2025-06-24 12:24:01,192 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:24:01,201 - pyskl - INFO - +mean_acc 0.7801 +2025-06-24 12:24:01,204 - pyskl - INFO - Epoch(val) [29][533] top1_acc: 0.8344, top5_acc: 0.9885, mean_class_accuracy: 0.7801 +2025-06-24 12:25:10,767 - pyskl - INFO - Epoch [30][100/1281] lr: 2.275e-02, eta: 12:39:59, time: 0.696, data_time: 0.195, memory: 4082, top1_acc: 0.8788, top5_acc: 0.9950, loss_cls: 0.5877, loss: 0.5877 +2025-06-24 12:25:36,001 - pyskl - INFO - Epoch [30][200/1281] lr: 2.274e-02, eta: 12:39:13, time: 0.252, data_time: 0.000, memory: 4082, top1_acc: 0.8862, top5_acc: 0.9975, loss_cls: 0.5511, loss: 0.5511 +2025-06-24 12:26:26,488 - pyskl - INFO - Epoch [30][300/1281] lr: 2.273e-02, eta: 12:40:10, time: 0.505, data_time: 0.001, memory: 4082, top1_acc: 0.8794, top5_acc: 0.9975, loss_cls: 0.5494, loss: 0.5494 +2025-06-24 12:27:15,947 - pyskl - INFO - Epoch [30][400/1281] lr: 2.272e-02, eta: 12:41:03, time: 0.495, data_time: 0.001, memory: 4082, top1_acc: 0.8562, top5_acc: 0.9888, loss_cls: 0.6518, loss: 0.6518 +2025-06-24 12:28:03,336 - pyskl - INFO - Epoch [30][500/1281] lr: 2.271e-02, eta: 12:41:47, time: 0.474, data_time: 0.000, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9944, loss_cls: 0.6479, loss: 0.6479 +2025-06-24 12:28:52,640 - pyskl - INFO - Epoch [30][600/1281] lr: 2.269e-02, eta: 12:42:38, time: 0.493, data_time: 0.000, memory: 4082, top1_acc: 0.8762, top5_acc: 0.9950, loss_cls: 0.5989, loss: 0.5989 +2025-06-24 12:29:42,560 - pyskl - INFO - Epoch [30][700/1281] lr: 2.268e-02, eta: 12:43:31, time: 0.499, data_time: 0.000, memory: 4082, top1_acc: 0.8656, top5_acc: 0.9938, loss_cls: 0.6210, loss: 0.6210 +2025-06-24 12:30:31,790 - pyskl - INFO - Epoch [30][800/1281] lr: 2.267e-02, eta: 12:44:20, time: 0.492, data_time: 0.000, memory: 4082, top1_acc: 0.8712, top5_acc: 0.9944, loss_cls: 0.6469, loss: 0.6469 +2025-06-24 12:31:20,653 - pyskl - INFO - Epoch [30][900/1281] lr: 2.266e-02, eta: 12:45:08, time: 0.489, data_time: 0.000, memory: 4082, top1_acc: 0.8575, top5_acc: 0.9894, loss_cls: 0.6812, loss: 0.6812 +2025-06-24 12:32:10,668 - pyskl - INFO - Epoch [30][1000/1281] lr: 2.265e-02, eta: 12:46:00, time: 0.500, data_time: 0.000, memory: 4082, top1_acc: 0.8650, top5_acc: 0.9931, loss_cls: 0.6434, loss: 0.6434 +2025-06-24 12:32:59,982 - pyskl - INFO - Epoch [30][1100/1281] lr: 2.263e-02, eta: 12:46:49, time: 0.493, data_time: 0.000, memory: 4082, top1_acc: 0.8738, top5_acc: 0.9925, loss_cls: 0.6349, loss: 0.6349 +2025-06-24 12:33:48,781 - pyskl - INFO - Epoch [30][1200/1281] lr: 2.262e-02, eta: 12:47:34, time: 0.488, data_time: 0.000, memory: 4082, top1_acc: 0.8812, top5_acc: 0.9944, loss_cls: 0.5866, loss: 0.5866 +2025-06-24 12:34:15,566 - pyskl - INFO - Saving checkpoint at 30 epochs +2025-06-24 12:35:15,944 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:35:16,016 - pyskl - INFO - +top1_acc 0.8344 +top5_acc 0.9878 +2025-06-24 12:35:16,016 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:35:16,025 - pyskl - INFO - +mean_acc 0.7740 +2025-06-24 12:35:16,028 - pyskl - INFO - Epoch(val) [30][533] top1_acc: 0.8344, top5_acc: 0.9878, mean_class_accuracy: 0.7740 +2025-06-24 12:36:45,291 - pyskl - INFO - Epoch [31][100/1281] lr: 2.260e-02, eta: 12:49:00, time: 0.893, data_time: 0.194, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9981, loss_cls: 0.7241, loss: 0.7241 +2025-06-24 12:37:36,451 - pyskl - INFO - Epoch [31][200/1281] lr: 2.259e-02, eta: 12:49:54, time: 0.512, data_time: 0.001, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9956, loss_cls: 0.7471, loss: 0.7471 +2025-06-24 12:38:28,952 - pyskl - INFO - Epoch [31][300/1281] lr: 2.258e-02, eta: 12:50:53, time: 0.525, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9950, loss_cls: 0.7891, loss: 0.7891 +2025-06-24 12:39:20,139 - pyskl - INFO - Epoch [31][400/1281] lr: 2.256e-02, eta: 12:51:46, time: 0.512, data_time: 0.001, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9938, loss_cls: 0.7261, loss: 0.7261 +2025-06-24 12:40:12,169 - pyskl - INFO - Epoch [31][500/1281] lr: 2.255e-02, eta: 12:52:41, time: 0.520, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9950, loss_cls: 0.7509, loss: 0.7509 +2025-06-24 12:41:03,703 - pyskl - INFO - Epoch [31][600/1281] lr: 2.254e-02, eta: 12:53:34, time: 0.515, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9962, loss_cls: 0.7659, loss: 0.7659 +2025-06-24 12:41:53,570 - pyskl - INFO - Epoch [31][700/1281] lr: 2.253e-02, eta: 12:54:21, time: 0.499, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9912, loss_cls: 0.8146, loss: 0.8146 +2025-06-24 12:42:44,886 - pyskl - INFO - Epoch [31][800/1281] lr: 2.252e-02, eta: 12:55:12, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9938, loss_cls: 0.7830, loss: 0.7830 +2025-06-24 12:43:25,331 - pyskl - INFO - Epoch [31][900/1281] lr: 2.250e-02, eta: 12:55:20, time: 0.404, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9944, loss_cls: 0.7881, loss: 0.7881 +2025-06-24 12:44:16,447 - pyskl - INFO - Epoch [31][1000/1281] lr: 2.249e-02, eta: 12:56:10, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9894, loss_cls: 0.8055, loss: 0.8055 +2025-06-24 12:44:42,735 - pyskl - INFO - Epoch [31][1100/1281] lr: 2.248e-02, eta: 12:55:23, time: 0.263, data_time: 0.000, memory: 4083, top1_acc: 0.8519, top5_acc: 0.9944, loss_cls: 0.8346, loss: 0.8346 +2025-06-24 12:45:34,251 - pyskl - INFO - Epoch [31][1200/1281] lr: 2.247e-02, eta: 12:56:14, time: 0.515, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9912, loss_cls: 0.8146, loss: 0.8146 +2025-06-24 12:46:16,352 - pyskl - INFO - Saving checkpoint at 31 epochs +2025-06-24 12:47:27,604 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:47:27,663 - pyskl - INFO - +top1_acc 0.8052 +top5_acc 0.9791 +2025-06-24 12:47:27,663 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:47:27,672 - pyskl - INFO - +mean_acc 0.7329 +2025-06-24 12:47:27,675 - pyskl - INFO - Epoch(val) [31][533] top1_acc: 0.8052, top5_acc: 0.9791, mean_class_accuracy: 0.7329 +2025-06-24 12:48:52,669 - pyskl - INFO - Epoch [32][100/1281] lr: 2.244e-02, eta: 12:57:12, time: 0.850, data_time: 0.199, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9925, loss_cls: 0.6506, loss: 0.6506 +2025-06-24 12:49:44,738 - pyskl - INFO - Epoch [32][200/1281] lr: 2.243e-02, eta: 12:58:03, time: 0.521, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9956, loss_cls: 0.6318, loss: 0.6318 +2025-06-24 12:50:37,304 - pyskl - INFO - Epoch [32][300/1281] lr: 2.242e-02, eta: 12:58:56, time: 0.526, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9938, loss_cls: 0.6757, loss: 0.6757 +2025-06-24 12:51:29,661 - pyskl - INFO - Epoch [32][400/1281] lr: 2.241e-02, eta: 12:59:47, time: 0.524, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9950, loss_cls: 0.7472, loss: 0.7472 +2025-06-24 12:52:19,096 - pyskl - INFO - Epoch [32][500/1281] lr: 2.239e-02, eta: 13:00:27, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9938, loss_cls: 0.7055, loss: 0.7055 +2025-06-24 12:52:51,062 - pyskl - INFO - Epoch [32][600/1281] lr: 2.238e-02, eta: 13:00:01, time: 0.320, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9944, loss_cls: 0.7420, loss: 0.7420 +2025-06-24 12:53:33,261 - pyskl - INFO - Epoch [32][700/1281] lr: 2.237e-02, eta: 13:00:12, time: 0.422, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9956, loss_cls: 0.6306, loss: 0.6306 +2025-06-24 12:54:17,455 - pyskl - INFO - Epoch [32][800/1281] lr: 2.236e-02, eta: 13:00:32, time: 0.442, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9956, loss_cls: 0.6222, loss: 0.6222 +2025-06-24 12:55:10,550 - pyskl - INFO - Epoch [32][900/1281] lr: 2.234e-02, eta: 13:01:24, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9950, loss_cls: 0.7318, loss: 0.7318 +2025-06-24 12:56:03,375 - pyskl - INFO - Epoch [32][1000/1281] lr: 2.233e-02, eta: 13:02:14, time: 0.528, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9925, loss_cls: 0.6822, loss: 0.6822 +2025-06-24 12:56:56,609 - pyskl - INFO - Epoch [32][1100/1281] lr: 2.232e-02, eta: 13:03:06, time: 0.532, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9950, loss_cls: 0.6863, loss: 0.6863 +2025-06-24 12:57:49,090 - pyskl - INFO - Epoch [32][1200/1281] lr: 2.231e-02, eta: 13:03:54, time: 0.525, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9931, loss_cls: 0.6982, loss: 0.6982 +2025-06-24 12:58:31,367 - pyskl - INFO - Saving checkpoint at 32 epochs +2025-06-24 12:59:42,639 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:59:42,709 - pyskl - INFO - +top1_acc 0.7586 +top5_acc 0.9772 +2025-06-24 12:59:42,709 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:59:42,717 - pyskl - INFO - +mean_acc 0.7199 +2025-06-24 12:59:42,720 - pyskl - INFO - Epoch(val) [32][533] top1_acc: 0.7586, top5_acc: 0.9772, mean_class_accuracy: 0.7199 +2025-06-24 13:01:07,239 - pyskl - INFO - Epoch [33][100/1281] lr: 2.228e-02, eta: 13:04:41, time: 0.845, data_time: 0.199, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9981, loss_cls: 0.6469, loss: 0.6469 +2025-06-24 13:01:36,693 - pyskl - INFO - Epoch [33][200/1281] lr: 2.227e-02, eta: 13:04:04, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9956, loss_cls: 0.6650, loss: 0.6650 +2025-06-24 13:02:28,144 - pyskl - INFO - Epoch [33][300/1281] lr: 2.226e-02, eta: 13:04:47, time: 0.514, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9956, loss_cls: 0.7144, loss: 0.7144 +2025-06-24 13:03:04,481 - pyskl - INFO - Epoch [33][400/1281] lr: 2.225e-02, eta: 13:04:34, time: 0.363, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9962, loss_cls: 0.6436, loss: 0.6436 +2025-06-24 13:03:56,678 - pyskl - INFO - Epoch [33][500/1281] lr: 2.223e-02, eta: 13:05:19, time: 0.522, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9931, loss_cls: 0.6776, loss: 0.6776 +2025-06-24 13:04:48,637 - pyskl - INFO - Epoch [33][600/1281] lr: 2.222e-02, eta: 13:06:03, time: 0.520, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9919, loss_cls: 0.7262, loss: 0.7262 +2025-06-24 13:05:40,426 - pyskl - INFO - Epoch [33][700/1281] lr: 2.221e-02, eta: 13:06:45, time: 0.518, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9956, loss_cls: 0.5991, loss: 0.5991 +2025-06-24 13:06:32,248 - pyskl - INFO - Epoch [33][800/1281] lr: 2.219e-02, eta: 13:07:27, time: 0.518, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9956, loss_cls: 0.6313, loss: 0.6313 +2025-06-24 13:07:23,749 - pyskl - INFO - Epoch [33][900/1281] lr: 2.218e-02, eta: 13:08:08, time: 0.515, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9938, loss_cls: 0.6485, loss: 0.6485 +2025-06-24 13:08:15,845 - pyskl - INFO - Epoch [33][1000/1281] lr: 2.217e-02, eta: 13:08:50, time: 0.521, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9969, loss_cls: 0.6659, loss: 0.6659 +2025-06-24 13:09:07,457 - pyskl - INFO - Epoch [33][1100/1281] lr: 2.216e-02, eta: 13:09:30, time: 0.516, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9931, loss_cls: 0.6778, loss: 0.6778 +2025-06-24 13:09:57,811 - pyskl - INFO - Epoch [33][1200/1281] lr: 2.214e-02, eta: 13:10:05, time: 0.504, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9962, loss_cls: 0.6548, loss: 0.6548 +2025-06-24 13:10:40,333 - pyskl - INFO - Saving checkpoint at 33 epochs +2025-06-24 13:11:49,321 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:11:49,392 - pyskl - INFO - +top1_acc 0.7963 +top5_acc 0.9849 +2025-06-24 13:11:49,392 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:11:49,400 - pyskl - INFO - +mean_acc 0.7320 +2025-06-24 13:11:49,402 - pyskl - INFO - Epoch(val) [33][533] top1_acc: 0.7963, top5_acc: 0.9849, mean_class_accuracy: 0.7320 +2025-06-24 13:12:57,690 - pyskl - INFO - Epoch [34][100/1281] lr: 2.212e-02, eta: 13:09:47, time: 0.683, data_time: 0.199, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9981, loss_cls: 0.6189, loss: 0.6189 +2025-06-24 13:13:48,549 - pyskl - INFO - Epoch [34][200/1281] lr: 2.211e-02, eta: 13:10:23, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9956, loss_cls: 0.6540, loss: 0.6540 +2025-06-24 13:14:39,464 - pyskl - INFO - Epoch [34][300/1281] lr: 2.209e-02, eta: 13:10:59, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9969, loss_cls: 0.6114, loss: 0.6114 +2025-06-24 13:15:30,427 - pyskl - INFO - Epoch [34][400/1281] lr: 2.208e-02, eta: 13:11:35, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9931, loss_cls: 0.6414, loss: 0.6414 +2025-06-24 13:16:22,747 - pyskl - INFO - Epoch [34][500/1281] lr: 2.207e-02, eta: 13:12:15, time: 0.523, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9956, loss_cls: 0.6054, loss: 0.6054 +2025-06-24 13:17:13,490 - pyskl - INFO - Epoch [34][600/1281] lr: 2.205e-02, eta: 13:12:49, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9969, loss_cls: 0.5864, loss: 0.5864 +2025-06-24 13:18:06,438 - pyskl - INFO - Epoch [34][700/1281] lr: 2.204e-02, eta: 13:13:30, time: 0.529, data_time: 0.000, memory: 4083, top1_acc: 0.8512, top5_acc: 0.9956, loss_cls: 0.7012, loss: 0.7012 +2025-06-24 13:18:58,555 - pyskl - INFO - Epoch [34][800/1281] lr: 2.203e-02, eta: 13:14:08, time: 0.521, data_time: 0.000, memory: 4083, top1_acc: 0.8525, top5_acc: 0.9912, loss_cls: 0.7150, loss: 0.7150 +2025-06-24 13:19:50,429 - pyskl - INFO - Epoch [34][900/1281] lr: 2.201e-02, eta: 13:14:45, time: 0.519, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9969, loss_cls: 0.7438, loss: 0.7438 +2025-06-24 13:20:31,869 - pyskl - INFO - Epoch [34][1000/1281] lr: 2.200e-02, eta: 13:14:45, time: 0.414, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9969, loss_cls: 0.6193, loss: 0.6193 +2025-06-24 13:21:23,052 - pyskl - INFO - Epoch [34][1100/1281] lr: 2.199e-02, eta: 13:15:19, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9950, loss_cls: 0.6315, loss: 0.6315 +2025-06-24 13:21:50,294 - pyskl - INFO - Epoch [34][1200/1281] lr: 2.197e-02, eta: 13:14:31, time: 0.272, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9912, loss_cls: 0.7216, loss: 0.7216 +2025-06-24 13:22:33,621 - pyskl - INFO - Saving checkpoint at 34 epochs +2025-06-24 13:23:44,375 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:23:44,430 - pyskl - INFO - +top1_acc 0.8356 +top5_acc 0.9887 +2025-06-24 13:23:44,430 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:23:44,437 - pyskl - INFO - +mean_acc 0.7765 +2025-06-24 13:23:44,439 - pyskl - INFO - Epoch(val) [34][533] top1_acc: 0.8356, top5_acc: 0.9887, mean_class_accuracy: 0.7765 +2025-06-24 13:25:09,162 - pyskl - INFO - Epoch [35][100/1281] lr: 2.195e-02, eta: 13:15:03, time: 0.847, data_time: 0.194, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9938, loss_cls: 0.6321, loss: 0.6321 +2025-06-24 13:26:01,424 - pyskl - INFO - Epoch [35][200/1281] lr: 2.194e-02, eta: 13:15:39, time: 0.523, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9981, loss_cls: 0.6047, loss: 0.6047 +2025-06-24 13:26:54,233 - pyskl - INFO - Epoch [35][300/1281] lr: 2.192e-02, eta: 13:16:17, time: 0.528, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9925, loss_cls: 0.6333, loss: 0.6333 +2025-06-24 13:27:45,158 - pyskl - INFO - Epoch [35][400/1281] lr: 2.191e-02, eta: 13:16:48, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9956, loss_cls: 0.6147, loss: 0.6147 +2025-06-24 13:28:37,165 - pyskl - INFO - Epoch [35][500/1281] lr: 2.190e-02, eta: 13:17:22, time: 0.520, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9956, loss_cls: 0.6607, loss: 0.6607 +2025-06-24 13:29:25,578 - pyskl - INFO - Epoch [35][600/1281] lr: 2.188e-02, eta: 13:17:44, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9906, loss_cls: 0.6430, loss: 0.6430 +2025-06-24 13:30:04,562 - pyskl - INFO - Epoch [35][700/1281] lr: 2.187e-02, eta: 13:17:34, time: 0.390, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9950, loss_cls: 0.6443, loss: 0.6443 +2025-06-24 13:30:39,714 - pyskl - INFO - Epoch [35][800/1281] lr: 2.185e-02, eta: 13:17:11, time: 0.352, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9938, loss_cls: 0.6338, loss: 0.6338 +2025-06-24 13:31:27,990 - pyskl - INFO - Epoch [35][900/1281] lr: 2.184e-02, eta: 13:17:31, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9962, loss_cls: 0.6411, loss: 0.6411 +2025-06-24 13:32:19,843 - pyskl - INFO - Epoch [35][1000/1281] lr: 2.183e-02, eta: 13:18:03, time: 0.519, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9956, loss_cls: 0.6201, loss: 0.6201 +2025-06-24 13:33:11,658 - pyskl - INFO - Epoch [35][1100/1281] lr: 2.181e-02, eta: 13:18:35, time: 0.518, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9975, loss_cls: 0.6167, loss: 0.6167 +2025-06-24 13:34:04,141 - pyskl - INFO - Epoch [35][1200/1281] lr: 2.180e-02, eta: 13:19:08, time: 0.525, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9969, loss_cls: 0.6146, loss: 0.6146 +2025-06-24 13:34:47,839 - pyskl - INFO - Saving checkpoint at 35 epochs +2025-06-24 13:35:58,794 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:35:58,852 - pyskl - INFO - +top1_acc 0.8375 +top5_acc 0.9891 +2025-06-24 13:35:58,852 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:35:58,859 - pyskl - INFO - +mean_acc 0.7717 +2025-06-24 13:35:58,862 - pyskl - INFO - Epoch(val) [35][533] top1_acc: 0.8375, top5_acc: 0.9891, mean_class_accuracy: 0.7717 +2025-06-24 13:37:23,340 - pyskl - INFO - Epoch [36][100/1281] lr: 2.178e-02, eta: 13:19:33, time: 0.845, data_time: 0.187, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9969, loss_cls: 0.6189, loss: 0.6189 +2025-06-24 13:38:14,938 - pyskl - INFO - Epoch [36][200/1281] lr: 2.176e-02, eta: 13:20:02, time: 0.516, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9956, loss_cls: 0.5856, loss: 0.5856 +2025-06-24 13:38:43,217 - pyskl - INFO - Epoch [36][300/1281] lr: 2.175e-02, eta: 13:19:16, time: 0.283, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9956, loss_cls: 0.6357, loss: 0.6357 +2025-06-24 13:39:33,710 - pyskl - INFO - Epoch [36][400/1281] lr: 2.173e-02, eta: 13:19:41, time: 0.505, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9975, loss_cls: 0.5810, loss: 0.5810 +2025-06-24 13:40:12,559 - pyskl - INFO - Epoch [36][500/1281] lr: 2.172e-02, eta: 13:19:28, time: 0.388, data_time: 0.001, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9944, loss_cls: 0.6382, loss: 0.6382 +2025-06-24 13:41:04,624 - pyskl - INFO - Epoch [36][600/1281] lr: 2.171e-02, eta: 13:19:58, time: 0.521, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9975, loss_cls: 0.6228, loss: 0.6228 +2025-06-24 13:41:56,370 - pyskl - INFO - Epoch [36][700/1281] lr: 2.169e-02, eta: 13:20:27, time: 0.517, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9925, loss_cls: 0.7163, loss: 0.7163 +2025-06-24 13:42:48,617 - pyskl - INFO - Epoch [36][800/1281] lr: 2.168e-02, eta: 13:20:57, time: 0.522, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9931, loss_cls: 0.6409, loss: 0.6409 +2025-06-24 13:43:41,157 - pyskl - INFO - Epoch [36][900/1281] lr: 2.167e-02, eta: 13:21:27, time: 0.525, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9956, loss_cls: 0.5869, loss: 0.5869 +2025-06-24 13:44:32,746 - pyskl - INFO - Epoch [36][1000/1281] lr: 2.165e-02, eta: 13:21:54, time: 0.516, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9956, loss_cls: 0.6531, loss: 0.6531 +2025-06-24 13:45:25,187 - pyskl - INFO - Epoch [36][1100/1281] lr: 2.164e-02, eta: 13:22:24, time: 0.524, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9944, loss_cls: 0.6506, loss: 0.6506 +2025-06-24 13:46:16,252 - pyskl - INFO - Epoch [36][1200/1281] lr: 2.162e-02, eta: 13:22:48, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9944, loss_cls: 0.6061, loss: 0.6061 +2025-06-24 13:46:59,405 - pyskl - INFO - Saving checkpoint at 36 epochs +2025-06-24 13:47:56,811 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:47:56,869 - pyskl - INFO - +top1_acc 0.8047 +top5_acc 0.9840 +2025-06-24 13:47:56,869 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:47:56,876 - pyskl - INFO - +mean_acc 0.7259 +2025-06-24 13:47:56,878 - pyskl - INFO - Epoch(val) [36][533] top1_acc: 0.8047, top5_acc: 0.9840, mean_class_accuracy: 0.7259 +2025-06-24 13:48:54,357 - pyskl - INFO - Epoch [37][100/1281] lr: 2.160e-02, eta: 13:21:42, time: 0.575, data_time: 0.198, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9944, loss_cls: 0.6105, loss: 0.6105 +2025-06-24 13:49:45,864 - pyskl - INFO - Epoch [37][200/1281] lr: 2.158e-02, eta: 13:22:07, time: 0.515, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9931, loss_cls: 0.6733, loss: 0.6733 +2025-06-24 13:50:36,352 - pyskl - INFO - Epoch [37][300/1281] lr: 2.157e-02, eta: 13:22:29, time: 0.505, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9981, loss_cls: 0.5822, loss: 0.5822 +2025-06-24 13:51:28,590 - pyskl - INFO - Epoch [37][400/1281] lr: 2.156e-02, eta: 13:22:56, time: 0.522, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9969, loss_cls: 0.5692, loss: 0.5692 +2025-06-24 13:52:20,461 - pyskl - INFO - Epoch [37][500/1281] lr: 2.154e-02, eta: 13:23:22, time: 0.519, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9962, loss_cls: 0.6253, loss: 0.6253 +2025-06-24 13:53:12,326 - pyskl - INFO - Epoch [37][600/1281] lr: 2.153e-02, eta: 13:23:47, time: 0.519, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9950, loss_cls: 0.6323, loss: 0.6323 +2025-06-24 13:54:04,105 - pyskl - INFO - Epoch [37][700/1281] lr: 2.151e-02, eta: 13:24:11, time: 0.518, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9956, loss_cls: 0.6516, loss: 0.6516 +2025-06-24 13:54:56,249 - pyskl - INFO - Epoch [37][800/1281] lr: 2.150e-02, eta: 13:24:37, time: 0.521, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9956, loss_cls: 0.5810, loss: 0.5810 +2025-06-24 13:55:47,327 - pyskl - INFO - Epoch [37][900/1281] lr: 2.149e-02, eta: 13:24:59, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9956, loss_cls: 0.5847, loss: 0.5847 +2025-06-24 13:56:38,595 - pyskl - INFO - Epoch [37][1000/1281] lr: 2.147e-02, eta: 13:25:21, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9950, loss_cls: 0.6276, loss: 0.6276 +2025-06-24 13:57:13,173 - pyskl - INFO - Epoch [37][1100/1281] lr: 2.146e-02, eta: 13:24:51, time: 0.346, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9931, loss_cls: 0.6702, loss: 0.6702 +2025-06-24 13:58:04,318 - pyskl - INFO - Epoch [37][1200/1281] lr: 2.144e-02, eta: 13:25:13, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9931, loss_cls: 0.6530, loss: 0.6530 +2025-06-24 13:58:27,574 - pyskl - INFO - Saving checkpoint at 37 epochs +2025-06-24 13:59:39,361 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:59:39,418 - pyskl - INFO - +top1_acc 0.8282 +top5_acc 0.9890 +2025-06-24 13:59:39,418 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:59:39,429 - pyskl - INFO - +mean_acc 0.7829 +2025-06-24 13:59:39,432 - pyskl - INFO - Epoch(val) [37][533] top1_acc: 0.8282, top5_acc: 0.9890, mean_class_accuracy: 0.7829 +2025-06-24 14:01:02,851 - pyskl - INFO - Epoch [38][100/1281] lr: 2.142e-02, eta: 13:25:22, time: 0.834, data_time: 0.198, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9944, loss_cls: 0.6174, loss: 0.6174 +2025-06-24 14:01:54,025 - pyskl - INFO - Epoch [38][200/1281] lr: 2.140e-02, eta: 13:25:43, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9956, loss_cls: 0.6038, loss: 0.6038 +2025-06-24 14:02:45,137 - pyskl - INFO - Epoch [38][300/1281] lr: 2.139e-02, eta: 13:26:03, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9944, loss_cls: 0.6418, loss: 0.6418 +2025-06-24 14:03:35,808 - pyskl - INFO - Epoch [38][400/1281] lr: 2.137e-02, eta: 13:26:21, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9981, loss_cls: 0.5834, loss: 0.5834 +2025-06-24 14:04:27,987 - pyskl - INFO - Epoch [38][500/1281] lr: 2.136e-02, eta: 13:26:44, time: 0.522, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9956, loss_cls: 0.5920, loss: 0.5920 +2025-06-24 14:05:19,446 - pyskl - INFO - Epoch [38][600/1281] lr: 2.134e-02, eta: 13:27:04, time: 0.515, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9988, loss_cls: 0.5699, loss: 0.5699 +2025-06-24 14:06:07,487 - pyskl - INFO - Epoch [38][700/1281] lr: 2.133e-02, eta: 13:27:14, time: 0.480, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9944, loss_cls: 0.6458, loss: 0.6458 +2025-06-24 14:06:46,203 - pyskl - INFO - Epoch [38][800/1281] lr: 2.132e-02, eta: 13:26:56, time: 0.387, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9956, loss_cls: 0.6161, loss: 0.6161 +2025-06-24 14:07:21,630 - pyskl - INFO - Epoch [38][900/1281] lr: 2.130e-02, eta: 13:26:27, time: 0.354, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9925, loss_cls: 0.6156, loss: 0.6156 +2025-06-24 14:08:08,825 - pyskl - INFO - Epoch [38][1000/1281] lr: 2.129e-02, eta: 13:26:34, time: 0.472, data_time: 0.001, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9962, loss_cls: 0.6201, loss: 0.6201 +2025-06-24 14:09:00,400 - pyskl - INFO - Epoch [38][1100/1281] lr: 2.127e-02, eta: 13:26:53, time: 0.516, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9956, loss_cls: 0.6048, loss: 0.6048 +2025-06-24 14:09:53,518 - pyskl - INFO - Epoch [38][1200/1281] lr: 2.126e-02, eta: 13:27:17, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9938, loss_cls: 0.6810, loss: 0.6810 +2025-06-24 14:10:37,092 - pyskl - INFO - Saving checkpoint at 38 epochs +2025-06-24 14:11:48,577 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:11:48,638 - pyskl - INFO - +top1_acc 0.8520 +top5_acc 0.9901 +2025-06-24 14:11:48,639 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:11:48,648 - pyskl - INFO - +mean_acc 0.8026 +2025-06-24 14:11:48,653 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_28.pth was removed +2025-06-24 14:11:48,886 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_38.pth. +2025-06-24 14:11:48,887 - pyskl - INFO - Best top1_acc is 0.8520 at 38 epoch. +2025-06-24 14:11:48,890 - pyskl - INFO - Epoch(val) [38][533] top1_acc: 0.8520, top5_acc: 0.9901, mean_class_accuracy: 0.8026 +2025-06-24 14:13:12,539 - pyskl - INFO - Epoch [39][100/1281] lr: 2.123e-02, eta: 13:27:22, time: 0.836, data_time: 0.192, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9962, loss_cls: 0.5648, loss: 0.5648 +2025-06-24 14:14:04,667 - pyskl - INFO - Epoch [39][200/1281] lr: 2.122e-02, eta: 13:27:42, time: 0.521, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9962, loss_cls: 0.5862, loss: 0.5862 +2025-06-24 14:14:55,987 - pyskl - INFO - Epoch [39][300/1281] lr: 2.120e-02, eta: 13:27:59, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9938, loss_cls: 0.6330, loss: 0.6330 +2025-06-24 14:15:24,085 - pyskl - INFO - Epoch [39][400/1281] lr: 2.119e-02, eta: 13:27:09, time: 0.281, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9962, loss_cls: 0.6280, loss: 0.6280 +2025-06-24 14:16:15,288 - pyskl - INFO - Epoch [39][500/1281] lr: 2.117e-02, eta: 13:27:25, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9950, loss_cls: 0.5627, loss: 0.5627 +2025-06-24 14:16:53,878 - pyskl - INFO - Epoch [39][600/1281] lr: 2.116e-02, eta: 13:27:05, time: 0.386, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9925, loss_cls: 0.6000, loss: 0.6000 +2025-06-24 14:17:45,619 - pyskl - INFO - Epoch [39][700/1281] lr: 2.114e-02, eta: 13:27:23, time: 0.517, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9962, loss_cls: 0.6337, loss: 0.6337 +2025-06-24 14:18:37,134 - pyskl - INFO - Epoch [39][800/1281] lr: 2.113e-02, eta: 13:27:40, time: 0.515, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9975, loss_cls: 0.5567, loss: 0.5567 +2025-06-24 14:19:29,927 - pyskl - INFO - Epoch [39][900/1281] lr: 2.111e-02, eta: 13:28:00, time: 0.528, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9956, loss_cls: 0.5939, loss: 0.5939 +2025-06-24 14:20:21,753 - pyskl - INFO - Epoch [39][1000/1281] lr: 2.110e-02, eta: 13:28:17, time: 0.518, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9969, loss_cls: 0.6342, loss: 0.6342 +2025-06-24 14:21:14,128 - pyskl - INFO - Epoch [39][1100/1281] lr: 2.108e-02, eta: 13:28:35, time: 0.524, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9944, loss_cls: 0.5697, loss: 0.5697 +2025-06-24 14:22:04,732 - pyskl - INFO - Epoch [39][1200/1281] lr: 2.107e-02, eta: 13:28:48, time: 0.506, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9969, loss_cls: 0.5846, loss: 0.5846 +2025-06-24 14:22:46,354 - pyskl - INFO - Saving checkpoint at 39 epochs +2025-06-24 14:23:58,001 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:23:58,057 - pyskl - INFO - +top1_acc 0.8675 +top5_acc 0.9919 +2025-06-24 14:23:58,057 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:23:58,064 - pyskl - INFO - +mean_acc 0.8112 +2025-06-24 14:23:58,068 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_38.pth was removed +2025-06-24 14:23:58,242 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_39.pth. +2025-06-24 14:23:58,243 - pyskl - INFO - Best top1_acc is 0.8675 at 39 epoch. +2025-06-24 14:23:58,245 - pyskl - INFO - Epoch(val) [39][533] top1_acc: 0.8675, top5_acc: 0.9919, mean_class_accuracy: 0.8112 +2025-06-24 14:24:56,869 - pyskl - INFO - Epoch [40][100/1281] lr: 2.104e-02, eta: 13:27:38, time: 0.586, data_time: 0.195, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9962, loss_cls: 0.5272, loss: 0.5272 +2025-06-24 14:25:32,376 - pyskl - INFO - Epoch [40][200/1281] lr: 2.103e-02, eta: 13:27:08, time: 0.355, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9975, loss_cls: 0.5288, loss: 0.5288 +2025-06-24 14:26:18,703 - pyskl - INFO - Epoch [40][300/1281] lr: 2.101e-02, eta: 13:27:08, time: 0.463, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9988, loss_cls: 0.5527, loss: 0.5527 +2025-06-24 14:27:08,807 - pyskl - INFO - Epoch [40][400/1281] lr: 2.100e-02, eta: 13:27:19, time: 0.501, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9969, loss_cls: 0.5583, loss: 0.5583 +2025-06-24 14:27:59,248 - pyskl - INFO - Epoch [40][500/1281] lr: 2.098e-02, eta: 13:27:30, time: 0.504, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9925, loss_cls: 0.6444, loss: 0.6444 +2025-06-24 14:28:49,742 - pyskl - INFO - Epoch [40][600/1281] lr: 2.097e-02, eta: 13:27:42, time: 0.505, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9950, loss_cls: 0.6560, loss: 0.6560 +2025-06-24 14:29:41,475 - pyskl - INFO - Epoch [40][700/1281] lr: 2.095e-02, eta: 13:27:57, time: 0.517, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9938, loss_cls: 0.6342, loss: 0.6342 +2025-06-24 14:30:32,352 - pyskl - INFO - Epoch [40][800/1281] lr: 2.094e-02, eta: 13:28:09, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9925, loss_cls: 0.6190, loss: 0.6190 +2025-06-24 14:31:24,007 - pyskl - INFO - Epoch [40][900/1281] lr: 2.092e-02, eta: 13:28:22, time: 0.517, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9969, loss_cls: 0.6177, loss: 0.6177 +2025-06-24 14:32:17,234 - pyskl - INFO - Epoch [40][1000/1281] lr: 2.091e-02, eta: 13:28:41, time: 0.532, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9962, loss_cls: 0.5626, loss: 0.5626 +2025-06-24 14:33:08,219 - pyskl - INFO - Epoch [40][1100/1281] lr: 2.089e-02, eta: 13:28:52, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9925, loss_cls: 0.6436, loss: 0.6436 +2025-06-24 14:33:54,341 - pyskl - INFO - Epoch [40][1200/1281] lr: 2.088e-02, eta: 13:28:50, time: 0.461, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9950, loss_cls: 0.6662, loss: 0.6662 +2025-06-24 14:34:20,955 - pyskl - INFO - Saving checkpoint at 40 epochs +2025-06-24 14:35:10,838 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:35:10,893 - pyskl - INFO - +top1_acc 0.8378 +top5_acc 0.9891 +2025-06-24 14:35:10,893 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:35:10,900 - pyskl - INFO - +mean_acc 0.7545 +2025-06-24 14:35:10,902 - pyskl - INFO - Epoch(val) [40][533] top1_acc: 0.8378, top5_acc: 0.9891, mean_class_accuracy: 0.7545 +2025-06-24 14:36:35,457 - pyskl - INFO - Epoch [41][100/1281] lr: 2.085e-02, eta: 13:28:49, time: 0.845, data_time: 0.198, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9888, loss_cls: 0.6599, loss: 0.6599 +2025-06-24 14:37:28,550 - pyskl - INFO - Epoch [41][200/1281] lr: 2.083e-02, eta: 13:29:05, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9988, loss_cls: 0.5117, loss: 0.5117 +2025-06-24 14:38:20,556 - pyskl - INFO - Epoch [41][300/1281] lr: 2.082e-02, eta: 13:29:18, time: 0.520, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9988, loss_cls: 0.4982, loss: 0.4982 +2025-06-24 14:39:12,232 - pyskl - INFO - Epoch [41][400/1281] lr: 2.080e-02, eta: 13:29:31, time: 0.517, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9956, loss_cls: 0.5953, loss: 0.5953 +2025-06-24 14:40:01,813 - pyskl - INFO - Epoch [41][500/1281] lr: 2.079e-02, eta: 13:29:37, time: 0.496, data_time: 0.001, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9962, loss_cls: 0.6356, loss: 0.6356 +2025-06-24 14:40:53,467 - pyskl - INFO - Epoch [41][600/1281] lr: 2.077e-02, eta: 13:29:48, time: 0.517, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9906, loss_cls: 0.6178, loss: 0.6178 +2025-06-24 14:41:44,279 - pyskl - INFO - Epoch [41][700/1281] lr: 2.076e-02, eta: 13:29:57, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9988, loss_cls: 0.5685, loss: 0.5685 +2025-06-24 14:42:38,093 - pyskl - INFO - Epoch [41][800/1281] lr: 2.074e-02, eta: 13:30:14, time: 0.538, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9931, loss_cls: 0.6905, loss: 0.6905 +2025-06-24 14:43:06,568 - pyskl - INFO - Epoch [41][900/1281] lr: 2.073e-02, eta: 13:29:23, time: 0.285, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9969, loss_cls: 0.5923, loss: 0.5923 +2025-06-24 14:43:57,747 - pyskl - INFO - Epoch [41][1000/1281] lr: 2.071e-02, eta: 13:29:32, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9950, loss_cls: 0.6142, loss: 0.6142 +2025-06-24 14:44:36,507 - pyskl - INFO - Epoch [41][1100/1281] lr: 2.070e-02, eta: 13:29:08, time: 0.388, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9962, loss_cls: 0.5558, loss: 0.5558 +2025-06-24 14:45:27,677 - pyskl - INFO - Epoch [41][1200/1281] lr: 2.068e-02, eta: 13:29:17, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9975, loss_cls: 0.5806, loss: 0.5806 +2025-06-24 14:46:09,487 - pyskl - INFO - Saving checkpoint at 41 epochs +2025-06-24 14:47:20,391 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:47:20,446 - pyskl - INFO - +top1_acc 0.8390 +top5_acc 0.9896 +2025-06-24 14:47:20,446 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:47:20,453 - pyskl - INFO - +mean_acc 0.7716 +2025-06-24 14:47:20,455 - pyskl - INFO - Epoch(val) [41][533] top1_acc: 0.8390, top5_acc: 0.9896, mean_class_accuracy: 0.7716 +2025-06-24 14:48:46,229 - pyskl - INFO - Epoch [42][100/1281] lr: 2.065e-02, eta: 13:29:15, time: 0.858, data_time: 0.194, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9956, loss_cls: 0.5821, loss: 0.5821 +2025-06-24 14:49:36,771 - pyskl - INFO - Epoch [42][200/1281] lr: 2.064e-02, eta: 13:29:22, time: 0.505, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9950, loss_cls: 0.6719, loss: 0.6719 +2025-06-24 14:50:28,990 - pyskl - INFO - Epoch [42][300/1281] lr: 2.062e-02, eta: 13:29:33, time: 0.522, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9956, loss_cls: 0.6694, loss: 0.6694 +2025-06-24 14:51:21,569 - pyskl - INFO - Epoch [42][400/1281] lr: 2.061e-02, eta: 13:29:45, time: 0.526, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9975, loss_cls: 0.6063, loss: 0.6063 +2025-06-24 14:51:59,694 - pyskl - INFO - Epoch [42][500/1281] lr: 2.059e-02, eta: 13:29:18, time: 0.381, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9944, loss_cls: 0.5689, loss: 0.5689 +2025-06-24 14:52:50,697 - pyskl - INFO - Epoch [42][600/1281] lr: 2.057e-02, eta: 13:29:26, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9925, loss_cls: 0.6194, loss: 0.6194 +2025-06-24 14:53:18,248 - pyskl - INFO - Epoch [42][700/1281] lr: 2.056e-02, eta: 13:28:31, time: 0.275, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9950, loss_cls: 0.5889, loss: 0.5889 +2025-06-24 14:54:06,586 - pyskl - INFO - Epoch [42][800/1281] lr: 2.054e-02, eta: 13:28:31, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9969, loss_cls: 0.5303, loss: 0.5303 +2025-06-24 14:54:54,868 - pyskl - INFO - Epoch [42][900/1281] lr: 2.053e-02, eta: 13:28:31, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9950, loss_cls: 0.5524, loss: 0.5524 +2025-06-24 14:55:43,142 - pyskl - INFO - Epoch [42][1000/1281] lr: 2.051e-02, eta: 13:28:31, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9962, loss_cls: 0.5632, loss: 0.5632 +2025-06-24 14:56:31,501 - pyskl - INFO - Epoch [42][1100/1281] lr: 2.050e-02, eta: 13:28:30, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9956, loss_cls: 0.5392, loss: 0.5392 +2025-06-24 14:57:19,770 - pyskl - INFO - Epoch [42][1200/1281] lr: 2.048e-02, eta: 13:28:29, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9962, loss_cls: 0.6016, loss: 0.6016 +2025-06-24 14:57:59,423 - pyskl - INFO - Saving checkpoint at 42 epochs +2025-06-24 14:59:00,007 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:59:00,074 - pyskl - INFO - +top1_acc 0.8150 +top5_acc 0.9879 +2025-06-24 14:59:00,074 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:59:00,082 - pyskl - INFO - +mean_acc 0.7686 +2025-06-24 14:59:00,084 - pyskl - INFO - Epoch(val) [42][533] top1_acc: 0.8150, top5_acc: 0.9879, mean_class_accuracy: 0.7686 +2025-06-24 15:00:19,793 - pyskl - INFO - Epoch [43][100/1281] lr: 2.045e-02, eta: 13:28:08, time: 0.797, data_time: 0.199, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9912, loss_cls: 0.6362, loss: 0.6362 +2025-06-24 15:01:08,031 - pyskl - INFO - Epoch [43][200/1281] lr: 2.044e-02, eta: 13:28:06, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9969, loss_cls: 0.5016, loss: 0.5016 +2025-06-24 15:01:56,557 - pyskl - INFO - Epoch [43][300/1281] lr: 2.042e-02, eta: 13:28:05, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9975, loss_cls: 0.5702, loss: 0.5702 +2025-06-24 15:02:44,891 - pyskl - INFO - Epoch [43][400/1281] lr: 2.040e-02, eta: 13:28:04, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9962, loss_cls: 0.5789, loss: 0.5789 +2025-06-24 15:03:15,841 - pyskl - INFO - Epoch [43][500/1281] lr: 2.039e-02, eta: 13:27:18, time: 0.309, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9981, loss_cls: 0.5864, loss: 0.5864 +2025-06-24 15:04:00,267 - pyskl - INFO - Epoch [43][600/1281] lr: 2.037e-02, eta: 13:27:06, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9962, loss_cls: 0.5472, loss: 0.5472 +2025-06-24 15:04:30,028 - pyskl - INFO - Epoch [43][700/1281] lr: 2.036e-02, eta: 13:26:18, time: 0.298, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9975, loss_cls: 0.5667, loss: 0.5667 +2025-06-24 15:05:18,961 - pyskl - INFO - Epoch [43][800/1281] lr: 2.034e-02, eta: 13:26:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9975, loss_cls: 0.6000, loss: 0.6000 +2025-06-24 15:06:07,964 - pyskl - INFO - Epoch [43][900/1281] lr: 2.033e-02, eta: 13:26:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9931, loss_cls: 0.5859, loss: 0.5859 +2025-06-24 15:06:57,028 - pyskl - INFO - Epoch [43][1000/1281] lr: 2.031e-02, eta: 13:26:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9988, loss_cls: 0.5057, loss: 0.5057 +2025-06-24 15:07:46,156 - pyskl - INFO - Epoch [43][1100/1281] lr: 2.029e-02, eta: 13:26:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9988, loss_cls: 0.5231, loss: 0.5231 +2025-06-24 15:08:35,356 - pyskl - INFO - Epoch [43][1200/1281] lr: 2.028e-02, eta: 13:26:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9950, loss_cls: 0.6262, loss: 0.6262 +2025-06-24 15:09:15,827 - pyskl - INFO - Saving checkpoint at 43 epochs +2025-06-24 15:10:15,616 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:10:15,694 - pyskl - INFO - +top1_acc 0.8540 +top5_acc 0.9920 +2025-06-24 15:10:15,694 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:10:15,705 - pyskl - INFO - +mean_acc 0.7864 +2025-06-24 15:10:15,708 - pyskl - INFO - Epoch(val) [43][533] top1_acc: 0.8540, top5_acc: 0.9920, mean_class_accuracy: 0.7864 +2025-06-24 15:11:35,475 - pyskl - INFO - Epoch [44][100/1281] lr: 2.025e-02, eta: 13:25:51, time: 0.798, data_time: 0.194, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9988, loss_cls: 0.5380, loss: 0.5380 +2025-06-24 15:12:24,613 - pyskl - INFO - Epoch [44][200/1281] lr: 2.023e-02, eta: 13:25:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9969, loss_cls: 0.5227, loss: 0.5227 +2025-06-24 15:13:13,409 - pyskl - INFO - Epoch [44][300/1281] lr: 2.022e-02, eta: 13:25:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9956, loss_cls: 0.5763, loss: 0.5763 +2025-06-24 15:14:02,715 - pyskl - INFO - Epoch [44][400/1281] lr: 2.020e-02, eta: 13:25:47, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9950, loss_cls: 0.6128, loss: 0.6128 +2025-06-24 15:14:30,209 - pyskl - INFO - Epoch [44][500/1281] lr: 2.018e-02, eta: 13:24:52, time: 0.275, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9969, loss_cls: 0.5600, loss: 0.5600 +2025-06-24 15:15:21,558 - pyskl - INFO - Epoch [44][600/1281] lr: 2.017e-02, eta: 13:24:56, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9981, loss_cls: 0.5640, loss: 0.5640 +2025-06-24 15:15:51,766 - pyskl - INFO - Epoch [44][700/1281] lr: 2.015e-02, eta: 13:24:08, time: 0.302, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9956, loss_cls: 0.5366, loss: 0.5366 +2025-06-24 15:16:40,829 - pyskl - INFO - Epoch [44][800/1281] lr: 2.014e-02, eta: 13:24:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9988, loss_cls: 0.5169, loss: 0.5169 +2025-06-24 15:17:29,789 - pyskl - INFO - Epoch [44][900/1281] lr: 2.012e-02, eta: 13:24:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9969, loss_cls: 0.6436, loss: 0.6436 +2025-06-24 15:18:18,485 - pyskl - INFO - Epoch [44][1000/1281] lr: 2.010e-02, eta: 13:24:00, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9969, loss_cls: 0.5784, loss: 0.5784 +2025-06-24 15:19:07,708 - pyskl - INFO - Epoch [44][1100/1281] lr: 2.009e-02, eta: 13:23:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9956, loss_cls: 0.6455, loss: 0.6455 +2025-06-24 15:19:56,872 - pyskl - INFO - Epoch [44][1200/1281] lr: 2.007e-02, eta: 13:23:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9981, loss_cls: 0.5942, loss: 0.5942 +2025-06-24 15:20:37,232 - pyskl - INFO - Saving checkpoint at 44 epochs +2025-06-24 15:21:36,573 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:21:36,640 - pyskl - INFO - +top1_acc 0.8388 +top5_acc 0.9891 +2025-06-24 15:21:36,640 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:21:36,648 - pyskl - INFO - +mean_acc 0.7798 +2025-06-24 15:21:36,650 - pyskl - INFO - Epoch(val) [44][533] top1_acc: 0.8388, top5_acc: 0.9891, mean_class_accuracy: 0.7798 +2025-06-24 15:22:56,702 - pyskl - INFO - Epoch [45][100/1281] lr: 2.004e-02, eta: 13:23:29, time: 0.800, data_time: 0.190, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9975, loss_cls: 0.5907, loss: 0.5907 +2025-06-24 15:23:45,557 - pyskl - INFO - Epoch [45][200/1281] lr: 2.003e-02, eta: 13:23:26, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9969, loss_cls: 0.5367, loss: 0.5367 +2025-06-24 15:24:34,569 - pyskl - INFO - Epoch [45][300/1281] lr: 2.001e-02, eta: 13:23:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9956, loss_cls: 0.5580, loss: 0.5580 +2025-06-24 15:25:23,325 - pyskl - INFO - Epoch [45][400/1281] lr: 1.999e-02, eta: 13:23:18, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9912, loss_cls: 0.6062, loss: 0.6062 +2025-06-24 15:25:51,363 - pyskl - INFO - Epoch [45][500/1281] lr: 1.998e-02, eta: 13:22:25, time: 0.280, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9931, loss_cls: 0.5785, loss: 0.5785 +2025-06-24 15:26:42,545 - pyskl - INFO - Epoch [45][600/1281] lr: 1.996e-02, eta: 13:22:26, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9981, loss_cls: 0.5682, loss: 0.5682 +2025-06-24 15:27:13,019 - pyskl - INFO - Epoch [45][700/1281] lr: 1.994e-02, eta: 13:21:38, time: 0.305, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9969, loss_cls: 0.5602, loss: 0.5602 +2025-06-24 15:28:02,137 - pyskl - INFO - Epoch [45][800/1281] lr: 1.993e-02, eta: 13:21:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9981, loss_cls: 0.5240, loss: 0.5240 +2025-06-24 15:28:50,765 - pyskl - INFO - Epoch [45][900/1281] lr: 1.991e-02, eta: 13:21:30, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9962, loss_cls: 0.5881, loss: 0.5881 +2025-06-24 15:29:39,708 - pyskl - INFO - Epoch [45][1000/1281] lr: 1.989e-02, eta: 13:21:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9975, loss_cls: 0.5155, loss: 0.5155 +2025-06-24 15:30:28,830 - pyskl - INFO - Epoch [45][1100/1281] lr: 1.988e-02, eta: 13:21:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9969, loss_cls: 0.5912, loss: 0.5912 +2025-06-24 15:31:17,931 - pyskl - INFO - Epoch [45][1200/1281] lr: 1.986e-02, eta: 13:21:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9962, loss_cls: 0.5664, loss: 0.5664 +2025-06-24 15:31:58,359 - pyskl - INFO - Saving checkpoint at 45 epochs +2025-06-24 15:32:57,207 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:32:57,265 - pyskl - INFO - +top1_acc 0.8565 +top5_acc 0.9899 +2025-06-24 15:32:57,266 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:32:57,272 - pyskl - INFO - +mean_acc 0.8143 +2025-06-24 15:32:57,274 - pyskl - INFO - Epoch(val) [45][533] top1_acc: 0.8565, top5_acc: 0.9899, mean_class_accuracy: 0.8143 +2025-06-24 15:34:17,598 - pyskl - INFO - Epoch [46][100/1281] lr: 1.983e-02, eta: 13:20:48, time: 0.803, data_time: 0.198, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9962, loss_cls: 0.5401, loss: 0.5401 +2025-06-24 15:35:06,632 - pyskl - INFO - Epoch [46][200/1281] lr: 1.981e-02, eta: 13:20:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9981, loss_cls: 0.4932, loss: 0.4932 +2025-06-24 15:35:55,815 - pyskl - INFO - Epoch [46][300/1281] lr: 1.980e-02, eta: 13:20:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9969, loss_cls: 0.5358, loss: 0.5358 +2025-06-24 15:36:45,119 - pyskl - INFO - Epoch [46][400/1281] lr: 1.978e-02, eta: 13:20:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9956, loss_cls: 0.5420, loss: 0.5420 +2025-06-24 15:37:12,411 - pyskl - INFO - Epoch [46][500/1281] lr: 1.976e-02, eta: 13:19:39, time: 0.273, data_time: 0.001, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9962, loss_cls: 0.4972, loss: 0.4972 +2025-06-24 15:38:03,423 - pyskl - INFO - Epoch [46][600/1281] lr: 1.975e-02, eta: 13:19:38, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9975, loss_cls: 0.5026, loss: 0.5026 +2025-06-24 15:38:33,690 - pyskl - INFO - Epoch [46][700/1281] lr: 1.973e-02, eta: 13:18:49, time: 0.303, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9969, loss_cls: 0.5388, loss: 0.5388 +2025-06-24 15:39:22,673 - pyskl - INFO - Epoch [46][800/1281] lr: 1.971e-02, eta: 13:18:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9962, loss_cls: 0.6379, loss: 0.6379 +2025-06-24 15:40:11,498 - pyskl - INFO - Epoch [46][900/1281] lr: 1.970e-02, eta: 13:18:37, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9956, loss_cls: 0.6046, loss: 0.6046 +2025-06-24 15:41:00,453 - pyskl - INFO - Epoch [46][1000/1281] lr: 1.968e-02, eta: 13:18:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9969, loss_cls: 0.5137, loss: 0.5137 +2025-06-24 15:41:49,814 - pyskl - INFO - Epoch [46][1100/1281] lr: 1.966e-02, eta: 13:18:26, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9944, loss_cls: 0.5715, loss: 0.5715 +2025-06-24 15:42:39,472 - pyskl - INFO - Epoch [46][1200/1281] lr: 1.965e-02, eta: 13:18:21, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9950, loss_cls: 0.6236, loss: 0.6236 +2025-06-24 15:43:19,842 - pyskl - INFO - Saving checkpoint at 46 epochs +2025-06-24 15:44:18,979 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:44:19,035 - pyskl - INFO - +top1_acc 0.8373 +top5_acc 0.9904 +2025-06-24 15:44:19,035 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:44:19,042 - pyskl - INFO - +mean_acc 0.7884 +2025-06-24 15:44:19,043 - pyskl - INFO - Epoch(val) [46][533] top1_acc: 0.8373, top5_acc: 0.9904, mean_class_accuracy: 0.7884 +2025-06-24 15:45:39,503 - pyskl - INFO - Epoch [47][100/1281] lr: 1.962e-02, eta: 13:17:51, time: 0.805, data_time: 0.200, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9962, loss_cls: 0.5483, loss: 0.5483 +2025-06-24 15:46:28,540 - pyskl - INFO - Epoch [47][200/1281] lr: 1.960e-02, eta: 13:17:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9956, loss_cls: 0.5313, loss: 0.5313 +2025-06-24 15:47:17,568 - pyskl - INFO - Epoch [47][300/1281] lr: 1.958e-02, eta: 13:17:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9962, loss_cls: 0.5526, loss: 0.5526 +2025-06-24 15:48:07,347 - pyskl - INFO - Epoch [47][400/1281] lr: 1.957e-02, eta: 13:17:33, time: 0.498, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9950, loss_cls: 0.5369, loss: 0.5369 +2025-06-24 15:48:34,812 - pyskl - INFO - Epoch [47][500/1281] lr: 1.955e-02, eta: 13:16:37, time: 0.275, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9956, loss_cls: 0.5637, loss: 0.5637 +2025-06-24 15:49:25,296 - pyskl - INFO - Epoch [47][600/1281] lr: 1.953e-02, eta: 13:16:34, time: 0.505, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9962, loss_cls: 0.5570, loss: 0.5570 +2025-06-24 15:49:55,437 - pyskl - INFO - Epoch [47][700/1281] lr: 1.952e-02, eta: 13:15:44, time: 0.301, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9969, loss_cls: 0.5217, loss: 0.5217 +2025-06-24 15:50:44,542 - pyskl - INFO - Epoch [47][800/1281] lr: 1.950e-02, eta: 13:15:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9969, loss_cls: 0.5871, loss: 0.5871 +2025-06-24 15:51:33,555 - pyskl - INFO - Epoch [47][900/1281] lr: 1.948e-02, eta: 13:15:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9925, loss_cls: 0.5522, loss: 0.5522 +2025-06-24 15:52:22,580 - pyskl - INFO - Epoch [47][1000/1281] lr: 1.947e-02, eta: 13:15:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9969, loss_cls: 0.5662, loss: 0.5662 +2025-06-24 15:53:11,286 - pyskl - INFO - Epoch [47][1100/1281] lr: 1.945e-02, eta: 13:15:14, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9931, loss_cls: 0.6077, loss: 0.6077 +2025-06-24 15:54:00,427 - pyskl - INFO - Epoch [47][1200/1281] lr: 1.943e-02, eta: 13:15:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9944, loss_cls: 0.5982, loss: 0.5982 +2025-06-24 15:54:40,882 - pyskl - INFO - Saving checkpoint at 47 epochs +2025-06-24 15:55:40,361 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:55:40,416 - pyskl - INFO - +top1_acc 0.8622 +top5_acc 0.9896 +2025-06-24 15:55:40,417 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:55:40,423 - pyskl - INFO - +mean_acc 0.8194 +2025-06-24 15:55:40,425 - pyskl - INFO - Epoch(val) [47][533] top1_acc: 0.8622, top5_acc: 0.9896, mean_class_accuracy: 0.8194 +2025-06-24 15:56:58,933 - pyskl - INFO - Epoch [48][100/1281] lr: 1.940e-02, eta: 13:14:30, time: 0.785, data_time: 0.188, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9962, loss_cls: 0.5167, loss: 0.5167 +2025-06-24 15:57:48,285 - pyskl - INFO - Epoch [48][200/1281] lr: 1.938e-02, eta: 13:14:23, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9969, loss_cls: 0.4800, loss: 0.4800 +2025-06-24 15:58:37,635 - pyskl - INFO - Epoch [48][300/1281] lr: 1.937e-02, eta: 13:14:15, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9988, loss_cls: 0.4969, loss: 0.4969 +2025-06-24 15:59:26,925 - pyskl - INFO - Epoch [48][400/1281] lr: 1.935e-02, eta: 13:14:07, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9962, loss_cls: 0.5085, loss: 0.5085 +2025-06-24 15:59:55,829 - pyskl - INFO - Epoch [48][500/1281] lr: 1.933e-02, eta: 13:13:15, time: 0.289, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9950, loss_cls: 0.5443, loss: 0.5443 +2025-06-24 16:00:47,017 - pyskl - INFO - Epoch [48][600/1281] lr: 1.932e-02, eta: 13:13:11, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9975, loss_cls: 0.5238, loss: 0.5238 +2025-06-24 16:01:15,237 - pyskl - INFO - Epoch [48][700/1281] lr: 1.930e-02, eta: 13:12:18, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9962, loss_cls: 0.5568, loss: 0.5568 +2025-06-24 16:02:03,979 - pyskl - INFO - Epoch [48][800/1281] lr: 1.928e-02, eta: 13:12:09, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9931, loss_cls: 0.5883, loss: 0.5883 +2025-06-24 16:02:52,955 - pyskl - INFO - Epoch [48][900/1281] lr: 1.926e-02, eta: 13:12:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9975, loss_cls: 0.5437, loss: 0.5437 +2025-06-24 16:03:41,770 - pyskl - INFO - Epoch [48][1000/1281] lr: 1.925e-02, eta: 13:11:50, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9956, loss_cls: 0.5398, loss: 0.5398 +2025-06-24 16:04:30,810 - pyskl - INFO - Epoch [48][1100/1281] lr: 1.923e-02, eta: 13:11:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9975, loss_cls: 0.5643, loss: 0.5643 +2025-06-24 16:05:19,820 - pyskl - INFO - Epoch [48][1200/1281] lr: 1.921e-02, eta: 13:11:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9938, loss_cls: 0.6197, loss: 0.6197 +2025-06-24 16:05:59,830 - pyskl - INFO - Saving checkpoint at 48 epochs +2025-06-24 16:06:59,377 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:06:59,448 - pyskl - INFO - +top1_acc 0.8613 +top5_acc 0.9905 +2025-06-24 16:06:59,448 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:06:59,457 - pyskl - INFO - +mean_acc 0.8192 +2025-06-24 16:06:59,459 - pyskl - INFO - Epoch(val) [48][533] top1_acc: 0.8613, top5_acc: 0.9905, mean_class_accuracy: 0.8192 +2025-06-24 16:08:19,809 - pyskl - INFO - Epoch [49][100/1281] lr: 1.918e-02, eta: 13:10:57, time: 0.803, data_time: 0.194, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9975, loss_cls: 0.5225, loss: 0.5225 +2025-06-24 16:09:08,858 - pyskl - INFO - Epoch [49][200/1281] lr: 1.916e-02, eta: 13:10:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9962, loss_cls: 0.6019, loss: 0.6019 +2025-06-24 16:09:57,869 - pyskl - INFO - Epoch [49][300/1281] lr: 1.915e-02, eta: 13:10:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9981, loss_cls: 0.4695, loss: 0.4695 +2025-06-24 16:10:46,855 - pyskl - INFO - Epoch [49][400/1281] lr: 1.913e-02, eta: 13:10:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9962, loss_cls: 0.4914, loss: 0.4914 +2025-06-24 16:11:15,821 - pyskl - INFO - Epoch [49][500/1281] lr: 1.911e-02, eta: 13:09:36, time: 0.290, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9956, loss_cls: 0.5184, loss: 0.5184 +2025-06-24 16:12:07,017 - pyskl - INFO - Epoch [49][600/1281] lr: 1.909e-02, eta: 13:09:31, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9988, loss_cls: 0.5325, loss: 0.5325 +2025-06-24 16:12:35,670 - pyskl - INFO - Epoch [49][700/1281] lr: 1.908e-02, eta: 13:08:38, time: 0.287, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9950, loss_cls: 0.5677, loss: 0.5677 +2025-06-24 16:13:24,516 - pyskl - INFO - Epoch [49][800/1281] lr: 1.906e-02, eta: 13:08:27, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9938, loss_cls: 0.5419, loss: 0.5419 +2025-06-24 16:14:13,613 - pyskl - INFO - Epoch [49][900/1281] lr: 1.904e-02, eta: 13:08:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9975, loss_cls: 0.5573, loss: 0.5573 +2025-06-24 16:15:03,068 - pyskl - INFO - Epoch [49][1000/1281] lr: 1.902e-02, eta: 13:08:08, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9962, loss_cls: 0.5898, loss: 0.5898 +2025-06-24 16:15:52,239 - pyskl - INFO - Epoch [49][1100/1281] lr: 1.901e-02, eta: 13:07:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9962, loss_cls: 0.5169, loss: 0.5169 +2025-06-24 16:16:41,272 - pyskl - INFO - Epoch [49][1200/1281] lr: 1.899e-02, eta: 13:07:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9981, loss_cls: 0.5540, loss: 0.5540 +2025-06-24 16:17:21,628 - pyskl - INFO - Saving checkpoint at 49 epochs +2025-06-24 16:18:21,899 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:18:21,958 - pyskl - INFO - +top1_acc 0.8657 +top5_acc 0.9924 +2025-06-24 16:18:21,958 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:18:21,966 - pyskl - INFO - +mean_acc 0.8200 +2025-06-24 16:18:21,973 - pyskl - INFO - Epoch(val) [49][533] top1_acc: 0.8657, top5_acc: 0.9924, mean_class_accuracy: 0.8200 +2025-06-24 16:19:42,060 - pyskl - INFO - Epoch [50][100/1281] lr: 1.896e-02, eta: 13:07:10, time: 0.801, data_time: 0.195, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9969, loss_cls: 0.4624, loss: 0.4624 +2025-06-24 16:20:31,040 - pyskl - INFO - Epoch [50][200/1281] lr: 1.894e-02, eta: 13:06:59, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9962, loss_cls: 0.5189, loss: 0.5189 +2025-06-24 16:21:20,109 - pyskl - INFO - Epoch [50][300/1281] lr: 1.892e-02, eta: 13:06:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9975, loss_cls: 0.5232, loss: 0.5232 +2025-06-24 16:22:09,212 - pyskl - INFO - Epoch [50][400/1281] lr: 1.891e-02, eta: 13:06:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9969, loss_cls: 0.5458, loss: 0.5458 +2025-06-24 16:22:36,478 - pyskl - INFO - Epoch [50][500/1281] lr: 1.889e-02, eta: 13:05:41, time: 0.273, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9988, loss_cls: 0.5133, loss: 0.5133 +2025-06-24 16:23:27,549 - pyskl - INFO - Epoch [50][600/1281] lr: 1.887e-02, eta: 13:05:34, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9950, loss_cls: 0.5621, loss: 0.5621 +2025-06-24 16:23:58,977 - pyskl - INFO - Epoch [50][700/1281] lr: 1.885e-02, eta: 13:04:47, time: 0.314, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9969, loss_cls: 0.5993, loss: 0.5993 +2025-06-24 16:24:48,114 - pyskl - INFO - Epoch [50][800/1281] lr: 1.884e-02, eta: 13:04:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9956, loss_cls: 0.6166, loss: 0.6166 +2025-06-24 16:25:36,657 - pyskl - INFO - Epoch [50][900/1281] lr: 1.882e-02, eta: 13:04:23, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9975, loss_cls: 0.5363, loss: 0.5363 +2025-06-24 16:26:25,473 - pyskl - INFO - Epoch [50][1000/1281] lr: 1.880e-02, eta: 13:04:11, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9950, loss_cls: 0.5372, loss: 0.5372 +2025-06-24 16:27:14,133 - pyskl - INFO - Epoch [50][1100/1281] lr: 1.878e-02, eta: 13:03:59, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9944, loss_cls: 0.5965, loss: 0.5965 +2025-06-24 16:28:03,090 - pyskl - INFO - Epoch [50][1200/1281] lr: 1.876e-02, eta: 13:03:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9962, loss_cls: 0.6104, loss: 0.6104 +2025-06-24 16:28:43,441 - pyskl - INFO - Saving checkpoint at 50 epochs +2025-06-24 16:29:42,564 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:29:42,622 - pyskl - INFO - +top1_acc 0.8451 +top5_acc 0.9916 +2025-06-24 16:29:42,622 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:29:42,629 - pyskl - INFO - +mean_acc 0.7959 +2025-06-24 16:29:42,632 - pyskl - INFO - Epoch(val) [50][533] top1_acc: 0.8451, top5_acc: 0.9916, mean_class_accuracy: 0.7959 +2025-06-24 16:31:03,827 - pyskl - INFO - Epoch [51][100/1281] lr: 1.873e-02, eta: 13:03:10, time: 0.812, data_time: 0.201, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9975, loss_cls: 0.4982, loss: 0.4982 +2025-06-24 16:31:53,012 - pyskl - INFO - Epoch [51][200/1281] lr: 1.871e-02, eta: 13:02:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9962, loss_cls: 0.4744, loss: 0.4744 +2025-06-24 16:32:41,840 - pyskl - INFO - Epoch [51][300/1281] lr: 1.870e-02, eta: 13:02:45, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9969, loss_cls: 0.4959, loss: 0.4959 +2025-06-24 16:33:31,114 - pyskl - INFO - Epoch [51][400/1281] lr: 1.868e-02, eta: 13:02:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9944, loss_cls: 0.4951, loss: 0.4951 +2025-06-24 16:33:58,410 - pyskl - INFO - Epoch [51][500/1281] lr: 1.866e-02, eta: 13:01:38, time: 0.273, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9962, loss_cls: 0.5399, loss: 0.5399 +2025-06-24 16:34:48,431 - pyskl - INFO - Epoch [51][600/1281] lr: 1.864e-02, eta: 13:01:27, time: 0.500, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9962, loss_cls: 0.5201, loss: 0.5201 +2025-06-24 16:35:20,187 - pyskl - INFO - Epoch [51][700/1281] lr: 1.863e-02, eta: 13:00:40, time: 0.318, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9962, loss_cls: 0.4796, loss: 0.4796 +2025-06-24 16:36:09,453 - pyskl - INFO - Epoch [51][800/1281] lr: 1.861e-02, eta: 13:00:28, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9969, loss_cls: 0.4827, loss: 0.4827 +2025-06-24 16:36:58,423 - pyskl - INFO - Epoch [51][900/1281] lr: 1.859e-02, eta: 13:00:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9962, loss_cls: 0.5352, loss: 0.5352 +2025-06-24 16:37:47,247 - pyskl - INFO - Epoch [51][1000/1281] lr: 1.857e-02, eta: 13:00:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9969, loss_cls: 0.4780, loss: 0.4780 +2025-06-24 16:38:36,107 - pyskl - INFO - Epoch [51][1100/1281] lr: 1.855e-02, eta: 12:59:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9988, loss_cls: 0.5551, loss: 0.5551 +2025-06-24 16:39:24,878 - pyskl - INFO - Epoch [51][1200/1281] lr: 1.854e-02, eta: 12:59:35, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9950, loss_cls: 0.5959, loss: 0.5959 +2025-06-24 16:40:05,437 - pyskl - INFO - Saving checkpoint at 51 epochs +2025-06-24 16:41:04,907 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:41:04,975 - pyskl - INFO - +top1_acc 0.8661 +top5_acc 0.9914 +2025-06-24 16:41:04,975 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:41:04,984 - pyskl - INFO - +mean_acc 0.8130 +2025-06-24 16:41:04,987 - pyskl - INFO - Epoch(val) [51][533] top1_acc: 0.8661, top5_acc: 0.9914, mean_class_accuracy: 0.8130 +2025-06-24 16:42:25,635 - pyskl - INFO - Epoch [52][100/1281] lr: 1.850e-02, eta: 12:58:55, time: 0.806, data_time: 0.200, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9956, loss_cls: 0.4953, loss: 0.4953 +2025-06-24 16:43:14,795 - pyskl - INFO - Epoch [52][200/1281] lr: 1.849e-02, eta: 12:58:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9988, loss_cls: 0.4640, loss: 0.4640 +2025-06-24 16:44:04,073 - pyskl - INFO - Epoch [52][300/1281] lr: 1.847e-02, eta: 12:58:29, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9969, loss_cls: 0.5348, loss: 0.5348 +2025-06-24 16:44:52,912 - pyskl - INFO - Epoch [52][400/1281] lr: 1.845e-02, eta: 12:58:15, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9988, loss_cls: 0.4838, loss: 0.4838 +2025-06-24 16:45:21,898 - pyskl - INFO - Epoch [52][500/1281] lr: 1.843e-02, eta: 12:57:23, time: 0.290, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9962, loss_cls: 0.5105, loss: 0.5105 +2025-06-24 16:46:09,516 - pyskl - INFO - Epoch [52][600/1281] lr: 1.841e-02, eta: 12:57:06, time: 0.476, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9969, loss_cls: 0.6004, loss: 0.6004 +2025-06-24 16:46:40,944 - pyskl - INFO - Epoch [52][700/1281] lr: 1.840e-02, eta: 12:56:19, time: 0.314, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9950, loss_cls: 0.5345, loss: 0.5345 +2025-06-24 16:47:30,019 - pyskl - INFO - Epoch [52][800/1281] lr: 1.838e-02, eta: 12:56:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9962, loss_cls: 0.5352, loss: 0.5352 +2025-06-24 16:48:19,063 - pyskl - INFO - Epoch [52][900/1281] lr: 1.836e-02, eta: 12:55:51, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9950, loss_cls: 0.5664, loss: 0.5664 +2025-06-24 16:49:08,171 - pyskl - INFO - Epoch [52][1000/1281] lr: 1.834e-02, eta: 12:55:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9938, loss_cls: 0.5640, loss: 0.5640 +2025-06-24 16:49:57,200 - pyskl - INFO - Epoch [52][1100/1281] lr: 1.832e-02, eta: 12:55:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9962, loss_cls: 0.5322, loss: 0.5322 +2025-06-24 16:50:46,059 - pyskl - INFO - Epoch [52][1200/1281] lr: 1.831e-02, eta: 12:55:08, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9938, loss_cls: 0.5835, loss: 0.5835 +2025-06-24 16:51:26,451 - pyskl - INFO - Saving checkpoint at 52 epochs +2025-06-24 16:52:25,771 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:52:25,826 - pyskl - INFO - +top1_acc 0.8628 +top5_acc 0.9919 +2025-06-24 16:52:25,826 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:52:25,833 - pyskl - INFO - +mean_acc 0.8289 +2025-06-24 16:52:25,835 - pyskl - INFO - Epoch(val) [52][533] top1_acc: 0.8628, top5_acc: 0.9919, mean_class_accuracy: 0.8289 +2025-06-24 16:53:46,930 - pyskl - INFO - Epoch [53][100/1281] lr: 1.827e-02, eta: 12:54:28, time: 0.811, data_time: 0.193, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9962, loss_cls: 0.5376, loss: 0.5376 +2025-06-24 16:54:36,462 - pyskl - INFO - Epoch [53][200/1281] lr: 1.826e-02, eta: 12:54:14, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9962, loss_cls: 0.5125, loss: 0.5125 +2025-06-24 16:55:25,825 - pyskl - INFO - Epoch [53][300/1281] lr: 1.824e-02, eta: 12:54:00, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9950, loss_cls: 0.5378, loss: 0.5378 +2025-06-24 16:56:15,121 - pyskl - INFO - Epoch [53][400/1281] lr: 1.822e-02, eta: 12:53:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9969, loss_cls: 0.4978, loss: 0.4978 +2025-06-24 16:56:43,150 - pyskl - INFO - Epoch [53][500/1281] lr: 1.820e-02, eta: 12:52:52, time: 0.280, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9962, loss_cls: 0.4834, loss: 0.4834 +2025-06-24 16:57:32,574 - pyskl - INFO - Epoch [53][600/1281] lr: 1.818e-02, eta: 12:52:38, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9969, loss_cls: 0.4693, loss: 0.4693 +2025-06-24 16:58:04,460 - pyskl - INFO - Epoch [53][700/1281] lr: 1.816e-02, eta: 12:51:51, time: 0.319, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9981, loss_cls: 0.5167, loss: 0.5167 +2025-06-24 16:58:53,937 - pyskl - INFO - Epoch [53][800/1281] lr: 1.815e-02, eta: 12:51:37, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9969, loss_cls: 0.4883, loss: 0.4883 +2025-06-24 16:59:42,976 - pyskl - INFO - Epoch [53][900/1281] lr: 1.813e-02, eta: 12:51:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9975, loss_cls: 0.5987, loss: 0.5987 +2025-06-24 17:00:31,868 - pyskl - INFO - Epoch [53][1000/1281] lr: 1.811e-02, eta: 12:51:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9956, loss_cls: 0.5217, loss: 0.5217 +2025-06-24 17:01:20,947 - pyskl - INFO - Epoch [53][1100/1281] lr: 1.809e-02, eta: 12:50:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9981, loss_cls: 0.4956, loss: 0.4956 +2025-06-24 17:02:09,800 - pyskl - INFO - Epoch [53][1200/1281] lr: 1.807e-02, eta: 12:50:35, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9975, loss_cls: 0.5022, loss: 0.5022 +2025-06-24 17:02:49,844 - pyskl - INFO - Saving checkpoint at 53 epochs +2025-06-24 17:03:49,885 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:03:49,956 - pyskl - INFO - +top1_acc 0.8750 +top5_acc 0.9914 +2025-06-24 17:03:49,956 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:03:49,963 - pyskl - INFO - +mean_acc 0.8313 +2025-06-24 17:03:49,967 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_39.pth was removed +2025-06-24 17:03:50,155 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_53.pth. +2025-06-24 17:03:50,155 - pyskl - INFO - Best top1_acc is 0.8750 at 53 epoch. +2025-06-24 17:03:50,158 - pyskl - INFO - Epoch(val) [53][533] top1_acc: 0.8750, top5_acc: 0.9914, mean_class_accuracy: 0.8313 +2025-06-24 17:05:10,337 - pyskl - INFO - Epoch [54][100/1281] lr: 1.804e-02, eta: 12:49:51, time: 0.802, data_time: 0.193, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9962, loss_cls: 0.5773, loss: 0.5773 +2025-06-24 17:05:59,339 - pyskl - INFO - Epoch [54][200/1281] lr: 1.802e-02, eta: 12:49:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9950, loss_cls: 0.4855, loss: 0.4855 +2025-06-24 17:06:48,814 - pyskl - INFO - Epoch [54][300/1281] lr: 1.800e-02, eta: 12:49:21, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9962, loss_cls: 0.4887, loss: 0.4887 +2025-06-24 17:07:38,002 - pyskl - INFO - Epoch [54][400/1281] lr: 1.798e-02, eta: 12:49:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9969, loss_cls: 0.4770, loss: 0.4770 +2025-06-24 17:08:08,562 - pyskl - INFO - Epoch [54][500/1281] lr: 1.797e-02, eta: 12:48:16, time: 0.306, data_time: 0.001, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9962, loss_cls: 0.4575, loss: 0.4575 +2025-06-24 17:08:54,012 - pyskl - INFO - Epoch [54][600/1281] lr: 1.795e-02, eta: 12:47:53, time: 0.454, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9975, loss_cls: 0.4843, loss: 0.4843 +2025-06-24 17:09:28,627 - pyskl - INFO - Epoch [54][700/1281] lr: 1.793e-02, eta: 12:47:11, time: 0.346, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9981, loss_cls: 0.5010, loss: 0.5010 +2025-06-24 17:10:17,820 - pyskl - INFO - Epoch [54][800/1281] lr: 1.791e-02, eta: 12:46:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9944, loss_cls: 0.4958, loss: 0.4958 +2025-06-24 17:11:06,901 - pyskl - INFO - Epoch [54][900/1281] lr: 1.789e-02, eta: 12:46:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9988, loss_cls: 0.4961, loss: 0.4961 +2025-06-24 17:11:55,911 - pyskl - INFO - Epoch [54][1000/1281] lr: 1.787e-02, eta: 12:46:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9988, loss_cls: 0.5080, loss: 0.5080 +2025-06-24 17:12:45,217 - pyskl - INFO - Epoch [54][1100/1281] lr: 1.786e-02, eta: 12:46:07, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9956, loss_cls: 0.5187, loss: 0.5187 +2025-06-24 17:13:34,019 - pyskl - INFO - Epoch [54][1200/1281] lr: 1.784e-02, eta: 12:45:50, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9975, loss_cls: 0.4404, loss: 0.4404 +2025-06-24 17:14:14,227 - pyskl - INFO - Saving checkpoint at 54 epochs +2025-06-24 17:15:14,110 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:15:14,177 - pyskl - INFO - +top1_acc 0.8532 +top5_acc 0.9903 +2025-06-24 17:15:14,177 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:15:14,185 - pyskl - INFO - +mean_acc 0.7894 +2025-06-24 17:15:14,188 - pyskl - INFO - Epoch(val) [54][533] top1_acc: 0.8532, top5_acc: 0.9903, mean_class_accuracy: 0.7894 +2025-06-24 17:16:34,702 - pyskl - INFO - Epoch [55][100/1281] lr: 1.780e-02, eta: 12:45:06, time: 0.805, data_time: 0.195, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9931, loss_cls: 0.5563, loss: 0.5563 +2025-06-24 17:17:23,826 - pyskl - INFO - Epoch [55][200/1281] lr: 1.779e-02, eta: 12:44:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9962, loss_cls: 0.5145, loss: 0.5145 +2025-06-24 17:18:12,962 - pyskl - INFO - Epoch [55][300/1281] lr: 1.777e-02, eta: 12:44:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9975, loss_cls: 0.4679, loss: 0.4679 +2025-06-24 17:19:02,099 - pyskl - INFO - Epoch [55][400/1281] lr: 1.775e-02, eta: 12:44:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9975, loss_cls: 0.4796, loss: 0.4796 +2025-06-24 17:19:34,467 - pyskl - INFO - Epoch [55][500/1281] lr: 1.773e-02, eta: 12:43:29, time: 0.324, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9975, loss_cls: 0.5041, loss: 0.5041 +2025-06-24 17:20:15,774 - pyskl - INFO - Epoch [55][600/1281] lr: 1.771e-02, eta: 12:42:59, time: 0.413, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9981, loss_cls: 0.5231, loss: 0.5231 +2025-06-24 17:20:52,221 - pyskl - INFO - Epoch [55][700/1281] lr: 1.769e-02, eta: 12:42:20, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9950, loss_cls: 0.5309, loss: 0.5309 +2025-06-24 17:21:41,436 - pyskl - INFO - Epoch [55][800/1281] lr: 1.767e-02, eta: 12:42:03, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9944, loss_cls: 0.5479, loss: 0.5479 +2025-06-24 17:22:30,436 - pyskl - INFO - Epoch [55][900/1281] lr: 1.766e-02, eta: 12:41:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9981, loss_cls: 0.5320, loss: 0.5320 +2025-06-24 17:23:19,880 - pyskl - INFO - Epoch [55][1000/1281] lr: 1.764e-02, eta: 12:41:29, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9975, loss_cls: 0.4689, loss: 0.4689 +2025-06-24 17:24:09,532 - pyskl - INFO - Epoch [55][1100/1281] lr: 1.762e-02, eta: 12:41:13, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9994, loss_cls: 0.5091, loss: 0.5091 +2025-06-24 17:24:58,901 - pyskl - INFO - Epoch [55][1200/1281] lr: 1.760e-02, eta: 12:40:56, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9956, loss_cls: 0.5849, loss: 0.5849 +2025-06-24 17:25:38,842 - pyskl - INFO - Saving checkpoint at 55 epochs +2025-06-24 17:26:38,171 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:26:38,228 - pyskl - INFO - +top1_acc 0.8653 +top5_acc 0.9914 +2025-06-24 17:26:38,228 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:26:38,235 - pyskl - INFO - +mean_acc 0.8143 +2025-06-24 17:26:38,237 - pyskl - INFO - Epoch(val) [55][533] top1_acc: 0.8653, top5_acc: 0.9914, mean_class_accuracy: 0.8143 +2025-06-24 17:27:58,201 - pyskl - INFO - Epoch [56][100/1281] lr: 1.757e-02, eta: 12:40:09, time: 0.800, data_time: 0.186, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9981, loss_cls: 0.4445, loss: 0.4445 +2025-06-24 17:28:47,068 - pyskl - INFO - Epoch [56][200/1281] lr: 1.755e-02, eta: 12:39:51, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9938, loss_cls: 0.5338, loss: 0.5338 +2025-06-24 17:29:35,927 - pyskl - INFO - Epoch [56][300/1281] lr: 1.753e-02, eta: 12:39:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9975, loss_cls: 0.4823, loss: 0.4823 +2025-06-24 17:30:23,682 - pyskl - INFO - Epoch [56][400/1281] lr: 1.751e-02, eta: 12:39:13, time: 0.478, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9981, loss_cls: 0.5101, loss: 0.5101 +2025-06-24 17:30:57,631 - pyskl - INFO - Epoch [56][500/1281] lr: 1.749e-02, eta: 12:38:29, time: 0.339, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9950, loss_cls: 0.4882, loss: 0.4882 +2025-06-24 17:31:37,351 - pyskl - INFO - Epoch [56][600/1281] lr: 1.747e-02, eta: 12:37:55, time: 0.397, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9969, loss_cls: 0.4524, loss: 0.4524 +2025-06-24 17:32:13,603 - pyskl - INFO - Epoch [56][700/1281] lr: 1.745e-02, eta: 12:37:16, time: 0.363, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9962, loss_cls: 0.4985, loss: 0.4985 +2025-06-24 17:33:02,931 - pyskl - INFO - Epoch [56][800/1281] lr: 1.743e-02, eta: 12:36:58, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 1.0000, loss_cls: 0.4919, loss: 0.4919 +2025-06-24 17:33:51,646 - pyskl - INFO - Epoch [56][900/1281] lr: 1.742e-02, eta: 12:36:39, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9962, loss_cls: 0.5326, loss: 0.5326 +2025-06-24 17:34:40,633 - pyskl - INFO - Epoch [56][1000/1281] lr: 1.740e-02, eta: 12:36:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9969, loss_cls: 0.4988, loss: 0.4988 +2025-06-24 17:35:29,457 - pyskl - INFO - Epoch [56][1100/1281] lr: 1.738e-02, eta: 12:36:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9962, loss_cls: 0.5194, loss: 0.5194 +2025-06-24 17:36:18,750 - pyskl - INFO - Epoch [56][1200/1281] lr: 1.736e-02, eta: 12:35:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.5201, loss: 0.5201 +2025-06-24 17:36:59,185 - pyskl - INFO - Saving checkpoint at 56 epochs +2025-06-24 17:37:57,803 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:37:57,891 - pyskl - INFO - +top1_acc 0.8566 +top5_acc 0.9899 +2025-06-24 17:37:57,891 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:37:57,898 - pyskl - INFO - +mean_acc 0.8088 +2025-06-24 17:37:57,900 - pyskl - INFO - Epoch(val) [56][533] top1_acc: 0.8566, top5_acc: 0.9899, mean_class_accuracy: 0.8088 +2025-06-24 17:39:17,482 - pyskl - INFO - Epoch [57][100/1281] lr: 1.733e-02, eta: 12:34:55, time: 0.796, data_time: 0.189, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9981, loss_cls: 0.4973, loss: 0.4973 +2025-06-24 17:40:06,195 - pyskl - INFO - Epoch [57][200/1281] lr: 1.731e-02, eta: 12:34:36, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9981, loss_cls: 0.4891, loss: 0.4891 +2025-06-24 17:40:55,269 - pyskl - INFO - Epoch [57][300/1281] lr: 1.729e-02, eta: 12:34:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9994, loss_cls: 0.4091, loss: 0.4091 +2025-06-24 17:41:44,359 - pyskl - INFO - Epoch [57][400/1281] lr: 1.727e-02, eta: 12:33:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9981, loss_cls: 0.4464, loss: 0.4464 +2025-06-24 17:42:15,957 - pyskl - INFO - Epoch [57][500/1281] lr: 1.725e-02, eta: 12:33:11, time: 0.316, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9969, loss_cls: 0.4484, loss: 0.4484 +2025-06-24 17:42:58,377 - pyskl - INFO - Epoch [57][600/1281] lr: 1.723e-02, eta: 12:32:41, time: 0.424, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9994, loss_cls: 0.5013, loss: 0.5013 +2025-06-24 17:43:33,135 - pyskl - INFO - Epoch [57][700/1281] lr: 1.721e-02, eta: 12:31:59, time: 0.348, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9962, loss_cls: 0.5247, loss: 0.5247 +2025-06-24 17:44:22,050 - pyskl - INFO - Epoch [57][800/1281] lr: 1.719e-02, eta: 12:31:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9956, loss_cls: 0.5164, loss: 0.5164 +2025-06-24 17:45:11,191 - pyskl - INFO - Epoch [57][900/1281] lr: 1.717e-02, eta: 12:31:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9956, loss_cls: 0.5207, loss: 0.5207 +2025-06-24 17:46:00,194 - pyskl - INFO - Epoch [57][1000/1281] lr: 1.716e-02, eta: 12:31:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9981, loss_cls: 0.4260, loss: 0.4260 +2025-06-24 17:46:49,173 - pyskl - INFO - Epoch [57][1100/1281] lr: 1.714e-02, eta: 12:30:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9975, loss_cls: 0.4719, loss: 0.4719 +2025-06-24 17:47:38,527 - pyskl - INFO - Epoch [57][1200/1281] lr: 1.712e-02, eta: 12:30:23, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9956, loss_cls: 0.5667, loss: 0.5667 +2025-06-24 17:48:18,955 - pyskl - INFO - Saving checkpoint at 57 epochs +2025-06-24 17:49:17,800 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:49:17,857 - pyskl - INFO - +top1_acc 0.8803 +top5_acc 0.9898 +2025-06-24 17:49:17,857 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:49:17,865 - pyskl - INFO - +mean_acc 0.8337 +2025-06-24 17:49:17,869 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_53.pth was removed +2025-06-24 17:49:18,064 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_57.pth. +2025-06-24 17:49:18,064 - pyskl - INFO - Best top1_acc is 0.8803 at 57 epoch. +2025-06-24 17:49:18,067 - pyskl - INFO - Epoch(val) [57][533] top1_acc: 0.8803, top5_acc: 0.9898, mean_class_accuracy: 0.8337 +2025-06-24 17:50:39,242 - pyskl - INFO - Epoch [58][100/1281] lr: 1.708e-02, eta: 12:29:36, time: 0.812, data_time: 0.196, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9981, loss_cls: 0.4115, loss: 0.4115 +2025-06-24 17:51:28,513 - pyskl - INFO - Epoch [58][200/1281] lr: 1.706e-02, eta: 12:29:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9981, loss_cls: 0.4340, loss: 0.4340 +2025-06-24 17:52:17,702 - pyskl - INFO - Epoch [58][300/1281] lr: 1.704e-02, eta: 12:28:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9969, loss_cls: 0.4588, loss: 0.4588 +2025-06-24 17:53:06,352 - pyskl - INFO - Epoch [58][400/1281] lr: 1.703e-02, eta: 12:28:37, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9981, loss_cls: 0.4258, loss: 0.4258 +2025-06-24 17:53:38,866 - pyskl - INFO - Epoch [58][500/1281] lr: 1.701e-02, eta: 12:27:51, time: 0.325, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9975, loss_cls: 0.4523, loss: 0.4523 +2025-06-24 17:54:20,205 - pyskl - INFO - Epoch [58][600/1281] lr: 1.699e-02, eta: 12:27:18, time: 0.413, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9988, loss_cls: 0.4939, loss: 0.4939 +2025-06-24 17:54:55,739 - pyskl - INFO - Epoch [58][700/1281] lr: 1.697e-02, eta: 12:26:37, time: 0.355, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4649, loss: 0.4649 +2025-06-24 17:55:44,993 - pyskl - INFO - Epoch [58][800/1281] lr: 1.695e-02, eta: 12:26:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9975, loss_cls: 0.4741, loss: 0.4741 +2025-06-24 17:56:34,004 - pyskl - INFO - Epoch [58][900/1281] lr: 1.693e-02, eta: 12:25:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9950, loss_cls: 0.5117, loss: 0.5117 +2025-06-24 17:57:23,129 - pyskl - INFO - Epoch [58][1000/1281] lr: 1.691e-02, eta: 12:25:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9988, loss_cls: 0.5177, loss: 0.5177 +2025-06-24 17:58:12,338 - pyskl - INFO - Epoch [58][1100/1281] lr: 1.689e-02, eta: 12:25:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9956, loss_cls: 0.5060, loss: 0.5060 +2025-06-24 17:59:01,745 - pyskl - INFO - Epoch [58][1200/1281] lr: 1.687e-02, eta: 12:24:58, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9956, loss_cls: 0.5205, loss: 0.5205 +2025-06-24 17:59:42,192 - pyskl - INFO - Saving checkpoint at 58 epochs +2025-06-24 18:00:41,293 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:00:41,350 - pyskl - INFO - +top1_acc 0.8777 +top5_acc 0.9926 +2025-06-24 18:00:41,350 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:00:41,357 - pyskl - INFO - +mean_acc 0.8381 +2025-06-24 18:00:41,359 - pyskl - INFO - Epoch(val) [58][533] top1_acc: 0.8777, top5_acc: 0.9926, mean_class_accuracy: 0.8381 +2025-06-24 18:01:59,540 - pyskl - INFO - Epoch [59][100/1281] lr: 1.684e-02, eta: 12:24:05, time: 0.782, data_time: 0.194, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9962, loss_cls: 0.4529, loss: 0.4529 +2025-06-24 18:02:48,519 - pyskl - INFO - Epoch [59][200/1281] lr: 1.682e-02, eta: 12:23:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9956, loss_cls: 0.5018, loss: 0.5018 +2025-06-24 18:03:37,734 - pyskl - INFO - Epoch [59][300/1281] lr: 1.680e-02, eta: 12:23:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9956, loss_cls: 0.4666, loss: 0.4666 +2025-06-24 18:04:26,855 - pyskl - INFO - Epoch [59][400/1281] lr: 1.678e-02, eta: 12:23:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9981, loss_cls: 0.4391, loss: 0.4391 +2025-06-24 18:04:56,704 - pyskl - INFO - Epoch [59][500/1281] lr: 1.676e-02, eta: 12:22:13, time: 0.298, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9988, loss_cls: 0.4268, loss: 0.4268 +2025-06-24 18:05:41,696 - pyskl - INFO - Epoch [59][600/1281] lr: 1.674e-02, eta: 12:21:46, time: 0.450, data_time: 0.001, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9988, loss_cls: 0.4908, loss: 0.4908 +2025-06-24 18:06:15,416 - pyskl - INFO - Epoch [59][700/1281] lr: 1.672e-02, eta: 12:21:02, time: 0.337, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9969, loss_cls: 0.4891, loss: 0.4891 +2025-06-24 18:07:04,546 - pyskl - INFO - Epoch [59][800/1281] lr: 1.670e-02, eta: 12:20:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9962, loss_cls: 0.4944, loss: 0.4944 +2025-06-24 18:07:53,720 - pyskl - INFO - Epoch [59][900/1281] lr: 1.668e-02, eta: 12:20:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9956, loss_cls: 0.5086, loss: 0.5086 +2025-06-24 18:08:42,812 - pyskl - INFO - Epoch [59][1000/1281] lr: 1.667e-02, eta: 12:20:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9956, loss_cls: 0.5102, loss: 0.5102 +2025-06-24 18:09:31,845 - pyskl - INFO - Epoch [59][1100/1281] lr: 1.665e-02, eta: 12:19:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9994, loss_cls: 0.5390, loss: 0.5390 +2025-06-24 18:10:21,162 - pyskl - INFO - Epoch [59][1200/1281] lr: 1.663e-02, eta: 12:19:18, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9969, loss_cls: 0.4364, loss: 0.4364 +2025-06-24 18:11:01,484 - pyskl - INFO - Saving checkpoint at 59 epochs +2025-06-24 18:12:00,625 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:12:00,687 - pyskl - INFO - +top1_acc 0.8811 +top5_acc 0.9913 +2025-06-24 18:12:00,687 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:12:00,694 - pyskl - INFO - +mean_acc 0.8318 +2025-06-24 18:12:00,698 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_57.pth was removed +2025-06-24 18:12:00,868 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_59.pth. +2025-06-24 18:12:00,868 - pyskl - INFO - Best top1_acc is 0.8811 at 59 epoch. +2025-06-24 18:12:00,871 - pyskl - INFO - Epoch(val) [59][533] top1_acc: 0.8811, top5_acc: 0.9913, mean_class_accuracy: 0.8318 +2025-06-24 18:13:21,612 - pyskl - INFO - Epoch [60][100/1281] lr: 1.659e-02, eta: 12:18:28, time: 0.807, data_time: 0.194, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9994, loss_cls: 0.3914, loss: 0.3914 +2025-06-24 18:14:10,386 - pyskl - INFO - Epoch [60][200/1281] lr: 1.657e-02, eta: 12:18:06, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9969, loss_cls: 0.4203, loss: 0.4203 +2025-06-24 18:14:59,421 - pyskl - INFO - Epoch [60][300/1281] lr: 1.655e-02, eta: 12:17:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9981, loss_cls: 0.4859, loss: 0.4859 +2025-06-24 18:15:48,555 - pyskl - INFO - Epoch [60][400/1281] lr: 1.653e-02, eta: 12:17:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9988, loss_cls: 0.4896, loss: 0.4896 +2025-06-24 18:16:20,647 - pyskl - INFO - Epoch [60][500/1281] lr: 1.651e-02, eta: 12:16:37, time: 0.321, data_time: 0.001, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9969, loss_cls: 0.4880, loss: 0.4880 +2025-06-24 18:17:02,397 - pyskl - INFO - Epoch [60][600/1281] lr: 1.650e-02, eta: 12:16:04, time: 0.417, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9969, loss_cls: 0.4343, loss: 0.4343 +2025-06-24 18:17:38,444 - pyskl - INFO - Epoch [60][700/1281] lr: 1.648e-02, eta: 12:15:23, time: 0.360, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9988, loss_cls: 0.4831, loss: 0.4831 +2025-06-24 18:18:27,617 - pyskl - INFO - Epoch [60][800/1281] lr: 1.646e-02, eta: 12:15:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9981, loss_cls: 0.4414, loss: 0.4414 +2025-06-24 18:19:16,694 - pyskl - INFO - Epoch [60][900/1281] lr: 1.644e-02, eta: 12:14:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9975, loss_cls: 0.4492, loss: 0.4492 +2025-06-24 18:20:06,070 - pyskl - INFO - Epoch [60][1000/1281] lr: 1.642e-02, eta: 12:14:19, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9975, loss_cls: 0.4500, loss: 0.4500 +2025-06-24 18:20:54,977 - pyskl - INFO - Epoch [60][1100/1281] lr: 1.640e-02, eta: 12:13:57, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9944, loss_cls: 0.5225, loss: 0.5225 +2025-06-24 18:21:44,184 - pyskl - INFO - Epoch [60][1200/1281] lr: 1.638e-02, eta: 12:13:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9969, loss_cls: 0.4957, loss: 0.4957 +2025-06-24 18:22:24,506 - pyskl - INFO - Saving checkpoint at 60 epochs +2025-06-24 18:23:24,632 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:23:24,688 - pyskl - INFO - +top1_acc 0.8541 +top5_acc 0.9860 +2025-06-24 18:23:24,688 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:23:24,696 - pyskl - INFO - +mean_acc 0.8227 +2025-06-24 18:23:24,698 - pyskl - INFO - Epoch(val) [60][533] top1_acc: 0.8541, top5_acc: 0.9860, mean_class_accuracy: 0.8227 +2025-06-24 18:24:44,729 - pyskl - INFO - Epoch [61][100/1281] lr: 1.634e-02, eta: 12:12:43, time: 0.800, data_time: 0.196, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9981, loss_cls: 0.4693, loss: 0.4693 +2025-06-24 18:25:34,095 - pyskl - INFO - Epoch [61][200/1281] lr: 1.632e-02, eta: 12:12:22, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9975, loss_cls: 0.4159, loss: 0.4159 +2025-06-24 18:26:23,469 - pyskl - INFO - Epoch [61][300/1281] lr: 1.630e-02, eta: 12:12:00, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4543, loss: 0.4543 +2025-06-24 18:27:10,516 - pyskl - INFO - Epoch [61][400/1281] lr: 1.629e-02, eta: 12:11:35, time: 0.470, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9994, loss_cls: 0.4981, loss: 0.4981 +2025-06-24 18:27:46,693 - pyskl - INFO - Epoch [61][500/1281] lr: 1.627e-02, eta: 12:10:54, time: 0.362, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9988, loss_cls: 0.4966, loss: 0.4966 +2025-06-24 18:28:24,154 - pyskl - INFO - Epoch [61][600/1281] lr: 1.625e-02, eta: 12:10:15, time: 0.375, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9969, loss_cls: 0.5060, loss: 0.5060 +2025-06-24 18:29:02,515 - pyskl - INFO - Epoch [61][700/1281] lr: 1.623e-02, eta: 12:09:37, time: 0.384, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9969, loss_cls: 0.4886, loss: 0.4886 +2025-06-24 18:29:51,838 - pyskl - INFO - Epoch [61][800/1281] lr: 1.621e-02, eta: 12:09:15, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9981, loss_cls: 0.4910, loss: 0.4910 +2025-06-24 18:30:40,733 - pyskl - INFO - Epoch [61][900/1281] lr: 1.619e-02, eta: 12:08:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9962, loss_cls: 0.5383, loss: 0.5383 +2025-06-24 18:31:29,688 - pyskl - INFO - Epoch [61][1000/1281] lr: 1.617e-02, eta: 12:08:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9962, loss_cls: 0.4553, loss: 0.4553 +2025-06-24 18:32:18,783 - pyskl - INFO - Epoch [61][1100/1281] lr: 1.615e-02, eta: 12:08:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9988, loss_cls: 0.4250, loss: 0.4250 +2025-06-24 18:33:08,001 - pyskl - INFO - Epoch [61][1200/1281] lr: 1.613e-02, eta: 12:07:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9969, loss_cls: 0.4953, loss: 0.4953 +2025-06-24 18:33:48,507 - pyskl - INFO - Saving checkpoint at 61 epochs +2025-06-24 18:34:48,084 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:34:48,141 - pyskl - INFO - +top1_acc 0.8392 +top5_acc 0.9867 +2025-06-24 18:34:48,142 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:34:48,150 - pyskl - INFO - +mean_acc 0.7928 +2025-06-24 18:34:48,153 - pyskl - INFO - Epoch(val) [61][533] top1_acc: 0.8392, top5_acc: 0.9867, mean_class_accuracy: 0.7928 +2025-06-24 18:36:07,228 - pyskl - INFO - Epoch [62][100/1281] lr: 1.609e-02, eta: 12:06:50, time: 0.791, data_time: 0.191, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9962, loss_cls: 0.5041, loss: 0.5041 +2025-06-24 18:36:56,271 - pyskl - INFO - Epoch [62][200/1281] lr: 1.607e-02, eta: 12:06:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9969, loss_cls: 0.4527, loss: 0.4527 +2025-06-24 18:37:45,466 - pyskl - INFO - Epoch [62][300/1281] lr: 1.605e-02, eta: 12:06:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9988, loss_cls: 0.4590, loss: 0.4590 +2025-06-24 18:38:32,089 - pyskl - INFO - Epoch [62][400/1281] lr: 1.603e-02, eta: 12:05:39, time: 0.466, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9969, loss_cls: 0.4671, loss: 0.4671 +2025-06-24 18:39:09,436 - pyskl - INFO - Epoch [62][500/1281] lr: 1.602e-02, eta: 12:04:59, time: 0.373, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9975, loss_cls: 0.4225, loss: 0.4225 +2025-06-24 18:39:45,578 - pyskl - INFO - Epoch [62][600/1281] lr: 1.600e-02, eta: 12:04:17, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9969, loss_cls: 0.4113, loss: 0.4113 +2025-06-24 18:40:23,103 - pyskl - INFO - Epoch [62][700/1281] lr: 1.598e-02, eta: 12:03:38, time: 0.375, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9962, loss_cls: 0.4701, loss: 0.4701 +2025-06-24 18:41:11,738 - pyskl - INFO - Epoch [62][800/1281] lr: 1.596e-02, eta: 12:03:14, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9969, loss_cls: 0.4635, loss: 0.4635 +2025-06-24 18:42:00,774 - pyskl - INFO - Epoch [62][900/1281] lr: 1.594e-02, eta: 12:02:51, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9994, loss_cls: 0.4800, loss: 0.4800 +2025-06-24 18:42:49,700 - pyskl - INFO - Epoch [62][1000/1281] lr: 1.592e-02, eta: 12:02:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9938, loss_cls: 0.4937, loss: 0.4937 +2025-06-24 18:43:38,911 - pyskl - INFO - Epoch [62][1100/1281] lr: 1.590e-02, eta: 12:02:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9944, loss_cls: 0.4902, loss: 0.4902 +2025-06-24 18:44:28,036 - pyskl - INFO - Epoch [62][1200/1281] lr: 1.588e-02, eta: 12:01:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9975, loss_cls: 0.4983, loss: 0.4983 +2025-06-24 18:45:08,255 - pyskl - INFO - Saving checkpoint at 62 epochs +2025-06-24 18:46:07,239 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:46:07,311 - pyskl - INFO - +top1_acc 0.8784 +top5_acc 0.9932 +2025-06-24 18:46:07,311 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:46:07,319 - pyskl - INFO - +mean_acc 0.8592 +2025-06-24 18:46:07,321 - pyskl - INFO - Epoch(val) [62][533] top1_acc: 0.8784, top5_acc: 0.9932, mean_class_accuracy: 0.8592 +2025-06-24 18:47:27,044 - pyskl - INFO - Epoch [63][100/1281] lr: 1.584e-02, eta: 12:00:47, time: 0.797, data_time: 0.194, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9975, loss_cls: 0.3970, loss: 0.3970 +2025-06-24 18:48:15,689 - pyskl - INFO - Epoch [63][200/1281] lr: 1.582e-02, eta: 12:00:23, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9981, loss_cls: 0.4340, loss: 0.4340 +2025-06-24 18:49:04,614 - pyskl - INFO - Epoch [63][300/1281] lr: 1.580e-02, eta: 12:00:00, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9994, loss_cls: 0.3979, loss: 0.3979 +2025-06-24 18:49:51,710 - pyskl - INFO - Epoch [63][400/1281] lr: 1.578e-02, eta: 11:59:34, time: 0.471, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.4972, loss: 0.4972 +2025-06-24 18:50:26,147 - pyskl - INFO - Epoch [63][500/1281] lr: 1.576e-02, eta: 11:58:49, time: 0.344, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9981, loss_cls: 0.4531, loss: 0.4531 +2025-06-24 18:51:05,210 - pyskl - INFO - Epoch [63][600/1281] lr: 1.574e-02, eta: 11:58:12, time: 0.391, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9988, loss_cls: 0.4291, loss: 0.4291 +2025-06-24 18:51:41,912 - pyskl - INFO - Epoch [63][700/1281] lr: 1.572e-02, eta: 11:57:31, time: 0.367, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9981, loss_cls: 0.4216, loss: 0.4216 +2025-06-24 18:52:31,157 - pyskl - INFO - Epoch [63][800/1281] lr: 1.570e-02, eta: 11:57:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9981, loss_cls: 0.4651, loss: 0.4651 +2025-06-24 18:53:20,367 - pyskl - INFO - Epoch [63][900/1281] lr: 1.568e-02, eta: 11:56:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9988, loss_cls: 0.5086, loss: 0.5086 +2025-06-24 18:54:09,303 - pyskl - INFO - Epoch [63][1000/1281] lr: 1.566e-02, eta: 11:56:20, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9931, loss_cls: 0.5171, loss: 0.5171 +2025-06-24 18:54:58,451 - pyskl - INFO - Epoch [63][1100/1281] lr: 1.564e-02, eta: 11:55:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9956, loss_cls: 0.4826, loss: 0.4826 +2025-06-24 18:55:47,637 - pyskl - INFO - Epoch [63][1200/1281] lr: 1.562e-02, eta: 11:55:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9981, loss_cls: 0.4847, loss: 0.4847 +2025-06-24 18:56:28,203 - pyskl - INFO - Saving checkpoint at 63 epochs +2025-06-24 18:57:27,904 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:57:27,959 - pyskl - INFO - +top1_acc 0.8691 +top5_acc 0.9907 +2025-06-24 18:57:27,959 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:57:27,966 - pyskl - INFO - +mean_acc 0.8185 +2025-06-24 18:57:27,968 - pyskl - INFO - Epoch(val) [63][533] top1_acc: 0.8691, top5_acc: 0.9907, mean_class_accuracy: 0.8185 +2025-06-24 18:58:46,854 - pyskl - INFO - Epoch [64][100/1281] lr: 1.559e-02, eta: 11:54:36, time: 0.789, data_time: 0.199, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9988, loss_cls: 0.4053, loss: 0.4053 +2025-06-24 18:59:36,145 - pyskl - INFO - Epoch [64][200/1281] lr: 1.557e-02, eta: 11:54:12, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9956, loss_cls: 0.4451, loss: 0.4451 +2025-06-24 19:00:25,154 - pyskl - INFO - Epoch [64][300/1281] lr: 1.555e-02, eta: 11:53:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.3849, loss: 0.3849 +2025-06-24 19:01:11,994 - pyskl - INFO - Epoch [64][400/1281] lr: 1.553e-02, eta: 11:53:21, time: 0.468, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9975, loss_cls: 0.4087, loss: 0.4087 +2025-06-24 19:01:47,174 - pyskl - INFO - Epoch [64][500/1281] lr: 1.551e-02, eta: 11:52:38, time: 0.352, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9988, loss_cls: 0.4551, loss: 0.4551 +2025-06-24 19:02:25,445 - pyskl - INFO - Epoch [64][600/1281] lr: 1.549e-02, eta: 11:51:59, time: 0.383, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9962, loss_cls: 0.4748, loss: 0.4748 +2025-06-24 19:03:03,015 - pyskl - INFO - Epoch [64][700/1281] lr: 1.547e-02, eta: 11:51:19, time: 0.376, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9962, loss_cls: 0.4055, loss: 0.4055 +2025-06-24 19:03:52,430 - pyskl - INFO - Epoch [64][800/1281] lr: 1.545e-02, eta: 11:50:55, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9956, loss_cls: 0.4634, loss: 0.4634 +2025-06-24 19:04:41,363 - pyskl - INFO - Epoch [64][900/1281] lr: 1.543e-02, eta: 11:50:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9981, loss_cls: 0.4612, loss: 0.4612 +2025-06-24 19:05:30,363 - pyskl - INFO - Epoch [64][1000/1281] lr: 1.541e-02, eta: 11:50:06, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9975, loss_cls: 0.4796, loss: 0.4796 +2025-06-24 19:06:19,562 - pyskl - INFO - Epoch [64][1100/1281] lr: 1.539e-02, eta: 11:49:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9956, loss_cls: 0.4702, loss: 0.4702 +2025-06-24 19:07:08,987 - pyskl - INFO - Epoch [64][1200/1281] lr: 1.537e-02, eta: 11:49:18, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9988, loss_cls: 0.4349, loss: 0.4349 +2025-06-24 19:07:49,593 - pyskl - INFO - Saving checkpoint at 64 epochs +2025-06-24 19:08:49,395 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:08:49,451 - pyskl - INFO - +top1_acc 0.8817 +top5_acc 0.9932 +2025-06-24 19:08:49,451 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:08:49,459 - pyskl - INFO - +mean_acc 0.8368 +2025-06-24 19:08:49,463 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_59.pth was removed +2025-06-24 19:08:49,712 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_64.pth. +2025-06-24 19:08:49,713 - pyskl - INFO - Best top1_acc is 0.8817 at 64 epoch. +2025-06-24 19:08:49,718 - pyskl - INFO - Epoch(val) [64][533] top1_acc: 0.8817, top5_acc: 0.9932, mean_class_accuracy: 0.8368 +2025-06-24 19:10:09,755 - pyskl - INFO - Epoch [65][100/1281] lr: 1.533e-02, eta: 11:48:22, time: 0.800, data_time: 0.193, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9981, loss_cls: 0.3819, loss: 0.3819 +2025-06-24 19:10:59,011 - pyskl - INFO - Epoch [65][200/1281] lr: 1.531e-02, eta: 11:47:58, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9994, loss_cls: 0.4650, loss: 0.4650 +2025-06-24 19:11:48,050 - pyskl - INFO - Epoch [65][300/1281] lr: 1.529e-02, eta: 11:47:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9988, loss_cls: 0.4505, loss: 0.4505 +2025-06-24 19:12:33,837 - pyskl - INFO - Epoch [65][400/1281] lr: 1.527e-02, eta: 11:47:04, time: 0.458, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9962, loss_cls: 0.3848, loss: 0.3848 +2025-06-24 19:13:14,303 - pyskl - INFO - Epoch [65][500/1281] lr: 1.526e-02, eta: 11:46:27, time: 0.405, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9988, loss_cls: 0.4190, loss: 0.4190 +2025-06-24 19:13:47,558 - pyskl - INFO - Epoch [65][600/1281] lr: 1.524e-02, eta: 11:45:42, time: 0.333, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9988, loss_cls: 0.4150, loss: 0.4150 +2025-06-24 19:14:28,281 - pyskl - INFO - Epoch [65][700/1281] lr: 1.522e-02, eta: 11:45:06, time: 0.407, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9994, loss_cls: 0.4292, loss: 0.4292 +2025-06-24 19:15:17,782 - pyskl - INFO - Epoch [65][800/1281] lr: 1.520e-02, eta: 11:44:41, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9962, loss_cls: 0.4515, loss: 0.4515 +2025-06-24 19:16:07,086 - pyskl - INFO - Epoch [65][900/1281] lr: 1.518e-02, eta: 11:44:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9988, loss_cls: 0.4709, loss: 0.4709 +2025-06-24 19:16:56,017 - pyskl - INFO - Epoch [65][1000/1281] lr: 1.516e-02, eta: 11:43:52, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9962, loss_cls: 0.4587, loss: 0.4587 +2025-06-24 19:17:45,069 - pyskl - INFO - Epoch [65][1100/1281] lr: 1.514e-02, eta: 11:43:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9981, loss_cls: 0.4260, loss: 0.4260 +2025-06-24 19:18:33,837 - pyskl - INFO - Epoch [65][1200/1281] lr: 1.512e-02, eta: 11:43:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9938, loss_cls: 0.5237, loss: 0.5237 +2025-06-24 19:19:14,214 - pyskl - INFO - Saving checkpoint at 65 epochs +2025-06-24 19:20:13,644 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:20:13,712 - pyskl - INFO - +top1_acc 0.8764 +top5_acc 0.9926 +2025-06-24 19:20:13,712 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:20:13,720 - pyskl - INFO - +mean_acc 0.8278 +2025-06-24 19:20:13,723 - pyskl - INFO - Epoch(val) [65][533] top1_acc: 0.8764, top5_acc: 0.9926, mean_class_accuracy: 0.8278 +2025-06-24 19:21:34,598 - pyskl - INFO - Epoch [66][100/1281] lr: 1.508e-02, eta: 11:42:05, time: 0.809, data_time: 0.197, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9988, loss_cls: 0.4144, loss: 0.4144 +2025-06-24 19:22:23,687 - pyskl - INFO - Epoch [66][200/1281] lr: 1.506e-02, eta: 11:41:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9988, loss_cls: 0.4258, loss: 0.4258 +2025-06-24 19:23:13,063 - pyskl - INFO - Epoch [66][300/1281] lr: 1.504e-02, eta: 11:41:15, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9975, loss_cls: 0.4363, loss: 0.4363 +2025-06-24 19:23:55,662 - pyskl - INFO - Epoch [66][400/1281] lr: 1.502e-02, eta: 11:40:41, time: 0.426, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9981, loss_cls: 0.4116, loss: 0.4116 +2025-06-24 19:24:40,032 - pyskl - INFO - Epoch [66][500/1281] lr: 1.500e-02, eta: 11:40:10, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9988, loss_cls: 0.4247, loss: 0.4247 +2025-06-24 19:25:09,188 - pyskl - INFO - Epoch [66][600/1281] lr: 1.498e-02, eta: 11:39:19, time: 0.292, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9981, loss_cls: 0.3823, loss: 0.3823 +2025-06-24 19:25:50,395 - pyskl - INFO - Epoch [66][700/1281] lr: 1.496e-02, eta: 11:38:43, time: 0.412, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9981, loss_cls: 0.4517, loss: 0.4517 +2025-06-24 19:26:39,699 - pyskl - INFO - Epoch [66][800/1281] lr: 1.494e-02, eta: 11:38:18, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9994, loss_cls: 0.4239, loss: 0.4239 +2025-06-24 19:27:29,003 - pyskl - INFO - Epoch [66][900/1281] lr: 1.492e-02, eta: 11:37:53, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9988, loss_cls: 0.4669, loss: 0.4669 +2025-06-24 19:28:17,621 - pyskl - INFO - Epoch [66][1000/1281] lr: 1.490e-02, eta: 11:37:26, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9969, loss_cls: 0.4301, loss: 0.4301 +2025-06-24 19:29:06,574 - pyskl - INFO - Epoch [66][1100/1281] lr: 1.488e-02, eta: 11:37:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9994, loss_cls: 0.4098, loss: 0.4098 +2025-06-24 19:29:56,203 - pyskl - INFO - Epoch [66][1200/1281] lr: 1.486e-02, eta: 11:36:36, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9981, loss_cls: 0.4633, loss: 0.4633 +2025-06-24 19:30:36,584 - pyskl - INFO - Saving checkpoint at 66 epochs +2025-06-24 19:31:36,229 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:31:36,291 - pyskl - INFO - +top1_acc 0.8661 +top5_acc 0.9919 +2025-06-24 19:31:36,291 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:31:36,300 - pyskl - INFO - +mean_acc 0.8079 +2025-06-24 19:31:36,315 - pyskl - INFO - Epoch(val) [66][533] top1_acc: 0.8661, top5_acc: 0.9919, mean_class_accuracy: 0.8079 +2025-06-24 19:32:56,285 - pyskl - INFO - Epoch [67][100/1281] lr: 1.482e-02, eta: 11:35:38, time: 0.800, data_time: 0.198, memory: 4083, top1_acc: 0.9287, top5_acc: 1.0000, loss_cls: 0.3885, loss: 0.3885 +2025-06-24 19:33:45,775 - pyskl - INFO - Epoch [67][200/1281] lr: 1.480e-02, eta: 11:35:12, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9988, loss_cls: 0.4376, loss: 0.4376 +2025-06-24 19:34:34,775 - pyskl - INFO - Epoch [67][300/1281] lr: 1.478e-02, eta: 11:34:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9969, loss_cls: 0.4349, loss: 0.4349 +2025-06-24 19:35:16,206 - pyskl - INFO - Epoch [67][400/1281] lr: 1.476e-02, eta: 11:34:11, time: 0.414, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9994, loss_cls: 0.3715, loss: 0.3715 +2025-06-24 19:36:02,066 - pyskl - INFO - Epoch [67][500/1281] lr: 1.474e-02, eta: 11:33:41, time: 0.459, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9975, loss_cls: 0.3983, loss: 0.3983 +2025-06-24 19:36:29,761 - pyskl - INFO - Epoch [67][600/1281] lr: 1.472e-02, eta: 11:32:48, time: 0.277, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9988, loss_cls: 0.3950, loss: 0.3950 +2025-06-24 19:37:12,422 - pyskl - INFO - Epoch [67][700/1281] lr: 1.470e-02, eta: 11:32:14, time: 0.427, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9988, loss_cls: 0.4160, loss: 0.4160 +2025-06-24 19:38:01,442 - pyskl - INFO - Epoch [67][800/1281] lr: 1.468e-02, eta: 11:31:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9981, loss_cls: 0.4139, loss: 0.4139 +2025-06-24 19:38:50,521 - pyskl - INFO - Epoch [67][900/1281] lr: 1.466e-02, eta: 11:31:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9969, loss_cls: 0.3981, loss: 0.3981 +2025-06-24 19:39:39,479 - pyskl - INFO - Epoch [67][1000/1281] lr: 1.464e-02, eta: 11:30:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9975, loss_cls: 0.4630, loss: 0.4630 +2025-06-24 19:40:28,569 - pyskl - INFO - Epoch [67][1100/1281] lr: 1.462e-02, eta: 11:30:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9981, loss_cls: 0.4235, loss: 0.4235 +2025-06-24 19:41:17,854 - pyskl - INFO - Epoch [67][1200/1281] lr: 1.460e-02, eta: 11:30:03, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9975, loss_cls: 0.4379, loss: 0.4379 +2025-06-24 19:41:57,998 - pyskl - INFO - Saving checkpoint at 67 epochs +2025-06-24 19:42:56,597 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:42:56,665 - pyskl - INFO - +top1_acc 0.8726 +top5_acc 0.9932 +2025-06-24 19:42:56,665 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:42:56,672 - pyskl - INFO - +mean_acc 0.8462 +2025-06-24 19:42:56,674 - pyskl - INFO - Epoch(val) [67][533] top1_acc: 0.8726, top5_acc: 0.9932, mean_class_accuracy: 0.8462 +2025-06-24 19:44:17,267 - pyskl - INFO - Epoch [68][100/1281] lr: 1.456e-02, eta: 11:29:05, time: 0.806, data_time: 0.201, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9975, loss_cls: 0.4506, loss: 0.4506 +2025-06-24 19:45:06,279 - pyskl - INFO - Epoch [68][200/1281] lr: 1.454e-02, eta: 11:28:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3703, loss: 0.3703 +2025-06-24 19:45:55,970 - pyskl - INFO - Epoch [68][300/1281] lr: 1.452e-02, eta: 11:28:13, time: 0.497, data_time: 0.001, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9988, loss_cls: 0.4287, loss: 0.4287 +2025-06-24 19:46:37,131 - pyskl - INFO - Epoch [68][400/1281] lr: 1.450e-02, eta: 11:27:37, time: 0.412, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9994, loss_cls: 0.3716, loss: 0.3716 +2025-06-24 19:47:24,501 - pyskl - INFO - Epoch [68][500/1281] lr: 1.448e-02, eta: 11:27:08, time: 0.474, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9975, loss_cls: 0.4494, loss: 0.4494 +2025-06-24 19:47:51,085 - pyskl - INFO - Epoch [68][600/1281] lr: 1.446e-02, eta: 11:26:14, time: 0.266, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9994, loss_cls: 0.4415, loss: 0.4415 +2025-06-24 19:48:33,795 - pyskl - INFO - Epoch [68][700/1281] lr: 1.444e-02, eta: 11:25:40, time: 0.427, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9981, loss_cls: 0.4030, loss: 0.4030 +2025-06-24 19:49:22,937 - pyskl - INFO - Epoch [68][800/1281] lr: 1.442e-02, eta: 11:25:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9994, loss_cls: 0.3403, loss: 0.3403 +2025-06-24 19:50:12,073 - pyskl - INFO - Epoch [68][900/1281] lr: 1.440e-02, eta: 11:24:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9981, loss_cls: 0.4091, loss: 0.4091 +2025-06-24 19:51:01,379 - pyskl - INFO - Epoch [68][1000/1281] lr: 1.438e-02, eta: 11:24:20, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9975, loss_cls: 0.4247, loss: 0.4247 +2025-06-24 19:51:50,216 - pyskl - INFO - Epoch [68][1100/1281] lr: 1.436e-02, eta: 11:23:53, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9981, loss_cls: 0.4645, loss: 0.4645 +2025-06-24 19:52:39,251 - pyskl - INFO - Epoch [68][1200/1281] lr: 1.434e-02, eta: 11:23:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9988, loss_cls: 0.3946, loss: 0.3946 +2025-06-24 19:53:19,767 - pyskl - INFO - Saving checkpoint at 68 epochs +2025-06-24 19:54:18,397 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:54:18,466 - pyskl - INFO - +top1_acc 0.8742 +top5_acc 0.9926 +2025-06-24 19:54:18,466 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:54:18,474 - pyskl - INFO - +mean_acc 0.8324 +2025-06-24 19:54:18,476 - pyskl - INFO - Epoch(val) [68][533] top1_acc: 0.8742, top5_acc: 0.9926, mean_class_accuracy: 0.8324 +2025-06-24 19:55:38,336 - pyskl - INFO - Epoch [69][100/1281] lr: 1.431e-02, eta: 11:22:27, time: 0.799, data_time: 0.191, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9981, loss_cls: 0.3359, loss: 0.3359 +2025-06-24 19:56:27,272 - pyskl - INFO - Epoch [69][200/1281] lr: 1.429e-02, eta: 11:22:00, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9975, loss_cls: 0.4175, loss: 0.4175 +2025-06-24 19:57:16,393 - pyskl - INFO - Epoch [69][300/1281] lr: 1.427e-02, eta: 11:21:33, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9988, loss_cls: 0.3641, loss: 0.3641 +2025-06-24 19:57:57,612 - pyskl - INFO - Epoch [69][400/1281] lr: 1.425e-02, eta: 11:20:56, time: 0.412, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9994, loss_cls: 0.3765, loss: 0.3765 +2025-06-24 19:58:45,950 - pyskl - INFO - Epoch [69][500/1281] lr: 1.423e-02, eta: 11:20:28, time: 0.483, data_time: 0.001, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9988, loss_cls: 0.4339, loss: 0.4339 +2025-06-24 19:59:11,859 - pyskl - INFO - Epoch [69][600/1281] lr: 1.420e-02, eta: 11:19:34, time: 0.259, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9981, loss_cls: 0.3813, loss: 0.3813 +2025-06-24 19:59:53,894 - pyskl - INFO - Epoch [69][700/1281] lr: 1.418e-02, eta: 11:18:58, time: 0.420, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4112, loss: 0.4112 +2025-06-24 20:00:43,085 - pyskl - INFO - Epoch [69][800/1281] lr: 1.416e-02, eta: 11:18:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9969, loss_cls: 0.4115, loss: 0.4115 +2025-06-24 20:01:31,935 - pyskl - INFO - Epoch [69][900/1281] lr: 1.414e-02, eta: 11:18:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9969, loss_cls: 0.4566, loss: 0.4566 +2025-06-24 20:02:20,933 - pyskl - INFO - Epoch [69][1000/1281] lr: 1.412e-02, eta: 11:17:36, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9956, loss_cls: 0.4652, loss: 0.4652 +2025-06-24 20:03:09,998 - pyskl - INFO - Epoch [69][1100/1281] lr: 1.410e-02, eta: 11:17:09, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9981, loss_cls: 0.4408, loss: 0.4408 +2025-06-24 20:03:59,105 - pyskl - INFO - Epoch [69][1200/1281] lr: 1.408e-02, eta: 11:16:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4362, loss: 0.4362 +2025-06-24 20:04:39,237 - pyskl - INFO - Saving checkpoint at 69 epochs +2025-06-24 20:05:38,707 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:05:38,766 - pyskl - INFO - +top1_acc 0.8756 +top5_acc 0.9890 +2025-06-24 20:05:38,766 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:05:38,776 - pyskl - INFO - +mean_acc 0.8340 +2025-06-24 20:05:38,779 - pyskl - INFO - Epoch(val) [69][533] top1_acc: 0.8756, top5_acc: 0.9890, mean_class_accuracy: 0.8340 +2025-06-24 20:06:59,509 - pyskl - INFO - Epoch [70][100/1281] lr: 1.405e-02, eta: 11:15:42, time: 0.807, data_time: 0.194, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9975, loss_cls: 0.4048, loss: 0.4048 +2025-06-24 20:07:48,696 - pyskl - INFO - Epoch [70][200/1281] lr: 1.403e-02, eta: 11:15:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9981, loss_cls: 0.3637, loss: 0.3637 +2025-06-24 20:08:37,637 - pyskl - INFO - Epoch [70][300/1281] lr: 1.401e-02, eta: 11:14:47, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9975, loss_cls: 0.3824, loss: 0.3824 +2025-06-24 20:09:17,554 - pyskl - INFO - Epoch [70][400/1281] lr: 1.399e-02, eta: 11:14:09, time: 0.399, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9975, loss_cls: 0.4000, loss: 0.4000 +2025-06-24 20:10:08,732 - pyskl - INFO - Epoch [70][500/1281] lr: 1.397e-02, eta: 11:13:44, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9988, loss_cls: 0.3551, loss: 0.3551 +2025-06-24 20:10:32,230 - pyskl - INFO - Epoch [70][600/1281] lr: 1.395e-02, eta: 11:12:47, time: 0.235, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.3949, loss: 0.3949 +2025-06-24 20:11:15,464 - pyskl - INFO - Epoch [70][700/1281] lr: 1.392e-02, eta: 11:12:12, time: 0.432, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9988, loss_cls: 0.4231, loss: 0.4231 +2025-06-24 20:12:04,801 - pyskl - INFO - Epoch [70][800/1281] lr: 1.390e-02, eta: 11:11:45, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9962, loss_cls: 0.4348, loss: 0.4348 +2025-06-24 20:12:53,958 - pyskl - INFO - Epoch [70][900/1281] lr: 1.388e-02, eta: 11:11:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9988, loss_cls: 0.4352, loss: 0.4352 +2025-06-24 20:13:43,100 - pyskl - INFO - Epoch [70][1000/1281] lr: 1.386e-02, eta: 11:10:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 1.0000, loss_cls: 0.4174, loss: 0.4174 +2025-06-24 20:14:32,251 - pyskl - INFO - Epoch [70][1100/1281] lr: 1.384e-02, eta: 11:10:22, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9962, loss_cls: 0.4787, loss: 0.4787 +2025-06-24 20:15:21,484 - pyskl - INFO - Epoch [70][1200/1281] lr: 1.382e-02, eta: 11:09:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 1.0000, loss_cls: 0.3962, loss: 0.3962 +2025-06-24 20:16:01,744 - pyskl - INFO - Saving checkpoint at 70 epochs +2025-06-24 20:17:01,625 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:17:01,682 - pyskl - INFO - +top1_acc 0.8713 +top5_acc 0.9918 +2025-06-24 20:17:01,682 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:17:01,689 - pyskl - INFO - +mean_acc 0.8298 +2025-06-24 20:17:01,691 - pyskl - INFO - Epoch(val) [70][533] top1_acc: 0.8713, top5_acc: 0.9918, mean_class_accuracy: 0.8298 +2025-06-24 20:18:21,887 - pyskl - INFO - Epoch [71][100/1281] lr: 1.379e-02, eta: 11:08:54, time: 0.802, data_time: 0.194, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3565, loss: 0.3565 +2025-06-24 20:19:11,000 - pyskl - INFO - Epoch [71][200/1281] lr: 1.377e-02, eta: 11:08:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9994, loss_cls: 0.3607, loss: 0.3607 +2025-06-24 20:20:00,211 - pyskl - INFO - Epoch [71][300/1281] lr: 1.375e-02, eta: 11:07:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9969, loss_cls: 0.3630, loss: 0.3630 +2025-06-24 20:20:39,553 - pyskl - INFO - Epoch [71][400/1281] lr: 1.373e-02, eta: 11:07:19, time: 0.393, data_time: 0.001, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9988, loss_cls: 0.3585, loss: 0.3585 +2025-06-24 20:21:30,717 - pyskl - INFO - Epoch [71][500/1281] lr: 1.371e-02, eta: 11:06:53, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9975, loss_cls: 0.4311, loss: 0.4311 +2025-06-24 20:21:53,911 - pyskl - INFO - Epoch [71][600/1281] lr: 1.368e-02, eta: 11:05:56, time: 0.232, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9950, loss_cls: 0.4694, loss: 0.4694 +2025-06-24 20:22:37,774 - pyskl - INFO - Epoch [71][700/1281] lr: 1.366e-02, eta: 11:05:22, time: 0.439, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9975, loss_cls: 0.3853, loss: 0.3853 +2025-06-24 20:23:27,101 - pyskl - INFO - Epoch [71][800/1281] lr: 1.364e-02, eta: 11:04:54, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 1.0000, loss_cls: 0.3943, loss: 0.3943 +2025-06-24 20:24:16,343 - pyskl - INFO - Epoch [71][900/1281] lr: 1.362e-02, eta: 11:04:26, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9969, loss_cls: 0.4287, loss: 0.4287 +2025-06-24 20:25:05,369 - pyskl - INFO - Epoch [71][1000/1281] lr: 1.360e-02, eta: 11:03:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.4079, loss: 0.4079 +2025-06-24 20:25:54,335 - pyskl - INFO - Epoch [71][1100/1281] lr: 1.358e-02, eta: 11:03:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9981, loss_cls: 0.3915, loss: 0.3915 +2025-06-24 20:26:43,423 - pyskl - INFO - Epoch [71][1200/1281] lr: 1.356e-02, eta: 11:03:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9988, loss_cls: 0.3798, loss: 0.3798 +2025-06-24 20:27:23,729 - pyskl - INFO - Saving checkpoint at 71 epochs +2025-06-24 20:28:23,414 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:28:23,482 - pyskl - INFO - +top1_acc 0.8998 +top5_acc 0.9948 +2025-06-24 20:28:23,482 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:28:23,490 - pyskl - INFO - +mean_acc 0.8648 +2025-06-24 20:28:23,495 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_64.pth was removed +2025-06-24 20:28:23,673 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_71.pth. +2025-06-24 20:28:23,673 - pyskl - INFO - Best top1_acc is 0.8998 at 71 epoch. +2025-06-24 20:28:23,675 - pyskl - INFO - Epoch(val) [71][533] top1_acc: 0.8998, top5_acc: 0.9948, mean_class_accuracy: 0.8648 +2025-06-24 20:29:44,662 - pyskl - INFO - Epoch [72][100/1281] lr: 1.353e-02, eta: 11:02:01, time: 0.810, data_time: 0.200, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9994, loss_cls: 0.3835, loss: 0.3835 +2025-06-24 20:30:33,806 - pyskl - INFO - Epoch [72][200/1281] lr: 1.351e-02, eta: 11:01:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9981, loss_cls: 0.3407, loss: 0.3407 +2025-06-24 20:31:23,023 - pyskl - INFO - Epoch [72][300/1281] lr: 1.349e-02, eta: 11:01:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2987, loss: 0.2987 +2025-06-24 20:32:00,210 - pyskl - INFO - Epoch [72][400/1281] lr: 1.346e-02, eta: 11:00:23, time: 0.372, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9981, loss_cls: 0.3659, loss: 0.3659 +2025-06-24 20:32:51,463 - pyskl - INFO - Epoch [72][500/1281] lr: 1.344e-02, eta: 10:59:57, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9981, loss_cls: 0.3695, loss: 0.3695 +2025-06-24 20:33:16,085 - pyskl - INFO - Epoch [72][600/1281] lr: 1.342e-02, eta: 10:59:01, time: 0.246, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.3994, loss: 0.3994 +2025-06-24 20:34:01,521 - pyskl - INFO - Epoch [72][700/1281] lr: 1.340e-02, eta: 10:58:28, time: 0.454, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9969, loss_cls: 0.3912, loss: 0.3912 +2025-06-24 20:34:50,867 - pyskl - INFO - Epoch [72][800/1281] lr: 1.338e-02, eta: 10:58:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9975, loss_cls: 0.3370, loss: 0.3370 +2025-06-24 20:35:39,894 - pyskl - INFO - Epoch [72][900/1281] lr: 1.336e-02, eta: 10:57:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9988, loss_cls: 0.3791, loss: 0.3791 +2025-06-24 20:36:28,911 - pyskl - INFO - Epoch [72][1000/1281] lr: 1.334e-02, eta: 10:57:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4707, loss: 0.4707 +2025-06-24 20:37:18,069 - pyskl - INFO - Epoch [72][1100/1281] lr: 1.332e-02, eta: 10:56:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9962, loss_cls: 0.4560, loss: 0.4560 +2025-06-24 20:38:07,373 - pyskl - INFO - Epoch [72][1200/1281] lr: 1.330e-02, eta: 10:56:05, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9956, loss_cls: 0.4643, loss: 0.4643 +2025-06-24 20:38:47,609 - pyskl - INFO - Saving checkpoint at 72 epochs +2025-06-24 20:39:46,374 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:39:46,455 - pyskl - INFO - +top1_acc 0.8825 +top5_acc 0.9920 +2025-06-24 20:39:46,455 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:39:46,463 - pyskl - INFO - +mean_acc 0.8366 +2025-06-24 20:39:46,465 - pyskl - INFO - Epoch(val) [72][533] top1_acc: 0.8825, top5_acc: 0.9920, mean_class_accuracy: 0.8366 +2025-06-24 20:41:07,805 - pyskl - INFO - Epoch [73][100/1281] lr: 1.326e-02, eta: 10:55:05, time: 0.813, data_time: 0.198, memory: 4083, top1_acc: 0.9250, top5_acc: 1.0000, loss_cls: 0.3839, loss: 0.3839 +2025-06-24 20:41:56,888 - pyskl - INFO - Epoch [73][200/1281] lr: 1.324e-02, eta: 10:54:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9981, loss_cls: 0.3857, loss: 0.3857 +2025-06-24 20:42:45,806 - pyskl - INFO - Epoch [73][300/1281] lr: 1.322e-02, eta: 10:54:07, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9975, loss_cls: 0.3760, loss: 0.3760 +2025-06-24 20:43:21,510 - pyskl - INFO - Epoch [73][400/1281] lr: 1.320e-02, eta: 10:53:24, time: 0.357, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9975, loss_cls: 0.3975, loss: 0.3975 +2025-06-24 20:44:12,433 - pyskl - INFO - Epoch [73][500/1281] lr: 1.318e-02, eta: 10:52:57, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9988, loss_cls: 0.3368, loss: 0.3368 +2025-06-24 20:44:37,032 - pyskl - INFO - Epoch [73][600/1281] lr: 1.316e-02, eta: 10:52:01, time: 0.246, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9981, loss_cls: 0.3546, loss: 0.3546 +2025-06-24 20:45:24,342 - pyskl - INFO - Epoch [73][700/1281] lr: 1.314e-02, eta: 10:51:30, time: 0.473, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9994, loss_cls: 0.3770, loss: 0.3770 +2025-06-24 20:46:13,228 - pyskl - INFO - Epoch [73][800/1281] lr: 1.312e-02, eta: 10:51:01, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9981, loss_cls: 0.4421, loss: 0.4421 +2025-06-24 20:47:02,064 - pyskl - INFO - Epoch [73][900/1281] lr: 1.310e-02, eta: 10:50:32, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9975, loss_cls: 0.4420, loss: 0.4420 +2025-06-24 20:47:51,069 - pyskl - INFO - Epoch [73][1000/1281] lr: 1.308e-02, eta: 10:50:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9975, loss_cls: 0.4099, loss: 0.4099 +2025-06-24 20:48:40,061 - pyskl - INFO - Epoch [73][1100/1281] lr: 1.306e-02, eta: 10:49:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9988, loss_cls: 0.4475, loss: 0.4475 +2025-06-24 20:49:29,060 - pyskl - INFO - Epoch [73][1200/1281] lr: 1.304e-02, eta: 10:49:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9994, loss_cls: 0.4013, loss: 0.4013 +2025-06-24 20:50:09,360 - pyskl - INFO - Saving checkpoint at 73 epochs +2025-06-24 20:51:08,403 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:51:08,474 - pyskl - INFO - +top1_acc 0.8726 +top5_acc 0.9910 +2025-06-24 20:51:08,474 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:51:08,482 - pyskl - INFO - +mean_acc 0.8435 +2025-06-24 20:51:08,484 - pyskl - INFO - Epoch(val) [73][533] top1_acc: 0.8726, top5_acc: 0.9910, mean_class_accuracy: 0.8435 +2025-06-24 20:52:28,973 - pyskl - INFO - Epoch [74][100/1281] lr: 1.300e-02, eta: 10:48:02, time: 0.805, data_time: 0.194, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9994, loss_cls: 0.3693, loss: 0.3693 +2025-06-24 20:53:17,992 - pyskl - INFO - Epoch [74][200/1281] lr: 1.298e-02, eta: 10:47:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9981, loss_cls: 0.3910, loss: 0.3910 +2025-06-24 20:54:07,108 - pyskl - INFO - Epoch [74][300/1281] lr: 1.296e-02, eta: 10:47:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4191, loss: 0.4191 +2025-06-24 20:54:40,644 - pyskl - INFO - Epoch [74][400/1281] lr: 1.294e-02, eta: 10:46:17, time: 0.335, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9969, loss_cls: 0.3723, loss: 0.3723 +2025-06-24 20:55:31,912 - pyskl - INFO - Epoch [74][500/1281] lr: 1.292e-02, eta: 10:45:50, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9994, loss_cls: 0.3399, loss: 0.3399 +2025-06-24 20:55:56,878 - pyskl - INFO - Epoch [74][600/1281] lr: 1.290e-02, eta: 10:44:56, time: 0.250, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9969, loss_cls: 0.3542, loss: 0.3542 +2025-06-24 20:56:44,708 - pyskl - INFO - Epoch [74][700/1281] lr: 1.288e-02, eta: 10:44:25, time: 0.478, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9981, loss_cls: 0.4425, loss: 0.4425 +2025-06-24 20:57:33,753 - pyskl - INFO - Epoch [74][800/1281] lr: 1.286e-02, eta: 10:43:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9981, loss_cls: 0.3996, loss: 0.3996 +2025-06-24 20:58:22,836 - pyskl - INFO - Epoch [74][900/1281] lr: 1.284e-02, eta: 10:43:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 1.0000, loss_cls: 0.3667, loss: 0.3667 +2025-06-24 20:59:12,185 - pyskl - INFO - Epoch [74][1000/1281] lr: 1.282e-02, eta: 10:42:56, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9962, loss_cls: 0.3978, loss: 0.3978 +2025-06-24 21:00:01,492 - pyskl - INFO - Epoch [74][1100/1281] lr: 1.280e-02, eta: 10:42:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9994, loss_cls: 0.3627, loss: 0.3627 +2025-06-24 21:00:50,991 - pyskl - INFO - Epoch [74][1200/1281] lr: 1.278e-02, eta: 10:41:58, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.3895, loss: 0.3895 +2025-06-24 21:01:31,500 - pyskl - INFO - Saving checkpoint at 74 epochs +2025-06-24 21:02:30,377 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:02:30,432 - pyskl - INFO - +top1_acc 0.8858 +top5_acc 0.9928 +2025-06-24 21:02:30,432 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:02:30,439 - pyskl - INFO - +mean_acc 0.8326 +2025-06-24 21:02:30,441 - pyskl - INFO - Epoch(val) [74][533] top1_acc: 0.8858, top5_acc: 0.9928, mean_class_accuracy: 0.8326 +2025-06-24 21:03:50,185 - pyskl - INFO - Epoch [75][100/1281] lr: 1.274e-02, eta: 10:40:55, time: 0.797, data_time: 0.197, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.2957, loss: 0.2957 +2025-06-24 21:04:39,351 - pyskl - INFO - Epoch [75][200/1281] lr: 1.272e-02, eta: 10:40:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3696, loss: 0.3696 +2025-06-24 21:05:28,534 - pyskl - INFO - Epoch [75][300/1281] lr: 1.270e-02, eta: 10:39:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3463, loss: 0.3463 +2025-06-24 21:06:02,759 - pyskl - INFO - Epoch [75][400/1281] lr: 1.268e-02, eta: 10:39:10, time: 0.342, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 1.0000, loss_cls: 0.3860, loss: 0.3860 +2025-06-24 21:06:53,813 - pyskl - INFO - Epoch [75][500/1281] lr: 1.266e-02, eta: 10:38:42, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.4289, loss: 0.4289 +2025-06-24 21:07:19,670 - pyskl - INFO - Epoch [75][600/1281] lr: 1.264e-02, eta: 10:37:49, time: 0.259, data_time: 0.001, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9994, loss_cls: 0.3756, loss: 0.3756 +2025-06-24 21:08:09,105 - pyskl - INFO - Epoch [75][700/1281] lr: 1.262e-02, eta: 10:37:19, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9962, loss_cls: 0.4317, loss: 0.4317 +2025-06-24 21:08:58,408 - pyskl - INFO - Epoch [75][800/1281] lr: 1.260e-02, eta: 10:36:50, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.3720, loss: 0.3720 +2025-06-24 21:09:47,683 - pyskl - INFO - Epoch [75][900/1281] lr: 1.258e-02, eta: 10:36:20, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9994, loss_cls: 0.4011, loss: 0.4011 +2025-06-24 21:10:36,821 - pyskl - INFO - Epoch [75][1000/1281] lr: 1.256e-02, eta: 10:35:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9975, loss_cls: 0.4089, loss: 0.4089 +2025-06-24 21:11:26,060 - pyskl - INFO - Epoch [75][1100/1281] lr: 1.254e-02, eta: 10:35:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9988, loss_cls: 0.3567, loss: 0.3567 +2025-06-24 21:12:15,353 - pyskl - INFO - Epoch [75][1200/1281] lr: 1.252e-02, eta: 10:34:50, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9994, loss_cls: 0.3795, loss: 0.3795 +2025-06-24 21:12:55,474 - pyskl - INFO - Saving checkpoint at 75 epochs +2025-06-24 21:13:54,999 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:13:55,056 - pyskl - INFO - +top1_acc 0.8837 +top5_acc 0.9933 +2025-06-24 21:13:55,056 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:13:55,063 - pyskl - INFO - +mean_acc 0.8433 +2025-06-24 21:13:55,065 - pyskl - INFO - Epoch(val) [75][533] top1_acc: 0.8837, top5_acc: 0.9933, mean_class_accuracy: 0.8433 +2025-06-24 21:15:15,249 - pyskl - INFO - Epoch [76][100/1281] lr: 1.248e-02, eta: 10:33:47, time: 0.802, data_time: 0.194, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9975, loss_cls: 0.4071, loss: 0.4071 +2025-06-24 21:16:04,761 - pyskl - INFO - Epoch [76][200/1281] lr: 1.246e-02, eta: 10:33:17, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9988, loss_cls: 0.3812, loss: 0.3812 +2025-06-24 21:16:54,148 - pyskl - INFO - Epoch [76][300/1281] lr: 1.244e-02, eta: 10:32:47, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9994, loss_cls: 0.3290, loss: 0.3290 +2025-06-24 21:17:25,480 - pyskl - INFO - Epoch [76][400/1281] lr: 1.242e-02, eta: 10:31:59, time: 0.313, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.2919, loss: 0.2919 +2025-06-24 21:18:16,573 - pyskl - INFO - Epoch [76][500/1281] lr: 1.240e-02, eta: 10:31:31, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 1.0000, loss_cls: 0.3793, loss: 0.3793 +2025-06-24 21:18:43,867 - pyskl - INFO - Epoch [76][600/1281] lr: 1.238e-02, eta: 10:30:39, time: 0.273, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.3462, loss: 0.3462 +2025-06-24 21:19:32,992 - pyskl - INFO - Epoch [76][700/1281] lr: 1.236e-02, eta: 10:30:09, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3493, loss: 0.3493 +2025-06-24 21:20:22,879 - pyskl - INFO - Epoch [76][800/1281] lr: 1.234e-02, eta: 10:29:39, time: 0.499, data_time: 0.001, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9944, loss_cls: 0.4104, loss: 0.4104 +2025-06-24 21:21:11,993 - pyskl - INFO - Epoch [76][900/1281] lr: 1.232e-02, eta: 10:29:09, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9969, loss_cls: 0.3651, loss: 0.3651 +2025-06-24 21:22:01,529 - pyskl - INFO - Epoch [76][1000/1281] lr: 1.230e-02, eta: 10:28:39, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9994, loss_cls: 0.3494, loss: 0.3494 +2025-06-24 21:22:50,911 - pyskl - INFO - Epoch [76][1100/1281] lr: 1.228e-02, eta: 10:28:09, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9988, loss_cls: 0.4062, loss: 0.4062 +2025-06-24 21:23:40,051 - pyskl - INFO - Epoch [76][1200/1281] lr: 1.225e-02, eta: 10:27:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9981, loss_cls: 0.3996, loss: 0.3996 +2025-06-24 21:24:20,240 - pyskl - INFO - Saving checkpoint at 76 epochs +2025-06-24 21:25:19,694 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:25:19,762 - pyskl - INFO - +top1_acc 0.8850 +top5_acc 0.9919 +2025-06-24 21:25:19,762 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:25:19,771 - pyskl - INFO - +mean_acc 0.8523 +2025-06-24 21:25:19,773 - pyskl - INFO - Epoch(val) [76][533] top1_acc: 0.8850, top5_acc: 0.9919, mean_class_accuracy: 0.8523 +2025-06-24 21:26:39,392 - pyskl - INFO - Epoch [77][100/1281] lr: 1.222e-02, eta: 10:26:34, time: 0.796, data_time: 0.191, memory: 4083, top1_acc: 0.9331, top5_acc: 1.0000, loss_cls: 0.3450, loss: 0.3450 +2025-06-24 21:27:28,205 - pyskl - INFO - Epoch [77][200/1281] lr: 1.220e-02, eta: 10:26:03, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3655, loss: 0.3655 +2025-06-24 21:28:17,459 - pyskl - INFO - Epoch [77][300/1281] lr: 1.218e-02, eta: 10:25:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 1.0000, loss_cls: 0.3120, loss: 0.3120 +2025-06-24 21:28:47,005 - pyskl - INFO - Epoch [77][400/1281] lr: 1.216e-02, eta: 10:24:43, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 1.0000, loss_cls: 0.3424, loss: 0.3424 +2025-06-24 21:29:38,210 - pyskl - INFO - Epoch [77][500/1281] lr: 1.214e-02, eta: 10:24:14, time: 0.512, data_time: 0.001, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9975, loss_cls: 0.3542, loss: 0.3542 +2025-06-24 21:30:05,596 - pyskl - INFO - Epoch [77][600/1281] lr: 1.212e-02, eta: 10:23:23, time: 0.274, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9994, loss_cls: 0.4025, loss: 0.4025 +2025-06-24 21:30:54,555 - pyskl - INFO - Epoch [77][700/1281] lr: 1.210e-02, eta: 10:22:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9994, loss_cls: 0.3442, loss: 0.3442 +2025-06-24 21:31:43,544 - pyskl - INFO - Epoch [77][800/1281] lr: 1.207e-02, eta: 10:22:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9988, loss_cls: 0.3531, loss: 0.3531 +2025-06-24 21:32:32,751 - pyskl - INFO - Epoch [77][900/1281] lr: 1.205e-02, eta: 10:21:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9994, loss_cls: 0.3918, loss: 0.3918 +2025-06-24 21:33:21,728 - pyskl - INFO - Epoch [77][1000/1281] lr: 1.203e-02, eta: 10:21:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9981, loss_cls: 0.3671, loss: 0.3671 +2025-06-24 21:34:10,910 - pyskl - INFO - Epoch [77][1100/1281] lr: 1.201e-02, eta: 10:20:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9969, loss_cls: 0.4183, loss: 0.4183 +2025-06-24 21:35:00,409 - pyskl - INFO - Epoch [77][1200/1281] lr: 1.199e-02, eta: 10:20:18, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9981, loss_cls: 0.3532, loss: 0.3532 +2025-06-24 21:35:40,540 - pyskl - INFO - Saving checkpoint at 77 epochs +2025-06-24 21:36:40,950 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:36:41,011 - pyskl - INFO - +top1_acc 0.8975 +top5_acc 0.9925 +2025-06-24 21:36:41,011 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:36:41,019 - pyskl - INFO - +mean_acc 0.8696 +2025-06-24 21:36:41,021 - pyskl - INFO - Epoch(val) [77][533] top1_acc: 0.8975, top5_acc: 0.9925, mean_class_accuracy: 0.8696 +2025-06-24 21:38:01,542 - pyskl - INFO - Epoch [78][100/1281] lr: 1.196e-02, eta: 10:19:14, time: 0.805, data_time: 0.193, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.3276, loss: 0.3276 +2025-06-24 21:38:50,596 - pyskl - INFO - Epoch [78][200/1281] lr: 1.194e-02, eta: 10:18:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3571, loss: 0.3571 +2025-06-24 21:39:39,684 - pyskl - INFO - Epoch [78][300/1281] lr: 1.192e-02, eta: 10:18:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.3167, loss: 0.3167 +2025-06-24 21:40:07,441 - pyskl - INFO - Epoch [78][400/1281] lr: 1.190e-02, eta: 10:17:21, time: 0.278, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9969, loss_cls: 0.3899, loss: 0.3899 +2025-06-24 21:40:58,469 - pyskl - INFO - Epoch [78][500/1281] lr: 1.187e-02, eta: 10:16:52, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9981, loss_cls: 0.3491, loss: 0.3491 +2025-06-24 21:41:29,673 - pyskl - INFO - Epoch [78][600/1281] lr: 1.185e-02, eta: 10:16:04, time: 0.312, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9975, loss_cls: 0.3983, loss: 0.3983 +2025-06-24 21:42:18,439 - pyskl - INFO - Epoch [78][700/1281] lr: 1.183e-02, eta: 10:15:33, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9981, loss_cls: 0.4630, loss: 0.4630 +2025-06-24 21:43:07,399 - pyskl - INFO - Epoch [78][800/1281] lr: 1.181e-02, eta: 10:15:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9994, loss_cls: 0.3512, loss: 0.3512 +2025-06-24 21:43:56,323 - pyskl - INFO - Epoch [78][900/1281] lr: 1.179e-02, eta: 10:14:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9981, loss_cls: 0.4142, loss: 0.4142 +2025-06-24 21:44:45,592 - pyskl - INFO - Epoch [78][1000/1281] lr: 1.177e-02, eta: 10:13:59, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9969, loss_cls: 0.3755, loss: 0.3755 +2025-06-24 21:45:34,533 - pyskl - INFO - Epoch [78][1100/1281] lr: 1.175e-02, eta: 10:13:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 1.0000, loss_cls: 0.3646, loss: 0.3646 +2025-06-24 21:46:23,638 - pyskl - INFO - Epoch [78][1200/1281] lr: 1.173e-02, eta: 10:12:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9975, loss_cls: 0.4092, loss: 0.4092 +2025-06-24 21:47:04,063 - pyskl - INFO - Saving checkpoint at 78 epochs +2025-06-24 21:48:03,031 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:48:03,088 - pyskl - INFO - +top1_acc 0.8801 +top5_acc 0.9910 +2025-06-24 21:48:03,088 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:48:03,095 - pyskl - INFO - +mean_acc 0.8479 +2025-06-24 21:48:03,097 - pyskl - INFO - Epoch(val) [78][533] top1_acc: 0.8801, top5_acc: 0.9910, mean_class_accuracy: 0.8479 +2025-06-24 21:49:22,183 - pyskl - INFO - Epoch [79][100/1281] lr: 1.169e-02, eta: 10:11:51, time: 0.791, data_time: 0.187, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9981, loss_cls: 0.3335, loss: 0.3335 +2025-06-24 21:50:11,474 - pyskl - INFO - Epoch [79][200/1281] lr: 1.167e-02, eta: 10:11:20, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9981, loss_cls: 0.3106, loss: 0.3106 +2025-06-24 21:51:00,769 - pyskl - INFO - Epoch [79][300/1281] lr: 1.165e-02, eta: 10:10:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9994, loss_cls: 0.3266, loss: 0.3266 +2025-06-24 21:51:29,135 - pyskl - INFO - Epoch [79][400/1281] lr: 1.163e-02, eta: 10:09:58, time: 0.284, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.3060, loss: 0.3060 +2025-06-24 21:52:19,362 - pyskl - INFO - Epoch [79][500/1281] lr: 1.161e-02, eta: 10:09:28, time: 0.502, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9988, loss_cls: 0.3325, loss: 0.3325 +2025-06-24 21:52:50,896 - pyskl - INFO - Epoch [79][600/1281] lr: 1.159e-02, eta: 10:08:40, time: 0.315, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.3989, loss: 0.3989 +2025-06-24 21:53:39,819 - pyskl - INFO - Epoch [79][700/1281] lr: 1.157e-02, eta: 10:08:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9994, loss_cls: 0.3607, loss: 0.3607 +2025-06-24 21:54:29,102 - pyskl - INFO - Epoch [79][800/1281] lr: 1.155e-02, eta: 10:07:37, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9981, loss_cls: 0.3109, loss: 0.3109 +2025-06-24 21:55:17,925 - pyskl - INFO - Epoch [79][900/1281] lr: 1.153e-02, eta: 10:07:06, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9981, loss_cls: 0.3277, loss: 0.3277 +2025-06-24 21:56:07,167 - pyskl - INFO - Epoch [79][1000/1281] lr: 1.151e-02, eta: 10:06:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9988, loss_cls: 0.3674, loss: 0.3674 +2025-06-24 21:56:56,318 - pyskl - INFO - Epoch [79][1100/1281] lr: 1.149e-02, eta: 10:06:03, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.3888, loss: 0.3888 +2025-06-24 21:57:45,551 - pyskl - INFO - Epoch [79][1200/1281] lr: 1.147e-02, eta: 10:05:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9988, loss_cls: 0.3717, loss: 0.3717 +2025-06-24 21:58:25,754 - pyskl - INFO - Saving checkpoint at 79 epochs +2025-06-24 21:59:25,571 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:59:25,627 - pyskl - INFO - +top1_acc 0.8953 +top5_acc 0.9931 +2025-06-24 21:59:25,627 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:59:25,634 - pyskl - INFO - +mean_acc 0.8580 +2025-06-24 21:59:25,636 - pyskl - INFO - Epoch(val) [79][533] top1_acc: 0.8953, top5_acc: 0.9931, mean_class_accuracy: 0.8580 +2025-06-24 22:00:46,444 - pyskl - INFO - Epoch [80][100/1281] lr: 1.143e-02, eta: 10:04:27, time: 0.808, data_time: 0.197, memory: 4083, top1_acc: 0.9431, top5_acc: 1.0000, loss_cls: 0.3066, loss: 0.3066 +2025-06-24 22:01:35,360 - pyskl - INFO - Epoch [80][200/1281] lr: 1.141e-02, eta: 10:03:55, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9994, loss_cls: 0.3513, loss: 0.3513 +2025-06-24 22:02:24,627 - pyskl - INFO - Epoch [80][300/1281] lr: 1.139e-02, eta: 10:03:23, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2944, loss: 0.2944 +2025-06-24 22:02:54,150 - pyskl - INFO - Epoch [80][400/1281] lr: 1.137e-02, eta: 10:02:34, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9988, loss_cls: 0.3882, loss: 0.3882 +2025-06-24 22:03:39,750 - pyskl - INFO - Epoch [80][500/1281] lr: 1.135e-02, eta: 10:01:59, time: 0.456, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.3610, loss: 0.3610 +2025-06-24 22:04:13,370 - pyskl - INFO - Epoch [80][600/1281] lr: 1.133e-02, eta: 10:01:13, time: 0.336, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9981, loss_cls: 0.3304, loss: 0.3304 +2025-06-24 22:05:02,549 - pyskl - INFO - Epoch [80][700/1281] lr: 1.131e-02, eta: 10:00:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9981, loss_cls: 0.2922, loss: 0.2922 +2025-06-24 22:05:51,834 - pyskl - INFO - Epoch [80][800/1281] lr: 1.129e-02, eta: 10:00:10, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.3028, loss: 0.3028 +2025-06-24 22:06:41,044 - pyskl - INFO - Epoch [80][900/1281] lr: 1.127e-02, eta: 9:59:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3194, loss: 0.3194 +2025-06-24 22:07:30,247 - pyskl - INFO - Epoch [80][1000/1281] lr: 1.125e-02, eta: 9:59:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9981, loss_cls: 0.3156, loss: 0.3156 +2025-06-24 22:08:18,900 - pyskl - INFO - Epoch [80][1100/1281] lr: 1.123e-02, eta: 9:58:34, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9981, loss_cls: 0.3442, loss: 0.3442 +2025-06-24 22:09:08,187 - pyskl - INFO - Epoch [80][1200/1281] lr: 1.121e-02, eta: 9:58:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9981, loss_cls: 0.3335, loss: 0.3335 +2025-06-24 22:09:48,456 - pyskl - INFO - Saving checkpoint at 80 epochs +2025-06-24 22:10:48,179 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:10:48,243 - pyskl - INFO - +top1_acc 0.8488 +top5_acc 0.9840 +2025-06-24 22:10:48,244 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:10:48,254 - pyskl - INFO - +mean_acc 0.7970 +2025-06-24 22:10:48,257 - pyskl - INFO - Epoch(val) [80][533] top1_acc: 0.8488, top5_acc: 0.9840, mean_class_accuracy: 0.7970 +2025-06-24 22:12:09,698 - pyskl - INFO - Epoch [81][100/1281] lr: 1.117e-02, eta: 9:56:58, time: 0.814, data_time: 0.199, memory: 4083, top1_acc: 0.9419, top5_acc: 1.0000, loss_cls: 0.3303, loss: 0.3303 +2025-06-24 22:12:58,785 - pyskl - INFO - Epoch [81][200/1281] lr: 1.115e-02, eta: 9:56:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9962, loss_cls: 0.3672, loss: 0.3672 +2025-06-24 22:13:47,153 - pyskl - INFO - Epoch [81][300/1281] lr: 1.113e-02, eta: 9:55:53, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9981, loss_cls: 0.3325, loss: 0.3325 +2025-06-24 22:14:20,726 - pyskl - INFO - Epoch [81][400/1281] lr: 1.111e-02, eta: 9:55:07, time: 0.336, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9981, loss_cls: 0.3463, loss: 0.3463 +2025-06-24 22:15:01,047 - pyskl - INFO - Epoch [81][500/1281] lr: 1.109e-02, eta: 9:54:28, time: 0.403, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 1.0000, loss_cls: 0.3698, loss: 0.3698 +2025-06-24 22:15:36,482 - pyskl - INFO - Epoch [81][600/1281] lr: 1.107e-02, eta: 9:53:44, time: 0.354, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9969, loss_cls: 0.3237, loss: 0.3237 +2025-06-24 22:16:25,836 - pyskl - INFO - Epoch [81][700/1281] lr: 1.105e-02, eta: 9:53:12, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9969, loss_cls: 0.3323, loss: 0.3323 +2025-06-24 22:17:14,920 - pyskl - INFO - Epoch [81][800/1281] lr: 1.103e-02, eta: 9:52:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.3140, loss: 0.3140 +2025-06-24 22:18:03,716 - pyskl - INFO - Epoch [81][900/1281] lr: 1.101e-02, eta: 9:52:07, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 1.0000, loss_cls: 0.3010, loss: 0.3010 +2025-06-24 22:18:53,059 - pyskl - INFO - Epoch [81][1000/1281] lr: 1.099e-02, eta: 9:51:35, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9988, loss_cls: 0.3021, loss: 0.3021 +2025-06-24 22:19:42,094 - pyskl - INFO - Epoch [81][1100/1281] lr: 1.097e-02, eta: 9:51:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 1.0000, loss_cls: 0.3335, loss: 0.3335 +2025-06-24 22:20:31,238 - pyskl - INFO - Epoch [81][1200/1281] lr: 1.095e-02, eta: 9:50:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.3203, loss: 0.3203 +2025-06-24 22:21:11,568 - pyskl - INFO - Saving checkpoint at 81 epochs +2025-06-24 22:22:10,916 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:22:10,984 - pyskl - INFO - +top1_acc 0.8937 +top5_acc 0.9930 +2025-06-24 22:22:10,984 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:22:10,992 - pyskl - INFO - +mean_acc 0.8604 +2025-06-24 22:22:10,995 - pyskl - INFO - Epoch(val) [81][533] top1_acc: 0.8937, top5_acc: 0.9930, mean_class_accuracy: 0.8604 +2025-06-24 22:23:30,531 - pyskl - INFO - Epoch [82][100/1281] lr: 1.091e-02, eta: 9:49:24, time: 0.795, data_time: 0.199, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.3178, loss: 0.3178 +2025-06-24 22:24:19,850 - pyskl - INFO - Epoch [82][200/1281] lr: 1.089e-02, eta: 9:48:52, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9994, loss_cls: 0.3322, loss: 0.3322 +2025-06-24 22:25:08,071 - pyskl - INFO - Epoch [82][300/1281] lr: 1.087e-02, eta: 9:48:19, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3270, loss: 0.3270 +2025-06-24 22:25:41,588 - pyskl - INFO - Epoch [82][400/1281] lr: 1.085e-02, eta: 9:47:33, time: 0.335, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2989, loss: 0.2989 +2025-06-24 22:26:21,552 - pyskl - INFO - Epoch [82][500/1281] lr: 1.083e-02, eta: 9:46:53, time: 0.400, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9981, loss_cls: 0.3426, loss: 0.3426 +2025-06-24 22:26:57,078 - pyskl - INFO - Epoch [82][600/1281] lr: 1.081e-02, eta: 9:46:09, time: 0.355, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3233, loss: 0.3233 +2025-06-24 22:27:45,992 - pyskl - INFO - Epoch [82][700/1281] lr: 1.079e-02, eta: 9:45:36, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9981, loss_cls: 0.3440, loss: 0.3440 +2025-06-24 22:28:35,233 - pyskl - INFO - Epoch [82][800/1281] lr: 1.077e-02, eta: 9:45:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9962, loss_cls: 0.3805, loss: 0.3805 +2025-06-24 22:29:24,505 - pyskl - INFO - Epoch [82][900/1281] lr: 1.075e-02, eta: 9:44:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3181, loss: 0.3181 +2025-06-24 22:30:13,969 - pyskl - INFO - Epoch [82][1000/1281] lr: 1.073e-02, eta: 9:43:59, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9981, loss_cls: 0.3653, loss: 0.3653 +2025-06-24 22:31:02,862 - pyskl - INFO - Epoch [82][1100/1281] lr: 1.071e-02, eta: 9:43:27, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3201, loss: 0.3201 +2025-06-24 22:31:51,655 - pyskl - INFO - Epoch [82][1200/1281] lr: 1.069e-02, eta: 9:42:54, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9975, loss_cls: 0.3832, loss: 0.3832 +2025-06-24 22:32:31,840 - pyskl - INFO - Saving checkpoint at 82 epochs +2025-06-24 22:33:31,841 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:33:31,910 - pyskl - INFO - +top1_acc 0.8892 +top5_acc 0.9927 +2025-06-24 22:33:31,910 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:33:31,918 - pyskl - INFO - +mean_acc 0.8601 +2025-06-24 22:33:31,920 - pyskl - INFO - Epoch(val) [82][533] top1_acc: 0.8892, top5_acc: 0.9927, mean_class_accuracy: 0.8601 +2025-06-24 22:34:52,695 - pyskl - INFO - Epoch [83][100/1281] lr: 1.065e-02, eta: 9:41:48, time: 0.808, data_time: 0.198, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9994, loss_cls: 0.3566, loss: 0.3566 +2025-06-24 22:35:42,148 - pyskl - INFO - Epoch [83][200/1281] lr: 1.063e-02, eta: 9:41:15, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2743, loss: 0.2743 +2025-06-24 22:36:28,601 - pyskl - INFO - Epoch [83][300/1281] lr: 1.061e-02, eta: 9:40:41, time: 0.465, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.2746, loss: 0.2746 +2025-06-24 22:37:07,122 - pyskl - INFO - Epoch [83][400/1281] lr: 1.059e-02, eta: 9:39:59, time: 0.385, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9988, loss_cls: 0.3200, loss: 0.3200 +2025-06-24 22:37:42,476 - pyskl - INFO - Epoch [83][500/1281] lr: 1.057e-02, eta: 9:39:15, time: 0.354, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.3020, loss: 0.3020 +2025-06-24 22:38:20,704 - pyskl - INFO - Epoch [83][600/1281] lr: 1.055e-02, eta: 9:38:33, time: 0.382, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 1.0000, loss_cls: 0.3323, loss: 0.3323 +2025-06-24 22:39:09,951 - pyskl - INFO - Epoch [83][700/1281] lr: 1.053e-02, eta: 9:38:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9975, loss_cls: 0.3228, loss: 0.3228 +2025-06-24 22:39:59,328 - pyskl - INFO - Epoch [83][800/1281] lr: 1.051e-02, eta: 9:37:28, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9988, loss_cls: 0.3101, loss: 0.3101 +2025-06-24 22:40:48,832 - pyskl - INFO - Epoch [83][900/1281] lr: 1.049e-02, eta: 9:36:56, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9981, loss_cls: 0.3313, loss: 0.3313 +2025-06-24 22:41:38,050 - pyskl - INFO - Epoch [83][1000/1281] lr: 1.047e-02, eta: 9:36:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 1.0000, loss_cls: 0.3233, loss: 0.3233 +2025-06-24 22:42:27,059 - pyskl - INFO - Epoch [83][1100/1281] lr: 1.045e-02, eta: 9:35:50, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.3033, loss: 0.3033 +2025-06-24 22:43:16,026 - pyskl - INFO - Epoch [83][1200/1281] lr: 1.043e-02, eta: 9:35:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9981, loss_cls: 0.2742, loss: 0.2742 +2025-06-24 22:43:56,381 - pyskl - INFO - Saving checkpoint at 83 epochs +2025-06-24 22:44:56,293 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:44:56,361 - pyskl - INFO - +top1_acc 0.9007 +top5_acc 0.9947 +2025-06-24 22:44:56,361 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:44:56,369 - pyskl - INFO - +mean_acc 0.8589 +2025-06-24 22:44:56,373 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_71.pth was removed +2025-06-24 22:44:56,575 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_83.pth. +2025-06-24 22:44:56,575 - pyskl - INFO - Best top1_acc is 0.9007 at 83 epoch. +2025-06-24 22:44:56,578 - pyskl - INFO - Epoch(val) [83][533] top1_acc: 0.9007, top5_acc: 0.9947, mean_class_accuracy: 0.8589 +2025-06-24 22:46:16,267 - pyskl - INFO - Epoch [84][100/1281] lr: 1.040e-02, eta: 9:34:10, time: 0.797, data_time: 0.190, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9988, loss_cls: 0.2786, loss: 0.2786 +2025-06-24 22:47:05,533 - pyskl - INFO - Epoch [84][200/1281] lr: 1.038e-02, eta: 9:33:37, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.2885, loss: 0.2885 +2025-06-24 22:47:50,769 - pyskl - INFO - Epoch [84][300/1281] lr: 1.036e-02, eta: 9:33:01, time: 0.452, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2705, loss: 0.2705 +2025-06-24 22:48:31,705 - pyskl - INFO - Epoch [84][400/1281] lr: 1.034e-02, eta: 9:32:21, time: 0.409, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3077, loss: 0.3077 +2025-06-24 22:49:04,456 - pyskl - INFO - Epoch [84][500/1281] lr: 1.031e-02, eta: 9:31:35, time: 0.328, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.3266, loss: 0.3266 +2025-06-24 22:49:43,975 - pyskl - INFO - Epoch [84][600/1281] lr: 1.029e-02, eta: 9:30:54, time: 0.395, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2771, loss: 0.2771 +2025-06-24 22:50:33,013 - pyskl - INFO - Epoch [84][700/1281] lr: 1.027e-02, eta: 9:30:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9981, loss_cls: 0.3057, loss: 0.3057 +2025-06-24 22:51:22,295 - pyskl - INFO - Epoch [84][800/1281] lr: 1.025e-02, eta: 9:29:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3161, loss: 0.3161 +2025-06-24 22:52:11,081 - pyskl - INFO - Epoch [84][900/1281] lr: 1.023e-02, eta: 9:29:14, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3079, loss: 0.3079 +2025-06-24 22:53:00,549 - pyskl - INFO - Epoch [84][1000/1281] lr: 1.021e-02, eta: 9:28:42, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2864, loss: 0.2864 +2025-06-24 22:53:49,587 - pyskl - INFO - Epoch [84][1100/1281] lr: 1.019e-02, eta: 9:28:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9988, loss_cls: 0.3102, loss: 0.3102 +2025-06-24 22:54:38,745 - pyskl - INFO - Epoch [84][1200/1281] lr: 1.017e-02, eta: 9:27:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.2932, loss: 0.2932 +2025-06-24 22:55:18,987 - pyskl - INFO - Saving checkpoint at 84 epochs +2025-06-24 22:56:18,251 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:56:18,307 - pyskl - INFO - +top1_acc 0.8875 +top5_acc 0.9938 +2025-06-24 22:56:18,307 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:56:18,314 - pyskl - INFO - +mean_acc 0.8450 +2025-06-24 22:56:18,316 - pyskl - INFO - Epoch(val) [84][533] top1_acc: 0.8875, top5_acc: 0.9938, mean_class_accuracy: 0.8450 +2025-06-24 22:57:38,357 - pyskl - INFO - Epoch [85][100/1281] lr: 1.014e-02, eta: 9:26:28, time: 0.800, data_time: 0.196, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2658, loss: 0.2658 +2025-06-24 22:58:27,421 - pyskl - INFO - Epoch [85][200/1281] lr: 1.012e-02, eta: 9:25:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2455, loss: 0.2455 +2025-06-24 22:59:11,518 - pyskl - INFO - Epoch [85][300/1281] lr: 1.010e-02, eta: 9:25:17, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9988, loss_cls: 0.2789, loss: 0.2789 +2025-06-24 22:59:55,262 - pyskl - INFO - Epoch [85][400/1281] lr: 1.008e-02, eta: 9:24:40, time: 0.437, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9981, loss_cls: 0.3244, loss: 0.3244 +2025-06-24 23:00:25,379 - pyskl - INFO - Epoch [85][500/1281] lr: 1.006e-02, eta: 9:23:52, time: 0.301, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2850, loss: 0.2850 +2025-06-24 23:01:05,537 - pyskl - INFO - Epoch [85][600/1281] lr: 1.004e-02, eta: 9:23:11, time: 0.402, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 1.0000, loss_cls: 0.3181, loss: 0.3181 +2025-06-24 23:01:54,741 - pyskl - INFO - Epoch [85][700/1281] lr: 1.002e-02, eta: 9:22:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9988, loss_cls: 0.3060, loss: 0.3060 +2025-06-24 23:02:44,103 - pyskl - INFO - Epoch [85][800/1281] lr: 9.998e-03, eta: 9:22:05, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.3027, loss: 0.3027 +2025-06-24 23:03:33,277 - pyskl - INFO - Epoch [85][900/1281] lr: 9.978e-03, eta: 9:21:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9994, loss_cls: 0.3381, loss: 0.3381 +2025-06-24 23:04:22,560 - pyskl - INFO - Epoch [85][1000/1281] lr: 9.958e-03, eta: 9:20:58, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9981, loss_cls: 0.3644, loss: 0.3644 +2025-06-24 23:05:11,668 - pyskl - INFO - Epoch [85][1100/1281] lr: 9.937e-03, eta: 9:20:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3159, loss: 0.3159 +2025-06-24 23:06:00,594 - pyskl - INFO - Epoch [85][1200/1281] lr: 9.917e-03, eta: 9:19:51, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9988, loss_cls: 0.2793, loss: 0.2793 +2025-06-24 23:06:40,947 - pyskl - INFO - Saving checkpoint at 85 epochs +2025-06-24 23:07:40,910 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:07:40,977 - pyskl - INFO - +top1_acc 0.9049 +top5_acc 0.9923 +2025-06-24 23:07:40,977 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:07:40,985 - pyskl - INFO - +mean_acc 0.8724 +2025-06-24 23:07:40,989 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_83.pth was removed +2025-06-24 23:07:41,187 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_85.pth. +2025-06-24 23:07:41,187 - pyskl - INFO - Best top1_acc is 0.9049 at 85 epoch. +2025-06-24 23:07:41,190 - pyskl - INFO - Epoch(val) [85][533] top1_acc: 0.9049, top5_acc: 0.9923, mean_class_accuracy: 0.8724 +2025-06-24 23:09:00,901 - pyskl - INFO - Epoch [86][100/1281] lr: 9.881e-03, eta: 9:18:43, time: 0.797, data_time: 0.191, memory: 4083, top1_acc: 0.9437, top5_acc: 1.0000, loss_cls: 0.2981, loss: 0.2981 +2025-06-24 23:09:50,244 - pyskl - INFO - Epoch [86][200/1281] lr: 9.861e-03, eta: 9:18:10, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2886, loss: 0.2886 +2025-06-24 23:10:33,110 - pyskl - INFO - Epoch [86][300/1281] lr: 9.841e-03, eta: 9:17:31, time: 0.429, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2495, loss: 0.2495 +2025-06-24 23:11:19,238 - pyskl - INFO - Epoch [86][400/1281] lr: 9.821e-03, eta: 9:16:55, time: 0.461, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.2667, loss: 0.2667 +2025-06-24 23:11:47,052 - pyskl - INFO - Epoch [86][500/1281] lr: 9.801e-03, eta: 9:16:05, time: 0.278, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2634, loss: 0.2634 +2025-06-24 23:12:28,449 - pyskl - INFO - Epoch [86][600/1281] lr: 9.781e-03, eta: 9:15:26, time: 0.414, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.2988, loss: 0.2988 +2025-06-24 23:13:17,527 - pyskl - INFO - Epoch [86][700/1281] lr: 9.762e-03, eta: 9:14:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9981, loss_cls: 0.3410, loss: 0.3410 +2025-06-24 23:14:06,566 - pyskl - INFO - Epoch [86][800/1281] lr: 9.742e-03, eta: 9:14:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 1.0000, loss_cls: 0.3150, loss: 0.3150 +2025-06-24 23:14:55,607 - pyskl - INFO - Epoch [86][900/1281] lr: 9.722e-03, eta: 9:13:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9988, loss_cls: 0.3589, loss: 0.3589 +2025-06-24 23:15:44,727 - pyskl - INFO - Epoch [86][1000/1281] lr: 9.702e-03, eta: 9:13:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9994, loss_cls: 0.3882, loss: 0.3882 +2025-06-24 23:16:33,631 - pyskl - INFO - Epoch [86][1100/1281] lr: 9.682e-03, eta: 9:12:37, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9981, loss_cls: 0.3374, loss: 0.3374 +2025-06-24 23:17:22,509 - pyskl - INFO - Epoch [86][1200/1281] lr: 9.662e-03, eta: 9:12:03, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2928, loss: 0.2928 +2025-06-24 23:18:02,817 - pyskl - INFO - Saving checkpoint at 86 epochs +2025-06-24 23:19:01,919 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:19:01,976 - pyskl - INFO - +top1_acc 0.9006 +top5_acc 0.9928 +2025-06-24 23:19:01,976 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:19:01,983 - pyskl - INFO - +mean_acc 0.8635 +2025-06-24 23:19:01,985 - pyskl - INFO - Epoch(val) [86][533] top1_acc: 0.9006, top5_acc: 0.9928, mean_class_accuracy: 0.8635 +2025-06-24 23:20:22,103 - pyskl - INFO - Epoch [87][100/1281] lr: 9.626e-03, eta: 9:10:55, time: 0.801, data_time: 0.194, memory: 4083, top1_acc: 0.9506, top5_acc: 1.0000, loss_cls: 0.2929, loss: 0.2929 +2025-06-24 23:21:11,317 - pyskl - INFO - Epoch [87][200/1281] lr: 9.606e-03, eta: 9:10:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2650, loss: 0.2650 +2025-06-24 23:21:53,576 - pyskl - INFO - Epoch [87][300/1281] lr: 9.586e-03, eta: 9:09:42, time: 0.423, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.2769, loss: 0.2769 +2025-06-24 23:22:41,957 - pyskl - INFO - Epoch [87][400/1281] lr: 9.566e-03, eta: 9:09:08, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 1.0000, loss_cls: 0.2722, loss: 0.2722 +2025-06-24 23:23:07,966 - pyskl - INFO - Epoch [87][500/1281] lr: 9.546e-03, eta: 9:08:17, time: 0.260, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2431, loss: 0.2431 +2025-06-24 23:23:51,383 - pyskl - INFO - Epoch [87][600/1281] lr: 9.527e-03, eta: 9:07:39, time: 0.434, data_time: 0.001, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9981, loss_cls: 0.3093, loss: 0.3093 +2025-06-24 23:24:41,031 - pyskl - INFO - Epoch [87][700/1281] lr: 9.507e-03, eta: 9:07:05, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9988, loss_cls: 0.3080, loss: 0.3080 +2025-06-24 23:25:30,466 - pyskl - INFO - Epoch [87][800/1281] lr: 9.487e-03, eta: 9:06:31, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9988, loss_cls: 0.2850, loss: 0.2850 +2025-06-24 23:26:19,627 - pyskl - INFO - Epoch [87][900/1281] lr: 9.467e-03, eta: 9:05:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9981, loss_cls: 0.3288, loss: 0.3288 +2025-06-24 23:27:08,999 - pyskl - INFO - Epoch [87][1000/1281] lr: 9.447e-03, eta: 9:05:24, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9988, loss_cls: 0.3206, loss: 0.3206 +2025-06-24 23:27:57,965 - pyskl - INFO - Epoch [87][1100/1281] lr: 9.427e-03, eta: 9:04:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9969, loss_cls: 0.3689, loss: 0.3689 +2025-06-24 23:28:46,997 - pyskl - INFO - Epoch [87][1200/1281] lr: 9.408e-03, eta: 9:04:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9988, loss_cls: 0.3261, loss: 0.3261 +2025-06-24 23:29:27,435 - pyskl - INFO - Saving checkpoint at 87 epochs +2025-06-24 23:30:27,095 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:30:27,151 - pyskl - INFO - +top1_acc 0.9061 +top5_acc 0.9931 +2025-06-24 23:30:27,151 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:30:27,158 - pyskl - INFO - +mean_acc 0.8772 +2025-06-24 23:30:27,162 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_85.pth was removed +2025-06-24 23:30:27,365 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_87.pth. +2025-06-24 23:30:27,365 - pyskl - INFO - Best top1_acc is 0.9061 at 87 epoch. +2025-06-24 23:30:27,369 - pyskl - INFO - Epoch(val) [87][533] top1_acc: 0.9061, top5_acc: 0.9931, mean_class_accuracy: 0.8772 +2025-06-24 23:31:47,922 - pyskl - INFO - Epoch [88][100/1281] lr: 9.372e-03, eta: 9:03:07, time: 0.805, data_time: 0.187, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2604, loss: 0.2604 +2025-06-24 23:32:37,321 - pyskl - INFO - Epoch [88][200/1281] lr: 9.352e-03, eta: 9:02:33, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2723, loss: 0.2723 +2025-06-24 23:33:15,349 - pyskl - INFO - Epoch [88][300/1281] lr: 9.332e-03, eta: 9:01:51, time: 0.380, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.2824, loss: 0.2824 +2025-06-24 23:34:06,310 - pyskl - INFO - Epoch [88][400/1281] lr: 9.312e-03, eta: 9:01:18, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9988, loss_cls: 0.2721, loss: 0.2721 +2025-06-24 23:34:30,571 - pyskl - INFO - Epoch [88][500/1281] lr: 9.293e-03, eta: 9:00:26, time: 0.243, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9994, loss_cls: 0.3124, loss: 0.3124 +2025-06-24 23:35:15,310 - pyskl - INFO - Epoch [88][600/1281] lr: 9.273e-03, eta: 8:59:49, time: 0.447, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.3383, loss: 0.3383 +2025-06-24 23:36:04,160 - pyskl - INFO - Epoch [88][700/1281] lr: 9.253e-03, eta: 8:59:14, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3222, loss: 0.3222 +2025-06-24 23:36:53,265 - pyskl - INFO - Epoch [88][800/1281] lr: 9.233e-03, eta: 8:58:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 1.0000, loss_cls: 0.3181, loss: 0.3181 +2025-06-24 23:37:42,592 - pyskl - INFO - Epoch [88][900/1281] lr: 9.214e-03, eta: 8:58:06, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.3055, loss: 0.3055 +2025-06-24 23:38:32,186 - pyskl - INFO - Epoch [88][1000/1281] lr: 9.194e-03, eta: 8:57:32, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9988, loss_cls: 0.2717, loss: 0.2717 +2025-06-24 23:39:21,348 - pyskl - INFO - Epoch [88][1100/1281] lr: 9.174e-03, eta: 8:56:58, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9500, top5_acc: 1.0000, loss_cls: 0.2998, loss: 0.2998 +2025-06-24 23:40:10,819 - pyskl - INFO - Epoch [88][1200/1281] lr: 9.155e-03, eta: 8:56:24, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.2895, loss: 0.2895 +2025-06-24 23:40:51,064 - pyskl - INFO - Saving checkpoint at 88 epochs +2025-06-24 23:41:49,874 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:41:49,931 - pyskl - INFO - +top1_acc 0.9036 +top5_acc 0.9937 +2025-06-24 23:41:49,931 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:41:49,938 - pyskl - INFO - +mean_acc 0.8735 +2025-06-24 23:41:49,941 - pyskl - INFO - Epoch(val) [88][533] top1_acc: 0.9036, top5_acc: 0.9937, mean_class_accuracy: 0.8735 +2025-06-24 23:43:10,390 - pyskl - INFO - Epoch [89][100/1281] lr: 9.119e-03, eta: 8:55:15, time: 0.804, data_time: 0.192, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.2226, loss: 0.2226 +2025-06-24 23:43:59,535 - pyskl - INFO - Epoch [89][200/1281] lr: 9.099e-03, eta: 8:54:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2426, loss: 0.2426 +2025-06-24 23:44:36,543 - pyskl - INFO - Epoch [89][300/1281] lr: 9.080e-03, eta: 8:53:58, time: 0.370, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2362, loss: 0.2362 +2025-06-24 23:45:27,886 - pyskl - INFO - Epoch [89][400/1281] lr: 9.060e-03, eta: 8:53:25, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2123, loss: 0.2123 +2025-06-24 23:45:52,383 - pyskl - INFO - Epoch [89][500/1281] lr: 9.040e-03, eta: 8:52:33, time: 0.245, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 1.0000, loss_cls: 0.2678, loss: 0.2678 +2025-06-24 23:46:38,934 - pyskl - INFO - Epoch [89][600/1281] lr: 9.021e-03, eta: 8:51:57, time: 0.465, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.2958, loss: 0.2958 +2025-06-24 23:47:28,131 - pyskl - INFO - Epoch [89][700/1281] lr: 9.001e-03, eta: 8:51:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9988, loss_cls: 0.3164, loss: 0.3164 +2025-06-24 23:48:17,346 - pyskl - INFO - Epoch [89][800/1281] lr: 8.982e-03, eta: 8:50:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2770, loss: 0.2770 +2025-06-24 23:49:06,452 - pyskl - INFO - Epoch [89][900/1281] lr: 8.962e-03, eta: 8:50:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2686, loss: 0.2686 +2025-06-24 23:49:55,513 - pyskl - INFO - Epoch [89][1000/1281] lr: 8.942e-03, eta: 8:49:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 1.0000, loss_cls: 0.2815, loss: 0.2815 +2025-06-24 23:50:44,671 - pyskl - INFO - Epoch [89][1100/1281] lr: 8.923e-03, eta: 8:49:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9988, loss_cls: 0.3200, loss: 0.3200 +2025-06-24 23:51:33,825 - pyskl - INFO - Epoch [89][1200/1281] lr: 8.903e-03, eta: 8:48:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9994, loss_cls: 0.2762, loss: 0.2762 +2025-06-24 23:52:14,154 - pyskl - INFO - Saving checkpoint at 89 epochs +2025-06-24 23:53:13,119 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:53:13,184 - pyskl - INFO - +top1_acc 0.9026 +top5_acc 0.9934 +2025-06-24 23:53:13,184 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:53:13,191 - pyskl - INFO - +mean_acc 0.8732 +2025-06-24 23:53:13,193 - pyskl - INFO - Epoch(val) [89][533] top1_acc: 0.9026, top5_acc: 0.9934, mean_class_accuracy: 0.8732 +2025-06-24 23:54:33,163 - pyskl - INFO - Epoch [90][100/1281] lr: 8.868e-03, eta: 8:47:21, time: 0.800, data_time: 0.192, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2705, loss: 0.2705 +2025-06-24 23:55:22,960 - pyskl - INFO - Epoch [90][200/1281] lr: 8.848e-03, eta: 8:46:47, time: 0.498, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 1.0000, loss_cls: 0.2680, loss: 0.2680 +2025-06-24 23:55:57,971 - pyskl - INFO - Epoch [90][300/1281] lr: 8.829e-03, eta: 8:46:02, time: 0.350, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2467, loss: 0.2467 +2025-06-24 23:56:49,097 - pyskl - INFO - Epoch [90][400/1281] lr: 8.809e-03, eta: 8:45:29, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9988, loss_cls: 0.2854, loss: 0.2854 +2025-06-24 23:57:13,552 - pyskl - INFO - Epoch [90][500/1281] lr: 8.790e-03, eta: 8:44:38, time: 0.245, data_time: 0.001, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9981, loss_cls: 0.2457, loss: 0.2457 +2025-06-24 23:58:00,102 - pyskl - INFO - Epoch [90][600/1281] lr: 8.770e-03, eta: 8:44:01, time: 0.465, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2780, loss: 0.2780 +2025-06-24 23:58:49,389 - pyskl - INFO - Epoch [90][700/1281] lr: 8.751e-03, eta: 8:43:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2882, loss: 0.2882 +2025-06-24 23:59:38,692 - pyskl - INFO - Epoch [90][800/1281] lr: 8.731e-03, eta: 8:42:52, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9994, loss_cls: 0.3262, loss: 0.3262 +2025-06-25 00:00:27,654 - pyskl - INFO - Epoch [90][900/1281] lr: 8.712e-03, eta: 8:42:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9994, loss_cls: 0.2803, loss: 0.2803 +2025-06-25 00:01:16,822 - pyskl - INFO - Epoch [90][1000/1281] lr: 8.692e-03, eta: 8:41:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9988, loss_cls: 0.2661, loss: 0.2661 +2025-06-25 00:02:06,053 - pyskl - INFO - Epoch [90][1100/1281] lr: 8.673e-03, eta: 8:41:07, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9988, loss_cls: 0.2691, loss: 0.2691 +2025-06-25 00:02:55,346 - pyskl - INFO - Epoch [90][1200/1281] lr: 8.653e-03, eta: 8:40:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.2616, loss: 0.2616 +2025-06-25 00:03:35,620 - pyskl - INFO - Saving checkpoint at 90 epochs +2025-06-25 00:04:34,035 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:04:34,096 - pyskl - INFO - +top1_acc 0.8904 +top5_acc 0.9939 +2025-06-25 00:04:34,096 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:04:34,103 - pyskl - INFO - +mean_acc 0.8517 +2025-06-25 00:04:34,104 - pyskl - INFO - Epoch(val) [90][533] top1_acc: 0.8904, top5_acc: 0.9939, mean_class_accuracy: 0.8517 +2025-06-25 00:05:54,376 - pyskl - INFO - Epoch [91][100/1281] lr: 8.618e-03, eta: 8:39:24, time: 0.803, data_time: 0.190, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9994, loss_cls: 0.2846, loss: 0.2846 +2025-06-25 00:06:43,427 - pyskl - INFO - Epoch [91][200/1281] lr: 8.599e-03, eta: 8:38:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2301, loss: 0.2301 +2025-06-25 00:07:18,538 - pyskl - INFO - Epoch [91][300/1281] lr: 8.579e-03, eta: 8:38:04, time: 0.351, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2301, loss: 0.2301 +2025-06-25 00:08:09,597 - pyskl - INFO - Epoch [91][400/1281] lr: 8.560e-03, eta: 8:37:31, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2640, loss: 0.2640 +2025-06-25 00:08:34,576 - pyskl - INFO - Epoch [91][500/1281] lr: 8.540e-03, eta: 8:36:40, time: 0.250, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 1.0000, loss_cls: 0.2706, loss: 0.2706 +2025-06-25 00:09:22,039 - pyskl - INFO - Epoch [91][600/1281] lr: 8.521e-03, eta: 8:36:04, time: 0.475, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9988, loss_cls: 0.2850, loss: 0.2850 +2025-06-25 00:10:11,028 - pyskl - INFO - Epoch [91][700/1281] lr: 8.502e-03, eta: 8:35:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9988, loss_cls: 0.2872, loss: 0.2872 +2025-06-25 00:11:00,216 - pyskl - INFO - Epoch [91][800/1281] lr: 8.482e-03, eta: 8:34:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2516, loss: 0.2516 +2025-06-25 00:11:49,280 - pyskl - INFO - Epoch [91][900/1281] lr: 8.463e-03, eta: 8:34:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9994, loss_cls: 0.2575, loss: 0.2575 +2025-06-25 00:12:38,327 - pyskl - INFO - Epoch [91][1000/1281] lr: 8.444e-03, eta: 8:33:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2542, loss: 0.2542 +2025-06-25 00:13:27,254 - pyskl - INFO - Epoch [91][1100/1281] lr: 8.424e-03, eta: 8:33:08, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2964, loss: 0.2964 +2025-06-25 00:14:16,249 - pyskl - INFO - Epoch [91][1200/1281] lr: 8.405e-03, eta: 8:32:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 0.9981, loss_cls: 0.2322, loss: 0.2322 +2025-06-25 00:14:56,276 - pyskl - INFO - Saving checkpoint at 91 epochs +2025-06-25 00:15:55,307 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:15:55,377 - pyskl - INFO - +top1_acc 0.9014 +top5_acc 0.9925 +2025-06-25 00:15:55,377 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:15:55,385 - pyskl - INFO - +mean_acc 0.8636 +2025-06-25 00:15:55,387 - pyskl - INFO - Epoch(val) [91][533] top1_acc: 0.9014, top5_acc: 0.9925, mean_class_accuracy: 0.8636 +2025-06-25 00:17:14,349 - pyskl - INFO - Epoch [92][100/1281] lr: 8.370e-03, eta: 8:31:23, time: 0.790, data_time: 0.186, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2725, loss: 0.2725 +2025-06-25 00:18:03,332 - pyskl - INFO - Epoch [92][200/1281] lr: 8.351e-03, eta: 8:30:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2499, loss: 0.2499 +2025-06-25 00:18:38,766 - pyskl - INFO - Epoch [92][300/1281] lr: 8.332e-03, eta: 8:30:04, time: 0.354, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9988, loss_cls: 0.3065, loss: 0.3065 +2025-06-25 00:19:29,850 - pyskl - INFO - Epoch [92][400/1281] lr: 8.312e-03, eta: 8:29:30, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2431, loss: 0.2431 +2025-06-25 00:19:54,425 - pyskl - INFO - Epoch [92][500/1281] lr: 8.293e-03, eta: 8:28:39, time: 0.246, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2311, loss: 0.2311 +2025-06-25 00:20:41,271 - pyskl - INFO - Epoch [92][600/1281] lr: 8.274e-03, eta: 8:28:02, time: 0.468, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9981, loss_cls: 0.3027, loss: 0.3027 +2025-06-25 00:21:30,508 - pyskl - INFO - Epoch [92][700/1281] lr: 8.255e-03, eta: 8:27:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2503, loss: 0.2503 +2025-06-25 00:22:19,600 - pyskl - INFO - Epoch [92][800/1281] lr: 8.235e-03, eta: 8:26:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9981, loss_cls: 0.2998, loss: 0.2998 +2025-06-25 00:23:08,433 - pyskl - INFO - Epoch [92][900/1281] lr: 8.216e-03, eta: 8:26:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2114, loss: 0.2114 +2025-06-25 00:23:57,637 - pyskl - INFO - Epoch [92][1000/1281] lr: 8.197e-03, eta: 8:25:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9988, loss_cls: 0.2523, loss: 0.2523 +2025-06-25 00:24:46,746 - pyskl - INFO - Epoch [92][1100/1281] lr: 8.178e-03, eta: 8:25:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2469, loss: 0.2469 +2025-06-25 00:25:35,971 - pyskl - INFO - Epoch [92][1200/1281] lr: 8.159e-03, eta: 8:24:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9969, loss_cls: 0.3216, loss: 0.3216 +2025-06-25 00:26:16,455 - pyskl - INFO - Saving checkpoint at 92 epochs +2025-06-25 00:27:15,041 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:27:15,109 - pyskl - INFO - +top1_acc 0.9028 +top5_acc 0.9931 +2025-06-25 00:27:15,109 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:27:15,117 - pyskl - INFO - +mean_acc 0.8768 +2025-06-25 00:27:15,119 - pyskl - INFO - Epoch(val) [92][533] top1_acc: 0.9028, top5_acc: 0.9931, mean_class_accuracy: 0.8768 +2025-06-25 00:28:34,061 - pyskl - INFO - Epoch [93][100/1281] lr: 8.124e-03, eta: 8:23:20, time: 0.789, data_time: 0.190, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2186, loss: 0.2186 +2025-06-25 00:29:23,404 - pyskl - INFO - Epoch [93][200/1281] lr: 8.105e-03, eta: 8:22:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2393, loss: 0.2393 +2025-06-25 00:29:59,918 - pyskl - INFO - Epoch [93][300/1281] lr: 8.086e-03, eta: 8:22:01, time: 0.365, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9988, loss_cls: 0.2300, loss: 0.2300 +2025-06-25 00:30:51,147 - pyskl - INFO - Epoch [93][400/1281] lr: 8.067e-03, eta: 8:21:27, time: 0.512, data_time: 0.001, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9994, loss_cls: 0.2384, loss: 0.2384 +2025-06-25 00:31:15,696 - pyskl - INFO - Epoch [93][500/1281] lr: 8.047e-03, eta: 8:20:36, time: 0.245, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9994, loss_cls: 0.2637, loss: 0.2637 +2025-06-25 00:32:01,890 - pyskl - INFO - Epoch [93][600/1281] lr: 8.028e-03, eta: 8:19:59, time: 0.462, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2642, loss: 0.2642 +2025-06-25 00:32:51,192 - pyskl - INFO - Epoch [93][700/1281] lr: 8.009e-03, eta: 8:19:23, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2762, loss: 0.2762 +2025-06-25 00:33:39,942 - pyskl - INFO - Epoch [93][800/1281] lr: 7.990e-03, eta: 8:18:48, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2771, loss: 0.2771 +2025-06-25 00:34:28,755 - pyskl - INFO - Epoch [93][900/1281] lr: 7.971e-03, eta: 8:18:12, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9988, loss_cls: 0.2442, loss: 0.2442 +2025-06-25 00:35:17,843 - pyskl - INFO - Epoch [93][1000/1281] lr: 7.952e-03, eta: 8:17:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2340, loss: 0.2340 +2025-06-25 00:36:06,692 - pyskl - INFO - Epoch [93][1100/1281] lr: 7.933e-03, eta: 8:17:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9981, loss_cls: 0.3080, loss: 0.3080 +2025-06-25 00:36:55,541 - pyskl - INFO - Epoch [93][1200/1281] lr: 7.914e-03, eta: 8:16:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.2837, loss: 0.2837 +2025-06-25 00:37:35,498 - pyskl - INFO - Saving checkpoint at 93 epochs +2025-06-25 00:38:33,553 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:38:33,610 - pyskl - INFO - +top1_acc 0.9024 +top5_acc 0.9913 +2025-06-25 00:38:33,610 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:38:33,619 - pyskl - INFO - +mean_acc 0.8707 +2025-06-25 00:38:33,622 - pyskl - INFO - Epoch(val) [93][533] top1_acc: 0.9024, top5_acc: 0.9913, mean_class_accuracy: 0.8707 +2025-06-25 00:39:52,883 - pyskl - INFO - Epoch [94][100/1281] lr: 7.880e-03, eta: 8:15:15, time: 0.793, data_time: 0.186, memory: 4083, top1_acc: 0.9775, top5_acc: 0.9994, loss_cls: 0.1765, loss: 0.1765 +2025-06-25 00:40:41,880 - pyskl - INFO - Epoch [94][200/1281] lr: 7.861e-03, eta: 8:14:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9994, loss_cls: 0.2259, loss: 0.2259 +2025-06-25 00:41:19,920 - pyskl - INFO - Epoch [94][300/1281] lr: 7.842e-03, eta: 8:13:56, time: 0.380, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2124, loss: 0.2124 +2025-06-25 00:42:11,057 - pyskl - INFO - Epoch [94][400/1281] lr: 7.823e-03, eta: 8:13:22, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1984, loss: 0.1984 +2025-06-25 00:42:35,228 - pyskl - INFO - Epoch [94][500/1281] lr: 7.804e-03, eta: 8:12:31, time: 0.242, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9981, loss_cls: 0.2252, loss: 0.2252 +2025-06-25 00:43:20,937 - pyskl - INFO - Epoch [94][600/1281] lr: 7.785e-03, eta: 8:11:53, time: 0.457, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9988, loss_cls: 0.2635, loss: 0.2635 +2025-06-25 00:44:09,885 - pyskl - INFO - Epoch [94][700/1281] lr: 7.766e-03, eta: 8:11:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9988, loss_cls: 0.2868, loss: 0.2868 +2025-06-25 00:44:58,906 - pyskl - INFO - Epoch [94][800/1281] lr: 7.747e-03, eta: 8:10:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9988, loss_cls: 0.2314, loss: 0.2314 +2025-06-25 00:45:48,050 - pyskl - INFO - Epoch [94][900/1281] lr: 7.728e-03, eta: 8:10:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2180, loss: 0.2180 +2025-06-25 00:46:37,307 - pyskl - INFO - Epoch [94][1000/1281] lr: 7.709e-03, eta: 8:09:30, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9975, loss_cls: 0.2471, loss: 0.2471 +2025-06-25 00:47:26,219 - pyskl - INFO - Epoch [94][1100/1281] lr: 7.690e-03, eta: 8:08:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9988, loss_cls: 0.2049, loss: 0.2049 +2025-06-25 00:48:15,310 - pyskl - INFO - Epoch [94][1200/1281] lr: 7.672e-03, eta: 8:08:19, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.2707, loss: 0.2707 +2025-06-25 00:48:55,232 - pyskl - INFO - Saving checkpoint at 94 epochs +2025-06-25 00:49:54,557 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:49:54,626 - pyskl - INFO - +top1_acc 0.9155 +top5_acc 0.9950 +2025-06-25 00:49:54,626 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:49:54,635 - pyskl - INFO - +mean_acc 0.8824 +2025-06-25 00:49:54,640 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_87.pth was removed +2025-06-25 00:49:55,001 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_94.pth. +2025-06-25 00:49:55,002 - pyskl - INFO - Best top1_acc is 0.9155 at 94 epoch. +2025-06-25 00:49:55,004 - pyskl - INFO - Epoch(val) [94][533] top1_acc: 0.9155, top5_acc: 0.9950, mean_class_accuracy: 0.8824 +2025-06-25 00:51:13,318 - pyskl - INFO - Epoch [95][100/1281] lr: 7.637e-03, eta: 8:07:07, time: 0.783, data_time: 0.190, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2447, loss: 0.2447 +2025-06-25 00:52:02,230 - pyskl - INFO - Epoch [95][200/1281] lr: 7.619e-03, eta: 8:06:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2101, loss: 0.2101 +2025-06-25 00:52:39,181 - pyskl - INFO - Epoch [95][300/1281] lr: 7.600e-03, eta: 8:05:48, time: 0.370, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9988, loss_cls: 0.2750, loss: 0.2750 +2025-06-25 00:53:30,231 - pyskl - INFO - Epoch [95][400/1281] lr: 7.581e-03, eta: 8:05:14, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9988, loss_cls: 0.2443, loss: 0.2443 +2025-06-25 00:53:54,932 - pyskl - INFO - Epoch [95][500/1281] lr: 7.562e-03, eta: 8:04:23, time: 0.247, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9994, loss_cls: 0.2316, loss: 0.2316 +2025-06-25 00:54:41,401 - pyskl - INFO - Epoch [95][600/1281] lr: 7.543e-03, eta: 8:03:46, time: 0.465, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2453, loss: 0.2453 +2025-06-25 00:55:30,673 - pyskl - INFO - Epoch [95][700/1281] lr: 7.525e-03, eta: 8:03:10, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2459, loss: 0.2459 +2025-06-25 00:56:20,041 - pyskl - INFO - Epoch [95][800/1281] lr: 7.506e-03, eta: 8:02:34, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2795, loss: 0.2795 +2025-06-25 00:57:08,984 - pyskl - INFO - Epoch [95][900/1281] lr: 7.487e-03, eta: 8:01:58, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2755, loss: 0.2755 +2025-06-25 00:57:58,627 - pyskl - INFO - Epoch [95][1000/1281] lr: 7.468e-03, eta: 8:01:22, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2445, loss: 0.2445 +2025-06-25 00:58:47,686 - pyskl - INFO - Epoch [95][1100/1281] lr: 7.450e-03, eta: 8:00:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2352, loss: 0.2352 +2025-06-25 00:59:36,820 - pyskl - INFO - Epoch [95][1200/1281] lr: 7.431e-03, eta: 8:00:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.2624, loss: 0.2624 +2025-06-25 01:00:17,245 - pyskl - INFO - Saving checkpoint at 95 epochs +2025-06-25 01:01:14,938 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:01:14,995 - pyskl - INFO - +top1_acc 0.9128 +top5_acc 0.9924 +2025-06-25 01:01:14,995 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:01:15,002 - pyskl - INFO - +mean_acc 0.8876 +2025-06-25 01:01:15,004 - pyskl - INFO - Epoch(val) [95][533] top1_acc: 0.9128, top5_acc: 0.9924, mean_class_accuracy: 0.8876 +2025-06-25 01:02:34,566 - pyskl - INFO - Epoch [96][100/1281] lr: 7.397e-03, eta: 7:59:00, time: 0.796, data_time: 0.186, memory: 4083, top1_acc: 0.9744, top5_acc: 0.9994, loss_cls: 0.2101, loss: 0.2101 +2025-06-25 01:03:23,551 - pyskl - INFO - Epoch [96][200/1281] lr: 7.379e-03, eta: 7:58:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1847, loss: 0.1847 +2025-06-25 01:04:00,048 - pyskl - INFO - Epoch [96][300/1281] lr: 7.360e-03, eta: 7:57:40, time: 0.365, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2348, loss: 0.2348 +2025-06-25 01:04:51,211 - pyskl - INFO - Epoch [96][400/1281] lr: 7.341e-03, eta: 7:57:05, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2041, loss: 0.2041 +2025-06-25 01:05:15,661 - pyskl - INFO - Epoch [96][500/1281] lr: 7.323e-03, eta: 7:56:15, time: 0.244, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9994, loss_cls: 0.2044, loss: 0.2044 +2025-06-25 01:06:00,208 - pyskl - INFO - Epoch [96][600/1281] lr: 7.304e-03, eta: 7:55:36, time: 0.445, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 0.9994, loss_cls: 0.1806, loss: 0.1806 +2025-06-25 01:06:49,027 - pyskl - INFO - Epoch [96][700/1281] lr: 7.286e-03, eta: 7:55:00, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.2092, loss: 0.2092 +2025-06-25 01:07:37,558 - pyskl - INFO - Epoch [96][800/1281] lr: 7.267e-03, eta: 7:54:23, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9981, loss_cls: 0.2552, loss: 0.2552 +2025-06-25 01:08:26,441 - pyskl - INFO - Epoch [96][900/1281] lr: 7.249e-03, eta: 7:53:47, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2401, loss: 0.2401 +2025-06-25 01:09:15,045 - pyskl - INFO - Epoch [96][1000/1281] lr: 7.230e-03, eta: 7:53:11, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2435, loss: 0.2435 +2025-06-25 01:10:04,137 - pyskl - INFO - Epoch [96][1100/1281] lr: 7.211e-03, eta: 7:52:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 1.0000, loss_cls: 0.2928, loss: 0.2928 +2025-06-25 01:10:52,716 - pyskl - INFO - Epoch [96][1200/1281] lr: 7.193e-03, eta: 7:51:58, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9994, loss_cls: 0.2332, loss: 0.2332 +2025-06-25 01:11:33,020 - pyskl - INFO - Saving checkpoint at 96 epochs +2025-06-25 01:12:30,654 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:12:30,714 - pyskl - INFO - +top1_acc 0.9075 +top5_acc 0.9945 +2025-06-25 01:12:30,714 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:12:30,721 - pyskl - INFO - +mean_acc 0.8730 +2025-06-25 01:12:30,723 - pyskl - INFO - Epoch(val) [96][533] top1_acc: 0.9075, top5_acc: 0.9945, mean_class_accuracy: 0.8730 +2025-06-25 01:13:50,206 - pyskl - INFO - Epoch [97][100/1281] lr: 7.159e-03, eta: 7:50:47, time: 0.795, data_time: 0.188, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9981, loss_cls: 0.2174, loss: 0.2174 +2025-06-25 01:14:39,259 - pyskl - INFO - Epoch [97][200/1281] lr: 7.141e-03, eta: 7:50:11, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2003, loss: 0.2003 +2025-06-25 01:15:18,559 - pyskl - INFO - Epoch [97][300/1281] lr: 7.123e-03, eta: 7:49:29, time: 0.393, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1875, loss: 0.1875 +2025-06-25 01:16:09,836 - pyskl - INFO - Epoch [97][400/1281] lr: 7.104e-03, eta: 7:48:54, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9994, loss_cls: 0.1962, loss: 0.1962 +2025-06-25 01:16:33,631 - pyskl - INFO - Epoch [97][500/1281] lr: 7.086e-03, eta: 7:48:03, time: 0.238, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1941, loss: 0.1941 +2025-06-25 01:17:17,865 - pyskl - INFO - Epoch [97][600/1281] lr: 7.067e-03, eta: 7:47:24, time: 0.442, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 0.9994, loss_cls: 0.1747, loss: 0.1747 +2025-06-25 01:18:06,607 - pyskl - INFO - Epoch [97][700/1281] lr: 7.049e-03, eta: 7:46:48, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2175, loss: 0.2175 +2025-06-25 01:18:55,148 - pyskl - INFO - Epoch [97][800/1281] lr: 7.030e-03, eta: 7:46:11, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9981, loss_cls: 0.2233, loss: 0.2233 +2025-06-25 01:19:44,489 - pyskl - INFO - Epoch [97][900/1281] lr: 7.012e-03, eta: 7:45:35, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2277, loss: 0.2277 +2025-06-25 01:20:33,717 - pyskl - INFO - Epoch [97][1000/1281] lr: 6.994e-03, eta: 7:44:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2562, loss: 0.2562 +2025-06-25 01:21:23,054 - pyskl - INFO - Epoch [97][1100/1281] lr: 6.975e-03, eta: 7:44:22, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9994, loss_cls: 0.2669, loss: 0.2669 +2025-06-25 01:22:11,635 - pyskl - INFO - Epoch [97][1200/1281] lr: 6.957e-03, eta: 7:43:45, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9994, loss_cls: 0.2578, loss: 0.2578 +2025-06-25 01:22:51,850 - pyskl - INFO - Saving checkpoint at 97 epochs +2025-06-25 01:23:49,601 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:23:49,656 - pyskl - INFO - +top1_acc 0.9167 +top5_acc 0.9951 +2025-06-25 01:23:49,657 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:23:49,663 - pyskl - INFO - +mean_acc 0.8861 +2025-06-25 01:23:49,667 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_94.pth was removed +2025-06-25 01:23:49,850 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_97.pth. +2025-06-25 01:23:49,850 - pyskl - INFO - Best top1_acc is 0.9167 at 97 epoch. +2025-06-25 01:23:49,853 - pyskl - INFO - Epoch(val) [97][533] top1_acc: 0.9167, top5_acc: 0.9951, mean_class_accuracy: 0.8861 +2025-06-25 01:25:08,197 - pyskl - INFO - Epoch [98][100/1281] lr: 6.924e-03, eta: 7:42:33, time: 0.783, data_time: 0.183, memory: 4083, top1_acc: 0.9719, top5_acc: 0.9994, loss_cls: 0.1880, loss: 0.1880 +2025-06-25 01:25:57,020 - pyskl - INFO - Epoch [98][200/1281] lr: 6.906e-03, eta: 7:41:57, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 0.9994, loss_cls: 0.1952, loss: 0.1952 +2025-06-25 01:26:37,598 - pyskl - INFO - Epoch [98][300/1281] lr: 6.887e-03, eta: 7:41:16, time: 0.406, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 0.9994, loss_cls: 0.1795, loss: 0.1795 +2025-06-25 01:27:28,751 - pyskl - INFO - Epoch [98][400/1281] lr: 6.869e-03, eta: 7:40:40, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1821, loss: 0.1821 +2025-06-25 01:27:52,199 - pyskl - INFO - Epoch [98][500/1281] lr: 6.851e-03, eta: 7:39:50, time: 0.234, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2115, loss: 0.2115 +2025-06-25 01:28:35,735 - pyskl - INFO - Epoch [98][600/1281] lr: 6.833e-03, eta: 7:39:10, time: 0.435, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9994, loss_cls: 0.2005, loss: 0.2005 +2025-06-25 01:29:24,444 - pyskl - INFO - Epoch [98][700/1281] lr: 6.814e-03, eta: 7:38:34, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1991, loss: 0.1991 +2025-06-25 01:30:12,979 - pyskl - INFO - Epoch [98][800/1281] lr: 6.796e-03, eta: 7:37:57, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2476, loss: 0.2476 +2025-06-25 01:31:02,053 - pyskl - INFO - Epoch [98][900/1281] lr: 6.778e-03, eta: 7:37:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2095, loss: 0.2095 +2025-06-25 01:31:50,758 - pyskl - INFO - Epoch [98][1000/1281] lr: 6.760e-03, eta: 7:36:43, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.2751, loss: 0.2751 +2025-06-25 01:32:39,696 - pyskl - INFO - Epoch [98][1100/1281] lr: 6.742e-03, eta: 7:36:07, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2516, loss: 0.2516 +2025-06-25 01:33:28,622 - pyskl - INFO - Epoch [98][1200/1281] lr: 6.724e-03, eta: 7:35:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9994, loss_cls: 0.2334, loss: 0.2334 +2025-06-25 01:34:08,700 - pyskl - INFO - Saving checkpoint at 98 epochs +2025-06-25 01:35:07,473 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:35:07,545 - pyskl - INFO - +top1_acc 0.9147 +top5_acc 0.9961 +2025-06-25 01:35:07,545 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:35:07,553 - pyskl - INFO - +mean_acc 0.8769 +2025-06-25 01:35:07,555 - pyskl - INFO - Epoch(val) [98][533] top1_acc: 0.9147, top5_acc: 0.9961, mean_class_accuracy: 0.8769 +2025-06-25 01:36:27,876 - pyskl - INFO - Epoch [99][100/1281] lr: 6.691e-03, eta: 7:34:19, time: 0.803, data_time: 0.188, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2319, loss: 0.2319 +2025-06-25 01:37:16,940 - pyskl - INFO - Epoch [99][200/1281] lr: 6.673e-03, eta: 7:33:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2125, loss: 0.2125 +2025-06-25 01:37:56,372 - pyskl - INFO - Epoch [99][300/1281] lr: 6.655e-03, eta: 7:33:00, time: 0.394, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 0.9994, loss_cls: 0.1776, loss: 0.1776 +2025-06-25 01:38:47,332 - pyskl - INFO - Epoch [99][400/1281] lr: 6.637e-03, eta: 7:32:25, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2070, loss: 0.2070 +2025-06-25 01:39:11,062 - pyskl - INFO - Epoch [99][500/1281] lr: 6.619e-03, eta: 7:31:35, time: 0.237, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2027, loss: 0.2027 +2025-06-25 01:39:54,273 - pyskl - INFO - Epoch [99][600/1281] lr: 6.601e-03, eta: 7:30:55, time: 0.432, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9988, loss_cls: 0.2540, loss: 0.2540 +2025-06-25 01:40:43,447 - pyskl - INFO - Epoch [99][700/1281] lr: 6.583e-03, eta: 7:30:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9988, loss_cls: 0.2267, loss: 0.2267 +2025-06-25 01:41:32,514 - pyskl - INFO - Epoch [99][800/1281] lr: 6.565e-03, eta: 7:29:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9988, loss_cls: 0.2198, loss: 0.2198 +2025-06-25 01:42:21,763 - pyskl - INFO - Epoch [99][900/1281] lr: 6.547e-03, eta: 7:29:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2585, loss: 0.2585 +2025-06-25 01:43:10,892 - pyskl - INFO - Epoch [99][1000/1281] lr: 6.529e-03, eta: 7:28:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 1.0000, loss_cls: 0.2922, loss: 0.2922 +2025-06-25 01:43:59,893 - pyskl - INFO - Epoch [99][1100/1281] lr: 6.511e-03, eta: 7:27:51, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2114, loss: 0.2114 +2025-06-25 01:44:48,790 - pyskl - INFO - Epoch [99][1200/1281] lr: 6.493e-03, eta: 7:27:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2049, loss: 0.2049 +2025-06-25 01:45:29,113 - pyskl - INFO - Saving checkpoint at 99 epochs +2025-06-25 01:46:27,022 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:46:27,078 - pyskl - INFO - +top1_acc 0.9015 +top5_acc 0.9926 +2025-06-25 01:46:27,078 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:46:27,085 - pyskl - INFO - +mean_acc 0.8712 +2025-06-25 01:46:27,087 - pyskl - INFO - Epoch(val) [99][533] top1_acc: 0.9015, top5_acc: 0.9926, mean_class_accuracy: 0.8712 +2025-06-25 01:47:45,693 - pyskl - INFO - Epoch [100][100/1281] lr: 6.460e-03, eta: 7:26:02, time: 0.786, data_time: 0.189, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9988, loss_cls: 0.2110, loss: 0.2110 +2025-06-25 01:48:34,421 - pyskl - INFO - Epoch [100][200/1281] lr: 6.442e-03, eta: 7:25:25, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9994, loss_cls: 0.2047, loss: 0.2047 +2025-06-25 01:49:16,418 - pyskl - INFO - Epoch [100][300/1281] lr: 6.425e-03, eta: 7:24:44, time: 0.420, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9994, loss_cls: 0.1937, loss: 0.1937 +2025-06-25 01:50:05,916 - pyskl - INFO - Epoch [100][400/1281] lr: 6.407e-03, eta: 7:24:08, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.1947, loss: 0.1947 +2025-06-25 01:50:30,874 - pyskl - INFO - Epoch [100][500/1281] lr: 6.389e-03, eta: 7:23:18, time: 0.250, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2215, loss: 0.2215 +2025-06-25 01:51:12,719 - pyskl - INFO - Epoch [100][600/1281] lr: 6.371e-03, eta: 7:22:38, time: 0.418, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2090, loss: 0.2090 +2025-06-25 01:52:01,526 - pyskl - INFO - Epoch [100][700/1281] lr: 6.353e-03, eta: 7:22:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2207, loss: 0.2207 +2025-06-25 01:52:50,349 - pyskl - INFO - Epoch [100][800/1281] lr: 6.336e-03, eta: 7:21:23, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9981, loss_cls: 0.2791, loss: 0.2791 +2025-06-25 01:53:39,636 - pyskl - INFO - Epoch [100][900/1281] lr: 6.318e-03, eta: 7:20:47, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.2094, loss: 0.2094 +2025-06-25 01:54:29,006 - pyskl - INFO - Epoch [100][1000/1281] lr: 6.300e-03, eta: 7:20:10, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9988, loss_cls: 0.2485, loss: 0.2485 +2025-06-25 01:55:17,976 - pyskl - INFO - Epoch [100][1100/1281] lr: 6.282e-03, eta: 7:19:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2076, loss: 0.2076 +2025-06-25 01:56:06,626 - pyskl - INFO - Epoch [100][1200/1281] lr: 6.265e-03, eta: 7:18:55, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.1903, loss: 0.1903 +2025-06-25 01:56:47,069 - pyskl - INFO - Saving checkpoint at 100 epochs +2025-06-25 01:57:45,389 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:57:45,445 - pyskl - INFO - +top1_acc 0.8948 +top5_acc 0.9920 +2025-06-25 01:57:45,445 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:57:45,452 - pyskl - INFO - +mean_acc 0.8598 +2025-06-25 01:57:45,453 - pyskl - INFO - Epoch(val) [100][533] top1_acc: 0.8948, top5_acc: 0.9920, mean_class_accuracy: 0.8598 +2025-06-25 01:59:04,711 - pyskl - INFO - Epoch [101][100/1281] lr: 6.232e-03, eta: 7:17:44, time: 0.793, data_time: 0.187, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9981, loss_cls: 0.2336, loss: 0.2336 +2025-06-25 01:59:53,458 - pyskl - INFO - Epoch [101][200/1281] lr: 6.215e-03, eta: 7:17:06, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9994, loss_cls: 0.1811, loss: 0.1811 +2025-06-25 02:00:35,763 - pyskl - INFO - Epoch [101][300/1281] lr: 6.197e-03, eta: 7:16:26, time: 0.423, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2115, loss: 0.2115 +2025-06-25 02:01:23,296 - pyskl - INFO - Epoch [101][400/1281] lr: 6.180e-03, eta: 7:15:48, time: 0.475, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 0.9994, loss_cls: 0.1851, loss: 0.1851 +2025-06-25 02:01:49,792 - pyskl - INFO - Epoch [101][500/1281] lr: 6.162e-03, eta: 7:15:00, time: 0.265, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1793, loss: 0.1793 +2025-06-25 02:02:31,447 - pyskl - INFO - Epoch [101][600/1281] lr: 6.144e-03, eta: 7:14:19, time: 0.417, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1648, loss: 0.1648 +2025-06-25 02:03:20,601 - pyskl - INFO - Epoch [101][700/1281] lr: 6.127e-03, eta: 7:13:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1835, loss: 0.1835 +2025-06-25 02:04:09,449 - pyskl - INFO - Epoch [101][800/1281] lr: 6.109e-03, eta: 7:13:05, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1707, loss: 0.1707 +2025-06-25 02:04:58,641 - pyskl - INFO - Epoch [101][900/1281] lr: 6.092e-03, eta: 7:12:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 0.9994, loss_cls: 0.2009, loss: 0.2009 +2025-06-25 02:05:47,611 - pyskl - INFO - Epoch [101][1000/1281] lr: 6.074e-03, eta: 7:11:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.1984, loss: 0.1984 +2025-06-25 02:06:36,713 - pyskl - INFO - Epoch [101][1100/1281] lr: 6.057e-03, eta: 7:11:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.2099, loss: 0.2099 +2025-06-25 02:07:25,989 - pyskl - INFO - Epoch [101][1200/1281] lr: 6.039e-03, eta: 7:10:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 0.9994, loss_cls: 0.1824, loss: 0.1824 +2025-06-25 02:08:06,101 - pyskl - INFO - Saving checkpoint at 101 epochs +2025-06-25 02:09:04,425 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:09:04,480 - pyskl - INFO - +top1_acc 0.9141 +top5_acc 0.9942 +2025-06-25 02:09:04,480 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:09:04,486 - pyskl - INFO - +mean_acc 0.8878 +2025-06-25 02:09:04,488 - pyskl - INFO - Epoch(val) [101][533] top1_acc: 0.9141, top5_acc: 0.9942, mean_class_accuracy: 0.8878 +2025-06-25 02:10:22,863 - pyskl - INFO - Epoch [102][100/1281] lr: 6.007e-03, eta: 7:09:23, time: 0.784, data_time: 0.189, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2599, loss: 0.2599 +2025-06-25 02:11:11,731 - pyskl - INFO - Epoch [102][200/1281] lr: 5.990e-03, eta: 7:08:46, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1610, loss: 0.1610 +2025-06-25 02:11:55,330 - pyskl - INFO - Epoch [102][300/1281] lr: 5.972e-03, eta: 7:08:06, time: 0.436, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 0.9994, loss_cls: 0.1648, loss: 0.1648 +2025-06-25 02:12:41,095 - pyskl - INFO - Epoch [102][400/1281] lr: 5.955e-03, eta: 7:07:27, time: 0.458, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.1987, loss: 0.1987 +2025-06-25 02:13:08,757 - pyskl - INFO - Epoch [102][500/1281] lr: 5.938e-03, eta: 7:06:40, time: 0.277, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2046, loss: 0.2046 +2025-06-25 02:13:50,981 - pyskl - INFO - Epoch [102][600/1281] lr: 5.920e-03, eta: 7:05:59, time: 0.422, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1490, loss: 0.1490 +2025-06-25 02:14:40,021 - pyskl - INFO - Epoch [102][700/1281] lr: 5.903e-03, eta: 7:05:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.1950, loss: 0.1950 +2025-06-25 02:15:28,910 - pyskl - INFO - Epoch [102][800/1281] lr: 5.886e-03, eta: 7:04:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 0.9988, loss_cls: 0.1907, loss: 0.1907 +2025-06-25 02:16:17,995 - pyskl - INFO - Epoch [102][900/1281] lr: 5.868e-03, eta: 7:04:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2122, loss: 0.2122 +2025-06-25 02:17:06,956 - pyskl - INFO - Epoch [102][1000/1281] lr: 5.851e-03, eta: 7:03:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 0.9994, loss_cls: 0.2046, loss: 0.2046 +2025-06-25 02:17:55,713 - pyskl - INFO - Epoch [102][1100/1281] lr: 5.834e-03, eta: 7:02:52, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.2058, loss: 0.2058 +2025-06-25 02:18:44,513 - pyskl - INFO - Epoch [102][1200/1281] lr: 5.816e-03, eta: 7:02:14, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2315, loss: 0.2315 +2025-06-25 02:19:24,378 - pyskl - INFO - Saving checkpoint at 102 epochs +2025-06-25 02:20:21,931 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:20:21,985 - pyskl - INFO - +top1_acc 0.9102 +top5_acc 0.9938 +2025-06-25 02:20:21,985 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:20:21,993 - pyskl - INFO - +mean_acc 0.8865 +2025-06-25 02:20:21,995 - pyskl - INFO - Epoch(val) [102][533] top1_acc: 0.9102, top5_acc: 0.9938, mean_class_accuracy: 0.8865 +2025-06-25 02:21:39,419 - pyskl - INFO - Epoch [103][100/1281] lr: 5.785e-03, eta: 7:01:01, time: 0.774, data_time: 0.190, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1840, loss: 0.1840 +2025-06-25 02:22:28,168 - pyskl - INFO - Epoch [103][200/1281] lr: 5.768e-03, eta: 7:00:24, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 0.9994, loss_cls: 0.1631, loss: 0.1631 +2025-06-25 02:23:12,591 - pyskl - INFO - Epoch [103][300/1281] lr: 5.751e-03, eta: 6:59:44, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.1851, loss: 0.1851 +2025-06-25 02:23:54,238 - pyskl - INFO - Epoch [103][400/1281] lr: 5.733e-03, eta: 6:59:03, time: 0.416, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9994, loss_cls: 0.2290, loss: 0.2290 +2025-06-25 02:24:25,833 - pyskl - INFO - Epoch [103][500/1281] lr: 5.716e-03, eta: 6:58:18, time: 0.316, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1752, loss: 0.1752 +2025-06-25 02:25:05,709 - pyskl - INFO - Epoch [103][600/1281] lr: 5.699e-03, eta: 6:57:36, time: 0.399, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1502, loss: 0.1502 +2025-06-25 02:25:54,764 - pyskl - INFO - Epoch [103][700/1281] lr: 5.682e-03, eta: 6:56:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 0.9994, loss_cls: 0.1697, loss: 0.1697 +2025-06-25 02:26:43,654 - pyskl - INFO - Epoch [103][800/1281] lr: 5.665e-03, eta: 6:56:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.1800, loss: 0.1800 +2025-06-25 02:27:32,326 - pyskl - INFO - Epoch [103][900/1281] lr: 5.648e-03, eta: 6:55:43, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 0.9994, loss_cls: 0.1595, loss: 0.1595 +2025-06-25 02:28:20,919 - pyskl - INFO - Epoch [103][1000/1281] lr: 5.631e-03, eta: 6:55:05, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9988, loss_cls: 0.1985, loss: 0.1985 +2025-06-25 02:29:09,851 - pyskl - INFO - Epoch [103][1100/1281] lr: 5.614e-03, eta: 6:54:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1988, loss: 0.1988 +2025-06-25 02:29:58,904 - pyskl - INFO - Epoch [103][1200/1281] lr: 5.597e-03, eta: 6:53:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1726, loss: 0.1726 +2025-06-25 02:30:38,810 - pyskl - INFO - Saving checkpoint at 103 epochs +2025-06-25 02:31:36,627 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:31:36,684 - pyskl - INFO - +top1_acc 0.9046 +top5_acc 0.9941 +2025-06-25 02:31:36,684 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:31:36,692 - pyskl - INFO - +mean_acc 0.8732 +2025-06-25 02:31:36,694 - pyskl - INFO - Epoch(val) [103][533] top1_acc: 0.9046, top5_acc: 0.9941, mean_class_accuracy: 0.8732 +2025-06-25 02:32:54,621 - pyskl - INFO - Epoch [104][100/1281] lr: 5.566e-03, eta: 6:52:37, time: 0.779, data_time: 0.182, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1711, loss: 0.1711 +2025-06-25 02:33:43,951 - pyskl - INFO - Epoch [104][200/1281] lr: 5.549e-03, eta: 6:52:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 0.9994, loss_cls: 0.1517, loss: 0.1517 +2025-06-25 02:34:31,470 - pyskl - INFO - Epoch [104][300/1281] lr: 5.532e-03, eta: 6:51:21, time: 0.475, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1310, loss: 0.1310 +2025-06-25 02:35:05,737 - pyskl - INFO - Epoch [104][400/1281] lr: 5.515e-03, eta: 6:50:37, time: 0.343, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1560, loss: 0.1560 +2025-06-25 02:35:44,494 - pyskl - INFO - Epoch [104][500/1281] lr: 5.498e-03, eta: 6:49:55, time: 0.388, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1537, loss: 0.1537 +2025-06-25 02:36:19,926 - pyskl - INFO - Epoch [104][600/1281] lr: 5.481e-03, eta: 6:49:11, time: 0.354, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.2204, loss: 0.2204 +2025-06-25 02:37:08,689 - pyskl - INFO - Epoch [104][700/1281] lr: 5.464e-03, eta: 6:48:33, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.1789, loss: 0.1789 +2025-06-25 02:37:57,752 - pyskl - INFO - Epoch [104][800/1281] lr: 5.447e-03, eta: 6:47:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.1877, loss: 0.1877 +2025-06-25 02:38:46,603 - pyskl - INFO - Epoch [104][900/1281] lr: 5.430e-03, eta: 6:47:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1694, loss: 0.1694 +2025-06-25 02:39:35,168 - pyskl - INFO - Epoch [104][1000/1281] lr: 5.413e-03, eta: 6:46:40, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1605, loss: 0.1605 +2025-06-25 02:40:23,984 - pyskl - INFO - Epoch [104][1100/1281] lr: 5.397e-03, eta: 6:46:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1710, loss: 0.1710 +2025-06-25 02:41:13,102 - pyskl - INFO - Epoch [104][1200/1281] lr: 5.380e-03, eta: 6:45:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 0.9994, loss_cls: 0.1476, loss: 0.1476 +2025-06-25 02:41:53,286 - pyskl - INFO - Saving checkpoint at 104 epochs +2025-06-25 02:42:51,398 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:42:51,463 - pyskl - INFO - +top1_acc 0.9137 +top5_acc 0.9940 +2025-06-25 02:42:51,464 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:42:51,471 - pyskl - INFO - +mean_acc 0.8847 +2025-06-25 02:42:51,472 - pyskl - INFO - Epoch(val) [104][533] top1_acc: 0.9137, top5_acc: 0.9940, mean_class_accuracy: 0.8847 +2025-06-25 02:44:09,982 - pyskl - INFO - Epoch [105][100/1281] lr: 5.349e-03, eta: 6:44:11, time: 0.785, data_time: 0.184, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1449, loss: 0.1449 +2025-06-25 02:44:59,426 - pyskl - INFO - Epoch [105][200/1281] lr: 5.333e-03, eta: 6:43:34, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1384, loss: 0.1384 +2025-06-25 02:45:48,562 - pyskl - INFO - Epoch [105][300/1281] lr: 5.316e-03, eta: 6:42:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1419, loss: 0.1419 +2025-06-25 02:46:19,022 - pyskl - INFO - Epoch [105][400/1281] lr: 5.299e-03, eta: 6:42:10, time: 0.305, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9988, loss_cls: 0.2091, loss: 0.2091 +2025-06-25 02:47:03,959 - pyskl - INFO - Epoch [105][500/1281] lr: 5.283e-03, eta: 6:41:30, time: 0.449, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1626, loss: 0.1626 +2025-06-25 02:47:36,933 - pyskl - INFO - Epoch [105][600/1281] lr: 5.266e-03, eta: 6:40:45, time: 0.330, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1405, loss: 0.1405 +2025-06-25 02:48:25,968 - pyskl - INFO - Epoch [105][700/1281] lr: 5.249e-03, eta: 6:40:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1557, loss: 0.1557 +2025-06-25 02:49:14,668 - pyskl - INFO - Epoch [105][800/1281] lr: 5.233e-03, eta: 6:39:30, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 0.9994, loss_cls: 0.1867, loss: 0.1867 +2025-06-25 02:50:03,503 - pyskl - INFO - Epoch [105][900/1281] lr: 5.216e-03, eta: 6:38:52, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1594, loss: 0.1594 +2025-06-25 02:50:52,465 - pyskl - INFO - Epoch [105][1000/1281] lr: 5.199e-03, eta: 6:38:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1560, loss: 0.1560 +2025-06-25 02:51:41,293 - pyskl - INFO - Epoch [105][1100/1281] lr: 5.183e-03, eta: 6:37:36, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 0.9994, loss_cls: 0.1938, loss: 0.1938 +2025-06-25 02:52:29,994 - pyskl - INFO - Epoch [105][1200/1281] lr: 5.166e-03, eta: 6:36:58, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1673, loss: 0.1673 +2025-06-25 02:53:10,117 - pyskl - INFO - Saving checkpoint at 105 epochs +2025-06-25 02:54:08,493 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:54:08,547 - pyskl - INFO - +top1_acc 0.9140 +top5_acc 0.9946 +2025-06-25 02:54:08,547 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:54:08,553 - pyskl - INFO - +mean_acc 0.8866 +2025-06-25 02:54:08,555 - pyskl - INFO - Epoch(val) [105][533] top1_acc: 0.9140, top5_acc: 0.9946, mean_class_accuracy: 0.8866 +2025-06-25 02:55:28,607 - pyskl - INFO - Epoch [106][100/1281] lr: 5.136e-03, eta: 6:35:45, time: 0.800, data_time: 0.187, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1558, loss: 0.1558 +2025-06-25 02:56:17,441 - pyskl - INFO - Epoch [106][200/1281] lr: 5.120e-03, eta: 6:35:07, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1341, loss: 0.1341 +2025-06-25 02:57:06,123 - pyskl - INFO - Epoch [106][300/1281] lr: 5.103e-03, eta: 6:34:29, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1132, loss: 0.1132 +2025-06-25 02:57:36,487 - pyskl - INFO - Epoch [106][400/1281] lr: 5.087e-03, eta: 6:33:43, time: 0.304, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1193, loss: 0.1193 +2025-06-25 02:58:21,796 - pyskl - INFO - Epoch [106][500/1281] lr: 5.070e-03, eta: 6:33:04, time: 0.453, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1120, loss: 0.1120 +2025-06-25 02:58:53,550 - pyskl - INFO - Epoch [106][600/1281] lr: 5.054e-03, eta: 6:32:18, time: 0.318, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1240, loss: 0.1240 +2025-06-25 02:59:42,396 - pyskl - INFO - Epoch [106][700/1281] lr: 5.038e-03, eta: 6:31:40, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1743, loss: 0.1743 +2025-06-25 03:00:31,767 - pyskl - INFO - Epoch [106][800/1281] lr: 5.021e-03, eta: 6:31:02, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.1551, loss: 0.1551 +2025-06-25 03:01:20,877 - pyskl - INFO - Epoch [106][900/1281] lr: 5.005e-03, eta: 6:30:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1252, loss: 0.1252 +2025-06-25 03:02:10,038 - pyskl - INFO - Epoch [106][1000/1281] lr: 4.988e-03, eta: 6:29:46, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 0.9988, loss_cls: 0.1602, loss: 0.1602 +2025-06-25 03:02:59,308 - pyskl - INFO - Epoch [106][1100/1281] lr: 4.972e-03, eta: 6:29:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1869, loss: 0.1869 +2025-06-25 03:03:48,233 - pyskl - INFO - Epoch [106][1200/1281] lr: 4.956e-03, eta: 6:28:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1827, loss: 0.1827 +2025-06-25 03:04:28,436 - pyskl - INFO - Saving checkpoint at 106 epochs +2025-06-25 03:05:26,163 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:05:26,228 - pyskl - INFO - +top1_acc 0.9101 +top5_acc 0.9959 +2025-06-25 03:05:26,228 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:05:26,238 - pyskl - INFO - +mean_acc 0.8822 +2025-06-25 03:05:26,241 - pyskl - INFO - Epoch(val) [106][533] top1_acc: 0.9101, top5_acc: 0.9959, mean_class_accuracy: 0.8822 +2025-06-25 03:06:45,069 - pyskl - INFO - Epoch [107][100/1281] lr: 4.926e-03, eta: 6:27:17, time: 0.788, data_time: 0.188, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1482, loss: 0.1482 +2025-06-25 03:07:34,093 - pyskl - INFO - Epoch [107][200/1281] lr: 4.910e-03, eta: 6:26:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 0.9994, loss_cls: 0.1701, loss: 0.1701 +2025-06-25 03:08:23,479 - pyskl - INFO - Epoch [107][300/1281] lr: 4.894e-03, eta: 6:26:01, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.1525, loss: 0.1525 +2025-06-25 03:08:52,248 - pyskl - INFO - Epoch [107][400/1281] lr: 4.878e-03, eta: 6:25:15, time: 0.288, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 0.9994, loss_cls: 0.1447, loss: 0.1447 +2025-06-25 03:09:43,042 - pyskl - INFO - Epoch [107][500/1281] lr: 4.862e-03, eta: 6:24:37, time: 0.508, data_time: 0.001, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1562, loss: 0.1562 +2025-06-25 03:10:12,676 - pyskl - INFO - Epoch [107][600/1281] lr: 4.845e-03, eta: 6:23:51, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1212, loss: 0.1212 +2025-06-25 03:11:01,268 - pyskl - INFO - Epoch [107][700/1281] lr: 4.829e-03, eta: 6:23:13, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1310, loss: 0.1310 +2025-06-25 03:11:50,350 - pyskl - INFO - Epoch [107][800/1281] lr: 4.813e-03, eta: 6:22:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1256, loss: 0.1256 +2025-06-25 03:12:38,775 - pyskl - INFO - Epoch [107][900/1281] lr: 4.797e-03, eta: 6:21:56, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1058, loss: 0.1058 +2025-06-25 03:13:27,708 - pyskl - INFO - Epoch [107][1000/1281] lr: 4.781e-03, eta: 6:21:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1373, loss: 0.1373 +2025-06-25 03:14:16,393 - pyskl - INFO - Epoch [107][1100/1281] lr: 4.765e-03, eta: 6:20:40, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1248, loss: 0.1248 +2025-06-25 03:15:05,040 - pyskl - INFO - Epoch [107][1200/1281] lr: 4.749e-03, eta: 6:20:01, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1292, loss: 0.1292 +2025-06-25 03:15:45,028 - pyskl - INFO - Saving checkpoint at 107 epochs +2025-06-25 03:16:43,105 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:16:43,159 - pyskl - INFO - +top1_acc 0.9248 +top5_acc 0.9959 +2025-06-25 03:16:43,159 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:16:43,165 - pyskl - INFO - +mean_acc 0.8960 +2025-06-25 03:16:43,170 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_97.pth was removed +2025-06-25 03:16:43,333 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_107.pth. +2025-06-25 03:16:43,333 - pyskl - INFO - Best top1_acc is 0.9248 at 107 epoch. +2025-06-25 03:16:43,336 - pyskl - INFO - Epoch(val) [107][533] top1_acc: 0.9248, top5_acc: 0.9959, mean_class_accuracy: 0.8960 +2025-06-25 03:18:01,934 - pyskl - INFO - Epoch [108][100/1281] lr: 4.720e-03, eta: 6:18:48, time: 0.786, data_time: 0.187, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1532, loss: 0.1532 +2025-06-25 03:18:51,095 - pyskl - INFO - Epoch [108][200/1281] lr: 4.704e-03, eta: 6:18:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1095, loss: 0.1095 +2025-06-25 03:19:39,804 - pyskl - INFO - Epoch [108][300/1281] lr: 4.688e-03, eta: 6:17:31, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 0.9994, loss_cls: 0.1170, loss: 0.1170 +2025-06-25 03:20:09,805 - pyskl - INFO - Epoch [108][400/1281] lr: 4.672e-03, eta: 6:16:46, time: 0.300, data_time: 0.001, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1519, loss: 0.1519 +2025-06-25 03:21:00,681 - pyskl - INFO - Epoch [108][500/1281] lr: 4.656e-03, eta: 6:16:08, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1575, loss: 0.1575 +2025-06-25 03:21:28,391 - pyskl - INFO - Epoch [108][600/1281] lr: 4.640e-03, eta: 6:15:21, time: 0.277, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1373, loss: 0.1373 +2025-06-25 03:22:17,296 - pyskl - INFO - Epoch [108][700/1281] lr: 4.624e-03, eta: 6:14:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1668, loss: 0.1668 +2025-06-25 03:23:05,970 - pyskl - INFO - Epoch [108][800/1281] lr: 4.608e-03, eta: 6:14:05, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1457, loss: 0.1457 +2025-06-25 03:23:55,052 - pyskl - INFO - Epoch [108][900/1281] lr: 4.593e-03, eta: 6:13:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.1151, loss: 0.1151 +2025-06-25 03:24:44,113 - pyskl - INFO - Epoch [108][1000/1281] lr: 4.577e-03, eta: 6:12:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.1919, loss: 0.1919 +2025-06-25 03:25:33,107 - pyskl - INFO - Epoch [108][1100/1281] lr: 4.561e-03, eta: 6:12:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1631, loss: 0.1631 +2025-06-25 03:26:22,088 - pyskl - INFO - Epoch [108][1200/1281] lr: 4.545e-03, eta: 6:11:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1340, loss: 0.1340 +2025-06-25 03:27:01,915 - pyskl - INFO - Saving checkpoint at 108 epochs +2025-06-25 03:28:00,598 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:28:00,669 - pyskl - INFO - +top1_acc 0.9079 +top5_acc 0.9955 +2025-06-25 03:28:00,669 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:28:00,676 - pyskl - INFO - +mean_acc 0.8656 +2025-06-25 03:28:00,678 - pyskl - INFO - Epoch(val) [108][533] top1_acc: 0.9079, top5_acc: 0.9955, mean_class_accuracy: 0.8656 +2025-06-25 03:29:20,129 - pyskl - INFO - Epoch [109][100/1281] lr: 4.517e-03, eta: 6:10:18, time: 0.794, data_time: 0.189, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1341, loss: 0.1341 +2025-06-25 03:30:08,802 - pyskl - INFO - Epoch [109][200/1281] lr: 4.501e-03, eta: 6:09:39, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1133, loss: 0.1133 +2025-06-25 03:30:57,636 - pyskl - INFO - Epoch [109][300/1281] lr: 4.485e-03, eta: 6:09:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1087, loss: 0.1087 +2025-06-25 03:31:27,330 - pyskl - INFO - Epoch [109][400/1281] lr: 4.470e-03, eta: 6:08:15, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1479, loss: 0.1479 +2025-06-25 03:32:18,218 - pyskl - INFO - Epoch [109][500/1281] lr: 4.454e-03, eta: 6:07:37, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1346, loss: 0.1346 +2025-06-25 03:32:46,517 - pyskl - INFO - Epoch [109][600/1281] lr: 4.438e-03, eta: 6:06:51, time: 0.283, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1325, loss: 0.1325 +2025-06-25 03:33:35,435 - pyskl - INFO - Epoch [109][700/1281] lr: 4.423e-03, eta: 6:06:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1185, loss: 0.1185 +2025-06-25 03:34:24,281 - pyskl - INFO - Epoch [109][800/1281] lr: 4.407e-03, eta: 6:05:34, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1200, loss: 0.1200 +2025-06-25 03:35:12,877 - pyskl - INFO - Epoch [109][900/1281] lr: 4.391e-03, eta: 6:04:55, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 0.9994, loss_cls: 0.1492, loss: 0.1492 +2025-06-25 03:36:01,787 - pyskl - INFO - Epoch [109][1000/1281] lr: 4.376e-03, eta: 6:04:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1493, loss: 0.1493 +2025-06-25 03:36:50,906 - pyskl - INFO - Epoch [109][1100/1281] lr: 4.360e-03, eta: 6:03:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.1061, loss: 0.1061 +2025-06-25 03:37:40,233 - pyskl - INFO - Epoch [109][1200/1281] lr: 4.345e-03, eta: 6:03:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1200, loss: 0.1200 +2025-06-25 03:38:20,122 - pyskl - INFO - Saving checkpoint at 109 epochs +2025-06-25 03:39:18,755 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:39:18,812 - pyskl - INFO - +top1_acc 0.9213 +top5_acc 0.9942 +2025-06-25 03:39:18,813 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:39:18,820 - pyskl - INFO - +mean_acc 0.8954 +2025-06-25 03:39:18,822 - pyskl - INFO - Epoch(val) [109][533] top1_acc: 0.9213, top5_acc: 0.9942, mean_class_accuracy: 0.8954 +2025-06-25 03:40:38,152 - pyskl - INFO - Epoch [110][100/1281] lr: 4.317e-03, eta: 6:01:47, time: 0.793, data_time: 0.189, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1290, loss: 0.1290 +2025-06-25 03:41:26,988 - pyskl - INFO - Epoch [110][200/1281] lr: 4.301e-03, eta: 6:01:08, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1168, loss: 0.1168 +2025-06-25 03:42:15,540 - pyskl - INFO - Epoch [110][300/1281] lr: 4.286e-03, eta: 6:00:29, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1157, loss: 0.1157 +2025-06-25 03:42:45,128 - pyskl - INFO - Epoch [110][400/1281] lr: 4.271e-03, eta: 5:59:43, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0998, loss: 0.0998 +2025-06-25 03:43:35,727 - pyskl - INFO - Epoch [110][500/1281] lr: 4.255e-03, eta: 5:59:05, time: 0.506, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0908, loss: 0.0908 +2025-06-25 03:44:03,905 - pyskl - INFO - Epoch [110][600/1281] lr: 4.240e-03, eta: 5:58:19, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1343, loss: 0.1343 +2025-06-25 03:44:52,874 - pyskl - INFO - Epoch [110][700/1281] lr: 4.225e-03, eta: 5:57:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1061, loss: 0.1061 +2025-06-25 03:45:41,814 - pyskl - INFO - Epoch [110][800/1281] lr: 4.209e-03, eta: 5:57:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1305, loss: 0.1305 +2025-06-25 03:46:30,685 - pyskl - INFO - Epoch [110][900/1281] lr: 4.194e-03, eta: 5:56:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1247, loss: 0.1247 +2025-06-25 03:47:19,725 - pyskl - INFO - Epoch [110][1000/1281] lr: 4.179e-03, eta: 5:55:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1151, loss: 0.1151 +2025-06-25 03:48:09,102 - pyskl - INFO - Epoch [110][1100/1281] lr: 4.164e-03, eta: 5:55:06, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.1085, loss: 0.1085 +2025-06-25 03:48:58,256 - pyskl - INFO - Epoch [110][1200/1281] lr: 4.148e-03, eta: 5:54:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1584, loss: 0.1584 +2025-06-25 03:49:38,489 - pyskl - INFO - Saving checkpoint at 110 epochs +2025-06-25 03:50:36,764 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:50:36,832 - pyskl - INFO - +top1_acc 0.9213 +top5_acc 0.9951 +2025-06-25 03:50:36,832 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:50:36,839 - pyskl - INFO - +mean_acc 0.8882 +2025-06-25 03:50:36,841 - pyskl - INFO - Epoch(val) [110][533] top1_acc: 0.9213, top5_acc: 0.9951, mean_class_accuracy: 0.8882 +2025-06-25 03:51:55,657 - pyskl - INFO - Epoch [111][100/1281] lr: 4.121e-03, eta: 5:53:14, time: 0.788, data_time: 0.187, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0930, loss: 0.0930 +2025-06-25 03:52:44,863 - pyskl - INFO - Epoch [111][200/1281] lr: 4.106e-03, eta: 5:52:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1044, loss: 0.1044 +2025-06-25 03:53:33,860 - pyskl - INFO - Epoch [111][300/1281] lr: 4.091e-03, eta: 5:51:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1028, loss: 0.1028 +2025-06-25 03:54:03,682 - pyskl - INFO - Epoch [111][400/1281] lr: 4.075e-03, eta: 5:51:11, time: 0.298, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1192, loss: 0.1192 +2025-06-25 03:54:54,483 - pyskl - INFO - Epoch [111][500/1281] lr: 4.060e-03, eta: 5:50:33, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1187, loss: 0.1187 +2025-06-25 03:55:21,508 - pyskl - INFO - Epoch [111][600/1281] lr: 4.045e-03, eta: 5:49:46, time: 0.270, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0947, loss: 0.0947 +2025-06-25 03:56:09,979 - pyskl - INFO - Epoch [111][700/1281] lr: 4.030e-03, eta: 5:49:07, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1298, loss: 0.1298 +2025-06-25 03:56:58,618 - pyskl - INFO - Epoch [111][800/1281] lr: 4.015e-03, eta: 5:48:28, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 0.9994, loss_cls: 0.1563, loss: 0.1563 +2025-06-25 03:57:47,328 - pyskl - INFO - Epoch [111][900/1281] lr: 4.000e-03, eta: 5:47:49, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1403, loss: 0.1403 +2025-06-25 03:58:36,501 - pyskl - INFO - Epoch [111][1000/1281] lr: 3.985e-03, eta: 5:47:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1498, loss: 0.1498 +2025-06-25 03:59:25,992 - pyskl - INFO - Epoch [111][1100/1281] lr: 3.970e-03, eta: 5:46:32, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1244, loss: 0.1244 +2025-06-25 04:00:14,762 - pyskl - INFO - Epoch [111][1200/1281] lr: 3.955e-03, eta: 5:45:53, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1386, loss: 0.1386 +2025-06-25 04:00:55,170 - pyskl - INFO - Saving checkpoint at 111 epochs +2025-06-25 04:01:53,881 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:01:53,938 - pyskl - INFO - +top1_acc 0.9139 +top5_acc 0.9925 +2025-06-25 04:01:53,938 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:01:53,945 - pyskl - INFO - +mean_acc 0.8878 +2025-06-25 04:01:53,947 - pyskl - INFO - Epoch(val) [111][533] top1_acc: 0.9139, top5_acc: 0.9925, mean_class_accuracy: 0.8878 +2025-06-25 04:03:15,036 - pyskl - INFO - Epoch [112][100/1281] lr: 3.928e-03, eta: 5:44:40, time: 0.811, data_time: 0.192, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1409, loss: 0.1409 +2025-06-25 04:04:03,990 - pyskl - INFO - Epoch [112][200/1281] lr: 3.914e-03, eta: 5:44:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1107, loss: 0.1107 +2025-06-25 04:04:52,984 - pyskl - INFO - Epoch [112][300/1281] lr: 3.899e-03, eta: 5:43:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.1094, loss: 0.1094 +2025-06-25 04:05:22,025 - pyskl - INFO - Epoch [112][400/1281] lr: 3.884e-03, eta: 5:42:36, time: 0.290, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1143, loss: 0.1143 +2025-06-25 04:06:12,823 - pyskl - INFO - Epoch [112][500/1281] lr: 3.869e-03, eta: 5:41:58, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1550, loss: 0.1550 +2025-06-25 04:06:42,049 - pyskl - INFO - Epoch [112][600/1281] lr: 3.854e-03, eta: 5:41:13, time: 0.292, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1197, loss: 0.1197 +2025-06-25 04:07:30,915 - pyskl - INFO - Epoch [112][700/1281] lr: 3.840e-03, eta: 5:40:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1055, loss: 0.1055 +2025-06-25 04:08:19,655 - pyskl - INFO - Epoch [112][800/1281] lr: 3.825e-03, eta: 5:39:55, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1139, loss: 0.1139 +2025-06-25 04:09:08,490 - pyskl - INFO - Epoch [112][900/1281] lr: 3.810e-03, eta: 5:39:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0863, loss: 0.0863 +2025-06-25 04:09:57,445 - pyskl - INFO - Epoch [112][1000/1281] lr: 3.795e-03, eta: 5:38:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1073, loss: 0.1073 +2025-06-25 04:10:46,502 - pyskl - INFO - Epoch [112][1100/1281] lr: 3.781e-03, eta: 5:37:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0880, loss: 0.0880 +2025-06-25 04:11:35,852 - pyskl - INFO - Epoch [112][1200/1281] lr: 3.766e-03, eta: 5:37:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1431, loss: 0.1431 +2025-06-25 04:12:16,091 - pyskl - INFO - Saving checkpoint at 112 epochs +2025-06-25 04:13:14,764 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:13:14,832 - pyskl - INFO - +top1_acc 0.9184 +top5_acc 0.9945 +2025-06-25 04:13:14,832 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:13:14,839 - pyskl - INFO - +mean_acc 0.9012 +2025-06-25 04:13:14,841 - pyskl - INFO - Epoch(val) [112][533] top1_acc: 0.9184, top5_acc: 0.9945, mean_class_accuracy: 0.9012 +2025-06-25 04:14:33,285 - pyskl - INFO - Epoch [113][100/1281] lr: 3.740e-03, eta: 5:36:05, time: 0.784, data_time: 0.185, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1288, loss: 0.1288 +2025-06-25 04:15:22,054 - pyskl - INFO - Epoch [113][200/1281] lr: 3.725e-03, eta: 5:35:26, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 0.9994, loss_cls: 0.1440, loss: 0.1440 +2025-06-25 04:16:10,979 - pyskl - INFO - Epoch [113][300/1281] lr: 3.711e-03, eta: 5:34:47, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1157, loss: 0.1157 +2025-06-25 04:16:40,227 - pyskl - INFO - Epoch [113][400/1281] lr: 3.696e-03, eta: 5:34:01, time: 0.292, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1123, loss: 0.1123 +2025-06-25 04:17:31,008 - pyskl - INFO - Epoch [113][500/1281] lr: 3.682e-03, eta: 5:33:23, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1068, loss: 0.1068 +2025-06-25 04:18:01,184 - pyskl - INFO - Epoch [113][600/1281] lr: 3.667e-03, eta: 5:32:37, time: 0.302, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0837, loss: 0.0837 +2025-06-25 04:18:49,840 - pyskl - INFO - Epoch [113][700/1281] lr: 3.653e-03, eta: 5:31:58, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0940, loss: 0.0940 +2025-06-25 04:19:38,683 - pyskl - INFO - Epoch [113][800/1281] lr: 3.638e-03, eta: 5:31:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1060, loss: 0.1060 +2025-06-25 04:20:27,851 - pyskl - INFO - Epoch [113][900/1281] lr: 3.624e-03, eta: 5:30:40, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0899, loss: 0.0899 +2025-06-25 04:21:16,937 - pyskl - INFO - Epoch [113][1000/1281] lr: 3.610e-03, eta: 5:30:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0773, loss: 0.0773 +2025-06-25 04:22:05,788 - pyskl - INFO - Epoch [113][1100/1281] lr: 3.595e-03, eta: 5:29:22, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1248, loss: 0.1248 +2025-06-25 04:22:54,784 - pyskl - INFO - Epoch [113][1200/1281] lr: 3.581e-03, eta: 5:28:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1141, loss: 0.1141 +2025-06-25 04:23:35,048 - pyskl - INFO - Saving checkpoint at 113 epochs +2025-06-25 04:24:33,433 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:24:33,489 - pyskl - INFO - +top1_acc 0.9088 +top5_acc 0.9928 +2025-06-25 04:24:33,489 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:24:33,496 - pyskl - INFO - +mean_acc 0.8830 +2025-06-25 04:24:33,497 - pyskl - INFO - Epoch(val) [113][533] top1_acc: 0.9088, top5_acc: 0.9928, mean_class_accuracy: 0.8830 +2025-06-25 04:25:51,958 - pyskl - INFO - Epoch [114][100/1281] lr: 3.555e-03, eta: 5:27:29, time: 0.785, data_time: 0.187, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1265, loss: 0.1265 +2025-06-25 04:26:40,755 - pyskl - INFO - Epoch [114][200/1281] lr: 3.541e-03, eta: 5:26:50, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0891, loss: 0.0891 +2025-06-25 04:27:29,889 - pyskl - INFO - Epoch [114][300/1281] lr: 3.526e-03, eta: 5:26:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0897, loss: 0.0897 +2025-06-25 04:27:58,651 - pyskl - INFO - Epoch [114][400/1281] lr: 3.512e-03, eta: 5:25:25, time: 0.288, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0996, loss: 0.0996 +2025-06-25 04:28:49,427 - pyskl - INFO - Epoch [114][500/1281] lr: 3.498e-03, eta: 5:24:46, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0949, loss: 0.0949 +2025-06-25 04:29:19,010 - pyskl - INFO - Epoch [114][600/1281] lr: 3.484e-03, eta: 5:24:01, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0947, loss: 0.0947 +2025-06-25 04:30:07,919 - pyskl - INFO - Epoch [114][700/1281] lr: 3.470e-03, eta: 5:23:22, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0851, loss: 0.0851 +2025-06-25 04:30:56,726 - pyskl - INFO - Epoch [114][800/1281] lr: 3.456e-03, eta: 5:22:43, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1070, loss: 0.1070 +2025-06-25 04:31:45,672 - pyskl - INFO - Epoch [114][900/1281] lr: 3.442e-03, eta: 5:22:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.1035, loss: 0.1035 +2025-06-25 04:32:34,411 - pyskl - INFO - Epoch [114][1000/1281] lr: 3.427e-03, eta: 5:21:24, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0740, loss: 0.0740 +2025-06-25 04:33:23,247 - pyskl - INFO - Epoch [114][1100/1281] lr: 3.413e-03, eta: 5:20:45, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.0996, loss: 0.0996 +2025-06-25 04:34:12,338 - pyskl - INFO - Epoch [114][1200/1281] lr: 3.399e-03, eta: 5:20:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1028, loss: 0.1028 +2025-06-25 04:34:52,837 - pyskl - INFO - Saving checkpoint at 114 epochs +2025-06-25 04:35:52,074 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:35:52,142 - pyskl - INFO - +top1_acc 0.9240 +top5_acc 0.9964 +2025-06-25 04:35:52,142 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:35:52,150 - pyskl - INFO - +mean_acc 0.8960 +2025-06-25 04:35:52,152 - pyskl - INFO - Epoch(val) [114][533] top1_acc: 0.9240, top5_acc: 0.9964, mean_class_accuracy: 0.8960 +2025-06-25 04:37:10,320 - pyskl - INFO - Epoch [115][100/1281] lr: 3.374e-03, eta: 5:18:51, time: 0.782, data_time: 0.186, memory: 4083, top1_acc: 0.9850, top5_acc: 0.9994, loss_cls: 0.1053, loss: 0.1053 +2025-06-25 04:37:59,282 - pyskl - INFO - Epoch [115][200/1281] lr: 3.360e-03, eta: 5:18:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0720, loss: 0.0720 +2025-06-25 04:38:48,741 - pyskl - INFO - Epoch [115][300/1281] lr: 3.346e-03, eta: 5:17:33, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0672, loss: 0.0672 +2025-06-25 04:39:17,386 - pyskl - INFO - Epoch [115][400/1281] lr: 3.332e-03, eta: 5:16:47, time: 0.286, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0757, loss: 0.0757 +2025-06-25 04:40:08,123 - pyskl - INFO - Epoch [115][500/1281] lr: 3.318e-03, eta: 5:16:09, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0635, loss: 0.0635 +2025-06-25 04:40:36,908 - pyskl - INFO - Epoch [115][600/1281] lr: 3.305e-03, eta: 5:15:23, time: 0.288, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0824, loss: 0.0824 +2025-06-25 04:41:25,757 - pyskl - INFO - Epoch [115][700/1281] lr: 3.291e-03, eta: 5:14:44, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0873, loss: 0.0873 +2025-06-25 04:42:14,678 - pyskl - INFO - Epoch [115][800/1281] lr: 3.277e-03, eta: 5:14:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0807, loss: 0.0807 +2025-06-25 04:43:03,479 - pyskl - INFO - Epoch [115][900/1281] lr: 3.263e-03, eta: 5:13:25, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0942, loss: 0.0942 +2025-06-25 04:43:52,613 - pyskl - INFO - Epoch [115][1000/1281] lr: 3.249e-03, eta: 5:12:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0950, loss: 0.0950 +2025-06-25 04:44:41,471 - pyskl - INFO - Epoch [115][1100/1281] lr: 3.236e-03, eta: 5:12:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0887, loss: 0.0887 +2025-06-25 04:45:30,907 - pyskl - INFO - Epoch [115][1200/1281] lr: 3.222e-03, eta: 5:11:27, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0888, loss: 0.0888 +2025-06-25 04:46:11,096 - pyskl - INFO - Saving checkpoint at 115 epochs +2025-06-25 04:47:09,499 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:47:09,555 - pyskl - INFO - +top1_acc 0.9242 +top5_acc 0.9951 +2025-06-25 04:47:09,556 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:47:09,562 - pyskl - INFO - +mean_acc 0.8920 +2025-06-25 04:47:09,564 - pyskl - INFO - Epoch(val) [115][533] top1_acc: 0.9242, top5_acc: 0.9951, mean_class_accuracy: 0.8920 +2025-06-25 04:48:27,798 - pyskl - INFO - Epoch [116][100/1281] lr: 3.197e-03, eta: 5:10:13, time: 0.782, data_time: 0.183, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1135, loss: 0.1135 +2025-06-25 04:49:16,793 - pyskl - INFO - Epoch [116][200/1281] lr: 3.184e-03, eta: 5:09:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0892, loss: 0.0892 +2025-06-25 04:50:05,744 - pyskl - INFO - Epoch [116][300/1281] lr: 3.170e-03, eta: 5:08:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 0.9994, loss_cls: 0.0890, loss: 0.0890 +2025-06-25 04:50:34,978 - pyskl - INFO - Epoch [116][400/1281] lr: 3.156e-03, eta: 5:08:09, time: 0.292, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0854, loss: 0.0854 +2025-06-25 04:51:25,737 - pyskl - INFO - Epoch [116][500/1281] lr: 3.143e-03, eta: 5:07:30, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0676, loss: 0.0676 +2025-06-25 04:51:54,855 - pyskl - INFO - Epoch [116][600/1281] lr: 3.129e-03, eta: 5:06:44, time: 0.291, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0769, loss: 0.0769 +2025-06-25 04:52:43,640 - pyskl - INFO - Epoch [116][700/1281] lr: 3.116e-03, eta: 5:06:05, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 0.9994, loss_cls: 0.0894, loss: 0.0894 +2025-06-25 04:53:32,636 - pyskl - INFO - Epoch [116][800/1281] lr: 3.102e-03, eta: 5:05:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0881, loss: 0.0881 +2025-06-25 04:54:21,205 - pyskl - INFO - Epoch [116][900/1281] lr: 3.089e-03, eta: 5:04:46, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0836, loss: 0.0836 +2025-06-25 04:55:10,101 - pyskl - INFO - Epoch [116][1000/1281] lr: 3.075e-03, eta: 5:04:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0665, loss: 0.0665 +2025-06-25 04:55:59,013 - pyskl - INFO - Epoch [116][1100/1281] lr: 3.062e-03, eta: 5:03:27, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0782, loss: 0.0782 +2025-06-25 04:56:48,510 - pyskl - INFO - Epoch [116][1200/1281] lr: 3.049e-03, eta: 5:02:48, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0750, loss: 0.0750 +2025-06-25 04:57:28,173 - pyskl - INFO - Saving checkpoint at 116 epochs +2025-06-25 04:58:26,095 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:58:26,165 - pyskl - INFO - +top1_acc 0.9328 +top5_acc 0.9965 +2025-06-25 04:58:26,165 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:58:26,173 - pyskl - INFO - +mean_acc 0.9114 +2025-06-25 04:58:26,178 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_107.pth was removed +2025-06-25 04:58:26,365 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_116.pth. +2025-06-25 04:58:26,365 - pyskl - INFO - Best top1_acc is 0.9328 at 116 epoch. +2025-06-25 04:58:26,368 - pyskl - INFO - Epoch(val) [116][533] top1_acc: 0.9328, top5_acc: 0.9965, mean_class_accuracy: 0.9114 +2025-06-25 04:59:45,546 - pyskl - INFO - Epoch [117][100/1281] lr: 3.024e-03, eta: 5:01:33, time: 0.792, data_time: 0.191, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0780, loss: 0.0780 +2025-06-25 05:00:34,087 - pyskl - INFO - Epoch [117][200/1281] lr: 3.011e-03, eta: 5:00:54, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0924, loss: 0.0924 +2025-06-25 05:01:22,982 - pyskl - INFO - Epoch [117][300/1281] lr: 2.998e-03, eta: 5:00:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1169, loss: 0.1169 +2025-06-25 05:01:51,469 - pyskl - INFO - Epoch [117][400/1281] lr: 2.984e-03, eta: 4:59:29, time: 0.285, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0942, loss: 0.0942 +2025-06-25 05:02:42,327 - pyskl - INFO - Epoch [117][500/1281] lr: 2.971e-03, eta: 4:58:50, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0826, loss: 0.0826 +2025-06-25 05:03:10,596 - pyskl - INFO - Epoch [117][600/1281] lr: 2.958e-03, eta: 4:58:04, time: 0.283, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0711, loss: 0.0711 +2025-06-25 05:03:59,430 - pyskl - INFO - Epoch [117][700/1281] lr: 2.945e-03, eta: 4:57:25, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0717, loss: 0.0717 +2025-06-25 05:04:48,354 - pyskl - INFO - Epoch [117][800/1281] lr: 2.932e-03, eta: 4:56:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0756, loss: 0.0756 +2025-06-25 05:05:37,455 - pyskl - INFO - Epoch [117][900/1281] lr: 2.919e-03, eta: 4:56:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 0.9994, loss_cls: 0.1023, loss: 0.1023 +2025-06-25 05:06:26,280 - pyskl - INFO - Epoch [117][1000/1281] lr: 2.905e-03, eta: 4:55:26, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 0.9994, loss_cls: 0.1052, loss: 0.1052 +2025-06-25 05:07:15,499 - pyskl - INFO - Epoch [117][1100/1281] lr: 2.892e-03, eta: 4:54:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.0985, loss: 0.0985 +2025-06-25 05:08:04,162 - pyskl - INFO - Epoch [117][1200/1281] lr: 2.879e-03, eta: 4:54:07, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0862, loss: 0.0862 +2025-06-25 05:08:44,477 - pyskl - INFO - Saving checkpoint at 117 epochs +2025-06-25 05:09:42,724 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:09:42,779 - pyskl - INFO - +top1_acc 0.9283 +top5_acc 0.9947 +2025-06-25 05:09:42,780 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:09:42,787 - pyskl - INFO - +mean_acc 0.9022 +2025-06-25 05:09:42,788 - pyskl - INFO - Epoch(val) [117][533] top1_acc: 0.9283, top5_acc: 0.9947, mean_class_accuracy: 0.9022 +2025-06-25 05:11:02,252 - pyskl - INFO - Epoch [118][100/1281] lr: 2.856e-03, eta: 4:52:53, time: 0.795, data_time: 0.185, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0884, loss: 0.0884 +2025-06-25 05:11:51,228 - pyskl - INFO - Epoch [118][200/1281] lr: 2.843e-03, eta: 4:52:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0699, loss: 0.0699 +2025-06-25 05:12:40,274 - pyskl - INFO - Epoch [118][300/1281] lr: 2.830e-03, eta: 4:51:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0776, loss: 0.0776 +2025-06-25 05:13:09,742 - pyskl - INFO - Epoch [118][400/1281] lr: 2.817e-03, eta: 4:50:48, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0866, loss: 0.0866 +2025-06-25 05:14:00,466 - pyskl - INFO - Epoch [118][500/1281] lr: 2.804e-03, eta: 4:50:09, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0637, loss: 0.0637 +2025-06-25 05:14:27,060 - pyskl - INFO - Epoch [118][600/1281] lr: 2.791e-03, eta: 4:49:23, time: 0.266, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0757, loss: 0.0757 +2025-06-25 05:15:15,799 - pyskl - INFO - Epoch [118][700/1281] lr: 2.778e-03, eta: 4:48:44, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0760, loss: 0.0760 +2025-06-25 05:16:04,520 - pyskl - INFO - Epoch [118][800/1281] lr: 2.765e-03, eta: 4:48:04, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0718, loss: 0.0718 +2025-06-25 05:16:53,405 - pyskl - INFO - Epoch [118][900/1281] lr: 2.753e-03, eta: 4:47:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0859, loss: 0.0859 +2025-06-25 05:17:42,583 - pyskl - INFO - Epoch [118][1000/1281] lr: 2.740e-03, eta: 4:46:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0566, loss: 0.0566 +2025-06-25 05:18:31,602 - pyskl - INFO - Epoch [118][1100/1281] lr: 2.727e-03, eta: 4:46:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0526, loss: 0.0526 +2025-06-25 05:19:20,459 - pyskl - INFO - Epoch [118][1200/1281] lr: 2.714e-03, eta: 4:45:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0763, loss: 0.0763 +2025-06-25 05:20:00,485 - pyskl - INFO - Saving checkpoint at 118 epochs +2025-06-25 05:20:58,641 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:20:58,697 - pyskl - INFO - +top1_acc 0.9276 +top5_acc 0.9955 +2025-06-25 05:20:58,697 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:20:58,704 - pyskl - INFO - +mean_acc 0.9008 +2025-06-25 05:20:58,706 - pyskl - INFO - Epoch(val) [118][533] top1_acc: 0.9276, top5_acc: 0.9955, mean_class_accuracy: 0.9008 +2025-06-25 05:22:18,786 - pyskl - INFO - Epoch [119][100/1281] lr: 2.691e-03, eta: 4:44:11, time: 0.801, data_time: 0.186, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0759, loss: 0.0759 +2025-06-25 05:23:07,391 - pyskl - INFO - Epoch [119][200/1281] lr: 2.679e-03, eta: 4:43:31, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0513, loss: 0.0513 +2025-06-25 05:23:56,266 - pyskl - INFO - Epoch [119][300/1281] lr: 2.666e-03, eta: 4:42:51, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0597, loss: 0.0597 +2025-06-25 05:24:27,676 - pyskl - INFO - Epoch [119][400/1281] lr: 2.653e-03, eta: 4:42:07, time: 0.314, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0657, loss: 0.0657 +2025-06-25 05:25:18,390 - pyskl - INFO - Epoch [119][500/1281] lr: 2.641e-03, eta: 4:41:27, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0579, loss: 0.0579 +2025-06-25 05:25:45,229 - pyskl - INFO - Epoch [119][600/1281] lr: 2.628e-03, eta: 4:40:42, time: 0.268, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0818, loss: 0.0818 +2025-06-25 05:26:34,447 - pyskl - INFO - Epoch [119][700/1281] lr: 2.616e-03, eta: 4:40:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0609, loss: 0.0609 +2025-06-25 05:27:22,791 - pyskl - INFO - Epoch [119][800/1281] lr: 2.603e-03, eta: 4:39:22, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0511, loss: 0.0511 +2025-06-25 05:28:11,675 - pyskl - INFO - Epoch [119][900/1281] lr: 2.591e-03, eta: 4:38:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0604, loss: 0.0604 +2025-06-25 05:29:00,463 - pyskl - INFO - Epoch [119][1000/1281] lr: 2.578e-03, eta: 4:38:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0678, loss: 0.0678 +2025-06-25 05:29:49,364 - pyskl - INFO - Epoch [119][1100/1281] lr: 2.566e-03, eta: 4:37:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 0.9994, loss_cls: 0.0680, loss: 0.0680 +2025-06-25 05:30:38,252 - pyskl - INFO - Epoch [119][1200/1281] lr: 2.554e-03, eta: 4:36:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 0.9994, loss_cls: 0.0719, loss: 0.0719 +2025-06-25 05:31:18,485 - pyskl - INFO - Saving checkpoint at 119 epochs +2025-06-25 05:32:16,645 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:32:16,701 - pyskl - INFO - +top1_acc 0.9291 +top5_acc 0.9954 +2025-06-25 05:32:16,701 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:32:16,708 - pyskl - INFO - +mean_acc 0.9070 +2025-06-25 05:32:16,710 - pyskl - INFO - Epoch(val) [119][533] top1_acc: 0.9291, top5_acc: 0.9954, mean_class_accuracy: 0.9070 +2025-06-25 05:33:36,430 - pyskl - INFO - Epoch [120][100/1281] lr: 2.531e-03, eta: 4:35:28, time: 0.797, data_time: 0.186, memory: 4083, top1_acc: 0.9862, top5_acc: 0.9994, loss_cls: 0.1084, loss: 0.1084 +2025-06-25 05:34:25,215 - pyskl - INFO - Epoch [120][200/1281] lr: 2.519e-03, eta: 4:34:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0598, loss: 0.0598 +2025-06-25 05:35:14,001 - pyskl - INFO - Epoch [120][300/1281] lr: 2.507e-03, eta: 4:34:09, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0664, loss: 0.0664 +2025-06-25 05:35:45,229 - pyskl - INFO - Epoch [120][400/1281] lr: 2.494e-03, eta: 4:33:24, time: 0.312, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0535, loss: 0.0535 +2025-06-25 05:36:36,104 - pyskl - INFO - Epoch [120][500/1281] lr: 2.482e-03, eta: 4:32:45, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0580, loss: 0.0580 +2025-06-25 05:37:02,968 - pyskl - INFO - Epoch [120][600/1281] lr: 2.470e-03, eta: 4:31:59, time: 0.269, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0601, loss: 0.0601 +2025-06-25 05:37:51,831 - pyskl - INFO - Epoch [120][700/1281] lr: 2.458e-03, eta: 4:31:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0557, loss: 0.0557 +2025-06-25 05:38:40,302 - pyskl - INFO - Epoch [120][800/1281] lr: 2.446e-03, eta: 4:30:39, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0614, loss: 0.0614 +2025-06-25 05:39:29,594 - pyskl - INFO - Epoch [120][900/1281] lr: 2.433e-03, eta: 4:29:59, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0737, loss: 0.0737 +2025-06-25 05:40:18,474 - pyskl - INFO - Epoch [120][1000/1281] lr: 2.421e-03, eta: 4:29:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0544, loss: 0.0544 +2025-06-25 05:41:07,492 - pyskl - INFO - Epoch [120][1100/1281] lr: 2.409e-03, eta: 4:28:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0752, loss: 0.0752 +2025-06-25 05:41:55,961 - pyskl - INFO - Epoch [120][1200/1281] lr: 2.397e-03, eta: 4:28:00, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0542, loss: 0.0542 +2025-06-25 05:42:36,336 - pyskl - INFO - Saving checkpoint at 120 epochs +2025-06-25 05:43:34,263 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:43:34,322 - pyskl - INFO - +top1_acc 0.9319 +top5_acc 0.9957 +2025-06-25 05:43:34,322 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:43:34,330 - pyskl - INFO - +mean_acc 0.9072 +2025-06-25 05:43:34,331 - pyskl - INFO - Epoch(val) [120][533] top1_acc: 0.9319, top5_acc: 0.9957, mean_class_accuracy: 0.9072 +2025-06-25 05:44:55,566 - pyskl - INFO - Epoch [121][100/1281] lr: 2.375e-03, eta: 4:26:45, time: 0.812, data_time: 0.186, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0455, loss: 0.0455 +2025-06-25 05:45:44,443 - pyskl - INFO - Epoch [121][200/1281] lr: 2.363e-03, eta: 4:26:05, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0463, loss: 0.0463 +2025-06-25 05:46:33,144 - pyskl - INFO - Epoch [121][300/1281] lr: 2.351e-03, eta: 4:25:25, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0460, loss: 0.0460 +2025-06-25 05:47:03,703 - pyskl - INFO - Epoch [121][400/1281] lr: 2.340e-03, eta: 4:24:41, time: 0.306, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0473, loss: 0.0473 +2025-06-25 05:47:54,343 - pyskl - INFO - Epoch [121][500/1281] lr: 2.328e-03, eta: 4:24:01, time: 0.506, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0401, loss: 0.0401 +2025-06-25 05:48:21,511 - pyskl - INFO - Epoch [121][600/1281] lr: 2.316e-03, eta: 4:23:16, time: 0.272, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0393, loss: 0.0393 +2025-06-25 05:49:10,545 - pyskl - INFO - Epoch [121][700/1281] lr: 2.304e-03, eta: 4:22:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0569, loss: 0.0569 +2025-06-25 05:49:59,332 - pyskl - INFO - Epoch [121][800/1281] lr: 2.292e-03, eta: 4:21:56, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0629, loss: 0.0629 +2025-06-25 05:50:47,800 - pyskl - INFO - Epoch [121][900/1281] lr: 2.280e-03, eta: 4:21:16, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0425, loss: 0.0425 +2025-06-25 05:51:36,660 - pyskl - INFO - Epoch [121][1000/1281] lr: 2.269e-03, eta: 4:20:36, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0476, loss: 0.0476 +2025-06-25 05:52:25,708 - pyskl - INFO - Epoch [121][1100/1281] lr: 2.257e-03, eta: 4:19:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0533, loss: 0.0533 +2025-06-25 05:53:14,645 - pyskl - INFO - Epoch [121][1200/1281] lr: 2.245e-03, eta: 4:19:16, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0357, loss: 0.0357 +2025-06-25 05:53:54,856 - pyskl - INFO - Saving checkpoint at 121 epochs +2025-06-25 05:54:53,216 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:54:53,287 - pyskl - INFO - +top1_acc 0.9343 +top5_acc 0.9955 +2025-06-25 05:54:53,287 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:54:53,296 - pyskl - INFO - +mean_acc 0.9076 +2025-06-25 05:54:53,301 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_116.pth was removed +2025-06-25 05:54:53,490 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_121.pth. +2025-06-25 05:54:53,490 - pyskl - INFO - Best top1_acc is 0.9343 at 121 epoch. +2025-06-25 05:54:53,493 - pyskl - INFO - Epoch(val) [121][533] top1_acc: 0.9343, top5_acc: 0.9955, mean_class_accuracy: 0.9076 +2025-06-25 05:56:12,925 - pyskl - INFO - Epoch [122][100/1281] lr: 2.224e-03, eta: 4:18:01, time: 0.794, data_time: 0.186, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0401, loss: 0.0401 +2025-06-25 05:57:01,465 - pyskl - INFO - Epoch [122][200/1281] lr: 2.212e-03, eta: 4:17:21, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0445, loss: 0.0445 +2025-06-25 05:57:50,179 - pyskl - INFO - Epoch [122][300/1281] lr: 2.201e-03, eta: 4:16:41, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0461, loss: 0.0461 +2025-06-25 05:58:20,942 - pyskl - INFO - Epoch [122][400/1281] lr: 2.189e-03, eta: 4:15:56, time: 0.308, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0495, loss: 0.0495 +2025-06-25 05:59:11,869 - pyskl - INFO - Epoch [122][500/1281] lr: 2.178e-03, eta: 4:15:16, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0710, loss: 0.0710 +2025-06-25 05:59:39,202 - pyskl - INFO - Epoch [122][600/1281] lr: 2.166e-03, eta: 4:14:31, time: 0.273, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0473, loss: 0.0473 +2025-06-25 06:00:27,949 - pyskl - INFO - Epoch [122][700/1281] lr: 2.155e-03, eta: 4:13:51, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0586, loss: 0.0586 +2025-06-25 06:01:17,261 - pyskl - INFO - Epoch [122][800/1281] lr: 2.143e-03, eta: 4:13:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0642, loss: 0.0642 +2025-06-25 06:02:05,979 - pyskl - INFO - Epoch [122][900/1281] lr: 2.132e-03, eta: 4:12:31, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 0.9994, loss_cls: 0.0829, loss: 0.0829 +2025-06-25 06:02:55,103 - pyskl - INFO - Epoch [122][1000/1281] lr: 2.120e-03, eta: 4:11:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0534, loss: 0.0534 +2025-06-25 06:03:44,247 - pyskl - INFO - Epoch [122][1100/1281] lr: 2.109e-03, eta: 4:11:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0622, loss: 0.0622 +2025-06-25 06:04:33,199 - pyskl - INFO - Epoch [122][1200/1281] lr: 2.098e-03, eta: 4:10:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0545, loss: 0.0545 +2025-06-25 06:05:13,157 - pyskl - INFO - Saving checkpoint at 122 epochs +2025-06-25 06:06:11,173 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:06:11,235 - pyskl - INFO - +top1_acc 0.9343 +top5_acc 0.9965 +2025-06-25 06:06:11,235 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:06:11,242 - pyskl - INFO - +mean_acc 0.9103 +2025-06-25 06:06:11,244 - pyskl - INFO - Epoch(val) [122][533] top1_acc: 0.9343, top5_acc: 0.9965, mean_class_accuracy: 0.9103 +2025-06-25 06:07:30,369 - pyskl - INFO - Epoch [123][100/1281] lr: 2.077e-03, eta: 4:09:16, time: 0.791, data_time: 0.183, memory: 4083, top1_acc: 0.9919, top5_acc: 0.9994, loss_cls: 0.0604, loss: 0.0604 +2025-06-25 06:08:19,203 - pyskl - INFO - Epoch [123][200/1281] lr: 2.066e-03, eta: 4:08:35, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0412, loss: 0.0412 +2025-06-25 06:09:08,311 - pyskl - INFO - Epoch [123][300/1281] lr: 2.055e-03, eta: 4:07:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0445, loss: 0.0445 +2025-06-25 06:09:39,882 - pyskl - INFO - Epoch [123][400/1281] lr: 2.044e-03, eta: 4:07:11, time: 0.316, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0580, loss: 0.0580 +2025-06-25 06:10:30,679 - pyskl - INFO - Epoch [123][500/1281] lr: 2.032e-03, eta: 4:06:31, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0428, loss: 0.0428 +2025-06-25 06:10:55,772 - pyskl - INFO - Epoch [123][600/1281] lr: 2.021e-03, eta: 4:05:46, time: 0.251, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0576, loss: 0.0576 +2025-06-25 06:11:44,539 - pyskl - INFO - Epoch [123][700/1281] lr: 2.010e-03, eta: 4:05:05, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0546, loss: 0.0546 +2025-06-25 06:12:33,341 - pyskl - INFO - Epoch [123][800/1281] lr: 1.999e-03, eta: 4:04:25, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0509, loss: 0.0509 +2025-06-25 06:13:22,329 - pyskl - INFO - Epoch [123][900/1281] lr: 1.988e-03, eta: 4:03:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0497, loss: 0.0497 +2025-06-25 06:14:11,185 - pyskl - INFO - Epoch [123][1000/1281] lr: 1.977e-03, eta: 4:03:05, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0462, loss: 0.0462 +2025-06-25 06:15:00,150 - pyskl - INFO - Epoch [123][1100/1281] lr: 1.966e-03, eta: 4:02:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0463, loss: 0.0463 +2025-06-25 06:15:49,033 - pyskl - INFO - Epoch [123][1200/1281] lr: 1.955e-03, eta: 4:01:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0559, loss: 0.0559 +2025-06-25 06:16:29,226 - pyskl - INFO - Saving checkpoint at 123 epochs +2025-06-25 06:17:27,918 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:17:27,972 - pyskl - INFO - +top1_acc 0.9337 +top5_acc 0.9952 +2025-06-25 06:17:27,973 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:17:27,979 - pyskl - INFO - +mean_acc 0.9065 +2025-06-25 06:17:27,981 - pyskl - INFO - Epoch(val) [123][533] top1_acc: 0.9337, top5_acc: 0.9952, mean_class_accuracy: 0.9065 +2025-06-25 06:18:47,373 - pyskl - INFO - Epoch [124][100/1281] lr: 1.935e-03, eta: 4:00:29, time: 0.794, data_time: 0.184, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0473, loss: 0.0473 +2025-06-25 06:19:36,291 - pyskl - INFO - Epoch [124][200/1281] lr: 1.924e-03, eta: 3:59:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0518, loss: 0.0518 +2025-06-25 06:20:25,012 - pyskl - INFO - Epoch [124][300/1281] lr: 1.913e-03, eta: 3:59:09, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0482, loss: 0.0482 +2025-06-25 06:20:56,806 - pyskl - INFO - Epoch [124][400/1281] lr: 1.902e-03, eta: 3:58:25, time: 0.318, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0355, loss: 0.0355 +2025-06-25 06:21:47,620 - pyskl - INFO - Epoch [124][500/1281] lr: 1.892e-03, eta: 3:57:45, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0394, loss: 0.0394 +2025-06-25 06:22:13,018 - pyskl - INFO - Epoch [124][600/1281] lr: 1.881e-03, eta: 3:56:59, time: 0.254, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0441, loss: 0.0441 +2025-06-25 06:23:01,743 - pyskl - INFO - Epoch [124][700/1281] lr: 1.870e-03, eta: 3:56:19, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0382, loss: 0.0382 +2025-06-25 06:23:50,665 - pyskl - INFO - Epoch [124][800/1281] lr: 1.859e-03, eta: 3:55:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0532, loss: 0.0532 +2025-06-25 06:24:39,638 - pyskl - INFO - Epoch [124][900/1281] lr: 1.849e-03, eta: 3:54:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0634, loss: 0.0634 +2025-06-25 06:25:28,409 - pyskl - INFO - Epoch [124][1000/1281] lr: 1.838e-03, eta: 3:54:18, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0819, loss: 0.0819 +2025-06-25 06:26:17,720 - pyskl - INFO - Epoch [124][1100/1281] lr: 1.827e-03, eta: 3:53:38, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0754, loss: 0.0754 +2025-06-25 06:27:06,873 - pyskl - INFO - Epoch [124][1200/1281] lr: 1.817e-03, eta: 3:52:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0441, loss: 0.0441 +2025-06-25 06:27:47,207 - pyskl - INFO - Saving checkpoint at 124 epochs +2025-06-25 06:28:44,872 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:28:44,927 - pyskl - INFO - +top1_acc 0.9342 +top5_acc 0.9966 +2025-06-25 06:28:44,927 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:28:44,934 - pyskl - INFO - +mean_acc 0.9062 +2025-06-25 06:28:44,936 - pyskl - INFO - Epoch(val) [124][533] top1_acc: 0.9342, top5_acc: 0.9966, mean_class_accuracy: 0.9062 +2025-06-25 06:30:05,262 - pyskl - INFO - Epoch [125][100/1281] lr: 1.797e-03, eta: 3:51:42, time: 0.803, data_time: 0.188, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0397, loss: 0.0397 +2025-06-25 06:30:53,976 - pyskl - INFO - Epoch [125][200/1281] lr: 1.787e-03, eta: 3:51:02, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0347, loss: 0.0347 +2025-06-25 06:31:42,799 - pyskl - INFO - Epoch [125][300/1281] lr: 1.776e-03, eta: 3:50:22, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0349, loss: 0.0349 +2025-06-25 06:32:15,476 - pyskl - INFO - Epoch [125][400/1281] lr: 1.766e-03, eta: 3:49:38, time: 0.327, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0370, loss: 0.0370 +2025-06-25 06:33:06,315 - pyskl - INFO - Epoch [125][500/1281] lr: 1.755e-03, eta: 3:48:58, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0329, loss: 0.0329 +2025-06-25 06:33:31,666 - pyskl - INFO - Epoch [125][600/1281] lr: 1.745e-03, eta: 3:48:12, time: 0.254, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0293, loss: 0.0293 +2025-06-25 06:34:20,339 - pyskl - INFO - Epoch [125][700/1281] lr: 1.735e-03, eta: 3:47:32, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0390, loss: 0.0390 +2025-06-25 06:35:09,256 - pyskl - INFO - Epoch [125][800/1281] lr: 1.724e-03, eta: 3:46:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0474, loss: 0.0474 +2025-06-25 06:35:58,149 - pyskl - INFO - Epoch [125][900/1281] lr: 1.714e-03, eta: 3:46:11, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0431, loss: 0.0431 +2025-06-25 06:36:47,154 - pyskl - INFO - Epoch [125][1000/1281] lr: 1.704e-03, eta: 3:45:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0330, loss: 0.0330 +2025-06-25 06:37:36,459 - pyskl - INFO - Epoch [125][1100/1281] lr: 1.693e-03, eta: 3:44:50, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0415, loss: 0.0415 +2025-06-25 06:38:25,634 - pyskl - INFO - Epoch [125][1200/1281] lr: 1.683e-03, eta: 3:44:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0291, loss: 0.0291 +2025-06-25 06:39:05,618 - pyskl - INFO - Saving checkpoint at 125 epochs +2025-06-25 06:40:04,026 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:40:04,085 - pyskl - INFO - +top1_acc 0.9282 +top5_acc 0.9962 +2025-06-25 06:40:04,085 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:40:04,094 - pyskl - INFO - +mean_acc 0.9054 +2025-06-25 06:40:04,096 - pyskl - INFO - Epoch(val) [125][533] top1_acc: 0.9282, top5_acc: 0.9962, mean_class_accuracy: 0.9054 +2025-06-25 06:41:25,181 - pyskl - INFO - Epoch [126][100/1281] lr: 1.665e-03, eta: 3:42:55, time: 0.811, data_time: 0.192, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0392, loss: 0.0392 +2025-06-25 06:42:13,812 - pyskl - INFO - Epoch [126][200/1281] lr: 1.654e-03, eta: 3:42:15, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0386, loss: 0.0386 +2025-06-25 06:43:02,766 - pyskl - INFO - Epoch [126][300/1281] lr: 1.644e-03, eta: 3:41:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0343, loss: 0.0343 +2025-06-25 06:43:33,546 - pyskl - INFO - Epoch [126][400/1281] lr: 1.634e-03, eta: 3:40:50, time: 0.308, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0431, loss: 0.0431 +2025-06-25 06:44:24,406 - pyskl - INFO - Epoch [126][500/1281] lr: 1.624e-03, eta: 3:40:10, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0408, loss: 0.0408 +2025-06-25 06:44:51,658 - pyskl - INFO - Epoch [126][600/1281] lr: 1.614e-03, eta: 3:39:25, time: 0.273, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0315, loss: 0.0315 +2025-06-25 06:45:40,641 - pyskl - INFO - Epoch [126][700/1281] lr: 1.604e-03, eta: 3:38:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0461, loss: 0.0461 +2025-06-25 06:46:29,400 - pyskl - INFO - Epoch [126][800/1281] lr: 1.594e-03, eta: 3:38:04, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0339, loss: 0.0339 +2025-06-25 06:47:17,960 - pyskl - INFO - Epoch [126][900/1281] lr: 1.584e-03, eta: 3:37:23, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0375, loss: 0.0375 +2025-06-25 06:48:06,883 - pyskl - INFO - Epoch [126][1000/1281] lr: 1.574e-03, eta: 3:36:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0352, loss: 0.0352 +2025-06-25 06:48:55,947 - pyskl - INFO - Epoch [126][1100/1281] lr: 1.564e-03, eta: 3:36:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0333, loss: 0.0333 +2025-06-25 06:49:44,691 - pyskl - INFO - Epoch [126][1200/1281] lr: 1.554e-03, eta: 3:35:22, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0306, loss: 0.0306 +2025-06-25 06:50:24,848 - pyskl - INFO - Saving checkpoint at 126 epochs +2025-06-25 06:51:23,020 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:51:23,092 - pyskl - INFO - +top1_acc 0.9371 +top5_acc 0.9961 +2025-06-25 06:51:23,092 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:51:23,100 - pyskl - INFO - +mean_acc 0.9128 +2025-06-25 06:51:23,105 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_121.pth was removed +2025-06-25 06:51:23,443 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_126.pth. +2025-06-25 06:51:23,443 - pyskl - INFO - Best top1_acc is 0.9371 at 126 epoch. +2025-06-25 06:51:23,446 - pyskl - INFO - Epoch(val) [126][533] top1_acc: 0.9371, top5_acc: 0.9961, mean_class_accuracy: 0.9128 +2025-06-25 06:52:43,639 - pyskl - INFO - Epoch [127][100/1281] lr: 1.536e-03, eta: 3:34:07, time: 0.802, data_time: 0.193, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0295, loss: 0.0295 +2025-06-25 06:53:32,222 - pyskl - INFO - Epoch [127][200/1281] lr: 1.527e-03, eta: 3:33:26, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0441, loss: 0.0441 +2025-06-25 06:54:21,408 - pyskl - INFO - Epoch [127][300/1281] lr: 1.517e-03, eta: 3:32:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0318, loss: 0.0318 +2025-06-25 06:54:50,856 - pyskl - INFO - Epoch [127][400/1281] lr: 1.507e-03, eta: 3:32:01, time: 0.294, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0347, loss: 0.0347 +2025-06-25 06:55:41,761 - pyskl - INFO - Epoch [127][500/1281] lr: 1.497e-03, eta: 3:31:21, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0356, loss: 0.0356 +2025-06-25 06:56:11,214 - pyskl - INFO - Epoch [127][600/1281] lr: 1.488e-03, eta: 3:30:37, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0329, loss: 0.0329 +2025-06-25 06:56:59,939 - pyskl - INFO - Epoch [127][700/1281] lr: 1.478e-03, eta: 3:29:56, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0298, loss: 0.0298 +2025-06-25 06:57:48,632 - pyskl - INFO - Epoch [127][800/1281] lr: 1.468e-03, eta: 3:29:15, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0256, loss: 0.0256 +2025-06-25 06:58:37,681 - pyskl - INFO - Epoch [127][900/1281] lr: 1.459e-03, eta: 3:28:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0366, loss: 0.0366 +2025-06-25 06:59:26,655 - pyskl - INFO - Epoch [127][1000/1281] lr: 1.449e-03, eta: 3:27:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0382, loss: 0.0382 +2025-06-25 07:00:15,328 - pyskl - INFO - Epoch [127][1100/1281] lr: 1.440e-03, eta: 3:27:13, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0421, loss: 0.0421 +2025-06-25 07:01:04,504 - pyskl - INFO - Epoch [127][1200/1281] lr: 1.430e-03, eta: 3:26:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0406, loss: 0.0406 +2025-06-25 07:01:44,633 - pyskl - INFO - Saving checkpoint at 127 epochs +2025-06-25 07:02:42,820 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:02:42,877 - pyskl - INFO - +top1_acc 0.9376 +top5_acc 0.9964 +2025-06-25 07:02:42,877 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:02:42,887 - pyskl - INFO - +mean_acc 0.9135 +2025-06-25 07:02:42,904 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_126.pth was removed +2025-06-25 07:02:43,088 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_127.pth. +2025-06-25 07:02:43,088 - pyskl - INFO - Best top1_acc is 0.9376 at 127 epoch. +2025-06-25 07:02:43,091 - pyskl - INFO - Epoch(val) [127][533] top1_acc: 0.9376, top5_acc: 0.9964, mean_class_accuracy: 0.9135 +2025-06-25 07:04:02,252 - pyskl - INFO - Epoch [128][100/1281] lr: 1.413e-03, eta: 3:25:17, time: 0.792, data_time: 0.186, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0298, loss: 0.0298 +2025-06-25 07:04:50,779 - pyskl - INFO - Epoch [128][200/1281] lr: 1.404e-03, eta: 3:24:36, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0270, loss: 0.0270 +2025-06-25 07:05:39,400 - pyskl - INFO - Epoch [128][300/1281] lr: 1.394e-03, eta: 3:23:56, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0269, loss: 0.0269 +2025-06-25 07:06:07,821 - pyskl - INFO - Epoch [128][400/1281] lr: 1.385e-03, eta: 3:23:11, time: 0.284, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0285, loss: 0.0285 +2025-06-25 07:06:58,526 - pyskl - INFO - Epoch [128][500/1281] lr: 1.376e-03, eta: 3:22:31, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0282, loss: 0.0282 +2025-06-25 07:07:27,881 - pyskl - INFO - Epoch [128][600/1281] lr: 1.366e-03, eta: 3:21:47, time: 0.294, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0271, loss: 0.0271 +2025-06-25 07:08:16,602 - pyskl - INFO - Epoch [128][700/1281] lr: 1.357e-03, eta: 3:21:06, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0320, loss: 0.0320 +2025-06-25 07:09:05,605 - pyskl - INFO - Epoch [128][800/1281] lr: 1.348e-03, eta: 3:20:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 07:09:54,625 - pyskl - INFO - Epoch [128][900/1281] lr: 1.339e-03, eta: 3:19:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0252, loss: 0.0252 +2025-06-25 07:10:43,821 - pyskl - INFO - Epoch [128][1000/1281] lr: 1.329e-03, eta: 3:19:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0228, loss: 0.0228 +2025-06-25 07:11:32,815 - pyskl - INFO - Epoch [128][1100/1281] lr: 1.320e-03, eta: 3:18:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 07:12:21,687 - pyskl - INFO - Epoch [128][1200/1281] lr: 1.311e-03, eta: 3:17:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0246, loss: 0.0246 +2025-06-25 07:13:02,144 - pyskl - INFO - Saving checkpoint at 128 epochs +2025-06-25 07:14:00,417 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:14:00,473 - pyskl - INFO - +top1_acc 0.9409 +top5_acc 0.9973 +2025-06-25 07:14:00,474 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:14:00,481 - pyskl - INFO - +mean_acc 0.9172 +2025-06-25 07:14:00,485 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_127.pth was removed +2025-06-25 07:14:00,841 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_128.pth. +2025-06-25 07:14:00,841 - pyskl - INFO - Best top1_acc is 0.9409 at 128 epoch. +2025-06-25 07:14:00,844 - pyskl - INFO - Epoch(val) [128][533] top1_acc: 0.9409, top5_acc: 0.9973, mean_class_accuracy: 0.9172 +2025-06-25 07:15:19,222 - pyskl - INFO - Epoch [129][100/1281] lr: 1.295e-03, eta: 3:16:27, time: 0.784, data_time: 0.192, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 07:16:07,785 - pyskl - INFO - Epoch [129][200/1281] lr: 1.286e-03, eta: 3:15:46, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 07:16:56,537 - pyskl - INFO - Epoch [129][300/1281] lr: 1.277e-03, eta: 3:15:05, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 07:17:26,062 - pyskl - INFO - Epoch [129][400/1281] lr: 1.268e-03, eta: 3:14:21, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0306, loss: 0.0306 +2025-06-25 07:18:16,731 - pyskl - INFO - Epoch [129][500/1281] lr: 1.259e-03, eta: 3:13:41, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0265, loss: 0.0265 +2025-06-25 07:18:43,833 - pyskl - INFO - Epoch [129][600/1281] lr: 1.250e-03, eta: 3:12:56, time: 0.271, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0251, loss: 0.0251 +2025-06-25 07:19:32,932 - pyskl - INFO - Epoch [129][700/1281] lr: 1.241e-03, eta: 3:12:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-25 07:20:21,799 - pyskl - INFO - Epoch [129][800/1281] lr: 1.232e-03, eta: 3:11:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 0.9994, loss_cls: 0.0311, loss: 0.0311 +2025-06-25 07:21:10,849 - pyskl - INFO - Epoch [129][900/1281] lr: 1.223e-03, eta: 3:10:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0276, loss: 0.0276 +2025-06-25 07:21:59,826 - pyskl - INFO - Epoch [129][1000/1281] lr: 1.214e-03, eta: 3:10:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-25 07:22:48,766 - pyskl - INFO - Epoch [129][1100/1281] lr: 1.206e-03, eta: 3:09:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 07:23:37,418 - pyskl - INFO - Epoch [129][1200/1281] lr: 1.197e-03, eta: 3:08:51, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0226, loss: 0.0226 +2025-06-25 07:24:17,619 - pyskl - INFO - Saving checkpoint at 129 epochs +2025-06-25 07:25:15,522 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:25:15,577 - pyskl - INFO - +top1_acc 0.9396 +top5_acc 0.9962 +2025-06-25 07:25:15,577 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:25:15,584 - pyskl - INFO - +mean_acc 0.9159 +2025-06-25 07:25:15,585 - pyskl - INFO - Epoch(val) [129][533] top1_acc: 0.9396, top5_acc: 0.9962, mean_class_accuracy: 0.9159 +2025-06-25 07:26:34,225 - pyskl - INFO - Epoch [130][100/1281] lr: 1.181e-03, eta: 3:07:35, time: 0.786, data_time: 0.192, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0239, loss: 0.0239 +2025-06-25 07:27:22,953 - pyskl - INFO - Epoch [130][200/1281] lr: 1.172e-03, eta: 3:06:55, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 07:28:11,823 - pyskl - INFO - Epoch [130][300/1281] lr: 1.164e-03, eta: 3:06:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0258, loss: 0.0258 +2025-06-25 07:28:44,160 - pyskl - INFO - Epoch [130][400/1281] lr: 1.155e-03, eta: 3:05:30, time: 0.323, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0235, loss: 0.0235 +2025-06-25 07:29:34,956 - pyskl - INFO - Epoch [130][500/1281] lr: 1.147e-03, eta: 3:04:49, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-25 07:30:01,166 - pyskl - INFO - Epoch [130][600/1281] lr: 1.138e-03, eta: 3:04:05, time: 0.262, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0303, loss: 0.0303 +2025-06-25 07:30:50,075 - pyskl - INFO - Epoch [130][700/1281] lr: 1.130e-03, eta: 3:03:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0309, loss: 0.0309 +2025-06-25 07:31:38,805 - pyskl - INFO - Epoch [130][800/1281] lr: 1.121e-03, eta: 3:02:43, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0280, loss: 0.0280 +2025-06-25 07:32:27,914 - pyskl - INFO - Epoch [130][900/1281] lr: 1.113e-03, eta: 3:02:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0271, loss: 0.0271 +2025-06-25 07:33:16,926 - pyskl - INFO - Epoch [130][1000/1281] lr: 1.104e-03, eta: 3:01:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-25 07:34:05,747 - pyskl - INFO - Epoch [130][1100/1281] lr: 1.096e-03, eta: 3:00:40, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0368, loss: 0.0368 +2025-06-25 07:34:54,393 - pyskl - INFO - Epoch [130][1200/1281] lr: 1.088e-03, eta: 2:59:59, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 07:35:34,437 - pyskl - INFO - Saving checkpoint at 130 epochs +2025-06-25 07:36:32,771 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:36:32,826 - pyskl - INFO - +top1_acc 0.9405 +top5_acc 0.9966 +2025-06-25 07:36:32,827 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:36:32,833 - pyskl - INFO - +mean_acc 0.9164 +2025-06-25 07:36:32,835 - pyskl - INFO - Epoch(val) [130][533] top1_acc: 0.9405, top5_acc: 0.9966, mean_class_accuracy: 0.9164 +2025-06-25 07:37:52,236 - pyskl - INFO - Epoch [131][100/1281] lr: 1.072e-03, eta: 2:58:44, time: 0.794, data_time: 0.192, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0232, loss: 0.0232 +2025-06-25 07:38:41,127 - pyskl - INFO - Epoch [131][200/1281] lr: 1.064e-03, eta: 2:58:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0259, loss: 0.0259 +2025-06-25 07:39:29,799 - pyskl - INFO - Epoch [131][300/1281] lr: 1.056e-03, eta: 2:57:22, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 07:40:02,191 - pyskl - INFO - Epoch [131][400/1281] lr: 1.048e-03, eta: 2:56:38, time: 0.324, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 07:40:52,860 - pyskl - INFO - Epoch [131][500/1281] lr: 1.040e-03, eta: 2:55:58, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0228, loss: 0.0228 +2025-06-25 07:41:18,133 - pyskl - INFO - Epoch [131][600/1281] lr: 1.031e-03, eta: 2:55:13, time: 0.253, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-25 07:42:06,621 - pyskl - INFO - Epoch [131][700/1281] lr: 1.023e-03, eta: 2:54:32, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 07:42:55,451 - pyskl - INFO - Epoch [131][800/1281] lr: 1.015e-03, eta: 2:53:51, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0293, loss: 0.0293 +2025-06-25 07:43:44,671 - pyskl - INFO - Epoch [131][900/1281] lr: 1.007e-03, eta: 2:53:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-25 07:44:33,332 - pyskl - INFO - Epoch [131][1000/1281] lr: 9.992e-04, eta: 2:52:29, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 07:45:21,950 - pyskl - INFO - Epoch [131][1100/1281] lr: 9.912e-04, eta: 2:51:48, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0235, loss: 0.0235 +2025-06-25 07:46:10,856 - pyskl - INFO - Epoch [131][1200/1281] lr: 9.832e-04, eta: 2:51:07, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 07:46:51,353 - pyskl - INFO - Saving checkpoint at 131 epochs +2025-06-25 07:47:49,285 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:47:49,339 - pyskl - INFO - +top1_acc 0.9419 +top5_acc 0.9962 +2025-06-25 07:47:49,339 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:47:49,349 - pyskl - INFO - +mean_acc 0.9210 +2025-06-25 07:47:49,353 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_128.pth was removed +2025-06-25 07:47:49,521 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_131.pth. +2025-06-25 07:47:49,521 - pyskl - INFO - Best top1_acc is 0.9419 at 131 epoch. +2025-06-25 07:47:49,524 - pyskl - INFO - Epoch(val) [131][533] top1_acc: 0.9419, top5_acc: 0.9962, mean_class_accuracy: 0.9210 +2025-06-25 07:49:07,510 - pyskl - INFO - Epoch [132][100/1281] lr: 9.689e-04, eta: 2:49:51, time: 0.780, data_time: 0.189, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-25 07:49:56,201 - pyskl - INFO - Epoch [132][200/1281] lr: 9.610e-04, eta: 2:49:10, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-25 07:50:45,119 - pyskl - INFO - Epoch [132][300/1281] lr: 9.532e-04, eta: 2:48:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 07:51:19,760 - pyskl - INFO - Epoch [132][400/1281] lr: 9.454e-04, eta: 2:47:46, time: 0.346, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 07:52:10,376 - pyskl - INFO - Epoch [132][500/1281] lr: 9.376e-04, eta: 2:47:05, time: 0.506, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 07:52:35,044 - pyskl - INFO - Epoch [132][600/1281] lr: 9.298e-04, eta: 2:46:20, time: 0.247, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0218, loss: 0.0218 +2025-06-25 07:53:22,597 - pyskl - INFO - Epoch [132][700/1281] lr: 9.221e-04, eta: 2:45:39, time: 0.476, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0281, loss: 0.0281 +2025-06-25 07:54:11,893 - pyskl - INFO - Epoch [132][800/1281] lr: 9.144e-04, eta: 2:44:58, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0226, loss: 0.0226 +2025-06-25 07:54:59,779 - pyskl - INFO - Epoch [132][900/1281] lr: 9.068e-04, eta: 2:44:17, time: 0.479, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 07:55:48,974 - pyskl - INFO - Epoch [132][1000/1281] lr: 8.991e-04, eta: 2:43:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0289, loss: 0.0289 +2025-06-25 07:56:38,017 - pyskl - INFO - Epoch [132][1100/1281] lr: 8.915e-04, eta: 2:42:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0291, loss: 0.0291 +2025-06-25 07:57:26,884 - pyskl - INFO - Epoch [132][1200/1281] lr: 8.840e-04, eta: 2:42:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0303, loss: 0.0303 +2025-06-25 07:58:07,216 - pyskl - INFO - Saving checkpoint at 132 epochs +2025-06-25 07:59:05,125 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:59:05,180 - pyskl - INFO - +top1_acc 0.9383 +top5_acc 0.9962 +2025-06-25 07:59:05,180 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:59:05,186 - pyskl - INFO - +mean_acc 0.9164 +2025-06-25 07:59:05,188 - pyskl - INFO - Epoch(val) [132][533] top1_acc: 0.9383, top5_acc: 0.9962, mean_class_accuracy: 0.9164 +2025-06-25 08:00:24,311 - pyskl - INFO - Epoch [133][100/1281] lr: 8.704e-04, eta: 2:40:58, time: 0.791, data_time: 0.187, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 08:01:12,846 - pyskl - INFO - Epoch [133][200/1281] lr: 8.629e-04, eta: 2:40:17, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 08:02:01,984 - pyskl - INFO - Epoch [133][300/1281] lr: 8.554e-04, eta: 2:39:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0261, loss: 0.0261 +2025-06-25 08:02:38,162 - pyskl - INFO - Epoch [133][400/1281] lr: 8.480e-04, eta: 2:38:53, time: 0.362, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0229, loss: 0.0229 +2025-06-25 08:03:28,845 - pyskl - INFO - Epoch [133][500/1281] lr: 8.406e-04, eta: 2:38:12, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 08:03:52,596 - pyskl - INFO - Epoch [133][600/1281] lr: 8.333e-04, eta: 2:37:27, time: 0.237, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0256, loss: 0.0256 +2025-06-25 08:04:37,401 - pyskl - INFO - Epoch [133][700/1281] lr: 8.260e-04, eta: 2:36:45, time: 0.448, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0235, loss: 0.0235 +2025-06-25 08:05:26,127 - pyskl - INFO - Epoch [133][800/1281] lr: 8.187e-04, eta: 2:36:04, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0277, loss: 0.0277 +2025-06-25 08:06:15,216 - pyskl - INFO - Epoch [133][900/1281] lr: 8.114e-04, eta: 2:35:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0222, loss: 0.0222 +2025-06-25 08:07:04,041 - pyskl - INFO - Epoch [133][1000/1281] lr: 8.042e-04, eta: 2:34:42, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0246, loss: 0.0246 +2025-06-25 08:07:52,818 - pyskl - INFO - Epoch [133][1100/1281] lr: 7.970e-04, eta: 2:34:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0292, loss: 0.0292 +2025-06-25 08:08:41,665 - pyskl - INFO - Epoch [133][1200/1281] lr: 7.898e-04, eta: 2:33:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0278, loss: 0.0278 +2025-06-25 08:09:21,911 - pyskl - INFO - Saving checkpoint at 133 epochs +2025-06-25 08:10:19,884 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:10:19,939 - pyskl - INFO - +top1_acc 0.9410 +top5_acc 0.9964 +2025-06-25 08:10:19,939 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:10:19,946 - pyskl - INFO - +mean_acc 0.9172 +2025-06-25 08:10:19,948 - pyskl - INFO - Epoch(val) [133][533] top1_acc: 0.9410, top5_acc: 0.9964, mean_class_accuracy: 0.9172 +2025-06-25 08:11:38,131 - pyskl - INFO - Epoch [134][100/1281] lr: 7.769e-04, eta: 2:32:04, time: 0.782, data_time: 0.189, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0276, loss: 0.0276 +2025-06-25 08:12:26,793 - pyskl - INFO - Epoch [134][200/1281] lr: 7.699e-04, eta: 2:31:22, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 08:13:15,756 - pyskl - INFO - Epoch [134][300/1281] lr: 7.628e-04, eta: 2:30:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0268, loss: 0.0268 +2025-06-25 08:13:54,896 - pyskl - INFO - Epoch [134][400/1281] lr: 7.558e-04, eta: 2:29:59, time: 0.391, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 08:14:45,192 - pyskl - INFO - Epoch [134][500/1281] lr: 7.488e-04, eta: 2:29:18, time: 0.503, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-25 08:15:08,228 - pyskl - INFO - Epoch [134][600/1281] lr: 7.419e-04, eta: 2:28:33, time: 0.230, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0274, loss: 0.0274 +2025-06-25 08:15:51,324 - pyskl - INFO - Epoch [134][700/1281] lr: 7.349e-04, eta: 2:27:51, time: 0.431, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0254, loss: 0.0254 +2025-06-25 08:16:40,088 - pyskl - INFO - Epoch [134][800/1281] lr: 7.281e-04, eta: 2:27:10, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0239, loss: 0.0239 +2025-06-25 08:17:28,910 - pyskl - INFO - Epoch [134][900/1281] lr: 7.212e-04, eta: 2:26:29, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0222, loss: 0.0222 +2025-06-25 08:18:17,657 - pyskl - INFO - Epoch [134][1000/1281] lr: 7.144e-04, eta: 2:25:47, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0237, loss: 0.0237 +2025-06-25 08:19:06,465 - pyskl - INFO - Epoch [134][1100/1281] lr: 7.076e-04, eta: 2:25:06, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-25 08:19:55,400 - pyskl - INFO - Epoch [134][1200/1281] lr: 7.008e-04, eta: 2:24:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0218, loss: 0.0218 +2025-06-25 08:20:35,402 - pyskl - INFO - Saving checkpoint at 134 epochs +2025-06-25 08:21:33,047 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:21:33,102 - pyskl - INFO - +top1_acc 0.9409 +top5_acc 0.9965 +2025-06-25 08:21:33,102 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:21:33,109 - pyskl - INFO - +mean_acc 0.9176 +2025-06-25 08:21:33,111 - pyskl - INFO - Epoch(val) [134][533] top1_acc: 0.9409, top5_acc: 0.9965, mean_class_accuracy: 0.9176 +2025-06-25 08:22:53,286 - pyskl - INFO - Epoch [135][100/1281] lr: 6.887e-04, eta: 2:23:09, time: 0.802, data_time: 0.187, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 08:23:42,254 - pyskl - INFO - Epoch [135][200/1281] lr: 6.820e-04, eta: 2:22:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-25 08:24:31,036 - pyskl - INFO - Epoch [135][300/1281] lr: 6.753e-04, eta: 2:21:46, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 08:25:11,563 - pyskl - INFO - Epoch [135][400/1281] lr: 6.687e-04, eta: 2:21:04, time: 0.405, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 08:26:01,590 - pyskl - INFO - Epoch [135][500/1281] lr: 6.622e-04, eta: 2:20:23, time: 0.500, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-25 08:26:25,128 - pyskl - INFO - Epoch [135][600/1281] lr: 6.556e-04, eta: 2:19:39, time: 0.235, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0268, loss: 0.0268 +2025-06-25 08:27:08,300 - pyskl - INFO - Epoch [135][700/1281] lr: 6.491e-04, eta: 2:18:57, time: 0.432, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 08:27:57,230 - pyskl - INFO - Epoch [135][800/1281] lr: 6.426e-04, eta: 2:18:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 08:28:46,316 - pyskl - INFO - Epoch [135][900/1281] lr: 6.362e-04, eta: 2:17:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 08:29:34,982 - pyskl - INFO - Epoch [135][1000/1281] lr: 6.297e-04, eta: 2:16:52, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-25 08:30:23,924 - pyskl - INFO - Epoch [135][1100/1281] lr: 6.233e-04, eta: 2:16:11, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-25 08:31:13,068 - pyskl - INFO - Epoch [135][1200/1281] lr: 6.170e-04, eta: 2:15:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0229, loss: 0.0229 +2025-06-25 08:31:53,188 - pyskl - INFO - Saving checkpoint at 135 epochs +2025-06-25 08:32:51,092 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:32:51,159 - pyskl - INFO - +top1_acc 0.9425 +top5_acc 0.9966 +2025-06-25 08:32:51,159 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:32:51,166 - pyskl - INFO - +mean_acc 0.9184 +2025-06-25 08:32:51,172 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_131.pth was removed +2025-06-25 08:32:51,345 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_135.pth. +2025-06-25 08:32:51,345 - pyskl - INFO - Best top1_acc is 0.9425 at 135 epoch. +2025-06-25 08:32:51,348 - pyskl - INFO - Epoch(val) [135][533] top1_acc: 0.9425, top5_acc: 0.9966, mean_class_accuracy: 0.9184 +2025-06-25 08:34:10,860 - pyskl - INFO - Epoch [136][100/1281] lr: 6.056e-04, eta: 2:14:14, time: 0.795, data_time: 0.186, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 08:34:59,395 - pyskl - INFO - Epoch [136][200/1281] lr: 5.993e-04, eta: 2:13:32, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 08:35:47,862 - pyskl - INFO - Epoch [136][300/1281] lr: 5.931e-04, eta: 2:12:51, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 08:36:28,311 - pyskl - INFO - Epoch [136][400/1281] lr: 5.868e-04, eta: 2:12:09, time: 0.404, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0232, loss: 0.0232 +2025-06-25 08:37:17,090 - pyskl - INFO - Epoch [136][500/1281] lr: 5.807e-04, eta: 2:11:27, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0235, loss: 0.0235 +2025-06-25 08:37:41,485 - pyskl - INFO - Epoch [136][600/1281] lr: 5.745e-04, eta: 2:10:43, time: 0.244, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0311, loss: 0.0311 +2025-06-25 08:38:24,116 - pyskl - INFO - Epoch [136][700/1281] lr: 5.684e-04, eta: 2:10:01, time: 0.426, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 08:39:13,309 - pyskl - INFO - Epoch [136][800/1281] lr: 5.623e-04, eta: 2:09:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0218, loss: 0.0218 +2025-06-25 08:40:01,817 - pyskl - INFO - Epoch [136][900/1281] lr: 5.563e-04, eta: 2:08:38, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 08:40:50,712 - pyskl - INFO - Epoch [136][1000/1281] lr: 5.503e-04, eta: 2:07:57, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-25 08:41:39,611 - pyskl - INFO - Epoch [136][1100/1281] lr: 5.443e-04, eta: 2:07:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 08:42:28,293 - pyskl - INFO - Epoch [136][1200/1281] lr: 5.384e-04, eta: 2:06:34, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 08:43:08,145 - pyskl - INFO - Saving checkpoint at 136 epochs +2025-06-25 08:44:05,911 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:44:05,966 - pyskl - INFO - +top1_acc 0.9428 +top5_acc 0.9966 +2025-06-25 08:44:05,966 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:44:05,972 - pyskl - INFO - +mean_acc 0.9197 +2025-06-25 08:44:05,976 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_135.pth was removed +2025-06-25 08:44:06,147 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_136.pth. +2025-06-25 08:44:06,148 - pyskl - INFO - Best top1_acc is 0.9428 at 136 epoch. +2025-06-25 08:44:06,150 - pyskl - INFO - Epoch(val) [136][533] top1_acc: 0.9428, top5_acc: 0.9966, mean_class_accuracy: 0.9197 +2025-06-25 08:45:24,511 - pyskl - INFO - Epoch [137][100/1281] lr: 5.277e-04, eta: 2:05:18, time: 0.784, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 08:46:13,517 - pyskl - INFO - Epoch [137][200/1281] lr: 5.218e-04, eta: 2:04:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 08:47:02,404 - pyskl - INFO - Epoch [137][300/1281] lr: 5.160e-04, eta: 2:03:55, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 08:47:45,436 - pyskl - INFO - Epoch [137][400/1281] lr: 5.102e-04, eta: 2:03:13, time: 0.430, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 08:48:32,211 - pyskl - INFO - Epoch [137][500/1281] lr: 5.044e-04, eta: 2:02:31, time: 0.468, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 08:48:58,320 - pyskl - INFO - Epoch [137][600/1281] lr: 4.987e-04, eta: 2:01:47, time: 0.261, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0257, loss: 0.0257 +2025-06-25 08:49:39,712 - pyskl - INFO - Epoch [137][700/1281] lr: 4.930e-04, eta: 2:01:05, time: 0.414, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 08:50:28,872 - pyskl - INFO - Epoch [137][800/1281] lr: 4.873e-04, eta: 2:00:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 08:51:17,908 - pyskl - INFO - Epoch [137][900/1281] lr: 4.817e-04, eta: 1:59:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 08:52:06,899 - pyskl - INFO - Epoch [137][1000/1281] lr: 4.761e-04, eta: 1:59:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 08:52:55,741 - pyskl - INFO - Epoch [137][1100/1281] lr: 4.705e-04, eta: 1:58:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 08:53:44,560 - pyskl - INFO - Epoch [137][1200/1281] lr: 4.650e-04, eta: 1:57:38, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0229, loss: 0.0229 +2025-06-25 08:54:24,843 - pyskl - INFO - Saving checkpoint at 137 epochs +2025-06-25 08:55:22,318 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:55:22,373 - pyskl - INFO - +top1_acc 0.9420 +top5_acc 0.9971 +2025-06-25 08:55:22,373 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:55:22,380 - pyskl - INFO - +mean_acc 0.9197 +2025-06-25 08:55:22,382 - pyskl - INFO - Epoch(val) [137][533] top1_acc: 0.9420, top5_acc: 0.9971, mean_class_accuracy: 0.9197 +2025-06-25 08:56:40,086 - pyskl - INFO - Epoch [138][100/1281] lr: 4.550e-04, eta: 1:56:21, time: 0.777, data_time: 0.184, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 08:57:28,861 - pyskl - INFO - Epoch [138][200/1281] lr: 4.496e-04, eta: 1:55:40, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 08:58:17,737 - pyskl - INFO - Epoch [138][300/1281] lr: 4.442e-04, eta: 1:54:58, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 08:59:02,980 - pyskl - INFO - Epoch [138][400/1281] lr: 4.388e-04, eta: 1:54:16, time: 0.452, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 08:59:43,647 - pyskl - INFO - Epoch [138][500/1281] lr: 4.334e-04, eta: 1:53:34, time: 0.407, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0265, loss: 0.0265 +2025-06-25 09:00:15,949 - pyskl - INFO - Epoch [138][600/1281] lr: 4.281e-04, eta: 1:52:51, time: 0.323, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 09:00:53,801 - pyskl - INFO - Epoch [138][700/1281] lr: 4.228e-04, eta: 1:52:08, time: 0.379, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 09:01:42,566 - pyskl - INFO - Epoch [138][800/1281] lr: 4.176e-04, eta: 1:51:27, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0222, loss: 0.0222 +2025-06-25 09:02:31,368 - pyskl - INFO - Epoch [138][900/1281] lr: 4.124e-04, eta: 1:50:45, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 09:03:20,180 - pyskl - INFO - Epoch [138][1000/1281] lr: 4.072e-04, eta: 1:50:04, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0237, loss: 0.0237 +2025-06-25 09:04:08,907 - pyskl - INFO - Epoch [138][1100/1281] lr: 4.020e-04, eta: 1:49:22, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 09:04:57,732 - pyskl - INFO - Epoch [138][1200/1281] lr: 3.969e-04, eta: 1:48:40, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 09:05:37,831 - pyskl - INFO - Saving checkpoint at 138 epochs +2025-06-25 09:06:35,737 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:06:35,805 - pyskl - INFO - +top1_acc 0.9406 +top5_acc 0.9973 +2025-06-25 09:06:35,805 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:06:35,814 - pyskl - INFO - +mean_acc 0.9178 +2025-06-25 09:06:35,816 - pyskl - INFO - Epoch(val) [138][533] top1_acc: 0.9406, top5_acc: 0.9973, mean_class_accuracy: 0.9178 +2025-06-25 09:07:55,149 - pyskl - INFO - Epoch [139][100/1281] lr: 3.877e-04, eta: 1:47:24, time: 0.793, data_time: 0.188, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 09:08:44,239 - pyskl - INFO - Epoch [139][200/1281] lr: 3.827e-04, eta: 1:46:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0255, loss: 0.0255 +2025-06-25 09:09:33,030 - pyskl - INFO - Epoch [139][300/1281] lr: 3.777e-04, eta: 1:46:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-25 09:10:20,755 - pyskl - INFO - Epoch [139][400/1281] lr: 3.727e-04, eta: 1:45:19, time: 0.477, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 09:10:56,752 - pyskl - INFO - Epoch [139][500/1281] lr: 3.678e-04, eta: 1:44:36, time: 0.360, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 09:11:33,674 - pyskl - INFO - Epoch [139][600/1281] lr: 3.628e-04, eta: 1:43:54, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 09:12:11,240 - pyskl - INFO - Epoch [139][700/1281] lr: 3.580e-04, eta: 1:43:11, time: 0.376, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 09:13:00,069 - pyskl - INFO - Epoch [139][800/1281] lr: 3.531e-04, eta: 1:42:30, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 09:13:48,945 - pyskl - INFO - Epoch [139][900/1281] lr: 3.483e-04, eta: 1:41:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 09:14:37,930 - pyskl - INFO - Epoch [139][1000/1281] lr: 3.436e-04, eta: 1:41:06, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 09:15:26,732 - pyskl - INFO - Epoch [139][1100/1281] lr: 3.388e-04, eta: 1:40:25, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-25 09:16:15,898 - pyskl - INFO - Epoch [139][1200/1281] lr: 3.341e-04, eta: 1:39:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 09:16:56,216 - pyskl - INFO - Saving checkpoint at 139 epochs +2025-06-25 09:17:54,082 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:17:54,137 - pyskl - INFO - +top1_acc 0.9440 +top5_acc 0.9971 +2025-06-25 09:17:54,138 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:17:54,145 - pyskl - INFO - +mean_acc 0.9227 +2025-06-25 09:17:54,149 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_136.pth was removed +2025-06-25 09:17:54,312 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_139.pth. +2025-06-25 09:17:54,313 - pyskl - INFO - Best top1_acc is 0.9440 at 139 epoch. +2025-06-25 09:17:54,315 - pyskl - INFO - Epoch(val) [139][533] top1_acc: 0.9440, top5_acc: 0.9971, mean_class_accuracy: 0.9227 +2025-06-25 09:19:14,649 - pyskl - INFO - Epoch [140][100/1281] lr: 3.257e-04, eta: 1:38:27, time: 0.803, data_time: 0.192, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 09:20:03,844 - pyskl - INFO - Epoch [140][200/1281] lr: 3.210e-04, eta: 1:37:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 09:20:52,571 - pyskl - INFO - Epoch [140][300/1281] lr: 3.165e-04, eta: 1:37:03, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 09:21:39,572 - pyskl - INFO - Epoch [140][400/1281] lr: 3.119e-04, eta: 1:36:21, time: 0.470, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 09:22:16,701 - pyskl - INFO - Epoch [140][500/1281] lr: 3.074e-04, eta: 1:35:39, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 09:22:52,430 - pyskl - INFO - Epoch [140][600/1281] lr: 3.029e-04, eta: 1:34:56, time: 0.357, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 09:23:30,489 - pyskl - INFO - Epoch [140][700/1281] lr: 2.984e-04, eta: 1:34:14, time: 0.381, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 09:24:19,722 - pyskl - INFO - Epoch [140][800/1281] lr: 2.940e-04, eta: 1:33:32, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-25 09:25:08,144 - pyskl - INFO - Epoch [140][900/1281] lr: 2.896e-04, eta: 1:32:50, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 09:25:56,826 - pyskl - INFO - Epoch [140][1000/1281] lr: 2.853e-04, eta: 1:32:08, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 09:26:45,669 - pyskl - INFO - Epoch [140][1100/1281] lr: 2.809e-04, eta: 1:31:27, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 09:27:34,782 - pyskl - INFO - Epoch [140][1200/1281] lr: 2.767e-04, eta: 1:30:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-25 09:28:15,559 - pyskl - INFO - Saving checkpoint at 140 epochs +2025-06-25 09:29:13,254 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:29:13,310 - pyskl - INFO - +top1_acc 0.9426 +top5_acc 0.9968 +2025-06-25 09:29:13,310 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:29:13,318 - pyskl - INFO - +mean_acc 0.9215 +2025-06-25 09:29:13,320 - pyskl - INFO - Epoch(val) [140][533] top1_acc: 0.9426, top5_acc: 0.9968, mean_class_accuracy: 0.9215 +2025-06-25 09:30:31,529 - pyskl - INFO - Epoch [141][100/1281] lr: 2.690e-04, eta: 1:29:29, time: 0.782, data_time: 0.189, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 09:31:20,476 - pyskl - INFO - Epoch [141][200/1281] lr: 2.648e-04, eta: 1:28:47, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 09:32:09,261 - pyskl - INFO - Epoch [141][300/1281] lr: 2.606e-04, eta: 1:28:05, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-25 09:32:57,429 - pyskl - INFO - Epoch [141][400/1281] lr: 2.565e-04, eta: 1:27:23, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0286, loss: 0.0286 +2025-06-25 09:33:31,481 - pyskl - INFO - Epoch [141][500/1281] lr: 2.524e-04, eta: 1:26:40, time: 0.340, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 09:34:10,395 - pyskl - INFO - Epoch [141][600/1281] lr: 2.483e-04, eta: 1:25:58, time: 0.389, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-25 09:34:47,277 - pyskl - INFO - Epoch [141][700/1281] lr: 2.443e-04, eta: 1:25:15, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 09:35:35,999 - pyskl - INFO - Epoch [141][800/1281] lr: 2.402e-04, eta: 1:24:33, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-25 09:36:24,848 - pyskl - INFO - Epoch [141][900/1281] lr: 2.363e-04, eta: 1:23:52, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 09:37:13,432 - pyskl - INFO - Epoch [141][1000/1281] lr: 2.323e-04, eta: 1:23:10, time: 0.486, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 09:38:02,008 - pyskl - INFO - Epoch [141][1100/1281] lr: 2.284e-04, eta: 1:22:28, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 09:38:51,057 - pyskl - INFO - Epoch [141][1200/1281] lr: 2.246e-04, eta: 1:21:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 09:39:31,320 - pyskl - INFO - Saving checkpoint at 141 epochs +2025-06-25 09:40:29,239 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:40:29,296 - pyskl - INFO - +top1_acc 0.9431 +top5_acc 0.9971 +2025-06-25 09:40:29,296 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:40:29,302 - pyskl - INFO - +mean_acc 0.9228 +2025-06-25 09:40:29,304 - pyskl - INFO - Epoch(val) [141][533] top1_acc: 0.9431, top5_acc: 0.9971, mean_class_accuracy: 0.9228 +2025-06-25 09:41:47,922 - pyskl - INFO - Epoch [142][100/1281] lr: 2.176e-04, eta: 1:20:30, time: 0.786, data_time: 0.191, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0218, loss: 0.0218 +2025-06-25 09:42:36,466 - pyskl - INFO - Epoch [142][200/1281] lr: 2.139e-04, eta: 1:19:48, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 09:43:25,545 - pyskl - INFO - Epoch [142][300/1281] lr: 2.101e-04, eta: 1:19:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 09:44:14,133 - pyskl - INFO - Epoch [142][400/1281] lr: 2.064e-04, eta: 1:18:24, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 09:44:45,778 - pyskl - INFO - Epoch [142][500/1281] lr: 2.027e-04, eta: 1:17:41, time: 0.316, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-25 09:45:26,751 - pyskl - INFO - Epoch [142][600/1281] lr: 1.991e-04, eta: 1:16:59, time: 0.410, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-25 09:46:02,163 - pyskl - INFO - Epoch [142][700/1281] lr: 1.954e-04, eta: 1:16:16, time: 0.354, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 09:46:50,682 - pyskl - INFO - Epoch [142][800/1281] lr: 1.919e-04, eta: 1:15:34, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-25 09:47:39,375 - pyskl - INFO - Epoch [142][900/1281] lr: 1.883e-04, eta: 1:14:52, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 09:48:28,341 - pyskl - INFO - Epoch [142][1000/1281] lr: 1.848e-04, eta: 1:14:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 09:49:17,205 - pyskl - INFO - Epoch [142][1100/1281] lr: 1.813e-04, eta: 1:13:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 09:50:06,159 - pyskl - INFO - Epoch [142][1200/1281] lr: 1.779e-04, eta: 1:12:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 09:50:46,469 - pyskl - INFO - Saving checkpoint at 142 epochs +2025-06-25 09:51:43,958 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:51:44,013 - pyskl - INFO - +top1_acc 0.9407 +top5_acc 0.9971 +2025-06-25 09:51:44,013 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:51:44,019 - pyskl - INFO - +mean_acc 0.9190 +2025-06-25 09:51:44,021 - pyskl - INFO - Epoch(val) [142][533] top1_acc: 0.9407, top5_acc: 0.9971, mean_class_accuracy: 0.9190 +2025-06-25 09:53:01,565 - pyskl - INFO - Epoch [143][100/1281] lr: 1.717e-04, eta: 1:11:30, time: 0.775, data_time: 0.183, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 09:53:50,487 - pyskl - INFO - Epoch [143][200/1281] lr: 1.683e-04, eta: 1:10:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 09:54:39,730 - pyskl - INFO - Epoch [143][300/1281] lr: 1.650e-04, eta: 1:10:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 09:55:28,619 - pyskl - INFO - Epoch [143][400/1281] lr: 1.617e-04, eta: 1:09:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 09:55:58,104 - pyskl - INFO - Epoch [143][500/1281] lr: 1.585e-04, eta: 1:08:42, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-25 09:56:45,009 - pyskl - INFO - Epoch [143][600/1281] lr: 1.552e-04, eta: 1:08:00, time: 0.469, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 09:57:17,609 - pyskl - INFO - Epoch [143][700/1281] lr: 1.520e-04, eta: 1:07:17, time: 0.326, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 09:58:07,034 - pyskl - INFO - Epoch [143][800/1281] lr: 1.489e-04, eta: 1:06:35, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 09:58:55,974 - pyskl - INFO - Epoch [143][900/1281] lr: 1.457e-04, eta: 1:05:53, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-25 09:59:44,858 - pyskl - INFO - Epoch [143][1000/1281] lr: 1.426e-04, eta: 1:05:11, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 10:00:33,568 - pyskl - INFO - Epoch [143][1100/1281] lr: 1.396e-04, eta: 1:04:29, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 10:01:22,531 - pyskl - INFO - Epoch [143][1200/1281] lr: 1.366e-04, eta: 1:03:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-25 10:02:02,913 - pyskl - INFO - Saving checkpoint at 143 epochs +2025-06-25 10:03:00,915 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:03:00,972 - pyskl - INFO - +top1_acc 0.9428 +top5_acc 0.9969 +2025-06-25 10:03:00,972 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:03:00,978 - pyskl - INFO - +mean_acc 0.9221 +2025-06-25 10:03:00,980 - pyskl - INFO - Epoch(val) [143][533] top1_acc: 0.9428, top5_acc: 0.9969, mean_class_accuracy: 0.9221 +2025-06-25 10:04:19,789 - pyskl - INFO - Epoch [144][100/1281] lr: 1.312e-04, eta: 1:02:30, time: 0.788, data_time: 0.187, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0251, loss: 0.0251 +2025-06-25 10:05:09,040 - pyskl - INFO - Epoch [144][200/1281] lr: 1.282e-04, eta: 1:01:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 10:05:57,985 - pyskl - INFO - Epoch [144][300/1281] lr: 1.253e-04, eta: 1:01:07, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 10:06:46,893 - pyskl - INFO - Epoch [144][400/1281] lr: 1.224e-04, eta: 1:00:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 10:07:16,186 - pyskl - INFO - Epoch [144][500/1281] lr: 1.196e-04, eta: 0:59:42, time: 0.293, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 10:08:03,587 - pyskl - INFO - Epoch [144][600/1281] lr: 1.168e-04, eta: 0:59:00, time: 0.474, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 10:08:36,151 - pyskl - INFO - Epoch [144][700/1281] lr: 1.140e-04, eta: 0:58:17, time: 0.326, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 10:09:25,114 - pyskl - INFO - Epoch [144][800/1281] lr: 1.113e-04, eta: 0:57:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 10:10:13,854 - pyskl - INFO - Epoch [144][900/1281] lr: 1.086e-04, eta: 0:56:53, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 10:11:02,899 - pyskl - INFO - Epoch [144][1000/1281] lr: 1.059e-04, eta: 0:56:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-25 10:11:52,113 - pyskl - INFO - Epoch [144][1100/1281] lr: 1.033e-04, eta: 0:55:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 10:12:41,131 - pyskl - INFO - Epoch [144][1200/1281] lr: 1.007e-04, eta: 0:54:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 10:13:21,313 - pyskl - INFO - Saving checkpoint at 144 epochs +2025-06-25 10:14:19,355 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:14:19,410 - pyskl - INFO - +top1_acc 0.9421 +top5_acc 0.9969 +2025-06-25 10:14:19,410 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:14:19,417 - pyskl - INFO - +mean_acc 0.9199 +2025-06-25 10:14:19,418 - pyskl - INFO - Epoch(val) [144][533] top1_acc: 0.9421, top5_acc: 0.9969, mean_class_accuracy: 0.9199 +2025-06-25 10:15:37,733 - pyskl - INFO - Epoch [145][100/1281] lr: 9.605e-05, eta: 0:53:30, time: 0.783, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 10:16:26,959 - pyskl - INFO - Epoch [145][200/1281] lr: 9.353e-05, eta: 0:52:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 10:17:15,617 - pyskl - INFO - Epoch [145][300/1281] lr: 9.106e-05, eta: 0:52:06, time: 0.487, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 10:18:04,480 - pyskl - INFO - Epoch [145][400/1281] lr: 8.861e-05, eta: 0:51:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0254, loss: 0.0254 +2025-06-25 10:18:31,638 - pyskl - INFO - Epoch [145][500/1281] lr: 8.620e-05, eta: 0:50:41, time: 0.272, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-25 10:19:21,501 - pyskl - INFO - Epoch [145][600/1281] lr: 8.382e-05, eta: 0:49:59, time: 0.499, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 10:19:52,222 - pyskl - INFO - Epoch [145][700/1281] lr: 8.147e-05, eta: 0:49:16, time: 0.307, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 10:20:41,218 - pyskl - INFO - Epoch [145][800/1281] lr: 7.916e-05, eta: 0:48:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 10:21:30,205 - pyskl - INFO - Epoch [145][900/1281] lr: 7.688e-05, eta: 0:47:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0226, loss: 0.0226 +2025-06-25 10:22:19,059 - pyskl - INFO - Epoch [145][1000/1281] lr: 7.463e-05, eta: 0:47:10, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 10:23:08,223 - pyskl - INFO - Epoch [145][1100/1281] lr: 7.242e-05, eta: 0:46:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 10:23:57,089 - pyskl - INFO - Epoch [145][1200/1281] lr: 7.024e-05, eta: 0:45:46, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 10:24:37,284 - pyskl - INFO - Saving checkpoint at 145 epochs +2025-06-25 10:25:35,029 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:25:35,084 - pyskl - INFO - +top1_acc 0.9417 +top5_acc 0.9969 +2025-06-25 10:25:35,084 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:25:35,091 - pyskl - INFO - +mean_acc 0.9193 +2025-06-25 10:25:35,092 - pyskl - INFO - Epoch(val) [145][533] top1_acc: 0.9417, top5_acc: 0.9969, mean_class_accuracy: 0.9193 +2025-06-25 10:26:54,053 - pyskl - INFO - Epoch [146][100/1281] lr: 6.638e-05, eta: 0:44:29, time: 0.790, data_time: 0.188, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 10:27:42,475 - pyskl - INFO - Epoch [146][200/1281] lr: 6.429e-05, eta: 0:43:47, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 10:28:31,243 - pyskl - INFO - Epoch [146][300/1281] lr: 6.224e-05, eta: 0:43:05, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 10:29:19,945 - pyskl - INFO - Epoch [146][400/1281] lr: 6.022e-05, eta: 0:42:23, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 10:29:47,610 - pyskl - INFO - Epoch [146][500/1281] lr: 5.823e-05, eta: 0:41:40, time: 0.277, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0232, loss: 0.0232 +2025-06-25 10:30:38,303 - pyskl - INFO - Epoch [146][600/1281] lr: 5.628e-05, eta: 0:40:58, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 10:31:08,321 - pyskl - INFO - Epoch [146][700/1281] lr: 5.436e-05, eta: 0:40:15, time: 0.300, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-25 10:31:57,381 - pyskl - INFO - Epoch [146][800/1281] lr: 5.247e-05, eta: 0:39:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 10:32:46,374 - pyskl - INFO - Epoch [146][900/1281] lr: 5.061e-05, eta: 0:38:51, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 10:33:35,233 - pyskl - INFO - Epoch [146][1000/1281] lr: 4.879e-05, eta: 0:38:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 10:34:24,432 - pyskl - INFO - Epoch [146][1100/1281] lr: 4.701e-05, eta: 0:37:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 10:35:13,335 - pyskl - INFO - Epoch [146][1200/1281] lr: 4.525e-05, eta: 0:36:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 10:35:53,743 - pyskl - INFO - Saving checkpoint at 146 epochs +2025-06-25 10:36:51,446 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:36:51,511 - pyskl - INFO - +top1_acc 0.9428 +top5_acc 0.9971 +2025-06-25 10:36:51,511 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:36:51,521 - pyskl - INFO - +mean_acc 0.9207 +2025-06-25 10:36:51,524 - pyskl - INFO - Epoch(val) [146][533] top1_acc: 0.9428, top5_acc: 0.9971, mean_class_accuracy: 0.9207 +2025-06-25 10:38:11,473 - pyskl - INFO - Epoch [147][100/1281] lr: 4.216e-05, eta: 0:35:28, time: 0.799, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 10:39:00,495 - pyskl - INFO - Epoch [147][200/1281] lr: 4.050e-05, eta: 0:34:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 10:39:49,168 - pyskl - INFO - Epoch [147][300/1281] lr: 3.887e-05, eta: 0:34:03, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 10:40:37,962 - pyskl - INFO - Epoch [147][400/1281] lr: 3.728e-05, eta: 0:33:21, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 10:41:05,978 - pyskl - INFO - Epoch [147][500/1281] lr: 3.572e-05, eta: 0:32:38, time: 0.280, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 10:41:56,791 - pyskl - INFO - Epoch [147][600/1281] lr: 3.419e-05, eta: 0:31:56, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 10:42:25,107 - pyskl - INFO - Epoch [147][700/1281] lr: 3.270e-05, eta: 0:31:14, time: 0.283, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 10:43:14,018 - pyskl - INFO - Epoch [147][800/1281] lr: 3.124e-05, eta: 0:30:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 10:44:03,188 - pyskl - INFO - Epoch [147][900/1281] lr: 2.981e-05, eta: 0:29:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 10:44:52,002 - pyskl - INFO - Epoch [147][1000/1281] lr: 2.842e-05, eta: 0:29:07, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0250, loss: 0.0250 +2025-06-25 10:45:40,693 - pyskl - INFO - Epoch [147][1100/1281] lr: 2.706e-05, eta: 0:28:25, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 10:46:30,106 - pyskl - INFO - Epoch [147][1200/1281] lr: 2.573e-05, eta: 0:27:42, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0218, loss: 0.0218 +2025-06-25 10:47:10,400 - pyskl - INFO - Saving checkpoint at 147 epochs +2025-06-25 10:48:08,376 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:48:08,431 - pyskl - INFO - +top1_acc 0.9413 +top5_acc 0.9967 +2025-06-25 10:48:08,431 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:48:08,438 - pyskl - INFO - +mean_acc 0.9201 +2025-06-25 10:48:08,439 - pyskl - INFO - Epoch(val) [147][533] top1_acc: 0.9413, top5_acc: 0.9967, mean_class_accuracy: 0.9201 +2025-06-25 10:49:26,201 - pyskl - INFO - Epoch [148][100/1281] lr: 2.341e-05, eta: 0:26:26, time: 0.778, data_time: 0.188, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 10:50:14,945 - pyskl - INFO - Epoch [148][200/1281] lr: 2.218e-05, eta: 0:25:44, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0268, loss: 0.0268 +2025-06-25 10:51:03,697 - pyskl - INFO - Epoch [148][300/1281] lr: 2.098e-05, eta: 0:25:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 10:51:52,574 - pyskl - INFO - Epoch [148][400/1281] lr: 1.981e-05, eta: 0:24:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0222, loss: 0.0222 +2025-06-25 10:52:22,993 - pyskl - INFO - Epoch [148][500/1281] lr: 1.868e-05, eta: 0:23:36, time: 0.304, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 10:53:13,601 - pyskl - INFO - Epoch [148][600/1281] lr: 1.758e-05, eta: 0:22:54, time: 0.506, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 10:53:41,507 - pyskl - INFO - Epoch [148][700/1281] lr: 1.651e-05, eta: 0:22:12, time: 0.279, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 10:54:30,489 - pyskl - INFO - Epoch [148][800/1281] lr: 1.548e-05, eta: 0:21:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 10:55:19,090 - pyskl - INFO - Epoch [148][900/1281] lr: 1.448e-05, eta: 0:20:47, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 10:56:07,736 - pyskl - INFO - Epoch [148][1000/1281] lr: 1.351e-05, eta: 0:20:05, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 10:56:56,746 - pyskl - INFO - Epoch [148][1100/1281] lr: 1.258e-05, eta: 0:19:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-25 10:57:45,763 - pyskl - INFO - Epoch [148][1200/1281] lr: 1.168e-05, eta: 0:18:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 10:58:26,097 - pyskl - INFO - Saving checkpoint at 148 epochs +2025-06-25 10:59:24,043 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:59:24,098 - pyskl - INFO - +top1_acc 0.9433 +top5_acc 0.9967 +2025-06-25 10:59:24,099 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:59:24,105 - pyskl - INFO - +mean_acc 0.9214 +2025-06-25 10:59:24,107 - pyskl - INFO - Epoch(val) [148][533] top1_acc: 0.9433, top5_acc: 0.9967, mean_class_accuracy: 0.9214 +2025-06-25 11:00:44,143 - pyskl - INFO - Epoch [149][100/1281] lr: 1.013e-05, eta: 0:17:23, time: 0.800, data_time: 0.186, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 11:01:32,992 - pyskl - INFO - Epoch [149][200/1281] lr: 9.328e-06, eta: 0:16:41, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 11:02:21,765 - pyskl - INFO - Epoch [149][300/1281] lr: 8.555e-06, eta: 0:15:59, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 11:03:10,842 - pyskl - INFO - Epoch [149][400/1281] lr: 7.816e-06, eta: 0:15:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 11:03:40,868 - pyskl - INFO - Epoch [149][500/1281] lr: 7.110e-06, eta: 0:14:34, time: 0.300, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 11:04:31,631 - pyskl - INFO - Epoch [149][600/1281] lr: 6.437e-06, eta: 0:13:52, time: 0.508, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 11:05:00,055 - pyskl - INFO - Epoch [149][700/1281] lr: 5.798e-06, eta: 0:13:09, time: 0.284, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 11:05:48,787 - pyskl - INFO - Epoch [149][800/1281] lr: 5.192e-06, eta: 0:12:27, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 11:06:37,658 - pyskl - INFO - Epoch [149][900/1281] lr: 4.620e-06, eta: 0:11:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-25 11:07:26,553 - pyskl - INFO - Epoch [149][1000/1281] lr: 4.081e-06, eta: 0:11:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 11:08:15,771 - pyskl - INFO - Epoch [149][1100/1281] lr: 3.576e-06, eta: 0:10:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 11:09:04,848 - pyskl - INFO - Epoch [149][1200/1281] lr: 3.104e-06, eta: 0:09:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 11:09:45,317 - pyskl - INFO - Saving checkpoint at 149 epochs +2025-06-25 11:10:43,541 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:10:43,597 - pyskl - INFO - +top1_acc 0.9421 +top5_acc 0.9966 +2025-06-25 11:10:43,597 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:10:43,603 - pyskl - INFO - +mean_acc 0.9212 +2025-06-25 11:10:43,605 - pyskl - INFO - Epoch(val) [149][533] top1_acc: 0.9421, top5_acc: 0.9966, mean_class_accuracy: 0.9212 +2025-06-25 11:12:01,595 - pyskl - INFO - Epoch [150][100/1281] lr: 2.334e-06, eta: 0:08:20, time: 0.780, data_time: 0.180, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 11:12:50,618 - pyskl - INFO - Epoch [150][200/1281] lr: 1.956e-06, eta: 0:07:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 11:13:39,830 - pyskl - INFO - Epoch [150][300/1281] lr: 1.611e-06, eta: 0:06:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 11:14:28,973 - pyskl - INFO - Epoch [150][400/1281] lr: 1.300e-06, eta: 0:06:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 11:15:00,059 - pyskl - INFO - Epoch [150][500/1281] lr: 1.022e-06, eta: 0:05:31, time: 0.311, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0246, loss: 0.0246 +2025-06-25 11:15:50,658 - pyskl - INFO - Epoch [150][600/1281] lr: 7.771e-07, eta: 0:04:48, time: 0.506, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 11:16:16,577 - pyskl - INFO - Epoch [150][700/1281] lr: 5.659e-07, eta: 0:04:06, time: 0.259, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 11:17:05,600 - pyskl - INFO - Epoch [150][800/1281] lr: 3.881e-07, eta: 0:03:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 11:17:54,734 - pyskl - INFO - Epoch [150][900/1281] lr: 2.438e-07, eta: 0:02:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 11:18:43,600 - pyskl - INFO - Epoch [150][1000/1281] lr: 1.329e-07, eta: 0:01:59, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 11:19:32,437 - pyskl - INFO - Epoch [150][1100/1281] lr: 5.534e-08, eta: 0:01:16, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 11:20:21,573 - pyskl - INFO - Epoch [150][1200/1281] lr: 1.123e-08, eta: 0:00:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-25 11:21:01,685 - pyskl - INFO - Saving checkpoint at 150 epochs +2025-06-25 11:21:59,661 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:21:59,731 - pyskl - INFO - +top1_acc 0.9431 +top5_acc 0.9965 +2025-06-25 11:21:59,731 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:21:59,740 - pyskl - INFO - +mean_acc 0.9212 +2025-06-25 11:21:59,743 - pyskl - INFO - Epoch(val) [150][533] top1_acc: 0.9431, top5_acc: 0.9965, mean_class_accuracy: 0.9212 +2025-06-25 11:22:04,281 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-25 11:29:36,949 - pyskl - INFO - Testing results of the last checkpoint +2025-06-25 11:29:36,949 - pyskl - INFO - top1_acc: 0.9431 +2025-06-25 11:29:36,949 - pyskl - INFO - top5_acc: 0.9973 +2025-06-25 11:29:36,949 - pyskl - INFO - mean_class_accuracy: 0.9221 +2025-06-25 11:29:36,950 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/finegym/b_2/best_top1_acc_epoch_139.pth +2025-06-25 11:37:19,917 - pyskl - INFO - Testing results of the best checkpoint +2025-06-25 11:37:19,918 - pyskl - INFO - top1_acc: 0.9457 +2025-06-25 11:37:19,918 - pyskl - INFO - top5_acc: 0.9974 +2025-06-25 11:37:19,918 - pyskl - INFO - mean_class_accuracy: 0.9259 diff --git a/finegym/b_2/20250624_084254.log.json b/finegym/b_2/20250624_084254.log.json new file mode 100644 index 0000000000000000000000000000000000000000..3cdba02b624f0bdbf05290c4e739925caba1474c --- /dev/null +++ b/finegym/b_2/20250624_084254.log.json @@ -0,0 +1,1951 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 1488861688, "config_name": "b_2.py", "work_dir": "b_2", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.17495, "top1_acc": 0.06813, "top5_acc": 0.24375, "loss_cls": 4.52317, "loss": 4.52317, "time": 0.38945} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.09375, "top5_acc": 0.35438, "loss_cls": 4.44336, "loss": 4.44336, "time": 0.2126} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.0875, "top5_acc": 0.38125, "loss_cls": 4.22169, "loss": 4.22169, "time": 0.21561} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.12562, "top5_acc": 0.43, "loss_cls": 4.0282, "loss": 4.0282, "time": 0.21647} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.025, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.15, "top5_acc": 0.48438, "loss_cls": 3.82706, "loss": 3.82706, "time": 0.21223} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.18625, "top5_acc": 0.51438, "loss_cls": 3.67205, "loss": 3.67205, "time": 0.21383} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.025, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.21812, "top5_acc": 0.55375, "loss_cls": 3.53991, "loss": 3.53991, "time": 0.21434} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.025, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.26125, "top5_acc": 0.61312, "loss_cls": 3.32536, "loss": 3.32536, "time": 0.21354} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.27062, "top5_acc": 0.62625, "loss_cls": 3.20199, "loss": 3.20199, "time": 0.21593} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.29125, "top5_acc": 0.6575, "loss_cls": 3.09403, "loss": 3.09403, "time": 0.21624} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.025, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.30625, "top5_acc": 0.6975, "loss_cls": 2.92135, "loss": 2.92135, "time": 0.21642} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.35688, "top5_acc": 0.73812, "loss_cls": 2.73537, "loss": 2.73537, "time": 0.21694} +{"mode": "val", "epoch": 1, "iter": 533, "lr": 0.025, "top1_acc": 0.39948, "top5_acc": 0.78794, "mean_class_accuracy": 0.21302} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.18442, "top1_acc": 0.39188, "top5_acc": 0.79938, "loss_cls": 2.53343, "loss": 2.53343, "time": 0.40002} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.39812, "top5_acc": 0.80812, "loss_cls": 2.50912, "loss": 2.50912, "time": 0.21829} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.43688, "top5_acc": 0.82688, "loss_cls": 2.3453, "loss": 2.3453, "time": 0.21674} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.46125, "top5_acc": 0.83312, "loss_cls": 2.30059, "loss": 2.30059, "time": 0.21659} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.02499, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.45125, "top5_acc": 0.84875, "loss_cls": 2.26941, "loss": 2.26941, "time": 0.21788} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.46, "top5_acc": 0.86688, "loss_cls": 2.16296, "loss": 2.16296, "time": 0.21657} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.48062, "top5_acc": 0.8725, "loss_cls": 2.13956, "loss": 2.13956, "time": 0.21604} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.02499, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.50438, "top5_acc": 0.87688, "loss_cls": 2.0691, "loss": 2.0691, "time": 0.21961} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.02499, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.50938, "top5_acc": 0.88125, "loss_cls": 2.022, "loss": 2.022, "time": 0.2168} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.02499, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.51125, "top5_acc": 0.89125, "loss_cls": 1.97766, "loss": 1.97766, "time": 0.21612} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.02499, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.5275, "top5_acc": 0.88688, "loss_cls": 1.93209, "loss": 1.93209, "time": 0.21834} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.02499, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.54937, "top5_acc": 0.89375, "loss_cls": 1.91343, "loss": 1.91343, "time": 0.21667} +{"mode": "val", "epoch": 2, "iter": 533, "lr": 0.02499, "top1_acc": 0.53937, "top5_acc": 0.90095, "mean_class_accuracy": 0.37268} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.02499, "memory": 4082, "data_time": 0.18695, "top1_acc": 0.5675, "top5_acc": 0.915, "loss_cls": 1.80825, "loss": 1.80825, "time": 0.40073} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.57063, "top5_acc": 0.915, "loss_cls": 1.78886, "loss": 1.78886, "time": 0.2141} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.58125, "top5_acc": 0.92812, "loss_cls": 1.73726, "loss": 1.73726, "time": 0.21541} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.02499, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.5875, "top5_acc": 0.92938, "loss_cls": 1.71594, "loss": 1.71594, "time": 0.21746} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.02498, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.57125, "top5_acc": 0.92, "loss_cls": 1.7391, "loss": 1.7391, "time": 0.21374} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.02498, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.60438, "top5_acc": 0.92562, "loss_cls": 1.68519, "loss": 1.68519, "time": 0.21239} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.02498, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.59938, "top5_acc": 0.94375, "loss_cls": 1.64089, "loss": 1.64089, "time": 0.21489} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.02498, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.60625, "top5_acc": 0.945, "loss_cls": 1.61545, "loss": 1.61545, "time": 0.21706} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.02498, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.63188, "top5_acc": 0.94438, "loss_cls": 1.55948, "loss": 1.55948, "time": 0.21483} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.02498, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.6425, "top5_acc": 0.95188, "loss_cls": 1.49731, "loss": 1.49731, "time": 0.21436} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.02498, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.615, "top5_acc": 0.9425, "loss_cls": 1.62831, "loss": 1.62831, "time": 0.21459} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.02498, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.6275, "top5_acc": 0.95, "loss_cls": 1.52238, "loss": 1.52238, "time": 0.21597} +{"mode": "val", "epoch": 3, "iter": 533, "lr": 0.02498, "top1_acc": 0.59911, "top5_acc": 0.93475, "mean_class_accuracy": 0.44537} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.02497, "memory": 4082, "data_time": 0.18161, "top1_acc": 0.64562, "top5_acc": 0.95375, "loss_cls": 1.4666, "loss": 1.4666, "time": 0.39966} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.02497, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.65062, "top5_acc": 0.95625, "loss_cls": 1.41887, "loss": 1.41887, "time": 0.21769} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.02497, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.66062, "top5_acc": 0.955, "loss_cls": 1.4052, "loss": 1.4052, "time": 0.21702} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.02497, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.645, "top5_acc": 0.95062, "loss_cls": 1.50635, "loss": 1.50635, "time": 0.21523} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.02497, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.62313, "top5_acc": 0.95438, "loss_cls": 1.48583, "loss": 1.48583, "time": 0.22066} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.02497, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.65062, "top5_acc": 0.9525, "loss_cls": 1.41999, "loss": 1.41999, "time": 0.21427} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.02497, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.67625, "top5_acc": 0.965, "loss_cls": 1.32614, "loss": 1.32614, "time": 0.21555} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.02496, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.68812, "top5_acc": 0.96562, "loss_cls": 1.36154, "loss": 1.36154, "time": 0.21753} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.02496, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.68062, "top5_acc": 0.96188, "loss_cls": 1.3752, "loss": 1.3752, "time": 0.21861} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.02496, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.6875, "top5_acc": 0.9625, "loss_cls": 1.32132, "loss": 1.32132, "time": 0.2173} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.02496, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.68938, "top5_acc": 0.95875, "loss_cls": 1.3502, "loss": 1.3502, "time": 0.2172} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.02496, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.69, "top5_acc": 0.95938, "loss_cls": 1.29171, "loss": 1.29171, "time": 0.21592} +{"mode": "val", "epoch": 4, "iter": 533, "lr": 0.02496, "top1_acc": 0.68396, "top5_acc": 0.95963, "mean_class_accuracy": 0.55857} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.02495, "memory": 4082, "data_time": 0.1817, "top1_acc": 0.69062, "top5_acc": 0.96625, "loss_cls": 1.28905, "loss": 1.28905, "time": 0.399} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.02495, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.67875, "top5_acc": 0.97188, "loss_cls": 1.27674, "loss": 1.27674, "time": 0.21952} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.02495, "memory": 4082, "data_time": 0.00054, "top1_acc": 0.67688, "top5_acc": 0.9675, "loss_cls": 1.29908, "loss": 1.29908, "time": 0.21533} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.02495, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.70188, "top5_acc": 0.96625, "loss_cls": 1.27221, "loss": 1.27221, "time": 0.2165} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.02495, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.70312, "top5_acc": 0.9725, "loss_cls": 1.24289, "loss": 1.24289, "time": 0.21555} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.02495, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.71125, "top5_acc": 0.97625, "loss_cls": 1.20539, "loss": 1.20539, "time": 0.21516} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.02494, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.70875, "top5_acc": 0.97125, "loss_cls": 1.23242, "loss": 1.23242, "time": 0.2172} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.02494, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.70875, "top5_acc": 0.965, "loss_cls": 1.22605, "loss": 1.22605, "time": 0.21681} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.02494, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.69688, "top5_acc": 0.97188, "loss_cls": 1.21821, "loss": 1.21821, "time": 0.21509} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.02494, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.71688, "top5_acc": 0.975, "loss_cls": 1.20636, "loss": 1.20636, "time": 0.21339} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.02494, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.71, "top5_acc": 0.96625, "loss_cls": 1.203, "loss": 1.203, "time": 0.21337} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.02493, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.72, "top5_acc": 0.97438, "loss_cls": 1.15162, "loss": 1.15162, "time": 0.21317} +{"mode": "val", "epoch": 5, "iter": 533, "lr": 0.02493, "top1_acc": 0.70802, "top5_acc": 0.96655, "mean_class_accuracy": 0.5733} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.02493, "memory": 4082, "data_time": 0.18934, "top1_acc": 0.72, "top5_acc": 0.97, "loss_cls": 1.17324, "loss": 1.17324, "time": 0.40462} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.02493, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.73312, "top5_acc": 0.975, "loss_cls": 1.16634, "loss": 1.16634, "time": 0.21679} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.02492, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.73188, "top5_acc": 0.97938, "loss_cls": 1.13296, "loss": 1.13296, "time": 0.21608} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.02492, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.72312, "top5_acc": 0.97625, "loss_cls": 1.1295, "loss": 1.1295, "time": 0.21224} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.02492, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.74312, "top5_acc": 0.97875, "loss_cls": 1.11482, "loss": 1.11482, "time": 0.21882} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.02492, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.74188, "top5_acc": 0.9775, "loss_cls": 1.10837, "loss": 1.10837, "time": 0.21804} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.02492, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.73125, "top5_acc": 0.97188, "loss_cls": 1.17108, "loss": 1.17108, "time": 0.21831} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.02491, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.74875, "top5_acc": 0.97812, "loss_cls": 1.0753, "loss": 1.0753, "time": 0.21628} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.02491, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.74125, "top5_acc": 0.97438, "loss_cls": 1.09615, "loss": 1.09615, "time": 0.21455} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.02491, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.71875, "top5_acc": 0.96812, "loss_cls": 1.19812, "loss": 1.19812, "time": 0.21844} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.02491, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7475, "top5_acc": 0.98188, "loss_cls": 1.07514, "loss": 1.07514, "time": 0.21419} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.0249, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.745, "top5_acc": 0.97688, "loss_cls": 1.10596, "loss": 1.10596, "time": 0.2166} +{"mode": "val", "epoch": 6, "iter": 533, "lr": 0.0249, "top1_acc": 0.70978, "top5_acc": 0.96679, "mean_class_accuracy": 0.59721} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0249, "memory": 4082, "data_time": 0.18319, "top1_acc": 0.74125, "top5_acc": 0.97438, "loss_cls": 1.11013, "loss": 1.11013, "time": 0.401} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0249, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.74688, "top5_acc": 0.97875, "loss_cls": 1.04987, "loss": 1.04987, "time": 0.21516} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.02489, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.76312, "top5_acc": 0.98688, "loss_cls": 1.00215, "loss": 1.00215, "time": 0.21783} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.02489, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.75, "top5_acc": 0.98312, "loss_cls": 1.07532, "loss": 1.07532, "time": 0.2153} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.02489, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.75312, "top5_acc": 0.98125, "loss_cls": 1.06118, "loss": 1.06118, "time": 0.21585} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.02489, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.74312, "top5_acc": 0.98188, "loss_cls": 1.08412, "loss": 1.08412, "time": 0.21529} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.02488, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.75438, "top5_acc": 0.975, "loss_cls": 1.0587, "loss": 1.0587, "time": 0.21442} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.02488, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.74938, "top5_acc": 0.97625, "loss_cls": 1.06875, "loss": 1.06875, "time": 0.21709} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.02488, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.74625, "top5_acc": 0.97875, "loss_cls": 1.03682, "loss": 1.03682, "time": 0.21401} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.02487, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.76, "top5_acc": 0.97938, "loss_cls": 1.06146, "loss": 1.06146, "time": 0.21585} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.02487, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.75188, "top5_acc": 0.98125, "loss_cls": 1.05986, "loss": 1.05986, "time": 0.21704} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.02487, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.75375, "top5_acc": 0.97625, "loss_cls": 1.06388, "loss": 1.06388, "time": 0.21804} +{"mode": "val", "epoch": 7, "iter": 533, "lr": 0.02487, "top1_acc": 0.71705, "top5_acc": 0.9716, "mean_class_accuracy": 0.62111} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.02486, "memory": 4082, "data_time": 0.18629, "top1_acc": 0.78375, "top5_acc": 0.98625, "loss_cls": 0.95674, "loss": 0.95674, "time": 0.40293} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.02486, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.7675, "top5_acc": 0.98188, "loss_cls": 0.98094, "loss": 0.98094, "time": 0.21465} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.02486, "memory": 4082, "data_time": 0.00068, "top1_acc": 0.75875, "top5_acc": 0.9825, "loss_cls": 1.03593, "loss": 1.03593, "time": 0.21648} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.02485, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.75, "top5_acc": 0.98125, "loss_cls": 1.03952, "loss": 1.03952, "time": 0.21554} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.02485, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.74812, "top5_acc": 0.98062, "loss_cls": 1.04994, "loss": 1.04994, "time": 0.21757} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.02485, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.75438, "top5_acc": 0.98375, "loss_cls": 1.00433, "loss": 1.00433, "time": 0.21536} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.02484, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.76812, "top5_acc": 0.98438, "loss_cls": 0.98836, "loss": 0.98836, "time": 0.21216} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.02484, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.76438, "top5_acc": 0.97812, "loss_cls": 1.02164, "loss": 1.02164, "time": 0.21437} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.02484, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.73375, "top5_acc": 0.97438, "loss_cls": 1.06685, "loss": 1.06685, "time": 0.21461} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.02483, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7725, "top5_acc": 0.98312, "loss_cls": 0.961, "loss": 0.961, "time": 0.21473} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.02483, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.78938, "top5_acc": 0.985, "loss_cls": 0.93933, "loss": 0.93933, "time": 0.21685} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.02483, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.75375, "top5_acc": 0.98188, "loss_cls": 1.0015, "loss": 1.0015, "time": 0.21673} +{"mode": "val", "epoch": 8, "iter": 533, "lr": 0.02482, "top1_acc": 0.72046, "top5_acc": 0.97524, "mean_class_accuracy": 0.63719} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.02482, "memory": 4082, "data_time": 0.18598, "top1_acc": 0.785, "top5_acc": 0.9875, "loss_cls": 0.93381, "loss": 0.93381, "time": 0.40184} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.02482, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.77938, "top5_acc": 0.98312, "loss_cls": 0.97576, "loss": 0.97576, "time": 0.21666} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.02481, "memory": 4082, "data_time": 0.00055, "top1_acc": 0.78062, "top5_acc": 0.98062, "loss_cls": 0.96324, "loss": 0.96324, "time": 0.21551} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.02481, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.77375, "top5_acc": 0.98062, "loss_cls": 1.00125, "loss": 1.00125, "time": 0.21381} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.02481, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.77, "top5_acc": 0.985, "loss_cls": 0.95352, "loss": 0.95352, "time": 0.21431} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.0248, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.76938, "top5_acc": 0.98375, "loss_cls": 0.96432, "loss": 0.96432, "time": 0.21549} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.0248, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.7875, "top5_acc": 0.98688, "loss_cls": 0.90616, "loss": 0.90616, "time": 0.21731} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.0248, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.78875, "top5_acc": 0.98312, "loss_cls": 0.97957, "loss": 0.97957, "time": 0.21483} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.02479, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.7625, "top5_acc": 0.97625, "loss_cls": 1.02024, "loss": 1.02024, "time": 0.21586} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.02479, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.77, "top5_acc": 0.98375, "loss_cls": 0.98039, "loss": 0.98039, "time": 0.21603} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.02479, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7825, "top5_acc": 0.98125, "loss_cls": 0.9392, "loss": 0.9392, "time": 0.22117} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.02478, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78125, "top5_acc": 0.9825, "loss_cls": 0.93064, "loss": 0.93064, "time": 0.21499} +{"mode": "val", "epoch": 9, "iter": 533, "lr": 0.02478, "top1_acc": 0.73219, "top5_acc": 0.97301, "mean_class_accuracy": 0.62642} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.02477, "memory": 4082, "data_time": 0.18793, "top1_acc": 0.785, "top5_acc": 0.98438, "loss_cls": 0.94682, "loss": 0.94682, "time": 0.4023} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.02477, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.765, "top5_acc": 0.98625, "loss_cls": 0.96157, "loss": 0.96157, "time": 0.21536} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.02477, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.78875, "top5_acc": 0.99125, "loss_cls": 0.90724, "loss": 0.90724, "time": 0.2166} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.02476, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7825, "top5_acc": 0.98188, "loss_cls": 0.92559, "loss": 0.92559, "time": 0.214} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.02476, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.77438, "top5_acc": 0.98188, "loss_cls": 0.93808, "loss": 0.93808, "time": 0.21802} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.02476, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.78, "top5_acc": 0.97875, "loss_cls": 0.96641, "loss": 0.96641, "time": 0.21417} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.02475, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78188, "top5_acc": 0.98688, "loss_cls": 0.9443, "loss": 0.9443, "time": 0.21372} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.02475, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7725, "top5_acc": 0.98562, "loss_cls": 0.96653, "loss": 0.96653, "time": 0.21484} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.02474, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77812, "top5_acc": 0.98062, "loss_cls": 0.95894, "loss": 0.95894, "time": 0.21754} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.02474, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78375, "top5_acc": 0.98875, "loss_cls": 0.92785, "loss": 0.92785, "time": 0.21573} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.02473, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.77812, "top5_acc": 0.98688, "loss_cls": 0.92282, "loss": 0.92282, "time": 0.21581} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.02473, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79562, "top5_acc": 0.98375, "loss_cls": 0.91831, "loss": 0.91831, "time": 0.21939} +{"mode": "val", "epoch": 10, "iter": 533, "lr": 0.02473, "top1_acc": 0.70731, "top5_acc": 0.96526, "mean_class_accuracy": 0.63019} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.02472, "memory": 4082, "data_time": 0.18441, "top1_acc": 0.78188, "top5_acc": 0.98312, "loss_cls": 0.9141, "loss": 0.9141, "time": 0.40017} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.02472, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.77625, "top5_acc": 0.98812, "loss_cls": 0.92166, "loss": 0.92166, "time": 0.21682} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.02471, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7925, "top5_acc": 0.99188, "loss_cls": 0.87237, "loss": 0.87237, "time": 0.21629} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.02471, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78688, "top5_acc": 0.98688, "loss_cls": 0.92784, "loss": 0.92784, "time": 0.21829} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.02471, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77, "top5_acc": 0.98312, "loss_cls": 0.96728, "loss": 0.96728, "time": 0.21882} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.0247, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.79062, "top5_acc": 0.98562, "loss_cls": 0.89052, "loss": 0.89052, "time": 0.21629} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.0247, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.81625, "top5_acc": 0.98562, "loss_cls": 0.83706, "loss": 0.83706, "time": 0.22018} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.02469, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.78062, "top5_acc": 0.985, "loss_cls": 0.90756, "loss": 0.90756, "time": 0.22016} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.02469, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.78438, "top5_acc": 0.9875, "loss_cls": 0.90621, "loss": 0.90621, "time": 0.2164} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.02468, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.77875, "top5_acc": 0.98375, "loss_cls": 0.9471, "loss": 0.9471, "time": 0.214} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.02468, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.79562, "top5_acc": 0.98875, "loss_cls": 0.87711, "loss": 0.87711, "time": 0.21648} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.02467, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.78875, "top5_acc": 0.98688, "loss_cls": 0.91697, "loss": 0.91697, "time": 0.21799} +{"mode": "val", "epoch": 11, "iter": 533, "lr": 0.02467, "top1_acc": 0.76036, "top5_acc": 0.97981, "mean_class_accuracy": 0.66114} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.02467, "memory": 4082, "data_time": 0.18935, "top1_acc": 0.785, "top5_acc": 0.98875, "loss_cls": 0.87633, "loss": 0.87633, "time": 0.40641} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.02466, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.80062, "top5_acc": 0.98938, "loss_cls": 0.86742, "loss": 0.86742, "time": 0.2186} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.02466, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.785, "top5_acc": 0.98812, "loss_cls": 0.91166, "loss": 0.91166, "time": 0.21604} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.02465, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79688, "top5_acc": 0.98688, "loss_cls": 0.87742, "loss": 0.87742, "time": 0.2181} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.02465, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80125, "top5_acc": 0.9875, "loss_cls": 0.84848, "loss": 0.84848, "time": 0.21993} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.02464, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80438, "top5_acc": 0.98562, "loss_cls": 0.88023, "loss": 0.88023, "time": 0.21527} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.02464, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79375, "top5_acc": 0.98938, "loss_cls": 0.91816, "loss": 0.91816, "time": 0.21669} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.02463, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8, "top5_acc": 0.98688, "loss_cls": 0.85843, "loss": 0.85843, "time": 0.21532} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.02463, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.8025, "top5_acc": 0.98938, "loss_cls": 0.85278, "loss": 0.85278, "time": 0.21292} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.02462, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.79812, "top5_acc": 0.98188, "loss_cls": 0.91991, "loss": 0.91991, "time": 0.21527} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.02462, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78938, "top5_acc": 0.98625, "loss_cls": 0.90409, "loss": 0.90409, "time": 0.21575} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.02461, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.795, "top5_acc": 0.985, "loss_cls": 0.89487, "loss": 0.89487, "time": 0.21441} +{"mode": "val", "epoch": 12, "iter": 533, "lr": 0.02461, "top1_acc": 0.79099, "top5_acc": 0.98322, "mean_class_accuracy": 0.6991} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.0246, "memory": 4082, "data_time": 0.18271, "top1_acc": 0.80625, "top5_acc": 0.9925, "loss_cls": 0.81862, "loss": 0.81862, "time": 0.40111} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.0246, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.805, "top5_acc": 0.99188, "loss_cls": 0.83912, "loss": 0.83912, "time": 0.21529} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.02459, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.79, "top5_acc": 0.98688, "loss_cls": 0.89782, "loss": 0.89782, "time": 0.21754} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.02459, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79062, "top5_acc": 0.99, "loss_cls": 0.86166, "loss": 0.86166, "time": 0.21367} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.02458, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8, "top5_acc": 0.99188, "loss_cls": 0.84093, "loss": 0.84093, "time": 0.21729} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.02458, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.8125, "top5_acc": 0.99125, "loss_cls": 0.79703, "loss": 0.79703, "time": 0.21474} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.02457, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81188, "top5_acc": 0.98688, "loss_cls": 0.83262, "loss": 0.83262, "time": 0.21673} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.02457, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.82, "top5_acc": 0.99, "loss_cls": 0.8209, "loss": 0.8209, "time": 0.21414} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.02456, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79, "top5_acc": 0.9875, "loss_cls": 0.89234, "loss": 0.89234, "time": 0.21416} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.02455, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.79188, "top5_acc": 0.98375, "loss_cls": 0.91353, "loss": 0.91353, "time": 0.2154} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.02455, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.81625, "top5_acc": 0.99, "loss_cls": 0.81992, "loss": 0.81992, "time": 0.21411} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.02454, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.81875, "top5_acc": 0.98875, "loss_cls": 0.81787, "loss": 0.81787, "time": 0.21772} +{"mode": "val", "epoch": 13, "iter": 533, "lr": 0.02454, "top1_acc": 0.79416, "top5_acc": 0.98568, "mean_class_accuracy": 0.68519} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.02453, "memory": 4082, "data_time": 0.1858, "top1_acc": 0.81188, "top5_acc": 0.99312, "loss_cls": 0.81327, "loss": 0.81327, "time": 0.40316} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.02453, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.825, "top5_acc": 0.99125, "loss_cls": 0.77817, "loss": 0.77817, "time": 0.21782} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.02452, "memory": 4082, "data_time": 0.00046, "top1_acc": 0.775, "top5_acc": 0.985, "loss_cls": 0.97551, "loss": 0.97551, "time": 0.21552} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.02452, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.79062, "top5_acc": 0.98875, "loss_cls": 0.90022, "loss": 0.90022, "time": 0.21444} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.02451, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.81562, "top5_acc": 0.99375, "loss_cls": 0.78884, "loss": 0.78884, "time": 0.21484} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.02451, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.7925, "top5_acc": 0.98875, "loss_cls": 0.86093, "loss": 0.86093, "time": 0.21391} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.0245, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81812, "top5_acc": 0.98938, "loss_cls": 0.8156, "loss": 0.8156, "time": 0.21261} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.02449, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.78, "top5_acc": 0.98562, "loss_cls": 0.93554, "loss": 0.93554, "time": 0.21572} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.02449, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.81938, "top5_acc": 0.98875, "loss_cls": 0.81492, "loss": 0.81492, "time": 0.21515} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.02448, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82375, "top5_acc": 0.99, "loss_cls": 0.79139, "loss": 0.79139, "time": 0.21568} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.02448, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81125, "top5_acc": 0.98625, "loss_cls": 0.84137, "loss": 0.84137, "time": 0.21389} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.02447, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.79062, "top5_acc": 0.98562, "loss_cls": 0.90667, "loss": 0.90667, "time": 0.21897} +{"mode": "val", "epoch": 14, "iter": 533, "lr": 0.02447, "top1_acc": 0.78441, "top5_acc": 0.98263, "mean_class_accuracy": 0.68422} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.02446, "memory": 4082, "data_time": 0.18512, "top1_acc": 0.825, "top5_acc": 0.99188, "loss_cls": 0.80458, "loss": 0.80458, "time": 0.40136} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.02445, "memory": 4082, "data_time": 0.00073, "top1_acc": 0.81562, "top5_acc": 0.99125, "loss_cls": 0.81064, "loss": 0.81064, "time": 0.21832} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.02445, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.81312, "top5_acc": 0.99062, "loss_cls": 0.83083, "loss": 0.83083, "time": 0.21503} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.02444, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83688, "top5_acc": 0.9875, "loss_cls": 0.76397, "loss": 0.76397, "time": 0.21556} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.02444, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.82312, "top5_acc": 0.99188, "loss_cls": 0.77785, "loss": 0.77785, "time": 0.21276} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.02443, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81562, "top5_acc": 0.985, "loss_cls": 0.83893, "loss": 0.83893, "time": 0.21502} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.02442, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83375, "top5_acc": 0.99062, "loss_cls": 0.80271, "loss": 0.80271, "time": 0.21593} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.02442, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81, "top5_acc": 0.98812, "loss_cls": 0.8344, "loss": 0.8344, "time": 0.21513} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.02441, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.80375, "top5_acc": 0.98625, "loss_cls": 0.86233, "loss": 0.86233, "time": 0.21522} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.02441, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81562, "top5_acc": 0.99188, "loss_cls": 0.78743, "loss": 0.78743, "time": 0.21535} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.0244, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81625, "top5_acc": 0.98875, "loss_cls": 0.822, "loss": 0.822, "time": 0.21467} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.02439, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.81125, "top5_acc": 0.99188, "loss_cls": 0.81628, "loss": 0.81628, "time": 0.21678} +{"mode": "val", "epoch": 15, "iter": 533, "lr": 0.02439, "top1_acc": 0.77655, "top5_acc": 0.97911, "mean_class_accuracy": 0.69} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.02438, "memory": 4082, "data_time": 0.17837, "top1_acc": 0.80625, "top5_acc": 0.98875, "loss_cls": 0.86145, "loss": 0.86145, "time": 0.39474} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.02438, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82875, "top5_acc": 0.98938, "loss_cls": 0.78665, "loss": 0.78665, "time": 0.21613} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.02437, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81562, "top5_acc": 0.9925, "loss_cls": 0.80344, "loss": 0.80344, "time": 0.21598} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.02436, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.8275, "top5_acc": 0.99375, "loss_cls": 0.79197, "loss": 0.79197, "time": 0.21633} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.02436, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8225, "top5_acc": 0.9875, "loss_cls": 0.78812, "loss": 0.78812, "time": 0.21244} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.02435, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82188, "top5_acc": 0.99438, "loss_cls": 0.77465, "loss": 0.77465, "time": 0.21483} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.02434, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83, "top5_acc": 0.99312, "loss_cls": 0.76371, "loss": 0.76371, "time": 0.21709} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.02434, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.8125, "top5_acc": 0.98625, "loss_cls": 0.80746, "loss": 0.80746, "time": 0.21372} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.02433, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83938, "top5_acc": 0.99062, "loss_cls": 0.73828, "loss": 0.73828, "time": 0.21728} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.02432, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.82312, "top5_acc": 0.99125, "loss_cls": 0.76487, "loss": 0.76487, "time": 0.21522} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.02432, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.815, "top5_acc": 0.99062, "loss_cls": 0.83927, "loss": 0.83927, "time": 0.21714} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.02431, "memory": 4082, "data_time": 0.0006, "top1_acc": 0.81188, "top5_acc": 0.99, "loss_cls": 0.83931, "loss": 0.83931, "time": 0.21638} +{"mode": "val", "epoch": 16, "iter": 533, "lr": 0.0243, "top1_acc": 0.77996, "top5_acc": 0.98157, "mean_class_accuracy": 0.70866} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.0243, "memory": 4082, "data_time": 0.19585, "top1_acc": 0.83312, "top5_acc": 0.99125, "loss_cls": 0.7598, "loss": 0.7598, "time": 0.41364} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.02429, "memory": 4082, "data_time": 0.00059, "top1_acc": 0.84875, "top5_acc": 0.98875, "loss_cls": 0.73211, "loss": 0.73211, "time": 0.22526} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.02428, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83625, "top5_acc": 0.99062, "loss_cls": 0.7813, "loss": 0.7813, "time": 0.36698} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.02428, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.83812, "top5_acc": 0.99125, "loss_cls": 0.75943, "loss": 0.75943, "time": 0.41544} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.02427, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82875, "top5_acc": 0.99, "loss_cls": 0.78948, "loss": 0.78948, "time": 0.41488} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.02426, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83188, "top5_acc": 0.9925, "loss_cls": 0.78008, "loss": 0.78008, "time": 0.41749} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.02426, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84312, "top5_acc": 0.99188, "loss_cls": 0.72765, "loss": 0.72765, "time": 0.41678} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.02425, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84, "top5_acc": 0.99438, "loss_cls": 0.73875, "loss": 0.73875, "time": 0.4157} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.02424, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83, "top5_acc": 0.99062, "loss_cls": 0.81781, "loss": 0.81781, "time": 0.41255} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.02424, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83188, "top5_acc": 0.98938, "loss_cls": 0.77247, "loss": 0.77247, "time": 0.41605} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.02423, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81812, "top5_acc": 0.9875, "loss_cls": 0.80727, "loss": 0.80727, "time": 0.41419} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.02422, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81875, "top5_acc": 0.9925, "loss_cls": 0.77817, "loss": 0.77817, "time": 0.41552} +{"mode": "val", "epoch": 17, "iter": 533, "lr": 0.02422, "top1_acc": 0.80261, "top5_acc": 0.98568, "mean_class_accuracy": 0.72394} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.02421, "memory": 4082, "data_time": 0.19289, "top1_acc": 0.82875, "top5_acc": 0.99188, "loss_cls": 0.75935, "loss": 0.75935, "time": 0.60984} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.0242, "memory": 4082, "data_time": 0.00051, "top1_acc": 0.85, "top5_acc": 0.99312, "loss_cls": 0.69326, "loss": 0.69326, "time": 0.29393} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.02419, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84625, "top5_acc": 0.99125, "loss_cls": 0.72617, "loss": 0.72617, "time": 0.36344} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.02419, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.8425, "top5_acc": 0.99438, "loss_cls": 0.73264, "loss": 0.73264, "time": 0.41536} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.02418, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83875, "top5_acc": 0.99375, "loss_cls": 0.7431, "loss": 0.7431, "time": 0.41383} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.02417, "memory": 4082, "data_time": 0.00055, "top1_acc": 0.83438, "top5_acc": 0.98562, "loss_cls": 0.77063, "loss": 0.77063, "time": 0.41538} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.02417, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.825, "top5_acc": 0.99062, "loss_cls": 0.80906, "loss": 0.80906, "time": 0.41404} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.02416, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82938, "top5_acc": 0.99, "loss_cls": 0.75638, "loss": 0.75638, "time": 0.41589} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.02415, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.81375, "top5_acc": 0.99312, "loss_cls": 0.81956, "loss": 0.81956, "time": 0.41499} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.02414, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.84375, "top5_acc": 0.99125, "loss_cls": 0.73376, "loss": 0.73376, "time": 0.41463} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.02414, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84312, "top5_acc": 0.99062, "loss_cls": 0.70627, "loss": 0.70627, "time": 0.415} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.02413, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83438, "top5_acc": 0.99062, "loss_cls": 0.7677, "loss": 0.7677, "time": 0.41425} +{"mode": "val", "epoch": 18, "iter": 533, "lr": 0.02412, "top1_acc": 0.785, "top5_acc": 0.97981, "mean_class_accuracy": 0.6993} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.02411, "memory": 4082, "data_time": 0.19327, "top1_acc": 0.85125, "top5_acc": 0.99312, "loss_cls": 0.71293, "loss": 0.71293, "time": 0.60554} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.02411, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.85188, "top5_acc": 0.98938, "loss_cls": 0.69807, "loss": 0.69807, "time": 0.29746} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.0241, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.84812, "top5_acc": 0.9925, "loss_cls": 0.69261, "loss": 0.69261, "time": 0.35719} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.02409, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85188, "top5_acc": 0.99438, "loss_cls": 0.70871, "loss": 0.70871, "time": 0.41488} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.02408, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83188, "top5_acc": 0.99688, "loss_cls": 0.72753, "loss": 0.72753, "time": 0.41376} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.02408, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.84188, "top5_acc": 0.9925, "loss_cls": 0.75125, "loss": 0.75125, "time": 0.41417} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.02407, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84375, "top5_acc": 0.99375, "loss_cls": 0.72596, "loss": 0.72596, "time": 0.41355} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.02406, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83562, "top5_acc": 0.99062, "loss_cls": 0.74297, "loss": 0.74297, "time": 0.41361} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.02405, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.84, "top5_acc": 0.99188, "loss_cls": 0.74373, "loss": 0.74373, "time": 0.41458} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.02405, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84188, "top5_acc": 0.9925, "loss_cls": 0.74198, "loss": 0.74198, "time": 0.41428} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.02404, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83438, "top5_acc": 0.98438, "loss_cls": 0.77977, "loss": 0.77977, "time": 0.41409} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.02403, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.85312, "top5_acc": 0.99188, "loss_cls": 0.70802, "loss": 0.70802, "time": 0.41453} +{"mode": "val", "epoch": 19, "iter": 533, "lr": 0.02402, "top1_acc": 0.79017, "top5_acc": 0.98568, "mean_class_accuracy": 0.70442} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.02402, "memory": 4082, "data_time": 0.19404, "top1_acc": 0.85188, "top5_acc": 0.99438, "loss_cls": 0.69859, "loss": 0.69859, "time": 0.60455} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.02401, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.83062, "top5_acc": 0.9925, "loss_cls": 0.752, "loss": 0.752, "time": 0.29955} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.024, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.83, "top5_acc": 0.99062, "loss_cls": 0.74734, "loss": 0.74734, "time": 0.36048} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.02399, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.825, "top5_acc": 0.98938, "loss_cls": 0.75793, "loss": 0.75793, "time": 0.41437} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.02398, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.84562, "top5_acc": 0.99375, "loss_cls": 0.7012, "loss": 0.7012, "time": 0.4162} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.02398, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83562, "top5_acc": 0.9925, "loss_cls": 0.77532, "loss": 0.77532, "time": 0.41563} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.02397, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8575, "top5_acc": 0.99125, "loss_cls": 0.65453, "loss": 0.65453, "time": 0.41586} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.02396, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82812, "top5_acc": 0.995, "loss_cls": 0.72617, "loss": 0.72617, "time": 0.41555} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.02395, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83125, "top5_acc": 0.99, "loss_cls": 0.76819, "loss": 0.76819, "time": 0.41398} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.02394, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84375, "top5_acc": 0.99, "loss_cls": 0.73438, "loss": 0.73438, "time": 0.41512} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.02393, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.83438, "top5_acc": 0.99, "loss_cls": 0.7242, "loss": 0.7242, "time": 0.41534} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.02393, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84438, "top5_acc": 0.99188, "loss_cls": 0.69681, "loss": 0.69681, "time": 0.4152} +{"mode": "val", "epoch": 20, "iter": 533, "lr": 0.02392, "top1_acc": 0.81939, "top5_acc": 0.98873, "mean_class_accuracy": 0.7431} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.02391, "memory": 4082, "data_time": 0.19406, "top1_acc": 0.8525, "top5_acc": 0.9925, "loss_cls": 0.69594, "loss": 0.69594, "time": 0.59551} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.0239, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85562, "top5_acc": 0.99188, "loss_cls": 0.69869, "loss": 0.69869, "time": 0.3071} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.02389, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8525, "top5_acc": 0.99062, "loss_cls": 0.70904, "loss": 0.70904, "time": 0.3482} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.02389, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82812, "top5_acc": 0.99312, "loss_cls": 0.74867, "loss": 0.74867, "time": 0.41493} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.02388, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.86, "top5_acc": 0.9975, "loss_cls": 0.67019, "loss": 0.67019, "time": 0.41736} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.02387, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81938, "top5_acc": 0.99438, "loss_cls": 0.76269, "loss": 0.76269, "time": 0.41546} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.02386, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83938, "top5_acc": 0.99562, "loss_cls": 0.70643, "loss": 0.70643, "time": 0.41455} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.02385, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.84438, "top5_acc": 0.99312, "loss_cls": 0.71578, "loss": 0.71578, "time": 0.41336} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.02384, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84875, "top5_acc": 0.99062, "loss_cls": 0.70725, "loss": 0.70725, "time": 0.42731} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.02383, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83875, "top5_acc": 0.9925, "loss_cls": 0.74209, "loss": 0.74209, "time": 0.41336} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.02383, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.83312, "top5_acc": 0.99312, "loss_cls": 0.73003, "loss": 0.73003, "time": 0.4147} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.02382, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85, "top5_acc": 0.99375, "loss_cls": 0.73118, "loss": 0.73118, "time": 0.41512} +{"mode": "val", "epoch": 21, "iter": 533, "lr": 0.02381, "top1_acc": 0.83793, "top5_acc": 0.98533, "mean_class_accuracy": 0.77169} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.0238, "memory": 4082, "data_time": 0.19704, "top1_acc": 0.87125, "top5_acc": 0.995, "loss_cls": 0.62277, "loss": 0.62277, "time": 0.58511} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.02379, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.85375, "top5_acc": 0.9925, "loss_cls": 0.67257, "loss": 0.67257, "time": 0.32103} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.02378, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85875, "top5_acc": 0.9925, "loss_cls": 0.68271, "loss": 0.68271, "time": 0.33491} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.02378, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.85438, "top5_acc": 0.99625, "loss_cls": 0.66173, "loss": 0.66173, "time": 0.41731} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.02377, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.865, "top5_acc": 0.99438, "loss_cls": 0.63336, "loss": 0.63336, "time": 0.41589} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.02376, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.85688, "top5_acc": 0.99312, "loss_cls": 0.67752, "loss": 0.67752, "time": 0.4141} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.02375, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.85312, "top5_acc": 0.99375, "loss_cls": 0.67896, "loss": 0.67896, "time": 0.41519} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.02374, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83625, "top5_acc": 0.995, "loss_cls": 0.69425, "loss": 0.69425, "time": 0.41395} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.02373, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85188, "top5_acc": 0.99062, "loss_cls": 0.69234, "loss": 0.69234, "time": 0.41646} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.02372, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83062, "top5_acc": 0.99438, "loss_cls": 0.75329, "loss": 0.75329, "time": 0.41494} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.02371, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83562, "top5_acc": 0.98938, "loss_cls": 0.7459, "loss": 0.7459, "time": 0.41475} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0237, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81938, "top5_acc": 0.9925, "loss_cls": 0.78518, "loss": 0.78518, "time": 0.4167} +{"mode": "val", "epoch": 22, "iter": 533, "lr": 0.0237, "top1_acc": 0.80894, "top5_acc": 0.98592, "mean_class_accuracy": 0.74248} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.02369, "memory": 4082, "data_time": 0.19362, "top1_acc": 0.86562, "top5_acc": 0.99938, "loss_cls": 0.61074, "loss": 0.61074, "time": 0.56845} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.02368, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.84125, "top5_acc": 0.99375, "loss_cls": 0.70252, "loss": 0.70252, "time": 0.33531} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.02367, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.84938, "top5_acc": 0.99438, "loss_cls": 0.65779, "loss": 0.65779, "time": 0.32832} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.02366, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.85375, "top5_acc": 0.9925, "loss_cls": 0.65978, "loss": 0.65978, "time": 0.41509} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.02365, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.88, "top5_acc": 0.99812, "loss_cls": 0.59364, "loss": 0.59364, "time": 0.42232} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.02364, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.86062, "top5_acc": 0.9975, "loss_cls": 0.63985, "loss": 0.63985, "time": 0.42526} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.02363, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84438, "top5_acc": 0.99375, "loss_cls": 0.70731, "loss": 0.70731, "time": 0.41798} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.02362, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.85062, "top5_acc": 0.98938, "loss_cls": 0.7091, "loss": 0.7091, "time": 0.4162} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.02361, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.84625, "top5_acc": 0.99375, "loss_cls": 0.69363, "loss": 0.69363, "time": 0.41518} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.0236, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.84625, "top5_acc": 0.99438, "loss_cls": 0.69521, "loss": 0.69521, "time": 0.4152} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.02359, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85688, "top5_acc": 0.99125, "loss_cls": 0.67387, "loss": 0.67387, "time": 0.41556} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.02359, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83562, "top5_acc": 0.99188, "loss_cls": 0.75297, "loss": 0.75297, "time": 0.41611} +{"mode": "val", "epoch": 23, "iter": 533, "lr": 0.02358, "top1_acc": 0.82373, "top5_acc": 0.98557, "mean_class_accuracy": 0.74015} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.02357, "memory": 4082, "data_time": 0.19957, "top1_acc": 0.85812, "top5_acc": 0.99375, "loss_cls": 0.66309, "loss": 0.66309, "time": 0.56358} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.02356, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.84438, "top5_acc": 0.995, "loss_cls": 0.67898, "loss": 0.67898, "time": 0.34585} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.02355, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.855, "top5_acc": 0.99375, "loss_cls": 0.64787, "loss": 0.64787, "time": 0.31971} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.02354, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.82938, "top5_acc": 0.99312, "loss_cls": 0.72802, "loss": 0.72802, "time": 0.41589} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.02353, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85375, "top5_acc": 0.99438, "loss_cls": 0.6961, "loss": 0.6961, "time": 0.41681} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.02352, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.86438, "top5_acc": 0.99438, "loss_cls": 0.66019, "loss": 0.66019, "time": 0.4167} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.02351, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.85562, "top5_acc": 0.99, "loss_cls": 0.6911, "loss": 0.6911, "time": 0.41494} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.0235, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84688, "top5_acc": 0.99438, "loss_cls": 0.65182, "loss": 0.65182, "time": 0.41453} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.02349, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.86562, "top5_acc": 0.99375, "loss_cls": 0.64053, "loss": 0.64053, "time": 0.41375} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.02348, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84438, "top5_acc": 0.99438, "loss_cls": 0.69355, "loss": 0.69355, "time": 0.41708} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.02347, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85812, "top5_acc": 0.99, "loss_cls": 0.6832, "loss": 0.6832, "time": 0.4175} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.02346, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85188, "top5_acc": 0.98812, "loss_cls": 0.67329, "loss": 0.67329, "time": 0.41672} +{"mode": "val", "epoch": 24, "iter": 533, "lr": 0.02345, "top1_acc": 0.81481, "top5_acc": 0.98662, "mean_class_accuracy": 0.74435} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.02344, "memory": 4082, "data_time": 0.19681, "top1_acc": 0.8675, "top5_acc": 0.99625, "loss_cls": 0.63938, "loss": 0.63938, "time": 0.55061} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.02343, "memory": 4082, "data_time": 0.00076, "top1_acc": 0.87375, "top5_acc": 0.99625, "loss_cls": 0.59009, "loss": 0.59009, "time": 0.35629} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.02342, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.865, "top5_acc": 0.99562, "loss_cls": 0.61934, "loss": 0.61934, "time": 0.31189} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.02341, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.85375, "top5_acc": 0.99188, "loss_cls": 0.66212, "loss": 0.66212, "time": 0.41668} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.0234, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86625, "top5_acc": 0.99312, "loss_cls": 0.64877, "loss": 0.64877, "time": 0.4152} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.02339, "memory": 4082, "data_time": 0.00071, "top1_acc": 0.85562, "top5_acc": 0.99375, "loss_cls": 0.65065, "loss": 0.65065, "time": 0.41396} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.02338, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8725, "top5_acc": 0.99562, "loss_cls": 0.61253, "loss": 0.61253, "time": 0.41685} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.02337, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.855, "top5_acc": 0.99062, "loss_cls": 0.64294, "loss": 0.64294, "time": 0.41553} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.02336, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.86062, "top5_acc": 0.9925, "loss_cls": 0.63905, "loss": 0.63905, "time": 0.41548} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.02335, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.85188, "top5_acc": 0.9925, "loss_cls": 0.65786, "loss": 0.65786, "time": 0.41422} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.02334, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.84688, "top5_acc": 0.99312, "loss_cls": 0.71882, "loss": 0.71882, "time": 0.41579} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.02333, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.84562, "top5_acc": 0.99188, "loss_cls": 0.72682, "loss": 0.72682, "time": 0.41455} +{"mode": "val", "epoch": 25, "iter": 533, "lr": 0.02333, "top1_acc": 0.79791, "top5_acc": 0.98451, "mean_class_accuracy": 0.72383} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.02332, "memory": 4082, "data_time": 0.20206, "top1_acc": 0.8725, "top5_acc": 0.99562, "loss_cls": 0.653, "loss": 0.653, "time": 0.5618} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.0233, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.85312, "top5_acc": 0.99438, "loss_cls": 0.67645, "loss": 0.67645, "time": 0.35394} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.02329, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.85938, "top5_acc": 0.99438, "loss_cls": 0.64528, "loss": 0.64528, "time": 0.33435} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.02328, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.86, "top5_acc": 0.99625, "loss_cls": 0.6642, "loss": 0.6642, "time": 0.43191} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.02327, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87562, "top5_acc": 0.99562, "loss_cls": 0.60538, "loss": 0.60538, "time": 0.41512} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.02326, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86938, "top5_acc": 0.99375, "loss_cls": 0.60822, "loss": 0.60822, "time": 0.41638} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.02325, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.86938, "top5_acc": 0.99312, "loss_cls": 0.61198, "loss": 0.61198, "time": 0.41491} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.02324, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.86688, "top5_acc": 0.995, "loss_cls": 0.62965, "loss": 0.62965, "time": 0.41368} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.02323, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85812, "top5_acc": 0.9925, "loss_cls": 0.65061, "loss": 0.65061, "time": 0.41522} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.02322, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8475, "top5_acc": 0.99688, "loss_cls": 0.69623, "loss": 0.69623, "time": 0.41659} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.02321, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85875, "top5_acc": 0.99625, "loss_cls": 0.65941, "loss": 0.65941, "time": 0.41512} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.0232, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8525, "top5_acc": 0.99, "loss_cls": 0.6988, "loss": 0.6988, "time": 0.41501} +{"mode": "val", "epoch": 26, "iter": 533, "lr": 0.02319, "top1_acc": 0.82279, "top5_acc": 0.98627, "mean_class_accuracy": 0.76018} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.02318, "memory": 4082, "data_time": 0.19866, "top1_acc": 0.87688, "top5_acc": 0.99625, "loss_cls": 0.60816, "loss": 0.60816, "time": 0.56547} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.02317, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.87438, "top5_acc": 0.99562, "loss_cls": 0.57414, "loss": 0.57414, "time": 0.34737} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.02316, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.86125, "top5_acc": 0.99625, "loss_cls": 0.61933, "loss": 0.61933, "time": 0.32578} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.02315, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.86562, "top5_acc": 0.99312, "loss_cls": 0.63995, "loss": 0.63995, "time": 0.41539} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.02314, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.86, "top5_acc": 0.9925, "loss_cls": 0.64635, "loss": 0.64635, "time": 0.42185} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.02313, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.88562, "top5_acc": 0.99562, "loss_cls": 0.57179, "loss": 0.57179, "time": 0.42899} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.02312, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8525, "top5_acc": 0.99375, "loss_cls": 0.67058, "loss": 0.67058, "time": 0.41475} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.02311, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86438, "top5_acc": 0.98875, "loss_cls": 0.65278, "loss": 0.65278, "time": 0.41587} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.0231, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.8625, "top5_acc": 0.995, "loss_cls": 0.65326, "loss": 0.65326, "time": 0.41362} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.02308, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.84688, "top5_acc": 0.99, "loss_cls": 0.67869, "loss": 0.67869, "time": 0.41703} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.02307, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.88, "top5_acc": 0.99688, "loss_cls": 0.59921, "loss": 0.59921, "time": 0.41469} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.02306, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84875, "top5_acc": 0.99562, "loss_cls": 0.68303, "loss": 0.68303, "time": 0.41629} +{"mode": "val", "epoch": 27, "iter": 533, "lr": 0.02305, "top1_acc": 0.8242, "top5_acc": 0.98134, "mean_class_accuracy": 0.75484} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.02304, "memory": 4082, "data_time": 0.19426, "top1_acc": 0.86125, "top5_acc": 0.995, "loss_cls": 0.64039, "loss": 0.64039, "time": 0.5482} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.02303, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86312, "top5_acc": 0.99375, "loss_cls": 0.65247, "loss": 0.65247, "time": 0.35998} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.02302, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.8725, "top5_acc": 0.99562, "loss_cls": 0.60711, "loss": 0.60711, "time": 0.31204} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.02301, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.86062, "top5_acc": 0.99375, "loss_cls": 0.66878, "loss": 0.66878, "time": 0.41412} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.023, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.87688, "top5_acc": 0.99438, "loss_cls": 0.57773, "loss": 0.57773, "time": 0.41778} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.02299, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86, "top5_acc": 0.98875, "loss_cls": 0.65714, "loss": 0.65714, "time": 0.41429} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.02298, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.8625, "top5_acc": 0.99562, "loss_cls": 0.62074, "loss": 0.62074, "time": 0.41449} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.02297, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.8675, "top5_acc": 0.99438, "loss_cls": 0.61963, "loss": 0.61963, "time": 0.41598} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.02295, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.85375, "top5_acc": 0.9925, "loss_cls": 0.67719, "loss": 0.67719, "time": 0.41566} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.02294, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.86688, "top5_acc": 0.9925, "loss_cls": 0.62979, "loss": 0.62979, "time": 0.41366} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.02293, "memory": 4082, "data_time": 0.00056, "top1_acc": 0.85688, "top5_acc": 0.99188, "loss_cls": 0.65416, "loss": 0.65416, "time": 0.41427} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.02292, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.85938, "top5_acc": 0.995, "loss_cls": 0.66657, "loss": 0.66657, "time": 0.4158} +{"mode": "val", "epoch": 28, "iter": 533, "lr": 0.02291, "top1_acc": 0.84392, "top5_acc": 0.98756, "mean_class_accuracy": 0.78196} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.0229, "memory": 4082, "data_time": 0.1975, "top1_acc": 0.88, "top5_acc": 0.99375, "loss_cls": 0.59064, "loss": 0.59064, "time": 0.55077} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.02289, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.86375, "top5_acc": 0.995, "loss_cls": 0.64205, "loss": 0.64205, "time": 0.35712} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.02288, "memory": 4082, "data_time": 0.00084, "top1_acc": 0.85, "top5_acc": 0.9925, "loss_cls": 0.69417, "loss": 0.69417, "time": 0.31251} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.02287, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.87688, "top5_acc": 0.99625, "loss_cls": 0.57734, "loss": 0.57734, "time": 0.41563} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.02285, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.87, "top5_acc": 0.99625, "loss_cls": 0.60549, "loss": 0.60549, "time": 0.41475} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.02284, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.86688, "top5_acc": 0.99188, "loss_cls": 0.63193, "loss": 0.63193, "time": 0.41474} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.02283, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.87688, "top5_acc": 0.99312, "loss_cls": 0.60608, "loss": 0.60608, "time": 0.41397} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.02282, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.86125, "top5_acc": 0.99688, "loss_cls": 0.61292, "loss": 0.61292, "time": 0.41471} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.02281, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.86812, "top5_acc": 0.99562, "loss_cls": 0.64048, "loss": 0.64048, "time": 0.41585} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.0228, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.865, "top5_acc": 0.99562, "loss_cls": 0.63818, "loss": 0.63818, "time": 0.41525} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.02279, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86875, "top5_acc": 0.99375, "loss_cls": 0.64011, "loss": 0.64011, "time": 0.41398} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.02277, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.87312, "top5_acc": 0.995, "loss_cls": 0.63458, "loss": 0.63458, "time": 0.41567} +{"mode": "val", "epoch": 29, "iter": 533, "lr": 0.02276, "top1_acc": 0.83441, "top5_acc": 0.9885, "mean_class_accuracy": 0.7801} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.02275, "memory": 4082, "data_time": 0.1954, "top1_acc": 0.87875, "top5_acc": 0.995, "loss_cls": 0.58766, "loss": 0.58766, "time": 0.69557} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.02274, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.88625, "top5_acc": 0.9975, "loss_cls": 0.5511, "loss": 0.5511, "time": 0.25233} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.02273, "memory": 4082, "data_time": 0.0006, "top1_acc": 0.87938, "top5_acc": 0.9975, "loss_cls": 0.54938, "loss": 0.54938, "time": 0.50487} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.02272, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.85625, "top5_acc": 0.98875, "loss_cls": 0.65185, "loss": 0.65185, "time": 0.49457} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.02271, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86375, "top5_acc": 0.99438, "loss_cls": 0.6479, "loss": 0.6479, "time": 0.47388} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.02269, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.87625, "top5_acc": 0.995, "loss_cls": 0.59892, "loss": 0.59892, "time": 0.49301} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.02268, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.86562, "top5_acc": 0.99375, "loss_cls": 0.62103, "loss": 0.62103, "time": 0.49921} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.02267, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.87125, "top5_acc": 0.99438, "loss_cls": 0.64694, "loss": 0.64694, "time": 0.4923} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.02266, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.8575, "top5_acc": 0.98938, "loss_cls": 0.68122, "loss": 0.68122, "time": 0.48861} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.02265, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.865, "top5_acc": 0.99312, "loss_cls": 0.64342, "loss": 0.64342, "time": 0.50016} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.02263, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.87375, "top5_acc": 0.9925, "loss_cls": 0.63494, "loss": 0.63494, "time": 0.49311} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.02262, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.88125, "top5_acc": 0.99438, "loss_cls": 0.58663, "loss": 0.58663, "time": 0.48798} +{"mode": "val", "epoch": 30, "iter": 533, "lr": 0.02261, "top1_acc": 0.83441, "top5_acc": 0.98779, "mean_class_accuracy": 0.77403} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.0226, "memory": 4083, "data_time": 0.19424, "top1_acc": 0.88875, "top5_acc": 0.99812, "loss_cls": 0.72412, "loss": 0.72412, "time": 0.89256} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.02259, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.86938, "top5_acc": 0.99562, "loss_cls": 0.7471, "loss": 0.7471, "time": 0.5116} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.02258, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85562, "top5_acc": 0.995, "loss_cls": 0.78914, "loss": 0.78914, "time": 0.525} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.02256, "memory": 4083, "data_time": 0.00066, "top1_acc": 0.87062, "top5_acc": 0.99375, "loss_cls": 0.72607, "loss": 0.72607, "time": 0.51186} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.02255, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.86938, "top5_acc": 0.995, "loss_cls": 0.75086, "loss": 0.75086, "time": 0.52029} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.02254, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87, "top5_acc": 0.99625, "loss_cls": 0.76588, "loss": 0.76588, "time": 0.51532} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.02253, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86625, "top5_acc": 0.99125, "loss_cls": 0.81464, "loss": 0.81464, "time": 0.49867} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.02252, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87188, "top5_acc": 0.99375, "loss_cls": 0.78297, "loss": 0.78297, "time": 0.51317} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0225, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.86438, "top5_acc": 0.99438, "loss_cls": 0.78805, "loss": 0.78805, "time": 0.40443} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.02249, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.87062, "top5_acc": 0.98938, "loss_cls": 0.80548, "loss": 0.80548, "time": 0.51116} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.02248, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.85188, "top5_acc": 0.99438, "loss_cls": 0.83458, "loss": 0.83458, "time": 0.26286} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.02247, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.86125, "top5_acc": 0.99125, "loss_cls": 0.81461, "loss": 0.81461, "time": 0.51514} +{"mode": "val", "epoch": 31, "iter": 533, "lr": 0.02246, "top1_acc": 0.80519, "top5_acc": 0.97911, "mean_class_accuracy": 0.73286} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.02244, "memory": 4083, "data_time": 0.19889, "top1_acc": 0.87812, "top5_acc": 0.9925, "loss_cls": 0.65062, "loss": 0.65062, "time": 0.84989} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.02243, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88875, "top5_acc": 0.99562, "loss_cls": 0.6318, "loss": 0.6318, "time": 0.52068} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.02242, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88625, "top5_acc": 0.99375, "loss_cls": 0.67567, "loss": 0.67567, "time": 0.52562} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.02241, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.86312, "top5_acc": 0.995, "loss_cls": 0.74724, "loss": 0.74724, "time": 0.52358} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.02239, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.865, "top5_acc": 0.99375, "loss_cls": 0.70551, "loss": 0.70551, "time": 0.49434} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.02238, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86375, "top5_acc": 0.99438, "loss_cls": 0.74198, "loss": 0.74198, "time": 0.31965} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.02237, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88188, "top5_acc": 0.99562, "loss_cls": 0.63058, "loss": 0.63058, "time": 0.42198} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.02236, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.88562, "top5_acc": 0.99562, "loss_cls": 0.62219, "loss": 0.62219, "time": 0.44194} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.02234, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86688, "top5_acc": 0.995, "loss_cls": 0.73184, "loss": 0.73184, "time": 0.53093} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.02233, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87688, "top5_acc": 0.9925, "loss_cls": 0.68216, "loss": 0.68216, "time": 0.52824} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.02232, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87812, "top5_acc": 0.995, "loss_cls": 0.68625, "loss": 0.68625, "time": 0.53234} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.02231, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85938, "top5_acc": 0.99312, "loss_cls": 0.69822, "loss": 0.69822, "time": 0.52478} +{"mode": "val", "epoch": 32, "iter": 533, "lr": 0.0223, "top1_acc": 0.7586, "top5_acc": 0.97723, "mean_class_accuracy": 0.71992} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.02228, "memory": 4083, "data_time": 0.19914, "top1_acc": 0.8725, "top5_acc": 0.99812, "loss_cls": 0.64693, "loss": 0.64693, "time": 0.84514} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.02227, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.8725, "top5_acc": 0.99562, "loss_cls": 0.66501, "loss": 0.66501, "time": 0.29452} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.02226, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.85812, "top5_acc": 0.99562, "loss_cls": 0.71444, "loss": 0.71444, "time": 0.51449} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.02225, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87562, "top5_acc": 0.99625, "loss_cls": 0.64356, "loss": 0.64356, "time": 0.36337} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.02223, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.87625, "top5_acc": 0.99312, "loss_cls": 0.67759, "loss": 0.67759, "time": 0.52196} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.02222, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.86438, "top5_acc": 0.99188, "loss_cls": 0.72619, "loss": 0.72619, "time": 0.51956} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.02221, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88875, "top5_acc": 0.99562, "loss_cls": 0.59907, "loss": 0.59907, "time": 0.5179} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.02219, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8925, "top5_acc": 0.99562, "loss_cls": 0.63125, "loss": 0.63125, "time": 0.51821} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.02218, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87562, "top5_acc": 0.99375, "loss_cls": 0.64846, "loss": 0.64846, "time": 0.515} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.02217, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86625, "top5_acc": 0.99688, "loss_cls": 0.66589, "loss": 0.66589, "time": 0.52093} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.02216, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87062, "top5_acc": 0.99312, "loss_cls": 0.6778, "loss": 0.6778, "time": 0.51612} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.02214, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.8625, "top5_acc": 0.99625, "loss_cls": 0.6548, "loss": 0.6548, "time": 0.50354} +{"mode": "val", "epoch": 33, "iter": 533, "lr": 0.02213, "top1_acc": 0.79627, "top5_acc": 0.98486, "mean_class_accuracy": 0.73203} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.02212, "memory": 4083, "data_time": 0.19858, "top1_acc": 0.88438, "top5_acc": 0.99812, "loss_cls": 0.61891, "loss": 0.61891, "time": 0.68283} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.02211, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.87312, "top5_acc": 0.99562, "loss_cls": 0.65401, "loss": 0.65401, "time": 0.50856} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.02209, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88875, "top5_acc": 0.99688, "loss_cls": 0.61136, "loss": 0.61136, "time": 0.50916} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.02208, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87312, "top5_acc": 0.99312, "loss_cls": 0.64143, "loss": 0.64143, "time": 0.50959} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.02207, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88375, "top5_acc": 0.99562, "loss_cls": 0.60542, "loss": 0.60542, "time": 0.52322} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.02205, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88562, "top5_acc": 0.99688, "loss_cls": 0.5864, "loss": 0.5864, "time": 0.50741} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.02204, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.85125, "top5_acc": 0.99562, "loss_cls": 0.70119, "loss": 0.70119, "time": 0.52947} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.02203, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.8525, "top5_acc": 0.99125, "loss_cls": 0.71498, "loss": 0.71498, "time": 0.52115} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.02201, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.85312, "top5_acc": 0.99688, "loss_cls": 0.74379, "loss": 0.74379, "time": 0.51871} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.022, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88625, "top5_acc": 0.99688, "loss_cls": 0.61928, "loss": 0.61928, "time": 0.41442} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.02199, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8825, "top5_acc": 0.995, "loss_cls": 0.6315, "loss": 0.6315, "time": 0.51182} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.02197, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86062, "top5_acc": 0.99125, "loss_cls": 0.7216, "loss": 0.7216, "time": 0.27239} +{"mode": "val", "epoch": 34, "iter": 533, "lr": 0.02196, "top1_acc": 0.83558, "top5_acc": 0.98873, "mean_class_accuracy": 0.77651} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.02195, "memory": 4083, "data_time": 0.19399, "top1_acc": 0.8825, "top5_acc": 0.99375, "loss_cls": 0.63208, "loss": 0.63208, "time": 0.84718} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.02194, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88438, "top5_acc": 0.99812, "loss_cls": 0.60465, "loss": 0.60465, "time": 0.52259} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.02192, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87688, "top5_acc": 0.9925, "loss_cls": 0.63333, "loss": 0.63333, "time": 0.52809} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.02191, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87875, "top5_acc": 0.99562, "loss_cls": 0.61474, "loss": 0.61474, "time": 0.50925} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.0219, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.865, "top5_acc": 0.99562, "loss_cls": 0.66067, "loss": 0.66067, "time": 0.52005} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.02188, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.88125, "top5_acc": 0.99062, "loss_cls": 0.64299, "loss": 0.64299, "time": 0.48413} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.02187, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86688, "top5_acc": 0.995, "loss_cls": 0.6443, "loss": 0.6443, "time": 0.3898} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.02185, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.87062, "top5_acc": 0.99375, "loss_cls": 0.63382, "loss": 0.63382, "time": 0.35154} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.02184, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87875, "top5_acc": 0.99625, "loss_cls": 0.64107, "loss": 0.64107, "time": 0.48273} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.02183, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.885, "top5_acc": 0.99562, "loss_cls": 0.62008, "loss": 0.62008, "time": 0.51854} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.02181, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88188, "top5_acc": 0.9975, "loss_cls": 0.61669, "loss": 0.61669, "time": 0.51812} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.0218, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88062, "top5_acc": 0.99688, "loss_cls": 0.61462, "loss": 0.61462, "time": 0.52482} +{"mode": "val", "epoch": 35, "iter": 533, "lr": 0.02179, "top1_acc": 0.83746, "top5_acc": 0.98909, "mean_class_accuracy": 0.77169} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.02178, "memory": 4083, "data_time": 0.18676, "top1_acc": 0.88625, "top5_acc": 0.99688, "loss_cls": 0.61888, "loss": 0.61888, "time": 0.84472} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.02176, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89562, "top5_acc": 0.99562, "loss_cls": 0.5856, "loss": 0.5856, "time": 0.51596} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.02175, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87688, "top5_acc": 0.99562, "loss_cls": 0.63569, "loss": 0.63569, "time": 0.28278} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.02173, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.88438, "top5_acc": 0.9975, "loss_cls": 0.58097, "loss": 0.58097, "time": 0.50495} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.02172, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.86688, "top5_acc": 0.99438, "loss_cls": 0.63819, "loss": 0.63819, "time": 0.38847} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.02171, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88938, "top5_acc": 0.9975, "loss_cls": 0.62276, "loss": 0.62276, "time": 0.52065} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.02169, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.865, "top5_acc": 0.9925, "loss_cls": 0.71635, "loss": 0.71635, "time": 0.51744} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.02168, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.88125, "top5_acc": 0.99312, "loss_cls": 0.64092, "loss": 0.64092, "time": 0.52247} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.02167, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89, "top5_acc": 0.99562, "loss_cls": 0.58687, "loss": 0.58687, "time": 0.52539} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.02165, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86875, "top5_acc": 0.99562, "loss_cls": 0.65308, "loss": 0.65308, "time": 0.51587} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.02164, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.87875, "top5_acc": 0.99438, "loss_cls": 0.65056, "loss": 0.65056, "time": 0.52441} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.02162, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88312, "top5_acc": 0.99438, "loss_cls": 0.60614, "loss": 0.60614, "time": 0.51065} +{"mode": "val", "epoch": 36, "iter": 533, "lr": 0.02161, "top1_acc": 0.80472, "top5_acc": 0.98404, "mean_class_accuracy": 0.72589} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.0216, "memory": 4083, "data_time": 0.19813, "top1_acc": 0.88062, "top5_acc": 0.99438, "loss_cls": 0.6105, "loss": 0.6105, "time": 0.57474} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.02158, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87062, "top5_acc": 0.99312, "loss_cls": 0.67327, "loss": 0.67327, "time": 0.51505} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.02157, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89188, "top5_acc": 0.99812, "loss_cls": 0.58224, "loss": 0.58224, "time": 0.50487} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.02156, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.89062, "top5_acc": 0.99688, "loss_cls": 0.56922, "loss": 0.56922, "time": 0.52236} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.02154, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.87562, "top5_acc": 0.99625, "loss_cls": 0.62531, "loss": 0.62531, "time": 0.51872} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.02153, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.87688, "top5_acc": 0.995, "loss_cls": 0.63232, "loss": 0.63232, "time": 0.51863} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.02151, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86625, "top5_acc": 0.99562, "loss_cls": 0.65165, "loss": 0.65165, "time": 0.51777} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0215, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.89312, "top5_acc": 0.99562, "loss_cls": 0.58104, "loss": 0.58104, "time": 0.52146} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.02149, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88688, "top5_acc": 0.99562, "loss_cls": 0.58468, "loss": 0.58468, "time": 0.51076} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.02147, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87625, "top5_acc": 0.995, "loss_cls": 0.6276, "loss": 0.6276, "time": 0.51265} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.02146, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86625, "top5_acc": 0.99312, "loss_cls": 0.6702, "loss": 0.6702, "time": 0.34579} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.02144, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87688, "top5_acc": 0.99312, "loss_cls": 0.653, "loss": 0.653, "time": 0.51145} +{"mode": "val", "epoch": 37, "iter": 533, "lr": 0.02143, "top1_acc": 0.82819, "top5_acc": 0.98897, "mean_class_accuracy": 0.7829} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.02142, "memory": 4083, "data_time": 0.19774, "top1_acc": 0.88875, "top5_acc": 0.99438, "loss_cls": 0.61739, "loss": 0.61739, "time": 0.83413} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.0214, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88625, "top5_acc": 0.99562, "loss_cls": 0.60377, "loss": 0.60377, "time": 0.51172} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.02139, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87438, "top5_acc": 0.99438, "loss_cls": 0.64185, "loss": 0.64185, "time": 0.5111} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.02137, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.88375, "top5_acc": 0.99812, "loss_cls": 0.58343, "loss": 0.58343, "time": 0.50671} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.02136, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88062, "top5_acc": 0.99562, "loss_cls": 0.59203, "loss": 0.59203, "time": 0.52177} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.02134, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.88312, "top5_acc": 0.99875, "loss_cls": 0.56994, "loss": 0.56994, "time": 0.5146} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.02133, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87812, "top5_acc": 0.99438, "loss_cls": 0.64576, "loss": 0.64576, "time": 0.48041} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.02132, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88625, "top5_acc": 0.99562, "loss_cls": 0.61609, "loss": 0.61609, "time": 0.38714} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.0213, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88188, "top5_acc": 0.9925, "loss_cls": 0.61564, "loss": 0.61564, "time": 0.35427} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.02129, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.87438, "top5_acc": 0.99625, "loss_cls": 0.62015, "loss": 0.62015, "time": 0.47193} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.02127, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88188, "top5_acc": 0.99562, "loss_cls": 0.60482, "loss": 0.60482, "time": 0.51574} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.02126, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87188, "top5_acc": 0.99375, "loss_cls": 0.68103, "loss": 0.68103, "time": 0.53118} +{"mode": "val", "epoch": 38, "iter": 533, "lr": 0.02125, "top1_acc": 0.85201, "top5_acc": 0.99014, "mean_class_accuracy": 0.80255} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.02123, "memory": 4083, "data_time": 0.19219, "top1_acc": 0.89438, "top5_acc": 0.99625, "loss_cls": 0.56481, "loss": 0.56481, "time": 0.83643} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.02122, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8825, "top5_acc": 0.99625, "loss_cls": 0.58624, "loss": 0.58624, "time": 0.52127} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.0212, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88125, "top5_acc": 0.99375, "loss_cls": 0.63296, "loss": 0.63296, "time": 0.51319} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.02119, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.88312, "top5_acc": 0.99625, "loss_cls": 0.62804, "loss": 0.62804, "time": 0.28097} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.02117, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.88625, "top5_acc": 0.995, "loss_cls": 0.56272, "loss": 0.56272, "time": 0.51202} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.02116, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88938, "top5_acc": 0.9925, "loss_cls": 0.60003, "loss": 0.60003, "time": 0.38586} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.02114, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.86688, "top5_acc": 0.99625, "loss_cls": 0.63367, "loss": 0.63367, "time": 0.51741} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.02113, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88938, "top5_acc": 0.9975, "loss_cls": 0.5567, "loss": 0.5567, "time": 0.51516} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.02111, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88938, "top5_acc": 0.99562, "loss_cls": 0.5939, "loss": 0.5939, "time": 0.5279} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.0211, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.87438, "top5_acc": 0.99688, "loss_cls": 0.63424, "loss": 0.63424, "time": 0.51827} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.02108, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89188, "top5_acc": 0.99438, "loss_cls": 0.56972, "loss": 0.56972, "time": 0.52374} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.02107, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88875, "top5_acc": 0.99688, "loss_cls": 0.58457, "loss": 0.58457, "time": 0.50604} +{"mode": "val", "epoch": 39, "iter": 533, "lr": 0.02106, "top1_acc": 0.8675, "top5_acc": 0.9919, "mean_class_accuracy": 0.81115} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.02104, "memory": 4083, "data_time": 0.1948, "top1_acc": 0.90562, "top5_acc": 0.99625, "loss_cls": 0.52717, "loss": 0.52717, "time": 0.58617} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.02103, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8975, "top5_acc": 0.9975, "loss_cls": 0.52883, "loss": 0.52883, "time": 0.35508} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.02101, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89938, "top5_acc": 0.99875, "loss_cls": 0.55268, "loss": 0.55268, "time": 0.46326} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.021, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8925, "top5_acc": 0.99688, "loss_cls": 0.55825, "loss": 0.55825, "time": 0.50103} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.02098, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.87188, "top5_acc": 0.9925, "loss_cls": 0.64441, "loss": 0.64441, "time": 0.50439} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.02097, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86875, "top5_acc": 0.995, "loss_cls": 0.65596, "loss": 0.65596, "time": 0.50494} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.02095, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8825, "top5_acc": 0.99375, "loss_cls": 0.63417, "loss": 0.63417, "time": 0.51732} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.02094, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88062, "top5_acc": 0.9925, "loss_cls": 0.619, "loss": 0.619, "time": 0.50875} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.02092, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.88, "top5_acc": 0.99688, "loss_cls": 0.6177, "loss": 0.6177, "time": 0.51652} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.02091, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.8925, "top5_acc": 0.99625, "loss_cls": 0.56263, "loss": 0.56263, "time": 0.53228} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.02089, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.875, "top5_acc": 0.9925, "loss_cls": 0.64364, "loss": 0.64364, "time": 0.50982} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.02088, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.865, "top5_acc": 0.995, "loss_cls": 0.6662, "loss": 0.6662, "time": 0.46124} +{"mode": "val", "epoch": 40, "iter": 533, "lr": 0.02086, "top1_acc": 0.83781, "top5_acc": 0.98909, "mean_class_accuracy": 0.75445} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.02085, "memory": 4083, "data_time": 0.1976, "top1_acc": 0.87562, "top5_acc": 0.98875, "loss_cls": 0.65987, "loss": 0.65987, "time": 0.8455} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.02083, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91062, "top5_acc": 0.99875, "loss_cls": 0.51173, "loss": 0.51173, "time": 0.53091} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.02082, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89938, "top5_acc": 0.99875, "loss_cls": 0.4982, "loss": 0.4982, "time": 0.52006} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.0208, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87562, "top5_acc": 0.99562, "loss_cls": 0.59528, "loss": 0.59528, "time": 0.51675} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.02079, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.875, "top5_acc": 0.99625, "loss_cls": 0.6356, "loss": 0.6356, "time": 0.4958} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.02077, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.88188, "top5_acc": 0.99062, "loss_cls": 0.61782, "loss": 0.61782, "time": 0.51651} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.02076, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88688, "top5_acc": 0.99875, "loss_cls": 0.56849, "loss": 0.56849, "time": 0.50812} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.02074, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8725, "top5_acc": 0.99312, "loss_cls": 0.69045, "loss": 0.69045, "time": 0.53812} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.02073, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89438, "top5_acc": 0.99688, "loss_cls": 0.59229, "loss": 0.59229, "time": 0.28476} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.02071, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87875, "top5_acc": 0.995, "loss_cls": 0.61421, "loss": 0.61421, "time": 0.51178} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.0207, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89688, "top5_acc": 0.99625, "loss_cls": 0.55585, "loss": 0.55585, "time": 0.38759} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.02068, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88812, "top5_acc": 0.9975, "loss_cls": 0.58061, "loss": 0.58061, "time": 0.51169} +{"mode": "val", "epoch": 41, "iter": 533, "lr": 0.02067, "top1_acc": 0.83899, "top5_acc": 0.98956, "mean_class_accuracy": 0.77158} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.02065, "memory": 4083, "data_time": 0.19423, "top1_acc": 0.88, "top5_acc": 0.99562, "loss_cls": 0.58207, "loss": 0.58207, "time": 0.85769} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.02064, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86188, "top5_acc": 0.995, "loss_cls": 0.67189, "loss": 0.67189, "time": 0.50539} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.02062, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8625, "top5_acc": 0.99562, "loss_cls": 0.66938, "loss": 0.66938, "time": 0.52219} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.02061, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88125, "top5_acc": 0.9975, "loss_cls": 0.60633, "loss": 0.60633, "time": 0.52578} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.02059, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89562, "top5_acc": 0.99438, "loss_cls": 0.56887, "loss": 0.56887, "time": 0.38125} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.02057, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88375, "top5_acc": 0.9925, "loss_cls": 0.6194, "loss": 0.6194, "time": 0.51002} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.02056, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.88375, "top5_acc": 0.995, "loss_cls": 0.58892, "loss": 0.58892, "time": 0.27549} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.02054, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.89625, "top5_acc": 0.99688, "loss_cls": 0.53026, "loss": 0.53026, "time": 0.48338} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.02053, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89688, "top5_acc": 0.995, "loss_cls": 0.55238, "loss": 0.55238, "time": 0.48282} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.02051, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88625, "top5_acc": 0.99625, "loss_cls": 0.56318, "loss": 0.56318, "time": 0.48272} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.0205, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8925, "top5_acc": 0.99562, "loss_cls": 0.53924, "loss": 0.53924, "time": 0.48358} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.02048, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88438, "top5_acc": 0.99625, "loss_cls": 0.6016, "loss": 0.6016, "time": 0.48268} +{"mode": "val", "epoch": 42, "iter": 533, "lr": 0.02047, "top1_acc": 0.81505, "top5_acc": 0.98791, "mean_class_accuracy": 0.76858} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.02045, "memory": 4083, "data_time": 0.19889, "top1_acc": 0.87812, "top5_acc": 0.99125, "loss_cls": 0.63617, "loss": 0.63617, "time": 0.79704} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.02044, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91, "top5_acc": 0.99688, "loss_cls": 0.50159, "loss": 0.50159, "time": 0.48235} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.02042, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8925, "top5_acc": 0.9975, "loss_cls": 0.57017, "loss": 0.57017, "time": 0.48527} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.0204, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88875, "top5_acc": 0.99625, "loss_cls": 0.5789, "loss": 0.5789, "time": 0.48334} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.02039, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88, "top5_acc": 0.99812, "loss_cls": 0.58636, "loss": 0.58636, "time": 0.30947} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.02037, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.89875, "top5_acc": 0.99625, "loss_cls": 0.54721, "loss": 0.54721, "time": 0.44426} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.02036, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88688, "top5_acc": 0.9975, "loss_cls": 0.56672, "loss": 0.56672, "time": 0.29761} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.02034, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.8775, "top5_acc": 0.9975, "loss_cls": 0.6, "loss": 0.6, "time": 0.48933} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.02033, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.88, "top5_acc": 0.99312, "loss_cls": 0.58593, "loss": 0.58593, "time": 0.49002} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.02031, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90812, "top5_acc": 0.99875, "loss_cls": 0.50568, "loss": 0.50568, "time": 0.49063} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.02029, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9025, "top5_acc": 0.99875, "loss_cls": 0.52307, "loss": 0.52307, "time": 0.49127} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.02028, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88375, "top5_acc": 0.995, "loss_cls": 0.62625, "loss": 0.62625, "time": 0.492} +{"mode": "val", "epoch": 43, "iter": 533, "lr": 0.02026, "top1_acc": 0.85401, "top5_acc": 0.99202, "mean_class_accuracy": 0.78638} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.02025, "memory": 4083, "data_time": 0.19431, "top1_acc": 0.89562, "top5_acc": 0.99875, "loss_cls": 0.53797, "loss": 0.53797, "time": 0.79759} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.02023, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.9, "top5_acc": 0.99688, "loss_cls": 0.52269, "loss": 0.52269, "time": 0.49138} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.02022, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88875, "top5_acc": 0.99562, "loss_cls": 0.57628, "loss": 0.57628, "time": 0.48795} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.0202, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87812, "top5_acc": 0.995, "loss_cls": 0.61278, "loss": 0.61278, "time": 0.49305} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.02018, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88312, "top5_acc": 0.99688, "loss_cls": 0.55996, "loss": 0.55996, "time": 0.27493} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.02017, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89188, "top5_acc": 0.99812, "loss_cls": 0.56399, "loss": 0.56399, "time": 0.51348} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.02015, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89938, "top5_acc": 0.99562, "loss_cls": 0.53658, "loss": 0.53658, "time": 0.30207} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.02014, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90188, "top5_acc": 0.99875, "loss_cls": 0.51689, "loss": 0.51689, "time": 0.49061} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.02012, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.8825, "top5_acc": 0.99688, "loss_cls": 0.64365, "loss": 0.64365, "time": 0.4896} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.0201, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88312, "top5_acc": 0.99688, "loss_cls": 0.57838, "loss": 0.57838, "time": 0.48695} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.02009, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8825, "top5_acc": 0.99562, "loss_cls": 0.6455, "loss": 0.6455, "time": 0.4922} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.02007, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88125, "top5_acc": 0.99812, "loss_cls": 0.5942, "loss": 0.5942, "time": 0.49164} +{"mode": "val", "epoch": 44, "iter": 533, "lr": 0.02006, "top1_acc": 0.83875, "top5_acc": 0.98909, "mean_class_accuracy": 0.77981} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.02004, "memory": 4083, "data_time": 0.19003, "top1_acc": 0.87562, "top5_acc": 0.9975, "loss_cls": 0.5907, "loss": 0.5907, "time": 0.80048} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.02003, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90562, "top5_acc": 0.99688, "loss_cls": 0.53675, "loss": 0.53675, "time": 0.48852} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.02001, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.8925, "top5_acc": 0.99562, "loss_cls": 0.558, "loss": 0.558, "time": 0.49011} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.01999, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88438, "top5_acc": 0.99125, "loss_cls": 0.60625, "loss": 0.60625, "time": 0.48754} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.01998, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.89062, "top5_acc": 0.99312, "loss_cls": 0.57855, "loss": 0.57855, "time": 0.28039} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.01996, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88625, "top5_acc": 0.99812, "loss_cls": 0.56821, "loss": 0.56821, "time": 0.51181} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.01994, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.895, "top5_acc": 0.99688, "loss_cls": 0.56015, "loss": 0.56015, "time": 0.30471} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.01993, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90812, "top5_acc": 0.99812, "loss_cls": 0.52403, "loss": 0.52403, "time": 0.49117} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.01991, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88188, "top5_acc": 0.99625, "loss_cls": 0.58811, "loss": 0.58811, "time": 0.48627} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.01989, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89562, "top5_acc": 0.9975, "loss_cls": 0.51552, "loss": 0.51552, "time": 0.48942} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.01988, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88875, "top5_acc": 0.99688, "loss_cls": 0.59116, "loss": 0.59116, "time": 0.49123} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.01986, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89375, "top5_acc": 0.99625, "loss_cls": 0.56637, "loss": 0.56637, "time": 0.491} +{"mode": "val", "epoch": 45, "iter": 533, "lr": 0.01985, "top1_acc": 0.85647, "top5_acc": 0.98991, "mean_class_accuracy": 0.81433} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.01983, "memory": 4083, "data_time": 0.19794, "top1_acc": 0.905, "top5_acc": 0.99625, "loss_cls": 0.54009, "loss": 0.54009, "time": 0.80318} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.01981, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91188, "top5_acc": 0.99812, "loss_cls": 0.49319, "loss": 0.49319, "time": 0.49033} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.0198, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9025, "top5_acc": 0.99688, "loss_cls": 0.53583, "loss": 0.53583, "time": 0.49181} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.01978, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.89625, "top5_acc": 0.99562, "loss_cls": 0.54199, "loss": 0.54199, "time": 0.49303} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.01976, "memory": 4083, "data_time": 0.00063, "top1_acc": 0.90688, "top5_acc": 0.99625, "loss_cls": 0.49723, "loss": 0.49723, "time": 0.27293} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.01975, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.89188, "top5_acc": 0.9975, "loss_cls": 0.50265, "loss": 0.50265, "time": 0.51011} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.01973, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89875, "top5_acc": 0.99688, "loss_cls": 0.53876, "loss": 0.53876, "time": 0.30265} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.01971, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.87562, "top5_acc": 0.99625, "loss_cls": 0.63795, "loss": 0.63795, "time": 0.48983} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.0197, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.88125, "top5_acc": 0.99562, "loss_cls": 0.60457, "loss": 0.60457, "time": 0.48823} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.01968, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90188, "top5_acc": 0.99688, "loss_cls": 0.51375, "loss": 0.51375, "time": 0.48956} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.01966, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89, "top5_acc": 0.99438, "loss_cls": 0.57146, "loss": 0.57146, "time": 0.49358} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.01965, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87875, "top5_acc": 0.995, "loss_cls": 0.62359, "loss": 0.62359, "time": 0.49658} +{"mode": "val", "epoch": 46, "iter": 533, "lr": 0.01963, "top1_acc": 0.83734, "top5_acc": 0.99038, "mean_class_accuracy": 0.78835} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.01962, "memory": 4083, "data_time": 0.20005, "top1_acc": 0.90312, "top5_acc": 0.99625, "loss_cls": 0.54834, "loss": 0.54834, "time": 0.80455} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.0196, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89875, "top5_acc": 0.99562, "loss_cls": 0.53127, "loss": 0.53127, "time": 0.49034} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.01958, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90125, "top5_acc": 0.99625, "loss_cls": 0.55265, "loss": 0.55265, "time": 0.49029} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.01957, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.89438, "top5_acc": 0.995, "loss_cls": 0.53691, "loss": 0.53691, "time": 0.49777} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.01955, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89188, "top5_acc": 0.99562, "loss_cls": 0.56366, "loss": 0.56366, "time": 0.27463} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.01953, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89562, "top5_acc": 0.99625, "loss_cls": 0.55696, "loss": 0.55696, "time": 0.50484} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.01952, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90375, "top5_acc": 0.99688, "loss_cls": 0.52168, "loss": 0.52168, "time": 0.30139} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.0195, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88125, "top5_acc": 0.99688, "loss_cls": 0.58715, "loss": 0.58715, "time": 0.49106} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.01948, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89375, "top5_acc": 0.9925, "loss_cls": 0.55221, "loss": 0.55221, "time": 0.49012} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.01947, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89375, "top5_acc": 0.99688, "loss_cls": 0.56619, "loss": 0.56619, "time": 0.49024} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.01945, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88312, "top5_acc": 0.99312, "loss_cls": 0.60769, "loss": 0.60769, "time": 0.48705} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.01943, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88438, "top5_acc": 0.99438, "loss_cls": 0.59824, "loss": 0.59824, "time": 0.4914} +{"mode": "val", "epoch": 47, "iter": 533, "lr": 0.01942, "top1_acc": 0.86222, "top5_acc": 0.98956, "mean_class_accuracy": 0.81939} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.0194, "memory": 4083, "data_time": 0.18809, "top1_acc": 0.90375, "top5_acc": 0.99625, "loss_cls": 0.51666, "loss": 0.51666, "time": 0.78503} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.01938, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91188, "top5_acc": 0.99688, "loss_cls": 0.48003, "loss": 0.48003, "time": 0.49349} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.01937, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90812, "top5_acc": 0.99875, "loss_cls": 0.49686, "loss": 0.49686, "time": 0.4935} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.01935, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89125, "top5_acc": 0.99625, "loss_cls": 0.5085, "loss": 0.5085, "time": 0.4929} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.01933, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89562, "top5_acc": 0.995, "loss_cls": 0.54428, "loss": 0.54428, "time": 0.28903} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.01932, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89938, "top5_acc": 0.9975, "loss_cls": 0.52376, "loss": 0.52376, "time": 0.51188} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.0193, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.89125, "top5_acc": 0.99625, "loss_cls": 0.55681, "loss": 0.55681, "time": 0.28216} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.01928, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89, "top5_acc": 0.99312, "loss_cls": 0.58831, "loss": 0.58831, "time": 0.48743} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.01926, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.88688, "top5_acc": 0.9975, "loss_cls": 0.54373, "loss": 0.54373, "time": 0.48974} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.01925, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89688, "top5_acc": 0.99562, "loss_cls": 0.53985, "loss": 0.53985, "time": 0.48816} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.01923, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.885, "top5_acc": 0.9975, "loss_cls": 0.56433, "loss": 0.56433, "time": 0.4904} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.01921, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88688, "top5_acc": 0.99375, "loss_cls": 0.61966, "loss": 0.61966, "time": 0.49009} +{"mode": "val", "epoch": 48, "iter": 533, "lr": 0.0192, "top1_acc": 0.86128, "top5_acc": 0.99049, "mean_class_accuracy": 0.81919} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.01918, "memory": 4083, "data_time": 0.19378, "top1_acc": 0.9025, "top5_acc": 0.9975, "loss_cls": 0.52247, "loss": 0.52247, "time": 0.80344} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.01916, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.885, "top5_acc": 0.99625, "loss_cls": 0.60194, "loss": 0.60194, "time": 0.49048} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.01915, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91312, "top5_acc": 0.99812, "loss_cls": 0.46947, "loss": 0.46947, "time": 0.4901} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.01913, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.90562, "top5_acc": 0.99625, "loss_cls": 0.49144, "loss": 0.49144, "time": 0.48985} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.01911, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89938, "top5_acc": 0.99562, "loss_cls": 0.51844, "loss": 0.51844, "time": 0.28965} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.01909, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89938, "top5_acc": 0.99875, "loss_cls": 0.53252, "loss": 0.53252, "time": 0.51195} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.01908, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88938, "top5_acc": 0.995, "loss_cls": 0.56767, "loss": 0.56767, "time": 0.28651} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.01906, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90188, "top5_acc": 0.99375, "loss_cls": 0.54194, "loss": 0.54194, "time": 0.48845} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.01904, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8925, "top5_acc": 0.9975, "loss_cls": 0.55734, "loss": 0.55734, "time": 0.49085} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.01902, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.88188, "top5_acc": 0.99625, "loss_cls": 0.58975, "loss": 0.58975, "time": 0.49466} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.01901, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8975, "top5_acc": 0.99625, "loss_cls": 0.51695, "loss": 0.51695, "time": 0.49169} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.01899, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89188, "top5_acc": 0.99812, "loss_cls": 0.55398, "loss": 0.55398, "time": 0.49034} +{"mode": "val", "epoch": 49, "iter": 533, "lr": 0.01898, "top1_acc": 0.86574, "top5_acc": 0.99237, "mean_class_accuracy": 0.81997} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.01896, "memory": 4083, "data_time": 0.19453, "top1_acc": 0.92062, "top5_acc": 0.99688, "loss_cls": 0.46243, "loss": 0.46243, "time": 0.80075} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.01894, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.895, "top5_acc": 0.99625, "loss_cls": 0.51888, "loss": 0.51888, "time": 0.48981} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.01892, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.90562, "top5_acc": 0.9975, "loss_cls": 0.52322, "loss": 0.52322, "time": 0.49068} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.01891, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.89562, "top5_acc": 0.99688, "loss_cls": 0.54577, "loss": 0.54577, "time": 0.49102} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.01889, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89125, "top5_acc": 0.99875, "loss_cls": 0.51329, "loss": 0.51329, "time": 0.27265} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.01887, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.89, "top5_acc": 0.995, "loss_cls": 0.56206, "loss": 0.56206, "time": 0.51071} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.01885, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88375, "top5_acc": 0.99688, "loss_cls": 0.59933, "loss": 0.59933, "time": 0.31427} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.01884, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88125, "top5_acc": 0.99562, "loss_cls": 0.61657, "loss": 0.61657, "time": 0.49134} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.01882, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89625, "top5_acc": 0.9975, "loss_cls": 0.53632, "loss": 0.53632, "time": 0.48544} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.0188, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.90375, "top5_acc": 0.995, "loss_cls": 0.5372, "loss": 0.5372, "time": 0.48813} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.01878, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88938, "top5_acc": 0.99438, "loss_cls": 0.59654, "loss": 0.59654, "time": 0.48661} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.01876, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88188, "top5_acc": 0.99625, "loss_cls": 0.61035, "loss": 0.61035, "time": 0.48957} +{"mode": "val", "epoch": 50, "iter": 533, "lr": 0.01875, "top1_acc": 0.84509, "top5_acc": 0.99155, "mean_class_accuracy": 0.7959} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.01873, "memory": 4083, "data_time": 0.2014, "top1_acc": 0.905, "top5_acc": 0.9975, "loss_cls": 0.49821, "loss": 0.49821, "time": 0.81191} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.01871, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.91062, "top5_acc": 0.99625, "loss_cls": 0.47436, "loss": 0.47436, "time": 0.49183} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.0187, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9125, "top5_acc": 0.99688, "loss_cls": 0.49591, "loss": 0.49591, "time": 0.48826} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.01868, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90125, "top5_acc": 0.99438, "loss_cls": 0.49509, "loss": 0.49509, "time": 0.49274} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.01866, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.89688, "top5_acc": 0.99625, "loss_cls": 0.53986, "loss": 0.53986, "time": 0.27293} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.01864, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.90375, "top5_acc": 0.99625, "loss_cls": 0.52008, "loss": 0.52008, "time": 0.50022} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.01863, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9175, "top5_acc": 0.99625, "loss_cls": 0.47964, "loss": 0.47964, "time": 0.31755} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.01861, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90188, "top5_acc": 0.99688, "loss_cls": 0.4827, "loss": 0.4827, "time": 0.49264} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.01859, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91125, "top5_acc": 0.99625, "loss_cls": 0.53515, "loss": 0.53515, "time": 0.4897} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.01857, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91438, "top5_acc": 0.99688, "loss_cls": 0.47799, "loss": 0.47799, "time": 0.48824} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.01855, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89312, "top5_acc": 0.99875, "loss_cls": 0.55514, "loss": 0.55514, "time": 0.48858} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.01854, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88938, "top5_acc": 0.995, "loss_cls": 0.59586, "loss": 0.59586, "time": 0.48772} +{"mode": "val", "epoch": 51, "iter": 533, "lr": 0.01852, "top1_acc": 0.8661, "top5_acc": 0.99143, "mean_class_accuracy": 0.81299} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.0185, "memory": 4083, "data_time": 0.20032, "top1_acc": 0.91125, "top5_acc": 0.99562, "loss_cls": 0.49527, "loss": 0.49527, "time": 0.80643} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.01849, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.90125, "top5_acc": 0.99875, "loss_cls": 0.46404, "loss": 0.46404, "time": 0.49158} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.01847, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.89562, "top5_acc": 0.99688, "loss_cls": 0.53481, "loss": 0.53481, "time": 0.49278} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.01845, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91062, "top5_acc": 0.99875, "loss_cls": 0.48382, "loss": 0.48382, "time": 0.48839} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.01843, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9075, "top5_acc": 0.99625, "loss_cls": 0.51054, "loss": 0.51054, "time": 0.28983} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.01841, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88188, "top5_acc": 0.99688, "loss_cls": 0.60043, "loss": 0.60043, "time": 0.4762} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.0184, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90125, "top5_acc": 0.995, "loss_cls": 0.53451, "loss": 0.53451, "time": 0.31425} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.01838, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.90562, "top5_acc": 0.99625, "loss_cls": 0.53523, "loss": 0.53523, "time": 0.49076} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.01836, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88812, "top5_acc": 0.995, "loss_cls": 0.56638, "loss": 0.56638, "time": 0.4904} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.01834, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89312, "top5_acc": 0.99375, "loss_cls": 0.56402, "loss": 0.56402, "time": 0.4911} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.01832, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90438, "top5_acc": 0.99625, "loss_cls": 0.53218, "loss": 0.53218, "time": 0.49029} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.01831, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.8725, "top5_acc": 0.99375, "loss_cls": 0.58346, "loss": 0.58346, "time": 0.48858} +{"mode": "val", "epoch": 52, "iter": 533, "lr": 0.01829, "top1_acc": 0.86281, "top5_acc": 0.9919, "mean_class_accuracy": 0.82889} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.01827, "memory": 4083, "data_time": 0.19338, "top1_acc": 0.90125, "top5_acc": 0.99625, "loss_cls": 0.53756, "loss": 0.53756, "time": 0.81089} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.01826, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90188, "top5_acc": 0.99625, "loss_cls": 0.5125, "loss": 0.5125, "time": 0.49531} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.01824, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88938, "top5_acc": 0.995, "loss_cls": 0.5378, "loss": 0.5378, "time": 0.49362} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.01822, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90938, "top5_acc": 0.99688, "loss_cls": 0.49783, "loss": 0.49783, "time": 0.49294} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.0182, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.905, "top5_acc": 0.99625, "loss_cls": 0.48336, "loss": 0.48336, "time": 0.28028} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.01818, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.90812, "top5_acc": 0.99688, "loss_cls": 0.46927, "loss": 0.46927, "time": 0.49424} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.01816, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.90688, "top5_acc": 0.99812, "loss_cls": 0.51673, "loss": 0.51673, "time": 0.31884} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.01815, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90812, "top5_acc": 0.99688, "loss_cls": 0.48826, "loss": 0.48826, "time": 0.49477} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.01813, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.87812, "top5_acc": 0.9975, "loss_cls": 0.59871, "loss": 0.59871, "time": 0.49038} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.01811, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.9075, "top5_acc": 0.99562, "loss_cls": 0.52173, "loss": 0.52173, "time": 0.48891} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.01809, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90375, "top5_acc": 0.99812, "loss_cls": 0.49564, "loss": 0.49564, "time": 0.49079} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.01807, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90562, "top5_acc": 0.9975, "loss_cls": 0.50225, "loss": 0.50225, "time": 0.48851} +{"mode": "val", "epoch": 53, "iter": 533, "lr": 0.01806, "top1_acc": 0.87501, "top5_acc": 0.99143, "mean_class_accuracy": 0.83128} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.01804, "memory": 4083, "data_time": 0.19324, "top1_acc": 0.89, "top5_acc": 0.99625, "loss_cls": 0.5773, "loss": 0.5773, "time": 0.80174} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.01802, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91938, "top5_acc": 0.995, "loss_cls": 0.4855, "loss": 0.4855, "time": 0.49001} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.018, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.905, "top5_acc": 0.99625, "loss_cls": 0.4887, "loss": 0.4887, "time": 0.49473} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.01798, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90812, "top5_acc": 0.99688, "loss_cls": 0.47701, "loss": 0.47701, "time": 0.49186} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.01797, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.92188, "top5_acc": 0.99625, "loss_cls": 0.45746, "loss": 0.45746, "time": 0.3056} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.01795, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90438, "top5_acc": 0.9975, "loss_cls": 0.48429, "loss": 0.48429, "time": 0.4545} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.01793, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90875, "top5_acc": 0.99812, "loss_cls": 0.50105, "loss": 0.50105, "time": 0.34613} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.01791, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90625, "top5_acc": 0.99438, "loss_cls": 0.49582, "loss": 0.49582, "time": 0.49191} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.01789, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.91125, "top5_acc": 0.99875, "loss_cls": 0.49612, "loss": 0.49612, "time": 0.49082} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.01787, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9075, "top5_acc": 0.99875, "loss_cls": 0.50796, "loss": 0.50796, "time": 0.49009} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.01786, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.91125, "top5_acc": 0.99562, "loss_cls": 0.51867, "loss": 0.51867, "time": 0.49304} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.01784, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92375, "top5_acc": 0.9975, "loss_cls": 0.44041, "loss": 0.44041, "time": 0.48802} +{"mode": "val", "epoch": 54, "iter": 533, "lr": 0.01782, "top1_acc": 0.85319, "top5_acc": 0.99026, "mean_class_accuracy": 0.78939} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.0178, "memory": 4083, "data_time": 0.19513, "top1_acc": 0.89688, "top5_acc": 0.99312, "loss_cls": 0.55626, "loss": 0.55626, "time": 0.80508} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.01779, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.905, "top5_acc": 0.99625, "loss_cls": 0.51446, "loss": 0.51446, "time": 0.49125} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.01777, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91375, "top5_acc": 0.9975, "loss_cls": 0.46791, "loss": 0.46791, "time": 0.49135} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.01775, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9125, "top5_acc": 0.9975, "loss_cls": 0.47956, "loss": 0.47956, "time": 0.49136} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.01773, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90125, "top5_acc": 0.9975, "loss_cls": 0.50412, "loss": 0.50412, "time": 0.32367} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.01771, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.9025, "top5_acc": 0.99812, "loss_cls": 0.52307, "loss": 0.52307, "time": 0.41306} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.01769, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89438, "top5_acc": 0.995, "loss_cls": 0.53092, "loss": 0.53092, "time": 0.36446} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.01767, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.90062, "top5_acc": 0.99438, "loss_cls": 0.54792, "loss": 0.54792, "time": 0.49214} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.01766, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89938, "top5_acc": 0.99812, "loss_cls": 0.53201, "loss": 0.53201, "time": 0.49} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.01764, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91188, "top5_acc": 0.9975, "loss_cls": 0.4689, "loss": 0.4689, "time": 0.49441} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.01762, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.895, "top5_acc": 0.99938, "loss_cls": 0.5091, "loss": 0.5091, "time": 0.49653} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.0176, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88688, "top5_acc": 0.99562, "loss_cls": 0.58492, "loss": 0.58492, "time": 0.49368} +{"mode": "val", "epoch": 55, "iter": 533, "lr": 0.01758, "top1_acc": 0.86527, "top5_acc": 0.99143, "mean_class_accuracy": 0.81429} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.01757, "memory": 4083, "data_time": 0.18581, "top1_acc": 0.92312, "top5_acc": 0.99812, "loss_cls": 0.44455, "loss": 0.44455, "time": 0.7996} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.01755, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90312, "top5_acc": 0.99375, "loss_cls": 0.53379, "loss": 0.53379, "time": 0.48865} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.01753, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9075, "top5_acc": 0.9975, "loss_cls": 0.48232, "loss": 0.48232, "time": 0.48858} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.01751, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.9, "top5_acc": 0.99812, "loss_cls": 0.51014, "loss": 0.51014, "time": 0.47755} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.01749, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9075, "top5_acc": 0.995, "loss_cls": 0.48822, "loss": 0.48822, "time": 0.33948} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.01747, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.91562, "top5_acc": 0.99688, "loss_cls": 0.45245, "loss": 0.45245, "time": 0.39719} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.01745, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.905, "top5_acc": 0.99625, "loss_cls": 0.49852, "loss": 0.49852, "time": 0.36251} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.01743, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90625, "top5_acc": 1.0, "loss_cls": 0.49186, "loss": 0.49186, "time": 0.49328} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.01742, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89812, "top5_acc": 0.99625, "loss_cls": 0.53261, "loss": 0.53261, "time": 0.48714} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.0174, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9025, "top5_acc": 0.99688, "loss_cls": 0.49875, "loss": 0.49875, "time": 0.48985} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.01738, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89875, "top5_acc": 0.99625, "loss_cls": 0.51937, "loss": 0.51937, "time": 0.48825} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.01736, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.52006, "loss": 0.52006, "time": 0.49291} +{"mode": "val", "epoch": 56, "iter": 533, "lr": 0.01734, "top1_acc": 0.85659, "top5_acc": 0.98991, "mean_class_accuracy": 0.80879} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.01733, "memory": 4083, "data_time": 0.18945, "top1_acc": 0.90312, "top5_acc": 0.99812, "loss_cls": 0.49734, "loss": 0.49734, "time": 0.79576} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.01731, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90938, "top5_acc": 0.99812, "loss_cls": 0.48914, "loss": 0.48914, "time": 0.48712} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.01729, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92375, "top5_acc": 0.99938, "loss_cls": 0.40911, "loss": 0.40911, "time": 0.49072} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.01727, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91875, "top5_acc": 0.99812, "loss_cls": 0.44645, "loss": 0.44645, "time": 0.4909} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.01725, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91688, "top5_acc": 0.99688, "loss_cls": 0.44836, "loss": 0.44836, "time": 0.31596} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.01723, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89938, "top5_acc": 0.99938, "loss_cls": 0.50129, "loss": 0.50129, "time": 0.4242} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.01721, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8975, "top5_acc": 0.99625, "loss_cls": 0.5247, "loss": 0.5247, "time": 0.34757} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.01719, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.90312, "top5_acc": 0.99562, "loss_cls": 0.5164, "loss": 0.5164, "time": 0.48913} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.01717, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.90562, "top5_acc": 0.99562, "loss_cls": 0.52072, "loss": 0.52072, "time": 0.49141} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.01716, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9275, "top5_acc": 0.99812, "loss_cls": 0.42604, "loss": 0.42604, "time": 0.49003} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.01714, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90875, "top5_acc": 0.9975, "loss_cls": 0.47191, "loss": 0.47191, "time": 0.48977} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.01712, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8825, "top5_acc": 0.99562, "loss_cls": 0.56673, "loss": 0.56673, "time": 0.49352} +{"mode": "val", "epoch": 57, "iter": 533, "lr": 0.0171, "top1_acc": 0.8803, "top5_acc": 0.98979, "mean_class_accuracy": 0.83375} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.01708, "memory": 4083, "data_time": 0.1963, "top1_acc": 0.92375, "top5_acc": 0.99812, "loss_cls": 0.41146, "loss": 0.41146, "time": 0.81167} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.01706, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.91125, "top5_acc": 0.99812, "loss_cls": 0.43404, "loss": 0.43404, "time": 0.49268} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.01704, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91375, "top5_acc": 0.99688, "loss_cls": 0.45876, "loss": 0.45876, "time": 0.49189} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.01703, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92312, "top5_acc": 0.99812, "loss_cls": 0.42578, "loss": 0.42578, "time": 0.48651} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.01701, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91438, "top5_acc": 0.9975, "loss_cls": 0.45232, "loss": 0.45232, "time": 0.32513} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.01699, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.89875, "top5_acc": 0.99875, "loss_cls": 0.49392, "loss": 0.49392, "time": 0.41338} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.01697, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.46485, "loss": 0.46485, "time": 0.35533} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.01695, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.90875, "top5_acc": 0.9975, "loss_cls": 0.47408, "loss": 0.47408, "time": 0.49251} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.01693, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9025, "top5_acc": 0.995, "loss_cls": 0.51175, "loss": 0.51175, "time": 0.4901} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.01691, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.90062, "top5_acc": 0.99875, "loss_cls": 0.51775, "loss": 0.51775, "time": 0.49126} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.01689, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90875, "top5_acc": 0.99562, "loss_cls": 0.50599, "loss": 0.50599, "time": 0.49207} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.01687, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9025, "top5_acc": 0.99562, "loss_cls": 0.52052, "loss": 0.52052, "time": 0.49406} +{"mode": "val", "epoch": 58, "iter": 533, "lr": 0.01686, "top1_acc": 0.87771, "top5_acc": 0.99261, "mean_class_accuracy": 0.83813} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.01684, "memory": 4083, "data_time": 0.19417, "top1_acc": 0.91688, "top5_acc": 0.99625, "loss_cls": 0.45293, "loss": 0.45293, "time": 0.78173} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.01682, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.90688, "top5_acc": 0.99562, "loss_cls": 0.50181, "loss": 0.50181, "time": 0.4898} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.0168, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91188, "top5_acc": 0.99562, "loss_cls": 0.46656, "loss": 0.46656, "time": 0.49215} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.01678, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92188, "top5_acc": 0.99812, "loss_cls": 0.43907, "loss": 0.43907, "time": 0.49119} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.01676, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.92, "top5_acc": 0.99875, "loss_cls": 0.42678, "loss": 0.42678, "time": 0.29847} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.01674, "memory": 4083, "data_time": 0.00075, "top1_acc": 0.9025, "top5_acc": 0.99875, "loss_cls": 0.49083, "loss": 0.49083, "time": 0.44993} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.01672, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.90875, "top5_acc": 0.99688, "loss_cls": 0.48909, "loss": 0.48909, "time": 0.33718} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.0167, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90938, "top5_acc": 0.99625, "loss_cls": 0.4944, "loss": 0.4944, "time": 0.4913} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.01668, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91125, "top5_acc": 0.99562, "loss_cls": 0.50862, "loss": 0.50862, "time": 0.49171} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.01667, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.90438, "top5_acc": 0.99562, "loss_cls": 0.51015, "loss": 0.51015, "time": 0.49092} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.01665, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.90438, "top5_acc": 0.99938, "loss_cls": 0.53899, "loss": 0.53899, "time": 0.49033} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.01663, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.92312, "top5_acc": 0.99688, "loss_cls": 0.43643, "loss": 0.43643, "time": 0.49315} +{"mode": "val", "epoch": 59, "iter": 533, "lr": 0.01661, "top1_acc": 0.88112, "top5_acc": 0.99132, "mean_class_accuracy": 0.83177} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.01659, "memory": 4083, "data_time": 0.19387, "top1_acc": 0.92625, "top5_acc": 0.99938, "loss_cls": 0.39138, "loss": 0.39138, "time": 0.80735} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.01657, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92375, "top5_acc": 0.99688, "loss_cls": 0.42031, "loss": 0.42031, "time": 0.48774} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.01655, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90562, "top5_acc": 0.99812, "loss_cls": 0.4859, "loss": 0.4859, "time": 0.49034} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.01653, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90312, "top5_acc": 0.99875, "loss_cls": 0.48956, "loss": 0.48956, "time": 0.49133} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.01651, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.9025, "top5_acc": 0.99688, "loss_cls": 0.48799, "loss": 0.48799, "time": 0.3209} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.0165, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92562, "top5_acc": 0.99688, "loss_cls": 0.4343, "loss": 0.4343, "time": 0.41749} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.01648, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.90938, "top5_acc": 0.99875, "loss_cls": 0.48305, "loss": 0.48305, "time": 0.36044} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.01646, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92, "top5_acc": 0.99812, "loss_cls": 0.44137, "loss": 0.44137, "time": 0.49175} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.01644, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9225, "top5_acc": 0.9975, "loss_cls": 0.44917, "loss": 0.44917, "time": 0.49075} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.01642, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.91438, "top5_acc": 0.9975, "loss_cls": 0.45, "loss": 0.45, "time": 0.49375} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.0164, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90062, "top5_acc": 0.99438, "loss_cls": 0.52251, "loss": 0.52251, "time": 0.48905} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.01638, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90312, "top5_acc": 0.99688, "loss_cls": 0.49573, "loss": 0.49573, "time": 0.49206} +{"mode": "val", "epoch": 60, "iter": 533, "lr": 0.01636, "top1_acc": 0.85413, "top5_acc": 0.98603, "mean_class_accuracy": 0.82267} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.01634, "memory": 4083, "data_time": 0.19557, "top1_acc": 0.91688, "top5_acc": 0.99812, "loss_cls": 0.46933, "loss": 0.46933, "time": 0.80026} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.01632, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.925, "top5_acc": 0.9975, "loss_cls": 0.4159, "loss": 0.4159, "time": 0.49364} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.0163, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.45426, "loss": 0.45426, "time": 0.49373} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.01629, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90562, "top5_acc": 0.99938, "loss_cls": 0.49809, "loss": 0.49809, "time": 0.47046} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.01627, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9, "top5_acc": 0.99875, "loss_cls": 0.49659, "loss": 0.49659, "time": 0.36176} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.01625, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90312, "top5_acc": 0.99688, "loss_cls": 0.50598, "loss": 0.50598, "time": 0.37461} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.01623, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90688, "top5_acc": 0.99688, "loss_cls": 0.48856, "loss": 0.48856, "time": 0.3836} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.01621, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.90625, "top5_acc": 0.99812, "loss_cls": 0.491, "loss": 0.491, "time": 0.49319} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.01619, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89688, "top5_acc": 0.99625, "loss_cls": 0.53825, "loss": 0.53825, "time": 0.48896} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.01617, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92062, "top5_acc": 0.99625, "loss_cls": 0.45532, "loss": 0.45532, "time": 0.48955} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.01615, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91562, "top5_acc": 0.99875, "loss_cls": 0.42499, "loss": 0.42499, "time": 0.49094} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.01613, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90562, "top5_acc": 0.99688, "loss_cls": 0.49534, "loss": 0.49534, "time": 0.49217} +{"mode": "val", "epoch": 61, "iter": 533, "lr": 0.01611, "top1_acc": 0.83922, "top5_acc": 0.98674, "mean_class_accuracy": 0.79276} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.01609, "memory": 4083, "data_time": 0.19082, "top1_acc": 0.90438, "top5_acc": 0.99625, "loss_cls": 0.50407, "loss": 0.50407, "time": 0.79071} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.01607, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91188, "top5_acc": 0.99688, "loss_cls": 0.45271, "loss": 0.45271, "time": 0.49041} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.01605, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91562, "top5_acc": 0.99875, "loss_cls": 0.45897, "loss": 0.45897, "time": 0.49195} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.01603, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91312, "top5_acc": 0.99688, "loss_cls": 0.4671, "loss": 0.4671, "time": 0.46621} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.01602, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.91312, "top5_acc": 0.9975, "loss_cls": 0.4225, "loss": 0.4225, "time": 0.37345} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.016, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.92375, "top5_acc": 0.99688, "loss_cls": 0.41132, "loss": 0.41132, "time": 0.36143} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.01598, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91562, "top5_acc": 0.99625, "loss_cls": 0.47011, "loss": 0.47011, "time": 0.37524} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.01596, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.91438, "top5_acc": 0.99688, "loss_cls": 0.4635, "loss": 0.4635, "time": 0.48635} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.01594, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9125, "top5_acc": 0.99938, "loss_cls": 0.48002, "loss": 0.48002, "time": 0.49035} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.01592, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.91375, "top5_acc": 0.99375, "loss_cls": 0.49371, "loss": 0.49371, "time": 0.48924} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.0159, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90812, "top5_acc": 0.99438, "loss_cls": 0.49019, "loss": 0.49019, "time": 0.49211} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.01588, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8975, "top5_acc": 0.9975, "loss_cls": 0.4983, "loss": 0.4983, "time": 0.49124} +{"mode": "val", "epoch": 62, "iter": 533, "lr": 0.01586, "top1_acc": 0.87842, "top5_acc": 0.99319, "mean_class_accuracy": 0.85921} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.01584, "memory": 4083, "data_time": 0.1941, "top1_acc": 0.93438, "top5_acc": 0.9975, "loss_cls": 0.39701, "loss": 0.39701, "time": 0.79718} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.01582, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.92188, "top5_acc": 0.99812, "loss_cls": 0.43401, "loss": 0.43401, "time": 0.48644} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.0158, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.93188, "top5_acc": 0.99938, "loss_cls": 0.39787, "loss": 0.39787, "time": 0.48924} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.01578, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.49721, "loss": 0.49721, "time": 0.47095} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.01576, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91312, "top5_acc": 0.99812, "loss_cls": 0.4531, "loss": 0.4531, "time": 0.34434} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.01574, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91562, "top5_acc": 0.99875, "loss_cls": 0.42908, "loss": 0.42908, "time": 0.39063} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.01572, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92875, "top5_acc": 0.99812, "loss_cls": 0.42156, "loss": 0.42156, "time": 0.36701} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.0157, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91375, "top5_acc": 0.99812, "loss_cls": 0.4651, "loss": 0.4651, "time": 0.49244} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.01568, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.9025, "top5_acc": 0.99875, "loss_cls": 0.50855, "loss": 0.50855, "time": 0.49208} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.01566, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9075, "top5_acc": 0.99312, "loss_cls": 0.51713, "loss": 0.51713, "time": 0.48937} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.01564, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90312, "top5_acc": 0.99562, "loss_cls": 0.48264, "loss": 0.48264, "time": 0.49145} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.01562, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90375, "top5_acc": 0.99812, "loss_cls": 0.48474, "loss": 0.48474, "time": 0.49186} +{"mode": "val", "epoch": 63, "iter": 533, "lr": 0.01561, "top1_acc": 0.86915, "top5_acc": 0.99073, "mean_class_accuracy": 0.81846} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.01559, "memory": 4083, "data_time": 0.19915, "top1_acc": 0.91938, "top5_acc": 0.99875, "loss_cls": 0.40528, "loss": 0.40528, "time": 0.78881} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.01557, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9175, "top5_acc": 0.99562, "loss_cls": 0.44512, "loss": 0.44512, "time": 0.49288} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.01555, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.38493, "loss": 0.38493, "time": 0.49009} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.01553, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.925, "top5_acc": 0.9975, "loss_cls": 0.40866, "loss": 0.40866, "time": 0.46839} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.01551, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9125, "top5_acc": 0.99875, "loss_cls": 0.45514, "loss": 0.45514, "time": 0.35177} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.01549, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9175, "top5_acc": 0.99625, "loss_cls": 0.47476, "loss": 0.47476, "time": 0.38272} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.01547, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93188, "top5_acc": 0.99625, "loss_cls": 0.40554, "loss": 0.40554, "time": 0.37566} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.01545, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9125, "top5_acc": 0.99562, "loss_cls": 0.46337, "loss": 0.46337, "time": 0.49416} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.01543, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.91312, "top5_acc": 0.99812, "loss_cls": 0.46123, "loss": 0.46123, "time": 0.48932} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.01541, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.90938, "top5_acc": 0.9975, "loss_cls": 0.4796, "loss": 0.4796, "time": 0.48998} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.01539, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91062, "top5_acc": 0.99562, "loss_cls": 0.47025, "loss": 0.47025, "time": 0.49199} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.01537, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92125, "top5_acc": 0.99875, "loss_cls": 0.43489, "loss": 0.43489, "time": 0.49424} +{"mode": "val", "epoch": 64, "iter": 533, "lr": 0.01535, "top1_acc": 0.8817, "top5_acc": 0.99319, "mean_class_accuracy": 0.83681} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.01533, "memory": 4083, "data_time": 0.19283, "top1_acc": 0.935, "top5_acc": 0.99812, "loss_cls": 0.38187, "loss": 0.38187, "time": 0.8003} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.01531, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.90812, "top5_acc": 0.99938, "loss_cls": 0.46499, "loss": 0.46499, "time": 0.49255} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.01529, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91375, "top5_acc": 0.99875, "loss_cls": 0.45046, "loss": 0.45046, "time": 0.49037} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.01527, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93125, "top5_acc": 0.99625, "loss_cls": 0.38475, "loss": 0.38475, "time": 0.45788} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.01526, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91812, "top5_acc": 0.99875, "loss_cls": 0.41902, "loss": 0.41902, "time": 0.40464} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.01524, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92812, "top5_acc": 0.99875, "loss_cls": 0.415, "loss": 0.415, "time": 0.33254} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.01522, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.92562, "top5_acc": 0.99938, "loss_cls": 0.42916, "loss": 0.42916, "time": 0.40722} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0152, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.915, "top5_acc": 0.99625, "loss_cls": 0.45153, "loss": 0.45153, "time": 0.49499} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.01518, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9175, "top5_acc": 0.99875, "loss_cls": 0.47088, "loss": 0.47088, "time": 0.49304} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.01516, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.91, "top5_acc": 0.99625, "loss_cls": 0.45872, "loss": 0.45872, "time": 0.4893} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.01514, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91438, "top5_acc": 0.99812, "loss_cls": 0.42595, "loss": 0.42595, "time": 0.49051} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.01512, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89875, "top5_acc": 0.99375, "loss_cls": 0.52374, "loss": 0.52374, "time": 0.48768} +{"mode": "val", "epoch": 65, "iter": 533, "lr": 0.0151, "top1_acc": 0.87642, "top5_acc": 0.99261, "mean_class_accuracy": 0.82782} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.01508, "memory": 4083, "data_time": 0.19667, "top1_acc": 0.91812, "top5_acc": 0.99875, "loss_cls": 0.41435, "loss": 0.41435, "time": 0.8087} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.01506, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91938, "top5_acc": 0.99875, "loss_cls": 0.42581, "loss": 0.42581, "time": 0.49088} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.01504, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90938, "top5_acc": 0.9975, "loss_cls": 0.4363, "loss": 0.4363, "time": 0.49375} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.01502, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.9325, "top5_acc": 0.99812, "loss_cls": 0.41165, "loss": 0.41165, "time": 0.42598} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.015, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92, "top5_acc": 0.99875, "loss_cls": 0.4247, "loss": 0.4247, "time": 0.44368} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.01498, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92812, "top5_acc": 0.99812, "loss_cls": 0.38233, "loss": 0.38233, "time": 0.29155} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.01496, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92, "top5_acc": 0.99812, "loss_cls": 0.45173, "loss": 0.45173, "time": 0.41206} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.01494, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9175, "top5_acc": 0.99938, "loss_cls": 0.42392, "loss": 0.42392, "time": 0.49303} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.01492, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.90812, "top5_acc": 0.99875, "loss_cls": 0.46694, "loss": 0.46694, "time": 0.49303} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.0149, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.91312, "top5_acc": 0.99688, "loss_cls": 0.43011, "loss": 0.43011, "time": 0.48616} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.01488, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92938, "top5_acc": 0.99938, "loss_cls": 0.40979, "loss": 0.40979, "time": 0.48953} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.01486, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91438, "top5_acc": 0.99812, "loss_cls": 0.46333, "loss": 0.46333, "time": 0.49628} +{"mode": "val", "epoch": 66, "iter": 533, "lr": 0.01484, "top1_acc": 0.8661, "top5_acc": 0.9919, "mean_class_accuracy": 0.8079} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.01482, "memory": 4083, "data_time": 0.19844, "top1_acc": 0.92875, "top5_acc": 1.0, "loss_cls": 0.38851, "loss": 0.38851, "time": 0.79964} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.0148, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.92125, "top5_acc": 0.99875, "loss_cls": 0.43757, "loss": 0.43757, "time": 0.49488} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.01478, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92438, "top5_acc": 0.99688, "loss_cls": 0.43489, "loss": 0.43489, "time": 0.48999} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.01476, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93062, "top5_acc": 0.99938, "loss_cls": 0.37151, "loss": 0.37151, "time": 0.4143} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.01474, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.935, "top5_acc": 0.9975, "loss_cls": 0.39827, "loss": 0.39827, "time": 0.45859} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.01472, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92312, "top5_acc": 0.99875, "loss_cls": 0.39502, "loss": 0.39502, "time": 0.27694} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.0147, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92, "top5_acc": 0.99875, "loss_cls": 0.41603, "loss": 0.41603, "time": 0.4266} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.01468, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92125, "top5_acc": 0.99812, "loss_cls": 0.41393, "loss": 0.41393, "time": 0.49019} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.01466, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92062, "top5_acc": 0.99688, "loss_cls": 0.39809, "loss": 0.39809, "time": 0.49078} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.01464, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90438, "top5_acc": 0.9975, "loss_cls": 0.46296, "loss": 0.46296, "time": 0.48957} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.01462, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.925, "top5_acc": 0.99812, "loss_cls": 0.4235, "loss": 0.4235, "time": 0.4909} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.0146, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9175, "top5_acc": 0.9975, "loss_cls": 0.43793, "loss": 0.43793, "time": 0.49284} +{"mode": "val", "epoch": 67, "iter": 533, "lr": 0.01458, "top1_acc": 0.87255, "top5_acc": 0.99319, "mean_class_accuracy": 0.8462} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.01456, "memory": 4083, "data_time": 0.20096, "top1_acc": 0.91625, "top5_acc": 0.9975, "loss_cls": 0.45062, "loss": 0.45062, "time": 0.80586} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.01454, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.37034, "loss": 0.37034, "time": 0.49009} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.01452, "memory": 4083, "data_time": 0.00078, "top1_acc": 0.91625, "top5_acc": 0.99875, "loss_cls": 0.42871, "loss": 0.42871, "time": 0.49692} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.0145, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.9275, "top5_acc": 0.99938, "loss_cls": 0.37164, "loss": 0.37164, "time": 0.41161} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.01448, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.90938, "top5_acc": 0.9975, "loss_cls": 0.44942, "loss": 0.44942, "time": 0.47367} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.01446, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9125, "top5_acc": 0.99938, "loss_cls": 0.44153, "loss": 0.44153, "time": 0.26584} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.01444, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93062, "top5_acc": 0.99812, "loss_cls": 0.40297, "loss": 0.40297, "time": 0.42708} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.01442, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94188, "top5_acc": 0.99938, "loss_cls": 0.34027, "loss": 0.34027, "time": 0.49142} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.0144, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92875, "top5_acc": 0.99812, "loss_cls": 0.40909, "loss": 0.40909, "time": 0.49134} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.01438, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91312, "top5_acc": 0.9975, "loss_cls": 0.42475, "loss": 0.42475, "time": 0.49306} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.01436, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90812, "top5_acc": 0.99812, "loss_cls": 0.46448, "loss": 0.46448, "time": 0.48837} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.01434, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93062, "top5_acc": 0.99875, "loss_cls": 0.39464, "loss": 0.39464, "time": 0.49033} +{"mode": "val", "epoch": 68, "iter": 533, "lr": 0.01433, "top1_acc": 0.87419, "top5_acc": 0.99261, "mean_class_accuracy": 0.83241} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.01431, "memory": 4083, "data_time": 0.1909, "top1_acc": 0.94875, "top5_acc": 0.99812, "loss_cls": 0.33593, "loss": 0.33593, "time": 0.79854} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.01429, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92, "top5_acc": 0.9975, "loss_cls": 0.41753, "loss": 0.41753, "time": 0.48935} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.01427, "memory": 4083, "data_time": 0.00075, "top1_acc": 0.93562, "top5_acc": 0.99875, "loss_cls": 0.36412, "loss": 0.36412, "time": 0.4912} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.01425, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.92875, "top5_acc": 0.99938, "loss_cls": 0.37651, "loss": 0.37651, "time": 0.41218} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.01423, "memory": 4083, "data_time": 0.00071, "top1_acc": 0.91562, "top5_acc": 0.99875, "loss_cls": 0.43387, "loss": 0.43387, "time": 0.48335} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.0142, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9275, "top5_acc": 0.99812, "loss_cls": 0.38132, "loss": 0.38132, "time": 0.25909} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.01418, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.41121, "loss": 0.41121, "time": 0.42033} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.01416, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9275, "top5_acc": 0.99688, "loss_cls": 0.41152, "loss": 0.41152, "time": 0.4919} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.01414, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91438, "top5_acc": 0.99688, "loss_cls": 0.45664, "loss": 0.45664, "time": 0.48851} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.01412, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.9125, "top5_acc": 0.99562, "loss_cls": 0.46525, "loss": 0.46525, "time": 0.48997} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.0141, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.90688, "top5_acc": 0.99812, "loss_cls": 0.44083, "loss": 0.44083, "time": 0.49064} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.01408, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.43621, "loss": 0.43621, "time": 0.49104} +{"mode": "val", "epoch": 69, "iter": 533, "lr": 0.01407, "top1_acc": 0.8756, "top5_acc": 0.98897, "mean_class_accuracy": 0.834} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.01405, "memory": 4083, "data_time": 0.19448, "top1_acc": 0.92938, "top5_acc": 0.9975, "loss_cls": 0.40478, "loss": 0.40478, "time": 0.80724} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.01403, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.935, "top5_acc": 0.99812, "loss_cls": 0.36373, "loss": 0.36373, "time": 0.49187} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.01401, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93062, "top5_acc": 0.9975, "loss_cls": 0.38241, "loss": 0.38241, "time": 0.48939} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.01399, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92125, "top5_acc": 0.9975, "loss_cls": 0.40003, "loss": 0.40003, "time": 0.39918} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.01397, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93438, "top5_acc": 0.99875, "loss_cls": 0.35507, "loss": 0.35507, "time": 0.51177} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.01395, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.39492, "loss": 0.39492, "time": 0.23497} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.01392, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91812, "top5_acc": 0.99875, "loss_cls": 0.42311, "loss": 0.42311, "time": 0.43232} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.0139, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92438, "top5_acc": 0.99625, "loss_cls": 0.43483, "loss": 0.43483, "time": 0.49335} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.01388, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91375, "top5_acc": 0.99875, "loss_cls": 0.43521, "loss": 0.43521, "time": 0.49158} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.01386, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91625, "top5_acc": 1.0, "loss_cls": 0.41739, "loss": 0.41739, "time": 0.4914} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.01384, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.905, "top5_acc": 0.99625, "loss_cls": 0.47874, "loss": 0.47874, "time": 0.49151} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.01382, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.935, "top5_acc": 1.0, "loss_cls": 0.3962, "loss": 0.3962, "time": 0.49233} +{"mode": "val", "epoch": 70, "iter": 533, "lr": 0.01381, "top1_acc": 0.87126, "top5_acc": 0.99179, "mean_class_accuracy": 0.8298} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.01379, "memory": 4083, "data_time": 0.19403, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.35651, "loss": 0.35651, "time": 0.80191} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.01377, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.93438, "top5_acc": 0.99938, "loss_cls": 0.36073, "loss": 0.36073, "time": 0.49112} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.01375, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.9375, "top5_acc": 0.99688, "loss_cls": 0.363, "loss": 0.363, "time": 0.4921} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.01373, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.9275, "top5_acc": 0.99875, "loss_cls": 0.3585, "loss": 0.3585, "time": 0.39342} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.01371, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.92125, "top5_acc": 0.9975, "loss_cls": 0.43107, "loss": 0.43107, "time": 0.51163} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.01368, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.90562, "top5_acc": 0.995, "loss_cls": 0.4694, "loss": 0.4694, "time": 0.23193} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.01366, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93312, "top5_acc": 0.9975, "loss_cls": 0.3853, "loss": 0.3853, "time": 0.43861} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.01364, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92562, "top5_acc": 1.0, "loss_cls": 0.39429, "loss": 0.39429, "time": 0.49325} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.01362, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91562, "top5_acc": 0.99688, "loss_cls": 0.42872, "loss": 0.42872, "time": 0.49243} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.0136, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.40794, "loss": 0.40794, "time": 0.49024} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.01358, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92, "top5_acc": 0.99812, "loss_cls": 0.39145, "loss": 0.39145, "time": 0.48966} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.01356, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.93062, "top5_acc": 0.99875, "loss_cls": 0.37981, "loss": 0.37981, "time": 0.49087} +{"mode": "val", "epoch": 71, "iter": 533, "lr": 0.01355, "top1_acc": 0.89978, "top5_acc": 0.99484, "mean_class_accuracy": 0.86476} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.01353, "memory": 4083, "data_time": 0.19955, "top1_acc": 0.93438, "top5_acc": 0.99938, "loss_cls": 0.38353, "loss": 0.38353, "time": 0.80982} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.01351, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9425, "top5_acc": 0.99812, "loss_cls": 0.34066, "loss": 0.34066, "time": 0.4914} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.01349, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.29867, "loss": 0.29867, "time": 0.49218} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.01346, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93312, "top5_acc": 0.99812, "loss_cls": 0.36586, "loss": 0.36586, "time": 0.37187} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.01344, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93812, "top5_acc": 0.99812, "loss_cls": 0.36948, "loss": 0.36948, "time": 0.51252} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.01342, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.39936, "loss": 0.39936, "time": 0.24621} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.0134, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.935, "top5_acc": 0.99688, "loss_cls": 0.39125, "loss": 0.39125, "time": 0.45435} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.01338, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94688, "top5_acc": 0.9975, "loss_cls": 0.33703, "loss": 0.33703, "time": 0.49344} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.01336, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93375, "top5_acc": 0.99875, "loss_cls": 0.37906, "loss": 0.37906, "time": 0.49027} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.01334, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.47068, "loss": 0.47068, "time": 0.49017} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.01332, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9175, "top5_acc": 0.99625, "loss_cls": 0.45602, "loss": 0.45602, "time": 0.49156} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.0133, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.91125, "top5_acc": 0.99562, "loss_cls": 0.46428, "loss": 0.46428, "time": 0.49304} +{"mode": "val", "epoch": 72, "iter": 533, "lr": 0.01329, "top1_acc": 0.88253, "top5_acc": 0.99202, "mean_class_accuracy": 0.8366} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.01326, "memory": 4083, "data_time": 0.19847, "top1_acc": 0.925, "top5_acc": 1.0, "loss_cls": 0.38393, "loss": 0.38393, "time": 0.81333} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.01324, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92938, "top5_acc": 0.99812, "loss_cls": 0.38572, "loss": 0.38572, "time": 0.49083} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.01322, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93812, "top5_acc": 0.9975, "loss_cls": 0.37605, "loss": 0.37605, "time": 0.48915} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.0132, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92625, "top5_acc": 0.9975, "loss_cls": 0.39752, "loss": 0.39752, "time": 0.35705} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.01318, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.93562, "top5_acc": 0.99875, "loss_cls": 0.33676, "loss": 0.33676, "time": 0.50922} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.01316, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.93438, "top5_acc": 0.99812, "loss_cls": 0.35459, "loss": 0.35459, "time": 0.24598} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.01314, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.92688, "top5_acc": 0.99938, "loss_cls": 0.37701, "loss": 0.37701, "time": 0.4731} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.01312, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91438, "top5_acc": 0.99812, "loss_cls": 0.44213, "loss": 0.44213, "time": 0.48885} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.0131, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91938, "top5_acc": 0.9975, "loss_cls": 0.44203, "loss": 0.44203, "time": 0.48834} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.01308, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92438, "top5_acc": 0.9975, "loss_cls": 0.40989, "loss": 0.40989, "time": 0.49004} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.01306, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.915, "top5_acc": 0.99875, "loss_cls": 0.44746, "loss": 0.44746, "time": 0.48992} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.01304, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92625, "top5_acc": 0.99938, "loss_cls": 0.40127, "loss": 0.40127, "time": 0.48997} +{"mode": "val", "epoch": 73, "iter": 533, "lr": 0.01302, "top1_acc": 0.87255, "top5_acc": 0.99096, "mean_class_accuracy": 0.84348} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.013, "memory": 4083, "data_time": 0.19387, "top1_acc": 0.93375, "top5_acc": 0.99938, "loss_cls": 0.36927, "loss": 0.36927, "time": 0.80484} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.01298, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92938, "top5_acc": 0.99812, "loss_cls": 0.39098, "loss": 0.39098, "time": 0.49017} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.01296, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.41905, "loss": 0.41905, "time": 0.49114} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.01294, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.92938, "top5_acc": 0.99688, "loss_cls": 0.37228, "loss": 0.37228, "time": 0.33536} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.01292, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.945, "top5_acc": 0.99938, "loss_cls": 0.33991, "loss": 0.33991, "time": 0.51268} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.0129, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9325, "top5_acc": 0.99688, "loss_cls": 0.35417, "loss": 0.35417, "time": 0.24964} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.01288, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91688, "top5_acc": 0.99812, "loss_cls": 0.44248, "loss": 0.44248, "time": 0.47829} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.01286, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9275, "top5_acc": 0.99812, "loss_cls": 0.39963, "loss": 0.39963, "time": 0.49044} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.01284, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92625, "top5_acc": 1.0, "loss_cls": 0.36668, "loss": 0.36668, "time": 0.49082} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.01282, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92875, "top5_acc": 0.99625, "loss_cls": 0.39782, "loss": 0.39782, "time": 0.49347} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.0128, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93312, "top5_acc": 0.99938, "loss_cls": 0.36268, "loss": 0.36268, "time": 0.49308} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.01278, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.38947, "loss": 0.38947, "time": 0.49498} +{"mode": "val", "epoch": 74, "iter": 533, "lr": 0.01276, "top1_acc": 0.88581, "top5_acc": 0.99284, "mean_class_accuracy": 0.83257} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.01274, "memory": 4083, "data_time": 0.19683, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.29569, "loss": 0.29569, "time": 0.79739} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.01272, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.36961, "loss": 0.36961, "time": 0.4916} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.0127, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.3463, "loss": 0.3463, "time": 0.49185} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.01268, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93188, "top5_acc": 1.0, "loss_cls": 0.386, "loss": 0.386, "time": 0.34226} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.01266, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.42891, "loss": 0.42891, "time": 0.51053} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.01264, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.92562, "top5_acc": 0.99938, "loss_cls": 0.37557, "loss": 0.37557, "time": 0.25856} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.01262, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92188, "top5_acc": 0.99625, "loss_cls": 0.43167, "loss": 0.43167, "time": 0.49435} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.0126, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.37204, "loss": 0.37204, "time": 0.49301} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.01258, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92688, "top5_acc": 0.99938, "loss_cls": 0.40106, "loss": 0.40106, "time": 0.49274} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.01256, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.92625, "top5_acc": 0.9975, "loss_cls": 0.40891, "loss": 0.40891, "time": 0.49137} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.01254, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93, "top5_acc": 0.99875, "loss_cls": 0.35674, "loss": 0.35674, "time": 0.49237} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.01252, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93, "top5_acc": 0.99938, "loss_cls": 0.37951, "loss": 0.37951, "time": 0.49294} +{"mode": "val", "epoch": 75, "iter": 533, "lr": 0.0125, "top1_acc": 0.8837, "top5_acc": 0.99331, "mean_class_accuracy": 0.84326} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.01248, "memory": 4083, "data_time": 0.19389, "top1_acc": 0.93062, "top5_acc": 0.9975, "loss_cls": 0.4071, "loss": 0.4071, "time": 0.8018} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.01246, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92312, "top5_acc": 0.99875, "loss_cls": 0.38118, "loss": 0.38118, "time": 0.49508} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.01244, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93938, "top5_acc": 0.99938, "loss_cls": 0.32902, "loss": 0.32902, "time": 0.49388} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.01242, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.29195, "loss": 0.29195, "time": 0.31331} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.0124, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9325, "top5_acc": 1.0, "loss_cls": 0.37926, "loss": 0.37926, "time": 0.51092} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.01238, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.34617, "loss": 0.34617, "time": 0.27293} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.01236, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.34934, "loss": 0.34934, "time": 0.49123} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.01234, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.92625, "top5_acc": 0.99438, "loss_cls": 0.41039, "loss": 0.41039, "time": 0.49886} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.01232, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93188, "top5_acc": 0.99688, "loss_cls": 0.36515, "loss": 0.36515, "time": 0.49114} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.0123, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93062, "top5_acc": 0.99938, "loss_cls": 0.34944, "loss": 0.34944, "time": 0.49535} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.01228, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92875, "top5_acc": 0.99875, "loss_cls": 0.40624, "loss": 0.40624, "time": 0.49381} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.01225, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92688, "top5_acc": 0.99812, "loss_cls": 0.39959, "loss": 0.39959, "time": 0.49139} +{"mode": "val", "epoch": 76, "iter": 533, "lr": 0.01224, "top1_acc": 0.88499, "top5_acc": 0.9919, "mean_class_accuracy": 0.85231} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.01222, "memory": 4083, "data_time": 0.19079, "top1_acc": 0.93312, "top5_acc": 1.0, "loss_cls": 0.34504, "loss": 0.34504, "time": 0.79613} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0122, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.36554, "loss": 0.36554, "time": 0.48813} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.01218, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94375, "top5_acc": 1.0, "loss_cls": 0.31199, "loss": 0.31199, "time": 0.49253} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.01216, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93625, "top5_acc": 1.0, "loss_cls": 0.34237, "loss": 0.34237, "time": 0.29544} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.01214, "memory": 4083, "data_time": 0.00062, "top1_acc": 0.93188, "top5_acc": 0.9975, "loss_cls": 0.35422, "loss": 0.35422, "time": 0.51204} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.01212, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92312, "top5_acc": 0.99938, "loss_cls": 0.40253, "loss": 0.40253, "time": 0.27385} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.0121, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93938, "top5_acc": 0.99938, "loss_cls": 0.34417, "loss": 0.34417, "time": 0.48957} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.01207, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93312, "top5_acc": 0.99875, "loss_cls": 0.35307, "loss": 0.35307, "time": 0.48989} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.01205, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92688, "top5_acc": 0.99938, "loss_cls": 0.39183, "loss": 0.39183, "time": 0.49207} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.01203, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93375, "top5_acc": 0.99812, "loss_cls": 0.36708, "loss": 0.36708, "time": 0.48975} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.01201, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92375, "top5_acc": 0.99688, "loss_cls": 0.41834, "loss": 0.41834, "time": 0.49182} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.01199, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.93875, "top5_acc": 0.99812, "loss_cls": 0.35319, "loss": 0.35319, "time": 0.49497} +{"mode": "val", "epoch": 77, "iter": 533, "lr": 0.01198, "top1_acc": 0.89755, "top5_acc": 0.99249, "mean_class_accuracy": 0.86962} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.01196, "memory": 4083, "data_time": 0.19264, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.32756, "loss": 0.32756, "time": 0.80517} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.01194, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.35711, "loss": 0.35711, "time": 0.49052} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.01192, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.31667, "loss": 0.31667, "time": 0.49088} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.0119, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92438, "top5_acc": 0.99688, "loss_cls": 0.38993, "loss": 0.38993, "time": 0.27756} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.01187, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93625, "top5_acc": 0.99812, "loss_cls": 0.34914, "loss": 0.34914, "time": 0.51027} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.01185, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92625, "top5_acc": 0.9975, "loss_cls": 0.39833, "loss": 0.39833, "time": 0.31203} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.01183, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91188, "top5_acc": 0.99812, "loss_cls": 0.46296, "loss": 0.46296, "time": 0.48764} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.01181, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.935, "top5_acc": 0.99938, "loss_cls": 0.35116, "loss": 0.35116, "time": 0.4896} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.01179, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9275, "top5_acc": 0.99812, "loss_cls": 0.41422, "loss": 0.41422, "time": 0.48923} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.01177, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.93562, "top5_acc": 0.99688, "loss_cls": 0.37549, "loss": 0.37549, "time": 0.49268} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.01175, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.935, "top5_acc": 1.0, "loss_cls": 0.36458, "loss": 0.36458, "time": 0.4894} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.01173, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.9175, "top5_acc": 0.9975, "loss_cls": 0.40921, "loss": 0.40921, "time": 0.49106} +{"mode": "val", "epoch": 78, "iter": 533, "lr": 0.01172, "top1_acc": 0.88006, "top5_acc": 0.99096, "mean_class_accuracy": 0.84785} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.01169, "memory": 4083, "data_time": 0.18743, "top1_acc": 0.93875, "top5_acc": 0.99812, "loss_cls": 0.33353, "loss": 0.33353, "time": 0.79075} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.01167, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.95312, "top5_acc": 0.99812, "loss_cls": 0.31064, "loss": 0.31064, "time": 0.49295} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.01165, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94, "top5_acc": 0.99938, "loss_cls": 0.32657, "loss": 0.32657, "time": 0.49294} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.01163, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.30596, "loss": 0.30596, "time": 0.28364} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.01161, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94125, "top5_acc": 0.99875, "loss_cls": 0.33253, "loss": 0.33253, "time": 0.50228} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.01159, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.39887, "loss": 0.39887, "time": 0.31532} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.01157, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.925, "top5_acc": 0.99938, "loss_cls": 0.36073, "loss": 0.36073, "time": 0.48921} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.01155, "memory": 4083, "data_time": 0.00066, "top1_acc": 0.95062, "top5_acc": 0.99812, "loss_cls": 0.3109, "loss": 0.3109, "time": 0.49281} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.01153, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93625, "top5_acc": 0.99812, "loss_cls": 0.32773, "loss": 0.32773, "time": 0.48824} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.01151, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93312, "top5_acc": 0.99875, "loss_cls": 0.36736, "loss": 0.36736, "time": 0.49235} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.01149, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.38884, "loss": 0.38884, "time": 0.49154} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.01147, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9375, "top5_acc": 0.99875, "loss_cls": 0.37168, "loss": 0.37168, "time": 0.49235} +{"mode": "val", "epoch": 79, "iter": 533, "lr": 0.01145, "top1_acc": 0.89532, "top5_acc": 0.99308, "mean_class_accuracy": 0.85797} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.01143, "memory": 4083, "data_time": 0.19677, "top1_acc": 0.94312, "top5_acc": 1.0, "loss_cls": 0.3066, "loss": 0.3066, "time": 0.80804} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.01141, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93812, "top5_acc": 0.99938, "loss_cls": 0.3513, "loss": 0.3513, "time": 0.48914} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.01139, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.29437, "loss": 0.29437, "time": 0.49266} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.01137, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93312, "top5_acc": 0.99875, "loss_cls": 0.38818, "loss": 0.38818, "time": 0.29522} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.01135, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.36099, "loss": 0.36099, "time": 0.456} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.01133, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93875, "top5_acc": 0.99812, "loss_cls": 0.33043, "loss": 0.33043, "time": 0.33618} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.01131, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94875, "top5_acc": 0.99812, "loss_cls": 0.29224, "loss": 0.29224, "time": 0.49178} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.01129, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.30283, "loss": 0.30283, "time": 0.49282} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.01127, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.31936, "loss": 0.31936, "time": 0.49211} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.01125, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94438, "top5_acc": 0.99812, "loss_cls": 0.31559, "loss": 0.31559, "time": 0.49201} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.01123, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94438, "top5_acc": 0.99812, "loss_cls": 0.34418, "loss": 0.34418, "time": 0.48652} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.01121, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93812, "top5_acc": 0.99812, "loss_cls": 0.33349, "loss": 0.33349, "time": 0.49287} +{"mode": "val", "epoch": 80, "iter": 533, "lr": 0.01119, "top1_acc": 0.84884, "top5_acc": 0.98404, "mean_class_accuracy": 0.79699} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.01117, "memory": 4083, "data_time": 0.19925, "top1_acc": 0.94188, "top5_acc": 1.0, "loss_cls": 0.33028, "loss": 0.33028, "time": 0.81435} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.01115, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.94, "top5_acc": 0.99625, "loss_cls": 0.36717, "loss": 0.36717, "time": 0.49086} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.01113, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9425, "top5_acc": 0.99812, "loss_cls": 0.33248, "loss": 0.33248, "time": 0.48366} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.01111, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.9375, "top5_acc": 0.99812, "loss_cls": 0.34631, "loss": 0.34631, "time": 0.3357} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.01109, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.93375, "top5_acc": 1.0, "loss_cls": 0.36984, "loss": 0.36984, "time": 0.40322} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.01107, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.94375, "top5_acc": 0.99688, "loss_cls": 0.32374, "loss": 0.32374, "time": 0.35434} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.01105, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94438, "top5_acc": 0.99688, "loss_cls": 0.33235, "loss": 0.33235, "time": 0.49354} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.01103, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.31403, "loss": 0.31403, "time": 0.49083} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.01101, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.95, "top5_acc": 1.0, "loss_cls": 0.301, "loss": 0.301, "time": 0.48795} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.01099, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95375, "top5_acc": 0.99875, "loss_cls": 0.30209, "loss": 0.30209, "time": 0.49343} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.01097, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93312, "top5_acc": 1.0, "loss_cls": 0.33349, "loss": 0.33349, "time": 0.49034} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.01095, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.32034, "loss": 0.32034, "time": 0.49143} +{"mode": "val", "epoch": 81, "iter": 533, "lr": 0.01093, "top1_acc": 0.89367, "top5_acc": 0.99296, "mean_class_accuracy": 0.86039} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.01091, "memory": 4083, "data_time": 0.19855, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.31776, "loss": 0.31776, "time": 0.79531} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.01089, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.94, "top5_acc": 0.99938, "loss_cls": 0.33218, "loss": 0.33218, "time": 0.49318} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.01087, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.32698, "loss": 0.32698, "time": 0.4822} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.01085, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.29887, "loss": 0.29887, "time": 0.33515} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.01083, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.9425, "top5_acc": 0.99812, "loss_cls": 0.34257, "loss": 0.34257, "time": 0.39964} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.01081, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.32327, "loss": 0.32327, "time": 0.35525} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.01079, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94562, "top5_acc": 0.99812, "loss_cls": 0.34401, "loss": 0.34401, "time": 0.48914} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.01077, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93, "top5_acc": 0.99625, "loss_cls": 0.38054, "loss": 0.38054, "time": 0.4924} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.01075, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.31813, "loss": 0.31813, "time": 0.4927} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.01073, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94125, "top5_acc": 0.99812, "loss_cls": 0.36527, "loss": 0.36527, "time": 0.49464} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.01071, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.32006, "loss": 0.32006, "time": 0.48891} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.01069, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93, "top5_acc": 0.9975, "loss_cls": 0.3832, "loss": 0.3832, "time": 0.48792} +{"mode": "val", "epoch": 82, "iter": 533, "lr": 0.01067, "top1_acc": 0.88921, "top5_acc": 0.99272, "mean_class_accuracy": 0.86011} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.01065, "memory": 4083, "data_time": 0.19761, "top1_acc": 0.93438, "top5_acc": 0.99938, "loss_cls": 0.35664, "loss": 0.35664, "time": 0.80769} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.01063, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.27432, "loss": 0.27432, "time": 0.49452} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.01061, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.27465, "loss": 0.27465, "time": 0.46452} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.01059, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.94562, "top5_acc": 0.99875, "loss_cls": 0.32, "loss": 0.32, "time": 0.3852} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.01057, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.30197, "loss": 0.30197, "time": 0.35354} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.01055, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93812, "top5_acc": 1.0, "loss_cls": 0.33232, "loss": 0.33232, "time": 0.38227} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.01053, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94188, "top5_acc": 0.9975, "loss_cls": 0.32279, "loss": 0.32279, "time": 0.49246} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.01051, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94312, "top5_acc": 0.99875, "loss_cls": 0.31007, "loss": 0.31007, "time": 0.49376} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.01049, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93375, "top5_acc": 0.99812, "loss_cls": 0.33132, "loss": 0.33132, "time": 0.49503} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.01047, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93938, "top5_acc": 1.0, "loss_cls": 0.32332, "loss": 0.32332, "time": 0.49214} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.01045, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.30335, "loss": 0.30335, "time": 0.4901} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.01043, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95688, "top5_acc": 0.99812, "loss_cls": 0.27418, "loss": 0.27418, "time": 0.48968} +{"mode": "val", "epoch": 83, "iter": 533, "lr": 0.01042, "top1_acc": 0.90072, "top5_acc": 0.99472, "mean_class_accuracy": 0.85885} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.0104, "memory": 4083, "data_time": 0.19014, "top1_acc": 0.96562, "top5_acc": 0.99875, "loss_cls": 0.27855, "loss": 0.27855, "time": 0.79685} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.01038, "memory": 4083, "data_time": 0.00063, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.28853, "loss": 0.28853, "time": 0.49264} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.01036, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.27053, "loss": 0.27053, "time": 0.45236} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.01034, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.3077, "loss": 0.3077, "time": 0.40935} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.01031, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.32656, "loss": 0.32656, "time": 0.32751} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.01029, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.27709, "loss": 0.27709, "time": 0.39516} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.01027, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94812, "top5_acc": 0.99812, "loss_cls": 0.30566, "loss": 0.30566, "time": 0.49038} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.01025, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.3161, "loss": 0.3161, "time": 0.49279} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.01023, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.30788, "loss": 0.30788, "time": 0.48787} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.01021, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.28639, "loss": 0.28639, "time": 0.49465} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.01019, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94625, "top5_acc": 0.99875, "loss_cls": 0.3102, "loss": 0.3102, "time": 0.49039} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.01017, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.29322, "loss": 0.29322, "time": 0.49158} +{"mode": "val", "epoch": 84, "iter": 533, "lr": 0.01016, "top1_acc": 0.88745, "top5_acc": 0.99378, "mean_class_accuracy": 0.84502} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.01014, "memory": 4083, "data_time": 0.19617, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.26584, "loss": 0.26584, "time": 0.80037} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.01012, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.24548, "loss": 0.24548, "time": 0.49063} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.0101, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95125, "top5_acc": 0.99875, "loss_cls": 0.27892, "loss": 0.27892, "time": 0.44096} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.01008, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.94625, "top5_acc": 0.99812, "loss_cls": 0.32444, "loss": 0.32444, "time": 0.43742} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.01006, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.28497, "loss": 0.28497, "time": 0.30116} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.01004, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94438, "top5_acc": 1.0, "loss_cls": 0.31806, "loss": 0.31806, "time": 0.40157} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.01002, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94875, "top5_acc": 0.99875, "loss_cls": 0.30605, "loss": 0.30605, "time": 0.49201} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.01, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.30273, "loss": 0.30273, "time": 0.49362} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.00998, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.94188, "top5_acc": 0.99938, "loss_cls": 0.33807, "loss": 0.33807, "time": 0.49175} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.00996, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93625, "top5_acc": 0.99812, "loss_cls": 0.36436, "loss": 0.36436, "time": 0.49281} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.00994, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.31586, "loss": 0.31586, "time": 0.49108} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.00992, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94812, "top5_acc": 0.99875, "loss_cls": 0.27926, "loss": 0.27926, "time": 0.48925} +{"mode": "val", "epoch": 85, "iter": 533, "lr": 0.0099, "top1_acc": 0.90494, "top5_acc": 0.99225, "mean_class_accuracy": 0.87239} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.00988, "memory": 4083, "data_time": 0.19147, "top1_acc": 0.94375, "top5_acc": 1.0, "loss_cls": 0.29809, "loss": 0.29809, "time": 0.79707} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.00986, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.28865, "loss": 0.28865, "time": 0.4934} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.00984, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.24947, "loss": 0.24947, "time": 0.42867} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.00982, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.26665, "loss": 0.26665, "time": 0.46124} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.0098, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.26335, "loss": 0.26335, "time": 0.27815} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.00978, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.29876, "loss": 0.29876, "time": 0.41396} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.00976, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.93438, "top5_acc": 0.99812, "loss_cls": 0.34096, "loss": 0.34096, "time": 0.49078} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.00974, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.9425, "top5_acc": 1.0, "loss_cls": 0.315, "loss": 0.315, "time": 0.49036} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.00972, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9375, "top5_acc": 0.99875, "loss_cls": 0.3589, "loss": 0.3589, "time": 0.4904} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.0097, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92625, "top5_acc": 0.99938, "loss_cls": 0.38817, "loss": 0.38817, "time": 0.4912} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.00968, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.94312, "top5_acc": 0.99812, "loss_cls": 0.33737, "loss": 0.33737, "time": 0.48903} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.00966, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.2928, "loss": 0.2928, "time": 0.48875} +{"mode": "val", "epoch": 86, "iter": 533, "lr": 0.00965, "top1_acc": 0.9006, "top5_acc": 0.99284, "mean_class_accuracy": 0.86347} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.00963, "memory": 4083, "data_time": 0.19437, "top1_acc": 0.95062, "top5_acc": 1.0, "loss_cls": 0.29292, "loss": 0.29292, "time": 0.80114} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.00961, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.26504, "loss": 0.26504, "time": 0.49212} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.00959, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.27692, "loss": 0.27692, "time": 0.42258} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.00957, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95188, "top5_acc": 1.0, "loss_cls": 0.27224, "loss": 0.27224, "time": 0.4838} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.00955, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.24311, "loss": 0.24311, "time": 0.26007} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.00953, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.94812, "top5_acc": 0.99812, "loss_cls": 0.30933, "loss": 0.30933, "time": 0.43416} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.00951, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9425, "top5_acc": 0.99875, "loss_cls": 0.30797, "loss": 0.30797, "time": 0.49646} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.00949, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9525, "top5_acc": 0.99875, "loss_cls": 0.285, "loss": 0.285, "time": 0.49435} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.00947, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93875, "top5_acc": 0.99812, "loss_cls": 0.32884, "loss": 0.32884, "time": 0.4916} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.00945, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94438, "top5_acc": 0.99875, "loss_cls": 0.32064, "loss": 0.32064, "time": 0.49371} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.00943, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9275, "top5_acc": 0.99688, "loss_cls": 0.36888, "loss": 0.36888, "time": 0.48965} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.00941, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.93812, "top5_acc": 0.99875, "loss_cls": 0.32611, "loss": 0.32611, "time": 0.49032} +{"mode": "val", "epoch": 87, "iter": 533, "lr": 0.00939, "top1_acc": 0.90611, "top5_acc": 0.99308, "mean_class_accuracy": 0.87719} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.00937, "memory": 4083, "data_time": 0.18706, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.26039, "loss": 0.26039, "time": 0.80547} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.00935, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.27228, "loss": 0.27228, "time": 0.49398} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.00933, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.28239, "loss": 0.28239, "time": 0.38027} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.00931, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95812, "top5_acc": 0.99875, "loss_cls": 0.2721, "loss": 0.2721, "time": 0.5096} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.00929, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93688, "top5_acc": 0.99938, "loss_cls": 0.31243, "loss": 0.31243, "time": 0.2426} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.00927, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.33828, "loss": 0.33828, "time": 0.44738} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.00925, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.3222, "loss": 0.3222, "time": 0.48846} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.00923, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.94625, "top5_acc": 1.0, "loss_cls": 0.31814, "loss": 0.31814, "time": 0.49107} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.00921, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.30555, "loss": 0.30555, "time": 0.49327} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.00919, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9525, "top5_acc": 0.99875, "loss_cls": 0.27173, "loss": 0.27173, "time": 0.49592} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.00917, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.95, "top5_acc": 1.0, "loss_cls": 0.29983, "loss": 0.29983, "time": 0.49161} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.00915, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.28954, "loss": 0.28954, "time": 0.49469} +{"mode": "val", "epoch": 88, "iter": 533, "lr": 0.00914, "top1_acc": 0.90365, "top5_acc": 0.99366, "mean_class_accuracy": 0.87354} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.00912, "memory": 4083, "data_time": 0.1917, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.22258, "loss": 0.22258, "time": 0.80445} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0091, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.24265, "loss": 0.24265, "time": 0.49144} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.00908, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.23619, "loss": 0.23619, "time": 0.37007} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.00906, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.21233, "loss": 0.21233, "time": 0.51342} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.00904, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94312, "top5_acc": 1.0, "loss_cls": 0.26776, "loss": 0.26776, "time": 0.24496} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.00902, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.29583, "loss": 0.29583, "time": 0.46549} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.009, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9425, "top5_acc": 0.99875, "loss_cls": 0.31645, "loss": 0.31645, "time": 0.49196} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.00898, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.27701, "loss": 0.27701, "time": 0.49215} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.00896, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.26862, "loss": 0.26862, "time": 0.49103} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.00894, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94625, "top5_acc": 1.0, "loss_cls": 0.28147, "loss": 0.28147, "time": 0.49063} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.00892, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.94188, "top5_acc": 0.99875, "loss_cls": 0.31999, "loss": 0.31999, "time": 0.49157} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.0089, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95812, "top5_acc": 0.99938, "loss_cls": 0.27623, "loss": 0.27623, "time": 0.49153} +{"mode": "val", "epoch": 89, "iter": 533, "lr": 0.00889, "top1_acc": 0.90259, "top5_acc": 0.99343, "mean_class_accuracy": 0.87318} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.00887, "memory": 4083, "data_time": 0.19246, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.27051, "loss": 0.27051, "time": 0.79966} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.00885, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95, "top5_acc": 1.0, "loss_cls": 0.26795, "loss": 0.26795, "time": 0.49795} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.00883, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.24672, "loss": 0.24672, "time": 0.35011} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.00881, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95375, "top5_acc": 0.99875, "loss_cls": 0.28538, "loss": 0.28538, "time": 0.51124} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.00879, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.95562, "top5_acc": 0.99812, "loss_cls": 0.24573, "loss": 0.24573, "time": 0.24454} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.00877, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.27797, "loss": 0.27797, "time": 0.46547} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.00875, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.28823, "loss": 0.28823, "time": 0.49288} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.00873, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93688, "top5_acc": 0.99938, "loss_cls": 0.32624, "loss": 0.32624, "time": 0.49301} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.00871, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95438, "top5_acc": 0.99938, "loss_cls": 0.2803, "loss": 0.2803, "time": 0.48962} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.00869, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.96188, "top5_acc": 0.99875, "loss_cls": 0.26611, "loss": 0.26611, "time": 0.49168} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.00867, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.95125, "top5_acc": 0.99875, "loss_cls": 0.26909, "loss": 0.26909, "time": 0.49229} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.00865, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.2616, "loss": 0.2616, "time": 0.49291} +{"mode": "val", "epoch": 90, "iter": 533, "lr": 0.00864, "top1_acc": 0.89039, "top5_acc": 0.9939, "mean_class_accuracy": 0.85169} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.00862, "memory": 4083, "data_time": 0.19035, "top1_acc": 0.95688, "top5_acc": 0.99938, "loss_cls": 0.28463, "loss": 0.28463, "time": 0.80266} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0086, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.2301, "loss": 0.2301, "time": 0.49051} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.00858, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.23009, "loss": 0.23009, "time": 0.3511} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.00856, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.26404, "loss": 0.26404, "time": 0.51057} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.00854, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.95188, "top5_acc": 1.0, "loss_cls": 0.27064, "loss": 0.27064, "time": 0.24979} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.00852, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.95, "top5_acc": 0.99875, "loss_cls": 0.28498, "loss": 0.28498, "time": 0.4746} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.0085, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95688, "top5_acc": 0.99875, "loss_cls": 0.28724, "loss": 0.28724, "time": 0.4899} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.00848, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.25159, "loss": 0.25159, "time": 0.49187} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.00846, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9575, "top5_acc": 0.99938, "loss_cls": 0.25752, "loss": 0.25752, "time": 0.49061} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.00844, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.25421, "loss": 0.25421, "time": 0.49048} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.00842, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.29644, "loss": 0.29644, "time": 0.48926} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.0084, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.96938, "top5_acc": 0.99812, "loss_cls": 0.23221, "loss": 0.23221, "time": 0.48995} +{"mode": "val", "epoch": 91, "iter": 533, "lr": 0.00839, "top1_acc": 0.90142, "top5_acc": 0.99249, "mean_class_accuracy": 0.8636} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.00837, "memory": 4083, "data_time": 0.18636, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.27253, "loss": 0.27253, "time": 0.78955} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.00835, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.24986, "loss": 0.24986, "time": 0.48984} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.00833, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94312, "top5_acc": 0.99875, "loss_cls": 0.30654, "loss": 0.30654, "time": 0.35433} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.00831, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.2431, "loss": 0.2431, "time": 0.51083} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.00829, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.23106, "loss": 0.23106, "time": 0.24574} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.00827, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94688, "top5_acc": 0.99812, "loss_cls": 0.30265, "loss": 0.30265, "time": 0.46845} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.00825, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.25033, "loss": 0.25033, "time": 0.49236} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.00824, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94938, "top5_acc": 0.99812, "loss_cls": 0.29978, "loss": 0.29978, "time": 0.49091} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.00822, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.21141, "loss": 0.21141, "time": 0.48832} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.0082, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95688, "top5_acc": 0.99875, "loss_cls": 0.25226, "loss": 0.25226, "time": 0.49202} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.00818, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.24691, "loss": 0.24691, "time": 0.49109} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.00816, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93562, "top5_acc": 0.99688, "loss_cls": 0.32158, "loss": 0.32158, "time": 0.49226} +{"mode": "val", "epoch": 92, "iter": 533, "lr": 0.00814, "top1_acc": 0.90283, "top5_acc": 0.99308, "mean_class_accuracy": 0.87675} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.00812, "memory": 4083, "data_time": 0.18954, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.21855, "loss": 0.21855, "time": 0.78931} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.0081, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.23929, "loss": 0.23929, "time": 0.49348} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.00809, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96438, "top5_acc": 0.99875, "loss_cls": 0.22995, "loss": 0.22995, "time": 0.36512} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.00807, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.96375, "top5_acc": 0.99938, "loss_cls": 0.23836, "loss": 0.23836, "time": 0.51228} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.00805, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.95812, "top5_acc": 0.99938, "loss_cls": 0.26373, "loss": 0.26373, "time": 0.24548} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.00803, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.26423, "loss": 0.26423, "time": 0.46194} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.00801, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.27624, "loss": 0.27624, "time": 0.49299} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.00799, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.27711, "loss": 0.27711, "time": 0.4875} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.00797, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.95812, "top5_acc": 0.99875, "loss_cls": 0.24417, "loss": 0.24417, "time": 0.48811} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.00795, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.23405, "loss": 0.23405, "time": 0.49089} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.00793, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.94625, "top5_acc": 0.99812, "loss_cls": 0.30803, "loss": 0.30803, "time": 0.48846} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.00791, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.28369, "loss": 0.28369, "time": 0.4885} +{"mode": "val", "epoch": 93, "iter": 533, "lr": 0.0079, "top1_acc": 0.90236, "top5_acc": 0.99132, "mean_class_accuracy": 0.87069} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.00788, "memory": 4083, "data_time": 0.18594, "top1_acc": 0.9775, "top5_acc": 0.99938, "loss_cls": 0.17646, "loss": 0.17646, "time": 0.79253} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.00786, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96625, "top5_acc": 0.99938, "loss_cls": 0.22586, "loss": 0.22586, "time": 0.48998} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.00784, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.21242, "loss": 0.21242, "time": 0.38039} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.00782, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.19844, "loss": 0.19844, "time": 0.51136} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.0078, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.96062, "top5_acc": 0.99812, "loss_cls": 0.22515, "loss": 0.22515, "time": 0.2417} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.00778, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.95625, "top5_acc": 0.99875, "loss_cls": 0.26352, "loss": 0.26352, "time": 0.45708} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.00777, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95062, "top5_acc": 0.99875, "loss_cls": 0.28676, "loss": 0.28676, "time": 0.48946} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.00775, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96625, "top5_acc": 0.99875, "loss_cls": 0.2314, "loss": 0.2314, "time": 0.4902} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.00773, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.21804, "loss": 0.21804, "time": 0.49143} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.00771, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96, "top5_acc": 0.9975, "loss_cls": 0.24715, "loss": 0.24715, "time": 0.49257} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.00769, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97062, "top5_acc": 0.99875, "loss_cls": 0.20489, "loss": 0.20489, "time": 0.4891} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.00767, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.27068, "loss": 0.27068, "time": 0.4909} +{"mode": "val", "epoch": 94, "iter": 533, "lr": 0.00766, "top1_acc": 0.9155, "top5_acc": 0.99495, "mean_class_accuracy": 0.88241} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.00764, "memory": 4083, "data_time": 0.19022, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.24469, "loss": 0.24469, "time": 0.78308} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.00762, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.21013, "loss": 0.21013, "time": 0.48912} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.0076, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95188, "top5_acc": 0.99875, "loss_cls": 0.275, "loss": 0.275, "time": 0.36951} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.00758, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95938, "top5_acc": 0.99875, "loss_cls": 0.24429, "loss": 0.24429, "time": 0.51049} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.00756, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96625, "top5_acc": 0.99938, "loss_cls": 0.23158, "loss": 0.23158, "time": 0.24701} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.00754, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.24528, "loss": 0.24528, "time": 0.46467} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.00752, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.24589, "loss": 0.24589, "time": 0.49268} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.00751, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.27953, "loss": 0.27953, "time": 0.49371} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.00749, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.27547, "loss": 0.27547, "time": 0.48943} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.00747, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.24453, "loss": 0.24453, "time": 0.49642} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.00745, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.2352, "loss": 0.2352, "time": 0.49057} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.00743, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.26244, "loss": 0.26244, "time": 0.49134} +{"mode": "val", "epoch": 95, "iter": 533, "lr": 0.00742, "top1_acc": 0.9128, "top5_acc": 0.99237, "mean_class_accuracy": 0.8876} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.0074, "memory": 4083, "data_time": 0.1865, "top1_acc": 0.97438, "top5_acc": 0.99938, "loss_cls": 0.21012, "loss": 0.21012, "time": 0.79556} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.00738, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.18472, "loss": 0.18472, "time": 0.48986} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.00736, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.23476, "loss": 0.23476, "time": 0.36496} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.00734, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.20407, "loss": 0.20407, "time": 0.51163} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.00732, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.97062, "top5_acc": 0.99938, "loss_cls": 0.20443, "loss": 0.20443, "time": 0.24449} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.0073, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9775, "top5_acc": 0.99938, "loss_cls": 0.18056, "loss": 0.18056, "time": 0.44545} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.00729, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.20918, "loss": 0.20918, "time": 0.4882} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.00727, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9575, "top5_acc": 0.99812, "loss_cls": 0.25522, "loss": 0.25522, "time": 0.4853} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.00725, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.24011, "loss": 0.24011, "time": 0.48882} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.00723, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.24347, "loss": 0.24347, "time": 0.48604} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.00721, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95, "top5_acc": 1.0, "loss_cls": 0.29285, "loss": 0.29285, "time": 0.49091} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.00719, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96375, "top5_acc": 0.99938, "loss_cls": 0.23318, "loss": 0.23318, "time": 0.48578} +{"mode": "val", "epoch": 96, "iter": 533, "lr": 0.00718, "top1_acc": 0.90752, "top5_acc": 0.99448, "mean_class_accuracy": 0.87297} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.00716, "memory": 4083, "data_time": 0.18762, "top1_acc": 0.965, "top5_acc": 0.99812, "loss_cls": 0.21735, "loss": 0.21735, "time": 0.79477} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.00714, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.20031, "loss": 0.20031, "time": 0.49054} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.00712, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.18748, "loss": 0.18748, "time": 0.39299} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.0071, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.96375, "top5_acc": 0.99938, "loss_cls": 0.19617, "loss": 0.19617, "time": 0.51275} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.00709, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.19414, "loss": 0.19414, "time": 0.23794} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.00707, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97562, "top5_acc": 0.99938, "loss_cls": 0.17471, "loss": 0.17471, "time": 0.44232} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.00705, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.21754, "loss": 0.21754, "time": 0.48741} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.00703, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.96625, "top5_acc": 0.99812, "loss_cls": 0.22329, "loss": 0.22329, "time": 0.48539} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.00701, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.22766, "loss": 0.22766, "time": 0.49341} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.00699, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.25622, "loss": 0.25622, "time": 0.49226} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.00698, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9575, "top5_acc": 0.99938, "loss_cls": 0.26693, "loss": 0.26693, "time": 0.49334} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.00696, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95812, "top5_acc": 0.99938, "loss_cls": 0.25783, "loss": 0.25783, "time": 0.48583} +{"mode": "val", "epoch": 97, "iter": 533, "lr": 0.00694, "top1_acc": 0.91668, "top5_acc": 0.99507, "mean_class_accuracy": 0.88609} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.00692, "memory": 4083, "data_time": 0.18328, "top1_acc": 0.97188, "top5_acc": 0.99938, "loss_cls": 0.18803, "loss": 0.18803, "time": 0.7834} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.00691, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.97188, "top5_acc": 0.99938, "loss_cls": 0.19519, "loss": 0.19519, "time": 0.4882} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.00689, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97562, "top5_acc": 0.99938, "loss_cls": 0.17954, "loss": 0.17954, "time": 0.4058} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.00687, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.18206, "loss": 0.18206, "time": 0.51151} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.00685, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.21149, "loss": 0.21149, "time": 0.23447} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.00683, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97062, "top5_acc": 0.99938, "loss_cls": 0.20052, "loss": 0.20052, "time": 0.43535} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.00681, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.19907, "loss": 0.19907, "time": 0.48709} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.0068, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.2476, "loss": 0.2476, "time": 0.48534} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.00678, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.20947, "loss": 0.20947, "time": 0.49073} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.00676, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.27506, "loss": 0.27506, "time": 0.48704} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.00674, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.25157, "loss": 0.25157, "time": 0.48935} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.00672, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96125, "top5_acc": 0.99938, "loss_cls": 0.23337, "loss": 0.23337, "time": 0.48927} +{"mode": "val", "epoch": 98, "iter": 533, "lr": 0.00671, "top1_acc": 0.91468, "top5_acc": 0.99613, "mean_class_accuracy": 0.87687} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.00669, "memory": 4083, "data_time": 0.18759, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.23194, "loss": 0.23194, "time": 0.80314} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.00667, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.21255, "loss": 0.21255, "time": 0.49062} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.00665, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97312, "top5_acc": 0.99938, "loss_cls": 0.17758, "loss": 0.17758, "time": 0.39433} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.00664, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.20699, "loss": 0.20699, "time": 0.50958} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.00662, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.20273, "loss": 0.20273, "time": 0.2373} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.0066, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.955, "top5_acc": 0.99875, "loss_cls": 0.25403, "loss": 0.25403, "time": 0.4321} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.00658, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96125, "top5_acc": 0.99875, "loss_cls": 0.2267, "loss": 0.2267, "time": 0.49173} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.00656, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96188, "top5_acc": 0.99875, "loss_cls": 0.21976, "loss": 0.21976, "time": 0.49066} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.00655, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.2585, "loss": 0.2585, "time": 0.49247} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.00653, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95062, "top5_acc": 1.0, "loss_cls": 0.29221, "loss": 0.29221, "time": 0.49129} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.00651, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.21144, "loss": 0.21144, "time": 0.49} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.00649, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.20491, "loss": 0.20491, "time": 0.48894} +{"mode": "val", "epoch": 99, "iter": 533, "lr": 0.00648, "top1_acc": 0.90154, "top5_acc": 0.99261, "mean_class_accuracy": 0.87122} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.00646, "memory": 4083, "data_time": 0.18863, "top1_acc": 0.96625, "top5_acc": 0.99875, "loss_cls": 0.21096, "loss": 0.21096, "time": 0.78601} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.00644, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96562, "top5_acc": 0.99938, "loss_cls": 0.20472, "loss": 0.20472, "time": 0.48725} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.00642, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.97062, "top5_acc": 0.99938, "loss_cls": 0.19366, "loss": 0.19366, "time": 0.41997} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.00641, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.19474, "loss": 0.19474, "time": 0.49496} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.00639, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.22151, "loss": 0.22151, "time": 0.24958} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.00637, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.20896, "loss": 0.20896, "time": 0.41844} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.00635, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.22072, "loss": 0.22072, "time": 0.48807} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.00634, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95, "top5_acc": 0.99812, "loss_cls": 0.27905, "loss": 0.27905, "time": 0.48822} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.00632, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.20943, "loss": 0.20943, "time": 0.49287} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.0063, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95562, "top5_acc": 0.99875, "loss_cls": 0.24848, "loss": 0.24848, "time": 0.49367} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.00628, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.20761, "loss": 0.20761, "time": 0.4897} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.00626, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.1903, "loss": 0.1903, "time": 0.48649} +{"mode": "val", "epoch": 100, "iter": 533, "lr": 0.00625, "top1_acc": 0.89485, "top5_acc": 0.99202, "mean_class_accuracy": 0.85984} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.00623, "memory": 4083, "data_time": 0.18661, "top1_acc": 0.96875, "top5_acc": 0.99812, "loss_cls": 0.23358, "loss": 0.23358, "time": 0.79251} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.00621, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.96875, "top5_acc": 0.99938, "loss_cls": 0.18109, "loss": 0.18109, "time": 0.48747} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.0062, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.21146, "loss": 0.21146, "time": 0.42305} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.00618, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.97188, "top5_acc": 0.99938, "loss_cls": 0.18512, "loss": 0.18512, "time": 0.47531} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.00616, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.17925, "loss": 0.17925, "time": 0.26496} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.00614, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.16477, "loss": 0.16477, "time": 0.41654} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.00613, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.18346, "loss": 0.18346, "time": 0.49151} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.00611, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.17074, "loss": 0.17074, "time": 0.48848} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.00609, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96812, "top5_acc": 0.99938, "loss_cls": 0.20091, "loss": 0.20091, "time": 0.49191} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.00607, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.19843, "loss": 0.19843, "time": 0.48969} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.00606, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.20993, "loss": 0.20993, "time": 0.49103} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.00604, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97188, "top5_acc": 0.99938, "loss_cls": 0.18237, "loss": 0.18237, "time": 0.49275} +{"mode": "val", "epoch": 101, "iter": 533, "lr": 0.00602, "top1_acc": 0.91409, "top5_acc": 0.99425, "mean_class_accuracy": 0.88781} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.00601, "memory": 4083, "data_time": 0.18885, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.25993, "loss": 0.25993, "time": 0.78369} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.00599, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.16095, "loss": 0.16095, "time": 0.48866} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.00597, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.97625, "top5_acc": 0.99938, "loss_cls": 0.1648, "loss": 0.1648, "time": 0.436} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.00596, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.19873, "loss": 0.19873, "time": 0.45763} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.00594, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.20462, "loss": 0.20462, "time": 0.27663} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.00592, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.14904, "loss": 0.14904, "time": 0.42223} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.0059, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.19497, "loss": 0.19497, "time": 0.49039} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.00589, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96938, "top5_acc": 0.99875, "loss_cls": 0.19066, "loss": 0.19066, "time": 0.48888} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.00587, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.21216, "loss": 0.21216, "time": 0.49083} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.00585, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96812, "top5_acc": 0.99938, "loss_cls": 0.20462, "loss": 0.20462, "time": 0.48961} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.00583, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.20581, "loss": 0.20581, "time": 0.48754} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.00582, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.23148, "loss": 0.23148, "time": 0.488} +{"mode": "val", "epoch": 102, "iter": 533, "lr": 0.0058, "top1_acc": 0.91022, "top5_acc": 0.99378, "mean_class_accuracy": 0.8865} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.00579, "memory": 4083, "data_time": 0.18997, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.18396, "loss": 0.18396, "time": 0.77417} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.00577, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97625, "top5_acc": 0.99938, "loss_cls": 0.16308, "loss": 0.16308, "time": 0.4875} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.00575, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.18508, "loss": 0.18508, "time": 0.44422} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.00573, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.96125, "top5_acc": 0.99938, "loss_cls": 0.22896, "loss": 0.22896, "time": 0.41645} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.00572, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.17516, "loss": 0.17516, "time": 0.31595} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.0057, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.15022, "loss": 0.15022, "time": 0.39874} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.00568, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97625, "top5_acc": 0.99938, "loss_cls": 0.16968, "loss": 0.16968, "time": 0.49055} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.00566, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.18005, "loss": 0.18005, "time": 0.48888} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.00565, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97688, "top5_acc": 0.99938, "loss_cls": 0.15949, "loss": 0.15949, "time": 0.48672} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.00563, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9675, "top5_acc": 0.99875, "loss_cls": 0.19847, "loss": 0.19847, "time": 0.48592} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.00561, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.19875, "loss": 0.19875, "time": 0.48928} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.0056, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.17263, "loss": 0.17263, "time": 0.49055} +{"mode": "val", "epoch": 103, "iter": 533, "lr": 0.00558, "top1_acc": 0.90459, "top5_acc": 0.99413, "mean_class_accuracy": 0.87321} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.00557, "memory": 4083, "data_time": 0.18242, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.17108, "loss": 0.17108, "time": 0.77922} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.00555, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.98, "top5_acc": 0.99938, "loss_cls": 0.15172, "loss": 0.15172, "time": 0.49327} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.00553, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.13096, "loss": 0.13096, "time": 0.4752} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.00551, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.15605, "loss": 0.15605, "time": 0.34265} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.0055, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.1537, "loss": 0.1537, "time": 0.38757} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.00548, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.22043, "loss": 0.22043, "time": 0.3543} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.00546, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.1789, "loss": 0.1789, "time": 0.48763} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.00545, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.18775, "loss": 0.18775, "time": 0.49061} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.00543, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.16941, "loss": 0.16941, "time": 0.4885} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.00541, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.16055, "loss": 0.16055, "time": 0.48565} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.0054, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.17102, "loss": 0.17102, "time": 0.48815} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.00538, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97938, "top5_acc": 0.99938, "loss_cls": 0.14763, "loss": 0.14763, "time": 0.49117} +{"mode": "val", "epoch": 104, "iter": 533, "lr": 0.00537, "top1_acc": 0.91374, "top5_acc": 0.99401, "mean_class_accuracy": 0.88467} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.00535, "memory": 4083, "data_time": 0.18389, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14485, "loss": 0.14485, "time": 0.78504} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.00533, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.13845, "loss": 0.13845, "time": 0.49442} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.00532, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14194, "loss": 0.14194, "time": 0.49137} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.0053, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96562, "top5_acc": 0.99875, "loss_cls": 0.20913, "loss": 0.20913, "time": 0.30457} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.00528, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.16264, "loss": 0.16264, "time": 0.44938} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.00527, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.14047, "loss": 0.14047, "time": 0.32971} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.00525, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.15567, "loss": 0.15567, "time": 0.49035} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.00523, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9725, "top5_acc": 0.99938, "loss_cls": 0.18675, "loss": 0.18675, "time": 0.487} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.00522, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.15938, "loss": 0.15938, "time": 0.48834} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.0052, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.15601, "loss": 0.15601, "time": 0.48962} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.00518, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.96812, "top5_acc": 0.99938, "loss_cls": 0.19377, "loss": 0.19377, "time": 0.48826} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.00517, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.16729, "loss": 0.16729, "time": 0.48701} +{"mode": "val", "epoch": 105, "iter": 533, "lr": 0.00515, "top1_acc": 0.91398, "top5_acc": 0.9946, "mean_class_accuracy": 0.88663} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.00514, "memory": 4083, "data_time": 0.18746, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.15576, "loss": 0.15576, "time": 0.80046} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.00512, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13408, "loss": 0.13408, "time": 0.48832} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.0051, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.11315, "loss": 0.11315, "time": 0.4868} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.00509, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11927, "loss": 0.11927, "time": 0.30363} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.00507, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11198, "loss": 0.11198, "time": 0.4531} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.00505, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12396, "loss": 0.12396, "time": 0.31753} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.00504, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.17427, "loss": 0.17427, "time": 0.48843} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.00502, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.15513, "loss": 0.15513, "time": 0.49372} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.005, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12522, "loss": 0.12522, "time": 0.49108} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.00499, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.97312, "top5_acc": 0.99875, "loss_cls": 0.16021, "loss": 0.16021, "time": 0.49161} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.00497, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.18685, "loss": 0.18685, "time": 0.49268} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.00496, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.18269, "loss": 0.18269, "time": 0.48926} +{"mode": "val", "epoch": 106, "iter": 533, "lr": 0.00494, "top1_acc": 0.9101, "top5_acc": 0.99589, "mean_class_accuracy": 0.8822} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.00493, "memory": 4083, "data_time": 0.18792, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.14817, "loss": 0.14817, "time": 0.78822} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.00491, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97625, "top5_acc": 0.99938, "loss_cls": 0.17008, "loss": 0.17008, "time": 0.49023} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.00489, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.15251, "loss": 0.15251, "time": 0.49385} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.00488, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.97812, "top5_acc": 0.99938, "loss_cls": 0.14471, "loss": 0.14471, "time": 0.28767} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.00486, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.1562, "loss": 0.1562, "time": 0.50793} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.00485, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.12122, "loss": 0.12122, "time": 0.29633} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.00483, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.13102, "loss": 0.13102, "time": 0.48591} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.00481, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.12556, "loss": 0.12556, "time": 0.49082} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.0048, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.10582, "loss": 0.10582, "time": 0.48422} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.00478, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.13731, "loss": 0.13731, "time": 0.48934} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.00476, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12476, "loss": 0.12476, "time": 0.48683} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.00475, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.12922, "loss": 0.12922, "time": 0.48647} +{"mode": "val", "epoch": 107, "iter": 533, "lr": 0.00474, "top1_acc": 0.92477, "top5_acc": 0.99589, "mean_class_accuracy": 0.89602} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.00472, "memory": 4083, "data_time": 0.18704, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.15319, "loss": 0.15319, "time": 0.78593} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0047, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.10953, "loss": 0.10953, "time": 0.4916} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.00469, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98312, "top5_acc": 0.99938, "loss_cls": 0.11702, "loss": 0.11702, "time": 0.48706} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.00467, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.1519, "loss": 0.1519, "time": 0.30002} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.00466, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.1575, "loss": 0.1575, "time": 0.50875} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.00464, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.13734, "loss": 0.13734, "time": 0.27707} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.00462, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.16682, "loss": 0.16682, "time": 0.48906} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.00461, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.14565, "loss": 0.14565, "time": 0.48672} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.00459, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.11506, "loss": 0.11506, "time": 0.49081} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.00458, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.19195, "loss": 0.19195, "time": 0.49062} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.00456, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.16313, "loss": 0.16313, "time": 0.48993} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.00455, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.13403, "loss": 0.13403, "time": 0.4898} +{"mode": "val", "epoch": 108, "iter": 533, "lr": 0.00453, "top1_acc": 0.90787, "top5_acc": 0.99554, "mean_class_accuracy": 0.8656} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.00452, "memory": 4083, "data_time": 0.18919, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.13409, "loss": 0.13409, "time": 0.79446} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.0045, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.11332, "loss": 0.11332, "time": 0.48672} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.00449, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.10872, "loss": 0.10872, "time": 0.48831} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.00447, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.14786, "loss": 0.14786, "time": 0.29695} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.00445, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.1346, "loss": 0.1346, "time": 0.50886} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.00444, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.13247, "loss": 0.13247, "time": 0.28298} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.00442, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.11854, "loss": 0.11854, "time": 0.48917} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.00441, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12003, "loss": 0.12003, "time": 0.48845} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.00439, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.97875, "top5_acc": 0.99938, "loss_cls": 0.14916, "loss": 0.14916, "time": 0.48595} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.00438, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.14931, "loss": 0.14931, "time": 0.4891} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.00436, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.10607, "loss": 0.10607, "time": 0.49116} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.00434, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11997, "loss": 0.11997, "time": 0.49327} +{"mode": "val", "epoch": 109, "iter": 533, "lr": 0.00433, "top1_acc": 0.92125, "top5_acc": 0.99425, "mean_class_accuracy": 0.89537} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.00432, "memory": 4083, "data_time": 0.18928, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12902, "loss": 0.12902, "time": 0.79323} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.0043, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11679, "loss": 0.11679, "time": 0.48836} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.00429, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.11572, "loss": 0.11572, "time": 0.48551} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.00427, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.09983, "loss": 0.09983, "time": 0.29587} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.00426, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.09083, "loss": 0.09083, "time": 0.50598} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.00424, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.13429, "loss": 0.13429, "time": 0.28177} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.00422, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.10608, "loss": 0.10608, "time": 0.48967} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.00421, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.13048, "loss": 0.13048, "time": 0.4894} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.00419, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.12469, "loss": 0.12469, "time": 0.48869} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.00418, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11511, "loss": 0.11511, "time": 0.4904} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.00416, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.1085, "loss": 0.1085, "time": 0.49377} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.00415, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.15845, "loss": 0.15845, "time": 0.49152} +{"mode": "val", "epoch": 110, "iter": 533, "lr": 0.00414, "top1_acc": 0.92125, "top5_acc": 0.99507, "mean_class_accuracy": 0.88825} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.00412, "memory": 4083, "data_time": 0.18741, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.09302, "loss": 0.09302, "time": 0.78807} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.00411, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.1044, "loss": 0.1044, "time": 0.49205} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.00409, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.10282, "loss": 0.10282, "time": 0.48995} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.00408, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11919, "loss": 0.11919, "time": 0.29822} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.00406, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11866, "loss": 0.11866, "time": 0.50801} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.00405, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.0947, "loss": 0.0947, "time": 0.27024} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.00403, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.12983, "loss": 0.12983, "time": 0.48468} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.00402, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97562, "top5_acc": 0.99938, "loss_cls": 0.15627, "loss": 0.15627, "time": 0.4864} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.004, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.1403, "loss": 0.1403, "time": 0.48706} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.00399, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.1498, "loss": 0.1498, "time": 0.49173} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.00397, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.12444, "loss": 0.12444, "time": 0.49489} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.00396, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.13858, "loss": 0.13858, "time": 0.48771} +{"mode": "val", "epoch": 111, "iter": 533, "lr": 0.00394, "top1_acc": 0.91386, "top5_acc": 0.99249, "mean_class_accuracy": 0.88781} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.00393, "memory": 4083, "data_time": 0.19156, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14086, "loss": 0.14086, "time": 0.81083} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.00391, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.11068, "loss": 0.11068, "time": 0.48953} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.0039, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.10938, "loss": 0.10938, "time": 0.48994} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.00388, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11427, "loss": 0.11427, "time": 0.29041} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.00387, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.15504, "loss": 0.15504, "time": 0.50796} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.00385, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11973, "loss": 0.11973, "time": 0.29224} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.00384, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.10552, "loss": 0.10552, "time": 0.48866} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.00382, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.11393, "loss": 0.11393, "time": 0.48738} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.00381, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.08635, "loss": 0.08635, "time": 0.48834} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.0038, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.10727, "loss": 0.10727, "time": 0.48954} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.00378, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08802, "loss": 0.08802, "time": 0.49056} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.00377, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.14305, "loss": 0.14305, "time": 0.49349} +{"mode": "val", "epoch": 112, "iter": 533, "lr": 0.00375, "top1_acc": 0.91844, "top5_acc": 0.99448, "mean_class_accuracy": 0.90122} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.00374, "memory": 4083, "data_time": 0.18485, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.12882, "loss": 0.12882, "time": 0.78439} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.00373, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98062, "top5_acc": 0.99938, "loss_cls": 0.14403, "loss": 0.14403, "time": 0.48768} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.00371, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11568, "loss": 0.11568, "time": 0.48924} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.0037, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.11233, "loss": 0.11233, "time": 0.29247} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.00368, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.10675, "loss": 0.10675, "time": 0.5078} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.00367, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.0837, "loss": 0.0837, "time": 0.30174} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.00365, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.09402, "loss": 0.09402, "time": 0.48656} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.00364, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10595, "loss": 0.10595, "time": 0.48842} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.00362, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08988, "loss": 0.08988, "time": 0.49167} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.00361, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07734, "loss": 0.07734, "time": 0.49085} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0036, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.12482, "loss": 0.12482, "time": 0.48852} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.00358, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.11406, "loss": 0.11406, "time": 0.48995} +{"mode": "val", "epoch": 113, "iter": 533, "lr": 0.00357, "top1_acc": 0.90881, "top5_acc": 0.99284, "mean_class_accuracy": 0.883} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.00355, "memory": 4083, "data_time": 0.18671, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.12648, "loss": 0.12648, "time": 0.78455} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.00354, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.08907, "loss": 0.08907, "time": 0.48797} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.00353, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.08969, "loss": 0.08969, "time": 0.49132} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.00351, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.09958, "loss": 0.09958, "time": 0.28762} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.0035, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.09492, "loss": 0.09492, "time": 0.50775} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.00348, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.09466, "loss": 0.09466, "time": 0.29581} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.00347, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08515, "loss": 0.08515, "time": 0.48909} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.00346, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.10699, "loss": 0.10699, "time": 0.48806} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.00344, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.10351, "loss": 0.10351, "time": 0.48945} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.00343, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07404, "loss": 0.07404, "time": 0.48739} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.00341, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.09964, "loss": 0.09964, "time": 0.48835} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.0034, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10277, "loss": 0.10277, "time": 0.49088} +{"mode": "val", "epoch": 114, "iter": 533, "lr": 0.00339, "top1_acc": 0.92395, "top5_acc": 0.99636, "mean_class_accuracy": 0.89601} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.00337, "memory": 4083, "data_time": 0.18615, "top1_acc": 0.985, "top5_acc": 0.99938, "loss_cls": 0.10533, "loss": 0.10533, "time": 0.78162} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.00336, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.07198, "loss": 0.07198, "time": 0.48962} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.00335, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06719, "loss": 0.06719, "time": 0.49458} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.00333, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.07569, "loss": 0.07569, "time": 0.28643} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.00332, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06346, "loss": 0.06346, "time": 0.50736} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.0033, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08243, "loss": 0.08243, "time": 0.28785} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.00329, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08729, "loss": 0.08729, "time": 0.48847} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.00328, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.08072, "loss": 0.08072, "time": 0.48919} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.00326, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.09424, "loss": 0.09424, "time": 0.48801} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.00325, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.09504, "loss": 0.09504, "time": 0.49133} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.00324, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08865, "loss": 0.08865, "time": 0.48857} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.00322, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08882, "loss": 0.08882, "time": 0.49434} +{"mode": "val", "epoch": 115, "iter": 533, "lr": 0.00321, "top1_acc": 0.92419, "top5_acc": 0.99507, "mean_class_accuracy": 0.89202} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.0032, "memory": 4083, "data_time": 0.18303, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11347, "loss": 0.11347, "time": 0.78228} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.00318, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.08917, "loss": 0.08917, "time": 0.48994} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.00317, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98812, "top5_acc": 0.99938, "loss_cls": 0.08904, "loss": 0.08904, "time": 0.48951} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.00316, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.08544, "loss": 0.08544, "time": 0.29233} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.00314, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06765, "loss": 0.06765, "time": 0.50758} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.00313, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07686, "loss": 0.07686, "time": 0.29116} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.00312, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99062, "top5_acc": 0.99938, "loss_cls": 0.08943, "loss": 0.08943, "time": 0.48785} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.0031, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.0881, "loss": 0.0881, "time": 0.48994} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.00309, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.0836, "loss": 0.0836, "time": 0.48568} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.00308, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.06648, "loss": 0.06648, "time": 0.48894} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.00306, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07819, "loss": 0.07819, "time": 0.48913} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.00305, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.07498, "loss": 0.07498, "time": 0.49496} +{"mode": "val", "epoch": 116, "iter": 533, "lr": 0.00304, "top1_acc": 0.93275, "top5_acc": 0.99648, "mean_class_accuracy": 0.91142} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.00302, "memory": 4083, "data_time": 0.19052, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.07797, "loss": 0.07797, "time": 0.79173} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.00301, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.0924, "loss": 0.0924, "time": 0.48539} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.003, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.11687, "loss": 0.11687, "time": 0.48893} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.00298, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.09417, "loss": 0.09417, "time": 0.28489} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.00297, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.08255, "loss": 0.08255, "time": 0.50856} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.00296, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07113, "loss": 0.07113, "time": 0.28269} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.00294, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.07167, "loss": 0.07167, "time": 0.48833} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.00293, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07562, "loss": 0.07562, "time": 0.48921} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.00292, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98375, "top5_acc": 0.99938, "loss_cls": 0.10226, "loss": 0.10226, "time": 0.49102} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.00291, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98688, "top5_acc": 0.99938, "loss_cls": 0.10518, "loss": 0.10518, "time": 0.48824} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.00289, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.09853, "loss": 0.09853, "time": 0.49217} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.00288, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08615, "loss": 0.08615, "time": 0.48662} +{"mode": "val", "epoch": 117, "iter": 533, "lr": 0.00287, "top1_acc": 0.92829, "top5_acc": 0.99472, "mean_class_accuracy": 0.90222} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.00286, "memory": 4083, "data_time": 0.18511, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08839, "loss": 0.08839, "time": 0.79456} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.00284, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06994, "loss": 0.06994, "time": 0.48977} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.00283, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.07756, "loss": 0.07756, "time": 0.49045} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.00282, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08662, "loss": 0.08662, "time": 0.29468} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.0028, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.06375, "loss": 0.06375, "time": 0.50722} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.00279, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07568, "loss": 0.07568, "time": 0.26593} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.00278, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07599, "loss": 0.07599, "time": 0.48738} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.00277, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07182, "loss": 0.07182, "time": 0.4872} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.00275, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08589, "loss": 0.08589, "time": 0.48885} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.00274, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.05661, "loss": 0.05661, "time": 0.49174} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.00273, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.05257, "loss": 0.05257, "time": 0.49021} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.00271, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.07633, "loss": 0.07633, "time": 0.48856} +{"mode": "val", "epoch": 118, "iter": 533, "lr": 0.0027, "top1_acc": 0.92759, "top5_acc": 0.99554, "mean_class_accuracy": 0.9008} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.00269, "memory": 4083, "data_time": 0.18602, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07589, "loss": 0.07589, "time": 0.80074} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.00268, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.05127, "loss": 0.05127, "time": 0.48604} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.00267, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05972, "loss": 0.05972, "time": 0.48875} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.00265, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06567, "loss": 0.06567, "time": 0.31408} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.00264, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05785, "loss": 0.05785, "time": 0.50713} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.00263, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08182, "loss": 0.08182, "time": 0.26837} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.00262, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.06092, "loss": 0.06092, "time": 0.49217} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.0026, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.05111, "loss": 0.05111, "time": 0.48344} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.00259, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06035, "loss": 0.06035, "time": 0.48883} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.00258, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.06782, "loss": 0.06782, "time": 0.48788} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.00257, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 0.99938, "loss_cls": 0.068, "loss": 0.068, "time": 0.489} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.00255, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99375, "top5_acc": 0.99938, "loss_cls": 0.07191, "loss": 0.07191, "time": 0.48886} +{"mode": "val", "epoch": 119, "iter": 533, "lr": 0.00254, "top1_acc": 0.92912, "top5_acc": 0.99542, "mean_class_accuracy": 0.90703} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.00253, "memory": 4083, "data_time": 0.18607, "top1_acc": 0.98625, "top5_acc": 0.99938, "loss_cls": 0.10839, "loss": 0.10839, "time": 0.79716} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.00252, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05982, "loss": 0.05982, "time": 0.48783} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.00251, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.0664, "loss": 0.0664, "time": 0.48785} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.00249, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.0535, "loss": 0.0535, "time": 0.31227} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.00248, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05805, "loss": 0.05805, "time": 0.50875} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.00247, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06009, "loss": 0.06009, "time": 0.26862} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.00246, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05566, "loss": 0.05566, "time": 0.48862} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.00245, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.06138, "loss": 0.06138, "time": 0.48471} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.00243, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07365, "loss": 0.07365, "time": 0.4929} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.00242, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05441, "loss": 0.05441, "time": 0.48878} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00241, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.07516, "loss": 0.07516, "time": 0.49019} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.0024, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.05424, "loss": 0.05424, "time": 0.48468} +{"mode": "val", "epoch": 120, "iter": 533, "lr": 0.00239, "top1_acc": 0.93193, "top5_acc": 0.99566, "mean_class_accuracy": 0.9072} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00238, "memory": 4083, "data_time": 0.18601, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04553, "loss": 0.04553, "time": 0.8123} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00236, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.04629, "loss": 0.04629, "time": 0.48877} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.00235, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04596, "loss": 0.04596, "time": 0.487} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00234, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04727, "loss": 0.04727, "time": 0.30557} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00233, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04013, "loss": 0.04013, "time": 0.50639} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00232, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03928, "loss": 0.03928, "time": 0.27165} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.0023, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05691, "loss": 0.05691, "time": 0.49035} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00229, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06292, "loss": 0.06292, "time": 0.48787} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.00228, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.04246, "loss": 0.04246, "time": 0.48467} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00227, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04761, "loss": 0.04761, "time": 0.4886} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00226, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.05328, "loss": 0.05328, "time": 0.49045} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00225, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03571, "loss": 0.03571, "time": 0.48937} +{"mode": "val", "epoch": 121, "iter": 533, "lr": 0.00224, "top1_acc": 0.93428, "top5_acc": 0.99554, "mean_class_accuracy": 0.90758} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00222, "memory": 4083, "data_time": 0.18562, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04008, "loss": 0.04008, "time": 0.79425} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00221, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04452, "loss": 0.04452, "time": 0.48539} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.0022, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04609, "loss": 0.04609, "time": 0.48713} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00219, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.04953, "loss": 0.04953, "time": 0.30764} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00218, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07104, "loss": 0.07104, "time": 0.50925} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00217, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04734, "loss": 0.04734, "time": 0.27332} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00215, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.0586, "loss": 0.0586, "time": 0.48746} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00214, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06423, "loss": 0.06423, "time": 0.49312} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.00213, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99062, "top5_acc": 0.99938, "loss_cls": 0.08289, "loss": 0.08289, "time": 0.48716} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00212, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05344, "loss": 0.05344, "time": 0.49125} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00211, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06223, "loss": 0.06223, "time": 0.49143} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.0021, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.0545, "loss": 0.0545, "time": 0.48949} +{"mode": "val", "epoch": 122, "iter": 533, "lr": 0.00209, "top1_acc": 0.93428, "top5_acc": 0.99648, "mean_class_accuracy": 0.91026} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00208, "memory": 4083, "data_time": 0.18255, "top1_acc": 0.99188, "top5_acc": 0.99938, "loss_cls": 0.06041, "loss": 0.06041, "time": 0.7912} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00207, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.04121, "loss": 0.04121, "time": 0.48832} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00205, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04449, "loss": 0.04449, "time": 0.49107} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00204, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.058, "loss": 0.058, "time": 0.31571} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00203, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04275, "loss": 0.04275, "time": 0.50796} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00202, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.05764, "loss": 0.05764, "time": 0.25091} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00201, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05458, "loss": 0.05458, "time": 0.48765} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.002, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05094, "loss": 0.05094, "time": 0.48803} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00199, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04966, "loss": 0.04966, "time": 0.48985} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.00198, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04623, "loss": 0.04623, "time": 0.48856} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00197, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04631, "loss": 0.04631, "time": 0.48966} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00195, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.05587, "loss": 0.05587, "time": 0.48883} +{"mode": "val", "epoch": 123, "iter": 533, "lr": 0.00195, "top1_acc": 0.93369, "top5_acc": 0.99519, "mean_class_accuracy": 0.90652} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00194, "memory": 4083, "data_time": 0.18379, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04731, "loss": 0.04731, "time": 0.79387} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00192, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05184, "loss": 0.05184, "time": 0.48916} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00191, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04823, "loss": 0.04823, "time": 0.48721} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.0019, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03551, "loss": 0.03551, "time": 0.31793} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00189, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03938, "loss": 0.03938, "time": 0.50813} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00188, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04409, "loss": 0.04409, "time": 0.25396} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00187, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03822, "loss": 0.03822, "time": 0.48725} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00186, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.0532, "loss": 0.0532, "time": 0.4892} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00185, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06341, "loss": 0.06341, "time": 0.48974} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00184, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08189, "loss": 0.08189, "time": 0.48769} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00183, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07543, "loss": 0.07543, "time": 0.49309} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.00182, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04405, "loss": 0.04405, "time": 0.49154} +{"mode": "val", "epoch": 124, "iter": 533, "lr": 0.00181, "top1_acc": 0.93416, "top5_acc": 0.9966, "mean_class_accuracy": 0.90623} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.0018, "memory": 4083, "data_time": 0.18845, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03969, "loss": 0.03969, "time": 0.80322} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.00179, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03471, "loss": 0.03471, "time": 0.48713} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00178, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03494, "loss": 0.03494, "time": 0.4882} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00177, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03699, "loss": 0.03699, "time": 0.32677} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00176, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03288, "loss": 0.03288, "time": 0.50838} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00175, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02927, "loss": 0.02927, "time": 0.25351} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00173, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03896, "loss": 0.03896, "time": 0.4867} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00172, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04744, "loss": 0.04744, "time": 0.48917} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.00171, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04312, "loss": 0.04312, "time": 0.48893} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.0017, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03298, "loss": 0.03298, "time": 0.49004} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00169, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04152, "loss": 0.04152, "time": 0.49304} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00168, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02915, "loss": 0.02915, "time": 0.49175} +{"mode": "val", "epoch": 125, "iter": 533, "lr": 0.00167, "top1_acc": 0.92818, "top5_acc": 0.99624, "mean_class_accuracy": 0.90544} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00166, "memory": 4083, "data_time": 0.19194, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03915, "loss": 0.03915, "time": 0.81079} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00165, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0386, "loss": 0.0386, "time": 0.48631} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00164, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03431, "loss": 0.03431, "time": 0.48953} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00163, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04306, "loss": 0.04306, "time": 0.30779} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00162, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04084, "loss": 0.04084, "time": 0.50859} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00161, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03149, "loss": 0.03149, "time": 0.27251} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0016, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04605, "loss": 0.04605, "time": 0.48982} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00159, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03389, "loss": 0.03389, "time": 0.48758} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00158, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03753, "loss": 0.03753, "time": 0.48558} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00157, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.03522, "loss": 0.03522, "time": 0.48922} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00156, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03333, "loss": 0.03333, "time": 0.49064} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00155, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03062, "loss": 0.03062, "time": 0.48743} +{"mode": "val", "epoch": 126, "iter": 533, "lr": 0.00155, "top1_acc": 0.9371, "top5_acc": 0.99613, "mean_class_accuracy": 0.91285} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00154, "memory": 4083, "data_time": 0.19251, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02954, "loss": 0.02954, "time": 0.80187} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00153, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04406, "loss": 0.04406, "time": 0.48583} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00152, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0318, "loss": 0.0318, "time": 0.49185} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00151, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03467, "loss": 0.03467, "time": 0.29447} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.0015, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03558, "loss": 0.03558, "time": 0.50905} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.00149, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03293, "loss": 0.03293, "time": 0.29452} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00148, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02976, "loss": 0.02976, "time": 0.48724} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00147, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02561, "loss": 0.02561, "time": 0.48692} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00146, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03661, "loss": 0.03661, "time": 0.49048} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00145, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03816, "loss": 0.03816, "time": 0.48973} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00144, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.0421, "loss": 0.0421, "time": 0.48672} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00143, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04062, "loss": 0.04062, "time": 0.49174} +{"mode": "val", "epoch": 127, "iter": 533, "lr": 0.00142, "top1_acc": 0.93757, "top5_acc": 0.99636, "mean_class_accuracy": 0.91351} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00141, "memory": 4083, "data_time": 0.18644, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02981, "loss": 0.02981, "time": 0.79156} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.0014, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02697, "loss": 0.02697, "time": 0.48527} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00139, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02694, "loss": 0.02694, "time": 0.4862} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00138, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02851, "loss": 0.02851, "time": 0.2842} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00138, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02818, "loss": 0.02818, "time": 0.50704} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00137, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02709, "loss": 0.02709, "time": 0.29353} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.00136, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03196, "loss": 0.03196, "time": 0.4872} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00135, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02474, "loss": 0.02474, "time": 0.49001} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00134, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0252, "loss": 0.0252, "time": 0.49019} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00133, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02276, "loss": 0.02276, "time": 0.49196} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00132, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02403, "loss": 0.02403, "time": 0.48994} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00131, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02463, "loss": 0.02463, "time": 0.4887} +{"mode": "val", "epoch": 128, "iter": 533, "lr": 0.0013, "top1_acc": 0.94085, "top5_acc": 0.9973, "mean_class_accuracy": 0.91715} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.00129, "memory": 4083, "data_time": 0.19164, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02159, "loss": 0.02159, "time": 0.78374} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00129, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02, "loss": 0.02, "time": 0.48561} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00128, "memory": 4083, "data_time": 0.00041, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02315, "loss": 0.02315, "time": 0.48751} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00127, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03061, "loss": 0.03061, "time": 0.29526} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00126, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0265, "loss": 0.0265, "time": 0.50668} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00125, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02511, "loss": 0.02511, "time": 0.271} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00124, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0238, "loss": 0.0238, "time": 0.49097} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00123, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99688, "top5_acc": 0.99938, "loss_cls": 0.03109, "loss": 0.03109, "time": 0.48866} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.00122, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02756, "loss": 0.02756, "time": 0.49051} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00121, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0242, "loss": 0.0242, "time": 0.48976} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00121, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02128, "loss": 0.02128, "time": 0.48938} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.0012, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0226, "loss": 0.0226, "time": 0.48651} +{"mode": "val", "epoch": 129, "iter": 533, "lr": 0.00119, "top1_acc": 0.93956, "top5_acc": 0.99624, "mean_class_accuracy": 0.91593} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00118, "memory": 4083, "data_time": 0.19191, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0239, "loss": 0.0239, "time": 0.78635} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00117, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02252, "loss": 0.02252, "time": 0.48727} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00116, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02575, "loss": 0.02575, "time": 0.48868} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00116, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02347, "loss": 0.02347, "time": 0.32337} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.00115, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02364, "loss": 0.02364, "time": 0.50795} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00114, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.0303, "loss": 0.0303, "time": 0.26209} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00113, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03093, "loss": 0.03093, "time": 0.48908} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00112, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02797, "loss": 0.02797, "time": 0.48729} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00111, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02707, "loss": 0.02707, "time": 0.49108} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.0011, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02424, "loss": 0.02424, "time": 0.4901} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.0011, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03677, "loss": 0.03677, "time": 0.48819} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00109, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0217, "loss": 0.0217, "time": 0.48646} +{"mode": "val", "epoch": 130, "iter": 533, "lr": 0.00108, "top1_acc": 0.9405, "top5_acc": 0.9966, "mean_class_accuracy": 0.91635} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00107, "memory": 4083, "data_time": 0.19232, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02322, "loss": 0.02322, "time": 0.79397} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.00106, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02593, "loss": 0.02593, "time": 0.48888} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00106, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0225, "loss": 0.0225, "time": 0.48672} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00105, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02103, "loss": 0.02103, "time": 0.32392} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00104, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02281, "loss": 0.02281, "time": 0.50668} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00103, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02379, "loss": 0.02379, "time": 0.25272} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00102, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02064, "loss": 0.02064, "time": 0.48486} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00102, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02933, "loss": 0.02933, "time": 0.48829} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00101, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02213, "loss": 0.02213, "time": 0.49219} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.001, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02402, "loss": 0.02402, "time": 0.4866} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.00099, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02352, "loss": 0.02352, "time": 0.48618} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00098, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02303, "loss": 0.02303, "time": 0.48905} +{"mode": "val", "epoch": 131, "iter": 533, "lr": 0.00098, "top1_acc": 0.94191, "top5_acc": 0.99624, "mean_class_accuracy": 0.92101} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.00097, "memory": 4083, "data_time": 0.18888, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02109, "loss": 0.02109, "time": 0.77982} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00096, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02055, "loss": 0.02055, "time": 0.48688} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00095, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02243, "loss": 0.02243, "time": 0.48917} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00095, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02124, "loss": 0.02124, "time": 0.34642} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00094, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0208, "loss": 0.0208, "time": 0.50615} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00093, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02179, "loss": 0.02179, "time": 0.24667} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00092, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02808, "loss": 0.02808, "time": 0.47552} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00091, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02259, "loss": 0.02259, "time": 0.49296} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00091, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02027, "loss": 0.02027, "time": 0.47883} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0009, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.02895, "loss": 0.02895, "time": 0.49195} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00089, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0291, "loss": 0.0291, "time": 0.49044} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00088, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03026, "loss": 0.03026, "time": 0.48866} +{"mode": "val", "epoch": 132, "iter": 533, "lr": 0.00088, "top1_acc": 0.93827, "top5_acc": 0.99624, "mean_class_accuracy": 0.91636} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.00087, "memory": 4083, "data_time": 0.18725, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02398, "loss": 0.02398, "time": 0.79117} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00086, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02101, "loss": 0.02101, "time": 0.48535} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00086, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02609, "loss": 0.02609, "time": 0.49135} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00085, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02287, "loss": 0.02287, "time": 0.36179} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00084, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02166, "loss": 0.02166, "time": 0.50682} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00083, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02563, "loss": 0.02563, "time": 0.2375} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00083, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02345, "loss": 0.02345, "time": 0.44805} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00082, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02769, "loss": 0.02769, "time": 0.48724} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00081, "memory": 4083, "data_time": 0.00048, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02221, "loss": 0.02221, "time": 0.49088} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.0008, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02464, "loss": 0.02464, "time": 0.48826} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0008, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02923, "loss": 0.02923, "time": 0.48775} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00079, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02778, "loss": 0.02778, "time": 0.48847} +{"mode": "val", "epoch": 133, "iter": 533, "lr": 0.00078, "top1_acc": 0.94097, "top5_acc": 0.99636, "mean_class_accuracy": 0.91717} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00078, "memory": 4083, "data_time": 0.18892, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02764, "loss": 0.02764, "time": 0.78177} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00077, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02254, "loss": 0.02254, "time": 0.48663} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00076, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02682, "loss": 0.02682, "time": 0.48961} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.00076, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02162, "loss": 0.02162, "time": 0.39139} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00075, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02204, "loss": 0.02204, "time": 0.50294} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00074, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02742, "loss": 0.02742, "time": 0.23037} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00073, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02542, "loss": 0.02542, "time": 0.43095} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00073, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02385, "loss": 0.02385, "time": 0.48764} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00072, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02225, "loss": 0.02225, "time": 0.4882} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00071, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0237, "loss": 0.0237, "time": 0.48747} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00071, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02206, "loss": 0.02206, "time": 0.48806} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.0007, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02176, "loss": 0.02176, "time": 0.48934} +{"mode": "val", "epoch": 134, "iter": 533, "lr": 0.0007, "top1_acc": 0.94085, "top5_acc": 0.99648, "mean_class_accuracy": 0.91758} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00069, "memory": 4083, "data_time": 0.18669, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02157, "loss": 0.02157, "time": 0.8017} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00068, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02091, "loss": 0.02091, "time": 0.48967} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00068, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02172, "loss": 0.02172, "time": 0.48778} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00067, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02174, "loss": 0.02174, "time": 0.40529} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00066, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02108, "loss": 0.02108, "time": 0.50024} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00066, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02682, "loss": 0.02682, "time": 0.23539} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00065, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02402, "loss": 0.02402, "time": 0.4317} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00064, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02254, "loss": 0.02254, "time": 0.48929} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.00064, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02144, "loss": 0.02144, "time": 0.49085} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00063, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02331, "loss": 0.02331, "time": 0.48665} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00062, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02361, "loss": 0.02361, "time": 0.48942} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00062, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02286, "loss": 0.02286, "time": 0.49143} +{"mode": "val", "epoch": 135, "iter": 533, "lr": 0.00061, "top1_acc": 0.9425, "top5_acc": 0.9966, "mean_class_accuracy": 0.91838} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00061, "memory": 4083, "data_time": 0.18562, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0244, "loss": 0.0244, "time": 0.79508} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.0006, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02095, "loss": 0.02095, "time": 0.48533} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00059, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0202, "loss": 0.0202, "time": 0.48466} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00059, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02321, "loss": 0.02321, "time": 0.4045} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.00058, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02347, "loss": 0.02347, "time": 0.48777} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.00057, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03105, "loss": 0.03105, "time": 0.24394} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00057, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02342, "loss": 0.02342, "time": 0.4263} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00056, "memory": 4083, "data_time": 0.00041, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02179, "loss": 0.02179, "time": 0.49191} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00056, "memory": 4083, "data_time": 0.00043, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01923, "loss": 0.01923, "time": 0.48508} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00055, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02048, "loss": 0.02048, "time": 0.48892} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00054, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02143, "loss": 0.02143, "time": 0.48901} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00054, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02063, "loss": 0.02063, "time": 0.4868} +{"mode": "val", "epoch": 136, "iter": 533, "lr": 0.00053, "top1_acc": 0.94285, "top5_acc": 0.9966, "mean_class_accuracy": 0.91969} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00053, "memory": 4083, "data_time": 0.18499, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01982, "loss": 0.01982, "time": 0.78355} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00052, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02061, "loss": 0.02061, "time": 0.49005} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00052, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01867, "loss": 0.01867, "time": 0.48886} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.00051, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01977, "loss": 0.01977, "time": 0.43031} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.0005, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02243, "loss": 0.02243, "time": 0.46772} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.0005, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02567, "loss": 0.02567, "time": 0.2611} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00049, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01933, "loss": 0.01933, "time": 0.41391} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00049, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01945, "loss": 0.01945, "time": 0.49159} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00048, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02025, "loss": 0.02025, "time": 0.49035} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00048, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02253, "loss": 0.02253, "time": 0.48991} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00047, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02082, "loss": 0.02082, "time": 0.48842} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00046, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02293, "loss": 0.02293, "time": 0.48818} +{"mode": "val", "epoch": 137, "iter": 533, "lr": 0.00046, "top1_acc": 0.94203, "top5_acc": 0.99707, "mean_class_accuracy": 0.91968} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00046, "memory": 4083, "data_time": 0.18394, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02007, "loss": 0.02007, "time": 0.77699} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00045, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01982, "loss": 0.01982, "time": 0.48774} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00044, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01994, "loss": 0.01994, "time": 0.48875} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00044, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02127, "loss": 0.02127, "time": 0.45242} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.00043, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02649, "loss": 0.02649, "time": 0.40665} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.00043, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01955, "loss": 0.01955, "time": 0.32303} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00042, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02156, "loss": 0.02156, "time": 0.37851} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00042, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02221, "loss": 0.02221, "time": 0.48763} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00041, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01981, "loss": 0.01981, "time": 0.488} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00041, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02371, "loss": 0.02371, "time": 0.48811} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.0004, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02345, "loss": 0.02345, "time": 0.48728} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.0004, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0216, "loss": 0.0216, "time": 0.48822} +{"mode": "val", "epoch": 138, "iter": 533, "lr": 0.00039, "top1_acc": 0.94062, "top5_acc": 0.9973, "mean_class_accuracy": 0.91777} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00039, "memory": 4083, "data_time": 0.18822, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02339, "loss": 0.02339, "time": 0.79328} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00038, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02551, "loss": 0.02551, "time": 0.49088} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00038, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02362, "loss": 0.02362, "time": 0.48791} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00037, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02141, "loss": 0.02141, "time": 0.47725} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00037, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01821, "loss": 0.01821, "time": 0.35995} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00036, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01989, "loss": 0.01989, "time": 0.36922} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00036, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01979, "loss": 0.01979, "time": 0.37565} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00035, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02064, "loss": 0.02064, "time": 0.48828} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00035, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02024, "loss": 0.02024, "time": 0.48874} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.00034, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01912, "loss": 0.01912, "time": 0.48983} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.00034, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02089, "loss": 0.02089, "time": 0.48803} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00033, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01877, "loss": 0.01877, "time": 0.49164} +{"mode": "val", "epoch": 139, "iter": 533, "lr": 0.00033, "top1_acc": 0.94402, "top5_acc": 0.99707, "mean_class_accuracy": 0.92273} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00033, "memory": 4083, "data_time": 0.19168, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02059, "loss": 0.02059, "time": 0.80328} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00032, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02337, "loss": 0.02337, "time": 0.49195} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.00032, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02026, "loss": 0.02026, "time": 0.48725} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.00031, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02063, "loss": 0.02063, "time": 0.47001} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00031, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01966, "loss": 0.01966, "time": 0.37127} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.0003, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0207, "loss": 0.0207, "time": 0.35729} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.0003, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01939, "loss": 0.01939, "time": 0.38057} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00029, "memory": 4083, "data_time": 0.00054, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02105, "loss": 0.02105, "time": 0.49234} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00029, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02015, "loss": 0.02015, "time": 0.48421} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00029, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02058, "loss": 0.02058, "time": 0.48682} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00028, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01974, "loss": 0.01974, "time": 0.4884} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00028, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02149, "loss": 0.02149, "time": 0.49114} +{"mode": "val", "epoch": 140, "iter": 533, "lr": 0.00027, "top1_acc": 0.94261, "top5_acc": 0.99683, "mean_class_accuracy": 0.92152} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00027, "memory": 4083, "data_time": 0.18909, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0187, "loss": 0.0187, "time": 0.78203} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00026, "memory": 4083, "data_time": 0.0004, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02038, "loss": 0.02038, "time": 0.48947} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00026, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02111, "loss": 0.02111, "time": 0.48784} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00026, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02859, "loss": 0.02859, "time": 0.48168} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00025, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01937, "loss": 0.01937, "time": 0.34049} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00025, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02049, "loss": 0.02049, "time": 0.38914} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00024, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0191, "loss": 0.0191, "time": 0.36881} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00024, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02089, "loss": 0.02089, "time": 0.4872} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00024, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0214, "loss": 0.0214, "time": 0.4885} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00023, "memory": 4083, "data_time": 0.00055, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01854, "loss": 0.01854, "time": 0.48582} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00023, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0189, "loss": 0.0189, "time": 0.48577} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00022, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02057, "loss": 0.02057, "time": 0.49046} +{"mode": "val", "epoch": 141, "iter": 533, "lr": 0.00022, "top1_acc": 0.94308, "top5_acc": 0.99707, "mean_class_accuracy": 0.92279} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00022, "memory": 4083, "data_time": 0.1911, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02181, "loss": 0.02181, "time": 0.78614} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00021, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02006, "loss": 0.02006, "time": 0.48543} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00021, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01935, "loss": 0.01935, "time": 0.49077} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00021, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0182, "loss": 0.0182, "time": 0.48587} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.0002, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02091, "loss": 0.02091, "time": 0.31643} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.0002, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0211, "loss": 0.0211, "time": 0.40973} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.0002, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01815, "loss": 0.01815, "time": 0.35409} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00019, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02152, "loss": 0.02152, "time": 0.48518} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00019, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02103, "loss": 0.02103, "time": 0.48693} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00018, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02038, "loss": 0.02038, "time": 0.48966} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00018, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01974, "loss": 0.01974, "time": 0.48863} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00018, "memory": 4083, "data_time": 0.00046, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01925, "loss": 0.01925, "time": 0.48952} +{"mode": "val", "epoch": 142, "iter": 533, "lr": 0.00018, "top1_acc": 0.94073, "top5_acc": 0.99707, "mean_class_accuracy": 0.91902} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.00017, "memory": 4083, "data_time": 0.18338, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01992, "loss": 0.01992, "time": 0.77539} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00017, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01915, "loss": 0.01915, "time": 0.48919} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00017, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01902, "loss": 0.01902, "time": 0.49243} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00016, "memory": 4083, "data_time": 0.0004, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02025, "loss": 0.02025, "time": 0.48887} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00016, "memory": 4083, "data_time": 0.00045, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02203, "loss": 0.02203, "time": 0.29484} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00016, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0203, "loss": 0.0203, "time": 0.46906} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00015, "memory": 4083, "data_time": 0.0004, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02121, "loss": 0.02121, "time": 0.32599} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00015, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02069, "loss": 0.02069, "time": 0.49424} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00015, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0209, "loss": 0.0209, "time": 0.48939} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00014, "memory": 4083, "data_time": 0.0004, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01888, "loss": 0.01888, "time": 0.48883} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00014, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01917, "loss": 0.01917, "time": 0.48709} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00014, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02361, "loss": 0.02361, "time": 0.48962} +{"mode": "val", "epoch": 143, "iter": 533, "lr": 0.00013, "top1_acc": 0.94285, "top5_acc": 0.99695, "mean_class_accuracy": 0.92213} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00013, "memory": 4083, "data_time": 0.18686, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02505, "loss": 0.02505, "time": 0.78804} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00013, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01963, "loss": 0.01963, "time": 0.49248} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00013, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01946, "loss": 0.01946, "time": 0.48946} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00012, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01973, "loss": 0.01973, "time": 0.48906} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00012, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02017, "loss": 0.02017, "time": 0.29293} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00012, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01943, "loss": 0.01943, "time": 0.47401} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00011, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01844, "loss": 0.01844, "time": 0.32562} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.00011, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02084, "loss": 0.02084, "time": 0.4896} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.00011, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0189, "loss": 0.0189, "time": 0.4874} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.00011, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02152, "loss": 0.02152, "time": 0.49046} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.0001, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02312, "loss": 0.02312, "time": 0.49213} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.0001, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01763, "loss": 0.01763, "time": 0.49017} +{"mode": "val", "epoch": 144, "iter": 533, "lr": 0.0001, "top1_acc": 0.94214, "top5_acc": 0.99695, "mean_class_accuracy": 0.91986} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.0001, "memory": 4083, "data_time": 0.18545, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01856, "loss": 0.01856, "time": 0.78309} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 9e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01914, "loss": 0.01914, "time": 0.49224} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 9e-05, "memory": 4083, "data_time": 0.00051, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01874, "loss": 0.01874, "time": 0.48659} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 9e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02542, "loss": 0.02542, "time": 0.4886} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 9e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02048, "loss": 0.02048, "time": 0.27157} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 8e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.018, "loss": 0.018, "time": 0.49863} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 8e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02013, "loss": 0.02013, "time": 0.30719} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 8e-05, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01892, "loss": 0.01892, "time": 0.48995} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 8e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02263, "loss": 0.02263, "time": 0.48987} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 7e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01959, "loss": 0.01959, "time": 0.48854} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 7e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02058, "loss": 0.02058, "time": 0.49161} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 7e-05, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02131, "loss": 0.02131, "time": 0.48866} +{"mode": "val", "epoch": 145, "iter": 533, "lr": 7e-05, "top1_acc": 0.94167, "top5_acc": 0.99695, "mean_class_accuracy": 0.91926} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 7e-05, "memory": 4083, "data_time": 0.18819, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02004, "loss": 0.02004, "time": 0.78956} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 6e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02055, "loss": 0.02055, "time": 0.4842} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 6e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02124, "loss": 0.02124, "time": 0.48767} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 6e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01964, "loss": 0.01964, "time": 0.48702} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 6e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02323, "loss": 0.02323, "time": 0.27664} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 6e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01945, "loss": 0.01945, "time": 0.50692} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 5e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02047, "loss": 0.02047, "time": 0.30017} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 5e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01899, "loss": 0.01899, "time": 0.49059} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 5e-05, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01827, "loss": 0.01827, "time": 0.48991} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 5e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01992, "loss": 0.01992, "time": 0.48859} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 5e-05, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02104, "loss": 0.02104, "time": 0.49198} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 5e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01949, "loss": 0.01949, "time": 0.48902} +{"mode": "val", "epoch": 146, "iter": 533, "lr": 4e-05, "top1_acc": 0.94285, "top5_acc": 0.99707, "mean_class_accuracy": 0.92066} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 4e-05, "memory": 4083, "data_time": 0.18563, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01928, "loss": 0.01928, "time": 0.79943} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 4e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01845, "loss": 0.01845, "time": 0.49021} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 4e-05, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0201, "loss": 0.0201, "time": 0.48672} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 4e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02041, "loss": 0.02041, "time": 0.48792} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 4e-05, "memory": 4083, "data_time": 0.00048, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02166, "loss": 0.02166, "time": 0.28016} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 3e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01918, "loss": 0.01918, "time": 0.50813} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 3e-05, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02076, "loss": 0.02076, "time": 0.28313} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 3e-05, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01871, "loss": 0.01871, "time": 0.48912} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 3e-05, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02237, "loss": 0.02237, "time": 0.49168} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 3e-05, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02499, "loss": 0.02499, "time": 0.48815} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 3e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01994, "loss": 0.01994, "time": 0.4869} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 3e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02184, "loss": 0.02184, "time": 0.49412} +{"mode": "val", "epoch": 147, "iter": 533, "lr": 2e-05, "top1_acc": 0.94132, "top5_acc": 0.99671, "mean_class_accuracy": 0.92008} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 2e-05, "memory": 4083, "data_time": 0.18846, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02079, "loss": 0.02079, "time": 0.77757} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 2e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02679, "loss": 0.02679, "time": 0.48742} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 2e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01983, "loss": 0.01983, "time": 0.48752} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 2e-05, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02224, "loss": 0.02224, "time": 0.48876} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 2e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02025, "loss": 0.02025, "time": 0.30419} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 2e-05, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0203, "loss": 0.0203, "time": 0.50607} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 2e-05, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01743, "loss": 0.01743, "time": 0.27904} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 2e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01912, "loss": 0.01912, "time": 0.48983} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 1e-05, "memory": 4083, "data_time": 0.00041, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01867, "loss": 0.01867, "time": 0.486} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 1e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01886, "loss": 0.01886, "time": 0.48645} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 1e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02086, "loss": 0.02086, "time": 0.49008} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 1e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0198, "loss": 0.0198, "time": 0.49015} +{"mode": "val", "epoch": 148, "iter": 533, "lr": 1e-05, "top1_acc": 0.94332, "top5_acc": 0.99671, "mean_class_accuracy": 0.9214} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 1e-05, "memory": 4083, "data_time": 0.18618, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01954, "loss": 0.01954, "time": 0.8003} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 1e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02084, "loss": 0.02084, "time": 0.48849} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0198, "loss": 0.0198, "time": 0.48772} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 1e-05, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01811, "loss": 0.01811, "time": 0.49077} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 1e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01904, "loss": 0.01904, "time": 0.30026} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 1e-05, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02237, "loss": 0.02237, "time": 0.50761} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 1e-05, "memory": 4083, "data_time": 0.00053, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0193, "loss": 0.0193, "time": 0.28423} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01899, "loss": 0.01899, "time": 0.48731} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02155, "loss": 0.02155, "time": 0.48869} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02117, "loss": 0.02117, "time": 0.48894} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01894, "loss": 0.01894, "time": 0.49219} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0217, "loss": 0.0217, "time": 0.49076} +{"mode": "val", "epoch": 149, "iter": 533, "lr": 0.0, "top1_acc": 0.94214, "top5_acc": 0.9966, "mean_class_accuracy": 0.92123} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 0.0, "memory": 4083, "data_time": 0.17992, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02098, "loss": 0.02098, "time": 0.77985} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 0.0, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01756, "loss": 0.01756, "time": 0.49021} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 0.0, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01889, "loss": 0.01889, "time": 0.49211} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 0.0, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01789, "loss": 0.01789, "time": 0.49142} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 0.0, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02456, "loss": 0.02456, "time": 0.31086} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 0.0, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0201, "loss": 0.0201, "time": 0.50598} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01931, "loss": 0.01931, "time": 0.25918} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 0.0, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01913, "loss": 0.01913, "time": 0.49021} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00046, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01952, "loss": 0.01952, "time": 0.49133} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01821, "loss": 0.01821, "time": 0.48866} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.0005, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01878, "loss": 0.01878, "time": 0.48836} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02095, "loss": 0.02095, "time": 0.49135} +{"mode": "val", "epoch": 150, "iter": 533, "lr": 0.0, "top1_acc": 0.94308, "top5_acc": 0.99648, "mean_class_accuracy": 0.92119} diff --git a/finegym/b_2/b_2.py b/finegym/b_2/b_2.py new file mode 100644 index 0000000000000000000000000000000000000000..20f59cd9bf429ebb234a7296d73e4f9a98aa2180 --- /dev/null +++ b/finegym/b_2/b_2.py @@ -0,0 +1,113 @@ +modality = 'b' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/b_2' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/finegym/b_2/best_pred.pkl b/finegym/b_2/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..d278440445e62ee3992e32ba9ed89c79a1b5572e --- /dev/null +++ b/finegym/b_2/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fd4b2d4a6b43b9e9c1cbe5d5db7b44799f971661080320e7c22858002bc99093 +size 5254226 diff --git a/finegym/b_2/best_top1_acc_epoch_139.pth b/finegym/b_2/best_top1_acc_epoch_139.pth new file mode 100644 index 0000000000000000000000000000000000000000..ee0a8818f223fb681403326d276b8a0941c8b4c6 --- /dev/null +++ b/finegym/b_2/best_top1_acc_epoch_139.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0f51a18f1d5646ec55ef9127d25424ab95aa47280c06a7b0323ddd25a679a3ff +size 31999601 diff --git a/finegym/b_3/20250624_084158.log b/finegym/b_3/20250624_084158.log new file mode 100644 index 0000000000000000000000000000000000000000..18d1d2bef3873acf57c37892cee549626c495cb1 --- /dev/null +++ b/finegym/b_3/20250624_084158.log @@ -0,0 +1,3471 @@ +2025-06-24 08:41:58,353 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2025-06-24 08:41:58,557 - pyskl - INFO - Config: modality = 'b' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/b_3' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2025-06-24 08:41:58,557 - pyskl - INFO - Set random seed to 335308573, deterministic: False +2025-06-24 08:42:00,004 - pyskl - INFO - 20484 videos remain after valid thresholding +2025-06-24 08:42:04,097 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-24 08:42:04,098 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3 +2025-06-24 08:42:04,098 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2025-06-24 08:42:04,098 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2025-06-24 08:42:04,098 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3 by HardDiskBackend. +2025-06-24 08:42:41,473 - pyskl - INFO - Epoch [1][100/1281] lr: 2.500e-02, eta: 19:56:11, time: 0.374, data_time: 0.162, memory: 4082, top1_acc: 0.0612, top5_acc: 0.2419, loss_cls: 4.4726, loss: 4.4726 +2025-06-24 08:43:02,488 - pyskl - INFO - Epoch [1][200/1281] lr: 2.500e-02, eta: 15:33:55, time: 0.210, data_time: 0.000, memory: 4082, top1_acc: 0.1100, top5_acc: 0.3375, loss_cls: 4.5090, loss: 4.5090 +2025-06-24 08:43:23,867 - pyskl - INFO - Epoch [1][300/1281] lr: 2.500e-02, eta: 14:10:08, time: 0.214, data_time: 0.001, memory: 4082, top1_acc: 0.1019, top5_acc: 0.3713, loss_cls: 4.2762, loss: 4.2762 +2025-06-24 08:43:45,104 - pyskl - INFO - Epoch [1][400/1281] lr: 2.500e-02, eta: 13:26:56, time: 0.212, data_time: 0.000, memory: 4082, top1_acc: 0.1037, top5_acc: 0.4019, loss_cls: 4.1223, loss: 4.1223 +2025-06-24 08:44:06,679 - pyskl - INFO - Epoch [1][500/1281] lr: 2.500e-02, eta: 13:03:02, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.1481, top5_acc: 0.4631, loss_cls: 3.8994, loss: 3.8994 +2025-06-24 08:44:28,441 - pyskl - INFO - Epoch [1][600/1281] lr: 2.500e-02, eta: 12:47:58, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.1756, top5_acc: 0.5238, loss_cls: 3.6457, loss: 3.6457 +2025-06-24 08:44:50,038 - pyskl - INFO - Epoch [1][700/1281] lr: 2.500e-02, eta: 12:36:22, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.2019, top5_acc: 0.5306, loss_cls: 3.6087, loss: 3.6087 +2025-06-24 08:45:11,542 - pyskl - INFO - Epoch [1][800/1281] lr: 2.500e-02, eta: 12:27:11, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.2394, top5_acc: 0.5931, loss_cls: 3.3308, loss: 3.3308 +2025-06-24 08:45:33,111 - pyskl - INFO - Epoch [1][900/1281] lr: 2.500e-02, eta: 12:20:13, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.2612, top5_acc: 0.6181, loss_cls: 3.2159, loss: 3.2159 +2025-06-24 08:45:55,271 - pyskl - INFO - Epoch [1][1000/1281] lr: 2.500e-02, eta: 12:16:26, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.2981, top5_acc: 0.6875, loss_cls: 3.0353, loss: 3.0353 +2025-06-24 08:46:16,864 - pyskl - INFO - Epoch [1][1100/1281] lr: 2.500e-02, eta: 12:11:38, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.3306, top5_acc: 0.7087, loss_cls: 2.8867, loss: 2.8867 +2025-06-24 08:46:38,687 - pyskl - INFO - Epoch [1][1200/1281] lr: 2.500e-02, eta: 12:08:11, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.3344, top5_acc: 0.7319, loss_cls: 2.7906, loss: 2.7906 +2025-06-24 08:46:57,052 - pyskl - INFO - Saving checkpoint at 1 epochs +2025-06-24 08:47:39,817 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 08:47:39,872 - pyskl - INFO - +top1_acc 0.3713 +top5_acc 0.7521 +2025-06-24 08:47:39,873 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 08:47:39,880 - pyskl - INFO - +mean_acc 0.1741 +2025-06-24 08:47:40,047 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2025-06-24 08:47:40,047 - pyskl - INFO - Best top1_acc is 0.3713 at 1 epoch. +2025-06-24 08:47:40,050 - pyskl - INFO - Epoch(val) [1][533] top1_acc: 0.3713, top5_acc: 0.7521, mean_class_accuracy: 0.1741 +2025-06-24 08:48:20,101 - pyskl - INFO - Epoch [2][100/1281] lr: 2.500e-02, eta: 12:04:21, time: 0.400, data_time: 0.185, memory: 4082, top1_acc: 0.3794, top5_acc: 0.7750, loss_cls: 2.5912, loss: 2.5912 +2025-06-24 08:48:41,741 - pyskl - INFO - Epoch [2][200/1281] lr: 2.500e-02, eta: 12:01:31, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.4219, top5_acc: 0.8037, loss_cls: 2.4540, loss: 2.4540 +2025-06-24 08:49:03,504 - pyskl - INFO - Epoch [2][300/1281] lr: 2.500e-02, eta: 11:59:15, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.4269, top5_acc: 0.8256, loss_cls: 2.3883, loss: 2.3883 +2025-06-24 08:49:25,314 - pyskl - INFO - Epoch [2][400/1281] lr: 2.500e-02, eta: 11:57:17, time: 0.218, data_time: 0.001, memory: 4082, top1_acc: 0.4219, top5_acc: 0.8219, loss_cls: 2.3825, loss: 2.3825 +2025-06-24 08:49:47,178 - pyskl - INFO - Epoch [2][500/1281] lr: 2.499e-02, eta: 11:55:36, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.4500, top5_acc: 0.8294, loss_cls: 2.2908, loss: 2.2908 +2025-06-24 08:50:08,898 - pyskl - INFO - Epoch [2][600/1281] lr: 2.499e-02, eta: 11:53:49, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.4544, top5_acc: 0.8569, loss_cls: 2.2045, loss: 2.2045 +2025-06-24 08:50:30,720 - pyskl - INFO - Epoch [2][700/1281] lr: 2.499e-02, eta: 11:52:21, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.4938, top5_acc: 0.8581, loss_cls: 2.1319, loss: 2.1319 +2025-06-24 08:50:52,616 - pyskl - INFO - Epoch [2][800/1281] lr: 2.499e-02, eta: 11:51:05, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.4994, top5_acc: 0.8644, loss_cls: 2.1104, loss: 2.1104 +2025-06-24 08:51:14,465 - pyskl - INFO - Epoch [2][900/1281] lr: 2.499e-02, eta: 11:49:51, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.4844, top5_acc: 0.8762, loss_cls: 2.0769, loss: 2.0769 +2025-06-24 08:51:36,045 - pyskl - INFO - Epoch [2][1000/1281] lr: 2.499e-02, eta: 11:48:18, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.5250, top5_acc: 0.8988, loss_cls: 1.9640, loss: 1.9640 +2025-06-24 08:51:57,769 - pyskl - INFO - Epoch [2][1100/1281] lr: 2.499e-02, eta: 11:47:03, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.5369, top5_acc: 0.9025, loss_cls: 1.9475, loss: 1.9475 +2025-06-24 08:52:19,753 - pyskl - INFO - Epoch [2][1200/1281] lr: 2.499e-02, eta: 11:46:12, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5238, top5_acc: 0.9038, loss_cls: 1.9329, loss: 1.9329 +2025-06-24 08:52:37,991 - pyskl - INFO - Saving checkpoint at 2 epochs +2025-06-24 08:53:21,279 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 08:53:21,332 - pyskl - INFO - +top1_acc 0.5258 +top5_acc 0.9012 +2025-06-24 08:53:21,332 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 08:53:21,338 - pyskl - INFO - +mean_acc 0.3436 +2025-06-24 08:53:21,342 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_1.pth was removed +2025-06-24 08:53:21,523 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2025-06-24 08:53:21,523 - pyskl - INFO - Best top1_acc is 0.5258 at 2 epoch. +2025-06-24 08:53:21,526 - pyskl - INFO - Epoch(val) [2][533] top1_acc: 0.5258, top5_acc: 0.9012, mean_class_accuracy: 0.3436 +2025-06-24 08:54:01,638 - pyskl - INFO - Epoch [3][100/1281] lr: 2.499e-02, eta: 11:45:08, time: 0.401, data_time: 0.182, memory: 4082, top1_acc: 0.5381, top5_acc: 0.8975, loss_cls: 1.8894, loss: 1.8894 +2025-06-24 08:54:23,606 - pyskl - INFO - Epoch [3][200/1281] lr: 2.499e-02, eta: 11:44:21, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5450, top5_acc: 0.9263, loss_cls: 1.8151, loss: 1.8151 +2025-06-24 08:54:45,370 - pyskl - INFO - Epoch [3][300/1281] lr: 2.499e-02, eta: 11:43:23, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.5644, top5_acc: 0.9331, loss_cls: 1.7533, loss: 1.7533 +2025-06-24 08:55:07,179 - pyskl - INFO - Epoch [3][400/1281] lr: 2.499e-02, eta: 11:42:29, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.5700, top5_acc: 0.9225, loss_cls: 1.7419, loss: 1.7419 +2025-06-24 08:55:29,277 - pyskl - INFO - Epoch [3][500/1281] lr: 2.498e-02, eta: 11:41:56, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.6000, top5_acc: 0.9325, loss_cls: 1.6817, loss: 1.6817 +2025-06-24 08:55:51,071 - pyskl - INFO - Epoch [3][600/1281] lr: 2.498e-02, eta: 11:41:05, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.5613, top5_acc: 0.9219, loss_cls: 1.7374, loss: 1.7374 +2025-06-24 08:56:13,256 - pyskl - INFO - Epoch [3][700/1281] lr: 2.498e-02, eta: 11:40:38, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.5787, top5_acc: 0.9275, loss_cls: 1.7121, loss: 1.7121 +2025-06-24 08:56:35,146 - pyskl - INFO - Epoch [3][800/1281] lr: 2.498e-02, eta: 11:39:55, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.5887, top5_acc: 0.9469, loss_cls: 1.6447, loss: 1.6447 +2025-06-24 08:56:57,084 - pyskl - INFO - Epoch [3][900/1281] lr: 2.498e-02, eta: 11:39:16, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6238, top5_acc: 0.9406, loss_cls: 1.5952, loss: 1.5952 +2025-06-24 08:57:18,877 - pyskl - INFO - Epoch [3][1000/1281] lr: 2.498e-02, eta: 11:38:30, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6150, top5_acc: 0.9394, loss_cls: 1.5650, loss: 1.5650 +2025-06-24 08:57:40,735 - pyskl - INFO - Epoch [3][1100/1281] lr: 2.498e-02, eta: 11:37:49, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6369, top5_acc: 0.9487, loss_cls: 1.5013, loss: 1.5013 +2025-06-24 08:58:02,528 - pyskl - INFO - Epoch [3][1200/1281] lr: 2.498e-02, eta: 11:37:06, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6344, top5_acc: 0.9406, loss_cls: 1.5737, loss: 1.5737 +2025-06-24 08:58:20,855 - pyskl - INFO - Saving checkpoint at 3 epochs +2025-06-24 08:59:04,331 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 08:59:04,387 - pyskl - INFO - +top1_acc 0.6410 +top5_acc 0.9502 +2025-06-24 08:59:04,387 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 08:59:04,393 - pyskl - INFO - +mean_acc 0.4690 +2025-06-24 08:59:04,397 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_2.pth was removed +2025-06-24 08:59:04,583 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2025-06-24 08:59:04,583 - pyskl - INFO - Best top1_acc is 0.6410 at 3 epoch. +2025-06-24 08:59:04,586 - pyskl - INFO - Epoch(val) [3][533] top1_acc: 0.6410, top5_acc: 0.9502, mean_class_accuracy: 0.4690 +2025-06-24 08:59:44,763 - pyskl - INFO - Epoch [4][100/1281] lr: 2.497e-02, eta: 11:36:25, time: 0.402, data_time: 0.182, memory: 4082, top1_acc: 0.6500, top5_acc: 0.9569, loss_cls: 1.4885, loss: 1.4885 +2025-06-24 09:00:06,425 - pyskl - INFO - Epoch [4][200/1281] lr: 2.497e-02, eta: 11:35:38, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6275, top5_acc: 0.9525, loss_cls: 1.5182, loss: 1.5182 +2025-06-24 09:00:28,230 - pyskl - INFO - Epoch [4][300/1281] lr: 2.497e-02, eta: 11:34:58, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6575, top5_acc: 0.9575, loss_cls: 1.4487, loss: 1.4487 +2025-06-24 09:00:50,120 - pyskl - INFO - Epoch [4][400/1281] lr: 2.497e-02, eta: 11:34:23, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6469, top5_acc: 0.9525, loss_cls: 1.4495, loss: 1.4495 +2025-06-24 09:01:12,027 - pyskl - INFO - Epoch [4][500/1281] lr: 2.497e-02, eta: 11:33:49, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6781, top5_acc: 0.9481, loss_cls: 1.4115, loss: 1.4115 +2025-06-24 09:01:33,769 - pyskl - INFO - Epoch [4][600/1281] lr: 2.497e-02, eta: 11:33:09, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6713, top5_acc: 0.9581, loss_cls: 1.4014, loss: 1.4014 +2025-06-24 09:01:55,440 - pyskl - INFO - Epoch [4][700/1281] lr: 2.497e-02, eta: 11:32:27, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6687, top5_acc: 0.9600, loss_cls: 1.4084, loss: 1.4084 +2025-06-24 09:02:17,061 - pyskl - INFO - Epoch [4][800/1281] lr: 2.496e-02, eta: 11:31:44, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6506, top5_acc: 0.9569, loss_cls: 1.4110, loss: 1.4110 +2025-06-24 09:02:38,563 - pyskl - INFO - Epoch [4][900/1281] lr: 2.496e-02, eta: 11:30:57, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.6775, top5_acc: 0.9550, loss_cls: 1.3629, loss: 1.3629 +2025-06-24 09:03:00,283 - pyskl - INFO - Epoch [4][1000/1281] lr: 2.496e-02, eta: 11:30:19, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6744, top5_acc: 0.9606, loss_cls: 1.3716, loss: 1.3716 +2025-06-24 09:03:21,862 - pyskl - INFO - Epoch [4][1100/1281] lr: 2.496e-02, eta: 11:29:37, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6681, top5_acc: 0.9563, loss_cls: 1.3565, loss: 1.3565 +2025-06-24 09:03:43,777 - pyskl - INFO - Epoch [4][1200/1281] lr: 2.496e-02, eta: 11:29:07, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7025, top5_acc: 0.9656, loss_cls: 1.2617, loss: 1.2617 +2025-06-24 09:04:02,336 - pyskl - INFO - Saving checkpoint at 4 epochs +2025-06-24 09:04:45,327 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:04:45,380 - pyskl - INFO - +top1_acc 0.6728 +top5_acc 0.9605 +2025-06-24 09:04:45,381 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:04:45,387 - pyskl - INFO - +mean_acc 0.5369 +2025-06-24 09:04:45,391 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_3.pth was removed +2025-06-24 09:04:45,625 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2025-06-24 09:04:45,626 - pyskl - INFO - Best top1_acc is 0.6728 at 4 epoch. +2025-06-24 09:04:45,628 - pyskl - INFO - Epoch(val) [4][533] top1_acc: 0.6728, top5_acc: 0.9605, mean_class_accuracy: 0.5369 +2025-06-24 09:05:25,446 - pyskl - INFO - Epoch [5][100/1281] lr: 2.495e-02, eta: 11:28:21, time: 0.398, data_time: 0.179, memory: 4082, top1_acc: 0.6806, top5_acc: 0.9650, loss_cls: 1.3018, loss: 1.3018 +2025-06-24 09:05:47,433 - pyskl - INFO - Epoch [5][200/1281] lr: 2.495e-02, eta: 11:27:55, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6869, top5_acc: 0.9631, loss_cls: 1.2875, loss: 1.2875 +2025-06-24 09:06:09,249 - pyskl - INFO - Epoch [5][300/1281] lr: 2.495e-02, eta: 11:27:23, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7025, top5_acc: 0.9637, loss_cls: 1.2858, loss: 1.2858 +2025-06-24 09:06:31,049 - pyskl - INFO - Epoch [5][400/1281] lr: 2.495e-02, eta: 11:26:51, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7188, top5_acc: 0.9725, loss_cls: 1.2067, loss: 1.2067 +2025-06-24 09:06:53,075 - pyskl - INFO - Epoch [5][500/1281] lr: 2.495e-02, eta: 11:26:27, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7262, top5_acc: 0.9719, loss_cls: 1.2033, loss: 1.2033 +2025-06-24 09:07:15,092 - pyskl - INFO - Epoch [5][600/1281] lr: 2.495e-02, eta: 11:26:03, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7312, top5_acc: 0.9712, loss_cls: 1.2160, loss: 1.2160 +2025-06-24 09:07:37,154 - pyskl - INFO - Epoch [5][700/1281] lr: 2.494e-02, eta: 11:25:40, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7163, top5_acc: 0.9712, loss_cls: 1.1892, loss: 1.1892 +2025-06-24 09:07:58,849 - pyskl - INFO - Epoch [5][800/1281] lr: 2.494e-02, eta: 11:25:06, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7025, top5_acc: 0.9700, loss_cls: 1.2450, loss: 1.2450 +2025-06-24 09:08:20,324 - pyskl - INFO - Epoch [5][900/1281] lr: 2.494e-02, eta: 11:24:26, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7094, top5_acc: 0.9688, loss_cls: 1.2107, loss: 1.2107 +2025-06-24 09:08:41,935 - pyskl - INFO - Epoch [5][1000/1281] lr: 2.494e-02, eta: 11:23:50, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7169, top5_acc: 0.9700, loss_cls: 1.1996, loss: 1.1996 +2025-06-24 09:09:03,684 - pyskl - INFO - Epoch [5][1100/1281] lr: 2.494e-02, eta: 11:23:19, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7050, top5_acc: 0.9731, loss_cls: 1.2231, loss: 1.2231 +2025-06-24 09:09:25,351 - pyskl - INFO - Epoch [5][1200/1281] lr: 2.493e-02, eta: 11:22:45, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7312, top5_acc: 0.9762, loss_cls: 1.1563, loss: 1.1563 +2025-06-24 09:09:43,564 - pyskl - INFO - Saving checkpoint at 5 epochs +2025-06-24 09:10:26,368 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:10:26,424 - pyskl - INFO - +top1_acc 0.6924 +top5_acc 0.9671 +2025-06-24 09:10:26,424 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:10:26,430 - pyskl - INFO - +mean_acc 0.5494 +2025-06-24 09:10:26,434 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_4.pth was removed +2025-06-24 09:10:26,610 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2025-06-24 09:10:26,610 - pyskl - INFO - Best top1_acc is 0.6924 at 5 epoch. +2025-06-24 09:10:26,613 - pyskl - INFO - Epoch(val) [5][533] top1_acc: 0.6924, top5_acc: 0.9671, mean_class_accuracy: 0.5494 +2025-06-24 09:11:06,667 - pyskl - INFO - Epoch [6][100/1281] lr: 2.493e-02, eta: 11:22:10, time: 0.400, data_time: 0.179, memory: 4082, top1_acc: 0.7269, top5_acc: 0.9738, loss_cls: 1.1695, loss: 1.1695 +2025-06-24 09:11:28,900 - pyskl - INFO - Epoch [6][200/1281] lr: 2.493e-02, eta: 11:21:53, time: 0.222, data_time: 0.001, memory: 4082, top1_acc: 0.7262, top5_acc: 0.9719, loss_cls: 1.1708, loss: 1.1708 +2025-06-24 09:11:50,854 - pyskl - INFO - Epoch [6][300/1281] lr: 2.492e-02, eta: 11:21:28, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7131, top5_acc: 0.9788, loss_cls: 1.1368, loss: 1.1368 +2025-06-24 09:12:12,531 - pyskl - INFO - Epoch [6][400/1281] lr: 2.492e-02, eta: 11:20:56, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7412, top5_acc: 0.9800, loss_cls: 1.1201, loss: 1.1201 +2025-06-24 09:12:34,732 - pyskl - INFO - Epoch [6][500/1281] lr: 2.492e-02, eta: 11:20:38, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7469, top5_acc: 0.9806, loss_cls: 1.0919, loss: 1.0919 +2025-06-24 09:12:56,550 - pyskl - INFO - Epoch [6][600/1281] lr: 2.492e-02, eta: 11:20:10, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7319, top5_acc: 0.9725, loss_cls: 1.1229, loss: 1.1229 +2025-06-24 09:13:18,219 - pyskl - INFO - Epoch [6][700/1281] lr: 2.492e-02, eta: 11:19:38, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7356, top5_acc: 0.9812, loss_cls: 1.1129, loss: 1.1129 +2025-06-24 09:13:40,000 - pyskl - INFO - Epoch [6][800/1281] lr: 2.491e-02, eta: 11:19:09, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7175, top5_acc: 0.9812, loss_cls: 1.1928, loss: 1.1928 +2025-06-24 09:14:01,822 - pyskl - INFO - Epoch [6][900/1281] lr: 2.491e-02, eta: 11:18:42, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7350, top5_acc: 0.9694, loss_cls: 1.1657, loss: 1.1657 +2025-06-24 09:14:23,446 - pyskl - INFO - Epoch [6][1000/1281] lr: 2.491e-02, eta: 11:18:10, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7612, top5_acc: 0.9800, loss_cls: 1.0292, loss: 1.0292 +2025-06-24 09:14:45,443 - pyskl - INFO - Epoch [6][1100/1281] lr: 2.491e-02, eta: 11:17:47, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7475, top5_acc: 0.9731, loss_cls: 1.0815, loss: 1.0815 +2025-06-24 09:15:07,196 - pyskl - INFO - Epoch [6][1200/1281] lr: 2.490e-02, eta: 11:17:18, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7238, top5_acc: 0.9744, loss_cls: 1.1398, loss: 1.1398 +2025-06-24 09:15:25,391 - pyskl - INFO - Saving checkpoint at 6 epochs +2025-06-24 09:16:08,323 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:16:08,377 - pyskl - INFO - +top1_acc 0.6831 +top5_acc 0.9561 +2025-06-24 09:16:08,378 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:16:08,384 - pyskl - INFO - +mean_acc 0.5456 +2025-06-24 09:16:08,386 - pyskl - INFO - Epoch(val) [6][533] top1_acc: 0.6831, top5_acc: 0.9561, mean_class_accuracy: 0.5456 +2025-06-24 09:16:48,855 - pyskl - INFO - Epoch [7][100/1281] lr: 2.490e-02, eta: 11:16:53, time: 0.405, data_time: 0.184, memory: 4082, top1_acc: 0.7375, top5_acc: 0.9825, loss_cls: 1.0883, loss: 1.0883 +2025-06-24 09:17:10,680 - pyskl - INFO - Epoch [7][200/1281] lr: 2.490e-02, eta: 11:16:26, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7606, top5_acc: 0.9812, loss_cls: 1.0000, loss: 1.0000 +2025-06-24 09:17:32,250 - pyskl - INFO - Epoch [7][300/1281] lr: 2.489e-02, eta: 11:15:54, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7681, top5_acc: 0.9825, loss_cls: 0.9861, loss: 0.9861 +2025-06-24 09:17:53,724 - pyskl - INFO - Epoch [7][400/1281] lr: 2.489e-02, eta: 11:15:19, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7506, top5_acc: 0.9794, loss_cls: 1.0864, loss: 1.0864 +2025-06-24 09:18:15,454 - pyskl - INFO - Epoch [7][500/1281] lr: 2.489e-02, eta: 11:14:51, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7538, top5_acc: 0.9775, loss_cls: 1.0483, loss: 1.0483 +2025-06-24 09:18:36,766 - pyskl - INFO - Epoch [7][600/1281] lr: 2.489e-02, eta: 11:14:13, time: 0.213, data_time: 0.000, memory: 4082, top1_acc: 0.7612, top5_acc: 0.9812, loss_cls: 0.9947, loss: 0.9947 +2025-06-24 09:18:58,864 - pyskl - INFO - Epoch [7][700/1281] lr: 2.488e-02, eta: 11:13:53, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7538, top5_acc: 0.9869, loss_cls: 1.0268, loss: 1.0268 +2025-06-24 09:19:20,399 - pyskl - INFO - Epoch [7][800/1281] lr: 2.488e-02, eta: 11:13:21, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7594, top5_acc: 0.9819, loss_cls: 1.0243, loss: 1.0243 +2025-06-24 09:19:42,317 - pyskl - INFO - Epoch [7][900/1281] lr: 2.488e-02, eta: 11:12:57, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7512, top5_acc: 0.9794, loss_cls: 1.0556, loss: 1.0556 +2025-06-24 09:20:03,733 - pyskl - INFO - Epoch [7][1000/1281] lr: 2.487e-02, eta: 11:12:23, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7575, top5_acc: 0.9875, loss_cls: 1.0051, loss: 1.0051 +2025-06-24 09:20:25,314 - pyskl - INFO - Epoch [7][1100/1281] lr: 2.487e-02, eta: 11:11:53, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7600, top5_acc: 0.9825, loss_cls: 1.0222, loss: 1.0222 +2025-06-24 09:20:46,995 - pyskl - INFO - Epoch [7][1200/1281] lr: 2.487e-02, eta: 11:11:24, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7556, top5_acc: 0.9800, loss_cls: 1.0318, loss: 1.0318 +2025-06-24 09:21:05,135 - pyskl - INFO - Saving checkpoint at 7 epochs +2025-06-24 09:21:48,163 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:21:48,220 - pyskl - INFO - +top1_acc 0.7240 +top5_acc 0.9687 +2025-06-24 09:21:48,220 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:21:48,227 - pyskl - INFO - +mean_acc 0.5950 +2025-06-24 09:21:48,231 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_5.pth was removed +2025-06-24 09:21:48,404 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2025-06-24 09:21:48,405 - pyskl - INFO - Best top1_acc is 0.7240 at 7 epoch. +2025-06-24 09:21:48,407 - pyskl - INFO - Epoch(val) [7][533] top1_acc: 0.7240, top5_acc: 0.9687, mean_class_accuracy: 0.5950 +2025-06-24 09:22:28,494 - pyskl - INFO - Epoch [8][100/1281] lr: 2.486e-02, eta: 11:10:50, time: 0.401, data_time: 0.182, memory: 4082, top1_acc: 0.7631, top5_acc: 0.9788, loss_cls: 1.0500, loss: 1.0500 +2025-06-24 09:22:50,541 - pyskl - INFO - Epoch [8][200/1281] lr: 2.486e-02, eta: 11:10:30, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7700, top5_acc: 0.9831, loss_cls: 1.0004, loss: 1.0004 +2025-06-24 09:23:12,333 - pyskl - INFO - Epoch [8][300/1281] lr: 2.486e-02, eta: 11:10:04, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7906, top5_acc: 0.9888, loss_cls: 0.9489, loss: 0.9489 +2025-06-24 09:23:34,084 - pyskl - INFO - Epoch [8][400/1281] lr: 2.485e-02, eta: 11:09:37, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7744, top5_acc: 0.9900, loss_cls: 0.9584, loss: 0.9584 +2025-06-24 09:23:55,719 - pyskl - INFO - Epoch [8][500/1281] lr: 2.485e-02, eta: 11:09:09, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7788, top5_acc: 0.9900, loss_cls: 0.9459, loss: 0.9459 +2025-06-24 09:24:17,599 - pyskl - INFO - Epoch [8][600/1281] lr: 2.485e-02, eta: 11:08:45, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7688, top5_acc: 0.9812, loss_cls: 0.9998, loss: 0.9998 +2025-06-24 09:24:39,325 - pyskl - INFO - Epoch [8][700/1281] lr: 2.484e-02, eta: 11:08:18, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7506, top5_acc: 0.9800, loss_cls: 1.0097, loss: 1.0097 +2025-06-24 09:25:00,970 - pyskl - INFO - Epoch [8][800/1281] lr: 2.484e-02, eta: 11:07:50, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8037, top5_acc: 0.9856, loss_cls: 0.9211, loss: 0.9211 +2025-06-24 09:25:23,072 - pyskl - INFO - Epoch [8][900/1281] lr: 2.484e-02, eta: 11:07:30, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7775, top5_acc: 0.9888, loss_cls: 0.9393, loss: 0.9393 +2025-06-24 09:25:44,853 - pyskl - INFO - Epoch [8][1000/1281] lr: 2.483e-02, eta: 11:07:05, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9856, loss_cls: 0.9036, loss: 0.9036 +2025-06-24 09:26:06,650 - pyskl - INFO - Epoch [8][1100/1281] lr: 2.483e-02, eta: 11:06:40, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7825, top5_acc: 0.9825, loss_cls: 0.9470, loss: 0.9470 +2025-06-24 09:26:28,523 - pyskl - INFO - Epoch [8][1200/1281] lr: 2.483e-02, eta: 11:06:16, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7725, top5_acc: 0.9856, loss_cls: 0.9767, loss: 0.9767 +2025-06-24 09:26:46,773 - pyskl - INFO - Saving checkpoint at 8 epochs +2025-06-24 09:27:30,137 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:27:30,195 - pyskl - INFO - +top1_acc 0.7836 +top5_acc 0.9798 +2025-06-24 09:27:30,195 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:27:30,203 - pyskl - INFO - +mean_acc 0.6870 +2025-06-24 09:27:30,207 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_7.pth was removed +2025-06-24 09:27:30,378 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_8.pth. +2025-06-24 09:27:30,378 - pyskl - INFO - Best top1_acc is 0.7836 at 8 epoch. +2025-06-24 09:27:30,381 - pyskl - INFO - Epoch(val) [8][533] top1_acc: 0.7836, top5_acc: 0.9798, mean_class_accuracy: 0.6870 +2025-06-24 09:28:10,243 - pyskl - INFO - Epoch [9][100/1281] lr: 2.482e-02, eta: 11:05:38, time: 0.399, data_time: 0.181, memory: 4082, top1_acc: 0.8037, top5_acc: 0.9881, loss_cls: 0.8610, loss: 0.8610 +2025-06-24 09:28:32,255 - pyskl - INFO - Epoch [9][200/1281] lr: 2.482e-02, eta: 11:05:17, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7863, top5_acc: 0.9888, loss_cls: 0.9166, loss: 0.9166 +2025-06-24 09:28:54,278 - pyskl - INFO - Epoch [9][300/1281] lr: 2.481e-02, eta: 11:04:56, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7750, top5_acc: 0.9838, loss_cls: 0.9574, loss: 0.9574 +2025-06-24 09:29:16,163 - pyskl - INFO - Epoch [9][400/1281] lr: 2.481e-02, eta: 11:04:32, time: 0.219, data_time: 0.001, memory: 4082, top1_acc: 0.7987, top5_acc: 0.9919, loss_cls: 0.8681, loss: 0.8681 +2025-06-24 09:29:38,231 - pyskl - INFO - Epoch [9][500/1281] lr: 2.481e-02, eta: 11:04:12, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7906, top5_acc: 0.9881, loss_cls: 0.9029, loss: 0.9029 +2025-06-24 09:29:59,905 - pyskl - INFO - Epoch [9][600/1281] lr: 2.480e-02, eta: 11:03:45, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7975, top5_acc: 0.9875, loss_cls: 0.9283, loss: 0.9283 +2025-06-24 09:30:21,458 - pyskl - INFO - Epoch [9][700/1281] lr: 2.480e-02, eta: 11:03:16, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7588, top5_acc: 0.9812, loss_cls: 0.9946, loss: 0.9946 +2025-06-24 09:30:43,591 - pyskl - INFO - Epoch [9][800/1281] lr: 2.480e-02, eta: 11:02:57, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7588, top5_acc: 0.9831, loss_cls: 0.9968, loss: 0.9968 +2025-06-24 09:31:05,580 - pyskl - INFO - Epoch [9][900/1281] lr: 2.479e-02, eta: 11:02:36, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7919, top5_acc: 0.9844, loss_cls: 0.9026, loss: 0.9026 +2025-06-24 09:31:27,167 - pyskl - INFO - Epoch [9][1000/1281] lr: 2.479e-02, eta: 11:02:08, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7863, top5_acc: 0.9862, loss_cls: 0.9290, loss: 0.9290 +2025-06-24 09:31:48,587 - pyskl - INFO - Epoch [9][1100/1281] lr: 2.479e-02, eta: 11:01:37, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9838, loss_cls: 0.9051, loss: 0.9051 +2025-06-24 09:32:10,387 - pyskl - INFO - Epoch [9][1200/1281] lr: 2.478e-02, eta: 11:01:12, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9831, loss_cls: 0.9081, loss: 0.9081 +2025-06-24 09:32:28,645 - pyskl - INFO - Saving checkpoint at 9 epochs +2025-06-24 09:33:11,355 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:33:11,422 - pyskl - INFO - +top1_acc 0.7586 +top5_acc 0.9754 +2025-06-24 09:33:11,422 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:33:11,430 - pyskl - INFO - +mean_acc 0.6656 +2025-06-24 09:33:11,432 - pyskl - INFO - Epoch(val) [9][533] top1_acc: 0.7586, top5_acc: 0.9754, mean_class_accuracy: 0.6656 +2025-06-24 09:33:51,552 - pyskl - INFO - Epoch [10][100/1281] lr: 2.477e-02, eta: 11:00:39, time: 0.401, data_time: 0.183, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9900, loss_cls: 0.8178, loss: 0.8178 +2025-06-24 09:34:13,653 - pyskl - INFO - Epoch [10][200/1281] lr: 2.477e-02, eta: 11:00:19, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9938, loss_cls: 0.8538, loss: 0.8538 +2025-06-24 09:34:35,807 - pyskl - INFO - Epoch [10][300/1281] lr: 2.477e-02, eta: 11:00:00, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7987, top5_acc: 0.9875, loss_cls: 0.8692, loss: 0.8692 +2025-06-24 09:34:57,856 - pyskl - INFO - Epoch [10][400/1281] lr: 2.476e-02, eta: 10:59:39, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8031, top5_acc: 0.9844, loss_cls: 0.8752, loss: 0.8752 +2025-06-24 09:35:19,950 - pyskl - INFO - Epoch [10][500/1281] lr: 2.476e-02, eta: 10:59:19, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7869, top5_acc: 0.9856, loss_cls: 0.9395, loss: 0.9395 +2025-06-24 09:35:41,740 - pyskl - INFO - Epoch [10][600/1281] lr: 2.476e-02, eta: 10:58:55, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9844, loss_cls: 0.9020, loss: 0.9020 +2025-06-24 09:36:03,714 - pyskl - INFO - Epoch [10][700/1281] lr: 2.475e-02, eta: 10:58:33, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8031, top5_acc: 0.9912, loss_cls: 0.8157, loss: 0.8157 +2025-06-24 09:36:25,510 - pyskl - INFO - Epoch [10][800/1281] lr: 2.475e-02, eta: 10:58:08, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9881, loss_cls: 0.8781, loss: 0.8781 +2025-06-24 09:36:47,206 - pyskl - INFO - Epoch [10][900/1281] lr: 2.474e-02, eta: 10:57:43, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8131, top5_acc: 0.9894, loss_cls: 0.8407, loss: 0.8407 +2025-06-24 09:37:08,917 - pyskl - INFO - Epoch [10][1000/1281] lr: 2.474e-02, eta: 10:57:17, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7887, top5_acc: 0.9888, loss_cls: 0.8969, loss: 0.8969 +2025-06-24 09:37:31,041 - pyskl - INFO - Epoch [10][1100/1281] lr: 2.473e-02, eta: 10:56:58, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8025, top5_acc: 0.9869, loss_cls: 0.8643, loss: 0.8643 +2025-06-24 09:37:52,683 - pyskl - INFO - Epoch [10][1200/1281] lr: 2.473e-02, eta: 10:56:31, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9888, loss_cls: 0.8420, loss: 0.8420 +2025-06-24 09:38:11,537 - pyskl - INFO - Saving checkpoint at 10 epochs +2025-06-24 09:38:54,467 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:38:54,533 - pyskl - INFO - +top1_acc 0.7518 +top5_acc 0.9729 +2025-06-24 09:38:54,533 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:38:54,540 - pyskl - INFO - +mean_acc 0.6424 +2025-06-24 09:38:54,542 - pyskl - INFO - Epoch(val) [10][533] top1_acc: 0.7518, top5_acc: 0.9729, mean_class_accuracy: 0.6424 +2025-06-24 09:39:34,143 - pyskl - INFO - Epoch [11][100/1281] lr: 2.472e-02, eta: 10:55:49, time: 0.396, data_time: 0.176, memory: 4082, top1_acc: 0.8087, top5_acc: 0.9894, loss_cls: 0.8468, loss: 0.8468 +2025-06-24 09:39:56,002 - pyskl - INFO - Epoch [11][200/1281] lr: 2.472e-02, eta: 10:55:26, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8056, top5_acc: 0.9838, loss_cls: 0.8797, loss: 0.8797 +2025-06-24 09:40:17,639 - pyskl - INFO - Epoch [11][300/1281] lr: 2.471e-02, eta: 10:55:00, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8125, top5_acc: 0.9888, loss_cls: 0.8520, loss: 0.8520 +2025-06-24 09:40:39,280 - pyskl - INFO - Epoch [11][400/1281] lr: 2.471e-02, eta: 10:54:34, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7963, top5_acc: 0.9888, loss_cls: 0.8416, loss: 0.8416 +2025-06-24 09:41:01,216 - pyskl - INFO - Epoch [11][500/1281] lr: 2.471e-02, eta: 10:54:12, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8200, top5_acc: 0.9925, loss_cls: 0.7988, loss: 0.7988 +2025-06-24 09:41:22,859 - pyskl - INFO - Epoch [11][600/1281] lr: 2.470e-02, eta: 10:53:46, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9875, loss_cls: 0.8241, loss: 0.8241 +2025-06-24 09:41:44,665 - pyskl - INFO - Epoch [11][700/1281] lr: 2.470e-02, eta: 10:53:22, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8025, top5_acc: 0.9856, loss_cls: 0.8492, loss: 0.8492 +2025-06-24 09:42:06,226 - pyskl - INFO - Epoch [11][800/1281] lr: 2.469e-02, eta: 10:52:55, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8213, top5_acc: 0.9888, loss_cls: 0.7913, loss: 0.7913 +2025-06-24 09:42:28,069 - pyskl - INFO - Epoch [11][900/1281] lr: 2.469e-02, eta: 10:52:32, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9919, loss_cls: 0.7635, loss: 0.7635 +2025-06-24 09:42:49,778 - pyskl - INFO - Epoch [11][1000/1281] lr: 2.468e-02, eta: 10:52:07, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8194, top5_acc: 0.9862, loss_cls: 0.8339, loss: 0.8339 +2025-06-24 09:43:11,665 - pyskl - INFO - Epoch [11][1100/1281] lr: 2.468e-02, eta: 10:51:44, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9875, loss_cls: 0.8483, loss: 0.8483 +2025-06-24 09:43:33,171 - pyskl - INFO - Epoch [11][1200/1281] lr: 2.467e-02, eta: 10:51:16, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9912, loss_cls: 0.7391, loss: 0.7391 +2025-06-24 09:43:51,625 - pyskl - INFO - Saving checkpoint at 11 epochs +2025-06-24 09:44:35,621 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:44:35,679 - pyskl - INFO - +top1_acc 0.7321 +top5_acc 0.9702 +2025-06-24 09:44:35,679 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:44:35,687 - pyskl - INFO - +mean_acc 0.6185 +2025-06-24 09:44:35,689 - pyskl - INFO - Epoch(val) [11][533] top1_acc: 0.7321, top5_acc: 0.9702, mean_class_accuracy: 0.6185 +2025-06-24 09:45:16,451 - pyskl - INFO - Epoch [12][100/1281] lr: 2.467e-02, eta: 10:50:50, time: 0.408, data_time: 0.187, memory: 4082, top1_acc: 0.8206, top5_acc: 0.9938, loss_cls: 0.7728, loss: 0.7728 +2025-06-24 09:45:38,650 - pyskl - INFO - Epoch [12][200/1281] lr: 2.466e-02, eta: 10:50:31, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9906, loss_cls: 0.7804, loss: 0.7804 +2025-06-24 09:46:00,786 - pyskl - INFO - Epoch [12][300/1281] lr: 2.466e-02, eta: 10:50:12, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9888, loss_cls: 0.7969, loss: 0.7969 +2025-06-24 09:46:22,747 - pyskl - INFO - Epoch [12][400/1281] lr: 2.465e-02, eta: 10:49:50, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9900, loss_cls: 0.8024, loss: 0.8024 +2025-06-24 09:46:44,586 - pyskl - INFO - Epoch [12][500/1281] lr: 2.465e-02, eta: 10:49:27, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8113, top5_acc: 0.9881, loss_cls: 0.8308, loss: 0.8308 +2025-06-24 09:47:06,187 - pyskl - INFO - Epoch [12][600/1281] lr: 2.464e-02, eta: 10:49:00, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9919, loss_cls: 0.7683, loss: 0.7683 +2025-06-24 09:47:28,227 - pyskl - INFO - Epoch [12][700/1281] lr: 2.464e-02, eta: 10:48:40, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9869, loss_cls: 0.7649, loss: 0.7649 +2025-06-24 09:47:49,963 - pyskl - INFO - Epoch [12][800/1281] lr: 2.463e-02, eta: 10:48:15, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9906, loss_cls: 0.7936, loss: 0.7936 +2025-06-24 09:48:11,965 - pyskl - INFO - Epoch [12][900/1281] lr: 2.463e-02, eta: 10:47:54, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9931, loss_cls: 0.7869, loss: 0.7869 +2025-06-24 09:48:33,606 - pyskl - INFO - Epoch [12][1000/1281] lr: 2.462e-02, eta: 10:47:28, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9894, loss_cls: 0.7984, loss: 0.7984 +2025-06-24 09:48:55,224 - pyskl - INFO - Epoch [12][1100/1281] lr: 2.462e-02, eta: 10:47:03, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9912, loss_cls: 0.7822, loss: 0.7822 +2025-06-24 09:49:17,165 - pyskl - INFO - Epoch [12][1200/1281] lr: 2.461e-02, eta: 10:46:41, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9900, loss_cls: 0.7976, loss: 0.7976 +2025-06-24 09:49:35,379 - pyskl - INFO - Saving checkpoint at 12 epochs +2025-06-24 09:50:17,818 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:50:17,873 - pyskl - INFO - +top1_acc 0.8013 +top5_acc 0.9857 +2025-06-24 09:50:17,873 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:50:17,880 - pyskl - INFO - +mean_acc 0.7040 +2025-06-24 09:50:17,884 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_8.pth was removed +2025-06-24 09:50:18,047 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_12.pth. +2025-06-24 09:50:18,047 - pyskl - INFO - Best top1_acc is 0.8013 at 12 epoch. +2025-06-24 09:50:18,050 - pyskl - INFO - Epoch(val) [12][533] top1_acc: 0.8013, top5_acc: 0.9857, mean_class_accuracy: 0.7040 +2025-06-24 09:50:57,817 - pyskl - INFO - Epoch [13][100/1281] lr: 2.460e-02, eta: 10:46:02, time: 0.398, data_time: 0.181, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9919, loss_cls: 0.7700, loss: 0.7700 +2025-06-24 09:51:20,345 - pyskl - INFO - Epoch [13][200/1281] lr: 2.460e-02, eta: 10:45:46, time: 0.225, data_time: 0.000, memory: 4082, top1_acc: 0.8231, top5_acc: 0.9944, loss_cls: 0.7650, loss: 0.7650 +2025-06-24 09:51:42,190 - pyskl - INFO - Epoch [13][300/1281] lr: 2.459e-02, eta: 10:45:23, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8031, top5_acc: 0.9912, loss_cls: 0.8340, loss: 0.8340 +2025-06-24 09:52:03,727 - pyskl - INFO - Epoch [13][400/1281] lr: 2.459e-02, eta: 10:44:57, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8275, top5_acc: 0.9919, loss_cls: 0.7658, loss: 0.7658 +2025-06-24 09:52:25,411 - pyskl - INFO - Epoch [13][500/1281] lr: 2.458e-02, eta: 10:44:32, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9919, loss_cls: 0.7356, loss: 0.7356 +2025-06-24 09:52:47,128 - pyskl - INFO - Epoch [13][600/1281] lr: 2.458e-02, eta: 10:44:08, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8381, top5_acc: 0.9931, loss_cls: 0.7450, loss: 0.7450 +2025-06-24 09:53:08,859 - pyskl - INFO - Epoch [13][700/1281] lr: 2.457e-02, eta: 10:43:43, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9919, loss_cls: 0.7175, loss: 0.7175 +2025-06-24 09:53:30,747 - pyskl - INFO - Epoch [13][800/1281] lr: 2.457e-02, eta: 10:43:21, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8206, top5_acc: 0.9938, loss_cls: 0.7811, loss: 0.7811 +2025-06-24 09:53:52,317 - pyskl - INFO - Epoch [13][900/1281] lr: 2.456e-02, eta: 10:42:55, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8131, top5_acc: 0.9856, loss_cls: 0.8467, loss: 0.8467 +2025-06-24 09:54:13,938 - pyskl - INFO - Epoch [13][1000/1281] lr: 2.455e-02, eta: 10:42:30, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9925, loss_cls: 0.8224, loss: 0.8224 +2025-06-24 09:54:36,026 - pyskl - INFO - Epoch [13][1100/1281] lr: 2.455e-02, eta: 10:42:10, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9912, loss_cls: 0.7708, loss: 0.7708 +2025-06-24 09:54:57,619 - pyskl - INFO - Epoch [13][1200/1281] lr: 2.454e-02, eta: 10:41:44, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9919, loss_cls: 0.7597, loss: 0.7597 +2025-06-24 09:55:16,133 - pyskl - INFO - Saving checkpoint at 13 epochs +2025-06-24 09:55:58,922 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:55:58,976 - pyskl - INFO - +top1_acc 0.7985 +top5_acc 0.9809 +2025-06-24 09:55:58,976 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:55:58,982 - pyskl - INFO - +mean_acc 0.7333 +2025-06-24 09:55:58,984 - pyskl - INFO - Epoch(val) [13][533] top1_acc: 0.7985, top5_acc: 0.9809, mean_class_accuracy: 0.7333 +2025-06-24 09:56:39,069 - pyskl - INFO - Epoch [14][100/1281] lr: 2.453e-02, eta: 10:41:08, time: 0.401, data_time: 0.181, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9925, loss_cls: 0.7564, loss: 0.7564 +2025-06-24 09:57:01,221 - pyskl - INFO - Epoch [14][200/1281] lr: 2.453e-02, eta: 10:40:49, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9925, loss_cls: 0.7284, loss: 0.7284 +2025-06-24 09:57:23,075 - pyskl - INFO - Epoch [14][300/1281] lr: 2.452e-02, eta: 10:40:26, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9931, loss_cls: 0.7463, loss: 0.7463 +2025-06-24 09:57:45,048 - pyskl - INFO - Epoch [14][400/1281] lr: 2.452e-02, eta: 10:40:04, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9900, loss_cls: 0.7583, loss: 0.7583 +2025-06-24 09:58:07,378 - pyskl - INFO - Epoch [14][500/1281] lr: 2.451e-02, eta: 10:39:47, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9912, loss_cls: 0.7263, loss: 0.7263 +2025-06-24 09:58:29,524 - pyskl - INFO - Epoch [14][600/1281] lr: 2.451e-02, eta: 10:39:27, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9862, loss_cls: 0.7573, loss: 0.7573 +2025-06-24 09:58:51,431 - pyskl - INFO - Epoch [14][700/1281] lr: 2.450e-02, eta: 10:39:04, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9900, loss_cls: 0.7489, loss: 0.7489 +2025-06-24 09:59:12,984 - pyskl - INFO - Epoch [14][800/1281] lr: 2.449e-02, eta: 10:38:39, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9894, loss_cls: 0.7612, loss: 0.7612 +2025-06-24 09:59:34,723 - pyskl - INFO - Epoch [14][900/1281] lr: 2.449e-02, eta: 10:38:15, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9931, loss_cls: 0.7695, loss: 0.7695 +2025-06-24 09:59:56,695 - pyskl - INFO - Epoch [14][1000/1281] lr: 2.448e-02, eta: 10:37:53, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9906, loss_cls: 0.7733, loss: 0.7733 +2025-06-24 10:00:18,314 - pyskl - INFO - Epoch [14][1100/1281] lr: 2.448e-02, eta: 10:37:28, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8144, top5_acc: 0.9919, loss_cls: 0.8028, loss: 0.8028 +2025-06-24 10:00:40,203 - pyskl - INFO - Epoch [14][1200/1281] lr: 2.447e-02, eta: 10:37:06, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8231, top5_acc: 0.9875, loss_cls: 0.7676, loss: 0.7676 +2025-06-24 10:00:58,530 - pyskl - INFO - Saving checkpoint at 14 epochs +2025-06-24 10:01:41,500 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:01:41,561 - pyskl - INFO - +top1_acc 0.7957 +top5_acc 0.9810 +2025-06-24 10:01:41,561 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:01:41,568 - pyskl - INFO - +mean_acc 0.7165 +2025-06-24 10:01:41,571 - pyskl - INFO - Epoch(val) [14][533] top1_acc: 0.7957, top5_acc: 0.9810, mean_class_accuracy: 0.7165 +2025-06-24 10:02:20,753 - pyskl - INFO - Epoch [15][100/1281] lr: 2.446e-02, eta: 10:36:21, time: 0.392, data_time: 0.169, memory: 4082, top1_acc: 0.8769, top5_acc: 0.9969, loss_cls: 0.6153, loss: 0.6153 +2025-06-24 10:02:42,596 - pyskl - INFO - Epoch [15][200/1281] lr: 2.445e-02, eta: 10:35:58, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9900, loss_cls: 0.7392, loss: 0.7392 +2025-06-24 10:03:04,688 - pyskl - INFO - Epoch [15][300/1281] lr: 2.445e-02, eta: 10:35:38, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9969, loss_cls: 0.6920, loss: 0.6920 +2025-06-24 10:03:26,325 - pyskl - INFO - Epoch [15][400/1281] lr: 2.444e-02, eta: 10:35:13, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9931, loss_cls: 0.7364, loss: 0.7364 +2025-06-24 10:03:47,782 - pyskl - INFO - Epoch [15][500/1281] lr: 2.444e-02, eta: 10:34:47, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9919, loss_cls: 0.7277, loss: 0.7277 +2025-06-24 10:04:09,802 - pyskl - INFO - Epoch [15][600/1281] lr: 2.443e-02, eta: 10:34:26, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8263, top5_acc: 0.9831, loss_cls: 0.8106, loss: 0.8106 +2025-06-24 10:04:31,679 - pyskl - INFO - Epoch [15][700/1281] lr: 2.442e-02, eta: 10:34:04, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8406, top5_acc: 0.9919, loss_cls: 0.7027, loss: 0.7027 +2025-06-24 10:04:53,648 - pyskl - INFO - Epoch [15][800/1281] lr: 2.442e-02, eta: 10:33:42, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9919, loss_cls: 0.7332, loss: 0.7332 +2025-06-24 10:05:15,051 - pyskl - INFO - Epoch [15][900/1281] lr: 2.441e-02, eta: 10:33:15, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9931, loss_cls: 0.7148, loss: 0.7148 +2025-06-24 10:05:36,617 - pyskl - INFO - Epoch [15][1000/1281] lr: 2.441e-02, eta: 10:32:50, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9888, loss_cls: 0.6920, loss: 0.6920 +2025-06-24 10:05:58,389 - pyskl - INFO - Epoch [15][1100/1281] lr: 2.440e-02, eta: 10:32:27, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9900, loss_cls: 0.7229, loss: 0.7229 +2025-06-24 10:06:20,467 - pyskl - INFO - Epoch [15][1200/1281] lr: 2.439e-02, eta: 10:32:06, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8631, top5_acc: 0.9919, loss_cls: 0.6530, loss: 0.6530 +2025-06-24 10:06:38,618 - pyskl - INFO - Saving checkpoint at 15 epochs +2025-06-24 10:07:21,496 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:07:21,570 - pyskl - INFO - +top1_acc 0.7712 +top5_acc 0.9791 +2025-06-24 10:07:21,570 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:07:21,577 - pyskl - INFO - +mean_acc 0.7057 +2025-06-24 10:07:21,579 - pyskl - INFO - Epoch(val) [15][533] top1_acc: 0.7712, top5_acc: 0.9791, mean_class_accuracy: 0.7057 +2025-06-24 10:08:01,989 - pyskl - INFO - Epoch [16][100/1281] lr: 2.438e-02, eta: 10:31:33, time: 0.404, data_time: 0.182, memory: 4082, top1_acc: 0.8594, top5_acc: 0.9912, loss_cls: 0.6934, loss: 0.6934 +2025-06-24 10:08:23,911 - pyskl - INFO - Epoch [16][200/1281] lr: 2.438e-02, eta: 10:31:11, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8344, top5_acc: 0.9938, loss_cls: 0.6830, loss: 0.6830 +2025-06-24 10:08:45,990 - pyskl - INFO - Epoch [16][300/1281] lr: 2.437e-02, eta: 10:30:51, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8400, top5_acc: 0.9950, loss_cls: 0.7066, loss: 0.7066 +2025-06-24 10:09:07,766 - pyskl - INFO - Epoch [16][400/1281] lr: 2.436e-02, eta: 10:30:27, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8562, top5_acc: 0.9944, loss_cls: 0.6945, loss: 0.6945 +2025-06-24 10:09:29,497 - pyskl - INFO - Epoch [16][500/1281] lr: 2.436e-02, eta: 10:30:04, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9938, loss_cls: 0.7254, loss: 0.7254 +2025-06-24 10:09:51,645 - pyskl - INFO - Epoch [16][600/1281] lr: 2.435e-02, eta: 10:29:44, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9919, loss_cls: 0.7326, loss: 0.7326 +2025-06-24 10:10:13,300 - pyskl - INFO - Epoch [16][700/1281] lr: 2.434e-02, eta: 10:29:19, time: 0.217, data_time: 0.001, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9925, loss_cls: 0.7079, loss: 0.7079 +2025-06-24 10:10:35,059 - pyskl - INFO - Epoch [16][800/1281] lr: 2.434e-02, eta: 10:28:56, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9938, loss_cls: 0.7072, loss: 0.7072 +2025-06-24 10:10:56,939 - pyskl - INFO - Epoch [16][900/1281] lr: 2.433e-02, eta: 10:28:34, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9900, loss_cls: 0.7080, loss: 0.7080 +2025-06-24 10:11:18,791 - pyskl - INFO - Epoch [16][1000/1281] lr: 2.432e-02, eta: 10:28:11, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9931, loss_cls: 0.7220, loss: 0.7220 +2025-06-24 10:11:40,411 - pyskl - INFO - Epoch [16][1100/1281] lr: 2.432e-02, eta: 10:27:47, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9900, loss_cls: 0.7717, loss: 0.7717 +2025-06-24 10:12:02,017 - pyskl - INFO - Epoch [16][1200/1281] lr: 2.431e-02, eta: 10:27:22, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9931, loss_cls: 0.7443, loss: 0.7443 +2025-06-24 10:12:20,614 - pyskl - INFO - Saving checkpoint at 16 epochs +2025-06-24 10:13:27,841 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:13:27,904 - pyskl - INFO - +top1_acc 0.8165 +top5_acc 0.9831 +2025-06-24 10:13:27,904 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:13:27,912 - pyskl - INFO - +mean_acc 0.7314 +2025-06-24 10:13:27,916 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_12.pth was removed +2025-06-24 10:13:28,095 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_16.pth. +2025-06-24 10:13:28,096 - pyskl - INFO - Best top1_acc is 0.8165 at 16 epoch. +2025-06-24 10:13:28,098 - pyskl - INFO - Epoch(val) [16][533] top1_acc: 0.8165, top5_acc: 0.9831, mean_class_accuracy: 0.7314 +2025-06-24 10:14:27,975 - pyskl - INFO - Epoch [17][100/1281] lr: 2.430e-02, eta: 10:29:31, time: 0.599, data_time: 0.184, memory: 4082, top1_acc: 0.8619, top5_acc: 0.9962, loss_cls: 0.6835, loss: 0.6835 +2025-06-24 10:15:09,300 - pyskl - INFO - Epoch [17][200/1281] lr: 2.429e-02, eta: 10:31:49, time: 0.413, data_time: 0.001, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9906, loss_cls: 0.6953, loss: 0.6953 +2025-06-24 10:15:50,782 - pyskl - INFO - Epoch [17][300/1281] lr: 2.428e-02, eta: 10:34:06, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9956, loss_cls: 0.6910, loss: 0.6910 +2025-06-24 10:16:32,089 - pyskl - INFO - Epoch [17][400/1281] lr: 2.428e-02, eta: 10:36:21, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9944, loss_cls: 0.6700, loss: 0.6700 +2025-06-24 10:17:13,826 - pyskl - INFO - Epoch [17][500/1281] lr: 2.427e-02, eta: 10:38:37, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9938, loss_cls: 0.6844, loss: 0.6844 +2025-06-24 10:17:55,318 - pyskl - INFO - Epoch [17][600/1281] lr: 2.426e-02, eta: 10:40:49, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8425, top5_acc: 0.9906, loss_cls: 0.7121, loss: 0.7121 +2025-06-24 10:18:36,753 - pyskl - INFO - Epoch [17][700/1281] lr: 2.426e-02, eta: 10:43:00, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9938, loss_cls: 0.7094, loss: 0.7094 +2025-06-24 10:19:18,050 - pyskl - INFO - Epoch [17][800/1281] lr: 2.425e-02, eta: 10:45:07, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9938, loss_cls: 0.6774, loss: 0.6774 +2025-06-24 10:19:59,520 - pyskl - INFO - Epoch [17][900/1281] lr: 2.424e-02, eta: 10:47:15, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8512, top5_acc: 0.9931, loss_cls: 0.6980, loss: 0.6980 +2025-06-24 10:20:40,778 - pyskl - INFO - Epoch [17][1000/1281] lr: 2.424e-02, eta: 10:49:19, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8556, top5_acc: 0.9919, loss_cls: 0.6673, loss: 0.6673 +2025-06-24 10:21:21,843 - pyskl - INFO - Epoch [17][1100/1281] lr: 2.423e-02, eta: 10:51:20, time: 0.411, data_time: 0.000, memory: 4082, top1_acc: 0.8375, top5_acc: 0.9919, loss_cls: 0.7194, loss: 0.7194 +2025-06-24 10:21:50,146 - pyskl - INFO - Epoch [17][1200/1281] lr: 2.422e-02, eta: 10:51:40, time: 0.283, data_time: 0.000, memory: 4082, top1_acc: 0.8512, top5_acc: 0.9888, loss_cls: 0.7074, loss: 0.7074 +2025-06-24 10:22:27,588 - pyskl - INFO - Saving checkpoint at 17 epochs +2025-06-24 10:23:34,681 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:23:34,750 - pyskl - INFO - +top1_acc 0.8298 +top5_acc 0.9863 +2025-06-24 10:23:34,750 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:23:34,758 - pyskl - INFO - +mean_acc 0.7617 +2025-06-24 10:23:34,763 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_16.pth was removed +2025-06-24 10:23:34,955 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_17.pth. +2025-06-24 10:23:34,956 - pyskl - INFO - Best top1_acc is 0.8298 at 17 epoch. +2025-06-24 10:23:34,958 - pyskl - INFO - Epoch(val) [17][533] top1_acc: 0.8298, top5_acc: 0.9863, mean_class_accuracy: 0.7617 +2025-06-24 10:24:36,513 - pyskl - INFO - Epoch [18][100/1281] lr: 2.421e-02, eta: 10:53:34, time: 0.615, data_time: 0.197, memory: 4082, top1_acc: 0.8781, top5_acc: 0.9919, loss_cls: 0.6051, loss: 0.6051 +2025-06-24 10:25:18,153 - pyskl - INFO - Epoch [18][200/1281] lr: 2.420e-02, eta: 10:55:35, time: 0.416, data_time: 0.001, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9956, loss_cls: 0.6520, loss: 0.6520 +2025-06-24 10:25:59,427 - pyskl - INFO - Epoch [18][300/1281] lr: 2.419e-02, eta: 10:57:32, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8644, top5_acc: 0.9962, loss_cls: 0.6115, loss: 0.6115 +2025-06-24 10:26:40,893 - pyskl - INFO - Epoch [18][400/1281] lr: 2.419e-02, eta: 10:59:29, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9944, loss_cls: 0.6870, loss: 0.6870 +2025-06-24 10:27:22,231 - pyskl - INFO - Epoch [18][500/1281] lr: 2.418e-02, eta: 11:01:23, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9919, loss_cls: 0.7159, loss: 0.7159 +2025-06-24 10:28:05,828 - pyskl - INFO - Epoch [18][600/1281] lr: 2.417e-02, eta: 11:03:33, time: 0.436, data_time: 0.000, memory: 4082, top1_acc: 0.8581, top5_acc: 0.9962, loss_cls: 0.6563, loss: 0.6563 +2025-06-24 10:28:47,133 - pyskl - INFO - Epoch [18][700/1281] lr: 2.417e-02, eta: 11:05:25, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9894, loss_cls: 0.7361, loss: 0.7361 +2025-06-24 10:29:28,537 - pyskl - INFO - Epoch [18][800/1281] lr: 2.416e-02, eta: 11:07:15, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9925, loss_cls: 0.7165, loss: 0.7165 +2025-06-24 10:30:09,888 - pyskl - INFO - Epoch [18][900/1281] lr: 2.415e-02, eta: 11:09:04, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9944, loss_cls: 0.6707, loss: 0.6707 +2025-06-24 10:30:51,307 - pyskl - INFO - Epoch [18][1000/1281] lr: 2.414e-02, eta: 11:10:52, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9931, loss_cls: 0.6632, loss: 0.6632 +2025-06-24 10:31:30,149 - pyskl - INFO - Epoch [18][1100/1281] lr: 2.414e-02, eta: 11:12:20, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.8594, top5_acc: 0.9956, loss_cls: 0.6640, loss: 0.6640 +2025-06-24 10:31:59,711 - pyskl - INFO - Epoch [18][1200/1281] lr: 2.413e-02, eta: 11:12:39, time: 0.296, data_time: 0.000, memory: 4082, top1_acc: 0.8556, top5_acc: 0.9938, loss_cls: 0.6874, loss: 0.6874 +2025-06-24 10:32:37,154 - pyskl - INFO - Saving checkpoint at 18 epochs +2025-06-24 10:33:47,499 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:33:47,556 - pyskl - INFO - +top1_acc 0.8501 +top5_acc 0.9876 +2025-06-24 10:33:47,556 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:33:47,564 - pyskl - INFO - +mean_acc 0.7818 +2025-06-24 10:33:47,568 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_17.pth was removed +2025-06-24 10:33:47,759 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_18.pth. +2025-06-24 10:33:47,759 - pyskl - INFO - Best top1_acc is 0.8501 at 18 epoch. +2025-06-24 10:33:47,763 - pyskl - INFO - Epoch(val) [18][533] top1_acc: 0.8501, top5_acc: 0.9876, mean_class_accuracy: 0.7818 +2025-06-24 10:34:48,467 - pyskl - INFO - Epoch [19][100/1281] lr: 2.411e-02, eta: 11:14:03, time: 0.607, data_time: 0.193, memory: 4082, top1_acc: 0.8712, top5_acc: 0.9950, loss_cls: 0.6127, loss: 0.6127 +2025-06-24 10:35:30,349 - pyskl - INFO - Epoch [19][200/1281] lr: 2.411e-02, eta: 11:15:50, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.8662, top5_acc: 0.9962, loss_cls: 0.6301, loss: 0.6301 +2025-06-24 10:36:11,809 - pyskl - INFO - Epoch [19][300/1281] lr: 2.410e-02, eta: 11:17:32, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8669, top5_acc: 0.9962, loss_cls: 0.6470, loss: 0.6470 +2025-06-24 10:36:53,240 - pyskl - INFO - Epoch [19][400/1281] lr: 2.409e-02, eta: 11:19:12, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8581, top5_acc: 0.9950, loss_cls: 0.6616, loss: 0.6616 +2025-06-24 10:37:34,766 - pyskl - INFO - Epoch [19][500/1281] lr: 2.408e-02, eta: 11:20:52, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9950, loss_cls: 0.6706, loss: 0.6706 +2025-06-24 10:38:16,047 - pyskl - INFO - Epoch [19][600/1281] lr: 2.408e-02, eta: 11:22:30, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9919, loss_cls: 0.6576, loss: 0.6576 +2025-06-24 10:38:57,499 - pyskl - INFO - Epoch [19][700/1281] lr: 2.407e-02, eta: 11:24:07, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9944, loss_cls: 0.6541, loss: 0.6541 +2025-06-24 10:39:38,978 - pyskl - INFO - Epoch [19][800/1281] lr: 2.406e-02, eta: 11:25:43, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9912, loss_cls: 0.7062, loss: 0.7062 +2025-06-24 10:40:20,551 - pyskl - INFO - Epoch [19][900/1281] lr: 2.405e-02, eta: 11:27:19, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9938, loss_cls: 0.7769, loss: 0.7769 +2025-06-24 10:41:01,959 - pyskl - INFO - Epoch [19][1000/1281] lr: 2.405e-02, eta: 11:28:52, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9956, loss_cls: 0.6374, loss: 0.6374 +2025-06-24 10:41:39,513 - pyskl - INFO - Epoch [19][1100/1281] lr: 2.404e-02, eta: 11:29:58, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9925, loss_cls: 0.6803, loss: 0.6803 +2025-06-24 10:42:09,515 - pyskl - INFO - Epoch [19][1200/1281] lr: 2.403e-02, eta: 11:30:10, time: 0.300, data_time: 0.000, memory: 4082, top1_acc: 0.8512, top5_acc: 0.9944, loss_cls: 0.6810, loss: 0.6810 +2025-06-24 10:42:46,843 - pyskl - INFO - Saving checkpoint at 19 epochs +2025-06-24 10:43:56,236 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:43:56,292 - pyskl - INFO - +top1_acc 0.7895 +top5_acc 0.9838 +2025-06-24 10:43:56,293 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:43:56,299 - pyskl - INFO - +mean_acc 0.7170 +2025-06-24 10:43:56,301 - pyskl - INFO - Epoch(val) [19][533] top1_acc: 0.7895, top5_acc: 0.9838, mean_class_accuracy: 0.7170 +2025-06-24 10:44:56,574 - pyskl - INFO - Epoch [20][100/1281] lr: 2.402e-02, eta: 11:31:13, time: 0.603, data_time: 0.187, memory: 4082, top1_acc: 0.8719, top5_acc: 0.9950, loss_cls: 0.6116, loss: 0.6116 +2025-06-24 10:45:38,356 - pyskl - INFO - Epoch [20][200/1281] lr: 2.401e-02, eta: 11:32:45, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8856, top5_acc: 0.9975, loss_cls: 0.5462, loss: 0.5462 +2025-06-24 10:46:19,843 - pyskl - INFO - Epoch [20][300/1281] lr: 2.400e-02, eta: 11:34:13, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8656, top5_acc: 0.9919, loss_cls: 0.6243, loss: 0.6243 +2025-06-24 10:47:01,179 - pyskl - INFO - Epoch [20][400/1281] lr: 2.399e-02, eta: 11:35:40, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8781, top5_acc: 0.9962, loss_cls: 0.6156, loss: 0.6156 +2025-06-24 10:47:42,580 - pyskl - INFO - Epoch [20][500/1281] lr: 2.398e-02, eta: 11:37:06, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8594, top5_acc: 0.9944, loss_cls: 0.6538, loss: 0.6538 +2025-06-24 10:48:24,034 - pyskl - INFO - Epoch [20][600/1281] lr: 2.398e-02, eta: 11:38:31, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9938, loss_cls: 0.6677, loss: 0.6677 +2025-06-24 10:49:05,474 - pyskl - INFO - Epoch [20][700/1281] lr: 2.397e-02, eta: 11:39:55, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9931, loss_cls: 0.6921, loss: 0.6921 +2025-06-24 10:49:46,956 - pyskl - INFO - Epoch [20][800/1281] lr: 2.396e-02, eta: 11:41:19, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9938, loss_cls: 0.7158, loss: 0.7158 +2025-06-24 10:50:28,506 - pyskl - INFO - Epoch [20][900/1281] lr: 2.395e-02, eta: 11:42:42, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8569, top5_acc: 0.9925, loss_cls: 0.7068, loss: 0.7068 +2025-06-24 10:51:09,721 - pyskl - INFO - Epoch [20][1000/1281] lr: 2.394e-02, eta: 11:44:02, time: 0.412, data_time: 0.000, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9938, loss_cls: 0.6855, loss: 0.6855 +2025-06-24 10:51:47,530 - pyskl - INFO - Epoch [20][1100/1281] lr: 2.393e-02, eta: 11:44:58, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8625, top5_acc: 0.9906, loss_cls: 0.6387, loss: 0.6387 +2025-06-24 10:52:17,681 - pyskl - INFO - Epoch [20][1200/1281] lr: 2.393e-02, eta: 11:45:04, time: 0.301, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9938, loss_cls: 0.6335, loss: 0.6335 +2025-06-24 10:52:54,798 - pyskl - INFO - Saving checkpoint at 20 epochs +2025-06-24 10:54:04,283 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:54:04,340 - pyskl - INFO - +top1_acc 0.7498 +top5_acc 0.9738 +2025-06-24 10:54:04,341 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:54:04,348 - pyskl - INFO - +mean_acc 0.6397 +2025-06-24 10:54:04,350 - pyskl - INFO - Epoch(val) [20][533] top1_acc: 0.7498, top5_acc: 0.9738, mean_class_accuracy: 0.6397 +2025-06-24 10:55:07,469 - pyskl - INFO - Epoch [21][100/1281] lr: 2.391e-02, eta: 11:46:09, time: 0.631, data_time: 0.200, memory: 4082, top1_acc: 0.8644, top5_acc: 0.9975, loss_cls: 0.6068, loss: 0.6068 +2025-06-24 10:55:49,001 - pyskl - INFO - Epoch [21][200/1281] lr: 2.390e-02, eta: 11:47:27, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8669, top5_acc: 0.9912, loss_cls: 0.6260, loss: 0.6260 +2025-06-24 10:56:30,364 - pyskl - INFO - Epoch [21][300/1281] lr: 2.389e-02, eta: 11:48:43, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8725, top5_acc: 0.9938, loss_cls: 0.6195, loss: 0.6195 +2025-06-24 10:57:11,708 - pyskl - INFO - Epoch [21][400/1281] lr: 2.389e-02, eta: 11:49:58, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8512, top5_acc: 0.9938, loss_cls: 0.6451, loss: 0.6451 +2025-06-24 10:57:53,110 - pyskl - INFO - Epoch [21][500/1281] lr: 2.388e-02, eta: 11:51:13, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8712, top5_acc: 0.9938, loss_cls: 0.6512, loss: 0.6512 +2025-06-24 10:58:34,361 - pyskl - INFO - Epoch [21][600/1281] lr: 2.387e-02, eta: 11:52:25, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8712, top5_acc: 0.9938, loss_cls: 0.6424, loss: 0.6424 +2025-06-24 10:59:15,679 - pyskl - INFO - Epoch [21][700/1281] lr: 2.386e-02, eta: 11:53:38, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8731, top5_acc: 0.9950, loss_cls: 0.6188, loss: 0.6188 +2025-06-24 10:59:57,101 - pyskl - INFO - Epoch [21][800/1281] lr: 2.385e-02, eta: 11:54:50, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8700, top5_acc: 0.9944, loss_cls: 0.5953, loss: 0.5953 +2025-06-24 11:00:38,556 - pyskl - INFO - Epoch [21][900/1281] lr: 2.384e-02, eta: 11:56:01, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9931, loss_cls: 0.6614, loss: 0.6614 +2025-06-24 11:01:19,869 - pyskl - INFO - Epoch [21][1000/1281] lr: 2.383e-02, eta: 11:57:11, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8800, top5_acc: 0.9969, loss_cls: 0.5992, loss: 0.5992 +2025-06-24 11:01:57,331 - pyskl - INFO - Epoch [21][1100/1281] lr: 2.383e-02, eta: 11:57:56, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9950, loss_cls: 0.6637, loss: 0.6637 +2025-06-24 11:02:28,105 - pyskl - INFO - Epoch [21][1200/1281] lr: 2.382e-02, eta: 11:57:59, time: 0.308, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9900, loss_cls: 0.7203, loss: 0.7203 +2025-06-24 11:03:05,059 - pyskl - INFO - Saving checkpoint at 21 epochs +2025-06-24 11:04:15,607 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:04:15,665 - pyskl - INFO - +top1_acc 0.8371 +top5_acc 0.9869 +2025-06-24 11:04:15,665 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:04:15,672 - pyskl - INFO - +mean_acc 0.7807 +2025-06-24 11:04:15,674 - pyskl - INFO - Epoch(val) [21][533] top1_acc: 0.8371, top5_acc: 0.9869, mean_class_accuracy: 0.7807 +2025-06-24 11:05:18,697 - pyskl - INFO - Epoch [22][100/1281] lr: 2.380e-02, eta: 11:58:49, time: 0.630, data_time: 0.197, memory: 4082, top1_acc: 0.8750, top5_acc: 0.9938, loss_cls: 0.6106, loss: 0.6106 +2025-06-24 11:06:00,972 - pyskl - INFO - Epoch [22][200/1281] lr: 2.379e-02, eta: 12:00:01, time: 0.423, data_time: 0.000, memory: 4082, top1_acc: 0.8675, top5_acc: 0.9938, loss_cls: 0.6098, loss: 0.6098 +2025-06-24 11:06:44,419 - pyskl - INFO - Epoch [22][300/1281] lr: 2.378e-02, eta: 12:01:20, time: 0.434, data_time: 0.000, memory: 4082, top1_acc: 0.8800, top5_acc: 0.9975, loss_cls: 0.5817, loss: 0.5817 +2025-06-24 11:07:28,050 - pyskl - INFO - Epoch [22][400/1281] lr: 2.378e-02, eta: 12:02:38, time: 0.436, data_time: 0.000, memory: 4082, top1_acc: 0.8625, top5_acc: 0.9981, loss_cls: 0.6290, loss: 0.6290 +2025-06-24 11:08:09,954 - pyskl - INFO - Epoch [22][500/1281] lr: 2.377e-02, eta: 12:03:46, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9962, loss_cls: 0.6444, loss: 0.6444 +2025-06-24 11:08:51,194 - pyskl - INFO - Epoch [22][600/1281] lr: 2.376e-02, eta: 12:04:49, time: 0.412, data_time: 0.000, memory: 4082, top1_acc: 0.8794, top5_acc: 0.9931, loss_cls: 0.6244, loss: 0.6244 +2025-06-24 11:09:32,559 - pyskl - INFO - Epoch [22][700/1281] lr: 2.375e-02, eta: 12:05:51, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8644, top5_acc: 0.9944, loss_cls: 0.6308, loss: 0.6308 +2025-06-24 11:10:13,924 - pyskl - INFO - Epoch [22][800/1281] lr: 2.374e-02, eta: 12:06:53, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8725, top5_acc: 0.9931, loss_cls: 0.6634, loss: 0.6634 +2025-06-24 11:10:55,288 - pyskl - INFO - Epoch [22][900/1281] lr: 2.373e-02, eta: 12:07:54, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8481, top5_acc: 0.9894, loss_cls: 0.7179, loss: 0.7179 +2025-06-24 11:11:36,609 - pyskl - INFO - Epoch [22][1000/1281] lr: 2.372e-02, eta: 12:08:55, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8806, top5_acc: 0.9956, loss_cls: 0.5623, loss: 0.5623 +2025-06-24 11:12:12,539 - pyskl - INFO - Epoch [22][1100/1281] lr: 2.371e-02, eta: 12:09:23, time: 0.359, data_time: 0.001, memory: 4082, top1_acc: 0.8631, top5_acc: 0.9919, loss_cls: 0.6724, loss: 0.6724 +2025-06-24 11:12:43,279 - pyskl - INFO - Epoch [22][1200/1281] lr: 2.370e-02, eta: 12:09:20, time: 0.307, data_time: 0.000, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9931, loss_cls: 0.6381, loss: 0.6381 +2025-06-24 11:13:20,075 - pyskl - INFO - Saving checkpoint at 22 epochs +2025-06-24 11:14:31,349 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:14:31,406 - pyskl - INFO - +top1_acc 0.8542 +top5_acc 0.9903 +2025-06-24 11:14:31,406 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:14:31,414 - pyskl - INFO - +mean_acc 0.7782 +2025-06-24 11:14:31,418 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_18.pth was removed +2025-06-24 11:14:31,606 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_22.pth. +2025-06-24 11:14:31,607 - pyskl - INFO - Best top1_acc is 0.8542 at 22 epoch. +2025-06-24 11:14:31,610 - pyskl - INFO - Epoch(val) [22][533] top1_acc: 0.8542, top5_acc: 0.9903, mean_class_accuracy: 0.7782 +2025-06-24 11:15:32,379 - pyskl - INFO - Epoch [23][100/1281] lr: 2.369e-02, eta: 12:09:44, time: 0.608, data_time: 0.193, memory: 4082, top1_acc: 0.8706, top5_acc: 0.9962, loss_cls: 0.5879, loss: 0.5879 +2025-06-24 11:16:13,879 - pyskl - INFO - Epoch [23][200/1281] lr: 2.368e-02, eta: 12:10:42, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8838, top5_acc: 0.9969, loss_cls: 0.5528, loss: 0.5528 +2025-06-24 11:16:55,418 - pyskl - INFO - Epoch [23][300/1281] lr: 2.367e-02, eta: 12:11:40, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8706, top5_acc: 0.9969, loss_cls: 0.5799, loss: 0.5799 +2025-06-24 11:17:36,967 - pyskl - INFO - Epoch [23][400/1281] lr: 2.366e-02, eta: 12:12:38, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8775, top5_acc: 0.9950, loss_cls: 0.6084, loss: 0.6084 +2025-06-24 11:18:18,261 - pyskl - INFO - Epoch [23][500/1281] lr: 2.365e-02, eta: 12:13:33, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8656, top5_acc: 0.9944, loss_cls: 0.6079, loss: 0.6079 +2025-06-24 11:18:59,845 - pyskl - INFO - Epoch [23][600/1281] lr: 2.364e-02, eta: 12:14:29, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8762, top5_acc: 0.9900, loss_cls: 0.6426, loss: 0.6426 +2025-06-24 11:19:41,136 - pyskl - INFO - Epoch [23][700/1281] lr: 2.363e-02, eta: 12:15:23, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8844, top5_acc: 0.9931, loss_cls: 0.6235, loss: 0.6235 +2025-06-24 11:20:22,680 - pyskl - INFO - Epoch [23][800/1281] lr: 2.362e-02, eta: 12:16:18, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8838, top5_acc: 0.9950, loss_cls: 0.5678, loss: 0.5678 +2025-06-24 11:21:04,091 - pyskl - INFO - Epoch [23][900/1281] lr: 2.361e-02, eta: 12:17:11, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8712, top5_acc: 0.9919, loss_cls: 0.6296, loss: 0.6296 +2025-06-24 11:21:45,271 - pyskl - INFO - Epoch [23][1000/1281] lr: 2.360e-02, eta: 12:18:03, time: 0.412, data_time: 0.000, memory: 4082, top1_acc: 0.8725, top5_acc: 0.9956, loss_cls: 0.6044, loss: 0.6044 +2025-06-24 11:22:22,041 - pyskl - INFO - Epoch [23][1100/1281] lr: 2.359e-02, eta: 12:18:29, time: 0.368, data_time: 0.000, memory: 4082, top1_acc: 0.8625, top5_acc: 0.9938, loss_cls: 0.6304, loss: 0.6304 +2025-06-24 11:22:53,533 - pyskl - INFO - Epoch [23][1200/1281] lr: 2.359e-02, eta: 12:18:26, time: 0.315, data_time: 0.000, memory: 4082, top1_acc: 0.8669, top5_acc: 0.9912, loss_cls: 0.6364, loss: 0.6364 +2025-06-24 11:23:29,773 - pyskl - INFO - Saving checkpoint at 23 epochs +2025-06-24 11:24:40,861 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:24:40,922 - pyskl - INFO - +top1_acc 0.8317 +top5_acc 0.9854 +2025-06-24 11:24:40,922 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:24:40,931 - pyskl - INFO - +mean_acc 0.7633 +2025-06-24 11:24:40,934 - pyskl - INFO - Epoch(val) [23][533] top1_acc: 0.8317, top5_acc: 0.9854, mean_class_accuracy: 0.7633 +2025-06-24 11:25:41,435 - pyskl - INFO - Epoch [24][100/1281] lr: 2.357e-02, eta: 12:18:38, time: 0.605, data_time: 0.191, memory: 4082, top1_acc: 0.8800, top5_acc: 0.9994, loss_cls: 0.5919, loss: 0.5919 +2025-06-24 11:26:22,952 - pyskl - INFO - Epoch [24][200/1281] lr: 2.356e-02, eta: 12:19:29, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8562, top5_acc: 0.9938, loss_cls: 0.6413, loss: 0.6413 +2025-06-24 11:27:04,365 - pyskl - INFO - Epoch [24][300/1281] lr: 2.355e-02, eta: 12:20:18, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8738, top5_acc: 0.9956, loss_cls: 0.6238, loss: 0.6238 +2025-06-24 11:27:45,921 - pyskl - INFO - Epoch [24][400/1281] lr: 2.354e-02, eta: 12:21:08, time: 0.416, data_time: 0.001, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9956, loss_cls: 0.6375, loss: 0.6375 +2025-06-24 11:28:27,393 - pyskl - INFO - Epoch [24][500/1281] lr: 2.353e-02, eta: 12:21:57, time: 0.415, data_time: 0.001, memory: 4082, top1_acc: 0.8538, top5_acc: 0.9900, loss_cls: 0.6589, loss: 0.6589 +2025-06-24 11:29:08,872 - pyskl - INFO - Epoch [24][600/1281] lr: 2.352e-02, eta: 12:22:45, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8675, top5_acc: 0.9938, loss_cls: 0.6543, loss: 0.6543 +2025-06-24 11:29:50,151 - pyskl - INFO - Epoch [24][700/1281] lr: 2.351e-02, eta: 12:23:32, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8900, top5_acc: 0.9962, loss_cls: 0.5404, loss: 0.5404 +2025-06-24 11:30:31,535 - pyskl - INFO - Epoch [24][800/1281] lr: 2.350e-02, eta: 12:24:18, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8769, top5_acc: 0.9931, loss_cls: 0.6213, loss: 0.6213 +2025-06-24 11:31:13,052 - pyskl - INFO - Epoch [24][900/1281] lr: 2.349e-02, eta: 12:25:05, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8688, top5_acc: 0.9969, loss_cls: 0.5747, loss: 0.5747 +2025-06-24 11:31:54,444 - pyskl - INFO - Epoch [24][1000/1281] lr: 2.348e-02, eta: 12:25:50, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8631, top5_acc: 0.9906, loss_cls: 0.6475, loss: 0.6475 +2025-06-24 11:32:31,227 - pyskl - INFO - Epoch [24][1100/1281] lr: 2.347e-02, eta: 12:26:11, time: 0.368, data_time: 0.000, memory: 4082, top1_acc: 0.8794, top5_acc: 0.9938, loss_cls: 0.5917, loss: 0.5917 +2025-06-24 11:33:01,802 - pyskl - INFO - Epoch [24][1200/1281] lr: 2.346e-02, eta: 12:25:58, time: 0.306, data_time: 0.000, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9962, loss_cls: 0.6330, loss: 0.6330 +2025-06-24 11:33:39,117 - pyskl - INFO - Saving checkpoint at 24 epochs +2025-06-24 11:34:49,446 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:34:49,502 - pyskl - INFO - +top1_acc 0.8403 +top5_acc 0.9896 +2025-06-24 11:34:49,502 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:34:49,510 - pyskl - INFO - +mean_acc 0.7694 +2025-06-24 11:34:49,512 - pyskl - INFO - Epoch(val) [24][533] top1_acc: 0.8403, top5_acc: 0.9896, mean_class_accuracy: 0.7694 +2025-06-24 11:35:50,555 - pyskl - INFO - Epoch [25][100/1281] lr: 2.344e-02, eta: 12:26:05, time: 0.610, data_time: 0.194, memory: 4082, top1_acc: 0.8944, top5_acc: 0.9962, loss_cls: 0.5158, loss: 0.5158 +2025-06-24 11:36:32,101 - pyskl - INFO - Epoch [25][200/1281] lr: 2.343e-02, eta: 12:26:49, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8806, top5_acc: 0.9956, loss_cls: 0.5417, loss: 0.5417 +2025-06-24 11:37:13,554 - pyskl - INFO - Epoch [25][300/1281] lr: 2.342e-02, eta: 12:27:32, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8738, top5_acc: 0.9962, loss_cls: 0.5874, loss: 0.5874 +2025-06-24 11:37:54,995 - pyskl - INFO - Epoch [25][400/1281] lr: 2.341e-02, eta: 12:28:15, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8938, top5_acc: 0.9975, loss_cls: 0.5205, loss: 0.5205 +2025-06-24 11:38:36,372 - pyskl - INFO - Epoch [25][500/1281] lr: 2.340e-02, eta: 12:28:56, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8850, top5_acc: 0.9919, loss_cls: 0.5789, loss: 0.5789 +2025-06-24 11:39:17,711 - pyskl - INFO - Epoch [25][600/1281] lr: 2.339e-02, eta: 12:29:37, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8738, top5_acc: 0.9925, loss_cls: 0.6243, loss: 0.6243 +2025-06-24 11:39:59,349 - pyskl - INFO - Epoch [25][700/1281] lr: 2.338e-02, eta: 12:30:19, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8725, top5_acc: 0.9919, loss_cls: 0.6059, loss: 0.6059 +2025-06-24 11:40:40,740 - pyskl - INFO - Epoch [25][800/1281] lr: 2.337e-02, eta: 12:30:59, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8788, top5_acc: 0.9981, loss_cls: 0.5945, loss: 0.5945 +2025-06-24 11:41:22,194 - pyskl - INFO - Epoch [25][900/1281] lr: 2.336e-02, eta: 12:31:39, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8594, top5_acc: 0.9969, loss_cls: 0.6705, loss: 0.6705 +2025-06-24 11:42:03,808 - pyskl - INFO - Epoch [25][1000/1281] lr: 2.335e-02, eta: 12:32:19, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9938, loss_cls: 0.6437, loss: 0.6437 +2025-06-24 11:42:41,383 - pyskl - INFO - Epoch [25][1100/1281] lr: 2.334e-02, eta: 12:32:38, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8800, top5_acc: 0.9906, loss_cls: 0.6120, loss: 0.6120 +2025-06-24 11:43:10,904 - pyskl - INFO - Epoch [25][1200/1281] lr: 2.333e-02, eta: 12:32:17, time: 0.295, data_time: 0.000, memory: 4082, top1_acc: 0.8700, top5_acc: 0.9944, loss_cls: 0.5978, loss: 0.5978 +2025-06-24 11:43:48,339 - pyskl - INFO - Saving checkpoint at 25 epochs +2025-06-24 11:44:58,130 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:44:58,184 - pyskl - INFO - +top1_acc 0.8187 +top5_acc 0.9881 +2025-06-24 11:44:58,185 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:44:58,192 - pyskl - INFO - +mean_acc 0.7542 +2025-06-24 11:44:58,193 - pyskl - INFO - Epoch(val) [25][533] top1_acc: 0.8187, top5_acc: 0.9881, mean_class_accuracy: 0.7542 +2025-06-24 11:45:59,922 - pyskl - INFO - Epoch [26][100/1281] lr: 2.332e-02, eta: 12:32:19, time: 0.617, data_time: 0.203, memory: 4082, top1_acc: 0.8850, top5_acc: 0.9950, loss_cls: 0.5757, loss: 0.5757 +2025-06-24 11:46:41,305 - pyskl - INFO - Epoch [26][200/1281] lr: 2.330e-02, eta: 12:32:57, time: 0.414, data_time: 0.001, memory: 4082, top1_acc: 0.8694, top5_acc: 0.9950, loss_cls: 0.6063, loss: 0.6063 +2025-06-24 11:47:22,710 - pyskl - INFO - Epoch [26][300/1281] lr: 2.329e-02, eta: 12:33:33, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8650, top5_acc: 0.9969, loss_cls: 0.5988, loss: 0.5988 +2025-06-24 11:48:04,121 - pyskl - INFO - Epoch [26][400/1281] lr: 2.328e-02, eta: 12:34:10, time: 0.414, data_time: 0.001, memory: 4082, top1_acc: 0.8631, top5_acc: 0.9931, loss_cls: 0.6201, loss: 0.6201 +2025-06-24 11:48:45,470 - pyskl - INFO - Epoch [26][500/1281] lr: 2.327e-02, eta: 12:34:45, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8781, top5_acc: 0.9925, loss_cls: 0.6072, loss: 0.6072 +2025-06-24 11:49:26,785 - pyskl - INFO - Epoch [26][600/1281] lr: 2.326e-02, eta: 12:35:20, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8762, top5_acc: 0.9938, loss_cls: 0.5898, loss: 0.5898 +2025-06-24 11:50:08,113 - pyskl - INFO - Epoch [26][700/1281] lr: 2.325e-02, eta: 12:35:55, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8838, top5_acc: 0.9981, loss_cls: 0.5485, loss: 0.5485 +2025-06-24 11:50:49,472 - pyskl - INFO - Epoch [26][800/1281] lr: 2.324e-02, eta: 12:36:29, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8769, top5_acc: 0.9944, loss_cls: 0.5719, loss: 0.5719 +2025-06-24 11:51:30,777 - pyskl - INFO - Epoch [26][900/1281] lr: 2.323e-02, eta: 12:37:02, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8812, top5_acc: 0.9969, loss_cls: 0.5477, loss: 0.5477 +2025-06-24 11:52:12,133 - pyskl - INFO - Epoch [26][1000/1281] lr: 2.322e-02, eta: 12:37:36, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8744, top5_acc: 0.9969, loss_cls: 0.5728, loss: 0.5728 +2025-06-24 11:52:49,232 - pyskl - INFO - Epoch [26][1100/1281] lr: 2.321e-02, eta: 12:37:48, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8525, top5_acc: 0.9925, loss_cls: 0.6811, loss: 0.6811 +2025-06-24 11:53:20,013 - pyskl - INFO - Epoch [26][1200/1281] lr: 2.320e-02, eta: 12:37:30, time: 0.308, data_time: 0.001, memory: 4082, top1_acc: 0.8769, top5_acc: 0.9925, loss_cls: 0.5801, loss: 0.5801 +2025-06-24 11:53:57,014 - pyskl - INFO - Saving checkpoint at 26 epochs +2025-06-24 11:55:08,062 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:55:08,132 - pyskl - INFO - +top1_acc 0.8487 +top5_acc 0.9891 +2025-06-24 11:55:08,132 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:55:08,141 - pyskl - INFO - +mean_acc 0.7743 +2025-06-24 11:55:08,143 - pyskl - INFO - Epoch(val) [26][533] top1_acc: 0.8487, top5_acc: 0.9891, mean_class_accuracy: 0.7743 +2025-06-24 11:56:10,689 - pyskl - INFO - Epoch [27][100/1281] lr: 2.318e-02, eta: 12:37:29, time: 0.625, data_time: 0.201, memory: 4082, top1_acc: 0.8875, top5_acc: 0.9938, loss_cls: 0.5667, loss: 0.5667 +2025-06-24 11:56:52,066 - pyskl - INFO - Epoch [27][200/1281] lr: 2.317e-02, eta: 12:38:01, time: 0.414, data_time: 0.001, memory: 4082, top1_acc: 0.8956, top5_acc: 0.9962, loss_cls: 0.5288, loss: 0.5288 +2025-06-24 11:57:33,547 - pyskl - INFO - Epoch [27][300/1281] lr: 2.316e-02, eta: 12:38:33, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8806, top5_acc: 0.9975, loss_cls: 0.5499, loss: 0.5499 +2025-06-24 11:58:14,964 - pyskl - INFO - Epoch [27][400/1281] lr: 2.315e-02, eta: 12:39:04, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8844, top5_acc: 0.9938, loss_cls: 0.5670, loss: 0.5670 +2025-06-24 11:58:56,405 - pyskl - INFO - Epoch [27][500/1281] lr: 2.314e-02, eta: 12:39:35, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8725, top5_acc: 0.9950, loss_cls: 0.5743, loss: 0.5743 +2025-06-24 11:59:37,891 - pyskl - INFO - Epoch [27][600/1281] lr: 2.313e-02, eta: 12:40:05, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8700, top5_acc: 0.9938, loss_cls: 0.6134, loss: 0.6134 +2025-06-24 12:00:19,337 - pyskl - INFO - Epoch [27][700/1281] lr: 2.312e-02, eta: 12:40:35, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8831, top5_acc: 0.9975, loss_cls: 0.5445, loss: 0.5445 +2025-06-24 12:01:00,700 - pyskl - INFO - Epoch [27][800/1281] lr: 2.311e-02, eta: 12:41:04, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8762, top5_acc: 0.9925, loss_cls: 0.5915, loss: 0.5915 +2025-06-24 12:01:42,134 - pyskl - INFO - Epoch [27][900/1281] lr: 2.310e-02, eta: 12:41:33, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8719, top5_acc: 0.9938, loss_cls: 0.6077, loss: 0.6077 +2025-06-24 12:02:23,545 - pyskl - INFO - Epoch [27][1000/1281] lr: 2.308e-02, eta: 12:42:02, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8662, top5_acc: 0.9925, loss_cls: 0.6205, loss: 0.6205 +2025-06-24 12:02:59,861 - pyskl - INFO - Epoch [27][1100/1281] lr: 2.307e-02, eta: 12:42:06, time: 0.363, data_time: 0.000, memory: 4082, top1_acc: 0.8900, top5_acc: 0.9956, loss_cls: 0.5079, loss: 0.5079 +2025-06-24 12:03:30,616 - pyskl - INFO - Epoch [27][1200/1281] lr: 2.306e-02, eta: 12:41:45, time: 0.308, data_time: 0.000, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9944, loss_cls: 0.6321, loss: 0.6321 +2025-06-24 12:04:07,331 - pyskl - INFO - Saving checkpoint at 27 epochs +2025-06-24 12:05:18,313 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:05:18,391 - pyskl - INFO - +top1_acc 0.8341 +top5_acc 0.9878 +2025-06-24 12:05:18,391 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:05:18,403 - pyskl - INFO - +mean_acc 0.7584 +2025-06-24 12:05:18,407 - pyskl - INFO - Epoch(val) [27][533] top1_acc: 0.8341, top5_acc: 0.9878, mean_class_accuracy: 0.7584 +2025-06-24 12:06:20,187 - pyskl - INFO - Epoch [28][100/1281] lr: 2.304e-02, eta: 12:41:35, time: 0.618, data_time: 0.203, memory: 4082, top1_acc: 0.8856, top5_acc: 0.9969, loss_cls: 0.5381, loss: 0.5381 +2025-06-24 12:07:01,671 - pyskl - INFO - Epoch [28][200/1281] lr: 2.303e-02, eta: 12:42:03, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8894, top5_acc: 0.9925, loss_cls: 0.5486, loss: 0.5486 +2025-06-24 12:07:43,138 - pyskl - INFO - Epoch [28][300/1281] lr: 2.302e-02, eta: 12:42:29, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8631, top5_acc: 0.9956, loss_cls: 0.6031, loss: 0.6031 +2025-06-24 12:08:24,692 - pyskl - INFO - Epoch [28][400/1281] lr: 2.301e-02, eta: 12:42:56, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8856, top5_acc: 0.9969, loss_cls: 0.5478, loss: 0.5478 +2025-06-24 12:09:06,006 - pyskl - INFO - Epoch [28][500/1281] lr: 2.300e-02, eta: 12:43:22, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8850, top5_acc: 0.9931, loss_cls: 0.5498, loss: 0.5498 +2025-06-24 12:09:47,449 - pyskl - INFO - Epoch [28][600/1281] lr: 2.299e-02, eta: 12:43:47, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.9000, top5_acc: 0.9956, loss_cls: 0.5373, loss: 0.5373 +2025-06-24 12:10:28,829 - pyskl - INFO - Epoch [28][700/1281] lr: 2.298e-02, eta: 12:44:12, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8719, top5_acc: 0.9950, loss_cls: 0.6131, loss: 0.6131 +2025-06-24 12:11:10,248 - pyskl - INFO - Epoch [28][800/1281] lr: 2.297e-02, eta: 12:44:37, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8775, top5_acc: 0.9944, loss_cls: 0.5961, loss: 0.5961 +2025-06-24 12:11:51,646 - pyskl - INFO - Epoch [28][900/1281] lr: 2.295e-02, eta: 12:45:01, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8775, top5_acc: 0.9969, loss_cls: 0.5597, loss: 0.5597 +2025-06-24 12:12:32,976 - pyskl - INFO - Epoch [28][1000/1281] lr: 2.294e-02, eta: 12:45:25, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8812, top5_acc: 0.9944, loss_cls: 0.6017, loss: 0.6017 +2025-06-24 12:13:09,125 - pyskl - INFO - Epoch [28][1100/1281] lr: 2.293e-02, eta: 12:45:26, time: 0.361, data_time: 0.000, memory: 4082, top1_acc: 0.8712, top5_acc: 0.9962, loss_cls: 0.6116, loss: 0.6116 +2025-06-24 12:13:40,965 - pyskl - INFO - Epoch [28][1200/1281] lr: 2.292e-02, eta: 12:45:07, time: 0.318, data_time: 0.000, memory: 4082, top1_acc: 0.8656, top5_acc: 0.9925, loss_cls: 0.6184, loss: 0.6184 +2025-06-24 12:14:16,840 - pyskl - INFO - Saving checkpoint at 28 epochs +2025-06-24 12:15:28,320 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:15:28,377 - pyskl - INFO - +top1_acc 0.8433 +top5_acc 0.9890 +2025-06-24 12:15:28,377 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:15:28,386 - pyskl - INFO - +mean_acc 0.7635 +2025-06-24 12:15:28,388 - pyskl - INFO - Epoch(val) [28][533] top1_acc: 0.8433, top5_acc: 0.9890, mean_class_accuracy: 0.7635 +2025-06-24 12:16:29,859 - pyskl - INFO - Epoch [29][100/1281] lr: 2.290e-02, eta: 12:44:50, time: 0.615, data_time: 0.200, memory: 4082, top1_acc: 0.8838, top5_acc: 0.9956, loss_cls: 0.5460, loss: 0.5460 +2025-06-24 12:17:11,199 - pyskl - INFO - Epoch [29][200/1281] lr: 2.289e-02, eta: 12:45:12, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.9025, top5_acc: 0.9988, loss_cls: 0.5085, loss: 0.5085 +2025-06-24 12:17:52,760 - pyskl - INFO - Epoch [29][300/1281] lr: 2.288e-02, eta: 12:45:35, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8912, top5_acc: 0.9969, loss_cls: 0.5285, loss: 0.5285 +2025-06-24 12:18:34,090 - pyskl - INFO - Epoch [29][400/1281] lr: 2.287e-02, eta: 12:45:57, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8812, top5_acc: 0.9950, loss_cls: 0.5744, loss: 0.5744 +2025-06-24 12:19:15,473 - pyskl - INFO - Epoch [29][500/1281] lr: 2.285e-02, eta: 12:46:18, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8812, top5_acc: 0.9938, loss_cls: 0.6147, loss: 0.6147 +2025-06-24 12:19:56,951 - pyskl - INFO - Epoch [29][600/1281] lr: 2.284e-02, eta: 12:46:40, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.9056, top5_acc: 0.9988, loss_cls: 0.4897, loss: 0.4897 +2025-06-24 12:20:38,332 - pyskl - INFO - Epoch [29][700/1281] lr: 2.283e-02, eta: 12:47:01, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8862, top5_acc: 0.9950, loss_cls: 0.5274, loss: 0.5274 +2025-06-24 12:21:19,898 - pyskl - INFO - Epoch [29][800/1281] lr: 2.282e-02, eta: 12:47:22, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8925, top5_acc: 0.9938, loss_cls: 0.5566, loss: 0.5566 +2025-06-24 12:22:01,291 - pyskl - INFO - Epoch [29][900/1281] lr: 2.281e-02, eta: 12:47:42, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8788, top5_acc: 0.9931, loss_cls: 0.5855, loss: 0.5855 +2025-06-24 12:22:42,623 - pyskl - INFO - Epoch [29][1000/1281] lr: 2.280e-02, eta: 12:48:02, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8819, top5_acc: 0.9962, loss_cls: 0.5600, loss: 0.5600 +2025-06-24 12:23:18,584 - pyskl - INFO - Epoch [29][1100/1281] lr: 2.279e-02, eta: 12:47:58, time: 0.360, data_time: 0.000, memory: 4082, top1_acc: 0.8681, top5_acc: 0.9956, loss_cls: 0.6217, loss: 0.6217 +2025-06-24 12:23:50,592 - pyskl - INFO - Epoch [29][1200/1281] lr: 2.277e-02, eta: 12:47:38, time: 0.320, data_time: 0.000, memory: 4082, top1_acc: 0.8800, top5_acc: 0.9950, loss_cls: 0.5622, loss: 0.5622 +2025-06-24 12:24:26,138 - pyskl - INFO - Saving checkpoint at 29 epochs +2025-06-24 12:25:38,170 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:25:38,227 - pyskl - INFO - +top1_acc 0.8093 +top5_acc 0.9835 +2025-06-24 12:25:38,227 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:25:38,235 - pyskl - INFO - +mean_acc 0.7359 +2025-06-24 12:25:38,237 - pyskl - INFO - Epoch(val) [29][533] top1_acc: 0.8093, top5_acc: 0.9835, mean_class_accuracy: 0.7359 +2025-06-24 12:26:47,646 - pyskl - INFO - Epoch [30][100/1281] lr: 2.275e-02, eta: 12:47:50, time: 0.694, data_time: 0.197, memory: 4082, top1_acc: 0.8875, top5_acc: 0.9969, loss_cls: 0.5374, loss: 0.5374 +2025-06-24 12:27:37,321 - pyskl - INFO - Epoch [30][200/1281] lr: 2.274e-02, eta: 12:48:43, time: 0.497, data_time: 0.001, memory: 4082, top1_acc: 0.8862, top5_acc: 0.9938, loss_cls: 0.5678, loss: 0.5678 +2025-06-24 12:28:25,924 - pyskl - INFO - Epoch [30][300/1281] lr: 2.273e-02, eta: 12:49:31, time: 0.486, data_time: 0.000, memory: 4082, top1_acc: 0.8781, top5_acc: 0.9931, loss_cls: 0.5803, loss: 0.5803 +2025-06-24 12:29:16,616 - pyskl - INFO - Epoch [30][400/1281] lr: 2.272e-02, eta: 12:50:27, time: 0.507, data_time: 0.000, memory: 4082, top1_acc: 0.8988, top5_acc: 0.9956, loss_cls: 0.5142, loss: 0.5142 +2025-06-24 12:30:06,943 - pyskl - INFO - Epoch [30][500/1281] lr: 2.271e-02, eta: 12:51:20, time: 0.503, data_time: 0.000, memory: 4082, top1_acc: 0.8850, top5_acc: 0.9931, loss_cls: 0.5687, loss: 0.5687 +2025-06-24 12:30:57,343 - pyskl - INFO - Epoch [30][600/1281] lr: 2.269e-02, eta: 12:52:14, time: 0.504, data_time: 0.000, memory: 4082, top1_acc: 0.8894, top5_acc: 0.9975, loss_cls: 0.5388, loss: 0.5388 +2025-06-24 12:31:48,667 - pyskl - INFO - Epoch [30][700/1281] lr: 2.268e-02, eta: 12:53:11, time: 0.513, data_time: 0.000, memory: 4082, top1_acc: 0.8725, top5_acc: 0.9969, loss_cls: 0.5816, loss: 0.5816 +2025-06-24 12:32:40,033 - pyskl - INFO - Epoch [30][800/1281] lr: 2.267e-02, eta: 12:54:07, time: 0.514, data_time: 0.000, memory: 4082, top1_acc: 0.8950, top5_acc: 0.9931, loss_cls: 0.5358, loss: 0.5358 +2025-06-24 12:33:09,828 - pyskl - INFO - Epoch [30][900/1281] lr: 2.266e-02, eta: 12:53:36, time: 0.298, data_time: 0.000, memory: 4082, top1_acc: 0.8912, top5_acc: 0.9938, loss_cls: 0.5500, loss: 0.5500 +2025-06-24 12:34:00,747 - pyskl - INFO - Epoch [30][1000/1281] lr: 2.265e-02, eta: 12:54:30, time: 0.509, data_time: 0.000, memory: 4082, top1_acc: 0.8762, top5_acc: 0.9969, loss_cls: 0.5601, loss: 0.5601 +2025-06-24 12:34:33,399 - pyskl - INFO - Epoch [30][1100/1281] lr: 2.263e-02, eta: 12:54:10, time: 0.326, data_time: 0.000, memory: 4082, top1_acc: 0.8731, top5_acc: 0.9981, loss_cls: 0.5736, loss: 0.5736 +2025-06-24 12:35:24,222 - pyskl - INFO - Epoch [30][1200/1281] lr: 2.262e-02, eta: 12:55:02, time: 0.508, data_time: 0.000, memory: 4082, top1_acc: 0.8994, top5_acc: 0.9925, loss_cls: 0.5220, loss: 0.5220 +2025-06-24 12:36:06,016 - pyskl - INFO - Saving checkpoint at 30 epochs +2025-06-24 12:37:17,390 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:37:17,448 - pyskl - INFO - +top1_acc 0.8357 +top5_acc 0.9871 +2025-06-24 12:37:17,448 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:37:17,456 - pyskl - INFO - +mean_acc 0.7821 +2025-06-24 12:37:17,458 - pyskl - INFO - Epoch(val) [30][533] top1_acc: 0.8357, top5_acc: 0.9871, mean_class_accuracy: 0.7821 +2025-06-24 12:38:53,486 - pyskl - INFO - Epoch [31][100/1281] lr: 2.260e-02, eta: 12:56:52, time: 0.960, data_time: 0.195, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9969, loss_cls: 0.7596, loss: 0.7596 +2025-06-24 12:39:46,966 - pyskl - INFO - Epoch [31][200/1281] lr: 2.259e-02, eta: 12:57:54, time: 0.535, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9944, loss_cls: 0.7014, loss: 0.7014 +2025-06-24 12:40:40,176 - pyskl - INFO - Epoch [31][300/1281] lr: 2.258e-02, eta: 12:58:54, time: 0.532, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9962, loss_cls: 0.7403, loss: 0.7403 +2025-06-24 12:41:32,248 - pyskl - INFO - Epoch [31][400/1281] lr: 2.256e-02, eta: 12:59:49, time: 0.521, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9969, loss_cls: 0.6760, loss: 0.6760 +2025-06-24 12:42:04,791 - pyskl - INFO - Epoch [31][500/1281] lr: 2.255e-02, eta: 12:59:26, time: 0.325, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9956, loss_cls: 0.7451, loss: 0.7451 +2025-06-24 12:42:55,767 - pyskl - INFO - Epoch [31][600/1281] lr: 2.254e-02, eta: 13:00:16, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9950, loss_cls: 0.7317, loss: 0.7317 +2025-06-24 12:43:29,522 - pyskl - INFO - Epoch [31][700/1281] lr: 2.253e-02, eta: 12:59:58, time: 0.338, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9950, loss_cls: 0.7527, loss: 0.7527 +2025-06-24 12:44:22,324 - pyskl - INFO - Epoch [31][800/1281] lr: 2.252e-02, eta: 13:00:54, time: 0.528, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9962, loss_cls: 0.8095, loss: 0.8095 +2025-06-24 12:45:14,760 - pyskl - INFO - Epoch [31][900/1281] lr: 2.250e-02, eta: 13:01:48, time: 0.524, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9931, loss_cls: 0.7727, loss: 0.7727 +2025-06-24 12:46:07,397 - pyskl - INFO - Epoch [31][1000/1281] lr: 2.249e-02, eta: 13:02:42, time: 0.526, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9962, loss_cls: 0.7031, loss: 0.7031 +2025-06-24 12:46:58,071 - pyskl - INFO - Epoch [31][1100/1281] lr: 2.248e-02, eta: 13:03:28, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9931, loss_cls: 0.8338, loss: 0.8338 +2025-06-24 12:47:49,987 - pyskl - INFO - Epoch [31][1200/1281] lr: 2.247e-02, eta: 13:04:19, time: 0.519, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9925, loss_cls: 0.8099, loss: 0.8099 +2025-06-24 12:48:33,553 - pyskl - INFO - Saving checkpoint at 31 epochs +2025-06-24 12:49:45,232 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:49:45,287 - pyskl - INFO - +top1_acc 0.8410 +top5_acc 0.9892 +2025-06-24 12:49:45,287 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:49:45,294 - pyskl - INFO - +mean_acc 0.7870 +2025-06-24 12:49:45,296 - pyskl - INFO - Epoch(val) [31][533] top1_acc: 0.8410, top5_acc: 0.9892, mean_class_accuracy: 0.7870 +2025-06-24 12:50:59,003 - pyskl - INFO - Epoch [32][100/1281] lr: 2.244e-02, eta: 13:04:31, time: 0.737, data_time: 0.207, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9981, loss_cls: 0.6500, loss: 0.6500 +2025-06-24 12:51:50,212 - pyskl - INFO - Epoch [32][200/1281] lr: 2.243e-02, eta: 13:05:18, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9956, loss_cls: 0.6847, loss: 0.6847 +2025-06-24 12:52:20,929 - pyskl - INFO - Epoch [32][300/1281] lr: 2.242e-02, eta: 13:04:46, time: 0.307, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9925, loss_cls: 0.7106, loss: 0.7106 +2025-06-24 12:53:14,754 - pyskl - INFO - Epoch [32][400/1281] lr: 2.241e-02, eta: 13:05:42, time: 0.538, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9975, loss_cls: 0.6868, loss: 0.6868 +2025-06-24 12:54:07,843 - pyskl - INFO - Epoch [32][500/1281] lr: 2.239e-02, eta: 13:06:34, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9969, loss_cls: 0.6310, loss: 0.6310 +2025-06-24 12:55:01,660 - pyskl - INFO - Epoch [32][600/1281] lr: 2.238e-02, eta: 13:07:29, time: 0.538, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9931, loss_cls: 0.6195, loss: 0.6195 +2025-06-24 12:55:55,356 - pyskl - INFO - Epoch [32][700/1281] lr: 2.237e-02, eta: 13:08:22, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9969, loss_cls: 0.6806, loss: 0.6806 +2025-06-24 12:56:48,433 - pyskl - INFO - Epoch [32][800/1281] lr: 2.236e-02, eta: 13:09:13, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9981, loss_cls: 0.6215, loss: 0.6215 +2025-06-24 12:57:42,637 - pyskl - INFO - Epoch [32][900/1281] lr: 2.234e-02, eta: 13:10:08, time: 0.542, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9944, loss_cls: 0.6444, loss: 0.6444 +2025-06-24 12:58:35,997 - pyskl - INFO - Epoch [32][1000/1281] lr: 2.233e-02, eta: 13:10:58, time: 0.534, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9956, loss_cls: 0.6922, loss: 0.6922 +2025-06-24 12:59:30,071 - pyskl - INFO - Epoch [32][1100/1281] lr: 2.232e-02, eta: 13:11:51, time: 0.541, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9938, loss_cls: 0.6454, loss: 0.6454 +2025-06-24 13:00:12,456 - pyskl - INFO - Epoch [32][1200/1281] lr: 2.231e-02, eta: 13:12:01, time: 0.424, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9962, loss_cls: 0.6020, loss: 0.6020 +2025-06-24 13:00:50,680 - pyskl - INFO - Saving checkpoint at 32 epochs +2025-06-24 13:01:51,626 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:01:51,700 - pyskl - INFO - +top1_acc 0.8359 +top5_acc 0.9859 +2025-06-24 13:01:51,701 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:01:51,712 - pyskl - INFO - +mean_acc 0.7698 +2025-06-24 13:01:51,715 - pyskl - INFO - Epoch(val) [32][533] top1_acc: 0.8359, top5_acc: 0.9859, mean_class_accuracy: 0.7698 +2025-06-24 13:03:19,132 - pyskl - INFO - Epoch [33][100/1281] lr: 2.228e-02, eta: 13:12:56, time: 0.874, data_time: 0.193, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9975, loss_cls: 0.5939, loss: 0.5939 +2025-06-24 13:04:13,055 - pyskl - INFO - Epoch [33][200/1281] lr: 2.227e-02, eta: 13:13:47, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9956, loss_cls: 0.6315, loss: 0.6315 +2025-06-24 13:05:06,788 - pyskl - INFO - Epoch [33][300/1281] lr: 2.226e-02, eta: 13:14:36, time: 0.537, data_time: 0.001, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9950, loss_cls: 0.5401, loss: 0.5401 +2025-06-24 13:05:59,557 - pyskl - INFO - Epoch [33][400/1281] lr: 2.225e-02, eta: 13:15:22, time: 0.528, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9962, loss_cls: 0.5529, loss: 0.5529 +2025-06-24 13:06:52,854 - pyskl - INFO - Epoch [33][500/1281] lr: 2.223e-02, eta: 13:16:09, time: 0.533, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9981, loss_cls: 0.6148, loss: 0.6148 +2025-06-24 13:07:47,228 - pyskl - INFO - Epoch [33][600/1281] lr: 2.222e-02, eta: 13:16:59, time: 0.544, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9944, loss_cls: 0.6388, loss: 0.6388 +2025-06-24 13:08:40,746 - pyskl - INFO - Epoch [33][700/1281] lr: 2.221e-02, eta: 13:17:46, time: 0.535, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9956, loss_cls: 0.5838, loss: 0.5838 +2025-06-24 13:09:14,316 - pyskl - INFO - Epoch [33][800/1281] lr: 2.219e-02, eta: 13:17:20, time: 0.336, data_time: 0.001, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9944, loss_cls: 0.5895, loss: 0.5895 +2025-06-24 13:10:05,277 - pyskl - INFO - Epoch [33][900/1281] lr: 2.218e-02, eta: 13:17:57, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9938, loss_cls: 0.6086, loss: 0.6086 +2025-06-24 13:10:40,107 - pyskl - INFO - Epoch [33][1000/1281] lr: 2.217e-02, eta: 13:17:36, time: 0.348, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9969, loss_cls: 0.5617, loss: 0.5617 +2025-06-24 13:11:34,467 - pyskl - INFO - Epoch [33][1100/1281] lr: 2.216e-02, eta: 13:18:24, time: 0.544, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9925, loss_cls: 0.6875, loss: 0.6875 +2025-06-24 13:12:28,221 - pyskl - INFO - Epoch [33][1200/1281] lr: 2.214e-02, eta: 13:19:10, time: 0.538, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9938, loss_cls: 0.5428, loss: 0.5428 +2025-06-24 13:13:11,739 - pyskl - INFO - Saving checkpoint at 33 epochs +2025-06-24 13:14:24,267 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:14:24,338 - pyskl - INFO - +top1_acc 0.8294 +top5_acc 0.9845 +2025-06-24 13:14:24,338 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:14:24,345 - pyskl - INFO - +mean_acc 0.7640 +2025-06-24 13:14:24,347 - pyskl - INFO - Epoch(val) [33][533] top1_acc: 0.8294, top5_acc: 0.9845, mean_class_accuracy: 0.7640 +2025-06-24 13:15:51,479 - pyskl - INFO - Epoch [34][100/1281] lr: 2.212e-02, eta: 13:19:55, time: 0.871, data_time: 0.195, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9988, loss_cls: 0.5538, loss: 0.5538 +2025-06-24 13:16:44,870 - pyskl - INFO - Epoch [34][200/1281] lr: 2.211e-02, eta: 13:20:38, time: 0.534, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9938, loss_cls: 0.5447, loss: 0.5447 +2025-06-24 13:17:38,822 - pyskl - INFO - Epoch [34][300/1281] lr: 2.209e-02, eta: 13:21:23, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9975, loss_cls: 0.5912, loss: 0.5912 +2025-06-24 13:18:08,250 - pyskl - INFO - Epoch [34][400/1281] lr: 2.208e-02, eta: 13:20:41, time: 0.294, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9950, loss_cls: 0.6062, loss: 0.6062 +2025-06-24 13:18:59,334 - pyskl - INFO - Epoch [34][500/1281] lr: 2.207e-02, eta: 13:21:15, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9950, loss_cls: 0.5554, loss: 0.5554 +2025-06-24 13:19:36,118 - pyskl - INFO - Epoch [34][600/1281] lr: 2.205e-02, eta: 13:20:59, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9988, loss_cls: 0.5643, loss: 0.5643 +2025-06-24 13:20:30,165 - pyskl - INFO - Epoch [34][700/1281] lr: 2.204e-02, eta: 13:21:43, time: 0.540, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9950, loss_cls: 0.6534, loss: 0.6534 +2025-06-24 13:21:23,385 - pyskl - INFO - Epoch [34][800/1281] lr: 2.203e-02, eta: 13:22:23, time: 0.532, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9956, loss_cls: 0.6227, loss: 0.6227 +2025-06-24 13:22:17,566 - pyskl - INFO - Epoch [34][900/1281] lr: 2.201e-02, eta: 13:23:07, time: 0.542, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9950, loss_cls: 0.5543, loss: 0.5543 +2025-06-24 13:23:11,222 - pyskl - INFO - Epoch [34][1000/1281] lr: 2.200e-02, eta: 13:23:47, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9950, loss_cls: 0.6440, loss: 0.6440 +2025-06-24 13:24:05,170 - pyskl - INFO - Epoch [34][1100/1281] lr: 2.199e-02, eta: 13:24:29, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9950, loss_cls: 0.5738, loss: 0.5738 +2025-06-24 13:24:58,544 - pyskl - INFO - Epoch [34][1200/1281] lr: 2.197e-02, eta: 13:25:08, time: 0.534, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9994, loss_cls: 0.5807, loss: 0.5807 +2025-06-24 13:25:41,377 - pyskl - INFO - Saving checkpoint at 34 epochs +2025-06-24 13:26:52,453 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:26:52,510 - pyskl - INFO - +top1_acc 0.8542 +top5_acc 0.9896 +2025-06-24 13:26:52,510 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:26:52,517 - pyskl - INFO - +mean_acc 0.7938 +2025-06-24 13:26:52,519 - pyskl - INFO - Epoch(val) [34][533] top1_acc: 0.8542, top5_acc: 0.9896, mean_class_accuracy: 0.7938 +2025-06-24 13:28:04,101 - pyskl - INFO - Epoch [35][100/1281] lr: 2.195e-02, eta: 13:24:53, time: 0.716, data_time: 0.194, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9944, loss_cls: 0.5693, loss: 0.5693 +2025-06-24 13:28:36,693 - pyskl - INFO - Epoch [35][200/1281] lr: 2.194e-02, eta: 13:24:20, time: 0.326, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9981, loss_cls: 0.6012, loss: 0.6012 +2025-06-24 13:29:30,318 - pyskl - INFO - Epoch [35][300/1281] lr: 2.192e-02, eta: 13:24:59, time: 0.536, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9988, loss_cls: 0.5536, loss: 0.5536 +2025-06-24 13:30:23,824 - pyskl - INFO - Epoch [35][400/1281] lr: 2.191e-02, eta: 13:25:37, time: 0.535, data_time: 0.001, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9931, loss_cls: 0.5939, loss: 0.5939 +2025-06-24 13:31:17,091 - pyskl - INFO - Epoch [35][500/1281] lr: 2.190e-02, eta: 13:26:14, time: 0.533, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9944, loss_cls: 0.5614, loss: 0.5614 +2025-06-24 13:32:10,658 - pyskl - INFO - Epoch [35][600/1281] lr: 2.188e-02, eta: 13:26:52, time: 0.536, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9944, loss_cls: 0.6210, loss: 0.6210 +2025-06-24 13:33:04,624 - pyskl - INFO - Epoch [35][700/1281] lr: 2.187e-02, eta: 13:27:30, time: 0.540, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9950, loss_cls: 0.5426, loss: 0.5426 +2025-06-24 13:33:58,376 - pyskl - INFO - Epoch [35][800/1281] lr: 2.185e-02, eta: 13:28:07, time: 0.538, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9981, loss_cls: 0.4814, loss: 0.4814 +2025-06-24 13:34:51,902 - pyskl - INFO - Epoch [35][900/1281] lr: 2.184e-02, eta: 13:28:43, time: 0.535, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9962, loss_cls: 0.5923, loss: 0.5923 +2025-06-24 13:35:45,458 - pyskl - INFO - Epoch [35][1000/1281] lr: 2.183e-02, eta: 13:29:19, time: 0.536, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9962, loss_cls: 0.5444, loss: 0.5444 +2025-06-24 13:36:26,192 - pyskl - INFO - Epoch [35][1100/1281] lr: 2.181e-02, eta: 13:29:12, time: 0.407, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9956, loss_cls: 0.6053, loss: 0.6053 +2025-06-24 13:37:17,196 - pyskl - INFO - Epoch [35][1200/1281] lr: 2.180e-02, eta: 13:29:38, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9956, loss_cls: 0.5796, loss: 0.5796 +2025-06-24 13:37:36,429 - pyskl - INFO - Saving checkpoint at 35 epochs +2025-06-24 13:38:48,338 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:38:48,402 - pyskl - INFO - +top1_acc 0.8365 +top5_acc 0.9889 +2025-06-24 13:38:48,403 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:38:48,409 - pyskl - INFO - +mean_acc 0.7997 +2025-06-24 13:38:48,411 - pyskl - INFO - Epoch(val) [35][533] top1_acc: 0.8365, top5_acc: 0.9889, mean_class_accuracy: 0.7997 +2025-06-24 13:40:12,921 - pyskl - INFO - Epoch [36][100/1281] lr: 2.178e-02, eta: 13:30:00, time: 0.845, data_time: 0.195, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9944, loss_cls: 0.5446, loss: 0.5446 +2025-06-24 13:41:06,583 - pyskl - INFO - Epoch [36][200/1281] lr: 2.176e-02, eta: 13:30:35, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9950, loss_cls: 0.5804, loss: 0.5804 +2025-06-24 13:41:59,788 - pyskl - INFO - Epoch [36][300/1281] lr: 2.175e-02, eta: 13:31:07, time: 0.532, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9981, loss_cls: 0.5673, loss: 0.5673 +2025-06-24 13:42:53,825 - pyskl - INFO - Epoch [36][400/1281] lr: 2.173e-02, eta: 13:31:42, time: 0.540, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9956, loss_cls: 0.5855, loss: 0.5855 +2025-06-24 13:43:47,268 - pyskl - INFO - Epoch [36][500/1281] lr: 2.172e-02, eta: 13:32:15, time: 0.534, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9969, loss_cls: 0.5480, loss: 0.5480 +2025-06-24 13:44:41,154 - pyskl - INFO - Epoch [36][600/1281] lr: 2.171e-02, eta: 13:32:48, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9988, loss_cls: 0.5388, loss: 0.5388 +2025-06-24 13:45:21,189 - pyskl - INFO - Epoch [36][700/1281] lr: 2.169e-02, eta: 13:32:37, time: 0.400, data_time: 0.001, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9962, loss_cls: 0.5813, loss: 0.5813 +2025-06-24 13:46:12,184 - pyskl - INFO - Epoch [36][800/1281] lr: 2.168e-02, eta: 13:33:01, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9962, loss_cls: 0.5077, loss: 0.5077 +2025-06-24 13:46:39,639 - pyskl - INFO - Epoch [36][900/1281] lr: 2.167e-02, eta: 13:32:09, time: 0.275, data_time: 0.001, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9950, loss_cls: 0.6018, loss: 0.6018 +2025-06-24 13:47:33,046 - pyskl - INFO - Epoch [36][1000/1281] lr: 2.165e-02, eta: 13:32:40, time: 0.534, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9975, loss_cls: 0.5961, loss: 0.5961 +2025-06-24 13:48:22,894 - pyskl - INFO - Epoch [36][1100/1281] lr: 2.164e-02, eta: 13:32:59, time: 0.498, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9962, loss_cls: 0.6079, loss: 0.6079 +2025-06-24 13:49:12,468 - pyskl - INFO - Epoch [36][1200/1281] lr: 2.162e-02, eta: 13:33:17, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9938, loss_cls: 0.5862, loss: 0.5862 +2025-06-24 13:49:56,457 - pyskl - INFO - Saving checkpoint at 36 epochs +2025-06-24 13:51:07,988 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:51:08,043 - pyskl - INFO - +top1_acc 0.8359 +top5_acc 0.9903 +2025-06-24 13:51:08,043 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:51:08,050 - pyskl - INFO - +mean_acc 0.7770 +2025-06-24 13:51:08,052 - pyskl - INFO - Epoch(val) [36][533] top1_acc: 0.8359, top5_acc: 0.9903, mean_class_accuracy: 0.7770 +2025-06-24 13:52:35,024 - pyskl - INFO - Epoch [37][100/1281] lr: 2.160e-02, eta: 13:33:40, time: 0.870, data_time: 0.201, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9962, loss_cls: 0.5571, loss: 0.5571 +2025-06-24 13:53:28,715 - pyskl - INFO - Epoch [37][200/1281] lr: 2.158e-02, eta: 13:34:10, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9962, loss_cls: 0.5680, loss: 0.5680 +2025-06-24 13:54:14,744 - pyskl - INFO - Epoch [37][300/1281] lr: 2.157e-02, eta: 13:34:16, time: 0.460, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9950, loss_cls: 0.6040, loss: 0.6040 +2025-06-24 13:55:00,826 - pyskl - INFO - Epoch [37][400/1281] lr: 2.156e-02, eta: 13:34:22, time: 0.461, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9969, loss_cls: 0.5396, loss: 0.5396 +2025-06-24 13:55:28,269 - pyskl - INFO - Epoch [37][500/1281] lr: 2.154e-02, eta: 13:33:29, time: 0.274, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9962, loss_cls: 0.4958, loss: 0.4958 +2025-06-24 13:56:21,012 - pyskl - INFO - Epoch [37][600/1281] lr: 2.153e-02, eta: 13:33:56, time: 0.527, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9969, loss_cls: 0.5150, loss: 0.5150 +2025-06-24 13:57:14,653 - pyskl - INFO - Epoch [37][700/1281] lr: 2.151e-02, eta: 13:34:24, time: 0.536, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9975, loss_cls: 0.5552, loss: 0.5552 +2025-06-24 13:58:07,793 - pyskl - INFO - Epoch [37][800/1281] lr: 2.150e-02, eta: 13:34:51, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9975, loss_cls: 0.5610, loss: 0.5610 +2025-06-24 13:59:01,297 - pyskl - INFO - Epoch [37][900/1281] lr: 2.149e-02, eta: 13:35:19, time: 0.535, data_time: 0.001, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9938, loss_cls: 0.5721, loss: 0.5721 +2025-06-24 13:59:55,683 - pyskl - INFO - Epoch [37][1000/1281] lr: 2.147e-02, eta: 13:35:49, time: 0.544, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9962, loss_cls: 0.6321, loss: 0.6321 +2025-06-24 14:00:49,882 - pyskl - INFO - Epoch [37][1100/1281] lr: 2.146e-02, eta: 13:36:18, time: 0.542, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9950, loss_cls: 0.5578, loss: 0.5578 +2025-06-24 14:01:44,098 - pyskl - INFO - Epoch [37][1200/1281] lr: 2.144e-02, eta: 13:36:46, time: 0.542, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9962, loss_cls: 0.5530, loss: 0.5530 +2025-06-24 14:02:27,730 - pyskl - INFO - Saving checkpoint at 37 epochs +2025-06-24 14:03:28,889 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:03:28,974 - pyskl - INFO - +top1_acc 0.8657 +top5_acc 0.9924 +2025-06-24 14:03:28,975 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:03:28,995 - pyskl - INFO - +mean_acc 0.8169 +2025-06-24 14:03:29,009 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_22.pth was removed +2025-06-24 14:03:29,199 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_37.pth. +2025-06-24 14:03:29,200 - pyskl - INFO - Best top1_acc is 0.8657 at 37 epoch. +2025-06-24 14:03:29,203 - pyskl - INFO - Epoch(val) [37][533] top1_acc: 0.8657, top5_acc: 0.9924, mean_class_accuracy: 0.8169 +2025-06-24 14:04:31,091 - pyskl - INFO - Epoch [38][100/1281] lr: 2.142e-02, eta: 13:35:47, time: 0.619, data_time: 0.196, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9994, loss_cls: 0.5109, loss: 0.5109 +2025-06-24 14:05:23,590 - pyskl - INFO - Epoch [38][200/1281] lr: 2.140e-02, eta: 13:36:10, time: 0.525, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9981, loss_cls: 0.4781, loss: 0.4781 +2025-06-24 14:06:16,854 - pyskl - INFO - Epoch [38][300/1281] lr: 2.139e-02, eta: 13:36:35, time: 0.533, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9969, loss_cls: 0.5423, loss: 0.5423 +2025-06-24 14:07:09,416 - pyskl - INFO - Epoch [38][400/1281] lr: 2.137e-02, eta: 13:36:57, time: 0.526, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9969, loss_cls: 0.5025, loss: 0.5025 +2025-06-24 14:08:03,725 - pyskl - INFO - Epoch [38][500/1281] lr: 2.136e-02, eta: 13:37:25, time: 0.543, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9969, loss_cls: 0.5449, loss: 0.5449 +2025-06-24 14:08:57,309 - pyskl - INFO - Epoch [38][600/1281] lr: 2.134e-02, eta: 13:37:49, time: 0.536, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9944, loss_cls: 0.5971, loss: 0.5971 +2025-06-24 14:09:50,109 - pyskl - INFO - Epoch [38][700/1281] lr: 2.133e-02, eta: 13:38:11, time: 0.528, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.5296, loss: 0.5296 +2025-06-24 14:10:43,564 - pyskl - INFO - Epoch [38][800/1281] lr: 2.132e-02, eta: 13:38:35, time: 0.535, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9981, loss_cls: 0.5400, loss: 0.5400 +2025-06-24 14:11:36,950 - pyskl - INFO - Epoch [38][900/1281] lr: 2.130e-02, eta: 13:38:59, time: 0.534, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9956, loss_cls: 0.5975, loss: 0.5975 +2025-06-24 14:12:27,177 - pyskl - INFO - Epoch [38][1000/1281] lr: 2.129e-02, eta: 13:39:12, time: 0.502, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9931, loss_cls: 0.5624, loss: 0.5624 +2025-06-24 14:13:06,312 - pyskl - INFO - Epoch [38][1100/1281] lr: 2.127e-02, eta: 13:38:53, time: 0.391, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9938, loss_cls: 0.6108, loss: 0.6108 +2025-06-24 14:13:40,976 - pyskl - INFO - Epoch [38][1200/1281] lr: 2.126e-02, eta: 13:38:20, time: 0.347, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9962, loss_cls: 0.5523, loss: 0.5523 +2025-06-24 14:14:22,468 - pyskl - INFO - Saving checkpoint at 38 epochs +2025-06-24 14:15:34,413 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:15:34,472 - pyskl - INFO - +top1_acc 0.8723 +top5_acc 0.9912 +2025-06-24 14:15:34,472 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:15:34,479 - pyskl - INFO - +mean_acc 0.8217 +2025-06-24 14:15:34,483 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_37.pth was removed +2025-06-24 14:15:34,809 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_38.pth. +2025-06-24 14:15:34,809 - pyskl - INFO - Best top1_acc is 0.8723 at 38 epoch. +2025-06-24 14:15:34,812 - pyskl - INFO - Epoch(val) [38][533] top1_acc: 0.8723, top5_acc: 0.9912, mean_class_accuracy: 0.8217 +2025-06-24 14:17:00,847 - pyskl - INFO - Epoch [39][100/1281] lr: 2.123e-02, eta: 13:38:29, time: 0.860, data_time: 0.203, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9981, loss_cls: 0.5088, loss: 0.5088 +2025-06-24 14:17:53,817 - pyskl - INFO - Epoch [39][200/1281] lr: 2.122e-02, eta: 13:38:49, time: 0.530, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9981, loss_cls: 0.4724, loss: 0.4724 +2025-06-24 14:18:46,506 - pyskl - INFO - Epoch [39][300/1281] lr: 2.120e-02, eta: 13:39:09, time: 0.527, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 1.0000, loss_cls: 0.4745, loss: 0.4745 +2025-06-24 14:19:40,141 - pyskl - INFO - Epoch [39][400/1281] lr: 2.119e-02, eta: 13:39:31, time: 0.536, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9975, loss_cls: 0.5278, loss: 0.5278 +2025-06-24 14:20:34,559 - pyskl - INFO - Epoch [39][500/1281] lr: 2.117e-02, eta: 13:39:55, time: 0.544, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9950, loss_cls: 0.5408, loss: 0.5408 +2025-06-24 14:21:21,114 - pyskl - INFO - Epoch [39][600/1281] lr: 2.116e-02, eta: 13:39:55, time: 0.466, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9988, loss_cls: 0.5344, loss: 0.5344 +2025-06-24 14:22:05,243 - pyskl - INFO - Epoch [39][700/1281] lr: 2.114e-02, eta: 13:39:49, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9962, loss_cls: 0.4975, loss: 0.4975 +2025-06-24 14:22:34,429 - pyskl - INFO - Epoch [39][800/1281] lr: 2.113e-02, eta: 13:38:59, time: 0.292, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9950, loss_cls: 0.5186, loss: 0.5186 +2025-06-24 14:23:26,208 - pyskl - INFO - Epoch [39][900/1281] lr: 2.111e-02, eta: 13:39:15, time: 0.518, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9962, loss_cls: 0.5401, loss: 0.5401 +2025-06-24 14:24:18,995 - pyskl - INFO - Epoch [39][1000/1281] lr: 2.110e-02, eta: 13:39:33, time: 0.528, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9956, loss_cls: 0.5587, loss: 0.5587 +2025-06-24 14:25:11,625 - pyskl - INFO - Epoch [39][1100/1281] lr: 2.108e-02, eta: 13:39:50, time: 0.526, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9975, loss_cls: 0.4794, loss: 0.4794 +2025-06-24 14:26:04,853 - pyskl - INFO - Epoch [39][1200/1281] lr: 2.107e-02, eta: 13:40:09, time: 0.532, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9950, loss_cls: 0.5666, loss: 0.5666 +2025-06-24 14:26:48,656 - pyskl - INFO - Saving checkpoint at 39 epochs +2025-06-24 14:28:00,455 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:28:00,543 - pyskl - INFO - +top1_acc 0.8538 +top5_acc 0.9908 +2025-06-24 14:28:00,543 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:28:00,552 - pyskl - INFO - +mean_acc 0.8109 +2025-06-24 14:28:00,554 - pyskl - INFO - Epoch(val) [39][533] top1_acc: 0.8538, top5_acc: 0.9908, mean_class_accuracy: 0.8109 +2025-06-24 14:29:28,045 - pyskl - INFO - Epoch [40][100/1281] lr: 2.104e-02, eta: 13:40:17, time: 0.875, data_time: 0.201, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9969, loss_cls: 0.5726, loss: 0.5726 +2025-06-24 14:30:14,859 - pyskl - INFO - Epoch [40][200/1281] lr: 2.103e-02, eta: 13:40:17, time: 0.468, data_time: 0.001, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9994, loss_cls: 0.4890, loss: 0.4890 +2025-06-24 14:30:58,509 - pyskl - INFO - Epoch [40][300/1281] lr: 2.101e-02, eta: 13:40:07, time: 0.436, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9969, loss_cls: 0.4694, loss: 0.4694 +2025-06-24 14:31:28,192 - pyskl - INFO - Epoch [40][400/1281] lr: 2.100e-02, eta: 13:39:19, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 1.0000, loss_cls: 0.5633, loss: 0.5633 +2025-06-24 14:32:19,165 - pyskl - INFO - Epoch [40][500/1281] lr: 2.098e-02, eta: 13:39:30, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9969, loss_cls: 0.5876, loss: 0.5876 +2025-06-24 14:33:12,394 - pyskl - INFO - Epoch [40][600/1281] lr: 2.097e-02, eta: 13:39:47, time: 0.532, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9975, loss_cls: 0.5187, loss: 0.5187 +2025-06-24 14:34:06,126 - pyskl - INFO - Epoch [40][700/1281] lr: 2.095e-02, eta: 13:40:05, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9950, loss_cls: 0.5369, loss: 0.5369 +2025-06-24 14:35:00,174 - pyskl - INFO - Epoch [40][800/1281] lr: 2.094e-02, eta: 13:40:24, time: 0.540, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9944, loss_cls: 0.5813, loss: 0.5813 +2025-06-24 14:35:53,558 - pyskl - INFO - Epoch [40][900/1281] lr: 2.092e-02, eta: 13:40:41, time: 0.534, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9956, loss_cls: 0.5808, loss: 0.5808 +2025-06-24 14:36:47,575 - pyskl - INFO - Epoch [40][1000/1281] lr: 2.091e-02, eta: 13:40:59, time: 0.540, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9962, loss_cls: 0.5608, loss: 0.5608 +2025-06-24 14:37:42,133 - pyskl - INFO - Epoch [40][1100/1281] lr: 2.089e-02, eta: 13:41:19, time: 0.546, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9975, loss_cls: 0.5648, loss: 0.5648 +2025-06-24 14:38:36,723 - pyskl - INFO - Epoch [40][1200/1281] lr: 2.088e-02, eta: 13:41:38, time: 0.546, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9950, loss_cls: 0.6091, loss: 0.6091 +2025-06-24 14:39:21,157 - pyskl - INFO - Saving checkpoint at 40 epochs +2025-06-24 14:40:33,823 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:40:33,880 - pyskl - INFO - +top1_acc 0.8398 +top5_acc 0.9898 +2025-06-24 14:40:33,880 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:40:33,888 - pyskl - INFO - +mean_acc 0.7937 +2025-06-24 14:40:33,890 - pyskl - INFO - Epoch(val) [40][533] top1_acc: 0.8398, top5_acc: 0.9898, mean_class_accuracy: 0.7937 +2025-06-24 14:41:44,911 - pyskl - INFO - Epoch [41][100/1281] lr: 2.085e-02, eta: 13:40:56, time: 0.710, data_time: 0.194, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9956, loss_cls: 0.5376, loss: 0.5376 +2025-06-24 14:42:38,834 - pyskl - INFO - Epoch [41][200/1281] lr: 2.083e-02, eta: 13:41:13, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9975, loss_cls: 0.4754, loss: 0.4754 +2025-06-24 14:43:31,999 - pyskl - INFO - Epoch [41][300/1281] lr: 2.082e-02, eta: 13:41:27, time: 0.532, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9962, loss_cls: 0.5234, loss: 0.5234 +2025-06-24 14:44:25,221 - pyskl - INFO - Epoch [41][400/1281] lr: 2.080e-02, eta: 13:41:42, time: 0.532, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9981, loss_cls: 0.4982, loss: 0.4982 +2025-06-24 14:45:19,420 - pyskl - INFO - Epoch [41][500/1281] lr: 2.079e-02, eta: 13:41:58, time: 0.542, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9962, loss_cls: 0.5744, loss: 0.5744 +2025-06-24 14:46:12,877 - pyskl - INFO - Epoch [41][600/1281] lr: 2.077e-02, eta: 13:42:13, time: 0.535, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9944, loss_cls: 0.5059, loss: 0.5059 +2025-06-24 14:47:06,319 - pyskl - INFO - Epoch [41][700/1281] lr: 2.076e-02, eta: 13:42:27, time: 0.534, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9931, loss_cls: 0.5115, loss: 0.5115 +2025-06-24 14:48:00,089 - pyskl - INFO - Epoch [41][800/1281] lr: 2.074e-02, eta: 13:42:42, time: 0.538, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9950, loss_cls: 0.5598, loss: 0.5598 +2025-06-24 14:48:53,987 - pyskl - INFO - Epoch [41][900/1281] lr: 2.073e-02, eta: 13:42:57, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9975, loss_cls: 0.5094, loss: 0.5094 +2025-06-24 14:49:24,275 - pyskl - INFO - Epoch [41][1000/1281] lr: 2.071e-02, eta: 13:42:08, time: 0.303, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9969, loss_cls: 0.5186, loss: 0.5186 +2025-06-24 14:50:07,584 - pyskl - INFO - Epoch [41][1100/1281] lr: 2.070e-02, eta: 13:41:54, time: 0.433, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9956, loss_cls: 0.5189, loss: 0.5189 +2025-06-24 14:50:47,827 - pyskl - INFO - Epoch [41][1200/1281] lr: 2.068e-02, eta: 13:41:32, time: 0.402, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9962, loss_cls: 0.5828, loss: 0.5828 +2025-06-24 14:51:26,966 - pyskl - INFO - Saving checkpoint at 41 epochs +2025-06-24 14:52:27,435 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:52:27,504 - pyskl - INFO - +top1_acc 0.8551 +top5_acc 0.9898 +2025-06-24 14:52:27,504 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:52:27,513 - pyskl - INFO - +mean_acc 0.8097 +2025-06-24 14:52:27,516 - pyskl - INFO - Epoch(val) [41][533] top1_acc: 0.8551, top5_acc: 0.9898, mean_class_accuracy: 0.8097 +2025-06-24 14:53:46,109 - pyskl - INFO - Epoch [42][100/1281] lr: 2.065e-02, eta: 13:41:07, time: 0.786, data_time: 0.198, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9981, loss_cls: 0.4485, loss: 0.4485 +2025-06-24 14:54:34,168 - pyskl - INFO - Epoch [42][200/1281] lr: 2.064e-02, eta: 13:41:06, time: 0.481, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9975, loss_cls: 0.4749, loss: 0.4749 +2025-06-24 14:55:22,006 - pyskl - INFO - Epoch [42][300/1281] lr: 2.062e-02, eta: 13:41:04, time: 0.478, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9944, loss_cls: 0.5380, loss: 0.5380 +2025-06-24 14:56:10,136 - pyskl - INFO - Epoch [42][400/1281] lr: 2.061e-02, eta: 13:41:02, time: 0.481, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9956, loss_cls: 0.4796, loss: 0.4796 +2025-06-24 14:56:57,936 - pyskl - INFO - Epoch [42][500/1281] lr: 2.059e-02, eta: 13:40:59, time: 0.478, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9969, loss_cls: 0.4689, loss: 0.4689 +2025-06-24 14:57:45,801 - pyskl - INFO - Epoch [42][600/1281] lr: 2.057e-02, eta: 13:40:56, time: 0.479, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9944, loss_cls: 0.6305, loss: 0.6305 +2025-06-24 14:58:33,872 - pyskl - INFO - Epoch [42][700/1281] lr: 2.056e-02, eta: 13:40:54, time: 0.481, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9962, loss_cls: 0.5253, loss: 0.5253 +2025-06-24 14:59:21,789 - pyskl - INFO - Epoch [42][800/1281] lr: 2.054e-02, eta: 13:40:51, time: 0.479, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9969, loss_cls: 0.5406, loss: 0.5406 +2025-06-24 14:59:59,048 - pyskl - INFO - Epoch [42][900/1281] lr: 2.053e-02, eta: 13:40:20, time: 0.373, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9950, loss_cls: 0.5460, loss: 0.5460 +2025-06-24 15:00:50,144 - pyskl - INFO - Epoch [42][1000/1281] lr: 2.051e-02, eta: 13:40:25, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9969, loss_cls: 0.4951, loss: 0.4951 +2025-06-24 15:01:13,597 - pyskl - INFO - Epoch [42][1100/1281] lr: 2.050e-02, eta: 13:39:18, time: 0.235, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9944, loss_cls: 0.5303, loss: 0.5303 +2025-06-24 15:01:54,488 - pyskl - INFO - Epoch [42][1200/1281] lr: 2.048e-02, eta: 13:38:57, time: 0.409, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9956, loss_cls: 0.5866, loss: 0.5866 +2025-06-24 15:02:34,360 - pyskl - INFO - Saving checkpoint at 42 epochs +2025-06-24 15:03:33,925 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:03:33,981 - pyskl - INFO - +top1_acc 0.8467 +top5_acc 0.9872 +2025-06-24 15:03:33,981 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:03:33,989 - pyskl - INFO - +mean_acc 0.7845 +2025-06-24 15:03:33,991 - pyskl - INFO - Epoch(val) [42][533] top1_acc: 0.8467, top5_acc: 0.9872, mean_class_accuracy: 0.7845 +2025-06-24 15:04:54,279 - pyskl - INFO - Epoch [43][100/1281] lr: 2.045e-02, eta: 13:38:34, time: 0.803, data_time: 0.197, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9956, loss_cls: 0.5299, loss: 0.5299 +2025-06-24 15:05:43,160 - pyskl - INFO - Epoch [43][200/1281] lr: 2.044e-02, eta: 13:38:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9956, loss_cls: 0.4510, loss: 0.4510 +2025-06-24 15:06:32,024 - pyskl - INFO - Epoch [43][300/1281] lr: 2.042e-02, eta: 13:38:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9969, loss_cls: 0.4775, loss: 0.4775 +2025-06-24 15:07:20,932 - pyskl - INFO - Epoch [43][400/1281] lr: 2.040e-02, eta: 13:38:29, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9106, top5_acc: 1.0000, loss_cls: 0.4775, loss: 0.4775 +2025-06-24 15:08:09,973 - pyskl - INFO - Epoch [43][500/1281] lr: 2.039e-02, eta: 13:38:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9956, loss_cls: 0.4556, loss: 0.4556 +2025-06-24 15:08:58,964 - pyskl - INFO - Epoch [43][600/1281] lr: 2.037e-02, eta: 13:38:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9981, loss_cls: 0.5479, loss: 0.5479 +2025-06-24 15:09:47,962 - pyskl - INFO - Epoch [43][700/1281] lr: 2.036e-02, eta: 13:38:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9975, loss_cls: 0.5116, loss: 0.5116 +2025-06-24 15:10:36,706 - pyskl - INFO - Epoch [43][800/1281] lr: 2.034e-02, eta: 13:38:21, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9950, loss_cls: 0.5567, loss: 0.5567 +2025-06-24 15:11:18,543 - pyskl - INFO - Epoch [43][900/1281] lr: 2.033e-02, eta: 13:38:01, time: 0.418, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9969, loss_cls: 0.5105, loss: 0.5105 +2025-06-24 15:12:02,330 - pyskl - INFO - Epoch [43][1000/1281] lr: 2.031e-02, eta: 13:37:45, time: 0.438, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9956, loss_cls: 0.4832, loss: 0.4832 +2025-06-24 15:12:31,920 - pyskl - INFO - Epoch [43][1100/1281] lr: 2.029e-02, eta: 13:36:54, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9925, loss_cls: 0.5743, loss: 0.5743 +2025-06-24 15:13:11,556 - pyskl - INFO - Epoch [43][1200/1281] lr: 2.028e-02, eta: 13:36:28, time: 0.396, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9950, loss_cls: 0.5513, loss: 0.5513 +2025-06-24 15:13:52,037 - pyskl - INFO - Saving checkpoint at 43 epochs +2025-06-24 15:14:52,179 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:14:52,237 - pyskl - INFO - +top1_acc 0.8553 +top5_acc 0.9893 +2025-06-24 15:14:52,237 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:14:52,244 - pyskl - INFO - +mean_acc 0.7858 +2025-06-24 15:14:52,246 - pyskl - INFO - Epoch(val) [43][533] top1_acc: 0.8553, top5_acc: 0.9893, mean_class_accuracy: 0.7858 +2025-06-24 15:16:11,200 - pyskl - INFO - Epoch [44][100/1281] lr: 2.025e-02, eta: 13:35:59, time: 0.789, data_time: 0.193, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9962, loss_cls: 0.4987, loss: 0.4987 +2025-06-24 15:17:00,021 - pyskl - INFO - Epoch [44][200/1281] lr: 2.023e-02, eta: 13:35:56, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9988, loss_cls: 0.4740, loss: 0.4740 +2025-06-24 15:17:48,950 - pyskl - INFO - Epoch [44][300/1281] lr: 2.022e-02, eta: 13:35:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9969, loss_cls: 0.4632, loss: 0.4632 +2025-06-24 15:18:37,755 - pyskl - INFO - Epoch [44][400/1281] lr: 2.020e-02, eta: 13:35:49, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9975, loss_cls: 0.5251, loss: 0.5251 +2025-06-24 15:19:26,925 - pyskl - INFO - Epoch [44][500/1281] lr: 2.018e-02, eta: 13:35:46, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9969, loss_cls: 0.4830, loss: 0.4830 +2025-06-24 15:20:15,994 - pyskl - INFO - Epoch [44][600/1281] lr: 2.017e-02, eta: 13:35:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9988, loss_cls: 0.5296, loss: 0.5296 +2025-06-24 15:21:04,700 - pyskl - INFO - Epoch [44][700/1281] lr: 2.015e-02, eta: 13:35:38, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9938, loss_cls: 0.5861, loss: 0.5861 +2025-06-24 15:21:53,544 - pyskl - INFO - Epoch [44][800/1281] lr: 2.014e-02, eta: 13:35:33, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9962, loss_cls: 0.5078, loss: 0.5078 +2025-06-24 15:22:37,971 - pyskl - INFO - Epoch [44][900/1281] lr: 2.012e-02, eta: 13:35:18, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9969, loss_cls: 0.5185, loss: 0.5185 +2025-06-24 15:23:19,162 - pyskl - INFO - Epoch [44][1000/1281] lr: 2.010e-02, eta: 13:34:55, time: 0.412, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9962, loss_cls: 0.4915, loss: 0.4915 +2025-06-24 15:23:51,708 - pyskl - INFO - Epoch [44][1100/1281] lr: 2.009e-02, eta: 13:34:11, time: 0.325, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9969, loss_cls: 0.5006, loss: 0.5006 +2025-06-24 15:24:31,134 - pyskl - INFO - Epoch [44][1200/1281] lr: 2.007e-02, eta: 13:33:43, time: 0.394, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9988, loss_cls: 0.4856, loss: 0.4856 +2025-06-24 15:25:11,584 - pyskl - INFO - Saving checkpoint at 44 epochs +2025-06-24 15:26:10,759 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:26:10,817 - pyskl - INFO - +top1_acc 0.8454 +top5_acc 0.9896 +2025-06-24 15:26:10,817 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:26:10,824 - pyskl - INFO - +mean_acc 0.7984 +2025-06-24 15:26:10,826 - pyskl - INFO - Epoch(val) [44][533] top1_acc: 0.8454, top5_acc: 0.9896, mean_class_accuracy: 0.7984 +2025-06-24 15:27:30,061 - pyskl - INFO - Epoch [45][100/1281] lr: 2.004e-02, eta: 13:33:13, time: 0.792, data_time: 0.197, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9981, loss_cls: 0.4721, loss: 0.4721 +2025-06-24 15:28:19,432 - pyskl - INFO - Epoch [45][200/1281] lr: 2.003e-02, eta: 13:33:09, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9975, loss_cls: 0.4943, loss: 0.4943 +2025-06-24 15:29:08,008 - pyskl - INFO - Epoch [45][300/1281] lr: 2.001e-02, eta: 13:33:03, time: 0.486, data_time: 0.001, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9969, loss_cls: 0.5108, loss: 0.5108 +2025-06-24 15:29:57,003 - pyskl - INFO - Epoch [45][400/1281] lr: 1.999e-02, eta: 13:32:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9981, loss_cls: 0.4676, loss: 0.4676 +2025-06-24 15:30:45,535 - pyskl - INFO - Epoch [45][500/1281] lr: 1.998e-02, eta: 13:32:52, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9962, loss_cls: 0.4533, loss: 0.4533 +2025-06-24 15:31:34,849 - pyskl - INFO - Epoch [45][600/1281] lr: 1.996e-02, eta: 13:32:47, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9981, loss_cls: 0.5140, loss: 0.5140 +2025-06-24 15:32:23,800 - pyskl - INFO - Epoch [45][700/1281] lr: 1.994e-02, eta: 13:32:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9950, loss_cls: 0.5370, loss: 0.5370 +2025-06-24 15:33:12,626 - pyskl - INFO - Epoch [45][800/1281] lr: 1.993e-02, eta: 13:32:35, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9956, loss_cls: 0.5274, loss: 0.5274 +2025-06-24 15:33:56,638 - pyskl - INFO - Epoch [45][900/1281] lr: 1.991e-02, eta: 13:32:18, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9962, loss_cls: 0.5062, loss: 0.5062 +2025-06-24 15:34:40,031 - pyskl - INFO - Epoch [45][1000/1281] lr: 1.989e-02, eta: 13:31:59, time: 0.434, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9950, loss_cls: 0.5547, loss: 0.5547 +2025-06-24 15:35:10,235 - pyskl - INFO - Epoch [45][1100/1281] lr: 1.988e-02, eta: 13:31:09, time: 0.302, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9988, loss_cls: 0.5313, loss: 0.5313 +2025-06-24 15:35:50,070 - pyskl - INFO - Epoch [45][1200/1281] lr: 1.986e-02, eta: 13:30:41, time: 0.398, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9944, loss_cls: 0.5074, loss: 0.5074 +2025-06-24 15:36:30,511 - pyskl - INFO - Saving checkpoint at 45 epochs +2025-06-24 15:37:30,029 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:37:30,087 - pyskl - INFO - +top1_acc 0.8711 +top5_acc 0.9900 +2025-06-24 15:37:30,087 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:37:30,097 - pyskl - INFO - +mean_acc 0.8278 +2025-06-24 15:37:30,100 - pyskl - INFO - Epoch(val) [45][533] top1_acc: 0.8711, top5_acc: 0.9900, mean_class_accuracy: 0.8278 +2025-06-24 15:38:50,623 - pyskl - INFO - Epoch [46][100/1281] lr: 1.983e-02, eta: 13:30:11, time: 0.805, data_time: 0.197, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9962, loss_cls: 0.4812, loss: 0.4812 +2025-06-24 15:39:39,330 - pyskl - INFO - Epoch [46][200/1281] lr: 1.981e-02, eta: 13:30:04, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9988, loss_cls: 0.4605, loss: 0.4605 +2025-06-24 15:40:28,065 - pyskl - INFO - Epoch [46][300/1281] lr: 1.980e-02, eta: 13:29:57, time: 0.487, data_time: 0.001, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9969, loss_cls: 0.4778, loss: 0.4778 +2025-06-24 15:41:17,351 - pyskl - INFO - Epoch [46][400/1281] lr: 1.978e-02, eta: 13:29:51, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9956, loss_cls: 0.5154, loss: 0.5154 +2025-06-24 15:42:06,707 - pyskl - INFO - Epoch [46][500/1281] lr: 1.976e-02, eta: 13:29:45, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9956, loss_cls: 0.4910, loss: 0.4910 +2025-06-24 15:42:55,732 - pyskl - INFO - Epoch [46][600/1281] lr: 1.975e-02, eta: 13:29:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9969, loss_cls: 0.5486, loss: 0.5486 +2025-06-24 15:43:44,700 - pyskl - INFO - Epoch [46][700/1281] lr: 1.973e-02, eta: 13:29:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9969, loss_cls: 0.5568, loss: 0.5568 +2025-06-24 15:44:33,533 - pyskl - INFO - Epoch [46][800/1281] lr: 1.971e-02, eta: 13:29:23, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9988, loss_cls: 0.4654, loss: 0.4654 +2025-06-24 15:45:16,354 - pyskl - INFO - Epoch [46][900/1281] lr: 1.970e-02, eta: 13:29:01, time: 0.428, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9956, loss_cls: 0.4948, loss: 0.4948 +2025-06-24 15:45:59,856 - pyskl - INFO - Epoch [46][1000/1281] lr: 1.968e-02, eta: 13:28:41, time: 0.435, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9956, loss_cls: 0.5022, loss: 0.5022 +2025-06-24 15:46:29,781 - pyskl - INFO - Epoch [46][1100/1281] lr: 1.966e-02, eta: 13:27:51, time: 0.299, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9969, loss_cls: 0.5075, loss: 0.5075 +2025-06-24 15:47:10,755 - pyskl - INFO - Epoch [46][1200/1281] lr: 1.965e-02, eta: 13:27:25, time: 0.410, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9969, loss_cls: 0.5415, loss: 0.5415 +2025-06-24 15:47:51,618 - pyskl - INFO - Saving checkpoint at 46 epochs +2025-06-24 15:48:51,215 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:48:51,276 - pyskl - INFO - +top1_acc 0.8452 +top5_acc 0.9865 +2025-06-24 15:48:51,276 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:48:51,283 - pyskl - INFO - +mean_acc 0.8168 +2025-06-24 15:48:51,285 - pyskl - INFO - Epoch(val) [46][533] top1_acc: 0.8452, top5_acc: 0.9865, mean_class_accuracy: 0.8168 +2025-06-24 15:50:12,209 - pyskl - INFO - Epoch [47][100/1281] lr: 1.962e-02, eta: 13:26:53, time: 0.809, data_time: 0.200, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9956, loss_cls: 0.4643, loss: 0.4643 +2025-06-24 15:51:01,005 - pyskl - INFO - Epoch [47][200/1281] lr: 1.960e-02, eta: 13:26:45, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9981, loss_cls: 0.4513, loss: 0.4513 +2025-06-24 15:51:49,340 - pyskl - INFO - Epoch [47][300/1281] lr: 1.958e-02, eta: 13:26:35, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9962, loss_cls: 0.5022, loss: 0.5022 +2025-06-24 15:52:38,217 - pyskl - INFO - Epoch [47][400/1281] lr: 1.957e-02, eta: 13:26:27, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9988, loss_cls: 0.4600, loss: 0.4600 +2025-06-24 15:53:27,205 - pyskl - INFO - Epoch [47][500/1281] lr: 1.955e-02, eta: 13:26:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9975, loss_cls: 0.4636, loss: 0.4636 +2025-06-24 15:54:16,370 - pyskl - INFO - Epoch [47][600/1281] lr: 1.953e-02, eta: 13:26:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9981, loss_cls: 0.5315, loss: 0.5315 +2025-06-24 15:55:05,864 - pyskl - INFO - Epoch [47][700/1281] lr: 1.952e-02, eta: 13:26:03, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9975, loss_cls: 0.5566, loss: 0.5566 +2025-06-24 15:55:55,057 - pyskl - INFO - Epoch [47][800/1281] lr: 1.950e-02, eta: 13:25:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9956, loss_cls: 0.5162, loss: 0.5162 +2025-06-24 15:56:37,092 - pyskl - INFO - Epoch [47][900/1281] lr: 1.948e-02, eta: 13:25:30, time: 0.420, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9994, loss_cls: 0.4845, loss: 0.4845 +2025-06-24 15:57:21,298 - pyskl - INFO - Epoch [47][1000/1281] lr: 1.947e-02, eta: 13:25:10, time: 0.442, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9969, loss_cls: 0.4650, loss: 0.4650 +2025-06-24 15:57:50,475 - pyskl - INFO - Epoch [47][1100/1281] lr: 1.945e-02, eta: 13:24:18, time: 0.292, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9931, loss_cls: 0.5185, loss: 0.5185 +2025-06-24 15:58:30,303 - pyskl - INFO - Epoch [47][1200/1281] lr: 1.943e-02, eta: 13:23:48, time: 0.398, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9981, loss_cls: 0.5064, loss: 0.5064 +2025-06-24 15:59:10,766 - pyskl - INFO - Saving checkpoint at 47 epochs +2025-06-24 16:00:10,499 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:00:10,567 - pyskl - INFO - +top1_acc 0.8715 +top5_acc 0.9900 +2025-06-24 16:00:10,567 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:00:10,577 - pyskl - INFO - +mean_acc 0.8385 +2025-06-24 16:00:10,579 - pyskl - INFO - Epoch(val) [47][533] top1_acc: 0.8715, top5_acc: 0.9900, mean_class_accuracy: 0.8385 +2025-06-24 16:01:31,058 - pyskl - INFO - Epoch [48][100/1281] lr: 1.940e-02, eta: 13:23:14, time: 0.805, data_time: 0.193, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9994, loss_cls: 0.4389, loss: 0.4389 +2025-06-24 16:02:20,183 - pyskl - INFO - Epoch [48][200/1281] lr: 1.938e-02, eta: 13:23:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9969, loss_cls: 0.4548, loss: 0.4548 +2025-06-24 16:03:09,098 - pyskl - INFO - Epoch [48][300/1281] lr: 1.937e-02, eta: 13:22:55, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9981, loss_cls: 0.4921, loss: 0.4921 +2025-06-24 16:03:57,996 - pyskl - INFO - Epoch [48][400/1281] lr: 1.935e-02, eta: 13:22:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9969, loss_cls: 0.5152, loss: 0.5152 +2025-06-24 16:04:47,104 - pyskl - INFO - Epoch [48][500/1281] lr: 1.933e-02, eta: 13:22:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9962, loss_cls: 0.4797, loss: 0.4797 +2025-06-24 16:05:35,940 - pyskl - INFO - Epoch [48][600/1281] lr: 1.932e-02, eta: 13:22:25, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9969, loss_cls: 0.5024, loss: 0.5024 +2025-06-24 16:06:24,681 - pyskl - INFO - Epoch [48][700/1281] lr: 1.930e-02, eta: 13:22:14, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9994, loss_cls: 0.4655, loss: 0.4655 +2025-06-24 16:07:13,347 - pyskl - INFO - Epoch [48][800/1281] lr: 1.928e-02, eta: 13:22:03, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9956, loss_cls: 0.4993, loss: 0.4993 +2025-06-24 16:07:57,313 - pyskl - INFO - Epoch [48][900/1281] lr: 1.926e-02, eta: 13:21:42, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9969, loss_cls: 0.4573, loss: 0.4573 +2025-06-24 16:08:38,072 - pyskl - INFO - Epoch [48][1000/1281] lr: 1.925e-02, eta: 13:21:14, time: 0.408, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9975, loss_cls: 0.5071, loss: 0.5071 +2025-06-24 16:09:10,532 - pyskl - INFO - Epoch [48][1100/1281] lr: 1.923e-02, eta: 13:20:29, time: 0.325, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9969, loss_cls: 0.4839, loss: 0.4839 +2025-06-24 16:09:48,539 - pyskl - INFO - Epoch [48][1200/1281] lr: 1.921e-02, eta: 13:19:55, time: 0.380, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9950, loss_cls: 0.5457, loss: 0.5457 +2025-06-24 16:10:28,790 - pyskl - INFO - Saving checkpoint at 48 epochs +2025-06-24 16:11:28,100 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:11:28,161 - pyskl - INFO - +top1_acc 0.8613 +top5_acc 0.9906 +2025-06-24 16:11:28,161 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:11:28,169 - pyskl - INFO - +mean_acc 0.8107 +2025-06-24 16:11:28,171 - pyskl - INFO - Epoch(val) [48][533] top1_acc: 0.8613, top5_acc: 0.9906, mean_class_accuracy: 0.8107 +2025-06-24 16:12:47,159 - pyskl - INFO - Epoch [49][100/1281] lr: 1.918e-02, eta: 13:19:15, time: 0.790, data_time: 0.191, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9988, loss_cls: 0.4526, loss: 0.4526 +2025-06-24 16:13:36,255 - pyskl - INFO - Epoch [49][200/1281] lr: 1.916e-02, eta: 13:19:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9975, loss_cls: 0.4691, loss: 0.4691 +2025-06-24 16:14:25,448 - pyskl - INFO - Epoch [49][300/1281] lr: 1.915e-02, eta: 13:18:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9981, loss_cls: 0.4408, loss: 0.4408 +2025-06-24 16:15:14,677 - pyskl - INFO - Epoch [49][400/1281] lr: 1.913e-02, eta: 13:18:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9944, loss_cls: 0.5145, loss: 0.5145 +2025-06-24 16:16:03,969 - pyskl - INFO - Epoch [49][500/1281] lr: 1.911e-02, eta: 13:18:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9962, loss_cls: 0.4835, loss: 0.4835 +2025-06-24 16:16:52,975 - pyskl - INFO - Epoch [49][600/1281] lr: 1.909e-02, eta: 13:18:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9981, loss_cls: 0.4729, loss: 0.4729 +2025-06-24 16:17:42,113 - pyskl - INFO - Epoch [49][700/1281] lr: 1.908e-02, eta: 13:18:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9944, loss_cls: 0.5066, loss: 0.5066 +2025-06-24 16:18:31,100 - pyskl - INFO - Epoch [49][800/1281] lr: 1.906e-02, eta: 13:17:59, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9969, loss_cls: 0.5362, loss: 0.5362 +2025-06-24 16:19:18,187 - pyskl - INFO - Epoch [49][900/1281] lr: 1.904e-02, eta: 13:17:43, time: 0.471, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9975, loss_cls: 0.4615, loss: 0.4615 +2025-06-24 16:19:52,679 - pyskl - INFO - Epoch [49][1000/1281] lr: 1.902e-02, eta: 13:17:01, time: 0.345, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9962, loss_cls: 0.4672, loss: 0.4672 +2025-06-24 16:20:31,628 - pyskl - INFO - Epoch [49][1100/1281] lr: 1.901e-02, eta: 13:16:29, time: 0.389, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9994, loss_cls: 0.5039, loss: 0.5039 +2025-06-24 16:21:08,084 - pyskl - INFO - Epoch [49][1200/1281] lr: 1.899e-02, eta: 13:15:51, time: 0.365, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9975, loss_cls: 0.4845, loss: 0.4845 +2025-06-24 16:21:48,341 - pyskl - INFO - Saving checkpoint at 49 epochs +2025-06-24 16:22:47,912 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:22:47,995 - pyskl - INFO - +top1_acc 0.8483 +top5_acc 0.9896 +2025-06-24 16:22:47,995 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:22:48,007 - pyskl - INFO - +mean_acc 0.8026 +2025-06-24 16:22:48,011 - pyskl - INFO - Epoch(val) [49][533] top1_acc: 0.8483, top5_acc: 0.9896, mean_class_accuracy: 0.8026 +2025-06-24 16:24:07,253 - pyskl - INFO - Epoch [50][100/1281] lr: 1.896e-02, eta: 13:15:10, time: 0.792, data_time: 0.195, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9969, loss_cls: 0.4953, loss: 0.4953 +2025-06-24 16:24:56,298 - pyskl - INFO - Epoch [50][200/1281] lr: 1.894e-02, eta: 13:14:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9956, loss_cls: 0.5398, loss: 0.5398 +2025-06-24 16:25:45,149 - pyskl - INFO - Epoch [50][300/1281] lr: 1.892e-02, eta: 13:14:45, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9950, loss_cls: 0.4519, loss: 0.4519 +2025-06-24 16:26:33,939 - pyskl - INFO - Epoch [50][400/1281] lr: 1.891e-02, eta: 13:14:32, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9975, loss_cls: 0.4344, loss: 0.4344 +2025-06-24 16:27:22,704 - pyskl - INFO - Epoch [50][500/1281] lr: 1.889e-02, eta: 13:14:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9969, loss_cls: 0.4715, loss: 0.4715 +2025-06-24 16:28:11,923 - pyskl - INFO - Epoch [50][600/1281] lr: 1.887e-02, eta: 13:14:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9962, loss_cls: 0.4706, loss: 0.4706 +2025-06-24 16:29:00,799 - pyskl - INFO - Epoch [50][700/1281] lr: 1.885e-02, eta: 13:13:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9981, loss_cls: 0.4248, loss: 0.4248 +2025-06-24 16:29:49,808 - pyskl - INFO - Epoch [50][800/1281] lr: 1.884e-02, eta: 13:13:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9975, loss_cls: 0.4611, loss: 0.4611 +2025-06-24 16:30:37,629 - pyskl - INFO - Epoch [50][900/1281] lr: 1.882e-02, eta: 13:13:26, time: 0.478, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9944, loss_cls: 0.5116, loss: 0.5116 +2025-06-24 16:31:11,668 - pyskl - INFO - Epoch [50][1000/1281] lr: 1.880e-02, eta: 13:12:43, time: 0.340, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9975, loss_cls: 0.4882, loss: 0.4882 +2025-06-24 16:31:50,813 - pyskl - INFO - Epoch [50][1100/1281] lr: 1.878e-02, eta: 13:12:10, time: 0.391, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9950, loss_cls: 0.5169, loss: 0.5169 +2025-06-24 16:32:25,897 - pyskl - INFO - Epoch [50][1200/1281] lr: 1.876e-02, eta: 13:11:29, time: 0.351, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9981, loss_cls: 0.4067, loss: 0.4067 +2025-06-24 16:33:06,122 - pyskl - INFO - Saving checkpoint at 50 epochs +2025-06-24 16:34:05,510 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:34:05,568 - pyskl - INFO - +top1_acc 0.8642 +top5_acc 0.9917 +2025-06-24 16:34:05,568 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:34:05,577 - pyskl - INFO - +mean_acc 0.8058 +2025-06-24 16:34:05,579 - pyskl - INFO - Epoch(val) [50][533] top1_acc: 0.8642, top5_acc: 0.9917, mean_class_accuracy: 0.8058 +2025-06-24 16:35:24,523 - pyskl - INFO - Epoch [51][100/1281] lr: 1.873e-02, eta: 13:10:46, time: 0.789, data_time: 0.187, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9962, loss_cls: 0.4952, loss: 0.4952 +2025-06-24 16:36:13,931 - pyskl - INFO - Epoch [51][200/1281] lr: 1.871e-02, eta: 13:10:34, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9962, loss_cls: 0.4257, loss: 0.4257 +2025-06-24 16:37:02,997 - pyskl - INFO - Epoch [51][300/1281] lr: 1.870e-02, eta: 13:10:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9969, loss_cls: 0.4009, loss: 0.4009 +2025-06-24 16:37:51,805 - pyskl - INFO - Epoch [51][400/1281] lr: 1.868e-02, eta: 13:10:06, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9988, loss_cls: 0.4250, loss: 0.4250 +2025-06-24 16:38:41,231 - pyskl - INFO - Epoch [51][500/1281] lr: 1.866e-02, eta: 13:09:54, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 1.0000, loss_cls: 0.4368, loss: 0.4368 +2025-06-24 16:39:30,248 - pyskl - INFO - Epoch [51][600/1281] lr: 1.864e-02, eta: 13:09:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9975, loss_cls: 0.4959, loss: 0.4959 +2025-06-24 16:40:19,962 - pyskl - INFO - Epoch [51][700/1281] lr: 1.863e-02, eta: 13:09:27, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9975, loss_cls: 0.4683, loss: 0.4683 +2025-06-24 16:41:08,869 - pyskl - INFO - Epoch [51][800/1281] lr: 1.861e-02, eta: 13:09:13, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9994, loss_cls: 0.4312, loss: 0.4312 +2025-06-24 16:41:58,030 - pyskl - INFO - Epoch [51][900/1281] lr: 1.859e-02, eta: 13:08:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9981, loss_cls: 0.4195, loss: 0.4195 +2025-06-24 16:42:27,540 - pyskl - INFO - Epoch [51][1000/1281] lr: 1.857e-02, eta: 13:08:07, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9969, loss_cls: 0.5029, loss: 0.5029 +2025-06-24 16:43:11,739 - pyskl - INFO - Epoch [51][1100/1281] lr: 1.855e-02, eta: 13:07:44, time: 0.442, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9981, loss_cls: 0.5058, loss: 0.5058 +2025-06-24 16:43:45,870 - pyskl - INFO - Epoch [51][1200/1281] lr: 1.854e-02, eta: 13:07:00, time: 0.341, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9931, loss_cls: 0.4886, loss: 0.4886 +2025-06-24 16:44:26,233 - pyskl - INFO - Saving checkpoint at 51 epochs +2025-06-24 16:45:26,471 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:45:26,540 - pyskl - INFO - +top1_acc 0.8689 +top5_acc 0.9926 +2025-06-24 16:45:26,540 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:45:26,549 - pyskl - INFO - +mean_acc 0.8192 +2025-06-24 16:45:26,552 - pyskl - INFO - Epoch(val) [51][533] top1_acc: 0.8689, top5_acc: 0.9926, mean_class_accuracy: 0.8192 +2025-06-24 16:46:45,988 - pyskl - INFO - Epoch [52][100/1281] lr: 1.850e-02, eta: 13:06:16, time: 0.794, data_time: 0.192, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9988, loss_cls: 0.4428, loss: 0.4428 +2025-06-24 16:47:34,854 - pyskl - INFO - Epoch [52][200/1281] lr: 1.849e-02, eta: 13:06:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9981, loss_cls: 0.3861, loss: 0.3861 +2025-06-24 16:48:23,432 - pyskl - INFO - Epoch [52][300/1281] lr: 1.847e-02, eta: 13:05:46, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9988, loss_cls: 0.4441, loss: 0.4441 +2025-06-24 16:49:12,575 - pyskl - INFO - Epoch [52][400/1281] lr: 1.845e-02, eta: 13:05:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4554, loss: 0.4554 +2025-06-24 16:50:01,679 - pyskl - INFO - Epoch [52][500/1281] lr: 1.843e-02, eta: 13:05:17, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9981, loss_cls: 0.4122, loss: 0.4122 +2025-06-24 16:50:50,458 - pyskl - INFO - Epoch [52][600/1281] lr: 1.841e-02, eta: 13:05:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9981, loss_cls: 0.4001, loss: 0.4001 +2025-06-24 16:51:39,595 - pyskl - INFO - Epoch [52][700/1281] lr: 1.840e-02, eta: 13:04:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9988, loss_cls: 0.4657, loss: 0.4657 +2025-06-24 16:52:28,334 - pyskl - INFO - Epoch [52][800/1281] lr: 1.838e-02, eta: 13:04:32, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9962, loss_cls: 0.5522, loss: 0.5522 +2025-06-24 16:53:17,476 - pyskl - INFO - Epoch [52][900/1281] lr: 1.836e-02, eta: 13:04:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9981, loss_cls: 0.5186, loss: 0.5186 +2025-06-24 16:53:48,446 - pyskl - INFO - Epoch [52][1000/1281] lr: 1.834e-02, eta: 13:03:27, time: 0.310, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9962, loss_cls: 0.4436, loss: 0.4436 +2025-06-24 16:54:31,409 - pyskl - INFO - Epoch [52][1100/1281] lr: 1.832e-02, eta: 13:03:00, time: 0.430, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9988, loss_cls: 0.4386, loss: 0.4386 +2025-06-24 16:55:05,217 - pyskl - INFO - Epoch [52][1200/1281] lr: 1.831e-02, eta: 13:02:16, time: 0.338, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9975, loss_cls: 0.4574, loss: 0.4574 +2025-06-24 16:55:45,573 - pyskl - INFO - Saving checkpoint at 52 epochs +2025-06-24 16:56:44,936 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:56:45,006 - pyskl - INFO - +top1_acc 0.8510 +top5_acc 0.9885 +2025-06-24 16:56:45,006 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:56:45,015 - pyskl - INFO - +mean_acc 0.8254 +2025-06-24 16:56:45,017 - pyskl - INFO - Epoch(val) [52][533] top1_acc: 0.8510, top5_acc: 0.9885, mean_class_accuracy: 0.8254 +2025-06-24 16:58:03,915 - pyskl - INFO - Epoch [53][100/1281] lr: 1.827e-02, eta: 13:01:30, time: 0.789, data_time: 0.191, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9994, loss_cls: 0.4083, loss: 0.4083 +2025-06-24 16:58:52,926 - pyskl - INFO - Epoch [53][200/1281] lr: 1.826e-02, eta: 13:01:14, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9988, loss_cls: 0.4238, loss: 0.4238 +2025-06-24 16:59:42,229 - pyskl - INFO - Epoch [53][300/1281] lr: 1.824e-02, eta: 13:00:59, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9994, loss_cls: 0.4150, loss: 0.4150 +2025-06-24 17:00:31,172 - pyskl - INFO - Epoch [53][400/1281] lr: 1.822e-02, eta: 13:00:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9975, loss_cls: 0.4639, loss: 0.4639 +2025-06-24 17:01:20,129 - pyskl - INFO - Epoch [53][500/1281] lr: 1.820e-02, eta: 13:00:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9988, loss_cls: 0.4249, loss: 0.4249 +2025-06-24 17:02:09,007 - pyskl - INFO - Epoch [53][600/1281] lr: 1.818e-02, eta: 13:00:11, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9975, loss_cls: 0.4700, loss: 0.4700 +2025-06-24 17:02:57,947 - pyskl - INFO - Epoch [53][700/1281] lr: 1.816e-02, eta: 12:59:55, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9962, loss_cls: 0.5008, loss: 0.5008 +2025-06-24 17:03:46,962 - pyskl - INFO - Epoch [53][800/1281] lr: 1.815e-02, eta: 12:59:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9994, loss_cls: 0.3845, loss: 0.3845 +2025-06-24 17:04:35,974 - pyskl - INFO - Epoch [53][900/1281] lr: 1.813e-02, eta: 12:59:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9962, loss_cls: 0.4620, loss: 0.4620 +2025-06-24 17:05:05,077 - pyskl - INFO - Epoch [53][1000/1281] lr: 1.811e-02, eta: 12:58:29, time: 0.291, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9962, loss_cls: 0.4493, loss: 0.4493 +2025-06-24 17:05:51,662 - pyskl - INFO - Epoch [53][1100/1281] lr: 1.809e-02, eta: 12:58:09, time: 0.466, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9950, loss_cls: 0.4831, loss: 0.4831 +2025-06-24 17:06:25,201 - pyskl - INFO - Epoch [53][1200/1281] lr: 1.807e-02, eta: 12:57:24, time: 0.335, data_time: 0.001, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9981, loss_cls: 0.4676, loss: 0.4676 +2025-06-24 17:07:05,567 - pyskl - INFO - Saving checkpoint at 53 epochs +2025-06-24 17:08:04,849 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:08:04,904 - pyskl - INFO - +top1_acc 0.8683 +top5_acc 0.9910 +2025-06-24 17:08:04,904 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:08:04,911 - pyskl - INFO - +mean_acc 0.8266 +2025-06-24 17:08:04,913 - pyskl - INFO - Epoch(val) [53][533] top1_acc: 0.8683, top5_acc: 0.9910, mean_class_accuracy: 0.8266 +2025-06-24 17:09:25,670 - pyskl - INFO - Epoch [54][100/1281] lr: 1.804e-02, eta: 12:56:39, time: 0.808, data_time: 0.193, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9969, loss_cls: 0.4152, loss: 0.4152 +2025-06-24 17:10:15,228 - pyskl - INFO - Epoch [54][200/1281] lr: 1.802e-02, eta: 12:56:24, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9975, loss_cls: 0.4331, loss: 0.4331 +2025-06-24 17:11:04,319 - pyskl - INFO - Epoch [54][300/1281] lr: 1.800e-02, eta: 12:56:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.4173, loss: 0.4173 +2025-06-24 17:11:53,282 - pyskl - INFO - Epoch [54][400/1281] lr: 1.798e-02, eta: 12:55:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9975, loss_cls: 0.4340, loss: 0.4340 +2025-06-24 17:12:42,180 - pyskl - INFO - Epoch [54][500/1281] lr: 1.797e-02, eta: 12:55:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9994, loss_cls: 0.3940, loss: 0.3940 +2025-06-24 17:13:31,102 - pyskl - INFO - Epoch [54][600/1281] lr: 1.795e-02, eta: 12:55:16, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9969, loss_cls: 0.4363, loss: 0.4363 +2025-06-24 17:14:19,881 - pyskl - INFO - Epoch [54][700/1281] lr: 1.793e-02, eta: 12:54:58, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9956, loss_cls: 0.5253, loss: 0.5253 +2025-06-24 17:15:08,731 - pyskl - INFO - Epoch [54][800/1281] lr: 1.791e-02, eta: 12:54:41, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9969, loss_cls: 0.4689, loss: 0.4689 +2025-06-24 17:15:57,689 - pyskl - INFO - Epoch [54][900/1281] lr: 1.789e-02, eta: 12:54:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9975, loss_cls: 0.5240, loss: 0.5240 +2025-06-24 17:16:27,647 - pyskl - INFO - Epoch [54][1000/1281] lr: 1.787e-02, eta: 12:53:32, time: 0.300, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9975, loss_cls: 0.5073, loss: 0.5073 +2025-06-24 17:17:11,691 - pyskl - INFO - Epoch [54][1100/1281] lr: 1.786e-02, eta: 12:53:06, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9969, loss_cls: 0.4296, loss: 0.4296 +2025-06-24 17:17:46,315 - pyskl - INFO - Epoch [54][1200/1281] lr: 1.784e-02, eta: 12:52:23, time: 0.346, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9950, loss_cls: 0.4896, loss: 0.4896 +2025-06-24 17:18:26,647 - pyskl - INFO - Saving checkpoint at 54 epochs +2025-06-24 17:19:25,653 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:19:25,708 - pyskl - INFO - +top1_acc 0.8755 +top5_acc 0.9919 +2025-06-24 17:19:25,708 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:19:25,715 - pyskl - INFO - +mean_acc 0.8199 +2025-06-24 17:19:25,719 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_38.pth was removed +2025-06-24 17:19:25,887 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_54.pth. +2025-06-24 17:19:25,888 - pyskl - INFO - Best top1_acc is 0.8755 at 54 epoch. +2025-06-24 17:19:25,890 - pyskl - INFO - Epoch(val) [54][533] top1_acc: 0.8755, top5_acc: 0.9919, mean_class_accuracy: 0.8199 +2025-06-24 17:20:45,757 - pyskl - INFO - Epoch [55][100/1281] lr: 1.780e-02, eta: 12:51:35, time: 0.799, data_time: 0.196, memory: 4083, top1_acc: 0.9169, top5_acc: 1.0000, loss_cls: 0.4372, loss: 0.4372 +2025-06-24 17:21:34,934 - pyskl - INFO - Epoch [55][200/1281] lr: 1.779e-02, eta: 12:51:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9988, loss_cls: 0.4217, loss: 0.4217 +2025-06-24 17:22:24,086 - pyskl - INFO - Epoch [55][300/1281] lr: 1.777e-02, eta: 12:51:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9981, loss_cls: 0.3978, loss: 0.3978 +2025-06-24 17:23:13,475 - pyskl - INFO - Epoch [55][400/1281] lr: 1.775e-02, eta: 12:50:43, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9988, loss_cls: 0.4395, loss: 0.4395 +2025-06-24 17:24:02,646 - pyskl - INFO - Epoch [55][500/1281] lr: 1.773e-02, eta: 12:50:26, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3846, loss: 0.3846 +2025-06-24 17:24:52,342 - pyskl - INFO - Epoch [55][600/1281] lr: 1.771e-02, eta: 12:50:09, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9988, loss_cls: 0.4633, loss: 0.4633 +2025-06-24 17:25:41,407 - pyskl - INFO - Epoch [55][700/1281] lr: 1.769e-02, eta: 12:49:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9981, loss_cls: 0.4357, loss: 0.4357 +2025-06-24 17:26:30,378 - pyskl - INFO - Epoch [55][800/1281] lr: 1.767e-02, eta: 12:49:33, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9962, loss_cls: 0.4660, loss: 0.4660 +2025-06-24 17:27:19,386 - pyskl - INFO - Epoch [55][900/1281] lr: 1.766e-02, eta: 12:49:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9962, loss_cls: 0.4697, loss: 0.4697 +2025-06-24 17:27:50,333 - pyskl - INFO - Epoch [55][1000/1281] lr: 1.764e-02, eta: 12:48:25, time: 0.309, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9988, loss_cls: 0.4512, loss: 0.4512 +2025-06-24 17:28:33,328 - pyskl - INFO - Epoch [55][1100/1281] lr: 1.762e-02, eta: 12:47:56, time: 0.430, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9975, loss_cls: 0.4541, loss: 0.4541 +2025-06-24 17:29:07,261 - pyskl - INFO - Epoch [55][1200/1281] lr: 1.760e-02, eta: 12:47:12, time: 0.339, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9988, loss_cls: 0.4105, loss: 0.4105 +2025-06-24 17:29:47,293 - pyskl - INFO - Saving checkpoint at 55 epochs +2025-06-24 17:30:46,495 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:30:46,553 - pyskl - INFO - +top1_acc 0.8694 +top5_acc 0.9918 +2025-06-24 17:30:46,553 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:30:46,560 - pyskl - INFO - +mean_acc 0.8260 +2025-06-24 17:30:46,562 - pyskl - INFO - Epoch(val) [55][533] top1_acc: 0.8694, top5_acc: 0.9918, mean_class_accuracy: 0.8260 +2025-06-24 17:32:06,815 - pyskl - INFO - Epoch [56][100/1281] lr: 1.757e-02, eta: 12:46:24, time: 0.802, data_time: 0.193, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4261, loss: 0.4261 +2025-06-24 17:32:55,833 - pyskl - INFO - Epoch [56][200/1281] lr: 1.755e-02, eta: 12:46:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9975, loss_cls: 0.4294, loss: 0.4294 +2025-06-24 17:33:44,793 - pyskl - INFO - Epoch [56][300/1281] lr: 1.753e-02, eta: 12:45:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9988, loss_cls: 0.4184, loss: 0.4184 +2025-06-24 17:34:33,732 - pyskl - INFO - Epoch [56][400/1281] lr: 1.751e-02, eta: 12:45:27, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9975, loss_cls: 0.3876, loss: 0.3876 +2025-06-24 17:35:22,541 - pyskl - INFO - Epoch [56][500/1281] lr: 1.749e-02, eta: 12:45:08, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9975, loss_cls: 0.4091, loss: 0.4091 +2025-06-24 17:36:11,780 - pyskl - INFO - Epoch [56][600/1281] lr: 1.747e-02, eta: 12:44:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9981, loss_cls: 0.4312, loss: 0.4312 +2025-06-24 17:37:00,870 - pyskl - INFO - Epoch [56][700/1281] lr: 1.745e-02, eta: 12:44:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4389, loss: 0.4389 +2025-06-24 17:37:49,592 - pyskl - INFO - Epoch [56][800/1281] lr: 1.743e-02, eta: 12:44:11, time: 0.487, data_time: 0.001, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9981, loss_cls: 0.4572, loss: 0.4572 +2025-06-24 17:38:38,512 - pyskl - INFO - Epoch [56][900/1281] lr: 1.742e-02, eta: 12:43:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9962, loss_cls: 0.4263, loss: 0.4263 +2025-06-24 17:39:09,638 - pyskl - INFO - Epoch [56][1000/1281] lr: 1.740e-02, eta: 12:43:02, time: 0.311, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9988, loss_cls: 0.4574, loss: 0.4574 +2025-06-24 17:39:52,828 - pyskl - INFO - Epoch [56][1100/1281] lr: 1.738e-02, eta: 12:42:33, time: 0.432, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9969, loss_cls: 0.4413, loss: 0.4413 +2025-06-24 17:40:26,296 - pyskl - INFO - Epoch [56][1200/1281] lr: 1.736e-02, eta: 12:41:48, time: 0.335, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9981, loss_cls: 0.4015, loss: 0.4015 +2025-06-24 17:41:06,781 - pyskl - INFO - Saving checkpoint at 56 epochs +2025-06-24 17:42:06,812 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:42:06,872 - pyskl - INFO - +top1_acc 0.8789 +top5_acc 0.9931 +2025-06-24 17:42:06,872 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:42:06,880 - pyskl - INFO - +mean_acc 0.8402 +2025-06-24 17:42:06,884 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_54.pth was removed +2025-06-24 17:42:07,075 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_56.pth. +2025-06-24 17:42:07,076 - pyskl - INFO - Best top1_acc is 0.8789 at 56 epoch. +2025-06-24 17:42:07,080 - pyskl - INFO - Epoch(val) [56][533] top1_acc: 0.8789, top5_acc: 0.9931, mean_class_accuracy: 0.8402 +2025-06-24 17:43:26,860 - pyskl - INFO - Epoch [57][100/1281] lr: 1.733e-02, eta: 12:40:58, time: 0.798, data_time: 0.191, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9969, loss_cls: 0.4217, loss: 0.4217 +2025-06-24 17:44:15,817 - pyskl - INFO - Epoch [57][200/1281] lr: 1.731e-02, eta: 12:40:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9981, loss_cls: 0.4472, loss: 0.4472 +2025-06-24 17:45:04,850 - pyskl - INFO - Epoch [57][300/1281] lr: 1.729e-02, eta: 12:40:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9969, loss_cls: 0.4272, loss: 0.4272 +2025-06-24 17:45:53,905 - pyskl - INFO - Epoch [57][400/1281] lr: 1.727e-02, eta: 12:39:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4275, loss: 0.4275 +2025-06-24 17:46:42,808 - pyskl - INFO - Epoch [57][500/1281] lr: 1.725e-02, eta: 12:39:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9988, loss_cls: 0.4289, loss: 0.4289 +2025-06-24 17:47:32,117 - pyskl - INFO - Epoch [57][600/1281] lr: 1.723e-02, eta: 12:39:20, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9969, loss_cls: 0.4347, loss: 0.4347 +2025-06-24 17:48:21,580 - pyskl - INFO - Epoch [57][700/1281] lr: 1.721e-02, eta: 12:39:01, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9981, loss_cls: 0.4489, loss: 0.4489 +2025-06-24 17:49:10,168 - pyskl - INFO - Epoch [57][800/1281] lr: 1.719e-02, eta: 12:38:40, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9988, loss_cls: 0.4751, loss: 0.4751 +2025-06-24 17:49:58,945 - pyskl - INFO - Epoch [57][900/1281] lr: 1.717e-02, eta: 12:38:20, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9956, loss_cls: 0.4565, loss: 0.4565 +2025-06-24 17:50:27,775 - pyskl - INFO - Epoch [57][1000/1281] lr: 1.716e-02, eta: 12:37:27, time: 0.288, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9969, loss_cls: 0.4399, loss: 0.4399 +2025-06-24 17:51:13,778 - pyskl - INFO - Epoch [57][1100/1281] lr: 1.714e-02, eta: 12:37:02, time: 0.460, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9956, loss_cls: 0.5016, loss: 0.5016 +2025-06-24 17:51:46,494 - pyskl - INFO - Epoch [57][1200/1281] lr: 1.712e-02, eta: 12:36:15, time: 0.327, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9975, loss_cls: 0.4676, loss: 0.4676 +2025-06-24 17:52:26,817 - pyskl - INFO - Saving checkpoint at 57 epochs +2025-06-24 17:53:26,220 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:53:26,276 - pyskl - INFO - +top1_acc 0.8703 +top5_acc 0.9870 +2025-06-24 17:53:26,276 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:53:26,287 - pyskl - INFO - +mean_acc 0.8280 +2025-06-24 17:53:26,290 - pyskl - INFO - Epoch(val) [57][533] top1_acc: 0.8703, top5_acc: 0.9870, mean_class_accuracy: 0.8280 +2025-06-24 17:54:45,210 - pyskl - INFO - Epoch [58][100/1281] lr: 1.708e-02, eta: 12:35:22, time: 0.789, data_time: 0.192, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.3863, loss: 0.3863 +2025-06-24 17:55:34,172 - pyskl - INFO - Epoch [58][200/1281] lr: 1.706e-02, eta: 12:35:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9981, loss_cls: 0.4204, loss: 0.4204 +2025-06-24 17:56:23,217 - pyskl - INFO - Epoch [58][300/1281] lr: 1.704e-02, eta: 12:34:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 1.0000, loss_cls: 0.4041, loss: 0.4041 +2025-06-24 17:57:12,099 - pyskl - INFO - Epoch [58][400/1281] lr: 1.703e-02, eta: 12:34:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9981, loss_cls: 0.4326, loss: 0.4326 +2025-06-24 17:58:00,933 - pyskl - INFO - Epoch [58][500/1281] lr: 1.701e-02, eta: 12:34:00, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9994, loss_cls: 0.3842, loss: 0.3842 +2025-06-24 17:58:50,031 - pyskl - INFO - Epoch [58][600/1281] lr: 1.699e-02, eta: 12:33:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9981, loss_cls: 0.3780, loss: 0.3780 +2025-06-24 17:59:39,206 - pyskl - INFO - Epoch [58][700/1281] lr: 1.697e-02, eta: 12:33:19, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9956, loss_cls: 0.4870, loss: 0.4870 +2025-06-24 18:00:28,217 - pyskl - INFO - Epoch [58][800/1281] lr: 1.695e-02, eta: 12:32:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9975, loss_cls: 0.4577, loss: 0.4577 +2025-06-24 18:01:17,253 - pyskl - INFO - Epoch [58][900/1281] lr: 1.693e-02, eta: 12:32:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9981, loss_cls: 0.4543, loss: 0.4543 +2025-06-24 18:01:44,721 - pyskl - INFO - Epoch [58][1000/1281] lr: 1.691e-02, eta: 12:31:42, time: 0.275, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9994, loss_cls: 0.4558, loss: 0.4558 +2025-06-24 18:02:33,913 - pyskl - INFO - Epoch [58][1100/1281] lr: 1.689e-02, eta: 12:31:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9950, loss_cls: 0.4342, loss: 0.4342 +2025-06-24 18:03:05,213 - pyskl - INFO - Epoch [58][1200/1281] lr: 1.687e-02, eta: 12:30:32, time: 0.313, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9981, loss_cls: 0.4238, loss: 0.4238 +2025-06-24 18:03:45,506 - pyskl - INFO - Saving checkpoint at 58 epochs +2025-06-24 18:04:44,918 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:04:44,976 - pyskl - INFO - +top1_acc 0.8826 +top5_acc 0.9910 +2025-06-24 18:04:44,976 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:04:44,983 - pyskl - INFO - +mean_acc 0.8344 +2025-06-24 18:04:44,987 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_56.pth was removed +2025-06-24 18:04:45,337 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_58.pth. +2025-06-24 18:04:45,338 - pyskl - INFO - Best top1_acc is 0.8826 at 58 epoch. +2025-06-24 18:04:45,340 - pyskl - INFO - Epoch(val) [58][533] top1_acc: 0.8826, top5_acc: 0.9910, mean_class_accuracy: 0.8344 +2025-06-24 18:06:04,825 - pyskl - INFO - Epoch [59][100/1281] lr: 1.684e-02, eta: 12:29:40, time: 0.795, data_time: 0.195, memory: 4083, top1_acc: 0.9400, top5_acc: 1.0000, loss_cls: 0.3662, loss: 0.3662 +2025-06-24 18:06:53,753 - pyskl - INFO - Epoch [59][200/1281] lr: 1.682e-02, eta: 12:29:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9981, loss_cls: 0.3847, loss: 0.3847 +2025-06-24 18:07:42,704 - pyskl - INFO - Epoch [59][300/1281] lr: 1.680e-02, eta: 12:28:57, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9981, loss_cls: 0.4284, loss: 0.4284 +2025-06-24 18:08:31,793 - pyskl - INFO - Epoch [59][400/1281] lr: 1.678e-02, eta: 12:28:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9956, loss_cls: 0.4111, loss: 0.4111 +2025-06-24 18:09:20,911 - pyskl - INFO - Epoch [59][500/1281] lr: 1.676e-02, eta: 12:28:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9975, loss_cls: 0.4490, loss: 0.4490 +2025-06-24 18:10:10,086 - pyskl - INFO - Epoch [59][600/1281] lr: 1.674e-02, eta: 12:27:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9981, loss_cls: 0.4084, loss: 0.4084 +2025-06-24 18:10:59,019 - pyskl - INFO - Epoch [59][700/1281] lr: 1.672e-02, eta: 12:27:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9981, loss_cls: 0.3962, loss: 0.3962 +2025-06-24 18:11:48,076 - pyskl - INFO - Epoch [59][800/1281] lr: 1.670e-02, eta: 12:27:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9981, loss_cls: 0.4331, loss: 0.4331 +2025-06-24 18:12:36,845 - pyskl - INFO - Epoch [59][900/1281] lr: 1.668e-02, eta: 12:26:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9975, loss_cls: 0.4671, loss: 0.4671 +2025-06-24 18:13:05,401 - pyskl - INFO - Epoch [59][1000/1281] lr: 1.667e-02, eta: 12:25:55, time: 0.286, data_time: 0.001, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9988, loss_cls: 0.4115, loss: 0.4115 +2025-06-24 18:13:54,179 - pyskl - INFO - Epoch [59][1100/1281] lr: 1.665e-02, eta: 12:25:33, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9962, loss_cls: 0.5067, loss: 0.5067 +2025-06-24 18:14:26,825 - pyskl - INFO - Epoch [59][1200/1281] lr: 1.663e-02, eta: 12:24:46, time: 0.326, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9962, loss_cls: 0.4299, loss: 0.4299 +2025-06-24 18:15:07,216 - pyskl - INFO - Saving checkpoint at 59 epochs +2025-06-24 18:16:06,070 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:16:06,125 - pyskl - INFO - +top1_acc 0.8812 +top5_acc 0.9918 +2025-06-24 18:16:06,126 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:16:06,133 - pyskl - INFO - +mean_acc 0.8406 +2025-06-24 18:16:06,135 - pyskl - INFO - Epoch(val) [59][533] top1_acc: 0.8812, top5_acc: 0.9918, mean_class_accuracy: 0.8406 +2025-06-24 18:17:25,106 - pyskl - INFO - Epoch [60][100/1281] lr: 1.659e-02, eta: 12:23:51, time: 0.790, data_time: 0.191, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.4102, loss: 0.4102 +2025-06-24 18:18:13,817 - pyskl - INFO - Epoch [60][200/1281] lr: 1.657e-02, eta: 12:23:29, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9994, loss_cls: 0.4002, loss: 0.4002 +2025-06-24 18:19:02,994 - pyskl - INFO - Epoch [60][300/1281] lr: 1.655e-02, eta: 12:23:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9969, loss_cls: 0.4009, loss: 0.4009 +2025-06-24 18:19:52,300 - pyskl - INFO - Epoch [60][400/1281] lr: 1.653e-02, eta: 12:22:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9994, loss_cls: 0.3527, loss: 0.3527 +2025-06-24 18:20:41,220 - pyskl - INFO - Epoch [60][500/1281] lr: 1.651e-02, eta: 12:22:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9981, loss_cls: 0.4042, loss: 0.4042 +2025-06-24 18:21:30,588 - pyskl - INFO - Epoch [60][600/1281] lr: 1.650e-02, eta: 12:22:02, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9981, loss_cls: 0.4067, loss: 0.4067 +2025-06-24 18:22:19,948 - pyskl - INFO - Epoch [60][700/1281] lr: 1.648e-02, eta: 12:21:40, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9975, loss_cls: 0.4200, loss: 0.4200 +2025-06-24 18:23:08,896 - pyskl - INFO - Epoch [60][800/1281] lr: 1.646e-02, eta: 12:21:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9950, loss_cls: 0.4822, loss: 0.4822 +2025-06-24 18:23:57,968 - pyskl - INFO - Epoch [60][900/1281] lr: 1.644e-02, eta: 12:20:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9975, loss_cls: 0.3871, loss: 0.3871 +2025-06-24 18:24:25,155 - pyskl - INFO - Epoch [60][1000/1281] lr: 1.642e-02, eta: 12:20:00, time: 0.272, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9994, loss_cls: 0.4285, loss: 0.4285 +2025-06-24 18:25:15,534 - pyskl - INFO - Epoch [60][1100/1281] lr: 1.640e-02, eta: 12:19:39, time: 0.504, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9994, loss_cls: 0.4053, loss: 0.4053 +2025-06-24 18:25:47,411 - pyskl - INFO - Epoch [60][1200/1281] lr: 1.638e-02, eta: 12:18:51, time: 0.319, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.4008, loss: 0.4008 +2025-06-24 18:26:28,030 - pyskl - INFO - Saving checkpoint at 60 epochs +2025-06-24 18:27:27,233 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:27:27,293 - pyskl - INFO - +top1_acc 0.8857 +top5_acc 0.9919 +2025-06-24 18:27:27,293 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:27:27,300 - pyskl - INFO - +mean_acc 0.8508 +2025-06-24 18:27:27,304 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_58.pth was removed +2025-06-24 18:27:27,479 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_60.pth. +2025-06-24 18:27:27,480 - pyskl - INFO - Best top1_acc is 0.8857 at 60 epoch. +2025-06-24 18:27:27,483 - pyskl - INFO - Epoch(val) [60][533] top1_acc: 0.8857, top5_acc: 0.9919, mean_class_accuracy: 0.8508 +2025-06-24 18:28:48,611 - pyskl - INFO - Epoch [61][100/1281] lr: 1.634e-02, eta: 12:17:59, time: 0.811, data_time: 0.197, memory: 4083, top1_acc: 0.9287, top5_acc: 1.0000, loss_cls: 0.3930, loss: 0.3930 +2025-06-24 18:29:38,076 - pyskl - INFO - Epoch [61][200/1281] lr: 1.632e-02, eta: 12:17:37, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9975, loss_cls: 0.4050, loss: 0.4050 +2025-06-24 18:30:27,013 - pyskl - INFO - Epoch [61][300/1281] lr: 1.630e-02, eta: 12:17:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9975, loss_cls: 0.4076, loss: 0.4076 +2025-06-24 18:31:16,007 - pyskl - INFO - Epoch [61][400/1281] lr: 1.629e-02, eta: 12:16:51, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9962, loss_cls: 0.4428, loss: 0.4428 +2025-06-24 18:32:04,960 - pyskl - INFO - Epoch [61][500/1281] lr: 1.627e-02, eta: 12:16:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.4033, loss: 0.4033 +2025-06-24 18:32:54,049 - pyskl - INFO - Epoch [61][600/1281] lr: 1.625e-02, eta: 12:16:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4296, loss: 0.4296 +2025-06-24 18:33:43,014 - pyskl - INFO - Epoch [61][700/1281] lr: 1.623e-02, eta: 12:15:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.4181, loss: 0.4181 +2025-06-24 18:34:31,887 - pyskl - INFO - Epoch [61][800/1281] lr: 1.621e-02, eta: 12:15:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9950, loss_cls: 0.4341, loss: 0.4341 +2025-06-24 18:35:21,069 - pyskl - INFO - Epoch [61][900/1281] lr: 1.619e-02, eta: 12:14:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9975, loss_cls: 0.4131, loss: 0.4131 +2025-06-24 18:35:50,138 - pyskl - INFO - Epoch [61][1000/1281] lr: 1.617e-02, eta: 12:14:04, time: 0.291, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9981, loss_cls: 0.4038, loss: 0.4038 +2025-06-24 18:36:36,416 - pyskl - INFO - Epoch [61][1100/1281] lr: 1.615e-02, eta: 12:13:37, time: 0.463, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9975, loss_cls: 0.4329, loss: 0.4329 +2025-06-24 18:37:09,341 - pyskl - INFO - Epoch [61][1200/1281] lr: 1.613e-02, eta: 12:12:50, time: 0.329, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9981, loss_cls: 0.4117, loss: 0.4117 +2025-06-24 18:37:49,904 - pyskl - INFO - Saving checkpoint at 61 epochs +2025-06-24 18:38:49,228 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:38:49,285 - pyskl - INFO - +top1_acc 0.8480 +top5_acc 0.9883 +2025-06-24 18:38:49,285 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:38:49,292 - pyskl - INFO - +mean_acc 0.8042 +2025-06-24 18:38:49,294 - pyskl - INFO - Epoch(val) [61][533] top1_acc: 0.8480, top5_acc: 0.9883, mean_class_accuracy: 0.8042 +2025-06-24 18:40:08,430 - pyskl - INFO - Epoch [62][100/1281] lr: 1.609e-02, eta: 12:11:54, time: 0.791, data_time: 0.193, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.3908, loss: 0.3908 +2025-06-24 18:40:57,878 - pyskl - INFO - Epoch [62][200/1281] lr: 1.607e-02, eta: 12:11:31, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9994, loss_cls: 0.3532, loss: 0.3532 +2025-06-24 18:41:47,131 - pyskl - INFO - Epoch [62][300/1281] lr: 1.605e-02, eta: 12:11:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9988, loss_cls: 0.4179, loss: 0.4179 +2025-06-24 18:42:35,845 - pyskl - INFO - Epoch [62][400/1281] lr: 1.603e-02, eta: 12:10:44, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9988, loss_cls: 0.4272, loss: 0.4272 +2025-06-24 18:43:24,817 - pyskl - INFO - Epoch [62][500/1281] lr: 1.602e-02, eta: 12:10:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9988, loss_cls: 0.3882, loss: 0.3882 +2025-06-24 18:44:13,842 - pyskl - INFO - Epoch [62][600/1281] lr: 1.600e-02, eta: 12:09:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9962, loss_cls: 0.3732, loss: 0.3732 +2025-06-24 18:45:02,678 - pyskl - INFO - Epoch [62][700/1281] lr: 1.598e-02, eta: 12:09:33, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9962, loss_cls: 0.4480, loss: 0.4480 +2025-06-24 18:45:51,803 - pyskl - INFO - Epoch [62][800/1281] lr: 1.596e-02, eta: 12:09:09, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9988, loss_cls: 0.4151, loss: 0.4151 +2025-06-24 18:46:40,853 - pyskl - INFO - Epoch [62][900/1281] lr: 1.594e-02, eta: 12:08:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9969, loss_cls: 0.4247, loss: 0.4247 +2025-06-24 18:47:11,015 - pyskl - INFO - Epoch [62][1000/1281] lr: 1.592e-02, eta: 12:07:55, time: 0.302, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9981, loss_cls: 0.3765, loss: 0.3765 +2025-06-24 18:47:56,893 - pyskl - INFO - Epoch [62][1100/1281] lr: 1.590e-02, eta: 12:07:26, time: 0.459, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9981, loss_cls: 0.4638, loss: 0.4638 +2025-06-24 18:48:28,427 - pyskl - INFO - Epoch [62][1200/1281] lr: 1.588e-02, eta: 12:06:38, time: 0.315, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9981, loss_cls: 0.4317, loss: 0.4317 +2025-06-24 18:49:08,572 - pyskl - INFO - Saving checkpoint at 62 epochs +2025-06-24 18:50:08,079 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:50:08,135 - pyskl - INFO - +top1_acc 0.9032 +top5_acc 0.9945 +2025-06-24 18:50:08,135 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:50:08,142 - pyskl - INFO - +mean_acc 0.8766 +2025-06-24 18:50:08,146 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_60.pth was removed +2025-06-24 18:50:08,322 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_62.pth. +2025-06-24 18:50:08,322 - pyskl - INFO - Best top1_acc is 0.9032 at 62 epoch. +2025-06-24 18:50:08,325 - pyskl - INFO - Epoch(val) [62][533] top1_acc: 0.9032, top5_acc: 0.9945, mean_class_accuracy: 0.8766 +2025-06-24 18:51:28,968 - pyskl - INFO - Epoch [63][100/1281] lr: 1.584e-02, eta: 12:05:43, time: 0.806, data_time: 0.197, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9969, loss_cls: 0.3484, loss: 0.3484 +2025-06-24 18:52:17,817 - pyskl - INFO - Epoch [63][200/1281] lr: 1.582e-02, eta: 12:05:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9988, loss_cls: 0.3426, loss: 0.3426 +2025-06-24 18:53:07,020 - pyskl - INFO - Epoch [63][300/1281] lr: 1.580e-02, eta: 12:04:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9994, loss_cls: 0.3288, loss: 0.3288 +2025-06-24 18:53:55,834 - pyskl - INFO - Epoch [63][400/1281] lr: 1.578e-02, eta: 12:04:30, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9975, loss_cls: 0.4020, loss: 0.4020 +2025-06-24 18:54:44,759 - pyskl - INFO - Epoch [63][500/1281] lr: 1.576e-02, eta: 12:04:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9975, loss_cls: 0.4097, loss: 0.4097 +2025-06-24 18:55:33,801 - pyskl - INFO - Epoch [63][600/1281] lr: 1.574e-02, eta: 12:03:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9981, loss_cls: 0.3989, loss: 0.3989 +2025-06-24 18:56:22,716 - pyskl - INFO - Epoch [63][700/1281] lr: 1.572e-02, eta: 12:03:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.3984, loss: 0.3984 +2025-06-24 18:57:11,380 - pyskl - INFO - Epoch [63][800/1281] lr: 1.570e-02, eta: 12:02:52, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9994, loss_cls: 0.3656, loss: 0.3656 +2025-06-24 18:58:00,095 - pyskl - INFO - Epoch [63][900/1281] lr: 1.568e-02, eta: 12:02:27, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9981, loss_cls: 0.3735, loss: 0.3735 +2025-06-24 18:58:27,267 - pyskl - INFO - Epoch [63][1000/1281] lr: 1.566e-02, eta: 12:01:33, time: 0.272, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9975, loss_cls: 0.4127, loss: 0.4127 +2025-06-24 18:59:16,876 - pyskl - INFO - Epoch [63][1100/1281] lr: 1.564e-02, eta: 12:01:09, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9969, loss_cls: 0.4357, loss: 0.4357 +2025-06-24 18:59:48,287 - pyskl - INFO - Epoch [63][1200/1281] lr: 1.562e-02, eta: 12:00:20, time: 0.314, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9969, loss_cls: 0.4000, loss: 0.4000 +2025-06-24 19:00:28,627 - pyskl - INFO - Saving checkpoint at 63 epochs +2025-06-24 19:01:28,092 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:01:28,147 - pyskl - INFO - +top1_acc 0.8826 +top5_acc 0.9901 +2025-06-24 19:01:28,148 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:01:28,154 - pyskl - INFO - +mean_acc 0.8425 +2025-06-24 19:01:28,155 - pyskl - INFO - Epoch(val) [63][533] top1_acc: 0.8826, top5_acc: 0.9901, mean_class_accuracy: 0.8425 +2025-06-24 19:02:48,093 - pyskl - INFO - Epoch [64][100/1281] lr: 1.559e-02, eta: 11:59:23, time: 0.799, data_time: 0.195, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3760, loss: 0.3760 +2025-06-24 19:03:37,548 - pyskl - INFO - Epoch [64][200/1281] lr: 1.557e-02, eta: 11:58:59, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9975, loss_cls: 0.3659, loss: 0.3659 +2025-06-24 19:04:27,002 - pyskl - INFO - Epoch [64][300/1281] lr: 1.555e-02, eta: 11:58:35, time: 0.495, data_time: 0.001, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9962, loss_cls: 0.3944, loss: 0.3944 +2025-06-24 19:05:16,098 - pyskl - INFO - Epoch [64][400/1281] lr: 1.553e-02, eta: 11:58:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9994, loss_cls: 0.3822, loss: 0.3822 +2025-06-24 19:06:05,146 - pyskl - INFO - Epoch [64][500/1281] lr: 1.551e-02, eta: 11:57:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9994, loss_cls: 0.3956, loss: 0.3956 +2025-06-24 19:06:54,290 - pyskl - INFO - Epoch [64][600/1281] lr: 1.549e-02, eta: 11:57:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.3986, loss: 0.3986 +2025-06-24 19:07:43,496 - pyskl - INFO - Epoch [64][700/1281] lr: 1.547e-02, eta: 11:56:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9994, loss_cls: 0.3485, loss: 0.3485 +2025-06-24 19:08:32,301 - pyskl - INFO - Epoch [64][800/1281] lr: 1.545e-02, eta: 11:56:31, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9981, loss_cls: 0.3807, loss: 0.3807 +2025-06-24 19:09:21,488 - pyskl - INFO - Epoch [64][900/1281] lr: 1.543e-02, eta: 11:56:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9975, loss_cls: 0.3674, loss: 0.3674 +2025-06-24 19:09:49,592 - pyskl - INFO - Epoch [64][1000/1281] lr: 1.541e-02, eta: 11:55:13, time: 0.281, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9969, loss_cls: 0.4359, loss: 0.4359 +2025-06-24 19:10:38,021 - pyskl - INFO - Epoch [64][1100/1281] lr: 1.539e-02, eta: 11:54:47, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9956, loss_cls: 0.4338, loss: 0.4338 +2025-06-24 19:11:10,106 - pyskl - INFO - Epoch [64][1200/1281] lr: 1.537e-02, eta: 11:53:59, time: 0.321, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9988, loss_cls: 0.4145, loss: 0.4145 +2025-06-24 19:11:50,229 - pyskl - INFO - Saving checkpoint at 64 epochs +2025-06-24 19:12:49,819 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:12:49,877 - pyskl - INFO - +top1_acc 0.8521 +top5_acc 0.9866 +2025-06-24 19:12:49,877 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:12:49,884 - pyskl - INFO - +mean_acc 0.8065 +2025-06-24 19:12:49,885 - pyskl - INFO - Epoch(val) [64][533] top1_acc: 0.8521, top5_acc: 0.9866, mean_class_accuracy: 0.8065 +2025-06-24 19:14:10,018 - pyskl - INFO - Epoch [65][100/1281] lr: 1.533e-02, eta: 11:53:01, time: 0.801, data_time: 0.198, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9962, loss_cls: 0.4159, loss: 0.4159 +2025-06-24 19:14:59,467 - pyskl - INFO - Epoch [65][200/1281] lr: 1.531e-02, eta: 11:52:37, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9975, loss_cls: 0.4270, loss: 0.4270 +2025-06-24 19:15:48,882 - pyskl - INFO - Epoch [65][300/1281] lr: 1.529e-02, eta: 11:52:12, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9975, loss_cls: 0.3296, loss: 0.3296 +2025-06-24 19:16:38,312 - pyskl - INFO - Epoch [65][400/1281] lr: 1.527e-02, eta: 11:51:47, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 1.0000, loss_cls: 0.3677, loss: 0.3677 +2025-06-24 19:17:27,539 - pyskl - INFO - Epoch [65][500/1281] lr: 1.526e-02, eta: 11:51:22, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9975, loss_cls: 0.3507, loss: 0.3507 +2025-06-24 19:18:17,076 - pyskl - INFO - Epoch [65][600/1281] lr: 1.524e-02, eta: 11:50:57, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3691, loss: 0.3691 +2025-06-24 19:19:06,098 - pyskl - INFO - Epoch [65][700/1281] lr: 1.522e-02, eta: 11:50:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 1.0000, loss_cls: 0.4247, loss: 0.4247 +2025-06-24 19:19:55,017 - pyskl - INFO - Epoch [65][800/1281] lr: 1.520e-02, eta: 11:50:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9975, loss_cls: 0.4216, loss: 0.4216 +2025-06-24 19:20:44,182 - pyskl - INFO - Epoch [65][900/1281] lr: 1.518e-02, eta: 11:49:40, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9956, loss_cls: 0.4054, loss: 0.4054 +2025-06-24 19:21:12,649 - pyskl - INFO - Epoch [65][1000/1281] lr: 1.516e-02, eta: 11:48:47, time: 0.285, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9956, loss_cls: 0.4067, loss: 0.4067 +2025-06-24 19:21:59,644 - pyskl - INFO - Epoch [65][1100/1281] lr: 1.514e-02, eta: 11:48:19, time: 0.470, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9988, loss_cls: 0.3532, loss: 0.3532 +2025-06-24 19:22:32,034 - pyskl - INFO - Epoch [65][1200/1281] lr: 1.512e-02, eta: 11:47:31, time: 0.324, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9981, loss_cls: 0.3980, loss: 0.3980 +2025-06-24 19:23:12,102 - pyskl - INFO - Saving checkpoint at 65 epochs +2025-06-24 19:24:10,969 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:24:11,036 - pyskl - INFO - +top1_acc 0.8599 +top5_acc 0.9869 +2025-06-24 19:24:11,037 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:24:11,045 - pyskl - INFO - +mean_acc 0.8111 +2025-06-24 19:24:11,047 - pyskl - INFO - Epoch(val) [65][533] top1_acc: 0.8599, top5_acc: 0.9869, mean_class_accuracy: 0.8111 +2025-06-24 19:25:31,658 - pyskl - INFO - Epoch [66][100/1281] lr: 1.508e-02, eta: 11:46:34, time: 0.806, data_time: 0.203, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9994, loss_cls: 0.4200, loss: 0.4200 +2025-06-24 19:26:20,922 - pyskl - INFO - Epoch [66][200/1281] lr: 1.506e-02, eta: 11:46:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9981, loss_cls: 0.3840, loss: 0.3840 +2025-06-24 19:27:09,565 - pyskl - INFO - Epoch [66][300/1281] lr: 1.504e-02, eta: 11:45:42, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9981, loss_cls: 0.3373, loss: 0.3373 +2025-06-24 19:27:58,568 - pyskl - INFO - Epoch [66][400/1281] lr: 1.502e-02, eta: 11:45:16, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9463, top5_acc: 1.0000, loss_cls: 0.3325, loss: 0.3325 +2025-06-24 19:28:47,443 - pyskl - INFO - Epoch [66][500/1281] lr: 1.500e-02, eta: 11:44:50, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9994, loss_cls: 0.3880, loss: 0.3880 +2025-06-24 19:29:36,937 - pyskl - INFO - Epoch [66][600/1281] lr: 1.498e-02, eta: 11:44:24, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9969, loss_cls: 0.3951, loss: 0.3951 +2025-06-24 19:30:26,015 - pyskl - INFO - Epoch [66][700/1281] lr: 1.496e-02, eta: 11:43:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9969, loss_cls: 0.3863, loss: 0.3863 +2025-06-24 19:31:15,261 - pyskl - INFO - Epoch [66][800/1281] lr: 1.494e-02, eta: 11:43:32, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9975, loss_cls: 0.3598, loss: 0.3598 +2025-06-24 19:32:04,190 - pyskl - INFO - Epoch [66][900/1281] lr: 1.492e-02, eta: 11:43:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9944, loss_cls: 0.4747, loss: 0.4747 +2025-06-24 19:32:32,350 - pyskl - INFO - Epoch [66][1000/1281] lr: 1.490e-02, eta: 11:42:13, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9994, loss_cls: 0.3883, loss: 0.3883 +2025-06-24 19:33:20,704 - pyskl - INFO - Epoch [66][1100/1281] lr: 1.488e-02, eta: 11:41:46, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3207, loss: 0.3207 +2025-06-24 19:33:51,688 - pyskl - INFO - Epoch [66][1200/1281] lr: 1.486e-02, eta: 11:40:56, time: 0.310, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9994, loss_cls: 0.4249, loss: 0.4249 +2025-06-24 19:34:31,812 - pyskl - INFO - Saving checkpoint at 66 epochs +2025-06-24 19:35:31,148 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:35:31,216 - pyskl - INFO - +top1_acc 0.8957 +top5_acc 0.9917 +2025-06-24 19:35:31,216 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:35:31,227 - pyskl - INFO - +mean_acc 0.8653 +2025-06-24 19:35:31,230 - pyskl - INFO - Epoch(val) [66][533] top1_acc: 0.8957, top5_acc: 0.9917, mean_class_accuracy: 0.8653 +2025-06-24 19:36:50,306 - pyskl - INFO - Epoch [67][100/1281] lr: 1.482e-02, eta: 11:39:56, time: 0.791, data_time: 0.198, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3765, loss: 0.3765 +2025-06-24 19:37:39,509 - pyskl - INFO - Epoch [67][200/1281] lr: 1.480e-02, eta: 11:39:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9988, loss_cls: 0.2847, loss: 0.2847 +2025-06-24 19:38:28,484 - pyskl - INFO - Epoch [67][300/1281] lr: 1.478e-02, eta: 11:39:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9981, loss_cls: 0.4145, loss: 0.4145 +2025-06-24 19:39:17,394 - pyskl - INFO - Epoch [67][400/1281] lr: 1.476e-02, eta: 11:38:37, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3650, loss: 0.3650 +2025-06-24 19:40:06,367 - pyskl - INFO - Epoch [67][500/1281] lr: 1.474e-02, eta: 11:38:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9988, loss_cls: 0.3628, loss: 0.3628 +2025-06-24 19:40:55,392 - pyskl - INFO - Epoch [67][600/1281] lr: 1.472e-02, eta: 11:37:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9975, loss_cls: 0.3712, loss: 0.3712 +2025-06-24 19:41:45,236 - pyskl - INFO - Epoch [67][700/1281] lr: 1.470e-02, eta: 11:37:18, time: 0.498, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 1.0000, loss_cls: 0.3732, loss: 0.3732 +2025-06-24 19:42:34,394 - pyskl - INFO - Epoch [67][800/1281] lr: 1.468e-02, eta: 11:36:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9988, loss_cls: 0.3705, loss: 0.3705 +2025-06-24 19:43:23,329 - pyskl - INFO - Epoch [67][900/1281] lr: 1.466e-02, eta: 11:36:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9962, loss_cls: 0.3641, loss: 0.3641 +2025-06-24 19:43:50,762 - pyskl - INFO - Epoch [67][1000/1281] lr: 1.464e-02, eta: 11:35:30, time: 0.274, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9981, loss_cls: 0.3739, loss: 0.3739 +2025-06-24 19:44:41,583 - pyskl - INFO - Epoch [67][1100/1281] lr: 1.462e-02, eta: 11:35:06, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 1.0000, loss_cls: 0.4400, loss: 0.4400 +2025-06-24 19:45:11,028 - pyskl - INFO - Epoch [67][1200/1281] lr: 1.460e-02, eta: 11:34:15, time: 0.294, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4336, loss: 0.4336 +2025-06-24 19:45:51,516 - pyskl - INFO - Saving checkpoint at 67 epochs +2025-06-24 19:46:50,434 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:46:50,493 - pyskl - INFO - +top1_acc 0.8902 +top5_acc 0.9938 +2025-06-24 19:46:50,493 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:46:50,500 - pyskl - INFO - +mean_acc 0.8458 +2025-06-24 19:46:50,503 - pyskl - INFO - Epoch(val) [67][533] top1_acc: 0.8902, top5_acc: 0.9938, mean_class_accuracy: 0.8458 +2025-06-24 19:48:10,743 - pyskl - INFO - Epoch [68][100/1281] lr: 1.456e-02, eta: 11:33:15, time: 0.802, data_time: 0.198, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3901, loss: 0.3901 +2025-06-24 19:48:59,722 - pyskl - INFO - Epoch [68][200/1281] lr: 1.454e-02, eta: 11:32:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9994, loss_cls: 0.3732, loss: 0.3732 +2025-06-24 19:49:48,475 - pyskl - INFO - Epoch [68][300/1281] lr: 1.452e-02, eta: 11:32:21, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9975, loss_cls: 0.3629, loss: 0.3629 +2025-06-24 19:50:37,586 - pyskl - INFO - Epoch [68][400/1281] lr: 1.450e-02, eta: 11:31:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9988, loss_cls: 0.3731, loss: 0.3731 +2025-06-24 19:51:26,569 - pyskl - INFO - Epoch [68][500/1281] lr: 1.448e-02, eta: 11:31:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.3241, loss: 0.3241 +2025-06-24 19:52:15,558 - pyskl - INFO - Epoch [68][600/1281] lr: 1.446e-02, eta: 11:31:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9988, loss_cls: 0.3944, loss: 0.3944 +2025-06-24 19:53:05,002 - pyskl - INFO - Epoch [68][700/1281] lr: 1.444e-02, eta: 11:30:33, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9975, loss_cls: 0.4169, loss: 0.4169 +2025-06-24 19:53:53,972 - pyskl - INFO - Epoch [68][800/1281] lr: 1.442e-02, eta: 11:30:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9988, loss_cls: 0.3841, loss: 0.3841 +2025-06-24 19:54:43,178 - pyskl - INFO - Epoch [68][900/1281] lr: 1.440e-02, eta: 11:29:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9981, loss_cls: 0.4119, loss: 0.4119 +2025-06-24 19:55:11,727 - pyskl - INFO - Epoch [68][1000/1281] lr: 1.438e-02, eta: 11:28:46, time: 0.285, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9975, loss_cls: 0.3801, loss: 0.3801 +2025-06-24 19:56:02,667 - pyskl - INFO - Epoch [68][1100/1281] lr: 1.436e-02, eta: 11:28:21, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9969, loss_cls: 0.4023, loss: 0.4023 +2025-06-24 19:56:30,220 - pyskl - INFO - Epoch [68][1200/1281] lr: 1.434e-02, eta: 11:27:28, time: 0.276, data_time: 0.001, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.3717, loss: 0.3717 +2025-06-24 19:57:10,467 - pyskl - INFO - Saving checkpoint at 68 epochs +2025-06-24 19:58:10,686 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:58:10,745 - pyskl - INFO - +top1_acc 0.8938 +top5_acc 0.9930 +2025-06-24 19:58:10,745 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:58:10,753 - pyskl - INFO - +mean_acc 0.8628 +2025-06-24 19:58:10,755 - pyskl - INFO - Epoch(val) [68][533] top1_acc: 0.8938, top5_acc: 0.9930, mean_class_accuracy: 0.8628 +2025-06-24 19:59:30,589 - pyskl - INFO - Epoch [69][100/1281] lr: 1.431e-02, eta: 11:26:27, time: 0.798, data_time: 0.191, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9969, loss_cls: 0.3693, loss: 0.3693 +2025-06-24 20:00:19,567 - pyskl - INFO - Epoch [69][200/1281] lr: 1.429e-02, eta: 11:26:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3289, loss: 0.3289 +2025-06-24 20:01:08,470 - pyskl - INFO - Epoch [69][300/1281] lr: 1.427e-02, eta: 11:25:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9988, loss_cls: 0.3431, loss: 0.3431 +2025-06-24 20:01:57,425 - pyskl - INFO - Epoch [69][400/1281] lr: 1.425e-02, eta: 11:25:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 1.0000, loss_cls: 0.3067, loss: 0.3067 +2025-06-24 20:02:46,447 - pyskl - INFO - Epoch [69][500/1281] lr: 1.423e-02, eta: 11:24:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9981, loss_cls: 0.3248, loss: 0.3248 +2025-06-24 20:03:35,692 - pyskl - INFO - Epoch [69][600/1281] lr: 1.420e-02, eta: 11:24:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9988, loss_cls: 0.3152, loss: 0.3152 +2025-06-24 20:04:24,451 - pyskl - INFO - Epoch [69][700/1281] lr: 1.418e-02, eta: 11:23:41, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9962, loss_cls: 0.3550, loss: 0.3550 +2025-06-24 20:05:13,575 - pyskl - INFO - Epoch [69][800/1281] lr: 1.416e-02, eta: 11:23:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9988, loss_cls: 0.3042, loss: 0.3042 +2025-06-24 20:06:02,397 - pyskl - INFO - Epoch [69][900/1281] lr: 1.414e-02, eta: 11:22:45, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.3108, loss: 0.3108 +2025-06-24 20:06:31,574 - pyskl - INFO - Epoch [69][1000/1281] lr: 1.412e-02, eta: 11:21:54, time: 0.292, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9981, loss_cls: 0.3494, loss: 0.3494 +2025-06-24 20:07:22,591 - pyskl - INFO - Epoch [69][1100/1281] lr: 1.410e-02, eta: 11:21:28, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9994, loss_cls: 0.4168, loss: 0.4168 +2025-06-24 20:07:49,304 - pyskl - INFO - Epoch [69][1200/1281] lr: 1.408e-02, eta: 11:20:34, time: 0.267, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9975, loss_cls: 0.4427, loss: 0.4427 +2025-06-24 20:08:29,864 - pyskl - INFO - Saving checkpoint at 69 epochs +2025-06-24 20:09:29,123 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:09:29,192 - pyskl - INFO - +top1_acc 0.8918 +top5_acc 0.9925 +2025-06-24 20:09:29,193 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:09:29,201 - pyskl - INFO - +mean_acc 0.8516 +2025-06-24 20:09:29,203 - pyskl - INFO - Epoch(val) [69][533] top1_acc: 0.8918, top5_acc: 0.9925, mean_class_accuracy: 0.8516 +2025-06-24 20:10:47,499 - pyskl - INFO - Epoch [70][100/1281] lr: 1.405e-02, eta: 11:19:31, time: 0.783, data_time: 0.196, memory: 4083, top1_acc: 0.9344, top5_acc: 1.0000, loss_cls: 0.3444, loss: 0.3444 +2025-06-24 20:11:36,675 - pyskl - INFO - Epoch [70][200/1281] lr: 1.403e-02, eta: 11:19:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9988, loss_cls: 0.3489, loss: 0.3489 +2025-06-24 20:12:25,745 - pyskl - INFO - Epoch [70][300/1281] lr: 1.401e-02, eta: 11:18:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9981, loss_cls: 0.3326, loss: 0.3326 +2025-06-24 20:13:14,980 - pyskl - INFO - Epoch [70][400/1281] lr: 1.399e-02, eta: 11:18:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9981, loss_cls: 0.3304, loss: 0.3304 +2025-06-24 20:14:04,301 - pyskl - INFO - Epoch [70][500/1281] lr: 1.397e-02, eta: 11:17:40, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9969, loss_cls: 0.4136, loss: 0.4136 +2025-06-24 20:14:53,625 - pyskl - INFO - Epoch [70][600/1281] lr: 1.395e-02, eta: 11:17:12, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9994, loss_cls: 0.3530, loss: 0.3530 +2025-06-24 20:15:42,345 - pyskl - INFO - Epoch [70][700/1281] lr: 1.392e-02, eta: 11:16:43, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9988, loss_cls: 0.3356, loss: 0.3356 +2025-06-24 20:16:31,156 - pyskl - INFO - Epoch [70][800/1281] lr: 1.390e-02, eta: 11:16:15, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9994, loss_cls: 0.4058, loss: 0.4058 +2025-06-24 20:17:20,265 - pyskl - INFO - Epoch [70][900/1281] lr: 1.388e-02, eta: 11:15:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9981, loss_cls: 0.3663, loss: 0.3663 +2025-06-24 20:17:53,215 - pyskl - INFO - Epoch [70][1000/1281] lr: 1.386e-02, eta: 11:15:00, time: 0.329, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3565, loss: 0.3565 +2025-06-24 20:18:44,289 - pyskl - INFO - Epoch [70][1100/1281] lr: 1.384e-02, eta: 11:14:34, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 1.0000, loss_cls: 0.3734, loss: 0.3734 +2025-06-24 20:19:09,426 - pyskl - INFO - Epoch [70][1200/1281] lr: 1.382e-02, eta: 11:13:38, time: 0.251, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9994, loss_cls: 0.3535, loss: 0.3535 +2025-06-24 20:19:48,643 - pyskl - INFO - Saving checkpoint at 70 epochs +2025-06-24 20:20:48,164 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:20:48,221 - pyskl - INFO - +top1_acc 0.8849 +top5_acc 0.9916 +2025-06-24 20:20:48,221 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:20:48,228 - pyskl - INFO - +mean_acc 0.8343 +2025-06-24 20:20:48,231 - pyskl - INFO - Epoch(val) [70][533] top1_acc: 0.8849, top5_acc: 0.9916, mean_class_accuracy: 0.8343 +2025-06-24 20:22:06,714 - pyskl - INFO - Epoch [71][100/1281] lr: 1.379e-02, eta: 11:12:35, time: 0.785, data_time: 0.196, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9969, loss_cls: 0.3424, loss: 0.3424 +2025-06-24 20:22:55,893 - pyskl - INFO - Epoch [71][200/1281] lr: 1.377e-02, eta: 11:12:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.3337, loss: 0.3337 +2025-06-24 20:23:44,799 - pyskl - INFO - Epoch [71][300/1281] lr: 1.375e-02, eta: 11:11:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3572, loss: 0.3572 +2025-06-24 20:24:33,831 - pyskl - INFO - Epoch [71][400/1281] lr: 1.373e-02, eta: 11:11:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9994, loss_cls: 0.3083, loss: 0.3083 +2025-06-24 20:25:23,027 - pyskl - INFO - Epoch [71][500/1281] lr: 1.371e-02, eta: 11:10:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9988, loss_cls: 0.3541, loss: 0.3541 +2025-06-24 20:26:11,709 - pyskl - INFO - Epoch [71][600/1281] lr: 1.368e-02, eta: 11:10:12, time: 0.487, data_time: 0.001, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9988, loss_cls: 0.3080, loss: 0.3080 +2025-06-24 20:27:00,812 - pyskl - INFO - Epoch [71][700/1281] lr: 1.366e-02, eta: 11:09:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9994, loss_cls: 0.3704, loss: 0.3704 +2025-06-24 20:27:50,155 - pyskl - INFO - Epoch [71][800/1281] lr: 1.364e-02, eta: 11:09:15, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 1.0000, loss_cls: 0.3235, loss: 0.3235 +2025-06-24 20:28:39,331 - pyskl - INFO - Epoch [71][900/1281] lr: 1.362e-02, eta: 11:08:46, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.3196, loss: 0.3196 +2025-06-24 20:29:14,184 - pyskl - INFO - Epoch [71][1000/1281] lr: 1.360e-02, eta: 11:08:01, time: 0.349, data_time: 0.001, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9981, loss_cls: 0.3426, loss: 0.3426 +2025-06-24 20:30:04,997 - pyskl - INFO - Epoch [71][1100/1281] lr: 1.358e-02, eta: 11:07:35, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9988, loss_cls: 0.3616, loss: 0.3616 +2025-06-24 20:30:29,350 - pyskl - INFO - Epoch [71][1200/1281] lr: 1.356e-02, eta: 11:06:38, time: 0.244, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9962, loss_cls: 0.4241, loss: 0.4241 +2025-06-24 20:31:06,288 - pyskl - INFO - Saving checkpoint at 71 epochs +2025-06-24 20:32:05,505 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:32:05,562 - pyskl - INFO - +top1_acc 0.8838 +top5_acc 0.9925 +2025-06-24 20:32:05,562 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:32:05,571 - pyskl - INFO - +mean_acc 0.8411 +2025-06-24 20:32:05,574 - pyskl - INFO - Epoch(val) [71][533] top1_acc: 0.8838, top5_acc: 0.9925, mean_class_accuracy: 0.8411 +2025-06-24 20:33:24,796 - pyskl - INFO - Epoch [72][100/1281] lr: 1.353e-02, eta: 11:05:35, time: 0.792, data_time: 0.188, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9981, loss_cls: 0.3523, loss: 0.3523 +2025-06-24 20:34:13,973 - pyskl - INFO - Epoch [72][200/1281] lr: 1.351e-02, eta: 11:05:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9981, loss_cls: 0.3422, loss: 0.3422 +2025-06-24 20:35:02,936 - pyskl - INFO - Epoch [72][300/1281] lr: 1.349e-02, eta: 11:04:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9988, loss_cls: 0.3158, loss: 0.3158 +2025-06-24 20:35:51,627 - pyskl - INFO - Epoch [72][400/1281] lr: 1.346e-02, eta: 11:04:08, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 1.0000, loss_cls: 0.3513, loss: 0.3513 +2025-06-24 20:36:40,542 - pyskl - INFO - Epoch [72][500/1281] lr: 1.344e-02, eta: 11:03:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.2928, loss: 0.2928 +2025-06-24 20:37:29,527 - pyskl - INFO - Epoch [72][600/1281] lr: 1.342e-02, eta: 11:03:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9988, loss_cls: 0.2970, loss: 0.2970 +2025-06-24 20:38:18,947 - pyskl - INFO - Epoch [72][700/1281] lr: 1.340e-02, eta: 11:02:41, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 1.0000, loss_cls: 0.3203, loss: 0.3203 +2025-06-24 20:39:07,953 - pyskl - INFO - Epoch [72][800/1281] lr: 1.338e-02, eta: 11:02:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9981, loss_cls: 0.3808, loss: 0.3808 +2025-06-24 20:39:56,803 - pyskl - INFO - Epoch [72][900/1281] lr: 1.336e-02, eta: 11:01:42, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3590, loss: 0.3590 +2025-06-24 20:40:34,107 - pyskl - INFO - Epoch [72][1000/1281] lr: 1.334e-02, eta: 11:01:00, time: 0.373, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9981, loss_cls: 0.3648, loss: 0.3648 +2025-06-24 20:41:24,995 - pyskl - INFO - Epoch [72][1100/1281] lr: 1.332e-02, eta: 11:00:33, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9988, loss_cls: 0.3712, loss: 0.3712 +2025-06-24 20:41:48,776 - pyskl - INFO - Epoch [72][1200/1281] lr: 1.330e-02, eta: 10:59:36, time: 0.238, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9969, loss_cls: 0.3588, loss: 0.3588 +2025-06-24 20:42:24,877 - pyskl - INFO - Saving checkpoint at 72 epochs +2025-06-24 20:43:23,755 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:43:23,814 - pyskl - INFO - +top1_acc 0.9060 +top5_acc 0.9934 +2025-06-24 20:43:23,815 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:43:23,822 - pyskl - INFO - +mean_acc 0.8784 +2025-06-24 20:43:23,826 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_62.pth was removed +2025-06-24 20:43:24,001 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_72.pth. +2025-06-24 20:43:24,001 - pyskl - INFO - Best top1_acc is 0.9060 at 72 epoch. +2025-06-24 20:43:24,005 - pyskl - INFO - Epoch(val) [72][533] top1_acc: 0.9060, top5_acc: 0.9934, mean_class_accuracy: 0.8784 +2025-06-24 20:44:42,961 - pyskl - INFO - Epoch [73][100/1281] lr: 1.326e-02, eta: 10:58:32, time: 0.790, data_time: 0.195, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9988, loss_cls: 0.3153, loss: 0.3153 +2025-06-24 20:45:31,994 - pyskl - INFO - Epoch [73][200/1281] lr: 1.324e-02, eta: 10:58:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9994, loss_cls: 0.3222, loss: 0.3222 +2025-06-24 20:46:20,943 - pyskl - INFO - Epoch [73][300/1281] lr: 1.322e-02, eta: 10:57:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9975, loss_cls: 0.3922, loss: 0.3922 +2025-06-24 20:47:09,973 - pyskl - INFO - Epoch [73][400/1281] lr: 1.320e-02, eta: 10:57:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9988, loss_cls: 0.3345, loss: 0.3345 +2025-06-24 20:47:58,915 - pyskl - INFO - Epoch [73][500/1281] lr: 1.318e-02, eta: 10:56:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3312, loss: 0.3312 +2025-06-24 20:48:47,890 - pyskl - INFO - Epoch [73][600/1281] lr: 1.316e-02, eta: 10:56:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9981, loss_cls: 0.3014, loss: 0.3014 +2025-06-24 20:49:36,880 - pyskl - INFO - Epoch [73][700/1281] lr: 1.314e-02, eta: 10:55:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9981, loss_cls: 0.3137, loss: 0.3137 +2025-06-24 20:50:26,069 - pyskl - INFO - Epoch [73][800/1281] lr: 1.312e-02, eta: 10:55:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9975, loss_cls: 0.3464, loss: 0.3464 +2025-06-24 20:51:15,075 - pyskl - INFO - Epoch [73][900/1281] lr: 1.310e-02, eta: 10:54:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 1.0000, loss_cls: 0.3119, loss: 0.3119 +2025-06-24 20:51:54,474 - pyskl - INFO - Epoch [73][1000/1281] lr: 1.308e-02, eta: 10:53:56, time: 0.394, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9969, loss_cls: 0.3439, loss: 0.3439 +2025-06-24 20:52:44,328 - pyskl - INFO - Epoch [73][1100/1281] lr: 1.306e-02, eta: 10:53:27, time: 0.499, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3329, loss: 0.3329 +2025-06-24 20:53:08,064 - pyskl - INFO - Epoch [73][1200/1281] lr: 1.304e-02, eta: 10:52:31, time: 0.237, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2921, loss: 0.2921 +2025-06-24 20:53:41,030 - pyskl - INFO - Saving checkpoint at 73 epochs +2025-06-24 20:54:39,828 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:54:39,885 - pyskl - INFO - +top1_acc 0.8924 +top5_acc 0.9935 +2025-06-24 20:54:39,885 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:54:39,892 - pyskl - INFO - +mean_acc 0.8500 +2025-06-24 20:54:39,894 - pyskl - INFO - Epoch(val) [73][533] top1_acc: 0.8924, top5_acc: 0.9935, mean_class_accuracy: 0.8500 +2025-06-24 20:56:00,767 - pyskl - INFO - Epoch [74][100/1281] lr: 1.300e-02, eta: 10:51:28, time: 0.809, data_time: 0.193, memory: 4083, top1_acc: 0.9556, top5_acc: 1.0000, loss_cls: 0.2824, loss: 0.2824 +2025-06-24 20:56:50,057 - pyskl - INFO - Epoch [74][200/1281] lr: 1.298e-02, eta: 10:50:59, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.3042, loss: 0.3042 +2025-06-24 20:57:39,094 - pyskl - INFO - Epoch [74][300/1281] lr: 1.296e-02, eta: 10:50:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9988, loss_cls: 0.3378, loss: 0.3378 +2025-06-24 20:58:28,257 - pyskl - INFO - Epoch [74][400/1281] lr: 1.294e-02, eta: 10:49:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.3197, loss: 0.3197 +2025-06-24 20:59:17,692 - pyskl - INFO - Epoch [74][500/1281] lr: 1.292e-02, eta: 10:49:30, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.3253, loss: 0.3253 +2025-06-24 21:00:06,970 - pyskl - INFO - Epoch [74][600/1281] lr: 1.290e-02, eta: 10:49:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 1.0000, loss_cls: 0.3819, loss: 0.3819 +2025-06-24 21:00:56,157 - pyskl - INFO - Epoch [74][700/1281] lr: 1.288e-02, eta: 10:48:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 1.0000, loss_cls: 0.3339, loss: 0.3339 +2025-06-24 21:01:44,592 - pyskl - INFO - Epoch [74][800/1281] lr: 1.286e-02, eta: 10:47:59, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.3059, loss: 0.3059 +2025-06-24 21:02:33,524 - pyskl - INFO - Epoch [74][900/1281] lr: 1.284e-02, eta: 10:47:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.3729, loss: 0.3729 +2025-06-24 21:03:16,571 - pyskl - INFO - Epoch [74][1000/1281] lr: 1.282e-02, eta: 10:46:53, time: 0.430, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9988, loss_cls: 0.3395, loss: 0.3395 +2025-06-24 21:04:00,418 - pyskl - INFO - Epoch [74][1100/1281] lr: 1.280e-02, eta: 10:46:17, time: 0.438, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9981, loss_cls: 0.3824, loss: 0.3824 +2025-06-24 21:04:30,162 - pyskl - INFO - Epoch [74][1200/1281] lr: 1.278e-02, eta: 10:45:27, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9975, loss_cls: 0.3845, loss: 0.3845 +2025-06-24 21:05:02,310 - pyskl - INFO - Saving checkpoint at 74 epochs +2025-06-24 21:06:00,974 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:06:01,039 - pyskl - INFO - +top1_acc 0.8943 +top5_acc 0.9920 +2025-06-24 21:06:01,039 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:06:01,050 - pyskl - INFO - +mean_acc 0.8601 +2025-06-24 21:06:01,052 - pyskl - INFO - Epoch(val) [74][533] top1_acc: 0.8943, top5_acc: 0.9920, mean_class_accuracy: 0.8601 +2025-06-24 21:07:22,299 - pyskl - INFO - Epoch [75][100/1281] lr: 1.274e-02, eta: 10:44:25, time: 0.812, data_time: 0.192, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3263, loss: 0.3263 +2025-06-24 21:08:11,485 - pyskl - INFO - Epoch [75][200/1281] lr: 1.272e-02, eta: 10:43:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9994, loss_cls: 0.3150, loss: 0.3150 +2025-06-24 21:09:00,382 - pyskl - INFO - Epoch [75][300/1281] lr: 1.270e-02, eta: 10:43:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9988, loss_cls: 0.3328, loss: 0.3328 +2025-06-24 21:09:49,625 - pyskl - INFO - Epoch [75][400/1281] lr: 1.268e-02, eta: 10:42:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9988, loss_cls: 0.2841, loss: 0.2841 +2025-06-24 21:10:38,800 - pyskl - INFO - Epoch [75][500/1281] lr: 1.266e-02, eta: 10:42:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 1.0000, loss_cls: 0.2974, loss: 0.2974 +2025-06-24 21:11:28,054 - pyskl - INFO - Epoch [75][600/1281] lr: 1.264e-02, eta: 10:41:54, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9981, loss_cls: 0.3242, loss: 0.3242 +2025-06-24 21:12:17,211 - pyskl - INFO - Epoch [75][700/1281] lr: 1.262e-02, eta: 10:41:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2789, loss: 0.2789 +2025-06-24 21:13:06,248 - pyskl - INFO - Epoch [75][800/1281] lr: 1.260e-02, eta: 10:40:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 1.0000, loss_cls: 0.2968, loss: 0.2968 +2025-06-24 21:13:55,316 - pyskl - INFO - Epoch [75][900/1281] lr: 1.258e-02, eta: 10:40:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.3410, loss: 0.3410 +2025-06-24 21:14:37,972 - pyskl - INFO - Epoch [75][1000/1281] lr: 1.256e-02, eta: 10:39:45, time: 0.427, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9988, loss_cls: 0.3229, loss: 0.3229 +2025-06-24 21:15:21,684 - pyskl - INFO - Epoch [75][1100/1281] lr: 1.254e-02, eta: 10:39:09, time: 0.437, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9994, loss_cls: 0.3481, loss: 0.3481 +2025-06-24 21:15:51,230 - pyskl - INFO - Epoch [75][1200/1281] lr: 1.252e-02, eta: 10:38:19, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 1.0000, loss_cls: 0.3894, loss: 0.3894 +2025-06-24 21:16:23,229 - pyskl - INFO - Saving checkpoint at 75 epochs +2025-06-24 21:17:21,536 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:17:21,593 - pyskl - INFO - +top1_acc 0.9027 +top5_acc 0.9923 +2025-06-24 21:17:21,594 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:17:21,601 - pyskl - INFO - +mean_acc 0.8648 +2025-06-24 21:17:21,604 - pyskl - INFO - Epoch(val) [75][533] top1_acc: 0.9027, top5_acc: 0.9923, mean_class_accuracy: 0.8648 +2025-06-24 21:18:41,631 - pyskl - INFO - Epoch [76][100/1281] lr: 1.248e-02, eta: 10:37:15, time: 0.800, data_time: 0.193, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.3414, loss: 0.3414 +2025-06-24 21:19:30,979 - pyskl - INFO - Epoch [76][200/1281] lr: 1.246e-02, eta: 10:36:45, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9988, loss_cls: 0.3375, loss: 0.3375 +2025-06-24 21:20:20,680 - pyskl - INFO - Epoch [76][300/1281] lr: 1.244e-02, eta: 10:36:15, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9975, loss_cls: 0.3566, loss: 0.3566 +2025-06-24 21:21:09,553 - pyskl - INFO - Epoch [76][400/1281] lr: 1.242e-02, eta: 10:35:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9975, loss_cls: 0.3008, loss: 0.3008 +2025-06-24 21:21:59,066 - pyskl - INFO - Epoch [76][500/1281] lr: 1.240e-02, eta: 10:35:13, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3105, loss: 0.3105 +2025-06-24 21:22:48,213 - pyskl - INFO - Epoch [76][600/1281] lr: 1.238e-02, eta: 10:34:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2763, loss: 0.2763 +2025-06-24 21:23:37,086 - pyskl - INFO - Epoch [76][700/1281] lr: 1.236e-02, eta: 10:34:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3507, loss: 0.3507 +2025-06-24 21:24:26,206 - pyskl - INFO - Epoch [76][800/1281] lr: 1.234e-02, eta: 10:33:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9981, loss_cls: 0.2933, loss: 0.2933 +2025-06-24 21:25:14,958 - pyskl - INFO - Epoch [76][900/1281] lr: 1.232e-02, eta: 10:33:09, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9969, loss_cls: 0.3267, loss: 0.3267 +2025-06-24 21:25:58,926 - pyskl - INFO - Epoch [76][1000/1281] lr: 1.230e-02, eta: 10:32:34, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9988, loss_cls: 0.3319, loss: 0.3319 +2025-06-24 21:26:40,616 - pyskl - INFO - Epoch [76][1100/1281] lr: 1.228e-02, eta: 10:31:55, time: 0.417, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9994, loss_cls: 0.3683, loss: 0.3683 +2025-06-24 21:27:12,637 - pyskl - INFO - Epoch [76][1200/1281] lr: 1.225e-02, eta: 10:31:08, time: 0.320, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9969, loss_cls: 0.3901, loss: 0.3901 +2025-06-24 21:27:43,264 - pyskl - INFO - Saving checkpoint at 76 epochs +2025-06-24 21:28:42,089 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:28:42,146 - pyskl - INFO - +top1_acc 0.8574 +top5_acc 0.9896 +2025-06-24 21:28:42,146 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:28:42,154 - pyskl - INFO - +mean_acc 0.8429 +2025-06-24 21:28:42,156 - pyskl - INFO - Epoch(val) [76][533] top1_acc: 0.8574, top5_acc: 0.9896, mean_class_accuracy: 0.8429 +2025-06-24 21:30:02,414 - pyskl - INFO - Epoch [77][100/1281] lr: 1.222e-02, eta: 10:30:03, time: 0.803, data_time: 0.193, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9981, loss_cls: 0.3483, loss: 0.3483 +2025-06-24 21:30:51,381 - pyskl - INFO - Epoch [77][200/1281] lr: 1.220e-02, eta: 10:29:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.2929, loss: 0.2929 +2025-06-24 21:31:40,086 - pyskl - INFO - Epoch [77][300/1281] lr: 1.218e-02, eta: 10:29:01, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9994, loss_cls: 0.3090, loss: 0.3090 +2025-06-24 21:32:29,152 - pyskl - INFO - Epoch [77][400/1281] lr: 1.216e-02, eta: 10:28:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.2916, loss: 0.2916 +2025-06-24 21:33:18,269 - pyskl - INFO - Epoch [77][500/1281] lr: 1.214e-02, eta: 10:27:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 1.0000, loss_cls: 0.3214, loss: 0.3214 +2025-06-24 21:34:07,494 - pyskl - INFO - Epoch [77][600/1281] lr: 1.212e-02, eta: 10:27:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 1.0000, loss_cls: 0.3082, loss: 0.3082 +2025-06-24 21:34:56,439 - pyskl - INFO - Epoch [77][700/1281] lr: 1.210e-02, eta: 10:26:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 1.0000, loss_cls: 0.2864, loss: 0.2864 +2025-06-24 21:35:45,440 - pyskl - INFO - Epoch [77][800/1281] lr: 1.207e-02, eta: 10:26:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.2744, loss: 0.2744 +2025-06-24 21:36:34,382 - pyskl - INFO - Epoch [77][900/1281] lr: 1.205e-02, eta: 10:25:53, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9988, loss_cls: 0.2888, loss: 0.2888 +2025-06-24 21:37:19,459 - pyskl - INFO - Epoch [77][1000/1281] lr: 1.203e-02, eta: 10:25:18, time: 0.451, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2823, loss: 0.2823 +2025-06-24 21:37:59,735 - pyskl - INFO - Epoch [77][1100/1281] lr: 1.201e-02, eta: 10:24:38, time: 0.403, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2980, loss: 0.2980 +2025-06-24 21:38:32,642 - pyskl - INFO - Epoch [77][1200/1281] lr: 1.199e-02, eta: 10:23:52, time: 0.329, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.3069, loss: 0.3069 +2025-06-24 21:39:01,895 - pyskl - INFO - Saving checkpoint at 77 epochs +2025-06-24 21:40:00,928 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:40:00,985 - pyskl - INFO - +top1_acc 0.8967 +top5_acc 0.9919 +2025-06-24 21:40:00,985 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:40:00,993 - pyskl - INFO - +mean_acc 0.8660 +2025-06-24 21:40:00,996 - pyskl - INFO - Epoch(val) [77][533] top1_acc: 0.8967, top5_acc: 0.9919, mean_class_accuracy: 0.8660 +2025-06-24 21:41:21,039 - pyskl - INFO - Epoch [78][100/1281] lr: 1.196e-02, eta: 10:22:46, time: 0.800, data_time: 0.192, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2276, loss: 0.2276 +2025-06-24 21:42:10,114 - pyskl - INFO - Epoch [78][200/1281] lr: 1.194e-02, eta: 10:22:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.2510, loss: 0.2510 +2025-06-24 21:42:59,077 - pyskl - INFO - Epoch [78][300/1281] lr: 1.192e-02, eta: 10:21:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2417, loss: 0.2417 +2025-06-24 21:43:47,581 - pyskl - INFO - Epoch [78][400/1281] lr: 1.190e-02, eta: 10:21:11, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.3367, loss: 0.3367 +2025-06-24 21:44:36,734 - pyskl - INFO - Epoch [78][500/1281] lr: 1.187e-02, eta: 10:20:40, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.3153, loss: 0.3153 +2025-06-24 21:45:25,791 - pyskl - INFO - Epoch [78][600/1281] lr: 1.185e-02, eta: 10:20:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 1.0000, loss_cls: 0.3145, loss: 0.3145 +2025-06-24 21:46:15,010 - pyskl - INFO - Epoch [78][700/1281] lr: 1.183e-02, eta: 10:19:37, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.3103, loss: 0.3103 +2025-06-24 21:47:04,151 - pyskl - INFO - Epoch [78][800/1281] lr: 1.181e-02, eta: 10:19:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9988, loss_cls: 0.3492, loss: 0.3492 +2025-06-24 21:47:53,388 - pyskl - INFO - Epoch [78][900/1281] lr: 1.179e-02, eta: 10:18:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.3352, loss: 0.3352 +2025-06-24 21:48:40,848 - pyskl - INFO - Epoch [78][1000/1281] lr: 1.177e-02, eta: 10:18:01, time: 0.475, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 1.0000, loss_cls: 0.3400, loss: 0.3400 +2025-06-24 21:49:15,486 - pyskl - INFO - Epoch [78][1100/1281] lr: 1.175e-02, eta: 10:17:15, time: 0.346, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9975, loss_cls: 0.3024, loss: 0.3024 +2025-06-24 21:49:54,364 - pyskl - INFO - Epoch [78][1200/1281] lr: 1.173e-02, eta: 10:16:34, time: 0.389, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9975, loss_cls: 0.3439, loss: 0.3439 +2025-06-24 21:50:21,125 - pyskl - INFO - Saving checkpoint at 78 epochs +2025-06-24 21:51:20,132 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:51:20,205 - pyskl - INFO - +top1_acc 0.8859 +top5_acc 0.9927 +2025-06-24 21:51:20,205 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:51:20,214 - pyskl - INFO - +mean_acc 0.8453 +2025-06-24 21:51:20,216 - pyskl - INFO - Epoch(val) [78][533] top1_acc: 0.8859, top5_acc: 0.9927, mean_class_accuracy: 0.8453 +2025-06-24 21:52:40,132 - pyskl - INFO - Epoch [79][100/1281] lr: 1.169e-02, eta: 10:15:28, time: 0.799, data_time: 0.194, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2554, loss: 0.2554 +2025-06-24 21:53:29,219 - pyskl - INFO - Epoch [79][200/1281] lr: 1.167e-02, eta: 10:14:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2287, loss: 0.2287 +2025-06-24 21:54:18,275 - pyskl - INFO - Epoch [79][300/1281] lr: 1.165e-02, eta: 10:14:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9981, loss_cls: 0.2975, loss: 0.2975 +2025-06-24 21:55:06,548 - pyskl - INFO - Epoch [79][400/1281] lr: 1.163e-02, eta: 10:13:52, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9988, loss_cls: 0.3186, loss: 0.3186 +2025-06-24 21:55:55,662 - pyskl - INFO - Epoch [79][500/1281] lr: 1.161e-02, eta: 10:13:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3185, loss: 0.3185 +2025-06-24 21:56:44,145 - pyskl - INFO - Epoch [79][600/1281] lr: 1.159e-02, eta: 10:12:48, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9988, loss_cls: 0.3068, loss: 0.3068 +2025-06-24 21:57:32,993 - pyskl - INFO - Epoch [79][700/1281] lr: 1.157e-02, eta: 10:12:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.3311, loss: 0.3311 +2025-06-24 21:58:21,933 - pyskl - INFO - Epoch [79][800/1281] lr: 1.155e-02, eta: 10:11:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9994, loss_cls: 0.3567, loss: 0.3567 +2025-06-24 21:59:11,154 - pyskl - INFO - Epoch [79][900/1281] lr: 1.153e-02, eta: 10:11:12, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.3431, loss: 0.3431 +2025-06-24 22:00:00,332 - pyskl - INFO - Epoch [79][1000/1281] lr: 1.151e-02, eta: 10:10:40, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9988, loss_cls: 0.3313, loss: 0.3313 +2025-06-24 22:00:30,616 - pyskl - INFO - Epoch [79][1100/1281] lr: 1.149e-02, eta: 10:09:50, time: 0.303, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9981, loss_cls: 0.3421, loss: 0.3421 +2025-06-24 22:01:14,277 - pyskl - INFO - Epoch [79][1200/1281] lr: 1.147e-02, eta: 10:09:13, time: 0.437, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2638, loss: 0.2638 +2025-06-24 22:01:37,949 - pyskl - INFO - Saving checkpoint at 79 epochs +2025-06-24 22:02:36,159 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:02:36,245 - pyskl - INFO - +top1_acc 0.8999 +top5_acc 0.9935 +2025-06-24 22:02:36,245 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:02:36,257 - pyskl - INFO - +mean_acc 0.8539 +2025-06-24 22:02:36,259 - pyskl - INFO - Epoch(val) [79][533] top1_acc: 0.8999, top5_acc: 0.9935, mean_class_accuracy: 0.8539 +2025-06-24 22:03:55,500 - pyskl - INFO - Epoch [80][100/1281] lr: 1.143e-02, eta: 10:08:07, time: 0.792, data_time: 0.192, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9962, loss_cls: 0.2377, loss: 0.2377 +2025-06-24 22:04:44,604 - pyskl - INFO - Epoch [80][200/1281] lr: 1.141e-02, eta: 10:07:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9981, loss_cls: 0.2611, loss: 0.2611 +2025-06-24 22:05:33,789 - pyskl - INFO - Epoch [80][300/1281] lr: 1.139e-02, eta: 10:07:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9994, loss_cls: 0.2433, loss: 0.2433 +2025-06-24 22:06:22,738 - pyskl - INFO - Epoch [80][400/1281] lr: 1.137e-02, eta: 10:06:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2478, loss: 0.2478 +2025-06-24 22:07:11,442 - pyskl - INFO - Epoch [80][500/1281] lr: 1.135e-02, eta: 10:05:57, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9988, loss_cls: 0.3129, loss: 0.3129 +2025-06-24 22:08:00,329 - pyskl - INFO - Epoch [80][600/1281] lr: 1.133e-02, eta: 10:05:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9962, loss_cls: 0.3349, loss: 0.3349 +2025-06-24 22:08:49,479 - pyskl - INFO - Epoch [80][700/1281] lr: 1.131e-02, eta: 10:04:53, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9975, loss_cls: 0.3229, loss: 0.3229 +2025-06-24 22:09:38,530 - pyskl - INFO - Epoch [80][800/1281] lr: 1.129e-02, eta: 10:04:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9988, loss_cls: 0.3107, loss: 0.3107 +2025-06-24 22:10:27,576 - pyskl - INFO - Epoch [80][900/1281] lr: 1.127e-02, eta: 10:03:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.3260, loss: 0.3260 +2025-06-24 22:11:16,731 - pyskl - INFO - Epoch [80][1000/1281] lr: 1.125e-02, eta: 10:03:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9994, loss_cls: 0.2724, loss: 0.2724 +2025-06-24 22:11:44,428 - pyskl - INFO - Epoch [80][1100/1281] lr: 1.123e-02, eta: 10:02:24, time: 0.277, data_time: 0.001, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2769, loss: 0.2769 +2025-06-24 22:12:35,273 - pyskl - INFO - Epoch [80][1200/1281] lr: 1.121e-02, eta: 10:01:53, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3242, loss: 0.3242 +2025-06-24 22:12:56,889 - pyskl - INFO - Saving checkpoint at 80 epochs +2025-06-24 22:13:55,554 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:13:55,610 - pyskl - INFO - +top1_acc 0.8869 +top5_acc 0.9908 +2025-06-24 22:13:55,610 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:13:55,616 - pyskl - INFO - +mean_acc 0.8586 +2025-06-24 22:13:55,618 - pyskl - INFO - Epoch(val) [80][533] top1_acc: 0.8869, top5_acc: 0.9908, mean_class_accuracy: 0.8586 +2025-06-24 22:15:16,666 - pyskl - INFO - Epoch [81][100/1281] lr: 1.117e-02, eta: 10:00:48, time: 0.810, data_time: 0.194, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9975, loss_cls: 0.3513, loss: 0.3513 +2025-06-24 22:16:05,642 - pyskl - INFO - Epoch [81][200/1281] lr: 1.115e-02, eta: 10:00:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2784, loss: 0.2784 +2025-06-24 22:16:54,563 - pyskl - INFO - Epoch [81][300/1281] lr: 1.113e-02, eta: 9:59:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2543, loss: 0.2543 +2025-06-24 22:17:43,592 - pyskl - INFO - Epoch [81][400/1281] lr: 1.111e-02, eta: 9:59:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 1.0000, loss_cls: 0.2833, loss: 0.2833 +2025-06-24 22:18:32,941 - pyskl - INFO - Epoch [81][500/1281] lr: 1.109e-02, eta: 9:58:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2673, loss: 0.2673 +2025-06-24 22:19:21,965 - pyskl - INFO - Epoch [81][600/1281] lr: 1.107e-02, eta: 9:58:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2902, loss: 0.2902 +2025-06-24 22:20:11,245 - pyskl - INFO - Epoch [81][700/1281] lr: 1.105e-02, eta: 9:57:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2735, loss: 0.2735 +2025-06-24 22:21:00,424 - pyskl - INFO - Epoch [81][800/1281] lr: 1.103e-02, eta: 9:56:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2689, loss: 0.2689 +2025-06-24 22:21:49,541 - pyskl - INFO - Epoch [81][900/1281] lr: 1.101e-02, eta: 9:56:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9988, loss_cls: 0.3290, loss: 0.3290 +2025-06-24 22:22:38,592 - pyskl - INFO - Epoch [81][1000/1281] lr: 1.099e-02, eta: 9:55:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.3001, loss: 0.3001 +2025-06-24 22:23:05,403 - pyskl - INFO - Epoch [81][1100/1281] lr: 1.097e-02, eta: 9:55:02, time: 0.268, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9969, loss_cls: 0.3269, loss: 0.3269 +2025-06-24 22:23:56,313 - pyskl - INFO - Epoch [81][1200/1281] lr: 1.095e-02, eta: 9:54:31, time: 0.509, data_time: 0.001, memory: 4083, top1_acc: 0.9450, top5_acc: 1.0000, loss_cls: 0.3014, loss: 0.3014 +2025-06-24 22:24:18,110 - pyskl - INFO - Saving checkpoint at 81 epochs +2025-06-24 22:25:16,991 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:25:17,053 - pyskl - INFO - +top1_acc 0.9114 +top5_acc 0.9947 +2025-06-24 22:25:17,053 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:25:17,061 - pyskl - INFO - +mean_acc 0.8743 +2025-06-24 22:25:17,065 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_72.pth was removed +2025-06-24 22:25:17,239 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_81.pth. +2025-06-24 22:25:17,239 - pyskl - INFO - Best top1_acc is 0.9114 at 81 epoch. +2025-06-24 22:25:17,242 - pyskl - INFO - Epoch(val) [81][533] top1_acc: 0.9114, top5_acc: 0.9947, mean_class_accuracy: 0.8743 +2025-06-24 22:26:36,275 - pyskl - INFO - Epoch [82][100/1281] lr: 1.091e-02, eta: 9:53:23, time: 0.790, data_time: 0.188, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9994, loss_cls: 0.2397, loss: 0.2397 +2025-06-24 22:27:25,368 - pyskl - INFO - Epoch [82][200/1281] lr: 1.089e-02, eta: 9:52:50, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9988, loss_cls: 0.2914, loss: 0.2914 +2025-06-24 22:28:14,390 - pyskl - INFO - Epoch [82][300/1281] lr: 1.087e-02, eta: 9:52:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9994, loss_cls: 0.2791, loss: 0.2791 +2025-06-24 22:29:03,453 - pyskl - INFO - Epoch [82][400/1281] lr: 1.085e-02, eta: 9:51:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2721, loss: 0.2721 +2025-06-24 22:29:52,638 - pyskl - INFO - Epoch [82][500/1281] lr: 1.083e-02, eta: 9:51:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2596, loss: 0.2596 +2025-06-24 22:30:41,631 - pyskl - INFO - Epoch [82][600/1281] lr: 1.081e-02, eta: 9:50:38, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9994, loss_cls: 0.2722, loss: 0.2722 +2025-06-24 22:31:30,279 - pyskl - INFO - Epoch [82][700/1281] lr: 1.079e-02, eta: 9:50:04, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2838, loss: 0.2838 +2025-06-24 22:32:19,287 - pyskl - INFO - Epoch [82][800/1281] lr: 1.077e-02, eta: 9:49:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9988, loss_cls: 0.2713, loss: 0.2713 +2025-06-24 22:33:08,473 - pyskl - INFO - Epoch [82][900/1281] lr: 1.075e-02, eta: 9:48:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2582, loss: 0.2582 +2025-06-24 22:33:57,504 - pyskl - INFO - Epoch [82][1000/1281] lr: 1.073e-02, eta: 9:48:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2801, loss: 0.2801 +2025-06-24 22:34:26,930 - pyskl - INFO - Epoch [82][1100/1281] lr: 1.071e-02, eta: 9:47:36, time: 0.294, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.2927, loss: 0.2927 +2025-06-24 22:35:17,696 - pyskl - INFO - Epoch [82][1200/1281] lr: 1.069e-02, eta: 9:47:04, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9981, loss_cls: 0.2973, loss: 0.2973 +2025-06-24 22:35:38,670 - pyskl - INFO - Saving checkpoint at 82 epochs +2025-06-24 22:36:37,002 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:36:37,070 - pyskl - INFO - +top1_acc 0.9153 +top5_acc 0.9934 +2025-06-24 22:36:37,070 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:36:37,079 - pyskl - INFO - +mean_acc 0.8916 +2025-06-24 22:36:37,084 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_81.pth was removed +2025-06-24 22:36:37,274 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_82.pth. +2025-06-24 22:36:37,275 - pyskl - INFO - Best top1_acc is 0.9153 at 82 epoch. +2025-06-24 22:36:37,278 - pyskl - INFO - Epoch(val) [82][533] top1_acc: 0.9153, top5_acc: 0.9934, mean_class_accuracy: 0.8916 +2025-06-24 22:37:56,747 - pyskl - INFO - Epoch [83][100/1281] lr: 1.065e-02, eta: 9:45:56, time: 0.795, data_time: 0.193, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9988, loss_cls: 0.2355, loss: 0.2355 +2025-06-24 22:38:45,728 - pyskl - INFO - Epoch [83][200/1281] lr: 1.063e-02, eta: 9:45:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2340, loss: 0.2340 +2025-06-24 22:39:34,881 - pyskl - INFO - Epoch [83][300/1281] lr: 1.061e-02, eta: 9:44:49, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.2169, loss: 0.2169 +2025-06-24 22:40:24,069 - pyskl - INFO - Epoch [83][400/1281] lr: 1.059e-02, eta: 9:44:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9988, loss_cls: 0.2573, loss: 0.2573 +2025-06-24 22:41:13,584 - pyskl - INFO - Epoch [83][500/1281] lr: 1.057e-02, eta: 9:43:43, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.2927, loss: 0.2927 +2025-06-24 22:42:02,387 - pyskl - INFO - Epoch [83][600/1281] lr: 1.055e-02, eta: 9:43:10, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9981, loss_cls: 0.3016, loss: 0.3016 +2025-06-24 22:42:51,351 - pyskl - INFO - Epoch [83][700/1281] lr: 1.053e-02, eta: 9:42:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9988, loss_cls: 0.3150, loss: 0.3150 +2025-06-24 22:43:40,042 - pyskl - INFO - Epoch [83][800/1281] lr: 1.051e-02, eta: 9:42:02, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 1.0000, loss_cls: 0.3251, loss: 0.3251 +2025-06-24 22:44:29,091 - pyskl - INFO - Epoch [83][900/1281] lr: 1.049e-02, eta: 9:41:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9988, loss_cls: 0.2792, loss: 0.2792 +2025-06-24 22:45:18,235 - pyskl - INFO - Epoch [83][1000/1281] lr: 1.047e-02, eta: 9:40:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3138, loss: 0.3138 +2025-06-24 22:45:46,974 - pyskl - INFO - Epoch [83][1100/1281] lr: 1.045e-02, eta: 9:40:05, time: 0.287, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9994, loss_cls: 0.3047, loss: 0.3047 +2025-06-24 22:46:38,108 - pyskl - INFO - Epoch [83][1200/1281] lr: 1.043e-02, eta: 9:39:34, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9988, loss_cls: 0.2826, loss: 0.2826 +2025-06-24 22:46:59,419 - pyskl - INFO - Saving checkpoint at 83 epochs +2025-06-24 22:47:58,125 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:47:58,184 - pyskl - INFO - +top1_acc 0.9155 +top5_acc 0.9939 +2025-06-24 22:47:58,184 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:47:58,192 - pyskl - INFO - +mean_acc 0.8816 +2025-06-24 22:47:58,196 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_82.pth was removed +2025-06-24 22:47:58,375 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_83.pth. +2025-06-24 22:47:58,375 - pyskl - INFO - Best top1_acc is 0.9155 at 83 epoch. +2025-06-24 22:47:58,381 - pyskl - INFO - Epoch(val) [83][533] top1_acc: 0.9155, top5_acc: 0.9939, mean_class_accuracy: 0.8816 +2025-06-24 22:49:17,060 - pyskl - INFO - Epoch [84][100/1281] lr: 1.040e-02, eta: 9:38:25, time: 0.787, data_time: 0.190, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2511, loss: 0.2511 +2025-06-24 22:50:06,267 - pyskl - INFO - Epoch [84][200/1281] lr: 1.038e-02, eta: 9:37:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2413, loss: 0.2413 +2025-06-24 22:50:55,202 - pyskl - INFO - Epoch [84][300/1281] lr: 1.036e-02, eta: 9:37:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2502, loss: 0.2502 +2025-06-24 22:51:44,485 - pyskl - INFO - Epoch [84][400/1281] lr: 1.034e-02, eta: 9:36:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9988, loss_cls: 0.2016, loss: 0.2016 +2025-06-24 22:52:33,279 - pyskl - INFO - Epoch [84][500/1281] lr: 1.031e-02, eta: 9:36:10, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2727, loss: 0.2727 +2025-06-24 22:53:22,645 - pyskl - INFO - Epoch [84][600/1281] lr: 1.029e-02, eta: 9:35:37, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9975, loss_cls: 0.3267, loss: 0.3267 +2025-06-24 22:54:11,466 - pyskl - INFO - Epoch [84][700/1281] lr: 1.027e-02, eta: 9:35:03, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 1.0000, loss_cls: 0.2893, loss: 0.2893 +2025-06-24 22:55:00,501 - pyskl - INFO - Epoch [84][800/1281] lr: 1.025e-02, eta: 9:34:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2629, loss: 0.2629 +2025-06-24 22:55:49,564 - pyskl - INFO - Epoch [84][900/1281] lr: 1.023e-02, eta: 9:33:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2635, loss: 0.2635 +2025-06-24 22:56:38,411 - pyskl - INFO - Epoch [84][1000/1281] lr: 1.021e-02, eta: 9:33:21, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2704, loss: 0.2704 +2025-06-24 22:57:07,726 - pyskl - INFO - Epoch [84][1100/1281] lr: 1.019e-02, eta: 9:32:32, time: 0.293, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3179, loss: 0.3179 +2025-06-24 22:57:58,522 - pyskl - INFO - Epoch [84][1200/1281] lr: 1.017e-02, eta: 9:31:59, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.3042, loss: 0.3042 +2025-06-24 22:58:19,660 - pyskl - INFO - Saving checkpoint at 84 epochs +2025-06-24 22:59:18,140 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:59:18,200 - pyskl - INFO - +top1_acc 0.8958 +top5_acc 0.9945 +2025-06-24 22:59:18,201 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:59:18,211 - pyskl - INFO - +mean_acc 0.8604 +2025-06-24 22:59:18,214 - pyskl - INFO - Epoch(val) [84][533] top1_acc: 0.8958, top5_acc: 0.9945, mean_class_accuracy: 0.8604 +2025-06-24 23:00:36,459 - pyskl - INFO - Epoch [85][100/1281] lr: 1.014e-02, eta: 9:30:50, time: 0.782, data_time: 0.190, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9981, loss_cls: 0.2654, loss: 0.2654 +2025-06-24 23:01:25,518 - pyskl - INFO - Epoch [85][200/1281] lr: 1.012e-02, eta: 9:30:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2126, loss: 0.2126 +2025-06-24 23:02:15,032 - pyskl - INFO - Epoch [85][300/1281] lr: 1.010e-02, eta: 9:29:42, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2506, loss: 0.2506 +2025-06-24 23:03:04,236 - pyskl - INFO - Epoch [85][400/1281] lr: 1.008e-02, eta: 9:29:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9994, loss_cls: 0.2711, loss: 0.2711 +2025-06-24 23:03:53,196 - pyskl - INFO - Epoch [85][500/1281] lr: 1.006e-02, eta: 9:28:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2531, loss: 0.2531 +2025-06-24 23:04:42,510 - pyskl - INFO - Epoch [85][600/1281] lr: 1.004e-02, eta: 9:28:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9988, loss_cls: 0.2941, loss: 0.2941 +2025-06-24 23:05:31,248 - pyskl - INFO - Epoch [85][700/1281] lr: 1.002e-02, eta: 9:27:26, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2546, loss: 0.2546 +2025-06-24 23:06:20,054 - pyskl - INFO - Epoch [85][800/1281] lr: 9.998e-03, eta: 9:26:52, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2488, loss: 0.2488 +2025-06-24 23:07:09,127 - pyskl - INFO - Epoch [85][900/1281] lr: 9.978e-03, eta: 9:26:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 1.0000, loss_cls: 0.3089, loss: 0.3089 +2025-06-24 23:07:58,254 - pyskl - INFO - Epoch [85][1000/1281] lr: 9.958e-03, eta: 9:25:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.2910, loss: 0.2910 +2025-06-24 23:08:28,088 - pyskl - INFO - Epoch [85][1100/1281] lr: 9.937e-03, eta: 9:24:55, time: 0.298, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.3009, loss: 0.3009 +2025-06-24 23:09:19,090 - pyskl - INFO - Epoch [85][1200/1281] lr: 9.917e-03, eta: 9:24:22, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9988, loss_cls: 0.2940, loss: 0.2940 +2025-06-24 23:09:39,970 - pyskl - INFO - Saving checkpoint at 85 epochs +2025-06-24 23:10:38,605 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:10:38,660 - pyskl - INFO - +top1_acc 0.8951 +top5_acc 0.9927 +2025-06-24 23:10:38,660 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:10:38,668 - pyskl - INFO - +mean_acc 0.8489 +2025-06-24 23:10:38,671 - pyskl - INFO - Epoch(val) [85][533] top1_acc: 0.8951, top5_acc: 0.9927, mean_class_accuracy: 0.8489 +2025-06-24 23:11:57,791 - pyskl - INFO - Epoch [86][100/1281] lr: 9.881e-03, eta: 9:23:13, time: 0.791, data_time: 0.198, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2643, loss: 0.2643 +2025-06-24 23:12:46,898 - pyskl - INFO - Epoch [86][200/1281] lr: 9.861e-03, eta: 9:22:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9994, loss_cls: 0.2236, loss: 0.2236 +2025-06-24 23:13:35,985 - pyskl - INFO - Epoch [86][300/1281] lr: 9.841e-03, eta: 9:22:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9994, loss_cls: 0.2489, loss: 0.2489 +2025-06-24 23:14:24,933 - pyskl - INFO - Epoch [86][400/1281] lr: 9.821e-03, eta: 9:21:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2239, loss: 0.2239 +2025-06-24 23:15:14,027 - pyskl - INFO - Epoch [86][500/1281] lr: 9.801e-03, eta: 9:20:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2097, loss: 0.2097 +2025-06-24 23:16:02,980 - pyskl - INFO - Epoch [86][600/1281] lr: 9.781e-03, eta: 9:20:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2320, loss: 0.2320 +2025-06-24 23:16:51,700 - pyskl - INFO - Epoch [86][700/1281] lr: 9.762e-03, eta: 9:19:47, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2578, loss: 0.2578 +2025-06-24 23:17:40,638 - pyskl - INFO - Epoch [86][800/1281] lr: 9.742e-03, eta: 9:19:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9994, loss_cls: 0.2487, loss: 0.2487 +2025-06-24 23:18:29,563 - pyskl - INFO - Epoch [86][900/1281] lr: 9.722e-03, eta: 9:18:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9994, loss_cls: 0.2534, loss: 0.2534 +2025-06-24 23:19:18,596 - pyskl - INFO - Epoch [86][1000/1281] lr: 9.702e-03, eta: 9:18:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2556, loss: 0.2556 +2025-06-24 23:19:48,242 - pyskl - INFO - Epoch [86][1100/1281] lr: 9.682e-03, eta: 9:17:14, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9981, loss_cls: 0.2989, loss: 0.2989 +2025-06-24 23:20:39,215 - pyskl - INFO - Epoch [86][1200/1281] lr: 9.662e-03, eta: 9:16:41, time: 0.510, data_time: 0.001, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9981, loss_cls: 0.2983, loss: 0.2983 +2025-06-24 23:20:59,907 - pyskl - INFO - Saving checkpoint at 86 epochs +2025-06-24 23:21:59,155 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:21:59,213 - pyskl - INFO - +top1_acc 0.9040 +top5_acc 0.9947 +2025-06-24 23:21:59,213 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:21:59,221 - pyskl - INFO - +mean_acc 0.8674 +2025-06-24 23:21:59,223 - pyskl - INFO - Epoch(val) [86][533] top1_acc: 0.9040, top5_acc: 0.9947, mean_class_accuracy: 0.8674 +2025-06-24 23:23:18,873 - pyskl - INFO - Epoch [87][100/1281] lr: 9.626e-03, eta: 9:15:32, time: 0.796, data_time: 0.189, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2363, loss: 0.2363 +2025-06-24 23:24:08,067 - pyskl - INFO - Epoch [87][200/1281] lr: 9.606e-03, eta: 9:14:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1934, loss: 0.1934 +2025-06-24 23:24:57,438 - pyskl - INFO - Epoch [87][300/1281] lr: 9.586e-03, eta: 9:14:23, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9994, loss_cls: 0.2165, loss: 0.2165 +2025-06-24 23:25:46,808 - pyskl - INFO - Epoch [87][400/1281] lr: 9.566e-03, eta: 9:13:49, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9994, loss_cls: 0.1890, loss: 0.1890 +2025-06-24 23:26:36,013 - pyskl - INFO - Epoch [87][500/1281] lr: 9.546e-03, eta: 9:13:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2164, loss: 0.2164 +2025-06-24 23:27:25,087 - pyskl - INFO - Epoch [87][600/1281] lr: 9.527e-03, eta: 9:12:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2309, loss: 0.2309 +2025-06-24 23:28:14,167 - pyskl - INFO - Epoch [87][700/1281] lr: 9.507e-03, eta: 9:12:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9988, loss_cls: 0.2523, loss: 0.2523 +2025-06-24 23:29:03,048 - pyskl - INFO - Epoch [87][800/1281] lr: 9.487e-03, eta: 9:11:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2747, loss: 0.2747 +2025-06-24 23:29:52,128 - pyskl - INFO - Epoch [87][900/1281] lr: 9.467e-03, eta: 9:10:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9988, loss_cls: 0.2682, loss: 0.2682 +2025-06-24 23:30:41,388 - pyskl - INFO - Epoch [87][1000/1281] lr: 9.447e-03, eta: 9:10:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2635, loss: 0.2635 +2025-06-24 23:31:09,527 - pyskl - INFO - Epoch [87][1100/1281] lr: 9.427e-03, eta: 9:09:31, time: 0.281, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2679, loss: 0.2679 +2025-06-24 23:32:00,316 - pyskl - INFO - Epoch [87][1200/1281] lr: 9.408e-03, eta: 9:08:58, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2723, loss: 0.2723 +2025-06-24 23:32:22,057 - pyskl - INFO - Saving checkpoint at 87 epochs +2025-06-24 23:33:20,573 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:33:20,631 - pyskl - INFO - +top1_acc 0.9201 +top5_acc 0.9951 +2025-06-24 23:33:20,631 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:33:20,639 - pyskl - INFO - +mean_acc 0.8835 +2025-06-24 23:33:20,643 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_83.pth was removed +2025-06-24 23:33:20,827 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_87.pth. +2025-06-24 23:33:20,828 - pyskl - INFO - Best top1_acc is 0.9201 at 87 epoch. +2025-06-24 23:33:20,831 - pyskl - INFO - Epoch(val) [87][533] top1_acc: 0.9201, top5_acc: 0.9951, mean_class_accuracy: 0.8835 +2025-06-24 23:34:40,702 - pyskl - INFO - Epoch [88][100/1281] lr: 9.372e-03, eta: 9:07:48, time: 0.799, data_time: 0.193, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2677, loss: 0.2677 +2025-06-24 23:35:29,492 - pyskl - INFO - Epoch [88][200/1281] lr: 9.352e-03, eta: 9:07:13, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2217, loss: 0.2217 +2025-06-24 23:36:18,304 - pyskl - INFO - Epoch [88][300/1281] lr: 9.332e-03, eta: 9:06:38, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2495, loss: 0.2495 +2025-06-24 23:37:06,921 - pyskl - INFO - Epoch [88][400/1281] lr: 9.312e-03, eta: 9:06:03, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9994, loss_cls: 0.2327, loss: 0.2327 +2025-06-24 23:37:56,567 - pyskl - INFO - Epoch [88][500/1281] lr: 9.293e-03, eta: 9:05:29, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9994, loss_cls: 0.2377, loss: 0.2377 +2025-06-24 23:38:45,388 - pyskl - INFO - Epoch [88][600/1281] lr: 9.273e-03, eta: 9:04:54, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2394, loss: 0.2394 +2025-06-24 23:39:34,673 - pyskl - INFO - Epoch [88][700/1281] lr: 9.253e-03, eta: 9:04:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2460, loss: 0.2460 +2025-06-24 23:40:23,640 - pyskl - INFO - Epoch [88][800/1281] lr: 9.233e-03, eta: 9:03:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2394, loss: 0.2394 +2025-06-24 23:41:12,438 - pyskl - INFO - Epoch [88][900/1281] lr: 9.214e-03, eta: 9:03:09, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9988, loss_cls: 0.2604, loss: 0.2604 +2025-06-24 23:42:01,066 - pyskl - INFO - Epoch [88][1000/1281] lr: 9.194e-03, eta: 9:02:33, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2638, loss: 0.2638 +2025-06-24 23:42:29,002 - pyskl - INFO - Epoch [88][1100/1281] lr: 9.174e-03, eta: 9:01:43, time: 0.279, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 1.0000, loss_cls: 0.2662, loss: 0.2662 +2025-06-24 23:43:20,067 - pyskl - INFO - Epoch [88][1200/1281] lr: 9.155e-03, eta: 9:01:10, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9988, loss_cls: 0.2712, loss: 0.2712 +2025-06-24 23:43:42,882 - pyskl - INFO - Saving checkpoint at 88 epochs +2025-06-24 23:44:41,375 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:44:41,431 - pyskl - INFO - +top1_acc 0.9123 +top5_acc 0.9933 +2025-06-24 23:44:41,431 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:44:41,439 - pyskl - INFO - +mean_acc 0.8822 +2025-06-24 23:44:41,441 - pyskl - INFO - Epoch(val) [88][533] top1_acc: 0.9123, top5_acc: 0.9933, mean_class_accuracy: 0.8822 +2025-06-24 23:46:00,713 - pyskl - INFO - Epoch [89][100/1281] lr: 9.119e-03, eta: 9:00:00, time: 0.793, data_time: 0.191, memory: 4083, top1_acc: 0.9756, top5_acc: 0.9994, loss_cls: 0.1849, loss: 0.1849 +2025-06-24 23:46:49,810 - pyskl - INFO - Epoch [89][200/1281] lr: 9.099e-03, eta: 8:59:25, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.1963, loss: 0.1963 +2025-06-24 23:47:38,619 - pyskl - INFO - Epoch [89][300/1281] lr: 9.080e-03, eta: 8:58:49, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2145, loss: 0.2145 +2025-06-24 23:48:27,705 - pyskl - INFO - Epoch [89][400/1281] lr: 9.060e-03, eta: 8:58:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1692, loss: 0.1692 +2025-06-24 23:49:16,273 - pyskl - INFO - Epoch [89][500/1281] lr: 9.040e-03, eta: 8:57:39, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.1942, loss: 0.1942 +2025-06-24 23:50:05,153 - pyskl - INFO - Epoch [89][600/1281] lr: 9.021e-03, eta: 8:57:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9988, loss_cls: 0.2382, loss: 0.2382 +2025-06-24 23:50:54,019 - pyskl - INFO - Epoch [89][700/1281] lr: 9.001e-03, eta: 8:56:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2258, loss: 0.2258 +2025-06-24 23:51:43,115 - pyskl - INFO - Epoch [89][800/1281] lr: 8.982e-03, eta: 8:55:53, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2681, loss: 0.2681 +2025-06-24 23:52:32,094 - pyskl - INFO - Epoch [89][900/1281] lr: 8.962e-03, eta: 8:55:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2346, loss: 0.2346 +2025-06-24 23:53:21,010 - pyskl - INFO - Epoch [89][1000/1281] lr: 8.942e-03, eta: 8:54:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2757, loss: 0.2757 +2025-06-24 23:53:49,018 - pyskl - INFO - Epoch [89][1100/1281] lr: 8.923e-03, eta: 8:53:52, time: 0.280, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2438, loss: 0.2438 +2025-06-24 23:54:39,853 - pyskl - INFO - Epoch [89][1200/1281] lr: 8.903e-03, eta: 8:53:18, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2588, loss: 0.2588 +2025-06-24 23:55:01,504 - pyskl - INFO - Saving checkpoint at 89 epochs +2025-06-24 23:56:00,092 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:56:00,153 - pyskl - INFO - +top1_acc 0.8994 +top5_acc 0.9916 +2025-06-24 23:56:00,153 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:56:00,162 - pyskl - INFO - +mean_acc 0.8742 +2025-06-24 23:56:00,165 - pyskl - INFO - Epoch(val) [89][533] top1_acc: 0.8994, top5_acc: 0.9916, mean_class_accuracy: 0.8742 +2025-06-24 23:57:20,015 - pyskl - INFO - Epoch [90][100/1281] lr: 8.868e-03, eta: 8:52:08, time: 0.798, data_time: 0.192, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9981, loss_cls: 0.2756, loss: 0.2756 +2025-06-24 23:58:09,154 - pyskl - INFO - Epoch [90][200/1281] lr: 8.848e-03, eta: 8:51:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2308, loss: 0.2308 +2025-06-24 23:58:58,802 - pyskl - INFO - Epoch [90][300/1281] lr: 8.829e-03, eta: 8:50:58, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1875, loss: 0.1875 +2025-06-24 23:59:47,776 - pyskl - INFO - Epoch [90][400/1281] lr: 8.809e-03, eta: 8:50:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2292, loss: 0.2292 +2025-06-25 00:00:36,778 - pyskl - INFO - Epoch [90][500/1281] lr: 8.790e-03, eta: 8:49:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2595, loss: 0.2595 +2025-06-25 00:01:26,263 - pyskl - INFO - Epoch [90][600/1281] lr: 8.770e-03, eta: 8:49:12, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3198, loss: 0.3198 +2025-06-25 00:02:15,489 - pyskl - INFO - Epoch [90][700/1281] lr: 8.751e-03, eta: 8:48:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2253, loss: 0.2253 +2025-06-25 00:03:04,572 - pyskl - INFO - Epoch [90][800/1281] lr: 8.731e-03, eta: 8:48:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2553, loss: 0.2553 +2025-06-25 00:03:53,573 - pyskl - INFO - Epoch [90][900/1281] lr: 8.712e-03, eta: 8:47:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9988, loss_cls: 0.2911, loss: 0.2911 +2025-06-25 00:04:42,470 - pyskl - INFO - Epoch [90][1000/1281] lr: 8.692e-03, eta: 8:46:50, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9988, loss_cls: 0.2408, loss: 0.2408 +2025-06-25 00:05:09,812 - pyskl - INFO - Epoch [90][1100/1281] lr: 8.673e-03, eta: 8:46:00, time: 0.273, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9994, loss_cls: 0.2514, loss: 0.2514 +2025-06-25 00:06:00,683 - pyskl - INFO - Epoch [90][1200/1281] lr: 8.653e-03, eta: 8:45:25, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2402, loss: 0.2402 +2025-06-25 00:06:22,319 - pyskl - INFO - Saving checkpoint at 90 epochs +2025-06-25 00:07:20,460 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:07:20,515 - pyskl - INFO - +top1_acc 0.8987 +top5_acc 0.9939 +2025-06-25 00:07:20,515 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:07:20,522 - pyskl - INFO - +mean_acc 0.8719 +2025-06-25 00:07:20,524 - pyskl - INFO - Epoch(val) [90][533] top1_acc: 0.8987, top5_acc: 0.9939, mean_class_accuracy: 0.8719 +2025-06-25 00:08:38,854 - pyskl - INFO - Epoch [91][100/1281] lr: 8.618e-03, eta: 8:44:14, time: 0.783, data_time: 0.190, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2157, loss: 0.2157 +2025-06-25 00:09:27,804 - pyskl - INFO - Epoch [91][200/1281] lr: 8.599e-03, eta: 8:43:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2201, loss: 0.2201 +2025-06-25 00:10:16,825 - pyskl - INFO - Epoch [91][300/1281] lr: 8.579e-03, eta: 8:43:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2352, loss: 0.2352 +2025-06-25 00:11:05,643 - pyskl - INFO - Epoch [91][400/1281] lr: 8.560e-03, eta: 8:42:27, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.2147, loss: 0.2147 +2025-06-25 00:11:54,720 - pyskl - INFO - Epoch [91][500/1281] lr: 8.540e-03, eta: 8:41:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2445, loss: 0.2445 +2025-06-25 00:12:43,166 - pyskl - INFO - Epoch [91][600/1281] lr: 8.521e-03, eta: 8:41:15, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2311, loss: 0.2311 +2025-06-25 00:13:31,768 - pyskl - INFO - Epoch [91][700/1281] lr: 8.502e-03, eta: 8:40:39, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2355, loss: 0.2355 +2025-06-25 00:14:20,598 - pyskl - INFO - Epoch [91][800/1281] lr: 8.482e-03, eta: 8:40:03, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2284, loss: 0.2284 +2025-06-25 00:15:09,632 - pyskl - INFO - Epoch [91][900/1281] lr: 8.463e-03, eta: 8:39:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.2153, loss: 0.2153 +2025-06-25 00:15:58,619 - pyskl - INFO - Epoch [91][1000/1281] lr: 8.444e-03, eta: 8:38:51, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9988, loss_cls: 0.2319, loss: 0.2319 +2025-06-25 00:16:28,874 - pyskl - INFO - Epoch [91][1100/1281] lr: 8.424e-03, eta: 8:38:03, time: 0.303, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9988, loss_cls: 0.2726, loss: 0.2726 +2025-06-25 00:17:19,824 - pyskl - INFO - Epoch [91][1200/1281] lr: 8.405e-03, eta: 8:37:29, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.2483, loss: 0.2483 +2025-06-25 00:17:41,073 - pyskl - INFO - Saving checkpoint at 91 epochs +2025-06-25 00:18:39,870 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:18:39,928 - pyskl - INFO - +top1_acc 0.9020 +top5_acc 0.9931 +2025-06-25 00:18:39,929 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:18:39,940 - pyskl - INFO - +mean_acc 0.8737 +2025-06-25 00:18:39,943 - pyskl - INFO - Epoch(val) [91][533] top1_acc: 0.9020, top5_acc: 0.9931, mean_class_accuracy: 0.8737 +2025-06-25 00:20:00,964 - pyskl - INFO - Epoch [92][100/1281] lr: 8.370e-03, eta: 8:36:19, time: 0.810, data_time: 0.196, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2402, loss: 0.2402 +2025-06-25 00:20:50,058 - pyskl - INFO - Epoch [92][200/1281] lr: 8.351e-03, eta: 8:35:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2126, loss: 0.2126 +2025-06-25 00:21:39,316 - pyskl - INFO - Epoch [92][300/1281] lr: 8.332e-03, eta: 8:35:07, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2185, loss: 0.2185 +2025-06-25 00:22:28,337 - pyskl - INFO - Epoch [92][400/1281] lr: 8.312e-03, eta: 8:34:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1926, loss: 0.1926 +2025-06-25 00:23:17,026 - pyskl - INFO - Epoch [92][500/1281] lr: 8.293e-03, eta: 8:33:55, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2312, loss: 0.2312 +2025-06-25 00:24:06,301 - pyskl - INFO - Epoch [92][600/1281] lr: 8.274e-03, eta: 8:33:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1924, loss: 0.1924 +2025-06-25 00:24:55,103 - pyskl - INFO - Epoch [92][700/1281] lr: 8.255e-03, eta: 8:32:43, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 0.9994, loss_cls: 0.1723, loss: 0.1723 +2025-06-25 00:25:44,147 - pyskl - INFO - Epoch [92][800/1281] lr: 8.235e-03, eta: 8:32:07, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2073, loss: 0.2073 +2025-06-25 00:26:32,394 - pyskl - INFO - Epoch [92][900/1281] lr: 8.216e-03, eta: 8:31:31, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2397, loss: 0.2397 +2025-06-25 00:27:21,001 - pyskl - INFO - Epoch [92][1000/1281] lr: 8.197e-03, eta: 8:30:54, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.2003, loss: 0.2003 +2025-06-25 00:27:48,357 - pyskl - INFO - Epoch [92][1100/1281] lr: 8.178e-03, eta: 8:30:04, time: 0.274, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1593, loss: 0.1593 +2025-06-25 00:28:39,227 - pyskl - INFO - Epoch [92][1200/1281] lr: 8.159e-03, eta: 8:29:29, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2589, loss: 0.2589 +2025-06-25 00:29:01,391 - pyskl - INFO - Saving checkpoint at 92 epochs +2025-06-25 00:29:59,991 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:30:00,061 - pyskl - INFO - +top1_acc 0.9060 +top5_acc 0.9924 +2025-06-25 00:30:00,061 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:30:00,069 - pyskl - INFO - +mean_acc 0.8796 +2025-06-25 00:30:00,072 - pyskl - INFO - Epoch(val) [92][533] top1_acc: 0.9060, top5_acc: 0.9924, mean_class_accuracy: 0.8796 +2025-06-25 00:31:20,489 - pyskl - INFO - Epoch [93][100/1281] lr: 8.124e-03, eta: 8:28:19, time: 0.804, data_time: 0.192, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1778, loss: 0.1778 +2025-06-25 00:32:09,356 - pyskl - INFO - Epoch [93][200/1281] lr: 8.105e-03, eta: 8:27:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 0.9994, loss_cls: 0.1855, loss: 0.1855 +2025-06-25 00:32:58,701 - pyskl - INFO - Epoch [93][300/1281] lr: 8.086e-03, eta: 8:27:07, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.1958, loss: 0.1958 +2025-06-25 00:33:47,814 - pyskl - INFO - Epoch [93][400/1281] lr: 8.067e-03, eta: 8:26:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.2112, loss: 0.2112 +2025-06-25 00:34:36,350 - pyskl - INFO - Epoch [93][500/1281] lr: 8.047e-03, eta: 8:25:54, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1922, loss: 0.1922 +2025-06-25 00:35:25,051 - pyskl - INFO - Epoch [93][600/1281] lr: 8.028e-03, eta: 8:25:18, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1947, loss: 0.1947 +2025-06-25 00:36:13,721 - pyskl - INFO - Epoch [93][700/1281] lr: 8.009e-03, eta: 8:24:41, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1860, loss: 0.1860 +2025-06-25 00:37:02,652 - pyskl - INFO - Epoch [93][800/1281] lr: 7.990e-03, eta: 8:24:05, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9994, loss_cls: 0.2005, loss: 0.2005 +2025-06-25 00:37:51,519 - pyskl - INFO - Epoch [93][900/1281] lr: 7.971e-03, eta: 8:23:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2180, loss: 0.2180 +2025-06-25 00:38:40,272 - pyskl - INFO - Epoch [93][1000/1281] lr: 7.952e-03, eta: 8:22:52, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2166, loss: 0.2166 +2025-06-25 00:39:07,359 - pyskl - INFO - Epoch [93][1100/1281] lr: 7.933e-03, eta: 8:22:02, time: 0.271, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1897, loss: 0.1897 +2025-06-25 00:39:58,188 - pyskl - INFO - Epoch [93][1200/1281] lr: 7.914e-03, eta: 8:21:27, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2134, loss: 0.2134 +2025-06-25 00:40:20,003 - pyskl - INFO - Saving checkpoint at 93 epochs +2025-06-25 00:41:18,265 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:41:18,327 - pyskl - INFO - +top1_acc 0.8983 +top5_acc 0.9906 +2025-06-25 00:41:18,327 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:41:18,334 - pyskl - INFO - +mean_acc 0.8647 +2025-06-25 00:41:18,336 - pyskl - INFO - Epoch(val) [93][533] top1_acc: 0.8983, top5_acc: 0.9906, mean_class_accuracy: 0.8647 +2025-06-25 00:42:38,663 - pyskl - INFO - Epoch [94][100/1281] lr: 7.880e-03, eta: 8:20:16, time: 0.803, data_time: 0.195, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.2076, loss: 0.2076 +2025-06-25 00:43:27,757 - pyskl - INFO - Epoch [94][200/1281] lr: 7.861e-03, eta: 8:19:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2252, loss: 0.2252 +2025-06-25 00:44:16,572 - pyskl - INFO - Epoch [94][300/1281] lr: 7.842e-03, eta: 8:19:03, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2375, loss: 0.2375 +2025-06-25 00:45:05,722 - pyskl - INFO - Epoch [94][400/1281] lr: 7.823e-03, eta: 8:18:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1794, loss: 0.1794 +2025-06-25 00:45:54,926 - pyskl - INFO - Epoch [94][500/1281] lr: 7.804e-03, eta: 8:17:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1550, loss: 0.1550 +2025-06-25 00:46:44,177 - pyskl - INFO - Epoch [94][600/1281] lr: 7.785e-03, eta: 8:17:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.1981, loss: 0.1981 +2025-06-25 00:47:33,338 - pyskl - INFO - Epoch [94][700/1281] lr: 7.766e-03, eta: 8:16:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9988, loss_cls: 0.2521, loss: 0.2521 +2025-06-25 00:48:22,094 - pyskl - INFO - Epoch [94][800/1281] lr: 7.747e-03, eta: 8:16:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2251, loss: 0.2251 +2025-06-25 00:49:11,283 - pyskl - INFO - Epoch [94][900/1281] lr: 7.728e-03, eta: 8:15:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1946, loss: 0.1946 +2025-06-25 00:50:00,080 - pyskl - INFO - Epoch [94][1000/1281] lr: 7.709e-03, eta: 8:14:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1660, loss: 0.1660 +2025-06-25 00:50:28,449 - pyskl - INFO - Epoch [94][1100/1281] lr: 7.690e-03, eta: 8:13:59, time: 0.284, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2029, loss: 0.2029 +2025-06-25 00:51:19,171 - pyskl - INFO - Epoch [94][1200/1281] lr: 7.672e-03, eta: 8:13:24, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2311, loss: 0.2311 +2025-06-25 00:51:40,741 - pyskl - INFO - Saving checkpoint at 94 epochs +2025-06-25 00:52:38,319 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:52:38,374 - pyskl - INFO - +top1_acc 0.9193 +top5_acc 0.9951 +2025-06-25 00:52:38,374 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:52:38,381 - pyskl - INFO - +mean_acc 0.8969 +2025-06-25 00:52:38,383 - pyskl - INFO - Epoch(val) [94][533] top1_acc: 0.9193, top5_acc: 0.9951, mean_class_accuracy: 0.8969 +2025-06-25 00:53:57,603 - pyskl - INFO - Epoch [95][100/1281] lr: 7.637e-03, eta: 8:12:12, time: 0.792, data_time: 0.187, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2151, loss: 0.2151 +2025-06-25 00:54:46,789 - pyskl - INFO - Epoch [95][200/1281] lr: 7.619e-03, eta: 8:11:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 0.9988, loss_cls: 0.1964, loss: 0.1964 +2025-06-25 00:55:36,020 - pyskl - INFO - Epoch [95][300/1281] lr: 7.600e-03, eta: 8:10:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1685, loss: 0.1685 +2025-06-25 00:56:25,336 - pyskl - INFO - Epoch [95][400/1281] lr: 7.581e-03, eta: 8:10:22, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1463, loss: 0.1463 +2025-06-25 00:57:14,469 - pyskl - INFO - Epoch [95][500/1281] lr: 7.562e-03, eta: 8:09:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.1690, loss: 0.1690 +2025-06-25 00:58:03,535 - pyskl - INFO - Epoch [95][600/1281] lr: 7.543e-03, eta: 8:09:09, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2337, loss: 0.2337 +2025-06-25 00:58:52,631 - pyskl - INFO - Epoch [95][700/1281] lr: 7.525e-03, eta: 8:08:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.2009, loss: 0.2009 +2025-06-25 00:59:41,415 - pyskl - INFO - Epoch [95][800/1281] lr: 7.506e-03, eta: 8:07:55, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9988, loss_cls: 0.2179, loss: 0.2179 +2025-06-25 01:00:30,296 - pyskl - INFO - Epoch [95][900/1281] lr: 7.487e-03, eta: 8:07:19, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2153, loss: 0.2153 +2025-06-25 01:01:19,160 - pyskl - INFO - Epoch [95][1000/1281] lr: 7.468e-03, eta: 8:06:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1955, loss: 0.1955 +2025-06-25 01:01:49,240 - pyskl - INFO - Epoch [95][1100/1281] lr: 7.450e-03, eta: 8:05:54, time: 0.301, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1767, loss: 0.1767 +2025-06-25 01:02:40,105 - pyskl - INFO - Epoch [95][1200/1281] lr: 7.431e-03, eta: 8:05:18, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2408, loss: 0.2408 +2025-06-25 01:03:01,439 - pyskl - INFO - Saving checkpoint at 95 epochs +2025-06-25 01:04:00,554 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:04:00,623 - pyskl - INFO - +top1_acc 0.9200 +top5_acc 0.9950 +2025-06-25 01:04:00,623 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:04:00,631 - pyskl - INFO - +mean_acc 0.8859 +2025-06-25 01:04:00,633 - pyskl - INFO - Epoch(val) [95][533] top1_acc: 0.9200, top5_acc: 0.9950, mean_class_accuracy: 0.8859 +2025-06-25 01:05:21,115 - pyskl - INFO - Epoch [96][100/1281] lr: 7.397e-03, eta: 8:04:07, time: 0.805, data_time: 0.187, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1668, loss: 0.1668 +2025-06-25 01:06:09,929 - pyskl - INFO - Epoch [96][200/1281] lr: 7.379e-03, eta: 8:03:30, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 0.9994, loss_cls: 0.1861, loss: 0.1861 +2025-06-25 01:06:58,716 - pyskl - INFO - Epoch [96][300/1281] lr: 7.360e-03, eta: 8:02:53, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 0.9994, loss_cls: 0.1952, loss: 0.1952 +2025-06-25 01:07:47,581 - pyskl - INFO - Epoch [96][400/1281] lr: 7.341e-03, eta: 8:02:16, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9994, loss_cls: 0.1860, loss: 0.1860 +2025-06-25 01:08:36,431 - pyskl - INFO - Epoch [96][500/1281] lr: 7.323e-03, eta: 8:01:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1855, loss: 0.1855 +2025-06-25 01:09:25,443 - pyskl - INFO - Epoch [96][600/1281] lr: 7.304e-03, eta: 8:01:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 0.9994, loss_cls: 0.1872, loss: 0.1872 +2025-06-25 01:10:14,153 - pyskl - INFO - Epoch [96][700/1281] lr: 7.286e-03, eta: 8:00:25, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1779, loss: 0.1779 +2025-06-25 01:11:03,089 - pyskl - INFO - Epoch [96][800/1281] lr: 7.267e-03, eta: 7:59:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2057, loss: 0.2057 +2025-06-25 01:11:52,061 - pyskl - INFO - Epoch [96][900/1281] lr: 7.249e-03, eta: 7:59:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9988, loss_cls: 0.2154, loss: 0.2154 +2025-06-25 01:12:40,855 - pyskl - INFO - Epoch [96][1000/1281] lr: 7.230e-03, eta: 7:58:34, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 0.9988, loss_cls: 0.2209, loss: 0.2209 +2025-06-25 01:13:10,008 - pyskl - INFO - Epoch [96][1100/1281] lr: 7.211e-03, eta: 7:57:46, time: 0.292, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.1951, loss: 0.1951 +2025-06-25 01:13:58,199 - pyskl - INFO - Epoch [96][1200/1281] lr: 7.193e-03, eta: 7:57:08, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9994, loss_cls: 0.2161, loss: 0.2161 +2025-06-25 01:14:20,648 - pyskl - INFO - Saving checkpoint at 96 epochs +2025-06-25 01:15:18,957 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:15:19,013 - pyskl - INFO - +top1_acc 0.9105 +top5_acc 0.9941 +2025-06-25 01:15:19,013 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:15:19,021 - pyskl - INFO - +mean_acc 0.8836 +2025-06-25 01:15:19,023 - pyskl - INFO - Epoch(val) [96][533] top1_acc: 0.9105, top5_acc: 0.9941, mean_class_accuracy: 0.8836 +2025-06-25 01:16:37,116 - pyskl - INFO - Epoch [97][100/1281] lr: 7.159e-03, eta: 7:55:55, time: 0.781, data_time: 0.185, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1617, loss: 0.1617 +2025-06-25 01:17:26,200 - pyskl - INFO - Epoch [97][200/1281] lr: 7.141e-03, eta: 7:55:18, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1732, loss: 0.1732 +2025-06-25 01:18:14,871 - pyskl - INFO - Epoch [97][300/1281] lr: 7.123e-03, eta: 7:54:41, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1786, loss: 0.1786 +2025-06-25 01:19:03,669 - pyskl - INFO - Epoch [97][400/1281] lr: 7.104e-03, eta: 7:54:04, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.2073, loss: 0.2073 +2025-06-25 01:19:52,543 - pyskl - INFO - Epoch [97][500/1281] lr: 7.086e-03, eta: 7:53:27, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1792, loss: 0.1792 +2025-06-25 01:20:41,502 - pyskl - INFO - Epoch [97][600/1281] lr: 7.067e-03, eta: 7:52:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 0.9994, loss_cls: 0.1924, loss: 0.1924 +2025-06-25 01:21:30,254 - pyskl - INFO - Epoch [97][700/1281] lr: 7.049e-03, eta: 7:52:12, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2219, loss: 0.2219 +2025-06-25 01:22:18,959 - pyskl - INFO - Epoch [97][800/1281] lr: 7.030e-03, eta: 7:51:35, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2441, loss: 0.2441 +2025-06-25 01:23:07,929 - pyskl - INFO - Epoch [97][900/1281] lr: 7.012e-03, eta: 7:50:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2080, loss: 0.2080 +2025-06-25 01:23:57,115 - pyskl - INFO - Epoch [97][1000/1281] lr: 6.994e-03, eta: 7:50:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2103, loss: 0.2103 +2025-06-25 01:24:25,953 - pyskl - INFO - Epoch [97][1100/1281] lr: 6.975e-03, eta: 7:49:32, time: 0.288, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2054, loss: 0.2054 +2025-06-25 01:25:16,791 - pyskl - INFO - Epoch [97][1200/1281] lr: 6.957e-03, eta: 7:48:56, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2074, loss: 0.2074 +2025-06-25 01:25:38,100 - pyskl - INFO - Saving checkpoint at 97 epochs +2025-06-25 01:26:36,720 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:26:36,774 - pyskl - INFO - +top1_acc 0.9142 +top5_acc 0.9939 +2025-06-25 01:26:36,774 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:26:36,780 - pyskl - INFO - +mean_acc 0.8861 +2025-06-25 01:26:36,782 - pyskl - INFO - Epoch(val) [97][533] top1_acc: 0.9142, top5_acc: 0.9939, mean_class_accuracy: 0.8861 +2025-06-25 01:27:57,791 - pyskl - INFO - Epoch [98][100/1281] lr: 6.924e-03, eta: 7:47:44, time: 0.810, data_time: 0.190, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1503, loss: 0.1503 +2025-06-25 01:28:46,963 - pyskl - INFO - Epoch [98][200/1281] lr: 6.906e-03, eta: 7:47:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1903, loss: 0.1903 +2025-06-25 01:29:35,942 - pyskl - INFO - Epoch [98][300/1281] lr: 6.887e-03, eta: 7:46:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1364, loss: 0.1364 +2025-06-25 01:30:24,594 - pyskl - INFO - Epoch [98][400/1281] lr: 6.869e-03, eta: 7:45:52, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1316, loss: 0.1316 +2025-06-25 01:31:13,319 - pyskl - INFO - Epoch [98][500/1281] lr: 6.851e-03, eta: 7:45:15, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1598, loss: 0.1598 +2025-06-25 01:32:02,277 - pyskl - INFO - Epoch [98][600/1281] lr: 6.833e-03, eta: 7:44:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 0.9994, loss_cls: 0.1630, loss: 0.1630 +2025-06-25 01:32:51,499 - pyskl - INFO - Epoch [98][700/1281] lr: 6.814e-03, eta: 7:44:00, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1865, loss: 0.1865 +2025-06-25 01:33:40,315 - pyskl - INFO - Epoch [98][800/1281] lr: 6.796e-03, eta: 7:43:23, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1959, loss: 0.1959 +2025-06-25 01:34:29,043 - pyskl - INFO - Epoch [98][900/1281] lr: 6.778e-03, eta: 7:42:45, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2180, loss: 0.2180 +2025-06-25 01:35:17,728 - pyskl - INFO - Epoch [98][1000/1281] lr: 6.760e-03, eta: 7:42:08, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1859, loss: 0.1859 +2025-06-25 01:35:46,612 - pyskl - INFO - Epoch [98][1100/1281] lr: 6.742e-03, eta: 7:41:20, time: 0.289, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1755, loss: 0.1755 +2025-06-25 01:36:34,709 - pyskl - INFO - Epoch [98][1200/1281] lr: 6.724e-03, eta: 7:40:42, time: 0.481, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1579, loss: 0.1579 +2025-06-25 01:37:00,587 - pyskl - INFO - Saving checkpoint at 98 epochs +2025-06-25 01:37:58,158 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:37:58,214 - pyskl - INFO - +top1_acc 0.9139 +top5_acc 0.9964 +2025-06-25 01:37:58,214 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:37:58,221 - pyskl - INFO - +mean_acc 0.8980 +2025-06-25 01:37:58,223 - pyskl - INFO - Epoch(val) [98][533] top1_acc: 0.9139, top5_acc: 0.9964, mean_class_accuracy: 0.8980 +2025-06-25 01:39:18,056 - pyskl - INFO - Epoch [99][100/1281] lr: 6.691e-03, eta: 7:39:29, time: 0.798, data_time: 0.188, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1512, loss: 0.1512 +2025-06-25 01:40:07,096 - pyskl - INFO - Epoch [99][200/1281] lr: 6.673e-03, eta: 7:38:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 0.9994, loss_cls: 0.1432, loss: 0.1432 +2025-06-25 01:40:55,693 - pyskl - INFO - Epoch [99][300/1281] lr: 6.655e-03, eta: 7:38:14, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1521, loss: 0.1521 +2025-06-25 01:41:44,227 - pyskl - INFO - Epoch [99][400/1281] lr: 6.637e-03, eta: 7:37:36, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.1822, loss: 0.1822 +2025-06-25 01:42:33,345 - pyskl - INFO - Epoch [99][500/1281] lr: 6.619e-03, eta: 7:36:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.1981, loss: 0.1981 +2025-06-25 01:43:22,759 - pyskl - INFO - Epoch [99][600/1281] lr: 6.601e-03, eta: 7:36:22, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1831, loss: 0.1831 +2025-06-25 01:44:11,552 - pyskl - INFO - Epoch [99][700/1281] lr: 6.583e-03, eta: 7:35:44, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1372, loss: 0.1372 +2025-06-25 01:45:00,467 - pyskl - INFO - Epoch [99][800/1281] lr: 6.565e-03, eta: 7:35:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1527, loss: 0.1527 +2025-06-25 01:45:49,180 - pyskl - INFO - Epoch [99][900/1281] lr: 6.547e-03, eta: 7:34:29, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.1955, loss: 0.1955 +2025-06-25 01:46:38,057 - pyskl - INFO - Epoch [99][1000/1281] lr: 6.529e-03, eta: 7:33:51, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1345, loss: 0.1345 +2025-06-25 01:47:08,895 - pyskl - INFO - Epoch [99][1100/1281] lr: 6.511e-03, eta: 7:33:04, time: 0.308, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1643, loss: 0.1643 +2025-06-25 01:47:54,654 - pyskl - INFO - Epoch [99][1200/1281] lr: 6.493e-03, eta: 7:32:25, time: 0.458, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1845, loss: 0.1845 +2025-06-25 01:48:20,575 - pyskl - INFO - Saving checkpoint at 99 epochs +2025-06-25 01:49:19,286 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:49:19,342 - pyskl - INFO - +top1_acc 0.9267 +top5_acc 0.9961 +2025-06-25 01:49:19,342 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:49:19,349 - pyskl - INFO - +mean_acc 0.9064 +2025-06-25 01:49:19,353 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_87.pth was removed +2025-06-25 01:49:19,540 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_99.pth. +2025-06-25 01:49:19,540 - pyskl - INFO - Best top1_acc is 0.9267 at 99 epoch. +2025-06-25 01:49:19,544 - pyskl - INFO - Epoch(val) [99][533] top1_acc: 0.9267, top5_acc: 0.9961, mean_class_accuracy: 0.9064 +2025-06-25 01:50:37,921 - pyskl - INFO - Epoch [100][100/1281] lr: 6.460e-03, eta: 7:31:11, time: 0.784, data_time: 0.190, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1365, loss: 0.1365 +2025-06-25 01:51:26,602 - pyskl - INFO - Epoch [100][200/1281] lr: 6.442e-03, eta: 7:30:33, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1533, loss: 0.1533 +2025-06-25 01:52:15,387 - pyskl - INFO - Epoch [100][300/1281] lr: 6.425e-03, eta: 7:29:56, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1874, loss: 0.1874 +2025-06-25 01:53:04,231 - pyskl - INFO - Epoch [100][400/1281] lr: 6.407e-03, eta: 7:29:18, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9994, loss_cls: 0.1828, loss: 0.1828 +2025-06-25 01:53:53,126 - pyskl - INFO - Epoch [100][500/1281] lr: 6.389e-03, eta: 7:28:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1561, loss: 0.1561 +2025-06-25 01:54:41,992 - pyskl - INFO - Epoch [100][600/1281] lr: 6.371e-03, eta: 7:28:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1620, loss: 0.1620 +2025-06-25 01:55:30,833 - pyskl - INFO - Epoch [100][700/1281] lr: 6.353e-03, eta: 7:27:24, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1393, loss: 0.1393 +2025-06-25 01:56:19,303 - pyskl - INFO - Epoch [100][800/1281] lr: 6.336e-03, eta: 7:26:46, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1357, loss: 0.1357 +2025-06-25 01:57:08,144 - pyskl - INFO - Epoch [100][900/1281] lr: 6.318e-03, eta: 7:26:08, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1201, loss: 0.1201 +2025-06-25 01:57:57,076 - pyskl - INFO - Epoch [100][1000/1281] lr: 6.300e-03, eta: 7:25:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9994, loss_cls: 0.1941, loss: 0.1941 +2025-06-25 01:58:27,067 - pyskl - INFO - Epoch [100][1100/1281] lr: 6.282e-03, eta: 7:24:43, time: 0.300, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1944, loss: 0.1944 +2025-06-25 01:59:12,441 - pyskl - INFO - Epoch [100][1200/1281] lr: 6.265e-03, eta: 7:24:04, time: 0.454, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.1868, loss: 0.1868 +2025-06-25 01:59:38,264 - pyskl - INFO - Saving checkpoint at 100 epochs +2025-06-25 02:00:36,918 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:00:36,976 - pyskl - INFO - +top1_acc 0.9137 +top5_acc 0.9946 +2025-06-25 02:00:36,976 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:00:36,984 - pyskl - INFO - +mean_acc 0.8732 +2025-06-25 02:00:36,987 - pyskl - INFO - Epoch(val) [100][533] top1_acc: 0.9137, top5_acc: 0.9946, mean_class_accuracy: 0.8732 +2025-06-25 02:01:57,160 - pyskl - INFO - Epoch [101][100/1281] lr: 6.232e-03, eta: 7:22:51, time: 0.802, data_time: 0.191, memory: 4083, top1_acc: 0.9681, top5_acc: 0.9994, loss_cls: 0.1884, loss: 0.1884 +2025-06-25 02:02:46,223 - pyskl - INFO - Epoch [101][200/1281] lr: 6.215e-03, eta: 7:22:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1303, loss: 0.1303 +2025-06-25 02:03:34,987 - pyskl - INFO - Epoch [101][300/1281] lr: 6.197e-03, eta: 7:21:35, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 0.9994, loss_cls: 0.1451, loss: 0.1451 +2025-06-25 02:04:23,649 - pyskl - INFO - Epoch [101][400/1281] lr: 6.180e-03, eta: 7:20:57, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1558, loss: 0.1558 +2025-06-25 02:05:12,254 - pyskl - INFO - Epoch [101][500/1281] lr: 6.162e-03, eta: 7:20:19, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 0.9994, loss_cls: 0.2005, loss: 0.2005 +2025-06-25 02:06:01,286 - pyskl - INFO - Epoch [101][600/1281] lr: 6.144e-03, eta: 7:19:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 0.9994, loss_cls: 0.1624, loss: 0.1624 +2025-06-25 02:06:49,978 - pyskl - INFO - Epoch [101][700/1281] lr: 6.127e-03, eta: 7:19:03, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1647, loss: 0.1647 +2025-06-25 02:07:39,188 - pyskl - INFO - Epoch [101][800/1281] lr: 6.109e-03, eta: 7:18:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 0.9994, loss_cls: 0.1474, loss: 0.1474 +2025-06-25 02:08:27,929 - pyskl - INFO - Epoch [101][900/1281] lr: 6.092e-03, eta: 7:17:47, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.1956, loss: 0.1956 +2025-06-25 02:09:16,616 - pyskl - INFO - Epoch [101][1000/1281] lr: 6.074e-03, eta: 7:17:09, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1865, loss: 0.1865 +2025-06-25 02:09:48,839 - pyskl - INFO - Epoch [101][1100/1281] lr: 6.057e-03, eta: 7:16:23, time: 0.322, data_time: 0.001, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1869, loss: 0.1869 +2025-06-25 02:10:30,260 - pyskl - INFO - Epoch [101][1200/1281] lr: 6.039e-03, eta: 7:15:41, time: 0.414, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1479, loss: 0.1479 +2025-06-25 02:10:55,989 - pyskl - INFO - Saving checkpoint at 101 epochs +2025-06-25 02:11:54,829 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:11:54,910 - pyskl - INFO - +top1_acc 0.9272 +top5_acc 0.9968 +2025-06-25 02:11:54,911 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:11:54,922 - pyskl - INFO - +mean_acc 0.9043 +2025-06-25 02:11:54,926 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_99.pth was removed +2025-06-25 02:11:55,107 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_101.pth. +2025-06-25 02:11:55,107 - pyskl - INFO - Best top1_acc is 0.9272 at 101 epoch. +2025-06-25 02:11:55,110 - pyskl - INFO - Epoch(val) [101][533] top1_acc: 0.9272, top5_acc: 0.9968, mean_class_accuracy: 0.9043 +2025-06-25 02:13:15,143 - pyskl - INFO - Epoch [102][100/1281] lr: 6.007e-03, eta: 7:14:28, time: 0.800, data_time: 0.191, memory: 4083, top1_acc: 0.9825, top5_acc: 0.9994, loss_cls: 0.1336, loss: 0.1336 +2025-06-25 02:14:04,151 - pyskl - INFO - Epoch [102][200/1281] lr: 5.990e-03, eta: 7:13:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 0.9994, loss_cls: 0.1523, loss: 0.1523 +2025-06-25 02:14:53,229 - pyskl - INFO - Epoch [102][300/1281] lr: 5.972e-03, eta: 7:13:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1536, loss: 0.1536 +2025-06-25 02:15:42,272 - pyskl - INFO - Epoch [102][400/1281] lr: 5.955e-03, eta: 7:12:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1473, loss: 0.1473 +2025-06-25 02:16:30,982 - pyskl - INFO - Epoch [102][500/1281] lr: 5.938e-03, eta: 7:11:56, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1314, loss: 0.1314 +2025-06-25 02:17:19,839 - pyskl - INFO - Epoch [102][600/1281] lr: 5.920e-03, eta: 7:11:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 0.9994, loss_cls: 0.1610, loss: 0.1610 +2025-06-25 02:18:08,882 - pyskl - INFO - Epoch [102][700/1281] lr: 5.903e-03, eta: 7:10:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1657, loss: 0.1657 +2025-06-25 02:18:57,680 - pyskl - INFO - Epoch [102][800/1281] lr: 5.886e-03, eta: 7:10:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 0.9994, loss_cls: 0.1459, loss: 0.1459 +2025-06-25 02:19:46,997 - pyskl - INFO - Epoch [102][900/1281] lr: 5.868e-03, eta: 7:09:23, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1305, loss: 0.1305 +2025-06-25 02:20:35,611 - pyskl - INFO - Epoch [102][1000/1281] lr: 5.851e-03, eta: 7:08:45, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1317, loss: 0.1317 +2025-06-25 02:21:08,012 - pyskl - INFO - Epoch [102][1100/1281] lr: 5.834e-03, eta: 7:07:59, time: 0.324, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1539, loss: 0.1539 +2025-06-25 02:21:49,338 - pyskl - INFO - Epoch [102][1200/1281] lr: 5.816e-03, eta: 7:07:17, time: 0.413, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1586, loss: 0.1586 +2025-06-25 02:22:14,808 - pyskl - INFO - Saving checkpoint at 102 epochs +2025-06-25 02:23:13,264 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:23:13,346 - pyskl - INFO - +top1_acc 0.9122 +top5_acc 0.9942 +2025-06-25 02:23:13,346 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:23:13,354 - pyskl - INFO - +mean_acc 0.8799 +2025-06-25 02:23:13,356 - pyskl - INFO - Epoch(val) [102][533] top1_acc: 0.9122, top5_acc: 0.9942, mean_class_accuracy: 0.8799 +2025-06-25 02:24:32,963 - pyskl - INFO - Epoch [103][100/1281] lr: 5.785e-03, eta: 7:06:04, time: 0.796, data_time: 0.193, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1428, loss: 0.1428 +2025-06-25 02:25:22,179 - pyskl - INFO - Epoch [103][200/1281] lr: 5.768e-03, eta: 7:05:26, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1273, loss: 0.1273 +2025-06-25 02:26:11,437 - pyskl - INFO - Epoch [103][300/1281] lr: 5.751e-03, eta: 7:04:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1208, loss: 0.1208 +2025-06-25 02:27:00,412 - pyskl - INFO - Epoch [103][400/1281] lr: 5.733e-03, eta: 7:04:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1643, loss: 0.1643 +2025-06-25 02:27:49,141 - pyskl - INFO - Epoch [103][500/1281] lr: 5.716e-03, eta: 7:03:31, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1505, loss: 0.1505 +2025-06-25 02:28:37,955 - pyskl - INFO - Epoch [103][600/1281] lr: 5.699e-03, eta: 7:02:53, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1084, loss: 0.1084 +2025-06-25 02:29:27,111 - pyskl - INFO - Epoch [103][700/1281] lr: 5.682e-03, eta: 7:02:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0978, loss: 0.0978 +2025-06-25 02:30:15,865 - pyskl - INFO - Epoch [103][800/1281] lr: 5.665e-03, eta: 7:01:36, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 0.9994, loss_cls: 0.1522, loss: 0.1522 +2025-06-25 02:31:04,592 - pyskl - INFO - Epoch [103][900/1281] lr: 5.648e-03, eta: 7:00:58, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1166, loss: 0.1166 +2025-06-25 02:31:53,344 - pyskl - INFO - Epoch [103][1000/1281] lr: 5.631e-03, eta: 7:00:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1287, loss: 0.1287 +2025-06-25 02:32:23,197 - pyskl - INFO - Epoch [103][1100/1281] lr: 5.614e-03, eta: 6:59:32, time: 0.299, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1536, loss: 0.1536 +2025-06-25 02:33:08,992 - pyskl - INFO - Epoch [103][1200/1281] lr: 5.597e-03, eta: 6:58:52, time: 0.458, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1656, loss: 0.1656 +2025-06-25 02:33:35,160 - pyskl - INFO - Saving checkpoint at 103 epochs +2025-06-25 02:34:33,038 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:34:33,093 - pyskl - INFO - +top1_acc 0.9157 +top5_acc 0.9965 +2025-06-25 02:34:33,094 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:34:33,100 - pyskl - INFO - +mean_acc 0.8849 +2025-06-25 02:34:33,102 - pyskl - INFO - Epoch(val) [103][533] top1_acc: 0.9157, top5_acc: 0.9965, mean_class_accuracy: 0.8849 +2025-06-25 02:35:53,648 - pyskl - INFO - Epoch [104][100/1281] lr: 5.566e-03, eta: 6:57:39, time: 0.805, data_time: 0.195, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1336, loss: 0.1336 +2025-06-25 02:36:42,443 - pyskl - INFO - Epoch [104][200/1281] lr: 5.549e-03, eta: 6:57:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1202, loss: 0.1202 +2025-06-25 02:37:31,331 - pyskl - INFO - Epoch [104][300/1281] lr: 5.532e-03, eta: 6:56:22, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1297, loss: 0.1297 +2025-06-25 02:38:20,365 - pyskl - INFO - Epoch [104][400/1281] lr: 5.515e-03, eta: 6:55:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1307, loss: 0.1307 +2025-06-25 02:39:09,274 - pyskl - INFO - Epoch [104][500/1281] lr: 5.498e-03, eta: 6:55:05, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1358, loss: 0.1358 +2025-06-25 02:39:58,315 - pyskl - INFO - Epoch [104][600/1281] lr: 5.481e-03, eta: 6:54:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1115, loss: 0.1115 +2025-06-25 02:40:47,475 - pyskl - INFO - Epoch [104][700/1281] lr: 5.464e-03, eta: 6:53:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1546, loss: 0.1546 +2025-06-25 02:41:36,725 - pyskl - INFO - Epoch [104][800/1281] lr: 5.447e-03, eta: 6:53:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1278, loss: 0.1278 +2025-06-25 02:42:25,609 - pyskl - INFO - Epoch [104][900/1281] lr: 5.430e-03, eta: 6:52:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1254, loss: 0.1254 +2025-06-25 02:43:14,362 - pyskl - INFO - Epoch [104][1000/1281] lr: 5.413e-03, eta: 6:51:53, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.1803, loss: 0.1803 +2025-06-25 02:43:46,453 - pyskl - INFO - Epoch [104][1100/1281] lr: 5.397e-03, eta: 6:51:07, time: 0.321, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1619, loss: 0.1619 +2025-06-25 02:44:28,172 - pyskl - INFO - Epoch [104][1200/1281] lr: 5.380e-03, eta: 6:50:25, time: 0.417, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1693, loss: 0.1693 +2025-06-25 02:44:54,464 - pyskl - INFO - Saving checkpoint at 104 epochs +2025-06-25 02:45:52,842 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:45:52,898 - pyskl - INFO - +top1_acc 0.9250 +top5_acc 0.9951 +2025-06-25 02:45:52,898 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:45:52,905 - pyskl - INFO - +mean_acc 0.8900 +2025-06-25 02:45:52,907 - pyskl - INFO - Epoch(val) [104][533] top1_acc: 0.9250, top5_acc: 0.9951, mean_class_accuracy: 0.8900 +2025-06-25 02:47:12,059 - pyskl - INFO - Epoch [105][100/1281] lr: 5.349e-03, eta: 6:49:11, time: 0.791, data_time: 0.197, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1155, loss: 0.1155 +2025-06-25 02:48:01,002 - pyskl - INFO - Epoch [105][200/1281] lr: 5.333e-03, eta: 6:48:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 0.9994, loss_cls: 0.1218, loss: 0.1218 +2025-06-25 02:48:49,944 - pyskl - INFO - Epoch [105][300/1281] lr: 5.316e-03, eta: 6:47:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 0.9994, loss_cls: 0.1371, loss: 0.1371 +2025-06-25 02:49:38,907 - pyskl - INFO - Epoch [105][400/1281] lr: 5.299e-03, eta: 6:47:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1138, loss: 0.1138 +2025-06-25 02:50:27,920 - pyskl - INFO - Epoch [105][500/1281] lr: 5.283e-03, eta: 6:46:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1515, loss: 0.1515 +2025-06-25 02:51:17,054 - pyskl - INFO - Epoch [105][600/1281] lr: 5.266e-03, eta: 6:45:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1515, loss: 0.1515 +2025-06-25 02:52:06,117 - pyskl - INFO - Epoch [105][700/1281] lr: 5.249e-03, eta: 6:45:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1248, loss: 0.1248 +2025-06-25 02:52:54,840 - pyskl - INFO - Epoch [105][800/1281] lr: 5.233e-03, eta: 6:44:41, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1123, loss: 0.1123 +2025-06-25 02:53:43,728 - pyskl - INFO - Epoch [105][900/1281] lr: 5.216e-03, eta: 6:44:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1396, loss: 0.1396 +2025-06-25 02:54:32,689 - pyskl - INFO - Epoch [105][1000/1281] lr: 5.199e-03, eta: 6:43:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 0.9994, loss_cls: 0.1500, loss: 0.1500 +2025-06-25 02:55:04,855 - pyskl - INFO - Epoch [105][1100/1281] lr: 5.183e-03, eta: 6:42:38, time: 0.322, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1440, loss: 0.1440 +2025-06-25 02:55:47,839 - pyskl - INFO - Epoch [105][1200/1281] lr: 5.166e-03, eta: 6:41:57, time: 0.430, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1256, loss: 0.1256 +2025-06-25 02:56:14,465 - pyskl - INFO - Saving checkpoint at 105 epochs +2025-06-25 02:57:12,563 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:57:12,617 - pyskl - INFO - +top1_acc 0.9248 +top5_acc 0.9948 +2025-06-25 02:57:12,618 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:57:12,624 - pyskl - INFO - +mean_acc 0.9045 +2025-06-25 02:57:12,626 - pyskl - INFO - Epoch(val) [105][533] top1_acc: 0.9248, top5_acc: 0.9948, mean_class_accuracy: 0.9045 +2025-06-25 02:58:31,406 - pyskl - INFO - Epoch [106][100/1281] lr: 5.136e-03, eta: 6:40:42, time: 0.788, data_time: 0.185, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0988, loss: 0.0988 +2025-06-25 02:59:20,217 - pyskl - INFO - Epoch [106][200/1281] lr: 5.120e-03, eta: 6:40:04, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 0.9994, loss_cls: 0.1125, loss: 0.1125 +2025-06-25 03:00:09,417 - pyskl - INFO - Epoch [106][300/1281] lr: 5.103e-03, eta: 6:39:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0965, loss: 0.0965 +2025-06-25 03:00:58,825 - pyskl - INFO - Epoch [106][400/1281] lr: 5.087e-03, eta: 6:38:46, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1114, loss: 0.1114 +2025-06-25 03:01:47,839 - pyskl - INFO - Epoch [106][500/1281] lr: 5.070e-03, eta: 6:38:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1127, loss: 0.1127 +2025-06-25 03:02:36,831 - pyskl - INFO - Epoch [106][600/1281] lr: 5.054e-03, eta: 6:37:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1130, loss: 0.1130 +2025-06-25 03:03:25,719 - pyskl - INFO - Epoch [106][700/1281] lr: 5.038e-03, eta: 6:36:50, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1117, loss: 0.1117 +2025-06-25 03:04:14,821 - pyskl - INFO - Epoch [106][800/1281] lr: 5.021e-03, eta: 6:36:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0964, loss: 0.0964 +2025-06-25 03:05:04,003 - pyskl - INFO - Epoch [106][900/1281] lr: 5.005e-03, eta: 6:35:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.1007, loss: 0.1007 +2025-06-25 03:05:52,873 - pyskl - INFO - Epoch [106][1000/1281] lr: 4.988e-03, eta: 6:34:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 0.9994, loss_cls: 0.1004, loss: 0.1004 +2025-06-25 03:06:22,384 - pyskl - INFO - Epoch [106][1100/1281] lr: 4.972e-03, eta: 6:34:07, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1520, loss: 0.1520 +2025-06-25 03:07:08,181 - pyskl - INFO - Epoch [106][1200/1281] lr: 4.956e-03, eta: 6:33:27, time: 0.458, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1456, loss: 0.1456 +2025-06-25 03:07:33,697 - pyskl - INFO - Saving checkpoint at 106 epochs +2025-06-25 03:08:32,042 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:08:32,110 - pyskl - INFO - +top1_acc 0.9257 +top5_acc 0.9957 +2025-06-25 03:08:32,110 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:08:32,117 - pyskl - INFO - +mean_acc 0.9055 +2025-06-25 03:08:32,119 - pyskl - INFO - Epoch(val) [106][533] top1_acc: 0.9257, top5_acc: 0.9957, mean_class_accuracy: 0.9055 +2025-06-25 03:09:51,844 - pyskl - INFO - Epoch [107][100/1281] lr: 4.926e-03, eta: 6:32:12, time: 0.797, data_time: 0.191, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1326, loss: 0.1326 +2025-06-25 03:10:40,767 - pyskl - INFO - Epoch [107][200/1281] lr: 4.910e-03, eta: 6:31:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1136, loss: 0.1136 +2025-06-25 03:11:29,837 - pyskl - INFO - Epoch [107][300/1281] lr: 4.894e-03, eta: 6:30:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1425, loss: 0.1425 +2025-06-25 03:12:18,726 - pyskl - INFO - Epoch [107][400/1281] lr: 4.878e-03, eta: 6:30:16, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1368, loss: 0.1368 +2025-06-25 03:13:07,320 - pyskl - INFO - Epoch [107][500/1281] lr: 4.862e-03, eta: 6:29:37, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1299, loss: 0.1299 +2025-06-25 03:13:56,560 - pyskl - INFO - Epoch [107][600/1281] lr: 4.845e-03, eta: 6:28:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 0.9994, loss_cls: 0.1404, loss: 0.1404 +2025-06-25 03:14:45,355 - pyskl - INFO - Epoch [107][700/1281] lr: 4.829e-03, eta: 6:28:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 0.9994, loss_cls: 0.1183, loss: 0.1183 +2025-06-25 03:15:34,295 - pyskl - INFO - Epoch [107][800/1281] lr: 4.813e-03, eta: 6:27:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1463, loss: 0.1463 +2025-06-25 03:16:22,906 - pyskl - INFO - Epoch [107][900/1281] lr: 4.797e-03, eta: 6:27:01, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1295, loss: 0.1295 +2025-06-25 03:17:11,800 - pyskl - INFO - Epoch [107][1000/1281] lr: 4.781e-03, eta: 6:26:22, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1380, loss: 0.1380 +2025-06-25 03:17:40,391 - pyskl - INFO - Epoch [107][1100/1281] lr: 4.765e-03, eta: 6:25:35, time: 0.286, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1297, loss: 0.1297 +2025-06-25 03:18:26,488 - pyskl - INFO - Epoch [107][1200/1281] lr: 4.749e-03, eta: 6:24:54, time: 0.461, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1104, loss: 0.1104 +2025-06-25 03:18:51,087 - pyskl - INFO - Saving checkpoint at 107 epochs +2025-06-25 03:19:49,235 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:19:49,295 - pyskl - INFO - +top1_acc 0.9275 +top5_acc 0.9960 +2025-06-25 03:19:49,295 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:19:49,305 - pyskl - INFO - +mean_acc 0.8987 +2025-06-25 03:19:49,309 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_101.pth was removed +2025-06-25 03:19:49,486 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_107.pth. +2025-06-25 03:19:49,487 - pyskl - INFO - Best top1_acc is 0.9275 at 107 epoch. +2025-06-25 03:19:49,490 - pyskl - INFO - Epoch(val) [107][533] top1_acc: 0.9275, top5_acc: 0.9960, mean_class_accuracy: 0.8987 +2025-06-25 03:21:09,748 - pyskl - INFO - Epoch [108][100/1281] lr: 4.720e-03, eta: 6:23:40, time: 0.803, data_time: 0.194, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1174, loss: 0.1174 +2025-06-25 03:21:58,608 - pyskl - INFO - Epoch [108][200/1281] lr: 4.704e-03, eta: 6:23:01, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1306, loss: 0.1306 +2025-06-25 03:22:47,479 - pyskl - INFO - Epoch [108][300/1281] lr: 4.688e-03, eta: 6:22:22, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1038, loss: 0.1038 +2025-06-25 03:23:36,520 - pyskl - INFO - Epoch [108][400/1281] lr: 4.672e-03, eta: 6:21:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1027, loss: 0.1027 +2025-06-25 03:24:25,402 - pyskl - INFO - Epoch [108][500/1281] lr: 4.656e-03, eta: 6:21:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1484, loss: 0.1484 +2025-06-25 03:25:14,605 - pyskl - INFO - Epoch [108][600/1281] lr: 4.640e-03, eta: 6:20:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1159, loss: 0.1159 +2025-06-25 03:26:03,355 - pyskl - INFO - Epoch [108][700/1281] lr: 4.624e-03, eta: 6:19:46, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0854, loss: 0.0854 +2025-06-25 03:26:52,380 - pyskl - INFO - Epoch [108][800/1281] lr: 4.608e-03, eta: 6:19:07, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1365, loss: 0.1365 +2025-06-25 03:27:41,320 - pyskl - INFO - Epoch [108][900/1281] lr: 4.593e-03, eta: 6:18:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0880, loss: 0.0880 +2025-06-25 03:28:30,387 - pyskl - INFO - Epoch [108][1000/1281] lr: 4.577e-03, eta: 6:17:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1102, loss: 0.1102 +2025-06-25 03:29:00,697 - pyskl - INFO - Epoch [108][1100/1281] lr: 4.561e-03, eta: 6:17:02, time: 0.303, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0939, loss: 0.0939 +2025-06-25 03:29:45,706 - pyskl - INFO - Epoch [108][1200/1281] lr: 4.545e-03, eta: 6:16:21, time: 0.450, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1251, loss: 0.1251 +2025-06-25 03:30:10,239 - pyskl - INFO - Saving checkpoint at 108 epochs +2025-06-25 03:31:08,512 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:31:08,572 - pyskl - INFO - +top1_acc 0.9161 +top5_acc 0.9927 +2025-06-25 03:31:08,572 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:31:08,579 - pyskl - INFO - +mean_acc 0.8834 +2025-06-25 03:31:08,580 - pyskl - INFO - Epoch(val) [108][533] top1_acc: 0.9161, top5_acc: 0.9927, mean_class_accuracy: 0.8834 +2025-06-25 03:32:27,642 - pyskl - INFO - Epoch [109][100/1281] lr: 4.517e-03, eta: 6:15:07, time: 0.791, data_time: 0.193, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1210, loss: 0.1210 +2025-06-25 03:33:16,380 - pyskl - INFO - Epoch [109][200/1281] lr: 4.501e-03, eta: 6:14:28, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1186, loss: 0.1186 +2025-06-25 03:34:05,370 - pyskl - INFO - Epoch [109][300/1281] lr: 4.485e-03, eta: 6:13:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0942, loss: 0.0942 +2025-06-25 03:34:54,378 - pyskl - INFO - Epoch [109][400/1281] lr: 4.470e-03, eta: 6:13:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0884, loss: 0.0884 +2025-06-25 03:35:43,854 - pyskl - INFO - Epoch [109][500/1281] lr: 4.454e-03, eta: 6:12:30, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1055, loss: 0.1055 +2025-06-25 03:36:33,047 - pyskl - INFO - Epoch [109][600/1281] lr: 4.438e-03, eta: 6:11:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0889, loss: 0.0889 +2025-06-25 03:37:22,483 - pyskl - INFO - Epoch [109][700/1281] lr: 4.423e-03, eta: 6:11:12, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1306, loss: 0.1306 +2025-06-25 03:38:11,300 - pyskl - INFO - Epoch [109][800/1281] lr: 4.407e-03, eta: 6:10:33, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1361, loss: 0.1361 +2025-06-25 03:39:00,473 - pyskl - INFO - Epoch [109][900/1281] lr: 4.391e-03, eta: 6:09:53, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1216, loss: 0.1216 +2025-06-25 03:39:49,563 - pyskl - INFO - Epoch [109][1000/1281] lr: 4.376e-03, eta: 6:09:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0912, loss: 0.0912 +2025-06-25 03:40:17,962 - pyskl - INFO - Epoch [109][1100/1281] lr: 4.360e-03, eta: 6:08:27, time: 0.284, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 0.9994, loss_cls: 0.1621, loss: 0.1621 +2025-06-25 03:41:05,680 - pyskl - INFO - Epoch [109][1200/1281] lr: 4.345e-03, eta: 6:07:47, time: 0.477, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 0.9994, loss_cls: 0.1286, loss: 0.1286 +2025-06-25 03:41:27,670 - pyskl - INFO - Saving checkpoint at 109 epochs +2025-06-25 03:42:25,926 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:42:25,987 - pyskl - INFO - +top1_acc 0.9276 +top5_acc 0.9953 +2025-06-25 03:42:25,987 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:42:25,994 - pyskl - INFO - +mean_acc 0.9106 +2025-06-25 03:42:25,998 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_107.pth was removed +2025-06-25 03:42:26,175 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_109.pth. +2025-06-25 03:42:26,175 - pyskl - INFO - Best top1_acc is 0.9276 at 109 epoch. +2025-06-25 03:42:26,178 - pyskl - INFO - Epoch(val) [109][533] top1_acc: 0.9276, top5_acc: 0.9953, mean_class_accuracy: 0.9106 +2025-06-25 03:43:46,618 - pyskl - INFO - Epoch [110][100/1281] lr: 4.317e-03, eta: 6:06:33, time: 0.804, data_time: 0.194, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1066, loss: 0.1066 +2025-06-25 03:44:35,740 - pyskl - INFO - Epoch [110][200/1281] lr: 4.301e-03, eta: 6:05:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0903, loss: 0.0903 +2025-06-25 03:45:24,729 - pyskl - INFO - Epoch [110][300/1281] lr: 4.286e-03, eta: 6:05:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1036, loss: 0.1036 +2025-06-25 03:46:13,671 - pyskl - INFO - Epoch [110][400/1281] lr: 4.271e-03, eta: 6:04:35, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0783, loss: 0.0783 +2025-06-25 03:47:02,849 - pyskl - INFO - Epoch [110][500/1281] lr: 4.255e-03, eta: 6:03:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0927, loss: 0.0927 +2025-06-25 03:47:51,885 - pyskl - INFO - Epoch [110][600/1281] lr: 4.240e-03, eta: 6:03:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1100, loss: 0.1100 +2025-06-25 03:48:40,812 - pyskl - INFO - Epoch [110][700/1281] lr: 4.225e-03, eta: 6:02:37, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1027, loss: 0.1027 +2025-06-25 03:49:30,077 - pyskl - INFO - Epoch [110][800/1281] lr: 4.209e-03, eta: 6:01:58, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0837, loss: 0.0837 +2025-06-25 03:50:19,175 - pyskl - INFO - Epoch [110][900/1281] lr: 4.194e-03, eta: 6:01:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 0.9994, loss_cls: 0.1148, loss: 0.1148 +2025-06-25 03:51:07,797 - pyskl - INFO - Epoch [110][1000/1281] lr: 4.179e-03, eta: 6:00:39, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.1010, loss: 0.1010 +2025-06-25 03:51:35,739 - pyskl - INFO - Epoch [110][1100/1281] lr: 4.164e-03, eta: 5:59:52, time: 0.279, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0966, loss: 0.0966 +2025-06-25 03:52:25,493 - pyskl - INFO - Epoch [110][1200/1281] lr: 4.148e-03, eta: 5:59:12, time: 0.498, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0874, loss: 0.0874 +2025-06-25 03:52:47,483 - pyskl - INFO - Saving checkpoint at 110 epochs +2025-06-25 03:53:45,507 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:53:45,562 - pyskl - INFO - +top1_acc 0.9302 +top5_acc 0.9962 +2025-06-25 03:53:45,562 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:53:45,569 - pyskl - INFO - +mean_acc 0.9040 +2025-06-25 03:53:45,573 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_109.pth was removed +2025-06-25 03:53:45,748 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_110.pth. +2025-06-25 03:53:45,749 - pyskl - INFO - Best top1_acc is 0.9302 at 110 epoch. +2025-06-25 03:53:45,751 - pyskl - INFO - Epoch(val) [110][533] top1_acc: 0.9302, top5_acc: 0.9962, mean_class_accuracy: 0.9040 +2025-06-25 03:55:04,853 - pyskl - INFO - Epoch [111][100/1281] lr: 4.121e-03, eta: 5:57:57, time: 0.791, data_time: 0.192, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0713, loss: 0.0713 +2025-06-25 03:55:53,515 - pyskl - INFO - Epoch [111][200/1281] lr: 4.106e-03, eta: 5:57:18, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0820, loss: 0.0820 +2025-06-25 03:56:42,607 - pyskl - INFO - Epoch [111][300/1281] lr: 4.091e-03, eta: 5:56:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0777, loss: 0.0777 +2025-06-25 03:57:31,206 - pyskl - INFO - Epoch [111][400/1281] lr: 4.075e-03, eta: 5:55:59, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0997, loss: 0.0997 +2025-06-25 03:58:20,427 - pyskl - INFO - Epoch [111][500/1281] lr: 4.060e-03, eta: 5:55:19, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 0.9994, loss_cls: 0.1256, loss: 0.1256 +2025-06-25 03:59:09,778 - pyskl - INFO - Epoch [111][600/1281] lr: 4.045e-03, eta: 5:54:40, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1047, loss: 0.1047 +2025-06-25 03:59:59,380 - pyskl - INFO - Epoch [111][700/1281] lr: 4.030e-03, eta: 5:54:01, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1014, loss: 0.1014 +2025-06-25 04:00:48,204 - pyskl - INFO - Epoch [111][800/1281] lr: 4.015e-03, eta: 5:53:21, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0986, loss: 0.0986 +2025-06-25 04:01:36,869 - pyskl - INFO - Epoch [111][900/1281] lr: 4.000e-03, eta: 5:52:41, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1126, loss: 0.1126 +2025-06-25 04:02:25,943 - pyskl - INFO - Epoch [111][1000/1281] lr: 3.985e-03, eta: 5:52:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1024, loss: 0.1024 +2025-06-25 04:02:53,821 - pyskl - INFO - Epoch [111][1100/1281] lr: 3.970e-03, eta: 5:51:15, time: 0.279, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1007, loss: 0.1007 +2025-06-25 04:03:44,504 - pyskl - INFO - Epoch [111][1200/1281] lr: 3.955e-03, eta: 5:50:36, time: 0.507, data_time: 0.001, memory: 4083, top1_acc: 0.9869, top5_acc: 0.9994, loss_cls: 0.1121, loss: 0.1121 +2025-06-25 04:04:06,253 - pyskl - INFO - Saving checkpoint at 111 epochs +2025-06-25 04:05:04,142 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:05:04,197 - pyskl - INFO - +top1_acc 0.9250 +top5_acc 0.9961 +2025-06-25 04:05:04,197 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:05:04,203 - pyskl - INFO - +mean_acc 0.8972 +2025-06-25 04:05:04,205 - pyskl - INFO - Epoch(val) [111][533] top1_acc: 0.9250, top5_acc: 0.9961, mean_class_accuracy: 0.8972 +2025-06-25 04:06:23,462 - pyskl - INFO - Epoch [112][100/1281] lr: 3.928e-03, eta: 5:49:21, time: 0.793, data_time: 0.191, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0868, loss: 0.0868 +2025-06-25 04:07:12,336 - pyskl - INFO - Epoch [112][200/1281] lr: 3.914e-03, eta: 5:48:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0885, loss: 0.0885 +2025-06-25 04:08:01,102 - pyskl - INFO - Epoch [112][300/1281] lr: 3.899e-03, eta: 5:48:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1170, loss: 0.1170 +2025-06-25 04:08:49,862 - pyskl - INFO - Epoch [112][400/1281] lr: 3.884e-03, eta: 5:47:22, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0902, loss: 0.0902 +2025-06-25 04:09:38,874 - pyskl - INFO - Epoch [112][500/1281] lr: 3.869e-03, eta: 5:46:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0738, loss: 0.0738 +2025-06-25 04:10:27,840 - pyskl - INFO - Epoch [112][600/1281] lr: 3.854e-03, eta: 5:46:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0891, loss: 0.0891 +2025-06-25 04:11:17,022 - pyskl - INFO - Epoch [112][700/1281] lr: 3.840e-03, eta: 5:45:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0990, loss: 0.0990 +2025-06-25 04:12:06,198 - pyskl - INFO - Epoch [112][800/1281] lr: 3.825e-03, eta: 5:44:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1016, loss: 0.1016 +2025-06-25 04:12:55,087 - pyskl - INFO - Epoch [112][900/1281] lr: 3.810e-03, eta: 5:44:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0788, loss: 0.0788 +2025-06-25 04:13:43,998 - pyskl - INFO - Epoch [112][1000/1281] lr: 3.795e-03, eta: 5:43:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0603, loss: 0.0603 +2025-06-25 04:14:12,842 - pyskl - INFO - Epoch [112][1100/1281] lr: 3.781e-03, eta: 5:42:37, time: 0.288, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0777, loss: 0.0777 +2025-06-25 04:15:03,547 - pyskl - INFO - Epoch [112][1200/1281] lr: 3.766e-03, eta: 5:41:58, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0812, loss: 0.0812 +2025-06-25 04:15:24,667 - pyskl - INFO - Saving checkpoint at 112 epochs +2025-06-25 04:16:22,964 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:16:23,027 - pyskl - INFO - +top1_acc 0.9306 +top5_acc 0.9973 +2025-06-25 04:16:23,027 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:16:23,033 - pyskl - INFO - +mean_acc 0.9042 +2025-06-25 04:16:23,037 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_110.pth was removed +2025-06-25 04:16:23,209 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_112.pth. +2025-06-25 04:16:23,209 - pyskl - INFO - Best top1_acc is 0.9306 at 112 epoch. +2025-06-25 04:16:23,212 - pyskl - INFO - Epoch(val) [112][533] top1_acc: 0.9306, top5_acc: 0.9973, mean_class_accuracy: 0.9042 +2025-06-25 04:17:42,682 - pyskl - INFO - Epoch [113][100/1281] lr: 3.740e-03, eta: 5:40:43, time: 0.795, data_time: 0.191, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0701, loss: 0.0701 +2025-06-25 04:18:31,480 - pyskl - INFO - Epoch [113][200/1281] lr: 3.725e-03, eta: 5:40:03, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1121, loss: 0.1121 +2025-06-25 04:19:20,112 - pyskl - INFO - Epoch [113][300/1281] lr: 3.711e-03, eta: 5:39:23, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0972, loss: 0.0972 +2025-06-25 04:20:08,999 - pyskl - INFO - Epoch [113][400/1281] lr: 3.696e-03, eta: 5:38:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0737, loss: 0.0737 +2025-06-25 04:20:58,525 - pyskl - INFO - Epoch [113][500/1281] lr: 3.682e-03, eta: 5:38:04, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0537, loss: 0.0537 +2025-06-25 04:21:47,395 - pyskl - INFO - Epoch [113][600/1281] lr: 3.667e-03, eta: 5:37:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0762, loss: 0.0762 +2025-06-25 04:22:36,533 - pyskl - INFO - Epoch [113][700/1281] lr: 3.653e-03, eta: 5:36:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0753, loss: 0.0753 +2025-06-25 04:23:25,305 - pyskl - INFO - Epoch [113][800/1281] lr: 3.638e-03, eta: 5:36:04, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 0.9994, loss_cls: 0.0975, loss: 0.0975 +2025-06-25 04:24:14,315 - pyskl - INFO - Epoch [113][900/1281] lr: 3.624e-03, eta: 5:35:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0835, loss: 0.0835 +2025-06-25 04:25:03,262 - pyskl - INFO - Epoch [113][1000/1281] lr: 3.610e-03, eta: 5:34:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0776, loss: 0.0776 +2025-06-25 04:25:31,428 - pyskl - INFO - Epoch [113][1100/1281] lr: 3.595e-03, eta: 5:33:58, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0820, loss: 0.0820 +2025-06-25 04:26:22,199 - pyskl - INFO - Epoch [113][1200/1281] lr: 3.581e-03, eta: 5:33:19, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0902, loss: 0.0902 +2025-06-25 04:26:43,867 - pyskl - INFO - Saving checkpoint at 113 epochs +2025-06-25 04:27:42,072 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:27:42,129 - pyskl - INFO - +top1_acc 0.9390 +top5_acc 0.9957 +2025-06-25 04:27:42,129 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:27:42,137 - pyskl - INFO - +mean_acc 0.9180 +2025-06-25 04:27:42,141 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_112.pth was removed +2025-06-25 04:27:42,323 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_113.pth. +2025-06-25 04:27:42,324 - pyskl - INFO - Best top1_acc is 0.9390 at 113 epoch. +2025-06-25 04:27:42,327 - pyskl - INFO - Epoch(val) [113][533] top1_acc: 0.9390, top5_acc: 0.9957, mean_class_accuracy: 0.9180 +2025-06-25 04:29:01,771 - pyskl - INFO - Epoch [114][100/1281] lr: 3.555e-03, eta: 5:32:03, time: 0.794, data_time: 0.192, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0860, loss: 0.0860 +2025-06-25 04:29:50,986 - pyskl - INFO - Epoch [114][200/1281] lr: 3.541e-03, eta: 5:31:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0768, loss: 0.0768 +2025-06-25 04:30:39,753 - pyskl - INFO - Epoch [114][300/1281] lr: 3.526e-03, eta: 5:30:44, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0927, loss: 0.0927 +2025-06-25 04:31:28,278 - pyskl - INFO - Epoch [114][400/1281] lr: 3.512e-03, eta: 5:30:04, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0780, loss: 0.0780 +2025-06-25 04:32:17,209 - pyskl - INFO - Epoch [114][500/1281] lr: 3.498e-03, eta: 5:29:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0843, loss: 0.0843 +2025-06-25 04:33:06,168 - pyskl - INFO - Epoch [114][600/1281] lr: 3.484e-03, eta: 5:28:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0811, loss: 0.0811 +2025-06-25 04:33:55,048 - pyskl - INFO - Epoch [114][700/1281] lr: 3.470e-03, eta: 5:28:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0669, loss: 0.0669 +2025-06-25 04:34:44,311 - pyskl - INFO - Epoch [114][800/1281] lr: 3.456e-03, eta: 5:27:24, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0819, loss: 0.0819 +2025-06-25 04:35:33,003 - pyskl - INFO - Epoch [114][900/1281] lr: 3.442e-03, eta: 5:26:44, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0805, loss: 0.0805 +2025-06-25 04:36:21,922 - pyskl - INFO - Epoch [114][1000/1281] lr: 3.427e-03, eta: 5:26:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0686, loss: 0.0686 +2025-06-25 04:36:50,257 - pyskl - INFO - Epoch [114][1100/1281] lr: 3.413e-03, eta: 5:25:17, time: 0.283, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0963, loss: 0.0963 +2025-06-25 04:37:41,111 - pyskl - INFO - Epoch [114][1200/1281] lr: 3.399e-03, eta: 5:24:38, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0765, loss: 0.0765 +2025-06-25 04:38:03,108 - pyskl - INFO - Saving checkpoint at 114 epochs +2025-06-25 04:39:00,956 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:39:01,011 - pyskl - INFO - +top1_acc 0.9342 +top5_acc 0.9968 +2025-06-25 04:39:01,012 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:39:01,018 - pyskl - INFO - +mean_acc 0.9101 +2025-06-25 04:39:01,020 - pyskl - INFO - Epoch(val) [114][533] top1_acc: 0.9342, top5_acc: 0.9968, mean_class_accuracy: 0.9101 +2025-06-25 04:40:19,982 - pyskl - INFO - Epoch [115][100/1281] lr: 3.374e-03, eta: 5:23:22, time: 0.790, data_time: 0.188, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0758, loss: 0.0758 +2025-06-25 04:41:09,189 - pyskl - INFO - Epoch [115][200/1281] lr: 3.360e-03, eta: 5:22:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0871, loss: 0.0871 +2025-06-25 04:41:57,958 - pyskl - INFO - Epoch [115][300/1281] lr: 3.346e-03, eta: 5:22:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 0.9994, loss_cls: 0.0935, loss: 0.0935 +2025-06-25 04:42:46,903 - pyskl - INFO - Epoch [115][400/1281] lr: 3.332e-03, eta: 5:21:22, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0624, loss: 0.0624 +2025-06-25 04:43:36,015 - pyskl - INFO - Epoch [115][500/1281] lr: 3.318e-03, eta: 5:20:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0590, loss: 0.0590 +2025-06-25 04:44:24,744 - pyskl - INFO - Epoch [115][600/1281] lr: 3.305e-03, eta: 5:20:02, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0622, loss: 0.0622 +2025-06-25 04:45:13,795 - pyskl - INFO - Epoch [115][700/1281] lr: 3.291e-03, eta: 5:19:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0816, loss: 0.0816 +2025-06-25 04:46:02,897 - pyskl - INFO - Epoch [115][800/1281] lr: 3.277e-03, eta: 5:18:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0819, loss: 0.0819 +2025-06-25 04:46:51,664 - pyskl - INFO - Epoch [115][900/1281] lr: 3.263e-03, eta: 5:18:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1333, loss: 0.1333 +2025-06-25 04:47:40,649 - pyskl - INFO - Epoch [115][1000/1281] lr: 3.249e-03, eta: 5:17:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0675, loss: 0.0675 +2025-06-25 04:48:09,216 - pyskl - INFO - Epoch [115][1100/1281] lr: 3.236e-03, eta: 5:16:35, time: 0.286, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 0.9994, loss_cls: 0.0891, loss: 0.0891 +2025-06-25 04:48:59,969 - pyskl - INFO - Epoch [115][1200/1281] lr: 3.222e-03, eta: 5:15:56, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0633, loss: 0.0633 +2025-06-25 04:49:21,691 - pyskl - INFO - Saving checkpoint at 115 epochs +2025-06-25 04:50:20,502 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:50:20,561 - pyskl - INFO - +top1_acc 0.9365 +top5_acc 0.9968 +2025-06-25 04:50:20,561 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:50:20,569 - pyskl - INFO - +mean_acc 0.9163 +2025-06-25 04:50:20,571 - pyskl - INFO - Epoch(val) [115][533] top1_acc: 0.9365, top5_acc: 0.9968, mean_class_accuracy: 0.9163 +2025-06-25 04:51:41,109 - pyskl - INFO - Epoch [116][100/1281] lr: 3.197e-03, eta: 5:14:40, time: 0.805, data_time: 0.191, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0605, loss: 0.0605 +2025-06-25 04:52:30,373 - pyskl - INFO - Epoch [116][200/1281] lr: 3.184e-03, eta: 5:14:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0554, loss: 0.0554 +2025-06-25 04:53:19,322 - pyskl - INFO - Epoch [116][300/1281] lr: 3.170e-03, eta: 5:13:20, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0548, loss: 0.0548 +2025-06-25 04:54:08,044 - pyskl - INFO - Epoch [116][400/1281] lr: 3.156e-03, eta: 5:12:40, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0557, loss: 0.0557 +2025-06-25 04:54:56,784 - pyskl - INFO - Epoch [116][500/1281] lr: 3.143e-03, eta: 5:12:00, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0593, loss: 0.0593 +2025-06-25 04:55:45,635 - pyskl - INFO - Epoch [116][600/1281] lr: 3.129e-03, eta: 5:11:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0435, loss: 0.0435 +2025-06-25 04:56:34,652 - pyskl - INFO - Epoch [116][700/1281] lr: 3.116e-03, eta: 5:10:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0615, loss: 0.0615 +2025-06-25 04:57:23,871 - pyskl - INFO - Epoch [116][800/1281] lr: 3.102e-03, eta: 5:09:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0777, loss: 0.0777 +2025-06-25 04:58:12,828 - pyskl - INFO - Epoch [116][900/1281] lr: 3.089e-03, eta: 5:09:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0503, loss: 0.0503 +2025-06-25 04:59:01,714 - pyskl - INFO - Epoch [116][1000/1281] lr: 3.075e-03, eta: 5:08:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0681, loss: 0.0681 +2025-06-25 04:59:30,085 - pyskl - INFO - Epoch [116][1100/1281] lr: 3.062e-03, eta: 5:07:52, time: 0.284, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0610, loss: 0.0610 +2025-06-25 05:00:18,891 - pyskl - INFO - Epoch [116][1200/1281] lr: 3.049e-03, eta: 5:07:12, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0801, loss: 0.0801 +2025-06-25 05:00:41,054 - pyskl - INFO - Saving checkpoint at 116 epochs +2025-06-25 05:01:39,266 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:01:39,321 - pyskl - INFO - +top1_acc 0.9291 +top5_acc 0.9953 +2025-06-25 05:01:39,321 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:01:39,327 - pyskl - INFO - +mean_acc 0.9086 +2025-06-25 05:01:39,329 - pyskl - INFO - Epoch(val) [116][533] top1_acc: 0.9291, top5_acc: 0.9953, mean_class_accuracy: 0.9086 +2025-06-25 05:02:59,069 - pyskl - INFO - Epoch [117][100/1281] lr: 3.024e-03, eta: 5:05:56, time: 0.797, data_time: 0.189, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0667, loss: 0.0667 +2025-06-25 05:03:48,025 - pyskl - INFO - Epoch [117][200/1281] lr: 3.011e-03, eta: 5:05:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0569, loss: 0.0569 +2025-06-25 05:04:37,122 - pyskl - INFO - Epoch [117][300/1281] lr: 2.998e-03, eta: 5:04:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0688, loss: 0.0688 +2025-06-25 05:05:26,139 - pyskl - INFO - Epoch [117][400/1281] lr: 2.984e-03, eta: 5:03:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0545, loss: 0.0545 +2025-06-25 05:06:15,034 - pyskl - INFO - Epoch [117][500/1281] lr: 2.971e-03, eta: 5:03:15, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0567, loss: 0.0567 +2025-06-25 05:07:04,047 - pyskl - INFO - Epoch [117][600/1281] lr: 2.958e-03, eta: 5:02:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0521, loss: 0.0521 +2025-06-25 05:07:53,125 - pyskl - INFO - Epoch [117][700/1281] lr: 2.945e-03, eta: 5:01:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0610, loss: 0.0610 +2025-06-25 05:08:42,198 - pyskl - INFO - Epoch [117][800/1281] lr: 2.932e-03, eta: 5:01:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0682, loss: 0.0682 +2025-06-25 05:09:30,974 - pyskl - INFO - Epoch [117][900/1281] lr: 2.919e-03, eta: 5:00:34, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0474, loss: 0.0474 +2025-06-25 05:10:19,968 - pyskl - INFO - Epoch [117][1000/1281] lr: 2.905e-03, eta: 4:59:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0533, loss: 0.0533 +2025-06-25 05:10:47,681 - pyskl - INFO - Epoch [117][1100/1281] lr: 2.892e-03, eta: 4:59:07, time: 0.277, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0585, loss: 0.0585 +2025-06-25 05:11:38,400 - pyskl - INFO - Epoch [117][1200/1281] lr: 2.879e-03, eta: 4:58:27, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0693, loss: 0.0693 +2025-06-25 05:12:00,212 - pyskl - INFO - Saving checkpoint at 117 epochs +2025-06-25 05:12:58,186 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:12:58,242 - pyskl - INFO - +top1_acc 0.9356 +top5_acc 0.9962 +2025-06-25 05:12:58,242 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:12:58,249 - pyskl - INFO - +mean_acc 0.9062 +2025-06-25 05:12:58,250 - pyskl - INFO - Epoch(val) [117][533] top1_acc: 0.9356, top5_acc: 0.9962, mean_class_accuracy: 0.9062 +2025-06-25 05:14:17,712 - pyskl - INFO - Epoch [118][100/1281] lr: 2.856e-03, eta: 4:57:11, time: 0.795, data_time: 0.190, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0410, loss: 0.0410 +2025-06-25 05:15:06,667 - pyskl - INFO - Epoch [118][200/1281] lr: 2.843e-03, eta: 4:56:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0430, loss: 0.0430 +2025-06-25 05:15:55,583 - pyskl - INFO - Epoch [118][300/1281] lr: 2.830e-03, eta: 4:55:51, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0482, loss: 0.0482 +2025-06-25 05:16:44,702 - pyskl - INFO - Epoch [118][400/1281] lr: 2.817e-03, eta: 4:55:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0652, loss: 0.0652 +2025-06-25 05:17:33,750 - pyskl - INFO - Epoch [118][500/1281] lr: 2.804e-03, eta: 4:54:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0481, loss: 0.0481 +2025-06-25 05:18:22,553 - pyskl - INFO - Epoch [118][600/1281] lr: 2.791e-03, eta: 4:53:49, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0613, loss: 0.0613 +2025-06-25 05:19:11,569 - pyskl - INFO - Epoch [118][700/1281] lr: 2.778e-03, eta: 4:53:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0488, loss: 0.0488 +2025-06-25 05:20:00,617 - pyskl - INFO - Epoch [118][800/1281] lr: 2.765e-03, eta: 4:52:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0513, loss: 0.0513 +2025-06-25 05:20:49,477 - pyskl - INFO - Epoch [118][900/1281] lr: 2.753e-03, eta: 4:51:48, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0608, loss: 0.0608 +2025-06-25 05:21:38,303 - pyskl - INFO - Epoch [118][1000/1281] lr: 2.740e-03, eta: 4:51:07, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0573, loss: 0.0573 +2025-06-25 05:22:06,410 - pyskl - INFO - Epoch [118][1100/1281] lr: 2.727e-03, eta: 4:50:21, time: 0.281, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0677, loss: 0.0677 +2025-06-25 05:22:57,116 - pyskl - INFO - Epoch [118][1200/1281] lr: 2.714e-03, eta: 4:49:41, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0692, loss: 0.0692 +2025-06-25 05:23:19,014 - pyskl - INFO - Saving checkpoint at 118 epochs +2025-06-25 05:24:16,975 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:24:17,043 - pyskl - INFO - +top1_acc 0.9409 +top5_acc 0.9967 +2025-06-25 05:24:17,043 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:24:17,051 - pyskl - INFO - +mean_acc 0.9240 +2025-06-25 05:24:17,056 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_113.pth was removed +2025-06-25 05:24:17,268 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2025-06-25 05:24:17,268 - pyskl - INFO - Best top1_acc is 0.9409 at 118 epoch. +2025-06-25 05:24:17,271 - pyskl - INFO - Epoch(val) [118][533] top1_acc: 0.9409, top5_acc: 0.9967, mean_class_accuracy: 0.9240 +2025-06-25 05:25:37,410 - pyskl - INFO - Epoch [119][100/1281] lr: 2.691e-03, eta: 4:48:25, time: 0.801, data_time: 0.194, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0375, loss: 0.0375 +2025-06-25 05:26:26,537 - pyskl - INFO - Epoch [119][200/1281] lr: 2.679e-03, eta: 4:47:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0513, loss: 0.0513 +2025-06-25 05:27:15,365 - pyskl - INFO - Epoch [119][300/1281] lr: 2.666e-03, eta: 4:47:04, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0378, loss: 0.0378 +2025-06-25 05:28:04,233 - pyskl - INFO - Epoch [119][400/1281] lr: 2.653e-03, eta: 4:46:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0349, loss: 0.0349 +2025-06-25 05:28:52,887 - pyskl - INFO - Epoch [119][500/1281] lr: 2.641e-03, eta: 4:45:43, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0520, loss: 0.0520 +2025-06-25 05:29:42,051 - pyskl - INFO - Epoch [119][600/1281] lr: 2.628e-03, eta: 4:45:03, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0432, loss: 0.0432 +2025-06-25 05:30:31,011 - pyskl - INFO - Epoch [119][700/1281] lr: 2.616e-03, eta: 4:44:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0461, loss: 0.0461 +2025-06-25 05:31:19,940 - pyskl - INFO - Epoch [119][800/1281] lr: 2.603e-03, eta: 4:43:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0633, loss: 0.0633 +2025-06-25 05:32:09,014 - pyskl - INFO - Epoch [119][900/1281] lr: 2.591e-03, eta: 4:43:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0536, loss: 0.0536 +2025-06-25 05:32:57,451 - pyskl - INFO - Epoch [119][1000/1281] lr: 2.578e-03, eta: 4:42:20, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0526, loss: 0.0526 +2025-06-25 05:33:25,301 - pyskl - INFO - Epoch [119][1100/1281] lr: 2.566e-03, eta: 4:41:34, time: 0.278, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0499, loss: 0.0499 +2025-06-25 05:34:16,128 - pyskl - INFO - Epoch [119][1200/1281] lr: 2.554e-03, eta: 4:40:54, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0536, loss: 0.0536 +2025-06-25 05:34:38,214 - pyskl - INFO - Saving checkpoint at 119 epochs +2025-06-25 05:35:36,014 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:35:36,083 - pyskl - INFO - +top1_acc 0.9378 +top5_acc 0.9964 +2025-06-25 05:35:36,083 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:35:36,091 - pyskl - INFO - +mean_acc 0.9150 +2025-06-25 05:35:36,093 - pyskl - INFO - Epoch(val) [119][533] top1_acc: 0.9378, top5_acc: 0.9964, mean_class_accuracy: 0.9150 +2025-06-25 05:36:56,343 - pyskl - INFO - Epoch [120][100/1281] lr: 2.531e-03, eta: 4:39:38, time: 0.802, data_time: 0.194, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0430, loss: 0.0430 +2025-06-25 05:37:45,382 - pyskl - INFO - Epoch [120][200/1281] lr: 2.519e-03, eta: 4:38:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0335, loss: 0.0335 +2025-06-25 05:38:33,930 - pyskl - INFO - Epoch [120][300/1281] lr: 2.507e-03, eta: 4:38:17, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0459, loss: 0.0459 +2025-06-25 05:39:22,774 - pyskl - INFO - Epoch [120][400/1281] lr: 2.494e-03, eta: 4:37:36, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0374, loss: 0.0374 +2025-06-25 05:40:11,849 - pyskl - INFO - Epoch [120][500/1281] lr: 2.482e-03, eta: 4:36:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0486, loss: 0.0486 +2025-06-25 05:41:01,070 - pyskl - INFO - Epoch [120][600/1281] lr: 2.470e-03, eta: 4:36:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0674, loss: 0.0674 +2025-06-25 05:41:49,860 - pyskl - INFO - Epoch [120][700/1281] lr: 2.458e-03, eta: 4:35:34, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0743, loss: 0.0743 +2025-06-25 05:42:38,899 - pyskl - INFO - Epoch [120][800/1281] lr: 2.446e-03, eta: 4:34:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0553, loss: 0.0553 +2025-06-25 05:43:27,848 - pyskl - INFO - Epoch [120][900/1281] lr: 2.433e-03, eta: 4:34:13, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0693, loss: 0.0693 +2025-06-25 05:44:16,622 - pyskl - INFO - Epoch [120][1000/1281] lr: 2.421e-03, eta: 4:33:32, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0585, loss: 0.0585 +2025-06-25 05:44:44,542 - pyskl - INFO - Epoch [120][1100/1281] lr: 2.409e-03, eta: 4:32:46, time: 0.279, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0630, loss: 0.0630 +2025-06-25 05:45:35,359 - pyskl - INFO - Epoch [120][1200/1281] lr: 2.397e-03, eta: 4:32:05, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0620, loss: 0.0620 +2025-06-25 05:45:57,423 - pyskl - INFO - Saving checkpoint at 120 epochs +2025-06-25 05:46:55,332 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:46:55,388 - pyskl - INFO - +top1_acc 0.9357 +top5_acc 0.9958 +2025-06-25 05:46:55,388 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:46:55,396 - pyskl - INFO - +mean_acc 0.9157 +2025-06-25 05:46:55,398 - pyskl - INFO - Epoch(val) [120][533] top1_acc: 0.9357, top5_acc: 0.9958, mean_class_accuracy: 0.9157 +2025-06-25 05:48:15,231 - pyskl - INFO - Epoch [121][100/1281] lr: 2.375e-03, eta: 4:30:49, time: 0.798, data_time: 0.188, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0693, loss: 0.0693 +2025-06-25 05:49:04,541 - pyskl - INFO - Epoch [121][200/1281] lr: 2.363e-03, eta: 4:30:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0423, loss: 0.0423 +2025-06-25 05:49:53,512 - pyskl - INFO - Epoch [121][300/1281] lr: 2.351e-03, eta: 4:29:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0412, loss: 0.0412 +2025-06-25 05:50:42,415 - pyskl - INFO - Epoch [121][400/1281] lr: 2.340e-03, eta: 4:28:47, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0418, loss: 0.0418 +2025-06-25 05:51:31,314 - pyskl - INFO - Epoch [121][500/1281] lr: 2.328e-03, eta: 4:28:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0571, loss: 0.0571 +2025-06-25 05:52:20,330 - pyskl - INFO - Epoch [121][600/1281] lr: 2.316e-03, eta: 4:27:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0460, loss: 0.0460 +2025-06-25 05:53:09,313 - pyskl - INFO - Epoch [121][700/1281] lr: 2.304e-03, eta: 4:26:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0421, loss: 0.0421 +2025-06-25 05:53:58,256 - pyskl - INFO - Epoch [121][800/1281] lr: 2.292e-03, eta: 4:26:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0386, loss: 0.0386 +2025-06-25 05:54:46,823 - pyskl - INFO - Epoch [121][900/1281] lr: 2.280e-03, eta: 4:25:23, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0572, loss: 0.0572 +2025-06-25 05:55:36,085 - pyskl - INFO - Epoch [121][1000/1281] lr: 2.269e-03, eta: 4:24:42, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0530, loss: 0.0530 +2025-06-25 05:56:05,021 - pyskl - INFO - Epoch [121][1100/1281] lr: 2.257e-03, eta: 4:23:57, time: 0.289, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0457, loss: 0.0457 +2025-06-25 05:56:55,754 - pyskl - INFO - Epoch [121][1200/1281] lr: 2.245e-03, eta: 4:23:16, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0525, loss: 0.0525 +2025-06-25 05:57:17,367 - pyskl - INFO - Saving checkpoint at 121 epochs +2025-06-25 05:58:15,568 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:58:15,636 - pyskl - INFO - +top1_acc 0.9380 +top5_acc 0.9959 +2025-06-25 05:58:15,636 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:58:15,644 - pyskl - INFO - +mean_acc 0.9163 +2025-06-25 05:58:15,646 - pyskl - INFO - Epoch(val) [121][533] top1_acc: 0.9380, top5_acc: 0.9959, mean_class_accuracy: 0.9163 +2025-06-25 05:59:34,496 - pyskl - INFO - Epoch [122][100/1281] lr: 2.224e-03, eta: 4:22:00, time: 0.788, data_time: 0.191, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0372, loss: 0.0372 +2025-06-25 06:00:23,400 - pyskl - INFO - Epoch [122][200/1281] lr: 2.212e-03, eta: 4:21:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0416, loss: 0.0416 +2025-06-25 06:01:12,672 - pyskl - INFO - Epoch [122][300/1281] lr: 2.201e-03, eta: 4:20:38, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0401, loss: 0.0401 +2025-06-25 06:02:01,490 - pyskl - INFO - Epoch [122][400/1281] lr: 2.189e-03, eta: 4:19:57, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0323, loss: 0.0323 +2025-06-25 06:02:50,814 - pyskl - INFO - Epoch [122][500/1281] lr: 2.178e-03, eta: 4:19:16, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0428, loss: 0.0428 +2025-06-25 06:03:40,072 - pyskl - INFO - Epoch [122][600/1281] lr: 2.166e-03, eta: 4:18:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0403, loss: 0.0403 +2025-06-25 06:04:29,246 - pyskl - INFO - Epoch [122][700/1281] lr: 2.155e-03, eta: 4:17:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0435, loss: 0.0435 +2025-06-25 06:05:18,196 - pyskl - INFO - Epoch [122][800/1281] lr: 2.143e-03, eta: 4:17:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0453, loss: 0.0453 +2025-06-25 06:06:07,082 - pyskl - INFO - Epoch [122][900/1281] lr: 2.132e-03, eta: 4:16:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0445, loss: 0.0445 +2025-06-25 06:06:55,897 - pyskl - INFO - Epoch [122][1000/1281] lr: 2.120e-03, eta: 4:15:52, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0394, loss: 0.0394 +2025-06-25 06:07:24,132 - pyskl - INFO - Epoch [122][1100/1281] lr: 2.109e-03, eta: 4:15:06, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0366, loss: 0.0366 +2025-06-25 06:08:14,853 - pyskl - INFO - Epoch [122][1200/1281] lr: 2.098e-03, eta: 4:14:25, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0401, loss: 0.0401 +2025-06-25 06:08:36,015 - pyskl - INFO - Saving checkpoint at 122 epochs +2025-06-25 06:09:34,073 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:09:34,141 - pyskl - INFO - +top1_acc 0.9370 +top5_acc 0.9960 +2025-06-25 06:09:34,141 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:09:34,151 - pyskl - INFO - +mean_acc 0.9203 +2025-06-25 06:09:34,153 - pyskl - INFO - Epoch(val) [122][533] top1_acc: 0.9370, top5_acc: 0.9960, mean_class_accuracy: 0.9203 +2025-06-25 06:10:53,842 - pyskl - INFO - Epoch [123][100/1281] lr: 2.077e-03, eta: 4:13:09, time: 0.797, data_time: 0.190, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0378, loss: 0.0378 +2025-06-25 06:11:42,837 - pyskl - INFO - Epoch [123][200/1281] lr: 2.066e-03, eta: 4:12:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0365, loss: 0.0365 +2025-06-25 06:12:31,895 - pyskl - INFO - Epoch [123][300/1281] lr: 2.055e-03, eta: 4:11:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0348, loss: 0.0348 +2025-06-25 06:13:20,895 - pyskl - INFO - Epoch [123][400/1281] lr: 2.044e-03, eta: 4:11:06, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0284, loss: 0.0284 +2025-06-25 06:14:09,927 - pyskl - INFO - Epoch [123][500/1281] lr: 2.032e-03, eta: 4:10:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0343, loss: 0.0343 +2025-06-25 06:14:59,124 - pyskl - INFO - Epoch [123][600/1281] lr: 2.021e-03, eta: 4:09:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0329, loss: 0.0329 +2025-06-25 06:15:48,019 - pyskl - INFO - Epoch [123][700/1281] lr: 2.010e-03, eta: 4:09:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 0.9994, loss_cls: 0.0479, loss: 0.0479 +2025-06-25 06:16:36,787 - pyskl - INFO - Epoch [123][800/1281] lr: 1.999e-03, eta: 4:08:22, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0384, loss: 0.0384 +2025-06-25 06:17:25,704 - pyskl - INFO - Epoch [123][900/1281] lr: 1.988e-03, eta: 4:07:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0319, loss: 0.0319 +2025-06-25 06:18:14,669 - pyskl - INFO - Epoch [123][1000/1281] lr: 1.977e-03, eta: 4:07:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0326, loss: 0.0326 +2025-06-25 06:18:43,024 - pyskl - INFO - Epoch [123][1100/1281] lr: 1.966e-03, eta: 4:06:14, time: 0.284, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0317, loss: 0.0317 +2025-06-25 06:19:33,873 - pyskl - INFO - Epoch [123][1200/1281] lr: 1.955e-03, eta: 4:05:34, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0327, loss: 0.0327 +2025-06-25 06:19:55,391 - pyskl - INFO - Saving checkpoint at 123 epochs +2025-06-25 06:20:53,292 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:20:53,349 - pyskl - INFO - +top1_acc 0.9391 +top5_acc 0.9965 +2025-06-25 06:20:53,349 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:20:53,356 - pyskl - INFO - +mean_acc 0.9217 +2025-06-25 06:20:53,358 - pyskl - INFO - Epoch(val) [123][533] top1_acc: 0.9391, top5_acc: 0.9965, mean_class_accuracy: 0.9217 +2025-06-25 06:22:11,782 - pyskl - INFO - Epoch [124][100/1281] lr: 1.935e-03, eta: 4:04:17, time: 0.784, data_time: 0.188, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0294, loss: 0.0294 +2025-06-25 06:23:00,784 - pyskl - INFO - Epoch [124][200/1281] lr: 1.924e-03, eta: 4:03:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0382, loss: 0.0382 +2025-06-25 06:23:49,721 - pyskl - INFO - Epoch [124][300/1281] lr: 1.913e-03, eta: 4:02:55, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0353, loss: 0.0353 +2025-06-25 06:24:38,609 - pyskl - INFO - Epoch [124][400/1281] lr: 1.902e-03, eta: 4:02:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0451, loss: 0.0451 +2025-06-25 06:25:27,529 - pyskl - INFO - Epoch [124][500/1281] lr: 1.892e-03, eta: 4:01:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0350, loss: 0.0350 +2025-06-25 06:26:16,408 - pyskl - INFO - Epoch [124][600/1281] lr: 1.881e-03, eta: 4:00:51, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0361, loss: 0.0361 +2025-06-25 06:27:05,385 - pyskl - INFO - Epoch [124][700/1281] lr: 1.870e-03, eta: 4:00:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 0.9994, loss_cls: 0.0426, loss: 0.0426 +2025-06-25 06:27:54,411 - pyskl - INFO - Epoch [124][800/1281] lr: 1.859e-03, eta: 3:59:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0272, loss: 0.0272 +2025-06-25 06:28:43,099 - pyskl - INFO - Epoch [124][900/1281] lr: 1.849e-03, eta: 3:58:48, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0259, loss: 0.0259 +2025-06-25 06:29:32,174 - pyskl - INFO - Epoch [124][1000/1281] lr: 1.838e-03, eta: 3:58:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0287, loss: 0.0287 +2025-06-25 06:30:02,038 - pyskl - INFO - Epoch [124][1100/1281] lr: 1.827e-03, eta: 3:57:22, time: 0.299, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0307, loss: 0.0307 +2025-06-25 06:30:52,794 - pyskl - INFO - Epoch [124][1200/1281] lr: 1.817e-03, eta: 3:56:41, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0255, loss: 0.0255 +2025-06-25 06:31:13,963 - pyskl - INFO - Saving checkpoint at 124 epochs +2025-06-25 06:32:12,041 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:32:12,096 - pyskl - INFO - +top1_acc 0.9437 +top5_acc 0.9967 +2025-06-25 06:32:12,096 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:32:12,103 - pyskl - INFO - +mean_acc 0.9247 +2025-06-25 06:32:12,107 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_118.pth was removed +2025-06-25 06:32:12,266 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_124.pth. +2025-06-25 06:32:12,266 - pyskl - INFO - Best top1_acc is 0.9437 at 124 epoch. +2025-06-25 06:32:12,269 - pyskl - INFO - Epoch(val) [124][533] top1_acc: 0.9437, top5_acc: 0.9967, mean_class_accuracy: 0.9247 +2025-06-25 06:33:30,842 - pyskl - INFO - Epoch [125][100/1281] lr: 1.797e-03, eta: 3:55:24, time: 0.786, data_time: 0.185, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0279, loss: 0.0279 +2025-06-25 06:34:19,971 - pyskl - INFO - Epoch [125][200/1281] lr: 1.787e-03, eta: 3:54:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0347, loss: 0.0347 +2025-06-25 06:35:08,788 - pyskl - INFO - Epoch [125][300/1281] lr: 1.776e-03, eta: 3:54:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0317, loss: 0.0317 +2025-06-25 06:35:57,517 - pyskl - INFO - Epoch [125][400/1281] lr: 1.766e-03, eta: 3:53:20, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0290, loss: 0.0290 +2025-06-25 06:36:46,634 - pyskl - INFO - Epoch [125][500/1281] lr: 1.755e-03, eta: 3:52:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0266, loss: 0.0266 +2025-06-25 06:37:36,181 - pyskl - INFO - Epoch [125][600/1281] lr: 1.745e-03, eta: 3:51:58, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0262, loss: 0.0262 +2025-06-25 06:38:25,369 - pyskl - INFO - Epoch [125][700/1281] lr: 1.735e-03, eta: 3:51:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0379, loss: 0.0379 +2025-06-25 06:39:14,331 - pyskl - INFO - Epoch [125][800/1281] lr: 1.724e-03, eta: 3:50:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0432, loss: 0.0432 +2025-06-25 06:40:02,602 - pyskl - INFO - Epoch [125][900/1281] lr: 1.714e-03, eta: 3:49:54, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0447, loss: 0.0447 +2025-06-25 06:40:51,428 - pyskl - INFO - Epoch [125][1000/1281] lr: 1.704e-03, eta: 3:49:13, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0401, loss: 0.0401 +2025-06-25 06:41:21,808 - pyskl - INFO - Epoch [125][1100/1281] lr: 1.693e-03, eta: 3:48:28, time: 0.304, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0308, loss: 0.0308 +2025-06-25 06:42:12,524 - pyskl - INFO - Epoch [125][1200/1281] lr: 1.683e-03, eta: 3:47:47, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0328, loss: 0.0328 +2025-06-25 06:42:33,151 - pyskl - INFO - Saving checkpoint at 125 epochs +2025-06-25 06:43:31,047 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:43:31,102 - pyskl - INFO - +top1_acc 0.9404 +top5_acc 0.9967 +2025-06-25 06:43:31,103 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:43:31,110 - pyskl - INFO - +mean_acc 0.9203 +2025-06-25 06:43:31,112 - pyskl - INFO - Epoch(val) [125][533] top1_acc: 0.9404, top5_acc: 0.9967, mean_class_accuracy: 0.9203 +2025-06-25 06:44:49,977 - pyskl - INFO - Epoch [126][100/1281] lr: 1.665e-03, eta: 3:46:30, time: 0.789, data_time: 0.186, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0286, loss: 0.0286 +2025-06-25 06:45:39,135 - pyskl - INFO - Epoch [126][200/1281] lr: 1.654e-03, eta: 3:45:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0280, loss: 0.0280 +2025-06-25 06:46:28,291 - pyskl - INFO - Epoch [126][300/1281] lr: 1.644e-03, eta: 3:45:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0293, loss: 0.0293 +2025-06-25 06:47:16,952 - pyskl - INFO - Epoch [126][400/1281] lr: 1.634e-03, eta: 3:44:26, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0327, loss: 0.0327 +2025-06-25 06:48:05,885 - pyskl - INFO - Epoch [126][500/1281] lr: 1.624e-03, eta: 3:43:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0307, loss: 0.0307 +2025-06-25 06:48:55,028 - pyskl - INFO - Epoch [126][600/1281] lr: 1.614e-03, eta: 3:43:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0250, loss: 0.0250 +2025-06-25 06:49:43,938 - pyskl - INFO - Epoch [126][700/1281] lr: 1.604e-03, eta: 3:42:22, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0251, loss: 0.0251 +2025-06-25 06:50:32,352 - pyskl - INFO - Epoch [126][800/1281] lr: 1.594e-03, eta: 3:41:41, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-25 06:51:21,082 - pyskl - INFO - Epoch [126][900/1281] lr: 1.584e-03, eta: 3:41:00, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0268, loss: 0.0268 +2025-06-25 06:52:09,963 - pyskl - INFO - Epoch [126][1000/1281] lr: 1.574e-03, eta: 3:40:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0303, loss: 0.0303 +2025-06-25 06:52:39,739 - pyskl - INFO - Epoch [126][1100/1281] lr: 1.564e-03, eta: 3:39:33, time: 0.298, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0374, loss: 0.0374 +2025-06-25 06:53:30,376 - pyskl - INFO - Epoch [126][1200/1281] lr: 1.554e-03, eta: 3:38:52, time: 0.506, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0350, loss: 0.0350 +2025-06-25 06:53:51,527 - pyskl - INFO - Saving checkpoint at 126 epochs +2025-06-25 06:54:49,740 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:54:49,809 - pyskl - INFO - +top1_acc 0.9434 +top5_acc 0.9972 +2025-06-25 06:54:49,809 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:54:49,818 - pyskl - INFO - +mean_acc 0.9251 +2025-06-25 06:54:49,820 - pyskl - INFO - Epoch(val) [126][533] top1_acc: 0.9434, top5_acc: 0.9972, mean_class_accuracy: 0.9251 +2025-06-25 06:56:08,472 - pyskl - INFO - Epoch [127][100/1281] lr: 1.536e-03, eta: 3:37:35, time: 0.786, data_time: 0.184, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0285, loss: 0.0285 +2025-06-25 06:56:57,330 - pyskl - INFO - Epoch [127][200/1281] lr: 1.527e-03, eta: 3:36:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0375, loss: 0.0375 +2025-06-25 06:57:46,191 - pyskl - INFO - Epoch [127][300/1281] lr: 1.517e-03, eta: 3:36:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0369, loss: 0.0369 +2025-06-25 06:58:35,296 - pyskl - INFO - Epoch [127][400/1281] lr: 1.507e-03, eta: 3:35:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0329, loss: 0.0329 +2025-06-25 06:59:24,217 - pyskl - INFO - Epoch [127][500/1281] lr: 1.497e-03, eta: 3:34:50, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0250, loss: 0.0250 +2025-06-25 07:00:12,900 - pyskl - INFO - Epoch [127][600/1281] lr: 1.488e-03, eta: 3:34:08, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0257, loss: 0.0257 +2025-06-25 07:01:02,110 - pyskl - INFO - Epoch [127][700/1281] lr: 1.478e-03, eta: 3:33:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0256, loss: 0.0256 +2025-06-25 07:01:50,822 - pyskl - INFO - Epoch [127][800/1281] lr: 1.468e-03, eta: 3:32:45, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0331, loss: 0.0331 +2025-06-25 07:02:39,446 - pyskl - INFO - Epoch [127][900/1281] lr: 1.459e-03, eta: 3:32:04, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0283, loss: 0.0283 +2025-06-25 07:03:28,166 - pyskl - INFO - Epoch [127][1000/1281] lr: 1.449e-03, eta: 3:31:22, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0323, loss: 0.0323 +2025-06-25 07:03:57,345 - pyskl - INFO - Epoch [127][1100/1281] lr: 1.440e-03, eta: 3:30:37, time: 0.292, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0280, loss: 0.0280 +2025-06-25 07:04:48,159 - pyskl - INFO - Epoch [127][1200/1281] lr: 1.430e-03, eta: 3:29:56, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 07:05:09,781 - pyskl - INFO - Saving checkpoint at 127 epochs +2025-06-25 07:06:07,865 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:06:07,922 - pyskl - INFO - +top1_acc 0.9419 +top5_acc 0.9967 +2025-06-25 07:06:07,922 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:06:07,929 - pyskl - INFO - +mean_acc 0.9223 +2025-06-25 07:06:07,930 - pyskl - INFO - Epoch(val) [127][533] top1_acc: 0.9419, top5_acc: 0.9967, mean_class_accuracy: 0.9223 +2025-06-25 07:07:27,376 - pyskl - INFO - Epoch [128][100/1281] lr: 1.413e-03, eta: 3:28:39, time: 0.794, data_time: 0.188, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0275, loss: 0.0275 +2025-06-25 07:08:16,102 - pyskl - INFO - Epoch [128][200/1281] lr: 1.404e-03, eta: 3:27:58, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0261, loss: 0.0261 +2025-06-25 07:09:04,857 - pyskl - INFO - Epoch [128][300/1281] lr: 1.394e-03, eta: 3:27:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 07:09:53,628 - pyskl - INFO - Epoch [128][400/1281] lr: 1.385e-03, eta: 3:26:34, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-25 07:10:42,811 - pyskl - INFO - Epoch [128][500/1281] lr: 1.376e-03, eta: 3:25:53, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-25 07:11:31,872 - pyskl - INFO - Epoch [128][600/1281] lr: 1.366e-03, eta: 3:25:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 07:12:20,741 - pyskl - INFO - Epoch [128][700/1281] lr: 1.357e-03, eta: 3:24:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 07:13:09,557 - pyskl - INFO - Epoch [128][800/1281] lr: 1.348e-03, eta: 3:23:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0314, loss: 0.0314 +2025-06-25 07:13:58,274 - pyskl - INFO - Epoch [128][900/1281] lr: 1.339e-03, eta: 3:23:07, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0239, loss: 0.0239 +2025-06-25 07:14:47,166 - pyskl - INFO - Epoch [128][1000/1281] lr: 1.329e-03, eta: 3:22:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0245, loss: 0.0245 +2025-06-25 07:15:15,889 - pyskl - INFO - Epoch [128][1100/1281] lr: 1.320e-03, eta: 3:21:40, time: 0.287, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0307, loss: 0.0307 +2025-06-25 07:16:06,628 - pyskl - INFO - Epoch [128][1200/1281] lr: 1.311e-03, eta: 3:20:59, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 07:16:29,065 - pyskl - INFO - Saving checkpoint at 128 epochs +2025-06-25 07:17:27,432 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:17:27,489 - pyskl - INFO - +top1_acc 0.9432 +top5_acc 0.9968 +2025-06-25 07:17:27,489 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:17:27,495 - pyskl - INFO - +mean_acc 0.9227 +2025-06-25 07:17:27,497 - pyskl - INFO - Epoch(val) [128][533] top1_acc: 0.9432, top5_acc: 0.9968, mean_class_accuracy: 0.9227 +2025-06-25 07:18:47,173 - pyskl - INFO - Epoch [129][100/1281] lr: 1.295e-03, eta: 3:19:42, time: 0.797, data_time: 0.189, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0227, loss: 0.0227 +2025-06-25 07:19:36,324 - pyskl - INFO - Epoch [129][200/1281] lr: 1.286e-03, eta: 3:19:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0334, loss: 0.0334 +2025-06-25 07:20:25,304 - pyskl - INFO - Epoch [129][300/1281] lr: 1.277e-03, eta: 3:18:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0246, loss: 0.0246 +2025-06-25 07:21:14,308 - pyskl - INFO - Epoch [129][400/1281] lr: 1.268e-03, eta: 3:17:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0232, loss: 0.0232 +2025-06-25 07:22:03,384 - pyskl - INFO - Epoch [129][500/1281] lr: 1.259e-03, eta: 3:16:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 07:22:52,683 - pyskl - INFO - Epoch [129][600/1281] lr: 1.250e-03, eta: 3:16:14, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0270, loss: 0.0270 +2025-06-25 07:23:41,670 - pyskl - INFO - Epoch [129][700/1281] lr: 1.241e-03, eta: 3:15:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0218, loss: 0.0218 +2025-06-25 07:24:30,813 - pyskl - INFO - Epoch [129][800/1281] lr: 1.232e-03, eta: 3:14:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0237, loss: 0.0237 +2025-06-25 07:25:19,506 - pyskl - INFO - Epoch [129][900/1281] lr: 1.223e-03, eta: 3:14:09, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 07:26:08,350 - pyskl - INFO - Epoch [129][1000/1281] lr: 1.214e-03, eta: 3:13:27, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0260, loss: 0.0260 +2025-06-25 07:26:36,855 - pyskl - INFO - Epoch [129][1100/1281] lr: 1.206e-03, eta: 3:12:42, time: 0.285, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 07:27:26,681 - pyskl - INFO - Epoch [129][1200/1281] lr: 1.197e-03, eta: 3:12:01, time: 0.498, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0276, loss: 0.0276 +2025-06-25 07:27:49,121 - pyskl - INFO - Saving checkpoint at 129 epochs +2025-06-25 07:28:47,127 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:28:47,191 - pyskl - INFO - +top1_acc 0.9431 +top5_acc 0.9968 +2025-06-25 07:28:47,191 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:28:47,199 - pyskl - INFO - +mean_acc 0.9255 +2025-06-25 07:28:47,201 - pyskl - INFO - Epoch(val) [129][533] top1_acc: 0.9431, top5_acc: 0.9968, mean_class_accuracy: 0.9255 +2025-06-25 07:30:07,384 - pyskl - INFO - Epoch [130][100/1281] lr: 1.181e-03, eta: 3:10:44, time: 0.802, data_time: 0.189, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0257, loss: 0.0257 +2025-06-25 07:30:56,560 - pyskl - INFO - Epoch [130][200/1281] lr: 1.172e-03, eta: 3:10:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 07:31:45,422 - pyskl - INFO - Epoch [130][300/1281] lr: 1.164e-03, eta: 3:09:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0276, loss: 0.0276 +2025-06-25 07:32:34,015 - pyskl - INFO - Epoch [130][400/1281] lr: 1.155e-03, eta: 3:08:39, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0282, loss: 0.0282 +2025-06-25 07:33:23,174 - pyskl - INFO - Epoch [130][500/1281] lr: 1.147e-03, eta: 3:07:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0228, loss: 0.0228 +2025-06-25 07:34:12,123 - pyskl - INFO - Epoch [130][600/1281] lr: 1.138e-03, eta: 3:07:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0228, loss: 0.0228 +2025-06-25 07:35:00,763 - pyskl - INFO - Epoch [130][700/1281] lr: 1.130e-03, eta: 3:06:34, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0227, loss: 0.0227 +2025-06-25 07:35:49,270 - pyskl - INFO - Epoch [130][800/1281] lr: 1.121e-03, eta: 3:05:52, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0275, loss: 0.0275 +2025-06-25 07:36:37,576 - pyskl - INFO - Epoch [130][900/1281] lr: 1.113e-03, eta: 3:05:10, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0287, loss: 0.0287 +2025-06-25 07:37:26,328 - pyskl - INFO - Epoch [130][1000/1281] lr: 1.104e-03, eta: 3:04:28, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 07:37:54,643 - pyskl - INFO - Epoch [130][1100/1281] lr: 1.096e-03, eta: 3:03:43, time: 0.283, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 07:38:45,047 - pyskl - INFO - Epoch [130][1200/1281] lr: 1.088e-03, eta: 3:03:02, time: 0.504, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 07:39:06,786 - pyskl - INFO - Saving checkpoint at 130 epochs +2025-06-25 07:40:04,976 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:40:05,035 - pyskl - INFO - +top1_acc 0.9454 +top5_acc 0.9971 +2025-06-25 07:40:05,035 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:40:05,041 - pyskl - INFO - +mean_acc 0.9283 +2025-06-25 07:40:05,045 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_124.pth was removed +2025-06-25 07:40:05,212 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_130.pth. +2025-06-25 07:40:05,212 - pyskl - INFO - Best top1_acc is 0.9454 at 130 epoch. +2025-06-25 07:40:05,215 - pyskl - INFO - Epoch(val) [130][533] top1_acc: 0.9454, top5_acc: 0.9971, mean_class_accuracy: 0.9283 +2025-06-25 07:41:24,025 - pyskl - INFO - Epoch [131][100/1281] lr: 1.072e-03, eta: 3:01:44, time: 0.788, data_time: 0.189, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0218, loss: 0.0218 +2025-06-25 07:42:12,825 - pyskl - INFO - Epoch [131][200/1281] lr: 1.064e-03, eta: 3:01:03, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 07:43:01,833 - pyskl - INFO - Epoch [131][300/1281] lr: 1.056e-03, eta: 3:00:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 07:43:50,653 - pyskl - INFO - Epoch [131][400/1281] lr: 1.048e-03, eta: 2:59:39, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 07:44:39,785 - pyskl - INFO - Epoch [131][500/1281] lr: 1.040e-03, eta: 2:58:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 07:45:28,554 - pyskl - INFO - Epoch [131][600/1281] lr: 1.031e-03, eta: 2:58:15, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-25 07:46:17,555 - pyskl - INFO - Epoch [131][700/1281] lr: 1.023e-03, eta: 2:57:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 07:47:06,939 - pyskl - INFO - Epoch [131][800/1281] lr: 1.015e-03, eta: 2:56:52, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0260, loss: 0.0260 +2025-06-25 07:47:55,673 - pyskl - INFO - Epoch [131][900/1281] lr: 1.007e-03, eta: 2:56:10, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0245, loss: 0.0245 +2025-06-25 07:48:44,644 - pyskl - INFO - Epoch [131][1000/1281] lr: 9.992e-04, eta: 2:55:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 07:49:13,577 - pyskl - INFO - Epoch [131][1100/1281] lr: 9.912e-04, eta: 2:54:43, time: 0.289, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 07:50:04,532 - pyskl - INFO - Epoch [131][1200/1281] lr: 9.832e-04, eta: 2:54:02, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-25 07:50:26,125 - pyskl - INFO - Saving checkpoint at 131 epochs +2025-06-25 07:51:24,272 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:51:24,333 - pyskl - INFO - +top1_acc 0.9453 +top5_acc 0.9966 +2025-06-25 07:51:24,333 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:51:24,340 - pyskl - INFO - +mean_acc 0.9262 +2025-06-25 07:51:24,341 - pyskl - INFO - Epoch(val) [131][533] top1_acc: 0.9453, top5_acc: 0.9966, mean_class_accuracy: 0.9262 +2025-06-25 07:52:44,518 - pyskl - INFO - Epoch [132][100/1281] lr: 9.689e-04, eta: 2:52:45, time: 0.802, data_time: 0.187, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 07:53:33,339 - pyskl - INFO - Epoch [132][200/1281] lr: 9.610e-04, eta: 2:52:03, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 07:54:22,624 - pyskl - INFO - Epoch [132][300/1281] lr: 9.532e-04, eta: 2:51:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 07:55:11,566 - pyskl - INFO - Epoch [132][400/1281] lr: 9.454e-04, eta: 2:50:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-25 07:56:00,783 - pyskl - INFO - Epoch [132][500/1281] lr: 9.376e-04, eta: 2:49:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 07:56:49,621 - pyskl - INFO - Epoch [132][600/1281] lr: 9.298e-04, eta: 2:49:15, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0219, loss: 0.0219 +2025-06-25 07:57:38,636 - pyskl - INFO - Epoch [132][700/1281] lr: 9.221e-04, eta: 2:48:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 07:58:27,469 - pyskl - INFO - Epoch [132][800/1281] lr: 9.144e-04, eta: 2:47:52, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0223, loss: 0.0223 +2025-06-25 07:59:16,096 - pyskl - INFO - Epoch [132][900/1281] lr: 9.068e-04, eta: 2:47:10, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 08:00:04,460 - pyskl - INFO - Epoch [132][1000/1281] lr: 8.991e-04, eta: 2:46:28, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-25 08:00:32,422 - pyskl - INFO - Epoch [132][1100/1281] lr: 8.915e-04, eta: 2:45:43, time: 0.280, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-25 08:01:23,102 - pyskl - INFO - Epoch [132][1200/1281] lr: 8.840e-04, eta: 2:45:01, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 08:01:44,551 - pyskl - INFO - Saving checkpoint at 132 epochs +2025-06-25 08:02:42,819 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:02:42,887 - pyskl - INFO - +top1_acc 0.9440 +top5_acc 0.9967 +2025-06-25 08:02:42,887 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:02:42,896 - pyskl - INFO - +mean_acc 0.9266 +2025-06-25 08:02:42,898 - pyskl - INFO - Epoch(val) [132][533] top1_acc: 0.9440, top5_acc: 0.9967, mean_class_accuracy: 0.9266 +2025-06-25 08:04:02,834 - pyskl - INFO - Epoch [133][100/1281] lr: 8.704e-04, eta: 2:43:44, time: 0.799, data_time: 0.186, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0246, loss: 0.0246 +2025-06-25 08:04:51,833 - pyskl - INFO - Epoch [133][200/1281] lr: 8.629e-04, eta: 2:43:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-25 08:05:40,861 - pyskl - INFO - Epoch [133][300/1281] lr: 8.554e-04, eta: 2:42:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 08:06:29,943 - pyskl - INFO - Epoch [133][400/1281] lr: 8.480e-04, eta: 2:41:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0219, loss: 0.0219 +2025-06-25 08:07:18,901 - pyskl - INFO - Epoch [133][500/1281] lr: 8.406e-04, eta: 2:40:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0223, loss: 0.0223 +2025-06-25 08:08:07,581 - pyskl - INFO - Epoch [133][600/1281] lr: 8.333e-04, eta: 2:40:14, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0278, loss: 0.0278 +2025-06-25 08:08:56,394 - pyskl - INFO - Epoch [133][700/1281] lr: 8.260e-04, eta: 2:39:32, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-25 08:09:45,143 - pyskl - INFO - Epoch [133][800/1281] lr: 8.187e-04, eta: 2:38:50, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 08:10:34,061 - pyskl - INFO - Epoch [133][900/1281] lr: 8.114e-04, eta: 2:38:08, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0281, loss: 0.0281 +2025-06-25 08:11:22,653 - pyskl - INFO - Epoch [133][1000/1281] lr: 8.042e-04, eta: 2:37:26, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 08:11:50,592 - pyskl - INFO - Epoch [133][1100/1281] lr: 7.970e-04, eta: 2:36:41, time: 0.279, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 08:12:41,432 - pyskl - INFO - Epoch [133][1200/1281] lr: 7.898e-04, eta: 2:35:59, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-25 08:13:03,006 - pyskl - INFO - Saving checkpoint at 133 epochs +2025-06-25 08:14:01,036 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:14:01,104 - pyskl - INFO - +top1_acc 0.9431 +top5_acc 0.9969 +2025-06-25 08:14:01,105 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:14:01,112 - pyskl - INFO - +mean_acc 0.9242 +2025-06-25 08:14:01,114 - pyskl - INFO - Epoch(val) [133][533] top1_acc: 0.9431, top5_acc: 0.9969, mean_class_accuracy: 0.9242 +2025-06-25 08:15:19,938 - pyskl - INFO - Epoch [134][100/1281] lr: 7.769e-04, eta: 2:34:42, time: 0.788, data_time: 0.188, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 08:16:08,670 - pyskl - INFO - Epoch [134][200/1281] lr: 7.699e-04, eta: 2:34:00, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-25 08:16:57,392 - pyskl - INFO - Epoch [134][300/1281] lr: 7.628e-04, eta: 2:33:18, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 08:17:46,505 - pyskl - INFO - Epoch [134][400/1281] lr: 7.558e-04, eta: 2:32:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 08:18:35,279 - pyskl - INFO - Epoch [134][500/1281] lr: 7.488e-04, eta: 2:31:54, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0275, loss: 0.0275 +2025-06-25 08:19:24,105 - pyskl - INFO - Epoch [134][600/1281] lr: 7.419e-04, eta: 2:31:12, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 08:20:12,956 - pyskl - INFO - Epoch [134][700/1281] lr: 7.349e-04, eta: 2:30:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 08:21:01,898 - pyskl - INFO - Epoch [134][800/1281] lr: 7.281e-04, eta: 2:29:47, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 08:21:50,708 - pyskl - INFO - Epoch [134][900/1281] lr: 7.212e-04, eta: 2:29:05, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 08:22:39,491 - pyskl - INFO - Epoch [134][1000/1281] lr: 7.144e-04, eta: 2:28:23, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0222, loss: 0.0222 +2025-06-25 08:23:09,147 - pyskl - INFO - Epoch [134][1100/1281] lr: 7.076e-04, eta: 2:27:39, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 08:23:59,959 - pyskl - INFO - Epoch [134][1200/1281] lr: 7.008e-04, eta: 2:26:57, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 08:24:20,835 - pyskl - INFO - Saving checkpoint at 134 epochs +2025-06-25 08:25:18,701 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:25:18,759 - pyskl - INFO - +top1_acc 0.9458 +top5_acc 0.9971 +2025-06-25 08:25:18,759 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:25:18,765 - pyskl - INFO - +mean_acc 0.9286 +2025-06-25 08:25:18,770 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_130.pth was removed +2025-06-25 08:25:18,955 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_134.pth. +2025-06-25 08:25:18,955 - pyskl - INFO - Best top1_acc is 0.9458 at 134 epoch. +2025-06-25 08:25:18,959 - pyskl - INFO - Epoch(val) [134][533] top1_acc: 0.9458, top5_acc: 0.9971, mean_class_accuracy: 0.9286 +2025-06-25 08:26:38,479 - pyskl - INFO - Epoch [135][100/1281] lr: 6.887e-04, eta: 2:25:39, time: 0.795, data_time: 0.194, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-25 08:27:27,417 - pyskl - INFO - Epoch [135][200/1281] lr: 6.820e-04, eta: 2:24:57, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0223, loss: 0.0223 +2025-06-25 08:28:16,222 - pyskl - INFO - Epoch [135][300/1281] lr: 6.753e-04, eta: 2:24:15, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0245, loss: 0.0245 +2025-06-25 08:29:05,350 - pyskl - INFO - Epoch [135][400/1281] lr: 6.687e-04, eta: 2:23:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 08:29:54,183 - pyskl - INFO - Epoch [135][500/1281] lr: 6.622e-04, eta: 2:22:51, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-25 08:30:43,186 - pyskl - INFO - Epoch [135][600/1281] lr: 6.556e-04, eta: 2:22:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 08:31:32,185 - pyskl - INFO - Epoch [135][700/1281] lr: 6.491e-04, eta: 2:21:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-25 08:32:21,051 - pyskl - INFO - Epoch [135][800/1281] lr: 6.426e-04, eta: 2:20:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 08:33:10,035 - pyskl - INFO - Epoch [135][900/1281] lr: 6.362e-04, eta: 2:20:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 08:33:58,754 - pyskl - INFO - Epoch [135][1000/1281] lr: 6.297e-04, eta: 2:19:20, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 08:34:27,091 - pyskl - INFO - Epoch [135][1100/1281] lr: 6.233e-04, eta: 2:18:35, time: 0.283, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 08:35:17,872 - pyskl - INFO - Epoch [135][1200/1281] lr: 6.170e-04, eta: 2:17:53, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 08:35:39,674 - pyskl - INFO - Saving checkpoint at 135 epochs +2025-06-25 08:36:37,577 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:36:37,633 - pyskl - INFO - +top1_acc 0.9454 +top5_acc 0.9971 +2025-06-25 08:36:37,633 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:36:37,641 - pyskl - INFO - +mean_acc 0.9261 +2025-06-25 08:36:37,643 - pyskl - INFO - Epoch(val) [135][533] top1_acc: 0.9454, top5_acc: 0.9971, mean_class_accuracy: 0.9261 +2025-06-25 08:37:57,471 - pyskl - INFO - Epoch [136][100/1281] lr: 6.056e-04, eta: 2:16:36, time: 0.798, data_time: 0.186, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-25 08:38:46,385 - pyskl - INFO - Epoch [136][200/1281] lr: 5.993e-04, eta: 2:15:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-25 08:39:35,283 - pyskl - INFO - Epoch [136][300/1281] lr: 5.931e-04, eta: 2:15:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 08:40:24,229 - pyskl - INFO - Epoch [136][400/1281] lr: 5.868e-04, eta: 2:14:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 08:41:13,259 - pyskl - INFO - Epoch [136][500/1281] lr: 5.807e-04, eta: 2:13:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-25 08:42:02,099 - pyskl - INFO - Epoch [136][600/1281] lr: 5.745e-04, eta: 2:13:05, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 08:42:50,798 - pyskl - INFO - Epoch [136][700/1281] lr: 5.684e-04, eta: 2:12:23, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 08:43:39,633 - pyskl - INFO - Epoch [136][800/1281] lr: 5.623e-04, eta: 2:11:40, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 08:44:28,600 - pyskl - INFO - Epoch [136][900/1281] lr: 5.563e-04, eta: 2:10:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-25 08:45:17,707 - pyskl - INFO - Epoch [136][1000/1281] lr: 5.503e-04, eta: 2:10:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 08:45:46,523 - pyskl - INFO - Epoch [136][1100/1281] lr: 5.443e-04, eta: 2:09:31, time: 0.288, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 08:46:37,451 - pyskl - INFO - Epoch [136][1200/1281] lr: 5.384e-04, eta: 2:08:49, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 08:46:58,758 - pyskl - INFO - Saving checkpoint at 136 epochs +2025-06-25 08:47:56,858 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:47:56,914 - pyskl - INFO - +top1_acc 0.9452 +top5_acc 0.9973 +2025-06-25 08:47:56,914 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:47:56,921 - pyskl - INFO - +mean_acc 0.9238 +2025-06-25 08:47:56,923 - pyskl - INFO - Epoch(val) [136][533] top1_acc: 0.9452, top5_acc: 0.9973, mean_class_accuracy: 0.9238 +2025-06-25 08:49:15,908 - pyskl - INFO - Epoch [137][100/1281] lr: 5.277e-04, eta: 2:07:32, time: 0.790, data_time: 0.185, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-25 08:50:04,893 - pyskl - INFO - Epoch [137][200/1281] lr: 5.218e-04, eta: 2:06:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 08:50:54,034 - pyskl - INFO - Epoch [137][300/1281] lr: 5.160e-04, eta: 2:06:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 08:51:42,707 - pyskl - INFO - Epoch [137][400/1281] lr: 5.102e-04, eta: 2:05:25, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 08:52:32,066 - pyskl - INFO - Epoch [137][500/1281] lr: 5.044e-04, eta: 2:04:43, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 08:53:21,154 - pyskl - INFO - Epoch [137][600/1281] lr: 4.987e-04, eta: 2:04:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 08:54:09,820 - pyskl - INFO - Epoch [137][700/1281] lr: 4.930e-04, eta: 2:03:18, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-25 08:54:58,847 - pyskl - INFO - Epoch [137][800/1281] lr: 4.873e-04, eta: 2:02:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0262, loss: 0.0262 +2025-06-25 08:55:47,776 - pyskl - INFO - Epoch [137][900/1281] lr: 4.817e-04, eta: 2:01:53, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 08:56:37,030 - pyskl - INFO - Epoch [137][1000/1281] lr: 4.761e-04, eta: 2:01:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 08:57:06,457 - pyskl - INFO - Epoch [137][1100/1281] lr: 4.705e-04, eta: 2:00:27, time: 0.294, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 08:57:57,196 - pyskl - INFO - Epoch [137][1200/1281] lr: 4.650e-04, eta: 1:59:45, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 08:58:19,188 - pyskl - INFO - Saving checkpoint at 137 epochs +2025-06-25 08:59:17,365 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:59:17,423 - pyskl - INFO - +top1_acc 0.9451 +top5_acc 0.9973 +2025-06-25 08:59:17,423 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:59:17,430 - pyskl - INFO - +mean_acc 0.9239 +2025-06-25 08:59:17,432 - pyskl - INFO - Epoch(val) [137][533] top1_acc: 0.9451, top5_acc: 0.9973, mean_class_accuracy: 0.9239 +2025-06-25 09:00:36,145 - pyskl - INFO - Epoch [138][100/1281] lr: 4.550e-04, eta: 1:58:27, time: 0.787, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 09:01:24,953 - pyskl - INFO - Epoch [138][200/1281] lr: 4.496e-04, eta: 1:57:45, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 09:02:13,860 - pyskl - INFO - Epoch [138][300/1281] lr: 4.442e-04, eta: 1:57:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 09:03:02,494 - pyskl - INFO - Epoch [138][400/1281] lr: 4.388e-04, eta: 1:56:20, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 09:03:51,514 - pyskl - INFO - Epoch [138][500/1281] lr: 4.334e-04, eta: 1:55:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 09:04:40,202 - pyskl - INFO - Epoch [138][600/1281] lr: 4.281e-04, eta: 1:54:55, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 09:05:29,167 - pyskl - INFO - Epoch [138][700/1281] lr: 4.228e-04, eta: 1:54:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 09:06:18,466 - pyskl - INFO - Epoch [138][800/1281] lr: 4.176e-04, eta: 1:53:30, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 09:07:07,263 - pyskl - INFO - Epoch [138][900/1281] lr: 4.124e-04, eta: 1:52:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-25 09:07:56,200 - pyskl - INFO - Epoch [138][1000/1281] lr: 4.072e-04, eta: 1:52:05, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0219, loss: 0.0219 +2025-06-25 09:08:24,957 - pyskl - INFO - Epoch [138][1100/1281] lr: 4.020e-04, eta: 1:51:21, time: 0.288, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0228, loss: 0.0228 +2025-06-25 09:09:15,663 - pyskl - INFO - Epoch [138][1200/1281] lr: 3.969e-04, eta: 1:50:39, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 09:09:37,092 - pyskl - INFO - Saving checkpoint at 138 epochs +2025-06-25 09:10:35,217 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:10:35,273 - pyskl - INFO - +top1_acc 0.9455 +top5_acc 0.9969 +2025-06-25 09:10:35,273 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:10:35,280 - pyskl - INFO - +mean_acc 0.9268 +2025-06-25 09:10:35,282 - pyskl - INFO - Epoch(val) [138][533] top1_acc: 0.9455, top5_acc: 0.9969, mean_class_accuracy: 0.9268 +2025-06-25 09:11:54,545 - pyskl - INFO - Epoch [139][100/1281] lr: 3.877e-04, eta: 1:49:21, time: 0.793, data_time: 0.190, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 09:12:43,633 - pyskl - INFO - Epoch [139][200/1281] lr: 3.827e-04, eta: 1:48:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 09:13:32,751 - pyskl - INFO - Epoch [139][300/1281] lr: 3.777e-04, eta: 1:47:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 09:14:21,937 - pyskl - INFO - Epoch [139][400/1281] lr: 3.727e-04, eta: 1:47:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 09:15:10,769 - pyskl - INFO - Epoch [139][500/1281] lr: 3.678e-04, eta: 1:46:31, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 09:15:59,897 - pyskl - INFO - Epoch [139][600/1281] lr: 3.628e-04, eta: 1:45:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 09:16:48,920 - pyskl - INFO - Epoch [139][700/1281] lr: 3.580e-04, eta: 1:45:06, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 09:17:37,742 - pyskl - INFO - Epoch [139][800/1281] lr: 3.531e-04, eta: 1:44:24, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 09:18:26,667 - pyskl - INFO - Epoch [139][900/1281] lr: 3.483e-04, eta: 1:43:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 09:19:15,337 - pyskl - INFO - Epoch [139][1000/1281] lr: 3.436e-04, eta: 1:42:59, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 09:19:44,575 - pyskl - INFO - Epoch [139][1100/1281] lr: 3.388e-04, eta: 1:42:15, time: 0.292, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 09:20:35,352 - pyskl - INFO - Epoch [139][1200/1281] lr: 3.341e-04, eta: 1:41:32, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 09:20:56,845 - pyskl - INFO - Saving checkpoint at 139 epochs +2025-06-25 09:21:54,716 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:21:54,772 - pyskl - INFO - +top1_acc 0.9475 +top5_acc 0.9974 +2025-06-25 09:21:54,772 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:21:54,778 - pyskl - INFO - +mean_acc 0.9287 +2025-06-25 09:21:54,782 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_134.pth was removed +2025-06-25 09:21:54,951 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_139.pth. +2025-06-25 09:21:54,951 - pyskl - INFO - Best top1_acc is 0.9475 at 139 epoch. +2025-06-25 09:21:54,954 - pyskl - INFO - Epoch(val) [139][533] top1_acc: 0.9475, top5_acc: 0.9974, mean_class_accuracy: 0.9287 +2025-06-25 09:23:12,491 - pyskl - INFO - Epoch [140][100/1281] lr: 3.257e-04, eta: 1:40:15, time: 0.775, data_time: 0.183, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 09:24:01,332 - pyskl - INFO - Epoch [140][200/1281] lr: 3.210e-04, eta: 1:39:32, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 09:24:50,334 - pyskl - INFO - Epoch [140][300/1281] lr: 3.165e-04, eta: 1:38:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 09:25:38,951 - pyskl - INFO - Epoch [140][400/1281] lr: 3.119e-04, eta: 1:38:07, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 09:26:27,815 - pyskl - INFO - Epoch [140][500/1281] lr: 3.074e-04, eta: 1:37:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 09:27:17,413 - pyskl - INFO - Epoch [140][600/1281] lr: 3.029e-04, eta: 1:36:42, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 09:28:06,515 - pyskl - INFO - Epoch [140][700/1281] lr: 2.984e-04, eta: 1:35:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 09:28:55,675 - pyskl - INFO - Epoch [140][800/1281] lr: 2.940e-04, eta: 1:35:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 09:29:44,741 - pyskl - INFO - Epoch [140][900/1281] lr: 2.896e-04, eta: 1:34:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 09:30:33,955 - pyskl - INFO - Epoch [140][1000/1281] lr: 2.853e-04, eta: 1:33:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 09:31:04,871 - pyskl - INFO - Epoch [140][1100/1281] lr: 2.809e-04, eta: 1:33:08, time: 0.309, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 09:31:55,544 - pyskl - INFO - Epoch [140][1200/1281] lr: 2.767e-04, eta: 1:32:25, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 09:32:16,120 - pyskl - INFO - Saving checkpoint at 140 epochs +2025-06-25 09:33:14,388 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:33:14,443 - pyskl - INFO - +top1_acc 0.9472 +top5_acc 0.9969 +2025-06-25 09:33:14,443 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:33:14,451 - pyskl - INFO - +mean_acc 0.9291 +2025-06-25 09:33:14,453 - pyskl - INFO - Epoch(val) [140][533] top1_acc: 0.9472, top5_acc: 0.9969, mean_class_accuracy: 0.9291 +2025-06-25 09:34:34,556 - pyskl - INFO - Epoch [141][100/1281] lr: 2.690e-04, eta: 1:31:08, time: 0.801, data_time: 0.185, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 09:35:23,331 - pyskl - INFO - Epoch [141][200/1281] lr: 2.648e-04, eta: 1:30:25, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 09:36:12,131 - pyskl - INFO - Epoch [141][300/1281] lr: 2.606e-04, eta: 1:29:42, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 09:37:00,958 - pyskl - INFO - Epoch [141][400/1281] lr: 2.565e-04, eta: 1:29:00, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0222, loss: 0.0222 +2025-06-25 09:37:49,901 - pyskl - INFO - Epoch [141][500/1281] lr: 2.524e-04, eta: 1:28:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 09:38:38,819 - pyskl - INFO - Epoch [141][600/1281] lr: 2.483e-04, eta: 1:27:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 09:39:27,758 - pyskl - INFO - Epoch [141][700/1281] lr: 2.443e-04, eta: 1:26:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 09:40:16,470 - pyskl - INFO - Epoch [141][800/1281] lr: 2.402e-04, eta: 1:26:09, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 09:41:05,750 - pyskl - INFO - Epoch [141][900/1281] lr: 2.363e-04, eta: 1:25:26, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 09:41:54,597 - pyskl - INFO - Epoch [141][1000/1281] lr: 2.323e-04, eta: 1:24:44, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 09:42:22,519 - pyskl - INFO - Epoch [141][1100/1281] lr: 2.284e-04, eta: 1:24:00, time: 0.279, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 09:43:13,288 - pyskl - INFO - Epoch [141][1200/1281] lr: 2.246e-04, eta: 1:23:17, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 09:43:34,923 - pyskl - INFO - Saving checkpoint at 141 epochs +2025-06-25 09:44:33,184 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:44:33,254 - pyskl - INFO - +top1_acc 0.9464 +top5_acc 0.9974 +2025-06-25 09:44:33,254 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:44:33,262 - pyskl - INFO - +mean_acc 0.9264 +2025-06-25 09:44:33,265 - pyskl - INFO - Epoch(val) [141][533] top1_acc: 0.9464, top5_acc: 0.9974, mean_class_accuracy: 0.9264 +2025-06-25 09:45:53,055 - pyskl - INFO - Epoch [142][100/1281] lr: 2.176e-04, eta: 1:21:59, time: 0.798, data_time: 0.190, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 09:46:41,975 - pyskl - INFO - Epoch [142][200/1281] lr: 2.139e-04, eta: 1:21:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 09:47:30,923 - pyskl - INFO - Epoch [142][300/1281] lr: 2.101e-04, eta: 1:20:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 09:48:20,064 - pyskl - INFO - Epoch [142][400/1281] lr: 2.064e-04, eta: 1:19:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 09:49:08,975 - pyskl - INFO - Epoch [142][500/1281] lr: 2.027e-04, eta: 1:19:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 09:49:58,046 - pyskl - INFO - Epoch [142][600/1281] lr: 1.991e-04, eta: 1:18:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 09:50:47,427 - pyskl - INFO - Epoch [142][700/1281] lr: 1.954e-04, eta: 1:17:43, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 09:51:36,452 - pyskl - INFO - Epoch [142][800/1281] lr: 1.919e-04, eta: 1:17:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 09:52:25,384 - pyskl - INFO - Epoch [142][900/1281] lr: 1.883e-04, eta: 1:16:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 09:53:14,232 - pyskl - INFO - Epoch [142][1000/1281] lr: 1.848e-04, eta: 1:15:35, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 09:53:41,154 - pyskl - INFO - Epoch [142][1100/1281] lr: 1.813e-04, eta: 1:14:51, time: 0.269, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 09:54:32,028 - pyskl - INFO - Epoch [142][1200/1281] lr: 1.779e-04, eta: 1:14:08, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 09:54:54,572 - pyskl - INFO - Saving checkpoint at 142 epochs +2025-06-25 09:55:52,564 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:55:52,639 - pyskl - INFO - +top1_acc 0.9473 +top5_acc 0.9973 +2025-06-25 09:55:52,639 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:55:52,647 - pyskl - INFO - +mean_acc 0.9265 +2025-06-25 09:55:52,649 - pyskl - INFO - Epoch(val) [142][533] top1_acc: 0.9473, top5_acc: 0.9973, mean_class_accuracy: 0.9265 +2025-06-25 09:57:10,855 - pyskl - INFO - Epoch [143][100/1281] lr: 1.717e-04, eta: 1:12:51, time: 0.782, data_time: 0.190, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 09:58:00,095 - pyskl - INFO - Epoch [143][200/1281] lr: 1.683e-04, eta: 1:12:08, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 09:58:49,155 - pyskl - INFO - Epoch [143][300/1281] lr: 1.650e-04, eta: 1:11:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 09:59:38,078 - pyskl - INFO - Epoch [143][400/1281] lr: 1.617e-04, eta: 1:10:42, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 10:00:27,171 - pyskl - INFO - Epoch [143][500/1281] lr: 1.585e-04, eta: 1:10:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 10:01:16,322 - pyskl - INFO - Epoch [143][600/1281] lr: 1.552e-04, eta: 1:09:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 10:02:05,403 - pyskl - INFO - Epoch [143][700/1281] lr: 1.520e-04, eta: 1:08:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 10:02:54,177 - pyskl - INFO - Epoch [143][800/1281] lr: 1.489e-04, eta: 1:07:51, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 10:03:42,918 - pyskl - INFO - Epoch [143][900/1281] lr: 1.457e-04, eta: 1:07:08, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 10:04:32,042 - pyskl - INFO - Epoch [143][1000/1281] lr: 1.426e-04, eta: 1:06:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-25 10:04:59,910 - pyskl - INFO - Epoch [143][1100/1281] lr: 1.396e-04, eta: 1:05:42, time: 0.279, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 10:05:50,771 - pyskl - INFO - Epoch [143][1200/1281] lr: 1.366e-04, eta: 1:04:59, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 10:06:12,853 - pyskl - INFO - Saving checkpoint at 143 epochs +2025-06-25 10:07:10,978 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:07:11,048 - pyskl - INFO - +top1_acc 0.9473 +top5_acc 0.9968 +2025-06-25 10:07:11,048 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:07:11,056 - pyskl - INFO - +mean_acc 0.9291 +2025-06-25 10:07:11,058 - pyskl - INFO - Epoch(val) [143][533] top1_acc: 0.9473, top5_acc: 0.9968, mean_class_accuracy: 0.9291 +2025-06-25 10:08:30,494 - pyskl - INFO - Epoch [144][100/1281] lr: 1.312e-04, eta: 1:03:41, time: 0.794, data_time: 0.192, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 10:09:19,194 - pyskl - INFO - Epoch [144][200/1281] lr: 1.282e-04, eta: 1:02:58, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 10:10:07,908 - pyskl - INFO - Epoch [144][300/1281] lr: 1.253e-04, eta: 1:02:15, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 10:10:57,006 - pyskl - INFO - Epoch [144][400/1281] lr: 1.224e-04, eta: 1:01:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 10:11:46,128 - pyskl - INFO - Epoch [144][500/1281] lr: 1.196e-04, eta: 1:00:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 10:12:35,629 - pyskl - INFO - Epoch [144][600/1281] lr: 1.168e-04, eta: 1:00:07, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 10:13:24,696 - pyskl - INFO - Epoch [144][700/1281] lr: 1.140e-04, eta: 0:59:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 10:14:14,131 - pyskl - INFO - Epoch [144][800/1281] lr: 1.113e-04, eta: 0:58:41, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-25 10:15:03,234 - pyskl - INFO - Epoch [144][900/1281] lr: 1.086e-04, eta: 0:57:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 10:15:52,478 - pyskl - INFO - Epoch [144][1000/1281] lr: 1.059e-04, eta: 0:57:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 10:16:19,386 - pyskl - INFO - Epoch [144][1100/1281] lr: 1.033e-04, eta: 0:56:32, time: 0.269, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 10:17:10,089 - pyskl - INFO - Epoch [144][1200/1281] lr: 1.007e-04, eta: 0:55:49, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 10:17:31,692 - pyskl - INFO - Saving checkpoint at 144 epochs +2025-06-25 10:18:29,783 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:18:29,839 - pyskl - INFO - +top1_acc 0.9466 +top5_acc 0.9971 +2025-06-25 10:18:29,839 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:18:29,846 - pyskl - INFO - +mean_acc 0.9274 +2025-06-25 10:18:29,847 - pyskl - INFO - Epoch(val) [144][533] top1_acc: 0.9466, top5_acc: 0.9971, mean_class_accuracy: 0.9274 +2025-06-25 10:19:48,089 - pyskl - INFO - Epoch [145][100/1281] lr: 9.605e-05, eta: 0:54:31, time: 0.782, data_time: 0.184, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 10:20:37,247 - pyskl - INFO - Epoch [145][200/1281] lr: 9.353e-05, eta: 0:53:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 10:21:26,260 - pyskl - INFO - Epoch [145][300/1281] lr: 9.106e-05, eta: 0:53:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 10:22:15,279 - pyskl - INFO - Epoch [145][400/1281] lr: 8.861e-05, eta: 0:52:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 10:23:04,380 - pyskl - INFO - Epoch [145][500/1281] lr: 8.620e-05, eta: 0:51:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 10:23:53,046 - pyskl - INFO - Epoch [145][600/1281] lr: 8.382e-05, eta: 0:50:56, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 10:24:41,987 - pyskl - INFO - Epoch [145][700/1281] lr: 8.147e-05, eta: 0:50:13, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 10:25:31,041 - pyskl - INFO - Epoch [145][800/1281] lr: 7.916e-05, eta: 0:49:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-25 10:26:19,894 - pyskl - INFO - Epoch [145][900/1281] lr: 7.688e-05, eta: 0:48:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 10:27:08,559 - pyskl - INFO - Epoch [145][1000/1281] lr: 7.463e-05, eta: 0:48:05, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 10:27:38,263 - pyskl - INFO - Epoch [145][1100/1281] lr: 7.242e-05, eta: 0:47:21, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 10:28:29,060 - pyskl - INFO - Epoch [145][1200/1281] lr: 7.024e-05, eta: 0:46:38, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 10:28:50,715 - pyskl - INFO - Saving checkpoint at 145 epochs +2025-06-25 10:29:48,815 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:29:48,870 - pyskl - INFO - +top1_acc 0.9460 +top5_acc 0.9972 +2025-06-25 10:29:48,870 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:29:48,877 - pyskl - INFO - +mean_acc 0.9264 +2025-06-25 10:29:48,878 - pyskl - INFO - Epoch(val) [145][533] top1_acc: 0.9460, top5_acc: 0.9972, mean_class_accuracy: 0.9264 +2025-06-25 10:31:07,702 - pyskl - INFO - Epoch [146][100/1281] lr: 6.638e-05, eta: 0:45:20, time: 0.788, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 10:31:56,971 - pyskl - INFO - Epoch [146][200/1281] lr: 6.429e-05, eta: 0:44:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 10:32:45,880 - pyskl - INFO - Epoch [146][300/1281] lr: 6.224e-05, eta: 0:43:54, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 10:33:34,886 - pyskl - INFO - Epoch [146][400/1281] lr: 6.022e-05, eta: 0:43:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 10:34:24,525 - pyskl - INFO - Epoch [146][500/1281] lr: 5.823e-05, eta: 0:42:28, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 10:35:13,314 - pyskl - INFO - Epoch [146][600/1281] lr: 5.628e-05, eta: 0:41:45, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 10:36:02,588 - pyskl - INFO - Epoch [146][700/1281] lr: 5.436e-05, eta: 0:41:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 10:36:51,494 - pyskl - INFO - Epoch [146][800/1281] lr: 5.247e-05, eta: 0:40:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 10:37:40,518 - pyskl - INFO - Epoch [146][900/1281] lr: 5.061e-05, eta: 0:39:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 10:38:29,538 - pyskl - INFO - Epoch [146][1000/1281] lr: 4.879e-05, eta: 0:38:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 10:38:58,625 - pyskl - INFO - Epoch [146][1100/1281] lr: 4.701e-05, eta: 0:38:10, time: 0.291, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 10:39:49,464 - pyskl - INFO - Epoch [146][1200/1281] lr: 4.525e-05, eta: 0:37:27, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 10:40:10,702 - pyskl - INFO - Saving checkpoint at 146 epochs +2025-06-25 10:41:08,898 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:41:08,962 - pyskl - INFO - +top1_acc 0.9463 +top5_acc 0.9971 +2025-06-25 10:41:08,962 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:41:08,976 - pyskl - INFO - +mean_acc 0.9249 +2025-06-25 10:41:08,980 - pyskl - INFO - Epoch(val) [146][533] top1_acc: 0.9463, top5_acc: 0.9971, mean_class_accuracy: 0.9249 +2025-06-25 10:42:27,806 - pyskl - INFO - Epoch [147][100/1281] lr: 4.216e-05, eta: 0:36:09, time: 0.788, data_time: 0.189, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 10:43:16,642 - pyskl - INFO - Epoch [147][200/1281] lr: 4.050e-05, eta: 0:35:26, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 10:44:05,627 - pyskl - INFO - Epoch [147][300/1281] lr: 3.887e-05, eta: 0:34:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 10:44:54,386 - pyskl - INFO - Epoch [147][400/1281] lr: 3.728e-05, eta: 0:33:59, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-25 10:45:43,189 - pyskl - INFO - Epoch [147][500/1281] lr: 3.572e-05, eta: 0:33:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 10:46:32,688 - pyskl - INFO - Epoch [147][600/1281] lr: 3.419e-05, eta: 0:32:33, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 10:47:22,048 - pyskl - INFO - Epoch [147][700/1281] lr: 3.270e-05, eta: 0:31:50, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 10:48:11,065 - pyskl - INFO - Epoch [147][800/1281] lr: 3.124e-05, eta: 0:31:07, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 10:49:00,213 - pyskl - INFO - Epoch [147][900/1281] lr: 2.981e-05, eta: 0:30:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 10:49:49,149 - pyskl - INFO - Epoch [147][1000/1281] lr: 2.842e-05, eta: 0:29:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 10:50:17,459 - pyskl - INFO - Epoch [147][1100/1281] lr: 2.706e-05, eta: 0:28:58, time: 0.283, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 10:51:08,280 - pyskl - INFO - Epoch [147][1200/1281] lr: 2.573e-05, eta: 0:28:15, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 10:51:30,054 - pyskl - INFO - Saving checkpoint at 147 epochs +2025-06-25 10:52:28,202 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:52:28,264 - pyskl - INFO - +top1_acc 0.9468 +top5_acc 0.9973 +2025-06-25 10:52:28,264 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:52:28,271 - pyskl - INFO - +mean_acc 0.9274 +2025-06-25 10:52:28,273 - pyskl - INFO - Epoch(val) [147][533] top1_acc: 0.9468, top5_acc: 0.9973, mean_class_accuracy: 0.9274 +2025-06-25 10:53:46,871 - pyskl - INFO - Epoch [148][100/1281] lr: 2.341e-05, eta: 0:26:56, time: 0.786, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 10:54:35,731 - pyskl - INFO - Epoch [148][200/1281] lr: 2.218e-05, eta: 0:26:13, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 10:55:24,529 - pyskl - INFO - Epoch [148][300/1281] lr: 2.098e-05, eta: 0:25:30, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 10:56:13,168 - pyskl - INFO - Epoch [148][400/1281] lr: 1.981e-05, eta: 0:24:47, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 10:57:02,148 - pyskl - INFO - Epoch [148][500/1281] lr: 1.868e-05, eta: 0:24:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 10:57:51,114 - pyskl - INFO - Epoch [148][600/1281] lr: 1.758e-05, eta: 0:23:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 10:58:40,168 - pyskl - INFO - Epoch [148][700/1281] lr: 1.651e-05, eta: 0:22:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 10:59:29,146 - pyskl - INFO - Epoch [148][800/1281] lr: 1.548e-05, eta: 0:21:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 11:00:18,176 - pyskl - INFO - Epoch [148][900/1281] lr: 1.448e-05, eta: 0:21:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 11:01:07,257 - pyskl - INFO - Epoch [148][1000/1281] lr: 1.351e-05, eta: 0:20:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0219, loss: 0.0219 +2025-06-25 11:01:36,631 - pyskl - INFO - Epoch [148][1100/1281] lr: 1.258e-05, eta: 0:19:45, time: 0.294, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 11:02:27,363 - pyskl - INFO - Epoch [148][1200/1281] lr: 1.168e-05, eta: 0:19:02, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 11:02:48,678 - pyskl - INFO - Saving checkpoint at 148 epochs +2025-06-25 11:03:46,919 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:03:46,974 - pyskl - INFO - +top1_acc 0.9478 +top5_acc 0.9974 +2025-06-25 11:03:46,974 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:03:46,980 - pyskl - INFO - +mean_acc 0.9287 +2025-06-25 11:03:46,984 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_139.pth was removed +2025-06-25 11:03:47,150 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_148.pth. +2025-06-25 11:03:47,150 - pyskl - INFO - Best top1_acc is 0.9478 at 148 epoch. +2025-06-25 11:03:47,153 - pyskl - INFO - Epoch(val) [148][533] top1_acc: 0.9478, top5_acc: 0.9974, mean_class_accuracy: 0.9287 +2025-06-25 11:05:06,693 - pyskl - INFO - Epoch [149][100/1281] lr: 1.013e-05, eta: 0:17:44, time: 0.795, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 11:05:55,530 - pyskl - INFO - Epoch [149][200/1281] lr: 9.328e-06, eta: 0:17:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 11:06:44,542 - pyskl - INFO - Epoch [149][300/1281] lr: 8.555e-06, eta: 0:16:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 11:07:33,512 - pyskl - INFO - Epoch [149][400/1281] lr: 7.816e-06, eta: 0:15:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 11:08:22,595 - pyskl - INFO - Epoch [149][500/1281] lr: 7.110e-06, eta: 0:14:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 11:09:11,823 - pyskl - INFO - Epoch [149][600/1281] lr: 6.437e-06, eta: 0:14:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 11:10:01,210 - pyskl - INFO - Epoch [149][700/1281] lr: 5.798e-06, eta: 0:13:25, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 11:10:50,126 - pyskl - INFO - Epoch [149][800/1281] lr: 5.192e-06, eta: 0:12:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 11:11:39,495 - pyskl - INFO - Epoch [149][900/1281] lr: 4.620e-06, eta: 0:11:58, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 11:12:28,682 - pyskl - INFO - Epoch [149][1000/1281] lr: 4.081e-06, eta: 0:11:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 11:12:57,101 - pyskl - INFO - Epoch [149][1100/1281] lr: 3.576e-06, eta: 0:10:32, time: 0.284, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 11:13:47,856 - pyskl - INFO - Epoch [149][1200/1281] lr: 3.104e-06, eta: 0:09:49, time: 0.508, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-25 11:14:09,106 - pyskl - INFO - Saving checkpoint at 149 epochs +2025-06-25 11:15:07,192 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:15:07,248 - pyskl - INFO - +top1_acc 0.9477 +top5_acc 0.9974 +2025-06-25 11:15:07,248 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:15:07,256 - pyskl - INFO - +mean_acc 0.9282 +2025-06-25 11:15:07,258 - pyskl - INFO - Epoch(val) [149][533] top1_acc: 0.9477, top5_acc: 0.9974, mean_class_accuracy: 0.9282 +2025-06-25 11:16:27,405 - pyskl - INFO - Epoch [150][100/1281] lr: 2.334e-06, eta: 0:08:30, time: 0.801, data_time: 0.187, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 11:17:16,259 - pyskl - INFO - Epoch [150][200/1281] lr: 1.956e-06, eta: 0:07:47, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 11:18:05,405 - pyskl - INFO - Epoch [150][300/1281] lr: 1.611e-06, eta: 0:07:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 11:18:54,284 - pyskl - INFO - Epoch [150][400/1281] lr: 1.300e-06, eta: 0:06:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 11:19:43,278 - pyskl - INFO - Epoch [150][500/1281] lr: 1.022e-06, eta: 0:05:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 11:20:32,318 - pyskl - INFO - Epoch [150][600/1281] lr: 7.771e-07, eta: 0:04:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 11:21:21,493 - pyskl - INFO - Epoch [150][700/1281] lr: 5.659e-07, eta: 0:04:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 11:22:10,344 - pyskl - INFO - Epoch [150][800/1281] lr: 3.881e-07, eta: 0:03:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 11:22:59,377 - pyskl - INFO - Epoch [150][900/1281] lr: 2.438e-07, eta: 0:02:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 11:23:47,952 - pyskl - INFO - Epoch [150][1000/1281] lr: 1.329e-07, eta: 0:02:01, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 11:24:17,422 - pyskl - INFO - Epoch [150][1100/1281] lr: 5.534e-08, eta: 0:01:18, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 11:25:06,483 - pyskl - INFO - Epoch [150][1200/1281] lr: 1.123e-08, eta: 0:00:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 11:25:28,898 - pyskl - INFO - Saving checkpoint at 150 epochs +2025-06-25 11:26:26,629 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:26:26,698 - pyskl - INFO - +top1_acc 0.9464 +top5_acc 0.9973 +2025-06-25 11:26:26,699 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:26:26,707 - pyskl - INFO - +mean_acc 0.9259 +2025-06-25 11:26:26,709 - pyskl - INFO - Epoch(val) [150][533] top1_acc: 0.9464, top5_acc: 0.9973, mean_class_accuracy: 0.9259 +2025-06-25 11:26:31,211 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-25 11:34:17,565 - pyskl - INFO - Testing results of the last checkpoint +2025-06-25 11:34:17,565 - pyskl - INFO - top1_acc: 0.9461 +2025-06-25 11:34:17,565 - pyskl - INFO - top5_acc: 0.9973 +2025-06-25 11:34:17,565 - pyskl - INFO - mean_class_accuracy: 0.9253 +2025-06-25 11:34:17,566 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/finegym/b_3/best_top1_acc_epoch_148.pth +2025-06-25 11:41:44,850 - pyskl - INFO - Testing results of the best checkpoint +2025-06-25 11:41:44,850 - pyskl - INFO - top1_acc: 0.9470 +2025-06-25 11:41:44,851 - pyskl - INFO - top5_acc: 0.9975 +2025-06-25 11:41:44,851 - pyskl - INFO - mean_class_accuracy: 0.9292 diff --git a/finegym/b_3/20250624_084158.log.json b/finegym/b_3/20250624_084158.log.json new file mode 100644 index 0000000000000000000000000000000000000000..36ad2dde3875727236fb57e02c56a0e3535e62d9 --- /dev/null +++ b/finegym/b_3/20250624_084158.log.json @@ -0,0 +1,1951 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 335308573, "config_name": "b_3.py", "work_dir": "b_3", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.1623, "top1_acc": 0.06125, "top5_acc": 0.24188, "loss_cls": 4.47259, "loss": 4.47259, "time": 0.37371} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.11, "top5_acc": 0.3375, "loss_cls": 4.50898, "loss": 4.50898, "time": 0.21014} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00057, "top1_acc": 0.10188, "top5_acc": 0.37125, "loss_cls": 4.27615, "loss": 4.27615, "time": 0.21378} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.10375, "top5_acc": 0.40188, "loss_cls": 4.12231, "loss": 4.12231, "time": 0.21237} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.025, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.14812, "top5_acc": 0.46312, "loss_cls": 3.8994, "loss": 3.8994, "time": 0.21573} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.025, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.17562, "top5_acc": 0.52375, "loss_cls": 3.64574, "loss": 3.64574, "time": 0.21761} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.20188, "top5_acc": 0.53062, "loss_cls": 3.60869, "loss": 3.60869, "time": 0.21596} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.23938, "top5_acc": 0.59312, "loss_cls": 3.33082, "loss": 3.33082, "time": 0.21502} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.26125, "top5_acc": 0.61812, "loss_cls": 3.2159, "loss": 3.2159, "time": 0.21569} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.29812, "top5_acc": 0.6875, "loss_cls": 3.03531, "loss": 3.03531, "time": 0.22159} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.025, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.33062, "top5_acc": 0.70875, "loss_cls": 2.88667, "loss": 2.88667, "time": 0.21593} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.025, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.33438, "top5_acc": 0.73188, "loss_cls": 2.7906, "loss": 2.7906, "time": 0.21822} +{"mode": "val", "epoch": 1, "iter": 533, "lr": 0.025, "top1_acc": 0.37132, "top5_acc": 0.75214, "mean_class_accuracy": 0.17411} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.18497, "top1_acc": 0.37938, "top5_acc": 0.775, "loss_cls": 2.59118, "loss": 2.59118, "time": 0.40047} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.42188, "top5_acc": 0.80375, "loss_cls": 2.45397, "loss": 2.45397, "time": 0.21639} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.42688, "top5_acc": 0.82562, "loss_cls": 2.38834, "loss": 2.38834, "time": 0.21762} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.42188, "top5_acc": 0.82188, "loss_cls": 2.38247, "loss": 2.38247, "time": 0.21809} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.02499, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.45, "top5_acc": 0.82938, "loss_cls": 2.29083, "loss": 2.29083, "time": 0.21863} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.45438, "top5_acc": 0.85688, "loss_cls": 2.20446, "loss": 2.20446, "time": 0.21719} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.02499, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.49375, "top5_acc": 0.85812, "loss_cls": 2.13193, "loss": 2.13193, "time": 0.21821} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.02499, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.49938, "top5_acc": 0.86438, "loss_cls": 2.11044, "loss": 2.11044, "time": 0.21895} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.02499, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.48438, "top5_acc": 0.87625, "loss_cls": 2.07689, "loss": 2.07689, "time": 0.21848} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.02499, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.525, "top5_acc": 0.89875, "loss_cls": 1.964, "loss": 1.964, "time": 0.21579} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.53688, "top5_acc": 0.9025, "loss_cls": 1.94754, "loss": 1.94754, "time": 0.21723} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.52375, "top5_acc": 0.90375, "loss_cls": 1.93293, "loss": 1.93293, "time": 0.21983} +{"mode": "val", "epoch": 2, "iter": 533, "lr": 0.02499, "top1_acc": 0.52576, "top5_acc": 0.90119, "mean_class_accuracy": 0.34355} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.02499, "memory": 4082, "data_time": 0.18164, "top1_acc": 0.53812, "top5_acc": 0.8975, "loss_cls": 1.88945, "loss": 1.88945, "time": 0.40108} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.545, "top5_acc": 0.92625, "loss_cls": 1.81507, "loss": 1.81507, "time": 0.21967} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.02499, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.56437, "top5_acc": 0.93312, "loss_cls": 1.75329, "loss": 1.75329, "time": 0.21763} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.02499, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.57, "top5_acc": 0.9225, "loss_cls": 1.74186, "loss": 1.74186, "time": 0.21808} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.02498, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.6, "top5_acc": 0.9325, "loss_cls": 1.68167, "loss": 1.68167, "time": 0.22097} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.02498, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.56125, "top5_acc": 0.92188, "loss_cls": 1.7374, "loss": 1.7374, "time": 0.21794} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.02498, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.57875, "top5_acc": 0.9275, "loss_cls": 1.71214, "loss": 1.71214, "time": 0.22185} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.02498, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.58875, "top5_acc": 0.94688, "loss_cls": 1.64468, "loss": 1.64468, "time": 0.21889} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.02498, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.62375, "top5_acc": 0.94062, "loss_cls": 1.59519, "loss": 1.59519, "time": 0.21937} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.02498, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.615, "top5_acc": 0.93938, "loss_cls": 1.56501, "loss": 1.56501, "time": 0.21792} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.02498, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.63688, "top5_acc": 0.94875, "loss_cls": 1.50135, "loss": 1.50135, "time": 0.21857} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.02498, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.63438, "top5_acc": 0.94062, "loss_cls": 1.57373, "loss": 1.57373, "time": 0.21792} +{"mode": "val", "epoch": 3, "iter": 533, "lr": 0.02498, "top1_acc": 0.641, "top5_acc": 0.95024, "mean_class_accuracy": 0.46902} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.02497, "memory": 4082, "data_time": 0.18238, "top1_acc": 0.65, "top5_acc": 0.95688, "loss_cls": 1.48854, "loss": 1.48854, "time": 0.40172} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.02497, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.6275, "top5_acc": 0.9525, "loss_cls": 1.51816, "loss": 1.51816, "time": 0.21662} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.02497, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.6575, "top5_acc": 0.9575, "loss_cls": 1.44871, "loss": 1.44871, "time": 0.21804} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.02497, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.64688, "top5_acc": 0.9525, "loss_cls": 1.44953, "loss": 1.44953, "time": 0.21889} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.02497, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.67812, "top5_acc": 0.94812, "loss_cls": 1.41147, "loss": 1.41147, "time": 0.21906} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.02497, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.67125, "top5_acc": 0.95812, "loss_cls": 1.40142, "loss": 1.40142, "time": 0.21741} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.02497, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.66875, "top5_acc": 0.96, "loss_cls": 1.4084, "loss": 1.4084, "time": 0.21671} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.02496, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.65062, "top5_acc": 0.95688, "loss_cls": 1.411, "loss": 1.411, "time": 0.21619} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.02496, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.6775, "top5_acc": 0.955, "loss_cls": 1.36291, "loss": 1.36291, "time": 0.21501} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.02496, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.67438, "top5_acc": 0.96062, "loss_cls": 1.37164, "loss": 1.37164, "time": 0.2172} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.02496, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.66812, "top5_acc": 0.95625, "loss_cls": 1.35653, "loss": 1.35653, "time": 0.21578} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.02496, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.7025, "top5_acc": 0.96562, "loss_cls": 1.26171, "loss": 1.26171, "time": 0.21914} +{"mode": "val", "epoch": 4, "iter": 533, "lr": 0.02496, "top1_acc": 0.67281, "top5_acc": 0.96045, "mean_class_accuracy": 0.53694} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.02495, "memory": 4082, "data_time": 0.17947, "top1_acc": 0.68062, "top5_acc": 0.965, "loss_cls": 1.30182, "loss": 1.30182, "time": 0.39813} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.02495, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.68688, "top5_acc": 0.96312, "loss_cls": 1.28752, "loss": 1.28752, "time": 0.21985} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.02495, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.7025, "top5_acc": 0.96375, "loss_cls": 1.28584, "loss": 1.28584, "time": 0.21815} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.02495, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.71875, "top5_acc": 0.9725, "loss_cls": 1.2067, "loss": 1.2067, "time": 0.21799} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.02495, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.72625, "top5_acc": 0.97188, "loss_cls": 1.20331, "loss": 1.20331, "time": 0.22025} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.02495, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.73125, "top5_acc": 0.97125, "loss_cls": 1.21595, "loss": 1.21595, "time": 0.22016} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.02494, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.71625, "top5_acc": 0.97125, "loss_cls": 1.1892, "loss": 1.1892, "time": 0.22061} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.02494, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.7025, "top5_acc": 0.97, "loss_cls": 1.24496, "loss": 1.24496, "time": 0.21695} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.02494, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.70938, "top5_acc": 0.96875, "loss_cls": 1.21071, "loss": 1.21071, "time": 0.21474} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.02494, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.71688, "top5_acc": 0.97, "loss_cls": 1.19957, "loss": 1.19957, "time": 0.21611} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.02494, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.705, "top5_acc": 0.97312, "loss_cls": 1.22311, "loss": 1.22311, "time": 0.21748} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.02493, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.73125, "top5_acc": 0.97625, "loss_cls": 1.1563, "loss": 1.1563, "time": 0.21667} +{"mode": "val", "epoch": 5, "iter": 533, "lr": 0.02493, "top1_acc": 0.69241, "top5_acc": 0.96714, "mean_class_accuracy": 0.54942} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.02493, "memory": 4082, "data_time": 0.1793, "top1_acc": 0.72688, "top5_acc": 0.97375, "loss_cls": 1.16955, "loss": 1.16955, "time": 0.40049} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.02493, "memory": 4082, "data_time": 0.00056, "top1_acc": 0.72625, "top5_acc": 0.97188, "loss_cls": 1.17078, "loss": 1.17078, "time": 0.22233} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.02492, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.71312, "top5_acc": 0.97875, "loss_cls": 1.1368, "loss": 1.1368, "time": 0.21953} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.02492, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.74125, "top5_acc": 0.98, "loss_cls": 1.12006, "loss": 1.12006, "time": 0.21676} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.02492, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.74688, "top5_acc": 0.98062, "loss_cls": 1.09192, "loss": 1.09192, "time": 0.222} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.02492, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.73188, "top5_acc": 0.9725, "loss_cls": 1.12291, "loss": 1.12291, "time": 0.21818} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.02492, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.73562, "top5_acc": 0.98125, "loss_cls": 1.11286, "loss": 1.11286, "time": 0.21667} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.02491, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.7175, "top5_acc": 0.98125, "loss_cls": 1.1928, "loss": 1.1928, "time": 0.21782} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.02491, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.735, "top5_acc": 0.96938, "loss_cls": 1.16575, "loss": 1.16575, "time": 0.21821} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.02491, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.76125, "top5_acc": 0.98, "loss_cls": 1.02916, "loss": 1.02916, "time": 0.21623} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.02491, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.7475, "top5_acc": 0.97312, "loss_cls": 1.08154, "loss": 1.08154, "time": 0.21996} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.0249, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.72375, "top5_acc": 0.97438, "loss_cls": 1.13977, "loss": 1.13977, "time": 0.21752} +{"mode": "val", "epoch": 6, "iter": 533, "lr": 0.0249, "top1_acc": 0.68314, "top5_acc": 0.95611, "mean_class_accuracy": 0.5456} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0249, "memory": 4082, "data_time": 0.18434, "top1_acc": 0.7375, "top5_acc": 0.9825, "loss_cls": 1.08827, "loss": 1.08827, "time": 0.40464} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0249, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.76062, "top5_acc": 0.98125, "loss_cls": 0.99996, "loss": 0.99996, "time": 0.21825} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.02489, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.76812, "top5_acc": 0.9825, "loss_cls": 0.98612, "loss": 0.98612, "time": 0.21569} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.02489, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.75062, "top5_acc": 0.97938, "loss_cls": 1.08638, "loss": 1.08638, "time": 0.21472} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.02489, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.75375, "top5_acc": 0.9775, "loss_cls": 1.04829, "loss": 1.04829, "time": 0.21729} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.02489, "memory": 4082, "data_time": 0.0002, "top1_acc": 0.76125, "top5_acc": 0.98125, "loss_cls": 0.99475, "loss": 0.99475, "time": 0.21312} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.02488, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.75375, "top5_acc": 0.98688, "loss_cls": 1.02682, "loss": 1.02682, "time": 0.22096} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.02488, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.75938, "top5_acc": 0.98188, "loss_cls": 1.02427, "loss": 1.02427, "time": 0.21535} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.02488, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.75125, "top5_acc": 0.97938, "loss_cls": 1.05557, "loss": 1.05557, "time": 0.21917} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.02487, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7575, "top5_acc": 0.9875, "loss_cls": 1.00506, "loss": 1.00506, "time": 0.21414} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.02487, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.76, "top5_acc": 0.9825, "loss_cls": 1.02221, "loss": 1.02221, "time": 0.2158} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.02487, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.75562, "top5_acc": 0.98, "loss_cls": 1.03179, "loss": 1.03179, "time": 0.2168} +{"mode": "val", "epoch": 7, "iter": 533, "lr": 0.02487, "top1_acc": 0.72398, "top5_acc": 0.96867, "mean_class_accuracy": 0.59505} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.02486, "memory": 4082, "data_time": 0.18164, "top1_acc": 0.76312, "top5_acc": 0.97875, "loss_cls": 1.04996, "loss": 1.04996, "time": 0.40082} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.02486, "memory": 4082, "data_time": 0.00046, "top1_acc": 0.77, "top5_acc": 0.98312, "loss_cls": 1.00043, "loss": 1.00043, "time": 0.22046} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.02486, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.79062, "top5_acc": 0.98875, "loss_cls": 0.94893, "loss": 0.94893, "time": 0.21791} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.02485, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.77438, "top5_acc": 0.99, "loss_cls": 0.95839, "loss": 0.95839, "time": 0.2175} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.02485, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.77875, "top5_acc": 0.99, "loss_cls": 0.94592, "loss": 0.94592, "time": 0.21633} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.02485, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.76875, "top5_acc": 0.98125, "loss_cls": 0.99976, "loss": 0.99976, "time": 0.2188} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.02484, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.75062, "top5_acc": 0.98, "loss_cls": 1.0097, "loss": 1.0097, "time": 0.21725} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.02484, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.80375, "top5_acc": 0.98562, "loss_cls": 0.92113, "loss": 0.92113, "time": 0.21644} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.02484, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7775, "top5_acc": 0.98875, "loss_cls": 0.93929, "loss": 0.93929, "time": 0.22102} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.02483, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.80062, "top5_acc": 0.98562, "loss_cls": 0.90361, "loss": 0.90361, "time": 0.2178} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.02483, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.7825, "top5_acc": 0.9825, "loss_cls": 0.94695, "loss": 0.94695, "time": 0.21797} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.02483, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.7725, "top5_acc": 0.98562, "loss_cls": 0.97675, "loss": 0.97675, "time": 0.21872} +{"mode": "val", "epoch": 8, "iter": 533, "lr": 0.02482, "top1_acc": 0.78359, "top5_acc": 0.97981, "mean_class_accuracy": 0.687} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.02482, "memory": 4082, "data_time": 0.18148, "top1_acc": 0.80375, "top5_acc": 0.98812, "loss_cls": 0.86099, "loss": 0.86099, "time": 0.39858} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.02482, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.78625, "top5_acc": 0.98875, "loss_cls": 0.91658, "loss": 0.91658, "time": 0.22011} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.02481, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.775, "top5_acc": 0.98375, "loss_cls": 0.95739, "loss": 0.95739, "time": 0.22022} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.02481, "memory": 4082, "data_time": 0.00056, "top1_acc": 0.79875, "top5_acc": 0.99188, "loss_cls": 0.8681, "loss": 0.8681, "time": 0.21884} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.02481, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79062, "top5_acc": 0.98812, "loss_cls": 0.90293, "loss": 0.90293, "time": 0.22067} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.0248, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.7975, "top5_acc": 0.9875, "loss_cls": 0.92831, "loss": 0.92831, "time": 0.21672} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.0248, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.75875, "top5_acc": 0.98125, "loss_cls": 0.99459, "loss": 0.99459, "time": 0.21553} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.0248, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.75875, "top5_acc": 0.98312, "loss_cls": 0.99683, "loss": 0.99683, "time": 0.22132} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.02479, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.79188, "top5_acc": 0.98438, "loss_cls": 0.90264, "loss": 0.90264, "time": 0.21988} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.02479, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78625, "top5_acc": 0.98625, "loss_cls": 0.92896, "loss": 0.92896, "time": 0.21587} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.02479, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.80062, "top5_acc": 0.98375, "loss_cls": 0.90514, "loss": 0.90514, "time": 0.21419} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.02478, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78812, "top5_acc": 0.98312, "loss_cls": 0.9081, "loss": 0.9081, "time": 0.21799} +{"mode": "val", "epoch": 9, "iter": 533, "lr": 0.02478, "top1_acc": 0.7586, "top5_acc": 0.97536, "mean_class_accuracy": 0.66557} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.02477, "memory": 4082, "data_time": 0.1832, "top1_acc": 0.8225, "top5_acc": 0.99, "loss_cls": 0.81785, "loss": 0.81785, "time": 0.40114} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.02477, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.79938, "top5_acc": 0.99375, "loss_cls": 0.85377, "loss": 0.85377, "time": 0.22101} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.02477, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79875, "top5_acc": 0.9875, "loss_cls": 0.8692, "loss": 0.8692, "time": 0.22153} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.02476, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80312, "top5_acc": 0.98438, "loss_cls": 0.87522, "loss": 0.87522, "time": 0.22048} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.02476, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.78688, "top5_acc": 0.98562, "loss_cls": 0.93955, "loss": 0.93955, "time": 0.22093} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.02476, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78938, "top5_acc": 0.98438, "loss_cls": 0.90199, "loss": 0.90199, "time": 0.2179} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.02475, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.80312, "top5_acc": 0.99125, "loss_cls": 0.81569, "loss": 0.81569, "time": 0.21972} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.02475, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80062, "top5_acc": 0.98812, "loss_cls": 0.87808, "loss": 0.87808, "time": 0.21795} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.02474, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81312, "top5_acc": 0.98938, "loss_cls": 0.84069, "loss": 0.84069, "time": 0.21695} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.02474, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.78875, "top5_acc": 0.98875, "loss_cls": 0.89686, "loss": 0.89686, "time": 0.2171} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.02473, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8025, "top5_acc": 0.98688, "loss_cls": 0.86434, "loss": 0.86434, "time": 0.22124} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.02473, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.81, "top5_acc": 0.98875, "loss_cls": 0.84204, "loss": 0.84204, "time": 0.21641} +{"mode": "val", "epoch": 10, "iter": 533, "lr": 0.02473, "top1_acc": 0.75179, "top5_acc": 0.97289, "mean_class_accuracy": 0.64245} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.02472, "memory": 4082, "data_time": 0.17598, "top1_acc": 0.80875, "top5_acc": 0.98938, "loss_cls": 0.84682, "loss": 0.84682, "time": 0.39596} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.02472, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.80562, "top5_acc": 0.98375, "loss_cls": 0.87968, "loss": 0.87968, "time": 0.21858} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.02471, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.8125, "top5_acc": 0.98875, "loss_cls": 0.85202, "loss": 0.85202, "time": 0.21635} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.02471, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79625, "top5_acc": 0.98875, "loss_cls": 0.84165, "loss": 0.84165, "time": 0.21641} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.02471, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82, "top5_acc": 0.9925, "loss_cls": 0.79883, "loss": 0.79883, "time": 0.21935} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.0247, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81688, "top5_acc": 0.9875, "loss_cls": 0.82405, "loss": 0.82405, "time": 0.21642} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.0247, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.8025, "top5_acc": 0.98562, "loss_cls": 0.84921, "loss": 0.84921, "time": 0.21806} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.02469, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82125, "top5_acc": 0.98875, "loss_cls": 0.79125, "loss": 0.79125, "time": 0.2156} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.02469, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82438, "top5_acc": 0.99188, "loss_cls": 0.76346, "loss": 0.76346, "time": 0.21841} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.02468, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81938, "top5_acc": 0.98625, "loss_cls": 0.83392, "loss": 0.83392, "time": 0.21709} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.02468, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.80812, "top5_acc": 0.9875, "loss_cls": 0.84827, "loss": 0.84827, "time": 0.21887} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.02467, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83625, "top5_acc": 0.99125, "loss_cls": 0.73913, "loss": 0.73913, "time": 0.21505} +{"mode": "val", "epoch": 11, "iter": 533, "lr": 0.02467, "top1_acc": 0.73207, "top5_acc": 0.97019, "mean_class_accuracy": 0.61847} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.02467, "memory": 4082, "data_time": 0.18723, "top1_acc": 0.82062, "top5_acc": 0.99375, "loss_cls": 0.77277, "loss": 0.77277, "time": 0.40757} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.02466, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.82688, "top5_acc": 0.99062, "loss_cls": 0.78042, "loss": 0.78042, "time": 0.22197} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.02466, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83188, "top5_acc": 0.98875, "loss_cls": 0.79694, "loss": 0.79694, "time": 0.22136} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.02465, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.82938, "top5_acc": 0.99, "loss_cls": 0.80244, "loss": 0.80244, "time": 0.2196} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.02465, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.81125, "top5_acc": 0.98812, "loss_cls": 0.83077, "loss": 0.83077, "time": 0.21838} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.02464, "memory": 4082, "data_time": 0.0002, "top1_acc": 0.83312, "top5_acc": 0.99188, "loss_cls": 0.76829, "loss": 0.76829, "time": 0.216} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.02464, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.82812, "top5_acc": 0.98688, "loss_cls": 0.76492, "loss": 0.76492, "time": 0.22039} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.02463, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.81375, "top5_acc": 0.99062, "loss_cls": 0.79355, "loss": 0.79355, "time": 0.21736} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.02463, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82438, "top5_acc": 0.99312, "loss_cls": 0.78687, "loss": 0.78687, "time": 0.22001} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.02462, "memory": 4082, "data_time": 0.0002, "top1_acc": 0.81875, "top5_acc": 0.98938, "loss_cls": 0.79836, "loss": 0.79836, "time": 0.21641} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.02462, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.835, "top5_acc": 0.99125, "loss_cls": 0.78217, "loss": 0.78217, "time": 0.21617} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.02461, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81188, "top5_acc": 0.99, "loss_cls": 0.79759, "loss": 0.79759, "time": 0.21941} +{"mode": "val", "epoch": 12, "iter": 533, "lr": 0.02461, "top1_acc": 0.80131, "top5_acc": 0.98568, "mean_class_accuracy": 0.70398} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.0246, "memory": 4082, "data_time": 0.18094, "top1_acc": 0.83312, "top5_acc": 0.99188, "loss_cls": 0.76995, "loss": 0.76995, "time": 0.39763} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.0246, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82312, "top5_acc": 0.99438, "loss_cls": 0.76501, "loss": 0.76501, "time": 0.22526} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.02459, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.80312, "top5_acc": 0.99125, "loss_cls": 0.83403, "loss": 0.83403, "time": 0.21844} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.02459, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.8275, "top5_acc": 0.99188, "loss_cls": 0.7658, "loss": 0.7658, "time": 0.21536} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.02458, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.84188, "top5_acc": 0.99188, "loss_cls": 0.7356, "loss": 0.7356, "time": 0.21684} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.02458, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83812, "top5_acc": 0.99312, "loss_cls": 0.74498, "loss": 0.74498, "time": 0.21716} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.02457, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.84375, "top5_acc": 0.99188, "loss_cls": 0.71753, "loss": 0.71753, "time": 0.2173} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.02457, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82062, "top5_acc": 0.99375, "loss_cls": 0.78108, "loss": 0.78108, "time": 0.21888} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.02456, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.81312, "top5_acc": 0.98562, "loss_cls": 0.84674, "loss": 0.84674, "time": 0.21569} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.02455, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.80938, "top5_acc": 0.9925, "loss_cls": 0.82242, "loss": 0.82242, "time": 0.2162} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.02455, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82812, "top5_acc": 0.99125, "loss_cls": 0.77083, "loss": 0.77083, "time": 0.22087} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.02454, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.835, "top5_acc": 0.99188, "loss_cls": 0.75972, "loss": 0.75972, "time": 0.21592} +{"mode": "val", "epoch": 13, "iter": 533, "lr": 0.02454, "top1_acc": 0.7985, "top5_acc": 0.98087, "mean_class_accuracy": 0.73333} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.02453, "memory": 4082, "data_time": 0.18059, "top1_acc": 0.82812, "top5_acc": 0.9925, "loss_cls": 0.75639, "loss": 0.75639, "time": 0.40081} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.02453, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85188, "top5_acc": 0.9925, "loss_cls": 0.72837, "loss": 0.72837, "time": 0.22151} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.02452, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.82812, "top5_acc": 0.99312, "loss_cls": 0.74634, "loss": 0.74634, "time": 0.21853} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.02452, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83312, "top5_acc": 0.99, "loss_cls": 0.7583, "loss": 0.7583, "time": 0.21972} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.02451, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.83938, "top5_acc": 0.99125, "loss_cls": 0.72625, "loss": 0.72625, "time": 0.22329} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.02451, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.84188, "top5_acc": 0.98625, "loss_cls": 0.75725, "loss": 0.75725, "time": 0.22145} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.0245, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83188, "top5_acc": 0.99, "loss_cls": 0.74887, "loss": 0.74887, "time": 0.21906} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.02449, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.8325, "top5_acc": 0.98938, "loss_cls": 0.76124, "loss": 0.76124, "time": 0.21552} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.02449, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.82562, "top5_acc": 0.99312, "loss_cls": 0.76949, "loss": 0.76949, "time": 0.21739} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.02448, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83562, "top5_acc": 0.99062, "loss_cls": 0.77335, "loss": 0.77335, "time": 0.21971} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.02448, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81438, "top5_acc": 0.99188, "loss_cls": 0.80278, "loss": 0.80278, "time": 0.21619} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.02447, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82312, "top5_acc": 0.9875, "loss_cls": 0.76757, "loss": 0.76757, "time": 0.21888} +{"mode": "val", "epoch": 14, "iter": 533, "lr": 0.02447, "top1_acc": 0.79568, "top5_acc": 0.98099, "mean_class_accuracy": 0.7165} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.02446, "memory": 4082, "data_time": 0.1694, "top1_acc": 0.87688, "top5_acc": 0.99688, "loss_cls": 0.61532, "loss": 0.61532, "time": 0.39178} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.02445, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83312, "top5_acc": 0.99, "loss_cls": 0.73918, "loss": 0.73918, "time": 0.21841} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.02445, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.85875, "top5_acc": 0.99688, "loss_cls": 0.69197, "loss": 0.69197, "time": 0.22092} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.02444, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83625, "top5_acc": 0.99312, "loss_cls": 0.73639, "loss": 0.73639, "time": 0.21636} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.02444, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.83938, "top5_acc": 0.99188, "loss_cls": 0.72773, "loss": 0.72773, "time": 0.21457} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.02443, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82625, "top5_acc": 0.98312, "loss_cls": 0.81064, "loss": 0.81064, "time": 0.22019} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.02442, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.84062, "top5_acc": 0.99188, "loss_cls": 0.70272, "loss": 0.70272, "time": 0.21876} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.02442, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83312, "top5_acc": 0.99188, "loss_cls": 0.73315, "loss": 0.73315, "time": 0.21968} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.02441, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.83562, "top5_acc": 0.99312, "loss_cls": 0.71479, "loss": 0.71479, "time": 0.21403} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.02441, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.86, "top5_acc": 0.98875, "loss_cls": 0.69195, "loss": 0.69195, "time": 0.21564} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.0244, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8475, "top5_acc": 0.99, "loss_cls": 0.72289, "loss": 0.72289, "time": 0.21771} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.02439, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86312, "top5_acc": 0.99188, "loss_cls": 0.65299, "loss": 0.65299, "time": 0.22078} +{"mode": "val", "epoch": 15, "iter": 533, "lr": 0.02439, "top1_acc": 0.77115, "top5_acc": 0.97911, "mean_class_accuracy": 0.70567} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.02438, "memory": 4082, "data_time": 0.182, "top1_acc": 0.85938, "top5_acc": 0.99125, "loss_cls": 0.6934, "loss": 0.6934, "time": 0.40405} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.02438, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83438, "top5_acc": 0.99375, "loss_cls": 0.683, "loss": 0.683, "time": 0.21921} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.02437, "memory": 4082, "data_time": 0.00049, "top1_acc": 0.84, "top5_acc": 0.995, "loss_cls": 0.70662, "loss": 0.70662, "time": 0.22079} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.02436, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85625, "top5_acc": 0.99438, "loss_cls": 0.69452, "loss": 0.69452, "time": 0.21775} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.02436, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.8475, "top5_acc": 0.99375, "loss_cls": 0.72545, "loss": 0.72545, "time": 0.2173} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.02435, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83688, "top5_acc": 0.99188, "loss_cls": 0.73257, "loss": 0.73257, "time": 0.22147} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.02434, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.84312, "top5_acc": 0.9925, "loss_cls": 0.70789, "loss": 0.70789, "time": 0.21654} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.02434, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.84188, "top5_acc": 0.99375, "loss_cls": 0.70723, "loss": 0.70723, "time": 0.21758} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.02433, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84625, "top5_acc": 0.99, "loss_cls": 0.70799, "loss": 0.70799, "time": 0.2188} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.02432, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84375, "top5_acc": 0.99312, "loss_cls": 0.722, "loss": 0.722, "time": 0.21851} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.02432, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.83375, "top5_acc": 0.99, "loss_cls": 0.77166, "loss": 0.77166, "time": 0.2162} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.02431, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83875, "top5_acc": 0.99312, "loss_cls": 0.74425, "loss": 0.74425, "time": 0.21605} +{"mode": "val", "epoch": 16, "iter": 533, "lr": 0.0243, "top1_acc": 0.81645, "top5_acc": 0.9831, "mean_class_accuracy": 0.7314} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.0243, "memory": 4082, "data_time": 0.18379, "top1_acc": 0.86188, "top5_acc": 0.99625, "loss_cls": 0.6835, "loss": 0.6835, "time": 0.5987} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.02429, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.84875, "top5_acc": 0.99062, "loss_cls": 0.69529, "loss": 0.69529, "time": 0.41323} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.02428, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.84875, "top5_acc": 0.99562, "loss_cls": 0.69096, "loss": 0.69096, "time": 0.41481} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.02428, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.85875, "top5_acc": 0.99438, "loss_cls": 0.67001, "loss": 0.67001, "time": 0.41306} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.02427, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.845, "top5_acc": 0.99375, "loss_cls": 0.68436, "loss": 0.68436, "time": 0.41738} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.02426, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.8425, "top5_acc": 0.99062, "loss_cls": 0.71212, "loss": 0.71212, "time": 0.41491} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.02426, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84688, "top5_acc": 0.99375, "loss_cls": 0.70944, "loss": 0.70944, "time": 0.41434} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.02425, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.84375, "top5_acc": 0.99375, "loss_cls": 0.67745, "loss": 0.67745, "time": 0.41296} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.02424, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85125, "top5_acc": 0.99312, "loss_cls": 0.69804, "loss": 0.69804, "time": 0.41468} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.02424, "memory": 4082, "data_time": 0.00049, "top1_acc": 0.85562, "top5_acc": 0.99188, "loss_cls": 0.66735, "loss": 0.66735, "time": 0.41258} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.02423, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.8375, "top5_acc": 0.99188, "loss_cls": 0.7194, "loss": 0.7194, "time": 0.41065} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.02422, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85125, "top5_acc": 0.98875, "loss_cls": 0.70736, "loss": 0.70736, "time": 0.28302} +{"mode": "val", "epoch": 17, "iter": 533, "lr": 0.02422, "top1_acc": 0.82983, "top5_acc": 0.98627, "mean_class_accuracy": 0.76166} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.02421, "memory": 4082, "data_time": 0.19736, "top1_acc": 0.87812, "top5_acc": 0.99188, "loss_cls": 0.60507, "loss": 0.60507, "time": 0.61549} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.0242, "memory": 4082, "data_time": 0.00056, "top1_acc": 0.85875, "top5_acc": 0.99562, "loss_cls": 0.65195, "loss": 0.65195, "time": 0.4164} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.02419, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.86438, "top5_acc": 0.99625, "loss_cls": 0.61147, "loss": 0.61147, "time": 0.41272} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.02419, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.84688, "top5_acc": 0.99438, "loss_cls": 0.68705, "loss": 0.68705, "time": 0.41465} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.02418, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.85062, "top5_acc": 0.99188, "loss_cls": 0.71594, "loss": 0.71594, "time": 0.41335} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.02417, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.85812, "top5_acc": 0.99625, "loss_cls": 0.65633, "loss": 0.65633, "time": 0.43597} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.02417, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.845, "top5_acc": 0.98938, "loss_cls": 0.73614, "loss": 0.73614, "time": 0.41303} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.02416, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.845, "top5_acc": 0.9925, "loss_cls": 0.71646, "loss": 0.71646, "time": 0.41405} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.02415, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.86375, "top5_acc": 0.99438, "loss_cls": 0.67073, "loss": 0.67073, "time": 0.4135} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.02414, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.86062, "top5_acc": 0.99312, "loss_cls": 0.66319, "loss": 0.66319, "time": 0.41417} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.02414, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85938, "top5_acc": 0.99562, "loss_cls": 0.66402, "loss": 0.66402, "time": 0.38843} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.02413, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.85562, "top5_acc": 0.99375, "loss_cls": 0.6874, "loss": 0.6874, "time": 0.29559} +{"mode": "val", "epoch": 18, "iter": 533, "lr": 0.02412, "top1_acc": 0.85013, "top5_acc": 0.98756, "mean_class_accuracy": 0.78184} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.02411, "memory": 4082, "data_time": 0.19311, "top1_acc": 0.87125, "top5_acc": 0.995, "loss_cls": 0.61274, "loss": 0.61274, "time": 0.60699} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.02411, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.86625, "top5_acc": 0.99625, "loss_cls": 0.63006, "loss": 0.63006, "time": 0.41882} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.0241, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86688, "top5_acc": 0.99625, "loss_cls": 0.64702, "loss": 0.64702, "time": 0.41457} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.02409, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.85812, "top5_acc": 0.995, "loss_cls": 0.66163, "loss": 0.66163, "time": 0.4143} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.02408, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.855, "top5_acc": 0.995, "loss_cls": 0.67056, "loss": 0.67056, "time": 0.41524} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.02408, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.86062, "top5_acc": 0.99188, "loss_cls": 0.65757, "loss": 0.65757, "time": 0.41281} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.02407, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.85062, "top5_acc": 0.99438, "loss_cls": 0.65412, "loss": 0.65412, "time": 0.41452} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.02406, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84625, "top5_acc": 0.99125, "loss_cls": 0.70623, "loss": 0.70623, "time": 0.41477} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.02405, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8225, "top5_acc": 0.99375, "loss_cls": 0.77693, "loss": 0.77693, "time": 0.41572} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.02405, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86375, "top5_acc": 0.99562, "loss_cls": 0.63741, "loss": 0.63741, "time": 0.41407} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.02404, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86375, "top5_acc": 0.9925, "loss_cls": 0.68029, "loss": 0.68029, "time": 0.37555} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.02403, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.85125, "top5_acc": 0.99438, "loss_cls": 0.68105, "loss": 0.68105, "time": 0.3} +{"mode": "val", "epoch": 19, "iter": 533, "lr": 0.02402, "top1_acc": 0.78946, "top5_acc": 0.9838, "mean_class_accuracy": 0.71703} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.02402, "memory": 4082, "data_time": 0.18734, "top1_acc": 0.87188, "top5_acc": 0.995, "loss_cls": 0.61162, "loss": 0.61162, "time": 0.60266} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.02401, "memory": 4082, "data_time": 0.00046, "top1_acc": 0.88562, "top5_acc": 0.9975, "loss_cls": 0.54621, "loss": 0.54621, "time": 0.41782} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.024, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86562, "top5_acc": 0.99188, "loss_cls": 0.62431, "loss": 0.62431, "time": 0.41485} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.02399, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.87812, "top5_acc": 0.99625, "loss_cls": 0.61559, "loss": 0.61559, "time": 0.41334} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.02398, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.85938, "top5_acc": 0.99438, "loss_cls": 0.65384, "loss": 0.65384, "time": 0.414} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.02398, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.85875, "top5_acc": 0.99375, "loss_cls": 0.66765, "loss": 0.66765, "time": 0.41455} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.02397, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85, "top5_acc": 0.99312, "loss_cls": 0.69213, "loss": 0.69213, "time": 0.41438} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.02396, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.83875, "top5_acc": 0.99375, "loss_cls": 0.71583, "loss": 0.71583, "time": 0.41479} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.02395, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.85688, "top5_acc": 0.9925, "loss_cls": 0.70677, "loss": 0.70677, "time": 0.4155} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.02394, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84875, "top5_acc": 0.99375, "loss_cls": 0.6855, "loss": 0.6855, "time": 0.41215} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.02393, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8625, "top5_acc": 0.99062, "loss_cls": 0.63867, "loss": 0.63867, "time": 0.37809} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.02393, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86, "top5_acc": 0.99375, "loss_cls": 0.6335, "loss": 0.6335, "time": 0.30149} +{"mode": "val", "epoch": 20, "iter": 533, "lr": 0.02392, "top1_acc": 0.74979, "top5_acc": 0.97383, "mean_class_accuracy": 0.63973} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.02391, "memory": 4082, "data_time": 0.19964, "top1_acc": 0.86438, "top5_acc": 0.9975, "loss_cls": 0.60676, "loss": 0.60676, "time": 0.63113} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.0239, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.86688, "top5_acc": 0.99125, "loss_cls": 0.62598, "loss": 0.62598, "time": 0.41531} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.02389, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8725, "top5_acc": 0.99375, "loss_cls": 0.61951, "loss": 0.61951, "time": 0.4136} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.02389, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.85125, "top5_acc": 0.99375, "loss_cls": 0.64507, "loss": 0.64507, "time": 0.41344} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.02388, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87125, "top5_acc": 0.99375, "loss_cls": 0.6512, "loss": 0.6512, "time": 0.414} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.02387, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.87125, "top5_acc": 0.99375, "loss_cls": 0.64244, "loss": 0.64244, "time": 0.41251} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.02386, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87312, "top5_acc": 0.995, "loss_cls": 0.61882, "loss": 0.61882, "time": 0.41319} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.02385, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.87, "top5_acc": 0.99438, "loss_cls": 0.5953, "loss": 0.5953, "time": 0.4142} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.02384, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.85875, "top5_acc": 0.99312, "loss_cls": 0.66139, "loss": 0.66139, "time": 0.41453} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.02383, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.88, "top5_acc": 0.99688, "loss_cls": 0.59925, "loss": 0.59925, "time": 0.41313} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.02383, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.855, "top5_acc": 0.995, "loss_cls": 0.66373, "loss": 0.66373, "time": 0.37463} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.02382, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84438, "top5_acc": 0.99, "loss_cls": 0.7203, "loss": 0.7203, "time": 0.30772} +{"mode": "val", "epoch": 21, "iter": 533, "lr": 0.02381, "top1_acc": 0.83711, "top5_acc": 0.98686, "mean_class_accuracy": 0.78074} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.0238, "memory": 4082, "data_time": 0.19679, "top1_acc": 0.875, "top5_acc": 0.99375, "loss_cls": 0.61062, "loss": 0.61062, "time": 0.63017} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.02379, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.8675, "top5_acc": 0.99375, "loss_cls": 0.60977, "loss": 0.60977, "time": 0.42273} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.02378, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.88, "top5_acc": 0.9975, "loss_cls": 0.5817, "loss": 0.5817, "time": 0.43448} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.02378, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.8625, "top5_acc": 0.99812, "loss_cls": 0.629, "loss": 0.629, "time": 0.43629} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.02377, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86, "top5_acc": 0.99625, "loss_cls": 0.64435, "loss": 0.64435, "time": 0.41903} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.02376, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.87938, "top5_acc": 0.99312, "loss_cls": 0.62437, "loss": 0.62437, "time": 0.41238} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.02375, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.86438, "top5_acc": 0.99438, "loss_cls": 0.63084, "loss": 0.63084, "time": 0.41366} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.02374, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8725, "top5_acc": 0.99312, "loss_cls": 0.66344, "loss": 0.66344, "time": 0.41364} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.02373, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.84812, "top5_acc": 0.98938, "loss_cls": 0.71791, "loss": 0.71791, "time": 0.41363} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.02372, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.88062, "top5_acc": 0.99562, "loss_cls": 0.56225, "loss": 0.56225, "time": 0.4132} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.02371, "memory": 4082, "data_time": 0.00058, "top1_acc": 0.86312, "top5_acc": 0.99188, "loss_cls": 0.67238, "loss": 0.67238, "time": 0.3593} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0237, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.86375, "top5_acc": 0.99312, "loss_cls": 0.63812, "loss": 0.63812, "time": 0.30739} +{"mode": "val", "epoch": 22, "iter": 533, "lr": 0.0237, "top1_acc": 0.85424, "top5_acc": 0.99026, "mean_class_accuracy": 0.77817} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.02369, "memory": 4082, "data_time": 0.1927, "top1_acc": 0.87062, "top5_acc": 0.99625, "loss_cls": 0.58786, "loss": 0.58786, "time": 0.60762} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.02368, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.88375, "top5_acc": 0.99688, "loss_cls": 0.55285, "loss": 0.55285, "time": 0.415} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.02367, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87062, "top5_acc": 0.99688, "loss_cls": 0.57993, "loss": 0.57993, "time": 0.41539} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.02366, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8775, "top5_acc": 0.995, "loss_cls": 0.6084, "loss": 0.6084, "time": 0.41548} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.02365, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.86562, "top5_acc": 0.99438, "loss_cls": 0.60786, "loss": 0.60786, "time": 0.41292} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.02364, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.87625, "top5_acc": 0.99, "loss_cls": 0.64264, "loss": 0.64264, "time": 0.41584} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.02363, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.88438, "top5_acc": 0.99312, "loss_cls": 0.62347, "loss": 0.62347, "time": 0.4129} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.02362, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.88375, "top5_acc": 0.995, "loss_cls": 0.56776, "loss": 0.56776, "time": 0.41542} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.02361, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.87125, "top5_acc": 0.99188, "loss_cls": 0.62963, "loss": 0.62963, "time": 0.4141} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.0236, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8725, "top5_acc": 0.99562, "loss_cls": 0.60437, "loss": 0.60437, "time": 0.4118} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.02359, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8625, "top5_acc": 0.99375, "loss_cls": 0.63035, "loss": 0.63035, "time": 0.3677} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.02359, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86688, "top5_acc": 0.99125, "loss_cls": 0.63643, "loss": 0.63643, "time": 0.31491} +{"mode": "val", "epoch": 23, "iter": 533, "lr": 0.02358, "top1_acc": 0.83171, "top5_acc": 0.98545, "mean_class_accuracy": 0.76326} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.02357, "memory": 4082, "data_time": 0.19133, "top1_acc": 0.88, "top5_acc": 0.99938, "loss_cls": 0.59189, "loss": 0.59189, "time": 0.60494} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.02356, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85625, "top5_acc": 0.99375, "loss_cls": 0.64127, "loss": 0.64127, "time": 0.41515} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.02355, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.87375, "top5_acc": 0.99562, "loss_cls": 0.62382, "loss": 0.62382, "time": 0.41414} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.02354, "memory": 4082, "data_time": 0.00055, "top1_acc": 0.86375, "top5_acc": 0.99562, "loss_cls": 0.63754, "loss": 0.63754, "time": 0.41553} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.02353, "memory": 4082, "data_time": 0.00054, "top1_acc": 0.85375, "top5_acc": 0.99, "loss_cls": 0.65888, "loss": 0.65888, "time": 0.41472} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.02352, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.8675, "top5_acc": 0.99375, "loss_cls": 0.6543, "loss": 0.6543, "time": 0.41476} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.02351, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.89, "top5_acc": 0.99625, "loss_cls": 0.54038, "loss": 0.54038, "time": 0.41278} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.0235, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.87688, "top5_acc": 0.99312, "loss_cls": 0.62126, "loss": 0.62126, "time": 0.41386} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.02349, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.86875, "top5_acc": 0.99688, "loss_cls": 0.57468, "loss": 0.57468, "time": 0.41515} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.02348, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.86312, "top5_acc": 0.99062, "loss_cls": 0.64745, "loss": 0.64745, "time": 0.4139} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.02347, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.87938, "top5_acc": 0.99375, "loss_cls": 0.59174, "loss": 0.59174, "time": 0.36784} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.02346, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.86062, "top5_acc": 0.99625, "loss_cls": 0.63298, "loss": 0.63298, "time": 0.30574} +{"mode": "val", "epoch": 24, "iter": 533, "lr": 0.02345, "top1_acc": 0.84028, "top5_acc": 0.98956, "mean_class_accuracy": 0.76941} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.02344, "memory": 4082, "data_time": 0.19435, "top1_acc": 0.89438, "top5_acc": 0.99625, "loss_cls": 0.51579, "loss": 0.51579, "time": 0.61037} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.02343, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.88062, "top5_acc": 0.99562, "loss_cls": 0.54168, "loss": 0.54168, "time": 0.41544} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.02342, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.87375, "top5_acc": 0.99625, "loss_cls": 0.58742, "loss": 0.58742, "time": 0.41454} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.02341, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.89375, "top5_acc": 0.9975, "loss_cls": 0.52046, "loss": 0.52046, "time": 0.41439} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.0234, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.885, "top5_acc": 0.99188, "loss_cls": 0.57895, "loss": 0.57895, "time": 0.41377} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.02339, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87375, "top5_acc": 0.9925, "loss_cls": 0.6243, "loss": 0.6243, "time": 0.41339} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.02338, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.8725, "top5_acc": 0.99188, "loss_cls": 0.60592, "loss": 0.60592, "time": 0.41636} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.02337, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.87875, "top5_acc": 0.99812, "loss_cls": 0.59451, "loss": 0.59451, "time": 0.41391} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.02336, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.85938, "top5_acc": 0.99688, "loss_cls": 0.67046, "loss": 0.67046, "time": 0.41452} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.02335, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86, "top5_acc": 0.99375, "loss_cls": 0.64372, "loss": 0.64372, "time": 0.41613} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.02334, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.88, "top5_acc": 0.99062, "loss_cls": 0.61199, "loss": 0.61199, "time": 0.37575} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.02333, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.87, "top5_acc": 0.99438, "loss_cls": 0.59781, "loss": 0.59781, "time": 0.29519} +{"mode": "val", "epoch": 25, "iter": 533, "lr": 0.02333, "top1_acc": 0.81868, "top5_acc": 0.98815, "mean_class_accuracy": 0.75421} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.02332, "memory": 4082, "data_time": 0.20298, "top1_acc": 0.885, "top5_acc": 0.995, "loss_cls": 0.5757, "loss": 0.5757, "time": 0.61723} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.0233, "memory": 4082, "data_time": 0.00062, "top1_acc": 0.86938, "top5_acc": 0.995, "loss_cls": 0.60632, "loss": 0.60632, "time": 0.41383} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.02329, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.865, "top5_acc": 0.99688, "loss_cls": 0.59879, "loss": 0.59879, "time": 0.41404} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.02328, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.86312, "top5_acc": 0.99312, "loss_cls": 0.62012, "loss": 0.62012, "time": 0.4141} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.02327, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87812, "top5_acc": 0.9925, "loss_cls": 0.6072, "loss": 0.6072, "time": 0.41347} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.02326, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.87625, "top5_acc": 0.99375, "loss_cls": 0.58983, "loss": 0.58983, "time": 0.41315} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.02325, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.88375, "top5_acc": 0.99812, "loss_cls": 0.54852, "loss": 0.54852, "time": 0.41328} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.02324, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.87688, "top5_acc": 0.99438, "loss_cls": 0.57194, "loss": 0.57194, "time": 0.41356} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.02323, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.88125, "top5_acc": 0.99688, "loss_cls": 0.54775, "loss": 0.54775, "time": 0.41305} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.02322, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.87438, "top5_acc": 0.99688, "loss_cls": 0.57283, "loss": 0.57283, "time": 0.41355} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.02321, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8525, "top5_acc": 0.9925, "loss_cls": 0.68113, "loss": 0.68113, "time": 0.371} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.0232, "memory": 4082, "data_time": 0.00066, "top1_acc": 0.87688, "top5_acc": 0.9925, "loss_cls": 0.58005, "loss": 0.58005, "time": 0.3078} +{"mode": "val", "epoch": 26, "iter": 533, "lr": 0.02319, "top1_acc": 0.84873, "top5_acc": 0.98909, "mean_class_accuracy": 0.77425} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.02318, "memory": 4082, "data_time": 0.20145, "top1_acc": 0.8875, "top5_acc": 0.99375, "loss_cls": 0.56671, "loss": 0.56671, "time": 0.6254} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.02317, "memory": 4082, "data_time": 0.00053, "top1_acc": 0.89562, "top5_acc": 0.99625, "loss_cls": 0.52881, "loss": 0.52881, "time": 0.41377} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.02316, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.88062, "top5_acc": 0.9975, "loss_cls": 0.54987, "loss": 0.54987, "time": 0.4148} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.02315, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.88438, "top5_acc": 0.99375, "loss_cls": 0.56705, "loss": 0.56705, "time": 0.41416} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.02314, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8725, "top5_acc": 0.995, "loss_cls": 0.57427, "loss": 0.57427, "time": 0.41439} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.02313, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.87, "top5_acc": 0.99375, "loss_cls": 0.61338, "loss": 0.61338, "time": 0.41486} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.02312, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.88312, "top5_acc": 0.9975, "loss_cls": 0.54455, "loss": 0.54455, "time": 0.41444} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.02311, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.87625, "top5_acc": 0.9925, "loss_cls": 0.59153, "loss": 0.59153, "time": 0.41364} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.0231, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.87188, "top5_acc": 0.99375, "loss_cls": 0.60769, "loss": 0.60769, "time": 0.41431} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.02308, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.86625, "top5_acc": 0.9925, "loss_cls": 0.62051, "loss": 0.62051, "time": 0.4141} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.02307, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.89, "top5_acc": 0.99562, "loss_cls": 0.50786, "loss": 0.50786, "time": 0.36317} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.02306, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.86062, "top5_acc": 0.99438, "loss_cls": 0.63212, "loss": 0.63212, "time": 0.30754} +{"mode": "val", "epoch": 27, "iter": 533, "lr": 0.02305, "top1_acc": 0.83406, "top5_acc": 0.98779, "mean_class_accuracy": 0.75835} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.02304, "memory": 4082, "data_time": 0.20254, "top1_acc": 0.88562, "top5_acc": 0.99688, "loss_cls": 0.53812, "loss": 0.53812, "time": 0.61773} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.02303, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.88938, "top5_acc": 0.9925, "loss_cls": 0.54859, "loss": 0.54859, "time": 0.41482} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.02302, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86312, "top5_acc": 0.99562, "loss_cls": 0.60305, "loss": 0.60305, "time": 0.41466} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.02301, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.88562, "top5_acc": 0.99688, "loss_cls": 0.54779, "loss": 0.54779, "time": 0.41554} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.023, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.885, "top5_acc": 0.99312, "loss_cls": 0.54984, "loss": 0.54984, "time": 0.41315} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.02299, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.9, "top5_acc": 0.99562, "loss_cls": 0.53732, "loss": 0.53732, "time": 0.41442} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.02298, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.87188, "top5_acc": 0.995, "loss_cls": 0.61306, "loss": 0.61306, "time": 0.41378} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.02297, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.8775, "top5_acc": 0.99438, "loss_cls": 0.59613, "loss": 0.59613, "time": 0.41418} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.02295, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8775, "top5_acc": 0.99688, "loss_cls": 0.55973, "loss": 0.55973, "time": 0.41396} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.02294, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.88125, "top5_acc": 0.99438, "loss_cls": 0.60166, "loss": 0.60166, "time": 0.41331} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.02293, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.87125, "top5_acc": 0.99625, "loss_cls": 0.61155, "loss": 0.61155, "time": 0.36148} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.02292, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.86562, "top5_acc": 0.9925, "loss_cls": 0.61836, "loss": 0.61836, "time": 0.31837} +{"mode": "val", "epoch": 28, "iter": 533, "lr": 0.02291, "top1_acc": 0.84333, "top5_acc": 0.98897, "mean_class_accuracy": 0.76353} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.0229, "memory": 4082, "data_time": 0.19983, "top1_acc": 0.88375, "top5_acc": 0.99562, "loss_cls": 0.54601, "loss": 0.54601, "time": 0.61466} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.02289, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.9025, "top5_acc": 0.99875, "loss_cls": 0.50853, "loss": 0.50853, "time": 0.41339} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.02288, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.89125, "top5_acc": 0.99688, "loss_cls": 0.52854, "loss": 0.52854, "time": 0.41559} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.02287, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.88125, "top5_acc": 0.995, "loss_cls": 0.57442, "loss": 0.57442, "time": 0.4133} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.02285, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.88125, "top5_acc": 0.99375, "loss_cls": 0.61468, "loss": 0.61468, "time": 0.41381} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.02284, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.90562, "top5_acc": 0.99875, "loss_cls": 0.48966, "loss": 0.48966, "time": 0.41478} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.02283, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.88625, "top5_acc": 0.995, "loss_cls": 0.52738, "loss": 0.52738, "time": 0.4138} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.02282, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8925, "top5_acc": 0.99375, "loss_cls": 0.55656, "loss": 0.55656, "time": 0.41565} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.02281, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87875, "top5_acc": 0.99312, "loss_cls": 0.58545, "loss": 0.58545, "time": 0.41392} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.0228, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.88188, "top5_acc": 0.99625, "loss_cls": 0.55996, "loss": 0.55996, "time": 0.41331} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.02279, "memory": 4082, "data_time": 0.00049, "top1_acc": 0.86812, "top5_acc": 0.99562, "loss_cls": 0.62167, "loss": 0.62167, "time": 0.35962} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.02277, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.88, "top5_acc": 0.995, "loss_cls": 0.56219, "loss": 0.56219, "time": 0.32007} +{"mode": "val", "epoch": 29, "iter": 533, "lr": 0.02276, "top1_acc": 0.80929, "top5_acc": 0.98345, "mean_class_accuracy": 0.73592} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.02275, "memory": 4082, "data_time": 0.1972, "top1_acc": 0.8875, "top5_acc": 0.99688, "loss_cls": 0.53736, "loss": 0.53736, "time": 0.69402} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.02274, "memory": 4082, "data_time": 0.00057, "top1_acc": 0.88625, "top5_acc": 0.99375, "loss_cls": 0.56778, "loss": 0.56778, "time": 0.49674} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.02273, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.87812, "top5_acc": 0.99312, "loss_cls": 0.58032, "loss": 0.58032, "time": 0.48603} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.02272, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.89875, "top5_acc": 0.99562, "loss_cls": 0.51416, "loss": 0.51416, "time": 0.50692} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.02271, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.885, "top5_acc": 0.99312, "loss_cls": 0.56865, "loss": 0.56865, "time": 0.50327} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.02269, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.88938, "top5_acc": 0.9975, "loss_cls": 0.53881, "loss": 0.53881, "time": 0.50398} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.02268, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.8725, "top5_acc": 0.99688, "loss_cls": 0.58161, "loss": 0.58161, "time": 0.51324} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.02267, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.895, "top5_acc": 0.99312, "loss_cls": 0.5358, "loss": 0.5358, "time": 0.51364} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.02266, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.89125, "top5_acc": 0.99375, "loss_cls": 0.55, "loss": 0.55, "time": 0.29796} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.02265, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.87625, "top5_acc": 0.99688, "loss_cls": 0.5601, "loss": 0.5601, "time": 0.50918} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.02263, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.87312, "top5_acc": 0.99812, "loss_cls": 0.57359, "loss": 0.57359, "time": 0.32644} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.02262, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.89938, "top5_acc": 0.9925, "loss_cls": 0.522, "loss": 0.522, "time": 0.50829} +{"mode": "val", "epoch": 30, "iter": 533, "lr": 0.02261, "top1_acc": 0.8357, "top5_acc": 0.98709, "mean_class_accuracy": 0.78214} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.0226, "memory": 4083, "data_time": 0.19499, "top1_acc": 0.88, "top5_acc": 0.99688, "loss_cls": 0.75958, "loss": 0.75958, "time": 0.9602} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.02259, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89438, "top5_acc": 0.99438, "loss_cls": 0.70138, "loss": 0.70138, "time": 0.53481} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.02258, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87938, "top5_acc": 0.99625, "loss_cls": 0.7403, "loss": 0.7403, "time": 0.53209} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.02256, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90062, "top5_acc": 0.99688, "loss_cls": 0.67604, "loss": 0.67604, "time": 0.5207} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.02255, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88312, "top5_acc": 0.99562, "loss_cls": 0.74509, "loss": 0.74509, "time": 0.32543} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.02254, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8825, "top5_acc": 0.995, "loss_cls": 0.73174, "loss": 0.73174, "time": 0.50975} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.02253, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.87875, "top5_acc": 0.995, "loss_cls": 0.75274, "loss": 0.75274, "time": 0.33754} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.02252, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87188, "top5_acc": 0.99625, "loss_cls": 0.80947, "loss": 0.80947, "time": 0.52801} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0225, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87938, "top5_acc": 0.99312, "loss_cls": 0.77273, "loss": 0.77273, "time": 0.52435} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.02249, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8875, "top5_acc": 0.99625, "loss_cls": 0.70309, "loss": 0.70309, "time": 0.52636} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.02248, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.86938, "top5_acc": 0.99312, "loss_cls": 0.83379, "loss": 0.83379, "time": 0.50672} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.02247, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86562, "top5_acc": 0.9925, "loss_cls": 0.80987, "loss": 0.80987, "time": 0.51916} +{"mode": "val", "epoch": 31, "iter": 533, "lr": 0.02246, "top1_acc": 0.84098, "top5_acc": 0.9892, "mean_class_accuracy": 0.78698} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.02244, "memory": 4083, "data_time": 0.20725, "top1_acc": 0.88625, "top5_acc": 0.99812, "loss_cls": 0.65003, "loss": 0.65003, "time": 0.73702} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.02243, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.87188, "top5_acc": 0.99562, "loss_cls": 0.6847, "loss": 0.6847, "time": 0.51208} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.02242, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.86375, "top5_acc": 0.9925, "loss_cls": 0.71057, "loss": 0.71057, "time": 0.30713} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.02241, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.87625, "top5_acc": 0.9975, "loss_cls": 0.68681, "loss": 0.68681, "time": 0.53825} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.02239, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.885, "top5_acc": 0.99688, "loss_cls": 0.63103, "loss": 0.63103, "time": 0.53089} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.02238, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.89062, "top5_acc": 0.99312, "loss_cls": 0.61946, "loss": 0.61946, "time": 0.53817} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.02237, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87812, "top5_acc": 0.99688, "loss_cls": 0.6806, "loss": 0.6806, "time": 0.53692} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.02236, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9, "top5_acc": 0.99812, "loss_cls": 0.62146, "loss": 0.62146, "time": 0.53079} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.02234, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88688, "top5_acc": 0.99438, "loss_cls": 0.64436, "loss": 0.64436, "time": 0.54203} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.02233, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87625, "top5_acc": 0.99562, "loss_cls": 0.69215, "loss": 0.69215, "time": 0.53359} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.02232, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8925, "top5_acc": 0.99375, "loss_cls": 0.6454, "loss": 0.6454, "time": 0.54073} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.02231, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9, "top5_acc": 0.99625, "loss_cls": 0.60196, "loss": 0.60196, "time": 0.42384} +{"mode": "val", "epoch": 32, "iter": 533, "lr": 0.0223, "top1_acc": 0.83593, "top5_acc": 0.98592, "mean_class_accuracy": 0.76984} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.02228, "memory": 4083, "data_time": 0.19266, "top1_acc": 0.89812, "top5_acc": 0.9975, "loss_cls": 0.59392, "loss": 0.59392, "time": 0.87412} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.02227, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87875, "top5_acc": 0.99562, "loss_cls": 0.63155, "loss": 0.63155, "time": 0.53921} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.02226, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.90375, "top5_acc": 0.995, "loss_cls": 0.54014, "loss": 0.54014, "time": 0.53732} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.02225, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.89375, "top5_acc": 0.99625, "loss_cls": 0.55295, "loss": 0.55295, "time": 0.52767} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.02223, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89312, "top5_acc": 0.99812, "loss_cls": 0.6148, "loss": 0.6148, "time": 0.53298} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.02222, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.88938, "top5_acc": 0.99438, "loss_cls": 0.63875, "loss": 0.63875, "time": 0.54373} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.02221, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89938, "top5_acc": 0.99562, "loss_cls": 0.58376, "loss": 0.58376, "time": 0.53515} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.02219, "memory": 4083, "data_time": 0.00065, "top1_acc": 0.88938, "top5_acc": 0.99438, "loss_cls": 0.58949, "loss": 0.58949, "time": 0.33571} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.02218, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89062, "top5_acc": 0.99375, "loss_cls": 0.60859, "loss": 0.60859, "time": 0.50961} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.02217, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89375, "top5_acc": 0.99688, "loss_cls": 0.56173, "loss": 0.56173, "time": 0.34828} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.02216, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.86812, "top5_acc": 0.9925, "loss_cls": 0.68748, "loss": 0.68748, "time": 0.54359} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.02214, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.90688, "top5_acc": 0.99375, "loss_cls": 0.54277, "loss": 0.54277, "time": 0.53753} +{"mode": "val", "epoch": 33, "iter": 533, "lr": 0.02213, "top1_acc": 0.82936, "top5_acc": 0.98451, "mean_class_accuracy": 0.76398} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.02212, "memory": 4083, "data_time": 0.19526, "top1_acc": 0.89062, "top5_acc": 0.99875, "loss_cls": 0.55382, "loss": 0.55382, "time": 0.87127} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.02211, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89875, "top5_acc": 0.99375, "loss_cls": 0.5447, "loss": 0.5447, "time": 0.5339} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.02209, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88125, "top5_acc": 0.9975, "loss_cls": 0.59121, "loss": 0.59121, "time": 0.53948} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.02208, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89438, "top5_acc": 0.995, "loss_cls": 0.60619, "loss": 0.60619, "time": 0.2943} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.02207, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.89, "top5_acc": 0.995, "loss_cls": 0.55538, "loss": 0.55538, "time": 0.51084} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.02205, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88438, "top5_acc": 0.99875, "loss_cls": 0.56428, "loss": 0.56428, "time": 0.36783} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.02204, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88188, "top5_acc": 0.995, "loss_cls": 0.65343, "loss": 0.65343, "time": 0.54045} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.02203, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87812, "top5_acc": 0.99562, "loss_cls": 0.62272, "loss": 0.62272, "time": 0.53219} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.02201, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89625, "top5_acc": 0.995, "loss_cls": 0.5543, "loss": 0.5543, "time": 0.54173} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.022, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.87062, "top5_acc": 0.995, "loss_cls": 0.64398, "loss": 0.64398, "time": 0.5366} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.02199, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9, "top5_acc": 0.995, "loss_cls": 0.57375, "loss": 0.57375, "time": 0.53949} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.02197, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88688, "top5_acc": 0.99938, "loss_cls": 0.58069, "loss": 0.58069, "time": 0.53371} +{"mode": "val", "epoch": 34, "iter": 533, "lr": 0.02196, "top1_acc": 0.85424, "top5_acc": 0.98956, "mean_class_accuracy": 0.79383} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.02195, "memory": 4083, "data_time": 0.19398, "top1_acc": 0.89875, "top5_acc": 0.99438, "loss_cls": 0.56933, "loss": 0.56933, "time": 0.71578} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.02194, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.87875, "top5_acc": 0.99812, "loss_cls": 0.6012, "loss": 0.6012, "time": 0.3259} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.02192, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89125, "top5_acc": 0.99875, "loss_cls": 0.55365, "loss": 0.55365, "time": 0.53625} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.02191, "memory": 4083, "data_time": 0.00063, "top1_acc": 0.88625, "top5_acc": 0.99312, "loss_cls": 0.59394, "loss": 0.59394, "time": 0.53505} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.0219, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.895, "top5_acc": 0.99438, "loss_cls": 0.56137, "loss": 0.56137, "time": 0.53264} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.02188, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88312, "top5_acc": 0.99438, "loss_cls": 0.62097, "loss": 0.62097, "time": 0.53568} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.02187, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8975, "top5_acc": 0.995, "loss_cls": 0.5426, "loss": 0.5426, "time": 0.53965} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.02185, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91188, "top5_acc": 0.99812, "loss_cls": 0.48142, "loss": 0.48142, "time": 0.53752} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.02184, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88562, "top5_acc": 0.99625, "loss_cls": 0.5923, "loss": 0.5923, "time": 0.53525} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.02183, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.89562, "top5_acc": 0.99625, "loss_cls": 0.5444, "loss": 0.5444, "time": 0.53555} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.02181, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.88438, "top5_acc": 0.99562, "loss_cls": 0.60531, "loss": 0.60531, "time": 0.40734} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.0218, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.8825, "top5_acc": 0.99562, "loss_cls": 0.57959, "loss": 0.57959, "time": 0.51003} +{"mode": "val", "epoch": 35, "iter": 533, "lr": 0.02179, "top1_acc": 0.83652, "top5_acc": 0.98885, "mean_class_accuracy": 0.79971} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.02178, "memory": 4083, "data_time": 0.19543, "top1_acc": 0.89938, "top5_acc": 0.99438, "loss_cls": 0.54459, "loss": 0.54459, "time": 0.84504} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.02176, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.8925, "top5_acc": 0.995, "loss_cls": 0.58037, "loss": 0.58037, "time": 0.53662} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.02175, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.89125, "top5_acc": 0.99812, "loss_cls": 0.56734, "loss": 0.56734, "time": 0.53202} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.02173, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.88625, "top5_acc": 0.99562, "loss_cls": 0.58549, "loss": 0.58549, "time": 0.54037} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.02172, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89625, "top5_acc": 0.99688, "loss_cls": 0.54803, "loss": 0.54803, "time": 0.53441} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.02171, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89688, "top5_acc": 0.99875, "loss_cls": 0.53882, "loss": 0.53882, "time": 0.53886} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.02169, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.89, "top5_acc": 0.99625, "loss_cls": 0.58125, "loss": 0.58125, "time": 0.40035} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.02168, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.90688, "top5_acc": 0.99625, "loss_cls": 0.50772, "loss": 0.50772, "time": 0.50994} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.02167, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.8825, "top5_acc": 0.995, "loss_cls": 0.60175, "loss": 0.60175, "time": 0.27453} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.02165, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88188, "top5_acc": 0.9975, "loss_cls": 0.59611, "loss": 0.59611, "time": 0.53407} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.02164, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88562, "top5_acc": 0.99625, "loss_cls": 0.60791, "loss": 0.60791, "time": 0.49847} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.02162, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8775, "top5_acc": 0.99375, "loss_cls": 0.58621, "loss": 0.58621, "time": 0.49574} +{"mode": "val", "epoch": 36, "iter": 533, "lr": 0.02161, "top1_acc": 0.83593, "top5_acc": 0.99026, "mean_class_accuracy": 0.77701} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.0216, "memory": 4083, "data_time": 0.20133, "top1_acc": 0.90062, "top5_acc": 0.99625, "loss_cls": 0.5571, "loss": 0.5571, "time": 0.86967} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.02158, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89, "top5_acc": 0.99625, "loss_cls": 0.56804, "loss": 0.56804, "time": 0.53689} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.02157, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88188, "top5_acc": 0.995, "loss_cls": 0.60402, "loss": 0.60402, "time": 0.46029} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.02156, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.8975, "top5_acc": 0.99688, "loss_cls": 0.53957, "loss": 0.53957, "time": 0.4608} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.02154, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.90125, "top5_acc": 0.99625, "loss_cls": 0.49582, "loss": 0.49582, "time": 0.27443} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.02153, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9025, "top5_acc": 0.99688, "loss_cls": 0.51501, "loss": 0.51501, "time": 0.52743} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.02151, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89188, "top5_acc": 0.9975, "loss_cls": 0.55519, "loss": 0.55519, "time": 0.53638} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0215, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.895, "top5_acc": 0.9975, "loss_cls": 0.56104, "loss": 0.56104, "time": 0.53136} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.02149, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.885, "top5_acc": 0.99375, "loss_cls": 0.57206, "loss": 0.57206, "time": 0.53509} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.02147, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.87812, "top5_acc": 0.99625, "loss_cls": 0.63214, "loss": 0.63214, "time": 0.54385} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.02146, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89688, "top5_acc": 0.995, "loss_cls": 0.55781, "loss": 0.55781, "time": 0.54197} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.02144, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89062, "top5_acc": 0.99625, "loss_cls": 0.55303, "loss": 0.55303, "time": 0.54215} +{"mode": "val", "epoch": 37, "iter": 533, "lr": 0.02143, "top1_acc": 0.86574, "top5_acc": 0.99237, "mean_class_accuracy": 0.8169} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.02142, "memory": 4083, "data_time": 0.19557, "top1_acc": 0.90938, "top5_acc": 0.99938, "loss_cls": 0.51093, "loss": 0.51093, "time": 0.61883} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.0214, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91375, "top5_acc": 0.99812, "loss_cls": 0.47809, "loss": 0.47809, "time": 0.52498} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.02139, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89375, "top5_acc": 0.99688, "loss_cls": 0.5423, "loss": 0.5423, "time": 0.53263} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.02137, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90875, "top5_acc": 0.99688, "loss_cls": 0.50246, "loss": 0.50246, "time": 0.5256} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.02136, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.89562, "top5_acc": 0.99688, "loss_cls": 0.54486, "loss": 0.54486, "time": 0.54309} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.02134, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.87812, "top5_acc": 0.99438, "loss_cls": 0.59706, "loss": 0.59706, "time": 0.53584} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.02133, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.52963, "loss": 0.52963, "time": 0.52799} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.02132, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.895, "top5_acc": 0.99812, "loss_cls": 0.54003, "loss": 0.54003, "time": 0.53452} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.0213, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.8825, "top5_acc": 0.99562, "loss_cls": 0.59753, "loss": 0.59753, "time": 0.53387} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.02129, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89062, "top5_acc": 0.99312, "loss_cls": 0.56241, "loss": 0.56241, "time": 0.50227} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.02127, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87812, "top5_acc": 0.99375, "loss_cls": 0.61081, "loss": 0.61081, "time": 0.39134} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.02126, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8925, "top5_acc": 0.99625, "loss_cls": 0.55233, "loss": 0.55233, "time": 0.34664} +{"mode": "val", "epoch": 38, "iter": 533, "lr": 0.02125, "top1_acc": 0.87232, "top5_acc": 0.9912, "mean_class_accuracy": 0.82171} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.02123, "memory": 4083, "data_time": 0.20292, "top1_acc": 0.90812, "top5_acc": 0.99812, "loss_cls": 0.50883, "loss": 0.50883, "time": 0.8603} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.02122, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.91375, "top5_acc": 0.99812, "loss_cls": 0.47236, "loss": 0.47236, "time": 0.5297} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.0212, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.90875, "top5_acc": 1.0, "loss_cls": 0.47448, "loss": 0.47448, "time": 0.52687} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.02119, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90625, "top5_acc": 0.9975, "loss_cls": 0.52783, "loss": 0.52783, "time": 0.53634} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.02117, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90125, "top5_acc": 0.995, "loss_cls": 0.54077, "loss": 0.54077, "time": 0.54418} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.02116, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90062, "top5_acc": 0.99875, "loss_cls": 0.53438, "loss": 0.53438, "time": 0.46554} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.02114, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9075, "top5_acc": 0.99625, "loss_cls": 0.49751, "loss": 0.49751, "time": 0.44128} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.02113, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9125, "top5_acc": 0.995, "loss_cls": 0.51856, "loss": 0.51856, "time": 0.29186} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.02111, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89562, "top5_acc": 0.99625, "loss_cls": 0.54008, "loss": 0.54008, "time": 0.51776} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.0211, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88938, "top5_acc": 0.99562, "loss_cls": 0.55871, "loss": 0.55871, "time": 0.52788} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.02108, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90812, "top5_acc": 0.9975, "loss_cls": 0.47944, "loss": 0.47944, "time": 0.52628} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.02107, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.89562, "top5_acc": 0.995, "loss_cls": 0.56663, "loss": 0.56663, "time": 0.53226} +{"mode": "val", "epoch": 39, "iter": 533, "lr": 0.02106, "top1_acc": 0.85377, "top5_acc": 0.99085, "mean_class_accuracy": 0.81093} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.02104, "memory": 4083, "data_time": 0.20121, "top1_acc": 0.88188, "top5_acc": 0.99688, "loss_cls": 0.57259, "loss": 0.57259, "time": 0.87484} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.02103, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.90875, "top5_acc": 0.99938, "loss_cls": 0.48901, "loss": 0.48901, "time": 0.46815} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.02101, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91562, "top5_acc": 0.99688, "loss_cls": 0.46943, "loss": 0.46943, "time": 0.43647} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.021, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.8925, "top5_acc": 1.0, "loss_cls": 0.56334, "loss": 0.56334, "time": 0.29685} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.02098, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87688, "top5_acc": 0.99688, "loss_cls": 0.58756, "loss": 0.58756, "time": 0.50972} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.02097, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89875, "top5_acc": 0.9975, "loss_cls": 0.51875, "loss": 0.51875, "time": 0.53227} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.02095, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90188, "top5_acc": 0.995, "loss_cls": 0.5369, "loss": 0.5369, "time": 0.53731} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.02094, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88625, "top5_acc": 0.99438, "loss_cls": 0.58132, "loss": 0.58132, "time": 0.54047} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.02092, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88812, "top5_acc": 0.99562, "loss_cls": 0.58082, "loss": 0.58082, "time": 0.53382} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.02091, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89938, "top5_acc": 0.99625, "loss_cls": 0.56075, "loss": 0.56075, "time": 0.54018} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.02089, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88938, "top5_acc": 0.9975, "loss_cls": 0.56478, "loss": 0.56478, "time": 0.54557} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.02088, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88812, "top5_acc": 0.995, "loss_cls": 0.60907, "loss": 0.60907, "time": 0.54588} +{"mode": "val", "epoch": 40, "iter": 533, "lr": 0.02086, "top1_acc": 0.83981, "top5_acc": 0.98979, "mean_class_accuracy": 0.7937} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.02085, "memory": 4083, "data_time": 0.19393, "top1_acc": 0.89938, "top5_acc": 0.99562, "loss_cls": 0.53759, "loss": 0.53759, "time": 0.71016} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.02083, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.91312, "top5_acc": 0.9975, "loss_cls": 0.47541, "loss": 0.47541, "time": 0.53923} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.02082, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.895, "top5_acc": 0.99625, "loss_cls": 0.52341, "loss": 0.52341, "time": 0.53164} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.0208, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.9075, "top5_acc": 0.99812, "loss_cls": 0.49817, "loss": 0.49817, "time": 0.53221} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.02079, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89188, "top5_acc": 0.99625, "loss_cls": 0.57435, "loss": 0.57435, "time": 0.54198} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.02077, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91125, "top5_acc": 0.99438, "loss_cls": 0.50586, "loss": 0.50586, "time": 0.53456} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.02076, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90812, "top5_acc": 0.99312, "loss_cls": 0.51152, "loss": 0.51152, "time": 0.53439} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.02074, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89375, "top5_acc": 0.995, "loss_cls": 0.55983, "loss": 0.55983, "time": 0.53771} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.02073, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90562, "top5_acc": 0.9975, "loss_cls": 0.50942, "loss": 0.50942, "time": 0.53898} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.02071, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89688, "top5_acc": 0.99688, "loss_cls": 0.51855, "loss": 0.51855, "time": 0.30287} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.0207, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90125, "top5_acc": 0.99562, "loss_cls": 0.51888, "loss": 0.51888, "time": 0.43309} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.02068, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8775, "top5_acc": 0.99625, "loss_cls": 0.58278, "loss": 0.58278, "time": 0.40241} +{"mode": "val", "epoch": 41, "iter": 533, "lr": 0.02067, "top1_acc": 0.85506, "top5_acc": 0.98979, "mean_class_accuracy": 0.80966} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.02065, "memory": 4083, "data_time": 0.19797, "top1_acc": 0.92062, "top5_acc": 0.99812, "loss_cls": 0.44853, "loss": 0.44853, "time": 0.78587} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.02064, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.9075, "top5_acc": 0.9975, "loss_cls": 0.47489, "loss": 0.47489, "time": 0.48059} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.02062, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.89938, "top5_acc": 0.99438, "loss_cls": 0.53802, "loss": 0.53802, "time": 0.47837} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.02061, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91438, "top5_acc": 0.99562, "loss_cls": 0.47963, "loss": 0.47963, "time": 0.48129} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.02059, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91, "top5_acc": 0.99688, "loss_cls": 0.46886, "loss": 0.46886, "time": 0.47799} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.02057, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.87, "top5_acc": 0.99438, "loss_cls": 0.63055, "loss": 0.63055, "time": 0.47863} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.02056, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9025, "top5_acc": 0.99625, "loss_cls": 0.52534, "loss": 0.52534, "time": 0.48071} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.02054, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89062, "top5_acc": 0.99688, "loss_cls": 0.54063, "loss": 0.54063, "time": 0.47916} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.02053, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90125, "top5_acc": 0.995, "loss_cls": 0.54601, "loss": 0.54601, "time": 0.37258} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.02051, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90438, "top5_acc": 0.99688, "loss_cls": 0.49508, "loss": 0.49508, "time": 0.51095} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.0205, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.90188, "top5_acc": 0.99438, "loss_cls": 0.53035, "loss": 0.53035, "time": 0.23453} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.02048, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87875, "top5_acc": 0.99562, "loss_cls": 0.58655, "loss": 0.58655, "time": 0.4089} +{"mode": "val", "epoch": 42, "iter": 533, "lr": 0.02047, "top1_acc": 0.84673, "top5_acc": 0.98721, "mean_class_accuracy": 0.78452} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.02045, "memory": 4083, "data_time": 0.19745, "top1_acc": 0.90125, "top5_acc": 0.99562, "loss_cls": 0.52985, "loss": 0.52985, "time": 0.8028} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.02044, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.92438, "top5_acc": 0.99562, "loss_cls": 0.45098, "loss": 0.45098, "time": 0.48883} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.02042, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9125, "top5_acc": 0.99688, "loss_cls": 0.4775, "loss": 0.4775, "time": 0.48861} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.0204, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.91062, "top5_acc": 1.0, "loss_cls": 0.47748, "loss": 0.47748, "time": 0.48909} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.02039, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91562, "top5_acc": 0.99562, "loss_cls": 0.45558, "loss": 0.45558, "time": 0.4904} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.02037, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89938, "top5_acc": 0.99812, "loss_cls": 0.54788, "loss": 0.54788, "time": 0.48991} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.02036, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90562, "top5_acc": 0.9975, "loss_cls": 0.5116, "loss": 0.5116, "time": 0.48997} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.02034, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89438, "top5_acc": 0.995, "loss_cls": 0.55673, "loss": 0.55673, "time": 0.48741} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.02033, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.905, "top5_acc": 0.99688, "loss_cls": 0.51049, "loss": 0.51049, "time": 0.41839} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.02031, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9075, "top5_acc": 0.99562, "loss_cls": 0.48324, "loss": 0.48324, "time": 0.43786} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.02029, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89438, "top5_acc": 0.9925, "loss_cls": 0.57428, "loss": 0.57428, "time": 0.29589} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.02028, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88812, "top5_acc": 0.995, "loss_cls": 0.55134, "loss": 0.55134, "time": 0.39635} +{"mode": "val", "epoch": 43, "iter": 533, "lr": 0.02026, "top1_acc": 0.8553, "top5_acc": 0.98932, "mean_class_accuracy": 0.78581} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.02025, "memory": 4083, "data_time": 0.19252, "top1_acc": 0.905, "top5_acc": 0.99625, "loss_cls": 0.49871, "loss": 0.49871, "time": 0.7895} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.02023, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.91, "top5_acc": 0.99875, "loss_cls": 0.47403, "loss": 0.47403, "time": 0.48819} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.02022, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91688, "top5_acc": 0.99688, "loss_cls": 0.46315, "loss": 0.46315, "time": 0.48927} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.0202, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89875, "top5_acc": 0.9975, "loss_cls": 0.52507, "loss": 0.52507, "time": 0.48804} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.02018, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90438, "top5_acc": 0.99688, "loss_cls": 0.48298, "loss": 0.48298, "time": 0.49168} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.02017, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8925, "top5_acc": 0.99875, "loss_cls": 0.52958, "loss": 0.52958, "time": 0.4907} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.02015, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89188, "top5_acc": 0.99375, "loss_cls": 0.58606, "loss": 0.58606, "time": 0.48704} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.02014, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.90125, "top5_acc": 0.99625, "loss_cls": 0.50777, "loss": 0.50777, "time": 0.48844} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.02012, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89625, "top5_acc": 0.99688, "loss_cls": 0.51849, "loss": 0.51849, "time": 0.44426} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.0201, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.90562, "top5_acc": 0.99625, "loss_cls": 0.49147, "loss": 0.49147, "time": 0.41189} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.02009, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.91062, "top5_acc": 0.99688, "loss_cls": 0.50064, "loss": 0.50064, "time": 0.32546} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.02007, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.90562, "top5_acc": 0.99875, "loss_cls": 0.48559, "loss": 0.48559, "time": 0.39424} +{"mode": "val", "epoch": 44, "iter": 533, "lr": 0.02006, "top1_acc": 0.84544, "top5_acc": 0.98956, "mean_class_accuracy": 0.7984} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.02004, "memory": 4083, "data_time": 0.19659, "top1_acc": 0.91125, "top5_acc": 0.99812, "loss_cls": 0.47205, "loss": 0.47205, "time": 0.79229} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.02003, "memory": 4083, "data_time": 0.00061, "top1_acc": 0.91, "top5_acc": 0.9975, "loss_cls": 0.49431, "loss": 0.49431, "time": 0.49369} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.02001, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.905, "top5_acc": 0.99688, "loss_cls": 0.51084, "loss": 0.51084, "time": 0.48577} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.01999, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.915, "top5_acc": 0.99812, "loss_cls": 0.46757, "loss": 0.46757, "time": 0.48994} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.01998, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91125, "top5_acc": 0.99625, "loss_cls": 0.45331, "loss": 0.45331, "time": 0.4853} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.01996, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90062, "top5_acc": 0.99812, "loss_cls": 0.514, "loss": 0.514, "time": 0.49314} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.01994, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9025, "top5_acc": 0.995, "loss_cls": 0.53703, "loss": 0.53703, "time": 0.48951} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.01993, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8925, "top5_acc": 0.99562, "loss_cls": 0.52739, "loss": 0.52739, "time": 0.48824} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.01991, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89438, "top5_acc": 0.99625, "loss_cls": 0.50622, "loss": 0.50622, "time": 0.44013} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.01989, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.89125, "top5_acc": 0.995, "loss_cls": 0.55471, "loss": 0.55471, "time": 0.43392} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.01988, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90312, "top5_acc": 0.99875, "loss_cls": 0.53133, "loss": 0.53133, "time": 0.30204} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.01986, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90688, "top5_acc": 0.99438, "loss_cls": 0.50735, "loss": 0.50735, "time": 0.39833} +{"mode": "val", "epoch": 45, "iter": 533, "lr": 0.01985, "top1_acc": 0.87114, "top5_acc": 0.99002, "mean_class_accuracy": 0.82778} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.01983, "memory": 4083, "data_time": 0.19739, "top1_acc": 0.91188, "top5_acc": 0.99625, "loss_cls": 0.48117, "loss": 0.48117, "time": 0.80515} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.01981, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.91438, "top5_acc": 0.99875, "loss_cls": 0.46053, "loss": 0.46053, "time": 0.48708} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.0198, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.90562, "top5_acc": 0.99688, "loss_cls": 0.47778, "loss": 0.47778, "time": 0.48731} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.01978, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90438, "top5_acc": 0.99562, "loss_cls": 0.51542, "loss": 0.51542, "time": 0.49287} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.01976, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.915, "top5_acc": 0.99562, "loss_cls": 0.491, "loss": 0.491, "time": 0.49356} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.01975, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88688, "top5_acc": 0.99688, "loss_cls": 0.54863, "loss": 0.54863, "time": 0.49023} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.01973, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89125, "top5_acc": 0.99688, "loss_cls": 0.55683, "loss": 0.55683, "time": 0.48968} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.01971, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.91438, "top5_acc": 0.99875, "loss_cls": 0.4654, "loss": 0.4654, "time": 0.48831} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.0197, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9075, "top5_acc": 0.99562, "loss_cls": 0.49485, "loss": 0.49485, "time": 0.4282} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.01968, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9075, "top5_acc": 0.99562, "loss_cls": 0.50219, "loss": 0.50219, "time": 0.435} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.01966, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.89938, "top5_acc": 0.99688, "loss_cls": 0.50746, "loss": 0.50746, "time": 0.29926} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.01965, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89375, "top5_acc": 0.99688, "loss_cls": 0.5415, "loss": 0.5415, "time": 0.40973} +{"mode": "val", "epoch": 46, "iter": 533, "lr": 0.01963, "top1_acc": 0.84521, "top5_acc": 0.9865, "mean_class_accuracy": 0.81678} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.01962, "memory": 4083, "data_time": 0.2, "top1_acc": 0.91375, "top5_acc": 0.99562, "loss_cls": 0.46435, "loss": 0.46435, "time": 0.80918} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.0196, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91562, "top5_acc": 0.99812, "loss_cls": 0.45126, "loss": 0.45126, "time": 0.48793} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.01958, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90312, "top5_acc": 0.99625, "loss_cls": 0.50217, "loss": 0.50217, "time": 0.48335} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.01957, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.915, "top5_acc": 0.99875, "loss_cls": 0.46, "loss": 0.46, "time": 0.48876} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.01955, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9125, "top5_acc": 0.9975, "loss_cls": 0.46363, "loss": 0.46363, "time": 0.48988} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.01953, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8925, "top5_acc": 0.99812, "loss_cls": 0.53145, "loss": 0.53145, "time": 0.49164} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.01952, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88812, "top5_acc": 0.9975, "loss_cls": 0.55659, "loss": 0.55659, "time": 0.49492} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.0195, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90438, "top5_acc": 0.99562, "loss_cls": 0.51618, "loss": 0.51618, "time": 0.49193} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.01948, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90438, "top5_acc": 0.99938, "loss_cls": 0.48454, "loss": 0.48454, "time": 0.42034} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.01947, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90812, "top5_acc": 0.99688, "loss_cls": 0.46498, "loss": 0.46498, "time": 0.44205} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.01945, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90438, "top5_acc": 0.99312, "loss_cls": 0.51852, "loss": 0.51852, "time": 0.29177} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.01943, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.90125, "top5_acc": 0.99812, "loss_cls": 0.50637, "loss": 0.50637, "time": 0.39826} +{"mode": "val", "epoch": 47, "iter": 533, "lr": 0.01942, "top1_acc": 0.87149, "top5_acc": 0.99002, "mean_class_accuracy": 0.83855} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.0194, "memory": 4083, "data_time": 0.19329, "top1_acc": 0.91625, "top5_acc": 0.99938, "loss_cls": 0.43885, "loss": 0.43885, "time": 0.80472} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.01938, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.92188, "top5_acc": 0.99688, "loss_cls": 0.45484, "loss": 0.45484, "time": 0.49125} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.01937, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.90688, "top5_acc": 0.99812, "loss_cls": 0.49212, "loss": 0.49212, "time": 0.48913} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.01935, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89812, "top5_acc": 0.99688, "loss_cls": 0.5152, "loss": 0.5152, "time": 0.48896} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.01933, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.90938, "top5_acc": 0.99625, "loss_cls": 0.47973, "loss": 0.47973, "time": 0.49109} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.01932, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90188, "top5_acc": 0.99688, "loss_cls": 0.50241, "loss": 0.50241, "time": 0.48834} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.0193, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.91438, "top5_acc": 0.99938, "loss_cls": 0.46546, "loss": 0.46546, "time": 0.48741} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.01928, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.90625, "top5_acc": 0.99562, "loss_cls": 0.49925, "loss": 0.49925, "time": 0.48666} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.01926, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91312, "top5_acc": 0.99688, "loss_cls": 0.45725, "loss": 0.45725, "time": 0.43965} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.01925, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90688, "top5_acc": 0.9975, "loss_cls": 0.50707, "loss": 0.50707, "time": 0.40756} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.01923, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.915, "top5_acc": 0.99688, "loss_cls": 0.48391, "loss": 0.48391, "time": 0.3246} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.01921, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90125, "top5_acc": 0.995, "loss_cls": 0.54572, "loss": 0.54572, "time": 0.38005} +{"mode": "val", "epoch": 48, "iter": 533, "lr": 0.0192, "top1_acc": 0.86128, "top5_acc": 0.99061, "mean_class_accuracy": 0.81069} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.01918, "memory": 4083, "data_time": 0.19129, "top1_acc": 0.9225, "top5_acc": 0.99875, "loss_cls": 0.45264, "loss": 0.45264, "time": 0.78982} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.01916, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.905, "top5_acc": 0.9975, "loss_cls": 0.46909, "loss": 0.46909, "time": 0.49095} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.01915, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91812, "top5_acc": 0.99812, "loss_cls": 0.4408, "loss": 0.4408, "time": 0.49194} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.01913, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.90625, "top5_acc": 0.99438, "loss_cls": 0.51453, "loss": 0.51453, "time": 0.49227} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.01911, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91188, "top5_acc": 0.99625, "loss_cls": 0.48353, "loss": 0.48353, "time": 0.49292} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.01909, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91, "top5_acc": 0.99812, "loss_cls": 0.47295, "loss": 0.47295, "time": 0.49005} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.01908, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90438, "top5_acc": 0.99438, "loss_cls": 0.50662, "loss": 0.50662, "time": 0.49136} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.01906, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.89125, "top5_acc": 0.99688, "loss_cls": 0.53625, "loss": 0.53625, "time": 0.48987} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.01904, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91875, "top5_acc": 0.9975, "loss_cls": 0.46153, "loss": 0.46153, "time": 0.47086} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.01902, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.915, "top5_acc": 0.99625, "loss_cls": 0.46719, "loss": 0.46719, "time": 0.34491} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.01901, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90438, "top5_acc": 0.99938, "loss_cls": 0.50387, "loss": 0.50387, "time": 0.38949} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.01899, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9125, "top5_acc": 0.9975, "loss_cls": 0.48454, "loss": 0.48454, "time": 0.36455} +{"mode": "val", "epoch": 49, "iter": 533, "lr": 0.01898, "top1_acc": 0.84826, "top5_acc": 0.98956, "mean_class_accuracy": 0.80261} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.01896, "memory": 4083, "data_time": 0.19463, "top1_acc": 0.90438, "top5_acc": 0.99688, "loss_cls": 0.49533, "loss": 0.49533, "time": 0.79234} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.01894, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.89188, "top5_acc": 0.99562, "loss_cls": 0.53979, "loss": 0.53979, "time": 0.49044} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.01892, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.915, "top5_acc": 0.995, "loss_cls": 0.45192, "loss": 0.45192, "time": 0.48852} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.01891, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9125, "top5_acc": 0.9975, "loss_cls": 0.43438, "loss": 0.43438, "time": 0.48787} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.01889, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91375, "top5_acc": 0.99688, "loss_cls": 0.47146, "loss": 0.47146, "time": 0.48766} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.01887, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92062, "top5_acc": 0.99625, "loss_cls": 0.47061, "loss": 0.47061, "time": 0.49217} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.01885, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92375, "top5_acc": 0.99812, "loss_cls": 0.42478, "loss": 0.42478, "time": 0.48873} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.01884, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.91438, "top5_acc": 0.9975, "loss_cls": 0.46114, "loss": 0.46114, "time": 0.49011} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.01882, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.905, "top5_acc": 0.99438, "loss_cls": 0.51161, "loss": 0.51161, "time": 0.4782} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.0188, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.90188, "top5_acc": 0.9975, "loss_cls": 0.48823, "loss": 0.48823, "time": 0.34033} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.01878, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90438, "top5_acc": 0.995, "loss_cls": 0.51693, "loss": 0.51693, "time": 0.39146} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.01876, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93188, "top5_acc": 0.99812, "loss_cls": 0.40672, "loss": 0.40672, "time": 0.35083} +{"mode": "val", "epoch": 50, "iter": 533, "lr": 0.01875, "top1_acc": 0.86422, "top5_acc": 0.99167, "mean_class_accuracy": 0.80582} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.01873, "memory": 4083, "data_time": 0.18655, "top1_acc": 0.90125, "top5_acc": 0.99625, "loss_cls": 0.49518, "loss": 0.49518, "time": 0.7894} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.01871, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9225, "top5_acc": 0.99625, "loss_cls": 0.42566, "loss": 0.42566, "time": 0.49407} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.0187, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.925, "top5_acc": 0.99688, "loss_cls": 0.40092, "loss": 0.40092, "time": 0.49064} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.01868, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.92188, "top5_acc": 0.99875, "loss_cls": 0.42497, "loss": 0.42497, "time": 0.48807} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.01866, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9175, "top5_acc": 1.0, "loss_cls": 0.43676, "loss": 0.43676, "time": 0.49426} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.01864, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.90688, "top5_acc": 0.9975, "loss_cls": 0.49593, "loss": 0.49593, "time": 0.49016} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.01863, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90562, "top5_acc": 0.9975, "loss_cls": 0.46833, "loss": 0.46833, "time": 0.49713} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.01861, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91938, "top5_acc": 0.99938, "loss_cls": 0.43116, "loss": 0.43116, "time": 0.48906} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.01859, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92625, "top5_acc": 0.99812, "loss_cls": 0.41955, "loss": 0.41955, "time": 0.49159} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.01857, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.90625, "top5_acc": 0.99688, "loss_cls": 0.50285, "loss": 0.50285, "time": 0.29511} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.01855, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.895, "top5_acc": 0.99812, "loss_cls": 0.50583, "loss": 0.50583, "time": 0.44198} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.01854, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.91, "top5_acc": 0.99312, "loss_cls": 0.48862, "loss": 0.48862, "time": 0.34131} +{"mode": "val", "epoch": 51, "iter": 533, "lr": 0.01852, "top1_acc": 0.86891, "top5_acc": 0.99261, "mean_class_accuracy": 0.81918} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.0185, "memory": 4083, "data_time": 0.19215, "top1_acc": 0.91875, "top5_acc": 0.99875, "loss_cls": 0.44278, "loss": 0.44278, "time": 0.79429} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.01849, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93, "top5_acc": 0.99812, "loss_cls": 0.38611, "loss": 0.38611, "time": 0.48865} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.01847, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.91, "top5_acc": 0.99875, "loss_cls": 0.44407, "loss": 0.44407, "time": 0.48579} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.01845, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.45536, "loss": 0.45536, "time": 0.49141} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.01843, "memory": 4083, "data_time": 0.00071, "top1_acc": 0.92688, "top5_acc": 0.99812, "loss_cls": 0.41217, "loss": 0.41217, "time": 0.49103} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.01841, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93375, "top5_acc": 0.99812, "loss_cls": 0.40012, "loss": 0.40012, "time": 0.48778} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.0184, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91, "top5_acc": 0.99875, "loss_cls": 0.46567, "loss": 0.46567, "time": 0.49135} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.01838, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.88938, "top5_acc": 0.99625, "loss_cls": 0.55216, "loss": 0.55216, "time": 0.48738} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.01836, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89812, "top5_acc": 0.99812, "loss_cls": 0.51863, "loss": 0.51863, "time": 0.49142} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.01834, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.91688, "top5_acc": 0.99625, "loss_cls": 0.44359, "loss": 0.44359, "time": 0.30968} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.01832, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91688, "top5_acc": 0.99875, "loss_cls": 0.43862, "loss": 0.43862, "time": 0.42965} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.01831, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9075, "top5_acc": 0.9975, "loss_cls": 0.45738, "loss": 0.45738, "time": 0.33807} +{"mode": "val", "epoch": 52, "iter": 533, "lr": 0.01829, "top1_acc": 0.85096, "top5_acc": 0.9885, "mean_class_accuracy": 0.82544} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.01827, "memory": 4083, "data_time": 0.19105, "top1_acc": 0.92875, "top5_acc": 0.99938, "loss_cls": 0.40826, "loss": 0.40826, "time": 0.78893} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.01826, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.92812, "top5_acc": 0.99875, "loss_cls": 0.42383, "loss": 0.42383, "time": 0.49009} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.01824, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92375, "top5_acc": 0.99938, "loss_cls": 0.41504, "loss": 0.41504, "time": 0.49302} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.01822, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91312, "top5_acc": 0.9975, "loss_cls": 0.46386, "loss": 0.46386, "time": 0.48942} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.0182, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91938, "top5_acc": 0.99875, "loss_cls": 0.42493, "loss": 0.42493, "time": 0.48956} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.01818, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90688, "top5_acc": 0.9975, "loss_cls": 0.47001, "loss": 0.47001, "time": 0.48876} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.01816, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90625, "top5_acc": 0.99625, "loss_cls": 0.50079, "loss": 0.50079, "time": 0.48939} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.01815, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93, "top5_acc": 0.99938, "loss_cls": 0.38447, "loss": 0.38447, "time": 0.49014} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.01813, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91125, "top5_acc": 0.99625, "loss_cls": 0.46204, "loss": 0.46204, "time": 0.49012} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.01811, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.915, "top5_acc": 0.99625, "loss_cls": 0.44929, "loss": 0.44929, "time": 0.29102} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.01809, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90875, "top5_acc": 0.995, "loss_cls": 0.48312, "loss": 0.48312, "time": 0.46583} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.01807, "memory": 4083, "data_time": 0.00094, "top1_acc": 0.915, "top5_acc": 0.99812, "loss_cls": 0.46757, "loss": 0.46757, "time": 0.33538} +{"mode": "val", "epoch": 53, "iter": 533, "lr": 0.01806, "top1_acc": 0.86833, "top5_acc": 0.99096, "mean_class_accuracy": 0.82662} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.01804, "memory": 4083, "data_time": 0.19327, "top1_acc": 0.92312, "top5_acc": 0.99688, "loss_cls": 0.4152, "loss": 0.4152, "time": 0.80751} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.01802, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91875, "top5_acc": 0.9975, "loss_cls": 0.43312, "loss": 0.43312, "time": 0.49558} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.018, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.41728, "loss": 0.41728, "time": 0.49089} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.01798, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91562, "top5_acc": 0.9975, "loss_cls": 0.43404, "loss": 0.43404, "time": 0.48963} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.01797, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93, "top5_acc": 0.99938, "loss_cls": 0.39396, "loss": 0.39396, "time": 0.48896} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.01795, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.915, "top5_acc": 0.99688, "loss_cls": 0.43629, "loss": 0.43629, "time": 0.48919} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.01793, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90312, "top5_acc": 0.99562, "loss_cls": 0.52532, "loss": 0.52532, "time": 0.4878} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.01791, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92375, "top5_acc": 0.99688, "loss_cls": 0.46895, "loss": 0.46895, "time": 0.48848} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.01789, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90125, "top5_acc": 0.9975, "loss_cls": 0.524, "loss": 0.524, "time": 0.48957} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.01787, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90125, "top5_acc": 0.9975, "loss_cls": 0.50726, "loss": 0.50726, "time": 0.29956} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.01786, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92812, "top5_acc": 0.99688, "loss_cls": 0.42961, "loss": 0.42961, "time": 0.44045} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.01784, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91062, "top5_acc": 0.995, "loss_cls": 0.48964, "loss": 0.48964, "time": 0.34623} +{"mode": "val", "epoch": 54, "iter": 533, "lr": 0.01782, "top1_acc": 0.87548, "top5_acc": 0.9919, "mean_class_accuracy": 0.81985} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.0178, "memory": 4083, "data_time": 0.19601, "top1_acc": 0.91688, "top5_acc": 1.0, "loss_cls": 0.43717, "loss": 0.43717, "time": 0.79862} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.01779, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91938, "top5_acc": 0.99875, "loss_cls": 0.42171, "loss": 0.42171, "time": 0.49175} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.01777, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.92875, "top5_acc": 0.99812, "loss_cls": 0.39784, "loss": 0.39784, "time": 0.49152} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.01775, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91938, "top5_acc": 0.99875, "loss_cls": 0.43946, "loss": 0.43946, "time": 0.49388} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.01773, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.38457, "loss": 0.38457, "time": 0.4917} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.01771, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.91875, "top5_acc": 0.99875, "loss_cls": 0.46334, "loss": 0.46334, "time": 0.49693} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.01769, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.91375, "top5_acc": 0.99812, "loss_cls": 0.43568, "loss": 0.43568, "time": 0.49066} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.01767, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.905, "top5_acc": 0.99625, "loss_cls": 0.466, "loss": 0.466, "time": 0.48971} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.01766, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.91438, "top5_acc": 0.99625, "loss_cls": 0.46973, "loss": 0.46973, "time": 0.49007} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.01764, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91875, "top5_acc": 0.99875, "loss_cls": 0.45118, "loss": 0.45118, "time": 0.30946} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.01762, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91875, "top5_acc": 0.9975, "loss_cls": 0.45413, "loss": 0.45413, "time": 0.42995} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.0176, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92688, "top5_acc": 0.99875, "loss_cls": 0.41051, "loss": 0.41051, "time": 0.3393} +{"mode": "val", "epoch": 55, "iter": 533, "lr": 0.01758, "top1_acc": 0.86938, "top5_acc": 0.99179, "mean_class_accuracy": 0.82595} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.01757, "memory": 4083, "data_time": 0.19271, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.42609, "loss": 0.42609, "time": 0.80247} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.01755, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93125, "top5_acc": 0.9975, "loss_cls": 0.4294, "loss": 0.4294, "time": 0.49017} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.01753, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.91938, "top5_acc": 0.99875, "loss_cls": 0.41844, "loss": 0.41844, "time": 0.48959} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.01751, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93, "top5_acc": 0.9975, "loss_cls": 0.38763, "loss": 0.38763, "time": 0.48938} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.01749, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92812, "top5_acc": 0.9975, "loss_cls": 0.40909, "loss": 0.40909, "time": 0.48809} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.01747, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9225, "top5_acc": 0.99812, "loss_cls": 0.43125, "loss": 0.43125, "time": 0.49237} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.01745, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.43888, "loss": 0.43888, "time": 0.49089} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.01743, "memory": 4083, "data_time": 0.00067, "top1_acc": 0.915, "top5_acc": 0.99812, "loss_cls": 0.45723, "loss": 0.45723, "time": 0.48721} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.01742, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.93125, "top5_acc": 0.99625, "loss_cls": 0.4263, "loss": 0.4263, "time": 0.4892} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.0174, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91625, "top5_acc": 0.99875, "loss_cls": 0.45742, "loss": 0.45742, "time": 0.31124} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.01738, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9225, "top5_acc": 0.99688, "loss_cls": 0.44131, "loss": 0.44131, "time": 0.43189} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.01736, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92188, "top5_acc": 0.99812, "loss_cls": 0.40152, "loss": 0.40152, "time": 0.33468} +{"mode": "val", "epoch": 56, "iter": 533, "lr": 0.01734, "top1_acc": 0.87889, "top5_acc": 0.99308, "mean_class_accuracy": 0.84019} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.01733, "memory": 4083, "data_time": 0.1912, "top1_acc": 0.92688, "top5_acc": 0.99688, "loss_cls": 0.42174, "loss": 0.42174, "time": 0.79774} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.01731, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91625, "top5_acc": 0.99812, "loss_cls": 0.44716, "loss": 0.44716, "time": 0.48954} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.01729, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92938, "top5_acc": 0.99688, "loss_cls": 0.42723, "loss": 0.42723, "time": 0.49033} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.01727, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.4275, "loss": 0.4275, "time": 0.49054} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.01725, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91625, "top5_acc": 0.99875, "loss_cls": 0.42893, "loss": 0.42893, "time": 0.48902} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.01723, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91562, "top5_acc": 0.99688, "loss_cls": 0.43469, "loss": 0.43469, "time": 0.49308} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.01721, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91688, "top5_acc": 0.99812, "loss_cls": 0.44889, "loss": 0.44889, "time": 0.49463} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.01719, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.90875, "top5_acc": 0.99875, "loss_cls": 0.47508, "loss": 0.47508, "time": 0.48587} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.01717, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91688, "top5_acc": 0.99562, "loss_cls": 0.45647, "loss": 0.45647, "time": 0.48775} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.01716, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91688, "top5_acc": 0.99688, "loss_cls": 0.43992, "loss": 0.43992, "time": 0.28829} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.01714, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90062, "top5_acc": 0.99562, "loss_cls": 0.50162, "loss": 0.50162, "time": 0.46003} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.01712, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91625, "top5_acc": 0.9975, "loss_cls": 0.46762, "loss": 0.46762, "time": 0.32714} +{"mode": "val", "epoch": 57, "iter": 533, "lr": 0.0171, "top1_acc": 0.87032, "top5_acc": 0.98697, "mean_class_accuracy": 0.82797} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.01708, "memory": 4083, "data_time": 0.19232, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.38626, "loss": 0.38626, "time": 0.78914} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.01706, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.91438, "top5_acc": 0.99812, "loss_cls": 0.42035, "loss": 0.42035, "time": 0.48963} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.01704, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92812, "top5_acc": 1.0, "loss_cls": 0.40413, "loss": 0.40413, "time": 0.49044} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.01703, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91625, "top5_acc": 0.99812, "loss_cls": 0.43256, "loss": 0.43256, "time": 0.4888} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.01701, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.935, "top5_acc": 0.99938, "loss_cls": 0.38418, "loss": 0.38418, "time": 0.48835} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.01699, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93375, "top5_acc": 0.99812, "loss_cls": 0.37804, "loss": 0.37804, "time": 0.49096} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.01697, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91062, "top5_acc": 0.99562, "loss_cls": 0.48702, "loss": 0.48702, "time": 0.49174} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.01695, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.92312, "top5_acc": 0.9975, "loss_cls": 0.45774, "loss": 0.45774, "time": 0.49011} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.01693, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.92062, "top5_acc": 0.99812, "loss_cls": 0.45429, "loss": 0.45429, "time": 0.49035} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.01691, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9125, "top5_acc": 0.99938, "loss_cls": 0.45578, "loss": 0.45578, "time": 0.27468} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.01689, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92, "top5_acc": 0.995, "loss_cls": 0.43415, "loss": 0.43415, "time": 0.49191} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.01687, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92188, "top5_acc": 0.99812, "loss_cls": 0.42379, "loss": 0.42379, "time": 0.31298} +{"mode": "val", "epoch": 58, "iter": 533, "lr": 0.01686, "top1_acc": 0.88264, "top5_acc": 0.99096, "mean_class_accuracy": 0.83441} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.01684, "memory": 4083, "data_time": 0.19461, "top1_acc": 0.94, "top5_acc": 1.0, "loss_cls": 0.36619, "loss": 0.36619, "time": 0.7948} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.01682, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9275, "top5_acc": 0.99812, "loss_cls": 0.38469, "loss": 0.38469, "time": 0.48926} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.0168, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92312, "top5_acc": 0.99812, "loss_cls": 0.42841, "loss": 0.42841, "time": 0.48949} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.01678, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92438, "top5_acc": 0.99562, "loss_cls": 0.4111, "loss": 0.4111, "time": 0.49089} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.01676, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92125, "top5_acc": 0.9975, "loss_cls": 0.44902, "loss": 0.44902, "time": 0.49118} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.01674, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92688, "top5_acc": 0.99812, "loss_cls": 0.40842, "loss": 0.40842, "time": 0.49174} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.01672, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.92812, "top5_acc": 0.99812, "loss_cls": 0.39616, "loss": 0.39616, "time": 0.48933} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.0167, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92562, "top5_acc": 0.99812, "loss_cls": 0.43314, "loss": 0.43314, "time": 0.49054} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.01668, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91312, "top5_acc": 0.9975, "loss_cls": 0.46714, "loss": 0.46714, "time": 0.4877} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.01667, "memory": 4083, "data_time": 0.00072, "top1_acc": 0.92438, "top5_acc": 0.99875, "loss_cls": 0.41152, "loss": 0.41152, "time": 0.28555} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.01665, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89625, "top5_acc": 0.99625, "loss_cls": 0.5067, "loss": 0.5067, "time": 0.48778} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.01663, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92062, "top5_acc": 0.99625, "loss_cls": 0.42993, "loss": 0.42993, "time": 0.32644} +{"mode": "val", "epoch": 59, "iter": 533, "lr": 0.01661, "top1_acc": 0.88123, "top5_acc": 0.99179, "mean_class_accuracy": 0.84055} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.01659, "memory": 4083, "data_time": 0.19106, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.41022, "loss": 0.41022, "time": 0.78967} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.01657, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.92812, "top5_acc": 0.99938, "loss_cls": 0.40024, "loss": 0.40024, "time": 0.4871} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.01655, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93625, "top5_acc": 0.99688, "loss_cls": 0.40086, "loss": 0.40086, "time": 0.49174} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.01653, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93625, "top5_acc": 0.99938, "loss_cls": 0.35274, "loss": 0.35274, "time": 0.49307} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.01651, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.925, "top5_acc": 0.99812, "loss_cls": 0.40416, "loss": 0.40416, "time": 0.4892} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.0165, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92, "top5_acc": 0.99812, "loss_cls": 0.40675, "loss": 0.40675, "time": 0.49366} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.01648, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92438, "top5_acc": 0.9975, "loss_cls": 0.41997, "loss": 0.41997, "time": 0.49359} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.01646, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.91, "top5_acc": 0.995, "loss_cls": 0.48216, "loss": 0.48216, "time": 0.48949} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.01644, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92812, "top5_acc": 0.9975, "loss_cls": 0.38707, "loss": 0.38707, "time": 0.4907} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.01642, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92562, "top5_acc": 0.99938, "loss_cls": 0.42855, "loss": 0.42855, "time": 0.27185} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.0164, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92625, "top5_acc": 0.99938, "loss_cls": 0.40532, "loss": 0.40532, "time": 0.5038} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.01638, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.40076, "loss": 0.40076, "time": 0.31875} +{"mode": "val", "epoch": 60, "iter": 533, "lr": 0.01636, "top1_acc": 0.88569, "top5_acc": 0.9919, "mean_class_accuracy": 0.85083} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.01634, "memory": 4083, "data_time": 0.19661, "top1_acc": 0.92875, "top5_acc": 1.0, "loss_cls": 0.39303, "loss": 0.39303, "time": 0.81122} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.01632, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92875, "top5_acc": 0.9975, "loss_cls": 0.40503, "loss": 0.40503, "time": 0.49464} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.0163, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93, "top5_acc": 0.9975, "loss_cls": 0.40758, "loss": 0.40758, "time": 0.48936} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.01629, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.92125, "top5_acc": 0.99625, "loss_cls": 0.44276, "loss": 0.44276, "time": 0.48993} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.01627, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.40335, "loss": 0.40335, "time": 0.48951} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.01625, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.42961, "loss": 0.42961, "time": 0.49089} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.01623, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.41813, "loss": 0.41813, "time": 0.48965} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.01621, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92, "top5_acc": 0.995, "loss_cls": 0.43411, "loss": 0.43411, "time": 0.48872} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.01619, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.92875, "top5_acc": 0.9975, "loss_cls": 0.41307, "loss": 0.41307, "time": 0.4918} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.01617, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92688, "top5_acc": 0.99812, "loss_cls": 0.40378, "loss": 0.40378, "time": 0.29068} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.01615, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9175, "top5_acc": 0.9975, "loss_cls": 0.43287, "loss": 0.43287, "time": 0.46279} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.01613, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92375, "top5_acc": 0.99812, "loss_cls": 0.41167, "loss": 0.41167, "time": 0.32923} +{"mode": "val", "epoch": 61, "iter": 533, "lr": 0.01611, "top1_acc": 0.84802, "top5_acc": 0.98826, "mean_class_accuracy": 0.80418} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.01609, "memory": 4083, "data_time": 0.19296, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.39076, "loss": 0.39076, "time": 0.79132} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.01607, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93938, "top5_acc": 0.99938, "loss_cls": 0.35321, "loss": 0.35321, "time": 0.49447} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.01605, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92438, "top5_acc": 0.99875, "loss_cls": 0.41794, "loss": 0.41794, "time": 0.4925} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.01603, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92, "top5_acc": 0.99875, "loss_cls": 0.4272, "loss": 0.4272, "time": 0.48714} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.01602, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93312, "top5_acc": 0.99875, "loss_cls": 0.38815, "loss": 0.38815, "time": 0.48973} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.016, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93438, "top5_acc": 0.99625, "loss_cls": 0.37322, "loss": 0.37322, "time": 0.49023} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.01598, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92, "top5_acc": 0.99625, "loss_cls": 0.44799, "loss": 0.44799, "time": 0.48836} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.01596, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92688, "top5_acc": 0.99875, "loss_cls": 0.41514, "loss": 0.41514, "time": 0.49123} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.01594, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.925, "top5_acc": 0.99688, "loss_cls": 0.42473, "loss": 0.42473, "time": 0.49048} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.01592, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93375, "top5_acc": 0.99812, "loss_cls": 0.37652, "loss": 0.37652, "time": 0.30163} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.0159, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.90312, "top5_acc": 0.99812, "loss_cls": 0.46379, "loss": 0.46379, "time": 0.45878} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.01588, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91688, "top5_acc": 0.99812, "loss_cls": 0.43171, "loss": 0.43171, "time": 0.31533} +{"mode": "val", "epoch": 62, "iter": 533, "lr": 0.01586, "top1_acc": 0.90318, "top5_acc": 0.99448, "mean_class_accuracy": 0.8766} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.01584, "memory": 4083, "data_time": 0.19691, "top1_acc": 0.94, "top5_acc": 0.99688, "loss_cls": 0.34841, "loss": 0.34841, "time": 0.80638} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.01582, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93938, "top5_acc": 0.99875, "loss_cls": 0.34257, "loss": 0.34257, "time": 0.48848} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.0158, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93625, "top5_acc": 0.99938, "loss_cls": 0.32885, "loss": 0.32885, "time": 0.49202} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.01578, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9225, "top5_acc": 0.9975, "loss_cls": 0.40202, "loss": 0.40202, "time": 0.48813} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.01576, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92438, "top5_acc": 0.9975, "loss_cls": 0.4097, "loss": 0.4097, "time": 0.48922} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.01574, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93, "top5_acc": 0.99812, "loss_cls": 0.3989, "loss": 0.3989, "time": 0.4904} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.01572, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.39837, "loss": 0.39837, "time": 0.48917} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.0157, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92625, "top5_acc": 0.99938, "loss_cls": 0.36557, "loss": 0.36557, "time": 0.48663} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.01568, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.92875, "top5_acc": 0.99812, "loss_cls": 0.37355, "loss": 0.37355, "time": 0.48714} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.01566, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92625, "top5_acc": 0.9975, "loss_cls": 0.41269, "loss": 0.41269, "time": 0.2717} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.01564, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.92312, "top5_acc": 0.99688, "loss_cls": 0.43575, "loss": 0.43575, "time": 0.49609} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.01562, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.92562, "top5_acc": 0.99688, "loss_cls": 0.39999, "loss": 0.39999, "time": 0.31409} +{"mode": "val", "epoch": 63, "iter": 533, "lr": 0.01561, "top1_acc": 0.88264, "top5_acc": 0.99014, "mean_class_accuracy": 0.84249} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.01559, "memory": 4083, "data_time": 0.19468, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.37599, "loss": 0.37599, "time": 0.79932} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.01557, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.935, "top5_acc": 0.9975, "loss_cls": 0.36593, "loss": 0.36593, "time": 0.49454} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.01555, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.93062, "top5_acc": 0.99625, "loss_cls": 0.39443, "loss": 0.39443, "time": 0.49453} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.01553, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93688, "top5_acc": 0.99938, "loss_cls": 0.38222, "loss": 0.38222, "time": 0.49095} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.01551, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92188, "top5_acc": 0.99938, "loss_cls": 0.39557, "loss": 0.39557, "time": 0.49047} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.01549, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.3986, "loss": 0.3986, "time": 0.49142} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.01547, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93812, "top5_acc": 0.99938, "loss_cls": 0.34854, "loss": 0.34854, "time": 0.49207} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.01545, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.92688, "top5_acc": 0.99812, "loss_cls": 0.38072, "loss": 0.38072, "time": 0.48805} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.01543, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9375, "top5_acc": 0.9975, "loss_cls": 0.36737, "loss": 0.36737, "time": 0.49186} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.01541, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92438, "top5_acc": 0.99688, "loss_cls": 0.43586, "loss": 0.43586, "time": 0.28101} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.01539, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91562, "top5_acc": 0.99562, "loss_cls": 0.43379, "loss": 0.43379, "time": 0.4843} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.01537, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9125, "top5_acc": 0.99875, "loss_cls": 0.41453, "loss": 0.41453, "time": 0.32083} +{"mode": "val", "epoch": 64, "iter": 533, "lr": 0.01535, "top1_acc": 0.85213, "top5_acc": 0.98662, "mean_class_accuracy": 0.80646} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.01533, "memory": 4083, "data_time": 0.19759, "top1_acc": 0.92438, "top5_acc": 0.99625, "loss_cls": 0.41591, "loss": 0.41591, "time": 0.80128} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.01531, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92125, "top5_acc": 0.9975, "loss_cls": 0.42701, "loss": 0.42701, "time": 0.49448} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.01529, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.93688, "top5_acc": 0.9975, "loss_cls": 0.32958, "loss": 0.32958, "time": 0.49414} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.01527, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.93125, "top5_acc": 1.0, "loss_cls": 0.36767, "loss": 0.36767, "time": 0.4943} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.01526, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.94, "top5_acc": 0.9975, "loss_cls": 0.35071, "loss": 0.35071, "time": 0.49225} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.01524, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.36914, "loss": 0.36914, "time": 0.49535} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.01522, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.925, "top5_acc": 1.0, "loss_cls": 0.42474, "loss": 0.42474, "time": 0.49022} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0152, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92, "top5_acc": 0.9975, "loss_cls": 0.42156, "loss": 0.42156, "time": 0.48919} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.01518, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92625, "top5_acc": 0.99562, "loss_cls": 0.40544, "loss": 0.40544, "time": 0.49164} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.01516, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92375, "top5_acc": 0.99562, "loss_cls": 0.40667, "loss": 0.40667, "time": 0.28464} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.01514, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94188, "top5_acc": 0.99875, "loss_cls": 0.35317, "loss": 0.35317, "time": 0.46997} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.01512, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.92625, "top5_acc": 0.99812, "loss_cls": 0.39797, "loss": 0.39797, "time": 0.32389} +{"mode": "val", "epoch": 65, "iter": 533, "lr": 0.0151, "top1_acc": 0.85988, "top5_acc": 0.98686, "mean_class_accuracy": 0.81106} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.01508, "memory": 4083, "data_time": 0.203, "top1_acc": 0.92562, "top5_acc": 0.99938, "loss_cls": 0.41996, "loss": 0.41996, "time": 0.80606} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.01506, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93062, "top5_acc": 0.99812, "loss_cls": 0.38403, "loss": 0.38403, "time": 0.49262} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.01504, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93938, "top5_acc": 0.99812, "loss_cls": 0.33728, "loss": 0.33728, "time": 0.48643} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.01502, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.94625, "top5_acc": 1.0, "loss_cls": 0.33247, "loss": 0.33247, "time": 0.49003} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.015, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92875, "top5_acc": 0.99938, "loss_cls": 0.38802, "loss": 0.38802, "time": 0.48873} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.01498, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93062, "top5_acc": 0.99688, "loss_cls": 0.39507, "loss": 0.39507, "time": 0.49492} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.01496, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93125, "top5_acc": 0.99688, "loss_cls": 0.38631, "loss": 0.38631, "time": 0.49078} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.01494, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92938, "top5_acc": 0.9975, "loss_cls": 0.35983, "loss": 0.35983, "time": 0.49245} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.01492, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91188, "top5_acc": 0.99438, "loss_cls": 0.47469, "loss": 0.47469, "time": 0.48928} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.0149, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93438, "top5_acc": 0.99938, "loss_cls": 0.38827, "loss": 0.38827, "time": 0.28158} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.01488, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.32066, "loss": 0.32066, "time": 0.48355} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.01486, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91312, "top5_acc": 0.99938, "loss_cls": 0.42492, "loss": 0.42492, "time": 0.30983} +{"mode": "val", "epoch": 66, "iter": 533, "lr": 0.01484, "top1_acc": 0.89567, "top5_acc": 0.99167, "mean_class_accuracy": 0.86534} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.01482, "memory": 4083, "data_time": 0.19783, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.37653, "loss": 0.37653, "time": 0.79065} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.0148, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95438, "top5_acc": 0.99875, "loss_cls": 0.2847, "loss": 0.2847, "time": 0.49201} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.01478, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91875, "top5_acc": 0.99812, "loss_cls": 0.41454, "loss": 0.41454, "time": 0.48976} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.01476, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.36497, "loss": 0.36497, "time": 0.48909} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.01474, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93688, "top5_acc": 0.99875, "loss_cls": 0.36279, "loss": 0.36279, "time": 0.48972} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.01472, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93375, "top5_acc": 0.9975, "loss_cls": 0.37124, "loss": 0.37124, "time": 0.49024} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.0147, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9325, "top5_acc": 1.0, "loss_cls": 0.37316, "loss": 0.37316, "time": 0.49843} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.01468, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92688, "top5_acc": 0.99875, "loss_cls": 0.37052, "loss": 0.37052, "time": 0.49156} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.01466, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93625, "top5_acc": 0.99625, "loss_cls": 0.36408, "loss": 0.36408, "time": 0.48936} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.01464, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92688, "top5_acc": 0.99812, "loss_cls": 0.37387, "loss": 0.37387, "time": 0.27433} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.01462, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91688, "top5_acc": 1.0, "loss_cls": 0.43998, "loss": 0.43998, "time": 0.5082} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.0146, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.43358, "loss": 0.43358, "time": 0.29444} +{"mode": "val", "epoch": 67, "iter": 533, "lr": 0.01458, "top1_acc": 0.89015, "top5_acc": 0.99378, "mean_class_accuracy": 0.84578} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.01456, "memory": 4083, "data_time": 0.1978, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.39007, "loss": 0.39007, "time": 0.80233} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.01454, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92875, "top5_acc": 0.99938, "loss_cls": 0.37316, "loss": 0.37316, "time": 0.48979} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.01452, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94062, "top5_acc": 0.9975, "loss_cls": 0.36293, "loss": 0.36293, "time": 0.4875} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.0145, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9275, "top5_acc": 0.99875, "loss_cls": 0.37308, "loss": 0.37308, "time": 0.49113} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.01448, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.32414, "loss": 0.32414, "time": 0.48979} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.01446, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.93562, "top5_acc": 0.99875, "loss_cls": 0.39441, "loss": 0.39441, "time": 0.4899} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.01444, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.925, "top5_acc": 0.9975, "loss_cls": 0.41687, "loss": 0.41687, "time": 0.49442} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.01442, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92312, "top5_acc": 0.99875, "loss_cls": 0.38406, "loss": 0.38406, "time": 0.48971} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.0144, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92625, "top5_acc": 0.99812, "loss_cls": 0.4119, "loss": 0.4119, "time": 0.49204} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.01438, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92625, "top5_acc": 0.9975, "loss_cls": 0.38005, "loss": 0.38005, "time": 0.28549} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.01436, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93, "top5_acc": 0.99688, "loss_cls": 0.40233, "loss": 0.40233, "time": 0.50939} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.01434, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.37171, "loss": 0.37171, "time": 0.27553} +{"mode": "val", "epoch": 68, "iter": 533, "lr": 0.01433, "top1_acc": 0.89379, "top5_acc": 0.99296, "mean_class_accuracy": 0.86284} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.01431, "memory": 4083, "data_time": 0.19081, "top1_acc": 0.93875, "top5_acc": 0.99688, "loss_cls": 0.36928, "loss": 0.36928, "time": 0.79829} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.01429, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.32886, "loss": 0.32886, "time": 0.48977} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.01427, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93812, "top5_acc": 0.99875, "loss_cls": 0.34306, "loss": 0.34306, "time": 0.48901} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.01425, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9475, "top5_acc": 1.0, "loss_cls": 0.30675, "loss": 0.30675, "time": 0.48955} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.01423, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93562, "top5_acc": 0.99812, "loss_cls": 0.32484, "loss": 0.32484, "time": 0.49019} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.0142, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94562, "top5_acc": 0.99875, "loss_cls": 0.31519, "loss": 0.31519, "time": 0.49245} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.01418, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93938, "top5_acc": 0.99625, "loss_cls": 0.35496, "loss": 0.35496, "time": 0.48759} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.01416, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95, "top5_acc": 0.99875, "loss_cls": 0.30416, "loss": 0.30416, "time": 0.49123} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.01414, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.3108, "loss": 0.3108, "time": 0.48821} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.01412, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93438, "top5_acc": 0.99812, "loss_cls": 0.34939, "loss": 0.34939, "time": 0.29177} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.0141, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92188, "top5_acc": 0.99938, "loss_cls": 0.41685, "loss": 0.41685, "time": 0.51016} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.01408, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9175, "top5_acc": 0.9975, "loss_cls": 0.44265, "loss": 0.44265, "time": 0.26712} +{"mode": "val", "epoch": 69, "iter": 533, "lr": 0.01407, "top1_acc": 0.8918, "top5_acc": 0.99249, "mean_class_accuracy": 0.85157} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.01405, "memory": 4083, "data_time": 0.19552, "top1_acc": 0.93438, "top5_acc": 1.0, "loss_cls": 0.34445, "loss": 0.34445, "time": 0.78289} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.01403, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93312, "top5_acc": 0.99875, "loss_cls": 0.3489, "loss": 0.3489, "time": 0.49177} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.01401, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94125, "top5_acc": 0.99812, "loss_cls": 0.33264, "loss": 0.33264, "time": 0.49069} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.01399, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94562, "top5_acc": 0.99812, "loss_cls": 0.33041, "loss": 0.33041, "time": 0.49233} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.01397, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93125, "top5_acc": 0.99688, "loss_cls": 0.41359, "loss": 0.41359, "time": 0.4932} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.01395, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93125, "top5_acc": 0.99938, "loss_cls": 0.35302, "loss": 0.35302, "time": 0.49324} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.01392, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94312, "top5_acc": 0.99875, "loss_cls": 0.33561, "loss": 0.33561, "time": 0.48718} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.0139, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92625, "top5_acc": 0.99938, "loss_cls": 0.40577, "loss": 0.40577, "time": 0.48812} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.01388, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93, "top5_acc": 0.99812, "loss_cls": 0.36628, "loss": 0.36628, "time": 0.49108} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.01386, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.3565, "loss": 0.3565, "time": 0.32949} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.01384, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92938, "top5_acc": 1.0, "loss_cls": 0.37337, "loss": 0.37337, "time": 0.51074} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.01382, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.93938, "top5_acc": 0.99938, "loss_cls": 0.35346, "loss": 0.35346, "time": 0.25136} +{"mode": "val", "epoch": 70, "iter": 533, "lr": 0.01381, "top1_acc": 0.88487, "top5_acc": 0.99155, "mean_class_accuracy": 0.83431} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.01379, "memory": 4083, "data_time": 0.19609, "top1_acc": 0.94125, "top5_acc": 0.99688, "loss_cls": 0.3424, "loss": 0.3424, "time": 0.78478} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.01377, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.33372, "loss": 0.33372, "time": 0.49178} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.01375, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.35725, "loss": 0.35725, "time": 0.48905} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.01373, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93938, "top5_acc": 0.99938, "loss_cls": 0.30831, "loss": 0.30831, "time": 0.49031} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.01371, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93812, "top5_acc": 0.99875, "loss_cls": 0.35407, "loss": 0.35407, "time": 0.49194} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.01368, "memory": 4083, "data_time": 0.00061, "top1_acc": 0.94312, "top5_acc": 0.99875, "loss_cls": 0.308, "loss": 0.308, "time": 0.48682} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.01366, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9275, "top5_acc": 0.99938, "loss_cls": 0.37036, "loss": 0.37036, "time": 0.49102} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.01364, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9425, "top5_acc": 1.0, "loss_cls": 0.3235, "loss": 0.3235, "time": 0.49343} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.01362, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.31962, "loss": 0.31962, "time": 0.49176} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.0136, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.93688, "top5_acc": 0.99812, "loss_cls": 0.34265, "loss": 0.34265, "time": 0.34852} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.01358, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93062, "top5_acc": 0.99875, "loss_cls": 0.36162, "loss": 0.36162, "time": 0.50812} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.01356, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92, "top5_acc": 0.99625, "loss_cls": 0.42405, "loss": 0.42405, "time": 0.24353} +{"mode": "val", "epoch": 71, "iter": 533, "lr": 0.01355, "top1_acc": 0.88382, "top5_acc": 0.99249, "mean_class_accuracy": 0.8411} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.01353, "memory": 4083, "data_time": 0.18832, "top1_acc": 0.93812, "top5_acc": 0.99812, "loss_cls": 0.35225, "loss": 0.35225, "time": 0.79216} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.01351, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.93625, "top5_acc": 0.99812, "loss_cls": 0.34222, "loss": 0.34222, "time": 0.49176} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.01349, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9475, "top5_acc": 0.99875, "loss_cls": 0.31581, "loss": 0.31581, "time": 0.48961} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.01346, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.94375, "top5_acc": 1.0, "loss_cls": 0.35133, "loss": 0.35133, "time": 0.4869} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.01344, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.29283, "loss": 0.29283, "time": 0.48915} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.01342, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94625, "top5_acc": 0.99875, "loss_cls": 0.29699, "loss": 0.29699, "time": 0.48983} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.0134, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94375, "top5_acc": 1.0, "loss_cls": 0.32033, "loss": 0.32033, "time": 0.49419} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.01338, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92625, "top5_acc": 0.99812, "loss_cls": 0.38079, "loss": 0.38079, "time": 0.49006} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.01336, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.35901, "loss": 0.35901, "time": 0.48849} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.01334, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9275, "top5_acc": 0.99812, "loss_cls": 0.36484, "loss": 0.36484, "time": 0.37303} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.01332, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93375, "top5_acc": 0.99875, "loss_cls": 0.3712, "loss": 0.3712, "time": 0.50888} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.0133, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.93562, "top5_acc": 0.99688, "loss_cls": 0.35877, "loss": 0.35877, "time": 0.2378} +{"mode": "val", "epoch": 72, "iter": 533, "lr": 0.01329, "top1_acc": 0.906, "top5_acc": 0.99343, "mean_class_accuracy": 0.8784} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.01326, "memory": 4083, "data_time": 0.19547, "top1_acc": 0.94438, "top5_acc": 0.99875, "loss_cls": 0.31531, "loss": 0.31531, "time": 0.78951} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.01324, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.94, "top5_acc": 0.99938, "loss_cls": 0.32223, "loss": 0.32223, "time": 0.49032} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.01322, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92312, "top5_acc": 0.9975, "loss_cls": 0.39215, "loss": 0.39215, "time": 0.48948} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.0132, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94562, "top5_acc": 0.99875, "loss_cls": 0.33446, "loss": 0.33446, "time": 0.49028} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.01318, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.33121, "loss": 0.33121, "time": 0.48942} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.01316, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95188, "top5_acc": 0.99812, "loss_cls": 0.3014, "loss": 0.3014, "time": 0.48974} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.01314, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95062, "top5_acc": 0.99812, "loss_cls": 0.31369, "loss": 0.31369, "time": 0.48988} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.01312, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9375, "top5_acc": 0.9975, "loss_cls": 0.34637, "loss": 0.34637, "time": 0.49189} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.0131, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94188, "top5_acc": 1.0, "loss_cls": 0.31187, "loss": 0.31187, "time": 0.49005} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.01308, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93812, "top5_acc": 0.99688, "loss_cls": 0.34391, "loss": 0.34391, "time": 0.39399} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.01306, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.33289, "loss": 0.33289, "time": 0.49852} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.01304, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.29214, "loss": 0.29214, "time": 0.23736} +{"mode": "val", "epoch": 73, "iter": 533, "lr": 0.01302, "top1_acc": 0.89238, "top5_acc": 0.99355, "mean_class_accuracy": 0.85005} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.013, "memory": 4083, "data_time": 0.19339, "top1_acc": 0.95562, "top5_acc": 1.0, "loss_cls": 0.28242, "loss": 0.28242, "time": 0.80867} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.01298, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.30417, "loss": 0.30417, "time": 0.49289} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.01296, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94438, "top5_acc": 0.99875, "loss_cls": 0.33783, "loss": 0.33783, "time": 0.49038} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.01294, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.3197, "loss": 0.3197, "time": 0.49162} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.01292, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.32525, "loss": 0.32525, "time": 0.49432} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.0129, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92812, "top5_acc": 1.0, "loss_cls": 0.38194, "loss": 0.38194, "time": 0.49279} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.01288, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94625, "top5_acc": 1.0, "loss_cls": 0.33392, "loss": 0.33392, "time": 0.49184} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.01286, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.30587, "loss": 0.30587, "time": 0.48436} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.01284, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.37294, "loss": 0.37294, "time": 0.4893} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.01282, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.94, "top5_acc": 0.99875, "loss_cls": 0.33951, "loss": 0.33951, "time": 0.43048} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.0128, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9325, "top5_acc": 0.99812, "loss_cls": 0.38245, "loss": 0.38245, "time": 0.43846} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.01278, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93125, "top5_acc": 0.9975, "loss_cls": 0.38446, "loss": 0.38446, "time": 0.29744} +{"mode": "val", "epoch": 74, "iter": 533, "lr": 0.01276, "top1_acc": 0.89426, "top5_acc": 0.99202, "mean_class_accuracy": 0.86006} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.01274, "memory": 4083, "data_time": 0.19226, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.32633, "loss": 0.32633, "time": 0.81242} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.01272, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.94, "top5_acc": 0.99938, "loss_cls": 0.31501, "loss": 0.31501, "time": 0.49185} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.0127, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.94, "top5_acc": 0.99875, "loss_cls": 0.33282, "loss": 0.33282, "time": 0.48895} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.01268, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95, "top5_acc": 0.99875, "loss_cls": 0.28414, "loss": 0.28414, "time": 0.49243} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.01266, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94938, "top5_acc": 1.0, "loss_cls": 0.29744, "loss": 0.29744, "time": 0.49173} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.01264, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94125, "top5_acc": 0.99812, "loss_cls": 0.3242, "loss": 0.3242, "time": 0.49254} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.01262, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.27887, "loss": 0.27887, "time": 0.49156} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.0126, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93312, "top5_acc": 1.0, "loss_cls": 0.2968, "loss": 0.2968, "time": 0.49036} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.01258, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.34105, "loss": 0.34105, "time": 0.49066} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.01256, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.94312, "top5_acc": 0.99875, "loss_cls": 0.32287, "loss": 0.32287, "time": 0.42658} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.01254, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94188, "top5_acc": 0.99938, "loss_cls": 0.3481, "loss": 0.3481, "time": 0.4371} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.01252, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92625, "top5_acc": 1.0, "loss_cls": 0.38936, "loss": 0.38936, "time": 0.29547} +{"mode": "val", "epoch": 75, "iter": 533, "lr": 0.0125, "top1_acc": 0.90271, "top5_acc": 0.99225, "mean_class_accuracy": 0.86479} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.01248, "memory": 4083, "data_time": 0.19344, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.34138, "loss": 0.34138, "time": 0.80021} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.01246, "memory": 4083, "data_time": 0.00085, "top1_acc": 0.94, "top5_acc": 0.99875, "loss_cls": 0.33748, "loss": 0.33748, "time": 0.49347} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.01244, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93625, "top5_acc": 0.9975, "loss_cls": 0.35659, "loss": 0.35659, "time": 0.49701} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.01242, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95062, "top5_acc": 0.9975, "loss_cls": 0.30082, "loss": 0.30082, "time": 0.48872} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.0124, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.31049, "loss": 0.31049, "time": 0.49512} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.01238, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.27626, "loss": 0.27626, "time": 0.49146} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.01236, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.35072, "loss": 0.35072, "time": 0.48874} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.01234, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94812, "top5_acc": 0.99812, "loss_cls": 0.29331, "loss": 0.29331, "time": 0.49119} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.01232, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.94, "top5_acc": 0.99688, "loss_cls": 0.32668, "loss": 0.32668, "time": 0.48751} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.0123, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9425, "top5_acc": 0.99875, "loss_cls": 0.3319, "loss": 0.3319, "time": 0.43967} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.01228, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93062, "top5_acc": 0.99938, "loss_cls": 0.36829, "loss": 0.36829, "time": 0.41687} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.01225, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92562, "top5_acc": 0.99688, "loss_cls": 0.3901, "loss": 0.3901, "time": 0.32022} +{"mode": "val", "epoch": 76, "iter": 533, "lr": 0.01224, "top1_acc": 0.85741, "top5_acc": 0.98956, "mean_class_accuracy": 0.84287} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.01222, "memory": 4083, "data_time": 0.19333, "top1_acc": 0.93625, "top5_acc": 0.99812, "loss_cls": 0.34832, "loss": 0.34832, "time": 0.80252} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0122, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.29294, "loss": 0.29294, "time": 0.48966} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.01218, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.94188, "top5_acc": 0.99938, "loss_cls": 0.30904, "loss": 0.30904, "time": 0.48703} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.01216, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.29157, "loss": 0.29157, "time": 0.49067} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.01214, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93438, "top5_acc": 1.0, "loss_cls": 0.32141, "loss": 0.32141, "time": 0.49116} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.01212, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.945, "top5_acc": 1.0, "loss_cls": 0.30821, "loss": 0.30821, "time": 0.49224} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.0121, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94188, "top5_acc": 1.0, "loss_cls": 0.28635, "loss": 0.28635, "time": 0.48942} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.01207, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.27444, "loss": 0.27444, "time": 0.49001} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.01205, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.95812, "top5_acc": 0.99875, "loss_cls": 0.28881, "loss": 0.28881, "time": 0.48939} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.01203, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.28229, "loss": 0.28229, "time": 0.45079} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.01201, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.29804, "loss": 0.29804, "time": 0.40273} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.01199, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.30687, "loss": 0.30687, "time": 0.32908} +{"mode": "val", "epoch": 77, "iter": 533, "lr": 0.01198, "top1_acc": 0.89673, "top5_acc": 0.9919, "mean_class_accuracy": 0.86601} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.01196, "memory": 4083, "data_time": 0.19232, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.22758, "loss": 0.22758, "time": 0.80039} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.01194, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.25099, "loss": 0.25099, "time": 0.49073} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.01192, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.24169, "loss": 0.24169, "time": 0.48961} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.0119, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.33674, "loss": 0.33674, "time": 0.48504} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.01187, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.31525, "loss": 0.31525, "time": 0.49152} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.01185, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94438, "top5_acc": 1.0, "loss_cls": 0.31447, "loss": 0.31447, "time": 0.49056} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.01183, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.31033, "loss": 0.31033, "time": 0.49219} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.01181, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93562, "top5_acc": 0.99875, "loss_cls": 0.34916, "loss": 0.34916, "time": 0.4914} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.01179, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.33524, "loss": 0.33524, "time": 0.49236} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.01177, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.935, "top5_acc": 1.0, "loss_cls": 0.33998, "loss": 0.33998, "time": 0.4746} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.01175, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95, "top5_acc": 0.9975, "loss_cls": 0.30238, "loss": 0.30238, "time": 0.34635} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.01173, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.93312, "top5_acc": 0.9975, "loss_cls": 0.3439, "loss": 0.3439, "time": 0.38879} +{"mode": "val", "epoch": 78, "iter": 533, "lr": 0.01172, "top1_acc": 0.88593, "top5_acc": 0.99272, "mean_class_accuracy": 0.84525} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.01169, "memory": 4083, "data_time": 0.19376, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.25538, "loss": 0.25538, "time": 0.79911} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.01167, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.22875, "loss": 0.22875, "time": 0.49084} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.01165, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94938, "top5_acc": 0.99812, "loss_cls": 0.2975, "loss": 0.2975, "time": 0.49056} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.01163, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.94812, "top5_acc": 0.99875, "loss_cls": 0.31862, "loss": 0.31862, "time": 0.48271} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.01161, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.31852, "loss": 0.31852, "time": 0.49114} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.01159, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94438, "top5_acc": 0.99875, "loss_cls": 0.30677, "loss": 0.30677, "time": 0.48482} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.01157, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.33108, "loss": 0.33108, "time": 0.48847} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.01155, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93125, "top5_acc": 0.99938, "loss_cls": 0.35673, "loss": 0.35673, "time": 0.48939} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.01153, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.34309, "loss": 0.34309, "time": 0.49219} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.01151, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9425, "top5_acc": 0.99875, "loss_cls": 0.33132, "loss": 0.33132, "time": 0.49177} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.01149, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.9425, "top5_acc": 0.99812, "loss_cls": 0.34207, "loss": 0.34207, "time": 0.30281} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.01147, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.26381, "loss": 0.26381, "time": 0.43662} +{"mode": "val", "epoch": 79, "iter": 533, "lr": 0.01145, "top1_acc": 0.89989, "top5_acc": 0.99355, "mean_class_accuracy": 0.85388} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.01143, "memory": 4083, "data_time": 0.19182, "top1_acc": 0.96562, "top5_acc": 0.99625, "loss_cls": 0.23773, "loss": 0.23773, "time": 0.79236} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.01141, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94875, "top5_acc": 0.99812, "loss_cls": 0.26112, "loss": 0.26112, "time": 0.49102} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.01139, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.96375, "top5_acc": 0.99938, "loss_cls": 0.24334, "loss": 0.24334, "time": 0.49183} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.01137, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.24783, "loss": 0.24783, "time": 0.48947} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.01135, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94688, "top5_acc": 0.99875, "loss_cls": 0.31286, "loss": 0.31286, "time": 0.48705} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.01133, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.94, "top5_acc": 0.99625, "loss_cls": 0.33489, "loss": 0.33489, "time": 0.48885} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.01131, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94625, "top5_acc": 0.9975, "loss_cls": 0.3229, "loss": 0.3229, "time": 0.49151} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.01129, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94938, "top5_acc": 0.99875, "loss_cls": 0.3107, "loss": 0.3107, "time": 0.4905} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.01127, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.32595, "loss": 0.32595, "time": 0.49044} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.01125, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95438, "top5_acc": 0.99938, "loss_cls": 0.2724, "loss": 0.2724, "time": 0.49155} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.01123, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.27693, "loss": 0.27693, "time": 0.27696} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.01121, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.32416, "loss": 0.32416, "time": 0.50844} +{"mode": "val", "epoch": 80, "iter": 533, "lr": 0.01119, "top1_acc": 0.88687, "top5_acc": 0.99085, "mean_class_accuracy": 0.85865} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.01117, "memory": 4083, "data_time": 0.19377, "top1_acc": 0.94062, "top5_acc": 0.9975, "loss_cls": 0.35132, "loss": 0.35132, "time": 0.81044} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.01115, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.27843, "loss": 0.27843, "time": 0.48975} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.01113, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.25426, "loss": 0.25426, "time": 0.4892} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.01111, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94625, "top5_acc": 1.0, "loss_cls": 0.28331, "loss": 0.28331, "time": 0.49027} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.01109, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.26732, "loss": 0.26732, "time": 0.49349} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.01107, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.29024, "loss": 0.29024, "time": 0.49022} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.01105, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.27354, "loss": 0.27354, "time": 0.4928} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.01103, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.26893, "loss": 0.26893, "time": 0.49176} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.01101, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93938, "top5_acc": 0.99875, "loss_cls": 0.32899, "loss": 0.32899, "time": 0.49118} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.01099, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.3001, "loss": 0.3001, "time": 0.4905} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.01097, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94375, "top5_acc": 0.99688, "loss_cls": 0.32686, "loss": 0.32686, "time": 0.26811} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.01095, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.945, "top5_acc": 1.0, "loss_cls": 0.30138, "loss": 0.30138, "time": 0.50908} +{"mode": "val", "epoch": 81, "iter": 533, "lr": 0.01093, "top1_acc": 0.9114, "top5_acc": 0.99472, "mean_class_accuracy": 0.87431} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.01091, "memory": 4083, "data_time": 0.18836, "top1_acc": 0.9575, "top5_acc": 0.99938, "loss_cls": 0.23965, "loss": 0.23965, "time": 0.79028} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.01089, "memory": 4083, "data_time": 0.00078, "top1_acc": 0.94812, "top5_acc": 0.99875, "loss_cls": 0.29143, "loss": 0.29143, "time": 0.49092} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.01087, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95438, "top5_acc": 0.99938, "loss_cls": 0.27913, "loss": 0.27913, "time": 0.4902} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.01085, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.27213, "loss": 0.27213, "time": 0.49063} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.01083, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.25956, "loss": 0.25956, "time": 0.49184} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.01081, "memory": 4083, "data_time": 0.00061, "top1_acc": 0.95438, "top5_acc": 0.99938, "loss_cls": 0.27217, "loss": 0.27217, "time": 0.48992} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.01079, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.28378, "loss": 0.28378, "time": 0.48647} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.01077, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.955, "top5_acc": 0.99875, "loss_cls": 0.27128, "loss": 0.27128, "time": 0.49007} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.01075, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.2582, "loss": 0.2582, "time": 0.49184} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.01073, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.28008, "loss": 0.28008, "time": 0.49032} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.01071, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.29268, "loss": 0.29268, "time": 0.29424} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.01069, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94688, "top5_acc": 0.99812, "loss_cls": 0.29729, "loss": 0.29729, "time": 0.50766} +{"mode": "val", "epoch": 82, "iter": 533, "lr": 0.01067, "top1_acc": 0.91527, "top5_acc": 0.99343, "mean_class_accuracy": 0.89163} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.01065, "memory": 4083, "data_time": 0.19268, "top1_acc": 0.9625, "top5_acc": 0.99875, "loss_cls": 0.23553, "loss": 0.23553, "time": 0.79463} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.01063, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.23399, "loss": 0.23399, "time": 0.4898} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.01061, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.21694, "loss": 0.21694, "time": 0.49151} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.01059, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95188, "top5_acc": 0.99875, "loss_cls": 0.25731, "loss": 0.25731, "time": 0.49189} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.01057, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.29267, "loss": 0.29267, "time": 0.49515} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.01055, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9475, "top5_acc": 0.99812, "loss_cls": 0.30158, "loss": 0.30158, "time": 0.488} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.01053, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.94125, "top5_acc": 0.99875, "loss_cls": 0.31498, "loss": 0.31498, "time": 0.48965} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.01051, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93875, "top5_acc": 1.0, "loss_cls": 0.32515, "loss": 0.32515, "time": 0.4869} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.01049, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95188, "top5_acc": 0.99875, "loss_cls": 0.27917, "loss": 0.27917, "time": 0.49046} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.01047, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.31376, "loss": 0.31376, "time": 0.49145} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.01045, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.945, "top5_acc": 0.99938, "loss_cls": 0.30472, "loss": 0.30472, "time": 0.28739} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.01043, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95188, "top5_acc": 0.99875, "loss_cls": 0.28261, "loss": 0.28261, "time": 0.51133} +{"mode": "val", "epoch": 83, "iter": 533, "lr": 0.01042, "top1_acc": 0.9155, "top5_acc": 0.9939, "mean_class_accuracy": 0.88161} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.0104, "memory": 4083, "data_time": 0.18995, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.25112, "loss": 0.25112, "time": 0.7867} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.01038, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.24134, "loss": 0.24134, "time": 0.49204} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.01036, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.25017, "loss": 0.25017, "time": 0.48936} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.01034, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96375, "top5_acc": 0.99875, "loss_cls": 0.20162, "loss": 0.20162, "time": 0.49281} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.01031, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.27274, "loss": 0.27274, "time": 0.48794} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.01029, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94188, "top5_acc": 0.9975, "loss_cls": 0.32667, "loss": 0.32667, "time": 0.49365} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.01027, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.945, "top5_acc": 1.0, "loss_cls": 0.28925, "loss": 0.28925, "time": 0.4882} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.01025, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.26286, "loss": 0.26286, "time": 0.49034} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.01023, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.26347, "loss": 0.26347, "time": 0.49062} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.01021, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.2704, "loss": 0.2704, "time": 0.48847} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.01019, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.31793, "loss": 0.31793, "time": 0.29314} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.01017, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.30424, "loss": 0.30424, "time": 0.50796} +{"mode": "val", "epoch": 84, "iter": 533, "lr": 0.01016, "top1_acc": 0.89579, "top5_acc": 0.99448, "mean_class_accuracy": 0.86036} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.01014, "memory": 4083, "data_time": 0.19002, "top1_acc": 0.95875, "top5_acc": 0.99812, "loss_cls": 0.2654, "loss": 0.2654, "time": 0.78238} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.01012, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.21261, "loss": 0.21261, "time": 0.49056} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.0101, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.25057, "loss": 0.25057, "time": 0.49514} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.01008, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95438, "top5_acc": 0.99938, "loss_cls": 0.27112, "loss": 0.27112, "time": 0.49203} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.01006, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.25306, "loss": 0.25306, "time": 0.48957} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.01004, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.95188, "top5_acc": 0.99875, "loss_cls": 0.29413, "loss": 0.29413, "time": 0.49315} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.01002, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.25457, "loss": 0.25457, "time": 0.48735} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.01, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.2488, "loss": 0.2488, "time": 0.48808} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.00998, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94375, "top5_acc": 1.0, "loss_cls": 0.30889, "loss": 0.30889, "time": 0.49072} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.00996, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.291, "loss": 0.291, "time": 0.49125} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.00994, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.30088, "loss": 0.30088, "time": 0.29834} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.00992, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94438, "top5_acc": 0.99875, "loss_cls": 0.29396, "loss": 0.29396, "time": 0.51002} +{"mode": "val", "epoch": 85, "iter": 533, "lr": 0.0099, "top1_acc": 0.89508, "top5_acc": 0.99272, "mean_class_accuracy": 0.84895} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.00988, "memory": 4083, "data_time": 0.19835, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.26433, "loss": 0.26433, "time": 0.79115} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.00986, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.95438, "top5_acc": 0.99938, "loss_cls": 0.22361, "loss": 0.22361, "time": 0.49105} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.00984, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95688, "top5_acc": 0.99938, "loss_cls": 0.24888, "loss": 0.24888, "time": 0.49087} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.00982, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.22394, "loss": 0.22394, "time": 0.48945} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.0098, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.20969, "loss": 0.20969, "time": 0.49095} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.00978, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.23204, "loss": 0.23204, "time": 0.48952} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.00976, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.2578, "loss": 0.2578, "time": 0.48718} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.00974, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.95812, "top5_acc": 0.99938, "loss_cls": 0.24868, "loss": 0.24868, "time": 0.48937} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.00972, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9575, "top5_acc": 0.99938, "loss_cls": 0.25342, "loss": 0.25342, "time": 0.48922} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.0097, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.25561, "loss": 0.25561, "time": 0.49034} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.00968, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.945, "top5_acc": 0.99812, "loss_cls": 0.29894, "loss": 0.29894, "time": 0.29646} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.00966, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.94688, "top5_acc": 0.99812, "loss_cls": 0.29831, "loss": 0.29831, "time": 0.50972} +{"mode": "val", "epoch": 86, "iter": 533, "lr": 0.00965, "top1_acc": 0.904, "top5_acc": 0.99472, "mean_class_accuracy": 0.86745} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.00963, "memory": 4083, "data_time": 0.18942, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.23627, "loss": 0.23627, "time": 0.79645} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.00961, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.19341, "loss": 0.19341, "time": 0.49193} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.00959, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96562, "top5_acc": 0.99938, "loss_cls": 0.21649, "loss": 0.21649, "time": 0.49368} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.00957, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97062, "top5_acc": 0.99938, "loss_cls": 0.18904, "loss": 0.18904, "time": 0.4937} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.00955, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.21635, "loss": 0.21635, "time": 0.49204} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.00953, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.23089, "loss": 0.23089, "time": 0.49072} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.00951, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95875, "top5_acc": 0.99875, "loss_cls": 0.25235, "loss": 0.25235, "time": 0.49081} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.00949, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.27473, "loss": 0.27473, "time": 0.48879} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.00947, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9575, "top5_acc": 0.99875, "loss_cls": 0.2682, "loss": 0.2682, "time": 0.4908} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.00945, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.2635, "loss": 0.2635, "time": 0.49258} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.00943, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.26789, "loss": 0.26789, "time": 0.28139} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.00941, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.27225, "loss": 0.27225, "time": 0.50789} +{"mode": "val", "epoch": 87, "iter": 533, "lr": 0.00939, "top1_acc": 0.92008, "top5_acc": 0.99507, "mean_class_accuracy": 0.88348} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.00937, "memory": 4083, "data_time": 0.19348, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.26767, "loss": 0.26767, "time": 0.79865} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.00935, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.22168, "loss": 0.22168, "time": 0.48789} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.00933, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.24954, "loss": 0.24954, "time": 0.48812} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.00931, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96625, "top5_acc": 0.99938, "loss_cls": 0.23273, "loss": 0.23273, "time": 0.48616} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.00929, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96, "top5_acc": 0.99938, "loss_cls": 0.23771, "loss": 0.23771, "time": 0.49645} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.00927, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.23943, "loss": 0.23943, "time": 0.48819} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.00925, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.24599, "loss": 0.24599, "time": 0.49285} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.00923, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.2394, "loss": 0.2394, "time": 0.48965} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.00921, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9575, "top5_acc": 0.99875, "loss_cls": 0.26044, "loss": 0.26044, "time": 0.48798} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.00919, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.26383, "loss": 0.26383, "time": 0.48627} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.00917, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95188, "top5_acc": 1.0, "loss_cls": 0.26622, "loss": 0.26622, "time": 0.27935} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.00915, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.9525, "top5_acc": 0.99875, "loss_cls": 0.27118, "loss": 0.27118, "time": 0.51065} +{"mode": "val", "epoch": 88, "iter": 533, "lr": 0.00914, "top1_acc": 0.91233, "top5_acc": 0.99331, "mean_class_accuracy": 0.88224} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.00912, "memory": 4083, "data_time": 0.19123, "top1_acc": 0.97562, "top5_acc": 0.99938, "loss_cls": 0.18491, "loss": 0.18491, "time": 0.79266} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0091, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.19632, "loss": 0.19632, "time": 0.49097} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.00908, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.21451, "loss": 0.21451, "time": 0.48808} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.00906, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.16919, "loss": 0.16919, "time": 0.49084} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.00904, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.19416, "loss": 0.19416, "time": 0.48567} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.00902, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96188, "top5_acc": 0.99875, "loss_cls": 0.23821, "loss": 0.23821, "time": 0.48879} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.009, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.22576, "loss": 0.22576, "time": 0.48865} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.00898, "memory": 4083, "data_time": 0.00075, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.26812, "loss": 0.26812, "time": 0.49095} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.00896, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.23455, "loss": 0.23455, "time": 0.48978} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.00894, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.27566, "loss": 0.27566, "time": 0.48913} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.00892, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.24383, "loss": 0.24383, "time": 0.28009} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.0089, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.25875, "loss": 0.25875, "time": 0.50834} +{"mode": "val", "epoch": 89, "iter": 533, "lr": 0.00889, "top1_acc": 0.89942, "top5_acc": 0.99155, "mean_class_accuracy": 0.87422} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.00887, "memory": 4083, "data_time": 0.19235, "top1_acc": 0.9525, "top5_acc": 0.99812, "loss_cls": 0.27565, "loss": 0.27565, "time": 0.79844} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.00885, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.23079, "loss": 0.23079, "time": 0.49138} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.00883, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.18753, "loss": 0.18753, "time": 0.49647} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.00881, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.22923, "loss": 0.22923, "time": 0.48974} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.00879, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.25946, "loss": 0.25946, "time": 0.49001} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.00877, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.31984, "loss": 0.31984, "time": 0.49484} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.00875, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.22534, "loss": 0.22534, "time": 0.49225} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.00873, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.25525, "loss": 0.25525, "time": 0.4908} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.00871, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.9575, "top5_acc": 0.99875, "loss_cls": 0.29112, "loss": 0.29112, "time": 0.49002} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.00869, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96125, "top5_acc": 0.99875, "loss_cls": 0.24077, "loss": 0.24077, "time": 0.48894} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.00867, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.95438, "top5_acc": 0.99938, "loss_cls": 0.25137, "loss": 0.25137, "time": 0.27343} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.00865, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.24015, "loss": 0.24015, "time": 0.50869} +{"mode": "val", "epoch": 90, "iter": 533, "lr": 0.00864, "top1_acc": 0.89872, "top5_acc": 0.9939, "mean_class_accuracy": 0.87194} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.00862, "memory": 4083, "data_time": 0.18953, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.21573, "loss": 0.21573, "time": 0.78325} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0086, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.22005, "loss": 0.22005, "time": 0.48949} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.00858, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.23518, "loss": 0.23518, "time": 0.4902} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.00856, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.21475, "loss": 0.21475, "time": 0.48818} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.00854, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.24451, "loss": 0.24451, "time": 0.49075} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.00852, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.23109, "loss": 0.23109, "time": 0.48446} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.0085, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.23551, "loss": 0.23551, "time": 0.48602} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.00848, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.22837, "loss": 0.22837, "time": 0.48826} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.00846, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.21526, "loss": 0.21526, "time": 0.49033} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.00844, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.96062, "top5_acc": 0.99875, "loss_cls": 0.23193, "loss": 0.23193, "time": 0.48988} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.00842, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95375, "top5_acc": 0.99875, "loss_cls": 0.27264, "loss": 0.27264, "time": 0.30254} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.0084, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.24833, "loss": 0.24833, "time": 0.50949} +{"mode": "val", "epoch": 91, "iter": 533, "lr": 0.00839, "top1_acc": 0.90201, "top5_acc": 0.99308, "mean_class_accuracy": 0.87374} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.00837, "memory": 4083, "data_time": 0.19574, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.24024, "loss": 0.24024, "time": 0.81013} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.00835, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.21262, "loss": 0.21262, "time": 0.49094} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.00833, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.21855, "loss": 0.21855, "time": 0.49258} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.00831, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.19258, "loss": 0.19258, "time": 0.4902} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.00829, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.23121, "loss": 0.23121, "time": 0.48688} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.00827, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.19241, "loss": 0.19241, "time": 0.49272} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.00825, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97562, "top5_acc": 0.99938, "loss_cls": 0.1723, "loss": 0.1723, "time": 0.48803} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.00824, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.20731, "loss": 0.20731, "time": 0.49043} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.00822, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.23968, "loss": 0.23968, "time": 0.48246} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.0082, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.20031, "loss": 0.20031, "time": 0.48606} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.00818, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.15928, "loss": 0.15928, "time": 0.27356} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.00816, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.25886, "loss": 0.25886, "time": 0.50869} +{"mode": "val", "epoch": 92, "iter": 533, "lr": 0.00814, "top1_acc": 0.906, "top5_acc": 0.99237, "mean_class_accuracy": 0.87964} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.00812, "memory": 4083, "data_time": 0.19176, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.17782, "loss": 0.17782, "time": 0.80411} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.0081, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97312, "top5_acc": 0.99938, "loss_cls": 0.1855, "loss": 0.1855, "time": 0.48866} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.00809, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.19575, "loss": 0.19575, "time": 0.49344} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.00807, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.21123, "loss": 0.21123, "time": 0.49112} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.00805, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.19217, "loss": 0.19217, "time": 0.48535} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.00803, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.19467, "loss": 0.19467, "time": 0.48699} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.00801, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.18597, "loss": 0.18597, "time": 0.48671} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.00799, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.96875, "top5_acc": 0.99938, "loss_cls": 0.20048, "loss": 0.20048, "time": 0.48929} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.00797, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.21799, "loss": 0.21799, "time": 0.48866} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.00795, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.21657, "loss": 0.21657, "time": 0.4875} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.00793, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.18967, "loss": 0.18967, "time": 0.27089} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.00791, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.21343, "loss": 0.21343, "time": 0.50829} +{"mode": "val", "epoch": 93, "iter": 533, "lr": 0.0079, "top1_acc": 0.89825, "top5_acc": 0.99061, "mean_class_accuracy": 0.86471} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.00788, "memory": 4083, "data_time": 0.19486, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.20756, "loss": 0.20756, "time": 0.80321} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.00786, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.22521, "loss": 0.22521, "time": 0.49091} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.00784, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.23747, "loss": 0.23747, "time": 0.48815} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.00782, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.17938, "loss": 0.17938, "time": 0.49149} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.0078, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.155, "loss": 0.155, "time": 0.49205} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.00778, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.19807, "loss": 0.19807, "time": 0.49248} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.00777, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95625, "top5_acc": 0.99875, "loss_cls": 0.25214, "loss": 0.25214, "time": 0.49162} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.00775, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.22511, "loss": 0.22511, "time": 0.48754} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.00773, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.1946, "loss": 0.1946, "time": 0.4919} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.00771, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.16598, "loss": 0.16598, "time": 0.48795} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.00769, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.20288, "loss": 0.20288, "time": 0.2837} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.00767, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.23108, "loss": 0.23108, "time": 0.50721} +{"mode": "val", "epoch": 94, "iter": 533, "lr": 0.00766, "top1_acc": 0.91926, "top5_acc": 0.99507, "mean_class_accuracy": 0.89689} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.00764, "memory": 4083, "data_time": 0.18694, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.21511, "loss": 0.21511, "time": 0.79215} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.00762, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96812, "top5_acc": 0.99875, "loss_cls": 0.19643, "loss": 0.19643, "time": 0.49183} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.0076, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.16853, "loss": 0.16853, "time": 0.49232} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.00758, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.14632, "loss": 0.14632, "time": 0.49315} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.00756, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.16899, "loss": 0.16899, "time": 0.49132} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.00754, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.23366, "loss": 0.23366, "time": 0.49064} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.00752, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.20086, "loss": 0.20086, "time": 0.49096} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.00751, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9625, "top5_acc": 0.99875, "loss_cls": 0.21787, "loss": 0.21787, "time": 0.48782} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.00749, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.21526, "loss": 0.21526, "time": 0.48881} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.00747, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.19547, "loss": 0.19547, "time": 0.48863} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.00745, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.17665, "loss": 0.17665, "time": 0.3008} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.00743, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.24079, "loss": 0.24079, "time": 0.50865} +{"mode": "val", "epoch": 95, "iter": 533, "lr": 0.00742, "top1_acc": 0.91996, "top5_acc": 0.99495, "mean_class_accuracy": 0.88593} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.0074, "memory": 4083, "data_time": 0.18714, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.16679, "loss": 0.16679, "time": 0.80478} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.00738, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97188, "top5_acc": 0.99938, "loss_cls": 0.18608, "loss": 0.18608, "time": 0.48812} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.00736, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9725, "top5_acc": 0.99938, "loss_cls": 0.19523, "loss": 0.19523, "time": 0.48785} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.00734, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97062, "top5_acc": 0.99938, "loss_cls": 0.18605, "loss": 0.18605, "time": 0.48865} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.00732, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.18553, "loss": 0.18553, "time": 0.4885} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.0073, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96938, "top5_acc": 0.99938, "loss_cls": 0.1872, "loss": 0.1872, "time": 0.49011} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.00729, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.1779, "loss": 0.1779, "time": 0.48709} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.00727, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.20574, "loss": 0.20574, "time": 0.48935} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.00725, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96562, "top5_acc": 0.99875, "loss_cls": 0.21542, "loss": 0.21542, "time": 0.48961} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.00723, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96812, "top5_acc": 0.99875, "loss_cls": 0.22086, "loss": 0.22086, "time": 0.48794} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.00721, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.19512, "loss": 0.19512, "time": 0.29152} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.00719, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.96375, "top5_acc": 0.99938, "loss_cls": 0.21615, "loss": 0.21615, "time": 0.48192} +{"mode": "val", "epoch": 96, "iter": 533, "lr": 0.00718, "top1_acc": 0.91046, "top5_acc": 0.99413, "mean_class_accuracy": 0.88361} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.00716, "memory": 4083, "data_time": 0.18533, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.16173, "loss": 0.16173, "time": 0.78089} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.00714, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.17318, "loss": 0.17318, "time": 0.49083} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.00712, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.17865, "loss": 0.17865, "time": 0.48669} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.0071, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.20732, "loss": 0.20732, "time": 0.48796} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.00709, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.17919, "loss": 0.17919, "time": 0.48875} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.00707, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97188, "top5_acc": 0.99938, "loss_cls": 0.19241, "loss": 0.19241, "time": 0.48957} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.00705, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.22189, "loss": 0.22189, "time": 0.48754} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.00703, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.24407, "loss": 0.24407, "time": 0.48704} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.00701, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.20804, "loss": 0.20804, "time": 0.48969} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.00699, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.2103, "loss": 0.2103, "time": 0.49185} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.00698, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.20542, "loss": 0.20542, "time": 0.28838} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.00696, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.20735, "loss": 0.20735, "time": 0.50837} +{"mode": "val", "epoch": 97, "iter": 533, "lr": 0.00694, "top1_acc": 0.91421, "top5_acc": 0.9939, "mean_class_accuracy": 0.88607} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.00692, "memory": 4083, "data_time": 0.18964, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.15029, "loss": 0.15029, "time": 0.81003} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.00691, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.19025, "loss": 0.19025, "time": 0.49171} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.00689, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.13644, "loss": 0.13644, "time": 0.48978} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.00687, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.13165, "loss": 0.13165, "time": 0.48652} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.00685, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.1598, "loss": 0.1598, "time": 0.48722} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.00683, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97875, "top5_acc": 0.99938, "loss_cls": 0.16305, "loss": 0.16305, "time": 0.4896} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.00681, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.18655, "loss": 0.18655, "time": 0.4922} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.0068, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.19586, "loss": 0.19586, "time": 0.48815} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.00678, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.21795, "loss": 0.21795, "time": 0.48728} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.00676, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.18594, "loss": 0.18594, "time": 0.48683} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.00674, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.17549, "loss": 0.17549, "time": 0.28884} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.00672, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.15786, "loss": 0.15786, "time": 0.48096} +{"mode": "val", "epoch": 98, "iter": 533, "lr": 0.00671, "top1_acc": 0.91386, "top5_acc": 0.99636, "mean_class_accuracy": 0.89799} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.00669, "memory": 4083, "data_time": 0.18848, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.15117, "loss": 0.15117, "time": 0.79827} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.00667, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98, "top5_acc": 0.99938, "loss_cls": 0.14322, "loss": 0.14322, "time": 0.4904} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.00665, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.15205, "loss": 0.15205, "time": 0.48596} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.00664, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.18222, "loss": 0.18222, "time": 0.48532} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.00662, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.19814, "loss": 0.19814, "time": 0.49118} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.0066, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.18314, "loss": 0.18314, "time": 0.49413} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.00658, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.13719, "loss": 0.13719, "time": 0.48792} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.00656, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.15268, "loss": 0.15268, "time": 0.48913} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.00655, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.19546, "loss": 0.19546, "time": 0.48714} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.00653, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.13446, "loss": 0.13446, "time": 0.48877} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.00651, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.16429, "loss": 0.16429, "time": 0.30837} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.00649, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.18453, "loss": 0.18453, "time": 0.45758} +{"mode": "val", "epoch": 99, "iter": 533, "lr": 0.00648, "top1_acc": 0.92665, "top5_acc": 0.99613, "mean_class_accuracy": 0.90642} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.00646, "memory": 4083, "data_time": 0.19025, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.13653, "loss": 0.13653, "time": 0.78369} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.00644, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.15328, "loss": 0.15328, "time": 0.48682} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.00642, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.18743, "loss": 0.18743, "time": 0.48784} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.00641, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.96875, "top5_acc": 0.99938, "loss_cls": 0.1828, "loss": 0.1828, "time": 0.48842} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.00639, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.15609, "loss": 0.15609, "time": 0.48894} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.00637, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.16199, "loss": 0.16199, "time": 0.48864} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.00635, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13929, "loss": 0.13929, "time": 0.48842} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.00634, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13567, "loss": 0.13567, "time": 0.48469} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.00632, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.12005, "loss": 0.12005, "time": 0.48838} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.0063, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.96562, "top5_acc": 0.99938, "loss_cls": 0.19408, "loss": 0.19408, "time": 0.48933} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.00628, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.19445, "loss": 0.19445, "time": 0.29991} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.00626, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.18683, "loss": 0.18683, "time": 0.45373} +{"mode": "val", "epoch": 100, "iter": 533, "lr": 0.00625, "top1_acc": 0.91374, "top5_acc": 0.9946, "mean_class_accuracy": 0.8732} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.00623, "memory": 4083, "data_time": 0.19139, "top1_acc": 0.96812, "top5_acc": 0.99938, "loss_cls": 0.18836, "loss": 0.18836, "time": 0.80166} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.00621, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13028, "loss": 0.13028, "time": 0.49063} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.0062, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98062, "top5_acc": 0.99938, "loss_cls": 0.14514, "loss": 0.14514, "time": 0.48762} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.00618, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.15578, "loss": 0.15578, "time": 0.48662} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.00616, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96812, "top5_acc": 0.99938, "loss_cls": 0.20048, "loss": 0.20048, "time": 0.48603} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.00614, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97625, "top5_acc": 0.99938, "loss_cls": 0.16237, "loss": 0.16237, "time": 0.49031} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.00613, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.1647, "loss": 0.1647, "time": 0.48692} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.00611, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98438, "top5_acc": 0.99938, "loss_cls": 0.14743, "loss": 0.14743, "time": 0.4921} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.00609, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.19562, "loss": 0.19562, "time": 0.4874} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.00607, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.18653, "loss": 0.18653, "time": 0.48685} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.00606, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.18685, "loss": 0.18685, "time": 0.32222} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.00604, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.14791, "loss": 0.14791, "time": 0.41422} +{"mode": "val", "epoch": 101, "iter": 533, "lr": 0.00602, "top1_acc": 0.92724, "top5_acc": 0.99683, "mean_class_accuracy": 0.90431} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.00601, "memory": 4083, "data_time": 0.19119, "top1_acc": 0.9825, "top5_acc": 0.99938, "loss_cls": 0.13362, "loss": 0.13362, "time": 0.80028} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.00599, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97562, "top5_acc": 0.99938, "loss_cls": 0.1523, "loss": 0.1523, "time": 0.49008} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.00597, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.15364, "loss": 0.15364, "time": 0.49077} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.00596, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14734, "loss": 0.14734, "time": 0.49041} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.00594, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.13144, "loss": 0.13144, "time": 0.48711} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.00592, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97438, "top5_acc": 0.99938, "loss_cls": 0.16102, "loss": 0.16102, "time": 0.48854} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.0059, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.16567, "loss": 0.16567, "time": 0.49043} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.00589, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98125, "top5_acc": 0.99938, "loss_cls": 0.14587, "loss": 0.14587, "time": 0.48797} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.00587, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.13052, "loss": 0.13052, "time": 0.49317} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.00585, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.13166, "loss": 0.13166, "time": 0.48613} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.00583, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.1539, "loss": 0.1539, "time": 0.324} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.00582, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.1586, "loss": 0.1586, "time": 0.41326} +{"mode": "val", "epoch": 102, "iter": 533, "lr": 0.0058, "top1_acc": 0.91222, "top5_acc": 0.99425, "mean_class_accuracy": 0.87988} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.00579, "memory": 4083, "data_time": 0.19292, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.14278, "loss": 0.14278, "time": 0.79601} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.00577, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.12735, "loss": 0.12735, "time": 0.49215} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.00575, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.12082, "loss": 0.12082, "time": 0.49258} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.00573, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.16428, "loss": 0.16428, "time": 0.48974} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.00572, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.15053, "loss": 0.15053, "time": 0.48728} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.0057, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.10845, "loss": 0.10845, "time": 0.48813} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.00568, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.09783, "loss": 0.09783, "time": 0.49154} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.00566, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97688, "top5_acc": 0.99938, "loss_cls": 0.15223, "loss": 0.15223, "time": 0.48753} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.00565, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.11658, "loss": 0.11658, "time": 0.48725} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.00563, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.12873, "loss": 0.12873, "time": 0.48753} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.00561, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.15357, "loss": 0.15357, "time": 0.29852} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.0056, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.16564, "loss": 0.16564, "time": 0.45794} +{"mode": "val", "epoch": 103, "iter": 533, "lr": 0.00558, "top1_acc": 0.91574, "top5_acc": 0.99648, "mean_class_accuracy": 0.88492} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.00557, "memory": 4083, "data_time": 0.19494, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13358, "loss": 0.13358, "time": 0.80542} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.00555, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.12016, "loss": 0.12016, "time": 0.48794} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.00553, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.1297, "loss": 0.1297, "time": 0.48887} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.00551, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.1307, "loss": 0.1307, "time": 0.49034} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.0055, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.13582, "loss": 0.13582, "time": 0.48908} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.00548, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.11152, "loss": 0.11152, "time": 0.4904} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.00546, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.15465, "loss": 0.15465, "time": 0.49159} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.00545, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12775, "loss": 0.12775, "time": 0.49249} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.00543, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.1254, "loss": 0.1254, "time": 0.48884} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.00541, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.18028, "loss": 0.18028, "time": 0.48752} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.0054, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.16193, "loss": 0.16193, "time": 0.3209} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.00538, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.16928, "loss": 0.16928, "time": 0.41718} +{"mode": "val", "epoch": 104, "iter": 533, "lr": 0.00537, "top1_acc": 0.92501, "top5_acc": 0.99507, "mean_class_accuracy": 0.88996} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.00535, "memory": 4083, "data_time": 0.19654, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11549, "loss": 0.11549, "time": 0.79128} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.00533, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98438, "top5_acc": 0.99938, "loss_cls": 0.12177, "loss": 0.12177, "time": 0.48944} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.00532, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9825, "top5_acc": 0.99938, "loss_cls": 0.13714, "loss": 0.13714, "time": 0.4894} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.0053, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11376, "loss": 0.11376, "time": 0.48962} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.00528, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.1515, "loss": 0.1515, "time": 0.49012} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.00527, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.15149, "loss": 0.15149, "time": 0.49133} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.00525, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.12476, "loss": 0.12476, "time": 0.49062} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.00523, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.11234, "loss": 0.11234, "time": 0.48722} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.00522, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.13965, "loss": 0.13965, "time": 0.48887} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.0052, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.97812, "top5_acc": 0.99938, "loss_cls": 0.14997, "loss": 0.14997, "time": 0.48961} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.00518, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.144, "loss": 0.144, "time": 0.32164} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.00517, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12563, "loss": 0.12563, "time": 0.42985} +{"mode": "val", "epoch": 105, "iter": 533, "lr": 0.00515, "top1_acc": 0.92477, "top5_acc": 0.99484, "mean_class_accuracy": 0.90446} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.00514, "memory": 4083, "data_time": 0.18473, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.09883, "loss": 0.09883, "time": 0.78775} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.00512, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.98812, "top5_acc": 0.99938, "loss_cls": 0.11246, "loss": 0.11246, "time": 0.4881} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.0051, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.09651, "loss": 0.09651, "time": 0.492} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.00509, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11144, "loss": 0.11144, "time": 0.49407} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.00507, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.11272, "loss": 0.11272, "time": 0.49013} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.00505, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.11295, "loss": 0.11295, "time": 0.48991} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.00504, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11174, "loss": 0.11174, "time": 0.48887} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.00502, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.09644, "loss": 0.09644, "time": 0.49101} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.005, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.10074, "loss": 0.10074, "time": 0.49182} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.00499, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.985, "top5_acc": 0.99938, "loss_cls": 0.10044, "loss": 0.10044, "time": 0.48869} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.00497, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.15199, "loss": 0.15199, "time": 0.29511} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.00496, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.14561, "loss": 0.14561, "time": 0.45796} +{"mode": "val", "epoch": 106, "iter": 533, "lr": 0.00494, "top1_acc": 0.92571, "top5_acc": 0.99566, "mean_class_accuracy": 0.90548} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.00493, "memory": 4083, "data_time": 0.19071, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13256, "loss": 0.13256, "time": 0.79719} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.00491, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.11362, "loss": 0.11362, "time": 0.48922} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.00489, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.14251, "loss": 0.14251, "time": 0.49068} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.00488, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.13682, "loss": 0.13682, "time": 0.4889} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.00486, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.12994, "loss": 0.12994, "time": 0.48592} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.00485, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98125, "top5_acc": 0.99938, "loss_cls": 0.14042, "loss": 0.14042, "time": 0.49239} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.00483, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98438, "top5_acc": 0.99938, "loss_cls": 0.11832, "loss": 0.11832, "time": 0.48796} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.00481, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.14628, "loss": 0.14628, "time": 0.48936} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.0048, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.1295, "loss": 0.1295, "time": 0.48612} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.00478, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13798, "loss": 0.13798, "time": 0.48893} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.00476, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.12972, "loss": 0.12972, "time": 0.2859} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.00475, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11038, "loss": 0.11038, "time": 0.46097} +{"mode": "val", "epoch": 107, "iter": 533, "lr": 0.00474, "top1_acc": 0.92747, "top5_acc": 0.99601, "mean_class_accuracy": 0.89865} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.00472, "memory": 4083, "data_time": 0.19423, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11738, "loss": 0.11738, "time": 0.80253} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0047, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.13064, "loss": 0.13064, "time": 0.4886} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.00469, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.10376, "loss": 0.10376, "time": 0.4887} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.00467, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.10267, "loss": 0.10267, "time": 0.49041} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.00466, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.14843, "loss": 0.14843, "time": 0.48881} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.00464, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.11594, "loss": 0.11594, "time": 0.49202} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.00462, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.08539, "loss": 0.08539, "time": 0.4875} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.00461, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13654, "loss": 0.13654, "time": 0.49024} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.00459, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.08801, "loss": 0.08801, "time": 0.48938} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.00458, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11015, "loss": 0.11015, "time": 0.49066} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.00456, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.09387, "loss": 0.09387, "time": 0.30308} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.00455, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.12509, "loss": 0.12509, "time": 0.45011} +{"mode": "val", "epoch": 108, "iter": 533, "lr": 0.00453, "top1_acc": 0.91609, "top5_acc": 0.99272, "mean_class_accuracy": 0.88343} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.00452, "memory": 4083, "data_time": 0.19279, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.12103, "loss": 0.12103, "time": 0.79054} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.0045, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.11855, "loss": 0.11855, "time": 0.4874} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.00449, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.09416, "loss": 0.09416, "time": 0.48989} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.00447, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08838, "loss": 0.08838, "time": 0.49007} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.00445, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.10549, "loss": 0.10549, "time": 0.49475} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.00444, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08886, "loss": 0.08886, "time": 0.49191} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.00442, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13059, "loss": 0.13059, "time": 0.49436} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.00441, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.13612, "loss": 0.13612, "time": 0.48817} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.00439, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.12159, "loss": 0.12159, "time": 0.49172} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.00438, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09119, "loss": 0.09119, "time": 0.49087} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.00436, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97562, "top5_acc": 0.99938, "loss_cls": 0.16211, "loss": 0.16211, "time": 0.28399} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.00434, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98375, "top5_acc": 0.99938, "loss_cls": 0.12865, "loss": 0.12865, "time": 0.47718} +{"mode": "val", "epoch": 109, "iter": 533, "lr": 0.00433, "top1_acc": 0.92759, "top5_acc": 0.99531, "mean_class_accuracy": 0.91062} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.00432, "memory": 4083, "data_time": 0.19418, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.10657, "loss": 0.10657, "time": 0.80433} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.0043, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.09032, "loss": 0.09032, "time": 0.49122} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.00429, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10363, "loss": 0.10363, "time": 0.4899} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.00427, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07828, "loss": 0.07828, "time": 0.48942} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.00426, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.09272, "loss": 0.09272, "time": 0.49171} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.00424, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10996, "loss": 0.10996, "time": 0.49041} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.00422, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.10272, "loss": 0.10272, "time": 0.48926} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.00421, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.08373, "loss": 0.08373, "time": 0.49265} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.00419, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98312, "top5_acc": 0.99938, "loss_cls": 0.11479, "loss": 0.11479, "time": 0.49098} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.00418, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.10104, "loss": 0.10104, "time": 0.48619} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.00416, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.09657, "loss": 0.09657, "time": 0.27942} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.00415, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.08736, "loss": 0.08736, "time": 0.49754} +{"mode": "val", "epoch": 110, "iter": 533, "lr": 0.00414, "top1_acc": 0.93017, "top5_acc": 0.99624, "mean_class_accuracy": 0.90401} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.00412, "memory": 4083, "data_time": 0.19176, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07134, "loss": 0.07134, "time": 0.79095} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.00411, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08199, "loss": 0.08199, "time": 0.4866} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.00409, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.07773, "loss": 0.07773, "time": 0.49093} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.00408, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.09974, "loss": 0.09974, "time": 0.48597} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.00406, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98, "top5_acc": 0.99938, "loss_cls": 0.12561, "loss": 0.12561, "time": 0.49219} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.00405, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.10469, "loss": 0.10469, "time": 0.49352} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.00403, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.10143, "loss": 0.10143, "time": 0.496} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.00402, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09865, "loss": 0.09865, "time": 0.48824} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.004, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11264, "loss": 0.11264, "time": 0.48665} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.00399, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.10242, "loss": 0.10242, "time": 0.49073} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.00397, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10072, "loss": 0.10072, "time": 0.27877} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.00396, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.98688, "top5_acc": 0.99938, "loss_cls": 0.11211, "loss": 0.11211, "time": 0.50682} +{"mode": "val", "epoch": 111, "iter": 533, "lr": 0.00394, "top1_acc": 0.92501, "top5_acc": 0.99613, "mean_class_accuracy": 0.89723} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.00393, "memory": 4083, "data_time": 0.19103, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.08675, "loss": 0.08675, "time": 0.79252} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.00391, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08854, "loss": 0.08854, "time": 0.48873} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.0039, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.11699, "loss": 0.11699, "time": 0.48765} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.00388, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.09018, "loss": 0.09018, "time": 0.48759} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.00387, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.07379, "loss": 0.07379, "time": 0.49012} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.00385, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08911, "loss": 0.08911, "time": 0.48965} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.00384, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.09902, "loss": 0.09902, "time": 0.49181} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.00382, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10164, "loss": 0.10164, "time": 0.49173} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.00381, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07883, "loss": 0.07883, "time": 0.4889} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.0038, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06034, "loss": 0.06034, "time": 0.48911} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.00378, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.07772, "loss": 0.07772, "time": 0.28844} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.00377, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.08117, "loss": 0.08117, "time": 0.50705} +{"mode": "val", "epoch": 112, "iter": 533, "lr": 0.00375, "top1_acc": 0.93064, "top5_acc": 0.9973, "mean_class_accuracy": 0.90424} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.00374, "memory": 4083, "data_time": 0.19064, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.07006, "loss": 0.07006, "time": 0.79466} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.00373, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.11205, "loss": 0.11205, "time": 0.48794} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.00371, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.09716, "loss": 0.09716, "time": 0.48633} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.0037, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.07366, "loss": 0.07366, "time": 0.48886} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.00368, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.05374, "loss": 0.05374, "time": 0.49526} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.00367, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07622, "loss": 0.07622, "time": 0.48868} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.00365, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07534, "loss": 0.07534, "time": 0.49139} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.00364, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98812, "top5_acc": 0.99938, "loss_cls": 0.0975, "loss": 0.0975, "time": 0.4877} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.00362, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08351, "loss": 0.08351, "time": 0.49009} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.00361, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.07764, "loss": 0.07764, "time": 0.48947} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0036, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08203, "loss": 0.08203, "time": 0.28165} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.00358, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.09018, "loss": 0.09018, "time": 0.5077} +{"mode": "val", "epoch": 113, "iter": 533, "lr": 0.00357, "top1_acc": 0.93897, "top5_acc": 0.99566, "mean_class_accuracy": 0.91795} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.00355, "memory": 4083, "data_time": 0.19219, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08601, "loss": 0.08601, "time": 0.79439} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.00354, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07684, "loss": 0.07684, "time": 0.49213} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.00353, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.09272, "loss": 0.09272, "time": 0.48767} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.00351, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.07796, "loss": 0.07796, "time": 0.48523} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.0035, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08432, "loss": 0.08432, "time": 0.4893} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.00348, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08108, "loss": 0.08108, "time": 0.48959} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.00347, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.06691, "loss": 0.06691, "time": 0.48879} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.00346, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08189, "loss": 0.08189, "time": 0.49262} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.00344, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08049, "loss": 0.08049, "time": 0.4869} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.00343, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.06856, "loss": 0.06856, "time": 0.48919} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.00341, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.09635, "loss": 0.09635, "time": 0.28335} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.0034, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07653, "loss": 0.07653, "time": 0.50853} +{"mode": "val", "epoch": 114, "iter": 533, "lr": 0.00339, "top1_acc": 0.93416, "top5_acc": 0.99683, "mean_class_accuracy": 0.91006} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.00337, "memory": 4083, "data_time": 0.18816, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.07575, "loss": 0.07575, "time": 0.78956} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.00336, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.08713, "loss": 0.08713, "time": 0.49207} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.00335, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98938, "top5_acc": 0.99938, "loss_cls": 0.09346, "loss": 0.09346, "time": 0.48767} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.00333, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.06241, "loss": 0.06241, "time": 0.48945} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.00332, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05897, "loss": 0.05897, "time": 0.4911} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.0033, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06216, "loss": 0.06216, "time": 0.48728} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.00329, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08161, "loss": 0.08161, "time": 0.49051} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.00328, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08186, "loss": 0.08186, "time": 0.49102} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.00326, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.13333, "loss": 0.13333, "time": 0.48764} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.00325, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.06745, "loss": 0.06745, "time": 0.48986} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.00324, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98875, "top5_acc": 0.99938, "loss_cls": 0.08911, "loss": 0.08911, "time": 0.28566} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.00322, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06326, "loss": 0.06326, "time": 0.50752} +{"mode": "val", "epoch": 115, "iter": 533, "lr": 0.00321, "top1_acc": 0.93651, "top5_acc": 0.99683, "mean_class_accuracy": 0.91632} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.0032, "memory": 4083, "data_time": 0.19131, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.0605, "loss": 0.0605, "time": 0.80533} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.00318, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05542, "loss": 0.05542, "time": 0.49264} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.00317, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05477, "loss": 0.05477, "time": 0.48948} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.00316, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05571, "loss": 0.05571, "time": 0.4872} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.00314, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05929, "loss": 0.05929, "time": 0.4874} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.00313, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04348, "loss": 0.04348, "time": 0.48849} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.00312, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06146, "loss": 0.06146, "time": 0.49017} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.0031, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07775, "loss": 0.07775, "time": 0.49218} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.00309, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.05027, "loss": 0.05027, "time": 0.48956} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.00308, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.06805, "loss": 0.06805, "time": 0.48886} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.00306, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06095, "loss": 0.06095, "time": 0.28371} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.00305, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.08005, "loss": 0.08005, "time": 0.48805} +{"mode": "val", "epoch": 116, "iter": 533, "lr": 0.00304, "top1_acc": 0.92912, "top5_acc": 0.99531, "mean_class_accuracy": 0.90859} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.00302, "memory": 4083, "data_time": 0.18879, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06666, "loss": 0.06666, "time": 0.79736} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.00301, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05689, "loss": 0.05689, "time": 0.48954} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.003, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06883, "loss": 0.06883, "time": 0.49096} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.00298, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05453, "loss": 0.05453, "time": 0.49017} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.00297, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05673, "loss": 0.05673, "time": 0.48894} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.00296, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05212, "loss": 0.05212, "time": 0.49009} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.00294, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.06099, "loss": 0.06099, "time": 0.49077} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.00293, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06816, "loss": 0.06816, "time": 0.49074} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.00292, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04742, "loss": 0.04742, "time": 0.48776} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.00291, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05331, "loss": 0.05331, "time": 0.48993} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.00289, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.0585, "loss": 0.0585, "time": 0.27712} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.00288, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.0693, "loss": 0.0693, "time": 0.50718} +{"mode": "val", "epoch": 117, "iter": 533, "lr": 0.00287, "top1_acc": 0.93557, "top5_acc": 0.99624, "mean_class_accuracy": 0.90623} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.00286, "memory": 4083, "data_time": 0.19044, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04097, "loss": 0.04097, "time": 0.79455} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.00284, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04299, "loss": 0.04299, "time": 0.48954} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.00283, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04816, "loss": 0.04816, "time": 0.48917} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.00282, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.0652, "loss": 0.0652, "time": 0.49118} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.0028, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04815, "loss": 0.04815, "time": 0.49047} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.00279, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06134, "loss": 0.06134, "time": 0.48802} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.00278, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0488, "loss": 0.0488, "time": 0.49017} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.00277, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.05126, "loss": 0.05126, "time": 0.49047} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.00275, "memory": 4083, "data_time": 0.00083, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06077, "loss": 0.06077, "time": 0.48857} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.00274, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05726, "loss": 0.05726, "time": 0.48825} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.00273, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06766, "loss": 0.06766, "time": 0.28108} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.00271, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06916, "loss": 0.06916, "time": 0.50706} +{"mode": "val", "epoch": 118, "iter": 533, "lr": 0.0027, "top1_acc": 0.94085, "top5_acc": 0.99671, "mean_class_accuracy": 0.92399} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.00269, "memory": 4083, "data_time": 0.19391, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03747, "loss": 0.03747, "time": 0.80133} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.00268, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05134, "loss": 0.05134, "time": 0.49125} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.00267, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03782, "loss": 0.03782, "time": 0.48828} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.00265, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03487, "loss": 0.03487, "time": 0.48869} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.00264, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05204, "loss": 0.05204, "time": 0.48652} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.00263, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04321, "loss": 0.04321, "time": 0.49163} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.00262, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.04605, "loss": 0.04605, "time": 0.4896} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.0026, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06325, "loss": 0.06325, "time": 0.48928} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.00259, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.05361, "loss": 0.05361, "time": 0.49074} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.00258, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05264, "loss": 0.05264, "time": 0.48435} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.00257, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04995, "loss": 0.04995, "time": 0.2785} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.00255, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05356, "loss": 0.05356, "time": 0.50826} +{"mode": "val", "epoch": 119, "iter": 533, "lr": 0.00254, "top1_acc": 0.9378, "top5_acc": 0.99636, "mean_class_accuracy": 0.91499} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.00253, "memory": 4083, "data_time": 0.19425, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04304, "loss": 0.04304, "time": 0.80244} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.00252, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03353, "loss": 0.03353, "time": 0.49038} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.00251, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04594, "loss": 0.04594, "time": 0.48547} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.00249, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03742, "loss": 0.03742, "time": 0.48844} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.00248, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04864, "loss": 0.04864, "time": 0.49073} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.00247, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06739, "loss": 0.06739, "time": 0.49221} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.00246, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07425, "loss": 0.07425, "time": 0.48789} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.00245, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05527, "loss": 0.05527, "time": 0.49038} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.00243, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06926, "loss": 0.06926, "time": 0.48948} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.00242, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05854, "loss": 0.05854, "time": 0.48773} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00241, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.06296, "loss": 0.06296, "time": 0.27921} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.0024, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06196, "loss": 0.06196, "time": 0.50816} +{"mode": "val", "epoch": 120, "iter": 533, "lr": 0.00239, "top1_acc": 0.93569, "top5_acc": 0.99578, "mean_class_accuracy": 0.91569} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00238, "memory": 4083, "data_time": 0.18812, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06925, "loss": 0.06925, "time": 0.79828} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00236, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.04227, "loss": 0.04227, "time": 0.49307} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.00235, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04125, "loss": 0.04125, "time": 0.48972} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00234, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04178, "loss": 0.04178, "time": 0.48903} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00233, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05709, "loss": 0.05709, "time": 0.48897} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00232, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04599, "loss": 0.04599, "time": 0.49015} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.0023, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04212, "loss": 0.04212, "time": 0.48983} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00229, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03864, "loss": 0.03864, "time": 0.48943} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.00228, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05719, "loss": 0.05719, "time": 0.48565} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00227, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05302, "loss": 0.05302, "time": 0.49262} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00226, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04571, "loss": 0.04571, "time": 0.28935} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00225, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.0525, "loss": 0.0525, "time": 0.50732} +{"mode": "val", "epoch": 121, "iter": 533, "lr": 0.00224, "top1_acc": 0.93804, "top5_acc": 0.99589, "mean_class_accuracy": 0.9163} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00222, "memory": 4083, "data_time": 0.19119, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03722, "loss": 0.03722, "time": 0.78846} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00221, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0416, "loss": 0.0416, "time": 0.48902} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.0022, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0401, "loss": 0.0401, "time": 0.49271} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00219, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03235, "loss": 0.03235, "time": 0.48816} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00218, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04283, "loss": 0.04283, "time": 0.49324} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00217, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.0403, "loss": 0.0403, "time": 0.49256} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00215, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04347, "loss": 0.04347, "time": 0.49174} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00214, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04531, "loss": 0.04531, "time": 0.48949} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.00213, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0445, "loss": 0.0445, "time": 0.48885} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00212, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03942, "loss": 0.03942, "time": 0.48814} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00211, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03663, "loss": 0.03663, "time": 0.28235} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.0021, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.0401, "loss": 0.0401, "time": 0.5072} +{"mode": "val", "epoch": 122, "iter": 533, "lr": 0.00209, "top1_acc": 0.93698, "top5_acc": 0.99601, "mean_class_accuracy": 0.92029} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00208, "memory": 4083, "data_time": 0.18971, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03784, "loss": 0.03784, "time": 0.79684} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00207, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03648, "loss": 0.03648, "time": 0.48994} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00205, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03477, "loss": 0.03477, "time": 0.49057} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00204, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02841, "loss": 0.02841, "time": 0.48999} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00203, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03432, "loss": 0.03432, "time": 0.49031} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00202, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03293, "loss": 0.03293, "time": 0.49196} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00201, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.995, "top5_acc": 0.99938, "loss_cls": 0.04793, "loss": 0.04793, "time": 0.48894} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.002, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03841, "loss": 0.03841, "time": 0.48768} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00199, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03185, "loss": 0.03185, "time": 0.48915} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.00198, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0326, "loss": 0.0326, "time": 0.48964} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00197, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03172, "loss": 0.03172, "time": 0.28355} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00195, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03268, "loss": 0.03268, "time": 0.50848} +{"mode": "val", "epoch": 123, "iter": 533, "lr": 0.00195, "top1_acc": 0.93909, "top5_acc": 0.99648, "mean_class_accuracy": 0.92167} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00194, "memory": 4083, "data_time": 0.18794, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02941, "loss": 0.02941, "time": 0.78417} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00192, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03819, "loss": 0.03819, "time": 0.49004} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00191, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03534, "loss": 0.03534, "time": 0.48935} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.0019, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04509, "loss": 0.04509, "time": 0.48888} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00189, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03496, "loss": 0.03496, "time": 0.48918} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00188, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03606, "loss": 0.03606, "time": 0.48878} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00187, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 0.99938, "loss_cls": 0.04257, "loss": 0.04257, "time": 0.48977} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00186, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0272, "loss": 0.0272, "time": 0.49025} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00185, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02591, "loss": 0.02591, "time": 0.48688} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00184, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02873, "loss": 0.02873, "time": 0.49074} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00183, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03067, "loss": 0.03067, "time": 0.29863} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.00182, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02552, "loss": 0.02552, "time": 0.50755} +{"mode": "val", "epoch": 124, "iter": 533, "lr": 0.00181, "top1_acc": 0.94367, "top5_acc": 0.99671, "mean_class_accuracy": 0.92466} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.0018, "memory": 4083, "data_time": 0.18455, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02795, "loss": 0.02795, "time": 0.78568} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.00179, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03473, "loss": 0.03473, "time": 0.49128} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00178, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03173, "loss": 0.03173, "time": 0.48817} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00177, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02901, "loss": 0.02901, "time": 0.48728} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00176, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0266, "loss": 0.0266, "time": 0.49116} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00175, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02624, "loss": 0.02624, "time": 0.49544} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00173, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.0379, "loss": 0.0379, "time": 0.49189} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00172, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04324, "loss": 0.04324, "time": 0.4896} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.00171, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04473, "loss": 0.04473, "time": 0.48269} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.0017, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04006, "loss": 0.04006, "time": 0.48825} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00169, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03082, "loss": 0.03082, "time": 0.30382} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00168, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03281, "loss": 0.03281, "time": 0.50715} +{"mode": "val", "epoch": 125, "iter": 533, "lr": 0.00167, "top1_acc": 0.94038, "top5_acc": 0.99671, "mean_class_accuracy": 0.92031} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00166, "memory": 4083, "data_time": 0.18586, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02861, "loss": 0.02861, "time": 0.78858} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00165, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02801, "loss": 0.02801, "time": 0.49159} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00164, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02927, "loss": 0.02927, "time": 0.49154} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00163, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03267, "loss": 0.03267, "time": 0.48662} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00162, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03066, "loss": 0.03066, "time": 0.48931} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00161, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02499, "loss": 0.02499, "time": 0.49142} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0016, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02511, "loss": 0.02511, "time": 0.48908} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00159, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.022, "loss": 0.022, "time": 0.48416} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00158, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02683, "loss": 0.02683, "time": 0.48729} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00157, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03031, "loss": 0.03031, "time": 0.4888} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00156, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03741, "loss": 0.03741, "time": 0.29775} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00155, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03499, "loss": 0.03499, "time": 0.50637} +{"mode": "val", "epoch": 126, "iter": 533, "lr": 0.00155, "top1_acc": 0.94343, "top5_acc": 0.99718, "mean_class_accuracy": 0.92511} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00154, "memory": 4083, "data_time": 0.18443, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02854, "loss": 0.02854, "time": 0.78645} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00153, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.0375, "loss": 0.0375, "time": 0.48858} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00152, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03686, "loss": 0.03686, "time": 0.48861} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00151, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0329, "loss": 0.0329, "time": 0.49105} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.0015, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02503, "loss": 0.02503, "time": 0.48919} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.00149, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02573, "loss": 0.02573, "time": 0.48681} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00148, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02556, "loss": 0.02556, "time": 0.49209} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00147, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03315, "loss": 0.03315, "time": 0.4871} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00146, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02834, "loss": 0.02834, "time": 0.48624} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00145, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03227, "loss": 0.03227, "time": 0.4872} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00144, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02801, "loss": 0.02801, "time": 0.29178} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00143, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02241, "loss": 0.02241, "time": 0.50814} +{"mode": "val", "epoch": 127, "iter": 533, "lr": 0.00142, "top1_acc": 0.94191, "top5_acc": 0.99671, "mean_class_accuracy": 0.92235} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00141, "memory": 4083, "data_time": 0.18801, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02754, "loss": 0.02754, "time": 0.7944} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.0014, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02606, "loss": 0.02606, "time": 0.48725} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00139, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02305, "loss": 0.02305, "time": 0.48755} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00138, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02381, "loss": 0.02381, "time": 0.48771} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00138, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02385, "loss": 0.02385, "time": 0.49182} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00137, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02342, "loss": 0.02342, "time": 0.4906} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.00136, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02241, "loss": 0.02241, "time": 0.48868} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00135, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03142, "loss": 0.03142, "time": 0.48813} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00134, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02391, "loss": 0.02391, "time": 0.48716} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00133, "memory": 4083, "data_time": 0.0004, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02449, "loss": 0.02449, "time": 0.48894} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00132, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03067, "loss": 0.03067, "time": 0.28721} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00131, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02438, "loss": 0.02438, "time": 0.50739} +{"mode": "val", "epoch": 128, "iter": 533, "lr": 0.0013, "top1_acc": 0.9432, "top5_acc": 0.99683, "mean_class_accuracy": 0.9227} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.00129, "memory": 4083, "data_time": 0.18909, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02274, "loss": 0.02274, "time": 0.79671} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00129, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03337, "loss": 0.03337, "time": 0.49149} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00128, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02464, "loss": 0.02464, "time": 0.48981} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00127, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02322, "loss": 0.02322, "time": 0.49002} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00126, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02009, "loss": 0.02009, "time": 0.49076} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00125, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02705, "loss": 0.02705, "time": 0.49298} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00124, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02185, "loss": 0.02185, "time": 0.48986} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00123, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02365, "loss": 0.02365, "time": 0.4914} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.00122, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02404, "loss": 0.02404, "time": 0.48695} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00121, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.026, "loss": 0.026, "time": 0.48843} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00121, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02061, "loss": 0.02061, "time": 0.28501} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.0012, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02757, "loss": 0.02757, "time": 0.49828} +{"mode": "val", "epoch": 129, "iter": 533, "lr": 0.00119, "top1_acc": 0.94308, "top5_acc": 0.99683, "mean_class_accuracy": 0.92546} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00118, "memory": 4083, "data_time": 0.18949, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02567, "loss": 0.02567, "time": 0.80178} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00117, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02296, "loss": 0.02296, "time": 0.49175} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00116, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02762, "loss": 0.02762, "time": 0.48859} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00116, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02823, "loss": 0.02823, "time": 0.48591} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.00115, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02276, "loss": 0.02276, "time": 0.4916} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00114, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02276, "loss": 0.02276, "time": 0.48949} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00113, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02266, "loss": 0.02266, "time": 0.48636} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00112, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0275, "loss": 0.0275, "time": 0.48509} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00111, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02868, "loss": 0.02868, "time": 0.48305} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.0011, "memory": 4083, "data_time": 0.0005, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02302, "loss": 0.02302, "time": 0.48751} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.0011, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02248, "loss": 0.02248, "time": 0.28314} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00109, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02077, "loss": 0.02077, "time": 0.50404} +{"mode": "val", "epoch": 130, "iter": 533, "lr": 0.00108, "top1_acc": 0.94543, "top5_acc": 0.99707, "mean_class_accuracy": 0.92827} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00107, "memory": 4083, "data_time": 0.18862, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02179, "loss": 0.02179, "time": 0.78805} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.00106, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02169, "loss": 0.02169, "time": 0.48799} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00106, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01959, "loss": 0.01959, "time": 0.49008} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00105, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02252, "loss": 0.02252, "time": 0.48818} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00104, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02116, "loss": 0.02116, "time": 0.49131} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00103, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02115, "loss": 0.02115, "time": 0.4877} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00102, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02014, "loss": 0.02014, "time": 0.49} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00102, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02602, "loss": 0.02602, "time": 0.49383} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00101, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02454, "loss": 0.02454, "time": 0.48733} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.001, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02103, "loss": 0.02103, "time": 0.48971} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.00099, "memory": 4083, "data_time": 0.00041, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02161, "loss": 0.02161, "time": 0.28932} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00098, "memory": 4083, "data_time": 0.00042, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02153, "loss": 0.02153, "time": 0.50954} +{"mode": "val", "epoch": 131, "iter": 533, "lr": 0.00098, "top1_acc": 0.94531, "top5_acc": 0.9966, "mean_class_accuracy": 0.92624} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.00097, "memory": 4083, "data_time": 0.18732, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0194, "loss": 0.0194, "time": 0.8017} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00096, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02033, "loss": 0.02033, "time": 0.48823} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00095, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02081, "loss": 0.02081, "time": 0.49282} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00095, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02416, "loss": 0.02416, "time": 0.48942} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00094, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02131, "loss": 0.02131, "time": 0.49217} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00093, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02192, "loss": 0.02192, "time": 0.48836} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00092, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01986, "loss": 0.01986, "time": 0.49013} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00091, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02228, "loss": 0.02228, "time": 0.48834} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00091, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02128, "loss": 0.02128, "time": 0.48624} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0009, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02113, "loss": 0.02113, "time": 0.48365} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00089, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02203, "loss": 0.02203, "time": 0.27962} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00088, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02018, "loss": 0.02018, "time": 0.5068} +{"mode": "val", "epoch": 132, "iter": 533, "lr": 0.00088, "top1_acc": 0.94402, "top5_acc": 0.99671, "mean_class_accuracy": 0.92663} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.00087, "memory": 4083, "data_time": 0.18575, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02462, "loss": 0.02462, "time": 0.7993} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00086, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02327, "loss": 0.02327, "time": 0.48998} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00086, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02298, "loss": 0.02298, "time": 0.49028} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00085, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02191, "loss": 0.02191, "time": 0.49082} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00084, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02234, "loss": 0.02234, "time": 0.48956} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00083, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02785, "loss": 0.02785, "time": 0.48679} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00083, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02089, "loss": 0.02089, "time": 0.48812} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00082, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02246, "loss": 0.02246, "time": 0.48749} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00081, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02813, "loss": 0.02813, "time": 0.48915} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.0008, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02103, "loss": 0.02103, "time": 0.48592} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0008, "memory": 4083, "data_time": 0.00069, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02101, "loss": 0.02101, "time": 0.27939} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00079, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02197, "loss": 0.02197, "time": 0.50839} +{"mode": "val", "epoch": 133, "iter": 533, "lr": 0.00078, "top1_acc": 0.94308, "top5_acc": 0.99695, "mean_class_accuracy": 0.92416} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00078, "memory": 4083, "data_time": 0.18783, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01985, "loss": 0.01985, "time": 0.78816} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00077, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02416, "loss": 0.02416, "time": 0.4873} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00076, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0214, "loss": 0.0214, "time": 0.48722} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.00076, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02123, "loss": 0.02123, "time": 0.49112} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00075, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02751, "loss": 0.02751, "time": 0.4877} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00074, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01909, "loss": 0.01909, "time": 0.48827} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00073, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02129, "loss": 0.02129, "time": 0.4885} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00073, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02062, "loss": 0.02062, "time": 0.4894} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00072, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02339, "loss": 0.02339, "time": 0.4881} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00071, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02216, "loss": 0.02216, "time": 0.48782} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00071, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0187, "loss": 0.0187, "time": 0.29655} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.0007, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02017, "loss": 0.02017, "time": 0.50812} +{"mode": "val", "epoch": 134, "iter": 533, "lr": 0.0007, "top1_acc": 0.94578, "top5_acc": 0.99707, "mean_class_accuracy": 0.92863} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00069, "memory": 4083, "data_time": 0.19371, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0233, "loss": 0.0233, "time": 0.79514} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00068, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02225, "loss": 0.02225, "time": 0.48937} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00068, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02453, "loss": 0.02453, "time": 0.48803} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00067, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01796, "loss": 0.01796, "time": 0.49127} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00066, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02357, "loss": 0.02357, "time": 0.48832} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00066, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01921, "loss": 0.01921, "time": 0.49002} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00065, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02215, "loss": 0.02215, "time": 0.48998} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00064, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02006, "loss": 0.02006, "time": 0.48865} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.00064, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02038, "loss": 0.02038, "time": 0.48985} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00063, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02026, "loss": 0.02026, "time": 0.48716} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00062, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02037, "loss": 0.02037, "time": 0.28337} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00062, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01858, "loss": 0.01858, "time": 0.50781} +{"mode": "val", "epoch": 135, "iter": 533, "lr": 0.00061, "top1_acc": 0.94543, "top5_acc": 0.99707, "mean_class_accuracy": 0.92609} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00061, "memory": 4083, "data_time": 0.18643, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02111, "loss": 0.02111, "time": 0.79824} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.0006, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0241, "loss": 0.0241, "time": 0.48913} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00059, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0195, "loss": 0.0195, "time": 0.48897} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00059, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02032, "loss": 0.02032, "time": 0.48945} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.00058, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.022, "loss": 0.022, "time": 0.49028} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.00057, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01775, "loss": 0.01775, "time": 0.48839} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00057, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01965, "loss": 0.01965, "time": 0.487} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00056, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01962, "loss": 0.01962, "time": 0.48834} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00056, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02333, "loss": 0.02333, "time": 0.48966} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00055, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02128, "loss": 0.02128, "time": 0.49103} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00054, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02039, "loss": 0.02039, "time": 0.28817} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00054, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01788, "loss": 0.01788, "time": 0.50927} +{"mode": "val", "epoch": 136, "iter": 533, "lr": 0.00053, "top1_acc": 0.94519, "top5_acc": 0.9973, "mean_class_accuracy": 0.92384} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00053, "memory": 4083, "data_time": 0.18491, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02212, "loss": 0.02212, "time": 0.78981} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00052, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02137, "loss": 0.02137, "time": 0.48983} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00052, "memory": 4083, "data_time": 0.00042, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01971, "loss": 0.01971, "time": 0.4914} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.00051, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01996, "loss": 0.01996, "time": 0.48672} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.0005, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01951, "loss": 0.01951, "time": 0.49359} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.0005, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01916, "loss": 0.01916, "time": 0.49087} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00049, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02207, "loss": 0.02207, "time": 0.48666} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00049, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02618, "loss": 0.02618, "time": 0.49025} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00048, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02306, "loss": 0.02306, "time": 0.48929} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00048, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02059, "loss": 0.02059, "time": 0.49254} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00047, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01941, "loss": 0.01941, "time": 0.29426} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00046, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01985, "loss": 0.01985, "time": 0.50738} +{"mode": "val", "epoch": 137, "iter": 533, "lr": 0.00046, "top1_acc": 0.94508, "top5_acc": 0.9973, "mean_class_accuracy": 0.92386} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00046, "memory": 4083, "data_time": 0.18516, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01847, "loss": 0.01847, "time": 0.78707} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00045, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02165, "loss": 0.02165, "time": 0.48806} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00044, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01942, "loss": 0.01942, "time": 0.48907} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00044, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01923, "loss": 0.01923, "time": 0.48633} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.00043, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01919, "loss": 0.01919, "time": 0.4902} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.00043, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02248, "loss": 0.02248, "time": 0.48685} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00042, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01806, "loss": 0.01806, "time": 0.48964} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00042, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01763, "loss": 0.01763, "time": 0.49298} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00041, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02105, "loss": 0.02105, "time": 0.48798} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00041, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02191, "loss": 0.02191, "time": 0.48937} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.0004, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0228, "loss": 0.0228, "time": 0.28757} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.0004, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02131, "loss": 0.02131, "time": 0.50705} +{"mode": "val", "epoch": 138, "iter": 533, "lr": 0.00039, "top1_acc": 0.94555, "top5_acc": 0.99695, "mean_class_accuracy": 0.9268} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00039, "memory": 4083, "data_time": 0.1896, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02156, "loss": 0.02156, "time": 0.79259} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00038, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01929, "loss": 0.01929, "time": 0.49087} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00038, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02298, "loss": 0.02298, "time": 0.49116} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00037, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01955, "loss": 0.01955, "time": 0.49187} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00037, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01846, "loss": 0.01846, "time": 0.48831} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00036, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01917, "loss": 0.01917, "time": 0.49127} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00036, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01891, "loss": 0.01891, "time": 0.49022} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00035, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01905, "loss": 0.01905, "time": 0.48819} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00035, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01772, "loss": 0.01772, "time": 0.48926} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.00034, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01793, "loss": 0.01793, "time": 0.48667} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.00034, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02126, "loss": 0.02126, "time": 0.2924} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00033, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01774, "loss": 0.01774, "time": 0.50776} +{"mode": "val", "epoch": 139, "iter": 533, "lr": 0.00033, "top1_acc": 0.94754, "top5_acc": 0.99742, "mean_class_accuracy": 0.92871} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00033, "memory": 4083, "data_time": 0.18265, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01868, "loss": 0.01868, "time": 0.77533} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00032, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01958, "loss": 0.01958, "time": 0.4884} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.00032, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01941, "loss": 0.01941, "time": 0.49001} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.00031, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02021, "loss": 0.02021, "time": 0.48616} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00031, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01979, "loss": 0.01979, "time": 0.48863} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.0003, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01923, "loss": 0.01923, "time": 0.49596} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.0003, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01785, "loss": 0.01785, "time": 0.49102} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00029, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01819, "loss": 0.01819, "time": 0.49157} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00029, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01932, "loss": 0.01932, "time": 0.49067} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00029, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01937, "loss": 0.01937, "time": 0.49212} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00028, "memory": 4083, "data_time": 0.00041, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01993, "loss": 0.01993, "time": 0.30916} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00028, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02116, "loss": 0.02116, "time": 0.50672} +{"mode": "val", "epoch": 140, "iter": 533, "lr": 0.00027, "top1_acc": 0.94719, "top5_acc": 0.99695, "mean_class_accuracy": 0.92905} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00027, "memory": 4083, "data_time": 0.18543, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01987, "loss": 0.01987, "time": 0.80098} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00026, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01909, "loss": 0.01909, "time": 0.48773} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00026, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01963, "loss": 0.01963, "time": 0.48801} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00026, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02216, "loss": 0.02216, "time": 0.48826} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00025, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02069, "loss": 0.02069, "time": 0.48942} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00025, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02, "loss": 0.02, "time": 0.48917} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00024, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0199, "loss": 0.0199, "time": 0.48938} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00024, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01935, "loss": 0.01935, "time": 0.48711} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00024, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01883, "loss": 0.01883, "time": 0.49279} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00023, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01763, "loss": 0.01763, "time": 0.48846} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00023, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0206, "loss": 0.0206, "time": 0.27923} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00022, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01784, "loss": 0.01784, "time": 0.50767} +{"mode": "val", "epoch": 141, "iter": 533, "lr": 0.00022, "top1_acc": 0.94637, "top5_acc": 0.99742, "mean_class_accuracy": 0.9264} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00022, "memory": 4083, "data_time": 0.18956, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01905, "loss": 0.01905, "time": 0.79783} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00021, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01762, "loss": 0.01762, "time": 0.48921} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00021, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01855, "loss": 0.01855, "time": 0.48925} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00021, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01907, "loss": 0.01907, "time": 0.49162} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.0002, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02028, "loss": 0.02028, "time": 0.4891} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.0002, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02056, "loss": 0.02056, "time": 0.49069} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.0002, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02143, "loss": 0.02143, "time": 0.49381} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00019, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01868, "loss": 0.01868, "time": 0.49024} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00019, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01958, "loss": 0.01958, "time": 0.48931} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00018, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01777, "loss": 0.01777, "time": 0.48846} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00018, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01918, "loss": 0.01918, "time": 0.26923} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00018, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01895, "loss": 0.01895, "time": 0.50872} +{"mode": "val", "epoch": 142, "iter": 533, "lr": 0.00018, "top1_acc": 0.94731, "top5_acc": 0.9973, "mean_class_accuracy": 0.92654} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.00017, "memory": 4083, "data_time": 0.18993, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01863, "loss": 0.01863, "time": 0.78201} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00017, "memory": 4083, "data_time": 0.00052, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02006, "loss": 0.02006, "time": 0.49238} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00017, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02083, "loss": 0.02083, "time": 0.49057} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00016, "memory": 4083, "data_time": 0.00054, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01805, "loss": 0.01805, "time": 0.48923} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00016, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02007, "loss": 0.02007, "time": 0.49093} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00016, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01816, "loss": 0.01816, "time": 0.49151} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00015, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01836, "loss": 0.01836, "time": 0.49079} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00015, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01925, "loss": 0.01925, "time": 0.48771} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00015, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0197, "loss": 0.0197, "time": 0.48741} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00014, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0211, "loss": 0.0211, "time": 0.49125} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00014, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01804, "loss": 0.01804, "time": 0.27867} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00014, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01938, "loss": 0.01938, "time": 0.5086} +{"mode": "val", "epoch": 143, "iter": 533, "lr": 0.00013, "top1_acc": 0.94731, "top5_acc": 0.99683, "mean_class_accuracy": 0.92905} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00013, "memory": 4083, "data_time": 0.19238, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01902, "loss": 0.01902, "time": 0.79431} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00013, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01824, "loss": 0.01824, "time": 0.48698} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00013, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01979, "loss": 0.01979, "time": 0.48714} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00012, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01967, "loss": 0.01967, "time": 0.49097} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00012, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02122, "loss": 0.02122, "time": 0.49122} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00012, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01836, "loss": 0.01836, "time": 0.495} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00011, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01893, "loss": 0.01893, "time": 0.49066} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.00011, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02053, "loss": 0.02053, "time": 0.49433} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.00011, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01955, "loss": 0.01955, "time": 0.49103} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.00011, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02024, "loss": 0.02024, "time": 0.49243} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.0001, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02067, "loss": 0.02067, "time": 0.26908} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.0001, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02031, "loss": 0.02031, "time": 0.50702} +{"mode": "val", "epoch": 144, "iter": 533, "lr": 0.0001, "top1_acc": 0.9466, "top5_acc": 0.99707, "mean_class_accuracy": 0.92737} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.0001, "memory": 4083, "data_time": 0.18441, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01971, "loss": 0.01971, "time": 0.78236} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 9e-05, "memory": 4083, "data_time": 0.00044, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02007, "loss": 0.02007, "time": 0.49158} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 9e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01915, "loss": 0.01915, "time": 0.49011} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 9e-05, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01877, "loss": 0.01877, "time": 0.49019} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 9e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01982, "loss": 0.01982, "time": 0.491} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 8e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01779, "loss": 0.01779, "time": 0.48664} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 8e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01911, "loss": 0.01911, "time": 0.4894} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 8e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02055, "loss": 0.02055, "time": 0.49055} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 8e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01796, "loss": 0.01796, "time": 0.48853} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 7e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01915, "loss": 0.01915, "time": 0.48664} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 7e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01951, "loss": 0.01951, "time": 0.29704} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 7e-05, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01858, "loss": 0.01858, "time": 0.50796} +{"mode": "val", "epoch": 145, "iter": 533, "lr": 7e-05, "top1_acc": 0.94602, "top5_acc": 0.99718, "mean_class_accuracy": 0.92644} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 7e-05, "memory": 4083, "data_time": 0.18624, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01884, "loss": 0.01884, "time": 0.78818} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 6e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0176, "loss": 0.0176, "time": 0.49269} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 6e-05, "memory": 4083, "data_time": 0.00053, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02035, "loss": 0.02035, "time": 0.48907} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 6e-05, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01982, "loss": 0.01982, "time": 0.49006} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 6e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01812, "loss": 0.01812, "time": 0.49638} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 6e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01817, "loss": 0.01817, "time": 0.48787} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 5e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01902, "loss": 0.01902, "time": 0.49274} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 5e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01902, "loss": 0.01902, "time": 0.48903} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 5e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01945, "loss": 0.01945, "time": 0.49025} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 5e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01999, "loss": 0.01999, "time": 0.49019} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 5e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0181, "loss": 0.0181, "time": 0.29088} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 5e-05, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01853, "loss": 0.01853, "time": 0.50838} +{"mode": "val", "epoch": 146, "iter": 533, "lr": 4e-05, "top1_acc": 0.94625, "top5_acc": 0.99707, "mean_class_accuracy": 0.92495} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 4e-05, "memory": 4083, "data_time": 0.18917, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01937, "loss": 0.01937, "time": 0.78818} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 4e-05, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01805, "loss": 0.01805, "time": 0.48837} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 4e-05, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02144, "loss": 0.02144, "time": 0.48984} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 4e-05, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02053, "loss": 0.02053, "time": 0.48758} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 4e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01886, "loss": 0.01886, "time": 0.48802} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 3e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02128, "loss": 0.02128, "time": 0.49498} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 3e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01761, "loss": 0.01761, "time": 0.49358} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 3e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0177, "loss": 0.0177, "time": 0.49016} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 3e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01896, "loss": 0.01896, "time": 0.49149} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 3e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01876, "loss": 0.01876, "time": 0.48933} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 3e-05, "memory": 4083, "data_time": 0.00055, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01623, "loss": 0.01623, "time": 0.28312} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 3e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02079, "loss": 0.02079, "time": 0.5082} +{"mode": "val", "epoch": 147, "iter": 533, "lr": 2e-05, "top1_acc": 0.94684, "top5_acc": 0.9973, "mean_class_accuracy": 0.92742} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 2e-05, "memory": 4083, "data_time": 0.1852, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01902, "loss": 0.01902, "time": 0.78594} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 2e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0194, "loss": 0.0194, "time": 0.48858} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 2e-05, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01784, "loss": 0.01784, "time": 0.48798} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 2e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01884, "loss": 0.01884, "time": 0.48636} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 2e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01803, "loss": 0.01803, "time": 0.48981} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 2e-05, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01787, "loss": 0.01787, "time": 0.48965} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 2e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01867, "loss": 0.01867, "time": 0.49053} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 2e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01812, "loss": 0.01812, "time": 0.48977} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 1e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02043, "loss": 0.02043, "time": 0.49027} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 1e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02185, "loss": 0.02185, "time": 0.4908} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 1e-05, "memory": 4083, "data_time": 0.00046, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01888, "loss": 0.01888, "time": 0.29376} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 1e-05, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01947, "loss": 0.01947, "time": 0.50732} +{"mode": "val", "epoch": 148, "iter": 533, "lr": 1e-05, "top1_acc": 0.94778, "top5_acc": 0.99742, "mean_class_accuracy": 0.9287} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 1e-05, "memory": 4083, "data_time": 0.18527, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0188, "loss": 0.0188, "time": 0.79535} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 1e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01839, "loss": 0.01839, "time": 0.48837} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 1e-05, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01947, "loss": 0.01947, "time": 0.49011} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 1e-05, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0184, "loss": 0.0184, "time": 0.48969} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 1e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01895, "loss": 0.01895, "time": 0.49082} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0187, "loss": 0.0187, "time": 0.49226} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 1e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01813, "loss": 0.01813, "time": 0.49385} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 1e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01844, "loss": 0.01844, "time": 0.48916} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0177, "loss": 0.0177, "time": 0.49367} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01803, "loss": 0.01803, "time": 0.49187} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01992, "loss": 0.01992, "time": 0.28419} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00051, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02146, "loss": 0.02146, "time": 0.50754} +{"mode": "val", "epoch": 149, "iter": 533, "lr": 0.0, "top1_acc": 0.94766, "top5_acc": 0.99742, "mean_class_accuracy": 0.92821} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 0.0, "memory": 4083, "data_time": 0.18675, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02003, "loss": 0.02003, "time": 0.80143} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 0.0, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01919, "loss": 0.01919, "time": 0.48852} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 0.0, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01997, "loss": 0.01997, "time": 0.49146} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 0.0, "memory": 4083, "data_time": 0.00045, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01878, "loss": 0.01878, "time": 0.48878} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 0.0, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01935, "loss": 0.01935, "time": 0.48992} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0192, "loss": 0.0192, "time": 0.4904} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 0.0, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01928, "loss": 0.01928, "time": 0.49174} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 0.0, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01938, "loss": 0.01938, "time": 0.4885} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01884, "loss": 0.01884, "time": 0.49032} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01908, "loss": 0.01908, "time": 0.48574} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01812, "loss": 0.01812, "time": 0.29469} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02024, "loss": 0.02024, "time": 0.49061} +{"mode": "val", "epoch": 150, "iter": 533, "lr": 0.0, "top1_acc": 0.94637, "top5_acc": 0.9973, "mean_class_accuracy": 0.92593} diff --git a/finegym/b_3/b_3.py b/finegym/b_3/b_3.py new file mode 100644 index 0000000000000000000000000000000000000000..f6b3e5ca7901aff22314939389d8eecc3f4fd2ad --- /dev/null +++ b/finegym/b_3/b_3.py @@ -0,0 +1,113 @@ +modality = 'b' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/b_3' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/finegym/b_3/best_pred.pkl b/finegym/b_3/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..b2e4ce5dcec70108786ff66952c0a9dd4b4d3303 --- /dev/null +++ b/finegym/b_3/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cbca7f85d66fa3978c79158c363760b4c13af9d2e0d01a5d21fa87704bfdd38b +size 5253922 diff --git a/finegym/b_3/best_top1_acc_epoch_148.pth b/finegym/b_3/best_top1_acc_epoch_148.pth new file mode 100644 index 0000000000000000000000000000000000000000..eefd7218ee98abb74a233af9110784a5958c1335 --- /dev/null +++ b/finegym/b_3/best_top1_acc_epoch_148.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce2ea406d71cf80ebf90b44e6b69876854ddefc870b0ee56bdc128eaff1303fc +size 31999601 diff --git a/finegym/bm/20250624_101409.log b/finegym/bm/20250624_101409.log new file mode 100644 index 0000000000000000000000000000000000000000..1fc0b153962213b75e5fee55f190135288e8e820 --- /dev/null +++ b/finegym/bm/20250624_101409.log @@ -0,0 +1,3489 @@ +2025-06-24 10:14:09,119 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2025-06-24 10:14:09,373 - pyskl - INFO - Config: modality = 'bm' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/bm' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2025-06-24 10:14:09,374 - pyskl - INFO - Set random seed to 1645946785, deterministic: False +2025-06-24 10:14:11,003 - pyskl - INFO - 20484 videos remain after valid thresholding +2025-06-24 10:14:17,191 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-24 10:14:17,192 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm +2025-06-24 10:14:17,192 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2025-06-24 10:14:17,192 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2025-06-24 10:14:17,192 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm by HardDiskBackend. +2025-06-24 10:15:19,744 - pyskl - INFO - Epoch [1][100/1281] lr: 2.500e-02, eta: 1 day, 9:22:01, time: 0.625, data_time: 0.203, memory: 4082, top1_acc: 0.0669, top5_acc: 0.2487, loss_cls: 4.5240, loss: 4.5240 +2025-06-24 10:16:01,247 - pyskl - INFO - Epoch [1][200/1281] lr: 2.500e-02, eta: 1 day, 3:44:17, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.0794, top5_acc: 0.3038, loss_cls: 4.5996, loss: 4.5996 +2025-06-24 10:16:42,927 - pyskl - INFO - Epoch [1][300/1281] lr: 2.500e-02, eta: 1 day, 1:53:11, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.1000, top5_acc: 0.3331, loss_cls: 4.4750, loss: 4.4750 +2025-06-24 10:17:24,639 - pyskl - INFO - Epoch [1][400/1281] lr: 2.500e-02, eta: 1 day, 0:57:32, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.1100, top5_acc: 0.3625, loss_cls: 4.3255, loss: 4.3255 +2025-06-24 10:18:06,262 - pyskl - INFO - Epoch [1][500/1281] lr: 2.500e-02, eta: 1 day, 0:23:18, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.1219, top5_acc: 0.3906, loss_cls: 4.1821, loss: 4.1821 +2025-06-24 10:18:47,693 - pyskl - INFO - Epoch [1][600/1281] lr: 2.500e-02, eta: 23:59:13, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.1300, top5_acc: 0.4219, loss_cls: 4.0994, loss: 4.0994 +2025-06-24 10:19:29,002 - pyskl - INFO - Epoch [1][700/1281] lr: 2.500e-02, eta: 23:41:16, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.1506, top5_acc: 0.4688, loss_cls: 3.8900, loss: 3.8900 +2025-06-24 10:20:10,657 - pyskl - INFO - Epoch [1][800/1281] lr: 2.500e-02, eta: 23:29:01, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.1900, top5_acc: 0.5244, loss_cls: 3.6852, loss: 3.6852 +2025-06-24 10:20:52,065 - pyskl - INFO - Epoch [1][900/1281] lr: 2.500e-02, eta: 23:18:27, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.2150, top5_acc: 0.5769, loss_cls: 3.4612, loss: 3.4612 +2025-06-24 10:21:33,553 - pyskl - INFO - Epoch [1][1000/1281] lr: 2.500e-02, eta: 23:10:07, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.2662, top5_acc: 0.6200, loss_cls: 3.3059, loss: 3.3059 +2025-06-24 10:22:06,568 - pyskl - INFO - Epoch [1][1100/1281] lr: 2.500e-02, eta: 22:38:39, time: 0.330, data_time: 0.000, memory: 4082, top1_acc: 0.2619, top5_acc: 0.6344, loss_cls: 3.2041, loss: 3.2041 +2025-06-24 10:22:41,935 - pyskl - INFO - Epoch [1][1200/1281] lr: 2.500e-02, eta: 22:18:34, time: 0.354, data_time: 0.000, memory: 4082, top1_acc: 0.3131, top5_acc: 0.6900, loss_cls: 2.9680, loss: 2.9680 +2025-06-24 10:23:14,095 - pyskl - INFO - Saving checkpoint at 1 epochs +2025-06-24 10:24:25,722 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:24:25,784 - pyskl - INFO - +top1_acc 0.2892 +top5_acc 0.6709 +2025-06-24 10:24:25,784 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:24:25,791 - pyskl - INFO - +mean_acc 0.1463 +2025-06-24 10:24:25,975 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2025-06-24 10:24:25,976 - pyskl - INFO - Best top1_acc is 0.2892 at 1 epoch. +2025-06-24 10:24:25,979 - pyskl - INFO - Epoch(val) [1][533] top1_acc: 0.2892, top5_acc: 0.6709, mean_class_accuracy: 0.1463 +2025-06-24 10:25:27,776 - pyskl - INFO - Epoch [2][100/1281] lr: 2.500e-02, eta: 21:44:17, time: 0.618, data_time: 0.199, memory: 4082, top1_acc: 0.3294, top5_acc: 0.7456, loss_cls: 2.8346, loss: 2.8346 +2025-06-24 10:26:09,180 - pyskl - INFO - Epoch [2][200/1281] lr: 2.500e-02, eta: 21:44:25, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.3319, top5_acc: 0.7431, loss_cls: 2.7833, loss: 2.7833 +2025-06-24 10:26:50,683 - pyskl - INFO - Epoch [2][300/1281] lr: 2.500e-02, eta: 21:44:38, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.3794, top5_acc: 0.7869, loss_cls: 2.6001, loss: 2.6001 +2025-06-24 10:27:32,159 - pyskl - INFO - Epoch [2][400/1281] lr: 2.500e-02, eta: 21:44:43, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.3937, top5_acc: 0.7900, loss_cls: 2.5469, loss: 2.5469 +2025-06-24 10:28:13,579 - pyskl - INFO - Epoch [2][500/1281] lr: 2.499e-02, eta: 21:44:35, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.3931, top5_acc: 0.7937, loss_cls: 2.4895, loss: 2.4895 +2025-06-24 10:28:55,224 - pyskl - INFO - Epoch [2][600/1281] lr: 2.499e-02, eta: 21:44:48, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.4100, top5_acc: 0.8094, loss_cls: 2.4359, loss: 2.4359 +2025-06-24 10:29:36,641 - pyskl - INFO - Epoch [2][700/1281] lr: 2.499e-02, eta: 21:44:32, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.4031, top5_acc: 0.8169, loss_cls: 2.3992, loss: 2.3992 +2025-06-24 10:30:18,137 - pyskl - INFO - Epoch [2][800/1281] lr: 2.499e-02, eta: 21:44:22, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.4163, top5_acc: 0.8275, loss_cls: 2.3293, loss: 2.3293 +2025-06-24 10:30:59,582 - pyskl - INFO - Epoch [2][900/1281] lr: 2.499e-02, eta: 21:44:04, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.4625, top5_acc: 0.8644, loss_cls: 2.1930, loss: 2.1930 +2025-06-24 10:31:41,029 - pyskl - INFO - Epoch [2][1000/1281] lr: 2.499e-02, eta: 21:43:44, time: 0.414, data_time: 0.001, memory: 4082, top1_acc: 0.4244, top5_acc: 0.8413, loss_cls: 2.2607, loss: 2.2607 +2025-06-24 10:32:13,483 - pyskl - INFO - Epoch [2][1100/1281] lr: 2.499e-02, eta: 21:31:26, time: 0.325, data_time: 0.001, memory: 4082, top1_acc: 0.4612, top5_acc: 0.8650, loss_cls: 2.1832, loss: 2.1832 +2025-06-24 10:32:49,504 - pyskl - INFO - Epoch [2][1200/1281] lr: 2.499e-02, eta: 21:24:37, time: 0.360, data_time: 0.000, memory: 4082, top1_acc: 0.4919, top5_acc: 0.8650, loss_cls: 2.1198, loss: 2.1198 +2025-06-24 10:33:20,938 - pyskl - INFO - Saving checkpoint at 2 epochs +2025-06-24 10:34:31,930 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:34:31,985 - pyskl - INFO - +top1_acc 0.4627 +top5_acc 0.8528 +2025-06-24 10:34:31,985 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:34:31,993 - pyskl - INFO - +mean_acc 0.2624 +2025-06-24 10:34:31,999 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_1.pth was removed +2025-06-24 10:34:32,231 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2025-06-24 10:34:32,231 - pyskl - INFO - Best top1_acc is 0.4627 at 2 epoch. +2025-06-24 10:34:32,235 - pyskl - INFO - Epoch(val) [2][533] top1_acc: 0.4627, top5_acc: 0.8528, mean_class_accuracy: 0.2624 +2025-06-24 10:35:33,610 - pyskl - INFO - Epoch [3][100/1281] lr: 2.499e-02, eta: 21:08:56, time: 0.614, data_time: 0.200, memory: 4082, top1_acc: 0.5044, top5_acc: 0.8806, loss_cls: 2.0531, loss: 2.0531 +2025-06-24 10:36:15,118 - pyskl - INFO - Epoch [3][200/1281] lr: 2.499e-02, eta: 21:09:47, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.4994, top5_acc: 0.8756, loss_cls: 2.0645, loss: 2.0645 +2025-06-24 10:36:56,525 - pyskl - INFO - Epoch [3][300/1281] lr: 2.499e-02, eta: 21:10:25, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.5012, top5_acc: 0.8919, loss_cls: 1.9961, loss: 1.9961 +2025-06-24 10:37:37,866 - pyskl - INFO - Epoch [3][400/1281] lr: 2.499e-02, eta: 21:10:53, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.4969, top5_acc: 0.9094, loss_cls: 1.9395, loss: 1.9395 +2025-06-24 10:38:19,239 - pyskl - INFO - Epoch [3][500/1281] lr: 2.498e-02, eta: 21:11:18, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.5425, top5_acc: 0.9062, loss_cls: 1.9004, loss: 1.9004 +2025-06-24 10:39:00,604 - pyskl - INFO - Epoch [3][600/1281] lr: 2.498e-02, eta: 21:11:39, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.5181, top5_acc: 0.9056, loss_cls: 1.9077, loss: 1.9077 +2025-06-24 10:39:42,063 - pyskl - INFO - Epoch [3][700/1281] lr: 2.498e-02, eta: 21:12:02, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.5344, top5_acc: 0.9094, loss_cls: 1.8703, loss: 1.8703 +2025-06-24 10:40:23,484 - pyskl - INFO - Epoch [3][800/1281] lr: 2.498e-02, eta: 21:12:18, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.5350, top5_acc: 0.9144, loss_cls: 1.8478, loss: 1.8478 +2025-06-24 10:41:04,896 - pyskl - INFO - Epoch [3][900/1281] lr: 2.498e-02, eta: 21:12:31, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.5500, top5_acc: 0.9113, loss_cls: 1.8127, loss: 1.8127 +2025-06-24 10:41:46,404 - pyskl - INFO - Epoch [3][1000/1281] lr: 2.498e-02, eta: 21:12:46, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.5537, top5_acc: 0.9113, loss_cls: 1.8322, loss: 1.8322 +2025-06-24 10:42:18,168 - pyskl - INFO - Epoch [3][1100/1281] lr: 2.498e-02, eta: 21:04:36, time: 0.318, data_time: 0.000, memory: 4082, top1_acc: 0.5469, top5_acc: 0.9169, loss_cls: 1.8100, loss: 1.8100 +2025-06-24 10:42:54,266 - pyskl - INFO - Epoch [3][1200/1281] lr: 2.498e-02, eta: 21:00:27, time: 0.361, data_time: 0.000, memory: 4082, top1_acc: 0.5425, top5_acc: 0.9200, loss_cls: 1.8006, loss: 1.8006 +2025-06-24 10:43:26,004 - pyskl - INFO - Saving checkpoint at 3 epochs +2025-06-24 10:44:37,860 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:44:37,919 - pyskl - INFO - +top1_acc 0.3984 +top5_acc 0.7809 +2025-06-24 10:44:37,919 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:44:37,930 - pyskl - INFO - +mean_acc 0.2448 +2025-06-24 10:44:37,933 - pyskl - INFO - Epoch(val) [3][533] top1_acc: 0.3984, top5_acc: 0.7809, mean_class_accuracy: 0.2448 +2025-06-24 10:45:39,218 - pyskl - INFO - Epoch [4][100/1281] lr: 2.497e-02, eta: 20:50:11, time: 0.613, data_time: 0.197, memory: 4082, top1_acc: 0.6100, top5_acc: 0.9325, loss_cls: 1.6559, loss: 1.6559 +2025-06-24 10:46:20,812 - pyskl - INFO - Epoch [4][200/1281] lr: 2.497e-02, eta: 20:50:52, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.5863, top5_acc: 0.9444, loss_cls: 1.6397, loss: 1.6397 +2025-06-24 10:47:02,260 - pyskl - INFO - Epoch [4][300/1281] lr: 2.497e-02, eta: 20:51:23, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.5981, top5_acc: 0.9431, loss_cls: 1.6487, loss: 1.6487 +2025-06-24 10:47:43,940 - pyskl - INFO - Epoch [4][400/1281] lr: 2.497e-02, eta: 20:52:00, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.5837, top5_acc: 0.9400, loss_cls: 1.6731, loss: 1.6731 +2025-06-24 10:48:25,497 - pyskl - INFO - Epoch [4][500/1281] lr: 2.497e-02, eta: 20:52:28, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.5763, top5_acc: 0.9313, loss_cls: 1.7002, loss: 1.7002 +2025-06-24 10:49:06,919 - pyskl - INFO - Epoch [4][600/1281] lr: 2.497e-02, eta: 20:52:48, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6144, top5_acc: 0.9431, loss_cls: 1.5949, loss: 1.5949 +2025-06-24 10:49:48,356 - pyskl - INFO - Epoch [4][700/1281] lr: 2.497e-02, eta: 20:53:05, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6175, top5_acc: 0.9463, loss_cls: 1.5439, loss: 1.5439 +2025-06-24 10:50:30,029 - pyskl - INFO - Epoch [4][800/1281] lr: 2.496e-02, eta: 20:53:29, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.6012, top5_acc: 0.9519, loss_cls: 1.5947, loss: 1.5947 +2025-06-24 10:51:11,462 - pyskl - INFO - Epoch [4][900/1281] lr: 2.496e-02, eta: 20:53:42, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6462, top5_acc: 0.9581, loss_cls: 1.4411, loss: 1.4411 +2025-06-24 10:51:53,071 - pyskl - INFO - Epoch [4][1000/1281] lr: 2.496e-02, eta: 20:53:58, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6294, top5_acc: 0.9369, loss_cls: 1.5424, loss: 1.5424 +2025-06-24 10:52:25,170 - pyskl - INFO - Epoch [4][1100/1281] lr: 2.496e-02, eta: 20:48:12, time: 0.321, data_time: 0.001, memory: 4082, top1_acc: 0.6312, top5_acc: 0.9525, loss_cls: 1.4917, loss: 1.4917 +2025-06-24 10:53:02,271 - pyskl - INFO - Epoch [4][1200/1281] lr: 2.496e-02, eta: 20:45:45, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.6375, top5_acc: 0.9563, loss_cls: 1.4911, loss: 1.4911 +2025-06-24 10:53:33,316 - pyskl - INFO - Saving checkpoint at 4 epochs +2025-06-24 10:54:44,659 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:54:44,717 - pyskl - INFO - +top1_acc 0.5659 +top5_acc 0.9296 +2025-06-24 10:54:44,717 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:54:44,725 - pyskl - INFO - +mean_acc 0.4331 +2025-06-24 10:54:44,729 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_2.pth was removed +2025-06-24 10:54:45,123 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2025-06-24 10:54:45,123 - pyskl - INFO - Best top1_acc is 0.5659 at 4 epoch. +2025-06-24 10:54:45,126 - pyskl - INFO - Epoch(val) [4][533] top1_acc: 0.5659, top5_acc: 0.9296, mean_class_accuracy: 0.4331 +2025-06-24 10:55:46,717 - pyskl - INFO - Epoch [5][100/1281] lr: 2.495e-02, eta: 20:38:09, time: 0.616, data_time: 0.196, memory: 4082, top1_acc: 0.6356, top5_acc: 0.9613, loss_cls: 1.4645, loss: 1.4645 +2025-06-24 10:56:28,186 - pyskl - INFO - Epoch [5][200/1281] lr: 2.495e-02, eta: 20:38:29, time: 0.415, data_time: 0.001, memory: 4082, top1_acc: 0.6631, top5_acc: 0.9644, loss_cls: 1.3958, loss: 1.3958 +2025-06-24 10:57:10,029 - pyskl - INFO - Epoch [5][300/1281] lr: 2.495e-02, eta: 20:39:01, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.6669, top5_acc: 0.9500, loss_cls: 1.4111, loss: 1.4111 +2025-06-24 10:57:51,496 - pyskl - INFO - Epoch [5][400/1281] lr: 2.495e-02, eta: 20:39:17, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6569, top5_acc: 0.9625, loss_cls: 1.4161, loss: 1.4161 +2025-06-24 10:58:32,968 - pyskl - INFO - Epoch [5][500/1281] lr: 2.495e-02, eta: 20:39:31, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6488, top5_acc: 0.9581, loss_cls: 1.4256, loss: 1.4256 +2025-06-24 10:59:14,796 - pyskl - INFO - Epoch [5][600/1281] lr: 2.495e-02, eta: 20:39:55, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.6600, top5_acc: 0.9631, loss_cls: 1.3890, loss: 1.3890 +2025-06-24 10:59:57,366 - pyskl - INFO - Epoch [5][700/1281] lr: 2.494e-02, eta: 20:40:40, time: 0.426, data_time: 0.000, memory: 4082, top1_acc: 0.6656, top5_acc: 0.9644, loss_cls: 1.3567, loss: 1.3567 +2025-06-24 11:00:38,752 - pyskl - INFO - Epoch [5][800/1281] lr: 2.494e-02, eta: 20:40:45, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6863, top5_acc: 0.9719, loss_cls: 1.2916, loss: 1.2916 +2025-06-24 11:01:20,204 - pyskl - INFO - Epoch [5][900/1281] lr: 2.494e-02, eta: 20:40:51, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6644, top5_acc: 0.9587, loss_cls: 1.3865, loss: 1.3865 +2025-06-24 11:02:01,736 - pyskl - INFO - Epoch [5][1000/1281] lr: 2.494e-02, eta: 20:40:57, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6837, top5_acc: 0.9631, loss_cls: 1.3559, loss: 1.3559 +2025-06-24 11:02:32,447 - pyskl - INFO - Epoch [5][1100/1281] lr: 2.494e-02, eta: 20:35:39, time: 0.307, data_time: 0.000, memory: 4082, top1_acc: 0.6756, top5_acc: 0.9587, loss_cls: 1.3653, loss: 1.3653 +2025-06-24 11:03:11,347 - pyskl - INFO - Epoch [5][1200/1281] lr: 2.493e-02, eta: 20:34:30, time: 0.389, data_time: 0.000, memory: 4082, top1_acc: 0.6844, top5_acc: 0.9650, loss_cls: 1.3359, loss: 1.3359 +2025-06-24 11:03:40,310 - pyskl - INFO - Saving checkpoint at 5 epochs +2025-06-24 11:04:52,074 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:04:52,142 - pyskl - INFO - +top1_acc 0.6475 +top5_acc 0.9529 +2025-06-24 11:04:52,142 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:04:52,150 - pyskl - INFO - +mean_acc 0.5230 +2025-06-24 11:04:52,155 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_4.pth was removed +2025-06-24 11:04:52,357 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2025-06-24 11:04:52,358 - pyskl - INFO - Best top1_acc is 0.6475 at 5 epoch. +2025-06-24 11:04:52,360 - pyskl - INFO - Epoch(val) [5][533] top1_acc: 0.6475, top5_acc: 0.9529, mean_class_accuracy: 0.5230 +2025-06-24 11:05:54,056 - pyskl - INFO - Epoch [6][100/1281] lr: 2.493e-02, eta: 20:28:20, time: 0.617, data_time: 0.200, memory: 4082, top1_acc: 0.7125, top5_acc: 0.9694, loss_cls: 1.2313, loss: 1.2313 +2025-06-24 11:06:35,747 - pyskl - INFO - Epoch [6][200/1281] lr: 2.493e-02, eta: 20:28:36, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7137, top5_acc: 0.9637, loss_cls: 1.2448, loss: 1.2448 +2025-06-24 11:07:17,291 - pyskl - INFO - Epoch [6][300/1281] lr: 2.492e-02, eta: 20:28:47, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6863, top5_acc: 0.9756, loss_cls: 1.2903, loss: 1.2903 +2025-06-24 11:07:58,789 - pyskl - INFO - Epoch [6][400/1281] lr: 2.492e-02, eta: 20:28:54, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6800, top5_acc: 0.9694, loss_cls: 1.2868, loss: 1.2868 +2025-06-24 11:08:40,185 - pyskl - INFO - Epoch [6][500/1281] lr: 2.492e-02, eta: 20:28:58, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6931, top5_acc: 0.9762, loss_cls: 1.2651, loss: 1.2651 +2025-06-24 11:09:21,716 - pyskl - INFO - Epoch [6][600/1281] lr: 2.492e-02, eta: 20:29:03, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6987, top5_acc: 0.9681, loss_cls: 1.2753, loss: 1.2753 +2025-06-24 11:10:03,300 - pyskl - INFO - Epoch [6][700/1281] lr: 2.492e-02, eta: 20:29:09, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6981, top5_acc: 0.9663, loss_cls: 1.2790, loss: 1.2790 +2025-06-24 11:10:44,740 - pyskl - INFO - Epoch [6][800/1281] lr: 2.491e-02, eta: 20:29:10, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7031, top5_acc: 0.9688, loss_cls: 1.2444, loss: 1.2444 +2025-06-24 11:11:26,373 - pyskl - INFO - Epoch [6][900/1281] lr: 2.491e-02, eta: 20:29:15, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6925, top5_acc: 0.9750, loss_cls: 1.2526, loss: 1.2526 +2025-06-24 11:12:07,837 - pyskl - INFO - Epoch [6][1000/1281] lr: 2.491e-02, eta: 20:29:14, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6987, top5_acc: 0.9637, loss_cls: 1.2544, loss: 1.2544 +2025-06-24 11:12:37,636 - pyskl - INFO - Epoch [6][1100/1281] lr: 2.491e-02, eta: 20:24:25, time: 0.298, data_time: 0.000, memory: 4082, top1_acc: 0.6769, top5_acc: 0.9650, loss_cls: 1.2922, loss: 1.2922 +2025-06-24 11:13:17,967 - pyskl - INFO - Epoch [6][1200/1281] lr: 2.490e-02, eta: 20:23:58, time: 0.403, data_time: 0.000, memory: 4082, top1_acc: 0.7019, top5_acc: 0.9688, loss_cls: 1.2509, loss: 1.2509 +2025-06-24 11:13:45,488 - pyskl - INFO - Saving checkpoint at 6 epochs +2025-06-24 11:14:56,463 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:14:56,527 - pyskl - INFO - +top1_acc 0.6774 +top5_acc 0.9680 +2025-06-24 11:14:56,527 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:14:56,534 - pyskl - INFO - +mean_acc 0.5539 +2025-06-24 11:14:56,538 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_5.pth was removed +2025-06-24 11:14:56,714 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_6.pth. +2025-06-24 11:14:56,714 - pyskl - INFO - Best top1_acc is 0.6774 at 6 epoch. +2025-06-24 11:14:56,717 - pyskl - INFO - Epoch(val) [6][533] top1_acc: 0.6774, top5_acc: 0.9680, mean_class_accuracy: 0.5539 +2025-06-24 11:15:58,759 - pyskl - INFO - Epoch [7][100/1281] lr: 2.490e-02, eta: 20:18:49, time: 0.620, data_time: 0.199, memory: 4082, top1_acc: 0.7206, top5_acc: 0.9775, loss_cls: 1.1744, loss: 1.1744 +2025-06-24 11:16:40,277 - pyskl - INFO - Epoch [7][200/1281] lr: 2.490e-02, eta: 20:18:53, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7238, top5_acc: 0.9725, loss_cls: 1.1469, loss: 1.1469 +2025-06-24 11:17:23,570 - pyskl - INFO - Epoch [7][300/1281] lr: 2.489e-02, eta: 20:19:36, time: 0.433, data_time: 0.000, memory: 4082, top1_acc: 0.7231, top5_acc: 0.9812, loss_cls: 1.1621, loss: 1.1621 +2025-06-24 11:18:05,568 - pyskl - INFO - Epoch [7][400/1281] lr: 2.489e-02, eta: 20:19:48, time: 0.420, data_time: 0.000, memory: 4082, top1_acc: 0.7288, top5_acc: 0.9725, loss_cls: 1.1506, loss: 1.1506 +2025-06-24 11:18:47,251 - pyskl - INFO - Epoch [7][500/1281] lr: 2.489e-02, eta: 20:19:51, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7056, top5_acc: 0.9719, loss_cls: 1.1979, loss: 1.1979 +2025-06-24 11:19:28,859 - pyskl - INFO - Epoch [7][600/1281] lr: 2.489e-02, eta: 20:19:52, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7106, top5_acc: 0.9725, loss_cls: 1.1959, loss: 1.1959 +2025-06-24 11:20:10,451 - pyskl - INFO - Epoch [7][700/1281] lr: 2.488e-02, eta: 20:19:51, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7144, top5_acc: 0.9750, loss_cls: 1.2031, loss: 1.2031 +2025-06-24 11:20:51,948 - pyskl - INFO - Epoch [7][800/1281] lr: 2.488e-02, eta: 20:19:48, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7031, top5_acc: 0.9731, loss_cls: 1.2427, loss: 1.2427 +2025-06-24 11:21:33,596 - pyskl - INFO - Epoch [7][900/1281] lr: 2.488e-02, eta: 20:19:46, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7400, top5_acc: 0.9781, loss_cls: 1.1270, loss: 1.1270 +2025-06-24 11:22:15,224 - pyskl - INFO - Epoch [7][1000/1281] lr: 2.487e-02, eta: 20:19:43, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7250, top5_acc: 0.9706, loss_cls: 1.1622, loss: 1.1622 +2025-06-24 11:22:43,681 - pyskl - INFO - Epoch [7][1100/1281] lr: 2.487e-02, eta: 20:15:05, time: 0.285, data_time: 0.000, memory: 4082, top1_acc: 0.7275, top5_acc: 0.9738, loss_cls: 1.1473, loss: 1.1473 +2025-06-24 11:23:24,809 - pyskl - INFO - Epoch [7][1200/1281] lr: 2.487e-02, eta: 20:14:53, time: 0.411, data_time: 0.000, memory: 4082, top1_acc: 0.7306, top5_acc: 0.9775, loss_cls: 1.1541, loss: 1.1541 +2025-06-24 11:23:51,495 - pyskl - INFO - Saving checkpoint at 7 epochs +2025-06-24 11:25:03,059 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:25:03,117 - pyskl - INFO - +top1_acc 0.6870 +top5_acc 0.9646 +2025-06-24 11:25:03,117 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:25:03,124 - pyskl - INFO - +mean_acc 0.5604 +2025-06-24 11:25:03,129 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_6.pth was removed +2025-06-24 11:25:03,327 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2025-06-24 11:25:03,328 - pyskl - INFO - Best top1_acc is 0.6870 at 7 epoch. +2025-06-24 11:25:03,332 - pyskl - INFO - Epoch(val) [7][533] top1_acc: 0.6870, top5_acc: 0.9646, mean_class_accuracy: 0.5604 +2025-06-24 11:26:05,010 - pyskl - INFO - Epoch [8][100/1281] lr: 2.486e-02, eta: 20:10:13, time: 0.617, data_time: 0.200, memory: 4082, top1_acc: 0.7412, top5_acc: 0.9838, loss_cls: 1.0727, loss: 1.0727 +2025-06-24 11:26:46,801 - pyskl - INFO - Epoch [8][200/1281] lr: 2.486e-02, eta: 20:10:16, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7212, top5_acc: 0.9775, loss_cls: 1.1676, loss: 1.1676 +2025-06-24 11:27:28,462 - pyskl - INFO - Epoch [8][300/1281] lr: 2.486e-02, eta: 20:10:15, time: 0.417, data_time: 0.001, memory: 4082, top1_acc: 0.7344, top5_acc: 0.9712, loss_cls: 1.1336, loss: 1.1336 +2025-06-24 11:28:09,998 - pyskl - INFO - Epoch [8][400/1281] lr: 2.485e-02, eta: 20:10:11, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7431, top5_acc: 0.9812, loss_cls: 1.1128, loss: 1.1128 +2025-06-24 11:28:51,413 - pyskl - INFO - Epoch [8][500/1281] lr: 2.485e-02, eta: 20:10:04, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7144, top5_acc: 0.9800, loss_cls: 1.1640, loss: 1.1640 +2025-06-24 11:29:32,904 - pyskl - INFO - Epoch [8][600/1281] lr: 2.485e-02, eta: 20:09:57, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7488, top5_acc: 0.9806, loss_cls: 1.0809, loss: 1.0809 +2025-06-24 11:30:14,505 - pyskl - INFO - Epoch [8][700/1281] lr: 2.484e-02, eta: 20:09:52, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7338, top5_acc: 0.9738, loss_cls: 1.1126, loss: 1.1126 +2025-06-24 11:30:56,300 - pyskl - INFO - Epoch [8][800/1281] lr: 2.484e-02, eta: 20:09:50, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7512, top5_acc: 0.9825, loss_cls: 1.0830, loss: 1.0830 +2025-06-24 11:31:37,839 - pyskl - INFO - Epoch [8][900/1281] lr: 2.484e-02, eta: 20:09:43, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7350, top5_acc: 0.9775, loss_cls: 1.1407, loss: 1.1407 +2025-06-24 11:32:19,435 - pyskl - INFO - Epoch [8][1000/1281] lr: 2.483e-02, eta: 20:09:35, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7462, top5_acc: 0.9775, loss_cls: 1.0856, loss: 1.0856 +2025-06-24 11:32:47,564 - pyskl - INFO - Epoch [8][1100/1281] lr: 2.483e-02, eta: 20:05:24, time: 0.281, data_time: 0.000, memory: 4082, top1_acc: 0.7381, top5_acc: 0.9844, loss_cls: 1.1041, loss: 1.1041 +2025-06-24 11:33:29,669 - pyskl - INFO - Epoch [8][1200/1281] lr: 2.483e-02, eta: 20:05:27, time: 0.421, data_time: 0.000, memory: 4082, top1_acc: 0.7400, top5_acc: 0.9725, loss_cls: 1.1115, loss: 1.1115 +2025-06-24 11:33:55,538 - pyskl - INFO - Saving checkpoint at 8 epochs +2025-06-24 11:35:06,450 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:35:06,515 - pyskl - INFO - +top1_acc 0.6835 +top5_acc 0.9650 +2025-06-24 11:35:06,515 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:35:06,523 - pyskl - INFO - +mean_acc 0.5895 +2025-06-24 11:35:06,529 - pyskl - INFO - Epoch(val) [8][533] top1_acc: 0.6835, top5_acc: 0.9650, mean_class_accuracy: 0.5895 +2025-06-24 11:36:07,870 - pyskl - INFO - Epoch [9][100/1281] lr: 2.482e-02, eta: 20:01:08, time: 0.613, data_time: 0.198, memory: 4082, top1_acc: 0.7481, top5_acc: 0.9856, loss_cls: 1.0415, loss: 1.0415 +2025-06-24 11:36:49,519 - pyskl - INFO - Epoch [9][200/1281] lr: 2.482e-02, eta: 20:01:04, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7531, top5_acc: 0.9831, loss_cls: 1.0570, loss: 1.0570 +2025-06-24 11:37:30,996 - pyskl - INFO - Epoch [9][300/1281] lr: 2.481e-02, eta: 20:00:55, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7569, top5_acc: 0.9750, loss_cls: 1.0887, loss: 1.0887 +2025-06-24 11:38:12,562 - pyskl - INFO - Epoch [9][400/1281] lr: 2.481e-02, eta: 20:00:48, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7400, top5_acc: 0.9806, loss_cls: 1.0792, loss: 1.0792 +2025-06-24 11:38:54,137 - pyskl - INFO - Epoch [9][500/1281] lr: 2.481e-02, eta: 20:00:40, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7475, top5_acc: 0.9850, loss_cls: 1.0579, loss: 1.0579 +2025-06-24 11:39:35,646 - pyskl - INFO - Epoch [9][600/1281] lr: 2.480e-02, eta: 20:00:30, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7444, top5_acc: 0.9844, loss_cls: 1.0797, loss: 1.0797 +2025-06-24 11:40:17,156 - pyskl - INFO - Epoch [9][700/1281] lr: 2.480e-02, eta: 20:00:20, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7612, top5_acc: 0.9794, loss_cls: 1.0512, loss: 1.0512 +2025-06-24 11:40:58,613 - pyskl - INFO - Epoch [9][800/1281] lr: 2.480e-02, eta: 20:00:08, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7331, top5_acc: 0.9806, loss_cls: 1.1137, loss: 1.1137 +2025-06-24 11:41:40,089 - pyskl - INFO - Epoch [9][900/1281] lr: 2.479e-02, eta: 19:59:56, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7506, top5_acc: 0.9800, loss_cls: 1.0526, loss: 1.0526 +2025-06-24 11:42:21,703 - pyskl - INFO - Epoch [9][1000/1281] lr: 2.479e-02, eta: 19:59:46, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7394, top5_acc: 0.9794, loss_cls: 1.1119, loss: 1.1119 +2025-06-24 11:42:49,383 - pyskl - INFO - Epoch [9][1100/1281] lr: 2.479e-02, eta: 19:55:53, time: 0.277, data_time: 0.000, memory: 4082, top1_acc: 0.7319, top5_acc: 0.9788, loss_cls: 1.1110, loss: 1.1110 +2025-06-24 11:43:30,798 - pyskl - INFO - Epoch [9][1200/1281] lr: 2.478e-02, eta: 19:55:41, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7188, top5_acc: 0.9706, loss_cls: 1.1642, loss: 1.1642 +2025-06-24 11:43:57,164 - pyskl - INFO - Saving checkpoint at 9 epochs +2025-06-24 11:45:08,657 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:45:08,713 - pyskl - INFO - +top1_acc 0.6958 +top5_acc 0.9674 +2025-06-24 11:45:08,713 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:45:08,721 - pyskl - INFO - +mean_acc 0.5641 +2025-06-24 11:45:08,725 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_7.pth was removed +2025-06-24 11:45:08,908 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_9.pth. +2025-06-24 11:45:08,909 - pyskl - INFO - Best top1_acc is 0.6958 at 9 epoch. +2025-06-24 11:45:08,912 - pyskl - INFO - Epoch(val) [9][533] top1_acc: 0.6958, top5_acc: 0.9674, mean_class_accuracy: 0.5641 +2025-06-24 11:46:12,436 - pyskl - INFO - Epoch [10][100/1281] lr: 2.477e-02, eta: 19:52:19, time: 0.635, data_time: 0.198, memory: 4082, top1_acc: 0.7475, top5_acc: 0.9794, loss_cls: 1.0833, loss: 1.0833 +2025-06-24 11:46:54,315 - pyskl - INFO - Epoch [10][200/1281] lr: 2.477e-02, eta: 19:52:14, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7488, top5_acc: 0.9844, loss_cls: 1.0795, loss: 1.0795 +2025-06-24 11:47:35,959 - pyskl - INFO - Epoch [10][300/1281] lr: 2.477e-02, eta: 19:52:05, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7644, top5_acc: 0.9819, loss_cls: 1.0385, loss: 1.0385 +2025-06-24 11:48:17,445 - pyskl - INFO - Epoch [10][400/1281] lr: 2.476e-02, eta: 19:51:53, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7556, top5_acc: 0.9812, loss_cls: 1.0602, loss: 1.0602 +2025-06-24 11:48:58,932 - pyskl - INFO - Epoch [10][500/1281] lr: 2.476e-02, eta: 19:51:40, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7575, top5_acc: 0.9794, loss_cls: 1.0381, loss: 1.0381 +2025-06-24 11:49:40,305 - pyskl - INFO - Epoch [10][600/1281] lr: 2.476e-02, eta: 19:51:25, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7525, top5_acc: 0.9800, loss_cls: 1.0947, loss: 1.0947 +2025-06-24 11:50:21,937 - pyskl - INFO - Epoch [10][700/1281] lr: 2.475e-02, eta: 19:51:14, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7562, top5_acc: 0.9850, loss_cls: 0.9992, loss: 0.9992 +2025-06-24 11:51:03,612 - pyskl - INFO - Epoch [10][800/1281] lr: 2.475e-02, eta: 19:51:03, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7694, top5_acc: 0.9850, loss_cls: 0.9984, loss: 0.9984 +2025-06-24 11:51:45,005 - pyskl - INFO - Epoch [10][900/1281] lr: 2.474e-02, eta: 19:50:47, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7344, top5_acc: 0.9788, loss_cls: 1.0948, loss: 1.0948 +2025-06-24 11:52:26,686 - pyskl - INFO - Epoch [10][1000/1281] lr: 2.474e-02, eta: 19:50:35, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7556, top5_acc: 0.9869, loss_cls: 1.0480, loss: 1.0480 +2025-06-24 11:52:55,266 - pyskl - INFO - Epoch [10][1100/1281] lr: 2.473e-02, eta: 19:47:16, time: 0.286, data_time: 0.000, memory: 4082, top1_acc: 0.7544, top5_acc: 0.9769, loss_cls: 1.0436, loss: 1.0436 +2025-06-24 11:53:36,170 - pyskl - INFO - Epoch [10][1200/1281] lr: 2.473e-02, eta: 19:46:53, time: 0.409, data_time: 0.000, memory: 4082, top1_acc: 0.7475, top5_acc: 0.9781, loss_cls: 1.0723, loss: 1.0723 +2025-06-24 11:54:03,080 - pyskl - INFO - Saving checkpoint at 10 epochs +2025-06-24 11:55:14,680 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:55:14,738 - pyskl - INFO - +top1_acc 0.7347 +top5_acc 0.9727 +2025-06-24 11:55:14,738 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:55:14,747 - pyskl - INFO - +mean_acc 0.6396 +2025-06-24 11:55:14,752 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_9.pth was removed +2025-06-24 11:55:14,940 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_10.pth. +2025-06-24 11:55:14,940 - pyskl - INFO - Best top1_acc is 0.7347 at 10 epoch. +2025-06-24 11:55:14,943 - pyskl - INFO - Epoch(val) [10][533] top1_acc: 0.7347, top5_acc: 0.9727, mean_class_accuracy: 0.6396 +2025-06-24 11:56:16,660 - pyskl - INFO - Epoch [11][100/1281] lr: 2.472e-02, eta: 19:43:21, time: 0.617, data_time: 0.202, memory: 4082, top1_acc: 0.7675, top5_acc: 0.9812, loss_cls: 1.0251, loss: 1.0251 +2025-06-24 11:56:58,340 - pyskl - INFO - Epoch [11][200/1281] lr: 2.472e-02, eta: 19:43:10, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7531, top5_acc: 0.9838, loss_cls: 1.0031, loss: 1.0031 +2025-06-24 11:57:41,529 - pyskl - INFO - Epoch [11][300/1281] lr: 2.471e-02, eta: 19:43:19, time: 0.432, data_time: 0.000, memory: 4082, top1_acc: 0.7788, top5_acc: 0.9825, loss_cls: 0.9614, loss: 0.9614 +2025-06-24 11:58:23,718 - pyskl - INFO - Epoch [11][400/1281] lr: 2.471e-02, eta: 19:43:13, time: 0.422, data_time: 0.000, memory: 4082, top1_acc: 0.7550, top5_acc: 0.9875, loss_cls: 1.0246, loss: 1.0246 +2025-06-24 11:59:05,328 - pyskl - INFO - Epoch [11][500/1281] lr: 2.471e-02, eta: 19:43:00, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7594, top5_acc: 0.9806, loss_cls: 1.0024, loss: 1.0024 +2025-06-24 11:59:46,752 - pyskl - INFO - Epoch [11][600/1281] lr: 2.470e-02, eta: 19:42:43, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7812, top5_acc: 0.9806, loss_cls: 1.0060, loss: 1.0060 +2025-06-24 12:00:28,215 - pyskl - INFO - Epoch [11][700/1281] lr: 2.470e-02, eta: 19:42:27, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7469, top5_acc: 0.9875, loss_cls: 1.0297, loss: 1.0297 +2025-06-24 12:01:09,864 - pyskl - INFO - Epoch [11][800/1281] lr: 2.469e-02, eta: 19:42:12, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7875, top5_acc: 0.9881, loss_cls: 0.9479, loss: 0.9479 +2025-06-24 12:01:51,425 - pyskl - INFO - Epoch [11][900/1281] lr: 2.469e-02, eta: 19:41:56, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7438, top5_acc: 0.9831, loss_cls: 1.0802, loss: 1.0802 +2025-06-24 12:02:32,915 - pyskl - INFO - Epoch [11][1000/1281] lr: 2.468e-02, eta: 19:41:39, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7738, top5_acc: 0.9800, loss_cls: 0.9994, loss: 0.9994 +2025-06-24 12:03:01,217 - pyskl - INFO - Epoch [11][1100/1281] lr: 2.468e-02, eta: 19:38:33, time: 0.283, data_time: 0.001, memory: 4082, top1_acc: 0.7669, top5_acc: 0.9825, loss_cls: 1.0027, loss: 1.0027 +2025-06-24 12:03:43,035 - pyskl - INFO - Epoch [11][1200/1281] lr: 2.467e-02, eta: 19:38:20, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7488, top5_acc: 0.9794, loss_cls: 1.0649, loss: 1.0649 +2025-06-24 12:04:08,761 - pyskl - INFO - Saving checkpoint at 11 epochs +2025-06-24 12:05:20,813 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:05:20,877 - pyskl - INFO - +top1_acc 0.7467 +top5_acc 0.9720 +2025-06-24 12:05:20,877 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:05:20,884 - pyskl - INFO - +mean_acc 0.6624 +2025-06-24 12:05:20,888 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_10.pth was removed +2025-06-24 12:05:21,071 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_11.pth. +2025-06-24 12:05:21,072 - pyskl - INFO - Best top1_acc is 0.7467 at 11 epoch. +2025-06-24 12:05:21,074 - pyskl - INFO - Epoch(val) [11][533] top1_acc: 0.7467, top5_acc: 0.9720, mean_class_accuracy: 0.6624 +2025-06-24 12:06:22,454 - pyskl - INFO - Epoch [12][100/1281] lr: 2.467e-02, eta: 19:34:57, time: 0.614, data_time: 0.200, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9844, loss_cls: 0.9181, loss: 0.9181 +2025-06-24 12:07:04,191 - pyskl - INFO - Epoch [12][200/1281] lr: 2.466e-02, eta: 19:34:44, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9875, loss_cls: 0.8955, loss: 0.8955 +2025-06-24 12:07:45,689 - pyskl - INFO - Epoch [12][300/1281] lr: 2.466e-02, eta: 19:34:27, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7844, top5_acc: 0.9844, loss_cls: 0.9872, loss: 0.9872 +2025-06-24 12:08:27,171 - pyskl - INFO - Epoch [12][400/1281] lr: 2.465e-02, eta: 19:34:10, time: 0.415, data_time: 0.001, memory: 4082, top1_acc: 0.7775, top5_acc: 0.9906, loss_cls: 0.9647, loss: 0.9647 +2025-06-24 12:09:08,655 - pyskl - INFO - Epoch [12][500/1281] lr: 2.465e-02, eta: 19:33:53, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7688, top5_acc: 0.9825, loss_cls: 0.9623, loss: 0.9623 +2025-06-24 12:09:50,359 - pyskl - INFO - Epoch [12][600/1281] lr: 2.464e-02, eta: 19:33:38, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7725, top5_acc: 0.9812, loss_cls: 0.9716, loss: 0.9716 +2025-06-24 12:10:31,678 - pyskl - INFO - Epoch [12][700/1281] lr: 2.464e-02, eta: 19:33:18, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7650, top5_acc: 0.9750, loss_cls: 1.0028, loss: 1.0028 +2025-06-24 12:11:13,054 - pyskl - INFO - Epoch [12][800/1281] lr: 2.463e-02, eta: 19:32:58, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7738, top5_acc: 0.9812, loss_cls: 1.0118, loss: 1.0118 +2025-06-24 12:11:54,589 - pyskl - INFO - Epoch [12][900/1281] lr: 2.463e-02, eta: 19:32:40, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7656, top5_acc: 0.9825, loss_cls: 1.0257, loss: 1.0257 +2025-06-24 12:12:36,098 - pyskl - INFO - Epoch [12][1000/1281] lr: 2.462e-02, eta: 19:32:21, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7512, top5_acc: 0.9844, loss_cls: 1.0303, loss: 1.0303 +2025-06-24 12:13:03,290 - pyskl - INFO - Epoch [12][1100/1281] lr: 2.462e-02, eta: 19:29:15, time: 0.272, data_time: 0.000, memory: 4082, top1_acc: 0.7612, top5_acc: 0.9831, loss_cls: 1.0217, loss: 1.0217 +2025-06-24 12:13:45,650 - pyskl - INFO - Epoch [12][1200/1281] lr: 2.461e-02, eta: 19:29:07, time: 0.424, data_time: 0.000, memory: 4082, top1_acc: 0.7700, top5_acc: 0.9819, loss_cls: 0.9577, loss: 0.9577 +2025-06-24 12:14:11,065 - pyskl - INFO - Saving checkpoint at 12 epochs +2025-06-24 12:15:22,506 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:15:22,563 - pyskl - INFO - +top1_acc 0.7384 +top5_acc 0.9688 +2025-06-24 12:15:22,563 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:15:22,572 - pyskl - INFO - +mean_acc 0.6478 +2025-06-24 12:15:22,574 - pyskl - INFO - Epoch(val) [12][533] top1_acc: 0.7384, top5_acc: 0.9688, mean_class_accuracy: 0.6478 +2025-06-24 12:16:23,548 - pyskl - INFO - Epoch [13][100/1281] lr: 2.460e-02, eta: 19:25:52, time: 0.610, data_time: 0.194, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9862, loss_cls: 0.8966, loss: 0.8966 +2025-06-24 12:17:04,948 - pyskl - INFO - Epoch [13][200/1281] lr: 2.460e-02, eta: 19:25:33, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7900, top5_acc: 0.9850, loss_cls: 0.9389, loss: 0.9389 +2025-06-24 12:17:46,522 - pyskl - INFO - Epoch [13][300/1281] lr: 2.459e-02, eta: 19:25:15, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9900, loss_cls: 0.8594, loss: 0.8594 +2025-06-24 12:18:27,887 - pyskl - INFO - Epoch [13][400/1281] lr: 2.459e-02, eta: 19:24:55, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7706, top5_acc: 0.9756, loss_cls: 0.9722, loss: 0.9722 +2025-06-24 12:19:09,310 - pyskl - INFO - Epoch [13][500/1281] lr: 2.458e-02, eta: 19:24:35, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7719, top5_acc: 0.9825, loss_cls: 0.9725, loss: 0.9725 +2025-06-24 12:19:50,919 - pyskl - INFO - Epoch [13][600/1281] lr: 2.458e-02, eta: 19:24:18, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7738, top5_acc: 0.9800, loss_cls: 1.0039, loss: 1.0039 +2025-06-24 12:20:32,448 - pyskl - INFO - Epoch [13][700/1281] lr: 2.457e-02, eta: 19:23:58, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7531, top5_acc: 0.9850, loss_cls: 1.0109, loss: 1.0109 +2025-06-24 12:21:13,926 - pyskl - INFO - Epoch [13][800/1281] lr: 2.457e-02, eta: 19:23:39, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7831, top5_acc: 0.9862, loss_cls: 0.9719, loss: 0.9719 +2025-06-24 12:21:55,495 - pyskl - INFO - Epoch [13][900/1281] lr: 2.456e-02, eta: 19:23:19, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7731, top5_acc: 0.9838, loss_cls: 0.9377, loss: 0.9377 +2025-06-24 12:22:36,829 - pyskl - INFO - Epoch [13][1000/1281] lr: 2.455e-02, eta: 19:22:57, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7569, top5_acc: 0.9838, loss_cls: 1.0063, loss: 1.0063 +2025-06-24 12:23:04,735 - pyskl - INFO - Epoch [13][1100/1281] lr: 2.455e-02, eta: 19:20:12, time: 0.279, data_time: 0.000, memory: 4082, top1_acc: 0.7819, top5_acc: 0.9844, loss_cls: 0.9602, loss: 0.9602 +2025-06-24 12:23:46,900 - pyskl - INFO - Epoch [13][1200/1281] lr: 2.454e-02, eta: 19:19:59, time: 0.422, data_time: 0.000, memory: 4082, top1_acc: 0.7719, top5_acc: 0.9850, loss_cls: 0.9474, loss: 0.9474 +2025-06-24 12:24:12,394 - pyskl - INFO - Saving checkpoint at 13 epochs +2025-06-24 12:25:13,721 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:25:13,787 - pyskl - INFO - +top1_acc 0.7509 +top5_acc 0.9783 +2025-06-24 12:25:13,788 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:25:13,798 - pyskl - INFO - +mean_acc 0.6598 +2025-06-24 12:25:13,804 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_11.pth was removed +2025-06-24 12:25:14,011 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_13.pth. +2025-06-24 12:25:14,011 - pyskl - INFO - Best top1_acc is 0.7509 at 13 epoch. +2025-06-24 12:25:14,015 - pyskl - INFO - Epoch(val) [13][533] top1_acc: 0.7509, top5_acc: 0.9783, mean_class_accuracy: 0.6598 +2025-06-24 12:26:13,329 - pyskl - INFO - Epoch [14][100/1281] lr: 2.453e-02, eta: 19:16:37, time: 0.593, data_time: 0.203, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9881, loss_cls: 0.9041, loss: 0.9041 +2025-06-24 12:26:52,446 - pyskl - INFO - Epoch [14][200/1281] lr: 2.453e-02, eta: 19:15:53, time: 0.391, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9869, loss_cls: 0.8832, loss: 0.8832 +2025-06-24 12:27:33,004 - pyskl - INFO - Epoch [14][300/1281] lr: 2.452e-02, eta: 19:15:24, time: 0.406, data_time: 0.000, memory: 4082, top1_acc: 0.7856, top5_acc: 0.9844, loss_cls: 0.9245, loss: 0.9245 +2025-06-24 12:28:13,797 - pyskl - INFO - Epoch [14][400/1281] lr: 2.452e-02, eta: 19:14:57, time: 0.408, data_time: 0.000, memory: 4082, top1_acc: 0.7588, top5_acc: 0.9900, loss_cls: 0.9529, loss: 0.9529 +2025-06-24 12:28:52,941 - pyskl - INFO - Epoch [14][500/1281] lr: 2.451e-02, eta: 19:14:13, time: 0.391, data_time: 0.000, memory: 4082, top1_acc: 0.7762, top5_acc: 0.9825, loss_cls: 0.9808, loss: 0.9808 +2025-06-24 12:29:32,291 - pyskl - INFO - Epoch [14][600/1281] lr: 2.451e-02, eta: 19:13:31, time: 0.394, data_time: 0.000, memory: 4082, top1_acc: 0.7831, top5_acc: 0.9819, loss_cls: 0.9663, loss: 0.9663 +2025-06-24 12:30:12,074 - pyskl - INFO - Epoch [14][700/1281] lr: 2.450e-02, eta: 19:12:53, time: 0.398, data_time: 0.000, memory: 4082, top1_acc: 0.7963, top5_acc: 0.9838, loss_cls: 0.9097, loss: 0.9097 +2025-06-24 12:30:51,435 - pyskl - INFO - Epoch [14][800/1281] lr: 2.449e-02, eta: 19:12:12, time: 0.394, data_time: 0.000, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9888, loss_cls: 0.9141, loss: 0.9141 +2025-06-24 12:31:31,412 - pyskl - INFO - Epoch [14][900/1281] lr: 2.449e-02, eta: 19:11:36, time: 0.400, data_time: 0.000, memory: 4082, top1_acc: 0.7944, top5_acc: 0.9862, loss_cls: 0.9132, loss: 0.9132 +2025-06-24 12:32:10,666 - pyskl - INFO - Epoch [14][1000/1281] lr: 2.448e-02, eta: 19:10:53, time: 0.393, data_time: 0.000, memory: 4082, top1_acc: 0.7812, top5_acc: 0.9819, loss_cls: 0.9396, loss: 0.9396 +2025-06-24 12:32:50,232 - pyskl - INFO - Epoch [14][1100/1281] lr: 2.448e-02, eta: 19:10:14, time: 0.396, data_time: 0.000, memory: 4082, top1_acc: 0.7869, top5_acc: 0.9819, loss_cls: 0.9036, loss: 0.9036 +2025-06-24 12:33:30,583 - pyskl - INFO - Epoch [14][1200/1281] lr: 2.447e-02, eta: 19:09:42, time: 0.403, data_time: 0.000, memory: 4082, top1_acc: 0.7738, top5_acc: 0.9844, loss_cls: 0.9279, loss: 0.9279 +2025-06-24 12:34:02,662 - pyskl - INFO - Saving checkpoint at 14 epochs +2025-06-24 12:35:03,192 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:35:03,258 - pyskl - INFO - +top1_acc 0.7302 +top5_acc 0.9620 +2025-06-24 12:35:03,258 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:35:03,270 - pyskl - INFO - +mean_acc 0.6531 +2025-06-24 12:35:03,274 - pyskl - INFO - Epoch(val) [14][533] top1_acc: 0.7302, top5_acc: 0.9620, mean_class_accuracy: 0.6531 +2025-06-24 12:35:46,357 - pyskl - INFO - Epoch [15][100/1281] lr: 2.446e-02, eta: 19:03:54, time: 0.431, data_time: 0.196, memory: 4082, top1_acc: 0.8025, top5_acc: 0.9900, loss_cls: 0.8604, loss: 0.8604 +2025-06-24 12:36:20,423 - pyskl - INFO - Epoch [15][200/1281] lr: 2.445e-02, eta: 19:02:23, time: 0.341, data_time: 0.000, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9894, loss_cls: 0.8451, loss: 0.8451 +2025-06-24 12:36:59,160 - pyskl - INFO - Epoch [15][300/1281] lr: 2.445e-02, eta: 19:01:38, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.7919, top5_acc: 0.9881, loss_cls: 0.9019, loss: 0.9019 +2025-06-24 12:37:37,761 - pyskl - INFO - Epoch [15][400/1281] lr: 2.444e-02, eta: 19:00:51, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8087, top5_acc: 0.9869, loss_cls: 0.8470, loss: 0.8470 +2025-06-24 12:38:15,510 - pyskl - INFO - Epoch [15][500/1281] lr: 2.444e-02, eta: 18:59:56, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.7944, top5_acc: 0.9850, loss_cls: 0.8992, loss: 0.8992 +2025-06-24 12:38:54,731 - pyskl - INFO - Epoch [15][600/1281] lr: 2.443e-02, eta: 18:59:15, time: 0.392, data_time: 0.000, memory: 4082, top1_acc: 0.7844, top5_acc: 0.9819, loss_cls: 0.9645, loss: 0.9645 +2025-06-24 12:39:32,848 - pyskl - INFO - Epoch [15][700/1281] lr: 2.442e-02, eta: 18:58:24, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.7837, top5_acc: 0.9881, loss_cls: 0.9255, loss: 0.9255 +2025-06-24 12:40:11,486 - pyskl - INFO - Epoch [15][800/1281] lr: 2.442e-02, eta: 18:57:38, time: 0.386, data_time: 0.001, memory: 4082, top1_acc: 0.7850, top5_acc: 0.9875, loss_cls: 0.9399, loss: 0.9399 +2025-06-24 12:40:49,842 - pyskl - INFO - Epoch [15][900/1281] lr: 2.441e-02, eta: 18:56:50, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.7812, top5_acc: 0.9825, loss_cls: 0.9560, loss: 0.9560 +2025-06-24 12:41:28,566 - pyskl - INFO - Epoch [15][1000/1281] lr: 2.441e-02, eta: 18:56:04, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9825, loss_cls: 0.9193, loss: 0.9193 +2025-06-24 12:42:07,002 - pyskl - INFO - Epoch [15][1100/1281] lr: 2.440e-02, eta: 18:55:17, time: 0.384, data_time: 0.001, memory: 4082, top1_acc: 0.7925, top5_acc: 0.9831, loss_cls: 0.9119, loss: 0.9119 +2025-06-24 12:42:45,783 - pyskl - INFO - Epoch [15][1200/1281] lr: 2.439e-02, eta: 18:54:32, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.7956, top5_acc: 0.9856, loss_cls: 0.8973, loss: 0.8973 +2025-06-24 12:43:18,234 - pyskl - INFO - Saving checkpoint at 15 epochs +2025-06-24 12:44:16,959 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:44:17,032 - pyskl - INFO - +top1_acc 0.7483 +top5_acc 0.9763 +2025-06-24 12:44:17,033 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:44:17,041 - pyskl - INFO - +mean_acc 0.6682 +2025-06-24 12:44:17,043 - pyskl - INFO - Epoch(val) [15][533] top1_acc: 0.7483, top5_acc: 0.9763, mean_class_accuracy: 0.6682 +2025-06-24 12:45:14,775 - pyskl - INFO - Epoch [16][100/1281] lr: 2.438e-02, eta: 18:51:20, time: 0.577, data_time: 0.193, memory: 4082, top1_acc: 0.8200, top5_acc: 0.9950, loss_cls: 0.7925, loss: 0.7925 +2025-06-24 12:45:52,803 - pyskl - INFO - Epoch [16][200/1281] lr: 2.438e-02, eta: 18:50:30, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.7913, top5_acc: 0.9869, loss_cls: 0.8889, loss: 0.8889 +2025-06-24 12:46:24,565 - pyskl - INFO - Epoch [16][300/1281] lr: 2.437e-02, eta: 18:48:44, time: 0.318, data_time: 0.000, memory: 4082, top1_acc: 0.7906, top5_acc: 0.9831, loss_cls: 0.9445, loss: 0.9445 +2025-06-24 12:47:00,111 - pyskl - INFO - Epoch [16][400/1281] lr: 2.436e-02, eta: 18:47:33, time: 0.355, data_time: 0.000, memory: 4082, top1_acc: 0.8087, top5_acc: 0.9869, loss_cls: 0.8760, loss: 0.8760 +2025-06-24 12:47:35,646 - pyskl - INFO - Epoch [16][500/1281] lr: 2.436e-02, eta: 18:46:21, time: 0.355, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9862, loss_cls: 0.8401, loss: 0.8401 +2025-06-24 12:47:59,207 - pyskl - INFO - Epoch [16][600/1281] lr: 2.435e-02, eta: 18:43:26, time: 0.236, data_time: 0.000, memory: 4082, top1_acc: 0.7956, top5_acc: 0.9881, loss_cls: 0.8887, loss: 0.8887 +2025-06-24 12:48:36,852 - pyskl - INFO - Epoch [16][700/1281] lr: 2.434e-02, eta: 18:42:34, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.7931, top5_acc: 0.9869, loss_cls: 0.8798, loss: 0.8798 +2025-06-24 12:49:14,722 - pyskl - INFO - Epoch [16][800/1281] lr: 2.434e-02, eta: 18:41:45, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.7812, top5_acc: 0.9875, loss_cls: 0.9171, loss: 0.9171 +2025-06-24 12:49:52,742 - pyskl - INFO - Epoch [16][900/1281] lr: 2.433e-02, eta: 18:40:56, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.7906, top5_acc: 0.9875, loss_cls: 0.8787, loss: 0.8787 +2025-06-24 12:50:30,749 - pyskl - INFO - Epoch [16][1000/1281] lr: 2.432e-02, eta: 18:40:08, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.7806, top5_acc: 0.9875, loss_cls: 0.9016, loss: 0.9016 +2025-06-24 12:51:09,162 - pyskl - INFO - Epoch [16][1100/1281] lr: 2.432e-02, eta: 18:39:23, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.7863, top5_acc: 0.9862, loss_cls: 0.9237, loss: 0.9237 +2025-06-24 12:51:47,367 - pyskl - INFO - Epoch [16][1200/1281] lr: 2.431e-02, eta: 18:38:37, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.7819, top5_acc: 0.9806, loss_cls: 0.9229, loss: 0.9229 +2025-06-24 12:52:19,452 - pyskl - INFO - Saving checkpoint at 16 epochs +2025-06-24 12:53:18,750 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:53:18,804 - pyskl - INFO - +top1_acc 0.7646 +top5_acc 0.9806 +2025-06-24 12:53:18,805 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:53:18,811 - pyskl - INFO - +mean_acc 0.6761 +2025-06-24 12:53:18,816 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_13.pth was removed +2025-06-24 12:53:19,022 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_16.pth. +2025-06-24 12:53:19,022 - pyskl - INFO - Best top1_acc is 0.7646 at 16 epoch. +2025-06-24 12:53:19,025 - pyskl - INFO - Epoch(val) [16][533] top1_acc: 0.7646, top5_acc: 0.9806, mean_class_accuracy: 0.6761 +2025-06-24 12:54:17,470 - pyskl - INFO - Epoch [17][100/1281] lr: 2.430e-02, eta: 18:35:43, time: 0.584, data_time: 0.197, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9919, loss_cls: 0.8524, loss: 0.8524 +2025-06-24 12:54:55,173 - pyskl - INFO - Epoch [17][200/1281] lr: 2.429e-02, eta: 18:34:53, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8025, top5_acc: 0.9856, loss_cls: 0.8598, loss: 0.8598 +2025-06-24 12:55:32,902 - pyskl - INFO - Epoch [17][300/1281] lr: 2.428e-02, eta: 18:34:04, time: 0.377, data_time: 0.001, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9888, loss_cls: 0.9025, loss: 0.9025 +2025-06-24 12:56:10,731 - pyskl - INFO - Epoch [17][400/1281] lr: 2.428e-02, eta: 18:33:15, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9875, loss_cls: 0.8966, loss: 0.8966 +2025-06-24 12:56:48,480 - pyskl - INFO - Epoch [17][500/1281] lr: 2.427e-02, eta: 18:32:26, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.7981, top5_acc: 0.9875, loss_cls: 0.8961, loss: 0.8961 +2025-06-24 12:57:26,130 - pyskl - INFO - Epoch [17][600/1281] lr: 2.426e-02, eta: 18:31:36, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9844, loss_cls: 0.8666, loss: 0.8666 +2025-06-24 12:58:04,068 - pyskl - INFO - Epoch [17][700/1281] lr: 2.426e-02, eta: 18:30:48, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.7969, top5_acc: 0.9888, loss_cls: 0.8976, loss: 0.8976 +2025-06-24 12:58:37,502 - pyskl - INFO - Epoch [17][800/1281] lr: 2.425e-02, eta: 18:29:25, time: 0.334, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9844, loss_cls: 0.9083, loss: 0.9083 +2025-06-24 12:59:10,649 - pyskl - INFO - Epoch [17][900/1281] lr: 2.424e-02, eta: 18:28:00, time: 0.331, data_time: 0.000, memory: 4082, top1_acc: 0.7913, top5_acc: 0.9838, loss_cls: 0.8982, loss: 0.8982 +2025-06-24 12:59:48,447 - pyskl - INFO - Epoch [17][1000/1281] lr: 2.424e-02, eta: 18:27:12, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8037, top5_acc: 0.9906, loss_cls: 0.8699, loss: 0.8699 +2025-06-24 13:00:12,002 - pyskl - INFO - Epoch [17][1100/1281] lr: 2.423e-02, eta: 18:24:31, time: 0.236, data_time: 0.000, memory: 4082, top1_acc: 0.7975, top5_acc: 0.9938, loss_cls: 0.8577, loss: 0.8577 +2025-06-24 13:00:48,207 - pyskl - INFO - Epoch [17][1200/1281] lr: 2.422e-02, eta: 18:23:32, time: 0.362, data_time: 0.000, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9844, loss_cls: 0.9311, loss: 0.9311 +2025-06-24 13:01:19,971 - pyskl - INFO - Saving checkpoint at 17 epochs +2025-06-24 13:02:19,530 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:02:19,597 - pyskl - INFO - +top1_acc 0.7800 +top5_acc 0.9806 +2025-06-24 13:02:19,598 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:02:19,606 - pyskl - INFO - +mean_acc 0.7213 +2025-06-24 13:02:19,610 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_16.pth was removed +2025-06-24 13:02:19,787 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_17.pth. +2025-06-24 13:02:19,787 - pyskl - INFO - Best top1_acc is 0.7800 at 17 epoch. +2025-06-24 13:02:19,790 - pyskl - INFO - Epoch(val) [17][533] top1_acc: 0.7800, top5_acc: 0.9806, mean_class_accuracy: 0.7213 +2025-06-24 13:03:17,983 - pyskl - INFO - Epoch [18][100/1281] lr: 2.421e-02, eta: 18:20:47, time: 0.582, data_time: 0.200, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9912, loss_cls: 0.7547, loss: 0.7547 +2025-06-24 13:03:56,235 - pyskl - INFO - Epoch [18][200/1281] lr: 2.420e-02, eta: 18:20:04, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9906, loss_cls: 0.7947, loss: 0.7947 +2025-06-24 13:04:34,857 - pyskl - INFO - Epoch [18][300/1281] lr: 2.419e-02, eta: 18:19:24, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8013, top5_acc: 0.9875, loss_cls: 0.8773, loss: 0.8773 +2025-06-24 13:05:12,803 - pyskl - INFO - Epoch [18][400/1281] lr: 2.419e-02, eta: 18:18:39, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.7975, top5_acc: 0.9838, loss_cls: 0.9037, loss: 0.9037 +2025-06-24 13:05:50,937 - pyskl - INFO - Epoch [18][500/1281] lr: 2.418e-02, eta: 18:17:55, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8044, top5_acc: 0.9906, loss_cls: 0.8502, loss: 0.8502 +2025-06-24 13:06:28,810 - pyskl - INFO - Epoch [18][600/1281] lr: 2.417e-02, eta: 18:17:09, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9862, loss_cls: 0.8839, loss: 0.8839 +2025-06-24 13:07:07,141 - pyskl - INFO - Epoch [18][700/1281] lr: 2.417e-02, eta: 18:16:27, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8131, top5_acc: 0.9894, loss_cls: 0.8472, loss: 0.8472 +2025-06-24 13:07:44,752 - pyskl - INFO - Epoch [18][800/1281] lr: 2.416e-02, eta: 18:15:40, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9881, loss_cls: 0.8435, loss: 0.8435 +2025-06-24 13:08:23,242 - pyskl - INFO - Epoch [18][900/1281] lr: 2.415e-02, eta: 18:14:59, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.7937, top5_acc: 0.9862, loss_cls: 0.9132, loss: 0.9132 +2025-06-24 13:09:01,496 - pyskl - INFO - Epoch [18][1000/1281] lr: 2.414e-02, eta: 18:14:16, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9925, loss_cls: 0.7578, loss: 0.7578 +2025-06-24 13:09:40,443 - pyskl - INFO - Epoch [18][1100/1281] lr: 2.414e-02, eta: 18:13:39, time: 0.389, data_time: 0.000, memory: 4082, top1_acc: 0.8106, top5_acc: 0.9888, loss_cls: 0.8641, loss: 0.8641 +2025-06-24 13:10:19,195 - pyskl - INFO - Epoch [18][1200/1281] lr: 2.413e-02, eta: 18:13:00, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9894, loss_cls: 0.8419, loss: 0.8419 +2025-06-24 13:10:46,414 - pyskl - INFO - Saving checkpoint at 18 epochs +2025-06-24 13:11:54,998 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:11:55,068 - pyskl - INFO - +top1_acc 0.7473 +top5_acc 0.9654 +2025-06-24 13:11:55,068 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:11:55,078 - pyskl - INFO - +mean_acc 0.6660 +2025-06-24 13:11:55,081 - pyskl - INFO - Epoch(val) [18][533] top1_acc: 0.7473, top5_acc: 0.9654, mean_class_accuracy: 0.6660 +2025-06-24 13:12:52,289 - pyskl - INFO - Epoch [19][100/1281] lr: 2.411e-02, eta: 18:10:15, time: 0.572, data_time: 0.194, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9919, loss_cls: 0.7668, loss: 0.7668 +2025-06-24 13:13:30,854 - pyskl - INFO - Epoch [19][200/1281] lr: 2.411e-02, eta: 18:09:35, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.7869, top5_acc: 0.9900, loss_cls: 0.9133, loss: 0.9133 +2025-06-24 13:14:09,590 - pyskl - INFO - Epoch [19][300/1281] lr: 2.410e-02, eta: 18:08:57, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9875, loss_cls: 0.8487, loss: 0.8487 +2025-06-24 13:14:48,225 - pyskl - INFO - Epoch [19][400/1281] lr: 2.409e-02, eta: 18:08:18, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9869, loss_cls: 0.8658, loss: 0.8658 +2025-06-24 13:15:26,706 - pyskl - INFO - Epoch [19][500/1281] lr: 2.408e-02, eta: 18:07:37, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8013, top5_acc: 0.9850, loss_cls: 0.9043, loss: 0.9043 +2025-06-24 13:16:04,800 - pyskl - INFO - Epoch [19][600/1281] lr: 2.408e-02, eta: 18:06:54, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9894, loss_cls: 0.7983, loss: 0.7983 +2025-06-24 13:16:43,338 - pyskl - INFO - Epoch [19][700/1281] lr: 2.407e-02, eta: 18:06:14, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9825, loss_cls: 0.8372, loss: 0.8372 +2025-06-24 13:17:21,433 - pyskl - INFO - Epoch [19][800/1281] lr: 2.406e-02, eta: 18:05:31, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.7975, top5_acc: 0.9900, loss_cls: 0.8636, loss: 0.8636 +2025-06-24 13:17:59,090 - pyskl - INFO - Epoch [19][900/1281] lr: 2.405e-02, eta: 18:04:45, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8031, top5_acc: 0.9856, loss_cls: 0.8575, loss: 0.8575 +2025-06-24 13:18:37,118 - pyskl - INFO - Epoch [19][1000/1281] lr: 2.405e-02, eta: 18:04:02, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9900, loss_cls: 0.7744, loss: 0.7744 +2025-06-24 13:19:15,103 - pyskl - INFO - Epoch [19][1100/1281] lr: 2.404e-02, eta: 18:03:18, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9894, loss_cls: 0.8072, loss: 0.8072 +2025-06-24 13:19:52,977 - pyskl - INFO - Epoch [19][1200/1281] lr: 2.403e-02, eta: 18:02:34, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.7975, top5_acc: 0.9888, loss_cls: 0.8854, loss: 0.8854 +2025-06-24 13:20:23,945 - pyskl - INFO - Saving checkpoint at 19 epochs +2025-06-24 13:21:22,870 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:21:22,927 - pyskl - INFO - +top1_acc 0.7808 +top5_acc 0.9791 +2025-06-24 13:21:22,927 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:21:22,934 - pyskl - INFO - +mean_acc 0.7036 +2025-06-24 13:21:22,938 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_17.pth was removed +2025-06-24 13:21:23,116 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_19.pth. +2025-06-24 13:21:23,116 - pyskl - INFO - Best top1_acc is 0.7808 at 19 epoch. +2025-06-24 13:21:23,119 - pyskl - INFO - Epoch(val) [19][533] top1_acc: 0.7808, top5_acc: 0.9791, mean_class_accuracy: 0.7036 +2025-06-24 13:22:20,187 - pyskl - INFO - Epoch [20][100/1281] lr: 2.402e-02, eta: 17:59:55, time: 0.571, data_time: 0.191, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9894, loss_cls: 0.8015, loss: 0.8015 +2025-06-24 13:22:47,313 - pyskl - INFO - Epoch [20][200/1281] lr: 2.401e-02, eta: 17:57:58, time: 0.271, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9925, loss_cls: 0.7618, loss: 0.7618 +2025-06-24 13:23:28,727 - pyskl - INFO - Epoch [20][300/1281] lr: 2.400e-02, eta: 17:57:38, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8063, top5_acc: 0.9850, loss_cls: 0.8419, loss: 0.8419 +2025-06-24 13:23:58,207 - pyskl - INFO - Epoch [20][400/1281] lr: 2.399e-02, eta: 17:55:58, time: 0.295, data_time: 0.000, memory: 4082, top1_acc: 0.8194, top5_acc: 0.9938, loss_cls: 0.7968, loss: 0.7968 +2025-06-24 13:24:25,598 - pyskl - INFO - Epoch [20][500/1281] lr: 2.398e-02, eta: 17:54:04, time: 0.274, data_time: 0.000, memory: 4082, top1_acc: 0.8413, top5_acc: 0.9881, loss_cls: 0.7485, loss: 0.7485 +2025-06-24 13:25:03,545 - pyskl - INFO - Epoch [20][600/1281] lr: 2.398e-02, eta: 17:53:22, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8063, top5_acc: 0.9806, loss_cls: 0.8756, loss: 0.8756 +2025-06-24 13:25:41,308 - pyskl - INFO - Epoch [20][700/1281] lr: 2.397e-02, eta: 17:52:38, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9838, loss_cls: 0.8137, loss: 0.8137 +2025-06-24 13:26:19,679 - pyskl - INFO - Epoch [20][800/1281] lr: 2.396e-02, eta: 17:51:59, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9862, loss_cls: 0.8628, loss: 0.8628 +2025-06-24 13:26:57,339 - pyskl - INFO - Epoch [20][900/1281] lr: 2.395e-02, eta: 17:51:15, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9894, loss_cls: 0.8336, loss: 0.8336 +2025-06-24 13:27:36,024 - pyskl - INFO - Epoch [20][1000/1281] lr: 2.394e-02, eta: 17:50:37, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9919, loss_cls: 0.7587, loss: 0.7587 +2025-06-24 13:28:14,000 - pyskl - INFO - Epoch [20][1100/1281] lr: 2.393e-02, eta: 17:49:55, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8200, top5_acc: 0.9894, loss_cls: 0.8194, loss: 0.8194 +2025-06-24 13:28:52,553 - pyskl - INFO - Epoch [20][1200/1281] lr: 2.393e-02, eta: 17:49:17, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9888, loss_cls: 0.8039, loss: 0.8039 +2025-06-24 13:29:23,535 - pyskl - INFO - Saving checkpoint at 20 epochs +2025-06-24 13:30:22,798 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:30:22,872 - pyskl - INFO - +top1_acc 0.7618 +top5_acc 0.9786 +2025-06-24 13:30:22,872 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:30:22,879 - pyskl - INFO - +mean_acc 0.6520 +2025-06-24 13:30:22,882 - pyskl - INFO - Epoch(val) [20][533] top1_acc: 0.7618, top5_acc: 0.9786, mean_class_accuracy: 0.6520 +2025-06-24 13:31:20,586 - pyskl - INFO - Epoch [21][100/1281] lr: 2.391e-02, eta: 17:46:50, time: 0.577, data_time: 0.192, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9900, loss_cls: 0.8140, loss: 0.8140 +2025-06-24 13:31:58,759 - pyskl - INFO - Epoch [21][200/1281] lr: 2.390e-02, eta: 17:46:09, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9938, loss_cls: 0.7087, loss: 0.7087 +2025-06-24 13:32:36,930 - pyskl - INFO - Epoch [21][300/1281] lr: 2.389e-02, eta: 17:45:29, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9862, loss_cls: 0.8044, loss: 0.8044 +2025-06-24 13:33:15,295 - pyskl - INFO - Epoch [21][400/1281] lr: 2.389e-02, eta: 17:44:50, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9844, loss_cls: 0.7843, loss: 0.7843 +2025-06-24 13:33:52,823 - pyskl - INFO - Epoch [21][500/1281] lr: 2.388e-02, eta: 17:44:06, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9906, loss_cls: 0.7499, loss: 0.7499 +2025-06-24 13:34:30,585 - pyskl - INFO - Epoch [21][600/1281] lr: 2.387e-02, eta: 17:43:23, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9850, loss_cls: 0.8096, loss: 0.8096 +2025-06-24 13:34:59,372 - pyskl - INFO - Epoch [21][700/1281] lr: 2.386e-02, eta: 17:41:43, time: 0.288, data_time: 0.000, memory: 4082, top1_acc: 0.8200, top5_acc: 0.9894, loss_cls: 0.7911, loss: 0.7911 +2025-06-24 13:35:38,625 - pyskl - INFO - Epoch [21][800/1281] lr: 2.385e-02, eta: 17:41:10, time: 0.393, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9875, loss_cls: 0.8509, loss: 0.8509 +2025-06-24 13:36:10,430 - pyskl - INFO - Epoch [21][900/1281] lr: 2.384e-02, eta: 17:39:50, time: 0.318, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9912, loss_cls: 0.8341, loss: 0.8341 +2025-06-24 13:36:36,749 - pyskl - INFO - Epoch [21][1000/1281] lr: 2.383e-02, eta: 17:37:57, time: 0.263, data_time: 0.000, memory: 4082, top1_acc: 0.8213, top5_acc: 0.9875, loss_cls: 0.8248, loss: 0.8248 +2025-06-24 13:37:14,248 - pyskl - INFO - Epoch [21][1100/1281] lr: 2.383e-02, eta: 17:37:13, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8381, top5_acc: 0.9931, loss_cls: 0.7402, loss: 0.7402 +2025-06-24 13:37:52,820 - pyskl - INFO - Epoch [21][1200/1281] lr: 2.382e-02, eta: 17:36:36, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.7931, top5_acc: 0.9850, loss_cls: 0.9086, loss: 0.9086 +2025-06-24 13:38:24,232 - pyskl - INFO - Saving checkpoint at 21 epochs +2025-06-24 13:39:23,807 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:39:23,875 - pyskl - INFO - +top1_acc 0.7963 +top5_acc 0.9850 +2025-06-24 13:39:23,875 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:39:23,883 - pyskl - INFO - +mean_acc 0.7199 +2025-06-24 13:39:23,888 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_19.pth was removed +2025-06-24 13:39:24,066 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_21.pth. +2025-06-24 13:39:24,066 - pyskl - INFO - Best top1_acc is 0.7963 at 21 epoch. +2025-06-24 13:39:24,069 - pyskl - INFO - Epoch(val) [21][533] top1_acc: 0.7963, top5_acc: 0.9850, mean_class_accuracy: 0.7199 +2025-06-24 13:40:21,666 - pyskl - INFO - Epoch [22][100/1281] lr: 2.380e-02, eta: 17:34:15, time: 0.576, data_time: 0.199, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9912, loss_cls: 0.7681, loss: 0.7681 +2025-06-24 13:41:00,428 - pyskl - INFO - Epoch [22][200/1281] lr: 2.379e-02, eta: 17:33:39, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.8406, top5_acc: 0.9875, loss_cls: 0.7867, loss: 0.7867 +2025-06-24 13:41:38,810 - pyskl - INFO - Epoch [22][300/1281] lr: 2.378e-02, eta: 17:33:01, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9875, loss_cls: 0.8066, loss: 0.8066 +2025-06-24 13:42:16,769 - pyskl - INFO - Epoch [22][400/1281] lr: 2.378e-02, eta: 17:32:21, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9912, loss_cls: 0.7559, loss: 0.7559 +2025-06-24 13:42:54,318 - pyskl - INFO - Epoch [22][500/1281] lr: 2.377e-02, eta: 17:31:38, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8144, top5_acc: 0.9900, loss_cls: 0.8158, loss: 0.8158 +2025-06-24 13:43:32,326 - pyskl - INFO - Epoch [22][600/1281] lr: 2.376e-02, eta: 17:30:58, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9900, loss_cls: 0.7828, loss: 0.7828 +2025-06-24 13:44:11,411 - pyskl - INFO - Epoch [22][700/1281] lr: 2.375e-02, eta: 17:30:25, time: 0.391, data_time: 0.000, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9900, loss_cls: 0.7016, loss: 0.7016 +2025-06-24 13:44:49,010 - pyskl - INFO - Epoch [22][800/1281] lr: 2.374e-02, eta: 17:29:42, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9844, loss_cls: 0.7912, loss: 0.7912 +2025-06-24 13:45:27,067 - pyskl - INFO - Epoch [22][900/1281] lr: 2.373e-02, eta: 17:29:02, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8037, top5_acc: 0.9862, loss_cls: 0.8570, loss: 0.8570 +2025-06-24 13:46:05,336 - pyskl - INFO - Epoch [22][1000/1281] lr: 2.372e-02, eta: 17:28:24, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9912, loss_cls: 0.8127, loss: 0.8127 +2025-06-24 13:46:43,796 - pyskl - INFO - Epoch [22][1100/1281] lr: 2.371e-02, eta: 17:27:46, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9912, loss_cls: 0.7974, loss: 0.7974 +2025-06-24 13:47:11,929 - pyskl - INFO - Epoch [22][1200/1281] lr: 2.370e-02, eta: 17:26:09, time: 0.281, data_time: 0.001, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9844, loss_cls: 0.8119, loss: 0.8119 +2025-06-24 13:47:43,138 - pyskl - INFO - Saving checkpoint at 22 epochs +2025-06-24 13:48:29,323 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:48:29,384 - pyskl - INFO - +top1_acc 0.7734 +top5_acc 0.9790 +2025-06-24 13:48:29,385 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:48:29,391 - pyskl - INFO - +mean_acc 0.7033 +2025-06-24 13:48:29,393 - pyskl - INFO - Epoch(val) [22][533] top1_acc: 0.7734, top5_acc: 0.9790, mean_class_accuracy: 0.7033 +2025-06-24 13:49:27,884 - pyskl - INFO - Epoch [23][100/1281] lr: 2.369e-02, eta: 17:23:57, time: 0.585, data_time: 0.199, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9869, loss_cls: 0.8359, loss: 0.8359 +2025-06-24 13:50:06,623 - pyskl - INFO - Epoch [23][200/1281] lr: 2.368e-02, eta: 17:23:22, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.8313, top5_acc: 0.9906, loss_cls: 0.7462, loss: 0.7462 +2025-06-24 13:50:45,229 - pyskl - INFO - Epoch [23][300/1281] lr: 2.367e-02, eta: 17:22:46, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9906, loss_cls: 0.7437, loss: 0.7437 +2025-06-24 13:51:23,150 - pyskl - INFO - Epoch [23][400/1281] lr: 2.366e-02, eta: 17:22:06, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9894, loss_cls: 0.7471, loss: 0.7471 +2025-06-24 13:52:00,790 - pyskl - INFO - Epoch [23][500/1281] lr: 2.365e-02, eta: 17:21:24, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9912, loss_cls: 0.7455, loss: 0.7455 +2025-06-24 13:52:39,167 - pyskl - INFO - Epoch [23][600/1281] lr: 2.364e-02, eta: 17:20:47, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9906, loss_cls: 0.7505, loss: 0.7505 +2025-06-24 13:53:17,423 - pyskl - INFO - Epoch [23][700/1281] lr: 2.363e-02, eta: 17:20:09, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9894, loss_cls: 0.7628, loss: 0.7628 +2025-06-24 13:53:55,763 - pyskl - INFO - Epoch [23][800/1281] lr: 2.362e-02, eta: 17:19:31, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9906, loss_cls: 0.8383, loss: 0.8383 +2025-06-24 13:54:33,281 - pyskl - INFO - Epoch [23][900/1281] lr: 2.361e-02, eta: 17:18:49, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8200, top5_acc: 0.9869, loss_cls: 0.8106, loss: 0.8106 +2025-06-24 13:55:11,704 - pyskl - INFO - Epoch [23][1000/1281] lr: 2.360e-02, eta: 17:18:12, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9906, loss_cls: 0.7132, loss: 0.7132 +2025-06-24 13:55:50,472 - pyskl - INFO - Epoch [23][1100/1281] lr: 2.359e-02, eta: 17:17:37, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9875, loss_cls: 0.7750, loss: 0.7750 +2025-06-24 13:56:29,172 - pyskl - INFO - Epoch [23][1200/1281] lr: 2.359e-02, eta: 17:17:01, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.8263, top5_acc: 0.9894, loss_cls: 0.7770, loss: 0.7770 +2025-06-24 13:57:00,536 - pyskl - INFO - Saving checkpoint at 23 epochs +2025-06-24 13:57:59,572 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:57:59,641 - pyskl - INFO - +top1_acc 0.7984 +top5_acc 0.9830 +2025-06-24 13:57:59,641 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:57:59,649 - pyskl - INFO - +mean_acc 0.7314 +2025-06-24 13:57:59,653 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_21.pth was removed +2025-06-24 13:57:59,864 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_23.pth. +2025-06-24 13:57:59,865 - pyskl - INFO - Best top1_acc is 0.7984 at 23 epoch. +2025-06-24 13:57:59,867 - pyskl - INFO - Epoch(val) [23][533] top1_acc: 0.7984, top5_acc: 0.9830, mean_class_accuracy: 0.7314 +2025-06-24 13:58:44,913 - pyskl - INFO - Epoch [24][100/1281] lr: 2.357e-02, eta: 17:13:39, time: 0.450, data_time: 0.196, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9944, loss_cls: 0.7287, loss: 0.7287 +2025-06-24 13:59:29,363 - pyskl - INFO - Epoch [24][200/1281] lr: 2.356e-02, eta: 17:13:36, time: 0.444, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9938, loss_cls: 0.7284, loss: 0.7284 +2025-06-24 13:59:56,220 - pyskl - INFO - Epoch [24][300/1281] lr: 2.355e-02, eta: 17:11:56, time: 0.269, data_time: 0.000, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9962, loss_cls: 0.7724, loss: 0.7724 +2025-06-24 14:00:26,562 - pyskl - INFO - Epoch [24][400/1281] lr: 2.354e-02, eta: 17:10:35, time: 0.303, data_time: 0.000, memory: 4082, top1_acc: 0.8400, top5_acc: 0.9875, loss_cls: 0.7358, loss: 0.7358 +2025-06-24 14:01:04,738 - pyskl - INFO - Epoch [24][500/1281] lr: 2.353e-02, eta: 17:09:57, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8344, top5_acc: 0.9856, loss_cls: 0.7661, loss: 0.7661 +2025-06-24 14:01:43,032 - pyskl - INFO - Epoch [24][600/1281] lr: 2.352e-02, eta: 17:09:20, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9906, loss_cls: 0.8078, loss: 0.8078 +2025-06-24 14:02:21,765 - pyskl - INFO - Epoch [24][700/1281] lr: 2.351e-02, eta: 17:08:46, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9931, loss_cls: 0.7666, loss: 0.7666 +2025-06-24 14:03:00,373 - pyskl - INFO - Epoch [24][800/1281] lr: 2.350e-02, eta: 17:08:10, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9862, loss_cls: 0.8097, loss: 0.8097 +2025-06-24 14:03:39,182 - pyskl - INFO - Epoch [24][900/1281] lr: 2.349e-02, eta: 17:07:36, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9906, loss_cls: 0.7638, loss: 0.7638 +2025-06-24 14:04:17,344 - pyskl - INFO - Epoch [24][1000/1281] lr: 2.348e-02, eta: 17:06:58, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8231, top5_acc: 0.9856, loss_cls: 0.7750, loss: 0.7750 +2025-06-24 14:04:55,724 - pyskl - INFO - Epoch [24][1100/1281] lr: 2.347e-02, eta: 17:06:21, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9869, loss_cls: 0.7895, loss: 0.7895 +2025-06-24 14:05:33,805 - pyskl - INFO - Epoch [24][1200/1281] lr: 2.346e-02, eta: 17:05:43, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9900, loss_cls: 0.7938, loss: 0.7938 +2025-06-24 14:06:05,677 - pyskl - INFO - Saving checkpoint at 24 epochs +2025-06-24 14:07:05,098 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:07:05,158 - pyskl - INFO - +top1_acc 0.7761 +top5_acc 0.9781 +2025-06-24 14:07:05,158 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:07:05,165 - pyskl - INFO - +mean_acc 0.7098 +2025-06-24 14:07:05,167 - pyskl - INFO - Epoch(val) [24][533] top1_acc: 0.7761, top5_acc: 0.9781, mean_class_accuracy: 0.7098 +2025-06-24 14:08:03,202 - pyskl - INFO - Epoch [25][100/1281] lr: 2.344e-02, eta: 17:03:37, time: 0.580, data_time: 0.195, memory: 4082, top1_acc: 0.8413, top5_acc: 0.9869, loss_cls: 0.7307, loss: 0.7307 +2025-06-24 14:08:41,822 - pyskl - INFO - Epoch [25][200/1281] lr: 2.343e-02, eta: 17:03:02, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9894, loss_cls: 0.7442, loss: 0.7442 +2025-06-24 14:09:19,361 - pyskl - INFO - Epoch [25][300/1281] lr: 2.342e-02, eta: 17:02:21, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8381, top5_acc: 0.9900, loss_cls: 0.7179, loss: 0.7179 +2025-06-24 14:09:57,425 - pyskl - INFO - Epoch [25][400/1281] lr: 2.341e-02, eta: 17:01:43, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8413, top5_acc: 0.9956, loss_cls: 0.6769, loss: 0.6769 +2025-06-24 14:10:34,998 - pyskl - INFO - Epoch [25][500/1281] lr: 2.340e-02, eta: 17:01:02, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9900, loss_cls: 0.7694, loss: 0.7694 +2025-06-24 14:10:59,770 - pyskl - INFO - Epoch [25][600/1281] lr: 2.339e-02, eta: 16:59:16, time: 0.248, data_time: 0.000, memory: 4082, top1_acc: 0.8425, top5_acc: 0.9925, loss_cls: 0.7340, loss: 0.7340 +2025-06-24 14:11:45,211 - pyskl - INFO - Epoch [25][700/1281] lr: 2.338e-02, eta: 16:59:15, time: 0.454, data_time: 0.000, memory: 4082, top1_acc: 0.8400, top5_acc: 0.9944, loss_cls: 0.7336, loss: 0.7336 +2025-06-24 14:12:08,377 - pyskl - INFO - Epoch [25][800/1281] lr: 2.337e-02, eta: 16:57:22, time: 0.232, data_time: 0.000, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9875, loss_cls: 0.7912, loss: 0.7912 +2025-06-24 14:12:40,224 - pyskl - INFO - Epoch [25][900/1281] lr: 2.336e-02, eta: 16:56:12, time: 0.318, data_time: 0.000, memory: 4082, top1_acc: 0.8175, top5_acc: 0.9862, loss_cls: 0.7991, loss: 0.7991 +2025-06-24 14:13:18,232 - pyskl - INFO - Epoch [25][1000/1281] lr: 2.335e-02, eta: 16:55:34, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9919, loss_cls: 0.7658, loss: 0.7658 +2025-06-24 14:13:56,265 - pyskl - INFO - Epoch [25][1100/1281] lr: 2.334e-02, eta: 16:54:57, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9894, loss_cls: 0.6909, loss: 0.6909 +2025-06-24 14:14:34,891 - pyskl - INFO - Epoch [25][1200/1281] lr: 2.333e-02, eta: 16:54:22, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9900, loss_cls: 0.7739, loss: 0.7739 +2025-06-24 14:15:06,623 - pyskl - INFO - Saving checkpoint at 25 epochs +2025-06-24 14:16:05,636 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:16:05,690 - pyskl - INFO - +top1_acc 0.7877 +top5_acc 0.9847 +2025-06-24 14:16:05,691 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:16:05,697 - pyskl - INFO - +mean_acc 0.7187 +2025-06-24 14:16:05,698 - pyskl - INFO - Epoch(val) [25][533] top1_acc: 0.7877, top5_acc: 0.9847, mean_class_accuracy: 0.7187 +2025-06-24 14:17:03,553 - pyskl - INFO - Epoch [26][100/1281] lr: 2.332e-02, eta: 16:52:19, time: 0.579, data_time: 0.199, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9906, loss_cls: 0.7282, loss: 0.7282 +2025-06-24 14:17:41,804 - pyskl - INFO - Epoch [26][200/1281] lr: 2.330e-02, eta: 16:51:42, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8512, top5_acc: 0.9900, loss_cls: 0.6602, loss: 0.6602 +2025-06-24 14:18:20,381 - pyskl - INFO - Epoch [26][300/1281] lr: 2.329e-02, eta: 16:51:07, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9912, loss_cls: 0.7089, loss: 0.7089 +2025-06-24 14:18:58,437 - pyskl - INFO - Epoch [26][400/1281] lr: 2.328e-02, eta: 16:50:30, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8263, top5_acc: 0.9875, loss_cls: 0.7751, loss: 0.7751 +2025-06-24 14:19:35,996 - pyskl - INFO - Epoch [26][500/1281] lr: 2.327e-02, eta: 16:49:50, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9881, loss_cls: 0.7948, loss: 0.7948 +2025-06-24 14:20:14,190 - pyskl - INFO - Epoch [26][600/1281] lr: 2.326e-02, eta: 16:49:13, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9906, loss_cls: 0.7580, loss: 0.7580 +2025-06-24 14:20:52,053 - pyskl - INFO - Epoch [26][700/1281] lr: 2.325e-02, eta: 16:48:35, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9944, loss_cls: 0.7535, loss: 0.7535 +2025-06-24 14:21:30,335 - pyskl - INFO - Epoch [26][800/1281] lr: 2.324e-02, eta: 16:47:58, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9912, loss_cls: 0.8213, loss: 0.8213 +2025-06-24 14:22:09,139 - pyskl - INFO - Epoch [26][900/1281] lr: 2.323e-02, eta: 16:47:24, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9888, loss_cls: 0.8330, loss: 0.8330 +2025-06-24 14:22:46,826 - pyskl - INFO - Epoch [26][1000/1281] lr: 2.322e-02, eta: 16:46:45, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8494, top5_acc: 0.9944, loss_cls: 0.6876, loss: 0.6876 +2025-06-24 14:23:13,488 - pyskl - INFO - Epoch [26][1100/1281] lr: 2.321e-02, eta: 16:45:13, time: 0.267, data_time: 0.000, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9969, loss_cls: 0.6806, loss: 0.6806 +2025-06-24 14:23:57,869 - pyskl - INFO - Epoch [26][1200/1281] lr: 2.320e-02, eta: 16:45:06, time: 0.444, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9938, loss_cls: 0.7155, loss: 0.7155 +2025-06-24 14:24:16,658 - pyskl - INFO - Saving checkpoint at 26 epochs +2025-06-24 14:25:16,305 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:25:16,376 - pyskl - INFO - +top1_acc 0.7988 +top5_acc 0.9832 +2025-06-24 14:25:16,376 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:25:16,383 - pyskl - INFO - +mean_acc 0.7320 +2025-06-24 14:25:16,388 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_23.pth was removed +2025-06-24 14:25:16,598 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_26.pth. +2025-06-24 14:25:16,599 - pyskl - INFO - Best top1_acc is 0.7988 at 26 epoch. +2025-06-24 14:25:16,603 - pyskl - INFO - Epoch(val) [26][533] top1_acc: 0.7988, top5_acc: 0.9832, mean_class_accuracy: 0.7320 +2025-06-24 14:26:15,244 - pyskl - INFO - Epoch [27][100/1281] lr: 2.318e-02, eta: 16:43:09, time: 0.586, data_time: 0.198, memory: 4082, top1_acc: 0.8512, top5_acc: 0.9956, loss_cls: 0.6912, loss: 0.6912 +2025-06-24 14:26:54,111 - pyskl - INFO - Epoch [27][200/1281] lr: 2.317e-02, eta: 16:42:36, time: 0.389, data_time: 0.000, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9925, loss_cls: 0.6607, loss: 0.6607 +2025-06-24 14:27:33,579 - pyskl - INFO - Epoch [27][300/1281] lr: 2.316e-02, eta: 16:42:05, time: 0.395, data_time: 0.000, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9919, loss_cls: 0.6987, loss: 0.6987 +2025-06-24 14:28:12,429 - pyskl - INFO - Epoch [27][400/1281] lr: 2.315e-02, eta: 16:41:32, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.8406, top5_acc: 0.9950, loss_cls: 0.7058, loss: 0.7058 +2025-06-24 14:28:51,219 - pyskl - INFO - Epoch [27][500/1281] lr: 2.314e-02, eta: 16:40:58, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9912, loss_cls: 0.7450, loss: 0.7450 +2025-06-24 14:29:29,315 - pyskl - INFO - Epoch [27][600/1281] lr: 2.313e-02, eta: 16:40:21, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8131, top5_acc: 0.9869, loss_cls: 0.8082, loss: 0.8082 +2025-06-24 14:30:07,842 - pyskl - INFO - Epoch [27][700/1281] lr: 2.312e-02, eta: 16:39:46, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9912, loss_cls: 0.8029, loss: 0.8029 +2025-06-24 14:30:46,996 - pyskl - INFO - Epoch [27][800/1281] lr: 2.311e-02, eta: 16:39:13, time: 0.392, data_time: 0.000, memory: 4082, top1_acc: 0.8375, top5_acc: 0.9894, loss_cls: 0.7510, loss: 0.7510 +2025-06-24 14:31:25,121 - pyskl - INFO - Epoch [27][900/1281] lr: 2.310e-02, eta: 16:38:36, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9950, loss_cls: 0.7024, loss: 0.7024 +2025-06-24 14:32:02,405 - pyskl - INFO - Epoch [27][1000/1281] lr: 2.308e-02, eta: 16:37:55, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8400, top5_acc: 0.9925, loss_cls: 0.7444, loss: 0.7444 +2025-06-24 14:32:40,635 - pyskl - INFO - Epoch [27][1100/1281] lr: 2.307e-02, eta: 16:37:19, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9900, loss_cls: 0.7346, loss: 0.7346 +2025-06-24 14:33:19,200 - pyskl - INFO - Epoch [27][1200/1281] lr: 2.306e-02, eta: 16:36:44, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9894, loss_cls: 0.7398, loss: 0.7398 +2025-06-24 14:33:50,944 - pyskl - INFO - Saving checkpoint at 27 epochs +2025-06-24 14:34:39,516 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:34:39,570 - pyskl - INFO - +top1_acc 0.6599 +top5_acc 0.9428 +2025-06-24 14:34:39,570 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:34:39,577 - pyskl - INFO - +mean_acc 0.5887 +2025-06-24 14:34:39,578 - pyskl - INFO - Epoch(val) [27][533] top1_acc: 0.6599, top5_acc: 0.9428, mean_class_accuracy: 0.5887 +2025-06-24 14:35:26,847 - pyskl - INFO - Epoch [28][100/1281] lr: 2.304e-02, eta: 16:33:58, time: 0.473, data_time: 0.195, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9912, loss_cls: 0.7604, loss: 0.7604 +2025-06-24 14:35:55,426 - pyskl - INFO - Epoch [28][200/1281] lr: 2.303e-02, eta: 16:32:38, time: 0.286, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9944, loss_cls: 0.6956, loss: 0.6956 +2025-06-24 14:36:33,773 - pyskl - INFO - Epoch [28][300/1281] lr: 2.302e-02, eta: 16:32:02, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9900, loss_cls: 0.7150, loss: 0.7150 +2025-06-24 14:37:11,658 - pyskl - INFO - Epoch [28][400/1281] lr: 2.301e-02, eta: 16:31:25, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8400, top5_acc: 0.9925, loss_cls: 0.7030, loss: 0.7030 +2025-06-24 14:37:49,673 - pyskl - INFO - Epoch [28][500/1281] lr: 2.300e-02, eta: 16:30:48, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9925, loss_cls: 0.7593, loss: 0.7593 +2025-06-24 14:38:27,443 - pyskl - INFO - Epoch [28][600/1281] lr: 2.299e-02, eta: 16:30:09, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9894, loss_cls: 0.7652, loss: 0.7652 +2025-06-24 14:39:05,645 - pyskl - INFO - Epoch [28][700/1281] lr: 2.298e-02, eta: 16:29:33, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8675, top5_acc: 0.9900, loss_cls: 0.6783, loss: 0.6783 +2025-06-24 14:39:45,396 - pyskl - INFO - Epoch [28][800/1281] lr: 2.297e-02, eta: 16:29:04, time: 0.397, data_time: 0.000, memory: 4082, top1_acc: 0.8237, top5_acc: 0.9919, loss_cls: 0.7802, loss: 0.7802 +2025-06-24 14:40:23,389 - pyskl - INFO - Epoch [28][900/1281] lr: 2.295e-02, eta: 16:28:26, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9894, loss_cls: 0.7327, loss: 0.7327 +2025-06-24 14:41:02,301 - pyskl - INFO - Epoch [28][1000/1281] lr: 2.294e-02, eta: 16:27:53, time: 0.389, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9894, loss_cls: 0.7669, loss: 0.7669 +2025-06-24 14:41:40,823 - pyskl - INFO - Epoch [28][1100/1281] lr: 2.293e-02, eta: 16:27:18, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9912, loss_cls: 0.7023, loss: 0.7023 +2025-06-24 14:42:18,375 - pyskl - INFO - Epoch [28][1200/1281] lr: 2.292e-02, eta: 16:26:39, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9875, loss_cls: 0.7626, loss: 0.7626 +2025-06-24 14:42:48,922 - pyskl - INFO - Saving checkpoint at 28 epochs +2025-06-24 14:43:48,099 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:43:48,157 - pyskl - INFO - +top1_acc 0.8113 +top5_acc 0.9836 +2025-06-24 14:43:48,157 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:43:48,164 - pyskl - INFO - +mean_acc 0.7589 +2025-06-24 14:43:48,169 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_26.pth was removed +2025-06-24 14:43:48,369 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_28.pth. +2025-06-24 14:43:48,370 - pyskl - INFO - Best top1_acc is 0.8113 at 28 epoch. +2025-06-24 14:43:48,372 - pyskl - INFO - Epoch(val) [28][533] top1_acc: 0.8113, top5_acc: 0.9836, mean_class_accuracy: 0.7589 +2025-06-24 14:44:46,077 - pyskl - INFO - Epoch [29][100/1281] lr: 2.290e-02, eta: 16:24:43, time: 0.577, data_time: 0.192, memory: 4082, top1_acc: 0.8625, top5_acc: 0.9931, loss_cls: 0.6577, loss: 0.6577 +2025-06-24 14:45:24,175 - pyskl - INFO - Epoch [29][200/1281] lr: 2.289e-02, eta: 16:24:07, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9950, loss_cls: 0.6844, loss: 0.6844 +2025-06-24 14:46:02,807 - pyskl - INFO - Epoch [29][300/1281] lr: 2.288e-02, eta: 16:23:32, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9862, loss_cls: 0.7304, loss: 0.7304 +2025-06-24 14:46:28,178 - pyskl - INFO - Epoch [29][400/1281] lr: 2.287e-02, eta: 16:22:01, time: 0.254, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9894, loss_cls: 0.8028, loss: 0.8028 +2025-06-24 14:47:13,322 - pyskl - INFO - Epoch [29][500/1281] lr: 2.285e-02, eta: 16:21:55, time: 0.451, data_time: 0.000, memory: 4082, top1_acc: 0.8456, top5_acc: 0.9944, loss_cls: 0.7131, loss: 0.7131 +2025-06-24 14:47:38,571 - pyskl - INFO - Epoch [29][600/1281] lr: 2.284e-02, eta: 16:20:23, time: 0.252, data_time: 0.000, memory: 4082, top1_acc: 0.8569, top5_acc: 0.9912, loss_cls: 0.6852, loss: 0.6852 +2025-06-24 14:48:07,998 - pyskl - INFO - Epoch [29][700/1281] lr: 2.283e-02, eta: 16:19:10, time: 0.294, data_time: 0.000, memory: 4082, top1_acc: 0.8575, top5_acc: 0.9906, loss_cls: 0.7032, loss: 0.7032 +2025-06-24 14:48:46,247 - pyskl - INFO - Epoch [29][800/1281] lr: 2.282e-02, eta: 16:18:34, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9931, loss_cls: 0.6814, loss: 0.6814 +2025-06-24 14:49:25,018 - pyskl - INFO - Epoch [29][900/1281] lr: 2.281e-02, eta: 16:18:01, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.8400, top5_acc: 0.9906, loss_cls: 0.6996, loss: 0.6996 +2025-06-24 14:50:03,120 - pyskl - INFO - Epoch [29][1000/1281] lr: 2.280e-02, eta: 16:17:24, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9881, loss_cls: 0.7786, loss: 0.7786 +2025-06-24 14:50:40,496 - pyskl - INFO - Epoch [29][1100/1281] lr: 2.279e-02, eta: 16:16:45, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9850, loss_cls: 0.7502, loss: 0.7502 +2025-06-24 14:51:18,345 - pyskl - INFO - Epoch [29][1200/1281] lr: 2.277e-02, eta: 16:16:07, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8562, top5_acc: 0.9912, loss_cls: 0.6822, loss: 0.6822 +2025-06-24 14:51:50,017 - pyskl - INFO - Saving checkpoint at 29 epochs +2025-06-24 14:52:49,026 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:52:49,081 - pyskl - INFO - +top1_acc 0.7996 +top5_acc 0.9820 +2025-06-24 14:52:49,081 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:52:49,089 - pyskl - INFO - +mean_acc 0.7465 +2025-06-24 14:52:49,091 - pyskl - INFO - Epoch(val) [29][533] top1_acc: 0.7996, top5_acc: 0.9820, mean_class_accuracy: 0.7465 +2025-06-24 14:53:57,425 - pyskl - INFO - Epoch [30][100/1281] lr: 2.275e-02, eta: 16:14:59, time: 0.683, data_time: 0.199, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9912, loss_cls: 0.6817, loss: 0.6817 +2025-06-24 14:54:45,753 - pyskl - INFO - Epoch [30][200/1281] lr: 2.274e-02, eta: 16:15:05, time: 0.483, data_time: 0.001, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9894, loss_cls: 0.7331, loss: 0.7331 +2025-06-24 14:55:33,928 - pyskl - INFO - Epoch [30][300/1281] lr: 2.273e-02, eta: 16:15:10, time: 0.482, data_time: 0.000, memory: 4082, top1_acc: 0.8494, top5_acc: 0.9912, loss_cls: 0.6897, loss: 0.6897 +2025-06-24 14:56:22,261 - pyskl - INFO - Epoch [30][400/1281] lr: 2.272e-02, eta: 16:15:16, time: 0.483, data_time: 0.000, memory: 4082, top1_acc: 0.8675, top5_acc: 0.9944, loss_cls: 0.6402, loss: 0.6402 +2025-06-24 14:57:10,627 - pyskl - INFO - Epoch [30][500/1281] lr: 2.271e-02, eta: 16:15:21, time: 0.484, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9938, loss_cls: 0.7017, loss: 0.7017 +2025-06-24 14:57:58,708 - pyskl - INFO - Epoch [30][600/1281] lr: 2.269e-02, eta: 16:15:25, time: 0.481, data_time: 0.000, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9944, loss_cls: 0.6731, loss: 0.6731 +2025-06-24 14:58:30,197 - pyskl - INFO - Epoch [30][700/1281] lr: 2.268e-02, eta: 16:14:21, time: 0.315, data_time: 0.000, memory: 4082, top1_acc: 0.8625, top5_acc: 0.9925, loss_cls: 0.6559, loss: 0.6559 +2025-06-24 14:59:12,126 - pyskl - INFO - Epoch [30][800/1281] lr: 2.267e-02, eta: 16:13:59, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9875, loss_cls: 0.7065, loss: 0.7065 +2025-06-24 14:59:44,374 - pyskl - INFO - Epoch [30][900/1281] lr: 2.266e-02, eta: 16:12:58, time: 0.322, data_time: 0.000, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9819, loss_cls: 0.7532, loss: 0.7532 +2025-06-24 15:00:32,929 - pyskl - INFO - Epoch [30][1000/1281] lr: 2.265e-02, eta: 16:13:04, time: 0.486, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9925, loss_cls: 0.7494, loss: 0.7494 +2025-06-24 15:01:21,201 - pyskl - INFO - Epoch [30][1100/1281] lr: 2.263e-02, eta: 16:13:07, time: 0.483, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9919, loss_cls: 0.7518, loss: 0.7518 +2025-06-24 15:02:09,570 - pyskl - INFO - Epoch [30][1200/1281] lr: 2.262e-02, eta: 16:13:11, time: 0.484, data_time: 0.000, memory: 4082, top1_acc: 0.8481, top5_acc: 0.9894, loss_cls: 0.6805, loss: 0.6805 +2025-06-24 15:02:48,884 - pyskl - INFO - Saving checkpoint at 30 epochs +2025-06-24 15:03:47,367 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:03:47,424 - pyskl - INFO - +top1_acc 0.7815 +top5_acc 0.9815 +2025-06-24 15:03:47,424 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:03:47,431 - pyskl - INFO - +mean_acc 0.7117 +2025-06-24 15:03:47,433 - pyskl - INFO - Epoch(val) [30][533] top1_acc: 0.7815, top5_acc: 0.9815, mean_class_accuracy: 0.7117 +2025-06-24 15:05:13,055 - pyskl - INFO - Epoch [31][100/1281] lr: 2.260e-02, eta: 16:13:10, time: 0.856, data_time: 0.198, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9944, loss_cls: 0.8132, loss: 0.8132 +2025-06-24 15:06:02,068 - pyskl - INFO - Epoch [31][200/1281] lr: 2.259e-02, eta: 16:13:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8381, top5_acc: 0.9912, loss_cls: 0.8458, loss: 0.8458 +2025-06-24 15:06:51,110 - pyskl - INFO - Epoch [31][300/1281] lr: 2.258e-02, eta: 16:13:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9938, loss_cls: 0.7964, loss: 0.7964 +2025-06-24 15:07:40,084 - pyskl - INFO - Epoch [31][400/1281] lr: 2.256e-02, eta: 16:13:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9938, loss_cls: 0.7957, loss: 0.7957 +2025-06-24 15:08:29,407 - pyskl - INFO - Epoch [31][500/1281] lr: 2.255e-02, eta: 16:13:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9931, loss_cls: 0.7905, loss: 0.7905 +2025-06-24 15:09:16,563 - pyskl - INFO - Epoch [31][600/1281] lr: 2.254e-02, eta: 16:13:30, time: 0.472, data_time: 0.000, memory: 4083, top1_acc: 0.8375, top5_acc: 0.9912, loss_cls: 0.8453, loss: 0.8453 +2025-06-24 15:09:50,808 - pyskl - INFO - Epoch [31][700/1281] lr: 2.253e-02, eta: 16:12:36, time: 0.342, data_time: 0.000, memory: 4083, top1_acc: 0.8506, top5_acc: 0.9906, loss_cls: 0.8391, loss: 0.8391 +2025-06-24 15:10:29,864 - pyskl - INFO - Epoch [31][800/1281] lr: 2.252e-02, eta: 16:12:02, time: 0.391, data_time: 0.000, memory: 4083, top1_acc: 0.8506, top5_acc: 0.9919, loss_cls: 0.8102, loss: 0.8102 +2025-06-24 15:11:05,623 - pyskl - INFO - Epoch [31][900/1281] lr: 2.250e-02, eta: 16:11:14, time: 0.358, data_time: 0.000, memory: 4083, top1_acc: 0.8444, top5_acc: 0.9925, loss_cls: 0.8776, loss: 0.8776 +2025-06-24 15:11:54,610 - pyskl - INFO - Epoch [31][1000/1281] lr: 2.249e-02, eta: 16:11:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9975, loss_cls: 0.8381, loss: 0.8381 +2025-06-24 15:12:43,739 - pyskl - INFO - Epoch [31][1100/1281] lr: 2.248e-02, eta: 16:11:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8313, top5_acc: 0.9875, loss_cls: 0.8864, loss: 0.8864 +2025-06-24 15:13:32,815 - pyskl - INFO - Epoch [31][1200/1281] lr: 2.247e-02, eta: 16:11:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9931, loss_cls: 0.7729, loss: 0.7729 +2025-06-24 15:14:13,007 - pyskl - INFO - Saving checkpoint at 31 epochs +2025-06-24 15:15:11,598 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:15:11,653 - pyskl - INFO - +top1_acc 0.8111 +top5_acc 0.9815 +2025-06-24 15:15:11,653 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:15:11,660 - pyskl - INFO - +mean_acc 0.7461 +2025-06-24 15:15:11,662 - pyskl - INFO - Epoch(val) [31][533] top1_acc: 0.8111, top5_acc: 0.9815, mean_class_accuracy: 0.7461 +2025-06-24 15:16:32,435 - pyskl - INFO - Epoch [32][100/1281] lr: 2.244e-02, eta: 16:11:01, time: 0.808, data_time: 0.192, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9944, loss_cls: 0.6873, loss: 0.6873 +2025-06-24 15:17:21,415 - pyskl - INFO - Epoch [32][200/1281] lr: 2.243e-02, eta: 16:11:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8325, top5_acc: 0.9919, loss_cls: 0.7922, loss: 0.7922 +2025-06-24 15:18:10,474 - pyskl - INFO - Epoch [32][300/1281] lr: 2.242e-02, eta: 16:11:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9894, loss_cls: 0.7452, loss: 0.7452 +2025-06-24 15:18:59,920 - pyskl - INFO - Epoch [32][400/1281] lr: 2.241e-02, eta: 16:11:11, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8431, top5_acc: 0.9938, loss_cls: 0.7845, loss: 0.7845 +2025-06-24 15:19:48,981 - pyskl - INFO - Epoch [32][500/1281] lr: 2.239e-02, eta: 16:11:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9919, loss_cls: 0.7351, loss: 0.7351 +2025-06-24 15:20:37,222 - pyskl - INFO - Epoch [32][600/1281] lr: 2.238e-02, eta: 16:11:12, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9906, loss_cls: 0.7938, loss: 0.7938 +2025-06-24 15:21:12,067 - pyskl - INFO - Epoch [32][700/1281] lr: 2.237e-02, eta: 16:10:20, time: 0.348, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9931, loss_cls: 0.7683, loss: 0.7683 +2025-06-24 15:21:50,990 - pyskl - INFO - Epoch [32][800/1281] lr: 2.236e-02, eta: 16:09:44, time: 0.389, data_time: 0.000, memory: 4083, top1_acc: 0.8519, top5_acc: 0.9912, loss_cls: 0.7362, loss: 0.7362 +2025-06-24 15:22:27,050 - pyskl - INFO - Epoch [32][900/1281] lr: 2.234e-02, eta: 16:08:57, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9956, loss_cls: 0.7501, loss: 0.7501 +2025-06-24 15:23:15,782 - pyskl - INFO - Epoch [32][1000/1281] lr: 2.233e-02, eta: 16:08:57, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9938, loss_cls: 0.7552, loss: 0.7552 +2025-06-24 15:24:04,626 - pyskl - INFO - Epoch [32][1100/1281] lr: 2.232e-02, eta: 16:08:57, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9900, loss_cls: 0.7656, loss: 0.7656 +2025-06-24 15:24:53,595 - pyskl - INFO - Epoch [32][1200/1281] lr: 2.231e-02, eta: 16:08:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8488, top5_acc: 0.9931, loss_cls: 0.7414, loss: 0.7414 +2025-06-24 15:25:33,734 - pyskl - INFO - Saving checkpoint at 32 epochs +2025-06-24 15:26:32,458 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:26:32,527 - pyskl - INFO - +top1_acc 0.7891 +top5_acc 0.9826 +2025-06-24 15:26:32,527 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:26:32,534 - pyskl - INFO - +mean_acc 0.7306 +2025-06-24 15:26:32,536 - pyskl - INFO - Epoch(val) [32][533] top1_acc: 0.7891, top5_acc: 0.9826, mean_class_accuracy: 0.7306 +2025-06-24 15:27:52,325 - pyskl - INFO - Epoch [33][100/1281] lr: 2.228e-02, eta: 16:08:26, time: 0.798, data_time: 0.197, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9962, loss_cls: 0.6343, loss: 0.6343 +2025-06-24 15:28:41,477 - pyskl - INFO - Epoch [33][200/1281] lr: 2.227e-02, eta: 16:08:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9931, loss_cls: 0.7035, loss: 0.7035 +2025-06-24 15:29:30,476 - pyskl - INFO - Epoch [33][300/1281] lr: 2.226e-02, eta: 16:08:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9938, loss_cls: 0.6792, loss: 0.6792 +2025-06-24 15:30:19,491 - pyskl - INFO - Epoch [33][400/1281] lr: 2.225e-02, eta: 16:08:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8344, top5_acc: 0.9894, loss_cls: 0.8087, loss: 0.8087 +2025-06-24 15:31:08,621 - pyskl - INFO - Epoch [33][500/1281] lr: 2.223e-02, eta: 16:08:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8512, top5_acc: 0.9912, loss_cls: 0.7198, loss: 0.7198 +2025-06-24 15:31:57,571 - pyskl - INFO - Epoch [33][600/1281] lr: 2.222e-02, eta: 16:08:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8387, top5_acc: 0.9894, loss_cls: 0.7883, loss: 0.7883 +2025-06-24 15:32:29,872 - pyskl - INFO - Epoch [33][700/1281] lr: 2.221e-02, eta: 16:07:24, time: 0.323, data_time: 0.000, memory: 4083, top1_acc: 0.8469, top5_acc: 0.9925, loss_cls: 0.7633, loss: 0.7633 +2025-06-24 15:33:11,353 - pyskl - INFO - Epoch [33][800/1281] lr: 2.219e-02, eta: 16:06:56, time: 0.415, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9944, loss_cls: 0.7057, loss: 0.7057 +2025-06-24 15:33:46,754 - pyskl - INFO - Epoch [33][900/1281] lr: 2.218e-02, eta: 16:06:06, time: 0.354, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9906, loss_cls: 0.7565, loss: 0.7565 +2025-06-24 15:34:35,774 - pyskl - INFO - Epoch [33][1000/1281] lr: 2.217e-02, eta: 16:06:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8319, top5_acc: 0.9925, loss_cls: 0.8009, loss: 0.8009 +2025-06-24 15:35:24,710 - pyskl - INFO - Epoch [33][1100/1281] lr: 2.216e-02, eta: 16:06:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8450, top5_acc: 0.9912, loss_cls: 0.7577, loss: 0.7577 +2025-06-24 15:36:14,330 - pyskl - INFO - Epoch [33][1200/1281] lr: 2.214e-02, eta: 16:06:03, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8381, top5_acc: 0.9931, loss_cls: 0.7706, loss: 0.7706 +2025-06-24 15:36:54,392 - pyskl - INFO - Saving checkpoint at 33 epochs +2025-06-24 15:37:53,566 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:37:53,621 - pyskl - INFO - +top1_acc 0.7938 +top5_acc 0.9835 +2025-06-24 15:37:53,621 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:37:53,627 - pyskl - INFO - +mean_acc 0.7276 +2025-06-24 15:37:53,629 - pyskl - INFO - Epoch(val) [33][533] top1_acc: 0.7938, top5_acc: 0.9835, mean_class_accuracy: 0.7276 +2025-06-24 15:39:13,838 - pyskl - INFO - Epoch [34][100/1281] lr: 2.212e-02, eta: 16:05:29, time: 0.802, data_time: 0.198, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9931, loss_cls: 0.6919, loss: 0.6919 +2025-06-24 15:40:02,625 - pyskl - INFO - Epoch [34][200/1281] lr: 2.211e-02, eta: 16:05:26, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9912, loss_cls: 0.6935, loss: 0.6935 +2025-06-24 15:40:51,539 - pyskl - INFO - Epoch [34][300/1281] lr: 2.209e-02, eta: 16:05:23, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9956, loss_cls: 0.6912, loss: 0.6912 +2025-06-24 15:41:40,936 - pyskl - INFO - Epoch [34][400/1281] lr: 2.208e-02, eta: 16:05:22, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9956, loss_cls: 0.6740, loss: 0.6740 +2025-06-24 15:42:30,522 - pyskl - INFO - Epoch [34][500/1281] lr: 2.207e-02, eta: 16:05:21, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9944, loss_cls: 0.6449, loss: 0.6449 +2025-06-24 15:43:19,197 - pyskl - INFO - Epoch [34][600/1281] lr: 2.205e-02, eta: 16:05:17, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8481, top5_acc: 0.9931, loss_cls: 0.7296, loss: 0.7296 +2025-06-24 15:43:51,715 - pyskl - INFO - Epoch [34][700/1281] lr: 2.204e-02, eta: 16:04:16, time: 0.325, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9931, loss_cls: 0.6890, loss: 0.6890 +2025-06-24 15:44:32,850 - pyskl - INFO - Epoch [34][800/1281] lr: 2.203e-02, eta: 16:03:46, time: 0.411, data_time: 0.000, memory: 4083, top1_acc: 0.8475, top5_acc: 0.9931, loss_cls: 0.7407, loss: 0.7407 +2025-06-24 15:45:07,717 - pyskl - INFO - Epoch [34][900/1281] lr: 2.201e-02, eta: 16:02:53, time: 0.349, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9919, loss_cls: 0.6834, loss: 0.6834 +2025-06-24 15:45:56,537 - pyskl - INFO - Epoch [34][1000/1281] lr: 2.200e-02, eta: 16:02:49, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8481, top5_acc: 0.9912, loss_cls: 0.7527, loss: 0.7527 +2025-06-24 15:46:45,714 - pyskl - INFO - Epoch [34][1100/1281] lr: 2.199e-02, eta: 16:02:46, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9950, loss_cls: 0.7046, loss: 0.7046 +2025-06-24 15:47:35,096 - pyskl - INFO - Epoch [34][1200/1281] lr: 2.197e-02, eta: 16:02:43, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9888, loss_cls: 0.7492, loss: 0.7492 +2025-06-24 15:48:15,863 - pyskl - INFO - Saving checkpoint at 34 epochs +2025-06-24 15:49:15,226 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:49:15,303 - pyskl - INFO - +top1_acc 0.8008 +top5_acc 0.9826 +2025-06-24 15:49:15,303 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:49:15,314 - pyskl - INFO - +mean_acc 0.7554 +2025-06-24 15:49:15,318 - pyskl - INFO - Epoch(val) [34][533] top1_acc: 0.8008, top5_acc: 0.9826, mean_class_accuracy: 0.7554 +2025-06-24 15:50:36,770 - pyskl - INFO - Epoch [35][100/1281] lr: 2.195e-02, eta: 16:02:11, time: 0.814, data_time: 0.196, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9925, loss_cls: 0.7201, loss: 0.7201 +2025-06-24 15:51:25,723 - pyskl - INFO - Epoch [35][200/1281] lr: 2.194e-02, eta: 16:02:06, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9962, loss_cls: 0.6738, loss: 0.6738 +2025-06-24 15:52:14,700 - pyskl - INFO - Epoch [35][300/1281] lr: 2.192e-02, eta: 16:02:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8512, top5_acc: 0.9931, loss_cls: 0.7161, loss: 0.7161 +2025-06-24 15:53:03,907 - pyskl - INFO - Epoch [35][400/1281] lr: 2.191e-02, eta: 16:01:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9975, loss_cls: 0.6027, loss: 0.6027 +2025-06-24 15:53:53,072 - pyskl - INFO - Epoch [35][500/1281] lr: 2.190e-02, eta: 16:01:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9919, loss_cls: 0.7236, loss: 0.7236 +2025-06-24 15:54:40,953 - pyskl - INFO - Epoch [35][600/1281] lr: 2.188e-02, eta: 16:01:43, time: 0.479, data_time: 0.000, memory: 4083, top1_acc: 0.8394, top5_acc: 0.9912, loss_cls: 0.7826, loss: 0.7826 +2025-06-24 15:55:14,416 - pyskl - INFO - Epoch [35][700/1281] lr: 2.187e-02, eta: 16:00:46, time: 0.335, data_time: 0.000, memory: 4083, top1_acc: 0.8469, top5_acc: 0.9944, loss_cls: 0.7274, loss: 0.7274 +2025-06-24 15:55:54,609 - pyskl - INFO - Epoch [35][800/1281] lr: 2.185e-02, eta: 16:00:11, time: 0.402, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9962, loss_cls: 0.6978, loss: 0.6978 +2025-06-24 15:56:31,890 - pyskl - INFO - Epoch [35][900/1281] lr: 2.184e-02, eta: 15:59:26, time: 0.373, data_time: 0.000, memory: 4083, top1_acc: 0.8538, top5_acc: 0.9900, loss_cls: 0.7393, loss: 0.7393 +2025-06-24 15:57:21,152 - pyskl - INFO - Epoch [35][1000/1281] lr: 2.183e-02, eta: 15:59:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9888, loss_cls: 0.7573, loss: 0.7573 +2025-06-24 15:58:10,508 - pyskl - INFO - Epoch [35][1100/1281] lr: 2.181e-02, eta: 15:59:16, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9906, loss_cls: 0.6766, loss: 0.6766 +2025-06-24 15:58:59,705 - pyskl - INFO - Epoch [35][1200/1281] lr: 2.180e-02, eta: 15:59:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9938, loss_cls: 0.7776, loss: 0.7776 +2025-06-24 15:59:40,202 - pyskl - INFO - Saving checkpoint at 35 epochs +2025-06-24 16:00:38,928 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:00:39,001 - pyskl - INFO - +top1_acc 0.8041 +top5_acc 0.9854 +2025-06-24 16:00:39,001 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:00:39,009 - pyskl - INFO - +mean_acc 0.7434 +2025-06-24 16:00:39,012 - pyskl - INFO - Epoch(val) [35][533] top1_acc: 0.8041, top5_acc: 0.9854, mean_class_accuracy: 0.7434 +2025-06-24 16:01:59,572 - pyskl - INFO - Epoch [36][100/1281] lr: 2.178e-02, eta: 15:58:33, time: 0.806, data_time: 0.194, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9956, loss_cls: 0.6402, loss: 0.6402 +2025-06-24 16:02:48,524 - pyskl - INFO - Epoch [36][200/1281] lr: 2.176e-02, eta: 15:58:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9956, loss_cls: 0.6951, loss: 0.6951 +2025-06-24 16:03:37,421 - pyskl - INFO - Epoch [36][300/1281] lr: 2.175e-02, eta: 15:58:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9938, loss_cls: 0.6423, loss: 0.6423 +2025-06-24 16:04:26,386 - pyskl - INFO - Epoch [36][400/1281] lr: 2.173e-02, eta: 15:58:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9938, loss_cls: 0.6233, loss: 0.6233 +2025-06-24 16:05:15,418 - pyskl - INFO - Epoch [36][500/1281] lr: 2.172e-02, eta: 15:58:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9925, loss_cls: 0.7273, loss: 0.7273 +2025-06-24 16:06:01,369 - pyskl - INFO - Epoch [36][600/1281] lr: 2.171e-02, eta: 15:57:47, time: 0.460, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9931, loss_cls: 0.6646, loss: 0.6646 +2025-06-24 16:06:37,884 - pyskl - INFO - Epoch [36][700/1281] lr: 2.169e-02, eta: 15:56:59, time: 0.365, data_time: 0.000, memory: 4083, top1_acc: 0.8475, top5_acc: 0.9944, loss_cls: 0.7309, loss: 0.7309 +2025-06-24 16:07:15,041 - pyskl - INFO - Epoch [36][800/1281] lr: 2.168e-02, eta: 15:56:14, time: 0.372, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9944, loss_cls: 0.7153, loss: 0.7153 +2025-06-24 16:07:52,167 - pyskl - INFO - Epoch [36][900/1281] lr: 2.167e-02, eta: 15:55:28, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9919, loss_cls: 0.7320, loss: 0.7320 +2025-06-24 16:08:41,380 - pyskl - INFO - Epoch [36][1000/1281] lr: 2.165e-02, eta: 15:55:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8438, top5_acc: 0.9925, loss_cls: 0.7588, loss: 0.7588 +2025-06-24 16:09:30,468 - pyskl - INFO - Epoch [36][1100/1281] lr: 2.164e-02, eta: 15:55:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9919, loss_cls: 0.6953, loss: 0.6953 +2025-06-24 16:10:19,348 - pyskl - INFO - Epoch [36][1200/1281] lr: 2.162e-02, eta: 15:55:05, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8519, top5_acc: 0.9888, loss_cls: 0.7349, loss: 0.7349 +2025-06-24 16:10:59,580 - pyskl - INFO - Saving checkpoint at 36 epochs +2025-06-24 16:11:58,472 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:11:58,546 - pyskl - INFO - +top1_acc 0.8363 +top5_acc 0.9878 +2025-06-24 16:11:58,547 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:11:58,556 - pyskl - INFO - +mean_acc 0.7763 +2025-06-24 16:11:58,562 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_28.pth was removed +2025-06-24 16:11:58,742 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_36.pth. +2025-06-24 16:11:58,742 - pyskl - INFO - Best top1_acc is 0.8363 at 36 epoch. +2025-06-24 16:11:58,745 - pyskl - INFO - Epoch(val) [36][533] top1_acc: 0.8363, top5_acc: 0.9878, mean_class_accuracy: 0.7763 +2025-06-24 16:13:18,605 - pyskl - INFO - Epoch [37][100/1281] lr: 2.160e-02, eta: 15:54:22, time: 0.799, data_time: 0.198, memory: 4083, top1_acc: 0.8500, top5_acc: 0.9950, loss_cls: 0.7179, loss: 0.7179 +2025-06-24 16:14:07,742 - pyskl - INFO - Epoch [37][200/1281] lr: 2.158e-02, eta: 15:54:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9944, loss_cls: 0.6858, loss: 0.6858 +2025-06-24 16:14:57,140 - pyskl - INFO - Epoch [37][300/1281] lr: 2.157e-02, eta: 15:54:07, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9956, loss_cls: 0.6294, loss: 0.6294 +2025-06-24 16:15:46,325 - pyskl - INFO - Epoch [37][400/1281] lr: 2.156e-02, eta: 15:53:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9950, loss_cls: 0.6875, loss: 0.6875 +2025-06-24 16:16:35,382 - pyskl - INFO - Epoch [37][500/1281] lr: 2.154e-02, eta: 15:53:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9919, loss_cls: 0.6990, loss: 0.6990 +2025-06-24 16:17:22,446 - pyskl - INFO - Epoch [37][600/1281] lr: 2.153e-02, eta: 15:53:34, time: 0.471, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9938, loss_cls: 0.6566, loss: 0.6566 +2025-06-24 16:17:55,287 - pyskl - INFO - Epoch [37][700/1281] lr: 2.151e-02, eta: 15:52:35, time: 0.328, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9950, loss_cls: 0.6933, loss: 0.6933 +2025-06-24 16:18:36,010 - pyskl - INFO - Epoch [37][800/1281] lr: 2.150e-02, eta: 15:52:00, time: 0.407, data_time: 0.001, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9931, loss_cls: 0.6922, loss: 0.6922 +2025-06-24 16:19:11,282 - pyskl - INFO - Epoch [37][900/1281] lr: 2.149e-02, eta: 15:51:08, time: 0.353, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9919, loss_cls: 0.6331, loss: 0.6331 +2025-06-24 16:20:00,119 - pyskl - INFO - Epoch [37][1000/1281] lr: 2.147e-02, eta: 15:50:58, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8500, top5_acc: 0.9925, loss_cls: 0.7476, loss: 0.7476 +2025-06-24 16:20:49,311 - pyskl - INFO - Epoch [37][1100/1281] lr: 2.146e-02, eta: 15:50:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8394, top5_acc: 0.9906, loss_cls: 0.7608, loss: 0.7608 +2025-06-24 16:21:38,250 - pyskl - INFO - Epoch [37][1200/1281] lr: 2.144e-02, eta: 15:50:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9962, loss_cls: 0.6180, loss: 0.6180 +2025-06-24 16:22:18,446 - pyskl - INFO - Saving checkpoint at 37 epochs +2025-06-24 16:23:17,407 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:23:17,462 - pyskl - INFO - +top1_acc 0.7770 +top5_acc 0.9762 +2025-06-24 16:23:17,462 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:23:17,469 - pyskl - INFO - +mean_acc 0.7224 +2025-06-24 16:23:17,470 - pyskl - INFO - Epoch(val) [37][533] top1_acc: 0.7770, top5_acc: 0.9762, mean_class_accuracy: 0.7224 +2025-06-24 16:24:37,379 - pyskl - INFO - Epoch [38][100/1281] lr: 2.142e-02, eta: 15:49:54, time: 0.799, data_time: 0.194, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9956, loss_cls: 0.6496, loss: 0.6496 +2025-06-24 16:25:26,501 - pyskl - INFO - Epoch [38][200/1281] lr: 2.140e-02, eta: 15:49:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9969, loss_cls: 0.6283, loss: 0.6283 +2025-06-24 16:26:15,181 - pyskl - INFO - Epoch [38][300/1281] lr: 2.139e-02, eta: 15:49:32, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9925, loss_cls: 0.6024, loss: 0.6024 +2025-06-24 16:27:04,300 - pyskl - INFO - Epoch [38][400/1281] lr: 2.137e-02, eta: 15:49:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9950, loss_cls: 0.6753, loss: 0.6753 +2025-06-24 16:27:53,379 - pyskl - INFO - Epoch [38][500/1281] lr: 2.136e-02, eta: 15:49:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9950, loss_cls: 0.6629, loss: 0.6629 +2025-06-24 16:28:42,285 - pyskl - INFO - Epoch [38][600/1281] lr: 2.134e-02, eta: 15:49:00, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9881, loss_cls: 0.6777, loss: 0.6777 +2025-06-24 16:29:13,804 - pyskl - INFO - Epoch [38][700/1281] lr: 2.133e-02, eta: 15:47:57, time: 0.315, data_time: 0.000, memory: 4083, top1_acc: 0.8506, top5_acc: 0.9950, loss_cls: 0.7226, loss: 0.7226 +2025-06-24 16:29:56,179 - pyskl - INFO - Epoch [38][800/1281] lr: 2.132e-02, eta: 15:47:26, time: 0.424, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9944, loss_cls: 0.7202, loss: 0.7202 +2025-06-24 16:30:30,333 - pyskl - INFO - Epoch [38][900/1281] lr: 2.130e-02, eta: 15:46:31, time: 0.342, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9912, loss_cls: 0.7302, loss: 0.7302 +2025-06-24 16:31:19,619 - pyskl - INFO - Epoch [38][1000/1281] lr: 2.129e-02, eta: 15:46:20, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9962, loss_cls: 0.6070, loss: 0.6070 +2025-06-24 16:32:08,665 - pyskl - INFO - Epoch [38][1100/1281] lr: 2.127e-02, eta: 15:46:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8500, top5_acc: 0.9931, loss_cls: 0.7237, loss: 0.7237 +2025-06-24 16:32:57,442 - pyskl - INFO - Epoch [38][1200/1281] lr: 2.126e-02, eta: 15:45:57, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9900, loss_cls: 0.7126, loss: 0.7126 +2025-06-24 16:33:38,192 - pyskl - INFO - Saving checkpoint at 38 epochs +2025-06-24 16:34:37,934 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:34:37,995 - pyskl - INFO - +top1_acc 0.8412 +top5_acc 0.9840 +2025-06-24 16:34:37,995 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:34:38,003 - pyskl - INFO - +mean_acc 0.7850 +2025-06-24 16:34:38,008 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_36.pth was removed +2025-06-24 16:34:38,182 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_38.pth. +2025-06-24 16:34:38,183 - pyskl - INFO - Best top1_acc is 0.8412 at 38 epoch. +2025-06-24 16:34:38,186 - pyskl - INFO - Epoch(val) [38][533] top1_acc: 0.8412, top5_acc: 0.9840, mean_class_accuracy: 0.7850 +2025-06-24 16:35:57,515 - pyskl - INFO - Epoch [39][100/1281] lr: 2.123e-02, eta: 15:45:08, time: 0.793, data_time: 0.193, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9938, loss_cls: 0.6444, loss: 0.6444 +2025-06-24 16:36:46,627 - pyskl - INFO - Epoch [39][200/1281] lr: 2.122e-02, eta: 15:44:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9956, loss_cls: 0.5751, loss: 0.5751 +2025-06-24 16:37:35,559 - pyskl - INFO - Epoch [39][300/1281] lr: 2.120e-02, eta: 15:44:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9925, loss_cls: 0.6649, loss: 0.6649 +2025-06-24 16:38:24,427 - pyskl - INFO - Epoch [39][400/1281] lr: 2.119e-02, eta: 15:44:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9919, loss_cls: 0.6886, loss: 0.6886 +2025-06-24 16:39:13,653 - pyskl - INFO - Epoch [39][500/1281] lr: 2.117e-02, eta: 15:44:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9919, loss_cls: 0.6972, loss: 0.6972 +2025-06-24 16:40:02,896 - pyskl - INFO - Epoch [39][600/1281] lr: 2.116e-02, eta: 15:44:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9969, loss_cls: 0.6083, loss: 0.6083 +2025-06-24 16:40:33,775 - pyskl - INFO - Epoch [39][700/1281] lr: 2.114e-02, eta: 15:43:04, time: 0.309, data_time: 0.001, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9906, loss_cls: 0.7167, loss: 0.7167 +2025-06-24 16:41:17,749 - pyskl - INFO - Epoch [39][800/1281] lr: 2.113e-02, eta: 15:42:36, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9944, loss_cls: 0.6829, loss: 0.6829 +2025-06-24 16:41:50,897 - pyskl - INFO - Epoch [39][900/1281] lr: 2.111e-02, eta: 15:41:38, time: 0.331, data_time: 0.000, memory: 4083, top1_acc: 0.8512, top5_acc: 0.9938, loss_cls: 0.7024, loss: 0.7024 +2025-06-24 16:42:39,885 - pyskl - INFO - Epoch [39][1000/1281] lr: 2.110e-02, eta: 15:41:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9919, loss_cls: 0.6636, loss: 0.6636 +2025-06-24 16:43:29,128 - pyskl - INFO - Epoch [39][1100/1281] lr: 2.108e-02, eta: 15:41:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9919, loss_cls: 0.6749, loss: 0.6749 +2025-06-24 16:44:18,265 - pyskl - INFO - Epoch [39][1200/1281] lr: 2.107e-02, eta: 15:41:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9950, loss_cls: 0.6628, loss: 0.6628 +2025-06-24 16:44:58,326 - pyskl - INFO - Saving checkpoint at 39 epochs +2025-06-24 16:45:58,124 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:45:58,210 - pyskl - INFO - +top1_acc 0.8152 +top5_acc 0.9833 +2025-06-24 16:45:58,211 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:45:58,219 - pyskl - INFO - +mean_acc 0.7758 +2025-06-24 16:45:58,221 - pyskl - INFO - Epoch(val) [39][533] top1_acc: 0.8152, top5_acc: 0.9833, mean_class_accuracy: 0.7758 +2025-06-24 16:47:19,738 - pyskl - INFO - Epoch [40][100/1281] lr: 2.104e-02, eta: 15:40:16, time: 0.815, data_time: 0.199, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9938, loss_cls: 0.5744, loss: 0.5744 +2025-06-24 16:48:08,364 - pyskl - INFO - Epoch [40][200/1281] lr: 2.103e-02, eta: 15:40:02, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9969, loss_cls: 0.5167, loss: 0.5167 +2025-06-24 16:48:57,377 - pyskl - INFO - Epoch [40][300/1281] lr: 2.101e-02, eta: 15:39:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9931, loss_cls: 0.6659, loss: 0.6659 +2025-06-24 16:49:46,381 - pyskl - INFO - Epoch [40][400/1281] lr: 2.100e-02, eta: 15:39:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9944, loss_cls: 0.6685, loss: 0.6685 +2025-06-24 16:50:35,688 - pyskl - INFO - Epoch [40][500/1281] lr: 2.098e-02, eta: 15:39:22, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9950, loss_cls: 0.6654, loss: 0.6654 +2025-06-24 16:51:24,912 - pyskl - INFO - Epoch [40][600/1281] lr: 2.097e-02, eta: 15:39:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9938, loss_cls: 0.6035, loss: 0.6035 +2025-06-24 16:51:56,220 - pyskl - INFO - Epoch [40][700/1281] lr: 2.095e-02, eta: 15:38:05, time: 0.313, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9919, loss_cls: 0.6730, loss: 0.6730 +2025-06-24 16:52:39,166 - pyskl - INFO - Epoch [40][800/1281] lr: 2.094e-02, eta: 15:37:34, time: 0.429, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9975, loss_cls: 0.7000, loss: 0.7000 +2025-06-24 16:53:13,940 - pyskl - INFO - Epoch [40][900/1281] lr: 2.092e-02, eta: 15:36:40, time: 0.348, data_time: 0.000, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9950, loss_cls: 0.6644, loss: 0.6644 +2025-06-24 16:54:03,448 - pyskl - INFO - Epoch [40][1000/1281] lr: 2.091e-02, eta: 15:36:27, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9944, loss_cls: 0.6733, loss: 0.6733 +2025-06-24 16:54:52,805 - pyskl - INFO - Epoch [40][1100/1281] lr: 2.089e-02, eta: 15:36:14, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9938, loss_cls: 0.6821, loss: 0.6821 +2025-06-24 16:55:42,288 - pyskl - INFO - Epoch [40][1200/1281] lr: 2.088e-02, eta: 15:36:01, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9931, loss_cls: 0.6808, loss: 0.6808 +2025-06-24 16:56:22,572 - pyskl - INFO - Saving checkpoint at 40 epochs +2025-06-24 16:57:21,865 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:57:21,922 - pyskl - INFO - +top1_acc 0.8141 +top5_acc 0.9845 +2025-06-24 16:57:21,922 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:57:21,929 - pyskl - INFO - +mean_acc 0.7564 +2025-06-24 16:57:21,931 - pyskl - INFO - Epoch(val) [40][533] top1_acc: 0.8141, top5_acc: 0.9845, mean_class_accuracy: 0.7564 +2025-06-24 16:58:42,505 - pyskl - INFO - Epoch [41][100/1281] lr: 2.085e-02, eta: 15:35:12, time: 0.806, data_time: 0.196, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9950, loss_cls: 0.5741, loss: 0.5741 +2025-06-24 16:59:31,739 - pyskl - INFO - Epoch [41][200/1281] lr: 2.083e-02, eta: 15:34:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9938, loss_cls: 0.6774, loss: 0.6774 +2025-06-24 17:00:20,613 - pyskl - INFO - Epoch [41][300/1281] lr: 2.082e-02, eta: 15:34:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9906, loss_cls: 0.6977, loss: 0.6977 +2025-06-24 17:01:09,572 - pyskl - INFO - Epoch [41][400/1281] lr: 2.080e-02, eta: 15:34:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9950, loss_cls: 0.6654, loss: 0.6654 +2025-06-24 17:01:58,600 - pyskl - INFO - Epoch [41][500/1281] lr: 2.079e-02, eta: 15:34:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9931, loss_cls: 0.6664, loss: 0.6664 +2025-06-24 17:02:47,393 - pyskl - INFO - Epoch [41][600/1281] lr: 2.077e-02, eta: 15:33:56, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9919, loss_cls: 0.7172, loss: 0.7172 +2025-06-24 17:03:17,065 - pyskl - INFO - Epoch [41][700/1281] lr: 2.076e-02, eta: 15:32:48, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9900, loss_cls: 0.6640, loss: 0.6640 +2025-06-24 17:04:01,953 - pyskl - INFO - Epoch [41][800/1281] lr: 2.074e-02, eta: 15:32:22, time: 0.449, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9944, loss_cls: 0.6527, loss: 0.6527 +2025-06-24 17:04:34,560 - pyskl - INFO - Epoch [41][900/1281] lr: 2.073e-02, eta: 15:31:22, time: 0.326, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9962, loss_cls: 0.6574, loss: 0.6574 +2025-06-24 17:05:23,481 - pyskl - INFO - Epoch [41][1000/1281] lr: 2.071e-02, eta: 15:31:07, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9975, loss_cls: 0.6669, loss: 0.6669 +2025-06-24 17:06:12,683 - pyskl - INFO - Epoch [41][1100/1281] lr: 2.070e-02, eta: 15:30:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9938, loss_cls: 0.6760, loss: 0.6760 +2025-06-24 17:07:02,295 - pyskl - INFO - Epoch [41][1200/1281] lr: 2.068e-02, eta: 15:30:37, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9944, loss_cls: 0.6818, loss: 0.6818 +2025-06-24 17:07:42,433 - pyskl - INFO - Saving checkpoint at 41 epochs +2025-06-24 17:08:41,374 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:08:41,433 - pyskl - INFO - +top1_acc 0.8216 +top5_acc 0.9827 +2025-06-24 17:08:41,433 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:08:41,440 - pyskl - INFO - +mean_acc 0.7863 +2025-06-24 17:08:41,442 - pyskl - INFO - Epoch(val) [41][533] top1_acc: 0.8216, top5_acc: 0.9827, mean_class_accuracy: 0.7863 +2025-06-24 17:10:01,391 - pyskl - INFO - Epoch [42][100/1281] lr: 2.065e-02, eta: 15:29:45, time: 0.799, data_time: 0.194, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9944, loss_cls: 0.7154, loss: 0.7154 +2025-06-24 17:10:50,269 - pyskl - INFO - Epoch [42][200/1281] lr: 2.064e-02, eta: 15:29:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9944, loss_cls: 0.6575, loss: 0.6575 +2025-06-24 17:11:39,256 - pyskl - INFO - Epoch [42][300/1281] lr: 2.062e-02, eta: 15:29:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9938, loss_cls: 0.6136, loss: 0.6136 +2025-06-24 17:12:28,355 - pyskl - INFO - Epoch [42][400/1281] lr: 2.061e-02, eta: 15:28:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9944, loss_cls: 0.6796, loss: 0.6796 +2025-06-24 17:13:17,525 - pyskl - INFO - Epoch [42][500/1281] lr: 2.059e-02, eta: 15:28:40, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9938, loss_cls: 0.6229, loss: 0.6229 +2025-06-24 17:14:06,298 - pyskl - INFO - Epoch [42][600/1281] lr: 2.057e-02, eta: 15:28:23, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9925, loss_cls: 0.6496, loss: 0.6496 +2025-06-24 17:14:32,890 - pyskl - INFO - Epoch [42][700/1281] lr: 2.056e-02, eta: 15:27:08, time: 0.266, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9956, loss_cls: 0.6243, loss: 0.6243 +2025-06-24 17:15:24,104 - pyskl - INFO - Epoch [42][800/1281] lr: 2.054e-02, eta: 15:26:57, time: 0.512, data_time: 0.001, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9919, loss_cls: 0.7025, loss: 0.7025 +2025-06-24 17:15:54,506 - pyskl - INFO - Epoch [42][900/1281] lr: 2.053e-02, eta: 15:25:51, time: 0.304, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9912, loss_cls: 0.6873, loss: 0.6873 +2025-06-24 17:16:43,556 - pyskl - INFO - Epoch [42][1000/1281] lr: 2.051e-02, eta: 15:25:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9938, loss_cls: 0.6806, loss: 0.6806 +2025-06-24 17:17:32,639 - pyskl - INFO - Epoch [42][1100/1281] lr: 2.050e-02, eta: 15:25:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9944, loss_cls: 0.6258, loss: 0.6258 +2025-06-24 17:18:21,928 - pyskl - INFO - Epoch [42][1200/1281] lr: 2.048e-02, eta: 15:25:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9900, loss_cls: 0.6381, loss: 0.6381 +2025-06-24 17:19:02,639 - pyskl - INFO - Saving checkpoint at 42 epochs +2025-06-24 17:20:01,997 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:20:02,052 - pyskl - INFO - +top1_acc 0.8261 +top5_acc 0.9790 +2025-06-24 17:20:02,052 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:20:02,059 - pyskl - INFO - +mean_acc 0.7918 +2025-06-24 17:20:02,061 - pyskl - INFO - Epoch(val) [42][533] top1_acc: 0.8261, top5_acc: 0.9790, mean_class_accuracy: 0.7918 +2025-06-24 17:21:21,311 - pyskl - INFO - Epoch [43][100/1281] lr: 2.045e-02, eta: 15:24:06, time: 0.792, data_time: 0.190, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9919, loss_cls: 0.6296, loss: 0.6296 +2025-06-24 17:22:10,336 - pyskl - INFO - Epoch [43][200/1281] lr: 2.044e-02, eta: 15:23:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9962, loss_cls: 0.6278, loss: 0.6278 +2025-06-24 17:22:59,620 - pyskl - INFO - Epoch [43][300/1281] lr: 2.042e-02, eta: 15:23:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9956, loss_cls: 0.6523, loss: 0.6523 +2025-06-24 17:23:49,195 - pyskl - INFO - Epoch [43][400/1281] lr: 2.040e-02, eta: 15:23:16, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9981, loss_cls: 0.5887, loss: 0.5887 +2025-06-24 17:24:38,684 - pyskl - INFO - Epoch [43][500/1281] lr: 2.039e-02, eta: 15:23:00, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9931, loss_cls: 0.6607, loss: 0.6607 +2025-06-24 17:25:27,575 - pyskl - INFO - Epoch [43][600/1281] lr: 2.037e-02, eta: 15:22:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9969, loss_cls: 0.6365, loss: 0.6365 +2025-06-24 17:25:55,451 - pyskl - INFO - Epoch [43][700/1281] lr: 2.036e-02, eta: 15:21:30, time: 0.279, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9912, loss_cls: 0.7120, loss: 0.7120 +2025-06-24 17:26:46,561 - pyskl - INFO - Epoch [43][800/1281] lr: 2.034e-02, eta: 15:21:18, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9944, loss_cls: 0.6315, loss: 0.6315 +2025-06-24 17:27:16,224 - pyskl - INFO - Epoch [43][900/1281] lr: 2.033e-02, eta: 15:20:11, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9950, loss_cls: 0.5835, loss: 0.5835 +2025-06-24 17:28:04,984 - pyskl - INFO - Epoch [43][1000/1281] lr: 2.031e-02, eta: 15:19:52, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9956, loss_cls: 0.6133, loss: 0.6133 +2025-06-24 17:28:53,909 - pyskl - INFO - Epoch [43][1100/1281] lr: 2.029e-02, eta: 15:19:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9944, loss_cls: 0.6317, loss: 0.6317 +2025-06-24 17:29:42,714 - pyskl - INFO - Epoch [43][1200/1281] lr: 2.028e-02, eta: 15:19:15, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9950, loss_cls: 0.5414, loss: 0.5414 +2025-06-24 17:30:23,613 - pyskl - INFO - Saving checkpoint at 43 epochs +2025-06-24 17:31:22,449 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:31:22,504 - pyskl - INFO - +top1_acc 0.8155 +top5_acc 0.9812 +2025-06-24 17:31:22,504 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:31:22,511 - pyskl - INFO - +mean_acc 0.7441 +2025-06-24 17:31:22,513 - pyskl - INFO - Epoch(val) [43][533] top1_acc: 0.8155, top5_acc: 0.9812, mean_class_accuracy: 0.7441 +2025-06-24 17:32:42,767 - pyskl - INFO - Epoch [44][100/1281] lr: 2.025e-02, eta: 15:18:21, time: 0.802, data_time: 0.193, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9969, loss_cls: 0.5758, loss: 0.5758 +2025-06-24 17:33:31,843 - pyskl - INFO - Epoch [44][200/1281] lr: 2.023e-02, eta: 15:18:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9919, loss_cls: 0.6534, loss: 0.6534 +2025-06-24 17:34:20,984 - pyskl - INFO - Epoch [44][300/1281] lr: 2.022e-02, eta: 15:17:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9931, loss_cls: 0.6582, loss: 0.6582 +2025-06-24 17:35:09,895 - pyskl - INFO - Epoch [44][400/1281] lr: 2.020e-02, eta: 15:17:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9938, loss_cls: 0.6762, loss: 0.6762 +2025-06-24 17:35:59,171 - pyskl - INFO - Epoch [44][500/1281] lr: 2.018e-02, eta: 15:17:07, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9969, loss_cls: 0.5563, loss: 0.5563 +2025-06-24 17:36:48,293 - pyskl - INFO - Epoch [44][600/1281] lr: 2.017e-02, eta: 15:16:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9962, loss_cls: 0.6497, loss: 0.6497 +2025-06-24 17:37:16,211 - pyskl - INFO - Epoch [44][700/1281] lr: 2.015e-02, eta: 15:15:38, time: 0.279, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9925, loss_cls: 0.5918, loss: 0.5918 +2025-06-24 17:38:07,330 - pyskl - INFO - Epoch [44][800/1281] lr: 2.014e-02, eta: 15:15:24, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9938, loss_cls: 0.6642, loss: 0.6642 +2025-06-24 17:38:37,899 - pyskl - INFO - Epoch [44][900/1281] lr: 2.012e-02, eta: 15:14:20, time: 0.306, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9956, loss_cls: 0.6038, loss: 0.6038 +2025-06-24 17:39:26,763 - pyskl - INFO - Epoch [44][1000/1281] lr: 2.010e-02, eta: 15:14:01, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9919, loss_cls: 0.6146, loss: 0.6146 +2025-06-24 17:40:15,486 - pyskl - INFO - Epoch [44][1100/1281] lr: 2.009e-02, eta: 15:13:41, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9919, loss_cls: 0.6475, loss: 0.6475 +2025-06-24 17:41:04,748 - pyskl - INFO - Epoch [44][1200/1281] lr: 2.007e-02, eta: 15:13:22, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9975, loss_cls: 0.6515, loss: 0.6515 +2025-06-24 17:41:45,437 - pyskl - INFO - Saving checkpoint at 44 epochs +2025-06-24 17:42:44,749 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:42:44,820 - pyskl - INFO - +top1_acc 0.8459 +top5_acc 0.9906 +2025-06-24 17:42:44,821 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:42:44,829 - pyskl - INFO - +mean_acc 0.8050 +2025-06-24 17:42:44,834 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_38.pth was removed +2025-06-24 17:42:45,060 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_44.pth. +2025-06-24 17:42:45,060 - pyskl - INFO - Best top1_acc is 0.8459 at 44 epoch. +2025-06-24 17:42:45,063 - pyskl - INFO - Epoch(val) [44][533] top1_acc: 0.8459, top5_acc: 0.9906, mean_class_accuracy: 0.8050 +2025-06-24 17:44:05,348 - pyskl - INFO - Epoch [45][100/1281] lr: 2.004e-02, eta: 15:12:26, time: 0.803, data_time: 0.196, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9956, loss_cls: 0.5963, loss: 0.5963 +2025-06-24 17:44:54,561 - pyskl - INFO - Epoch [45][200/1281] lr: 2.003e-02, eta: 15:12:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9956, loss_cls: 0.5867, loss: 0.5867 +2025-06-24 17:45:43,645 - pyskl - INFO - Epoch [45][300/1281] lr: 2.001e-02, eta: 15:11:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9925, loss_cls: 0.6508, loss: 0.6508 +2025-06-24 17:46:32,570 - pyskl - INFO - Epoch [45][400/1281] lr: 1.999e-02, eta: 15:11:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9925, loss_cls: 0.6015, loss: 0.6015 +2025-06-24 17:47:21,773 - pyskl - INFO - Epoch [45][500/1281] lr: 1.998e-02, eta: 15:11:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9981, loss_cls: 0.5960, loss: 0.5960 +2025-06-24 17:48:11,009 - pyskl - INFO - Epoch [45][600/1281] lr: 1.996e-02, eta: 15:10:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9931, loss_cls: 0.6839, loss: 0.6839 +2025-06-24 17:48:39,647 - pyskl - INFO - Epoch [45][700/1281] lr: 1.994e-02, eta: 15:09:40, time: 0.286, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9938, loss_cls: 0.6263, loss: 0.6263 +2025-06-24 17:49:28,774 - pyskl - INFO - Epoch [45][800/1281] lr: 1.993e-02, eta: 15:09:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9950, loss_cls: 0.6613, loss: 0.6613 +2025-06-24 17:49:59,519 - pyskl - INFO - Epoch [45][900/1281] lr: 1.991e-02, eta: 15:08:17, time: 0.307, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9938, loss_cls: 0.6269, loss: 0.6269 +2025-06-24 17:50:48,567 - pyskl - INFO - Epoch [45][1000/1281] lr: 1.989e-02, eta: 15:07:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9925, loss_cls: 0.6397, loss: 0.6397 +2025-06-24 17:51:37,964 - pyskl - INFO - Epoch [45][1100/1281] lr: 1.988e-02, eta: 15:07:38, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9912, loss_cls: 0.6457, loss: 0.6457 +2025-06-24 17:52:27,034 - pyskl - INFO - Epoch [45][1200/1281] lr: 1.986e-02, eta: 15:07:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9931, loss_cls: 0.6574, loss: 0.6574 +2025-06-24 17:53:07,108 - pyskl - INFO - Saving checkpoint at 45 epochs +2025-06-24 17:54:06,562 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:54:06,620 - pyskl - INFO - +top1_acc 0.8524 +top5_acc 0.9897 +2025-06-24 17:54:06,620 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:54:06,627 - pyskl - INFO - +mean_acc 0.8083 +2025-06-24 17:54:06,631 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_44.pth was removed +2025-06-24 17:54:06,798 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_45.pth. +2025-06-24 17:54:06,798 - pyskl - INFO - Best top1_acc is 0.8524 at 45 epoch. +2025-06-24 17:54:06,801 - pyskl - INFO - Epoch(val) [45][533] top1_acc: 0.8524, top5_acc: 0.9897, mean_class_accuracy: 0.8083 +2025-06-24 17:55:25,728 - pyskl - INFO - Epoch [46][100/1281] lr: 1.983e-02, eta: 15:06:18, time: 0.789, data_time: 0.194, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9975, loss_cls: 0.5396, loss: 0.5396 +2025-06-24 17:56:14,809 - pyskl - INFO - Epoch [46][200/1281] lr: 1.981e-02, eta: 15:05:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9950, loss_cls: 0.5765, loss: 0.5765 +2025-06-24 17:57:03,956 - pyskl - INFO - Epoch [46][300/1281] lr: 1.980e-02, eta: 15:05:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9962, loss_cls: 0.6001, loss: 0.6001 +2025-06-24 17:57:53,001 - pyskl - INFO - Epoch [46][400/1281] lr: 1.978e-02, eta: 15:05:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9919, loss_cls: 0.6455, loss: 0.6455 +2025-06-24 17:58:42,338 - pyskl - INFO - Epoch [46][500/1281] lr: 1.976e-02, eta: 15:04:56, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9906, loss_cls: 0.6323, loss: 0.6323 +2025-06-24 17:59:31,818 - pyskl - INFO - Epoch [46][600/1281] lr: 1.975e-02, eta: 15:04:36, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9950, loss_cls: 0.5902, loss: 0.5902 +2025-06-24 17:59:59,376 - pyskl - INFO - Epoch [46][700/1281] lr: 1.973e-02, eta: 15:03:26, time: 0.276, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9962, loss_cls: 0.6340, loss: 0.6340 +2025-06-24 18:00:50,518 - pyskl - INFO - Epoch [46][800/1281] lr: 1.971e-02, eta: 15:03:10, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9950, loss_cls: 0.5886, loss: 0.5886 +2025-06-24 18:01:22,253 - pyskl - INFO - Epoch [46][900/1281] lr: 1.970e-02, eta: 15:02:09, time: 0.317, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9944, loss_cls: 0.6315, loss: 0.6315 +2025-06-24 18:02:11,315 - pyskl - INFO - Epoch [46][1000/1281] lr: 1.968e-02, eta: 15:01:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9931, loss_cls: 0.6074, loss: 0.6074 +2025-06-24 18:03:00,308 - pyskl - INFO - Epoch [46][1100/1281] lr: 1.966e-02, eta: 15:01:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9931, loss_cls: 0.6573, loss: 0.6573 +2025-06-24 18:03:49,498 - pyskl - INFO - Epoch [46][1200/1281] lr: 1.965e-02, eta: 15:01:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9925, loss_cls: 0.6296, loss: 0.6296 +2025-06-24 18:04:29,839 - pyskl - INFO - Saving checkpoint at 46 epochs +2025-06-24 18:05:29,400 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:05:29,476 - pyskl - INFO - +top1_acc 0.8264 +top5_acc 0.9862 +2025-06-24 18:05:29,476 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:05:29,486 - pyskl - INFO - +mean_acc 0.7719 +2025-06-24 18:05:29,489 - pyskl - INFO - Epoch(val) [46][533] top1_acc: 0.8264, top5_acc: 0.9862, mean_class_accuracy: 0.7719 +2025-06-24 18:06:48,942 - pyskl - INFO - Epoch [47][100/1281] lr: 1.962e-02, eta: 15:00:06, time: 0.794, data_time: 0.193, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9944, loss_cls: 0.5362, loss: 0.5362 +2025-06-24 18:07:37,820 - pyskl - INFO - Epoch [47][200/1281] lr: 1.960e-02, eta: 14:59:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.5138, loss: 0.5138 +2025-06-24 18:08:26,583 - pyskl - INFO - Epoch [47][300/1281] lr: 1.958e-02, eta: 14:59:22, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9912, loss_cls: 0.6470, loss: 0.6470 +2025-06-24 18:09:15,512 - pyskl - INFO - Epoch [47][400/1281] lr: 1.957e-02, eta: 14:59:00, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9969, loss_cls: 0.5992, loss: 0.5992 +2025-06-24 18:10:04,810 - pyskl - INFO - Epoch [47][500/1281] lr: 1.955e-02, eta: 14:58:39, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9950, loss_cls: 0.6121, loss: 0.6121 +2025-06-24 18:10:54,092 - pyskl - INFO - Epoch [47][600/1281] lr: 1.953e-02, eta: 14:58:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9944, loss_cls: 0.5656, loss: 0.5656 +2025-06-24 18:11:23,187 - pyskl - INFO - Epoch [47][700/1281] lr: 1.952e-02, eta: 14:57:11, time: 0.291, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9975, loss_cls: 0.5333, loss: 0.5333 +2025-06-24 18:12:11,343 - pyskl - INFO - Epoch [47][800/1281] lr: 1.950e-02, eta: 14:56:47, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9944, loss_cls: 0.6136, loss: 0.6136 +2025-06-24 18:12:42,201 - pyskl - INFO - Epoch [47][900/1281] lr: 1.948e-02, eta: 14:55:45, time: 0.309, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9950, loss_cls: 0.6619, loss: 0.6619 +2025-06-24 18:13:31,321 - pyskl - INFO - Epoch [47][1000/1281] lr: 1.947e-02, eta: 14:55:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9950, loss_cls: 0.6361, loss: 0.6361 +2025-06-24 18:14:20,163 - pyskl - INFO - Epoch [47][1100/1281] lr: 1.945e-02, eta: 14:55:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9975, loss_cls: 0.6270, loss: 0.6270 +2025-06-24 18:15:09,250 - pyskl - INFO - Epoch [47][1200/1281] lr: 1.943e-02, eta: 14:54:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9956, loss_cls: 0.5621, loss: 0.5621 +2025-06-24 18:15:49,588 - pyskl - INFO - Saving checkpoint at 47 epochs +2025-06-24 18:16:48,791 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:16:48,854 - pyskl - INFO - +top1_acc 0.8490 +top5_acc 0.9883 +2025-06-24 18:16:48,854 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:16:48,862 - pyskl - INFO - +mean_acc 0.7949 +2025-06-24 18:16:48,864 - pyskl - INFO - Epoch(val) [47][533] top1_acc: 0.8490, top5_acc: 0.9883, mean_class_accuracy: 0.7949 +2025-06-24 18:18:07,977 - pyskl - INFO - Epoch [48][100/1281] lr: 1.940e-02, eta: 14:53:37, time: 0.791, data_time: 0.195, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9988, loss_cls: 0.5184, loss: 0.5184 +2025-06-24 18:18:57,113 - pyskl - INFO - Epoch [48][200/1281] lr: 1.938e-02, eta: 14:53:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9962, loss_cls: 0.5426, loss: 0.5426 +2025-06-24 18:19:46,408 - pyskl - INFO - Epoch [48][300/1281] lr: 1.937e-02, eta: 14:52:53, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9950, loss_cls: 0.5817, loss: 0.5817 +2025-06-24 18:20:35,358 - pyskl - INFO - Epoch [48][400/1281] lr: 1.935e-02, eta: 14:52:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9956, loss_cls: 0.5589, loss: 0.5589 +2025-06-24 18:21:24,490 - pyskl - INFO - Epoch [48][500/1281] lr: 1.933e-02, eta: 14:52:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9944, loss_cls: 0.5952, loss: 0.5952 +2025-06-24 18:22:13,687 - pyskl - INFO - Epoch [48][600/1281] lr: 1.932e-02, eta: 14:51:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9931, loss_cls: 0.6615, loss: 0.6615 +2025-06-24 18:22:41,799 - pyskl - INFO - Epoch [48][700/1281] lr: 1.930e-02, eta: 14:50:37, time: 0.281, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9975, loss_cls: 0.5700, loss: 0.5700 +2025-06-24 18:23:32,981 - pyskl - INFO - Epoch [48][800/1281] lr: 1.928e-02, eta: 14:50:19, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9956, loss_cls: 0.5699, loss: 0.5699 +2025-06-24 18:24:01,529 - pyskl - INFO - Epoch [48][900/1281] lr: 1.926e-02, eta: 14:49:12, time: 0.285, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9969, loss_cls: 0.5941, loss: 0.5941 +2025-06-24 18:24:50,701 - pyskl - INFO - Epoch [48][1000/1281] lr: 1.925e-02, eta: 14:48:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9969, loss_cls: 0.5775, loss: 0.5775 +2025-06-24 18:25:40,036 - pyskl - INFO - Epoch [48][1100/1281] lr: 1.923e-02, eta: 14:48:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9938, loss_cls: 0.6410, loss: 0.6410 +2025-06-24 18:26:29,384 - pyskl - INFO - Epoch [48][1200/1281] lr: 1.921e-02, eta: 14:48:05, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9919, loss_cls: 0.6374, loss: 0.6374 +2025-06-24 18:27:09,648 - pyskl - INFO - Saving checkpoint at 48 epochs +2025-06-24 18:28:08,726 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:28:08,784 - pyskl - INFO - +top1_acc 0.8339 +top5_acc 0.9842 +2025-06-24 18:28:08,784 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:28:08,791 - pyskl - INFO - +mean_acc 0.7760 +2025-06-24 18:28:08,793 - pyskl - INFO - Epoch(val) [48][533] top1_acc: 0.8339, top5_acc: 0.9842, mean_class_accuracy: 0.7760 +2025-06-24 18:29:28,251 - pyskl - INFO - Epoch [49][100/1281] lr: 1.918e-02, eta: 14:47:03, time: 0.795, data_time: 0.192, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9931, loss_cls: 0.5494, loss: 0.5494 +2025-06-24 18:30:17,234 - pyskl - INFO - Epoch [49][200/1281] lr: 1.916e-02, eta: 14:46:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9950, loss_cls: 0.5263, loss: 0.5263 +2025-06-24 18:31:06,133 - pyskl - INFO - Epoch [49][300/1281] lr: 1.915e-02, eta: 14:46:16, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9944, loss_cls: 0.5677, loss: 0.5677 +2025-06-24 18:31:55,235 - pyskl - INFO - Epoch [49][400/1281] lr: 1.913e-02, eta: 14:45:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9975, loss_cls: 0.5528, loss: 0.5528 +2025-06-24 18:32:44,367 - pyskl - INFO - Epoch [49][500/1281] lr: 1.911e-02, eta: 14:45:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9938, loss_cls: 0.5764, loss: 0.5764 +2025-06-24 18:33:33,604 - pyskl - INFO - Epoch [49][600/1281] lr: 1.909e-02, eta: 14:45:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9981, loss_cls: 0.5538, loss: 0.5538 +2025-06-24 18:34:03,797 - pyskl - INFO - Epoch [49][700/1281] lr: 1.908e-02, eta: 14:44:03, time: 0.302, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9962, loss_cls: 0.5957, loss: 0.5957 +2025-06-24 18:34:54,861 - pyskl - INFO - Epoch [49][800/1281] lr: 1.906e-02, eta: 14:43:44, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9931, loss_cls: 0.6203, loss: 0.6203 +2025-06-24 18:35:22,889 - pyskl - INFO - Epoch [49][900/1281] lr: 1.904e-02, eta: 14:42:36, time: 0.280, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9912, loss_cls: 0.6943, loss: 0.6943 +2025-06-24 18:36:12,154 - pyskl - INFO - Epoch [49][1000/1281] lr: 1.902e-02, eta: 14:42:13, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9962, loss_cls: 0.5891, loss: 0.5891 +2025-06-24 18:37:01,201 - pyskl - INFO - Epoch [49][1100/1281] lr: 1.901e-02, eta: 14:41:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9962, loss_cls: 0.5926, loss: 0.5926 +2025-06-24 18:37:50,367 - pyskl - INFO - Epoch [49][1200/1281] lr: 1.899e-02, eta: 14:41:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9919, loss_cls: 0.5884, loss: 0.5884 +2025-06-24 18:38:30,708 - pyskl - INFO - Saving checkpoint at 49 epochs +2025-06-24 18:39:29,720 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:39:29,789 - pyskl - INFO - +top1_acc 0.8290 +top5_acc 0.9835 +2025-06-24 18:39:29,789 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:39:29,797 - pyskl - INFO - +mean_acc 0.7731 +2025-06-24 18:39:29,799 - pyskl - INFO - Epoch(val) [49][533] top1_acc: 0.8290, top5_acc: 0.9835, mean_class_accuracy: 0.7731 +2025-06-24 18:40:49,745 - pyskl - INFO - Epoch [50][100/1281] lr: 1.896e-02, eta: 14:40:24, time: 0.799, data_time: 0.195, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9944, loss_cls: 0.5927, loss: 0.5927 +2025-06-24 18:41:38,738 - pyskl - INFO - Epoch [50][200/1281] lr: 1.894e-02, eta: 14:40:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9975, loss_cls: 0.5435, loss: 0.5435 +2025-06-24 18:42:27,765 - pyskl - INFO - Epoch [50][300/1281] lr: 1.892e-02, eta: 14:39:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9938, loss_cls: 0.5756, loss: 0.5756 +2025-06-24 18:43:16,722 - pyskl - INFO - Epoch [50][400/1281] lr: 1.891e-02, eta: 14:39:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9988, loss_cls: 0.5338, loss: 0.5338 +2025-06-24 18:44:05,997 - pyskl - INFO - Epoch [50][500/1281] lr: 1.889e-02, eta: 14:38:47, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9938, loss_cls: 0.5218, loss: 0.5218 +2025-06-24 18:44:54,989 - pyskl - INFO - Epoch [50][600/1281] lr: 1.887e-02, eta: 14:38:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9975, loss_cls: 0.5398, loss: 0.5398 +2025-06-24 18:45:23,898 - pyskl - INFO - Epoch [50][700/1281] lr: 1.885e-02, eta: 14:37:17, time: 0.289, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9956, loss_cls: 0.5628, loss: 0.5628 +2025-06-24 18:46:14,746 - pyskl - INFO - Epoch [50][800/1281] lr: 1.884e-02, eta: 14:36:57, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9950, loss_cls: 0.5835, loss: 0.5835 +2025-06-24 18:46:42,807 - pyskl - INFO - Epoch [50][900/1281] lr: 1.882e-02, eta: 14:35:50, time: 0.281, data_time: 0.001, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9950, loss_cls: 0.6000, loss: 0.6000 +2025-06-24 18:47:32,012 - pyskl - INFO - Epoch [50][1000/1281] lr: 1.880e-02, eta: 14:35:26, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9938, loss_cls: 0.6666, loss: 0.6666 +2025-06-24 18:48:21,069 - pyskl - INFO - Epoch [50][1100/1281] lr: 1.878e-02, eta: 14:35:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9950, loss_cls: 0.6236, loss: 0.6236 +2025-06-24 18:49:10,023 - pyskl - INFO - Epoch [50][1200/1281] lr: 1.876e-02, eta: 14:34:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9944, loss_cls: 0.6193, loss: 0.6193 +2025-06-24 18:49:50,563 - pyskl - INFO - Saving checkpoint at 50 epochs +2025-06-24 18:50:50,554 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:50:50,610 - pyskl - INFO - +top1_acc 0.8345 +top5_acc 0.9843 +2025-06-24 18:50:50,610 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:50:50,617 - pyskl - INFO - +mean_acc 0.7970 +2025-06-24 18:50:50,619 - pyskl - INFO - Epoch(val) [50][533] top1_acc: 0.8345, top5_acc: 0.9843, mean_class_accuracy: 0.7970 +2025-06-24 18:52:09,867 - pyskl - INFO - Epoch [51][100/1281] lr: 1.873e-02, eta: 14:33:32, time: 0.792, data_time: 0.193, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9950, loss_cls: 0.5828, loss: 0.5828 +2025-06-24 18:52:58,576 - pyskl - INFO - Epoch [51][200/1281] lr: 1.871e-02, eta: 14:33:07, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9975, loss_cls: 0.5933, loss: 0.5933 +2025-06-24 18:53:47,108 - pyskl - INFO - Epoch [51][300/1281] lr: 1.870e-02, eta: 14:32:41, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9981, loss_cls: 0.5452, loss: 0.5452 +2025-06-24 18:54:36,340 - pyskl - INFO - Epoch [51][400/1281] lr: 1.868e-02, eta: 14:32:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9944, loss_cls: 0.5395, loss: 0.5395 +2025-06-24 18:55:25,458 - pyskl - INFO - Epoch [51][500/1281] lr: 1.866e-02, eta: 14:31:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9975, loss_cls: 0.5787, loss: 0.5787 +2025-06-24 18:56:14,633 - pyskl - INFO - Epoch [51][600/1281] lr: 1.864e-02, eta: 14:31:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9975, loss_cls: 0.6139, loss: 0.6139 +2025-06-24 18:56:44,057 - pyskl - INFO - Epoch [51][700/1281] lr: 1.863e-02, eta: 14:30:23, time: 0.294, data_time: 0.001, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9938, loss_cls: 0.5914, loss: 0.5914 +2025-06-24 18:57:35,034 - pyskl - INFO - Epoch [51][800/1281] lr: 1.861e-02, eta: 14:30:01, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9944, loss_cls: 0.5483, loss: 0.5483 +2025-06-24 18:58:03,039 - pyskl - INFO - Epoch [51][900/1281] lr: 1.859e-02, eta: 14:28:55, time: 0.280, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9969, loss_cls: 0.5191, loss: 0.5191 +2025-06-24 18:58:52,305 - pyskl - INFO - Epoch [51][1000/1281] lr: 1.857e-02, eta: 14:28:30, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9962, loss_cls: 0.5483, loss: 0.5483 +2025-06-24 18:59:41,544 - pyskl - INFO - Epoch [51][1100/1281] lr: 1.855e-02, eta: 14:28:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9912, loss_cls: 0.6123, loss: 0.6123 +2025-06-24 19:00:30,997 - pyskl - INFO - Epoch [51][1200/1281] lr: 1.854e-02, eta: 14:27:40, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9975, loss_cls: 0.5479, loss: 0.5479 +2025-06-24 19:01:11,154 - pyskl - INFO - Saving checkpoint at 51 epochs +2025-06-24 19:02:10,396 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:02:10,463 - pyskl - INFO - +top1_acc 0.8325 +top5_acc 0.9891 +2025-06-24 19:02:10,463 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:02:10,471 - pyskl - INFO - +mean_acc 0.8085 +2025-06-24 19:02:10,473 - pyskl - INFO - Epoch(val) [51][533] top1_acc: 0.8325, top5_acc: 0.9891, mean_class_accuracy: 0.8085 +2025-06-24 19:03:30,769 - pyskl - INFO - Epoch [52][100/1281] lr: 1.850e-02, eta: 14:26:38, time: 0.803, data_time: 0.190, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9969, loss_cls: 0.5657, loss: 0.5657 +2025-06-24 19:04:19,785 - pyskl - INFO - Epoch [52][200/1281] lr: 1.849e-02, eta: 14:26:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9981, loss_cls: 0.5430, loss: 0.5430 +2025-06-24 19:05:08,797 - pyskl - INFO - Epoch [52][300/1281] lr: 1.847e-02, eta: 14:25:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9956, loss_cls: 0.5124, loss: 0.5124 +2025-06-24 19:05:57,871 - pyskl - INFO - Epoch [52][400/1281] lr: 1.845e-02, eta: 14:25:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9956, loss_cls: 0.5916, loss: 0.5916 +2025-06-24 19:06:47,384 - pyskl - INFO - Epoch [52][500/1281] lr: 1.843e-02, eta: 14:24:56, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9969, loss_cls: 0.5396, loss: 0.5396 +2025-06-24 19:07:36,679 - pyskl - INFO - Epoch [52][600/1281] lr: 1.841e-02, eta: 14:24:31, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9919, loss_cls: 0.6305, loss: 0.6305 +2025-06-24 19:08:05,660 - pyskl - INFO - Epoch [52][700/1281] lr: 1.840e-02, eta: 14:23:26, time: 0.290, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9931, loss_cls: 0.5997, loss: 0.5997 +2025-06-24 19:08:56,640 - pyskl - INFO - Epoch [52][800/1281] lr: 1.838e-02, eta: 14:23:04, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9969, loss_cls: 0.5167, loss: 0.5167 +2025-06-24 19:09:23,591 - pyskl - INFO - Epoch [52][900/1281] lr: 1.836e-02, eta: 14:21:56, time: 0.269, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9931, loss_cls: 0.6149, loss: 0.6149 +2025-06-24 19:10:12,716 - pyskl - INFO - Epoch [52][1000/1281] lr: 1.834e-02, eta: 14:21:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9931, loss_cls: 0.6271, loss: 0.6271 +2025-06-24 19:11:01,994 - pyskl - INFO - Epoch [52][1100/1281] lr: 1.832e-02, eta: 14:21:05, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9938, loss_cls: 0.5916, loss: 0.5916 +2025-06-24 19:11:51,515 - pyskl - INFO - Epoch [52][1200/1281] lr: 1.831e-02, eta: 14:20:40, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9894, loss_cls: 0.6727, loss: 0.6727 +2025-06-24 19:12:31,798 - pyskl - INFO - Saving checkpoint at 52 epochs +2025-06-24 19:13:30,089 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:13:30,147 - pyskl - INFO - +top1_acc 0.8388 +top5_acc 0.9872 +2025-06-24 19:13:30,147 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:13:30,154 - pyskl - INFO - +mean_acc 0.7815 +2025-06-24 19:13:30,155 - pyskl - INFO - Epoch(val) [52][533] top1_acc: 0.8388, top5_acc: 0.9872, mean_class_accuracy: 0.7815 +2025-06-24 19:14:49,641 - pyskl - INFO - Epoch [53][100/1281] lr: 1.827e-02, eta: 14:19:35, time: 0.795, data_time: 0.189, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9950, loss_cls: 0.5528, loss: 0.5528 +2025-06-24 19:15:39,044 - pyskl - INFO - Epoch [53][200/1281] lr: 1.826e-02, eta: 14:19:09, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9956, loss_cls: 0.5356, loss: 0.5356 +2025-06-24 19:16:28,120 - pyskl - INFO - Epoch [53][300/1281] lr: 1.824e-02, eta: 14:18:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9962, loss_cls: 0.5013, loss: 0.5013 +2025-06-24 19:17:17,173 - pyskl - INFO - Epoch [53][400/1281] lr: 1.822e-02, eta: 14:18:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9981, loss_cls: 0.5397, loss: 0.5397 +2025-06-24 19:18:06,300 - pyskl - INFO - Epoch [53][500/1281] lr: 1.820e-02, eta: 14:17:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9962, loss_cls: 0.5480, loss: 0.5480 +2025-06-24 19:18:55,508 - pyskl - INFO - Epoch [53][600/1281] lr: 1.818e-02, eta: 14:17:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9962, loss_cls: 0.5484, loss: 0.5484 +2025-06-24 19:19:27,653 - pyskl - INFO - Epoch [53][700/1281] lr: 1.816e-02, eta: 14:16:26, time: 0.321, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9975, loss_cls: 0.5755, loss: 0.5755 +2025-06-24 19:20:18,631 - pyskl - INFO - Epoch [53][800/1281] lr: 1.815e-02, eta: 14:16:03, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9969, loss_cls: 0.6134, loss: 0.6134 +2025-06-24 19:20:43,891 - pyskl - INFO - Epoch [53][900/1281] lr: 1.813e-02, eta: 14:14:53, time: 0.253, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9944, loss_cls: 0.6180, loss: 0.6180 +2025-06-24 19:21:31,619 - pyskl - INFO - Epoch [53][1000/1281] lr: 1.811e-02, eta: 14:14:24, time: 0.477, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9944, loss_cls: 0.6031, loss: 0.6031 +2025-06-24 19:22:20,702 - pyskl - INFO - Epoch [53][1100/1281] lr: 1.809e-02, eta: 14:13:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9988, loss_cls: 0.5435, loss: 0.5435 +2025-06-24 19:23:10,110 - pyskl - INFO - Epoch [53][1200/1281] lr: 1.807e-02, eta: 14:13:31, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9944, loss_cls: 0.5918, loss: 0.5918 +2025-06-24 19:23:50,922 - pyskl - INFO - Saving checkpoint at 53 epochs +2025-06-24 19:24:50,233 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:24:50,289 - pyskl - INFO - +top1_acc 0.8271 +top5_acc 0.9857 +2025-06-24 19:24:50,289 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:24:50,297 - pyskl - INFO - +mean_acc 0.7827 +2025-06-24 19:24:50,299 - pyskl - INFO - Epoch(val) [53][533] top1_acc: 0.8271, top5_acc: 0.9857, mean_class_accuracy: 0.7827 +2025-06-24 19:26:11,211 - pyskl - INFO - Epoch [54][100/1281] lr: 1.804e-02, eta: 14:12:28, time: 0.809, data_time: 0.197, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9944, loss_cls: 0.5949, loss: 0.5949 +2025-06-24 19:27:00,350 - pyskl - INFO - Epoch [54][200/1281] lr: 1.802e-02, eta: 14:12:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9975, loss_cls: 0.4757, loss: 0.4757 +2025-06-24 19:27:49,509 - pyskl - INFO - Epoch [54][300/1281] lr: 1.800e-02, eta: 14:11:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9969, loss_cls: 0.5047, loss: 0.5047 +2025-06-24 19:28:38,680 - pyskl - INFO - Epoch [54][400/1281] lr: 1.798e-02, eta: 14:11:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9956, loss_cls: 0.5211, loss: 0.5211 +2025-06-24 19:29:28,057 - pyskl - INFO - Epoch [54][500/1281] lr: 1.797e-02, eta: 14:10:41, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9950, loss_cls: 0.5221, loss: 0.5221 +2025-06-24 19:30:17,364 - pyskl - INFO - Epoch [54][600/1281] lr: 1.795e-02, eta: 14:10:15, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9956, loss_cls: 0.5561, loss: 0.5561 +2025-06-24 19:30:49,693 - pyskl - INFO - Epoch [54][700/1281] lr: 1.793e-02, eta: 14:09:17, time: 0.323, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9944, loss_cls: 0.5905, loss: 0.5905 +2025-06-24 19:31:40,639 - pyskl - INFO - Epoch [54][800/1281] lr: 1.791e-02, eta: 14:08:53, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9962, loss_cls: 0.5094, loss: 0.5094 +2025-06-24 19:32:05,512 - pyskl - INFO - Epoch [54][900/1281] lr: 1.789e-02, eta: 14:07:43, time: 0.249, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9956, loss_cls: 0.6694, loss: 0.6694 +2025-06-24 19:32:52,860 - pyskl - INFO - Epoch [54][1000/1281] lr: 1.787e-02, eta: 14:07:12, time: 0.473, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9969, loss_cls: 0.5563, loss: 0.5563 +2025-06-24 19:33:41,841 - pyskl - INFO - Epoch [54][1100/1281] lr: 1.786e-02, eta: 14:06:45, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9944, loss_cls: 0.5665, loss: 0.5665 +2025-06-24 19:34:30,825 - pyskl - INFO - Epoch [54][1200/1281] lr: 1.784e-02, eta: 14:06:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9956, loss_cls: 0.5794, loss: 0.5794 +2025-06-24 19:35:10,860 - pyskl - INFO - Saving checkpoint at 54 epochs +2025-06-24 19:36:10,326 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:36:10,380 - pyskl - INFO - +top1_acc 0.8157 +top5_acc 0.9864 +2025-06-24 19:36:10,380 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:36:10,388 - pyskl - INFO - +mean_acc 0.7609 +2025-06-24 19:36:10,390 - pyskl - INFO - Epoch(val) [54][533] top1_acc: 0.8157, top5_acc: 0.9864, mean_class_accuracy: 0.7609 +2025-06-24 19:37:29,943 - pyskl - INFO - Epoch [55][100/1281] lr: 1.780e-02, eta: 14:05:11, time: 0.795, data_time: 0.192, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9956, loss_cls: 0.4853, loss: 0.4853 +2025-06-24 19:38:19,150 - pyskl - INFO - Epoch [55][200/1281] lr: 1.779e-02, eta: 14:04:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9969, loss_cls: 0.5183, loss: 0.5183 +2025-06-24 19:39:08,131 - pyskl - INFO - Epoch [55][300/1281] lr: 1.777e-02, eta: 14:04:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9931, loss_cls: 0.6075, loss: 0.6075 +2025-06-24 19:39:57,020 - pyskl - INFO - Epoch [55][400/1281] lr: 1.775e-02, eta: 14:03:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9950, loss_cls: 0.5375, loss: 0.5375 +2025-06-24 19:40:45,709 - pyskl - INFO - Epoch [55][500/1281] lr: 1.773e-02, eta: 14:03:20, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9962, loss_cls: 0.5352, loss: 0.5352 +2025-06-24 19:41:35,028 - pyskl - INFO - Epoch [55][600/1281] lr: 1.771e-02, eta: 14:02:53, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9925, loss_cls: 0.5892, loss: 0.5892 +2025-06-24 19:42:09,245 - pyskl - INFO - Epoch [55][700/1281] lr: 1.769e-02, eta: 14:01:59, time: 0.342, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9975, loss_cls: 0.5812, loss: 0.5812 +2025-06-24 19:43:00,332 - pyskl - INFO - Epoch [55][800/1281] lr: 1.767e-02, eta: 14:01:35, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9969, loss_cls: 0.5796, loss: 0.5796 +2025-06-24 19:43:24,715 - pyskl - INFO - Epoch [55][900/1281] lr: 1.766e-02, eta: 14:00:24, time: 0.244, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9925, loss_cls: 0.5848, loss: 0.5848 +2025-06-24 19:44:11,873 - pyskl - INFO - Epoch [55][1000/1281] lr: 1.764e-02, eta: 13:59:53, time: 0.472, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9956, loss_cls: 0.5167, loss: 0.5167 +2025-06-24 19:45:00,836 - pyskl - INFO - Epoch [55][1100/1281] lr: 1.762e-02, eta: 13:59:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9950, loss_cls: 0.5394, loss: 0.5394 +2025-06-24 19:45:50,515 - pyskl - INFO - Epoch [55][1200/1281] lr: 1.760e-02, eta: 13:58:58, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9969, loss_cls: 0.5503, loss: 0.5503 +2025-06-24 19:46:31,023 - pyskl - INFO - Saving checkpoint at 55 epochs +2025-06-24 19:47:30,878 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:47:30,939 - pyskl - INFO - +top1_acc 0.8427 +top5_acc 0.9878 +2025-06-24 19:47:30,939 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:47:30,946 - pyskl - INFO - +mean_acc 0.7919 +2025-06-24 19:47:30,947 - pyskl - INFO - Epoch(val) [55][533] top1_acc: 0.8427, top5_acc: 0.9878, mean_class_accuracy: 0.7919 +2025-06-24 19:48:50,586 - pyskl - INFO - Epoch [56][100/1281] lr: 1.757e-02, eta: 13:57:52, time: 0.796, data_time: 0.193, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9981, loss_cls: 0.4768, loss: 0.4768 +2025-06-24 19:49:39,657 - pyskl - INFO - Epoch [56][200/1281] lr: 1.755e-02, eta: 13:57:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9969, loss_cls: 0.5370, loss: 0.5370 +2025-06-24 19:50:28,775 - pyskl - INFO - Epoch [56][300/1281] lr: 1.753e-02, eta: 13:56:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9981, loss_cls: 0.5286, loss: 0.5286 +2025-06-24 19:51:17,777 - pyskl - INFO - Epoch [56][400/1281] lr: 1.751e-02, eta: 13:56:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9938, loss_cls: 0.5940, loss: 0.5940 +2025-06-24 19:52:06,464 - pyskl - INFO - Epoch [56][500/1281] lr: 1.749e-02, eta: 13:55:58, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9975, loss_cls: 0.5477, loss: 0.5477 +2025-06-24 19:52:55,490 - pyskl - INFO - Epoch [56][600/1281] lr: 1.747e-02, eta: 13:55:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9956, loss_cls: 0.5295, loss: 0.5295 +2025-06-24 19:53:30,867 - pyskl - INFO - Epoch [56][700/1281] lr: 1.745e-02, eta: 13:54:38, time: 0.354, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9975, loss_cls: 0.5095, loss: 0.5095 +2025-06-24 19:54:21,816 - pyskl - INFO - Epoch [56][800/1281] lr: 1.743e-02, eta: 13:54:13, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9950, loss_cls: 0.5930, loss: 0.5930 +2025-06-24 19:54:46,734 - pyskl - INFO - Epoch [56][900/1281] lr: 1.742e-02, eta: 13:53:04, time: 0.249, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9956, loss_cls: 0.5390, loss: 0.5390 +2025-06-24 19:55:33,449 - pyskl - INFO - Epoch [56][1000/1281] lr: 1.740e-02, eta: 13:52:31, time: 0.467, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9956, loss_cls: 0.5627, loss: 0.5627 +2025-06-24 19:56:22,414 - pyskl - INFO - Epoch [56][1100/1281] lr: 1.738e-02, eta: 13:52:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9969, loss_cls: 0.5847, loss: 0.5847 +2025-06-24 19:57:11,508 - pyskl - INFO - Epoch [56][1200/1281] lr: 1.736e-02, eta: 13:51:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9956, loss_cls: 0.5255, loss: 0.5255 +2025-06-24 19:57:51,698 - pyskl - INFO - Saving checkpoint at 56 epochs +2025-06-24 19:58:51,007 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:58:51,068 - pyskl - INFO - +top1_acc 0.8579 +top5_acc 0.9899 +2025-06-24 19:58:51,068 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:58:51,077 - pyskl - INFO - +mean_acc 0.8194 +2025-06-24 19:58:51,082 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_45.pth was removed +2025-06-24 19:58:51,280 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_56.pth. +2025-06-24 19:58:51,281 - pyskl - INFO - Best top1_acc is 0.8579 at 56 epoch. +2025-06-24 19:58:51,284 - pyskl - INFO - Epoch(val) [56][533] top1_acc: 0.8579, top5_acc: 0.9899, mean_class_accuracy: 0.8194 +2025-06-24 20:00:11,787 - pyskl - INFO - Epoch [57][100/1281] lr: 1.733e-02, eta: 13:50:29, time: 0.805, data_time: 0.190, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9969, loss_cls: 0.5345, loss: 0.5345 +2025-06-24 20:01:00,558 - pyskl - INFO - Epoch [57][200/1281] lr: 1.731e-02, eta: 13:50:00, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9956, loss_cls: 0.5056, loss: 0.5056 +2025-06-24 20:01:49,596 - pyskl - INFO - Epoch [57][300/1281] lr: 1.729e-02, eta: 13:49:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9962, loss_cls: 0.5298, loss: 0.5298 +2025-06-24 20:02:38,534 - pyskl - INFO - Epoch [57][400/1281] lr: 1.727e-02, eta: 13:49:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9956, loss_cls: 0.4843, loss: 0.4843 +2025-06-24 20:03:27,899 - pyskl - INFO - Epoch [57][500/1281] lr: 1.725e-02, eta: 13:48:34, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9962, loss_cls: 0.5657, loss: 0.5657 +2025-06-24 20:04:16,644 - pyskl - INFO - Epoch [57][600/1281] lr: 1.723e-02, eta: 13:48:04, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9925, loss_cls: 0.5520, loss: 0.5520 +2025-06-24 20:04:50,852 - pyskl - INFO - Epoch [57][700/1281] lr: 1.721e-02, eta: 13:47:11, time: 0.342, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9950, loss_cls: 0.5336, loss: 0.5336 +2025-06-24 20:05:41,728 - pyskl - INFO - Epoch [57][800/1281] lr: 1.719e-02, eta: 13:46:45, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9944, loss_cls: 0.5170, loss: 0.5170 +2025-06-24 20:06:06,357 - pyskl - INFO - Epoch [57][900/1281] lr: 1.717e-02, eta: 13:45:36, time: 0.246, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9938, loss_cls: 0.5697, loss: 0.5697 +2025-06-24 20:06:52,198 - pyskl - INFO - Epoch [57][1000/1281] lr: 1.716e-02, eta: 13:45:01, time: 0.458, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9962, loss_cls: 0.5543, loss: 0.5543 +2025-06-24 20:07:41,336 - pyskl - INFO - Epoch [57][1100/1281] lr: 1.714e-02, eta: 13:44:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9956, loss_cls: 0.5301, loss: 0.5301 +2025-06-24 20:08:30,369 - pyskl - INFO - Epoch [57][1200/1281] lr: 1.712e-02, eta: 13:44:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9950, loss_cls: 0.5328, loss: 0.5328 +2025-06-24 20:09:10,420 - pyskl - INFO - Saving checkpoint at 57 epochs +2025-06-24 20:10:09,650 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:10:09,705 - pyskl - INFO - +top1_acc 0.8490 +top5_acc 0.9879 +2025-06-24 20:10:09,706 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:10:09,713 - pyskl - INFO - +mean_acc 0.7968 +2025-06-24 20:10:09,715 - pyskl - INFO - Epoch(val) [57][533] top1_acc: 0.8490, top5_acc: 0.9879, mean_class_accuracy: 0.7968 +2025-06-24 20:11:30,046 - pyskl - INFO - Epoch [58][100/1281] lr: 1.708e-02, eta: 13:42:57, time: 0.803, data_time: 0.192, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9962, loss_cls: 0.5500, loss: 0.5500 +2025-06-24 20:12:19,391 - pyskl - INFO - Epoch [58][200/1281] lr: 1.706e-02, eta: 13:42:28, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9981, loss_cls: 0.5142, loss: 0.5142 +2025-06-24 20:13:08,573 - pyskl - INFO - Epoch [58][300/1281] lr: 1.704e-02, eta: 13:41:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9962, loss_cls: 0.4922, loss: 0.4922 +2025-06-24 20:13:57,877 - pyskl - INFO - Epoch [58][400/1281] lr: 1.703e-02, eta: 13:41:30, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9969, loss_cls: 0.4628, loss: 0.4628 +2025-06-24 20:14:47,126 - pyskl - INFO - Epoch [58][500/1281] lr: 1.701e-02, eta: 13:41:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9988, loss_cls: 0.4891, loss: 0.4891 +2025-06-24 20:15:36,199 - pyskl - INFO - Epoch [58][600/1281] lr: 1.699e-02, eta: 13:40:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9956, loss_cls: 0.5243, loss: 0.5243 +2025-06-24 20:16:12,144 - pyskl - INFO - Epoch [58][700/1281] lr: 1.697e-02, eta: 13:39:41, time: 0.359, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9950, loss_cls: 0.5518, loss: 0.5518 +2025-06-24 20:17:03,087 - pyskl - INFO - Epoch [58][800/1281] lr: 1.695e-02, eta: 13:39:15, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9944, loss_cls: 0.5121, loss: 0.5121 +2025-06-24 20:17:27,380 - pyskl - INFO - Epoch [58][900/1281] lr: 1.693e-02, eta: 13:38:06, time: 0.243, data_time: 0.001, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9919, loss_cls: 0.5879, loss: 0.5879 +2025-06-24 20:18:13,458 - pyskl - INFO - Epoch [58][1000/1281] lr: 1.691e-02, eta: 13:37:32, time: 0.461, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9988, loss_cls: 0.5145, loss: 0.5145 +2025-06-24 20:19:02,686 - pyskl - INFO - Epoch [58][1100/1281] lr: 1.689e-02, eta: 13:37:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9969, loss_cls: 0.5499, loss: 0.5499 +2025-06-24 20:19:51,786 - pyskl - INFO - Epoch [58][1200/1281] lr: 1.687e-02, eta: 13:36:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9969, loss_cls: 0.5190, loss: 0.5190 +2025-06-24 20:20:32,073 - pyskl - INFO - Saving checkpoint at 58 epochs +2025-06-24 20:21:31,500 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:21:31,556 - pyskl - INFO - +top1_acc 0.8583 +top5_acc 0.9877 +2025-06-24 20:21:31,556 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:21:31,563 - pyskl - INFO - +mean_acc 0.8218 +2025-06-24 20:21:31,567 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_56.pth was removed +2025-06-24 20:21:31,739 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_58.pth. +2025-06-24 20:21:31,739 - pyskl - INFO - Best top1_acc is 0.8583 at 58 epoch. +2025-06-24 20:21:31,741 - pyskl - INFO - Epoch(val) [58][533] top1_acc: 0.8583, top5_acc: 0.9877, mean_class_accuracy: 0.8218 +2025-06-24 20:22:51,132 - pyskl - INFO - Epoch [59][100/1281] lr: 1.684e-02, eta: 13:35:24, time: 0.794, data_time: 0.195, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9988, loss_cls: 0.4481, loss: 0.4481 +2025-06-24 20:23:40,336 - pyskl - INFO - Epoch [59][200/1281] lr: 1.682e-02, eta: 13:34:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9981, loss_cls: 0.5078, loss: 0.5078 +2025-06-24 20:24:29,475 - pyskl - INFO - Epoch [59][300/1281] lr: 1.680e-02, eta: 13:34:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9988, loss_cls: 0.4723, loss: 0.4723 +2025-06-24 20:25:18,613 - pyskl - INFO - Epoch [59][400/1281] lr: 1.678e-02, eta: 13:33:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9975, loss_cls: 0.5013, loss: 0.5013 +2025-06-24 20:26:07,574 - pyskl - INFO - Epoch [59][500/1281] lr: 1.676e-02, eta: 13:33:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9981, loss_cls: 0.5335, loss: 0.5335 +2025-06-24 20:26:56,612 - pyskl - INFO - Epoch [59][600/1281] lr: 1.674e-02, eta: 13:32:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9944, loss_cls: 0.5323, loss: 0.5323 +2025-06-24 20:27:33,279 - pyskl - INFO - Epoch [59][700/1281] lr: 1.672e-02, eta: 13:32:06, time: 0.367, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9962, loss_cls: 0.5484, loss: 0.5484 +2025-06-24 20:28:24,369 - pyskl - INFO - Epoch [59][800/1281] lr: 1.670e-02, eta: 13:31:39, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9969, loss_cls: 0.5786, loss: 0.5786 +2025-06-24 20:28:47,842 - pyskl - INFO - Epoch [59][900/1281] lr: 1.668e-02, eta: 13:30:30, time: 0.235, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9956, loss_cls: 0.4933, loss: 0.4933 +2025-06-24 20:29:31,931 - pyskl - INFO - Epoch [59][1000/1281] lr: 1.667e-02, eta: 13:29:52, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9919, loss_cls: 0.6106, loss: 0.6106 +2025-06-24 20:30:21,130 - pyskl - INFO - Epoch [59][1100/1281] lr: 1.665e-02, eta: 13:29:22, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9962, loss_cls: 0.5480, loss: 0.5480 +2025-06-24 20:31:09,681 - pyskl - INFO - Epoch [59][1200/1281] lr: 1.663e-02, eta: 13:28:51, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9969, loss_cls: 0.5701, loss: 0.5701 +2025-06-24 20:31:50,440 - pyskl - INFO - Saving checkpoint at 59 epochs +2025-06-24 20:32:50,089 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:32:50,149 - pyskl - INFO - +top1_acc 0.8386 +top5_acc 0.9858 +2025-06-24 20:32:50,149 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:32:50,156 - pyskl - INFO - +mean_acc 0.8095 +2025-06-24 20:32:50,158 - pyskl - INFO - Epoch(val) [59][533] top1_acc: 0.8386, top5_acc: 0.9858, mean_class_accuracy: 0.8095 +2025-06-24 20:34:11,410 - pyskl - INFO - Epoch [60][100/1281] lr: 1.659e-02, eta: 13:27:45, time: 0.812, data_time: 0.193, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9988, loss_cls: 0.4896, loss: 0.4896 +2025-06-24 20:35:00,670 - pyskl - INFO - Epoch [60][200/1281] lr: 1.657e-02, eta: 13:27:15, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9931, loss_cls: 0.5044, loss: 0.5044 +2025-06-24 20:35:49,711 - pyskl - INFO - Epoch [60][300/1281] lr: 1.655e-02, eta: 13:26:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9956, loss_cls: 0.4988, loss: 0.4988 +2025-06-24 20:36:38,830 - pyskl - INFO - Epoch [60][400/1281] lr: 1.653e-02, eta: 13:26:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9981, loss_cls: 0.5035, loss: 0.5035 +2025-06-24 20:37:27,559 - pyskl - INFO - Epoch [60][500/1281] lr: 1.651e-02, eta: 13:25:44, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9962, loss_cls: 0.4818, loss: 0.4818 +2025-06-24 20:38:16,704 - pyskl - INFO - Epoch [60][600/1281] lr: 1.650e-02, eta: 13:25:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9975, loss_cls: 0.4522, loss: 0.4522 +2025-06-24 20:38:55,250 - pyskl - INFO - Epoch [60][700/1281] lr: 1.648e-02, eta: 13:24:27, time: 0.385, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9969, loss_cls: 0.5028, loss: 0.5028 +2025-06-24 20:39:46,403 - pyskl - INFO - Epoch [60][800/1281] lr: 1.646e-02, eta: 13:24:00, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9956, loss_cls: 0.5161, loss: 0.5161 +2025-06-24 20:40:09,809 - pyskl - INFO - Epoch [60][900/1281] lr: 1.644e-02, eta: 13:22:51, time: 0.234, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9944, loss_cls: 0.5111, loss: 0.5111 +2025-06-24 20:40:53,068 - pyskl - INFO - Epoch [60][1000/1281] lr: 1.642e-02, eta: 13:22:12, time: 0.433, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9981, loss_cls: 0.5132, loss: 0.5132 +2025-06-24 20:41:42,079 - pyskl - INFO - Epoch [60][1100/1281] lr: 1.640e-02, eta: 13:21:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9950, loss_cls: 0.5091, loss: 0.5091 +2025-06-24 20:42:31,112 - pyskl - INFO - Epoch [60][1200/1281] lr: 1.638e-02, eta: 13:21:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9956, loss_cls: 0.5485, loss: 0.5485 +2025-06-24 20:43:11,248 - pyskl - INFO - Saving checkpoint at 60 epochs +2025-06-24 20:44:10,472 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:44:10,530 - pyskl - INFO - +top1_acc 0.8537 +top5_acc 0.9865 +2025-06-24 20:44:10,530 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:44:10,538 - pyskl - INFO - +mean_acc 0.7989 +2025-06-24 20:44:10,540 - pyskl - INFO - Epoch(val) [60][533] top1_acc: 0.8537, top5_acc: 0.9865, mean_class_accuracy: 0.7989 +2025-06-24 20:45:28,781 - pyskl - INFO - Epoch [61][100/1281] lr: 1.634e-02, eta: 13:19:59, time: 0.782, data_time: 0.193, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9969, loss_cls: 0.5074, loss: 0.5074 +2025-06-24 20:46:17,623 - pyskl - INFO - Epoch [61][200/1281] lr: 1.632e-02, eta: 13:19:28, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9981, loss_cls: 0.4980, loss: 0.4980 +2025-06-24 20:47:06,552 - pyskl - INFO - Epoch [61][300/1281] lr: 1.630e-02, eta: 13:18:57, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9988, loss_cls: 0.4599, loss: 0.4599 +2025-06-24 20:47:55,490 - pyskl - INFO - Epoch [61][400/1281] lr: 1.629e-02, eta: 13:18:27, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9981, loss_cls: 0.4577, loss: 0.4577 +2025-06-24 20:48:44,517 - pyskl - INFO - Epoch [61][500/1281] lr: 1.627e-02, eta: 13:17:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9912, loss_cls: 0.5301, loss: 0.5301 +2025-06-24 20:49:33,369 - pyskl - INFO - Epoch [61][600/1281] lr: 1.625e-02, eta: 13:17:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9988, loss_cls: 0.5182, loss: 0.5182 +2025-06-24 20:50:14,916 - pyskl - INFO - Epoch [61][700/1281] lr: 1.623e-02, eta: 13:16:43, time: 0.415, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9962, loss_cls: 0.5057, loss: 0.5057 +2025-06-24 20:51:01,610 - pyskl - INFO - Epoch [61][800/1281] lr: 1.621e-02, eta: 13:16:08, time: 0.467, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9975, loss_cls: 0.4894, loss: 0.4894 +2025-06-24 20:51:28,365 - pyskl - INFO - Epoch [61][900/1281] lr: 1.619e-02, eta: 13:15:04, time: 0.268, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9944, loss_cls: 0.5564, loss: 0.5564 +2025-06-24 20:52:09,842 - pyskl - INFO - Epoch [61][1000/1281] lr: 1.617e-02, eta: 13:14:22, time: 0.415, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9969, loss_cls: 0.4997, loss: 0.4997 +2025-06-24 20:52:58,923 - pyskl - INFO - Epoch [61][1100/1281] lr: 1.615e-02, eta: 13:13:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9969, loss_cls: 0.5158, loss: 0.5158 +2025-06-24 20:53:47,906 - pyskl - INFO - Epoch [61][1200/1281] lr: 1.613e-02, eta: 13:13:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9975, loss_cls: 0.5299, loss: 0.5299 +2025-06-24 20:54:28,444 - pyskl - INFO - Saving checkpoint at 61 epochs +2025-06-24 20:55:28,250 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:55:28,307 - pyskl - INFO - +top1_acc 0.8406 +top5_acc 0.9857 +2025-06-24 20:55:28,307 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:55:28,314 - pyskl - INFO - +mean_acc 0.7917 +2025-06-24 20:55:28,317 - pyskl - INFO - Epoch(val) [61][533] top1_acc: 0.8406, top5_acc: 0.9857, mean_class_accuracy: 0.7917 +2025-06-24 20:56:48,152 - pyskl - INFO - Epoch [62][100/1281] lr: 1.609e-02, eta: 13:12:11, time: 0.798, data_time: 0.187, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9962, loss_cls: 0.5362, loss: 0.5362 +2025-06-24 20:57:37,156 - pyskl - INFO - Epoch [62][200/1281] lr: 1.607e-02, eta: 13:11:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9988, loss_cls: 0.4974, loss: 0.4974 +2025-06-24 20:58:26,282 - pyskl - INFO - Epoch [62][300/1281] lr: 1.605e-02, eta: 13:11:09, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9988, loss_cls: 0.4613, loss: 0.4613 +2025-06-24 20:59:15,708 - pyskl - INFO - Epoch [62][400/1281] lr: 1.603e-02, eta: 13:10:38, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9969, loss_cls: 0.5053, loss: 0.5053 +2025-06-24 21:00:04,914 - pyskl - INFO - Epoch [62][500/1281] lr: 1.602e-02, eta: 13:10:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9969, loss_cls: 0.4779, loss: 0.4779 +2025-06-24 21:00:54,384 - pyskl - INFO - Epoch [62][600/1281] lr: 1.600e-02, eta: 13:09:37, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9969, loss_cls: 0.4210, loss: 0.4210 +2025-06-24 21:01:36,484 - pyskl - INFO - Epoch [62][700/1281] lr: 1.598e-02, eta: 13:08:55, time: 0.421, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9969, loss_cls: 0.4551, loss: 0.4551 +2025-06-24 21:02:22,364 - pyskl - INFO - Epoch [62][800/1281] lr: 1.596e-02, eta: 13:08:20, time: 0.459, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9975, loss_cls: 0.5065, loss: 0.5065 +2025-06-24 21:02:49,815 - pyskl - INFO - Epoch [62][900/1281] lr: 1.594e-02, eta: 13:07:17, time: 0.275, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9975, loss_cls: 0.4515, loss: 0.4515 +2025-06-24 21:03:32,019 - pyskl - INFO - Epoch [62][1000/1281] lr: 1.592e-02, eta: 13:06:36, time: 0.422, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9969, loss_cls: 0.4845, loss: 0.4845 +2025-06-24 21:04:21,243 - pyskl - INFO - Epoch [62][1100/1281] lr: 1.590e-02, eta: 13:06:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9962, loss_cls: 0.5078, loss: 0.5078 +2025-06-24 21:05:10,271 - pyskl - INFO - Epoch [62][1200/1281] lr: 1.588e-02, eta: 13:05:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9988, loss_cls: 0.4760, loss: 0.4760 +2025-06-24 21:05:50,829 - pyskl - INFO - Saving checkpoint at 62 epochs +2025-06-24 21:06:49,083 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:06:49,140 - pyskl - INFO - +top1_acc 0.8366 +top5_acc 0.9844 +2025-06-24 21:06:49,140 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:06:49,147 - pyskl - INFO - +mean_acc 0.7957 +2025-06-24 21:06:49,150 - pyskl - INFO - Epoch(val) [62][533] top1_acc: 0.8366, top5_acc: 0.9844, mean_class_accuracy: 0.7957 +2025-06-24 21:08:08,599 - pyskl - INFO - Epoch [63][100/1281] lr: 1.584e-02, eta: 13:04:23, time: 0.794, data_time: 0.188, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9956, loss_cls: 0.4971, loss: 0.4971 +2025-06-24 21:08:57,886 - pyskl - INFO - Epoch [63][200/1281] lr: 1.582e-02, eta: 13:03:52, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9950, loss_cls: 0.4834, loss: 0.4834 +2025-06-24 21:09:47,185 - pyskl - INFO - Epoch [63][300/1281] lr: 1.580e-02, eta: 13:03:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9988, loss_cls: 0.4580, loss: 0.4580 +2025-06-24 21:10:36,320 - pyskl - INFO - Epoch [63][400/1281] lr: 1.578e-02, eta: 13:02:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9988, loss_cls: 0.4186, loss: 0.4186 +2025-06-24 21:11:25,587 - pyskl - INFO - Epoch [63][500/1281] lr: 1.576e-02, eta: 13:02:18, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9981, loss_cls: 0.4813, loss: 0.4813 +2025-06-24 21:12:14,879 - pyskl - INFO - Epoch [63][600/1281] lr: 1.574e-02, eta: 13:01:47, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9950, loss_cls: 0.5388, loss: 0.5388 +2025-06-24 21:12:58,739 - pyskl - INFO - Epoch [63][700/1281] lr: 1.572e-02, eta: 13:01:08, time: 0.439, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9969, loss_cls: 0.4978, loss: 0.4978 +2025-06-24 21:13:39,701 - pyskl - INFO - Epoch [63][800/1281] lr: 1.570e-02, eta: 13:00:25, time: 0.410, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9956, loss_cls: 0.5200, loss: 0.5200 +2025-06-24 21:14:12,369 - pyskl - INFO - Epoch [63][900/1281] lr: 1.568e-02, eta: 12:59:30, time: 0.327, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9956, loss_cls: 0.5246, loss: 0.5246 +2025-06-24 21:14:52,078 - pyskl - INFO - Epoch [63][1000/1281] lr: 1.566e-02, eta: 12:58:45, time: 0.397, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9969, loss_cls: 0.5091, loss: 0.5091 +2025-06-24 21:15:41,409 - pyskl - INFO - Epoch [63][1100/1281] lr: 1.564e-02, eta: 12:58:14, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9969, loss_cls: 0.5107, loss: 0.5107 +2025-06-24 21:16:30,913 - pyskl - INFO - Epoch [63][1200/1281] lr: 1.562e-02, eta: 12:57:43, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9969, loss_cls: 0.4916, loss: 0.4916 +2025-06-24 21:17:11,367 - pyskl - INFO - Saving checkpoint at 63 epochs +2025-06-24 21:18:10,292 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:18:10,360 - pyskl - INFO - +top1_acc 0.8511 +top5_acc 0.9896 +2025-06-24 21:18:10,360 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:18:10,368 - pyskl - INFO - +mean_acc 0.8234 +2025-06-24 21:18:10,370 - pyskl - INFO - Epoch(val) [63][533] top1_acc: 0.8511, top5_acc: 0.9896, mean_class_accuracy: 0.8234 +2025-06-24 21:19:29,562 - pyskl - INFO - Epoch [64][100/1281] lr: 1.559e-02, eta: 12:56:32, time: 0.792, data_time: 0.196, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9994, loss_cls: 0.4598, loss: 0.4598 +2025-06-24 21:20:18,939 - pyskl - INFO - Epoch [64][200/1281] lr: 1.557e-02, eta: 12:56:00, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9988, loss_cls: 0.4693, loss: 0.4693 +2025-06-24 21:21:08,130 - pyskl - INFO - Epoch [64][300/1281] lr: 1.555e-02, eta: 12:55:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9975, loss_cls: 0.4609, loss: 0.4609 +2025-06-24 21:21:57,566 - pyskl - INFO - Epoch [64][400/1281] lr: 1.553e-02, eta: 12:54:57, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9981, loss_cls: 0.4399, loss: 0.4399 +2025-06-24 21:22:46,994 - pyskl - INFO - Epoch [64][500/1281] lr: 1.551e-02, eta: 12:54:25, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.4201, loss: 0.4201 +2025-06-24 21:23:36,136 - pyskl - INFO - Epoch [64][600/1281] lr: 1.549e-02, eta: 12:53:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9969, loss_cls: 0.5178, loss: 0.5178 +2025-06-24 21:24:21,974 - pyskl - INFO - Epoch [64][700/1281] lr: 1.547e-02, eta: 12:53:17, time: 0.458, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9969, loss_cls: 0.5001, loss: 0.5001 +2025-06-24 21:24:59,494 - pyskl - INFO - Epoch [64][800/1281] lr: 1.545e-02, eta: 12:52:29, time: 0.375, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9975, loss_cls: 0.5764, loss: 0.5764 +2025-06-24 21:25:35,606 - pyskl - INFO - Epoch [64][900/1281] lr: 1.543e-02, eta: 12:51:39, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9962, loss_cls: 0.5572, loss: 0.5572 +2025-06-24 21:26:13,446 - pyskl - INFO - Epoch [64][1000/1281] lr: 1.541e-02, eta: 12:50:52, time: 0.378, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9912, loss_cls: 0.5697, loss: 0.5697 +2025-06-24 21:27:02,188 - pyskl - INFO - Epoch [64][1100/1281] lr: 1.539e-02, eta: 12:50:19, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9969, loss_cls: 0.5401, loss: 0.5401 +2025-06-24 21:27:51,449 - pyskl - INFO - Epoch [64][1200/1281] lr: 1.537e-02, eta: 12:49:47, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9975, loss_cls: 0.4437, loss: 0.4437 +2025-06-24 21:28:31,702 - pyskl - INFO - Saving checkpoint at 64 epochs +2025-06-24 21:29:30,467 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:29:30,540 - pyskl - INFO - +top1_acc 0.8578 +top5_acc 0.9900 +2025-06-24 21:29:30,540 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:29:30,552 - pyskl - INFO - +mean_acc 0.8192 +2025-06-24 21:29:30,554 - pyskl - INFO - Epoch(val) [64][533] top1_acc: 0.8578, top5_acc: 0.9900, mean_class_accuracy: 0.8192 +2025-06-24 21:30:51,114 - pyskl - INFO - Epoch [65][100/1281] lr: 1.533e-02, eta: 12:48:38, time: 0.806, data_time: 0.189, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9975, loss_cls: 0.4173, loss: 0.4173 +2025-06-24 21:31:40,095 - pyskl - INFO - Epoch [65][200/1281] lr: 1.531e-02, eta: 12:48:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9956, loss_cls: 0.5102, loss: 0.5102 +2025-06-24 21:32:29,300 - pyskl - INFO - Epoch [65][300/1281] lr: 1.529e-02, eta: 12:47:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9988, loss_cls: 0.4519, loss: 0.4519 +2025-06-24 21:33:18,256 - pyskl - INFO - Epoch [65][400/1281] lr: 1.527e-02, eta: 12:47:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9969, loss_cls: 0.5459, loss: 0.5459 +2025-06-24 21:34:07,490 - pyskl - INFO - Epoch [65][500/1281] lr: 1.526e-02, eta: 12:46:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9981, loss_cls: 0.4965, loss: 0.4965 +2025-06-24 21:34:56,882 - pyskl - INFO - Epoch [65][600/1281] lr: 1.524e-02, eta: 12:45:56, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9981, loss_cls: 0.4448, loss: 0.4448 +2025-06-24 21:35:42,498 - pyskl - INFO - Epoch [65][700/1281] lr: 1.522e-02, eta: 12:45:19, time: 0.456, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9956, loss_cls: 0.4499, loss: 0.4499 +2025-06-24 21:36:19,582 - pyskl - INFO - Epoch [65][800/1281] lr: 1.520e-02, eta: 12:44:31, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9981, loss_cls: 0.4382, loss: 0.4382 +2025-06-24 21:36:55,879 - pyskl - INFO - Epoch [65][900/1281] lr: 1.518e-02, eta: 12:43:42, time: 0.363, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9975, loss_cls: 0.5187, loss: 0.5187 +2025-06-24 21:37:31,639 - pyskl - INFO - Epoch [65][1000/1281] lr: 1.516e-02, eta: 12:42:52, time: 0.358, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9988, loss_cls: 0.5312, loss: 0.5312 +2025-06-24 21:38:20,692 - pyskl - INFO - Epoch [65][1100/1281] lr: 1.514e-02, eta: 12:42:19, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9962, loss_cls: 0.5317, loss: 0.5317 +2025-06-24 21:39:09,818 - pyskl - INFO - Epoch [65][1200/1281] lr: 1.512e-02, eta: 12:41:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9950, loss_cls: 0.5280, loss: 0.5280 +2025-06-24 21:39:50,063 - pyskl - INFO - Saving checkpoint at 65 epochs +2025-06-24 21:40:48,406 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:40:48,461 - pyskl - INFO - +top1_acc 0.8781 +top5_acc 0.9916 +2025-06-24 21:40:48,461 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:40:48,468 - pyskl - INFO - +mean_acc 0.8381 +2025-06-24 21:40:48,472 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_58.pth was removed +2025-06-24 21:40:48,676 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_65.pth. +2025-06-24 21:40:48,676 - pyskl - INFO - Best top1_acc is 0.8781 at 65 epoch. +2025-06-24 21:40:48,678 - pyskl - INFO - Epoch(val) [65][533] top1_acc: 0.8781, top5_acc: 0.9916, mean_class_accuracy: 0.8381 +2025-06-24 21:42:08,598 - pyskl - INFO - Epoch [66][100/1281] lr: 1.508e-02, eta: 12:40:36, time: 0.799, data_time: 0.187, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9975, loss_cls: 0.4815, loss: 0.4815 +2025-06-24 21:42:57,619 - pyskl - INFO - Epoch [66][200/1281] lr: 1.506e-02, eta: 12:40:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9981, loss_cls: 0.4946, loss: 0.4946 +2025-06-24 21:43:46,568 - pyskl - INFO - Epoch [66][300/1281] lr: 1.504e-02, eta: 12:39:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9975, loss_cls: 0.4118, loss: 0.4118 +2025-06-24 21:44:35,788 - pyskl - INFO - Epoch [66][400/1281] lr: 1.502e-02, eta: 12:38:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9969, loss_cls: 0.4686, loss: 0.4686 +2025-06-24 21:45:24,767 - pyskl - INFO - Epoch [66][500/1281] lr: 1.500e-02, eta: 12:38:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9975, loss_cls: 0.4667, loss: 0.4667 +2025-06-24 21:46:13,781 - pyskl - INFO - Epoch [66][600/1281] lr: 1.498e-02, eta: 12:37:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9956, loss_cls: 0.4818, loss: 0.4818 +2025-06-24 21:47:02,605 - pyskl - INFO - Epoch [66][700/1281] lr: 1.496e-02, eta: 12:37:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4519, loss: 0.4519 +2025-06-24 21:47:33,875 - pyskl - INFO - Epoch [66][800/1281] lr: 1.494e-02, eta: 12:36:23, time: 0.313, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9975, loss_cls: 0.4982, loss: 0.4982 +2025-06-24 21:48:16,292 - pyskl - INFO - Epoch [66][900/1281] lr: 1.492e-02, eta: 12:35:41, time: 0.424, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9988, loss_cls: 0.4755, loss: 0.4755 +2025-06-24 21:48:51,357 - pyskl - INFO - Epoch [66][1000/1281] lr: 1.490e-02, eta: 12:34:50, time: 0.351, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9938, loss_cls: 0.5087, loss: 0.5087 +2025-06-24 21:49:40,491 - pyskl - INFO - Epoch [66][1100/1281] lr: 1.488e-02, eta: 12:34:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9931, loss_cls: 0.5924, loss: 0.5924 +2025-06-24 21:50:29,768 - pyskl - INFO - Epoch [66][1200/1281] lr: 1.486e-02, eta: 12:33:45, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9969, loss_cls: 0.5197, loss: 0.5197 +2025-06-24 21:51:10,162 - pyskl - INFO - Saving checkpoint at 66 epochs +2025-06-24 21:52:09,288 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:52:09,359 - pyskl - INFO - +top1_acc 0.8603 +top5_acc 0.9918 +2025-06-24 21:52:09,359 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:52:09,367 - pyskl - INFO - +mean_acc 0.8175 +2025-06-24 21:52:09,369 - pyskl - INFO - Epoch(val) [66][533] top1_acc: 0.8603, top5_acc: 0.9918, mean_class_accuracy: 0.8175 +2025-06-24 21:53:28,531 - pyskl - INFO - Epoch [67][100/1281] lr: 1.482e-02, eta: 12:32:33, time: 0.792, data_time: 0.190, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9981, loss_cls: 0.4033, loss: 0.4033 +2025-06-24 21:54:17,783 - pyskl - INFO - Epoch [67][200/1281] lr: 1.480e-02, eta: 12:32:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9994, loss_cls: 0.4274, loss: 0.4274 +2025-06-24 21:55:06,600 - pyskl - INFO - Epoch [67][300/1281] lr: 1.478e-02, eta: 12:31:27, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9988, loss_cls: 0.4644, loss: 0.4644 +2025-06-24 21:55:55,818 - pyskl - INFO - Epoch [67][400/1281] lr: 1.476e-02, eta: 12:30:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9981, loss_cls: 0.4181, loss: 0.4181 +2025-06-24 21:56:45,013 - pyskl - INFO - Epoch [67][500/1281] lr: 1.474e-02, eta: 12:30:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9969, loss_cls: 0.4943, loss: 0.4943 +2025-06-24 21:57:34,284 - pyskl - INFO - Epoch [67][600/1281] lr: 1.472e-02, eta: 12:29:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9969, loss_cls: 0.4776, loss: 0.4776 +2025-06-24 21:58:23,287 - pyskl - INFO - Epoch [67][700/1281] lr: 1.470e-02, eta: 12:29:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9962, loss_cls: 0.4813, loss: 0.4813 +2025-06-24 21:58:53,188 - pyskl - INFO - Epoch [67][800/1281] lr: 1.468e-02, eta: 12:28:17, time: 0.299, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9975, loss_cls: 0.4377, loss: 0.4377 +2025-06-24 21:59:37,419 - pyskl - INFO - Epoch [67][900/1281] lr: 1.466e-02, eta: 12:27:38, time: 0.442, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9975, loss_cls: 0.4331, loss: 0.4331 +2025-06-24 22:00:09,722 - pyskl - INFO - Epoch [67][1000/1281] lr: 1.464e-02, eta: 12:26:44, time: 0.323, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9969, loss_cls: 0.5369, loss: 0.5369 +2025-06-24 22:00:58,159 - pyskl - INFO - Epoch [67][1100/1281] lr: 1.462e-02, eta: 12:26:10, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9931, loss_cls: 0.5454, loss: 0.5454 +2025-06-24 22:01:47,778 - pyskl - INFO - Epoch [67][1200/1281] lr: 1.460e-02, eta: 12:25:37, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9969, loss_cls: 0.5589, loss: 0.5589 +2025-06-24 22:02:28,076 - pyskl - INFO - Saving checkpoint at 67 epochs +2025-06-24 22:03:27,560 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:03:27,618 - pyskl - INFO - +top1_acc 0.8670 +top5_acc 0.9893 +2025-06-24 22:03:27,618 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:03:27,625 - pyskl - INFO - +mean_acc 0.8168 +2025-06-24 22:03:27,628 - pyskl - INFO - Epoch(val) [67][533] top1_acc: 0.8670, top5_acc: 0.9893, mean_class_accuracy: 0.8168 +2025-06-24 22:04:47,539 - pyskl - INFO - Epoch [68][100/1281] lr: 1.456e-02, eta: 12:24:26, time: 0.799, data_time: 0.193, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9981, loss_cls: 0.4325, loss: 0.4325 +2025-06-24 22:05:37,036 - pyskl - INFO - Epoch [68][200/1281] lr: 1.454e-02, eta: 12:23:53, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9981, loss_cls: 0.4329, loss: 0.4329 +2025-06-24 22:06:26,699 - pyskl - INFO - Epoch [68][300/1281] lr: 1.452e-02, eta: 12:23:20, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.3633, loss: 0.3633 +2025-06-24 22:07:15,997 - pyskl - INFO - Epoch [68][400/1281] lr: 1.450e-02, eta: 12:22:47, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9956, loss_cls: 0.4167, loss: 0.4167 +2025-06-24 22:08:04,711 - pyskl - INFO - Epoch [68][500/1281] lr: 1.448e-02, eta: 12:22:13, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9988, loss_cls: 0.4354, loss: 0.4354 +2025-06-24 22:08:53,951 - pyskl - INFO - Epoch [68][600/1281] lr: 1.446e-02, eta: 12:21:40, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9969, loss_cls: 0.4456, loss: 0.4456 +2025-06-24 22:09:43,013 - pyskl - INFO - Epoch [68][700/1281] lr: 1.444e-02, eta: 12:21:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9962, loss_cls: 0.4762, loss: 0.4762 +2025-06-24 22:10:11,335 - pyskl - INFO - Epoch [68][800/1281] lr: 1.442e-02, eta: 12:20:07, time: 0.283, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9975, loss_cls: 0.4342, loss: 0.4342 +2025-06-24 22:10:58,976 - pyskl - INFO - Epoch [68][900/1281] lr: 1.440e-02, eta: 12:19:32, time: 0.476, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9981, loss_cls: 0.4527, loss: 0.4527 +2025-06-24 22:11:29,411 - pyskl - INFO - Epoch [68][1000/1281] lr: 1.438e-02, eta: 12:18:36, time: 0.304, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9969, loss_cls: 0.4898, loss: 0.4898 +2025-06-24 22:12:18,172 - pyskl - INFO - Epoch [68][1100/1281] lr: 1.436e-02, eta: 12:18:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9975, loss_cls: 0.4966, loss: 0.4966 +2025-06-24 22:13:07,182 - pyskl - INFO - Epoch [68][1200/1281] lr: 1.434e-02, eta: 12:17:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9956, loss_cls: 0.4927, loss: 0.4927 +2025-06-24 22:13:47,372 - pyskl - INFO - Saving checkpoint at 68 epochs +2025-06-24 22:14:46,522 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:14:46,584 - pyskl - INFO - +top1_acc 0.8328 +top5_acc 0.9846 +2025-06-24 22:14:46,584 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:14:46,592 - pyskl - INFO - +mean_acc 0.7822 +2025-06-24 22:14:46,594 - pyskl - INFO - Epoch(val) [68][533] top1_acc: 0.8328, top5_acc: 0.9846, mean_class_accuracy: 0.7822 +2025-06-24 22:16:07,572 - pyskl - INFO - Epoch [69][100/1281] lr: 1.431e-02, eta: 12:16:17, time: 0.810, data_time: 0.199, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9975, loss_cls: 0.4074, loss: 0.4074 +2025-06-24 22:16:56,770 - pyskl - INFO - Epoch [69][200/1281] lr: 1.429e-02, eta: 12:15:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9938, loss_cls: 0.4352, loss: 0.4352 +2025-06-24 22:17:45,690 - pyskl - INFO - Epoch [69][300/1281] lr: 1.427e-02, eta: 12:15:10, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9969, loss_cls: 0.4485, loss: 0.4485 +2025-06-24 22:18:34,920 - pyskl - INFO - Epoch [69][400/1281] lr: 1.425e-02, eta: 12:14:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9988, loss_cls: 0.4480, loss: 0.4480 +2025-06-24 22:19:23,950 - pyskl - INFO - Epoch [69][500/1281] lr: 1.423e-02, eta: 12:14:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9981, loss_cls: 0.4243, loss: 0.4243 +2025-06-24 22:20:13,019 - pyskl - INFO - Epoch [69][600/1281] lr: 1.420e-02, eta: 12:13:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9988, loss_cls: 0.4152, loss: 0.4152 +2025-06-24 22:21:02,090 - pyskl - INFO - Epoch [69][700/1281] lr: 1.418e-02, eta: 12:12:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9981, loss_cls: 0.4382, loss: 0.4382 +2025-06-24 22:21:29,107 - pyskl - INFO - Epoch [69][800/1281] lr: 1.416e-02, eta: 12:11:55, time: 0.270, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9944, loss_cls: 0.5022, loss: 0.5022 +2025-06-24 22:22:20,015 - pyskl - INFO - Epoch [69][900/1281] lr: 1.414e-02, eta: 12:11:23, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9981, loss_cls: 0.4535, loss: 0.4535 +2025-06-24 22:22:49,535 - pyskl - INFO - Epoch [69][1000/1281] lr: 1.412e-02, eta: 12:10:26, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9975, loss_cls: 0.5059, loss: 0.5059 +2025-06-24 22:23:38,459 - pyskl - INFO - Epoch [69][1100/1281] lr: 1.410e-02, eta: 12:09:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9944, loss_cls: 0.5034, loss: 0.5034 +2025-06-24 22:24:27,734 - pyskl - INFO - Epoch [69][1200/1281] lr: 1.408e-02, eta: 12:09:18, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9988, loss_cls: 0.4350, loss: 0.4350 +2025-06-24 22:25:08,104 - pyskl - INFO - Saving checkpoint at 69 epochs +2025-06-24 22:26:07,568 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:26:07,625 - pyskl - INFO - +top1_acc 0.8522 +top5_acc 0.9894 +2025-06-24 22:26:07,625 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:26:07,632 - pyskl - INFO - +mean_acc 0.8025 +2025-06-24 22:26:07,634 - pyskl - INFO - Epoch(val) [69][533] top1_acc: 0.8522, top5_acc: 0.9894, mean_class_accuracy: 0.8025 +2025-06-24 22:27:28,364 - pyskl - INFO - Epoch [70][100/1281] lr: 1.405e-02, eta: 12:08:07, time: 0.807, data_time: 0.195, memory: 4083, top1_acc: 0.9325, top5_acc: 1.0000, loss_cls: 0.3892, loss: 0.3892 +2025-06-24 22:28:17,084 - pyskl - INFO - Epoch [70][200/1281] lr: 1.403e-02, eta: 12:07:32, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9981, loss_cls: 0.4275, loss: 0.4275 +2025-06-24 22:29:05,756 - pyskl - INFO - Epoch [70][300/1281] lr: 1.401e-02, eta: 12:06:58, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9981, loss_cls: 0.4398, loss: 0.4398 +2025-06-24 22:29:55,232 - pyskl - INFO - Epoch [70][400/1281] lr: 1.399e-02, eta: 12:06:24, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9981, loss_cls: 0.4257, loss: 0.4257 +2025-06-24 22:30:44,377 - pyskl - INFO - Epoch [70][500/1281] lr: 1.397e-02, eta: 12:05:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9981, loss_cls: 0.3469, loss: 0.3469 +2025-06-24 22:31:33,063 - pyskl - INFO - Epoch [70][600/1281] lr: 1.395e-02, eta: 12:05:16, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9969, loss_cls: 0.4353, loss: 0.4353 +2025-06-24 22:32:22,497 - pyskl - INFO - Epoch [70][700/1281] lr: 1.392e-02, eta: 12:04:42, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9962, loss_cls: 0.4787, loss: 0.4787 +2025-06-24 22:32:49,489 - pyskl - INFO - Epoch [70][800/1281] lr: 1.390e-02, eta: 12:03:42, time: 0.270, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9988, loss_cls: 0.3970, loss: 0.3970 +2025-06-24 22:33:40,491 - pyskl - INFO - Epoch [70][900/1281] lr: 1.388e-02, eta: 12:03:10, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9981, loss_cls: 0.4574, loss: 0.4574 +2025-06-24 22:34:08,689 - pyskl - INFO - Epoch [70][1000/1281] lr: 1.386e-02, eta: 12:02:12, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9994, loss_cls: 0.4652, loss: 0.4652 +2025-06-24 22:34:57,577 - pyskl - INFO - Epoch [70][1100/1281] lr: 1.384e-02, eta: 12:01:37, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9969, loss_cls: 0.4363, loss: 0.4363 +2025-06-24 22:35:46,530 - pyskl - INFO - Epoch [70][1200/1281] lr: 1.382e-02, eta: 12:01:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9969, loss_cls: 0.4544, loss: 0.4544 +2025-06-24 22:36:27,154 - pyskl - INFO - Saving checkpoint at 70 epochs +2025-06-24 22:37:25,957 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:37:26,013 - pyskl - INFO - +top1_acc 0.8572 +top5_acc 0.9887 +2025-06-24 22:37:26,013 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:37:26,020 - pyskl - INFO - +mean_acc 0.8226 +2025-06-24 22:37:26,021 - pyskl - INFO - Epoch(val) [70][533] top1_acc: 0.8572, top5_acc: 0.9887, mean_class_accuracy: 0.8226 +2025-06-24 22:38:46,222 - pyskl - INFO - Epoch [71][100/1281] lr: 1.379e-02, eta: 11:59:51, time: 0.802, data_time: 0.191, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9975, loss_cls: 0.4223, loss: 0.4223 +2025-06-24 22:39:35,716 - pyskl - INFO - Epoch [71][200/1281] lr: 1.377e-02, eta: 11:59:17, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9988, loss_cls: 0.3524, loss: 0.3524 +2025-06-24 22:40:25,020 - pyskl - INFO - Epoch [71][300/1281] lr: 1.375e-02, eta: 11:58:43, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9988, loss_cls: 0.3893, loss: 0.3893 +2025-06-24 22:41:14,501 - pyskl - INFO - Epoch [71][400/1281] lr: 1.373e-02, eta: 11:58:09, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9969, loss_cls: 0.3868, loss: 0.3868 +2025-06-24 22:42:03,384 - pyskl - INFO - Epoch [71][500/1281] lr: 1.371e-02, eta: 11:57:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9969, loss_cls: 0.4132, loss: 0.4132 +2025-06-24 22:42:52,469 - pyskl - INFO - Epoch [71][600/1281] lr: 1.368e-02, eta: 11:57:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9938, loss_cls: 0.4484, loss: 0.4484 +2025-06-24 22:43:41,557 - pyskl - INFO - Epoch [71][700/1281] lr: 1.366e-02, eta: 11:56:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9988, loss_cls: 0.4947, loss: 0.4947 +2025-06-24 22:44:11,469 - pyskl - INFO - Epoch [71][800/1281] lr: 1.364e-02, eta: 11:55:29, time: 0.299, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9969, loss_cls: 0.4464, loss: 0.4464 +2025-06-24 22:45:02,641 - pyskl - INFO - Epoch [71][900/1281] lr: 1.362e-02, eta: 11:54:57, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9981, loss_cls: 0.4377, loss: 0.4377 +2025-06-24 22:45:29,262 - pyskl - INFO - Epoch [71][1000/1281] lr: 1.360e-02, eta: 11:53:57, time: 0.266, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9969, loss_cls: 0.4084, loss: 0.4084 +2025-06-24 22:46:18,799 - pyskl - INFO - Epoch [71][1100/1281] lr: 1.358e-02, eta: 11:53:23, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9988, loss_cls: 0.4185, loss: 0.4185 +2025-06-24 22:47:07,989 - pyskl - INFO - Epoch [71][1200/1281] lr: 1.356e-02, eta: 11:52:49, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9975, loss_cls: 0.4810, loss: 0.4810 +2025-06-24 22:47:48,354 - pyskl - INFO - Saving checkpoint at 71 epochs +2025-06-24 22:48:47,320 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:48:47,379 - pyskl - INFO - +top1_acc 0.8662 +top5_acc 0.9873 +2025-06-24 22:48:47,379 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:48:47,386 - pyskl - INFO - +mean_acc 0.8284 +2025-06-24 22:48:47,388 - pyskl - INFO - Epoch(val) [71][533] top1_acc: 0.8662, top5_acc: 0.9873, mean_class_accuracy: 0.8284 +2025-06-24 22:50:07,500 - pyskl - INFO - Epoch [72][100/1281] lr: 1.353e-02, eta: 11:51:37, time: 0.801, data_time: 0.192, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9969, loss_cls: 0.3654, loss: 0.3654 +2025-06-24 22:50:56,167 - pyskl - INFO - Epoch [72][200/1281] lr: 1.351e-02, eta: 11:51:01, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9975, loss_cls: 0.3909, loss: 0.3909 +2025-06-24 22:51:45,467 - pyskl - INFO - Epoch [72][300/1281] lr: 1.349e-02, eta: 11:50:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9988, loss_cls: 0.4438, loss: 0.4438 +2025-06-24 22:52:34,787 - pyskl - INFO - Epoch [72][400/1281] lr: 1.346e-02, eta: 11:49:53, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9962, loss_cls: 0.4688, loss: 0.4688 +2025-06-24 22:53:24,102 - pyskl - INFO - Epoch [72][500/1281] lr: 1.344e-02, eta: 11:49:18, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3980, loss: 0.3980 +2025-06-24 22:54:13,108 - pyskl - INFO - Epoch [72][600/1281] lr: 1.342e-02, eta: 11:48:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9988, loss_cls: 0.3747, loss: 0.3747 +2025-06-24 22:55:02,277 - pyskl - INFO - Epoch [72][700/1281] lr: 1.340e-02, eta: 11:48:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9962, loss_cls: 0.4067, loss: 0.4067 +2025-06-24 22:55:33,220 - pyskl - INFO - Epoch [72][800/1281] lr: 1.338e-02, eta: 11:47:14, time: 0.309, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9981, loss_cls: 0.4425, loss: 0.4425 +2025-06-24 22:56:24,231 - pyskl - INFO - Epoch [72][900/1281] lr: 1.336e-02, eta: 11:46:41, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9988, loss_cls: 0.4352, loss: 0.4352 +2025-06-24 22:56:50,298 - pyskl - INFO - Epoch [72][1000/1281] lr: 1.334e-02, eta: 11:45:41, time: 0.261, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9969, loss_cls: 0.4060, loss: 0.4060 +2025-06-24 22:57:39,346 - pyskl - INFO - Epoch [72][1100/1281] lr: 1.332e-02, eta: 11:45:06, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9981, loss_cls: 0.4232, loss: 0.4232 +2025-06-24 22:58:28,365 - pyskl - INFO - Epoch [72][1200/1281] lr: 1.330e-02, eta: 11:44:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9994, loss_cls: 0.4837, loss: 0.4837 +2025-06-24 22:59:08,363 - pyskl - INFO - Saving checkpoint at 72 epochs +2025-06-24 23:00:06,961 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:00:07,029 - pyskl - INFO - +top1_acc 0.8581 +top5_acc 0.9890 +2025-06-24 23:00:07,030 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:00:07,053 - pyskl - INFO - +mean_acc 0.8026 +2025-06-24 23:00:07,056 - pyskl - INFO - Epoch(val) [72][533] top1_acc: 0.8581, top5_acc: 0.9890, mean_class_accuracy: 0.8026 +2025-06-24 23:01:27,994 - pyskl - INFO - Epoch [73][100/1281] lr: 1.326e-02, eta: 11:43:20, time: 0.809, data_time: 0.194, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9988, loss_cls: 0.3831, loss: 0.3831 +2025-06-24 23:02:17,479 - pyskl - INFO - Epoch [73][200/1281] lr: 1.324e-02, eta: 11:42:45, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9994, loss_cls: 0.4136, loss: 0.4136 +2025-06-24 23:03:06,668 - pyskl - INFO - Epoch [73][300/1281] lr: 1.322e-02, eta: 11:42:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9994, loss_cls: 0.3756, loss: 0.3756 +2025-06-24 23:03:55,582 - pyskl - INFO - Epoch [73][400/1281] lr: 1.320e-02, eta: 11:41:35, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4090, loss: 0.4090 +2025-06-24 23:04:44,686 - pyskl - INFO - Epoch [73][500/1281] lr: 1.318e-02, eta: 11:41:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9994, loss_cls: 0.3626, loss: 0.3626 +2025-06-24 23:05:33,738 - pyskl - INFO - Epoch [73][600/1281] lr: 1.316e-02, eta: 11:40:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9981, loss_cls: 0.3662, loss: 0.3662 +2025-06-24 23:06:22,883 - pyskl - INFO - Epoch [73][700/1281] lr: 1.314e-02, eta: 11:39:50, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9294, top5_acc: 1.0000, loss_cls: 0.3943, loss: 0.3943 +2025-06-24 23:06:54,493 - pyskl - INFO - Epoch [73][800/1281] lr: 1.312e-02, eta: 11:38:56, time: 0.316, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9944, loss_cls: 0.5273, loss: 0.5273 +2025-06-24 23:07:45,517 - pyskl - INFO - Epoch [73][900/1281] lr: 1.310e-02, eta: 11:38:23, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9981, loss_cls: 0.4329, loss: 0.4329 +2025-06-24 23:08:10,608 - pyskl - INFO - Epoch [73][1000/1281] lr: 1.308e-02, eta: 11:37:23, time: 0.251, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9962, loss_cls: 0.4031, loss: 0.4031 +2025-06-24 23:08:58,454 - pyskl - INFO - Epoch [73][1100/1281] lr: 1.306e-02, eta: 11:36:46, time: 0.478, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4276, loss: 0.4276 +2025-06-24 23:09:47,781 - pyskl - INFO - Epoch [73][1200/1281] lr: 1.304e-02, eta: 11:36:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9956, loss_cls: 0.4805, loss: 0.4805 +2025-06-24 23:10:28,400 - pyskl - INFO - Saving checkpoint at 73 epochs +2025-06-24 23:11:27,253 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:11:27,316 - pyskl - INFO - +top1_acc 0.8645 +top5_acc 0.9870 +2025-06-24 23:11:27,316 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:11:27,324 - pyskl - INFO - +mean_acc 0.8358 +2025-06-24 23:11:27,327 - pyskl - INFO - Epoch(val) [73][533] top1_acc: 0.8645, top5_acc: 0.9870, mean_class_accuracy: 0.8358 +2025-06-24 23:12:48,128 - pyskl - INFO - Epoch [74][100/1281] lr: 1.300e-02, eta: 11:34:59, time: 0.808, data_time: 0.194, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9994, loss_cls: 0.3526, loss: 0.3526 +2025-06-24 23:13:37,077 - pyskl - INFO - Epoch [74][200/1281] lr: 1.298e-02, eta: 11:34:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9988, loss_cls: 0.3730, loss: 0.3730 +2025-06-24 23:14:26,171 - pyskl - INFO - Epoch [74][300/1281] lr: 1.296e-02, eta: 11:33:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9994, loss_cls: 0.4213, loss: 0.4213 +2025-06-24 23:15:15,266 - pyskl - INFO - Epoch [74][400/1281] lr: 1.294e-02, eta: 11:33:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9969, loss_cls: 0.4675, loss: 0.4675 +2025-06-24 23:16:04,210 - pyskl - INFO - Epoch [74][500/1281] lr: 1.292e-02, eta: 11:32:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9969, loss_cls: 0.3978, loss: 0.3978 +2025-06-24 23:16:53,148 - pyskl - INFO - Epoch [74][600/1281] lr: 1.290e-02, eta: 11:32:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9988, loss_cls: 0.4184, loss: 0.4184 +2025-06-24 23:17:42,144 - pyskl - INFO - Epoch [74][700/1281] lr: 1.288e-02, eta: 11:31:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9962, loss_cls: 0.4492, loss: 0.4492 +2025-06-24 23:18:15,315 - pyskl - INFO - Epoch [74][800/1281] lr: 1.286e-02, eta: 11:30:35, time: 0.332, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9994, loss_cls: 0.3739, loss: 0.3739 +2025-06-24 23:19:06,397 - pyskl - INFO - Epoch [74][900/1281] lr: 1.284e-02, eta: 11:30:02, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9988, loss_cls: 0.4115, loss: 0.4115 +2025-06-24 23:19:31,453 - pyskl - INFO - Epoch [74][1000/1281] lr: 1.282e-02, eta: 11:29:01, time: 0.251, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9988, loss_cls: 0.4333, loss: 0.4333 +2025-06-24 23:20:19,171 - pyskl - INFO - Epoch [74][1100/1281] lr: 1.280e-02, eta: 11:28:25, time: 0.477, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4116, loss: 0.4116 +2025-06-24 23:21:08,424 - pyskl - INFO - Epoch [74][1200/1281] lr: 1.278e-02, eta: 11:27:49, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9969, loss_cls: 0.4537, loss: 0.4537 +2025-06-24 23:21:48,608 - pyskl - INFO - Saving checkpoint at 74 epochs +2025-06-24 23:22:46,753 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:22:46,808 - pyskl - INFO - +top1_acc 0.8776 +top5_acc 0.9907 +2025-06-24 23:22:46,808 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:22:46,815 - pyskl - INFO - +mean_acc 0.8356 +2025-06-24 23:22:46,816 - pyskl - INFO - Epoch(val) [74][533] top1_acc: 0.8776, top5_acc: 0.9907, mean_class_accuracy: 0.8356 +2025-06-24 23:24:06,306 - pyskl - INFO - Epoch [75][100/1281] lr: 1.274e-02, eta: 11:26:36, time: 0.795, data_time: 0.192, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9988, loss_cls: 0.3782, loss: 0.3782 +2025-06-24 23:24:55,876 - pyskl - INFO - Epoch [75][200/1281] lr: 1.272e-02, eta: 11:26:01, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9988, loss_cls: 0.3472, loss: 0.3472 +2025-06-24 23:25:45,388 - pyskl - INFO - Epoch [75][300/1281] lr: 1.270e-02, eta: 11:25:26, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3474, loss: 0.3474 +2025-06-24 23:26:34,508 - pyskl - INFO - Epoch [75][400/1281] lr: 1.268e-02, eta: 11:24:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 1.0000, loss_cls: 0.4047, loss: 0.4047 +2025-06-24 23:27:23,651 - pyskl - INFO - Epoch [75][500/1281] lr: 1.266e-02, eta: 11:24:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9975, loss_cls: 0.4348, loss: 0.4348 +2025-06-24 23:28:12,783 - pyskl - INFO - Epoch [75][600/1281] lr: 1.264e-02, eta: 11:23:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9981, loss_cls: 0.4375, loss: 0.4375 +2025-06-24 23:29:01,709 - pyskl - INFO - Epoch [75][700/1281] lr: 1.262e-02, eta: 11:23:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9988, loss_cls: 0.4316, loss: 0.4316 +2025-06-24 23:29:37,573 - pyskl - INFO - Epoch [75][800/1281] lr: 1.260e-02, eta: 11:22:14, time: 0.359, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9981, loss_cls: 0.4221, loss: 0.4221 +2025-06-24 23:30:28,523 - pyskl - INFO - Epoch [75][900/1281] lr: 1.258e-02, eta: 11:21:40, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9975, loss_cls: 0.3775, loss: 0.3775 +2025-06-24 23:30:52,430 - pyskl - INFO - Epoch [75][1000/1281] lr: 1.256e-02, eta: 11:20:39, time: 0.239, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9944, loss_cls: 0.4566, loss: 0.4566 +2025-06-24 23:31:36,972 - pyskl - INFO - Epoch [75][1100/1281] lr: 1.254e-02, eta: 11:19:59, time: 0.445, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9981, loss_cls: 0.4091, loss: 0.4091 +2025-06-24 23:32:26,494 - pyskl - INFO - Epoch [75][1200/1281] lr: 1.252e-02, eta: 11:19:24, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9975, loss_cls: 0.3592, loss: 0.3592 +2025-06-24 23:33:06,861 - pyskl - INFO - Saving checkpoint at 75 epochs +2025-06-24 23:34:05,752 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:34:05,811 - pyskl - INFO - +top1_acc 0.7935 +top5_acc 0.9824 +2025-06-24 23:34:05,811 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:34:05,819 - pyskl - INFO - +mean_acc 0.7593 +2025-06-24 23:34:05,821 - pyskl - INFO - Epoch(val) [75][533] top1_acc: 0.7935, top5_acc: 0.9824, mean_class_accuracy: 0.7593 +2025-06-24 23:35:26,040 - pyskl - INFO - Epoch [76][100/1281] lr: 1.248e-02, eta: 11:18:11, time: 0.802, data_time: 0.194, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9981, loss_cls: 0.3547, loss: 0.3547 +2025-06-24 23:36:14,574 - pyskl - INFO - Epoch [76][200/1281] lr: 1.246e-02, eta: 11:17:34, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 1.0000, loss_cls: 0.3474, loss: 0.3474 +2025-06-24 23:37:03,593 - pyskl - INFO - Epoch [76][300/1281] lr: 1.244e-02, eta: 11:16:59, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9975, loss_cls: 0.4195, loss: 0.4195 +2025-06-24 23:37:53,019 - pyskl - INFO - Epoch [76][400/1281] lr: 1.242e-02, eta: 11:16:23, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9969, loss_cls: 0.3425, loss: 0.3425 +2025-06-24 23:38:42,439 - pyskl - INFO - Epoch [76][500/1281] lr: 1.240e-02, eta: 11:15:48, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9994, loss_cls: 0.3537, loss: 0.3537 +2025-06-24 23:39:31,742 - pyskl - INFO - Epoch [76][600/1281] lr: 1.238e-02, eta: 11:15:12, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9988, loss_cls: 0.3947, loss: 0.3947 +2025-06-24 23:40:21,093 - pyskl - INFO - Epoch [76][700/1281] lr: 1.236e-02, eta: 11:14:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 1.0000, loss_cls: 0.4069, loss: 0.4069 +2025-06-24 23:40:59,287 - pyskl - INFO - Epoch [76][800/1281] lr: 1.234e-02, eta: 11:13:50, time: 0.382, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9969, loss_cls: 0.4217, loss: 0.4217 +2025-06-24 23:41:50,302 - pyskl - INFO - Epoch [76][900/1281] lr: 1.232e-02, eta: 11:13:16, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9988, loss_cls: 0.3755, loss: 0.3755 +2025-06-24 23:42:13,669 - pyskl - INFO - Epoch [76][1000/1281] lr: 1.230e-02, eta: 11:12:15, time: 0.234, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.3911, loss: 0.3911 +2025-06-24 23:42:57,677 - pyskl - INFO - Epoch [76][1100/1281] lr: 1.228e-02, eta: 11:11:34, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.3983, loss: 0.3983 +2025-06-24 23:43:46,692 - pyskl - INFO - Epoch [76][1200/1281] lr: 1.225e-02, eta: 11:10:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9981, loss_cls: 0.3987, loss: 0.3987 +2025-06-24 23:44:27,341 - pyskl - INFO - Saving checkpoint at 76 epochs +2025-06-24 23:45:25,478 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:45:25,539 - pyskl - INFO - +top1_acc 0.8572 +top5_acc 0.9911 +2025-06-24 23:45:25,539 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:45:25,547 - pyskl - INFO - +mean_acc 0.8244 +2025-06-24 23:45:25,549 - pyskl - INFO - Epoch(val) [76][533] top1_acc: 0.8572, top5_acc: 0.9911, mean_class_accuracy: 0.8244 +2025-06-24 23:46:45,413 - pyskl - INFO - Epoch [77][100/1281] lr: 1.222e-02, eta: 11:09:44, time: 0.799, data_time: 0.188, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9981, loss_cls: 0.3759, loss: 0.3759 +2025-06-24 23:47:34,546 - pyskl - INFO - Epoch [77][200/1281] lr: 1.220e-02, eta: 11:09:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9981, loss_cls: 0.3770, loss: 0.3770 +2025-06-24 23:48:23,714 - pyskl - INFO - Epoch [77][300/1281] lr: 1.218e-02, eta: 11:08:32, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 1.0000, loss_cls: 0.3506, loss: 0.3506 +2025-06-24 23:49:12,774 - pyskl - INFO - Epoch [77][400/1281] lr: 1.216e-02, eta: 11:07:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9975, loss_cls: 0.4137, loss: 0.4137 +2025-06-24 23:50:01,870 - pyskl - INFO - Epoch [77][500/1281] lr: 1.214e-02, eta: 11:07:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9988, loss_cls: 0.3908, loss: 0.3908 +2025-06-24 23:50:50,581 - pyskl - INFO - Epoch [77][600/1281] lr: 1.212e-02, eta: 11:06:43, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9975, loss_cls: 0.3485, loss: 0.3485 +2025-06-24 23:51:39,682 - pyskl - INFO - Epoch [77][700/1281] lr: 1.210e-02, eta: 11:06:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9956, loss_cls: 0.4217, loss: 0.4217 +2025-06-24 23:52:20,291 - pyskl - INFO - Epoch [77][800/1281] lr: 1.207e-02, eta: 11:05:23, time: 0.406, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9988, loss_cls: 0.4215, loss: 0.4215 +2025-06-24 23:53:10,208 - pyskl - INFO - Epoch [77][900/1281] lr: 1.205e-02, eta: 11:04:48, time: 0.499, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9981, loss_cls: 0.4232, loss: 0.4232 +2025-06-24 23:53:34,132 - pyskl - INFO - Epoch [77][1000/1281] lr: 1.203e-02, eta: 11:03:48, time: 0.239, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3937, loss: 0.3937 +2025-06-24 23:54:17,034 - pyskl - INFO - Epoch [77][1100/1281] lr: 1.201e-02, eta: 11:03:05, time: 0.429, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9969, loss_cls: 0.3997, loss: 0.3997 +2025-06-24 23:55:06,621 - pyskl - INFO - Epoch [77][1200/1281] lr: 1.199e-02, eta: 11:02:30, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4356, loss: 0.4356 +2025-06-24 23:55:46,999 - pyskl - INFO - Saving checkpoint at 77 epochs +2025-06-24 23:56:46,142 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:56:46,210 - pyskl - INFO - +top1_acc 0.8670 +top5_acc 0.9894 +2025-06-24 23:56:46,210 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:56:46,219 - pyskl - INFO - +mean_acc 0.8310 +2025-06-24 23:56:46,222 - pyskl - INFO - Epoch(val) [77][533] top1_acc: 0.8670, top5_acc: 0.9894, mean_class_accuracy: 0.8310 +2025-06-24 23:58:05,994 - pyskl - INFO - Epoch [78][100/1281] lr: 1.196e-02, eta: 11:01:16, time: 0.798, data_time: 0.191, memory: 4083, top1_acc: 0.9356, top5_acc: 1.0000, loss_cls: 0.3704, loss: 0.3704 +2025-06-24 23:58:55,335 - pyskl - INFO - Epoch [78][200/1281] lr: 1.194e-02, eta: 11:00:40, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9981, loss_cls: 0.3770, loss: 0.3770 +2025-06-24 23:59:44,576 - pyskl - INFO - Epoch [78][300/1281] lr: 1.192e-02, eta: 11:00:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9988, loss_cls: 0.3651, loss: 0.3651 +2025-06-25 00:00:33,570 - pyskl - INFO - Epoch [78][400/1281] lr: 1.190e-02, eta: 10:59:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.3278, loss: 0.3278 +2025-06-25 00:01:22,775 - pyskl - INFO - Epoch [78][500/1281] lr: 1.187e-02, eta: 10:58:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9975, loss_cls: 0.3791, loss: 0.3791 +2025-06-25 00:02:12,026 - pyskl - INFO - Epoch [78][600/1281] lr: 1.185e-02, eta: 10:58:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9988, loss_cls: 0.3747, loss: 0.3747 +2025-06-25 00:03:01,152 - pyskl - INFO - Epoch [78][700/1281] lr: 1.183e-02, eta: 10:57:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9988, loss_cls: 0.3640, loss: 0.3640 +2025-06-25 00:03:41,683 - pyskl - INFO - Epoch [78][800/1281] lr: 1.181e-02, eta: 10:56:54, time: 0.405, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9975, loss_cls: 0.4367, loss: 0.4367 +2025-06-25 00:04:31,964 - pyskl - INFO - Epoch [78][900/1281] lr: 1.179e-02, eta: 10:56:19, time: 0.503, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9969, loss_cls: 0.4080, loss: 0.4080 +2025-06-25 00:04:55,554 - pyskl - INFO - Epoch [78][1000/1281] lr: 1.177e-02, eta: 10:55:19, time: 0.236, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9962, loss_cls: 0.4143, loss: 0.4143 +2025-06-25 00:05:38,160 - pyskl - INFO - Epoch [78][1100/1281] lr: 1.175e-02, eta: 10:54:36, time: 0.426, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9962, loss_cls: 0.4118, loss: 0.4118 +2025-06-25 00:06:27,318 - pyskl - INFO - Epoch [78][1200/1281] lr: 1.173e-02, eta: 10:54:00, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9981, loss_cls: 0.3875, loss: 0.3875 +2025-06-25 00:07:07,541 - pyskl - INFO - Saving checkpoint at 78 epochs +2025-06-25 00:08:06,545 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:08:06,601 - pyskl - INFO - +top1_acc 0.8760 +top5_acc 0.9879 +2025-06-25 00:08:06,601 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:08:06,607 - pyskl - INFO - +mean_acc 0.8448 +2025-06-25 00:08:06,608 - pyskl - INFO - Epoch(val) [78][533] top1_acc: 0.8760, top5_acc: 0.9879, mean_class_accuracy: 0.8448 +2025-06-25 00:09:25,910 - pyskl - INFO - Epoch [79][100/1281] lr: 1.169e-02, eta: 10:52:45, time: 0.793, data_time: 0.194, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3344, loss: 0.3344 +2025-06-25 00:10:14,876 - pyskl - INFO - Epoch [79][200/1281] lr: 1.167e-02, eta: 10:52:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3014, loss: 0.3014 +2025-06-25 00:11:04,075 - pyskl - INFO - Epoch [79][300/1281] lr: 1.165e-02, eta: 10:51:32, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9975, loss_cls: 0.3836, loss: 0.3836 +2025-06-25 00:11:53,188 - pyskl - INFO - Epoch [79][400/1281] lr: 1.163e-02, eta: 10:50:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9981, loss_cls: 0.3291, loss: 0.3291 +2025-06-25 00:12:42,223 - pyskl - INFO - Epoch [79][500/1281] lr: 1.161e-02, eta: 10:50:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9975, loss_cls: 0.3634, loss: 0.3634 +2025-06-25 00:13:31,148 - pyskl - INFO - Epoch [79][600/1281] lr: 1.159e-02, eta: 10:49:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9994, loss_cls: 0.4013, loss: 0.4013 +2025-06-25 00:14:20,408 - pyskl - INFO - Epoch [79][700/1281] lr: 1.157e-02, eta: 10:49:06, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9994, loss_cls: 0.3688, loss: 0.3688 +2025-06-25 00:15:01,835 - pyskl - INFO - Epoch [79][800/1281] lr: 1.155e-02, eta: 10:48:22, time: 0.414, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.3297, loss: 0.3297 +2025-06-25 00:15:49,550 - pyskl - INFO - Epoch [79][900/1281] lr: 1.153e-02, eta: 10:47:45, time: 0.477, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9981, loss_cls: 0.4291, loss: 0.4291 +2025-06-25 00:16:15,908 - pyskl - INFO - Epoch [79][1000/1281] lr: 1.151e-02, eta: 10:46:47, time: 0.264, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9994, loss_cls: 0.3818, loss: 0.3818 +2025-06-25 00:16:59,162 - pyskl - INFO - Epoch [79][1100/1281] lr: 1.149e-02, eta: 10:46:05, time: 0.433, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9981, loss_cls: 0.3656, loss: 0.3656 +2025-06-25 00:17:48,264 - pyskl - INFO - Epoch [79][1200/1281] lr: 1.147e-02, eta: 10:45:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9981, loss_cls: 0.3720, loss: 0.3720 +2025-06-25 00:18:28,356 - pyskl - INFO - Saving checkpoint at 79 epochs +2025-06-25 00:19:26,606 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:19:26,661 - pyskl - INFO - +top1_acc 0.8627 +top5_acc 0.9886 +2025-06-25 00:19:26,661 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:19:26,668 - pyskl - INFO - +mean_acc 0.8323 +2025-06-25 00:19:26,670 - pyskl - INFO - Epoch(val) [79][533] top1_acc: 0.8627, top5_acc: 0.9886, mean_class_accuracy: 0.8323 +2025-06-25 00:20:47,222 - pyskl - INFO - Epoch [80][100/1281] lr: 1.143e-02, eta: 10:44:15, time: 0.805, data_time: 0.194, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.3845, loss: 0.3845 +2025-06-25 00:21:36,368 - pyskl - INFO - Epoch [80][200/1281] lr: 1.141e-02, eta: 10:43:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 1.0000, loss_cls: 0.3586, loss: 0.3586 +2025-06-25 00:22:25,382 - pyskl - INFO - Epoch [80][300/1281] lr: 1.139e-02, eta: 10:43:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9981, loss_cls: 0.3412, loss: 0.3412 +2025-06-25 00:23:14,375 - pyskl - INFO - Epoch [80][400/1281] lr: 1.137e-02, eta: 10:42:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9988, loss_cls: 0.4066, loss: 0.4066 +2025-06-25 00:24:03,522 - pyskl - INFO - Epoch [80][500/1281] lr: 1.135e-02, eta: 10:41:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9981, loss_cls: 0.3166, loss: 0.3166 +2025-06-25 00:24:52,734 - pyskl - INFO - Epoch [80][600/1281] lr: 1.133e-02, eta: 10:41:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9975, loss_cls: 0.3063, loss: 0.3063 +2025-06-25 00:25:41,859 - pyskl - INFO - Epoch [80][700/1281] lr: 1.131e-02, eta: 10:40:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9994, loss_cls: 0.3008, loss: 0.3008 +2025-06-25 00:26:22,593 - pyskl - INFO - Epoch [80][800/1281] lr: 1.129e-02, eta: 10:39:50, time: 0.407, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3376, loss: 0.3376 +2025-06-25 00:27:11,283 - pyskl - INFO - Epoch [80][900/1281] lr: 1.127e-02, eta: 10:39:13, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9975, loss_cls: 0.4101, loss: 0.4101 +2025-06-25 00:27:36,318 - pyskl - INFO - Epoch [80][1000/1281] lr: 1.125e-02, eta: 10:38:15, time: 0.250, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 1.0000, loss_cls: 0.3197, loss: 0.3197 +2025-06-25 00:28:20,187 - pyskl - INFO - Epoch [80][1100/1281] lr: 1.123e-02, eta: 10:37:34, time: 0.439, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9981, loss_cls: 0.3279, loss: 0.3279 +2025-06-25 00:29:09,499 - pyskl - INFO - Epoch [80][1200/1281] lr: 1.121e-02, eta: 10:36:57, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9975, loss_cls: 0.3846, loss: 0.3846 +2025-06-25 00:29:50,163 - pyskl - INFO - Saving checkpoint at 80 epochs +2025-06-25 00:30:48,575 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:30:48,631 - pyskl - INFO - +top1_acc 0.8774 +top5_acc 0.9900 +2025-06-25 00:30:48,632 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:30:48,639 - pyskl - INFO - +mean_acc 0.8417 +2025-06-25 00:30:48,641 - pyskl - INFO - Epoch(val) [80][533] top1_acc: 0.8774, top5_acc: 0.9900, mean_class_accuracy: 0.8417 +2025-06-25 00:32:09,333 - pyskl - INFO - Epoch [81][100/1281] lr: 1.117e-02, eta: 10:35:43, time: 0.807, data_time: 0.191, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9988, loss_cls: 0.2957, loss: 0.2957 +2025-06-25 00:32:58,526 - pyskl - INFO - Epoch [81][200/1281] lr: 1.115e-02, eta: 10:35:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 1.0000, loss_cls: 0.3337, loss: 0.3337 +2025-06-25 00:33:47,275 - pyskl - INFO - Epoch [81][300/1281] lr: 1.113e-02, eta: 10:34:29, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9988, loss_cls: 0.3164, loss: 0.3164 +2025-06-25 00:34:36,143 - pyskl - INFO - Epoch [81][400/1281] lr: 1.111e-02, eta: 10:33:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9981, loss_cls: 0.3560, loss: 0.3560 +2025-06-25 00:35:25,149 - pyskl - INFO - Epoch [81][500/1281] lr: 1.109e-02, eta: 10:33:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 1.0000, loss_cls: 0.3253, loss: 0.3253 +2025-06-25 00:36:13,966 - pyskl - INFO - Epoch [81][600/1281] lr: 1.107e-02, eta: 10:32:38, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 1.0000, loss_cls: 0.3897, loss: 0.3897 +2025-06-25 00:37:02,863 - pyskl - INFO - Epoch [81][700/1281] lr: 1.105e-02, eta: 10:32:00, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9981, loss_cls: 0.3567, loss: 0.3567 +2025-06-25 00:37:42,481 - pyskl - INFO - Epoch [81][800/1281] lr: 1.103e-02, eta: 10:31:15, time: 0.396, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9975, loss_cls: 0.3746, loss: 0.3746 +2025-06-25 00:38:33,496 - pyskl - INFO - Epoch [81][900/1281] lr: 1.101e-02, eta: 10:30:40, time: 0.510, data_time: 0.001, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.3839, loss: 0.3839 +2025-06-25 00:38:57,677 - pyskl - INFO - Epoch [81][1000/1281] lr: 1.099e-02, eta: 10:29:41, time: 0.242, data_time: 0.001, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9981, loss_cls: 0.4216, loss: 0.4216 +2025-06-25 00:39:42,704 - pyskl - INFO - Epoch [81][1100/1281] lr: 1.097e-02, eta: 10:29:01, time: 0.450, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9988, loss_cls: 0.3941, loss: 0.3941 +2025-06-25 00:40:31,615 - pyskl - INFO - Epoch [81][1200/1281] lr: 1.095e-02, eta: 10:28:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9981, loss_cls: 0.3531, loss: 0.3531 +2025-06-25 00:41:11,565 - pyskl - INFO - Saving checkpoint at 81 epochs +2025-06-25 00:42:10,073 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:42:10,151 - pyskl - INFO - +top1_acc 0.8749 +top5_acc 0.9910 +2025-06-25 00:42:10,152 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:42:10,160 - pyskl - INFO - +mean_acc 0.8319 +2025-06-25 00:42:10,163 - pyskl - INFO - Epoch(val) [81][533] top1_acc: 0.8749, top5_acc: 0.9910, mean_class_accuracy: 0.8319 +2025-06-25 00:43:29,255 - pyskl - INFO - Epoch [82][100/1281] lr: 1.091e-02, eta: 10:27:08, time: 0.791, data_time: 0.189, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9981, loss_cls: 0.3509, loss: 0.3509 +2025-06-25 00:44:18,166 - pyskl - INFO - Epoch [82][200/1281] lr: 1.089e-02, eta: 10:26:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3593, loss: 0.3593 +2025-06-25 00:45:07,147 - pyskl - INFO - Epoch [82][300/1281] lr: 1.087e-02, eta: 10:25:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9975, loss_cls: 0.3840, loss: 0.3840 +2025-06-25 00:45:56,343 - pyskl - INFO - Epoch [82][400/1281] lr: 1.085e-02, eta: 10:25:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.3141, loss: 0.3141 +2025-06-25 00:46:45,652 - pyskl - INFO - Epoch [82][500/1281] lr: 1.083e-02, eta: 10:24:39, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9988, loss_cls: 0.2967, loss: 0.2967 +2025-06-25 00:47:34,618 - pyskl - INFO - Epoch [82][600/1281] lr: 1.081e-02, eta: 10:24:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3521, loss: 0.3521 +2025-06-25 00:48:23,542 - pyskl - INFO - Epoch [82][700/1281] lr: 1.079e-02, eta: 10:23:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9975, loss_cls: 0.3293, loss: 0.3293 +2025-06-25 00:49:02,721 - pyskl - INFO - Epoch [82][800/1281] lr: 1.077e-02, eta: 10:22:39, time: 0.392, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3590, loss: 0.3590 +2025-06-25 00:49:53,683 - pyskl - INFO - Epoch [82][900/1281] lr: 1.075e-02, eta: 10:22:03, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9988, loss_cls: 0.3531, loss: 0.3531 +2025-06-25 00:50:16,712 - pyskl - INFO - Epoch [82][1000/1281] lr: 1.073e-02, eta: 10:21:04, time: 0.230, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3543, loss: 0.3543 +2025-06-25 00:51:01,006 - pyskl - INFO - Epoch [82][1100/1281] lr: 1.071e-02, eta: 10:20:23, time: 0.443, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9988, loss_cls: 0.4042, loss: 0.4042 +2025-06-25 00:51:49,959 - pyskl - INFO - Epoch [82][1200/1281] lr: 1.069e-02, eta: 10:19:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 1.0000, loss_cls: 0.3774, loss: 0.3774 +2025-06-25 00:52:29,920 - pyskl - INFO - Saving checkpoint at 82 epochs +2025-06-25 00:53:28,283 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:53:28,338 - pyskl - INFO - +top1_acc 0.8708 +top5_acc 0.9885 +2025-06-25 00:53:28,339 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:53:28,346 - pyskl - INFO - +mean_acc 0.8236 +2025-06-25 00:53:28,348 - pyskl - INFO - Epoch(val) [82][533] top1_acc: 0.8708, top5_acc: 0.9885, mean_class_accuracy: 0.8236 +2025-06-25 00:54:48,231 - pyskl - INFO - Epoch [83][100/1281] lr: 1.065e-02, eta: 10:18:31, time: 0.799, data_time: 0.186, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9975, loss_cls: 0.3693, loss: 0.3693 +2025-06-25 00:55:37,520 - pyskl - INFO - Epoch [83][200/1281] lr: 1.063e-02, eta: 10:17:54, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9988, loss_cls: 0.3233, loss: 0.3233 +2025-06-25 00:56:26,874 - pyskl - INFO - Epoch [83][300/1281] lr: 1.061e-02, eta: 10:17:16, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9988, loss_cls: 0.3242, loss: 0.3242 +2025-06-25 00:57:15,994 - pyskl - INFO - Epoch [83][400/1281] lr: 1.059e-02, eta: 10:16:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9994, loss_cls: 0.3705, loss: 0.3705 +2025-06-25 00:58:05,511 - pyskl - INFO - Epoch [83][500/1281] lr: 1.057e-02, eta: 10:16:02, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9981, loss_cls: 0.3407, loss: 0.3407 +2025-06-25 00:58:54,661 - pyskl - INFO - Epoch [83][600/1281] lr: 1.055e-02, eta: 10:15:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9994, loss_cls: 0.3259, loss: 0.3259 +2025-06-25 00:59:43,723 - pyskl - INFO - Epoch [83][700/1281] lr: 1.053e-02, eta: 10:14:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9975, loss_cls: 0.3375, loss: 0.3375 +2025-06-25 01:00:23,790 - pyskl - INFO - Epoch [83][800/1281] lr: 1.051e-02, eta: 10:14:02, time: 0.401, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9994, loss_cls: 0.3255, loss: 0.3255 +2025-06-25 01:01:14,743 - pyskl - INFO - Epoch [83][900/1281] lr: 1.049e-02, eta: 10:13:26, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9969, loss_cls: 0.3318, loss: 0.3318 +2025-06-25 01:01:37,992 - pyskl - INFO - Epoch [83][1000/1281] lr: 1.047e-02, eta: 10:12:28, time: 0.232, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9988, loss_cls: 0.3880, loss: 0.3880 +2025-06-25 01:02:21,942 - pyskl - INFO - Epoch [83][1100/1281] lr: 1.045e-02, eta: 10:11:46, time: 0.439, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9994, loss_cls: 0.3959, loss: 0.3959 +2025-06-25 01:03:10,786 - pyskl - INFO - Epoch [83][1200/1281] lr: 1.043e-02, eta: 10:11:09, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9994, loss_cls: 0.3851, loss: 0.3851 +2025-06-25 01:03:50,792 - pyskl - INFO - Saving checkpoint at 83 epochs +2025-06-25 01:04:49,602 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:04:49,658 - pyskl - INFO - +top1_acc 0.8743 +top5_acc 0.9896 +2025-06-25 01:04:49,658 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:04:49,666 - pyskl - INFO - +mean_acc 0.8454 +2025-06-25 01:04:49,668 - pyskl - INFO - Epoch(val) [83][533] top1_acc: 0.8743, top5_acc: 0.9896, mean_class_accuracy: 0.8454 +2025-06-25 01:06:10,925 - pyskl - INFO - Epoch [84][100/1281] lr: 1.040e-02, eta: 10:09:54, time: 0.812, data_time: 0.190, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2876, loss: 0.2876 +2025-06-25 01:06:59,699 - pyskl - INFO - Epoch [84][200/1281] lr: 1.038e-02, eta: 10:09:17, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.2773, loss: 0.2773 +2025-06-25 01:07:48,351 - pyskl - INFO - Epoch [84][300/1281] lr: 1.036e-02, eta: 10:08:39, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.2613, loss: 0.2613 +2025-06-25 01:08:37,121 - pyskl - INFO - Epoch [84][400/1281] lr: 1.034e-02, eta: 10:08:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9988, loss_cls: 0.2957, loss: 0.2957 +2025-06-25 01:09:25,960 - pyskl - INFO - Epoch [84][500/1281] lr: 1.031e-02, eta: 10:07:23, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.2905, loss: 0.2905 +2025-06-25 01:10:14,754 - pyskl - INFO - Epoch [84][600/1281] lr: 1.029e-02, eta: 10:06:45, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9988, loss_cls: 0.2908, loss: 0.2908 +2025-06-25 01:11:03,592 - pyskl - INFO - Epoch [84][700/1281] lr: 1.027e-02, eta: 10:06:07, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9988, loss_cls: 0.3264, loss: 0.3264 +2025-06-25 01:11:41,374 - pyskl - INFO - Epoch [84][800/1281] lr: 1.025e-02, eta: 10:05:21, time: 0.378, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9988, loss_cls: 0.3214, loss: 0.3214 +2025-06-25 01:12:32,308 - pyskl - INFO - Epoch [84][900/1281] lr: 1.023e-02, eta: 10:04:45, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9981, loss_cls: 0.3405, loss: 0.3405 +2025-06-25 01:12:56,714 - pyskl - INFO - Epoch [84][1000/1281] lr: 1.021e-02, eta: 10:03:47, time: 0.244, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9994, loss_cls: 0.3625, loss: 0.3625 +2025-06-25 01:13:42,417 - pyskl - INFO - Epoch [84][1100/1281] lr: 1.019e-02, eta: 10:03:07, time: 0.457, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9981, loss_cls: 0.3167, loss: 0.3167 +2025-06-25 01:14:31,403 - pyskl - INFO - Epoch [84][1200/1281] lr: 1.017e-02, eta: 10:02:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9981, loss_cls: 0.3434, loss: 0.3434 +2025-06-25 01:15:11,645 - pyskl - INFO - Saving checkpoint at 84 epochs +2025-06-25 01:16:10,143 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:16:10,210 - pyskl - INFO - +top1_acc 0.8825 +top5_acc 0.9908 +2025-06-25 01:16:10,210 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:16:10,218 - pyskl - INFO - +mean_acc 0.8418 +2025-06-25 01:16:10,223 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_65.pth was removed +2025-06-25 01:16:10,418 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_84.pth. +2025-06-25 01:16:10,418 - pyskl - INFO - Best top1_acc is 0.8825 at 84 epoch. +2025-06-25 01:16:10,421 - pyskl - INFO - Epoch(val) [84][533] top1_acc: 0.8825, top5_acc: 0.9908, mean_class_accuracy: 0.8418 +2025-06-25 01:17:30,612 - pyskl - INFO - Epoch [85][100/1281] lr: 1.014e-02, eta: 10:01:14, time: 0.802, data_time: 0.185, memory: 4083, top1_acc: 0.9425, top5_acc: 1.0000, loss_cls: 0.3137, loss: 0.3137 +2025-06-25 01:18:19,193 - pyskl - INFO - Epoch [85][200/1281] lr: 1.012e-02, eta: 10:00:36, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 1.0000, loss_cls: 0.3064, loss: 0.3064 +2025-06-25 01:19:08,018 - pyskl - INFO - Epoch [85][300/1281] lr: 1.010e-02, eta: 9:59:58, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9981, loss_cls: 0.3014, loss: 0.3014 +2025-06-25 01:19:57,341 - pyskl - INFO - Epoch [85][400/1281] lr: 1.008e-02, eta: 9:59:20, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2827, loss: 0.2827 +2025-06-25 01:20:46,682 - pyskl - INFO - Epoch [85][500/1281] lr: 1.006e-02, eta: 9:58:43, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9975, loss_cls: 0.3351, loss: 0.3351 +2025-06-25 01:21:35,701 - pyskl - INFO - Epoch [85][600/1281] lr: 1.004e-02, eta: 9:58:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 1.0000, loss_cls: 0.3302, loss: 0.3302 +2025-06-25 01:22:24,356 - pyskl - INFO - Epoch [85][700/1281] lr: 1.002e-02, eta: 9:57:27, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9994, loss_cls: 0.3104, loss: 0.3104 +2025-06-25 01:23:01,230 - pyskl - INFO - Epoch [85][800/1281] lr: 9.998e-03, eta: 9:56:40, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.3282, loss: 0.3282 +2025-06-25 01:23:52,114 - pyskl - INFO - Epoch [85][900/1281] lr: 9.978e-03, eta: 9:56:03, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.2785, loss: 0.2785 +2025-06-25 01:24:16,719 - pyskl - INFO - Epoch [85][1000/1281] lr: 9.958e-03, eta: 9:55:06, time: 0.246, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9981, loss_cls: 0.3058, loss: 0.3058 +2025-06-25 01:25:03,313 - pyskl - INFO - Epoch [85][1100/1281] lr: 9.937e-03, eta: 9:54:27, time: 0.466, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9981, loss_cls: 0.3691, loss: 0.3691 +2025-06-25 01:25:52,127 - pyskl - INFO - Epoch [85][1200/1281] lr: 9.917e-03, eta: 9:53:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9975, loss_cls: 0.4000, loss: 0.4000 +2025-06-25 01:26:31,702 - pyskl - INFO - Saving checkpoint at 85 epochs +2025-06-25 01:27:29,608 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:27:29,676 - pyskl - INFO - +top1_acc 0.8742 +top5_acc 0.9901 +2025-06-25 01:27:29,676 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:27:29,685 - pyskl - INFO - +mean_acc 0.8304 +2025-06-25 01:27:29,687 - pyskl - INFO - Epoch(val) [85][533] top1_acc: 0.8742, top5_acc: 0.9901, mean_class_accuracy: 0.8304 +2025-06-25 01:28:49,862 - pyskl - INFO - Epoch [86][100/1281] lr: 9.881e-03, eta: 9:52:33, time: 0.802, data_time: 0.189, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2886, loss: 0.2886 +2025-06-25 01:29:38,520 - pyskl - INFO - Epoch [86][200/1281] lr: 9.861e-03, eta: 9:51:55, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 1.0000, loss_cls: 0.3104, loss: 0.3104 +2025-06-25 01:30:27,155 - pyskl - INFO - Epoch [86][300/1281] lr: 9.841e-03, eta: 9:51:17, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3154, loss: 0.3154 +2025-06-25 01:31:16,109 - pyskl - INFO - Epoch [86][400/1281] lr: 9.821e-03, eta: 9:50:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2719, loss: 0.2719 +2025-06-25 01:32:04,774 - pyskl - INFO - Epoch [86][500/1281] lr: 9.801e-03, eta: 9:50:00, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9981, loss_cls: 0.3269, loss: 0.3269 +2025-06-25 01:32:53,950 - pyskl - INFO - Epoch [86][600/1281] lr: 9.781e-03, eta: 9:49:22, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9994, loss_cls: 0.3460, loss: 0.3460 +2025-06-25 01:33:42,777 - pyskl - INFO - Epoch [86][700/1281] lr: 9.762e-03, eta: 9:48:44, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9994, loss_cls: 0.3611, loss: 0.3611 +2025-06-25 01:34:18,679 - pyskl - INFO - Epoch [86][800/1281] lr: 9.742e-03, eta: 9:47:56, time: 0.359, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2916, loss: 0.2916 +2025-06-25 01:35:09,691 - pyskl - INFO - Epoch [86][900/1281] lr: 9.722e-03, eta: 9:47:20, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9975, loss_cls: 0.3583, loss: 0.3583 +2025-06-25 01:35:34,219 - pyskl - INFO - Epoch [86][1000/1281] lr: 9.702e-03, eta: 9:46:23, time: 0.245, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9994, loss_cls: 0.3636, loss: 0.3636 +2025-06-25 01:36:20,459 - pyskl - INFO - Epoch [86][1100/1281] lr: 9.682e-03, eta: 9:45:43, time: 0.462, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9981, loss_cls: 0.3805, loss: 0.3805 +2025-06-25 01:37:09,616 - pyskl - INFO - Epoch [86][1200/1281] lr: 9.662e-03, eta: 9:45:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9988, loss_cls: 0.3581, loss: 0.3581 +2025-06-25 01:37:49,481 - pyskl - INFO - Saving checkpoint at 86 epochs +2025-06-25 01:38:47,936 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:38:47,991 - pyskl - INFO - +top1_acc 0.8770 +top5_acc 0.9911 +2025-06-25 01:38:47,991 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:38:47,998 - pyskl - INFO - +mean_acc 0.8318 +2025-06-25 01:38:48,000 - pyskl - INFO - Epoch(val) [86][533] top1_acc: 0.8770, top5_acc: 0.9911, mean_class_accuracy: 0.8318 +2025-06-25 01:40:08,580 - pyskl - INFO - Epoch [87][100/1281] lr: 9.626e-03, eta: 9:43:50, time: 0.806, data_time: 0.188, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9981, loss_cls: 0.3256, loss: 0.3256 +2025-06-25 01:40:57,210 - pyskl - INFO - Epoch [87][200/1281] lr: 9.606e-03, eta: 9:43:11, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2279, loss: 0.2279 +2025-06-25 01:41:46,203 - pyskl - INFO - Epoch [87][300/1281] lr: 9.586e-03, eta: 9:42:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2781, loss: 0.2781 +2025-06-25 01:42:35,314 - pyskl - INFO - Epoch [87][400/1281] lr: 9.566e-03, eta: 9:41:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2491, loss: 0.2491 +2025-06-25 01:43:24,844 - pyskl - INFO - Epoch [87][500/1281] lr: 9.546e-03, eta: 9:41:17, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2432, loss: 0.2432 +2025-06-25 01:44:13,632 - pyskl - INFO - Epoch [87][600/1281] lr: 9.527e-03, eta: 9:40:39, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9988, loss_cls: 0.2706, loss: 0.2706 +2025-06-25 01:45:02,543 - pyskl - INFO - Epoch [87][700/1281] lr: 9.507e-03, eta: 9:40:00, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9969, loss_cls: 0.3032, loss: 0.3032 +2025-06-25 01:45:38,926 - pyskl - INFO - Epoch [87][800/1281] lr: 9.487e-03, eta: 9:39:13, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2982, loss: 0.2982 +2025-06-25 01:46:30,060 - pyskl - INFO - Epoch [87][900/1281] lr: 9.467e-03, eta: 9:38:36, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9994, loss_cls: 0.2745, loss: 0.2745 +2025-06-25 01:46:55,001 - pyskl - INFO - Epoch [87][1000/1281] lr: 9.447e-03, eta: 9:37:40, time: 0.249, data_time: 0.001, memory: 4083, top1_acc: 0.9481, top5_acc: 1.0000, loss_cls: 0.3172, loss: 0.3172 +2025-06-25 01:47:42,303 - pyskl - INFO - Epoch [87][1100/1281] lr: 9.427e-03, eta: 9:37:01, time: 0.473, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3132, loss: 0.3132 +2025-06-25 01:48:31,002 - pyskl - INFO - Epoch [87][1200/1281] lr: 9.408e-03, eta: 9:36:22, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 1.0000, loss_cls: 0.3754, loss: 0.3754 +2025-06-25 01:49:11,095 - pyskl - INFO - Saving checkpoint at 87 epochs +2025-06-25 01:50:09,459 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:50:09,527 - pyskl - INFO - +top1_acc 0.8865 +top5_acc 0.9925 +2025-06-25 01:50:09,528 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:50:09,535 - pyskl - INFO - +mean_acc 0.8514 +2025-06-25 01:50:09,540 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_84.pth was removed +2025-06-25 01:50:09,735 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_87.pth. +2025-06-25 01:50:09,736 - pyskl - INFO - Best top1_acc is 0.8865 at 87 epoch. +2025-06-25 01:50:09,738 - pyskl - INFO - Epoch(val) [87][533] top1_acc: 0.8865, top5_acc: 0.9925, mean_class_accuracy: 0.8514 +2025-06-25 01:51:30,326 - pyskl - INFO - Epoch [88][100/1281] lr: 9.372e-03, eta: 9:35:07, time: 0.806, data_time: 0.188, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.2978, loss: 0.2978 +2025-06-25 01:52:19,023 - pyskl - INFO - Epoch [88][200/1281] lr: 9.352e-03, eta: 9:34:29, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2571, loss: 0.2571 +2025-06-25 01:53:08,182 - pyskl - INFO - Epoch [88][300/1281] lr: 9.332e-03, eta: 9:33:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9988, loss_cls: 0.2398, loss: 0.2398 +2025-06-25 01:53:57,192 - pyskl - INFO - Epoch [88][400/1281] lr: 9.312e-03, eta: 9:33:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.2751, loss: 0.2751 +2025-06-25 01:54:46,591 - pyskl - INFO - Epoch [88][500/1281] lr: 9.293e-03, eta: 9:32:34, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9994, loss_cls: 0.2934, loss: 0.2934 +2025-06-25 01:55:35,379 - pyskl - INFO - Epoch [88][600/1281] lr: 9.273e-03, eta: 9:31:55, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9981, loss_cls: 0.2883, loss: 0.2883 +2025-06-25 01:56:24,115 - pyskl - INFO - Epoch [88][700/1281] lr: 9.253e-03, eta: 9:31:17, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.2987, loss: 0.2987 +2025-06-25 01:56:58,270 - pyskl - INFO - Epoch [88][800/1281] lr: 9.233e-03, eta: 9:30:28, time: 0.342, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 1.0000, loss_cls: 0.3022, loss: 0.3022 +2025-06-25 01:57:49,229 - pyskl - INFO - Epoch [88][900/1281] lr: 9.214e-03, eta: 9:29:50, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2585, loss: 0.2585 +2025-06-25 01:58:14,187 - pyskl - INFO - Epoch [88][1000/1281] lr: 9.194e-03, eta: 9:28:55, time: 0.250, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9994, loss_cls: 0.3464, loss: 0.3464 +2025-06-25 01:59:01,797 - pyskl - INFO - Epoch [88][1100/1281] lr: 9.174e-03, eta: 9:28:15, time: 0.476, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9969, loss_cls: 0.3668, loss: 0.3668 +2025-06-25 01:59:50,986 - pyskl - INFO - Epoch [88][1200/1281] lr: 9.155e-03, eta: 9:27:37, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.3070, loss: 0.3070 +2025-06-25 02:00:31,162 - pyskl - INFO - Saving checkpoint at 88 epochs +2025-06-25 02:01:29,290 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:01:29,357 - pyskl - INFO - +top1_acc 0.8790 +top5_acc 0.9887 +2025-06-25 02:01:29,358 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:01:29,366 - pyskl - INFO - +mean_acc 0.8449 +2025-06-25 02:01:29,368 - pyskl - INFO - Epoch(val) [88][533] top1_acc: 0.8790, top5_acc: 0.9887, mean_class_accuracy: 0.8449 +2025-06-25 02:02:50,183 - pyskl - INFO - Epoch [89][100/1281] lr: 9.119e-03, eta: 9:26:22, time: 0.808, data_time: 0.185, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2486, loss: 0.2486 +2025-06-25 02:03:39,040 - pyskl - INFO - Epoch [89][200/1281] lr: 9.099e-03, eta: 9:25:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2666, loss: 0.2666 +2025-06-25 02:04:28,049 - pyskl - INFO - Epoch [89][300/1281] lr: 9.080e-03, eta: 9:25:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2557, loss: 0.2557 +2025-06-25 02:05:17,109 - pyskl - INFO - Epoch [89][400/1281] lr: 9.060e-03, eta: 9:24:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3162, loss: 0.3162 +2025-06-25 02:06:06,561 - pyskl - INFO - Epoch [89][500/1281] lr: 9.040e-03, eta: 9:23:48, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2891, loss: 0.2891 +2025-06-25 02:06:55,419 - pyskl - INFO - Epoch [89][600/1281] lr: 9.021e-03, eta: 9:23:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2926, loss: 0.2926 +2025-06-25 02:07:44,592 - pyskl - INFO - Epoch [89][700/1281] lr: 9.001e-03, eta: 9:22:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9981, loss_cls: 0.2914, loss: 0.2914 +2025-06-25 02:08:18,068 - pyskl - INFO - Epoch [89][800/1281] lr: 8.982e-03, eta: 9:21:41, time: 0.335, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9981, loss_cls: 0.2864, loss: 0.2864 +2025-06-25 02:09:08,809 - pyskl - INFO - Epoch [89][900/1281] lr: 8.962e-03, eta: 9:21:04, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.2999, loss: 0.2999 +2025-06-25 02:09:33,873 - pyskl - INFO - Epoch [89][1000/1281] lr: 8.942e-03, eta: 9:20:09, time: 0.251, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2679, loss: 0.2679 +2025-06-25 02:10:22,381 - pyskl - INFO - Epoch [89][1100/1281] lr: 8.923e-03, eta: 9:19:30, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.3080, loss: 0.3080 +2025-06-25 02:11:11,238 - pyskl - INFO - Epoch [89][1200/1281] lr: 8.903e-03, eta: 9:18:51, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9988, loss_cls: 0.2572, loss: 0.2572 +2025-06-25 02:11:51,498 - pyskl - INFO - Saving checkpoint at 89 epochs +2025-06-25 02:12:49,177 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:12:49,233 - pyskl - INFO - +top1_acc 0.8799 +top5_acc 0.9890 +2025-06-25 02:12:49,233 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:12:49,240 - pyskl - INFO - +mean_acc 0.8400 +2025-06-25 02:12:49,242 - pyskl - INFO - Epoch(val) [89][533] top1_acc: 0.8799, top5_acc: 0.9890, mean_class_accuracy: 0.8400 +2025-06-25 02:14:08,559 - pyskl - INFO - Epoch [90][100/1281] lr: 8.868e-03, eta: 9:17:35, time: 0.793, data_time: 0.185, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.2992, loss: 0.2992 +2025-06-25 02:14:57,676 - pyskl - INFO - Epoch [90][200/1281] lr: 8.848e-03, eta: 9:16:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.2840, loss: 0.2840 +2025-06-25 02:15:46,623 - pyskl - INFO - Epoch [90][300/1281] lr: 8.829e-03, eta: 9:16:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2651, loss: 0.2651 +2025-06-25 02:16:35,657 - pyskl - INFO - Epoch [90][400/1281] lr: 8.809e-03, eta: 9:15:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2396, loss: 0.2396 +2025-06-25 02:17:24,497 - pyskl - INFO - Epoch [90][500/1281] lr: 8.790e-03, eta: 9:15:00, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9994, loss_cls: 0.2102, loss: 0.2102 +2025-06-25 02:18:13,280 - pyskl - INFO - Epoch [90][600/1281] lr: 8.770e-03, eta: 9:14:21, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2426, loss: 0.2426 +2025-06-25 02:19:02,279 - pyskl - INFO - Epoch [90][700/1281] lr: 8.751e-03, eta: 9:13:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 1.0000, loss_cls: 0.3182, loss: 0.3182 +2025-06-25 02:19:36,112 - pyskl - INFO - Epoch [90][800/1281] lr: 8.731e-03, eta: 9:12:53, time: 0.338, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9981, loss_cls: 0.2894, loss: 0.2894 +2025-06-25 02:20:26,922 - pyskl - INFO - Epoch [90][900/1281] lr: 8.712e-03, eta: 9:12:16, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9981, loss_cls: 0.2795, loss: 0.2795 +2025-06-25 02:20:52,980 - pyskl - INFO - Epoch [90][1000/1281] lr: 8.692e-03, eta: 9:11:21, time: 0.261, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.3180, loss: 0.3180 +2025-06-25 02:21:41,383 - pyskl - INFO - Epoch [90][1100/1281] lr: 8.673e-03, eta: 9:10:42, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9988, loss_cls: 0.3868, loss: 0.3868 +2025-06-25 02:22:30,161 - pyskl - INFO - Epoch [90][1200/1281] lr: 8.653e-03, eta: 9:10:03, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.3010, loss: 0.3010 +2025-06-25 02:23:10,036 - pyskl - INFO - Saving checkpoint at 90 epochs +2025-06-25 02:24:08,269 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:24:08,324 - pyskl - INFO - +top1_acc 0.8802 +top5_acc 0.9897 +2025-06-25 02:24:08,324 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:24:08,331 - pyskl - INFO - +mean_acc 0.8612 +2025-06-25 02:24:08,333 - pyskl - INFO - Epoch(val) [90][533] top1_acc: 0.8802, top5_acc: 0.9897, mean_class_accuracy: 0.8612 +2025-06-25 02:25:27,686 - pyskl - INFO - Epoch [91][100/1281] lr: 8.618e-03, eta: 9:08:47, time: 0.793, data_time: 0.190, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.2884, loss: 0.2884 +2025-06-25 02:26:16,857 - pyskl - INFO - Epoch [91][200/1281] lr: 8.599e-03, eta: 9:08:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3051, loss: 0.3051 +2025-06-25 02:27:05,582 - pyskl - INFO - Epoch [91][300/1281] lr: 8.579e-03, eta: 9:07:29, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2551, loss: 0.2551 +2025-06-25 02:27:54,237 - pyskl - INFO - Epoch [91][400/1281] lr: 8.560e-03, eta: 9:06:50, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9975, loss_cls: 0.2839, loss: 0.2839 +2025-06-25 02:28:42,996 - pyskl - INFO - Epoch [91][500/1281] lr: 8.540e-03, eta: 9:06:11, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.2838, loss: 0.2838 +2025-06-25 02:29:32,009 - pyskl - INFO - Epoch [91][600/1281] lr: 8.521e-03, eta: 9:05:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9988, loss_cls: 0.3169, loss: 0.3169 +2025-06-25 02:30:20,895 - pyskl - INFO - Epoch [91][700/1281] lr: 8.502e-03, eta: 9:04:53, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2434, loss: 0.2434 +2025-06-25 02:30:52,660 - pyskl - INFO - Epoch [91][800/1281] lr: 8.482e-03, eta: 9:04:03, time: 0.318, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2368, loss: 0.2368 +2025-06-25 02:31:43,787 - pyskl - INFO - Epoch [91][900/1281] lr: 8.463e-03, eta: 9:03:25, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9988, loss_cls: 0.2891, loss: 0.2891 +2025-06-25 02:32:13,054 - pyskl - INFO - Epoch [91][1000/1281] lr: 8.444e-03, eta: 9:02:33, time: 0.293, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.2784, loss: 0.2784 +2025-06-25 02:33:01,973 - pyskl - INFO - Epoch [91][1100/1281] lr: 8.424e-03, eta: 9:01:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2845, loss: 0.2845 +2025-06-25 02:33:51,314 - pyskl - INFO - Epoch [91][1200/1281] lr: 8.405e-03, eta: 9:01:15, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 1.0000, loss_cls: 0.3289, loss: 0.3289 +2025-06-25 02:34:31,239 - pyskl - INFO - Saving checkpoint at 91 epochs +2025-06-25 02:35:29,524 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:35:29,592 - pyskl - INFO - +top1_acc 0.8748 +top5_acc 0.9876 +2025-06-25 02:35:29,593 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:35:29,600 - pyskl - INFO - +mean_acc 0.8431 +2025-06-25 02:35:29,602 - pyskl - INFO - Epoch(val) [91][533] top1_acc: 0.8748, top5_acc: 0.9876, mean_class_accuracy: 0.8431 +2025-06-25 02:36:50,294 - pyskl - INFO - Epoch [92][100/1281] lr: 8.370e-03, eta: 9:00:00, time: 0.807, data_time: 0.190, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2573, loss: 0.2573 +2025-06-25 02:37:39,149 - pyskl - INFO - Epoch [92][200/1281] lr: 8.351e-03, eta: 8:59:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 0.9994, loss_cls: 0.2257, loss: 0.2257 +2025-06-25 02:38:28,105 - pyskl - INFO - Epoch [92][300/1281] lr: 8.332e-03, eta: 8:58:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2668, loss: 0.2668 +2025-06-25 02:39:16,717 - pyskl - INFO - Epoch [92][400/1281] lr: 8.312e-03, eta: 8:58:02, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9988, loss_cls: 0.2983, loss: 0.2983 +2025-06-25 02:40:05,478 - pyskl - INFO - Epoch [92][500/1281] lr: 8.293e-03, eta: 8:57:23, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2222, loss: 0.2222 +2025-06-25 02:40:54,305 - pyskl - INFO - Epoch [92][600/1281] lr: 8.274e-03, eta: 8:56:44, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2556, loss: 0.2556 +2025-06-25 02:41:43,615 - pyskl - INFO - Epoch [92][700/1281] lr: 8.255e-03, eta: 8:56:05, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2890, loss: 0.2890 +2025-06-25 02:42:11,156 - pyskl - INFO - Epoch [92][800/1281] lr: 8.235e-03, eta: 8:55:12, time: 0.275, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9994, loss_cls: 0.2802, loss: 0.2802 +2025-06-25 02:43:01,819 - pyskl - INFO - Epoch [92][900/1281] lr: 8.216e-03, eta: 8:54:34, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2748, loss: 0.2748 +2025-06-25 02:43:34,295 - pyskl - INFO - Epoch [92][1000/1281] lr: 8.197e-03, eta: 8:53:45, time: 0.325, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2386, loss: 0.2386 +2025-06-25 02:44:23,455 - pyskl - INFO - Epoch [92][1100/1281] lr: 8.178e-03, eta: 8:53:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2583, loss: 0.2583 +2025-06-25 02:45:12,648 - pyskl - INFO - Epoch [92][1200/1281] lr: 8.159e-03, eta: 8:52:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2156, loss: 0.2156 +2025-06-25 02:45:53,064 - pyskl - INFO - Saving checkpoint at 92 epochs +2025-06-25 02:46:51,756 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:46:51,812 - pyskl - INFO - +top1_acc 0.8863 +top5_acc 0.9911 +2025-06-25 02:46:51,812 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:46:51,818 - pyskl - INFO - +mean_acc 0.8531 +2025-06-25 02:46:51,820 - pyskl - INFO - Epoch(val) [92][533] top1_acc: 0.8863, top5_acc: 0.9911, mean_class_accuracy: 0.8531 +2025-06-25 02:48:11,707 - pyskl - INFO - Epoch [93][100/1281] lr: 8.124e-03, eta: 8:51:10, time: 0.799, data_time: 0.189, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2733, loss: 0.2733 +2025-06-25 02:49:00,439 - pyskl - INFO - Epoch [93][200/1281] lr: 8.105e-03, eta: 8:50:31, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.2080, loss: 0.2080 +2025-06-25 02:49:49,324 - pyskl - INFO - Epoch [93][300/1281] lr: 8.086e-03, eta: 8:49:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9994, loss_cls: 0.2782, loss: 0.2782 +2025-06-25 02:50:38,280 - pyskl - INFO - Epoch [93][400/1281] lr: 8.067e-03, eta: 8:49:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2787, loss: 0.2787 +2025-06-25 02:51:26,962 - pyskl - INFO - Epoch [93][500/1281] lr: 8.047e-03, eta: 8:48:33, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9994, loss_cls: 0.2363, loss: 0.2363 +2025-06-25 02:52:15,905 - pyskl - INFO - Epoch [93][600/1281] lr: 8.028e-03, eta: 8:47:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9988, loss_cls: 0.2448, loss: 0.2448 +2025-06-25 02:53:04,552 - pyskl - INFO - Epoch [93][700/1281] lr: 8.009e-03, eta: 8:47:14, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2562, loss: 0.2562 +2025-06-25 02:53:33,556 - pyskl - INFO - Epoch [93][800/1281] lr: 7.990e-03, eta: 8:46:23, time: 0.290, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2470, loss: 0.2470 +2025-06-25 02:54:20,660 - pyskl - INFO - Epoch [93][900/1281] lr: 7.971e-03, eta: 8:45:42, time: 0.471, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2708, loss: 0.2708 +2025-06-25 02:54:53,896 - pyskl - INFO - Epoch [93][1000/1281] lr: 7.952e-03, eta: 8:44:53, time: 0.332, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2675, loss: 0.2675 +2025-06-25 02:55:42,726 - pyskl - INFO - Epoch [93][1100/1281] lr: 7.933e-03, eta: 8:44:14, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9994, loss_cls: 0.2665, loss: 0.2665 +2025-06-25 02:56:31,506 - pyskl - INFO - Epoch [93][1200/1281] lr: 7.914e-03, eta: 8:43:34, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2673, loss: 0.2673 +2025-06-25 02:57:11,472 - pyskl - INFO - Saving checkpoint at 93 epochs +2025-06-25 02:58:10,356 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:58:10,412 - pyskl - INFO - +top1_acc 0.8857 +top5_acc 0.9923 +2025-06-25 02:58:10,412 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:58:10,419 - pyskl - INFO - +mean_acc 0.8609 +2025-06-25 02:58:10,421 - pyskl - INFO - Epoch(val) [93][533] top1_acc: 0.8857, top5_acc: 0.9923, mean_class_accuracy: 0.8609 +2025-06-25 02:59:31,099 - pyskl - INFO - Epoch [94][100/1281] lr: 7.880e-03, eta: 8:42:18, time: 0.807, data_time: 0.191, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9994, loss_cls: 0.2226, loss: 0.2226 +2025-06-25 03:00:20,309 - pyskl - INFO - Epoch [94][200/1281] lr: 7.861e-03, eta: 8:41:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2205, loss: 0.2205 +2025-06-25 03:01:09,450 - pyskl - INFO - Epoch [94][300/1281] lr: 7.842e-03, eta: 8:41:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9988, loss_cls: 0.2457, loss: 0.2457 +2025-06-25 03:01:58,889 - pyskl - INFO - Epoch [94][400/1281] lr: 7.823e-03, eta: 8:40:21, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2134, loss: 0.2134 +2025-06-25 03:02:47,988 - pyskl - INFO - Epoch [94][500/1281] lr: 7.804e-03, eta: 8:39:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9988, loss_cls: 0.2815, loss: 0.2815 +2025-06-25 03:03:37,049 - pyskl - INFO - Epoch [94][600/1281] lr: 7.785e-03, eta: 8:39:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9994, loss_cls: 0.3037, loss: 0.3037 +2025-06-25 03:04:25,991 - pyskl - INFO - Epoch [94][700/1281] lr: 7.766e-03, eta: 8:38:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2454, loss: 0.2454 +2025-06-25 03:04:58,799 - pyskl - INFO - Epoch [94][800/1281] lr: 7.747e-03, eta: 8:37:34, time: 0.328, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2138, loss: 0.2138 +2025-06-25 03:05:40,477 - pyskl - INFO - Epoch [94][900/1281] lr: 7.728e-03, eta: 8:36:50, time: 0.417, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9994, loss_cls: 0.2431, loss: 0.2431 +2025-06-25 03:06:17,298 - pyskl - INFO - Epoch [94][1000/1281] lr: 7.709e-03, eta: 8:36:03, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2634, loss: 0.2634 +2025-06-25 03:07:06,143 - pyskl - INFO - Epoch [94][1100/1281] lr: 7.690e-03, eta: 8:35:24, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.2662, loss: 0.2662 +2025-06-25 03:07:55,341 - pyskl - INFO - Epoch [94][1200/1281] lr: 7.672e-03, eta: 8:34:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2353, loss: 0.2353 +2025-06-25 03:08:35,741 - pyskl - INFO - Saving checkpoint at 94 epochs +2025-06-25 03:09:34,255 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:09:34,315 - pyskl - INFO - +top1_acc 0.8911 +top5_acc 0.9911 +2025-06-25 03:09:34,315 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:09:34,322 - pyskl - INFO - +mean_acc 0.8594 +2025-06-25 03:09:34,326 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_87.pth was removed +2025-06-25 03:09:34,540 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_94.pth. +2025-06-25 03:09:34,540 - pyskl - INFO - Best top1_acc is 0.8911 at 94 epoch. +2025-06-25 03:09:34,543 - pyskl - INFO - Epoch(val) [94][533] top1_acc: 0.8911, top5_acc: 0.9911, mean_class_accuracy: 0.8594 +2025-06-25 03:10:53,059 - pyskl - INFO - Epoch [95][100/1281] lr: 7.637e-03, eta: 8:33:27, time: 0.785, data_time: 0.187, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2217, loss: 0.2217 +2025-06-25 03:11:41,964 - pyskl - INFO - Epoch [95][200/1281] lr: 7.619e-03, eta: 8:32:47, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9988, loss_cls: 0.2026, loss: 0.2026 +2025-06-25 03:12:30,944 - pyskl - INFO - Epoch [95][300/1281] lr: 7.600e-03, eta: 8:32:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9994, loss_cls: 0.2141, loss: 0.2141 +2025-06-25 03:13:19,847 - pyskl - INFO - Epoch [95][400/1281] lr: 7.581e-03, eta: 8:31:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2309, loss: 0.2309 +2025-06-25 03:14:08,610 - pyskl - INFO - Epoch [95][500/1281] lr: 7.562e-03, eta: 8:30:49, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2560, loss: 0.2560 +2025-06-25 03:14:57,283 - pyskl - INFO - Epoch [95][600/1281] lr: 7.543e-03, eta: 8:30:09, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 1.0000, loss_cls: 0.2400, loss: 0.2400 +2025-06-25 03:15:44,998 - pyskl - INFO - Epoch [95][700/1281] lr: 7.525e-03, eta: 8:29:29, time: 0.477, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2197, loss: 0.2197 +2025-06-25 03:16:20,589 - pyskl - INFO - Epoch [95][800/1281] lr: 7.506e-03, eta: 8:28:41, time: 0.356, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2438, loss: 0.2438 +2025-06-25 03:16:58,471 - pyskl - INFO - Epoch [95][900/1281] lr: 7.487e-03, eta: 8:27:55, time: 0.379, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9981, loss_cls: 0.2484, loss: 0.2484 +2025-06-25 03:17:36,546 - pyskl - INFO - Epoch [95][1000/1281] lr: 7.468e-03, eta: 8:27:10, time: 0.381, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2893, loss: 0.2893 +2025-06-25 03:18:25,517 - pyskl - INFO - Epoch [95][1100/1281] lr: 7.450e-03, eta: 8:26:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9994, loss_cls: 0.2991, loss: 0.2991 +2025-06-25 03:19:14,500 - pyskl - INFO - Epoch [95][1200/1281] lr: 7.431e-03, eta: 8:25:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2430, loss: 0.2430 +2025-06-25 03:19:54,522 - pyskl - INFO - Saving checkpoint at 95 epochs +2025-06-25 03:20:53,280 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:20:53,348 - pyskl - INFO - +top1_acc 0.8700 +top5_acc 0.9878 +2025-06-25 03:20:53,349 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:20:53,357 - pyskl - INFO - +mean_acc 0.8414 +2025-06-25 03:20:53,359 - pyskl - INFO - Epoch(val) [95][533] top1_acc: 0.8700, top5_acc: 0.9878, mean_class_accuracy: 0.8414 +2025-06-25 03:22:12,419 - pyskl - INFO - Epoch [96][100/1281] lr: 7.397e-03, eta: 8:24:33, time: 0.791, data_time: 0.190, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2403, loss: 0.2403 +2025-06-25 03:23:01,101 - pyskl - INFO - Epoch [96][200/1281] lr: 7.379e-03, eta: 8:23:53, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1720, loss: 0.1720 +2025-06-25 03:23:50,221 - pyskl - INFO - Epoch [96][300/1281] lr: 7.360e-03, eta: 8:23:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1729, loss: 0.1729 +2025-06-25 03:24:39,204 - pyskl - INFO - Epoch [96][400/1281] lr: 7.341e-03, eta: 8:22:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 0.9994, loss_cls: 0.1689, loss: 0.1689 +2025-06-25 03:25:28,277 - pyskl - INFO - Epoch [96][500/1281] lr: 7.323e-03, eta: 8:21:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2087, loss: 0.2087 +2025-06-25 03:26:17,179 - pyskl - INFO - Epoch [96][600/1281] lr: 7.304e-03, eta: 8:21:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2029, loss: 0.2029 +2025-06-25 03:27:03,304 - pyskl - INFO - Epoch [96][700/1281] lr: 7.286e-03, eta: 8:20:34, time: 0.461, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1712, loss: 0.1712 +2025-06-25 03:27:41,149 - pyskl - INFO - Epoch [96][800/1281] lr: 7.267e-03, eta: 8:19:48, time: 0.378, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2123, loss: 0.2123 +2025-06-25 03:28:16,731 - pyskl - INFO - Epoch [96][900/1281] lr: 7.249e-03, eta: 8:19:00, time: 0.356, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2600, loss: 0.2600 +2025-06-25 03:28:55,177 - pyskl - INFO - Epoch [96][1000/1281] lr: 7.230e-03, eta: 8:18:15, time: 0.384, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2143, loss: 0.2143 +2025-06-25 03:29:44,187 - pyskl - INFO - Epoch [96][1100/1281] lr: 7.211e-03, eta: 8:17:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2307, loss: 0.2307 +2025-06-25 03:30:32,706 - pyskl - INFO - Epoch [96][1200/1281] lr: 7.193e-03, eta: 8:16:55, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2642, loss: 0.2642 +2025-06-25 03:31:12,838 - pyskl - INFO - Saving checkpoint at 96 epochs +2025-06-25 03:32:11,199 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:32:11,255 - pyskl - INFO - +top1_acc 0.8740 +top5_acc 0.9891 +2025-06-25 03:32:11,255 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:32:11,261 - pyskl - INFO - +mean_acc 0.8416 +2025-06-25 03:32:11,262 - pyskl - INFO - Epoch(val) [96][533] top1_acc: 0.8740, top5_acc: 0.9891, mean_class_accuracy: 0.8416 +2025-06-25 03:33:29,607 - pyskl - INFO - Epoch [97][100/1281] lr: 7.159e-03, eta: 8:15:37, time: 0.783, data_time: 0.186, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2498, loss: 0.2498 +2025-06-25 03:34:18,426 - pyskl - INFO - Epoch [97][200/1281] lr: 7.141e-03, eta: 8:14:58, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2006, loss: 0.2006 +2025-06-25 03:35:07,514 - pyskl - INFO - Epoch [97][300/1281] lr: 7.123e-03, eta: 8:14:18, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2388, loss: 0.2388 +2025-06-25 03:35:56,374 - pyskl - INFO - Epoch [97][400/1281] lr: 7.104e-03, eta: 8:13:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1914, loss: 0.1914 +2025-06-25 03:36:45,302 - pyskl - INFO - Epoch [97][500/1281] lr: 7.086e-03, eta: 8:12:58, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1982, loss: 0.1982 +2025-06-25 03:37:34,805 - pyskl - INFO - Epoch [97][600/1281] lr: 7.067e-03, eta: 8:12:19, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1873, loss: 0.1873 +2025-06-25 03:38:21,183 - pyskl - INFO - Epoch [97][700/1281] lr: 7.049e-03, eta: 8:11:37, time: 0.464, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1818, loss: 0.1818 +2025-06-25 03:38:58,598 - pyskl - INFO - Epoch [97][800/1281] lr: 7.030e-03, eta: 8:10:51, time: 0.374, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.2030, loss: 0.2030 +2025-06-25 03:39:34,597 - pyskl - INFO - Epoch [97][900/1281] lr: 7.012e-03, eta: 8:10:04, time: 0.360, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.1966, loss: 0.1966 +2025-06-25 03:40:12,841 - pyskl - INFO - Epoch [97][1000/1281] lr: 6.994e-03, eta: 8:09:19, time: 0.382, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1982, loss: 0.1982 +2025-06-25 03:41:01,600 - pyskl - INFO - Epoch [97][1100/1281] lr: 6.975e-03, eta: 8:08:39, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9975, loss_cls: 0.2332, loss: 0.2332 +2025-06-25 03:41:50,276 - pyskl - INFO - Epoch [97][1200/1281] lr: 6.957e-03, eta: 8:07:59, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2114, loss: 0.2114 +2025-06-25 03:42:30,292 - pyskl - INFO - Saving checkpoint at 97 epochs +2025-06-25 03:43:28,991 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:43:29,046 - pyskl - INFO - +top1_acc 0.8880 +top5_acc 0.9900 +2025-06-25 03:43:29,046 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:43:29,053 - pyskl - INFO - +mean_acc 0.8637 +2025-06-25 03:43:29,054 - pyskl - INFO - Epoch(val) [97][533] top1_acc: 0.8880, top5_acc: 0.9900, mean_class_accuracy: 0.8637 +2025-06-25 03:44:47,014 - pyskl - INFO - Epoch [98][100/1281] lr: 6.924e-03, eta: 8:06:41, time: 0.780, data_time: 0.184, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2229, loss: 0.2229 +2025-06-25 03:45:36,012 - pyskl - INFO - Epoch [98][200/1281] lr: 6.906e-03, eta: 8:06:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1782, loss: 0.1782 +2025-06-25 03:46:24,868 - pyskl - INFO - Epoch [98][300/1281] lr: 6.887e-03, eta: 8:05:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1554, loss: 0.1554 +2025-06-25 03:47:13,885 - pyskl - INFO - Epoch [98][400/1281] lr: 6.869e-03, eta: 8:04:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1648, loss: 0.1648 +2025-06-25 03:48:03,152 - pyskl - INFO - Epoch [98][500/1281] lr: 6.851e-03, eta: 8:04:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1771, loss: 0.1771 +2025-06-25 03:48:52,391 - pyskl - INFO - Epoch [98][600/1281] lr: 6.833e-03, eta: 8:03:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1605, loss: 0.1605 +2025-06-25 03:49:39,320 - pyskl - INFO - Epoch [98][700/1281] lr: 6.814e-03, eta: 8:02:40, time: 0.469, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2259, loss: 0.2259 +2025-06-25 03:50:15,467 - pyskl - INFO - Epoch [98][800/1281] lr: 6.796e-03, eta: 8:01:54, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2380, loss: 0.2380 +2025-06-25 03:50:52,471 - pyskl - INFO - Epoch [98][900/1281] lr: 6.778e-03, eta: 8:01:07, time: 0.370, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2155, loss: 0.2155 +2025-06-25 03:51:30,324 - pyskl - INFO - Epoch [98][1000/1281] lr: 6.760e-03, eta: 8:00:21, time: 0.379, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9988, loss_cls: 0.1821, loss: 0.1821 +2025-06-25 03:52:19,450 - pyskl - INFO - Epoch [98][1100/1281] lr: 6.742e-03, eta: 7:59:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2331, loss: 0.2331 +2025-06-25 03:53:08,517 - pyskl - INFO - Epoch [98][1200/1281] lr: 6.724e-03, eta: 7:59:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2427, loss: 0.2427 +2025-06-25 03:53:48,802 - pyskl - INFO - Saving checkpoint at 98 epochs +2025-06-25 03:54:47,404 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:54:47,460 - pyskl - INFO - +top1_acc 0.8991 +top5_acc 0.9935 +2025-06-25 03:54:47,460 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:54:47,466 - pyskl - INFO - +mean_acc 0.8655 +2025-06-25 03:54:47,470 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_94.pth was removed +2025-06-25 03:54:47,643 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_98.pth. +2025-06-25 03:54:47,644 - pyskl - INFO - Best top1_acc is 0.8991 at 98 epoch. +2025-06-25 03:54:47,647 - pyskl - INFO - Epoch(val) [98][533] top1_acc: 0.8991, top5_acc: 0.9935, mean_class_accuracy: 0.8655 +2025-06-25 03:56:06,613 - pyskl - INFO - Epoch [99][100/1281] lr: 6.691e-03, eta: 7:57:44, time: 0.790, data_time: 0.190, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1821, loss: 0.1821 +2025-06-25 03:56:55,199 - pyskl - INFO - Epoch [99][200/1281] lr: 6.673e-03, eta: 7:57:04, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1783, loss: 0.1783 +2025-06-25 03:57:43,883 - pyskl - INFO - Epoch [99][300/1281] lr: 6.655e-03, eta: 7:56:24, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1846, loss: 0.1846 +2025-06-25 03:58:33,047 - pyskl - INFO - Epoch [99][400/1281] lr: 6.637e-03, eta: 7:55:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9994, loss_cls: 0.2234, loss: 0.2234 +2025-06-25 03:59:22,479 - pyskl - INFO - Epoch [99][500/1281] lr: 6.619e-03, eta: 7:55:04, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1872, loss: 0.1872 +2025-06-25 04:00:11,345 - pyskl - INFO - Epoch [99][600/1281] lr: 6.601e-03, eta: 7:54:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1883, loss: 0.1883 +2025-06-25 04:00:57,054 - pyskl - INFO - Epoch [99][700/1281] lr: 6.583e-03, eta: 7:53:42, time: 0.457, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1689, loss: 0.1689 +2025-06-25 04:01:36,474 - pyskl - INFO - Epoch [99][800/1281] lr: 6.565e-03, eta: 7:52:57, time: 0.394, data_time: 0.001, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2353, loss: 0.2353 +2025-06-25 04:02:10,356 - pyskl - INFO - Epoch [99][900/1281] lr: 6.547e-03, eta: 7:52:09, time: 0.339, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2348, loss: 0.2348 +2025-06-25 04:02:47,983 - pyskl - INFO - Epoch [99][1000/1281] lr: 6.529e-03, eta: 7:51:23, time: 0.376, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2142, loss: 0.2142 +2025-06-25 04:03:37,045 - pyskl - INFO - Epoch [99][1100/1281] lr: 6.511e-03, eta: 7:50:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1951, loss: 0.1951 +2025-06-25 04:04:26,103 - pyskl - INFO - Epoch [99][1200/1281] lr: 6.493e-03, eta: 7:50:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9988, loss_cls: 0.2071, loss: 0.2071 +2025-06-25 04:05:06,244 - pyskl - INFO - Saving checkpoint at 99 epochs +2025-06-25 04:06:04,781 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:06:04,849 - pyskl - INFO - +top1_acc 0.8931 +top5_acc 0.9928 +2025-06-25 04:06:04,849 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:06:04,857 - pyskl - INFO - +mean_acc 0.8606 +2025-06-25 04:06:04,859 - pyskl - INFO - Epoch(val) [99][533] top1_acc: 0.8931, top5_acc: 0.9928, mean_class_accuracy: 0.8606 +2025-06-25 04:07:23,074 - pyskl - INFO - Epoch [100][100/1281] lr: 6.460e-03, eta: 7:48:45, time: 0.782, data_time: 0.185, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.1914, loss: 0.1914 +2025-06-25 04:08:11,791 - pyskl - INFO - Epoch [100][200/1281] lr: 6.442e-03, eta: 7:48:05, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 0.9994, loss_cls: 0.1933, loss: 0.1933 +2025-06-25 04:09:00,712 - pyskl - INFO - Epoch [100][300/1281] lr: 6.425e-03, eta: 7:47:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1564, loss: 0.1564 +2025-06-25 04:09:49,671 - pyskl - INFO - Epoch [100][400/1281] lr: 6.407e-03, eta: 7:46:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1611, loss: 0.1611 +2025-06-25 04:10:38,620 - pyskl - INFO - Epoch [100][500/1281] lr: 6.389e-03, eta: 7:46:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1739, loss: 0.1739 +2025-06-25 04:11:27,871 - pyskl - INFO - Epoch [100][600/1281] lr: 6.371e-03, eta: 7:45:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1740, loss: 0.1740 +2025-06-25 04:12:15,996 - pyskl - INFO - Epoch [100][700/1281] lr: 6.353e-03, eta: 7:44:44, time: 0.481, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2120, loss: 0.2120 +2025-06-25 04:12:50,311 - pyskl - INFO - Epoch [100][800/1281] lr: 6.336e-03, eta: 7:43:56, time: 0.343, data_time: 0.001, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9994, loss_cls: 0.2082, loss: 0.2082 +2025-06-25 04:13:29,370 - pyskl - INFO - Epoch [100][900/1281] lr: 6.318e-03, eta: 7:43:11, time: 0.391, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2434, loss: 0.2434 +2025-06-25 04:14:06,797 - pyskl - INFO - Epoch [100][1000/1281] lr: 6.300e-03, eta: 7:42:25, time: 0.374, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1847, loss: 0.1847 +2025-06-25 04:14:55,820 - pyskl - INFO - Epoch [100][1100/1281] lr: 6.282e-03, eta: 7:41:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1722, loss: 0.1722 +2025-06-25 04:15:44,478 - pyskl - INFO - Epoch [100][1200/1281] lr: 6.265e-03, eta: 7:41:04, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2260, loss: 0.2260 +2025-06-25 04:16:24,613 - pyskl - INFO - Saving checkpoint at 100 epochs +2025-06-25 04:17:22,706 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:17:22,784 - pyskl - INFO - +top1_acc 0.8885 +top5_acc 0.9918 +2025-06-25 04:17:22,784 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:17:22,791 - pyskl - INFO - +mean_acc 0.8519 +2025-06-25 04:17:22,792 - pyskl - INFO - Epoch(val) [100][533] top1_acc: 0.8885, top5_acc: 0.9918, mean_class_accuracy: 0.8519 +2025-06-25 04:18:40,583 - pyskl - INFO - Epoch [101][100/1281] lr: 6.232e-03, eta: 7:39:46, time: 0.778, data_time: 0.186, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1807, loss: 0.1807 +2025-06-25 04:19:29,458 - pyskl - INFO - Epoch [101][200/1281] lr: 6.215e-03, eta: 7:39:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1667, loss: 0.1667 +2025-06-25 04:20:18,411 - pyskl - INFO - Epoch [101][300/1281] lr: 6.197e-03, eta: 7:38:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2099, loss: 0.2099 +2025-06-25 04:21:07,767 - pyskl - INFO - Epoch [101][400/1281] lr: 6.180e-03, eta: 7:37:46, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1553, loss: 0.1553 +2025-06-25 04:21:56,487 - pyskl - INFO - Epoch [101][500/1281] lr: 6.162e-03, eta: 7:37:05, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 0.9988, loss_cls: 0.1539, loss: 0.1539 +2025-06-25 04:22:45,419 - pyskl - INFO - Epoch [101][600/1281] lr: 6.144e-03, eta: 7:36:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2057, loss: 0.2057 +2025-06-25 04:23:34,443 - pyskl - INFO - Epoch [101][700/1281] lr: 6.127e-03, eta: 7:35:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2080, loss: 0.2080 +2025-06-25 04:24:07,363 - pyskl - INFO - Epoch [101][800/1281] lr: 6.109e-03, eta: 7:34:56, time: 0.329, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9988, loss_cls: 0.2472, loss: 0.2472 +2025-06-25 04:24:47,852 - pyskl - INFO - Epoch [101][900/1281] lr: 6.092e-03, eta: 7:34:12, time: 0.405, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.2025, loss: 0.2025 +2025-06-25 04:25:24,793 - pyskl - INFO - Epoch [101][1000/1281] lr: 6.074e-03, eta: 7:33:26, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1702, loss: 0.1702 +2025-06-25 04:26:13,458 - pyskl - INFO - Epoch [101][1100/1281] lr: 6.057e-03, eta: 7:32:45, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2007, loss: 0.2007 +2025-06-25 04:27:02,396 - pyskl - INFO - Epoch [101][1200/1281] lr: 6.039e-03, eta: 7:32:05, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9988, loss_cls: 0.2700, loss: 0.2700 +2025-06-25 04:27:42,570 - pyskl - INFO - Saving checkpoint at 101 epochs +2025-06-25 04:28:40,667 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:28:40,727 - pyskl - INFO - +top1_acc 0.8957 +top5_acc 0.9931 +2025-06-25 04:28:40,728 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:28:40,735 - pyskl - INFO - +mean_acc 0.8684 +2025-06-25 04:28:40,737 - pyskl - INFO - Epoch(val) [101][533] top1_acc: 0.8957, top5_acc: 0.9931, mean_class_accuracy: 0.8684 +2025-06-25 04:30:00,513 - pyskl - INFO - Epoch [102][100/1281] lr: 6.007e-03, eta: 7:30:48, time: 0.798, data_time: 0.185, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1475, loss: 0.1475 +2025-06-25 04:30:48,931 - pyskl - INFO - Epoch [102][200/1281] lr: 5.990e-03, eta: 7:30:07, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1711, loss: 0.1711 +2025-06-25 04:31:37,820 - pyskl - INFO - Epoch [102][300/1281] lr: 5.972e-03, eta: 7:29:27, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1544, loss: 0.1544 +2025-06-25 04:32:26,691 - pyskl - INFO - Epoch [102][400/1281] lr: 5.955e-03, eta: 7:28:46, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9994, loss_cls: 0.1838, loss: 0.1838 +2025-06-25 04:33:15,482 - pyskl - INFO - Epoch [102][500/1281] lr: 5.938e-03, eta: 7:28:06, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1749, loss: 0.1749 +2025-06-25 04:34:04,561 - pyskl - INFO - Epoch [102][600/1281] lr: 5.920e-03, eta: 7:27:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2131, loss: 0.2131 +2025-06-25 04:34:52,718 - pyskl - INFO - Epoch [102][700/1281] lr: 5.903e-03, eta: 7:26:45, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2178, loss: 0.2178 +2025-06-25 04:35:25,751 - pyskl - INFO - Epoch [102][800/1281] lr: 5.886e-03, eta: 7:25:57, time: 0.330, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1957, loss: 0.1957 +2025-06-25 04:36:06,076 - pyskl - INFO - Epoch [102][900/1281] lr: 5.868e-03, eta: 7:25:12, time: 0.403, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.1938, loss: 0.1938 +2025-06-25 04:36:43,155 - pyskl - INFO - Epoch [102][1000/1281] lr: 5.851e-03, eta: 7:24:26, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9994, loss_cls: 0.1848, loss: 0.1848 +2025-06-25 04:37:31,814 - pyskl - INFO - Epoch [102][1100/1281] lr: 5.834e-03, eta: 7:23:45, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2275, loss: 0.2275 +2025-06-25 04:38:21,079 - pyskl - INFO - Epoch [102][1200/1281] lr: 5.816e-03, eta: 7:23:05, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1975, loss: 0.1975 +2025-06-25 04:39:01,418 - pyskl - INFO - Saving checkpoint at 102 epochs +2025-06-25 04:39:59,536 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:39:59,592 - pyskl - INFO - +top1_acc 0.8857 +top5_acc 0.9893 +2025-06-25 04:39:59,592 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:39:59,599 - pyskl - INFO - +mean_acc 0.8608 +2025-06-25 04:39:59,600 - pyskl - INFO - Epoch(val) [102][533] top1_acc: 0.8857, top5_acc: 0.9893, mean_class_accuracy: 0.8608 +2025-06-25 04:41:18,971 - pyskl - INFO - Epoch [103][100/1281] lr: 5.785e-03, eta: 7:21:47, time: 0.794, data_time: 0.180, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1803, loss: 0.1803 +2025-06-25 04:42:07,808 - pyskl - INFO - Epoch [103][200/1281] lr: 5.768e-03, eta: 7:21:07, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1391, loss: 0.1391 +2025-06-25 04:42:56,814 - pyskl - INFO - Epoch [103][300/1281] lr: 5.751e-03, eta: 7:20:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1452, loss: 0.1452 +2025-06-25 04:43:45,792 - pyskl - INFO - Epoch [103][400/1281] lr: 5.733e-03, eta: 7:19:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1515, loss: 0.1515 +2025-06-25 04:44:34,631 - pyskl - INFO - Epoch [103][500/1281] lr: 5.716e-03, eta: 7:19:05, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1626, loss: 0.1626 +2025-06-25 04:45:23,990 - pyskl - INFO - Epoch [103][600/1281] lr: 5.699e-03, eta: 7:18:25, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1426, loss: 0.1426 +2025-06-25 04:46:11,544 - pyskl - INFO - Epoch [103][700/1281] lr: 5.682e-03, eta: 7:17:44, time: 0.476, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1707, loss: 0.1707 +2025-06-25 04:46:46,781 - pyskl - INFO - Epoch [103][800/1281] lr: 5.665e-03, eta: 7:16:57, time: 0.352, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1835, loss: 0.1835 +2025-06-25 04:47:24,599 - pyskl - INFO - Epoch [103][900/1281] lr: 5.648e-03, eta: 7:16:11, time: 0.378, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1686, loss: 0.1686 +2025-06-25 04:48:02,168 - pyskl - INFO - Epoch [103][1000/1281] lr: 5.631e-03, eta: 7:15:25, time: 0.376, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.1757, loss: 0.1757 +2025-06-25 04:48:50,688 - pyskl - INFO - Epoch [103][1100/1281] lr: 5.614e-03, eta: 7:14:45, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.1896, loss: 0.1896 +2025-06-25 04:49:39,383 - pyskl - INFO - Epoch [103][1200/1281] lr: 5.597e-03, eta: 7:14:04, time: 0.487, data_time: 0.001, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1604, loss: 0.1604 +2025-06-25 04:50:19,479 - pyskl - INFO - Saving checkpoint at 103 epochs +2025-06-25 04:51:18,361 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:51:18,415 - pyskl - INFO - +top1_acc 0.8948 +top5_acc 0.9921 +2025-06-25 04:51:18,415 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:51:18,421 - pyskl - INFO - +mean_acc 0.8716 +2025-06-25 04:51:18,423 - pyskl - INFO - Epoch(val) [103][533] top1_acc: 0.8948, top5_acc: 0.9921, mean_class_accuracy: 0.8716 +2025-06-25 04:52:36,309 - pyskl - INFO - Epoch [104][100/1281] lr: 5.566e-03, eta: 7:12:46, time: 0.779, data_time: 0.183, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1517, loss: 0.1517 +2025-06-25 04:53:24,833 - pyskl - INFO - Epoch [104][200/1281] lr: 5.549e-03, eta: 7:12:05, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2083, loss: 0.2083 +2025-06-25 04:54:13,895 - pyskl - INFO - Epoch [104][300/1281] lr: 5.532e-03, eta: 7:11:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1809, loss: 0.1809 +2025-06-25 04:55:02,606 - pyskl - INFO - Epoch [104][400/1281] lr: 5.515e-03, eta: 7:10:44, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1718, loss: 0.1718 +2025-06-25 04:55:51,632 - pyskl - INFO - Epoch [104][500/1281] lr: 5.498e-03, eta: 7:10:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1399, loss: 0.1399 +2025-06-25 04:56:41,135 - pyskl - INFO - Epoch [104][600/1281] lr: 5.481e-03, eta: 7:09:23, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1765, loss: 0.1765 +2025-06-25 04:57:28,381 - pyskl - INFO - Epoch [104][700/1281] lr: 5.464e-03, eta: 7:08:41, time: 0.472, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 0.9994, loss_cls: 0.1695, loss: 0.1695 +2025-06-25 04:58:04,268 - pyskl - INFO - Epoch [104][800/1281] lr: 5.447e-03, eta: 7:07:55, time: 0.359, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1159, loss: 0.1159 +2025-06-25 04:58:41,634 - pyskl - INFO - Epoch [104][900/1281] lr: 5.430e-03, eta: 7:07:09, time: 0.374, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.1650, loss: 0.1650 +2025-06-25 04:59:18,718 - pyskl - INFO - Epoch [104][1000/1281] lr: 5.413e-03, eta: 7:06:23, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1781, loss: 0.1781 +2025-06-25 05:00:07,379 - pyskl - INFO - Epoch [104][1100/1281] lr: 5.397e-03, eta: 7:05:42, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 0.9994, loss_cls: 0.1412, loss: 0.1412 +2025-06-25 05:00:55,647 - pyskl - INFO - Epoch [104][1200/1281] lr: 5.380e-03, eta: 7:05:01, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1662, loss: 0.1662 +2025-06-25 05:01:35,641 - pyskl - INFO - Saving checkpoint at 104 epochs +2025-06-25 05:02:34,167 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:02:34,231 - pyskl - INFO - +top1_acc 0.8967 +top5_acc 0.9907 +2025-06-25 05:02:34,231 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:02:34,238 - pyskl - INFO - +mean_acc 0.8642 +2025-06-25 05:02:34,239 - pyskl - INFO - Epoch(val) [104][533] top1_acc: 0.8967, top5_acc: 0.9907, mean_class_accuracy: 0.8642 +2025-06-25 05:03:53,494 - pyskl - INFO - Epoch [105][100/1281] lr: 5.349e-03, eta: 7:03:43, time: 0.792, data_time: 0.188, memory: 4083, top1_acc: 0.9725, top5_acc: 0.9994, loss_cls: 0.1595, loss: 0.1595 +2025-06-25 05:04:42,505 - pyskl - INFO - Epoch [105][200/1281] lr: 5.333e-03, eta: 7:03:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1548, loss: 0.1548 +2025-06-25 05:05:31,138 - pyskl - INFO - Epoch [105][300/1281] lr: 5.316e-03, eta: 7:02:22, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1364, loss: 0.1364 +2025-06-25 05:06:19,806 - pyskl - INFO - Epoch [105][400/1281] lr: 5.299e-03, eta: 7:01:41, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1555, loss: 0.1555 +2025-06-25 05:07:09,108 - pyskl - INFO - Epoch [105][500/1281] lr: 5.283e-03, eta: 7:01:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1522, loss: 0.1522 +2025-06-25 05:07:57,778 - pyskl - INFO - Epoch [105][600/1281] lr: 5.266e-03, eta: 7:00:20, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 0.9994, loss_cls: 0.1685, loss: 0.1685 +2025-06-25 05:08:45,097 - pyskl - INFO - Epoch [105][700/1281] lr: 5.249e-03, eta: 6:59:38, time: 0.473, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1416, loss: 0.1416 +2025-06-25 05:09:22,660 - pyskl - INFO - Epoch [105][800/1281] lr: 5.233e-03, eta: 6:58:52, time: 0.376, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 0.9994, loss_cls: 0.1986, loss: 0.1986 +2025-06-25 05:09:58,416 - pyskl - INFO - Epoch [105][900/1281] lr: 5.216e-03, eta: 6:58:06, time: 0.358, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1788, loss: 0.1788 +2025-06-25 05:10:36,328 - pyskl - INFO - Epoch [105][1000/1281] lr: 5.199e-03, eta: 6:57:20, time: 0.379, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1771, loss: 0.1771 +2025-06-25 05:11:25,239 - pyskl - INFO - Epoch [105][1100/1281] lr: 5.183e-03, eta: 6:56:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1549, loss: 0.1549 +2025-06-25 05:12:14,340 - pyskl - INFO - Epoch [105][1200/1281] lr: 5.166e-03, eta: 6:55:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1581, loss: 0.1581 +2025-06-25 05:12:54,450 - pyskl - INFO - Saving checkpoint at 105 epochs +2025-06-25 05:13:52,854 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:13:52,909 - pyskl - INFO - +top1_acc 0.9026 +top5_acc 0.9931 +2025-06-25 05:13:52,909 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:13:52,916 - pyskl - INFO - +mean_acc 0.8773 +2025-06-25 05:13:52,920 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_98.pth was removed +2025-06-25 05:13:53,094 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_105.pth. +2025-06-25 05:13:53,095 - pyskl - INFO - Best top1_acc is 0.9026 at 105 epoch. +2025-06-25 05:13:53,097 - pyskl - INFO - Epoch(val) [105][533] top1_acc: 0.9026, top5_acc: 0.9931, mean_class_accuracy: 0.8773 +2025-06-25 05:15:13,390 - pyskl - INFO - Epoch [106][100/1281] lr: 5.136e-03, eta: 6:54:42, time: 0.803, data_time: 0.188, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1214, loss: 0.1214 +2025-06-25 05:16:02,507 - pyskl - INFO - Epoch [106][200/1281] lr: 5.120e-03, eta: 6:54:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 0.9994, loss_cls: 0.1296, loss: 0.1296 +2025-06-25 05:16:51,460 - pyskl - INFO - Epoch [106][300/1281] lr: 5.103e-03, eta: 6:53:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1484, loss: 0.1484 +2025-06-25 05:17:40,625 - pyskl - INFO - Epoch [106][400/1281] lr: 5.087e-03, eta: 6:52:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1619, loss: 0.1619 +2025-06-25 05:18:29,626 - pyskl - INFO - Epoch [106][500/1281] lr: 5.070e-03, eta: 6:51:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1439, loss: 0.1439 +2025-06-25 05:19:18,458 - pyskl - INFO - Epoch [106][600/1281] lr: 5.054e-03, eta: 6:51:18, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1304, loss: 0.1304 +2025-06-25 05:20:03,193 - pyskl - INFO - Epoch [106][700/1281] lr: 5.038e-03, eta: 6:50:35, time: 0.447, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1126, loss: 0.1126 +2025-06-25 05:20:44,258 - pyskl - INFO - Epoch [106][800/1281] lr: 5.021e-03, eta: 6:49:51, time: 0.411, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9994, loss_cls: 0.1681, loss: 0.1681 +2025-06-25 05:21:16,296 - pyskl - INFO - Epoch [106][900/1281] lr: 5.005e-03, eta: 6:49:03, time: 0.320, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1488, loss: 0.1488 +2025-06-25 05:21:56,007 - pyskl - INFO - Epoch [106][1000/1281] lr: 4.988e-03, eta: 6:48:18, time: 0.397, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1466, loss: 0.1466 +2025-06-25 05:22:44,648 - pyskl - INFO - Epoch [106][1100/1281] lr: 4.972e-03, eta: 6:47:37, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1628, loss: 0.1628 +2025-06-25 05:23:33,724 - pyskl - INFO - Epoch [106][1200/1281] lr: 4.956e-03, eta: 6:46:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1525, loss: 0.1525 +2025-06-25 05:24:13,890 - pyskl - INFO - Saving checkpoint at 106 epochs +2025-06-25 05:25:11,812 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:25:11,867 - pyskl - INFO - +top1_acc 0.9120 +top5_acc 0.9939 +2025-06-25 05:25:11,867 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:25:11,874 - pyskl - INFO - +mean_acc 0.8787 +2025-06-25 05:25:11,879 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_105.pth was removed +2025-06-25 05:25:12,057 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_106.pth. +2025-06-25 05:25:12,057 - pyskl - INFO - Best top1_acc is 0.9120 at 106 epoch. +2025-06-25 05:25:12,059 - pyskl - INFO - Epoch(val) [106][533] top1_acc: 0.9120, top5_acc: 0.9939, mean_class_accuracy: 0.8787 +2025-06-25 05:26:32,047 - pyskl - INFO - Epoch [107][100/1281] lr: 4.926e-03, eta: 6:45:39, time: 0.800, data_time: 0.183, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1495, loss: 0.1495 +2025-06-25 05:27:20,830 - pyskl - INFO - Epoch [107][200/1281] lr: 4.910e-03, eta: 6:44:58, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1064, loss: 0.1064 +2025-06-25 05:28:09,254 - pyskl - INFO - Epoch [107][300/1281] lr: 4.894e-03, eta: 6:44:16, time: 0.484, data_time: 0.001, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1065, loss: 0.1065 +2025-06-25 05:28:58,000 - pyskl - INFO - Epoch [107][400/1281] lr: 4.878e-03, eta: 6:43:35, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1006, loss: 0.1006 +2025-06-25 05:29:46,934 - pyskl - INFO - Epoch [107][500/1281] lr: 4.862e-03, eta: 6:42:55, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1147, loss: 0.1147 +2025-06-25 05:30:35,815 - pyskl - INFO - Epoch [107][600/1281] lr: 4.845e-03, eta: 6:42:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1347, loss: 0.1347 +2025-06-25 05:31:20,813 - pyskl - INFO - Epoch [107][700/1281] lr: 4.829e-03, eta: 6:41:31, time: 0.450, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1056, loss: 0.1056 +2025-06-25 05:32:02,261 - pyskl - INFO - Epoch [107][800/1281] lr: 4.813e-03, eta: 6:40:47, time: 0.414, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1579, loss: 0.1579 +2025-06-25 05:32:33,975 - pyskl - INFO - Epoch [107][900/1281] lr: 4.797e-03, eta: 6:39:59, time: 0.317, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1731, loss: 0.1731 +2025-06-25 05:33:13,713 - pyskl - INFO - Epoch [107][1000/1281] lr: 4.781e-03, eta: 6:39:14, time: 0.397, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1431, loss: 0.1431 +2025-06-25 05:34:02,814 - pyskl - INFO - Epoch [107][1100/1281] lr: 4.765e-03, eta: 6:38:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1544, loss: 0.1544 +2025-06-25 05:34:51,482 - pyskl - INFO - Epoch [107][1200/1281] lr: 4.749e-03, eta: 6:37:52, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1186, loss: 0.1186 +2025-06-25 05:35:31,611 - pyskl - INFO - Saving checkpoint at 107 epochs +2025-06-25 05:36:29,970 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:36:30,025 - pyskl - INFO - +top1_acc 0.8923 +top5_acc 0.9899 +2025-06-25 05:36:30,025 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:36:30,031 - pyskl - INFO - +mean_acc 0.8603 +2025-06-25 05:36:30,033 - pyskl - INFO - Epoch(val) [107][533] top1_acc: 0.8923, top5_acc: 0.9899, mean_class_accuracy: 0.8603 +2025-06-25 05:37:48,836 - pyskl - INFO - Epoch [108][100/1281] lr: 4.720e-03, eta: 6:36:34, time: 0.788, data_time: 0.183, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1285, loss: 0.1285 +2025-06-25 05:38:37,875 - pyskl - INFO - Epoch [108][200/1281] lr: 4.704e-03, eta: 6:35:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.1018, loss: 0.1018 +2025-06-25 05:39:27,146 - pyskl - INFO - Epoch [108][300/1281] lr: 4.688e-03, eta: 6:35:12, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1398, loss: 0.1398 +2025-06-25 05:40:16,032 - pyskl - INFO - Epoch [108][400/1281] lr: 4.672e-03, eta: 6:34:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1341, loss: 0.1341 +2025-06-25 05:41:04,983 - pyskl - INFO - Epoch [108][500/1281] lr: 4.656e-03, eta: 6:33:50, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1459, loss: 0.1459 +2025-06-25 05:41:53,993 - pyskl - INFO - Epoch [108][600/1281] lr: 4.640e-03, eta: 6:33:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1371, loss: 0.1371 +2025-06-25 05:42:38,844 - pyskl - INFO - Epoch [108][700/1281] lr: 4.624e-03, eta: 6:32:26, time: 0.449, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1050, loss: 0.1050 +2025-06-25 05:43:21,199 - pyskl - INFO - Epoch [108][800/1281] lr: 4.608e-03, eta: 6:31:43, time: 0.424, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1318, loss: 0.1318 +2025-06-25 05:43:51,791 - pyskl - INFO - Epoch [108][900/1281] lr: 4.593e-03, eta: 6:30:54, time: 0.306, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1111, loss: 0.1111 +2025-06-25 05:44:30,255 - pyskl - INFO - Epoch [108][1000/1281] lr: 4.577e-03, eta: 6:30:09, time: 0.385, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1495, loss: 0.1495 +2025-06-25 05:45:19,237 - pyskl - INFO - Epoch [108][1100/1281] lr: 4.561e-03, eta: 6:29:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1635, loss: 0.1635 +2025-06-25 05:46:08,630 - pyskl - INFO - Epoch [108][1200/1281] lr: 4.545e-03, eta: 6:28:47, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1394, loss: 0.1394 +2025-06-25 05:46:48,851 - pyskl - INFO - Saving checkpoint at 108 epochs +2025-06-25 05:47:47,099 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:47:47,172 - pyskl - INFO - +top1_acc 0.8992 +top5_acc 0.9930 +2025-06-25 05:47:47,172 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:47:47,180 - pyskl - INFO - +mean_acc 0.8713 +2025-06-25 05:47:47,182 - pyskl - INFO - Epoch(val) [108][533] top1_acc: 0.8992, top5_acc: 0.9930, mean_class_accuracy: 0.8713 +2025-06-25 05:49:07,000 - pyskl - INFO - Epoch [109][100/1281] lr: 4.517e-03, eta: 6:27:30, time: 0.798, data_time: 0.182, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1343, loss: 0.1343 +2025-06-25 05:49:55,969 - pyskl - INFO - Epoch [109][200/1281] lr: 4.501e-03, eta: 6:26:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1506, loss: 0.1506 +2025-06-25 05:50:44,881 - pyskl - INFO - Epoch [109][300/1281] lr: 4.485e-03, eta: 6:26:07, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1323, loss: 0.1323 +2025-06-25 05:51:33,751 - pyskl - INFO - Epoch [109][400/1281] lr: 4.470e-03, eta: 6:25:26, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1437, loss: 0.1437 +2025-06-25 05:52:22,751 - pyskl - INFO - Epoch [109][500/1281] lr: 4.454e-03, eta: 6:24:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1196, loss: 0.1196 +2025-06-25 05:53:11,775 - pyskl - INFO - Epoch [109][600/1281] lr: 4.438e-03, eta: 6:24:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 0.9994, loss_cls: 0.1274, loss: 0.1274 +2025-06-25 05:53:57,081 - pyskl - INFO - Epoch [109][700/1281] lr: 4.423e-03, eta: 6:23:21, time: 0.453, data_time: 0.001, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1088, loss: 0.1088 +2025-06-25 05:54:37,193 - pyskl - INFO - Epoch [109][800/1281] lr: 4.407e-03, eta: 6:22:37, time: 0.401, data_time: 0.001, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1612, loss: 0.1612 +2025-06-25 05:55:10,166 - pyskl - INFO - Epoch [109][900/1281] lr: 4.391e-03, eta: 6:21:50, time: 0.330, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1440, loss: 0.1440 +2025-06-25 05:55:48,859 - pyskl - INFO - Epoch [109][1000/1281] lr: 4.376e-03, eta: 6:21:05, time: 0.387, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 0.9994, loss_cls: 0.1310, loss: 0.1310 +2025-06-25 05:56:37,762 - pyskl - INFO - Epoch [109][1100/1281] lr: 4.360e-03, eta: 6:20:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1086, loss: 0.1086 +2025-06-25 05:57:26,835 - pyskl - INFO - Epoch [109][1200/1281] lr: 4.345e-03, eta: 6:19:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1080, loss: 0.1080 +2025-06-25 05:58:06,825 - pyskl - INFO - Saving checkpoint at 109 epochs +2025-06-25 05:59:04,085 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:59:04,139 - pyskl - INFO - +top1_acc 0.9042 +top5_acc 0.9928 +2025-06-25 05:59:04,139 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:59:04,145 - pyskl - INFO - +mean_acc 0.8757 +2025-06-25 05:59:04,147 - pyskl - INFO - Epoch(val) [109][533] top1_acc: 0.9042, top5_acc: 0.9928, mean_class_accuracy: 0.8757 +2025-06-25 06:00:24,541 - pyskl - INFO - Epoch [110][100/1281] lr: 4.317e-03, eta: 6:18:25, time: 0.804, data_time: 0.188, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0819, loss: 0.0819 +2025-06-25 06:01:13,782 - pyskl - INFO - Epoch [110][200/1281] lr: 4.301e-03, eta: 6:17:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0798, loss: 0.0798 +2025-06-25 06:02:02,564 - pyskl - INFO - Epoch [110][300/1281] lr: 4.286e-03, eta: 6:17:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1016, loss: 0.1016 +2025-06-25 06:02:51,730 - pyskl - INFO - Epoch [110][400/1281] lr: 4.271e-03, eta: 6:16:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1518, loss: 0.1518 +2025-06-25 06:03:40,783 - pyskl - INFO - Epoch [110][500/1281] lr: 4.255e-03, eta: 6:15:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1060, loss: 0.1060 +2025-06-25 06:04:29,755 - pyskl - INFO - Epoch [110][600/1281] lr: 4.240e-03, eta: 6:14:59, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1588, loss: 0.1588 +2025-06-25 06:05:15,049 - pyskl - INFO - Epoch [110][700/1281] lr: 4.225e-03, eta: 6:14:16, time: 0.453, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1276, loss: 0.1276 +2025-06-25 06:05:55,376 - pyskl - INFO - Epoch [110][800/1281] lr: 4.209e-03, eta: 6:13:32, time: 0.403, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1279, loss: 0.1279 +2025-06-25 06:06:28,112 - pyskl - INFO - Epoch [110][900/1281] lr: 4.194e-03, eta: 6:12:44, time: 0.327, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1340, loss: 0.1340 +2025-06-25 06:07:08,008 - pyskl - INFO - Epoch [110][1000/1281] lr: 4.179e-03, eta: 6:12:00, time: 0.399, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1349, loss: 0.1349 +2025-06-25 06:07:56,663 - pyskl - INFO - Epoch [110][1100/1281] lr: 4.164e-03, eta: 6:11:18, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1659, loss: 0.1659 +2025-06-25 06:08:45,769 - pyskl - INFO - Epoch [110][1200/1281] lr: 4.148e-03, eta: 6:10:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1307, loss: 0.1307 +2025-06-25 06:09:26,187 - pyskl - INFO - Saving checkpoint at 110 epochs +2025-06-25 06:10:24,846 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:10:24,901 - pyskl - INFO - +top1_acc 0.9044 +top5_acc 0.9932 +2025-06-25 06:10:24,901 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:10:24,908 - pyskl - INFO - +mean_acc 0.8815 +2025-06-25 06:10:24,909 - pyskl - INFO - Epoch(val) [110][533] top1_acc: 0.9044, top5_acc: 0.9932, mean_class_accuracy: 0.8815 +2025-06-25 06:11:44,556 - pyskl - INFO - Epoch [111][100/1281] lr: 4.121e-03, eta: 6:09:19, time: 0.796, data_time: 0.189, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1037, loss: 0.1037 +2025-06-25 06:12:33,351 - pyskl - INFO - Epoch [111][200/1281] lr: 4.106e-03, eta: 6:08:38, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0858, loss: 0.0858 +2025-06-25 06:13:22,312 - pyskl - INFO - Epoch [111][300/1281] lr: 4.091e-03, eta: 6:07:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.0970, loss: 0.0970 +2025-06-25 06:14:11,215 - pyskl - INFO - Epoch [111][400/1281] lr: 4.075e-03, eta: 6:07:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0895, loss: 0.0895 +2025-06-25 06:15:00,166 - pyskl - INFO - Epoch [111][500/1281] lr: 4.060e-03, eta: 6:06:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0819, loss: 0.0819 +2025-06-25 06:15:49,044 - pyskl - INFO - Epoch [111][600/1281] lr: 4.045e-03, eta: 6:05:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1074, loss: 0.1074 +2025-06-25 06:16:32,461 - pyskl - INFO - Epoch [111][700/1281] lr: 4.030e-03, eta: 6:05:09, time: 0.434, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0960, loss: 0.0960 +2025-06-25 06:17:14,594 - pyskl - INFO - Epoch [111][800/1281] lr: 4.015e-03, eta: 6:04:25, time: 0.421, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1051, loss: 0.1051 +2025-06-25 06:17:45,655 - pyskl - INFO - Epoch [111][900/1281] lr: 4.000e-03, eta: 6:03:38, time: 0.311, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1145, loss: 0.1145 +2025-06-25 06:18:25,966 - pyskl - INFO - Epoch [111][1000/1281] lr: 3.985e-03, eta: 6:02:53, time: 0.403, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 0.9994, loss_cls: 0.1032, loss: 0.1032 +2025-06-25 06:19:14,636 - pyskl - INFO - Epoch [111][1100/1281] lr: 3.970e-03, eta: 6:02:12, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0949, loss: 0.0949 +2025-06-25 06:20:03,521 - pyskl - INFO - Epoch [111][1200/1281] lr: 3.955e-03, eta: 6:01:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0983, loss: 0.0983 +2025-06-25 06:20:43,797 - pyskl - INFO - Saving checkpoint at 111 epochs +2025-06-25 06:21:41,935 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:21:41,990 - pyskl - INFO - +top1_acc 0.8954 +top5_acc 0.9913 +2025-06-25 06:21:41,990 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:21:41,996 - pyskl - INFO - +mean_acc 0.8612 +2025-06-25 06:21:41,998 - pyskl - INFO - Epoch(val) [111][533] top1_acc: 0.8954, top5_acc: 0.9913, mean_class_accuracy: 0.8612 +2025-06-25 06:23:01,253 - pyskl - INFO - Epoch [112][100/1281] lr: 3.928e-03, eta: 6:00:12, time: 0.793, data_time: 0.183, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1265, loss: 0.1265 +2025-06-25 06:23:50,203 - pyskl - INFO - Epoch [112][200/1281] lr: 3.914e-03, eta: 5:59:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1109, loss: 0.1109 +2025-06-25 06:24:39,166 - pyskl - INFO - Epoch [112][300/1281] lr: 3.899e-03, eta: 5:58:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1047, loss: 0.1047 +2025-06-25 06:25:27,926 - pyskl - INFO - Epoch [112][400/1281] lr: 3.884e-03, eta: 5:58:08, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1222, loss: 0.1222 +2025-06-25 06:26:17,240 - pyskl - INFO - Epoch [112][500/1281] lr: 3.869e-03, eta: 5:57:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0882, loss: 0.0882 +2025-06-25 06:27:06,387 - pyskl - INFO - Epoch [112][600/1281] lr: 3.854e-03, eta: 5:56:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1082, loss: 0.1082 +2025-06-25 06:27:50,569 - pyskl - INFO - Epoch [112][700/1281] lr: 3.840e-03, eta: 5:56:02, time: 0.442, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1103, loss: 0.1103 +2025-06-25 06:28:35,804 - pyskl - INFO - Epoch [112][800/1281] lr: 3.825e-03, eta: 5:55:19, time: 0.452, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1333, loss: 0.1333 +2025-06-25 06:29:04,239 - pyskl - INFO - Epoch [112][900/1281] lr: 3.810e-03, eta: 5:54:31, time: 0.284, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0886, loss: 0.0886 +2025-06-25 06:29:45,670 - pyskl - INFO - Epoch [112][1000/1281] lr: 3.795e-03, eta: 5:53:47, time: 0.414, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 0.9994, loss_cls: 0.1133, loss: 0.1133 +2025-06-25 06:30:34,484 - pyskl - INFO - Epoch [112][1100/1281] lr: 3.781e-03, eta: 5:53:05, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1132, loss: 0.1132 +2025-06-25 06:31:23,147 - pyskl - INFO - Epoch [112][1200/1281] lr: 3.766e-03, eta: 5:52:24, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0995, loss: 0.0995 +2025-06-25 06:32:03,192 - pyskl - INFO - Saving checkpoint at 112 epochs +2025-06-25 06:33:00,967 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:33:01,022 - pyskl - INFO - +top1_acc 0.9033 +top5_acc 0.9916 +2025-06-25 06:33:01,022 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:33:01,028 - pyskl - INFO - +mean_acc 0.8716 +2025-06-25 06:33:01,030 - pyskl - INFO - Epoch(val) [112][533] top1_acc: 0.9033, top5_acc: 0.9916, mean_class_accuracy: 0.8716 +2025-06-25 06:34:20,332 - pyskl - INFO - Epoch [113][100/1281] lr: 3.740e-03, eta: 5:51:06, time: 0.793, data_time: 0.184, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1111, loss: 0.1111 +2025-06-25 06:35:09,232 - pyskl - INFO - Epoch [113][200/1281] lr: 3.725e-03, eta: 5:50:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1294, loss: 0.1294 +2025-06-25 06:35:58,146 - pyskl - INFO - Epoch [113][300/1281] lr: 3.711e-03, eta: 5:49:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0819, loss: 0.0819 +2025-06-25 06:36:47,142 - pyskl - INFO - Epoch [113][400/1281] lr: 3.696e-03, eta: 5:49:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1064, loss: 0.1064 +2025-06-25 06:37:36,460 - pyskl - INFO - Epoch [113][500/1281] lr: 3.682e-03, eta: 5:48:20, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1391, loss: 0.1391 +2025-06-25 06:38:25,640 - pyskl - INFO - Epoch [113][600/1281] lr: 3.667e-03, eta: 5:47:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.1055, loss: 0.1055 +2025-06-25 06:39:09,232 - pyskl - INFO - Epoch [113][700/1281] lr: 3.653e-03, eta: 5:46:55, time: 0.436, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0885, loss: 0.0885 +2025-06-25 06:39:52,638 - pyskl - INFO - Epoch [113][800/1281] lr: 3.638e-03, eta: 5:46:12, time: 0.434, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0813, loss: 0.0813 +2025-06-25 06:40:22,423 - pyskl - INFO - Epoch [113][900/1281] lr: 3.624e-03, eta: 5:45:24, time: 0.298, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0909, loss: 0.0909 +2025-06-25 06:41:01,814 - pyskl - INFO - Epoch [113][1000/1281] lr: 3.610e-03, eta: 5:44:39, time: 0.394, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1148, loss: 0.1148 +2025-06-25 06:41:50,481 - pyskl - INFO - Epoch [113][1100/1281] lr: 3.595e-03, eta: 5:43:57, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 0.9994, loss_cls: 0.1078, loss: 0.1078 +2025-06-25 06:42:39,296 - pyskl - INFO - Epoch [113][1200/1281] lr: 3.581e-03, eta: 5:43:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1242, loss: 0.1242 +2025-06-25 06:43:19,317 - pyskl - INFO - Saving checkpoint at 113 epochs +2025-06-25 06:44:17,934 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:44:17,989 - pyskl - INFO - +top1_acc 0.9061 +top5_acc 0.9923 +2025-06-25 06:44:17,989 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:44:17,995 - pyskl - INFO - +mean_acc 0.8799 +2025-06-25 06:44:17,997 - pyskl - INFO - Epoch(val) [113][533] top1_acc: 0.9061, top5_acc: 0.9923, mean_class_accuracy: 0.8799 +2025-06-25 06:45:37,224 - pyskl - INFO - Epoch [114][100/1281] lr: 3.555e-03, eta: 5:41:58, time: 0.792, data_time: 0.183, memory: 4083, top1_acc: 0.9844, top5_acc: 0.9994, loss_cls: 0.1100, loss: 0.1100 +2025-06-25 06:46:26,015 - pyskl - INFO - Epoch [114][200/1281] lr: 3.541e-03, eta: 5:41:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0843, loss: 0.0843 +2025-06-25 06:47:15,032 - pyskl - INFO - Epoch [114][300/1281] lr: 3.526e-03, eta: 5:40:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1276, loss: 0.1276 +2025-06-25 06:48:03,889 - pyskl - INFO - Epoch [114][400/1281] lr: 3.512e-03, eta: 5:39:53, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0940, loss: 0.0940 +2025-06-25 06:48:53,024 - pyskl - INFO - Epoch [114][500/1281] lr: 3.498e-03, eta: 5:39:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0784, loss: 0.0784 +2025-06-25 06:49:41,808 - pyskl - INFO - Epoch [114][600/1281] lr: 3.484e-03, eta: 5:38:30, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0822, loss: 0.0822 +2025-06-25 06:50:27,019 - pyskl - INFO - Epoch [114][700/1281] lr: 3.470e-03, eta: 5:37:47, time: 0.452, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0905, loss: 0.0905 +2025-06-25 06:51:06,718 - pyskl - INFO - Epoch [114][800/1281] lr: 3.456e-03, eta: 5:37:02, time: 0.397, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0785, loss: 0.0785 +2025-06-25 06:51:40,249 - pyskl - INFO - Epoch [114][900/1281] lr: 3.442e-03, eta: 5:36:16, time: 0.335, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0764, loss: 0.0764 +2025-06-25 06:52:18,358 - pyskl - INFO - Epoch [114][1000/1281] lr: 3.427e-03, eta: 5:35:31, time: 0.381, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0897, loss: 0.0897 +2025-06-25 06:53:06,965 - pyskl - INFO - Epoch [114][1100/1281] lr: 3.413e-03, eta: 5:34:49, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0884, loss: 0.0884 +2025-06-25 06:53:55,670 - pyskl - INFO - Epoch [114][1200/1281] lr: 3.399e-03, eta: 5:34:07, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0886, loss: 0.0886 +2025-06-25 06:54:35,657 - pyskl - INFO - Saving checkpoint at 114 epochs +2025-06-25 06:55:33,447 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:55:33,522 - pyskl - INFO - +top1_acc 0.9096 +top5_acc 0.9919 +2025-06-25 06:55:33,522 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:55:33,530 - pyskl - INFO - +mean_acc 0.8849 +2025-06-25 06:55:33,532 - pyskl - INFO - Epoch(val) [114][533] top1_acc: 0.9096, top5_acc: 0.9919, mean_class_accuracy: 0.8849 +2025-06-25 06:56:51,663 - pyskl - INFO - Epoch [115][100/1281] lr: 3.374e-03, eta: 5:32:48, time: 0.781, data_time: 0.183, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0702, loss: 0.0702 +2025-06-25 06:57:40,286 - pyskl - INFO - Epoch [115][200/1281] lr: 3.360e-03, eta: 5:32:07, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0744, loss: 0.0744 +2025-06-25 06:58:29,382 - pyskl - INFO - Epoch [115][300/1281] lr: 3.346e-03, eta: 5:31:25, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.0865, loss: 0.0865 +2025-06-25 06:59:18,223 - pyskl - INFO - Epoch [115][400/1281] lr: 3.332e-03, eta: 5:30:43, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0737, loss: 0.0737 +2025-06-25 07:00:06,999 - pyskl - INFO - Epoch [115][500/1281] lr: 3.318e-03, eta: 5:30:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0642, loss: 0.0642 +2025-06-25 07:00:56,145 - pyskl - INFO - Epoch [115][600/1281] lr: 3.305e-03, eta: 5:29:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0724, loss: 0.0724 +2025-06-25 07:01:44,243 - pyskl - INFO - Epoch [115][700/1281] lr: 3.291e-03, eta: 5:28:38, time: 0.481, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0830, loss: 0.0830 +2025-06-25 07:02:18,426 - pyskl - INFO - Epoch [115][800/1281] lr: 3.277e-03, eta: 5:27:52, time: 0.342, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1007, loss: 0.1007 +2025-06-25 07:02:57,711 - pyskl - INFO - Epoch [115][900/1281] lr: 3.263e-03, eta: 5:27:07, time: 0.393, data_time: 0.001, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1022, loss: 0.1022 +2025-06-25 07:03:34,068 - pyskl - INFO - Epoch [115][1000/1281] lr: 3.249e-03, eta: 5:26:21, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0949, loss: 0.0949 +2025-06-25 07:04:22,632 - pyskl - INFO - Epoch [115][1100/1281] lr: 3.236e-03, eta: 5:25:40, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0877, loss: 0.0877 +2025-06-25 07:05:11,431 - pyskl - INFO - Epoch [115][1200/1281] lr: 3.222e-03, eta: 5:24:58, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0925, loss: 0.0925 +2025-06-25 07:05:51,755 - pyskl - INFO - Saving checkpoint at 115 epochs +2025-06-25 07:06:50,007 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:06:50,063 - pyskl - INFO - +top1_acc 0.9099 +top5_acc 0.9925 +2025-06-25 07:06:50,063 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:06:50,070 - pyskl - INFO - +mean_acc 0.8890 +2025-06-25 07:06:50,072 - pyskl - INFO - Epoch(val) [115][533] top1_acc: 0.9099, top5_acc: 0.9925, mean_class_accuracy: 0.8890 +2025-06-25 07:08:08,817 - pyskl - INFO - Epoch [116][100/1281] lr: 3.197e-03, eta: 5:23:39, time: 0.787, data_time: 0.187, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0682, loss: 0.0682 +2025-06-25 07:08:57,754 - pyskl - INFO - Epoch [116][200/1281] lr: 3.184e-03, eta: 5:22:58, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0505, loss: 0.0505 +2025-06-25 07:09:46,790 - pyskl - INFO - Epoch [116][300/1281] lr: 3.170e-03, eta: 5:22:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0464, loss: 0.0464 +2025-06-25 07:10:36,055 - pyskl - INFO - Epoch [116][400/1281] lr: 3.156e-03, eta: 5:21:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0492, loss: 0.0492 +2025-06-25 07:11:24,934 - pyskl - INFO - Epoch [116][500/1281] lr: 3.143e-03, eta: 5:20:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0577, loss: 0.0577 +2025-06-25 07:12:13,872 - pyskl - INFO - Epoch [116][600/1281] lr: 3.129e-03, eta: 5:20:11, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0706, loss: 0.0706 +2025-06-25 07:13:01,951 - pyskl - INFO - Epoch [116][700/1281] lr: 3.116e-03, eta: 5:19:29, time: 0.481, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0627, loss: 0.0627 +2025-06-25 07:13:37,057 - pyskl - INFO - Epoch [116][800/1281] lr: 3.102e-03, eta: 5:18:43, time: 0.351, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0851, loss: 0.0851 +2025-06-25 07:14:16,032 - pyskl - INFO - Epoch [116][900/1281] lr: 3.089e-03, eta: 5:17:58, time: 0.390, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0740, loss: 0.0740 +2025-06-25 07:14:53,883 - pyskl - INFO - Epoch [116][1000/1281] lr: 3.075e-03, eta: 5:17:13, time: 0.378, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0694, loss: 0.0694 +2025-06-25 07:15:42,667 - pyskl - INFO - Epoch [116][1100/1281] lr: 3.062e-03, eta: 5:16:31, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0769, loss: 0.0769 +2025-06-25 07:16:31,243 - pyskl - INFO - Epoch [116][1200/1281] lr: 3.049e-03, eta: 5:15:49, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0827, loss: 0.0827 +2025-06-25 07:17:11,152 - pyskl - INFO - Saving checkpoint at 116 epochs +2025-06-25 07:18:09,182 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:18:09,248 - pyskl - INFO - +top1_acc 0.9127 +top5_acc 0.9923 +2025-06-25 07:18:09,248 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:18:09,256 - pyskl - INFO - +mean_acc 0.8842 +2025-06-25 07:18:09,261 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_106.pth was removed +2025-06-25 07:18:09,426 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_116.pth. +2025-06-25 07:18:09,426 - pyskl - INFO - Best top1_acc is 0.9127 at 116 epoch. +2025-06-25 07:18:09,429 - pyskl - INFO - Epoch(val) [116][533] top1_acc: 0.9127, top5_acc: 0.9923, mean_class_accuracy: 0.8842 +2025-06-25 07:19:28,959 - pyskl - INFO - Epoch [117][100/1281] lr: 3.024e-03, eta: 5:14:31, time: 0.795, data_time: 0.186, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0609, loss: 0.0609 +2025-06-25 07:20:17,842 - pyskl - INFO - Epoch [117][200/1281] lr: 3.011e-03, eta: 5:13:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0839, loss: 0.0839 +2025-06-25 07:21:06,917 - pyskl - INFO - Epoch [117][300/1281] lr: 2.998e-03, eta: 5:13:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0718, loss: 0.0718 +2025-06-25 07:21:55,962 - pyskl - INFO - Epoch [117][400/1281] lr: 2.984e-03, eta: 5:12:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 0.9994, loss_cls: 0.0881, loss: 0.0881 +2025-06-25 07:22:44,791 - pyskl - INFO - Epoch [117][500/1281] lr: 2.971e-03, eta: 5:11:43, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0627, loss: 0.0627 +2025-06-25 07:23:33,538 - pyskl - INFO - Epoch [117][600/1281] lr: 2.958e-03, eta: 5:11:02, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0509, loss: 0.0509 +2025-06-25 07:24:19,445 - pyskl - INFO - Epoch [117][700/1281] lr: 2.945e-03, eta: 5:10:19, time: 0.459, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 0.9994, loss_cls: 0.0673, loss: 0.0673 +2025-06-25 07:25:01,105 - pyskl - INFO - Epoch [117][800/1281] lr: 2.932e-03, eta: 5:09:35, time: 0.417, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0601, loss: 0.0601 +2025-06-25 07:25:33,247 - pyskl - INFO - Epoch [117][900/1281] lr: 2.919e-03, eta: 5:08:48, time: 0.321, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0772, loss: 0.0772 +2025-06-25 07:26:13,815 - pyskl - INFO - Epoch [117][1000/1281] lr: 2.905e-03, eta: 5:08:04, time: 0.406, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0585, loss: 0.0585 +2025-06-25 07:27:02,524 - pyskl - INFO - Epoch [117][1100/1281] lr: 2.892e-03, eta: 5:07:22, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0434, loss: 0.0434 +2025-06-25 07:27:50,928 - pyskl - INFO - Epoch [117][1200/1281] lr: 2.879e-03, eta: 5:06:40, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.0932, loss: 0.0932 +2025-06-25 07:28:31,266 - pyskl - INFO - Saving checkpoint at 117 epochs +2025-06-25 07:29:29,811 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:29:29,866 - pyskl - INFO - +top1_acc 0.9123 +top5_acc 0.9926 +2025-06-25 07:29:29,866 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:29:29,872 - pyskl - INFO - +mean_acc 0.8771 +2025-06-25 07:29:29,874 - pyskl - INFO - Epoch(val) [117][533] top1_acc: 0.9123, top5_acc: 0.9926, mean_class_accuracy: 0.8771 +2025-06-25 07:30:48,128 - pyskl - INFO - Epoch [118][100/1281] lr: 2.856e-03, eta: 5:05:21, time: 0.782, data_time: 0.178, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0683, loss: 0.0683 +2025-06-25 07:31:36,826 - pyskl - INFO - Epoch [118][200/1281] lr: 2.843e-03, eta: 5:04:39, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0509, loss: 0.0509 +2025-06-25 07:32:25,924 - pyskl - INFO - Epoch [118][300/1281] lr: 2.830e-03, eta: 5:03:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0541, loss: 0.0541 +2025-06-25 07:33:14,971 - pyskl - INFO - Epoch [118][400/1281] lr: 2.817e-03, eta: 5:03:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0555, loss: 0.0555 +2025-06-25 07:34:03,794 - pyskl - INFO - Epoch [118][500/1281] lr: 2.804e-03, eta: 5:02:34, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0731, loss: 0.0731 +2025-06-25 07:34:52,433 - pyskl - INFO - Epoch [118][600/1281] lr: 2.791e-03, eta: 5:01:51, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0570, loss: 0.0570 +2025-06-25 07:35:36,849 - pyskl - INFO - Epoch [118][700/1281] lr: 2.778e-03, eta: 5:01:08, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0540, loss: 0.0540 +2025-06-25 07:36:17,957 - pyskl - INFO - Epoch [118][800/1281] lr: 2.765e-03, eta: 5:00:24, time: 0.411, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0610, loss: 0.0610 +2025-06-25 07:36:50,886 - pyskl - INFO - Epoch [118][900/1281] lr: 2.753e-03, eta: 4:59:38, time: 0.329, data_time: 0.001, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0617, loss: 0.0617 +2025-06-25 07:37:31,631 - pyskl - INFO - Epoch [118][1000/1281] lr: 2.740e-03, eta: 4:58:54, time: 0.407, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 0.9994, loss_cls: 0.0669, loss: 0.0669 +2025-06-25 07:38:20,684 - pyskl - INFO - Epoch [118][1100/1281] lr: 2.727e-03, eta: 4:58:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0687, loss: 0.0687 +2025-06-25 07:39:09,269 - pyskl - INFO - Epoch [118][1200/1281] lr: 2.714e-03, eta: 4:57:30, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0590, loss: 0.0590 +2025-06-25 07:39:49,516 - pyskl - INFO - Saving checkpoint at 118 epochs +2025-06-25 07:40:47,152 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:40:47,209 - pyskl - INFO - +top1_acc 0.9152 +top5_acc 0.9926 +2025-06-25 07:40:47,209 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:40:47,216 - pyskl - INFO - +mean_acc 0.8862 +2025-06-25 07:40:47,220 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_116.pth was removed +2025-06-25 07:40:47,384 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2025-06-25 07:40:47,384 - pyskl - INFO - Best top1_acc is 0.9152 at 118 epoch. +2025-06-25 07:40:47,387 - pyskl - INFO - Epoch(val) [118][533] top1_acc: 0.9152, top5_acc: 0.9926, mean_class_accuracy: 0.8862 +2025-06-25 07:42:07,109 - pyskl - INFO - Epoch [119][100/1281] lr: 2.691e-03, eta: 4:56:11, time: 0.797, data_time: 0.184, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0502, loss: 0.0502 +2025-06-25 07:42:55,962 - pyskl - INFO - Epoch [119][200/1281] lr: 2.679e-03, eta: 4:55:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0553, loss: 0.0553 +2025-06-25 07:43:45,162 - pyskl - INFO - Epoch [119][300/1281] lr: 2.666e-03, eta: 4:54:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0459, loss: 0.0459 +2025-06-25 07:44:33,788 - pyskl - INFO - Epoch [119][400/1281] lr: 2.653e-03, eta: 4:54:05, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0431, loss: 0.0431 +2025-06-25 07:45:22,451 - pyskl - INFO - Epoch [119][500/1281] lr: 2.641e-03, eta: 4:53:23, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0384, loss: 0.0384 +2025-06-25 07:46:11,337 - pyskl - INFO - Epoch [119][600/1281] lr: 2.628e-03, eta: 4:52:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0769, loss: 0.0769 +2025-06-25 07:46:55,177 - pyskl - INFO - Epoch [119][700/1281] lr: 2.616e-03, eta: 4:51:58, time: 0.438, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0761, loss: 0.0761 +2025-06-25 07:47:38,731 - pyskl - INFO - Epoch [119][800/1281] lr: 2.603e-03, eta: 4:51:14, time: 0.436, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0695, loss: 0.0695 +2025-06-25 07:48:08,096 - pyskl - INFO - Epoch [119][900/1281] lr: 2.591e-03, eta: 4:50:27, time: 0.294, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0812, loss: 0.0812 +2025-06-25 07:48:50,991 - pyskl - INFO - Epoch [119][1000/1281] lr: 2.578e-03, eta: 4:49:43, time: 0.429, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0521, loss: 0.0521 +2025-06-25 07:49:39,720 - pyskl - INFO - Epoch [119][1100/1281] lr: 2.566e-03, eta: 4:49:01, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0740, loss: 0.0740 +2025-06-25 07:50:28,417 - pyskl - INFO - Epoch [119][1200/1281] lr: 2.554e-03, eta: 4:48:19, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0695, loss: 0.0695 +2025-06-25 07:51:08,809 - pyskl - INFO - Saving checkpoint at 119 epochs +2025-06-25 07:52:06,301 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:52:06,361 - pyskl - INFO - +top1_acc 0.9098 +top5_acc 0.9928 +2025-06-25 07:52:06,361 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:52:06,367 - pyskl - INFO - +mean_acc 0.8808 +2025-06-25 07:52:06,369 - pyskl - INFO - Epoch(val) [119][533] top1_acc: 0.9098, top5_acc: 0.9928, mean_class_accuracy: 0.8808 +2025-06-25 07:53:26,500 - pyskl - INFO - Epoch [120][100/1281] lr: 2.531e-03, eta: 4:47:01, time: 0.801, data_time: 0.186, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0702, loss: 0.0702 +2025-06-25 07:54:15,748 - pyskl - INFO - Epoch [120][200/1281] lr: 2.519e-03, eta: 4:46:19, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0519, loss: 0.0519 +2025-06-25 07:55:04,624 - pyskl - INFO - Epoch [120][300/1281] lr: 2.507e-03, eta: 4:45:37, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0485, loss: 0.0485 +2025-06-25 07:55:53,879 - pyskl - INFO - Epoch [120][400/1281] lr: 2.494e-03, eta: 4:44:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0614, loss: 0.0614 +2025-06-25 07:56:42,902 - pyskl - INFO - Epoch [120][500/1281] lr: 2.482e-03, eta: 4:44:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0411, loss: 0.0411 +2025-06-25 07:57:31,775 - pyskl - INFO - Epoch [120][600/1281] lr: 2.470e-03, eta: 4:43:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0453, loss: 0.0453 +2025-06-25 07:58:12,867 - pyskl - INFO - Epoch [120][700/1281] lr: 2.458e-03, eta: 4:42:47, time: 0.411, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0633, loss: 0.0633 +2025-06-25 07:59:03,205 - pyskl - INFO - Epoch [120][800/1281] lr: 2.446e-03, eta: 4:42:05, time: 0.503, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0473, loss: 0.0473 +2025-06-25 07:59:26,862 - pyskl - INFO - Epoch [120][900/1281] lr: 2.433e-03, eta: 4:41:16, time: 0.237, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0415, loss: 0.0415 +2025-06-25 08:00:10,582 - pyskl - INFO - Epoch [120][1000/1281] lr: 2.421e-03, eta: 4:40:33, time: 0.437, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0509, loss: 0.0509 +2025-06-25 08:00:59,303 - pyskl - INFO - Epoch [120][1100/1281] lr: 2.409e-03, eta: 4:39:51, time: 0.487, data_time: 0.001, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0565, loss: 0.0565 +2025-06-25 08:01:48,141 - pyskl - INFO - Epoch [120][1200/1281] lr: 2.397e-03, eta: 4:39:09, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0408, loss: 0.0408 +2025-06-25 08:02:28,410 - pyskl - INFO - Saving checkpoint at 120 epochs +2025-06-25 08:03:26,574 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:03:26,641 - pyskl - INFO - +top1_acc 0.9187 +top5_acc 0.9934 +2025-06-25 08:03:26,641 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:03:26,648 - pyskl - INFO - +mean_acc 0.8934 +2025-06-25 08:03:26,653 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_118.pth was removed +2025-06-25 08:03:26,866 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_120.pth. +2025-06-25 08:03:26,867 - pyskl - INFO - Best top1_acc is 0.9187 at 120 epoch. +2025-06-25 08:03:26,869 - pyskl - INFO - Epoch(val) [120][533] top1_acc: 0.9187, top5_acc: 0.9934, mean_class_accuracy: 0.8934 +2025-06-25 08:04:47,652 - pyskl - INFO - Epoch [121][100/1281] lr: 2.375e-03, eta: 4:37:50, time: 0.808, data_time: 0.185, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0533, loss: 0.0533 +2025-06-25 08:05:36,409 - pyskl - INFO - Epoch [121][200/1281] lr: 2.363e-03, eta: 4:37:08, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0344, loss: 0.0344 +2025-06-25 08:06:25,529 - pyskl - INFO - Epoch [121][300/1281] lr: 2.351e-03, eta: 4:36:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0431, loss: 0.0431 +2025-06-25 08:07:14,341 - pyskl - INFO - Epoch [121][400/1281] lr: 2.340e-03, eta: 4:35:44, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0505, loss: 0.0505 +2025-06-25 08:08:03,085 - pyskl - INFO - Epoch [121][500/1281] lr: 2.328e-03, eta: 4:35:02, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0413, loss: 0.0413 +2025-06-25 08:08:51,964 - pyskl - INFO - Epoch [121][600/1281] lr: 2.316e-03, eta: 4:34:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0414, loss: 0.0414 +2025-06-25 08:09:29,889 - pyskl - INFO - Epoch [121][700/1281] lr: 2.304e-03, eta: 4:33:35, time: 0.379, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0485, loss: 0.0485 +2025-06-25 08:10:20,775 - pyskl - INFO - Epoch [121][800/1281] lr: 2.292e-03, eta: 4:32:53, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0361, loss: 0.0361 +2025-06-25 08:10:44,788 - pyskl - INFO - Epoch [121][900/1281] lr: 2.280e-03, eta: 4:32:05, time: 0.240, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0393, loss: 0.0393 +2025-06-25 08:11:30,617 - pyskl - INFO - Epoch [121][1000/1281] lr: 2.269e-03, eta: 4:31:22, time: 0.458, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0524, loss: 0.0524 +2025-06-25 08:12:19,522 - pyskl - INFO - Epoch [121][1100/1281] lr: 2.257e-03, eta: 4:30:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0589, loss: 0.0589 +2025-06-25 08:13:08,360 - pyskl - INFO - Epoch [121][1200/1281] lr: 2.245e-03, eta: 4:29:57, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0611, loss: 0.0611 +2025-06-25 08:13:48,472 - pyskl - INFO - Saving checkpoint at 121 epochs +2025-06-25 08:14:46,670 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:14:46,726 - pyskl - INFO - +top1_acc 0.9223 +top5_acc 0.9941 +2025-06-25 08:14:46,726 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:14:46,733 - pyskl - INFO - +mean_acc 0.8979 +2025-06-25 08:14:46,737 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_120.pth was removed +2025-06-25 08:14:46,904 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_121.pth. +2025-06-25 08:14:46,905 - pyskl - INFO - Best top1_acc is 0.9223 at 121 epoch. +2025-06-25 08:14:46,907 - pyskl - INFO - Epoch(val) [121][533] top1_acc: 0.9223, top5_acc: 0.9941, mean_class_accuracy: 0.8979 +2025-06-25 08:16:05,912 - pyskl - INFO - Epoch [122][100/1281] lr: 2.224e-03, eta: 4:28:39, time: 0.790, data_time: 0.182, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0375, loss: 0.0375 +2025-06-25 08:16:54,774 - pyskl - INFO - Epoch [122][200/1281] lr: 2.212e-03, eta: 4:27:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0466, loss: 0.0466 +2025-06-25 08:17:43,610 - pyskl - INFO - Epoch [122][300/1281] lr: 2.201e-03, eta: 4:27:14, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0310, loss: 0.0310 +2025-06-25 08:18:32,295 - pyskl - INFO - Epoch [122][400/1281] lr: 2.189e-03, eta: 4:26:32, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0467, loss: 0.0467 +2025-06-25 08:19:21,160 - pyskl - INFO - Epoch [122][500/1281] lr: 2.178e-03, eta: 4:25:50, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0515, loss: 0.0515 +2025-06-25 08:20:10,103 - pyskl - INFO - Epoch [122][600/1281] lr: 2.166e-03, eta: 4:25:07, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0364, loss: 0.0364 +2025-06-25 08:20:45,587 - pyskl - INFO - Epoch [122][700/1281] lr: 2.155e-03, eta: 4:24:22, time: 0.355, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0287, loss: 0.0287 +2025-06-25 08:21:36,545 - pyskl - INFO - Epoch [122][800/1281] lr: 2.143e-03, eta: 4:23:40, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0297, loss: 0.0297 +2025-06-25 08:22:01,185 - pyskl - INFO - Epoch [122][900/1281] lr: 2.132e-03, eta: 4:22:52, time: 0.246, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0293, loss: 0.0293 +2025-06-25 08:22:47,900 - pyskl - INFO - Epoch [122][1000/1281] lr: 2.120e-03, eta: 4:22:09, time: 0.467, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0377, loss: 0.0377 +2025-06-25 08:23:36,421 - pyskl - INFO - Epoch [122][1100/1281] lr: 2.109e-03, eta: 4:21:27, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0332, loss: 0.0332 +2025-06-25 08:24:25,240 - pyskl - INFO - Epoch [122][1200/1281] lr: 2.098e-03, eta: 4:20:45, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0389, loss: 0.0389 +2025-06-25 08:25:05,425 - pyskl - INFO - Saving checkpoint at 122 epochs +2025-06-25 08:26:03,760 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:26:03,815 - pyskl - INFO - +top1_acc 0.9200 +top5_acc 0.9941 +2025-06-25 08:26:03,816 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:26:03,822 - pyskl - INFO - +mean_acc 0.8966 +2025-06-25 08:26:03,823 - pyskl - INFO - Epoch(val) [122][533] top1_acc: 0.9200, top5_acc: 0.9941, mean_class_accuracy: 0.8966 +2025-06-25 08:27:23,036 - pyskl - INFO - Epoch [123][100/1281] lr: 2.077e-03, eta: 4:19:26, time: 0.792, data_time: 0.185, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0314, loss: 0.0314 +2025-06-25 08:28:11,821 - pyskl - INFO - Epoch [123][200/1281] lr: 2.066e-03, eta: 4:18:44, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0351, loss: 0.0351 +2025-06-25 08:29:00,848 - pyskl - INFO - Epoch [123][300/1281] lr: 2.055e-03, eta: 4:18:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0330, loss: 0.0330 +2025-06-25 08:29:49,702 - pyskl - INFO - Epoch [123][400/1281] lr: 2.044e-03, eta: 4:17:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0293, loss: 0.0293 +2025-06-25 08:30:38,783 - pyskl - INFO - Epoch [123][500/1281] lr: 2.032e-03, eta: 4:16:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0412, loss: 0.0412 +2025-06-25 08:31:27,769 - pyskl - INFO - Epoch [123][600/1281] lr: 2.021e-03, eta: 4:15:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0300, loss: 0.0300 +2025-06-25 08:32:03,422 - pyskl - INFO - Epoch [123][700/1281] lr: 2.010e-03, eta: 4:15:09, time: 0.357, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0416, loss: 0.0416 +2025-06-25 08:32:54,279 - pyskl - INFO - Epoch [123][800/1281] lr: 1.999e-03, eta: 4:14:27, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0406, loss: 0.0406 +2025-06-25 08:33:18,933 - pyskl - INFO - Epoch [123][900/1281] lr: 1.988e-03, eta: 4:13:40, time: 0.247, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0634, loss: 0.0634 +2025-06-25 08:34:05,539 - pyskl - INFO - Epoch [123][1000/1281] lr: 1.977e-03, eta: 4:12:57, time: 0.466, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0440, loss: 0.0440 +2025-06-25 08:34:54,115 - pyskl - INFO - Epoch [123][1100/1281] lr: 1.966e-03, eta: 4:12:14, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0421, loss: 0.0421 +2025-06-25 08:35:42,560 - pyskl - INFO - Epoch [123][1200/1281] lr: 1.955e-03, eta: 4:11:32, time: 0.484, data_time: 0.001, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0312, loss: 0.0312 +2025-06-25 08:36:22,428 - pyskl - INFO - Saving checkpoint at 123 epochs +2025-06-25 08:37:20,861 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:37:20,918 - pyskl - INFO - +top1_acc 0.9198 +top5_acc 0.9927 +2025-06-25 08:37:20,919 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:37:20,925 - pyskl - INFO - +mean_acc 0.8947 +2025-06-25 08:37:20,927 - pyskl - INFO - Epoch(val) [123][533] top1_acc: 0.9198, top5_acc: 0.9927, mean_class_accuracy: 0.8947 +2025-06-25 08:38:40,221 - pyskl - INFO - Epoch [124][100/1281] lr: 1.935e-03, eta: 4:10:13, time: 0.793, data_time: 0.187, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0330, loss: 0.0330 +2025-06-25 08:39:28,925 - pyskl - INFO - Epoch [124][200/1281] lr: 1.924e-03, eta: 4:09:31, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0402, loss: 0.0402 +2025-06-25 08:40:17,823 - pyskl - INFO - Epoch [124][300/1281] lr: 1.913e-03, eta: 4:08:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0286, loss: 0.0286 +2025-06-25 08:41:06,875 - pyskl - INFO - Epoch [124][400/1281] lr: 1.902e-03, eta: 4:08:06, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0402, loss: 0.0402 +2025-06-25 08:41:55,770 - pyskl - INFO - Epoch [124][500/1281] lr: 1.892e-03, eta: 4:07:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0292, loss: 0.0292 +2025-06-25 08:42:44,345 - pyskl - INFO - Epoch [124][600/1281] lr: 1.881e-03, eta: 4:06:41, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0336, loss: 0.0336 +2025-06-25 08:43:18,968 - pyskl - INFO - Epoch [124][700/1281] lr: 1.870e-03, eta: 4:05:56, time: 0.346, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0355, loss: 0.0355 +2025-06-25 08:44:09,950 - pyskl - INFO - Epoch [124][800/1281] lr: 1.859e-03, eta: 4:05:14, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-25 08:44:34,983 - pyskl - INFO - Epoch [124][900/1281] lr: 1.849e-03, eta: 4:04:26, time: 0.250, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-25 08:45:23,103 - pyskl - INFO - Epoch [124][1000/1281] lr: 1.838e-03, eta: 4:03:44, time: 0.481, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0312, loss: 0.0312 +2025-06-25 08:46:12,055 - pyskl - INFO - Epoch [124][1100/1281] lr: 1.827e-03, eta: 4:03:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0291, loss: 0.0291 +2025-06-25 08:47:00,928 - pyskl - INFO - Epoch [124][1200/1281] lr: 1.817e-03, eta: 4:02:19, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0302, loss: 0.0302 +2025-06-25 08:47:41,256 - pyskl - INFO - Saving checkpoint at 124 epochs +2025-06-25 08:48:38,774 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:48:38,829 - pyskl - INFO - +top1_acc 0.9223 +top5_acc 0.9950 +2025-06-25 08:48:38,829 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:48:38,836 - pyskl - INFO - +mean_acc 0.8959 +2025-06-25 08:48:38,837 - pyskl - INFO - Epoch(val) [124][533] top1_acc: 0.9223, top5_acc: 0.9950, mean_class_accuracy: 0.8959 +2025-06-25 08:49:58,542 - pyskl - INFO - Epoch [125][100/1281] lr: 1.797e-03, eta: 4:01:00, time: 0.797, data_time: 0.185, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0253, loss: 0.0253 +2025-06-25 08:50:47,561 - pyskl - INFO - Epoch [125][200/1281] lr: 1.787e-03, eta: 4:00:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0292, loss: 0.0292 +2025-06-25 08:51:36,496 - pyskl - INFO - Epoch [125][300/1281] lr: 1.776e-03, eta: 3:59:36, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0246, loss: 0.0246 +2025-06-25 08:52:25,598 - pyskl - INFO - Epoch [125][400/1281] lr: 1.766e-03, eta: 3:58:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0388, loss: 0.0388 +2025-06-25 08:53:14,329 - pyskl - INFO - Epoch [125][500/1281] lr: 1.755e-03, eta: 3:58:11, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0254, loss: 0.0254 +2025-06-25 08:54:03,168 - pyskl - INFO - Epoch [125][600/1281] lr: 1.745e-03, eta: 3:57:28, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0251, loss: 0.0251 +2025-06-25 08:54:36,887 - pyskl - INFO - Epoch [125][700/1281] lr: 1.735e-03, eta: 3:56:43, time: 0.337, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0271, loss: 0.0271 +2025-06-25 08:55:27,691 - pyskl - INFO - Epoch [125][800/1281] lr: 1.724e-03, eta: 3:56:00, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 08:55:54,289 - pyskl - INFO - Epoch [125][900/1281] lr: 1.714e-03, eta: 3:55:13, time: 0.266, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-25 08:56:42,996 - pyskl - INFO - Epoch [125][1000/1281] lr: 1.704e-03, eta: 3:54:31, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0235, loss: 0.0235 +2025-06-25 08:57:31,803 - pyskl - INFO - Epoch [125][1100/1281] lr: 1.693e-03, eta: 3:53:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 08:58:20,676 - pyskl - INFO - Epoch [125][1200/1281] lr: 1.683e-03, eta: 3:53:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0248, loss: 0.0248 +2025-06-25 08:59:00,762 - pyskl - INFO - Saving checkpoint at 125 epochs +2025-06-25 08:59:58,895 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:59:58,953 - pyskl - INFO - +top1_acc 0.9245 +top5_acc 0.9948 +2025-06-25 08:59:58,954 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:59:58,960 - pyskl - INFO - +mean_acc 0.9000 +2025-06-25 08:59:58,964 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_121.pth was removed +2025-06-25 08:59:59,137 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_125.pth. +2025-06-25 08:59:59,137 - pyskl - INFO - Best top1_acc is 0.9245 at 125 epoch. +2025-06-25 08:59:59,139 - pyskl - INFO - Epoch(val) [125][533] top1_acc: 0.9245, top5_acc: 0.9948, mean_class_accuracy: 0.9000 +2025-06-25 09:01:19,522 - pyskl - INFO - Epoch [126][100/1281] lr: 1.665e-03, eta: 3:51:47, time: 0.804, data_time: 0.188, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0276, loss: 0.0276 +2025-06-25 09:02:08,436 - pyskl - INFO - Epoch [126][200/1281] lr: 1.654e-03, eta: 3:51:05, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 09:02:57,146 - pyskl - INFO - Epoch [126][300/1281] lr: 1.644e-03, eta: 3:50:22, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 09:03:45,920 - pyskl - INFO - Epoch [126][400/1281] lr: 1.634e-03, eta: 3:49:40, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 09:04:34,931 - pyskl - INFO - Epoch [126][500/1281] lr: 1.624e-03, eta: 3:48:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0294, loss: 0.0294 +2025-06-25 09:05:23,684 - pyskl - INFO - Epoch [126][600/1281] lr: 1.614e-03, eta: 3:48:15, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 09:05:53,407 - pyskl - INFO - Epoch [126][700/1281] lr: 1.604e-03, eta: 3:47:28, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0264, loss: 0.0264 +2025-06-25 09:06:44,392 - pyskl - INFO - Epoch [126][800/1281] lr: 1.594e-03, eta: 3:46:46, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-25 09:07:13,896 - pyskl - INFO - Epoch [126][900/1281] lr: 1.584e-03, eta: 3:46:00, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0331, loss: 0.0331 +2025-06-25 09:08:02,440 - pyskl - INFO - Epoch [126][1000/1281] lr: 1.574e-03, eta: 3:45:17, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0290, loss: 0.0290 +2025-06-25 09:08:51,587 - pyskl - INFO - Epoch [126][1100/1281] lr: 1.564e-03, eta: 3:44:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0314, loss: 0.0314 +2025-06-25 09:09:40,301 - pyskl - INFO - Epoch [126][1200/1281] lr: 1.554e-03, eta: 3:43:52, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0255, loss: 0.0255 +2025-06-25 09:10:20,584 - pyskl - INFO - Saving checkpoint at 126 epochs +2025-06-25 09:11:18,267 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:11:18,332 - pyskl - INFO - +top1_acc 0.9242 +top5_acc 0.9939 +2025-06-25 09:11:18,332 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:11:18,339 - pyskl - INFO - +mean_acc 0.9003 +2025-06-25 09:11:18,342 - pyskl - INFO - Epoch(val) [126][533] top1_acc: 0.9242, top5_acc: 0.9939, mean_class_accuracy: 0.9003 +2025-06-25 09:12:38,149 - pyskl - INFO - Epoch [127][100/1281] lr: 1.536e-03, eta: 3:42:34, time: 0.798, data_time: 0.187, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0263, loss: 0.0263 +2025-06-25 09:13:26,854 - pyskl - INFO - Epoch [127][200/1281] lr: 1.527e-03, eta: 3:41:51, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 09:14:15,895 - pyskl - INFO - Epoch [127][300/1281] lr: 1.517e-03, eta: 3:41:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0226, loss: 0.0226 +2025-06-25 09:15:04,783 - pyskl - INFO - Epoch [127][400/1281] lr: 1.507e-03, eta: 3:40:26, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0218, loss: 0.0218 +2025-06-25 09:15:53,912 - pyskl - INFO - Epoch [127][500/1281] lr: 1.497e-03, eta: 3:39:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 09:16:42,927 - pyskl - INFO - Epoch [127][600/1281] lr: 1.488e-03, eta: 3:39:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 09:17:12,000 - pyskl - INFO - Epoch [127][700/1281] lr: 1.478e-03, eta: 3:38:14, time: 0.291, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-25 09:18:02,871 - pyskl - INFO - Epoch [127][800/1281] lr: 1.468e-03, eta: 3:37:32, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 09:18:31,693 - pyskl - INFO - Epoch [127][900/1281] lr: 1.459e-03, eta: 3:36:46, time: 0.288, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-25 09:19:20,477 - pyskl - INFO - Epoch [127][1000/1281] lr: 1.449e-03, eta: 3:36:03, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-25 09:20:09,783 - pyskl - INFO - Epoch [127][1100/1281] lr: 1.440e-03, eta: 3:35:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0260, loss: 0.0260 +2025-06-25 09:20:58,491 - pyskl - INFO - Epoch [127][1200/1281] lr: 1.430e-03, eta: 3:34:38, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0245, loss: 0.0245 +2025-06-25 09:21:38,691 - pyskl - INFO - Saving checkpoint at 127 epochs +2025-06-25 09:22:37,073 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:22:37,140 - pyskl - INFO - +top1_acc 0.9229 +top5_acc 0.9945 +2025-06-25 09:22:37,140 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:22:37,147 - pyskl - INFO - +mean_acc 0.9019 +2025-06-25 09:22:37,149 - pyskl - INFO - Epoch(val) [127][533] top1_acc: 0.9229, top5_acc: 0.9945, mean_class_accuracy: 0.9019 +2025-06-25 09:23:55,805 - pyskl - INFO - Epoch [128][100/1281] lr: 1.413e-03, eta: 3:33:19, time: 0.787, data_time: 0.182, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 09:24:45,056 - pyskl - INFO - Epoch [128][200/1281] lr: 1.404e-03, eta: 3:32:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0250, loss: 0.0250 +2025-06-25 09:25:33,839 - pyskl - INFO - Epoch [128][300/1281] lr: 1.394e-03, eta: 3:31:54, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 09:26:22,690 - pyskl - INFO - Epoch [128][400/1281] lr: 1.385e-03, eta: 3:31:11, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 09:27:11,897 - pyskl - INFO - Epoch [128][500/1281] lr: 1.376e-03, eta: 3:30:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 09:28:01,011 - pyskl - INFO - Epoch [128][600/1281] lr: 1.366e-03, eta: 3:29:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 09:28:30,624 - pyskl - INFO - Epoch [128][700/1281] lr: 1.357e-03, eta: 3:29:00, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-25 09:29:21,333 - pyskl - INFO - Epoch [128][800/1281] lr: 1.348e-03, eta: 3:28:18, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 09:29:51,307 - pyskl - INFO - Epoch [128][900/1281] lr: 1.339e-03, eta: 3:27:32, time: 0.300, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 09:30:40,298 - pyskl - INFO - Epoch [128][1000/1281] lr: 1.329e-03, eta: 3:26:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0296, loss: 0.0296 +2025-06-25 09:31:29,209 - pyskl - INFO - Epoch [128][1100/1281] lr: 1.320e-03, eta: 3:26:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 09:32:17,964 - pyskl - INFO - Epoch [128][1200/1281] lr: 1.311e-03, eta: 3:25:24, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0222, loss: 0.0222 +2025-06-25 09:32:57,913 - pyskl - INFO - Saving checkpoint at 128 epochs +2025-06-25 09:33:55,817 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:33:55,873 - pyskl - INFO - +top1_acc 0.9250 +top5_acc 0.9944 +2025-06-25 09:33:55,873 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:33:55,880 - pyskl - INFO - +mean_acc 0.9014 +2025-06-25 09:33:55,884 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_125.pth was removed +2025-06-25 09:33:56,059 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_128.pth. +2025-06-25 09:33:56,059 - pyskl - INFO - Best top1_acc is 0.9250 at 128 epoch. +2025-06-25 09:33:56,062 - pyskl - INFO - Epoch(val) [128][533] top1_acc: 0.9250, top5_acc: 0.9944, mean_class_accuracy: 0.9014 +2025-06-25 09:35:14,510 - pyskl - INFO - Epoch [129][100/1281] lr: 1.295e-03, eta: 3:24:05, time: 0.784, data_time: 0.184, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0218, loss: 0.0218 +2025-06-25 09:36:03,277 - pyskl - INFO - Epoch [129][200/1281] lr: 1.286e-03, eta: 3:23:22, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-25 09:36:52,397 - pyskl - INFO - Epoch [129][300/1281] lr: 1.277e-03, eta: 3:22:39, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9988, top5_acc: 0.9994, loss_cls: 0.0321, loss: 0.0321 +2025-06-25 09:37:41,152 - pyskl - INFO - Epoch [129][400/1281] lr: 1.268e-03, eta: 3:21:56, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 09:38:29,962 - pyskl - INFO - Epoch [129][500/1281] lr: 1.259e-03, eta: 3:21:14, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0276, loss: 0.0276 +2025-06-25 09:39:18,988 - pyskl - INFO - Epoch [129][600/1281] lr: 1.250e-03, eta: 3:20:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-25 09:39:47,611 - pyskl - INFO - Epoch [129][700/1281] lr: 1.241e-03, eta: 3:19:45, time: 0.286, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0277, loss: 0.0277 +2025-06-25 09:40:38,344 - pyskl - INFO - Epoch [129][800/1281] lr: 1.232e-03, eta: 3:19:02, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0226, loss: 0.0226 +2025-06-25 09:41:09,007 - pyskl - INFO - Epoch [129][900/1281] lr: 1.223e-03, eta: 3:18:17, time: 0.307, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-25 09:41:57,634 - pyskl - INFO - Epoch [129][1000/1281] lr: 1.214e-03, eta: 3:17:34, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 09:42:46,294 - pyskl - INFO - Epoch [129][1100/1281] lr: 1.206e-03, eta: 3:16:51, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0279, loss: 0.0279 +2025-06-25 09:43:35,277 - pyskl - INFO - Epoch [129][1200/1281] lr: 1.197e-03, eta: 3:16:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 09:44:15,079 - pyskl - INFO - Saving checkpoint at 129 epochs +2025-06-25 09:45:13,164 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:45:13,226 - pyskl - INFO - +top1_acc 0.9241 +top5_acc 0.9944 +2025-06-25 09:45:13,226 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:45:13,233 - pyskl - INFO - +mean_acc 0.8982 +2025-06-25 09:45:13,235 - pyskl - INFO - Epoch(val) [129][533] top1_acc: 0.9241, top5_acc: 0.9944, mean_class_accuracy: 0.8982 +2025-06-25 09:46:32,010 - pyskl - INFO - Epoch [130][100/1281] lr: 1.181e-03, eta: 3:14:49, time: 0.788, data_time: 0.190, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 09:47:20,837 - pyskl - INFO - Epoch [130][200/1281] lr: 1.172e-03, eta: 3:14:07, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0256, loss: 0.0256 +2025-06-25 09:48:09,780 - pyskl - INFO - Epoch [130][300/1281] lr: 1.164e-03, eta: 3:13:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-25 09:48:58,681 - pyskl - INFO - Epoch [130][400/1281] lr: 1.155e-03, eta: 3:12:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 09:49:47,504 - pyskl - INFO - Epoch [130][500/1281] lr: 1.147e-03, eta: 3:11:58, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 09:50:36,710 - pyskl - INFO - Epoch [130][600/1281] lr: 1.138e-03, eta: 3:11:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 09:51:04,900 - pyskl - INFO - Epoch [130][700/1281] lr: 1.130e-03, eta: 3:10:29, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-25 09:51:54,854 - pyskl - INFO - Epoch [130][800/1281] lr: 1.121e-03, eta: 3:09:47, time: 0.500, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0249, loss: 0.0249 +2025-06-25 09:52:28,831 - pyskl - INFO - Epoch [130][900/1281] lr: 1.113e-03, eta: 3:09:02, time: 0.340, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 09:53:17,658 - pyskl - INFO - Epoch [130][1000/1281] lr: 1.104e-03, eta: 3:08:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 09:54:06,654 - pyskl - INFO - Epoch [130][1100/1281] lr: 1.096e-03, eta: 3:07:36, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-25 09:54:55,839 - pyskl - INFO - Epoch [130][1200/1281] lr: 1.088e-03, eta: 3:06:53, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 09:55:35,876 - pyskl - INFO - Saving checkpoint at 130 epochs +2025-06-25 09:56:34,028 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:56:34,084 - pyskl - INFO - +top1_acc 0.9252 +top5_acc 0.9952 +2025-06-25 09:56:34,084 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:56:34,091 - pyskl - INFO - +mean_acc 0.9016 +2025-06-25 09:56:34,095 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_128.pth was removed +2025-06-25 09:56:34,315 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_130.pth. +2025-06-25 09:56:34,315 - pyskl - INFO - Best top1_acc is 0.9252 at 130 epoch. +2025-06-25 09:56:34,318 - pyskl - INFO - Epoch(val) [130][533] top1_acc: 0.9252, top5_acc: 0.9952, mean_class_accuracy: 0.9016 +2025-06-25 09:57:53,117 - pyskl - INFO - Epoch [131][100/1281] lr: 1.072e-03, eta: 3:05:34, time: 0.788, data_time: 0.184, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 09:58:41,359 - pyskl - INFO - Epoch [131][200/1281] lr: 1.064e-03, eta: 3:04:51, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 09:59:30,242 - pyskl - INFO - Epoch [131][300/1281] lr: 1.056e-03, eta: 3:04:08, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 10:00:19,013 - pyskl - INFO - Epoch [131][400/1281] lr: 1.048e-03, eta: 3:03:26, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0309, loss: 0.0309 +2025-06-25 10:01:07,933 - pyskl - INFO - Epoch [131][500/1281] lr: 1.040e-03, eta: 3:02:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 10:01:57,089 - pyskl - INFO - Epoch [131][600/1281] lr: 1.031e-03, eta: 3:02:00, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-25 10:02:26,642 - pyskl - INFO - Epoch [131][700/1281] lr: 1.023e-03, eta: 3:01:14, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 10:03:12,859 - pyskl - INFO - Epoch [131][800/1281] lr: 1.015e-03, eta: 3:00:31, time: 0.462, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 10:03:47,064 - pyskl - INFO - Epoch [131][900/1281] lr: 1.007e-03, eta: 2:59:46, time: 0.342, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0280, loss: 0.0280 +2025-06-25 10:04:36,067 - pyskl - INFO - Epoch [131][1000/1281] lr: 9.992e-04, eta: 2:59:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 10:05:25,266 - pyskl - INFO - Epoch [131][1100/1281] lr: 9.912e-04, eta: 2:58:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 10:06:14,097 - pyskl - INFO - Epoch [131][1200/1281] lr: 9.832e-04, eta: 2:57:37, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 10:06:54,228 - pyskl - INFO - Saving checkpoint at 131 epochs +2025-06-25 10:07:52,324 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:07:52,381 - pyskl - INFO - +top1_acc 0.9263 +top5_acc 0.9954 +2025-06-25 10:07:52,381 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:07:52,388 - pyskl - INFO - +mean_acc 0.9043 +2025-06-25 10:07:52,392 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_130.pth was removed +2025-06-25 10:07:52,593 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_131.pth. +2025-06-25 10:07:52,594 - pyskl - INFO - Best top1_acc is 0.9263 at 131 epoch. +2025-06-25 10:07:52,596 - pyskl - INFO - Epoch(val) [131][533] top1_acc: 0.9263, top5_acc: 0.9954, mean_class_accuracy: 0.9043 +2025-06-25 10:09:11,303 - pyskl - INFO - Epoch [132][100/1281] lr: 9.689e-04, eta: 2:56:18, time: 0.787, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 10:10:00,186 - pyskl - INFO - Epoch [132][200/1281] lr: 9.610e-04, eta: 2:55:35, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-25 10:10:49,250 - pyskl - INFO - Epoch [132][300/1281] lr: 9.532e-04, eta: 2:54:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 10:11:38,228 - pyskl - INFO - Epoch [132][400/1281] lr: 9.454e-04, eta: 2:54:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 10:12:27,489 - pyskl - INFO - Epoch [132][500/1281] lr: 9.376e-04, eta: 2:53:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 10:13:16,453 - pyskl - INFO - Epoch [132][600/1281] lr: 9.298e-04, eta: 2:52:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 10:13:47,570 - pyskl - INFO - Epoch [132][700/1281] lr: 9.221e-04, eta: 2:51:58, time: 0.311, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 10:14:31,824 - pyskl - INFO - Epoch [132][800/1281] lr: 9.144e-04, eta: 2:51:15, time: 0.443, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 10:15:06,910 - pyskl - INFO - Epoch [132][900/1281] lr: 9.068e-04, eta: 2:50:30, time: 0.351, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-25 10:15:55,910 - pyskl - INFO - Epoch [132][1000/1281] lr: 8.991e-04, eta: 2:49:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 10:16:44,963 - pyskl - INFO - Epoch [132][1100/1281] lr: 8.915e-04, eta: 2:49:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 10:17:33,659 - pyskl - INFO - Epoch [132][1200/1281] lr: 8.840e-04, eta: 2:48:21, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 10:18:13,726 - pyskl - INFO - Saving checkpoint at 132 epochs +2025-06-25 10:19:11,798 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:19:11,859 - pyskl - INFO - +top1_acc 0.9263 +top5_acc 0.9947 +2025-06-25 10:19:11,859 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:19:11,867 - pyskl - INFO - +mean_acc 0.9053 +2025-06-25 10:19:11,869 - pyskl - INFO - Epoch(val) [132][533] top1_acc: 0.9263, top5_acc: 0.9947, mean_class_accuracy: 0.9053 +2025-06-25 10:20:31,005 - pyskl - INFO - Epoch [133][100/1281] lr: 8.704e-04, eta: 2:47:02, time: 0.791, data_time: 0.182, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 10:21:19,907 - pyskl - INFO - Epoch [133][200/1281] lr: 8.629e-04, eta: 2:46:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 10:22:08,781 - pyskl - INFO - Epoch [133][300/1281] lr: 8.554e-04, eta: 2:45:36, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 10:22:57,903 - pyskl - INFO - Epoch [133][400/1281] lr: 8.480e-04, eta: 2:44:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 10:23:46,806 - pyskl - INFO - Epoch [133][500/1281] lr: 8.406e-04, eta: 2:44:10, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 10:24:35,840 - pyskl - INFO - Epoch [133][600/1281] lr: 8.333e-04, eta: 2:43:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 10:25:07,892 - pyskl - INFO - Epoch [133][700/1281] lr: 8.260e-04, eta: 2:42:42, time: 0.321, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 10:25:49,112 - pyskl - INFO - Epoch [133][800/1281] lr: 8.187e-04, eta: 2:41:58, time: 0.412, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 10:26:25,008 - pyskl - INFO - Epoch [133][900/1281] lr: 8.114e-04, eta: 2:41:14, time: 0.359, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 10:27:13,635 - pyskl - INFO - Epoch [133][1000/1281] lr: 8.042e-04, eta: 2:40:31, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 10:28:02,503 - pyskl - INFO - Epoch [133][1100/1281] lr: 7.970e-04, eta: 2:39:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 10:28:51,211 - pyskl - INFO - Epoch [133][1200/1281] lr: 7.898e-04, eta: 2:39:05, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 10:29:31,188 - pyskl - INFO - Saving checkpoint at 133 epochs +2025-06-25 10:30:29,373 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:30:29,428 - pyskl - INFO - +top1_acc 0.9271 +top5_acc 0.9947 +2025-06-25 10:30:29,429 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:30:29,435 - pyskl - INFO - +mean_acc 0.9056 +2025-06-25 10:30:29,439 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_131.pth was removed +2025-06-25 10:30:29,607 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_133.pth. +2025-06-25 10:30:29,608 - pyskl - INFO - Best top1_acc is 0.9271 at 133 epoch. +2025-06-25 10:30:29,610 - pyskl - INFO - Epoch(val) [133][533] top1_acc: 0.9271, top5_acc: 0.9947, mean_class_accuracy: 0.9056 +2025-06-25 10:31:48,548 - pyskl - INFO - Epoch [134][100/1281] lr: 7.769e-04, eta: 2:37:46, time: 0.789, data_time: 0.186, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 10:32:37,517 - pyskl - INFO - Epoch [134][200/1281] lr: 7.699e-04, eta: 2:37:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 10:33:26,523 - pyskl - INFO - Epoch [134][300/1281] lr: 7.628e-04, eta: 2:36:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0219, loss: 0.0219 +2025-06-25 10:34:15,562 - pyskl - INFO - Epoch [134][400/1281] lr: 7.558e-04, eta: 2:35:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 10:35:04,524 - pyskl - INFO - Epoch [134][500/1281] lr: 7.488e-04, eta: 2:34:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-25 10:35:53,279 - pyskl - INFO - Epoch [134][600/1281] lr: 7.419e-04, eta: 2:34:10, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 10:36:28,007 - pyskl - INFO - Epoch [134][700/1281] lr: 7.349e-04, eta: 2:33:26, time: 0.347, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 10:37:06,231 - pyskl - INFO - Epoch [134][800/1281] lr: 7.281e-04, eta: 2:32:41, time: 0.382, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 10:37:43,020 - pyskl - INFO - Epoch [134][900/1281] lr: 7.212e-04, eta: 2:31:57, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 10:38:31,919 - pyskl - INFO - Epoch [134][1000/1281] lr: 7.144e-04, eta: 2:31:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 10:39:21,055 - pyskl - INFO - Epoch [134][1100/1281] lr: 7.076e-04, eta: 2:30:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 10:40:09,629 - pyskl - INFO - Epoch [134][1200/1281] lr: 7.008e-04, eta: 2:29:48, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 10:40:49,755 - pyskl - INFO - Saving checkpoint at 134 epochs +2025-06-25 10:41:47,997 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:41:48,053 - pyskl - INFO - +top1_acc 0.9279 +top5_acc 0.9950 +2025-06-25 10:41:48,054 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:41:48,061 - pyskl - INFO - +mean_acc 0.9040 +2025-06-25 10:41:48,065 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_133.pth was removed +2025-06-25 10:41:48,232 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_134.pth. +2025-06-25 10:41:48,232 - pyskl - INFO - Best top1_acc is 0.9279 at 134 epoch. +2025-06-25 10:41:48,235 - pyskl - INFO - Epoch(val) [134][533] top1_acc: 0.9279, top5_acc: 0.9950, mean_class_accuracy: 0.9040 +2025-06-25 10:43:08,146 - pyskl - INFO - Epoch [135][100/1281] lr: 6.887e-04, eta: 2:28:29, time: 0.799, data_time: 0.190, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 10:43:56,802 - pyskl - INFO - Epoch [135][200/1281] lr: 6.820e-04, eta: 2:27:46, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 10:44:45,683 - pyskl - INFO - Epoch [135][300/1281] lr: 6.753e-04, eta: 2:27:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 10:45:34,456 - pyskl - INFO - Epoch [135][400/1281] lr: 6.687e-04, eta: 2:26:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 10:46:23,809 - pyskl - INFO - Epoch [135][500/1281] lr: 6.622e-04, eta: 2:25:36, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 10:47:11,067 - pyskl - INFO - Epoch [135][600/1281] lr: 6.556e-04, eta: 2:24:53, time: 0.473, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 10:47:47,154 - pyskl - INFO - Epoch [135][700/1281] lr: 6.491e-04, eta: 2:24:09, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 10:48:24,090 - pyskl - INFO - Epoch [135][800/1281] lr: 6.426e-04, eta: 2:23:24, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 10:49:02,750 - pyskl - INFO - Epoch [135][900/1281] lr: 6.362e-04, eta: 2:22:40, time: 0.387, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 10:49:51,520 - pyskl - INFO - Epoch [135][1000/1281] lr: 6.297e-04, eta: 2:21:57, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0259, loss: 0.0259 +2025-06-25 10:50:40,251 - pyskl - INFO - Epoch [135][1100/1281] lr: 6.233e-04, eta: 2:21:14, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 10:51:28,954 - pyskl - INFO - Epoch [135][1200/1281] lr: 6.170e-04, eta: 2:20:31, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 10:52:09,202 - pyskl - INFO - Saving checkpoint at 135 epochs +2025-06-25 10:53:06,864 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:53:06,919 - pyskl - INFO - +top1_acc 0.9252 +top5_acc 0.9945 +2025-06-25 10:53:06,920 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:53:06,927 - pyskl - INFO - +mean_acc 0.9017 +2025-06-25 10:53:06,929 - pyskl - INFO - Epoch(val) [135][533] top1_acc: 0.9252, top5_acc: 0.9945, mean_class_accuracy: 0.9017 +2025-06-25 10:54:25,155 - pyskl - INFO - Epoch [136][100/1281] lr: 6.056e-04, eta: 2:19:11, time: 0.782, data_time: 0.183, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 10:55:13,761 - pyskl - INFO - Epoch [136][200/1281] lr: 5.993e-04, eta: 2:18:28, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 10:56:02,853 - pyskl - INFO - Epoch [136][300/1281] lr: 5.931e-04, eta: 2:17:45, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 10:56:51,866 - pyskl - INFO - Epoch [136][400/1281] lr: 5.868e-04, eta: 2:17:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 10:57:40,763 - pyskl - INFO - Epoch [136][500/1281] lr: 5.807e-04, eta: 2:16:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 10:58:27,404 - pyskl - INFO - Epoch [136][600/1281] lr: 5.745e-04, eta: 2:15:35, time: 0.466, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 10:59:05,123 - pyskl - INFO - Epoch [136][700/1281] lr: 5.684e-04, eta: 2:14:51, time: 0.377, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 10:59:40,462 - pyskl - INFO - Epoch [136][800/1281] lr: 5.623e-04, eta: 2:14:06, time: 0.353, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 11:00:19,119 - pyskl - INFO - Epoch [136][900/1281] lr: 5.563e-04, eta: 2:13:22, time: 0.387, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 11:01:08,175 - pyskl - INFO - Epoch [136][1000/1281] lr: 5.503e-04, eta: 2:12:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 11:01:56,801 - pyskl - INFO - Epoch [136][1100/1281] lr: 5.443e-04, eta: 2:11:56, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 11:02:45,828 - pyskl - INFO - Epoch [136][1200/1281] lr: 5.384e-04, eta: 2:11:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 11:03:26,020 - pyskl - INFO - Saving checkpoint at 136 epochs +2025-06-25 11:04:24,066 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:04:24,134 - pyskl - INFO - +top1_acc 0.9259 +top5_acc 0.9945 +2025-06-25 11:04:24,135 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:04:24,142 - pyskl - INFO - +mean_acc 0.9038 +2025-06-25 11:04:24,144 - pyskl - INFO - Epoch(val) [136][533] top1_acc: 0.9259, top5_acc: 0.9945, mean_class_accuracy: 0.9038 +2025-06-25 11:05:43,472 - pyskl - INFO - Epoch [137][100/1281] lr: 5.277e-04, eta: 2:09:54, time: 0.793, data_time: 0.181, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 11:06:32,231 - pyskl - INFO - Epoch [137][200/1281] lr: 5.218e-04, eta: 2:09:11, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 11:07:21,083 - pyskl - INFO - Epoch [137][300/1281] lr: 5.160e-04, eta: 2:08:27, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 11:08:10,380 - pyskl - INFO - Epoch [137][400/1281] lr: 5.102e-04, eta: 2:07:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 11:08:59,429 - pyskl - INFO - Epoch [137][500/1281] lr: 5.044e-04, eta: 2:07:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 11:09:46,396 - pyskl - INFO - Epoch [137][600/1281] lr: 4.987e-04, eta: 2:06:18, time: 0.470, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 11:10:22,741 - pyskl - INFO - Epoch [137][700/1281] lr: 4.930e-04, eta: 2:05:33, time: 0.363, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 11:10:59,156 - pyskl - INFO - Epoch [137][800/1281] lr: 4.873e-04, eta: 2:04:49, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 11:11:38,547 - pyskl - INFO - Epoch [137][900/1281] lr: 4.817e-04, eta: 2:04:05, time: 0.394, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 11:12:27,717 - pyskl - INFO - Epoch [137][1000/1281] lr: 4.761e-04, eta: 2:03:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 11:13:16,615 - pyskl - INFO - Epoch [137][1100/1281] lr: 4.705e-04, eta: 2:02:38, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 11:14:05,918 - pyskl - INFO - Epoch [137][1200/1281] lr: 4.650e-04, eta: 2:01:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 11:14:46,209 - pyskl - INFO - Saving checkpoint at 137 epochs +2025-06-25 11:15:44,602 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:15:44,666 - pyskl - INFO - +top1_acc 0.9288 +top5_acc 0.9944 +2025-06-25 11:15:44,666 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:15:44,675 - pyskl - INFO - +mean_acc 0.9064 +2025-06-25 11:15:44,681 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_134.pth was removed +2025-06-25 11:15:44,865 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_137.pth. +2025-06-25 11:15:44,865 - pyskl - INFO - Best top1_acc is 0.9288 at 137 epoch. +2025-06-25 11:15:44,868 - pyskl - INFO - Epoch(val) [137][533] top1_acc: 0.9288, top5_acc: 0.9944, mean_class_accuracy: 0.9064 +2025-06-25 11:17:04,147 - pyskl - INFO - Epoch [138][100/1281] lr: 4.550e-04, eta: 2:00:36, time: 0.793, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 11:17:53,260 - pyskl - INFO - Epoch [138][200/1281] lr: 4.496e-04, eta: 1:59:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 11:18:42,147 - pyskl - INFO - Epoch [138][300/1281] lr: 4.442e-04, eta: 1:59:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 11:19:30,993 - pyskl - INFO - Epoch [138][400/1281] lr: 4.388e-04, eta: 1:58:26, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 11:20:20,123 - pyskl - INFO - Epoch [138][500/1281] lr: 4.334e-04, eta: 1:57:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 11:21:04,490 - pyskl - INFO - Epoch [138][600/1281] lr: 4.281e-04, eta: 1:56:59, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 11:21:47,315 - pyskl - INFO - Epoch [138][700/1281] lr: 4.228e-04, eta: 1:56:16, time: 0.428, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 11:22:17,705 - pyskl - INFO - Epoch [138][800/1281] lr: 4.176e-04, eta: 1:55:31, time: 0.304, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 11:23:19,691 - pyskl - INFO - Epoch [138][900/1281] lr: 4.124e-04, eta: 1:54:48, time: 0.620, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-25 11:24:28,955 - pyskl - INFO - Epoch [138][1000/1281] lr: 4.072e-04, eta: 1:54:07, time: 0.693, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 11:25:40,308 - pyskl - INFO - Epoch [138][1100/1281] lr: 4.020e-04, eta: 1:53:26, time: 0.714, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 11:26:50,410 - pyskl - INFO - Epoch [138][1200/1281] lr: 3.969e-04, eta: 1:52:44, time: 0.701, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 11:27:48,183 - pyskl - INFO - Saving checkpoint at 138 epochs +2025-06-25 11:29:02,174 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:29:02,241 - pyskl - INFO - +top1_acc 0.9303 +top5_acc 0.9948 +2025-06-25 11:29:02,241 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:29:02,249 - pyskl - INFO - +mean_acc 0.9075 +2025-06-25 11:29:02,254 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_137.pth was removed +2025-06-25 11:29:02,430 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_138.pth. +2025-06-25 11:29:02,431 - pyskl - INFO - Best top1_acc is 0.9303 at 138 epoch. +2025-06-25 11:29:02,433 - pyskl - INFO - Epoch(val) [138][533] top1_acc: 0.9303, top5_acc: 0.9948, mean_class_accuracy: 0.9075 +2025-06-25 11:30:06,767 - pyskl - INFO - Epoch [139][100/1281] lr: 3.877e-04, eta: 1:51:24, time: 0.643, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 11:31:14,866 - pyskl - INFO - Epoch [139][200/1281] lr: 3.827e-04, eta: 1:50:42, time: 0.681, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 11:32:23,939 - pyskl - INFO - Epoch [139][300/1281] lr: 3.777e-04, eta: 1:50:00, time: 0.691, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 11:33:34,136 - pyskl - INFO - Epoch [139][400/1281] lr: 3.727e-04, eta: 1:49:19, time: 0.702, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 11:34:43,981 - pyskl - INFO - Epoch [139][500/1281] lr: 3.678e-04, eta: 1:48:37, time: 0.698, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 11:35:55,215 - pyskl - INFO - Epoch [139][600/1281] lr: 3.628e-04, eta: 1:47:56, time: 0.712, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 11:37:05,817 - pyskl - INFO - Epoch [139][700/1281] lr: 3.580e-04, eta: 1:47:14, time: 0.706, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 11:37:36,545 - pyskl - INFO - Epoch [139][800/1281] lr: 3.531e-04, eta: 1:46:29, time: 0.307, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 11:37:58,402 - pyskl - INFO - Epoch [139][900/1281] lr: 3.483e-04, eta: 1:45:43, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 11:38:20,374 - pyskl - INFO - Epoch [139][1000/1281] lr: 3.436e-04, eta: 1:44:58, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 11:38:42,717 - pyskl - INFO - Epoch [139][1100/1281] lr: 3.388e-04, eta: 1:44:12, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 11:39:04,514 - pyskl - INFO - Epoch [139][1200/1281] lr: 3.341e-04, eta: 1:43:27, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 11:39:22,980 - pyskl - INFO - Saving checkpoint at 139 epochs +2025-06-25 11:40:05,480 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:40:05,534 - pyskl - INFO - +top1_acc 0.9297 +top5_acc 0.9953 +2025-06-25 11:40:05,534 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:40:05,541 - pyskl - INFO - +mean_acc 0.9074 +2025-06-25 11:40:05,542 - pyskl - INFO - Epoch(val) [139][533] top1_acc: 0.9297, top5_acc: 0.9953, mean_class_accuracy: 0.9074 +2025-06-25 11:40:46,795 - pyskl - INFO - Epoch [140][100/1281] lr: 3.257e-04, eta: 1:42:04, time: 0.412, data_time: 0.182, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 11:41:08,863 - pyskl - INFO - Epoch [140][200/1281] lr: 3.210e-04, eta: 1:41:19, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 11:41:30,911 - pyskl - INFO - Epoch [140][300/1281] lr: 3.165e-04, eta: 1:40:34, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 11:41:52,786 - pyskl - INFO - Epoch [140][400/1281] lr: 3.119e-04, eta: 1:39:48, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 11:42:15,139 - pyskl - INFO - Epoch [140][500/1281] lr: 3.074e-04, eta: 1:39:03, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 11:42:36,935 - pyskl - INFO - Epoch [140][600/1281] lr: 3.029e-04, eta: 1:38:17, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0144, loss: 0.0144 +2025-06-25 11:42:59,157 - pyskl - INFO - Epoch [140][700/1281] lr: 2.984e-04, eta: 1:37:32, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 11:43:21,741 - pyskl - INFO - Epoch [140][800/1281] lr: 2.940e-04, eta: 1:36:47, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 11:43:43,633 - pyskl - INFO - Epoch [140][900/1281] lr: 2.896e-04, eta: 1:36:01, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 11:44:05,520 - pyskl - INFO - Epoch [140][1000/1281] lr: 2.853e-04, eta: 1:35:16, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 11:44:27,344 - pyskl - INFO - Epoch [140][1100/1281] lr: 2.809e-04, eta: 1:34:31, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 11:44:49,276 - pyskl - INFO - Epoch [140][1200/1281] lr: 2.767e-04, eta: 1:33:46, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 11:45:07,607 - pyskl - INFO - Saving checkpoint at 140 epochs +2025-06-25 11:45:50,442 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:45:50,497 - pyskl - INFO - +top1_acc 0.9286 +top5_acc 0.9953 +2025-06-25 11:45:50,498 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:45:50,504 - pyskl - INFO - +mean_acc 0.9049 +2025-06-25 11:45:50,506 - pyskl - INFO - Epoch(val) [140][533] top1_acc: 0.9286, top5_acc: 0.9953, mean_class_accuracy: 0.9049 +2025-06-25 11:46:31,234 - pyskl - INFO - Epoch [141][100/1281] lr: 2.690e-04, eta: 1:32:24, time: 0.407, data_time: 0.177, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 11:46:53,220 - pyskl - INFO - Epoch [141][200/1281] lr: 2.648e-04, eta: 1:31:39, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0146, loss: 0.0146 +2025-06-25 11:47:15,243 - pyskl - INFO - Epoch [141][300/1281] lr: 2.606e-04, eta: 1:30:54, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 11:47:37,314 - pyskl - INFO - Epoch [141][400/1281] lr: 2.565e-04, eta: 1:30:09, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 11:47:59,425 - pyskl - INFO - Epoch [141][500/1281] lr: 2.524e-04, eta: 1:29:24, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 11:48:21,482 - pyskl - INFO - Epoch [141][600/1281] lr: 2.483e-04, eta: 1:28:39, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 11:48:43,458 - pyskl - INFO - Epoch [141][700/1281] lr: 2.443e-04, eta: 1:27:54, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 11:49:05,363 - pyskl - INFO - Epoch [141][800/1281] lr: 2.402e-04, eta: 1:27:09, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0145, loss: 0.0145 +2025-06-25 11:49:27,281 - pyskl - INFO - Epoch [141][900/1281] lr: 2.363e-04, eta: 1:26:24, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 11:49:49,212 - pyskl - INFO - Epoch [141][1000/1281] lr: 2.323e-04, eta: 1:25:39, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 11:50:11,181 - pyskl - INFO - Epoch [141][1100/1281] lr: 2.284e-04, eta: 1:24:54, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 11:50:33,191 - pyskl - INFO - Epoch [141][1200/1281] lr: 2.246e-04, eta: 1:24:09, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 11:50:51,809 - pyskl - INFO - Saving checkpoint at 141 epochs +2025-06-25 11:51:34,495 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:51:34,549 - pyskl - INFO - +top1_acc 0.9276 +top5_acc 0.9950 +2025-06-25 11:51:34,549 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:51:34,556 - pyskl - INFO - +mean_acc 0.9051 +2025-06-25 11:51:34,557 - pyskl - INFO - Epoch(val) [141][533] top1_acc: 0.9276, top5_acc: 0.9950, mean_class_accuracy: 0.9051 +2025-06-25 11:52:15,707 - pyskl - INFO - Epoch [142][100/1281] lr: 2.176e-04, eta: 1:22:48, time: 0.411, data_time: 0.181, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 11:52:37,762 - pyskl - INFO - Epoch [142][200/1281] lr: 2.139e-04, eta: 1:22:03, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 11:53:00,515 - pyskl - INFO - Epoch [142][300/1281] lr: 2.101e-04, eta: 1:21:18, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 11:53:22,743 - pyskl - INFO - Epoch [142][400/1281] lr: 2.064e-04, eta: 1:20:33, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 11:53:44,662 - pyskl - INFO - Epoch [142][500/1281] lr: 2.027e-04, eta: 1:19:49, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 11:54:06,982 - pyskl - INFO - Epoch [142][600/1281] lr: 1.991e-04, eta: 1:19:04, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 11:54:29,172 - pyskl - INFO - Epoch [142][700/1281] lr: 1.954e-04, eta: 1:18:19, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 11:54:51,417 - pyskl - INFO - Epoch [142][800/1281] lr: 1.919e-04, eta: 1:17:35, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 11:55:13,548 - pyskl - INFO - Epoch [142][900/1281] lr: 1.883e-04, eta: 1:16:50, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 11:55:35,512 - pyskl - INFO - Epoch [142][1000/1281] lr: 1.848e-04, eta: 1:16:05, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 11:55:57,514 - pyskl - INFO - Epoch [142][1100/1281] lr: 1.813e-04, eta: 1:15:21, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 11:56:19,342 - pyskl - INFO - Epoch [142][1200/1281] lr: 1.779e-04, eta: 1:14:36, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 11:56:38,292 - pyskl - INFO - Saving checkpoint at 142 epochs +2025-06-25 11:57:20,593 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:57:20,647 - pyskl - INFO - +top1_acc 0.9284 +top5_acc 0.9952 +2025-06-25 11:57:20,647 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:57:20,653 - pyskl - INFO - +mean_acc 0.9058 +2025-06-25 11:57:20,655 - pyskl - INFO - Epoch(val) [142][533] top1_acc: 0.9284, top5_acc: 0.9952, mean_class_accuracy: 0.9058 +2025-06-25 11:58:02,167 - pyskl - INFO - Epoch [143][100/1281] lr: 1.717e-04, eta: 1:13:16, time: 0.415, data_time: 0.182, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 11:58:24,031 - pyskl - INFO - Epoch [143][200/1281] lr: 1.683e-04, eta: 1:12:31, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 11:58:45,939 - pyskl - INFO - Epoch [143][300/1281] lr: 1.650e-04, eta: 1:11:47, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 11:59:08,317 - pyskl - INFO - Epoch [143][400/1281] lr: 1.617e-04, eta: 1:11:02, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 11:59:30,229 - pyskl - INFO - Epoch [143][500/1281] lr: 1.585e-04, eta: 1:10:18, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 11:59:52,205 - pyskl - INFO - Epoch [143][600/1281] lr: 1.552e-04, eta: 1:09:34, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 12:00:14,436 - pyskl - INFO - Epoch [143][700/1281] lr: 1.520e-04, eta: 1:08:49, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 12:00:36,794 - pyskl - INFO - Epoch [143][800/1281] lr: 1.489e-04, eta: 1:08:05, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 12:00:59,000 - pyskl - INFO - Epoch [143][900/1281] lr: 1.457e-04, eta: 1:07:21, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:01:20,967 - pyskl - INFO - Epoch [143][1000/1281] lr: 1.426e-04, eta: 1:06:36, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 12:01:43,112 - pyskl - INFO - Epoch [143][1100/1281] lr: 1.396e-04, eta: 1:05:52, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 12:02:05,002 - pyskl - INFO - Epoch [143][1200/1281] lr: 1.366e-04, eta: 1:05:08, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 12:02:23,490 - pyskl - INFO - Saving checkpoint at 143 epochs +2025-06-25 12:03:05,418 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:03:05,489 - pyskl - INFO - +top1_acc 0.9291 +top5_acc 0.9950 +2025-06-25 12:03:05,490 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:03:05,500 - pyskl - INFO - +mean_acc 0.9090 +2025-06-25 12:03:05,503 - pyskl - INFO - Epoch(val) [143][533] top1_acc: 0.9291, top5_acc: 0.9950, mean_class_accuracy: 0.9090 +2025-06-25 12:03:46,441 - pyskl - INFO - Epoch [144][100/1281] lr: 1.312e-04, eta: 1:03:48, time: 0.409, data_time: 0.175, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 12:04:08,243 - pyskl - INFO - Epoch [144][200/1281] lr: 1.282e-04, eta: 1:03:04, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 12:04:30,365 - pyskl - INFO - Epoch [144][300/1281] lr: 1.253e-04, eta: 1:02:19, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 12:04:52,497 - pyskl - INFO - Epoch [144][400/1281] lr: 1.224e-04, eta: 1:01:35, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 12:05:14,574 - pyskl - INFO - Epoch [144][500/1281] lr: 1.196e-04, eta: 1:00:51, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 12:05:36,667 - pyskl - INFO - Epoch [144][600/1281] lr: 1.168e-04, eta: 1:00:07, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 12:05:58,566 - pyskl - INFO - Epoch [144][700/1281] lr: 1.140e-04, eta: 0:59:23, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:06:21,029 - pyskl - INFO - Epoch [144][800/1281] lr: 1.113e-04, eta: 0:58:39, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 12:06:43,028 - pyskl - INFO - Epoch [144][900/1281] lr: 1.086e-04, eta: 0:57:55, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 12:07:04,895 - pyskl - INFO - Epoch [144][1000/1281] lr: 1.059e-04, eta: 0:57:11, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 12:07:26,674 - pyskl - INFO - Epoch [144][1100/1281] lr: 1.033e-04, eta: 0:56:27, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 12:07:48,630 - pyskl - INFO - Epoch [144][1200/1281] lr: 1.007e-04, eta: 0:55:43, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 12:08:06,863 - pyskl - INFO - Saving checkpoint at 144 epochs +2025-06-25 12:08:48,821 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:08:48,888 - pyskl - INFO - +top1_acc 0.9279 +top5_acc 0.9952 +2025-06-25 12:08:48,888 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:08:48,895 - pyskl - INFO - +mean_acc 0.9071 +2025-06-25 12:08:48,897 - pyskl - INFO - Epoch(val) [144][533] top1_acc: 0.9279, top5_acc: 0.9952, mean_class_accuracy: 0.9071 +2025-06-25 12:09:29,839 - pyskl - INFO - Epoch [145][100/1281] lr: 9.605e-05, eta: 0:54:24, time: 0.409, data_time: 0.178, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 12:09:51,521 - pyskl - INFO - Epoch [145][200/1281] lr: 9.353e-05, eta: 0:53:40, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 12:10:13,476 - pyskl - INFO - Epoch [145][300/1281] lr: 9.106e-05, eta: 0:52:56, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 12:10:35,592 - pyskl - INFO - Epoch [145][400/1281] lr: 8.861e-05, eta: 0:52:12, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 12:10:57,684 - pyskl - INFO - Epoch [145][500/1281] lr: 8.620e-05, eta: 0:51:28, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 12:11:19,722 - pyskl - INFO - Epoch [145][600/1281] lr: 8.382e-05, eta: 0:50:44, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 12:11:41,609 - pyskl - INFO - Epoch [145][700/1281] lr: 8.147e-05, eta: 0:50:01, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 12:12:03,495 - pyskl - INFO - Epoch [145][800/1281] lr: 7.916e-05, eta: 0:49:17, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 12:12:25,261 - pyskl - INFO - Epoch [145][900/1281] lr: 7.688e-05, eta: 0:48:33, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 12:12:47,362 - pyskl - INFO - Epoch [145][1000/1281] lr: 7.463e-05, eta: 0:47:49, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 12:13:09,148 - pyskl - INFO - Epoch [145][1100/1281] lr: 7.242e-05, eta: 0:47:06, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:13:31,226 - pyskl - INFO - Epoch [145][1200/1281] lr: 7.024e-05, eta: 0:46:22, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 12:13:49,916 - pyskl - INFO - Saving checkpoint at 145 epochs +2025-06-25 12:14:33,270 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:14:33,324 - pyskl - INFO - +top1_acc 0.9283 +top5_acc 0.9948 +2025-06-25 12:14:33,324 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:14:33,331 - pyskl - INFO - +mean_acc 0.9063 +2025-06-25 12:14:33,332 - pyskl - INFO - Epoch(val) [145][533] top1_acc: 0.9283, top5_acc: 0.9948, mean_class_accuracy: 0.9063 +2025-06-25 12:15:13,712 - pyskl - INFO - Epoch [146][100/1281] lr: 6.638e-05, eta: 0:45:03, time: 0.404, data_time: 0.176, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:15:35,519 - pyskl - INFO - Epoch [146][200/1281] lr: 6.429e-05, eta: 0:44:20, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 12:15:57,472 - pyskl - INFO - Epoch [146][300/1281] lr: 6.224e-05, eta: 0:43:36, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 12:16:19,223 - pyskl - INFO - Epoch [146][400/1281] lr: 6.022e-05, eta: 0:42:53, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:16:41,258 - pyskl - INFO - Epoch [146][500/1281] lr: 5.823e-05, eta: 0:42:09, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 12:17:03,335 - pyskl - INFO - Epoch [146][600/1281] lr: 5.628e-05, eta: 0:41:26, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 12:17:25,543 - pyskl - INFO - Epoch [146][700/1281] lr: 5.436e-05, eta: 0:40:42, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 12:17:47,522 - pyskl - INFO - Epoch [146][800/1281] lr: 5.247e-05, eta: 0:39:59, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 12:18:09,246 - pyskl - INFO - Epoch [146][900/1281] lr: 5.061e-05, eta: 0:39:15, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 12:18:31,180 - pyskl - INFO - Epoch [146][1000/1281] lr: 4.879e-05, eta: 0:38:32, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 12:18:52,956 - pyskl - INFO - Epoch [146][1100/1281] lr: 4.701e-05, eta: 0:37:48, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:19:14,501 - pyskl - INFO - Epoch [146][1200/1281] lr: 4.525e-05, eta: 0:37:05, time: 0.215, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 12:19:32,673 - pyskl - INFO - Saving checkpoint at 146 epochs +2025-06-25 12:20:15,269 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:20:15,335 - pyskl - INFO - +top1_acc 0.9274 +top5_acc 0.9952 +2025-06-25 12:20:15,335 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:20:15,343 - pyskl - INFO - +mean_acc 0.9039 +2025-06-25 12:20:15,345 - pyskl - INFO - Epoch(val) [146][533] top1_acc: 0.9274, top5_acc: 0.9952, mean_class_accuracy: 0.9039 +2025-06-25 12:20:56,459 - pyskl - INFO - Epoch [147][100/1281] lr: 4.216e-05, eta: 0:35:47, time: 0.411, data_time: 0.177, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 12:21:18,147 - pyskl - INFO - Epoch [147][200/1281] lr: 4.050e-05, eta: 0:35:03, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 12:21:40,207 - pyskl - INFO - Epoch [147][300/1281] lr: 3.887e-05, eta: 0:34:20, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0146, loss: 0.0146 +2025-06-25 12:22:02,394 - pyskl - INFO - Epoch [147][400/1281] lr: 3.728e-05, eta: 0:33:37, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 12:22:24,288 - pyskl - INFO - Epoch [147][500/1281] lr: 3.572e-05, eta: 0:32:54, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 12:22:46,223 - pyskl - INFO - Epoch [147][600/1281] lr: 3.419e-05, eta: 0:32:11, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 12:23:08,288 - pyskl - INFO - Epoch [147][700/1281] lr: 3.270e-05, eta: 0:31:27, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 12:23:30,289 - pyskl - INFO - Epoch [147][800/1281] lr: 3.124e-05, eta: 0:30:44, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 12:23:52,466 - pyskl - INFO - Epoch [147][900/1281] lr: 2.981e-05, eta: 0:30:01, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 12:24:14,667 - pyskl - INFO - Epoch [147][1000/1281] lr: 2.842e-05, eta: 0:29:18, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 12:24:36,530 - pyskl - INFO - Epoch [147][1100/1281] lr: 2.706e-05, eta: 0:28:35, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 12:24:58,348 - pyskl - INFO - Epoch [147][1200/1281] lr: 2.573e-05, eta: 0:27:52, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 12:25:16,730 - pyskl - INFO - Saving checkpoint at 147 epochs +2025-06-25 12:25:58,772 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:25:58,824 - pyskl - INFO - +top1_acc 0.9290 +top5_acc 0.9951 +2025-06-25 12:25:58,824 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:25:58,830 - pyskl - INFO - +mean_acc 0.9064 +2025-06-25 12:25:58,831 - pyskl - INFO - Epoch(val) [147][533] top1_acc: 0.9290, top5_acc: 0.9951, mean_class_accuracy: 0.9064 +2025-06-25 12:26:39,492 - pyskl - INFO - Epoch [148][100/1281] lr: 2.341e-05, eta: 0:26:34, time: 0.407, data_time: 0.174, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 12:27:01,258 - pyskl - INFO - Epoch [148][200/1281] lr: 2.218e-05, eta: 0:25:51, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:27:23,456 - pyskl - INFO - Epoch [148][300/1281] lr: 2.098e-05, eta: 0:25:08, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 12:27:45,595 - pyskl - INFO - Epoch [148][400/1281] lr: 1.981e-05, eta: 0:24:25, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:28:08,086 - pyskl - INFO - Epoch [148][500/1281] lr: 1.868e-05, eta: 0:23:42, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 12:28:30,254 - pyskl - INFO - Epoch [148][600/1281] lr: 1.758e-05, eta: 0:22:59, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 12:28:52,594 - pyskl - INFO - Epoch [148][700/1281] lr: 1.651e-05, eta: 0:22:16, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0137, loss: 0.0137 +2025-06-25 12:29:14,375 - pyskl - INFO - Epoch [148][800/1281] lr: 1.548e-05, eta: 0:21:33, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:29:36,123 - pyskl - INFO - Epoch [148][900/1281] lr: 1.448e-05, eta: 0:20:51, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 12:29:57,849 - pyskl - INFO - Epoch [148][1000/1281] lr: 1.351e-05, eta: 0:20:08, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:30:19,719 - pyskl - INFO - Epoch [148][1100/1281] lr: 1.258e-05, eta: 0:19:25, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 12:30:41,606 - pyskl - INFO - Epoch [148][1200/1281] lr: 1.168e-05, eta: 0:18:42, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 12:31:00,086 - pyskl - INFO - Saving checkpoint at 148 epochs +2025-06-25 12:31:42,346 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:31:42,400 - pyskl - INFO - +top1_acc 0.9306 +top5_acc 0.9950 +2025-06-25 12:31:42,400 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:31:42,406 - pyskl - INFO - +mean_acc 0.9085 +2025-06-25 12:31:42,410 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_138.pth was removed +2025-06-25 12:31:42,568 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_148.pth. +2025-06-25 12:31:42,569 - pyskl - INFO - Best top1_acc is 0.9306 at 148 epoch. +2025-06-25 12:31:42,571 - pyskl - INFO - Epoch(val) [148][533] top1_acc: 0.9306, top5_acc: 0.9950, mean_class_accuracy: 0.9085 +2025-06-25 12:32:23,800 - pyskl - INFO - Epoch [149][100/1281] lr: 1.013e-05, eta: 0:17:25, time: 0.412, data_time: 0.177, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 12:32:46,056 - pyskl - INFO - Epoch [149][200/1281] lr: 9.328e-06, eta: 0:16:42, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0139, loss: 0.0139 +2025-06-25 12:33:08,243 - pyskl - INFO - Epoch [149][300/1281] lr: 8.555e-06, eta: 0:15:59, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:33:30,853 - pyskl - INFO - Epoch [149][400/1281] lr: 7.816e-06, eta: 0:15:17, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 12:33:52,954 - pyskl - INFO - Epoch [149][500/1281] lr: 7.110e-06, eta: 0:14:34, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 12:34:14,720 - pyskl - INFO - Epoch [149][600/1281] lr: 6.437e-06, eta: 0:13:52, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 12:34:36,903 - pyskl - INFO - Epoch [149][700/1281] lr: 5.798e-06, eta: 0:13:09, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:34:58,833 - pyskl - INFO - Epoch [149][800/1281] lr: 5.192e-06, eta: 0:12:26, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:35:20,689 - pyskl - INFO - Epoch [149][900/1281] lr: 4.620e-06, eta: 0:11:44, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:35:42,694 - pyskl - INFO - Epoch [149][1000/1281] lr: 4.081e-06, eta: 0:11:01, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 12:36:04,355 - pyskl - INFO - Epoch [149][1100/1281] lr: 3.576e-06, eta: 0:10:19, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 12:36:26,296 - pyskl - INFO - Epoch [149][1200/1281] lr: 3.104e-06, eta: 0:09:36, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 12:36:44,943 - pyskl - INFO - Saving checkpoint at 149 epochs +2025-06-25 12:37:27,339 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:37:27,397 - pyskl - INFO - +top1_acc 0.9279 +top5_acc 0.9951 +2025-06-25 12:37:27,397 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:37:27,404 - pyskl - INFO - +mean_acc 0.9044 +2025-06-25 12:37:27,405 - pyskl - INFO - Epoch(val) [149][533] top1_acc: 0.9279, top5_acc: 0.9951, mean_class_accuracy: 0.9044 +2025-06-25 12:38:08,385 - pyskl - INFO - Epoch [150][100/1281] lr: 2.334e-06, eta: 0:08:19, time: 0.410, data_time: 0.177, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 12:38:30,262 - pyskl - INFO - Epoch [150][200/1281] lr: 1.956e-06, eta: 0:07:37, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:38:52,302 - pyskl - INFO - Epoch [150][300/1281] lr: 1.611e-06, eta: 0:06:54, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 12:39:14,228 - pyskl - INFO - Epoch [150][400/1281] lr: 1.300e-06, eta: 0:06:12, time: 0.219, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 12:39:36,368 - pyskl - INFO - Epoch [150][500/1281] lr: 1.022e-06, eta: 0:05:30, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 12:39:58,204 - pyskl - INFO - Epoch [150][600/1281] lr: 7.771e-07, eta: 0:04:47, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 12:40:20,568 - pyskl - INFO - Epoch [150][700/1281] lr: 5.659e-07, eta: 0:04:05, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 12:40:42,611 - pyskl - INFO - Epoch [150][800/1281] lr: 3.881e-07, eta: 0:03:23, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 12:41:04,742 - pyskl - INFO - Epoch [150][900/1281] lr: 2.438e-07, eta: 0:02:40, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 12:41:26,483 - pyskl - INFO - Epoch [150][1000/1281] lr: 1.329e-07, eta: 0:01:58, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 12:41:48,098 - pyskl - INFO - Epoch [150][1100/1281] lr: 5.534e-08, eta: 0:01:16, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 12:42:10,012 - pyskl - INFO - Epoch [150][1200/1281] lr: 1.123e-08, eta: 0:00:34, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:42:28,759 - pyskl - INFO - Saving checkpoint at 150 epochs +2025-06-25 12:43:11,531 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:43:11,586 - pyskl - INFO - +top1_acc 0.9281 +top5_acc 0.9952 +2025-06-25 12:43:11,586 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:43:11,592 - pyskl - INFO - +mean_acc 0.9057 +2025-06-25 12:43:11,594 - pyskl - INFO - Epoch(val) [150][533] top1_acc: 0.9281, top5_acc: 0.9952, mean_class_accuracy: 0.9057 +2025-06-25 12:43:15,928 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-25 12:48:21,455 - pyskl - INFO - Testing results of the last checkpoint +2025-06-25 12:48:21,455 - pyskl - INFO - top1_acc: 0.9330 +2025-06-25 12:48:21,455 - pyskl - INFO - top5_acc: 0.9955 +2025-06-25 12:48:21,455 - pyskl - INFO - mean_class_accuracy: 0.9113 +2025-06-25 12:48:21,456 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/finegym/bm/best_top1_acc_epoch_148.pth +2025-06-25 12:53:27,897 - pyskl - INFO - Testing results of the best checkpoint +2025-06-25 12:53:27,897 - pyskl - INFO - top1_acc: 0.9352 +2025-06-25 12:53:27,897 - pyskl - INFO - top5_acc: 0.9951 +2025-06-25 12:53:27,897 - pyskl - INFO - mean_class_accuracy: 0.9137 diff --git a/finegym/bm/20250624_101409.log.json b/finegym/bm/20250624_101409.log.json new file mode 100644 index 0000000000000000000000000000000000000000..e01fadb084ea7a749279fdcb134fe86780b7f643 --- /dev/null +++ b/finegym/bm/20250624_101409.log.json @@ -0,0 +1,1951 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 1645946785, "config_name": "bm.py", "work_dir": "bm", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.20251, "top1_acc": 0.06688, "top5_acc": 0.24875, "loss_cls": 4.52402, "loss": 4.52402, "time": 0.62547} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.07938, "top5_acc": 0.30375, "loss_cls": 4.59963, "loss": 4.59963, "time": 0.41499} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.1, "top5_acc": 0.33312, "loss_cls": 4.47499, "loss": 4.47499, "time": 0.4168} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.11, "top5_acc": 0.3625, "loss_cls": 4.32548, "loss": 4.32548, "time": 0.41711} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.025, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.12188, "top5_acc": 0.39062, "loss_cls": 4.18212, "loss": 4.18212, "time": 0.41623} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.13, "top5_acc": 0.42188, "loss_cls": 4.09937, "loss": 4.09937, "time": 0.41429} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.15062, "top5_acc": 0.46875, "loss_cls": 3.89004, "loss": 3.89004, "time": 0.41307} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.025, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.19, "top5_acc": 0.52438, "loss_cls": 3.68524, "loss": 3.68524, "time": 0.41657} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.215, "top5_acc": 0.57688, "loss_cls": 3.46118, "loss": 3.46118, "time": 0.41407} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.26625, "top5_acc": 0.62, "loss_cls": 3.30592, "loss": 3.30592, "time": 0.41488} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.26188, "top5_acc": 0.63438, "loss_cls": 3.20411, "loss": 3.20411, "time": 0.33015} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.31312, "top5_acc": 0.69, "loss_cls": 2.96798, "loss": 2.96798, "time": 0.35366} +{"mode": "val", "epoch": 1, "iter": 533, "lr": 0.025, "top1_acc": 0.28917, "top5_acc": 0.67093, "mean_class_accuracy": 0.1463} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.19944, "top1_acc": 0.32938, "top5_acc": 0.74562, "loss_cls": 2.83457, "loss": 2.83457, "time": 0.61786} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.33188, "top5_acc": 0.74312, "loss_cls": 2.78334, "loss": 2.78334, "time": 0.41403} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.37938, "top5_acc": 0.78688, "loss_cls": 2.60005, "loss": 2.60005, "time": 0.41502} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.39375, "top5_acc": 0.79, "loss_cls": 2.54687, "loss": 2.54687, "time": 0.41475} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.02499, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.39312, "top5_acc": 0.79375, "loss_cls": 2.4895, "loss": 2.4895, "time": 0.41418} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.02499, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.41, "top5_acc": 0.80938, "loss_cls": 2.43589, "loss": 2.43589, "time": 0.41645} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.02499, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.40312, "top5_acc": 0.81688, "loss_cls": 2.3992, "loss": 2.3992, "time": 0.41416} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.02499, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.41625, "top5_acc": 0.8275, "loss_cls": 2.32933, "loss": 2.32933, "time": 0.41495} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.02499, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.4625, "top5_acc": 0.86438, "loss_cls": 2.19301, "loss": 2.19301, "time": 0.41445} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.02499, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.42438, "top5_acc": 0.84125, "loss_cls": 2.26069, "loss": 2.26069, "time": 0.41445} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.02499, "memory": 4082, "data_time": 0.00066, "top1_acc": 0.46125, "top5_acc": 0.865, "loss_cls": 2.18321, "loss": 2.18321, "time": 0.32455} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.49188, "top5_acc": 0.865, "loss_cls": 2.11976, "loss": 2.11976, "time": 0.36019} +{"mode": "val", "epoch": 2, "iter": 533, "lr": 0.02499, "top1_acc": 0.46274, "top5_acc": 0.85283, "mean_class_accuracy": 0.26239} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.02499, "memory": 4082, "data_time": 0.19999, "top1_acc": 0.50438, "top5_acc": 0.88062, "loss_cls": 2.05309, "loss": 2.05309, "time": 0.61367} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.02499, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.49938, "top5_acc": 0.87562, "loss_cls": 2.0645, "loss": 2.0645, "time": 0.41507} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.02499, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.50125, "top5_acc": 0.89188, "loss_cls": 1.99612, "loss": 1.99612, "time": 0.41405} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.02499, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.49688, "top5_acc": 0.90938, "loss_cls": 1.93947, "loss": 1.93947, "time": 0.4134} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.02498, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.5425, "top5_acc": 0.90625, "loss_cls": 1.90038, "loss": 1.90038, "time": 0.41372} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.02498, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.51812, "top5_acc": 0.90562, "loss_cls": 1.90773, "loss": 1.90773, "time": 0.41364} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.02498, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.53438, "top5_acc": 0.90938, "loss_cls": 1.87034, "loss": 1.87034, "time": 0.41458} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.02498, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.535, "top5_acc": 0.91438, "loss_cls": 1.84784, "loss": 1.84784, "time": 0.41421} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.02498, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.55, "top5_acc": 0.91125, "loss_cls": 1.8127, "loss": 1.8127, "time": 0.41411} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.02498, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.55375, "top5_acc": 0.91125, "loss_cls": 1.83225, "loss": 1.83225, "time": 0.41506} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.02498, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.54688, "top5_acc": 0.91688, "loss_cls": 1.80998, "loss": 1.80998, "time": 0.31764} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.02498, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.5425, "top5_acc": 0.92, "loss_cls": 1.80062, "loss": 1.80062, "time": 0.36097} +{"mode": "val", "epoch": 3, "iter": 533, "lr": 0.02498, "top1_acc": 0.39843, "top5_acc": 0.78089, "mean_class_accuracy": 0.24483} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.02497, "memory": 4082, "data_time": 0.19671, "top1_acc": 0.61, "top5_acc": 0.9325, "loss_cls": 1.65585, "loss": 1.65585, "time": 0.61281} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.02497, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.58625, "top5_acc": 0.94438, "loss_cls": 1.63969, "loss": 1.63969, "time": 0.41593} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.02497, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.59812, "top5_acc": 0.94312, "loss_cls": 1.6487, "loss": 1.6487, "time": 0.41446} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.02497, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.58375, "top5_acc": 0.94, "loss_cls": 1.67311, "loss": 1.67311, "time": 0.41679} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.02497, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.57625, "top5_acc": 0.93125, "loss_cls": 1.70024, "loss": 1.70024, "time": 0.41555} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.02497, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.61438, "top5_acc": 0.94312, "loss_cls": 1.59493, "loss": 1.59493, "time": 0.41422} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.02497, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.6175, "top5_acc": 0.94625, "loss_cls": 1.54392, "loss": 1.54392, "time": 0.41436} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.02496, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.60125, "top5_acc": 0.95188, "loss_cls": 1.59469, "loss": 1.59469, "time": 0.41672} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.02496, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.64625, "top5_acc": 0.95812, "loss_cls": 1.44112, "loss": 1.44112, "time": 0.41433} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.02496, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.62938, "top5_acc": 0.93688, "loss_cls": 1.54238, "loss": 1.54238, "time": 0.41608} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.02496, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.63125, "top5_acc": 0.9525, "loss_cls": 1.49166, "loss": 1.49166, "time": 0.32098} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.02496, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.6375, "top5_acc": 0.95625, "loss_cls": 1.49114, "loss": 1.49114, "time": 0.37098} +{"mode": "val", "epoch": 4, "iter": 533, "lr": 0.02496, "top1_acc": 0.5659, "top5_acc": 0.92959, "mean_class_accuracy": 0.43312} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.02495, "memory": 4082, "data_time": 0.19562, "top1_acc": 0.63562, "top5_acc": 0.96125, "loss_cls": 1.46449, "loss": 1.46449, "time": 0.61585} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.02495, "memory": 4082, "data_time": 0.00063, "top1_acc": 0.66312, "top5_acc": 0.96438, "loss_cls": 1.39578, "loss": 1.39578, "time": 0.41468} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.02495, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.66688, "top5_acc": 0.95, "loss_cls": 1.41114, "loss": 1.41114, "time": 0.41843} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.02495, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.65688, "top5_acc": 0.9625, "loss_cls": 1.41606, "loss": 1.41606, "time": 0.41465} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.02495, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.64875, "top5_acc": 0.95812, "loss_cls": 1.42565, "loss": 1.42565, "time": 0.41471} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.02495, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.66, "top5_acc": 0.96312, "loss_cls": 1.38898, "loss": 1.38898, "time": 0.41828} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.02494, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.66562, "top5_acc": 0.96438, "loss_cls": 1.35665, "loss": 1.35665, "time": 0.42567} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.02494, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.68625, "top5_acc": 0.97188, "loss_cls": 1.29162, "loss": 1.29162, "time": 0.41387} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.02494, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.66438, "top5_acc": 0.95875, "loss_cls": 1.38652, "loss": 1.38652, "time": 0.41449} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.02494, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.68375, "top5_acc": 0.96312, "loss_cls": 1.35586, "loss": 1.35586, "time": 0.41531} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.02494, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.67562, "top5_acc": 0.95875, "loss_cls": 1.36525, "loss": 1.36525, "time": 0.30712} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.02493, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.68438, "top5_acc": 0.965, "loss_cls": 1.33592, "loss": 1.33592, "time": 0.38899} +{"mode": "val", "epoch": 5, "iter": 533, "lr": 0.02493, "top1_acc": 0.64746, "top5_acc": 0.95294, "mean_class_accuracy": 0.52298} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.02493, "memory": 4082, "data_time": 0.20008, "top1_acc": 0.7125, "top5_acc": 0.96938, "loss_cls": 1.23129, "loss": 1.23129, "time": 0.6169} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.02493, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.71375, "top5_acc": 0.96375, "loss_cls": 1.24483, "loss": 1.24483, "time": 0.4169} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.02492, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.68625, "top5_acc": 0.97562, "loss_cls": 1.29026, "loss": 1.29026, "time": 0.41543} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.02492, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.68, "top5_acc": 0.96938, "loss_cls": 1.28681, "loss": 1.28681, "time": 0.41497} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.02492, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.69312, "top5_acc": 0.97625, "loss_cls": 1.26506, "loss": 1.26506, "time": 0.41394} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.02492, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.69875, "top5_acc": 0.96812, "loss_cls": 1.27532, "loss": 1.27532, "time": 0.41531} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.02492, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.69812, "top5_acc": 0.96625, "loss_cls": 1.27903, "loss": 1.27903, "time": 0.41584} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.02491, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.70312, "top5_acc": 0.96875, "loss_cls": 1.24444, "loss": 1.24444, "time": 0.41438} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.02491, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.6925, "top5_acc": 0.975, "loss_cls": 1.25264, "loss": 1.25264, "time": 0.41632} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.02491, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.69875, "top5_acc": 0.96375, "loss_cls": 1.25441, "loss": 1.25441, "time": 0.41462} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.02491, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.67688, "top5_acc": 0.965, "loss_cls": 1.29215, "loss": 1.29215, "time": 0.29801} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.0249, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.70188, "top5_acc": 0.96875, "loss_cls": 1.25091, "loss": 1.25091, "time": 0.40327} +{"mode": "val", "epoch": 6, "iter": 533, "lr": 0.0249, "top1_acc": 0.67739, "top5_acc": 0.96796, "mean_class_accuracy": 0.55386} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0249, "memory": 4082, "data_time": 0.19893, "top1_acc": 0.72062, "top5_acc": 0.9775, "loss_cls": 1.17443, "loss": 1.17443, "time": 0.62036} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0249, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.72375, "top5_acc": 0.9725, "loss_cls": 1.1469, "loss": 1.1469, "time": 0.41517} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.02489, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.72312, "top5_acc": 0.98125, "loss_cls": 1.16211, "loss": 1.16211, "time": 0.43292} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.02489, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.72875, "top5_acc": 0.9725, "loss_cls": 1.1506, "loss": 1.1506, "time": 0.41997} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.02489, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.70562, "top5_acc": 0.97188, "loss_cls": 1.19788, "loss": 1.19788, "time": 0.41681} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.02489, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.71062, "top5_acc": 0.9725, "loss_cls": 1.19593, "loss": 1.19593, "time": 0.41608} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.02488, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.71438, "top5_acc": 0.975, "loss_cls": 1.20313, "loss": 1.20313, "time": 0.41591} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.02488, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.70312, "top5_acc": 0.97312, "loss_cls": 1.2427, "loss": 1.2427, "time": 0.41497} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.02488, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.74, "top5_acc": 0.97812, "loss_cls": 1.12701, "loss": 1.12701, "time": 0.41646} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.02487, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.725, "top5_acc": 0.97062, "loss_cls": 1.16216, "loss": 1.16216, "time": 0.41628} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.02487, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7275, "top5_acc": 0.97375, "loss_cls": 1.14733, "loss": 1.14733, "time": 0.28457} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.02487, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.73062, "top5_acc": 0.9775, "loss_cls": 1.15411, "loss": 1.15411, "time": 0.41125} +{"mode": "val", "epoch": 7, "iter": 533, "lr": 0.02487, "top1_acc": 0.68701, "top5_acc": 0.96456, "mean_class_accuracy": 0.56035} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.02486, "memory": 4082, "data_time": 0.20013, "top1_acc": 0.74125, "top5_acc": 0.98375, "loss_cls": 1.07271, "loss": 1.07271, "time": 0.61671} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.02486, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.72125, "top5_acc": 0.9775, "loss_cls": 1.16758, "loss": 1.16758, "time": 0.41789} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.02486, "memory": 4082, "data_time": 0.00056, "top1_acc": 0.73438, "top5_acc": 0.97125, "loss_cls": 1.13356, "loss": 1.13356, "time": 0.41661} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.02485, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.74312, "top5_acc": 0.98125, "loss_cls": 1.11277, "loss": 1.11277, "time": 0.41534} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.02485, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.71438, "top5_acc": 0.98, "loss_cls": 1.16404, "loss": 1.16404, "time": 0.41417} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.02485, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.74875, "top5_acc": 0.98062, "loss_cls": 1.08092, "loss": 1.08092, "time": 0.41489} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.02484, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.73375, "top5_acc": 0.97375, "loss_cls": 1.11259, "loss": 1.11259, "time": 0.416} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.02484, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.75125, "top5_acc": 0.9825, "loss_cls": 1.08301, "loss": 1.08301, "time": 0.41794} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.02484, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.735, "top5_acc": 0.9775, "loss_cls": 1.14069, "loss": 1.14069, "time": 0.41539} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.02483, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.74625, "top5_acc": 0.9775, "loss_cls": 1.08563, "loss": 1.08563, "time": 0.41595} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.02483, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.73812, "top5_acc": 0.98438, "loss_cls": 1.1041, "loss": 1.1041, "time": 0.28129} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.02483, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.74, "top5_acc": 0.9725, "loss_cls": 1.11145, "loss": 1.11145, "time": 0.42102} +{"mode": "val", "epoch": 8, "iter": 533, "lr": 0.02482, "top1_acc": 0.68349, "top5_acc": 0.96503, "mean_class_accuracy": 0.5895} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.02482, "memory": 4082, "data_time": 0.19777, "top1_acc": 0.74812, "top5_acc": 0.98562, "loss_cls": 1.04152, "loss": 1.04152, "time": 0.61335} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.02482, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.75312, "top5_acc": 0.98312, "loss_cls": 1.05702, "loss": 1.05702, "time": 0.41648} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.02481, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.75688, "top5_acc": 0.975, "loss_cls": 1.08873, "loss": 1.08873, "time": 0.41475} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.02481, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.74, "top5_acc": 0.98062, "loss_cls": 1.07916, "loss": 1.07916, "time": 0.41567} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.02481, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.7475, "top5_acc": 0.985, "loss_cls": 1.05791, "loss": 1.05791, "time": 0.41574} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.0248, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.74438, "top5_acc": 0.98438, "loss_cls": 1.07966, "loss": 1.07966, "time": 0.41507} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.0248, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.76125, "top5_acc": 0.97938, "loss_cls": 1.05119, "loss": 1.05119, "time": 0.41509} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.0248, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.73312, "top5_acc": 0.98062, "loss_cls": 1.11371, "loss": 1.11371, "time": 0.41457} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.02479, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.75062, "top5_acc": 0.98, "loss_cls": 1.05264, "loss": 1.05264, "time": 0.41475} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.02479, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.73938, "top5_acc": 0.97938, "loss_cls": 1.1119, "loss": 1.1119, "time": 0.41613} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.02479, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.73188, "top5_acc": 0.97875, "loss_cls": 1.11104, "loss": 1.11104, "time": 0.27681} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.02478, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.71875, "top5_acc": 0.97062, "loss_cls": 1.1642, "loss": 1.1642, "time": 0.41414} +{"mode": "val", "epoch": 9, "iter": 533, "lr": 0.02478, "top1_acc": 0.69581, "top5_acc": 0.96737, "mean_class_accuracy": 0.56409} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.02477, "memory": 4082, "data_time": 0.19805, "top1_acc": 0.7475, "top5_acc": 0.97938, "loss_cls": 1.0833, "loss": 1.0833, "time": 0.6352} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.02477, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.74875, "top5_acc": 0.98438, "loss_cls": 1.07955, "loss": 1.07955, "time": 0.41875} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.02477, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.76438, "top5_acc": 0.98188, "loss_cls": 1.03849, "loss": 1.03849, "time": 0.41643} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.02476, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.75562, "top5_acc": 0.98125, "loss_cls": 1.06016, "loss": 1.06016, "time": 0.41487} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.02476, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.7575, "top5_acc": 0.97938, "loss_cls": 1.03807, "loss": 1.03807, "time": 0.41485} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.02476, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.7525, "top5_acc": 0.98, "loss_cls": 1.0947, "loss": 1.0947, "time": 0.41373} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.02475, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.75625, "top5_acc": 0.985, "loss_cls": 0.99922, "loss": 0.99922, "time": 0.41629} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.02475, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.76938, "top5_acc": 0.985, "loss_cls": 0.99839, "loss": 0.99839, "time": 0.41675} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.02474, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.73438, "top5_acc": 0.97875, "loss_cls": 1.09476, "loss": 1.09476, "time": 0.41393} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.02474, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.75562, "top5_acc": 0.98688, "loss_cls": 1.04804, "loss": 1.04804, "time": 0.41679} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.02473, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.75438, "top5_acc": 0.97688, "loss_cls": 1.04362, "loss": 1.04362, "time": 0.2858} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.02473, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7475, "top5_acc": 0.97812, "loss_cls": 1.07231, "loss": 1.07231, "time": 0.409} +{"mode": "val", "epoch": 10, "iter": 533, "lr": 0.02473, "top1_acc": 0.73466, "top5_acc": 0.97266, "mean_class_accuracy": 0.63959} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.02472, "memory": 4082, "data_time": 0.20181, "top1_acc": 0.7675, "top5_acc": 0.98125, "loss_cls": 1.02514, "loss": 1.02514, "time": 0.61711} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.02472, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.75312, "top5_acc": 0.98375, "loss_cls": 1.00309, "loss": 1.00309, "time": 0.41678} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.02471, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.77875, "top5_acc": 0.9825, "loss_cls": 0.96139, "loss": 0.96139, "time": 0.43189} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.02471, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.755, "top5_acc": 0.9875, "loss_cls": 1.02463, "loss": 1.02463, "time": 0.42188} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.02471, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.75938, "top5_acc": 0.98062, "loss_cls": 1.00243, "loss": 1.00243, "time": 0.41607} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.0247, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.78125, "top5_acc": 0.98062, "loss_cls": 1.00596, "loss": 1.00596, "time": 0.41423} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.0247, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.74688, "top5_acc": 0.9875, "loss_cls": 1.02965, "loss": 1.02965, "time": 0.41464} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.02469, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.7875, "top5_acc": 0.98812, "loss_cls": 0.94795, "loss": 0.94795, "time": 0.41643} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.02469, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.74375, "top5_acc": 0.98312, "loss_cls": 1.08023, "loss": 1.08023, "time": 0.41559} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.02468, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77375, "top5_acc": 0.98, "loss_cls": 0.99935, "loss": 0.99935, "time": 0.41492} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.02468, "memory": 4082, "data_time": 0.00053, "top1_acc": 0.76688, "top5_acc": 0.9825, "loss_cls": 1.00268, "loss": 1.00268, "time": 0.28301} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.02467, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.74875, "top5_acc": 0.97938, "loss_cls": 1.06494, "loss": 1.06494, "time": 0.41817} +{"mode": "val", "epoch": 11, "iter": 533, "lr": 0.02467, "top1_acc": 0.74674, "top5_acc": 0.97195, "mean_class_accuracy": 0.66239} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.02467, "memory": 4082, "data_time": 0.19973, "top1_acc": 0.80188, "top5_acc": 0.98438, "loss_cls": 0.91806, "loss": 0.91806, "time": 0.61374} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.02466, "memory": 4082, "data_time": 0.00046, "top1_acc": 0.80062, "top5_acc": 0.9875, "loss_cls": 0.89548, "loss": 0.89548, "time": 0.41735} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.02466, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.78438, "top5_acc": 0.98438, "loss_cls": 0.98719, "loss": 0.98719, "time": 0.41475} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.02465, "memory": 4082, "data_time": 0.00058, "top1_acc": 0.7775, "top5_acc": 0.99062, "loss_cls": 0.96471, "loss": 0.96471, "time": 0.41503} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.02465, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.76875, "top5_acc": 0.9825, "loss_cls": 0.96226, "loss": 0.96226, "time": 0.41483} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.02464, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.7725, "top5_acc": 0.98125, "loss_cls": 0.97157, "loss": 0.97157, "time": 0.41705} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.02464, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.765, "top5_acc": 0.975, "loss_cls": 1.00284, "loss": 1.00284, "time": 0.41317} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.02463, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.77375, "top5_acc": 0.98125, "loss_cls": 1.01182, "loss": 1.01182, "time": 0.41374} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.02463, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.76562, "top5_acc": 0.9825, "loss_cls": 1.02571, "loss": 1.02571, "time": 0.41535} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.02462, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.75125, "top5_acc": 0.98438, "loss_cls": 1.03033, "loss": 1.03033, "time": 0.41507} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.02462, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.76125, "top5_acc": 0.98312, "loss_cls": 1.02174, "loss": 1.02174, "time": 0.27193} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.02461, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.77, "top5_acc": 0.98188, "loss_cls": 0.95773, "loss": 0.95773, "time": 0.42358} +{"mode": "val", "epoch": 12, "iter": 533, "lr": 0.02461, "top1_acc": 0.73841, "top5_acc": 0.96878, "mean_class_accuracy": 0.64785} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.0246, "memory": 4082, "data_time": 0.19419, "top1_acc": 0.78938, "top5_acc": 0.98625, "loss_cls": 0.89655, "loss": 0.89655, "time": 0.60969} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.0246, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.79, "top5_acc": 0.985, "loss_cls": 0.93885, "loss": 0.93885, "time": 0.41398} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.02459, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.78938, "top5_acc": 0.99, "loss_cls": 0.85944, "loss": 0.85944, "time": 0.41574} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.02459, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77062, "top5_acc": 0.97562, "loss_cls": 0.97224, "loss": 0.97224, "time": 0.41363} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.02458, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.77188, "top5_acc": 0.9825, "loss_cls": 0.97251, "loss": 0.97251, "time": 0.41422} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.02458, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.77375, "top5_acc": 0.98, "loss_cls": 1.00389, "loss": 1.00389, "time": 0.41608} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.02457, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.75312, "top5_acc": 0.985, "loss_cls": 1.01091, "loss": 1.01091, "time": 0.4153} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.02457, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78312, "top5_acc": 0.98625, "loss_cls": 0.97187, "loss": 0.97187, "time": 0.41477} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.02456, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77312, "top5_acc": 0.98375, "loss_cls": 0.9377, "loss": 0.9377, "time": 0.41567} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.02455, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.75688, "top5_acc": 0.98375, "loss_cls": 1.00635, "loss": 1.00635, "time": 0.41334} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.02455, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.78188, "top5_acc": 0.98438, "loss_cls": 0.96022, "loss": 0.96022, "time": 0.27906} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.02454, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77188, "top5_acc": 0.985, "loss_cls": 0.94737, "loss": 0.94737, "time": 0.42163} +{"mode": "val", "epoch": 13, "iter": 533, "lr": 0.02454, "top1_acc": 0.75085, "top5_acc": 0.97829, "mean_class_accuracy": 0.65982} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.02453, "memory": 4082, "data_time": 0.20329, "top1_acc": 0.78938, "top5_acc": 0.98812, "loss_cls": 0.90408, "loss": 0.90408, "time": 0.59306} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.02453, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.8, "top5_acc": 0.98688, "loss_cls": 0.88318, "loss": 0.88318, "time": 0.39118} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.02452, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.78562, "top5_acc": 0.98438, "loss_cls": 0.92449, "loss": 0.92449, "time": 0.40556} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.02452, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.75875, "top5_acc": 0.99, "loss_cls": 0.95291, "loss": 0.95291, "time": 0.40791} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.02451, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.77625, "top5_acc": 0.9825, "loss_cls": 0.98082, "loss": 0.98082, "time": 0.39144} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.02451, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.78312, "top5_acc": 0.98188, "loss_cls": 0.96635, "loss": 0.96635, "time": 0.39351} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.0245, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79625, "top5_acc": 0.98375, "loss_cls": 0.90971, "loss": 0.90971, "time": 0.39781} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.02449, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78812, "top5_acc": 0.98875, "loss_cls": 0.91414, "loss": 0.91414, "time": 0.39359} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.02449, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.79438, "top5_acc": 0.98625, "loss_cls": 0.91318, "loss": 0.91318, "time": 0.39976} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.02448, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78125, "top5_acc": 0.98188, "loss_cls": 0.93961, "loss": 0.93961, "time": 0.39254} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.02448, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78688, "top5_acc": 0.98188, "loss_cls": 0.90362, "loss": 0.90362, "time": 0.39566} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.02447, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77375, "top5_acc": 0.98438, "loss_cls": 0.92789, "loss": 0.92789, "time": 0.40347} +{"mode": "val", "epoch": 14, "iter": 533, "lr": 0.02447, "top1_acc": 0.7302, "top5_acc": 0.96198, "mean_class_accuracy": 0.6531} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.02446, "memory": 4082, "data_time": 0.19558, "top1_acc": 0.8025, "top5_acc": 0.99, "loss_cls": 0.8604, "loss": 0.8604, "time": 0.43077} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.02445, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.81, "top5_acc": 0.98938, "loss_cls": 0.84511, "loss": 0.84511, "time": 0.3405} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.02445, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.79188, "top5_acc": 0.98812, "loss_cls": 0.90194, "loss": 0.90194, "time": 0.3875} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.02444, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.80875, "top5_acc": 0.98688, "loss_cls": 0.84705, "loss": 0.84705, "time": 0.38599} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.02444, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.79438, "top5_acc": 0.985, "loss_cls": 0.89919, "loss": 0.89919, "time": 0.3775} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.02443, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78438, "top5_acc": 0.98188, "loss_cls": 0.96454, "loss": 0.96454, "time": 0.3922} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.02442, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78375, "top5_acc": 0.98812, "loss_cls": 0.92546, "loss": 0.92546, "time": 0.38116} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.02442, "memory": 4082, "data_time": 0.00058, "top1_acc": 0.785, "top5_acc": 0.9875, "loss_cls": 0.93991, "loss": 0.93991, "time": 0.38637} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.02441, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.78125, "top5_acc": 0.9825, "loss_cls": 0.95599, "loss": 0.95599, "time": 0.38355} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.02441, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.78812, "top5_acc": 0.9825, "loss_cls": 0.91933, "loss": 0.91933, "time": 0.38723} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.0244, "memory": 4082, "data_time": 0.00083, "top1_acc": 0.7925, "top5_acc": 0.98312, "loss_cls": 0.91187, "loss": 0.91187, "time": 0.38434} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.02439, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79562, "top5_acc": 0.98562, "loss_cls": 0.89734, "loss": 0.89734, "time": 0.3878} +{"mode": "val", "epoch": 15, "iter": 533, "lr": 0.02439, "top1_acc": 0.74827, "top5_acc": 0.97629, "mean_class_accuracy": 0.66825} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.02438, "memory": 4082, "data_time": 0.19308, "top1_acc": 0.82, "top5_acc": 0.995, "loss_cls": 0.79252, "loss": 0.79252, "time": 0.57726} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.02438, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.79125, "top5_acc": 0.98688, "loss_cls": 0.88886, "loss": 0.88886, "time": 0.38027} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.02437, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79062, "top5_acc": 0.98312, "loss_cls": 0.94453, "loss": 0.94453, "time": 0.31762} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.02436, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.80875, "top5_acc": 0.98688, "loss_cls": 0.87597, "loss": 0.87597, "time": 0.35543} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.02436, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.79938, "top5_acc": 0.98625, "loss_cls": 0.84006, "loss": 0.84006, "time": 0.35536} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.02435, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.79562, "top5_acc": 0.98812, "loss_cls": 0.88866, "loss": 0.88866, "time": 0.2356} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.02434, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79312, "top5_acc": 0.98688, "loss_cls": 0.87979, "loss": 0.87979, "time": 0.37644} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.02434, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.78125, "top5_acc": 0.9875, "loss_cls": 0.91712, "loss": 0.91712, "time": 0.3787} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.02433, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79062, "top5_acc": 0.9875, "loss_cls": 0.87869, "loss": 0.87869, "time": 0.38018} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.02432, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.78062, "top5_acc": 0.9875, "loss_cls": 0.90159, "loss": 0.90159, "time": 0.38005} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.02432, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.78625, "top5_acc": 0.98625, "loss_cls": 0.92366, "loss": 0.92366, "time": 0.38413} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.02431, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78188, "top5_acc": 0.98062, "loss_cls": 0.92292, "loss": 0.92292, "time": 0.38204} +{"mode": "val", "epoch": 16, "iter": 533, "lr": 0.0243, "top1_acc": 0.76458, "top5_acc": 0.98064, "mean_class_accuracy": 0.67608} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.0243, "memory": 4082, "data_time": 0.19731, "top1_acc": 0.80188, "top5_acc": 0.99188, "loss_cls": 0.85243, "loss": 0.85243, "time": 0.5844} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.02429, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8025, "top5_acc": 0.98562, "loss_cls": 0.85981, "loss": 0.85981, "time": 0.37701} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.02428, "memory": 4082, "data_time": 0.00058, "top1_acc": 0.78938, "top5_acc": 0.98875, "loss_cls": 0.90253, "loss": 0.90253, "time": 0.37729} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.02428, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78938, "top5_acc": 0.9875, "loss_cls": 0.89659, "loss": 0.89659, "time": 0.37827} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.02427, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.79812, "top5_acc": 0.9875, "loss_cls": 0.89614, "loss": 0.89614, "time": 0.37748} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.02426, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.815, "top5_acc": 0.98438, "loss_cls": 0.86658, "loss": 0.86658, "time": 0.37649} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.02426, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79688, "top5_acc": 0.98875, "loss_cls": 0.89762, "loss": 0.89762, "time": 0.37938} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.02425, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79938, "top5_acc": 0.98438, "loss_cls": 0.90827, "loss": 0.90827, "time": 0.33433} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.02424, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79125, "top5_acc": 0.98375, "loss_cls": 0.89824, "loss": 0.89824, "time": 0.33144} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.02424, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80375, "top5_acc": 0.99062, "loss_cls": 0.86994, "loss": 0.86994, "time": 0.37799} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.02423, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.7975, "top5_acc": 0.99375, "loss_cls": 0.85774, "loss": 0.85774, "time": 0.23554} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.02422, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78938, "top5_acc": 0.98438, "loss_cls": 0.93113, "loss": 0.93113, "time": 0.36204} +{"mode": "val", "epoch": 17, "iter": 533, "lr": 0.02422, "top1_acc": 0.77996, "top5_acc": 0.98064, "mean_class_accuracy": 0.72128} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.02421, "memory": 4082, "data_time": 0.2003, "top1_acc": 0.83375, "top5_acc": 0.99125, "loss_cls": 0.75471, "loss": 0.75471, "time": 0.58182} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.0242, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.81375, "top5_acc": 0.99062, "loss_cls": 0.79471, "loss": 0.79471, "time": 0.38257} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.02419, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80125, "top5_acc": 0.9875, "loss_cls": 0.87726, "loss": 0.87726, "time": 0.38621} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.02419, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7975, "top5_acc": 0.98375, "loss_cls": 0.9037, "loss": 0.9037, "time": 0.37943} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.02418, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80438, "top5_acc": 0.99062, "loss_cls": 0.85021, "loss": 0.85021, "time": 0.38135} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.02417, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.80188, "top5_acc": 0.98625, "loss_cls": 0.88387, "loss": 0.88387, "time": 0.37872} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.02417, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.81312, "top5_acc": 0.98938, "loss_cls": 0.84715, "loss": 0.84715, "time": 0.3833} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.02416, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81375, "top5_acc": 0.98812, "loss_cls": 0.8435, "loss": 0.8435, "time": 0.37608} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.02415, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79375, "top5_acc": 0.98625, "loss_cls": 0.91318, "loss": 0.91318, "time": 0.38491} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.02414, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.83938, "top5_acc": 0.9925, "loss_cls": 0.75775, "loss": 0.75775, "time": 0.38253} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.02414, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81062, "top5_acc": 0.98875, "loss_cls": 0.86406, "loss": 0.86406, "time": 0.38946} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.02413, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80938, "top5_acc": 0.98938, "loss_cls": 0.84193, "loss": 0.84193, "time": 0.38751} +{"mode": "val", "epoch": 18, "iter": 533, "lr": 0.02412, "top1_acc": 0.74733, "top5_acc": 0.96538, "mean_class_accuracy": 0.66597} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.02411, "memory": 4082, "data_time": 0.19362, "top1_acc": 0.83312, "top5_acc": 0.99188, "loss_cls": 0.76676, "loss": 0.76676, "time": 0.57203} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.02411, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.78688, "top5_acc": 0.99, "loss_cls": 0.91329, "loss": 0.91329, "time": 0.38563} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.0241, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.80812, "top5_acc": 0.9875, "loss_cls": 0.84872, "loss": 0.84872, "time": 0.38735} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.02409, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.80938, "top5_acc": 0.98688, "loss_cls": 0.86576, "loss": 0.86576, "time": 0.38635} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.02408, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.80125, "top5_acc": 0.985, "loss_cls": 0.90429, "loss": 0.90429, "time": 0.3848} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.02408, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82938, "top5_acc": 0.98938, "loss_cls": 0.79829, "loss": 0.79829, "time": 0.38093} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.02407, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.80812, "top5_acc": 0.9825, "loss_cls": 0.83723, "loss": 0.83723, "time": 0.38538} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.02406, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.7975, "top5_acc": 0.99, "loss_cls": 0.86358, "loss": 0.86358, "time": 0.38093} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.02405, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.80312, "top5_acc": 0.98562, "loss_cls": 0.85751, "loss": 0.85751, "time": 0.37654} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.02405, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8325, "top5_acc": 0.99, "loss_cls": 0.77444, "loss": 0.77444, "time": 0.38029} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.02404, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.82438, "top5_acc": 0.98938, "loss_cls": 0.8072, "loss": 0.8072, "time": 0.37983} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.02403, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.7975, "top5_acc": 0.98875, "loss_cls": 0.88535, "loss": 0.88535, "time": 0.37875} +{"mode": "val", "epoch": 19, "iter": 533, "lr": 0.02402, "top1_acc": 0.78078, "top5_acc": 0.97911, "mean_class_accuracy": 0.70363} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.02402, "memory": 4082, "data_time": 0.1908, "top1_acc": 0.82438, "top5_acc": 0.98938, "loss_cls": 0.80146, "loss": 0.80146, "time": 0.57064} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.02401, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83, "top5_acc": 0.9925, "loss_cls": 0.7618, "loss": 0.7618, "time": 0.27125} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.024, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80625, "top5_acc": 0.985, "loss_cls": 0.84185, "loss": 0.84185, "time": 0.41412} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.02399, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.81938, "top5_acc": 0.99375, "loss_cls": 0.79681, "loss": 0.79681, "time": 0.2948} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.02398, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.84125, "top5_acc": 0.98812, "loss_cls": 0.74846, "loss": 0.74846, "time": 0.2739} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.02398, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.80625, "top5_acc": 0.98062, "loss_cls": 0.87561, "loss": 0.87561, "time": 0.37947} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.02397, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8225, "top5_acc": 0.98375, "loss_cls": 0.81375, "loss": 0.81375, "time": 0.37762} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.02396, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.80062, "top5_acc": 0.98625, "loss_cls": 0.86277, "loss": 0.86277, "time": 0.3837} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.02395, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.80938, "top5_acc": 0.98938, "loss_cls": 0.83356, "loss": 0.83356, "time": 0.3766} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.02394, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84875, "top5_acc": 0.99188, "loss_cls": 0.75869, "loss": 0.75869, "time": 0.38681} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.02393, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.82, "top5_acc": 0.98938, "loss_cls": 0.81939, "loss": 0.81939, "time": 0.37975} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.02393, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.81812, "top5_acc": 0.98875, "loss_cls": 0.80394, "loss": 0.80394, "time": 0.38551} +{"mode": "val", "epoch": 20, "iter": 533, "lr": 0.02392, "top1_acc": 0.76177, "top5_acc": 0.97864, "mean_class_accuracy": 0.652} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.02391, "memory": 4082, "data_time": 0.19165, "top1_acc": 0.815, "top5_acc": 0.99, "loss_cls": 0.81403, "loss": 0.81403, "time": 0.57699} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.0239, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83062, "top5_acc": 0.99375, "loss_cls": 0.70874, "loss": 0.70874, "time": 0.38169} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.02389, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.83188, "top5_acc": 0.98625, "loss_cls": 0.80443, "loss": 0.80443, "time": 0.38172} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.02389, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82438, "top5_acc": 0.98438, "loss_cls": 0.78429, "loss": 0.78429, "time": 0.38365} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.02388, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82875, "top5_acc": 0.99062, "loss_cls": 0.74985, "loss": 0.74985, "time": 0.37527} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.02387, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.81875, "top5_acc": 0.985, "loss_cls": 0.8096, "loss": 0.8096, "time": 0.37761} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.02386, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82, "top5_acc": 0.98938, "loss_cls": 0.79115, "loss": 0.79115, "time": 0.28787} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.02385, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81375, "top5_acc": 0.9875, "loss_cls": 0.85088, "loss": 0.85088, "time": 0.39251} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.02384, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81375, "top5_acc": 0.99125, "loss_cls": 0.83414, "loss": 0.83414, "time": 0.31805} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.02383, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82125, "top5_acc": 0.9875, "loss_cls": 0.82478, "loss": 0.82478, "time": 0.26317} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.02383, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.83812, "top5_acc": 0.99312, "loss_cls": 0.74024, "loss": 0.74024, "time": 0.37499} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.02382, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79312, "top5_acc": 0.985, "loss_cls": 0.90859, "loss": 0.90859, "time": 0.38572} +{"mode": "val", "epoch": 21, "iter": 533, "lr": 0.02381, "top1_acc": 0.79627, "top5_acc": 0.98498, "mean_class_accuracy": 0.71989} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.0238, "memory": 4082, "data_time": 0.1987, "top1_acc": 0.82438, "top5_acc": 0.99125, "loss_cls": 0.76812, "loss": 0.76812, "time": 0.57592} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.02379, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.84062, "top5_acc": 0.9875, "loss_cls": 0.78666, "loss": 0.78666, "time": 0.3876} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.02378, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.82188, "top5_acc": 0.9875, "loss_cls": 0.80664, "loss": 0.80664, "time": 0.38381} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.02378, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83375, "top5_acc": 0.99125, "loss_cls": 0.75586, "loss": 0.75586, "time": 0.37959} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.02377, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81438, "top5_acc": 0.99, "loss_cls": 0.81579, "loss": 0.81579, "time": 0.37549} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.02376, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82438, "top5_acc": 0.99, "loss_cls": 0.78284, "loss": 0.78284, "time": 0.38007} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.02375, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.85, "top5_acc": 0.99, "loss_cls": 0.70157, "loss": 0.70157, "time": 0.39082} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.02374, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82875, "top5_acc": 0.98438, "loss_cls": 0.79116, "loss": 0.79116, "time": 0.376} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.02373, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.80375, "top5_acc": 0.98625, "loss_cls": 0.85697, "loss": 0.85697, "time": 0.38051} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.02372, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81, "top5_acc": 0.99125, "loss_cls": 0.81268, "loss": 0.81268, "time": 0.38269} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.02371, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.825, "top5_acc": 0.99125, "loss_cls": 0.79742, "loss": 0.79742, "time": 0.38458} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0237, "memory": 4082, "data_time": 0.00061, "top1_acc": 0.82562, "top5_acc": 0.98438, "loss_cls": 0.8119, "loss": 0.8119, "time": 0.28133} +{"mode": "val", "epoch": 22, "iter": 533, "lr": 0.0237, "top1_acc": 0.77338, "top5_acc": 0.97899, "mean_class_accuracy": 0.70331} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.02369, "memory": 4082, "data_time": 0.19895, "top1_acc": 0.81, "top5_acc": 0.98688, "loss_cls": 0.83594, "loss": 0.83594, "time": 0.58486} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.02368, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83125, "top5_acc": 0.99062, "loss_cls": 0.74622, "loss": 0.74622, "time": 0.38738} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.02367, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83875, "top5_acc": 0.99062, "loss_cls": 0.7437, "loss": 0.7437, "time": 0.38605} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.02366, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82812, "top5_acc": 0.98938, "loss_cls": 0.74707, "loss": 0.74707, "time": 0.37918} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.02365, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82938, "top5_acc": 0.99125, "loss_cls": 0.74548, "loss": 0.74548, "time": 0.37642} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.02364, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.83375, "top5_acc": 0.99062, "loss_cls": 0.75045, "loss": 0.75045, "time": 0.38376} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.02363, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8325, "top5_acc": 0.98938, "loss_cls": 0.7628, "loss": 0.7628, "time": 0.38254} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.02362, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.80938, "top5_acc": 0.99062, "loss_cls": 0.83825, "loss": 0.83825, "time": 0.38341} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.02361, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82, "top5_acc": 0.98688, "loss_cls": 0.81065, "loss": 0.81065, "time": 0.37517} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.0236, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84625, "top5_acc": 0.99062, "loss_cls": 0.71318, "loss": 0.71318, "time": 0.38422} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.02359, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83188, "top5_acc": 0.9875, "loss_cls": 0.77503, "loss": 0.77503, "time": 0.38767} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.02359, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.82625, "top5_acc": 0.98938, "loss_cls": 0.77704, "loss": 0.77704, "time": 0.387} +{"mode": "val", "epoch": 23, "iter": 533, "lr": 0.02358, "top1_acc": 0.79838, "top5_acc": 0.98298, "mean_class_accuracy": 0.73141} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.02357, "memory": 4082, "data_time": 0.19557, "top1_acc": 0.84312, "top5_acc": 0.99438, "loss_cls": 0.72867, "loss": 0.72867, "time": 0.4504} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.02356, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83312, "top5_acc": 0.99375, "loss_cls": 0.72837, "loss": 0.72837, "time": 0.44448} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.02355, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.825, "top5_acc": 0.99625, "loss_cls": 0.77241, "loss": 0.77241, "time": 0.26857} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.02354, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.84, "top5_acc": 0.9875, "loss_cls": 0.73577, "loss": 0.73577, "time": 0.30341} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.02353, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83438, "top5_acc": 0.98562, "loss_cls": 0.76609, "loss": 0.76609, "time": 0.38174} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.02352, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.81688, "top5_acc": 0.99062, "loss_cls": 0.8078, "loss": 0.8078, "time": 0.38295} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.02351, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.825, "top5_acc": 0.99312, "loss_cls": 0.76662, "loss": 0.76662, "time": 0.38732} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.0235, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.81688, "top5_acc": 0.98625, "loss_cls": 0.80971, "loss": 0.80971, "time": 0.38607} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.02349, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.82562, "top5_acc": 0.99062, "loss_cls": 0.76379, "loss": 0.76379, "time": 0.38807} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.02348, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82312, "top5_acc": 0.98562, "loss_cls": 0.77503, "loss": 0.77503, "time": 0.38159} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.02347, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83, "top5_acc": 0.98688, "loss_cls": 0.78951, "loss": 0.78951, "time": 0.38382} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.02346, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.83375, "top5_acc": 0.99, "loss_cls": 0.79378, "loss": 0.79378, "time": 0.3808} +{"mode": "val", "epoch": 24, "iter": 533, "lr": 0.02345, "top1_acc": 0.77608, "top5_acc": 0.97805, "mean_class_accuracy": 0.70976} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.02344, "memory": 4082, "data_time": 0.19503, "top1_acc": 0.84125, "top5_acc": 0.98688, "loss_cls": 0.73069, "loss": 0.73069, "time": 0.5803} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.02343, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.83062, "top5_acc": 0.98938, "loss_cls": 0.74421, "loss": 0.74421, "time": 0.38619} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.02342, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83812, "top5_acc": 0.99, "loss_cls": 0.71792, "loss": 0.71792, "time": 0.37537} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.02341, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84125, "top5_acc": 0.99562, "loss_cls": 0.67693, "loss": 0.67693, "time": 0.38063} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.0234, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83562, "top5_acc": 0.99, "loss_cls": 0.76939, "loss": 0.76939, "time": 0.37572} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.02339, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.8425, "top5_acc": 0.9925, "loss_cls": 0.73396, "loss": 0.73396, "time": 0.2477} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.02338, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.84, "top5_acc": 0.99438, "loss_cls": 0.73358, "loss": 0.73358, "time": 0.45441} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.02337, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81688, "top5_acc": 0.9875, "loss_cls": 0.79118, "loss": 0.79118, "time": 0.23165} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.02336, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.8175, "top5_acc": 0.98625, "loss_cls": 0.79908, "loss": 0.79908, "time": 0.31846} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.02335, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.82562, "top5_acc": 0.99188, "loss_cls": 0.76576, "loss": 0.76576, "time": 0.38008} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.02334, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85062, "top5_acc": 0.98938, "loss_cls": 0.69094, "loss": 0.69094, "time": 0.38032} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.02333, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81812, "top5_acc": 0.99, "loss_cls": 0.77391, "loss": 0.77391, "time": 0.38624} +{"mode": "val", "epoch": 25, "iter": 533, "lr": 0.02333, "top1_acc": 0.7877, "top5_acc": 0.98474, "mean_class_accuracy": 0.71871} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.02332, "memory": 4082, "data_time": 0.19947, "top1_acc": 0.845, "top5_acc": 0.99062, "loss_cls": 0.72817, "loss": 0.72817, "time": 0.5785} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.0233, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85125, "top5_acc": 0.99, "loss_cls": 0.66016, "loss": 0.66016, "time": 0.3825} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.02329, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.85, "top5_acc": 0.99125, "loss_cls": 0.70892, "loss": 0.70892, "time": 0.38576} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.02328, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82625, "top5_acc": 0.9875, "loss_cls": 0.77508, "loss": 0.77508, "time": 0.38056} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.02327, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82188, "top5_acc": 0.98812, "loss_cls": 0.79481, "loss": 0.79481, "time": 0.37556} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.02326, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.82562, "top5_acc": 0.99062, "loss_cls": 0.75801, "loss": 0.75801, "time": 0.38195} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.02325, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.8325, "top5_acc": 0.99438, "loss_cls": 0.75348, "loss": 0.75348, "time": 0.37861} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.02324, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80812, "top5_acc": 0.99125, "loss_cls": 0.82132, "loss": 0.82132, "time": 0.38279} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.02323, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.815, "top5_acc": 0.98875, "loss_cls": 0.83303, "loss": 0.83303, "time": 0.38806} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.02322, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.84938, "top5_acc": 0.99438, "loss_cls": 0.6876, "loss": 0.6876, "time": 0.37686} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.02321, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.855, "top5_acc": 0.99688, "loss_cls": 0.68062, "loss": 0.68062, "time": 0.2666} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.0232, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82938, "top5_acc": 0.99375, "loss_cls": 0.71546, "loss": 0.71546, "time": 0.44382} +{"mode": "val", "epoch": 26, "iter": 533, "lr": 0.02319, "top1_acc": 0.79885, "top5_acc": 0.98322, "mean_class_accuracy": 0.73198} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.02318, "memory": 4082, "data_time": 0.19775, "top1_acc": 0.85125, "top5_acc": 0.99562, "loss_cls": 0.69121, "loss": 0.69121, "time": 0.58634} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.02317, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.84875, "top5_acc": 0.9925, "loss_cls": 0.66067, "loss": 0.66067, "time": 0.38865} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.02316, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.84625, "top5_acc": 0.99188, "loss_cls": 0.69868, "loss": 0.69868, "time": 0.39468} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.02315, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84062, "top5_acc": 0.995, "loss_cls": 0.70582, "loss": 0.70582, "time": 0.38848} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.02314, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.845, "top5_acc": 0.99125, "loss_cls": 0.74504, "loss": 0.74504, "time": 0.3879} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.02313, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81312, "top5_acc": 0.98688, "loss_cls": 0.80817, "loss": 0.80817, "time": 0.38095} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.02312, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.83, "top5_acc": 0.99125, "loss_cls": 0.80288, "loss": 0.80288, "time": 0.38524} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.02311, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8375, "top5_acc": 0.98938, "loss_cls": 0.75095, "loss": 0.75095, "time": 0.39154} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.0231, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84188, "top5_acc": 0.995, "loss_cls": 0.70236, "loss": 0.70236, "time": 0.38124} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.02308, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84, "top5_acc": 0.9925, "loss_cls": 0.74442, "loss": 0.74442, "time": 0.37284} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.02307, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83688, "top5_acc": 0.99, "loss_cls": 0.73456, "loss": 0.73456, "time": 0.38227} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.02306, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.83188, "top5_acc": 0.98938, "loss_cls": 0.73982, "loss": 0.73982, "time": 0.38567} +{"mode": "val", "epoch": 27, "iter": 533, "lr": 0.02305, "top1_acc": 0.6599, "top5_acc": 0.94285, "mean_class_accuracy": 0.58871} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.02304, "memory": 4082, "data_time": 0.19451, "top1_acc": 0.83, "top5_acc": 0.99125, "loss_cls": 0.76043, "loss": 0.76043, "time": 0.47265} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.02303, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.84188, "top5_acc": 0.99438, "loss_cls": 0.69564, "loss": 0.69564, "time": 0.28577} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.02302, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.84188, "top5_acc": 0.99, "loss_cls": 0.71497, "loss": 0.71497, "time": 0.38347} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.02301, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.84, "top5_acc": 0.9925, "loss_cls": 0.70296, "loss": 0.70296, "time": 0.37884} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.023, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83938, "top5_acc": 0.9925, "loss_cls": 0.75929, "loss": 0.75929, "time": 0.38015} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.02299, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82188, "top5_acc": 0.98938, "loss_cls": 0.76515, "loss": 0.76515, "time": 0.37767} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.02298, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.8675, "top5_acc": 0.99, "loss_cls": 0.67831, "loss": 0.67831, "time": 0.38202} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.02297, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.82375, "top5_acc": 0.99188, "loss_cls": 0.78023, "loss": 0.78023, "time": 0.39749} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.02295, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.84312, "top5_acc": 0.98938, "loss_cls": 0.73266, "loss": 0.73266, "time": 0.37993} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.02294, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.82812, "top5_acc": 0.98938, "loss_cls": 0.76689, "loss": 0.76689, "time": 0.38911} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.02293, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8475, "top5_acc": 0.99125, "loss_cls": 0.70229, "loss": 0.70229, "time": 0.38523} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.02292, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82875, "top5_acc": 0.9875, "loss_cls": 0.76262, "loss": 0.76262, "time": 0.37551} +{"mode": "val", "epoch": 28, "iter": 533, "lr": 0.02291, "top1_acc": 0.81129, "top5_acc": 0.98357, "mean_class_accuracy": 0.7589} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.0229, "memory": 4082, "data_time": 0.19241, "top1_acc": 0.8625, "top5_acc": 0.99312, "loss_cls": 0.65768, "loss": 0.65768, "time": 0.57699} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.02289, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84312, "top5_acc": 0.995, "loss_cls": 0.68442, "loss": 0.68442, "time": 0.38096} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.02288, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83688, "top5_acc": 0.98625, "loss_cls": 0.73043, "loss": 0.73043, "time": 0.38631} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.02287, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.81812, "top5_acc": 0.98938, "loss_cls": 0.8028, "loss": 0.8028, "time": 0.2537} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.02285, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.84562, "top5_acc": 0.99438, "loss_cls": 0.71313, "loss": 0.71313, "time": 0.45144} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.02284, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85688, "top5_acc": 0.99125, "loss_cls": 0.68519, "loss": 0.68519, "time": 0.2525} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.02283, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.8575, "top5_acc": 0.99062, "loss_cls": 0.70325, "loss": 0.70325, "time": 0.29425} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.02282, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.84688, "top5_acc": 0.99312, "loss_cls": 0.68138, "loss": 0.68138, "time": 0.38242} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.02281, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.84, "top5_acc": 0.99062, "loss_cls": 0.69962, "loss": 0.69962, "time": 0.38777} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.0228, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83312, "top5_acc": 0.98812, "loss_cls": 0.77864, "loss": 0.77864, "time": 0.381} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.02279, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.84312, "top5_acc": 0.985, "loss_cls": 0.75024, "loss": 0.75024, "time": 0.37376} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.02277, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.85625, "top5_acc": 0.99125, "loss_cls": 0.68215, "loss": 0.68215, "time": 0.37846} +{"mode": "val", "epoch": 29, "iter": 533, "lr": 0.02276, "top1_acc": 0.79955, "top5_acc": 0.98204, "mean_class_accuracy": 0.74654} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.02275, "memory": 4082, "data_time": 0.19942, "top1_acc": 0.84875, "top5_acc": 0.99125, "loss_cls": 0.68174, "loss": 0.68174, "time": 0.68327} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.02274, "memory": 4082, "data_time": 0.00055, "top1_acc": 0.83188, "top5_acc": 0.98938, "loss_cls": 0.73309, "loss": 0.73309, "time": 0.48327} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.02273, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84938, "top5_acc": 0.99125, "loss_cls": 0.68969, "loss": 0.68969, "time": 0.48172} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.02272, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8675, "top5_acc": 0.99438, "loss_cls": 0.64023, "loss": 0.64023, "time": 0.48333} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.02271, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83375, "top5_acc": 0.99375, "loss_cls": 0.70165, "loss": 0.70165, "time": 0.48366} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.02269, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85, "top5_acc": 0.99438, "loss_cls": 0.67309, "loss": 0.67309, "time": 0.4808} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.02268, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8625, "top5_acc": 0.9925, "loss_cls": 0.65589, "loss": 0.65589, "time": 0.31486} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.02267, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84625, "top5_acc": 0.9875, "loss_cls": 0.70646, "loss": 0.70646, "time": 0.41931} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.02266, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.845, "top5_acc": 0.98188, "loss_cls": 0.75323, "loss": 0.75323, "time": 0.32246} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.02265, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83875, "top5_acc": 0.9925, "loss_cls": 0.74938, "loss": 0.74938, "time": 0.48555} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.02263, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8225, "top5_acc": 0.99188, "loss_cls": 0.75175, "loss": 0.75175, "time": 0.48272} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.02262, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.84812, "top5_acc": 0.98938, "loss_cls": 0.6805, "loss": 0.6805, "time": 0.48368} +{"mode": "val", "epoch": 30, "iter": 533, "lr": 0.02261, "top1_acc": 0.78148, "top5_acc": 0.98146, "mean_class_accuracy": 0.7117} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.0226, "memory": 4083, "data_time": 0.19826, "top1_acc": 0.8575, "top5_acc": 0.99438, "loss_cls": 0.81316, "loss": 0.81316, "time": 0.85617} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.02259, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.83812, "top5_acc": 0.99125, "loss_cls": 0.84585, "loss": 0.84585, "time": 0.49013} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.02258, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8625, "top5_acc": 0.99375, "loss_cls": 0.79641, "loss": 0.79641, "time": 0.49041} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.02256, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.85562, "top5_acc": 0.99375, "loss_cls": 0.79572, "loss": 0.79572, "time": 0.48972} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.02255, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85688, "top5_acc": 0.99312, "loss_cls": 0.79055, "loss": 0.79055, "time": 0.49322} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.02254, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8375, "top5_acc": 0.99125, "loss_cls": 0.84533, "loss": 0.84533, "time": 0.47155} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.02253, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.85062, "top5_acc": 0.99062, "loss_cls": 0.83907, "loss": 0.83907, "time": 0.34243} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.02252, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85062, "top5_acc": 0.99188, "loss_cls": 0.81021, "loss": 0.81021, "time": 0.39057} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0225, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.84438, "top5_acc": 0.9925, "loss_cls": 0.87764, "loss": 0.87764, "time": 0.35756} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.02249, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.8425, "top5_acc": 0.9975, "loss_cls": 0.83811, "loss": 0.83811, "time": 0.48987} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.02248, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.83125, "top5_acc": 0.9875, "loss_cls": 0.8864, "loss": 0.8864, "time": 0.49127} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.02247, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8675, "top5_acc": 0.99312, "loss_cls": 0.77291, "loss": 0.77291, "time": 0.49077} +{"mode": "val", "epoch": 31, "iter": 533, "lr": 0.02246, "top1_acc": 0.81106, "top5_acc": 0.98146, "mean_class_accuracy": 0.74614} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.02244, "memory": 4083, "data_time": 0.19191, "top1_acc": 0.87312, "top5_acc": 0.99438, "loss_cls": 0.68727, "loss": 0.68727, "time": 0.80769} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.02243, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8325, "top5_acc": 0.99188, "loss_cls": 0.79221, "loss": 0.79221, "time": 0.48978} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.02242, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84938, "top5_acc": 0.98938, "loss_cls": 0.74522, "loss": 0.74522, "time": 0.49058} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.02241, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84312, "top5_acc": 0.99375, "loss_cls": 0.78446, "loss": 0.78446, "time": 0.49445} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.02239, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.85875, "top5_acc": 0.99188, "loss_cls": 0.73512, "loss": 0.73512, "time": 0.49059} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.02238, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8425, "top5_acc": 0.99062, "loss_cls": 0.79381, "loss": 0.79381, "time": 0.48242} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.02237, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85312, "top5_acc": 0.99312, "loss_cls": 0.76834, "loss": 0.76834, "time": 0.34843} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.02236, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.85188, "top5_acc": 0.99125, "loss_cls": 0.7362, "loss": 0.7362, "time": 0.38924} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.02234, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.855, "top5_acc": 0.99562, "loss_cls": 0.75007, "loss": 0.75007, "time": 0.36059} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.02233, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85688, "top5_acc": 0.99375, "loss_cls": 0.75518, "loss": 0.75518, "time": 0.4873} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.02232, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8625, "top5_acc": 0.99, "loss_cls": 0.76557, "loss": 0.76557, "time": 0.48843} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.02231, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.84875, "top5_acc": 0.99312, "loss_cls": 0.74141, "loss": 0.74141, "time": 0.48967} +{"mode": "val", "epoch": 32, "iter": 533, "lr": 0.0223, "top1_acc": 0.78911, "top5_acc": 0.98263, "mean_class_accuracy": 0.7306} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.02228, "memory": 4083, "data_time": 0.19682, "top1_acc": 0.87938, "top5_acc": 0.99625, "loss_cls": 0.63425, "loss": 0.63425, "time": 0.79784} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.02227, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8725, "top5_acc": 0.99312, "loss_cls": 0.70347, "loss": 0.70347, "time": 0.49149} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.02226, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87125, "top5_acc": 0.99375, "loss_cls": 0.67924, "loss": 0.67924, "time": 0.48998} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.02225, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.83438, "top5_acc": 0.98938, "loss_cls": 0.80874, "loss": 0.80874, "time": 0.49016} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.02223, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85125, "top5_acc": 0.99125, "loss_cls": 0.71985, "loss": 0.71985, "time": 0.49127} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.02222, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.83875, "top5_acc": 0.98938, "loss_cls": 0.78826, "loss": 0.78826, "time": 0.48951} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.02221, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.84688, "top5_acc": 0.9925, "loss_cls": 0.76333, "loss": 0.76333, "time": 0.32298} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.02219, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86438, "top5_acc": 0.99438, "loss_cls": 0.70569, "loss": 0.70569, "time": 0.41482} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.02218, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.8425, "top5_acc": 0.99062, "loss_cls": 0.7565, "loss": 0.7565, "time": 0.354} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.02217, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.83188, "top5_acc": 0.9925, "loss_cls": 0.80093, "loss": 0.80093, "time": 0.49019} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.02216, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.845, "top5_acc": 0.99125, "loss_cls": 0.75768, "loss": 0.75768, "time": 0.48935} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.02214, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.83812, "top5_acc": 0.99312, "loss_cls": 0.77056, "loss": 0.77056, "time": 0.49619} +{"mode": "val", "epoch": 33, "iter": 533, "lr": 0.02213, "top1_acc": 0.7938, "top5_acc": 0.98345, "mean_class_accuracy": 0.72756} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.02212, "memory": 4083, "data_time": 0.19815, "top1_acc": 0.87188, "top5_acc": 0.99312, "loss_cls": 0.69185, "loss": 0.69185, "time": 0.80204} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.02211, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86188, "top5_acc": 0.99125, "loss_cls": 0.69351, "loss": 0.69351, "time": 0.48786} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.02209, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.86812, "top5_acc": 0.99562, "loss_cls": 0.69119, "loss": 0.69119, "time": 0.48912} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.02208, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86, "top5_acc": 0.99562, "loss_cls": 0.674, "loss": 0.674, "time": 0.49396} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.02207, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87125, "top5_acc": 0.99438, "loss_cls": 0.64493, "loss": 0.64493, "time": 0.49584} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.02205, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84812, "top5_acc": 0.99312, "loss_cls": 0.72963, "loss": 0.72963, "time": 0.48675} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.02204, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85875, "top5_acc": 0.99312, "loss_cls": 0.68899, "loss": 0.68899, "time": 0.32517} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.02203, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.8475, "top5_acc": 0.99312, "loss_cls": 0.74074, "loss": 0.74074, "time": 0.41134} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.02201, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.865, "top5_acc": 0.99188, "loss_cls": 0.68336, "loss": 0.68336, "time": 0.34866} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.022, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.84812, "top5_acc": 0.99125, "loss_cls": 0.75271, "loss": 0.75271, "time": 0.4882} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.02199, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85625, "top5_acc": 0.995, "loss_cls": 0.70456, "loss": 0.70456, "time": 0.49174} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.02197, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85312, "top5_acc": 0.98875, "loss_cls": 0.74922, "loss": 0.74922, "time": 0.49382} +{"mode": "val", "epoch": 34, "iter": 533, "lr": 0.02196, "top1_acc": 0.80084, "top5_acc": 0.98263, "mean_class_accuracy": 0.75536} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.02195, "memory": 4083, "data_time": 0.19578, "top1_acc": 0.86188, "top5_acc": 0.9925, "loss_cls": 0.72013, "loss": 0.72013, "time": 0.81446} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.02194, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86, "top5_acc": 0.99625, "loss_cls": 0.6738, "loss": 0.6738, "time": 0.48953} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.02192, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85125, "top5_acc": 0.99312, "loss_cls": 0.7161, "loss": 0.7161, "time": 0.48976} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.02191, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88188, "top5_acc": 0.9975, "loss_cls": 0.60269, "loss": 0.60269, "time": 0.49207} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.0219, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.865, "top5_acc": 0.99188, "loss_cls": 0.7236, "loss": 0.7236, "time": 0.49164} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.02188, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.83938, "top5_acc": 0.99125, "loss_cls": 0.78257, "loss": 0.78257, "time": 0.4788} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.02187, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.84688, "top5_acc": 0.99438, "loss_cls": 0.72738, "loss": 0.72738, "time": 0.33461} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.02185, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.85812, "top5_acc": 0.99625, "loss_cls": 0.69778, "loss": 0.69778, "time": 0.40192} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.02184, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.85375, "top5_acc": 0.99, "loss_cls": 0.73931, "loss": 0.73931, "time": 0.3728} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.02183, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8575, "top5_acc": 0.98875, "loss_cls": 0.75729, "loss": 0.75729, "time": 0.4926} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.02181, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.86188, "top5_acc": 0.99062, "loss_cls": 0.67659, "loss": 0.67659, "time": 0.49357} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.0218, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8425, "top5_acc": 0.99375, "loss_cls": 0.7776, "loss": 0.7776, "time": 0.49197} +{"mode": "val", "epoch": 35, "iter": 533, "lr": 0.02179, "top1_acc": 0.80413, "top5_acc": 0.98545, "mean_class_accuracy": 0.74337} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.02178, "memory": 4083, "data_time": 0.19354, "top1_acc": 0.87375, "top5_acc": 0.99562, "loss_cls": 0.64024, "loss": 0.64024, "time": 0.80554} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.02176, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86062, "top5_acc": 0.99562, "loss_cls": 0.69512, "loss": 0.69512, "time": 0.48951} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.02175, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87375, "top5_acc": 0.99375, "loss_cls": 0.64232, "loss": 0.64232, "time": 0.48896} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.02173, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8775, "top5_acc": 0.99375, "loss_cls": 0.62333, "loss": 0.62333, "time": 0.48963} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.02172, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84562, "top5_acc": 0.9925, "loss_cls": 0.7273, "loss": 0.7273, "time": 0.49031} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.02171, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87, "top5_acc": 0.99312, "loss_cls": 0.66465, "loss": 0.66465, "time": 0.45952} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.02169, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8475, "top5_acc": 0.99438, "loss_cls": 0.73088, "loss": 0.73088, "time": 0.36513} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.02168, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86375, "top5_acc": 0.99438, "loss_cls": 0.71532, "loss": 0.71532, "time": 0.37156} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.02167, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.84938, "top5_acc": 0.99188, "loss_cls": 0.732, "loss": 0.732, "time": 0.37125} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.02165, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.84375, "top5_acc": 0.9925, "loss_cls": 0.7588, "loss": 0.7588, "time": 0.49211} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.02164, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85875, "top5_acc": 0.99188, "loss_cls": 0.69534, "loss": 0.69534, "time": 0.49089} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.02162, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85188, "top5_acc": 0.98875, "loss_cls": 0.73488, "loss": 0.73488, "time": 0.48878} +{"mode": "val", "epoch": 36, "iter": 533, "lr": 0.02161, "top1_acc": 0.83629, "top5_acc": 0.98779, "mean_class_accuracy": 0.77626} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.0216, "memory": 4083, "data_time": 0.19754, "top1_acc": 0.85, "top5_acc": 0.995, "loss_cls": 0.71792, "loss": 0.71792, "time": 0.79855} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.02158, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86188, "top5_acc": 0.99438, "loss_cls": 0.68578, "loss": 0.68578, "time": 0.49137} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.02157, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.875, "top5_acc": 0.99562, "loss_cls": 0.62938, "loss": 0.62938, "time": 0.49395} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.02156, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85875, "top5_acc": 0.995, "loss_cls": 0.68746, "loss": 0.68746, "time": 0.49185} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.02154, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86938, "top5_acc": 0.99188, "loss_cls": 0.69898, "loss": 0.69898, "time": 0.49056} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.02153, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8725, "top5_acc": 0.99375, "loss_cls": 0.65664, "loss": 0.65664, "time": 0.47064} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.02151, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.85875, "top5_acc": 0.995, "loss_cls": 0.69333, "loss": 0.69333, "time": 0.32839} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0215, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.86438, "top5_acc": 0.99312, "loss_cls": 0.69218, "loss": 0.69218, "time": 0.40723} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.02149, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87438, "top5_acc": 0.99188, "loss_cls": 0.63308, "loss": 0.63308, "time": 0.35272} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.02147, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85, "top5_acc": 0.9925, "loss_cls": 0.74762, "loss": 0.74762, "time": 0.48835} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.02146, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.83938, "top5_acc": 0.99062, "loss_cls": 0.7608, "loss": 0.7608, "time": 0.49192} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.02144, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88562, "top5_acc": 0.99625, "loss_cls": 0.61802, "loss": 0.61802, "time": 0.48938} +{"mode": "val", "epoch": 37, "iter": 533, "lr": 0.02143, "top1_acc": 0.77702, "top5_acc": 0.97618, "mean_class_accuracy": 0.72242} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.02142, "memory": 4083, "data_time": 0.19448, "top1_acc": 0.87938, "top5_acc": 0.99562, "loss_cls": 0.64957, "loss": 0.64957, "time": 0.79903} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.0214, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.875, "top5_acc": 0.99688, "loss_cls": 0.62831, "loss": 0.62831, "time": 0.49121} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.02139, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87938, "top5_acc": 0.9925, "loss_cls": 0.60243, "loss": 0.60243, "time": 0.48678} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.02137, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.86875, "top5_acc": 0.995, "loss_cls": 0.67526, "loss": 0.67526, "time": 0.49119} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.02136, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86438, "top5_acc": 0.995, "loss_cls": 0.66289, "loss": 0.66289, "time": 0.49077} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.02134, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86938, "top5_acc": 0.98812, "loss_cls": 0.67765, "loss": 0.67765, "time": 0.48907} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.02133, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85062, "top5_acc": 0.995, "loss_cls": 0.72264, "loss": 0.72264, "time": 0.31517} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.02132, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.855, "top5_acc": 0.99438, "loss_cls": 0.72016, "loss": 0.72016, "time": 0.42375} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.0213, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86438, "top5_acc": 0.99125, "loss_cls": 0.73016, "loss": 0.73016, "time": 0.34153} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.02129, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87375, "top5_acc": 0.99625, "loss_cls": 0.60696, "loss": 0.60696, "time": 0.49285} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.02127, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85, "top5_acc": 0.99312, "loss_cls": 0.72374, "loss": 0.72374, "time": 0.49044} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.02126, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8675, "top5_acc": 0.99, "loss_cls": 0.71262, "loss": 0.71262, "time": 0.48777} +{"mode": "val", "epoch": 38, "iter": 533, "lr": 0.02125, "top1_acc": 0.84122, "top5_acc": 0.98404, "mean_class_accuracy": 0.78503} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.02123, "memory": 4083, "data_time": 0.19272, "top1_acc": 0.86812, "top5_acc": 0.99375, "loss_cls": 0.6444, "loss": 0.6444, "time": 0.79322} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.02122, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88812, "top5_acc": 0.99562, "loss_cls": 0.57507, "loss": 0.57507, "time": 0.49111} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.0212, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87312, "top5_acc": 0.9925, "loss_cls": 0.66495, "loss": 0.66495, "time": 0.48931} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.02119, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86375, "top5_acc": 0.99188, "loss_cls": 0.6886, "loss": 0.6886, "time": 0.48866} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.02117, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87, "top5_acc": 0.99188, "loss_cls": 0.69719, "loss": 0.69719, "time": 0.49226} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.02116, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87625, "top5_acc": 0.99688, "loss_cls": 0.60834, "loss": 0.60834, "time": 0.49242} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.02114, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.84938, "top5_acc": 0.99062, "loss_cls": 0.71673, "loss": 0.71673, "time": 0.30877} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.02113, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86, "top5_acc": 0.99438, "loss_cls": 0.68285, "loss": 0.68285, "time": 0.43974} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.02111, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85125, "top5_acc": 0.99375, "loss_cls": 0.70238, "loss": 0.70238, "time": 0.33146} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.0211, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87688, "top5_acc": 0.99188, "loss_cls": 0.66356, "loss": 0.66356, "time": 0.48988} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.02108, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8725, "top5_acc": 0.99188, "loss_cls": 0.67494, "loss": 0.67494, "time": 0.49242} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.02107, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.86625, "top5_acc": 0.995, "loss_cls": 0.66281, "loss": 0.66281, "time": 0.49137} +{"mode": "val", "epoch": 39, "iter": 533, "lr": 0.02106, "top1_acc": 0.81516, "top5_acc": 0.98334, "mean_class_accuracy": 0.77583} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.02104, "memory": 4083, "data_time": 0.19929, "top1_acc": 0.895, "top5_acc": 0.99375, "loss_cls": 0.57445, "loss": 0.57445, "time": 0.81511} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.02103, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.905, "top5_acc": 0.99688, "loss_cls": 0.51672, "loss": 0.51672, "time": 0.48625} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.02101, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.86812, "top5_acc": 0.99312, "loss_cls": 0.66591, "loss": 0.66591, "time": 0.49012} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.021, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8675, "top5_acc": 0.99438, "loss_cls": 0.66852, "loss": 0.66852, "time": 0.49003} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.02098, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87125, "top5_acc": 0.995, "loss_cls": 0.66536, "loss": 0.66536, "time": 0.49306} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.02097, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8875, "top5_acc": 0.99375, "loss_cls": 0.60351, "loss": 0.60351, "time": 0.49221} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.02095, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85812, "top5_acc": 0.99188, "loss_cls": 0.67302, "loss": 0.67302, "time": 0.31307} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.02094, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8575, "top5_acc": 0.9975, "loss_cls": 0.70002, "loss": 0.70002, "time": 0.42948} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.02092, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.85688, "top5_acc": 0.995, "loss_cls": 0.66444, "loss": 0.66444, "time": 0.34773} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.02091, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87, "top5_acc": 0.99438, "loss_cls": 0.67331, "loss": 0.67331, "time": 0.49505} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.02089, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8625, "top5_acc": 0.99375, "loss_cls": 0.68207, "loss": 0.68207, "time": 0.49358} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.02088, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86812, "top5_acc": 0.99312, "loss_cls": 0.68078, "loss": 0.68078, "time": 0.4948} +{"mode": "val", "epoch": 40, "iter": 533, "lr": 0.02086, "top1_acc": 0.81411, "top5_acc": 0.98451, "mean_class_accuracy": 0.75644} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.02085, "memory": 4083, "data_time": 0.19558, "top1_acc": 0.88562, "top5_acc": 0.995, "loss_cls": 0.57413, "loss": 0.57413, "time": 0.80569} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.02083, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86375, "top5_acc": 0.99375, "loss_cls": 0.6774, "loss": 0.6774, "time": 0.49234} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.02082, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85688, "top5_acc": 0.99062, "loss_cls": 0.69765, "loss": 0.69765, "time": 0.48871} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.0208, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87062, "top5_acc": 0.995, "loss_cls": 0.6654, "loss": 0.6654, "time": 0.48959} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.02079, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.86875, "top5_acc": 0.99312, "loss_cls": 0.66639, "loss": 0.66639, "time": 0.49028} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.02077, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.8625, "top5_acc": 0.99188, "loss_cls": 0.71725, "loss": 0.71725, "time": 0.48792} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.02076, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87625, "top5_acc": 0.99, "loss_cls": 0.664, "loss": 0.664, "time": 0.29671} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.02074, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.87125, "top5_acc": 0.99438, "loss_cls": 0.65272, "loss": 0.65272, "time": 0.44887} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.02073, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86125, "top5_acc": 0.99625, "loss_cls": 0.65743, "loss": 0.65743, "time": 0.32607} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.02071, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.86812, "top5_acc": 0.9975, "loss_cls": 0.66694, "loss": 0.66694, "time": 0.48917} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.0207, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85812, "top5_acc": 0.99375, "loss_cls": 0.67603, "loss": 0.67603, "time": 0.49203} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.02068, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.86938, "top5_acc": 0.99438, "loss_cls": 0.6818, "loss": 0.6818, "time": 0.49612} +{"mode": "val", "epoch": 41, "iter": 533, "lr": 0.02067, "top1_acc": 0.82162, "top5_acc": 0.98275, "mean_class_accuracy": 0.78631} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.02065, "memory": 4083, "data_time": 0.19406, "top1_acc": 0.86562, "top5_acc": 0.99438, "loss_cls": 0.71542, "loss": 0.71542, "time": 0.79943} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.02064, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87375, "top5_acc": 0.99438, "loss_cls": 0.65752, "loss": 0.65752, "time": 0.48879} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.02062, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.88375, "top5_acc": 0.99375, "loss_cls": 0.61356, "loss": 0.61356, "time": 0.48986} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.02061, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.86125, "top5_acc": 0.99438, "loss_cls": 0.6796, "loss": 0.6796, "time": 0.49098} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.02059, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.87875, "top5_acc": 0.99375, "loss_cls": 0.62286, "loss": 0.62286, "time": 0.49169} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.02057, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87, "top5_acc": 0.9925, "loss_cls": 0.64957, "loss": 0.64957, "time": 0.48772} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.02056, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.87438, "top5_acc": 0.99562, "loss_cls": 0.62426, "loss": 0.62426, "time": 0.26592} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.02054, "memory": 4083, "data_time": 0.00075, "top1_acc": 0.85812, "top5_acc": 0.99188, "loss_cls": 0.7025, "loss": 0.7025, "time": 0.51212} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.02053, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86688, "top5_acc": 0.99125, "loss_cls": 0.6873, "loss": 0.6873, "time": 0.304} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.02051, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86188, "top5_acc": 0.99375, "loss_cls": 0.68055, "loss": 0.68055, "time": 0.49051} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.0205, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87688, "top5_acc": 0.99438, "loss_cls": 0.62575, "loss": 0.62575, "time": 0.49082} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.02048, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86938, "top5_acc": 0.99, "loss_cls": 0.63812, "loss": 0.63812, "time": 0.49287} +{"mode": "val", "epoch": 42, "iter": 533, "lr": 0.02047, "top1_acc": 0.82608, "top5_acc": 0.97899, "mean_class_accuracy": 0.79179} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.02045, "memory": 4083, "data_time": 0.18969, "top1_acc": 0.88125, "top5_acc": 0.99188, "loss_cls": 0.62956, "loss": 0.62956, "time": 0.79245} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.02044, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87125, "top5_acc": 0.99625, "loss_cls": 0.62778, "loss": 0.62778, "time": 0.49022} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.02042, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87312, "top5_acc": 0.99562, "loss_cls": 0.65227, "loss": 0.65227, "time": 0.49285} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.0204, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8925, "top5_acc": 0.99812, "loss_cls": 0.58868, "loss": 0.58868, "time": 0.49575} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.02039, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.86, "top5_acc": 0.99312, "loss_cls": 0.66072, "loss": 0.66072, "time": 0.49487} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.02037, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.87188, "top5_acc": 0.99688, "loss_cls": 0.63654, "loss": 0.63654, "time": 0.48888} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.02036, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.85875, "top5_acc": 0.99125, "loss_cls": 0.71196, "loss": 0.71196, "time": 0.27876} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.02034, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8725, "top5_acc": 0.99438, "loss_cls": 0.63153, "loss": 0.63153, "time": 0.5111} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.02033, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88625, "top5_acc": 0.995, "loss_cls": 0.58348, "loss": 0.58348, "time": 0.2966} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.02031, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88625, "top5_acc": 0.99562, "loss_cls": 0.61331, "loss": 0.61331, "time": 0.48759} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.02029, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87125, "top5_acc": 0.99438, "loss_cls": 0.63174, "loss": 0.63174, "time": 0.48926} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.02028, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8925, "top5_acc": 0.995, "loss_cls": 0.54136, "loss": 0.54136, "time": 0.48802} +{"mode": "val", "epoch": 43, "iter": 533, "lr": 0.02026, "top1_acc": 0.81551, "top5_acc": 0.98122, "mean_class_accuracy": 0.74415} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.02025, "memory": 4083, "data_time": 0.1929, "top1_acc": 0.8875, "top5_acc": 0.99688, "loss_cls": 0.57577, "loss": 0.57577, "time": 0.80247} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.02023, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86562, "top5_acc": 0.99188, "loss_cls": 0.65342, "loss": 0.65342, "time": 0.49077} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.02022, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86812, "top5_acc": 0.99312, "loss_cls": 0.65819, "loss": 0.65819, "time": 0.49137} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.0202, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.86688, "top5_acc": 0.99375, "loss_cls": 0.67617, "loss": 0.67617, "time": 0.48912} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.02018, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.89562, "top5_acc": 0.99688, "loss_cls": 0.55626, "loss": 0.55626, "time": 0.49276} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.02017, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.875, "top5_acc": 0.99625, "loss_cls": 0.64974, "loss": 0.64974, "time": 0.4912} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.02015, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88625, "top5_acc": 0.9925, "loss_cls": 0.59181, "loss": 0.59181, "time": 0.27918} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.02014, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.86625, "top5_acc": 0.99375, "loss_cls": 0.66418, "loss": 0.66418, "time": 0.51118} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.02012, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.8825, "top5_acc": 0.99562, "loss_cls": 0.60377, "loss": 0.60377, "time": 0.30568} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.0201, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87938, "top5_acc": 0.99188, "loss_cls": 0.61461, "loss": 0.61461, "time": 0.48863} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.02009, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85625, "top5_acc": 0.99188, "loss_cls": 0.64749, "loss": 0.64749, "time": 0.48721} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.02007, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87062, "top5_acc": 0.9975, "loss_cls": 0.65152, "loss": 0.65152, "time": 0.4926} +{"mode": "val", "epoch": 44, "iter": 533, "lr": 0.02006, "top1_acc": 0.84591, "top5_acc": 0.99061, "mean_class_accuracy": 0.80502} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.02004, "memory": 4083, "data_time": 0.19631, "top1_acc": 0.88375, "top5_acc": 0.99562, "loss_cls": 0.59629, "loss": 0.59629, "time": 0.80281} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.02003, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87812, "top5_acc": 0.99562, "loss_cls": 0.58671, "loss": 0.58671, "time": 0.4921} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.02001, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.87562, "top5_acc": 0.9925, "loss_cls": 0.65082, "loss": 0.65082, "time": 0.49082} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.01999, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88312, "top5_acc": 0.9925, "loss_cls": 0.60146, "loss": 0.60146, "time": 0.48926} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.01998, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87938, "top5_acc": 0.99812, "loss_cls": 0.59602, "loss": 0.59602, "time": 0.49202} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.01996, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.86438, "top5_acc": 0.99312, "loss_cls": 0.68391, "loss": 0.68391, "time": 0.49236} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.01994, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8775, "top5_acc": 0.99375, "loss_cls": 0.62634, "loss": 0.62634, "time": 0.28636} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.01993, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87438, "top5_acc": 0.995, "loss_cls": 0.66132, "loss": 0.66132, "time": 0.49126} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.01991, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86938, "top5_acc": 0.99375, "loss_cls": 0.62689, "loss": 0.62689, "time": 0.30744} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.01989, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87562, "top5_acc": 0.9925, "loss_cls": 0.63972, "loss": 0.63972, "time": 0.49047} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.01988, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.8725, "top5_acc": 0.99125, "loss_cls": 0.64574, "loss": 0.64574, "time": 0.49397} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.01986, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88, "top5_acc": 0.99312, "loss_cls": 0.65737, "loss": 0.65737, "time": 0.4907} +{"mode": "val", "epoch": 45, "iter": 533, "lr": 0.01985, "top1_acc": 0.85236, "top5_acc": 0.98967, "mean_class_accuracy": 0.80828} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.01983, "memory": 4083, "data_time": 0.19394, "top1_acc": 0.89312, "top5_acc": 0.9975, "loss_cls": 0.53961, "loss": 0.53961, "time": 0.78922} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.01981, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88812, "top5_acc": 0.995, "loss_cls": 0.57646, "loss": 0.57646, "time": 0.4908} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.0198, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8725, "top5_acc": 0.99625, "loss_cls": 0.60013, "loss": 0.60013, "time": 0.49146} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.01978, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87438, "top5_acc": 0.99188, "loss_cls": 0.64545, "loss": 0.64545, "time": 0.49044} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.01976, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8675, "top5_acc": 0.99062, "loss_cls": 0.63232, "loss": 0.63232, "time": 0.49337} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.01975, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88625, "top5_acc": 0.995, "loss_cls": 0.59018, "loss": 0.59018, "time": 0.49479} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.01973, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.86625, "top5_acc": 0.99625, "loss_cls": 0.63401, "loss": 0.63401, "time": 0.27557} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.01971, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.87938, "top5_acc": 0.995, "loss_cls": 0.58855, "loss": 0.58855, "time": 0.5114} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.0197, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.86875, "top5_acc": 0.99438, "loss_cls": 0.63148, "loss": 0.63148, "time": 0.31735} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.01968, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89125, "top5_acc": 0.99312, "loss_cls": 0.60741, "loss": 0.60741, "time": 0.49061} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.01966, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86125, "top5_acc": 0.99312, "loss_cls": 0.65735, "loss": 0.65735, "time": 0.48992} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.01965, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87438, "top5_acc": 0.9925, "loss_cls": 0.62965, "loss": 0.62965, "time": 0.49189} +{"mode": "val", "epoch": 46, "iter": 533, "lr": 0.01963, "top1_acc": 0.82643, "top5_acc": 0.98615, "mean_class_accuracy": 0.77186} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.01962, "memory": 4083, "data_time": 0.19285, "top1_acc": 0.89812, "top5_acc": 0.99438, "loss_cls": 0.53615, "loss": 0.53615, "time": 0.79446} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.0196, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.51378, "loss": 0.51378, "time": 0.48879} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.01958, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87312, "top5_acc": 0.99125, "loss_cls": 0.647, "loss": 0.647, "time": 0.48761} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.01957, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88125, "top5_acc": 0.99688, "loss_cls": 0.59919, "loss": 0.59919, "time": 0.48928} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.01955, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.8925, "top5_acc": 0.995, "loss_cls": 0.61211, "loss": 0.61211, "time": 0.49296} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.01953, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.895, "top5_acc": 0.99438, "loss_cls": 0.56563, "loss": 0.56563, "time": 0.49282} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.01952, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.89625, "top5_acc": 0.9975, "loss_cls": 0.53327, "loss": 0.53327, "time": 0.29094} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.0195, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.87438, "top5_acc": 0.99438, "loss_cls": 0.61359, "loss": 0.61359, "time": 0.48156} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.01948, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87062, "top5_acc": 0.995, "loss_cls": 0.66186, "loss": 0.66186, "time": 0.30857} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.01947, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.88, "top5_acc": 0.995, "loss_cls": 0.63605, "loss": 0.63605, "time": 0.49118} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.01945, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87312, "top5_acc": 0.9975, "loss_cls": 0.62696, "loss": 0.62696, "time": 0.48843} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.01943, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89625, "top5_acc": 0.99562, "loss_cls": 0.56212, "loss": 0.56212, "time": 0.49086} +{"mode": "val", "epoch": 47, "iter": 533, "lr": 0.01942, "top1_acc": 0.84896, "top5_acc": 0.98826, "mean_class_accuracy": 0.79487} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.0194, "memory": 4083, "data_time": 0.19523, "top1_acc": 0.9, "top5_acc": 0.99875, "loss_cls": 0.51839, "loss": 0.51839, "time": 0.79107} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.01938, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89938, "top5_acc": 0.99625, "loss_cls": 0.54264, "loss": 0.54264, "time": 0.49134} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.01937, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88812, "top5_acc": 0.995, "loss_cls": 0.58171, "loss": 0.58171, "time": 0.49296} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.01935, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.89812, "top5_acc": 0.99562, "loss_cls": 0.55886, "loss": 0.55886, "time": 0.48948} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.01933, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.87875, "top5_acc": 0.99438, "loss_cls": 0.59521, "loss": 0.59521, "time": 0.4913} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.01932, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.86938, "top5_acc": 0.99312, "loss_cls": 0.66149, "loss": 0.66149, "time": 0.49196} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.0193, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.885, "top5_acc": 0.9975, "loss_cls": 0.57, "loss": 0.57, "time": 0.28113} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.01928, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88312, "top5_acc": 0.99562, "loss_cls": 0.56991, "loss": 0.56991, "time": 0.51181} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.01926, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.88938, "top5_acc": 0.99688, "loss_cls": 0.59411, "loss": 0.59411, "time": 0.28547} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.01925, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89938, "top5_acc": 0.99688, "loss_cls": 0.57747, "loss": 0.57747, "time": 0.49171} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.01923, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87312, "top5_acc": 0.99375, "loss_cls": 0.64095, "loss": 0.64095, "time": 0.49335} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.01921, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87062, "top5_acc": 0.99188, "loss_cls": 0.63743, "loss": 0.63743, "time": 0.49347} +{"mode": "val", "epoch": 48, "iter": 533, "lr": 0.0192, "top1_acc": 0.83394, "top5_acc": 0.98416, "mean_class_accuracy": 0.77605} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.01918, "memory": 4083, "data_time": 0.19247, "top1_acc": 0.9, "top5_acc": 0.99312, "loss_cls": 0.5494, "loss": 0.5494, "time": 0.79453} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.01916, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9, "top5_acc": 0.995, "loss_cls": 0.52626, "loss": 0.52626, "time": 0.48982} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.01915, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8875, "top5_acc": 0.99438, "loss_cls": 0.56765, "loss": 0.56765, "time": 0.48897} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.01913, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89188, "top5_acc": 0.9975, "loss_cls": 0.55282, "loss": 0.55282, "time": 0.491} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.01911, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89, "top5_acc": 0.99375, "loss_cls": 0.57643, "loss": 0.57643, "time": 0.49133} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.01909, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8875, "top5_acc": 0.99812, "loss_cls": 0.55377, "loss": 0.55377, "time": 0.49236} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.01908, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88562, "top5_acc": 0.99625, "loss_cls": 0.5957, "loss": 0.5957, "time": 0.30192} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.01906, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.88375, "top5_acc": 0.99312, "loss_cls": 0.62033, "loss": 0.62033, "time": 0.51063} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.01904, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.86125, "top5_acc": 0.99125, "loss_cls": 0.6943, "loss": 0.6943, "time": 0.28025} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.01902, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.885, "top5_acc": 0.99625, "loss_cls": 0.58906, "loss": 0.58906, "time": 0.49267} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.01901, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87062, "top5_acc": 0.99625, "loss_cls": 0.59261, "loss": 0.59261, "time": 0.49046} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.01899, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.88125, "top5_acc": 0.99188, "loss_cls": 0.58841, "loss": 0.58841, "time": 0.49165} +{"mode": "val", "epoch": 49, "iter": 533, "lr": 0.01898, "top1_acc": 0.82901, "top5_acc": 0.98345, "mean_class_accuracy": 0.77313} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.01896, "memory": 4083, "data_time": 0.19523, "top1_acc": 0.88875, "top5_acc": 0.99438, "loss_cls": 0.59273, "loss": 0.59273, "time": 0.7994} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.01894, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89438, "top5_acc": 0.9975, "loss_cls": 0.5435, "loss": 0.5435, "time": 0.4899} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.01892, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88375, "top5_acc": 0.99375, "loss_cls": 0.57561, "loss": 0.57561, "time": 0.49028} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.01891, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89688, "top5_acc": 0.99875, "loss_cls": 0.53382, "loss": 0.53382, "time": 0.48956} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.01889, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90625, "top5_acc": 0.99375, "loss_cls": 0.52185, "loss": 0.52185, "time": 0.49275} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.01887, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89125, "top5_acc": 0.9975, "loss_cls": 0.53981, "loss": 0.53981, "time": 0.48989} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.01885, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88312, "top5_acc": 0.99562, "loss_cls": 0.56279, "loss": 0.56279, "time": 0.2891} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.01884, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88625, "top5_acc": 0.995, "loss_cls": 0.58346, "loss": 0.58346, "time": 0.50848} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.01882, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.875, "top5_acc": 0.995, "loss_cls": 0.6, "loss": 0.6, "time": 0.28059} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.0188, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.865, "top5_acc": 0.99375, "loss_cls": 0.66657, "loss": 0.66657, "time": 0.49203} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.01878, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87875, "top5_acc": 0.995, "loss_cls": 0.62358, "loss": 0.62358, "time": 0.49057} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.01876, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.875, "top5_acc": 0.99438, "loss_cls": 0.61934, "loss": 0.61934, "time": 0.48954} +{"mode": "val", "epoch": 50, "iter": 533, "lr": 0.01875, "top1_acc": 0.83453, "top5_acc": 0.98427, "mean_class_accuracy": 0.79696} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.01873, "memory": 4083, "data_time": 0.19288, "top1_acc": 0.88125, "top5_acc": 0.995, "loss_cls": 0.58276, "loss": 0.58276, "time": 0.79243} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.01871, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88188, "top5_acc": 0.9975, "loss_cls": 0.59334, "loss": 0.59334, "time": 0.48706} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.0187, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89312, "top5_acc": 0.99812, "loss_cls": 0.54519, "loss": 0.54519, "time": 0.48533} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.01868, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89625, "top5_acc": 0.99438, "loss_cls": 0.53945, "loss": 0.53945, "time": 0.49231} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.01866, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.885, "top5_acc": 0.9975, "loss_cls": 0.57871, "loss": 0.57871, "time": 0.49116} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.01864, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88062, "top5_acc": 0.9975, "loss_cls": 0.61393, "loss": 0.61393, "time": 0.49172} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.01863, "memory": 4083, "data_time": 0.00066, "top1_acc": 0.88688, "top5_acc": 0.99375, "loss_cls": 0.59139, "loss": 0.59139, "time": 0.29425} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.01861, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89938, "top5_acc": 0.99438, "loss_cls": 0.54833, "loss": 0.54833, "time": 0.50977} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.01859, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9025, "top5_acc": 0.99688, "loss_cls": 0.51909, "loss": 0.51909, "time": 0.28003} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.01857, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88812, "top5_acc": 0.99625, "loss_cls": 0.54825, "loss": 0.54825, "time": 0.49266} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.01855, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88188, "top5_acc": 0.99125, "loss_cls": 0.61228, "loss": 0.61228, "time": 0.49238} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.01854, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89125, "top5_acc": 0.9975, "loss_cls": 0.54786, "loss": 0.54786, "time": 0.49452} +{"mode": "val", "epoch": 51, "iter": 533, "lr": 0.01852, "top1_acc": 0.83253, "top5_acc": 0.98909, "mean_class_accuracy": 0.80846} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.0185, "memory": 4083, "data_time": 0.19027, "top1_acc": 0.88188, "top5_acc": 0.99688, "loss_cls": 0.56566, "loss": 0.56566, "time": 0.80288} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.01849, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.89312, "top5_acc": 0.99812, "loss_cls": 0.54304, "loss": 0.54304, "time": 0.49017} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.01847, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90312, "top5_acc": 0.99562, "loss_cls": 0.51235, "loss": 0.51235, "time": 0.4901} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.01845, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88625, "top5_acc": 0.99562, "loss_cls": 0.59158, "loss": 0.59158, "time": 0.49074} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.01843, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89125, "top5_acc": 0.99688, "loss_cls": 0.53963, "loss": 0.53963, "time": 0.49511} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.01841, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87625, "top5_acc": 0.99188, "loss_cls": 0.63049, "loss": 0.63049, "time": 0.49295} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.0184, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.87875, "top5_acc": 0.99312, "loss_cls": 0.59967, "loss": 0.59967, "time": 0.28973} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.01838, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.90438, "top5_acc": 0.99688, "loss_cls": 0.51673, "loss": 0.51673, "time": 0.50987} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.01836, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.88188, "top5_acc": 0.99312, "loss_cls": 0.61488, "loss": 0.61488, "time": 0.2695} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.01834, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.87375, "top5_acc": 0.99312, "loss_cls": 0.62711, "loss": 0.62711, "time": 0.49124} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.01832, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.88938, "top5_acc": 0.99375, "loss_cls": 0.59158, "loss": 0.59158, "time": 0.49278} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.01831, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8675, "top5_acc": 0.98938, "loss_cls": 0.67273, "loss": 0.67273, "time": 0.49518} +{"mode": "val", "epoch": 52, "iter": 533, "lr": 0.01829, "top1_acc": 0.83875, "top5_acc": 0.98721, "mean_class_accuracy": 0.78146} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.01827, "memory": 4083, "data_time": 0.18872, "top1_acc": 0.895, "top5_acc": 0.995, "loss_cls": 0.5528, "loss": 0.5528, "time": 0.79479} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.01826, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9025, "top5_acc": 0.99562, "loss_cls": 0.53558, "loss": 0.53558, "time": 0.49403} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.01824, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90938, "top5_acc": 0.99625, "loss_cls": 0.50128, "loss": 0.50128, "time": 0.49076} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.01822, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9025, "top5_acc": 0.99812, "loss_cls": 0.53967, "loss": 0.53967, "time": 0.49052} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.0182, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89625, "top5_acc": 0.99625, "loss_cls": 0.54797, "loss": 0.54797, "time": 0.49125} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.01818, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89938, "top5_acc": 0.99625, "loss_cls": 0.54837, "loss": 0.54837, "time": 0.49206} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.01816, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.88438, "top5_acc": 0.9975, "loss_cls": 0.57548, "loss": 0.57548, "time": 0.32146} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.01815, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.8775, "top5_acc": 0.99688, "loss_cls": 0.61342, "loss": 0.61342, "time": 0.50977} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.01813, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87875, "top5_acc": 0.99438, "loss_cls": 0.61798, "loss": 0.61798, "time": 0.25259} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.01811, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88188, "top5_acc": 0.99438, "loss_cls": 0.6031, "loss": 0.6031, "time": 0.47727} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.01809, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.89312, "top5_acc": 0.99875, "loss_cls": 0.54349, "loss": 0.54349, "time": 0.49081} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.01807, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.885, "top5_acc": 0.99438, "loss_cls": 0.5918, "loss": 0.5918, "time": 0.49408} +{"mode": "val", "epoch": 53, "iter": 533, "lr": 0.01806, "top1_acc": 0.82713, "top5_acc": 0.98568, "mean_class_accuracy": 0.78265} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.01804, "memory": 4083, "data_time": 0.19669, "top1_acc": 0.88062, "top5_acc": 0.99438, "loss_cls": 0.59489, "loss": 0.59489, "time": 0.80906} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.01802, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.91312, "top5_acc": 0.9975, "loss_cls": 0.47569, "loss": 0.47569, "time": 0.49137} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.018, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.89625, "top5_acc": 0.99688, "loss_cls": 0.50472, "loss": 0.50472, "time": 0.4916} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.01798, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90188, "top5_acc": 0.99562, "loss_cls": 0.52111, "loss": 0.52111, "time": 0.4917} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.01797, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90125, "top5_acc": 0.995, "loss_cls": 0.52211, "loss": 0.52211, "time": 0.49377} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.01795, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.8925, "top5_acc": 0.99562, "loss_cls": 0.55613, "loss": 0.55613, "time": 0.49305} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.01793, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87875, "top5_acc": 0.99438, "loss_cls": 0.59054, "loss": 0.59054, "time": 0.32329} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.01791, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90938, "top5_acc": 0.99625, "loss_cls": 0.50941, "loss": 0.50941, "time": 0.50945} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.01789, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.86625, "top5_acc": 0.99562, "loss_cls": 0.66943, "loss": 0.66943, "time": 0.24872} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.01787, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89062, "top5_acc": 0.99688, "loss_cls": 0.55626, "loss": 0.55626, "time": 0.47346} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.01786, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.88938, "top5_acc": 0.99438, "loss_cls": 0.56646, "loss": 0.56646, "time": 0.4898} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.01784, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89125, "top5_acc": 0.99562, "loss_cls": 0.5794, "loss": 0.5794, "time": 0.48984} +{"mode": "val", "epoch": 54, "iter": 533, "lr": 0.01782, "top1_acc": 0.81575, "top5_acc": 0.98639, "mean_class_accuracy": 0.76091} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.0178, "memory": 4083, "data_time": 0.19174, "top1_acc": 0.9075, "top5_acc": 0.99562, "loss_cls": 0.48533, "loss": 0.48533, "time": 0.79548} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.01779, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9, "top5_acc": 0.99688, "loss_cls": 0.51835, "loss": 0.51835, "time": 0.49206} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.01777, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87625, "top5_acc": 0.99312, "loss_cls": 0.60747, "loss": 0.60747, "time": 0.4898} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.01775, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88438, "top5_acc": 0.995, "loss_cls": 0.5375, "loss": 0.5375, "time": 0.48886} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.01773, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89938, "top5_acc": 0.99625, "loss_cls": 0.53516, "loss": 0.53516, "time": 0.48689} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.01771, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.88312, "top5_acc": 0.9925, "loss_cls": 0.58922, "loss": 0.58922, "time": 0.4932} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.01769, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88875, "top5_acc": 0.9975, "loss_cls": 0.58124, "loss": 0.58124, "time": 0.34216} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.01767, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88812, "top5_acc": 0.99688, "loss_cls": 0.57959, "loss": 0.57959, "time": 0.51086} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.01766, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88188, "top5_acc": 0.9925, "loss_cls": 0.5848, "loss": 0.5848, "time": 0.24382} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.01764, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.90688, "top5_acc": 0.99562, "loss_cls": 0.51669, "loss": 0.51669, "time": 0.47157} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.01762, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.88812, "top5_acc": 0.995, "loss_cls": 0.53936, "loss": 0.53936, "time": 0.48961} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.0176, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.885, "top5_acc": 0.99688, "loss_cls": 0.55034, "loss": 0.55034, "time": 0.49679} +{"mode": "val", "epoch": 55, "iter": 533, "lr": 0.01758, "top1_acc": 0.84274, "top5_acc": 0.98779, "mean_class_accuracy": 0.79192} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.01757, "memory": 4083, "data_time": 0.19344, "top1_acc": 0.90875, "top5_acc": 0.99812, "loss_cls": 0.47677, "loss": 0.47677, "time": 0.79633} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.01755, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88688, "top5_acc": 0.99688, "loss_cls": 0.53704, "loss": 0.53704, "time": 0.4907} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.01753, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88875, "top5_acc": 0.99812, "loss_cls": 0.52865, "loss": 0.52865, "time": 0.49117} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.01751, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88875, "top5_acc": 0.99375, "loss_cls": 0.594, "loss": 0.594, "time": 0.49001} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.01749, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89188, "top5_acc": 0.9975, "loss_cls": 0.54769, "loss": 0.54769, "time": 0.48686} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.01747, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90375, "top5_acc": 0.99562, "loss_cls": 0.52951, "loss": 0.52951, "time": 0.49024} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.01745, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.8975, "top5_acc": 0.9975, "loss_cls": 0.5095, "loss": 0.5095, "time": 0.35378} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.01743, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.885, "top5_acc": 0.995, "loss_cls": 0.59304, "loss": 0.59304, "time": 0.50948} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.01742, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8975, "top5_acc": 0.99562, "loss_cls": 0.53903, "loss": 0.53903, "time": 0.24916} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.0174, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88875, "top5_acc": 0.99562, "loss_cls": 0.56272, "loss": 0.56272, "time": 0.46714} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.01738, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.885, "top5_acc": 0.99688, "loss_cls": 0.58468, "loss": 0.58468, "time": 0.48965} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.01736, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.905, "top5_acc": 0.99562, "loss_cls": 0.52551, "loss": 0.52551, "time": 0.49092} +{"mode": "val", "epoch": 56, "iter": 533, "lr": 0.01734, "top1_acc": 0.85788, "top5_acc": 0.98991, "mean_class_accuracy": 0.81936} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.01733, "memory": 4083, "data_time": 0.19033, "top1_acc": 0.89688, "top5_acc": 0.99688, "loss_cls": 0.53454, "loss": 0.53454, "time": 0.80498} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.01731, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9075, "top5_acc": 0.99562, "loss_cls": 0.5056, "loss": 0.5056, "time": 0.48769} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.01729, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88875, "top5_acc": 0.99625, "loss_cls": 0.52979, "loss": 0.52979, "time": 0.49039} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.01727, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90688, "top5_acc": 0.99562, "loss_cls": 0.48435, "loss": 0.48435, "time": 0.48937} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.01725, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89188, "top5_acc": 0.99625, "loss_cls": 0.56572, "loss": 0.56572, "time": 0.49363} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.01723, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.905, "top5_acc": 0.9925, "loss_cls": 0.55202, "loss": 0.55202, "time": 0.48744} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.01721, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.89875, "top5_acc": 0.995, "loss_cls": 0.53355, "loss": 0.53355, "time": 0.34208} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.01719, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90812, "top5_acc": 0.99438, "loss_cls": 0.51701, "loss": 0.51701, "time": 0.50876} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.01717, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.8875, "top5_acc": 0.99375, "loss_cls": 0.5697, "loss": 0.5697, "time": 0.24627} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.01716, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9, "top5_acc": 0.99625, "loss_cls": 0.5543, "loss": 0.5543, "time": 0.4584} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.01714, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90875, "top5_acc": 0.99562, "loss_cls": 0.53012, "loss": 0.53012, "time": 0.49137} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.01712, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90062, "top5_acc": 0.995, "loss_cls": 0.53281, "loss": 0.53281, "time": 0.49032} +{"mode": "val", "epoch": 57, "iter": 533, "lr": 0.0171, "top1_acc": 0.84896, "top5_acc": 0.98791, "mean_class_accuracy": 0.79683} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.01708, "memory": 4083, "data_time": 0.19172, "top1_acc": 0.89688, "top5_acc": 0.99625, "loss_cls": 0.55, "loss": 0.55, "time": 0.80326} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.01706, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90062, "top5_acc": 0.99812, "loss_cls": 0.51417, "loss": 0.51417, "time": 0.49344} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.01704, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91125, "top5_acc": 0.99625, "loss_cls": 0.49219, "loss": 0.49219, "time": 0.49182} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.01703, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91562, "top5_acc": 0.99688, "loss_cls": 0.46282, "loss": 0.46282, "time": 0.49304} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.01701, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90312, "top5_acc": 0.99875, "loss_cls": 0.48908, "loss": 0.48908, "time": 0.49247} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.01699, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.89562, "top5_acc": 0.99562, "loss_cls": 0.52431, "loss": 0.52431, "time": 0.49073} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.01697, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89125, "top5_acc": 0.995, "loss_cls": 0.55183, "loss": 0.55183, "time": 0.35944} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.01695, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9, "top5_acc": 0.99438, "loss_cls": 0.51209, "loss": 0.51209, "time": 0.50942} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.01693, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.89, "top5_acc": 0.99188, "loss_cls": 0.58793, "loss": 0.58793, "time": 0.24293} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.01691, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89438, "top5_acc": 0.99875, "loss_cls": 0.51447, "loss": 0.51447, "time": 0.46075} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.01689, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88812, "top5_acc": 0.99688, "loss_cls": 0.54988, "loss": 0.54988, "time": 0.49228} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.01687, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89688, "top5_acc": 0.99688, "loss_cls": 0.51902, "loss": 0.51902, "time": 0.49101} +{"mode": "val", "epoch": 58, "iter": 533, "lr": 0.01686, "top1_acc": 0.85835, "top5_acc": 0.98768, "mean_class_accuracy": 0.82175} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.01684, "memory": 4083, "data_time": 0.19511, "top1_acc": 0.90812, "top5_acc": 0.99875, "loss_cls": 0.44812, "loss": 0.44812, "time": 0.7938} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.01682, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90625, "top5_acc": 0.99812, "loss_cls": 0.50781, "loss": 0.50781, "time": 0.49204} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.0168, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91125, "top5_acc": 0.99875, "loss_cls": 0.47231, "loss": 0.47231, "time": 0.49137} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.01678, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89188, "top5_acc": 0.9975, "loss_cls": 0.50125, "loss": 0.50125, "time": 0.49137} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.01676, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.89875, "top5_acc": 0.99812, "loss_cls": 0.53346, "loss": 0.53346, "time": 0.4896} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.01674, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90188, "top5_acc": 0.99438, "loss_cls": 0.53233, "loss": 0.53233, "time": 0.49037} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.01672, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8825, "top5_acc": 0.99625, "loss_cls": 0.54842, "loss": 0.54842, "time": 0.36666} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.0167, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88688, "top5_acc": 0.99688, "loss_cls": 0.57856, "loss": 0.57856, "time": 0.51089} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.01668, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91375, "top5_acc": 0.99562, "loss_cls": 0.49327, "loss": 0.49327, "time": 0.23473} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.01667, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88, "top5_acc": 0.99188, "loss_cls": 0.6106, "loss": 0.6106, "time": 0.44088} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.01665, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89812, "top5_acc": 0.99625, "loss_cls": 0.54804, "loss": 0.54804, "time": 0.49198} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.01663, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88688, "top5_acc": 0.99688, "loss_cls": 0.57007, "loss": 0.57007, "time": 0.48551} +{"mode": "val", "epoch": 59, "iter": 533, "lr": 0.01661, "top1_acc": 0.83863, "top5_acc": 0.9858, "mean_class_accuracy": 0.80946} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.01659, "memory": 4083, "data_time": 0.19281, "top1_acc": 0.91, "top5_acc": 0.99875, "loss_cls": 0.48962, "loss": 0.48962, "time": 0.81247} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.01657, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89375, "top5_acc": 0.99312, "loss_cls": 0.50444, "loss": 0.50444, "time": 0.49259} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.01655, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91812, "top5_acc": 0.99562, "loss_cls": 0.49879, "loss": 0.49879, "time": 0.49039} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.01653, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.90375, "top5_acc": 0.99812, "loss_cls": 0.5035, "loss": 0.5035, "time": 0.49119} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.01651, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91188, "top5_acc": 0.99625, "loss_cls": 0.48183, "loss": 0.48183, "time": 0.48727} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.0165, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91188, "top5_acc": 0.9975, "loss_cls": 0.45224, "loss": 0.45224, "time": 0.49143} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.01648, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.90938, "top5_acc": 0.99688, "loss_cls": 0.5028, "loss": 0.5028, "time": 0.38547} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.01646, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90562, "top5_acc": 0.99562, "loss_cls": 0.51608, "loss": 0.51608, "time": 0.51152} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.01644, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90812, "top5_acc": 0.99438, "loss_cls": 0.51108, "loss": 0.51108, "time": 0.23405} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.01642, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89688, "top5_acc": 0.99812, "loss_cls": 0.51324, "loss": 0.51324, "time": 0.43258} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.0164, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.89812, "top5_acc": 0.995, "loss_cls": 0.50908, "loss": 0.50908, "time": 0.4901} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.01638, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89375, "top5_acc": 0.99562, "loss_cls": 0.54851, "loss": 0.54851, "time": 0.49032} +{"mode": "val", "epoch": 60, "iter": 533, "lr": 0.01636, "top1_acc": 0.85366, "top5_acc": 0.9865, "mean_class_accuracy": 0.79891} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.01634, "memory": 4083, "data_time": 0.19291, "top1_acc": 0.91312, "top5_acc": 0.99688, "loss_cls": 0.50742, "loss": 0.50742, "time": 0.78236} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.01632, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.90312, "top5_acc": 0.99812, "loss_cls": 0.49802, "loss": 0.49802, "time": 0.48842} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.0163, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92188, "top5_acc": 0.99875, "loss_cls": 0.45986, "loss": 0.45986, "time": 0.48927} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.01629, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91625, "top5_acc": 0.99812, "loss_cls": 0.45765, "loss": 0.45765, "time": 0.48939} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.01627, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90562, "top5_acc": 0.99125, "loss_cls": 0.53005, "loss": 0.53005, "time": 0.49025} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.01625, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.895, "top5_acc": 0.99875, "loss_cls": 0.51816, "loss": 0.51816, "time": 0.48852} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.01623, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91312, "top5_acc": 0.99625, "loss_cls": 0.50572, "loss": 0.50572, "time": 0.41547} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.01621, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91, "top5_acc": 0.9975, "loss_cls": 0.48944, "loss": 0.48944, "time": 0.46691} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.01619, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.89688, "top5_acc": 0.99438, "loss_cls": 0.55641, "loss": 0.55641, "time": 0.26756} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.01617, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90938, "top5_acc": 0.99688, "loss_cls": 0.49972, "loss": 0.49972, "time": 0.41475} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.01615, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8975, "top5_acc": 0.99688, "loss_cls": 0.51578, "loss": 0.51578, "time": 0.49078} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.01613, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89875, "top5_acc": 0.9975, "loss_cls": 0.52987, "loss": 0.52987, "time": 0.48983} +{"mode": "val", "epoch": 61, "iter": 533, "lr": 0.01611, "top1_acc": 0.84063, "top5_acc": 0.98568, "mean_class_accuracy": 0.79169} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.01609, "memory": 4083, "data_time": 0.18699, "top1_acc": 0.90562, "top5_acc": 0.99625, "loss_cls": 0.5362, "loss": 0.5362, "time": 0.79829} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.01607, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.91188, "top5_acc": 0.99875, "loss_cls": 0.4974, "loss": 0.4974, "time": 0.49005} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.01605, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.90938, "top5_acc": 0.99875, "loss_cls": 0.46131, "loss": 0.46131, "time": 0.49125} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.01603, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89562, "top5_acc": 0.99688, "loss_cls": 0.50528, "loss": 0.50528, "time": 0.49423} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.01602, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90938, "top5_acc": 0.99688, "loss_cls": 0.47795, "loss": 0.47795, "time": 0.49205} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.016, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92688, "top5_acc": 0.99688, "loss_cls": 0.42099, "loss": 0.42099, "time": 0.4947} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.01598, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91125, "top5_acc": 0.99688, "loss_cls": 0.45512, "loss": 0.45512, "time": 0.421} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.01596, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9, "top5_acc": 0.9975, "loss_cls": 0.50645, "loss": 0.50645, "time": 0.45878} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.01594, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9075, "top5_acc": 0.9975, "loss_cls": 0.45154, "loss": 0.45154, "time": 0.27451} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.01592, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91688, "top5_acc": 0.99688, "loss_cls": 0.48446, "loss": 0.48446, "time": 0.42203} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.0159, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9075, "top5_acc": 0.99625, "loss_cls": 0.50782, "loss": 0.50782, "time": 0.49222} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.01588, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91375, "top5_acc": 0.99875, "loss_cls": 0.47602, "loss": 0.47602, "time": 0.49027} +{"mode": "val", "epoch": 62, "iter": 533, "lr": 0.01586, "top1_acc": 0.83664, "top5_acc": 0.98439, "mean_class_accuracy": 0.79568} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.01584, "memory": 4083, "data_time": 0.18802, "top1_acc": 0.91375, "top5_acc": 0.99562, "loss_cls": 0.49707, "loss": 0.49707, "time": 0.79444} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.01582, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91312, "top5_acc": 0.995, "loss_cls": 0.48339, "loss": 0.48339, "time": 0.49288} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.0158, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90875, "top5_acc": 0.99875, "loss_cls": 0.45797, "loss": 0.45797, "time": 0.49297} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.01578, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9225, "top5_acc": 0.99875, "loss_cls": 0.41856, "loss": 0.41856, "time": 0.49132} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.01576, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91375, "top5_acc": 0.99812, "loss_cls": 0.4813, "loss": 0.4813, "time": 0.49268} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.01574, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89688, "top5_acc": 0.995, "loss_cls": 0.53878, "loss": 0.53878, "time": 0.4929} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.01572, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91125, "top5_acc": 0.99688, "loss_cls": 0.49776, "loss": 0.49776, "time": 0.4386} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.0157, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.90812, "top5_acc": 0.99562, "loss_cls": 0.52, "loss": 0.52, "time": 0.40961} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.01568, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.89625, "top5_acc": 0.99562, "loss_cls": 0.52458, "loss": 0.52458, "time": 0.32667} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.01566, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90312, "top5_acc": 0.99688, "loss_cls": 0.50907, "loss": 0.50907, "time": 0.39709} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.01564, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9025, "top5_acc": 0.99688, "loss_cls": 0.51072, "loss": 0.51072, "time": 0.4933} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.01562, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90562, "top5_acc": 0.99688, "loss_cls": 0.4916, "loss": 0.4916, "time": 0.49503} +{"mode": "val", "epoch": 63, "iter": 533, "lr": 0.01561, "top1_acc": 0.85107, "top5_acc": 0.98956, "mean_class_accuracy": 0.82343} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.01559, "memory": 4083, "data_time": 0.19601, "top1_acc": 0.91375, "top5_acc": 0.99938, "loss_cls": 0.4598, "loss": 0.4598, "time": 0.79184} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.01557, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9125, "top5_acc": 0.99875, "loss_cls": 0.46926, "loss": 0.46926, "time": 0.49379} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.01555, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90875, "top5_acc": 0.9975, "loss_cls": 0.4609, "loss": 0.4609, "time": 0.4919} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.01553, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91562, "top5_acc": 0.99812, "loss_cls": 0.43986, "loss": 0.43986, "time": 0.49435} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.01551, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.42012, "loss": 0.42012, "time": 0.49427} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.01549, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89938, "top5_acc": 0.99688, "loss_cls": 0.51782, "loss": 0.51782, "time": 0.49138} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.01547, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.895, "top5_acc": 0.99688, "loss_cls": 0.50014, "loss": 0.50014, "time": 0.4584} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.01545, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.88938, "top5_acc": 0.9975, "loss_cls": 0.57642, "loss": 0.57642, "time": 0.37519} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.01543, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8975, "top5_acc": 0.99625, "loss_cls": 0.55721, "loss": 0.55721, "time": 0.36111} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.01541, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90062, "top5_acc": 0.99125, "loss_cls": 0.56969, "loss": 0.56969, "time": 0.37838} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.01539, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8975, "top5_acc": 0.99688, "loss_cls": 0.5401, "loss": 0.5401, "time": 0.48742} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.01537, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92125, "top5_acc": 0.9975, "loss_cls": 0.44369, "loss": 0.44369, "time": 0.49259} +{"mode": "val", "epoch": 64, "iter": 533, "lr": 0.01535, "top1_acc": 0.85776, "top5_acc": 0.99002, "mean_class_accuracy": 0.81924} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.01533, "memory": 4083, "data_time": 0.18919, "top1_acc": 0.93312, "top5_acc": 0.9975, "loss_cls": 0.41729, "loss": 0.41729, "time": 0.80554} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.01531, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9025, "top5_acc": 0.99562, "loss_cls": 0.51024, "loss": 0.51024, "time": 0.48979} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.01529, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91625, "top5_acc": 0.99875, "loss_cls": 0.45186, "loss": 0.45186, "time": 0.49204} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.01527, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90188, "top5_acc": 0.99688, "loss_cls": 0.54591, "loss": 0.54591, "time": 0.48955} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.01526, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.905, "top5_acc": 0.99812, "loss_cls": 0.49647, "loss": 0.49647, "time": 0.49232} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.01524, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91625, "top5_acc": 0.99812, "loss_cls": 0.44478, "loss": 0.44478, "time": 0.49392} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.01522, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91438, "top5_acc": 0.99562, "loss_cls": 0.44987, "loss": 0.44987, "time": 0.45615} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0152, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91125, "top5_acc": 0.99812, "loss_cls": 0.43822, "loss": 0.43822, "time": 0.37081} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.01518, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.89875, "top5_acc": 0.9975, "loss_cls": 0.51868, "loss": 0.51868, "time": 0.36299} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.01516, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89812, "top5_acc": 0.99875, "loss_cls": 0.53117, "loss": 0.53117, "time": 0.35757} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.01514, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90438, "top5_acc": 0.99625, "loss_cls": 0.53174, "loss": 0.53174, "time": 0.49054} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.01512, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9, "top5_acc": 0.995, "loss_cls": 0.52804, "loss": 0.52804, "time": 0.49124} +{"mode": "val", "epoch": 65, "iter": 533, "lr": 0.0151, "top1_acc": 0.87807, "top5_acc": 0.99155, "mean_class_accuracy": 0.83809} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.01508, "memory": 4083, "data_time": 0.18714, "top1_acc": 0.9125, "top5_acc": 0.9975, "loss_cls": 0.48147, "loss": 0.48147, "time": 0.79915} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.01506, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.90312, "top5_acc": 0.99812, "loss_cls": 0.4946, "loss": 0.4946, "time": 0.4902} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.01504, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92312, "top5_acc": 0.9975, "loss_cls": 0.41179, "loss": 0.41179, "time": 0.48948} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.01502, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91375, "top5_acc": 0.99688, "loss_cls": 0.46857, "loss": 0.46857, "time": 0.49219} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.015, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90938, "top5_acc": 0.9975, "loss_cls": 0.46669, "loss": 0.46669, "time": 0.48978} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.01498, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91, "top5_acc": 0.99562, "loss_cls": 0.48183, "loss": 0.48183, "time": 0.49013} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.01496, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.45195, "loss": 0.45195, "time": 0.48822} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.01494, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.90062, "top5_acc": 0.9975, "loss_cls": 0.49817, "loss": 0.49817, "time": 0.31268} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.01492, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90438, "top5_acc": 0.99875, "loss_cls": 0.47547, "loss": 0.47547, "time": 0.42418} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.0149, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89438, "top5_acc": 0.99375, "loss_cls": 0.50873, "loss": 0.50873, "time": 0.35063} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.01488, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88625, "top5_acc": 0.99312, "loss_cls": 0.59239, "loss": 0.59239, "time": 0.49131} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.01486, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90188, "top5_acc": 0.99688, "loss_cls": 0.51971, "loss": 0.51971, "time": 0.49278} +{"mode": "val", "epoch": 66, "iter": 533, "lr": 0.01484, "top1_acc": 0.86035, "top5_acc": 0.99179, "mean_class_accuracy": 0.81751} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.01482, "memory": 4083, "data_time": 0.19004, "top1_acc": 0.925, "top5_acc": 0.99812, "loss_cls": 0.40331, "loss": 0.40331, "time": 0.79156} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.0148, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91812, "top5_acc": 0.99938, "loss_cls": 0.42741, "loss": 0.42741, "time": 0.49251} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.01478, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91625, "top5_acc": 0.99875, "loss_cls": 0.46435, "loss": 0.46435, "time": 0.48816} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.01476, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91938, "top5_acc": 0.99812, "loss_cls": 0.41809, "loss": 0.41809, "time": 0.49218} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.01474, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.90062, "top5_acc": 0.99688, "loss_cls": 0.49431, "loss": 0.49431, "time": 0.49193} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.01472, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90312, "top5_acc": 0.99688, "loss_cls": 0.47759, "loss": 0.47759, "time": 0.49266} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.0147, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9075, "top5_acc": 0.99625, "loss_cls": 0.48128, "loss": 0.48128, "time": 0.49006} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.01468, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91562, "top5_acc": 0.9975, "loss_cls": 0.4377, "loss": 0.4377, "time": 0.299} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.01466, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91562, "top5_acc": 0.9975, "loss_cls": 0.43306, "loss": 0.43306, "time": 0.44231} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.01464, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89625, "top5_acc": 0.99688, "loss_cls": 0.53693, "loss": 0.53693, "time": 0.32302} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.01462, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.90812, "top5_acc": 0.99312, "loss_cls": 0.54538, "loss": 0.54538, "time": 0.48434} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.0146, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88625, "top5_acc": 0.99688, "loss_cls": 0.55887, "loss": 0.55887, "time": 0.4962} +{"mode": "val", "epoch": 67, "iter": 533, "lr": 0.01458, "top1_acc": 0.86703, "top5_acc": 0.98932, "mean_class_accuracy": 0.81682} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.01456, "memory": 4083, "data_time": 0.19291, "top1_acc": 0.91812, "top5_acc": 0.99812, "loss_cls": 0.43253, "loss": 0.43253, "time": 0.79907} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.01454, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91562, "top5_acc": 0.99812, "loss_cls": 0.43286, "loss": 0.43286, "time": 0.49495} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.01452, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.3633, "loss": 0.3633, "time": 0.49662} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.0145, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92688, "top5_acc": 0.99562, "loss_cls": 0.41671, "loss": 0.41671, "time": 0.49298} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.01448, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91875, "top5_acc": 0.99875, "loss_cls": 0.43543, "loss": 0.43543, "time": 0.48712} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.01446, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.91812, "top5_acc": 0.99688, "loss_cls": 0.44557, "loss": 0.44557, "time": 0.4924} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.01444, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91562, "top5_acc": 0.99625, "loss_cls": 0.47619, "loss": 0.47619, "time": 0.49061} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.01442, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91438, "top5_acc": 0.9975, "loss_cls": 0.43419, "loss": 0.43419, "time": 0.28319} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.0144, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91938, "top5_acc": 0.99812, "loss_cls": 0.45267, "loss": 0.45267, "time": 0.47642} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.01438, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90812, "top5_acc": 0.99688, "loss_cls": 0.48979, "loss": 0.48979, "time": 0.30435} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.01436, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90062, "top5_acc": 0.9975, "loss_cls": 0.4966, "loss": 0.4966, "time": 0.48759} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.01434, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.9075, "top5_acc": 0.99562, "loss_cls": 0.49269, "loss": 0.49269, "time": 0.49008} +{"mode": "val", "epoch": 68, "iter": 533, "lr": 0.01433, "top1_acc": 0.83277, "top5_acc": 0.98463, "mean_class_accuracy": 0.78217} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.01431, "memory": 4083, "data_time": 0.1988, "top1_acc": 0.92375, "top5_acc": 0.9975, "loss_cls": 0.4074, "loss": 0.4074, "time": 0.80973} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.01429, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.925, "top5_acc": 0.99375, "loss_cls": 0.43523, "loss": 0.43523, "time": 0.49196} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.01427, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91062, "top5_acc": 0.99688, "loss_cls": 0.44851, "loss": 0.44851, "time": 0.48921} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.01425, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.91438, "top5_acc": 0.99875, "loss_cls": 0.44804, "loss": 0.44804, "time": 0.49227} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.01423, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.91688, "top5_acc": 0.99812, "loss_cls": 0.42426, "loss": 0.42426, "time": 0.4903} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.0142, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91688, "top5_acc": 0.99875, "loss_cls": 0.41522, "loss": 0.41522, "time": 0.49069} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.01418, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91688, "top5_acc": 0.99812, "loss_cls": 0.4382, "loss": 0.4382, "time": 0.49069} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.01416, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.90625, "top5_acc": 0.99438, "loss_cls": 0.5022, "loss": 0.5022, "time": 0.27017} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.01414, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90812, "top5_acc": 0.99812, "loss_cls": 0.45346, "loss": 0.45346, "time": 0.50907} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.01412, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90438, "top5_acc": 0.9975, "loss_cls": 0.50588, "loss": 0.50588, "time": 0.29519} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.0141, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90125, "top5_acc": 0.99438, "loss_cls": 0.50345, "loss": 0.50345, "time": 0.48923} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.01408, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91375, "top5_acc": 0.99875, "loss_cls": 0.43499, "loss": 0.43499, "time": 0.49273} +{"mode": "val", "epoch": 69, "iter": 533, "lr": 0.01407, "top1_acc": 0.85225, "top5_acc": 0.98944, "mean_class_accuracy": 0.80249} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.01405, "memory": 4083, "data_time": 0.19468, "top1_acc": 0.9325, "top5_acc": 1.0, "loss_cls": 0.38916, "loss": 0.38916, "time": 0.80726} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.01403, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92062, "top5_acc": 0.99812, "loss_cls": 0.42752, "loss": 0.42752, "time": 0.48719} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.01401, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.925, "top5_acc": 0.99812, "loss_cls": 0.43977, "loss": 0.43977, "time": 0.4867} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.01399, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.9175, "top5_acc": 0.99812, "loss_cls": 0.4257, "loss": 0.4257, "time": 0.49476} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.01397, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94312, "top5_acc": 0.99812, "loss_cls": 0.34686, "loss": 0.34686, "time": 0.49142} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.01395, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92625, "top5_acc": 0.99688, "loss_cls": 0.43529, "loss": 0.43529, "time": 0.48685} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.01392, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90562, "top5_acc": 0.99625, "loss_cls": 0.47871, "loss": 0.47871, "time": 0.49434} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.0139, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.92438, "top5_acc": 0.99875, "loss_cls": 0.39702, "loss": 0.39702, "time": 0.26993} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.01388, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.92125, "top5_acc": 0.99812, "loss_cls": 0.45736, "loss": 0.45736, "time": 0.51001} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.01386, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91, "top5_acc": 0.99938, "loss_cls": 0.46523, "loss": 0.46523, "time": 0.28197} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.01384, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91188, "top5_acc": 0.99688, "loss_cls": 0.43632, "loss": 0.43632, "time": 0.48887} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.01382, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91625, "top5_acc": 0.99688, "loss_cls": 0.45441, "loss": 0.45441, "time": 0.48951} +{"mode": "val", "epoch": 70, "iter": 533, "lr": 0.01381, "top1_acc": 0.85718, "top5_acc": 0.98873, "mean_class_accuracy": 0.82257} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.01379, "memory": 4083, "data_time": 0.19051, "top1_acc": 0.92312, "top5_acc": 0.9975, "loss_cls": 0.42228, "loss": 0.42228, "time": 0.80194} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.01377, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93438, "top5_acc": 0.99875, "loss_cls": 0.35244, "loss": 0.35244, "time": 0.49494} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.01375, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92562, "top5_acc": 0.99875, "loss_cls": 0.38929, "loss": 0.38929, "time": 0.49303} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.01373, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9325, "top5_acc": 0.99688, "loss_cls": 0.38676, "loss": 0.38676, "time": 0.4948} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.01371, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92375, "top5_acc": 0.99688, "loss_cls": 0.41323, "loss": 0.41323, "time": 0.48882} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.01368, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9175, "top5_acc": 0.99375, "loss_cls": 0.44843, "loss": 0.44843, "time": 0.49085} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.01366, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91125, "top5_acc": 0.99875, "loss_cls": 0.4947, "loss": 0.4947, "time": 0.49086} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.01364, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91938, "top5_acc": 0.99688, "loss_cls": 0.44637, "loss": 0.44637, "time": 0.29912} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.01362, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92125, "top5_acc": 0.99812, "loss_cls": 0.43775, "loss": 0.43775, "time": 0.51172} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.0136, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91938, "top5_acc": 0.99688, "loss_cls": 0.40839, "loss": 0.40839, "time": 0.2662} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.01358, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91812, "top5_acc": 0.99875, "loss_cls": 0.41847, "loss": 0.41847, "time": 0.49536} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.01356, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.90188, "top5_acc": 0.9975, "loss_cls": 0.48103, "loss": 0.48103, "time": 0.49189} +{"mode": "val", "epoch": 71, "iter": 533, "lr": 0.01355, "top1_acc": 0.86621, "top5_acc": 0.98733, "mean_class_accuracy": 0.82836} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.01353, "memory": 4083, "data_time": 0.19165, "top1_acc": 0.93438, "top5_acc": 0.99688, "loss_cls": 0.36541, "loss": 0.36541, "time": 0.80107} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.01351, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93062, "top5_acc": 0.9975, "loss_cls": 0.39089, "loss": 0.39089, "time": 0.48666} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.01349, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91188, "top5_acc": 0.99875, "loss_cls": 0.44377, "loss": 0.44377, "time": 0.493} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.01346, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90938, "top5_acc": 0.99625, "loss_cls": 0.46882, "loss": 0.46882, "time": 0.49319} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.01344, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.39803, "loss": 0.39803, "time": 0.49314} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.01342, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92688, "top5_acc": 0.99875, "loss_cls": 0.37471, "loss": 0.37471, "time": 0.49005} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.0134, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.92375, "top5_acc": 0.99625, "loss_cls": 0.40672, "loss": 0.40672, "time": 0.49167} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.01338, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.915, "top5_acc": 0.99812, "loss_cls": 0.4425, "loss": 0.4425, "time": 0.30944} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.01336, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91625, "top5_acc": 0.99875, "loss_cls": 0.43516, "loss": 0.43516, "time": 0.5101} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.01334, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9275, "top5_acc": 0.99688, "loss_cls": 0.40598, "loss": 0.40598, "time": 0.26066} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.01332, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92188, "top5_acc": 0.99812, "loss_cls": 0.42322, "loss": 0.42322, "time": 0.49047} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.0133, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.90375, "top5_acc": 0.99938, "loss_cls": 0.48373, "loss": 0.48373, "time": 0.49019} +{"mode": "val", "epoch": 72, "iter": 533, "lr": 0.01329, "top1_acc": 0.85812, "top5_acc": 0.98897, "mean_class_accuracy": 0.80257} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.01326, "memory": 4083, "data_time": 0.19379, "top1_acc": 0.93062, "top5_acc": 0.99875, "loss_cls": 0.38307, "loss": 0.38307, "time": 0.80927} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.01324, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9275, "top5_acc": 0.99938, "loss_cls": 0.41358, "loss": 0.41358, "time": 0.49482} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.01322, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92188, "top5_acc": 0.99938, "loss_cls": 0.37559, "loss": 0.37559, "time": 0.49188} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.0132, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.40896, "loss": 0.40896, "time": 0.48912} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.01318, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.93062, "top5_acc": 0.99938, "loss_cls": 0.36264, "loss": 0.36264, "time": 0.49104} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.01316, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9325, "top5_acc": 0.99812, "loss_cls": 0.36619, "loss": 0.36619, "time": 0.4905} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.01314, "memory": 4083, "data_time": 0.00062, "top1_acc": 0.92938, "top5_acc": 1.0, "loss_cls": 0.39433, "loss": 0.39433, "time": 0.49146} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.01312, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89938, "top5_acc": 0.99438, "loss_cls": 0.52726, "loss": 0.52726, "time": 0.3161} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.0131, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92375, "top5_acc": 0.99812, "loss_cls": 0.43294, "loss": 0.43294, "time": 0.51022} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.01308, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92062, "top5_acc": 0.99625, "loss_cls": 0.40309, "loss": 0.40309, "time": 0.25091} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.01306, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.42762, "loss": 0.42762, "time": 0.47845} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.01304, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91312, "top5_acc": 0.99562, "loss_cls": 0.48047, "loss": 0.48047, "time": 0.49325} +{"mode": "val", "epoch": 73, "iter": 533, "lr": 0.01302, "top1_acc": 0.86445, "top5_acc": 0.98697, "mean_class_accuracy": 0.83583} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.013, "memory": 4083, "data_time": 0.19414, "top1_acc": 0.93375, "top5_acc": 0.99938, "loss_cls": 0.35256, "loss": 0.35256, "time": 0.80793} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.01298, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93125, "top5_acc": 0.99875, "loss_cls": 0.37303, "loss": 0.37303, "time": 0.4895} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.01296, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91688, "top5_acc": 0.99938, "loss_cls": 0.42129, "loss": 0.42129, "time": 0.49093} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.01294, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91438, "top5_acc": 0.99688, "loss_cls": 0.46752, "loss": 0.46752, "time": 0.49094} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.01292, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92438, "top5_acc": 0.99688, "loss_cls": 0.39778, "loss": 0.39778, "time": 0.48943} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.0129, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91812, "top5_acc": 0.99875, "loss_cls": 0.4184, "loss": 0.4184, "time": 0.48937} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.01288, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.915, "top5_acc": 0.99625, "loss_cls": 0.44919, "loss": 0.44919, "time": 0.48995} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.01286, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92875, "top5_acc": 0.99938, "loss_cls": 0.37395, "loss": 0.37395, "time": 0.33171} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.01284, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91438, "top5_acc": 0.99875, "loss_cls": 0.41149, "loss": 0.41149, "time": 0.51081} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.01282, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.91062, "top5_acc": 0.99875, "loss_cls": 0.43328, "loss": 0.43328, "time": 0.25055} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.0128, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.41157, "loss": 0.41157, "time": 0.47716} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.01278, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91625, "top5_acc": 0.99688, "loss_cls": 0.45372, "loss": 0.45372, "time": 0.49253} +{"mode": "val", "epoch": 74, "iter": 533, "lr": 0.01276, "top1_acc": 0.8776, "top5_acc": 0.99073, "mean_class_accuracy": 0.83564} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.01274, "memory": 4083, "data_time": 0.19238, "top1_acc": 0.93438, "top5_acc": 0.99875, "loss_cls": 0.3782, "loss": 0.3782, "time": 0.79485} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.01272, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93625, "top5_acc": 0.99875, "loss_cls": 0.34717, "loss": 0.34717, "time": 0.49569} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.0127, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.34736, "loss": 0.34736, "time": 0.4951} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.01268, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92062, "top5_acc": 1.0, "loss_cls": 0.40472, "loss": 0.40472, "time": 0.4912} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.01266, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9225, "top5_acc": 0.9975, "loss_cls": 0.43478, "loss": 0.43478, "time": 0.49142} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.01264, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9175, "top5_acc": 0.99812, "loss_cls": 0.43755, "loss": 0.43755, "time": 0.4913} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.01262, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.91812, "top5_acc": 0.99875, "loss_cls": 0.43157, "loss": 0.43157, "time": 0.48925} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.0126, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9225, "top5_acc": 0.99812, "loss_cls": 0.42209, "loss": 0.42209, "time": 0.35864} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.01258, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92812, "top5_acc": 0.9975, "loss_cls": 0.37748, "loss": 0.37748, "time": 0.5095} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.01256, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91375, "top5_acc": 0.99438, "loss_cls": 0.45663, "loss": 0.45663, "time": 0.23906} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.01254, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92312, "top5_acc": 0.99812, "loss_cls": 0.40905, "loss": 0.40905, "time": 0.44542} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.01252, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9425, "top5_acc": 0.9975, "loss_cls": 0.35917, "loss": 0.35917, "time": 0.4952} +{"mode": "val", "epoch": 75, "iter": 533, "lr": 0.0125, "top1_acc": 0.79345, "top5_acc": 0.9824, "mean_class_accuracy": 0.75928} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.01248, "memory": 4083, "data_time": 0.19368, "top1_acc": 0.93312, "top5_acc": 0.99812, "loss_cls": 0.35471, "loss": 0.35471, "time": 0.80214} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.01246, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93688, "top5_acc": 1.0, "loss_cls": 0.34745, "loss": 0.34745, "time": 0.48533} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.01244, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91938, "top5_acc": 0.9975, "loss_cls": 0.41952, "loss": 0.41952, "time": 0.49018} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.01242, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94062, "top5_acc": 0.99688, "loss_cls": 0.34251, "loss": 0.34251, "time": 0.49425} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.0124, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93312, "top5_acc": 0.99938, "loss_cls": 0.35369, "loss": 0.35369, "time": 0.49419} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.01238, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93062, "top5_acc": 0.99875, "loss_cls": 0.39471, "loss": 0.39471, "time": 0.49302} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.01236, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9225, "top5_acc": 1.0, "loss_cls": 0.40685, "loss": 0.40685, "time": 0.4935} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.01234, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9225, "top5_acc": 0.99688, "loss_cls": 0.42168, "loss": 0.42168, "time": 0.38194} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.01232, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.92562, "top5_acc": 0.99875, "loss_cls": 0.37547, "loss": 0.37547, "time": 0.51015} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.0123, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.39111, "loss": 0.39111, "time": 0.23366} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.01228, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.39828, "loss": 0.39828, "time": 0.44005} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.01225, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9325, "top5_acc": 0.99812, "loss_cls": 0.39874, "loss": 0.39874, "time": 0.49014} +{"mode": "val", "epoch": 76, "iter": 533, "lr": 0.01224, "top1_acc": 0.85718, "top5_acc": 0.99108, "mean_class_accuracy": 0.82443} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.01222, "memory": 4083, "data_time": 0.18769, "top1_acc": 0.93562, "top5_acc": 0.99812, "loss_cls": 0.37586, "loss": 0.37586, "time": 0.79857} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0122, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93312, "top5_acc": 0.99812, "loss_cls": 0.37704, "loss": 0.37704, "time": 0.49134} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.01218, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93312, "top5_acc": 1.0, "loss_cls": 0.35063, "loss": 0.35063, "time": 0.49165} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.01216, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92688, "top5_acc": 0.9975, "loss_cls": 0.41366, "loss": 0.41366, "time": 0.49062} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.01214, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92875, "top5_acc": 0.99875, "loss_cls": 0.39082, "loss": 0.39082, "time": 0.49095} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.01212, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93688, "top5_acc": 0.9975, "loss_cls": 0.34846, "loss": 0.34846, "time": 0.48708} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.0121, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91875, "top5_acc": 0.99562, "loss_cls": 0.42169, "loss": 0.42169, "time": 0.49102} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.01207, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.9225, "top5_acc": 0.99875, "loss_cls": 0.42146, "loss": 0.42146, "time": 0.40608} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.01205, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91688, "top5_acc": 0.99812, "loss_cls": 0.42318, "loss": 0.42318, "time": 0.49915} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.01203, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.39372, "loss": 0.39372, "time": 0.23924} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.01201, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92375, "top5_acc": 0.99688, "loss_cls": 0.39966, "loss": 0.39966, "time": 0.42901} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.01199, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.43558, "loss": 0.43558, "time": 0.49586} +{"mode": "val", "epoch": 77, "iter": 533, "lr": 0.01198, "top1_acc": 0.86703, "top5_acc": 0.98944, "mean_class_accuracy": 0.83105} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.01196, "memory": 4083, "data_time": 0.19098, "top1_acc": 0.93562, "top5_acc": 1.0, "loss_cls": 0.37037, "loss": 0.37037, "time": 0.79767} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.01194, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93875, "top5_acc": 0.99812, "loss_cls": 0.37703, "loss": 0.37703, "time": 0.4934} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.01192, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93812, "top5_acc": 0.99875, "loss_cls": 0.36515, "loss": 0.36515, "time": 0.49238} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.0119, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.32784, "loss": 0.32784, "time": 0.48995} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.01187, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93125, "top5_acc": 0.9975, "loss_cls": 0.37906, "loss": 0.37906, "time": 0.49204} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.01185, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93375, "top5_acc": 0.99875, "loss_cls": 0.37469, "loss": 0.37469, "time": 0.49248} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.01183, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93062, "top5_acc": 0.99875, "loss_cls": 0.364, "loss": 0.364, "time": 0.49127} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.01181, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91125, "top5_acc": 0.9975, "loss_cls": 0.43673, "loss": 0.43673, "time": 0.40531} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.01179, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92625, "top5_acc": 0.99688, "loss_cls": 0.40804, "loss": 0.40804, "time": 0.50278} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.01177, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93062, "top5_acc": 0.99625, "loss_cls": 0.41429, "loss": 0.41429, "time": 0.23591} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.01175, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92125, "top5_acc": 0.99625, "loss_cls": 0.41185, "loss": 0.41185, "time": 0.42605} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.01173, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92875, "top5_acc": 0.99812, "loss_cls": 0.38746, "loss": 0.38746, "time": 0.49156} +{"mode": "val", "epoch": 78, "iter": 533, "lr": 0.01172, "top1_acc": 0.87595, "top5_acc": 0.98791, "mean_class_accuracy": 0.84478} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.01169, "memory": 4083, "data_time": 0.19442, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.3344, "loss": 0.3344, "time": 0.79295} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.01167, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.30145, "loss": 0.30145, "time": 0.48967} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.01165, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92688, "top5_acc": 0.9975, "loss_cls": 0.38355, "loss": 0.38355, "time": 0.49197} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.01163, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93375, "top5_acc": 0.99812, "loss_cls": 0.32908, "loss": 0.32908, "time": 0.49113} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.01161, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.935, "top5_acc": 0.9975, "loss_cls": 0.36338, "loss": 0.36338, "time": 0.49035} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.01159, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92375, "top5_acc": 0.99938, "loss_cls": 0.40125, "loss": 0.40125, "time": 0.48924} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.01157, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93375, "top5_acc": 0.99938, "loss_cls": 0.36877, "loss": 0.36877, "time": 0.49257} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.01155, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.32966, "loss": 0.32966, "time": 0.41428} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.01153, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92, "top5_acc": 0.99812, "loss_cls": 0.4291, "loss": 0.4291, "time": 0.47714} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.01151, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9325, "top5_acc": 0.99938, "loss_cls": 0.38178, "loss": 0.38178, "time": 0.26357} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.01149, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93625, "top5_acc": 0.99812, "loss_cls": 0.36561, "loss": 0.36561, "time": 0.43251} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.01147, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.935, "top5_acc": 0.99812, "loss_cls": 0.37198, "loss": 0.37198, "time": 0.49103} +{"mode": "val", "epoch": 79, "iter": 533, "lr": 0.01145, "top1_acc": 0.86269, "top5_acc": 0.98862, "mean_class_accuracy": 0.83226} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.01143, "memory": 4083, "data_time": 0.19428, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.38446, "loss": 0.38446, "time": 0.80548} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.01141, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93375, "top5_acc": 1.0, "loss_cls": 0.35864, "loss": 0.35864, "time": 0.49145} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.01139, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93375, "top5_acc": 0.99812, "loss_cls": 0.34117, "loss": 0.34117, "time": 0.49013} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.01137, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91938, "top5_acc": 0.99875, "loss_cls": 0.40657, "loss": 0.40657, "time": 0.48991} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.01135, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9425, "top5_acc": 0.99812, "loss_cls": 0.31658, "loss": 0.31658, "time": 0.49146} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.01133, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94562, "top5_acc": 0.9975, "loss_cls": 0.30633, "loss": 0.30633, "time": 0.49209} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.01131, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94688, "top5_acc": 0.99938, "loss_cls": 0.30085, "loss": 0.30085, "time": 0.49124} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.01129, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.33759, "loss": 0.33759, "time": 0.40735} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.01127, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.92375, "top5_acc": 0.9975, "loss_cls": 0.41012, "loss": 0.41012, "time": 0.48688} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.01125, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94062, "top5_acc": 1.0, "loss_cls": 0.31968, "loss": 0.31968, "time": 0.25036} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.01123, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94875, "top5_acc": 0.99812, "loss_cls": 0.3279, "loss": 0.3279, "time": 0.43868} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.01121, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93, "top5_acc": 0.9975, "loss_cls": 0.38455, "loss": 0.38455, "time": 0.49311} +{"mode": "val", "epoch": 80, "iter": 533, "lr": 0.01119, "top1_acc": 0.87736, "top5_acc": 0.99002, "mean_class_accuracy": 0.84166} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.01117, "memory": 4083, "data_time": 0.19126, "top1_acc": 0.94938, "top5_acc": 0.99875, "loss_cls": 0.29571, "loss": 0.29571, "time": 0.80688} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.01115, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93188, "top5_acc": 1.0, "loss_cls": 0.33367, "loss": 0.33367, "time": 0.49192} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.01113, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94875, "top5_acc": 0.99875, "loss_cls": 0.31641, "loss": 0.31641, "time": 0.48746} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.01111, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93938, "top5_acc": 0.99812, "loss_cls": 0.35602, "loss": 0.35602, "time": 0.48869} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.01109, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93562, "top5_acc": 1.0, "loss_cls": 0.3253, "loss": 0.3253, "time": 0.49004} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.01107, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9225, "top5_acc": 1.0, "loss_cls": 0.38967, "loss": 0.38967, "time": 0.48815} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.01105, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93688, "top5_acc": 0.99812, "loss_cls": 0.35667, "loss": 0.35667, "time": 0.48898} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.01103, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9275, "top5_acc": 0.9975, "loss_cls": 0.37457, "loss": 0.37457, "time": 0.39617} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.01101, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.38394, "loss": 0.38394, "time": 0.51014} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.01099, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.91812, "top5_acc": 0.99812, "loss_cls": 0.42162, "loss": 0.42162, "time": 0.2418} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.01097, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93, "top5_acc": 0.99875, "loss_cls": 0.39406, "loss": 0.39406, "time": 0.45026} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.01095, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92688, "top5_acc": 0.99812, "loss_cls": 0.3531, "loss": 0.3531, "time": 0.4891} +{"mode": "val", "epoch": 81, "iter": 533, "lr": 0.01093, "top1_acc": 0.8749, "top5_acc": 0.99096, "mean_class_accuracy": 0.83189} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.01091, "memory": 4083, "data_time": 0.18922, "top1_acc": 0.9375, "top5_acc": 0.99812, "loss_cls": 0.35094, "loss": 0.35094, "time": 0.79087} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.01089, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.35929, "loss": 0.35929, "time": 0.48907} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.01087, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93062, "top5_acc": 0.9975, "loss_cls": 0.38396, "loss": 0.38396, "time": 0.48983} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.01085, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.31408, "loss": 0.31408, "time": 0.49194} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.01083, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94312, "top5_acc": 0.99875, "loss_cls": 0.29671, "loss": 0.29671, "time": 0.49309} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.01081, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.35211, "loss": 0.35211, "time": 0.48965} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.01079, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93812, "top5_acc": 0.9975, "loss_cls": 0.32933, "loss": 0.32933, "time": 0.48922} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.01077, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.35899, "loss": 0.35899, "time": 0.3918} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.01075, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93438, "top5_acc": 0.99875, "loss_cls": 0.35309, "loss": 0.35309, "time": 0.50961} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.01073, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.35429, "loss": 0.35429, "time": 0.23029} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.01071, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93, "top5_acc": 0.99875, "loss_cls": 0.40415, "loss": 0.40415, "time": 0.44292} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.01069, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92625, "top5_acc": 1.0, "loss_cls": 0.37743, "loss": 0.37743, "time": 0.48953} +{"mode": "val", "epoch": 82, "iter": 533, "lr": 0.01067, "top1_acc": 0.87079, "top5_acc": 0.9885, "mean_class_accuracy": 0.8236} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.01065, "memory": 4083, "data_time": 0.18628, "top1_acc": 0.93875, "top5_acc": 0.9975, "loss_cls": 0.36931, "loss": 0.36931, "time": 0.79879} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.01063, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95, "top5_acc": 0.99875, "loss_cls": 0.32326, "loss": 0.32326, "time": 0.49286} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.01061, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94312, "top5_acc": 0.99875, "loss_cls": 0.32424, "loss": 0.32424, "time": 0.49356} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.01059, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92688, "top5_acc": 0.99938, "loss_cls": 0.37053, "loss": 0.37053, "time": 0.49117} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.01057, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93375, "top5_acc": 0.99812, "loss_cls": 0.3407, "loss": 0.3407, "time": 0.49517} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.01055, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.935, "top5_acc": 0.99938, "loss_cls": 0.32587, "loss": 0.32587, "time": 0.49147} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.01053, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93688, "top5_acc": 0.9975, "loss_cls": 0.33754, "loss": 0.33754, "time": 0.4906} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.01051, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94062, "top5_acc": 0.99938, "loss_cls": 0.32552, "loss": 0.32552, "time": 0.40068} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.01049, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94062, "top5_acc": 0.99688, "loss_cls": 0.33177, "loss": 0.33177, "time": 0.50952} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.01047, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92312, "top5_acc": 0.99875, "loss_cls": 0.38799, "loss": 0.38799, "time": 0.23248} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.01045, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.925, "top5_acc": 0.99938, "loss_cls": 0.3959, "loss": 0.3959, "time": 0.43949} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.01043, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92312, "top5_acc": 0.99938, "loss_cls": 0.3851, "loss": 0.3851, "time": 0.48843} +{"mode": "val", "epoch": 83, "iter": 533, "lr": 0.01042, "top1_acc": 0.87431, "top5_acc": 0.98956, "mean_class_accuracy": 0.84536} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.0104, "memory": 4083, "data_time": 0.18978, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.28755, "loss": 0.28755, "time": 0.81249} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.01038, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.27733, "loss": 0.27733, "time": 0.48775} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.01036, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.26134, "loss": 0.26134, "time": 0.48651} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.01034, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94438, "top5_acc": 0.99875, "loss_cls": 0.29569, "loss": 0.29569, "time": 0.48768} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.01031, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.29054, "loss": 0.29054, "time": 0.48839} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.01029, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95062, "top5_acc": 0.99875, "loss_cls": 0.29081, "loss": 0.29081, "time": 0.48793} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.01027, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.94188, "top5_acc": 0.99875, "loss_cls": 0.32639, "loss": 0.32639, "time": 0.48837} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.01025, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9425, "top5_acc": 0.99875, "loss_cls": 0.32141, "loss": 0.32141, "time": 0.37782} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.01023, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93812, "top5_acc": 0.99812, "loss_cls": 0.34051, "loss": 0.34051, "time": 0.50933} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.01021, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93375, "top5_acc": 0.99938, "loss_cls": 0.36248, "loss": 0.36248, "time": 0.24405} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.01019, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.94312, "top5_acc": 0.99812, "loss_cls": 0.31674, "loss": 0.31674, "time": 0.45702} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.01017, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93875, "top5_acc": 0.99812, "loss_cls": 0.3434, "loss": 0.3434, "time": 0.48984} +{"mode": "val", "epoch": 84, "iter": 533, "lr": 0.01016, "top1_acc": 0.88253, "top5_acc": 0.99085, "mean_class_accuracy": 0.84185} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.01014, "memory": 4083, "data_time": 0.18526, "top1_acc": 0.9425, "top5_acc": 1.0, "loss_cls": 0.31369, "loss": 0.31369, "time": 0.80186} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.01012, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94438, "top5_acc": 1.0, "loss_cls": 0.30635, "loss": 0.30635, "time": 0.48581} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.0101, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95125, "top5_acc": 0.99812, "loss_cls": 0.30144, "loss": 0.30144, "time": 0.48824} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.01008, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.28269, "loss": 0.28269, "time": 0.49322} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.01006, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94062, "top5_acc": 0.9975, "loss_cls": 0.33514, "loss": 0.33514, "time": 0.4934} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.01004, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.945, "top5_acc": 1.0, "loss_cls": 0.3302, "loss": 0.3302, "time": 0.49017} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.01002, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94, "top5_acc": 0.99938, "loss_cls": 0.31036, "loss": 0.31036, "time": 0.48654} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.01, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.32819, "loss": 0.32819, "time": 0.36874} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.00998, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.2785, "loss": 0.2785, "time": 0.50883} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.00996, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94312, "top5_acc": 0.99812, "loss_cls": 0.30578, "loss": 0.30578, "time": 0.24604} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.00994, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92875, "top5_acc": 0.99812, "loss_cls": 0.36912, "loss": 0.36912, "time": 0.46592} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.00992, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.92438, "top5_acc": 0.9975, "loss_cls": 0.4, "loss": 0.4, "time": 0.48814} +{"mode": "val", "epoch": 85, "iter": 533, "lr": 0.0099, "top1_acc": 0.87419, "top5_acc": 0.99014, "mean_class_accuracy": 0.83043} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.00988, "memory": 4083, "data_time": 0.18873, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.2886, "loss": 0.2886, "time": 0.8017} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.00986, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9475, "top5_acc": 1.0, "loss_cls": 0.31036, "loss": 0.31036, "time": 0.48657} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.00984, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.31545, "loss": 0.31545, "time": 0.48634} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.00982, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.2719, "loss": 0.2719, "time": 0.48953} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.0098, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94062, "top5_acc": 0.99812, "loss_cls": 0.3269, "loss": 0.3269, "time": 0.48663} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.00978, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93812, "top5_acc": 0.99938, "loss_cls": 0.34598, "loss": 0.34598, "time": 0.49177} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.00976, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93125, "top5_acc": 0.99938, "loss_cls": 0.36113, "loss": 0.36113, "time": 0.48826} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.00974, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.2916, "loss": 0.2916, "time": 0.35901} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.00972, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93812, "top5_acc": 0.9975, "loss_cls": 0.3583, "loss": 0.3583, "time": 0.51011} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.0097, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93625, "top5_acc": 0.99938, "loss_cls": 0.36358, "loss": 0.36358, "time": 0.24528} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.00968, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92812, "top5_acc": 0.99812, "loss_cls": 0.38049, "loss": 0.38049, "time": 0.46239} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.00966, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92688, "top5_acc": 0.99875, "loss_cls": 0.35812, "loss": 0.35812, "time": 0.49157} +{"mode": "val", "epoch": 86, "iter": 533, "lr": 0.00965, "top1_acc": 0.87701, "top5_acc": 0.99108, "mean_class_accuracy": 0.83182} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.00963, "memory": 4083, "data_time": 0.18843, "top1_acc": 0.94375, "top5_acc": 0.99812, "loss_cls": 0.32564, "loss": 0.32564, "time": 0.80575} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.00961, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.22794, "loss": 0.22794, "time": 0.48629} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.00959, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.27807, "loss": 0.27807, "time": 0.48992} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.00957, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.24909, "loss": 0.24909, "time": 0.4911} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.00955, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.24318, "loss": 0.24318, "time": 0.49528} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.00953, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94812, "top5_acc": 0.99875, "loss_cls": 0.2706, "loss": 0.2706, "time": 0.48789} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.00951, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94438, "top5_acc": 0.99688, "loss_cls": 0.30323, "loss": 0.30323, "time": 0.48908} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.00949, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.29822, "loss": 0.29822, "time": 0.36384} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.00947, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9575, "top5_acc": 0.99938, "loss_cls": 0.27447, "loss": 0.27447, "time": 0.51133} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.00945, "memory": 4083, "data_time": 0.00064, "top1_acc": 0.94812, "top5_acc": 1.0, "loss_cls": 0.31717, "loss": 0.31717, "time": 0.24939} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.00943, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.31322, "loss": 0.31322, "time": 0.473} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.00941, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.92812, "top5_acc": 1.0, "loss_cls": 0.37544, "loss": 0.37544, "time": 0.48697} +{"mode": "val", "epoch": 87, "iter": 533, "lr": 0.00939, "top1_acc": 0.88652, "top5_acc": 0.99249, "mean_class_accuracy": 0.85142} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.00937, "memory": 4083, "data_time": 0.18837, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.29781, "loss": 0.29781, "time": 0.80582} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.00935, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.25706, "loss": 0.25706, "time": 0.48698} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.00933, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96, "top5_acc": 0.99875, "loss_cls": 0.2398, "loss": 0.2398, "time": 0.49157} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.00931, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.27512, "loss": 0.27512, "time": 0.4901} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.00929, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.945, "top5_acc": 0.99938, "loss_cls": 0.29344, "loss": 0.29344, "time": 0.49398} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.00927, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93875, "top5_acc": 0.99812, "loss_cls": 0.28829, "loss": 0.28829, "time": 0.48786} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.00925, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.2987, "loss": 0.2987, "time": 0.48735} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.00923, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94062, "top5_acc": 1.0, "loss_cls": 0.30221, "loss": 0.30221, "time": 0.34155} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.00921, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.25854, "loss": 0.25854, "time": 0.50958} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.00919, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.935, "top5_acc": 0.99938, "loss_cls": 0.34635, "loss": 0.34635, "time": 0.24957} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.00917, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93188, "top5_acc": 0.99688, "loss_cls": 0.36678, "loss": 0.36678, "time": 0.4761} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.00915, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.30698, "loss": 0.30698, "time": 0.49188} +{"mode": "val", "epoch": 88, "iter": 533, "lr": 0.00914, "top1_acc": 0.879, "top5_acc": 0.98873, "mean_class_accuracy": 0.84489} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.00912, "memory": 4083, "data_time": 0.18502, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.24857, "loss": 0.24857, "time": 0.80809} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0091, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.26662, "loss": 0.26662, "time": 0.48857} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.00908, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.25569, "loss": 0.25569, "time": 0.49006} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.00906, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.31616, "loss": 0.31616, "time": 0.4906} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.00904, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.28908, "loss": 0.28908, "time": 0.49451} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.00902, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.29255, "loss": 0.29255, "time": 0.48859} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.009, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94688, "top5_acc": 0.99812, "loss_cls": 0.29136, "loss": 0.29136, "time": 0.49171} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.00898, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.95125, "top5_acc": 0.99812, "loss_cls": 0.28636, "loss": 0.28636, "time": 0.33476} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.00896, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.29989, "loss": 0.29989, "time": 0.5074} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.00894, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.26793, "loss": 0.26793, "time": 0.25063} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.00892, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.30802, "loss": 0.30802, "time": 0.48507} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.0089, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9575, "top5_acc": 0.99875, "loss_cls": 0.25718, "loss": 0.25718, "time": 0.48857} +{"mode": "val", "epoch": 89, "iter": 533, "lr": 0.00889, "top1_acc": 0.87994, "top5_acc": 0.98897, "mean_class_accuracy": 0.84001} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.00887, "memory": 4083, "data_time": 0.18537, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.29919, "loss": 0.29919, "time": 0.79313} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.00885, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.284, "loss": 0.284, "time": 0.49116} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.00883, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.26507, "loss": 0.26507, "time": 0.48944} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.00881, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.23957, "loss": 0.23957, "time": 0.49034} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.00879, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96688, "top5_acc": 0.99938, "loss_cls": 0.21018, "loss": 0.21018, "time": 0.4884} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.00877, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.24264, "loss": 0.24264, "time": 0.48781} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.00875, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94188, "top5_acc": 1.0, "loss_cls": 0.31815, "loss": 0.31815, "time": 0.48999} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.00873, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95438, "top5_acc": 0.99812, "loss_cls": 0.28939, "loss": 0.28939, "time": 0.33833} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.00871, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95688, "top5_acc": 0.99812, "loss_cls": 0.27947, "loss": 0.27947, "time": 0.50809} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.00869, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.31796, "loss": 0.31796, "time": 0.26055} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.00867, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93, "top5_acc": 0.99875, "loss_cls": 0.38683, "loss": 0.38683, "time": 0.48403} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.00865, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.30096, "loss": 0.30096, "time": 0.48778} +{"mode": "val", "epoch": 90, "iter": 533, "lr": 0.00864, "top1_acc": 0.88018, "top5_acc": 0.98967, "mean_class_accuracy": 0.86124} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.00862, "memory": 4083, "data_time": 0.18952, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.28838, "loss": 0.28838, "time": 0.79347} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0086, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.30513, "loss": 0.30513, "time": 0.4917} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.00858, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.25511, "loss": 0.25511, "time": 0.48726} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.00856, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94812, "top5_acc": 0.9975, "loss_cls": 0.28392, "loss": 0.28392, "time": 0.48653} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.00854, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.28383, "loss": 0.28383, "time": 0.48759} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.00852, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94, "top5_acc": 0.99875, "loss_cls": 0.31693, "loss": 0.31693, "time": 0.49011} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.0085, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.24338, "loss": 0.24338, "time": 0.48887} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.00848, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.23675, "loss": 0.23675, "time": 0.31765} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.00846, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9475, "top5_acc": 0.99875, "loss_cls": 0.28915, "loss": 0.28915, "time": 0.51126} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.00844, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.27836, "loss": 0.27836, "time": 0.29265} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.00842, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.28455, "loss": 0.28455, "time": 0.48918} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.0084, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93938, "top5_acc": 1.0, "loss_cls": 0.32889, "loss": 0.32889, "time": 0.4934} +{"mode": "val", "epoch": 91, "iter": 533, "lr": 0.00839, "top1_acc": 0.87478, "top5_acc": 0.98756, "mean_class_accuracy": 0.84309} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.00837, "memory": 4083, "data_time": 0.19013, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.25733, "loss": 0.25733, "time": 0.80685} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.00835, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96812, "top5_acc": 0.99938, "loss_cls": 0.22571, "loss": 0.22571, "time": 0.48855} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.00833, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.26676, "loss": 0.26676, "time": 0.48954} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.00831, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94188, "top5_acc": 0.99875, "loss_cls": 0.29835, "loss": 0.29835, "time": 0.48613} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.00829, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.22218, "loss": 0.22218, "time": 0.48758} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.00827, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.25562, "loss": 0.25562, "time": 0.48828} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.00825, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.289, "loss": 0.289, "time": 0.4931} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.00824, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94188, "top5_acc": 0.99938, "loss_cls": 0.28019, "loss": 0.28019, "time": 0.27539} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.00822, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.2748, "loss": 0.2748, "time": 0.50663} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.0082, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.23862, "loss": 0.23862, "time": 0.32475} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.00818, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.25826, "loss": 0.25826, "time": 0.49159} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.00816, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.21565, "loss": 0.21565, "time": 0.49191} +{"mode": "val", "epoch": 92, "iter": 533, "lr": 0.00814, "top1_acc": 0.88628, "top5_acc": 0.99108, "mean_class_accuracy": 0.85314} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.00812, "memory": 4083, "data_time": 0.18931, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.27331, "loss": 0.27331, "time": 0.79882} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.0081, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.20805, "loss": 0.20805, "time": 0.48729} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.00809, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.945, "top5_acc": 0.99938, "loss_cls": 0.27821, "loss": 0.27821, "time": 0.48884} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.00807, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.27866, "loss": 0.27866, "time": 0.48953} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.00805, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96, "top5_acc": 0.99938, "loss_cls": 0.23634, "loss": 0.23634, "time": 0.48682} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.00803, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96, "top5_acc": 0.99875, "loss_cls": 0.24481, "loss": 0.24481, "time": 0.48942} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.00801, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.25621, "loss": 0.25621, "time": 0.48646} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.00799, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.24695, "loss": 0.24695, "time": 0.29006} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.00797, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.27081, "loss": 0.27081, "time": 0.47103} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.00795, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.26745, "loss": 0.26745, "time": 0.33235} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.00793, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9575, "top5_acc": 0.99938, "loss_cls": 0.26653, "loss": 0.26653, "time": 0.48829} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.00791, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.26734, "loss": 0.26734, "time": 0.48778} +{"mode": "val", "epoch": 93, "iter": 533, "lr": 0.0079, "top1_acc": 0.88569, "top5_acc": 0.99225, "mean_class_accuracy": 0.86093} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.00788, "memory": 4083, "data_time": 0.19104, "top1_acc": 0.96625, "top5_acc": 0.99938, "loss_cls": 0.2226, "loss": 0.2226, "time": 0.80674} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.00786, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.22046, "loss": 0.22046, "time": 0.49208} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.00784, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95562, "top5_acc": 0.99875, "loss_cls": 0.2457, "loss": 0.2457, "time": 0.49139} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.00782, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.21336, "loss": 0.21336, "time": 0.49438} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.0078, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95, "top5_acc": 0.99875, "loss_cls": 0.28149, "loss": 0.28149, "time": 0.49099} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.00778, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94188, "top5_acc": 0.99938, "loss_cls": 0.30371, "loss": 0.30371, "time": 0.4906} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.00777, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.24542, "loss": 0.24542, "time": 0.48941} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.00775, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.21377, "loss": 0.21377, "time": 0.32806} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.00773, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.95688, "top5_acc": 0.99938, "loss_cls": 0.2431, "loss": 0.2431, "time": 0.41678} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.00771, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.26337, "loss": 0.26337, "time": 0.36819} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.00769, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.26621, "loss": 0.26621, "time": 0.48844} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.00767, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.23532, "loss": 0.23532, "time": 0.49197} +{"mode": "val", "epoch": 94, "iter": 533, "lr": 0.00766, "top1_acc": 0.89109, "top5_acc": 0.99108, "mean_class_accuracy": 0.8594} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.00764, "memory": 4083, "data_time": 0.18699, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.22171, "loss": 0.22171, "time": 0.78511} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.00762, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96875, "top5_acc": 0.99875, "loss_cls": 0.20259, "loss": 0.20259, "time": 0.48904} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.0076, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96125, "top5_acc": 0.99938, "loss_cls": 0.21412, "loss": 0.21412, "time": 0.48978} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.00758, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.23088, "loss": 0.23088, "time": 0.48904} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.00756, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.25596, "loss": 0.25596, "time": 0.48762} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.00754, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95562, "top5_acc": 1.0, "loss_cls": 0.23997, "loss": 0.23997, "time": 0.48672} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.00752, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.21966, "loss": 0.21966, "time": 0.47714} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.00751, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.24385, "loss": 0.24385, "time": 0.35588} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.00749, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95812, "top5_acc": 0.99812, "loss_cls": 0.24843, "loss": 0.24843, "time": 0.37882} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.00747, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.28934, "loss": 0.28934, "time": 0.38073} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.00745, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94062, "top5_acc": 0.99938, "loss_cls": 0.29915, "loss": 0.29915, "time": 0.48972} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.00743, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.24303, "loss": 0.24303, "time": 0.48981} +{"mode": "val", "epoch": 95, "iter": 533, "lr": 0.00742, "top1_acc": 0.86997, "top5_acc": 0.98779, "mean_class_accuracy": 0.84137} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.0074, "memory": 4083, "data_time": 0.19007, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.24025, "loss": 0.24025, "time": 0.79054} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.00738, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.17195, "loss": 0.17195, "time": 0.48682} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.00736, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.17286, "loss": 0.17286, "time": 0.49119} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.00734, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9725, "top5_acc": 0.99938, "loss_cls": 0.16892, "loss": 0.16892, "time": 0.48982} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.00732, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.20871, "loss": 0.20871, "time": 0.4907} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.0073, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.20294, "loss": 0.20294, "time": 0.48902} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.00729, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.17121, "loss": 0.17121, "time": 0.46125} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.00727, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.21235, "loss": 0.21235, "time": 0.37843} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.00725, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.26002, "loss": 0.26002, "time": 0.35582} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.00723, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.2143, "loss": 0.2143, "time": 0.38446} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.00721, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.23067, "loss": 0.23067, "time": 0.49009} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.00719, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.26419, "loss": 0.26419, "time": 0.48518} +{"mode": "val", "epoch": 96, "iter": 533, "lr": 0.00718, "top1_acc": 0.87396, "top5_acc": 0.98909, "mean_class_accuracy": 0.84161} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.00716, "memory": 4083, "data_time": 0.18622, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.24975, "loss": 0.24975, "time": 0.78339} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.00714, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.20061, "loss": 0.20061, "time": 0.48819} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.00712, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.23884, "loss": 0.23884, "time": 0.49086} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.0071, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.19143, "loss": 0.19143, "time": 0.48861} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.00709, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.19819, "loss": 0.19819, "time": 0.48926} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.00707, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.18734, "loss": 0.18734, "time": 0.49504} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.00705, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.18182, "loss": 0.18182, "time": 0.46377} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.00703, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.20299, "loss": 0.20299, "time": 0.37412} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.00701, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.19664, "loss": 0.19664, "time": 0.35999} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.00699, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.19816, "loss": 0.19816, "time": 0.38243} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.00698, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95938, "top5_acc": 0.9975, "loss_cls": 0.23319, "loss": 0.23319, "time": 0.48757} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.00696, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.21142, "loss": 0.21142, "time": 0.48676} +{"mode": "val", "epoch": 97, "iter": 533, "lr": 0.00694, "top1_acc": 0.88804, "top5_acc": 0.99002, "mean_class_accuracy": 0.86368} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.00692, "memory": 4083, "data_time": 0.18353, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.22288, "loss": 0.22288, "time": 0.77955} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.00691, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.17816, "loss": 0.17816, "time": 0.48997} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.00689, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.15541, "loss": 0.15541, "time": 0.48855} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.00687, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.16475, "loss": 0.16475, "time": 0.49016} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.00685, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.1771, "loss": 0.1771, "time": 0.49264} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.00683, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.16048, "loss": 0.16048, "time": 0.4924} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.00681, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.22593, "loss": 0.22593, "time": 0.46929} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.0068, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.23797, "loss": 0.23797, "time": 0.36144} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.00678, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.21552, "loss": 0.21552, "time": 0.37005} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.00676, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97, "top5_acc": 0.99875, "loss_cls": 0.1821, "loss": 0.1821, "time": 0.37851} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.00674, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.23308, "loss": 0.23308, "time": 0.49125} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.00672, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.24274, "loss": 0.24274, "time": 0.49065} +{"mode": "val", "epoch": 98, "iter": 533, "lr": 0.00671, "top1_acc": 0.89907, "top5_acc": 0.99355, "mean_class_accuracy": 0.86545} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.00669, "memory": 4083, "data_time": 0.19035, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.1821, "loss": 0.1821, "time": 0.78959} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.00667, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.17827, "loss": 0.17827, "time": 0.48585} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.00665, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.18463, "loss": 0.18463, "time": 0.48682} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.00664, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95812, "top5_acc": 0.99938, "loss_cls": 0.22341, "loss": 0.22341, "time": 0.49163} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.00662, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.18718, "loss": 0.18718, "time": 0.49432} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.0066, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.18828, "loss": 0.18828, "time": 0.48864} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.00658, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.16887, "loss": 0.16887, "time": 0.4571} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.00656, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.23534, "loss": 0.23534, "time": 0.39418} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.00655, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.23476, "loss": 0.23476, "time": 0.33881} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.00653, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.21418, "loss": 0.21418, "time": 0.37627} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.00651, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.19512, "loss": 0.19512, "time": 0.49059} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.00649, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9625, "top5_acc": 0.99875, "loss_cls": 0.20714, "loss": 0.20714, "time": 0.49058} +{"mode": "val", "epoch": 99, "iter": 533, "lr": 0.00648, "top1_acc": 0.89309, "top5_acc": 0.99284, "mean_class_accuracy": 0.86056} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.00646, "memory": 4083, "data_time": 0.18538, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.19143, "loss": 0.19143, "time": 0.7821} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.00644, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.96938, "top5_acc": 0.99938, "loss_cls": 0.19327, "loss": 0.19327, "time": 0.48714} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.00642, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.15644, "loss": 0.15644, "time": 0.48922} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.00641, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.16108, "loss": 0.16108, "time": 0.48956} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.00639, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.17393, "loss": 0.17393, "time": 0.4895} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.00637, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.17403, "loss": 0.17403, "time": 0.49248} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.00635, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.21204, "loss": 0.21204, "time": 0.48127} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.00634, "memory": 4083, "data_time": 0.00067, "top1_acc": 0.96688, "top5_acc": 0.99938, "loss_cls": 0.20815, "loss": 0.20815, "time": 0.34312} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.00632, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.24337, "loss": 0.24337, "time": 0.39061} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.0063, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.18469, "loss": 0.18469, "time": 0.37425} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.00628, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.17218, "loss": 0.17218, "time": 0.49022} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.00626, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.22597, "loss": 0.22597, "time": 0.48656} +{"mode": "val", "epoch": 100, "iter": 533, "lr": 0.00625, "top1_acc": 0.88851, "top5_acc": 0.99179, "mean_class_accuracy": 0.85186} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.00623, "memory": 4083, "data_time": 0.18625, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.18075, "loss": 0.18075, "time": 0.77786} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.00621, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.16671, "loss": 0.16671, "time": 0.48874} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.0062, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.20986, "loss": 0.20986, "time": 0.4895} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.00618, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.15528, "loss": 0.15528, "time": 0.49356} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.00616, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9775, "top5_acc": 0.99875, "loss_cls": 0.1539, "loss": 0.1539, "time": 0.48719} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.00614, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.20571, "loss": 0.20571, "time": 0.48932} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.00613, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.20805, "loss": 0.20805, "time": 0.49024} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.00611, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95375, "top5_acc": 0.99875, "loss_cls": 0.24724, "loss": 0.24724, "time": 0.32917} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.00609, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.20251, "loss": 0.20251, "time": 0.40489} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.00607, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.17023, "loss": 0.17023, "time": 0.3694} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.00606, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.20065, "loss": 0.20065, "time": 0.48663} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.00604, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94938, "top5_acc": 0.99875, "loss_cls": 0.27003, "loss": 0.27003, "time": 0.48938} +{"mode": "val", "epoch": 101, "iter": 533, "lr": 0.00602, "top1_acc": 0.89567, "top5_acc": 0.99308, "mean_class_accuracy": 0.8684} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.00601, "memory": 4083, "data_time": 0.18525, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.14748, "loss": 0.14748, "time": 0.79772} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.00599, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.17106, "loss": 0.17106, "time": 0.48417} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.00597, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.15443, "loss": 0.15443, "time": 0.48887} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.00596, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96625, "top5_acc": 0.99938, "loss_cls": 0.18375, "loss": 0.18375, "time": 0.4887} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.00594, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.17495, "loss": 0.17495, "time": 0.48789} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.00592, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.21313, "loss": 0.21313, "time": 0.49079} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.0059, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.21777, "loss": 0.21777, "time": 0.48156} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.00589, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.19567, "loss": 0.19567, "time": 0.33032} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.00587, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.1938, "loss": 0.1938, "time": 0.40326} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.00585, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96875, "top5_acc": 0.99938, "loss_cls": 0.18477, "loss": 0.18477, "time": 0.37078} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.00583, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.22749, "loss": 0.22749, "time": 0.48659} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.00582, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.19747, "loss": 0.19747, "time": 0.49261} +{"mode": "val", "epoch": 102, "iter": 533, "lr": 0.0058, "top1_acc": 0.88569, "top5_acc": 0.98932, "mean_class_accuracy": 0.86085} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.00579, "memory": 4083, "data_time": 0.18005, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.18032, "loss": 0.18032, "time": 0.79366} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.00577, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.13911, "loss": 0.13911, "time": 0.48835} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.00575, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.14523, "loss": 0.14523, "time": 0.49005} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.00573, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.1515, "loss": 0.1515, "time": 0.48976} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.00572, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.16261, "loss": 0.16261, "time": 0.48839} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.0057, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.1426, "loss": 0.1426, "time": 0.49358} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.00568, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.17071, "loss": 0.17071, "time": 0.47553} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.00566, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.18354, "loss": 0.18354, "time": 0.35234} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.00565, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.16863, "loss": 0.16863, "time": 0.37819} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.00563, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.17565, "loss": 0.17565, "time": 0.37567} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.00561, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.18959, "loss": 0.18959, "time": 0.48519} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.0056, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.16043, "loss": 0.16043, "time": 0.48695} +{"mode": "val", "epoch": 103, "iter": 533, "lr": 0.00558, "top1_acc": 0.89485, "top5_acc": 0.99214, "mean_class_accuracy": 0.87158} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.00557, "memory": 4083, "data_time": 0.18265, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.15172, "loss": 0.15172, "time": 0.77881} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.00555, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.20833, "loss": 0.20833, "time": 0.48523} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.00553, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.18093, "loss": 0.18093, "time": 0.49061} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.00551, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.17184, "loss": 0.17184, "time": 0.48709} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.0055, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.13995, "loss": 0.13995, "time": 0.49026} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.00548, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.17652, "loss": 0.17652, "time": 0.49502} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.00546, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.97625, "top5_acc": 0.99938, "loss_cls": 0.16954, "loss": 0.16954, "time": 0.47245} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.00545, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.11586, "loss": 0.11586, "time": 0.35884} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.00543, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.16499, "loss": 0.16499, "time": 0.37368} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.00541, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.17805, "loss": 0.17805, "time": 0.37082} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.0054, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.97875, "top5_acc": 0.99938, "loss_cls": 0.14123, "loss": 0.14123, "time": 0.48659} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.00538, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.16623, "loss": 0.16623, "time": 0.48268} +{"mode": "val", "epoch": 104, "iter": 533, "lr": 0.00537, "top1_acc": 0.89673, "top5_acc": 0.99073, "mean_class_accuracy": 0.86415} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.00535, "memory": 4083, "data_time": 0.18841, "top1_acc": 0.9725, "top5_acc": 0.99938, "loss_cls": 0.15946, "loss": 0.15946, "time": 0.79249} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.00533, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.15477, "loss": 0.15477, "time": 0.4901} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.00532, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.13643, "loss": 0.13643, "time": 0.48634} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.0053, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.15548, "loss": 0.15548, "time": 0.48665} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.00528, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.15222, "loss": 0.15222, "time": 0.49303} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.00527, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97312, "top5_acc": 0.99938, "loss_cls": 0.16847, "loss": 0.16847, "time": 0.48669} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.00525, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.14165, "loss": 0.14165, "time": 0.47317} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.00523, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96812, "top5_acc": 0.99938, "loss_cls": 0.19862, "loss": 0.19862, "time": 0.37561} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.00522, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.17877, "loss": 0.17877, "time": 0.35756} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.0052, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.17714, "loss": 0.17714, "time": 0.3791} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.00518, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.15485, "loss": 0.15485, "time": 0.48912} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.00517, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.15809, "loss": 0.15809, "time": 0.491} +{"mode": "val", "epoch": 105, "iter": 533, "lr": 0.00515, "top1_acc": 0.90259, "top5_acc": 0.99308, "mean_class_accuracy": 0.87731} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.00514, "memory": 4083, "data_time": 0.18783, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.12137, "loss": 0.12137, "time": 0.80286} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.00512, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.985, "top5_acc": 0.99938, "loss_cls": 0.12955, "loss": 0.12955, "time": 0.49115} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.0051, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.14839, "loss": 0.14839, "time": 0.48953} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.00509, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.16194, "loss": 0.16194, "time": 0.49166} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.00507, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.14385, "loss": 0.14385, "time": 0.48999} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.00505, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.13038, "loss": 0.13038, "time": 0.48833} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.00504, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.11259, "loss": 0.11259, "time": 0.44734} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.00502, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97062, "top5_acc": 0.99938, "loss_cls": 0.16806, "loss": 0.16806, "time": 0.41064} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.005, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.14877, "loss": 0.14877, "time": 0.32039} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.00499, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.14659, "loss": 0.14659, "time": 0.3971} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.00497, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.16276, "loss": 0.16276, "time": 0.4864} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.00496, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.15252, "loss": 0.15252, "time": 0.49074} +{"mode": "val", "epoch": 106, "iter": 533, "lr": 0.00494, "top1_acc": 0.91198, "top5_acc": 0.9939, "mean_class_accuracy": 0.87873} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.00493, "memory": 4083, "data_time": 0.18331, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.14953, "loss": 0.14953, "time": 0.79983} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.00491, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.10645, "loss": 0.10645, "time": 0.48782} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.00489, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.10646, "loss": 0.10646, "time": 0.48423} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.00488, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10057, "loss": 0.10057, "time": 0.48746} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.00486, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.1147, "loss": 0.1147, "time": 0.48933} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.00485, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.13466, "loss": 0.13466, "time": 0.48878} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.00483, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.10564, "loss": 0.10564, "time": 0.44999} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.00481, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.15788, "loss": 0.15788, "time": 0.41446} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.0048, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.17313, "loss": 0.17313, "time": 0.31715} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.00478, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.14315, "loss": 0.14315, "time": 0.39737} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.00476, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.15444, "loss": 0.15444, "time": 0.491} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.00475, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11856, "loss": 0.11856, "time": 0.48666} +{"mode": "val", "epoch": 107, "iter": 533, "lr": 0.00474, "top1_acc": 0.89227, "top5_acc": 0.98991, "mean_class_accuracy": 0.86027} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.00472, "memory": 4083, "data_time": 0.18296, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.1285, "loss": 0.1285, "time": 0.78798} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0047, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.10182, "loss": 0.10182, "time": 0.49039} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.00469, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.13976, "loss": 0.13976, "time": 0.4927} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.00467, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13407, "loss": 0.13407, "time": 0.48885} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.00466, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14592, "loss": 0.14592, "time": 0.4895} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.00464, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.13709, "loss": 0.13709, "time": 0.49008} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.00462, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10495, "loss": 0.10495, "time": 0.44852} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.00461, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.1318, "loss": 0.1318, "time": 0.42352} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.00459, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.11107, "loss": 0.11107, "time": 0.30594} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.00458, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.1495, "loss": 0.1495, "time": 0.38463} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.00456, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.16346, "loss": 0.16346, "time": 0.4898} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.00455, "memory": 4083, "data_time": 0.00068, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.13939, "loss": 0.13939, "time": 0.49391} +{"mode": "val", "epoch": 108, "iter": 533, "lr": 0.00453, "top1_acc": 0.89919, "top5_acc": 0.99296, "mean_class_accuracy": 0.87131} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.00452, "memory": 4083, "data_time": 0.18166, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.13434, "loss": 0.13434, "time": 0.79813} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.0045, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.15063, "loss": 0.15063, "time": 0.48969} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.00449, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.13227, "loss": 0.13227, "time": 0.4891} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.00447, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.1437, "loss": 0.1437, "time": 0.4887} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.00445, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.11958, "loss": 0.11958, "time": 0.48997} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.00444, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9825, "top5_acc": 0.99938, "loss_cls": 0.12737, "loss": 0.12737, "time": 0.49024} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.00442, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.10877, "loss": 0.10877, "time": 0.45306} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.00441, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.16119, "loss": 0.16119, "time": 0.40109} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.00439, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.14402, "loss": 0.14402, "time": 0.32975} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.00438, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98, "top5_acc": 0.99938, "loss_cls": 0.13101, "loss": 0.13101, "time": 0.38692} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.00436, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.10865, "loss": 0.10865, "time": 0.489} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.00434, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10803, "loss": 0.10803, "time": 0.49074} +{"mode": "val", "epoch": 109, "iter": 533, "lr": 0.00433, "top1_acc": 0.90424, "top5_acc": 0.99284, "mean_class_accuracy": 0.87569} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.00432, "memory": 4083, "data_time": 0.18785, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08188, "loss": 0.08188, "time": 0.80389} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.0043, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07984, "loss": 0.07984, "time": 0.49241} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.00429, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10159, "loss": 0.10159, "time": 0.48781} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.00427, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.15178, "loss": 0.15178, "time": 0.49164} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.00426, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.106, "loss": 0.106, "time": 0.49052} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.00424, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.15882, "loss": 0.15882, "time": 0.48969} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.00422, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.12758, "loss": 0.12758, "time": 0.45296} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.00421, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.12787, "loss": 0.12787, "time": 0.40325} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.00419, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.13398, "loss": 0.13398, "time": 0.32735} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.00418, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.13488, "loss": 0.13488, "time": 0.39894} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.00416, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.16594, "loss": 0.16594, "time": 0.48656} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.00415, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.13065, "loss": 0.13065, "time": 0.49105} +{"mode": "val", "epoch": 110, "iter": 533, "lr": 0.00414, "top1_acc": 0.90435, "top5_acc": 0.99319, "mean_class_accuracy": 0.88146} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.00412, "memory": 4083, "data_time": 0.18852, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.1037, "loss": 0.1037, "time": 0.79642} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.00411, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08577, "loss": 0.08577, "time": 0.48792} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.00409, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.09702, "loss": 0.09702, "time": 0.48962} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.00408, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.08949, "loss": 0.08949, "time": 0.48902} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.00406, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.08185, "loss": 0.08185, "time": 0.48951} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.00405, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.10741, "loss": 0.10741, "time": 0.48876} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.00403, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.09605, "loss": 0.09605, "time": 0.43418} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.00402, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.10507, "loss": 0.10507, "time": 0.42131} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.004, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.11446, "loss": 0.11446, "time": 0.31062} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.00399, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98562, "top5_acc": 0.99938, "loss_cls": 0.10316, "loss": 0.10316, "time": 0.4031} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.00397, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.09489, "loss": 0.09489, "time": 0.48669} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.00396, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.09828, "loss": 0.09828, "time": 0.48883} +{"mode": "val", "epoch": 111, "iter": 533, "lr": 0.00394, "top1_acc": 0.89543, "top5_acc": 0.99132, "mean_class_accuracy": 0.86119} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.00393, "memory": 4083, "data_time": 0.18313, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.12653, "loss": 0.12653, "time": 0.7925} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.00391, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.11086, "loss": 0.11086, "time": 0.48947} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.0039, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10472, "loss": 0.10472, "time": 0.48964} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.00388, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.12218, "loss": 0.12218, "time": 0.48759} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.00387, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08817, "loss": 0.08817, "time": 0.49313} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.00385, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10821, "loss": 0.10821, "time": 0.49145} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.00384, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.11029, "loss": 0.11029, "time": 0.44183} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.00382, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13326, "loss": 0.13326, "time": 0.45234} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.00381, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08855, "loss": 0.08855, "time": 0.28435} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.0038, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98438, "top5_acc": 0.99938, "loss_cls": 0.11325, "loss": 0.11325, "time": 0.41431} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.00378, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11316, "loss": 0.11316, "time": 0.4881} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.00377, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.09947, "loss": 0.09947, "time": 0.48663} +{"mode": "val", "epoch": 112, "iter": 533, "lr": 0.00375, "top1_acc": 0.9033, "top5_acc": 0.99155, "mean_class_accuracy": 0.87159} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.00374, "memory": 4083, "data_time": 0.18443, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.11112, "loss": 0.11112, "time": 0.79298} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.00373, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.12937, "loss": 0.12937, "time": 0.48898} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.00371, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.08187, "loss": 0.08187, "time": 0.48913} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.0037, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.10641, "loss": 0.10641, "time": 0.48995} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.00368, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.13906, "loss": 0.13906, "time": 0.49318} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.00367, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.10554, "loss": 0.10554, "time": 0.49178} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.00365, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08848, "loss": 0.08848, "time": 0.43593} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.00364, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08128, "loss": 0.08128, "time": 0.43404} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.00362, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.09086, "loss": 0.09086, "time": 0.29785} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.00361, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11476, "loss": 0.11476, "time": 0.3939} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0036, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.98625, "top5_acc": 0.99938, "loss_cls": 0.10777, "loss": 0.10777, "time": 0.48666} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.00358, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.12423, "loss": 0.12423, "time": 0.48812} +{"mode": "val", "epoch": 113, "iter": 533, "lr": 0.00357, "top1_acc": 0.90611, "top5_acc": 0.99225, "mean_class_accuracy": 0.87992} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.00355, "memory": 4083, "data_time": 0.18299, "top1_acc": 0.98438, "top5_acc": 0.99938, "loss_cls": 0.10995, "loss": 0.10995, "time": 0.79222} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.00354, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08432, "loss": 0.08432, "time": 0.4879} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.00353, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.12759, "loss": 0.12759, "time": 0.49015} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.00351, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.094, "loss": 0.094, "time": 0.48857} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.0035, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.07843, "loss": 0.07843, "time": 0.49134} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.00348, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08221, "loss": 0.08221, "time": 0.48782} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.00347, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.09047, "loss": 0.09047, "time": 0.45211} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.00346, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.07852, "loss": 0.07852, "time": 0.39698} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.00344, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07637, "loss": 0.07637, "time": 0.3353} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.00343, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08973, "loss": 0.08973, "time": 0.38107} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.00341, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.0884, "loss": 0.0884, "time": 0.48608} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.0034, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.08861, "loss": 0.08861, "time": 0.48703} +{"mode": "val", "epoch": 114, "iter": 533, "lr": 0.00339, "top1_acc": 0.90964, "top5_acc": 0.9919, "mean_class_accuracy": 0.88492} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.00337, "memory": 4083, "data_time": 0.18305, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.07023, "loss": 0.07023, "time": 0.78126} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.00336, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.07437, "loss": 0.07437, "time": 0.48622} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.00335, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.08647, "loss": 0.08647, "time": 0.49094} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.00333, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07367, "loss": 0.07367, "time": 0.4884} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.00332, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06423, "loss": 0.06423, "time": 0.48775} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.0033, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07237, "loss": 0.07237, "time": 0.49144} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.00329, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08303, "loss": 0.08303, "time": 0.48099} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.00328, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10066, "loss": 0.10066, "time": 0.3418} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.00326, "memory": 4083, "data_time": 0.00085, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.10223, "loss": 0.10223, "time": 0.39286} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.00325, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09495, "loss": 0.09495, "time": 0.36355} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.00324, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08769, "loss": 0.08769, "time": 0.48563} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.00322, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.09251, "loss": 0.09251, "time": 0.48798} +{"mode": "val", "epoch": 115, "iter": 533, "lr": 0.00321, "top1_acc": 0.90987, "top5_acc": 0.99249, "mean_class_accuracy": 0.889} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.0032, "memory": 4083, "data_time": 0.18686, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.0682, "loss": 0.0682, "time": 0.78739} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.00318, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05053, "loss": 0.05053, "time": 0.48938} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.00317, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04638, "loss": 0.04638, "time": 0.49034} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.00316, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.04925, "loss": 0.04925, "time": 0.49265} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.00314, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.05772, "loss": 0.05772, "time": 0.48876} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.00313, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07057, "loss": 0.07057, "time": 0.48939} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.00312, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06274, "loss": 0.06274, "time": 0.48079} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.0031, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.08505, "loss": 0.08505, "time": 0.35104} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.00309, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07399, "loss": 0.07399, "time": 0.38975} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.00308, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06937, "loss": 0.06937, "time": 0.37848} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.00306, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.07686, "loss": 0.07686, "time": 0.48785} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.00305, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08274, "loss": 0.08274, "time": 0.48575} +{"mode": "val", "epoch": 116, "iter": 533, "lr": 0.00304, "top1_acc": 0.91269, "top5_acc": 0.99225, "mean_class_accuracy": 0.8842} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.00302, "memory": 4083, "data_time": 0.18644, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06094, "loss": 0.06094, "time": 0.79525} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.00301, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.08386, "loss": 0.08386, "time": 0.4888} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.003, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07182, "loss": 0.07182, "time": 0.49075} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.00298, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98562, "top5_acc": 0.99938, "loss_cls": 0.08806, "loss": 0.08806, "time": 0.49045} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.00297, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06272, "loss": 0.06272, "time": 0.48825} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.00296, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.05093, "loss": 0.05093, "time": 0.48748} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.00294, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99188, "top5_acc": 0.99938, "loss_cls": 0.06728, "loss": 0.06728, "time": 0.45908} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.00293, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.06009, "loss": 0.06009, "time": 0.41658} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.00292, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.07721, "loss": 0.07721, "time": 0.32142} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.00291, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05847, "loss": 0.05847, "time": 0.40567} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.00289, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04343, "loss": 0.04343, "time": 0.48707} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.00288, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.09319, "loss": 0.09319, "time": 0.48404} +{"mode": "val", "epoch": 117, "iter": 533, "lr": 0.00287, "top1_acc": 0.91233, "top5_acc": 0.99261, "mean_class_accuracy": 0.8771} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.00286, "memory": 4083, "data_time": 0.17834, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06829, "loss": 0.06829, "time": 0.78248} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.00284, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.05093, "loss": 0.05093, "time": 0.48698} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.00283, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.05411, "loss": 0.05411, "time": 0.49097} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.00282, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05553, "loss": 0.05553, "time": 0.49046} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.0028, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07314, "loss": 0.07314, "time": 0.4882} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.00279, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05701, "loss": 0.05701, "time": 0.48638} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.00278, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05403, "loss": 0.05403, "time": 0.44417} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.00277, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06096, "loss": 0.06096, "time": 0.41106} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.00275, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.06173, "loss": 0.06173, "time": 0.32929} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.00274, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99438, "top5_acc": 0.99938, "loss_cls": 0.06694, "loss": 0.06694, "time": 0.40745} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.00273, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06873, "loss": 0.06873, "time": 0.49051} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.00271, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05902, "loss": 0.05902, "time": 0.48584} +{"mode": "val", "epoch": 118, "iter": 533, "lr": 0.0027, "top1_acc": 0.91515, "top5_acc": 0.99261, "mean_class_accuracy": 0.8862} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.00269, "memory": 4083, "data_time": 0.18399, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.05017, "loss": 0.05017, "time": 0.79717} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.00268, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05531, "loss": 0.05531, "time": 0.4885} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.00267, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.04592, "loss": 0.04592, "time": 0.49202} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.00265, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.04306, "loss": 0.04306, "time": 0.48625} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.00264, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03835, "loss": 0.03835, "time": 0.48663} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.00263, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.07693, "loss": 0.07693, "time": 0.48884} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.00262, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07612, "loss": 0.07612, "time": 0.43839} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.0026, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.06949, "loss": 0.06949, "time": 0.43553} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.00259, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.08121, "loss": 0.08121, "time": 0.29365} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.00258, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05212, "loss": 0.05212, "time": 0.42894} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.00257, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.07402, "loss": 0.07402, "time": 0.48728} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.00255, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.0695, "loss": 0.0695, "time": 0.48696} +{"mode": "val", "epoch": 119, "iter": 533, "lr": 0.00254, "top1_acc": 0.90975, "top5_acc": 0.99284, "mean_class_accuracy": 0.88081} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.00253, "memory": 4083, "data_time": 0.18563, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.0702, "loss": 0.0702, "time": 0.80126} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.00252, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.0519, "loss": 0.0519, "time": 0.49247} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.00251, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04851, "loss": 0.04851, "time": 0.48875} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.00249, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06136, "loss": 0.06136, "time": 0.49252} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.00248, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04115, "loss": 0.04115, "time": 0.49023} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.00247, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04532, "loss": 0.04532, "time": 0.48871} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.00246, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06328, "loss": 0.06328, "time": 0.41093} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.00245, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04733, "loss": 0.04733, "time": 0.50336} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.00243, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.04152, "loss": 0.04152, "time": 0.23656} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.00242, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05088, "loss": 0.05088, "time": 0.43719} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00241, "memory": 4083, "data_time": 0.00063, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05647, "loss": 0.05647, "time": 0.48718} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.0024, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.04083, "loss": 0.04083, "time": 0.4884} +{"mode": "val", "epoch": 120, "iter": 533, "lr": 0.00239, "top1_acc": 0.91867, "top5_acc": 0.99343, "mean_class_accuracy": 0.89345} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00238, "memory": 4083, "data_time": 0.18526, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05334, "loss": 0.05334, "time": 0.80777} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00236, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.0344, "loss": 0.0344, "time": 0.48756} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.00235, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.04311, "loss": 0.04311, "time": 0.4912} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00234, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.05053, "loss": 0.05053, "time": 0.48812} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00233, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04133, "loss": 0.04133, "time": 0.48744} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00232, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04143, "loss": 0.04143, "time": 0.48878} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.0023, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.04849, "loss": 0.04849, "time": 0.37925} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00229, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03605, "loss": 0.03605, "time": 0.50884} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.00228, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03934, "loss": 0.03934, "time": 0.24012} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00227, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05243, "loss": 0.05243, "time": 0.45827} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00226, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05891, "loss": 0.05891, "time": 0.48904} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00225, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.0611, "loss": 0.0611, "time": 0.48837} +{"mode": "val", "epoch": 121, "iter": 533, "lr": 0.00224, "top1_acc": 0.92231, "top5_acc": 0.99413, "mean_class_accuracy": 0.89795} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00222, "memory": 4083, "data_time": 0.1818, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03745, "loss": 0.03745, "time": 0.78999} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00221, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04657, "loss": 0.04657, "time": 0.4886} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.0022, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.031, "loss": 0.031, "time": 0.48836} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00219, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04671, "loss": 0.04671, "time": 0.48684} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00218, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05151, "loss": 0.05151, "time": 0.48864} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00217, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03641, "loss": 0.03641, "time": 0.48941} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00215, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02871, "loss": 0.02871, "time": 0.35485} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00214, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02968, "loss": 0.02968, "time": 0.50957} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.00213, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02928, "loss": 0.02928, "time": 0.24639} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00212, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03773, "loss": 0.03773, "time": 0.46714} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00211, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03317, "loss": 0.03317, "time": 0.48521} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.0021, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03891, "loss": 0.03891, "time": 0.48818} +{"mode": "val", "epoch": 122, "iter": 533, "lr": 0.00209, "top1_acc": 0.91996, "top5_acc": 0.99413, "mean_class_accuracy": 0.8966} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00208, "memory": 4083, "data_time": 0.185, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03138, "loss": 0.03138, "time": 0.79207} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00207, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03512, "loss": 0.03512, "time": 0.48785} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00205, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03304, "loss": 0.03304, "time": 0.49026} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00204, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02933, "loss": 0.02933, "time": 0.48854} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00203, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04124, "loss": 0.04124, "time": 0.49081} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00202, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03004, "loss": 0.03004, "time": 0.48985} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00201, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04157, "loss": 0.04157, "time": 0.35652} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.002, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04062, "loss": 0.04062, "time": 0.50856} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00199, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06342, "loss": 0.06342, "time": 0.24653} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.00198, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04403, "loss": 0.04403, "time": 0.46605} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00197, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04207, "loss": 0.04207, "time": 0.48573} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00195, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03116, "loss": 0.03116, "time": 0.48447} +{"mode": "val", "epoch": 123, "iter": 533, "lr": 0.00195, "top1_acc": 0.91985, "top5_acc": 0.99272, "mean_class_accuracy": 0.89473} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00194, "memory": 4083, "data_time": 0.18673, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03299, "loss": 0.03299, "time": 0.7929} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00192, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04016, "loss": 0.04016, "time": 0.48703} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00191, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02861, "loss": 0.02861, "time": 0.48897} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.0019, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04025, "loss": 0.04025, "time": 0.4905} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00189, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02921, "loss": 0.02921, "time": 0.48895} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00188, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03363, "loss": 0.03363, "time": 0.48575} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00187, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03548, "loss": 0.03548, "time": 0.34623} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00186, "memory": 4083, "data_time": 0.0004, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02375, "loss": 0.02375, "time": 0.50981} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00185, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02365, "loss": 0.02365, "time": 0.25033} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00184, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03115, "loss": 0.03115, "time": 0.48118} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00183, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02906, "loss": 0.02906, "time": 0.4895} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.00182, "memory": 4083, "data_time": 0.0007, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03023, "loss": 0.03023, "time": 0.48873} +{"mode": "val", "epoch": 124, "iter": 533, "lr": 0.00181, "top1_acc": 0.92231, "top5_acc": 0.99495, "mean_class_accuracy": 0.89588} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.0018, "memory": 4083, "data_time": 0.18462, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02527, "loss": 0.02527, "time": 0.79699} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.00179, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02921, "loss": 0.02921, "time": 0.49019} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00178, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02462, "loss": 0.02462, "time": 0.48934} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00177, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.03878, "loss": 0.03878, "time": 0.49099} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00176, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02539, "loss": 0.02539, "time": 0.48732} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00175, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02508, "loss": 0.02508, "time": 0.48838} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00173, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02713, "loss": 0.02713, "time": 0.33718} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00172, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02469, "loss": 0.02469, "time": 0.50803} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.00171, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02411, "loss": 0.02411, "time": 0.26595} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.0017, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02347, "loss": 0.02347, "time": 0.48706} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00169, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01932, "loss": 0.01932, "time": 0.48807} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00168, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02477, "loss": 0.02477, "time": 0.48873} +{"mode": "val", "epoch": 125, "iter": 533, "lr": 0.00167, "top1_acc": 0.92454, "top5_acc": 0.99484, "mean_class_accuracy": 0.90002} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00166, "memory": 4083, "data_time": 0.18764, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0276, "loss": 0.0276, "time": 0.80377} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00165, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.021, "loss": 0.021, "time": 0.48914} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00164, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02074, "loss": 0.02074, "time": 0.48709} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00163, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02074, "loss": 0.02074, "time": 0.48773} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00162, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02941, "loss": 0.02941, "time": 0.49009} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00161, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02251, "loss": 0.02251, "time": 0.48753} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0016, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02637, "loss": 0.02637, "time": 0.29722} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00159, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02201, "loss": 0.02201, "time": 0.50984} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00158, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03311, "loss": 0.03311, "time": 0.29504} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00157, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02897, "loss": 0.02897, "time": 0.48543} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00156, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03136, "loss": 0.03136, "time": 0.49146} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00155, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02546, "loss": 0.02546, "time": 0.48713} +{"mode": "val", "epoch": 126, "iter": 533, "lr": 0.00155, "top1_acc": 0.92419, "top5_acc": 0.9939, "mean_class_accuracy": 0.90032} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00154, "memory": 4083, "data_time": 0.18658, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02632, "loss": 0.02632, "time": 0.79801} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00153, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01994, "loss": 0.01994, "time": 0.48706} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00152, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02255, "loss": 0.02255, "time": 0.49039} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00151, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02178, "loss": 0.02178, "time": 0.48886} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.0015, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01771, "loss": 0.01771, "time": 0.49129} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.00149, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01696, "loss": 0.01696, "time": 0.49015} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00148, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02153, "loss": 0.02153, "time": 0.29072} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00147, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01851, "loss": 0.01851, "time": 0.50871} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00146, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02356, "loss": 0.02356, "time": 0.28821} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00145, "memory": 4083, "data_time": 0.00061, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02413, "loss": 0.02413, "time": 0.48784} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00144, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.026, "loss": 0.026, "time": 0.49305} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00143, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02447, "loss": 0.02447, "time": 0.48705} +{"mode": "val", "epoch": 127, "iter": 533, "lr": 0.00142, "top1_acc": 0.9229, "top5_acc": 0.99448, "mean_class_accuracy": 0.90195} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00141, "memory": 4083, "data_time": 0.18241, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01974, "loss": 0.01974, "time": 0.7865} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.0014, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02495, "loss": 0.02495, "time": 0.4925} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00139, "memory": 4083, "data_time": 0.00065, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0213, "loss": 0.0213, "time": 0.48783} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00138, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01952, "loss": 0.01952, "time": 0.48848} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00138, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02079, "loss": 0.02079, "time": 0.49207} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00137, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01903, "loss": 0.01903, "time": 0.49112} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.00136, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02086, "loss": 0.02086, "time": 0.29613} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00135, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02344, "loss": 0.02344, "time": 0.50709} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00134, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01886, "loss": 0.01886, "time": 0.29974} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00133, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.02959, "loss": 0.02959, "time": 0.48988} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00132, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02165, "loss": 0.02165, "time": 0.48911} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00131, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0222, "loss": 0.0222, "time": 0.48754} +{"mode": "val", "epoch": 128, "iter": 533, "lr": 0.0013, "top1_acc": 0.92501, "top5_acc": 0.99437, "mean_class_accuracy": 0.90142} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.00129, "memory": 4083, "data_time": 0.18367, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02181, "loss": 0.02181, "time": 0.78442} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00129, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02408, "loss": 0.02408, "time": 0.48768} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00128, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.99875, "top5_acc": 0.99938, "loss_cls": 0.0321, "loss": 0.0321, "time": 0.49119} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00127, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02298, "loss": 0.02298, "time": 0.48754} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00126, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.0276, "loss": 0.0276, "time": 0.4881} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00125, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.0241, "loss": 0.0241, "time": 0.49024} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00124, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02766, "loss": 0.02766, "time": 0.28623} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00123, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02255, "loss": 0.02255, "time": 0.50732} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.00122, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02204, "loss": 0.02204, "time": 0.30661} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00121, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02428, "loss": 0.02428, "time": 0.48626} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00121, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02787, "loss": 0.02787, "time": 0.48661} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.0012, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02029, "loss": 0.02029, "time": 0.48981} +{"mode": "val", "epoch": 129, "iter": 533, "lr": 0.00119, "top1_acc": 0.92407, "top5_acc": 0.99437, "mean_class_accuracy": 0.89825} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00118, "memory": 4083, "data_time": 0.18988, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01884, "loss": 0.01884, "time": 0.78769} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00117, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0256, "loss": 0.0256, "time": 0.48825} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00116, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02214, "loss": 0.02214, "time": 0.48944} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00116, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02018, "loss": 0.02018, "time": 0.489} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.00115, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02247, "loss": 0.02247, "time": 0.4882} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00114, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01846, "loss": 0.01846, "time": 0.49207} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00113, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02052, "loss": 0.02052, "time": 0.28189} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00112, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02489, "loss": 0.02489, "time": 0.49954} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00111, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01963, "loss": 0.01963, "time": 0.33976} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.0011, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0196, "loss": 0.0196, "time": 0.48825} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.0011, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02213, "loss": 0.02213, "time": 0.48997} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00109, "memory": 4083, "data_time": 0.00043, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02139, "loss": 0.02139, "time": 0.49183} +{"mode": "val", "epoch": 130, "iter": 533, "lr": 0.00108, "top1_acc": 0.92524, "top5_acc": 0.99519, "mean_class_accuracy": 0.90158} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00107, "memory": 4083, "data_time": 0.18357, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02073, "loss": 0.02073, "time": 0.78794} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.00106, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01777, "loss": 0.01777, "time": 0.4824} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00106, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0178, "loss": 0.0178, "time": 0.48881} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00105, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03095, "loss": 0.03095, "time": 0.48771} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00104, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02426, "loss": 0.02426, "time": 0.4892} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00103, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0233, "loss": 0.0233, "time": 0.49156} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00102, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02027, "loss": 0.02027, "time": 0.29552} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00102, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01997, "loss": 0.01997, "time": 0.46217} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00101, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02801, "loss": 0.02801, "time": 0.34201} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.001, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0201, "loss": 0.0201, "time": 0.49003} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.00099, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01794, "loss": 0.01794, "time": 0.49198} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00098, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01911, "loss": 0.01911, "time": 0.48832} +{"mode": "val", "epoch": 131, "iter": 533, "lr": 0.00098, "top1_acc": 0.9263, "top5_acc": 0.99542, "mean_class_accuracy": 0.90434} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.00097, "memory": 4083, "data_time": 0.18511, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01779, "loss": 0.01779, "time": 0.78701} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00096, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02092, "loss": 0.02092, "time": 0.48881} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00095, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02063, "loss": 0.02063, "time": 0.49065} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00095, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0217, "loss": 0.0217, "time": 0.48976} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00094, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02118, "loss": 0.02118, "time": 0.49261} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00093, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01821, "loss": 0.01821, "time": 0.48963} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00092, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01909, "loss": 0.01909, "time": 0.31116} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00091, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01606, "loss": 0.01606, "time": 0.44254} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00091, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02054, "loss": 0.02054, "time": 0.35085} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0009, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01993, "loss": 0.01993, "time": 0.48997} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00089, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02082, "loss": 0.02082, "time": 0.49053} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00088, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01788, "loss": 0.01788, "time": 0.48695} +{"mode": "val", "epoch": 132, "iter": 533, "lr": 0.00088, "top1_acc": 0.9263, "top5_acc": 0.99472, "mean_class_accuracy": 0.90532} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.00087, "memory": 4083, "data_time": 0.182, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01855, "loss": 0.01855, "time": 0.7913} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00086, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01726, "loss": 0.01726, "time": 0.48902} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00086, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0202, "loss": 0.0202, "time": 0.48874} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00085, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01833, "loss": 0.01833, "time": 0.49119} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00084, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01991, "loss": 0.01991, "time": 0.48903} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00083, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01767, "loss": 0.01767, "time": 0.49035} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00083, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02012, "loss": 0.02012, "time": 0.32051} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00082, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02121, "loss": 0.02121, "time": 0.41219} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00081, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01648, "loss": 0.01648, "time": 0.35894} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.0008, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01631, "loss": 0.01631, "time": 0.48624} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0008, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01512, "loss": 0.01512, "time": 0.48867} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00079, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02244, "loss": 0.02244, "time": 0.48709} +{"mode": "val", "epoch": 133, "iter": 533, "lr": 0.00078, "top1_acc": 0.92712, "top5_acc": 0.99472, "mean_class_accuracy": 0.90556} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00078, "memory": 4083, "data_time": 0.18592, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02136, "loss": 0.02136, "time": 0.78933} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00077, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01797, "loss": 0.01797, "time": 0.48967} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00076, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02189, "loss": 0.02189, "time": 0.49005} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.00076, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02017, "loss": 0.02017, "time": 0.49039} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00075, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02379, "loss": 0.02379, "time": 0.48959} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00074, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01996, "loss": 0.01996, "time": 0.48756} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00073, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01738, "loss": 0.01738, "time": 0.34725} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00073, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0174, "loss": 0.0174, "time": 0.38225} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00072, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01701, "loss": 0.01701, "time": 0.36787} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00071, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01554, "loss": 0.01554, "time": 0.48899} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00071, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0174, "loss": 0.0174, "time": 0.49134} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.0007, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01638, "loss": 0.01638, "time": 0.48576} +{"mode": "val", "epoch": 134, "iter": 533, "lr": 0.0007, "top1_acc": 0.92794, "top5_acc": 0.99495, "mean_class_accuracy": 0.90397} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00069, "memory": 4083, "data_time": 0.1905, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0177, "loss": 0.0177, "time": 0.79904} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00068, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01731, "loss": 0.01731, "time": 0.48656} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00068, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01676, "loss": 0.01676, "time": 0.48881} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00067, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01899, "loss": 0.01899, "time": 0.48769} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00066, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0176, "loss": 0.0176, "time": 0.49354} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00066, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01864, "loss": 0.01864, "time": 0.47258} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00065, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01803, "loss": 0.01803, "time": 0.36082} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00064, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01792, "loss": 0.01792, "time": 0.36938} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.00064, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01518, "loss": 0.01518, "time": 0.3866} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00063, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0259, "loss": 0.0259, "time": 0.48768} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00062, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01708, "loss": 0.01708, "time": 0.48729} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00062, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01672, "loss": 0.01672, "time": 0.48704} +{"mode": "val", "epoch": 135, "iter": 533, "lr": 0.00061, "top1_acc": 0.92524, "top5_acc": 0.99448, "mean_class_accuracy": 0.90165} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00061, "memory": 4083, "data_time": 0.18322, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01854, "loss": 0.01854, "time": 0.7822} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.0006, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0174, "loss": 0.0174, "time": 0.48606} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00059, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02314, "loss": 0.02314, "time": 0.49091} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00059, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01549, "loss": 0.01549, "time": 0.49012} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.00058, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0173, "loss": 0.0173, "time": 0.48896} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.00057, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01978, "loss": 0.01978, "time": 0.4664} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00057, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02005, "loss": 0.02005, "time": 0.37718} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00056, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01627, "loss": 0.01627, "time": 0.35338} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00056, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01834, "loss": 0.01834, "time": 0.38655} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00055, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01598, "loss": 0.01598, "time": 0.49056} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00054, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01669, "loss": 0.01669, "time": 0.48625} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00054, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.016, "loss": 0.016, "time": 0.49025} +{"mode": "val", "epoch": 136, "iter": 533, "lr": 0.00053, "top1_acc": 0.92595, "top5_acc": 0.99448, "mean_class_accuracy": 0.90384} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00053, "memory": 4083, "data_time": 0.18133, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01613, "loss": 0.01613, "time": 0.79323} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00052, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01752, "loss": 0.01752, "time": 0.48758} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00052, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01789, "loss": 0.01789, "time": 0.48848} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.00051, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01617, "loss": 0.01617, "time": 0.49298} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.0005, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02097, "loss": 0.02097, "time": 0.49043} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.0005, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01842, "loss": 0.01842, "time": 0.46969} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00049, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01566, "loss": 0.01566, "time": 0.36343} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00049, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01693, "loss": 0.01693, "time": 0.36415} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00048, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01589, "loss": 0.01589, "time": 0.39388} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00048, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01713, "loss": 0.01713, "time": 0.49172} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00047, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01964, "loss": 0.01964, "time": 0.48896} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00046, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01603, "loss": 0.01603, "time": 0.49302} +{"mode": "val", "epoch": 137, "iter": 533, "lr": 0.00046, "top1_acc": 0.92876, "top5_acc": 0.99437, "mean_class_accuracy": 0.90644} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00046, "memory": 4083, "data_time": 0.18633, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0184, "loss": 0.0184, "time": 0.79275} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00045, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01868, "loss": 0.01868, "time": 0.49111} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00044, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01673, "loss": 0.01673, "time": 0.48886} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00044, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01752, "loss": 0.01752, "time": 0.48844} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.00043, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01678, "loss": 0.01678, "time": 0.49127} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.00043, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01525, "loss": 0.01525, "time": 0.44368} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00042, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01755, "loss": 0.01755, "time": 0.42824} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00042, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01711, "loss": 0.01711, "time": 0.30389} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00041, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02146, "loss": 0.02146, "time": 0.61982} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00041, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01697, "loss": 0.01697, "time": 0.69264} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.0004, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01593, "loss": 0.01593, "time": 0.71353} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.0004, "memory": 4083, "data_time": 0.00061, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01846, "loss": 0.01846, "time": 0.70101} +{"mode": "val", "epoch": 138, "iter": 533, "lr": 0.00039, "top1_acc": 0.93029, "top5_acc": 0.99484, "mean_class_accuracy": 0.90752} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00039, "memory": 4083, "data_time": 0.18484, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01519, "loss": 0.01519, "time": 0.64328} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00038, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01798, "loss": 0.01798, "time": 0.68098} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00038, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01666, "loss": 0.01666, "time": 0.69072} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00037, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0149, "loss": 0.0149, "time": 0.70195} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00037, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01646, "loss": 0.01646, "time": 0.69845} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00036, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01664, "loss": 0.01664, "time": 0.71232} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00036, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0164, "loss": 0.0164, "time": 0.70601} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00035, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01683, "loss": 0.01683, "time": 0.30728} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00035, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01647, "loss": 0.01647, "time": 0.21856} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.00034, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01598, "loss": 0.01598, "time": 0.21971} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.00034, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01578, "loss": 0.01578, "time": 0.22343} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00033, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01493, "loss": 0.01493, "time": 0.21796} +{"mode": "val", "epoch": 139, "iter": 533, "lr": 0.00033, "top1_acc": 0.9297, "top5_acc": 0.99531, "mean_class_accuracy": 0.90737} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00033, "memory": 4083, "data_time": 0.18244, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01626, "loss": 0.01626, "time": 0.41249} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00032, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01639, "loss": 0.01639, "time": 0.22066} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.00032, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01604, "loss": 0.01604, "time": 0.22048} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.00031, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01631, "loss": 0.01631, "time": 0.21874} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00031, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0191, "loss": 0.0191, "time": 0.22352} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.0003, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01439, "loss": 0.01439, "time": 0.21795} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.0003, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01654, "loss": 0.01654, "time": 0.22221} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00029, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01596, "loss": 0.01596, "time": 0.22584} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00029, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01898, "loss": 0.01898, "time": 0.21891} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00029, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01857, "loss": 0.01857, "time": 0.21887} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00028, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01611, "loss": 0.01611, "time": 0.21823} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00028, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0202, "loss": 0.0202, "time": 0.21931} +{"mode": "val", "epoch": 140, "iter": 533, "lr": 0.00027, "top1_acc": 0.92865, "top5_acc": 0.99531, "mean_class_accuracy": 0.90492} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00027, "memory": 4083, "data_time": 0.17666, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01501, "loss": 0.01501, "time": 0.40724} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00026, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01456, "loss": 0.01456, "time": 0.21985} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00026, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01759, "loss": 0.01759, "time": 0.22022} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00026, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01626, "loss": 0.01626, "time": 0.2207} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00025, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01728, "loss": 0.01728, "time": 0.2211} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00025, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01769, "loss": 0.01769, "time": 0.22056} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00024, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01633, "loss": 0.01633, "time": 0.21975} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00024, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01448, "loss": 0.01448, "time": 0.21904} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00024, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01607, "loss": 0.01607, "time": 0.21917} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00023, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01639, "loss": 0.01639, "time": 0.21931} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00023, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01684, "loss": 0.01684, "time": 0.21968} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00022, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01693, "loss": 0.01693, "time": 0.22009} +{"mode": "val", "epoch": 141, "iter": 533, "lr": 0.00022, "top1_acc": 0.92759, "top5_acc": 0.99495, "mean_class_accuracy": 0.9051} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00022, "memory": 4083, "data_time": 0.1807, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01889, "loss": 0.01889, "time": 0.41146} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00021, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01574, "loss": 0.01574, "time": 0.22053} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00021, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01666, "loss": 0.01666, "time": 0.22753} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00021, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01546, "loss": 0.01546, "time": 0.22227} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.0002, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01575, "loss": 0.01575, "time": 0.21918} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.0002, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01551, "loss": 0.01551, "time": 0.22319} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.0002, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01537, "loss": 0.01537, "time": 0.22189} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00019, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01758, "loss": 0.01758, "time": 0.22244} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00019, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01759, "loss": 0.01759, "time": 0.22131} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00018, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01484, "loss": 0.01484, "time": 0.21963} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00018, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0184, "loss": 0.0184, "time": 0.22001} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00018, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01622, "loss": 0.01622, "time": 0.21827} +{"mode": "val", "epoch": 142, "iter": 533, "lr": 0.00018, "top1_acc": 0.92841, "top5_acc": 0.99519, "mean_class_accuracy": 0.90584} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.00017, "memory": 4083, "data_time": 0.18213, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02022, "loss": 0.02022, "time": 0.41507} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00017, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0156, "loss": 0.0156, "time": 0.21863} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00017, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02015, "loss": 0.02015, "time": 0.21907} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00016, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01686, "loss": 0.01686, "time": 0.22377} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00016, "memory": 4083, "data_time": 0.00042, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01549, "loss": 0.01549, "time": 0.21912} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00016, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0168, "loss": 0.0168, "time": 0.21975} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00015, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01732, "loss": 0.01732, "time": 0.2223} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00015, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01913, "loss": 0.01913, "time": 0.22358} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00015, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01528, "loss": 0.01528, "time": 0.22205} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00014, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0158, "loss": 0.0158, "time": 0.21966} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00014, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01698, "loss": 0.01698, "time": 0.22144} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00014, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01677, "loss": 0.01677, "time": 0.21889} +{"mode": "val", "epoch": 143, "iter": 533, "lr": 0.00013, "top1_acc": 0.92912, "top5_acc": 0.99495, "mean_class_accuracy": 0.90896} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00013, "memory": 4083, "data_time": 0.17522, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01508, "loss": 0.01508, "time": 0.40931} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00013, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01506, "loss": 0.01506, "time": 0.21802} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00013, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01433, "loss": 0.01433, "time": 0.22121} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00012, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0165, "loss": 0.0165, "time": 0.22131} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00012, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02082, "loss": 0.02082, "time": 0.22076} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00012, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01644, "loss": 0.01644, "time": 0.22092} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00011, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01617, "loss": 0.01617, "time": 0.21898} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.00011, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01511, "loss": 0.01511, "time": 0.22462} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.00011, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01523, "loss": 0.01523, "time": 0.21998} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.00011, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01748, "loss": 0.01748, "time": 0.21867} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.0001, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0182, "loss": 0.0182, "time": 0.21778} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.0001, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01829, "loss": 0.01829, "time": 0.21955} +{"mode": "val", "epoch": 144, "iter": 533, "lr": 0.0001, "top1_acc": 0.92794, "top5_acc": 0.99519, "mean_class_accuracy": 0.9071} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.0001, "memory": 4083, "data_time": 0.17839, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01594, "loss": 0.01594, "time": 0.40938} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 9e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01573, "loss": 0.01573, "time": 0.2168} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 9e-05, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01587, "loss": 0.01587, "time": 0.21954} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 9e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01502, "loss": 0.01502, "time": 0.22116} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 9e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01521, "loss": 0.01521, "time": 0.22091} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 8e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01643, "loss": 0.01643, "time": 0.22037} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 8e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01643, "loss": 0.01643, "time": 0.21886} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 8e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01519, "loss": 0.01519, "time": 0.21885} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 8e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01604, "loss": 0.01604, "time": 0.21765} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 7e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01566, "loss": 0.01566, "time": 0.22101} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 7e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01623, "loss": 0.01623, "time": 0.21785} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 7e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01577, "loss": 0.01577, "time": 0.22077} +{"mode": "val", "epoch": 145, "iter": 533, "lr": 7e-05, "top1_acc": 0.92829, "top5_acc": 0.99484, "mean_class_accuracy": 0.90629} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 7e-05, "memory": 4083, "data_time": 0.17606, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0162, "loss": 0.0162, "time": 0.40376} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 6e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01552, "loss": 0.01552, "time": 0.21806} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 6e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01493, "loss": 0.01493, "time": 0.21953} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 6e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01621, "loss": 0.01621, "time": 0.2175} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 6e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01514, "loss": 0.01514, "time": 0.22033} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 6e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01487, "loss": 0.01487, "time": 0.22077} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 5e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01762, "loss": 0.01762, "time": 0.22208} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 5e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01689, "loss": 0.01689, "time": 0.21978} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 5e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01713, "loss": 0.01713, "time": 0.21723} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 5e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01588, "loss": 0.01588, "time": 0.21933} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 5e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0154, "loss": 0.0154, "time": 0.21777} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 5e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01558, "loss": 0.01558, "time": 0.21543} +{"mode": "val", "epoch": 146, "iter": 533, "lr": 4e-05, "top1_acc": 0.92736, "top5_acc": 0.99519, "mean_class_accuracy": 0.90386} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 4e-05, "memory": 4083, "data_time": 0.17681, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02029, "loss": 0.02029, "time": 0.4111} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 4e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01857, "loss": 0.01857, "time": 0.21686} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 4e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01459, "loss": 0.01459, "time": 0.2206} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 4e-05, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01988, "loss": 0.01988, "time": 0.22186} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 4e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02174, "loss": 0.02174, "time": 0.21893} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 3e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01563, "loss": 0.01563, "time": 0.21934} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 3e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0187, "loss": 0.0187, "time": 0.22064} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 3e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0157, "loss": 0.0157, "time": 0.22} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 3e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01518, "loss": 0.01518, "time": 0.22177} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 3e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01615, "loss": 0.01615, "time": 0.22199} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 3e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01601, "loss": 0.01601, "time": 0.21862} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 3e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01498, "loss": 0.01498, "time": 0.21818} +{"mode": "val", "epoch": 147, "iter": 533, "lr": 2e-05, "top1_acc": 0.929, "top5_acc": 0.99507, "mean_class_accuracy": 0.90642} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 2e-05, "memory": 4083, "data_time": 0.17358, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01816, "loss": 0.01816, "time": 0.40656} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 2e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01535, "loss": 0.01535, "time": 0.21766} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 2e-05, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01579, "loss": 0.01579, "time": 0.22197} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 2e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01536, "loss": 0.01536, "time": 0.22138} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 2e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01548, "loss": 0.01548, "time": 0.2249} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 2e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01478, "loss": 0.01478, "time": 0.22167} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 2e-05, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01372, "loss": 0.01372, "time": 0.22339} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 2e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01621, "loss": 0.01621, "time": 0.2178} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 1e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01693, "loss": 0.01693, "time": 0.21747} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 1e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01538, "loss": 0.01538, "time": 0.21726} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01803, "loss": 0.01803, "time": 0.21869} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01728, "loss": 0.01728, "time": 0.21887} +{"mode": "val", "epoch": 148, "iter": 533, "lr": 1e-05, "top1_acc": 0.93064, "top5_acc": 0.99495, "mean_class_accuracy": 0.90848} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 1e-05, "memory": 4083, "data_time": 0.17736, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01896, "loss": 0.01896, "time": 0.41224} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0139, "loss": 0.0139, "time": 0.22255} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 1e-05, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01534, "loss": 0.01534, "time": 0.22186} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 1e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.015, "loss": 0.015, "time": 0.2261} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 1e-05, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01551, "loss": 0.01551, "time": 0.221} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 1e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01546, "loss": 0.01546, "time": 0.21765} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01619, "loss": 0.01619, "time": 0.22182} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 1e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01532, "loss": 0.01532, "time": 0.21929} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01537, "loss": 0.01537, "time": 0.21856} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01722, "loss": 0.01722, "time": 0.22004} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.016, "loss": 0.016, "time": 0.21661} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01697, "loss": 0.01697, "time": 0.2194} +{"mode": "val", "epoch": 149, "iter": 533, "lr": 0.0, "top1_acc": 0.92794, "top5_acc": 0.99507, "mean_class_accuracy": 0.9044} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 0.0, "memory": 4083, "data_time": 0.1771, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01473, "loss": 0.01473, "time": 0.40975} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 0.0, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01538, "loss": 0.01538, "time": 0.21877} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 0.0, "memory": 4083, "data_time": 0.00041, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01472, "loss": 0.01472, "time": 0.22038} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 0.0, "memory": 4083, "data_time": 0.00063, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01631, "loss": 0.01631, "time": 0.21926} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 0.0, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0164, "loss": 0.0164, "time": 0.22139} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01762, "loss": 0.01762, "time": 0.21836} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 0.0, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01569, "loss": 0.01569, "time": 0.22363} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01482, "loss": 0.01482, "time": 0.22042} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0149, "loss": 0.0149, "time": 0.22131} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01548, "loss": 0.01548, "time": 0.2174} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00019, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01769, "loss": 0.01769, "time": 0.21615} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01542, "loss": 0.01542, "time": 0.21913} +{"mode": "val", "epoch": 150, "iter": 533, "lr": 0.0, "top1_acc": 0.92806, "top5_acc": 0.99519, "mean_class_accuracy": 0.90574} diff --git a/finegym/bm/best_pred.pkl b/finegym/bm/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..5b9914482b12d45aba7b6b76406184a3996b49af --- /dev/null +++ b/finegym/bm/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b1d334b3d197936a0bdf6324091c167c42a4ef18795428df4b50b17b258ce90e +size 5257893 diff --git a/finegym/bm/best_top1_acc_epoch_148.pth b/finegym/bm/best_top1_acc_epoch_148.pth new file mode 100644 index 0000000000000000000000000000000000000000..e56e801d6465ad238f66aa729cd1c4102f3341f6 --- /dev/null +++ b/finegym/bm/best_top1_acc_epoch_148.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85f8fa42d235cb630ac931492b8a6a08835fea0cd97221f3df638526806f91e3 +size 31999601 diff --git a/finegym/bm/bm.py b/finegym/bm/bm.py new file mode 100644 index 0000000000000000000000000000000000000000..f4f2ae4ef1b805371eb7558aa14865b8dda8e277 --- /dev/null +++ b/finegym/bm/bm.py @@ -0,0 +1,113 @@ +modality = 'bm' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/bm' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/finegym/finegym_ensemble.py b/finegym/finegym_ensemble.py new file mode 100644 index 0000000000000000000000000000000000000000..4cf257188e3a7334f3efd198bd6f4c8d42ead322 --- /dev/null +++ b/finegym/finegym_ensemble.py @@ -0,0 +1,68 @@ +from mmcv import load +import sys +# Note: please adjust the relative path according to the actual situation. +sys.path.append('../..') +from aclnet.smp import * + + +j_1 = load('j_1/best_pred.pkl') +b_1 = load('b_1/best_pred.pkl') +k_1 = load('k_1/best_pred.pkl') +jm = load('jm/best_pred.pkl') +bm = load('bm/best_pred.pkl') +km = load('km/best_pred.pkl') +j_2 = load('j_2/best_pred.pkl') +b_2 = load('b_2/best_pred.pkl') +k_2 = load('k_2/best_pred.pkl') +j_3 = load('j_3/best_pred.pkl') +b_3 = load('b_3/best_pred.pkl') +k_3 = load('k_3/best_pred.pkl') +label = load_label('/data/finegym/gym_hrnet.pkl', 'val') + + +""" +*************** +InfoGCN v0: +j jm b bm k km +2S: 95.48 +4S: 95.80 +6S: 96.01 +*************** +""" +print('InfoGCN v0:') +print('j jm b bm k km') +print('2S') +fused = comb([j_1, b_1], [1, 1]) +print('Top-1', top1(fused, label)) + +print('4S') +fused = comb([j_1, b_1, jm, bm], [9, 9, 5, 5]) +print('Top-1', top1(fused, label)) + +print('6S') +fused = comb([j_1, b_1, k_1, jm, bm, km], [9, 9, 9, 5, 5, 5]) +print('Top-1', top1(fused, label)) + + +""" +*************** +HD-GCN v1: +j b j b j b +2S: 95.48 +4S: 95.79 +6S: 95.93 +*************** +""" +print('HD-GCN v1:') +print('j b j b j b') +print('2S') +fused = comb([j_1, b_1], [1, 1]) +print('Top-1', top1(fused, label)) + +print('4S') +fused = comb([j_1, b_1, j_2, b_2], [4, 4, 3, 3]) +print('Top-1', top1(fused, label)) + +print('6S') +fused = comb([j_1, b_1, j_2, b_2, j_3, b_3], [4, 4, 3, 3, 8, 8]) +print('Top-1', top1(fused, label)) diff --git a/finegym/j_1/20250624_084414.log b/finegym/j_1/20250624_084414.log new file mode 100644 index 0000000000000000000000000000000000000000..92470c45d03f36f39f57d98ceb9856af844f0c1b --- /dev/null +++ b/finegym/j_1/20250624_084414.log @@ -0,0 +1,3483 @@ +2025-06-24 08:44:14,843 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2025-06-24 08:44:15,056 - pyskl - INFO - Config: modality = 'j' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/j_1' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2025-06-24 08:44:15,056 - pyskl - INFO - Set random seed to 1637075804, deterministic: False +2025-06-24 08:44:16,542 - pyskl - INFO - 20484 videos remain after valid thresholding +2025-06-24 08:44:20,851 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-24 08:44:20,852 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1 +2025-06-24 08:44:20,852 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2025-06-24 08:44:20,852 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2025-06-24 08:44:20,853 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1 by HardDiskBackend. +2025-06-24 08:45:00,894 - pyskl - INFO - Epoch [1][100/1281] lr: 2.500e-02, eta: 21:21:31, time: 0.400, data_time: 0.183, memory: 4082, top1_acc: 0.0544, top5_acc: 0.2144, loss_cls: 4.6133, loss: 4.6133 +2025-06-24 08:45:22,886 - pyskl - INFO - Epoch [1][200/1281] lr: 2.500e-02, eta: 16:32:11, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.0737, top5_acc: 0.2894, loss_cls: 4.6321, loss: 4.6321 +2025-06-24 08:45:44,572 - pyskl - INFO - Epoch [1][300/1281] lr: 2.500e-02, eta: 14:52:14, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.0931, top5_acc: 0.3594, loss_cls: 4.3957, loss: 4.3957 +2025-06-24 08:46:06,578 - pyskl - INFO - Epoch [1][400/1281] lr: 2.500e-02, eta: 14:04:38, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.1431, top5_acc: 0.4256, loss_cls: 4.1255, loss: 4.1255 +2025-06-24 08:46:28,263 - pyskl - INFO - Epoch [1][500/1281] lr: 2.500e-02, eta: 13:33:53, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.1700, top5_acc: 0.5056, loss_cls: 3.8258, loss: 3.8258 +2025-06-24 08:46:50,223 - pyskl - INFO - Epoch [1][600/1281] lr: 2.500e-02, eta: 13:14:43, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.1900, top5_acc: 0.5387, loss_cls: 3.6372, loss: 3.6372 +2025-06-24 08:47:12,369 - pyskl - INFO - Epoch [1][700/1281] lr: 2.500e-02, eta: 13:01:47, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.2300, top5_acc: 0.5919, loss_cls: 3.4500, loss: 3.4500 +2025-06-24 08:47:34,238 - pyskl - INFO - Epoch [1][800/1281] lr: 2.500e-02, eta: 12:50:52, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.2531, top5_acc: 0.5988, loss_cls: 3.3461, loss: 3.3461 +2025-06-24 08:47:56,153 - pyskl - INFO - Epoch [1][900/1281] lr: 2.500e-02, eta: 12:42:28, time: 0.219, data_time: 0.001, memory: 4082, top1_acc: 0.3113, top5_acc: 0.6606, loss_cls: 3.0386, loss: 3.0386 +2025-06-24 08:48:17,988 - pyskl - INFO - Epoch [1][1000/1281] lr: 2.500e-02, eta: 12:35:26, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.3231, top5_acc: 0.7044, loss_cls: 2.9264, loss: 2.9264 +2025-06-24 08:48:39,844 - pyskl - INFO - Epoch [1][1100/1281] lr: 2.500e-02, eta: 12:29:39, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.3325, top5_acc: 0.6937, loss_cls: 2.8763, loss: 2.8763 +2025-06-24 08:49:01,703 - pyskl - INFO - Epoch [1][1200/1281] lr: 2.500e-02, eta: 12:24:48, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.3706, top5_acc: 0.7519, loss_cls: 2.7033, loss: 2.7033 +2025-06-24 08:49:19,966 - pyskl - INFO - Saving checkpoint at 1 epochs +2025-06-24 08:50:02,681 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 08:50:02,733 - pyskl - INFO - +top1_acc 0.3808 +top5_acc 0.7645 +2025-06-24 08:50:02,733 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 08:50:02,739 - pyskl - INFO - +mean_acc 0.1944 +2025-06-24 08:50:02,906 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2025-06-24 08:50:02,907 - pyskl - INFO - Best top1_acc is 0.3808 at 1 epoch. +2025-06-24 08:50:02,910 - pyskl - INFO - Epoch(val) [1][533] top1_acc: 0.3808, top5_acc: 0.7645, mean_class_accuracy: 0.1944 +2025-06-24 08:50:42,870 - pyskl - INFO - Epoch [2][100/1281] lr: 2.500e-02, eta: 12:18:33, time: 0.400, data_time: 0.180, memory: 4082, top1_acc: 0.3987, top5_acc: 0.7869, loss_cls: 2.5347, loss: 2.5347 +2025-06-24 08:51:05,029 - pyskl - INFO - Epoch [2][200/1281] lr: 2.500e-02, eta: 12:15:52, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.4331, top5_acc: 0.8156, loss_cls: 2.3908, loss: 2.3908 +2025-06-24 08:51:26,884 - pyskl - INFO - Epoch [2][300/1281] lr: 2.500e-02, eta: 12:12:52, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.4300, top5_acc: 0.8069, loss_cls: 2.3931, loss: 2.3931 +2025-06-24 08:51:49,124 - pyskl - INFO - Epoch [2][400/1281] lr: 2.500e-02, eta: 12:10:54, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.4437, top5_acc: 0.8381, loss_cls: 2.2461, loss: 2.2461 +2025-06-24 08:52:11,250 - pyskl - INFO - Epoch [2][500/1281] lr: 2.499e-02, eta: 12:08:55, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.4675, top5_acc: 0.8562, loss_cls: 2.2174, loss: 2.2174 +2025-06-24 08:52:33,382 - pyskl - INFO - Epoch [2][600/1281] lr: 2.499e-02, eta: 12:07:07, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.4506, top5_acc: 0.8506, loss_cls: 2.2194, loss: 2.2194 +2025-06-24 08:52:55,157 - pyskl - INFO - Epoch [2][700/1281] lr: 2.499e-02, eta: 12:04:53, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.4850, top5_acc: 0.8694, loss_cls: 2.1080, loss: 2.1080 +2025-06-24 08:53:17,023 - pyskl - INFO - Epoch [2][800/1281] lr: 2.499e-02, eta: 12:02:58, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.4888, top5_acc: 0.8712, loss_cls: 2.0742, loss: 2.0742 +2025-06-24 08:53:39,131 - pyskl - INFO - Epoch [2][900/1281] lr: 2.499e-02, eta: 12:01:33, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.4988, top5_acc: 0.8869, loss_cls: 2.0067, loss: 2.0067 +2025-06-24 08:54:00,700 - pyskl - INFO - Epoch [2][1000/1281] lr: 2.499e-02, eta: 11:59:29, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.4975, top5_acc: 0.8844, loss_cls: 2.0255, loss: 2.0255 +2025-06-24 08:54:22,608 - pyskl - INFO - Epoch [2][1100/1281] lr: 2.499e-02, eta: 11:58:00, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.5062, top5_acc: 0.8756, loss_cls: 2.0218, loss: 2.0218 +2025-06-24 08:54:44,364 - pyskl - INFO - Epoch [2][1200/1281] lr: 2.499e-02, eta: 11:56:25, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.4963, top5_acc: 0.8906, loss_cls: 1.9848, loss: 1.9848 +2025-06-24 08:55:03,056 - pyskl - INFO - Saving checkpoint at 2 epochs +2025-06-24 08:55:46,057 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 08:55:46,118 - pyskl - INFO - +top1_acc 0.4577 +top5_acc 0.8659 +2025-06-24 08:55:46,118 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 08:55:46,125 - pyskl - INFO - +mean_acc 0.2881 +2025-06-24 08:55:46,129 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_1.pth was removed +2025-06-24 08:55:46,306 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2025-06-24 08:55:46,306 - pyskl - INFO - Best top1_acc is 0.4577 at 2 epoch. +2025-06-24 08:55:46,309 - pyskl - INFO - Epoch(val) [2][533] top1_acc: 0.4577, top5_acc: 0.8659, mean_class_accuracy: 0.2881 +2025-06-24 08:56:26,483 - pyskl - INFO - Epoch [3][100/1281] lr: 2.499e-02, eta: 11:54:43, time: 0.402, data_time: 0.183, memory: 4082, top1_acc: 0.5050, top5_acc: 0.8819, loss_cls: 1.9811, loss: 1.9811 +2025-06-24 08:56:48,208 - pyskl - INFO - Epoch [3][200/1281] lr: 2.499e-02, eta: 11:53:18, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.5181, top5_acc: 0.9075, loss_cls: 1.8467, loss: 1.8467 +2025-06-24 08:57:09,850 - pyskl - INFO - Epoch [3][300/1281] lr: 2.499e-02, eta: 11:51:52, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.5025, top5_acc: 0.8931, loss_cls: 1.9370, loss: 1.9370 +2025-06-24 08:57:31,569 - pyskl - INFO - Epoch [3][400/1281] lr: 2.499e-02, eta: 11:50:36, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.5450, top5_acc: 0.9087, loss_cls: 1.8543, loss: 1.8543 +2025-06-24 08:57:53,259 - pyskl - INFO - Epoch [3][500/1281] lr: 2.498e-02, eta: 11:49:21, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.5481, top5_acc: 0.9038, loss_cls: 1.8296, loss: 1.8296 +2025-06-24 08:58:14,824 - pyskl - INFO - Epoch [3][600/1281] lr: 2.498e-02, eta: 11:48:02, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.5481, top5_acc: 0.9031, loss_cls: 1.8284, loss: 1.8284 +2025-06-24 08:58:36,588 - pyskl - INFO - Epoch [3][700/1281] lr: 2.498e-02, eta: 11:46:58, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.5569, top5_acc: 0.9275, loss_cls: 1.7495, loss: 1.7495 +2025-06-24 08:58:58,370 - pyskl - INFO - Epoch [3][800/1281] lr: 2.498e-02, eta: 11:45:58, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.5369, top5_acc: 0.9137, loss_cls: 1.7888, loss: 1.7888 +2025-06-24 08:59:20,340 - pyskl - INFO - Epoch [3][900/1281] lr: 2.498e-02, eta: 11:45:10, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5625, top5_acc: 0.9250, loss_cls: 1.7115, loss: 1.7115 +2025-06-24 08:59:42,389 - pyskl - INFO - Epoch [3][1000/1281] lr: 2.498e-02, eta: 11:44:27, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5837, top5_acc: 0.9350, loss_cls: 1.6843, loss: 1.6843 +2025-06-24 09:00:04,609 - pyskl - INFO - Epoch [3][1100/1281] lr: 2.498e-02, eta: 11:43:55, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.5831, top5_acc: 0.9175, loss_cls: 1.6985, loss: 1.6985 +2025-06-24 09:00:26,592 - pyskl - INFO - Epoch [3][1200/1281] lr: 2.498e-02, eta: 11:43:11, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5631, top5_acc: 0.9375, loss_cls: 1.6724, loss: 1.6724 +2025-06-24 09:00:45,243 - pyskl - INFO - Saving checkpoint at 3 epochs +2025-06-24 09:01:28,343 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:01:28,410 - pyskl - INFO - +top1_acc 0.5482 +top5_acc 0.9068 +2025-06-24 09:01:28,411 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:01:28,418 - pyskl - INFO - +mean_acc 0.3776 +2025-06-24 09:01:28,423 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_2.pth was removed +2025-06-24 09:01:28,615 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2025-06-24 09:01:28,615 - pyskl - INFO - Best top1_acc is 0.5482 at 3 epoch. +2025-06-24 09:01:28,618 - pyskl - INFO - Epoch(val) [3][533] top1_acc: 0.5482, top5_acc: 0.9068, mean_class_accuracy: 0.3776 +2025-06-24 09:02:08,514 - pyskl - INFO - Epoch [4][100/1281] lr: 2.497e-02, eta: 11:42:00, time: 0.399, data_time: 0.181, memory: 4082, top1_acc: 0.5831, top5_acc: 0.9375, loss_cls: 1.6466, loss: 1.6466 +2025-06-24 09:02:30,302 - pyskl - INFO - Epoch [4][200/1281] lr: 2.497e-02, eta: 11:41:10, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.5875, top5_acc: 0.9331, loss_cls: 1.6118, loss: 1.6118 +2025-06-24 09:02:51,825 - pyskl - INFO - Epoch [4][300/1281] lr: 2.497e-02, eta: 11:40:09, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.5944, top5_acc: 0.9350, loss_cls: 1.6365, loss: 1.6365 +2025-06-24 09:03:13,564 - pyskl - INFO - Epoch [4][400/1281] lr: 2.497e-02, eta: 11:39:20, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6094, top5_acc: 0.9450, loss_cls: 1.5415, loss: 1.5415 +2025-06-24 09:03:35,456 - pyskl - INFO - Epoch [4][500/1281] lr: 2.497e-02, eta: 11:38:39, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.5825, top5_acc: 0.9300, loss_cls: 1.6152, loss: 1.6152 +2025-06-24 09:03:57,259 - pyskl - INFO - Epoch [4][600/1281] lr: 2.497e-02, eta: 11:37:55, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6181, top5_acc: 0.9356, loss_cls: 1.5518, loss: 1.5518 +2025-06-24 09:04:19,227 - pyskl - INFO - Epoch [4][700/1281] lr: 2.497e-02, eta: 11:37:18, time: 0.220, data_time: 0.001, memory: 4082, top1_acc: 0.6031, top5_acc: 0.9500, loss_cls: 1.5644, loss: 1.5644 +2025-06-24 09:04:40,826 - pyskl - INFO - Epoch [4][800/1281] lr: 2.496e-02, eta: 11:36:27, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6206, top5_acc: 0.9456, loss_cls: 1.5425, loss: 1.5425 +2025-06-24 09:05:03,129 - pyskl - INFO - Epoch [4][900/1281] lr: 2.496e-02, eta: 11:36:06, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.5975, top5_acc: 0.9469, loss_cls: 1.5680, loss: 1.5680 +2025-06-24 09:05:24,947 - pyskl - INFO - Epoch [4][1000/1281] lr: 2.496e-02, eta: 11:35:25, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6106, top5_acc: 0.9437, loss_cls: 1.5642, loss: 1.5642 +2025-06-24 09:05:46,798 - pyskl - INFO - Epoch [4][1100/1281] lr: 2.496e-02, eta: 11:34:47, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6212, top5_acc: 0.9406, loss_cls: 1.5449, loss: 1.5449 +2025-06-24 09:06:08,691 - pyskl - INFO - Epoch [4][1200/1281] lr: 2.496e-02, eta: 11:34:11, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6131, top5_acc: 0.9475, loss_cls: 1.5393, loss: 1.5393 +2025-06-24 09:06:27,143 - pyskl - INFO - Saving checkpoint at 4 epochs +2025-06-24 09:07:09,971 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:07:10,030 - pyskl - INFO - +top1_acc 0.6047 +top5_acc 0.9400 +2025-06-24 09:07:10,030 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:07:10,036 - pyskl - INFO - +mean_acc 0.4253 +2025-06-24 09:07:10,041 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_3.pth was removed +2025-06-24 09:07:10,219 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2025-06-24 09:07:10,219 - pyskl - INFO - Best top1_acc is 0.6047 at 4 epoch. +2025-06-24 09:07:10,221 - pyskl - INFO - Epoch(val) [4][533] top1_acc: 0.6047, top5_acc: 0.9400, mean_class_accuracy: 0.4253 +2025-06-24 09:07:50,354 - pyskl - INFO - Epoch [5][100/1281] lr: 2.495e-02, eta: 11:33:25, time: 0.401, data_time: 0.183, memory: 4082, top1_acc: 0.6519, top5_acc: 0.9619, loss_cls: 1.4386, loss: 1.4386 +2025-06-24 09:08:12,305 - pyskl - INFO - Epoch [5][200/1281] lr: 2.495e-02, eta: 11:32:52, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6338, top5_acc: 0.9556, loss_cls: 1.4616, loss: 1.4616 +2025-06-24 09:08:33,966 - pyskl - INFO - Epoch [5][300/1281] lr: 2.495e-02, eta: 11:32:09, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6531, top5_acc: 0.9600, loss_cls: 1.4093, loss: 1.4093 +2025-06-24 09:08:55,802 - pyskl - INFO - Epoch [5][400/1281] lr: 2.495e-02, eta: 11:31:33, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6212, top5_acc: 0.9463, loss_cls: 1.5072, loss: 1.5072 +2025-06-24 09:09:17,784 - pyskl - INFO - Epoch [5][500/1281] lr: 2.495e-02, eta: 11:31:02, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6250, top5_acc: 0.9487, loss_cls: 1.4623, loss: 1.4623 +2025-06-24 09:09:39,390 - pyskl - INFO - Epoch [5][600/1281] lr: 2.495e-02, eta: 11:30:20, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6406, top5_acc: 0.9594, loss_cls: 1.4624, loss: 1.4624 +2025-06-24 09:10:01,128 - pyskl - INFO - Epoch [5][700/1281] lr: 2.494e-02, eta: 11:29:42, time: 0.217, data_time: 0.001, memory: 4082, top1_acc: 0.6581, top5_acc: 0.9531, loss_cls: 1.3995, loss: 1.3995 +2025-06-24 09:10:23,033 - pyskl - INFO - Epoch [5][800/1281] lr: 2.494e-02, eta: 11:29:10, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6700, top5_acc: 0.9669, loss_cls: 1.3494, loss: 1.3494 +2025-06-24 09:10:45,126 - pyskl - INFO - Epoch [5][900/1281] lr: 2.494e-02, eta: 11:28:45, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.6613, top5_acc: 0.9637, loss_cls: 1.3644, loss: 1.3644 +2025-06-24 09:11:07,247 - pyskl - INFO - Epoch [5][1000/1281] lr: 2.494e-02, eta: 11:28:20, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.6444, top5_acc: 0.9525, loss_cls: 1.4353, loss: 1.4353 +2025-06-24 09:11:29,391 - pyskl - INFO - Epoch [5][1100/1281] lr: 2.494e-02, eta: 11:27:56, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.6562, top5_acc: 0.9637, loss_cls: 1.3545, loss: 1.3545 +2025-06-24 09:11:51,046 - pyskl - INFO - Epoch [5][1200/1281] lr: 2.493e-02, eta: 11:27:18, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6650, top5_acc: 0.9606, loss_cls: 1.4010, loss: 1.4010 +2025-06-24 09:12:09,293 - pyskl - INFO - Saving checkpoint at 5 epochs +2025-06-24 09:12:51,720 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:12:51,775 - pyskl - INFO - +top1_acc 0.6235 +top5_acc 0.9501 +2025-06-24 09:12:51,775 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:12:51,781 - pyskl - INFO - +mean_acc 0.4813 +2025-06-24 09:12:51,785 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_4.pth was removed +2025-06-24 09:12:51,985 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2025-06-24 09:12:51,986 - pyskl - INFO - Best top1_acc is 0.6235 at 5 epoch. +2025-06-24 09:12:51,990 - pyskl - INFO - Epoch(val) [5][533] top1_acc: 0.6235, top5_acc: 0.9501, mean_class_accuracy: 0.4813 +2025-06-24 09:13:31,874 - pyskl - INFO - Epoch [6][100/1281] lr: 2.493e-02, eta: 11:26:29, time: 0.399, data_time: 0.181, memory: 4082, top1_acc: 0.6831, top5_acc: 0.9644, loss_cls: 1.2861, loss: 1.2861 +2025-06-24 09:13:53,781 - pyskl - INFO - Epoch [6][200/1281] lr: 2.493e-02, eta: 11:25:59, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6775, top5_acc: 0.9694, loss_cls: 1.2989, loss: 1.2989 +2025-06-24 09:14:15,536 - pyskl - INFO - Epoch [6][300/1281] lr: 2.492e-02, eta: 11:25:25, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6919, top5_acc: 0.9625, loss_cls: 1.3253, loss: 1.3253 +2025-06-24 09:14:37,427 - pyskl - INFO - Epoch [6][400/1281] lr: 2.492e-02, eta: 11:24:55, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6756, top5_acc: 0.9619, loss_cls: 1.3359, loss: 1.3359 +2025-06-24 09:14:59,002 - pyskl - INFO - Epoch [6][500/1281] lr: 2.492e-02, eta: 11:24:17, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6694, top5_acc: 0.9587, loss_cls: 1.3386, loss: 1.3386 +2025-06-24 09:15:20,902 - pyskl - INFO - Epoch [6][600/1281] lr: 2.492e-02, eta: 11:23:48, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6619, top5_acc: 0.9663, loss_cls: 1.3619, loss: 1.3619 +2025-06-24 09:15:42,561 - pyskl - INFO - Epoch [6][700/1281] lr: 2.492e-02, eta: 11:23:13, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6744, top5_acc: 0.9637, loss_cls: 1.2943, loss: 1.2943 +2025-06-24 09:16:04,313 - pyskl - INFO - Epoch [6][800/1281] lr: 2.491e-02, eta: 11:22:40, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6931, top5_acc: 0.9675, loss_cls: 1.2699, loss: 1.2699 +2025-06-24 09:16:26,436 - pyskl - INFO - Epoch [6][900/1281] lr: 2.491e-02, eta: 11:22:17, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.6806, top5_acc: 0.9669, loss_cls: 1.3109, loss: 1.3109 +2025-06-24 09:16:48,188 - pyskl - INFO - Epoch [6][1000/1281] lr: 2.491e-02, eta: 11:21:45, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6769, top5_acc: 0.9694, loss_cls: 1.2811, loss: 1.2811 +2025-06-24 09:17:09,888 - pyskl - INFO - Epoch [6][1100/1281] lr: 2.491e-02, eta: 11:21:12, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6844, top5_acc: 0.9581, loss_cls: 1.3147, loss: 1.3147 +2025-06-24 09:17:31,854 - pyskl - INFO - Epoch [6][1200/1281] lr: 2.490e-02, eta: 11:20:46, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6850, top5_acc: 0.9712, loss_cls: 1.2538, loss: 1.2538 +2025-06-24 09:17:50,226 - pyskl - INFO - Saving checkpoint at 6 epochs +2025-06-24 09:18:32,722 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:18:32,798 - pyskl - INFO - +top1_acc 0.6428 +top5_acc 0.9502 +2025-06-24 09:18:32,798 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:18:32,806 - pyskl - INFO - +mean_acc 0.5004 +2025-06-24 09:18:32,810 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_5.pth was removed +2025-06-24 09:18:33,016 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_6.pth. +2025-06-24 09:18:33,017 - pyskl - INFO - Best top1_acc is 0.6428 at 6 epoch. +2025-06-24 09:18:33,019 - pyskl - INFO - Epoch(val) [6][533] top1_acc: 0.6428, top5_acc: 0.9502, mean_class_accuracy: 0.5004 +2025-06-24 09:19:13,166 - pyskl - INFO - Epoch [7][100/1281] lr: 2.490e-02, eta: 11:20:08, time: 0.401, data_time: 0.183, memory: 4082, top1_acc: 0.6831, top5_acc: 0.9731, loss_cls: 1.2765, loss: 1.2765 +2025-06-24 09:19:35,201 - pyskl - INFO - Epoch [7][200/1281] lr: 2.490e-02, eta: 11:19:43, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7069, top5_acc: 0.9719, loss_cls: 1.2234, loss: 1.2234 +2025-06-24 09:19:57,291 - pyskl - INFO - Epoch [7][300/1281] lr: 2.489e-02, eta: 11:19:20, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.6887, top5_acc: 0.9744, loss_cls: 1.2267, loss: 1.2267 +2025-06-24 09:20:19,015 - pyskl - INFO - Epoch [7][400/1281] lr: 2.489e-02, eta: 11:18:49, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6975, top5_acc: 0.9644, loss_cls: 1.2346, loss: 1.2346 +2025-06-24 09:20:40,931 - pyskl - INFO - Epoch [7][500/1281] lr: 2.489e-02, eta: 11:18:22, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7050, top5_acc: 0.9694, loss_cls: 1.2066, loss: 1.2066 +2025-06-24 09:21:02,569 - pyskl - INFO - Epoch [7][600/1281] lr: 2.489e-02, eta: 11:17:49, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6813, top5_acc: 0.9619, loss_cls: 1.3036, loss: 1.3036 +2025-06-24 09:21:24,592 - pyskl - INFO - Epoch [7][700/1281] lr: 2.488e-02, eta: 11:17:25, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6987, top5_acc: 0.9700, loss_cls: 1.2376, loss: 1.2376 +2025-06-24 09:21:46,367 - pyskl - INFO - Epoch [7][800/1281] lr: 2.488e-02, eta: 11:16:55, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6975, top5_acc: 0.9769, loss_cls: 1.2328, loss: 1.2328 +2025-06-24 09:22:08,489 - pyskl - INFO - Epoch [7][900/1281] lr: 2.488e-02, eta: 11:16:33, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7069, top5_acc: 0.9712, loss_cls: 1.1935, loss: 1.1935 +2025-06-24 09:22:30,594 - pyskl - INFO - Epoch [7][1000/1281] lr: 2.487e-02, eta: 11:16:11, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7025, top5_acc: 0.9744, loss_cls: 1.2029, loss: 1.2029 +2025-06-24 09:22:52,402 - pyskl - INFO - Epoch [7][1100/1281] lr: 2.487e-02, eta: 11:15:42, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7063, top5_acc: 0.9731, loss_cls: 1.2022, loss: 1.2022 +2025-06-24 09:23:14,403 - pyskl - INFO - Epoch [7][1200/1281] lr: 2.487e-02, eta: 11:15:18, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7231, top5_acc: 0.9831, loss_cls: 1.1410, loss: 1.1410 +2025-06-24 09:23:32,916 - pyskl - INFO - Saving checkpoint at 7 epochs +2025-06-24 09:24:16,451 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:24:16,504 - pyskl - INFO - +top1_acc 0.6713 +top5_acc 0.9614 +2025-06-24 09:24:16,504 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:24:16,510 - pyskl - INFO - +mean_acc 0.5276 +2025-06-24 09:24:16,514 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_6.pth was removed +2025-06-24 09:24:16,694 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2025-06-24 09:24:16,694 - pyskl - INFO - Best top1_acc is 0.6713 at 7 epoch. +2025-06-24 09:24:16,697 - pyskl - INFO - Epoch(val) [7][533] top1_acc: 0.6713, top5_acc: 0.9614, mean_class_accuracy: 0.5276 +2025-06-24 09:24:56,099 - pyskl - INFO - Epoch [8][100/1281] lr: 2.486e-02, eta: 11:14:25, time: 0.394, data_time: 0.175, memory: 4082, top1_acc: 0.7212, top5_acc: 0.9769, loss_cls: 1.1374, loss: 1.1374 +2025-06-24 09:25:17,774 - pyskl - INFO - Epoch [8][200/1281] lr: 2.486e-02, eta: 11:13:55, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7094, top5_acc: 0.9650, loss_cls: 1.1931, loss: 1.1931 +2025-06-24 09:25:39,499 - pyskl - INFO - Epoch [8][300/1281] lr: 2.486e-02, eta: 11:13:25, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7238, top5_acc: 0.9800, loss_cls: 1.1135, loss: 1.1135 +2025-06-24 09:26:01,480 - pyskl - INFO - Epoch [8][400/1281] lr: 2.485e-02, eta: 11:13:01, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7312, top5_acc: 0.9712, loss_cls: 1.1413, loss: 1.1413 +2025-06-24 09:26:23,234 - pyskl - INFO - Epoch [8][500/1281] lr: 2.485e-02, eta: 11:12:32, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7056, top5_acc: 0.9712, loss_cls: 1.1931, loss: 1.1931 +2025-06-24 09:26:45,015 - pyskl - INFO - Epoch [8][600/1281] lr: 2.485e-02, eta: 11:12:04, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7369, top5_acc: 0.9719, loss_cls: 1.1188, loss: 1.1188 +2025-06-24 09:27:06,884 - pyskl - INFO - Epoch [8][700/1281] lr: 2.484e-02, eta: 11:11:38, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7294, top5_acc: 0.9706, loss_cls: 1.1041, loss: 1.1041 +2025-06-24 09:27:28,952 - pyskl - INFO - Epoch [8][800/1281] lr: 2.484e-02, eta: 11:11:16, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.6913, top5_acc: 0.9700, loss_cls: 1.2329, loss: 1.2329 +2025-06-24 09:27:50,780 - pyskl - INFO - Epoch [8][900/1281] lr: 2.484e-02, eta: 11:10:49, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7394, top5_acc: 0.9731, loss_cls: 1.1152, loss: 1.1152 +2025-06-24 09:28:12,449 - pyskl - INFO - Epoch [8][1000/1281] lr: 2.483e-02, eta: 11:10:19, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7300, top5_acc: 0.9694, loss_cls: 1.1592, loss: 1.1592 +2025-06-24 09:28:34,284 - pyskl - INFO - Epoch [8][1100/1281] lr: 2.483e-02, eta: 11:09:53, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7300, top5_acc: 0.9731, loss_cls: 1.0954, loss: 1.0954 +2025-06-24 09:28:56,075 - pyskl - INFO - Epoch [8][1200/1281] lr: 2.483e-02, eta: 11:09:26, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7194, top5_acc: 0.9731, loss_cls: 1.1639, loss: 1.1639 +2025-06-24 09:29:14,312 - pyskl - INFO - Saving checkpoint at 8 epochs +2025-06-24 09:29:57,135 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:29:57,190 - pyskl - INFO - +top1_acc 0.7066 +top5_acc 0.9622 +2025-06-24 09:29:57,190 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:29:57,196 - pyskl - INFO - +mean_acc 0.5939 +2025-06-24 09:29:57,200 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_7.pth was removed +2025-06-24 09:29:57,376 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_8.pth. +2025-06-24 09:29:57,376 - pyskl - INFO - Best top1_acc is 0.7066 at 8 epoch. +2025-06-24 09:29:57,379 - pyskl - INFO - Epoch(val) [8][533] top1_acc: 0.7066, top5_acc: 0.9622, mean_class_accuracy: 0.5939 +2025-06-24 09:30:37,088 - pyskl - INFO - Epoch [9][100/1281] lr: 2.482e-02, eta: 11:08:41, time: 0.397, data_time: 0.177, memory: 4082, top1_acc: 0.7412, top5_acc: 0.9838, loss_cls: 1.0646, loss: 1.0646 +2025-06-24 09:30:59,059 - pyskl - INFO - Epoch [9][200/1281] lr: 2.482e-02, eta: 11:08:18, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7431, top5_acc: 0.9781, loss_cls: 1.0673, loss: 1.0673 +2025-06-24 09:31:20,875 - pyskl - INFO - Epoch [9][300/1281] lr: 2.481e-02, eta: 11:07:51, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7419, top5_acc: 0.9756, loss_cls: 1.0977, loss: 1.0977 +2025-06-24 09:31:42,386 - pyskl - INFO - Epoch [9][400/1281] lr: 2.481e-02, eta: 11:07:20, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7200, top5_acc: 0.9769, loss_cls: 1.1458, loss: 1.1458 +2025-06-24 09:32:04,039 - pyskl - INFO - Epoch [9][500/1281] lr: 2.481e-02, eta: 11:06:51, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7137, top5_acc: 0.9769, loss_cls: 1.1296, loss: 1.1296 +2025-06-24 09:32:25,915 - pyskl - INFO - Epoch [9][600/1281] lr: 2.480e-02, eta: 11:06:26, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7231, top5_acc: 0.9725, loss_cls: 1.1132, loss: 1.1132 +2025-06-24 09:32:47,930 - pyskl - INFO - Epoch [9][700/1281] lr: 2.480e-02, eta: 11:06:03, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7219, top5_acc: 0.9725, loss_cls: 1.1316, loss: 1.1316 +2025-06-24 09:33:09,748 - pyskl - INFO - Epoch [9][800/1281] lr: 2.480e-02, eta: 11:05:37, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7075, top5_acc: 0.9769, loss_cls: 1.1767, loss: 1.1767 +2025-06-24 09:33:31,796 - pyskl - INFO - Epoch [9][900/1281] lr: 2.479e-02, eta: 11:05:15, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7438, top5_acc: 0.9781, loss_cls: 1.0985, loss: 1.0985 +2025-06-24 09:33:53,239 - pyskl - INFO - Epoch [9][1000/1281] lr: 2.479e-02, eta: 11:04:43, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7206, top5_acc: 0.9731, loss_cls: 1.1206, loss: 1.1206 +2025-06-24 09:34:15,025 - pyskl - INFO - Epoch [9][1100/1281] lr: 2.479e-02, eta: 11:04:17, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7281, top5_acc: 0.9756, loss_cls: 1.1329, loss: 1.1329 +2025-06-24 09:34:36,555 - pyskl - INFO - Epoch [9][1200/1281] lr: 2.478e-02, eta: 11:03:47, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7369, top5_acc: 0.9769, loss_cls: 1.0998, loss: 1.0998 +2025-06-24 09:34:54,743 - pyskl - INFO - Saving checkpoint at 9 epochs +2025-06-24 09:35:37,637 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:35:37,704 - pyskl - INFO - +top1_acc 0.6932 +top5_acc 0.9727 +2025-06-24 09:35:37,704 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:35:37,712 - pyskl - INFO - +mean_acc 0.5639 +2025-06-24 09:35:37,714 - pyskl - INFO - Epoch(val) [9][533] top1_acc: 0.6932, top5_acc: 0.9727, mean_class_accuracy: 0.5639 +2025-06-24 09:36:18,011 - pyskl - INFO - Epoch [10][100/1281] lr: 2.477e-02, eta: 11:03:13, time: 0.403, data_time: 0.182, memory: 4082, top1_acc: 0.7512, top5_acc: 0.9819, loss_cls: 1.0398, loss: 1.0398 +2025-06-24 09:36:39,865 - pyskl - INFO - Epoch [10][200/1281] lr: 2.477e-02, eta: 11:02:48, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7450, top5_acc: 0.9738, loss_cls: 1.0751, loss: 1.0751 +2025-06-24 09:37:01,951 - pyskl - INFO - Epoch [10][300/1281] lr: 2.477e-02, eta: 11:02:27, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7406, top5_acc: 0.9800, loss_cls: 1.0689, loss: 1.0689 +2025-06-24 09:37:23,792 - pyskl - INFO - Epoch [10][400/1281] lr: 2.476e-02, eta: 11:02:01, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7325, top5_acc: 0.9819, loss_cls: 1.0582, loss: 1.0582 +2025-06-24 09:37:45,716 - pyskl - INFO - Epoch [10][500/1281] lr: 2.476e-02, eta: 11:01:38, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7462, top5_acc: 0.9819, loss_cls: 1.0576, loss: 1.0576 +2025-06-24 09:38:07,691 - pyskl - INFO - Epoch [10][600/1281] lr: 2.476e-02, eta: 11:01:15, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7319, top5_acc: 0.9712, loss_cls: 1.1086, loss: 1.1086 +2025-06-24 09:38:29,651 - pyskl - INFO - Epoch [10][700/1281] lr: 2.475e-02, eta: 11:00:51, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7431, top5_acc: 0.9800, loss_cls: 1.0409, loss: 1.0409 +2025-06-24 09:38:51,328 - pyskl - INFO - Epoch [10][800/1281] lr: 2.475e-02, eta: 11:00:24, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7219, top5_acc: 0.9781, loss_cls: 1.1275, loss: 1.1275 +2025-06-24 09:39:13,326 - pyskl - INFO - Epoch [10][900/1281] lr: 2.474e-02, eta: 11:00:01, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7388, top5_acc: 0.9775, loss_cls: 1.1041, loss: 1.1041 +2025-06-24 09:39:35,245 - pyskl - INFO - Epoch [10][1000/1281] lr: 2.474e-02, eta: 10:59:38, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7312, top5_acc: 0.9725, loss_cls: 1.0973, loss: 1.0973 +2025-06-24 09:39:57,306 - pyskl - INFO - Epoch [10][1100/1281] lr: 2.473e-02, eta: 10:59:16, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7356, top5_acc: 0.9831, loss_cls: 1.0853, loss: 1.0853 +2025-06-24 09:40:19,314 - pyskl - INFO - Epoch [10][1200/1281] lr: 2.473e-02, eta: 10:58:54, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7362, top5_acc: 0.9769, loss_cls: 1.1207, loss: 1.1207 +2025-06-24 09:40:37,593 - pyskl - INFO - Saving checkpoint at 10 epochs +2025-06-24 09:41:19,882 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:41:19,938 - pyskl - INFO - +top1_acc 0.6999 +top5_acc 0.9657 +2025-06-24 09:41:19,938 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:41:19,945 - pyskl - INFO - +mean_acc 0.5751 +2025-06-24 09:41:19,947 - pyskl - INFO - Epoch(val) [10][533] top1_acc: 0.6999, top5_acc: 0.9657, mean_class_accuracy: 0.5751 +2025-06-24 09:41:59,992 - pyskl - INFO - Epoch [11][100/1281] lr: 2.472e-02, eta: 10:58:16, time: 0.400, data_time: 0.183, memory: 4082, top1_acc: 0.7606, top5_acc: 0.9844, loss_cls: 1.0140, loss: 1.0140 +2025-06-24 09:42:21,672 - pyskl - INFO - Epoch [11][200/1281] lr: 2.472e-02, eta: 10:57:49, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7375, top5_acc: 0.9762, loss_cls: 1.0444, loss: 1.0444 +2025-06-24 09:42:43,574 - pyskl - INFO - Epoch [11][300/1281] lr: 2.471e-02, eta: 10:57:25, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7662, top5_acc: 0.9844, loss_cls: 0.9799, loss: 0.9799 +2025-06-24 09:43:05,437 - pyskl - INFO - Epoch [11][400/1281] lr: 2.471e-02, eta: 10:57:01, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7431, top5_acc: 0.9781, loss_cls: 1.0562, loss: 1.0562 +2025-06-24 09:43:27,159 - pyskl - INFO - Epoch [11][500/1281] lr: 2.471e-02, eta: 10:56:35, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7456, top5_acc: 0.9794, loss_cls: 1.0343, loss: 1.0343 +2025-06-24 09:43:48,826 - pyskl - INFO - Epoch [11][600/1281] lr: 2.470e-02, eta: 10:56:08, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7481, top5_acc: 0.9769, loss_cls: 1.0764, loss: 1.0764 +2025-06-24 09:44:10,587 - pyskl - INFO - Epoch [11][700/1281] lr: 2.470e-02, eta: 10:55:42, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7581, top5_acc: 0.9744, loss_cls: 1.0535, loss: 1.0535 +2025-06-24 09:44:32,249 - pyskl - INFO - Epoch [11][800/1281] lr: 2.469e-02, eta: 10:55:16, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7625, top5_acc: 0.9775, loss_cls: 1.0451, loss: 1.0451 +2025-06-24 09:44:53,968 - pyskl - INFO - Epoch [11][900/1281] lr: 2.469e-02, eta: 10:54:50, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7412, top5_acc: 0.9769, loss_cls: 1.0670, loss: 1.0670 +2025-06-24 09:45:15,276 - pyskl - INFO - Epoch [11][1000/1281] lr: 2.468e-02, eta: 10:54:18, time: 0.213, data_time: 0.000, memory: 4082, top1_acc: 0.7581, top5_acc: 0.9794, loss_cls: 1.0156, loss: 1.0156 +2025-06-24 09:45:37,197 - pyskl - INFO - Epoch [11][1100/1281] lr: 2.468e-02, eta: 10:53:55, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7550, top5_acc: 0.9888, loss_cls: 1.0058, loss: 1.0058 +2025-06-24 09:45:59,157 - pyskl - INFO - Epoch [11][1200/1281] lr: 2.467e-02, eta: 10:53:32, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7206, top5_acc: 0.9712, loss_cls: 1.1469, loss: 1.1469 +2025-06-24 09:46:17,393 - pyskl - INFO - Saving checkpoint at 11 epochs +2025-06-24 09:47:00,595 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:47:00,650 - pyskl - INFO - +top1_acc 0.7329 +top5_acc 0.9791 +2025-06-24 09:47:00,650 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:47:00,656 - pyskl - INFO - +mean_acc 0.6203 +2025-06-24 09:47:00,660 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_8.pth was removed +2025-06-24 09:47:00,829 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_11.pth. +2025-06-24 09:47:00,830 - pyskl - INFO - Best top1_acc is 0.7329 at 11 epoch. +2025-06-24 09:47:00,832 - pyskl - INFO - Epoch(val) [11][533] top1_acc: 0.7329, top5_acc: 0.9791, mean_class_accuracy: 0.6203 +2025-06-24 09:47:40,723 - pyskl - INFO - Epoch [12][100/1281] lr: 2.467e-02, eta: 10:52:53, time: 0.399, data_time: 0.182, memory: 4082, top1_acc: 0.7650, top5_acc: 0.9856, loss_cls: 0.9761, loss: 0.9761 +2025-06-24 09:48:02,342 - pyskl - INFO - Epoch [12][200/1281] lr: 2.466e-02, eta: 10:52:26, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7544, top5_acc: 0.9862, loss_cls: 1.0111, loss: 1.0111 +2025-06-24 09:48:23,989 - pyskl - INFO - Epoch [12][300/1281] lr: 2.466e-02, eta: 10:52:00, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7744, top5_acc: 0.9844, loss_cls: 0.9553, loss: 0.9553 +2025-06-24 09:48:45,558 - pyskl - INFO - Epoch [12][400/1281] lr: 2.465e-02, eta: 10:51:32, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7681, top5_acc: 0.9800, loss_cls: 0.9991, loss: 0.9991 +2025-06-24 09:49:06,987 - pyskl - INFO - Epoch [12][500/1281] lr: 2.465e-02, eta: 10:51:03, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7412, top5_acc: 0.9756, loss_cls: 1.0454, loss: 1.0454 +2025-06-24 09:49:28,772 - pyskl - INFO - Epoch [12][600/1281] lr: 2.464e-02, eta: 10:50:39, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7719, top5_acc: 0.9831, loss_cls: 0.9870, loss: 0.9870 +2025-06-24 09:49:50,332 - pyskl - INFO - Epoch [12][700/1281] lr: 2.464e-02, eta: 10:50:11, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7462, top5_acc: 0.9800, loss_cls: 1.0381, loss: 1.0381 +2025-06-24 09:50:12,414 - pyskl - INFO - Epoch [12][800/1281] lr: 2.463e-02, eta: 10:49:50, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7331, top5_acc: 0.9819, loss_cls: 1.0575, loss: 1.0575 +2025-06-24 09:50:34,236 - pyskl - INFO - Epoch [12][900/1281] lr: 2.463e-02, eta: 10:49:26, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7619, top5_acc: 0.9781, loss_cls: 1.0243, loss: 1.0243 +2025-06-24 09:50:55,911 - pyskl - INFO - Epoch [12][1000/1281] lr: 2.462e-02, eta: 10:49:01, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7506, top5_acc: 0.9794, loss_cls: 1.0506, loss: 1.0506 +2025-06-24 09:51:17,873 - pyskl - INFO - Epoch [12][1100/1281] lr: 2.462e-02, eta: 10:48:38, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7556, top5_acc: 0.9756, loss_cls: 1.0291, loss: 1.0291 +2025-06-24 09:51:40,089 - pyskl - INFO - Epoch [12][1200/1281] lr: 2.461e-02, eta: 10:48:19, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7331, top5_acc: 0.9775, loss_cls: 1.1149, loss: 1.1149 +2025-06-24 09:51:58,471 - pyskl - INFO - Saving checkpoint at 12 epochs +2025-06-24 09:52:41,147 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:52:41,215 - pyskl - INFO - +top1_acc 0.7040 +top5_acc 0.9693 +2025-06-24 09:52:41,215 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:52:41,222 - pyskl - INFO - +mean_acc 0.5990 +2025-06-24 09:52:41,224 - pyskl - INFO - Epoch(val) [12][533] top1_acc: 0.7040, top5_acc: 0.9693, mean_class_accuracy: 0.5990 +2025-06-24 09:53:20,557 - pyskl - INFO - Epoch [13][100/1281] lr: 2.460e-02, eta: 10:47:34, time: 0.393, data_time: 0.177, memory: 4082, top1_acc: 0.7681, top5_acc: 0.9831, loss_cls: 0.9809, loss: 0.9809 +2025-06-24 09:53:42,436 - pyskl - INFO - Epoch [13][200/1281] lr: 2.460e-02, eta: 10:47:10, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7700, top5_acc: 0.9875, loss_cls: 0.9454, loss: 0.9454 +2025-06-24 09:54:04,108 - pyskl - INFO - Epoch [13][300/1281] lr: 2.459e-02, eta: 10:46:45, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7612, top5_acc: 0.9838, loss_cls: 0.9723, loss: 0.9723 +2025-06-24 09:54:26,059 - pyskl - INFO - Epoch [13][400/1281] lr: 2.459e-02, eta: 10:46:22, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7644, top5_acc: 0.9806, loss_cls: 0.9761, loss: 0.9761 +2025-06-24 09:54:47,839 - pyskl - INFO - Epoch [13][500/1281] lr: 2.458e-02, eta: 10:45:58, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7544, top5_acc: 0.9825, loss_cls: 1.0455, loss: 1.0455 +2025-06-24 09:55:09,811 - pyskl - INFO - Epoch [13][600/1281] lr: 2.458e-02, eta: 10:45:36, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7450, top5_acc: 0.9838, loss_cls: 1.0089, loss: 1.0089 +2025-06-24 09:55:31,663 - pyskl - INFO - Epoch [13][700/1281] lr: 2.457e-02, eta: 10:45:12, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7625, top5_acc: 0.9844, loss_cls: 0.9965, loss: 0.9965 +2025-06-24 09:55:53,411 - pyskl - INFO - Epoch [13][800/1281] lr: 2.457e-02, eta: 10:44:48, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7625, top5_acc: 0.9838, loss_cls: 0.9828, loss: 0.9828 +2025-06-24 09:56:15,482 - pyskl - INFO - Epoch [13][900/1281] lr: 2.456e-02, eta: 10:44:27, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7575, top5_acc: 0.9794, loss_cls: 1.0177, loss: 1.0177 +2025-06-24 09:56:37,380 - pyskl - INFO - Epoch [13][1000/1281] lr: 2.455e-02, eta: 10:44:04, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7712, top5_acc: 0.9812, loss_cls: 0.9710, loss: 0.9710 +2025-06-24 09:56:59,657 - pyskl - INFO - Epoch [13][1100/1281] lr: 2.455e-02, eta: 10:43:45, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.7494, top5_acc: 0.9831, loss_cls: 1.0170, loss: 1.0170 +2025-06-24 09:57:21,806 - pyskl - INFO - Epoch [13][1200/1281] lr: 2.454e-02, eta: 10:43:25, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7762, top5_acc: 0.9731, loss_cls: 1.0442, loss: 1.0442 +2025-06-24 09:57:40,215 - pyskl - INFO - Saving checkpoint at 13 epochs +2025-06-24 09:58:22,693 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:58:22,750 - pyskl - INFO - +top1_acc 0.7229 +top5_acc 0.9721 +2025-06-24 09:58:22,750 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:58:22,758 - pyskl - INFO - +mean_acc 0.6146 +2025-06-24 09:58:22,759 - pyskl - INFO - Epoch(val) [13][533] top1_acc: 0.7229, top5_acc: 0.9721, mean_class_accuracy: 0.6146 +2025-06-24 09:59:02,593 - pyskl - INFO - Epoch [14][100/1281] lr: 2.453e-02, eta: 10:42:45, time: 0.398, data_time: 0.182, memory: 4082, top1_acc: 0.7675, top5_acc: 0.9844, loss_cls: 0.9766, loss: 0.9766 +2025-06-24 09:59:24,250 - pyskl - INFO - Epoch [14][200/1281] lr: 2.453e-02, eta: 10:42:20, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7731, top5_acc: 0.9812, loss_cls: 0.9495, loss: 0.9495 +2025-06-24 09:59:46,167 - pyskl - INFO - Epoch [14][300/1281] lr: 2.452e-02, eta: 10:41:57, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7863, top5_acc: 0.9894, loss_cls: 0.8829, loss: 0.8829 +2025-06-24 10:00:07,749 - pyskl - INFO - Epoch [14][400/1281] lr: 2.452e-02, eta: 10:41:31, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7644, top5_acc: 0.9825, loss_cls: 0.9649, loss: 0.9649 +2025-06-24 10:00:29,278 - pyskl - INFO - Epoch [14][500/1281] lr: 2.451e-02, eta: 10:41:04, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7575, top5_acc: 0.9831, loss_cls: 1.0135, loss: 1.0135 +2025-06-24 10:00:50,927 - pyskl - INFO - Epoch [14][600/1281] lr: 2.451e-02, eta: 10:40:39, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7625, top5_acc: 0.9856, loss_cls: 1.0074, loss: 1.0074 +2025-06-24 10:01:13,106 - pyskl - INFO - Epoch [14][700/1281] lr: 2.450e-02, eta: 10:40:19, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7694, top5_acc: 0.9862, loss_cls: 1.0012, loss: 1.0012 +2025-06-24 10:01:34,900 - pyskl - INFO - Epoch [14][800/1281] lr: 2.449e-02, eta: 10:39:55, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7644, top5_acc: 0.9825, loss_cls: 0.9636, loss: 0.9636 +2025-06-24 10:01:56,679 - pyskl - INFO - Epoch [14][900/1281] lr: 2.449e-02, eta: 10:39:31, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7656, top5_acc: 0.9775, loss_cls: 1.0293, loss: 1.0293 +2025-06-24 10:02:18,403 - pyskl - INFO - Epoch [14][1000/1281] lr: 2.448e-02, eta: 10:39:07, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7438, top5_acc: 0.9769, loss_cls: 1.0500, loss: 1.0500 +2025-06-24 10:02:40,095 - pyskl - INFO - Epoch [14][1100/1281] lr: 2.448e-02, eta: 10:38:42, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7681, top5_acc: 0.9800, loss_cls: 1.0065, loss: 1.0065 +2025-06-24 10:03:02,081 - pyskl - INFO - Epoch [14][1200/1281] lr: 2.447e-02, eta: 10:38:20, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7750, top5_acc: 0.9838, loss_cls: 0.9735, loss: 0.9735 +2025-06-24 10:03:20,119 - pyskl - INFO - Saving checkpoint at 14 epochs +2025-06-24 10:04:02,616 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:04:02,684 - pyskl - INFO - +top1_acc 0.7189 +top5_acc 0.9777 +2025-06-24 10:04:02,684 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:04:02,692 - pyskl - INFO - +mean_acc 0.6111 +2025-06-24 10:04:02,694 - pyskl - INFO - Epoch(val) [14][533] top1_acc: 0.7189, top5_acc: 0.9777, mean_class_accuracy: 0.6111 +2025-06-24 10:04:43,088 - pyskl - INFO - Epoch [15][100/1281] lr: 2.446e-02, eta: 10:37:46, time: 0.404, data_time: 0.187, memory: 4082, top1_acc: 0.7631, top5_acc: 0.9850, loss_cls: 0.9675, loss: 0.9675 +2025-06-24 10:05:04,998 - pyskl - INFO - Epoch [15][200/1281] lr: 2.445e-02, eta: 10:37:23, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7631, top5_acc: 0.9788, loss_cls: 1.0004, loss: 1.0004 +2025-06-24 10:05:26,726 - pyskl - INFO - Epoch [15][300/1281] lr: 2.445e-02, eta: 10:36:59, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7875, top5_acc: 0.9856, loss_cls: 0.8978, loss: 0.8978 +2025-06-24 10:05:48,595 - pyskl - INFO - Epoch [15][400/1281] lr: 2.444e-02, eta: 10:36:36, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7750, top5_acc: 0.9844, loss_cls: 0.9598, loss: 0.9598 +2025-06-24 10:06:10,167 - pyskl - INFO - Epoch [15][500/1281] lr: 2.444e-02, eta: 10:36:10, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9806, loss_cls: 0.9404, loss: 0.9404 +2025-06-24 10:06:31,795 - pyskl - INFO - Epoch [15][600/1281] lr: 2.443e-02, eta: 10:35:45, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7775, top5_acc: 0.9756, loss_cls: 0.9840, loss: 0.9840 +2025-06-24 10:06:53,835 - pyskl - INFO - Epoch [15][700/1281] lr: 2.442e-02, eta: 10:35:24, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7631, top5_acc: 0.9825, loss_cls: 0.9994, loss: 0.9994 +2025-06-24 10:07:15,697 - pyskl - INFO - Epoch [15][800/1281] lr: 2.442e-02, eta: 10:35:01, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7594, top5_acc: 0.9881, loss_cls: 0.9786, loss: 0.9786 +2025-06-24 10:07:37,508 - pyskl - INFO - Epoch [15][900/1281] lr: 2.441e-02, eta: 10:34:37, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7688, top5_acc: 0.9894, loss_cls: 0.9617, loss: 0.9617 +2025-06-24 10:07:59,537 - pyskl - INFO - Epoch [15][1000/1281] lr: 2.441e-02, eta: 10:34:16, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7719, top5_acc: 0.9869, loss_cls: 0.9374, loss: 0.9374 +2025-06-24 10:08:21,337 - pyskl - INFO - Epoch [15][1100/1281] lr: 2.440e-02, eta: 10:33:52, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7631, top5_acc: 0.9844, loss_cls: 0.9703, loss: 0.9703 +2025-06-24 10:08:43,322 - pyskl - INFO - Epoch [15][1200/1281] lr: 2.439e-02, eta: 10:33:31, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7581, top5_acc: 0.9856, loss_cls: 0.9888, loss: 0.9888 +2025-06-24 10:09:01,922 - pyskl - INFO - Saving checkpoint at 15 epochs +2025-06-24 10:09:44,718 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:09:44,784 - pyskl - INFO - +top1_acc 0.7093 +top5_acc 0.9715 +2025-06-24 10:09:44,784 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:09:44,792 - pyskl - INFO - +mean_acc 0.5803 +2025-06-24 10:09:44,794 - pyskl - INFO - Epoch(val) [15][533] top1_acc: 0.7093, top5_acc: 0.9715, mean_class_accuracy: 0.5803 +2025-06-24 10:10:25,019 - pyskl - INFO - Epoch [16][100/1281] lr: 2.438e-02, eta: 10:32:55, time: 0.402, data_time: 0.181, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9881, loss_cls: 0.9467, loss: 0.9467 +2025-06-24 10:10:46,881 - pyskl - INFO - Epoch [16][200/1281] lr: 2.438e-02, eta: 10:32:32, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7688, top5_acc: 0.9875, loss_cls: 0.9193, loss: 0.9193 +2025-06-24 10:11:08,792 - pyskl - INFO - Epoch [16][300/1281] lr: 2.437e-02, eta: 10:32:09, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7837, top5_acc: 0.9862, loss_cls: 0.9298, loss: 0.9298 +2025-06-24 10:11:30,528 - pyskl - INFO - Epoch [16][400/1281] lr: 2.436e-02, eta: 10:31:45, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9819, loss_cls: 0.8876, loss: 0.8876 +2025-06-24 10:11:52,150 - pyskl - INFO - Epoch [16][500/1281] lr: 2.436e-02, eta: 10:31:20, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7919, top5_acc: 0.9881, loss_cls: 0.9030, loss: 0.9030 +2025-06-24 10:12:14,060 - pyskl - INFO - Epoch [16][600/1281] lr: 2.435e-02, eta: 10:30:58, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7706, top5_acc: 0.9788, loss_cls: 0.9930, loss: 0.9930 +2025-06-24 10:12:36,400 - pyskl - INFO - Epoch [16][700/1281] lr: 2.434e-02, eta: 10:30:39, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.7656, top5_acc: 0.9894, loss_cls: 0.9450, loss: 0.9450 +2025-06-24 10:12:58,386 - pyskl - INFO - Epoch [16][800/1281] lr: 2.434e-02, eta: 10:30:17, time: 0.220, data_time: 0.001, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9869, loss_cls: 0.8990, loss: 0.8990 +2025-06-24 10:13:20,181 - pyskl - INFO - Epoch [16][900/1281] lr: 2.433e-02, eta: 10:29:54, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7544, top5_acc: 0.9838, loss_cls: 0.9742, loss: 0.9742 +2025-06-24 10:13:42,268 - pyskl - INFO - Epoch [16][1000/1281] lr: 2.432e-02, eta: 10:29:33, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7806, top5_acc: 0.9769, loss_cls: 0.9893, loss: 0.9893 +2025-06-24 10:14:04,020 - pyskl - INFO - Epoch [16][1100/1281] lr: 2.432e-02, eta: 10:29:09, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7762, top5_acc: 0.9831, loss_cls: 0.9296, loss: 0.9296 +2025-06-24 10:14:26,302 - pyskl - INFO - Epoch [16][1200/1281] lr: 2.431e-02, eta: 10:28:50, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.7725, top5_acc: 0.9844, loss_cls: 0.9539, loss: 0.9539 +2025-06-24 10:14:45,190 - pyskl - INFO - Saving checkpoint at 16 epochs +2025-06-24 10:15:29,652 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:15:29,721 - pyskl - INFO - +top1_acc 0.7428 +top5_acc 0.9796 +2025-06-24 10:15:29,721 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:15:29,730 - pyskl - INFO - +mean_acc 0.6423 +2025-06-24 10:15:29,734 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_11.pth was removed +2025-06-24 10:15:29,934 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_16.pth. +2025-06-24 10:15:29,934 - pyskl - INFO - Best top1_acc is 0.7428 at 16 epoch. +2025-06-24 10:15:29,939 - pyskl - INFO - Epoch(val) [16][533] top1_acc: 0.7428, top5_acc: 0.9796, mean_class_accuracy: 0.6423 +2025-06-24 10:16:11,256 - pyskl - INFO - Epoch [17][100/1281] lr: 2.430e-02, eta: 10:28:23, time: 0.413, data_time: 0.192, memory: 4082, top1_acc: 0.7812, top5_acc: 0.9806, loss_cls: 0.9624, loss: 0.9624 +2025-06-24 10:16:33,336 - pyskl - INFO - Epoch [17][200/1281] lr: 2.429e-02, eta: 10:28:02, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9888, loss_cls: 0.8368, loss: 0.8368 +2025-06-24 10:16:55,312 - pyskl - INFO - Epoch [17][300/1281] lr: 2.428e-02, eta: 10:27:40, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7788, top5_acc: 0.9788, loss_cls: 0.9542, loss: 0.9542 +2025-06-24 10:17:17,114 - pyskl - INFO - Epoch [17][400/1281] lr: 2.428e-02, eta: 10:27:16, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9875, loss_cls: 0.8834, loss: 0.8834 +2025-06-24 10:17:39,084 - pyskl - INFO - Epoch [17][500/1281] lr: 2.427e-02, eta: 10:26:54, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7975, top5_acc: 0.9844, loss_cls: 0.8854, loss: 0.8854 +2025-06-24 10:18:00,895 - pyskl - INFO - Epoch [17][600/1281] lr: 2.426e-02, eta: 10:26:31, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7662, top5_acc: 0.9788, loss_cls: 0.9914, loss: 0.9914 +2025-06-24 10:18:22,956 - pyskl - INFO - Epoch [17][700/1281] lr: 2.426e-02, eta: 10:26:09, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7806, top5_acc: 0.9819, loss_cls: 0.9598, loss: 0.9598 +2025-06-24 10:18:44,949 - pyskl - INFO - Epoch [17][800/1281] lr: 2.425e-02, eta: 10:25:48, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7937, top5_acc: 0.9862, loss_cls: 0.9001, loss: 0.9001 +2025-06-24 10:19:06,833 - pyskl - INFO - Epoch [17][900/1281] lr: 2.424e-02, eta: 10:25:25, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7837, top5_acc: 0.9850, loss_cls: 0.9262, loss: 0.9262 +2025-06-24 10:19:28,765 - pyskl - INFO - Epoch [17][1000/1281] lr: 2.424e-02, eta: 10:25:03, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9888, loss_cls: 0.8525, loss: 0.8525 +2025-06-24 10:19:50,639 - pyskl - INFO - Epoch [17][1100/1281] lr: 2.423e-02, eta: 10:24:40, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7819, top5_acc: 0.9831, loss_cls: 0.9291, loss: 0.9291 +2025-06-24 10:20:12,620 - pyskl - INFO - Epoch [17][1200/1281] lr: 2.422e-02, eta: 10:24:18, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7594, top5_acc: 0.9838, loss_cls: 1.0049, loss: 1.0049 +2025-06-24 10:20:31,167 - pyskl - INFO - Saving checkpoint at 17 epochs +2025-06-24 10:21:14,548 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:21:14,614 - pyskl - INFO - +top1_acc 0.7409 +top5_acc 0.9809 +2025-06-24 10:21:14,614 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:21:14,622 - pyskl - INFO - +mean_acc 0.6205 +2025-06-24 10:21:14,624 - pyskl - INFO - Epoch(val) [17][533] top1_acc: 0.7409, top5_acc: 0.9809, mean_class_accuracy: 0.6205 +2025-06-24 10:21:55,658 - pyskl - INFO - Epoch [18][100/1281] lr: 2.421e-02, eta: 10:23:48, time: 0.410, data_time: 0.191, memory: 4082, top1_acc: 0.7944, top5_acc: 0.9850, loss_cls: 0.8737, loss: 0.8737 +2025-06-24 10:22:17,607 - pyskl - INFO - Epoch [18][200/1281] lr: 2.420e-02, eta: 10:23:26, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7694, top5_acc: 0.9888, loss_cls: 0.9302, loss: 0.9302 +2025-06-24 10:22:39,417 - pyskl - INFO - Epoch [18][300/1281] lr: 2.419e-02, eta: 10:23:02, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7919, top5_acc: 0.9888, loss_cls: 0.8481, loss: 0.8481 +2025-06-24 10:23:01,382 - pyskl - INFO - Epoch [18][400/1281] lr: 2.419e-02, eta: 10:22:40, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7950, top5_acc: 0.9875, loss_cls: 0.8811, loss: 0.8811 +2025-06-24 10:23:23,713 - pyskl - INFO - Epoch [18][500/1281] lr: 2.418e-02, eta: 10:22:21, time: 0.223, data_time: 0.001, memory: 4082, top1_acc: 0.7975, top5_acc: 0.9881, loss_cls: 0.8790, loss: 0.8790 +2025-06-24 10:23:45,562 - pyskl - INFO - Epoch [18][600/1281] lr: 2.417e-02, eta: 10:21:58, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7944, top5_acc: 0.9838, loss_cls: 0.9121, loss: 0.9121 +2025-06-24 10:24:07,896 - pyskl - INFO - Epoch [18][700/1281] lr: 2.417e-02, eta: 10:21:39, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.7800, top5_acc: 0.9838, loss_cls: 0.9389, loss: 0.9389 +2025-06-24 10:24:29,966 - pyskl - INFO - Epoch [18][800/1281] lr: 2.416e-02, eta: 10:21:17, time: 0.221, data_time: 0.001, memory: 4082, top1_acc: 0.7831, top5_acc: 0.9838, loss_cls: 0.9323, loss: 0.9323 +2025-06-24 10:24:52,120 - pyskl - INFO - Epoch [18][900/1281] lr: 2.415e-02, eta: 10:20:56, time: 0.222, data_time: 0.001, memory: 4082, top1_acc: 0.7906, top5_acc: 0.9844, loss_cls: 0.8931, loss: 0.8931 +2025-06-24 10:25:14,303 - pyskl - INFO - Epoch [18][1000/1281] lr: 2.414e-02, eta: 10:20:36, time: 0.222, data_time: 0.001, memory: 4082, top1_acc: 0.8063, top5_acc: 0.9888, loss_cls: 0.8713, loss: 0.8713 +2025-06-24 10:25:36,419 - pyskl - INFO - Epoch [18][1100/1281] lr: 2.414e-02, eta: 10:20:15, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8031, top5_acc: 0.9819, loss_cls: 0.8852, loss: 0.8852 +2025-06-24 10:25:58,582 - pyskl - INFO - Epoch [18][1200/1281] lr: 2.413e-02, eta: 10:19:54, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7731, top5_acc: 0.9819, loss_cls: 0.9339, loss: 0.9339 +2025-06-24 10:26:17,245 - pyskl - INFO - Saving checkpoint at 18 epochs +2025-06-24 10:27:01,790 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:27:01,854 - pyskl - INFO - +top1_acc 0.7178 +top5_acc 0.9688 +2025-06-24 10:27:01,854 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:27:01,861 - pyskl - INFO - +mean_acc 0.5926 +2025-06-24 10:27:01,862 - pyskl - INFO - Epoch(val) [18][533] top1_acc: 0.7178, top5_acc: 0.9688, mean_class_accuracy: 0.5926 +2025-06-24 10:27:43,550 - pyskl - INFO - Epoch [19][100/1281] lr: 2.411e-02, eta: 10:19:28, time: 0.417, data_time: 0.197, memory: 4082, top1_acc: 0.7931, top5_acc: 0.9906, loss_cls: 0.8809, loss: 0.8809 +2025-06-24 10:28:05,378 - pyskl - INFO - Epoch [19][200/1281] lr: 2.411e-02, eta: 10:19:05, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7856, top5_acc: 0.9856, loss_cls: 0.8968, loss: 0.8968 +2025-06-24 10:28:27,164 - pyskl - INFO - Epoch [19][300/1281] lr: 2.410e-02, eta: 10:18:42, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7875, top5_acc: 0.9856, loss_cls: 0.8831, loss: 0.8831 +2025-06-24 10:28:49,105 - pyskl - INFO - Epoch [19][400/1281] lr: 2.409e-02, eta: 10:18:19, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9869, loss_cls: 0.8730, loss: 0.8730 +2025-06-24 10:29:10,960 - pyskl - INFO - Epoch [19][500/1281] lr: 2.408e-02, eta: 10:17:56, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7969, top5_acc: 0.9869, loss_cls: 0.8294, loss: 0.8294 +2025-06-24 10:29:32,666 - pyskl - INFO - Epoch [19][600/1281] lr: 2.408e-02, eta: 10:17:32, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7969, top5_acc: 0.9825, loss_cls: 0.8551, loss: 0.8551 +2025-06-24 10:29:54,444 - pyskl - INFO - Epoch [19][700/1281] lr: 2.407e-02, eta: 10:17:09, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8075, top5_acc: 0.9838, loss_cls: 0.8844, loss: 0.8844 +2025-06-24 10:30:16,341 - pyskl - INFO - Epoch [19][800/1281] lr: 2.406e-02, eta: 10:16:46, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7781, top5_acc: 0.9844, loss_cls: 0.8988, loss: 0.8988 +2025-06-24 10:30:38,135 - pyskl - INFO - Epoch [19][900/1281] lr: 2.405e-02, eta: 10:16:23, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7706, top5_acc: 0.9838, loss_cls: 0.9523, loss: 0.9523 +2025-06-24 10:30:59,837 - pyskl - INFO - Epoch [19][1000/1281] lr: 2.405e-02, eta: 10:15:59, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7981, top5_acc: 0.9875, loss_cls: 0.9060, loss: 0.9060 +2025-06-24 10:31:21,894 - pyskl - INFO - Epoch [19][1100/1281] lr: 2.404e-02, eta: 10:15:37, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7900, top5_acc: 0.9894, loss_cls: 0.8655, loss: 0.8655 +2025-06-24 10:31:43,859 - pyskl - INFO - Epoch [19][1200/1281] lr: 2.403e-02, eta: 10:15:15, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8113, top5_acc: 0.9838, loss_cls: 0.8162, loss: 0.8162 +2025-06-24 10:32:02,358 - pyskl - INFO - Saving checkpoint at 19 epochs +2025-06-24 10:32:46,079 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:32:46,132 - pyskl - INFO - +top1_acc 0.7830 +top5_acc 0.9856 +2025-06-24 10:32:46,133 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:32:46,140 - pyskl - INFO - +mean_acc 0.6800 +2025-06-24 10:32:46,144 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_16.pth was removed +2025-06-24 10:32:46,314 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_19.pth. +2025-06-24 10:32:46,315 - pyskl - INFO - Best top1_acc is 0.7830 at 19 epoch. +2025-06-24 10:32:46,317 - pyskl - INFO - Epoch(val) [19][533] top1_acc: 0.7830, top5_acc: 0.9856, mean_class_accuracy: 0.6800 +2025-06-24 10:33:27,561 - pyskl - INFO - Epoch [20][100/1281] lr: 2.402e-02, eta: 10:14:45, time: 0.412, data_time: 0.189, memory: 4082, top1_acc: 0.8013, top5_acc: 0.9906, loss_cls: 0.8267, loss: 0.8267 +2025-06-24 10:33:49,459 - pyskl - INFO - Epoch [20][200/1281] lr: 2.401e-02, eta: 10:14:22, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7831, top5_acc: 0.9894, loss_cls: 0.8795, loss: 0.8795 +2025-06-24 10:34:11,677 - pyskl - INFO - Epoch [20][300/1281] lr: 2.400e-02, eta: 10:14:02, time: 0.222, data_time: 0.001, memory: 4082, top1_acc: 0.8231, top5_acc: 0.9900, loss_cls: 0.8056, loss: 0.8056 +2025-06-24 10:34:33,714 - pyskl - INFO - Epoch [20][400/1281] lr: 2.399e-02, eta: 10:13:40, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9906, loss_cls: 0.8759, loss: 0.8759 +2025-06-24 10:34:55,863 - pyskl - INFO - Epoch [20][500/1281] lr: 2.398e-02, eta: 10:13:19, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7913, top5_acc: 0.9869, loss_cls: 0.9037, loss: 0.9037 +2025-06-24 10:35:17,849 - pyskl - INFO - Epoch [20][600/1281] lr: 2.398e-02, eta: 10:12:57, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7987, top5_acc: 0.9875, loss_cls: 0.8620, loss: 0.8620 +2025-06-24 10:35:39,581 - pyskl - INFO - Epoch [20][700/1281] lr: 2.397e-02, eta: 10:12:33, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9881, loss_cls: 0.8249, loss: 0.8249 +2025-06-24 10:36:01,763 - pyskl - INFO - Epoch [20][800/1281] lr: 2.396e-02, eta: 10:12:13, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7644, top5_acc: 0.9838, loss_cls: 0.9477, loss: 0.9477 +2025-06-24 10:36:23,708 - pyskl - INFO - Epoch [20][900/1281] lr: 2.395e-02, eta: 10:11:50, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8069, top5_acc: 0.9881, loss_cls: 0.8703, loss: 0.8703 +2025-06-24 10:36:45,753 - pyskl - INFO - Epoch [20][1000/1281] lr: 2.394e-02, eta: 10:11:29, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7981, top5_acc: 0.9875, loss_cls: 0.8641, loss: 0.8641 +2025-06-24 10:37:07,586 - pyskl - INFO - Epoch [20][1100/1281] lr: 2.393e-02, eta: 10:11:06, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9862, loss_cls: 0.8352, loss: 0.8352 +2025-06-24 10:37:29,273 - pyskl - INFO - Epoch [20][1200/1281] lr: 2.393e-02, eta: 10:10:42, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9906, loss_cls: 0.8393, loss: 0.8393 +2025-06-24 10:37:48,021 - pyskl - INFO - Saving checkpoint at 20 epochs +2025-06-24 10:38:31,793 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:38:31,860 - pyskl - INFO - +top1_acc 0.7632 +top5_acc 0.9843 +2025-06-24 10:38:31,860 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:38:31,868 - pyskl - INFO - +mean_acc 0.6485 +2025-06-24 10:38:31,870 - pyskl - INFO - Epoch(val) [20][533] top1_acc: 0.7632, top5_acc: 0.9843, mean_class_accuracy: 0.6485 +2025-06-24 10:39:12,886 - pyskl - INFO - Epoch [21][100/1281] lr: 2.391e-02, eta: 10:10:10, time: 0.410, data_time: 0.190, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9925, loss_cls: 0.8211, loss: 0.8211 +2025-06-24 10:39:34,855 - pyskl - INFO - Epoch [21][200/1281] lr: 2.390e-02, eta: 10:09:47, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9881, loss_cls: 0.7896, loss: 0.7896 +2025-06-24 10:39:56,788 - pyskl - INFO - Epoch [21][300/1281] lr: 2.389e-02, eta: 10:09:25, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7850, top5_acc: 0.9875, loss_cls: 0.8796, loss: 0.8796 +2025-06-24 10:40:18,907 - pyskl - INFO - Epoch [21][400/1281] lr: 2.389e-02, eta: 10:09:04, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9912, loss_cls: 0.8304, loss: 0.8304 +2025-06-24 10:40:40,671 - pyskl - INFO - Epoch [21][500/1281] lr: 2.388e-02, eta: 10:08:40, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8237, top5_acc: 0.9888, loss_cls: 0.7767, loss: 0.7767 +2025-06-24 10:41:02,813 - pyskl - INFO - Epoch [21][600/1281] lr: 2.387e-02, eta: 10:08:19, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7750, top5_acc: 0.9888, loss_cls: 0.9043, loss: 0.9043 +2025-06-24 10:41:24,729 - pyskl - INFO - Epoch [21][700/1281] lr: 2.386e-02, eta: 10:07:57, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8069, top5_acc: 0.9900, loss_cls: 0.8506, loss: 0.8506 +2025-06-24 10:41:46,735 - pyskl - INFO - Epoch [21][800/1281] lr: 2.385e-02, eta: 10:07:35, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9900, loss_cls: 0.8084, loss: 0.8084 +2025-06-24 10:42:08,775 - pyskl - INFO - Epoch [21][900/1281] lr: 2.384e-02, eta: 10:07:13, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8013, top5_acc: 0.9794, loss_cls: 0.8981, loss: 0.8981 +2025-06-24 10:42:30,930 - pyskl - INFO - Epoch [21][1000/1281] lr: 2.383e-02, eta: 10:06:52, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7987, top5_acc: 0.9881, loss_cls: 0.8500, loss: 0.8500 +2025-06-24 10:42:52,831 - pyskl - INFO - Epoch [21][1100/1281] lr: 2.383e-02, eta: 10:06:29, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8063, top5_acc: 0.9888, loss_cls: 0.8492, loss: 0.8492 +2025-06-24 10:43:15,258 - pyskl - INFO - Epoch [21][1200/1281] lr: 2.382e-02, eta: 10:06:10, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.7925, top5_acc: 0.9869, loss_cls: 0.8798, loss: 0.8798 +2025-06-24 10:43:33,802 - pyskl - INFO - Saving checkpoint at 21 epochs +2025-06-24 10:44:18,686 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:44:18,742 - pyskl - INFO - +top1_acc 0.7530 +top5_acc 0.9795 +2025-06-24 10:44:18,742 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:44:18,749 - pyskl - INFO - +mean_acc 0.6495 +2025-06-24 10:44:18,752 - pyskl - INFO - Epoch(val) [21][533] top1_acc: 0.7530, top5_acc: 0.9795, mean_class_accuracy: 0.6495 +2025-06-24 10:45:00,442 - pyskl - INFO - Epoch [22][100/1281] lr: 2.380e-02, eta: 10:05:42, time: 0.417, data_time: 0.192, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9894, loss_cls: 0.8426, loss: 0.8426 +2025-06-24 10:45:22,812 - pyskl - INFO - Epoch [22][200/1281] lr: 2.379e-02, eta: 10:05:22, time: 0.224, data_time: 0.001, memory: 4082, top1_acc: 0.8275, top5_acc: 0.9925, loss_cls: 0.7858, loss: 0.7858 +2025-06-24 10:45:44,545 - pyskl - INFO - Epoch [22][300/1281] lr: 2.378e-02, eta: 10:04:58, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9938, loss_cls: 0.7859, loss: 0.7859 +2025-06-24 10:46:06,451 - pyskl - INFO - Epoch [22][400/1281] lr: 2.378e-02, eta: 10:04:36, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7969, top5_acc: 0.9888, loss_cls: 0.8501, loss: 0.8501 +2025-06-24 10:46:28,903 - pyskl - INFO - Epoch [22][500/1281] lr: 2.377e-02, eta: 10:04:16, time: 0.225, data_time: 0.000, memory: 4082, top1_acc: 0.8056, top5_acc: 0.9900, loss_cls: 0.8382, loss: 0.8382 +2025-06-24 10:46:51,036 - pyskl - INFO - Epoch [22][600/1281] lr: 2.376e-02, eta: 10:03:55, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9881, loss_cls: 0.7835, loss: 0.7835 +2025-06-24 10:47:13,001 - pyskl - INFO - Epoch [22][700/1281] lr: 2.375e-02, eta: 10:03:33, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8013, top5_acc: 0.9888, loss_cls: 0.8615, loss: 0.8615 +2025-06-24 10:47:35,132 - pyskl - INFO - Epoch [22][800/1281] lr: 2.374e-02, eta: 10:03:11, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7937, top5_acc: 0.9850, loss_cls: 0.8732, loss: 0.8732 +2025-06-24 10:47:57,035 - pyskl - INFO - Epoch [22][900/1281] lr: 2.373e-02, eta: 10:02:49, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7944, top5_acc: 0.9862, loss_cls: 0.8795, loss: 0.8795 +2025-06-24 10:48:18,919 - pyskl - INFO - Epoch [22][1000/1281] lr: 2.372e-02, eta: 10:02:26, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8063, top5_acc: 0.9894, loss_cls: 0.8341, loss: 0.8341 +2025-06-24 10:48:40,882 - pyskl - INFO - Epoch [22][1100/1281] lr: 2.371e-02, eta: 10:02:04, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9931, loss_cls: 0.7338, loss: 0.7338 +2025-06-24 10:49:02,766 - pyskl - INFO - Epoch [22][1200/1281] lr: 2.370e-02, eta: 10:01:41, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8031, top5_acc: 0.9844, loss_cls: 0.8216, loss: 0.8216 +2025-06-24 10:49:21,445 - pyskl - INFO - Saving checkpoint at 22 epochs +2025-06-24 10:50:05,157 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:50:05,213 - pyskl - INFO - +top1_acc 0.7270 +top5_acc 0.9764 +2025-06-24 10:50:05,213 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:50:05,219 - pyskl - INFO - +mean_acc 0.6334 +2025-06-24 10:50:05,221 - pyskl - INFO - Epoch(val) [22][533] top1_acc: 0.7270, top5_acc: 0.9764, mean_class_accuracy: 0.6334 +2025-06-24 10:50:47,167 - pyskl - INFO - Epoch [23][100/1281] lr: 2.369e-02, eta: 10:01:13, time: 0.419, data_time: 0.198, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9931, loss_cls: 0.7885, loss: 0.7885 +2025-06-24 10:51:09,105 - pyskl - INFO - Epoch [23][200/1281] lr: 2.368e-02, eta: 10:00:51, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9925, loss_cls: 0.7862, loss: 0.7862 +2025-06-24 10:51:31,001 - pyskl - INFO - Epoch [23][300/1281] lr: 2.367e-02, eta: 10:00:28, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9931, loss_cls: 0.7535, loss: 0.7535 +2025-06-24 10:51:52,944 - pyskl - INFO - Epoch [23][400/1281] lr: 2.366e-02, eta: 10:00:06, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9881, loss_cls: 0.7279, loss: 0.7279 +2025-06-24 10:52:14,988 - pyskl - INFO - Epoch [23][500/1281] lr: 2.365e-02, eta: 9:59:44, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7869, top5_acc: 0.9806, loss_cls: 0.9137, loss: 0.9137 +2025-06-24 10:52:37,063 - pyskl - INFO - Epoch [23][600/1281] lr: 2.364e-02, eta: 9:59:22, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8106, top5_acc: 0.9925, loss_cls: 0.7956, loss: 0.7956 +2025-06-24 10:52:59,165 - pyskl - INFO - Epoch [23][700/1281] lr: 2.363e-02, eta: 9:59:01, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8087, top5_acc: 0.9862, loss_cls: 0.8287, loss: 0.8287 +2025-06-24 10:53:21,348 - pyskl - INFO - Epoch [23][800/1281] lr: 2.362e-02, eta: 9:58:40, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8113, top5_acc: 0.9888, loss_cls: 0.7845, loss: 0.7845 +2025-06-24 10:53:43,573 - pyskl - INFO - Epoch [23][900/1281] lr: 2.361e-02, eta: 9:58:19, time: 0.222, data_time: 0.001, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9919, loss_cls: 0.8166, loss: 0.8166 +2025-06-24 10:54:05,576 - pyskl - INFO - Epoch [23][1000/1281] lr: 2.360e-02, eta: 9:57:57, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9912, loss_cls: 0.8328, loss: 0.8328 +2025-06-24 10:54:27,614 - pyskl - INFO - Epoch [23][1100/1281] lr: 2.359e-02, eta: 9:57:35, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7956, top5_acc: 0.9819, loss_cls: 0.8915, loss: 0.8915 +2025-06-24 10:54:49,353 - pyskl - INFO - Epoch [23][1200/1281] lr: 2.359e-02, eta: 9:57:11, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7963, top5_acc: 0.9894, loss_cls: 0.8395, loss: 0.8395 +2025-06-24 10:55:08,254 - pyskl - INFO - Saving checkpoint at 23 epochs +2025-06-24 10:55:52,610 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:55:52,666 - pyskl - INFO - +top1_acc 0.7095 +top5_acc 0.9613 +2025-06-24 10:55:52,666 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:55:52,673 - pyskl - INFO - +mean_acc 0.6056 +2025-06-24 10:55:52,675 - pyskl - INFO - Epoch(val) [23][533] top1_acc: 0.7095, top5_acc: 0.9613, mean_class_accuracy: 0.6056 +2025-06-24 10:56:34,020 - pyskl - INFO - Epoch [24][100/1281] lr: 2.357e-02, eta: 9:56:40, time: 0.413, data_time: 0.191, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9844, loss_cls: 0.8237, loss: 0.8237 +2025-06-24 10:56:55,797 - pyskl - INFO - Epoch [24][200/1281] lr: 2.356e-02, eta: 9:56:16, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8125, top5_acc: 0.9888, loss_cls: 0.8262, loss: 0.8262 +2025-06-24 10:57:17,784 - pyskl - INFO - Epoch [24][300/1281] lr: 2.355e-02, eta: 9:55:54, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9906, loss_cls: 0.7623, loss: 0.7623 +2025-06-24 10:57:39,725 - pyskl - INFO - Epoch [24][400/1281] lr: 2.354e-02, eta: 9:55:32, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9950, loss_cls: 0.7814, loss: 0.7814 +2025-06-24 10:58:01,741 - pyskl - INFO - Epoch [24][500/1281] lr: 2.353e-02, eta: 9:55:10, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9894, loss_cls: 0.7899, loss: 0.7899 +2025-06-24 10:58:23,584 - pyskl - INFO - Epoch [24][600/1281] lr: 2.352e-02, eta: 9:54:47, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8206, top5_acc: 0.9900, loss_cls: 0.7889, loss: 0.7889 +2025-06-24 10:58:45,450 - pyskl - INFO - Epoch [24][700/1281] lr: 2.351e-02, eta: 9:54:24, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8044, top5_acc: 0.9894, loss_cls: 0.8630, loss: 0.8630 +2025-06-24 10:59:07,689 - pyskl - INFO - Epoch [24][800/1281] lr: 2.350e-02, eta: 9:54:03, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9888, loss_cls: 0.8804, loss: 0.8804 +2025-06-24 10:59:29,581 - pyskl - INFO - Epoch [24][900/1281] lr: 2.349e-02, eta: 9:53:40, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9925, loss_cls: 0.7623, loss: 0.7623 +2025-06-24 10:59:51,483 - pyskl - INFO - Epoch [24][1000/1281] lr: 2.348e-02, eta: 9:53:18, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8263, top5_acc: 0.9919, loss_cls: 0.7642, loss: 0.7642 +2025-06-24 11:00:13,438 - pyskl - INFO - Epoch [24][1100/1281] lr: 2.347e-02, eta: 9:52:55, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8069, top5_acc: 0.9862, loss_cls: 0.8418, loss: 0.8418 +2025-06-24 11:00:35,346 - pyskl - INFO - Epoch [24][1200/1281] lr: 2.346e-02, eta: 9:52:33, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8069, top5_acc: 0.9831, loss_cls: 0.8723, loss: 0.8723 +2025-06-24 11:00:54,102 - pyskl - INFO - Saving checkpoint at 24 epochs +2025-06-24 11:01:38,227 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:01:38,296 - pyskl - INFO - +top1_acc 0.7757 +top5_acc 0.9822 +2025-06-24 11:01:38,296 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:01:38,305 - pyskl - INFO - +mean_acc 0.6751 +2025-06-24 11:01:38,307 - pyskl - INFO - Epoch(val) [24][533] top1_acc: 0.7757, top5_acc: 0.9822, mean_class_accuracy: 0.6751 +2025-06-24 11:02:19,744 - pyskl - INFO - Epoch [25][100/1281] lr: 2.344e-02, eta: 9:52:01, time: 0.414, data_time: 0.194, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9881, loss_cls: 0.8239, loss: 0.8239 +2025-06-24 11:02:41,988 - pyskl - INFO - Epoch [25][200/1281] lr: 2.343e-02, eta: 9:51:40, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9944, loss_cls: 0.7313, loss: 0.7313 +2025-06-24 11:03:03,917 - pyskl - INFO - Epoch [25][300/1281] lr: 2.342e-02, eta: 9:51:18, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8050, top5_acc: 0.9906, loss_cls: 0.7833, loss: 0.7833 +2025-06-24 11:03:26,299 - pyskl - INFO - Epoch [25][400/1281] lr: 2.341e-02, eta: 9:50:58, time: 0.224, data_time: 0.001, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9938, loss_cls: 0.8037, loss: 0.8037 +2025-06-24 11:03:48,021 - pyskl - INFO - Epoch [25][500/1281] lr: 2.340e-02, eta: 9:50:34, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9931, loss_cls: 0.7863, loss: 0.7863 +2025-06-24 11:04:10,246 - pyskl - INFO - Epoch [25][600/1281] lr: 2.339e-02, eta: 9:50:13, time: 0.222, data_time: 0.001, memory: 4082, top1_acc: 0.7987, top5_acc: 0.9831, loss_cls: 0.8706, loss: 0.8706 +2025-06-24 11:04:32,314 - pyskl - INFO - Epoch [25][700/1281] lr: 2.338e-02, eta: 9:49:51, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9888, loss_cls: 0.7533, loss: 0.7533 +2025-06-24 11:04:54,200 - pyskl - INFO - Epoch [25][800/1281] lr: 2.337e-02, eta: 9:49:29, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8125, top5_acc: 0.9919, loss_cls: 0.8021, loss: 0.8021 +2025-06-24 11:05:16,538 - pyskl - INFO - Epoch [25][900/1281] lr: 2.336e-02, eta: 9:49:08, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9919, loss_cls: 0.8122, loss: 0.8122 +2025-06-24 11:05:38,780 - pyskl - INFO - Epoch [25][1000/1281] lr: 2.335e-02, eta: 9:48:47, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8163, top5_acc: 0.9900, loss_cls: 0.8030, loss: 0.8030 +2025-06-24 11:06:01,153 - pyskl - INFO - Epoch [25][1100/1281] lr: 2.334e-02, eta: 9:48:27, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.7875, top5_acc: 0.9881, loss_cls: 0.8561, loss: 0.8561 +2025-06-24 11:06:23,275 - pyskl - INFO - Epoch [25][1200/1281] lr: 2.333e-02, eta: 9:48:05, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9906, loss_cls: 0.7981, loss: 0.7981 +2025-06-24 11:06:41,976 - pyskl - INFO - Saving checkpoint at 25 epochs +2025-06-24 11:07:25,649 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:07:25,705 - pyskl - INFO - +top1_acc 0.7233 +top5_acc 0.9710 +2025-06-24 11:07:25,705 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:07:25,711 - pyskl - INFO - +mean_acc 0.6288 +2025-06-24 11:07:25,713 - pyskl - INFO - Epoch(val) [25][533] top1_acc: 0.7233, top5_acc: 0.9710, mean_class_accuracy: 0.6288 +2025-06-24 11:08:07,493 - pyskl - INFO - Epoch [26][100/1281] lr: 2.332e-02, eta: 9:47:35, time: 0.418, data_time: 0.195, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9919, loss_cls: 0.7394, loss: 0.7394 +2025-06-24 11:08:29,545 - pyskl - INFO - Epoch [26][200/1281] lr: 2.330e-02, eta: 9:47:13, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8237, top5_acc: 0.9931, loss_cls: 0.7357, loss: 0.7357 +2025-06-24 11:08:51,480 - pyskl - INFO - Epoch [26][300/1281] lr: 2.329e-02, eta: 9:46:51, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9956, loss_cls: 0.7278, loss: 0.7278 +2025-06-24 11:09:13,499 - pyskl - INFO - Epoch [26][400/1281] lr: 2.328e-02, eta: 9:46:29, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8344, top5_acc: 0.9900, loss_cls: 0.7511, loss: 0.7511 +2025-06-24 11:09:35,572 - pyskl - INFO - Epoch [26][500/1281] lr: 2.327e-02, eta: 9:46:07, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9919, loss_cls: 0.7740, loss: 0.7740 +2025-06-24 11:09:57,408 - pyskl - INFO - Epoch [26][600/1281] lr: 2.326e-02, eta: 9:45:44, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9925, loss_cls: 0.7757, loss: 0.7757 +2025-06-24 11:10:19,193 - pyskl - INFO - Epoch [26][700/1281] lr: 2.325e-02, eta: 9:45:21, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8163, top5_acc: 0.9894, loss_cls: 0.8208, loss: 0.8208 +2025-06-24 11:10:41,226 - pyskl - INFO - Epoch [26][800/1281] lr: 2.324e-02, eta: 9:44:59, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9906, loss_cls: 0.7877, loss: 0.7877 +2025-06-24 11:11:03,211 - pyskl - INFO - Epoch [26][900/1281] lr: 2.323e-02, eta: 9:44:36, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8056, top5_acc: 0.9894, loss_cls: 0.8184, loss: 0.8184 +2025-06-24 11:11:25,425 - pyskl - INFO - Epoch [26][1000/1281] lr: 2.322e-02, eta: 9:44:15, time: 0.222, data_time: 0.001, memory: 4082, top1_acc: 0.8175, top5_acc: 0.9844, loss_cls: 0.8341, loss: 0.8341 +2025-06-24 11:11:47,550 - pyskl - INFO - Epoch [26][1100/1281] lr: 2.321e-02, eta: 9:43:54, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8213, top5_acc: 0.9894, loss_cls: 0.7800, loss: 0.7800 +2025-06-24 11:12:09,466 - pyskl - INFO - Epoch [26][1200/1281] lr: 2.320e-02, eta: 9:43:31, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9925, loss_cls: 0.7743, loss: 0.7743 +2025-06-24 11:12:28,130 - pyskl - INFO - Saving checkpoint at 26 epochs +2025-06-24 11:13:12,218 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:13:12,287 - pyskl - INFO - +top1_acc 0.7932 +top5_acc 0.9864 +2025-06-24 11:13:12,287 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:13:12,297 - pyskl - INFO - +mean_acc 0.6996 +2025-06-24 11:13:12,303 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_19.pth was removed +2025-06-24 11:13:12,559 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_26.pth. +2025-06-24 11:13:12,559 - pyskl - INFO - Best top1_acc is 0.7932 at 26 epoch. +2025-06-24 11:13:12,563 - pyskl - INFO - Epoch(val) [26][533] top1_acc: 0.7932, top5_acc: 0.9864, mean_class_accuracy: 0.6996 +2025-06-24 11:13:54,299 - pyskl - INFO - Epoch [27][100/1281] lr: 2.318e-02, eta: 9:43:00, time: 0.417, data_time: 0.194, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9931, loss_cls: 0.7390, loss: 0.7390 +2025-06-24 11:14:16,420 - pyskl - INFO - Epoch [27][200/1281] lr: 2.317e-02, eta: 9:42:38, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9900, loss_cls: 0.6712, loss: 0.6712 +2025-06-24 11:14:38,382 - pyskl - INFO - Epoch [27][300/1281] lr: 2.316e-02, eta: 9:42:16, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9938, loss_cls: 0.7374, loss: 0.7374 +2025-06-24 11:15:00,614 - pyskl - INFO - Epoch [27][400/1281] lr: 2.315e-02, eta: 9:41:55, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8194, top5_acc: 0.9912, loss_cls: 0.7804, loss: 0.7804 +2025-06-24 11:15:22,765 - pyskl - INFO - Epoch [27][500/1281] lr: 2.314e-02, eta: 9:41:33, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8313, top5_acc: 0.9956, loss_cls: 0.7253, loss: 0.7253 +2025-06-24 11:15:44,921 - pyskl - INFO - Epoch [27][600/1281] lr: 2.313e-02, eta: 9:41:12, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9931, loss_cls: 0.7448, loss: 0.7448 +2025-06-24 11:16:07,301 - pyskl - INFO - Epoch [27][700/1281] lr: 2.312e-02, eta: 9:40:51, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9912, loss_cls: 0.7844, loss: 0.7844 +2025-06-24 11:16:29,383 - pyskl - INFO - Epoch [27][800/1281] lr: 2.311e-02, eta: 9:40:29, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9950, loss_cls: 0.7087, loss: 0.7087 +2025-06-24 11:16:51,221 - pyskl - INFO - Epoch [27][900/1281] lr: 2.310e-02, eta: 9:40:07, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9900, loss_cls: 0.8010, loss: 0.8010 +2025-06-24 11:17:13,537 - pyskl - INFO - Epoch [27][1000/1281] lr: 2.308e-02, eta: 9:39:46, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9975, loss_cls: 0.7146, loss: 0.7146 +2025-06-24 11:17:35,500 - pyskl - INFO - Epoch [27][1100/1281] lr: 2.307e-02, eta: 9:39:23, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9912, loss_cls: 0.7625, loss: 0.7625 +2025-06-24 11:17:57,306 - pyskl - INFO - Epoch [27][1200/1281] lr: 2.306e-02, eta: 9:39:00, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9900, loss_cls: 0.7597, loss: 0.7597 +2025-06-24 11:18:15,982 - pyskl - INFO - Saving checkpoint at 27 epochs +2025-06-24 11:19:00,373 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:19:00,441 - pyskl - INFO - +top1_acc 0.7754 +top5_acc 0.9824 +2025-06-24 11:19:00,442 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:19:00,452 - pyskl - INFO - +mean_acc 0.6984 +2025-06-24 11:19:00,455 - pyskl - INFO - Epoch(val) [27][533] top1_acc: 0.7754, top5_acc: 0.9824, mean_class_accuracy: 0.6984 +2025-06-24 11:19:42,413 - pyskl - INFO - Epoch [28][100/1281] lr: 2.304e-02, eta: 9:38:30, time: 0.420, data_time: 0.197, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9894, loss_cls: 0.7486, loss: 0.7486 +2025-06-24 11:20:04,439 - pyskl - INFO - Epoch [28][200/1281] lr: 2.303e-02, eta: 9:38:08, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8494, top5_acc: 0.9900, loss_cls: 0.6906, loss: 0.6906 +2025-06-24 11:20:26,509 - pyskl - INFO - Epoch [28][300/1281] lr: 2.302e-02, eta: 9:37:46, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9969, loss_cls: 0.6930, loss: 0.6930 +2025-06-24 11:20:48,545 - pyskl - INFO - Epoch [28][400/1281] lr: 2.301e-02, eta: 9:37:24, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8194, top5_acc: 0.9919, loss_cls: 0.7562, loss: 0.7562 +2025-06-24 11:21:10,254 - pyskl - INFO - Epoch [28][500/1281] lr: 2.300e-02, eta: 9:37:00, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9906, loss_cls: 0.7414, loss: 0.7414 +2025-06-24 11:21:32,299 - pyskl - INFO - Epoch [28][600/1281] lr: 2.299e-02, eta: 9:36:38, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9869, loss_cls: 0.7894, loss: 0.7894 +2025-06-24 11:21:54,145 - pyskl - INFO - Epoch [28][700/1281] lr: 2.298e-02, eta: 9:36:15, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8263, top5_acc: 0.9881, loss_cls: 0.7501, loss: 0.7501 +2025-06-24 11:22:16,032 - pyskl - INFO - Epoch [28][800/1281] lr: 2.297e-02, eta: 9:35:52, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9912, loss_cls: 0.7492, loss: 0.7492 +2025-06-24 11:22:38,014 - pyskl - INFO - Epoch [28][900/1281] lr: 2.295e-02, eta: 9:35:30, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9906, loss_cls: 0.7365, loss: 0.7365 +2025-06-24 11:23:00,157 - pyskl - INFO - Epoch [28][1000/1281] lr: 2.294e-02, eta: 9:35:08, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9931, loss_cls: 0.7491, loss: 0.7491 +2025-06-24 11:23:22,410 - pyskl - INFO - Epoch [28][1100/1281] lr: 2.293e-02, eta: 9:34:47, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9894, loss_cls: 0.7931, loss: 0.7931 +2025-06-24 11:23:44,444 - pyskl - INFO - Epoch [28][1200/1281] lr: 2.292e-02, eta: 9:34:25, time: 0.220, data_time: 0.001, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9931, loss_cls: 0.7503, loss: 0.7503 +2025-06-24 11:24:03,230 - pyskl - INFO - Saving checkpoint at 28 epochs +2025-06-24 11:24:47,697 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:24:47,768 - pyskl - INFO - +top1_acc 0.7843 +top5_acc 0.9871 +2025-06-24 11:24:47,768 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:24:47,777 - pyskl - INFO - +mean_acc 0.7261 +2025-06-24 11:24:47,780 - pyskl - INFO - Epoch(val) [28][533] top1_acc: 0.7843, top5_acc: 0.9871, mean_class_accuracy: 0.7261 +2025-06-24 11:25:29,925 - pyskl - INFO - Epoch [29][100/1281] lr: 2.290e-02, eta: 9:33:55, time: 0.421, data_time: 0.200, memory: 4082, top1_acc: 0.8425, top5_acc: 0.9906, loss_cls: 0.7347, loss: 0.7347 +2025-06-24 11:25:52,182 - pyskl - INFO - Epoch [29][200/1281] lr: 2.289e-02, eta: 9:33:34, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.8594, top5_acc: 0.9925, loss_cls: 0.6671, loss: 0.6671 +2025-06-24 11:26:14,727 - pyskl - INFO - Epoch [29][300/1281] lr: 2.288e-02, eta: 9:33:14, time: 0.225, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9875, loss_cls: 0.7295, loss: 0.7295 +2025-06-24 11:26:36,751 - pyskl - INFO - Epoch [29][400/1281] lr: 2.287e-02, eta: 9:32:52, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9900, loss_cls: 0.7913, loss: 0.7913 +2025-06-24 11:26:58,505 - pyskl - INFO - Epoch [29][500/1281] lr: 2.285e-02, eta: 9:32:29, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9931, loss_cls: 0.7013, loss: 0.7013 +2025-06-24 11:27:20,775 - pyskl - INFO - Epoch [29][600/1281] lr: 2.284e-02, eta: 9:32:07, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9956, loss_cls: 0.6914, loss: 0.6914 +2025-06-24 11:27:42,967 - pyskl - INFO - Epoch [29][700/1281] lr: 2.283e-02, eta: 9:31:46, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9938, loss_cls: 0.7549, loss: 0.7549 +2025-06-24 11:28:05,089 - pyskl - INFO - Epoch [29][800/1281] lr: 2.282e-02, eta: 9:31:24, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9881, loss_cls: 0.7400, loss: 0.7400 +2025-06-24 11:28:26,869 - pyskl - INFO - Epoch [29][900/1281] lr: 2.281e-02, eta: 9:31:01, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9875, loss_cls: 0.7643, loss: 0.7643 +2025-06-24 11:28:48,665 - pyskl - INFO - Epoch [29][1000/1281] lr: 2.280e-02, eta: 9:30:38, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9912, loss_cls: 0.7235, loss: 0.7235 +2025-06-24 11:29:10,660 - pyskl - INFO - Epoch [29][1100/1281] lr: 2.279e-02, eta: 9:30:16, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8263, top5_acc: 0.9881, loss_cls: 0.7578, loss: 0.7578 +2025-06-24 11:29:32,791 - pyskl - INFO - Epoch [29][1200/1281] lr: 2.277e-02, eta: 9:29:54, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8263, top5_acc: 0.9925, loss_cls: 0.7562, loss: 0.7562 +2025-06-24 11:29:51,301 - pyskl - INFO - Saving checkpoint at 29 epochs +2025-06-24 11:30:35,896 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:30:35,952 - pyskl - INFO - +top1_acc 0.7429 +top5_acc 0.9757 +2025-06-24 11:30:35,952 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:30:35,958 - pyskl - INFO - +mean_acc 0.6727 +2025-06-24 11:30:35,960 - pyskl - INFO - Epoch(val) [29][533] top1_acc: 0.7429, top5_acc: 0.9757, mean_class_accuracy: 0.6727 +2025-06-24 11:31:18,223 - pyskl - INFO - Epoch [30][100/1281] lr: 2.275e-02, eta: 9:29:24, time: 0.423, data_time: 0.192, memory: 4082, top1_acc: 0.8425, top5_acc: 0.9900, loss_cls: 0.7224, loss: 0.7224 +2025-06-24 11:31:41,121 - pyskl - INFO - Epoch [30][200/1281] lr: 2.274e-02, eta: 9:29:05, time: 0.229, data_time: 0.000, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9944, loss_cls: 0.6789, loss: 0.6789 +2025-06-24 11:32:03,778 - pyskl - INFO - Epoch [30][300/1281] lr: 2.273e-02, eta: 9:28:46, time: 0.227, data_time: 0.000, memory: 4082, top1_acc: 0.8625, top5_acc: 0.9950, loss_cls: 0.6436, loss: 0.6436 +2025-06-24 11:32:26,563 - pyskl - INFO - Epoch [30][400/1281] lr: 2.272e-02, eta: 9:28:27, time: 0.228, data_time: 0.000, memory: 4082, top1_acc: 0.8263, top5_acc: 0.9881, loss_cls: 0.7754, loss: 0.7754 +2025-06-24 11:32:49,542 - pyskl - INFO - Epoch [30][500/1281] lr: 2.271e-02, eta: 9:28:08, time: 0.230, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9950, loss_cls: 0.7497, loss: 0.7497 +2025-06-24 11:33:11,973 - pyskl - INFO - Epoch [30][600/1281] lr: 2.269e-02, eta: 9:27:48, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.8425, top5_acc: 0.9850, loss_cls: 0.7288, loss: 0.7288 +2025-06-24 11:33:34,803 - pyskl - INFO - Epoch [30][700/1281] lr: 2.268e-02, eta: 9:27:29, time: 0.228, data_time: 0.000, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9912, loss_cls: 0.7031, loss: 0.7031 +2025-06-24 11:33:58,025 - pyskl - INFO - Epoch [30][800/1281] lr: 2.267e-02, eta: 9:27:11, time: 0.232, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9938, loss_cls: 0.7376, loss: 0.7376 +2025-06-24 11:34:20,721 - pyskl - INFO - Epoch [30][900/1281] lr: 2.266e-02, eta: 9:26:52, time: 0.227, data_time: 0.000, memory: 4082, top1_acc: 0.8381, top5_acc: 0.9919, loss_cls: 0.7161, loss: 0.7161 +2025-06-24 11:34:43,724 - pyskl - INFO - Epoch [30][1000/1281] lr: 2.265e-02, eta: 9:26:34, time: 0.230, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9894, loss_cls: 0.7409, loss: 0.7409 +2025-06-24 11:35:06,420 - pyskl - INFO - Epoch [30][1100/1281] lr: 2.263e-02, eta: 9:26:14, time: 0.227, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9925, loss_cls: 0.7347, loss: 0.7347 +2025-06-24 11:35:29,371 - pyskl - INFO - Epoch [30][1200/1281] lr: 2.262e-02, eta: 9:25:55, time: 0.229, data_time: 0.001, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9956, loss_cls: 0.7185, loss: 0.7185 +2025-06-24 11:35:48,878 - pyskl - INFO - Saving checkpoint at 30 epochs +2025-06-24 11:36:33,135 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:36:33,203 - pyskl - INFO - +top1_acc 0.7881 +top5_acc 0.9864 +2025-06-24 11:36:33,203 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:36:33,212 - pyskl - INFO - +mean_acc 0.6909 +2025-06-24 11:36:33,214 - pyskl - INFO - Epoch(val) [30][533] top1_acc: 0.7881, top5_acc: 0.9864, mean_class_accuracy: 0.6909 +2025-06-24 11:37:16,075 - pyskl - INFO - Epoch [31][100/1281] lr: 2.260e-02, eta: 9:25:27, time: 0.429, data_time: 0.191, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9906, loss_cls: 0.7950, loss: 0.7950 +2025-06-24 11:37:38,857 - pyskl - INFO - Epoch [31][200/1281] lr: 2.259e-02, eta: 9:25:08, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8337, top5_acc: 0.9919, loss_cls: 0.8934, loss: 0.8934 +2025-06-24 11:38:01,380 - pyskl - INFO - Epoch [31][300/1281] lr: 2.258e-02, eta: 9:24:47, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8406, top5_acc: 0.9938, loss_cls: 0.8331, loss: 0.8331 +2025-06-24 11:38:23,653 - pyskl - INFO - Epoch [31][400/1281] lr: 2.256e-02, eta: 9:24:26, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8431, top5_acc: 0.9950, loss_cls: 0.8140, loss: 0.8140 +2025-06-24 11:38:46,085 - pyskl - INFO - Epoch [31][500/1281] lr: 2.255e-02, eta: 9:24:05, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8256, top5_acc: 0.9919, loss_cls: 0.8729, loss: 0.8729 +2025-06-24 11:39:08,387 - pyskl - INFO - Epoch [31][600/1281] lr: 2.254e-02, eta: 9:23:44, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8375, top5_acc: 0.9919, loss_cls: 0.8685, loss: 0.8685 +2025-06-24 11:39:31,036 - pyskl - INFO - Epoch [31][700/1281] lr: 2.253e-02, eta: 9:23:24, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8475, top5_acc: 0.9938, loss_cls: 0.8467, loss: 0.8467 +2025-06-24 11:39:53,367 - pyskl - INFO - Epoch [31][800/1281] lr: 2.252e-02, eta: 9:23:03, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8525, top5_acc: 0.9931, loss_cls: 0.7986, loss: 0.7986 +2025-06-24 11:40:15,847 - pyskl - INFO - Epoch [31][900/1281] lr: 2.250e-02, eta: 9:22:42, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8313, top5_acc: 0.9906, loss_cls: 0.8729, loss: 0.8729 +2025-06-24 11:40:38,607 - pyskl - INFO - Epoch [31][1000/1281] lr: 2.249e-02, eta: 9:22:23, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8350, top5_acc: 0.9894, loss_cls: 0.8459, loss: 0.8459 +2025-06-24 11:41:01,175 - pyskl - INFO - Epoch [31][1100/1281] lr: 2.248e-02, eta: 9:22:02, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8306, top5_acc: 0.9881, loss_cls: 0.8883, loss: 0.8883 +2025-06-24 11:41:23,565 - pyskl - INFO - Epoch [31][1200/1281] lr: 2.247e-02, eta: 9:21:42, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8444, top5_acc: 0.9956, loss_cls: 0.8524, loss: 0.8524 +2025-06-24 11:41:42,523 - pyskl - INFO - Saving checkpoint at 31 epochs +2025-06-24 11:42:26,869 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:42:26,935 - pyskl - INFO - +top1_acc 0.7835 +top5_acc 0.9827 +2025-06-24 11:42:26,935 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:42:26,944 - pyskl - INFO - +mean_acc 0.6860 +2025-06-24 11:42:26,946 - pyskl - INFO - Epoch(val) [31][533] top1_acc: 0.7835, top5_acc: 0.9827, mean_class_accuracy: 0.6860 +2025-06-24 11:43:10,419 - pyskl - INFO - Epoch [32][100/1281] lr: 2.244e-02, eta: 9:21:15, time: 0.435, data_time: 0.198, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9931, loss_cls: 0.7924, loss: 0.7924 +2025-06-24 11:43:33,076 - pyskl - INFO - Epoch [32][200/1281] lr: 2.243e-02, eta: 9:20:55, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8381, top5_acc: 0.9938, loss_cls: 0.7849, loss: 0.7849 +2025-06-24 11:43:55,747 - pyskl - INFO - Epoch [32][300/1281] lr: 2.242e-02, eta: 9:20:35, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8469, top5_acc: 0.9925, loss_cls: 0.7517, loss: 0.7517 +2025-06-24 11:44:18,696 - pyskl - INFO - Epoch [32][400/1281] lr: 2.241e-02, eta: 9:20:16, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.8519, top5_acc: 0.9950, loss_cls: 0.7214, loss: 0.7214 +2025-06-24 11:44:41,306 - pyskl - INFO - Epoch [32][500/1281] lr: 2.239e-02, eta: 9:19:56, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8469, top5_acc: 0.9950, loss_cls: 0.7739, loss: 0.7739 +2025-06-24 11:45:03,592 - pyskl - INFO - Epoch [32][600/1281] lr: 2.238e-02, eta: 9:19:34, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8319, top5_acc: 0.9944, loss_cls: 0.8178, loss: 0.8178 +2025-06-24 11:45:26,663 - pyskl - INFO - Epoch [32][700/1281] lr: 2.237e-02, eta: 9:19:16, time: 0.231, data_time: 0.001, memory: 4083, top1_acc: 0.8287, top5_acc: 0.9906, loss_cls: 0.8378, loss: 0.8378 +2025-06-24 11:45:49,090 - pyskl - INFO - Epoch [32][800/1281] lr: 2.236e-02, eta: 9:18:55, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8363, top5_acc: 0.9931, loss_cls: 0.7830, loss: 0.7830 +2025-06-24 11:46:11,432 - pyskl - INFO - Epoch [32][900/1281] lr: 2.234e-02, eta: 9:18:33, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8281, top5_acc: 0.9888, loss_cls: 0.8418, loss: 0.8418 +2025-06-24 11:46:34,254 - pyskl - INFO - Epoch [32][1000/1281] lr: 2.233e-02, eta: 9:18:14, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9919, loss_cls: 0.7770, loss: 0.7770 +2025-06-24 11:46:56,795 - pyskl - INFO - Epoch [32][1100/1281] lr: 2.232e-02, eta: 9:17:53, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8413, top5_acc: 0.9912, loss_cls: 0.7864, loss: 0.7864 +2025-06-24 11:47:19,045 - pyskl - INFO - Epoch [32][1200/1281] lr: 2.231e-02, eta: 9:17:32, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8363, top5_acc: 0.9962, loss_cls: 0.8114, loss: 0.8114 +2025-06-24 11:47:38,250 - pyskl - INFO - Saving checkpoint at 32 epochs +2025-06-24 11:48:22,597 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:48:22,654 - pyskl - INFO - +top1_acc 0.7910 +top5_acc 0.9851 +2025-06-24 11:48:22,654 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:48:22,661 - pyskl - INFO - +mean_acc 0.7207 +2025-06-24 11:48:22,663 - pyskl - INFO - Epoch(val) [32][533] top1_acc: 0.7910, top5_acc: 0.9851, mean_class_accuracy: 0.7207 +2025-06-24 11:49:06,484 - pyskl - INFO - Epoch [33][100/1281] lr: 2.228e-02, eta: 9:17:06, time: 0.438, data_time: 0.199, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9931, loss_cls: 0.7137, loss: 0.7137 +2025-06-24 11:49:29,057 - pyskl - INFO - Epoch [33][200/1281] lr: 2.227e-02, eta: 9:16:45, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9931, loss_cls: 0.7258, loss: 0.7258 +2025-06-24 11:49:51,634 - pyskl - INFO - Epoch [33][300/1281] lr: 2.226e-02, eta: 9:16:25, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8406, top5_acc: 0.9919, loss_cls: 0.7377, loss: 0.7377 +2025-06-24 11:50:14,159 - pyskl - INFO - Epoch [33][400/1281] lr: 2.225e-02, eta: 9:16:04, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8406, top5_acc: 0.9912, loss_cls: 0.7893, loss: 0.7893 +2025-06-24 11:50:36,887 - pyskl - INFO - Epoch [33][500/1281] lr: 2.223e-02, eta: 9:15:44, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8538, top5_acc: 0.9944, loss_cls: 0.7378, loss: 0.7378 +2025-06-24 11:50:59,263 - pyskl - INFO - Epoch [33][600/1281] lr: 2.222e-02, eta: 9:15:23, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8381, top5_acc: 0.9906, loss_cls: 0.7911, loss: 0.7911 +2025-06-24 11:51:21,886 - pyskl - INFO - Epoch [33][700/1281] lr: 2.221e-02, eta: 9:15:02, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9956, loss_cls: 0.7809, loss: 0.7809 +2025-06-24 11:51:44,374 - pyskl - INFO - Epoch [33][800/1281] lr: 2.219e-02, eta: 9:14:42, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8263, top5_acc: 0.9931, loss_cls: 0.8178, loss: 0.8178 +2025-06-24 11:52:06,889 - pyskl - INFO - Epoch [33][900/1281] lr: 2.218e-02, eta: 9:14:21, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9906, loss_cls: 0.7799, loss: 0.7799 +2025-06-24 11:52:29,242 - pyskl - INFO - Epoch [33][1000/1281] lr: 2.217e-02, eta: 9:13:59, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9912, loss_cls: 0.7343, loss: 0.7343 +2025-06-24 11:52:51,936 - pyskl - INFO - Epoch [33][1100/1281] lr: 2.216e-02, eta: 9:13:39, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8419, top5_acc: 0.9900, loss_cls: 0.8080, loss: 0.8080 +2025-06-24 11:53:14,375 - pyskl - INFO - Epoch [33][1200/1281] lr: 2.214e-02, eta: 9:13:18, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8406, top5_acc: 0.9956, loss_cls: 0.7389, loss: 0.7389 +2025-06-24 11:53:33,347 - pyskl - INFO - Saving checkpoint at 33 epochs +2025-06-24 11:54:17,459 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:54:17,514 - pyskl - INFO - +top1_acc 0.8106 +top5_acc 0.9886 +2025-06-24 11:54:17,515 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:54:17,522 - pyskl - INFO - +mean_acc 0.7293 +2025-06-24 11:54:17,526 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_26.pth was removed +2025-06-24 11:54:17,696 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_33.pth. +2025-06-24 11:54:17,697 - pyskl - INFO - Best top1_acc is 0.8106 at 33 epoch. +2025-06-24 11:54:17,699 - pyskl - INFO - Epoch(val) [33][533] top1_acc: 0.8106, top5_acc: 0.9886, mean_class_accuracy: 0.7293 +2025-06-24 11:55:00,520 - pyskl - INFO - Epoch [34][100/1281] lr: 2.212e-02, eta: 9:12:48, time: 0.428, data_time: 0.187, memory: 4083, top1_acc: 0.8450, top5_acc: 0.9969, loss_cls: 0.7141, loss: 0.7141 +2025-06-24 11:55:23,301 - pyskl - INFO - Epoch [34][200/1281] lr: 2.211e-02, eta: 9:12:28, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9975, loss_cls: 0.6914, loss: 0.6914 +2025-06-24 11:55:46,181 - pyskl - INFO - Epoch [34][300/1281] lr: 2.209e-02, eta: 9:12:08, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9950, loss_cls: 0.6482, loss: 0.6482 +2025-06-24 11:56:08,965 - pyskl - INFO - Epoch [34][400/1281] lr: 2.208e-02, eta: 9:11:48, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9900, loss_cls: 0.7440, loss: 0.7440 +2025-06-24 11:56:31,364 - pyskl - INFO - Epoch [34][500/1281] lr: 2.207e-02, eta: 9:11:27, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8506, top5_acc: 0.9912, loss_cls: 0.7258, loss: 0.7258 +2025-06-24 11:56:53,944 - pyskl - INFO - Epoch [34][600/1281] lr: 2.205e-02, eta: 9:11:06, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9925, loss_cls: 0.7395, loss: 0.7395 +2025-06-24 11:57:16,446 - pyskl - INFO - Epoch [34][700/1281] lr: 2.204e-02, eta: 9:10:46, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8406, top5_acc: 0.9900, loss_cls: 0.7871, loss: 0.7871 +2025-06-24 11:57:38,791 - pyskl - INFO - Epoch [34][800/1281] lr: 2.203e-02, eta: 9:10:24, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9906, loss_cls: 0.7485, loss: 0.7485 +2025-06-24 11:58:01,670 - pyskl - INFO - Epoch [34][900/1281] lr: 2.201e-02, eta: 9:10:04, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.8406, top5_acc: 0.9925, loss_cls: 0.7463, loss: 0.7463 +2025-06-24 11:58:24,240 - pyskl - INFO - Epoch [34][1000/1281] lr: 2.200e-02, eta: 9:09:44, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8469, top5_acc: 0.9938, loss_cls: 0.7671, loss: 0.7671 +2025-06-24 11:58:46,625 - pyskl - INFO - Epoch [34][1100/1281] lr: 2.199e-02, eta: 9:09:22, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8237, top5_acc: 0.9919, loss_cls: 0.8341, loss: 0.8341 +2025-06-24 11:59:09,076 - pyskl - INFO - Epoch [34][1200/1281] lr: 2.197e-02, eta: 9:09:01, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8213, top5_acc: 0.9938, loss_cls: 0.8102, loss: 0.8102 +2025-06-24 11:59:28,012 - pyskl - INFO - Saving checkpoint at 34 epochs +2025-06-24 12:00:12,129 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:00:12,190 - pyskl - INFO - +top1_acc 0.7776 +top5_acc 0.9825 +2025-06-24 12:00:12,190 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:00:12,197 - pyskl - INFO - +mean_acc 0.6798 +2025-06-24 12:00:12,199 - pyskl - INFO - Epoch(val) [34][533] top1_acc: 0.7776, top5_acc: 0.9825, mean_class_accuracy: 0.6798 +2025-06-24 12:00:54,476 - pyskl - INFO - Epoch [35][100/1281] lr: 2.195e-02, eta: 9:08:28, time: 0.423, data_time: 0.188, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9969, loss_cls: 0.6987, loss: 0.6987 +2025-06-24 12:01:16,757 - pyskl - INFO - Epoch [35][200/1281] lr: 2.194e-02, eta: 9:08:07, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9938, loss_cls: 0.7012, loss: 0.7012 +2025-06-24 12:01:39,283 - pyskl - INFO - Epoch [35][300/1281] lr: 2.192e-02, eta: 9:07:46, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8506, top5_acc: 0.9925, loss_cls: 0.7275, loss: 0.7275 +2025-06-24 12:02:01,794 - pyskl - INFO - Epoch [35][400/1281] lr: 2.191e-02, eta: 9:07:25, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8287, top5_acc: 0.9900, loss_cls: 0.7570, loss: 0.7570 +2025-06-24 12:02:24,240 - pyskl - INFO - Epoch [35][500/1281] lr: 2.190e-02, eta: 9:07:03, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8375, top5_acc: 0.9900, loss_cls: 0.7952, loss: 0.7952 +2025-06-24 12:02:46,715 - pyskl - INFO - Epoch [35][600/1281] lr: 2.188e-02, eta: 9:06:42, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9938, loss_cls: 0.7141, loss: 0.7141 +2025-06-24 12:03:09,378 - pyskl - INFO - Epoch [35][700/1281] lr: 2.187e-02, eta: 9:06:22, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9931, loss_cls: 0.7236, loss: 0.7236 +2025-06-24 12:03:31,890 - pyskl - INFO - Epoch [35][800/1281] lr: 2.185e-02, eta: 9:06:01, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8512, top5_acc: 0.9931, loss_cls: 0.7550, loss: 0.7550 +2025-06-24 12:03:54,367 - pyskl - INFO - Epoch [35][900/1281] lr: 2.184e-02, eta: 9:05:40, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8469, top5_acc: 0.9912, loss_cls: 0.7404, loss: 0.7404 +2025-06-24 12:04:16,908 - pyskl - INFO - Epoch [35][1000/1281] lr: 2.183e-02, eta: 9:05:19, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9931, loss_cls: 0.7186, loss: 0.7186 +2025-06-24 12:04:39,647 - pyskl - INFO - Epoch [35][1100/1281] lr: 2.181e-02, eta: 9:04:59, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9894, loss_cls: 0.7160, loss: 0.7160 +2025-06-24 12:05:01,775 - pyskl - INFO - Epoch [35][1200/1281] lr: 2.180e-02, eta: 9:04:36, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8369, top5_acc: 0.9944, loss_cls: 0.7538, loss: 0.7538 +2025-06-24 12:05:20,767 - pyskl - INFO - Saving checkpoint at 35 epochs +2025-06-24 12:06:05,524 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:06:05,591 - pyskl - INFO - +top1_acc 0.8277 +top5_acc 0.9910 +2025-06-24 12:06:05,591 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:06:05,601 - pyskl - INFO - +mean_acc 0.7497 +2025-06-24 12:06:05,605 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_33.pth was removed +2025-06-24 12:06:05,825 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_35.pth. +2025-06-24 12:06:05,826 - pyskl - INFO - Best top1_acc is 0.8277 at 35 epoch. +2025-06-24 12:06:05,828 - pyskl - INFO - Epoch(val) [35][533] top1_acc: 0.8277, top5_acc: 0.9910, mean_class_accuracy: 0.7497 +2025-06-24 12:06:48,765 - pyskl - INFO - Epoch [36][100/1281] lr: 2.178e-02, eta: 9:04:05, time: 0.429, data_time: 0.193, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9962, loss_cls: 0.6171, loss: 0.6171 +2025-06-24 12:07:11,462 - pyskl - INFO - Epoch [36][200/1281] lr: 2.176e-02, eta: 9:03:45, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9944, loss_cls: 0.6753, loss: 0.6753 +2025-06-24 12:07:33,870 - pyskl - INFO - Epoch [36][300/1281] lr: 2.175e-02, eta: 9:03:23, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9919, loss_cls: 0.6959, loss: 0.6959 +2025-06-24 12:07:56,436 - pyskl - INFO - Epoch [36][400/1281] lr: 2.173e-02, eta: 9:03:02, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8525, top5_acc: 0.9925, loss_cls: 0.7282, loss: 0.7282 +2025-06-24 12:08:18,929 - pyskl - INFO - Epoch [36][500/1281] lr: 2.172e-02, eta: 9:02:41, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9975, loss_cls: 0.6523, loss: 0.6523 +2025-06-24 12:08:41,344 - pyskl - INFO - Epoch [36][600/1281] lr: 2.171e-02, eta: 9:02:20, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 0.8325, top5_acc: 0.9900, loss_cls: 0.7667, loss: 0.7667 +2025-06-24 12:09:04,059 - pyskl - INFO - Epoch [36][700/1281] lr: 2.169e-02, eta: 9:01:59, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9950, loss_cls: 0.6953, loss: 0.6953 +2025-06-24 12:09:26,820 - pyskl - INFO - Epoch [36][800/1281] lr: 2.168e-02, eta: 9:01:39, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8488, top5_acc: 0.9906, loss_cls: 0.7325, loss: 0.7325 +2025-06-24 12:09:49,242 - pyskl - INFO - Epoch [36][900/1281] lr: 2.167e-02, eta: 9:01:18, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8444, top5_acc: 0.9931, loss_cls: 0.7427, loss: 0.7427 +2025-06-24 12:10:11,446 - pyskl - INFO - Epoch [36][1000/1281] lr: 2.165e-02, eta: 9:00:56, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8450, top5_acc: 0.9938, loss_cls: 0.7721, loss: 0.7721 +2025-06-24 12:10:33,882 - pyskl - INFO - Epoch [36][1100/1281] lr: 2.164e-02, eta: 9:00:34, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8419, top5_acc: 0.9969, loss_cls: 0.7307, loss: 0.7307 +2025-06-24 12:10:56,203 - pyskl - INFO - Epoch [36][1200/1281] lr: 2.162e-02, eta: 9:00:12, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9931, loss_cls: 0.7311, loss: 0.7311 +2025-06-24 12:11:15,188 - pyskl - INFO - Saving checkpoint at 36 epochs +2025-06-24 12:11:59,537 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:11:59,592 - pyskl - INFO - +top1_acc 0.7763 +top5_acc 0.9805 +2025-06-24 12:11:59,592 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:11:59,598 - pyskl - INFO - +mean_acc 0.7211 +2025-06-24 12:11:59,600 - pyskl - INFO - Epoch(val) [36][533] top1_acc: 0.7763, top5_acc: 0.9805, mean_class_accuracy: 0.7211 +2025-06-24 12:12:42,564 - pyskl - INFO - Epoch [37][100/1281] lr: 2.160e-02, eta: 8:59:41, time: 0.430, data_time: 0.194, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9962, loss_cls: 0.6947, loss: 0.6947 +2025-06-24 12:13:05,354 - pyskl - INFO - Epoch [37][200/1281] lr: 2.158e-02, eta: 8:59:21, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9962, loss_cls: 0.6779, loss: 0.6779 +2025-06-24 12:13:28,047 - pyskl - INFO - Epoch [37][300/1281] lr: 2.157e-02, eta: 8:59:00, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9969, loss_cls: 0.6826, loss: 0.6826 +2025-06-24 12:13:50,315 - pyskl - INFO - Epoch [37][400/1281] lr: 2.156e-02, eta: 8:58:38, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9962, loss_cls: 0.6550, loss: 0.6550 +2025-06-24 12:14:12,907 - pyskl - INFO - Epoch [37][500/1281] lr: 2.154e-02, eta: 8:58:17, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9938, loss_cls: 0.6861, loss: 0.6861 +2025-06-24 12:14:35,584 - pyskl - INFO - Epoch [37][600/1281] lr: 2.153e-02, eta: 8:57:57, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9938, loss_cls: 0.7124, loss: 0.7124 +2025-06-24 12:14:57,955 - pyskl - INFO - Epoch [37][700/1281] lr: 2.151e-02, eta: 8:57:35, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9944, loss_cls: 0.6276, loss: 0.6276 +2025-06-24 12:15:20,655 - pyskl - INFO - Epoch [37][800/1281] lr: 2.150e-02, eta: 8:57:14, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8462, top5_acc: 0.9931, loss_cls: 0.7148, loss: 0.7148 +2025-06-24 12:15:43,205 - pyskl - INFO - Epoch [37][900/1281] lr: 2.149e-02, eta: 8:56:53, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9950, loss_cls: 0.6888, loss: 0.6888 +2025-06-24 12:16:06,191 - pyskl - INFO - Epoch [37][1000/1281] lr: 2.147e-02, eta: 8:56:33, time: 0.230, data_time: 0.000, memory: 4083, top1_acc: 0.8438, top5_acc: 0.9938, loss_cls: 0.7482, loss: 0.7482 +2025-06-24 12:16:28,610 - pyskl - INFO - Epoch [37][1100/1281] lr: 2.146e-02, eta: 8:56:12, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9888, loss_cls: 0.7568, loss: 0.7568 +2025-06-24 12:16:51,369 - pyskl - INFO - Epoch [37][1200/1281] lr: 2.144e-02, eta: 8:55:51, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9925, loss_cls: 0.7228, loss: 0.7228 +2025-06-24 12:17:10,546 - pyskl - INFO - Saving checkpoint at 37 epochs +2025-06-24 12:17:53,887 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:17:53,956 - pyskl - INFO - +top1_acc 0.7775 +top5_acc 0.9843 +2025-06-24 12:17:53,956 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:17:53,963 - pyskl - INFO - +mean_acc 0.6946 +2025-06-24 12:17:53,966 - pyskl - INFO - Epoch(val) [37][533] top1_acc: 0.7775, top5_acc: 0.9843, mean_class_accuracy: 0.6946 +2025-06-24 12:18:37,273 - pyskl - INFO - Epoch [38][100/1281] lr: 2.142e-02, eta: 8:55:21, time: 0.433, data_time: 0.198, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9950, loss_cls: 0.6699, loss: 0.6699 +2025-06-24 12:18:59,711 - pyskl - INFO - Epoch [38][200/1281] lr: 2.140e-02, eta: 8:54:59, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9969, loss_cls: 0.6697, loss: 0.6697 +2025-06-24 12:19:22,093 - pyskl - INFO - Epoch [38][300/1281] lr: 2.139e-02, eta: 8:54:38, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9912, loss_cls: 0.6759, loss: 0.6759 +2025-06-24 12:19:44,278 - pyskl - INFO - Epoch [38][400/1281] lr: 2.137e-02, eta: 8:54:15, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9931, loss_cls: 0.6785, loss: 0.6785 +2025-06-24 12:20:06,923 - pyskl - INFO - Epoch [38][500/1281] lr: 2.136e-02, eta: 8:53:54, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9938, loss_cls: 0.7094, loss: 0.7094 +2025-06-24 12:20:29,372 - pyskl - INFO - Epoch [38][600/1281] lr: 2.134e-02, eta: 8:53:33, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9962, loss_cls: 0.6554, loss: 0.6554 +2025-06-24 12:20:51,846 - pyskl - INFO - Epoch [38][700/1281] lr: 2.133e-02, eta: 8:53:12, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9950, loss_cls: 0.6695, loss: 0.6695 +2025-06-24 12:21:14,292 - pyskl - INFO - Epoch [38][800/1281] lr: 2.132e-02, eta: 8:52:50, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9906, loss_cls: 0.7103, loss: 0.7103 +2025-06-24 12:21:36,711 - pyskl - INFO - Epoch [38][900/1281] lr: 2.130e-02, eta: 8:52:28, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9969, loss_cls: 0.6868, loss: 0.6868 +2025-06-24 12:21:59,146 - pyskl - INFO - Epoch [38][1000/1281] lr: 2.129e-02, eta: 8:52:07, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8469, top5_acc: 0.9950, loss_cls: 0.7260, loss: 0.7260 +2025-06-24 12:22:21,641 - pyskl - INFO - Epoch [38][1100/1281] lr: 2.127e-02, eta: 8:51:46, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8488, top5_acc: 0.9931, loss_cls: 0.6962, loss: 0.6962 +2025-06-24 12:22:44,057 - pyskl - INFO - Epoch [38][1200/1281] lr: 2.126e-02, eta: 8:51:24, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9888, loss_cls: 0.7036, loss: 0.7036 +2025-06-24 12:23:03,124 - pyskl - INFO - Saving checkpoint at 38 epochs +2025-06-24 12:23:47,097 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:23:47,152 - pyskl - INFO - +top1_acc 0.8040 +top5_acc 0.9838 +2025-06-24 12:23:47,152 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:23:47,159 - pyskl - INFO - +mean_acc 0.7503 +2025-06-24 12:23:47,160 - pyskl - INFO - Epoch(val) [38][533] top1_acc: 0.8040, top5_acc: 0.9838, mean_class_accuracy: 0.7503 +2025-06-24 12:24:30,268 - pyskl - INFO - Epoch [39][100/1281] lr: 2.123e-02, eta: 8:50:52, time: 0.431, data_time: 0.188, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9925, loss_cls: 0.6162, loss: 0.6162 +2025-06-24 12:24:52,964 - pyskl - INFO - Epoch [39][200/1281] lr: 2.122e-02, eta: 8:50:31, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9944, loss_cls: 0.6532, loss: 0.6532 +2025-06-24 12:25:15,043 - pyskl - INFO - Epoch [39][300/1281] lr: 2.120e-02, eta: 8:50:09, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9975, loss_cls: 0.6576, loss: 0.6576 +2025-06-24 12:25:37,671 - pyskl - INFO - Epoch [39][400/1281] lr: 2.119e-02, eta: 8:49:48, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9962, loss_cls: 0.6910, loss: 0.6910 +2025-06-24 12:26:00,294 - pyskl - INFO - Epoch [39][500/1281] lr: 2.117e-02, eta: 8:49:27, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9938, loss_cls: 0.6966, loss: 0.6966 +2025-06-24 12:26:22,683 - pyskl - INFO - Epoch [39][600/1281] lr: 2.116e-02, eta: 8:49:05, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8512, top5_acc: 0.9944, loss_cls: 0.7098, loss: 0.7098 +2025-06-24 12:26:45,190 - pyskl - INFO - Epoch [39][700/1281] lr: 2.114e-02, eta: 8:48:44, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9925, loss_cls: 0.6972, loss: 0.6972 +2025-06-24 12:27:08,084 - pyskl - INFO - Epoch [39][800/1281] lr: 2.113e-02, eta: 8:48:23, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9925, loss_cls: 0.6522, loss: 0.6522 +2025-06-24 12:27:30,682 - pyskl - INFO - Epoch [39][900/1281] lr: 2.111e-02, eta: 8:48:02, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9925, loss_cls: 0.6881, loss: 0.6881 +2025-06-24 12:27:53,085 - pyskl - INFO - Epoch [39][1000/1281] lr: 2.110e-02, eta: 8:47:41, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8400, top5_acc: 0.9912, loss_cls: 0.7432, loss: 0.7432 +2025-06-24 12:28:15,689 - pyskl - INFO - Epoch [39][1100/1281] lr: 2.108e-02, eta: 8:47:19, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8512, top5_acc: 0.9962, loss_cls: 0.6998, loss: 0.6998 +2025-06-24 12:28:38,205 - pyskl - INFO - Epoch [39][1200/1281] lr: 2.107e-02, eta: 8:46:58, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9950, loss_cls: 0.6675, loss: 0.6675 +2025-06-24 12:28:57,173 - pyskl - INFO - Saving checkpoint at 39 epochs +2025-06-24 12:29:41,142 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:29:41,215 - pyskl - INFO - +top1_acc 0.8101 +top5_acc 0.9858 +2025-06-24 12:29:41,215 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:29:41,223 - pyskl - INFO - +mean_acc 0.7492 +2025-06-24 12:29:41,226 - pyskl - INFO - Epoch(val) [39][533] top1_acc: 0.8101, top5_acc: 0.9858, mean_class_accuracy: 0.7492 +2025-06-24 12:30:23,813 - pyskl - INFO - Epoch [40][100/1281] lr: 2.104e-02, eta: 8:46:24, time: 0.426, data_time: 0.189, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9931, loss_cls: 0.6308, loss: 0.6308 +2025-06-24 12:30:46,216 - pyskl - INFO - Epoch [40][200/1281] lr: 2.103e-02, eta: 8:46:03, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9931, loss_cls: 0.6569, loss: 0.6569 +2025-06-24 12:31:08,444 - pyskl - INFO - Epoch [40][300/1281] lr: 2.101e-02, eta: 8:45:41, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9925, loss_cls: 0.6975, loss: 0.6975 +2025-06-24 12:31:30,864 - pyskl - INFO - Epoch [40][400/1281] lr: 2.100e-02, eta: 8:45:19, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9931, loss_cls: 0.6254, loss: 0.6254 +2025-06-24 12:31:53,064 - pyskl - INFO - Epoch [40][500/1281] lr: 2.098e-02, eta: 8:44:56, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9944, loss_cls: 0.6621, loss: 0.6621 +2025-06-24 12:32:15,344 - pyskl - INFO - Epoch [40][600/1281] lr: 2.097e-02, eta: 8:44:34, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9956, loss_cls: 0.6662, loss: 0.6662 +2025-06-24 12:32:37,943 - pyskl - INFO - Epoch [40][700/1281] lr: 2.095e-02, eta: 8:44:13, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9944, loss_cls: 0.6809, loss: 0.6809 +2025-06-24 12:33:00,438 - pyskl - INFO - Epoch [40][800/1281] lr: 2.094e-02, eta: 8:43:52, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8525, top5_acc: 0.9938, loss_cls: 0.7187, loss: 0.7187 +2025-06-24 12:33:22,846 - pyskl - INFO - Epoch [40][900/1281] lr: 2.092e-02, eta: 8:43:30, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9912, loss_cls: 0.6528, loss: 0.6528 +2025-06-24 12:33:45,337 - pyskl - INFO - Epoch [40][1000/1281] lr: 2.091e-02, eta: 8:43:08, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9956, loss_cls: 0.7064, loss: 0.7064 +2025-06-24 12:34:08,187 - pyskl - INFO - Epoch [40][1100/1281] lr: 2.089e-02, eta: 8:42:48, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9938, loss_cls: 0.7118, loss: 0.7118 +2025-06-24 12:34:31,210 - pyskl - INFO - Epoch [40][1200/1281] lr: 2.088e-02, eta: 8:42:28, time: 0.230, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9925, loss_cls: 0.6843, loss: 0.6843 +2025-06-24 12:34:49,871 - pyskl - INFO - Saving checkpoint at 40 epochs +2025-06-24 12:35:34,271 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:35:34,327 - pyskl - INFO - +top1_acc 0.8289 +top5_acc 0.9877 +2025-06-24 12:35:34,327 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:35:34,335 - pyskl - INFO - +mean_acc 0.7517 +2025-06-24 12:35:34,339 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_35.pth was removed +2025-06-24 12:35:34,511 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_40.pth. +2025-06-24 12:35:34,512 - pyskl - INFO - Best top1_acc is 0.8289 at 40 epoch. +2025-06-24 12:35:34,514 - pyskl - INFO - Epoch(val) [40][533] top1_acc: 0.8289, top5_acc: 0.9877, mean_class_accuracy: 0.7517 +2025-06-24 12:36:16,899 - pyskl - INFO - Epoch [41][100/1281] lr: 2.085e-02, eta: 8:41:53, time: 0.424, data_time: 0.185, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9975, loss_cls: 0.6603, loss: 0.6603 +2025-06-24 12:36:39,315 - pyskl - INFO - Epoch [41][200/1281] lr: 2.083e-02, eta: 8:41:32, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9962, loss_cls: 0.5943, loss: 0.5943 +2025-06-24 12:37:01,952 - pyskl - INFO - Epoch [41][300/1281] lr: 2.082e-02, eta: 8:41:11, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9938, loss_cls: 0.6403, loss: 0.6403 +2025-06-24 12:37:25,009 - pyskl - INFO - Epoch [41][400/1281] lr: 2.080e-02, eta: 8:40:51, time: 0.231, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9956, loss_cls: 0.6490, loss: 0.6490 +2025-06-24 12:37:47,348 - pyskl - INFO - Epoch [41][500/1281] lr: 2.079e-02, eta: 8:40:29, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9938, loss_cls: 0.6494, loss: 0.6494 +2025-06-24 12:38:09,757 - pyskl - INFO - Epoch [41][600/1281] lr: 2.077e-02, eta: 8:40:07, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9962, loss_cls: 0.6459, loss: 0.6459 +2025-06-24 12:38:32,457 - pyskl - INFO - Epoch [41][700/1281] lr: 2.076e-02, eta: 8:39:46, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9956, loss_cls: 0.6786, loss: 0.6786 +2025-06-24 12:38:55,148 - pyskl - INFO - Epoch [41][800/1281] lr: 2.074e-02, eta: 8:39:25, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9969, loss_cls: 0.6615, loss: 0.6615 +2025-06-24 12:39:17,532 - pyskl - INFO - Epoch [41][900/1281] lr: 2.073e-02, eta: 8:39:03, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9950, loss_cls: 0.6606, loss: 0.6606 +2025-06-24 12:39:39,928 - pyskl - INFO - Epoch [41][1000/1281] lr: 2.071e-02, eta: 8:38:41, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9938, loss_cls: 0.6565, loss: 0.6565 +2025-06-24 12:40:02,453 - pyskl - INFO - Epoch [41][1100/1281] lr: 2.070e-02, eta: 8:38:20, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9956, loss_cls: 0.6770, loss: 0.6770 +2025-06-24 12:40:24,959 - pyskl - INFO - Epoch [41][1200/1281] lr: 2.068e-02, eta: 8:37:58, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9956, loss_cls: 0.6947, loss: 0.6947 +2025-06-24 12:40:43,862 - pyskl - INFO - Saving checkpoint at 41 epochs +2025-06-24 12:41:28,133 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:41:28,195 - pyskl - INFO - +top1_acc 0.8040 +top5_acc 0.9858 +2025-06-24 12:41:28,195 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:41:28,202 - pyskl - INFO - +mean_acc 0.7442 +2025-06-24 12:41:28,204 - pyskl - INFO - Epoch(val) [41][533] top1_acc: 0.8040, top5_acc: 0.9858, mean_class_accuracy: 0.7442 +2025-06-24 12:42:11,649 - pyskl - INFO - Epoch [42][100/1281] lr: 2.065e-02, eta: 8:37:26, time: 0.434, data_time: 0.195, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9956, loss_cls: 0.6753, loss: 0.6753 +2025-06-24 12:42:33,938 - pyskl - INFO - Epoch [42][200/1281] lr: 2.064e-02, eta: 8:37:04, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9950, loss_cls: 0.6458, loss: 0.6458 +2025-06-24 12:42:56,279 - pyskl - INFO - Epoch [42][300/1281] lr: 2.062e-02, eta: 8:36:42, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9969, loss_cls: 0.6010, loss: 0.6010 +2025-06-24 12:43:19,047 - pyskl - INFO - Epoch [42][400/1281] lr: 2.061e-02, eta: 8:36:21, time: 0.228, data_time: 0.001, memory: 4083, top1_acc: 0.8500, top5_acc: 0.9925, loss_cls: 0.7417, loss: 0.7417 +2025-06-24 12:43:41,507 - pyskl - INFO - Epoch [42][500/1281] lr: 2.059e-02, eta: 8:35:59, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8469, top5_acc: 0.9912, loss_cls: 0.7212, loss: 0.7212 +2025-06-24 12:44:04,047 - pyskl - INFO - Epoch [42][600/1281] lr: 2.057e-02, eta: 8:35:38, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9944, loss_cls: 0.6945, loss: 0.6945 +2025-06-24 12:44:26,544 - pyskl - INFO - Epoch [42][700/1281] lr: 2.056e-02, eta: 8:35:16, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9944, loss_cls: 0.6833, loss: 0.6833 +2025-06-24 12:44:49,138 - pyskl - INFO - Epoch [42][800/1281] lr: 2.054e-02, eta: 8:34:55, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9956, loss_cls: 0.6454, loss: 0.6454 +2025-06-24 12:45:11,597 - pyskl - INFO - Epoch [42][900/1281] lr: 2.053e-02, eta: 8:34:33, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9944, loss_cls: 0.6628, loss: 0.6628 +2025-06-24 12:45:33,884 - pyskl - INFO - Epoch [42][1000/1281] lr: 2.051e-02, eta: 8:34:11, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8519, top5_acc: 0.9919, loss_cls: 0.6995, loss: 0.6995 +2025-06-24 12:45:56,212 - pyskl - INFO - Epoch [42][1100/1281] lr: 2.050e-02, eta: 8:33:49, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9925, loss_cls: 0.6874, loss: 0.6874 +2025-06-24 12:46:18,571 - pyskl - INFO - Epoch [42][1200/1281] lr: 2.048e-02, eta: 8:33:27, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9962, loss_cls: 0.6471, loss: 0.6471 +2025-06-24 12:46:37,624 - pyskl - INFO - Saving checkpoint at 42 epochs +2025-06-24 12:47:21,690 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:47:21,753 - pyskl - INFO - +top1_acc 0.8033 +top5_acc 0.9857 +2025-06-24 12:47:21,754 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:47:21,761 - pyskl - INFO - +mean_acc 0.7174 +2025-06-24 12:47:21,763 - pyskl - INFO - Epoch(val) [42][533] top1_acc: 0.8033, top5_acc: 0.9857, mean_class_accuracy: 0.7174 +2025-06-24 12:48:04,943 - pyskl - INFO - Epoch [43][100/1281] lr: 2.045e-02, eta: 8:32:54, time: 0.432, data_time: 0.195, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9944, loss_cls: 0.6254, loss: 0.6254 +2025-06-24 12:48:27,227 - pyskl - INFO - Epoch [43][200/1281] lr: 2.044e-02, eta: 8:32:32, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9956, loss_cls: 0.5699, loss: 0.5699 +2025-06-24 12:48:49,539 - pyskl - INFO - Epoch [43][300/1281] lr: 2.042e-02, eta: 8:32:10, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9950, loss_cls: 0.5695, loss: 0.5695 +2025-06-24 12:49:12,031 - pyskl - INFO - Epoch [43][400/1281] lr: 2.040e-02, eta: 8:31:48, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9944, loss_cls: 0.6362, loss: 0.6362 +2025-06-24 12:49:34,280 - pyskl - INFO - Epoch [43][500/1281] lr: 2.039e-02, eta: 8:31:26, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9906, loss_cls: 0.7241, loss: 0.7241 +2025-06-24 12:49:57,159 - pyskl - INFO - Epoch [43][600/1281] lr: 2.037e-02, eta: 8:31:05, time: 0.229, data_time: 0.001, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9931, loss_cls: 0.6754, loss: 0.6754 +2025-06-24 12:50:19,902 - pyskl - INFO - Epoch [43][700/1281] lr: 2.036e-02, eta: 8:30:44, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9944, loss_cls: 0.6258, loss: 0.6258 +2025-06-24 12:50:42,281 - pyskl - INFO - Epoch [43][800/1281] lr: 2.034e-02, eta: 8:30:22, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9931, loss_cls: 0.6483, loss: 0.6483 +2025-06-24 12:51:04,962 - pyskl - INFO - Epoch [43][900/1281] lr: 2.033e-02, eta: 8:30:01, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9956, loss_cls: 0.6494, loss: 0.6494 +2025-06-24 12:51:27,429 - pyskl - INFO - Epoch [43][1000/1281] lr: 2.031e-02, eta: 8:29:39, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9956, loss_cls: 0.6572, loss: 0.6572 +2025-06-24 12:51:49,870 - pyskl - INFO - Epoch [43][1100/1281] lr: 2.029e-02, eta: 8:29:17, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9925, loss_cls: 0.7048, loss: 0.7048 +2025-06-24 12:52:12,257 - pyskl - INFO - Epoch [43][1200/1281] lr: 2.028e-02, eta: 8:28:55, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9938, loss_cls: 0.6202, loss: 0.6202 +2025-06-24 12:52:31,438 - pyskl - INFO - Saving checkpoint at 43 epochs +2025-06-24 12:53:15,502 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:53:15,564 - pyskl - INFO - +top1_acc 0.8025 +top5_acc 0.9838 +2025-06-24 12:53:15,564 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:53:15,572 - pyskl - INFO - +mean_acc 0.7472 +2025-06-24 12:53:15,574 - pyskl - INFO - Epoch(val) [43][533] top1_acc: 0.8025, top5_acc: 0.9838, mean_class_accuracy: 0.7472 +2025-06-24 12:53:58,643 - pyskl - INFO - Epoch [44][100/1281] lr: 2.025e-02, eta: 8:28:22, time: 0.431, data_time: 0.193, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9956, loss_cls: 0.6025, loss: 0.6025 +2025-06-24 12:54:21,184 - pyskl - INFO - Epoch [44][200/1281] lr: 2.023e-02, eta: 8:28:00, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9950, loss_cls: 0.5328, loss: 0.5328 +2025-06-24 12:54:43,692 - pyskl - INFO - Epoch [44][300/1281] lr: 2.022e-02, eta: 8:27:39, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9962, loss_cls: 0.6061, loss: 0.6061 +2025-06-24 12:55:06,279 - pyskl - INFO - Epoch [44][400/1281] lr: 2.020e-02, eta: 8:27:17, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9950, loss_cls: 0.6273, loss: 0.6273 +2025-06-24 12:55:28,970 - pyskl - INFO - Epoch [44][500/1281] lr: 2.018e-02, eta: 8:26:56, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9944, loss_cls: 0.6352, loss: 0.6352 +2025-06-24 12:55:51,767 - pyskl - INFO - Epoch [44][600/1281] lr: 2.017e-02, eta: 8:26:35, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9962, loss_cls: 0.6711, loss: 0.6711 +2025-06-24 12:56:14,545 - pyskl - INFO - Epoch [44][700/1281] lr: 2.015e-02, eta: 8:26:14, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9912, loss_cls: 0.7018, loss: 0.7018 +2025-06-24 12:56:37,024 - pyskl - INFO - Epoch [44][800/1281] lr: 2.014e-02, eta: 8:25:52, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9938, loss_cls: 0.6875, loss: 0.6875 +2025-06-24 12:56:59,403 - pyskl - INFO - Epoch [44][900/1281] lr: 2.012e-02, eta: 8:25:30, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9956, loss_cls: 0.6156, loss: 0.6156 +2025-06-24 12:57:21,808 - pyskl - INFO - Epoch [44][1000/1281] lr: 2.010e-02, eta: 8:25:08, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9981, loss_cls: 0.5924, loss: 0.5924 +2025-06-24 12:57:44,177 - pyskl - INFO - Epoch [44][1100/1281] lr: 2.009e-02, eta: 8:24:46, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9975, loss_cls: 0.6223, loss: 0.6223 +2025-06-24 12:58:06,704 - pyskl - INFO - Epoch [44][1200/1281] lr: 2.007e-02, eta: 8:24:24, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9931, loss_cls: 0.7099, loss: 0.7099 +2025-06-24 12:58:25,977 - pyskl - INFO - Saving checkpoint at 44 epochs +2025-06-24 12:59:11,100 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:59:11,171 - pyskl - INFO - +top1_acc 0.8147 +top5_acc 0.9870 +2025-06-24 12:59:11,171 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:59:11,180 - pyskl - INFO - +mean_acc 0.7179 +2025-06-24 12:59:11,183 - pyskl - INFO - Epoch(val) [44][533] top1_acc: 0.8147, top5_acc: 0.9870, mean_class_accuracy: 0.7179 +2025-06-24 12:59:54,188 - pyskl - INFO - Epoch [45][100/1281] lr: 2.004e-02, eta: 8:23:50, time: 0.430, data_time: 0.194, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9962, loss_cls: 0.6155, loss: 0.6155 +2025-06-24 13:00:16,819 - pyskl - INFO - Epoch [45][200/1281] lr: 2.003e-02, eta: 8:23:29, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9944, loss_cls: 0.5939, loss: 0.5939 +2025-06-24 13:00:39,283 - pyskl - INFO - Epoch [45][300/1281] lr: 2.001e-02, eta: 8:23:07, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9962, loss_cls: 0.5960, loss: 0.5960 +2025-06-24 13:01:01,316 - pyskl - INFO - Epoch [45][400/1281] lr: 1.999e-02, eta: 8:22:44, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9931, loss_cls: 0.7269, loss: 0.7269 +2025-06-24 13:01:24,419 - pyskl - INFO - Epoch [45][500/1281] lr: 1.998e-02, eta: 8:22:24, time: 0.231, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9962, loss_cls: 0.6238, loss: 0.6238 +2025-06-24 13:01:47,090 - pyskl - INFO - Epoch [45][600/1281] lr: 1.996e-02, eta: 8:22:03, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9956, loss_cls: 0.6562, loss: 0.6562 +2025-06-24 13:02:09,399 - pyskl - INFO - Epoch [45][700/1281] lr: 1.994e-02, eta: 8:21:40, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9919, loss_cls: 0.6563, loss: 0.6563 +2025-06-24 13:02:32,048 - pyskl - INFO - Epoch [45][800/1281] lr: 1.993e-02, eta: 8:21:19, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9975, loss_cls: 0.6161, loss: 0.6161 +2025-06-24 13:02:54,691 - pyskl - INFO - Epoch [45][900/1281] lr: 1.991e-02, eta: 8:20:58, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9956, loss_cls: 0.6364, loss: 0.6364 +2025-06-24 13:03:17,121 - pyskl - INFO - Epoch [45][1000/1281] lr: 1.989e-02, eta: 8:20:36, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9944, loss_cls: 0.6165, loss: 0.6165 +2025-06-24 13:03:39,684 - pyskl - INFO - Epoch [45][1100/1281] lr: 1.988e-02, eta: 8:20:14, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9956, loss_cls: 0.6342, loss: 0.6342 +2025-06-24 13:04:01,858 - pyskl - INFO - Epoch [45][1200/1281] lr: 1.986e-02, eta: 8:19:51, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9944, loss_cls: 0.6549, loss: 0.6549 +2025-06-24 13:04:21,093 - pyskl - INFO - Saving checkpoint at 45 epochs +2025-06-24 13:05:05,263 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:05:05,318 - pyskl - INFO - +top1_acc 0.8162 +top5_acc 0.9872 +2025-06-24 13:05:05,318 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:05:05,325 - pyskl - INFO - +mean_acc 0.7432 +2025-06-24 13:05:05,326 - pyskl - INFO - Epoch(val) [45][533] top1_acc: 0.8162, top5_acc: 0.9872, mean_class_accuracy: 0.7432 +2025-06-24 13:05:48,426 - pyskl - INFO - Epoch [46][100/1281] lr: 1.983e-02, eta: 8:19:17, time: 0.431, data_time: 0.191, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9981, loss_cls: 0.5841, loss: 0.5841 +2025-06-24 13:06:11,009 - pyskl - INFO - Epoch [46][200/1281] lr: 1.981e-02, eta: 8:18:56, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9981, loss_cls: 0.6087, loss: 0.6087 +2025-06-24 13:06:33,563 - pyskl - INFO - Epoch [46][300/1281] lr: 1.980e-02, eta: 8:18:34, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9944, loss_cls: 0.5632, loss: 0.5632 +2025-06-24 13:06:55,943 - pyskl - INFO - Epoch [46][400/1281] lr: 1.978e-02, eta: 8:18:12, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9938, loss_cls: 0.5971, loss: 0.5971 +2025-06-24 13:07:18,691 - pyskl - INFO - Epoch [46][500/1281] lr: 1.976e-02, eta: 8:17:51, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9950, loss_cls: 0.6774, loss: 0.6774 +2025-06-24 13:07:41,005 - pyskl - INFO - Epoch [46][600/1281] lr: 1.975e-02, eta: 8:17:29, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9975, loss_cls: 0.5939, loss: 0.5939 +2025-06-24 13:08:03,241 - pyskl - INFO - Epoch [46][700/1281] lr: 1.973e-02, eta: 8:17:06, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9925, loss_cls: 0.6663, loss: 0.6663 +2025-06-24 13:08:25,762 - pyskl - INFO - Epoch [46][800/1281] lr: 1.971e-02, eta: 8:16:44, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9962, loss_cls: 0.6318, loss: 0.6318 +2025-06-24 13:08:47,985 - pyskl - INFO - Epoch [46][900/1281] lr: 1.970e-02, eta: 8:16:22, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8488, top5_acc: 0.9925, loss_cls: 0.6977, loss: 0.6977 +2025-06-24 13:09:10,170 - pyskl - INFO - Epoch [46][1000/1281] lr: 1.968e-02, eta: 8:15:59, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9925, loss_cls: 0.6821, loss: 0.6821 +2025-06-24 13:09:32,524 - pyskl - INFO - Epoch [46][1100/1281] lr: 1.966e-02, eta: 8:15:37, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9919, loss_cls: 0.6759, loss: 0.6759 +2025-06-24 13:09:54,791 - pyskl - INFO - Epoch [46][1200/1281] lr: 1.965e-02, eta: 8:15:15, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9956, loss_cls: 0.5899, loss: 0.5899 +2025-06-24 13:10:13,904 - pyskl - INFO - Saving checkpoint at 46 epochs +2025-06-24 13:10:58,869 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:10:58,926 - pyskl - INFO - +top1_acc 0.7881 +top5_acc 0.9867 +2025-06-24 13:10:58,927 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:10:58,934 - pyskl - INFO - +mean_acc 0.7532 +2025-06-24 13:10:58,936 - pyskl - INFO - Epoch(val) [46][533] top1_acc: 0.7881, top5_acc: 0.9867, mean_class_accuracy: 0.7532 +2025-06-24 13:11:41,576 - pyskl - INFO - Epoch [47][100/1281] lr: 1.962e-02, eta: 8:14:40, time: 0.426, data_time: 0.188, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9950, loss_cls: 0.6178, loss: 0.6178 +2025-06-24 13:12:04,411 - pyskl - INFO - Epoch [47][200/1281] lr: 1.960e-02, eta: 8:14:19, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9950, loss_cls: 0.6044, loss: 0.6044 +2025-06-24 13:12:26,954 - pyskl - INFO - Epoch [47][300/1281] lr: 1.958e-02, eta: 8:13:57, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9969, loss_cls: 0.5883, loss: 0.5883 +2025-06-24 13:12:49,423 - pyskl - INFO - Epoch [47][400/1281] lr: 1.957e-02, eta: 8:13:35, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9975, loss_cls: 0.6509, loss: 0.6509 +2025-06-24 13:13:12,045 - pyskl - INFO - Epoch [47][500/1281] lr: 1.955e-02, eta: 8:13:13, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9944, loss_cls: 0.5513, loss: 0.5513 +2025-06-24 13:13:34,897 - pyskl - INFO - Epoch [47][600/1281] lr: 1.953e-02, eta: 8:12:52, time: 0.229, data_time: 0.001, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9962, loss_cls: 0.5840, loss: 0.5840 +2025-06-24 13:13:57,267 - pyskl - INFO - Epoch [47][700/1281] lr: 1.952e-02, eta: 8:12:30, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9956, loss_cls: 0.6414, loss: 0.6414 +2025-06-24 13:14:19,347 - pyskl - INFO - Epoch [47][800/1281] lr: 1.950e-02, eta: 8:12:07, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9962, loss_cls: 0.6045, loss: 0.6045 +2025-06-24 13:14:42,021 - pyskl - INFO - Epoch [47][900/1281] lr: 1.948e-02, eta: 8:11:46, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9969, loss_cls: 0.6138, loss: 0.6138 +2025-06-24 13:15:04,391 - pyskl - INFO - Epoch [47][1000/1281] lr: 1.947e-02, eta: 8:11:24, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9944, loss_cls: 0.5990, loss: 0.5990 +2025-06-24 13:15:27,046 - pyskl - INFO - Epoch [47][1100/1281] lr: 1.945e-02, eta: 8:11:02, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9900, loss_cls: 0.6888, loss: 0.6888 +2025-06-24 13:15:49,321 - pyskl - INFO - Epoch [47][1200/1281] lr: 1.943e-02, eta: 8:10:40, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9956, loss_cls: 0.6409, loss: 0.6409 +2025-06-24 13:16:08,300 - pyskl - INFO - Saving checkpoint at 47 epochs +2025-06-24 13:16:52,231 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:16:52,299 - pyskl - INFO - +top1_acc 0.8150 +top5_acc 0.9878 +2025-06-24 13:16:52,299 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:16:52,307 - pyskl - INFO - +mean_acc 0.7422 +2025-06-24 13:16:52,309 - pyskl - INFO - Epoch(val) [47][533] top1_acc: 0.8150, top5_acc: 0.9878, mean_class_accuracy: 0.7422 +2025-06-24 13:17:35,672 - pyskl - INFO - Epoch [48][100/1281] lr: 1.940e-02, eta: 8:10:06, time: 0.434, data_time: 0.198, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9975, loss_cls: 0.5705, loss: 0.5705 +2025-06-24 13:17:58,162 - pyskl - INFO - Epoch [48][200/1281] lr: 1.938e-02, eta: 8:09:44, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9969, loss_cls: 0.5604, loss: 0.5604 +2025-06-24 13:18:20,615 - pyskl - INFO - Epoch [48][300/1281] lr: 1.937e-02, eta: 8:09:22, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9975, loss_cls: 0.5264, loss: 0.5264 +2025-06-24 13:18:42,840 - pyskl - INFO - Epoch [48][400/1281] lr: 1.935e-02, eta: 8:09:00, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9969, loss_cls: 0.5615, loss: 0.5615 +2025-06-24 13:19:05,225 - pyskl - INFO - Epoch [48][500/1281] lr: 1.933e-02, eta: 8:08:38, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9969, loss_cls: 0.5788, loss: 0.5788 +2025-06-24 13:19:27,623 - pyskl - INFO - Epoch [48][600/1281] lr: 1.932e-02, eta: 8:08:16, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9938, loss_cls: 0.6446, loss: 0.6446 +2025-06-24 13:19:50,161 - pyskl - INFO - Epoch [48][700/1281] lr: 1.930e-02, eta: 8:07:54, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9969, loss_cls: 0.5593, loss: 0.5593 +2025-06-24 13:20:12,542 - pyskl - INFO - Epoch [48][800/1281] lr: 1.928e-02, eta: 8:07:32, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9944, loss_cls: 0.5608, loss: 0.5608 +2025-06-24 13:20:35,212 - pyskl - INFO - Epoch [48][900/1281] lr: 1.926e-02, eta: 8:07:10, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9969, loss_cls: 0.6106, loss: 0.6106 +2025-06-24 13:20:57,805 - pyskl - INFO - Epoch [48][1000/1281] lr: 1.925e-02, eta: 8:06:48, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9919, loss_cls: 0.6595, loss: 0.6595 +2025-06-24 13:21:20,113 - pyskl - INFO - Epoch [48][1100/1281] lr: 1.923e-02, eta: 8:06:26, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9969, loss_cls: 0.5964, loss: 0.5964 +2025-06-24 13:21:42,433 - pyskl - INFO - Epoch [48][1200/1281] lr: 1.921e-02, eta: 8:06:04, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9944, loss_cls: 0.6486, loss: 0.6486 +2025-06-24 13:22:01,334 - pyskl - INFO - Saving checkpoint at 48 epochs +2025-06-24 13:22:45,253 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:22:45,308 - pyskl - INFO - +top1_acc 0.8364 +top5_acc 0.9891 +2025-06-24 13:22:45,308 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:22:45,315 - pyskl - INFO - +mean_acc 0.7817 +2025-06-24 13:22:45,319 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_40.pth was removed +2025-06-24 13:22:45,515 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_48.pth. +2025-06-24 13:22:45,516 - pyskl - INFO - Best top1_acc is 0.8364 at 48 epoch. +2025-06-24 13:22:45,518 - pyskl - INFO - Epoch(val) [48][533] top1_acc: 0.8364, top5_acc: 0.9891, mean_class_accuracy: 0.7817 +2025-06-24 13:23:28,329 - pyskl - INFO - Epoch [49][100/1281] lr: 1.918e-02, eta: 8:05:29, time: 0.428, data_time: 0.191, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9950, loss_cls: 0.6370, loss: 0.6370 +2025-06-24 13:23:50,580 - pyskl - INFO - Epoch [49][200/1281] lr: 1.916e-02, eta: 8:05:06, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9969, loss_cls: 0.5536, loss: 0.5536 +2025-06-24 13:24:13,230 - pyskl - INFO - Epoch [49][300/1281] lr: 1.915e-02, eta: 8:04:45, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9944, loss_cls: 0.6000, loss: 0.6000 +2025-06-24 13:24:35,750 - pyskl - INFO - Epoch [49][400/1281] lr: 1.913e-02, eta: 8:04:23, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9969, loss_cls: 0.5991, loss: 0.5991 +2025-06-24 13:24:58,256 - pyskl - INFO - Epoch [49][500/1281] lr: 1.911e-02, eta: 8:04:01, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9969, loss_cls: 0.5649, loss: 0.5649 +2025-06-24 13:25:20,651 - pyskl - INFO - Epoch [49][600/1281] lr: 1.909e-02, eta: 8:03:39, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9938, loss_cls: 0.5662, loss: 0.5662 +2025-06-24 13:25:43,149 - pyskl - INFO - Epoch [49][700/1281] lr: 1.908e-02, eta: 8:03:17, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9975, loss_cls: 0.5603, loss: 0.5603 +2025-06-24 13:26:05,973 - pyskl - INFO - Epoch [49][800/1281] lr: 1.906e-02, eta: 8:02:55, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9931, loss_cls: 0.5787, loss: 0.5787 +2025-06-24 13:26:28,498 - pyskl - INFO - Epoch [49][900/1281] lr: 1.904e-02, eta: 8:02:34, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9950, loss_cls: 0.6058, loss: 0.6058 +2025-06-24 13:26:50,837 - pyskl - INFO - Epoch [49][1000/1281] lr: 1.902e-02, eta: 8:02:11, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9975, loss_cls: 0.6468, loss: 0.6468 +2025-06-24 13:27:13,344 - pyskl - INFO - Epoch [49][1100/1281] lr: 1.901e-02, eta: 8:01:49, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9950, loss_cls: 0.6592, loss: 0.6592 +2025-06-24 13:27:35,778 - pyskl - INFO - Epoch [49][1200/1281] lr: 1.899e-02, eta: 8:01:27, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9969, loss_cls: 0.6399, loss: 0.6399 +2025-06-24 13:27:54,605 - pyskl - INFO - Saving checkpoint at 49 epochs +2025-06-24 13:28:38,229 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:28:38,284 - pyskl - INFO - +top1_acc 0.8134 +top5_acc 0.9863 +2025-06-24 13:28:38,284 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:28:38,291 - pyskl - INFO - +mean_acc 0.7636 +2025-06-24 13:28:38,292 - pyskl - INFO - Epoch(val) [49][533] top1_acc: 0.8134, top5_acc: 0.9863, mean_class_accuracy: 0.7636 +2025-06-24 13:29:20,871 - pyskl - INFO - Epoch [50][100/1281] lr: 1.896e-02, eta: 8:00:51, time: 0.426, data_time: 0.189, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9969, loss_cls: 0.5424, loss: 0.5424 +2025-06-24 13:29:43,768 - pyskl - INFO - Epoch [50][200/1281] lr: 1.894e-02, eta: 8:00:30, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9956, loss_cls: 0.5408, loss: 0.5408 +2025-06-24 13:30:06,234 - pyskl - INFO - Epoch [50][300/1281] lr: 1.892e-02, eta: 8:00:08, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9962, loss_cls: 0.5622, loss: 0.5622 +2025-06-24 13:30:28,753 - pyskl - INFO - Epoch [50][400/1281] lr: 1.891e-02, eta: 7:59:46, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9925, loss_cls: 0.5683, loss: 0.5683 +2025-06-24 13:30:51,780 - pyskl - INFO - Epoch [50][500/1281] lr: 1.889e-02, eta: 7:59:26, time: 0.230, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9962, loss_cls: 0.6136, loss: 0.6136 +2025-06-24 13:31:14,600 - pyskl - INFO - Epoch [50][600/1281] lr: 1.887e-02, eta: 7:59:04, time: 0.228, data_time: 0.001, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9950, loss_cls: 0.5945, loss: 0.5945 +2025-06-24 13:31:36,851 - pyskl - INFO - Epoch [50][700/1281] lr: 1.885e-02, eta: 7:58:42, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9962, loss_cls: 0.5851, loss: 0.5851 +2025-06-24 13:31:59,114 - pyskl - INFO - Epoch [50][800/1281] lr: 1.884e-02, eta: 7:58:19, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9969, loss_cls: 0.6218, loss: 0.6218 +2025-06-24 13:32:22,046 - pyskl - INFO - Epoch [50][900/1281] lr: 1.882e-02, eta: 7:57:58, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9962, loss_cls: 0.6225, loss: 0.6225 +2025-06-24 13:32:44,274 - pyskl - INFO - Epoch [50][1000/1281] lr: 1.880e-02, eta: 7:57:36, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9950, loss_cls: 0.5873, loss: 0.5873 +2025-06-24 13:33:06,891 - pyskl - INFO - Epoch [50][1100/1281] lr: 1.878e-02, eta: 7:57:14, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9956, loss_cls: 0.6169, loss: 0.6169 +2025-06-24 13:33:29,574 - pyskl - INFO - Epoch [50][1200/1281] lr: 1.876e-02, eta: 7:56:52, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9956, loss_cls: 0.6055, loss: 0.6055 +2025-06-24 13:33:48,484 - pyskl - INFO - Saving checkpoint at 50 epochs +2025-06-24 13:34:32,370 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:34:32,438 - pyskl - INFO - +top1_acc 0.8192 +top5_acc 0.9858 +2025-06-24 13:34:32,438 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:34:32,447 - pyskl - INFO - +mean_acc 0.7616 +2025-06-24 13:34:32,450 - pyskl - INFO - Epoch(val) [50][533] top1_acc: 0.8192, top5_acc: 0.9858, mean_class_accuracy: 0.7616 +2025-06-24 13:35:14,779 - pyskl - INFO - Epoch [51][100/1281] lr: 1.873e-02, eta: 7:56:16, time: 0.423, data_time: 0.185, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9981, loss_cls: 0.5465, loss: 0.5465 +2025-06-24 13:35:37,383 - pyskl - INFO - Epoch [51][200/1281] lr: 1.871e-02, eta: 7:55:54, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9969, loss_cls: 0.5641, loss: 0.5641 +2025-06-24 13:36:00,057 - pyskl - INFO - Epoch [51][300/1281] lr: 1.870e-02, eta: 7:55:32, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9956, loss_cls: 0.6515, loss: 0.6515 +2025-06-24 13:36:22,614 - pyskl - INFO - Epoch [51][400/1281] lr: 1.868e-02, eta: 7:55:11, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9962, loss_cls: 0.5658, loss: 0.5658 +2025-06-24 13:36:45,101 - pyskl - INFO - Epoch [51][500/1281] lr: 1.866e-02, eta: 7:54:49, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9975, loss_cls: 0.6169, loss: 0.6169 +2025-06-24 13:37:07,466 - pyskl - INFO - Epoch [51][600/1281] lr: 1.864e-02, eta: 7:54:26, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9969, loss_cls: 0.6346, loss: 0.6346 +2025-06-24 13:37:29,975 - pyskl - INFO - Epoch [51][700/1281] lr: 1.863e-02, eta: 7:54:04, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9950, loss_cls: 0.5919, loss: 0.5919 +2025-06-24 13:37:52,458 - pyskl - INFO - Epoch [51][800/1281] lr: 1.861e-02, eta: 7:53:42, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9919, loss_cls: 0.6174, loss: 0.6174 +2025-06-24 13:38:14,987 - pyskl - INFO - Epoch [51][900/1281] lr: 1.859e-02, eta: 7:53:20, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9919, loss_cls: 0.5982, loss: 0.5982 +2025-06-24 13:38:37,086 - pyskl - INFO - Epoch [51][1000/1281] lr: 1.857e-02, eta: 7:52:58, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9950, loss_cls: 0.5532, loss: 0.5532 +2025-06-24 13:38:59,191 - pyskl - INFO - Epoch [51][1100/1281] lr: 1.855e-02, eta: 7:52:35, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9962, loss_cls: 0.5811, loss: 0.5811 +2025-06-24 13:39:21,504 - pyskl - INFO - Epoch [51][1200/1281] lr: 1.854e-02, eta: 7:52:12, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9962, loss_cls: 0.6470, loss: 0.6470 +2025-06-24 13:39:40,451 - pyskl - INFO - Saving checkpoint at 51 epochs +2025-06-24 13:40:24,601 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:40:24,664 - pyskl - INFO - +top1_acc 0.8439 +top5_acc 0.9869 +2025-06-24 13:40:24,664 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:40:24,671 - pyskl - INFO - +mean_acc 0.7795 +2025-06-24 13:40:24,675 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_48.pth was removed +2025-06-24 13:40:24,841 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_51.pth. +2025-06-24 13:40:24,842 - pyskl - INFO - Best top1_acc is 0.8439 at 51 epoch. +2025-06-24 13:40:24,845 - pyskl - INFO - Epoch(val) [51][533] top1_acc: 0.8439, top5_acc: 0.9869, mean_class_accuracy: 0.7795 +2025-06-24 13:41:07,110 - pyskl - INFO - Epoch [52][100/1281] lr: 1.850e-02, eta: 7:51:36, time: 0.423, data_time: 0.189, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9988, loss_cls: 0.5183, loss: 0.5183 +2025-06-24 13:41:29,708 - pyskl - INFO - Epoch [52][200/1281] lr: 1.849e-02, eta: 7:51:14, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9969, loss_cls: 0.5455, loss: 0.5455 +2025-06-24 13:41:52,207 - pyskl - INFO - Epoch [52][300/1281] lr: 1.847e-02, eta: 7:50:52, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9931, loss_cls: 0.6143, loss: 0.6143 +2025-06-24 13:42:14,379 - pyskl - INFO - Epoch [52][400/1281] lr: 1.845e-02, eta: 7:50:29, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9981, loss_cls: 0.5844, loss: 0.5844 +2025-06-24 13:42:36,542 - pyskl - INFO - Epoch [52][500/1281] lr: 1.843e-02, eta: 7:50:07, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9925, loss_cls: 0.6415, loss: 0.6415 +2025-06-24 13:42:58,679 - pyskl - INFO - Epoch [52][600/1281] lr: 1.841e-02, eta: 7:49:44, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9975, loss_cls: 0.5452, loss: 0.5452 +2025-06-24 13:43:20,974 - pyskl - INFO - Epoch [52][700/1281] lr: 1.840e-02, eta: 7:49:21, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9962, loss_cls: 0.5531, loss: 0.5531 +2025-06-24 13:43:43,300 - pyskl - INFO - Epoch [52][800/1281] lr: 1.838e-02, eta: 7:48:59, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9969, loss_cls: 0.5744, loss: 0.5744 +2025-06-24 13:44:05,759 - pyskl - INFO - Epoch [52][900/1281] lr: 1.836e-02, eta: 7:48:37, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9962, loss_cls: 0.5986, loss: 0.5986 +2025-06-24 13:44:28,200 - pyskl - INFO - Epoch [52][1000/1281] lr: 1.834e-02, eta: 7:48:15, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9975, loss_cls: 0.5955, loss: 0.5955 +2025-06-24 13:44:50,633 - pyskl - INFO - Epoch [52][1100/1281] lr: 1.832e-02, eta: 7:47:53, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9938, loss_cls: 0.6077, loss: 0.6077 +2025-06-24 13:45:12,967 - pyskl - INFO - Epoch [52][1200/1281] lr: 1.831e-02, eta: 7:47:30, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9969, loss_cls: 0.5869, loss: 0.5869 +2025-06-24 13:45:32,160 - pyskl - INFO - Saving checkpoint at 52 epochs +2025-06-24 13:46:15,882 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:46:15,953 - pyskl - INFO - +top1_acc 0.8601 +top5_acc 0.9924 +2025-06-24 13:46:15,954 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:46:15,962 - pyskl - INFO - +mean_acc 0.8168 +2025-06-24 13:46:15,966 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_51.pth was removed +2025-06-24 13:46:16,179 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_52.pth. +2025-06-24 13:46:16,180 - pyskl - INFO - Best top1_acc is 0.8601 at 52 epoch. +2025-06-24 13:46:16,184 - pyskl - INFO - Epoch(val) [52][533] top1_acc: 0.8601, top5_acc: 0.9924, mean_class_accuracy: 0.8168 +2025-06-24 13:47:00,049 - pyskl - INFO - Epoch [53][100/1281] lr: 1.827e-02, eta: 7:46:56, time: 0.439, data_time: 0.196, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9969, loss_cls: 0.5014, loss: 0.5014 +2025-06-24 13:47:22,744 - pyskl - INFO - Epoch [53][200/1281] lr: 1.826e-02, eta: 7:46:35, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9950, loss_cls: 0.5272, loss: 0.5272 +2025-06-24 13:47:45,701 - pyskl - INFO - Epoch [53][300/1281] lr: 1.824e-02, eta: 7:46:14, time: 0.230, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9950, loss_cls: 0.5737, loss: 0.5737 +2025-06-24 13:48:08,358 - pyskl - INFO - Epoch [53][400/1281] lr: 1.822e-02, eta: 7:45:52, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9969, loss_cls: 0.5953, loss: 0.5953 +2025-06-24 13:48:30,991 - pyskl - INFO - Epoch [53][500/1281] lr: 1.820e-02, eta: 7:45:30, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9962, loss_cls: 0.6134, loss: 0.6134 +2025-06-24 13:48:53,683 - pyskl - INFO - Epoch [53][600/1281] lr: 1.818e-02, eta: 7:45:08, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9906, loss_cls: 0.5778, loss: 0.5778 +2025-06-24 13:49:16,313 - pyskl - INFO - Epoch [53][700/1281] lr: 1.816e-02, eta: 7:44:47, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9950, loss_cls: 0.5286, loss: 0.5286 +2025-06-24 13:49:38,706 - pyskl - INFO - Epoch [53][800/1281] lr: 1.815e-02, eta: 7:44:24, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9931, loss_cls: 0.6409, loss: 0.6409 +2025-06-24 13:50:01,221 - pyskl - INFO - Epoch [53][900/1281] lr: 1.813e-02, eta: 7:44:02, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9975, loss_cls: 0.6026, loss: 0.6026 +2025-06-24 13:50:23,727 - pyskl - INFO - Epoch [53][1000/1281] lr: 1.811e-02, eta: 7:43:40, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9962, loss_cls: 0.5285, loss: 0.5285 +2025-06-24 13:50:45,937 - pyskl - INFO - Epoch [53][1100/1281] lr: 1.809e-02, eta: 7:43:18, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9950, loss_cls: 0.6578, loss: 0.6578 +2025-06-24 13:51:08,337 - pyskl - INFO - Epoch [53][1200/1281] lr: 1.807e-02, eta: 7:42:56, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9925, loss_cls: 0.6238, loss: 0.6238 +2025-06-24 13:51:27,291 - pyskl - INFO - Saving checkpoint at 53 epochs +2025-06-24 13:52:10,869 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:52:10,927 - pyskl - INFO - +top1_acc 0.8451 +top5_acc 0.9918 +2025-06-24 13:52:10,927 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:52:10,934 - pyskl - INFO - +mean_acc 0.7913 +2025-06-24 13:52:10,936 - pyskl - INFO - Epoch(val) [53][533] top1_acc: 0.8451, top5_acc: 0.9918, mean_class_accuracy: 0.7913 +2025-06-24 13:52:53,812 - pyskl - INFO - Epoch [54][100/1281] lr: 1.804e-02, eta: 7:42:20, time: 0.429, data_time: 0.193, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9975, loss_cls: 0.5651, loss: 0.5651 +2025-06-24 13:53:16,082 - pyskl - INFO - Epoch [54][200/1281] lr: 1.802e-02, eta: 7:41:57, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9956, loss_cls: 0.5751, loss: 0.5751 +2025-06-24 13:53:38,714 - pyskl - INFO - Epoch [54][300/1281] lr: 1.800e-02, eta: 7:41:35, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9969, loss_cls: 0.5028, loss: 0.5028 +2025-06-24 13:54:01,251 - pyskl - INFO - Epoch [54][400/1281] lr: 1.798e-02, eta: 7:41:13, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9962, loss_cls: 0.5531, loss: 0.5531 +2025-06-24 13:54:23,820 - pyskl - INFO - Epoch [54][500/1281] lr: 1.797e-02, eta: 7:40:51, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9950, loss_cls: 0.5875, loss: 0.5875 +2025-06-24 13:54:46,198 - pyskl - INFO - Epoch [54][600/1281] lr: 1.795e-02, eta: 7:40:29, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9962, loss_cls: 0.5271, loss: 0.5271 +2025-06-24 13:55:08,431 - pyskl - INFO - Epoch [54][700/1281] lr: 1.793e-02, eta: 7:40:06, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9981, loss_cls: 0.5431, loss: 0.5431 +2025-06-24 13:55:31,098 - pyskl - INFO - Epoch [54][800/1281] lr: 1.791e-02, eta: 7:39:45, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9962, loss_cls: 0.6539, loss: 0.6539 +2025-06-24 13:55:53,673 - pyskl - INFO - Epoch [54][900/1281] lr: 1.789e-02, eta: 7:39:23, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9944, loss_cls: 0.5790, loss: 0.5790 +2025-06-24 13:56:16,124 - pyskl - INFO - Epoch [54][1000/1281] lr: 1.787e-02, eta: 7:39:01, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9981, loss_cls: 0.5269, loss: 0.5269 +2025-06-24 13:56:38,443 - pyskl - INFO - Epoch [54][1100/1281] lr: 1.786e-02, eta: 7:38:38, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9981, loss_cls: 0.6027, loss: 0.6027 +2025-06-24 13:57:01,372 - pyskl - INFO - Epoch [54][1200/1281] lr: 1.784e-02, eta: 7:38:17, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9956, loss_cls: 0.5674, loss: 0.5674 +2025-06-24 13:57:20,303 - pyskl - INFO - Saving checkpoint at 54 epochs +2025-06-24 13:58:04,256 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:58:04,312 - pyskl - INFO - +top1_acc 0.8163 +top5_acc 0.9852 +2025-06-24 13:58:04,312 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:58:04,320 - pyskl - INFO - +mean_acc 0.7672 +2025-06-24 13:58:04,322 - pyskl - INFO - Epoch(val) [54][533] top1_acc: 0.8163, top5_acc: 0.9852, mean_class_accuracy: 0.7672 +2025-06-24 13:58:48,286 - pyskl - INFO - Epoch [55][100/1281] lr: 1.780e-02, eta: 7:37:43, time: 0.440, data_time: 0.201, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9962, loss_cls: 0.5396, loss: 0.5396 +2025-06-24 13:59:10,897 - pyskl - INFO - Epoch [55][200/1281] lr: 1.779e-02, eta: 7:37:21, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9962, loss_cls: 0.5228, loss: 0.5228 +2025-06-24 13:59:32,948 - pyskl - INFO - Epoch [55][300/1281] lr: 1.777e-02, eta: 7:36:58, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9981, loss_cls: 0.4759, loss: 0.4759 +2025-06-24 13:59:55,744 - pyskl - INFO - Epoch [55][400/1281] lr: 1.775e-02, eta: 7:36:36, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9975, loss_cls: 0.5113, loss: 0.5113 +2025-06-24 14:00:18,255 - pyskl - INFO - Epoch [55][500/1281] lr: 1.773e-02, eta: 7:36:14, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9950, loss_cls: 0.5870, loss: 0.5870 +2025-06-24 14:00:40,570 - pyskl - INFO - Epoch [55][600/1281] lr: 1.771e-02, eta: 7:35:52, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9975, loss_cls: 0.5970, loss: 0.5970 +2025-06-24 14:01:02,892 - pyskl - INFO - Epoch [55][700/1281] lr: 1.769e-02, eta: 7:35:30, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9938, loss_cls: 0.5751, loss: 0.5751 +2025-06-24 14:01:25,123 - pyskl - INFO - Epoch [55][800/1281] lr: 1.767e-02, eta: 7:35:07, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9969, loss_cls: 0.5592, loss: 0.5592 +2025-06-24 14:01:47,497 - pyskl - INFO - Epoch [55][900/1281] lr: 1.766e-02, eta: 7:34:45, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9938, loss_cls: 0.6259, loss: 0.6259 +2025-06-24 14:02:09,687 - pyskl - INFO - Epoch [55][1000/1281] lr: 1.764e-02, eta: 7:34:22, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9969, loss_cls: 0.5808, loss: 0.5808 +2025-06-24 14:02:31,842 - pyskl - INFO - Epoch [55][1100/1281] lr: 1.762e-02, eta: 7:33:59, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9981, loss_cls: 0.5434, loss: 0.5434 +2025-06-24 14:02:54,286 - pyskl - INFO - Epoch [55][1200/1281] lr: 1.760e-02, eta: 7:33:37, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9944, loss_cls: 0.5684, loss: 0.5684 +2025-06-24 14:03:12,965 - pyskl - INFO - Saving checkpoint at 55 epochs +2025-06-24 14:03:56,746 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:03:56,803 - pyskl - INFO - +top1_acc 0.8453 +top5_acc 0.9878 +2025-06-24 14:03:56,803 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:03:56,813 - pyskl - INFO - +mean_acc 0.7751 +2025-06-24 14:03:56,815 - pyskl - INFO - Epoch(val) [55][533] top1_acc: 0.8453, top5_acc: 0.9878, mean_class_accuracy: 0.7751 +2025-06-24 14:04:39,913 - pyskl - INFO - Epoch [56][100/1281] lr: 1.757e-02, eta: 7:33:01, time: 0.431, data_time: 0.193, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9969, loss_cls: 0.5536, loss: 0.5536 +2025-06-24 14:05:02,559 - pyskl - INFO - Epoch [56][200/1281] lr: 1.755e-02, eta: 7:32:39, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9981, loss_cls: 0.5496, loss: 0.5496 +2025-06-24 14:05:25,152 - pyskl - INFO - Epoch [56][300/1281] lr: 1.753e-02, eta: 7:32:17, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9975, loss_cls: 0.4711, loss: 0.4711 +2025-06-24 14:05:47,330 - pyskl - INFO - Epoch [56][400/1281] lr: 1.751e-02, eta: 7:31:55, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9969, loss_cls: 0.5584, loss: 0.5584 +2025-06-24 14:06:09,729 - pyskl - INFO - Epoch [56][500/1281] lr: 1.749e-02, eta: 7:31:32, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9956, loss_cls: 0.5342, loss: 0.5342 +2025-06-24 14:06:32,464 - pyskl - INFO - Epoch [56][600/1281] lr: 1.747e-02, eta: 7:31:11, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9950, loss_cls: 0.5534, loss: 0.5534 +2025-06-24 14:06:54,721 - pyskl - INFO - Epoch [56][700/1281] lr: 1.745e-02, eta: 7:30:48, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9962, loss_cls: 0.5499, loss: 0.5499 +2025-06-24 14:07:17,181 - pyskl - INFO - Epoch [56][800/1281] lr: 1.743e-02, eta: 7:30:26, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9962, loss_cls: 0.6123, loss: 0.6123 +2025-06-24 14:07:39,847 - pyskl - INFO - Epoch [56][900/1281] lr: 1.742e-02, eta: 7:30:04, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9950, loss_cls: 0.5826, loss: 0.5826 +2025-06-24 14:08:02,525 - pyskl - INFO - Epoch [56][1000/1281] lr: 1.740e-02, eta: 7:29:42, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9994, loss_cls: 0.5223, loss: 0.5223 +2025-06-24 14:08:24,767 - pyskl - INFO - Epoch [56][1100/1281] lr: 1.738e-02, eta: 7:29:20, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9956, loss_cls: 0.5683, loss: 0.5683 +2025-06-24 14:08:46,984 - pyskl - INFO - Epoch [56][1200/1281] lr: 1.736e-02, eta: 7:28:57, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9950, loss_cls: 0.5778, loss: 0.5778 +2025-06-24 14:09:06,101 - pyskl - INFO - Saving checkpoint at 56 epochs +2025-06-24 14:09:49,724 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:09:49,806 - pyskl - INFO - +top1_acc 0.8479 +top5_acc 0.9918 +2025-06-24 14:09:49,806 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:09:49,814 - pyskl - INFO - +mean_acc 0.7760 +2025-06-24 14:09:49,816 - pyskl - INFO - Epoch(val) [56][533] top1_acc: 0.8479, top5_acc: 0.9918, mean_class_accuracy: 0.7760 +2025-06-24 14:10:33,599 - pyskl - INFO - Epoch [57][100/1281] lr: 1.733e-02, eta: 7:28:22, time: 0.438, data_time: 0.202, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9969, loss_cls: 0.5728, loss: 0.5728 +2025-06-24 14:10:56,014 - pyskl - INFO - Epoch [57][200/1281] lr: 1.731e-02, eta: 7:28:00, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9988, loss_cls: 0.4186, loss: 0.4186 +2025-06-24 14:11:18,848 - pyskl - INFO - Epoch [57][300/1281] lr: 1.729e-02, eta: 7:27:39, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9981, loss_cls: 0.4796, loss: 0.4796 +2025-06-24 14:11:41,296 - pyskl - INFO - Epoch [57][400/1281] lr: 1.727e-02, eta: 7:27:16, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9988, loss_cls: 0.5111, loss: 0.5111 +2025-06-24 14:12:04,087 - pyskl - INFO - Epoch [57][500/1281] lr: 1.725e-02, eta: 7:26:55, time: 0.228, data_time: 0.001, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9969, loss_cls: 0.6138, loss: 0.6138 +2025-06-24 14:12:26,771 - pyskl - INFO - Epoch [57][600/1281] lr: 1.723e-02, eta: 7:26:33, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9981, loss_cls: 0.5707, loss: 0.5707 +2025-06-24 14:12:49,434 - pyskl - INFO - Epoch [57][700/1281] lr: 1.721e-02, eta: 7:26:11, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9969, loss_cls: 0.6107, loss: 0.6107 +2025-06-24 14:13:11,879 - pyskl - INFO - Epoch [57][800/1281] lr: 1.719e-02, eta: 7:25:49, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9950, loss_cls: 0.6151, loss: 0.6151 +2025-06-24 14:13:34,071 - pyskl - INFO - Epoch [57][900/1281] lr: 1.717e-02, eta: 7:25:26, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9944, loss_cls: 0.6042, loss: 0.6042 +2025-06-24 14:13:56,586 - pyskl - INFO - Epoch [57][1000/1281] lr: 1.716e-02, eta: 7:25:04, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9975, loss_cls: 0.5507, loss: 0.5507 +2025-06-24 14:14:18,977 - pyskl - INFO - Epoch [57][1100/1281] lr: 1.714e-02, eta: 7:24:42, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9944, loss_cls: 0.5431, loss: 0.5431 +2025-06-24 14:14:41,524 - pyskl - INFO - Epoch [57][1200/1281] lr: 1.712e-02, eta: 7:24:20, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9906, loss_cls: 0.5895, loss: 0.5895 +2025-06-24 14:15:00,365 - pyskl - INFO - Saving checkpoint at 57 epochs +2025-06-24 14:15:44,311 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:15:44,393 - pyskl - INFO - +top1_acc 0.8541 +top5_acc 0.9923 +2025-06-24 14:15:44,393 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:15:44,402 - pyskl - INFO - +mean_acc 0.7832 +2025-06-24 14:15:44,405 - pyskl - INFO - Epoch(val) [57][533] top1_acc: 0.8541, top5_acc: 0.9923, mean_class_accuracy: 0.7832 +2025-06-24 14:16:27,058 - pyskl - INFO - Epoch [58][100/1281] lr: 1.708e-02, eta: 7:23:43, time: 0.426, data_time: 0.191, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9969, loss_cls: 0.5087, loss: 0.5087 +2025-06-24 14:16:49,652 - pyskl - INFO - Epoch [58][200/1281] lr: 1.706e-02, eta: 7:23:21, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9981, loss_cls: 0.4875, loss: 0.4875 +2025-06-24 14:17:12,349 - pyskl - INFO - Epoch [58][300/1281] lr: 1.704e-02, eta: 7:22:59, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9981, loss_cls: 0.4971, loss: 0.4971 +2025-06-24 14:17:34,626 - pyskl - INFO - Epoch [58][400/1281] lr: 1.703e-02, eta: 7:22:36, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9975, loss_cls: 0.4771, loss: 0.4771 +2025-06-24 14:17:56,989 - pyskl - INFO - Epoch [58][500/1281] lr: 1.701e-02, eta: 7:22:14, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9969, loss_cls: 0.4955, loss: 0.4955 +2025-06-24 14:18:19,431 - pyskl - INFO - Epoch [58][600/1281] lr: 1.699e-02, eta: 7:21:52, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9944, loss_cls: 0.5507, loss: 0.5507 +2025-06-24 14:18:42,517 - pyskl - INFO - Epoch [58][700/1281] lr: 1.697e-02, eta: 7:21:30, time: 0.231, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9969, loss_cls: 0.5310, loss: 0.5310 +2025-06-24 14:19:05,018 - pyskl - INFO - Epoch [58][800/1281] lr: 1.695e-02, eta: 7:21:08, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9950, loss_cls: 0.4789, loss: 0.4789 +2025-06-24 14:19:27,628 - pyskl - INFO - Epoch [58][900/1281] lr: 1.693e-02, eta: 7:20:46, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9969, loss_cls: 0.5357, loss: 0.5357 +2025-06-24 14:19:49,997 - pyskl - INFO - Epoch [58][1000/1281] lr: 1.691e-02, eta: 7:20:24, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9956, loss_cls: 0.6146, loss: 0.6146 +2025-06-24 14:20:12,328 - pyskl - INFO - Epoch [58][1100/1281] lr: 1.689e-02, eta: 7:20:02, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9969, loss_cls: 0.5776, loss: 0.5776 +2025-06-24 14:20:34,647 - pyskl - INFO - Epoch [58][1200/1281] lr: 1.687e-02, eta: 7:19:39, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9975, loss_cls: 0.5431, loss: 0.5431 +2025-06-24 14:20:53,655 - pyskl - INFO - Saving checkpoint at 58 epochs +2025-06-24 14:21:36,667 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:21:36,721 - pyskl - INFO - +top1_acc 0.8169 +top5_acc 0.9885 +2025-06-24 14:21:36,722 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:21:36,728 - pyskl - INFO - +mean_acc 0.7495 +2025-06-24 14:21:36,730 - pyskl - INFO - Epoch(val) [58][533] top1_acc: 0.8169, top5_acc: 0.9885, mean_class_accuracy: 0.7495 +2025-06-24 14:22:19,243 - pyskl - INFO - Epoch [59][100/1281] lr: 1.684e-02, eta: 7:19:02, time: 0.425, data_time: 0.188, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9969, loss_cls: 0.5282, loss: 0.5282 +2025-06-24 14:22:41,827 - pyskl - INFO - Epoch [59][200/1281] lr: 1.682e-02, eta: 7:18:40, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9969, loss_cls: 0.5535, loss: 0.5535 +2025-06-24 14:23:04,688 - pyskl - INFO - Epoch [59][300/1281] lr: 1.680e-02, eta: 7:18:18, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9994, loss_cls: 0.4765, loss: 0.4765 +2025-06-24 14:23:27,087 - pyskl - INFO - Epoch [59][400/1281] lr: 1.678e-02, eta: 7:17:56, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9988, loss_cls: 0.4889, loss: 0.4889 +2025-06-24 14:23:49,968 - pyskl - INFO - Epoch [59][500/1281] lr: 1.676e-02, eta: 7:17:34, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9962, loss_cls: 0.5857, loss: 0.5857 +2025-06-24 14:24:12,368 - pyskl - INFO - Epoch [59][600/1281] lr: 1.674e-02, eta: 7:17:12, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9969, loss_cls: 0.5285, loss: 0.5285 +2025-06-24 14:24:35,285 - pyskl - INFO - Epoch [59][700/1281] lr: 1.672e-02, eta: 7:16:50, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9981, loss_cls: 0.4988, loss: 0.4988 +2025-06-24 14:24:58,126 - pyskl - INFO - Epoch [59][800/1281] lr: 1.670e-02, eta: 7:16:29, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9975, loss_cls: 0.4698, loss: 0.4698 +2025-06-24 14:25:20,667 - pyskl - INFO - Epoch [59][900/1281] lr: 1.668e-02, eta: 7:16:07, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9962, loss_cls: 0.5454, loss: 0.5454 +2025-06-24 14:25:43,677 - pyskl - INFO - Epoch [59][1000/1281] lr: 1.667e-02, eta: 7:15:45, time: 0.230, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9988, loss_cls: 0.5334, loss: 0.5334 +2025-06-24 14:26:06,280 - pyskl - INFO - Epoch [59][1100/1281] lr: 1.665e-02, eta: 7:15:23, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9962, loss_cls: 0.5227, loss: 0.5227 +2025-06-24 14:26:29,051 - pyskl - INFO - Epoch [59][1200/1281] lr: 1.663e-02, eta: 7:15:01, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9975, loss_cls: 0.5625, loss: 0.5625 +2025-06-24 14:26:48,597 - pyskl - INFO - Saving checkpoint at 59 epochs +2025-06-24 14:27:33,232 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:27:33,290 - pyskl - INFO - +top1_acc 0.8350 +top5_acc 0.9871 +2025-06-24 14:27:33,290 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:27:33,298 - pyskl - INFO - +mean_acc 0.7741 +2025-06-24 14:27:33,301 - pyskl - INFO - Epoch(val) [59][533] top1_acc: 0.8350, top5_acc: 0.9871, mean_class_accuracy: 0.7741 +2025-06-24 14:28:16,472 - pyskl - INFO - Epoch [60][100/1281] lr: 1.659e-02, eta: 7:14:25, time: 0.432, data_time: 0.195, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9988, loss_cls: 0.4939, loss: 0.4939 +2025-06-24 14:28:39,104 - pyskl - INFO - Epoch [60][200/1281] lr: 1.657e-02, eta: 7:14:03, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9981, loss_cls: 0.4177, loss: 0.4177 +2025-06-24 14:29:01,387 - pyskl - INFO - Epoch [60][300/1281] lr: 1.655e-02, eta: 7:13:41, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9981, loss_cls: 0.4908, loss: 0.4908 +2025-06-24 14:29:23,957 - pyskl - INFO - Epoch [60][400/1281] lr: 1.653e-02, eta: 7:13:18, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9981, loss_cls: 0.4749, loss: 0.4749 +2025-06-24 14:29:46,602 - pyskl - INFO - Epoch [60][500/1281] lr: 1.651e-02, eta: 7:12:56, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9969, loss_cls: 0.5247, loss: 0.5247 +2025-06-24 14:30:09,164 - pyskl - INFO - Epoch [60][600/1281] lr: 1.650e-02, eta: 7:12:34, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9981, loss_cls: 0.4494, loss: 0.4494 +2025-06-24 14:30:31,645 - pyskl - INFO - Epoch [60][700/1281] lr: 1.648e-02, eta: 7:12:12, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9956, loss_cls: 0.5075, loss: 0.5075 +2025-06-24 14:30:54,112 - pyskl - INFO - Epoch [60][800/1281] lr: 1.646e-02, eta: 7:11:50, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9956, loss_cls: 0.5405, loss: 0.5405 +2025-06-24 14:31:16,635 - pyskl - INFO - Epoch [60][900/1281] lr: 1.644e-02, eta: 7:11:28, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9981, loss_cls: 0.5483, loss: 0.5483 +2025-06-24 14:31:38,964 - pyskl - INFO - Epoch [60][1000/1281] lr: 1.642e-02, eta: 7:11:05, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9975, loss_cls: 0.4925, loss: 0.4925 +2025-06-24 14:32:01,392 - pyskl - INFO - Epoch [60][1100/1281] lr: 1.640e-02, eta: 7:10:43, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9962, loss_cls: 0.5660, loss: 0.5660 +2025-06-24 14:32:23,803 - pyskl - INFO - Epoch [60][1200/1281] lr: 1.638e-02, eta: 7:10:21, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9981, loss_cls: 0.5190, loss: 0.5190 +2025-06-24 14:32:42,642 - pyskl - INFO - Saving checkpoint at 60 epochs +2025-06-24 14:33:26,177 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:33:26,245 - pyskl - INFO - +top1_acc 0.8172 +top5_acc 0.9878 +2025-06-24 14:33:26,245 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:33:26,253 - pyskl - INFO - +mean_acc 0.7487 +2025-06-24 14:33:26,255 - pyskl - INFO - Epoch(val) [60][533] top1_acc: 0.8172, top5_acc: 0.9878, mean_class_accuracy: 0.7487 +2025-06-24 14:34:09,576 - pyskl - INFO - Epoch [61][100/1281] lr: 1.634e-02, eta: 7:09:44, time: 0.433, data_time: 0.194, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9975, loss_cls: 0.4709, loss: 0.4709 +2025-06-24 14:34:31,809 - pyskl - INFO - Epoch [61][200/1281] lr: 1.632e-02, eta: 7:09:22, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9988, loss_cls: 0.4824, loss: 0.4824 +2025-06-24 14:34:54,562 - pyskl - INFO - Epoch [61][300/1281] lr: 1.630e-02, eta: 7:09:00, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9981, loss_cls: 0.4925, loss: 0.4925 +2025-06-24 14:35:17,071 - pyskl - INFO - Epoch [61][400/1281] lr: 1.629e-02, eta: 7:08:38, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.4783, loss: 0.4783 +2025-06-24 14:35:39,513 - pyskl - INFO - Epoch [61][500/1281] lr: 1.627e-02, eta: 7:08:15, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9956, loss_cls: 0.5194, loss: 0.5194 +2025-06-24 14:36:01,962 - pyskl - INFO - Epoch [61][600/1281] lr: 1.625e-02, eta: 7:07:53, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9956, loss_cls: 0.5488, loss: 0.5488 +2025-06-24 14:36:24,519 - pyskl - INFO - Epoch [61][700/1281] lr: 1.623e-02, eta: 7:07:31, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9988, loss_cls: 0.5079, loss: 0.5079 +2025-06-24 14:36:46,951 - pyskl - INFO - Epoch [61][800/1281] lr: 1.621e-02, eta: 7:07:09, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9956, loss_cls: 0.4871, loss: 0.4871 +2025-06-24 14:37:09,248 - pyskl - INFO - Epoch [61][900/1281] lr: 1.619e-02, eta: 7:06:46, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9962, loss_cls: 0.5283, loss: 0.5283 +2025-06-24 14:37:31,540 - pyskl - INFO - Epoch [61][1000/1281] lr: 1.617e-02, eta: 7:06:24, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9975, loss_cls: 0.5309, loss: 0.5309 +2025-06-24 14:37:53,750 - pyskl - INFO - Epoch [61][1100/1281] lr: 1.615e-02, eta: 7:06:01, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9969, loss_cls: 0.4754, loss: 0.4754 +2025-06-24 14:38:16,150 - pyskl - INFO - Epoch [61][1200/1281] lr: 1.613e-02, eta: 7:05:39, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9975, loss_cls: 0.5007, loss: 0.5007 +2025-06-24 14:38:35,031 - pyskl - INFO - Saving checkpoint at 61 epochs +2025-06-24 14:39:19,128 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:39:19,184 - pyskl - INFO - +top1_acc 0.8430 +top5_acc 0.9901 +2025-06-24 14:39:19,184 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:39:19,191 - pyskl - INFO - +mean_acc 0.7806 +2025-06-24 14:39:19,193 - pyskl - INFO - Epoch(val) [61][533] top1_acc: 0.8430, top5_acc: 0.9901, mean_class_accuracy: 0.7806 +2025-06-24 14:40:02,537 - pyskl - INFO - Epoch [62][100/1281] lr: 1.609e-02, eta: 7:05:02, time: 0.433, data_time: 0.199, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9975, loss_cls: 0.4573, loss: 0.4573 +2025-06-24 14:40:24,867 - pyskl - INFO - Epoch [62][200/1281] lr: 1.607e-02, eta: 7:04:40, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9962, loss_cls: 0.5471, loss: 0.5471 +2025-06-24 14:40:47,497 - pyskl - INFO - Epoch [62][300/1281] lr: 1.605e-02, eta: 7:04:18, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9962, loss_cls: 0.5020, loss: 0.5020 +2025-06-24 14:41:10,153 - pyskl - INFO - Epoch [62][400/1281] lr: 1.603e-02, eta: 7:03:56, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9956, loss_cls: 0.4648, loss: 0.4648 +2025-06-24 14:41:32,541 - pyskl - INFO - Epoch [62][500/1281] lr: 1.602e-02, eta: 7:03:33, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9969, loss_cls: 0.4460, loss: 0.4460 +2025-06-24 14:41:54,954 - pyskl - INFO - Epoch [62][600/1281] lr: 1.600e-02, eta: 7:03:11, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9962, loss_cls: 0.4996, loss: 0.4996 +2025-06-24 14:42:17,983 - pyskl - INFO - Epoch [62][700/1281] lr: 1.598e-02, eta: 7:02:49, time: 0.230, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9969, loss_cls: 0.4689, loss: 0.4689 +2025-06-24 14:42:40,282 - pyskl - INFO - Epoch [62][800/1281] lr: 1.596e-02, eta: 7:02:27, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9981, loss_cls: 0.5153, loss: 0.5153 +2025-06-24 14:43:03,038 - pyskl - INFO - Epoch [62][900/1281] lr: 1.594e-02, eta: 7:02:05, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9962, loss_cls: 0.4802, loss: 0.4802 +2025-06-24 14:43:25,774 - pyskl - INFO - Epoch [62][1000/1281] lr: 1.592e-02, eta: 7:01:43, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9962, loss_cls: 0.5382, loss: 0.5382 +2025-06-24 14:43:48,331 - pyskl - INFO - Epoch [62][1100/1281] lr: 1.590e-02, eta: 7:01:21, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9969, loss_cls: 0.5582, loss: 0.5582 +2025-06-24 14:44:10,936 - pyskl - INFO - Epoch [62][1200/1281] lr: 1.588e-02, eta: 7:00:59, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9944, loss_cls: 0.5754, loss: 0.5754 +2025-06-24 14:44:30,035 - pyskl - INFO - Saving checkpoint at 62 epochs +2025-06-24 14:45:14,249 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:45:14,306 - pyskl - INFO - +top1_acc 0.8517 +top5_acc 0.9907 +2025-06-24 14:45:14,306 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:45:14,314 - pyskl - INFO - +mean_acc 0.8190 +2025-06-24 14:45:14,316 - pyskl - INFO - Epoch(val) [62][533] top1_acc: 0.8517, top5_acc: 0.9907, mean_class_accuracy: 0.8190 +2025-06-24 14:45:56,926 - pyskl - INFO - Epoch [63][100/1281] lr: 1.584e-02, eta: 7:00:21, time: 0.426, data_time: 0.187, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9988, loss_cls: 0.4489, loss: 0.4489 +2025-06-24 14:46:19,569 - pyskl - INFO - Epoch [63][200/1281] lr: 1.582e-02, eta: 6:59:59, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9975, loss_cls: 0.4850, loss: 0.4850 +2025-06-24 14:46:42,208 - pyskl - INFO - Epoch [63][300/1281] lr: 1.580e-02, eta: 6:59:37, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9975, loss_cls: 0.5236, loss: 0.5236 +2025-06-24 14:47:04,341 - pyskl - INFO - Epoch [63][400/1281] lr: 1.578e-02, eta: 6:59:14, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9925, loss_cls: 0.5197, loss: 0.5197 +2025-06-24 14:47:26,664 - pyskl - INFO - Epoch [63][500/1281] lr: 1.576e-02, eta: 6:58:52, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9988, loss_cls: 0.4828, loss: 0.4828 +2025-06-24 14:47:49,372 - pyskl - INFO - Epoch [63][600/1281] lr: 1.574e-02, eta: 6:58:30, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9962, loss_cls: 0.5404, loss: 0.5404 +2025-06-24 14:48:11,714 - pyskl - INFO - Epoch [63][700/1281] lr: 1.572e-02, eta: 6:58:07, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9975, loss_cls: 0.4730, loss: 0.4730 +2025-06-24 14:48:33,984 - pyskl - INFO - Epoch [63][800/1281] lr: 1.570e-02, eta: 6:57:45, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9969, loss_cls: 0.4820, loss: 0.4820 +2025-06-24 14:48:56,300 - pyskl - INFO - Epoch [63][900/1281] lr: 1.568e-02, eta: 6:57:22, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9938, loss_cls: 0.5247, loss: 0.5247 +2025-06-24 14:49:18,661 - pyskl - INFO - Epoch [63][1000/1281] lr: 1.566e-02, eta: 6:57:00, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9994, loss_cls: 0.4706, loss: 0.4706 +2025-06-24 14:49:40,700 - pyskl - INFO - Epoch [63][1100/1281] lr: 1.564e-02, eta: 6:56:37, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9950, loss_cls: 0.5510, loss: 0.5510 +2025-06-24 14:50:02,983 - pyskl - INFO - Epoch [63][1200/1281] lr: 1.562e-02, eta: 6:56:15, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9938, loss_cls: 0.5012, loss: 0.5012 +2025-06-24 14:50:22,049 - pyskl - INFO - Saving checkpoint at 63 epochs +2025-06-24 14:51:05,945 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:51:06,006 - pyskl - INFO - +top1_acc 0.8297 +top5_acc 0.9911 +2025-06-24 14:51:06,007 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:51:06,014 - pyskl - INFO - +mean_acc 0.7793 +2025-06-24 14:51:06,016 - pyskl - INFO - Epoch(val) [63][533] top1_acc: 0.8297, top5_acc: 0.9911, mean_class_accuracy: 0.7793 +2025-06-24 14:51:49,462 - pyskl - INFO - Epoch [64][100/1281] lr: 1.559e-02, eta: 6:55:38, time: 0.434, data_time: 0.199, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9981, loss_cls: 0.5086, loss: 0.5086 +2025-06-24 14:52:12,102 - pyskl - INFO - Epoch [64][200/1281] lr: 1.557e-02, eta: 6:55:16, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9956, loss_cls: 0.4745, loss: 0.4745 +2025-06-24 14:52:34,631 - pyskl - INFO - Epoch [64][300/1281] lr: 1.555e-02, eta: 6:54:54, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9962, loss_cls: 0.4792, loss: 0.4792 +2025-06-24 14:52:57,126 - pyskl - INFO - Epoch [64][400/1281] lr: 1.553e-02, eta: 6:54:31, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9981, loss_cls: 0.5073, loss: 0.5073 +2025-06-24 14:53:19,832 - pyskl - INFO - Epoch [64][500/1281] lr: 1.551e-02, eta: 6:54:09, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9931, loss_cls: 0.5373, loss: 0.5373 +2025-06-24 14:53:42,314 - pyskl - INFO - Epoch [64][600/1281] lr: 1.549e-02, eta: 6:53:47, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9975, loss_cls: 0.5256, loss: 0.5256 +2025-06-24 14:54:04,767 - pyskl - INFO - Epoch [64][700/1281] lr: 1.547e-02, eta: 6:53:25, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9981, loss_cls: 0.4854, loss: 0.4854 +2025-06-24 14:54:27,306 - pyskl - INFO - Epoch [64][800/1281] lr: 1.545e-02, eta: 6:53:03, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9969, loss_cls: 0.5818, loss: 0.5818 +2025-06-24 14:54:49,625 - pyskl - INFO - Epoch [64][900/1281] lr: 1.543e-02, eta: 6:52:40, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9975, loss_cls: 0.5291, loss: 0.5291 +2025-06-24 14:55:12,163 - pyskl - INFO - Epoch [64][1000/1281] lr: 1.541e-02, eta: 6:52:18, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9956, loss_cls: 0.5290, loss: 0.5290 +2025-06-24 14:55:35,065 - pyskl - INFO - Epoch [64][1100/1281] lr: 1.539e-02, eta: 6:51:56, time: 0.229, data_time: 0.001, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9981, loss_cls: 0.4910, loss: 0.4910 +2025-06-24 14:55:57,493 - pyskl - INFO - Epoch [64][1200/1281] lr: 1.537e-02, eta: 6:51:34, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9981, loss_cls: 0.5319, loss: 0.5319 +2025-06-24 14:56:16,447 - pyskl - INFO - Saving checkpoint at 64 epochs +2025-06-24 14:57:00,354 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:57:00,410 - pyskl - INFO - +top1_acc 0.8600 +top5_acc 0.9914 +2025-06-24 14:57:00,410 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:57:00,417 - pyskl - INFO - +mean_acc 0.8290 +2025-06-24 14:57:00,419 - pyskl - INFO - Epoch(val) [64][533] top1_acc: 0.8600, top5_acc: 0.9914, mean_class_accuracy: 0.8290 +2025-06-24 14:57:43,253 - pyskl - INFO - Epoch [65][100/1281] lr: 1.533e-02, eta: 6:50:56, time: 0.428, data_time: 0.192, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9988, loss_cls: 0.4437, loss: 0.4437 +2025-06-24 14:58:05,832 - pyskl - INFO - Epoch [65][200/1281] lr: 1.531e-02, eta: 6:50:34, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9975, loss_cls: 0.4853, loss: 0.4853 +2025-06-24 14:58:28,472 - pyskl - INFO - Epoch [65][300/1281] lr: 1.529e-02, eta: 6:50:12, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9988, loss_cls: 0.4875, loss: 0.4875 +2025-06-24 14:58:50,743 - pyskl - INFO - Epoch [65][400/1281] lr: 1.527e-02, eta: 6:49:50, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9981, loss_cls: 0.4353, loss: 0.4353 +2025-06-24 14:59:12,916 - pyskl - INFO - Epoch [65][500/1281] lr: 1.526e-02, eta: 6:49:27, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9975, loss_cls: 0.4792, loss: 0.4792 +2025-06-24 14:59:35,288 - pyskl - INFO - Epoch [65][600/1281] lr: 1.524e-02, eta: 6:49:04, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9969, loss_cls: 0.4860, loss: 0.4860 +2025-06-24 14:59:57,544 - pyskl - INFO - Epoch [65][700/1281] lr: 1.522e-02, eta: 6:48:42, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9994, loss_cls: 0.4133, loss: 0.4133 +2025-06-24 15:00:19,740 - pyskl - INFO - Epoch [65][800/1281] lr: 1.520e-02, eta: 6:48:19, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9962, loss_cls: 0.4817, loss: 0.4817 +2025-06-24 15:00:42,099 - pyskl - INFO - Epoch [65][900/1281] lr: 1.518e-02, eta: 6:47:57, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9975, loss_cls: 0.4819, loss: 0.4819 +2025-06-24 15:01:04,517 - pyskl - INFO - Epoch [65][1000/1281] lr: 1.516e-02, eta: 6:47:34, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9962, loss_cls: 0.5938, loss: 0.5938 +2025-06-24 15:01:27,041 - pyskl - INFO - Epoch [65][1100/1281] lr: 1.514e-02, eta: 6:47:12, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9981, loss_cls: 0.5160, loss: 0.5160 +2025-06-24 15:01:49,415 - pyskl - INFO - Epoch [65][1200/1281] lr: 1.512e-02, eta: 6:46:50, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9975, loss_cls: 0.5052, loss: 0.5052 +2025-06-24 15:02:08,585 - pyskl - INFO - Saving checkpoint at 65 epochs +2025-06-24 15:02:53,013 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:02:53,083 - pyskl - INFO - +top1_acc 0.8122 +top5_acc 0.9822 +2025-06-24 15:02:53,083 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:02:53,091 - pyskl - INFO - +mean_acc 0.7635 +2025-06-24 15:02:53,094 - pyskl - INFO - Epoch(val) [65][533] top1_acc: 0.8122, top5_acc: 0.9822, mean_class_accuracy: 0.7635 +2025-06-24 15:03:36,408 - pyskl - INFO - Epoch [66][100/1281] lr: 1.508e-02, eta: 6:46:13, time: 0.433, data_time: 0.196, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9969, loss_cls: 0.4291, loss: 0.4291 +2025-06-24 15:03:58,921 - pyskl - INFO - Epoch [66][200/1281] lr: 1.506e-02, eta: 6:45:50, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9994, loss_cls: 0.4494, loss: 0.4494 +2025-06-24 15:04:21,198 - pyskl - INFO - Epoch [66][300/1281] lr: 1.504e-02, eta: 6:45:28, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9975, loss_cls: 0.4815, loss: 0.4815 +2025-06-24 15:04:43,688 - pyskl - INFO - Epoch [66][400/1281] lr: 1.502e-02, eta: 6:45:06, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9988, loss_cls: 0.4844, loss: 0.4844 +2025-06-24 15:05:06,525 - pyskl - INFO - Epoch [66][500/1281] lr: 1.500e-02, eta: 6:44:44, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9975, loss_cls: 0.4879, loss: 0.4879 +2025-06-24 15:05:28,838 - pyskl - INFO - Epoch [66][600/1281] lr: 1.498e-02, eta: 6:44:21, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9988, loss_cls: 0.4731, loss: 0.4731 +2025-06-24 15:05:51,539 - pyskl - INFO - Epoch [66][700/1281] lr: 1.496e-02, eta: 6:43:59, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9975, loss_cls: 0.4190, loss: 0.4190 +2025-06-24 15:06:14,033 - pyskl - INFO - Epoch [66][800/1281] lr: 1.494e-02, eta: 6:43:37, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9988, loss_cls: 0.4464, loss: 0.4464 +2025-06-24 15:06:36,353 - pyskl - INFO - Epoch [66][900/1281] lr: 1.492e-02, eta: 6:43:14, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9956, loss_cls: 0.5388, loss: 0.5388 +2025-06-24 15:06:58,868 - pyskl - INFO - Epoch [66][1000/1281] lr: 1.490e-02, eta: 6:42:52, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9969, loss_cls: 0.5255, loss: 0.5255 +2025-06-24 15:07:21,506 - pyskl - INFO - Epoch [66][1100/1281] lr: 1.488e-02, eta: 6:42:30, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9938, loss_cls: 0.5408, loss: 0.5408 +2025-06-24 15:07:44,066 - pyskl - INFO - Epoch [66][1200/1281] lr: 1.486e-02, eta: 6:42:08, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9969, loss_cls: 0.5379, loss: 0.5379 +2025-06-24 15:08:03,016 - pyskl - INFO - Saving checkpoint at 66 epochs +2025-06-24 15:08:47,130 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:08:47,198 - pyskl - INFO - +top1_acc 0.8567 +top5_acc 0.9913 +2025-06-24 15:08:47,198 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:08:47,207 - pyskl - INFO - +mean_acc 0.8148 +2025-06-24 15:08:47,210 - pyskl - INFO - Epoch(val) [66][533] top1_acc: 0.8567, top5_acc: 0.9913, mean_class_accuracy: 0.8148 +2025-06-24 15:09:30,243 - pyskl - INFO - Epoch [67][100/1281] lr: 1.482e-02, eta: 6:41:30, time: 0.430, data_time: 0.191, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.4077, loss: 0.4077 +2025-06-24 15:09:52,609 - pyskl - INFO - Epoch [67][200/1281] lr: 1.480e-02, eta: 6:41:08, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9969, loss_cls: 0.4189, loss: 0.4189 +2025-06-24 15:10:15,077 - pyskl - INFO - Epoch [67][300/1281] lr: 1.478e-02, eta: 6:40:46, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9969, loss_cls: 0.4450, loss: 0.4450 +2025-06-24 15:10:37,736 - pyskl - INFO - Epoch [67][400/1281] lr: 1.476e-02, eta: 6:40:23, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9988, loss_cls: 0.4883, loss: 0.4883 +2025-06-24 15:10:59,809 - pyskl - INFO - Epoch [67][500/1281] lr: 1.474e-02, eta: 6:40:01, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9994, loss_cls: 0.4650, loss: 0.4650 +2025-06-24 15:11:22,135 - pyskl - INFO - Epoch [67][600/1281] lr: 1.472e-02, eta: 6:39:38, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9988, loss_cls: 0.4572, loss: 0.4572 +2025-06-24 15:11:44,459 - pyskl - INFO - Epoch [67][700/1281] lr: 1.470e-02, eta: 6:39:16, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9981, loss_cls: 0.4788, loss: 0.4788 +2025-06-24 15:12:06,777 - pyskl - INFO - Epoch [67][800/1281] lr: 1.468e-02, eta: 6:38:53, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9969, loss_cls: 0.4810, loss: 0.4810 +2025-06-24 15:12:29,128 - pyskl - INFO - Epoch [67][900/1281] lr: 1.466e-02, eta: 6:38:31, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4296, loss: 0.4296 +2025-06-24 15:12:51,627 - pyskl - INFO - Epoch [67][1000/1281] lr: 1.464e-02, eta: 6:38:08, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9981, loss_cls: 0.5214, loss: 0.5214 +2025-06-24 15:13:13,886 - pyskl - INFO - Epoch [67][1100/1281] lr: 1.462e-02, eta: 6:37:46, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9975, loss_cls: 0.4659, loss: 0.4659 +2025-06-24 15:13:36,233 - pyskl - INFO - Epoch [67][1200/1281] lr: 1.460e-02, eta: 6:37:23, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9944, loss_cls: 0.5011, loss: 0.5011 +2025-06-24 15:13:55,490 - pyskl - INFO - Saving checkpoint at 67 epochs +2025-06-24 15:14:39,510 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:14:39,566 - pyskl - INFO - +top1_acc 0.8717 +top5_acc 0.9926 +2025-06-24 15:14:39,566 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:14:39,573 - pyskl - INFO - +mean_acc 0.8239 +2025-06-24 15:14:39,578 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_52.pth was removed +2025-06-24 15:14:39,814 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_67.pth. +2025-06-24 15:14:39,814 - pyskl - INFO - Best top1_acc is 0.8717 at 67 epoch. +2025-06-24 15:14:39,816 - pyskl - INFO - Epoch(val) [67][533] top1_acc: 0.8717, top5_acc: 0.9926, mean_class_accuracy: 0.8239 +2025-06-24 15:15:22,555 - pyskl - INFO - Epoch [68][100/1281] lr: 1.456e-02, eta: 6:36:45, time: 0.427, data_time: 0.191, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9981, loss_cls: 0.4276, loss: 0.4276 +2025-06-24 15:15:44,968 - pyskl - INFO - Epoch [68][200/1281] lr: 1.454e-02, eta: 6:36:23, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 1.0000, loss_cls: 0.4960, loss: 0.4960 +2025-06-24 15:16:07,239 - pyskl - INFO - Epoch [68][300/1281] lr: 1.452e-02, eta: 6:36:00, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9981, loss_cls: 0.4517, loss: 0.4517 +2025-06-24 15:16:29,659 - pyskl - INFO - Epoch [68][400/1281] lr: 1.450e-02, eta: 6:35:38, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9988, loss_cls: 0.4299, loss: 0.4299 +2025-06-24 15:16:52,063 - pyskl - INFO - Epoch [68][500/1281] lr: 1.448e-02, eta: 6:35:15, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9975, loss_cls: 0.4921, loss: 0.4921 +2025-06-24 15:17:14,645 - pyskl - INFO - Epoch [68][600/1281] lr: 1.446e-02, eta: 6:34:53, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9988, loss_cls: 0.4977, loss: 0.4977 +2025-06-24 15:17:37,010 - pyskl - INFO - Epoch [68][700/1281] lr: 1.444e-02, eta: 6:34:31, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9981, loss_cls: 0.4311, loss: 0.4311 +2025-06-24 15:17:59,766 - pyskl - INFO - Epoch [68][800/1281] lr: 1.442e-02, eta: 6:34:09, time: 0.228, data_time: 0.001, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9994, loss_cls: 0.4545, loss: 0.4545 +2025-06-24 15:18:22,309 - pyskl - INFO - Epoch [68][900/1281] lr: 1.440e-02, eta: 6:33:47, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9969, loss_cls: 0.4598, loss: 0.4598 +2025-06-24 15:18:44,830 - pyskl - INFO - Epoch [68][1000/1281] lr: 1.438e-02, eta: 6:33:24, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9994, loss_cls: 0.4640, loss: 0.4640 +2025-06-24 15:19:06,943 - pyskl - INFO - Epoch [68][1100/1281] lr: 1.436e-02, eta: 6:33:02, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9981, loss_cls: 0.4386, loss: 0.4386 +2025-06-24 15:19:29,476 - pyskl - INFO - Epoch [68][1200/1281] lr: 1.434e-02, eta: 6:32:39, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9981, loss_cls: 0.4094, loss: 0.4094 +2025-06-24 15:19:48,552 - pyskl - INFO - Saving checkpoint at 68 epochs +2025-06-24 15:20:32,318 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:20:32,373 - pyskl - INFO - +top1_acc 0.8519 +top5_acc 0.9924 +2025-06-24 15:20:32,373 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:20:32,379 - pyskl - INFO - +mean_acc 0.8141 +2025-06-24 15:20:32,381 - pyskl - INFO - Epoch(val) [68][533] top1_acc: 0.8519, top5_acc: 0.9924, mean_class_accuracy: 0.8141 +2025-06-24 15:21:15,233 - pyskl - INFO - Epoch [69][100/1281] lr: 1.431e-02, eta: 6:32:01, time: 0.428, data_time: 0.193, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9969, loss_cls: 0.4419, loss: 0.4419 +2025-06-24 15:21:37,742 - pyskl - INFO - Epoch [69][200/1281] lr: 1.429e-02, eta: 6:31:39, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9988, loss_cls: 0.4346, loss: 0.4346 +2025-06-24 15:22:00,574 - pyskl - INFO - Epoch [69][300/1281] lr: 1.427e-02, eta: 6:31:17, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9981, loss_cls: 0.4447, loss: 0.4447 +2025-06-24 15:22:22,904 - pyskl - INFO - Epoch [69][400/1281] lr: 1.425e-02, eta: 6:30:55, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9981, loss_cls: 0.4300, loss: 0.4300 +2025-06-24 15:22:45,303 - pyskl - INFO - Epoch [69][500/1281] lr: 1.423e-02, eta: 6:30:32, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 1.0000, loss_cls: 0.4388, loss: 0.4388 +2025-06-24 15:23:07,737 - pyskl - INFO - Epoch [69][600/1281] lr: 1.420e-02, eta: 6:30:10, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9981, loss_cls: 0.5272, loss: 0.5272 +2025-06-24 15:23:30,180 - pyskl - INFO - Epoch [69][700/1281] lr: 1.418e-02, eta: 6:29:47, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9975, loss_cls: 0.4671, loss: 0.4671 +2025-06-24 15:23:52,892 - pyskl - INFO - Epoch [69][800/1281] lr: 1.416e-02, eta: 6:29:25, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9988, loss_cls: 0.4416, loss: 0.4416 +2025-06-24 15:24:15,454 - pyskl - INFO - Epoch [69][900/1281] lr: 1.414e-02, eta: 6:29:03, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9981, loss_cls: 0.4576, loss: 0.4576 +2025-06-24 15:24:37,884 - pyskl - INFO - Epoch [69][1000/1281] lr: 1.412e-02, eta: 6:28:41, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9988, loss_cls: 0.4552, loss: 0.4552 +2025-06-24 15:25:00,340 - pyskl - INFO - Epoch [69][1100/1281] lr: 1.410e-02, eta: 6:28:18, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9981, loss_cls: 0.4774, loss: 0.4774 +2025-06-24 15:25:22,881 - pyskl - INFO - Epoch [69][1200/1281] lr: 1.408e-02, eta: 6:27:56, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9981, loss_cls: 0.4895, loss: 0.4895 +2025-06-24 15:25:42,086 - pyskl - INFO - Saving checkpoint at 69 epochs +2025-06-24 15:26:26,161 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:26:26,225 - pyskl - INFO - +top1_acc 0.8498 +top5_acc 0.9903 +2025-06-24 15:26:26,225 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:26:26,233 - pyskl - INFO - +mean_acc 0.7899 +2025-06-24 15:26:26,236 - pyskl - INFO - Epoch(val) [69][533] top1_acc: 0.8498, top5_acc: 0.9903, mean_class_accuracy: 0.7899 +2025-06-24 15:27:09,442 - pyskl - INFO - Epoch [70][100/1281] lr: 1.405e-02, eta: 6:27:19, time: 0.432, data_time: 0.196, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9994, loss_cls: 0.4297, loss: 0.4297 +2025-06-24 15:27:31,976 - pyskl - INFO - Epoch [70][200/1281] lr: 1.403e-02, eta: 6:26:56, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9962, loss_cls: 0.4855, loss: 0.4855 +2025-06-24 15:27:54,507 - pyskl - INFO - Epoch [70][300/1281] lr: 1.401e-02, eta: 6:26:34, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9994, loss_cls: 0.4564, loss: 0.4564 +2025-06-24 15:28:16,816 - pyskl - INFO - Epoch [70][400/1281] lr: 1.399e-02, eta: 6:26:12, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9969, loss_cls: 0.4635, loss: 0.4635 +2025-06-24 15:28:39,288 - pyskl - INFO - Epoch [70][500/1281] lr: 1.397e-02, eta: 6:25:49, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9981, loss_cls: 0.4727, loss: 0.4727 +2025-06-24 15:29:01,734 - pyskl - INFO - Epoch [70][600/1281] lr: 1.395e-02, eta: 6:25:27, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9975, loss_cls: 0.4522, loss: 0.4522 +2025-06-24 15:29:24,581 - pyskl - INFO - Epoch [70][700/1281] lr: 1.392e-02, eta: 6:25:05, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9950, loss_cls: 0.5057, loss: 0.5057 +2025-06-24 15:29:46,869 - pyskl - INFO - Epoch [70][800/1281] lr: 1.390e-02, eta: 6:24:42, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9994, loss_cls: 0.4464, loss: 0.4464 +2025-06-24 15:30:09,374 - pyskl - INFO - Epoch [70][900/1281] lr: 1.388e-02, eta: 6:24:20, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9969, loss_cls: 0.4428, loss: 0.4428 +2025-06-24 15:30:31,649 - pyskl - INFO - Epoch [70][1000/1281] lr: 1.386e-02, eta: 6:23:57, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9988, loss_cls: 0.4782, loss: 0.4782 +2025-06-24 15:30:54,061 - pyskl - INFO - Epoch [70][1100/1281] lr: 1.384e-02, eta: 6:23:35, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9994, loss_cls: 0.4941, loss: 0.4941 +2025-06-24 15:31:16,416 - pyskl - INFO - Epoch [70][1200/1281] lr: 1.382e-02, eta: 6:23:12, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9988, loss_cls: 0.4424, loss: 0.4424 +2025-06-24 15:31:35,275 - pyskl - INFO - Saving checkpoint at 70 epochs +2025-06-24 15:32:18,955 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:32:19,012 - pyskl - INFO - +top1_acc 0.8625 +top5_acc 0.9933 +2025-06-24 15:32:19,012 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:32:19,022 - pyskl - INFO - +mean_acc 0.7964 +2025-06-24 15:32:19,025 - pyskl - INFO - Epoch(val) [70][533] top1_acc: 0.8625, top5_acc: 0.9933, mean_class_accuracy: 0.7964 +2025-06-24 15:33:01,754 - pyskl - INFO - Epoch [71][100/1281] lr: 1.379e-02, eta: 6:22:34, time: 0.427, data_time: 0.190, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3831, loss: 0.3831 +2025-06-24 15:33:24,651 - pyskl - INFO - Epoch [71][200/1281] lr: 1.377e-02, eta: 6:22:12, time: 0.229, data_time: 0.001, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3776, loss: 0.3776 +2025-06-24 15:33:47,086 - pyskl - INFO - Epoch [71][300/1281] lr: 1.375e-02, eta: 6:21:50, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9981, loss_cls: 0.4107, loss: 0.4107 +2025-06-24 15:34:09,579 - pyskl - INFO - Epoch [71][400/1281] lr: 1.373e-02, eta: 6:21:28, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9994, loss_cls: 0.4820, loss: 0.4820 +2025-06-24 15:34:31,929 - pyskl - INFO - Epoch [71][500/1281] lr: 1.371e-02, eta: 6:21:05, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9994, loss_cls: 0.4427, loss: 0.4427 +2025-06-24 15:34:54,044 - pyskl - INFO - Epoch [71][600/1281] lr: 1.368e-02, eta: 6:20:42, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9969, loss_cls: 0.4493, loss: 0.4493 +2025-06-24 15:35:16,635 - pyskl - INFO - Epoch [71][700/1281] lr: 1.366e-02, eta: 6:20:20, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9994, loss_cls: 0.4297, loss: 0.4297 +2025-06-24 15:35:39,141 - pyskl - INFO - Epoch [71][800/1281] lr: 1.364e-02, eta: 6:19:58, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9975, loss_cls: 0.4668, loss: 0.4668 +2025-06-24 15:36:01,365 - pyskl - INFO - Epoch [71][900/1281] lr: 1.362e-02, eta: 6:19:35, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9975, loss_cls: 0.4128, loss: 0.4128 +2025-06-24 15:36:23,680 - pyskl - INFO - Epoch [71][1000/1281] lr: 1.360e-02, eta: 6:19:13, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 1.0000, loss_cls: 0.4212, loss: 0.4212 +2025-06-24 15:36:46,124 - pyskl - INFO - Epoch [71][1100/1281] lr: 1.358e-02, eta: 6:18:50, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9994, loss_cls: 0.4090, loss: 0.4090 +2025-06-24 15:37:08,546 - pyskl - INFO - Epoch [71][1200/1281] lr: 1.356e-02, eta: 6:18:28, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9962, loss_cls: 0.4967, loss: 0.4967 +2025-06-24 15:37:27,499 - pyskl - INFO - Saving checkpoint at 71 epochs +2025-06-24 15:38:11,465 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:38:11,528 - pyskl - INFO - +top1_acc 0.8446 +top5_acc 0.9920 +2025-06-24 15:38:11,528 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:38:11,537 - pyskl - INFO - +mean_acc 0.7976 +2025-06-24 15:38:11,539 - pyskl - INFO - Epoch(val) [71][533] top1_acc: 0.8446, top5_acc: 0.9920, mean_class_accuracy: 0.7976 +2025-06-24 15:38:53,888 - pyskl - INFO - Epoch [72][100/1281] lr: 1.353e-02, eta: 6:17:49, time: 0.423, data_time: 0.186, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.3787, loss: 0.3787 +2025-06-24 15:39:16,599 - pyskl - INFO - Epoch [72][200/1281] lr: 1.351e-02, eta: 6:17:27, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3641, loss: 0.3641 +2025-06-24 15:39:38,906 - pyskl - INFO - Epoch [72][300/1281] lr: 1.349e-02, eta: 6:17:05, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9975, loss_cls: 0.3630, loss: 0.3630 +2025-06-24 15:40:01,783 - pyskl - INFO - Epoch [72][400/1281] lr: 1.346e-02, eta: 6:16:43, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9994, loss_cls: 0.4003, loss: 0.4003 +2025-06-24 15:40:24,313 - pyskl - INFO - Epoch [72][500/1281] lr: 1.344e-02, eta: 6:16:20, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.4043, loss: 0.4043 +2025-06-24 15:40:46,665 - pyskl - INFO - Epoch [72][600/1281] lr: 1.342e-02, eta: 6:15:58, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9975, loss_cls: 0.4418, loss: 0.4418 +2025-06-24 15:41:09,258 - pyskl - INFO - Epoch [72][700/1281] lr: 1.340e-02, eta: 6:15:36, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9975, loss_cls: 0.4449, loss: 0.4449 +2025-06-24 15:41:31,733 - pyskl - INFO - Epoch [72][800/1281] lr: 1.338e-02, eta: 6:15:13, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9975, loss_cls: 0.4696, loss: 0.4696 +2025-06-24 15:41:54,167 - pyskl - INFO - Epoch [72][900/1281] lr: 1.336e-02, eta: 6:14:51, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9975, loss_cls: 0.4638, loss: 0.4638 +2025-06-24 15:42:16,528 - pyskl - INFO - Epoch [72][1000/1281] lr: 1.334e-02, eta: 6:14:28, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9981, loss_cls: 0.4500, loss: 0.4500 +2025-06-24 15:42:38,848 - pyskl - INFO - Epoch [72][1100/1281] lr: 1.332e-02, eta: 6:14:06, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9969, loss_cls: 0.4709, loss: 0.4709 +2025-06-24 15:43:01,124 - pyskl - INFO - Epoch [72][1200/1281] lr: 1.330e-02, eta: 6:13:43, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9988, loss_cls: 0.3805, loss: 0.3805 +2025-06-24 15:43:20,009 - pyskl - INFO - Saving checkpoint at 72 epochs +2025-06-24 15:44:04,427 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:44:04,483 - pyskl - INFO - +top1_acc 0.8484 +top5_acc 0.9894 +2025-06-24 15:44:04,483 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:44:04,491 - pyskl - INFO - +mean_acc 0.8108 +2025-06-24 15:44:04,492 - pyskl - INFO - Epoch(val) [72][533] top1_acc: 0.8484, top5_acc: 0.9894, mean_class_accuracy: 0.8108 +2025-06-24 15:44:47,841 - pyskl - INFO - Epoch [73][100/1281] lr: 1.326e-02, eta: 6:13:06, time: 0.433, data_time: 0.193, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9988, loss_cls: 0.3922, loss: 0.3922 +2025-06-24 15:45:10,297 - pyskl - INFO - Epoch [73][200/1281] lr: 1.324e-02, eta: 6:12:43, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 1.0000, loss_cls: 0.4144, loss: 0.4144 +2025-06-24 15:45:32,868 - pyskl - INFO - Epoch [73][300/1281] lr: 1.322e-02, eta: 6:12:21, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9988, loss_cls: 0.3313, loss: 0.3313 +2025-06-24 15:45:55,453 - pyskl - INFO - Epoch [73][400/1281] lr: 1.320e-02, eta: 6:11:59, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9988, loss_cls: 0.4220, loss: 0.4220 +2025-06-24 15:46:18,118 - pyskl - INFO - Epoch [73][500/1281] lr: 1.318e-02, eta: 6:11:36, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3951, loss: 0.3951 +2025-06-24 15:46:40,770 - pyskl - INFO - Epoch [73][600/1281] lr: 1.316e-02, eta: 6:11:14, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9969, loss_cls: 0.4486, loss: 0.4486 +2025-06-24 15:47:03,028 - pyskl - INFO - Epoch [73][700/1281] lr: 1.314e-02, eta: 6:10:52, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9962, loss_cls: 0.4419, loss: 0.4419 +2025-06-24 15:47:25,656 - pyskl - INFO - Epoch [73][800/1281] lr: 1.312e-02, eta: 6:10:29, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9969, loss_cls: 0.4278, loss: 0.4278 +2025-06-24 15:47:48,469 - pyskl - INFO - Epoch [73][900/1281] lr: 1.310e-02, eta: 6:10:07, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9975, loss_cls: 0.4487, loss: 0.4487 +2025-06-24 15:48:11,394 - pyskl - INFO - Epoch [73][1000/1281] lr: 1.308e-02, eta: 6:09:46, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4425, loss: 0.4425 +2025-06-24 15:48:33,983 - pyskl - INFO - Epoch [73][1100/1281] lr: 1.306e-02, eta: 6:09:23, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9975, loss_cls: 0.4735, loss: 0.4735 +2025-06-24 15:48:56,290 - pyskl - INFO - Epoch [73][1200/1281] lr: 1.304e-02, eta: 6:09:01, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9969, loss_cls: 0.4465, loss: 0.4465 +2025-06-24 15:49:15,432 - pyskl - INFO - Saving checkpoint at 73 epochs +2025-06-24 15:50:00,378 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:50:00,447 - pyskl - INFO - +top1_acc 0.8654 +top5_acc 0.9927 +2025-06-24 15:50:00,447 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:50:00,456 - pyskl - INFO - +mean_acc 0.8099 +2025-06-24 15:50:00,458 - pyskl - INFO - Epoch(val) [73][533] top1_acc: 0.8654, top5_acc: 0.9927, mean_class_accuracy: 0.8099 +2025-06-24 15:50:43,575 - pyskl - INFO - Epoch [74][100/1281] lr: 1.300e-02, eta: 6:08:23, time: 0.431, data_time: 0.193, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9994, loss_cls: 0.3784, loss: 0.3784 +2025-06-24 15:51:06,203 - pyskl - INFO - Epoch [74][200/1281] lr: 1.298e-02, eta: 6:08:01, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9994, loss_cls: 0.3811, loss: 0.3811 +2025-06-24 15:51:28,581 - pyskl - INFO - Epoch [74][300/1281] lr: 1.296e-02, eta: 6:07:38, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9988, loss_cls: 0.4004, loss: 0.4004 +2025-06-24 15:51:51,381 - pyskl - INFO - Epoch [74][400/1281] lr: 1.294e-02, eta: 6:07:16, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9994, loss_cls: 0.3989, loss: 0.3989 +2025-06-24 15:52:14,054 - pyskl - INFO - Epoch [74][500/1281] lr: 1.292e-02, eta: 6:06:54, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9981, loss_cls: 0.3864, loss: 0.3864 +2025-06-24 15:52:36,659 - pyskl - INFO - Epoch [74][600/1281] lr: 1.290e-02, eta: 6:06:32, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9975, loss_cls: 0.4760, loss: 0.4760 +2025-06-24 15:52:59,156 - pyskl - INFO - Epoch [74][700/1281] lr: 1.288e-02, eta: 6:06:09, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9988, loss_cls: 0.4466, loss: 0.4466 +2025-06-24 15:53:21,495 - pyskl - INFO - Epoch [74][800/1281] lr: 1.286e-02, eta: 6:05:47, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9988, loss_cls: 0.4192, loss: 0.4192 +2025-06-24 15:53:44,144 - pyskl - INFO - Epoch [74][900/1281] lr: 1.284e-02, eta: 6:05:24, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9962, loss_cls: 0.4539, loss: 0.4539 +2025-06-24 15:54:06,442 - pyskl - INFO - Epoch [74][1000/1281] lr: 1.282e-02, eta: 6:05:02, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9988, loss_cls: 0.4375, loss: 0.4375 +2025-06-24 15:54:28,845 - pyskl - INFO - Epoch [74][1100/1281] lr: 1.280e-02, eta: 6:04:39, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9962, loss_cls: 0.4816, loss: 0.4816 +2025-06-24 15:54:51,567 - pyskl - INFO - Epoch [74][1200/1281] lr: 1.278e-02, eta: 6:04:17, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9981, loss_cls: 0.4494, loss: 0.4494 +2025-06-24 15:55:10,671 - pyskl - INFO - Saving checkpoint at 74 epochs +2025-06-24 15:55:54,268 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:55:54,325 - pyskl - INFO - +top1_acc 0.8506 +top5_acc 0.9876 +2025-06-24 15:55:54,325 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:55:54,333 - pyskl - INFO - +mean_acc 0.7979 +2025-06-24 15:55:54,335 - pyskl - INFO - Epoch(val) [74][533] top1_acc: 0.8506, top5_acc: 0.9876, mean_class_accuracy: 0.7979 +2025-06-24 15:56:38,070 - pyskl - INFO - Epoch [75][100/1281] lr: 1.274e-02, eta: 6:03:40, time: 0.437, data_time: 0.198, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9975, loss_cls: 0.4255, loss: 0.4255 +2025-06-24 15:57:00,599 - pyskl - INFO - Epoch [75][200/1281] lr: 1.272e-02, eta: 6:03:18, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9981, loss_cls: 0.3894, loss: 0.3894 +2025-06-24 15:57:23,004 - pyskl - INFO - Epoch [75][300/1281] lr: 1.270e-02, eta: 6:02:55, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3437, loss: 0.3437 +2025-06-24 15:57:45,783 - pyskl - INFO - Epoch [75][400/1281] lr: 1.268e-02, eta: 6:02:33, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.3920, loss: 0.3920 +2025-06-24 15:58:08,159 - pyskl - INFO - Epoch [75][500/1281] lr: 1.266e-02, eta: 6:02:11, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9981, loss_cls: 0.3950, loss: 0.3950 +2025-06-24 15:58:30,630 - pyskl - INFO - Epoch [75][600/1281] lr: 1.264e-02, eta: 6:01:48, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9969, loss_cls: 0.4648, loss: 0.4648 +2025-06-24 15:58:53,123 - pyskl - INFO - Epoch [75][700/1281] lr: 1.262e-02, eta: 6:01:26, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9981, loss_cls: 0.4464, loss: 0.4464 +2025-06-24 15:59:15,798 - pyskl - INFO - Epoch [75][800/1281] lr: 1.260e-02, eta: 6:01:04, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9962, loss_cls: 0.4618, loss: 0.4618 +2025-06-24 15:59:38,498 - pyskl - INFO - Epoch [75][900/1281] lr: 1.258e-02, eta: 6:00:41, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9969, loss_cls: 0.4046, loss: 0.4046 +2025-06-24 16:00:00,802 - pyskl - INFO - Epoch [75][1000/1281] lr: 1.256e-02, eta: 6:00:19, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9931, loss_cls: 0.4605, loss: 0.4605 +2025-06-24 16:00:23,002 - pyskl - INFO - Epoch [75][1100/1281] lr: 1.254e-02, eta: 5:59:56, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9975, loss_cls: 0.4162, loss: 0.4162 +2025-06-24 16:00:45,639 - pyskl - INFO - Epoch [75][1200/1281] lr: 1.252e-02, eta: 5:59:34, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9981, loss_cls: 0.4616, loss: 0.4616 +2025-06-24 16:01:04,469 - pyskl - INFO - Saving checkpoint at 75 epochs +2025-06-24 16:01:48,184 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:01:48,253 - pyskl - INFO - +top1_acc 0.8652 +top5_acc 0.9923 +2025-06-24 16:01:48,253 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:01:48,262 - pyskl - INFO - +mean_acc 0.8286 +2025-06-24 16:01:48,265 - pyskl - INFO - Epoch(val) [75][533] top1_acc: 0.8652, top5_acc: 0.9923, mean_class_accuracy: 0.8286 +2025-06-24 16:02:30,716 - pyskl - INFO - Epoch [76][100/1281] lr: 1.248e-02, eta: 5:58:55, time: 0.424, data_time: 0.190, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9988, loss_cls: 0.3184, loss: 0.3184 +2025-06-24 16:02:53,308 - pyskl - INFO - Epoch [76][200/1281] lr: 1.246e-02, eta: 5:58:33, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3617, loss: 0.3617 +2025-06-24 16:03:15,511 - pyskl - INFO - Epoch [76][300/1281] lr: 1.244e-02, eta: 5:58:10, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9981, loss_cls: 0.4111, loss: 0.4111 +2025-06-24 16:03:38,034 - pyskl - INFO - Epoch [76][400/1281] lr: 1.242e-02, eta: 5:57:48, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3425, loss: 0.3425 +2025-06-24 16:04:00,568 - pyskl - INFO - Epoch [76][500/1281] lr: 1.240e-02, eta: 5:57:25, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.3932, loss: 0.3932 +2025-06-24 16:04:23,168 - pyskl - INFO - Epoch [76][600/1281] lr: 1.238e-02, eta: 5:57:03, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9988, loss_cls: 0.3618, loss: 0.3618 +2025-06-24 16:04:45,544 - pyskl - INFO - Epoch [76][700/1281] lr: 1.236e-02, eta: 5:56:41, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9975, loss_cls: 0.3944, loss: 0.3944 +2025-06-24 16:05:07,910 - pyskl - INFO - Epoch [76][800/1281] lr: 1.234e-02, eta: 5:56:18, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 1.0000, loss_cls: 0.4490, loss: 0.4490 +2025-06-24 16:05:30,386 - pyskl - INFO - Epoch [76][900/1281] lr: 1.232e-02, eta: 5:55:56, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.4159, loss: 0.4159 +2025-06-24 16:05:52,682 - pyskl - INFO - Epoch [76][1000/1281] lr: 1.230e-02, eta: 5:55:33, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9975, loss_cls: 0.4115, loss: 0.4115 +2025-06-24 16:06:15,154 - pyskl - INFO - Epoch [76][1100/1281] lr: 1.228e-02, eta: 5:55:11, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9981, loss_cls: 0.4612, loss: 0.4612 +2025-06-24 16:06:37,511 - pyskl - INFO - Epoch [76][1200/1281] lr: 1.225e-02, eta: 5:54:48, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9988, loss_cls: 0.4243, loss: 0.4243 +2025-06-24 16:06:56,300 - pyskl - INFO - Saving checkpoint at 76 epochs +2025-06-24 16:07:40,567 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:07:40,627 - pyskl - INFO - +top1_acc 0.8346 +top5_acc 0.9879 +2025-06-24 16:07:40,627 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:07:40,635 - pyskl - INFO - +mean_acc 0.7957 +2025-06-24 16:07:40,637 - pyskl - INFO - Epoch(val) [76][533] top1_acc: 0.8346, top5_acc: 0.9879, mean_class_accuracy: 0.7957 +2025-06-24 16:08:23,743 - pyskl - INFO - Epoch [77][100/1281] lr: 1.222e-02, eta: 5:54:10, time: 0.431, data_time: 0.194, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9988, loss_cls: 0.3940, loss: 0.3940 +2025-06-24 16:08:46,099 - pyskl - INFO - Epoch [77][200/1281] lr: 1.220e-02, eta: 5:53:48, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9994, loss_cls: 0.3532, loss: 0.3532 +2025-06-24 16:09:08,367 - pyskl - INFO - Epoch [77][300/1281] lr: 1.218e-02, eta: 5:53:25, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9994, loss_cls: 0.3966, loss: 0.3966 +2025-06-24 16:09:30,899 - pyskl - INFO - Epoch [77][400/1281] lr: 1.216e-02, eta: 5:53:03, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9988, loss_cls: 0.3833, loss: 0.3833 +2025-06-24 16:09:53,244 - pyskl - INFO - Epoch [77][500/1281] lr: 1.214e-02, eta: 5:52:40, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9988, loss_cls: 0.3690, loss: 0.3690 +2025-06-24 16:10:15,629 - pyskl - INFO - Epoch [77][600/1281] lr: 1.212e-02, eta: 5:52:18, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9981, loss_cls: 0.4389, loss: 0.4389 +2025-06-24 16:10:38,127 - pyskl - INFO - Epoch [77][700/1281] lr: 1.210e-02, eta: 5:51:55, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9988, loss_cls: 0.4497, loss: 0.4497 +2025-06-24 16:11:00,934 - pyskl - INFO - Epoch [77][800/1281] lr: 1.207e-02, eta: 5:51:33, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9994, loss_cls: 0.3743, loss: 0.3743 +2025-06-24 16:11:23,546 - pyskl - INFO - Epoch [77][900/1281] lr: 1.205e-02, eta: 5:51:11, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9969, loss_cls: 0.3757, loss: 0.3757 +2025-06-24 16:11:45,904 - pyskl - INFO - Epoch [77][1000/1281] lr: 1.203e-02, eta: 5:50:48, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9981, loss_cls: 0.3843, loss: 0.3843 +2025-06-24 16:12:08,666 - pyskl - INFO - Epoch [77][1100/1281] lr: 1.201e-02, eta: 5:50:26, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9981, loss_cls: 0.4210, loss: 0.4210 +2025-06-24 16:12:31,165 - pyskl - INFO - Epoch [77][1200/1281] lr: 1.199e-02, eta: 5:50:04, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 1.0000, loss_cls: 0.4000, loss: 0.4000 +2025-06-24 16:12:50,135 - pyskl - INFO - Saving checkpoint at 77 epochs +2025-06-24 16:13:34,064 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:13:34,131 - pyskl - INFO - +top1_acc 0.8713 +top5_acc 0.9919 +2025-06-24 16:13:34,131 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:13:34,139 - pyskl - INFO - +mean_acc 0.8119 +2025-06-24 16:13:34,141 - pyskl - INFO - Epoch(val) [77][533] top1_acc: 0.8713, top5_acc: 0.9919, mean_class_accuracy: 0.8119 +2025-06-24 16:14:16,774 - pyskl - INFO - Epoch [78][100/1281] lr: 1.196e-02, eta: 5:49:25, time: 0.426, data_time: 0.190, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.3311, loss: 0.3311 +2025-06-24 16:14:39,271 - pyskl - INFO - Epoch [78][200/1281] lr: 1.194e-02, eta: 5:49:03, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9994, loss_cls: 0.3431, loss: 0.3431 +2025-06-24 16:15:02,031 - pyskl - INFO - Epoch [78][300/1281] lr: 1.192e-02, eta: 5:48:40, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 1.0000, loss_cls: 0.3551, loss: 0.3551 +2025-06-24 16:15:24,669 - pyskl - INFO - Epoch [78][400/1281] lr: 1.190e-02, eta: 5:48:18, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9975, loss_cls: 0.3979, loss: 0.3979 +2025-06-24 16:15:47,233 - pyskl - INFO - Epoch [78][500/1281] lr: 1.187e-02, eta: 5:47:56, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 1.0000, loss_cls: 0.3266, loss: 0.3266 +2025-06-24 16:16:09,891 - pyskl - INFO - Epoch [78][600/1281] lr: 1.185e-02, eta: 5:47:34, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9994, loss_cls: 0.3530, loss: 0.3530 +2025-06-24 16:16:32,109 - pyskl - INFO - Epoch [78][700/1281] lr: 1.183e-02, eta: 5:47:11, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9994, loss_cls: 0.4370, loss: 0.4370 +2025-06-24 16:16:54,699 - pyskl - INFO - Epoch [78][800/1281] lr: 1.181e-02, eta: 5:46:49, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9988, loss_cls: 0.4128, loss: 0.4128 +2025-06-24 16:17:17,364 - pyskl - INFO - Epoch [78][900/1281] lr: 1.179e-02, eta: 5:46:26, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9988, loss_cls: 0.3433, loss: 0.3433 +2025-06-24 16:17:39,986 - pyskl - INFO - Epoch [78][1000/1281] lr: 1.177e-02, eta: 5:46:04, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9950, loss_cls: 0.4256, loss: 0.4256 +2025-06-24 16:18:02,511 - pyskl - INFO - Epoch [78][1100/1281] lr: 1.175e-02, eta: 5:45:42, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9962, loss_cls: 0.4103, loss: 0.4103 +2025-06-24 16:18:24,852 - pyskl - INFO - Epoch [78][1200/1281] lr: 1.173e-02, eta: 5:45:19, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9994, loss_cls: 0.3676, loss: 0.3676 +2025-06-24 16:18:43,994 - pyskl - INFO - Saving checkpoint at 78 epochs +2025-06-24 16:19:27,894 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:19:27,953 - pyskl - INFO - +top1_acc 0.8758 +top5_acc 0.9913 +2025-06-24 16:19:27,954 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:19:27,962 - pyskl - INFO - +mean_acc 0.8275 +2025-06-24 16:19:27,970 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_67.pth was removed +2025-06-24 16:19:28,241 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_78.pth. +2025-06-24 16:19:28,241 - pyskl - INFO - Best top1_acc is 0.8758 at 78 epoch. +2025-06-24 16:19:28,245 - pyskl - INFO - Epoch(val) [78][533] top1_acc: 0.8758, top5_acc: 0.9913, mean_class_accuracy: 0.8275 +2025-06-24 16:20:11,044 - pyskl - INFO - Epoch [79][100/1281] lr: 1.169e-02, eta: 5:44:41, time: 0.428, data_time: 0.194, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3601, loss: 0.3601 +2025-06-24 16:20:33,804 - pyskl - INFO - Epoch [79][200/1281] lr: 1.167e-02, eta: 5:44:18, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.3663, loss: 0.3663 +2025-06-24 16:20:56,360 - pyskl - INFO - Epoch [79][300/1281] lr: 1.165e-02, eta: 5:43:56, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.3827, loss: 0.3827 +2025-06-24 16:21:18,676 - pyskl - INFO - Epoch [79][400/1281] lr: 1.163e-02, eta: 5:43:34, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 1.0000, loss_cls: 0.3446, loss: 0.3446 +2025-06-24 16:21:41,162 - pyskl - INFO - Epoch [79][500/1281] lr: 1.161e-02, eta: 5:43:11, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9975, loss_cls: 0.3899, loss: 0.3899 +2025-06-24 16:22:03,886 - pyskl - INFO - Epoch [79][600/1281] lr: 1.159e-02, eta: 5:42:49, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.3142, loss: 0.3142 +2025-06-24 16:22:26,414 - pyskl - INFO - Epoch [79][700/1281] lr: 1.157e-02, eta: 5:42:27, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3512, loss: 0.3512 +2025-06-24 16:22:48,740 - pyskl - INFO - Epoch [79][800/1281] lr: 1.155e-02, eta: 5:42:04, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9969, loss_cls: 0.3999, loss: 0.3999 +2025-06-24 16:23:11,229 - pyskl - INFO - Epoch [79][900/1281] lr: 1.153e-02, eta: 5:41:42, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9988, loss_cls: 0.3711, loss: 0.3711 +2025-06-24 16:23:33,996 - pyskl - INFO - Epoch [79][1000/1281] lr: 1.151e-02, eta: 5:41:19, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9981, loss_cls: 0.3246, loss: 0.3246 +2025-06-24 16:23:56,311 - pyskl - INFO - Epoch [79][1100/1281] lr: 1.149e-02, eta: 5:40:57, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9988, loss_cls: 0.4131, loss: 0.4131 +2025-06-24 16:24:18,913 - pyskl - INFO - Epoch [79][1200/1281] lr: 1.147e-02, eta: 5:40:35, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9981, loss_cls: 0.4107, loss: 0.4107 +2025-06-24 16:24:38,207 - pyskl - INFO - Saving checkpoint at 79 epochs +2025-06-24 16:25:22,112 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:25:22,180 - pyskl - INFO - +top1_acc 0.8473 +top5_acc 0.9912 +2025-06-24 16:25:22,180 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:25:22,188 - pyskl - INFO - +mean_acc 0.7841 +2025-06-24 16:25:22,191 - pyskl - INFO - Epoch(val) [79][533] top1_acc: 0.8473, top5_acc: 0.9912, mean_class_accuracy: 0.7841 +2025-06-24 16:26:05,294 - pyskl - INFO - Epoch [80][100/1281] lr: 1.143e-02, eta: 5:39:56, time: 0.431, data_time: 0.196, memory: 4083, top1_acc: 0.9281, top5_acc: 1.0000, loss_cls: 0.3532, loss: 0.3532 +2025-06-24 16:26:28,016 - pyskl - INFO - Epoch [80][200/1281] lr: 1.141e-02, eta: 5:39:34, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9988, loss_cls: 0.3334, loss: 0.3334 +2025-06-24 16:26:50,918 - pyskl - INFO - Epoch [80][300/1281] lr: 1.139e-02, eta: 5:39:12, time: 0.229, data_time: 0.001, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3421, loss: 0.3421 +2025-06-24 16:27:13,304 - pyskl - INFO - Epoch [80][400/1281] lr: 1.137e-02, eta: 5:38:49, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9994, loss_cls: 0.3770, loss: 0.3770 +2025-06-24 16:27:35,746 - pyskl - INFO - Epoch [80][500/1281] lr: 1.135e-02, eta: 5:38:27, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9988, loss_cls: 0.3767, loss: 0.3767 +2025-06-24 16:27:58,566 - pyskl - INFO - Epoch [80][600/1281] lr: 1.133e-02, eta: 5:38:05, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9981, loss_cls: 0.4146, loss: 0.4146 +2025-06-24 16:28:21,018 - pyskl - INFO - Epoch [80][700/1281] lr: 1.131e-02, eta: 5:37:42, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 1.0000, loss_cls: 0.3386, loss: 0.3386 +2025-06-24 16:28:43,462 - pyskl - INFO - Epoch [80][800/1281] lr: 1.129e-02, eta: 5:37:20, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9988, loss_cls: 0.3701, loss: 0.3701 +2025-06-24 16:29:06,029 - pyskl - INFO - Epoch [80][900/1281] lr: 1.127e-02, eta: 5:36:57, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9994, loss_cls: 0.3368, loss: 0.3368 +2025-06-24 16:29:28,244 - pyskl - INFO - Epoch [80][1000/1281] lr: 1.125e-02, eta: 5:36:35, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3644, loss: 0.3644 +2025-06-24 16:29:50,502 - pyskl - INFO - Epoch [80][1100/1281] lr: 1.123e-02, eta: 5:36:12, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9956, loss_cls: 0.4128, loss: 0.4128 +2025-06-24 16:30:13,024 - pyskl - INFO - Epoch [80][1200/1281] lr: 1.121e-02, eta: 5:35:50, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9969, loss_cls: 0.4119, loss: 0.4119 +2025-06-24 16:30:31,763 - pyskl - INFO - Saving checkpoint at 80 epochs +2025-06-24 16:31:15,479 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:31:15,534 - pyskl - INFO - +top1_acc 0.8670 +top5_acc 0.9924 +2025-06-24 16:31:15,534 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:31:15,541 - pyskl - INFO - +mean_acc 0.8385 +2025-06-24 16:31:15,543 - pyskl - INFO - Epoch(val) [80][533] top1_acc: 0.8670, top5_acc: 0.9924, mean_class_accuracy: 0.8385 +2025-06-24 16:31:58,048 - pyskl - INFO - Epoch [81][100/1281] lr: 1.117e-02, eta: 5:35:11, time: 0.425, data_time: 0.185, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9988, loss_cls: 0.3041, loss: 0.3041 +2025-06-24 16:32:20,460 - pyskl - INFO - Epoch [81][200/1281] lr: 1.115e-02, eta: 5:34:48, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 1.0000, loss_cls: 0.3346, loss: 0.3346 +2025-06-24 16:32:43,024 - pyskl - INFO - Epoch [81][300/1281] lr: 1.113e-02, eta: 5:34:26, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9962, loss_cls: 0.3976, loss: 0.3976 +2025-06-24 16:33:05,319 - pyskl - INFO - Epoch [81][400/1281] lr: 1.111e-02, eta: 5:34:03, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9981, loss_cls: 0.3746, loss: 0.3746 +2025-06-24 16:33:27,709 - pyskl - INFO - Epoch [81][500/1281] lr: 1.109e-02, eta: 5:33:41, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9988, loss_cls: 0.3643, loss: 0.3643 +2025-06-24 16:33:50,365 - pyskl - INFO - Epoch [81][600/1281] lr: 1.107e-02, eta: 5:33:19, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9975, loss_cls: 0.4179, loss: 0.4179 +2025-06-24 16:34:12,887 - pyskl - INFO - Epoch [81][700/1281] lr: 1.105e-02, eta: 5:32:56, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9988, loss_cls: 0.3842, loss: 0.3842 +2025-06-24 16:34:35,166 - pyskl - INFO - Epoch [81][800/1281] lr: 1.103e-02, eta: 5:32:34, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9969, loss_cls: 0.3598, loss: 0.3598 +2025-06-24 16:34:57,854 - pyskl - INFO - Epoch [81][900/1281] lr: 1.101e-02, eta: 5:32:11, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9988, loss_cls: 0.3641, loss: 0.3641 +2025-06-24 16:35:20,223 - pyskl - INFO - Epoch [81][1000/1281] lr: 1.099e-02, eta: 5:31:49, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9975, loss_cls: 0.3635, loss: 0.3635 +2025-06-24 16:35:42,348 - pyskl - INFO - Epoch [81][1100/1281] lr: 1.097e-02, eta: 5:31:26, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9994, loss_cls: 0.3823, loss: 0.3823 +2025-06-24 16:36:04,861 - pyskl - INFO - Epoch [81][1200/1281] lr: 1.095e-02, eta: 5:31:04, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9988, loss_cls: 0.4118, loss: 0.4118 +2025-06-24 16:36:23,498 - pyskl - INFO - Saving checkpoint at 81 epochs +2025-06-24 16:37:07,230 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:37:07,286 - pyskl - INFO - +top1_acc 0.8749 +top5_acc 0.9926 +2025-06-24 16:37:07,286 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:37:07,293 - pyskl - INFO - +mean_acc 0.8367 +2025-06-24 16:37:07,295 - pyskl - INFO - Epoch(val) [81][533] top1_acc: 0.8749, top5_acc: 0.9926, mean_class_accuracy: 0.8367 +2025-06-24 16:37:50,535 - pyskl - INFO - Epoch [82][100/1281] lr: 1.091e-02, eta: 5:30:25, time: 0.432, data_time: 0.193, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3290, loss: 0.3290 +2025-06-24 16:38:13,035 - pyskl - INFO - Epoch [82][200/1281] lr: 1.089e-02, eta: 5:30:03, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9994, loss_cls: 0.3624, loss: 0.3624 +2025-06-24 16:38:35,719 - pyskl - INFO - Epoch [82][300/1281] lr: 1.087e-02, eta: 5:29:41, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9981, loss_cls: 0.3158, loss: 0.3158 +2025-06-24 16:38:58,271 - pyskl - INFO - Epoch [82][400/1281] lr: 1.085e-02, eta: 5:29:18, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9981, loss_cls: 0.3396, loss: 0.3396 +2025-06-24 16:39:20,799 - pyskl - INFO - Epoch [82][500/1281] lr: 1.083e-02, eta: 5:28:56, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9981, loss_cls: 0.3885, loss: 0.3885 +2025-06-24 16:39:43,259 - pyskl - INFO - Epoch [82][600/1281] lr: 1.081e-02, eta: 5:28:33, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9994, loss_cls: 0.3498, loss: 0.3498 +2025-06-24 16:40:05,911 - pyskl - INFO - Epoch [82][700/1281] lr: 1.079e-02, eta: 5:28:11, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 1.0000, loss_cls: 0.3245, loss: 0.3245 +2025-06-24 16:40:28,790 - pyskl - INFO - Epoch [82][800/1281] lr: 1.077e-02, eta: 5:27:49, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 1.0000, loss_cls: 0.3406, loss: 0.3406 +2025-06-24 16:40:50,947 - pyskl - INFO - Epoch [82][900/1281] lr: 1.075e-02, eta: 5:27:26, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.3314, loss: 0.3314 +2025-06-24 16:41:13,279 - pyskl - INFO - Epoch [82][1000/1281] lr: 1.073e-02, eta: 5:27:04, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9975, loss_cls: 0.4418, loss: 0.4418 +2025-06-24 16:41:35,881 - pyskl - INFO - Epoch [82][1100/1281] lr: 1.071e-02, eta: 5:26:41, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 1.0000, loss_cls: 0.3695, loss: 0.3695 +2025-06-24 16:41:58,093 - pyskl - INFO - Epoch [82][1200/1281] lr: 1.069e-02, eta: 5:26:19, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9981, loss_cls: 0.4207, loss: 0.4207 +2025-06-24 16:42:17,426 - pyskl - INFO - Saving checkpoint at 82 epochs +2025-06-24 16:43:00,788 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:43:00,853 - pyskl - INFO - +top1_acc 0.8810 +top5_acc 0.9921 +2025-06-24 16:43:00,853 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:43:00,860 - pyskl - INFO - +mean_acc 0.8381 +2025-06-24 16:43:00,864 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_78.pth was removed +2025-06-24 16:43:01,030 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_82.pth. +2025-06-24 16:43:01,031 - pyskl - INFO - Best top1_acc is 0.8810 at 82 epoch. +2025-06-24 16:43:01,034 - pyskl - INFO - Epoch(val) [82][533] top1_acc: 0.8810, top5_acc: 0.9921, mean_class_accuracy: 0.8381 +2025-06-24 16:43:44,096 - pyskl - INFO - Epoch [83][100/1281] lr: 1.065e-02, eta: 5:25:40, time: 0.431, data_time: 0.193, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9988, loss_cls: 0.3275, loss: 0.3275 +2025-06-24 16:44:06,432 - pyskl - INFO - Epoch [83][200/1281] lr: 1.063e-02, eta: 5:25:17, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9988, loss_cls: 0.2925, loss: 0.2925 +2025-06-24 16:44:29,104 - pyskl - INFO - Epoch [83][300/1281] lr: 1.061e-02, eta: 5:24:55, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 1.0000, loss_cls: 0.3685, loss: 0.3685 +2025-06-24 16:44:51,803 - pyskl - INFO - Epoch [83][400/1281] lr: 1.059e-02, eta: 5:24:33, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.9394, top5_acc: 1.0000, loss_cls: 0.3463, loss: 0.3463 +2025-06-24 16:45:14,421 - pyskl - INFO - Epoch [83][500/1281] lr: 1.057e-02, eta: 5:24:11, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.3433, loss: 0.3433 +2025-06-24 16:45:36,826 - pyskl - INFO - Epoch [83][600/1281] lr: 1.055e-02, eta: 5:23:48, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 1.0000, loss_cls: 0.3891, loss: 0.3891 +2025-06-24 16:45:59,242 - pyskl - INFO - Epoch [83][700/1281] lr: 1.053e-02, eta: 5:23:26, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9981, loss_cls: 0.3659, loss: 0.3659 +2025-06-24 16:46:21,982 - pyskl - INFO - Epoch [83][800/1281] lr: 1.051e-02, eta: 5:23:03, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9988, loss_cls: 0.3434, loss: 0.3434 +2025-06-24 16:46:44,282 - pyskl - INFO - Epoch [83][900/1281] lr: 1.049e-02, eta: 5:22:41, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9994, loss_cls: 0.3703, loss: 0.3703 +2025-06-24 16:47:06,671 - pyskl - INFO - Epoch [83][1000/1281] lr: 1.047e-02, eta: 5:22:18, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9988, loss_cls: 0.3194, loss: 0.3194 +2025-06-24 16:47:29,139 - pyskl - INFO - Epoch [83][1100/1281] lr: 1.045e-02, eta: 5:21:56, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3794, loss: 0.3794 +2025-06-24 16:47:51,645 - pyskl - INFO - Epoch [83][1200/1281] lr: 1.043e-02, eta: 5:21:33, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3634, loss: 0.3634 +2025-06-24 16:48:10,921 - pyskl - INFO - Saving checkpoint at 83 epochs +2025-06-24 16:48:54,331 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:48:54,388 - pyskl - INFO - +top1_acc 0.8792 +top5_acc 0.9934 +2025-06-24 16:48:54,388 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:48:54,396 - pyskl - INFO - +mean_acc 0.8317 +2025-06-24 16:48:54,399 - pyskl - INFO - Epoch(val) [83][533] top1_acc: 0.8792, top5_acc: 0.9934, mean_class_accuracy: 0.8317 +2025-06-24 16:49:37,563 - pyskl - INFO - Epoch [84][100/1281] lr: 1.040e-02, eta: 5:20:55, time: 0.432, data_time: 0.193, memory: 4083, top1_acc: 0.9363, top5_acc: 1.0000, loss_cls: 0.3630, loss: 0.3630 +2025-06-24 16:49:59,985 - pyskl - INFO - Epoch [84][200/1281] lr: 1.038e-02, eta: 5:20:32, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 1.0000, loss_cls: 0.2971, loss: 0.2971 +2025-06-24 16:50:22,576 - pyskl - INFO - Epoch [84][300/1281] lr: 1.036e-02, eta: 5:20:10, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9988, loss_cls: 0.3246, loss: 0.3246 +2025-06-24 16:50:44,944 - pyskl - INFO - Epoch [84][400/1281] lr: 1.034e-02, eta: 5:19:47, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3174, loss: 0.3174 +2025-06-24 16:51:07,803 - pyskl - INFO - Epoch [84][500/1281] lr: 1.031e-02, eta: 5:19:25, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9988, loss_cls: 0.3446, loss: 0.3446 +2025-06-24 16:51:30,240 - pyskl - INFO - Epoch [84][600/1281] lr: 1.029e-02, eta: 5:19:03, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9975, loss_cls: 0.3861, loss: 0.3861 +2025-06-24 16:51:53,375 - pyskl - INFO - Epoch [84][700/1281] lr: 1.027e-02, eta: 5:18:41, time: 0.231, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9981, loss_cls: 0.2841, loss: 0.2841 +2025-06-24 16:52:15,725 - pyskl - INFO - Epoch [84][800/1281] lr: 1.025e-02, eta: 5:18:18, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 1.0000, loss_cls: 0.3198, loss: 0.3198 +2025-06-24 16:52:38,177 - pyskl - INFO - Epoch [84][900/1281] lr: 1.023e-02, eta: 5:17:56, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9981, loss_cls: 0.3434, loss: 0.3434 +2025-06-24 16:53:00,680 - pyskl - INFO - Epoch [84][1000/1281] lr: 1.021e-02, eta: 5:17:33, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9994, loss_cls: 0.3110, loss: 0.3110 +2025-06-24 16:53:23,320 - pyskl - INFO - Epoch [84][1100/1281] lr: 1.019e-02, eta: 5:17:11, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3745, loss: 0.3745 +2025-06-24 16:53:45,922 - pyskl - INFO - Epoch [84][1200/1281] lr: 1.017e-02, eta: 5:16:49, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9994, loss_cls: 0.3302, loss: 0.3302 +2025-06-24 16:54:04,810 - pyskl - INFO - Saving checkpoint at 84 epochs +2025-06-24 16:54:48,364 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:54:48,423 - pyskl - INFO - +top1_acc 0.8674 +top5_acc 0.9937 +2025-06-24 16:54:48,423 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:54:48,430 - pyskl - INFO - +mean_acc 0.8318 +2025-06-24 16:54:48,432 - pyskl - INFO - Epoch(val) [84][533] top1_acc: 0.8674, top5_acc: 0.9937, mean_class_accuracy: 0.8318 +2025-06-24 16:55:31,458 - pyskl - INFO - Epoch [85][100/1281] lr: 1.014e-02, eta: 5:16:10, time: 0.430, data_time: 0.191, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2695, loss: 0.2695 +2025-06-24 16:55:53,623 - pyskl - INFO - Epoch [85][200/1281] lr: 1.012e-02, eta: 5:15:47, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.3189, loss: 0.3189 +2025-06-24 16:56:16,260 - pyskl - INFO - Epoch [85][300/1281] lr: 1.010e-02, eta: 5:15:25, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9988, loss_cls: 0.3032, loss: 0.3032 +2025-06-24 16:56:38,920 - pyskl - INFO - Epoch [85][400/1281] lr: 1.008e-02, eta: 5:15:03, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9975, loss_cls: 0.2876, loss: 0.2876 +2025-06-24 16:57:01,343 - pyskl - INFO - Epoch [85][500/1281] lr: 1.006e-02, eta: 5:14:40, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9994, loss_cls: 0.3219, loss: 0.3219 +2025-06-24 16:57:23,499 - pyskl - INFO - Epoch [85][600/1281] lr: 1.004e-02, eta: 5:14:17, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9981, loss_cls: 0.3156, loss: 0.3156 +2025-06-24 16:57:46,173 - pyskl - INFO - Epoch [85][700/1281] lr: 1.002e-02, eta: 5:13:55, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.9381, top5_acc: 1.0000, loss_cls: 0.3274, loss: 0.3274 +2025-06-24 16:58:08,735 - pyskl - INFO - Epoch [85][800/1281] lr: 9.998e-03, eta: 5:13:33, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.3141, loss: 0.3141 +2025-06-24 16:58:31,279 - pyskl - INFO - Epoch [85][900/1281] lr: 9.978e-03, eta: 5:13:10, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 1.0000, loss_cls: 0.3621, loss: 0.3621 +2025-06-24 16:58:54,065 - pyskl - INFO - Epoch [85][1000/1281] lr: 9.958e-03, eta: 5:12:48, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9981, loss_cls: 0.3949, loss: 0.3949 +2025-06-24 16:59:16,508 - pyskl - INFO - Epoch [85][1100/1281] lr: 9.937e-03, eta: 5:12:26, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.3160, loss: 0.3160 +2025-06-24 16:59:39,092 - pyskl - INFO - Epoch [85][1200/1281] lr: 9.917e-03, eta: 5:12:03, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9994, loss_cls: 0.3546, loss: 0.3546 +2025-06-24 16:59:58,141 - pyskl - INFO - Saving checkpoint at 85 epochs +2025-06-24 17:00:41,976 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:00:42,051 - pyskl - INFO - +top1_acc 0.8801 +top5_acc 0.9926 +2025-06-24 17:00:42,051 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:00:42,059 - pyskl - INFO - +mean_acc 0.8452 +2025-06-24 17:00:42,061 - pyskl - INFO - Epoch(val) [85][533] top1_acc: 0.8801, top5_acc: 0.9926, mean_class_accuracy: 0.8452 +2025-06-24 17:01:24,708 - pyskl - INFO - Epoch [86][100/1281] lr: 9.881e-03, eta: 5:11:24, time: 0.426, data_time: 0.190, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9962, loss_cls: 0.3274, loss: 0.3274 +2025-06-24 17:01:47,355 - pyskl - INFO - Epoch [86][200/1281] lr: 9.861e-03, eta: 5:11:02, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.2592, loss: 0.2592 +2025-06-24 17:02:09,812 - pyskl - INFO - Epoch [86][300/1281] lr: 9.841e-03, eta: 5:10:39, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9988, loss_cls: 0.3009, loss: 0.3009 +2025-06-24 17:02:32,334 - pyskl - INFO - Epoch [86][400/1281] lr: 9.821e-03, eta: 5:10:17, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2671, loss: 0.2671 +2025-06-24 17:02:54,811 - pyskl - INFO - Epoch [86][500/1281] lr: 9.801e-03, eta: 5:09:54, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2885, loss: 0.2885 +2025-06-24 17:03:17,454 - pyskl - INFO - Epoch [86][600/1281] lr: 9.781e-03, eta: 5:09:32, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2571, loss: 0.2571 +2025-06-24 17:03:39,815 - pyskl - INFO - Epoch [86][700/1281] lr: 9.762e-03, eta: 5:09:09, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9994, loss_cls: 0.2957, loss: 0.2957 +2025-06-24 17:04:02,374 - pyskl - INFO - Epoch [86][800/1281] lr: 9.742e-03, eta: 5:08:47, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 1.0000, loss_cls: 0.2649, loss: 0.2649 +2025-06-24 17:04:24,661 - pyskl - INFO - Epoch [86][900/1281] lr: 9.722e-03, eta: 5:08:24, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.3061, loss: 0.3061 +2025-06-24 17:04:47,249 - pyskl - INFO - Epoch [86][1000/1281] lr: 9.702e-03, eta: 5:08:02, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.3066, loss: 0.3066 +2025-06-24 17:05:09,722 - pyskl - INFO - Epoch [86][1100/1281] lr: 9.682e-03, eta: 5:07:40, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9994, loss_cls: 0.3340, loss: 0.3340 +2025-06-24 17:05:31,976 - pyskl - INFO - Epoch [86][1200/1281] lr: 9.662e-03, eta: 5:07:17, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9994, loss_cls: 0.3747, loss: 0.3747 +2025-06-24 17:05:50,792 - pyskl - INFO - Saving checkpoint at 86 epochs +2025-06-24 17:06:34,507 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:06:34,562 - pyskl - INFO - +top1_acc 0.8747 +top5_acc 0.9919 +2025-06-24 17:06:34,563 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:06:34,569 - pyskl - INFO - +mean_acc 0.8216 +2025-06-24 17:06:34,570 - pyskl - INFO - Epoch(val) [86][533] top1_acc: 0.8747, top5_acc: 0.9919, mean_class_accuracy: 0.8216 +2025-06-24 17:07:17,508 - pyskl - INFO - Epoch [87][100/1281] lr: 9.626e-03, eta: 5:06:38, time: 0.429, data_time: 0.192, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.2875, loss: 0.2875 +2025-06-24 17:07:40,065 - pyskl - INFO - Epoch [87][200/1281] lr: 9.606e-03, eta: 5:06:16, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9988, loss_cls: 0.2951, loss: 0.2951 +2025-06-24 17:08:02,741 - pyskl - INFO - Epoch [87][300/1281] lr: 9.586e-03, eta: 5:05:53, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2658, loss: 0.2658 +2025-06-24 17:08:25,092 - pyskl - INFO - Epoch [87][400/1281] lr: 9.566e-03, eta: 5:05:31, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9994, loss_cls: 0.3102, loss: 0.3102 +2025-06-24 17:08:47,190 - pyskl - INFO - Epoch [87][500/1281] lr: 9.546e-03, eta: 5:05:08, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2759, loss: 0.2759 +2025-06-24 17:09:09,963 - pyskl - INFO - Epoch [87][600/1281] lr: 9.527e-03, eta: 5:04:46, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9975, loss_cls: 0.2940, loss: 0.2940 +2025-06-24 17:09:32,379 - pyskl - INFO - Epoch [87][700/1281] lr: 9.507e-03, eta: 5:04:23, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9994, loss_cls: 0.3364, loss: 0.3364 +2025-06-24 17:09:54,667 - pyskl - INFO - Epoch [87][800/1281] lr: 9.487e-03, eta: 5:04:01, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9969, loss_cls: 0.3228, loss: 0.3228 +2025-06-24 17:10:17,350 - pyskl - INFO - Epoch [87][900/1281] lr: 9.467e-03, eta: 5:03:38, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.2983, loss: 0.2983 +2025-06-24 17:10:39,965 - pyskl - INFO - Epoch [87][1000/1281] lr: 9.447e-03, eta: 5:03:16, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9994, loss_cls: 0.3316, loss: 0.3316 +2025-06-24 17:11:02,572 - pyskl - INFO - Epoch [87][1100/1281] lr: 9.427e-03, eta: 5:02:54, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3314, loss: 0.3314 +2025-06-24 17:11:24,894 - pyskl - INFO - Epoch [87][1200/1281] lr: 9.408e-03, eta: 5:02:31, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9981, loss_cls: 0.2945, loss: 0.2945 +2025-06-24 17:11:43,601 - pyskl - INFO - Saving checkpoint at 87 epochs +2025-06-24 17:12:27,761 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:12:27,831 - pyskl - INFO - +top1_acc 0.8815 +top5_acc 0.9926 +2025-06-24 17:12:27,832 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:12:27,840 - pyskl - INFO - +mean_acc 0.8376 +2025-06-24 17:12:27,845 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_82.pth was removed +2025-06-24 17:12:28,068 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_87.pth. +2025-06-24 17:12:28,068 - pyskl - INFO - Best top1_acc is 0.8815 at 87 epoch. +2025-06-24 17:12:28,077 - pyskl - INFO - Epoch(val) [87][533] top1_acc: 0.8815, top5_acc: 0.9926, mean_class_accuracy: 0.8376 +2025-06-24 17:13:10,824 - pyskl - INFO - Epoch [88][100/1281] lr: 9.372e-03, eta: 5:01:52, time: 0.427, data_time: 0.192, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2575, loss: 0.2575 +2025-06-24 17:13:33,469 - pyskl - INFO - Epoch [88][200/1281] lr: 9.352e-03, eta: 5:01:29, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.2944, loss: 0.2944 +2025-06-24 17:13:55,961 - pyskl - INFO - Epoch [88][300/1281] lr: 9.332e-03, eta: 5:01:07, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9988, loss_cls: 0.3195, loss: 0.3195 +2025-06-24 17:14:18,479 - pyskl - INFO - Epoch [88][400/1281] lr: 9.312e-03, eta: 5:00:45, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9994, loss_cls: 0.2650, loss: 0.2650 +2025-06-24 17:14:41,125 - pyskl - INFO - Epoch [88][500/1281] lr: 9.293e-03, eta: 5:00:22, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9988, loss_cls: 0.2968, loss: 0.2968 +2025-06-24 17:15:03,557 - pyskl - INFO - Epoch [88][600/1281] lr: 9.273e-03, eta: 5:00:00, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.2980, loss: 0.2980 +2025-06-24 17:15:26,345 - pyskl - INFO - Epoch [88][700/1281] lr: 9.253e-03, eta: 4:59:37, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9994, loss_cls: 0.3707, loss: 0.3707 +2025-06-24 17:15:48,802 - pyskl - INFO - Epoch [88][800/1281] lr: 9.233e-03, eta: 4:59:15, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3593, loss: 0.3593 +2025-06-24 17:16:11,434 - pyskl - INFO - Epoch [88][900/1281] lr: 9.214e-03, eta: 4:58:53, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9988, loss_cls: 0.3609, loss: 0.3609 +2025-06-24 17:16:33,844 - pyskl - INFO - Epoch [88][1000/1281] lr: 9.194e-03, eta: 4:58:30, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9994, loss_cls: 0.3427, loss: 0.3427 +2025-06-24 17:16:56,171 - pyskl - INFO - Epoch [88][1100/1281] lr: 9.174e-03, eta: 4:58:08, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9988, loss_cls: 0.3541, loss: 0.3541 +2025-06-24 17:17:18,692 - pyskl - INFO - Epoch [88][1200/1281] lr: 9.155e-03, eta: 4:57:45, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9988, loss_cls: 0.3441, loss: 0.3441 +2025-06-24 17:17:37,558 - pyskl - INFO - Saving checkpoint at 88 epochs +2025-06-24 17:18:21,327 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:18:21,383 - pyskl - INFO - +top1_acc 0.8654 +top5_acc 0.9912 +2025-06-24 17:18:21,383 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:18:21,390 - pyskl - INFO - +mean_acc 0.8145 +2025-06-24 17:18:21,392 - pyskl - INFO - Epoch(val) [88][533] top1_acc: 0.8654, top5_acc: 0.9912, mean_class_accuracy: 0.8145 +2025-06-24 17:19:04,584 - pyskl - INFO - Epoch [89][100/1281] lr: 9.119e-03, eta: 4:57:06, time: 0.432, data_time: 0.194, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9988, loss_cls: 0.3153, loss: 0.3153 +2025-06-24 17:19:27,304 - pyskl - INFO - Epoch [89][200/1281] lr: 9.099e-03, eta: 4:56:44, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9988, loss_cls: 0.2968, loss: 0.2968 +2025-06-24 17:19:49,669 - pyskl - INFO - Epoch [89][300/1281] lr: 9.080e-03, eta: 4:56:21, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2554, loss: 0.2554 +2025-06-24 17:20:11,989 - pyskl - INFO - Epoch [89][400/1281] lr: 9.060e-03, eta: 4:55:59, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2428, loss: 0.2428 +2025-06-24 17:20:34,666 - pyskl - INFO - Epoch [89][500/1281] lr: 9.040e-03, eta: 4:55:36, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9994, loss_cls: 0.2685, loss: 0.2685 +2025-06-24 17:20:57,140 - pyskl - INFO - Epoch [89][600/1281] lr: 9.021e-03, eta: 4:55:14, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2640, loss: 0.2640 +2025-06-24 17:21:19,651 - pyskl - INFO - Epoch [89][700/1281] lr: 9.001e-03, eta: 4:54:52, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2762, loss: 0.2762 +2025-06-24 17:21:42,102 - pyskl - INFO - Epoch [89][800/1281] lr: 8.982e-03, eta: 4:54:29, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.3155, loss: 0.3155 +2025-06-24 17:22:04,616 - pyskl - INFO - Epoch [89][900/1281] lr: 8.962e-03, eta: 4:54:07, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 1.0000, loss_cls: 0.2850, loss: 0.2850 +2025-06-24 17:22:27,216 - pyskl - INFO - Epoch [89][1000/1281] lr: 8.942e-03, eta: 4:53:44, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2820, loss: 0.2820 +2025-06-24 17:22:49,667 - pyskl - INFO - Epoch [89][1100/1281] lr: 8.923e-03, eta: 4:53:22, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9975, loss_cls: 0.3071, loss: 0.3071 +2025-06-24 17:23:12,098 - pyskl - INFO - Epoch [89][1200/1281] lr: 8.903e-03, eta: 4:52:59, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9981, loss_cls: 0.3132, loss: 0.3132 +2025-06-24 17:23:30,989 - pyskl - INFO - Saving checkpoint at 89 epochs +2025-06-24 17:24:15,035 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:24:15,090 - pyskl - INFO - +top1_acc 0.8743 +top5_acc 0.9891 +2025-06-24 17:24:15,090 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:24:15,096 - pyskl - INFO - +mean_acc 0.8350 +2025-06-24 17:24:15,098 - pyskl - INFO - Epoch(val) [89][533] top1_acc: 0.8743, top5_acc: 0.9891, mean_class_accuracy: 0.8350 +2025-06-24 17:24:57,526 - pyskl - INFO - Epoch [90][100/1281] lr: 8.868e-03, eta: 4:52:20, time: 0.424, data_time: 0.188, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9988, loss_cls: 0.2929, loss: 0.2929 +2025-06-24 17:25:20,487 - pyskl - INFO - Epoch [90][200/1281] lr: 8.848e-03, eta: 4:51:58, time: 0.230, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 1.0000, loss_cls: 0.2670, loss: 0.2670 +2025-06-24 17:25:43,263 - pyskl - INFO - Epoch [90][300/1281] lr: 8.829e-03, eta: 4:51:35, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9994, loss_cls: 0.2441, loss: 0.2441 +2025-06-24 17:26:05,989 - pyskl - INFO - Epoch [90][400/1281] lr: 8.809e-03, eta: 4:51:13, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9988, loss_cls: 0.2601, loss: 0.2601 +2025-06-24 17:26:28,263 - pyskl - INFO - Epoch [90][500/1281] lr: 8.790e-03, eta: 4:50:50, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9988, loss_cls: 0.2631, loss: 0.2631 +2025-06-24 17:26:51,212 - pyskl - INFO - Epoch [90][600/1281] lr: 8.770e-03, eta: 4:50:28, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9988, loss_cls: 0.2925, loss: 0.2925 +2025-06-24 17:27:13,788 - pyskl - INFO - Epoch [90][700/1281] lr: 8.751e-03, eta: 4:50:06, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2477, loss: 0.2477 +2025-06-24 17:27:36,432 - pyskl - INFO - Epoch [90][800/1281] lr: 8.731e-03, eta: 4:49:43, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 1.0000, loss_cls: 0.2640, loss: 0.2640 +2025-06-24 17:27:58,855 - pyskl - INFO - Epoch [90][900/1281] lr: 8.712e-03, eta: 4:49:21, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9994, loss_cls: 0.3244, loss: 0.3244 +2025-06-24 17:28:21,195 - pyskl - INFO - Epoch [90][1000/1281] lr: 8.692e-03, eta: 4:48:58, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 1.0000, loss_cls: 0.3832, loss: 0.3832 +2025-06-24 17:28:43,740 - pyskl - INFO - Epoch [90][1100/1281] lr: 8.673e-03, eta: 4:48:36, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 1.0000, loss_cls: 0.2845, loss: 0.2845 +2025-06-24 17:29:05,977 - pyskl - INFO - Epoch [90][1200/1281] lr: 8.653e-03, eta: 4:48:13, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2730, loss: 0.2730 +2025-06-24 17:29:24,757 - pyskl - INFO - Saving checkpoint at 90 epochs +2025-06-24 17:30:08,161 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:30:08,217 - pyskl - INFO - +top1_acc 0.8852 +top5_acc 0.9918 +2025-06-24 17:30:08,217 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:30:08,224 - pyskl - INFO - +mean_acc 0.8490 +2025-06-24 17:30:08,228 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_87.pth was removed +2025-06-24 17:30:08,395 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_90.pth. +2025-06-24 17:30:08,395 - pyskl - INFO - Best top1_acc is 0.8852 at 90 epoch. +2025-06-24 17:30:08,398 - pyskl - INFO - Epoch(val) [90][533] top1_acc: 0.8852, top5_acc: 0.9918, mean_class_accuracy: 0.8490 +2025-06-24 17:30:51,587 - pyskl - INFO - Epoch [91][100/1281] lr: 8.618e-03, eta: 4:47:34, time: 0.432, data_time: 0.191, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2598, loss: 0.2598 +2025-06-24 17:31:14,212 - pyskl - INFO - Epoch [91][200/1281] lr: 8.599e-03, eta: 4:47:12, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2215, loss: 0.2215 +2025-06-24 17:31:36,602 - pyskl - INFO - Epoch [91][300/1281] lr: 8.579e-03, eta: 4:46:49, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 1.0000, loss_cls: 0.2896, loss: 0.2896 +2025-06-24 17:31:59,157 - pyskl - INFO - Epoch [91][400/1281] lr: 8.560e-03, eta: 4:46:27, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9994, loss_cls: 0.2483, loss: 0.2483 +2025-06-24 17:32:21,463 - pyskl - INFO - Epoch [91][500/1281] lr: 8.540e-03, eta: 4:46:04, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 1.0000, loss_cls: 0.2936, loss: 0.2936 +2025-06-24 17:32:44,237 - pyskl - INFO - Epoch [91][600/1281] lr: 8.521e-03, eta: 4:45:42, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9988, loss_cls: 0.2960, loss: 0.2960 +2025-06-24 17:33:06,911 - pyskl - INFO - Epoch [91][700/1281] lr: 8.502e-03, eta: 4:45:20, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 1.0000, loss_cls: 0.3106, loss: 0.3106 +2025-06-24 17:33:29,492 - pyskl - INFO - Epoch [91][800/1281] lr: 8.482e-03, eta: 4:44:57, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2395, loss: 0.2395 +2025-06-24 17:33:52,271 - pyskl - INFO - Epoch [91][900/1281] lr: 8.463e-03, eta: 4:44:35, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 1.0000, loss_cls: 0.2831, loss: 0.2831 +2025-06-24 17:34:14,675 - pyskl - INFO - Epoch [91][1000/1281] lr: 8.444e-03, eta: 4:44:12, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.2716, loss: 0.2716 +2025-06-24 17:34:37,227 - pyskl - INFO - Epoch [91][1100/1281] lr: 8.424e-03, eta: 4:43:50, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9975, loss_cls: 0.3336, loss: 0.3336 +2025-06-24 17:34:59,789 - pyskl - INFO - Epoch [91][1200/1281] lr: 8.405e-03, eta: 4:43:28, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9988, loss_cls: 0.2887, loss: 0.2887 +2025-06-24 17:35:18,652 - pyskl - INFO - Saving checkpoint at 91 epochs +2025-06-24 17:36:02,572 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:36:02,627 - pyskl - INFO - +top1_acc 0.8762 +top5_acc 0.9932 +2025-06-24 17:36:02,627 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:36:02,637 - pyskl - INFO - +mean_acc 0.8387 +2025-06-24 17:36:02,640 - pyskl - INFO - Epoch(val) [91][533] top1_acc: 0.8762, top5_acc: 0.9932, mean_class_accuracy: 0.8387 +2025-06-24 17:36:45,517 - pyskl - INFO - Epoch [92][100/1281] lr: 8.370e-03, eta: 4:42:48, time: 0.429, data_time: 0.192, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9994, loss_cls: 0.3065, loss: 0.3065 +2025-06-24 17:37:08,331 - pyskl - INFO - Epoch [92][200/1281] lr: 8.351e-03, eta: 4:42:26, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2642, loss: 0.2642 +2025-06-24 17:37:30,811 - pyskl - INFO - Epoch [92][300/1281] lr: 8.332e-03, eta: 4:42:04, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2312, loss: 0.2312 +2025-06-24 17:37:53,537 - pyskl - INFO - Epoch [92][400/1281] lr: 8.312e-03, eta: 4:41:41, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2391, loss: 0.2391 +2025-06-24 17:38:16,493 - pyskl - INFO - Epoch [92][500/1281] lr: 8.293e-03, eta: 4:41:19, time: 0.230, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2662, loss: 0.2662 +2025-06-24 17:38:39,136 - pyskl - INFO - Epoch [92][600/1281] lr: 8.274e-03, eta: 4:40:57, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3372, loss: 0.3372 +2025-06-24 17:39:01,783 - pyskl - INFO - Epoch [92][700/1281] lr: 8.255e-03, eta: 4:40:34, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9981, loss_cls: 0.2849, loss: 0.2849 +2025-06-24 17:39:24,276 - pyskl - INFO - Epoch [92][800/1281] lr: 8.235e-03, eta: 4:40:12, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 1.0000, loss_cls: 0.3064, loss: 0.3064 +2025-06-24 17:39:46,725 - pyskl - INFO - Epoch [92][900/1281] lr: 8.216e-03, eta: 4:39:49, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.2861, loss: 0.2861 +2025-06-24 17:40:09,312 - pyskl - INFO - Epoch [92][1000/1281] lr: 8.197e-03, eta: 4:39:27, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2669, loss: 0.2669 +2025-06-24 17:40:31,726 - pyskl - INFO - Epoch [92][1100/1281] lr: 8.178e-03, eta: 4:39:04, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 1.0000, loss_cls: 0.3348, loss: 0.3348 +2025-06-24 17:40:54,163 - pyskl - INFO - Epoch [92][1200/1281] lr: 8.159e-03, eta: 4:38:42, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9988, loss_cls: 0.2820, loss: 0.2820 +2025-06-24 17:41:13,372 - pyskl - INFO - Saving checkpoint at 92 epochs +2025-06-24 17:41:57,630 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:41:57,686 - pyskl - INFO - +top1_acc 0.8892 +top5_acc 0.9928 +2025-06-24 17:41:57,687 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:41:57,693 - pyskl - INFO - +mean_acc 0.8528 +2025-06-24 17:41:57,697 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_90.pth was removed +2025-06-24 17:41:57,866 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_92.pth. +2025-06-24 17:41:57,866 - pyskl - INFO - Best top1_acc is 0.8892 at 92 epoch. +2025-06-24 17:41:57,869 - pyskl - INFO - Epoch(val) [92][533] top1_acc: 0.8892, top5_acc: 0.9928, mean_class_accuracy: 0.8528 +2025-06-24 17:42:40,498 - pyskl - INFO - Epoch [93][100/1281] lr: 8.124e-03, eta: 4:38:02, time: 0.426, data_time: 0.191, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2203, loss: 0.2203 +2025-06-24 17:43:02,980 - pyskl - INFO - Epoch [93][200/1281] lr: 8.105e-03, eta: 4:37:40, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2348, loss: 0.2348 +2025-06-24 17:43:25,445 - pyskl - INFO - Epoch [93][300/1281] lr: 8.086e-03, eta: 4:37:17, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2371, loss: 0.2371 +2025-06-24 17:43:48,094 - pyskl - INFO - Epoch [93][400/1281] lr: 8.067e-03, eta: 4:36:55, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.2051, loss: 0.2051 +2025-06-24 17:44:10,537 - pyskl - INFO - Epoch [93][500/1281] lr: 8.047e-03, eta: 4:36:33, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.2088, loss: 0.2088 +2025-06-24 17:44:32,864 - pyskl - INFO - Epoch [93][600/1281] lr: 8.028e-03, eta: 4:36:10, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2482, loss: 0.2482 +2025-06-24 17:44:55,364 - pyskl - INFO - Epoch [93][700/1281] lr: 8.009e-03, eta: 4:35:47, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2351, loss: 0.2351 +2025-06-24 17:45:18,166 - pyskl - INFO - Epoch [93][800/1281] lr: 7.990e-03, eta: 4:35:25, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2582, loss: 0.2582 +2025-06-24 17:45:40,390 - pyskl - INFO - Epoch [93][900/1281] lr: 7.971e-03, eta: 4:35:03, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9975, loss_cls: 0.2639, loss: 0.2639 +2025-06-24 17:46:02,987 - pyskl - INFO - Epoch [93][1000/1281] lr: 7.952e-03, eta: 4:34:40, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2341, loss: 0.2341 +2025-06-24 17:46:25,670 - pyskl - INFO - Epoch [93][1100/1281] lr: 7.933e-03, eta: 4:34:18, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 1.0000, loss_cls: 0.2497, loss: 0.2497 +2025-06-24 17:46:48,173 - pyskl - INFO - Epoch [93][1200/1281] lr: 7.914e-03, eta: 4:33:55, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9994, loss_cls: 0.2934, loss: 0.2934 +2025-06-24 17:47:06,925 - pyskl - INFO - Saving checkpoint at 93 epochs +2025-06-24 17:47:51,064 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:47:51,126 - pyskl - INFO - +top1_acc 0.8816 +top5_acc 0.9937 +2025-06-24 17:47:51,127 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:47:51,150 - pyskl - INFO - +mean_acc 0.8505 +2025-06-24 17:47:51,153 - pyskl - INFO - Epoch(val) [93][533] top1_acc: 0.8816, top5_acc: 0.9937, mean_class_accuracy: 0.8505 +2025-06-24 17:48:33,889 - pyskl - INFO - Epoch [94][100/1281] lr: 7.880e-03, eta: 4:33:16, time: 0.427, data_time: 0.190, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2384, loss: 0.2384 +2025-06-24 17:48:56,258 - pyskl - INFO - Epoch [94][200/1281] lr: 7.861e-03, eta: 4:32:53, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2466, loss: 0.2466 +2025-06-24 17:49:18,687 - pyskl - INFO - Epoch [94][300/1281] lr: 7.842e-03, eta: 4:32:31, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2377, loss: 0.2377 +2025-06-24 17:49:41,585 - pyskl - INFO - Epoch [94][400/1281] lr: 7.823e-03, eta: 4:32:09, time: 0.229, data_time: 0.001, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9988, loss_cls: 0.2681, loss: 0.2681 +2025-06-24 17:50:04,156 - pyskl - INFO - Epoch [94][500/1281] lr: 7.804e-03, eta: 4:31:46, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9994, loss_cls: 0.2602, loss: 0.2602 +2025-06-24 17:50:26,945 - pyskl - INFO - Epoch [94][600/1281] lr: 7.785e-03, eta: 4:31:24, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2588, loss: 0.2588 +2025-06-24 17:50:49,105 - pyskl - INFO - Epoch [94][700/1281] lr: 7.766e-03, eta: 4:31:01, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9988, loss_cls: 0.2447, loss: 0.2447 +2025-06-24 17:51:11,554 - pyskl - INFO - Epoch [94][800/1281] lr: 7.747e-03, eta: 4:30:39, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2542, loss: 0.2542 +2025-06-24 17:51:34,011 - pyskl - INFO - Epoch [94][900/1281] lr: 7.728e-03, eta: 4:30:16, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.3070, loss: 0.3070 +2025-06-24 17:51:56,412 - pyskl - INFO - Epoch [94][1000/1281] lr: 7.709e-03, eta: 4:29:54, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 1.0000, loss_cls: 0.2709, loss: 0.2709 +2025-06-24 17:52:18,956 - pyskl - INFO - Epoch [94][1100/1281] lr: 7.690e-03, eta: 4:29:31, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2785, loss: 0.2785 +2025-06-24 17:52:41,525 - pyskl - INFO - Epoch [94][1200/1281] lr: 7.672e-03, eta: 4:29:09, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9988, loss_cls: 0.3107, loss: 0.3107 +2025-06-24 17:53:00,558 - pyskl - INFO - Saving checkpoint at 94 epochs +2025-06-24 17:53:44,638 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:53:44,693 - pyskl - INFO - +top1_acc 0.8889 +top5_acc 0.9921 +2025-06-24 17:53:44,694 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:53:44,701 - pyskl - INFO - +mean_acc 0.8488 +2025-06-24 17:53:44,703 - pyskl - INFO - Epoch(val) [94][533] top1_acc: 0.8889, top5_acc: 0.9921, mean_class_accuracy: 0.8488 +2025-06-24 17:54:27,078 - pyskl - INFO - Epoch [95][100/1281] lr: 7.637e-03, eta: 4:28:29, time: 0.424, data_time: 0.187, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2115, loss: 0.2115 +2025-06-24 17:54:49,721 - pyskl - INFO - Epoch [95][200/1281] lr: 7.619e-03, eta: 4:28:07, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2040, loss: 0.2040 +2025-06-24 17:55:12,134 - pyskl - INFO - Epoch [95][300/1281] lr: 7.600e-03, eta: 4:27:44, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1886, loss: 0.1886 +2025-06-24 17:55:34,681 - pyskl - INFO - Epoch [95][400/1281] lr: 7.581e-03, eta: 4:27:22, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2080, loss: 0.2080 +2025-06-24 17:55:56,961 - pyskl - INFO - Epoch [95][500/1281] lr: 7.562e-03, eta: 4:26:59, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9981, loss_cls: 0.2657, loss: 0.2657 +2025-06-24 17:56:19,113 - pyskl - INFO - Epoch [95][600/1281] lr: 7.543e-03, eta: 4:26:36, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9988, loss_cls: 0.2483, loss: 0.2483 +2025-06-24 17:56:41,786 - pyskl - INFO - Epoch [95][700/1281] lr: 7.525e-03, eta: 4:26:14, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 1.0000, loss_cls: 0.2548, loss: 0.2548 +2025-06-24 17:57:03,888 - pyskl - INFO - Epoch [95][800/1281] lr: 7.506e-03, eta: 4:25:51, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9988, loss_cls: 0.2059, loss: 0.2059 +2025-06-24 17:57:26,194 - pyskl - INFO - Epoch [95][900/1281] lr: 7.487e-03, eta: 4:25:29, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9994, loss_cls: 0.2059, loss: 0.2059 +2025-06-24 17:57:48,517 - pyskl - INFO - Epoch [95][1000/1281] lr: 7.468e-03, eta: 4:25:06, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2362, loss: 0.2362 +2025-06-24 17:58:10,761 - pyskl - INFO - Epoch [95][1100/1281] lr: 7.450e-03, eta: 4:24:43, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2550, loss: 0.2550 +2025-06-24 17:58:33,280 - pyskl - INFO - Epoch [95][1200/1281] lr: 7.431e-03, eta: 4:24:21, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2493, loss: 0.2493 +2025-06-24 17:58:51,949 - pyskl - INFO - Saving checkpoint at 95 epochs +2025-06-24 17:59:35,982 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:59:36,039 - pyskl - INFO - +top1_acc 0.8822 +top5_acc 0.9935 +2025-06-24 17:59:36,040 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:59:36,047 - pyskl - INFO - +mean_acc 0.8412 +2025-06-24 17:59:36,049 - pyskl - INFO - Epoch(val) [95][533] top1_acc: 0.8822, top5_acc: 0.9935, mean_class_accuracy: 0.8412 +2025-06-24 18:00:19,055 - pyskl - INFO - Epoch [96][100/1281] lr: 7.397e-03, eta: 4:23:42, time: 0.430, data_time: 0.194, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9994, loss_cls: 0.2073, loss: 0.2073 +2025-06-24 18:00:41,679 - pyskl - INFO - Epoch [96][200/1281] lr: 7.379e-03, eta: 4:23:19, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1913, loss: 0.1913 +2025-06-24 18:01:04,128 - pyskl - INFO - Epoch [96][300/1281] lr: 7.360e-03, eta: 4:22:57, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1844, loss: 0.1844 +2025-06-24 18:01:26,771 - pyskl - INFO - Epoch [96][400/1281] lr: 7.341e-03, eta: 4:22:34, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2310, loss: 0.2310 +2025-06-24 18:01:49,429 - pyskl - INFO - Epoch [96][500/1281] lr: 7.323e-03, eta: 4:22:12, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2281, loss: 0.2281 +2025-06-24 18:02:12,036 - pyskl - INFO - Epoch [96][600/1281] lr: 7.304e-03, eta: 4:21:49, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9994, loss_cls: 0.2169, loss: 0.2169 +2025-06-24 18:02:34,725 - pyskl - INFO - Epoch [96][700/1281] lr: 7.286e-03, eta: 4:21:27, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2200, loss: 0.2200 +2025-06-24 18:02:57,263 - pyskl - INFO - Epoch [96][800/1281] lr: 7.267e-03, eta: 4:21:05, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2282, loss: 0.2282 +2025-06-24 18:03:19,822 - pyskl - INFO - Epoch [96][900/1281] lr: 7.249e-03, eta: 4:20:42, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.3092, loss: 0.3092 +2025-06-24 18:03:42,224 - pyskl - INFO - Epoch [96][1000/1281] lr: 7.230e-03, eta: 4:20:20, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.2828, loss: 0.2828 +2025-06-24 18:04:04,695 - pyskl - INFO - Epoch [96][1100/1281] lr: 7.211e-03, eta: 4:19:57, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9988, loss_cls: 0.2622, loss: 0.2622 +2025-06-24 18:04:26,968 - pyskl - INFO - Epoch [96][1200/1281] lr: 7.193e-03, eta: 4:19:34, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9994, loss_cls: 0.2480, loss: 0.2480 +2025-06-24 18:04:46,050 - pyskl - INFO - Saving checkpoint at 96 epochs +2025-06-24 18:05:30,069 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:05:30,125 - pyskl - INFO - +top1_acc 0.8880 +top5_acc 0.9899 +2025-06-24 18:05:30,125 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:05:30,131 - pyskl - INFO - +mean_acc 0.8617 +2025-06-24 18:05:30,133 - pyskl - INFO - Epoch(val) [96][533] top1_acc: 0.8880, top5_acc: 0.9899, mean_class_accuracy: 0.8617 +2025-06-24 18:06:12,696 - pyskl - INFO - Epoch [97][100/1281] lr: 7.159e-03, eta: 4:18:55, time: 0.426, data_time: 0.189, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2176, loss: 0.2176 +2025-06-24 18:06:35,161 - pyskl - INFO - Epoch [97][200/1281] lr: 7.141e-03, eta: 4:18:32, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1876, loss: 0.1876 +2025-06-24 18:06:57,791 - pyskl - INFO - Epoch [97][300/1281] lr: 7.123e-03, eta: 4:18:10, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.1934, loss: 0.1934 +2025-06-24 18:07:20,155 - pyskl - INFO - Epoch [97][400/1281] lr: 7.104e-03, eta: 4:17:47, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2097, loss: 0.2097 +2025-06-24 18:07:42,373 - pyskl - INFO - Epoch [97][500/1281] lr: 7.086e-03, eta: 4:17:25, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9988, loss_cls: 0.2591, loss: 0.2591 +2025-06-24 18:08:04,952 - pyskl - INFO - Epoch [97][600/1281] lr: 7.067e-03, eta: 4:17:02, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9988, loss_cls: 0.2477, loss: 0.2477 +2025-06-24 18:08:27,121 - pyskl - INFO - Epoch [97][700/1281] lr: 7.049e-03, eta: 4:16:40, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 0.9994, loss_cls: 0.1806, loss: 0.1806 +2025-06-24 18:08:49,743 - pyskl - INFO - Epoch [97][800/1281] lr: 7.030e-03, eta: 4:16:17, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2111, loss: 0.2111 +2025-06-24 18:09:12,360 - pyskl - INFO - Epoch [97][900/1281] lr: 7.012e-03, eta: 4:15:55, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2139, loss: 0.2139 +2025-06-24 18:09:34,921 - pyskl - INFO - Epoch [97][1000/1281] lr: 6.994e-03, eta: 4:15:32, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2235, loss: 0.2235 +2025-06-24 18:09:57,297 - pyskl - INFO - Epoch [97][1100/1281] lr: 6.975e-03, eta: 4:15:10, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2141, loss: 0.2141 +2025-06-24 18:10:19,782 - pyskl - INFO - Epoch [97][1200/1281] lr: 6.957e-03, eta: 4:14:47, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2221, loss: 0.2221 +2025-06-24 18:10:38,600 - pyskl - INFO - Saving checkpoint at 97 epochs +2025-06-24 18:11:23,044 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:11:23,101 - pyskl - INFO - +top1_acc 0.8958 +top5_acc 0.9920 +2025-06-24 18:11:23,101 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:11:23,109 - pyskl - INFO - +mean_acc 0.8597 +2025-06-24 18:11:23,113 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_92.pth was removed +2025-06-24 18:11:23,281 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_97.pth. +2025-06-24 18:11:23,281 - pyskl - INFO - Best top1_acc is 0.8958 at 97 epoch. +2025-06-24 18:11:23,283 - pyskl - INFO - Epoch(val) [97][533] top1_acc: 0.8958, top5_acc: 0.9920, mean_class_accuracy: 0.8597 +2025-06-24 18:12:05,269 - pyskl - INFO - Epoch [98][100/1281] lr: 6.924e-03, eta: 4:14:07, time: 0.420, data_time: 0.181, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2101, loss: 0.2101 +2025-06-24 18:12:28,128 - pyskl - INFO - Epoch [98][200/1281] lr: 6.906e-03, eta: 4:13:45, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 1.0000, loss_cls: 0.2529, loss: 0.2529 +2025-06-24 18:12:50,924 - pyskl - INFO - Epoch [98][300/1281] lr: 6.887e-03, eta: 4:13:23, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9994, loss_cls: 0.2162, loss: 0.2162 +2025-06-24 18:13:13,445 - pyskl - INFO - Epoch [98][400/1281] lr: 6.869e-03, eta: 4:13:00, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2241, loss: 0.2241 +2025-06-24 18:13:35,789 - pyskl - INFO - Epoch [98][500/1281] lr: 6.851e-03, eta: 4:12:38, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2253, loss: 0.2253 +2025-06-24 18:13:58,493 - pyskl - INFO - Epoch [98][600/1281] lr: 6.833e-03, eta: 4:12:15, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.1804, loss: 0.1804 +2025-06-24 18:14:20,976 - pyskl - INFO - Epoch [98][700/1281] lr: 6.814e-03, eta: 4:11:53, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.2037, loss: 0.2037 +2025-06-24 18:14:43,248 - pyskl - INFO - Epoch [98][800/1281] lr: 6.796e-03, eta: 4:11:30, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2093, loss: 0.2093 +2025-06-24 18:15:05,715 - pyskl - INFO - Epoch [98][900/1281] lr: 6.778e-03, eta: 4:11:08, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2169, loss: 0.2169 +2025-06-24 18:15:28,124 - pyskl - INFO - Epoch [98][1000/1281] lr: 6.760e-03, eta: 4:10:45, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2288, loss: 0.2288 +2025-06-24 18:15:50,505 - pyskl - INFO - Epoch [98][1100/1281] lr: 6.742e-03, eta: 4:10:22, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2204, loss: 0.2204 +2025-06-24 18:16:13,047 - pyskl - INFO - Epoch [98][1200/1281] lr: 6.724e-03, eta: 4:10:00, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2275, loss: 0.2275 +2025-06-24 18:16:32,064 - pyskl - INFO - Saving checkpoint at 98 epochs +2025-06-24 18:17:15,458 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:17:15,529 - pyskl - INFO - +top1_acc 0.8909 +top5_acc 0.9944 +2025-06-24 18:17:15,529 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:17:15,540 - pyskl - INFO - +mean_acc 0.8562 +2025-06-24 18:17:15,544 - pyskl - INFO - Epoch(val) [98][533] top1_acc: 0.8909, top5_acc: 0.9944, mean_class_accuracy: 0.8562 +2025-06-24 18:17:58,341 - pyskl - INFO - Epoch [99][100/1281] lr: 6.691e-03, eta: 4:09:20, time: 0.428, data_time: 0.192, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1816, loss: 0.1816 +2025-06-24 18:18:20,874 - pyskl - INFO - Epoch [99][200/1281] lr: 6.673e-03, eta: 4:08:58, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1705, loss: 0.1705 +2025-06-24 18:18:43,377 - pyskl - INFO - Epoch [99][300/1281] lr: 6.655e-03, eta: 4:08:35, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.1859, loss: 0.1859 +2025-06-24 18:19:05,777 - pyskl - INFO - Epoch [99][400/1281] lr: 6.637e-03, eta: 4:08:13, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.1942, loss: 0.1942 +2025-06-24 18:19:28,238 - pyskl - INFO - Epoch [99][500/1281] lr: 6.619e-03, eta: 4:07:50, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2166, loss: 0.2166 +2025-06-24 18:19:50,670 - pyskl - INFO - Epoch [99][600/1281] lr: 6.601e-03, eta: 4:07:28, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1803, loss: 0.1803 +2025-06-24 18:20:13,297 - pyskl - INFO - Epoch [99][700/1281] lr: 6.583e-03, eta: 4:07:05, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1620, loss: 0.1620 +2025-06-24 18:20:35,682 - pyskl - INFO - Epoch [99][800/1281] lr: 6.565e-03, eta: 4:06:43, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.1998, loss: 0.1998 +2025-06-24 18:20:58,072 - pyskl - INFO - Epoch [99][900/1281] lr: 6.547e-03, eta: 4:06:20, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3028, loss: 0.3028 +2025-06-24 18:21:20,392 - pyskl - INFO - Epoch [99][1000/1281] lr: 6.529e-03, eta: 4:05:58, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9981, loss_cls: 0.3138, loss: 0.3138 +2025-06-24 18:21:42,873 - pyskl - INFO - Epoch [99][1100/1281] lr: 6.511e-03, eta: 4:05:35, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9994, loss_cls: 0.2399, loss: 0.2399 +2025-06-24 18:22:05,127 - pyskl - INFO - Epoch [99][1200/1281] lr: 6.493e-03, eta: 4:05:13, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1749, loss: 0.1749 +2025-06-24 18:22:24,101 - pyskl - INFO - Saving checkpoint at 99 epochs +2025-06-24 18:23:08,644 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:23:08,713 - pyskl - INFO - +top1_acc 0.8998 +top5_acc 0.9944 +2025-06-24 18:23:08,713 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:23:08,723 - pyskl - INFO - +mean_acc 0.8716 +2025-06-24 18:23:08,729 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_97.pth was removed +2025-06-24 18:23:08,911 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_99.pth. +2025-06-24 18:23:08,912 - pyskl - INFO - Best top1_acc is 0.8998 at 99 epoch. +2025-06-24 18:23:08,915 - pyskl - INFO - Epoch(val) [99][533] top1_acc: 0.8998, top5_acc: 0.9944, mean_class_accuracy: 0.8716 +2025-06-24 18:23:51,996 - pyskl - INFO - Epoch [100][100/1281] lr: 6.460e-03, eta: 4:04:33, time: 0.431, data_time: 0.195, memory: 4083, top1_acc: 0.9694, top5_acc: 0.9994, loss_cls: 0.1945, loss: 0.1945 +2025-06-24 18:24:14,619 - pyskl - INFO - Epoch [100][200/1281] lr: 6.442e-03, eta: 4:04:11, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1733, loss: 0.1733 +2025-06-24 18:24:37,281 - pyskl - INFO - Epoch [100][300/1281] lr: 6.425e-03, eta: 4:03:48, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2018, loss: 0.2018 +2025-06-24 18:24:59,369 - pyskl - INFO - Epoch [100][400/1281] lr: 6.407e-03, eta: 4:03:26, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2036, loss: 0.2036 +2025-06-24 18:25:22,129 - pyskl - INFO - Epoch [100][500/1281] lr: 6.389e-03, eta: 4:03:03, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1602, loss: 0.1602 +2025-06-24 18:25:44,688 - pyskl - INFO - Epoch [100][600/1281] lr: 6.371e-03, eta: 4:02:41, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.1850, loss: 0.1850 +2025-06-24 18:26:07,083 - pyskl - INFO - Epoch [100][700/1281] lr: 6.353e-03, eta: 4:02:18, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 0.9994, loss_cls: 0.1633, loss: 0.1633 +2025-06-24 18:26:29,475 - pyskl - INFO - Epoch [100][800/1281] lr: 6.336e-03, eta: 4:01:56, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1718, loss: 0.1718 +2025-06-24 18:26:52,052 - pyskl - INFO - Epoch [100][900/1281] lr: 6.318e-03, eta: 4:01:33, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1903, loss: 0.1903 +2025-06-24 18:27:14,334 - pyskl - INFO - Epoch [100][1000/1281] lr: 6.300e-03, eta: 4:01:11, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2428, loss: 0.2428 +2025-06-24 18:27:36,849 - pyskl - INFO - Epoch [100][1100/1281] lr: 6.282e-03, eta: 4:00:48, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9988, loss_cls: 0.2278, loss: 0.2278 +2025-06-24 18:27:59,712 - pyskl - INFO - Epoch [100][1200/1281] lr: 6.265e-03, eta: 4:00:26, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1952, loss: 0.1952 +2025-06-24 18:28:18,855 - pyskl - INFO - Saving checkpoint at 100 epochs +2025-06-24 18:29:02,686 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:29:02,766 - pyskl - INFO - +top1_acc 0.8945 +top5_acc 0.9923 +2025-06-24 18:29:02,767 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:29:02,774 - pyskl - INFO - +mean_acc 0.8457 +2025-06-24 18:29:02,776 - pyskl - INFO - Epoch(val) [100][533] top1_acc: 0.8945, top5_acc: 0.9923, mean_class_accuracy: 0.8457 +2025-06-24 18:29:45,457 - pyskl - INFO - Epoch [101][100/1281] lr: 6.232e-03, eta: 3:59:46, time: 0.427, data_time: 0.188, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1770, loss: 0.1770 +2025-06-24 18:30:07,896 - pyskl - INFO - Epoch [101][200/1281] lr: 6.215e-03, eta: 3:59:24, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1511, loss: 0.1511 +2025-06-24 18:30:30,287 - pyskl - INFO - Epoch [101][300/1281] lr: 6.197e-03, eta: 3:59:01, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.1984, loss: 0.1984 +2025-06-24 18:30:52,752 - pyskl - INFO - Epoch [101][400/1281] lr: 6.180e-03, eta: 3:58:38, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2157, loss: 0.2157 +2025-06-24 18:31:14,868 - pyskl - INFO - Epoch [101][500/1281] lr: 6.162e-03, eta: 3:58:16, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1814, loss: 0.1814 +2025-06-24 18:31:36,965 - pyskl - INFO - Epoch [101][600/1281] lr: 6.144e-03, eta: 3:57:53, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1706, loss: 0.1706 +2025-06-24 18:31:59,383 - pyskl - INFO - Epoch [101][700/1281] lr: 6.127e-03, eta: 3:57:31, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.1994, loss: 0.1994 +2025-06-24 18:32:21,830 - pyskl - INFO - Epoch [101][800/1281] lr: 6.109e-03, eta: 3:57:08, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2188, loss: 0.2188 +2025-06-24 18:32:44,028 - pyskl - INFO - Epoch [101][900/1281] lr: 6.092e-03, eta: 3:56:45, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2394, loss: 0.2394 +2025-06-24 18:33:06,344 - pyskl - INFO - Epoch [101][1000/1281] lr: 6.074e-03, eta: 3:56:23, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2120, loss: 0.2120 +2025-06-24 18:33:28,415 - pyskl - INFO - Epoch [101][1100/1281] lr: 6.057e-03, eta: 3:56:00, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.1973, loss: 0.1973 +2025-06-24 18:33:50,891 - pyskl - INFO - Epoch [101][1200/1281] lr: 6.039e-03, eta: 3:55:38, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1935, loss: 0.1935 +2025-06-24 18:34:09,861 - pyskl - INFO - Saving checkpoint at 101 epochs +2025-06-24 18:34:53,370 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:34:53,439 - pyskl - INFO - +top1_acc 0.8876 +top5_acc 0.9926 +2025-06-24 18:34:53,439 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:34:53,447 - pyskl - INFO - +mean_acc 0.8494 +2025-06-24 18:34:53,449 - pyskl - INFO - Epoch(val) [101][533] top1_acc: 0.8876, top5_acc: 0.9926, mean_class_accuracy: 0.8494 +2025-06-24 18:35:36,255 - pyskl - INFO - Epoch [102][100/1281] lr: 6.007e-03, eta: 3:54:58, time: 0.428, data_time: 0.190, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1673, loss: 0.1673 +2025-06-24 18:35:58,857 - pyskl - INFO - Epoch [102][200/1281] lr: 5.990e-03, eta: 3:54:35, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 0.9994, loss_cls: 0.1589, loss: 0.1589 +2025-06-24 18:36:21,507 - pyskl - INFO - Epoch [102][300/1281] lr: 5.972e-03, eta: 3:54:13, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1465, loss: 0.1465 +2025-06-24 18:36:44,056 - pyskl - INFO - Epoch [102][400/1281] lr: 5.955e-03, eta: 3:53:51, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1244, loss: 0.1244 +2025-06-24 18:37:06,553 - pyskl - INFO - Epoch [102][500/1281] lr: 5.938e-03, eta: 3:53:28, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1799, loss: 0.1799 +2025-06-24 18:37:28,976 - pyskl - INFO - Epoch [102][600/1281] lr: 5.920e-03, eta: 3:53:06, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9988, loss_cls: 0.2047, loss: 0.2047 +2025-06-24 18:37:51,371 - pyskl - INFO - Epoch [102][700/1281] lr: 5.903e-03, eta: 3:52:43, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2023, loss: 0.2023 +2025-06-24 18:38:13,654 - pyskl - INFO - Epoch [102][800/1281] lr: 5.886e-03, eta: 3:52:20, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1899, loss: 0.1899 +2025-06-24 18:38:36,006 - pyskl - INFO - Epoch [102][900/1281] lr: 5.868e-03, eta: 3:51:58, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1785, loss: 0.1785 +2025-06-24 18:38:58,645 - pyskl - INFO - Epoch [102][1000/1281] lr: 5.851e-03, eta: 3:51:35, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1915, loss: 0.1915 +2025-06-24 18:39:21,148 - pyskl - INFO - Epoch [102][1100/1281] lr: 5.834e-03, eta: 3:51:13, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2425, loss: 0.2425 +2025-06-24 18:39:43,702 - pyskl - INFO - Epoch [102][1200/1281] lr: 5.816e-03, eta: 3:50:50, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9994, loss_cls: 0.2011, loss: 0.2011 +2025-06-24 18:40:02,678 - pyskl - INFO - Saving checkpoint at 102 epochs +2025-06-24 18:40:46,166 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:40:46,226 - pyskl - INFO - +top1_acc 0.8959 +top5_acc 0.9934 +2025-06-24 18:40:46,226 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:40:46,234 - pyskl - INFO - +mean_acc 0.8615 +2025-06-24 18:40:46,236 - pyskl - INFO - Epoch(val) [102][533] top1_acc: 0.8959, top5_acc: 0.9934, mean_class_accuracy: 0.8615 +2025-06-24 18:41:29,133 - pyskl - INFO - Epoch [103][100/1281] lr: 5.785e-03, eta: 3:50:11, time: 0.429, data_time: 0.193, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1450, loss: 0.1450 +2025-06-24 18:41:51,583 - pyskl - INFO - Epoch [103][200/1281] lr: 5.768e-03, eta: 3:49:48, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1528, loss: 0.1528 +2025-06-24 18:42:14,229 - pyskl - INFO - Epoch [103][300/1281] lr: 5.751e-03, eta: 3:49:26, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1624, loss: 0.1624 +2025-06-24 18:42:36,454 - pyskl - INFO - Epoch [103][400/1281] lr: 5.733e-03, eta: 3:49:03, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1605, loss: 0.1605 +2025-06-24 18:42:58,711 - pyskl - INFO - Epoch [103][500/1281] lr: 5.716e-03, eta: 3:48:41, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1787, loss: 0.1787 +2025-06-24 18:43:21,089 - pyskl - INFO - Epoch [103][600/1281] lr: 5.699e-03, eta: 3:48:18, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1694, loss: 0.1694 +2025-06-24 18:43:43,596 - pyskl - INFO - Epoch [103][700/1281] lr: 5.682e-03, eta: 3:47:55, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 0.9994, loss_cls: 0.1318, loss: 0.1318 +2025-06-24 18:44:06,159 - pyskl - INFO - Epoch [103][800/1281] lr: 5.665e-03, eta: 3:47:33, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1619, loss: 0.1619 +2025-06-24 18:44:28,412 - pyskl - INFO - Epoch [103][900/1281] lr: 5.648e-03, eta: 3:47:10, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1466, loss: 0.1466 +2025-06-24 18:44:50,751 - pyskl - INFO - Epoch [103][1000/1281] lr: 5.631e-03, eta: 3:46:48, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 0.9994, loss_cls: 0.1597, loss: 0.1597 +2025-06-24 18:45:13,339 - pyskl - INFO - Epoch [103][1100/1281] lr: 5.614e-03, eta: 3:46:25, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.1951, loss: 0.1951 +2025-06-24 18:45:35,787 - pyskl - INFO - Epoch [103][1200/1281] lr: 5.597e-03, eta: 3:46:03, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1763, loss: 0.1763 +2025-06-24 18:45:54,543 - pyskl - INFO - Saving checkpoint at 103 epochs +2025-06-24 18:46:38,695 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:46:38,750 - pyskl - INFO - +top1_acc 0.8943 +top5_acc 0.9933 +2025-06-24 18:46:38,750 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:46:38,757 - pyskl - INFO - +mean_acc 0.8591 +2025-06-24 18:46:38,759 - pyskl - INFO - Epoch(val) [103][533] top1_acc: 0.8943, top5_acc: 0.9933, mean_class_accuracy: 0.8591 +2025-06-24 18:47:22,100 - pyskl - INFO - Epoch [104][100/1281] lr: 5.566e-03, eta: 3:45:23, time: 0.433, data_time: 0.194, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1435, loss: 0.1435 +2025-06-24 18:47:44,430 - pyskl - INFO - Epoch [104][200/1281] lr: 5.549e-03, eta: 3:45:01, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 0.9994, loss_cls: 0.1548, loss: 0.1548 +2025-06-24 18:48:07,283 - pyskl - INFO - Epoch [104][300/1281] lr: 5.532e-03, eta: 3:44:38, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0963, loss: 0.0963 +2025-06-24 18:48:29,611 - pyskl - INFO - Epoch [104][400/1281] lr: 5.515e-03, eta: 3:44:16, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1575, loss: 0.1575 +2025-06-24 18:48:52,167 - pyskl - INFO - Epoch [104][500/1281] lr: 5.498e-03, eta: 3:43:53, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9988, loss_cls: 0.2065, loss: 0.2065 +2025-06-24 18:49:14,520 - pyskl - INFO - Epoch [104][600/1281] lr: 5.481e-03, eta: 3:43:31, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1787, loss: 0.1787 +2025-06-24 18:49:36,982 - pyskl - INFO - Epoch [104][700/1281] lr: 5.464e-03, eta: 3:43:08, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2031, loss: 0.2031 +2025-06-24 18:49:59,370 - pyskl - INFO - Epoch [104][800/1281] lr: 5.447e-03, eta: 3:42:46, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1933, loss: 0.1933 +2025-06-24 18:50:22,037 - pyskl - INFO - Epoch [104][900/1281] lr: 5.430e-03, eta: 3:42:23, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1726, loss: 0.1726 +2025-06-24 18:50:44,499 - pyskl - INFO - Epoch [104][1000/1281] lr: 5.413e-03, eta: 3:42:01, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1616, loss: 0.1616 +2025-06-24 18:51:06,988 - pyskl - INFO - Epoch [104][1100/1281] lr: 5.397e-03, eta: 3:41:38, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.1961, loss: 0.1961 +2025-06-24 18:51:29,345 - pyskl - INFO - Epoch [104][1200/1281] lr: 5.380e-03, eta: 3:41:16, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9988, loss_cls: 0.1919, loss: 0.1919 +2025-06-24 18:51:48,216 - pyskl - INFO - Saving checkpoint at 104 epochs +2025-06-24 18:52:32,700 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:52:32,755 - pyskl - INFO - +top1_acc 0.9029 +top5_acc 0.9928 +2025-06-24 18:52:32,755 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:52:32,761 - pyskl - INFO - +mean_acc 0.8700 +2025-06-24 18:52:32,765 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_99.pth was removed +2025-06-24 18:52:32,942 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_104.pth. +2025-06-24 18:52:32,942 - pyskl - INFO - Best top1_acc is 0.9029 at 104 epoch. +2025-06-24 18:52:32,945 - pyskl - INFO - Epoch(val) [104][533] top1_acc: 0.9029, top5_acc: 0.9928, mean_class_accuracy: 0.8700 +2025-06-24 18:53:15,407 - pyskl - INFO - Epoch [105][100/1281] lr: 5.349e-03, eta: 3:40:36, time: 0.425, data_time: 0.188, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1451, loss: 0.1451 +2025-06-24 18:53:38,158 - pyskl - INFO - Epoch [105][200/1281] lr: 5.333e-03, eta: 3:40:13, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1180, loss: 0.1180 +2025-06-24 18:54:00,447 - pyskl - INFO - Epoch [105][300/1281] lr: 5.316e-03, eta: 3:39:51, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1432, loss: 0.1432 +2025-06-24 18:54:22,911 - pyskl - INFO - Epoch [105][400/1281] lr: 5.299e-03, eta: 3:39:28, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1632, loss: 0.1632 +2025-06-24 18:54:45,228 - pyskl - INFO - Epoch [105][500/1281] lr: 5.283e-03, eta: 3:39:06, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1883, loss: 0.1883 +2025-06-24 18:55:07,996 - pyskl - INFO - Epoch [105][600/1281] lr: 5.266e-03, eta: 3:38:43, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 0.9994, loss_cls: 0.1703, loss: 0.1703 +2025-06-24 18:55:30,356 - pyskl - INFO - Epoch [105][700/1281] lr: 5.249e-03, eta: 3:38:21, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1799, loss: 0.1799 +2025-06-24 18:55:52,691 - pyskl - INFO - Epoch [105][800/1281] lr: 5.233e-03, eta: 3:37:58, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1470, loss: 0.1470 +2025-06-24 18:56:15,038 - pyskl - INFO - Epoch [105][900/1281] lr: 5.216e-03, eta: 3:37:36, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 0.9994, loss_cls: 0.1536, loss: 0.1536 +2025-06-24 18:56:37,739 - pyskl - INFO - Epoch [105][1000/1281] lr: 5.199e-03, eta: 3:37:13, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.1865, loss: 0.1865 +2025-06-24 18:57:00,453 - pyskl - INFO - Epoch [105][1100/1281] lr: 5.183e-03, eta: 3:36:51, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2146, loss: 0.2146 +2025-06-24 18:57:22,899 - pyskl - INFO - Epoch [105][1200/1281] lr: 5.166e-03, eta: 3:36:28, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1845, loss: 0.1845 +2025-06-24 18:57:41,862 - pyskl - INFO - Saving checkpoint at 105 epochs +2025-06-24 18:58:26,190 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:58:26,256 - pyskl - INFO - +top1_acc 0.9018 +top5_acc 0.9938 +2025-06-24 18:58:26,256 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:58:26,264 - pyskl - INFO - +mean_acc 0.8762 +2025-06-24 18:58:26,266 - pyskl - INFO - Epoch(val) [105][533] top1_acc: 0.9018, top5_acc: 0.9938, mean_class_accuracy: 0.8762 +2025-06-24 18:59:08,709 - pyskl - INFO - Epoch [106][100/1281] lr: 5.136e-03, eta: 3:35:48, time: 0.424, data_time: 0.189, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1293, loss: 0.1293 +2025-06-24 18:59:31,327 - pyskl - INFO - Epoch [106][200/1281] lr: 5.120e-03, eta: 3:35:26, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1184, loss: 0.1184 +2025-06-24 18:59:53,964 - pyskl - INFO - Epoch [106][300/1281] lr: 5.103e-03, eta: 3:35:03, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 0.9994, loss_cls: 0.1379, loss: 0.1379 +2025-06-24 19:00:16,438 - pyskl - INFO - Epoch [106][400/1281] lr: 5.087e-03, eta: 3:34:41, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.1135, loss: 0.1135 +2025-06-24 19:00:39,102 - pyskl - INFO - Epoch [106][500/1281] lr: 5.070e-03, eta: 3:34:18, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1502, loss: 0.1502 +2025-06-24 19:01:01,422 - pyskl - INFO - Epoch [106][600/1281] lr: 5.054e-03, eta: 3:33:56, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1666, loss: 0.1666 +2025-06-24 19:01:23,909 - pyskl - INFO - Epoch [106][700/1281] lr: 5.038e-03, eta: 3:33:33, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1507, loss: 0.1507 +2025-06-24 19:01:46,631 - pyskl - INFO - Epoch [106][800/1281] lr: 5.021e-03, eta: 3:33:11, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2113, loss: 0.2113 +2025-06-24 19:02:08,705 - pyskl - INFO - Epoch [106][900/1281] lr: 5.005e-03, eta: 3:32:48, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1857, loss: 0.1857 +2025-06-24 19:02:31,240 - pyskl - INFO - Epoch [106][1000/1281] lr: 4.988e-03, eta: 3:32:26, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1656, loss: 0.1656 +2025-06-24 19:02:53,976 - pyskl - INFO - Epoch [106][1100/1281] lr: 4.972e-03, eta: 3:32:03, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1687, loss: 0.1687 +2025-06-24 19:03:16,334 - pyskl - INFO - Epoch [106][1200/1281] lr: 4.956e-03, eta: 3:31:41, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.1843, loss: 0.1843 +2025-06-24 19:03:35,390 - pyskl - INFO - Saving checkpoint at 106 epochs +2025-06-24 19:04:19,239 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:04:19,295 - pyskl - INFO - +top1_acc 0.9042 +top5_acc 0.9937 +2025-06-24 19:04:19,295 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:04:19,302 - pyskl - INFO - +mean_acc 0.8763 +2025-06-24 19:04:19,307 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_104.pth was removed +2025-06-24 19:04:19,680 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_106.pth. +2025-06-24 19:04:19,680 - pyskl - INFO - Best top1_acc is 0.9042 at 106 epoch. +2025-06-24 19:04:19,683 - pyskl - INFO - Epoch(val) [106][533] top1_acc: 0.9042, top5_acc: 0.9937, mean_class_accuracy: 0.8763 +2025-06-24 19:05:02,163 - pyskl - INFO - Epoch [107][100/1281] lr: 4.926e-03, eta: 3:31:01, time: 0.425, data_time: 0.188, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1171, loss: 0.1171 +2025-06-24 19:05:24,604 - pyskl - INFO - Epoch [107][200/1281] lr: 4.910e-03, eta: 3:30:38, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1243, loss: 0.1243 +2025-06-24 19:05:47,159 - pyskl - INFO - Epoch [107][300/1281] lr: 4.894e-03, eta: 3:30:16, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1175, loss: 0.1175 +2025-06-24 19:06:09,553 - pyskl - INFO - Epoch [107][400/1281] lr: 4.878e-03, eta: 3:29:53, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1186, loss: 0.1186 +2025-06-24 19:06:32,058 - pyskl - INFO - Epoch [107][500/1281] lr: 4.862e-03, eta: 3:29:31, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1152, loss: 0.1152 +2025-06-24 19:06:54,719 - pyskl - INFO - Epoch [107][600/1281] lr: 4.845e-03, eta: 3:29:08, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1299, loss: 0.1299 +2025-06-24 19:07:17,233 - pyskl - INFO - Epoch [107][700/1281] lr: 4.829e-03, eta: 3:28:46, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.1589, loss: 0.1589 +2025-06-24 19:07:39,652 - pyskl - INFO - Epoch [107][800/1281] lr: 4.813e-03, eta: 3:28:23, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1636, loss: 0.1636 +2025-06-24 19:08:02,313 - pyskl - INFO - Epoch [107][900/1281] lr: 4.797e-03, eta: 3:28:01, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1372, loss: 0.1372 +2025-06-24 19:08:24,561 - pyskl - INFO - Epoch [107][1000/1281] lr: 4.781e-03, eta: 3:27:38, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1399, loss: 0.1399 +2025-06-24 19:08:46,899 - pyskl - INFO - Epoch [107][1100/1281] lr: 4.765e-03, eta: 3:27:16, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1420, loss: 0.1420 +2025-06-24 19:09:09,418 - pyskl - INFO - Epoch [107][1200/1281] lr: 4.749e-03, eta: 3:26:53, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1822, loss: 0.1822 +2025-06-24 19:09:28,315 - pyskl - INFO - Saving checkpoint at 107 epochs +2025-06-24 19:10:12,556 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:10:12,624 - pyskl - INFO - +top1_acc 0.8980 +top5_acc 0.9941 +2025-06-24 19:10:12,624 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:10:12,632 - pyskl - INFO - +mean_acc 0.8650 +2025-06-24 19:10:12,634 - pyskl - INFO - Epoch(val) [107][533] top1_acc: 0.8980, top5_acc: 0.9941, mean_class_accuracy: 0.8650 +2025-06-24 19:10:54,958 - pyskl - INFO - Epoch [108][100/1281] lr: 4.720e-03, eta: 3:26:13, time: 0.423, data_time: 0.187, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1581, loss: 0.1581 +2025-06-24 19:11:17,449 - pyskl - INFO - Epoch [108][200/1281] lr: 4.704e-03, eta: 3:25:50, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1198, loss: 0.1198 +2025-06-24 19:11:40,128 - pyskl - INFO - Epoch [108][300/1281] lr: 4.688e-03, eta: 3:25:28, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1252, loss: 0.1252 +2025-06-24 19:12:02,336 - pyskl - INFO - Epoch [108][400/1281] lr: 4.672e-03, eta: 3:25:05, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 0.9994, loss_cls: 0.1418, loss: 0.1418 +2025-06-24 19:12:24,624 - pyskl - INFO - Epoch [108][500/1281] lr: 4.656e-03, eta: 3:24:43, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1230, loss: 0.1230 +2025-06-24 19:12:47,014 - pyskl - INFO - Epoch [108][600/1281] lr: 4.640e-03, eta: 3:24:20, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1174, loss: 0.1174 +2025-06-24 19:13:09,968 - pyskl - INFO - Epoch [108][700/1281] lr: 4.624e-03, eta: 3:23:58, time: 0.230, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1693, loss: 0.1693 +2025-06-24 19:13:32,189 - pyskl - INFO - Epoch [108][800/1281] lr: 4.608e-03, eta: 3:23:35, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1757, loss: 0.1757 +2025-06-24 19:13:54,958 - pyskl - INFO - Epoch [108][900/1281] lr: 4.593e-03, eta: 3:23:13, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1714, loss: 0.1714 +2025-06-24 19:14:17,491 - pyskl - INFO - Epoch [108][1000/1281] lr: 4.577e-03, eta: 3:22:50, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1576, loss: 0.1576 +2025-06-24 19:14:40,099 - pyskl - INFO - Epoch [108][1100/1281] lr: 4.561e-03, eta: 3:22:28, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 0.9994, loss_cls: 0.1631, loss: 0.1631 +2025-06-24 19:15:02,674 - pyskl - INFO - Epoch [108][1200/1281] lr: 4.545e-03, eta: 3:22:06, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1624, loss: 0.1624 +2025-06-24 19:15:21,906 - pyskl - INFO - Saving checkpoint at 108 epochs +2025-06-24 19:16:05,494 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:16:05,550 - pyskl - INFO - +top1_acc 0.9086 +top5_acc 0.9934 +2025-06-24 19:16:05,550 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:16:05,557 - pyskl - INFO - +mean_acc 0.8695 +2025-06-24 19:16:05,561 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_106.pth was removed +2025-06-24 19:16:05,732 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_108.pth. +2025-06-24 19:16:05,732 - pyskl - INFO - Best top1_acc is 0.9086 at 108 epoch. +2025-06-24 19:16:05,735 - pyskl - INFO - Epoch(val) [108][533] top1_acc: 0.9086, top5_acc: 0.9934, mean_class_accuracy: 0.8695 +2025-06-24 19:16:49,102 - pyskl - INFO - Epoch [109][100/1281] lr: 4.517e-03, eta: 3:21:26, time: 0.434, data_time: 0.197, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1215, loss: 0.1215 +2025-06-24 19:17:11,295 - pyskl - INFO - Epoch [109][200/1281] lr: 4.501e-03, eta: 3:21:03, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1205, loss: 0.1205 +2025-06-24 19:17:33,950 - pyskl - INFO - Epoch [109][300/1281] lr: 4.485e-03, eta: 3:20:41, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1393, loss: 0.1393 +2025-06-24 19:17:56,412 - pyskl - INFO - Epoch [109][400/1281] lr: 4.470e-03, eta: 3:20:18, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1578, loss: 0.1578 +2025-06-24 19:18:19,042 - pyskl - INFO - Epoch [109][500/1281] lr: 4.454e-03, eta: 3:19:56, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1336, loss: 0.1336 +2025-06-24 19:18:41,760 - pyskl - INFO - Epoch [109][600/1281] lr: 4.438e-03, eta: 3:19:33, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 0.9994, loss_cls: 0.1341, loss: 0.1341 +2025-06-24 19:19:04,416 - pyskl - INFO - Epoch [109][700/1281] lr: 4.423e-03, eta: 3:19:11, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1389, loss: 0.1389 +2025-06-24 19:19:27,124 - pyskl - INFO - Epoch [109][800/1281] lr: 4.407e-03, eta: 3:18:48, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1296, loss: 0.1296 +2025-06-24 19:19:49,283 - pyskl - INFO - Epoch [109][900/1281] lr: 4.391e-03, eta: 3:18:26, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1220, loss: 0.1220 +2025-06-24 19:20:11,888 - pyskl - INFO - Epoch [109][1000/1281] lr: 4.376e-03, eta: 3:18:03, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0996, loss: 0.0996 +2025-06-24 19:20:34,557 - pyskl - INFO - Epoch [109][1100/1281] lr: 4.360e-03, eta: 3:17:41, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 0.9994, loss_cls: 0.0781, loss: 0.0781 +2025-06-24 19:20:56,977 - pyskl - INFO - Epoch [109][1200/1281] lr: 4.345e-03, eta: 3:17:18, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1150, loss: 0.1150 +2025-06-24 19:21:16,274 - pyskl - INFO - Saving checkpoint at 109 epochs +2025-06-24 19:22:00,059 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:22:00,127 - pyskl - INFO - +top1_acc 0.8946 +top5_acc 0.9925 +2025-06-24 19:22:00,128 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:22:00,138 - pyskl - INFO - +mean_acc 0.8607 +2025-06-24 19:22:00,141 - pyskl - INFO - Epoch(val) [109][533] top1_acc: 0.8946, top5_acc: 0.9925, mean_class_accuracy: 0.8607 +2025-06-24 19:22:42,280 - pyskl - INFO - Epoch [110][100/1281] lr: 4.317e-03, eta: 3:16:38, time: 0.421, data_time: 0.189, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1044, loss: 0.1044 +2025-06-24 19:23:04,893 - pyskl - INFO - Epoch [110][200/1281] lr: 4.301e-03, eta: 3:16:16, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.1093, loss: 0.1093 +2025-06-24 19:23:27,313 - pyskl - INFO - Epoch [110][300/1281] lr: 4.286e-03, eta: 3:15:53, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1006, loss: 0.1006 +2025-06-24 19:23:49,648 - pyskl - INFO - Epoch [110][400/1281] lr: 4.271e-03, eta: 3:15:31, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0877, loss: 0.0877 +2025-06-24 19:24:12,416 - pyskl - INFO - Epoch [110][500/1281] lr: 4.255e-03, eta: 3:15:08, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1082, loss: 0.1082 +2025-06-24 19:24:35,209 - pyskl - INFO - Epoch [110][600/1281] lr: 4.240e-03, eta: 3:14:46, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1020, loss: 0.1020 +2025-06-24 19:24:57,399 - pyskl - INFO - Epoch [110][700/1281] lr: 4.225e-03, eta: 3:14:23, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1200, loss: 0.1200 +2025-06-24 19:25:19,879 - pyskl - INFO - Epoch [110][800/1281] lr: 4.209e-03, eta: 3:14:01, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 0.9994, loss_cls: 0.1103, loss: 0.1103 +2025-06-24 19:25:42,452 - pyskl - INFO - Epoch [110][900/1281] lr: 4.194e-03, eta: 3:13:38, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0959, loss: 0.0959 +2025-06-24 19:26:05,101 - pyskl - INFO - Epoch [110][1000/1281] lr: 4.179e-03, eta: 3:13:16, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0878, loss: 0.0878 +2025-06-24 19:26:27,756 - pyskl - INFO - Epoch [110][1100/1281] lr: 4.164e-03, eta: 3:12:53, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1030, loss: 0.1030 +2025-06-24 19:26:50,068 - pyskl - INFO - Epoch [110][1200/1281] lr: 4.148e-03, eta: 3:12:31, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1326, loss: 0.1326 +2025-06-24 19:27:08,962 - pyskl - INFO - Saving checkpoint at 110 epochs +2025-06-24 19:27:53,292 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:27:53,360 - pyskl - INFO - +top1_acc 0.9067 +top5_acc 0.9944 +2025-06-24 19:27:53,360 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:27:53,370 - pyskl - INFO - +mean_acc 0.8774 +2025-06-24 19:27:53,372 - pyskl - INFO - Epoch(val) [110][533] top1_acc: 0.9067, top5_acc: 0.9944, mean_class_accuracy: 0.8774 +2025-06-24 19:28:35,566 - pyskl - INFO - Epoch [111][100/1281] lr: 4.121e-03, eta: 3:11:50, time: 0.422, data_time: 0.185, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.1051, loss: 0.1051 +2025-06-24 19:28:58,294 - pyskl - INFO - Epoch [111][200/1281] lr: 4.106e-03, eta: 3:11:28, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0694, loss: 0.0694 +2025-06-24 19:29:20,719 - pyskl - INFO - Epoch [111][300/1281] lr: 4.091e-03, eta: 3:11:05, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 0.9994, loss_cls: 0.1102, loss: 0.1102 +2025-06-24 19:29:43,287 - pyskl - INFO - Epoch [111][400/1281] lr: 4.075e-03, eta: 3:10:43, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0964, loss: 0.0964 +2025-06-24 19:30:06,041 - pyskl - INFO - Epoch [111][500/1281] lr: 4.060e-03, eta: 3:10:21, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1216, loss: 0.1216 +2025-06-24 19:30:28,696 - pyskl - INFO - Epoch [111][600/1281] lr: 4.045e-03, eta: 3:09:58, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1162, loss: 0.1162 +2025-06-24 19:30:51,216 - pyskl - INFO - Epoch [111][700/1281] lr: 4.030e-03, eta: 3:09:36, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.1024, loss: 0.1024 +2025-06-24 19:31:13,490 - pyskl - INFO - Epoch [111][800/1281] lr: 4.015e-03, eta: 3:09:13, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1121, loss: 0.1121 +2025-06-24 19:31:36,116 - pyskl - INFO - Epoch [111][900/1281] lr: 4.000e-03, eta: 3:08:51, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 0.9994, loss_cls: 0.1090, loss: 0.1090 +2025-06-24 19:31:58,491 - pyskl - INFO - Epoch [111][1000/1281] lr: 3.985e-03, eta: 3:08:28, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1512, loss: 0.1512 +2025-06-24 19:32:20,987 - pyskl - INFO - Epoch [111][1100/1281] lr: 3.970e-03, eta: 3:08:05, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0999, loss: 0.0999 +2025-06-24 19:32:43,482 - pyskl - INFO - Epoch [111][1200/1281] lr: 3.955e-03, eta: 3:07:43, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0976, loss: 0.0976 +2025-06-24 19:33:02,468 - pyskl - INFO - Saving checkpoint at 111 epochs +2025-06-24 19:33:46,604 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:33:46,659 - pyskl - INFO - +top1_acc 0.9071 +top5_acc 0.9928 +2025-06-24 19:33:46,659 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:33:46,666 - pyskl - INFO - +mean_acc 0.8792 +2025-06-24 19:33:46,668 - pyskl - INFO - Epoch(val) [111][533] top1_acc: 0.9071, top5_acc: 0.9928, mean_class_accuracy: 0.8792 +2025-06-24 19:34:29,142 - pyskl - INFO - Epoch [112][100/1281] lr: 3.928e-03, eta: 3:07:03, time: 0.425, data_time: 0.187, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0825, loss: 0.0825 +2025-06-24 19:34:52,024 - pyskl - INFO - Epoch [112][200/1281] lr: 3.914e-03, eta: 3:06:40, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.1014, loss: 0.1014 +2025-06-24 19:35:14,398 - pyskl - INFO - Epoch [112][300/1281] lr: 3.899e-03, eta: 3:06:18, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0946, loss: 0.0946 +2025-06-24 19:35:37,192 - pyskl - INFO - Epoch [112][400/1281] lr: 3.884e-03, eta: 3:05:55, time: 0.228, data_time: 0.001, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0815, loss: 0.0815 +2025-06-24 19:35:59,799 - pyskl - INFO - Epoch [112][500/1281] lr: 3.869e-03, eta: 3:05:33, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0895, loss: 0.0895 +2025-06-24 19:36:22,188 - pyskl - INFO - Epoch [112][600/1281] lr: 3.854e-03, eta: 3:05:10, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0847, loss: 0.0847 +2025-06-24 19:36:44,591 - pyskl - INFO - Epoch [112][700/1281] lr: 3.840e-03, eta: 3:04:48, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.0994, loss: 0.0994 +2025-06-24 19:37:07,322 - pyskl - INFO - Epoch [112][800/1281] lr: 3.825e-03, eta: 3:04:25, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1224, loss: 0.1224 +2025-06-24 19:37:29,919 - pyskl - INFO - Epoch [112][900/1281] lr: 3.810e-03, eta: 3:04:03, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1364, loss: 0.1364 +2025-06-24 19:37:52,361 - pyskl - INFO - Epoch [112][1000/1281] lr: 3.795e-03, eta: 3:03:40, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0903, loss: 0.0903 +2025-06-24 19:38:14,718 - pyskl - INFO - Epoch [112][1100/1281] lr: 3.781e-03, eta: 3:03:18, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0932, loss: 0.0932 +2025-06-24 19:38:36,931 - pyskl - INFO - Epoch [112][1200/1281] lr: 3.766e-03, eta: 3:02:55, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1299, loss: 0.1299 +2025-06-24 19:38:55,799 - pyskl - INFO - Saving checkpoint at 112 epochs +2025-06-24 19:39:39,519 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:39:39,574 - pyskl - INFO - +top1_acc 0.9017 +top5_acc 0.9933 +2025-06-24 19:39:39,574 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:39:39,582 - pyskl - INFO - +mean_acc 0.8697 +2025-06-24 19:39:39,584 - pyskl - INFO - Epoch(val) [112][533] top1_acc: 0.9017, top5_acc: 0.9933, mean_class_accuracy: 0.8697 +2025-06-24 19:40:21,970 - pyskl - INFO - Epoch [113][100/1281] lr: 3.740e-03, eta: 3:02:15, time: 0.424, data_time: 0.187, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0951, loss: 0.0951 +2025-06-24 19:40:44,442 - pyskl - INFO - Epoch [113][200/1281] lr: 3.725e-03, eta: 3:01:53, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0896, loss: 0.0896 +2025-06-24 19:41:07,010 - pyskl - INFO - Epoch [113][300/1281] lr: 3.711e-03, eta: 3:01:30, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1078, loss: 0.1078 +2025-06-24 19:41:29,690 - pyskl - INFO - Epoch [113][400/1281] lr: 3.696e-03, eta: 3:01:08, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0778, loss: 0.0778 +2025-06-24 19:41:52,323 - pyskl - INFO - Epoch [113][500/1281] lr: 3.682e-03, eta: 3:00:45, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0926, loss: 0.0926 +2025-06-24 19:42:14,872 - pyskl - INFO - Epoch [113][600/1281] lr: 3.667e-03, eta: 3:00:23, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0849, loss: 0.0849 +2025-06-24 19:42:37,247 - pyskl - INFO - Epoch [113][700/1281] lr: 3.653e-03, eta: 3:00:00, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 0.9994, loss_cls: 0.1042, loss: 0.1042 +2025-06-24 19:42:59,946 - pyskl - INFO - Epoch [113][800/1281] lr: 3.638e-03, eta: 2:59:38, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0785, loss: 0.0785 +2025-06-24 19:43:22,516 - pyskl - INFO - Epoch [113][900/1281] lr: 3.624e-03, eta: 2:59:15, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0745, loss: 0.0745 +2025-06-24 19:43:45,063 - pyskl - INFO - Epoch [113][1000/1281] lr: 3.610e-03, eta: 2:58:53, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0871, loss: 0.0871 +2025-06-24 19:44:07,406 - pyskl - INFO - Epoch [113][1100/1281] lr: 3.595e-03, eta: 2:58:30, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1049, loss: 0.1049 +2025-06-24 19:44:29,732 - pyskl - INFO - Epoch [113][1200/1281] lr: 3.581e-03, eta: 2:58:07, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0957, loss: 0.0957 +2025-06-24 19:44:48,856 - pyskl - INFO - Saving checkpoint at 113 epochs +2025-06-24 19:45:32,322 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:45:32,379 - pyskl - INFO - +top1_acc 0.9013 +top5_acc 0.9938 +2025-06-24 19:45:32,379 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:45:32,389 - pyskl - INFO - +mean_acc 0.8625 +2025-06-24 19:45:32,392 - pyskl - INFO - Epoch(val) [113][533] top1_acc: 0.9013, top5_acc: 0.9938, mean_class_accuracy: 0.8625 +2025-06-24 19:46:15,155 - pyskl - INFO - Epoch [114][100/1281] lr: 3.555e-03, eta: 2:57:27, time: 0.428, data_time: 0.192, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1070, loss: 0.1070 +2025-06-24 19:46:37,841 - pyskl - INFO - Epoch [114][200/1281] lr: 3.541e-03, eta: 2:57:05, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0776, loss: 0.0776 +2025-06-24 19:47:00,441 - pyskl - INFO - Epoch [114][300/1281] lr: 3.526e-03, eta: 2:56:42, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0702, loss: 0.0702 +2025-06-24 19:47:22,978 - pyskl - INFO - Epoch [114][400/1281] lr: 3.512e-03, eta: 2:56:20, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0692, loss: 0.0692 +2025-06-24 19:47:45,797 - pyskl - INFO - Epoch [114][500/1281] lr: 3.498e-03, eta: 2:55:58, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0842, loss: 0.0842 +2025-06-24 19:48:08,599 - pyskl - INFO - Epoch [114][600/1281] lr: 3.484e-03, eta: 2:55:35, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0856, loss: 0.0856 +2025-06-24 19:48:31,299 - pyskl - INFO - Epoch [114][700/1281] lr: 3.470e-03, eta: 2:55:13, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0808, loss: 0.0808 +2025-06-24 19:48:53,988 - pyskl - INFO - Epoch [114][800/1281] lr: 3.456e-03, eta: 2:54:50, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.0945, loss: 0.0945 +2025-06-24 19:49:16,549 - pyskl - INFO - Epoch [114][900/1281] lr: 3.442e-03, eta: 2:54:28, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0959, loss: 0.0959 +2025-06-24 19:49:39,094 - pyskl - INFO - Epoch [114][1000/1281] lr: 3.427e-03, eta: 2:54:05, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0614, loss: 0.0614 +2025-06-24 19:50:01,722 - pyskl - INFO - Epoch [114][1100/1281] lr: 3.413e-03, eta: 2:53:43, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0799, loss: 0.0799 +2025-06-24 19:50:24,197 - pyskl - INFO - Epoch [114][1200/1281] lr: 3.399e-03, eta: 2:53:20, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0921, loss: 0.0921 +2025-06-24 19:50:43,276 - pyskl - INFO - Saving checkpoint at 114 epochs +2025-06-24 19:51:26,987 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:51:27,059 - pyskl - INFO - +top1_acc 0.9059 +top5_acc 0.9946 +2025-06-24 19:51:27,059 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:51:27,069 - pyskl - INFO - +mean_acc 0.8667 +2025-06-24 19:51:27,072 - pyskl - INFO - Epoch(val) [114][533] top1_acc: 0.9059, top5_acc: 0.9946, mean_class_accuracy: 0.8667 +2025-06-24 19:52:09,438 - pyskl - INFO - Epoch [115][100/1281] lr: 3.374e-03, eta: 2:52:40, time: 0.424, data_time: 0.187, memory: 4083, top1_acc: 0.9950, top5_acc: 0.9994, loss_cls: 0.0612, loss: 0.0612 +2025-06-24 19:52:31,813 - pyskl - INFO - Epoch [115][200/1281] lr: 3.360e-03, eta: 2:52:17, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0562, loss: 0.0562 +2025-06-24 19:52:54,340 - pyskl - INFO - Epoch [115][300/1281] lr: 3.346e-03, eta: 2:51:55, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0483, loss: 0.0483 +2025-06-24 19:53:16,922 - pyskl - INFO - Epoch [115][400/1281] lr: 3.332e-03, eta: 2:51:32, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0636, loss: 0.0636 +2025-06-24 19:53:39,504 - pyskl - INFO - Epoch [115][500/1281] lr: 3.318e-03, eta: 2:51:10, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0836, loss: 0.0836 +2025-06-24 19:54:01,963 - pyskl - INFO - Epoch [115][600/1281] lr: 3.305e-03, eta: 2:50:47, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0624, loss: 0.0624 +2025-06-24 19:54:24,383 - pyskl - INFO - Epoch [115][700/1281] lr: 3.291e-03, eta: 2:50:25, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0546, loss: 0.0546 +2025-06-24 19:54:46,954 - pyskl - INFO - Epoch [115][800/1281] lr: 3.277e-03, eta: 2:50:02, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0787, loss: 0.0787 +2025-06-24 19:55:09,371 - pyskl - INFO - Epoch [115][900/1281] lr: 3.263e-03, eta: 2:49:40, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0874, loss: 0.0874 +2025-06-24 19:55:31,751 - pyskl - INFO - Epoch [115][1000/1281] lr: 3.249e-03, eta: 2:49:17, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0779, loss: 0.0779 +2025-06-24 19:55:53,996 - pyskl - INFO - Epoch [115][1100/1281] lr: 3.236e-03, eta: 2:48:55, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0559, loss: 0.0559 +2025-06-24 19:56:16,350 - pyskl - INFO - Epoch [115][1200/1281] lr: 3.222e-03, eta: 2:48:32, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0576, loss: 0.0576 +2025-06-24 19:56:35,285 - pyskl - INFO - Saving checkpoint at 115 epochs +2025-06-24 19:57:18,897 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:57:18,963 - pyskl - INFO - +top1_acc 0.9108 +top5_acc 0.9931 +2025-06-24 19:57:18,963 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:57:18,974 - pyskl - INFO - +mean_acc 0.8758 +2025-06-24 19:57:18,981 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_108.pth was removed +2025-06-24 19:57:19,207 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_115.pth. +2025-06-24 19:57:19,207 - pyskl - INFO - Best top1_acc is 0.9108 at 115 epoch. +2025-06-24 19:57:19,211 - pyskl - INFO - Epoch(val) [115][533] top1_acc: 0.9108, top5_acc: 0.9931, mean_class_accuracy: 0.8758 +2025-06-24 19:58:02,494 - pyskl - INFO - Epoch [116][100/1281] lr: 3.197e-03, eta: 2:47:52, time: 0.433, data_time: 0.195, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0789, loss: 0.0789 +2025-06-24 19:58:25,568 - pyskl - INFO - Epoch [116][200/1281] lr: 3.184e-03, eta: 2:47:30, time: 0.231, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0662, loss: 0.0662 +2025-06-24 19:58:48,125 - pyskl - INFO - Epoch [116][300/1281] lr: 3.170e-03, eta: 2:47:07, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0544, loss: 0.0544 +2025-06-24 19:59:10,657 - pyskl - INFO - Epoch [116][400/1281] lr: 3.156e-03, eta: 2:46:45, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0562, loss: 0.0562 +2025-06-24 19:59:33,100 - pyskl - INFO - Epoch [116][500/1281] lr: 3.143e-03, eta: 2:46:22, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0430, loss: 0.0430 +2025-06-24 19:59:55,475 - pyskl - INFO - Epoch [116][600/1281] lr: 3.129e-03, eta: 2:46:00, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0569, loss: 0.0569 +2025-06-24 20:00:18,140 - pyskl - INFO - Epoch [116][700/1281] lr: 3.116e-03, eta: 2:45:37, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0631, loss: 0.0631 +2025-06-24 20:00:40,700 - pyskl - INFO - Epoch [116][800/1281] lr: 3.102e-03, eta: 2:45:15, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0744, loss: 0.0744 +2025-06-24 20:01:02,971 - pyskl - INFO - Epoch [116][900/1281] lr: 3.089e-03, eta: 2:44:52, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0608, loss: 0.0608 +2025-06-24 20:01:25,401 - pyskl - INFO - Epoch [116][1000/1281] lr: 3.075e-03, eta: 2:44:29, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0607, loss: 0.0607 +2025-06-24 20:01:47,832 - pyskl - INFO - Epoch [116][1100/1281] lr: 3.062e-03, eta: 2:44:07, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0790, loss: 0.0790 +2025-06-24 20:02:10,269 - pyskl - INFO - Epoch [116][1200/1281] lr: 3.049e-03, eta: 2:43:44, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0712, loss: 0.0712 +2025-06-24 20:02:29,406 - pyskl - INFO - Saving checkpoint at 116 epochs +2025-06-24 20:03:12,663 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:03:12,720 - pyskl - INFO - +top1_acc 0.9110 +top5_acc 0.9944 +2025-06-24 20:03:12,721 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:03:12,727 - pyskl - INFO - +mean_acc 0.8856 +2025-06-24 20:03:12,732 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_115.pth was removed +2025-06-24 20:03:12,903 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_116.pth. +2025-06-24 20:03:12,904 - pyskl - INFO - Best top1_acc is 0.9110 at 116 epoch. +2025-06-24 20:03:12,906 - pyskl - INFO - Epoch(val) [116][533] top1_acc: 0.9110, top5_acc: 0.9944, mean_class_accuracy: 0.8856 +2025-06-24 20:03:54,722 - pyskl - INFO - Epoch [117][100/1281] lr: 3.024e-03, eta: 2:43:04, time: 0.418, data_time: 0.185, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0652, loss: 0.0652 +2025-06-24 20:04:17,230 - pyskl - INFO - Epoch [117][200/1281] lr: 3.011e-03, eta: 2:42:41, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0878, loss: 0.0878 +2025-06-24 20:04:39,736 - pyskl - INFO - Epoch [117][300/1281] lr: 2.998e-03, eta: 2:42:19, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0695, loss: 0.0695 +2025-06-24 20:05:02,332 - pyskl - INFO - Epoch [117][400/1281] lr: 2.984e-03, eta: 2:41:56, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0765, loss: 0.0765 +2025-06-24 20:05:24,828 - pyskl - INFO - Epoch [117][500/1281] lr: 2.971e-03, eta: 2:41:34, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0665, loss: 0.0665 +2025-06-24 20:05:47,325 - pyskl - INFO - Epoch [117][600/1281] lr: 2.958e-03, eta: 2:41:11, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0641, loss: 0.0641 +2025-06-24 20:06:09,939 - pyskl - INFO - Epoch [117][700/1281] lr: 2.945e-03, eta: 2:40:49, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0597, loss: 0.0597 +2025-06-24 20:06:32,471 - pyskl - INFO - Epoch [117][800/1281] lr: 2.932e-03, eta: 2:40:26, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0650, loss: 0.0650 +2025-06-24 20:06:55,055 - pyskl - INFO - Epoch [117][900/1281] lr: 2.919e-03, eta: 2:40:04, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0694, loss: 0.0694 +2025-06-24 20:07:17,389 - pyskl - INFO - Epoch [117][1000/1281] lr: 2.905e-03, eta: 2:39:41, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0665, loss: 0.0665 +2025-06-24 20:07:40,012 - pyskl - INFO - Epoch [117][1100/1281] lr: 2.892e-03, eta: 2:39:19, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0628, loss: 0.0628 +2025-06-24 20:08:02,483 - pyskl - INFO - Epoch [117][1200/1281] lr: 2.879e-03, eta: 2:38:56, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0798, loss: 0.0798 +2025-06-24 20:08:21,418 - pyskl - INFO - Saving checkpoint at 117 epochs +2025-06-24 20:09:05,115 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:09:05,176 - pyskl - INFO - +top1_acc 0.9152 +top5_acc 0.9932 +2025-06-24 20:09:05,176 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:09:05,183 - pyskl - INFO - +mean_acc 0.8761 +2025-06-24 20:09:05,187 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_116.pth was removed +2025-06-24 20:09:05,357 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_117.pth. +2025-06-24 20:09:05,357 - pyskl - INFO - Best top1_acc is 0.9152 at 117 epoch. +2025-06-24 20:09:05,360 - pyskl - INFO - Epoch(val) [117][533] top1_acc: 0.9152, top5_acc: 0.9932, mean_class_accuracy: 0.8761 +2025-06-24 20:09:48,191 - pyskl - INFO - Epoch [118][100/1281] lr: 2.856e-03, eta: 2:38:16, time: 0.428, data_time: 0.187, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0569, loss: 0.0569 +2025-06-24 20:10:10,504 - pyskl - INFO - Epoch [118][200/1281] lr: 2.843e-03, eta: 2:37:54, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0661, loss: 0.0661 +2025-06-24 20:10:33,128 - pyskl - INFO - Epoch [118][300/1281] lr: 2.830e-03, eta: 2:37:31, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0551, loss: 0.0551 +2025-06-24 20:10:55,692 - pyskl - INFO - Epoch [118][400/1281] lr: 2.817e-03, eta: 2:37:09, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0527, loss: 0.0527 +2025-06-24 20:11:18,261 - pyskl - INFO - Epoch [118][500/1281] lr: 2.804e-03, eta: 2:36:46, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0642, loss: 0.0642 +2025-06-24 20:11:40,884 - pyskl - INFO - Epoch [118][600/1281] lr: 2.791e-03, eta: 2:36:24, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0457, loss: 0.0457 +2025-06-24 20:12:03,586 - pyskl - INFO - Epoch [118][700/1281] lr: 2.778e-03, eta: 2:36:01, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0593, loss: 0.0593 +2025-06-24 20:12:25,838 - pyskl - INFO - Epoch [118][800/1281] lr: 2.765e-03, eta: 2:35:38, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0411, loss: 0.0411 +2025-06-24 20:12:48,271 - pyskl - INFO - Epoch [118][900/1281] lr: 2.753e-03, eta: 2:35:16, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0403, loss: 0.0403 +2025-06-24 20:13:10,728 - pyskl - INFO - Epoch [118][1000/1281] lr: 2.740e-03, eta: 2:34:53, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0379, loss: 0.0379 +2025-06-24 20:13:33,307 - pyskl - INFO - Epoch [118][1100/1281] lr: 2.727e-03, eta: 2:34:31, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0481, loss: 0.0481 +2025-06-24 20:13:55,856 - pyskl - INFO - Epoch [118][1200/1281] lr: 2.714e-03, eta: 2:34:08, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0640, loss: 0.0640 +2025-06-24 20:14:14,679 - pyskl - INFO - Saving checkpoint at 118 epochs +2025-06-24 20:14:58,604 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:14:58,670 - pyskl - INFO - +top1_acc 0.9215 +top5_acc 0.9941 +2025-06-24 20:14:58,670 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:14:58,679 - pyskl - INFO - +mean_acc 0.8941 +2025-06-24 20:14:58,685 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_117.pth was removed +2025-06-24 20:14:58,862 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2025-06-24 20:14:58,862 - pyskl - INFO - Best top1_acc is 0.9215 at 118 epoch. +2025-06-24 20:14:58,865 - pyskl - INFO - Epoch(val) [118][533] top1_acc: 0.9215, top5_acc: 0.9941, mean_class_accuracy: 0.8941 +2025-06-24 20:15:41,179 - pyskl - INFO - Epoch [119][100/1281] lr: 2.691e-03, eta: 2:33:28, time: 0.423, data_time: 0.191, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0409, loss: 0.0409 +2025-06-24 20:16:03,982 - pyskl - INFO - Epoch [119][200/1281] lr: 2.679e-03, eta: 2:33:06, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0485, loss: 0.0485 +2025-06-24 20:16:26,498 - pyskl - INFO - Epoch [119][300/1281] lr: 2.666e-03, eta: 2:32:43, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0407, loss: 0.0407 +2025-06-24 20:16:48,826 - pyskl - INFO - Epoch [119][400/1281] lr: 2.653e-03, eta: 2:32:21, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0639, loss: 0.0639 +2025-06-24 20:17:11,466 - pyskl - INFO - Epoch [119][500/1281] lr: 2.641e-03, eta: 2:31:58, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 0.9994, loss_cls: 0.0620, loss: 0.0620 +2025-06-24 20:17:33,883 - pyskl - INFO - Epoch [119][600/1281] lr: 2.628e-03, eta: 2:31:35, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0406, loss: 0.0406 +2025-06-24 20:17:56,207 - pyskl - INFO - Epoch [119][700/1281] lr: 2.616e-03, eta: 2:31:13, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0390, loss: 0.0390 +2025-06-24 20:18:18,405 - pyskl - INFO - Epoch [119][800/1281] lr: 2.603e-03, eta: 2:30:50, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0330, loss: 0.0330 +2025-06-24 20:18:40,769 - pyskl - INFO - Epoch [119][900/1281] lr: 2.591e-03, eta: 2:30:28, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0491, loss: 0.0491 +2025-06-24 20:19:03,333 - pyskl - INFO - Epoch [119][1000/1281] lr: 2.578e-03, eta: 2:30:05, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0498, loss: 0.0498 +2025-06-24 20:19:25,707 - pyskl - INFO - Epoch [119][1100/1281] lr: 2.566e-03, eta: 2:29:43, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0352, loss: 0.0352 +2025-06-24 20:19:48,018 - pyskl - INFO - Epoch [119][1200/1281] lr: 2.554e-03, eta: 2:29:20, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0566, loss: 0.0566 +2025-06-24 20:20:07,160 - pyskl - INFO - Saving checkpoint at 119 epochs +2025-06-24 20:20:50,658 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:20:50,715 - pyskl - INFO - +top1_acc 0.9168 +top5_acc 0.9945 +2025-06-24 20:20:50,715 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:20:50,722 - pyskl - INFO - +mean_acc 0.8852 +2025-06-24 20:20:50,724 - pyskl - INFO - Epoch(val) [119][533] top1_acc: 0.9168, top5_acc: 0.9945, mean_class_accuracy: 0.8852 +2025-06-24 20:21:33,387 - pyskl - INFO - Epoch [120][100/1281] lr: 2.531e-03, eta: 2:28:40, time: 0.427, data_time: 0.190, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0437, loss: 0.0437 +2025-06-24 20:21:55,952 - pyskl - INFO - Epoch [120][200/1281] lr: 2.519e-03, eta: 2:28:17, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0427, loss: 0.0427 +2025-06-24 20:22:18,787 - pyskl - INFO - Epoch [120][300/1281] lr: 2.507e-03, eta: 2:27:55, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0497, loss: 0.0497 +2025-06-24 20:22:41,224 - pyskl - INFO - Epoch [120][400/1281] lr: 2.494e-03, eta: 2:27:32, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0400, loss: 0.0400 +2025-06-24 20:23:03,638 - pyskl - INFO - Epoch [120][500/1281] lr: 2.482e-03, eta: 2:27:10, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0454, loss: 0.0454 +2025-06-24 20:23:26,282 - pyskl - INFO - Epoch [120][600/1281] lr: 2.470e-03, eta: 2:26:47, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0385, loss: 0.0385 +2025-06-24 20:23:48,668 - pyskl - INFO - Epoch [120][700/1281] lr: 2.458e-03, eta: 2:26:25, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0497, loss: 0.0497 +2025-06-24 20:24:11,029 - pyskl - INFO - Epoch [120][800/1281] lr: 2.446e-03, eta: 2:26:02, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0516, loss: 0.0516 +2025-06-24 20:24:33,357 - pyskl - INFO - Epoch [120][900/1281] lr: 2.433e-03, eta: 2:25:40, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0451, loss: 0.0451 +2025-06-24 20:24:55,854 - pyskl - INFO - Epoch [120][1000/1281] lr: 2.421e-03, eta: 2:25:17, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0352, loss: 0.0352 +2025-06-24 20:25:18,002 - pyskl - INFO - Epoch [120][1100/1281] lr: 2.409e-03, eta: 2:24:55, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0506, loss: 0.0506 +2025-06-24 20:25:40,448 - pyskl - INFO - Epoch [120][1200/1281] lr: 2.397e-03, eta: 2:24:32, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0371, loss: 0.0371 +2025-06-24 20:25:59,528 - pyskl - INFO - Saving checkpoint at 120 epochs +2025-06-24 20:26:42,968 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:26:43,023 - pyskl - INFO - +top1_acc 0.9153 +top5_acc 0.9925 +2025-06-24 20:26:43,023 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:26:43,030 - pyskl - INFO - +mean_acc 0.8827 +2025-06-24 20:26:43,031 - pyskl - INFO - Epoch(val) [120][533] top1_acc: 0.9153, top5_acc: 0.9925, mean_class_accuracy: 0.8827 +2025-06-24 20:27:25,906 - pyskl - INFO - Epoch [121][100/1281] lr: 2.375e-03, eta: 2:23:52, time: 0.429, data_time: 0.190, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0758, loss: 0.0758 +2025-06-24 20:27:48,483 - pyskl - INFO - Epoch [121][200/1281] lr: 2.363e-03, eta: 2:23:29, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0469, loss: 0.0469 +2025-06-24 20:28:10,867 - pyskl - INFO - Epoch [121][300/1281] lr: 2.351e-03, eta: 2:23:07, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0568, loss: 0.0568 +2025-06-24 20:28:33,447 - pyskl - INFO - Epoch [121][400/1281] lr: 2.340e-03, eta: 2:22:44, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0456, loss: 0.0456 +2025-06-24 20:28:55,955 - pyskl - INFO - Epoch [121][500/1281] lr: 2.328e-03, eta: 2:22:22, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0432, loss: 0.0432 +2025-06-24 20:29:18,558 - pyskl - INFO - Epoch [121][600/1281] lr: 2.316e-03, eta: 2:21:59, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0397, loss: 0.0397 +2025-06-24 20:29:41,003 - pyskl - INFO - Epoch [121][700/1281] lr: 2.304e-03, eta: 2:21:37, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0476, loss: 0.0476 +2025-06-24 20:30:03,425 - pyskl - INFO - Epoch [121][800/1281] lr: 2.292e-03, eta: 2:21:14, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0395, loss: 0.0395 +2025-06-24 20:30:26,119 - pyskl - INFO - Epoch [121][900/1281] lr: 2.280e-03, eta: 2:20:52, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0363, loss: 0.0363 +2025-06-24 20:30:48,387 - pyskl - INFO - Epoch [121][1000/1281] lr: 2.269e-03, eta: 2:20:29, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0315, loss: 0.0315 +2025-06-24 20:31:10,810 - pyskl - INFO - Epoch [121][1100/1281] lr: 2.257e-03, eta: 2:20:06, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0424, loss: 0.0424 +2025-06-24 20:31:33,331 - pyskl - INFO - Epoch [121][1200/1281] lr: 2.245e-03, eta: 2:19:44, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0431, loss: 0.0431 +2025-06-24 20:31:51,762 - pyskl - INFO - Saving checkpoint at 121 epochs +2025-06-24 20:32:35,830 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:32:35,885 - pyskl - INFO - +top1_acc 0.9218 +top5_acc 0.9952 +2025-06-24 20:32:35,885 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:32:35,892 - pyskl - INFO - +mean_acc 0.8918 +2025-06-24 20:32:35,896 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_118.pth was removed +2025-06-24 20:32:36,089 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_121.pth. +2025-06-24 20:32:36,089 - pyskl - INFO - Best top1_acc is 0.9218 at 121 epoch. +2025-06-24 20:32:36,092 - pyskl - INFO - Epoch(val) [121][533] top1_acc: 0.9218, top5_acc: 0.9952, mean_class_accuracy: 0.8918 +2025-06-24 20:33:18,916 - pyskl - INFO - Epoch [122][100/1281] lr: 2.224e-03, eta: 2:19:04, time: 0.428, data_time: 0.194, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0361, loss: 0.0361 +2025-06-24 20:33:41,358 - pyskl - INFO - Epoch [122][200/1281] lr: 2.212e-03, eta: 2:18:41, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0335, loss: 0.0335 +2025-06-24 20:34:03,916 - pyskl - INFO - Epoch [122][300/1281] lr: 2.201e-03, eta: 2:18:19, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0307, loss: 0.0307 +2025-06-24 20:34:26,363 - pyskl - INFO - Epoch [122][400/1281] lr: 2.189e-03, eta: 2:17:56, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0418, loss: 0.0418 +2025-06-24 20:34:48,835 - pyskl - INFO - Epoch [122][500/1281] lr: 2.178e-03, eta: 2:17:34, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0389, loss: 0.0389 +2025-06-24 20:35:11,128 - pyskl - INFO - Epoch [122][600/1281] lr: 2.166e-03, eta: 2:17:11, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0320, loss: 0.0320 +2025-06-24 20:35:33,421 - pyskl - INFO - Epoch [122][700/1281] lr: 2.155e-03, eta: 2:16:48, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0322, loss: 0.0322 +2025-06-24 20:35:55,762 - pyskl - INFO - Epoch [122][800/1281] lr: 2.143e-03, eta: 2:16:26, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0382, loss: 0.0382 +2025-06-24 20:36:18,329 - pyskl - INFO - Epoch [122][900/1281] lr: 2.132e-03, eta: 2:16:03, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0456, loss: 0.0456 +2025-06-24 20:36:40,729 - pyskl - INFO - Epoch [122][1000/1281] lr: 2.120e-03, eta: 2:15:41, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0632, loss: 0.0632 +2025-06-24 20:37:02,829 - pyskl - INFO - Epoch [122][1100/1281] lr: 2.109e-03, eta: 2:15:18, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0497, loss: 0.0497 +2025-06-24 20:37:25,534 - pyskl - INFO - Epoch [122][1200/1281] lr: 2.098e-03, eta: 2:14:56, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0427, loss: 0.0427 +2025-06-24 20:37:44,434 - pyskl - INFO - Saving checkpoint at 122 epochs +2025-06-24 20:38:27,794 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:38:27,849 - pyskl - INFO - +top1_acc 0.9228 +top5_acc 0.9942 +2025-06-24 20:38:27,849 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:38:27,857 - pyskl - INFO - +mean_acc 0.8959 +2025-06-24 20:38:27,861 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_121.pth was removed +2025-06-24 20:38:28,039 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_122.pth. +2025-06-24 20:38:28,039 - pyskl - INFO - Best top1_acc is 0.9228 at 122 epoch. +2025-06-24 20:38:28,042 - pyskl - INFO - Epoch(val) [122][533] top1_acc: 0.9228, top5_acc: 0.9942, mean_class_accuracy: 0.8959 +2025-06-24 20:39:10,996 - pyskl - INFO - Epoch [123][100/1281] lr: 2.077e-03, eta: 2:14:15, time: 0.429, data_time: 0.192, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0352, loss: 0.0352 +2025-06-24 20:39:33,402 - pyskl - INFO - Epoch [123][200/1281] lr: 2.066e-03, eta: 2:13:53, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0323, loss: 0.0323 +2025-06-24 20:39:55,981 - pyskl - INFO - Epoch [123][300/1281] lr: 2.055e-03, eta: 2:13:30, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0284, loss: 0.0284 +2025-06-24 20:40:18,419 - pyskl - INFO - Epoch [123][400/1281] lr: 2.044e-03, eta: 2:13:08, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0371, loss: 0.0371 +2025-06-24 20:40:41,066 - pyskl - INFO - Epoch [123][500/1281] lr: 2.032e-03, eta: 2:12:45, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0365, loss: 0.0365 +2025-06-24 20:41:03,474 - pyskl - INFO - Epoch [123][600/1281] lr: 2.021e-03, eta: 2:12:23, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0298, loss: 0.0298 +2025-06-24 20:41:25,833 - pyskl - INFO - Epoch [123][700/1281] lr: 2.010e-03, eta: 2:12:00, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0395, loss: 0.0395 +2025-06-24 20:41:48,558 - pyskl - INFO - Epoch [123][800/1281] lr: 1.999e-03, eta: 2:11:38, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0369, loss: 0.0369 +2025-06-24 20:42:11,149 - pyskl - INFO - Epoch [123][900/1281] lr: 1.988e-03, eta: 2:11:15, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0473, loss: 0.0473 +2025-06-24 20:42:33,827 - pyskl - INFO - Epoch [123][1000/1281] lr: 1.977e-03, eta: 2:10:53, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0407, loss: 0.0407 +2025-06-24 20:42:56,492 - pyskl - INFO - Epoch [123][1100/1281] lr: 1.966e-03, eta: 2:10:30, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0355, loss: 0.0355 +2025-06-24 20:43:18,723 - pyskl - INFO - Epoch [123][1200/1281] lr: 1.955e-03, eta: 2:10:08, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0449, loss: 0.0449 +2025-06-24 20:43:38,143 - pyskl - INFO - Saving checkpoint at 123 epochs +2025-06-24 20:44:22,204 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:44:22,276 - pyskl - INFO - +top1_acc 0.9207 +top5_acc 0.9938 +2025-06-24 20:44:22,276 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:44:22,287 - pyskl - INFO - +mean_acc 0.8962 +2025-06-24 20:44:22,289 - pyskl - INFO - Epoch(val) [123][533] top1_acc: 0.9207, top5_acc: 0.9938, mean_class_accuracy: 0.8962 +2025-06-24 20:45:05,326 - pyskl - INFO - Epoch [124][100/1281] lr: 1.935e-03, eta: 2:09:27, time: 0.430, data_time: 0.194, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0266, loss: 0.0266 +2025-06-24 20:45:27,911 - pyskl - INFO - Epoch [124][200/1281] lr: 1.924e-03, eta: 2:09:05, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0422, loss: 0.0422 +2025-06-24 20:45:50,478 - pyskl - INFO - Epoch [124][300/1281] lr: 1.913e-03, eta: 2:08:42, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0276, loss: 0.0276 +2025-06-24 20:46:12,948 - pyskl - INFO - Epoch [124][400/1281] lr: 1.902e-03, eta: 2:08:20, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0256, loss: 0.0256 +2025-06-24 20:46:35,293 - pyskl - INFO - Epoch [124][500/1281] lr: 1.892e-03, eta: 2:07:57, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0268, loss: 0.0268 +2025-06-24 20:46:58,092 - pyskl - INFO - Epoch [124][600/1281] lr: 1.881e-03, eta: 2:07:35, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0245, loss: 0.0245 +2025-06-24 20:47:20,416 - pyskl - INFO - Epoch [124][700/1281] lr: 1.870e-03, eta: 2:07:12, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0295, loss: 0.0295 +2025-06-24 20:47:43,054 - pyskl - INFO - Epoch [124][800/1281] lr: 1.859e-03, eta: 2:06:50, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0253, loss: 0.0253 +2025-06-24 20:48:05,683 - pyskl - INFO - Epoch [124][900/1281] lr: 1.849e-03, eta: 2:06:27, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-24 20:48:27,796 - pyskl - INFO - Epoch [124][1000/1281] lr: 1.838e-03, eta: 2:06:05, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0268, loss: 0.0268 +2025-06-24 20:48:50,218 - pyskl - INFO - Epoch [124][1100/1281] lr: 1.827e-03, eta: 2:05:42, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 0.9994, loss_cls: 0.0500, loss: 0.0500 +2025-06-24 20:49:12,555 - pyskl - INFO - Epoch [124][1200/1281] lr: 1.817e-03, eta: 2:05:19, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0465, loss: 0.0465 +2025-06-24 20:49:31,450 - pyskl - INFO - Saving checkpoint at 124 epochs +2025-06-24 20:50:15,162 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:50:15,217 - pyskl - INFO - +top1_acc 0.9223 +top5_acc 0.9941 +2025-06-24 20:50:15,217 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:50:15,224 - pyskl - INFO - +mean_acc 0.8958 +2025-06-24 20:50:15,226 - pyskl - INFO - Epoch(val) [124][533] top1_acc: 0.9223, top5_acc: 0.9941, mean_class_accuracy: 0.8958 +2025-06-24 20:50:57,980 - pyskl - INFO - Epoch [125][100/1281] lr: 1.797e-03, eta: 2:04:39, time: 0.427, data_time: 0.189, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0291, loss: 0.0291 +2025-06-24 20:51:20,493 - pyskl - INFO - Epoch [125][200/1281] lr: 1.787e-03, eta: 2:04:17, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0276, loss: 0.0276 +2025-06-24 20:51:42,712 - pyskl - INFO - Epoch [125][300/1281] lr: 1.776e-03, eta: 2:03:54, time: 0.222, data_time: 0.001, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0339, loss: 0.0339 +2025-06-24 20:52:05,215 - pyskl - INFO - Epoch [125][400/1281] lr: 1.766e-03, eta: 2:03:31, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0288, loss: 0.0288 +2025-06-24 20:52:27,489 - pyskl - INFO - Epoch [125][500/1281] lr: 1.755e-03, eta: 2:03:09, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0286, loss: 0.0286 +2025-06-24 20:52:49,681 - pyskl - INFO - Epoch [125][600/1281] lr: 1.745e-03, eta: 2:02:46, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0289, loss: 0.0289 +2025-06-24 20:53:12,192 - pyskl - INFO - Epoch [125][700/1281] lr: 1.735e-03, eta: 2:02:24, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0307, loss: 0.0307 +2025-06-24 20:53:34,812 - pyskl - INFO - Epoch [125][800/1281] lr: 1.724e-03, eta: 2:02:01, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0272, loss: 0.0272 +2025-06-24 20:53:57,394 - pyskl - INFO - Epoch [125][900/1281] lr: 1.714e-03, eta: 2:01:39, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0319, loss: 0.0319 +2025-06-24 20:54:19,926 - pyskl - INFO - Epoch [125][1000/1281] lr: 1.704e-03, eta: 2:01:16, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0288, loss: 0.0288 +2025-06-24 20:54:42,332 - pyskl - INFO - Epoch [125][1100/1281] lr: 1.693e-03, eta: 2:00:54, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0293, loss: 0.0293 +2025-06-24 20:55:04,976 - pyskl - INFO - Epoch [125][1200/1281] lr: 1.683e-03, eta: 2:00:31, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0250, loss: 0.0250 +2025-06-24 20:55:24,199 - pyskl - INFO - Saving checkpoint at 125 epochs +2025-06-24 20:56:08,548 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:56:08,606 - pyskl - INFO - +top1_acc 0.9243 +top5_acc 0.9940 +2025-06-24 20:56:08,606 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:56:08,613 - pyskl - INFO - +mean_acc 0.8971 +2025-06-24 20:56:08,618 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_122.pth was removed +2025-06-24 20:56:08,830 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_125.pth. +2025-06-24 20:56:08,830 - pyskl - INFO - Best top1_acc is 0.9243 at 125 epoch. +2025-06-24 20:56:08,833 - pyskl - INFO - Epoch(val) [125][533] top1_acc: 0.9243, top5_acc: 0.9940, mean_class_accuracy: 0.8971 +2025-06-24 20:56:51,718 - pyskl - INFO - Epoch [126][100/1281] lr: 1.665e-03, eta: 1:59:51, time: 0.429, data_time: 0.193, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0246, loss: 0.0246 +2025-06-24 20:57:14,349 - pyskl - INFO - Epoch [126][200/1281] lr: 1.654e-03, eta: 1:59:28, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-24 20:57:36,815 - pyskl - INFO - Epoch [126][300/1281] lr: 1.644e-03, eta: 1:59:06, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0226, loss: 0.0226 +2025-06-24 20:57:59,334 - pyskl - INFO - Epoch [126][400/1281] lr: 1.634e-03, eta: 1:58:43, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-24 20:58:21,725 - pyskl - INFO - Epoch [126][500/1281] lr: 1.624e-03, eta: 1:58:21, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-24 20:58:44,071 - pyskl - INFO - Epoch [126][600/1281] lr: 1.614e-03, eta: 1:57:58, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0290, loss: 0.0290 +2025-06-24 20:59:06,445 - pyskl - INFO - Epoch [126][700/1281] lr: 1.604e-03, eta: 1:57:36, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0301, loss: 0.0301 +2025-06-24 20:59:28,854 - pyskl - INFO - Epoch [126][800/1281] lr: 1.594e-03, eta: 1:57:13, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0281, loss: 0.0281 +2025-06-24 20:59:51,270 - pyskl - INFO - Epoch [126][900/1281] lr: 1.584e-03, eta: 1:56:51, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0245, loss: 0.0245 +2025-06-24 21:00:13,925 - pyskl - INFO - Epoch [126][1000/1281] lr: 1.574e-03, eta: 1:56:28, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-24 21:00:36,411 - pyskl - INFO - Epoch [126][1100/1281] lr: 1.564e-03, eta: 1:56:05, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0223, loss: 0.0223 +2025-06-24 21:00:58,944 - pyskl - INFO - Epoch [126][1200/1281] lr: 1.554e-03, eta: 1:55:43, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-24 21:01:17,936 - pyskl - INFO - Saving checkpoint at 126 epochs +2025-06-24 21:02:02,023 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:02:02,080 - pyskl - INFO - +top1_acc 0.9284 +top5_acc 0.9946 +2025-06-24 21:02:02,081 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:02:02,090 - pyskl - INFO - +mean_acc 0.8987 +2025-06-24 21:02:02,096 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_125.pth was removed +2025-06-24 21:02:02,278 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_126.pth. +2025-06-24 21:02:02,279 - pyskl - INFO - Best top1_acc is 0.9284 at 126 epoch. +2025-06-24 21:02:02,281 - pyskl - INFO - Epoch(val) [126][533] top1_acc: 0.9284, top5_acc: 0.9946, mean_class_accuracy: 0.8987 +2025-06-24 21:02:45,222 - pyskl - INFO - Epoch [127][100/1281] lr: 1.536e-03, eta: 1:55:03, time: 0.429, data_time: 0.193, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0237, loss: 0.0237 +2025-06-24 21:03:08,035 - pyskl - INFO - Epoch [127][200/1281] lr: 1.527e-03, eta: 1:54:40, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-24 21:03:31,123 - pyskl - INFO - Epoch [127][300/1281] lr: 1.517e-03, eta: 1:54:18, time: 0.231, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0237, loss: 0.0237 +2025-06-24 21:03:53,492 - pyskl - INFO - Epoch [127][400/1281] lr: 1.507e-03, eta: 1:53:55, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0271, loss: 0.0271 +2025-06-24 21:04:15,983 - pyskl - INFO - Epoch [127][500/1281] lr: 1.497e-03, eta: 1:53:33, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-24 21:04:38,401 - pyskl - INFO - Epoch [127][600/1281] lr: 1.488e-03, eta: 1:53:10, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-24 21:05:00,735 - pyskl - INFO - Epoch [127][700/1281] lr: 1.478e-03, eta: 1:52:48, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0281, loss: 0.0281 +2025-06-24 21:05:23,439 - pyskl - INFO - Epoch [127][800/1281] lr: 1.468e-03, eta: 1:52:25, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0251, loss: 0.0251 +2025-06-24 21:05:45,876 - pyskl - INFO - Epoch [127][900/1281] lr: 1.459e-03, eta: 1:52:02, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-24 21:06:08,373 - pyskl - INFO - Epoch [127][1000/1281] lr: 1.449e-03, eta: 1:51:40, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-24 21:06:30,997 - pyskl - INFO - Epoch [127][1100/1281] lr: 1.440e-03, eta: 1:51:17, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-24 21:06:53,333 - pyskl - INFO - Epoch [127][1200/1281] lr: 1.430e-03, eta: 1:50:55, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0237, loss: 0.0237 +2025-06-24 21:07:12,348 - pyskl - INFO - Saving checkpoint at 127 epochs +2025-06-24 21:07:55,643 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:07:55,698 - pyskl - INFO - +top1_acc 0.9263 +top5_acc 0.9946 +2025-06-24 21:07:55,699 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:07:55,706 - pyskl - INFO - +mean_acc 0.8986 +2025-06-24 21:07:55,708 - pyskl - INFO - Epoch(val) [127][533] top1_acc: 0.9263, top5_acc: 0.9946, mean_class_accuracy: 0.8986 +2025-06-24 21:08:38,012 - pyskl - INFO - Epoch [128][100/1281] lr: 1.413e-03, eta: 1:50:14, time: 0.423, data_time: 0.187, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-24 21:09:00,466 - pyskl - INFO - Epoch [128][200/1281] lr: 1.404e-03, eta: 1:49:52, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0318, loss: 0.0318 +2025-06-24 21:09:23,045 - pyskl - INFO - Epoch [128][300/1281] lr: 1.394e-03, eta: 1:49:29, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0256, loss: 0.0256 +2025-06-24 21:09:45,908 - pyskl - INFO - Epoch [128][400/1281] lr: 1.385e-03, eta: 1:49:07, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0263, loss: 0.0263 +2025-06-24 21:10:08,219 - pyskl - INFO - Epoch [128][500/1281] lr: 1.376e-03, eta: 1:48:44, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-24 21:10:30,605 - pyskl - INFO - Epoch [128][600/1281] lr: 1.366e-03, eta: 1:48:22, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0258, loss: 0.0258 +2025-06-24 21:10:52,938 - pyskl - INFO - Epoch [128][700/1281] lr: 1.357e-03, eta: 1:47:59, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0261, loss: 0.0261 +2025-06-24 21:11:15,428 - pyskl - INFO - Epoch [128][800/1281] lr: 1.348e-03, eta: 1:47:37, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-24 21:11:37,904 - pyskl - INFO - Epoch [128][900/1281] lr: 1.339e-03, eta: 1:47:14, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-24 21:12:00,277 - pyskl - INFO - Epoch [128][1000/1281] lr: 1.329e-03, eta: 1:46:52, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0310, loss: 0.0310 +2025-06-24 21:12:22,658 - pyskl - INFO - Epoch [128][1100/1281] lr: 1.320e-03, eta: 1:46:29, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0275, loss: 0.0275 +2025-06-24 21:12:45,011 - pyskl - INFO - Epoch [128][1200/1281] lr: 1.311e-03, eta: 1:46:06, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0250, loss: 0.0250 +2025-06-24 21:13:03,715 - pyskl - INFO - Saving checkpoint at 128 epochs +2025-06-24 21:13:46,688 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:13:46,786 - pyskl - INFO - +top1_acc 0.9272 +top5_acc 0.9941 +2025-06-24 21:13:46,786 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:13:46,795 - pyskl - INFO - +mean_acc 0.8999 +2025-06-24 21:13:46,797 - pyskl - INFO - Epoch(val) [128][533] top1_acc: 0.9272, top5_acc: 0.9941, mean_class_accuracy: 0.8999 +2025-06-24 21:14:29,093 - pyskl - INFO - Epoch [129][100/1281] lr: 1.295e-03, eta: 1:45:26, time: 0.423, data_time: 0.187, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-24 21:14:51,686 - pyskl - INFO - Epoch [129][200/1281] lr: 1.286e-03, eta: 1:45:03, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0219, loss: 0.0219 +2025-06-24 21:15:14,193 - pyskl - INFO - Epoch [129][300/1281] lr: 1.277e-03, eta: 1:44:41, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-24 21:15:36,466 - pyskl - INFO - Epoch [129][400/1281] lr: 1.268e-03, eta: 1:44:18, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-24 21:15:59,223 - pyskl - INFO - Epoch [129][500/1281] lr: 1.259e-03, eta: 1:43:56, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0252, loss: 0.0252 +2025-06-24 21:16:21,580 - pyskl - INFO - Epoch [129][600/1281] lr: 1.250e-03, eta: 1:43:33, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-24 21:16:44,233 - pyskl - INFO - Epoch [129][700/1281] lr: 1.241e-03, eta: 1:43:11, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0249, loss: 0.0249 +2025-06-24 21:17:06,742 - pyskl - INFO - Epoch [129][800/1281] lr: 1.232e-03, eta: 1:42:48, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-24 21:17:29,356 - pyskl - INFO - Epoch [129][900/1281] lr: 1.223e-03, eta: 1:42:26, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-24 21:17:51,741 - pyskl - INFO - Epoch [129][1000/1281] lr: 1.214e-03, eta: 1:42:03, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-24 21:18:14,342 - pyskl - INFO - Epoch [129][1100/1281] lr: 1.206e-03, eta: 1:41:41, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0299, loss: 0.0299 +2025-06-24 21:18:36,991 - pyskl - INFO - Epoch [129][1200/1281] lr: 1.197e-03, eta: 1:41:18, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-24 21:18:56,147 - pyskl - INFO - Saving checkpoint at 129 epochs +2025-06-24 21:19:39,917 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:19:39,971 - pyskl - INFO - +top1_acc 0.9261 +top5_acc 0.9946 +2025-06-24 21:19:39,972 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:19:39,979 - pyskl - INFO - +mean_acc 0.8987 +2025-06-24 21:19:39,981 - pyskl - INFO - Epoch(val) [129][533] top1_acc: 0.9261, top5_acc: 0.9946, mean_class_accuracy: 0.8987 +2025-06-24 21:20:22,762 - pyskl - INFO - Epoch [130][100/1281] lr: 1.181e-03, eta: 1:40:38, time: 0.428, data_time: 0.192, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-24 21:20:45,104 - pyskl - INFO - Epoch [130][200/1281] lr: 1.172e-03, eta: 1:40:15, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-24 21:21:07,644 - pyskl - INFO - Epoch [130][300/1281] lr: 1.164e-03, eta: 1:39:53, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-24 21:21:30,154 - pyskl - INFO - Epoch [130][400/1281] lr: 1.155e-03, eta: 1:39:30, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-24 21:21:52,705 - pyskl - INFO - Epoch [130][500/1281] lr: 1.147e-03, eta: 1:39:08, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0262, loss: 0.0262 +2025-06-24 21:22:15,163 - pyskl - INFO - Epoch [130][600/1281] lr: 1.138e-03, eta: 1:38:45, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-24 21:22:37,355 - pyskl - INFO - Epoch [130][700/1281] lr: 1.130e-03, eta: 1:38:22, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-24 21:23:00,218 - pyskl - INFO - Epoch [130][800/1281] lr: 1.121e-03, eta: 1:38:00, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-24 21:23:22,787 - pyskl - INFO - Epoch [130][900/1281] lr: 1.113e-03, eta: 1:37:37, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0229, loss: 0.0229 +2025-06-24 21:23:45,022 - pyskl - INFO - Epoch [130][1000/1281] lr: 1.104e-03, eta: 1:37:15, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-24 21:24:07,483 - pyskl - INFO - Epoch [130][1100/1281] lr: 1.096e-03, eta: 1:36:52, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-24 21:24:29,943 - pyskl - INFO - Epoch [130][1200/1281] lr: 1.088e-03, eta: 1:36:30, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0219, loss: 0.0219 +2025-06-24 21:24:49,173 - pyskl - INFO - Saving checkpoint at 130 epochs +2025-06-24 21:25:33,579 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:25:33,639 - pyskl - INFO - +top1_acc 0.9263 +top5_acc 0.9946 +2025-06-24 21:25:33,640 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:25:33,647 - pyskl - INFO - +mean_acc 0.8999 +2025-06-24 21:25:33,649 - pyskl - INFO - Epoch(val) [130][533] top1_acc: 0.9263, top5_acc: 0.9946, mean_class_accuracy: 0.8999 +2025-06-24 21:26:15,712 - pyskl - INFO - Epoch [131][100/1281] lr: 1.072e-03, eta: 1:35:49, time: 0.421, data_time: 0.183, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-24 21:26:38,187 - pyskl - INFO - Epoch [131][200/1281] lr: 1.064e-03, eta: 1:35:27, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0235, loss: 0.0235 +2025-06-24 21:27:00,599 - pyskl - INFO - Epoch [131][300/1281] lr: 1.056e-03, eta: 1:35:04, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-24 21:27:23,161 - pyskl - INFO - Epoch [131][400/1281] lr: 1.048e-03, eta: 1:34:42, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-24 21:27:45,511 - pyskl - INFO - Epoch [131][500/1281] lr: 1.040e-03, eta: 1:34:19, time: 0.223, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-24 21:28:07,812 - pyskl - INFO - Epoch [131][600/1281] lr: 1.031e-03, eta: 1:33:56, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-24 21:28:29,936 - pyskl - INFO - Epoch [131][700/1281] lr: 1.023e-03, eta: 1:33:34, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-24 21:28:52,428 - pyskl - INFO - Epoch [131][800/1281] lr: 1.015e-03, eta: 1:33:11, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-24 21:29:14,635 - pyskl - INFO - Epoch [131][900/1281] lr: 1.007e-03, eta: 1:32:49, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-24 21:29:37,302 - pyskl - INFO - Epoch [131][1000/1281] lr: 9.992e-04, eta: 1:32:26, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-24 21:29:59,602 - pyskl - INFO - Epoch [131][1100/1281] lr: 9.912e-04, eta: 1:32:04, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-24 21:30:22,174 - pyskl - INFO - Epoch [131][1200/1281] lr: 9.832e-04, eta: 1:31:41, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-24 21:30:40,908 - pyskl - INFO - Saving checkpoint at 131 epochs +2025-06-24 21:31:24,643 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:31:24,706 - pyskl - INFO - +top1_acc 0.9281 +top5_acc 0.9941 +2025-06-24 21:31:24,707 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:31:24,717 - pyskl - INFO - +mean_acc 0.9018 +2025-06-24 21:31:24,720 - pyskl - INFO - Epoch(val) [131][533] top1_acc: 0.9281, top5_acc: 0.9941, mean_class_accuracy: 0.9018 +2025-06-24 21:32:06,641 - pyskl - INFO - Epoch [132][100/1281] lr: 9.689e-04, eta: 1:31:00, time: 0.419, data_time: 0.184, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0219, loss: 0.0219 +2025-06-24 21:32:28,963 - pyskl - INFO - Epoch [132][200/1281] lr: 9.610e-04, eta: 1:30:38, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-24 21:32:51,383 - pyskl - INFO - Epoch [132][300/1281] lr: 9.532e-04, eta: 1:30:15, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-24 21:33:13,932 - pyskl - INFO - Epoch [132][400/1281] lr: 9.454e-04, eta: 1:29:53, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-24 21:33:36,391 - pyskl - INFO - Epoch [132][500/1281] lr: 9.376e-04, eta: 1:29:30, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-24 21:33:58,758 - pyskl - INFO - Epoch [132][600/1281] lr: 9.298e-04, eta: 1:29:08, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-24 21:34:21,625 - pyskl - INFO - Epoch [132][700/1281] lr: 9.221e-04, eta: 1:28:45, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-24 21:34:43,958 - pyskl - INFO - Epoch [132][800/1281] lr: 9.144e-04, eta: 1:28:23, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-24 21:35:06,534 - pyskl - INFO - Epoch [132][900/1281] lr: 9.068e-04, eta: 1:28:00, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-24 21:35:29,165 - pyskl - INFO - Epoch [132][1000/1281] lr: 8.991e-04, eta: 1:27:38, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-24 21:35:51,776 - pyskl - INFO - Epoch [132][1100/1281] lr: 8.915e-04, eta: 1:27:15, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0223, loss: 0.0223 +2025-06-24 21:36:14,595 - pyskl - INFO - Epoch [132][1200/1281] lr: 8.840e-04, eta: 1:26:53, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-24 21:36:33,547 - pyskl - INFO - Saving checkpoint at 132 epochs +2025-06-24 21:37:17,461 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:37:17,517 - pyskl - INFO - +top1_acc 0.9276 +top5_acc 0.9948 +2025-06-24 21:37:17,517 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:37:17,524 - pyskl - INFO - +mean_acc 0.9013 +2025-06-24 21:37:17,526 - pyskl - INFO - Epoch(val) [132][533] top1_acc: 0.9276, top5_acc: 0.9948, mean_class_accuracy: 0.9013 +2025-06-24 21:38:00,626 - pyskl - INFO - Epoch [133][100/1281] lr: 8.704e-04, eta: 1:26:12, time: 0.431, data_time: 0.193, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-24 21:38:23,356 - pyskl - INFO - Epoch [133][200/1281] lr: 8.629e-04, eta: 1:25:50, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-24 21:38:45,863 - pyskl - INFO - Epoch [133][300/1281] lr: 8.554e-04, eta: 1:25:27, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-24 21:39:08,124 - pyskl - INFO - Epoch [133][400/1281] lr: 8.480e-04, eta: 1:25:05, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-24 21:39:30,611 - pyskl - INFO - Epoch [133][500/1281] lr: 8.406e-04, eta: 1:24:42, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-24 21:39:52,916 - pyskl - INFO - Epoch [133][600/1281] lr: 8.333e-04, eta: 1:24:20, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-24 21:40:15,707 - pyskl - INFO - Epoch [133][700/1281] lr: 8.260e-04, eta: 1:23:57, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-24 21:40:38,187 - pyskl - INFO - Epoch [133][800/1281] lr: 8.187e-04, eta: 1:23:34, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-24 21:41:00,740 - pyskl - INFO - Epoch [133][900/1281] lr: 8.114e-04, eta: 1:23:12, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-24 21:41:23,279 - pyskl - INFO - Epoch [133][1000/1281] lr: 8.042e-04, eta: 1:22:49, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-24 21:41:46,037 - pyskl - INFO - Epoch [133][1100/1281] lr: 7.970e-04, eta: 1:22:27, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0246, loss: 0.0246 +2025-06-24 21:42:08,470 - pyskl - INFO - Epoch [133][1200/1281] lr: 7.898e-04, eta: 1:22:04, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-24 21:42:27,394 - pyskl - INFO - Saving checkpoint at 133 epochs +2025-06-24 21:43:10,704 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:43:10,761 - pyskl - INFO - +top1_acc 0.9285 +top5_acc 0.9945 +2025-06-24 21:43:10,761 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:43:10,770 - pyskl - INFO - +mean_acc 0.9052 +2025-06-24 21:43:10,774 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_126.pth was removed +2025-06-24 21:43:10,959 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_133.pth. +2025-06-24 21:43:10,960 - pyskl - INFO - Best top1_acc is 0.9285 at 133 epoch. +2025-06-24 21:43:10,962 - pyskl - INFO - Epoch(val) [133][533] top1_acc: 0.9285, top5_acc: 0.9945, mean_class_accuracy: 0.9052 +2025-06-24 21:43:53,413 - pyskl - INFO - Epoch [134][100/1281] lr: 7.769e-04, eta: 1:21:24, time: 0.424, data_time: 0.184, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-24 21:44:16,016 - pyskl - INFO - Epoch [134][200/1281] lr: 7.699e-04, eta: 1:21:01, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0226, loss: 0.0226 +2025-06-24 21:44:38,601 - pyskl - INFO - Epoch [134][300/1281] lr: 7.628e-04, eta: 1:20:39, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-24 21:45:01,032 - pyskl - INFO - Epoch [134][400/1281] lr: 7.558e-04, eta: 1:20:16, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-24 21:45:23,182 - pyskl - INFO - Epoch [134][500/1281] lr: 7.488e-04, eta: 1:19:54, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-24 21:45:45,636 - pyskl - INFO - Epoch [134][600/1281] lr: 7.419e-04, eta: 1:19:31, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-24 21:46:07,977 - pyskl - INFO - Epoch [134][700/1281] lr: 7.349e-04, eta: 1:19:09, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-24 21:46:30,387 - pyskl - INFO - Epoch [134][800/1281] lr: 7.281e-04, eta: 1:18:46, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-24 21:46:53,040 - pyskl - INFO - Epoch [134][900/1281] lr: 7.212e-04, eta: 1:18:23, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-24 21:47:15,533 - pyskl - INFO - Epoch [134][1000/1281] lr: 7.144e-04, eta: 1:18:01, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-24 21:47:38,176 - pyskl - INFO - Epoch [134][1100/1281] lr: 7.076e-04, eta: 1:17:38, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-24 21:48:00,591 - pyskl - INFO - Epoch [134][1200/1281] lr: 7.008e-04, eta: 1:17:16, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-24 21:48:19,655 - pyskl - INFO - Saving checkpoint at 134 epochs +2025-06-24 21:49:03,200 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:49:03,268 - pyskl - INFO - +top1_acc 0.9296 +top5_acc 0.9947 +2025-06-24 21:49:03,268 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:49:03,275 - pyskl - INFO - +mean_acc 0.9054 +2025-06-24 21:49:03,279 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_133.pth was removed +2025-06-24 21:49:03,601 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_134.pth. +2025-06-24 21:49:03,601 - pyskl - INFO - Best top1_acc is 0.9296 at 134 epoch. +2025-06-24 21:49:03,605 - pyskl - INFO - Epoch(val) [134][533] top1_acc: 0.9296, top5_acc: 0.9947, mean_class_accuracy: 0.9054 +2025-06-24 21:49:45,835 - pyskl - INFO - Epoch [135][100/1281] lr: 6.887e-04, eta: 1:16:35, time: 0.422, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-24 21:50:08,202 - pyskl - INFO - Epoch [135][200/1281] lr: 6.820e-04, eta: 1:16:13, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0229, loss: 0.0229 +2025-06-24 21:50:30,846 - pyskl - INFO - Epoch [135][300/1281] lr: 6.753e-04, eta: 1:15:50, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-24 21:50:53,102 - pyskl - INFO - Epoch [135][400/1281] lr: 6.687e-04, eta: 1:15:28, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-24 21:51:15,568 - pyskl - INFO - Epoch [135][500/1281] lr: 6.622e-04, eta: 1:15:05, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-24 21:51:38,019 - pyskl - INFO - Epoch [135][600/1281] lr: 6.556e-04, eta: 1:14:43, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-24 21:52:00,634 - pyskl - INFO - Epoch [135][700/1281] lr: 6.491e-04, eta: 1:14:20, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-24 21:52:22,897 - pyskl - INFO - Epoch [135][800/1281] lr: 6.426e-04, eta: 1:13:57, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-24 21:52:45,321 - pyskl - INFO - Epoch [135][900/1281] lr: 6.362e-04, eta: 1:13:35, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-24 21:53:08,160 - pyskl - INFO - Epoch [135][1000/1281] lr: 6.297e-04, eta: 1:13:12, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-24 21:53:30,317 - pyskl - INFO - Epoch [135][1100/1281] lr: 6.233e-04, eta: 1:12:50, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-24 21:53:52,610 - pyskl - INFO - Epoch [135][1200/1281] lr: 6.170e-04, eta: 1:12:27, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-24 21:54:11,896 - pyskl - INFO - Saving checkpoint at 135 epochs +2025-06-24 21:54:55,264 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:54:55,321 - pyskl - INFO - +top1_acc 0.9291 +top5_acc 0.9947 +2025-06-24 21:54:55,321 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:54:55,327 - pyskl - INFO - +mean_acc 0.9035 +2025-06-24 21:54:55,328 - pyskl - INFO - Epoch(val) [135][533] top1_acc: 0.9291, top5_acc: 0.9947, mean_class_accuracy: 0.9035 +2025-06-24 21:55:38,022 - pyskl - INFO - Epoch [136][100/1281] lr: 6.056e-04, eta: 1:11:47, time: 0.427, data_time: 0.190, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-24 21:56:00,639 - pyskl - INFO - Epoch [136][200/1281] lr: 5.993e-04, eta: 1:11:24, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-24 21:56:23,378 - pyskl - INFO - Epoch [136][300/1281] lr: 5.931e-04, eta: 1:11:02, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-24 21:56:45,922 - pyskl - INFO - Epoch [136][400/1281] lr: 5.868e-04, eta: 1:10:39, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-24 21:57:08,747 - pyskl - INFO - Epoch [136][500/1281] lr: 5.807e-04, eta: 1:10:17, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-24 21:57:31,268 - pyskl - INFO - Epoch [136][600/1281] lr: 5.745e-04, eta: 1:09:54, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-24 21:57:53,751 - pyskl - INFO - Epoch [136][700/1281] lr: 5.684e-04, eta: 1:09:32, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-24 21:58:16,278 - pyskl - INFO - Epoch [136][800/1281] lr: 5.623e-04, eta: 1:09:09, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-24 21:58:38,557 - pyskl - INFO - Epoch [136][900/1281] lr: 5.563e-04, eta: 1:08:46, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0248, loss: 0.0248 +2025-06-24 21:59:00,801 - pyskl - INFO - Epoch [136][1000/1281] lr: 5.503e-04, eta: 1:08:24, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-24 21:59:23,205 - pyskl - INFO - Epoch [136][1100/1281] lr: 5.443e-04, eta: 1:08:01, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-24 21:59:45,759 - pyskl - INFO - Epoch [136][1200/1281] lr: 5.384e-04, eta: 1:07:39, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-24 22:00:04,742 - pyskl - INFO - Saving checkpoint at 136 epochs +2025-06-24 22:00:48,216 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:00:48,284 - pyskl - INFO - +top1_acc 0.9278 +top5_acc 0.9945 +2025-06-24 22:00:48,284 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:00:48,293 - pyskl - INFO - +mean_acc 0.9020 +2025-06-24 22:00:48,295 - pyskl - INFO - Epoch(val) [136][533] top1_acc: 0.9278, top5_acc: 0.9945, mean_class_accuracy: 0.9020 +2025-06-24 22:01:31,141 - pyskl - INFO - Epoch [137][100/1281] lr: 5.277e-04, eta: 1:06:58, time: 0.428, data_time: 0.191, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-24 22:01:53,989 - pyskl - INFO - Epoch [137][200/1281] lr: 5.218e-04, eta: 1:06:36, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-24 22:02:16,216 - pyskl - INFO - Epoch [137][300/1281] lr: 5.160e-04, eta: 1:06:13, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-24 22:02:38,792 - pyskl - INFO - Epoch [137][400/1281] lr: 5.102e-04, eta: 1:05:51, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-24 22:03:01,610 - pyskl - INFO - Epoch [137][500/1281] lr: 5.044e-04, eta: 1:05:28, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-24 22:03:23,950 - pyskl - INFO - Epoch [137][600/1281] lr: 4.987e-04, eta: 1:05:06, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-24 22:03:46,113 - pyskl - INFO - Epoch [137][700/1281] lr: 4.930e-04, eta: 1:04:43, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-24 22:04:08,566 - pyskl - INFO - Epoch [137][800/1281] lr: 4.873e-04, eta: 1:04:20, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-24 22:04:30,985 - pyskl - INFO - Epoch [137][900/1281] lr: 4.817e-04, eta: 1:03:58, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-24 22:04:53,243 - pyskl - INFO - Epoch [137][1000/1281] lr: 4.761e-04, eta: 1:03:35, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-24 22:05:15,694 - pyskl - INFO - Epoch [137][1100/1281] lr: 4.705e-04, eta: 1:03:13, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-24 22:05:38,165 - pyskl - INFO - Epoch [137][1200/1281] lr: 4.650e-04, eta: 1:02:50, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-24 22:05:56,920 - pyskl - INFO - Saving checkpoint at 137 epochs +2025-06-24 22:06:41,046 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:06:41,105 - pyskl - INFO - +top1_acc 0.9305 +top5_acc 0.9948 +2025-06-24 22:06:41,105 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:06:41,112 - pyskl - INFO - +mean_acc 0.9045 +2025-06-24 22:06:41,115 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_134.pth was removed +2025-06-24 22:06:41,289 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_137.pth. +2025-06-24 22:06:41,289 - pyskl - INFO - Best top1_acc is 0.9305 at 137 epoch. +2025-06-24 22:06:41,292 - pyskl - INFO - Epoch(val) [137][533] top1_acc: 0.9305, top5_acc: 0.9948, mean_class_accuracy: 0.9045 +2025-06-24 22:07:24,235 - pyskl - INFO - Epoch [138][100/1281] lr: 4.550e-04, eta: 1:02:10, time: 0.429, data_time: 0.193, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-24 22:07:46,877 - pyskl - INFO - Epoch [138][200/1281] lr: 4.496e-04, eta: 1:01:47, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-24 22:08:09,410 - pyskl - INFO - Epoch [138][300/1281] lr: 4.442e-04, eta: 1:01:25, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-24 22:08:32,019 - pyskl - INFO - Epoch [138][400/1281] lr: 4.388e-04, eta: 1:01:02, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-24 22:08:54,343 - pyskl - INFO - Epoch [138][500/1281] lr: 4.334e-04, eta: 1:00:40, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-24 22:09:16,616 - pyskl - INFO - Epoch [138][600/1281] lr: 4.281e-04, eta: 1:00:17, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-24 22:09:39,193 - pyskl - INFO - Epoch [138][700/1281] lr: 4.228e-04, eta: 0:59:54, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-24 22:10:01,794 - pyskl - INFO - Epoch [138][800/1281] lr: 4.176e-04, eta: 0:59:32, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-24 22:10:24,163 - pyskl - INFO - Epoch [138][900/1281] lr: 4.124e-04, eta: 0:59:09, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-24 22:10:46,768 - pyskl - INFO - Epoch [138][1000/1281] lr: 4.072e-04, eta: 0:58:47, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-24 22:11:09,374 - pyskl - INFO - Epoch [138][1100/1281] lr: 4.020e-04, eta: 0:58:24, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-24 22:11:31,616 - pyskl - INFO - Epoch [138][1200/1281] lr: 3.969e-04, eta: 0:58:02, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-24 22:11:50,609 - pyskl - INFO - Saving checkpoint at 138 epochs +2025-06-24 22:12:33,868 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:12:33,924 - pyskl - INFO - +top1_acc 0.9302 +top5_acc 0.9946 +2025-06-24 22:12:33,924 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:12:33,931 - pyskl - INFO - +mean_acc 0.9033 +2025-06-24 22:12:33,933 - pyskl - INFO - Epoch(val) [138][533] top1_acc: 0.9302, top5_acc: 0.9946, mean_class_accuracy: 0.9033 +2025-06-24 22:13:17,083 - pyskl - INFO - Epoch [139][100/1281] lr: 3.877e-04, eta: 0:57:21, time: 0.431, data_time: 0.196, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-24 22:13:39,608 - pyskl - INFO - Epoch [139][200/1281] lr: 3.827e-04, eta: 0:56:59, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-24 22:14:01,826 - pyskl - INFO - Epoch [139][300/1281] lr: 3.777e-04, eta: 0:56:36, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-24 22:14:24,104 - pyskl - INFO - Epoch [139][400/1281] lr: 3.727e-04, eta: 0:56:14, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-24 22:14:46,749 - pyskl - INFO - Epoch [139][500/1281] lr: 3.678e-04, eta: 0:55:51, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-24 22:15:09,412 - pyskl - INFO - Epoch [139][600/1281] lr: 3.628e-04, eta: 0:55:28, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-24 22:15:31,697 - pyskl - INFO - Epoch [139][700/1281] lr: 3.580e-04, eta: 0:55:06, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-24 22:15:54,115 - pyskl - INFO - Epoch [139][800/1281] lr: 3.531e-04, eta: 0:54:43, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-24 22:16:16,625 - pyskl - INFO - Epoch [139][900/1281] lr: 3.483e-04, eta: 0:54:21, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-24 22:16:38,988 - pyskl - INFO - Epoch [139][1000/1281] lr: 3.436e-04, eta: 0:53:58, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-24 22:17:01,886 - pyskl - INFO - Epoch [139][1100/1281] lr: 3.388e-04, eta: 0:53:36, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-24 22:17:24,087 - pyskl - INFO - Epoch [139][1200/1281] lr: 3.341e-04, eta: 0:53:13, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-24 22:17:43,119 - pyskl - INFO - Saving checkpoint at 139 epochs +2025-06-24 22:18:27,227 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:18:27,294 - pyskl - INFO - +top1_acc 0.9295 +top5_acc 0.9946 +2025-06-24 22:18:27,295 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:18:27,302 - pyskl - INFO - +mean_acc 0.9058 +2025-06-24 22:18:27,304 - pyskl - INFO - Epoch(val) [139][533] top1_acc: 0.9295, top5_acc: 0.9946, mean_class_accuracy: 0.9058 +2025-06-24 22:19:10,072 - pyskl - INFO - Epoch [140][100/1281] lr: 3.257e-04, eta: 0:52:33, time: 0.428, data_time: 0.191, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-24 22:19:32,530 - pyskl - INFO - Epoch [140][200/1281] lr: 3.210e-04, eta: 0:52:10, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-24 22:19:55,025 - pyskl - INFO - Epoch [140][300/1281] lr: 3.165e-04, eta: 0:51:48, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-24 22:20:17,690 - pyskl - INFO - Epoch [140][400/1281] lr: 3.119e-04, eta: 0:51:25, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-24 22:20:40,131 - pyskl - INFO - Epoch [140][500/1281] lr: 3.074e-04, eta: 0:51:02, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-24 22:21:02,521 - pyskl - INFO - Epoch [140][600/1281] lr: 3.029e-04, eta: 0:50:40, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-24 22:21:24,878 - pyskl - INFO - Epoch [140][700/1281] lr: 2.984e-04, eta: 0:50:17, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-24 22:21:47,356 - pyskl - INFO - Epoch [140][800/1281] lr: 2.940e-04, eta: 0:49:55, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-24 22:22:09,910 - pyskl - INFO - Epoch [140][900/1281] lr: 2.896e-04, eta: 0:49:32, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-24 22:22:32,374 - pyskl - INFO - Epoch [140][1000/1281] lr: 2.853e-04, eta: 0:49:10, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-24 22:22:54,469 - pyskl - INFO - Epoch [140][1100/1281] lr: 2.809e-04, eta: 0:48:47, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-24 22:23:16,977 - pyskl - INFO - Epoch [140][1200/1281] lr: 2.767e-04, eta: 0:48:25, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-24 22:23:36,061 - pyskl - INFO - Saving checkpoint at 140 epochs +2025-06-24 22:24:19,612 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:24:19,681 - pyskl - INFO - +top1_acc 0.9304 +top5_acc 0.9947 +2025-06-24 22:24:19,682 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:24:19,689 - pyskl - INFO - +mean_acc 0.9052 +2025-06-24 22:24:19,691 - pyskl - INFO - Epoch(val) [140][533] top1_acc: 0.9304, top5_acc: 0.9947, mean_class_accuracy: 0.9052 +2025-06-24 22:25:02,083 - pyskl - INFO - Epoch [141][100/1281] lr: 2.690e-04, eta: 0:47:44, time: 0.424, data_time: 0.192, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-24 22:25:24,672 - pyskl - INFO - Epoch [141][200/1281] lr: 2.648e-04, eta: 0:47:21, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-24 22:25:47,013 - pyskl - INFO - Epoch [141][300/1281] lr: 2.606e-04, eta: 0:46:59, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-24 22:26:09,359 - pyskl - INFO - Epoch [141][400/1281] lr: 2.565e-04, eta: 0:46:36, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-24 22:26:31,891 - pyskl - INFO - Epoch [141][500/1281] lr: 2.524e-04, eta: 0:46:14, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-24 22:26:54,467 - pyskl - INFO - Epoch [141][600/1281] lr: 2.483e-04, eta: 0:45:51, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-24 22:27:17,284 - pyskl - INFO - Epoch [141][700/1281] lr: 2.443e-04, eta: 0:45:29, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-24 22:27:39,642 - pyskl - INFO - Epoch [141][800/1281] lr: 2.402e-04, eta: 0:45:06, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-24 22:28:01,903 - pyskl - INFO - Epoch [141][900/1281] lr: 2.363e-04, eta: 0:44:44, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-24 22:28:24,464 - pyskl - INFO - Epoch [141][1000/1281] lr: 2.323e-04, eta: 0:44:21, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-24 22:28:46,959 - pyskl - INFO - Epoch [141][1100/1281] lr: 2.284e-04, eta: 0:43:59, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-24 22:29:09,244 - pyskl - INFO - Epoch [141][1200/1281] lr: 2.246e-04, eta: 0:43:36, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-24 22:29:28,059 - pyskl - INFO - Saving checkpoint at 141 epochs +2025-06-24 22:30:12,027 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:30:12,085 - pyskl - INFO - +top1_acc 0.9290 +top5_acc 0.9953 +2025-06-24 22:30:12,085 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:30:12,093 - pyskl - INFO - +mean_acc 0.9000 +2025-06-24 22:30:12,095 - pyskl - INFO - Epoch(val) [141][533] top1_acc: 0.9290, top5_acc: 0.9953, mean_class_accuracy: 0.9000 +2025-06-24 22:30:54,885 - pyskl - INFO - Epoch [142][100/1281] lr: 2.176e-04, eta: 0:42:55, time: 0.428, data_time: 0.192, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-24 22:31:17,415 - pyskl - INFO - Epoch [142][200/1281] lr: 2.139e-04, eta: 0:42:33, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-24 22:31:39,985 - pyskl - INFO - Epoch [142][300/1281] lr: 2.101e-04, eta: 0:42:10, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-24 22:32:02,480 - pyskl - INFO - Epoch [142][400/1281] lr: 2.064e-04, eta: 0:41:48, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-24 22:32:24,872 - pyskl - INFO - Epoch [142][500/1281] lr: 2.027e-04, eta: 0:41:25, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-24 22:32:47,436 - pyskl - INFO - Epoch [142][600/1281] lr: 1.991e-04, eta: 0:41:03, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-24 22:33:09,778 - pyskl - INFO - Epoch [142][700/1281] lr: 1.954e-04, eta: 0:40:40, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-24 22:33:32,210 - pyskl - INFO - Epoch [142][800/1281] lr: 1.919e-04, eta: 0:40:18, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-24 22:33:54,606 - pyskl - INFO - Epoch [142][900/1281] lr: 1.883e-04, eta: 0:39:55, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-24 22:34:17,219 - pyskl - INFO - Epoch [142][1000/1281] lr: 1.848e-04, eta: 0:39:32, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-24 22:34:39,603 - pyskl - INFO - Epoch [142][1100/1281] lr: 1.813e-04, eta: 0:39:10, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-24 22:35:02,168 - pyskl - INFO - Epoch [142][1200/1281] lr: 1.779e-04, eta: 0:38:47, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-24 22:35:21,127 - pyskl - INFO - Saving checkpoint at 142 epochs +2025-06-24 22:36:05,092 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:36:05,148 - pyskl - INFO - +top1_acc 0.9313 +top5_acc 0.9947 +2025-06-24 22:36:05,148 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:36:05,154 - pyskl - INFO - +mean_acc 0.9062 +2025-06-24 22:36:05,159 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_137.pth was removed +2025-06-24 22:36:05,368 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_142.pth. +2025-06-24 22:36:05,368 - pyskl - INFO - Best top1_acc is 0.9313 at 142 epoch. +2025-06-24 22:36:05,371 - pyskl - INFO - Epoch(val) [142][533] top1_acc: 0.9313, top5_acc: 0.9947, mean_class_accuracy: 0.9062 +2025-06-24 22:36:47,706 - pyskl - INFO - Epoch [143][100/1281] lr: 1.717e-04, eta: 0:38:07, time: 0.423, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-24 22:37:10,061 - pyskl - INFO - Epoch [143][200/1281] lr: 1.683e-04, eta: 0:37:44, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-24 22:37:32,555 - pyskl - INFO - Epoch [143][300/1281] lr: 1.650e-04, eta: 0:37:22, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-24 22:37:55,156 - pyskl - INFO - Epoch [143][400/1281] lr: 1.617e-04, eta: 0:36:59, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-24 22:38:17,722 - pyskl - INFO - Epoch [143][500/1281] lr: 1.585e-04, eta: 0:36:37, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-24 22:38:40,081 - pyskl - INFO - Epoch [143][600/1281] lr: 1.552e-04, eta: 0:36:14, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-24 22:39:02,299 - pyskl - INFO - Epoch [143][700/1281] lr: 1.520e-04, eta: 0:35:51, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-24 22:39:25,052 - pyskl - INFO - Epoch [143][800/1281] lr: 1.489e-04, eta: 0:35:29, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-24 22:39:47,893 - pyskl - INFO - Epoch [143][900/1281] lr: 1.457e-04, eta: 0:35:06, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-24 22:40:10,226 - pyskl - INFO - Epoch [143][1000/1281] lr: 1.426e-04, eta: 0:34:44, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-24 22:40:33,193 - pyskl - INFO - Epoch [143][1100/1281] lr: 1.396e-04, eta: 0:34:21, time: 0.230, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-24 22:40:55,315 - pyskl - INFO - Epoch [143][1200/1281] lr: 1.366e-04, eta: 0:33:59, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-24 22:41:14,031 - pyskl - INFO - Saving checkpoint at 143 epochs +2025-06-24 22:41:57,811 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:41:57,870 - pyskl - INFO - +top1_acc 0.9303 +top5_acc 0.9951 +2025-06-24 22:41:57,870 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:41:57,880 - pyskl - INFO - +mean_acc 0.9027 +2025-06-24 22:41:57,883 - pyskl - INFO - Epoch(val) [143][533] top1_acc: 0.9303, top5_acc: 0.9951, mean_class_accuracy: 0.9027 +2025-06-24 22:42:40,829 - pyskl - INFO - Epoch [144][100/1281] lr: 1.312e-04, eta: 0:33:18, time: 0.429, data_time: 0.191, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-24 22:43:03,529 - pyskl - INFO - Epoch [144][200/1281] lr: 1.282e-04, eta: 0:32:56, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-24 22:43:25,955 - pyskl - INFO - Epoch [144][300/1281] lr: 1.253e-04, eta: 0:32:33, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-24 22:43:48,207 - pyskl - INFO - Epoch [144][400/1281] lr: 1.224e-04, eta: 0:32:10, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-24 22:44:10,442 - pyskl - INFO - Epoch [144][500/1281] lr: 1.196e-04, eta: 0:31:48, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-24 22:44:33,169 - pyskl - INFO - Epoch [144][600/1281] lr: 1.168e-04, eta: 0:31:25, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-24 22:44:55,516 - pyskl - INFO - Epoch [144][700/1281] lr: 1.140e-04, eta: 0:31:03, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-24 22:45:17,836 - pyskl - INFO - Epoch [144][800/1281] lr: 1.113e-04, eta: 0:30:40, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-24 22:45:40,046 - pyskl - INFO - Epoch [144][900/1281] lr: 1.086e-04, eta: 0:30:18, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-24 22:46:02,473 - pyskl - INFO - Epoch [144][1000/1281] lr: 1.059e-04, eta: 0:29:55, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-24 22:46:25,129 - pyskl - INFO - Epoch [144][1100/1281] lr: 1.033e-04, eta: 0:29:33, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-24 22:46:47,841 - pyskl - INFO - Epoch [144][1200/1281] lr: 1.007e-04, eta: 0:29:10, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-24 22:47:06,640 - pyskl - INFO - Saving checkpoint at 144 epochs +2025-06-24 22:47:50,957 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:47:51,019 - pyskl - INFO - +top1_acc 0.9298 +top5_acc 0.9950 +2025-06-24 22:47:51,019 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:47:51,029 - pyskl - INFO - +mean_acc 0.9028 +2025-06-24 22:47:51,033 - pyskl - INFO - Epoch(val) [144][533] top1_acc: 0.9298, top5_acc: 0.9950, mean_class_accuracy: 0.9028 +2025-06-24 22:48:34,032 - pyskl - INFO - Epoch [145][100/1281] lr: 9.605e-05, eta: 0:28:29, time: 0.430, data_time: 0.195, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-24 22:48:56,604 - pyskl - INFO - Epoch [145][200/1281] lr: 9.353e-05, eta: 0:28:07, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-24 22:49:19,119 - pyskl - INFO - Epoch [145][300/1281] lr: 9.106e-05, eta: 0:27:44, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-24 22:49:41,602 - pyskl - INFO - Epoch [145][400/1281] lr: 8.861e-05, eta: 0:27:22, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-24 22:50:04,024 - pyskl - INFO - Epoch [145][500/1281] lr: 8.620e-05, eta: 0:26:59, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-24 22:50:26,170 - pyskl - INFO - Epoch [145][600/1281] lr: 8.382e-05, eta: 0:26:37, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-24 22:50:48,395 - pyskl - INFO - Epoch [145][700/1281] lr: 8.147e-05, eta: 0:26:14, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-24 22:51:11,032 - pyskl - INFO - Epoch [145][800/1281] lr: 7.916e-05, eta: 0:25:52, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-24 22:51:33,378 - pyskl - INFO - Epoch [145][900/1281] lr: 7.688e-05, eta: 0:25:29, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-24 22:51:55,989 - pyskl - INFO - Epoch [145][1000/1281] lr: 7.463e-05, eta: 0:25:06, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-24 22:52:18,116 - pyskl - INFO - Epoch [145][1100/1281] lr: 7.242e-05, eta: 0:24:44, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-24 22:52:40,345 - pyskl - INFO - Epoch [145][1200/1281] lr: 7.024e-05, eta: 0:24:21, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-24 22:52:59,452 - pyskl - INFO - Saving checkpoint at 145 epochs +2025-06-24 22:53:43,003 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:53:43,058 - pyskl - INFO - +top1_acc 0.9298 +top5_acc 0.9951 +2025-06-24 22:53:43,058 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:53:43,066 - pyskl - INFO - +mean_acc 0.9043 +2025-06-24 22:53:43,068 - pyskl - INFO - Epoch(val) [145][533] top1_acc: 0.9298, top5_acc: 0.9951, mean_class_accuracy: 0.9043 +2025-06-24 22:54:25,840 - pyskl - INFO - Epoch [146][100/1281] lr: 6.638e-05, eta: 0:23:41, time: 0.428, data_time: 0.191, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-24 22:54:48,213 - pyskl - INFO - Epoch [146][200/1281] lr: 6.429e-05, eta: 0:23:18, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-24 22:55:10,988 - pyskl - INFO - Epoch [146][300/1281] lr: 6.224e-05, eta: 0:22:56, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-24 22:55:33,217 - pyskl - INFO - Epoch [146][400/1281] lr: 6.022e-05, eta: 0:22:33, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-24 22:55:55,967 - pyskl - INFO - Epoch [146][500/1281] lr: 5.823e-05, eta: 0:22:10, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-24 22:56:18,159 - pyskl - INFO - Epoch [146][600/1281] lr: 5.628e-05, eta: 0:21:48, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-24 22:56:41,066 - pyskl - INFO - Epoch [146][700/1281] lr: 5.436e-05, eta: 0:21:25, time: 0.229, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-24 22:57:03,332 - pyskl - INFO - Epoch [146][800/1281] lr: 5.247e-05, eta: 0:21:03, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-24 22:57:25,539 - pyskl - INFO - Epoch [146][900/1281] lr: 5.061e-05, eta: 0:20:40, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-24 22:57:47,938 - pyskl - INFO - Epoch [146][1000/1281] lr: 4.879e-05, eta: 0:20:18, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-24 22:58:10,411 - pyskl - INFO - Epoch [146][1100/1281] lr: 4.701e-05, eta: 0:19:55, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-24 22:58:33,391 - pyskl - INFO - Epoch [146][1200/1281] lr: 4.525e-05, eta: 0:19:33, time: 0.230, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-24 22:58:52,420 - pyskl - INFO - Saving checkpoint at 146 epochs +2025-06-24 22:59:35,962 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:59:36,035 - pyskl - INFO - +top1_acc 0.9304 +top5_acc 0.9954 +2025-06-24 22:59:36,035 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:59:36,046 - pyskl - INFO - +mean_acc 0.9042 +2025-06-24 22:59:36,049 - pyskl - INFO - Epoch(val) [146][533] top1_acc: 0.9304, top5_acc: 0.9954, mean_class_accuracy: 0.9042 +2025-06-24 23:00:19,419 - pyskl - INFO - Epoch [147][100/1281] lr: 4.216e-05, eta: 0:18:52, time: 0.434, data_time: 0.197, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-24 23:00:41,802 - pyskl - INFO - Epoch [147][200/1281] lr: 4.050e-05, eta: 0:18:29, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-24 23:01:04,187 - pyskl - INFO - Epoch [147][300/1281] lr: 3.887e-05, eta: 0:18:07, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-24 23:01:26,664 - pyskl - INFO - Epoch [147][400/1281] lr: 3.728e-05, eta: 0:17:44, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-24 23:01:49,201 - pyskl - INFO - Epoch [147][500/1281] lr: 3.572e-05, eta: 0:17:22, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-24 23:02:11,856 - pyskl - INFO - Epoch [147][600/1281] lr: 3.419e-05, eta: 0:16:59, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-24 23:02:34,439 - pyskl - INFO - Epoch [147][700/1281] lr: 3.270e-05, eta: 0:16:37, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-24 23:02:56,808 - pyskl - INFO - Epoch [147][800/1281] lr: 3.124e-05, eta: 0:16:14, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-24 23:03:19,317 - pyskl - INFO - Epoch [147][900/1281] lr: 2.981e-05, eta: 0:15:52, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-24 23:03:41,750 - pyskl - INFO - Epoch [147][1000/1281] lr: 2.842e-05, eta: 0:15:29, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-24 23:04:03,963 - pyskl - INFO - Epoch [147][1100/1281] lr: 2.706e-05, eta: 0:15:07, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-24 23:04:26,580 - pyskl - INFO - Epoch [147][1200/1281] lr: 2.573e-05, eta: 0:14:44, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-24 23:04:46,110 - pyskl - INFO - Saving checkpoint at 147 epochs +2025-06-24 23:05:29,892 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:05:29,960 - pyskl - INFO - +top1_acc 0.9311 +top5_acc 0.9953 +2025-06-24 23:05:29,960 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:05:29,969 - pyskl - INFO - +mean_acc 0.9050 +2025-06-24 23:05:29,972 - pyskl - INFO - Epoch(val) [147][533] top1_acc: 0.9311, top5_acc: 0.9953, mean_class_accuracy: 0.9050 +2025-06-24 23:06:12,806 - pyskl - INFO - Epoch [148][100/1281] lr: 2.341e-05, eta: 0:14:03, time: 0.428, data_time: 0.192, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-24 23:06:35,158 - pyskl - INFO - Epoch [148][200/1281] lr: 2.218e-05, eta: 0:13:41, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-24 23:06:57,743 - pyskl - INFO - Epoch [148][300/1281] lr: 2.098e-05, eta: 0:13:18, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-24 23:07:20,396 - pyskl - INFO - Epoch [148][400/1281] lr: 1.981e-05, eta: 0:12:56, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-24 23:07:42,857 - pyskl - INFO - Epoch [148][500/1281] lr: 1.868e-05, eta: 0:12:33, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-24 23:08:05,601 - pyskl - INFO - Epoch [148][600/1281] lr: 1.758e-05, eta: 0:12:11, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-24 23:08:28,070 - pyskl - INFO - Epoch [148][700/1281] lr: 1.651e-05, eta: 0:11:48, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-24 23:08:50,531 - pyskl - INFO - Epoch [148][800/1281] lr: 1.548e-05, eta: 0:11:25, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-24 23:09:13,096 - pyskl - INFO - Epoch [148][900/1281] lr: 1.448e-05, eta: 0:11:03, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-24 23:09:35,829 - pyskl - INFO - Epoch [148][1000/1281] lr: 1.351e-05, eta: 0:10:40, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-24 23:09:58,339 - pyskl - INFO - Epoch [148][1100/1281] lr: 1.258e-05, eta: 0:10:18, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-24 23:10:20,747 - pyskl - INFO - Epoch [148][1200/1281] lr: 1.168e-05, eta: 0:09:55, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-24 23:10:39,773 - pyskl - INFO - Saving checkpoint at 148 epochs +2025-06-24 23:11:24,128 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:11:24,194 - pyskl - INFO - +top1_acc 0.9305 +top5_acc 0.9947 +2025-06-24 23:11:24,194 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:11:24,201 - pyskl - INFO - +mean_acc 0.9053 +2025-06-24 23:11:24,203 - pyskl - INFO - Epoch(val) [148][533] top1_acc: 0.9305, top5_acc: 0.9947, mean_class_accuracy: 0.9053 +2025-06-24 23:12:07,058 - pyskl - INFO - Epoch [149][100/1281] lr: 1.013e-05, eta: 0:09:15, time: 0.429, data_time: 0.194, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-24 23:12:29,743 - pyskl - INFO - Epoch [149][200/1281] lr: 9.328e-06, eta: 0:08:52, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-24 23:12:52,442 - pyskl - INFO - Epoch [149][300/1281] lr: 8.555e-06, eta: 0:08:29, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-24 23:13:15,275 - pyskl - INFO - Epoch [149][400/1281] lr: 7.816e-06, eta: 0:08:07, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-24 23:13:37,821 - pyskl - INFO - Epoch [149][500/1281] lr: 7.110e-06, eta: 0:07:44, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-24 23:14:00,381 - pyskl - INFO - Epoch [149][600/1281] lr: 6.437e-06, eta: 0:07:22, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-24 23:14:22,682 - pyskl - INFO - Epoch [149][700/1281] lr: 5.798e-06, eta: 0:06:59, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-24 23:14:45,384 - pyskl - INFO - Epoch [149][800/1281] lr: 5.192e-06, eta: 0:06:37, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-24 23:15:07,601 - pyskl - INFO - Epoch [149][900/1281] lr: 4.620e-06, eta: 0:06:14, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-24 23:15:29,867 - pyskl - INFO - Epoch [149][1000/1281] lr: 4.081e-06, eta: 0:05:52, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0222, loss: 0.0222 +2025-06-24 23:15:52,037 - pyskl - INFO - Epoch [149][1100/1281] lr: 3.576e-06, eta: 0:05:29, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-24 23:16:14,698 - pyskl - INFO - Epoch [149][1200/1281] lr: 3.104e-06, eta: 0:05:07, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-24 23:16:33,441 - pyskl - INFO - Saving checkpoint at 149 epochs +2025-06-24 23:17:17,079 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:17:17,137 - pyskl - INFO - +top1_acc 0.9302 +top5_acc 0.9953 +2025-06-24 23:17:17,138 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:17:17,148 - pyskl - INFO - +mean_acc 0.9041 +2025-06-24 23:17:17,151 - pyskl - INFO - Epoch(val) [149][533] top1_acc: 0.9302, top5_acc: 0.9953, mean_class_accuracy: 0.9041 +2025-06-24 23:18:00,395 - pyskl - INFO - Epoch [150][100/1281] lr: 2.334e-06, eta: 0:04:26, time: 0.432, data_time: 0.194, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-24 23:18:22,772 - pyskl - INFO - Epoch [150][200/1281] lr: 1.956e-06, eta: 0:04:03, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-24 23:18:45,388 - pyskl - INFO - Epoch [150][300/1281] lr: 1.611e-06, eta: 0:03:41, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-24 23:19:07,972 - pyskl - INFO - Epoch [150][400/1281] lr: 1.300e-06, eta: 0:03:18, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-24 23:19:30,496 - pyskl - INFO - Epoch [150][500/1281] lr: 1.022e-06, eta: 0:02:56, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-24 23:19:53,011 - pyskl - INFO - Epoch [150][600/1281] lr: 7.771e-07, eta: 0:02:33, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-24 23:20:15,655 - pyskl - INFO - Epoch [150][700/1281] lr: 5.659e-07, eta: 0:02:10, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-24 23:20:38,184 - pyskl - INFO - Epoch [150][800/1281] lr: 3.881e-07, eta: 0:01:48, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-24 23:21:00,603 - pyskl - INFO - Epoch [150][900/1281] lr: 2.438e-07, eta: 0:01:25, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-24 23:21:23,122 - pyskl - INFO - Epoch [150][1000/1281] lr: 1.329e-07, eta: 0:01:03, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-24 23:21:45,446 - pyskl - INFO - Epoch [150][1100/1281] lr: 5.534e-08, eta: 0:00:40, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-24 23:22:07,948 - pyskl - INFO - Epoch [150][1200/1281] lr: 1.123e-08, eta: 0:00:18, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-24 23:22:26,869 - pyskl - INFO - Saving checkpoint at 150 epochs +2025-06-24 23:23:10,772 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:23:10,842 - pyskl - INFO - +top1_acc 0.9296 +top5_acc 0.9952 +2025-06-24 23:23:10,842 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:23:10,850 - pyskl - INFO - +mean_acc 0.9025 +2025-06-24 23:23:10,852 - pyskl - INFO - Epoch(val) [150][533] top1_acc: 0.9296, top5_acc: 0.9952, mean_class_accuracy: 0.9025 +2025-06-24 23:23:15,325 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-24 23:28:38,227 - pyskl - INFO - Testing results of the last checkpoint +2025-06-24 23:28:38,227 - pyskl - INFO - top1_acc: 0.9338 +2025-06-24 23:28:38,227 - pyskl - INFO - top5_acc: 0.9965 +2025-06-24 23:28:38,227 - pyskl - INFO - mean_class_accuracy: 0.9096 +2025-06-24 23:28:38,228 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/finegym/j_1/best_top1_acc_epoch_142.pth +2025-06-24 23:34:03,032 - pyskl - INFO - Testing results of the best checkpoint +2025-06-24 23:34:03,032 - pyskl - INFO - top1_acc: 0.9331 +2025-06-24 23:34:03,032 - pyskl - INFO - top5_acc: 0.9961 +2025-06-24 23:34:03,032 - pyskl - INFO - mean_class_accuracy: 0.9105 diff --git a/finegym/j_1/20250624_084414.log.json b/finegym/j_1/20250624_084414.log.json new file mode 100644 index 0000000000000000000000000000000000000000..cfccbec071e5bcc034b0c41a40f62a50a447d5ba --- /dev/null +++ b/finegym/j_1/20250624_084414.log.json @@ -0,0 +1,1951 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 1637075804, "config_name": "j_1.py", "work_dir": "j_1", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.18277, "top1_acc": 0.05438, "top5_acc": 0.21438, "loss_cls": 4.61331, "loss": 4.61331, "time": 0.40037} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.07375, "top5_acc": 0.28938, "loss_cls": 4.63214, "loss": 4.63214, "time": 0.21991} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.09312, "top5_acc": 0.35938, "loss_cls": 4.39568, "loss": 4.39568, "time": 0.21685} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.14312, "top5_acc": 0.42562, "loss_cls": 4.12547, "loss": 4.12547, "time": 0.22004} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.17, "top5_acc": 0.50562, "loss_cls": 3.82584, "loss": 3.82584, "time": 0.21685} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.19, "top5_acc": 0.53875, "loss_cls": 3.6372, "loss": 3.6372, "time": 0.21959} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.23, "top5_acc": 0.59188, "loss_cls": 3.45, "loss": 3.45, "time": 0.22146} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.25312, "top5_acc": 0.59875, "loss_cls": 3.34608, "loss": 3.34608, "time": 0.21868} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.025, "memory": 4082, "data_time": 0.00053, "top1_acc": 0.31125, "top5_acc": 0.66062, "loss_cls": 3.03856, "loss": 3.03856, "time": 0.21914} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.32312, "top5_acc": 0.70438, "loss_cls": 2.92642, "loss": 2.92642, "time": 0.21834} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.025, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.3325, "top5_acc": 0.69375, "loss_cls": 2.87633, "loss": 2.87633, "time": 0.21855} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.37062, "top5_acc": 0.75188, "loss_cls": 2.70327, "loss": 2.70327, "time": 0.21859} +{"mode": "val", "epoch": 1, "iter": 533, "lr": 0.025, "top1_acc": 0.38082, "top5_acc": 0.76446, "mean_class_accuracy": 0.19444} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.18028, "top1_acc": 0.39875, "top5_acc": 0.78688, "loss_cls": 2.53471, "loss": 2.53471, "time": 0.39954} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.43312, "top5_acc": 0.81562, "loss_cls": 2.39079, "loss": 2.39079, "time": 0.22158} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.43, "top5_acc": 0.80688, "loss_cls": 2.39309, "loss": 2.39309, "time": 0.21854} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.44375, "top5_acc": 0.83812, "loss_cls": 2.24608, "loss": 2.24608, "time": 0.22239} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.02499, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.4675, "top5_acc": 0.85625, "loss_cls": 2.21737, "loss": 2.21737, "time": 0.22124} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.02499, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.45062, "top5_acc": 0.85062, "loss_cls": 2.21942, "loss": 2.21942, "time": 0.22132} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.02499, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.485, "top5_acc": 0.86938, "loss_cls": 2.10798, "loss": 2.10798, "time": 0.21774} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.02499, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.48875, "top5_acc": 0.87125, "loss_cls": 2.07425, "loss": 2.07425, "time": 0.21866} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.02499, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.49875, "top5_acc": 0.88688, "loss_cls": 2.00665, "loss": 2.00665, "time": 0.22107} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.4975, "top5_acc": 0.88438, "loss_cls": 2.0255, "loss": 2.0255, "time": 0.21568} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.02499, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.50625, "top5_acc": 0.87562, "loss_cls": 2.02183, "loss": 2.02183, "time": 0.21908} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.02499, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.49625, "top5_acc": 0.89062, "loss_cls": 1.98483, "loss": 1.98483, "time": 0.21755} +{"mode": "val", "epoch": 2, "iter": 533, "lr": 0.02499, "top1_acc": 0.45769, "top5_acc": 0.86586, "mean_class_accuracy": 0.28812} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.02499, "memory": 4082, "data_time": 0.18336, "top1_acc": 0.505, "top5_acc": 0.88188, "loss_cls": 1.9811, "loss": 1.9811, "time": 0.40169} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.02499, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.51812, "top5_acc": 0.9075, "loss_cls": 1.84668, "loss": 1.84668, "time": 0.21725} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.02499, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.5025, "top5_acc": 0.89312, "loss_cls": 1.93696, "loss": 1.93696, "time": 0.21641} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.545, "top5_acc": 0.90875, "loss_cls": 1.85429, "loss": 1.85429, "time": 0.21718} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.02498, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.54812, "top5_acc": 0.90375, "loss_cls": 1.82963, "loss": 1.82963, "time": 0.21688} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.02498, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.54812, "top5_acc": 0.90312, "loss_cls": 1.82837, "loss": 1.82837, "time": 0.21565} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.02498, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.55688, "top5_acc": 0.9275, "loss_cls": 1.74946, "loss": 1.74946, "time": 0.21763} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.02498, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.53688, "top5_acc": 0.91375, "loss_cls": 1.7888, "loss": 1.7888, "time": 0.21781} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.02498, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.5625, "top5_acc": 0.925, "loss_cls": 1.71147, "loss": 1.71147, "time": 0.21969} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.02498, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.58375, "top5_acc": 0.935, "loss_cls": 1.68432, "loss": 1.68432, "time": 0.22048} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.02498, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.58312, "top5_acc": 0.9175, "loss_cls": 1.69849, "loss": 1.69849, "time": 0.2222} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.02498, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.56312, "top5_acc": 0.9375, "loss_cls": 1.67237, "loss": 1.67237, "time": 0.21982} +{"mode": "val", "epoch": 3, "iter": 533, "lr": 0.02498, "top1_acc": 0.54818, "top5_acc": 0.90682, "mean_class_accuracy": 0.37762} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.02497, "memory": 4082, "data_time": 0.18055, "top1_acc": 0.58312, "top5_acc": 0.9375, "loss_cls": 1.64664, "loss": 1.64664, "time": 0.39892} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.02497, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.5875, "top5_acc": 0.93312, "loss_cls": 1.61179, "loss": 1.61179, "time": 0.21787} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.02497, "memory": 4082, "data_time": 0.0002, "top1_acc": 0.59438, "top5_acc": 0.935, "loss_cls": 1.63655, "loss": 1.63655, "time": 0.21522} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.02497, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.60938, "top5_acc": 0.945, "loss_cls": 1.54152, "loss": 1.54152, "time": 0.21737} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.02497, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.5825, "top5_acc": 0.93, "loss_cls": 1.61516, "loss": 1.61516, "time": 0.21892} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.02497, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.61812, "top5_acc": 0.93562, "loss_cls": 1.5518, "loss": 1.5518, "time": 0.21801} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.02497, "memory": 4082, "data_time": 0.00082, "top1_acc": 0.60312, "top5_acc": 0.95, "loss_cls": 1.56438, "loss": 1.56438, "time": 0.21967} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.02496, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.62062, "top5_acc": 0.94562, "loss_cls": 1.5425, "loss": 1.5425, "time": 0.21599} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.02496, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.5975, "top5_acc": 0.94688, "loss_cls": 1.56801, "loss": 1.56801, "time": 0.22302} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.02496, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.61062, "top5_acc": 0.94375, "loss_cls": 1.56416, "loss": 1.56416, "time": 0.21817} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.02496, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.62125, "top5_acc": 0.94062, "loss_cls": 1.54493, "loss": 1.54493, "time": 0.21851} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.02496, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.61312, "top5_acc": 0.9475, "loss_cls": 1.53932, "loss": 1.53932, "time": 0.21891} +{"mode": "val", "epoch": 4, "iter": 533, "lr": 0.02496, "top1_acc": 0.60474, "top5_acc": 0.94003, "mean_class_accuracy": 0.42526} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.02495, "memory": 4082, "data_time": 0.18289, "top1_acc": 0.65188, "top5_acc": 0.96188, "loss_cls": 1.43856, "loss": 1.43856, "time": 0.40128} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.02495, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.63375, "top5_acc": 0.95562, "loss_cls": 1.46162, "loss": 1.46162, "time": 0.2195} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.02495, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.65312, "top5_acc": 0.96, "loss_cls": 1.40935, "loss": 1.40935, "time": 0.21659} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.02495, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.62125, "top5_acc": 0.94625, "loss_cls": 1.50724, "loss": 1.50724, "time": 0.21835} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.02495, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.625, "top5_acc": 0.94875, "loss_cls": 1.46227, "loss": 1.46227, "time": 0.2198} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.02495, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.64062, "top5_acc": 0.95938, "loss_cls": 1.46236, "loss": 1.46236, "time": 0.21606} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.02494, "memory": 4082, "data_time": 0.00062, "top1_acc": 0.65812, "top5_acc": 0.95312, "loss_cls": 1.39951, "loss": 1.39951, "time": 0.21737} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.02494, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.67, "top5_acc": 0.96688, "loss_cls": 1.34939, "loss": 1.34939, "time": 0.21904} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.02494, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.66125, "top5_acc": 0.96375, "loss_cls": 1.3644, "loss": 1.3644, "time": 0.22093} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.02494, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.64438, "top5_acc": 0.9525, "loss_cls": 1.43526, "loss": 1.43526, "time": 0.2212} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.02494, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.65625, "top5_acc": 0.96375, "loss_cls": 1.35454, "loss": 1.35454, "time": 0.22143} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.02493, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.665, "top5_acc": 0.96062, "loss_cls": 1.40101, "loss": 1.40101, "time": 0.21654} +{"mode": "val", "epoch": 5, "iter": 533, "lr": 0.02493, "top1_acc": 0.62352, "top5_acc": 0.95012, "mean_class_accuracy": 0.48133} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.02493, "memory": 4082, "data_time": 0.18115, "top1_acc": 0.68312, "top5_acc": 0.96438, "loss_cls": 1.28613, "loss": 1.28613, "time": 0.3988} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.02493, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.6775, "top5_acc": 0.96938, "loss_cls": 1.29893, "loss": 1.29893, "time": 0.21905} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.02492, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.69188, "top5_acc": 0.9625, "loss_cls": 1.32533, "loss": 1.32533, "time": 0.21754} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.02492, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.67562, "top5_acc": 0.96188, "loss_cls": 1.33585, "loss": 1.33585, "time": 0.2189} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.02492, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.66938, "top5_acc": 0.95875, "loss_cls": 1.3386, "loss": 1.3386, "time": 0.21574} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.02492, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.66188, "top5_acc": 0.96625, "loss_cls": 1.3619, "loss": 1.3619, "time": 0.21899} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.02492, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.67438, "top5_acc": 0.96375, "loss_cls": 1.29435, "loss": 1.29435, "time": 0.21659} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.02491, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.69312, "top5_acc": 0.9675, "loss_cls": 1.26994, "loss": 1.26994, "time": 0.21751} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.02491, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.68062, "top5_acc": 0.96688, "loss_cls": 1.31091, "loss": 1.31091, "time": 0.22122} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.02491, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.67688, "top5_acc": 0.96938, "loss_cls": 1.28107, "loss": 1.28107, "time": 0.21751} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.02491, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.68438, "top5_acc": 0.95812, "loss_cls": 1.31472, "loss": 1.31472, "time": 0.21699} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.0249, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.685, "top5_acc": 0.97125, "loss_cls": 1.25377, "loss": 1.25377, "time": 0.21965} +{"mode": "val", "epoch": 6, "iter": 533, "lr": 0.0249, "top1_acc": 0.64276, "top5_acc": 0.95024, "mean_class_accuracy": 0.50045} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0249, "memory": 4082, "data_time": 0.18275, "top1_acc": 0.68312, "top5_acc": 0.97312, "loss_cls": 1.27648, "loss": 1.27648, "time": 0.40143} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0249, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.70688, "top5_acc": 0.97188, "loss_cls": 1.22341, "loss": 1.22341, "time": 0.22034} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.02489, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.68875, "top5_acc": 0.97438, "loss_cls": 1.22671, "loss": 1.22671, "time": 0.22089} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.02489, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.6975, "top5_acc": 0.96438, "loss_cls": 1.2346, "loss": 1.2346, "time": 0.21723} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.02489, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.705, "top5_acc": 0.96938, "loss_cls": 1.20657, "loss": 1.20657, "time": 0.21915} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.02489, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.68125, "top5_acc": 0.96188, "loss_cls": 1.30361, "loss": 1.30361, "time": 0.21638} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.02488, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.69875, "top5_acc": 0.97, "loss_cls": 1.23756, "loss": 1.23756, "time": 0.22022} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.02488, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.6975, "top5_acc": 0.97688, "loss_cls": 1.2328, "loss": 1.2328, "time": 0.21774} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.02488, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.70688, "top5_acc": 0.97125, "loss_cls": 1.19346, "loss": 1.19346, "time": 0.22121} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.02487, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7025, "top5_acc": 0.97438, "loss_cls": 1.2029, "loss": 1.2029, "time": 0.22104} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.02487, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.70625, "top5_acc": 0.97312, "loss_cls": 1.20216, "loss": 1.20216, "time": 0.21807} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.02487, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.72312, "top5_acc": 0.98312, "loss_cls": 1.14098, "loss": 1.14098, "time": 0.22001} +{"mode": "val", "epoch": 7, "iter": 533, "lr": 0.02487, "top1_acc": 0.67128, "top5_acc": 0.96139, "mean_class_accuracy": 0.52756} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.02486, "memory": 4082, "data_time": 0.17522, "top1_acc": 0.72125, "top5_acc": 0.97688, "loss_cls": 1.13739, "loss": 1.13739, "time": 0.39398} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.02486, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.70938, "top5_acc": 0.965, "loss_cls": 1.19306, "loss": 1.19306, "time": 0.21673} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.02486, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.72375, "top5_acc": 0.98, "loss_cls": 1.11353, "loss": 1.11353, "time": 0.21724} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.02485, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.73125, "top5_acc": 0.97125, "loss_cls": 1.14133, "loss": 1.14133, "time": 0.2198} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.02485, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.70562, "top5_acc": 0.97125, "loss_cls": 1.19313, "loss": 1.19313, "time": 0.21753} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.02485, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.73688, "top5_acc": 0.97188, "loss_cls": 1.11876, "loss": 1.11876, "time": 0.21781} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.02484, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.72938, "top5_acc": 0.97062, "loss_cls": 1.10405, "loss": 1.10405, "time": 0.21868} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.02484, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.69125, "top5_acc": 0.97, "loss_cls": 1.23285, "loss": 1.23285, "time": 0.22066} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.02484, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.73938, "top5_acc": 0.97312, "loss_cls": 1.11524, "loss": 1.11524, "time": 0.21828} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.02483, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.73, "top5_acc": 0.96938, "loss_cls": 1.15919, "loss": 1.15919, "time": 0.21668} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.02483, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.73, "top5_acc": 0.97312, "loss_cls": 1.09543, "loss": 1.09543, "time": 0.21835} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.02483, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.71938, "top5_acc": 0.97312, "loss_cls": 1.16392, "loss": 1.16392, "time": 0.2179} +{"mode": "val", "epoch": 8, "iter": 533, "lr": 0.02482, "top1_acc": 0.70661, "top5_acc": 0.96221, "mean_class_accuracy": 0.59394} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.02482, "memory": 4082, "data_time": 0.17711, "top1_acc": 0.74125, "top5_acc": 0.98375, "loss_cls": 1.06462, "loss": 1.06462, "time": 0.39705} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.02482, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.74312, "top5_acc": 0.97812, "loss_cls": 1.06729, "loss": 1.06729, "time": 0.2197} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.02481, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.74188, "top5_acc": 0.97562, "loss_cls": 1.09775, "loss": 1.09775, "time": 0.21815} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.02481, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.72, "top5_acc": 0.97688, "loss_cls": 1.14578, "loss": 1.14578, "time": 0.2151} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.02481, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.71375, "top5_acc": 0.97688, "loss_cls": 1.12959, "loss": 1.12959, "time": 0.21652} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.0248, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.72312, "top5_acc": 0.9725, "loss_cls": 1.11317, "loss": 1.11317, "time": 0.21876} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.0248, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.72188, "top5_acc": 0.9725, "loss_cls": 1.13155, "loss": 1.13155, "time": 0.22014} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.0248, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.7075, "top5_acc": 0.97688, "loss_cls": 1.17666, "loss": 1.17666, "time": 0.21817} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.02479, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.74375, "top5_acc": 0.97812, "loss_cls": 1.09848, "loss": 1.09848, "time": 0.22047} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.02479, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.72062, "top5_acc": 0.97312, "loss_cls": 1.12064, "loss": 1.12064, "time": 0.21442} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.02479, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.72812, "top5_acc": 0.97562, "loss_cls": 1.13293, "loss": 1.13293, "time": 0.21785} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.02478, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.73688, "top5_acc": 0.97688, "loss_cls": 1.09979, "loss": 1.09979, "time": 0.21529} +{"mode": "val", "epoch": 9, "iter": 533, "lr": 0.02478, "top1_acc": 0.69323, "top5_acc": 0.97266, "mean_class_accuracy": 0.56387} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.02477, "memory": 4082, "data_time": 0.18151, "top1_acc": 0.75125, "top5_acc": 0.98188, "loss_cls": 1.03983, "loss": 1.03983, "time": 0.40293} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.02477, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.745, "top5_acc": 0.97375, "loss_cls": 1.07512, "loss": 1.07512, "time": 0.21853} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.02477, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.74062, "top5_acc": 0.98, "loss_cls": 1.06894, "loss": 1.06894, "time": 0.22085} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.02476, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.7325, "top5_acc": 0.98188, "loss_cls": 1.05816, "loss": 1.05816, "time": 0.2184} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.02476, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.74625, "top5_acc": 0.98188, "loss_cls": 1.05764, "loss": 1.05764, "time": 0.21924} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.02476, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.73188, "top5_acc": 0.97125, "loss_cls": 1.10858, "loss": 1.10858, "time": 0.21974} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.02475, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.74312, "top5_acc": 0.98, "loss_cls": 1.04085, "loss": 1.04085, "time": 0.21959} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.02475, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.72188, "top5_acc": 0.97812, "loss_cls": 1.12748, "loss": 1.12748, "time": 0.21675} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.02474, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.73875, "top5_acc": 0.9775, "loss_cls": 1.10408, "loss": 1.10408, "time": 0.21998} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.02474, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.73125, "top5_acc": 0.9725, "loss_cls": 1.09729, "loss": 1.09729, "time": 0.21917} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.02473, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.73562, "top5_acc": 0.98312, "loss_cls": 1.08534, "loss": 1.08534, "time": 0.22062} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.02473, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.73625, "top5_acc": 0.97688, "loss_cls": 1.1207, "loss": 1.1207, "time": 0.22007} +{"mode": "val", "epoch": 10, "iter": 533, "lr": 0.02473, "top1_acc": 0.69992, "top5_acc": 0.96573, "mean_class_accuracy": 0.57508} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.02472, "memory": 4082, "data_time": 0.18342, "top1_acc": 0.76062, "top5_acc": 0.98438, "loss_cls": 1.01405, "loss": 1.01405, "time": 0.40041} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.02472, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.7375, "top5_acc": 0.97625, "loss_cls": 1.04439, "loss": 1.04439, "time": 0.21679} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.02471, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.76625, "top5_acc": 0.98438, "loss_cls": 0.97995, "loss": 0.97995, "time": 0.21901} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.02471, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.74312, "top5_acc": 0.97812, "loss_cls": 1.05618, "loss": 1.05618, "time": 0.21862} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.02471, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.74562, "top5_acc": 0.97938, "loss_cls": 1.03434, "loss": 1.03434, "time": 0.21721} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.0247, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.74812, "top5_acc": 0.97688, "loss_cls": 1.07645, "loss": 1.07645, "time": 0.21666} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.0247, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.75812, "top5_acc": 0.97438, "loss_cls": 1.05353, "loss": 1.05353, "time": 0.2176} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.02469, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.7625, "top5_acc": 0.9775, "loss_cls": 1.04513, "loss": 1.04513, "time": 0.21662} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.02469, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.74125, "top5_acc": 0.97688, "loss_cls": 1.06705, "loss": 1.06705, "time": 0.21718} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.02468, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.75812, "top5_acc": 0.97938, "loss_cls": 1.01556, "loss": 1.01556, "time": 0.21308} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.02468, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.755, "top5_acc": 0.98875, "loss_cls": 1.00582, "loss": 1.00582, "time": 0.2192} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.02467, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.72062, "top5_acc": 0.97125, "loss_cls": 1.14691, "loss": 1.14691, "time": 0.21959} +{"mode": "val", "epoch": 11, "iter": 533, "lr": 0.02467, "top1_acc": 0.7329, "top5_acc": 0.97911, "mean_class_accuracy": 0.6203} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.02467, "memory": 4082, "data_time": 0.18172, "top1_acc": 0.765, "top5_acc": 0.98562, "loss_cls": 0.97609, "loss": 0.97609, "time": 0.39887} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.02466, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.75438, "top5_acc": 0.98625, "loss_cls": 1.01113, "loss": 1.01113, "time": 0.21618} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.02466, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.77438, "top5_acc": 0.98438, "loss_cls": 0.9553, "loss": 0.9553, "time": 0.21646} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.02465, "memory": 4082, "data_time": 0.0002, "top1_acc": 0.76812, "top5_acc": 0.98, "loss_cls": 0.99908, "loss": 0.99908, "time": 0.21568} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.02465, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.74125, "top5_acc": 0.97562, "loss_cls": 1.04542, "loss": 1.04542, "time": 0.21429} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.02464, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.77188, "top5_acc": 0.98312, "loss_cls": 0.98696, "loss": 0.98696, "time": 0.21784} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.02464, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.74625, "top5_acc": 0.98, "loss_cls": 1.03808, "loss": 1.03808, "time": 0.21558} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.02463, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.73312, "top5_acc": 0.98188, "loss_cls": 1.05749, "loss": 1.05749, "time": 0.22082} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.02463, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.76188, "top5_acc": 0.97812, "loss_cls": 1.02426, "loss": 1.02426, "time": 0.21821} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.02462, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.75062, "top5_acc": 0.97938, "loss_cls": 1.05058, "loss": 1.05058, "time": 0.21674} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.02462, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.75562, "top5_acc": 0.97562, "loss_cls": 1.02914, "loss": 1.02914, "time": 0.21961} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.02461, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.73312, "top5_acc": 0.9775, "loss_cls": 1.11492, "loss": 1.11492, "time": 0.22216} +{"mode": "val", "epoch": 12, "iter": 533, "lr": 0.02461, "top1_acc": 0.70403, "top5_acc": 0.96925, "mean_class_accuracy": 0.59896} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.0246, "memory": 4082, "data_time": 0.17676, "top1_acc": 0.76812, "top5_acc": 0.98312, "loss_cls": 0.98093, "loss": 0.98093, "time": 0.39328} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.0246, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.77, "top5_acc": 0.9875, "loss_cls": 0.9454, "loss": 0.9454, "time": 0.21878} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.02459, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.76125, "top5_acc": 0.98375, "loss_cls": 0.97235, "loss": 0.97235, "time": 0.21671} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.02459, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.76438, "top5_acc": 0.98062, "loss_cls": 0.97606, "loss": 0.97606, "time": 0.2195} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.02458, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.75438, "top5_acc": 0.9825, "loss_cls": 1.04553, "loss": 1.04553, "time": 0.21779} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.02458, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.745, "top5_acc": 0.98375, "loss_cls": 1.00887, "loss": 1.00887, "time": 0.21971} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.02457, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.7625, "top5_acc": 0.98438, "loss_cls": 0.99647, "loss": 0.99647, "time": 0.21851} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.02457, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.7625, "top5_acc": 0.98375, "loss_cls": 0.98284, "loss": 0.98284, "time": 0.21747} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.02456, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.7575, "top5_acc": 0.97938, "loss_cls": 1.01771, "loss": 1.01771, "time": 0.22071} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.02455, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.77125, "top5_acc": 0.98125, "loss_cls": 0.97098, "loss": 0.97098, "time": 0.21896} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.02455, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.74938, "top5_acc": 0.98312, "loss_cls": 1.017, "loss": 1.017, "time": 0.22277} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.02454, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.77625, "top5_acc": 0.97312, "loss_cls": 1.04415, "loss": 1.04415, "time": 0.22147} +{"mode": "val", "epoch": 13, "iter": 533, "lr": 0.02454, "top1_acc": 0.72292, "top5_acc": 0.97207, "mean_class_accuracy": 0.61457} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.02453, "memory": 4082, "data_time": 0.18235, "top1_acc": 0.7675, "top5_acc": 0.98438, "loss_cls": 0.97656, "loss": 0.97656, "time": 0.3983} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.02453, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.77312, "top5_acc": 0.98125, "loss_cls": 0.9495, "loss": 0.9495, "time": 0.21655} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.02452, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78625, "top5_acc": 0.98938, "loss_cls": 0.88292, "loss": 0.88292, "time": 0.21917} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.02452, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.76438, "top5_acc": 0.9825, "loss_cls": 0.96488, "loss": 0.96488, "time": 0.2158} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.02451, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7575, "top5_acc": 0.98312, "loss_cls": 1.01354, "loss": 1.01354, "time": 0.21528} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.02451, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.7625, "top5_acc": 0.98562, "loss_cls": 1.00743, "loss": 1.00743, "time": 0.21649} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.0245, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.76938, "top5_acc": 0.98625, "loss_cls": 1.00117, "loss": 1.00117, "time": 0.22178} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.02449, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.76438, "top5_acc": 0.9825, "loss_cls": 0.96358, "loss": 0.96358, "time": 0.21793} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.02449, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.76562, "top5_acc": 0.9775, "loss_cls": 1.0293, "loss": 1.0293, "time": 0.21778} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.02448, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.74375, "top5_acc": 0.97688, "loss_cls": 1.04995, "loss": 1.04995, "time": 0.21724} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.02448, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.76812, "top5_acc": 0.98, "loss_cls": 1.00653, "loss": 1.00653, "time": 0.21691} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.02447, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.775, "top5_acc": 0.98375, "loss_cls": 0.97354, "loss": 0.97354, "time": 0.21985} +{"mode": "val", "epoch": 14, "iter": 533, "lr": 0.02447, "top1_acc": 0.71893, "top5_acc": 0.9777, "mean_class_accuracy": 0.61108} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.02446, "memory": 4082, "data_time": 0.18699, "top1_acc": 0.76312, "top5_acc": 0.985, "loss_cls": 0.96752, "loss": 0.96752, "time": 0.4039} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.02445, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.76312, "top5_acc": 0.97875, "loss_cls": 1.00044, "loss": 1.00044, "time": 0.21908} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.02445, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7875, "top5_acc": 0.98562, "loss_cls": 0.89779, "loss": 0.89779, "time": 0.21727} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.02444, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.775, "top5_acc": 0.98438, "loss_cls": 0.95982, "loss": 0.95982, "time": 0.21868} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.02444, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.78812, "top5_acc": 0.98062, "loss_cls": 0.94042, "loss": 0.94042, "time": 0.21572} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.02443, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.7775, "top5_acc": 0.97562, "loss_cls": 0.98399, "loss": 0.98399, "time": 0.21627} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.02442, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.76312, "top5_acc": 0.9825, "loss_cls": 0.99943, "loss": 0.99943, "time": 0.2204} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.02442, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.75938, "top5_acc": 0.98812, "loss_cls": 0.9786, "loss": 0.9786, "time": 0.21862} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.02441, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.76875, "top5_acc": 0.98938, "loss_cls": 0.9617, "loss": 0.9617, "time": 0.21809} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.02441, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.77188, "top5_acc": 0.98688, "loss_cls": 0.9374, "loss": 0.9374, "time": 0.22028} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.0244, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.76312, "top5_acc": 0.98438, "loss_cls": 0.97026, "loss": 0.97026, "time": 0.218} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.02439, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.75812, "top5_acc": 0.98562, "loss_cls": 0.98879, "loss": 0.98879, "time": 0.21983} +{"mode": "val", "epoch": 15, "iter": 533, "lr": 0.02439, "top1_acc": 0.70931, "top5_acc": 0.97148, "mean_class_accuracy": 0.58026} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.02438, "memory": 4082, "data_time": 0.1811, "top1_acc": 0.78812, "top5_acc": 0.98812, "loss_cls": 0.94668, "loss": 0.94668, "time": 0.40219} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.02438, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.76875, "top5_acc": 0.9875, "loss_cls": 0.91928, "loss": 0.91928, "time": 0.21861} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.02437, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.78375, "top5_acc": 0.98625, "loss_cls": 0.9298, "loss": 0.9298, "time": 0.2191} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.02436, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80062, "top5_acc": 0.98188, "loss_cls": 0.88758, "loss": 0.88758, "time": 0.21734} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.02436, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.79188, "top5_acc": 0.98812, "loss_cls": 0.903, "loss": 0.903, "time": 0.21622} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.02435, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.77062, "top5_acc": 0.97875, "loss_cls": 0.99297, "loss": 0.99297, "time": 0.21909} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.02434, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.76562, "top5_acc": 0.98938, "loss_cls": 0.94499, "loss": 0.94499, "time": 0.22339} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.02434, "memory": 4082, "data_time": 0.00098, "top1_acc": 0.78812, "top5_acc": 0.98688, "loss_cls": 0.89903, "loss": 0.89903, "time": 0.21986} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.02433, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.75438, "top5_acc": 0.98375, "loss_cls": 0.97418, "loss": 0.97418, "time": 0.21794} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.02432, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.78062, "top5_acc": 0.97688, "loss_cls": 0.98931, "loss": 0.98931, "time": 0.22086} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.02432, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.77625, "top5_acc": 0.98312, "loss_cls": 0.92957, "loss": 0.92957, "time": 0.21751} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.02431, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.7725, "top5_acc": 0.98438, "loss_cls": 0.95385, "loss": 0.95385, "time": 0.22281} +{"mode": "val", "epoch": 16, "iter": 533, "lr": 0.0243, "top1_acc": 0.74275, "top5_acc": 0.97958, "mean_class_accuracy": 0.64226} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.0243, "memory": 4082, "data_time": 0.19192, "top1_acc": 0.78125, "top5_acc": 0.98062, "loss_cls": 0.96241, "loss": 0.96241, "time": 0.4131} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.02429, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8, "top5_acc": 0.98875, "loss_cls": 0.83677, "loss": 0.83677, "time": 0.22079} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.02428, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.77875, "top5_acc": 0.97875, "loss_cls": 0.95424, "loss": 0.95424, "time": 0.21975} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.02428, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78938, "top5_acc": 0.9875, "loss_cls": 0.88344, "loss": 0.88344, "time": 0.21801} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.02427, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.7975, "top5_acc": 0.98438, "loss_cls": 0.88538, "loss": 0.88538, "time": 0.21969} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.02426, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.76625, "top5_acc": 0.97875, "loss_cls": 0.99138, "loss": 0.99138, "time": 0.2181} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.02426, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78062, "top5_acc": 0.98188, "loss_cls": 0.95983, "loss": 0.95983, "time": 0.2206} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.02425, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.79375, "top5_acc": 0.98625, "loss_cls": 0.9001, "loss": 0.9001, "time": 0.21993} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.02424, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78375, "top5_acc": 0.985, "loss_cls": 0.9262, "loss": 0.9262, "time": 0.21883} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.02424, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.79938, "top5_acc": 0.98875, "loss_cls": 0.85255, "loss": 0.85255, "time": 0.2193} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.02423, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.78188, "top5_acc": 0.98312, "loss_cls": 0.92908, "loss": 0.92908, "time": 0.21874} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.02422, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.75938, "top5_acc": 0.98375, "loss_cls": 1.00486, "loss": 1.00486, "time": 0.2198} +{"mode": "val", "epoch": 17, "iter": 533, "lr": 0.02422, "top1_acc": 0.74088, "top5_acc": 0.98087, "mean_class_accuracy": 0.62054} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.02421, "memory": 4082, "data_time": 0.19107, "top1_acc": 0.79438, "top5_acc": 0.985, "loss_cls": 0.87368, "loss": 0.87368, "time": 0.41028} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.0242, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.76938, "top5_acc": 0.98875, "loss_cls": 0.93024, "loss": 0.93024, "time": 0.21949} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.02419, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79188, "top5_acc": 0.98875, "loss_cls": 0.84813, "loss": 0.84813, "time": 0.21809} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.02419, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.795, "top5_acc": 0.9875, "loss_cls": 0.88115, "loss": 0.88115, "time": 0.21964} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.02418, "memory": 4082, "data_time": 0.00061, "top1_acc": 0.7975, "top5_acc": 0.98812, "loss_cls": 0.87901, "loss": 0.87901, "time": 0.2233} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.02417, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79438, "top5_acc": 0.98375, "loss_cls": 0.91212, "loss": 0.91212, "time": 0.21848} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.02417, "memory": 4082, "data_time": 0.00049, "top1_acc": 0.78, "top5_acc": 0.98375, "loss_cls": 0.93887, "loss": 0.93887, "time": 0.22334} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.02416, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.78312, "top5_acc": 0.98375, "loss_cls": 0.93227, "loss": 0.93227, "time": 0.22069} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.02415, "memory": 4082, "data_time": 0.00054, "top1_acc": 0.79062, "top5_acc": 0.98438, "loss_cls": 0.8931, "loss": 0.8931, "time": 0.22153} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.02414, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.80625, "top5_acc": 0.98875, "loss_cls": 0.8713, "loss": 0.8713, "time": 0.22182} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.02414, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.80312, "top5_acc": 0.98188, "loss_cls": 0.88525, "loss": 0.88525, "time": 0.22115} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.02413, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.77312, "top5_acc": 0.98188, "loss_cls": 0.93389, "loss": 0.93389, "time": 0.22163} +{"mode": "val", "epoch": 18, "iter": 533, "lr": 0.02412, "top1_acc": 0.71776, "top5_acc": 0.96878, "mean_class_accuracy": 0.59262} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.02411, "memory": 4082, "data_time": 0.19652, "top1_acc": 0.79312, "top5_acc": 0.99062, "loss_cls": 0.88086, "loss": 0.88086, "time": 0.41683} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.02411, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78562, "top5_acc": 0.98562, "loss_cls": 0.89685, "loss": 0.89685, "time": 0.21828} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.0241, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.7875, "top5_acc": 0.98562, "loss_cls": 0.88307, "loss": 0.88307, "time": 0.21785} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.02409, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80188, "top5_acc": 0.98688, "loss_cls": 0.87299, "loss": 0.87299, "time": 0.2194} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.02408, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79688, "top5_acc": 0.98688, "loss_cls": 0.82944, "loss": 0.82944, "time": 0.21854} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.02408, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79688, "top5_acc": 0.9825, "loss_cls": 0.85509, "loss": 0.85509, "time": 0.21705} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.02407, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.8075, "top5_acc": 0.98375, "loss_cls": 0.88442, "loss": 0.88442, "time": 0.21777} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.02406, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.77812, "top5_acc": 0.98438, "loss_cls": 0.89884, "loss": 0.89884, "time": 0.21896} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.02405, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.77062, "top5_acc": 0.98375, "loss_cls": 0.95235, "loss": 0.95235, "time": 0.21793} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.02405, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79812, "top5_acc": 0.9875, "loss_cls": 0.906, "loss": 0.906, "time": 0.21702} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.02404, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.79, "top5_acc": 0.98938, "loss_cls": 0.86554, "loss": 0.86554, "time": 0.22056} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.02403, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81125, "top5_acc": 0.98375, "loss_cls": 0.8162, "loss": 0.8162, "time": 0.21965} +{"mode": "val", "epoch": 19, "iter": 533, "lr": 0.02402, "top1_acc": 0.78301, "top5_acc": 0.98557, "mean_class_accuracy": 0.68002} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.02402, "memory": 4082, "data_time": 0.18877, "top1_acc": 0.80125, "top5_acc": 0.99062, "loss_cls": 0.82667, "loss": 0.82667, "time": 0.41239} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.02401, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78312, "top5_acc": 0.98938, "loss_cls": 0.87951, "loss": 0.87951, "time": 0.21898} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.024, "memory": 4082, "data_time": 0.00065, "top1_acc": 0.82312, "top5_acc": 0.99, "loss_cls": 0.80563, "loss": 0.80563, "time": 0.22216} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.02399, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.78938, "top5_acc": 0.99062, "loss_cls": 0.8759, "loss": 0.8759, "time": 0.22037} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.02398, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.79125, "top5_acc": 0.98688, "loss_cls": 0.90373, "loss": 0.90373, "time": 0.22148} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.02398, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.79875, "top5_acc": 0.9875, "loss_cls": 0.86201, "loss": 0.86201, "time": 0.21985} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.02397, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.81688, "top5_acc": 0.98812, "loss_cls": 0.82493, "loss": 0.82493, "time": 0.21732} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.02396, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.76438, "top5_acc": 0.98375, "loss_cls": 0.94767, "loss": 0.94767, "time": 0.2218} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.02395, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80688, "top5_acc": 0.98812, "loss_cls": 0.87027, "loss": 0.87027, "time": 0.21945} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.02394, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79812, "top5_acc": 0.9875, "loss_cls": 0.86412, "loss": 0.86412, "time": 0.22044} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.02393, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.80938, "top5_acc": 0.98625, "loss_cls": 0.83521, "loss": 0.83521, "time": 0.21832} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.02393, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79938, "top5_acc": 0.99062, "loss_cls": 0.83927, "loss": 0.83927, "time": 0.21686} +{"mode": "val", "epoch": 20, "iter": 533, "lr": 0.02392, "top1_acc": 0.76317, "top5_acc": 0.98427, "mean_class_accuracy": 0.64846} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.02391, "memory": 4082, "data_time": 0.18988, "top1_acc": 0.82875, "top5_acc": 0.9925, "loss_cls": 0.82112, "loss": 0.82112, "time": 0.41008} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.0239, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82688, "top5_acc": 0.98812, "loss_cls": 0.78963, "loss": 0.78963, "time": 0.21966} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.02389, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.785, "top5_acc": 0.9875, "loss_cls": 0.87956, "loss": 0.87956, "time": 0.21933} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.02389, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80188, "top5_acc": 0.99125, "loss_cls": 0.83044, "loss": 0.83044, "time": 0.22119} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.02388, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82375, "top5_acc": 0.98875, "loss_cls": 0.77673, "loss": 0.77673, "time": 0.21762} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.02387, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.775, "top5_acc": 0.98875, "loss_cls": 0.90426, "loss": 0.90426, "time": 0.22142} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.02386, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80688, "top5_acc": 0.99, "loss_cls": 0.85063, "loss": 0.85063, "time": 0.21915} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.02385, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.80812, "top5_acc": 0.99, "loss_cls": 0.80841, "loss": 0.80841, "time": 0.22005} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.02384, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.80125, "top5_acc": 0.97938, "loss_cls": 0.89811, "loss": 0.89811, "time": 0.22039} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.02383, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.79875, "top5_acc": 0.98812, "loss_cls": 0.84998, "loss": 0.84998, "time": 0.22154} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.02383, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80625, "top5_acc": 0.98875, "loss_cls": 0.84916, "loss": 0.84916, "time": 0.219} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.02382, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.7925, "top5_acc": 0.98688, "loss_cls": 0.87978, "loss": 0.87978, "time": 0.22426} +{"mode": "val", "epoch": 21, "iter": 533, "lr": 0.02381, "top1_acc": 0.75296, "top5_acc": 0.97946, "mean_class_accuracy": 0.64951} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.0238, "memory": 4082, "data_time": 0.19223, "top1_acc": 0.81688, "top5_acc": 0.98938, "loss_cls": 0.84259, "loss": 0.84259, "time": 0.41686} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.02379, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.8275, "top5_acc": 0.9925, "loss_cls": 0.78582, "loss": 0.78582, "time": 0.22369} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.02378, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.815, "top5_acc": 0.99375, "loss_cls": 0.78593, "loss": 0.78593, "time": 0.21733} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.02378, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.79688, "top5_acc": 0.98875, "loss_cls": 0.85014, "loss": 0.85014, "time": 0.21905} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.02377, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.80562, "top5_acc": 0.99, "loss_cls": 0.83819, "loss": 0.83819, "time": 0.22451} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.02376, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8225, "top5_acc": 0.98812, "loss_cls": 0.78352, "loss": 0.78352, "time": 0.22133} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.02375, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.80125, "top5_acc": 0.98875, "loss_cls": 0.86148, "loss": 0.86148, "time": 0.21963} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.02374, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79375, "top5_acc": 0.985, "loss_cls": 0.87318, "loss": 0.87318, "time": 0.22131} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.02373, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.79438, "top5_acc": 0.98625, "loss_cls": 0.87945, "loss": 0.87945, "time": 0.21902} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.02372, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80625, "top5_acc": 0.98938, "loss_cls": 0.83407, "loss": 0.83407, "time": 0.21883} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.02371, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.825, "top5_acc": 0.99312, "loss_cls": 0.73375, "loss": 0.73375, "time": 0.21962} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0237, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80312, "top5_acc": 0.98438, "loss_cls": 0.82162, "loss": 0.82162, "time": 0.21884} +{"mode": "val", "epoch": 22, "iter": 533, "lr": 0.0237, "top1_acc": 0.72703, "top5_acc": 0.97641, "mean_class_accuracy": 0.63343} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.02369, "memory": 4082, "data_time": 0.19761, "top1_acc": 0.8225, "top5_acc": 0.99312, "loss_cls": 0.78845, "loss": 0.78845, "time": 0.41942} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.02368, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.8225, "top5_acc": 0.9925, "loss_cls": 0.78625, "loss": 0.78625, "time": 0.21936} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.02367, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82188, "top5_acc": 0.99312, "loss_cls": 0.75346, "loss": 0.75346, "time": 0.21895} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.02366, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83375, "top5_acc": 0.98812, "loss_cls": 0.72786, "loss": 0.72786, "time": 0.21942} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.02365, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.78688, "top5_acc": 0.98062, "loss_cls": 0.91371, "loss": 0.91371, "time": 0.22043} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.02364, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.81062, "top5_acc": 0.9925, "loss_cls": 0.79558, "loss": 0.79558, "time": 0.22074} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.02363, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.80875, "top5_acc": 0.98625, "loss_cls": 0.82868, "loss": 0.82868, "time": 0.22101} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.02362, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81125, "top5_acc": 0.98875, "loss_cls": 0.78451, "loss": 0.78451, "time": 0.22182} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.02361, "memory": 4082, "data_time": 0.00055, "top1_acc": 0.80188, "top5_acc": 0.99188, "loss_cls": 0.8166, "loss": 0.8166, "time": 0.22224} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.0236, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81375, "top5_acc": 0.99125, "loss_cls": 0.83279, "loss": 0.83279, "time": 0.22002} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.02359, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.79562, "top5_acc": 0.98188, "loss_cls": 0.89151, "loss": 0.89151, "time": 0.22038} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.02359, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79625, "top5_acc": 0.98938, "loss_cls": 0.83949, "loss": 0.83949, "time": 0.21738} +{"mode": "val", "epoch": 23, "iter": 533, "lr": 0.02358, "top1_acc": 0.70954, "top5_acc": 0.96127, "mean_class_accuracy": 0.60561} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.02357, "memory": 4082, "data_time": 0.19091, "top1_acc": 0.81188, "top5_acc": 0.98438, "loss_cls": 0.82372, "loss": 0.82372, "time": 0.4134} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.02356, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.8125, "top5_acc": 0.98875, "loss_cls": 0.82616, "loss": 0.82616, "time": 0.21776} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.02355, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81875, "top5_acc": 0.99062, "loss_cls": 0.7623, "loss": 0.7623, "time": 0.21986} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.02354, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81188, "top5_acc": 0.995, "loss_cls": 0.7814, "loss": 0.7814, "time": 0.21941} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.02353, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81562, "top5_acc": 0.98938, "loss_cls": 0.78985, "loss": 0.78985, "time": 0.22014} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.02352, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.82062, "top5_acc": 0.99, "loss_cls": 0.78893, "loss": 0.78893, "time": 0.21842} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.02351, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80438, "top5_acc": 0.98938, "loss_cls": 0.863, "loss": 0.863, "time": 0.21865} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.0235, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78938, "top5_acc": 0.98875, "loss_cls": 0.88036, "loss": 0.88036, "time": 0.22237} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.02349, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83562, "top5_acc": 0.9925, "loss_cls": 0.76228, "loss": 0.76228, "time": 0.21892} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.02348, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82625, "top5_acc": 0.99188, "loss_cls": 0.7642, "loss": 0.7642, "time": 0.219} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.02347, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.80688, "top5_acc": 0.98625, "loss_cls": 0.84185, "loss": 0.84185, "time": 0.21954} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.02346, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80688, "top5_acc": 0.98312, "loss_cls": 0.87231, "loss": 0.87231, "time": 0.21907} +{"mode": "val", "epoch": 24, "iter": 533, "lr": 0.02345, "top1_acc": 0.77573, "top5_acc": 0.98216, "mean_class_accuracy": 0.67514} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.02344, "memory": 4082, "data_time": 0.19405, "top1_acc": 0.81562, "top5_acc": 0.98812, "loss_cls": 0.82394, "loss": 0.82394, "time": 0.41432} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.02343, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82438, "top5_acc": 0.99438, "loss_cls": 0.73128, "loss": 0.73128, "time": 0.22243} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.02342, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.805, "top5_acc": 0.99062, "loss_cls": 0.78326, "loss": 0.78326, "time": 0.21928} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.02341, "memory": 4082, "data_time": 0.00056, "top1_acc": 0.81, "top5_acc": 0.99375, "loss_cls": 0.8037, "loss": 0.8037, "time": 0.22382} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.0234, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8225, "top5_acc": 0.99312, "loss_cls": 0.78634, "loss": 0.78634, "time": 0.2172} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.02339, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.79875, "top5_acc": 0.98312, "loss_cls": 0.87062, "loss": 0.87062, "time": 0.22224} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.02338, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82688, "top5_acc": 0.98875, "loss_cls": 0.75325, "loss": 0.75325, "time": 0.22067} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.02337, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.8125, "top5_acc": 0.99188, "loss_cls": 0.80209, "loss": 0.80209, "time": 0.21885} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.02336, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.81875, "top5_acc": 0.99188, "loss_cls": 0.81218, "loss": 0.81218, "time": 0.22332} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.02335, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.81625, "top5_acc": 0.99, "loss_cls": 0.80304, "loss": 0.80304, "time": 0.22241} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.02334, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7875, "top5_acc": 0.98812, "loss_cls": 0.85609, "loss": 0.85609, "time": 0.22372} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.02333, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8225, "top5_acc": 0.99062, "loss_cls": 0.79807, "loss": 0.79807, "time": 0.22121} +{"mode": "val", "epoch": 25, "iter": 533, "lr": 0.02333, "top1_acc": 0.72327, "top5_acc": 0.97101, "mean_class_accuracy": 0.62883} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.02332, "memory": 4082, "data_time": 0.19543, "top1_acc": 0.83, "top5_acc": 0.99188, "loss_cls": 0.73936, "loss": 0.73936, "time": 0.41775} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.0233, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82375, "top5_acc": 0.99312, "loss_cls": 0.73572, "loss": 0.73572, "time": 0.2205} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.02329, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82938, "top5_acc": 0.99562, "loss_cls": 0.72783, "loss": 0.72783, "time": 0.21935} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.02328, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.83438, "top5_acc": 0.99, "loss_cls": 0.7511, "loss": 0.7511, "time": 0.22018} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.02327, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83625, "top5_acc": 0.99188, "loss_cls": 0.77401, "loss": 0.77401, "time": 0.22072} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.02326, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.8225, "top5_acc": 0.9925, "loss_cls": 0.77571, "loss": 0.77571, "time": 0.21835} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.02325, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81625, "top5_acc": 0.98938, "loss_cls": 0.82076, "loss": 0.82076, "time": 0.21785} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.02324, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82438, "top5_acc": 0.99062, "loss_cls": 0.78771, "loss": 0.78771, "time": 0.22032} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.02323, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.80562, "top5_acc": 0.98938, "loss_cls": 0.8184, "loss": 0.8184, "time": 0.21983} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.02322, "memory": 4082, "data_time": 0.00053, "top1_acc": 0.8175, "top5_acc": 0.98438, "loss_cls": 0.83415, "loss": 0.83415, "time": 0.22214} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.02321, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82125, "top5_acc": 0.98938, "loss_cls": 0.78004, "loss": 0.78004, "time": 0.22124} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.0232, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8225, "top5_acc": 0.9925, "loss_cls": 0.77431, "loss": 0.77431, "time": 0.21915} +{"mode": "val", "epoch": 26, "iter": 533, "lr": 0.02319, "top1_acc": 0.79322, "top5_acc": 0.98639, "mean_class_accuracy": 0.69957} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.02318, "memory": 4082, "data_time": 0.19431, "top1_acc": 0.83, "top5_acc": 0.99312, "loss_cls": 0.73904, "loss": 0.73904, "time": 0.4173} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.02317, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.8475, "top5_acc": 0.99, "loss_cls": 0.67117, "loss": 0.67117, "time": 0.22121} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.02316, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82875, "top5_acc": 0.99375, "loss_cls": 0.73741, "loss": 0.73741, "time": 0.2196} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.02315, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.81938, "top5_acc": 0.99125, "loss_cls": 0.78039, "loss": 0.78039, "time": 0.22232} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.02314, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83125, "top5_acc": 0.99562, "loss_cls": 0.7253, "loss": 0.7253, "time": 0.2215} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.02313, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82688, "top5_acc": 0.99312, "loss_cls": 0.74478, "loss": 0.74478, "time": 0.22155} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.02312, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.82812, "top5_acc": 0.99125, "loss_cls": 0.78443, "loss": 0.78443, "time": 0.22378} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.02311, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83062, "top5_acc": 0.995, "loss_cls": 0.70873, "loss": 0.70873, "time": 0.22081} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.0231, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.81688, "top5_acc": 0.99, "loss_cls": 0.80104, "loss": 0.80104, "time": 0.21838} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.02308, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8225, "top5_acc": 0.9975, "loss_cls": 0.7146, "loss": 0.7146, "time": 0.22315} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.02307, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81688, "top5_acc": 0.99125, "loss_cls": 0.76247, "loss": 0.76247, "time": 0.21962} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.02306, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82562, "top5_acc": 0.99, "loss_cls": 0.75973, "loss": 0.75973, "time": 0.21806} +{"mode": "val", "epoch": 27, "iter": 533, "lr": 0.02305, "top1_acc": 0.77538, "top5_acc": 0.9824, "mean_class_accuracy": 0.69844} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.02304, "memory": 4082, "data_time": 0.19707, "top1_acc": 0.835, "top5_acc": 0.98938, "loss_cls": 0.7486, "loss": 0.7486, "time": 0.41953} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.02303, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84938, "top5_acc": 0.99, "loss_cls": 0.69061, "loss": 0.69061, "time": 0.22025} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.02302, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.84688, "top5_acc": 0.99688, "loss_cls": 0.69302, "loss": 0.69302, "time": 0.22069} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.02301, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81938, "top5_acc": 0.99188, "loss_cls": 0.75623, "loss": 0.75623, "time": 0.22034} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.023, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83938, "top5_acc": 0.99062, "loss_cls": 0.74136, "loss": 0.74136, "time": 0.21709} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.02299, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81875, "top5_acc": 0.98688, "loss_cls": 0.78943, "loss": 0.78943, "time": 0.22044} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.02298, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82625, "top5_acc": 0.98812, "loss_cls": 0.75006, "loss": 0.75006, "time": 0.21845} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.02297, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83188, "top5_acc": 0.99125, "loss_cls": 0.74922, "loss": 0.74922, "time": 0.21886} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.02295, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8325, "top5_acc": 0.99062, "loss_cls": 0.73651, "loss": 0.73651, "time": 0.2198} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.02294, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.835, "top5_acc": 0.99312, "loss_cls": 0.74909, "loss": 0.74909, "time": 0.22143} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.02293, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83, "top5_acc": 0.98938, "loss_cls": 0.79313, "loss": 0.79313, "time": 0.22252} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.02292, "memory": 4082, "data_time": 0.00056, "top1_acc": 0.82875, "top5_acc": 0.99312, "loss_cls": 0.75034, "loss": 0.75034, "time": 0.22034} +{"mode": "val", "epoch": 28, "iter": 533, "lr": 0.02291, "top1_acc": 0.7843, "top5_acc": 0.98709, "mean_class_accuracy": 0.72612} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.0229, "memory": 4082, "data_time": 0.20013, "top1_acc": 0.8425, "top5_acc": 0.99062, "loss_cls": 0.73472, "loss": 0.73472, "time": 0.42138} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.02289, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.85938, "top5_acc": 0.9925, "loss_cls": 0.66715, "loss": 0.66715, "time": 0.22256} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.02288, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.83875, "top5_acc": 0.9875, "loss_cls": 0.72951, "loss": 0.72951, "time": 0.22544} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.02287, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.81562, "top5_acc": 0.99, "loss_cls": 0.7913, "loss": 0.7913, "time": 0.22023} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.02285, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85, "top5_acc": 0.99312, "loss_cls": 0.7013, "loss": 0.7013, "time": 0.21754} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.02284, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.83312, "top5_acc": 0.99562, "loss_cls": 0.6914, "loss": 0.6914, "time": 0.22268} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.02283, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.8325, "top5_acc": 0.99375, "loss_cls": 0.75489, "loss": 0.75489, "time": 0.22192} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.02282, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.83938, "top5_acc": 0.98812, "loss_cls": 0.73998, "loss": 0.73998, "time": 0.22121} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.02281, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.82688, "top5_acc": 0.9875, "loss_cls": 0.76432, "loss": 0.76432, "time": 0.21779} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.0228, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82688, "top5_acc": 0.99125, "loss_cls": 0.72349, "loss": 0.72349, "time": 0.21796} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.02279, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.82625, "top5_acc": 0.98812, "loss_cls": 0.75783, "loss": 0.75783, "time": 0.21994} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.02277, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.82625, "top5_acc": 0.9925, "loss_cls": 0.7562, "loss": 0.7562, "time": 0.2213} +{"mode": "val", "epoch": 29, "iter": 533, "lr": 0.02276, "top1_acc": 0.74287, "top5_acc": 0.97571, "mean_class_accuracy": 0.67265} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.02275, "memory": 4082, "data_time": 0.19209, "top1_acc": 0.8425, "top5_acc": 0.99, "loss_cls": 0.72237, "loss": 0.72237, "time": 0.42259} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.02274, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85, "top5_acc": 0.99438, "loss_cls": 0.6789, "loss": 0.6789, "time": 0.22896} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.02273, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8625, "top5_acc": 0.995, "loss_cls": 0.64357, "loss": 0.64357, "time": 0.22656} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.02272, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82625, "top5_acc": 0.98812, "loss_cls": 0.77544, "loss": 0.77544, "time": 0.22784} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.02271, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.83062, "top5_acc": 0.995, "loss_cls": 0.74967, "loss": 0.74967, "time": 0.22978} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.02269, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8425, "top5_acc": 0.985, "loss_cls": 0.72883, "loss": 0.72883, "time": 0.22429} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.02268, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84312, "top5_acc": 0.99125, "loss_cls": 0.70308, "loss": 0.70308, "time": 0.22828} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.02267, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.82438, "top5_acc": 0.99375, "loss_cls": 0.73762, "loss": 0.73762, "time": 0.23223} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.02266, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83812, "top5_acc": 0.99188, "loss_cls": 0.71613, "loss": 0.71613, "time": 0.22695} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.02265, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.83062, "top5_acc": 0.98938, "loss_cls": 0.74088, "loss": 0.74088, "time": 0.23002} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.02263, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.835, "top5_acc": 0.9925, "loss_cls": 0.73469, "loss": 0.73469, "time": 0.22695} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.02262, "memory": 4082, "data_time": 0.00054, "top1_acc": 0.84438, "top5_acc": 0.99562, "loss_cls": 0.71846, "loss": 0.71846, "time": 0.2295} +{"mode": "val", "epoch": 30, "iter": 533, "lr": 0.02261, "top1_acc": 0.78805, "top5_acc": 0.98639, "mean_class_accuracy": 0.69091} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.0226, "memory": 4083, "data_time": 0.19086, "top1_acc": 0.86, "top5_acc": 0.99062, "loss_cls": 0.79498, "loss": 0.79498, "time": 0.42857} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.02259, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.83375, "top5_acc": 0.99188, "loss_cls": 0.89336, "loss": 0.89336, "time": 0.22781} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.02258, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84062, "top5_acc": 0.99375, "loss_cls": 0.83313, "loss": 0.83313, "time": 0.22522} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.02256, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.84312, "top5_acc": 0.995, "loss_cls": 0.81398, "loss": 0.81398, "time": 0.22272} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.02255, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.82562, "top5_acc": 0.99188, "loss_cls": 0.87294, "loss": 0.87294, "time": 0.22431} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.02254, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8375, "top5_acc": 0.99188, "loss_cls": 0.86851, "loss": 0.86851, "time": 0.22301} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.02253, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8475, "top5_acc": 0.99375, "loss_cls": 0.84667, "loss": 0.84667, "time": 0.22648} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.02252, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.8525, "top5_acc": 0.99312, "loss_cls": 0.79865, "loss": 0.79865, "time": 0.2233} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0225, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.83125, "top5_acc": 0.99062, "loss_cls": 0.87291, "loss": 0.87291, "time": 0.2248} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.02249, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.835, "top5_acc": 0.98938, "loss_cls": 0.84593, "loss": 0.84593, "time": 0.22759} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.02248, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.83062, "top5_acc": 0.98812, "loss_cls": 0.8883, "loss": 0.8883, "time": 0.22567} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.02247, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84438, "top5_acc": 0.99562, "loss_cls": 0.85243, "loss": 0.85243, "time": 0.2239} +{"mode": "val", "epoch": 31, "iter": 533, "lr": 0.02246, "top1_acc": 0.78348, "top5_acc": 0.98275, "mean_class_accuracy": 0.686} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.02244, "memory": 4083, "data_time": 0.19822, "top1_acc": 0.8425, "top5_acc": 0.99312, "loss_cls": 0.79236, "loss": 0.79236, "time": 0.43468} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.02243, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.83812, "top5_acc": 0.99375, "loss_cls": 0.78491, "loss": 0.78491, "time": 0.22656} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.02242, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.84688, "top5_acc": 0.9925, "loss_cls": 0.75169, "loss": 0.75169, "time": 0.2267} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.02241, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.85188, "top5_acc": 0.995, "loss_cls": 0.72143, "loss": 0.72143, "time": 0.22948} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.02239, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.84688, "top5_acc": 0.995, "loss_cls": 0.77394, "loss": 0.77394, "time": 0.2261} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.02238, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.83188, "top5_acc": 0.99438, "loss_cls": 0.81775, "loss": 0.81775, "time": 0.22284} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.02237, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.82875, "top5_acc": 0.99062, "loss_cls": 0.83778, "loss": 0.83778, "time": 0.2307} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.02236, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.83625, "top5_acc": 0.99312, "loss_cls": 0.78302, "loss": 0.78302, "time": 0.22426} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.02234, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.82812, "top5_acc": 0.98875, "loss_cls": 0.84184, "loss": 0.84184, "time": 0.22341} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.02233, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.86, "top5_acc": 0.99188, "loss_cls": 0.77696, "loss": 0.77696, "time": 0.22821} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.02232, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.84125, "top5_acc": 0.99125, "loss_cls": 0.78642, "loss": 0.78642, "time": 0.2254} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.02231, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.83625, "top5_acc": 0.99625, "loss_cls": 0.81144, "loss": 0.81144, "time": 0.22249} +{"mode": "val", "epoch": 32, "iter": 533, "lr": 0.0223, "top1_acc": 0.79099, "top5_acc": 0.9851, "mean_class_accuracy": 0.72072} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.02228, "memory": 4083, "data_time": 0.19919, "top1_acc": 0.8575, "top5_acc": 0.99312, "loss_cls": 0.7137, "loss": 0.7137, "time": 0.43816} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.02227, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.86062, "top5_acc": 0.99312, "loss_cls": 0.72576, "loss": 0.72576, "time": 0.22573} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.02226, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.84062, "top5_acc": 0.99188, "loss_cls": 0.73773, "loss": 0.73773, "time": 0.22575} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.02225, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.84062, "top5_acc": 0.99125, "loss_cls": 0.78926, "loss": 0.78926, "time": 0.22524} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.02223, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.85375, "top5_acc": 0.99438, "loss_cls": 0.73779, "loss": 0.73779, "time": 0.22727} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.02222, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.83812, "top5_acc": 0.99062, "loss_cls": 0.79107, "loss": 0.79107, "time": 0.22375} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.02221, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.84938, "top5_acc": 0.99562, "loss_cls": 0.78087, "loss": 0.78087, "time": 0.22622} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.02219, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.82625, "top5_acc": 0.99312, "loss_cls": 0.8178, "loss": 0.8178, "time": 0.22488} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.02218, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8425, "top5_acc": 0.99062, "loss_cls": 0.77987, "loss": 0.77987, "time": 0.22514} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.02217, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.84562, "top5_acc": 0.99125, "loss_cls": 0.73432, "loss": 0.73432, "time": 0.22352} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.02216, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84188, "top5_acc": 0.99, "loss_cls": 0.80797, "loss": 0.80797, "time": 0.22693} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.02214, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.84062, "top5_acc": 0.99562, "loss_cls": 0.73895, "loss": 0.73895, "time": 0.22438} +{"mode": "val", "epoch": 33, "iter": 533, "lr": 0.02213, "top1_acc": 0.81059, "top5_acc": 0.98862, "mean_class_accuracy": 0.7293} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.02212, "memory": 4083, "data_time": 0.18674, "top1_acc": 0.845, "top5_acc": 0.99688, "loss_cls": 0.7141, "loss": 0.7141, "time": 0.42814} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.02211, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8575, "top5_acc": 0.9975, "loss_cls": 0.69143, "loss": 0.69143, "time": 0.22782} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.02209, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8725, "top5_acc": 0.995, "loss_cls": 0.64819, "loss": 0.64819, "time": 0.22879} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.02208, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.84562, "top5_acc": 0.99, "loss_cls": 0.74399, "loss": 0.74399, "time": 0.22783} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.02207, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.85062, "top5_acc": 0.99125, "loss_cls": 0.72575, "loss": 0.72575, "time": 0.22397} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.02205, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85625, "top5_acc": 0.9925, "loss_cls": 0.73945, "loss": 0.73945, "time": 0.22579} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.02204, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84062, "top5_acc": 0.99, "loss_cls": 0.78713, "loss": 0.78713, "time": 0.22502} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.02203, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.85875, "top5_acc": 0.99062, "loss_cls": 0.74847, "loss": 0.74847, "time": 0.22344} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.02201, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.84062, "top5_acc": 0.9925, "loss_cls": 0.74634, "loss": 0.74634, "time": 0.22878} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.022, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84688, "top5_acc": 0.99375, "loss_cls": 0.76706, "loss": 0.76706, "time": 0.22569} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.02199, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.82375, "top5_acc": 0.99188, "loss_cls": 0.83407, "loss": 0.83407, "time": 0.22384} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.02197, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.82125, "top5_acc": 0.99375, "loss_cls": 0.81017, "loss": 0.81017, "time": 0.2245} +{"mode": "val", "epoch": 34, "iter": 533, "lr": 0.02196, "top1_acc": 0.77761, "top5_acc": 0.98251, "mean_class_accuracy": 0.67977} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.02195, "memory": 4083, "data_time": 0.18786, "top1_acc": 0.8575, "top5_acc": 0.99688, "loss_cls": 0.69871, "loss": 0.69871, "time": 0.42273} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.02194, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.85312, "top5_acc": 0.99375, "loss_cls": 0.70124, "loss": 0.70124, "time": 0.2228} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.02192, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.85062, "top5_acc": 0.9925, "loss_cls": 0.7275, "loss": 0.7275, "time": 0.22525} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.02191, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.82875, "top5_acc": 0.99, "loss_cls": 0.75704, "loss": 0.75704, "time": 0.2251} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.0219, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8375, "top5_acc": 0.99, "loss_cls": 0.79519, "loss": 0.79519, "time": 0.22445} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.02188, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.855, "top5_acc": 0.99375, "loss_cls": 0.71406, "loss": 0.71406, "time": 0.22474} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.02187, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.8425, "top5_acc": 0.99312, "loss_cls": 0.72358, "loss": 0.72358, "time": 0.22663} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.02185, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.85125, "top5_acc": 0.99312, "loss_cls": 0.75498, "loss": 0.75498, "time": 0.22511} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.02184, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.84688, "top5_acc": 0.99125, "loss_cls": 0.74036, "loss": 0.74036, "time": 0.22476} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.02183, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.84938, "top5_acc": 0.99312, "loss_cls": 0.7186, "loss": 0.7186, "time": 0.2254} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.02181, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.84938, "top5_acc": 0.98938, "loss_cls": 0.71596, "loss": 0.71596, "time": 0.22739} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.0218, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.83688, "top5_acc": 0.99438, "loss_cls": 0.75382, "loss": 0.75382, "time": 0.22127} +{"mode": "val", "epoch": 35, "iter": 533, "lr": 0.02179, "top1_acc": 0.82772, "top5_acc": 0.99096, "mean_class_accuracy": 0.74967} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.02178, "memory": 4083, "data_time": 0.19305, "top1_acc": 0.87938, "top5_acc": 0.99625, "loss_cls": 0.61714, "loss": 0.61714, "time": 0.42932} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.02176, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.86312, "top5_acc": 0.99438, "loss_cls": 0.67527, "loss": 0.67527, "time": 0.22696} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.02175, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86188, "top5_acc": 0.99188, "loss_cls": 0.69587, "loss": 0.69587, "time": 0.22407} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.02173, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8525, "top5_acc": 0.9925, "loss_cls": 0.7282, "loss": 0.7282, "time": 0.22565} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.02172, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.865, "top5_acc": 0.9975, "loss_cls": 0.65226, "loss": 0.65226, "time": 0.22492} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.02171, "memory": 4083, "data_time": 0.00083, "top1_acc": 0.8325, "top5_acc": 0.99, "loss_cls": 0.76671, "loss": 0.76671, "time": 0.22414} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.02169, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86438, "top5_acc": 0.995, "loss_cls": 0.69529, "loss": 0.69529, "time": 0.22713} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.02168, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84875, "top5_acc": 0.99062, "loss_cls": 0.7325, "loss": 0.7325, "time": 0.22761} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.02167, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.84438, "top5_acc": 0.99312, "loss_cls": 0.74274, "loss": 0.74274, "time": 0.22421} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.02165, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.845, "top5_acc": 0.99375, "loss_cls": 0.77214, "loss": 0.77214, "time": 0.22203} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.02164, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.84188, "top5_acc": 0.99688, "loss_cls": 0.73066, "loss": 0.73066, "time": 0.22436} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.02162, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.865, "top5_acc": 0.99312, "loss_cls": 0.73106, "loss": 0.73106, "time": 0.2232} +{"mode": "val", "epoch": 36, "iter": 533, "lr": 0.02161, "top1_acc": 0.77632, "top5_acc": 0.98052, "mean_class_accuracy": 0.72109} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.0216, "memory": 4083, "data_time": 0.1939, "top1_acc": 0.85812, "top5_acc": 0.99625, "loss_cls": 0.69466, "loss": 0.69466, "time": 0.4296} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.02158, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86875, "top5_acc": 0.99625, "loss_cls": 0.67791, "loss": 0.67791, "time": 0.22789} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.02157, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.865, "top5_acc": 0.99688, "loss_cls": 0.68265, "loss": 0.68265, "time": 0.22693} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.02156, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86125, "top5_acc": 0.99625, "loss_cls": 0.65496, "loss": 0.65496, "time": 0.22266} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.02154, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86375, "top5_acc": 0.99375, "loss_cls": 0.68613, "loss": 0.68613, "time": 0.22592} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.02153, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84938, "top5_acc": 0.99375, "loss_cls": 0.71245, "loss": 0.71245, "time": 0.22676} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.02151, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.875, "top5_acc": 0.99438, "loss_cls": 0.6276, "loss": 0.6276, "time": 0.22371} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0215, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.84625, "top5_acc": 0.99312, "loss_cls": 0.71475, "loss": 0.71475, "time": 0.22698} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.02149, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.85625, "top5_acc": 0.995, "loss_cls": 0.68885, "loss": 0.68885, "time": 0.22549} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.02147, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.84375, "top5_acc": 0.99375, "loss_cls": 0.74816, "loss": 0.74816, "time": 0.22986} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.02146, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.8425, "top5_acc": 0.98875, "loss_cls": 0.75676, "loss": 0.75676, "time": 0.22418} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.02144, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.855, "top5_acc": 0.9925, "loss_cls": 0.72283, "loss": 0.72283, "time": 0.22758} +{"mode": "val", "epoch": 37, "iter": 533, "lr": 0.02143, "top1_acc": 0.77749, "top5_acc": 0.98427, "mean_class_accuracy": 0.69462} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.02142, "memory": 4083, "data_time": 0.19848, "top1_acc": 0.86188, "top5_acc": 0.995, "loss_cls": 0.66989, "loss": 0.66989, "time": 0.43303} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.0214, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86875, "top5_acc": 0.99688, "loss_cls": 0.66965, "loss": 0.66965, "time": 0.22437} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.02139, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86062, "top5_acc": 0.99125, "loss_cls": 0.67591, "loss": 0.67591, "time": 0.22381} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.02137, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.86562, "top5_acc": 0.99312, "loss_cls": 0.67853, "loss": 0.67853, "time": 0.22185} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.02136, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.855, "top5_acc": 0.99375, "loss_cls": 0.70936, "loss": 0.70936, "time": 0.22644} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.02134, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86875, "top5_acc": 0.99625, "loss_cls": 0.65544, "loss": 0.65544, "time": 0.22448} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.02133, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.86125, "top5_acc": 0.995, "loss_cls": 0.66948, "loss": 0.66948, "time": 0.22474} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.02132, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86438, "top5_acc": 0.99062, "loss_cls": 0.71032, "loss": 0.71032, "time": 0.22445} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.0213, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.86062, "top5_acc": 0.99688, "loss_cls": 0.68683, "loss": 0.68683, "time": 0.22419} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.02129, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.84688, "top5_acc": 0.995, "loss_cls": 0.72596, "loss": 0.72596, "time": 0.22434} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.02127, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.84875, "top5_acc": 0.99312, "loss_cls": 0.69622, "loss": 0.69622, "time": 0.22495} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.02126, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85625, "top5_acc": 0.98875, "loss_cls": 0.70358, "loss": 0.70358, "time": 0.22415} +{"mode": "val", "epoch": 38, "iter": 533, "lr": 0.02125, "top1_acc": 0.80401, "top5_acc": 0.9838, "mean_class_accuracy": 0.7503} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.02123, "memory": 4083, "data_time": 0.1876, "top1_acc": 0.87, "top5_acc": 0.9925, "loss_cls": 0.61623, "loss": 0.61623, "time": 0.43103} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.02122, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.86125, "top5_acc": 0.99438, "loss_cls": 0.65323, "loss": 0.65323, "time": 0.22695} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.0212, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.855, "top5_acc": 0.9975, "loss_cls": 0.65762, "loss": 0.65762, "time": 0.22078} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.02119, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.855, "top5_acc": 0.99625, "loss_cls": 0.69098, "loss": 0.69098, "time": 0.22627} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.02117, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.865, "top5_acc": 0.99375, "loss_cls": 0.69662, "loss": 0.69662, "time": 0.22622} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.02116, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.85125, "top5_acc": 0.99438, "loss_cls": 0.70976, "loss": 0.70976, "time": 0.22388} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.02114, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.85312, "top5_acc": 0.9925, "loss_cls": 0.69719, "loss": 0.69719, "time": 0.22506} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.02113, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87375, "top5_acc": 0.9925, "loss_cls": 0.65215, "loss": 0.65215, "time": 0.22894} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.02111, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.85688, "top5_acc": 0.9925, "loss_cls": 0.68812, "loss": 0.68812, "time": 0.22596} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.0211, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.84, "top5_acc": 0.99125, "loss_cls": 0.74318, "loss": 0.74318, "time": 0.22403} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.02108, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.85125, "top5_acc": 0.99625, "loss_cls": 0.69976, "loss": 0.69976, "time": 0.22603} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.02107, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86625, "top5_acc": 0.995, "loss_cls": 0.66748, "loss": 0.66748, "time": 0.22516} +{"mode": "val", "epoch": 39, "iter": 533, "lr": 0.02106, "top1_acc": 0.81012, "top5_acc": 0.9858, "mean_class_accuracy": 0.7492} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.02104, "memory": 4083, "data_time": 0.18914, "top1_acc": 0.87938, "top5_acc": 0.99312, "loss_cls": 0.6308, "loss": 0.6308, "time": 0.42581} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.02103, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87062, "top5_acc": 0.99312, "loss_cls": 0.65692, "loss": 0.65692, "time": 0.22402} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.02101, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86375, "top5_acc": 0.9925, "loss_cls": 0.69745, "loss": 0.69745, "time": 0.22227} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.021, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8775, "top5_acc": 0.99312, "loss_cls": 0.62544, "loss": 0.62544, "time": 0.2242} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.02098, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.86688, "top5_acc": 0.99438, "loss_cls": 0.66207, "loss": 0.66207, "time": 0.22199} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.02097, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87062, "top5_acc": 0.99562, "loss_cls": 0.66622, "loss": 0.66622, "time": 0.22279} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.02095, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.85875, "top5_acc": 0.99438, "loss_cls": 0.68089, "loss": 0.68089, "time": 0.22598} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.02094, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8525, "top5_acc": 0.99375, "loss_cls": 0.71871, "loss": 0.71871, "time": 0.22494} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.02092, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87062, "top5_acc": 0.99125, "loss_cls": 0.65283, "loss": 0.65283, "time": 0.22407} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.02091, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.85812, "top5_acc": 0.99562, "loss_cls": 0.70637, "loss": 0.70637, "time": 0.2249} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.02089, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85562, "top5_acc": 0.99375, "loss_cls": 0.7118, "loss": 0.7118, "time": 0.22849} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.02088, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.8675, "top5_acc": 0.9925, "loss_cls": 0.68426, "loss": 0.68426, "time": 0.23022} +{"mode": "val", "epoch": 40, "iter": 533, "lr": 0.02086, "top1_acc": 0.82889, "top5_acc": 0.98768, "mean_class_accuracy": 0.75166} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.02085, "memory": 4083, "data_time": 0.18475, "top1_acc": 0.87, "top5_acc": 0.9975, "loss_cls": 0.66033, "loss": 0.66033, "time": 0.4238} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.02083, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88375, "top5_acc": 0.99625, "loss_cls": 0.59431, "loss": 0.59431, "time": 0.22415} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.02082, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.8675, "top5_acc": 0.99375, "loss_cls": 0.64034, "loss": 0.64034, "time": 0.22635} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.0208, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.85938, "top5_acc": 0.99562, "loss_cls": 0.64903, "loss": 0.64903, "time": 0.23056} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.02079, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8675, "top5_acc": 0.99375, "loss_cls": 0.64937, "loss": 0.64937, "time": 0.22339} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.02077, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86438, "top5_acc": 0.99625, "loss_cls": 0.64592, "loss": 0.64592, "time": 0.22407} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.02076, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.86562, "top5_acc": 0.99562, "loss_cls": 0.67862, "loss": 0.67862, "time": 0.227} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.02074, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.86312, "top5_acc": 0.99688, "loss_cls": 0.66153, "loss": 0.66153, "time": 0.2269} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.02073, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85688, "top5_acc": 0.995, "loss_cls": 0.66056, "loss": 0.66056, "time": 0.22384} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.02071, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86875, "top5_acc": 0.99375, "loss_cls": 0.65654, "loss": 0.65654, "time": 0.22395} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.0207, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85938, "top5_acc": 0.99562, "loss_cls": 0.67697, "loss": 0.67697, "time": 0.22524} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.02068, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86, "top5_acc": 0.99562, "loss_cls": 0.6947, "loss": 0.6947, "time": 0.22505} +{"mode": "val", "epoch": 41, "iter": 533, "lr": 0.02067, "top1_acc": 0.80401, "top5_acc": 0.9858, "mean_class_accuracy": 0.74418} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.02065, "memory": 4083, "data_time": 0.19494, "top1_acc": 0.85562, "top5_acc": 0.99562, "loss_cls": 0.6753, "loss": 0.6753, "time": 0.43441} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.02064, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87188, "top5_acc": 0.995, "loss_cls": 0.64585, "loss": 0.64585, "time": 0.22288} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.02062, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87812, "top5_acc": 0.99688, "loss_cls": 0.60102, "loss": 0.60102, "time": 0.2234} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.02061, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.85, "top5_acc": 0.9925, "loss_cls": 0.74166, "loss": 0.74166, "time": 0.22767} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.02059, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.84688, "top5_acc": 0.99125, "loss_cls": 0.72121, "loss": 0.72121, "time": 0.22459} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.02057, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.86562, "top5_acc": 0.99438, "loss_cls": 0.69452, "loss": 0.69452, "time": 0.22539} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.02056, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87438, "top5_acc": 0.99438, "loss_cls": 0.68332, "loss": 0.68332, "time": 0.22497} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.02054, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85688, "top5_acc": 0.99562, "loss_cls": 0.64541, "loss": 0.64541, "time": 0.22592} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.02053, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87188, "top5_acc": 0.99438, "loss_cls": 0.66277, "loss": 0.66277, "time": 0.22458} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.02051, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85188, "top5_acc": 0.99188, "loss_cls": 0.69951, "loss": 0.69951, "time": 0.22286} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.0205, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.8575, "top5_acc": 0.9925, "loss_cls": 0.6874, "loss": 0.6874, "time": 0.22328} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.02048, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86312, "top5_acc": 0.99625, "loss_cls": 0.64712, "loss": 0.64712, "time": 0.22357} +{"mode": "val", "epoch": 42, "iter": 533, "lr": 0.02047, "top1_acc": 0.80331, "top5_acc": 0.98568, "mean_class_accuracy": 0.71745} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.02045, "memory": 4083, "data_time": 0.19459, "top1_acc": 0.87562, "top5_acc": 0.99438, "loss_cls": 0.62543, "loss": 0.62543, "time": 0.43176} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.02044, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88812, "top5_acc": 0.99562, "loss_cls": 0.56989, "loss": 0.56989, "time": 0.22283} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.02042, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88812, "top5_acc": 0.995, "loss_cls": 0.56946, "loss": 0.56946, "time": 0.22312} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.0204, "memory": 4083, "data_time": 0.00061, "top1_acc": 0.87625, "top5_acc": 0.99438, "loss_cls": 0.63615, "loss": 0.63615, "time": 0.2249} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.02039, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85938, "top5_acc": 0.99062, "loss_cls": 0.72411, "loss": 0.72411, "time": 0.22248} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.02037, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.85812, "top5_acc": 0.99312, "loss_cls": 0.67538, "loss": 0.67538, "time": 0.22879} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.02036, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.86438, "top5_acc": 0.99438, "loss_cls": 0.62583, "loss": 0.62583, "time": 0.22741} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.02034, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86562, "top5_acc": 0.99312, "loss_cls": 0.64825, "loss": 0.64825, "time": 0.22379} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.02033, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86625, "top5_acc": 0.99562, "loss_cls": 0.64943, "loss": 0.64943, "time": 0.2268} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.02031, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87375, "top5_acc": 0.99562, "loss_cls": 0.65722, "loss": 0.65722, "time": 0.22466} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.02029, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86062, "top5_acc": 0.9925, "loss_cls": 0.70475, "loss": 0.70475, "time": 0.2244} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.02028, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.87688, "top5_acc": 0.99375, "loss_cls": 0.6202, "loss": 0.6202, "time": 0.22386} +{"mode": "val", "epoch": 43, "iter": 533, "lr": 0.02026, "top1_acc": 0.80249, "top5_acc": 0.9838, "mean_class_accuracy": 0.74717} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.02025, "memory": 4083, "data_time": 0.1928, "top1_acc": 0.88125, "top5_acc": 0.99562, "loss_cls": 0.60251, "loss": 0.60251, "time": 0.43063} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.02023, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89125, "top5_acc": 0.995, "loss_cls": 0.53284, "loss": 0.53284, "time": 0.2254} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.02022, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.87812, "top5_acc": 0.99625, "loss_cls": 0.60608, "loss": 0.60608, "time": 0.22508} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.0202, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.875, "top5_acc": 0.995, "loss_cls": 0.62728, "loss": 0.62728, "time": 0.22585} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.02018, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.87625, "top5_acc": 0.99438, "loss_cls": 0.63517, "loss": 0.63517, "time": 0.2269} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.02017, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.86, "top5_acc": 0.99625, "loss_cls": 0.67108, "loss": 0.67108, "time": 0.22797} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.02015, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8625, "top5_acc": 0.99125, "loss_cls": 0.70178, "loss": 0.70178, "time": 0.22775} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.02014, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84938, "top5_acc": 0.99375, "loss_cls": 0.68748, "loss": 0.68748, "time": 0.22479} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.02012, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8675, "top5_acc": 0.99562, "loss_cls": 0.6156, "loss": 0.6156, "time": 0.22378} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.0201, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.875, "top5_acc": 0.99812, "loss_cls": 0.59241, "loss": 0.59241, "time": 0.22403} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.02009, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87812, "top5_acc": 0.9975, "loss_cls": 0.62234, "loss": 0.62234, "time": 0.22369} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.02007, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.855, "top5_acc": 0.99312, "loss_cls": 0.7099, "loss": 0.7099, "time": 0.22526} +{"mode": "val", "epoch": 44, "iter": 533, "lr": 0.02006, "top1_acc": 0.81469, "top5_acc": 0.98697, "mean_class_accuracy": 0.71794} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.02004, "memory": 4083, "data_time": 0.19358, "top1_acc": 0.87812, "top5_acc": 0.99625, "loss_cls": 0.61547, "loss": 0.61547, "time": 0.42999} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.02003, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8875, "top5_acc": 0.99438, "loss_cls": 0.59386, "loss": 0.59386, "time": 0.2263} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.02001, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8825, "top5_acc": 0.99625, "loss_cls": 0.59601, "loss": 0.59601, "time": 0.22463} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.01999, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.84562, "top5_acc": 0.99312, "loss_cls": 0.72694, "loss": 0.72694, "time": 0.22032} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.01998, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87938, "top5_acc": 0.99625, "loss_cls": 0.62384, "loss": 0.62384, "time": 0.23103} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.01996, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86625, "top5_acc": 0.99562, "loss_cls": 0.65618, "loss": 0.65618, "time": 0.2267} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.01994, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8625, "top5_acc": 0.99188, "loss_cls": 0.65629, "loss": 0.65629, "time": 0.22308} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.01993, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.87625, "top5_acc": 0.9975, "loss_cls": 0.61605, "loss": 0.61605, "time": 0.22648} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.01991, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87, "top5_acc": 0.99562, "loss_cls": 0.63642, "loss": 0.63642, "time": 0.22642} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.01989, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87812, "top5_acc": 0.99438, "loss_cls": 0.61654, "loss": 0.61654, "time": 0.2243} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.01988, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.87875, "top5_acc": 0.99562, "loss_cls": 0.63422, "loss": 0.63422, "time": 0.22562} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.01986, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86375, "top5_acc": 0.99438, "loss_cls": 0.65489, "loss": 0.65489, "time": 0.22173} +{"mode": "val", "epoch": 45, "iter": 533, "lr": 0.01985, "top1_acc": 0.81622, "top5_acc": 0.98721, "mean_class_accuracy": 0.74317} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.01983, "memory": 4083, "data_time": 0.19081, "top1_acc": 0.88188, "top5_acc": 0.99812, "loss_cls": 0.58413, "loss": 0.58413, "time": 0.43095} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.01981, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87062, "top5_acc": 0.99812, "loss_cls": 0.60871, "loss": 0.60871, "time": 0.22583} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.0198, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8825, "top5_acc": 0.99438, "loss_cls": 0.5632, "loss": 0.5632, "time": 0.22553} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.01978, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87938, "top5_acc": 0.99375, "loss_cls": 0.59709, "loss": 0.59709, "time": 0.22379} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.01976, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86688, "top5_acc": 0.995, "loss_cls": 0.67738, "loss": 0.67738, "time": 0.22746} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.01975, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8775, "top5_acc": 0.9975, "loss_cls": 0.59393, "loss": 0.59393, "time": 0.22313} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.01973, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8575, "top5_acc": 0.9925, "loss_cls": 0.66631, "loss": 0.66631, "time": 0.22235} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.01971, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87188, "top5_acc": 0.99625, "loss_cls": 0.63182, "loss": 0.63182, "time": 0.22521} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.0197, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.84875, "top5_acc": 0.9925, "loss_cls": 0.69769, "loss": 0.69769, "time": 0.22221} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.01968, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85875, "top5_acc": 0.9925, "loss_cls": 0.68214, "loss": 0.68214, "time": 0.22184} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.01966, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86875, "top5_acc": 0.99188, "loss_cls": 0.67592, "loss": 0.67592, "time": 0.22353} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.01965, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8825, "top5_acc": 0.99562, "loss_cls": 0.58986, "loss": 0.58986, "time": 0.22266} +{"mode": "val", "epoch": 46, "iter": 533, "lr": 0.01963, "top1_acc": 0.78805, "top5_acc": 0.98674, "mean_class_accuracy": 0.75318} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.01962, "memory": 4083, "data_time": 0.18838, "top1_acc": 0.87312, "top5_acc": 0.995, "loss_cls": 0.61782, "loss": 0.61782, "time": 0.42635} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.0196, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.875, "top5_acc": 0.995, "loss_cls": 0.60444, "loss": 0.60444, "time": 0.22834} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.01958, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88438, "top5_acc": 0.99688, "loss_cls": 0.58831, "loss": 0.58831, "time": 0.22542} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.01957, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86438, "top5_acc": 0.9975, "loss_cls": 0.65088, "loss": 0.65088, "time": 0.22468} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.01955, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.895, "top5_acc": 0.99438, "loss_cls": 0.55128, "loss": 0.55128, "time": 0.22621} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.01953, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.875, "top5_acc": 0.99625, "loss_cls": 0.58395, "loss": 0.58395, "time": 0.22851} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.01952, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86625, "top5_acc": 0.99562, "loss_cls": 0.64144, "loss": 0.64144, "time": 0.22368} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.0195, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88438, "top5_acc": 0.99625, "loss_cls": 0.60452, "loss": 0.60452, "time": 0.2208} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.01948, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.88438, "top5_acc": 0.99688, "loss_cls": 0.61377, "loss": 0.61377, "time": 0.22673} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.01947, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88125, "top5_acc": 0.99438, "loss_cls": 0.59901, "loss": 0.59901, "time": 0.22369} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.01945, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.855, "top5_acc": 0.99, "loss_cls": 0.68879, "loss": 0.68879, "time": 0.22655} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.01943, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86875, "top5_acc": 0.99562, "loss_cls": 0.64091, "loss": 0.64091, "time": 0.22274} +{"mode": "val", "epoch": 47, "iter": 533, "lr": 0.01942, "top1_acc": 0.81505, "top5_acc": 0.98779, "mean_class_accuracy": 0.74223} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.0194, "memory": 4083, "data_time": 0.19781, "top1_acc": 0.88812, "top5_acc": 0.9975, "loss_cls": 0.57054, "loss": 0.57054, "time": 0.43358} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.01938, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88812, "top5_acc": 0.99688, "loss_cls": 0.56038, "loss": 0.56038, "time": 0.2249} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.01937, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.89938, "top5_acc": 0.9975, "loss_cls": 0.52638, "loss": 0.52638, "time": 0.22452} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.01935, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88312, "top5_acc": 0.99688, "loss_cls": 0.56152, "loss": 0.56152, "time": 0.22224} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.01933, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.88188, "top5_acc": 0.99688, "loss_cls": 0.57883, "loss": 0.57883, "time": 0.22384} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.01932, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87125, "top5_acc": 0.99375, "loss_cls": 0.64464, "loss": 0.64464, "time": 0.22397} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.0193, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.89375, "top5_acc": 0.99688, "loss_cls": 0.55932, "loss": 0.55932, "time": 0.22537} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.01928, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89438, "top5_acc": 0.99438, "loss_cls": 0.56079, "loss": 0.56079, "time": 0.2238} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.01926, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.88188, "top5_acc": 0.99688, "loss_cls": 0.61062, "loss": 0.61062, "time": 0.22669} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.01925, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86062, "top5_acc": 0.99188, "loss_cls": 0.65952, "loss": 0.65952, "time": 0.22593} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.01923, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8775, "top5_acc": 0.99688, "loss_cls": 0.59635, "loss": 0.59635, "time": 0.22306} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.01921, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.85938, "top5_acc": 0.99438, "loss_cls": 0.64861, "loss": 0.64861, "time": 0.2232} +{"mode": "val", "epoch": 48, "iter": 533, "lr": 0.0192, "top1_acc": 0.8364, "top5_acc": 0.98909, "mean_class_accuracy": 0.78166} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.01918, "memory": 4083, "data_time": 0.19139, "top1_acc": 0.87062, "top5_acc": 0.995, "loss_cls": 0.63703, "loss": 0.63703, "time": 0.42806} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.01916, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89312, "top5_acc": 0.99688, "loss_cls": 0.55364, "loss": 0.55364, "time": 0.2225} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.01915, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88688, "top5_acc": 0.99438, "loss_cls": 0.60003, "loss": 0.60003, "time": 0.22649} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.01913, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87938, "top5_acc": 0.99688, "loss_cls": 0.59908, "loss": 0.59908, "time": 0.2252} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.01911, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88875, "top5_acc": 0.99688, "loss_cls": 0.5649, "loss": 0.5649, "time": 0.22505} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.01909, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89375, "top5_acc": 0.99375, "loss_cls": 0.56616, "loss": 0.56616, "time": 0.22394} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.01908, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89312, "top5_acc": 0.9975, "loss_cls": 0.56028, "loss": 0.56028, "time": 0.22497} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.01906, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.8875, "top5_acc": 0.99312, "loss_cls": 0.57866, "loss": 0.57866, "time": 0.22823} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.01904, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86375, "top5_acc": 0.995, "loss_cls": 0.60577, "loss": 0.60577, "time": 0.22525} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.01902, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87562, "top5_acc": 0.9975, "loss_cls": 0.64678, "loss": 0.64678, "time": 0.22337} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.01901, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8575, "top5_acc": 0.995, "loss_cls": 0.65921, "loss": 0.65921, "time": 0.22507} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.01899, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85938, "top5_acc": 0.99688, "loss_cls": 0.63986, "loss": 0.63986, "time": 0.22433} +{"mode": "val", "epoch": 49, "iter": 533, "lr": 0.01898, "top1_acc": 0.8134, "top5_acc": 0.98627, "mean_class_accuracy": 0.76356} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.01896, "memory": 4083, "data_time": 0.189, "top1_acc": 0.88375, "top5_acc": 0.99688, "loss_cls": 0.54244, "loss": 0.54244, "time": 0.42575} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.01894, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.89625, "top5_acc": 0.99562, "loss_cls": 0.54077, "loss": 0.54077, "time": 0.22895} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.01892, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.87438, "top5_acc": 0.99625, "loss_cls": 0.56218, "loss": 0.56218, "time": 0.22465} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.01891, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8875, "top5_acc": 0.9925, "loss_cls": 0.56828, "loss": 0.56828, "time": 0.22518} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.01889, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.87188, "top5_acc": 0.99625, "loss_cls": 0.6136, "loss": 0.6136, "time": 0.23026} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.01887, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.88562, "top5_acc": 0.995, "loss_cls": 0.59453, "loss": 0.59453, "time": 0.22819} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.01885, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88562, "top5_acc": 0.99625, "loss_cls": 0.58512, "loss": 0.58512, "time": 0.2225} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.01884, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.86875, "top5_acc": 0.99688, "loss_cls": 0.62177, "loss": 0.62177, "time": 0.22263} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.01882, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87312, "top5_acc": 0.99625, "loss_cls": 0.62248, "loss": 0.62248, "time": 0.22931} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.0188, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88062, "top5_acc": 0.995, "loss_cls": 0.58733, "loss": 0.58733, "time": 0.22228} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.01878, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87125, "top5_acc": 0.99562, "loss_cls": 0.61694, "loss": 0.61694, "time": 0.22615} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.01876, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86875, "top5_acc": 0.99562, "loss_cls": 0.60547, "loss": 0.60547, "time": 0.22682} +{"mode": "val", "epoch": 50, "iter": 533, "lr": 0.01875, "top1_acc": 0.81915, "top5_acc": 0.9858, "mean_class_accuracy": 0.7616} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.01873, "memory": 4083, "data_time": 0.18512, "top1_acc": 0.89438, "top5_acc": 0.99812, "loss_cls": 0.54653, "loss": 0.54653, "time": 0.42325} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.01871, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89, "top5_acc": 0.99688, "loss_cls": 0.5641, "loss": 0.5641, "time": 0.22604} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.0187, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86562, "top5_acc": 0.99562, "loss_cls": 0.65153, "loss": 0.65153, "time": 0.22672} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.01868, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.8875, "top5_acc": 0.99625, "loss_cls": 0.56575, "loss": 0.56575, "time": 0.22556} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.01866, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8675, "top5_acc": 0.9975, "loss_cls": 0.61693, "loss": 0.61693, "time": 0.22487} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.01864, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86562, "top5_acc": 0.99688, "loss_cls": 0.63463, "loss": 0.63463, "time": 0.22364} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.01863, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8775, "top5_acc": 0.995, "loss_cls": 0.59192, "loss": 0.59192, "time": 0.22508} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.01861, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88562, "top5_acc": 0.99188, "loss_cls": 0.61744, "loss": 0.61744, "time": 0.22481} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.01859, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87812, "top5_acc": 0.99188, "loss_cls": 0.59825, "loss": 0.59825, "time": 0.22529} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.01857, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89312, "top5_acc": 0.995, "loss_cls": 0.55322, "loss": 0.55322, "time": 0.22099} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.01855, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.875, "top5_acc": 0.99625, "loss_cls": 0.58108, "loss": 0.58108, "time": 0.22103} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.01854, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86312, "top5_acc": 0.99625, "loss_cls": 0.64705, "loss": 0.64705, "time": 0.22312} +{"mode": "val", "epoch": 51, "iter": 533, "lr": 0.01852, "top1_acc": 0.84392, "top5_acc": 0.98686, "mean_class_accuracy": 0.77953} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.0185, "memory": 4083, "data_time": 0.18939, "top1_acc": 0.89312, "top5_acc": 0.99875, "loss_cls": 0.51834, "loss": 0.51834, "time": 0.42258} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.01849, "memory": 4083, "data_time": 0.00061, "top1_acc": 0.89375, "top5_acc": 0.99688, "loss_cls": 0.54547, "loss": 0.54547, "time": 0.22597} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.01847, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88875, "top5_acc": 0.99312, "loss_cls": 0.61429, "loss": 0.61429, "time": 0.22499} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.01845, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88562, "top5_acc": 0.99812, "loss_cls": 0.58442, "loss": 0.58442, "time": 0.22171} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.01843, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86875, "top5_acc": 0.9925, "loss_cls": 0.64152, "loss": 0.64152, "time": 0.22161} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.01841, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.89125, "top5_acc": 0.9975, "loss_cls": 0.54524, "loss": 0.54524, "time": 0.22136} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.0184, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89, "top5_acc": 0.99625, "loss_cls": 0.55309, "loss": 0.55309, "time": 0.22295} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.01838, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.885, "top5_acc": 0.99688, "loss_cls": 0.5744, "loss": 0.5744, "time": 0.22325} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.01836, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87625, "top5_acc": 0.99625, "loss_cls": 0.59861, "loss": 0.59861, "time": 0.22457} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.01834, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88188, "top5_acc": 0.9975, "loss_cls": 0.59547, "loss": 0.59547, "time": 0.2244} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.01832, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87375, "top5_acc": 0.99375, "loss_cls": 0.60773, "loss": 0.60773, "time": 0.22433} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.01831, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88125, "top5_acc": 0.99688, "loss_cls": 0.58693, "loss": 0.58693, "time": 0.22333} +{"mode": "val", "epoch": 52, "iter": 533, "lr": 0.01829, "top1_acc": 0.86011, "top5_acc": 0.99237, "mean_class_accuracy": 0.81684} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.01827, "memory": 4083, "data_time": 0.19618, "top1_acc": 0.90188, "top5_acc": 0.99688, "loss_cls": 0.50136, "loss": 0.50136, "time": 0.4386} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.01826, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89062, "top5_acc": 0.995, "loss_cls": 0.52718, "loss": 0.52718, "time": 0.22694} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.01824, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88312, "top5_acc": 0.995, "loss_cls": 0.57374, "loss": 0.57374, "time": 0.22956} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.01822, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.885, "top5_acc": 0.99688, "loss_cls": 0.59526, "loss": 0.59526, "time": 0.22656} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.0182, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87812, "top5_acc": 0.99625, "loss_cls": 0.61343, "loss": 0.61343, "time": 0.22632} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.01818, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89, "top5_acc": 0.99062, "loss_cls": 0.57783, "loss": 0.57783, "time": 0.22691} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.01816, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90375, "top5_acc": 0.995, "loss_cls": 0.52855, "loss": 0.52855, "time": 0.22629} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.01815, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.85812, "top5_acc": 0.99312, "loss_cls": 0.64088, "loss": 0.64088, "time": 0.22392} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.01813, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8725, "top5_acc": 0.9975, "loss_cls": 0.60265, "loss": 0.60265, "time": 0.22515} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.01811, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88312, "top5_acc": 0.99625, "loss_cls": 0.52846, "loss": 0.52846, "time": 0.22505} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.01809, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86625, "top5_acc": 0.995, "loss_cls": 0.6578, "loss": 0.6578, "time": 0.22209} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.01807, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87438, "top5_acc": 0.9925, "loss_cls": 0.62379, "loss": 0.62379, "time": 0.22399} +{"mode": "val", "epoch": 53, "iter": 533, "lr": 0.01806, "top1_acc": 0.84509, "top5_acc": 0.99179, "mean_class_accuracy": 0.79125} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.01804, "memory": 4083, "data_time": 0.19346, "top1_acc": 0.8875, "top5_acc": 0.9975, "loss_cls": 0.56513, "loss": 0.56513, "time": 0.42871} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.01802, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8775, "top5_acc": 0.99562, "loss_cls": 0.57507, "loss": 0.57507, "time": 0.2227} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.018, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89812, "top5_acc": 0.99688, "loss_cls": 0.5028, "loss": 0.5028, "time": 0.22631} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.01798, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88562, "top5_acc": 0.99625, "loss_cls": 0.55305, "loss": 0.55305, "time": 0.22536} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.01797, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88625, "top5_acc": 0.995, "loss_cls": 0.58747, "loss": 0.58747, "time": 0.22568} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.01795, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89625, "top5_acc": 0.99625, "loss_cls": 0.52707, "loss": 0.52707, "time": 0.22378} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.01793, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90125, "top5_acc": 0.99812, "loss_cls": 0.54309, "loss": 0.54309, "time": 0.22232} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.01791, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.86625, "top5_acc": 0.99625, "loss_cls": 0.65393, "loss": 0.65393, "time": 0.22667} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.01789, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88812, "top5_acc": 0.99438, "loss_cls": 0.57902, "loss": 0.57902, "time": 0.22574} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.01787, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89188, "top5_acc": 0.99812, "loss_cls": 0.52688, "loss": 0.52688, "time": 0.22449} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.01786, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88188, "top5_acc": 0.99812, "loss_cls": 0.60267, "loss": 0.60267, "time": 0.22319} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.01784, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89, "top5_acc": 0.99562, "loss_cls": 0.5674, "loss": 0.5674, "time": 0.22927} +{"mode": "val", "epoch": 54, "iter": 533, "lr": 0.01782, "top1_acc": 0.81634, "top5_acc": 0.98521, "mean_class_accuracy": 0.76715} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.0178, "memory": 4083, "data_time": 0.20083, "top1_acc": 0.8925, "top5_acc": 0.99625, "loss_cls": 0.53961, "loss": 0.53961, "time": 0.43959} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.01779, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90062, "top5_acc": 0.99625, "loss_cls": 0.52284, "loss": 0.52284, "time": 0.2261} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.01777, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9025, "top5_acc": 0.99812, "loss_cls": 0.47594, "loss": 0.47594, "time": 0.22049} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.01775, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.90188, "top5_acc": 0.9975, "loss_cls": 0.51126, "loss": 0.51126, "time": 0.22795} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.01773, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.88562, "top5_acc": 0.995, "loss_cls": 0.58703, "loss": 0.58703, "time": 0.22511} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.01771, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.875, "top5_acc": 0.9975, "loss_cls": 0.59699, "loss": 0.59699, "time": 0.22313} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.01769, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89438, "top5_acc": 0.99375, "loss_cls": 0.57505, "loss": 0.57505, "time": 0.22322} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.01767, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88438, "top5_acc": 0.99688, "loss_cls": 0.55916, "loss": 0.55916, "time": 0.2223} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.01766, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86688, "top5_acc": 0.99375, "loss_cls": 0.62594, "loss": 0.62594, "time": 0.22372} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.01764, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87812, "top5_acc": 0.99688, "loss_cls": 0.58082, "loss": 0.58082, "time": 0.2219} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.01762, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89125, "top5_acc": 0.99812, "loss_cls": 0.54341, "loss": 0.54341, "time": 0.22154} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.0176, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8925, "top5_acc": 0.99438, "loss_cls": 0.56842, "loss": 0.56842, "time": 0.22444} +{"mode": "val", "epoch": 55, "iter": 533, "lr": 0.01758, "top1_acc": 0.84532, "top5_acc": 0.98779, "mean_class_accuracy": 0.77506} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.01757, "memory": 4083, "data_time": 0.19326, "top1_acc": 0.8925, "top5_acc": 0.99688, "loss_cls": 0.55358, "loss": 0.55358, "time": 0.43093} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.01755, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.89062, "top5_acc": 0.99812, "loss_cls": 0.54964, "loss": 0.54964, "time": 0.22645} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.01753, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.90875, "top5_acc": 0.9975, "loss_cls": 0.47113, "loss": 0.47113, "time": 0.22592} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.01751, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88062, "top5_acc": 0.99688, "loss_cls": 0.55844, "loss": 0.55844, "time": 0.22178} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.01749, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.8975, "top5_acc": 0.99562, "loss_cls": 0.53422, "loss": 0.53422, "time": 0.22398} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.01747, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.89188, "top5_acc": 0.995, "loss_cls": 0.55339, "loss": 0.55339, "time": 0.22734} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.01745, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89438, "top5_acc": 0.99625, "loss_cls": 0.54986, "loss": 0.54986, "time": 0.22256} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.01743, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87125, "top5_acc": 0.99625, "loss_cls": 0.61235, "loss": 0.61235, "time": 0.22459} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.01742, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87938, "top5_acc": 0.995, "loss_cls": 0.58256, "loss": 0.58256, "time": 0.22665} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.0174, "memory": 4083, "data_time": 0.00069, "top1_acc": 0.9, "top5_acc": 0.99938, "loss_cls": 0.52234, "loss": 0.52234, "time": 0.22677} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.01738, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88625, "top5_acc": 0.99562, "loss_cls": 0.5683, "loss": 0.5683, "time": 0.22242} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.01736, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88438, "top5_acc": 0.995, "loss_cls": 0.57784, "loss": 0.57784, "time": 0.22215} +{"mode": "val", "epoch": 56, "iter": 533, "lr": 0.01734, "top1_acc": 0.84791, "top5_acc": 0.99179, "mean_class_accuracy": 0.77602} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.01733, "memory": 4083, "data_time": 0.20152, "top1_acc": 0.88938, "top5_acc": 0.99688, "loss_cls": 0.57284, "loss": 0.57284, "time": 0.43778} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.01731, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92125, "top5_acc": 0.99875, "loss_cls": 0.4186, "loss": 0.4186, "time": 0.22414} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.01729, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89812, "top5_acc": 0.99812, "loss_cls": 0.47964, "loss": 0.47964, "time": 0.22833} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.01727, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89562, "top5_acc": 0.99875, "loss_cls": 0.51111, "loss": 0.51111, "time": 0.22447} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.01725, "memory": 4083, "data_time": 0.00081, "top1_acc": 0.8725, "top5_acc": 0.99688, "loss_cls": 0.61377, "loss": 0.61377, "time": 0.2279} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.01723, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88562, "top5_acc": 0.99812, "loss_cls": 0.5707, "loss": 0.5707, "time": 0.22683} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.01721, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.86812, "top5_acc": 0.99688, "loss_cls": 0.61069, "loss": 0.61069, "time": 0.22662} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.01719, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88312, "top5_acc": 0.995, "loss_cls": 0.61514, "loss": 0.61514, "time": 0.22444} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.01717, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88312, "top5_acc": 0.99438, "loss_cls": 0.60416, "loss": 0.60416, "time": 0.22191} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.01716, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88125, "top5_acc": 0.9975, "loss_cls": 0.55072, "loss": 0.55072, "time": 0.22514} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.01714, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89188, "top5_acc": 0.99438, "loss_cls": 0.5431, "loss": 0.5431, "time": 0.2239} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.01712, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.89125, "top5_acc": 0.99062, "loss_cls": 0.58946, "loss": 0.58946, "time": 0.22545} +{"mode": "val", "epoch": 57, "iter": 533, "lr": 0.0171, "top1_acc": 0.85413, "top5_acc": 0.99225, "mean_class_accuracy": 0.78319} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.01708, "memory": 4083, "data_time": 0.19053, "top1_acc": 0.89562, "top5_acc": 0.99688, "loss_cls": 0.50866, "loss": 0.50866, "time": 0.42648} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.01706, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89562, "top5_acc": 0.99812, "loss_cls": 0.48747, "loss": 0.48747, "time": 0.22592} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.01704, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.90312, "top5_acc": 0.99812, "loss_cls": 0.4971, "loss": 0.4971, "time": 0.22696} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.01703, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90188, "top5_acc": 0.9975, "loss_cls": 0.47706, "loss": 0.47706, "time": 0.22276} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.01701, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.90688, "top5_acc": 0.99688, "loss_cls": 0.49545, "loss": 0.49545, "time": 0.22363} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.01699, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88938, "top5_acc": 0.99438, "loss_cls": 0.55069, "loss": 0.55069, "time": 0.22441} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.01697, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89812, "top5_acc": 0.99688, "loss_cls": 0.53099, "loss": 0.53099, "time": 0.23084} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.01695, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90875, "top5_acc": 0.995, "loss_cls": 0.47891, "loss": 0.47891, "time": 0.22501} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.01693, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89, "top5_acc": 0.99688, "loss_cls": 0.53572, "loss": 0.53572, "time": 0.22609} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.01691, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87688, "top5_acc": 0.99562, "loss_cls": 0.61461, "loss": 0.61461, "time": 0.22368} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.01689, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88625, "top5_acc": 0.99688, "loss_cls": 0.57763, "loss": 0.57763, "time": 0.22331} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.01687, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89812, "top5_acc": 0.9975, "loss_cls": 0.54309, "loss": 0.54309, "time": 0.22317} +{"mode": "val", "epoch": 58, "iter": 533, "lr": 0.01686, "top1_acc": 0.81692, "top5_acc": 0.9885, "mean_class_accuracy": 0.74949} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.01684, "memory": 4083, "data_time": 0.18806, "top1_acc": 0.90812, "top5_acc": 0.99688, "loss_cls": 0.52821, "loss": 0.52821, "time": 0.42508} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.01682, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89188, "top5_acc": 0.99688, "loss_cls": 0.55349, "loss": 0.55349, "time": 0.22583} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.0168, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.90188, "top5_acc": 0.99938, "loss_cls": 0.47647, "loss": 0.47647, "time": 0.22857} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.01678, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9125, "top5_acc": 0.99875, "loss_cls": 0.48895, "loss": 0.48895, "time": 0.22399} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.01676, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88188, "top5_acc": 0.99625, "loss_cls": 0.5857, "loss": 0.5857, "time": 0.22881} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.01674, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89562, "top5_acc": 0.99688, "loss_cls": 0.52854, "loss": 0.52854, "time": 0.22399} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.01672, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89688, "top5_acc": 0.99812, "loss_cls": 0.49879, "loss": 0.49879, "time": 0.22917} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.0167, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.90438, "top5_acc": 0.9975, "loss_cls": 0.4698, "loss": 0.4698, "time": 0.22834} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.01668, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89812, "top5_acc": 0.99625, "loss_cls": 0.54541, "loss": 0.54541, "time": 0.22541} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.01667, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89375, "top5_acc": 0.99875, "loss_cls": 0.53343, "loss": 0.53343, "time": 0.23008} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.01665, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90188, "top5_acc": 0.99625, "loss_cls": 0.52266, "loss": 0.52266, "time": 0.22603} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.01663, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88062, "top5_acc": 0.9975, "loss_cls": 0.56247, "loss": 0.56247, "time": 0.2277} +{"mode": "val", "epoch": 59, "iter": 533, "lr": 0.01661, "top1_acc": 0.835, "top5_acc": 0.98709, "mean_class_accuracy": 0.77411} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.01659, "memory": 4083, "data_time": 0.19505, "top1_acc": 0.90125, "top5_acc": 0.99875, "loss_cls": 0.49389, "loss": 0.49389, "time": 0.43166} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.01657, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.925, "top5_acc": 0.99812, "loss_cls": 0.41765, "loss": 0.41765, "time": 0.2263} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.01655, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9, "top5_acc": 0.99812, "loss_cls": 0.49079, "loss": 0.49079, "time": 0.22283} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.01653, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.90562, "top5_acc": 0.99812, "loss_cls": 0.47492, "loss": 0.47492, "time": 0.22569} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.01651, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89062, "top5_acc": 0.99688, "loss_cls": 0.52471, "loss": 0.52471, "time": 0.22644} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.0165, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91938, "top5_acc": 0.99812, "loss_cls": 0.4494, "loss": 0.4494, "time": 0.22561} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.01648, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.895, "top5_acc": 0.99562, "loss_cls": 0.50753, "loss": 0.50753, "time": 0.2248} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.01646, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88438, "top5_acc": 0.99562, "loss_cls": 0.54047, "loss": 0.54047, "time": 0.22466} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.01644, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89438, "top5_acc": 0.99812, "loss_cls": 0.54834, "loss": 0.54834, "time": 0.22523} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.01642, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.905, "top5_acc": 0.9975, "loss_cls": 0.49248, "loss": 0.49248, "time": 0.22329} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.0164, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88312, "top5_acc": 0.99625, "loss_cls": 0.56603, "loss": 0.56603, "time": 0.22427} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.01638, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89625, "top5_acc": 0.99812, "loss_cls": 0.51899, "loss": 0.51899, "time": 0.2241} +{"mode": "val", "epoch": 60, "iter": 533, "lr": 0.01636, "top1_acc": 0.81716, "top5_acc": 0.98779, "mean_class_accuracy": 0.74867} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.01634, "memory": 4083, "data_time": 0.19443, "top1_acc": 0.9075, "top5_acc": 0.9975, "loss_cls": 0.47091, "loss": 0.47091, "time": 0.43316} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.01632, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90812, "top5_acc": 0.99875, "loss_cls": 0.4824, "loss": 0.4824, "time": 0.22232} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.0163, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9125, "top5_acc": 0.99812, "loss_cls": 0.49249, "loss": 0.49249, "time": 0.22752} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.01629, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.47831, "loss": 0.47831, "time": 0.22508} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.01627, "memory": 4083, "data_time": 0.00064, "top1_acc": 0.90375, "top5_acc": 0.99562, "loss_cls": 0.51943, "loss": 0.51943, "time": 0.22441} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.01625, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89438, "top5_acc": 0.99562, "loss_cls": 0.54877, "loss": 0.54877, "time": 0.22448} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.01623, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.89875, "top5_acc": 0.99875, "loss_cls": 0.50786, "loss": 0.50786, "time": 0.22555} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.01621, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90625, "top5_acc": 0.99562, "loss_cls": 0.48708, "loss": 0.48708, "time": 0.22432} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.01619, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89688, "top5_acc": 0.99625, "loss_cls": 0.52834, "loss": 0.52834, "time": 0.22296} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.01617, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90562, "top5_acc": 0.9975, "loss_cls": 0.5309, "loss": 0.5309, "time": 0.22292} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.01615, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.90875, "top5_acc": 0.99688, "loss_cls": 0.47541, "loss": 0.47541, "time": 0.22208} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.01613, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89312, "top5_acc": 0.9975, "loss_cls": 0.50071, "loss": 0.50071, "time": 0.224} +{"mode": "val", "epoch": 61, "iter": 533, "lr": 0.01611, "top1_acc": 0.84298, "top5_acc": 0.99014, "mean_class_accuracy": 0.78063} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.01609, "memory": 4083, "data_time": 0.19893, "top1_acc": 0.91188, "top5_acc": 0.9975, "loss_cls": 0.45725, "loss": 0.45725, "time": 0.43339} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.01607, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89438, "top5_acc": 0.99625, "loss_cls": 0.5471, "loss": 0.5471, "time": 0.22329} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.01605, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9025, "top5_acc": 0.99625, "loss_cls": 0.50202, "loss": 0.50202, "time": 0.22629} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.01603, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90938, "top5_acc": 0.99562, "loss_cls": 0.46482, "loss": 0.46482, "time": 0.22655} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.01602, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91812, "top5_acc": 0.99688, "loss_cls": 0.44604, "loss": 0.44604, "time": 0.22388} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.016, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89375, "top5_acc": 0.99625, "loss_cls": 0.49962, "loss": 0.49962, "time": 0.22412} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.01598, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90562, "top5_acc": 0.99688, "loss_cls": 0.46886, "loss": 0.46886, "time": 0.23028} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.01596, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89938, "top5_acc": 0.99812, "loss_cls": 0.51529, "loss": 0.51529, "time": 0.22299} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.01594, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90625, "top5_acc": 0.99625, "loss_cls": 0.48019, "loss": 0.48019, "time": 0.22754} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.01592, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.88312, "top5_acc": 0.99625, "loss_cls": 0.53817, "loss": 0.53817, "time": 0.22736} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.0159, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88812, "top5_acc": 0.99688, "loss_cls": 0.55823, "loss": 0.55823, "time": 0.22556} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.01588, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88938, "top5_acc": 0.99438, "loss_cls": 0.57536, "loss": 0.57536, "time": 0.22604} +{"mode": "val", "epoch": 62, "iter": 533, "lr": 0.01586, "top1_acc": 0.85166, "top5_acc": 0.99073, "mean_class_accuracy": 0.81904} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.01584, "memory": 4083, "data_time": 0.18658, "top1_acc": 0.91188, "top5_acc": 0.99875, "loss_cls": 0.44886, "loss": 0.44886, "time": 0.42605} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.01582, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91375, "top5_acc": 0.9975, "loss_cls": 0.48502, "loss": 0.48502, "time": 0.22641} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.0158, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.89, "top5_acc": 0.9975, "loss_cls": 0.52365, "loss": 0.52365, "time": 0.22638} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.01578, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90625, "top5_acc": 0.9925, "loss_cls": 0.5197, "loss": 0.5197, "time": 0.22132} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.01576, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.89438, "top5_acc": 0.99875, "loss_cls": 0.48276, "loss": 0.48276, "time": 0.22323} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.01574, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.90062, "top5_acc": 0.99625, "loss_cls": 0.54036, "loss": 0.54036, "time": 0.22707} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.01572, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89812, "top5_acc": 0.9975, "loss_cls": 0.47302, "loss": 0.47302, "time": 0.22341} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.0157, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90938, "top5_acc": 0.99688, "loss_cls": 0.48195, "loss": 0.48195, "time": 0.22268} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.01568, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90125, "top5_acc": 0.99375, "loss_cls": 0.52474, "loss": 0.52474, "time": 0.22315} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.01566, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8975, "top5_acc": 0.99938, "loss_cls": 0.4706, "loss": 0.4706, "time": 0.22361} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.01564, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89438, "top5_acc": 0.995, "loss_cls": 0.55101, "loss": 0.55101, "time": 0.22037} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.01562, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9075, "top5_acc": 0.99375, "loss_cls": 0.50123, "loss": 0.50123, "time": 0.22282} +{"mode": "val", "epoch": 63, "iter": 533, "lr": 0.01561, "top1_acc": 0.82971, "top5_acc": 0.99108, "mean_class_accuracy": 0.77927} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.01559, "memory": 4083, "data_time": 0.1989, "top1_acc": 0.8975, "top5_acc": 0.99812, "loss_cls": 0.50857, "loss": 0.50857, "time": 0.43441} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.01557, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.89938, "top5_acc": 0.99562, "loss_cls": 0.47447, "loss": 0.47447, "time": 0.22639} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.01555, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90875, "top5_acc": 0.99625, "loss_cls": 0.47921, "loss": 0.47921, "time": 0.22528} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.01553, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90688, "top5_acc": 0.99812, "loss_cls": 0.50727, "loss": 0.50727, "time": 0.22494} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.01551, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88812, "top5_acc": 0.99312, "loss_cls": 0.53729, "loss": 0.53729, "time": 0.22706} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.01549, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.8975, "top5_acc": 0.9975, "loss_cls": 0.52565, "loss": 0.52565, "time": 0.2248} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.01547, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9025, "top5_acc": 0.99812, "loss_cls": 0.48543, "loss": 0.48543, "time": 0.22452} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.01545, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.875, "top5_acc": 0.99688, "loss_cls": 0.58184, "loss": 0.58184, "time": 0.22538} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.01543, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89375, "top5_acc": 0.9975, "loss_cls": 0.5291, "loss": 0.5291, "time": 0.22313} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.01541, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89688, "top5_acc": 0.99562, "loss_cls": 0.52903, "loss": 0.52903, "time": 0.22542} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.01539, "memory": 4083, "data_time": 0.00071, "top1_acc": 0.89562, "top5_acc": 0.99812, "loss_cls": 0.49097, "loss": 0.49097, "time": 0.22902} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.01537, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89312, "top5_acc": 0.99812, "loss_cls": 0.5319, "loss": 0.5319, "time": 0.22427} +{"mode": "val", "epoch": 64, "iter": 533, "lr": 0.01535, "top1_acc": 0.85999, "top5_acc": 0.99143, "mean_class_accuracy": 0.82895} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.01533, "memory": 4083, "data_time": 0.19158, "top1_acc": 0.90875, "top5_acc": 0.99875, "loss_cls": 0.44374, "loss": 0.44374, "time": 0.42829} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.01531, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.90375, "top5_acc": 0.9975, "loss_cls": 0.48529, "loss": 0.48529, "time": 0.22578} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.01529, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90188, "top5_acc": 0.99875, "loss_cls": 0.48746, "loss": 0.48746, "time": 0.2264} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.01527, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91688, "top5_acc": 0.99812, "loss_cls": 0.43531, "loss": 0.43531, "time": 0.22269} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.01526, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91125, "top5_acc": 0.9975, "loss_cls": 0.47917, "loss": 0.47917, "time": 0.22173} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.01524, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9125, "top5_acc": 0.99688, "loss_cls": 0.48605, "loss": 0.48605, "time": 0.22371} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.01522, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91562, "top5_acc": 0.99938, "loss_cls": 0.41328, "loss": 0.41328, "time": 0.22255} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0152, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90438, "top5_acc": 0.99625, "loss_cls": 0.4817, "loss": 0.4817, "time": 0.22195} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.01518, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9025, "top5_acc": 0.9975, "loss_cls": 0.48186, "loss": 0.48186, "time": 0.22359} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.01516, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88188, "top5_acc": 0.99625, "loss_cls": 0.5938, "loss": 0.5938, "time": 0.22416} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.01514, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.905, "top5_acc": 0.99812, "loss_cls": 0.516, "loss": 0.516, "time": 0.22523} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.01512, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89875, "top5_acc": 0.9975, "loss_cls": 0.50517, "loss": 0.50517, "time": 0.22373} +{"mode": "val", "epoch": 65, "iter": 533, "lr": 0.0151, "top1_acc": 0.81223, "top5_acc": 0.98216, "mean_class_accuracy": 0.76348} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.01508, "memory": 4083, "data_time": 0.19618, "top1_acc": 0.92188, "top5_acc": 0.99688, "loss_cls": 0.42909, "loss": 0.42909, "time": 0.43309} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.01506, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90938, "top5_acc": 0.99938, "loss_cls": 0.44937, "loss": 0.44937, "time": 0.22512} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.01504, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.90125, "top5_acc": 0.9975, "loss_cls": 0.48154, "loss": 0.48154, "time": 0.22276} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.01502, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90125, "top5_acc": 0.99875, "loss_cls": 0.48445, "loss": 0.48445, "time": 0.22489} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.015, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.91188, "top5_acc": 0.9975, "loss_cls": 0.48791, "loss": 0.48791, "time": 0.22835} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.01498, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90875, "top5_acc": 0.99875, "loss_cls": 0.47308, "loss": 0.47308, "time": 0.22313} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.01496, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.92125, "top5_acc": 0.9975, "loss_cls": 0.419, "loss": 0.419, "time": 0.227} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.01494, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91812, "top5_acc": 0.99875, "loss_cls": 0.44638, "loss": 0.44638, "time": 0.22493} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.01492, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.89188, "top5_acc": 0.99562, "loss_cls": 0.53879, "loss": 0.53879, "time": 0.22319} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.0149, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89375, "top5_acc": 0.99688, "loss_cls": 0.52548, "loss": 0.52548, "time": 0.22514} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.01488, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8925, "top5_acc": 0.99375, "loss_cls": 0.54082, "loss": 0.54082, "time": 0.22637} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.01486, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.89938, "top5_acc": 0.99688, "loss_cls": 0.53792, "loss": 0.53792, "time": 0.22559} +{"mode": "val", "epoch": 66, "iter": 533, "lr": 0.01484, "top1_acc": 0.85671, "top5_acc": 0.99132, "mean_class_accuracy": 0.81481} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.01482, "memory": 4083, "data_time": 0.19077, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.40774, "loss": 0.40774, "time": 0.43027} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.0148, "memory": 4083, "data_time": 0.00071, "top1_acc": 0.92062, "top5_acc": 0.99688, "loss_cls": 0.41893, "loss": 0.41893, "time": 0.22365} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.01478, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91438, "top5_acc": 0.99688, "loss_cls": 0.445, "loss": 0.445, "time": 0.22467} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.01476, "memory": 4083, "data_time": 0.00073, "top1_acc": 0.90625, "top5_acc": 0.99875, "loss_cls": 0.48833, "loss": 0.48833, "time": 0.22658} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.01474, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.915, "top5_acc": 0.99938, "loss_cls": 0.46502, "loss": 0.46502, "time": 0.22072} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.01472, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91, "top5_acc": 0.99875, "loss_cls": 0.4572, "loss": 0.4572, "time": 0.22325} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.0147, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90125, "top5_acc": 0.99812, "loss_cls": 0.47877, "loss": 0.47877, "time": 0.22323} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.01468, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90938, "top5_acc": 0.99688, "loss_cls": 0.481, "loss": 0.481, "time": 0.22317} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.01466, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.4296, "loss": 0.4296, "time": 0.22351} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.01464, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89688, "top5_acc": 0.99812, "loss_cls": 0.52136, "loss": 0.52136, "time": 0.22498} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.01462, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.91062, "top5_acc": 0.9975, "loss_cls": 0.46595, "loss": 0.46595, "time": 0.22259} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.0146, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.905, "top5_acc": 0.99438, "loss_cls": 0.50112, "loss": 0.50112, "time": 0.22346} +{"mode": "val", "epoch": 67, "iter": 533, "lr": 0.01458, "top1_acc": 0.87173, "top5_acc": 0.99261, "mean_class_accuracy": 0.82392} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.01456, "memory": 4083, "data_time": 0.19092, "top1_acc": 0.91875, "top5_acc": 0.99812, "loss_cls": 0.42758, "loss": 0.42758, "time": 0.42734} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.01454, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.89875, "top5_acc": 1.0, "loss_cls": 0.49603, "loss": 0.49603, "time": 0.22411} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.01452, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91125, "top5_acc": 0.99812, "loss_cls": 0.45165, "loss": 0.45165, "time": 0.22271} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.0145, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.91062, "top5_acc": 0.99875, "loss_cls": 0.42986, "loss": 0.42986, "time": 0.22419} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.01448, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89938, "top5_acc": 0.9975, "loss_cls": 0.4921, "loss": 0.4921, "time": 0.22403} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.01446, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90375, "top5_acc": 0.99875, "loss_cls": 0.49768, "loss": 0.49768, "time": 0.22582} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.01444, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.91938, "top5_acc": 0.99812, "loss_cls": 0.43111, "loss": 0.43111, "time": 0.22364} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.01442, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.91688, "top5_acc": 0.99938, "loss_cls": 0.45451, "loss": 0.45451, "time": 0.22754} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.0144, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91312, "top5_acc": 0.99688, "loss_cls": 0.45982, "loss": 0.45982, "time": 0.22543} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.01438, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91438, "top5_acc": 0.99938, "loss_cls": 0.46405, "loss": 0.46405, "time": 0.2252} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.01436, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.91625, "top5_acc": 0.99812, "loss_cls": 0.43856, "loss": 0.43856, "time": 0.22112} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.01434, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92938, "top5_acc": 0.99812, "loss_cls": 0.40938, "loss": 0.40938, "time": 0.22533} +{"mode": "val", "epoch": 68, "iter": 533, "lr": 0.01433, "top1_acc": 0.8519, "top5_acc": 0.99237, "mean_class_accuracy": 0.81412} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.01431, "memory": 4083, "data_time": 0.19296, "top1_acc": 0.91375, "top5_acc": 0.99688, "loss_cls": 0.44186, "loss": 0.44186, "time": 0.42843} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.01429, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.91375, "top5_acc": 0.99875, "loss_cls": 0.43464, "loss": 0.43464, "time": 0.22508} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.01427, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91125, "top5_acc": 0.99812, "loss_cls": 0.44473, "loss": 0.44473, "time": 0.22831} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.01425, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91312, "top5_acc": 0.99812, "loss_cls": 0.43002, "loss": 0.43002, "time": 0.22329} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.01423, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9125, "top5_acc": 1.0, "loss_cls": 0.4388, "loss": 0.4388, "time": 0.22398} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.0142, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88812, "top5_acc": 0.99812, "loss_cls": 0.52724, "loss": 0.52724, "time": 0.22434} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.01418, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91062, "top5_acc": 0.9975, "loss_cls": 0.46713, "loss": 0.46713, "time": 0.22442} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.01416, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91688, "top5_acc": 0.99875, "loss_cls": 0.44162, "loss": 0.44162, "time": 0.22711} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.01414, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90938, "top5_acc": 0.99812, "loss_cls": 0.45764, "loss": 0.45764, "time": 0.22562} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.01412, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91438, "top5_acc": 0.99875, "loss_cls": 0.45521, "loss": 0.45521, "time": 0.2243} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.0141, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90562, "top5_acc": 0.99812, "loss_cls": 0.4774, "loss": 0.4774, "time": 0.22454} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.01408, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.90125, "top5_acc": 0.99812, "loss_cls": 0.48949, "loss": 0.48949, "time": 0.2254} +{"mode": "val", "epoch": 69, "iter": 533, "lr": 0.01407, "top1_acc": 0.84978, "top5_acc": 0.99026, "mean_class_accuracy": 0.78986} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.01405, "memory": 4083, "data_time": 0.19616, "top1_acc": 0.92062, "top5_acc": 0.99938, "loss_cls": 0.42975, "loss": 0.42975, "time": 0.43202} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.01403, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.90625, "top5_acc": 0.99625, "loss_cls": 0.48545, "loss": 0.48545, "time": 0.22533} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.01401, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90625, "top5_acc": 0.99938, "loss_cls": 0.45639, "loss": 0.45639, "time": 0.22529} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.01399, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9075, "top5_acc": 0.99688, "loss_cls": 0.46348, "loss": 0.46348, "time": 0.22308} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.01397, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.90438, "top5_acc": 0.99812, "loss_cls": 0.47269, "loss": 0.47269, "time": 0.22471} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.01395, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91, "top5_acc": 0.9975, "loss_cls": 0.4522, "loss": 0.4522, "time": 0.22445} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.01392, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.90438, "top5_acc": 0.995, "loss_cls": 0.50573, "loss": 0.50573, "time": 0.22847} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.0139, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.91, "top5_acc": 0.99938, "loss_cls": 0.44642, "loss": 0.44642, "time": 0.22287} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.01388, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92125, "top5_acc": 0.99688, "loss_cls": 0.44279, "loss": 0.44279, "time": 0.22504} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.01386, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.90375, "top5_acc": 0.99875, "loss_cls": 0.47819, "loss": 0.47819, "time": 0.22274} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.01384, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90062, "top5_acc": 0.99938, "loss_cls": 0.49405, "loss": 0.49405, "time": 0.22412} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.01382, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.915, "top5_acc": 0.99875, "loss_cls": 0.44243, "loss": 0.44243, "time": 0.22354} +{"mode": "val", "epoch": 70, "iter": 533, "lr": 0.01381, "top1_acc": 0.86246, "top5_acc": 0.99331, "mean_class_accuracy": 0.79644} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.01379, "memory": 4083, "data_time": 0.19046, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.38306, "loss": 0.38306, "time": 0.42725} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.01377, "memory": 4083, "data_time": 0.00063, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.37765, "loss": 0.37765, "time": 0.22896} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.01375, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93, "top5_acc": 0.99812, "loss_cls": 0.41069, "loss": 0.41069, "time": 0.22434} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.01373, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.90188, "top5_acc": 0.99938, "loss_cls": 0.48198, "loss": 0.48198, "time": 0.22493} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.01371, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90938, "top5_acc": 0.99938, "loss_cls": 0.4427, "loss": 0.4427, "time": 0.22349} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.01368, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90938, "top5_acc": 0.99688, "loss_cls": 0.4493, "loss": 0.4493, "time": 0.22114} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.01366, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.91625, "top5_acc": 0.99938, "loss_cls": 0.42969, "loss": 0.42969, "time": 0.2259} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.01364, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9125, "top5_acc": 0.9975, "loss_cls": 0.46678, "loss": 0.46678, "time": 0.22505} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.01362, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92312, "top5_acc": 0.9975, "loss_cls": 0.41277, "loss": 0.41277, "time": 0.22224} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.0136, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.9175, "top5_acc": 1.0, "loss_cls": 0.42124, "loss": 0.42124, "time": 0.22314} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.01358, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92188, "top5_acc": 0.99938, "loss_cls": 0.40903, "loss": 0.40903, "time": 0.22443} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.01356, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90375, "top5_acc": 0.99625, "loss_cls": 0.49669, "loss": 0.49669, "time": 0.22421} +{"mode": "val", "epoch": 71, "iter": 533, "lr": 0.01355, "top1_acc": 0.84462, "top5_acc": 0.99202, "mean_class_accuracy": 0.79756} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.01353, "memory": 4083, "data_time": 0.18638, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.37866, "loss": 0.37866, "time": 0.42342} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.01351, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.36414, "loss": 0.36414, "time": 0.2271} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.01349, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93, "top5_acc": 0.9975, "loss_cls": 0.363, "loss": 0.363, "time": 0.22306} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.01346, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.92688, "top5_acc": 0.99938, "loss_cls": 0.40026, "loss": 0.40026, "time": 0.22876} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.01344, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.40427, "loss": 0.40427, "time": 0.22529} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.01342, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91812, "top5_acc": 0.9975, "loss_cls": 0.44178, "loss": 0.44178, "time": 0.22351} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.0134, "memory": 4083, "data_time": 0.00062, "top1_acc": 0.92062, "top5_acc": 0.9975, "loss_cls": 0.44494, "loss": 0.44494, "time": 0.22593} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.01338, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90562, "top5_acc": 0.9975, "loss_cls": 0.46957, "loss": 0.46957, "time": 0.22475} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.01336, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.91875, "top5_acc": 0.9975, "loss_cls": 0.46376, "loss": 0.46376, "time": 0.22433} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.01334, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9175, "top5_acc": 0.99812, "loss_cls": 0.44996, "loss": 0.44996, "time": 0.22361} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.01332, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90312, "top5_acc": 0.99688, "loss_cls": 0.47091, "loss": 0.47091, "time": 0.22319} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.0133, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.93812, "top5_acc": 0.99875, "loss_cls": 0.38047, "loss": 0.38047, "time": 0.22275} +{"mode": "val", "epoch": 72, "iter": 533, "lr": 0.01329, "top1_acc": 0.84837, "top5_acc": 0.98944, "mean_class_accuracy": 0.81084} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.01326, "memory": 4083, "data_time": 0.19345, "top1_acc": 0.92188, "top5_acc": 0.99875, "loss_cls": 0.39217, "loss": 0.39217, "time": 0.43344} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.01324, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92375, "top5_acc": 1.0, "loss_cls": 0.41437, "loss": 0.41437, "time": 0.22454} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.01322, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94125, "top5_acc": 0.99875, "loss_cls": 0.33131, "loss": 0.33131, "time": 0.22569} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.0132, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.91688, "top5_acc": 0.99875, "loss_cls": 0.42205, "loss": 0.42205, "time": 0.22584} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.01318, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.39511, "loss": 0.39511, "time": 0.22664} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.01316, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91812, "top5_acc": 0.99688, "loss_cls": 0.44858, "loss": 0.44858, "time": 0.22651} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.01314, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9125, "top5_acc": 0.99625, "loss_cls": 0.44186, "loss": 0.44186, "time": 0.22258} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.01312, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92625, "top5_acc": 0.99688, "loss_cls": 0.42775, "loss": 0.42775, "time": 0.22627} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.0131, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9175, "top5_acc": 0.9975, "loss_cls": 0.44869, "loss": 0.44869, "time": 0.22811} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.01308, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.44249, "loss": 0.44249, "time": 0.22924} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.01306, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89938, "top5_acc": 0.9975, "loss_cls": 0.47348, "loss": 0.47348, "time": 0.22589} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.01304, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90562, "top5_acc": 0.99688, "loss_cls": 0.44648, "loss": 0.44648, "time": 0.22305} +{"mode": "val", "epoch": 73, "iter": 533, "lr": 0.01302, "top1_acc": 0.86539, "top5_acc": 0.99272, "mean_class_accuracy": 0.80994} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.013, "memory": 4083, "data_time": 0.19297, "top1_acc": 0.92938, "top5_acc": 0.99938, "loss_cls": 0.37836, "loss": 0.37836, "time": 0.43112} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.01298, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.92625, "top5_acc": 0.99938, "loss_cls": 0.38107, "loss": 0.38107, "time": 0.22626} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.01296, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92, "top5_acc": 0.99875, "loss_cls": 0.40042, "loss": 0.40042, "time": 0.22378} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.01294, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92375, "top5_acc": 0.99938, "loss_cls": 0.3989, "loss": 0.3989, "time": 0.22798} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.01292, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.93188, "top5_acc": 0.99812, "loss_cls": 0.38642, "loss": 0.38642, "time": 0.22673} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.0129, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91188, "top5_acc": 0.9975, "loss_cls": 0.47599, "loss": 0.47599, "time": 0.22604} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.01288, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91312, "top5_acc": 0.99875, "loss_cls": 0.44657, "loss": 0.44657, "time": 0.22496} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.01286, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92, "top5_acc": 0.99875, "loss_cls": 0.41917, "loss": 0.41917, "time": 0.22339} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.01284, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91062, "top5_acc": 0.99625, "loss_cls": 0.45393, "loss": 0.45393, "time": 0.22649} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.01282, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.91125, "top5_acc": 0.99875, "loss_cls": 0.43752, "loss": 0.43752, "time": 0.22297} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.0128, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9, "top5_acc": 0.99625, "loss_cls": 0.48162, "loss": 0.48162, "time": 0.22402} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.01278, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91375, "top5_acc": 0.99812, "loss_cls": 0.44942, "loss": 0.44942, "time": 0.22721} +{"mode": "val", "epoch": 74, "iter": 533, "lr": 0.01276, "top1_acc": 0.8506, "top5_acc": 0.98756, "mean_class_accuracy": 0.79787} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.01274, "memory": 4083, "data_time": 0.19758, "top1_acc": 0.92125, "top5_acc": 0.9975, "loss_cls": 0.42549, "loss": 0.42549, "time": 0.4373} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.01272, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92062, "top5_acc": 0.99812, "loss_cls": 0.38936, "loss": 0.38936, "time": 0.22529} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.0127, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.3437, "loss": 0.3437, "time": 0.22404} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.01268, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.39199, "loss": 0.39199, "time": 0.22778} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.01266, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.92312, "top5_acc": 0.99812, "loss_cls": 0.39498, "loss": 0.39498, "time": 0.22375} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.01264, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91625, "top5_acc": 0.99688, "loss_cls": 0.46482, "loss": 0.46482, "time": 0.2247} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.01262, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91875, "top5_acc": 0.99812, "loss_cls": 0.4464, "loss": 0.4464, "time": 0.22493} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.0126, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91375, "top5_acc": 0.99625, "loss_cls": 0.46184, "loss": 0.46184, "time": 0.22674} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.01258, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.93062, "top5_acc": 0.99688, "loss_cls": 0.40464, "loss": 0.40464, "time": 0.22699} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.01256, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91938, "top5_acc": 0.99312, "loss_cls": 0.46049, "loss": 0.46049, "time": 0.22304} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.01254, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.925, "top5_acc": 0.9975, "loss_cls": 0.41617, "loss": 0.41617, "time": 0.22199} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.01252, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.90625, "top5_acc": 0.99812, "loss_cls": 0.46157, "loss": 0.46157, "time": 0.22636} +{"mode": "val", "epoch": 75, "iter": 533, "lr": 0.0125, "top1_acc": 0.86516, "top5_acc": 0.99225, "mean_class_accuracy": 0.82864} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.01248, "memory": 4083, "data_time": 0.18985, "top1_acc": 0.94812, "top5_acc": 0.99875, "loss_cls": 0.31839, "loss": 0.31839, "time": 0.42446} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.01246, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.36172, "loss": 0.36172, "time": 0.22592} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.01244, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92312, "top5_acc": 0.99812, "loss_cls": 0.41112, "loss": 0.41112, "time": 0.22202} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.01242, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.34247, "loss": 0.34247, "time": 0.22522} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.0124, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.39322, "loss": 0.39322, "time": 0.22533} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.01238, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.93, "top5_acc": 0.99875, "loss_cls": 0.36184, "loss": 0.36184, "time": 0.22599} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.01236, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.92812, "top5_acc": 0.9975, "loss_cls": 0.39444, "loss": 0.39444, "time": 0.22374} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.01234, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90688, "top5_acc": 1.0, "loss_cls": 0.44899, "loss": 0.44899, "time": 0.22366} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.01232, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.41594, "loss": 0.41594, "time": 0.22475} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.0123, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92438, "top5_acc": 0.9975, "loss_cls": 0.41146, "loss": 0.41146, "time": 0.22295} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.01228, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.90375, "top5_acc": 0.99812, "loss_cls": 0.46122, "loss": 0.46122, "time": 0.22472} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.01225, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9225, "top5_acc": 0.99875, "loss_cls": 0.42431, "loss": 0.42431, "time": 0.22356} +{"mode": "val", "epoch": 76, "iter": 533, "lr": 0.01224, "top1_acc": 0.83464, "top5_acc": 0.98791, "mean_class_accuracy": 0.79574} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.01222, "memory": 4083, "data_time": 0.19404, "top1_acc": 0.92438, "top5_acc": 0.99875, "loss_cls": 0.39401, "loss": 0.39401, "time": 0.43101} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0122, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94188, "top5_acc": 0.99938, "loss_cls": 0.35322, "loss": 0.35322, "time": 0.22355} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.01218, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92312, "top5_acc": 0.99938, "loss_cls": 0.39657, "loss": 0.39657, "time": 0.22268} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.01216, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9275, "top5_acc": 0.99875, "loss_cls": 0.38326, "loss": 0.38326, "time": 0.22531} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.01214, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9275, "top5_acc": 0.99875, "loss_cls": 0.36896, "loss": 0.36896, "time": 0.22344} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.01212, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92062, "top5_acc": 0.99812, "loss_cls": 0.43893, "loss": 0.43893, "time": 0.22385} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.0121, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91438, "top5_acc": 0.99875, "loss_cls": 0.44968, "loss": 0.44968, "time": 0.22497} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.01207, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.93062, "top5_acc": 0.99938, "loss_cls": 0.37428, "loss": 0.37428, "time": 0.22806} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.01205, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92812, "top5_acc": 0.99688, "loss_cls": 0.37567, "loss": 0.37567, "time": 0.2261} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.01203, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93, "top5_acc": 0.99812, "loss_cls": 0.38433, "loss": 0.38433, "time": 0.22358} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.01201, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.92188, "top5_acc": 0.99812, "loss_cls": 0.42097, "loss": 0.42097, "time": 0.22762} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.01199, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92688, "top5_acc": 1.0, "loss_cls": 0.40005, "loss": 0.40005, "time": 0.22498} +{"mode": "val", "epoch": 77, "iter": 533, "lr": 0.01198, "top1_acc": 0.87126, "top5_acc": 0.9919, "mean_class_accuracy": 0.81186} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.01196, "memory": 4083, "data_time": 0.18954, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.33107, "loss": 0.33107, "time": 0.42628} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.01194, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93312, "top5_acc": 0.99938, "loss_cls": 0.34308, "loss": 0.34308, "time": 0.22496} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.01192, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93375, "top5_acc": 1.0, "loss_cls": 0.35511, "loss": 0.35511, "time": 0.22759} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.0119, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92438, "top5_acc": 0.9975, "loss_cls": 0.39789, "loss": 0.39789, "time": 0.22638} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.01187, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94062, "top5_acc": 1.0, "loss_cls": 0.32658, "loss": 0.32658, "time": 0.22563} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.01185, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.92938, "top5_acc": 0.99938, "loss_cls": 0.35302, "loss": 0.35302, "time": 0.22657} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.01183, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.91562, "top5_acc": 0.99938, "loss_cls": 0.43696, "loss": 0.43696, "time": 0.22217} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.01181, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9225, "top5_acc": 0.99875, "loss_cls": 0.41283, "loss": 0.41283, "time": 0.22589} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.01179, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94, "top5_acc": 0.99875, "loss_cls": 0.34328, "loss": 0.34328, "time": 0.22665} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.01177, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.915, "top5_acc": 0.995, "loss_cls": 0.42559, "loss": 0.42559, "time": 0.22621} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.01175, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93188, "top5_acc": 0.99625, "loss_cls": 0.41034, "loss": 0.41034, "time": 0.22524} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.01173, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9275, "top5_acc": 0.99938, "loss_cls": 0.36765, "loss": 0.36765, "time": 0.2234} +{"mode": "val", "epoch": 78, "iter": 533, "lr": 0.01172, "top1_acc": 0.87584, "top5_acc": 0.99132, "mean_class_accuracy": 0.82745} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.01169, "memory": 4083, "data_time": 0.19355, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.36009, "loss": 0.36009, "time": 0.42795} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.01167, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.36632, "loss": 0.36632, "time": 0.22759} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.01165, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.38268, "loss": 0.38268, "time": 0.22555} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.01163, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94375, "top5_acc": 1.0, "loss_cls": 0.34461, "loss": 0.34461, "time": 0.22315} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.01161, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.925, "top5_acc": 0.9975, "loss_cls": 0.38987, "loss": 0.38987, "time": 0.22484} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.01159, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.31422, "loss": 0.31422, "time": 0.22724} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.01157, "memory": 4083, "data_time": 0.00064, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.35119, "loss": 0.35119, "time": 0.22527} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.01155, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.92125, "top5_acc": 0.99688, "loss_cls": 0.39993, "loss": 0.39993, "time": 0.22325} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.01153, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92688, "top5_acc": 0.99875, "loss_cls": 0.37106, "loss": 0.37106, "time": 0.22489} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.01151, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.945, "top5_acc": 0.99812, "loss_cls": 0.32464, "loss": 0.32464, "time": 0.22766} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.01149, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.91938, "top5_acc": 0.99875, "loss_cls": 0.41313, "loss": 0.41313, "time": 0.22315} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.01147, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92188, "top5_acc": 0.99812, "loss_cls": 0.41071, "loss": 0.41071, "time": 0.226} +{"mode": "val", "epoch": 79, "iter": 533, "lr": 0.01145, "top1_acc": 0.84732, "top5_acc": 0.9912, "mean_class_accuracy": 0.78408} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.01143, "memory": 4083, "data_time": 0.19564, "top1_acc": 0.92812, "top5_acc": 1.0, "loss_cls": 0.35324, "loss": 0.35324, "time": 0.43098} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.01141, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.94188, "top5_acc": 0.99875, "loss_cls": 0.33344, "loss": 0.33344, "time": 0.2272} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.01139, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.3421, "loss": 0.3421, "time": 0.22902} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.01137, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93062, "top5_acc": 0.99938, "loss_cls": 0.37703, "loss": 0.37703, "time": 0.22386} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.01135, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94, "top5_acc": 0.99875, "loss_cls": 0.37665, "loss": 0.37665, "time": 0.22441} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.01133, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92625, "top5_acc": 0.99812, "loss_cls": 0.41458, "loss": 0.41458, "time": 0.22819} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.01131, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9425, "top5_acc": 1.0, "loss_cls": 0.33858, "loss": 0.33858, "time": 0.22452} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.01129, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9325, "top5_acc": 0.99875, "loss_cls": 0.37014, "loss": 0.37014, "time": 0.22442} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.01127, "memory": 4083, "data_time": 0.00061, "top1_acc": 0.935, "top5_acc": 0.99938, "loss_cls": 0.3368, "loss": 0.3368, "time": 0.22567} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.01125, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.36439, "loss": 0.36439, "time": 0.22214} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.01123, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92688, "top5_acc": 0.99562, "loss_cls": 0.41282, "loss": 0.41282, "time": 0.22257} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.01121, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92875, "top5_acc": 0.99688, "loss_cls": 0.41186, "loss": 0.41186, "time": 0.22522} +{"mode": "val", "epoch": 80, "iter": 533, "lr": 0.01119, "top1_acc": 0.86703, "top5_acc": 0.99237, "mean_class_accuracy": 0.8385} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.01117, "memory": 4083, "data_time": 0.18496, "top1_acc": 0.95, "top5_acc": 0.99875, "loss_cls": 0.30409, "loss": 0.30409, "time": 0.425} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.01115, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93625, "top5_acc": 1.0, "loss_cls": 0.33457, "loss": 0.33457, "time": 0.22412} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.01113, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92812, "top5_acc": 0.99625, "loss_cls": 0.39764, "loss": 0.39764, "time": 0.22563} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.01111, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92875, "top5_acc": 0.99812, "loss_cls": 0.37456, "loss": 0.37456, "time": 0.22294} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.01109, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93, "top5_acc": 0.99875, "loss_cls": 0.36427, "loss": 0.36427, "time": 0.22389} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.01107, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92062, "top5_acc": 0.9975, "loss_cls": 0.41789, "loss": 0.41789, "time": 0.22655} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.01105, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92812, "top5_acc": 0.99875, "loss_cls": 0.38417, "loss": 0.38417, "time": 0.2252} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.01103, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93688, "top5_acc": 0.99688, "loss_cls": 0.35976, "loss": 0.35976, "time": 0.22278} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.01101, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93438, "top5_acc": 0.99875, "loss_cls": 0.3641, "loss": 0.3641, "time": 0.22687} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.01099, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93688, "top5_acc": 0.9975, "loss_cls": 0.36352, "loss": 0.36352, "time": 0.22368} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.01097, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92812, "top5_acc": 0.99938, "loss_cls": 0.38225, "loss": 0.38225, "time": 0.22124} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.01095, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92562, "top5_acc": 0.99875, "loss_cls": 0.41184, "loss": 0.41184, "time": 0.22512} +{"mode": "val", "epoch": 81, "iter": 533, "lr": 0.01093, "top1_acc": 0.8749, "top5_acc": 0.99261, "mean_class_accuracy": 0.8367} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.01091, "memory": 4083, "data_time": 0.19331, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.32905, "loss": 0.32905, "time": 0.43235} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.01089, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92938, "top5_acc": 0.99938, "loss_cls": 0.36236, "loss": 0.36236, "time": 0.22498} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.01087, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94312, "top5_acc": 0.99812, "loss_cls": 0.3158, "loss": 0.3158, "time": 0.22684} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.01085, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93438, "top5_acc": 0.99812, "loss_cls": 0.33957, "loss": 0.33957, "time": 0.2255} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.01083, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.935, "top5_acc": 0.99812, "loss_cls": 0.38845, "loss": 0.38845, "time": 0.22528} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.01081, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93688, "top5_acc": 0.99938, "loss_cls": 0.34983, "loss": 0.34983, "time": 0.22459} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.01079, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.94438, "top5_acc": 1.0, "loss_cls": 0.32447, "loss": 0.32447, "time": 0.2265} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.01077, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.935, "top5_acc": 1.0, "loss_cls": 0.34059, "loss": 0.34059, "time": 0.22878} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.01075, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.33138, "loss": 0.33138, "time": 0.22157} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.01073, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91812, "top5_acc": 0.9975, "loss_cls": 0.44184, "loss": 0.44184, "time": 0.22331} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.01071, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93375, "top5_acc": 1.0, "loss_cls": 0.36948, "loss": 0.36948, "time": 0.22601} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.01069, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9225, "top5_acc": 0.99812, "loss_cls": 0.42073, "loss": 0.42073, "time": 0.22212} +{"mode": "val", "epoch": 82, "iter": 533, "lr": 0.01067, "top1_acc": 0.881, "top5_acc": 0.99214, "mean_class_accuracy": 0.83813} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.01065, "memory": 4083, "data_time": 0.19297, "top1_acc": 0.94312, "top5_acc": 0.99875, "loss_cls": 0.32753, "loss": 0.32753, "time": 0.43058} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.01063, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94812, "top5_acc": 0.99875, "loss_cls": 0.29245, "loss": 0.29245, "time": 0.22335} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.01061, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93, "top5_acc": 1.0, "loss_cls": 0.36855, "loss": 0.36855, "time": 0.22671} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.01059, "memory": 4083, "data_time": 0.00072, "top1_acc": 0.93938, "top5_acc": 1.0, "loss_cls": 0.34634, "loss": 0.34634, "time": 0.22698} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.01057, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.34325, "loss": 0.34325, "time": 0.22617} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.01055, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93062, "top5_acc": 1.0, "loss_cls": 0.3891, "loss": 0.3891, "time": 0.22404} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.01053, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92938, "top5_acc": 0.99812, "loss_cls": 0.36589, "loss": 0.36589, "time": 0.22415} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.01051, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94, "top5_acc": 0.99875, "loss_cls": 0.34343, "loss": 0.34343, "time": 0.22739} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.01049, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92625, "top5_acc": 0.99938, "loss_cls": 0.37028, "loss": 0.37028, "time": 0.223} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.01047, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94562, "top5_acc": 0.99875, "loss_cls": 0.31935, "loss": 0.31935, "time": 0.22388} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.01045, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.37941, "loss": 0.37941, "time": 0.22467} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.01043, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.36338, "loss": 0.36338, "time": 0.22505} +{"mode": "val", "epoch": 83, "iter": 533, "lr": 0.01042, "top1_acc": 0.87924, "top5_acc": 0.99343, "mean_class_accuracy": 0.83168} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.0104, "memory": 4083, "data_time": 0.19347, "top1_acc": 0.93625, "top5_acc": 1.0, "loss_cls": 0.36301, "loss": 0.36301, "time": 0.43159} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.01038, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.94375, "top5_acc": 1.0, "loss_cls": 0.29707, "loss": 0.29707, "time": 0.22421} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.01036, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94438, "top5_acc": 0.99875, "loss_cls": 0.3246, "loss": 0.3246, "time": 0.22591} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.01034, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.31743, "loss": 0.31743, "time": 0.22365} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.01031, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93562, "top5_acc": 0.99875, "loss_cls": 0.34465, "loss": 0.34465, "time": 0.22859} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.01029, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93, "top5_acc": 0.9975, "loss_cls": 0.38609, "loss": 0.38609, "time": 0.22436} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.01027, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.95312, "top5_acc": 0.99812, "loss_cls": 0.28411, "loss": 0.28411, "time": 0.23134} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.01025, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94438, "top5_acc": 1.0, "loss_cls": 0.31979, "loss": 0.31979, "time": 0.22348} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.01023, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94188, "top5_acc": 0.99812, "loss_cls": 0.34341, "loss": 0.34341, "time": 0.22452} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.01021, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94062, "top5_acc": 0.99938, "loss_cls": 0.31095, "loss": 0.31095, "time": 0.22502} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.01019, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.37454, "loss": 0.37454, "time": 0.22639} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.01017, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.93938, "top5_acc": 0.99938, "loss_cls": 0.33016, "loss": 0.33016, "time": 0.22602} +{"mode": "val", "epoch": 84, "iter": 533, "lr": 0.01016, "top1_acc": 0.86739, "top5_acc": 0.99366, "mean_class_accuracy": 0.83178} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.01014, "memory": 4083, "data_time": 0.19096, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.26947, "loss": 0.26947, "time": 0.43021} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.01012, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.31888, "loss": 0.31888, "time": 0.22164} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.0101, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9425, "top5_acc": 0.99875, "loss_cls": 0.30316, "loss": 0.30316, "time": 0.22636} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.01008, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95062, "top5_acc": 0.9975, "loss_cls": 0.28763, "loss": 0.28763, "time": 0.22659} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.01006, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.945, "top5_acc": 0.99938, "loss_cls": 0.32195, "loss": 0.32195, "time": 0.22422} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.01004, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94875, "top5_acc": 0.99812, "loss_cls": 0.31559, "loss": 0.31559, "time": 0.22155} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.01002, "memory": 4083, "data_time": 0.00062, "top1_acc": 0.93812, "top5_acc": 1.0, "loss_cls": 0.32742, "loss": 0.32742, "time": 0.22672} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.01, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.31414, "loss": 0.31414, "time": 0.22561} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.00998, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92625, "top5_acc": 1.0, "loss_cls": 0.36214, "loss": 0.36214, "time": 0.22544} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.00996, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9175, "top5_acc": 0.99812, "loss_cls": 0.39489, "loss": 0.39489, "time": 0.22785} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.00994, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.31596, "loss": 0.31596, "time": 0.22442} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.00992, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.93438, "top5_acc": 0.99938, "loss_cls": 0.35463, "loss": 0.35463, "time": 0.22584} +{"mode": "val", "epoch": 85, "iter": 533, "lr": 0.0099, "top1_acc": 0.88006, "top5_acc": 0.99261, "mean_class_accuracy": 0.84517} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.00988, "memory": 4083, "data_time": 0.1899, "top1_acc": 0.94375, "top5_acc": 0.99625, "loss_cls": 0.3274, "loss": 0.3274, "time": 0.42641} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.00986, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.25924, "loss": 0.25924, "time": 0.22647} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.00984, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95062, "top5_acc": 0.99875, "loss_cls": 0.30086, "loss": 0.30086, "time": 0.22456} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.00982, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.26705, "loss": 0.26705, "time": 0.22521} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.0098, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.28849, "loss": 0.28849, "time": 0.22477} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.00978, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.25709, "loss": 0.25709, "time": 0.22642} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.00976, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.945, "top5_acc": 0.99938, "loss_cls": 0.2957, "loss": 0.2957, "time": 0.22361} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.00974, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95188, "top5_acc": 1.0, "loss_cls": 0.2649, "loss": 0.2649, "time": 0.22558} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.00972, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.3061, "loss": 0.3061, "time": 0.22286} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.0097, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.30656, "loss": 0.30656, "time": 0.22587} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.00968, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.93812, "top5_acc": 0.99938, "loss_cls": 0.334, "loss": 0.334, "time": 0.22473} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.00966, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92812, "top5_acc": 0.99938, "loss_cls": 0.37475, "loss": 0.37475, "time": 0.22252} +{"mode": "val", "epoch": 86, "iter": 533, "lr": 0.00965, "top1_acc": 0.87466, "top5_acc": 0.9919, "mean_class_accuracy": 0.82156} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.00963, "memory": 4083, "data_time": 0.19203, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.28753, "loss": 0.28753, "time": 0.42933} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.00961, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.95062, "top5_acc": 0.99875, "loss_cls": 0.29509, "loss": 0.29509, "time": 0.22556} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.00959, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.26576, "loss": 0.26576, "time": 0.22675} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.00957, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.945, "top5_acc": 0.99938, "loss_cls": 0.31023, "loss": 0.31023, "time": 0.22351} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.00955, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.27587, "loss": 0.27587, "time": 0.22097} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.00953, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94812, "top5_acc": 0.9975, "loss_cls": 0.29404, "loss": 0.29404, "time": 0.22771} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.00951, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.935, "top5_acc": 0.99938, "loss_cls": 0.33643, "loss": 0.33643, "time": 0.22416} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.00949, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94062, "top5_acc": 0.99688, "loss_cls": 0.32278, "loss": 0.32278, "time": 0.22287} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.00947, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.29832, "loss": 0.29832, "time": 0.22682} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.00945, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93375, "top5_acc": 0.99938, "loss_cls": 0.33159, "loss": 0.33159, "time": 0.22614} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.00943, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.33141, "loss": 0.33141, "time": 0.22607} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.00941, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95188, "top5_acc": 0.99812, "loss_cls": 0.29455, "loss": 0.29455, "time": 0.22321} +{"mode": "val", "epoch": 87, "iter": 533, "lr": 0.00939, "top1_acc": 0.88147, "top5_acc": 0.99261, "mean_class_accuracy": 0.83756} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.00937, "memory": 4083, "data_time": 0.19249, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.2575, "loss": 0.2575, "time": 0.4274} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.00935, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.29436, "loss": 0.29436, "time": 0.22644} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.00933, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94188, "top5_acc": 0.99875, "loss_cls": 0.31945, "loss": 0.31945, "time": 0.2249} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.00931, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.95438, "top5_acc": 0.99938, "loss_cls": 0.26503, "loss": 0.26503, "time": 0.22517} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.00929, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94438, "top5_acc": 0.99875, "loss_cls": 0.29676, "loss": 0.29676, "time": 0.22646} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.00927, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.29804, "loss": 0.29804, "time": 0.2243} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.00925, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93438, "top5_acc": 0.99938, "loss_cls": 0.37067, "loss": 0.37067, "time": 0.22788} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.00923, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.35934, "loss": 0.35934, "time": 0.22455} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.00921, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92812, "top5_acc": 0.99875, "loss_cls": 0.36094, "loss": 0.36094, "time": 0.22631} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.00919, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.93062, "top5_acc": 0.99938, "loss_cls": 0.34275, "loss": 0.34275, "time": 0.2241} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.00917, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93375, "top5_acc": 0.99875, "loss_cls": 0.35413, "loss": 0.35413, "time": 0.22326} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.00915, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9375, "top5_acc": 0.99875, "loss_cls": 0.34414, "loss": 0.34414, "time": 0.2252} +{"mode": "val", "epoch": 88, "iter": 533, "lr": 0.00914, "top1_acc": 0.86539, "top5_acc": 0.9912, "mean_class_accuracy": 0.81455} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.00912, "memory": 4083, "data_time": 0.19367, "top1_acc": 0.94312, "top5_acc": 0.99875, "loss_cls": 0.31528, "loss": 0.31528, "time": 0.43187} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0091, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.95062, "top5_acc": 0.99875, "loss_cls": 0.29676, "loss": 0.29676, "time": 0.22719} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.00908, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.25537, "loss": 0.25537, "time": 0.22365} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.00906, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.24281, "loss": 0.24281, "time": 0.22318} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.00904, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9575, "top5_acc": 0.99938, "loss_cls": 0.26848, "loss": 0.26848, "time": 0.22677} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.00902, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.26398, "loss": 0.26398, "time": 0.22473} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.009, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.27617, "loss": 0.27617, "time": 0.2251} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.00898, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.31554, "loss": 0.31554, "time": 0.22451} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.00896, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94938, "top5_acc": 1.0, "loss_cls": 0.285, "loss": 0.285, "time": 0.22513} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.00894, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.28198, "loss": 0.28198, "time": 0.22599} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.00892, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94375, "top5_acc": 0.9975, "loss_cls": 0.30705, "loss": 0.30705, "time": 0.22451} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.0089, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9425, "top5_acc": 0.99812, "loss_cls": 0.31324, "loss": 0.31324, "time": 0.22429} +{"mode": "val", "epoch": 89, "iter": 533, "lr": 0.00889, "top1_acc": 0.87431, "top5_acc": 0.98909, "mean_class_accuracy": 0.83498} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.00887, "memory": 4083, "data_time": 0.18819, "top1_acc": 0.94625, "top5_acc": 0.99875, "loss_cls": 0.29294, "loss": 0.29294, "time": 0.42424} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.00885, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.95062, "top5_acc": 1.0, "loss_cls": 0.26697, "loss": 0.26697, "time": 0.22959} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.00883, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.95688, "top5_acc": 0.99938, "loss_cls": 0.24408, "loss": 0.24408, "time": 0.22775} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.00881, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.9525, "top5_acc": 0.99875, "loss_cls": 0.26014, "loss": 0.26014, "time": 0.22725} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.00879, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95188, "top5_acc": 0.99875, "loss_cls": 0.26306, "loss": 0.26306, "time": 0.22273} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.00877, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.94562, "top5_acc": 0.99875, "loss_cls": 0.29255, "loss": 0.29255, "time": 0.22948} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.00875, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.24766, "loss": 0.24766, "time": 0.22574} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.00873, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.94938, "top5_acc": 1.0, "loss_cls": 0.26397, "loss": 0.26397, "time": 0.22644} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.00871, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94062, "top5_acc": 0.99938, "loss_cls": 0.32437, "loss": 0.32437, "time": 0.22422} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.00869, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9275, "top5_acc": 1.0, "loss_cls": 0.38316, "loss": 0.38316, "time": 0.22339} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.00867, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95, "top5_acc": 1.0, "loss_cls": 0.28448, "loss": 0.28448, "time": 0.22545} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.00865, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.27301, "loss": 0.27301, "time": 0.22236} +{"mode": "val", "epoch": 90, "iter": 533, "lr": 0.00864, "top1_acc": 0.88522, "top5_acc": 0.99179, "mean_class_accuracy": 0.84899} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.00862, "memory": 4083, "data_time": 0.19083, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.25975, "loss": 0.25975, "time": 0.43184} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0086, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.22146, "loss": 0.22146, "time": 0.22624} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.00858, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94375, "top5_acc": 1.0, "loss_cls": 0.28963, "loss": 0.28963, "time": 0.22389} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.00856, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96375, "top5_acc": 0.99938, "loss_cls": 0.24827, "loss": 0.24827, "time": 0.22554} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.00854, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94312, "top5_acc": 1.0, "loss_cls": 0.29359, "loss": 0.29359, "time": 0.22306} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.00852, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95, "top5_acc": 0.99875, "loss_cls": 0.29602, "loss": 0.29602, "time": 0.22772} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.0085, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9425, "top5_acc": 1.0, "loss_cls": 0.31063, "loss": 0.31063, "time": 0.22673} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.00848, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.23951, "loss": 0.23951, "time": 0.22581} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.00846, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.9475, "top5_acc": 1.0, "loss_cls": 0.28314, "loss": 0.28314, "time": 0.22778} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.00844, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.27157, "loss": 0.27157, "time": 0.22402} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.00842, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.94125, "top5_acc": 0.9975, "loss_cls": 0.33361, "loss": 0.33361, "time": 0.22551} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.0084, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.95, "top5_acc": 0.99875, "loss_cls": 0.28873, "loss": 0.28873, "time": 0.22562} +{"mode": "val", "epoch": 91, "iter": 533, "lr": 0.00839, "top1_acc": 0.87619, "top5_acc": 0.99319, "mean_class_accuracy": 0.83874} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.00837, "memory": 4083, "data_time": 0.19175, "top1_acc": 0.94062, "top5_acc": 0.99938, "loss_cls": 0.30649, "loss": 0.30649, "time": 0.42871} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.00835, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.26423, "loss": 0.26423, "time": 0.22813} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.00833, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.2312, "loss": 0.2312, "time": 0.22478} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.00831, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.23913, "loss": 0.23913, "time": 0.22726} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.00829, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.26619, "loss": 0.26619, "time": 0.22955} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.00827, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.33715, "loss": 0.33715, "time": 0.22642} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.00825, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95125, "top5_acc": 0.99812, "loss_cls": 0.28491, "loss": 0.28491, "time": 0.22647} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.00824, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94312, "top5_acc": 1.0, "loss_cls": 0.30638, "loss": 0.30638, "time": 0.22492} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.00822, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.28613, "loss": 0.28613, "time": 0.22449} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.0082, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.26687, "loss": 0.26687, "time": 0.22585} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.00818, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93438, "top5_acc": 1.0, "loss_cls": 0.33483, "loss": 0.33483, "time": 0.22413} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.00816, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95312, "top5_acc": 0.99875, "loss_cls": 0.28205, "loss": 0.28205, "time": 0.22436} +{"mode": "val", "epoch": 92, "iter": 533, "lr": 0.00814, "top1_acc": 0.88921, "top5_acc": 0.99284, "mean_class_accuracy": 0.85277} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.00812, "memory": 4083, "data_time": 0.19093, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.22029, "loss": 0.22029, "time": 0.42624} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.0081, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.23476, "loss": 0.23476, "time": 0.22482} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.00809, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.23713, "loss": 0.23713, "time": 0.22464} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.00807, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.20514, "loss": 0.20514, "time": 0.22647} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.00805, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.20883, "loss": 0.20883, "time": 0.22442} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.00803, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.24817, "loss": 0.24817, "time": 0.22327} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.00801, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.23509, "loss": 0.23509, "time": 0.22499} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.00799, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.25819, "loss": 0.25819, "time": 0.22801} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.00797, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95188, "top5_acc": 0.9975, "loss_cls": 0.26388, "loss": 0.26388, "time": 0.22223} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.00795, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.23415, "loss": 0.23415, "time": 0.22596} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.00793, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95062, "top5_acc": 1.0, "loss_cls": 0.24965, "loss": 0.24965, "time": 0.22682} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.00791, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.945, "top5_acc": 0.99938, "loss_cls": 0.29338, "loss": 0.29338, "time": 0.22503} +{"mode": "val", "epoch": 93, "iter": 533, "lr": 0.0079, "top1_acc": 0.88159, "top5_acc": 0.99366, "mean_class_accuracy": 0.85054} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.00788, "memory": 4083, "data_time": 0.19039, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.23839, "loss": 0.23839, "time": 0.42731} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.00786, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.24658, "loss": 0.24658, "time": 0.22368} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.00784, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.23769, "loss": 0.23769, "time": 0.22428} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.00782, "memory": 4083, "data_time": 0.00069, "top1_acc": 0.95062, "top5_acc": 0.99875, "loss_cls": 0.26808, "loss": 0.26808, "time": 0.22896} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.0078, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95438, "top5_acc": 0.99938, "loss_cls": 0.26021, "loss": 0.26021, "time": 0.2257} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.00778, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.25876, "loss": 0.25876, "time": 0.22789} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.00777, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96062, "top5_acc": 0.99875, "loss_cls": 0.24465, "loss": 0.24465, "time": 0.22159} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.00775, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.25416, "loss": 0.25416, "time": 0.22447} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.00773, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.30705, "loss": 0.30705, "time": 0.22456} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.00771, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.95, "top5_acc": 1.0, "loss_cls": 0.27088, "loss": 0.27088, "time": 0.224} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.00769, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.27846, "loss": 0.27846, "time": 0.22543} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.00767, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94625, "top5_acc": 0.99875, "loss_cls": 0.31073, "loss": 0.31073, "time": 0.22568} +{"mode": "val", "epoch": 94, "iter": 533, "lr": 0.00766, "top1_acc": 0.88886, "top5_acc": 0.99214, "mean_class_accuracy": 0.84881} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.00764, "memory": 4083, "data_time": 0.1865, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.21148, "loss": 0.21148, "time": 0.42371} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.00762, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.204, "loss": 0.204, "time": 0.22642} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.0076, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.18864, "loss": 0.18864, "time": 0.22412} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.00758, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.20797, "loss": 0.20797, "time": 0.22546} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.00756, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.955, "top5_acc": 0.99812, "loss_cls": 0.26567, "loss": 0.26567, "time": 0.2228} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.00754, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9575, "top5_acc": 0.99875, "loss_cls": 0.24828, "loss": 0.24828, "time": 0.2215} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.00752, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95062, "top5_acc": 1.0, "loss_cls": 0.2548, "loss": 0.2548, "time": 0.22672} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.00751, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97375, "top5_acc": 0.99875, "loss_cls": 0.20591, "loss": 0.20591, "time": 0.22101} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.00749, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96625, "top5_acc": 0.99938, "loss_cls": 0.20587, "loss": 0.20587, "time": 0.22305} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.00747, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.23622, "loss": 0.23622, "time": 0.22321} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.00745, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.25495, "loss": 0.25495, "time": 0.22244} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.00743, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.2493, "loss": 0.2493, "time": 0.22518} +{"mode": "val", "epoch": 95, "iter": 533, "lr": 0.00742, "top1_acc": 0.88217, "top5_acc": 0.99355, "mean_class_accuracy": 0.84125} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.0074, "memory": 4083, "data_time": 0.19403, "top1_acc": 0.96875, "top5_acc": 0.99938, "loss_cls": 0.20726, "loss": 0.20726, "time": 0.43002} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.00738, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.19131, "loss": 0.19131, "time": 0.22622} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.00736, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.18443, "loss": 0.18443, "time": 0.22448} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.00734, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.23104, "loss": 0.23104, "time": 0.22643} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.00732, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.22812, "loss": 0.22812, "time": 0.22658} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.0073, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96688, "top5_acc": 0.99938, "loss_cls": 0.21693, "loss": 0.21693, "time": 0.22605} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.00729, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.22, "loss": 0.22, "time": 0.22689} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.00727, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.22817, "loss": 0.22817, "time": 0.22537} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.00725, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.3092, "loss": 0.3092, "time": 0.22558} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.00723, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.28277, "loss": 0.28277, "time": 0.22398} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.00721, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.95188, "top5_acc": 0.99875, "loss_cls": 0.26221, "loss": 0.26221, "time": 0.22474} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.00719, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95812, "top5_acc": 0.99938, "loss_cls": 0.24795, "loss": 0.24795, "time": 0.22273} +{"mode": "val", "epoch": 96, "iter": 533, "lr": 0.00718, "top1_acc": 0.88804, "top5_acc": 0.98991, "mean_class_accuracy": 0.86168} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.00716, "memory": 4083, "data_time": 0.18913, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.21759, "loss": 0.21759, "time": 0.42558} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.00714, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.1876, "loss": 0.1876, "time": 0.22464} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.00712, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.19343, "loss": 0.19343, "time": 0.22629} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.0071, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.20975, "loss": 0.20975, "time": 0.22363} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.00709, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95438, "top5_acc": 0.99875, "loss_cls": 0.25906, "loss": 0.25906, "time": 0.22217} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.00707, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96, "top5_acc": 0.99875, "loss_cls": 0.24766, "loss": 0.24766, "time": 0.22579} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.00705, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97438, "top5_acc": 0.99938, "loss_cls": 0.18062, "loss": 0.18062, "time": 0.22167} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.00703, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.21107, "loss": 0.21107, "time": 0.22621} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.00701, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.21385, "loss": 0.21385, "time": 0.22617} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.00699, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.22345, "loss": 0.22345, "time": 0.2256} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.00698, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.21407, "loss": 0.21407, "time": 0.22376} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.00696, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.22206, "loss": 0.22206, "time": 0.22483} +{"mode": "val", "epoch": 97, "iter": 533, "lr": 0.00694, "top1_acc": 0.89579, "top5_acc": 0.99202, "mean_class_accuracy": 0.85971} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.00692, "memory": 4083, "data_time": 0.18076, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.21015, "loss": 0.21015, "time": 0.41969} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.00691, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.95062, "top5_acc": 1.0, "loss_cls": 0.25293, "loss": 0.25293, "time": 0.22869} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.00689, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.96562, "top5_acc": 0.99938, "loss_cls": 0.21616, "loss": 0.21616, "time": 0.22795} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.00687, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.22411, "loss": 0.22411, "time": 0.2252} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.00685, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.22527, "loss": 0.22527, "time": 0.22343} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.00683, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.18035, "loss": 0.18035, "time": 0.22703} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.00681, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.20371, "loss": 0.20371, "time": 0.22482} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.0068, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.2093, "loss": 0.2093, "time": 0.22272} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.00678, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.21687, "loss": 0.21687, "time": 0.22466} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.00676, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.22883, "loss": 0.22883, "time": 0.22409} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.00674, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.22043, "loss": 0.22043, "time": 0.2238} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.00672, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.22746, "loss": 0.22746, "time": 0.22541} +{"mode": "val", "epoch": 98, "iter": 533, "lr": 0.00671, "top1_acc": 0.89086, "top5_acc": 0.99437, "mean_class_accuracy": 0.85622} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.00669, "memory": 4083, "data_time": 0.19162, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.18157, "loss": 0.18157, "time": 0.42791} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.00667, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.17051, "loss": 0.17051, "time": 0.22531} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.00665, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.18588, "loss": 0.18588, "time": 0.22503} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.00664, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.19423, "loss": 0.19423, "time": 0.22399} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.00662, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.21655, "loss": 0.21655, "time": 0.2246} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.0066, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.18025, "loss": 0.18025, "time": 0.2243} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.00658, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.16196, "loss": 0.16196, "time": 0.22626} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.00656, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.19976, "loss": 0.19976, "time": 0.22383} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.00655, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.30282, "loss": 0.30282, "time": 0.2239} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.00653, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94625, "top5_acc": 0.99812, "loss_cls": 0.31385, "loss": 0.31385, "time": 0.22318} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.00651, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96688, "top5_acc": 0.99938, "loss_cls": 0.23995, "loss": 0.23995, "time": 0.22481} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.00649, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.17493, "loss": 0.17493, "time": 0.22253} +{"mode": "val", "epoch": 99, "iter": 533, "lr": 0.00648, "top1_acc": 0.89978, "top5_acc": 0.99437, "mean_class_accuracy": 0.87162} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.00646, "memory": 4083, "data_time": 0.19478, "top1_acc": 0.96938, "top5_acc": 0.99938, "loss_cls": 0.19452, "loss": 0.19452, "time": 0.43076} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.00644, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.17326, "loss": 0.17326, "time": 0.22623} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.00642, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.2018, "loss": 0.2018, "time": 0.22661} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.00641, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.20362, "loss": 0.20362, "time": 0.22088} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.00639, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.16023, "loss": 0.16023, "time": 0.22758} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.00637, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.18503, "loss": 0.18503, "time": 0.22559} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.00635, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97625, "top5_acc": 0.99938, "loss_cls": 0.16327, "loss": 0.16327, "time": 0.22394} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.00634, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.17183, "loss": 0.17183, "time": 0.22391} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.00632, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.19032, "loss": 0.19032, "time": 0.22576} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.0063, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.2428, "loss": 0.2428, "time": 0.22281} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.00628, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96188, "top5_acc": 0.99875, "loss_cls": 0.22781, "loss": 0.22781, "time": 0.22515} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.00626, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.19523, "loss": 0.19523, "time": 0.22862} +{"mode": "val", "epoch": 100, "iter": 533, "lr": 0.00625, "top1_acc": 0.8945, "top5_acc": 0.99225, "mean_class_accuracy": 0.84573} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.00623, "memory": 4083, "data_time": 0.18812, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.177, "loss": 0.177, "time": 0.42677} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.00621, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.15112, "loss": 0.15112, "time": 0.22437} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.0062, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.19838, "loss": 0.19838, "time": 0.2239} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.00618, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.21567, "loss": 0.21567, "time": 0.22465} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.00616, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.18139, "loss": 0.18139, "time": 0.22115} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.00614, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.17064, "loss": 0.17064, "time": 0.22096} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.00613, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.19944, "loss": 0.19944, "time": 0.22417} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.00611, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.21882, "loss": 0.21882, "time": 0.22447} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.00609, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.23938, "loss": 0.23938, "time": 0.22197} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.00607, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.21201, "loss": 0.21201, "time": 0.22316} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.00606, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.19732, "loss": 0.19732, "time": 0.22069} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.00604, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.19354, "loss": 0.19354, "time": 0.22476} +{"mode": "val", "epoch": 101, "iter": 533, "lr": 0.00602, "top1_acc": 0.88757, "top5_acc": 0.99261, "mean_class_accuracy": 0.84941} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.00601, "memory": 4083, "data_time": 0.19033, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.16729, "loss": 0.16729, "time": 0.428} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.00599, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.97812, "top5_acc": 0.99938, "loss_cls": 0.15892, "loss": 0.15892, "time": 0.22602} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.00597, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.14654, "loss": 0.14654, "time": 0.22649} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.00596, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.12435, "loss": 0.12435, "time": 0.22548} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.00594, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.17995, "loss": 0.17995, "time": 0.22496} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.00592, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96562, "top5_acc": 0.99875, "loss_cls": 0.20473, "loss": 0.20473, "time": 0.22423} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.0059, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.20232, "loss": 0.20232, "time": 0.22394} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.00589, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.18994, "loss": 0.18994, "time": 0.2228} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.00587, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.17848, "loss": 0.17848, "time": 0.22353} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.00585, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.19153, "loss": 0.19153, "time": 0.22638} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.00583, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.2425, "loss": 0.2425, "time": 0.22503} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.00582, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96688, "top5_acc": 0.99938, "loss_cls": 0.20115, "loss": 0.20115, "time": 0.22552} +{"mode": "val", "epoch": 102, "iter": 533, "lr": 0.0058, "top1_acc": 0.8959, "top5_acc": 0.99343, "mean_class_accuracy": 0.86147} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.00579, "memory": 4083, "data_time": 0.19332, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.145, "loss": 0.145, "time": 0.42892} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.00577, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.15281, "loss": 0.15281, "time": 0.22449} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.00575, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.16245, "loss": 0.16245, "time": 0.22645} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.00573, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.16045, "loss": 0.16045, "time": 0.22224} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.00572, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.17868, "loss": 0.17868, "time": 0.22256} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.0057, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.16945, "loss": 0.16945, "time": 0.22377} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.00568, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98375, "top5_acc": 0.99938, "loss_cls": 0.13181, "loss": 0.13181, "time": 0.22506} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.00566, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.16189, "loss": 0.16189, "time": 0.22561} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.00565, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.14655, "loss": 0.14655, "time": 0.22253} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.00563, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9775, "top5_acc": 0.99938, "loss_cls": 0.15965, "loss": 0.15965, "time": 0.22339} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.00561, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.19511, "loss": 0.19511, "time": 0.22587} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.0056, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.17628, "loss": 0.17628, "time": 0.22447} +{"mode": "val", "epoch": 103, "iter": 533, "lr": 0.00558, "top1_acc": 0.89426, "top5_acc": 0.99331, "mean_class_accuracy": 0.8591} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.00557, "memory": 4083, "data_time": 0.19447, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.1435, "loss": 0.1435, "time": 0.43337} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.00555, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98, "top5_acc": 0.99938, "loss_cls": 0.15475, "loss": 0.15475, "time": 0.22329} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.00553, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.0963, "loss": 0.0963, "time": 0.22851} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.00551, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.15755, "loss": 0.15755, "time": 0.22327} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.0055, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9625, "top5_acc": 0.99875, "loss_cls": 0.20648, "loss": 0.20648, "time": 0.22555} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.00548, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.17868, "loss": 0.17868, "time": 0.22352} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.00546, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.20309, "loss": 0.20309, "time": 0.22461} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.00545, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.1933, "loss": 0.1933, "time": 0.22387} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.00543, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.17259, "loss": 0.17259, "time": 0.22666} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.00541, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.16164, "loss": 0.16164, "time": 0.22461} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.0054, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.1961, "loss": 0.1961, "time": 0.22488} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.00538, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96438, "top5_acc": 0.99875, "loss_cls": 0.19189, "loss": 0.19189, "time": 0.22356} +{"mode": "val", "epoch": 104, "iter": 533, "lr": 0.00537, "top1_acc": 0.90295, "top5_acc": 0.99284, "mean_class_accuracy": 0.87004} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.00535, "memory": 4083, "data_time": 0.18812, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.14505, "loss": 0.14505, "time": 0.42457} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.00533, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.11798, "loss": 0.11798, "time": 0.2275} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.00532, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.14319, "loss": 0.14319, "time": 0.22288} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.0053, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.16317, "loss": 0.16317, "time": 0.22462} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.00528, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.18831, "loss": 0.18831, "time": 0.22317} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.00527, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97438, "top5_acc": 0.99938, "loss_cls": 0.17027, "loss": 0.17027, "time": 0.22767} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.00525, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.1799, "loss": 0.1799, "time": 0.22359} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.00523, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.147, "loss": 0.147, "time": 0.22335} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.00522, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98062, "top5_acc": 0.99938, "loss_cls": 0.15357, "loss": 0.15357, "time": 0.22341} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.0052, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.1865, "loss": 0.1865, "time": 0.227} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.00518, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.21458, "loss": 0.21458, "time": 0.22713} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.00517, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.18448, "loss": 0.18448, "time": 0.22445} +{"mode": "val", "epoch": 105, "iter": 533, "lr": 0.00515, "top1_acc": 0.90177, "top5_acc": 0.99378, "mean_class_accuracy": 0.87617} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.00514, "memory": 4083, "data_time": 0.18936, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12926, "loss": 0.12926, "time": 0.42435} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.00512, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.11838, "loss": 0.11838, "time": 0.22617} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.0051, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97688, "top5_acc": 0.99938, "loss_cls": 0.1379, "loss": 0.1379, "time": 0.22635} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.00509, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.11351, "loss": 0.11351, "time": 0.22474} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.00507, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.15019, "loss": 0.15019, "time": 0.22662} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.00505, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.16662, "loss": 0.16662, "time": 0.22319} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.00504, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.15075, "loss": 0.15075, "time": 0.22485} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.00502, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.21125, "loss": 0.21125, "time": 0.22722} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.005, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.18571, "loss": 0.18571, "time": 0.22073} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.00499, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.16562, "loss": 0.16562, "time": 0.22535} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.00497, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.16873, "loss": 0.16873, "time": 0.22735} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.00496, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.18432, "loss": 0.18432, "time": 0.22357} +{"mode": "val", "epoch": 106, "iter": 533, "lr": 0.00494, "top1_acc": 0.90424, "top5_acc": 0.99366, "mean_class_accuracy": 0.87633} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.00493, "memory": 4083, "data_time": 0.18813, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.11707, "loss": 0.11707, "time": 0.42476} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.00491, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.12433, "loss": 0.12433, "time": 0.2244} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.00489, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.11748, "loss": 0.11748, "time": 0.22554} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.00488, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.11857, "loss": 0.11857, "time": 0.22393} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.00486, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.11518, "loss": 0.11518, "time": 0.22505} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.00485, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.12994, "loss": 0.12994, "time": 0.22659} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.00483, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.15889, "loss": 0.15889, "time": 0.22514} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.00481, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.16362, "loss": 0.16362, "time": 0.22418} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.0048, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13718, "loss": 0.13718, "time": 0.22659} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.00478, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.1399, "loss": 0.1399, "time": 0.22248} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.00476, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.14198, "loss": 0.14198, "time": 0.22337} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.00475, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.18225, "loss": 0.18225, "time": 0.22518} +{"mode": "val", "epoch": 107, "iter": 533, "lr": 0.00474, "top1_acc": 0.89802, "top5_acc": 0.99413, "mean_class_accuracy": 0.86502} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.00472, "memory": 4083, "data_time": 0.18711, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.15806, "loss": 0.15806, "time": 0.42319} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0047, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.11976, "loss": 0.11976, "time": 0.2249} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.00469, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.12519, "loss": 0.12519, "time": 0.22679} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.00467, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97688, "top5_acc": 0.99938, "loss_cls": 0.14184, "loss": 0.14184, "time": 0.22206} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.00466, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12303, "loss": 0.12303, "time": 0.22287} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.00464, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11745, "loss": 0.11745, "time": 0.22388} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.00462, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.16933, "loss": 0.16933, "time": 0.22954} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.00461, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.17573, "loss": 0.17573, "time": 0.2222} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.00459, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.17139, "loss": 0.17139, "time": 0.22769} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.00458, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.15762, "loss": 0.15762, "time": 0.22532} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.00456, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9725, "top5_acc": 0.99938, "loss_cls": 0.16306, "loss": 0.16306, "time": 0.22607} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.00455, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.16243, "loss": 0.16243, "time": 0.22575} +{"mode": "val", "epoch": 108, "iter": 533, "lr": 0.00453, "top1_acc": 0.90858, "top5_acc": 0.99343, "mean_class_accuracy": 0.8695} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.00452, "memory": 4083, "data_time": 0.19672, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.12153, "loss": 0.12153, "time": 0.43363} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.0045, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.12046, "loss": 0.12046, "time": 0.22193} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.00449, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.13931, "loss": 0.13931, "time": 0.22654} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.00447, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.15781, "loss": 0.15781, "time": 0.22461} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.00445, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.13359, "loss": 0.13359, "time": 0.22629} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.00444, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98125, "top5_acc": 0.99938, "loss_cls": 0.13407, "loss": 0.13407, "time": 0.22717} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.00442, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.13889, "loss": 0.13889, "time": 0.22655} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.00441, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12958, "loss": 0.12958, "time": 0.22707} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.00439, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.12198, "loss": 0.12198, "time": 0.22158} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.00438, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.09957, "loss": 0.09957, "time": 0.22604} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.00436, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9925, "top5_acc": 0.99938, "loss_cls": 0.07806, "loss": 0.07806, "time": 0.22669} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.00434, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.11501, "loss": 0.11501, "time": 0.22418} +{"mode": "val", "epoch": 109, "iter": 533, "lr": 0.00433, "top1_acc": 0.89461, "top5_acc": 0.99249, "mean_class_accuracy": 0.86071} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.00432, "memory": 4083, "data_time": 0.18854, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10441, "loss": 0.10441, "time": 0.42132} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.0043, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.10931, "loss": 0.10931, "time": 0.22612} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.00429, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.10061, "loss": 0.10061, "time": 0.22419} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.00427, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.08767, "loss": 0.08767, "time": 0.22334} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.00426, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.10818, "loss": 0.10818, "time": 0.22767} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.00424, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10197, "loss": 0.10197, "time": 0.22792} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.00422, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12001, "loss": 0.12001, "time": 0.2219} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.00421, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98562, "top5_acc": 0.99938, "loss_cls": 0.11026, "loss": 0.11026, "time": 0.22479} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.00419, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09592, "loss": 0.09592, "time": 0.22571} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.00418, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.08784, "loss": 0.08784, "time": 0.22649} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.00416, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.103, "loss": 0.103, "time": 0.22653} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.00415, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.13264, "loss": 0.13264, "time": 0.22312} +{"mode": "val", "epoch": 110, "iter": 533, "lr": 0.00414, "top1_acc": 0.9067, "top5_acc": 0.99437, "mean_class_accuracy": 0.87737} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.00412, "memory": 4083, "data_time": 0.1851, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.10514, "loss": 0.10514, "time": 0.42189} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.00411, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.06944, "loss": 0.06944, "time": 0.22728} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.00409, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98375, "top5_acc": 0.99938, "loss_cls": 0.1102, "loss": 0.1102, "time": 0.22424} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.00408, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.09636, "loss": 0.09636, "time": 0.22567} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.00406, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.12158, "loss": 0.12158, "time": 0.22753} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.00405, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.11619, "loss": 0.11619, "time": 0.22653} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.00403, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.10236, "loss": 0.10236, "time": 0.2252} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.00402, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.11211, "loss": 0.11211, "time": 0.22273} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.004, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.98375, "top5_acc": 0.99938, "loss_cls": 0.10901, "loss": 0.10901, "time": 0.22625} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.00399, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.15122, "loss": 0.15122, "time": 0.22368} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.00397, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.09995, "loss": 0.09995, "time": 0.22501} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.00396, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.09765, "loss": 0.09765, "time": 0.22494} +{"mode": "val", "epoch": 111, "iter": 533, "lr": 0.00394, "top1_acc": 0.90705, "top5_acc": 0.99284, "mean_class_accuracy": 0.87918} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.00393, "memory": 4083, "data_time": 0.18721, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08249, "loss": 0.08249, "time": 0.4247} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.00391, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.10142, "loss": 0.10142, "time": 0.22881} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.0039, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09458, "loss": 0.09458, "time": 0.22373} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.00388, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08153, "loss": 0.08153, "time": 0.22793} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.00387, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.08948, "loss": 0.08948, "time": 0.22606} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.00385, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08465, "loss": 0.08465, "time": 0.22387} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.00384, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.0994, "loss": 0.0994, "time": 0.22403} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.00382, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.1224, "loss": 0.1224, "time": 0.2273} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.00381, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.1364, "loss": 0.1364, "time": 0.22597} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.0038, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.09035, "loss": 0.09035, "time": 0.22441} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.00378, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.09316, "loss": 0.09316, "time": 0.22356} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.00377, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.12987, "loss": 0.12987, "time": 0.22213} +{"mode": "val", "epoch": 112, "iter": 533, "lr": 0.00375, "top1_acc": 0.90165, "top5_acc": 0.99331, "mean_class_accuracy": 0.86974} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.00374, "memory": 4083, "data_time": 0.18744, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.09515, "loss": 0.09515, "time": 0.4238} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.00373, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08962, "loss": 0.08962, "time": 0.22471} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.00371, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.10778, "loss": 0.10778, "time": 0.22568} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.0037, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.07782, "loss": 0.07782, "time": 0.22679} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.00368, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.09256, "loss": 0.09256, "time": 0.22632} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.00367, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08491, "loss": 0.08491, "time": 0.22548} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.00365, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98688, "top5_acc": 0.99938, "loss_cls": 0.10415, "loss": 0.10415, "time": 0.22374} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.00364, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.07848, "loss": 0.07848, "time": 0.22698} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.00362, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.07447, "loss": 0.07447, "time": 0.22569} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.00361, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08715, "loss": 0.08715, "time": 0.22547} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0036, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.10492, "loss": 0.10492, "time": 0.22341} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.00358, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.09568, "loss": 0.09568, "time": 0.22326} +{"mode": "val", "epoch": 113, "iter": 533, "lr": 0.00357, "top1_acc": 0.9013, "top5_acc": 0.99378, "mean_class_accuracy": 0.86248} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.00355, "memory": 4083, "data_time": 0.19176, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.10697, "loss": 0.10697, "time": 0.42757} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.00354, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07763, "loss": 0.07763, "time": 0.22684} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.00353, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.07024, "loss": 0.07024, "time": 0.22599} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.00351, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.0692, "loss": 0.0692, "time": 0.22536} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.0035, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.08419, "loss": 0.08419, "time": 0.22818} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.00348, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08556, "loss": 0.08556, "time": 0.22801} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.00347, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08077, "loss": 0.08077, "time": 0.22699} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.00346, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.09451, "loss": 0.09451, "time": 0.22688} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.00344, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.09587, "loss": 0.09587, "time": 0.2256} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.00343, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.06141, "loss": 0.06141, "time": 0.22544} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.00341, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07993, "loss": 0.07993, "time": 0.22627} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.0034, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.09206, "loss": 0.09206, "time": 0.22474} +{"mode": "val", "epoch": 114, "iter": 533, "lr": 0.00339, "top1_acc": 0.90588, "top5_acc": 0.9946, "mean_class_accuracy": 0.86667} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.00337, "memory": 4083, "data_time": 0.18724, "top1_acc": 0.995, "top5_acc": 0.99938, "loss_cls": 0.06118, "loss": 0.06118, "time": 0.4236} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.00336, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05621, "loss": 0.05621, "time": 0.22375} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.00335, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04827, "loss": 0.04827, "time": 0.22526} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.00333, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.06356, "loss": 0.06356, "time": 0.22581} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.00332, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08358, "loss": 0.08358, "time": 0.22582} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.0033, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.06242, "loss": 0.06242, "time": 0.22458} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.00329, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.0546, "loss": 0.0546, "time": 0.2242} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.00328, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.07871, "loss": 0.07871, "time": 0.2257} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.00326, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.08737, "loss": 0.08737, "time": 0.22416} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.00325, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07794, "loss": 0.07794, "time": 0.22379} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.00324, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05593, "loss": 0.05593, "time": 0.22244} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.00322, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.0576, "loss": 0.0576, "time": 0.22353} +{"mode": "val", "epoch": 115, "iter": 533, "lr": 0.00321, "top1_acc": 0.91081, "top5_acc": 0.99308, "mean_class_accuracy": 0.87576} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.0032, "memory": 4083, "data_time": 0.19462, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.0789, "loss": 0.0789, "time": 0.43278} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.00318, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06621, "loss": 0.06621, "time": 0.23072} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.00317, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05442, "loss": 0.05442, "time": 0.22557} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.00316, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.0562, "loss": 0.0562, "time": 0.2253} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.00314, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.04304, "loss": 0.04304, "time": 0.22443} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.00313, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05692, "loss": 0.05692, "time": 0.22375} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.00312, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.0631, "loss": 0.0631, "time": 0.22663} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.0031, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07443, "loss": 0.07443, "time": 0.22559} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.00309, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.0608, "loss": 0.0608, "time": 0.2227} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.00308, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06072, "loss": 0.06072, "time": 0.2243} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.00306, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.07896, "loss": 0.07896, "time": 0.22429} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.00305, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07125, "loss": 0.07125, "time": 0.22436} +{"mode": "val", "epoch": 116, "iter": 533, "lr": 0.00304, "top1_acc": 0.91104, "top5_acc": 0.99437, "mean_class_accuracy": 0.88563} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.00302, "memory": 4083, "data_time": 0.18526, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.06518, "loss": 0.06518, "time": 0.41811} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.00301, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.08785, "loss": 0.08785, "time": 0.22507} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.003, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06948, "loss": 0.06948, "time": 0.22506} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.00298, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.07649, "loss": 0.07649, "time": 0.22594} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.00297, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06652, "loss": 0.06652, "time": 0.22496} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.00296, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06412, "loss": 0.06412, "time": 0.22496} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.00294, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05968, "loss": 0.05968, "time": 0.22613} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.00293, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06495, "loss": 0.06495, "time": 0.22531} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.00292, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06941, "loss": 0.06941, "time": 0.22583} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.00291, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06655, "loss": 0.06655, "time": 0.22334} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.00289, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.06278, "loss": 0.06278, "time": 0.22621} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.00288, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.07984, "loss": 0.07984, "time": 0.22471} +{"mode": "val", "epoch": 117, "iter": 533, "lr": 0.00287, "top1_acc": 0.91515, "top5_acc": 0.99319, "mean_class_accuracy": 0.87605} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.00286, "memory": 4083, "data_time": 0.18709, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05693, "loss": 0.05693, "time": 0.42827} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.00284, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06612, "loss": 0.06612, "time": 0.22312} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.00283, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05509, "loss": 0.05509, "time": 0.22622} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.00282, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05274, "loss": 0.05274, "time": 0.22563} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.0028, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.06424, "loss": 0.06424, "time": 0.22568} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.00279, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.0457, "loss": 0.0457, "time": 0.22622} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.00278, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05929, "loss": 0.05929, "time": 0.22701} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.00277, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.04115, "loss": 0.04115, "time": 0.22252} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.00275, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0403, "loss": 0.0403, "time": 0.22432} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.00274, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03792, "loss": 0.03792, "time": 0.22457} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.00273, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.0481, "loss": 0.0481, "time": 0.22573} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.00271, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06399, "loss": 0.06399, "time": 0.22549} +{"mode": "val", "epoch": 118, "iter": 533, "lr": 0.0027, "top1_acc": 0.92149, "top5_acc": 0.99413, "mean_class_accuracy": 0.89407} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.00269, "memory": 4083, "data_time": 0.1905, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04087, "loss": 0.04087, "time": 0.4231} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.00268, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04853, "loss": 0.04853, "time": 0.22802} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.00267, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04066, "loss": 0.04066, "time": 0.22515} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.00265, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06394, "loss": 0.06394, "time": 0.22327} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.00264, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.995, "top5_acc": 0.99938, "loss_cls": 0.06201, "loss": 0.06201, "time": 0.22639} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.00263, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04059, "loss": 0.04059, "time": 0.22416} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.00262, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03898, "loss": 0.03898, "time": 0.22323} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.0026, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03298, "loss": 0.03298, "time": 0.22198} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.00259, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04908, "loss": 0.04908, "time": 0.22363} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.00258, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04978, "loss": 0.04978, "time": 0.22564} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.00257, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03522, "loss": 0.03522, "time": 0.22372} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.00255, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05661, "loss": 0.05661, "time": 0.22311} +{"mode": "val", "epoch": 119, "iter": 533, "lr": 0.00254, "top1_acc": 0.91679, "top5_acc": 0.99448, "mean_class_accuracy": 0.88518} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.00253, "memory": 4083, "data_time": 0.18983, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04368, "loss": 0.04368, "time": 0.42658} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.00252, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.0427, "loss": 0.0427, "time": 0.22564} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.00251, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.0497, "loss": 0.0497, "time": 0.22833} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.00249, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.04003, "loss": 0.04003, "time": 0.22436} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.00248, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04535, "loss": 0.04535, "time": 0.22414} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.00247, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03847, "loss": 0.03847, "time": 0.22643} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.00246, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.0497, "loss": 0.0497, "time": 0.22385} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.00245, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05161, "loss": 0.05161, "time": 0.22351} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.00243, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0451, "loss": 0.0451, "time": 0.22336} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.00242, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03521, "loss": 0.03521, "time": 0.22496} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00241, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05055, "loss": 0.05055, "time": 0.22148} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.0024, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03707, "loss": 0.03707, "time": 0.22444} +{"mode": "val", "epoch": 120, "iter": 533, "lr": 0.00239, "top1_acc": 0.91527, "top5_acc": 0.99249, "mean_class_accuracy": 0.88269} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00238, "memory": 4083, "data_time": 0.18996, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.0758, "loss": 0.0758, "time": 0.42871} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00236, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04692, "loss": 0.04692, "time": 0.22576} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.00235, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05682, "loss": 0.05682, "time": 0.22383} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00234, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04557, "loss": 0.04557, "time": 0.22579} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00233, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04317, "loss": 0.04317, "time": 0.22507} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00232, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03965, "loss": 0.03965, "time": 0.22602} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.0023, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04765, "loss": 0.04765, "time": 0.22444} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00229, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03953, "loss": 0.03953, "time": 0.22421} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.00228, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03634, "loss": 0.03634, "time": 0.22693} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00227, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03146, "loss": 0.03146, "time": 0.22268} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00226, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04245, "loss": 0.04245, "time": 0.22422} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00225, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04315, "loss": 0.04315, "time": 0.2252} +{"mode": "val", "epoch": 121, "iter": 533, "lr": 0.00224, "top1_acc": 0.92184, "top5_acc": 0.99519, "mean_class_accuracy": 0.89176} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00222, "memory": 4083, "data_time": 0.19369, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03612, "loss": 0.03612, "time": 0.42818} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00221, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03353, "loss": 0.03353, "time": 0.2244} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.0022, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03074, "loss": 0.03074, "time": 0.22558} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00219, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04176, "loss": 0.04176, "time": 0.22446} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00218, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03889, "loss": 0.03889, "time": 0.2247} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00217, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03198, "loss": 0.03198, "time": 0.22293} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00215, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03222, "loss": 0.03222, "time": 0.22292} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00214, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03822, "loss": 0.03822, "time": 0.22341} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.00213, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04563, "loss": 0.04563, "time": 0.22565} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00212, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06319, "loss": 0.06319, "time": 0.22399} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00211, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04974, "loss": 0.04974, "time": 0.22087} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.0021, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04273, "loss": 0.04273, "time": 0.22717} +{"mode": "val", "epoch": 122, "iter": 533, "lr": 0.00209, "top1_acc": 0.92278, "top5_acc": 0.99425, "mean_class_accuracy": 0.89593} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00208, "memory": 4083, "data_time": 0.19204, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03524, "loss": 0.03524, "time": 0.4295} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00207, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03226, "loss": 0.03226, "time": 0.22397} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00205, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02843, "loss": 0.02843, "time": 0.22586} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00204, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03706, "loss": 0.03706, "time": 0.22437} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00203, "memory": 4083, "data_time": 0.0007, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03651, "loss": 0.03651, "time": 0.22646} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00202, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02978, "loss": 0.02978, "time": 0.22407} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00201, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.03945, "loss": 0.03945, "time": 0.22359} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.002, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0369, "loss": 0.0369, "time": 0.22724} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00199, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04727, "loss": 0.04727, "time": 0.2259} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.00198, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04071, "loss": 0.04071, "time": 0.22677} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00197, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03547, "loss": 0.03547, "time": 0.22664} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00195, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04492, "loss": 0.04492, "time": 0.2223} +{"mode": "val", "epoch": 123, "iter": 533, "lr": 0.00195, "top1_acc": 0.92067, "top5_acc": 0.99378, "mean_class_accuracy": 0.89622} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00194, "memory": 4083, "data_time": 0.19368, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02658, "loss": 0.02658, "time": 0.4303} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00192, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.0422, "loss": 0.0422, "time": 0.22584} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00191, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02756, "loss": 0.02756, "time": 0.22566} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.0019, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02557, "loss": 0.02557, "time": 0.22469} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00189, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02681, "loss": 0.02681, "time": 0.22344} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00188, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0245, "loss": 0.0245, "time": 0.22798} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00187, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02946, "loss": 0.02946, "time": 0.22324} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00186, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02528, "loss": 0.02528, "time": 0.22637} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00185, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0238, "loss": 0.0238, "time": 0.22628} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00184, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02678, "loss": 0.02678, "time": 0.22112} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00183, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99625, "top5_acc": 0.99938, "loss_cls": 0.05, "loss": 0.05, "time": 0.22422} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.00182, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04649, "loss": 0.04649, "time": 0.22336} +{"mode": "val", "epoch": 124, "iter": 533, "lr": 0.00181, "top1_acc": 0.92231, "top5_acc": 0.99413, "mean_class_accuracy": 0.89575} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.0018, "memory": 4083, "data_time": 0.18895, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02914, "loss": 0.02914, "time": 0.4275} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.00179, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02762, "loss": 0.02762, "time": 0.22512} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00178, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03389, "loss": 0.03389, "time": 0.22218} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00177, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0288, "loss": 0.0288, "time": 0.22502} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00176, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02856, "loss": 0.02856, "time": 0.22272} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00175, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02887, "loss": 0.02887, "time": 0.22192} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00173, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03067, "loss": 0.03067, "time": 0.22509} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00172, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02717, "loss": 0.02717, "time": 0.2262} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.00171, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03186, "loss": 0.03186, "time": 0.22581} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.0017, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02878, "loss": 0.02878, "time": 0.22531} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00169, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0293, "loss": 0.0293, "time": 0.22405} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00168, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02502, "loss": 0.02502, "time": 0.22644} +{"mode": "val", "epoch": 125, "iter": 533, "lr": 0.00167, "top1_acc": 0.9243, "top5_acc": 0.99401, "mean_class_accuracy": 0.8971} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00166, "memory": 4083, "data_time": 0.19289, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02456, "loss": 0.02456, "time": 0.4288} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00165, "memory": 4083, "data_time": 0.00042, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02202, "loss": 0.02202, "time": 0.2263} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00164, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02256, "loss": 0.02256, "time": 0.22466} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00163, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02309, "loss": 0.02309, "time": 0.22518} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00162, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02106, "loss": 0.02106, "time": 0.2239} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00161, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.02896, "loss": 0.02896, "time": 0.22346} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0016, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03012, "loss": 0.03012, "time": 0.22373} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00159, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02811, "loss": 0.02811, "time": 0.22408} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00158, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02447, "loss": 0.02447, "time": 0.22416} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00157, "memory": 4083, "data_time": 0.00042, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02362, "loss": 0.02362, "time": 0.22655} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00156, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02226, "loss": 0.02226, "time": 0.22485} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00155, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02246, "loss": 0.02246, "time": 0.22532} +{"mode": "val", "epoch": 126, "iter": 533, "lr": 0.00155, "top1_acc": 0.92841, "top5_acc": 0.9946, "mean_class_accuracy": 0.89867} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00154, "memory": 4083, "data_time": 0.19315, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02372, "loss": 0.02372, "time": 0.42936} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00153, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02356, "loss": 0.02356, "time": 0.22812} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00152, "memory": 4083, "data_time": 0.00078, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02375, "loss": 0.02375, "time": 0.23087} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00151, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02711, "loss": 0.02711, "time": 0.22368} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.0015, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02201, "loss": 0.02201, "time": 0.2249} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.00149, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0216, "loss": 0.0216, "time": 0.22417} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00148, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02812, "loss": 0.02812, "time": 0.22333} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00147, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02514, "loss": 0.02514, "time": 0.22703} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00146, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02327, "loss": 0.02327, "time": 0.22436} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00145, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02333, "loss": 0.02333, "time": 0.22496} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00144, "memory": 4083, "data_time": 0.0004, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01999, "loss": 0.01999, "time": 0.22624} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00143, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02367, "loss": 0.02367, "time": 0.22335} +{"mode": "val", "epoch": 127, "iter": 533, "lr": 0.00142, "top1_acc": 0.9263, "top5_acc": 0.9946, "mean_class_accuracy": 0.89864} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00141, "memory": 4083, "data_time": 0.18711, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02064, "loss": 0.02064, "time": 0.42299} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.0014, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03182, "loss": 0.03182, "time": 0.22453} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00139, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02561, "loss": 0.02561, "time": 0.22578} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00138, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02633, "loss": 0.02633, "time": 0.22861} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00138, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02242, "loss": 0.02242, "time": 0.22311} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00137, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02576, "loss": 0.02576, "time": 0.22385} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.00136, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02605, "loss": 0.02605, "time": 0.22332} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00135, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02301, "loss": 0.02301, "time": 0.2249} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00134, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01959, "loss": 0.01959, "time": 0.22475} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00133, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03099, "loss": 0.03099, "time": 0.22372} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00132, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02753, "loss": 0.02753, "time": 0.2238} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00131, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02499, "loss": 0.02499, "time": 0.22353} +{"mode": "val", "epoch": 128, "iter": 533, "lr": 0.0013, "top1_acc": 0.92724, "top5_acc": 0.99413, "mean_class_accuracy": 0.89989} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.00129, "memory": 4083, "data_time": 0.18731, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02143, "loss": 0.02143, "time": 0.42291} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00129, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02186, "loss": 0.02186, "time": 0.22592} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00128, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02116, "loss": 0.02116, "time": 0.22506} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00127, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01941, "loss": 0.01941, "time": 0.22272} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00126, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02519, "loss": 0.02519, "time": 0.22756} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00125, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02426, "loss": 0.02426, "time": 0.22356} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00124, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02491, "loss": 0.02491, "time": 0.22652} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00123, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02124, "loss": 0.02124, "time": 0.22507} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.00122, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01958, "loss": 0.01958, "time": 0.22614} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00121, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02363, "loss": 0.02363, "time": 0.22383} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00121, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0299, "loss": 0.0299, "time": 0.226} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.0012, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02082, "loss": 0.02082, "time": 0.22648} +{"mode": "val", "epoch": 129, "iter": 533, "lr": 0.00119, "top1_acc": 0.92607, "top5_acc": 0.9946, "mean_class_accuracy": 0.89868} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00118, "memory": 4083, "data_time": 0.19213, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01914, "loss": 0.01914, "time": 0.42775} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00117, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02466, "loss": 0.02466, "time": 0.22342} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00116, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0234, "loss": 0.0234, "time": 0.2254} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00116, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02088, "loss": 0.02088, "time": 0.22508} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.00115, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02622, "loss": 0.02622, "time": 0.22551} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00114, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02172, "loss": 0.02172, "time": 0.22457} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00113, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02204, "loss": 0.02204, "time": 0.22185} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00112, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02431, "loss": 0.02431, "time": 0.22867} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00111, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02291, "loss": 0.02291, "time": 0.22569} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.0011, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02002, "loss": 0.02002, "time": 0.22234} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.0011, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02056, "loss": 0.02056, "time": 0.22459} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00109, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02189, "loss": 0.02189, "time": 0.2246} +{"mode": "val", "epoch": 130, "iter": 533, "lr": 0.00108, "top1_acc": 0.9263, "top5_acc": 0.9946, "mean_class_accuracy": 0.89993} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00107, "memory": 4083, "data_time": 0.18308, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02248, "loss": 0.02248, "time": 0.42057} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.00106, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02354, "loss": 0.02354, "time": 0.22475} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00106, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02171, "loss": 0.02171, "time": 0.2241} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00105, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02358, "loss": 0.02358, "time": 0.22562} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00104, "memory": 4083, "data_time": 0.00056, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02148, "loss": 0.02148, "time": 0.22349} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00103, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02144, "loss": 0.02144, "time": 0.22301} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00102, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02049, "loss": 0.02049, "time": 0.22123} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00102, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01949, "loss": 0.01949, "time": 0.22491} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00101, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0206, "loss": 0.0206, "time": 0.22206} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.001, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01956, "loss": 0.01956, "time": 0.22666} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.00099, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02007, "loss": 0.02007, "time": 0.22298} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00098, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01855, "loss": 0.01855, "time": 0.22572} +{"mode": "val", "epoch": 131, "iter": 533, "lr": 0.00098, "top1_acc": 0.92806, "top5_acc": 0.99413, "mean_class_accuracy": 0.90182} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.00097, "memory": 4083, "data_time": 0.18378, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02194, "loss": 0.02194, "time": 0.41915} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00096, "memory": 4083, "data_time": 0.00042, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01901, "loss": 0.01901, "time": 0.2232} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00095, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02033, "loss": 0.02033, "time": 0.22419} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00095, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02093, "loss": 0.02093, "time": 0.22548} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00094, "memory": 4083, "data_time": 0.00056, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01748, "loss": 0.01748, "time": 0.22458} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00093, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01929, "loss": 0.01929, "time": 0.22367} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00092, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0197, "loss": 0.0197, "time": 0.22866} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00091, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02023, "loss": 0.02023, "time": 0.22333} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00091, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02002, "loss": 0.02002, "time": 0.22574} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0009, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02037, "loss": 0.02037, "time": 0.22631} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00089, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02231, "loss": 0.02231, "time": 0.2261} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00088, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01994, "loss": 0.01994, "time": 0.22817} +{"mode": "val", "epoch": 132, "iter": 533, "lr": 0.00088, "top1_acc": 0.92759, "top5_acc": 0.99484, "mean_class_accuracy": 0.90128} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.00087, "memory": 4083, "data_time": 0.19339, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02154, "loss": 0.02154, "time": 0.43095} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00086, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02076, "loss": 0.02076, "time": 0.22729} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00086, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02121, "loss": 0.02121, "time": 0.22506} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00085, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01932, "loss": 0.01932, "time": 0.2226} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00084, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0197, "loss": 0.0197, "time": 0.22486} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00083, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02242, "loss": 0.02242, "time": 0.22304} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00083, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01964, "loss": 0.01964, "time": 0.22789} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00082, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01977, "loss": 0.01977, "time": 0.2248} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00081, "memory": 4083, "data_time": 0.00041, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01882, "loss": 0.01882, "time": 0.22552} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.0008, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02157, "loss": 0.02157, "time": 0.22539} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0008, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0246, "loss": 0.0246, "time": 0.22757} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00079, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01926, "loss": 0.01926, "time": 0.22432} +{"mode": "val", "epoch": 133, "iter": 533, "lr": 0.00078, "top1_acc": 0.92853, "top5_acc": 0.99448, "mean_class_accuracy": 0.90515} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00078, "memory": 4083, "data_time": 0.18381, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01987, "loss": 0.01987, "time": 0.42445} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00077, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02265, "loss": 0.02265, "time": 0.22603} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00076, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01816, "loss": 0.01816, "time": 0.22584} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.00076, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02056, "loss": 0.02056, "time": 0.2243} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00075, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01935, "loss": 0.01935, "time": 0.22149} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00074, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01994, "loss": 0.01994, "time": 0.22453} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00073, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02068, "loss": 0.02068, "time": 0.2234} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00073, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01951, "loss": 0.01951, "time": 0.22408} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00072, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02163, "loss": 0.02163, "time": 0.22654} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00071, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01934, "loss": 0.01934, "time": 0.22491} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00071, "memory": 4083, "data_time": 0.00043, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02004, "loss": 0.02004, "time": 0.22643} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.0007, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01953, "loss": 0.01953, "time": 0.22415} +{"mode": "val", "epoch": 134, "iter": 533, "lr": 0.0007, "top1_acc": 0.92959, "top5_acc": 0.99472, "mean_class_accuracy": 0.90544} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00069, "memory": 4083, "data_time": 0.1858, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01815, "loss": 0.01815, "time": 0.42224} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00068, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02286, "loss": 0.02286, "time": 0.22366} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00068, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01944, "loss": 0.01944, "time": 0.22643} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00067, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02241, "loss": 0.02241, "time": 0.22255} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00066, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02095, "loss": 0.02095, "time": 0.22464} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00066, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01909, "loss": 0.01909, "time": 0.22451} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00065, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01788, "loss": 0.01788, "time": 0.22614} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00064, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0178, "loss": 0.0178, "time": 0.22262} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.00064, "memory": 4083, "data_time": 0.00062, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0191, "loss": 0.0191, "time": 0.22424} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00063, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01953, "loss": 0.01953, "time": 0.22836} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00062, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02095, "loss": 0.02095, "time": 0.22159} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00062, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01867, "loss": 0.01867, "time": 0.22291} +{"mode": "val", "epoch": 135, "iter": 533, "lr": 0.00061, "top1_acc": 0.92912, "top5_acc": 0.99472, "mean_class_accuracy": 0.9035} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00061, "memory": 4083, "data_time": 0.18963, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01889, "loss": 0.01889, "time": 0.42689} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.0006, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01927, "loss": 0.01927, "time": 0.22615} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00059, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01927, "loss": 0.01927, "time": 0.22738} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00059, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01854, "loss": 0.01854, "time": 0.22544} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.00058, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01904, "loss": 0.01904, "time": 0.22823} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.00057, "memory": 4083, "data_time": 0.0004, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01972, "loss": 0.01972, "time": 0.22521} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00057, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01887, "loss": 0.01887, "time": 0.22482} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00056, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02035, "loss": 0.02035, "time": 0.22527} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00056, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02482, "loss": 0.02482, "time": 0.22278} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00055, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02013, "loss": 0.02013, "time": 0.22244} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00054, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01831, "loss": 0.01831, "time": 0.22403} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00054, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0191, "loss": 0.0191, "time": 0.22553} +{"mode": "val", "epoch": 136, "iter": 533, "lr": 0.00053, "top1_acc": 0.92783, "top5_acc": 0.99448, "mean_class_accuracy": 0.90199} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00053, "memory": 4083, "data_time": 0.19076, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01718, "loss": 0.01718, "time": 0.42841} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00052, "memory": 4083, "data_time": 0.00042, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0191, "loss": 0.0191, "time": 0.22846} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00052, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01962, "loss": 0.01962, "time": 0.22226} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.00051, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01908, "loss": 0.01908, "time": 0.22576} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.0005, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01777, "loss": 0.01777, "time": 0.22817} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.0005, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01752, "loss": 0.01752, "time": 0.22339} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00049, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01946, "loss": 0.01946, "time": 0.22162} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00049, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01809, "loss": 0.01809, "time": 0.22452} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00048, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01883, "loss": 0.01883, "time": 0.22419} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00048, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02364, "loss": 0.02364, "time": 0.22257} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00047, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01942, "loss": 0.01942, "time": 0.22451} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00046, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01899, "loss": 0.01899, "time": 0.22469} +{"mode": "val", "epoch": 137, "iter": 533, "lr": 0.00046, "top1_acc": 0.93052, "top5_acc": 0.99484, "mean_class_accuracy": 0.90446} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00046, "memory": 4083, "data_time": 0.19339, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01922, "loss": 0.01922, "time": 0.42939} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00045, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01951, "loss": 0.01951, "time": 0.22641} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00044, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01972, "loss": 0.01972, "time": 0.22532} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00044, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01945, "loss": 0.01945, "time": 0.22608} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.00043, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01863, "loss": 0.01863, "time": 0.22323} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.00043, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01708, "loss": 0.01708, "time": 0.22272} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00042, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01851, "loss": 0.01851, "time": 0.22576} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00042, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01879, "loss": 0.01879, "time": 0.22601} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00041, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01725, "loss": 0.01725, "time": 0.22368} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00041, "memory": 4083, "data_time": 0.00044, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01879, "loss": 0.01879, "time": 0.22605} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.0004, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01755, "loss": 0.01755, "time": 0.22604} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.0004, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01918, "loss": 0.01918, "time": 0.22241} +{"mode": "val", "epoch": 138, "iter": 533, "lr": 0.00039, "top1_acc": 0.93017, "top5_acc": 0.9946, "mean_class_accuracy": 0.90329} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00039, "memory": 4083, "data_time": 0.19551, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0193, "loss": 0.0193, "time": 0.43145} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00038, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0193, "loss": 0.0193, "time": 0.22523} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00038, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0187, "loss": 0.0187, "time": 0.22217} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00037, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0187, "loss": 0.0187, "time": 0.22278} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00037, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01833, "loss": 0.01833, "time": 0.22644} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00036, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01886, "loss": 0.01886, "time": 0.22661} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00036, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02008, "loss": 0.02008, "time": 0.22284} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00035, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01794, "loss": 0.01794, "time": 0.22418} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00035, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01758, "loss": 0.01758, "time": 0.2251} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.00034, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01712, "loss": 0.01712, "time": 0.22362} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.00034, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01918, "loss": 0.01918, "time": 0.22897} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00033, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01917, "loss": 0.01917, "time": 0.222} +{"mode": "val", "epoch": 139, "iter": 533, "lr": 0.00033, "top1_acc": 0.92947, "top5_acc": 0.9946, "mean_class_accuracy": 0.90577} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00033, "memory": 4083, "data_time": 0.19102, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01857, "loss": 0.01857, "time": 0.42763} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00032, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01966, "loss": 0.01966, "time": 0.22456} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.00032, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01903, "loss": 0.01903, "time": 0.22495} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.00031, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01964, "loss": 0.01964, "time": 0.22664} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00031, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01869, "loss": 0.01869, "time": 0.22441} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.0003, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01881, "loss": 0.01881, "time": 0.22388} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.0003, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01823, "loss": 0.01823, "time": 0.22357} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00029, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01765, "loss": 0.01765, "time": 0.22477} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00029, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01923, "loss": 0.01923, "time": 0.22553} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00029, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01794, "loss": 0.01794, "time": 0.22464} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00028, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01798, "loss": 0.01798, "time": 0.22094} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00028, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01877, "loss": 0.01877, "time": 0.22507} +{"mode": "val", "epoch": 140, "iter": 533, "lr": 0.00027, "top1_acc": 0.93041, "top5_acc": 0.99472, "mean_class_accuracy": 0.90515} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00027, "memory": 4083, "data_time": 0.19155, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0189, "loss": 0.0189, "time": 0.42387} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00026, "memory": 4083, "data_time": 0.00046, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01897, "loss": 0.01897, "time": 0.22588} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00026, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02096, "loss": 0.02096, "time": 0.22341} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00026, "memory": 4083, "data_time": 0.00046, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01736, "loss": 0.01736, "time": 0.22344} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00025, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01829, "loss": 0.01829, "time": 0.22531} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00025, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01769, "loss": 0.01769, "time": 0.22575} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00024, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01718, "loss": 0.01718, "time": 0.22816} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00024, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01782, "loss": 0.01782, "time": 0.22358} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00024, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01813, "loss": 0.01813, "time": 0.2226} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00023, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01903, "loss": 0.01903, "time": 0.22561} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00023, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01821, "loss": 0.01821, "time": 0.22494} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00022, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01918, "loss": 0.01918, "time": 0.22285} +{"mode": "val", "epoch": 141, "iter": 533, "lr": 0.00022, "top1_acc": 0.929, "top5_acc": 0.99531, "mean_class_accuracy": 0.89999} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00022, "memory": 4083, "data_time": 0.19197, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01851, "loss": 0.01851, "time": 0.42786} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00021, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02045, "loss": 0.02045, "time": 0.22529} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00021, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01888, "loss": 0.01888, "time": 0.22569} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00021, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0187, "loss": 0.0187, "time": 0.22494} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.0002, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01971, "loss": 0.01971, "time": 0.22391} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.0002, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0174, "loss": 0.0174, "time": 0.22562} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.0002, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01744, "loss": 0.01744, "time": 0.22341} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00019, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01859, "loss": 0.01859, "time": 0.22432} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00019, "memory": 4083, "data_time": 0.00061, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0159, "loss": 0.0159, "time": 0.22395} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00018, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01875, "loss": 0.01875, "time": 0.22612} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00018, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01803, "loss": 0.01803, "time": 0.22383} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00018, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01827, "loss": 0.01827, "time": 0.22564} +{"mode": "val", "epoch": 142, "iter": 533, "lr": 0.00018, "top1_acc": 0.93135, "top5_acc": 0.99472, "mean_class_accuracy": 0.90619} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.00017, "memory": 4083, "data_time": 0.1851, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01731, "loss": 0.01731, "time": 0.4233} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00017, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01833, "loss": 0.01833, "time": 0.22354} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00017, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01801, "loss": 0.01801, "time": 0.22494} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00016, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01932, "loss": 0.01932, "time": 0.22599} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00016, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01753, "loss": 0.01753, "time": 0.22565} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00016, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01867, "loss": 0.01867, "time": 0.22359} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00015, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01866, "loss": 0.01866, "time": 0.22217} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00015, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01809, "loss": 0.01809, "time": 0.22751} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00015, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01887, "loss": 0.01887, "time": 0.22841} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00014, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01851, "loss": 0.01851, "time": 0.22331} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00014, "memory": 4083, "data_time": 0.00047, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01772, "loss": 0.01772, "time": 0.22967} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00014, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01695, "loss": 0.01695, "time": 0.22122} +{"mode": "val", "epoch": 143, "iter": 533, "lr": 0.00013, "top1_acc": 0.93029, "top5_acc": 0.99507, "mean_class_accuracy": 0.90265} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00013, "memory": 4083, "data_time": 0.1914, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01847, "loss": 0.01847, "time": 0.4294} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00013, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0195, "loss": 0.0195, "time": 0.22699} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00013, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01852, "loss": 0.01852, "time": 0.22425} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00012, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01803, "loss": 0.01803, "time": 0.22251} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00012, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01749, "loss": 0.01749, "time": 0.22234} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00012, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01756, "loss": 0.01756, "time": 0.22725} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00011, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01735, "loss": 0.01735, "time": 0.22347} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.00011, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01972, "loss": 0.01972, "time": 0.22319} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.00011, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.018, "loss": 0.018, "time": 0.22209} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.00011, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01938, "loss": 0.01938, "time": 0.22426} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.0001, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01827, "loss": 0.01827, "time": 0.22656} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.0001, "memory": 4083, "data_time": 0.00041, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01992, "loss": 0.01992, "time": 0.2271} +{"mode": "val", "epoch": 144, "iter": 533, "lr": 0.0001, "top1_acc": 0.92982, "top5_acc": 0.99495, "mean_class_accuracy": 0.90281} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.0001, "memory": 4083, "data_time": 0.19512, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01937, "loss": 0.01937, "time": 0.42989} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 9e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01682, "loss": 0.01682, "time": 0.2257} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 9e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01807, "loss": 0.01807, "time": 0.22514} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 9e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01703, "loss": 0.01703, "time": 0.22482} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 9e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.018, "loss": 0.018, "time": 0.22421} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 8e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01809, "loss": 0.01809, "time": 0.22145} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 8e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01845, "loss": 0.01845, "time": 0.22224} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 8e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01769, "loss": 0.01769, "time": 0.22636} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 8e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01833, "loss": 0.01833, "time": 0.22345} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 7e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02005, "loss": 0.02005, "time": 0.2261} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 7e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01773, "loss": 0.01773, "time": 0.22126} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 7e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01889, "loss": 0.01889, "time": 0.22228} +{"mode": "val", "epoch": 145, "iter": 533, "lr": 7e-05, "top1_acc": 0.92982, "top5_acc": 0.99507, "mean_class_accuracy": 0.9043} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 7e-05, "memory": 4083, "data_time": 0.19093, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01882, "loss": 0.01882, "time": 0.42768} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 6e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01893, "loss": 0.01893, "time": 0.22372} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 6e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0187, "loss": 0.0187, "time": 0.22774} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 6e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01599, "loss": 0.01599, "time": 0.22228} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 6e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0185, "loss": 0.0185, "time": 0.2275} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 6e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01802, "loss": 0.01802, "time": 0.22191} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 5e-05, "memory": 4083, "data_time": 0.00057, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01895, "loss": 0.01895, "time": 0.22905} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 5e-05, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01796, "loss": 0.01796, "time": 0.22265} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 5e-05, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01752, "loss": 0.01752, "time": 0.22206} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 5e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01725, "loss": 0.01725, "time": 0.22399} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 5e-05, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01844, "loss": 0.01844, "time": 0.22472} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 5e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01776, "loss": 0.01776, "time": 0.22979} +{"mode": "val", "epoch": 146, "iter": 533, "lr": 4e-05, "top1_acc": 0.93041, "top5_acc": 0.99542, "mean_class_accuracy": 0.90418} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 4e-05, "memory": 4083, "data_time": 0.19662, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01767, "loss": 0.01767, "time": 0.43365} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 4e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01841, "loss": 0.01841, "time": 0.22382} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 4e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01632, "loss": 0.01632, "time": 0.22384} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 4e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01856, "loss": 0.01856, "time": 0.22476} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 4e-05, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01701, "loss": 0.01701, "time": 0.22536} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 3e-05, "memory": 4083, "data_time": 0.00051, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01859, "loss": 0.01859, "time": 0.22654} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 3e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02068, "loss": 0.02068, "time": 0.22582} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 3e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0185, "loss": 0.0185, "time": 0.22368} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 3e-05, "memory": 4083, "data_time": 0.00062, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0173, "loss": 0.0173, "time": 0.22508} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 3e-05, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01807, "loss": 0.01807, "time": 0.22432} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 3e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0185, "loss": 0.0185, "time": 0.22212} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 3e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01861, "loss": 0.01861, "time": 0.22617} +{"mode": "val", "epoch": 147, "iter": 533, "lr": 2e-05, "top1_acc": 0.93111, "top5_acc": 0.99531, "mean_class_accuracy": 0.90496} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 2e-05, "memory": 4083, "data_time": 0.19156, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01761, "loss": 0.01761, "time": 0.42828} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 2e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01902, "loss": 0.01902, "time": 0.22351} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 2e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0188, "loss": 0.0188, "time": 0.22584} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 2e-05, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01736, "loss": 0.01736, "time": 0.22651} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 2e-05, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01693, "loss": 0.01693, "time": 0.2246} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 2e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01719, "loss": 0.01719, "time": 0.22744} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 2e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01766, "loss": 0.01766, "time": 0.22468} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 2e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01828, "loss": 0.01828, "time": 0.2246} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 1e-05, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01926, "loss": 0.01926, "time": 0.22563} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 1e-05, "memory": 4083, "data_time": 0.0005, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01696, "loss": 0.01696, "time": 0.22732} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 1e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0202, "loss": 0.0202, "time": 0.2251} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 1e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02025, "loss": 0.02025, "time": 0.22407} +{"mode": "val", "epoch": 148, "iter": 533, "lr": 1e-05, "top1_acc": 0.93052, "top5_acc": 0.99472, "mean_class_accuracy": 0.90535} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 1e-05, "memory": 4083, "data_time": 0.19428, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02152, "loss": 0.02152, "time": 0.4285} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 1e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0186, "loss": 0.0186, "time": 0.22684} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 1e-05, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01645, "loss": 0.01645, "time": 0.22699} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 1e-05, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01853, "loss": 0.01853, "time": 0.22831} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 1e-05, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01901, "loss": 0.01901, "time": 0.22545} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 1e-05, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0174, "loss": 0.0174, "time": 0.22559} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 1e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01918, "loss": 0.01918, "time": 0.223} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 1e-05, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01819, "loss": 0.01819, "time": 0.22701} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01882, "loss": 0.01882, "time": 0.22216} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02219, "loss": 0.02219, "time": 0.22266} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0189, "loss": 0.0189, "time": 0.22169} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01865, "loss": 0.01865, "time": 0.22661} +{"mode": "val", "epoch": 149, "iter": 533, "lr": 0.0, "top1_acc": 0.93017, "top5_acc": 0.99531, "mean_class_accuracy": 0.90407} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 0.0, "memory": 4083, "data_time": 0.19407, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01811, "loss": 0.01811, "time": 0.4324} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 0.0, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01908, "loss": 0.01908, "time": 0.22375} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 0.0, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01844, "loss": 0.01844, "time": 0.22615} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 0.0, "memory": 4083, "data_time": 0.00044, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01754, "loss": 0.01754, "time": 0.22583} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 0.0, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01819, "loss": 0.01819, "time": 0.22523} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 0.0, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01878, "loss": 0.01878, "time": 0.22514} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 0.0, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01869, "loss": 0.01869, "time": 0.22643} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 0.0, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01888, "loss": 0.01888, "time": 0.22528} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00069, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01876, "loss": 0.01876, "time": 0.22419} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01745, "loss": 0.01745, "time": 0.22518} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01723, "loss": 0.01723, "time": 0.22323} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01816, "loss": 0.01816, "time": 0.22501} +{"mode": "val", "epoch": 150, "iter": 533, "lr": 0.0, "top1_acc": 0.92959, "top5_acc": 0.99519, "mean_class_accuracy": 0.90251} diff --git a/finegym/j_1/best_pred.pkl b/finegym/j_1/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..6cdbeaf60243bf377fd8609b302e2bed41efa0f3 --- /dev/null +++ b/finegym/j_1/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bb1cca8720d20096212d90cab32335f99ac86240e99869f7c685395e485ccdc5 +size 5256570 diff --git a/finegym/j_1/best_top1_acc_epoch_150.pth b/finegym/j_1/best_top1_acc_epoch_150.pth new file mode 100644 index 0000000000000000000000000000000000000000..a94c7c8da1424a1fec45d9b0869f01a6cf52cb00 --- /dev/null +++ b/finegym/j_1/best_top1_acc_epoch_150.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a991fcc19307ac0de3c0b7784ad8260dfaa7db5843114bc0673db17aad5b6e5b +size 16118201 diff --git a/finegym/j_1/j_1.py b/finegym/j_1/j_1.py new file mode 100644 index 0000000000000000000000000000000000000000..44fd2026e128db9a512ff82e544eaab174bd9c38 --- /dev/null +++ b/finegym/j_1/j_1.py @@ -0,0 +1,113 @@ +modality = 'j' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/j_1' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/finegym/j_2/20250624_084315.log b/finegym/j_2/20250624_084315.log new file mode 100644 index 0000000000000000000000000000000000000000..a8634ee31f603cf07ce670ad1bcbe6ff2265f247 --- /dev/null +++ b/finegym/j_2/20250624_084315.log @@ -0,0 +1,3489 @@ +2025-06-24 08:43:15,818 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2025-06-24 08:43:16,029 - pyskl - INFO - Config: modality = 'j' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/j_2' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2025-06-24 08:43:16,029 - pyskl - INFO - Set random seed to 2014598172, deterministic: False +2025-06-24 08:43:17,543 - pyskl - INFO - 20484 videos remain after valid thresholding +2025-06-24 08:43:21,762 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-24 08:43:21,763 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2 +2025-06-24 08:43:21,763 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2025-06-24 08:43:21,763 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2025-06-24 08:43:21,763 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2 by HardDiskBackend. +2025-06-24 08:44:01,820 - pyskl - INFO - Epoch [1][100/1281] lr: 2.500e-02, eta: 21:22:00, time: 0.401, data_time: 0.181, memory: 4082, top1_acc: 0.0656, top5_acc: 0.2181, loss_cls: 4.5750, loss: 4.5750 +2025-06-24 08:44:24,081 - pyskl - INFO - Epoch [1][200/1281] lr: 2.500e-02, eta: 16:36:44, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.0775, top5_acc: 0.2856, loss_cls: 4.6853, loss: 4.6853 +2025-06-24 08:44:45,960 - pyskl - INFO - Epoch [1][300/1281] lr: 2.500e-02, eta: 14:57:19, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.0794, top5_acc: 0.3088, loss_cls: 4.5749, loss: 4.5749 +2025-06-24 08:45:07,858 - pyskl - INFO - Epoch [1][400/1281] lr: 2.500e-02, eta: 14:07:35, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.1062, top5_acc: 0.3300, loss_cls: 4.3757, loss: 4.3757 +2025-06-24 08:45:29,793 - pyskl - INFO - Epoch [1][500/1281] lr: 2.500e-02, eta: 13:37:50, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.1069, top5_acc: 0.4081, loss_cls: 4.1837, loss: 4.1837 +2025-06-24 08:45:51,359 - pyskl - INFO - Epoch [1][600/1281] lr: 2.500e-02, eta: 13:15:55, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.1575, top5_acc: 0.4869, loss_cls: 3.8831, loss: 3.8831 +2025-06-24 08:46:13,020 - pyskl - INFO - Epoch [1][700/1281] lr: 2.500e-02, eta: 13:00:35, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.1975, top5_acc: 0.5281, loss_cls: 3.6775, loss: 3.6775 +2025-06-24 08:46:34,614 - pyskl - INFO - Epoch [1][800/1281] lr: 2.500e-02, eta: 12:48:44, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.2500, top5_acc: 0.5906, loss_cls: 3.4479, loss: 3.4479 +2025-06-24 08:46:56,234 - pyskl - INFO - Epoch [1][900/1281] lr: 2.500e-02, eta: 12:39:32, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.2637, top5_acc: 0.6506, loss_cls: 3.2225, loss: 3.2225 +2025-06-24 08:47:18,039 - pyskl - INFO - Epoch [1][1000/1281] lr: 2.500e-02, eta: 12:32:41, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.3000, top5_acc: 0.6825, loss_cls: 3.0404, loss: 3.0404 +2025-06-24 08:47:39,421 - pyskl - INFO - Epoch [1][1100/1281] lr: 2.500e-02, eta: 12:25:48, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.3331, top5_acc: 0.7300, loss_cls: 2.8771, loss: 2.8771 +2025-06-24 08:48:00,951 - pyskl - INFO - Epoch [1][1200/1281] lr: 2.500e-02, eta: 12:20:23, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.3519, top5_acc: 0.7331, loss_cls: 2.8156, loss: 2.8156 +2025-06-24 08:48:18,840 - pyskl - INFO - Saving checkpoint at 1 epochs +2025-06-24 08:49:02,512 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 08:49:02,565 - pyskl - INFO - +top1_acc 0.3733 +top5_acc 0.7557 +2025-06-24 08:49:02,565 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 08:49:02,571 - pyskl - INFO - +mean_acc 0.1867 +2025-06-24 08:49:02,735 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2025-06-24 08:49:02,735 - pyskl - INFO - Best top1_acc is 0.3733 at 1 epoch. +2025-06-24 08:49:02,738 - pyskl - INFO - Epoch(val) [1][533] top1_acc: 0.3733, top5_acc: 0.7557, mean_class_accuracy: 0.1867 +2025-06-24 08:49:42,815 - pyskl - INFO - Epoch [2][100/1281] lr: 2.500e-02, eta: 12:15:00, time: 0.401, data_time: 0.186, memory: 4082, top1_acc: 0.4037, top5_acc: 0.8000, loss_cls: 2.5859, loss: 2.5859 +2025-06-24 08:50:04,454 - pyskl - INFO - Epoch [2][200/1281] lr: 2.500e-02, eta: 12:11:26, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.3931, top5_acc: 0.7981, loss_cls: 2.5437, loss: 2.5437 +2025-06-24 08:50:26,067 - pyskl - INFO - Epoch [2][300/1281] lr: 2.500e-02, eta: 12:08:14, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.4169, top5_acc: 0.7981, loss_cls: 2.4532, loss: 2.4532 +2025-06-24 08:50:47,527 - pyskl - INFO - Epoch [2][400/1281] lr: 2.500e-02, eta: 12:05:04, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.4325, top5_acc: 0.8169, loss_cls: 2.3528, loss: 2.3528 +2025-06-24 08:51:09,317 - pyskl - INFO - Epoch [2][500/1281] lr: 2.499e-02, eta: 12:02:49, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.4412, top5_acc: 0.8175, loss_cls: 2.3815, loss: 2.3815 +2025-06-24 08:51:31,077 - pyskl - INFO - Epoch [2][600/1281] lr: 2.499e-02, eta: 12:00:43, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.4688, top5_acc: 0.8444, loss_cls: 2.2147, loss: 2.2147 +2025-06-24 08:51:52,845 - pyskl - INFO - Epoch [2][700/1281] lr: 2.499e-02, eta: 11:58:48, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.4681, top5_acc: 0.8387, loss_cls: 2.2344, loss: 2.2344 +2025-06-24 08:52:14,233 - pyskl - INFO - Epoch [2][800/1281] lr: 2.499e-02, eta: 11:56:27, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.4738, top5_acc: 0.8500, loss_cls: 2.1650, loss: 2.1650 +2025-06-24 08:52:35,624 - pyskl - INFO - Epoch [2][900/1281] lr: 2.499e-02, eta: 11:54:18, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.4875, top5_acc: 0.8750, loss_cls: 2.0780, loss: 2.0780 +2025-06-24 08:52:57,153 - pyskl - INFO - Epoch [2][1000/1281] lr: 2.499e-02, eta: 11:52:29, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.4963, top5_acc: 0.8744, loss_cls: 2.0608, loss: 2.0608 +2025-06-24 08:53:18,445 - pyskl - INFO - Epoch [2][1100/1281] lr: 2.499e-02, eta: 11:50:29, time: 0.213, data_time: 0.000, memory: 4082, top1_acc: 0.4900, top5_acc: 0.8775, loss_cls: 1.9650, loss: 1.9650 +2025-06-24 08:53:40,032 - pyskl - INFO - Epoch [2][1200/1281] lr: 2.499e-02, eta: 11:49:00, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.5062, top5_acc: 0.8806, loss_cls: 1.9728, loss: 1.9728 +2025-06-24 08:53:58,336 - pyskl - INFO - Saving checkpoint at 2 epochs +2025-06-24 08:54:41,982 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 08:54:42,047 - pyskl - INFO - +top1_acc 0.5036 +top5_acc 0.8852 +2025-06-24 08:54:42,047 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 08:54:42,055 - pyskl - INFO - +mean_acc 0.3013 +2025-06-24 08:54:42,059 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_1.pth was removed +2025-06-24 08:54:42,248 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2025-06-24 08:54:42,249 - pyskl - INFO - Best top1_acc is 0.5036 at 2 epoch. +2025-06-24 08:54:42,252 - pyskl - INFO - Epoch(val) [2][533] top1_acc: 0.5036, top5_acc: 0.8852, mean_class_accuracy: 0.3013 +2025-06-24 08:55:22,176 - pyskl - INFO - Epoch [3][100/1281] lr: 2.499e-02, eta: 11:47:31, time: 0.399, data_time: 0.184, memory: 4082, top1_acc: 0.5244, top5_acc: 0.9081, loss_cls: 1.8993, loss: 1.8993 +2025-06-24 08:55:43,823 - pyskl - INFO - Epoch [3][200/1281] lr: 2.499e-02, eta: 11:46:17, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.5419, top5_acc: 0.9069, loss_cls: 1.8481, loss: 1.8481 +2025-06-24 08:56:05,446 - pyskl - INFO - Epoch [3][300/1281] lr: 2.499e-02, eta: 11:45:04, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.5537, top5_acc: 0.9144, loss_cls: 1.7968, loss: 1.7968 +2025-06-24 08:56:26,839 - pyskl - INFO - Epoch [3][400/1281] lr: 2.499e-02, eta: 11:43:41, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.5463, top5_acc: 0.9137, loss_cls: 1.7933, loss: 1.7933 +2025-06-24 08:56:48,368 - pyskl - INFO - Epoch [3][500/1281] lr: 2.498e-02, eta: 11:42:30, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.5487, top5_acc: 0.9156, loss_cls: 1.7571, loss: 1.7571 +2025-06-24 08:57:09,789 - pyskl - INFO - Epoch [3][600/1281] lr: 2.498e-02, eta: 11:41:15, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.5669, top5_acc: 0.9287, loss_cls: 1.7285, loss: 1.7285 +2025-06-24 08:57:31,262 - pyskl - INFO - Epoch [3][700/1281] lr: 2.498e-02, eta: 11:40:07, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.5375, top5_acc: 0.9131, loss_cls: 1.8409, loss: 1.8409 +2025-06-24 08:57:52,825 - pyskl - INFO - Epoch [3][800/1281] lr: 2.498e-02, eta: 11:39:07, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.5537, top5_acc: 0.9269, loss_cls: 1.7342, loss: 1.7342 +2025-06-24 08:58:14,179 - pyskl - INFO - Epoch [3][900/1281] lr: 2.498e-02, eta: 11:37:58, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.5713, top5_acc: 0.9137, loss_cls: 1.7465, loss: 1.7465 +2025-06-24 08:58:35,811 - pyskl - INFO - Epoch [3][1000/1281] lr: 2.498e-02, eta: 11:37:06, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.5675, top5_acc: 0.9331, loss_cls: 1.6750, loss: 1.6750 +2025-06-24 08:58:57,131 - pyskl - INFO - Epoch [3][1100/1281] lr: 2.498e-02, eta: 11:35:59, time: 0.213, data_time: 0.000, memory: 4082, top1_acc: 0.5669, top5_acc: 0.9206, loss_cls: 1.7088, loss: 1.7088 +2025-06-24 08:59:18,678 - pyskl - INFO - Epoch [3][1200/1281] lr: 2.498e-02, eta: 11:35:07, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.5887, top5_acc: 0.9256, loss_cls: 1.6703, loss: 1.6703 +2025-06-24 08:59:36,969 - pyskl - INFO - Saving checkpoint at 3 epochs +2025-06-24 09:00:20,026 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:00:20,081 - pyskl - INFO - +top1_acc 0.6037 +top5_acc 0.9441 +2025-06-24 09:00:20,081 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:00:20,087 - pyskl - INFO - +mean_acc 0.4094 +2025-06-24 09:00:20,091 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_2.pth was removed +2025-06-24 09:00:20,274 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2025-06-24 09:00:20,275 - pyskl - INFO - Best top1_acc is 0.6037 at 3 epoch. +2025-06-24 09:00:20,278 - pyskl - INFO - Epoch(val) [3][533] top1_acc: 0.6037, top5_acc: 0.9441, mean_class_accuracy: 0.4094 +2025-06-24 09:01:00,796 - pyskl - INFO - Epoch [4][100/1281] lr: 2.497e-02, eta: 11:34:48, time: 0.405, data_time: 0.187, memory: 4082, top1_acc: 0.6119, top5_acc: 0.9500, loss_cls: 1.5109, loss: 1.5109 +2025-06-24 09:01:22,340 - pyskl - INFO - Epoch [4][200/1281] lr: 2.497e-02, eta: 11:33:57, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.5981, top5_acc: 0.9494, loss_cls: 1.5870, loss: 1.5870 +2025-06-24 09:01:43,771 - pyskl - INFO - Epoch [4][300/1281] lr: 2.497e-02, eta: 11:33:03, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.6019, top5_acc: 0.9437, loss_cls: 1.5731, loss: 1.5731 +2025-06-24 09:02:05,185 - pyskl - INFO - Epoch [4][400/1281] lr: 2.497e-02, eta: 11:32:10, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.6012, top5_acc: 0.9425, loss_cls: 1.5556, loss: 1.5556 +2025-06-24 09:02:26,743 - pyskl - INFO - Epoch [4][500/1281] lr: 2.497e-02, eta: 11:31:24, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6100, top5_acc: 0.9469, loss_cls: 1.5308, loss: 1.5308 +2025-06-24 09:02:48,085 - pyskl - INFO - Epoch [4][600/1281] lr: 2.497e-02, eta: 11:30:30, time: 0.213, data_time: 0.000, memory: 4082, top1_acc: 0.6094, top5_acc: 0.9356, loss_cls: 1.5719, loss: 1.5719 +2025-06-24 09:03:09,535 - pyskl - INFO - Epoch [4][700/1281] lr: 2.497e-02, eta: 11:29:43, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.6125, top5_acc: 0.9506, loss_cls: 1.5310, loss: 1.5310 +2025-06-24 09:03:31,107 - pyskl - INFO - Epoch [4][800/1281] lr: 2.496e-02, eta: 11:29:01, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6344, top5_acc: 0.9419, loss_cls: 1.5095, loss: 1.5095 +2025-06-24 09:03:52,732 - pyskl - INFO - Epoch [4][900/1281] lr: 2.496e-02, eta: 11:28:22, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6044, top5_acc: 0.9437, loss_cls: 1.5787, loss: 1.5787 +2025-06-24 09:04:14,605 - pyskl - INFO - Epoch [4][1000/1281] lr: 2.496e-02, eta: 11:27:54, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6125, top5_acc: 0.9394, loss_cls: 1.5489, loss: 1.5489 +2025-06-24 09:04:36,060 - pyskl - INFO - Epoch [4][1100/1281] lr: 2.496e-02, eta: 11:27:10, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.6169, top5_acc: 0.9519, loss_cls: 1.4860, loss: 1.4860 +2025-06-24 09:04:57,571 - pyskl - INFO - Epoch [4][1200/1281] lr: 2.496e-02, eta: 11:26:28, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.6288, top5_acc: 0.9556, loss_cls: 1.4785, loss: 1.4785 +2025-06-24 09:05:15,944 - pyskl - INFO - Saving checkpoint at 4 epochs +2025-06-24 09:05:59,236 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:05:59,291 - pyskl - INFO - +top1_acc 0.6191 +top5_acc 0.9466 +2025-06-24 09:05:59,291 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:05:59,297 - pyskl - INFO - +mean_acc 0.4740 +2025-06-24 09:05:59,301 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_3.pth was removed +2025-06-24 09:05:59,474 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2025-06-24 09:05:59,474 - pyskl - INFO - Best top1_acc is 0.6191 at 4 epoch. +2025-06-24 09:05:59,477 - pyskl - INFO - Epoch(val) [4][533] top1_acc: 0.6191, top5_acc: 0.9466, mean_class_accuracy: 0.4740 +2025-06-24 09:06:39,406 - pyskl - INFO - Epoch [5][100/1281] lr: 2.495e-02, eta: 11:25:51, time: 0.399, data_time: 0.184, memory: 4082, top1_acc: 0.6562, top5_acc: 0.9600, loss_cls: 1.3953, loss: 1.3953 +2025-06-24 09:07:00,869 - pyskl - INFO - Epoch [5][200/1281] lr: 2.495e-02, eta: 11:25:10, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.6587, top5_acc: 0.9525, loss_cls: 1.3704, loss: 1.3704 +2025-06-24 09:07:22,676 - pyskl - INFO - Epoch [5][300/1281] lr: 2.495e-02, eta: 11:24:41, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6375, top5_acc: 0.9594, loss_cls: 1.4242, loss: 1.4242 +2025-06-24 09:07:44,202 - pyskl - INFO - Epoch [5][400/1281] lr: 2.495e-02, eta: 11:24:03, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.6569, top5_acc: 0.9544, loss_cls: 1.4177, loss: 1.4177 +2025-06-24 09:08:05,720 - pyskl - INFO - Epoch [5][500/1281] lr: 2.495e-02, eta: 11:23:25, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.6813, top5_acc: 0.9525, loss_cls: 1.3543, loss: 1.3543 +2025-06-24 09:08:27,265 - pyskl - INFO - Epoch [5][600/1281] lr: 2.495e-02, eta: 11:22:49, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.6444, top5_acc: 0.9494, loss_cls: 1.4250, loss: 1.4250 +2025-06-24 09:08:48,753 - pyskl - INFO - Epoch [5][700/1281] lr: 2.494e-02, eta: 11:22:11, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.6619, top5_acc: 0.9625, loss_cls: 1.3497, loss: 1.3497 +2025-06-24 09:09:10,145 - pyskl - INFO - Epoch [5][800/1281] lr: 2.494e-02, eta: 11:21:31, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.6587, top5_acc: 0.9663, loss_cls: 1.3757, loss: 1.3757 +2025-06-24 09:09:31,571 - pyskl - INFO - Epoch [5][900/1281] lr: 2.494e-02, eta: 11:20:53, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.6806, top5_acc: 0.9650, loss_cls: 1.3288, loss: 1.3288 +2025-06-24 09:09:53,307 - pyskl - INFO - Epoch [5][1000/1281] lr: 2.494e-02, eta: 11:20:24, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6750, top5_acc: 0.9569, loss_cls: 1.3358, loss: 1.3358 +2025-06-24 09:10:14,951 - pyskl - INFO - Epoch [5][1100/1281] lr: 2.494e-02, eta: 11:19:53, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6613, top5_acc: 0.9544, loss_cls: 1.3669, loss: 1.3669 +2025-06-24 09:10:36,784 - pyskl - INFO - Epoch [5][1200/1281] lr: 2.493e-02, eta: 11:19:28, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6763, top5_acc: 0.9587, loss_cls: 1.3430, loss: 1.3430 +2025-06-24 09:10:55,167 - pyskl - INFO - Saving checkpoint at 5 epochs +2025-06-24 09:11:38,552 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:11:38,620 - pyskl - INFO - +top1_acc 0.6280 +top5_acc 0.9379 +2025-06-24 09:11:38,620 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:11:38,626 - pyskl - INFO - +mean_acc 0.4935 +2025-06-24 09:11:38,631 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_4.pth was removed +2025-06-24 09:11:38,805 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2025-06-24 09:11:38,805 - pyskl - INFO - Best top1_acc is 0.6280 at 5 epoch. +2025-06-24 09:11:38,808 - pyskl - INFO - Epoch(val) [5][533] top1_acc: 0.6280, top5_acc: 0.9379, mean_class_accuracy: 0.4935 +2025-06-24 09:12:18,960 - pyskl - INFO - Epoch [6][100/1281] lr: 2.493e-02, eta: 11:19:01, time: 0.401, data_time: 0.185, memory: 4082, top1_acc: 0.6869, top5_acc: 0.9619, loss_cls: 1.2753, loss: 1.2753 +2025-06-24 09:12:40,796 - pyskl - INFO - Epoch [6][200/1281] lr: 2.493e-02, eta: 11:18:36, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6831, top5_acc: 0.9731, loss_cls: 1.2439, loss: 1.2439 +2025-06-24 09:13:02,775 - pyskl - INFO - Epoch [6][300/1281] lr: 2.492e-02, eta: 11:18:15, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6619, top5_acc: 0.9587, loss_cls: 1.3472, loss: 1.3472 +2025-06-24 09:13:24,685 - pyskl - INFO - Epoch [6][400/1281] lr: 2.492e-02, eta: 11:17:52, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6887, top5_acc: 0.9619, loss_cls: 1.2360, loss: 1.2360 +2025-06-24 09:13:46,260 - pyskl - INFO - Epoch [6][500/1281] lr: 2.492e-02, eta: 11:17:20, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6931, top5_acc: 0.9556, loss_cls: 1.2301, loss: 1.2301 +2025-06-24 09:14:08,048 - pyskl - INFO - Epoch [6][600/1281] lr: 2.492e-02, eta: 11:16:54, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6744, top5_acc: 0.9600, loss_cls: 1.3237, loss: 1.3237 +2025-06-24 09:14:29,876 - pyskl - INFO - Epoch [6][700/1281] lr: 2.492e-02, eta: 11:16:29, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6825, top5_acc: 0.9631, loss_cls: 1.3188, loss: 1.3188 +2025-06-24 09:14:51,867 - pyskl - INFO - Epoch [6][800/1281] lr: 2.491e-02, eta: 11:16:09, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7006, top5_acc: 0.9719, loss_cls: 1.2048, loss: 1.2048 +2025-06-24 09:15:13,763 - pyskl - INFO - Epoch [6][900/1281] lr: 2.491e-02, eta: 11:15:46, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7006, top5_acc: 0.9694, loss_cls: 1.2165, loss: 1.2165 +2025-06-24 09:15:35,574 - pyskl - INFO - Epoch [6][1000/1281] lr: 2.491e-02, eta: 11:15:21, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6813, top5_acc: 0.9637, loss_cls: 1.2799, loss: 1.2799 +2025-06-24 09:15:57,331 - pyskl - INFO - Epoch [6][1100/1281] lr: 2.491e-02, eta: 11:14:55, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7019, top5_acc: 0.9725, loss_cls: 1.2031, loss: 1.2031 +2025-06-24 09:16:19,240 - pyskl - INFO - Epoch [6][1200/1281] lr: 2.490e-02, eta: 11:14:32, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7081, top5_acc: 0.9750, loss_cls: 1.1644, loss: 1.1644 +2025-06-24 09:16:37,521 - pyskl - INFO - Saving checkpoint at 6 epochs +2025-06-24 09:17:20,767 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:17:20,821 - pyskl - INFO - +top1_acc 0.6871 +top5_acc 0.9711 +2025-06-24 09:17:20,821 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:17:20,827 - pyskl - INFO - +mean_acc 0.5368 +2025-06-24 09:17:20,831 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_5.pth was removed +2025-06-24 09:17:21,015 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_6.pth. +2025-06-24 09:17:21,015 - pyskl - INFO - Best top1_acc is 0.6871 at 6 epoch. +2025-06-24 09:17:21,018 - pyskl - INFO - Epoch(val) [6][533] top1_acc: 0.6871, top5_acc: 0.9711, mean_class_accuracy: 0.5368 +2025-06-24 09:18:01,540 - pyskl - INFO - Epoch [7][100/1281] lr: 2.490e-02, eta: 11:14:12, time: 0.405, data_time: 0.187, memory: 4082, top1_acc: 0.7031, top5_acc: 0.9694, loss_cls: 1.2034, loss: 1.2034 +2025-06-24 09:18:23,276 - pyskl - INFO - Epoch [7][200/1281] lr: 2.490e-02, eta: 11:13:45, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7206, top5_acc: 0.9700, loss_cls: 1.1497, loss: 1.1497 +2025-06-24 09:18:44,995 - pyskl - INFO - Epoch [7][300/1281] lr: 2.489e-02, eta: 11:13:18, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7106, top5_acc: 0.9663, loss_cls: 1.2011, loss: 1.2011 +2025-06-24 09:19:06,897 - pyskl - INFO - Epoch [7][400/1281] lr: 2.489e-02, eta: 11:12:55, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6987, top5_acc: 0.9688, loss_cls: 1.2265, loss: 1.2265 +2025-06-24 09:19:28,844 - pyskl - INFO - Epoch [7][500/1281] lr: 2.489e-02, eta: 11:12:34, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7050, top5_acc: 0.9719, loss_cls: 1.2033, loss: 1.2033 +2025-06-24 09:19:50,500 - pyskl - INFO - Epoch [7][600/1281] lr: 2.489e-02, eta: 11:12:05, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6987, top5_acc: 0.9750, loss_cls: 1.1965, loss: 1.1965 +2025-06-24 09:20:12,324 - pyskl - INFO - Epoch [7][700/1281] lr: 2.488e-02, eta: 11:11:41, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7256, top5_acc: 0.9644, loss_cls: 1.1749, loss: 1.1749 +2025-06-24 09:20:34,118 - pyskl - INFO - Epoch [7][800/1281] lr: 2.488e-02, eta: 11:11:16, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7013, top5_acc: 0.9688, loss_cls: 1.1902, loss: 1.1902 +2025-06-24 09:20:55,825 - pyskl - INFO - Epoch [7][900/1281] lr: 2.488e-02, eta: 11:10:50, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7150, top5_acc: 0.9644, loss_cls: 1.2016, loss: 1.2016 +2025-06-24 09:21:17,444 - pyskl - INFO - Epoch [7][1000/1281] lr: 2.487e-02, eta: 11:10:21, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7194, top5_acc: 0.9738, loss_cls: 1.1655, loss: 1.1655 +2025-06-24 09:21:39,133 - pyskl - INFO - Epoch [7][1100/1281] lr: 2.487e-02, eta: 11:09:54, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7362, top5_acc: 0.9788, loss_cls: 1.1095, loss: 1.1095 +2025-06-24 09:22:00,951 - pyskl - INFO - Epoch [7][1200/1281] lr: 2.487e-02, eta: 11:09:30, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7175, top5_acc: 0.9725, loss_cls: 1.1513, loss: 1.1513 +2025-06-24 09:22:19,338 - pyskl - INFO - Saving checkpoint at 7 epochs +2025-06-24 09:23:02,261 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:23:02,318 - pyskl - INFO - +top1_acc 0.6469 +top5_acc 0.9600 +2025-06-24 09:23:02,318 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:23:02,324 - pyskl - INFO - +mean_acc 0.4986 +2025-06-24 09:23:02,325 - pyskl - INFO - Epoch(val) [7][533] top1_acc: 0.6469, top5_acc: 0.9600, mean_class_accuracy: 0.4986 +2025-06-24 09:23:42,704 - pyskl - INFO - Epoch [8][100/1281] lr: 2.486e-02, eta: 11:09:05, time: 0.404, data_time: 0.185, memory: 4082, top1_acc: 0.7350, top5_acc: 0.9688, loss_cls: 1.1304, loss: 1.1304 +2025-06-24 09:24:04,520 - pyskl - INFO - Epoch [8][200/1281] lr: 2.486e-02, eta: 11:08:40, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7275, top5_acc: 0.9706, loss_cls: 1.1682, loss: 1.1682 +2025-06-24 09:24:26,086 - pyskl - INFO - Epoch [8][300/1281] lr: 2.486e-02, eta: 11:08:11, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7181, top5_acc: 0.9781, loss_cls: 1.1471, loss: 1.1471 +2025-06-24 09:24:47,784 - pyskl - INFO - Epoch [8][400/1281] lr: 2.485e-02, eta: 11:07:45, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7300, top5_acc: 0.9750, loss_cls: 1.1398, loss: 1.1398 +2025-06-24 09:25:09,557 - pyskl - INFO - Epoch [8][500/1281] lr: 2.485e-02, eta: 11:07:20, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7575, top5_acc: 0.9756, loss_cls: 1.0688, loss: 1.0688 +2025-06-24 09:25:31,071 - pyskl - INFO - Epoch [8][600/1281] lr: 2.485e-02, eta: 11:06:51, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7275, top5_acc: 0.9750, loss_cls: 1.1134, loss: 1.1134 +2025-06-24 09:25:52,951 - pyskl - INFO - Epoch [8][700/1281] lr: 2.484e-02, eta: 11:06:28, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7113, top5_acc: 0.9794, loss_cls: 1.1427, loss: 1.1427 +2025-06-24 09:26:14,848 - pyskl - INFO - Epoch [8][800/1281] lr: 2.484e-02, eta: 11:06:06, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7163, top5_acc: 0.9688, loss_cls: 1.1537, loss: 1.1537 +2025-06-24 09:26:36,520 - pyskl - INFO - Epoch [8][900/1281] lr: 2.484e-02, eta: 11:05:40, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7238, top5_acc: 0.9738, loss_cls: 1.1508, loss: 1.1508 +2025-06-24 09:26:58,407 - pyskl - INFO - Epoch [8][1000/1281] lr: 2.483e-02, eta: 11:05:17, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7319, top5_acc: 0.9756, loss_cls: 1.0953, loss: 1.0953 +2025-06-24 09:27:19,854 - pyskl - INFO - Epoch [8][1100/1281] lr: 2.483e-02, eta: 11:04:47, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7137, top5_acc: 0.9700, loss_cls: 1.1546, loss: 1.1546 +2025-06-24 09:27:41,435 - pyskl - INFO - Epoch [8][1200/1281] lr: 2.483e-02, eta: 11:04:19, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7225, top5_acc: 0.9788, loss_cls: 1.1237, loss: 1.1237 +2025-06-24 09:27:59,962 - pyskl - INFO - Saving checkpoint at 8 epochs +2025-06-24 09:28:43,287 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:28:43,352 - pyskl - INFO - +top1_acc 0.6858 +top5_acc 0.9661 +2025-06-24 09:28:43,352 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:28:43,360 - pyskl - INFO - +mean_acc 0.5621 +2025-06-24 09:28:43,362 - pyskl - INFO - Epoch(val) [8][533] top1_acc: 0.6858, top5_acc: 0.9661, mean_class_accuracy: 0.5621 +2025-06-24 09:29:24,087 - pyskl - INFO - Epoch [9][100/1281] lr: 2.482e-02, eta: 11:03:58, time: 0.407, data_time: 0.188, memory: 4082, top1_acc: 0.7506, top5_acc: 0.9806, loss_cls: 1.0676, loss: 1.0676 +2025-06-24 09:29:45,764 - pyskl - INFO - Epoch [9][200/1281] lr: 2.482e-02, eta: 11:03:32, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7675, top5_acc: 0.9800, loss_cls: 1.0013, loss: 1.0013 +2025-06-24 09:30:07,524 - pyskl - INFO - Epoch [9][300/1281] lr: 2.481e-02, eta: 11:03:08, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7475, top5_acc: 0.9812, loss_cls: 1.0523, loss: 1.0523 +2025-06-24 09:30:29,182 - pyskl - INFO - Epoch [9][400/1281] lr: 2.481e-02, eta: 11:02:42, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7500, top5_acc: 0.9788, loss_cls: 1.0741, loss: 1.0741 +2025-06-24 09:30:50,900 - pyskl - INFO - Epoch [9][500/1281] lr: 2.481e-02, eta: 11:02:16, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7438, top5_acc: 0.9769, loss_cls: 1.0915, loss: 1.0915 +2025-06-24 09:31:12,519 - pyskl - INFO - Epoch [9][600/1281] lr: 2.480e-02, eta: 11:01:50, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7481, top5_acc: 0.9744, loss_cls: 1.0895, loss: 1.0895 +2025-06-24 09:31:34,596 - pyskl - INFO - Epoch [9][700/1281] lr: 2.480e-02, eta: 11:01:31, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7356, top5_acc: 0.9781, loss_cls: 1.0973, loss: 1.0973 +2025-06-24 09:31:56,201 - pyskl - INFO - Epoch [9][800/1281] lr: 2.480e-02, eta: 11:01:04, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7506, top5_acc: 0.9775, loss_cls: 1.0570, loss: 1.0570 +2025-06-24 09:32:17,503 - pyskl - INFO - Epoch [9][900/1281] lr: 2.479e-02, eta: 11:00:32, time: 0.213, data_time: 0.000, memory: 4082, top1_acc: 0.7325, top5_acc: 0.9762, loss_cls: 1.0914, loss: 1.0914 +2025-06-24 09:32:39,195 - pyskl - INFO - Epoch [9][1000/1281] lr: 2.479e-02, eta: 11:00:07, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7381, top5_acc: 0.9744, loss_cls: 1.0921, loss: 1.0921 +2025-06-24 09:33:00,776 - pyskl - INFO - Epoch [9][1100/1281] lr: 2.479e-02, eta: 10:59:40, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7312, top5_acc: 0.9812, loss_cls: 1.0994, loss: 1.0994 +2025-06-24 09:33:22,606 - pyskl - INFO - Epoch [9][1200/1281] lr: 2.478e-02, eta: 10:59:17, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7469, top5_acc: 0.9794, loss_cls: 1.0896, loss: 1.0896 +2025-06-24 09:33:41,175 - pyskl - INFO - Saving checkpoint at 9 epochs +2025-06-24 09:34:24,249 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:34:24,317 - pyskl - INFO - +top1_acc 0.7138 +top5_acc 0.9749 +2025-06-24 09:34:24,318 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:34:24,324 - pyskl - INFO - +mean_acc 0.5738 +2025-06-24 09:34:24,328 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_6.pth was removed +2025-06-24 09:34:24,505 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_9.pth. +2025-06-24 09:34:24,505 - pyskl - INFO - Best top1_acc is 0.7138 at 9 epoch. +2025-06-24 09:34:24,507 - pyskl - INFO - Epoch(val) [9][533] top1_acc: 0.7138, top5_acc: 0.9749, mean_class_accuracy: 0.5738 +2025-06-24 09:35:04,283 - pyskl - INFO - Epoch [10][100/1281] lr: 2.477e-02, eta: 10:58:40, time: 0.398, data_time: 0.180, memory: 4082, top1_acc: 0.7681, top5_acc: 0.9831, loss_cls: 0.9840, loss: 0.9840 +2025-06-24 09:35:25,930 - pyskl - INFO - Epoch [10][200/1281] lr: 2.477e-02, eta: 10:58:14, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7650, top5_acc: 0.9794, loss_cls: 0.9924, loss: 0.9924 +2025-06-24 09:35:47,801 - pyskl - INFO - Epoch [10][300/1281] lr: 2.477e-02, eta: 10:57:52, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7438, top5_acc: 0.9819, loss_cls: 1.0525, loss: 1.0525 +2025-06-24 09:36:09,735 - pyskl - INFO - Epoch [10][400/1281] lr: 2.476e-02, eta: 10:57:31, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7356, top5_acc: 0.9825, loss_cls: 1.0775, loss: 1.0775 +2025-06-24 09:36:31,473 - pyskl - INFO - Epoch [10][500/1281] lr: 2.476e-02, eta: 10:57:07, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7538, top5_acc: 0.9762, loss_cls: 1.0352, loss: 1.0352 +2025-06-24 09:36:53,294 - pyskl - INFO - Epoch [10][600/1281] lr: 2.476e-02, eta: 10:56:44, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7481, top5_acc: 0.9744, loss_cls: 1.0707, loss: 1.0707 +2025-06-24 09:37:14,928 - pyskl - INFO - Epoch [10][700/1281] lr: 2.475e-02, eta: 10:56:18, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7506, top5_acc: 0.9825, loss_cls: 1.0188, loss: 1.0188 +2025-06-24 09:37:36,695 - pyskl - INFO - Epoch [10][800/1281] lr: 2.475e-02, eta: 10:55:54, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7562, top5_acc: 0.9819, loss_cls: 1.0523, loss: 1.0523 +2025-06-24 09:37:58,518 - pyskl - INFO - Epoch [10][900/1281] lr: 2.474e-02, eta: 10:55:32, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7450, top5_acc: 0.9794, loss_cls: 1.0822, loss: 1.0822 +2025-06-24 09:38:20,015 - pyskl - INFO - Epoch [10][1000/1281] lr: 2.474e-02, eta: 10:55:04, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7475, top5_acc: 0.9738, loss_cls: 1.1044, loss: 1.1044 +2025-06-24 09:38:41,708 - pyskl - INFO - Epoch [10][1100/1281] lr: 2.473e-02, eta: 10:54:40, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7662, top5_acc: 0.9769, loss_cls: 1.0319, loss: 1.0319 +2025-06-24 09:39:03,751 - pyskl - INFO - Epoch [10][1200/1281] lr: 2.473e-02, eta: 10:54:20, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7475, top5_acc: 0.9775, loss_cls: 1.0467, loss: 1.0467 +2025-06-24 09:39:22,097 - pyskl - INFO - Saving checkpoint at 10 epochs +2025-06-24 09:40:04,660 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:40:04,715 - pyskl - INFO - +top1_acc 0.6868 +top5_acc 0.9717 +2025-06-24 09:40:04,715 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:40:04,721 - pyskl - INFO - +mean_acc 0.5661 +2025-06-24 09:40:04,722 - pyskl - INFO - Epoch(val) [10][533] top1_acc: 0.6868, top5_acc: 0.9717, mean_class_accuracy: 0.5661 +2025-06-24 09:40:43,639 - pyskl - INFO - Epoch [11][100/1281] lr: 2.472e-02, eta: 10:53:31, time: 0.389, data_time: 0.174, memory: 4082, top1_acc: 0.7619, top5_acc: 0.9825, loss_cls: 0.9991, loss: 0.9991 +2025-06-24 09:41:05,541 - pyskl - INFO - Epoch [11][200/1281] lr: 2.472e-02, eta: 10:53:09, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7456, top5_acc: 0.9775, loss_cls: 1.0285, loss: 1.0285 +2025-06-24 09:41:27,221 - pyskl - INFO - Epoch [11][300/1281] lr: 2.471e-02, eta: 10:52:45, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7506, top5_acc: 0.9756, loss_cls: 1.0461, loss: 1.0461 +2025-06-24 09:41:48,896 - pyskl - INFO - Epoch [11][400/1281] lr: 2.471e-02, eta: 10:52:20, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7375, top5_acc: 0.9831, loss_cls: 1.0911, loss: 1.0911 +2025-06-24 09:42:10,545 - pyskl - INFO - Epoch [11][500/1281] lr: 2.471e-02, eta: 10:51:55, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7588, top5_acc: 0.9850, loss_cls: 1.0284, loss: 1.0284 +2025-06-24 09:42:32,179 - pyskl - INFO - Epoch [11][600/1281] lr: 2.470e-02, eta: 10:51:30, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7569, top5_acc: 0.9800, loss_cls: 1.0611, loss: 1.0611 +2025-06-24 09:42:53,820 - pyskl - INFO - Epoch [11][700/1281] lr: 2.470e-02, eta: 10:51:05, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7444, top5_acc: 0.9750, loss_cls: 1.1106, loss: 1.1106 +2025-06-24 09:43:15,735 - pyskl - INFO - Epoch [11][800/1281] lr: 2.469e-02, eta: 10:50:44, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7738, top5_acc: 0.9800, loss_cls: 0.9774, loss: 0.9774 +2025-06-24 09:43:37,484 - pyskl - INFO - Epoch [11][900/1281] lr: 2.469e-02, eta: 10:50:20, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7725, top5_acc: 0.9781, loss_cls: 1.0147, loss: 1.0147 +2025-06-24 09:43:59,103 - pyskl - INFO - Epoch [11][1000/1281] lr: 2.468e-02, eta: 10:49:55, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7406, top5_acc: 0.9800, loss_cls: 1.0265, loss: 1.0265 +2025-06-24 09:44:21,144 - pyskl - INFO - Epoch [11][1100/1281] lr: 2.468e-02, eta: 10:49:36, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7638, top5_acc: 0.9825, loss_cls: 0.9962, loss: 0.9962 +2025-06-24 09:44:42,924 - pyskl - INFO - Epoch [11][1200/1281] lr: 2.467e-02, eta: 10:49:13, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7262, top5_acc: 0.9856, loss_cls: 1.0882, loss: 1.0882 +2025-06-24 09:45:01,178 - pyskl - INFO - Saving checkpoint at 11 epochs +2025-06-24 09:45:44,377 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:45:44,430 - pyskl - INFO - +top1_acc 0.6883 +top5_acc 0.9664 +2025-06-24 09:45:44,431 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:45:44,436 - pyskl - INFO - +mean_acc 0.5484 +2025-06-24 09:45:44,438 - pyskl - INFO - Epoch(val) [11][533] top1_acc: 0.6883, top5_acc: 0.9664, mean_class_accuracy: 0.5484 +2025-06-24 09:46:25,544 - pyskl - INFO - Epoch [12][100/1281] lr: 2.467e-02, eta: 10:48:52, time: 0.411, data_time: 0.189, memory: 4082, top1_acc: 0.7675, top5_acc: 0.9800, loss_cls: 0.9850, loss: 0.9850 +2025-06-24 09:46:47,645 - pyskl - INFO - Epoch [12][200/1281] lr: 2.466e-02, eta: 10:48:33, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7588, top5_acc: 0.9800, loss_cls: 1.0332, loss: 1.0332 +2025-06-24 09:47:09,354 - pyskl - INFO - Epoch [12][300/1281] lr: 2.466e-02, eta: 10:48:09, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7306, top5_acc: 0.9856, loss_cls: 1.0964, loss: 1.0964 +2025-06-24 09:47:31,051 - pyskl - INFO - Epoch [12][400/1281] lr: 2.465e-02, eta: 10:47:45, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7512, top5_acc: 0.9812, loss_cls: 1.0783, loss: 1.0783 +2025-06-24 09:47:52,792 - pyskl - INFO - Epoch [12][500/1281] lr: 2.465e-02, eta: 10:47:22, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7400, top5_acc: 0.9825, loss_cls: 1.0339, loss: 1.0339 +2025-06-24 09:48:14,388 - pyskl - INFO - Epoch [12][600/1281] lr: 2.464e-02, eta: 10:46:56, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7350, top5_acc: 0.9775, loss_cls: 1.0483, loss: 1.0483 +2025-06-24 09:48:36,086 - pyskl - INFO - Epoch [12][700/1281] lr: 2.464e-02, eta: 10:46:32, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7575, top5_acc: 0.9744, loss_cls: 1.0285, loss: 1.0285 +2025-06-24 09:48:57,731 - pyskl - INFO - Epoch [12][800/1281] lr: 2.463e-02, eta: 10:46:08, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7806, top5_acc: 0.9825, loss_cls: 0.9306, loss: 0.9306 +2025-06-24 09:49:19,468 - pyskl - INFO - Epoch [12][900/1281] lr: 2.463e-02, eta: 10:45:44, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7506, top5_acc: 0.9719, loss_cls: 1.0828, loss: 1.0828 +2025-06-24 09:49:40,772 - pyskl - INFO - Epoch [12][1000/1281] lr: 2.462e-02, eta: 10:45:16, time: 0.213, data_time: 0.000, memory: 4082, top1_acc: 0.7588, top5_acc: 0.9831, loss_cls: 1.0093, loss: 1.0093 +2025-06-24 09:50:02,588 - pyskl - INFO - Epoch [12][1100/1281] lr: 2.462e-02, eta: 10:44:53, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7656, top5_acc: 0.9819, loss_cls: 1.0177, loss: 1.0177 +2025-06-24 09:50:24,369 - pyskl - INFO - Epoch [12][1200/1281] lr: 2.461e-02, eta: 10:44:30, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7481, top5_acc: 0.9781, loss_cls: 1.0637, loss: 1.0637 +2025-06-24 09:50:42,475 - pyskl - INFO - Saving checkpoint at 12 epochs +2025-06-24 09:51:25,644 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:51:25,711 - pyskl - INFO - +top1_acc 0.7270 +top5_acc 0.9721 +2025-06-24 09:51:25,711 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:51:25,719 - pyskl - INFO - +mean_acc 0.6137 +2025-06-24 09:51:25,725 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_9.pth was removed +2025-06-24 09:51:25,966 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_12.pth. +2025-06-24 09:51:25,966 - pyskl - INFO - Best top1_acc is 0.7270 at 12 epoch. +2025-06-24 09:51:25,969 - pyskl - INFO - Epoch(val) [12][533] top1_acc: 0.7270, top5_acc: 0.9721, mean_class_accuracy: 0.6137 +2025-06-24 09:52:05,309 - pyskl - INFO - Epoch [13][100/1281] lr: 2.460e-02, eta: 10:43:48, time: 0.393, data_time: 0.178, memory: 4082, top1_acc: 0.7500, top5_acc: 0.9812, loss_cls: 1.0569, loss: 1.0569 +2025-06-24 09:52:26,998 - pyskl - INFO - Epoch [13][200/1281] lr: 2.460e-02, eta: 10:43:24, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7662, top5_acc: 0.9850, loss_cls: 0.9772, loss: 0.9772 +2025-06-24 09:52:48,217 - pyskl - INFO - Epoch [13][300/1281] lr: 2.459e-02, eta: 10:42:55, time: 0.212, data_time: 0.000, memory: 4082, top1_acc: 0.7594, top5_acc: 0.9838, loss_cls: 1.0084, loss: 1.0084 +2025-06-24 09:53:09,701 - pyskl - INFO - Epoch [13][400/1281] lr: 2.459e-02, eta: 10:42:29, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7706, top5_acc: 0.9844, loss_cls: 0.9610, loss: 0.9610 +2025-06-24 09:53:31,347 - pyskl - INFO - Epoch [13][500/1281] lr: 2.458e-02, eta: 10:42:05, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7375, top5_acc: 0.9812, loss_cls: 1.0695, loss: 1.0695 +2025-06-24 09:53:53,160 - pyskl - INFO - Epoch [13][600/1281] lr: 2.458e-02, eta: 10:41:42, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7731, top5_acc: 0.9825, loss_cls: 0.9476, loss: 0.9476 +2025-06-24 09:54:14,859 - pyskl - INFO - Epoch [13][700/1281] lr: 2.457e-02, eta: 10:41:19, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7638, top5_acc: 0.9800, loss_cls: 1.0002, loss: 1.0002 +2025-06-24 09:54:36,494 - pyskl - INFO - Epoch [13][800/1281] lr: 2.457e-02, eta: 10:40:55, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7700, top5_acc: 0.9825, loss_cls: 1.0034, loss: 1.0034 +2025-06-24 09:54:57,872 - pyskl - INFO - Epoch [13][900/1281] lr: 2.456e-02, eta: 10:40:28, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7694, top5_acc: 0.9856, loss_cls: 0.9648, loss: 0.9648 +2025-06-24 09:55:19,423 - pyskl - INFO - Epoch [13][1000/1281] lr: 2.455e-02, eta: 10:40:03, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7762, top5_acc: 0.9781, loss_cls: 0.9741, loss: 0.9741 +2025-06-24 09:55:41,155 - pyskl - INFO - Epoch [13][1100/1281] lr: 2.455e-02, eta: 10:39:40, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7538, top5_acc: 0.9788, loss_cls: 1.0177, loss: 1.0177 +2025-06-24 09:56:02,715 - pyskl - INFO - Epoch [13][1200/1281] lr: 2.454e-02, eta: 10:39:15, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7750, top5_acc: 0.9800, loss_cls: 0.9875, loss: 0.9875 +2025-06-24 09:56:20,746 - pyskl - INFO - Saving checkpoint at 13 epochs +2025-06-24 09:57:03,558 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:57:03,611 - pyskl - INFO - +top1_acc 0.7270 +top5_acc 0.9766 +2025-06-24 09:57:03,611 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:57:03,618 - pyskl - INFO - +mean_acc 0.6028 +2025-06-24 09:57:03,620 - pyskl - INFO - Epoch(val) [13][533] top1_acc: 0.7270, top5_acc: 0.9766, mean_class_accuracy: 0.6028 +2025-06-24 09:57:43,504 - pyskl - INFO - Epoch [14][100/1281] lr: 2.453e-02, eta: 10:38:39, time: 0.399, data_time: 0.184, memory: 4082, top1_acc: 0.7675, top5_acc: 0.9862, loss_cls: 0.9557, loss: 0.9557 +2025-06-24 09:58:05,188 - pyskl - INFO - Epoch [14][200/1281] lr: 2.453e-02, eta: 10:38:15, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7544, top5_acc: 0.9850, loss_cls: 1.0271, loss: 1.0271 +2025-06-24 09:58:26,818 - pyskl - INFO - Epoch [14][300/1281] lr: 2.452e-02, eta: 10:37:51, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9806, loss_cls: 0.9576, loss: 0.9576 +2025-06-24 09:58:48,317 - pyskl - INFO - Epoch [14][400/1281] lr: 2.452e-02, eta: 10:37:26, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7931, top5_acc: 0.9862, loss_cls: 0.9389, loss: 0.9389 +2025-06-24 09:59:09,918 - pyskl - INFO - Epoch [14][500/1281] lr: 2.451e-02, eta: 10:37:01, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7875, top5_acc: 0.9825, loss_cls: 0.9012, loss: 0.9012 +2025-06-24 09:59:31,709 - pyskl - INFO - Epoch [14][600/1281] lr: 2.451e-02, eta: 10:36:39, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7681, top5_acc: 0.9844, loss_cls: 0.9822, loss: 0.9822 +2025-06-24 09:59:53,562 - pyskl - INFO - Epoch [14][700/1281] lr: 2.450e-02, eta: 10:36:17, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7744, top5_acc: 0.9838, loss_cls: 0.9814, loss: 0.9814 +2025-06-24 10:00:15,247 - pyskl - INFO - Epoch [14][800/1281] lr: 2.449e-02, eta: 10:35:54, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7550, top5_acc: 0.9800, loss_cls: 1.0266, loss: 1.0266 +2025-06-24 10:00:37,067 - pyskl - INFO - Epoch [14][900/1281] lr: 2.449e-02, eta: 10:35:32, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7556, top5_acc: 0.9819, loss_cls: 1.0316, loss: 1.0316 +2025-06-24 10:00:58,588 - pyskl - INFO - Epoch [14][1000/1281] lr: 2.448e-02, eta: 10:35:07, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7656, top5_acc: 0.9850, loss_cls: 1.0041, loss: 1.0041 +2025-06-24 10:01:20,645 - pyskl - INFO - Epoch [14][1100/1281] lr: 2.448e-02, eta: 10:34:47, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7656, top5_acc: 0.9856, loss_cls: 0.9897, loss: 0.9897 +2025-06-24 10:01:42,127 - pyskl - INFO - Epoch [14][1200/1281] lr: 2.447e-02, eta: 10:34:22, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7625, top5_acc: 0.9775, loss_cls: 0.9840, loss: 0.9840 +2025-06-24 10:02:00,248 - pyskl - INFO - Saving checkpoint at 14 epochs +2025-06-24 10:02:42,708 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:02:42,772 - pyskl - INFO - +top1_acc 0.6872 +top5_acc 0.9575 +2025-06-24 10:02:42,773 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:02:42,780 - pyskl - INFO - +mean_acc 0.5507 +2025-06-24 10:02:42,782 - pyskl - INFO - Epoch(val) [14][533] top1_acc: 0.6872, top5_acc: 0.9575, mean_class_accuracy: 0.5507 +2025-06-24 10:03:22,802 - pyskl - INFO - Epoch [15][100/1281] lr: 2.446e-02, eta: 10:33:47, time: 0.400, data_time: 0.183, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9838, loss_cls: 0.9251, loss: 0.9251 +2025-06-24 10:03:44,516 - pyskl - INFO - Epoch [15][200/1281] lr: 2.445e-02, eta: 10:33:24, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7725, top5_acc: 0.9844, loss_cls: 0.9658, loss: 0.9658 +2025-06-24 10:04:06,332 - pyskl - INFO - Epoch [15][300/1281] lr: 2.445e-02, eta: 10:33:02, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7731, top5_acc: 0.9844, loss_cls: 0.9514, loss: 0.9514 +2025-06-24 10:04:28,367 - pyskl - INFO - Epoch [15][400/1281] lr: 2.444e-02, eta: 10:32:42, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7712, top5_acc: 0.9825, loss_cls: 0.9685, loss: 0.9685 +2025-06-24 10:04:49,955 - pyskl - INFO - Epoch [15][500/1281] lr: 2.444e-02, eta: 10:32:18, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7750, top5_acc: 0.9894, loss_cls: 0.9420, loss: 0.9420 +2025-06-24 10:05:11,987 - pyskl - INFO - Epoch [15][600/1281] lr: 2.443e-02, eta: 10:31:58, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7887, top5_acc: 0.9825, loss_cls: 0.9158, loss: 0.9158 +2025-06-24 10:05:33,504 - pyskl - INFO - Epoch [15][700/1281] lr: 2.442e-02, eta: 10:31:33, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7806, top5_acc: 0.9838, loss_cls: 0.9303, loss: 0.9303 +2025-06-24 10:05:55,074 - pyskl - INFO - Epoch [15][800/1281] lr: 2.442e-02, eta: 10:31:08, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7619, top5_acc: 0.9844, loss_cls: 0.9943, loss: 0.9943 +2025-06-24 10:06:16,583 - pyskl - INFO - Epoch [15][900/1281] lr: 2.441e-02, eta: 10:30:43, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7606, top5_acc: 0.9788, loss_cls: 1.0186, loss: 1.0186 +2025-06-24 10:06:38,060 - pyskl - INFO - Epoch [15][1000/1281] lr: 2.441e-02, eta: 10:30:18, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7869, top5_acc: 0.9862, loss_cls: 0.8992, loss: 0.8992 +2025-06-24 10:07:00,067 - pyskl - INFO - Epoch [15][1100/1281] lr: 2.440e-02, eta: 10:29:58, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7806, top5_acc: 0.9844, loss_cls: 0.9056, loss: 0.9056 +2025-06-24 10:07:21,906 - pyskl - INFO - Epoch [15][1200/1281] lr: 2.439e-02, eta: 10:29:36, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9850, loss_cls: 0.9031, loss: 0.9031 +2025-06-24 10:07:40,289 - pyskl - INFO - Saving checkpoint at 15 epochs +2025-06-24 10:08:23,220 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:08:23,274 - pyskl - INFO - +top1_acc 0.7025 +top5_acc 0.9617 +2025-06-24 10:08:23,274 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:08:23,280 - pyskl - INFO - +mean_acc 0.6019 +2025-06-24 10:08:23,282 - pyskl - INFO - Epoch(val) [15][533] top1_acc: 0.7025, top5_acc: 0.9617, mean_class_accuracy: 0.6019 +2025-06-24 10:09:03,132 - pyskl - INFO - Epoch [16][100/1281] lr: 2.438e-02, eta: 10:29:00, time: 0.398, data_time: 0.180, memory: 4082, top1_acc: 0.8031, top5_acc: 0.9888, loss_cls: 0.8864, loss: 0.8864 +2025-06-24 10:09:24,949 - pyskl - INFO - Epoch [16][200/1281] lr: 2.438e-02, eta: 10:28:38, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7775, top5_acc: 0.9812, loss_cls: 0.9772, loss: 0.9772 +2025-06-24 10:09:46,869 - pyskl - INFO - Epoch [16][300/1281] lr: 2.437e-02, eta: 10:28:17, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7906, top5_acc: 0.9900, loss_cls: 0.8857, loss: 0.8857 +2025-06-24 10:10:08,787 - pyskl - INFO - Epoch [16][400/1281] lr: 2.436e-02, eta: 10:27:55, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7769, top5_acc: 0.9844, loss_cls: 0.9673, loss: 0.9673 +2025-06-24 10:10:30,530 - pyskl - INFO - Epoch [16][500/1281] lr: 2.436e-02, eta: 10:27:33, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7738, top5_acc: 0.9844, loss_cls: 0.9575, loss: 0.9575 +2025-06-24 10:10:52,237 - pyskl - INFO - Epoch [16][600/1281] lr: 2.435e-02, eta: 10:27:10, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7950, top5_acc: 0.9850, loss_cls: 0.9288, loss: 0.9288 +2025-06-24 10:11:14,156 - pyskl - INFO - Epoch [16][700/1281] lr: 2.434e-02, eta: 10:26:49, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7756, top5_acc: 0.9888, loss_cls: 0.9156, loss: 0.9156 +2025-06-24 10:11:35,725 - pyskl - INFO - Epoch [16][800/1281] lr: 2.434e-02, eta: 10:26:25, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7694, top5_acc: 0.9825, loss_cls: 0.9453, loss: 0.9453 +2025-06-24 10:11:57,617 - pyskl - INFO - Epoch [16][900/1281] lr: 2.433e-02, eta: 10:26:03, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7656, top5_acc: 0.9825, loss_cls: 0.9752, loss: 0.9752 +2025-06-24 10:12:19,394 - pyskl - INFO - Epoch [16][1000/1281] lr: 2.432e-02, eta: 10:25:41, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7744, top5_acc: 0.9794, loss_cls: 0.9914, loss: 0.9914 +2025-06-24 10:12:41,495 - pyskl - INFO - Epoch [16][1100/1281] lr: 2.432e-02, eta: 10:25:21, time: 0.221, data_time: 0.001, memory: 4082, top1_acc: 0.7738, top5_acc: 0.9862, loss_cls: 0.9561, loss: 0.9561 +2025-06-24 10:13:03,378 - pyskl - INFO - Epoch [16][1200/1281] lr: 2.431e-02, eta: 10:25:00, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7750, top5_acc: 0.9831, loss_cls: 0.9426, loss: 0.9426 +2025-06-24 10:13:21,480 - pyskl - INFO - Saving checkpoint at 16 epochs +2025-06-24 10:14:04,372 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:14:04,439 - pyskl - INFO - +top1_acc 0.7119 +top5_acc 0.9727 +2025-06-24 10:14:04,439 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:14:04,448 - pyskl - INFO - +mean_acc 0.6005 +2025-06-24 10:14:04,450 - pyskl - INFO - Epoch(val) [16][533] top1_acc: 0.7119, top5_acc: 0.9727, mean_class_accuracy: 0.6005 +2025-06-24 10:14:46,223 - pyskl - INFO - Epoch [17][100/1281] lr: 2.430e-02, eta: 10:24:39, time: 0.418, data_time: 0.193, memory: 4082, top1_acc: 0.7756, top5_acc: 0.9831, loss_cls: 1.0174, loss: 1.0174 +2025-06-24 10:15:13,258 - pyskl - INFO - Epoch [17][200/1281] lr: 2.429e-02, eta: 10:25:00, time: 0.270, data_time: 0.000, memory: 4082, top1_acc: 0.7744, top5_acc: 0.9862, loss_cls: 0.9404, loss: 0.9404 +2025-06-24 10:15:55,562 - pyskl - INFO - Epoch [17][300/1281] lr: 2.428e-02, eta: 10:27:27, time: 0.423, data_time: 0.000, memory: 4082, top1_acc: 0.7788, top5_acc: 0.9850, loss_cls: 0.9263, loss: 0.9263 +2025-06-24 10:16:37,202 - pyskl - INFO - Epoch [17][400/1281] lr: 2.428e-02, eta: 10:29:46, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7963, top5_acc: 0.9888, loss_cls: 0.8863, loss: 0.8863 +2025-06-24 10:17:18,793 - pyskl - INFO - Epoch [17][500/1281] lr: 2.427e-02, eta: 10:32:03, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9875, loss_cls: 0.8702, loss: 0.8702 +2025-06-24 10:18:00,259 - pyskl - INFO - Epoch [17][600/1281] lr: 2.426e-02, eta: 10:34:17, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9844, loss_cls: 0.8832, loss: 0.8832 +2025-06-24 10:18:41,724 - pyskl - INFO - Epoch [17][700/1281] lr: 2.426e-02, eta: 10:36:30, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8025, top5_acc: 0.9856, loss_cls: 0.9164, loss: 0.9164 +2025-06-24 10:19:23,346 - pyskl - INFO - Epoch [17][800/1281] lr: 2.425e-02, eta: 10:38:42, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7850, top5_acc: 0.9819, loss_cls: 0.9154, loss: 0.9154 +2025-06-24 10:20:05,004 - pyskl - INFO - Epoch [17][900/1281] lr: 2.424e-02, eta: 10:40:53, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7831, top5_acc: 0.9869, loss_cls: 0.9315, loss: 0.9315 +2025-06-24 10:20:46,510 - pyskl - INFO - Epoch [17][1000/1281] lr: 2.424e-02, eta: 10:43:02, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7719, top5_acc: 0.9869, loss_cls: 0.9386, loss: 0.9386 +2025-06-24 10:21:27,961 - pyskl - INFO - Epoch [17][1100/1281] lr: 2.423e-02, eta: 10:45:08, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7950, top5_acc: 0.9856, loss_cls: 0.8860, loss: 0.8860 +2025-06-24 10:22:09,729 - pyskl - INFO - Epoch [17][1200/1281] lr: 2.422e-02, eta: 10:47:15, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7719, top5_acc: 0.9812, loss_cls: 0.9912, loss: 0.9912 +2025-06-24 10:22:44,147 - pyskl - INFO - Saving checkpoint at 17 epochs +2025-06-24 10:23:49,145 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:23:49,202 - pyskl - INFO - +top1_acc 0.6667 +top5_acc 0.9461 +2025-06-24 10:23:49,203 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:23:49,210 - pyskl - INFO - +mean_acc 0.5837 +2025-06-24 10:23:49,212 - pyskl - INFO - Epoch(val) [17][533] top1_acc: 0.6667, top5_acc: 0.9461, mean_class_accuracy: 0.5837 +2025-06-24 10:24:49,816 - pyskl - INFO - Epoch [18][100/1281] lr: 2.421e-02, eta: 10:49:04, time: 0.606, data_time: 0.193, memory: 4082, top1_acc: 0.8044, top5_acc: 0.9888, loss_cls: 0.8737, loss: 0.8737 +2025-06-24 10:25:15,653 - pyskl - INFO - Epoch [18][200/1281] lr: 2.420e-02, eta: 10:49:04, time: 0.258, data_time: 0.000, memory: 4082, top1_acc: 0.7863, top5_acc: 0.9869, loss_cls: 0.8759, loss: 0.8759 +2025-06-24 10:25:57,135 - pyskl - INFO - Epoch [18][300/1281] lr: 2.419e-02, eta: 10:51:05, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7987, top5_acc: 0.9888, loss_cls: 0.8703, loss: 0.8703 +2025-06-24 10:26:38,855 - pyskl - INFO - Epoch [18][400/1281] lr: 2.419e-02, eta: 10:53:05, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7900, top5_acc: 0.9838, loss_cls: 0.9063, loss: 0.9063 +2025-06-24 10:27:20,465 - pyskl - INFO - Epoch [18][500/1281] lr: 2.418e-02, eta: 10:55:04, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9894, loss_cls: 0.8280, loss: 0.8280 +2025-06-24 10:28:02,023 - pyskl - INFO - Epoch [18][600/1281] lr: 2.417e-02, eta: 10:57:01, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7844, top5_acc: 0.9856, loss_cls: 0.9381, loss: 0.9381 +2025-06-24 10:28:43,698 - pyskl - INFO - Epoch [18][700/1281] lr: 2.417e-02, eta: 10:58:57, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7863, top5_acc: 0.9844, loss_cls: 0.9112, loss: 0.9112 +2025-06-24 10:29:25,593 - pyskl - INFO - Epoch [18][800/1281] lr: 2.416e-02, eta: 11:00:53, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7856, top5_acc: 0.9838, loss_cls: 0.9233, loss: 0.9233 +2025-06-24 10:30:07,430 - pyskl - INFO - Epoch [18][900/1281] lr: 2.415e-02, eta: 11:02:47, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7781, top5_acc: 0.9800, loss_cls: 0.9708, loss: 0.9708 +2025-06-24 10:30:48,994 - pyskl - INFO - Epoch [18][1000/1281] lr: 2.414e-02, eta: 11:04:39, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7981, top5_acc: 0.9906, loss_cls: 0.8662, loss: 0.8662 +2025-06-24 10:31:31,696 - pyskl - INFO - Epoch [18][1100/1281] lr: 2.414e-02, eta: 11:06:37, time: 0.427, data_time: 0.000, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9819, loss_cls: 0.8876, loss: 0.8876 +2025-06-24 10:32:15,097 - pyskl - INFO - Epoch [18][1200/1281] lr: 2.413e-02, eta: 11:08:39, time: 0.434, data_time: 0.000, memory: 4082, top1_acc: 0.7937, top5_acc: 0.9831, loss_cls: 0.9129, loss: 0.9129 +2025-06-24 10:32:49,562 - pyskl - INFO - Saving checkpoint at 18 epochs +2025-06-24 10:33:56,314 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:33:56,373 - pyskl - INFO - +top1_acc 0.7568 +top5_acc 0.9792 +2025-06-24 10:33:56,373 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:33:56,380 - pyskl - INFO - +mean_acc 0.6375 +2025-06-24 10:33:56,384 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_12.pth was removed +2025-06-24 10:33:56,572 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_18.pth. +2025-06-24 10:33:56,572 - pyskl - INFO - Best top1_acc is 0.7568 at 18 epoch. +2025-06-24 10:33:56,574 - pyskl - INFO - Epoch(val) [18][533] top1_acc: 0.7568, top5_acc: 0.9792, mean_class_accuracy: 0.6375 +2025-06-24 10:34:58,914 - pyskl - INFO - Epoch [19][100/1281] lr: 2.411e-02, eta: 11:10:17, time: 0.623, data_time: 0.197, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9881, loss_cls: 0.8839, loss: 0.8839 +2025-06-24 10:35:24,978 - pyskl - INFO - Epoch [19][200/1281] lr: 2.411e-02, eta: 11:10:10, time: 0.261, data_time: 0.000, memory: 4082, top1_acc: 0.8069, top5_acc: 0.9894, loss_cls: 0.8462, loss: 0.8462 +2025-06-24 10:36:08,460 - pyskl - INFO - Epoch [19][300/1281] lr: 2.410e-02, eta: 11:12:08, time: 0.435, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9881, loss_cls: 0.8308, loss: 0.8308 +2025-06-24 10:36:50,063 - pyskl - INFO - Epoch [19][400/1281] lr: 2.409e-02, eta: 11:13:52, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7725, top5_acc: 0.9844, loss_cls: 0.9563, loss: 0.9563 +2025-06-24 10:37:31,506 - pyskl - INFO - Epoch [19][500/1281] lr: 2.408e-02, eta: 11:15:33, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7906, top5_acc: 0.9888, loss_cls: 0.9069, loss: 0.9069 +2025-06-24 10:38:12,902 - pyskl - INFO - Epoch [19][600/1281] lr: 2.408e-02, eta: 11:17:12, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9881, loss_cls: 0.8737, loss: 0.8737 +2025-06-24 10:38:54,540 - pyskl - INFO - Epoch [19][700/1281] lr: 2.407e-02, eta: 11:18:53, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7694, top5_acc: 0.9838, loss_cls: 0.9630, loss: 0.9630 +2025-06-24 10:39:36,052 - pyskl - INFO - Epoch [19][800/1281] lr: 2.406e-02, eta: 11:20:31, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7887, top5_acc: 0.9888, loss_cls: 0.9056, loss: 0.9056 +2025-06-24 10:40:17,465 - pyskl - INFO - Epoch [19][900/1281] lr: 2.405e-02, eta: 11:22:07, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7963, top5_acc: 0.9850, loss_cls: 0.9195, loss: 0.9195 +2025-06-24 10:40:58,993 - pyskl - INFO - Epoch [19][1000/1281] lr: 2.405e-02, eta: 11:23:43, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7869, top5_acc: 0.9850, loss_cls: 0.9122, loss: 0.9122 +2025-06-24 10:41:40,453 - pyskl - INFO - Epoch [19][1100/1281] lr: 2.404e-02, eta: 11:25:17, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7837, top5_acc: 0.9819, loss_cls: 0.9331, loss: 0.9331 +2025-06-24 10:42:22,814 - pyskl - INFO - Epoch [19][1200/1281] lr: 2.403e-02, eta: 11:26:56, time: 0.424, data_time: 0.000, memory: 4082, top1_acc: 0.8050, top5_acc: 0.9906, loss_cls: 0.8777, loss: 0.8777 +2025-06-24 10:42:56,979 - pyskl - INFO - Saving checkpoint at 19 epochs +2025-06-24 10:44:04,307 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:44:04,362 - pyskl - INFO - +top1_acc 0.7385 +top5_acc 0.9741 +2025-06-24 10:44:04,362 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:44:04,368 - pyskl - INFO - +mean_acc 0.6238 +2025-06-24 10:44:04,370 - pyskl - INFO - Epoch(val) [19][533] top1_acc: 0.7385, top5_acc: 0.9741, mean_class_accuracy: 0.6238 +2025-06-24 10:45:07,103 - pyskl - INFO - Epoch [20][100/1281] lr: 2.402e-02, eta: 11:28:17, time: 0.627, data_time: 0.191, memory: 4082, top1_acc: 0.7825, top5_acc: 0.9881, loss_cls: 0.9395, loss: 0.9395 +2025-06-24 10:45:31,506 - pyskl - INFO - Epoch [20][200/1281] lr: 2.401e-02, eta: 11:27:51, time: 0.244, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9862, loss_cls: 0.8542, loss: 0.8542 +2025-06-24 10:46:13,005 - pyskl - INFO - Epoch [20][300/1281] lr: 2.400e-02, eta: 11:29:21, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9912, loss_cls: 0.8206, loss: 0.8206 +2025-06-24 10:46:54,479 - pyskl - INFO - Epoch [20][400/1281] lr: 2.399e-02, eta: 11:30:50, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7981, top5_acc: 0.9838, loss_cls: 0.8874, loss: 0.8874 +2025-06-24 10:47:35,966 - pyskl - INFO - Epoch [20][500/1281] lr: 2.398e-02, eta: 11:32:18, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7837, top5_acc: 0.9938, loss_cls: 0.8716, loss: 0.8716 +2025-06-24 10:48:17,464 - pyskl - INFO - Epoch [20][600/1281] lr: 2.398e-02, eta: 11:33:45, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8025, top5_acc: 0.9869, loss_cls: 0.8930, loss: 0.8930 +2025-06-24 10:48:59,087 - pyskl - INFO - Epoch [20][700/1281] lr: 2.397e-02, eta: 11:35:12, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8037, top5_acc: 0.9844, loss_cls: 0.8750, loss: 0.8750 +2025-06-24 10:49:40,617 - pyskl - INFO - Epoch [20][800/1281] lr: 2.396e-02, eta: 11:36:37, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7706, top5_acc: 0.9838, loss_cls: 0.9834, loss: 0.9834 +2025-06-24 10:50:22,197 - pyskl - INFO - Epoch [20][900/1281] lr: 2.395e-02, eta: 11:38:01, time: 0.416, data_time: 0.001, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9906, loss_cls: 0.8940, loss: 0.8940 +2025-06-24 10:51:03,709 - pyskl - INFO - Epoch [20][1000/1281] lr: 2.394e-02, eta: 11:39:24, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7950, top5_acc: 0.9838, loss_cls: 0.8915, loss: 0.8915 +2025-06-24 10:51:45,206 - pyskl - INFO - Epoch [20][1100/1281] lr: 2.393e-02, eta: 11:40:46, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7937, top5_acc: 0.9875, loss_cls: 0.8880, loss: 0.8880 +2025-06-24 10:52:26,844 - pyskl - INFO - Epoch [20][1200/1281] lr: 2.393e-02, eta: 11:42:08, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9838, loss_cls: 0.9069, loss: 0.9069 +2025-06-24 10:53:01,067 - pyskl - INFO - Saving checkpoint at 20 epochs +2025-06-24 10:54:09,722 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:54:09,791 - pyskl - INFO - +top1_acc 0.7730 +top5_acc 0.9846 +2025-06-24 10:54:09,791 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:54:09,799 - pyskl - INFO - +mean_acc 0.6669 +2025-06-24 10:54:09,804 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_18.pth was removed +2025-06-24 10:54:09,972 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_20.pth. +2025-06-24 10:54:09,972 - pyskl - INFO - Best top1_acc is 0.7730 at 20 epoch. +2025-06-24 10:54:09,974 - pyskl - INFO - Epoch(val) [20][533] top1_acc: 0.7730, top5_acc: 0.9846, mean_class_accuracy: 0.6669 +2025-06-24 10:55:14,994 - pyskl - INFO - Epoch [21][100/1281] lr: 2.391e-02, eta: 11:43:27, time: 0.650, data_time: 0.195, memory: 4082, top1_acc: 0.8313, top5_acc: 0.9938, loss_cls: 0.7533, loss: 0.7533 +2025-06-24 10:55:38,190 - pyskl - INFO - Epoch [21][200/1281] lr: 2.390e-02, eta: 11:42:47, time: 0.232, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9925, loss_cls: 0.7587, loss: 0.7587 +2025-06-24 10:56:22,040 - pyskl - INFO - Epoch [21][300/1281] lr: 2.389e-02, eta: 11:44:21, time: 0.438, data_time: 0.000, memory: 4082, top1_acc: 0.7981, top5_acc: 0.9888, loss_cls: 0.8998, loss: 0.8998 +2025-06-24 10:57:03,956 - pyskl - INFO - Epoch [21][400/1281] lr: 2.389e-02, eta: 11:45:40, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7869, top5_acc: 0.9825, loss_cls: 0.9104, loss: 0.9104 +2025-06-24 10:57:45,445 - pyskl - INFO - Epoch [21][500/1281] lr: 2.388e-02, eta: 11:46:57, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8063, top5_acc: 0.9881, loss_cls: 0.8292, loss: 0.8292 +2025-06-24 10:58:26,754 - pyskl - INFO - Epoch [21][600/1281] lr: 2.387e-02, eta: 11:48:11, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8075, top5_acc: 0.9869, loss_cls: 0.8538, loss: 0.8538 +2025-06-24 10:59:08,221 - pyskl - INFO - Epoch [21][700/1281] lr: 2.386e-02, eta: 11:49:25, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7906, top5_acc: 0.9838, loss_cls: 0.9226, loss: 0.9226 +2025-06-24 10:59:49,645 - pyskl - INFO - Epoch [21][800/1281] lr: 2.385e-02, eta: 11:50:38, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7844, top5_acc: 0.9819, loss_cls: 0.9185, loss: 0.9185 +2025-06-24 11:00:31,076 - pyskl - INFO - Epoch [21][900/1281] lr: 2.384e-02, eta: 11:51:51, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8075, top5_acc: 0.9825, loss_cls: 0.8361, loss: 0.8361 +2025-06-24 11:01:12,506 - pyskl - INFO - Epoch [21][1000/1281] lr: 2.383e-02, eta: 11:53:02, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9862, loss_cls: 0.8814, loss: 0.8814 +2025-06-24 11:01:54,098 - pyskl - INFO - Epoch [21][1100/1281] lr: 2.383e-02, eta: 11:54:14, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7906, top5_acc: 0.9862, loss_cls: 0.9329, loss: 0.9329 +2025-06-24 11:02:35,502 - pyskl - INFO - Epoch [21][1200/1281] lr: 2.382e-02, eta: 11:55:23, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7900, top5_acc: 0.9894, loss_cls: 0.8773, loss: 0.8773 +2025-06-24 11:03:09,726 - pyskl - INFO - Saving checkpoint at 21 epochs +2025-06-24 11:04:20,541 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:04:20,608 - pyskl - INFO - +top1_acc 0.7564 +top5_acc 0.9789 +2025-06-24 11:04:20,608 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:04:20,617 - pyskl - INFO - +mean_acc 0.6414 +2025-06-24 11:04:20,619 - pyskl - INFO - Epoch(val) [21][533] top1_acc: 0.7564, top5_acc: 0.9789, mean_class_accuracy: 0.6414 +2025-06-24 11:05:25,627 - pyskl - INFO - Epoch [22][100/1281] lr: 2.380e-02, eta: 11:56:27, time: 0.650, data_time: 0.193, memory: 4082, top1_acc: 0.8025, top5_acc: 0.9881, loss_cls: 0.8558, loss: 0.8558 +2025-06-24 11:05:49,933 - pyskl - INFO - Epoch [22][200/1281] lr: 2.379e-02, eta: 11:55:50, time: 0.243, data_time: 0.001, memory: 4082, top1_acc: 0.8063, top5_acc: 0.9888, loss_cls: 0.8367, loss: 0.8367 +2025-06-24 11:06:31,495 - pyskl - INFO - Epoch [22][300/1281] lr: 2.378e-02, eta: 11:56:58, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9919, loss_cls: 0.8469, loss: 0.8469 +2025-06-24 11:07:13,121 - pyskl - INFO - Epoch [22][400/1281] lr: 2.378e-02, eta: 11:58:06, time: 0.416, data_time: 0.001, memory: 4082, top1_acc: 0.7931, top5_acc: 0.9862, loss_cls: 0.8580, loss: 0.8580 +2025-06-24 11:07:54,634 - pyskl - INFO - Epoch [22][500/1281] lr: 2.377e-02, eta: 11:59:12, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9919, loss_cls: 0.8634, loss: 0.8634 +2025-06-24 11:08:36,001 - pyskl - INFO - Epoch [22][600/1281] lr: 2.376e-02, eta: 12:00:17, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8069, top5_acc: 0.9875, loss_cls: 0.8747, loss: 0.8747 +2025-06-24 11:09:17,678 - pyskl - INFO - Epoch [22][700/1281] lr: 2.375e-02, eta: 12:01:23, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9875, loss_cls: 0.8566, loss: 0.8566 +2025-06-24 11:09:59,281 - pyskl - INFO - Epoch [22][800/1281] lr: 2.374e-02, eta: 12:02:27, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8013, top5_acc: 0.9888, loss_cls: 0.8667, loss: 0.8667 +2025-06-24 11:10:40,742 - pyskl - INFO - Epoch [22][900/1281] lr: 2.373e-02, eta: 12:03:30, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9925, loss_cls: 0.8084, loss: 0.8084 +2025-06-24 11:11:22,205 - pyskl - INFO - Epoch [22][1000/1281] lr: 2.372e-02, eta: 12:04:32, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8037, top5_acc: 0.9900, loss_cls: 0.8417, loss: 0.8417 +2025-06-24 11:12:05,532 - pyskl - INFO - Epoch [22][1100/1281] lr: 2.371e-02, eta: 12:05:45, time: 0.433, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9856, loss_cls: 0.8528, loss: 0.8528 +2025-06-24 11:12:47,724 - pyskl - INFO - Epoch [22][1200/1281] lr: 2.370e-02, eta: 12:06:50, time: 0.422, data_time: 0.000, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9912, loss_cls: 0.8333, loss: 0.8333 +2025-06-24 11:13:21,983 - pyskl - INFO - Saving checkpoint at 22 epochs +2025-06-24 11:14:32,779 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:14:32,834 - pyskl - INFO - +top1_acc 0.7497 +top5_acc 0.9748 +2025-06-24 11:14:32,834 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:14:32,841 - pyskl - INFO - +mean_acc 0.6816 +2025-06-24 11:14:32,842 - pyskl - INFO - Epoch(val) [22][533] top1_acc: 0.7497, top5_acc: 0.9748, mean_class_accuracy: 0.6816 +2025-06-24 11:15:38,296 - pyskl - INFO - Epoch [23][100/1281] lr: 2.369e-02, eta: 12:07:42, time: 0.654, data_time: 0.198, memory: 4082, top1_acc: 0.8075, top5_acc: 0.9931, loss_cls: 0.8227, loss: 0.8227 +2025-06-24 11:16:01,351 - pyskl - INFO - Epoch [23][200/1281] lr: 2.368e-02, eta: 12:06:55, time: 0.231, data_time: 0.001, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9894, loss_cls: 0.7833, loss: 0.7833 +2025-06-24 11:16:42,700 - pyskl - INFO - Epoch [23][300/1281] lr: 2.367e-02, eta: 12:07:52, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8050, top5_acc: 0.9844, loss_cls: 0.8853, loss: 0.8853 +2025-06-24 11:17:24,151 - pyskl - INFO - Epoch [23][400/1281] lr: 2.366e-02, eta: 12:08:50, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7969, top5_acc: 0.9925, loss_cls: 0.8818, loss: 0.8818 +2025-06-24 11:18:05,484 - pyskl - INFO - Epoch [23][500/1281] lr: 2.365e-02, eta: 12:09:47, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9875, loss_cls: 0.8696, loss: 0.8696 +2025-06-24 11:18:46,829 - pyskl - INFO - Epoch [23][600/1281] lr: 2.364e-02, eta: 12:10:43, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8087, top5_acc: 0.9862, loss_cls: 0.8252, loss: 0.8252 +2025-06-24 11:19:28,569 - pyskl - INFO - Epoch [23][700/1281] lr: 2.363e-02, eta: 12:11:40, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9856, loss_cls: 0.8542, loss: 0.8542 +2025-06-24 11:20:10,032 - pyskl - INFO - Epoch [23][800/1281] lr: 2.362e-02, eta: 12:12:35, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8056, top5_acc: 0.9856, loss_cls: 0.8363, loss: 0.8363 +2025-06-24 11:20:51,488 - pyskl - INFO - Epoch [23][900/1281] lr: 2.361e-02, eta: 12:13:30, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8125, top5_acc: 0.9888, loss_cls: 0.8225, loss: 0.8225 +2025-06-24 11:21:32,950 - pyskl - INFO - Epoch [23][1000/1281] lr: 2.360e-02, eta: 12:14:23, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7769, top5_acc: 0.9831, loss_cls: 0.9352, loss: 0.9352 +2025-06-24 11:22:14,373 - pyskl - INFO - Epoch [23][1100/1281] lr: 2.359e-02, eta: 12:15:16, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9919, loss_cls: 0.8312, loss: 0.8312 +2025-06-24 11:22:55,873 - pyskl - INFO - Epoch [23][1200/1281] lr: 2.359e-02, eta: 12:16:09, time: 0.415, data_time: 0.001, memory: 4082, top1_acc: 0.8050, top5_acc: 0.9894, loss_cls: 0.8343, loss: 0.8343 +2025-06-24 11:23:30,313 - pyskl - INFO - Saving checkpoint at 23 epochs +2025-06-24 11:24:41,825 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:24:41,880 - pyskl - INFO - +top1_acc 0.7068 +top5_acc 0.9628 +2025-06-24 11:24:41,880 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:24:41,887 - pyskl - INFO - +mean_acc 0.6046 +2025-06-24 11:24:41,889 - pyskl - INFO - Epoch(val) [23][533] top1_acc: 0.7068, top5_acc: 0.9628, mean_class_accuracy: 0.6046 +2025-06-24 11:25:47,334 - pyskl - INFO - Epoch [24][100/1281] lr: 2.357e-02, eta: 12:16:50, time: 0.654, data_time: 0.198, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9875, loss_cls: 0.8733, loss: 0.8733 +2025-06-24 11:26:10,853 - pyskl - INFO - Epoch [24][200/1281] lr: 2.356e-02, eta: 12:16:02, time: 0.235, data_time: 0.000, memory: 4082, top1_acc: 0.8313, top5_acc: 0.9919, loss_cls: 0.7643, loss: 0.7643 +2025-06-24 11:26:51,766 - pyskl - INFO - Epoch [24][300/1281] lr: 2.355e-02, eta: 12:16:50, time: 0.409, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9906, loss_cls: 0.8293, loss: 0.8293 +2025-06-24 11:27:33,319 - pyskl - INFO - Epoch [24][400/1281] lr: 2.354e-02, eta: 12:17:41, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8050, top5_acc: 0.9850, loss_cls: 0.8745, loss: 0.8745 +2025-06-24 11:28:14,796 - pyskl - INFO - Epoch [24][500/1281] lr: 2.353e-02, eta: 12:18:30, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8206, top5_acc: 0.9869, loss_cls: 0.7930, loss: 0.7930 +2025-06-24 11:28:56,238 - pyskl - INFO - Epoch [24][600/1281] lr: 2.352e-02, eta: 12:19:19, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9888, loss_cls: 0.8022, loss: 0.8022 +2025-06-24 11:29:37,776 - pyskl - INFO - Epoch [24][700/1281] lr: 2.351e-02, eta: 12:20:08, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9906, loss_cls: 0.7850, loss: 0.7850 +2025-06-24 11:30:19,503 - pyskl - INFO - Epoch [24][800/1281] lr: 2.350e-02, eta: 12:20:57, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8144, top5_acc: 0.9900, loss_cls: 0.8224, loss: 0.8224 +2025-06-24 11:31:01,026 - pyskl - INFO - Epoch [24][900/1281] lr: 2.349e-02, eta: 12:21:45, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8163, top5_acc: 0.9869, loss_cls: 0.8279, loss: 0.8279 +2025-06-24 11:31:42,687 - pyskl - INFO - Epoch [24][1000/1281] lr: 2.348e-02, eta: 12:22:32, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8106, top5_acc: 0.9894, loss_cls: 0.8036, loss: 0.8036 +2025-06-24 11:32:24,270 - pyskl - INFO - Epoch [24][1100/1281] lr: 2.347e-02, eta: 12:23:19, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9894, loss_cls: 0.7973, loss: 0.7973 +2025-06-24 11:33:06,056 - pyskl - INFO - Epoch [24][1200/1281] lr: 2.346e-02, eta: 12:24:06, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7900, top5_acc: 0.9838, loss_cls: 0.8930, loss: 0.8930 +2025-06-24 11:33:41,307 - pyskl - INFO - Saving checkpoint at 24 epochs +2025-06-24 11:34:52,707 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:34:52,777 - pyskl - INFO - +top1_acc 0.7769 +top5_acc 0.9843 +2025-06-24 11:34:52,778 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:34:52,787 - pyskl - INFO - +mean_acc 0.6587 +2025-06-24 11:34:52,792 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_20.pth was removed +2025-06-24 11:34:52,991 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_24.pth. +2025-06-24 11:34:52,992 - pyskl - INFO - Best top1_acc is 0.7769 at 24 epoch. +2025-06-24 11:34:52,995 - pyskl - INFO - Epoch(val) [24][533] top1_acc: 0.7769, top5_acc: 0.9843, mean_class_accuracy: 0.6587 +2025-06-24 11:35:58,674 - pyskl - INFO - Epoch [25][100/1281] lr: 2.344e-02, eta: 12:24:38, time: 0.657, data_time: 0.201, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9888, loss_cls: 0.7768, loss: 0.7768 +2025-06-24 11:36:23,045 - pyskl - INFO - Epoch [25][200/1281] lr: 2.343e-02, eta: 12:23:53, time: 0.244, data_time: 0.000, memory: 4082, top1_acc: 0.8069, top5_acc: 0.9869, loss_cls: 0.8232, loss: 0.8232 +2025-06-24 11:37:03,857 - pyskl - INFO - Epoch [25][300/1281] lr: 2.342e-02, eta: 12:24:33, time: 0.408, data_time: 0.000, memory: 4082, top1_acc: 0.7963, top5_acc: 0.9875, loss_cls: 0.8525, loss: 0.8525 +2025-06-24 11:37:45,243 - pyskl - INFO - Epoch [25][400/1281] lr: 2.341e-02, eta: 12:25:16, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9906, loss_cls: 0.7770, loss: 0.7770 +2025-06-24 11:38:26,796 - pyskl - INFO - Epoch [25][500/1281] lr: 2.340e-02, eta: 12:25:59, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8087, top5_acc: 0.9844, loss_cls: 0.8405, loss: 0.8405 +2025-06-24 11:39:08,195 - pyskl - INFO - Epoch [25][600/1281] lr: 2.339e-02, eta: 12:26:41, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8069, top5_acc: 0.9856, loss_cls: 0.8353, loss: 0.8353 +2025-06-24 11:39:49,751 - pyskl - INFO - Epoch [25][700/1281] lr: 2.338e-02, eta: 12:27:23, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9919, loss_cls: 0.7696, loss: 0.7696 +2025-06-24 11:40:31,405 - pyskl - INFO - Epoch [25][800/1281] lr: 2.337e-02, eta: 12:28:05, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8163, top5_acc: 0.9894, loss_cls: 0.8115, loss: 0.8115 +2025-06-24 11:41:12,772 - pyskl - INFO - Epoch [25][900/1281] lr: 2.336e-02, eta: 12:28:45, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9900, loss_cls: 0.8255, loss: 0.8255 +2025-06-24 11:41:54,110 - pyskl - INFO - Epoch [25][1000/1281] lr: 2.335e-02, eta: 12:29:25, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8163, top5_acc: 0.9838, loss_cls: 0.8347, loss: 0.8347 +2025-06-24 11:42:35,434 - pyskl - INFO - Epoch [25][1100/1281] lr: 2.334e-02, eta: 12:30:04, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8237, top5_acc: 0.9875, loss_cls: 0.8063, loss: 0.8063 +2025-06-24 11:43:17,165 - pyskl - INFO - Epoch [25][1200/1281] lr: 2.333e-02, eta: 12:30:44, time: 0.417, data_time: 0.001, memory: 4082, top1_acc: 0.8050, top5_acc: 0.9912, loss_cls: 0.8335, loss: 0.8335 +2025-06-24 11:43:51,569 - pyskl - INFO - Saving checkpoint at 25 epochs +2025-06-24 11:45:03,706 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:45:03,764 - pyskl - INFO - +top1_acc 0.7627 +top5_acc 0.9792 +2025-06-24 11:45:03,764 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:45:03,772 - pyskl - INFO - +mean_acc 0.6636 +2025-06-24 11:45:03,775 - pyskl - INFO - Epoch(val) [25][533] top1_acc: 0.7627, top5_acc: 0.9792, mean_class_accuracy: 0.6636 +2025-06-24 11:46:09,459 - pyskl - INFO - Epoch [26][100/1281] lr: 2.332e-02, eta: 12:31:07, time: 0.657, data_time: 0.200, memory: 4082, top1_acc: 0.8263, top5_acc: 0.9888, loss_cls: 0.7979, loss: 0.7979 +2025-06-24 11:46:33,676 - pyskl - INFO - Epoch [26][200/1281] lr: 2.330e-02, eta: 12:30:19, time: 0.242, data_time: 0.000, memory: 4082, top1_acc: 0.8194, top5_acc: 0.9956, loss_cls: 0.7678, loss: 0.7678 +2025-06-24 11:47:14,289 - pyskl - INFO - Epoch [26][300/1281] lr: 2.329e-02, eta: 12:30:52, time: 0.406, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9919, loss_cls: 0.7522, loss: 0.7522 +2025-06-24 11:47:55,844 - pyskl - INFO - Epoch [26][400/1281] lr: 2.328e-02, eta: 12:31:30, time: 0.416, data_time: 0.001, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9888, loss_cls: 0.7759, loss: 0.7759 +2025-06-24 11:48:37,308 - pyskl - INFO - Epoch [26][500/1281] lr: 2.327e-02, eta: 12:32:07, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8231, top5_acc: 0.9825, loss_cls: 0.8117, loss: 0.8117 +2025-06-24 11:49:18,767 - pyskl - INFO - Epoch [26][600/1281] lr: 2.326e-02, eta: 12:32:43, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9869, loss_cls: 0.8568, loss: 0.8568 +2025-06-24 11:50:00,114 - pyskl - INFO - Epoch [26][700/1281] lr: 2.325e-02, eta: 12:33:18, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8263, top5_acc: 0.9856, loss_cls: 0.7791, loss: 0.7791 +2025-06-24 11:50:41,637 - pyskl - INFO - Epoch [26][800/1281] lr: 2.324e-02, eta: 12:33:54, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8213, top5_acc: 0.9844, loss_cls: 0.8275, loss: 0.8275 +2025-06-24 11:51:23,041 - pyskl - INFO - Epoch [26][900/1281] lr: 2.323e-02, eta: 12:34:28, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9912, loss_cls: 0.8049, loss: 0.8049 +2025-06-24 11:52:04,559 - pyskl - INFO - Epoch [26][1000/1281] lr: 2.322e-02, eta: 12:35:03, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9850, loss_cls: 0.8150, loss: 0.8150 +2025-06-24 11:52:46,122 - pyskl - INFO - Epoch [26][1100/1281] lr: 2.321e-02, eta: 12:35:37, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8125, top5_acc: 0.9919, loss_cls: 0.8172, loss: 0.8172 +2025-06-24 11:53:27,666 - pyskl - INFO - Epoch [26][1200/1281] lr: 2.320e-02, eta: 12:36:11, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9881, loss_cls: 0.7919, loss: 0.7919 +2025-06-24 11:54:02,020 - pyskl - INFO - Saving checkpoint at 26 epochs +2025-06-24 11:55:14,387 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:55:14,447 - pyskl - INFO - +top1_acc 0.7422 +top5_acc 0.9701 +2025-06-24 11:55:14,447 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:55:14,458 - pyskl - INFO - +mean_acc 0.6648 +2025-06-24 11:55:14,462 - pyskl - INFO - Epoch(val) [26][533] top1_acc: 0.7422, top5_acc: 0.9701, mean_class_accuracy: 0.6648 +2025-06-24 11:56:19,877 - pyskl - INFO - Epoch [27][100/1281] lr: 2.318e-02, eta: 12:36:25, time: 0.654, data_time: 0.199, memory: 4082, top1_acc: 0.8213, top5_acc: 0.9919, loss_cls: 0.7712, loss: 0.7712 +2025-06-24 11:56:44,012 - pyskl - INFO - Epoch [27][200/1281] lr: 2.317e-02, eta: 12:35:35, time: 0.241, data_time: 0.001, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9906, loss_cls: 0.7784, loss: 0.7784 +2025-06-24 11:57:24,719 - pyskl - INFO - Epoch [27][300/1281] lr: 2.316e-02, eta: 12:36:04, time: 0.407, data_time: 0.000, memory: 4082, top1_acc: 0.8131, top5_acc: 0.9900, loss_cls: 0.7898, loss: 0.7898 +2025-06-24 11:58:06,236 - pyskl - INFO - Epoch [27][400/1281] lr: 2.315e-02, eta: 12:36:36, time: 0.415, data_time: 0.001, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9912, loss_cls: 0.7448, loss: 0.7448 +2025-06-24 11:58:47,765 - pyskl - INFO - Epoch [27][500/1281] lr: 2.314e-02, eta: 12:37:07, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9875, loss_cls: 0.8130, loss: 0.8130 +2025-06-24 11:59:29,350 - pyskl - INFO - Epoch [27][600/1281] lr: 2.313e-02, eta: 12:37:39, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8213, top5_acc: 0.9894, loss_cls: 0.7623, loss: 0.7623 +2025-06-24 12:00:11,133 - pyskl - INFO - Epoch [27][700/1281] lr: 2.312e-02, eta: 12:38:11, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8087, top5_acc: 0.9869, loss_cls: 0.8317, loss: 0.8317 +2025-06-24 12:00:54,125 - pyskl - INFO - Epoch [27][800/1281] lr: 2.311e-02, eta: 12:38:48, time: 0.430, data_time: 0.000, memory: 4082, top1_acc: 0.7987, top5_acc: 0.9900, loss_cls: 0.8384, loss: 0.8384 +2025-06-24 12:01:37,779 - pyskl - INFO - Epoch [27][900/1281] lr: 2.310e-02, eta: 12:39:28, time: 0.437, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9894, loss_cls: 0.7672, loss: 0.7672 +2025-06-24 12:02:19,329 - pyskl - INFO - Epoch [27][1000/1281] lr: 2.308e-02, eta: 12:39:57, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9912, loss_cls: 0.8331, loss: 0.8331 +2025-06-24 12:03:00,930 - pyskl - INFO - Epoch [27][1100/1281] lr: 2.307e-02, eta: 12:40:27, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9850, loss_cls: 0.8486, loss: 0.8486 +2025-06-24 12:03:42,401 - pyskl - INFO - Epoch [27][1200/1281] lr: 2.306e-02, eta: 12:40:55, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7856, top5_acc: 0.9862, loss_cls: 0.9079, loss: 0.9079 +2025-06-24 12:04:16,799 - pyskl - INFO - Saving checkpoint at 27 epochs +2025-06-24 12:05:28,434 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:05:28,503 - pyskl - INFO - +top1_acc 0.7552 +top5_acc 0.9728 +2025-06-24 12:05:28,503 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:05:28,518 - pyskl - INFO - +mean_acc 0.6561 +2025-06-24 12:05:28,521 - pyskl - INFO - Epoch(val) [27][533] top1_acc: 0.7552, top5_acc: 0.9728, mean_class_accuracy: 0.6561 +2025-06-24 12:06:33,698 - pyskl - INFO - Epoch [28][100/1281] lr: 2.304e-02, eta: 12:41:01, time: 0.652, data_time: 0.196, memory: 4082, top1_acc: 0.8237, top5_acc: 0.9862, loss_cls: 0.8065, loss: 0.8065 +2025-06-24 12:06:58,320 - pyskl - INFO - Epoch [28][200/1281] lr: 2.303e-02, eta: 12:40:12, time: 0.246, data_time: 0.000, memory: 4082, top1_acc: 0.8200, top5_acc: 0.9888, loss_cls: 0.7640, loss: 0.7640 +2025-06-24 12:07:41,330 - pyskl - INFO - Epoch [28][300/1281] lr: 2.302e-02, eta: 12:40:46, time: 0.430, data_time: 0.000, memory: 4082, top1_acc: 0.8275, top5_acc: 0.9912, loss_cls: 0.7463, loss: 0.7463 +2025-06-24 12:08:22,821 - pyskl - INFO - Epoch [28][400/1281] lr: 2.301e-02, eta: 12:41:13, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9919, loss_cls: 0.7741, loss: 0.7741 +2025-06-24 12:09:04,161 - pyskl - INFO - Epoch [28][500/1281] lr: 2.300e-02, eta: 12:41:39, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9912, loss_cls: 0.7296, loss: 0.7296 +2025-06-24 12:09:45,594 - pyskl - INFO - Epoch [28][600/1281] lr: 2.299e-02, eta: 12:42:05, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8200, top5_acc: 0.9931, loss_cls: 0.7863, loss: 0.7863 +2025-06-24 12:10:26,940 - pyskl - INFO - Epoch [28][700/1281] lr: 2.298e-02, eta: 12:42:30, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8263, top5_acc: 0.9906, loss_cls: 0.8068, loss: 0.8068 +2025-06-24 12:11:08,337 - pyskl - INFO - Epoch [28][800/1281] lr: 2.297e-02, eta: 12:42:55, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8344, top5_acc: 0.9888, loss_cls: 0.7342, loss: 0.7342 +2025-06-24 12:11:49,693 - pyskl - INFO - Epoch [28][900/1281] lr: 2.295e-02, eta: 12:43:19, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9925, loss_cls: 0.7966, loss: 0.7966 +2025-06-24 12:12:31,340 - pyskl - INFO - Epoch [28][1000/1281] lr: 2.294e-02, eta: 12:43:45, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9931, loss_cls: 0.7915, loss: 0.7915 +2025-06-24 12:13:12,766 - pyskl - INFO - Epoch [28][1100/1281] lr: 2.293e-02, eta: 12:44:09, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9900, loss_cls: 0.8163, loss: 0.8163 +2025-06-24 12:13:54,218 - pyskl - INFO - Epoch [28][1200/1281] lr: 2.292e-02, eta: 12:44:33, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9919, loss_cls: 0.7578, loss: 0.7578 +2025-06-24 12:14:28,523 - pyskl - INFO - Saving checkpoint at 28 epochs +2025-06-24 12:15:39,157 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:15:39,214 - pyskl - INFO - +top1_acc 0.7873 +top5_acc 0.9835 +2025-06-24 12:15:39,214 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:15:39,223 - pyskl - INFO - +mean_acc 0.6946 +2025-06-24 12:15:39,228 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_24.pth was removed +2025-06-24 12:15:39,413 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_28.pth. +2025-06-24 12:15:39,413 - pyskl - INFO - Best top1_acc is 0.7873 at 28 epoch. +2025-06-24 12:15:39,416 - pyskl - INFO - Epoch(val) [28][533] top1_acc: 0.7873, top5_acc: 0.9835, mean_class_accuracy: 0.6946 +2025-06-24 12:16:44,937 - pyskl - INFO - Epoch [29][100/1281] lr: 2.290e-02, eta: 12:44:33, time: 0.655, data_time: 0.200, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9919, loss_cls: 0.7503, loss: 0.7503 +2025-06-24 12:17:10,272 - pyskl - INFO - Epoch [29][200/1281] lr: 2.289e-02, eta: 12:43:47, time: 0.253, data_time: 0.001, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9875, loss_cls: 0.7995, loss: 0.7995 +2025-06-24 12:17:50,687 - pyskl - INFO - Epoch [29][300/1281] lr: 2.288e-02, eta: 12:44:05, time: 0.404, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9912, loss_cls: 0.7361, loss: 0.7361 +2025-06-24 12:18:32,257 - pyskl - INFO - Epoch [29][400/1281] lr: 2.287e-02, eta: 12:44:28, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8206, top5_acc: 0.9931, loss_cls: 0.7929, loss: 0.7929 +2025-06-24 12:19:13,804 - pyskl - INFO - Epoch [29][500/1281] lr: 2.285e-02, eta: 12:44:50, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9919, loss_cls: 0.7788, loss: 0.7788 +2025-06-24 12:19:55,288 - pyskl - INFO - Epoch [29][600/1281] lr: 2.284e-02, eta: 12:45:12, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8275, top5_acc: 0.9862, loss_cls: 0.7686, loss: 0.7686 +2025-06-24 12:20:36,697 - pyskl - INFO - Epoch [29][700/1281] lr: 2.283e-02, eta: 12:45:33, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8013, top5_acc: 0.9825, loss_cls: 0.8200, loss: 0.8200 +2025-06-24 12:21:18,483 - pyskl - INFO - Epoch [29][800/1281] lr: 2.282e-02, eta: 12:45:56, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8231, top5_acc: 0.9944, loss_cls: 0.7652, loss: 0.7652 +2025-06-24 12:21:59,953 - pyskl - INFO - Epoch [29][900/1281] lr: 2.281e-02, eta: 12:46:17, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8344, top5_acc: 0.9869, loss_cls: 0.8076, loss: 0.8076 +2025-06-24 12:22:41,486 - pyskl - INFO - Epoch [29][1000/1281] lr: 2.280e-02, eta: 12:46:37, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9925, loss_cls: 0.7824, loss: 0.7824 +2025-06-24 12:23:22,927 - pyskl - INFO - Epoch [29][1100/1281] lr: 2.279e-02, eta: 12:46:57, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.8063, top5_acc: 0.9856, loss_cls: 0.8189, loss: 0.8189 +2025-06-24 12:24:04,378 - pyskl - INFO - Epoch [29][1200/1281] lr: 2.277e-02, eta: 12:47:17, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7963, top5_acc: 0.9931, loss_cls: 0.8617, loss: 0.8617 +2025-06-24 12:24:38,808 - pyskl - INFO - Saving checkpoint at 29 epochs +2025-06-24 12:25:49,749 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:25:49,807 - pyskl - INFO - +top1_acc 0.7476 +top5_acc 0.9708 +2025-06-24 12:25:49,807 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:25:49,814 - pyskl - INFO - +mean_acc 0.6470 +2025-06-24 12:25:49,817 - pyskl - INFO - Epoch(val) [29][533] top1_acc: 0.7476, top5_acc: 0.9708, mean_class_accuracy: 0.6470 +2025-06-24 12:26:59,458 - pyskl - INFO - Epoch [30][100/1281] lr: 2.275e-02, eta: 12:47:29, time: 0.696, data_time: 0.200, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9919, loss_cls: 0.7739, loss: 0.7739 +2025-06-24 12:27:35,780 - pyskl - INFO - Epoch [30][200/1281] lr: 2.274e-02, eta: 12:47:27, time: 0.363, data_time: 0.000, memory: 4082, top1_acc: 0.8525, top5_acc: 0.9938, loss_cls: 0.6941, loss: 0.6941 +2025-06-24 12:28:24,248 - pyskl - INFO - Epoch [30][300/1281] lr: 2.273e-02, eta: 12:48:15, time: 0.485, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9950, loss_cls: 0.7296, loss: 0.7296 +2025-06-24 12:29:13,648 - pyskl - INFO - Epoch [30][400/1281] lr: 2.272e-02, eta: 12:49:06, time: 0.494, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9912, loss_cls: 0.7331, loss: 0.7331 +2025-06-24 12:30:01,717 - pyskl - INFO - Epoch [30][500/1281] lr: 2.271e-02, eta: 12:49:50, time: 0.481, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9944, loss_cls: 0.7444, loss: 0.7444 +2025-06-24 12:30:50,615 - pyskl - INFO - Epoch [30][600/1281] lr: 2.269e-02, eta: 12:50:38, time: 0.489, data_time: 0.000, memory: 4082, top1_acc: 0.8163, top5_acc: 0.9888, loss_cls: 0.7789, loss: 0.7789 +2025-06-24 12:31:39,348 - pyskl - INFO - Epoch [30][700/1281] lr: 2.268e-02, eta: 12:51:25, time: 0.487, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9912, loss_cls: 0.7858, loss: 0.7858 +2025-06-24 12:32:27,295 - pyskl - INFO - Epoch [30][800/1281] lr: 2.267e-02, eta: 12:52:08, time: 0.479, data_time: 0.000, memory: 4082, top1_acc: 0.8175, top5_acc: 0.9906, loss_cls: 0.7961, loss: 0.7961 +2025-06-24 12:33:16,202 - pyskl - INFO - Epoch [30][900/1281] lr: 2.266e-02, eta: 12:52:54, time: 0.489, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9888, loss_cls: 0.7973, loss: 0.7973 +2025-06-24 12:34:05,906 - pyskl - INFO - Epoch [30][1000/1281] lr: 2.265e-02, eta: 12:53:43, time: 0.497, data_time: 0.000, memory: 4082, top1_acc: 0.8194, top5_acc: 0.9900, loss_cls: 0.7802, loss: 0.7802 +2025-06-24 12:34:54,488 - pyskl - INFO - Epoch [30][1100/1281] lr: 2.263e-02, eta: 12:54:27, time: 0.486, data_time: 0.000, memory: 4082, top1_acc: 0.8175, top5_acc: 0.9875, loss_cls: 0.7847, loss: 0.7847 +2025-06-24 12:35:42,590 - pyskl - INFO - Epoch [30][1200/1281] lr: 2.262e-02, eta: 12:55:09, time: 0.481, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9944, loss_cls: 0.8163, loss: 0.8163 +2025-06-24 12:36:04,333 - pyskl - INFO - Saving checkpoint at 30 epochs +2025-06-24 12:36:54,708 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:36:54,771 - pyskl - INFO - +top1_acc 0.8141 +top5_acc 0.9867 +2025-06-24 12:36:54,771 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:36:54,782 - pyskl - INFO - +mean_acc 0.7317 +2025-06-24 12:36:54,787 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_28.pth was removed +2025-06-24 12:36:54,962 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_30.pth. +2025-06-24 12:36:54,963 - pyskl - INFO - Best top1_acc is 0.8141 at 30 epoch. +2025-06-24 12:36:54,965 - pyskl - INFO - Epoch(val) [30][533] top1_acc: 0.8141, top5_acc: 0.9867, mean_class_accuracy: 0.7317 +2025-06-24 12:38:19,271 - pyskl - INFO - Epoch [31][100/1281] lr: 2.260e-02, eta: 12:56:12, time: 0.843, data_time: 0.193, memory: 4083, top1_acc: 0.8375, top5_acc: 0.9919, loss_cls: 0.8863, loss: 0.8863 +2025-06-24 12:39:10,587 - pyskl - INFO - Epoch [31][200/1281] lr: 2.259e-02, eta: 12:57:05, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8181, top5_acc: 0.9938, loss_cls: 0.9130, loss: 0.9130 +2025-06-24 12:40:02,926 - pyskl - INFO - Epoch [31][300/1281] lr: 2.258e-02, eta: 12:58:02, time: 0.523, data_time: 0.000, memory: 4083, top1_acc: 0.8131, top5_acc: 0.9906, loss_cls: 0.9577, loss: 0.9577 +2025-06-24 12:40:52,424 - pyskl - INFO - Epoch [31][400/1281] lr: 2.256e-02, eta: 12:58:47, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8269, top5_acc: 0.9906, loss_cls: 0.9353, loss: 0.9353 +2025-06-24 12:41:41,538 - pyskl - INFO - Epoch [31][500/1281] lr: 2.255e-02, eta: 12:59:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8219, top5_acc: 0.9931, loss_cls: 0.9259, loss: 0.9259 +2025-06-24 12:42:32,590 - pyskl - INFO - Epoch [31][600/1281] lr: 2.254e-02, eta: 13:00:20, time: 0.511, data_time: 0.001, memory: 4083, top1_acc: 0.8163, top5_acc: 0.9894, loss_cls: 0.9791, loss: 0.9791 +2025-06-24 12:43:22,935 - pyskl - INFO - Epoch [31][700/1281] lr: 2.253e-02, eta: 13:01:06, time: 0.503, data_time: 0.000, memory: 4083, top1_acc: 0.8281, top5_acc: 0.9888, loss_cls: 0.8999, loss: 0.8999 +2025-06-24 12:44:14,179 - pyskl - INFO - Epoch [31][800/1281] lr: 2.252e-02, eta: 13:01:56, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8438, top5_acc: 0.9912, loss_cls: 0.8485, loss: 0.8485 +2025-06-24 12:44:55,400 - pyskl - INFO - Epoch [31][900/1281] lr: 2.250e-02, eta: 13:02:06, time: 0.412, data_time: 0.000, memory: 4083, top1_acc: 0.8287, top5_acc: 0.9875, loss_cls: 0.9250, loss: 0.9250 +2025-06-24 12:45:45,568 - pyskl - INFO - Epoch [31][1000/1281] lr: 2.249e-02, eta: 13:02:51, time: 0.502, data_time: 0.000, memory: 4083, top1_acc: 0.8406, top5_acc: 0.9906, loss_cls: 0.8643, loss: 0.8643 +2025-06-24 12:46:11,072 - pyskl - INFO - Epoch [31][1100/1281] lr: 2.248e-02, eta: 13:02:00, time: 0.255, data_time: 0.001, memory: 4083, top1_acc: 0.8344, top5_acc: 0.9888, loss_cls: 0.8831, loss: 0.8831 +2025-06-24 12:47:00,715 - pyskl - INFO - Epoch [31][1200/1281] lr: 2.247e-02, eta: 13:02:42, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8256, top5_acc: 0.9894, loss_cls: 0.9078, loss: 0.9078 +2025-06-24 12:47:42,705 - pyskl - INFO - Saving checkpoint at 31 epochs +2025-06-24 12:48:54,113 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:48:54,178 - pyskl - INFO - +top1_acc 0.7739 +top5_acc 0.9800 +2025-06-24 12:48:54,178 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:48:54,186 - pyskl - INFO - +mean_acc 0.6973 +2025-06-24 12:48:54,188 - pyskl - INFO - Epoch(val) [31][533] top1_acc: 0.7739, top5_acc: 0.9800, mean_class_accuracy: 0.6973 +2025-06-24 12:50:19,760 - pyskl - INFO - Epoch [32][100/1281] lr: 2.244e-02, eta: 13:03:40, time: 0.856, data_time: 0.190, memory: 4083, top1_acc: 0.8375, top5_acc: 0.9919, loss_cls: 0.8249, loss: 0.8249 +2025-06-24 12:51:10,773 - pyskl - INFO - Epoch [32][200/1281] lr: 2.243e-02, eta: 13:04:26, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8488, top5_acc: 0.9912, loss_cls: 0.8147, loss: 0.8147 +2025-06-24 12:52:01,773 - pyskl - INFO - Epoch [32][300/1281] lr: 2.242e-02, eta: 13:05:12, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8137, top5_acc: 0.9944, loss_cls: 0.9079, loss: 0.9079 +2025-06-24 12:52:52,126 - pyskl - INFO - Epoch [32][400/1281] lr: 2.241e-02, eta: 13:05:54, time: 0.504, data_time: 0.001, memory: 4083, top1_acc: 0.8337, top5_acc: 0.9906, loss_cls: 0.8159, loss: 0.8159 +2025-06-24 12:53:42,905 - pyskl - INFO - Epoch [32][500/1281] lr: 2.239e-02, eta: 13:06:38, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.8287, top5_acc: 0.9925, loss_cls: 0.8361, loss: 0.8361 +2025-06-24 12:54:11,986 - pyskl - INFO - Epoch [32][600/1281] lr: 2.238e-02, eta: 13:05:59, time: 0.291, data_time: 0.000, memory: 4083, top1_acc: 0.8331, top5_acc: 0.9912, loss_cls: 0.8410, loss: 0.8410 +2025-06-24 12:55:03,092 - pyskl - INFO - Epoch [32][700/1281] lr: 2.237e-02, eta: 13:06:44, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8250, top5_acc: 0.9900, loss_cls: 0.8362, loss: 0.8362 +2025-06-24 12:55:39,020 - pyskl - INFO - Epoch [32][800/1281] lr: 2.236e-02, eta: 13:06:31, time: 0.359, data_time: 0.000, memory: 4083, top1_acc: 0.8237, top5_acc: 0.9869, loss_cls: 0.9097, loss: 0.9097 +2025-06-24 12:56:30,345 - pyskl - INFO - Epoch [32][900/1281] lr: 2.234e-02, eta: 13:07:15, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8269, top5_acc: 0.9906, loss_cls: 0.8575, loss: 0.8575 +2025-06-24 12:57:20,581 - pyskl - INFO - Epoch [32][1000/1281] lr: 2.233e-02, eta: 13:07:55, time: 0.502, data_time: 0.000, memory: 4083, top1_acc: 0.8050, top5_acc: 0.9931, loss_cls: 0.9169, loss: 0.9169 +2025-06-24 12:58:12,013 - pyskl - INFO - Epoch [32][1100/1281] lr: 2.232e-02, eta: 13:08:38, time: 0.514, data_time: 0.000, memory: 4083, top1_acc: 0.8337, top5_acc: 0.9938, loss_cls: 0.8603, loss: 0.8603 +2025-06-24 12:59:03,224 - pyskl - INFO - Epoch [32][1200/1281] lr: 2.231e-02, eta: 13:09:21, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8175, top5_acc: 0.9894, loss_cls: 0.8949, loss: 0.8949 +2025-06-24 12:59:46,258 - pyskl - INFO - Saving checkpoint at 32 epochs +2025-06-24 13:00:57,561 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:00:57,635 - pyskl - INFO - +top1_acc 0.7689 +top5_acc 0.9812 +2025-06-24 13:00:57,635 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:00:57,643 - pyskl - INFO - +mean_acc 0.7005 +2025-06-24 13:00:57,646 - pyskl - INFO - Epoch(val) [32][533] top1_acc: 0.7689, top5_acc: 0.9812, mean_class_accuracy: 0.7005 +2025-06-24 13:02:20,025 - pyskl - INFO - Epoch [33][100/1281] lr: 2.228e-02, eta: 13:09:59, time: 0.824, data_time: 0.194, memory: 4083, top1_acc: 0.8325, top5_acc: 0.9944, loss_cls: 0.7744, loss: 0.7744 +2025-06-24 13:03:05,037 - pyskl - INFO - Epoch [33][200/1281] lr: 2.227e-02, eta: 13:10:17, time: 0.450, data_time: 0.000, memory: 4083, top1_acc: 0.8444, top5_acc: 0.9925, loss_cls: 0.7667, loss: 0.7667 +2025-06-24 13:03:46,431 - pyskl - INFO - Epoch [33][300/1281] lr: 2.226e-02, eta: 13:10:22, time: 0.414, data_time: 0.000, memory: 4083, top1_acc: 0.8275, top5_acc: 0.9881, loss_cls: 0.8008, loss: 0.8008 +2025-06-24 13:04:18,795 - pyskl - INFO - Epoch [33][400/1281] lr: 2.225e-02, eta: 13:09:54, time: 0.324, data_time: 0.000, memory: 4083, top1_acc: 0.8069, top5_acc: 0.9856, loss_cls: 0.9251, loss: 0.9251 +2025-06-24 13:05:07,264 - pyskl - INFO - Epoch [33][500/1281] lr: 2.223e-02, eta: 13:10:25, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.8187, top5_acc: 0.9850, loss_cls: 0.8551, loss: 0.8551 +2025-06-24 13:05:57,663 - pyskl - INFO - Epoch [33][600/1281] lr: 2.222e-02, eta: 13:11:02, time: 0.504, data_time: 0.000, memory: 4083, top1_acc: 0.8419, top5_acc: 0.9950, loss_cls: 0.7880, loss: 0.7880 +2025-06-24 13:06:48,553 - pyskl - INFO - Epoch [33][700/1281] lr: 2.221e-02, eta: 13:11:40, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.8213, top5_acc: 0.9894, loss_cls: 0.8255, loss: 0.8255 +2025-06-24 13:07:38,522 - pyskl - INFO - Epoch [33][800/1281] lr: 2.219e-02, eta: 13:12:15, time: 0.500, data_time: 0.000, memory: 4083, top1_acc: 0.8306, top5_acc: 0.9906, loss_cls: 0.8248, loss: 0.8248 +2025-06-24 13:08:27,924 - pyskl - INFO - Epoch [33][900/1281] lr: 2.218e-02, eta: 13:12:47, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8325, top5_acc: 0.9925, loss_cls: 0.8022, loss: 0.8022 +2025-06-24 13:09:19,230 - pyskl - INFO - Epoch [33][1000/1281] lr: 2.217e-02, eta: 13:13:25, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8169, top5_acc: 0.9925, loss_cls: 0.8385, loss: 0.8385 +2025-06-24 13:10:10,512 - pyskl - INFO - Epoch [33][1100/1281] lr: 2.216e-02, eta: 13:14:03, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.7919, top5_acc: 0.9850, loss_cls: 0.9678, loss: 0.9678 +2025-06-24 13:11:00,652 - pyskl - INFO - Epoch [33][1200/1281] lr: 2.214e-02, eta: 13:14:37, time: 0.501, data_time: 0.000, memory: 4083, top1_acc: 0.8300, top5_acc: 0.9925, loss_cls: 0.7960, loss: 0.7960 +2025-06-24 13:11:40,469 - pyskl - INFO - Saving checkpoint at 33 epochs +2025-06-24 13:12:40,432 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:12:40,510 - pyskl - INFO - +top1_acc 0.7692 +top5_acc 0.9791 +2025-06-24 13:12:40,510 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:12:40,518 - pyskl - INFO - +mean_acc 0.6960 +2025-06-24 13:12:40,520 - pyskl - INFO - Epoch(val) [33][533] top1_acc: 0.7692, top5_acc: 0.9791, mean_class_accuracy: 0.6960 +2025-06-24 13:13:41,851 - pyskl - INFO - Epoch [34][100/1281] lr: 2.212e-02, eta: 13:13:53, time: 0.613, data_time: 0.194, memory: 4083, top1_acc: 0.8538, top5_acc: 0.9919, loss_cls: 0.7390, loss: 0.7390 +2025-06-24 13:14:32,071 - pyskl - INFO - Epoch [34][200/1281] lr: 2.211e-02, eta: 13:14:26, time: 0.502, data_time: 0.000, memory: 4083, top1_acc: 0.8300, top5_acc: 0.9944, loss_cls: 0.8127, loss: 0.8127 +2025-06-24 13:15:24,184 - pyskl - INFO - Epoch [34][300/1281] lr: 2.209e-02, eta: 13:15:05, time: 0.521, data_time: 0.000, memory: 4083, top1_acc: 0.8219, top5_acc: 0.9888, loss_cls: 0.8619, loss: 0.8619 +2025-06-24 13:16:16,045 - pyskl - INFO - Epoch [34][400/1281] lr: 2.208e-02, eta: 13:15:43, time: 0.519, data_time: 0.000, memory: 4083, top1_acc: 0.8512, top5_acc: 0.9912, loss_cls: 0.7634, loss: 0.7634 +2025-06-24 13:17:06,488 - pyskl - INFO - Epoch [34][500/1281] lr: 2.207e-02, eta: 13:16:16, time: 0.504, data_time: 0.001, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9900, loss_cls: 0.7792, loss: 0.7792 +2025-06-24 13:17:57,269 - pyskl - INFO - Epoch [34][600/1281] lr: 2.205e-02, eta: 13:16:49, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.8356, top5_acc: 0.9875, loss_cls: 0.8187, loss: 0.8187 +2025-06-24 13:18:47,605 - pyskl - INFO - Epoch [34][700/1281] lr: 2.204e-02, eta: 13:17:21, time: 0.503, data_time: 0.000, memory: 4083, top1_acc: 0.8281, top5_acc: 0.9938, loss_cls: 0.8155, loss: 0.8155 +2025-06-24 13:19:38,555 - pyskl - INFO - Epoch [34][800/1281] lr: 2.203e-02, eta: 13:17:54, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.8387, top5_acc: 0.9869, loss_cls: 0.8212, loss: 0.8212 +2025-06-24 13:20:29,439 - pyskl - INFO - Epoch [34][900/1281] lr: 2.201e-02, eta: 13:18:27, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.8337, top5_acc: 0.9938, loss_cls: 0.8016, loss: 0.8016 +2025-06-24 13:21:21,068 - pyskl - INFO - Epoch [34][1000/1281] lr: 2.200e-02, eta: 13:19:02, time: 0.516, data_time: 0.000, memory: 4083, top1_acc: 0.8325, top5_acc: 0.9962, loss_cls: 0.8013, loss: 0.8013 +2025-06-24 13:22:00,483 - pyskl - INFO - Epoch [34][1100/1281] lr: 2.199e-02, eta: 13:18:54, time: 0.394, data_time: 0.000, memory: 4083, top1_acc: 0.8369, top5_acc: 0.9850, loss_cls: 0.8381, loss: 0.8381 +2025-06-24 13:22:51,792 - pyskl - INFO - Epoch [34][1200/1281] lr: 2.197e-02, eta: 13:19:27, time: 0.513, data_time: 0.001, memory: 4083, top1_acc: 0.8250, top5_acc: 0.9888, loss_cls: 0.8308, loss: 0.8308 +2025-06-24 13:23:10,961 - pyskl - INFO - Saving checkpoint at 34 epochs +2025-06-24 13:24:22,364 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:24:22,420 - pyskl - INFO - +top1_acc 0.7750 +top5_acc 0.9743 +2025-06-24 13:24:22,420 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:24:22,427 - pyskl - INFO - +mean_acc 0.6839 +2025-06-24 13:24:22,429 - pyskl - INFO - Epoch(val) [34][533] top1_acc: 0.7750, top5_acc: 0.9743, mean_class_accuracy: 0.6839 +2025-06-24 13:25:45,576 - pyskl - INFO - Epoch [35][100/1281] lr: 2.195e-02, eta: 13:19:53, time: 0.831, data_time: 0.188, memory: 4083, top1_acc: 0.8306, top5_acc: 0.9938, loss_cls: 0.8022, loss: 0.8022 +2025-06-24 13:26:37,920 - pyskl - INFO - Epoch [35][200/1281] lr: 2.194e-02, eta: 13:20:29, time: 0.524, data_time: 0.001, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9906, loss_cls: 0.7330, loss: 0.7330 +2025-06-24 13:27:28,382 - pyskl - INFO - Epoch [35][300/1281] lr: 2.192e-02, eta: 13:20:58, time: 0.505, data_time: 0.000, memory: 4083, top1_acc: 0.8438, top5_acc: 0.9950, loss_cls: 0.7282, loss: 0.7282 +2025-06-24 13:28:20,878 - pyskl - INFO - Epoch [35][400/1281] lr: 2.191e-02, eta: 13:21:33, time: 0.525, data_time: 0.000, memory: 4083, top1_acc: 0.8319, top5_acc: 0.9931, loss_cls: 0.7768, loss: 0.7768 +2025-06-24 13:29:11,948 - pyskl - INFO - Epoch [35][500/1281] lr: 2.190e-02, eta: 13:22:03, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8413, top5_acc: 0.9938, loss_cls: 0.7487, loss: 0.7487 +2025-06-24 13:30:03,286 - pyskl - INFO - Epoch [35][600/1281] lr: 2.188e-02, eta: 13:22:34, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8469, top5_acc: 0.9912, loss_cls: 0.7457, loss: 0.7457 +2025-06-24 13:30:54,602 - pyskl - INFO - Epoch [35][700/1281] lr: 2.187e-02, eta: 13:23:04, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8294, top5_acc: 0.9900, loss_cls: 0.8205, loss: 0.8205 +2025-06-24 13:31:27,185 - pyskl - INFO - Epoch [35][800/1281] lr: 2.185e-02, eta: 13:22:31, time: 0.326, data_time: 0.000, memory: 4083, top1_acc: 0.8387, top5_acc: 0.9900, loss_cls: 0.8332, loss: 0.8332 +2025-06-24 13:32:08,474 - pyskl - INFO - Epoch [35][900/1281] lr: 2.184e-02, eta: 13:22:28, time: 0.413, data_time: 0.000, memory: 4083, top1_acc: 0.8313, top5_acc: 0.9931, loss_cls: 0.7849, loss: 0.7849 +2025-06-24 13:32:52,030 - pyskl - INFO - Epoch [35][1000/1281] lr: 2.183e-02, eta: 13:22:31, time: 0.436, data_time: 0.000, memory: 4083, top1_acc: 0.8369, top5_acc: 0.9856, loss_cls: 0.8131, loss: 0.8131 +2025-06-24 13:33:44,991 - pyskl - INFO - Epoch [35][1100/1281] lr: 2.181e-02, eta: 13:23:06, time: 0.530, data_time: 0.000, memory: 4083, top1_acc: 0.8431, top5_acc: 0.9919, loss_cls: 0.7511, loss: 0.7511 +2025-06-24 13:34:37,847 - pyskl - INFO - Epoch [35][1200/1281] lr: 2.180e-02, eta: 13:23:40, time: 0.529, data_time: 0.000, memory: 4083, top1_acc: 0.8394, top5_acc: 0.9894, loss_cls: 0.7753, loss: 0.7753 +2025-06-24 13:35:21,323 - pyskl - INFO - Saving checkpoint at 35 epochs +2025-06-24 13:36:32,461 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:36:32,532 - pyskl - INFO - +top1_acc 0.7653 +top5_acc 0.9785 +2025-06-24 13:36:32,533 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:36:32,541 - pyskl - INFO - +mean_acc 0.6852 +2025-06-24 13:36:32,544 - pyskl - INFO - Epoch(val) [35][533] top1_acc: 0.7653, top5_acc: 0.9785, mean_class_accuracy: 0.6852 +2025-06-24 13:37:57,433 - pyskl - INFO - Epoch [36][100/1281] lr: 2.178e-02, eta: 13:24:05, time: 0.849, data_time: 0.201, memory: 4083, top1_acc: 0.8406, top5_acc: 0.9962, loss_cls: 0.7275, loss: 0.7275 +2025-06-24 13:38:48,656 - pyskl - INFO - Epoch [36][200/1281] lr: 2.176e-02, eta: 13:24:32, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8512, top5_acc: 0.9938, loss_cls: 0.7095, loss: 0.7095 +2025-06-24 13:39:40,381 - pyskl - INFO - Epoch [36][300/1281] lr: 2.175e-02, eta: 13:25:01, time: 0.517, data_time: 0.000, memory: 4083, top1_acc: 0.8450, top5_acc: 0.9900, loss_cls: 0.7532, loss: 0.7532 +2025-06-24 13:40:12,365 - pyskl - INFO - Epoch [36][400/1281] lr: 2.173e-02, eta: 13:24:25, time: 0.320, data_time: 0.000, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9938, loss_cls: 0.7544, loss: 0.7544 +2025-06-24 13:41:03,552 - pyskl - INFO - Epoch [36][500/1281] lr: 2.172e-02, eta: 13:24:52, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8431, top5_acc: 0.9962, loss_cls: 0.7698, loss: 0.7698 +2025-06-24 13:41:36,441 - pyskl - INFO - Epoch [36][600/1281] lr: 2.171e-02, eta: 13:24:19, time: 0.329, data_time: 0.000, memory: 4083, top1_acc: 0.8356, top5_acc: 0.9906, loss_cls: 0.8183, loss: 0.8183 +2025-06-24 13:42:27,305 - pyskl - INFO - Epoch [36][700/1281] lr: 2.169e-02, eta: 13:24:44, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.8419, top5_acc: 0.9912, loss_cls: 0.7543, loss: 0.7543 +2025-06-24 13:43:17,186 - pyskl - INFO - Epoch [36][800/1281] lr: 2.168e-02, eta: 13:25:05, time: 0.499, data_time: 0.000, memory: 4083, top1_acc: 0.8387, top5_acc: 0.9888, loss_cls: 0.7753, loss: 0.7753 +2025-06-24 13:44:09,423 - pyskl - INFO - Epoch [36][900/1281] lr: 2.167e-02, eta: 13:25:34, time: 0.522, data_time: 0.000, memory: 4083, top1_acc: 0.8306, top5_acc: 0.9906, loss_cls: 0.7984, loss: 0.7984 +2025-06-24 13:45:01,150 - pyskl - INFO - Epoch [36][1000/1281] lr: 2.165e-02, eta: 13:26:01, time: 0.517, data_time: 0.000, memory: 4083, top1_acc: 0.8325, top5_acc: 0.9912, loss_cls: 0.7974, loss: 0.7974 +2025-06-24 13:45:51,544 - pyskl - INFO - Epoch [36][1100/1281] lr: 2.164e-02, eta: 13:26:23, time: 0.504, data_time: 0.000, memory: 4083, top1_acc: 0.8213, top5_acc: 0.9900, loss_cls: 0.8275, loss: 0.8275 +2025-06-24 13:46:41,708 - pyskl - INFO - Epoch [36][1200/1281] lr: 2.162e-02, eta: 13:26:44, time: 0.502, data_time: 0.000, memory: 4083, top1_acc: 0.8325, top5_acc: 0.9888, loss_cls: 0.8267, loss: 0.8267 +2025-06-24 13:47:23,400 - pyskl - INFO - Saving checkpoint at 36 epochs +2025-06-24 13:48:34,832 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:48:34,889 - pyskl - INFO - +top1_acc 0.7869 +top5_acc 0.9862 +2025-06-24 13:48:34,889 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:48:34,896 - pyskl - INFO - +mean_acc 0.7204 +2025-06-24 13:48:34,898 - pyskl - INFO - Epoch(val) [36][533] top1_acc: 0.7869, top5_acc: 0.9862, mean_class_accuracy: 0.7204 +2025-06-24 13:49:38,037 - pyskl - INFO - Epoch [37][100/1281] lr: 2.160e-02, eta: 13:25:54, time: 0.631, data_time: 0.199, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9938, loss_cls: 0.7261, loss: 0.7261 +2025-06-24 13:50:19,499 - pyskl - INFO - Epoch [37][200/1281] lr: 2.158e-02, eta: 13:25:47, time: 0.415, data_time: 0.000, memory: 4083, top1_acc: 0.8469, top5_acc: 0.9944, loss_cls: 0.7270, loss: 0.7270 +2025-06-24 13:51:02,338 - pyskl - INFO - Epoch [37][300/1281] lr: 2.157e-02, eta: 13:25:44, time: 0.428, data_time: 0.000, memory: 4083, top1_acc: 0.8281, top5_acc: 0.9938, loss_cls: 0.7871, loss: 0.7871 +2025-06-24 13:51:54,677 - pyskl - INFO - Epoch [37][400/1281] lr: 2.156e-02, eta: 13:26:11, time: 0.523, data_time: 0.000, memory: 4083, top1_acc: 0.8419, top5_acc: 0.9888, loss_cls: 0.7877, loss: 0.7877 +2025-06-24 13:52:46,024 - pyskl - INFO - Epoch [37][500/1281] lr: 2.154e-02, eta: 13:26:35, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8375, top5_acc: 0.9969, loss_cls: 0.7580, loss: 0.7580 +2025-06-24 13:53:38,870 - pyskl - INFO - Epoch [37][600/1281] lr: 2.153e-02, eta: 13:27:02, time: 0.528, data_time: 0.000, memory: 4083, top1_acc: 0.8213, top5_acc: 0.9912, loss_cls: 0.8204, loss: 0.8204 +2025-06-24 13:54:30,209 - pyskl - INFO - Epoch [37][700/1281] lr: 2.151e-02, eta: 13:27:25, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8506, top5_acc: 0.9925, loss_cls: 0.7663, loss: 0.7663 +2025-06-24 13:55:21,873 - pyskl - INFO - Epoch [37][800/1281] lr: 2.150e-02, eta: 13:27:48, time: 0.517, data_time: 0.000, memory: 4083, top1_acc: 0.8375, top5_acc: 0.9900, loss_cls: 0.8095, loss: 0.8095 +2025-06-24 13:56:12,517 - pyskl - INFO - Epoch [37][900/1281] lr: 2.149e-02, eta: 13:28:08, time: 0.506, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9919, loss_cls: 0.7684, loss: 0.7684 +2025-06-24 13:57:04,763 - pyskl - INFO - Epoch [37][1000/1281] lr: 2.147e-02, eta: 13:28:33, time: 0.522, data_time: 0.000, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9925, loss_cls: 0.7534, loss: 0.7534 +2025-06-24 13:57:56,564 - pyskl - INFO - Epoch [37][1100/1281] lr: 2.146e-02, eta: 13:28:56, time: 0.518, data_time: 0.000, memory: 4083, top1_acc: 0.8169, top5_acc: 0.9881, loss_cls: 0.9031, loss: 0.9031 +2025-06-24 13:58:41,050 - pyskl - INFO - Epoch [37][1200/1281] lr: 2.144e-02, eta: 13:28:56, time: 0.445, data_time: 0.000, memory: 4083, top1_acc: 0.8294, top5_acc: 0.9938, loss_cls: 0.7899, loss: 0.7899 +2025-06-24 13:59:10,201 - pyskl - INFO - Saving checkpoint at 37 epochs +2025-06-24 14:00:00,360 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:00:00,417 - pyskl - INFO - +top1_acc 0.7794 +top5_acc 0.9846 +2025-06-24 14:00:00,417 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:00:00,424 - pyskl - INFO - +mean_acc 0.6931 +2025-06-24 14:00:00,426 - pyskl - INFO - Epoch(val) [37][533] top1_acc: 0.7794, top5_acc: 0.9846, mean_class_accuracy: 0.6931 +2025-06-24 14:01:23,502 - pyskl - INFO - Epoch [38][100/1281] lr: 2.142e-02, eta: 13:29:04, time: 0.831, data_time: 0.190, memory: 4083, top1_acc: 0.8481, top5_acc: 0.9969, loss_cls: 0.7311, loss: 0.7311 +2025-06-24 14:02:15,418 - pyskl - INFO - Epoch [38][200/1281] lr: 2.140e-02, eta: 13:29:26, time: 0.519, data_time: 0.000, memory: 4083, top1_acc: 0.8381, top5_acc: 0.9906, loss_cls: 0.7736, loss: 0.7736 +2025-06-24 14:03:05,727 - pyskl - INFO - Epoch [38][300/1281] lr: 2.139e-02, eta: 13:29:43, time: 0.503, data_time: 0.000, memory: 4083, top1_acc: 0.8363, top5_acc: 0.9912, loss_cls: 0.7680, loss: 0.7680 +2025-06-24 14:03:55,211 - pyskl - INFO - Epoch [38][400/1281] lr: 2.137e-02, eta: 13:29:57, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9919, loss_cls: 0.7079, loss: 0.7079 +2025-06-24 14:04:45,753 - pyskl - INFO - Epoch [38][500/1281] lr: 2.136e-02, eta: 13:30:14, time: 0.505, data_time: 0.000, memory: 4083, top1_acc: 0.8450, top5_acc: 0.9938, loss_cls: 0.7445, loss: 0.7445 +2025-06-24 14:05:38,533 - pyskl - INFO - Epoch [38][600/1281] lr: 2.134e-02, eta: 13:30:38, time: 0.528, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9938, loss_cls: 0.6650, loss: 0.6650 +2025-06-24 14:06:31,752 - pyskl - INFO - Epoch [38][700/1281] lr: 2.133e-02, eta: 13:31:02, time: 0.532, data_time: 0.001, memory: 4083, top1_acc: 0.8337, top5_acc: 0.9912, loss_cls: 0.7729, loss: 0.7729 +2025-06-24 14:07:23,106 - pyskl - INFO - Epoch [38][800/1281] lr: 2.132e-02, eta: 13:31:21, time: 0.514, data_time: 0.000, memory: 4083, top1_acc: 0.8406, top5_acc: 0.9900, loss_cls: 0.7679, loss: 0.7679 +2025-06-24 14:07:54,923 - pyskl - INFO - Epoch [38][900/1281] lr: 2.130e-02, eta: 13:30:41, time: 0.318, data_time: 0.000, memory: 4083, top1_acc: 0.8319, top5_acc: 0.9931, loss_cls: 0.8265, loss: 0.8265 +2025-06-24 14:08:45,944 - pyskl - INFO - Epoch [38][1000/1281] lr: 2.129e-02, eta: 13:30:59, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8400, top5_acc: 0.9950, loss_cls: 0.7690, loss: 0.7690 +2025-06-24 14:09:20,605 - pyskl - INFO - Epoch [38][1100/1281] lr: 2.127e-02, eta: 13:30:27, time: 0.347, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9956, loss_cls: 0.7300, loss: 0.7300 +2025-06-24 14:10:10,716 - pyskl - INFO - Epoch [38][1200/1281] lr: 2.126e-02, eta: 13:30:41, time: 0.501, data_time: 0.000, memory: 4083, top1_acc: 0.8438, top5_acc: 0.9862, loss_cls: 0.7983, loss: 0.7983 +2025-06-24 14:10:53,120 - pyskl - INFO - Saving checkpoint at 38 epochs +2025-06-24 14:12:04,428 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:12:04,504 - pyskl - INFO - +top1_acc 0.7680 +top5_acc 0.9798 +2025-06-24 14:12:04,504 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:12:04,512 - pyskl - INFO - +mean_acc 0.6683 +2025-06-24 14:12:04,514 - pyskl - INFO - Epoch(val) [38][533] top1_acc: 0.7680, top5_acc: 0.9798, mean_class_accuracy: 0.6683 +2025-06-24 14:13:26,749 - pyskl - INFO - Epoch [39][100/1281] lr: 2.123e-02, eta: 13:30:41, time: 0.822, data_time: 0.196, memory: 4083, top1_acc: 0.8462, top5_acc: 0.9944, loss_cls: 0.7546, loss: 0.7546 +2025-06-24 14:14:18,358 - pyskl - INFO - Epoch [39][200/1281] lr: 2.122e-02, eta: 13:30:59, time: 0.516, data_time: 0.000, memory: 4083, top1_acc: 0.8506, top5_acc: 0.9969, loss_cls: 0.7082, loss: 0.7082 +2025-06-24 14:15:09,332 - pyskl - INFO - Epoch [39][300/1281] lr: 2.120e-02, eta: 13:31:15, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9962, loss_cls: 0.6731, loss: 0.6731 +2025-06-24 14:16:01,141 - pyskl - INFO - Epoch [39][400/1281] lr: 2.119e-02, eta: 13:31:33, time: 0.518, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9912, loss_cls: 0.7158, loss: 0.7158 +2025-06-24 14:16:47,538 - pyskl - INFO - Epoch [39][500/1281] lr: 2.117e-02, eta: 13:31:35, time: 0.464, data_time: 0.000, memory: 4083, top1_acc: 0.8213, top5_acc: 0.9919, loss_cls: 0.7924, loss: 0.7924 +2025-06-24 14:17:27,977 - pyskl - INFO - Epoch [39][600/1281] lr: 2.116e-02, eta: 13:31:19, time: 0.404, data_time: 0.000, memory: 4083, top1_acc: 0.8313, top5_acc: 0.9875, loss_cls: 0.8326, loss: 0.8326 +2025-06-24 14:18:01,395 - pyskl - INFO - Epoch [39][700/1281] lr: 2.114e-02, eta: 13:30:43, time: 0.334, data_time: 0.000, memory: 4083, top1_acc: 0.8462, top5_acc: 0.9906, loss_cls: 0.7627, loss: 0.7627 +2025-06-24 14:18:49,933 - pyskl - INFO - Epoch [39][800/1281] lr: 2.113e-02, eta: 13:30:51, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.8406, top5_acc: 0.9900, loss_cls: 0.7437, loss: 0.7437 +2025-06-24 14:19:41,492 - pyskl - INFO - Epoch [39][900/1281] lr: 2.111e-02, eta: 13:31:07, time: 0.516, data_time: 0.000, memory: 4083, top1_acc: 0.8356, top5_acc: 0.9888, loss_cls: 0.7964, loss: 0.7964 +2025-06-24 14:20:32,113 - pyskl - INFO - Epoch [39][1000/1281] lr: 2.110e-02, eta: 13:31:20, time: 0.506, data_time: 0.000, memory: 4083, top1_acc: 0.8419, top5_acc: 0.9925, loss_cls: 0.7744, loss: 0.7744 +2025-06-24 14:21:22,622 - pyskl - INFO - Epoch [39][1100/1281] lr: 2.108e-02, eta: 13:31:33, time: 0.505, data_time: 0.000, memory: 4083, top1_acc: 0.8381, top5_acc: 0.9950, loss_cls: 0.7735, loss: 0.7735 +2025-06-24 14:22:13,917 - pyskl - INFO - Epoch [39][1200/1281] lr: 2.107e-02, eta: 13:31:47, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8381, top5_acc: 0.9938, loss_cls: 0.8057, loss: 0.8057 +2025-06-24 14:22:55,857 - pyskl - INFO - Saving checkpoint at 39 epochs +2025-06-24 14:24:06,303 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:24:06,368 - pyskl - INFO - +top1_acc 0.7978 +top5_acc 0.9862 +2025-06-24 14:24:06,368 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:24:06,375 - pyskl - INFO - +mean_acc 0.7046 +2025-06-24 14:24:06,377 - pyskl - INFO - Epoch(val) [39][533] top1_acc: 0.7978, top5_acc: 0.9862, mean_class_accuracy: 0.7046 +2025-06-24 14:25:28,321 - pyskl - INFO - Epoch [40][100/1281] lr: 2.104e-02, eta: 13:31:42, time: 0.819, data_time: 0.191, memory: 4083, top1_acc: 0.8419, top5_acc: 0.9950, loss_cls: 0.7488, loss: 0.7488 +2025-06-24 14:26:03,213 - pyskl - INFO - Epoch [40][200/1281] lr: 2.103e-02, eta: 13:31:10, time: 0.349, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9925, loss_cls: 0.7189, loss: 0.7189 +2025-06-24 14:26:54,415 - pyskl - INFO - Epoch [40][300/1281] lr: 2.101e-02, eta: 13:31:23, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8444, top5_acc: 0.9912, loss_cls: 0.7745, loss: 0.7745 +2025-06-24 14:27:25,021 - pyskl - INFO - Epoch [40][400/1281] lr: 2.100e-02, eta: 13:30:38, time: 0.306, data_time: 0.000, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9919, loss_cls: 0.7593, loss: 0.7593 +2025-06-24 14:28:15,995 - pyskl - INFO - Epoch [40][500/1281] lr: 2.098e-02, eta: 13:30:51, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9938, loss_cls: 0.6932, loss: 0.6932 +2025-06-24 14:29:07,667 - pyskl - INFO - Epoch [40][600/1281] lr: 2.097e-02, eta: 13:31:05, time: 0.517, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9919, loss_cls: 0.6946, loss: 0.6946 +2025-06-24 14:29:58,576 - pyskl - INFO - Epoch [40][700/1281] lr: 2.095e-02, eta: 13:31:17, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.8369, top5_acc: 0.9944, loss_cls: 0.7557, loss: 0.7557 +2025-06-24 14:30:49,191 - pyskl - INFO - Epoch [40][800/1281] lr: 2.094e-02, eta: 13:31:28, time: 0.506, data_time: 0.000, memory: 4083, top1_acc: 0.8500, top5_acc: 0.9925, loss_cls: 0.7187, loss: 0.7187 +2025-06-24 14:31:40,526 - pyskl - INFO - Epoch [40][900/1281] lr: 2.092e-02, eta: 13:31:40, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8450, top5_acc: 0.9906, loss_cls: 0.7377, loss: 0.7377 +2025-06-24 14:32:32,201 - pyskl - INFO - Epoch [40][1000/1281] lr: 2.091e-02, eta: 13:31:53, time: 0.517, data_time: 0.000, memory: 4083, top1_acc: 0.8512, top5_acc: 0.9919, loss_cls: 0.7393, loss: 0.7393 +2025-06-24 14:33:22,689 - pyskl - INFO - Epoch [40][1100/1281] lr: 2.089e-02, eta: 13:32:03, time: 0.505, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9925, loss_cls: 0.7143, loss: 0.7143 +2025-06-24 14:34:14,291 - pyskl - INFO - Epoch [40][1200/1281] lr: 2.088e-02, eta: 13:32:15, time: 0.516, data_time: 0.000, memory: 4083, top1_acc: 0.8525, top5_acc: 0.9900, loss_cls: 0.7603, loss: 0.7603 +2025-06-24 14:34:55,771 - pyskl - INFO - Saving checkpoint at 40 epochs +2025-06-24 14:35:50,933 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:35:50,988 - pyskl - INFO - +top1_acc 0.8011 +top5_acc 0.9823 +2025-06-24 14:35:50,988 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:35:50,996 - pyskl - INFO - +mean_acc 0.7139 +2025-06-24 14:35:50,998 - pyskl - INFO - Epoch(val) [40][533] top1_acc: 0.8011, top5_acc: 0.9823, mean_class_accuracy: 0.7139 +2025-06-24 14:36:42,853 - pyskl - INFO - Epoch [41][100/1281] lr: 2.085e-02, eta: 13:30:44, time: 0.519, data_time: 0.192, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9950, loss_cls: 0.6694, loss: 0.6694 +2025-06-24 14:37:33,502 - pyskl - INFO - Epoch [41][200/1281] lr: 2.083e-02, eta: 13:30:53, time: 0.506, data_time: 0.000, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9925, loss_cls: 0.7214, loss: 0.7214 +2025-06-24 14:38:25,767 - pyskl - INFO - Epoch [41][300/1281] lr: 2.082e-02, eta: 13:31:07, time: 0.523, data_time: 0.000, memory: 4083, top1_acc: 0.8369, top5_acc: 0.9938, loss_cls: 0.7504, loss: 0.7504 +2025-06-24 14:39:18,388 - pyskl - INFO - Epoch [41][400/1281] lr: 2.080e-02, eta: 13:31:21, time: 0.526, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9912, loss_cls: 0.6634, loss: 0.6634 +2025-06-24 14:40:09,546 - pyskl - INFO - Epoch [41][500/1281] lr: 2.079e-02, eta: 13:31:31, time: 0.512, data_time: 0.001, memory: 4083, top1_acc: 0.8450, top5_acc: 0.9919, loss_cls: 0.7576, loss: 0.7576 +2025-06-24 14:41:00,826 - pyskl - INFO - Epoch [41][600/1281] lr: 2.077e-02, eta: 13:31:41, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8331, top5_acc: 0.9950, loss_cls: 0.7874, loss: 0.7874 +2025-06-24 14:41:51,888 - pyskl - INFO - Epoch [41][700/1281] lr: 2.076e-02, eta: 13:31:51, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9912, loss_cls: 0.7161, loss: 0.7161 +2025-06-24 14:42:43,276 - pyskl - INFO - Epoch [41][800/1281] lr: 2.074e-02, eta: 13:32:01, time: 0.514, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9925, loss_cls: 0.7166, loss: 0.7166 +2025-06-24 14:43:35,805 - pyskl - INFO - Epoch [41][900/1281] lr: 2.073e-02, eta: 13:32:14, time: 0.525, data_time: 0.000, memory: 4083, top1_acc: 0.8275, top5_acc: 0.9925, loss_cls: 0.8157, loss: 0.8157 +2025-06-24 14:44:27,455 - pyskl - INFO - Epoch [41][1000/1281] lr: 2.071e-02, eta: 13:32:24, time: 0.516, data_time: 0.000, memory: 4083, top1_acc: 0.8519, top5_acc: 0.9888, loss_cls: 0.7627, loss: 0.7627 +2025-06-24 14:44:54,781 - pyskl - INFO - Epoch [41][1100/1281] lr: 2.070e-02, eta: 13:31:29, time: 0.273, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9944, loss_cls: 0.6921, loss: 0.6921 +2025-06-24 14:45:45,914 - pyskl - INFO - Epoch [41][1200/1281] lr: 2.068e-02, eta: 13:31:38, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8287, top5_acc: 0.9881, loss_cls: 0.8223, loss: 0.8223 +2025-06-24 14:46:16,516 - pyskl - INFO - Saving checkpoint at 41 epochs +2025-06-24 14:47:27,082 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:47:27,152 - pyskl - INFO - +top1_acc 0.7762 +top5_acc 0.9836 +2025-06-24 14:47:27,152 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:47:27,161 - pyskl - INFO - +mean_acc 0.7159 +2025-06-24 14:47:27,163 - pyskl - INFO - Epoch(val) [41][533] top1_acc: 0.7762, top5_acc: 0.9836, mean_class_accuracy: 0.7159 +2025-06-24 14:48:50,219 - pyskl - INFO - Epoch [42][100/1281] lr: 2.065e-02, eta: 13:31:28, time: 0.830, data_time: 0.194, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9944, loss_cls: 0.7037, loss: 0.7037 +2025-06-24 14:49:41,623 - pyskl - INFO - Epoch [42][200/1281] lr: 2.064e-02, eta: 13:31:36, time: 0.514, data_time: 0.000, memory: 4083, top1_acc: 0.8481, top5_acc: 0.9919, loss_cls: 0.7340, loss: 0.7340 +2025-06-24 14:50:34,189 - pyskl - INFO - Epoch [42][300/1281] lr: 2.062e-02, eta: 13:31:48, time: 0.526, data_time: 0.000, memory: 4083, top1_acc: 0.8538, top5_acc: 0.9944, loss_cls: 0.7150, loss: 0.7150 +2025-06-24 14:51:26,345 - pyskl - INFO - Epoch [42][400/1281] lr: 2.061e-02, eta: 13:31:58, time: 0.522, data_time: 0.000, memory: 4083, top1_acc: 0.8438, top5_acc: 0.9931, loss_cls: 0.7570, loss: 0.7570 +2025-06-24 14:52:16,300 - pyskl - INFO - Epoch [42][500/1281] lr: 2.059e-02, eta: 13:32:03, time: 0.500, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9875, loss_cls: 0.7589, loss: 0.7589 +2025-06-24 14:53:08,187 - pyskl - INFO - Epoch [42][600/1281] lr: 2.057e-02, eta: 13:32:12, time: 0.519, data_time: 0.000, memory: 4083, top1_acc: 0.8512, top5_acc: 0.9925, loss_cls: 0.7257, loss: 0.7257 +2025-06-24 14:53:47,791 - pyskl - INFO - Epoch [42][700/1281] lr: 2.056e-02, eta: 13:31:49, time: 0.396, data_time: 0.000, memory: 4083, top1_acc: 0.8400, top5_acc: 0.9950, loss_cls: 0.7491, loss: 0.7491 +2025-06-24 14:54:38,216 - pyskl - INFO - Epoch [42][800/1281] lr: 2.054e-02, eta: 13:31:54, time: 0.504, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9956, loss_cls: 0.6643, loss: 0.6643 +2025-06-24 14:55:03,457 - pyskl - INFO - Epoch [42][900/1281] lr: 2.053e-02, eta: 13:30:53, time: 0.252, data_time: 0.000, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9925, loss_cls: 0.7289, loss: 0.7289 +2025-06-24 14:55:51,550 - pyskl - INFO - Epoch [42][1000/1281] lr: 2.051e-02, eta: 13:30:52, time: 0.481, data_time: 0.000, memory: 4083, top1_acc: 0.8544, top5_acc: 0.9925, loss_cls: 0.7672, loss: 0.7672 +2025-06-24 14:56:39,599 - pyskl - INFO - Epoch [42][1100/1281] lr: 2.050e-02, eta: 13:30:50, time: 0.480, data_time: 0.000, memory: 4083, top1_acc: 0.8350, top5_acc: 0.9925, loss_cls: 0.7845, loss: 0.7845 +2025-06-24 14:57:27,763 - pyskl - INFO - Epoch [42][1200/1281] lr: 2.048e-02, eta: 13:30:48, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9938, loss_cls: 0.7031, loss: 0.7031 +2025-06-24 14:58:07,329 - pyskl - INFO - Saving checkpoint at 42 epochs +2025-06-24 14:59:07,313 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:59:07,369 - pyskl - INFO - +top1_acc 0.7423 +top5_acc 0.9739 +2025-06-24 14:59:07,369 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:59:07,376 - pyskl - INFO - +mean_acc 0.6576 +2025-06-24 14:59:07,378 - pyskl - INFO - Epoch(val) [42][533] top1_acc: 0.7423, top5_acc: 0.9739, mean_class_accuracy: 0.6576 +2025-06-24 15:00:26,582 - pyskl - INFO - Epoch [43][100/1281] lr: 2.045e-02, eta: 13:30:25, time: 0.792, data_time: 0.194, memory: 4083, top1_acc: 0.8394, top5_acc: 0.9925, loss_cls: 0.7545, loss: 0.7545 +2025-06-24 15:01:14,895 - pyskl - INFO - Epoch [43][200/1281] lr: 2.044e-02, eta: 13:30:23, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9950, loss_cls: 0.7048, loss: 0.7048 +2025-06-24 15:02:03,310 - pyskl - INFO - Epoch [43][300/1281] lr: 2.042e-02, eta: 13:30:22, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.8538, top5_acc: 0.9962, loss_cls: 0.7004, loss: 0.7004 +2025-06-24 15:02:51,421 - pyskl - INFO - Epoch [43][400/1281] lr: 2.040e-02, eta: 13:30:19, time: 0.481, data_time: 0.000, memory: 4083, top1_acc: 0.8462, top5_acc: 0.9931, loss_cls: 0.7095, loss: 0.7095 +2025-06-24 15:03:39,733 - pyskl - INFO - Epoch [43][500/1281] lr: 2.039e-02, eta: 13:30:17, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9944, loss_cls: 0.7164, loss: 0.7164 +2025-06-24 15:04:28,061 - pyskl - INFO - Epoch [43][600/1281] lr: 2.037e-02, eta: 13:30:15, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.8375, top5_acc: 0.9962, loss_cls: 0.7757, loss: 0.7757 +2025-06-24 15:04:57,506 - pyskl - INFO - Epoch [43][700/1281] lr: 2.036e-02, eta: 13:29:25, time: 0.294, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9962, loss_cls: 0.7298, loss: 0.7298 +2025-06-24 15:05:48,714 - pyskl - INFO - Epoch [43][800/1281] lr: 2.034e-02, eta: 13:29:30, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8475, top5_acc: 0.9931, loss_cls: 0.7188, loss: 0.7188 +2025-06-24 15:06:14,673 - pyskl - INFO - Epoch [43][900/1281] lr: 2.033e-02, eta: 13:28:31, time: 0.260, data_time: 0.000, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9912, loss_cls: 0.7435, loss: 0.7435 +2025-06-24 15:07:00,957 - pyskl - INFO - Epoch [43][1000/1281] lr: 2.031e-02, eta: 13:28:23, time: 0.463, data_time: 0.000, memory: 4083, top1_acc: 0.8413, top5_acc: 0.9938, loss_cls: 0.7911, loss: 0.7911 +2025-06-24 15:07:50,038 - pyskl - INFO - Epoch [43][1100/1281] lr: 2.029e-02, eta: 13:28:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8500, top5_acc: 0.9969, loss_cls: 0.7275, loss: 0.7275 +2025-06-24 15:08:39,367 - pyskl - INFO - Epoch [43][1200/1281] lr: 2.028e-02, eta: 13:28:22, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8462, top5_acc: 0.9925, loss_cls: 0.7508, loss: 0.7508 +2025-06-24 15:09:19,617 - pyskl - INFO - Saving checkpoint at 43 epochs +2025-06-24 15:10:19,114 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:10:19,177 - pyskl - INFO - +top1_acc 0.8088 +top5_acc 0.9845 +2025-06-24 15:10:19,178 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:10:19,185 - pyskl - INFO - +mean_acc 0.7436 +2025-06-24 15:10:19,188 - pyskl - INFO - Epoch(val) [43][533] top1_acc: 0.8088, top5_acc: 0.9845, mean_class_accuracy: 0.7436 +2025-06-24 15:11:40,331 - pyskl - INFO - Epoch [44][100/1281] lr: 2.025e-02, eta: 13:28:00, time: 0.811, data_time: 0.198, memory: 4083, top1_acc: 0.8363, top5_acc: 0.9944, loss_cls: 0.7377, loss: 0.7377 +2025-06-24 15:12:29,314 - pyskl - INFO - Epoch [44][200/1281] lr: 2.023e-02, eta: 13:27:59, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8538, top5_acc: 0.9950, loss_cls: 0.6935, loss: 0.6935 +2025-06-24 15:13:18,462 - pyskl - INFO - Epoch [44][300/1281] lr: 2.022e-02, eta: 13:27:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9962, loss_cls: 0.6356, loss: 0.6356 +2025-06-24 15:14:07,597 - pyskl - INFO - Epoch [44][400/1281] lr: 2.020e-02, eta: 13:27:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9938, loss_cls: 0.7123, loss: 0.7123 +2025-06-24 15:14:56,552 - pyskl - INFO - Epoch [44][500/1281] lr: 2.018e-02, eta: 13:27:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9931, loss_cls: 0.6624, loss: 0.6624 +2025-06-24 15:15:45,573 - pyskl - INFO - Epoch [44][600/1281] lr: 2.017e-02, eta: 13:27:51, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9969, loss_cls: 0.6449, loss: 0.6449 +2025-06-24 15:16:19,759 - pyskl - INFO - Epoch [44][700/1281] lr: 2.015e-02, eta: 13:27:12, time: 0.342, data_time: 0.000, memory: 4083, top1_acc: 0.8481, top5_acc: 0.9931, loss_cls: 0.7527, loss: 0.7527 +2025-06-24 15:17:10,960 - pyskl - INFO - Epoch [44][800/1281] lr: 2.014e-02, eta: 13:27:15, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8525, top5_acc: 0.9931, loss_cls: 0.7204, loss: 0.7204 +2025-06-24 15:17:35,636 - pyskl - INFO - Epoch [44][900/1281] lr: 2.012e-02, eta: 13:26:13, time: 0.247, data_time: 0.000, memory: 4083, top1_acc: 0.8475, top5_acc: 0.9919, loss_cls: 0.7410, loss: 0.7410 +2025-06-24 15:18:22,117 - pyskl - INFO - Epoch [44][1000/1281] lr: 2.010e-02, eta: 13:26:04, time: 0.465, data_time: 0.000, memory: 4083, top1_acc: 0.8375, top5_acc: 0.9912, loss_cls: 0.7756, loss: 0.7756 +2025-06-24 15:19:11,011 - pyskl - INFO - Epoch [44][1100/1281] lr: 2.009e-02, eta: 13:26:00, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9925, loss_cls: 0.7316, loss: 0.7316 +2025-06-24 15:19:59,872 - pyskl - INFO - Epoch [44][1200/1281] lr: 2.007e-02, eta: 13:25:57, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9944, loss_cls: 0.7141, loss: 0.7141 +2025-06-24 15:20:40,357 - pyskl - INFO - Saving checkpoint at 44 epochs +2025-06-24 15:21:39,791 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:21:39,874 - pyskl - INFO - +top1_acc 0.7741 +top5_acc 0.9804 +2025-06-24 15:21:39,874 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:21:39,883 - pyskl - INFO - +mean_acc 0.6818 +2025-06-24 15:21:39,885 - pyskl - INFO - Epoch(val) [44][533] top1_acc: 0.7741, top5_acc: 0.9804, mean_class_accuracy: 0.6818 +2025-06-24 15:23:00,031 - pyskl - INFO - Epoch [45][100/1281] lr: 2.004e-02, eta: 13:25:30, time: 0.801, data_time: 0.192, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9981, loss_cls: 0.6574, loss: 0.6574 +2025-06-24 15:23:49,222 - pyskl - INFO - Epoch [45][200/1281] lr: 2.003e-02, eta: 13:25:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9975, loss_cls: 0.6532, loss: 0.6532 +2025-06-24 15:24:38,460 - pyskl - INFO - Epoch [45][300/1281] lr: 2.001e-02, eta: 13:25:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9944, loss_cls: 0.6768, loss: 0.6768 +2025-06-24 15:25:27,719 - pyskl - INFO - Epoch [45][400/1281] lr: 1.999e-02, eta: 13:25:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9894, loss_cls: 0.6812, loss: 0.6812 +2025-06-24 15:26:16,686 - pyskl - INFO - Epoch [45][500/1281] lr: 1.998e-02, eta: 13:25:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9944, loss_cls: 0.6720, loss: 0.6720 +2025-06-24 15:27:05,578 - pyskl - INFO - Epoch [45][600/1281] lr: 1.996e-02, eta: 13:25:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9938, loss_cls: 0.6413, loss: 0.6413 +2025-06-24 15:27:40,262 - pyskl - INFO - Epoch [45][700/1281] lr: 1.994e-02, eta: 13:24:34, time: 0.347, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9969, loss_cls: 0.6833, loss: 0.6833 +2025-06-24 15:28:31,523 - pyskl - INFO - Epoch [45][800/1281] lr: 1.993e-02, eta: 13:24:35, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8419, top5_acc: 0.9950, loss_cls: 0.7683, loss: 0.7683 +2025-06-24 15:28:56,289 - pyskl - INFO - Epoch [45][900/1281] lr: 1.991e-02, eta: 13:23:33, time: 0.248, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9950, loss_cls: 0.6847, loss: 0.6847 +2025-06-24 15:29:42,684 - pyskl - INFO - Epoch [45][1000/1281] lr: 1.989e-02, eta: 13:23:23, time: 0.464, data_time: 0.000, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9950, loss_cls: 0.7259, loss: 0.7259 +2025-06-24 15:30:31,882 - pyskl - INFO - Epoch [45][1100/1281] lr: 1.988e-02, eta: 13:23:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9919, loss_cls: 0.7536, loss: 0.7536 +2025-06-24 15:31:20,917 - pyskl - INFO - Epoch [45][1200/1281] lr: 1.986e-02, eta: 13:23:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9931, loss_cls: 0.7471, loss: 0.7471 +2025-06-24 15:32:01,473 - pyskl - INFO - Saving checkpoint at 45 epochs +2025-06-24 15:33:00,443 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:33:00,498 - pyskl - INFO - +top1_acc 0.8003 +top5_acc 0.9836 +2025-06-24 15:33:00,498 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:33:00,504 - pyskl - INFO - +mean_acc 0.7092 +2025-06-24 15:33:00,506 - pyskl - INFO - Epoch(val) [45][533] top1_acc: 0.8003, top5_acc: 0.9836, mean_class_accuracy: 0.7092 +2025-06-24 15:34:21,212 - pyskl - INFO - Epoch [46][100/1281] lr: 1.983e-02, eta: 13:22:46, time: 0.807, data_time: 0.195, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9925, loss_cls: 0.6585, loss: 0.6585 +2025-06-24 15:35:10,219 - pyskl - INFO - Epoch [46][200/1281] lr: 1.981e-02, eta: 13:22:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9981, loss_cls: 0.6418, loss: 0.6418 +2025-06-24 15:35:59,852 - pyskl - INFO - Epoch [46][300/1281] lr: 1.980e-02, eta: 13:22:36, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9938, loss_cls: 0.6808, loss: 0.6808 +2025-06-24 15:36:49,263 - pyskl - INFO - Epoch [46][400/1281] lr: 1.978e-02, eta: 13:22:32, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9912, loss_cls: 0.6780, loss: 0.6780 +2025-06-24 15:37:38,350 - pyskl - INFO - Epoch [46][500/1281] lr: 1.976e-02, eta: 13:22:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9938, loss_cls: 0.6634, loss: 0.6634 +2025-06-24 15:38:27,365 - pyskl - INFO - Epoch [46][600/1281] lr: 1.975e-02, eta: 13:22:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9950, loss_cls: 0.6588, loss: 0.6588 +2025-06-24 15:39:02,172 - pyskl - INFO - Epoch [46][700/1281] lr: 1.973e-02, eta: 13:21:42, time: 0.348, data_time: 0.000, memory: 4083, top1_acc: 0.8237, top5_acc: 0.9931, loss_cls: 0.8227, loss: 0.8227 +2025-06-24 15:39:53,367 - pyskl - INFO - Epoch [46][800/1281] lr: 1.971e-02, eta: 13:21:41, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8500, top5_acc: 0.9925, loss_cls: 0.7362, loss: 0.7362 +2025-06-24 15:40:18,582 - pyskl - INFO - Epoch [46][900/1281] lr: 1.970e-02, eta: 13:20:40, time: 0.252, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9931, loss_cls: 0.6386, loss: 0.6386 +2025-06-24 15:41:05,910 - pyskl - INFO - Epoch [46][1000/1281] lr: 1.968e-02, eta: 13:20:30, time: 0.473, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9938, loss_cls: 0.6676, loss: 0.6676 +2025-06-24 15:41:55,372 - pyskl - INFO - Epoch [46][1100/1281] lr: 1.966e-02, eta: 13:20:25, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9925, loss_cls: 0.7322, loss: 0.7322 +2025-06-24 15:42:44,830 - pyskl - INFO - Epoch [46][1200/1281] lr: 1.965e-02, eta: 13:20:19, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8444, top5_acc: 0.9894, loss_cls: 0.7477, loss: 0.7477 +2025-06-24 15:43:25,185 - pyskl - INFO - Saving checkpoint at 46 epochs +2025-06-24 15:44:24,817 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:44:24,874 - pyskl - INFO - +top1_acc 0.8004 +top5_acc 0.9818 +2025-06-24 15:44:24,874 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:44:24,882 - pyskl - INFO - +mean_acc 0.7250 +2025-06-24 15:44:24,884 - pyskl - INFO - Epoch(val) [46][533] top1_acc: 0.8004, top5_acc: 0.9818, mean_class_accuracy: 0.7250 +2025-06-24 15:45:44,898 - pyskl - INFO - Epoch [47][100/1281] lr: 1.962e-02, eta: 13:19:47, time: 0.800, data_time: 0.197, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9950, loss_cls: 0.6363, loss: 0.6363 +2025-06-24 15:46:33,950 - pyskl - INFO - Epoch [47][200/1281] lr: 1.960e-02, eta: 13:19:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9944, loss_cls: 0.6696, loss: 0.6696 +2025-06-24 15:47:23,124 - pyskl - INFO - Epoch [47][300/1281] lr: 1.958e-02, eta: 13:19:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9981, loss_cls: 0.6550, loss: 0.6550 +2025-06-24 15:48:12,748 - pyskl - INFO - Epoch [47][400/1281] lr: 1.957e-02, eta: 13:19:28, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9944, loss_cls: 0.6812, loss: 0.6812 +2025-06-24 15:49:01,714 - pyskl - INFO - Epoch [47][500/1281] lr: 1.955e-02, eta: 13:19:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8538, top5_acc: 0.9919, loss_cls: 0.6978, loss: 0.6978 +2025-06-24 15:49:50,977 - pyskl - INFO - Epoch [47][600/1281] lr: 1.953e-02, eta: 13:19:14, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9962, loss_cls: 0.6925, loss: 0.6925 +2025-06-24 15:50:24,882 - pyskl - INFO - Epoch [47][700/1281] lr: 1.952e-02, eta: 13:18:33, time: 0.339, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9919, loss_cls: 0.7132, loss: 0.7132 +2025-06-24 15:51:16,038 - pyskl - INFO - Epoch [47][800/1281] lr: 1.950e-02, eta: 13:18:30, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9969, loss_cls: 0.6900, loss: 0.6900 +2025-06-24 15:51:40,878 - pyskl - INFO - Epoch [47][900/1281] lr: 1.948e-02, eta: 13:17:29, time: 0.248, data_time: 0.001, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9938, loss_cls: 0.6883, loss: 0.6883 +2025-06-24 15:52:28,503 - pyskl - INFO - Epoch [47][1000/1281] lr: 1.947e-02, eta: 13:17:18, time: 0.476, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9950, loss_cls: 0.6501, loss: 0.6501 +2025-06-24 15:53:17,178 - pyskl - INFO - Epoch [47][1100/1281] lr: 1.945e-02, eta: 13:17:09, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8419, top5_acc: 0.9912, loss_cls: 0.7224, loss: 0.7224 +2025-06-24 15:54:06,472 - pyskl - INFO - Epoch [47][1200/1281] lr: 1.943e-02, eta: 13:17:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9931, loss_cls: 0.7237, loss: 0.7237 +2025-06-24 15:54:47,131 - pyskl - INFO - Saving checkpoint at 47 epochs +2025-06-24 15:55:46,079 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:55:46,136 - pyskl - INFO - +top1_acc 0.8175 +top5_acc 0.9894 +2025-06-24 15:55:46,136 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:55:46,143 - pyskl - INFO - +mean_acc 0.7534 +2025-06-24 15:55:46,148 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_30.pth was removed +2025-06-24 15:55:46,337 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_47.pth. +2025-06-24 15:55:46,337 - pyskl - INFO - Best top1_acc is 0.8175 at 47 epoch. +2025-06-24 15:55:46,339 - pyskl - INFO - Epoch(val) [47][533] top1_acc: 0.8175, top5_acc: 0.9894, mean_class_accuracy: 0.7534 +2025-06-24 15:57:06,833 - pyskl - INFO - Epoch [48][100/1281] lr: 1.940e-02, eta: 13:16:29, time: 0.805, data_time: 0.197, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9962, loss_cls: 0.6425, loss: 0.6425 +2025-06-24 15:57:56,133 - pyskl - INFO - Epoch [48][200/1281] lr: 1.938e-02, eta: 13:16:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9950, loss_cls: 0.6119, loss: 0.6119 +2025-06-24 15:58:45,569 - pyskl - INFO - Epoch [48][300/1281] lr: 1.937e-02, eta: 13:16:13, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9956, loss_cls: 0.7058, loss: 0.7058 +2025-06-24 15:59:34,621 - pyskl - INFO - Epoch [48][400/1281] lr: 1.935e-02, eta: 13:16:05, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9944, loss_cls: 0.7057, loss: 0.7057 +2025-06-24 16:00:23,602 - pyskl - INFO - Epoch [48][500/1281] lr: 1.933e-02, eta: 13:15:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9931, loss_cls: 0.6163, loss: 0.6163 +2025-06-24 16:01:12,712 - pyskl - INFO - Epoch [48][600/1281] lr: 1.932e-02, eta: 13:15:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9919, loss_cls: 0.7175, loss: 0.7175 +2025-06-24 16:01:46,647 - pyskl - INFO - Epoch [48][700/1281] lr: 1.930e-02, eta: 13:15:06, time: 0.339, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9938, loss_cls: 0.6737, loss: 0.6737 +2025-06-24 16:02:37,727 - pyskl - INFO - Epoch [48][800/1281] lr: 1.928e-02, eta: 13:15:01, time: 0.511, data_time: 0.001, memory: 4083, top1_acc: 0.8413, top5_acc: 0.9919, loss_cls: 0.7625, loss: 0.7625 +2025-06-24 16:03:02,506 - pyskl - INFO - Epoch [48][900/1281] lr: 1.926e-02, eta: 13:14:00, time: 0.248, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9931, loss_cls: 0.6860, loss: 0.6860 +2025-06-24 16:03:49,414 - pyskl - INFO - Epoch [48][1000/1281] lr: 1.925e-02, eta: 13:13:46, time: 0.469, data_time: 0.001, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9956, loss_cls: 0.6147, loss: 0.6147 +2025-06-24 16:04:38,406 - pyskl - INFO - Epoch [48][1100/1281] lr: 1.923e-02, eta: 13:13:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8413, top5_acc: 0.9938, loss_cls: 0.7347, loss: 0.7347 +2025-06-24 16:05:27,511 - pyskl - INFO - Epoch [48][1200/1281] lr: 1.921e-02, eta: 13:13:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9938, loss_cls: 0.6677, loss: 0.6677 +2025-06-24 16:06:07,718 - pyskl - INFO - Saving checkpoint at 48 epochs +2025-06-24 16:07:06,666 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:07:06,721 - pyskl - INFO - +top1_acc 0.7816 +top5_acc 0.9809 +2025-06-24 16:07:06,722 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:07:06,729 - pyskl - INFO - +mean_acc 0.7079 +2025-06-24 16:07:06,732 - pyskl - INFO - Epoch(val) [48][533] top1_acc: 0.7816, top5_acc: 0.9809, mean_class_accuracy: 0.7079 +2025-06-24 16:08:26,731 - pyskl - INFO - Epoch [49][100/1281] lr: 1.918e-02, eta: 13:12:51, time: 0.800, data_time: 0.196, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9981, loss_cls: 0.6341, loss: 0.6341 +2025-06-24 16:09:15,983 - pyskl - INFO - Epoch [49][200/1281] lr: 1.916e-02, eta: 13:12:42, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9969, loss_cls: 0.5924, loss: 0.5924 +2025-06-24 16:10:05,051 - pyskl - INFO - Epoch [49][300/1281] lr: 1.915e-02, eta: 13:12:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9925, loss_cls: 0.6925, loss: 0.6925 +2025-06-24 16:10:54,159 - pyskl - INFO - Epoch [49][400/1281] lr: 1.913e-02, eta: 13:12:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9950, loss_cls: 0.6623, loss: 0.6623 +2025-06-24 16:11:43,178 - pyskl - INFO - Epoch [49][500/1281] lr: 1.911e-02, eta: 13:12:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9938, loss_cls: 0.6400, loss: 0.6400 +2025-06-24 16:12:32,259 - pyskl - INFO - Epoch [49][600/1281] lr: 1.909e-02, eta: 13:12:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9888, loss_cls: 0.6977, loss: 0.6977 +2025-06-24 16:13:07,781 - pyskl - INFO - Epoch [49][700/1281] lr: 1.908e-02, eta: 13:11:23, time: 0.355, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9944, loss_cls: 0.6915, loss: 0.6915 +2025-06-24 16:13:58,826 - pyskl - INFO - Epoch [49][800/1281] lr: 1.906e-02, eta: 13:11:17, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9931, loss_cls: 0.7063, loss: 0.7063 +2025-06-24 16:14:23,436 - pyskl - INFO - Epoch [49][900/1281] lr: 1.904e-02, eta: 13:10:15, time: 0.246, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9962, loss_cls: 0.6956, loss: 0.6956 +2025-06-24 16:15:10,401 - pyskl - INFO - Epoch [49][1000/1281] lr: 1.902e-02, eta: 13:10:00, time: 0.470, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9925, loss_cls: 0.6723, loss: 0.6723 +2025-06-24 16:15:59,368 - pyskl - INFO - Epoch [49][1100/1281] lr: 1.901e-02, eta: 13:09:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8544, top5_acc: 0.9931, loss_cls: 0.6975, loss: 0.6975 +2025-06-24 16:16:48,070 - pyskl - INFO - Epoch [49][1200/1281] lr: 1.899e-02, eta: 13:09:38, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9938, loss_cls: 0.6917, loss: 0.6917 +2025-06-24 16:17:28,563 - pyskl - INFO - Saving checkpoint at 49 epochs +2025-06-24 16:18:27,601 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:18:27,671 - pyskl - INFO - +top1_acc 0.7784 +top5_acc 0.9783 +2025-06-24 16:18:27,671 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:18:27,679 - pyskl - INFO - +mean_acc 0.7014 +2025-06-24 16:18:27,682 - pyskl - INFO - Epoch(val) [49][533] top1_acc: 0.7784, top5_acc: 0.9783, mean_class_accuracy: 0.7014 +2025-06-24 16:19:46,473 - pyskl - INFO - Epoch [50][100/1281] lr: 1.896e-02, eta: 13:08:57, time: 0.788, data_time: 0.190, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9950, loss_cls: 0.6150, loss: 0.6150 +2025-06-24 16:20:35,461 - pyskl - INFO - Epoch [50][200/1281] lr: 1.894e-02, eta: 13:08:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9962, loss_cls: 0.6008, loss: 0.6008 +2025-06-24 16:21:24,636 - pyskl - INFO - Epoch [50][300/1281] lr: 1.892e-02, eta: 13:08:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9944, loss_cls: 0.6075, loss: 0.6075 +2025-06-24 16:22:13,892 - pyskl - INFO - Epoch [50][400/1281] lr: 1.891e-02, eta: 13:08:24, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9956, loss_cls: 0.6489, loss: 0.6489 +2025-06-24 16:23:02,870 - pyskl - INFO - Epoch [50][500/1281] lr: 1.889e-02, eta: 13:08:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9975, loss_cls: 0.5742, loss: 0.5742 +2025-06-24 16:23:51,495 - pyskl - INFO - Epoch [50][600/1281] lr: 1.887e-02, eta: 13:08:00, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9925, loss_cls: 0.6820, loss: 0.6820 +2025-06-24 16:24:29,173 - pyskl - INFO - Epoch [50][700/1281] lr: 1.885e-02, eta: 13:07:25, time: 0.377, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9956, loss_cls: 0.6436, loss: 0.6436 +2025-06-24 16:25:20,207 - pyskl - INFO - Epoch [50][800/1281] lr: 1.884e-02, eta: 13:07:18, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9975, loss_cls: 0.5958, loss: 0.5958 +2025-06-24 16:25:43,891 - pyskl - INFO - Epoch [50][900/1281] lr: 1.882e-02, eta: 13:06:15, time: 0.237, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9944, loss_cls: 0.6771, loss: 0.6771 +2025-06-24 16:26:27,929 - pyskl - INFO - Epoch [50][1000/1281] lr: 1.880e-02, eta: 13:05:53, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9944, loss_cls: 0.6550, loss: 0.6550 +2025-06-24 16:27:16,787 - pyskl - INFO - Epoch [50][1100/1281] lr: 1.878e-02, eta: 13:05:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9919, loss_cls: 0.7154, loss: 0.7154 +2025-06-24 16:28:05,883 - pyskl - INFO - Epoch [50][1200/1281] lr: 1.876e-02, eta: 13:05:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9944, loss_cls: 0.7059, loss: 0.7059 +2025-06-24 16:28:45,924 - pyskl - INFO - Saving checkpoint at 50 epochs +2025-06-24 16:29:45,041 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:29:45,109 - pyskl - INFO - +top1_acc 0.7943 +top5_acc 0.9854 +2025-06-24 16:29:45,109 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:29:45,116 - pyskl - INFO - +mean_acc 0.7098 +2025-06-24 16:29:45,118 - pyskl - INFO - Epoch(val) [50][533] top1_acc: 0.7943, top5_acc: 0.9854, mean_class_accuracy: 0.7098 +2025-06-24 16:31:04,619 - pyskl - INFO - Epoch [51][100/1281] lr: 1.873e-02, eta: 13:04:47, time: 0.795, data_time: 0.195, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9938, loss_cls: 0.6613, loss: 0.6613 +2025-06-24 16:31:53,818 - pyskl - INFO - Epoch [51][200/1281] lr: 1.871e-02, eta: 13:04:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9938, loss_cls: 0.6436, loss: 0.6436 +2025-06-24 16:32:43,045 - pyskl - INFO - Epoch [51][300/1281] lr: 1.870e-02, eta: 13:04:23, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9931, loss_cls: 0.6321, loss: 0.6321 +2025-06-24 16:33:32,110 - pyskl - INFO - Epoch [51][400/1281] lr: 1.868e-02, eta: 13:04:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9969, loss_cls: 0.6624, loss: 0.6624 +2025-06-24 16:34:21,205 - pyskl - INFO - Epoch [51][500/1281] lr: 1.866e-02, eta: 13:03:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9969, loss_cls: 0.6339, loss: 0.6339 +2025-06-24 16:35:10,659 - pyskl - INFO - Epoch [51][600/1281] lr: 1.864e-02, eta: 13:03:46, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9950, loss_cls: 0.6414, loss: 0.6414 +2025-06-24 16:35:49,955 - pyskl - INFO - Epoch [51][700/1281] lr: 1.863e-02, eta: 13:03:14, time: 0.393, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9925, loss_cls: 0.6882, loss: 0.6882 +2025-06-24 16:36:40,980 - pyskl - INFO - Epoch [51][800/1281] lr: 1.861e-02, eta: 13:03:05, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9950, loss_cls: 0.6615, loss: 0.6615 +2025-06-24 16:37:04,553 - pyskl - INFO - Epoch [51][900/1281] lr: 1.859e-02, eta: 13:02:02, time: 0.236, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9944, loss_cls: 0.6789, loss: 0.6789 +2025-06-24 16:37:48,860 - pyskl - INFO - Epoch [51][1000/1281] lr: 1.857e-02, eta: 13:01:39, time: 0.443, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9938, loss_cls: 0.6843, loss: 0.6843 +2025-06-24 16:38:38,033 - pyskl - INFO - Epoch [51][1100/1281] lr: 1.855e-02, eta: 13:01:26, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9962, loss_cls: 0.6288, loss: 0.6288 +2025-06-24 16:39:27,047 - pyskl - INFO - Epoch [51][1200/1281] lr: 1.854e-02, eta: 13:01:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9938, loss_cls: 0.6877, loss: 0.6877 +2025-06-24 16:40:07,305 - pyskl - INFO - Saving checkpoint at 51 epochs +2025-06-24 16:41:05,613 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:41:05,670 - pyskl - INFO - +top1_acc 0.8018 +top5_acc 0.9838 +2025-06-24 16:41:05,670 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:41:05,677 - pyskl - INFO - +mean_acc 0.7391 +2025-06-24 16:41:05,679 - pyskl - INFO - Epoch(val) [51][533] top1_acc: 0.8018, top5_acc: 0.9838, mean_class_accuracy: 0.7391 +2025-06-24 16:42:24,152 - pyskl - INFO - Epoch [52][100/1281] lr: 1.850e-02, eta: 13:00:29, time: 0.785, data_time: 0.191, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9975, loss_cls: 0.6215, loss: 0.6215 +2025-06-24 16:43:13,354 - pyskl - INFO - Epoch [52][200/1281] lr: 1.849e-02, eta: 13:00:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9950, loss_cls: 0.6165, loss: 0.6165 +2025-06-24 16:44:02,571 - pyskl - INFO - Epoch [52][300/1281] lr: 1.847e-02, eta: 13:00:02, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9938, loss_cls: 0.6317, loss: 0.6317 +2025-06-24 16:44:51,450 - pyskl - INFO - Epoch [52][400/1281] lr: 1.845e-02, eta: 12:59:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9962, loss_cls: 0.5827, loss: 0.5827 +2025-06-24 16:45:40,312 - pyskl - INFO - Epoch [52][500/1281] lr: 1.843e-02, eta: 12:59:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9938, loss_cls: 0.6548, loss: 0.6548 +2025-06-24 16:46:29,077 - pyskl - INFO - Epoch [52][600/1281] lr: 1.841e-02, eta: 12:59:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8538, top5_acc: 0.9950, loss_cls: 0.6989, loss: 0.6989 +2025-06-24 16:47:10,338 - pyskl - INFO - Epoch [52][700/1281] lr: 1.840e-02, eta: 12:58:50, time: 0.413, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9950, loss_cls: 0.6436, loss: 0.6436 +2025-06-24 16:47:59,097 - pyskl - INFO - Epoch [52][800/1281] lr: 1.838e-02, eta: 12:58:35, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9931, loss_cls: 0.6868, loss: 0.6868 +2025-06-24 16:48:25,226 - pyskl - INFO - Epoch [52][900/1281] lr: 1.836e-02, eta: 12:57:37, time: 0.261, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9962, loss_cls: 0.6454, loss: 0.6454 +2025-06-24 16:49:08,912 - pyskl - INFO - Epoch [52][1000/1281] lr: 1.834e-02, eta: 12:57:13, time: 0.437, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9944, loss_cls: 0.6477, loss: 0.6477 +2025-06-24 16:49:57,713 - pyskl - INFO - Epoch [52][1100/1281] lr: 1.832e-02, eta: 12:56:58, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9956, loss_cls: 0.6162, loss: 0.6162 +2025-06-24 16:50:46,800 - pyskl - INFO - Epoch [52][1200/1281] lr: 1.831e-02, eta: 12:56:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8444, top5_acc: 0.9944, loss_cls: 0.7338, loss: 0.7338 +2025-06-24 16:51:27,068 - pyskl - INFO - Saving checkpoint at 52 epochs +2025-06-24 16:52:25,929 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:52:25,984 - pyskl - INFO - +top1_acc 0.7919 +top5_acc 0.9857 +2025-06-24 16:52:25,984 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:52:25,990 - pyskl - INFO - +mean_acc 0.7088 +2025-06-24 16:52:25,992 - pyskl - INFO - Epoch(val) [52][533] top1_acc: 0.7919, top5_acc: 0.9857, mean_class_accuracy: 0.7088 +2025-06-24 16:53:45,967 - pyskl - INFO - Epoch [53][100/1281] lr: 1.827e-02, eta: 12:56:01, time: 0.800, data_time: 0.192, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9969, loss_cls: 0.5883, loss: 0.5883 +2025-06-24 16:54:35,480 - pyskl - INFO - Epoch [53][200/1281] lr: 1.826e-02, eta: 12:55:47, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9944, loss_cls: 0.6113, loss: 0.6113 +2025-06-24 16:55:24,890 - pyskl - INFO - Epoch [53][300/1281] lr: 1.824e-02, eta: 12:55:32, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9962, loss_cls: 0.5919, loss: 0.5919 +2025-06-24 16:56:14,153 - pyskl - INFO - Epoch [53][400/1281] lr: 1.822e-02, eta: 12:55:18, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9994, loss_cls: 0.5424, loss: 0.5424 +2025-06-24 16:57:02,982 - pyskl - INFO - Epoch [53][500/1281] lr: 1.820e-02, eta: 12:55:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9925, loss_cls: 0.6140, loss: 0.6140 +2025-06-24 16:57:52,134 - pyskl - INFO - Epoch [53][600/1281] lr: 1.818e-02, eta: 12:54:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9944, loss_cls: 0.6020, loss: 0.6020 +2025-06-24 16:58:32,858 - pyskl - INFO - Epoch [53][700/1281] lr: 1.816e-02, eta: 12:54:17, time: 0.407, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9938, loss_cls: 0.6323, loss: 0.6323 +2025-06-24 16:59:20,339 - pyskl - INFO - Epoch [53][800/1281] lr: 1.815e-02, eta: 12:53:59, time: 0.475, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9975, loss_cls: 0.6605, loss: 0.6605 +2025-06-24 16:59:46,865 - pyskl - INFO - Epoch [53][900/1281] lr: 1.813e-02, eta: 12:53:02, time: 0.265, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9950, loss_cls: 0.6206, loss: 0.6206 +2025-06-24 17:00:29,430 - pyskl - INFO - Epoch [53][1000/1281] lr: 1.811e-02, eta: 12:52:34, time: 0.426, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9950, loss_cls: 0.6886, loss: 0.6886 +2025-06-24 17:01:18,481 - pyskl - INFO - Epoch [53][1100/1281] lr: 1.809e-02, eta: 12:52:19, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9925, loss_cls: 0.7161, loss: 0.7161 +2025-06-24 17:02:07,534 - pyskl - INFO - Epoch [53][1200/1281] lr: 1.807e-02, eta: 12:52:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8450, top5_acc: 0.9919, loss_cls: 0.7687, loss: 0.7687 +2025-06-24 17:02:47,637 - pyskl - INFO - Saving checkpoint at 53 epochs +2025-06-24 17:03:46,635 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:03:46,705 - pyskl - INFO - +top1_acc 0.8089 +top5_acc 0.9830 +2025-06-24 17:03:46,705 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:03:46,712 - pyskl - INFO - +mean_acc 0.7440 +2025-06-24 17:03:46,714 - pyskl - INFO - Epoch(val) [53][533] top1_acc: 0.8089, top5_acc: 0.9830, mean_class_accuracy: 0.7440 +2025-06-24 17:05:06,301 - pyskl - INFO - Epoch [54][100/1281] lr: 1.804e-02, eta: 12:51:18, time: 0.796, data_time: 0.193, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9931, loss_cls: 0.6529, loss: 0.6529 +2025-06-24 17:05:55,415 - pyskl - INFO - Epoch [54][200/1281] lr: 1.802e-02, eta: 12:51:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9956, loss_cls: 0.6826, loss: 0.6826 +2025-06-24 17:06:44,855 - pyskl - INFO - Epoch [54][300/1281] lr: 1.800e-02, eta: 12:50:47, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9956, loss_cls: 0.5831, loss: 0.5831 +2025-06-24 17:07:34,220 - pyskl - INFO - Epoch [54][400/1281] lr: 1.798e-02, eta: 12:50:32, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9956, loss_cls: 0.6049, loss: 0.6049 +2025-06-24 17:08:22,618 - pyskl - INFO - Epoch [54][500/1281] lr: 1.797e-02, eta: 12:50:14, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9931, loss_cls: 0.6817, loss: 0.6817 +2025-06-24 17:09:11,589 - pyskl - INFO - Epoch [54][600/1281] lr: 1.795e-02, eta: 12:49:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9931, loss_cls: 0.6636, loss: 0.6636 +2025-06-24 17:09:54,193 - pyskl - INFO - Epoch [54][700/1281] lr: 1.793e-02, eta: 12:49:30, time: 0.426, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9962, loss_cls: 0.6498, loss: 0.6498 +2025-06-24 17:10:39,210 - pyskl - INFO - Epoch [54][800/1281] lr: 1.791e-02, eta: 12:49:06, time: 0.450, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9969, loss_cls: 0.5670, loss: 0.5670 +2025-06-24 17:11:07,870 - pyskl - INFO - Epoch [54][900/1281] lr: 1.789e-02, eta: 12:48:13, time: 0.287, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9931, loss_cls: 0.6547, loss: 0.6547 +2025-06-24 17:11:49,582 - pyskl - INFO - Epoch [54][1000/1281] lr: 1.787e-02, eta: 12:47:44, time: 0.417, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9975, loss_cls: 0.6163, loss: 0.6163 +2025-06-24 17:12:38,276 - pyskl - INFO - Epoch [54][1100/1281] lr: 1.786e-02, eta: 12:47:27, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9925, loss_cls: 0.6734, loss: 0.6734 +2025-06-24 17:13:27,145 - pyskl - INFO - Epoch [54][1200/1281] lr: 1.784e-02, eta: 12:47:10, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9956, loss_cls: 0.6714, loss: 0.6714 +2025-06-24 17:14:07,615 - pyskl - INFO - Saving checkpoint at 54 epochs +2025-06-24 17:15:06,313 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:15:06,370 - pyskl - INFO - +top1_acc 0.8131 +top5_acc 0.9871 +2025-06-24 17:15:06,370 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:15:06,377 - pyskl - INFO - +mean_acc 0.7229 +2025-06-24 17:15:06,379 - pyskl - INFO - Epoch(val) [54][533] top1_acc: 0.8131, top5_acc: 0.9871, mean_class_accuracy: 0.7229 +2025-06-24 17:16:26,351 - pyskl - INFO - Epoch [55][100/1281] lr: 1.780e-02, eta: 12:46:24, time: 0.800, data_time: 0.191, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9950, loss_cls: 0.5648, loss: 0.5648 +2025-06-24 17:17:15,233 - pyskl - INFO - Epoch [55][200/1281] lr: 1.779e-02, eta: 12:46:07, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9969, loss_cls: 0.6189, loss: 0.6189 +2025-06-24 17:18:04,556 - pyskl - INFO - Epoch [55][300/1281] lr: 1.777e-02, eta: 12:45:50, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9938, loss_cls: 0.6469, loss: 0.6469 +2025-06-24 17:18:53,936 - pyskl - INFO - Epoch [55][400/1281] lr: 1.775e-02, eta: 12:45:34, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9950, loss_cls: 0.6175, loss: 0.6175 +2025-06-24 17:19:42,901 - pyskl - INFO - Epoch [55][500/1281] lr: 1.773e-02, eta: 12:45:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9931, loss_cls: 0.6617, loss: 0.6617 +2025-06-24 17:20:31,671 - pyskl - INFO - Epoch [55][600/1281] lr: 1.771e-02, eta: 12:44:59, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9950, loss_cls: 0.6811, loss: 0.6811 +2025-06-24 17:21:15,590 - pyskl - INFO - Epoch [55][700/1281] lr: 1.769e-02, eta: 12:44:32, time: 0.439, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9956, loss_cls: 0.6245, loss: 0.6245 +2025-06-24 17:21:57,534 - pyskl - INFO - Epoch [55][800/1281] lr: 1.767e-02, eta: 12:44:03, time: 0.419, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9956, loss_cls: 0.6278, loss: 0.6278 +2025-06-24 17:22:29,287 - pyskl - INFO - Epoch [55][900/1281] lr: 1.766e-02, eta: 12:43:15, time: 0.318, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9956, loss_cls: 0.6318, loss: 0.6318 +2025-06-24 17:23:09,772 - pyskl - INFO - Epoch [55][1000/1281] lr: 1.764e-02, eta: 12:42:43, time: 0.405, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9969, loss_cls: 0.6053, loss: 0.6053 +2025-06-24 17:23:59,202 - pyskl - INFO - Epoch [55][1100/1281] lr: 1.762e-02, eta: 12:42:26, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9950, loss_cls: 0.5782, loss: 0.5782 +2025-06-24 17:24:48,262 - pyskl - INFO - Epoch [55][1200/1281] lr: 1.760e-02, eta: 12:42:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9981, loss_cls: 0.6295, loss: 0.6295 +2025-06-24 17:25:28,473 - pyskl - INFO - Saving checkpoint at 55 epochs +2025-06-24 17:26:27,045 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:26:27,111 - pyskl - INFO - +top1_acc 0.8219 +top5_acc 0.9852 +2025-06-24 17:26:27,112 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:26:27,119 - pyskl - INFO - +mean_acc 0.7536 +2025-06-24 17:26:27,124 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_47.pth was removed +2025-06-24 17:26:27,305 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_55.pth. +2025-06-24 17:26:27,306 - pyskl - INFO - Best top1_acc is 0.8219 at 55 epoch. +2025-06-24 17:26:27,308 - pyskl - INFO - Epoch(val) [55][533] top1_acc: 0.8219, top5_acc: 0.9852, mean_class_accuracy: 0.7536 +2025-06-24 17:27:45,354 - pyskl - INFO - Epoch [56][100/1281] lr: 1.757e-02, eta: 12:41:18, time: 0.780, data_time: 0.182, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9975, loss_cls: 0.5980, loss: 0.5980 +2025-06-24 17:28:34,435 - pyskl - INFO - Epoch [56][200/1281] lr: 1.755e-02, eta: 12:41:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9962, loss_cls: 0.5676, loss: 0.5676 +2025-06-24 17:29:23,568 - pyskl - INFO - Epoch [56][300/1281] lr: 1.753e-02, eta: 12:40:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9956, loss_cls: 0.5828, loss: 0.5828 +2025-06-24 17:30:12,831 - pyskl - INFO - Epoch [56][400/1281] lr: 1.751e-02, eta: 12:40:24, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9938, loss_cls: 0.5900, loss: 0.5900 +2025-06-24 17:31:01,962 - pyskl - INFO - Epoch [56][500/1281] lr: 1.749e-02, eta: 12:40:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9962, loss_cls: 0.5976, loss: 0.5976 +2025-06-24 17:31:51,106 - pyskl - INFO - Epoch [56][600/1281] lr: 1.747e-02, eta: 12:39:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9944, loss_cls: 0.6469, loss: 0.6469 +2025-06-24 17:32:37,116 - pyskl - INFO - Epoch [56][700/1281] lr: 1.745e-02, eta: 12:39:25, time: 0.460, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9912, loss_cls: 0.6188, loss: 0.6188 +2025-06-24 17:33:13,068 - pyskl - INFO - Epoch [56][800/1281] lr: 1.743e-02, eta: 12:38:45, time: 0.359, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9969, loss_cls: 0.5981, loss: 0.5981 +2025-06-24 17:33:50,651 - pyskl - INFO - Epoch [56][900/1281] lr: 1.742e-02, eta: 12:38:07, time: 0.376, data_time: 0.001, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9962, loss_cls: 0.6664, loss: 0.6664 +2025-06-24 17:34:26,928 - pyskl - INFO - Epoch [56][1000/1281] lr: 1.740e-02, eta: 12:37:27, time: 0.363, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9931, loss_cls: 0.6707, loss: 0.6707 +2025-06-24 17:35:15,802 - pyskl - INFO - Epoch [56][1100/1281] lr: 1.738e-02, eta: 12:37:08, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9969, loss_cls: 0.6381, loss: 0.6381 +2025-06-24 17:36:04,960 - pyskl - INFO - Epoch [56][1200/1281] lr: 1.736e-02, eta: 12:36:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9956, loss_cls: 0.6193, loss: 0.6193 +2025-06-24 17:36:45,263 - pyskl - INFO - Saving checkpoint at 56 epochs +2025-06-24 17:37:44,128 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:37:44,200 - pyskl - INFO - +top1_acc 0.8338 +top5_acc 0.9885 +2025-06-24 17:37:44,200 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:37:44,211 - pyskl - INFO - +mean_acc 0.7681 +2025-06-24 17:37:44,217 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_55.pth was removed +2025-06-24 17:37:44,456 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_56.pth. +2025-06-24 17:37:44,457 - pyskl - INFO - Best top1_acc is 0.8338 at 56 epoch. +2025-06-24 17:37:44,462 - pyskl - INFO - Epoch(val) [56][533] top1_acc: 0.8338, top5_acc: 0.9885, mean_class_accuracy: 0.7681 +2025-06-24 17:39:04,378 - pyskl - INFO - Epoch [57][100/1281] lr: 1.733e-02, eta: 12:36:01, time: 0.799, data_time: 0.197, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9988, loss_cls: 0.5306, loss: 0.5306 +2025-06-24 17:39:53,418 - pyskl - INFO - Epoch [57][200/1281] lr: 1.731e-02, eta: 12:35:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9969, loss_cls: 0.5484, loss: 0.5484 +2025-06-24 17:40:42,401 - pyskl - INFO - Epoch [57][300/1281] lr: 1.729e-02, eta: 12:35:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9988, loss_cls: 0.5408, loss: 0.5408 +2025-06-24 17:41:31,345 - pyskl - INFO - Epoch [57][400/1281] lr: 1.727e-02, eta: 12:35:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9962, loss_cls: 0.5922, loss: 0.5922 +2025-06-24 17:42:21,005 - pyskl - INFO - Epoch [57][500/1281] lr: 1.725e-02, eta: 12:34:46, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9944, loss_cls: 0.6103, loss: 0.6103 +2025-06-24 17:43:10,028 - pyskl - INFO - Epoch [57][600/1281] lr: 1.723e-02, eta: 12:34:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9950, loss_cls: 0.6656, loss: 0.6656 +2025-06-24 17:43:58,785 - pyskl - INFO - Epoch [57][700/1281] lr: 1.721e-02, eta: 12:34:07, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9969, loss_cls: 0.6279, loss: 0.6279 +2025-06-24 17:44:31,802 - pyskl - INFO - Epoch [57][800/1281] lr: 1.719e-02, eta: 12:33:22, time: 0.330, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9950, loss_cls: 0.6117, loss: 0.6117 +2025-06-24 17:45:12,520 - pyskl - INFO - Epoch [57][900/1281] lr: 1.717e-02, eta: 12:32:49, time: 0.407, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9919, loss_cls: 0.6123, loss: 0.6123 +2025-06-24 17:45:48,841 - pyskl - INFO - Epoch [57][1000/1281] lr: 1.716e-02, eta: 12:32:09, time: 0.363, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9969, loss_cls: 0.6056, loss: 0.6056 +2025-06-24 17:46:37,732 - pyskl - INFO - Epoch [57][1100/1281] lr: 1.714e-02, eta: 12:31:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9956, loss_cls: 0.6420, loss: 0.6420 +2025-06-24 17:47:26,648 - pyskl - INFO - Epoch [57][1200/1281] lr: 1.712e-02, eta: 12:31:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9981, loss_cls: 0.6455, loss: 0.6455 +2025-06-24 17:48:07,266 - pyskl - INFO - Saving checkpoint at 57 epochs +2025-06-24 17:49:05,733 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:49:05,800 - pyskl - INFO - +top1_acc 0.8215 +top5_acc 0.9851 +2025-06-24 17:49:05,800 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:49:05,808 - pyskl - INFO - +mean_acc 0.7473 +2025-06-24 17:49:05,810 - pyskl - INFO - Epoch(val) [57][533] top1_acc: 0.8215, top5_acc: 0.9851, mean_class_accuracy: 0.7473 +2025-06-24 17:50:24,851 - pyskl - INFO - Epoch [58][100/1281] lr: 1.708e-02, eta: 12:30:38, time: 0.790, data_time: 0.187, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9956, loss_cls: 0.5713, loss: 0.5713 +2025-06-24 17:51:14,045 - pyskl - INFO - Epoch [58][200/1281] lr: 1.706e-02, eta: 12:30:19, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9975, loss_cls: 0.5447, loss: 0.5447 +2025-06-24 17:52:03,901 - pyskl - INFO - Epoch [58][300/1281] lr: 1.704e-02, eta: 12:30:00, time: 0.499, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9975, loss_cls: 0.5592, loss: 0.5592 +2025-06-24 17:52:53,054 - pyskl - INFO - Epoch [58][400/1281] lr: 1.703e-02, eta: 12:29:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9938, loss_cls: 0.5919, loss: 0.5919 +2025-06-24 17:53:42,120 - pyskl - INFO - Epoch [58][500/1281] lr: 1.701e-02, eta: 12:29:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9969, loss_cls: 0.5706, loss: 0.5706 +2025-06-24 17:54:31,339 - pyskl - INFO - Epoch [58][600/1281] lr: 1.699e-02, eta: 12:29:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9969, loss_cls: 0.6161, loss: 0.6161 +2025-06-24 17:55:20,328 - pyskl - INFO - Epoch [58][700/1281] lr: 1.697e-02, eta: 12:28:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9912, loss_cls: 0.6736, loss: 0.6736 +2025-06-24 17:55:52,173 - pyskl - INFO - Epoch [58][800/1281] lr: 1.695e-02, eta: 12:27:53, time: 0.318, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9938, loss_cls: 0.6497, loss: 0.6497 +2025-06-24 17:56:34,784 - pyskl - INFO - Epoch [58][900/1281] lr: 1.693e-02, eta: 12:27:23, time: 0.426, data_time: 0.001, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9969, loss_cls: 0.6486, loss: 0.6486 +2025-06-24 17:57:08,156 - pyskl - INFO - Epoch [58][1000/1281] lr: 1.691e-02, eta: 12:26:38, time: 0.334, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9944, loss_cls: 0.6144, loss: 0.6144 +2025-06-24 17:57:57,086 - pyskl - INFO - Epoch [58][1100/1281] lr: 1.689e-02, eta: 12:26:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9944, loss_cls: 0.6225, loss: 0.6225 +2025-06-24 17:58:46,314 - pyskl - INFO - Epoch [58][1200/1281] lr: 1.687e-02, eta: 12:25:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9944, loss_cls: 0.5581, loss: 0.5581 +2025-06-24 17:59:26,523 - pyskl - INFO - Saving checkpoint at 58 epochs +2025-06-24 18:00:25,449 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:00:25,522 - pyskl - INFO - +top1_acc 0.7636 +top5_acc 0.9749 +2025-06-24 18:00:25,523 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:00:25,532 - pyskl - INFO - +mean_acc 0.7173 +2025-06-24 18:00:25,535 - pyskl - INFO - Epoch(val) [58][533] top1_acc: 0.7636, top5_acc: 0.9749, mean_class_accuracy: 0.7173 +2025-06-24 18:01:46,785 - pyskl - INFO - Epoch [59][100/1281] lr: 1.684e-02, eta: 12:25:09, time: 0.812, data_time: 0.200, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9950, loss_cls: 0.5933, loss: 0.5933 +2025-06-24 18:02:35,589 - pyskl - INFO - Epoch [59][200/1281] lr: 1.682e-02, eta: 12:24:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9975, loss_cls: 0.5434, loss: 0.5434 +2025-06-24 18:03:24,717 - pyskl - INFO - Epoch [59][300/1281] lr: 1.680e-02, eta: 12:24:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9950, loss_cls: 0.5866, loss: 0.5866 +2025-06-24 18:04:14,015 - pyskl - INFO - Epoch [59][400/1281] lr: 1.678e-02, eta: 12:24:07, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9975, loss_cls: 0.6283, loss: 0.6283 +2025-06-24 18:05:03,115 - pyskl - INFO - Epoch [59][500/1281] lr: 1.676e-02, eta: 12:23:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9994, loss_cls: 0.5577, loss: 0.5577 +2025-06-24 18:05:52,288 - pyskl - INFO - Epoch [59][600/1281] lr: 1.674e-02, eta: 12:23:26, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9950, loss_cls: 0.5676, loss: 0.5676 +2025-06-24 18:06:41,023 - pyskl - INFO - Epoch [59][700/1281] lr: 1.672e-02, eta: 12:23:04, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9969, loss_cls: 0.5811, loss: 0.5811 +2025-06-24 18:07:11,274 - pyskl - INFO - Epoch [59][800/1281] lr: 1.670e-02, eta: 12:22:14, time: 0.303, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9975, loss_cls: 0.5446, loss: 0.5446 +2025-06-24 18:07:56,102 - pyskl - INFO - Epoch [59][900/1281] lr: 1.668e-02, eta: 12:21:47, time: 0.448, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9944, loss_cls: 0.6092, loss: 0.6092 +2025-06-24 18:08:30,353 - pyskl - INFO - Epoch [59][1000/1281] lr: 1.667e-02, eta: 12:21:03, time: 0.342, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9969, loss_cls: 0.5960, loss: 0.5960 +2025-06-24 18:09:19,363 - pyskl - INFO - Epoch [59][1100/1281] lr: 1.665e-02, eta: 12:20:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9956, loss_cls: 0.5937, loss: 0.5937 +2025-06-24 18:10:08,771 - pyskl - INFO - Epoch [59][1200/1281] lr: 1.663e-02, eta: 12:20:21, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9956, loss_cls: 0.6701, loss: 0.6701 +2025-06-24 18:10:49,328 - pyskl - INFO - Saving checkpoint at 59 epochs +2025-06-24 18:11:47,999 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:11:48,062 - pyskl - INFO - +top1_acc 0.8257 +top5_acc 0.9836 +2025-06-24 18:11:48,062 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:11:48,070 - pyskl - INFO - +mean_acc 0.7472 +2025-06-24 18:11:48,072 - pyskl - INFO - Epoch(val) [59][533] top1_acc: 0.8257, top5_acc: 0.9836, mean_class_accuracy: 0.7472 +2025-06-24 18:13:06,983 - pyskl - INFO - Epoch [60][100/1281] lr: 1.659e-02, eta: 12:19:28, time: 0.789, data_time: 0.193, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9956, loss_cls: 0.6260, loss: 0.6260 +2025-06-24 18:13:55,838 - pyskl - INFO - Epoch [60][200/1281] lr: 1.657e-02, eta: 12:19:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9981, loss_cls: 0.5490, loss: 0.5490 +2025-06-24 18:14:44,732 - pyskl - INFO - Epoch [60][300/1281] lr: 1.655e-02, eta: 12:18:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9950, loss_cls: 0.5495, loss: 0.5495 +2025-06-24 18:15:33,854 - pyskl - INFO - Epoch [60][400/1281] lr: 1.653e-02, eta: 12:18:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9975, loss_cls: 0.5543, loss: 0.5543 +2025-06-24 18:16:22,764 - pyskl - INFO - Epoch [60][500/1281] lr: 1.651e-02, eta: 12:18:01, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9931, loss_cls: 0.5914, loss: 0.5914 +2025-06-24 18:17:11,568 - pyskl - INFO - Epoch [60][600/1281] lr: 1.650e-02, eta: 12:17:40, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9938, loss_cls: 0.6411, loss: 0.6411 +2025-06-24 18:18:00,632 - pyskl - INFO - Epoch [60][700/1281] lr: 1.648e-02, eta: 12:17:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9962, loss_cls: 0.5988, loss: 0.5988 +2025-06-24 18:18:30,613 - pyskl - INFO - Epoch [60][800/1281] lr: 1.646e-02, eta: 12:16:27, time: 0.300, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9969, loss_cls: 0.6078, loss: 0.6078 +2025-06-24 18:19:16,783 - pyskl - INFO - Epoch [60][900/1281] lr: 1.644e-02, eta: 12:16:01, time: 0.462, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9944, loss_cls: 0.6147, loss: 0.6147 +2025-06-24 18:19:50,473 - pyskl - INFO - Epoch [60][1000/1281] lr: 1.642e-02, eta: 12:15:16, time: 0.337, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9988, loss_cls: 0.5556, loss: 0.5556 +2025-06-24 18:20:39,562 - pyskl - INFO - Epoch [60][1100/1281] lr: 1.640e-02, eta: 12:14:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9950, loss_cls: 0.6150, loss: 0.6150 +2025-06-24 18:21:28,684 - pyskl - INFO - Epoch [60][1200/1281] lr: 1.638e-02, eta: 12:14:33, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9956, loss_cls: 0.5700, loss: 0.5700 +2025-06-24 18:22:09,186 - pyskl - INFO - Saving checkpoint at 60 epochs +2025-06-24 18:23:08,609 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:23:08,667 - pyskl - INFO - +top1_acc 0.8077 +top5_acc 0.9878 +2025-06-24 18:23:08,668 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:23:08,675 - pyskl - INFO - +mean_acc 0.7374 +2025-06-24 18:23:08,677 - pyskl - INFO - Epoch(val) [60][533] top1_acc: 0.8077, top5_acc: 0.9878, mean_class_accuracy: 0.7374 +2025-06-24 18:24:27,443 - pyskl - INFO - Epoch [61][100/1281] lr: 1.634e-02, eta: 12:13:38, time: 0.788, data_time: 0.197, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9962, loss_cls: 0.5408, loss: 0.5408 +2025-06-24 18:25:16,986 - pyskl - INFO - Epoch [61][200/1281] lr: 1.632e-02, eta: 12:13:17, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9975, loss_cls: 0.5197, loss: 0.5197 +2025-06-24 18:26:05,662 - pyskl - INFO - Epoch [61][300/1281] lr: 1.630e-02, eta: 12:12:54, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9981, loss_cls: 0.5080, loss: 0.5080 +2025-06-24 18:26:54,504 - pyskl - INFO - Epoch [61][400/1281] lr: 1.629e-02, eta: 12:12:32, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9969, loss_cls: 0.5870, loss: 0.5870 +2025-06-24 18:27:43,151 - pyskl - INFO - Epoch [61][500/1281] lr: 1.627e-02, eta: 12:12:09, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9962, loss_cls: 0.5966, loss: 0.5966 +2025-06-24 18:28:31,891 - pyskl - INFO - Epoch [61][600/1281] lr: 1.625e-02, eta: 12:11:46, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9938, loss_cls: 0.5633, loss: 0.5633 +2025-06-24 18:29:21,336 - pyskl - INFO - Epoch [61][700/1281] lr: 1.623e-02, eta: 12:11:24, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9956, loss_cls: 0.5651, loss: 0.5651 +2025-06-24 18:29:51,758 - pyskl - INFO - Epoch [61][800/1281] lr: 1.621e-02, eta: 12:10:34, time: 0.304, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9950, loss_cls: 0.6118, loss: 0.6118 +2025-06-24 18:30:37,304 - pyskl - INFO - Epoch [61][900/1281] lr: 1.619e-02, eta: 12:10:07, time: 0.455, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9969, loss_cls: 0.6011, loss: 0.6011 +2025-06-24 18:31:10,030 - pyskl - INFO - Epoch [61][1000/1281] lr: 1.617e-02, eta: 12:09:20, time: 0.327, data_time: 0.000, memory: 4083, top1_acc: 0.8506, top5_acc: 0.9931, loss_cls: 0.6910, loss: 0.6910 +2025-06-24 18:31:59,051 - pyskl - INFO - Epoch [61][1100/1281] lr: 1.615e-02, eta: 12:08:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9981, loss_cls: 0.5371, loss: 0.5371 +2025-06-24 18:32:48,083 - pyskl - INFO - Epoch [61][1200/1281] lr: 1.613e-02, eta: 12:08:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9975, loss_cls: 0.5420, loss: 0.5420 +2025-06-24 18:33:28,435 - pyskl - INFO - Saving checkpoint at 61 epochs +2025-06-24 18:34:27,370 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:34:27,441 - pyskl - INFO - +top1_acc 0.8193 +top5_acc 0.9865 +2025-06-24 18:34:27,441 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:34:27,450 - pyskl - INFO - +mean_acc 0.7474 +2025-06-24 18:34:27,452 - pyskl - INFO - Epoch(val) [61][533] top1_acc: 0.8193, top5_acc: 0.9865, mean_class_accuracy: 0.7474 +2025-06-24 18:35:46,730 - pyskl - INFO - Epoch [62][100/1281] lr: 1.609e-02, eta: 12:07:40, time: 0.793, data_time: 0.190, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9962, loss_cls: 0.5721, loss: 0.5721 +2025-06-24 18:36:35,640 - pyskl - INFO - Epoch [62][200/1281] lr: 1.607e-02, eta: 12:07:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9994, loss_cls: 0.4215, loss: 0.4215 +2025-06-24 18:37:24,932 - pyskl - INFO - Epoch [62][300/1281] lr: 1.605e-02, eta: 12:06:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9975, loss_cls: 0.5476, loss: 0.5476 +2025-06-24 18:38:14,404 - pyskl - INFO - Epoch [62][400/1281] lr: 1.603e-02, eta: 12:06:32, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9969, loss_cls: 0.5600, loss: 0.5600 +2025-06-24 18:39:03,421 - pyskl - INFO - Epoch [62][500/1281] lr: 1.602e-02, eta: 12:06:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9975, loss_cls: 0.5623, loss: 0.5623 +2025-06-24 18:39:52,521 - pyskl - INFO - Epoch [62][600/1281] lr: 1.600e-02, eta: 12:05:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9950, loss_cls: 0.6169, loss: 0.6169 +2025-06-24 18:40:41,490 - pyskl - INFO - Epoch [62][700/1281] lr: 1.598e-02, eta: 12:05:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9912, loss_cls: 0.5796, loss: 0.5796 +2025-06-24 18:41:10,132 - pyskl - INFO - Epoch [62][800/1281] lr: 1.596e-02, eta: 12:04:31, time: 0.286, data_time: 0.001, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9956, loss_cls: 0.5456, loss: 0.5456 +2025-06-24 18:41:58,451 - pyskl - INFO - Epoch [62][900/1281] lr: 1.594e-02, eta: 12:04:07, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9975, loss_cls: 0.6066, loss: 0.6066 +2025-06-24 18:42:29,240 - pyskl - INFO - Epoch [62][1000/1281] lr: 1.592e-02, eta: 12:03:17, time: 0.308, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9956, loss_cls: 0.6285, loss: 0.6285 +2025-06-24 18:43:18,417 - pyskl - INFO - Epoch [62][1100/1281] lr: 1.590e-02, eta: 12:02:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9950, loss_cls: 0.6356, loss: 0.6356 +2025-06-24 18:44:07,688 - pyskl - INFO - Epoch [62][1200/1281] lr: 1.588e-02, eta: 12:02:31, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9956, loss_cls: 0.5934, loss: 0.5934 +2025-06-24 18:44:48,256 - pyskl - INFO - Saving checkpoint at 62 epochs +2025-06-24 18:45:47,074 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:45:47,143 - pyskl - INFO - +top1_acc 0.8433 +top5_acc 0.9906 +2025-06-24 18:45:47,143 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:45:47,151 - pyskl - INFO - +mean_acc 0.7801 +2025-06-24 18:45:47,155 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_56.pth was removed +2025-06-24 18:45:47,345 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_62.pth. +2025-06-24 18:45:47,346 - pyskl - INFO - Best top1_acc is 0.8433 at 62 epoch. +2025-06-24 18:45:47,348 - pyskl - INFO - Epoch(val) [62][533] top1_acc: 0.8433, top5_acc: 0.9906, mean_class_accuracy: 0.7801 +2025-06-24 18:47:07,676 - pyskl - INFO - Epoch [63][100/1281] lr: 1.584e-02, eta: 12:01:37, time: 0.803, data_time: 0.196, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9988, loss_cls: 0.5110, loss: 0.5110 +2025-06-24 18:47:57,045 - pyskl - INFO - Epoch [63][200/1281] lr: 1.582e-02, eta: 12:01:14, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9944, loss_cls: 0.4926, loss: 0.4926 +2025-06-24 18:48:46,274 - pyskl - INFO - Epoch [63][300/1281] lr: 1.580e-02, eta: 12:00:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9988, loss_cls: 0.4624, loss: 0.4624 +2025-06-24 18:49:35,274 - pyskl - INFO - Epoch [63][400/1281] lr: 1.578e-02, eta: 12:00:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9981, loss_cls: 0.5477, loss: 0.5477 +2025-06-24 18:50:24,297 - pyskl - INFO - Epoch [63][500/1281] lr: 1.576e-02, eta: 12:00:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9956, loss_cls: 0.4952, loss: 0.4952 +2025-06-24 18:51:13,348 - pyskl - INFO - Epoch [63][600/1281] lr: 1.574e-02, eta: 11:59:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9969, loss_cls: 0.5863, loss: 0.5863 +2025-06-24 18:52:02,291 - pyskl - INFO - Epoch [63][700/1281] lr: 1.572e-02, eta: 11:59:16, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9956, loss_cls: 0.5778, loss: 0.5778 +2025-06-24 18:52:32,019 - pyskl - INFO - Epoch [63][800/1281] lr: 1.570e-02, eta: 11:58:25, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9912, loss_cls: 0.6324, loss: 0.6324 +2025-06-24 18:53:20,363 - pyskl - INFO - Epoch [63][900/1281] lr: 1.568e-02, eta: 11:58:00, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9969, loss_cls: 0.5714, loss: 0.5714 +2025-06-24 18:53:52,389 - pyskl - INFO - Epoch [63][1000/1281] lr: 1.566e-02, eta: 11:57:13, time: 0.320, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9925, loss_cls: 0.6053, loss: 0.6053 +2025-06-24 18:54:41,649 - pyskl - INFO - Epoch [63][1100/1281] lr: 1.564e-02, eta: 11:56:49, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9981, loss_cls: 0.5292, loss: 0.5292 +2025-06-24 18:55:30,849 - pyskl - INFO - Epoch [63][1200/1281] lr: 1.562e-02, eta: 11:56:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9962, loss_cls: 0.5583, loss: 0.5583 +2025-06-24 18:56:11,436 - pyskl - INFO - Saving checkpoint at 63 epochs +2025-06-24 18:57:11,287 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:57:11,357 - pyskl - INFO - +top1_acc 0.8047 +top5_acc 0.9874 +2025-06-24 18:57:11,358 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:57:11,369 - pyskl - INFO - +mean_acc 0.7420 +2025-06-24 18:57:11,373 - pyskl - INFO - Epoch(val) [63][533] top1_acc: 0.8047, top5_acc: 0.9874, mean_class_accuracy: 0.7420 +2025-06-24 18:58:31,594 - pyskl - INFO - Epoch [64][100/1281] lr: 1.559e-02, eta: 11:55:30, time: 0.802, data_time: 0.187, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9981, loss_cls: 0.5676, loss: 0.5676 +2025-06-24 18:59:20,913 - pyskl - INFO - Epoch [64][200/1281] lr: 1.557e-02, eta: 11:55:06, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9956, loss_cls: 0.5527, loss: 0.5527 +2025-06-24 19:00:10,114 - pyskl - INFO - Epoch [64][300/1281] lr: 1.555e-02, eta: 11:54:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 1.0000, loss_cls: 0.4624, loss: 0.4624 +2025-06-24 19:00:59,122 - pyskl - INFO - Epoch [64][400/1281] lr: 1.553e-02, eta: 11:54:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9975, loss_cls: 0.5396, loss: 0.5396 +2025-06-24 19:01:48,157 - pyskl - INFO - Epoch [64][500/1281] lr: 1.551e-02, eta: 11:53:53, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9969, loss_cls: 0.5530, loss: 0.5530 +2025-06-24 19:02:37,272 - pyskl - INFO - Epoch [64][600/1281] lr: 1.549e-02, eta: 11:53:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9981, loss_cls: 0.5936, loss: 0.5936 +2025-06-24 19:03:26,683 - pyskl - INFO - Epoch [64][700/1281] lr: 1.547e-02, eta: 11:53:05, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9962, loss_cls: 0.5972, loss: 0.5972 +2025-06-24 19:03:57,003 - pyskl - INFO - Epoch [64][800/1281] lr: 1.545e-02, eta: 11:52:15, time: 0.303, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9962, loss_cls: 0.5459, loss: 0.5459 +2025-06-24 19:04:42,765 - pyskl - INFO - Epoch [64][900/1281] lr: 1.543e-02, eta: 11:51:46, time: 0.458, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9938, loss_cls: 0.5712, loss: 0.5712 +2025-06-24 19:05:15,566 - pyskl - INFO - Epoch [64][1000/1281] lr: 1.541e-02, eta: 11:51:00, time: 0.328, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9975, loss_cls: 0.5753, loss: 0.5753 +2025-06-24 19:06:04,519 - pyskl - INFO - Epoch [64][1100/1281] lr: 1.539e-02, eta: 11:50:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9962, loss_cls: 0.5156, loss: 0.5156 +2025-06-24 19:06:53,786 - pyskl - INFO - Epoch [64][1200/1281] lr: 1.537e-02, eta: 11:50:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9962, loss_cls: 0.5380, loss: 0.5380 +2025-06-24 19:07:34,215 - pyskl - INFO - Saving checkpoint at 64 epochs +2025-06-24 19:08:33,552 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:08:33,608 - pyskl - INFO - +top1_acc 0.8341 +top5_acc 0.9883 +2025-06-24 19:08:33,608 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:08:33,615 - pyskl - INFO - +mean_acc 0.7772 +2025-06-24 19:08:33,618 - pyskl - INFO - Epoch(val) [64][533] top1_acc: 0.8341, top5_acc: 0.9883, mean_class_accuracy: 0.7772 +2025-06-24 19:09:53,627 - pyskl - INFO - Epoch [65][100/1281] lr: 1.533e-02, eta: 11:49:14, time: 0.800, data_time: 0.192, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9956, loss_cls: 0.5196, loss: 0.5196 +2025-06-24 19:10:43,171 - pyskl - INFO - Epoch [65][200/1281] lr: 1.531e-02, eta: 11:48:50, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9950, loss_cls: 0.5352, loss: 0.5352 +2025-06-24 19:11:32,284 - pyskl - INFO - Epoch [65][300/1281] lr: 1.529e-02, eta: 11:48:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9969, loss_cls: 0.5482, loss: 0.5482 +2025-06-24 19:12:21,561 - pyskl - INFO - Epoch [65][400/1281] lr: 1.527e-02, eta: 11:48:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9962, loss_cls: 0.5149, loss: 0.5149 +2025-06-24 19:13:10,809 - pyskl - INFO - Epoch [65][500/1281] lr: 1.526e-02, eta: 11:47:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9938, loss_cls: 0.5685, loss: 0.5685 +2025-06-24 19:13:59,698 - pyskl - INFO - Epoch [65][600/1281] lr: 1.524e-02, eta: 11:47:11, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9962, loss_cls: 0.5716, loss: 0.5716 +2025-06-24 19:14:48,816 - pyskl - INFO - Epoch [65][700/1281] lr: 1.522e-02, eta: 11:46:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9975, loss_cls: 0.5534, loss: 0.5534 +2025-06-24 19:15:19,314 - pyskl - INFO - Epoch [65][800/1281] lr: 1.520e-02, eta: 11:45:56, time: 0.305, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9969, loss_cls: 0.5755, loss: 0.5755 +2025-06-24 19:16:04,645 - pyskl - INFO - Epoch [65][900/1281] lr: 1.518e-02, eta: 11:45:26, time: 0.453, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9969, loss_cls: 0.5147, loss: 0.5147 +2025-06-24 19:16:38,328 - pyskl - INFO - Epoch [65][1000/1281] lr: 1.516e-02, eta: 11:44:41, time: 0.337, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9944, loss_cls: 0.5884, loss: 0.5884 +2025-06-24 19:17:27,550 - pyskl - INFO - Epoch [65][1100/1281] lr: 1.514e-02, eta: 11:44:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9962, loss_cls: 0.5775, loss: 0.5775 +2025-06-24 19:18:17,076 - pyskl - INFO - Epoch [65][1200/1281] lr: 1.512e-02, eta: 11:43:51, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9956, loss_cls: 0.5671, loss: 0.5671 +2025-06-24 19:18:57,661 - pyskl - INFO - Saving checkpoint at 65 epochs +2025-06-24 19:19:56,725 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:19:56,798 - pyskl - INFO - +top1_acc 0.8507 +top5_acc 0.9914 +2025-06-24 19:19:56,798 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:19:56,806 - pyskl - INFO - +mean_acc 0.7950 +2025-06-24 19:19:56,811 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_62.pth was removed +2025-06-24 19:19:57,027 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_65.pth. +2025-06-24 19:19:57,027 - pyskl - INFO - Best top1_acc is 0.8507 at 65 epoch. +2025-06-24 19:19:57,030 - pyskl - INFO - Epoch(val) [65][533] top1_acc: 0.8507, top5_acc: 0.9914, mean_class_accuracy: 0.7950 +2025-06-24 19:21:15,926 - pyskl - INFO - Epoch [66][100/1281] lr: 1.508e-02, eta: 11:42:53, time: 0.789, data_time: 0.192, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9994, loss_cls: 0.4698, loss: 0.4698 +2025-06-24 19:22:04,865 - pyskl - INFO - Epoch [66][200/1281] lr: 1.506e-02, eta: 11:42:27, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9950, loss_cls: 0.5525, loss: 0.5525 +2025-06-24 19:22:54,193 - pyskl - INFO - Epoch [66][300/1281] lr: 1.504e-02, eta: 11:42:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9988, loss_cls: 0.5012, loss: 0.5012 +2025-06-24 19:23:43,781 - pyskl - INFO - Epoch [66][400/1281] lr: 1.502e-02, eta: 11:41:37, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9981, loss_cls: 0.5245, loss: 0.5245 +2025-06-24 19:24:32,255 - pyskl - INFO - Epoch [66][500/1281] lr: 1.500e-02, eta: 11:41:11, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9962, loss_cls: 0.5570, loss: 0.5570 +2025-06-24 19:25:21,350 - pyskl - INFO - Epoch [66][600/1281] lr: 1.498e-02, eta: 11:40:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9944, loss_cls: 0.5369, loss: 0.5369 +2025-06-24 19:26:10,998 - pyskl - INFO - Epoch [66][700/1281] lr: 1.496e-02, eta: 11:40:20, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9950, loss_cls: 0.5094, loss: 0.5094 +2025-06-24 19:26:39,708 - pyskl - INFO - Epoch [66][800/1281] lr: 1.494e-02, eta: 11:39:29, time: 0.287, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9956, loss_cls: 0.5053, loss: 0.5053 +2025-06-24 19:27:26,851 - pyskl - INFO - Epoch [66][900/1281] lr: 1.492e-02, eta: 11:39:00, time: 0.471, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9925, loss_cls: 0.6114, loss: 0.6114 +2025-06-24 19:27:59,679 - pyskl - INFO - Epoch [66][1000/1281] lr: 1.490e-02, eta: 11:38:14, time: 0.328, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9944, loss_cls: 0.5596, loss: 0.5596 +2025-06-24 19:28:48,851 - pyskl - INFO - Epoch [66][1100/1281] lr: 1.488e-02, eta: 11:37:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9969, loss_cls: 0.5867, loss: 0.5867 +2025-06-24 19:29:37,930 - pyskl - INFO - Epoch [66][1200/1281] lr: 1.486e-02, eta: 11:37:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9956, loss_cls: 0.5447, loss: 0.5447 +2025-06-24 19:30:18,487 - pyskl - INFO - Saving checkpoint at 66 epochs +2025-06-24 19:31:16,985 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:31:17,040 - pyskl - INFO - +top1_acc 0.8583 +top5_acc 0.9916 +2025-06-24 19:31:17,041 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:31:17,047 - pyskl - INFO - +mean_acc 0.7929 +2025-06-24 19:31:17,051 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_65.pth was removed +2025-06-24 19:31:17,233 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_66.pth. +2025-06-24 19:31:17,234 - pyskl - INFO - Best top1_acc is 0.8583 at 66 epoch. +2025-06-24 19:31:17,236 - pyskl - INFO - Epoch(val) [66][533] top1_acc: 0.8583, top5_acc: 0.9916, mean_class_accuracy: 0.7929 +2025-06-24 19:32:35,433 - pyskl - INFO - Epoch [67][100/1281] lr: 1.482e-02, eta: 11:36:22, time: 0.782, data_time: 0.191, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9988, loss_cls: 0.4848, loss: 0.4848 +2025-06-24 19:33:24,673 - pyskl - INFO - Epoch [67][200/1281] lr: 1.480e-02, eta: 11:35:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9975, loss_cls: 0.4794, loss: 0.4794 +2025-06-24 19:34:13,507 - pyskl - INFO - Epoch [67][300/1281] lr: 1.478e-02, eta: 11:35:30, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9981, loss_cls: 0.5298, loss: 0.5298 +2025-06-24 19:35:02,250 - pyskl - INFO - Epoch [67][400/1281] lr: 1.476e-02, eta: 11:35:04, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9969, loss_cls: 0.5455, loss: 0.5455 +2025-06-24 19:35:51,416 - pyskl - INFO - Epoch [67][500/1281] lr: 1.474e-02, eta: 11:34:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9969, loss_cls: 0.5599, loss: 0.5599 +2025-06-24 19:36:40,273 - pyskl - INFO - Epoch [67][600/1281] lr: 1.472e-02, eta: 11:34:11, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9950, loss_cls: 0.5812, loss: 0.5812 +2025-06-24 19:37:29,683 - pyskl - INFO - Epoch [67][700/1281] lr: 1.470e-02, eta: 11:33:45, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9988, loss_cls: 0.4467, loss: 0.4467 +2025-06-24 19:37:56,504 - pyskl - INFO - Epoch [67][800/1281] lr: 1.468e-02, eta: 11:32:51, time: 0.268, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9969, loss_cls: 0.5288, loss: 0.5288 +2025-06-24 19:38:47,497 - pyskl - INFO - Epoch [67][900/1281] lr: 1.466e-02, eta: 11:32:28, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9962, loss_cls: 0.5568, loss: 0.5568 +2025-06-24 19:39:16,987 - pyskl - INFO - Epoch [67][1000/1281] lr: 1.464e-02, eta: 11:31:37, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9981, loss_cls: 0.5451, loss: 0.5451 +2025-06-24 19:40:05,437 - pyskl - INFO - Epoch [67][1100/1281] lr: 1.462e-02, eta: 11:31:10, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9962, loss_cls: 0.4801, loss: 0.4801 +2025-06-24 19:40:54,466 - pyskl - INFO - Epoch [67][1200/1281] lr: 1.460e-02, eta: 11:30:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9956, loss_cls: 0.5822, loss: 0.5822 +2025-06-24 19:41:35,435 - pyskl - INFO - Saving checkpoint at 67 epochs +2025-06-24 19:42:34,028 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:42:34,082 - pyskl - INFO - +top1_acc 0.8411 +top5_acc 0.9880 +2025-06-24 19:42:34,083 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:42:34,095 - pyskl - INFO - +mean_acc 0.7704 +2025-06-24 19:42:34,098 - pyskl - INFO - Epoch(val) [67][533] top1_acc: 0.8411, top5_acc: 0.9880, mean_class_accuracy: 0.7704 +2025-06-24 19:43:53,392 - pyskl - INFO - Epoch [68][100/1281] lr: 1.456e-02, eta: 11:29:44, time: 0.793, data_time: 0.192, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9975, loss_cls: 0.4754, loss: 0.4754 +2025-06-24 19:44:42,328 - pyskl - INFO - Epoch [68][200/1281] lr: 1.454e-02, eta: 11:29:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9981, loss_cls: 0.4363, loss: 0.4363 +2025-06-24 19:45:31,451 - pyskl - INFO - Epoch [68][300/1281] lr: 1.452e-02, eta: 11:28:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9956, loss_cls: 0.5025, loss: 0.5025 +2025-06-24 19:46:21,166 - pyskl - INFO - Epoch [68][400/1281] lr: 1.450e-02, eta: 11:28:25, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9975, loss_cls: 0.4883, loss: 0.4883 +2025-06-24 19:47:10,473 - pyskl - INFO - Epoch [68][500/1281] lr: 1.448e-02, eta: 11:27:58, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9956, loss_cls: 0.5808, loss: 0.5808 +2025-06-24 19:47:59,562 - pyskl - INFO - Epoch [68][600/1281] lr: 1.446e-02, eta: 11:27:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9950, loss_cls: 0.4364, loss: 0.4364 +2025-06-24 19:48:48,592 - pyskl - INFO - Epoch [68][700/1281] lr: 1.444e-02, eta: 11:27:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9956, loss_cls: 0.5448, loss: 0.5448 +2025-06-24 19:49:17,769 - pyskl - INFO - Epoch [68][800/1281] lr: 1.442e-02, eta: 11:26:14, time: 0.292, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9988, loss_cls: 0.5242, loss: 0.5242 +2025-06-24 19:50:08,817 - pyskl - INFO - Epoch [68][900/1281] lr: 1.440e-02, eta: 11:25:49, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9975, loss_cls: 0.5425, loss: 0.5425 +2025-06-24 19:50:37,398 - pyskl - INFO - Epoch [68][1000/1281] lr: 1.438e-02, eta: 11:24:58, time: 0.286, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9969, loss_cls: 0.5467, loss: 0.5467 +2025-06-24 19:51:26,105 - pyskl - INFO - Epoch [68][1100/1281] lr: 1.436e-02, eta: 11:24:30, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9962, loss_cls: 0.5594, loss: 0.5594 +2025-06-24 19:52:15,104 - pyskl - INFO - Epoch [68][1200/1281] lr: 1.434e-02, eta: 11:24:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9962, loss_cls: 0.6325, loss: 0.6325 +2025-06-24 19:52:55,632 - pyskl - INFO - Saving checkpoint at 68 epochs +2025-06-24 19:53:54,976 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:53:55,044 - pyskl - INFO - +top1_acc 0.8459 +top5_acc 0.9885 +2025-06-24 19:53:55,045 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:53:55,054 - pyskl - INFO - +mean_acc 0.7861 +2025-06-24 19:53:55,056 - pyskl - INFO - Epoch(val) [68][533] top1_acc: 0.8459, top5_acc: 0.9885, mean_class_accuracy: 0.7861 +2025-06-24 19:55:15,713 - pyskl - INFO - Epoch [69][100/1281] lr: 1.431e-02, eta: 11:23:05, time: 0.807, data_time: 0.191, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9969, loss_cls: 0.4098, loss: 0.4098 +2025-06-24 19:56:04,807 - pyskl - INFO - Epoch [69][200/1281] lr: 1.429e-02, eta: 11:22:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9981, loss_cls: 0.4603, loss: 0.4603 +2025-06-24 19:56:54,465 - pyskl - INFO - Epoch [69][300/1281] lr: 1.427e-02, eta: 11:22:11, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9969, loss_cls: 0.5174, loss: 0.5174 +2025-06-24 19:57:43,137 - pyskl - INFO - Epoch [69][400/1281] lr: 1.425e-02, eta: 11:21:44, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9956, loss_cls: 0.5754, loss: 0.5754 +2025-06-24 19:58:31,568 - pyskl - INFO - Epoch [69][500/1281] lr: 1.423e-02, eta: 11:21:16, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9975, loss_cls: 0.5238, loss: 0.5238 +2025-06-24 19:59:20,371 - pyskl - INFO - Epoch [69][600/1281] lr: 1.420e-02, eta: 11:20:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9962, loss_cls: 0.5266, loss: 0.5266 +2025-06-24 20:00:09,443 - pyskl - INFO - Epoch [69][700/1281] lr: 1.418e-02, eta: 11:20:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9975, loss_cls: 0.4867, loss: 0.4867 +2025-06-24 20:00:38,122 - pyskl - INFO - Epoch [69][800/1281] lr: 1.416e-02, eta: 11:19:30, time: 0.287, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9988, loss_cls: 0.4894, loss: 0.4894 +2025-06-24 20:01:29,447 - pyskl - INFO - Epoch [69][900/1281] lr: 1.414e-02, eta: 11:19:05, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9925, loss_cls: 0.5822, loss: 0.5822 +2025-06-24 20:01:59,260 - pyskl - INFO - Epoch [69][1000/1281] lr: 1.412e-02, eta: 11:18:15, time: 0.298, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9969, loss_cls: 0.5275, loss: 0.5275 +2025-06-24 20:02:48,389 - pyskl - INFO - Epoch [69][1100/1281] lr: 1.410e-02, eta: 11:17:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9981, loss_cls: 0.5638, loss: 0.5638 +2025-06-24 20:03:37,688 - pyskl - INFO - Epoch [69][1200/1281] lr: 1.408e-02, eta: 11:17:20, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9962, loss_cls: 0.5244, loss: 0.5244 +2025-06-24 20:04:17,768 - pyskl - INFO - Saving checkpoint at 69 epochs +2025-06-24 20:05:16,293 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:05:16,351 - pyskl - INFO - +top1_acc 0.8295 +top5_acc 0.9886 +2025-06-24 20:05:16,351 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:05:16,359 - pyskl - INFO - +mean_acc 0.7716 +2025-06-24 20:05:16,360 - pyskl - INFO - Epoch(val) [69][533] top1_acc: 0.8295, top5_acc: 0.9886, mean_class_accuracy: 0.7716 +2025-06-24 20:06:36,197 - pyskl - INFO - Epoch [70][100/1281] lr: 1.405e-02, eta: 11:16:20, time: 0.798, data_time: 0.194, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9975, loss_cls: 0.4589, loss: 0.4589 +2025-06-24 20:07:24,931 - pyskl - INFO - Epoch [70][200/1281] lr: 1.403e-02, eta: 11:15:52, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4404, loss: 0.4404 +2025-06-24 20:08:14,503 - pyskl - INFO - Epoch [70][300/1281] lr: 1.401e-02, eta: 11:15:25, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9975, loss_cls: 0.5362, loss: 0.5362 +2025-06-24 20:09:03,970 - pyskl - INFO - Epoch [70][400/1281] lr: 1.399e-02, eta: 11:14:58, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9994, loss_cls: 0.4545, loss: 0.4545 +2025-06-24 20:09:52,677 - pyskl - INFO - Epoch [70][500/1281] lr: 1.397e-02, eta: 11:14:30, time: 0.487, data_time: 0.001, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9981, loss_cls: 0.5010, loss: 0.5010 +2025-06-24 20:10:41,596 - pyskl - INFO - Epoch [70][600/1281] lr: 1.395e-02, eta: 11:14:02, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9950, loss_cls: 0.5494, loss: 0.5494 +2025-06-24 20:11:30,615 - pyskl - INFO - Epoch [70][700/1281] lr: 1.392e-02, eta: 11:13:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9912, loss_cls: 0.5290, loss: 0.5290 +2025-06-24 20:11:59,588 - pyskl - INFO - Epoch [70][800/1281] lr: 1.390e-02, eta: 11:12:43, time: 0.290, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9956, loss_cls: 0.5437, loss: 0.5437 +2025-06-24 20:12:50,830 - pyskl - INFO - Epoch [70][900/1281] lr: 1.388e-02, eta: 11:12:18, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9975, loss_cls: 0.5438, loss: 0.5438 +2025-06-24 20:13:20,854 - pyskl - INFO - Epoch [70][1000/1281] lr: 1.386e-02, eta: 11:11:28, time: 0.300, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9975, loss_cls: 0.5555, loss: 0.5555 +2025-06-24 20:14:10,250 - pyskl - INFO - Epoch [70][1100/1281] lr: 1.384e-02, eta: 11:11:00, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9975, loss_cls: 0.5036, loss: 0.5036 +2025-06-24 20:14:59,417 - pyskl - INFO - Epoch [70][1200/1281] lr: 1.382e-02, eta: 11:10:32, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9969, loss_cls: 0.5093, loss: 0.5093 +2025-06-24 20:15:39,433 - pyskl - INFO - Saving checkpoint at 70 epochs +2025-06-24 20:16:38,701 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:16:38,758 - pyskl - INFO - +top1_acc 0.8477 +top5_acc 0.9910 +2025-06-24 20:16:38,758 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:16:38,766 - pyskl - INFO - +mean_acc 0.7925 +2025-06-24 20:16:38,768 - pyskl - INFO - Epoch(val) [70][533] top1_acc: 0.8477, top5_acc: 0.9910, mean_class_accuracy: 0.7925 +2025-06-24 20:17:57,683 - pyskl - INFO - Epoch [71][100/1281] lr: 1.379e-02, eta: 11:09:30, time: 0.789, data_time: 0.193, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9994, loss_cls: 0.4578, loss: 0.4578 +2025-06-24 20:18:46,937 - pyskl - INFO - Epoch [71][200/1281] lr: 1.377e-02, eta: 11:09:03, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9975, loss_cls: 0.4476, loss: 0.4476 +2025-06-24 20:19:36,138 - pyskl - INFO - Epoch [71][300/1281] lr: 1.375e-02, eta: 11:08:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9994, loss_cls: 0.4695, loss: 0.4695 +2025-06-24 20:20:25,002 - pyskl - INFO - Epoch [71][400/1281] lr: 1.373e-02, eta: 11:08:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9969, loss_cls: 0.5247, loss: 0.5247 +2025-06-24 20:21:14,039 - pyskl - INFO - Epoch [71][500/1281] lr: 1.371e-02, eta: 11:07:38, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9962, loss_cls: 0.5205, loss: 0.5205 +2025-06-24 20:22:03,279 - pyskl - INFO - Epoch [71][600/1281] lr: 1.368e-02, eta: 11:07:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9988, loss_cls: 0.4387, loss: 0.4387 +2025-06-24 20:22:52,442 - pyskl - INFO - Epoch [71][700/1281] lr: 1.366e-02, eta: 11:06:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9962, loss_cls: 0.5166, loss: 0.5166 +2025-06-24 20:23:20,499 - pyskl - INFO - Epoch [71][800/1281] lr: 1.364e-02, eta: 11:05:50, time: 0.281, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9962, loss_cls: 0.5333, loss: 0.5333 +2025-06-24 20:24:11,524 - pyskl - INFO - Epoch [71][900/1281] lr: 1.362e-02, eta: 11:05:24, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9988, loss_cls: 0.5113, loss: 0.5113 +2025-06-24 20:24:41,939 - pyskl - INFO - Epoch [71][1000/1281] lr: 1.360e-02, eta: 11:04:35, time: 0.304, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9981, loss_cls: 0.4637, loss: 0.4637 +2025-06-24 20:25:31,157 - pyskl - INFO - Epoch [71][1100/1281] lr: 1.358e-02, eta: 11:04:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9981, loss_cls: 0.5297, loss: 0.5297 +2025-06-24 20:26:20,034 - pyskl - INFO - Epoch [71][1200/1281] lr: 1.356e-02, eta: 11:03:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9950, loss_cls: 0.5147, loss: 0.5147 +2025-06-24 20:27:00,436 - pyskl - INFO - Saving checkpoint at 71 epochs +2025-06-24 20:27:59,542 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:27:59,599 - pyskl - INFO - +top1_acc 0.8383 +top5_acc 0.9921 +2025-06-24 20:27:59,599 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:27:59,606 - pyskl - INFO - +mean_acc 0.7816 +2025-06-24 20:27:59,608 - pyskl - INFO - Epoch(val) [71][533] top1_acc: 0.8383, top5_acc: 0.9921, mean_class_accuracy: 0.7816 +2025-06-24 20:29:17,459 - pyskl - INFO - Epoch [72][100/1281] lr: 1.353e-02, eta: 11:02:34, time: 0.778, data_time: 0.190, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9988, loss_cls: 0.4456, loss: 0.4456 +2025-06-24 20:30:06,607 - pyskl - INFO - Epoch [72][200/1281] lr: 1.351e-02, eta: 11:02:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9994, loss_cls: 0.4458, loss: 0.4458 +2025-06-24 20:30:55,667 - pyskl - INFO - Epoch [72][300/1281] lr: 1.349e-02, eta: 11:01:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9988, loss_cls: 0.4654, loss: 0.4654 +2025-06-24 20:31:44,760 - pyskl - INFO - Epoch [72][400/1281] lr: 1.346e-02, eta: 11:01:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9981, loss_cls: 0.4555, loss: 0.4555 +2025-06-24 20:32:33,743 - pyskl - INFO - Epoch [72][500/1281] lr: 1.344e-02, eta: 11:00:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9981, loss_cls: 0.4655, loss: 0.4655 +2025-06-24 20:33:22,592 - pyskl - INFO - Epoch [72][600/1281] lr: 1.342e-02, eta: 11:00:11, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9969, loss_cls: 0.4826, loss: 0.4826 +2025-06-24 20:34:11,915 - pyskl - INFO - Epoch [72][700/1281] lr: 1.340e-02, eta: 10:59:42, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9994, loss_cls: 0.4837, loss: 0.4837 +2025-06-24 20:34:40,575 - pyskl - INFO - Epoch [72][800/1281] lr: 1.338e-02, eta: 10:58:51, time: 0.287, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9975, loss_cls: 0.5527, loss: 0.5527 +2025-06-24 20:35:31,817 - pyskl - INFO - Epoch [72][900/1281] lr: 1.336e-02, eta: 10:58:25, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9981, loss_cls: 0.4946, loss: 0.4946 +2025-06-24 20:36:00,241 - pyskl - INFO - Epoch [72][1000/1281] lr: 1.334e-02, eta: 10:57:33, time: 0.284, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9969, loss_cls: 0.5099, loss: 0.5099 +2025-06-24 20:36:49,350 - pyskl - INFO - Epoch [72][1100/1281] lr: 1.332e-02, eta: 10:57:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9969, loss_cls: 0.5362, loss: 0.5362 +2025-06-24 20:37:38,569 - pyskl - INFO - Epoch [72][1200/1281] lr: 1.330e-02, eta: 10:56:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9962, loss_cls: 0.5037, loss: 0.5037 +2025-06-24 20:38:19,082 - pyskl - INFO - Saving checkpoint at 72 epochs +2025-06-24 20:39:18,099 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:39:18,156 - pyskl - INFO - +top1_acc 0.8356 +top5_acc 0.9887 +2025-06-24 20:39:18,157 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:39:18,163 - pyskl - INFO - +mean_acc 0.7634 +2025-06-24 20:39:18,165 - pyskl - INFO - Epoch(val) [72][533] top1_acc: 0.8356, top5_acc: 0.9887, mean_class_accuracy: 0.7634 +2025-06-24 20:40:38,157 - pyskl - INFO - Epoch [73][100/1281] lr: 1.326e-02, eta: 10:55:34, time: 0.800, data_time: 0.195, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9988, loss_cls: 0.4565, loss: 0.4565 +2025-06-24 20:41:27,133 - pyskl - INFO - Epoch [73][200/1281] lr: 1.324e-02, eta: 10:55:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 1.0000, loss_cls: 0.4521, loss: 0.4521 +2025-06-24 20:42:16,423 - pyskl - INFO - Epoch [73][300/1281] lr: 1.322e-02, eta: 10:54:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9994, loss_cls: 0.4645, loss: 0.4645 +2025-06-24 20:43:05,139 - pyskl - INFO - Epoch [73][400/1281] lr: 1.320e-02, eta: 10:54:07, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9975, loss_cls: 0.4848, loss: 0.4848 +2025-06-24 20:43:54,164 - pyskl - INFO - Epoch [73][500/1281] lr: 1.318e-02, eta: 10:53:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9988, loss_cls: 0.4173, loss: 0.4173 +2025-06-24 20:44:42,990 - pyskl - INFO - Epoch [73][600/1281] lr: 1.316e-02, eta: 10:53:08, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9994, loss_cls: 0.5112, loss: 0.5112 +2025-06-24 20:45:31,980 - pyskl - INFO - Epoch [73][700/1281] lr: 1.314e-02, eta: 10:52:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9981, loss_cls: 0.5293, loss: 0.5293 +2025-06-24 20:46:01,433 - pyskl - INFO - Epoch [73][800/1281] lr: 1.312e-02, eta: 10:51:49, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9988, loss_cls: 0.4613, loss: 0.4613 +2025-06-24 20:46:52,492 - pyskl - INFO - Epoch [73][900/1281] lr: 1.310e-02, eta: 10:51:21, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9975, loss_cls: 0.4872, loss: 0.4872 +2025-06-24 20:47:20,986 - pyskl - INFO - Epoch [73][1000/1281] lr: 1.308e-02, eta: 10:50:30, time: 0.285, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9994, loss_cls: 0.5062, loss: 0.5062 +2025-06-24 20:48:09,947 - pyskl - INFO - Epoch [73][1100/1281] lr: 1.306e-02, eta: 10:50:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9994, loss_cls: 0.5002, loss: 0.5002 +2025-06-24 20:48:59,004 - pyskl - INFO - Epoch [73][1200/1281] lr: 1.304e-02, eta: 10:49:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9981, loss_cls: 0.4928, loss: 0.4928 +2025-06-24 20:49:39,234 - pyskl - INFO - Saving checkpoint at 73 epochs +2025-06-24 20:50:37,829 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:50:37,885 - pyskl - INFO - +top1_acc 0.8508 +top5_acc 0.9908 +2025-06-24 20:50:37,885 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:50:37,891 - pyskl - INFO - +mean_acc 0.7926 +2025-06-24 20:50:37,893 - pyskl - INFO - Epoch(val) [73][533] top1_acc: 0.8508, top5_acc: 0.9908, mean_class_accuracy: 0.7926 +2025-06-24 20:51:58,921 - pyskl - INFO - Epoch [74][100/1281] lr: 1.300e-02, eta: 10:48:30, time: 0.810, data_time: 0.192, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9994, loss_cls: 0.4048, loss: 0.4048 +2025-06-24 20:52:47,805 - pyskl - INFO - Epoch [74][200/1281] lr: 1.298e-02, eta: 10:48:01, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9962, loss_cls: 0.4369, loss: 0.4369 +2025-06-24 20:53:36,896 - pyskl - INFO - Epoch [74][300/1281] lr: 1.296e-02, eta: 10:47:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9994, loss_cls: 0.4071, loss: 0.4071 +2025-06-24 20:54:25,731 - pyskl - INFO - Epoch [74][400/1281] lr: 1.294e-02, eta: 10:47:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9969, loss_cls: 0.4649, loss: 0.4649 +2025-06-24 20:55:14,956 - pyskl - INFO - Epoch [74][500/1281] lr: 1.292e-02, eta: 10:46:32, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9994, loss_cls: 0.4574, loss: 0.4574 +2025-06-24 20:56:04,207 - pyskl - INFO - Epoch [74][600/1281] lr: 1.290e-02, eta: 10:46:03, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9950, loss_cls: 0.5127, loss: 0.5127 +2025-06-24 20:56:53,458 - pyskl - INFO - Epoch [74][700/1281] lr: 1.288e-02, eta: 10:45:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9994, loss_cls: 0.4258, loss: 0.4258 +2025-06-24 20:57:22,147 - pyskl - INFO - Epoch [74][800/1281] lr: 1.286e-02, eta: 10:44:42, time: 0.287, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 1.0000, loss_cls: 0.4658, loss: 0.4658 +2025-06-24 20:58:13,264 - pyskl - INFO - Epoch [74][900/1281] lr: 1.284e-02, eta: 10:44:15, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9975, loss_cls: 0.4766, loss: 0.4766 +2025-06-24 20:58:41,809 - pyskl - INFO - Epoch [74][1000/1281] lr: 1.282e-02, eta: 10:43:24, time: 0.285, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9962, loss_cls: 0.4469, loss: 0.4469 +2025-06-24 20:59:31,069 - pyskl - INFO - Epoch [74][1100/1281] lr: 1.280e-02, eta: 10:42:54, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9969, loss_cls: 0.5368, loss: 0.5368 +2025-06-24 21:00:20,069 - pyskl - INFO - Epoch [74][1200/1281] lr: 1.278e-02, eta: 10:42:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9988, loss_cls: 0.4410, loss: 0.4410 +2025-06-24 21:01:01,078 - pyskl - INFO - Saving checkpoint at 74 epochs +2025-06-24 21:01:59,591 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:01:59,646 - pyskl - INFO - +top1_acc 0.8522 +top5_acc 0.9912 +2025-06-24 21:01:59,646 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:01:59,653 - pyskl - INFO - +mean_acc 0.7840 +2025-06-24 21:01:59,655 - pyskl - INFO - Epoch(val) [74][533] top1_acc: 0.8522, top5_acc: 0.9912, mean_class_accuracy: 0.7840 +2025-06-24 21:03:19,915 - pyskl - INFO - Epoch [75][100/1281] lr: 1.274e-02, eta: 10:41:22, time: 0.803, data_time: 0.192, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9994, loss_cls: 0.4000, loss: 0.4000 +2025-06-24 21:04:08,963 - pyskl - INFO - Epoch [75][200/1281] lr: 1.272e-02, eta: 10:40:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4394, loss: 0.4394 +2025-06-24 21:04:58,465 - pyskl - INFO - Epoch [75][300/1281] lr: 1.270e-02, eta: 10:40:22, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9975, loss_cls: 0.4545, loss: 0.4545 +2025-06-24 21:05:47,951 - pyskl - INFO - Epoch [75][400/1281] lr: 1.268e-02, eta: 10:39:53, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9969, loss_cls: 0.4153, loss: 0.4153 +2025-06-24 21:06:36,953 - pyskl - INFO - Epoch [75][500/1281] lr: 1.266e-02, eta: 10:39:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9975, loss_cls: 0.4561, loss: 0.4561 +2025-06-24 21:07:26,118 - pyskl - INFO - Epoch [75][600/1281] lr: 1.264e-02, eta: 10:38:53, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9962, loss_cls: 0.5546, loss: 0.5546 +2025-06-24 21:08:15,483 - pyskl - INFO - Epoch [75][700/1281] lr: 1.262e-02, eta: 10:38:23, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9988, loss_cls: 0.5071, loss: 0.5071 +2025-06-24 21:08:44,738 - pyskl - INFO - Epoch [75][800/1281] lr: 1.260e-02, eta: 10:37:33, time: 0.293, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9969, loss_cls: 0.4535, loss: 0.4535 +2025-06-24 21:09:35,823 - pyskl - INFO - Epoch [75][900/1281] lr: 1.258e-02, eta: 10:37:05, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4617, loss: 0.4617 +2025-06-24 21:10:03,964 - pyskl - INFO - Epoch [75][1000/1281] lr: 1.256e-02, eta: 10:36:14, time: 0.281, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9975, loss_cls: 0.4819, loss: 0.4819 +2025-06-24 21:10:53,073 - pyskl - INFO - Epoch [75][1100/1281] lr: 1.254e-02, eta: 10:35:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9975, loss_cls: 0.4741, loss: 0.4741 +2025-06-24 21:11:42,475 - pyskl - INFO - Epoch [75][1200/1281] lr: 1.252e-02, eta: 10:35:14, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9962, loss_cls: 0.4835, loss: 0.4835 +2025-06-24 21:12:22,744 - pyskl - INFO - Saving checkpoint at 75 epochs +2025-06-24 21:13:22,126 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:13:22,198 - pyskl - INFO - +top1_acc 0.8405 +top5_acc 0.9885 +2025-06-24 21:13:22,198 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:13:22,205 - pyskl - INFO - +mean_acc 0.7950 +2025-06-24 21:13:22,207 - pyskl - INFO - Epoch(val) [75][533] top1_acc: 0.8405, top5_acc: 0.9885, mean_class_accuracy: 0.7950 +2025-06-24 21:14:41,177 - pyskl - INFO - Epoch [76][100/1281] lr: 1.248e-02, eta: 10:34:09, time: 0.790, data_time: 0.188, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9994, loss_cls: 0.4624, loss: 0.4624 +2025-06-24 21:15:30,564 - pyskl - INFO - Epoch [76][200/1281] lr: 1.246e-02, eta: 10:33:39, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9969, loss_cls: 0.4684, loss: 0.4684 +2025-06-24 21:16:20,270 - pyskl - INFO - Epoch [76][300/1281] lr: 1.244e-02, eta: 10:33:10, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9969, loss_cls: 0.4369, loss: 0.4369 +2025-06-24 21:17:09,342 - pyskl - INFO - Epoch [76][400/1281] lr: 1.242e-02, eta: 10:32:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9975, loss_cls: 0.4338, loss: 0.4338 +2025-06-24 21:17:58,481 - pyskl - INFO - Epoch [76][500/1281] lr: 1.240e-02, eta: 10:32:09, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9969, loss_cls: 0.4972, loss: 0.4972 +2025-06-24 21:18:47,546 - pyskl - INFO - Epoch [76][600/1281] lr: 1.238e-02, eta: 10:31:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9975, loss_cls: 0.4922, loss: 0.4922 +2025-06-24 21:19:36,882 - pyskl - INFO - Epoch [76][700/1281] lr: 1.236e-02, eta: 10:31:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9975, loss_cls: 0.4938, loss: 0.4938 +2025-06-24 21:20:06,776 - pyskl - INFO - Epoch [76][800/1281] lr: 1.234e-02, eta: 10:30:19, time: 0.299, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9988, loss_cls: 0.4285, loss: 0.4285 +2025-06-24 21:20:57,999 - pyskl - INFO - Epoch [76][900/1281] lr: 1.232e-02, eta: 10:29:51, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 1.0000, loss_cls: 0.4671, loss: 0.4671 +2025-06-24 21:21:27,317 - pyskl - INFO - Epoch [76][1000/1281] lr: 1.230e-02, eta: 10:29:01, time: 0.293, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9975, loss_cls: 0.4541, loss: 0.4541 +2025-06-24 21:22:16,879 - pyskl - INFO - Epoch [76][1100/1281] lr: 1.228e-02, eta: 10:28:31, time: 0.496, data_time: 0.001, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9969, loss_cls: 0.4497, loss: 0.4497 +2025-06-24 21:23:05,898 - pyskl - INFO - Epoch [76][1200/1281] lr: 1.225e-02, eta: 10:28:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9988, loss_cls: 0.4894, loss: 0.4894 +2025-06-24 21:23:46,225 - pyskl - INFO - Saving checkpoint at 76 epochs +2025-06-24 21:24:44,855 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:24:44,915 - pyskl - INFO - +top1_acc 0.8518 +top5_acc 0.9904 +2025-06-24 21:24:44,915 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:24:44,922 - pyskl - INFO - +mean_acc 0.8010 +2025-06-24 21:24:44,924 - pyskl - INFO - Epoch(val) [76][533] top1_acc: 0.8518, top5_acc: 0.9904, mean_class_accuracy: 0.8010 +2025-06-24 21:26:04,333 - pyskl - INFO - Epoch [77][100/1281] lr: 1.222e-02, eta: 10:26:56, time: 0.794, data_time: 0.191, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9994, loss_cls: 0.4155, loss: 0.4155 +2025-06-24 21:26:53,441 - pyskl - INFO - Epoch [77][200/1281] lr: 1.220e-02, eta: 10:26:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3643, loss: 0.3643 +2025-06-24 21:27:42,667 - pyskl - INFO - Epoch [77][300/1281] lr: 1.218e-02, eta: 10:25:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9981, loss_cls: 0.4489, loss: 0.4489 +2025-06-24 21:28:31,355 - pyskl - INFO - Epoch [77][400/1281] lr: 1.216e-02, eta: 10:25:23, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9981, loss_cls: 0.4927, loss: 0.4927 +2025-06-24 21:29:20,390 - pyskl - INFO - Epoch [77][500/1281] lr: 1.214e-02, eta: 10:24:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9994, loss_cls: 0.4072, loss: 0.4072 +2025-06-24 21:30:09,417 - pyskl - INFO - Epoch [77][600/1281] lr: 1.212e-02, eta: 10:24:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9981, loss_cls: 0.4426, loss: 0.4426 +2025-06-24 21:30:58,244 - pyskl - INFO - Epoch [77][700/1281] lr: 1.210e-02, eta: 10:23:51, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9988, loss_cls: 0.3970, loss: 0.3970 +2025-06-24 21:31:27,548 - pyskl - INFO - Epoch [77][800/1281] lr: 1.207e-02, eta: 10:23:01, time: 0.293, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9988, loss_cls: 0.4495, loss: 0.4495 +2025-06-24 21:32:18,643 - pyskl - INFO - Epoch [77][900/1281] lr: 1.205e-02, eta: 10:22:32, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9988, loss_cls: 0.4733, loss: 0.4733 +2025-06-24 21:32:46,264 - pyskl - INFO - Epoch [77][1000/1281] lr: 1.203e-02, eta: 10:21:41, time: 0.276, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9994, loss_cls: 0.4418, loss: 0.4418 +2025-06-24 21:33:35,504 - pyskl - INFO - Epoch [77][1100/1281] lr: 1.201e-02, eta: 10:21:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9981, loss_cls: 0.4069, loss: 0.4069 +2025-06-24 21:34:24,712 - pyskl - INFO - Epoch [77][1200/1281] lr: 1.199e-02, eta: 10:20:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9962, loss_cls: 0.4642, loss: 0.4642 +2025-06-24 21:35:05,508 - pyskl - INFO - Saving checkpoint at 77 epochs +2025-06-24 21:36:04,710 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:36:04,768 - pyskl - INFO - +top1_acc 0.8497 +top5_acc 0.9878 +2025-06-24 21:36:04,768 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:36:04,775 - pyskl - INFO - +mean_acc 0.8184 +2025-06-24 21:36:04,777 - pyskl - INFO - Epoch(val) [77][533] top1_acc: 0.8497, top5_acc: 0.9878, mean_class_accuracy: 0.8184 +2025-06-24 21:37:23,817 - pyskl - INFO - Epoch [78][100/1281] lr: 1.196e-02, eta: 10:19:34, time: 0.790, data_time: 0.190, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.4195, loss: 0.4195 +2025-06-24 21:38:12,917 - pyskl - INFO - Epoch [78][200/1281] lr: 1.194e-02, eta: 10:19:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9981, loss_cls: 0.4038, loss: 0.4038 +2025-06-24 21:39:02,144 - pyskl - INFO - Epoch [78][300/1281] lr: 1.192e-02, eta: 10:18:32, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9994, loss_cls: 0.3839, loss: 0.3839 +2025-06-24 21:39:51,104 - pyskl - INFO - Epoch [78][400/1281] lr: 1.190e-02, eta: 10:18:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9981, loss_cls: 0.4345, loss: 0.4345 +2025-06-24 21:40:40,212 - pyskl - INFO - Epoch [78][500/1281] lr: 1.187e-02, eta: 10:17:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9969, loss_cls: 0.4313, loss: 0.4313 +2025-06-24 21:41:29,341 - pyskl - INFO - Epoch [78][600/1281] lr: 1.185e-02, eta: 10:16:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9969, loss_cls: 0.4242, loss: 0.4242 +2025-06-24 21:42:18,420 - pyskl - INFO - Epoch [78][700/1281] lr: 1.183e-02, eta: 10:16:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4315, loss: 0.4315 +2025-06-24 21:42:48,842 - pyskl - INFO - Epoch [78][800/1281] lr: 1.181e-02, eta: 10:15:38, time: 0.304, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 1.0000, loss_cls: 0.4549, loss: 0.4549 +2025-06-24 21:43:40,082 - pyskl - INFO - Epoch [78][900/1281] lr: 1.179e-02, eta: 10:15:09, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9975, loss_cls: 0.4479, loss: 0.4479 +2025-06-24 21:44:07,837 - pyskl - INFO - Epoch [78][1000/1281] lr: 1.177e-02, eta: 10:14:18, time: 0.278, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9975, loss_cls: 0.4645, loss: 0.4645 +2025-06-24 21:44:56,723 - pyskl - INFO - Epoch [78][1100/1281] lr: 1.175e-02, eta: 10:13:46, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9988, loss_cls: 0.4419, loss: 0.4419 +2025-06-24 21:45:46,046 - pyskl - INFO - Epoch [78][1200/1281] lr: 1.173e-02, eta: 10:13:15, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9975, loss_cls: 0.4229, loss: 0.4229 +2025-06-24 21:46:26,656 - pyskl - INFO - Saving checkpoint at 78 epochs +2025-06-24 21:47:26,089 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:47:26,145 - pyskl - INFO - +top1_acc 0.8522 +top5_acc 0.9887 +2025-06-24 21:47:26,145 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:47:26,152 - pyskl - INFO - +mean_acc 0.8038 +2025-06-24 21:47:26,153 - pyskl - INFO - Epoch(val) [78][533] top1_acc: 0.8522, top5_acc: 0.9887, mean_class_accuracy: 0.8038 +2025-06-24 21:48:45,852 - pyskl - INFO - Epoch [79][100/1281] lr: 1.169e-02, eta: 10:12:10, time: 0.797, data_time: 0.188, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9975, loss_cls: 0.4243, loss: 0.4243 +2025-06-24 21:49:35,315 - pyskl - INFO - Epoch [79][200/1281] lr: 1.167e-02, eta: 10:11:39, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9994, loss_cls: 0.3673, loss: 0.3673 +2025-06-24 21:50:24,542 - pyskl - INFO - Epoch [79][300/1281] lr: 1.165e-02, eta: 10:11:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 1.0000, loss_cls: 0.3828, loss: 0.3828 +2025-06-24 21:51:13,663 - pyskl - INFO - Epoch [79][400/1281] lr: 1.163e-02, eta: 10:10:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9981, loss_cls: 0.4421, loss: 0.4421 +2025-06-24 21:52:02,985 - pyskl - INFO - Epoch [79][500/1281] lr: 1.161e-02, eta: 10:10:05, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9969, loss_cls: 0.4585, loss: 0.4585 +2025-06-24 21:52:52,036 - pyskl - INFO - Epoch [79][600/1281] lr: 1.159e-02, eta: 10:09:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9969, loss_cls: 0.4554, loss: 0.4554 +2025-06-24 21:53:41,055 - pyskl - INFO - Epoch [79][700/1281] lr: 1.157e-02, eta: 10:09:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9988, loss_cls: 0.4226, loss: 0.4226 +2025-06-24 21:54:10,718 - pyskl - INFO - Epoch [79][800/1281] lr: 1.155e-02, eta: 10:08:13, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9969, loss_cls: 0.4394, loss: 0.4394 +2025-06-24 21:55:01,632 - pyskl - INFO - Epoch [79][900/1281] lr: 1.153e-02, eta: 10:07:43, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9988, loss_cls: 0.4102, loss: 0.4102 +2025-06-24 21:55:30,530 - pyskl - INFO - Epoch [79][1000/1281] lr: 1.151e-02, eta: 10:06:53, time: 0.289, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9981, loss_cls: 0.4327, loss: 0.4327 +2025-06-24 21:56:19,962 - pyskl - INFO - Epoch [79][1100/1281] lr: 1.149e-02, eta: 10:06:22, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9975, loss_cls: 0.4446, loss: 0.4446 +2025-06-24 21:57:09,089 - pyskl - INFO - Epoch [79][1200/1281] lr: 1.147e-02, eta: 10:05:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9994, loss_cls: 0.4939, loss: 0.4939 +2025-06-24 21:57:49,576 - pyskl - INFO - Saving checkpoint at 79 epochs +2025-06-24 21:58:48,346 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:58:48,414 - pyskl - INFO - +top1_acc 0.8587 +top5_acc 0.9907 +2025-06-24 21:58:48,414 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:58:48,423 - pyskl - INFO - +mean_acc 0.7979 +2025-06-24 21:58:48,427 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_66.pth was removed +2025-06-24 21:58:48,621 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_79.pth. +2025-06-24 21:58:48,621 - pyskl - INFO - Best top1_acc is 0.8587 at 79 epoch. +2025-06-24 21:58:48,624 - pyskl - INFO - Epoch(val) [79][533] top1_acc: 0.8587, top5_acc: 0.9907, mean_class_accuracy: 0.7979 +2025-06-24 22:00:09,552 - pyskl - INFO - Epoch [80][100/1281] lr: 1.143e-02, eta: 10:04:45, time: 0.809, data_time: 0.191, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9981, loss_cls: 0.4059, loss: 0.4059 +2025-06-24 22:00:58,440 - pyskl - INFO - Epoch [80][200/1281] lr: 1.141e-02, eta: 10:04:13, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9994, loss_cls: 0.4233, loss: 0.4233 +2025-06-24 22:01:47,717 - pyskl - INFO - Epoch [80][300/1281] lr: 1.139e-02, eta: 10:03:42, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9981, loss_cls: 0.3801, loss: 0.3801 +2025-06-24 22:02:37,151 - pyskl - INFO - Epoch [80][400/1281] lr: 1.137e-02, eta: 10:03:10, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9994, loss_cls: 0.4244, loss: 0.4244 +2025-06-24 22:03:26,180 - pyskl - INFO - Epoch [80][500/1281] lr: 1.135e-02, eta: 10:02:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9994, loss_cls: 0.3841, loss: 0.3841 +2025-06-24 22:04:15,179 - pyskl - INFO - Epoch [80][600/1281] lr: 1.133e-02, eta: 10:02:06, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9994, loss_cls: 0.4053, loss: 0.4053 +2025-06-24 22:05:04,544 - pyskl - INFO - Epoch [80][700/1281] lr: 1.131e-02, eta: 10:01:35, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9975, loss_cls: 0.4466, loss: 0.4466 +2025-06-24 22:05:32,539 - pyskl - INFO - Epoch [80][800/1281] lr: 1.129e-02, eta: 10:00:44, time: 0.280, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9956, loss_cls: 0.3930, loss: 0.3930 +2025-06-24 22:06:23,761 - pyskl - INFO - Epoch [80][900/1281] lr: 1.127e-02, eta: 10:00:14, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9994, loss_cls: 0.4467, loss: 0.4467 +2025-06-24 22:06:53,558 - pyskl - INFO - Epoch [80][1000/1281] lr: 1.125e-02, eta: 9:59:25, time: 0.298, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9981, loss_cls: 0.4367, loss: 0.4367 +2025-06-24 22:07:42,432 - pyskl - INFO - Epoch [80][1100/1281] lr: 1.123e-02, eta: 9:58:53, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9994, loss_cls: 0.4220, loss: 0.4220 +2025-06-24 22:08:31,102 - pyskl - INFO - Epoch [80][1200/1281] lr: 1.121e-02, eta: 9:58:21, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9956, loss_cls: 0.4135, loss: 0.4135 +2025-06-24 22:09:11,375 - pyskl - INFO - Saving checkpoint at 80 epochs +2025-06-24 22:10:11,056 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:10:11,112 - pyskl - INFO - +top1_acc 0.8588 +top5_acc 0.9871 +2025-06-24 22:10:11,112 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:10:11,119 - pyskl - INFO - +mean_acc 0.7909 +2025-06-24 22:10:11,123 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_79.pth was removed +2025-06-24 22:10:11,304 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_80.pth. +2025-06-24 22:10:11,305 - pyskl - INFO - Best top1_acc is 0.8588 at 80 epoch. +2025-06-24 22:10:11,307 - pyskl - INFO - Epoch(val) [80][533] top1_acc: 0.8588, top5_acc: 0.9871, mean_class_accuracy: 0.7909 +2025-06-24 22:11:30,710 - pyskl - INFO - Epoch [81][100/1281] lr: 1.117e-02, eta: 9:57:14, time: 0.794, data_time: 0.186, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9988, loss_cls: 0.3626, loss: 0.3626 +2025-06-24 22:12:20,086 - pyskl - INFO - Epoch [81][200/1281] lr: 1.115e-02, eta: 9:56:42, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9994, loss_cls: 0.3847, loss: 0.3847 +2025-06-24 22:13:08,876 - pyskl - INFO - Epoch [81][300/1281] lr: 1.113e-02, eta: 9:56:10, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9975, loss_cls: 0.4381, loss: 0.4381 +2025-06-24 22:13:58,130 - pyskl - INFO - Epoch [81][400/1281] lr: 1.111e-02, eta: 9:55:38, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9969, loss_cls: 0.4007, loss: 0.4007 +2025-06-24 22:14:46,815 - pyskl - INFO - Epoch [81][500/1281] lr: 1.109e-02, eta: 9:55:05, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9969, loss_cls: 0.4853, loss: 0.4853 +2025-06-24 22:15:35,673 - pyskl - INFO - Epoch [81][600/1281] lr: 1.107e-02, eta: 9:54:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9988, loss_cls: 0.4339, loss: 0.4339 +2025-06-24 22:16:24,861 - pyskl - INFO - Epoch [81][700/1281] lr: 1.105e-02, eta: 9:54:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9981, loss_cls: 0.4214, loss: 0.4214 +2025-06-24 22:16:53,058 - pyskl - INFO - Epoch [81][800/1281] lr: 1.103e-02, eta: 9:53:11, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9994, loss_cls: 0.3428, loss: 0.3428 +2025-06-24 22:17:44,160 - pyskl - INFO - Epoch [81][900/1281] lr: 1.101e-02, eta: 9:52:40, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9994, loss_cls: 0.3812, loss: 0.3812 +2025-06-24 22:18:14,164 - pyskl - INFO - Epoch [81][1000/1281] lr: 1.099e-02, eta: 9:51:51, time: 0.300, data_time: 0.001, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9975, loss_cls: 0.4251, loss: 0.4251 +2025-06-24 22:19:03,117 - pyskl - INFO - Epoch [81][1100/1281] lr: 1.097e-02, eta: 9:51:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9988, loss_cls: 0.4219, loss: 0.4219 +2025-06-24 22:19:52,248 - pyskl - INFO - Epoch [81][1200/1281] lr: 1.095e-02, eta: 9:50:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9956, loss_cls: 0.3908, loss: 0.3908 +2025-06-24 22:20:32,802 - pyskl - INFO - Saving checkpoint at 81 epochs +2025-06-24 22:21:31,612 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:21:31,670 - pyskl - INFO - +top1_acc 0.8404 +top5_acc 0.9869 +2025-06-24 22:21:31,670 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:21:31,677 - pyskl - INFO - +mean_acc 0.7788 +2025-06-24 22:21:31,679 - pyskl - INFO - Epoch(val) [81][533] top1_acc: 0.8404, top5_acc: 0.9869, mean_class_accuracy: 0.7788 +2025-06-24 22:22:51,467 - pyskl - INFO - Epoch [82][100/1281] lr: 1.091e-02, eta: 9:49:40, time: 0.798, data_time: 0.190, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9988, loss_cls: 0.4209, loss: 0.4209 +2025-06-24 22:23:40,677 - pyskl - INFO - Epoch [82][200/1281] lr: 1.089e-02, eta: 9:49:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9988, loss_cls: 0.4030, loss: 0.4030 +2025-06-24 22:24:29,483 - pyskl - INFO - Epoch [82][300/1281] lr: 1.087e-02, eta: 9:48:35, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9975, loss_cls: 0.3858, loss: 0.3858 +2025-06-24 22:25:18,790 - pyskl - INFO - Epoch [82][400/1281] lr: 1.085e-02, eta: 9:48:03, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9969, loss_cls: 0.3889, loss: 0.3889 +2025-06-24 22:26:07,961 - pyskl - INFO - Epoch [82][500/1281] lr: 1.083e-02, eta: 9:47:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 1.0000, loss_cls: 0.3680, loss: 0.3680 +2025-06-24 22:26:57,629 - pyskl - INFO - Epoch [82][600/1281] lr: 1.081e-02, eta: 9:46:58, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3661, loss: 0.3661 +2025-06-24 22:27:46,565 - pyskl - INFO - Epoch [82][700/1281] lr: 1.079e-02, eta: 9:46:26, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9975, loss_cls: 0.3927, loss: 0.3927 +2025-06-24 22:28:14,891 - pyskl - INFO - Epoch [82][800/1281] lr: 1.077e-02, eta: 9:45:36, time: 0.283, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.4132, loss: 0.4132 +2025-06-24 22:29:05,941 - pyskl - INFO - Epoch [82][900/1281] lr: 1.075e-02, eta: 9:45:05, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9988, loss_cls: 0.4087, loss: 0.4087 +2025-06-24 22:29:37,545 - pyskl - INFO - Epoch [82][1000/1281] lr: 1.073e-02, eta: 9:44:17, time: 0.316, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9994, loss_cls: 0.3726, loss: 0.3726 +2025-06-24 22:30:26,669 - pyskl - INFO - Epoch [82][1100/1281] lr: 1.071e-02, eta: 9:43:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9988, loss_cls: 0.4439, loss: 0.4439 +2025-06-24 22:31:15,506 - pyskl - INFO - Epoch [82][1200/1281] lr: 1.069e-02, eta: 9:43:12, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9994, loss_cls: 0.4115, loss: 0.4115 +2025-06-24 22:31:55,840 - pyskl - INFO - Saving checkpoint at 82 epochs +2025-06-24 22:32:54,706 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:32:54,761 - pyskl - INFO - +top1_acc 0.8507 +top5_acc 0.9878 +2025-06-24 22:32:54,761 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:32:54,768 - pyskl - INFO - +mean_acc 0.7949 +2025-06-24 22:32:54,770 - pyskl - INFO - Epoch(val) [82][533] top1_acc: 0.8507, top5_acc: 0.9878, mean_class_accuracy: 0.7949 +2025-06-24 22:34:15,862 - pyskl - INFO - Epoch [83][100/1281] lr: 1.065e-02, eta: 9:42:06, time: 0.811, data_time: 0.196, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9988, loss_cls: 0.3985, loss: 0.3985 +2025-06-24 22:35:05,162 - pyskl - INFO - Epoch [83][200/1281] lr: 1.063e-02, eta: 9:41:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 1.0000, loss_cls: 0.3769, loss: 0.3769 +2025-06-24 22:35:54,572 - pyskl - INFO - Epoch [83][300/1281] lr: 1.061e-02, eta: 9:41:01, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9994, loss_cls: 0.3874, loss: 0.3874 +2025-06-24 22:36:43,854 - pyskl - INFO - Epoch [83][400/1281] lr: 1.059e-02, eta: 9:40:29, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9988, loss_cls: 0.4384, loss: 0.4384 +2025-06-24 22:37:33,116 - pyskl - INFO - Epoch [83][500/1281] lr: 1.057e-02, eta: 9:39:56, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3514, loss: 0.3514 +2025-06-24 22:38:22,166 - pyskl - INFO - Epoch [83][600/1281] lr: 1.055e-02, eta: 9:39:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9981, loss_cls: 0.4362, loss: 0.4362 +2025-06-24 22:39:11,470 - pyskl - INFO - Epoch [83][700/1281] lr: 1.053e-02, eta: 9:38:50, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9994, loss_cls: 0.3779, loss: 0.3779 +2025-06-24 22:39:41,022 - pyskl - INFO - Epoch [83][800/1281] lr: 1.051e-02, eta: 9:38:01, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9994, loss_cls: 0.4052, loss: 0.4052 +2025-06-24 22:40:27,676 - pyskl - INFO - Epoch [83][900/1281] lr: 1.049e-02, eta: 9:37:26, time: 0.467, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9969, loss_cls: 0.4598, loss: 0.4598 +2025-06-24 22:41:02,205 - pyskl - INFO - Epoch [83][1000/1281] lr: 1.047e-02, eta: 9:36:42, time: 0.345, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9981, loss_cls: 0.4201, loss: 0.4201 +2025-06-24 22:41:50,618 - pyskl - INFO - Epoch [83][1100/1281] lr: 1.045e-02, eta: 9:36:08, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9975, loss_cls: 0.4227, loss: 0.4227 +2025-06-24 22:42:39,503 - pyskl - INFO - Epoch [83][1200/1281] lr: 1.043e-02, eta: 9:35:35, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9981, loss_cls: 0.3760, loss: 0.3760 +2025-06-24 22:43:19,559 - pyskl - INFO - Saving checkpoint at 83 epochs +2025-06-24 22:44:18,117 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:44:18,179 - pyskl - INFO - +top1_acc 0.8768 +top5_acc 0.9930 +2025-06-24 22:44:18,179 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:44:18,188 - pyskl - INFO - +mean_acc 0.8261 +2025-06-24 22:44:18,192 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_80.pth was removed +2025-06-24 22:44:18,380 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_83.pth. +2025-06-24 22:44:18,380 - pyskl - INFO - Best top1_acc is 0.8768 at 83 epoch. +2025-06-24 22:44:18,383 - pyskl - INFO - Epoch(val) [83][533] top1_acc: 0.8768, top5_acc: 0.9930, mean_class_accuracy: 0.8261 +2025-06-24 22:45:38,387 - pyskl - INFO - Epoch [84][100/1281] lr: 1.040e-02, eta: 9:34:28, time: 0.800, data_time: 0.188, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9994, loss_cls: 0.3916, loss: 0.3916 +2025-06-24 22:46:27,641 - pyskl - INFO - Epoch [84][200/1281] lr: 1.038e-02, eta: 9:33:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 1.0000, loss_cls: 0.3447, loss: 0.3447 +2025-06-24 22:47:16,361 - pyskl - INFO - Epoch [84][300/1281] lr: 1.036e-02, eta: 9:33:22, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9988, loss_cls: 0.3529, loss: 0.3529 +2025-06-24 22:48:05,613 - pyskl - INFO - Epoch [84][400/1281] lr: 1.034e-02, eta: 9:32:49, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9975, loss_cls: 0.3903, loss: 0.3903 +2025-06-24 22:48:54,949 - pyskl - INFO - Epoch [84][500/1281] lr: 1.031e-02, eta: 9:32:16, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9975, loss_cls: 0.4067, loss: 0.4067 +2025-06-24 22:49:44,122 - pyskl - INFO - Epoch [84][600/1281] lr: 1.029e-02, eta: 9:31:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9988, loss_cls: 0.3265, loss: 0.3265 +2025-06-24 22:50:33,275 - pyskl - INFO - Epoch [84][700/1281] lr: 1.027e-02, eta: 9:31:09, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9988, loss_cls: 0.3972, loss: 0.3972 +2025-06-24 22:51:02,761 - pyskl - INFO - Epoch [84][800/1281] lr: 1.025e-02, eta: 9:30:21, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 1.0000, loss_cls: 0.3412, loss: 0.3412 +2025-06-24 22:51:48,903 - pyskl - INFO - Epoch [84][900/1281] lr: 1.023e-02, eta: 9:29:45, time: 0.461, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9994, loss_cls: 0.3913, loss: 0.3913 +2025-06-24 22:52:22,382 - pyskl - INFO - Epoch [84][1000/1281] lr: 1.021e-02, eta: 9:28:59, time: 0.335, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9994, loss_cls: 0.3828, loss: 0.3828 +2025-06-24 22:53:11,561 - pyskl - INFO - Epoch [84][1100/1281] lr: 1.019e-02, eta: 9:28:26, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.4257, loss: 0.4257 +2025-06-24 22:54:00,351 - pyskl - INFO - Epoch [84][1200/1281] lr: 1.017e-02, eta: 9:27:53, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 1.0000, loss_cls: 0.4190, loss: 0.4190 +2025-06-24 22:54:41,036 - pyskl - INFO - Saving checkpoint at 84 epochs +2025-06-24 22:55:40,129 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:55:40,196 - pyskl - INFO - +top1_acc 0.8346 +top5_acc 0.9907 +2025-06-24 22:55:40,196 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:55:40,205 - pyskl - INFO - +mean_acc 0.7879 +2025-06-24 22:55:40,208 - pyskl - INFO - Epoch(val) [84][533] top1_acc: 0.8346, top5_acc: 0.9907, mean_class_accuracy: 0.7879 +2025-06-24 22:57:01,458 - pyskl - INFO - Epoch [85][100/1281] lr: 1.014e-02, eta: 9:26:46, time: 0.812, data_time: 0.191, memory: 4083, top1_acc: 0.9375, top5_acc: 1.0000, loss_cls: 0.3584, loss: 0.3584 +2025-06-24 22:57:50,962 - pyskl - INFO - Epoch [85][200/1281] lr: 1.012e-02, eta: 9:26:13, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9994, loss_cls: 0.3510, loss: 0.3510 +2025-06-24 22:58:40,233 - pyskl - INFO - Epoch [85][300/1281] lr: 1.010e-02, eta: 9:25:40, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 1.0000, loss_cls: 0.2898, loss: 0.2898 +2025-06-24 22:59:29,337 - pyskl - INFO - Epoch [85][400/1281] lr: 1.008e-02, eta: 9:25:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9994, loss_cls: 0.3452, loss: 0.3452 +2025-06-24 23:00:18,494 - pyskl - INFO - Epoch [85][500/1281] lr: 1.006e-02, eta: 9:24:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9981, loss_cls: 0.3513, loss: 0.3513 +2025-06-24 23:01:07,673 - pyskl - INFO - Epoch [85][600/1281] lr: 1.004e-02, eta: 9:24:00, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3567, loss: 0.3567 +2025-06-24 23:01:56,902 - pyskl - INFO - Epoch [85][700/1281] lr: 1.002e-02, eta: 9:23:26, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9988, loss_cls: 0.4069, loss: 0.4069 +2025-06-24 23:02:29,487 - pyskl - INFO - Epoch [85][800/1281] lr: 9.998e-03, eta: 9:22:40, time: 0.326, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9988, loss_cls: 0.3764, loss: 0.3764 +2025-06-24 23:03:11,103 - pyskl - INFO - Epoch [85][900/1281] lr: 9.978e-03, eta: 9:22:01, time: 0.416, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9988, loss_cls: 0.3123, loss: 0.3123 +2025-06-24 23:03:46,637 - pyskl - INFO - Epoch [85][1000/1281] lr: 9.958e-03, eta: 9:21:17, time: 0.355, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9981, loss_cls: 0.3132, loss: 0.3132 +2025-06-24 23:04:36,142 - pyskl - INFO - Epoch [85][1100/1281] lr: 9.937e-03, eta: 9:20:44, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9969, loss_cls: 0.4276, loss: 0.4276 +2025-06-24 23:05:25,210 - pyskl - INFO - Epoch [85][1200/1281] lr: 9.917e-03, eta: 9:20:10, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9962, loss_cls: 0.4129, loss: 0.4129 +2025-06-24 23:06:05,377 - pyskl - INFO - Saving checkpoint at 85 epochs +2025-06-24 23:07:04,108 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:07:04,178 - pyskl - INFO - +top1_acc 0.8571 +top5_acc 0.9910 +2025-06-24 23:07:04,178 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:07:04,188 - pyskl - INFO - +mean_acc 0.8009 +2025-06-24 23:07:04,190 - pyskl - INFO - Epoch(val) [85][533] top1_acc: 0.8571, top5_acc: 0.9910, mean_class_accuracy: 0.8009 +2025-06-24 23:08:23,168 - pyskl - INFO - Epoch [86][100/1281] lr: 9.881e-03, eta: 9:19:02, time: 0.790, data_time: 0.186, memory: 4083, top1_acc: 0.9519, top5_acc: 1.0000, loss_cls: 0.2921, loss: 0.2921 +2025-06-24 23:09:12,060 - pyskl - INFO - Epoch [86][200/1281] lr: 9.861e-03, eta: 9:18:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 1.0000, loss_cls: 0.3483, loss: 0.3483 +2025-06-24 23:10:01,579 - pyskl - INFO - Epoch [86][300/1281] lr: 9.841e-03, eta: 9:17:54, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9981, loss_cls: 0.3950, loss: 0.3950 +2025-06-24 23:10:51,036 - pyskl - INFO - Epoch [86][400/1281] lr: 9.821e-03, eta: 9:17:21, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 1.0000, loss_cls: 0.3701, loss: 0.3701 +2025-06-24 23:11:40,034 - pyskl - INFO - Epoch [86][500/1281] lr: 9.801e-03, eta: 9:16:47, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9988, loss_cls: 0.3232, loss: 0.3232 +2025-06-24 23:12:29,221 - pyskl - INFO - Epoch [86][600/1281] lr: 9.781e-03, eta: 9:16:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3245, loss: 0.3245 +2025-06-24 23:13:18,448 - pyskl - INFO - Epoch [86][700/1281] lr: 9.762e-03, eta: 9:15:40, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9988, loss_cls: 0.4203, loss: 0.4203 +2025-06-24 23:13:47,294 - pyskl - INFO - Epoch [86][800/1281] lr: 9.742e-03, eta: 9:14:51, time: 0.288, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9988, loss_cls: 0.4292, loss: 0.4292 +2025-06-24 23:14:33,048 - pyskl - INFO - Epoch [86][900/1281] lr: 9.722e-03, eta: 9:14:14, time: 0.458, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9988, loss_cls: 0.3413, loss: 0.3413 +2025-06-24 23:15:05,655 - pyskl - INFO - Epoch [86][1000/1281] lr: 9.702e-03, eta: 9:13:28, time: 0.326, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9975, loss_cls: 0.4012, loss: 0.4012 +2025-06-24 23:15:54,795 - pyskl - INFO - Epoch [86][1100/1281] lr: 9.682e-03, eta: 9:12:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9988, loss_cls: 0.3782, loss: 0.3782 +2025-06-24 23:16:43,745 - pyskl - INFO - Epoch [86][1200/1281] lr: 9.662e-03, eta: 9:12:20, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9969, loss_cls: 0.4223, loss: 0.4223 +2025-06-24 23:17:24,032 - pyskl - INFO - Saving checkpoint at 86 epochs +2025-06-24 23:18:23,459 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:18:23,526 - pyskl - INFO - +top1_acc 0.8426 +top5_acc 0.9900 +2025-06-24 23:18:23,526 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:18:23,534 - pyskl - INFO - +mean_acc 0.7808 +2025-06-24 23:18:23,536 - pyskl - INFO - Epoch(val) [86][533] top1_acc: 0.8426, top5_acc: 0.9900, mean_class_accuracy: 0.7808 +2025-06-24 23:19:43,443 - pyskl - INFO - Epoch [87][100/1281] lr: 9.626e-03, eta: 9:11:12, time: 0.799, data_time: 0.189, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3486, loss: 0.3486 +2025-06-24 23:20:32,467 - pyskl - INFO - Epoch [87][200/1281] lr: 9.606e-03, eta: 9:10:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9994, loss_cls: 0.3705, loss: 0.3705 +2025-06-24 23:21:21,617 - pyskl - INFO - Epoch [87][300/1281] lr: 9.586e-03, eta: 9:10:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 1.0000, loss_cls: 0.3477, loss: 0.3477 +2025-06-24 23:22:10,609 - pyskl - INFO - Epoch [87][400/1281] lr: 9.566e-03, eta: 9:09:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.3205, loss: 0.3205 +2025-06-24 23:22:59,454 - pyskl - INFO - Epoch [87][500/1281] lr: 9.546e-03, eta: 9:08:56, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9994, loss_cls: 0.3885, loss: 0.3885 +2025-06-24 23:23:48,368 - pyskl - INFO - Epoch [87][600/1281] lr: 9.527e-03, eta: 9:08:22, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9981, loss_cls: 0.3635, loss: 0.3635 +2025-06-24 23:24:38,080 - pyskl - INFO - Epoch [87][700/1281] lr: 9.507e-03, eta: 9:07:48, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 1.0000, loss_cls: 0.3397, loss: 0.3397 +2025-06-24 23:25:06,697 - pyskl - INFO - Epoch [87][800/1281] lr: 9.487e-03, eta: 9:06:59, time: 0.286, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 1.0000, loss_cls: 0.3907, loss: 0.3907 +2025-06-24 23:25:54,240 - pyskl - INFO - Epoch [87][900/1281] lr: 9.467e-03, eta: 9:06:24, time: 0.475, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9994, loss_cls: 0.3904, loss: 0.3904 +2025-06-24 23:26:27,571 - pyskl - INFO - Epoch [87][1000/1281] lr: 9.447e-03, eta: 9:05:38, time: 0.333, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9988, loss_cls: 0.3709, loss: 0.3709 +2025-06-24 23:27:16,795 - pyskl - INFO - Epoch [87][1100/1281] lr: 9.427e-03, eta: 9:05:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9975, loss_cls: 0.4089, loss: 0.4089 +2025-06-24 23:28:05,898 - pyskl - INFO - Epoch [87][1200/1281] lr: 9.408e-03, eta: 9:04:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9988, loss_cls: 0.3837, loss: 0.3837 +2025-06-24 23:28:46,113 - pyskl - INFO - Saving checkpoint at 87 epochs +2025-06-24 23:29:45,218 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:29:45,274 - pyskl - INFO - +top1_acc 0.8726 +top5_acc 0.9924 +2025-06-24 23:29:45,275 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:29:45,285 - pyskl - INFO - +mean_acc 0.8301 +2025-06-24 23:29:45,287 - pyskl - INFO - Epoch(val) [87][533] top1_acc: 0.8726, top5_acc: 0.9924, mean_class_accuracy: 0.8301 +2025-06-24 23:31:05,514 - pyskl - INFO - Epoch [88][100/1281] lr: 9.372e-03, eta: 9:03:22, time: 0.802, data_time: 0.193, memory: 4083, top1_acc: 0.9463, top5_acc: 1.0000, loss_cls: 0.3301, loss: 0.3301 +2025-06-24 23:31:54,807 - pyskl - INFO - Epoch [88][200/1281] lr: 9.352e-03, eta: 9:02:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.3402, loss: 0.3402 +2025-06-24 23:32:44,264 - pyskl - INFO - Epoch [88][300/1281] lr: 9.332e-03, eta: 9:02:14, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.3055, loss: 0.3055 +2025-06-24 23:33:33,187 - pyskl - INFO - Epoch [88][400/1281] lr: 9.312e-03, eta: 9:01:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9981, loss_cls: 0.3711, loss: 0.3711 +2025-06-24 23:34:22,008 - pyskl - INFO - Epoch [88][500/1281] lr: 9.293e-03, eta: 9:01:05, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9975, loss_cls: 0.3581, loss: 0.3581 +2025-06-24 23:35:10,922 - pyskl - INFO - Epoch [88][600/1281] lr: 9.273e-03, eta: 9:00:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.3974, loss: 0.3974 +2025-06-24 23:35:59,859 - pyskl - INFO - Epoch [88][700/1281] lr: 9.253e-03, eta: 8:59:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9981, loss_cls: 0.3408, loss: 0.3408 +2025-06-24 23:36:30,198 - pyskl - INFO - Epoch [88][800/1281] lr: 9.233e-03, eta: 8:59:08, time: 0.303, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9988, loss_cls: 0.3673, loss: 0.3673 +2025-06-24 23:37:14,977 - pyskl - INFO - Epoch [88][900/1281] lr: 9.214e-03, eta: 8:58:31, time: 0.448, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9994, loss_cls: 0.3224, loss: 0.3224 +2025-06-24 23:37:49,534 - pyskl - INFO - Epoch [88][1000/1281] lr: 9.194e-03, eta: 8:57:46, time: 0.346, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9981, loss_cls: 0.3569, loss: 0.3569 +2025-06-24 23:38:39,049 - pyskl - INFO - Epoch [88][1100/1281] lr: 9.174e-03, eta: 8:57:12, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9994, loss_cls: 0.3678, loss: 0.3678 +2025-06-24 23:39:28,288 - pyskl - INFO - Epoch [88][1200/1281] lr: 9.155e-03, eta: 8:56:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9994, loss_cls: 0.3336, loss: 0.3336 +2025-06-24 23:40:08,837 - pyskl - INFO - Saving checkpoint at 88 epochs +2025-06-24 23:41:07,768 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:41:07,835 - pyskl - INFO - +top1_acc 0.8723 +top5_acc 0.9921 +2025-06-24 23:41:07,836 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:41:07,843 - pyskl - INFO - +mean_acc 0.8205 +2025-06-24 23:41:07,845 - pyskl - INFO - Epoch(val) [88][533] top1_acc: 0.8723, top5_acc: 0.9921, mean_class_accuracy: 0.8205 +2025-06-24 23:42:27,228 - pyskl - INFO - Epoch [89][100/1281] lr: 9.119e-03, eta: 8:55:29, time: 0.794, data_time: 0.187, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9994, loss_cls: 0.3230, loss: 0.3230 +2025-06-24 23:43:16,575 - pyskl - INFO - Epoch [89][200/1281] lr: 9.099e-03, eta: 8:54:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2756, loss: 0.2756 +2025-06-24 23:44:05,464 - pyskl - INFO - Epoch [89][300/1281] lr: 9.080e-03, eta: 8:54:20, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.3226, loss: 0.3226 +2025-06-24 23:44:54,763 - pyskl - INFO - Epoch [89][400/1281] lr: 9.060e-03, eta: 8:53:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.3291, loss: 0.3291 +2025-06-24 23:45:43,887 - pyskl - INFO - Epoch [89][500/1281] lr: 9.040e-03, eta: 8:53:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9988, loss_cls: 0.3565, loss: 0.3565 +2025-06-24 23:46:33,151 - pyskl - INFO - Epoch [89][600/1281] lr: 9.021e-03, eta: 8:52:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9981, loss_cls: 0.3394, loss: 0.3394 +2025-06-24 23:47:22,257 - pyskl - INFO - Epoch [89][700/1281] lr: 9.001e-03, eta: 8:52:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9994, loss_cls: 0.3708, loss: 0.3708 +2025-06-24 23:47:54,038 - pyskl - INFO - Epoch [89][800/1281] lr: 8.982e-03, eta: 8:51:15, time: 0.318, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3685, loss: 0.3685 +2025-06-24 23:48:36,627 - pyskl - INFO - Epoch [89][900/1281] lr: 8.962e-03, eta: 8:50:36, time: 0.426, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9981, loss_cls: 0.3293, loss: 0.3293 +2025-06-24 23:49:12,773 - pyskl - INFO - Epoch [89][1000/1281] lr: 8.942e-03, eta: 8:49:53, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9988, loss_cls: 0.3612, loss: 0.3612 +2025-06-24 23:50:01,695 - pyskl - INFO - Epoch [89][1100/1281] lr: 8.923e-03, eta: 8:49:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9981, loss_cls: 0.3323, loss: 0.3323 +2025-06-24 23:50:50,717 - pyskl - INFO - Epoch [89][1200/1281] lr: 8.903e-03, eta: 8:48:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9975, loss_cls: 0.4102, loss: 0.4102 +2025-06-24 23:51:30,412 - pyskl - INFO - Saving checkpoint at 89 epochs +2025-06-24 23:52:29,606 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:52:29,661 - pyskl - INFO - +top1_acc 0.8844 +top5_acc 0.9934 +2025-06-24 23:52:29,661 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:52:29,668 - pyskl - INFO - +mean_acc 0.8399 +2025-06-24 23:52:29,672 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_83.pth was removed +2025-06-24 23:52:29,853 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_89.pth. +2025-06-24 23:52:29,853 - pyskl - INFO - Best top1_acc is 0.8844 at 89 epoch. +2025-06-24 23:52:29,856 - pyskl - INFO - Epoch(val) [89][533] top1_acc: 0.8844, top5_acc: 0.9934, mean_class_accuracy: 0.8399 +2025-06-24 23:53:51,063 - pyskl - INFO - Epoch [90][100/1281] lr: 8.868e-03, eta: 8:47:35, time: 0.812, data_time: 0.197, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2402, loss: 0.2402 +2025-06-24 23:54:40,627 - pyskl - INFO - Epoch [90][200/1281] lr: 8.848e-03, eta: 8:47:01, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2758, loss: 0.2758 +2025-06-24 23:55:30,097 - pyskl - INFO - Epoch [90][300/1281] lr: 8.829e-03, eta: 8:46:26, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2785, loss: 0.2785 +2025-06-24 23:56:19,297 - pyskl - INFO - Epoch [90][400/1281] lr: 8.809e-03, eta: 8:45:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2778, loss: 0.2778 +2025-06-24 23:57:08,325 - pyskl - INFO - Epoch [90][500/1281] lr: 8.790e-03, eta: 8:45:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 1.0000, loss_cls: 0.3323, loss: 0.3323 +2025-06-24 23:57:57,123 - pyskl - INFO - Epoch [90][600/1281] lr: 8.770e-03, eta: 8:44:41, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.3414, loss: 0.3414 +2025-06-24 23:58:43,592 - pyskl - INFO - Epoch [90][700/1281] lr: 8.751e-03, eta: 8:44:05, time: 0.465, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9994, loss_cls: 0.3313, loss: 0.3313 +2025-06-24 23:59:21,243 - pyskl - INFO - Epoch [90][800/1281] lr: 8.731e-03, eta: 8:43:22, time: 0.376, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9988, loss_cls: 0.3394, loss: 0.3394 +2025-06-24 23:59:57,016 - pyskl - INFO - Epoch [90][900/1281] lr: 8.712e-03, eta: 8:42:38, time: 0.358, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9994, loss_cls: 0.3327, loss: 0.3327 +2025-06-25 00:00:35,776 - pyskl - INFO - Epoch [90][1000/1281] lr: 8.692e-03, eta: 8:41:57, time: 0.388, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4273, loss: 0.4273 +2025-06-25 00:01:25,254 - pyskl - INFO - Epoch [90][1100/1281] lr: 8.673e-03, eta: 8:41:22, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3404, loss: 0.3404 +2025-06-25 00:02:14,536 - pyskl - INFO - Epoch [90][1200/1281] lr: 8.653e-03, eta: 8:40:47, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 1.0000, loss_cls: 0.3596, loss: 0.3596 +2025-06-25 00:02:54,856 - pyskl - INFO - Saving checkpoint at 90 epochs +2025-06-25 00:03:53,972 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:03:54,030 - pyskl - INFO - +top1_acc 0.8778 +top5_acc 0.9920 +2025-06-25 00:03:54,030 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:03:54,037 - pyskl - INFO - +mean_acc 0.8316 +2025-06-25 00:03:54,039 - pyskl - INFO - Epoch(val) [90][533] top1_acc: 0.8778, top5_acc: 0.9920, mean_class_accuracy: 0.8316 +2025-06-25 00:05:14,202 - pyskl - INFO - Epoch [91][100/1281] lr: 8.618e-03, eta: 8:39:38, time: 0.802, data_time: 0.189, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9988, loss_cls: 0.2548, loss: 0.2548 +2025-06-25 00:06:03,269 - pyskl - INFO - Epoch [91][200/1281] lr: 8.599e-03, eta: 8:39:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.3015, loss: 0.3015 +2025-06-25 00:06:52,271 - pyskl - INFO - Epoch [91][300/1281] lr: 8.579e-03, eta: 8:38:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.3045, loss: 0.3045 +2025-06-25 00:07:41,665 - pyskl - INFO - Epoch [91][400/1281] lr: 8.560e-03, eta: 8:37:53, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.3026, loss: 0.3026 +2025-06-25 00:08:30,213 - pyskl - INFO - Epoch [91][500/1281] lr: 8.540e-03, eta: 8:37:18, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9981, loss_cls: 0.3150, loss: 0.3150 +2025-06-25 00:09:19,130 - pyskl - INFO - Epoch [91][600/1281] lr: 8.521e-03, eta: 8:36:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3343, loss: 0.3343 +2025-06-25 00:10:04,107 - pyskl - INFO - Epoch [91][700/1281] lr: 8.502e-03, eta: 8:36:05, time: 0.450, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 1.0000, loss_cls: 0.3234, loss: 0.3234 +2025-06-25 00:10:44,715 - pyskl - INFO - Epoch [91][800/1281] lr: 8.482e-03, eta: 8:35:24, time: 0.406, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.3036, loss: 0.3036 +2025-06-25 00:11:17,524 - pyskl - INFO - Epoch [91][900/1281] lr: 8.463e-03, eta: 8:34:38, time: 0.328, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9988, loss_cls: 0.3453, loss: 0.3453 +2025-06-25 00:11:56,147 - pyskl - INFO - Epoch [91][1000/1281] lr: 8.444e-03, eta: 8:33:56, time: 0.386, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9988, loss_cls: 0.3578, loss: 0.3578 +2025-06-25 00:12:45,171 - pyskl - INFO - Epoch [91][1100/1281] lr: 8.424e-03, eta: 8:33:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3234, loss: 0.3234 +2025-06-25 00:13:34,235 - pyskl - INFO - Epoch [91][1200/1281] lr: 8.405e-03, eta: 8:32:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9988, loss_cls: 0.3743, loss: 0.3743 +2025-06-25 00:14:14,236 - pyskl - INFO - Saving checkpoint at 91 epochs +2025-06-25 00:15:13,222 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:15:13,279 - pyskl - INFO - +top1_acc 0.8733 +top5_acc 0.9917 +2025-06-25 00:15:13,279 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:15:13,287 - pyskl - INFO - +mean_acc 0.8286 +2025-06-25 00:15:13,289 - pyskl - INFO - Epoch(val) [91][533] top1_acc: 0.8733, top5_acc: 0.9917, mean_class_accuracy: 0.8286 +2025-06-25 00:16:34,170 - pyskl - INFO - Epoch [92][100/1281] lr: 8.370e-03, eta: 8:31:37, time: 0.809, data_time: 0.188, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9994, loss_cls: 0.2500, loss: 0.2500 +2025-06-25 00:17:23,148 - pyskl - INFO - Epoch [92][200/1281] lr: 8.351e-03, eta: 8:31:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.2852, loss: 0.2852 +2025-06-25 00:18:12,096 - pyskl - INFO - Epoch [92][300/1281] lr: 8.332e-03, eta: 8:30:26, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2658, loss: 0.2658 +2025-06-25 00:19:01,173 - pyskl - INFO - Epoch [92][400/1281] lr: 8.312e-03, eta: 8:29:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9981, loss_cls: 0.3370, loss: 0.3370 +2025-06-25 00:19:50,351 - pyskl - INFO - Epoch [92][500/1281] lr: 8.293e-03, eta: 8:29:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9988, loss_cls: 0.2999, loss: 0.2999 +2025-06-25 00:20:39,308 - pyskl - INFO - Epoch [92][600/1281] lr: 8.274e-03, eta: 8:28:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9994, loss_cls: 0.3653, loss: 0.3653 +2025-06-25 00:21:23,917 - pyskl - INFO - Epoch [92][700/1281] lr: 8.255e-03, eta: 8:28:02, time: 0.446, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9994, loss_cls: 0.3498, loss: 0.3498 +2025-06-25 00:22:07,288 - pyskl - INFO - Epoch [92][800/1281] lr: 8.235e-03, eta: 8:27:23, time: 0.434, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2935, loss: 0.2935 +2025-06-25 00:22:37,185 - pyskl - INFO - Epoch [92][900/1281] lr: 8.216e-03, eta: 8:26:35, time: 0.299, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3083, loss: 0.3083 +2025-06-25 00:23:18,068 - pyskl - INFO - Epoch [92][1000/1281] lr: 8.197e-03, eta: 8:25:55, time: 0.409, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3152, loss: 0.3152 +2025-06-25 00:24:07,438 - pyskl - INFO - Epoch [92][1100/1281] lr: 8.178e-03, eta: 8:25:20, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3484, loss: 0.3484 +2025-06-25 00:24:56,631 - pyskl - INFO - Epoch [92][1200/1281] lr: 8.159e-03, eta: 8:24:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 1.0000, loss_cls: 0.3315, loss: 0.3315 +2025-06-25 00:25:36,954 - pyskl - INFO - Saving checkpoint at 92 epochs +2025-06-25 00:26:37,317 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:26:37,380 - pyskl - INFO - +top1_acc 0.8639 +top5_acc 0.9904 +2025-06-25 00:26:37,380 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:26:37,387 - pyskl - INFO - +mean_acc 0.8081 +2025-06-25 00:26:37,389 - pyskl - INFO - Epoch(val) [92][533] top1_acc: 0.8639, top5_acc: 0.9904, mean_class_accuracy: 0.8081 +2025-06-25 00:27:56,805 - pyskl - INFO - Epoch [93][100/1281] lr: 8.124e-03, eta: 8:23:34, time: 0.794, data_time: 0.194, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2415, loss: 0.2415 +2025-06-25 00:28:45,863 - pyskl - INFO - Epoch [93][200/1281] lr: 8.105e-03, eta: 8:22:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2622, loss: 0.2622 +2025-06-25 00:29:35,171 - pyskl - INFO - Epoch [93][300/1281] lr: 8.086e-03, eta: 8:22:23, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9988, loss_cls: 0.2902, loss: 0.2902 +2025-06-25 00:30:24,238 - pyskl - INFO - Epoch [93][400/1281] lr: 8.067e-03, eta: 8:21:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2735, loss: 0.2735 +2025-06-25 00:31:12,934 - pyskl - INFO - Epoch [93][500/1281] lr: 8.047e-03, eta: 8:21:12, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.2916, loss: 0.2916 +2025-06-25 00:32:01,893 - pyskl - INFO - Epoch [93][600/1281] lr: 8.028e-03, eta: 8:20:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9994, loss_cls: 0.3187, loss: 0.3187 +2025-06-25 00:32:44,193 - pyskl - INFO - Epoch [93][700/1281] lr: 8.009e-03, eta: 8:19:57, time: 0.423, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9988, loss_cls: 0.2729, loss: 0.2729 +2025-06-25 00:33:30,883 - pyskl - INFO - Epoch [93][800/1281] lr: 7.990e-03, eta: 8:19:19, time: 0.467, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9994, loss_cls: 0.3309, loss: 0.3309 +2025-06-25 00:33:57,980 - pyskl - INFO - Epoch [93][900/1281] lr: 7.971e-03, eta: 8:18:30, time: 0.271, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.3083, loss: 0.3083 +2025-06-25 00:34:39,802 - pyskl - INFO - Epoch [93][1000/1281] lr: 7.952e-03, eta: 8:17:50, time: 0.418, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9988, loss_cls: 0.3198, loss: 0.3198 +2025-06-25 00:35:28,833 - pyskl - INFO - Epoch [93][1100/1281] lr: 7.933e-03, eta: 8:17:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.3202, loss: 0.3202 +2025-06-25 00:36:17,465 - pyskl - INFO - Epoch [93][1200/1281] lr: 7.914e-03, eta: 8:16:39, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 1.0000, loss_cls: 0.2968, loss: 0.2968 +2025-06-25 00:36:57,883 - pyskl - INFO - Saving checkpoint at 93 epochs +2025-06-25 00:37:57,417 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:37:57,494 - pyskl - INFO - +top1_acc 0.8674 +top5_acc 0.9927 +2025-06-25 00:37:57,495 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:37:57,503 - pyskl - INFO - +mean_acc 0.8055 +2025-06-25 00:37:57,506 - pyskl - INFO - Epoch(val) [93][533] top1_acc: 0.8674, top5_acc: 0.9927, mean_class_accuracy: 0.8055 +2025-06-25 00:39:17,226 - pyskl - INFO - Epoch [94][100/1281] lr: 7.880e-03, eta: 8:15:28, time: 0.797, data_time: 0.186, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2548, loss: 0.2548 +2025-06-25 00:40:06,068 - pyskl - INFO - Epoch [94][200/1281] lr: 7.861e-03, eta: 8:14:53, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2949, loss: 0.2949 +2025-06-25 00:40:54,882 - pyskl - INFO - Epoch [94][300/1281] lr: 7.842e-03, eta: 8:14:17, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.2787, loss: 0.2787 +2025-06-25 00:41:44,084 - pyskl - INFO - Epoch [94][400/1281] lr: 7.823e-03, eta: 8:13:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2722, loss: 0.2722 +2025-06-25 00:42:33,222 - pyskl - INFO - Epoch [94][500/1281] lr: 7.804e-03, eta: 8:13:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.3076, loss: 0.3076 +2025-06-25 00:43:22,287 - pyskl - INFO - Epoch [94][600/1281] lr: 7.785e-03, eta: 8:12:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.2931, loss: 0.2931 +2025-06-25 00:44:04,070 - pyskl - INFO - Epoch [94][700/1281] lr: 7.766e-03, eta: 8:11:49, time: 0.418, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 1.0000, loss_cls: 0.3099, loss: 0.3099 +2025-06-25 00:44:54,702 - pyskl - INFO - Epoch [94][800/1281] lr: 7.747e-03, eta: 8:11:14, time: 0.506, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.3083, loss: 0.3083 +2025-06-25 00:45:18,550 - pyskl - INFO - Epoch [94][900/1281] lr: 7.728e-03, eta: 8:10:23, time: 0.238, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9981, loss_cls: 0.2992, loss: 0.2992 +2025-06-25 00:46:03,356 - pyskl - INFO - Epoch [94][1000/1281] lr: 7.709e-03, eta: 8:09:45, time: 0.448, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2462, loss: 0.2462 +2025-06-25 00:46:52,516 - pyskl - INFO - Epoch [94][1100/1281] lr: 7.690e-03, eta: 8:09:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2766, loss: 0.2766 +2025-06-25 00:47:41,487 - pyskl - INFO - Epoch [94][1200/1281] lr: 7.672e-03, eta: 8:08:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 1.0000, loss_cls: 0.3055, loss: 0.3055 +2025-06-25 00:48:21,994 - pyskl - INFO - Saving checkpoint at 94 epochs +2025-06-25 00:49:21,145 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:49:21,213 - pyskl - INFO - +top1_acc 0.8675 +top5_acc 0.9917 +2025-06-25 00:49:21,213 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:49:21,220 - pyskl - INFO - +mean_acc 0.8262 +2025-06-25 00:49:21,222 - pyskl - INFO - Epoch(val) [94][533] top1_acc: 0.8675, top5_acc: 0.9917, mean_class_accuracy: 0.8262 +2025-06-25 00:50:41,662 - pyskl - INFO - Epoch [95][100/1281] lr: 7.637e-03, eta: 8:07:23, time: 0.804, data_time: 0.187, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9994, loss_cls: 0.2650, loss: 0.2650 +2025-06-25 00:51:31,230 - pyskl - INFO - Epoch [95][200/1281] lr: 7.619e-03, eta: 8:06:48, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9994, loss_cls: 0.2306, loss: 0.2306 +2025-06-25 00:52:20,096 - pyskl - INFO - Epoch [95][300/1281] lr: 7.600e-03, eta: 8:06:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9988, loss_cls: 0.2636, loss: 0.2636 +2025-06-25 00:53:09,019 - pyskl - INFO - Epoch [95][400/1281] lr: 7.581e-03, eta: 8:05:35, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2760, loss: 0.2760 +2025-06-25 00:53:58,124 - pyskl - INFO - Epoch [95][500/1281] lr: 7.562e-03, eta: 8:05:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9994, loss_cls: 0.2716, loss: 0.2716 +2025-06-25 00:54:47,269 - pyskl - INFO - Epoch [95][600/1281] lr: 7.543e-03, eta: 8:04:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 1.0000, loss_cls: 0.3057, loss: 0.3057 +2025-06-25 00:55:24,688 - pyskl - INFO - Epoch [95][700/1281] lr: 7.525e-03, eta: 8:03:41, time: 0.374, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 1.0000, loss_cls: 0.3343, loss: 0.3343 +2025-06-25 00:56:15,638 - pyskl - INFO - Epoch [95][800/1281] lr: 7.506e-03, eta: 8:03:06, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2829, loss: 0.2829 +2025-06-25 00:56:40,327 - pyskl - INFO - Epoch [95][900/1281] lr: 7.487e-03, eta: 8:02:15, time: 0.247, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2658, loss: 0.2658 +2025-06-25 00:57:26,776 - pyskl - INFO - Epoch [95][1000/1281] lr: 7.468e-03, eta: 8:01:38, time: 0.464, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9994, loss_cls: 0.3313, loss: 0.3313 +2025-06-25 00:58:16,223 - pyskl - INFO - Epoch [95][1100/1281] lr: 7.450e-03, eta: 8:01:02, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9981, loss_cls: 0.3251, loss: 0.3251 +2025-06-25 00:59:05,333 - pyskl - INFO - Epoch [95][1200/1281] lr: 7.431e-03, eta: 8:00:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.2996, loss: 0.2996 +2025-06-25 00:59:45,836 - pyskl - INFO - Saving checkpoint at 95 epochs +2025-06-25 01:00:44,394 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:00:44,449 - pyskl - INFO - +top1_acc 0.8893 +top5_acc 0.9933 +2025-06-25 01:00:44,449 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:00:44,456 - pyskl - INFO - +mean_acc 0.8470 +2025-06-25 01:00:44,460 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_89.pth was removed +2025-06-25 01:00:44,634 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_95.pth. +2025-06-25 01:00:44,634 - pyskl - INFO - Best top1_acc is 0.8893 at 95 epoch. +2025-06-25 01:00:44,637 - pyskl - INFO - Epoch(val) [95][533] top1_acc: 0.8893, top5_acc: 0.9933, mean_class_accuracy: 0.8470 +2025-06-25 01:02:02,544 - pyskl - INFO - Epoch [96][100/1281] lr: 7.397e-03, eta: 7:59:14, time: 0.779, data_time: 0.184, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2463, loss: 0.2463 +2025-06-25 01:02:51,463 - pyskl - INFO - Epoch [96][200/1281] lr: 7.379e-03, eta: 7:58:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2525, loss: 0.2525 +2025-06-25 01:03:40,466 - pyskl - INFO - Epoch [96][300/1281] lr: 7.360e-03, eta: 7:58:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9988, loss_cls: 0.2389, loss: 0.2389 +2025-06-25 01:04:29,530 - pyskl - INFO - Epoch [96][400/1281] lr: 7.341e-03, eta: 7:57:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 0.9994, loss_cls: 0.2161, loss: 0.2161 +2025-06-25 01:05:18,695 - pyskl - INFO - Epoch [96][500/1281] lr: 7.323e-03, eta: 7:56:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2613, loss: 0.2613 +2025-06-25 01:06:07,483 - pyskl - INFO - Epoch [96][600/1281] lr: 7.304e-03, eta: 7:56:13, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2424, loss: 0.2424 +2025-06-25 01:06:43,946 - pyskl - INFO - Epoch [96][700/1281] lr: 7.286e-03, eta: 7:55:30, time: 0.365, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2755, loss: 0.2755 +2025-06-25 01:07:34,912 - pyskl - INFO - Epoch [96][800/1281] lr: 7.267e-03, eta: 7:54:54, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9988, loss_cls: 0.2528, loss: 0.2528 +2025-06-25 01:07:59,588 - pyskl - INFO - Epoch [96][900/1281] lr: 7.249e-03, eta: 7:54:04, time: 0.247, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2701, loss: 0.2701 +2025-06-25 01:08:46,114 - pyskl - INFO - Epoch [96][1000/1281] lr: 7.230e-03, eta: 7:53:27, time: 0.465, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9988, loss_cls: 0.2958, loss: 0.2958 +2025-06-25 01:09:35,137 - pyskl - INFO - Epoch [96][1100/1281] lr: 7.211e-03, eta: 7:52:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9981, loss_cls: 0.2854, loss: 0.2854 +2025-06-25 01:10:23,999 - pyskl - INFO - Epoch [96][1200/1281] lr: 7.193e-03, eta: 7:52:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 1.0000, loss_cls: 0.2825, loss: 0.2825 +2025-06-25 01:11:04,179 - pyskl - INFO - Saving checkpoint at 96 epochs +2025-06-25 01:12:02,856 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:12:02,912 - pyskl - INFO - +top1_acc 0.8911 +top5_acc 0.9930 +2025-06-25 01:12:02,912 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:12:02,919 - pyskl - INFO - +mean_acc 0.8461 +2025-06-25 01:12:02,923 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_95.pth was removed +2025-06-25 01:12:03,102 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_96.pth. +2025-06-25 01:12:03,102 - pyskl - INFO - Best top1_acc is 0.8911 at 96 epoch. +2025-06-25 01:12:03,105 - pyskl - INFO - Epoch(val) [96][533] top1_acc: 0.8911, top5_acc: 0.9930, mean_class_accuracy: 0.8461 +2025-06-25 01:13:23,386 - pyskl - INFO - Epoch [97][100/1281] lr: 7.159e-03, eta: 7:51:03, time: 0.803, data_time: 0.183, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.2163, loss: 0.2163 +2025-06-25 01:14:11,952 - pyskl - INFO - Epoch [97][200/1281] lr: 7.141e-03, eta: 7:50:27, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2298, loss: 0.2298 +2025-06-25 01:15:00,636 - pyskl - INFO - Epoch [97][300/1281] lr: 7.123e-03, eta: 7:49:50, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.2081, loss: 0.2081 +2025-06-25 01:15:49,691 - pyskl - INFO - Epoch [97][400/1281] lr: 7.104e-03, eta: 7:49:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2436, loss: 0.2436 +2025-06-25 01:16:38,514 - pyskl - INFO - Epoch [97][500/1281] lr: 7.086e-03, eta: 7:48:37, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2448, loss: 0.2448 +2025-06-25 01:17:27,634 - pyskl - INFO - Epoch [97][600/1281] lr: 7.067e-03, eta: 7:48:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2456, loss: 0.2456 +2025-06-25 01:18:01,947 - pyskl - INFO - Epoch [97][700/1281] lr: 7.049e-03, eta: 7:47:16, time: 0.343, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2539, loss: 0.2539 +2025-06-25 01:18:52,764 - pyskl - INFO - Epoch [97][800/1281] lr: 7.030e-03, eta: 7:46:41, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 1.0000, loss_cls: 0.3080, loss: 0.3080 +2025-06-25 01:19:18,204 - pyskl - INFO - Epoch [97][900/1281] lr: 7.012e-03, eta: 7:45:51, time: 0.254, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9988, loss_cls: 0.2856, loss: 0.2856 +2025-06-25 01:20:06,666 - pyskl - INFO - Epoch [97][1000/1281] lr: 6.994e-03, eta: 7:45:14, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9994, loss_cls: 0.2973, loss: 0.2973 +2025-06-25 01:20:55,866 - pyskl - INFO - Epoch [97][1100/1281] lr: 6.975e-03, eta: 7:44:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9994, loss_cls: 0.2628, loss: 0.2628 +2025-06-25 01:21:44,689 - pyskl - INFO - Epoch [97][1200/1281] lr: 6.957e-03, eta: 7:44:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2553, loss: 0.2553 +2025-06-25 01:22:24,866 - pyskl - INFO - Saving checkpoint at 97 epochs +2025-06-25 01:23:22,798 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:23:22,861 - pyskl - INFO - +top1_acc 0.8722 +top5_acc 0.9900 +2025-06-25 01:23:22,861 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:23:22,867 - pyskl - INFO - +mean_acc 0.8193 +2025-06-25 01:23:22,869 - pyskl - INFO - Epoch(val) [97][533] top1_acc: 0.8722, top5_acc: 0.9900, mean_class_accuracy: 0.8193 +2025-06-25 01:24:42,820 - pyskl - INFO - Epoch [98][100/1281] lr: 6.924e-03, eta: 7:42:50, time: 0.799, data_time: 0.190, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.2240, loss: 0.2240 +2025-06-25 01:25:31,633 - pyskl - INFO - Epoch [98][200/1281] lr: 6.906e-03, eta: 7:42:14, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2174, loss: 0.2174 +2025-06-25 01:26:20,819 - pyskl - INFO - Epoch [98][300/1281] lr: 6.887e-03, eta: 7:41:37, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.2324, loss: 0.2324 +2025-06-25 01:27:09,568 - pyskl - INFO - Epoch [98][400/1281] lr: 6.869e-03, eta: 7:41:00, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.2024, loss: 0.2024 +2025-06-25 01:27:58,332 - pyskl - INFO - Epoch [98][500/1281] lr: 6.851e-03, eta: 7:40:23, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2105, loss: 0.2105 +2025-06-25 01:28:47,685 - pyskl - INFO - Epoch [98][600/1281] lr: 6.833e-03, eta: 7:39:47, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 1.0000, loss_cls: 0.2344, loss: 0.2344 +2025-06-25 01:29:20,360 - pyskl - INFO - Epoch [98][700/1281] lr: 6.814e-03, eta: 7:39:02, time: 0.327, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2505, loss: 0.2505 +2025-06-25 01:30:11,298 - pyskl - INFO - Epoch [98][800/1281] lr: 6.796e-03, eta: 7:38:26, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9988, loss_cls: 0.2707, loss: 0.2707 +2025-06-25 01:30:36,987 - pyskl - INFO - Epoch [98][900/1281] lr: 6.778e-03, eta: 7:37:37, time: 0.257, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9988, loss_cls: 0.2299, loss: 0.2299 +2025-06-25 01:31:25,816 - pyskl - INFO - Epoch [98][1000/1281] lr: 6.760e-03, eta: 7:37:00, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.2658, loss: 0.2658 +2025-06-25 01:32:14,528 - pyskl - INFO - Epoch [98][1100/1281] lr: 6.742e-03, eta: 7:36:23, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9988, loss_cls: 0.2838, loss: 0.2838 +2025-06-25 01:33:03,779 - pyskl - INFO - Epoch [98][1200/1281] lr: 6.724e-03, eta: 7:35:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2519, loss: 0.2519 +2025-06-25 01:33:44,016 - pyskl - INFO - Saving checkpoint at 98 epochs +2025-06-25 01:34:42,609 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:34:42,663 - pyskl - INFO - +top1_acc 0.8904 +top5_acc 0.9931 +2025-06-25 01:34:42,664 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:34:42,670 - pyskl - INFO - +mean_acc 0.8447 +2025-06-25 01:34:42,672 - pyskl - INFO - Epoch(val) [98][533] top1_acc: 0.8904, top5_acc: 0.9931, mean_class_accuracy: 0.8447 +2025-06-25 01:36:00,199 - pyskl - INFO - Epoch [99][100/1281] lr: 6.691e-03, eta: 7:34:34, time: 0.775, data_time: 0.186, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.1956, loss: 0.1956 +2025-06-25 01:36:49,529 - pyskl - INFO - Epoch [99][200/1281] lr: 6.673e-03, eta: 7:33:57, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.2044, loss: 0.2044 +2025-06-25 01:37:38,386 - pyskl - INFO - Epoch [99][300/1281] lr: 6.655e-03, eta: 7:33:20, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2325, loss: 0.2325 +2025-06-25 01:38:27,470 - pyskl - INFO - Epoch [99][400/1281] lr: 6.637e-03, eta: 7:32:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2049, loss: 0.2049 +2025-06-25 01:39:16,810 - pyskl - INFO - Epoch [99][500/1281] lr: 6.619e-03, eta: 7:32:07, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9994, loss_cls: 0.2422, loss: 0.2422 +2025-06-25 01:40:05,588 - pyskl - INFO - Epoch [99][600/1281] lr: 6.601e-03, eta: 7:31:30, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2163, loss: 0.2163 +2025-06-25 01:40:38,681 - pyskl - INFO - Epoch [99][700/1281] lr: 6.583e-03, eta: 7:30:45, time: 0.331, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 1.0000, loss_cls: 0.3115, loss: 0.3115 +2025-06-25 01:41:29,785 - pyskl - INFO - Epoch [99][800/1281] lr: 6.565e-03, eta: 7:30:09, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9988, loss_cls: 0.2698, loss: 0.2698 +2025-06-25 01:41:55,987 - pyskl - INFO - Epoch [99][900/1281] lr: 6.547e-03, eta: 7:29:20, time: 0.262, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2336, loss: 0.2336 +2025-06-25 01:42:45,118 - pyskl - INFO - Epoch [99][1000/1281] lr: 6.529e-03, eta: 7:28:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 1.0000, loss_cls: 0.2786, loss: 0.2786 +2025-06-25 01:43:34,471 - pyskl - INFO - Epoch [99][1100/1281] lr: 6.511e-03, eta: 7:28:07, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9988, loss_cls: 0.2688, loss: 0.2688 +2025-06-25 01:44:23,281 - pyskl - INFO - Epoch [99][1200/1281] lr: 6.493e-03, eta: 7:27:30, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2506, loss: 0.2506 +2025-06-25 01:45:03,518 - pyskl - INFO - Saving checkpoint at 99 epochs +2025-06-25 01:46:01,547 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:46:01,615 - pyskl - INFO - +top1_acc 0.8946 +top5_acc 0.9935 +2025-06-25 01:46:01,615 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:46:01,623 - pyskl - INFO - +mean_acc 0.8490 +2025-06-25 01:46:01,628 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_96.pth was removed +2025-06-25 01:46:01,818 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_99.pth. +2025-06-25 01:46:01,818 - pyskl - INFO - Best top1_acc is 0.8946 at 99 epoch. +2025-06-25 01:46:01,821 - pyskl - INFO - Epoch(val) [99][533] top1_acc: 0.8946, top5_acc: 0.9935, mean_class_accuracy: 0.8490 +2025-06-25 01:47:21,113 - pyskl - INFO - Epoch [100][100/1281] lr: 6.460e-03, eta: 7:26:18, time: 0.793, data_time: 0.183, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2242, loss: 0.2242 +2025-06-25 01:48:10,038 - pyskl - INFO - Epoch [100][200/1281] lr: 6.442e-03, eta: 7:25:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9988, loss_cls: 0.2465, loss: 0.2465 +2025-06-25 01:48:58,881 - pyskl - INFO - Epoch [100][300/1281] lr: 6.425e-03, eta: 7:25:04, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2139, loss: 0.2139 +2025-06-25 01:49:47,925 - pyskl - INFO - Epoch [100][400/1281] lr: 6.407e-03, eta: 7:24:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2161, loss: 0.2161 +2025-06-25 01:50:36,670 - pyskl - INFO - Epoch [100][500/1281] lr: 6.389e-03, eta: 7:23:50, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1717, loss: 0.1717 +2025-06-25 01:51:25,512 - pyskl - INFO - Epoch [100][600/1281] lr: 6.371e-03, eta: 7:23:13, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9994, loss_cls: 0.2132, loss: 0.2132 +2025-06-25 01:51:57,673 - pyskl - INFO - Epoch [100][700/1281] lr: 6.353e-03, eta: 7:22:27, time: 0.322, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2328, loss: 0.2328 +2025-06-25 01:52:48,762 - pyskl - INFO - Epoch [100][800/1281] lr: 6.336e-03, eta: 7:21:51, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2636, loss: 0.2636 +2025-06-25 01:53:14,879 - pyskl - INFO - Epoch [100][900/1281] lr: 6.318e-03, eta: 7:21:02, time: 0.261, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 1.0000, loss_cls: 0.2502, loss: 0.2502 +2025-06-25 01:54:03,818 - pyskl - INFO - Epoch [100][1000/1281] lr: 6.300e-03, eta: 7:20:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9994, loss_cls: 0.2341, loss: 0.2341 +2025-06-25 01:54:52,763 - pyskl - INFO - Epoch [100][1100/1281] lr: 6.282e-03, eta: 7:19:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.2542, loss: 0.2542 +2025-06-25 01:55:41,787 - pyskl - INFO - Epoch [100][1200/1281] lr: 6.265e-03, eta: 7:19:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2878, loss: 0.2878 +2025-06-25 01:56:21,846 - pyskl - INFO - Saving checkpoint at 100 epochs +2025-06-25 01:57:20,584 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:57:20,643 - pyskl - INFO - +top1_acc 0.8779 +top5_acc 0.9921 +2025-06-25 01:57:20,643 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:57:20,650 - pyskl - INFO - +mean_acc 0.8260 +2025-06-25 01:57:20,652 - pyskl - INFO - Epoch(val) [100][533] top1_acc: 0.8779, top5_acc: 0.9921, mean_class_accuracy: 0.8260 +2025-06-25 01:58:39,773 - pyskl - INFO - Epoch [101][100/1281] lr: 6.232e-03, eta: 7:17:59, time: 0.791, data_time: 0.185, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9994, loss_cls: 0.2440, loss: 0.2440 +2025-06-25 01:59:28,712 - pyskl - INFO - Epoch [101][200/1281] lr: 6.215e-03, eta: 7:17:22, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.2038, loss: 0.2038 +2025-06-25 02:00:17,708 - pyskl - INFO - Epoch [101][300/1281] lr: 6.197e-03, eta: 7:16:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2148, loss: 0.2148 +2025-06-25 02:01:06,422 - pyskl - INFO - Epoch [101][400/1281] lr: 6.180e-03, eta: 7:16:07, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2271, loss: 0.2271 +2025-06-25 02:01:55,192 - pyskl - INFO - Epoch [101][500/1281] lr: 6.162e-03, eta: 7:15:30, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9988, loss_cls: 0.2357, loss: 0.2357 +2025-06-25 02:02:43,889 - pyskl - INFO - Epoch [101][600/1281] lr: 6.144e-03, eta: 7:14:53, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2176, loss: 0.2176 +2025-06-25 02:03:16,007 - pyskl - INFO - Epoch [101][700/1281] lr: 6.127e-03, eta: 7:14:07, time: 0.321, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2521, loss: 0.2521 +2025-06-25 02:04:06,971 - pyskl - INFO - Epoch [101][800/1281] lr: 6.109e-03, eta: 7:13:31, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2385, loss: 0.2385 +2025-06-25 02:04:33,996 - pyskl - INFO - Epoch [101][900/1281] lr: 6.092e-03, eta: 7:12:43, time: 0.270, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2017, loss: 0.2017 +2025-06-25 02:05:23,119 - pyskl - INFO - Epoch [101][1000/1281] lr: 6.074e-03, eta: 7:12:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.2069, loss: 0.2069 +2025-06-25 02:06:12,376 - pyskl - INFO - Epoch [101][1100/1281] lr: 6.057e-03, eta: 7:11:28, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.2062, loss: 0.2062 +2025-06-25 02:07:01,294 - pyskl - INFO - Epoch [101][1200/1281] lr: 6.039e-03, eta: 7:10:51, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1978, loss: 0.1978 +2025-06-25 02:07:41,695 - pyskl - INFO - Saving checkpoint at 101 epochs +2025-06-25 02:08:39,667 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:08:39,734 - pyskl - INFO - +top1_acc 0.8802 +top5_acc 0.9904 +2025-06-25 02:08:39,734 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:08:39,742 - pyskl - INFO - +mean_acc 0.8530 +2025-06-25 02:08:39,744 - pyskl - INFO - Epoch(val) [101][533] top1_acc: 0.8802, top5_acc: 0.9904, mean_class_accuracy: 0.8530 +2025-06-25 02:09:58,750 - pyskl - INFO - Epoch [102][100/1281] lr: 6.007e-03, eta: 7:09:39, time: 0.790, data_time: 0.186, memory: 4083, top1_acc: 0.9756, top5_acc: 0.9994, loss_cls: 0.1927, loss: 0.1927 +2025-06-25 02:10:48,065 - pyskl - INFO - Epoch [102][200/1281] lr: 5.990e-03, eta: 7:09:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1629, loss: 0.1629 +2025-06-25 02:11:36,934 - pyskl - INFO - Epoch [102][300/1281] lr: 5.972e-03, eta: 7:08:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1942, loss: 0.1942 +2025-06-25 02:12:26,027 - pyskl - INFO - Epoch [102][400/1281] lr: 5.955e-03, eta: 7:07:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.1963, loss: 0.1963 +2025-06-25 02:13:14,750 - pyskl - INFO - Epoch [102][500/1281] lr: 5.938e-03, eta: 7:07:09, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9994, loss_cls: 0.2080, loss: 0.2080 +2025-06-25 02:14:03,684 - pyskl - INFO - Epoch [102][600/1281] lr: 5.920e-03, eta: 7:06:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9988, loss_cls: 0.2540, loss: 0.2540 +2025-06-25 02:14:35,439 - pyskl - INFO - Epoch [102][700/1281] lr: 5.903e-03, eta: 7:05:46, time: 0.318, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2475, loss: 0.2475 +2025-06-25 02:15:26,576 - pyskl - INFO - Epoch [102][800/1281] lr: 5.886e-03, eta: 7:05:10, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2251, loss: 0.2251 +2025-06-25 02:15:54,795 - pyskl - INFO - Epoch [102][900/1281] lr: 5.868e-03, eta: 7:04:22, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9994, loss_cls: 0.2196, loss: 0.2196 +2025-06-25 02:16:43,770 - pyskl - INFO - Epoch [102][1000/1281] lr: 5.851e-03, eta: 7:03:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 0.9994, loss_cls: 0.2069, loss: 0.2069 +2025-06-25 02:17:32,665 - pyskl - INFO - Epoch [102][1100/1281] lr: 5.834e-03, eta: 7:03:08, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.2270, loss: 0.2270 +2025-06-25 02:18:21,627 - pyskl - INFO - Epoch [102][1200/1281] lr: 5.816e-03, eta: 7:02:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9988, loss_cls: 0.2704, loss: 0.2704 +2025-06-25 02:19:02,301 - pyskl - INFO - Saving checkpoint at 102 epochs +2025-06-25 02:20:01,324 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:20:01,384 - pyskl - INFO - +top1_acc 0.8747 +top5_acc 0.9903 +2025-06-25 02:20:01,385 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:20:01,392 - pyskl - INFO - +mean_acc 0.8318 +2025-06-25 02:20:01,393 - pyskl - INFO - Epoch(val) [102][533] top1_acc: 0.8747, top5_acc: 0.9903, mean_class_accuracy: 0.8318 +2025-06-25 02:21:21,575 - pyskl - INFO - Epoch [103][100/1281] lr: 5.785e-03, eta: 7:01:18, time: 0.802, data_time: 0.184, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.2143, loss: 0.2143 +2025-06-25 02:22:10,652 - pyskl - INFO - Epoch [103][200/1281] lr: 5.768e-03, eta: 7:00:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1545, loss: 0.1545 +2025-06-25 02:22:59,866 - pyskl - INFO - Epoch [103][300/1281] lr: 5.751e-03, eta: 7:00:03, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 0.9994, loss_cls: 0.1772, loss: 0.1772 +2025-06-25 02:23:48,465 - pyskl - INFO - Epoch [103][400/1281] lr: 5.733e-03, eta: 6:59:25, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2029, loss: 0.2029 +2025-06-25 02:24:37,404 - pyskl - INFO - Epoch [103][500/1281] lr: 5.716e-03, eta: 6:58:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1737, loss: 0.1737 +2025-06-25 02:25:26,162 - pyskl - INFO - Epoch [103][600/1281] lr: 5.699e-03, eta: 6:58:10, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9994, loss_cls: 0.2068, loss: 0.2068 +2025-06-25 02:25:53,814 - pyskl - INFO - Epoch [103][700/1281] lr: 5.682e-03, eta: 6:57:23, time: 0.277, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9988, loss_cls: 0.2343, loss: 0.2343 +2025-06-25 02:26:44,874 - pyskl - INFO - Epoch [103][800/1281] lr: 5.665e-03, eta: 6:56:46, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2295, loss: 0.2295 +2025-06-25 02:27:16,187 - pyskl - INFO - Epoch [103][900/1281] lr: 5.648e-03, eta: 6:56:00, time: 0.313, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.1987, loss: 0.1987 +2025-06-25 02:28:04,819 - pyskl - INFO - Epoch [103][1000/1281] lr: 5.631e-03, eta: 6:55:23, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.2040, loss: 0.2040 +2025-06-25 02:28:53,760 - pyskl - INFO - Epoch [103][1100/1281] lr: 5.614e-03, eta: 6:54:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1906, loss: 0.1906 +2025-06-25 02:29:42,813 - pyskl - INFO - Epoch [103][1200/1281] lr: 5.597e-03, eta: 6:54:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1782, loss: 0.1782 +2025-06-25 02:30:22,856 - pyskl - INFO - Saving checkpoint at 103 epochs +2025-06-25 02:31:21,774 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:31:21,832 - pyskl - INFO - +top1_acc 0.8913 +top5_acc 0.9935 +2025-06-25 02:31:21,832 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:31:21,839 - pyskl - INFO - +mean_acc 0.8449 +2025-06-25 02:31:21,841 - pyskl - INFO - Epoch(val) [103][533] top1_acc: 0.8913, top5_acc: 0.9935, mean_class_accuracy: 0.8449 +2025-06-25 02:32:41,760 - pyskl - INFO - Epoch [104][100/1281] lr: 5.566e-03, eta: 6:52:55, time: 0.799, data_time: 0.185, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1705, loss: 0.1705 +2025-06-25 02:33:31,249 - pyskl - INFO - Epoch [104][200/1281] lr: 5.549e-03, eta: 6:52:18, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 0.9994, loss_cls: 0.1567, loss: 0.1567 +2025-06-25 02:34:20,044 - pyskl - INFO - Epoch [104][300/1281] lr: 5.532e-03, eta: 6:51:40, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 0.9994, loss_cls: 0.1641, loss: 0.1641 +2025-06-25 02:35:08,929 - pyskl - INFO - Epoch [104][400/1281] lr: 5.515e-03, eta: 6:51:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2326, loss: 0.2326 +2025-06-25 02:35:57,608 - pyskl - INFO - Epoch [104][500/1281] lr: 5.498e-03, eta: 6:50:24, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2590, loss: 0.2590 +2025-06-25 02:36:46,737 - pyskl - INFO - Epoch [104][600/1281] lr: 5.481e-03, eta: 6:49:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2086, loss: 0.2086 +2025-06-25 02:37:17,792 - pyskl - INFO - Epoch [104][700/1281] lr: 5.464e-03, eta: 6:49:01, time: 0.311, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2077, loss: 0.2077 +2025-06-25 02:38:02,598 - pyskl - INFO - Epoch [104][800/1281] lr: 5.447e-03, eta: 6:48:21, time: 0.448, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1828, loss: 0.1828 +2025-06-25 02:38:36,025 - pyskl - INFO - Epoch [104][900/1281] lr: 5.430e-03, eta: 6:47:36, time: 0.334, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2216, loss: 0.2216 +2025-06-25 02:39:25,082 - pyskl - INFO - Epoch [104][1000/1281] lr: 5.413e-03, eta: 6:46:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1987, loss: 0.1987 +2025-06-25 02:40:13,938 - pyskl - INFO - Epoch [104][1100/1281] lr: 5.397e-03, eta: 6:46:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2053, loss: 0.2053 +2025-06-25 02:41:02,887 - pyskl - INFO - Epoch [104][1200/1281] lr: 5.380e-03, eta: 6:45:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 0.9994, loss_cls: 0.1897, loss: 0.1897 +2025-06-25 02:41:43,580 - pyskl - INFO - Saving checkpoint at 104 epochs +2025-06-25 02:42:42,246 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:42:42,302 - pyskl - INFO - +top1_acc 0.8958 +top5_acc 0.9918 +2025-06-25 02:42:42,302 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:42:42,311 - pyskl - INFO - +mean_acc 0.8538 +2025-06-25 02:42:42,317 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_99.pth was removed +2025-06-25 02:42:42,508 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_104.pth. +2025-06-25 02:42:42,508 - pyskl - INFO - Best top1_acc is 0.8958 at 104 epoch. +2025-06-25 02:42:42,511 - pyskl - INFO - Epoch(val) [104][533] top1_acc: 0.8958, top5_acc: 0.9918, mean_class_accuracy: 0.8538 +2025-06-25 02:44:01,548 - pyskl - INFO - Epoch [105][100/1281] lr: 5.349e-03, eta: 6:44:30, time: 0.790, data_time: 0.186, memory: 4083, top1_acc: 0.9744, top5_acc: 0.9994, loss_cls: 0.1787, loss: 0.1787 +2025-06-25 02:44:50,951 - pyskl - INFO - Epoch [105][200/1281] lr: 5.333e-03, eta: 6:43:52, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1832, loss: 0.1832 +2025-06-25 02:45:39,726 - pyskl - INFO - Epoch [105][300/1281] lr: 5.316e-03, eta: 6:43:14, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 0.9994, loss_cls: 0.1521, loss: 0.1521 +2025-06-25 02:46:28,768 - pyskl - INFO - Epoch [105][400/1281] lr: 5.299e-03, eta: 6:42:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1627, loss: 0.1627 +2025-06-25 02:47:17,445 - pyskl - INFO - Epoch [105][500/1281] lr: 5.283e-03, eta: 6:41:58, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 0.9994, loss_cls: 0.1379, loss: 0.1379 +2025-06-25 02:48:06,399 - pyskl - INFO - Epoch [105][600/1281] lr: 5.266e-03, eta: 6:41:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1697, loss: 0.1697 +2025-06-25 02:48:38,276 - pyskl - INFO - Epoch [105][700/1281] lr: 5.249e-03, eta: 6:40:35, time: 0.319, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1546, loss: 0.1546 +2025-06-25 02:49:21,524 - pyskl - INFO - Epoch [105][800/1281] lr: 5.233e-03, eta: 6:39:55, time: 0.432, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1527, loss: 0.1527 +2025-06-25 02:49:56,992 - pyskl - INFO - Epoch [105][900/1281] lr: 5.216e-03, eta: 6:39:11, time: 0.355, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1805, loss: 0.1805 +2025-06-25 02:50:46,140 - pyskl - INFO - Epoch [105][1000/1281] lr: 5.199e-03, eta: 6:38:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9994, loss_cls: 0.1860, loss: 0.1860 +2025-06-25 02:51:35,091 - pyskl - INFO - Epoch [105][1100/1281] lr: 5.183e-03, eta: 6:37:55, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2167, loss: 0.2167 +2025-06-25 02:52:24,143 - pyskl - INFO - Epoch [105][1200/1281] lr: 5.166e-03, eta: 6:37:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 1.0000, loss_cls: 0.2228, loss: 0.2228 +2025-06-25 02:53:04,195 - pyskl - INFO - Saving checkpoint at 105 epochs +2025-06-25 02:54:02,768 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:54:02,836 - pyskl - INFO - +top1_acc 0.8883 +top5_acc 0.9910 +2025-06-25 02:54:02,836 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:54:02,844 - pyskl - INFO - +mean_acc 0.8524 +2025-06-25 02:54:02,846 - pyskl - INFO - Epoch(val) [105][533] top1_acc: 0.8883, top5_acc: 0.9910, mean_class_accuracy: 0.8524 +2025-06-25 02:55:22,557 - pyskl - INFO - Epoch [106][100/1281] lr: 5.136e-03, eta: 6:36:05, time: 0.797, data_time: 0.193, memory: 4083, top1_acc: 0.9775, top5_acc: 0.9994, loss_cls: 0.1761, loss: 0.1761 +2025-06-25 02:56:11,591 - pyskl - INFO - Epoch [106][200/1281] lr: 5.120e-03, eta: 6:35:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1435, loss: 0.1435 +2025-06-25 02:57:00,376 - pyskl - INFO - Epoch [106][300/1281] lr: 5.103e-03, eta: 6:34:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1359, loss: 0.1359 +2025-06-25 02:57:49,350 - pyskl - INFO - Epoch [106][400/1281] lr: 5.087e-03, eta: 6:34:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1786, loss: 0.1786 +2025-06-25 02:58:38,172 - pyskl - INFO - Epoch [106][500/1281] lr: 5.070e-03, eta: 6:33:32, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1696, loss: 0.1696 +2025-06-25 02:59:26,429 - pyskl - INFO - Epoch [106][600/1281] lr: 5.054e-03, eta: 6:32:54, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1850, loss: 0.1850 +2025-06-25 03:00:01,736 - pyskl - INFO - Epoch [106][700/1281] lr: 5.038e-03, eta: 6:32:10, time: 0.353, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 0.9994, loss_cls: 0.1682, loss: 0.1682 +2025-06-25 03:00:39,665 - pyskl - INFO - Epoch [106][800/1281] lr: 5.021e-03, eta: 6:31:27, time: 0.379, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9988, loss_cls: 0.2151, loss: 0.2151 +2025-06-25 03:01:17,352 - pyskl - INFO - Epoch [106][900/1281] lr: 5.005e-03, eta: 6:30:44, time: 0.377, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1712, loss: 0.1712 +2025-06-25 03:02:06,490 - pyskl - INFO - Epoch [106][1000/1281] lr: 4.988e-03, eta: 6:30:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1582, loss: 0.1582 +2025-06-25 03:02:55,946 - pyskl - INFO - Epoch [106][1100/1281] lr: 4.972e-03, eta: 6:29:28, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1792, loss: 0.1792 +2025-06-25 03:03:44,672 - pyskl - INFO - Epoch [106][1200/1281] lr: 4.956e-03, eta: 6:28:50, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1537, loss: 0.1537 +2025-06-25 03:04:24,699 - pyskl - INFO - Saving checkpoint at 106 epochs +2025-06-25 03:05:22,812 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:05:22,868 - pyskl - INFO - +top1_acc 0.8818 +top5_acc 0.9934 +2025-06-25 03:05:22,868 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:05:22,875 - pyskl - INFO - +mean_acc 0.8352 +2025-06-25 03:05:22,876 - pyskl - INFO - Epoch(val) [106][533] top1_acc: 0.8818, top5_acc: 0.9934, mean_class_accuracy: 0.8352 +2025-06-25 03:06:41,652 - pyskl - INFO - Epoch [107][100/1281] lr: 4.926e-03, eta: 6:27:37, time: 0.788, data_time: 0.186, memory: 4083, top1_acc: 0.9794, top5_acc: 0.9994, loss_cls: 0.1583, loss: 0.1583 +2025-06-25 03:07:30,792 - pyskl - INFO - Epoch [107][200/1281] lr: 4.910e-03, eta: 6:26:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1550, loss: 0.1550 +2025-06-25 03:08:20,014 - pyskl - INFO - Epoch [107][300/1281] lr: 4.894e-03, eta: 6:26:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1351, loss: 0.1351 +2025-06-25 03:09:08,737 - pyskl - INFO - Epoch [107][400/1281] lr: 4.878e-03, eta: 6:25:42, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1243, loss: 0.1243 +2025-06-25 03:09:57,683 - pyskl - INFO - Epoch [107][500/1281] lr: 4.862e-03, eta: 6:25:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 0.9994, loss_cls: 0.1084, loss: 0.1084 +2025-06-25 03:10:45,591 - pyskl - INFO - Epoch [107][600/1281] lr: 4.845e-03, eta: 6:24:26, time: 0.479, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 0.9994, loss_cls: 0.1329, loss: 0.1329 +2025-06-25 03:11:22,685 - pyskl - INFO - Epoch [107][700/1281] lr: 4.829e-03, eta: 6:23:42, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1421, loss: 0.1421 +2025-06-25 03:11:58,870 - pyskl - INFO - Epoch [107][800/1281] lr: 4.813e-03, eta: 6:22:59, time: 0.362, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1692, loss: 0.1692 +2025-06-25 03:12:37,094 - pyskl - INFO - Epoch [107][900/1281] lr: 4.797e-03, eta: 6:22:16, time: 0.382, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1746, loss: 0.1746 +2025-06-25 03:13:25,992 - pyskl - INFO - Epoch [107][1000/1281] lr: 4.781e-03, eta: 6:21:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1756, loss: 0.1756 +2025-06-25 03:14:14,987 - pyskl - INFO - Epoch [107][1100/1281] lr: 4.765e-03, eta: 6:21:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1741, loss: 0.1741 +2025-06-25 03:15:03,865 - pyskl - INFO - Epoch [107][1200/1281] lr: 4.749e-03, eta: 6:20:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1449, loss: 0.1449 +2025-06-25 03:15:43,998 - pyskl - INFO - Saving checkpoint at 107 epochs +2025-06-25 03:16:42,535 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:16:42,612 - pyskl - INFO - +top1_acc 0.8925 +top5_acc 0.9919 +2025-06-25 03:16:42,612 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:16:42,621 - pyskl - INFO - +mean_acc 0.8519 +2025-06-25 03:16:42,624 - pyskl - INFO - Epoch(val) [107][533] top1_acc: 0.8925, top5_acc: 0.9919, mean_class_accuracy: 0.8519 +2025-06-25 03:18:02,336 - pyskl - INFO - Epoch [108][100/1281] lr: 4.720e-03, eta: 6:19:08, time: 0.797, data_time: 0.185, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1632, loss: 0.1632 +2025-06-25 03:18:51,781 - pyskl - INFO - Epoch [108][200/1281] lr: 4.704e-03, eta: 6:18:30, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 0.9994, loss_cls: 0.1451, loss: 0.1451 +2025-06-25 03:19:40,805 - pyskl - INFO - Epoch [108][300/1281] lr: 4.688e-03, eta: 6:17:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1503, loss: 0.1503 +2025-06-25 03:20:29,639 - pyskl - INFO - Epoch [108][400/1281] lr: 4.672e-03, eta: 6:17:14, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1262, loss: 0.1262 +2025-06-25 03:21:18,540 - pyskl - INFO - Epoch [108][500/1281] lr: 4.656e-03, eta: 6:16:35, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1852, loss: 0.1852 +2025-06-25 03:22:04,860 - pyskl - INFO - Epoch [108][600/1281] lr: 4.640e-03, eta: 6:15:56, time: 0.463, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1702, loss: 0.1702 +2025-06-25 03:22:44,194 - pyskl - INFO - Epoch [108][700/1281] lr: 4.624e-03, eta: 6:15:13, time: 0.393, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 0.9994, loss_cls: 0.1461, loss: 0.1461 +2025-06-25 03:23:18,329 - pyskl - INFO - Epoch [108][800/1281] lr: 4.608e-03, eta: 6:14:29, time: 0.341, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 0.9994, loss_cls: 0.1570, loss: 0.1570 +2025-06-25 03:23:58,034 - pyskl - INFO - Epoch [108][900/1281] lr: 4.593e-03, eta: 6:13:47, time: 0.397, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1173, loss: 0.1173 +2025-06-25 03:24:47,272 - pyskl - INFO - Epoch [108][1000/1281] lr: 4.577e-03, eta: 6:13:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1429, loss: 0.1429 +2025-06-25 03:25:36,375 - pyskl - INFO - Epoch [108][1100/1281] lr: 4.561e-03, eta: 6:12:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1763, loss: 0.1763 +2025-06-25 03:26:25,490 - pyskl - INFO - Epoch [108][1200/1281] lr: 4.545e-03, eta: 6:11:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1516, loss: 0.1516 +2025-06-25 03:27:05,548 - pyskl - INFO - Saving checkpoint at 108 epochs +2025-06-25 03:28:04,098 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:28:04,154 - pyskl - INFO - +top1_acc 0.8934 +top5_acc 0.9947 +2025-06-25 03:28:04,154 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:28:04,161 - pyskl - INFO - +mean_acc 0.8506 +2025-06-25 03:28:04,162 - pyskl - INFO - Epoch(val) [108][533] top1_acc: 0.8934, top5_acc: 0.9947, mean_class_accuracy: 0.8506 +2025-06-25 03:29:23,868 - pyskl - INFO - Epoch [109][100/1281] lr: 4.517e-03, eta: 6:10:39, time: 0.797, data_time: 0.186, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1657, loss: 0.1657 +2025-06-25 03:30:12,903 - pyskl - INFO - Epoch [109][200/1281] lr: 4.501e-03, eta: 6:10:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 0.9994, loss_cls: 0.1446, loss: 0.1446 +2025-06-25 03:31:01,497 - pyskl - INFO - Epoch [109][300/1281] lr: 4.485e-03, eta: 6:09:22, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 0.9994, loss_cls: 0.1240, loss: 0.1240 +2025-06-25 03:31:50,508 - pyskl - INFO - Epoch [109][400/1281] lr: 4.470e-03, eta: 6:08:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1210, loss: 0.1210 +2025-06-25 03:32:39,645 - pyskl - INFO - Epoch [109][500/1281] lr: 4.454e-03, eta: 6:08:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1211, loss: 0.1211 +2025-06-25 03:33:25,064 - pyskl - INFO - Epoch [109][600/1281] lr: 4.438e-03, eta: 6:07:25, time: 0.454, data_time: 0.001, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1321, loss: 0.1321 +2025-06-25 03:34:06,784 - pyskl - INFO - Epoch [109][700/1281] lr: 4.423e-03, eta: 6:06:44, time: 0.417, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1385, loss: 0.1385 +2025-06-25 03:34:38,420 - pyskl - INFO - Epoch [109][800/1281] lr: 4.407e-03, eta: 6:05:58, time: 0.316, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1404, loss: 0.1404 +2025-06-25 03:35:19,431 - pyskl - INFO - Epoch [109][900/1281] lr: 4.391e-03, eta: 6:05:17, time: 0.410, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.1048, loss: 0.1048 +2025-06-25 03:36:08,456 - pyskl - INFO - Epoch [109][1000/1281] lr: 4.376e-03, eta: 6:04:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1540, loss: 0.1540 +2025-06-25 03:36:57,676 - pyskl - INFO - Epoch [109][1100/1281] lr: 4.360e-03, eta: 6:04:00, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1675, loss: 0.1675 +2025-06-25 03:37:46,609 - pyskl - INFO - Epoch [109][1200/1281] lr: 4.345e-03, eta: 6:03:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1888, loss: 0.1888 +2025-06-25 03:38:26,826 - pyskl - INFO - Saving checkpoint at 109 epochs +2025-06-25 03:39:25,284 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:39:25,375 - pyskl - INFO - +top1_acc 0.8926 +top5_acc 0.9920 +2025-06-25 03:39:25,375 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:39:25,387 - pyskl - INFO - +mean_acc 0.8518 +2025-06-25 03:39:25,391 - pyskl - INFO - Epoch(val) [109][533] top1_acc: 0.8926, top5_acc: 0.9920, mean_class_accuracy: 0.8518 +2025-06-25 03:40:44,918 - pyskl - INFO - Epoch [110][100/1281] lr: 4.317e-03, eta: 6:02:08, time: 0.795, data_time: 0.188, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1477, loss: 0.1477 +2025-06-25 03:41:33,688 - pyskl - INFO - Epoch [110][200/1281] lr: 4.301e-03, eta: 6:01:29, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1298, loss: 0.1298 +2025-06-25 03:42:22,388 - pyskl - INFO - Epoch [110][300/1281] lr: 4.286e-03, eta: 6:00:50, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1111, loss: 0.1111 +2025-06-25 03:43:11,269 - pyskl - INFO - Epoch [110][400/1281] lr: 4.271e-03, eta: 6:00:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1083, loss: 0.1083 +2025-06-25 03:44:00,248 - pyskl - INFO - Epoch [110][500/1281] lr: 4.255e-03, eta: 5:59:33, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1182, loss: 0.1182 +2025-06-25 03:44:43,834 - pyskl - INFO - Epoch [110][600/1281] lr: 4.240e-03, eta: 5:58:52, time: 0.436, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1133, loss: 0.1133 +2025-06-25 03:45:30,304 - pyskl - INFO - Epoch [110][700/1281] lr: 4.225e-03, eta: 5:58:13, time: 0.465, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1386, loss: 0.1386 +2025-06-25 03:45:57,455 - pyskl - INFO - Epoch [110][800/1281] lr: 4.209e-03, eta: 5:57:26, time: 0.272, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1349, loss: 0.1349 +2025-06-25 03:46:39,589 - pyskl - INFO - Epoch [110][900/1281] lr: 4.194e-03, eta: 5:56:45, time: 0.421, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1455, loss: 0.1455 +2025-06-25 03:47:28,773 - pyskl - INFO - Epoch [110][1000/1281] lr: 4.179e-03, eta: 5:56:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1351, loss: 0.1351 +2025-06-25 03:48:17,477 - pyskl - INFO - Epoch [110][1100/1281] lr: 4.164e-03, eta: 5:55:27, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1425, loss: 0.1425 +2025-06-25 03:49:06,484 - pyskl - INFO - Epoch [110][1200/1281] lr: 4.148e-03, eta: 5:54:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.1753, loss: 0.1753 +2025-06-25 03:49:46,839 - pyskl - INFO - Saving checkpoint at 110 epochs +2025-06-25 03:50:44,899 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:50:44,954 - pyskl - INFO - +top1_acc 0.8994 +top5_acc 0.9933 +2025-06-25 03:50:44,955 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:50:44,962 - pyskl - INFO - +mean_acc 0.8658 +2025-06-25 03:50:44,966 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_104.pth was removed +2025-06-25 03:50:45,180 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_110.pth. +2025-06-25 03:50:45,180 - pyskl - INFO - Best top1_acc is 0.8994 at 110 epoch. +2025-06-25 03:50:45,183 - pyskl - INFO - Epoch(val) [110][533] top1_acc: 0.8994, top5_acc: 0.9933, mean_class_accuracy: 0.8658 +2025-06-25 03:52:04,900 - pyskl - INFO - Epoch [111][100/1281] lr: 4.121e-03, eta: 5:53:35, time: 0.797, data_time: 0.189, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1749, loss: 0.1749 +2025-06-25 03:52:54,263 - pyskl - INFO - Epoch [111][200/1281] lr: 4.106e-03, eta: 5:52:56, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1249, loss: 0.1249 +2025-06-25 03:53:43,195 - pyskl - INFO - Epoch [111][300/1281] lr: 4.091e-03, eta: 5:52:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1586, loss: 0.1586 +2025-06-25 03:54:31,866 - pyskl - INFO - Epoch [111][400/1281] lr: 4.075e-03, eta: 5:51:39, time: 0.487, data_time: 0.001, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1254, loss: 0.1254 +2025-06-25 03:55:20,725 - pyskl - INFO - Epoch [111][500/1281] lr: 4.060e-03, eta: 5:51:00, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1109, loss: 0.1109 +2025-06-25 03:56:03,808 - pyskl - INFO - Epoch [111][600/1281] lr: 4.045e-03, eta: 5:50:19, time: 0.431, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1631, loss: 0.1631 +2025-06-25 03:56:49,803 - pyskl - INFO - Epoch [111][700/1281] lr: 4.030e-03, eta: 5:49:39, time: 0.460, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1159, loss: 0.1159 +2025-06-25 03:57:17,732 - pyskl - INFO - Epoch [111][800/1281] lr: 4.015e-03, eta: 5:48:53, time: 0.279, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1393, loss: 0.1393 +2025-06-25 03:57:59,194 - pyskl - INFO - Epoch [111][900/1281] lr: 4.000e-03, eta: 5:48:11, time: 0.415, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1483, loss: 0.1483 +2025-06-25 03:58:48,415 - pyskl - INFO - Epoch [111][1000/1281] lr: 3.985e-03, eta: 5:47:32, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1410, loss: 0.1410 +2025-06-25 03:59:37,780 - pyskl - INFO - Epoch [111][1100/1281] lr: 3.970e-03, eta: 5:46:54, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1267, loss: 0.1267 +2025-06-25 04:00:26,944 - pyskl - INFO - Epoch [111][1200/1281] lr: 3.955e-03, eta: 5:46:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 0.9994, loss_cls: 0.1649, loss: 0.1649 +2025-06-25 04:01:07,021 - pyskl - INFO - Saving checkpoint at 111 epochs +2025-06-25 04:02:05,743 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:02:05,799 - pyskl - INFO - +top1_acc 0.8998 +top5_acc 0.9946 +2025-06-25 04:02:05,800 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:02:05,807 - pyskl - INFO - +mean_acc 0.8543 +2025-06-25 04:02:05,811 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_110.pth was removed +2025-06-25 04:02:05,982 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_111.pth. +2025-06-25 04:02:05,982 - pyskl - INFO - Best top1_acc is 0.8998 at 111 epoch. +2025-06-25 04:02:05,985 - pyskl - INFO - Epoch(val) [111][533] top1_acc: 0.8998, top5_acc: 0.9946, mean_class_accuracy: 0.8543 +2025-06-25 04:03:24,224 - pyskl - INFO - Epoch [112][100/1281] lr: 3.928e-03, eta: 5:45:01, time: 0.782, data_time: 0.187, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1237, loss: 0.1237 +2025-06-25 04:04:13,326 - pyskl - INFO - Epoch [112][200/1281] lr: 3.914e-03, eta: 5:44:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1271, loss: 0.1271 +2025-06-25 04:05:02,321 - pyskl - INFO - Epoch [112][300/1281] lr: 3.899e-03, eta: 5:43:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1394, loss: 0.1394 +2025-06-25 04:05:51,603 - pyskl - INFO - Epoch [112][400/1281] lr: 3.884e-03, eta: 5:43:04, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1199, loss: 0.1199 +2025-06-25 04:06:40,620 - pyskl - INFO - Epoch [112][500/1281] lr: 3.869e-03, eta: 5:42:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0927, loss: 0.0927 +2025-06-25 04:07:24,234 - pyskl - INFO - Epoch [112][600/1281] lr: 3.854e-03, eta: 5:41:44, time: 0.436, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0995, loss: 0.0995 +2025-06-25 04:08:08,216 - pyskl - INFO - Epoch [112][700/1281] lr: 3.840e-03, eta: 5:41:04, time: 0.440, data_time: 0.001, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1371, loss: 0.1371 +2025-06-25 04:08:38,270 - pyskl - INFO - Epoch [112][800/1281] lr: 3.825e-03, eta: 5:40:18, time: 0.301, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1456, loss: 0.1456 +2025-06-25 04:09:19,925 - pyskl - INFO - Epoch [112][900/1281] lr: 3.810e-03, eta: 5:39:37, time: 0.417, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1352, loss: 0.1352 +2025-06-25 04:10:08,735 - pyskl - INFO - Epoch [112][1000/1281] lr: 3.795e-03, eta: 5:38:58, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1215, loss: 0.1215 +2025-06-25 04:10:57,697 - pyskl - INFO - Epoch [112][1100/1281] lr: 3.781e-03, eta: 5:38:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1437, loss: 0.1437 +2025-06-25 04:11:47,131 - pyskl - INFO - Epoch [112][1200/1281] lr: 3.766e-03, eta: 5:37:40, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1117, loss: 0.1117 +2025-06-25 04:12:27,240 - pyskl - INFO - Saving checkpoint at 112 epochs +2025-06-25 04:13:25,878 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:13:25,933 - pyskl - INFO - +top1_acc 0.9094 +top5_acc 0.9934 +2025-06-25 04:13:25,934 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:13:25,941 - pyskl - INFO - +mean_acc 0.8792 +2025-06-25 04:13:25,945 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_111.pth was removed +2025-06-25 04:13:26,130 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_112.pth. +2025-06-25 04:13:26,130 - pyskl - INFO - Best top1_acc is 0.9094 at 112 epoch. +2025-06-25 04:13:26,133 - pyskl - INFO - Epoch(val) [112][533] top1_acc: 0.9094, top5_acc: 0.9934, mean_class_accuracy: 0.8792 +2025-06-25 04:14:46,251 - pyskl - INFO - Epoch [113][100/1281] lr: 3.740e-03, eta: 5:36:26, time: 0.801, data_time: 0.186, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1229, loss: 0.1229 +2025-06-25 04:15:35,266 - pyskl - INFO - Epoch [113][200/1281] lr: 3.725e-03, eta: 5:35:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1182, loss: 0.1182 +2025-06-25 04:16:24,120 - pyskl - INFO - Epoch [113][300/1281] lr: 3.711e-03, eta: 5:35:08, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0957, loss: 0.0957 +2025-06-25 04:17:13,059 - pyskl - INFO - Epoch [113][400/1281] lr: 3.696e-03, eta: 5:34:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1416, loss: 0.1416 +2025-06-25 04:18:02,236 - pyskl - INFO - Epoch [113][500/1281] lr: 3.682e-03, eta: 5:33:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0959, loss: 0.0959 +2025-06-25 04:18:44,666 - pyskl - INFO - Epoch [113][600/1281] lr: 3.667e-03, eta: 5:33:09, time: 0.424, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1061, loss: 0.1061 +2025-06-25 04:19:30,687 - pyskl - INFO - Epoch [113][700/1281] lr: 3.653e-03, eta: 5:32:29, time: 0.460, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1150, loss: 0.1150 +2025-06-25 04:19:58,319 - pyskl - INFO - Epoch [113][800/1281] lr: 3.638e-03, eta: 5:31:42, time: 0.276, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0938, loss: 0.0938 +2025-06-25 04:20:41,776 - pyskl - INFO - Epoch [113][900/1281] lr: 3.624e-03, eta: 5:31:01, time: 0.435, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1169, loss: 0.1169 +2025-06-25 04:21:30,560 - pyskl - INFO - Epoch [113][1000/1281] lr: 3.610e-03, eta: 5:30:22, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1324, loss: 0.1324 +2025-06-25 04:22:19,924 - pyskl - INFO - Epoch [113][1100/1281] lr: 3.595e-03, eta: 5:29:43, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.1038, loss: 0.1038 +2025-06-25 04:23:09,080 - pyskl - INFO - Epoch [113][1200/1281] lr: 3.581e-03, eta: 5:29:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1084, loss: 0.1084 +2025-06-25 04:23:49,333 - pyskl - INFO - Saving checkpoint at 113 epochs +2025-06-25 04:24:47,429 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:24:47,488 - pyskl - INFO - +top1_acc 0.9069 +top5_acc 0.9926 +2025-06-25 04:24:47,488 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:24:47,496 - pyskl - INFO - +mean_acc 0.8750 +2025-06-25 04:24:47,499 - pyskl - INFO - Epoch(val) [113][533] top1_acc: 0.9069, top5_acc: 0.9926, mean_class_accuracy: 0.8750 +2025-06-25 04:26:06,701 - pyskl - INFO - Epoch [114][100/1281] lr: 3.555e-03, eta: 5:27:50, time: 0.792, data_time: 0.185, memory: 4083, top1_acc: 0.9944, top5_acc: 0.9994, loss_cls: 0.0700, loss: 0.0700 +2025-06-25 04:26:56,020 - pyskl - INFO - Epoch [114][200/1281] lr: 3.541e-03, eta: 5:27:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.1021, loss: 0.1021 +2025-06-25 04:27:44,960 - pyskl - INFO - Epoch [114][300/1281] lr: 3.526e-03, eta: 5:26:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1043, loss: 0.1043 +2025-06-25 04:28:33,573 - pyskl - INFO - Epoch [114][400/1281] lr: 3.512e-03, eta: 5:25:53, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0821, loss: 0.0821 +2025-06-25 04:29:22,529 - pyskl - INFO - Epoch [114][500/1281] lr: 3.498e-03, eta: 5:25:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0818, loss: 0.0818 +2025-06-25 04:30:05,130 - pyskl - INFO - Epoch [114][600/1281] lr: 3.484e-03, eta: 5:24:32, time: 0.426, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1101, loss: 0.1101 +2025-06-25 04:30:52,981 - pyskl - INFO - Epoch [114][700/1281] lr: 3.470e-03, eta: 5:23:52, time: 0.478, data_time: 0.001, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1161, loss: 0.1161 +2025-06-25 04:31:19,434 - pyskl - INFO - Epoch [114][800/1281] lr: 3.456e-03, eta: 5:23:06, time: 0.265, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1256, loss: 0.1256 +2025-06-25 04:32:01,243 - pyskl - INFO - Epoch [114][900/1281] lr: 3.442e-03, eta: 5:22:24, time: 0.418, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1236, loss: 0.1236 +2025-06-25 04:32:49,993 - pyskl - INFO - Epoch [114][1000/1281] lr: 3.427e-03, eta: 5:21:45, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0758, loss: 0.0758 +2025-06-25 04:33:39,247 - pyskl - INFO - Epoch [114][1100/1281] lr: 3.413e-03, eta: 5:21:06, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1094, loss: 0.1094 +2025-06-25 04:34:28,202 - pyskl - INFO - Epoch [114][1200/1281] lr: 3.399e-03, eta: 5:20:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1145, loss: 0.1145 +2025-06-25 04:35:08,358 - pyskl - INFO - Saving checkpoint at 114 epochs +2025-06-25 04:36:07,560 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:36:07,616 - pyskl - INFO - +top1_acc 0.9093 +top5_acc 0.9935 +2025-06-25 04:36:07,616 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:36:07,625 - pyskl - INFO - +mean_acc 0.8714 +2025-06-25 04:36:07,627 - pyskl - INFO - Epoch(val) [114][533] top1_acc: 0.9093, top5_acc: 0.9935, mean_class_accuracy: 0.8714 +2025-06-25 04:37:26,943 - pyskl - INFO - Epoch [115][100/1281] lr: 3.374e-03, eta: 5:19:13, time: 0.793, data_time: 0.184, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0684, loss: 0.0684 +2025-06-25 04:38:16,140 - pyskl - INFO - Epoch [115][200/1281] lr: 3.360e-03, eta: 5:18:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0741, loss: 0.0741 +2025-06-25 04:39:05,670 - pyskl - INFO - Epoch [115][300/1281] lr: 3.346e-03, eta: 5:17:54, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0909, loss: 0.0909 +2025-06-25 04:39:54,814 - pyskl - INFO - Epoch [115][400/1281] lr: 3.332e-03, eta: 5:17:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0768, loss: 0.0768 +2025-06-25 04:40:43,814 - pyskl - INFO - Epoch [115][500/1281] lr: 3.318e-03, eta: 5:16:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0632, loss: 0.0632 +2025-06-25 04:41:25,129 - pyskl - INFO - Epoch [115][600/1281] lr: 3.305e-03, eta: 5:15:54, time: 0.413, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0740, loss: 0.0740 +2025-06-25 04:42:15,525 - pyskl - INFO - Epoch [115][700/1281] lr: 3.291e-03, eta: 5:15:15, time: 0.504, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0521, loss: 0.0521 +2025-06-25 04:42:39,773 - pyskl - INFO - Epoch [115][800/1281] lr: 3.277e-03, eta: 5:14:28, time: 0.242, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0509, loss: 0.0509 +2025-06-25 04:43:24,091 - pyskl - INFO - Epoch [115][900/1281] lr: 3.263e-03, eta: 5:13:47, time: 0.443, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0604, loss: 0.0604 +2025-06-25 04:44:13,158 - pyskl - INFO - Epoch [115][1000/1281] lr: 3.249e-03, eta: 5:13:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0845, loss: 0.0845 +2025-06-25 04:45:02,291 - pyskl - INFO - Epoch [115][1100/1281] lr: 3.236e-03, eta: 5:12:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0971, loss: 0.0971 +2025-06-25 04:45:51,503 - pyskl - INFO - Epoch [115][1200/1281] lr: 3.222e-03, eta: 5:11:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0897, loss: 0.0897 +2025-06-25 04:46:31,589 - pyskl - INFO - Saving checkpoint at 115 epochs +2025-06-25 04:47:30,346 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:47:30,402 - pyskl - INFO - +top1_acc 0.9052 +top5_acc 0.9921 +2025-06-25 04:47:30,403 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:47:30,409 - pyskl - INFO - +mean_acc 0.8706 +2025-06-25 04:47:30,411 - pyskl - INFO - Epoch(val) [115][533] top1_acc: 0.9052, top5_acc: 0.9921, mean_class_accuracy: 0.8706 +2025-06-25 04:48:48,694 - pyskl - INFO - Epoch [116][100/1281] lr: 3.197e-03, eta: 5:10:34, time: 0.783, data_time: 0.185, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0731, loss: 0.0731 +2025-06-25 04:49:37,675 - pyskl - INFO - Epoch [116][200/1281] lr: 3.184e-03, eta: 5:09:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0666, loss: 0.0666 +2025-06-25 04:50:26,590 - pyskl - INFO - Epoch [116][300/1281] lr: 3.170e-03, eta: 5:09:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0698, loss: 0.0698 +2025-06-25 04:51:15,314 - pyskl - INFO - Epoch [116][400/1281] lr: 3.156e-03, eta: 5:08:36, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0719, loss: 0.0719 +2025-06-25 04:52:04,200 - pyskl - INFO - Epoch [116][500/1281] lr: 3.143e-03, eta: 5:07:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0985, loss: 0.0985 +2025-06-25 04:52:43,989 - pyskl - INFO - Epoch [116][600/1281] lr: 3.129e-03, eta: 5:07:14, time: 0.398, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1195, loss: 0.1195 +2025-06-25 04:53:35,013 - pyskl - INFO - Epoch [116][700/1281] lr: 3.116e-03, eta: 5:06:35, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0913, loss: 0.0913 +2025-06-25 04:53:58,314 - pyskl - INFO - Epoch [116][800/1281] lr: 3.102e-03, eta: 5:05:48, time: 0.233, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0935, loss: 0.0935 +2025-06-25 04:54:42,490 - pyskl - INFO - Epoch [116][900/1281] lr: 3.089e-03, eta: 5:05:07, time: 0.442, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0788, loss: 0.0788 +2025-06-25 04:55:31,683 - pyskl - INFO - Epoch [116][1000/1281] lr: 3.075e-03, eta: 5:04:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 0.9994, loss_cls: 0.0815, loss: 0.0815 +2025-06-25 04:56:20,764 - pyskl - INFO - Epoch [116][1100/1281] lr: 3.062e-03, eta: 5:03:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0751, loss: 0.0751 +2025-06-25 04:57:10,149 - pyskl - INFO - Epoch [116][1200/1281] lr: 3.049e-03, eta: 5:03:09, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0898, loss: 0.0898 +2025-06-25 04:57:50,506 - pyskl - INFO - Saving checkpoint at 116 epochs +2025-06-25 04:58:48,824 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:58:48,879 - pyskl - INFO - +top1_acc 0.9132 +top5_acc 0.9940 +2025-06-25 04:58:48,879 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:58:48,885 - pyskl - INFO - +mean_acc 0.8796 +2025-06-25 04:58:48,889 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_112.pth was removed +2025-06-25 04:58:49,204 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_116.pth. +2025-06-25 04:58:49,204 - pyskl - INFO - Best top1_acc is 0.9132 at 116 epoch. +2025-06-25 04:58:49,207 - pyskl - INFO - Epoch(val) [116][533] top1_acc: 0.9132, top5_acc: 0.9940, mean_class_accuracy: 0.8796 +2025-06-25 05:00:08,813 - pyskl - INFO - Epoch [117][100/1281] lr: 3.024e-03, eta: 5:01:55, time: 0.796, data_time: 0.189, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0674, loss: 0.0674 +2025-06-25 05:00:57,486 - pyskl - INFO - Epoch [117][200/1281] lr: 3.011e-03, eta: 5:01:15, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0709, loss: 0.0709 +2025-06-25 05:01:46,361 - pyskl - INFO - Epoch [117][300/1281] lr: 2.998e-03, eta: 5:00:35, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0693, loss: 0.0693 +2025-06-25 05:02:35,396 - pyskl - INFO - Epoch [117][400/1281] lr: 2.984e-03, eta: 4:59:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0626, loss: 0.0626 +2025-06-25 05:03:24,221 - pyskl - INFO - Epoch [117][500/1281] lr: 2.971e-03, eta: 4:59:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0564, loss: 0.0564 +2025-06-25 05:04:03,179 - pyskl - INFO - Epoch [117][600/1281] lr: 2.958e-03, eta: 4:58:34, time: 0.390, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0672, loss: 0.0672 +2025-06-25 05:04:54,340 - pyskl - INFO - Epoch [117][700/1281] lr: 2.945e-03, eta: 4:57:55, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0786, loss: 0.0786 +2025-06-25 05:05:18,236 - pyskl - INFO - Epoch [117][800/1281] lr: 2.932e-03, eta: 4:57:08, time: 0.239, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 0.9994, loss_cls: 0.0736, loss: 0.0736 +2025-06-25 05:06:03,191 - pyskl - INFO - Epoch [117][900/1281] lr: 2.919e-03, eta: 4:56:27, time: 0.450, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0650, loss: 0.0650 +2025-06-25 05:06:52,334 - pyskl - INFO - Epoch [117][1000/1281] lr: 2.905e-03, eta: 4:55:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0662, loss: 0.0662 +2025-06-25 05:07:41,456 - pyskl - INFO - Epoch [117][1100/1281] lr: 2.892e-03, eta: 4:55:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0912, loss: 0.0912 +2025-06-25 05:08:30,635 - pyskl - INFO - Epoch [117][1200/1281] lr: 2.879e-03, eta: 4:54:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0670, loss: 0.0670 +2025-06-25 05:09:11,040 - pyskl - INFO - Saving checkpoint at 117 epochs +2025-06-25 05:10:09,414 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:10:09,469 - pyskl - INFO - +top1_acc 0.9156 +top5_acc 0.9944 +2025-06-25 05:10:09,469 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:10:09,475 - pyskl - INFO - +mean_acc 0.8827 +2025-06-25 05:10:09,479 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_116.pth was removed +2025-06-25 05:10:09,648 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_117.pth. +2025-06-25 05:10:09,649 - pyskl - INFO - Best top1_acc is 0.9156 at 117 epoch. +2025-06-25 05:10:09,651 - pyskl - INFO - Epoch(val) [117][533] top1_acc: 0.9156, top5_acc: 0.9944, mean_class_accuracy: 0.8827 +2025-06-25 05:11:27,659 - pyskl - INFO - Epoch [118][100/1281] lr: 2.856e-03, eta: 4:53:13, time: 0.780, data_time: 0.186, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0710, loss: 0.0710 +2025-06-25 05:12:16,655 - pyskl - INFO - Epoch [118][200/1281] lr: 2.843e-03, eta: 4:52:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0488, loss: 0.0488 +2025-06-25 05:13:05,713 - pyskl - INFO - Epoch [118][300/1281] lr: 2.830e-03, eta: 4:51:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0664, loss: 0.0664 +2025-06-25 05:13:55,209 - pyskl - INFO - Epoch [118][400/1281] lr: 2.817e-03, eta: 4:51:15, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0537, loss: 0.0537 +2025-06-25 05:14:44,483 - pyskl - INFO - Epoch [118][500/1281] lr: 2.804e-03, eta: 4:50:35, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0834, loss: 0.0834 +2025-06-25 05:15:23,238 - pyskl - INFO - Epoch [118][600/1281] lr: 2.791e-03, eta: 4:49:52, time: 0.388, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0650, loss: 0.0650 +2025-06-25 05:16:14,245 - pyskl - INFO - Epoch [118][700/1281] lr: 2.778e-03, eta: 4:49:13, time: 0.510, data_time: 0.001, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0745, loss: 0.0745 +2025-06-25 05:16:38,417 - pyskl - INFO - Epoch [118][800/1281] lr: 2.765e-03, eta: 4:48:27, time: 0.242, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0539, loss: 0.0539 +2025-06-25 05:17:25,417 - pyskl - INFO - Epoch [118][900/1281] lr: 2.753e-03, eta: 4:47:46, time: 0.470, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0640, loss: 0.0640 +2025-06-25 05:18:14,293 - pyskl - INFO - Epoch [118][1000/1281] lr: 2.740e-03, eta: 4:47:07, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0587, loss: 0.0587 +2025-06-25 05:19:03,670 - pyskl - INFO - Epoch [118][1100/1281] lr: 2.727e-03, eta: 4:46:27, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0720, loss: 0.0720 +2025-06-25 05:19:52,591 - pyskl - INFO - Epoch [118][1200/1281] lr: 2.714e-03, eta: 4:45:47, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0621, loss: 0.0621 +2025-06-25 05:20:32,859 - pyskl - INFO - Saving checkpoint at 118 epochs +2025-06-25 05:21:31,371 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:21:31,426 - pyskl - INFO - +top1_acc 0.9146 +top5_acc 0.9928 +2025-06-25 05:21:31,426 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:21:31,433 - pyskl - INFO - +mean_acc 0.8820 +2025-06-25 05:21:31,435 - pyskl - INFO - Epoch(val) [118][533] top1_acc: 0.9146, top5_acc: 0.9928, mean_class_accuracy: 0.8820 +2025-06-25 05:22:50,550 - pyskl - INFO - Epoch [119][100/1281] lr: 2.691e-03, eta: 4:44:32, time: 0.791, data_time: 0.188, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0709, loss: 0.0709 +2025-06-25 05:23:39,368 - pyskl - INFO - Epoch [119][200/1281] lr: 2.679e-03, eta: 4:43:53, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0803, loss: 0.0803 +2025-06-25 05:24:28,319 - pyskl - INFO - Epoch [119][300/1281] lr: 2.666e-03, eta: 4:43:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0874, loss: 0.0874 +2025-06-25 05:25:17,587 - pyskl - INFO - Epoch [119][400/1281] lr: 2.653e-03, eta: 4:42:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0623, loss: 0.0623 +2025-06-25 05:26:06,610 - pyskl - INFO - Epoch [119][500/1281] lr: 2.641e-03, eta: 4:41:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0709, loss: 0.0709 +2025-06-25 05:26:41,666 - pyskl - INFO - Epoch [119][600/1281] lr: 2.628e-03, eta: 4:41:10, time: 0.351, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0777, loss: 0.0777 +2025-06-25 05:27:32,513 - pyskl - INFO - Epoch [119][700/1281] lr: 2.616e-03, eta: 4:40:30, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 0.9994, loss_cls: 0.0872, loss: 0.0872 +2025-06-25 05:27:57,263 - pyskl - INFO - Epoch [119][800/1281] lr: 2.603e-03, eta: 4:39:44, time: 0.247, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0763, loss: 0.0763 +2025-06-25 05:28:44,148 - pyskl - INFO - Epoch [119][900/1281] lr: 2.591e-03, eta: 4:39:04, time: 0.469, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 0.9994, loss_cls: 0.0772, loss: 0.0772 +2025-06-25 05:29:33,183 - pyskl - INFO - Epoch [119][1000/1281] lr: 2.578e-03, eta: 4:38:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0575, loss: 0.0575 +2025-06-25 05:30:21,863 - pyskl - INFO - Epoch [119][1100/1281] lr: 2.566e-03, eta: 4:37:44, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0696, loss: 0.0696 +2025-06-25 05:31:11,077 - pyskl - INFO - Epoch [119][1200/1281] lr: 2.554e-03, eta: 4:37:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0517, loss: 0.0517 +2025-06-25 05:31:51,544 - pyskl - INFO - Saving checkpoint at 119 epochs +2025-06-25 05:32:50,574 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:32:50,633 - pyskl - INFO - +top1_acc 0.9125 +top5_acc 0.9934 +2025-06-25 05:32:50,633 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:32:50,641 - pyskl - INFO - +mean_acc 0.8779 +2025-06-25 05:32:50,643 - pyskl - INFO - Epoch(val) [119][533] top1_acc: 0.9125, top5_acc: 0.9934, mean_class_accuracy: 0.8779 +2025-06-25 05:34:09,504 - pyskl - INFO - Epoch [120][100/1281] lr: 2.531e-03, eta: 4:35:49, time: 0.789, data_time: 0.188, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0519, loss: 0.0519 +2025-06-25 05:34:58,217 - pyskl - INFO - Epoch [120][200/1281] lr: 2.519e-03, eta: 4:35:09, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0488, loss: 0.0488 +2025-06-25 05:35:47,178 - pyskl - INFO - Epoch [120][300/1281] lr: 2.507e-03, eta: 4:34:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0372, loss: 0.0372 +2025-06-25 05:36:36,019 - pyskl - INFO - Epoch [120][400/1281] lr: 2.494e-03, eta: 4:33:49, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0375, loss: 0.0375 +2025-06-25 05:37:25,273 - pyskl - INFO - Epoch [120][500/1281] lr: 2.482e-03, eta: 4:33:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0500, loss: 0.0500 +2025-06-25 05:38:00,545 - pyskl - INFO - Epoch [120][600/1281] lr: 2.470e-03, eta: 4:32:26, time: 0.353, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 0.9994, loss_cls: 0.0698, loss: 0.0698 +2025-06-25 05:38:51,607 - pyskl - INFO - Epoch [120][700/1281] lr: 2.458e-03, eta: 4:31:47, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0585, loss: 0.0585 +2025-06-25 05:39:16,854 - pyskl - INFO - Epoch [120][800/1281] lr: 2.446e-03, eta: 4:31:01, time: 0.252, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0613, loss: 0.0613 +2025-06-25 05:40:04,740 - pyskl - INFO - Epoch [120][900/1281] lr: 2.433e-03, eta: 4:30:20, time: 0.479, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0628, loss: 0.0628 +2025-06-25 05:40:53,811 - pyskl - INFO - Epoch [120][1000/1281] lr: 2.421e-03, eta: 4:29:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0658, loss: 0.0658 +2025-06-25 05:41:42,124 - pyskl - INFO - Epoch [120][1100/1281] lr: 2.409e-03, eta: 4:29:00, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0844, loss: 0.0844 +2025-06-25 05:42:31,558 - pyskl - INFO - Epoch [120][1200/1281] lr: 2.397e-03, eta: 4:28:20, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0585, loss: 0.0585 +2025-06-25 05:43:11,974 - pyskl - INFO - Saving checkpoint at 120 epochs +2025-06-25 05:44:11,181 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:44:11,248 - pyskl - INFO - +top1_acc 0.9061 +top5_acc 0.9947 +2025-06-25 05:44:11,248 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:44:11,256 - pyskl - INFO - +mean_acc 0.8825 +2025-06-25 05:44:11,258 - pyskl - INFO - Epoch(val) [120][533] top1_acc: 0.9061, top5_acc: 0.9947, mean_class_accuracy: 0.8825 +2025-06-25 05:45:29,897 - pyskl - INFO - Epoch [121][100/1281] lr: 2.375e-03, eta: 4:27:05, time: 0.786, data_time: 0.188, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0571, loss: 0.0571 +2025-06-25 05:46:18,786 - pyskl - INFO - Epoch [121][200/1281] lr: 2.363e-03, eta: 4:26:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0604, loss: 0.0604 +2025-06-25 05:47:08,207 - pyskl - INFO - Epoch [121][300/1281] lr: 2.351e-03, eta: 4:25:45, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0423, loss: 0.0423 +2025-06-25 05:47:57,239 - pyskl - INFO - Epoch [121][400/1281] lr: 2.340e-03, eta: 4:25:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0385, loss: 0.0385 +2025-06-25 05:48:46,184 - pyskl - INFO - Epoch [121][500/1281] lr: 2.328e-03, eta: 4:24:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0470, loss: 0.0470 +2025-06-25 05:49:21,145 - pyskl - INFO - Epoch [121][600/1281] lr: 2.316e-03, eta: 4:23:42, time: 0.350, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0502, loss: 0.0502 +2025-06-25 05:50:12,124 - pyskl - INFO - Epoch [121][700/1281] lr: 2.304e-03, eta: 4:23:02, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0406, loss: 0.0406 +2025-06-25 05:50:37,151 - pyskl - INFO - Epoch [121][800/1281] lr: 2.292e-03, eta: 4:22:16, time: 0.250, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0373, loss: 0.0373 +2025-06-25 05:51:24,402 - pyskl - INFO - Epoch [121][900/1281] lr: 2.280e-03, eta: 4:21:36, time: 0.472, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0480, loss: 0.0480 +2025-06-25 05:52:13,358 - pyskl - INFO - Epoch [121][1000/1281] lr: 2.269e-03, eta: 4:20:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0430, loss: 0.0430 +2025-06-25 05:53:02,352 - pyskl - INFO - Epoch [121][1100/1281] lr: 2.257e-03, eta: 4:20:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0528, loss: 0.0528 +2025-06-25 05:53:51,475 - pyskl - INFO - Epoch [121][1200/1281] lr: 2.245e-03, eta: 4:19:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0551, loss: 0.0551 +2025-06-25 05:54:31,734 - pyskl - INFO - Saving checkpoint at 121 epochs +2025-06-25 05:55:30,514 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:55:30,569 - pyskl - INFO - +top1_acc 0.9148 +top5_acc 0.9939 +2025-06-25 05:55:30,569 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:55:30,576 - pyskl - INFO - +mean_acc 0.8802 +2025-06-25 05:55:30,578 - pyskl - INFO - Epoch(val) [121][533] top1_acc: 0.9148, top5_acc: 0.9939, mean_class_accuracy: 0.8802 +2025-06-25 05:56:48,876 - pyskl - INFO - Epoch [122][100/1281] lr: 2.224e-03, eta: 4:18:20, time: 0.783, data_time: 0.185, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0442, loss: 0.0442 +2025-06-25 05:57:37,815 - pyskl - INFO - Epoch [122][200/1281] lr: 2.212e-03, eta: 4:17:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0412, loss: 0.0412 +2025-06-25 05:58:26,778 - pyskl - INFO - Epoch [122][300/1281] lr: 2.201e-03, eta: 4:17:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0535, loss: 0.0535 +2025-06-25 05:59:15,734 - pyskl - INFO - Epoch [122][400/1281] lr: 2.189e-03, eta: 4:16:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0540, loss: 0.0540 +2025-06-25 06:00:04,624 - pyskl - INFO - Epoch [122][500/1281] lr: 2.178e-03, eta: 4:15:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0480, loss: 0.0480 +2025-06-25 06:00:39,708 - pyskl - INFO - Epoch [122][600/1281] lr: 2.166e-03, eta: 4:14:56, time: 0.351, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0418, loss: 0.0418 +2025-06-25 06:01:30,785 - pyskl - INFO - Epoch [122][700/1281] lr: 2.155e-03, eta: 4:14:17, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0423, loss: 0.0423 +2025-06-25 06:01:56,068 - pyskl - INFO - Epoch [122][800/1281] lr: 2.143e-03, eta: 4:13:31, time: 0.253, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0405, loss: 0.0405 +2025-06-25 06:02:44,916 - pyskl - INFO - Epoch [122][900/1281] lr: 2.132e-03, eta: 4:12:51, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0438, loss: 0.0438 +2025-06-25 06:03:34,121 - pyskl - INFO - Epoch [122][1000/1281] lr: 2.120e-03, eta: 4:12:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0672, loss: 0.0672 +2025-06-25 06:04:23,478 - pyskl - INFO - Epoch [122][1100/1281] lr: 2.109e-03, eta: 4:11:30, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0485, loss: 0.0485 +2025-06-25 06:05:12,081 - pyskl - INFO - Epoch [122][1200/1281] lr: 2.098e-03, eta: 4:10:50, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0468, loss: 0.0468 +2025-06-25 06:05:52,406 - pyskl - INFO - Saving checkpoint at 122 epochs +2025-06-25 06:06:50,757 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:06:50,811 - pyskl - INFO - +top1_acc 0.9169 +top5_acc 0.9948 +2025-06-25 06:06:50,812 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:06:50,818 - pyskl - INFO - +mean_acc 0.8862 +2025-06-25 06:06:50,822 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_117.pth was removed +2025-06-25 06:06:51,000 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_122.pth. +2025-06-25 06:06:51,001 - pyskl - INFO - Best top1_acc is 0.9169 at 122 epoch. +2025-06-25 06:06:51,003 - pyskl - INFO - Epoch(val) [122][533] top1_acc: 0.9169, top5_acc: 0.9948, mean_class_accuracy: 0.8862 +2025-06-25 06:08:09,684 - pyskl - INFO - Epoch [123][100/1281] lr: 2.077e-03, eta: 4:09:35, time: 0.787, data_time: 0.184, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0354, loss: 0.0354 +2025-06-25 06:08:58,420 - pyskl - INFO - Epoch [123][200/1281] lr: 2.066e-03, eta: 4:08:55, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0488, loss: 0.0488 +2025-06-25 06:09:47,780 - pyskl - INFO - Epoch [123][300/1281] lr: 2.055e-03, eta: 4:08:14, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0455, loss: 0.0455 +2025-06-25 06:10:36,812 - pyskl - INFO - Epoch [123][400/1281] lr: 2.044e-03, eta: 4:07:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0412, loss: 0.0412 +2025-06-25 06:11:25,892 - pyskl - INFO - Epoch [123][500/1281] lr: 2.032e-03, eta: 4:06:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0365, loss: 0.0365 +2025-06-25 06:11:59,687 - pyskl - INFO - Epoch [123][600/1281] lr: 2.021e-03, eta: 4:06:10, time: 0.338, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0404, loss: 0.0404 +2025-06-25 06:12:50,735 - pyskl - INFO - Epoch [123][700/1281] lr: 2.010e-03, eta: 4:05:30, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0385, loss: 0.0385 +2025-06-25 06:13:16,309 - pyskl - INFO - Epoch [123][800/1281] lr: 1.999e-03, eta: 4:04:45, time: 0.256, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0444, loss: 0.0444 +2025-06-25 06:14:05,049 - pyskl - INFO - Epoch [123][900/1281] lr: 1.988e-03, eta: 4:04:04, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0620, loss: 0.0620 +2025-06-25 06:14:54,655 - pyskl - INFO - Epoch [123][1000/1281] lr: 1.977e-03, eta: 4:03:24, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0544, loss: 0.0544 +2025-06-25 06:15:43,768 - pyskl - INFO - Epoch [123][1100/1281] lr: 1.966e-03, eta: 4:02:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0554, loss: 0.0554 +2025-06-25 06:16:32,392 - pyskl - INFO - Epoch [123][1200/1281] lr: 1.955e-03, eta: 4:02:04, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0573, loss: 0.0573 +2025-06-25 06:17:12,516 - pyskl - INFO - Saving checkpoint at 123 epochs +2025-06-25 06:18:11,333 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:18:11,389 - pyskl - INFO - +top1_acc 0.9169 +top5_acc 0.9951 +2025-06-25 06:18:11,389 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:18:11,395 - pyskl - INFO - +mean_acc 0.8846 +2025-06-25 06:18:11,397 - pyskl - INFO - Epoch(val) [123][533] top1_acc: 0.9169, top5_acc: 0.9951, mean_class_accuracy: 0.8846 +2025-06-25 06:19:30,418 - pyskl - INFO - Epoch [124][100/1281] lr: 1.935e-03, eta: 4:00:48, time: 0.790, data_time: 0.186, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0442, loss: 0.0442 +2025-06-25 06:20:19,284 - pyskl - INFO - Epoch [124][200/1281] lr: 1.924e-03, eta: 4:00:08, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0383, loss: 0.0383 +2025-06-25 06:21:08,114 - pyskl - INFO - Epoch [124][300/1281] lr: 1.913e-03, eta: 3:59:28, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0328, loss: 0.0328 +2025-06-25 06:21:57,367 - pyskl - INFO - Epoch [124][400/1281] lr: 1.902e-03, eta: 3:58:47, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0452, loss: 0.0452 +2025-06-25 06:22:46,199 - pyskl - INFO - Epoch [124][500/1281] lr: 1.892e-03, eta: 3:58:07, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0582, loss: 0.0582 +2025-06-25 06:23:18,993 - pyskl - INFO - Epoch [124][600/1281] lr: 1.881e-03, eta: 3:57:23, time: 0.328, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0460, loss: 0.0460 +2025-06-25 06:24:10,159 - pyskl - INFO - Epoch [124][700/1281] lr: 1.870e-03, eta: 3:56:43, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0494, loss: 0.0494 +2025-06-25 06:24:35,731 - pyskl - INFO - Epoch [124][800/1281] lr: 1.859e-03, eta: 3:55:58, time: 0.256, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0395, loss: 0.0395 +2025-06-25 06:25:24,725 - pyskl - INFO - Epoch [124][900/1281] lr: 1.849e-03, eta: 3:55:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0362, loss: 0.0362 +2025-06-25 06:26:13,840 - pyskl - INFO - Epoch [124][1000/1281] lr: 1.838e-03, eta: 3:54:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0416, loss: 0.0416 +2025-06-25 06:27:02,910 - pyskl - INFO - Epoch [124][1100/1281] lr: 1.827e-03, eta: 3:53:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0364, loss: 0.0364 +2025-06-25 06:27:52,205 - pyskl - INFO - Epoch [124][1200/1281] lr: 1.817e-03, eta: 3:53:16, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0413, loss: 0.0413 +2025-06-25 06:28:32,308 - pyskl - INFO - Saving checkpoint at 124 epochs +2025-06-25 06:29:31,671 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:29:31,727 - pyskl - INFO - +top1_acc 0.9174 +top5_acc 0.9953 +2025-06-25 06:29:31,728 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:29:31,735 - pyskl - INFO - +mean_acc 0.8877 +2025-06-25 06:29:31,738 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_122.pth was removed +2025-06-25 06:29:31,930 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_124.pth. +2025-06-25 06:29:31,930 - pyskl - INFO - Best top1_acc is 0.9174 at 124 epoch. +2025-06-25 06:29:31,933 - pyskl - INFO - Epoch(val) [124][533] top1_acc: 0.9174, top5_acc: 0.9953, mean_class_accuracy: 0.8877 +2025-06-25 06:30:50,919 - pyskl - INFO - Epoch [125][100/1281] lr: 1.797e-03, eta: 3:52:01, time: 0.790, data_time: 0.189, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0299, loss: 0.0299 +2025-06-25 06:31:39,928 - pyskl - INFO - Epoch [125][200/1281] lr: 1.787e-03, eta: 3:51:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0299, loss: 0.0299 +2025-06-25 06:32:29,038 - pyskl - INFO - Epoch [125][300/1281] lr: 1.776e-03, eta: 3:50:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0297, loss: 0.0297 +2025-06-25 06:33:18,142 - pyskl - INFO - Epoch [125][400/1281] lr: 1.766e-03, eta: 3:50:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0337, loss: 0.0337 +2025-06-25 06:34:07,282 - pyskl - INFO - Epoch [125][500/1281] lr: 1.755e-03, eta: 3:49:19, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0341, loss: 0.0341 +2025-06-25 06:34:39,366 - pyskl - INFO - Epoch [125][600/1281] lr: 1.745e-03, eta: 3:48:35, time: 0.321, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0284, loss: 0.0284 +2025-06-25 06:35:30,353 - pyskl - INFO - Epoch [125][700/1281] lr: 1.735e-03, eta: 3:47:55, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0291, loss: 0.0291 +2025-06-25 06:35:57,508 - pyskl - INFO - Epoch [125][800/1281] lr: 1.724e-03, eta: 3:47:10, time: 0.272, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0354, loss: 0.0354 +2025-06-25 06:36:46,592 - pyskl - INFO - Epoch [125][900/1281] lr: 1.714e-03, eta: 3:46:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0368, loss: 0.0368 +2025-06-25 06:37:35,453 - pyskl - INFO - Epoch [125][1000/1281] lr: 1.704e-03, eta: 3:45:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0386, loss: 0.0386 +2025-06-25 06:38:24,540 - pyskl - INFO - Epoch [125][1100/1281] lr: 1.693e-03, eta: 3:45:09, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0313, loss: 0.0313 +2025-06-25 06:39:13,535 - pyskl - INFO - Epoch [125][1200/1281] lr: 1.683e-03, eta: 3:44:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0477, loss: 0.0477 +2025-06-25 06:39:53,580 - pyskl - INFO - Saving checkpoint at 125 epochs +2025-06-25 06:40:53,159 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:40:53,215 - pyskl - INFO - +top1_acc 0.9203 +top5_acc 0.9944 +2025-06-25 06:40:53,215 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:40:53,222 - pyskl - INFO - +mean_acc 0.8893 +2025-06-25 06:40:53,226 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_124.pth was removed +2025-06-25 06:40:53,395 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_125.pth. +2025-06-25 06:40:53,395 - pyskl - INFO - Best top1_acc is 0.9203 at 125 epoch. +2025-06-25 06:40:53,398 - pyskl - INFO - Epoch(val) [125][533] top1_acc: 0.9203, top5_acc: 0.9944, mean_class_accuracy: 0.8893 +2025-06-25 06:42:12,889 - pyskl - INFO - Epoch [126][100/1281] lr: 1.665e-03, eta: 3:43:13, time: 0.795, data_time: 0.190, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0318, loss: 0.0318 +2025-06-25 06:43:01,822 - pyskl - INFO - Epoch [126][200/1281] lr: 1.654e-03, eta: 3:42:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0281, loss: 0.0281 +2025-06-25 06:43:50,962 - pyskl - INFO - Epoch [126][300/1281] lr: 1.644e-03, eta: 3:41:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0305, loss: 0.0305 +2025-06-25 06:44:39,749 - pyskl - INFO - Epoch [126][400/1281] lr: 1.634e-03, eta: 3:41:11, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0350, loss: 0.0350 +2025-06-25 06:45:28,858 - pyskl - INFO - Epoch [126][500/1281] lr: 1.624e-03, eta: 3:40:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0327, loss: 0.0327 +2025-06-25 06:45:58,352 - pyskl - INFO - Epoch [126][600/1281] lr: 1.614e-03, eta: 3:39:46, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0331, loss: 0.0331 +2025-06-25 06:46:49,317 - pyskl - INFO - Epoch [126][700/1281] lr: 1.604e-03, eta: 3:39:06, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0347, loss: 0.0347 +2025-06-25 06:47:19,194 - pyskl - INFO - Epoch [126][800/1281] lr: 1.594e-03, eta: 3:38:22, time: 0.299, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0321, loss: 0.0321 +2025-06-25 06:48:08,251 - pyskl - INFO - Epoch [126][900/1281] lr: 1.584e-03, eta: 3:37:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0294, loss: 0.0294 +2025-06-25 06:48:57,127 - pyskl - INFO - Epoch [126][1000/1281] lr: 1.574e-03, eta: 3:37:00, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0314, loss: 0.0314 +2025-06-25 06:49:46,085 - pyskl - INFO - Epoch [126][1100/1281] lr: 1.564e-03, eta: 3:36:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0363, loss: 0.0363 +2025-06-25 06:50:35,002 - pyskl - INFO - Epoch [126][1200/1281] lr: 1.554e-03, eta: 3:35:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0314, loss: 0.0314 +2025-06-25 06:51:15,004 - pyskl - INFO - Saving checkpoint at 126 epochs +2025-06-25 06:52:13,938 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:52:13,994 - pyskl - INFO - +top1_acc 0.9202 +top5_acc 0.9957 +2025-06-25 06:52:13,994 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:52:14,001 - pyskl - INFO - +mean_acc 0.8877 +2025-06-25 06:52:14,003 - pyskl - INFO - Epoch(val) [126][533] top1_acc: 0.9202, top5_acc: 0.9957, mean_class_accuracy: 0.8877 +2025-06-25 06:53:32,797 - pyskl - INFO - Epoch [127][100/1281] lr: 1.536e-03, eta: 3:34:24, time: 0.788, data_time: 0.190, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0304, loss: 0.0304 +2025-06-25 06:54:21,831 - pyskl - INFO - Epoch [127][200/1281] lr: 1.527e-03, eta: 3:33:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0304, loss: 0.0304 +2025-06-25 06:55:10,935 - pyskl - INFO - Epoch [127][300/1281] lr: 1.517e-03, eta: 3:33:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0306, loss: 0.0306 +2025-06-25 06:55:59,713 - pyskl - INFO - Epoch [127][400/1281] lr: 1.507e-03, eta: 3:32:22, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0268, loss: 0.0268 +2025-06-25 06:56:48,582 - pyskl - INFO - Epoch [127][500/1281] lr: 1.497e-03, eta: 3:31:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0293, loss: 0.0293 +2025-06-25 06:57:17,371 - pyskl - INFO - Epoch [127][600/1281] lr: 1.488e-03, eta: 3:30:57, time: 0.288, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0305, loss: 0.0305 +2025-06-25 06:58:08,343 - pyskl - INFO - Epoch [127][700/1281] lr: 1.478e-03, eta: 3:30:16, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0279, loss: 0.0279 +2025-06-25 06:58:39,313 - pyskl - INFO - Epoch [127][800/1281] lr: 1.468e-03, eta: 3:29:32, time: 0.310, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0313, loss: 0.0313 +2025-06-25 06:59:28,150 - pyskl - INFO - Epoch [127][900/1281] lr: 1.459e-03, eta: 3:28:51, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0263, loss: 0.0263 +2025-06-25 07:00:17,318 - pyskl - INFO - Epoch [127][1000/1281] lr: 1.449e-03, eta: 3:28:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0300, loss: 0.0300 +2025-06-25 07:01:06,498 - pyskl - INFO - Epoch [127][1100/1281] lr: 1.440e-03, eta: 3:27:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0268, loss: 0.0268 +2025-06-25 07:01:54,898 - pyskl - INFO - Epoch [127][1200/1281] lr: 1.430e-03, eta: 3:26:49, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0327, loss: 0.0327 +2025-06-25 07:02:34,737 - pyskl - INFO - Saving checkpoint at 127 epochs +2025-06-25 07:03:33,863 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:03:33,919 - pyskl - INFO - +top1_acc 0.9249 +top5_acc 0.9953 +2025-06-25 07:03:33,919 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:03:33,926 - pyskl - INFO - +mean_acc 0.8968 +2025-06-25 07:03:33,930 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_125.pth was removed +2025-06-25 07:03:34,102 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_127.pth. +2025-06-25 07:03:34,102 - pyskl - INFO - Best top1_acc is 0.9249 at 127 epoch. +2025-06-25 07:03:34,105 - pyskl - INFO - Epoch(val) [127][533] top1_acc: 0.9249, top5_acc: 0.9953, mean_class_accuracy: 0.8968 +2025-06-25 07:04:56,215 - pyskl - INFO - Epoch [128][100/1281] lr: 1.413e-03, eta: 3:25:34, time: 0.821, data_time: 0.198, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0257, loss: 0.0257 +2025-06-25 07:05:45,378 - pyskl - INFO - Epoch [128][200/1281] lr: 1.404e-03, eta: 3:24:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0258, loss: 0.0258 +2025-06-25 07:06:33,994 - pyskl - INFO - Epoch [128][300/1281] lr: 1.394e-03, eta: 3:24:13, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0259, loss: 0.0259 +2025-06-25 07:07:22,911 - pyskl - INFO - Epoch [128][400/1281] lr: 1.385e-03, eta: 3:23:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0274, loss: 0.0274 +2025-06-25 07:08:11,769 - pyskl - INFO - Epoch [128][500/1281] lr: 1.376e-03, eta: 3:22:51, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0257, loss: 0.0257 +2025-06-25 07:08:41,880 - pyskl - INFO - Epoch [128][600/1281] lr: 1.366e-03, eta: 3:22:07, time: 0.301, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0267, loss: 0.0267 +2025-06-25 07:09:26,370 - pyskl - INFO - Epoch [128][700/1281] lr: 1.357e-03, eta: 3:21:25, time: 0.445, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0318, loss: 0.0318 +2025-06-25 07:09:59,490 - pyskl - INFO - Epoch [128][800/1281] lr: 1.348e-03, eta: 3:20:42, time: 0.331, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0322, loss: 0.0322 +2025-06-25 07:10:48,746 - pyskl - INFO - Epoch [128][900/1281] lr: 1.339e-03, eta: 3:20:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0277, loss: 0.0277 +2025-06-25 07:11:37,930 - pyskl - INFO - Epoch [128][1000/1281] lr: 1.329e-03, eta: 3:19:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0270, loss: 0.0270 +2025-06-25 07:12:26,719 - pyskl - INFO - Epoch [128][1100/1281] lr: 1.320e-03, eta: 3:18:40, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0293, loss: 0.0293 +2025-06-25 07:13:15,523 - pyskl - INFO - Epoch [128][1200/1281] lr: 1.311e-03, eta: 3:17:59, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0305, loss: 0.0305 +2025-06-25 07:13:55,558 - pyskl - INFO - Saving checkpoint at 128 epochs +2025-06-25 07:14:54,959 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:14:55,028 - pyskl - INFO - +top1_acc 0.9249 +top5_acc 0.9958 +2025-06-25 07:14:55,028 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:14:55,038 - pyskl - INFO - +mean_acc 0.8965 +2025-06-25 07:14:55,041 - pyskl - INFO - Epoch(val) [128][533] top1_acc: 0.9249, top5_acc: 0.9958, mean_class_accuracy: 0.8965 +2025-06-25 07:16:15,456 - pyskl - INFO - Epoch [129][100/1281] lr: 1.295e-03, eta: 3:16:43, time: 0.804, data_time: 0.192, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0287, loss: 0.0287 +2025-06-25 07:17:04,391 - pyskl - INFO - Epoch [129][200/1281] lr: 1.286e-03, eta: 3:16:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0255, loss: 0.0255 +2025-06-25 07:17:53,385 - pyskl - INFO - Epoch [129][300/1281] lr: 1.277e-03, eta: 3:15:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0255, loss: 0.0255 +2025-06-25 07:18:42,420 - pyskl - INFO - Epoch [129][400/1281] lr: 1.268e-03, eta: 3:14:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0251, loss: 0.0251 +2025-06-25 07:19:31,190 - pyskl - INFO - Epoch [129][500/1281] lr: 1.259e-03, eta: 3:14:00, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0287, loss: 0.0287 +2025-06-25 07:20:03,636 - pyskl - INFO - Epoch [129][600/1281] lr: 1.250e-03, eta: 3:13:16, time: 0.324, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0278, loss: 0.0278 +2025-06-25 07:20:45,812 - pyskl - INFO - Epoch [129][700/1281] lr: 1.241e-03, eta: 3:12:34, time: 0.422, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0278, loss: 0.0278 +2025-06-25 07:21:20,888 - pyskl - INFO - Epoch [129][800/1281] lr: 1.232e-03, eta: 3:11:51, time: 0.351, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 07:22:09,475 - pyskl - INFO - Epoch [129][900/1281] lr: 1.223e-03, eta: 3:11:10, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0266, loss: 0.0266 +2025-06-25 07:22:58,592 - pyskl - INFO - Epoch [129][1000/1281] lr: 1.214e-03, eta: 3:10:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0280, loss: 0.0280 +2025-06-25 07:23:47,451 - pyskl - INFO - Epoch [129][1100/1281] lr: 1.206e-03, eta: 3:09:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0264, loss: 0.0264 +2025-06-25 07:24:36,738 - pyskl - INFO - Epoch [129][1200/1281] lr: 1.197e-03, eta: 3:09:07, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0263, loss: 0.0263 +2025-06-25 07:25:16,701 - pyskl - INFO - Saving checkpoint at 129 epochs +2025-06-25 07:26:15,848 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:26:15,904 - pyskl - INFO - +top1_acc 0.9252 +top5_acc 0.9958 +2025-06-25 07:26:15,904 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:26:15,911 - pyskl - INFO - +mean_acc 0.8962 +2025-06-25 07:26:15,915 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_127.pth was removed +2025-06-25 07:26:16,098 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_129.pth. +2025-06-25 07:26:16,098 - pyskl - INFO - Best top1_acc is 0.9252 at 129 epoch. +2025-06-25 07:26:16,101 - pyskl - INFO - Epoch(val) [129][533] top1_acc: 0.9252, top5_acc: 0.9958, mean_class_accuracy: 0.8962 +2025-06-25 07:27:36,006 - pyskl - INFO - Epoch [130][100/1281] lr: 1.181e-03, eta: 3:07:52, time: 0.799, data_time: 0.189, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0271, loss: 0.0271 +2025-06-25 07:28:25,040 - pyskl - INFO - Epoch [130][200/1281] lr: 1.172e-03, eta: 3:07:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0250, loss: 0.0250 +2025-06-25 07:29:13,780 - pyskl - INFO - Epoch [130][300/1281] lr: 1.164e-03, eta: 3:06:30, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0279, loss: 0.0279 +2025-06-25 07:30:02,975 - pyskl - INFO - Epoch [130][400/1281] lr: 1.155e-03, eta: 3:05:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0286, loss: 0.0286 +2025-06-25 07:30:51,792 - pyskl - INFO - Epoch [130][500/1281] lr: 1.147e-03, eta: 3:05:08, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0271, loss: 0.0271 +2025-06-25 07:31:24,766 - pyskl - INFO - Epoch [130][600/1281] lr: 1.138e-03, eta: 3:04:24, time: 0.330, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0271, loss: 0.0271 +2025-06-25 07:32:05,281 - pyskl - INFO - Epoch [130][700/1281] lr: 1.130e-03, eta: 3:03:42, time: 0.405, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0264, loss: 0.0264 +2025-06-25 07:32:39,965 - pyskl - INFO - Epoch [130][800/1281] lr: 1.121e-03, eta: 3:02:59, time: 0.347, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 07:33:29,342 - pyskl - INFO - Epoch [130][900/1281] lr: 1.113e-03, eta: 3:02:18, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0272, loss: 0.0272 +2025-06-25 07:34:18,245 - pyskl - INFO - Epoch [130][1000/1281] lr: 1.104e-03, eta: 3:01:37, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0246, loss: 0.0246 +2025-06-25 07:35:07,101 - pyskl - INFO - Epoch [130][1100/1281] lr: 1.096e-03, eta: 3:00:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0252, loss: 0.0252 +2025-06-25 07:35:56,083 - pyskl - INFO - Epoch [130][1200/1281] lr: 1.088e-03, eta: 3:00:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0264, loss: 0.0264 +2025-06-25 07:36:35,980 - pyskl - INFO - Saving checkpoint at 130 epochs +2025-06-25 07:37:34,849 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:37:34,916 - pyskl - INFO - +top1_acc 0.9232 +top5_acc 0.9953 +2025-06-25 07:37:34,917 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:37:34,925 - pyskl - INFO - +mean_acc 0.8903 +2025-06-25 07:37:34,927 - pyskl - INFO - Epoch(val) [130][533] top1_acc: 0.9232, top5_acc: 0.9953, mean_class_accuracy: 0.8903 +2025-06-25 07:38:55,860 - pyskl - INFO - Epoch [131][100/1281] lr: 1.072e-03, eta: 2:58:59, time: 0.809, data_time: 0.196, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0267, loss: 0.0267 +2025-06-25 07:39:44,871 - pyskl - INFO - Epoch [131][200/1281] lr: 1.064e-03, eta: 2:58:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0298, loss: 0.0298 +2025-06-25 07:40:33,824 - pyskl - INFO - Epoch [131][300/1281] lr: 1.056e-03, eta: 2:57:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0296, loss: 0.0296 +2025-06-25 07:41:22,664 - pyskl - INFO - Epoch [131][400/1281] lr: 1.048e-03, eta: 2:56:56, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0313, loss: 0.0313 +2025-06-25 07:42:11,210 - pyskl - INFO - Epoch [131][500/1281] lr: 1.040e-03, eta: 2:56:15, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0284, loss: 0.0284 +2025-06-25 07:42:46,126 - pyskl - INFO - Epoch [131][600/1281] lr: 1.031e-03, eta: 2:55:32, time: 0.349, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0325, loss: 0.0325 +2025-06-25 07:43:24,660 - pyskl - INFO - Epoch [131][700/1281] lr: 1.023e-03, eta: 2:54:49, time: 0.385, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0258, loss: 0.0258 +2025-06-25 07:44:01,892 - pyskl - INFO - Epoch [131][800/1281] lr: 1.015e-03, eta: 2:54:07, time: 0.372, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0267, loss: 0.0267 +2025-06-25 07:44:50,584 - pyskl - INFO - Epoch [131][900/1281] lr: 1.007e-03, eta: 2:53:25, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0342, loss: 0.0342 +2025-06-25 07:45:39,556 - pyskl - INFO - Epoch [131][1000/1281] lr: 9.992e-04, eta: 2:52:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0255, loss: 0.0255 +2025-06-25 07:46:28,595 - pyskl - INFO - Epoch [131][1100/1281] lr: 9.912e-04, eta: 2:52:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0264, loss: 0.0264 +2025-06-25 07:47:17,856 - pyskl - INFO - Epoch [131][1200/1281] lr: 9.832e-04, eta: 2:51:22, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0276, loss: 0.0276 +2025-06-25 07:47:57,922 - pyskl - INFO - Saving checkpoint at 131 epochs +2025-06-25 07:48:56,711 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:48:56,767 - pyskl - INFO - +top1_acc 0.9251 +top5_acc 0.9959 +2025-06-25 07:48:56,767 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:48:56,775 - pyskl - INFO - +mean_acc 0.8974 +2025-06-25 07:48:56,777 - pyskl - INFO - Epoch(val) [131][533] top1_acc: 0.9251, top5_acc: 0.9959, mean_class_accuracy: 0.8974 +2025-06-25 07:50:18,036 - pyskl - INFO - Epoch [132][100/1281] lr: 9.689e-04, eta: 2:50:07, time: 0.813, data_time: 0.197, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0255, loss: 0.0255 +2025-06-25 07:51:07,287 - pyskl - INFO - Epoch [132][200/1281] lr: 9.610e-04, eta: 2:49:26, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-25 07:51:56,552 - pyskl - INFO - Epoch [132][300/1281] lr: 9.532e-04, eta: 2:48:45, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0248, loss: 0.0248 +2025-06-25 07:52:45,373 - pyskl - INFO - Epoch [132][400/1281] lr: 9.454e-04, eta: 2:48:03, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0252, loss: 0.0252 +2025-06-25 07:53:31,019 - pyskl - INFO - Epoch [132][500/1281] lr: 9.376e-04, eta: 2:47:22, time: 0.456, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0255, loss: 0.0255 +2025-06-25 07:54:10,154 - pyskl - INFO - Epoch [132][600/1281] lr: 9.298e-04, eta: 2:46:39, time: 0.391, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0260, loss: 0.0260 +2025-06-25 07:54:44,404 - pyskl - INFO - Epoch [132][700/1281] lr: 9.221e-04, eta: 2:45:56, time: 0.343, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0271, loss: 0.0271 +2025-06-25 07:55:21,891 - pyskl - INFO - Epoch [132][800/1281] lr: 9.144e-04, eta: 2:45:13, time: 0.375, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0318, loss: 0.0318 +2025-06-25 07:56:10,965 - pyskl - INFO - Epoch [132][900/1281] lr: 9.068e-04, eta: 2:44:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0288, loss: 0.0288 +2025-06-25 07:56:59,886 - pyskl - INFO - Epoch [132][1000/1281] lr: 8.991e-04, eta: 2:43:51, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0269, loss: 0.0269 +2025-06-25 07:57:48,600 - pyskl - INFO - Epoch [132][1100/1281] lr: 8.915e-04, eta: 2:43:10, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 07:58:37,712 - pyskl - INFO - Epoch [132][1200/1281] lr: 8.840e-04, eta: 2:42:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0264, loss: 0.0264 +2025-06-25 07:59:17,442 - pyskl - INFO - Saving checkpoint at 132 epochs +2025-06-25 08:00:16,093 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:00:16,162 - pyskl - INFO - +top1_acc 0.9267 +top5_acc 0.9953 +2025-06-25 08:00:16,162 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:00:16,169 - pyskl - INFO - +mean_acc 0.8984 +2025-06-25 08:00:16,174 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_129.pth was removed +2025-06-25 08:00:16,374 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_132.pth. +2025-06-25 08:00:16,375 - pyskl - INFO - Best top1_acc is 0.9267 at 132 epoch. +2025-06-25 08:00:16,377 - pyskl - INFO - Epoch(val) [132][533] top1_acc: 0.9267, top5_acc: 0.9953, mean_class_accuracy: 0.8984 +2025-06-25 08:01:37,888 - pyskl - INFO - Epoch [133][100/1281] lr: 8.704e-04, eta: 2:41:13, time: 0.815, data_time: 0.191, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0248, loss: 0.0248 +2025-06-25 08:02:26,903 - pyskl - INFO - Epoch [133][200/1281] lr: 8.629e-04, eta: 2:40:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0267, loss: 0.0267 +2025-06-25 08:03:15,857 - pyskl - INFO - Epoch [133][300/1281] lr: 8.554e-04, eta: 2:39:51, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0263, loss: 0.0263 +2025-06-25 08:04:05,100 - pyskl - INFO - Epoch [133][400/1281] lr: 8.480e-04, eta: 2:39:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0249, loss: 0.0249 +2025-06-25 08:04:50,720 - pyskl - INFO - Epoch [133][500/1281] lr: 8.406e-04, eta: 2:38:28, time: 0.456, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0258, loss: 0.0258 +2025-06-25 08:05:30,841 - pyskl - INFO - Epoch [133][600/1281] lr: 8.333e-04, eta: 2:37:45, time: 0.401, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0266, loss: 0.0266 +2025-06-25 08:06:04,112 - pyskl - INFO - Epoch [133][700/1281] lr: 8.260e-04, eta: 2:37:02, time: 0.333, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0239, loss: 0.0239 +2025-06-25 08:06:43,073 - pyskl - INFO - Epoch [133][800/1281] lr: 8.187e-04, eta: 2:36:19, time: 0.390, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0260, loss: 0.0260 +2025-06-25 08:07:32,038 - pyskl - INFO - Epoch [133][900/1281] lr: 8.114e-04, eta: 2:35:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0250, loss: 0.0250 +2025-06-25 08:08:20,439 - pyskl - INFO - Epoch [133][1000/1281] lr: 8.042e-04, eta: 2:34:57, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0249, loss: 0.0249 +2025-06-25 08:09:09,259 - pyskl - INFO - Epoch [133][1100/1281] lr: 7.970e-04, eta: 2:34:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 08:09:58,003 - pyskl - INFO - Epoch [133][1200/1281] lr: 7.898e-04, eta: 2:33:34, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 08:10:38,115 - pyskl - INFO - Saving checkpoint at 133 epochs +2025-06-25 08:11:36,871 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:11:36,941 - pyskl - INFO - +top1_acc 0.9268 +top5_acc 0.9954 +2025-06-25 08:11:36,941 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:11:36,949 - pyskl - INFO - +mean_acc 0.8996 +2025-06-25 08:11:36,954 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_132.pth was removed +2025-06-25 08:11:37,134 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_133.pth. +2025-06-25 08:11:37,134 - pyskl - INFO - Best top1_acc is 0.9268 at 133 epoch. +2025-06-25 08:11:37,137 - pyskl - INFO - Epoch(val) [133][533] top1_acc: 0.9268, top5_acc: 0.9954, mean_class_accuracy: 0.8996 +2025-06-25 08:12:59,173 - pyskl - INFO - Epoch [134][100/1281] lr: 7.769e-04, eta: 2:32:19, time: 0.820, data_time: 0.193, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 08:13:47,986 - pyskl - INFO - Epoch [134][200/1281] lr: 7.699e-04, eta: 2:31:37, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0287, loss: 0.0287 +2025-06-25 08:14:37,048 - pyskl - INFO - Epoch [134][300/1281] lr: 7.628e-04, eta: 2:30:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0292, loss: 0.0292 +2025-06-25 08:15:26,491 - pyskl - INFO - Epoch [134][400/1281] lr: 7.558e-04, eta: 2:30:15, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0317, loss: 0.0317 +2025-06-25 08:16:09,497 - pyskl - INFO - Epoch [134][500/1281] lr: 7.488e-04, eta: 2:29:33, time: 0.430, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0246, loss: 0.0246 +2025-06-25 08:16:55,973 - pyskl - INFO - Epoch [134][600/1281] lr: 7.419e-04, eta: 2:28:51, time: 0.465, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0253, loss: 0.0253 +2025-06-25 08:17:23,552 - pyskl - INFO - Epoch [134][700/1281] lr: 7.349e-04, eta: 2:28:07, time: 0.276, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0274, loss: 0.0274 +2025-06-25 08:18:05,749 - pyskl - INFO - Epoch [134][800/1281] lr: 7.281e-04, eta: 2:27:25, time: 0.422, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0249, loss: 0.0249 +2025-06-25 08:18:54,389 - pyskl - INFO - Epoch [134][900/1281] lr: 7.212e-04, eta: 2:26:44, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0246, loss: 0.0246 +2025-06-25 08:19:43,186 - pyskl - INFO - Epoch [134][1000/1281] lr: 7.144e-04, eta: 2:26:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0254, loss: 0.0254 +2025-06-25 08:20:31,941 - pyskl - INFO - Epoch [134][1100/1281] lr: 7.076e-04, eta: 2:25:21, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0261, loss: 0.0261 +2025-06-25 08:21:20,822 - pyskl - INFO - Epoch [134][1200/1281] lr: 7.008e-04, eta: 2:24:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 08:22:00,964 - pyskl - INFO - Saving checkpoint at 134 epochs +2025-06-25 08:22:59,942 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:22:59,998 - pyskl - INFO - +top1_acc 0.9265 +top5_acc 0.9954 +2025-06-25 08:22:59,998 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:23:00,005 - pyskl - INFO - +mean_acc 0.8973 +2025-06-25 08:23:00,007 - pyskl - INFO - Epoch(val) [134][533] top1_acc: 0.9265, top5_acc: 0.9954, mean_class_accuracy: 0.8973 +2025-06-25 08:24:21,612 - pyskl - INFO - Epoch [135][100/1281] lr: 6.887e-04, eta: 2:23:24, time: 0.816, data_time: 0.190, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0245, loss: 0.0245 +2025-06-25 08:25:10,855 - pyskl - INFO - Epoch [135][200/1281] lr: 6.820e-04, eta: 2:22:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0246, loss: 0.0246 +2025-06-25 08:25:59,653 - pyskl - INFO - Epoch [135][300/1281] lr: 6.753e-04, eta: 2:22:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 08:26:48,558 - pyskl - INFO - Epoch [135][400/1281] lr: 6.687e-04, eta: 2:21:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0262, loss: 0.0262 +2025-06-25 08:27:28,213 - pyskl - INFO - Epoch [135][500/1281] lr: 6.622e-04, eta: 2:20:37, time: 0.397, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0257, loss: 0.0257 +2025-06-25 08:28:19,470 - pyskl - INFO - Epoch [135][600/1281] lr: 6.556e-04, eta: 2:19:56, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0272, loss: 0.0272 +2025-06-25 08:28:43,117 - pyskl - INFO - Epoch [135][700/1281] lr: 6.491e-04, eta: 2:19:12, time: 0.236, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0267, loss: 0.0267 +2025-06-25 08:29:27,136 - pyskl - INFO - Epoch [135][800/1281] lr: 6.426e-04, eta: 2:18:30, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 08:30:15,805 - pyskl - INFO - Epoch [135][900/1281] lr: 6.362e-04, eta: 2:17:48, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0318, loss: 0.0318 +2025-06-25 08:31:04,918 - pyskl - INFO - Epoch [135][1000/1281] lr: 6.297e-04, eta: 2:17:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 08:31:54,209 - pyskl - INFO - Epoch [135][1100/1281] lr: 6.233e-04, eta: 2:16:25, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0253, loss: 0.0253 +2025-06-25 08:32:43,002 - pyskl - INFO - Epoch [135][1200/1281] lr: 6.170e-04, eta: 2:15:44, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0252, loss: 0.0252 +2025-06-25 08:33:22,796 - pyskl - INFO - Saving checkpoint at 135 epochs +2025-06-25 08:34:21,584 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:34:21,652 - pyskl - INFO - +top1_acc 0.9258 +top5_acc 0.9954 +2025-06-25 08:34:21,652 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:34:21,660 - pyskl - INFO - +mean_acc 0.8976 +2025-06-25 08:34:21,662 - pyskl - INFO - Epoch(val) [135][533] top1_acc: 0.9258, top5_acc: 0.9954, mean_class_accuracy: 0.8976 +2025-06-25 08:35:41,821 - pyskl - INFO - Epoch [136][100/1281] lr: 6.056e-04, eta: 2:14:28, time: 0.802, data_time: 0.189, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-25 08:36:30,674 - pyskl - INFO - Epoch [136][200/1281] lr: 5.993e-04, eta: 2:13:46, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0281, loss: 0.0281 +2025-06-25 08:37:19,470 - pyskl - INFO - Epoch [136][300/1281] lr: 5.931e-04, eta: 2:13:05, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0239, loss: 0.0239 +2025-06-25 08:38:08,717 - pyskl - INFO - Epoch [136][400/1281] lr: 5.868e-04, eta: 2:12:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 08:38:47,524 - pyskl - INFO - Epoch [136][500/1281] lr: 5.807e-04, eta: 2:11:41, time: 0.388, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0270, loss: 0.0270 +2025-06-25 08:39:38,589 - pyskl - INFO - Epoch [136][600/1281] lr: 5.745e-04, eta: 2:11:00, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0274, loss: 0.0274 +2025-06-25 08:40:02,071 - pyskl - INFO - Epoch [136][700/1281] lr: 5.684e-04, eta: 2:10:15, time: 0.235, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0249, loss: 0.0249 +2025-06-25 08:40:46,285 - pyskl - INFO - Epoch [136][800/1281] lr: 5.623e-04, eta: 2:09:33, time: 0.442, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0265, loss: 0.0265 +2025-06-25 08:41:35,060 - pyskl - INFO - Epoch [136][900/1281] lr: 5.563e-04, eta: 2:08:52, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0263, loss: 0.0263 +2025-06-25 08:42:24,000 - pyskl - INFO - Epoch [136][1000/1281] lr: 5.503e-04, eta: 2:08:10, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0245, loss: 0.0245 +2025-06-25 08:43:12,986 - pyskl - INFO - Epoch [136][1100/1281] lr: 5.443e-04, eta: 2:07:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0257, loss: 0.0257 +2025-06-25 08:44:01,993 - pyskl - INFO - Epoch [136][1200/1281] lr: 5.384e-04, eta: 2:06:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-25 08:44:42,055 - pyskl - INFO - Saving checkpoint at 136 epochs +2025-06-25 08:45:40,827 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:45:40,882 - pyskl - INFO - +top1_acc 0.9254 +top5_acc 0.9958 +2025-06-25 08:45:40,882 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:45:40,890 - pyskl - INFO - +mean_acc 0.8960 +2025-06-25 08:45:40,892 - pyskl - INFO - Epoch(val) [136][533] top1_acc: 0.9254, top5_acc: 0.9958, mean_class_accuracy: 0.8960 +2025-06-25 08:47:00,411 - pyskl - INFO - Epoch [137][100/1281] lr: 5.277e-04, eta: 2:05:31, time: 0.795, data_time: 0.187, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0253, loss: 0.0253 +2025-06-25 08:47:49,595 - pyskl - INFO - Epoch [137][200/1281] lr: 5.218e-04, eta: 2:04:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0256, loss: 0.0256 +2025-06-25 08:48:38,552 - pyskl - INFO - Epoch [137][300/1281] lr: 5.160e-04, eta: 2:04:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 08:49:27,360 - pyskl - INFO - Epoch [137][400/1281] lr: 5.102e-04, eta: 2:03:26, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-25 08:50:07,361 - pyskl - INFO - Epoch [137][500/1281] lr: 5.044e-04, eta: 2:02:44, time: 0.400, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 08:50:58,320 - pyskl - INFO - Epoch [137][600/1281] lr: 4.987e-04, eta: 2:02:03, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0232, loss: 0.0232 +2025-06-25 08:51:21,963 - pyskl - INFO - Epoch [137][700/1281] lr: 4.930e-04, eta: 2:01:19, time: 0.236, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 08:52:07,408 - pyskl - INFO - Epoch [137][800/1281] lr: 4.873e-04, eta: 2:00:37, time: 0.454, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-25 08:52:56,240 - pyskl - INFO - Epoch [137][900/1281] lr: 4.817e-04, eta: 1:59:55, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-25 08:53:45,163 - pyskl - INFO - Epoch [137][1000/1281] lr: 4.761e-04, eta: 1:59:13, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0263, loss: 0.0263 +2025-06-25 08:54:34,148 - pyskl - INFO - Epoch [137][1100/1281] lr: 4.705e-04, eta: 1:58:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 08:55:23,305 - pyskl - INFO - Epoch [137][1200/1281] lr: 4.650e-04, eta: 1:57:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 08:56:03,649 - pyskl - INFO - Saving checkpoint at 137 epochs +2025-06-25 08:57:02,447 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:57:02,514 - pyskl - INFO - +top1_acc 0.9267 +top5_acc 0.9952 +2025-06-25 08:57:02,514 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:57:02,522 - pyskl - INFO - +mean_acc 0.8966 +2025-06-25 08:57:02,524 - pyskl - INFO - Epoch(val) [137][533] top1_acc: 0.9267, top5_acc: 0.9952, mean_class_accuracy: 0.8966 +2025-06-25 08:58:21,575 - pyskl - INFO - Epoch [138][100/1281] lr: 4.550e-04, eta: 1:56:34, time: 0.790, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-25 08:59:10,615 - pyskl - INFO - Epoch [138][200/1281] lr: 4.496e-04, eta: 1:55:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0261, loss: 0.0261 +2025-06-25 08:59:59,199 - pyskl - INFO - Epoch [138][300/1281] lr: 4.442e-04, eta: 1:55:11, time: 0.486, data_time: 0.001, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0266, loss: 0.0266 +2025-06-25 09:00:48,115 - pyskl - INFO - Epoch [138][400/1281] lr: 4.388e-04, eta: 1:54:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0262, loss: 0.0262 +2025-06-25 09:01:26,325 - pyskl - INFO - Epoch [138][500/1281] lr: 4.334e-04, eta: 1:53:46, time: 0.382, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0239, loss: 0.0239 +2025-06-25 09:02:17,419 - pyskl - INFO - Epoch [138][600/1281] lr: 4.281e-04, eta: 1:53:05, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-25 09:02:41,756 - pyskl - INFO - Epoch [138][700/1281] lr: 4.228e-04, eta: 1:52:21, time: 0.243, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0252, loss: 0.0252 +2025-06-25 09:03:27,819 - pyskl - INFO - Epoch [138][800/1281] lr: 4.176e-04, eta: 1:51:39, time: 0.461, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 09:04:16,907 - pyskl - INFO - Epoch [138][900/1281] lr: 4.124e-04, eta: 1:50:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0248, loss: 0.0248 +2025-06-25 09:05:05,827 - pyskl - INFO - Epoch [138][1000/1281] lr: 4.072e-04, eta: 1:50:16, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0237, loss: 0.0237 +2025-06-25 09:05:54,842 - pyskl - INFO - Epoch [138][1100/1281] lr: 4.020e-04, eta: 1:49:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0255, loss: 0.0255 +2025-06-25 09:06:43,735 - pyskl - INFO - Epoch [138][1200/1281] lr: 3.969e-04, eta: 1:48:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 09:07:23,859 - pyskl - INFO - Saving checkpoint at 138 epochs +2025-06-25 09:08:22,953 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:08:23,008 - pyskl - INFO - +top1_acc 0.9271 +top5_acc 0.9950 +2025-06-25 09:08:23,008 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:08:23,015 - pyskl - INFO - +mean_acc 0.8980 +2025-06-25 09:08:23,020 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_133.pth was removed +2025-06-25 09:08:23,187 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_138.pth. +2025-06-25 09:08:23,187 - pyskl - INFO - Best top1_acc is 0.9271 at 138 epoch. +2025-06-25 09:08:23,190 - pyskl - INFO - Epoch(val) [138][533] top1_acc: 0.9271, top5_acc: 0.9950, mean_class_accuracy: 0.8980 +2025-06-25 09:09:42,791 - pyskl - INFO - Epoch [139][100/1281] lr: 3.877e-04, eta: 1:47:36, time: 0.796, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0249, loss: 0.0249 +2025-06-25 09:10:31,735 - pyskl - INFO - Epoch [139][200/1281] lr: 3.827e-04, eta: 1:46:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0249, loss: 0.0249 +2025-06-25 09:11:21,029 - pyskl - INFO - Epoch [139][300/1281] lr: 3.777e-04, eta: 1:46:13, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-25 09:12:09,852 - pyskl - INFO - Epoch [139][400/1281] lr: 3.727e-04, eta: 1:45:31, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 09:12:44,886 - pyskl - INFO - Epoch [139][500/1281] lr: 3.678e-04, eta: 1:44:48, time: 0.350, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-25 09:13:35,921 - pyskl - INFO - Epoch [139][600/1281] lr: 3.628e-04, eta: 1:44:07, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-25 09:14:00,612 - pyskl - INFO - Epoch [139][700/1281] lr: 3.580e-04, eta: 1:43:23, time: 0.247, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0249, loss: 0.0249 +2025-06-25 09:14:48,926 - pyskl - INFO - Epoch [139][800/1281] lr: 3.531e-04, eta: 1:42:41, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-25 09:15:37,818 - pyskl - INFO - Epoch [139][900/1281] lr: 3.483e-04, eta: 1:41:59, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0255, loss: 0.0255 +2025-06-25 09:16:26,939 - pyskl - INFO - Epoch [139][1000/1281] lr: 3.436e-04, eta: 1:41:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 09:17:15,974 - pyskl - INFO - Epoch [139][1100/1281] lr: 3.388e-04, eta: 1:40:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 09:18:04,970 - pyskl - INFO - Epoch [139][1200/1281] lr: 3.341e-04, eta: 1:39:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0227, loss: 0.0227 +2025-06-25 09:18:45,410 - pyskl - INFO - Saving checkpoint at 139 epochs +2025-06-25 09:19:44,753 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:19:44,810 - pyskl - INFO - +top1_acc 0.9267 +top5_acc 0.9954 +2025-06-25 09:19:44,811 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:19:44,818 - pyskl - INFO - +mean_acc 0.8971 +2025-06-25 09:19:44,820 - pyskl - INFO - Epoch(val) [139][533] top1_acc: 0.9267, top5_acc: 0.9954, mean_class_accuracy: 0.8971 +2025-06-25 09:21:03,946 - pyskl - INFO - Epoch [140][100/1281] lr: 3.257e-04, eta: 1:38:38, time: 0.791, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 09:21:53,041 - pyskl - INFO - Epoch [140][200/1281] lr: 3.210e-04, eta: 1:37:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0248, loss: 0.0248 +2025-06-25 09:22:41,927 - pyskl - INFO - Epoch [140][300/1281] lr: 3.165e-04, eta: 1:37:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0249, loss: 0.0249 +2025-06-25 09:23:31,011 - pyskl - INFO - Epoch [140][400/1281] lr: 3.119e-04, eta: 1:36:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0227, loss: 0.0227 +2025-06-25 09:24:05,028 - pyskl - INFO - Epoch [140][500/1281] lr: 3.074e-04, eta: 1:35:49, time: 0.340, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0259, loss: 0.0259 +2025-06-25 09:24:55,900 - pyskl - INFO - Epoch [140][600/1281] lr: 3.029e-04, eta: 1:35:08, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0257, loss: 0.0257 +2025-06-25 09:25:21,240 - pyskl - INFO - Epoch [140][700/1281] lr: 2.984e-04, eta: 1:34:24, time: 0.253, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-25 09:26:10,133 - pyskl - INFO - Epoch [140][800/1281] lr: 2.940e-04, eta: 1:33:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-25 09:26:59,539 - pyskl - INFO - Epoch [140][900/1281] lr: 2.896e-04, eta: 1:33:01, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 09:27:48,447 - pyskl - INFO - Epoch [140][1000/1281] lr: 2.853e-04, eta: 1:32:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-25 09:28:37,333 - pyskl - INFO - Epoch [140][1100/1281] lr: 2.809e-04, eta: 1:31:37, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 09:29:26,499 - pyskl - INFO - Epoch [140][1200/1281] lr: 2.767e-04, eta: 1:30:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 09:30:07,180 - pyskl - INFO - Saving checkpoint at 140 epochs +2025-06-25 09:31:05,771 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:31:05,826 - pyskl - INFO - +top1_acc 0.9259 +top5_acc 0.9960 +2025-06-25 09:31:05,826 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:31:05,833 - pyskl - INFO - +mean_acc 0.8954 +2025-06-25 09:31:05,835 - pyskl - INFO - Epoch(val) [140][533] top1_acc: 0.9259, top5_acc: 0.9960, mean_class_accuracy: 0.8954 +2025-06-25 09:32:24,257 - pyskl - INFO - Epoch [141][100/1281] lr: 2.690e-04, eta: 1:29:39, time: 0.784, data_time: 0.187, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 09:33:12,800 - pyskl - INFO - Epoch [141][200/1281] lr: 2.648e-04, eta: 1:28:57, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-25 09:34:01,207 - pyskl - INFO - Epoch [141][300/1281] lr: 2.606e-04, eta: 1:28:15, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 09:34:50,204 - pyskl - INFO - Epoch [141][400/1281] lr: 2.565e-04, eta: 1:27:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 09:35:23,487 - pyskl - INFO - Epoch [141][500/1281] lr: 2.524e-04, eta: 1:26:50, time: 0.333, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 09:36:14,376 - pyskl - INFO - Epoch [141][600/1281] lr: 2.483e-04, eta: 1:26:08, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 09:36:40,619 - pyskl - INFO - Epoch [141][700/1281] lr: 2.443e-04, eta: 1:25:25, time: 0.262, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0252, loss: 0.0252 +2025-06-25 09:37:29,535 - pyskl - INFO - Epoch [141][800/1281] lr: 2.402e-04, eta: 1:24:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0237, loss: 0.0237 +2025-06-25 09:38:18,437 - pyskl - INFO - Epoch [141][900/1281] lr: 2.363e-04, eta: 1:24:01, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-25 09:39:07,752 - pyskl - INFO - Epoch [141][1000/1281] lr: 2.323e-04, eta: 1:23:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-25 09:39:57,036 - pyskl - INFO - Epoch [141][1100/1281] lr: 2.284e-04, eta: 1:22:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0239, loss: 0.0239 +2025-06-25 09:40:45,921 - pyskl - INFO - Epoch [141][1200/1281] lr: 2.246e-04, eta: 1:21:55, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0235, loss: 0.0235 +2025-06-25 09:41:26,495 - pyskl - INFO - Saving checkpoint at 141 epochs +2025-06-25 09:42:25,229 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:42:25,295 - pyskl - INFO - +top1_acc 0.9279 +top5_acc 0.9957 +2025-06-25 09:42:25,295 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:42:25,305 - pyskl - INFO - +mean_acc 0.8986 +2025-06-25 09:42:25,310 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_138.pth was removed +2025-06-25 09:42:25,523 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_141.pth. +2025-06-25 09:42:25,524 - pyskl - INFO - Best top1_acc is 0.9279 at 141 epoch. +2025-06-25 09:42:25,526 - pyskl - INFO - Epoch(val) [141][533] top1_acc: 0.9279, top5_acc: 0.9957, mean_class_accuracy: 0.8986 +2025-06-25 09:43:43,419 - pyskl - INFO - Epoch [142][100/1281] lr: 2.176e-04, eta: 1:20:39, time: 0.779, data_time: 0.183, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0239, loss: 0.0239 +2025-06-25 09:44:32,169 - pyskl - INFO - Epoch [142][200/1281] lr: 2.139e-04, eta: 1:19:57, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-25 09:45:20,849 - pyskl - INFO - Epoch [142][300/1281] lr: 2.101e-04, eta: 1:19:15, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0226, loss: 0.0226 +2025-06-25 09:46:09,691 - pyskl - INFO - Epoch [142][400/1281] lr: 2.064e-04, eta: 1:18:33, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0228, loss: 0.0228 +2025-06-25 09:46:42,251 - pyskl - INFO - Epoch [142][500/1281] lr: 2.027e-04, eta: 1:17:50, time: 0.326, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 09:47:33,095 - pyskl - INFO - Epoch [142][600/1281] lr: 1.991e-04, eta: 1:17:08, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 09:47:58,413 - pyskl - INFO - Epoch [142][700/1281] lr: 1.954e-04, eta: 1:16:25, time: 0.253, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 09:48:47,225 - pyskl - INFO - Epoch [142][800/1281] lr: 1.919e-04, eta: 1:15:43, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0235, loss: 0.0235 +2025-06-25 09:49:36,390 - pyskl - INFO - Epoch [142][900/1281] lr: 1.883e-04, eta: 1:15:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0252, loss: 0.0252 +2025-06-25 09:50:25,689 - pyskl - INFO - Epoch [142][1000/1281] lr: 1.848e-04, eta: 1:14:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0252, loss: 0.0252 +2025-06-25 09:51:14,691 - pyskl - INFO - Epoch [142][1100/1281] lr: 1.813e-04, eta: 1:13:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0249, loss: 0.0249 +2025-06-25 09:52:03,769 - pyskl - INFO - Epoch [142][1200/1281] lr: 1.779e-04, eta: 1:12:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0246, loss: 0.0246 +2025-06-25 09:52:44,259 - pyskl - INFO - Saving checkpoint at 142 epochs +2025-06-25 09:53:42,781 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:53:42,864 - pyskl - INFO - +top1_acc 0.9275 +top5_acc 0.9958 +2025-06-25 09:53:42,864 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:53:42,873 - pyskl - INFO - +mean_acc 0.8986 +2025-06-25 09:53:42,875 - pyskl - INFO - Epoch(val) [142][533] top1_acc: 0.9275, top5_acc: 0.9958, mean_class_accuracy: 0.8986 +2025-06-25 09:55:02,269 - pyskl - INFO - Epoch [143][100/1281] lr: 1.717e-04, eta: 1:11:39, time: 0.794, data_time: 0.183, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 09:55:51,504 - pyskl - INFO - Epoch [143][200/1281] lr: 1.683e-04, eta: 1:10:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0254, loss: 0.0254 +2025-06-25 09:56:40,350 - pyskl - INFO - Epoch [143][300/1281] lr: 1.650e-04, eta: 1:10:15, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-25 09:57:29,516 - pyskl - INFO - Epoch [143][400/1281] lr: 1.617e-04, eta: 1:09:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0245, loss: 0.0245 +2025-06-25 09:58:01,545 - pyskl - INFO - Epoch [143][500/1281] lr: 1.585e-04, eta: 1:08:50, time: 0.320, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0266, loss: 0.0266 +2025-06-25 09:58:52,521 - pyskl - INFO - Epoch [143][600/1281] lr: 1.552e-04, eta: 1:08:08, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0256, loss: 0.0256 +2025-06-25 09:59:17,999 - pyskl - INFO - Epoch [143][700/1281] lr: 1.520e-04, eta: 1:07:24, time: 0.255, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 10:00:06,247 - pyskl - INFO - Epoch [143][800/1281] lr: 1.489e-04, eta: 1:06:42, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-25 10:00:54,818 - pyskl - INFO - Epoch [143][900/1281] lr: 1.457e-04, eta: 1:06:00, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0239, loss: 0.0239 +2025-06-25 10:01:43,899 - pyskl - INFO - Epoch [143][1000/1281] lr: 1.426e-04, eta: 1:05:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0227, loss: 0.0227 +2025-06-25 10:02:32,898 - pyskl - INFO - Epoch [143][1100/1281] lr: 1.396e-04, eta: 1:04:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0266, loss: 0.0266 +2025-06-25 10:03:21,812 - pyskl - INFO - Epoch [143][1200/1281] lr: 1.366e-04, eta: 1:03:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0253, loss: 0.0253 +2025-06-25 10:04:01,886 - pyskl - INFO - Saving checkpoint at 143 epochs +2025-06-25 10:05:00,716 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:05:00,771 - pyskl - INFO - +top1_acc 0.9258 +top5_acc 0.9959 +2025-06-25 10:05:00,771 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:05:00,778 - pyskl - INFO - +mean_acc 0.8976 +2025-06-25 10:05:00,779 - pyskl - INFO - Epoch(val) [143][533] top1_acc: 0.9258, top5_acc: 0.9959, mean_class_accuracy: 0.8976 +2025-06-25 10:06:18,015 - pyskl - INFO - Epoch [144][100/1281] lr: 1.312e-04, eta: 1:02:37, time: 0.772, data_time: 0.183, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0219, loss: 0.0219 +2025-06-25 10:07:07,018 - pyskl - INFO - Epoch [144][200/1281] lr: 1.282e-04, eta: 1:01:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0229, loss: 0.0229 +2025-06-25 10:07:55,981 - pyskl - INFO - Epoch [144][300/1281] lr: 1.253e-04, eta: 1:01:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0249, loss: 0.0249 +2025-06-25 10:08:44,829 - pyskl - INFO - Epoch [144][400/1281] lr: 1.224e-04, eta: 1:00:31, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 10:09:19,913 - pyskl - INFO - Epoch [144][500/1281] lr: 1.196e-04, eta: 0:59:49, time: 0.351, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 10:10:10,716 - pyskl - INFO - Epoch [144][600/1281] lr: 1.168e-04, eta: 0:59:07, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0235, loss: 0.0235 +2025-06-25 10:10:34,587 - pyskl - INFO - Epoch [144][700/1281] lr: 1.140e-04, eta: 0:58:23, time: 0.239, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 10:11:20,508 - pyskl - INFO - Epoch [144][800/1281] lr: 1.113e-04, eta: 0:57:41, time: 0.459, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0252, loss: 0.0252 +2025-06-25 10:12:09,391 - pyskl - INFO - Epoch [144][900/1281] lr: 1.086e-04, eta: 0:56:59, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 10:12:58,646 - pyskl - INFO - Epoch [144][1000/1281] lr: 1.059e-04, eta: 0:56:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0227, loss: 0.0227 +2025-06-25 10:13:47,816 - pyskl - INFO - Epoch [144][1100/1281] lr: 1.033e-04, eta: 0:55:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0229, loss: 0.0229 +2025-06-25 10:14:37,103 - pyskl - INFO - Epoch [144][1200/1281] lr: 1.007e-04, eta: 0:54:53, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0246, loss: 0.0246 +2025-06-25 10:15:17,155 - pyskl - INFO - Saving checkpoint at 144 epochs +2025-06-25 10:16:15,296 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:16:15,358 - pyskl - INFO - +top1_acc 0.9281 +top5_acc 0.9955 +2025-06-25 10:16:15,359 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:16:15,366 - pyskl - INFO - +mean_acc 0.9011 +2025-06-25 10:16:15,370 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_141.pth was removed +2025-06-25 10:16:15,532 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_144.pth. +2025-06-25 10:16:15,533 - pyskl - INFO - Best top1_acc is 0.9281 at 144 epoch. +2025-06-25 10:16:15,535 - pyskl - INFO - Epoch(val) [144][533] top1_acc: 0.9281, top5_acc: 0.9955, mean_class_accuracy: 0.9011 +2025-06-25 10:17:35,483 - pyskl - INFO - Epoch [145][100/1281] lr: 9.605e-05, eta: 0:53:36, time: 0.799, data_time: 0.182, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0249, loss: 0.0249 +2025-06-25 10:18:24,433 - pyskl - INFO - Epoch [145][200/1281] lr: 9.353e-05, eta: 0:52:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0253, loss: 0.0253 +2025-06-25 10:19:13,486 - pyskl - INFO - Epoch [145][300/1281] lr: 9.106e-05, eta: 0:52:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 10:20:02,504 - pyskl - INFO - Epoch [145][400/1281] lr: 8.861e-05, eta: 0:51:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0235, loss: 0.0235 +2025-06-25 10:20:39,595 - pyskl - INFO - Epoch [145][500/1281] lr: 8.620e-05, eta: 0:50:47, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 10:21:30,444 - pyskl - INFO - Epoch [145][600/1281] lr: 8.382e-05, eta: 0:50:05, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-25 10:21:54,292 - pyskl - INFO - Epoch [145][700/1281] lr: 8.147e-05, eta: 0:49:22, time: 0.238, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 10:22:40,602 - pyskl - INFO - Epoch [145][800/1281] lr: 7.916e-05, eta: 0:48:40, time: 0.463, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-25 10:23:29,730 - pyskl - INFO - Epoch [145][900/1281] lr: 7.688e-05, eta: 0:47:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 10:24:18,728 - pyskl - INFO - Epoch [145][1000/1281] lr: 7.463e-05, eta: 0:47:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0235, loss: 0.0235 +2025-06-25 10:25:07,799 - pyskl - INFO - Epoch [145][1100/1281] lr: 7.242e-05, eta: 0:46:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 10:25:56,934 - pyskl - INFO - Epoch [145][1200/1281] lr: 7.024e-05, eta: 0:45:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0253, loss: 0.0253 +2025-06-25 10:26:37,129 - pyskl - INFO - Saving checkpoint at 145 epochs +2025-06-25 10:27:35,219 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:27:35,277 - pyskl - INFO - +top1_acc 0.9262 +top5_acc 0.9954 +2025-06-25 10:27:35,277 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:27:35,287 - pyskl - INFO - +mean_acc 0.8961 +2025-06-25 10:27:35,290 - pyskl - INFO - Epoch(val) [145][533] top1_acc: 0.9262, top5_acc: 0.9954, mean_class_accuracy: 0.8961 +2025-06-25 10:28:54,635 - pyskl - INFO - Epoch [146][100/1281] lr: 6.638e-05, eta: 0:44:34, time: 0.793, data_time: 0.181, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0245, loss: 0.0245 +2025-06-25 10:29:43,509 - pyskl - INFO - Epoch [146][200/1281] lr: 6.429e-05, eta: 0:43:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0228, loss: 0.0228 +2025-06-25 10:30:32,571 - pyskl - INFO - Epoch [146][300/1281] lr: 6.224e-05, eta: 0:43:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 10:31:21,607 - pyskl - INFO - Epoch [146][400/1281] lr: 6.022e-05, eta: 0:42:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0252, loss: 0.0252 +2025-06-25 10:31:58,076 - pyskl - INFO - Epoch [146][500/1281] lr: 5.823e-05, eta: 0:41:45, time: 0.365, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0237, loss: 0.0237 +2025-06-25 10:32:48,997 - pyskl - INFO - Epoch [146][600/1281] lr: 5.628e-05, eta: 0:41:03, time: 0.509, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0246, loss: 0.0246 +2025-06-25 10:33:13,326 - pyskl - INFO - Epoch [146][700/1281] lr: 5.436e-05, eta: 0:40:20, time: 0.243, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 10:33:59,422 - pyskl - INFO - Epoch [146][800/1281] lr: 5.247e-05, eta: 0:39:37, time: 0.461, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 10:34:48,887 - pyskl - INFO - Epoch [146][900/1281] lr: 5.061e-05, eta: 0:38:55, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 10:35:37,807 - pyskl - INFO - Epoch [146][1000/1281] lr: 4.879e-05, eta: 0:38:13, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0252, loss: 0.0252 +2025-06-25 10:36:27,114 - pyskl - INFO - Epoch [146][1100/1281] lr: 4.701e-05, eta: 0:37:31, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-25 10:37:16,002 - pyskl - INFO - Epoch [146][1200/1281] lr: 4.525e-05, eta: 0:36:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 10:37:56,407 - pyskl - INFO - Saving checkpoint at 146 epochs +2025-06-25 10:38:54,413 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:38:54,477 - pyskl - INFO - +top1_acc 0.9263 +top5_acc 0.9958 +2025-06-25 10:38:54,477 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:38:54,484 - pyskl - INFO - +mean_acc 0.8965 +2025-06-25 10:38:54,486 - pyskl - INFO - Epoch(val) [146][533] top1_acc: 0.9263, top5_acc: 0.9958, mean_class_accuracy: 0.8965 +2025-06-25 10:40:14,620 - pyskl - INFO - Epoch [147][100/1281] lr: 4.216e-05, eta: 0:35:32, time: 0.801, data_time: 0.189, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0235, loss: 0.0235 +2025-06-25 10:41:03,379 - pyskl - INFO - Epoch [147][200/1281] lr: 4.050e-05, eta: 0:34:50, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0229, loss: 0.0229 +2025-06-25 10:41:52,426 - pyskl - INFO - Epoch [147][300/1281] lr: 3.887e-05, eta: 0:34:07, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 10:42:40,979 - pyskl - INFO - Epoch [147][400/1281] lr: 3.728e-05, eta: 0:33:25, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0251, loss: 0.0251 +2025-06-25 10:43:17,603 - pyskl - INFO - Epoch [147][500/1281] lr: 3.572e-05, eta: 0:32:42, time: 0.366, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0227, loss: 0.0227 +2025-06-25 10:44:08,512 - pyskl - INFO - Epoch [147][600/1281] lr: 3.419e-05, eta: 0:32:00, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0239, loss: 0.0239 +2025-06-25 10:44:31,938 - pyskl - INFO - Epoch [147][700/1281] lr: 3.270e-05, eta: 0:31:17, time: 0.234, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0255, loss: 0.0255 +2025-06-25 10:45:16,613 - pyskl - INFO - Epoch [147][800/1281] lr: 3.124e-05, eta: 0:30:35, time: 0.447, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 10:46:05,940 - pyskl - INFO - Epoch [147][900/1281] lr: 2.981e-05, eta: 0:29:53, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0261, loss: 0.0261 +2025-06-25 10:46:55,369 - pyskl - INFO - Epoch [147][1000/1281] lr: 2.842e-05, eta: 0:29:10, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0256, loss: 0.0256 +2025-06-25 10:47:44,612 - pyskl - INFO - Epoch [147][1100/1281] lr: 2.706e-05, eta: 0:28:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0223, loss: 0.0223 +2025-06-25 10:48:33,673 - pyskl - INFO - Epoch [147][1200/1281] lr: 2.573e-05, eta: 0:27:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 10:49:14,079 - pyskl - INFO - Saving checkpoint at 147 epochs +2025-06-25 10:50:12,091 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:50:12,146 - pyskl - INFO - +top1_acc 0.9276 +top5_acc 0.9957 +2025-06-25 10:50:12,146 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:50:12,152 - pyskl - INFO - +mean_acc 0.8993 +2025-06-25 10:50:12,154 - pyskl - INFO - Epoch(val) [147][533] top1_acc: 0.9276, top5_acc: 0.9957, mean_class_accuracy: 0.8993 +2025-06-25 10:51:32,290 - pyskl - INFO - Epoch [148][100/1281] lr: 2.341e-05, eta: 0:26:29, time: 0.801, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0227, loss: 0.0227 +2025-06-25 10:52:21,292 - pyskl - INFO - Epoch [148][200/1281] lr: 2.218e-05, eta: 0:25:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0245, loss: 0.0245 +2025-06-25 10:53:10,340 - pyskl - INFO - Epoch [148][300/1281] lr: 2.098e-05, eta: 0:25:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0251, loss: 0.0251 +2025-06-25 10:53:59,329 - pyskl - INFO - Epoch [148][400/1281] lr: 1.981e-05, eta: 0:24:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 10:54:37,344 - pyskl - INFO - Epoch [148][500/1281] lr: 1.868e-05, eta: 0:23:39, time: 0.380, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 10:55:28,404 - pyskl - INFO - Epoch [148][600/1281] lr: 1.758e-05, eta: 0:22:57, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-25 10:55:51,933 - pyskl - INFO - Epoch [148][700/1281] lr: 1.651e-05, eta: 0:22:14, time: 0.235, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0239, loss: 0.0239 +2025-06-25 10:56:35,097 - pyskl - INFO - Epoch [148][800/1281] lr: 1.548e-05, eta: 0:21:32, time: 0.432, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 10:57:24,285 - pyskl - INFO - Epoch [148][900/1281] lr: 1.448e-05, eta: 0:20:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 10:58:13,087 - pyskl - INFO - Epoch [148][1000/1281] lr: 1.351e-05, eta: 0:20:07, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 10:59:01,960 - pyskl - INFO - Epoch [148][1100/1281] lr: 1.258e-05, eta: 0:19:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0245, loss: 0.0245 +2025-06-25 10:59:51,155 - pyskl - INFO - Epoch [148][1200/1281] lr: 1.168e-05, eta: 0:18:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0251, loss: 0.0251 +2025-06-25 11:00:31,530 - pyskl - INFO - Saving checkpoint at 148 epochs +2025-06-25 11:01:29,753 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:01:29,809 - pyskl - INFO - +top1_acc 0.9269 +top5_acc 0.9954 +2025-06-25 11:01:29,809 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:01:29,816 - pyskl - INFO - +mean_acc 0.8977 +2025-06-25 11:01:29,818 - pyskl - INFO - Epoch(val) [148][533] top1_acc: 0.9269, top5_acc: 0.9954, mean_class_accuracy: 0.8977 +2025-06-25 11:02:48,753 - pyskl - INFO - Epoch [149][100/1281] lr: 1.013e-05, eta: 0:17:25, time: 0.789, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 11:03:37,828 - pyskl - INFO - Epoch [149][200/1281] lr: 9.328e-06, eta: 0:16:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 11:04:27,288 - pyskl - INFO - Epoch [149][300/1281] lr: 8.555e-06, eta: 0:16:01, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0237, loss: 0.0237 +2025-06-25 11:05:16,599 - pyskl - INFO - Epoch [149][400/1281] lr: 7.816e-06, eta: 0:15:18, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-25 11:05:57,957 - pyskl - INFO - Epoch [149][500/1281] lr: 7.110e-06, eta: 0:14:36, time: 0.414, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0254, loss: 0.0254 +2025-06-25 11:06:45,440 - pyskl - INFO - Epoch [149][600/1281] lr: 6.437e-06, eta: 0:13:53, time: 0.475, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 11:07:11,107 - pyskl - INFO - Epoch [149][700/1281] lr: 5.798e-06, eta: 0:13:11, time: 0.257, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0228, loss: 0.0228 +2025-06-25 11:07:54,011 - pyskl - INFO - Epoch [149][800/1281] lr: 5.192e-06, eta: 0:12:28, time: 0.429, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 11:08:43,165 - pyskl - INFO - Epoch [149][900/1281] lr: 4.620e-06, eta: 0:11:46, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 11:09:32,453 - pyskl - INFO - Epoch [149][1000/1281] lr: 4.081e-06, eta: 0:11:03, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0228, loss: 0.0228 +2025-06-25 11:10:21,731 - pyskl - INFO - Epoch [149][1100/1281] lr: 3.576e-06, eta: 0:10:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 11:11:10,911 - pyskl - INFO - Epoch [149][1200/1281] lr: 3.104e-06, eta: 0:09:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-25 11:11:51,456 - pyskl - INFO - Saving checkpoint at 149 epochs +2025-06-25 11:12:49,508 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:12:49,563 - pyskl - INFO - +top1_acc 0.9278 +top5_acc 0.9957 +2025-06-25 11:12:49,563 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:12:49,570 - pyskl - INFO - +mean_acc 0.8983 +2025-06-25 11:12:49,572 - pyskl - INFO - Epoch(val) [149][533] top1_acc: 0.9278, top5_acc: 0.9957, mean_class_accuracy: 0.8983 +2025-06-25 11:14:10,392 - pyskl - INFO - Epoch [150][100/1281] lr: 2.334e-06, eta: 0:08:21, time: 0.808, data_time: 0.184, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-25 11:14:59,255 - pyskl - INFO - Epoch [150][200/1281] lr: 1.956e-06, eta: 0:07:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 11:15:48,451 - pyskl - INFO - Epoch [150][300/1281] lr: 1.611e-06, eta: 0:06:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-25 11:16:37,393 - pyskl - INFO - Epoch [150][400/1281] lr: 1.300e-06, eta: 0:06:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0250, loss: 0.0250 +2025-06-25 11:17:18,116 - pyskl - INFO - Epoch [150][500/1281] lr: 1.022e-06, eta: 0:05:31, time: 0.407, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0227, loss: 0.0227 +2025-06-25 11:18:06,334 - pyskl - INFO - Epoch [150][600/1281] lr: 7.771e-07, eta: 0:04:49, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0228, loss: 0.0228 +2025-06-25 11:18:31,384 - pyskl - INFO - Epoch [150][700/1281] lr: 5.659e-07, eta: 0:04:06, time: 0.251, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 11:19:13,293 - pyskl - INFO - Epoch [150][800/1281] lr: 3.881e-07, eta: 0:03:24, time: 0.419, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-25 11:20:01,965 - pyskl - INFO - Epoch [150][900/1281] lr: 2.438e-07, eta: 0:02:41, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0219, loss: 0.0219 +2025-06-25 11:20:50,992 - pyskl - INFO - Epoch [150][1000/1281] lr: 1.329e-07, eta: 0:01:59, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 11:21:40,535 - pyskl - INFO - Epoch [150][1100/1281] lr: 5.534e-08, eta: 0:01:16, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0229, loss: 0.0229 +2025-06-25 11:22:29,470 - pyskl - INFO - Epoch [150][1200/1281] lr: 1.123e-08, eta: 0:00:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0235, loss: 0.0235 +2025-06-25 11:23:09,658 - pyskl - INFO - Saving checkpoint at 150 epochs +2025-06-25 11:24:07,644 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:24:07,700 - pyskl - INFO - +top1_acc 0.9285 +top5_acc 0.9954 +2025-06-25 11:24:07,700 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:24:07,707 - pyskl - INFO - +mean_acc 0.8989 +2025-06-25 11:24:07,711 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_144.pth was removed +2025-06-25 11:24:07,872 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_150.pth. +2025-06-25 11:24:07,872 - pyskl - INFO - Best top1_acc is 0.9285 at 150 epoch. +2025-06-25 11:24:07,875 - pyskl - INFO - Epoch(val) [150][533] top1_acc: 0.9285, top5_acc: 0.9954, mean_class_accuracy: 0.8989 +2025-06-25 11:24:12,298 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-25 11:31:53,568 - pyskl - INFO - Testing results of the last checkpoint +2025-06-25 11:31:53,569 - pyskl - INFO - top1_acc: 0.9310 +2025-06-25 11:31:53,569 - pyskl - INFO - top5_acc: 0.9962 +2025-06-25 11:31:53,569 - pyskl - INFO - mean_class_accuracy: 0.9028 +2025-06-25 11:31:53,570 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/finegym/j_2/best_top1_acc_epoch_150.pth +2025-06-25 11:39:35,340 - pyskl - INFO - Testing results of the best checkpoint +2025-06-25 11:39:35,340 - pyskl - INFO - top1_acc: 0.9310 +2025-06-25 11:39:35,340 - pyskl - INFO - top5_acc: 0.9962 +2025-06-25 11:39:35,340 - pyskl - INFO - mean_class_accuracy: 0.9028 diff --git a/finegym/j_2/20250624_084315.log.json b/finegym/j_2/20250624_084315.log.json new file mode 100644 index 0000000000000000000000000000000000000000..43479c613ca0c4f5acc00e1968487e7c3bc563ff --- /dev/null +++ b/finegym/j_2/20250624_084315.log.json @@ -0,0 +1,1951 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 2014598172, "config_name": "j_2.py", "work_dir": "j_2", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.18134, "top1_acc": 0.06562, "top5_acc": 0.21812, "loss_cls": 4.57497, "loss": 4.57497, "time": 0.40052} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.0775, "top5_acc": 0.28562, "loss_cls": 4.68533, "loss": 4.68533, "time": 0.2226} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.07938, "top5_acc": 0.30875, "loss_cls": 4.57493, "loss": 4.57493, "time": 0.21878} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.10625, "top5_acc": 0.33, "loss_cls": 4.37572, "loss": 4.37572, "time": 0.21897} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.025, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.10688, "top5_acc": 0.40812, "loss_cls": 4.18371, "loss": 4.18371, "time": 0.21933} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.1575, "top5_acc": 0.48688, "loss_cls": 3.8831, "loss": 3.8831, "time": 0.21565} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.1975, "top5_acc": 0.52812, "loss_cls": 3.67753, "loss": 3.67753, "time": 0.21661} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.025, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.25, "top5_acc": 0.59062, "loss_cls": 3.44788, "loss": 3.44788, "time": 0.21592} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.025, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.26375, "top5_acc": 0.65062, "loss_cls": 3.22254, "loss": 3.22254, "time": 0.21619} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.3, "top5_acc": 0.6825, "loss_cls": 3.04043, "loss": 3.04043, "time": 0.21804} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.025, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.33312, "top5_acc": 0.73, "loss_cls": 2.87715, "loss": 2.87715, "time": 0.21382} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.35188, "top5_acc": 0.73312, "loss_cls": 2.8156, "loss": 2.8156, "time": 0.21529} +{"mode": "val", "epoch": 1, "iter": 533, "lr": 0.025, "top1_acc": 0.37331, "top5_acc": 0.75566, "mean_class_accuracy": 0.1867} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.1855, "top1_acc": 0.40375, "top5_acc": 0.8, "loss_cls": 2.58594, "loss": 2.58594, "time": 0.40073} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.39312, "top5_acc": 0.79812, "loss_cls": 2.54369, "loss": 2.54369, "time": 0.21638} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.41688, "top5_acc": 0.79812, "loss_cls": 2.45318, "loss": 2.45318, "time": 0.21612} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.4325, "top5_acc": 0.81688, "loss_cls": 2.35284, "loss": 2.35284, "time": 0.21458} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.02499, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.44125, "top5_acc": 0.8175, "loss_cls": 2.38149, "loss": 2.38149, "time": 0.2179} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.02499, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.46875, "top5_acc": 0.84438, "loss_cls": 2.21465, "loss": 2.21465, "time": 0.21759} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.02499, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.46812, "top5_acc": 0.83875, "loss_cls": 2.23439, "loss": 2.23439, "time": 0.21767} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.47375, "top5_acc": 0.85, "loss_cls": 2.16497, "loss": 2.16497, "time": 0.21388} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.02499, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.4875, "top5_acc": 0.875, "loss_cls": 2.078, "loss": 2.078, "time": 0.2139} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.02499, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.49625, "top5_acc": 0.87438, "loss_cls": 2.06077, "loss": 2.06077, "time": 0.21527} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.49, "top5_acc": 0.8775, "loss_cls": 1.96498, "loss": 1.96498, "time": 0.21292} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.02499, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.50625, "top5_acc": 0.88062, "loss_cls": 1.97284, "loss": 1.97284, "time": 0.21586} +{"mode": "val", "epoch": 2, "iter": 533, "lr": 0.02499, "top1_acc": 0.50358, "top5_acc": 0.88522, "mean_class_accuracy": 0.30125} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.02499, "memory": 4082, "data_time": 0.18385, "top1_acc": 0.52438, "top5_acc": 0.90812, "loss_cls": 1.89934, "loss": 1.89934, "time": 0.39919} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.02499, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.54188, "top5_acc": 0.90688, "loss_cls": 1.84807, "loss": 1.84807, "time": 0.21647} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.02499, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.55375, "top5_acc": 0.91438, "loss_cls": 1.79681, "loss": 1.79681, "time": 0.21622} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.02499, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.54625, "top5_acc": 0.91375, "loss_cls": 1.79331, "loss": 1.79331, "time": 0.21392} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.02498, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.54875, "top5_acc": 0.91562, "loss_cls": 1.75714, "loss": 1.75714, "time": 0.21528} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.02498, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.56688, "top5_acc": 0.92875, "loss_cls": 1.72854, "loss": 1.72854, "time": 0.2142} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.02498, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.5375, "top5_acc": 0.91312, "loss_cls": 1.84089, "loss": 1.84089, "time": 0.21472} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.02498, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.55375, "top5_acc": 0.92688, "loss_cls": 1.73422, "loss": 1.73422, "time": 0.21563} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.02498, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.57125, "top5_acc": 0.91375, "loss_cls": 1.74645, "loss": 1.74645, "time": 0.21352} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.02498, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.5675, "top5_acc": 0.93312, "loss_cls": 1.67501, "loss": 1.67501, "time": 0.21632} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.02498, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.56688, "top5_acc": 0.92062, "loss_cls": 1.70882, "loss": 1.70882, "time": 0.21319} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.02498, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.58875, "top5_acc": 0.92562, "loss_cls": 1.67028, "loss": 1.67028, "time": 0.21546} +{"mode": "val", "epoch": 3, "iter": 533, "lr": 0.02498, "top1_acc": 0.60369, "top5_acc": 0.94414, "mean_class_accuracy": 0.40936} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.02497, "memory": 4082, "data_time": 0.18729, "top1_acc": 0.61187, "top5_acc": 0.95, "loss_cls": 1.51087, "loss": 1.51087, "time": 0.40514} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.02497, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.59812, "top5_acc": 0.94938, "loss_cls": 1.58698, "loss": 1.58698, "time": 0.21543} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.02497, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.60188, "top5_acc": 0.94375, "loss_cls": 1.57311, "loss": 1.57311, "time": 0.2143} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.02497, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.60125, "top5_acc": 0.9425, "loss_cls": 1.55563, "loss": 1.55563, "time": 0.21412} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.02497, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.61, "top5_acc": 0.94688, "loss_cls": 1.53083, "loss": 1.53083, "time": 0.21558} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.02497, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.60938, "top5_acc": 0.93562, "loss_cls": 1.57189, "loss": 1.57189, "time": 0.21341} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.02497, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.6125, "top5_acc": 0.95062, "loss_cls": 1.53097, "loss": 1.53097, "time": 0.21449} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.02496, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.63438, "top5_acc": 0.94188, "loss_cls": 1.50948, "loss": 1.50948, "time": 0.21571} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.02496, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.60438, "top5_acc": 0.94375, "loss_cls": 1.57869, "loss": 1.57869, "time": 0.21624} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.02496, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.6125, "top5_acc": 0.93938, "loss_cls": 1.5489, "loss": 1.5489, "time": 0.21873} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.02496, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.61687, "top5_acc": 0.95188, "loss_cls": 1.48599, "loss": 1.48599, "time": 0.21453} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.02496, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.62875, "top5_acc": 0.95562, "loss_cls": 1.47851, "loss": 1.47851, "time": 0.2151} +{"mode": "val", "epoch": 4, "iter": 533, "lr": 0.02496, "top1_acc": 0.61906, "top5_acc": 0.9466, "mean_class_accuracy": 0.47399} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.02495, "memory": 4082, "data_time": 0.18396, "top1_acc": 0.65625, "top5_acc": 0.96, "loss_cls": 1.39525, "loss": 1.39525, "time": 0.39924} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.02495, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.65875, "top5_acc": 0.9525, "loss_cls": 1.37038, "loss": 1.37038, "time": 0.21463} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.02495, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.6375, "top5_acc": 0.95938, "loss_cls": 1.42422, "loss": 1.42422, "time": 0.21806} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.02495, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.65688, "top5_acc": 0.95438, "loss_cls": 1.41767, "loss": 1.41767, "time": 0.21525} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.02495, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.68125, "top5_acc": 0.9525, "loss_cls": 1.35429, "loss": 1.35429, "time": 0.21517} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.02495, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.64438, "top5_acc": 0.94938, "loss_cls": 1.42496, "loss": 1.42496, "time": 0.21544} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.02494, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.66188, "top5_acc": 0.9625, "loss_cls": 1.34974, "loss": 1.34974, "time": 0.21487} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.02494, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.65875, "top5_acc": 0.96625, "loss_cls": 1.3757, "loss": 1.3757, "time": 0.21391} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.02494, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.68062, "top5_acc": 0.965, "loss_cls": 1.32884, "loss": 1.32884, "time": 0.21425} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.02494, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.675, "top5_acc": 0.95688, "loss_cls": 1.33581, "loss": 1.33581, "time": 0.21735} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.02494, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.66125, "top5_acc": 0.95438, "loss_cls": 1.36688, "loss": 1.36688, "time": 0.21643} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.02493, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.67625, "top5_acc": 0.95875, "loss_cls": 1.34298, "loss": 1.34298, "time": 0.21833} +{"mode": "val", "epoch": 5, "iter": 533, "lr": 0.02493, "top1_acc": 0.62798, "top5_acc": 0.93792, "mean_class_accuracy": 0.49351} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.02493, "memory": 4082, "data_time": 0.18479, "top1_acc": 0.68688, "top5_acc": 0.96188, "loss_cls": 1.27527, "loss": 1.27527, "time": 0.40147} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.02493, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.68312, "top5_acc": 0.97312, "loss_cls": 1.24387, "loss": 1.24387, "time": 0.21835} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.02492, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.66188, "top5_acc": 0.95875, "loss_cls": 1.34721, "loss": 1.34721, "time": 0.21978} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.02492, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.68875, "top5_acc": 0.96188, "loss_cls": 1.23604, "loss": 1.23604, "time": 0.2191} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.02492, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.69312, "top5_acc": 0.95562, "loss_cls": 1.23006, "loss": 1.23006, "time": 0.21574} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.02492, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.67438, "top5_acc": 0.96, "loss_cls": 1.32366, "loss": 1.32366, "time": 0.21788} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.02492, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.6825, "top5_acc": 0.96312, "loss_cls": 1.31879, "loss": 1.31879, "time": 0.21826} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.02491, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.70062, "top5_acc": 0.97188, "loss_cls": 1.20477, "loss": 1.20477, "time": 0.21991} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.02491, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.70062, "top5_acc": 0.96938, "loss_cls": 1.21655, "loss": 1.21655, "time": 0.21895} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.02491, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.68125, "top5_acc": 0.96375, "loss_cls": 1.2799, "loss": 1.2799, "time": 0.2181} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.02491, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.70188, "top5_acc": 0.9725, "loss_cls": 1.2031, "loss": 1.2031, "time": 0.21756} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.0249, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.70812, "top5_acc": 0.975, "loss_cls": 1.16445, "loss": 1.16445, "time": 0.21908} +{"mode": "val", "epoch": 6, "iter": 533, "lr": 0.0249, "top1_acc": 0.68713, "top5_acc": 0.97113, "mean_class_accuracy": 0.53681} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0249, "memory": 4082, "data_time": 0.18688, "top1_acc": 0.70312, "top5_acc": 0.96938, "loss_cls": 1.20336, "loss": 1.20336, "time": 0.40518} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0249, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.72062, "top5_acc": 0.97, "loss_cls": 1.14967, "loss": 1.14967, "time": 0.21735} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.02489, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.71062, "top5_acc": 0.96625, "loss_cls": 1.20109, "loss": 1.20109, "time": 0.21719} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.02489, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.69875, "top5_acc": 0.96875, "loss_cls": 1.22645, "loss": 1.22645, "time": 0.21901} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.02489, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.705, "top5_acc": 0.97188, "loss_cls": 1.20326, "loss": 1.20326, "time": 0.21946} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.02489, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.69875, "top5_acc": 0.975, "loss_cls": 1.19654, "loss": 1.19654, "time": 0.21655} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.02488, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.72562, "top5_acc": 0.96438, "loss_cls": 1.17492, "loss": 1.17492, "time": 0.21824} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.02488, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.70125, "top5_acc": 0.96875, "loss_cls": 1.1902, "loss": 1.1902, "time": 0.21793} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.02488, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.715, "top5_acc": 0.96438, "loss_cls": 1.20159, "loss": 1.20159, "time": 0.21706} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.02487, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.71938, "top5_acc": 0.97375, "loss_cls": 1.16552, "loss": 1.16552, "time": 0.21618} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.02487, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.73625, "top5_acc": 0.97875, "loss_cls": 1.10955, "loss": 1.10955, "time": 0.21689} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.02487, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.7175, "top5_acc": 0.9725, "loss_cls": 1.15125, "loss": 1.15125, "time": 0.21817} +{"mode": "val", "epoch": 7, "iter": 533, "lr": 0.02487, "top1_acc": 0.64687, "top5_acc": 0.95998, "mean_class_accuracy": 0.49859} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.02486, "memory": 4082, "data_time": 0.18527, "top1_acc": 0.735, "top5_acc": 0.96875, "loss_cls": 1.13041, "loss": 1.13041, "time": 0.40374} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.02486, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7275, "top5_acc": 0.97062, "loss_cls": 1.16818, "loss": 1.16818, "time": 0.21815} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.02486, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.71812, "top5_acc": 0.97812, "loss_cls": 1.14707, "loss": 1.14707, "time": 0.21565} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.02485, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.73, "top5_acc": 0.975, "loss_cls": 1.13978, "loss": 1.13978, "time": 0.21698} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.02485, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.7575, "top5_acc": 0.97562, "loss_cls": 1.0688, "loss": 1.0688, "time": 0.21772} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.02485, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.7275, "top5_acc": 0.975, "loss_cls": 1.11344, "loss": 1.11344, "time": 0.21513} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.02484, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.71125, "top5_acc": 0.97938, "loss_cls": 1.14269, "loss": 1.14269, "time": 0.21879} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.02484, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.71625, "top5_acc": 0.96875, "loss_cls": 1.15366, "loss": 1.15366, "time": 0.21896} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.02484, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.72375, "top5_acc": 0.97375, "loss_cls": 1.15079, "loss": 1.15079, "time": 0.21672} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.02483, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.73188, "top5_acc": 0.97562, "loss_cls": 1.09526, "loss": 1.09526, "time": 0.21886} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.02483, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.71375, "top5_acc": 0.97, "loss_cls": 1.15456, "loss": 1.15456, "time": 0.21447} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.02483, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7225, "top5_acc": 0.97875, "loss_cls": 1.12371, "loss": 1.12371, "time": 0.2158} +{"mode": "val", "epoch": 8, "iter": 533, "lr": 0.02482, "top1_acc": 0.68583, "top5_acc": 0.96608, "mean_class_accuracy": 0.56212} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.02482, "memory": 4082, "data_time": 0.18769, "top1_acc": 0.75062, "top5_acc": 0.98062, "loss_cls": 1.06759, "loss": 1.06759, "time": 0.40721} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.02482, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.7675, "top5_acc": 0.98, "loss_cls": 1.0013, "loss": 1.0013, "time": 0.21676} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.02481, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.7475, "top5_acc": 0.98125, "loss_cls": 1.0523, "loss": 1.0523, "time": 0.21759} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.02481, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.75, "top5_acc": 0.97875, "loss_cls": 1.07413, "loss": 1.07413, "time": 0.21657} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.02481, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.74375, "top5_acc": 0.97688, "loss_cls": 1.09152, "loss": 1.09152, "time": 0.21717} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.0248, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.74812, "top5_acc": 0.97438, "loss_cls": 1.08951, "loss": 1.08951, "time": 0.21618} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.0248, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.73562, "top5_acc": 0.97812, "loss_cls": 1.09729, "loss": 1.09729, "time": 0.22076} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.0248, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.75062, "top5_acc": 0.9775, "loss_cls": 1.05703, "loss": 1.05703, "time": 0.21603} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.02479, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7325, "top5_acc": 0.97625, "loss_cls": 1.09142, "loss": 1.09142, "time": 0.21302} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.02479, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.73812, "top5_acc": 0.97438, "loss_cls": 1.09209, "loss": 1.09209, "time": 0.21691} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.02479, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.73125, "top5_acc": 0.98125, "loss_cls": 1.09937, "loss": 1.09937, "time": 0.21581} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.02478, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.74688, "top5_acc": 0.97938, "loss_cls": 1.0896, "loss": 1.0896, "time": 0.21829} +{"mode": "val", "epoch": 9, "iter": 533, "lr": 0.02478, "top1_acc": 0.71377, "top5_acc": 0.97489, "mean_class_accuracy": 0.57383} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.02477, "memory": 4082, "data_time": 0.18031, "top1_acc": 0.76812, "top5_acc": 0.98312, "loss_cls": 0.984, "loss": 0.984, "time": 0.39771} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.02477, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.765, "top5_acc": 0.97938, "loss_cls": 0.99238, "loss": 0.99238, "time": 0.21647} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.02477, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.74375, "top5_acc": 0.98188, "loss_cls": 1.05247, "loss": 1.05247, "time": 0.21869} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.02476, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.73562, "top5_acc": 0.9825, "loss_cls": 1.07748, "loss": 1.07748, "time": 0.21934} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.02476, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.75375, "top5_acc": 0.97625, "loss_cls": 1.0352, "loss": 1.0352, "time": 0.21737} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.02476, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.74812, "top5_acc": 0.97438, "loss_cls": 1.0707, "loss": 1.0707, "time": 0.2182} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.02475, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.75062, "top5_acc": 0.9825, "loss_cls": 1.01876, "loss": 1.01876, "time": 0.21634} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.02475, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.75625, "top5_acc": 0.98188, "loss_cls": 1.05226, "loss": 1.05226, "time": 0.21765} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.02474, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.745, "top5_acc": 0.97938, "loss_cls": 1.08221, "loss": 1.08221, "time": 0.21823} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.02474, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.7475, "top5_acc": 0.97375, "loss_cls": 1.10444, "loss": 1.10444, "time": 0.21497} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.02473, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.76625, "top5_acc": 0.97688, "loss_cls": 1.03194, "loss": 1.03194, "time": 0.21692} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.02473, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.7475, "top5_acc": 0.9775, "loss_cls": 1.04669, "loss": 1.04669, "time": 0.22042} +{"mode": "val", "epoch": 10, "iter": 533, "lr": 0.02473, "top1_acc": 0.68677, "top5_acc": 0.97172, "mean_class_accuracy": 0.56614} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.02472, "memory": 4082, "data_time": 0.17355, "top1_acc": 0.76188, "top5_acc": 0.9825, "loss_cls": 0.99911, "loss": 0.99911, "time": 0.38912} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.02472, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.74562, "top5_acc": 0.9775, "loss_cls": 1.02846, "loss": 1.02846, "time": 0.21901} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.02471, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.75062, "top5_acc": 0.97562, "loss_cls": 1.04614, "loss": 1.04614, "time": 0.2168} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.02471, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.7375, "top5_acc": 0.98312, "loss_cls": 1.09113, "loss": 1.09113, "time": 0.21674} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.02471, "memory": 4082, "data_time": 0.0002, "top1_acc": 0.75875, "top5_acc": 0.985, "loss_cls": 1.02843, "loss": 1.02843, "time": 0.21648} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.0247, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.75688, "top5_acc": 0.98, "loss_cls": 1.06109, "loss": 1.06109, "time": 0.21634} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.0247, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.74438, "top5_acc": 0.975, "loss_cls": 1.11058, "loss": 1.11058, "time": 0.2164} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.02469, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.77375, "top5_acc": 0.98, "loss_cls": 0.97744, "loss": 0.97744, "time": 0.21914} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.02469, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.7725, "top5_acc": 0.97812, "loss_cls": 1.01473, "loss": 1.01473, "time": 0.21749} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.02468, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.74062, "top5_acc": 0.98, "loss_cls": 1.02653, "loss": 1.02653, "time": 0.21618} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.02468, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.76375, "top5_acc": 0.9825, "loss_cls": 0.99619, "loss": 0.99619, "time": 0.2204} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.02467, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.72625, "top5_acc": 0.98562, "loss_cls": 1.08819, "loss": 1.08819, "time": 0.21779} +{"mode": "val", "epoch": 11, "iter": 533, "lr": 0.02467, "top1_acc": 0.6883, "top5_acc": 0.96644, "mean_class_accuracy": 0.54842} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.02467, "memory": 4082, "data_time": 0.18931, "top1_acc": 0.7675, "top5_acc": 0.98, "loss_cls": 0.98502, "loss": 0.98502, "time": 0.41102} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.02466, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.75875, "top5_acc": 0.98, "loss_cls": 1.03321, "loss": 1.03321, "time": 0.221} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.02466, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.73062, "top5_acc": 0.98562, "loss_cls": 1.09645, "loss": 1.09645, "time": 0.21708} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.02465, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.75125, "top5_acc": 0.98125, "loss_cls": 1.07832, "loss": 1.07832, "time": 0.21697} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.02465, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.74, "top5_acc": 0.9825, "loss_cls": 1.03392, "loss": 1.03392, "time": 0.21739} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.02464, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.735, "top5_acc": 0.9775, "loss_cls": 1.04829, "loss": 1.04829, "time": 0.21595} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.02464, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7575, "top5_acc": 0.97438, "loss_cls": 1.02848, "loss": 1.02848, "time": 0.21698} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.02463, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78062, "top5_acc": 0.9825, "loss_cls": 0.93063, "loss": 0.93063, "time": 0.21643} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.02463, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.75062, "top5_acc": 0.97188, "loss_cls": 1.08281, "loss": 1.08281, "time": 0.21737} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.02462, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.75875, "top5_acc": 0.98312, "loss_cls": 1.00929, "loss": 1.00929, "time": 0.21303} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.02462, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.76562, "top5_acc": 0.98188, "loss_cls": 1.01767, "loss": 1.01767, "time": 0.21815} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.02461, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.74812, "top5_acc": 0.97812, "loss_cls": 1.06366, "loss": 1.06366, "time": 0.2178} +{"mode": "val", "epoch": 12, "iter": 533, "lr": 0.02461, "top1_acc": 0.72703, "top5_acc": 0.97207, "mean_class_accuracy": 0.61367} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.0246, "memory": 4082, "data_time": 0.17836, "top1_acc": 0.75, "top5_acc": 0.98125, "loss_cls": 1.05689, "loss": 1.05689, "time": 0.39336} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.0246, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.76625, "top5_acc": 0.985, "loss_cls": 0.97725, "loss": 0.97725, "time": 0.21688} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.02459, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.75938, "top5_acc": 0.98375, "loss_cls": 1.00838, "loss": 1.00838, "time": 0.21219} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.02459, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.77062, "top5_acc": 0.98438, "loss_cls": 0.96103, "loss": 0.96103, "time": 0.21482} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.02458, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.7375, "top5_acc": 0.98125, "loss_cls": 1.06947, "loss": 1.06947, "time": 0.21645} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.02458, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.77312, "top5_acc": 0.9825, "loss_cls": 0.94758, "loss": 0.94758, "time": 0.21812} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.02457, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.76375, "top5_acc": 0.98, "loss_cls": 1.00024, "loss": 1.00024, "time": 0.21698} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.02457, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.77, "top5_acc": 0.9825, "loss_cls": 1.00343, "loss": 1.00343, "time": 0.21635} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.02456, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.76938, "top5_acc": 0.98562, "loss_cls": 0.96485, "loss": 0.96485, "time": 0.21377} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.02455, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.77625, "top5_acc": 0.97812, "loss_cls": 0.97414, "loss": 0.97414, "time": 0.2155} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.02455, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.75375, "top5_acc": 0.97875, "loss_cls": 1.01773, "loss": 1.01773, "time": 0.21731} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.02454, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.775, "top5_acc": 0.98, "loss_cls": 0.98753, "loss": 0.98753, "time": 0.2156} +{"mode": "val", "epoch": 13, "iter": 533, "lr": 0.02454, "top1_acc": 0.72703, "top5_acc": 0.97665, "mean_class_accuracy": 0.60284} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.02453, "memory": 4082, "data_time": 0.18352, "top1_acc": 0.7675, "top5_acc": 0.98625, "loss_cls": 0.95565, "loss": 0.95565, "time": 0.39879} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.02453, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.75438, "top5_acc": 0.985, "loss_cls": 1.02709, "loss": 1.02709, "time": 0.21683} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.02452, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78938, "top5_acc": 0.98062, "loss_cls": 0.9576, "loss": 0.9576, "time": 0.21629} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.02452, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.79312, "top5_acc": 0.98625, "loss_cls": 0.93894, "loss": 0.93894, "time": 0.21499} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.02451, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.7875, "top5_acc": 0.9825, "loss_cls": 0.90122, "loss": 0.90122, "time": 0.216} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.02451, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.76812, "top5_acc": 0.98438, "loss_cls": 0.98224, "loss": 0.98224, "time": 0.2179} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.0245, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.77438, "top5_acc": 0.98375, "loss_cls": 0.98144, "loss": 0.98144, "time": 0.21852} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.02449, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.755, "top5_acc": 0.98, "loss_cls": 1.02664, "loss": 1.02664, "time": 0.21683} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.02449, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.75562, "top5_acc": 0.98188, "loss_cls": 1.03163, "loss": 1.03163, "time": 0.21819} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.02448, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.76562, "top5_acc": 0.985, "loss_cls": 1.00412, "loss": 1.00412, "time": 0.21521} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.02448, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.76562, "top5_acc": 0.98562, "loss_cls": 0.9897, "loss": 0.9897, "time": 0.22056} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.02447, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.7625, "top5_acc": 0.9775, "loss_cls": 0.98398, "loss": 0.98398, "time": 0.21481} +{"mode": "val", "epoch": 14, "iter": 533, "lr": 0.02447, "top1_acc": 0.68724, "top5_acc": 0.95752, "mean_class_accuracy": 0.55073} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.02446, "memory": 4082, "data_time": 0.18252, "top1_acc": 0.78812, "top5_acc": 0.98375, "loss_cls": 0.92505, "loss": 0.92505, "time": 0.40015} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.02445, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.7725, "top5_acc": 0.98438, "loss_cls": 0.96584, "loss": 0.96584, "time": 0.21713} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.02445, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.77312, "top5_acc": 0.98438, "loss_cls": 0.95138, "loss": 0.95138, "time": 0.21815} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.02444, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.77125, "top5_acc": 0.9825, "loss_cls": 0.96847, "loss": 0.96847, "time": 0.22035} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.02444, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.775, "top5_acc": 0.98938, "loss_cls": 0.94199, "loss": 0.94199, "time": 0.21587} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.02443, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.78875, "top5_acc": 0.9825, "loss_cls": 0.91579, "loss": 0.91579, "time": 0.22031} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.02442, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.78062, "top5_acc": 0.98375, "loss_cls": 0.93027, "loss": 0.93027, "time": 0.21516} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.02442, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.76188, "top5_acc": 0.98438, "loss_cls": 0.9943, "loss": 0.9943, "time": 0.21569} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.02441, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.76062, "top5_acc": 0.97875, "loss_cls": 1.01863, "loss": 1.01863, "time": 0.21508} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.02441, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.78688, "top5_acc": 0.98625, "loss_cls": 0.89919, "loss": 0.89919, "time": 0.21476} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.0244, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78062, "top5_acc": 0.98438, "loss_cls": 0.90556, "loss": 0.90556, "time": 0.22006} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.02439, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.78812, "top5_acc": 0.985, "loss_cls": 0.90307, "loss": 0.90307, "time": 0.21838} +{"mode": "val", "epoch": 15, "iter": 533, "lr": 0.02439, "top1_acc": 0.7025, "top5_acc": 0.96174, "mean_class_accuracy": 0.60195} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.02438, "memory": 4082, "data_time": 0.17978, "top1_acc": 0.80312, "top5_acc": 0.98875, "loss_cls": 0.88645, "loss": 0.88645, "time": 0.39846} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.02438, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.7775, "top5_acc": 0.98125, "loss_cls": 0.97722, "loss": 0.97722, "time": 0.21816} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.02437, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79062, "top5_acc": 0.99, "loss_cls": 0.88571, "loss": 0.88571, "time": 0.21919} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.02436, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.77688, "top5_acc": 0.98438, "loss_cls": 0.96733, "loss": 0.96733, "time": 0.21917} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.02436, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.77375, "top5_acc": 0.98438, "loss_cls": 0.95754, "loss": 0.95754, "time": 0.21742} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.02435, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.795, "top5_acc": 0.985, "loss_cls": 0.92884, "loss": 0.92884, "time": 0.21706} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.02434, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.77562, "top5_acc": 0.98875, "loss_cls": 0.91561, "loss": 0.91561, "time": 0.21918} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.02434, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.76938, "top5_acc": 0.9825, "loss_cls": 0.94532, "loss": 0.94532, "time": 0.21568} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.02433, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.76562, "top5_acc": 0.9825, "loss_cls": 0.97518, "loss": 0.97518, "time": 0.21891} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.02432, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.77438, "top5_acc": 0.97938, "loss_cls": 0.99143, "loss": 0.99143, "time": 0.21776} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.02432, "memory": 4082, "data_time": 0.00066, "top1_acc": 0.77375, "top5_acc": 0.98625, "loss_cls": 0.95608, "loss": 0.95608, "time": 0.221} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.02431, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.775, "top5_acc": 0.98312, "loss_cls": 0.94264, "loss": 0.94264, "time": 0.21882} +{"mode": "val", "epoch": 16, "iter": 533, "lr": 0.0243, "top1_acc": 0.71189, "top5_acc": 0.97266, "mean_class_accuracy": 0.60047} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.0243, "memory": 4082, "data_time": 0.19272, "top1_acc": 0.77562, "top5_acc": 0.98312, "loss_cls": 1.01742, "loss": 1.01742, "time": 0.41768} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.02429, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77438, "top5_acc": 0.98625, "loss_cls": 0.94036, "loss": 0.94036, "time": 0.27033} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.02428, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.77875, "top5_acc": 0.985, "loss_cls": 0.92628, "loss": 0.92628, "time": 0.42303} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.02428, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.79625, "top5_acc": 0.98875, "loss_cls": 0.88625, "loss": 0.88625, "time": 0.41639} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.02427, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.80062, "top5_acc": 0.9875, "loss_cls": 0.87023, "loss": 0.87023, "time": 0.4159} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.02426, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.80188, "top5_acc": 0.98438, "loss_cls": 0.88325, "loss": 0.88325, "time": 0.41464} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.02426, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8025, "top5_acc": 0.98562, "loss_cls": 0.91637, "loss": 0.91637, "time": 0.41465} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.02425, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.785, "top5_acc": 0.98188, "loss_cls": 0.91543, "loss": 0.91543, "time": 0.41621} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.02424, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78312, "top5_acc": 0.98688, "loss_cls": 0.93148, "loss": 0.93148, "time": 0.41658} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.02424, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.77188, "top5_acc": 0.98688, "loss_cls": 0.93857, "loss": 0.93857, "time": 0.41505} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.02423, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.795, "top5_acc": 0.98562, "loss_cls": 0.886, "loss": 0.886, "time": 0.4145} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.02422, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.77188, "top5_acc": 0.98125, "loss_cls": 0.99124, "loss": 0.99124, "time": 0.41756} +{"mode": "val", "epoch": 17, "iter": 533, "lr": 0.02422, "top1_acc": 0.66671, "top5_acc": 0.94613, "mean_class_accuracy": 0.58366} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.02421, "memory": 4082, "data_time": 0.19312, "top1_acc": 0.80438, "top5_acc": 0.98875, "loss_cls": 0.87365, "loss": 0.87365, "time": 0.606} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.0242, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.78625, "top5_acc": 0.98688, "loss_cls": 0.87592, "loss": 0.87592, "time": 0.25835} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.02419, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.79875, "top5_acc": 0.98875, "loss_cls": 0.87028, "loss": 0.87028, "time": 0.41482} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.02419, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79, "top5_acc": 0.98375, "loss_cls": 0.90628, "loss": 0.90628, "time": 0.41719} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.02418, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81875, "top5_acc": 0.98938, "loss_cls": 0.82804, "loss": 0.82804, "time": 0.41609} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.02417, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.78438, "top5_acc": 0.98562, "loss_cls": 0.9381, "loss": 0.9381, "time": 0.41556} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.02417, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78625, "top5_acc": 0.98438, "loss_cls": 0.91118, "loss": 0.91118, "time": 0.41673} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.02416, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78562, "top5_acc": 0.98375, "loss_cls": 0.92329, "loss": 0.92329, "time": 0.41896} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.02415, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77812, "top5_acc": 0.98, "loss_cls": 0.97085, "loss": 0.97085, "time": 0.41834} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.02414, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79812, "top5_acc": 0.99062, "loss_cls": 0.86622, "loss": 0.86622, "time": 0.41563} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.02414, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.80062, "top5_acc": 0.98188, "loss_cls": 0.88761, "loss": 0.88761, "time": 0.42704} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.02413, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.79375, "top5_acc": 0.98312, "loss_cls": 0.91293, "loss": 0.91293, "time": 0.434} +{"mode": "val", "epoch": 18, "iter": 533, "lr": 0.02412, "top1_acc": 0.75684, "top5_acc": 0.97923, "mean_class_accuracy": 0.63746} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.02411, "memory": 4082, "data_time": 0.19684, "top1_acc": 0.8, "top5_acc": 0.98812, "loss_cls": 0.88395, "loss": 0.88395, "time": 0.62335} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.02411, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80688, "top5_acc": 0.98938, "loss_cls": 0.8462, "loss": 0.8462, "time": 0.26063} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.0241, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81375, "top5_acc": 0.98812, "loss_cls": 0.83084, "loss": 0.83084, "time": 0.4348} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.02409, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.7725, "top5_acc": 0.98438, "loss_cls": 0.95635, "loss": 0.95635, "time": 0.41601} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.02408, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79062, "top5_acc": 0.98875, "loss_cls": 0.90691, "loss": 0.90691, "time": 0.41441} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.02408, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.78938, "top5_acc": 0.98812, "loss_cls": 0.87369, "loss": 0.87369, "time": 0.41395} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.02407, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.76938, "top5_acc": 0.98375, "loss_cls": 0.96296, "loss": 0.96296, "time": 0.41639} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.02406, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78875, "top5_acc": 0.98875, "loss_cls": 0.90555, "loss": 0.90555, "time": 0.41509} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.02405, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79625, "top5_acc": 0.985, "loss_cls": 0.91947, "loss": 0.91947, "time": 0.41411} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.02405, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.78688, "top5_acc": 0.985, "loss_cls": 0.9122, "loss": 0.9122, "time": 0.41529} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.02404, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78375, "top5_acc": 0.98188, "loss_cls": 0.93308, "loss": 0.93308, "time": 0.41458} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.02403, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.805, "top5_acc": 0.99062, "loss_cls": 0.87766, "loss": 0.87766, "time": 0.42361} +{"mode": "val", "epoch": 19, "iter": 533, "lr": 0.02402, "top1_acc": 0.73853, "top5_acc": 0.97406, "mean_class_accuracy": 0.6238} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.02402, "memory": 4082, "data_time": 0.19127, "top1_acc": 0.7825, "top5_acc": 0.98812, "loss_cls": 0.9395, "loss": 0.9395, "time": 0.62729} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.02401, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.80188, "top5_acc": 0.98625, "loss_cls": 0.85416, "loss": 0.85416, "time": 0.24402} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.024, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81, "top5_acc": 0.99125, "loss_cls": 0.82063, "loss": 0.82063, "time": 0.41497} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.02399, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.79812, "top5_acc": 0.98375, "loss_cls": 0.88739, "loss": 0.88739, "time": 0.41473} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.02398, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78375, "top5_acc": 0.99375, "loss_cls": 0.8716, "loss": 0.8716, "time": 0.41486} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.02398, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.8025, "top5_acc": 0.98688, "loss_cls": 0.89302, "loss": 0.89302, "time": 0.41496} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.02397, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80375, "top5_acc": 0.98438, "loss_cls": 0.87501, "loss": 0.87501, "time": 0.41621} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.02396, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.77062, "top5_acc": 0.98375, "loss_cls": 0.98341, "loss": 0.98341, "time": 0.41529} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.02395, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.78938, "top5_acc": 0.99062, "loss_cls": 0.89399, "loss": 0.89399, "time": 0.4158} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.02394, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.795, "top5_acc": 0.98375, "loss_cls": 0.89145, "loss": 0.89145, "time": 0.41511} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.02393, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79375, "top5_acc": 0.9875, "loss_cls": 0.88797, "loss": 0.88797, "time": 0.41497} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.02393, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.8, "top5_acc": 0.98375, "loss_cls": 0.90688, "loss": 0.90688, "time": 0.41638} +{"mode": "val", "epoch": 20, "iter": 533, "lr": 0.02392, "top1_acc": 0.77303, "top5_acc": 0.98463, "mean_class_accuracy": 0.66688} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.02391, "memory": 4082, "data_time": 0.19461, "top1_acc": 0.83125, "top5_acc": 0.99375, "loss_cls": 0.75327, "loss": 0.75327, "time": 0.65013} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.0239, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.8325, "top5_acc": 0.9925, "loss_cls": 0.75874, "loss": 0.75874, "time": 0.23196} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.02389, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.79812, "top5_acc": 0.98875, "loss_cls": 0.89985, "loss": 0.89985, "time": 0.43848} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.02389, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.78688, "top5_acc": 0.9825, "loss_cls": 0.91038, "loss": 0.91038, "time": 0.41915} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.02388, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.80625, "top5_acc": 0.98812, "loss_cls": 0.8292, "loss": 0.8292, "time": 0.41488} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.02387, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8075, "top5_acc": 0.98688, "loss_cls": 0.85384, "loss": 0.85384, "time": 0.41308} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.02386, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.79062, "top5_acc": 0.98375, "loss_cls": 0.92264, "loss": 0.92264, "time": 0.41467} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.02385, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78438, "top5_acc": 0.98188, "loss_cls": 0.91845, "loss": 0.91845, "time": 0.41422} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.02384, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8075, "top5_acc": 0.9825, "loss_cls": 0.83613, "loss": 0.83613, "time": 0.4143} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.02383, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80188, "top5_acc": 0.98625, "loss_cls": 0.88144, "loss": 0.88144, "time": 0.41429} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.02383, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.79062, "top5_acc": 0.98625, "loss_cls": 0.93292, "loss": 0.93292, "time": 0.41592} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.02382, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.79, "top5_acc": 0.98938, "loss_cls": 0.87727, "loss": 0.87727, "time": 0.41402} +{"mode": "val", "epoch": 21, "iter": 533, "lr": 0.02381, "top1_acc": 0.75637, "top5_acc": 0.97888, "mean_class_accuracy": 0.64139} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.0238, "memory": 4082, "data_time": 0.19305, "top1_acc": 0.8025, "top5_acc": 0.98812, "loss_cls": 0.85578, "loss": 0.85578, "time": 0.65002} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.02379, "memory": 4082, "data_time": 0.00058, "top1_acc": 0.80625, "top5_acc": 0.98875, "loss_cls": 0.83669, "loss": 0.83669, "time": 0.24304} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.02378, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81812, "top5_acc": 0.99188, "loss_cls": 0.84695, "loss": 0.84695, "time": 0.41561} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.02378, "memory": 4082, "data_time": 0.00066, "top1_acc": 0.79312, "top5_acc": 0.98625, "loss_cls": 0.85796, "loss": 0.85796, "time": 0.41624} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.02377, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.81, "top5_acc": 0.99188, "loss_cls": 0.86336, "loss": 0.86336, "time": 0.41513} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.02376, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80688, "top5_acc": 0.9875, "loss_cls": 0.87474, "loss": 0.87474, "time": 0.41366} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.02375, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.81188, "top5_acc": 0.9875, "loss_cls": 0.85658, "loss": 0.85658, "time": 0.41677} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.02374, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80125, "top5_acc": 0.98875, "loss_cls": 0.86665, "loss": 0.86665, "time": 0.41601} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.02373, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.815, "top5_acc": 0.9925, "loss_cls": 0.80837, "loss": 0.80837, "time": 0.4146} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.02372, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.80375, "top5_acc": 0.99, "loss_cls": 0.84168, "loss": 0.84168, "time": 0.41464} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.02371, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.80812, "top5_acc": 0.98562, "loss_cls": 0.85282, "loss": 0.85282, "time": 0.43326} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0237, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.81562, "top5_acc": 0.99125, "loss_cls": 0.83331, "loss": 0.83331, "time": 0.42191} +{"mode": "val", "epoch": 22, "iter": 533, "lr": 0.0237, "top1_acc": 0.74968, "top5_acc": 0.97477, "mean_class_accuracy": 0.68162} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.02369, "memory": 4082, "data_time": 0.19794, "top1_acc": 0.8075, "top5_acc": 0.99312, "loss_cls": 0.82272, "loss": 0.82272, "time": 0.65446} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.02368, "memory": 4082, "data_time": 0.00054, "top1_acc": 0.82188, "top5_acc": 0.98938, "loss_cls": 0.78335, "loss": 0.78335, "time": 0.23057} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.02367, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.805, "top5_acc": 0.98438, "loss_cls": 0.88528, "loss": 0.88528, "time": 0.41347} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.02366, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.79688, "top5_acc": 0.9925, "loss_cls": 0.88182, "loss": 0.88182, "time": 0.4145} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.02365, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80062, "top5_acc": 0.9875, "loss_cls": 0.86961, "loss": 0.86961, "time": 0.41331} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.02364, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.80875, "top5_acc": 0.98625, "loss_cls": 0.82525, "loss": 0.82525, "time": 0.41345} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.02363, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81812, "top5_acc": 0.98562, "loss_cls": 0.85421, "loss": 0.85421, "time": 0.41739} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.02362, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.80562, "top5_acc": 0.98562, "loss_cls": 0.83627, "loss": 0.83627, "time": 0.41463} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.02361, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8125, "top5_acc": 0.98875, "loss_cls": 0.82253, "loss": 0.82253, "time": 0.41454} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.0236, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.77688, "top5_acc": 0.98312, "loss_cls": 0.93525, "loss": 0.93525, "time": 0.41461} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.02359, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.79938, "top5_acc": 0.99188, "loss_cls": 0.83119, "loss": 0.83119, "time": 0.41424} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.02359, "memory": 4082, "data_time": 0.00061, "top1_acc": 0.805, "top5_acc": 0.98938, "loss_cls": 0.83429, "loss": 0.83429, "time": 0.41497} +{"mode": "val", "epoch": 23, "iter": 533, "lr": 0.02358, "top1_acc": 0.70684, "top5_acc": 0.9628, "mean_class_accuracy": 0.60462} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.02357, "memory": 4082, "data_time": 0.19783, "top1_acc": 0.80188, "top5_acc": 0.9875, "loss_cls": 0.8733, "loss": 0.8733, "time": 0.65437} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.02356, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.83125, "top5_acc": 0.99188, "loss_cls": 0.76433, "loss": 0.76433, "time": 0.2352} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.02355, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.80812, "top5_acc": 0.99062, "loss_cls": 0.82932, "loss": 0.82932, "time": 0.4091} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.02354, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.805, "top5_acc": 0.985, "loss_cls": 0.87449, "loss": 0.87449, "time": 0.41553} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.02353, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.82062, "top5_acc": 0.98688, "loss_cls": 0.79301, "loss": 0.79301, "time": 0.41475} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.02352, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.81, "top5_acc": 0.98875, "loss_cls": 0.80217, "loss": 0.80217, "time": 0.41441} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.02351, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.815, "top5_acc": 0.99062, "loss_cls": 0.78499, "loss": 0.78499, "time": 0.41537} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.0235, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.81438, "top5_acc": 0.99, "loss_cls": 0.82244, "loss": 0.82244, "time": 0.41727} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.02349, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81625, "top5_acc": 0.98688, "loss_cls": 0.82794, "loss": 0.82794, "time": 0.41524} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.02348, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81062, "top5_acc": 0.98938, "loss_cls": 0.80361, "loss": 0.80361, "time": 0.41659} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.02347, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.81812, "top5_acc": 0.98938, "loss_cls": 0.7973, "loss": 0.7973, "time": 0.41582} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.02346, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.79, "top5_acc": 0.98375, "loss_cls": 0.89298, "loss": 0.89298, "time": 0.41785} +{"mode": "val", "epoch": 24, "iter": 533, "lr": 0.02345, "top1_acc": 0.7769, "top5_acc": 0.98427, "mean_class_accuracy": 0.6587} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.02344, "memory": 4082, "data_time": 0.2014, "top1_acc": 0.8225, "top5_acc": 0.98875, "loss_cls": 0.77684, "loss": 0.77684, "time": 0.65674} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.02343, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80688, "top5_acc": 0.98688, "loss_cls": 0.82319, "loss": 0.82319, "time": 0.2437} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.02342, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.79625, "top5_acc": 0.9875, "loss_cls": 0.85249, "loss": 0.85249, "time": 0.4081} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.02341, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.82875, "top5_acc": 0.99062, "loss_cls": 0.77696, "loss": 0.77696, "time": 0.41384} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.0234, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80875, "top5_acc": 0.98438, "loss_cls": 0.84054, "loss": 0.84054, "time": 0.41552} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.02339, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80688, "top5_acc": 0.98562, "loss_cls": 0.83529, "loss": 0.83529, "time": 0.41399} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.02338, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.81375, "top5_acc": 0.99188, "loss_cls": 0.76956, "loss": 0.76956, "time": 0.41554} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.02337, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81625, "top5_acc": 0.98938, "loss_cls": 0.81149, "loss": 0.81149, "time": 0.41653} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.02336, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.815, "top5_acc": 0.99, "loss_cls": 0.8255, "loss": 0.8255, "time": 0.41367} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.02335, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81625, "top5_acc": 0.98375, "loss_cls": 0.83474, "loss": 0.83474, "time": 0.41337} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.02334, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82375, "top5_acc": 0.9875, "loss_cls": 0.80635, "loss": 0.80635, "time": 0.41325} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.02333, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.805, "top5_acc": 0.99125, "loss_cls": 0.8335, "loss": 0.8335, "time": 0.41729} +{"mode": "val", "epoch": 25, "iter": 533, "lr": 0.02333, "top1_acc": 0.7627, "top5_acc": 0.97923, "mean_class_accuracy": 0.66355} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.02332, "memory": 4082, "data_time": 0.20036, "top1_acc": 0.82625, "top5_acc": 0.98875, "loss_cls": 0.79786, "loss": 0.79786, "time": 0.65679} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.0233, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.81938, "top5_acc": 0.99562, "loss_cls": 0.7678, "loss": 0.7678, "time": 0.24216} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.02329, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82938, "top5_acc": 0.99188, "loss_cls": 0.75221, "loss": 0.75221, "time": 0.4061} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.02328, "memory": 4082, "data_time": 0.0006, "top1_acc": 0.82875, "top5_acc": 0.98875, "loss_cls": 0.77591, "loss": 0.77591, "time": 0.41555} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.02327, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82312, "top5_acc": 0.9825, "loss_cls": 0.81171, "loss": 0.81171, "time": 0.41462} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.02326, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.80938, "top5_acc": 0.98688, "loss_cls": 0.8568, "loss": 0.8568, "time": 0.41459} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.02325, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82625, "top5_acc": 0.98562, "loss_cls": 0.7791, "loss": 0.7791, "time": 0.41347} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.02324, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.82125, "top5_acc": 0.98438, "loss_cls": 0.82751, "loss": 0.82751, "time": 0.41522} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.02323, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.815, "top5_acc": 0.99125, "loss_cls": 0.80486, "loss": 0.80486, "time": 0.41403} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.02322, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81812, "top5_acc": 0.985, "loss_cls": 0.81499, "loss": 0.81499, "time": 0.41516} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.02321, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.8125, "top5_acc": 0.99188, "loss_cls": 0.81723, "loss": 0.81723, "time": 0.41562} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.0232, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.8225, "top5_acc": 0.98812, "loss_cls": 0.79188, "loss": 0.79188, "time": 0.41545} +{"mode": "val", "epoch": 26, "iter": 533, "lr": 0.02319, "top1_acc": 0.74217, "top5_acc": 0.97007, "mean_class_accuracy": 0.66483} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.02318, "memory": 4082, "data_time": 0.19858, "top1_acc": 0.82125, "top5_acc": 0.99188, "loss_cls": 0.77119, "loss": 0.77119, "time": 0.65408} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.02317, "memory": 4082, "data_time": 0.00063, "top1_acc": 0.83062, "top5_acc": 0.99062, "loss_cls": 0.77836, "loss": 0.77836, "time": 0.24135} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.02316, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81312, "top5_acc": 0.99, "loss_cls": 0.78981, "loss": 0.78981, "time": 0.40704} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.02315, "memory": 4082, "data_time": 0.00064, "top1_acc": 0.83688, "top5_acc": 0.99125, "loss_cls": 0.74478, "loss": 0.74478, "time": 0.41518} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.02314, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81188, "top5_acc": 0.9875, "loss_cls": 0.81305, "loss": 0.81305, "time": 0.41526} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.02313, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82125, "top5_acc": 0.98938, "loss_cls": 0.76225, "loss": 0.76225, "time": 0.41585} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.02312, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.80875, "top5_acc": 0.98688, "loss_cls": 0.83174, "loss": 0.83174, "time": 0.41782} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.02311, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.79875, "top5_acc": 0.99, "loss_cls": 0.83837, "loss": 0.83837, "time": 0.42991} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.0231, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81812, "top5_acc": 0.98938, "loss_cls": 0.76716, "loss": 0.76716, "time": 0.43656} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.02308, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.79938, "top5_acc": 0.99125, "loss_cls": 0.8331, "loss": 0.8331, "time": 0.41547} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.02307, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.80812, "top5_acc": 0.985, "loss_cls": 0.8486, "loss": 0.8486, "time": 0.41602} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.02306, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78562, "top5_acc": 0.98625, "loss_cls": 0.90789, "loss": 0.90789, "time": 0.4147} +{"mode": "val", "epoch": 27, "iter": 533, "lr": 0.02305, "top1_acc": 0.75519, "top5_acc": 0.97277, "mean_class_accuracy": 0.6561} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.02304, "memory": 4082, "data_time": 0.19631, "top1_acc": 0.82375, "top5_acc": 0.98625, "loss_cls": 0.80654, "loss": 0.80654, "time": 0.65169} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.02303, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.82, "top5_acc": 0.98875, "loss_cls": 0.76397, "loss": 0.76397, "time": 0.24622} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.02302, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8275, "top5_acc": 0.99125, "loss_cls": 0.74625, "loss": 0.74625, "time": 0.43008} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.02301, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.82438, "top5_acc": 0.99188, "loss_cls": 0.77409, "loss": 0.77409, "time": 0.41491} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.023, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83, "top5_acc": 0.99125, "loss_cls": 0.72955, "loss": 0.72955, "time": 0.41339} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.02299, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.82, "top5_acc": 0.99312, "loss_cls": 0.78627, "loss": 0.78627, "time": 0.41432} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.02298, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82625, "top5_acc": 0.99062, "loss_cls": 0.80677, "loss": 0.80677, "time": 0.41345} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.02297, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83438, "top5_acc": 0.98875, "loss_cls": 0.73415, "loss": 0.73415, "time": 0.41397} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.02295, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80812, "top5_acc": 0.9925, "loss_cls": 0.79663, "loss": 0.79663, "time": 0.41354} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.02294, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81, "top5_acc": 0.99312, "loss_cls": 0.79147, "loss": 0.79147, "time": 0.41648} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.02293, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81875, "top5_acc": 0.99, "loss_cls": 0.81632, "loss": 0.81632, "time": 0.41426} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.02292, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83562, "top5_acc": 0.99188, "loss_cls": 0.7578, "loss": 0.7578, "time": 0.4145} +{"mode": "val", "epoch": 28, "iter": 533, "lr": 0.02291, "top1_acc": 0.78735, "top5_acc": 0.98345, "mean_class_accuracy": 0.69462} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.0229, "memory": 4082, "data_time": 0.19982, "top1_acc": 0.82938, "top5_acc": 0.99188, "loss_cls": 0.75028, "loss": 0.75028, "time": 0.65516} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.02289, "memory": 4082, "data_time": 0.00056, "top1_acc": 0.81812, "top5_acc": 0.9875, "loss_cls": 0.7995, "loss": 0.7995, "time": 0.25334} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.02288, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83312, "top5_acc": 0.99125, "loss_cls": 0.73613, "loss": 0.73613, "time": 0.40413} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.02287, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82062, "top5_acc": 0.99312, "loss_cls": 0.79295, "loss": 0.79295, "time": 0.4157} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.02285, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83062, "top5_acc": 0.99188, "loss_cls": 0.77884, "loss": 0.77884, "time": 0.41545} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.02284, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.8275, "top5_acc": 0.98625, "loss_cls": 0.76857, "loss": 0.76857, "time": 0.41484} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.02283, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80125, "top5_acc": 0.9825, "loss_cls": 0.82, "loss": 0.82, "time": 0.41407} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.02282, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.82312, "top5_acc": 0.99438, "loss_cls": 0.76519, "loss": 0.76519, "time": 0.41784} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.02281, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.83438, "top5_acc": 0.98688, "loss_cls": 0.80756, "loss": 0.80756, "time": 0.41471} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.0228, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81875, "top5_acc": 0.9925, "loss_cls": 0.7824, "loss": 0.7824, "time": 0.41532} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.02279, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.80625, "top5_acc": 0.98562, "loss_cls": 0.81886, "loss": 0.81886, "time": 0.41439} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.02277, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.79625, "top5_acc": 0.99312, "loss_cls": 0.8617, "loss": 0.8617, "time": 0.41449} +{"mode": "val", "epoch": 29, "iter": 533, "lr": 0.02276, "top1_acc": 0.74756, "top5_acc": 0.97078, "mean_class_accuracy": 0.64699} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.02275, "memory": 4082, "data_time": 0.19956, "top1_acc": 0.82438, "top5_acc": 0.99188, "loss_cls": 0.77387, "loss": 0.77387, "time": 0.69637} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.02274, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.8525, "top5_acc": 0.99375, "loss_cls": 0.69407, "loss": 0.69407, "time": 0.3632} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.02273, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8225, "top5_acc": 0.995, "loss_cls": 0.72964, "loss": 0.72964, "time": 0.48465} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.02272, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83938, "top5_acc": 0.99125, "loss_cls": 0.7331, "loss": 0.7331, "time": 0.494} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.02271, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83188, "top5_acc": 0.99438, "loss_cls": 0.74439, "loss": 0.74439, "time": 0.48068} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.02269, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81625, "top5_acc": 0.98875, "loss_cls": 0.77888, "loss": 0.77888, "time": 0.48896} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.02268, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80938, "top5_acc": 0.99125, "loss_cls": 0.78582, "loss": 0.78582, "time": 0.48733} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.02267, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.8175, "top5_acc": 0.99062, "loss_cls": 0.7961, "loss": 0.7961, "time": 0.47946} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.02266, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.80938, "top5_acc": 0.98875, "loss_cls": 0.79731, "loss": 0.79731, "time": 0.48906} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.02265, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.81938, "top5_acc": 0.99, "loss_cls": 0.78022, "loss": 0.78022, "time": 0.49704} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.02263, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8175, "top5_acc": 0.9875, "loss_cls": 0.78472, "loss": 0.78472, "time": 0.48582} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.02262, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.80938, "top5_acc": 0.99438, "loss_cls": 0.81633, "loss": 0.81633, "time": 0.48101} +{"mode": "val", "epoch": 30, "iter": 533, "lr": 0.02261, "top1_acc": 0.81411, "top5_acc": 0.98674, "mean_class_accuracy": 0.73172} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.0226, "memory": 4083, "data_time": 0.19315, "top1_acc": 0.8375, "top5_acc": 0.99188, "loss_cls": 0.88631, "loss": 0.88631, "time": 0.84301} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.02259, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.81812, "top5_acc": 0.99375, "loss_cls": 0.91301, "loss": 0.91301, "time": 0.51314} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.02258, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.81312, "top5_acc": 0.99062, "loss_cls": 0.95766, "loss": 0.95766, "time": 0.52339} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.02256, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.82688, "top5_acc": 0.99062, "loss_cls": 0.93531, "loss": 0.93531, "time": 0.49496} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.02255, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.82188, "top5_acc": 0.99312, "loss_cls": 0.92595, "loss": 0.92595, "time": 0.49113} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.02254, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.81625, "top5_acc": 0.98938, "loss_cls": 0.97914, "loss": 0.97914, "time": 0.51051} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.02253, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.82812, "top5_acc": 0.98875, "loss_cls": 0.89991, "loss": 0.89991, "time": 0.50344} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.02252, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.84375, "top5_acc": 0.99125, "loss_cls": 0.84852, "loss": 0.84852, "time": 0.51243} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0225, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.82875, "top5_acc": 0.9875, "loss_cls": 0.92504, "loss": 0.92504, "time": 0.4122} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.02249, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.84062, "top5_acc": 0.99062, "loss_cls": 0.86426, "loss": 0.86426, "time": 0.50165} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.02248, "memory": 4083, "data_time": 0.00072, "top1_acc": 0.83438, "top5_acc": 0.98875, "loss_cls": 0.88315, "loss": 0.88315, "time": 0.25503} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.02247, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.82562, "top5_acc": 0.98938, "loss_cls": 0.90779, "loss": 0.90779, "time": 0.49643} +{"mode": "val", "epoch": 31, "iter": 533, "lr": 0.02246, "top1_acc": 0.77385, "top5_acc": 0.98005, "mean_class_accuracy": 0.6973} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.02244, "memory": 4083, "data_time": 0.19006, "top1_acc": 0.8375, "top5_acc": 0.99188, "loss_cls": 0.82492, "loss": 0.82492, "time": 0.85568} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.02243, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84875, "top5_acc": 0.99125, "loss_cls": 0.81475, "loss": 0.81475, "time": 0.51012} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.02242, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.81375, "top5_acc": 0.99438, "loss_cls": 0.90788, "loss": 0.90788, "time": 0.50998} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.02241, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.83375, "top5_acc": 0.99062, "loss_cls": 0.81587, "loss": 0.81587, "time": 0.50352} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.02239, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.82875, "top5_acc": 0.9925, "loss_cls": 0.83611, "loss": 0.83611, "time": 0.50777} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.02238, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.83312, "top5_acc": 0.99125, "loss_cls": 0.84101, "loss": 0.84101, "time": 0.29081} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.02237, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.825, "top5_acc": 0.99, "loss_cls": 0.83621, "loss": 0.83621, "time": 0.51105} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.02236, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.82375, "top5_acc": 0.98688, "loss_cls": 0.90973, "loss": 0.90973, "time": 0.35926} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.02234, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.82688, "top5_acc": 0.99062, "loss_cls": 0.85749, "loss": 0.85749, "time": 0.51326} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.02233, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.805, "top5_acc": 0.99312, "loss_cls": 0.9169, "loss": 0.9169, "time": 0.50236} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.02232, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.83375, "top5_acc": 0.99375, "loss_cls": 0.86028, "loss": 0.86028, "time": 0.5143} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.02231, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8175, "top5_acc": 0.98938, "loss_cls": 0.8949, "loss": 0.8949, "time": 0.51209} +{"mode": "val", "epoch": 32, "iter": 533, "lr": 0.0223, "top1_acc": 0.76892, "top5_acc": 0.98122, "mean_class_accuracy": 0.70048} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.02228, "memory": 4083, "data_time": 0.19408, "top1_acc": 0.8325, "top5_acc": 0.99438, "loss_cls": 0.77441, "loss": 0.77441, "time": 0.82374} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.02227, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.84438, "top5_acc": 0.9925, "loss_cls": 0.76675, "loss": 0.76675, "time": 0.45012} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.02226, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.8275, "top5_acc": 0.98812, "loss_cls": 0.80082, "loss": 0.80082, "time": 0.41392} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.02225, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.80688, "top5_acc": 0.98562, "loss_cls": 0.92508, "loss": 0.92508, "time": 0.32363} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.02223, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.81875, "top5_acc": 0.985, "loss_cls": 0.85508, "loss": 0.85508, "time": 0.48469} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.02222, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.84188, "top5_acc": 0.995, "loss_cls": 0.78804, "loss": 0.78804, "time": 0.50395} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.02221, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.82125, "top5_acc": 0.98938, "loss_cls": 0.82549, "loss": 0.82549, "time": 0.50891} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.02219, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.83062, "top5_acc": 0.99062, "loss_cls": 0.82483, "loss": 0.82483, "time": 0.49968} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.02218, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8325, "top5_acc": 0.9925, "loss_cls": 0.80219, "loss": 0.80219, "time": 0.49402} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.02217, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.81688, "top5_acc": 0.9925, "loss_cls": 0.83851, "loss": 0.83851, "time": 0.51304} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.02216, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.79188, "top5_acc": 0.985, "loss_cls": 0.96779, "loss": 0.96779, "time": 0.51282} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.02214, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.83, "top5_acc": 0.9925, "loss_cls": 0.79602, "loss": 0.79602, "time": 0.50139} +{"mode": "val", "epoch": 33, "iter": 533, "lr": 0.02213, "top1_acc": 0.76916, "top5_acc": 0.97911, "mean_class_accuracy": 0.69602} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.02212, "memory": 4083, "data_time": 0.19386, "top1_acc": 0.85375, "top5_acc": 0.99188, "loss_cls": 0.73896, "loss": 0.73896, "time": 0.61326} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.02211, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.83, "top5_acc": 0.99438, "loss_cls": 0.81266, "loss": 0.81266, "time": 0.50217} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.02209, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.82188, "top5_acc": 0.98875, "loss_cls": 0.86195, "loss": 0.86195, "time": 0.52113} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.02208, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85125, "top5_acc": 0.99125, "loss_cls": 0.76338, "loss": 0.76338, "time": 0.51858} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.02207, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.8425, "top5_acc": 0.99, "loss_cls": 0.77918, "loss": 0.77918, "time": 0.50442} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.02205, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.83562, "top5_acc": 0.9875, "loss_cls": 0.81865, "loss": 0.81865, "time": 0.5078} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.02204, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.82812, "top5_acc": 0.99375, "loss_cls": 0.81545, "loss": 0.81545, "time": 0.50334} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.02203, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.83875, "top5_acc": 0.98688, "loss_cls": 0.82116, "loss": 0.82116, "time": 0.50949} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.02201, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.83375, "top5_acc": 0.99375, "loss_cls": 0.8016, "loss": 0.8016, "time": 0.50885} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.022, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8325, "top5_acc": 0.99625, "loss_cls": 0.80127, "loss": 0.80127, "time": 0.51629} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.02199, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.83688, "top5_acc": 0.985, "loss_cls": 0.83806, "loss": 0.83806, "time": 0.39413} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.02197, "memory": 4083, "data_time": 0.00066, "top1_acc": 0.825, "top5_acc": 0.98875, "loss_cls": 0.83079, "loss": 0.83079, "time": 0.51309} +{"mode": "val", "epoch": 34, "iter": 533, "lr": 0.02196, "top1_acc": 0.77503, "top5_acc": 0.9743, "mean_class_accuracy": 0.68394} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.02195, "memory": 4083, "data_time": 0.18821, "top1_acc": 0.83062, "top5_acc": 0.99375, "loss_cls": 0.80216, "loss": 0.80216, "time": 0.83131} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.02194, "memory": 4083, "data_time": 0.00071, "top1_acc": 0.85812, "top5_acc": 0.99062, "loss_cls": 0.73298, "loss": 0.73298, "time": 0.52352} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.02192, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.84375, "top5_acc": 0.995, "loss_cls": 0.72822, "loss": 0.72822, "time": 0.50461} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.02191, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.83188, "top5_acc": 0.99312, "loss_cls": 0.77678, "loss": 0.77678, "time": 0.52497} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.0219, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.84125, "top5_acc": 0.99375, "loss_cls": 0.74872, "loss": 0.74872, "time": 0.51068} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.02188, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.84688, "top5_acc": 0.99125, "loss_cls": 0.74574, "loss": 0.74574, "time": 0.51337} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.02187, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.82938, "top5_acc": 0.99, "loss_cls": 0.82051, "loss": 0.82051, "time": 0.51316} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.02185, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.83875, "top5_acc": 0.99, "loss_cls": 0.83318, "loss": 0.83318, "time": 0.32581} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.02184, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.83125, "top5_acc": 0.99312, "loss_cls": 0.7849, "loss": 0.7849, "time": 0.41289} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.02183, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.83688, "top5_acc": 0.98562, "loss_cls": 0.8131, "loss": 0.8131, "time": 0.43555} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.02181, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84312, "top5_acc": 0.99188, "loss_cls": 0.75107, "loss": 0.75107, "time": 0.5296} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.0218, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.83938, "top5_acc": 0.98938, "loss_cls": 0.77526, "loss": 0.77526, "time": 0.52853} +{"mode": "val", "epoch": 35, "iter": 533, "lr": 0.02179, "top1_acc": 0.76529, "top5_acc": 0.97852, "mean_class_accuracy": 0.6852} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.02178, "memory": 4083, "data_time": 0.20066, "top1_acc": 0.84062, "top5_acc": 0.99625, "loss_cls": 0.72754, "loss": 0.72754, "time": 0.84883} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.02176, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.85125, "top5_acc": 0.99375, "loss_cls": 0.7095, "loss": 0.7095, "time": 0.51223} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.02175, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.845, "top5_acc": 0.99, "loss_cls": 0.75325, "loss": 0.75325, "time": 0.51723} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.02173, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.84562, "top5_acc": 0.99375, "loss_cls": 0.75436, "loss": 0.75436, "time": 0.31984} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.02172, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.84312, "top5_acc": 0.99625, "loss_cls": 0.76984, "loss": 0.76984, "time": 0.51186} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.02171, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.83562, "top5_acc": 0.99062, "loss_cls": 0.81826, "loss": 0.81826, "time": 0.32889} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.02169, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.84188, "top5_acc": 0.99125, "loss_cls": 0.75426, "loss": 0.75426, "time": 0.50861} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.02168, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.83875, "top5_acc": 0.98875, "loss_cls": 0.77527, "loss": 0.77527, "time": 0.49881} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.02167, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.83062, "top5_acc": 0.99062, "loss_cls": 0.79836, "loss": 0.79836, "time": 0.52235} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.02165, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8325, "top5_acc": 0.99125, "loss_cls": 0.7974, "loss": 0.7974, "time": 0.51727} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.02164, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.82125, "top5_acc": 0.99, "loss_cls": 0.82754, "loss": 0.82754, "time": 0.50393} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.02162, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.8325, "top5_acc": 0.98875, "loss_cls": 0.82666, "loss": 0.82666, "time": 0.50163} +{"mode": "val", "epoch": 36, "iter": 533, "lr": 0.02161, "top1_acc": 0.78688, "top5_acc": 0.98615, "mean_class_accuracy": 0.72042} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.0216, "memory": 4083, "data_time": 0.19881, "top1_acc": 0.855, "top5_acc": 0.99375, "loss_cls": 0.72605, "loss": 0.72605, "time": 0.63134} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.02158, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84688, "top5_acc": 0.99438, "loss_cls": 0.72699, "loss": 0.72699, "time": 0.41462} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.02157, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.82812, "top5_acc": 0.99375, "loss_cls": 0.78706, "loss": 0.78706, "time": 0.42838} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.02156, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84188, "top5_acc": 0.98875, "loss_cls": 0.78765, "loss": 0.78765, "time": 0.52337} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.02154, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8375, "top5_acc": 0.99688, "loss_cls": 0.75799, "loss": 0.75799, "time": 0.51346} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.02153, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.82125, "top5_acc": 0.99125, "loss_cls": 0.82036, "loss": 0.82036, "time": 0.52846} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.02151, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85062, "top5_acc": 0.9925, "loss_cls": 0.76633, "loss": 0.76633, "time": 0.51337} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0215, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8375, "top5_acc": 0.99, "loss_cls": 0.80951, "loss": 0.80951, "time": 0.51663} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.02149, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8425, "top5_acc": 0.99188, "loss_cls": 0.76844, "loss": 0.76844, "time": 0.50645} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.02147, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.84938, "top5_acc": 0.9925, "loss_cls": 0.75342, "loss": 0.75342, "time": 0.52244} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.02146, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.81688, "top5_acc": 0.98812, "loss_cls": 0.90315, "loss": 0.90315, "time": 0.51801} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.02144, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.82938, "top5_acc": 0.99375, "loss_cls": 0.78992, "loss": 0.78992, "time": 0.44485} +{"mode": "val", "epoch": 37, "iter": 533, "lr": 0.02143, "top1_acc": 0.77937, "top5_acc": 0.98463, "mean_class_accuracy": 0.69313} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.02142, "memory": 4083, "data_time": 0.19034, "top1_acc": 0.84812, "top5_acc": 0.99688, "loss_cls": 0.7311, "loss": 0.7311, "time": 0.8307} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.0214, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.83812, "top5_acc": 0.99062, "loss_cls": 0.77355, "loss": 0.77355, "time": 0.51915} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.02139, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.83625, "top5_acc": 0.99125, "loss_cls": 0.76805, "loss": 0.76805, "time": 0.50309} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.02137, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8625, "top5_acc": 0.99188, "loss_cls": 0.70789, "loss": 0.70789, "time": 0.49483} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.02136, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.845, "top5_acc": 0.99375, "loss_cls": 0.74451, "loss": 0.74451, "time": 0.5054} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.02134, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.86938, "top5_acc": 0.99375, "loss_cls": 0.66497, "loss": 0.66497, "time": 0.5278} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.02133, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.83375, "top5_acc": 0.99125, "loss_cls": 0.77288, "loss": 0.77288, "time": 0.53218} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.02132, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84062, "top5_acc": 0.99, "loss_cls": 0.76787, "loss": 0.76787, "time": 0.51353} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.0213, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.83188, "top5_acc": 0.99312, "loss_cls": 0.82647, "loss": 0.82647, "time": 0.31817} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.02129, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.84, "top5_acc": 0.995, "loss_cls": 0.76895, "loss": 0.76895, "time": 0.5102} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.02127, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.855, "top5_acc": 0.99562, "loss_cls": 0.73004, "loss": 0.73004, "time": 0.34658} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.02126, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.84375, "top5_acc": 0.98625, "loss_cls": 0.79832, "loss": 0.79832, "time": 0.50111} +{"mode": "val", "epoch": 38, "iter": 533, "lr": 0.02125, "top1_acc": 0.76798, "top5_acc": 0.97981, "mean_class_accuracy": 0.6683} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.02123, "memory": 4083, "data_time": 0.19636, "top1_acc": 0.84625, "top5_acc": 0.99438, "loss_cls": 0.75457, "loss": 0.75457, "time": 0.8223} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.02122, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85062, "top5_acc": 0.99688, "loss_cls": 0.70823, "loss": 0.70823, "time": 0.51606} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.0212, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8625, "top5_acc": 0.99625, "loss_cls": 0.67307, "loss": 0.67307, "time": 0.50974} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.02119, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.855, "top5_acc": 0.99125, "loss_cls": 0.71576, "loss": 0.71576, "time": 0.51808} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.02117, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.82125, "top5_acc": 0.99188, "loss_cls": 0.7924, "loss": 0.7924, "time": 0.46396} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.02116, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.83125, "top5_acc": 0.9875, "loss_cls": 0.83262, "loss": 0.83262, "time": 0.40437} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.02114, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.84625, "top5_acc": 0.99062, "loss_cls": 0.76267, "loss": 0.76267, "time": 0.33418} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.02113, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.84062, "top5_acc": 0.99, "loss_cls": 0.74374, "loss": 0.74374, "time": 0.48537} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.02111, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.83562, "top5_acc": 0.98875, "loss_cls": 0.79645, "loss": 0.79645, "time": 0.51559} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.0211, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84188, "top5_acc": 0.9925, "loss_cls": 0.77436, "loss": 0.77436, "time": 0.5062} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.02108, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.83812, "top5_acc": 0.995, "loss_cls": 0.77353, "loss": 0.77353, "time": 0.50509} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.02107, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.83812, "top5_acc": 0.99375, "loss_cls": 0.80572, "loss": 0.80572, "time": 0.51293} +{"mode": "val", "epoch": 39, "iter": 533, "lr": 0.02106, "top1_acc": 0.79779, "top5_acc": 0.98615, "mean_class_accuracy": 0.70462} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.02104, "memory": 4083, "data_time": 0.19073, "top1_acc": 0.84188, "top5_acc": 0.995, "loss_cls": 0.74884, "loss": 0.74884, "time": 0.81939} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.02103, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.86, "top5_acc": 0.9925, "loss_cls": 0.71885, "loss": 0.71885, "time": 0.34891} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.02101, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84438, "top5_acc": 0.99125, "loss_cls": 0.77449, "loss": 0.77449, "time": 0.51201} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.021, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.84562, "top5_acc": 0.99188, "loss_cls": 0.75932, "loss": 0.75932, "time": 0.30604} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.02098, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.86188, "top5_acc": 0.99375, "loss_cls": 0.69325, "loss": 0.69325, "time": 0.50974} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.02097, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85812, "top5_acc": 0.99188, "loss_cls": 0.69464, "loss": 0.69464, "time": 0.51671} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.02095, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.83688, "top5_acc": 0.99438, "loss_cls": 0.75568, "loss": 0.75568, "time": 0.50909} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.02094, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.85, "top5_acc": 0.9925, "loss_cls": 0.71873, "loss": 0.71873, "time": 0.50612} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.02092, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.845, "top5_acc": 0.99062, "loss_cls": 0.73772, "loss": 0.73772, "time": 0.51335} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.02091, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85125, "top5_acc": 0.99188, "loss_cls": 0.73927, "loss": 0.73927, "time": 0.51674} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.02089, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85562, "top5_acc": 0.9925, "loss_cls": 0.71426, "loss": 0.71426, "time": 0.50488} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.02088, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8525, "top5_acc": 0.99, "loss_cls": 0.76028, "loss": 0.76028, "time": 0.51601} +{"mode": "val", "epoch": 40, "iter": 533, "lr": 0.02086, "top1_acc": 0.80108, "top5_acc": 0.98228, "mean_class_accuracy": 0.71389} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.02085, "memory": 4083, "data_time": 0.19163, "top1_acc": 0.88, "top5_acc": 0.995, "loss_cls": 0.66941, "loss": 0.66941, "time": 0.5185} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.02083, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85688, "top5_acc": 0.9925, "loss_cls": 0.72143, "loss": 0.72143, "time": 0.50648} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.02082, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.83688, "top5_acc": 0.99375, "loss_cls": 0.75039, "loss": 0.75039, "time": 0.52263} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.0208, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87125, "top5_acc": 0.99125, "loss_cls": 0.66336, "loss": 0.66336, "time": 0.5262} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.02079, "memory": 4083, "data_time": 0.00066, "top1_acc": 0.845, "top5_acc": 0.99188, "loss_cls": 0.75758, "loss": 0.75758, "time": 0.51157} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.02077, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.83312, "top5_acc": 0.995, "loss_cls": 0.78742, "loss": 0.78742, "time": 0.51278} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.02076, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85812, "top5_acc": 0.99125, "loss_cls": 0.71611, "loss": 0.71611, "time": 0.51063} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.02074, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86312, "top5_acc": 0.9925, "loss_cls": 0.71656, "loss": 0.71656, "time": 0.51386} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.02073, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8275, "top5_acc": 0.9925, "loss_cls": 0.8157, "loss": 0.8157, "time": 0.52529} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.02071, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85188, "top5_acc": 0.98875, "loss_cls": 0.7627, "loss": 0.7627, "time": 0.51648} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.0207, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86312, "top5_acc": 0.99438, "loss_cls": 0.69208, "loss": 0.69208, "time": 0.27328} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.02068, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.82875, "top5_acc": 0.98812, "loss_cls": 0.82225, "loss": 0.82225, "time": 0.51132} +{"mode": "val", "epoch": 41, "iter": 533, "lr": 0.02067, "top1_acc": 0.7762, "top5_acc": 0.98357, "mean_class_accuracy": 0.71592} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.02065, "memory": 4083, "data_time": 0.19399, "top1_acc": 0.8575, "top5_acc": 0.99438, "loss_cls": 0.70375, "loss": 0.70375, "time": 0.8305} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.02064, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.84812, "top5_acc": 0.99188, "loss_cls": 0.73399, "loss": 0.73399, "time": 0.51404} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.02062, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85375, "top5_acc": 0.99438, "loss_cls": 0.71496, "loss": 0.71496, "time": 0.52563} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.02061, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84375, "top5_acc": 0.99312, "loss_cls": 0.75696, "loss": 0.75696, "time": 0.52157} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.02059, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.855, "top5_acc": 0.9875, "loss_cls": 0.75892, "loss": 0.75892, "time": 0.4995} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.02057, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.85125, "top5_acc": 0.9925, "loss_cls": 0.72571, "loss": 0.72571, "time": 0.5189} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.02056, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84, "top5_acc": 0.995, "loss_cls": 0.74905, "loss": 0.74905, "time": 0.39603} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.02054, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86375, "top5_acc": 0.99562, "loss_cls": 0.66428, "loss": 0.66428, "time": 0.50424} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.02053, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.84562, "top5_acc": 0.9925, "loss_cls": 0.72888, "loss": 0.72888, "time": 0.2524} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.02051, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85438, "top5_acc": 0.9925, "loss_cls": 0.76718, "loss": 0.76718, "time": 0.48093} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.0205, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.835, "top5_acc": 0.9925, "loss_cls": 0.78454, "loss": 0.78454, "time": 0.48049} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.02048, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85312, "top5_acc": 0.99375, "loss_cls": 0.70307, "loss": 0.70307, "time": 0.48163} +{"mode": "val", "epoch": 42, "iter": 533, "lr": 0.02047, "top1_acc": 0.74228, "top5_acc": 0.97395, "mean_class_accuracy": 0.65756} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.02045, "memory": 4083, "data_time": 0.19398, "top1_acc": 0.83938, "top5_acc": 0.9925, "loss_cls": 0.75452, "loss": 0.75452, "time": 0.79199} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.02044, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.855, "top5_acc": 0.995, "loss_cls": 0.7048, "loss": 0.7048, "time": 0.48312} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.02042, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.85375, "top5_acc": 0.99625, "loss_cls": 0.70038, "loss": 0.70038, "time": 0.48414} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.0204, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.84625, "top5_acc": 0.99312, "loss_cls": 0.70949, "loss": 0.70949, "time": 0.4811} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.02039, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.85312, "top5_acc": 0.99438, "loss_cls": 0.71638, "loss": 0.71638, "time": 0.4831} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.02037, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8375, "top5_acc": 0.99625, "loss_cls": 0.77572, "loss": 0.77572, "time": 0.48326} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.02036, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85562, "top5_acc": 0.99625, "loss_cls": 0.72977, "loss": 0.72977, "time": 0.29446} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.02034, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8475, "top5_acc": 0.99312, "loss_cls": 0.71884, "loss": 0.71884, "time": 0.51207} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.02033, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.84938, "top5_acc": 0.99125, "loss_cls": 0.74353, "loss": 0.74353, "time": 0.25958} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.02031, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.84125, "top5_acc": 0.99375, "loss_cls": 0.79106, "loss": 0.79106, "time": 0.46283} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.02029, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85, "top5_acc": 0.99688, "loss_cls": 0.72749, "loss": 0.72749, "time": 0.4908} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.02028, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.84625, "top5_acc": 0.9925, "loss_cls": 0.75077, "loss": 0.75077, "time": 0.49326} +{"mode": "val", "epoch": 43, "iter": 533, "lr": 0.02026, "top1_acc": 0.80883, "top5_acc": 0.98451, "mean_class_accuracy": 0.74356} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.02025, "memory": 4083, "data_time": 0.19801, "top1_acc": 0.83625, "top5_acc": 0.99438, "loss_cls": 0.73771, "loss": 0.73771, "time": 0.81138} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.02023, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85375, "top5_acc": 0.995, "loss_cls": 0.69352, "loss": 0.69352, "time": 0.48982} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.02022, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86688, "top5_acc": 0.99625, "loss_cls": 0.63556, "loss": 0.63556, "time": 0.49147} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.0202, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85562, "top5_acc": 0.99375, "loss_cls": 0.71226, "loss": 0.71226, "time": 0.49135} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.02018, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87062, "top5_acc": 0.99312, "loss_cls": 0.66242, "loss": 0.66242, "time": 0.48954} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.02017, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87312, "top5_acc": 0.99688, "loss_cls": 0.64493, "loss": 0.64493, "time": 0.49018} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.02015, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.84812, "top5_acc": 0.99312, "loss_cls": 0.75268, "loss": 0.75268, "time": 0.34187} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.02014, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8525, "top5_acc": 0.99312, "loss_cls": 0.72036, "loss": 0.72036, "time": 0.512} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.02012, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8475, "top5_acc": 0.99188, "loss_cls": 0.74102, "loss": 0.74102, "time": 0.24675} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.0201, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8375, "top5_acc": 0.99125, "loss_cls": 0.77556, "loss": 0.77556, "time": 0.46481} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.02009, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.85312, "top5_acc": 0.9925, "loss_cls": 0.73159, "loss": 0.73159, "time": 0.48893} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.02007, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.86438, "top5_acc": 0.99438, "loss_cls": 0.71413, "loss": 0.71413, "time": 0.48861} +{"mode": "val", "epoch": 44, "iter": 533, "lr": 0.02006, "top1_acc": 0.77409, "top5_acc": 0.9804, "mean_class_accuracy": 0.68177} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.02004, "memory": 4083, "data_time": 0.19216, "top1_acc": 0.87062, "top5_acc": 0.99812, "loss_cls": 0.65742, "loss": 0.65742, "time": 0.80137} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.02003, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8775, "top5_acc": 0.9975, "loss_cls": 0.65321, "loss": 0.65321, "time": 0.49191} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.02001, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.85312, "top5_acc": 0.99438, "loss_cls": 0.6768, "loss": 0.6768, "time": 0.49235} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.01999, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87562, "top5_acc": 0.98938, "loss_cls": 0.68118, "loss": 0.68118, "time": 0.49258} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.01998, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86938, "top5_acc": 0.99438, "loss_cls": 0.67198, "loss": 0.67198, "time": 0.48967} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.01996, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87938, "top5_acc": 0.99375, "loss_cls": 0.64132, "loss": 0.64132, "time": 0.48891} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.01994, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86125, "top5_acc": 0.99688, "loss_cls": 0.68335, "loss": 0.68335, "time": 0.34684} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.01993, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.84188, "top5_acc": 0.995, "loss_cls": 0.76835, "loss": 0.76835, "time": 0.51259} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.01991, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87, "top5_acc": 0.995, "loss_cls": 0.68472, "loss": 0.68472, "time": 0.24766} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.01989, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.84562, "top5_acc": 0.995, "loss_cls": 0.72591, "loss": 0.72591, "time": 0.46393} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.01988, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8425, "top5_acc": 0.99188, "loss_cls": 0.75355, "loss": 0.75355, "time": 0.49197} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.01986, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8425, "top5_acc": 0.99312, "loss_cls": 0.74707, "loss": 0.74707, "time": 0.49033} +{"mode": "val", "epoch": 45, "iter": 533, "lr": 0.01985, "top1_acc": 0.80026, "top5_acc": 0.98357, "mean_class_accuracy": 0.70922} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.01983, "memory": 4083, "data_time": 0.19475, "top1_acc": 0.86312, "top5_acc": 0.9925, "loss_cls": 0.65849, "loss": 0.65849, "time": 0.80701} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.01981, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86625, "top5_acc": 0.99812, "loss_cls": 0.64179, "loss": 0.64179, "time": 0.49006} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.0198, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85625, "top5_acc": 0.99375, "loss_cls": 0.68076, "loss": 0.68076, "time": 0.49631} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.01978, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.8625, "top5_acc": 0.99125, "loss_cls": 0.678, "loss": 0.678, "time": 0.49411} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.01976, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8675, "top5_acc": 0.99375, "loss_cls": 0.66335, "loss": 0.66335, "time": 0.49085} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.01975, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.87, "top5_acc": 0.995, "loss_cls": 0.65884, "loss": 0.65884, "time": 0.49015} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.01973, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.82375, "top5_acc": 0.99312, "loss_cls": 0.82271, "loss": 0.82271, "time": 0.34806} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.01971, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85, "top5_acc": 0.9925, "loss_cls": 0.73624, "loss": 0.73624, "time": 0.51194} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.0197, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.87125, "top5_acc": 0.99312, "loss_cls": 0.63857, "loss": 0.63857, "time": 0.25215} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.01968, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86438, "top5_acc": 0.99375, "loss_cls": 0.66765, "loss": 0.66765, "time": 0.47325} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.01966, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85812, "top5_acc": 0.9925, "loss_cls": 0.73218, "loss": 0.73218, "time": 0.49462} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.01965, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.84438, "top5_acc": 0.98938, "loss_cls": 0.74774, "loss": 0.74774, "time": 0.49457} +{"mode": "val", "epoch": 46, "iter": 533, "lr": 0.01963, "top1_acc": 0.80038, "top5_acc": 0.98181, "mean_class_accuracy": 0.72497} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.01962, "memory": 4083, "data_time": 0.197, "top1_acc": 0.88312, "top5_acc": 0.995, "loss_cls": 0.63628, "loss": 0.63628, "time": 0.8001} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.0196, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.86, "top5_acc": 0.99438, "loss_cls": 0.66959, "loss": 0.66959, "time": 0.4905} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.01958, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87625, "top5_acc": 0.99812, "loss_cls": 0.655, "loss": 0.655, "time": 0.49174} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.01957, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.85312, "top5_acc": 0.99438, "loss_cls": 0.68119, "loss": 0.68119, "time": 0.49623} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.01955, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.85375, "top5_acc": 0.99188, "loss_cls": 0.69777, "loss": 0.69777, "time": 0.48965} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.01953, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87625, "top5_acc": 0.99625, "loss_cls": 0.69248, "loss": 0.69248, "time": 0.49263} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.01952, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.85312, "top5_acc": 0.99188, "loss_cls": 0.71317, "loss": 0.71317, "time": 0.33903} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.0195, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.86375, "top5_acc": 0.99688, "loss_cls": 0.69003, "loss": 0.69003, "time": 0.51155} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.01948, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.85875, "top5_acc": 0.99375, "loss_cls": 0.68828, "loss": 0.68828, "time": 0.2484} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.01947, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87562, "top5_acc": 0.995, "loss_cls": 0.65009, "loss": 0.65009, "time": 0.47623} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.01945, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.84188, "top5_acc": 0.99125, "loss_cls": 0.72245, "loss": 0.72245, "time": 0.48675} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.01943, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.855, "top5_acc": 0.99312, "loss_cls": 0.72373, "loss": 0.72373, "time": 0.49293} +{"mode": "val", "epoch": 47, "iter": 533, "lr": 0.01942, "top1_acc": 0.81751, "top5_acc": 0.98944, "mean_class_accuracy": 0.75338} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.0194, "memory": 4083, "data_time": 0.19731, "top1_acc": 0.87875, "top5_acc": 0.99625, "loss_cls": 0.64246, "loss": 0.64246, "time": 0.80489} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.01938, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87312, "top5_acc": 0.995, "loss_cls": 0.61191, "loss": 0.61191, "time": 0.49299} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.01937, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86, "top5_acc": 0.99562, "loss_cls": 0.70576, "loss": 0.70576, "time": 0.49434} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.01935, "memory": 4083, "data_time": 0.00062, "top1_acc": 0.85875, "top5_acc": 0.99438, "loss_cls": 0.70575, "loss": 0.70575, "time": 0.49052} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.01933, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88188, "top5_acc": 0.99312, "loss_cls": 0.61634, "loss": 0.61634, "time": 0.4898} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.01932, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84562, "top5_acc": 0.99188, "loss_cls": 0.71751, "loss": 0.71751, "time": 0.49109} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.0193, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8575, "top5_acc": 0.99375, "loss_cls": 0.67371, "loss": 0.67371, "time": 0.33934} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.01928, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.84125, "top5_acc": 0.99188, "loss_cls": 0.76254, "loss": 0.76254, "time": 0.51079} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.01926, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86625, "top5_acc": 0.99312, "loss_cls": 0.68605, "loss": 0.68605, "time": 0.24779} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.01925, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.88, "top5_acc": 0.99562, "loss_cls": 0.61466, "loss": 0.61466, "time": 0.46907} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.01923, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.84125, "top5_acc": 0.99375, "loss_cls": 0.73469, "loss": 0.73469, "time": 0.48991} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.01921, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.86562, "top5_acc": 0.99375, "loss_cls": 0.66772, "loss": 0.66772, "time": 0.49105} +{"mode": "val", "epoch": 48, "iter": 533, "lr": 0.0192, "top1_acc": 0.7816, "top5_acc": 0.98087, "mean_class_accuracy": 0.70786} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.01918, "memory": 4083, "data_time": 0.19597, "top1_acc": 0.86125, "top5_acc": 0.99812, "loss_cls": 0.63409, "loss": 0.63409, "time": 0.79995} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.01916, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8825, "top5_acc": 0.99688, "loss_cls": 0.59238, "loss": 0.59238, "time": 0.4925} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.01915, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85938, "top5_acc": 0.9925, "loss_cls": 0.69253, "loss": 0.69253, "time": 0.49066} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.01913, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86688, "top5_acc": 0.995, "loss_cls": 0.66231, "loss": 0.66231, "time": 0.49107} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.01911, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87062, "top5_acc": 0.99375, "loss_cls": 0.64, "loss": 0.64, "time": 0.49019} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.01909, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.865, "top5_acc": 0.98875, "loss_cls": 0.69766, "loss": 0.69766, "time": 0.49073} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.01908, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86625, "top5_acc": 0.99438, "loss_cls": 0.69147, "loss": 0.69147, "time": 0.35523} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.01906, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85625, "top5_acc": 0.99312, "loss_cls": 0.70628, "loss": 0.70628, "time": 0.51044} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.01904, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.86, "top5_acc": 0.99625, "loss_cls": 0.69565, "loss": 0.69565, "time": 0.24609} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.01902, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87125, "top5_acc": 0.9925, "loss_cls": 0.67228, "loss": 0.67228, "time": 0.46964} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.01901, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85438, "top5_acc": 0.99312, "loss_cls": 0.6975, "loss": 0.6975, "time": 0.48966} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.01899, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86125, "top5_acc": 0.99375, "loss_cls": 0.69165, "loss": 0.69165, "time": 0.48701} +{"mode": "val", "epoch": 49, "iter": 533, "lr": 0.01898, "top1_acc": 0.77843, "top5_acc": 0.97829, "mean_class_accuracy": 0.7014} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.01896, "memory": 4083, "data_time": 0.1901, "top1_acc": 0.87812, "top5_acc": 0.995, "loss_cls": 0.61496, "loss": 0.61496, "time": 0.78783} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.01894, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87875, "top5_acc": 0.99625, "loss_cls": 0.60078, "loss": 0.60078, "time": 0.48989} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.01892, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.875, "top5_acc": 0.99438, "loss_cls": 0.60747, "loss": 0.60747, "time": 0.49174} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.01891, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87875, "top5_acc": 0.99562, "loss_cls": 0.64886, "loss": 0.64886, "time": 0.49253} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.01889, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88938, "top5_acc": 0.9975, "loss_cls": 0.57418, "loss": 0.57418, "time": 0.48977} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.01887, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.86438, "top5_acc": 0.9925, "loss_cls": 0.68196, "loss": 0.68196, "time": 0.48625} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.01885, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87125, "top5_acc": 0.99562, "loss_cls": 0.64355, "loss": 0.64355, "time": 0.37678} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.01884, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.87938, "top5_acc": 0.9975, "loss_cls": 0.59583, "loss": 0.59583, "time": 0.51033} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.01882, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.865, "top5_acc": 0.99438, "loss_cls": 0.67705, "loss": 0.67705, "time": 0.23682} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.0188, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86938, "top5_acc": 0.99438, "loss_cls": 0.65501, "loss": 0.65501, "time": 0.44038} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.01878, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.855, "top5_acc": 0.99188, "loss_cls": 0.71542, "loss": 0.71542, "time": 0.48857} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.01876, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.85875, "top5_acc": 0.99438, "loss_cls": 0.7059, "loss": 0.7059, "time": 0.49095} +{"mode": "val", "epoch": 50, "iter": 533, "lr": 0.01875, "top1_acc": 0.79427, "top5_acc": 0.98545, "mean_class_accuracy": 0.70982} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.01873, "memory": 4083, "data_time": 0.19483, "top1_acc": 0.87, "top5_acc": 0.99375, "loss_cls": 0.6613, "loss": 0.6613, "time": 0.79495} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.01871, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.86688, "top5_acc": 0.99375, "loss_cls": 0.64359, "loss": 0.64359, "time": 0.492} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.0187, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.86375, "top5_acc": 0.99312, "loss_cls": 0.63205, "loss": 0.63205, "time": 0.49225} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.01868, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8625, "top5_acc": 0.99688, "loss_cls": 0.66243, "loss": 0.66243, "time": 0.49065} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.01866, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87, "top5_acc": 0.99688, "loss_cls": 0.6339, "loss": 0.6339, "time": 0.49094} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.01864, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.86688, "top5_acc": 0.995, "loss_cls": 0.64139, "loss": 0.64139, "time": 0.49453} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.01863, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86062, "top5_acc": 0.9925, "loss_cls": 0.68816, "loss": 0.68816, "time": 0.39296} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.01861, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86125, "top5_acc": 0.995, "loss_cls": 0.66154, "loss": 0.66154, "time": 0.51024} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.01859, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87, "top5_acc": 0.99438, "loss_cls": 0.67889, "loss": 0.67889, "time": 0.23572} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.01857, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86312, "top5_acc": 0.99375, "loss_cls": 0.68434, "loss": 0.68434, "time": 0.44306} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.01855, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.875, "top5_acc": 0.99625, "loss_cls": 0.62878, "loss": 0.62878, "time": 0.49172} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.01854, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85562, "top5_acc": 0.99375, "loss_cls": 0.68774, "loss": 0.68774, "time": 0.49013} +{"mode": "val", "epoch": 51, "iter": 533, "lr": 0.01852, "top1_acc": 0.80178, "top5_acc": 0.9838, "mean_class_accuracy": 0.73913} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.0185, "memory": 4083, "data_time": 0.19071, "top1_acc": 0.88375, "top5_acc": 0.9975, "loss_cls": 0.62145, "loss": 0.62145, "time": 0.78468} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.01849, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88562, "top5_acc": 0.995, "loss_cls": 0.61654, "loss": 0.61654, "time": 0.49199} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.01847, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.87562, "top5_acc": 0.99375, "loss_cls": 0.63175, "loss": 0.63175, "time": 0.49218} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.01845, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.88375, "top5_acc": 0.99625, "loss_cls": 0.58267, "loss": 0.58267, "time": 0.48877} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.01843, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.87375, "top5_acc": 0.99375, "loss_cls": 0.65476, "loss": 0.65476, "time": 0.48863} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.01841, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.85375, "top5_acc": 0.995, "loss_cls": 0.69888, "loss": 0.69888, "time": 0.48762} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.0184, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.86625, "top5_acc": 0.995, "loss_cls": 0.64357, "loss": 0.64357, "time": 0.41262} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.01838, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86125, "top5_acc": 0.99312, "loss_cls": 0.68682, "loss": 0.68682, "time": 0.48757} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.01836, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.86625, "top5_acc": 0.99625, "loss_cls": 0.64545, "loss": 0.64545, "time": 0.26128} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.01834, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.8725, "top5_acc": 0.99438, "loss_cls": 0.64765, "loss": 0.64765, "time": 0.43685} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.01832, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87625, "top5_acc": 0.99562, "loss_cls": 0.61619, "loss": 0.61619, "time": 0.48801} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.01831, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84438, "top5_acc": 0.99438, "loss_cls": 0.73378, "loss": 0.73378, "time": 0.49085} +{"mode": "val", "epoch": 52, "iter": 533, "lr": 0.01829, "top1_acc": 0.79193, "top5_acc": 0.98568, "mean_class_accuracy": 0.70885} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.01827, "memory": 4083, "data_time": 0.19242, "top1_acc": 0.88375, "top5_acc": 0.99688, "loss_cls": 0.58829, "loss": 0.58829, "time": 0.79969} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.01826, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87812, "top5_acc": 0.99438, "loss_cls": 0.61133, "loss": 0.61133, "time": 0.49512} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.01824, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88062, "top5_acc": 0.99625, "loss_cls": 0.59193, "loss": 0.59193, "time": 0.49411} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.01822, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89438, "top5_acc": 0.99938, "loss_cls": 0.54239, "loss": 0.54239, "time": 0.49262} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.0182, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87875, "top5_acc": 0.9925, "loss_cls": 0.61404, "loss": 0.61404, "time": 0.48826} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.01818, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8825, "top5_acc": 0.99438, "loss_cls": 0.60204, "loss": 0.60204, "time": 0.49153} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.01816, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.87438, "top5_acc": 0.99375, "loss_cls": 0.6323, "loss": 0.6323, "time": 0.40723} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.01815, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8675, "top5_acc": 0.9975, "loss_cls": 0.66053, "loss": 0.66053, "time": 0.47479} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.01813, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.88062, "top5_acc": 0.995, "loss_cls": 0.62063, "loss": 0.62063, "time": 0.26527} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.01811, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85625, "top5_acc": 0.995, "loss_cls": 0.68856, "loss": 0.68856, "time": 0.42561} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.01809, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.85625, "top5_acc": 0.9925, "loss_cls": 0.71608, "loss": 0.71608, "time": 0.49052} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.01807, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.845, "top5_acc": 0.99188, "loss_cls": 0.7687, "loss": 0.7687, "time": 0.49051} +{"mode": "val", "epoch": 53, "iter": 533, "lr": 0.01806, "top1_acc": 0.80894, "top5_acc": 0.98298, "mean_class_accuracy": 0.74397} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.01804, "memory": 4083, "data_time": 0.19324, "top1_acc": 0.86062, "top5_acc": 0.99312, "loss_cls": 0.65291, "loss": 0.65291, "time": 0.79582} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.01802, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85562, "top5_acc": 0.99562, "loss_cls": 0.68262, "loss": 0.68262, "time": 0.49113} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.018, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88312, "top5_acc": 0.99562, "loss_cls": 0.58312, "loss": 0.58312, "time": 0.49437} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.01798, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87875, "top5_acc": 0.99562, "loss_cls": 0.60488, "loss": 0.60488, "time": 0.49366} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.01797, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8625, "top5_acc": 0.99312, "loss_cls": 0.68172, "loss": 0.68172, "time": 0.48396} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.01795, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.875, "top5_acc": 0.99312, "loss_cls": 0.6636, "loss": 0.6636, "time": 0.48969} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.01793, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86312, "top5_acc": 0.99625, "loss_cls": 0.64983, "loss": 0.64983, "time": 0.42604} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.01791, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89312, "top5_acc": 0.99688, "loss_cls": 0.567, "loss": 0.567, "time": 0.45015} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.01789, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86625, "top5_acc": 0.99312, "loss_cls": 0.65469, "loss": 0.65469, "time": 0.2866} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.01787, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8725, "top5_acc": 0.9975, "loss_cls": 0.61627, "loss": 0.61627, "time": 0.41711} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.01786, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85562, "top5_acc": 0.9925, "loss_cls": 0.67338, "loss": 0.67338, "time": 0.48694} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.01784, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86812, "top5_acc": 0.99562, "loss_cls": 0.67145, "loss": 0.67145, "time": 0.48868} +{"mode": "val", "epoch": 54, "iter": 533, "lr": 0.01782, "top1_acc": 0.81305, "top5_acc": 0.98709, "mean_class_accuracy": 0.72287} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.0178, "memory": 4083, "data_time": 0.19111, "top1_acc": 0.89188, "top5_acc": 0.995, "loss_cls": 0.56484, "loss": 0.56484, "time": 0.79965} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.01779, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87625, "top5_acc": 0.99688, "loss_cls": 0.6189, "loss": 0.6189, "time": 0.48883} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.01777, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86688, "top5_acc": 0.99375, "loss_cls": 0.64689, "loss": 0.64689, "time": 0.49321} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.01775, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.87562, "top5_acc": 0.995, "loss_cls": 0.61755, "loss": 0.61755, "time": 0.49378} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.01773, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.86438, "top5_acc": 0.99312, "loss_cls": 0.66168, "loss": 0.66168, "time": 0.48965} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.01771, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.855, "top5_acc": 0.995, "loss_cls": 0.68106, "loss": 0.68106, "time": 0.48769} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.01769, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87125, "top5_acc": 0.99562, "loss_cls": 0.62454, "loss": 0.62454, "time": 0.43918} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.01767, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87938, "top5_acc": 0.99562, "loss_cls": 0.62775, "loss": 0.62775, "time": 0.41942} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.01766, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.875, "top5_acc": 0.99562, "loss_cls": 0.63181, "loss": 0.63181, "time": 0.31754} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.01764, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.86688, "top5_acc": 0.99688, "loss_cls": 0.60526, "loss": 0.60526, "time": 0.40484} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.01762, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88438, "top5_acc": 0.995, "loss_cls": 0.5782, "loss": 0.5782, "time": 0.49428} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.0176, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86562, "top5_acc": 0.99812, "loss_cls": 0.62952, "loss": 0.62952, "time": 0.49059} +{"mode": "val", "epoch": 55, "iter": 533, "lr": 0.01758, "top1_acc": 0.82185, "top5_acc": 0.98521, "mean_class_accuracy": 0.75356} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.01757, "memory": 4083, "data_time": 0.18237, "top1_acc": 0.88188, "top5_acc": 0.9975, "loss_cls": 0.59802, "loss": 0.59802, "time": 0.78042} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.01755, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88938, "top5_acc": 0.99625, "loss_cls": 0.5676, "loss": 0.5676, "time": 0.4908} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.01753, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88812, "top5_acc": 0.99562, "loss_cls": 0.58276, "loss": 0.58276, "time": 0.49131} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.01751, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.87812, "top5_acc": 0.99375, "loss_cls": 0.58996, "loss": 0.58996, "time": 0.49261} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.01749, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88312, "top5_acc": 0.99625, "loss_cls": 0.59756, "loss": 0.59756, "time": 0.49131} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.01747, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87312, "top5_acc": 0.99438, "loss_cls": 0.64688, "loss": 0.64688, "time": 0.49142} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.01745, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87938, "top5_acc": 0.99125, "loss_cls": 0.61884, "loss": 0.61884, "time": 0.4601} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.01743, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.8825, "top5_acc": 0.99688, "loss_cls": 0.59807, "loss": 0.59807, "time": 0.3595} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.01742, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.8725, "top5_acc": 0.99625, "loss_cls": 0.66641, "loss": 0.66641, "time": 0.37583} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.0174, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.85812, "top5_acc": 0.99312, "loss_cls": 0.67068, "loss": 0.67068, "time": 0.36275} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.01738, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.87062, "top5_acc": 0.99688, "loss_cls": 0.6381, "loss": 0.6381, "time": 0.4887} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.01736, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.87375, "top5_acc": 0.99562, "loss_cls": 0.61926, "loss": 0.61926, "time": 0.49161} +{"mode": "val", "epoch": 56, "iter": 533, "lr": 0.01734, "top1_acc": 0.83382, "top5_acc": 0.9885, "mean_class_accuracy": 0.76813} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.01733, "memory": 4083, "data_time": 0.19665, "top1_acc": 0.89, "top5_acc": 0.99875, "loss_cls": 0.53059, "loss": 0.53059, "time": 0.79909} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.01731, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.89375, "top5_acc": 0.99688, "loss_cls": 0.54844, "loss": 0.54844, "time": 0.49037} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.01729, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88438, "top5_acc": 0.99875, "loss_cls": 0.54082, "loss": 0.54082, "time": 0.48983} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.01727, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88, "top5_acc": 0.99625, "loss_cls": 0.59217, "loss": 0.59217, "time": 0.48944} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.01725, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88, "top5_acc": 0.99438, "loss_cls": 0.61033, "loss": 0.61033, "time": 0.49658} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.01723, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.87062, "top5_acc": 0.995, "loss_cls": 0.66564, "loss": 0.66564, "time": 0.49023} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.01721, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88188, "top5_acc": 0.99688, "loss_cls": 0.62787, "loss": 0.62787, "time": 0.48756} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.01719, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88188, "top5_acc": 0.995, "loss_cls": 0.61168, "loss": 0.61168, "time": 0.33014} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.01717, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.87812, "top5_acc": 0.99188, "loss_cls": 0.61233, "loss": 0.61233, "time": 0.40719} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.01716, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88188, "top5_acc": 0.99688, "loss_cls": 0.60563, "loss": 0.60563, "time": 0.36318} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.01714, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87438, "top5_acc": 0.99562, "loss_cls": 0.64203, "loss": 0.64203, "time": 0.48892} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.01712, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86562, "top5_acc": 0.99812, "loss_cls": 0.64545, "loss": 0.64545, "time": 0.48916} +{"mode": "val", "epoch": 57, "iter": 533, "lr": 0.0171, "top1_acc": 0.8215, "top5_acc": 0.9851, "mean_class_accuracy": 0.74731} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.01708, "memory": 4083, "data_time": 0.18719, "top1_acc": 0.89, "top5_acc": 0.99562, "loss_cls": 0.57126, "loss": 0.57126, "time": 0.79034} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.01706, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89, "top5_acc": 0.9975, "loss_cls": 0.54471, "loss": 0.54471, "time": 0.49195} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.01704, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88938, "top5_acc": 0.9975, "loss_cls": 0.55921, "loss": 0.55921, "time": 0.49855} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.01703, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8825, "top5_acc": 0.99375, "loss_cls": 0.59189, "loss": 0.59189, "time": 0.4915} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.01701, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89, "top5_acc": 0.99688, "loss_cls": 0.57057, "loss": 0.57057, "time": 0.49065} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.01699, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87, "top5_acc": 0.99688, "loss_cls": 0.61612, "loss": 0.61612, "time": 0.4922} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.01697, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86875, "top5_acc": 0.99125, "loss_cls": 0.67356, "loss": 0.67356, "time": 0.48986} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.01695, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.87875, "top5_acc": 0.99375, "loss_cls": 0.64975, "loss": 0.64975, "time": 0.31845} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.01693, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.87125, "top5_acc": 0.99688, "loss_cls": 0.64858, "loss": 0.64858, "time": 0.42611} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.01691, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.87812, "top5_acc": 0.99438, "loss_cls": 0.61436, "loss": 0.61436, "time": 0.33369} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.01689, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88, "top5_acc": 0.99438, "loss_cls": 0.62253, "loss": 0.62253, "time": 0.4893} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.01687, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.8975, "top5_acc": 0.99438, "loss_cls": 0.5581, "loss": 0.5581, "time": 0.49227} +{"mode": "val", "epoch": 58, "iter": 533, "lr": 0.01686, "top1_acc": 0.76364, "top5_acc": 0.97489, "mean_class_accuracy": 0.71733} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.01684, "memory": 4083, "data_time": 0.19984, "top1_acc": 0.89062, "top5_acc": 0.995, "loss_cls": 0.59334, "loss": 0.59334, "time": 0.81245} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.01682, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89812, "top5_acc": 0.9975, "loss_cls": 0.54337, "loss": 0.54337, "time": 0.48804} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.0168, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.87438, "top5_acc": 0.995, "loss_cls": 0.58661, "loss": 0.58661, "time": 0.49126} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.01678, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.865, "top5_acc": 0.9975, "loss_cls": 0.62826, "loss": 0.62826, "time": 0.49298} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.01676, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89375, "top5_acc": 0.99938, "loss_cls": 0.5577, "loss": 0.5577, "time": 0.49099} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.01674, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.8875, "top5_acc": 0.995, "loss_cls": 0.56762, "loss": 0.56762, "time": 0.49172} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.01672, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88312, "top5_acc": 0.99688, "loss_cls": 0.58109, "loss": 0.58109, "time": 0.48732} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.0167, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89, "top5_acc": 0.9975, "loss_cls": 0.54464, "loss": 0.54464, "time": 0.30251} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.01668, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.87188, "top5_acc": 0.99438, "loss_cls": 0.60918, "loss": 0.60918, "time": 0.44828} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.01667, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88, "top5_acc": 0.99688, "loss_cls": 0.59601, "loss": 0.59601, "time": 0.3425} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.01665, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88188, "top5_acc": 0.99562, "loss_cls": 0.59372, "loss": 0.59372, "time": 0.49009} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.01663, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.855, "top5_acc": 0.99562, "loss_cls": 0.67014, "loss": 0.67014, "time": 0.49406} +{"mode": "val", "epoch": 59, "iter": 533, "lr": 0.01661, "top1_acc": 0.82572, "top5_acc": 0.98357, "mean_class_accuracy": 0.74721} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.01659, "memory": 4083, "data_time": 0.19296, "top1_acc": 0.87812, "top5_acc": 0.99562, "loss_cls": 0.62605, "loss": 0.62605, "time": 0.78905} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.01657, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89375, "top5_acc": 0.99812, "loss_cls": 0.54904, "loss": 0.54904, "time": 0.48855} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.01655, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88812, "top5_acc": 0.995, "loss_cls": 0.54949, "loss": 0.54949, "time": 0.48892} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.01653, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89188, "top5_acc": 0.9975, "loss_cls": 0.55435, "loss": 0.55435, "time": 0.4912} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.01651, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88688, "top5_acc": 0.99312, "loss_cls": 0.59142, "loss": 0.59142, "time": 0.4891} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.0165, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.86812, "top5_acc": 0.99375, "loss_cls": 0.64112, "loss": 0.64112, "time": 0.48803} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.01648, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88375, "top5_acc": 0.99625, "loss_cls": 0.59883, "loss": 0.59883, "time": 0.49064} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.01646, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88312, "top5_acc": 0.99688, "loss_cls": 0.60776, "loss": 0.60776, "time": 0.29979} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.01644, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.87688, "top5_acc": 0.99438, "loss_cls": 0.61469, "loss": 0.61469, "time": 0.46169} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.01642, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89125, "top5_acc": 0.99875, "loss_cls": 0.55563, "loss": 0.55563, "time": 0.33688} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.0164, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88188, "top5_acc": 0.995, "loss_cls": 0.61502, "loss": 0.61502, "time": 0.49088} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.01638, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.8875, "top5_acc": 0.99562, "loss_cls": 0.57002, "loss": 0.57002, "time": 0.49123} +{"mode": "val", "epoch": 60, "iter": 533, "lr": 0.01636, "top1_acc": 0.80765, "top5_acc": 0.98779, "mean_class_accuracy": 0.73744} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.01634, "memory": 4083, "data_time": 0.19748, "top1_acc": 0.89812, "top5_acc": 0.99625, "loss_cls": 0.54084, "loss": 0.54084, "time": 0.78761} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.01632, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89312, "top5_acc": 0.9975, "loss_cls": 0.51973, "loss": 0.51973, "time": 0.49542} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.0163, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89312, "top5_acc": 0.99812, "loss_cls": 0.50805, "loss": 0.50805, "time": 0.48673} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.01629, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88312, "top5_acc": 0.99688, "loss_cls": 0.58703, "loss": 0.58703, "time": 0.48842} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.01627, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87875, "top5_acc": 0.99625, "loss_cls": 0.59663, "loss": 0.59663, "time": 0.48647} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.01625, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89438, "top5_acc": 0.99375, "loss_cls": 0.56331, "loss": 0.56331, "time": 0.48739} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.01623, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89312, "top5_acc": 0.99562, "loss_cls": 0.56514, "loss": 0.56514, "time": 0.49444} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.01621, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.87438, "top5_acc": 0.995, "loss_cls": 0.61176, "loss": 0.61176, "time": 0.3042} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.01619, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.88062, "top5_acc": 0.99688, "loss_cls": 0.60107, "loss": 0.60107, "time": 0.45547} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.01617, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.85062, "top5_acc": 0.99312, "loss_cls": 0.691, "loss": 0.691, "time": 0.32725} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.01615, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90125, "top5_acc": 0.99812, "loss_cls": 0.53707, "loss": 0.53707, "time": 0.49019} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.01613, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89562, "top5_acc": 0.9975, "loss_cls": 0.54204, "loss": 0.54204, "time": 0.49032} +{"mode": "val", "epoch": 61, "iter": 533, "lr": 0.01611, "top1_acc": 0.81927, "top5_acc": 0.9865, "mean_class_accuracy": 0.74743} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.01609, "memory": 4083, "data_time": 0.19003, "top1_acc": 0.88688, "top5_acc": 0.99625, "loss_cls": 0.57208, "loss": 0.57208, "time": 0.79273} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.01607, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.92438, "top5_acc": 0.99938, "loss_cls": 0.42148, "loss": 0.42148, "time": 0.48909} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.01605, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.89812, "top5_acc": 0.9975, "loss_cls": 0.54765, "loss": 0.54765, "time": 0.4929} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.01603, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89188, "top5_acc": 0.99688, "loss_cls": 0.56001, "loss": 0.56001, "time": 0.49472} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.01602, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89188, "top5_acc": 0.9975, "loss_cls": 0.56229, "loss": 0.56229, "time": 0.49015} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.016, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87625, "top5_acc": 0.995, "loss_cls": 0.61688, "loss": 0.61688, "time": 0.49099} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.01598, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.88875, "top5_acc": 0.99125, "loss_cls": 0.57957, "loss": 0.57957, "time": 0.48969} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.01596, "memory": 4083, "data_time": 0.00071, "top1_acc": 0.8975, "top5_acc": 0.99562, "loss_cls": 0.54565, "loss": 0.54565, "time": 0.2864} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.01594, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.87938, "top5_acc": 0.9975, "loss_cls": 0.60663, "loss": 0.60663, "time": 0.4832} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.01592, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.87438, "top5_acc": 0.99562, "loss_cls": 0.62851, "loss": 0.62851, "time": 0.30787} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.0159, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8675, "top5_acc": 0.995, "loss_cls": 0.63557, "loss": 0.63557, "time": 0.49176} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.01588, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87938, "top5_acc": 0.99562, "loss_cls": 0.5934, "loss": 0.5934, "time": 0.49271} +{"mode": "val", "epoch": 62, "iter": 533, "lr": 0.01586, "top1_acc": 0.84333, "top5_acc": 0.99061, "mean_class_accuracy": 0.78014} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.01584, "memory": 4083, "data_time": 0.19592, "top1_acc": 0.9125, "top5_acc": 0.99875, "loss_cls": 0.51097, "loss": 0.51097, "time": 0.80323} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.01582, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90812, "top5_acc": 0.99438, "loss_cls": 0.49262, "loss": 0.49262, "time": 0.49367} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.0158, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90562, "top5_acc": 0.99875, "loss_cls": 0.46244, "loss": 0.46244, "time": 0.49226} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.01578, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.88562, "top5_acc": 0.99812, "loss_cls": 0.54771, "loss": 0.54771, "time": 0.49002} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.01576, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9125, "top5_acc": 0.99562, "loss_cls": 0.49517, "loss": 0.49517, "time": 0.49021} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.01574, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.87938, "top5_acc": 0.99688, "loss_cls": 0.58632, "loss": 0.58632, "time": 0.49048} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.01572, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.8875, "top5_acc": 0.99562, "loss_cls": 0.57782, "loss": 0.57782, "time": 0.48942} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.0157, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87562, "top5_acc": 0.99125, "loss_cls": 0.63241, "loss": 0.63241, "time": 0.29729} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.01568, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88812, "top5_acc": 0.99688, "loss_cls": 0.57142, "loss": 0.57142, "time": 0.48343} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.01566, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87312, "top5_acc": 0.9925, "loss_cls": 0.60533, "loss": 0.60533, "time": 0.32025} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.01564, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8975, "top5_acc": 0.99812, "loss_cls": 0.52916, "loss": 0.52916, "time": 0.49259} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.01562, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89188, "top5_acc": 0.99625, "loss_cls": 0.5583, "loss": 0.5583, "time": 0.49199} +{"mode": "val", "epoch": 63, "iter": 533, "lr": 0.01561, "top1_acc": 0.80472, "top5_acc": 0.98744, "mean_class_accuracy": 0.74203} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.01559, "memory": 4083, "data_time": 0.1869, "top1_acc": 0.88875, "top5_acc": 0.99812, "loss_cls": 0.56759, "loss": 0.56759, "time": 0.80213} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.01557, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89625, "top5_acc": 0.99562, "loss_cls": 0.5527, "loss": 0.5527, "time": 0.4932} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.01555, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91125, "top5_acc": 1.0, "loss_cls": 0.46235, "loss": 0.46235, "time": 0.492} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.01553, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89188, "top5_acc": 0.9975, "loss_cls": 0.53959, "loss": 0.53959, "time": 0.49006} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.01551, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.8925, "top5_acc": 0.99688, "loss_cls": 0.55305, "loss": 0.55305, "time": 0.49036} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.01549, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.86938, "top5_acc": 0.99812, "loss_cls": 0.5936, "loss": 0.5936, "time": 0.49114} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.01547, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88062, "top5_acc": 0.99625, "loss_cls": 0.59722, "loss": 0.59722, "time": 0.49409} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.01545, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89188, "top5_acc": 0.99625, "loss_cls": 0.54588, "loss": 0.54588, "time": 0.30318} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.01543, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89, "top5_acc": 0.99375, "loss_cls": 0.5712, "loss": 0.5712, "time": 0.45763} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.01541, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.885, "top5_acc": 0.9975, "loss_cls": 0.57526, "loss": 0.57526, "time": 0.328} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.01539, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90562, "top5_acc": 0.99625, "loss_cls": 0.51564, "loss": 0.51564, "time": 0.48951} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.01537, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89, "top5_acc": 0.99625, "loss_cls": 0.53803, "loss": 0.53803, "time": 0.49267} +{"mode": "val", "epoch": 64, "iter": 533, "lr": 0.01535, "top1_acc": 0.83406, "top5_acc": 0.98826, "mean_class_accuracy": 0.77721} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.01533, "memory": 4083, "data_time": 0.19206, "top1_acc": 0.89875, "top5_acc": 0.99562, "loss_cls": 0.51959, "loss": 0.51959, "time": 0.80004} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.01531, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.90375, "top5_acc": 0.995, "loss_cls": 0.53521, "loss": 0.53521, "time": 0.49545} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.01529, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88875, "top5_acc": 0.99688, "loss_cls": 0.54819, "loss": 0.54819, "time": 0.49111} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.01527, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9025, "top5_acc": 0.99625, "loss_cls": 0.51495, "loss": 0.51495, "time": 0.49277} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.01526, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89312, "top5_acc": 0.99375, "loss_cls": 0.5685, "loss": 0.5685, "time": 0.49247} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.01524, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89, "top5_acc": 0.99625, "loss_cls": 0.57163, "loss": 0.57163, "time": 0.48888} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.01522, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89438, "top5_acc": 0.9975, "loss_cls": 0.55345, "loss": 0.55345, "time": 0.49117} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0152, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.88938, "top5_acc": 0.99688, "loss_cls": 0.5755, "loss": 0.5755, "time": 0.30497} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.01518, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.89812, "top5_acc": 0.99688, "loss_cls": 0.51471, "loss": 0.51471, "time": 0.4533} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.01516, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.88188, "top5_acc": 0.99438, "loss_cls": 0.58843, "loss": 0.58843, "time": 0.33681} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.01514, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.88562, "top5_acc": 0.99625, "loss_cls": 0.57746, "loss": 0.57746, "time": 0.49221} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.01512, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8925, "top5_acc": 0.99562, "loss_cls": 0.56706, "loss": 0.56706, "time": 0.49526} +{"mode": "val", "epoch": 65, "iter": 533, "lr": 0.0151, "top1_acc": 0.85072, "top5_acc": 0.99143, "mean_class_accuracy": 0.79505} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.01508, "memory": 4083, "data_time": 0.1915, "top1_acc": 0.90562, "top5_acc": 0.99938, "loss_cls": 0.46978, "loss": 0.46978, "time": 0.78892} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.01506, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8875, "top5_acc": 0.995, "loss_cls": 0.55251, "loss": 0.55251, "time": 0.48938} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.01504, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9, "top5_acc": 0.99875, "loss_cls": 0.5012, "loss": 0.5012, "time": 0.49326} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.01502, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.89312, "top5_acc": 0.99812, "loss_cls": 0.52449, "loss": 0.52449, "time": 0.49587} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.015, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.88062, "top5_acc": 0.99625, "loss_cls": 0.557, "loss": 0.557, "time": 0.48474} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.01498, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90062, "top5_acc": 0.99438, "loss_cls": 0.53693, "loss": 0.53693, "time": 0.49094} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.01496, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90188, "top5_acc": 0.995, "loss_cls": 0.50944, "loss": 0.50944, "time": 0.49647} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.01494, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.90562, "top5_acc": 0.99562, "loss_cls": 0.50531, "loss": 0.50531, "time": 0.28709} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.01492, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.88312, "top5_acc": 0.9925, "loss_cls": 0.61142, "loss": 0.61142, "time": 0.47143} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.0149, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8975, "top5_acc": 0.99438, "loss_cls": 0.55957, "loss": 0.55957, "time": 0.32827} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.01488, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88812, "top5_acc": 0.99688, "loss_cls": 0.58669, "loss": 0.58669, "time": 0.49171} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.01486, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89812, "top5_acc": 0.99562, "loss_cls": 0.54467, "loss": 0.54467, "time": 0.49078} +{"mode": "val", "epoch": 66, "iter": 533, "lr": 0.01484, "top1_acc": 0.85835, "top5_acc": 0.99155, "mean_class_accuracy": 0.79287} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.01482, "memory": 4083, "data_time": 0.19097, "top1_acc": 0.90562, "top5_acc": 0.99875, "loss_cls": 0.48476, "loss": 0.48476, "time": 0.78191} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.0148, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90875, "top5_acc": 0.9975, "loss_cls": 0.47939, "loss": 0.47939, "time": 0.4924} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.01478, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8925, "top5_acc": 0.99812, "loss_cls": 0.52982, "loss": 0.52982, "time": 0.48833} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.01476, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89, "top5_acc": 0.99688, "loss_cls": 0.54548, "loss": 0.54548, "time": 0.48742} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.01474, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88875, "top5_acc": 0.99688, "loss_cls": 0.5599, "loss": 0.5599, "time": 0.49164} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.01472, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.88312, "top5_acc": 0.995, "loss_cls": 0.58116, "loss": 0.58116, "time": 0.48857} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.0147, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91688, "top5_acc": 0.99875, "loss_cls": 0.44666, "loss": 0.44666, "time": 0.49408} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.01468, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89625, "top5_acc": 0.99688, "loss_cls": 0.52882, "loss": 0.52882, "time": 0.26822} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.01466, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88875, "top5_acc": 0.99625, "loss_cls": 0.55682, "loss": 0.55682, "time": 0.50993} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.01464, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89188, "top5_acc": 0.99812, "loss_cls": 0.54507, "loss": 0.54507, "time": 0.29488} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.01462, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.90438, "top5_acc": 0.99625, "loss_cls": 0.48014, "loss": 0.48014, "time": 0.4845} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.0146, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88625, "top5_acc": 0.99562, "loss_cls": 0.58222, "loss": 0.58222, "time": 0.49028} +{"mode": "val", "epoch": 67, "iter": 533, "lr": 0.01458, "top1_acc": 0.8411, "top5_acc": 0.98803, "mean_class_accuracy": 0.77039} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.01456, "memory": 4083, "data_time": 0.19234, "top1_acc": 0.905, "top5_acc": 0.9975, "loss_cls": 0.47539, "loss": 0.47539, "time": 0.79282} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.01454, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91812, "top5_acc": 0.99812, "loss_cls": 0.43631, "loss": 0.43631, "time": 0.48937} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.01452, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90938, "top5_acc": 0.99562, "loss_cls": 0.50248, "loss": 0.50248, "time": 0.49122} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.0145, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90438, "top5_acc": 0.9975, "loss_cls": 0.48835, "loss": 0.48835, "time": 0.49714} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.01448, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.88875, "top5_acc": 0.99562, "loss_cls": 0.58084, "loss": 0.58084, "time": 0.49306} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.01446, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9225, "top5_acc": 0.995, "loss_cls": 0.43637, "loss": 0.43637, "time": 0.49086} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.01444, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88938, "top5_acc": 0.99562, "loss_cls": 0.54477, "loss": 0.54477, "time": 0.49031} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.01442, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90125, "top5_acc": 0.99875, "loss_cls": 0.5242, "loss": 0.5242, "time": 0.29177} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.0144, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.895, "top5_acc": 0.9975, "loss_cls": 0.54245, "loss": 0.54245, "time": 0.51046} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.01438, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.89, "top5_acc": 0.99688, "loss_cls": 0.54671, "loss": 0.54671, "time": 0.28581} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.01436, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89562, "top5_acc": 0.99625, "loss_cls": 0.55935, "loss": 0.55935, "time": 0.48705} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.01434, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86688, "top5_acc": 0.99625, "loss_cls": 0.63252, "loss": 0.63252, "time": 0.49} +{"mode": "val", "epoch": 68, "iter": 533, "lr": 0.01433, "top1_acc": 0.84591, "top5_acc": 0.9885, "mean_class_accuracy": 0.78609} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.01431, "memory": 4083, "data_time": 0.19069, "top1_acc": 0.93, "top5_acc": 0.99688, "loss_cls": 0.40978, "loss": 0.40978, "time": 0.80651} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.01429, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91125, "top5_acc": 0.99812, "loss_cls": 0.46028, "loss": 0.46028, "time": 0.49093} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.01427, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.905, "top5_acc": 0.99688, "loss_cls": 0.51739, "loss": 0.51739, "time": 0.49656} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.01425, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88562, "top5_acc": 0.99562, "loss_cls": 0.57545, "loss": 0.57545, "time": 0.4867} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.01423, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.89438, "top5_acc": 0.9975, "loss_cls": 0.52384, "loss": 0.52384, "time": 0.48431} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.0142, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90125, "top5_acc": 0.99625, "loss_cls": 0.52664, "loss": 0.52664, "time": 0.48797} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.01418, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.90812, "top5_acc": 0.9975, "loss_cls": 0.48666, "loss": 0.48666, "time": 0.49072} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.01416, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90562, "top5_acc": 0.99875, "loss_cls": 0.48936, "loss": 0.48936, "time": 0.28679} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.01414, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.88125, "top5_acc": 0.9925, "loss_cls": 0.58217, "loss": 0.58217, "time": 0.51323} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.01412, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89625, "top5_acc": 0.99688, "loss_cls": 0.52753, "loss": 0.52753, "time": 0.29811} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.0141, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89, "top5_acc": 0.99812, "loss_cls": 0.56376, "loss": 0.56376, "time": 0.4913} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.01408, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89562, "top5_acc": 0.99625, "loss_cls": 0.52437, "loss": 0.52437, "time": 0.49298} +{"mode": "val", "epoch": 69, "iter": 533, "lr": 0.01407, "top1_acc": 0.82948, "top5_acc": 0.98862, "mean_class_accuracy": 0.77158} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.01405, "memory": 4083, "data_time": 0.19415, "top1_acc": 0.9175, "top5_acc": 0.9975, "loss_cls": 0.45893, "loss": 0.45893, "time": 0.79831} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.01403, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.44042, "loss": 0.44042, "time": 0.48733} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.01401, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89188, "top5_acc": 0.9975, "loss_cls": 0.53618, "loss": 0.53618, "time": 0.49572} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.01399, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.915, "top5_acc": 0.99938, "loss_cls": 0.45446, "loss": 0.45446, "time": 0.49464} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.01397, "memory": 4083, "data_time": 0.00078, "top1_acc": 0.89125, "top5_acc": 0.99812, "loss_cls": 0.50095, "loss": 0.50095, "time": 0.48707} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.01395, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.89438, "top5_acc": 0.995, "loss_cls": 0.54939, "loss": 0.54939, "time": 0.48918} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.01392, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90188, "top5_acc": 0.99125, "loss_cls": 0.52902, "loss": 0.52902, "time": 0.49016} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.0139, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88938, "top5_acc": 0.99562, "loss_cls": 0.54371, "loss": 0.54371, "time": 0.28974} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.01388, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88625, "top5_acc": 0.9975, "loss_cls": 0.54381, "loss": 0.54381, "time": 0.51241} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.01386, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.885, "top5_acc": 0.9975, "loss_cls": 0.5555, "loss": 0.5555, "time": 0.30022} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.01384, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89938, "top5_acc": 0.9975, "loss_cls": 0.50357, "loss": 0.50357, "time": 0.49394} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.01382, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9, "top5_acc": 0.99688, "loss_cls": 0.5093, "loss": 0.5093, "time": 0.49167} +{"mode": "val", "epoch": 70, "iter": 533, "lr": 0.01381, "top1_acc": 0.84767, "top5_acc": 0.99096, "mean_class_accuracy": 0.79247} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.01379, "memory": 4083, "data_time": 0.19336, "top1_acc": 0.915, "top5_acc": 0.99938, "loss_cls": 0.45779, "loss": 0.45779, "time": 0.7891} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.01377, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91, "top5_acc": 0.9975, "loss_cls": 0.44755, "loss": 0.44755, "time": 0.49251} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.01375, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9125, "top5_acc": 0.99938, "loss_cls": 0.4695, "loss": 0.4695, "time": 0.492} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.01373, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.89875, "top5_acc": 0.99688, "loss_cls": 0.52467, "loss": 0.52467, "time": 0.48864} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.01371, "memory": 4083, "data_time": 0.00075, "top1_acc": 0.89812, "top5_acc": 0.99625, "loss_cls": 0.52047, "loss": 0.52047, "time": 0.49036} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.01368, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.91625, "top5_acc": 0.99875, "loss_cls": 0.43872, "loss": 0.43872, "time": 0.49238} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.01366, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9, "top5_acc": 0.99625, "loss_cls": 0.51656, "loss": 0.51656, "time": 0.49163} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.01364, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89562, "top5_acc": 0.99625, "loss_cls": 0.53334, "loss": 0.53334, "time": 0.28057} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.01362, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90625, "top5_acc": 0.99875, "loss_cls": 0.51129, "loss": 0.51129, "time": 0.51024} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.0136, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.91312, "top5_acc": 0.99812, "loss_cls": 0.46366, "loss": 0.46366, "time": 0.30414} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.01358, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89562, "top5_acc": 0.99812, "loss_cls": 0.52972, "loss": 0.52972, "time": 0.49215} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.01356, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.90062, "top5_acc": 0.995, "loss_cls": 0.51467, "loss": 0.51467, "time": 0.48878} +{"mode": "val", "epoch": 71, "iter": 533, "lr": 0.01355, "top1_acc": 0.83828, "top5_acc": 0.99214, "mean_class_accuracy": 0.78165} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.01353, "memory": 4083, "data_time": 0.18951, "top1_acc": 0.92125, "top5_acc": 0.99875, "loss_cls": 0.44558, "loss": 0.44558, "time": 0.77846} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.01351, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91438, "top5_acc": 0.99938, "loss_cls": 0.44581, "loss": 0.44581, "time": 0.49147} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.01349, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90438, "top5_acc": 0.99875, "loss_cls": 0.46539, "loss": 0.46539, "time": 0.49058} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.01346, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92, "top5_acc": 0.99812, "loss_cls": 0.45549, "loss": 0.45549, "time": 0.49092} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.01344, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.91125, "top5_acc": 0.99812, "loss_cls": 0.46549, "loss": 0.46549, "time": 0.48982} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.01342, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9025, "top5_acc": 0.99688, "loss_cls": 0.48256, "loss": 0.48256, "time": 0.48846} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.0134, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.90062, "top5_acc": 0.99938, "loss_cls": 0.48375, "loss": 0.48375, "time": 0.49323} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.01338, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8925, "top5_acc": 0.9975, "loss_cls": 0.55273, "loss": 0.55273, "time": 0.2866} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.01336, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90062, "top5_acc": 0.99812, "loss_cls": 0.4946, "loss": 0.4946, "time": 0.51241} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.01334, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90312, "top5_acc": 0.99688, "loss_cls": 0.50993, "loss": 0.50993, "time": 0.28422} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.01332, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89, "top5_acc": 0.99688, "loss_cls": 0.53622, "loss": 0.53622, "time": 0.49107} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.0133, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89438, "top5_acc": 0.99625, "loss_cls": 0.5037, "loss": 0.5037, "time": 0.49219} +{"mode": "val", "epoch": 72, "iter": 533, "lr": 0.01329, "top1_acc": 0.83558, "top5_acc": 0.98873, "mean_class_accuracy": 0.76344} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.01326, "memory": 4083, "data_time": 0.19497, "top1_acc": 0.90875, "top5_acc": 0.99875, "loss_cls": 0.45653, "loss": 0.45653, "time": 0.79987} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.01324, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9125, "top5_acc": 1.0, "loss_cls": 0.45212, "loss": 0.45212, "time": 0.48974} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.01322, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.91125, "top5_acc": 0.99938, "loss_cls": 0.46449, "loss": 0.46449, "time": 0.4929} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.0132, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90312, "top5_acc": 0.9975, "loss_cls": 0.48479, "loss": 0.48479, "time": 0.48716} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.01318, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91688, "top5_acc": 0.99875, "loss_cls": 0.41731, "loss": 0.41731, "time": 0.49024} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.01316, "memory": 4083, "data_time": 0.0007, "top1_acc": 0.89938, "top5_acc": 0.99938, "loss_cls": 0.51119, "loss": 0.51119, "time": 0.48825} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.01314, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90312, "top5_acc": 0.99812, "loss_cls": 0.52928, "loss": 0.52928, "time": 0.48988} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.01312, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.91375, "top5_acc": 0.99875, "loss_cls": 0.46127, "loss": 0.46127, "time": 0.29454} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.0131, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89375, "top5_acc": 0.9975, "loss_cls": 0.4872, "loss": 0.4872, "time": 0.51058} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.01308, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90438, "top5_acc": 0.99938, "loss_cls": 0.50615, "loss": 0.50615, "time": 0.28493} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.01306, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89875, "top5_acc": 0.99938, "loss_cls": 0.5002, "loss": 0.5002, "time": 0.48961} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.01304, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89812, "top5_acc": 0.99812, "loss_cls": 0.49283, "loss": 0.49283, "time": 0.49055} +{"mode": "val", "epoch": 73, "iter": 533, "lr": 0.01302, "top1_acc": 0.85084, "top5_acc": 0.99085, "mean_class_accuracy": 0.7926} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.013, "memory": 4083, "data_time": 0.19208, "top1_acc": 0.92875, "top5_acc": 0.99938, "loss_cls": 0.40476, "loss": 0.40476, "time": 0.81023} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.01298, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92312, "top5_acc": 0.99625, "loss_cls": 0.43689, "loss": 0.43689, "time": 0.48883} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.01296, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92, "top5_acc": 0.99938, "loss_cls": 0.40711, "loss": 0.40711, "time": 0.49091} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.01294, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9125, "top5_acc": 0.99688, "loss_cls": 0.4649, "loss": 0.4649, "time": 0.48833} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.01292, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9125, "top5_acc": 0.99938, "loss_cls": 0.45736, "loss": 0.45736, "time": 0.49223} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.0129, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.895, "top5_acc": 0.995, "loss_cls": 0.51267, "loss": 0.51267, "time": 0.49252} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.01288, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9225, "top5_acc": 0.99938, "loss_cls": 0.42576, "loss": 0.42576, "time": 0.49251} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.01286, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9025, "top5_acc": 1.0, "loss_cls": 0.46581, "loss": 0.46581, "time": 0.28688} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.01284, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9025, "top5_acc": 0.9975, "loss_cls": 0.47659, "loss": 0.47659, "time": 0.51116} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.01282, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91312, "top5_acc": 0.99625, "loss_cls": 0.44692, "loss": 0.44692, "time": 0.28544} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.0128, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88, "top5_acc": 0.99688, "loss_cls": 0.53678, "loss": 0.53678, "time": 0.4926} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.01278, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91688, "top5_acc": 0.99875, "loss_cls": 0.44104, "loss": 0.44104, "time": 0.48999} +{"mode": "val", "epoch": 74, "iter": 533, "lr": 0.01276, "top1_acc": 0.85225, "top5_acc": 0.9912, "mean_class_accuracy": 0.78405} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.01274, "memory": 4083, "data_time": 0.19243, "top1_acc": 0.93375, "top5_acc": 0.99938, "loss_cls": 0.39998, "loss": 0.39998, "time": 0.80255} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.01272, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.43939, "loss": 0.43939, "time": 0.49047} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.0127, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.91375, "top5_acc": 0.9975, "loss_cls": 0.45446, "loss": 0.45446, "time": 0.49501} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.01268, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91812, "top5_acc": 0.99688, "loss_cls": 0.41531, "loss": 0.41531, "time": 0.49486} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.01266, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91438, "top5_acc": 0.9975, "loss_cls": 0.45614, "loss": 0.45614, "time": 0.48999} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.01264, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88812, "top5_acc": 0.99625, "loss_cls": 0.55457, "loss": 0.55457, "time": 0.49166} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.01262, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89625, "top5_acc": 0.99875, "loss_cls": 0.50705, "loss": 0.50705, "time": 0.49365} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.0126, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.90875, "top5_acc": 0.99688, "loss_cls": 0.45354, "loss": 0.45354, "time": 0.29254} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.01258, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.46167, "loss": 0.46167, "time": 0.51085} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.01256, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9, "top5_acc": 0.9975, "loss_cls": 0.48192, "loss": 0.48192, "time": 0.2814} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.01254, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90875, "top5_acc": 0.9975, "loss_cls": 0.47406, "loss": 0.47406, "time": 0.49107} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.01252, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90188, "top5_acc": 0.99625, "loss_cls": 0.48349, "loss": 0.48349, "time": 0.49401} +{"mode": "val", "epoch": 75, "iter": 533, "lr": 0.0125, "top1_acc": 0.84051, "top5_acc": 0.9885, "mean_class_accuracy": 0.79496} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.01248, "memory": 4083, "data_time": 0.18811, "top1_acc": 0.91375, "top5_acc": 0.99938, "loss_cls": 0.46243, "loss": 0.46243, "time": 0.78966} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.01246, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90938, "top5_acc": 0.99688, "loss_cls": 0.46843, "loss": 0.46843, "time": 0.49386} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.01244, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92062, "top5_acc": 0.99688, "loss_cls": 0.43693, "loss": 0.43693, "time": 0.49705} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.01242, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92125, "top5_acc": 0.9975, "loss_cls": 0.43376, "loss": 0.43376, "time": 0.49071} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.0124, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.895, "top5_acc": 0.99688, "loss_cls": 0.49725, "loss": 0.49725, "time": 0.49136} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.01238, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.90438, "top5_acc": 0.9975, "loss_cls": 0.49218, "loss": 0.49218, "time": 0.49065} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.01236, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.90125, "top5_acc": 0.9975, "loss_cls": 0.49379, "loss": 0.49379, "time": 0.49336} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.01234, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.9225, "top5_acc": 0.99875, "loss_cls": 0.42848, "loss": 0.42848, "time": 0.29894} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.01232, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91, "top5_acc": 1.0, "loss_cls": 0.46714, "loss": 0.46714, "time": 0.51222} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.0123, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91812, "top5_acc": 0.9975, "loss_cls": 0.45406, "loss": 0.45406, "time": 0.29315} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.01228, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.92312, "top5_acc": 0.99688, "loss_cls": 0.44972, "loss": 0.44972, "time": 0.49563} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.01225, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90938, "top5_acc": 0.99875, "loss_cls": 0.48938, "loss": 0.48938, "time": 0.49018} +{"mode": "val", "epoch": 76, "iter": 533, "lr": 0.01224, "top1_acc": 0.85178, "top5_acc": 0.99038, "mean_class_accuracy": 0.80103} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.01222, "memory": 4083, "data_time": 0.19107, "top1_acc": 0.92312, "top5_acc": 0.99938, "loss_cls": 0.4155, "loss": 0.4155, "time": 0.79402} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0122, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.36428, "loss": 0.36428, "time": 0.49107} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.01218, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90875, "top5_acc": 0.99812, "loss_cls": 0.44887, "loss": 0.44887, "time": 0.49225} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.01216, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.90625, "top5_acc": 0.99812, "loss_cls": 0.49273, "loss": 0.49273, "time": 0.48689} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.01214, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9275, "top5_acc": 0.99938, "loss_cls": 0.40716, "loss": 0.40716, "time": 0.49035} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.01212, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91062, "top5_acc": 0.99812, "loss_cls": 0.44255, "loss": 0.44255, "time": 0.49026} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.0121, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.92438, "top5_acc": 0.99875, "loss_cls": 0.39702, "loss": 0.39702, "time": 0.48826} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.01207, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.91625, "top5_acc": 0.99875, "loss_cls": 0.44951, "loss": 0.44951, "time": 0.29304} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.01205, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91875, "top5_acc": 0.99875, "loss_cls": 0.47334, "loss": 0.47334, "time": 0.51094} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.01203, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9175, "top5_acc": 0.99938, "loss_cls": 0.44185, "loss": 0.44185, "time": 0.2762} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.01201, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92875, "top5_acc": 0.99812, "loss_cls": 0.40695, "loss": 0.40695, "time": 0.49237} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.01199, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91312, "top5_acc": 0.99625, "loss_cls": 0.4642, "loss": 0.4642, "time": 0.49209} +{"mode": "val", "epoch": 77, "iter": 533, "lr": 0.01198, "top1_acc": 0.84967, "top5_acc": 0.98779, "mean_class_accuracy": 0.81843} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.01196, "memory": 4083, "data_time": 0.18973, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.41946, "loss": 0.41946, "time": 0.79035} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.01194, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92688, "top5_acc": 0.99812, "loss_cls": 0.40379, "loss": 0.40379, "time": 0.49099} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.01192, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.93, "top5_acc": 0.99938, "loss_cls": 0.38386, "loss": 0.38386, "time": 0.49225} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.0119, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91688, "top5_acc": 0.99812, "loss_cls": 0.4345, "loss": 0.4345, "time": 0.4896} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.01187, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9225, "top5_acc": 0.99688, "loss_cls": 0.43134, "loss": 0.43134, "time": 0.49108} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.01185, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91625, "top5_acc": 0.99688, "loss_cls": 0.42418, "loss": 0.42418, "time": 0.49128} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.01183, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.4315, "loss": 0.4315, "time": 0.49079} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.01181, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.90625, "top5_acc": 1.0, "loss_cls": 0.45494, "loss": 0.45494, "time": 0.3042} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.01179, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92062, "top5_acc": 0.9975, "loss_cls": 0.44787, "loss": 0.44787, "time": 0.51239} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.01177, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90312, "top5_acc": 0.9975, "loss_cls": 0.46448, "loss": 0.46448, "time": 0.27754} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.01175, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91625, "top5_acc": 0.99875, "loss_cls": 0.44187, "loss": 0.44187, "time": 0.48886} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.01173, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92875, "top5_acc": 0.9975, "loss_cls": 0.42295, "loss": 0.42295, "time": 0.49321} +{"mode": "val", "epoch": 78, "iter": 533, "lr": 0.01172, "top1_acc": 0.85225, "top5_acc": 0.98873, "mean_class_accuracy": 0.80381} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.01169, "memory": 4083, "data_time": 0.18848, "top1_acc": 0.925, "top5_acc": 0.9975, "loss_cls": 0.42432, "loss": 0.42432, "time": 0.79694} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.01167, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.935, "top5_acc": 0.99938, "loss_cls": 0.36732, "loss": 0.36732, "time": 0.4946} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.01165, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92625, "top5_acc": 1.0, "loss_cls": 0.38285, "loss": 0.38285, "time": 0.49227} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.01163, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91438, "top5_acc": 0.99812, "loss_cls": 0.44214, "loss": 0.44214, "time": 0.49119} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.01161, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.915, "top5_acc": 0.99688, "loss_cls": 0.45845, "loss": 0.45845, "time": 0.49323} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.01159, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91688, "top5_acc": 0.99688, "loss_cls": 0.45541, "loss": 0.45541, "time": 0.49049} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.01157, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91875, "top5_acc": 0.99875, "loss_cls": 0.42256, "loss": 0.42256, "time": 0.49019} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.01155, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.9225, "top5_acc": 0.99688, "loss_cls": 0.43941, "loss": 0.43941, "time": 0.29662} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.01153, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92562, "top5_acc": 0.99875, "loss_cls": 0.41021, "loss": 0.41021, "time": 0.50914} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.01151, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92562, "top5_acc": 0.99812, "loss_cls": 0.43266, "loss": 0.43266, "time": 0.28896} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.01149, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.91562, "top5_acc": 0.9975, "loss_cls": 0.44459, "loss": 0.44459, "time": 0.49431} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.01147, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90625, "top5_acc": 0.99938, "loss_cls": 0.49391, "loss": 0.49391, "time": 0.49127} +{"mode": "val", "epoch": 79, "iter": 533, "lr": 0.01145, "top1_acc": 0.8587, "top5_acc": 0.99073, "mean_class_accuracy": 0.79788} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.01143, "memory": 4083, "data_time": 0.19075, "top1_acc": 0.9325, "top5_acc": 0.99812, "loss_cls": 0.40587, "loss": 0.40587, "time": 0.8092} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.01141, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.92, "top5_acc": 0.99938, "loss_cls": 0.42333, "loss": 0.42333, "time": 0.48889} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.01139, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93688, "top5_acc": 0.99812, "loss_cls": 0.38008, "loss": 0.38008, "time": 0.49276} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.01137, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91812, "top5_acc": 0.99938, "loss_cls": 0.42438, "loss": 0.42438, "time": 0.4943} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.01135, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92938, "top5_acc": 0.99938, "loss_cls": 0.38409, "loss": 0.38409, "time": 0.49031} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.01133, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92188, "top5_acc": 0.99938, "loss_cls": 0.40533, "loss": 0.40533, "time": 0.48998} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.01131, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9175, "top5_acc": 0.9975, "loss_cls": 0.44656, "loss": 0.44656, "time": 0.49364} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.01129, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92688, "top5_acc": 0.99562, "loss_cls": 0.39304, "loss": 0.39304, "time": 0.27994} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.01127, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9125, "top5_acc": 0.99938, "loss_cls": 0.44672, "loss": 0.44672, "time": 0.5122} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.01125, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.915, "top5_acc": 0.99812, "loss_cls": 0.43672, "loss": 0.43672, "time": 0.29797} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.01123, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.925, "top5_acc": 0.99938, "loss_cls": 0.42204, "loss": 0.42204, "time": 0.48872} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.01121, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.925, "top5_acc": 0.99562, "loss_cls": 0.41353, "loss": 0.41353, "time": 0.4867} +{"mode": "val", "epoch": 80, "iter": 533, "lr": 0.01119, "top1_acc": 0.85882, "top5_acc": 0.98709, "mean_class_accuracy": 0.79094} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.01117, "memory": 4083, "data_time": 0.18592, "top1_acc": 0.93438, "top5_acc": 0.99875, "loss_cls": 0.36259, "loss": 0.36259, "time": 0.79396} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.01115, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.925, "top5_acc": 0.99938, "loss_cls": 0.38469, "loss": 0.38469, "time": 0.49376} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.01113, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91062, "top5_acc": 0.9975, "loss_cls": 0.43815, "loss": 0.43815, "time": 0.4879} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.01111, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.92875, "top5_acc": 0.99688, "loss_cls": 0.40075, "loss": 0.40075, "time": 0.49252} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.01109, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.91062, "top5_acc": 0.99688, "loss_cls": 0.48533, "loss": 0.48533, "time": 0.48682} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.01107, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91938, "top5_acc": 0.99875, "loss_cls": 0.43389, "loss": 0.43389, "time": 0.48859} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.01105, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91938, "top5_acc": 0.99812, "loss_cls": 0.42139, "loss": 0.42139, "time": 0.49188} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.01103, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94062, "top5_acc": 0.99938, "loss_cls": 0.34279, "loss": 0.34279, "time": 0.28195} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.01101, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.92938, "top5_acc": 0.99938, "loss_cls": 0.38123, "loss": 0.38123, "time": 0.51102} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.01099, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.91938, "top5_acc": 0.9975, "loss_cls": 0.42511, "loss": 0.42511, "time": 0.30003} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.01097, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92062, "top5_acc": 0.99875, "loss_cls": 0.42186, "loss": 0.42186, "time": 0.4895} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.01095, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93875, "top5_acc": 0.99562, "loss_cls": 0.39083, "loss": 0.39083, "time": 0.49132} +{"mode": "val", "epoch": 81, "iter": 533, "lr": 0.01093, "top1_acc": 0.84039, "top5_acc": 0.98686, "mean_class_accuracy": 0.77881} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.01091, "memory": 4083, "data_time": 0.18954, "top1_acc": 0.92312, "top5_acc": 0.99875, "loss_cls": 0.42089, "loss": 0.42089, "time": 0.79783} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.01089, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92, "top5_acc": 0.99875, "loss_cls": 0.40303, "loss": 0.40303, "time": 0.49208} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.01087, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92812, "top5_acc": 0.9975, "loss_cls": 0.38581, "loss": 0.38581, "time": 0.48805} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.01085, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.93188, "top5_acc": 0.99688, "loss_cls": 0.38888, "loss": 0.38888, "time": 0.49306} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.01083, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93062, "top5_acc": 1.0, "loss_cls": 0.36803, "loss": 0.36803, "time": 0.4917} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.01081, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.36612, "loss": 0.36612, "time": 0.49665} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.01079, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9325, "top5_acc": 0.9975, "loss_cls": 0.39272, "loss": 0.39272, "time": 0.48937} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.01077, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.41319, "loss": 0.41319, "time": 0.28326} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.01075, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93062, "top5_acc": 0.99875, "loss_cls": 0.40868, "loss": 0.40868, "time": 0.51049} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.01073, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92938, "top5_acc": 0.99938, "loss_cls": 0.37259, "loss": 0.37259, "time": 0.31603} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.01071, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91938, "top5_acc": 0.99875, "loss_cls": 0.44394, "loss": 0.44394, "time": 0.49122} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.01069, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92375, "top5_acc": 0.99938, "loss_cls": 0.41152, "loss": 0.41152, "time": 0.48837} +{"mode": "val", "epoch": 82, "iter": 533, "lr": 0.01067, "top1_acc": 0.85072, "top5_acc": 0.98779, "mean_class_accuracy": 0.79494} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.01065, "memory": 4083, "data_time": 0.19561, "top1_acc": 0.93625, "top5_acc": 0.99875, "loss_cls": 0.39852, "loss": 0.39852, "time": 0.81085} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.01063, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93125, "top5_acc": 1.0, "loss_cls": 0.37688, "loss": 0.37688, "time": 0.493} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.01061, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.925, "top5_acc": 0.99938, "loss_cls": 0.38738, "loss": 0.38738, "time": 0.4941} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.01059, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91375, "top5_acc": 0.99875, "loss_cls": 0.4384, "loss": 0.4384, "time": 0.4928} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.01057, "memory": 4083, "data_time": 0.0007, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.3514, "loss": 0.3514, "time": 0.49263} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.01055, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91375, "top5_acc": 0.99812, "loss_cls": 0.43619, "loss": 0.43619, "time": 0.49048} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.01053, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93312, "top5_acc": 0.99938, "loss_cls": 0.37795, "loss": 0.37795, "time": 0.49304} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.01051, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.92375, "top5_acc": 0.99938, "loss_cls": 0.40517, "loss": 0.40517, "time": 0.29551} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.01049, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92062, "top5_acc": 0.99688, "loss_cls": 0.45976, "loss": 0.45976, "time": 0.46654} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.01047, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92375, "top5_acc": 0.99812, "loss_cls": 0.4201, "loss": 0.4201, "time": 0.34528} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.01045, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9225, "top5_acc": 0.9975, "loss_cls": 0.42272, "loss": 0.42272, "time": 0.48411} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.01043, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93312, "top5_acc": 0.99812, "loss_cls": 0.37597, "loss": 0.37597, "time": 0.48886} +{"mode": "val", "epoch": 83, "iter": 533, "lr": 0.01042, "top1_acc": 0.87678, "top5_acc": 0.99296, "mean_class_accuracy": 0.8261} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.0104, "memory": 4083, "data_time": 0.1883, "top1_acc": 0.92875, "top5_acc": 0.99938, "loss_cls": 0.39163, "loss": 0.39163, "time": 0.8} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.01038, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9475, "top5_acc": 1.0, "loss_cls": 0.34469, "loss": 0.34469, "time": 0.49252} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.01036, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93625, "top5_acc": 0.99875, "loss_cls": 0.35293, "loss": 0.35293, "time": 0.48719} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.01034, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93375, "top5_acc": 0.9975, "loss_cls": 0.39027, "loss": 0.39027, "time": 0.49252} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.01031, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.92438, "top5_acc": 0.9975, "loss_cls": 0.40665, "loss": 0.40665, "time": 0.49333} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.01029, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.94188, "top5_acc": 0.99875, "loss_cls": 0.32648, "loss": 0.32648, "time": 0.49173} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.01027, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93, "top5_acc": 0.99875, "loss_cls": 0.39719, "loss": 0.39719, "time": 0.4915} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.01025, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9475, "top5_acc": 1.0, "loss_cls": 0.34123, "loss": 0.34123, "time": 0.29487} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.01023, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93125, "top5_acc": 0.99938, "loss_cls": 0.39128, "loss": 0.39128, "time": 0.46142} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.01021, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92625, "top5_acc": 0.99938, "loss_cls": 0.38278, "loss": 0.38278, "time": 0.33478} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.01019, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.4257, "loss": 0.4257, "time": 0.49177} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.01017, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92, "top5_acc": 1.0, "loss_cls": 0.41897, "loss": 0.41897, "time": 0.48787} +{"mode": "val", "epoch": 84, "iter": 533, "lr": 0.01016, "top1_acc": 0.83464, "top5_acc": 0.99073, "mean_class_accuracy": 0.78788} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.01014, "memory": 4083, "data_time": 0.19125, "top1_acc": 0.9375, "top5_acc": 1.0, "loss_cls": 0.35836, "loss": 0.35836, "time": 0.81244} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.01012, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.935, "top5_acc": 0.99938, "loss_cls": 0.35104, "loss": 0.35104, "time": 0.49503} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.0101, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9475, "top5_acc": 1.0, "loss_cls": 0.2898, "loss": 0.2898, "time": 0.4927} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.01008, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.94, "top5_acc": 0.99938, "loss_cls": 0.34522, "loss": 0.34522, "time": 0.49103} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.01006, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9325, "top5_acc": 0.99812, "loss_cls": 0.35133, "loss": 0.35133, "time": 0.49155} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.01004, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.35667, "loss": 0.35667, "time": 0.49179} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.01002, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92438, "top5_acc": 0.99875, "loss_cls": 0.40686, "loss": 0.40686, "time": 0.49226} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.01, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93375, "top5_acc": 0.99875, "loss_cls": 0.37636, "loss": 0.37636, "time": 0.32583} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.00998, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95062, "top5_acc": 0.99875, "loss_cls": 0.31226, "loss": 0.31226, "time": 0.41617} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.00996, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95312, "top5_acc": 0.99812, "loss_cls": 0.3132, "loss": 0.3132, "time": 0.35533} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.00994, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9175, "top5_acc": 0.99688, "loss_cls": 0.42764, "loss": 0.42764, "time": 0.49504} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.00992, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.9275, "top5_acc": 0.99625, "loss_cls": 0.41291, "loss": 0.41291, "time": 0.49068} +{"mode": "val", "epoch": 85, "iter": 533, "lr": 0.0099, "top1_acc": 0.85706, "top5_acc": 0.99096, "mean_class_accuracy": 0.80085} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.00988, "memory": 4083, "data_time": 0.18643, "top1_acc": 0.95188, "top5_acc": 1.0, "loss_cls": 0.29214, "loss": 0.29214, "time": 0.78972} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.00986, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93438, "top5_acc": 1.0, "loss_cls": 0.34826, "loss": 0.34826, "time": 0.48892} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.00984, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92688, "top5_acc": 0.99812, "loss_cls": 0.39496, "loss": 0.39496, "time": 0.49517} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.00982, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93125, "top5_acc": 1.0, "loss_cls": 0.37013, "loss": 0.37013, "time": 0.49456} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.0098, "memory": 4083, "data_time": 0.00069, "top1_acc": 0.93688, "top5_acc": 0.99875, "loss_cls": 0.32324, "loss": 0.32324, "time": 0.48998} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.00978, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.3245, "loss": 0.3245, "time": 0.49185} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.00976, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92062, "top5_acc": 0.99875, "loss_cls": 0.42028, "loss": 0.42028, "time": 0.49225} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.00974, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92, "top5_acc": 0.99875, "loss_cls": 0.42924, "loss": 0.42924, "time": 0.28846} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.00972, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94188, "top5_acc": 0.99875, "loss_cls": 0.34128, "loss": 0.34128, "time": 0.45754} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.0097, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92812, "top5_acc": 0.9975, "loss_cls": 0.4012, "loss": 0.4012, "time": 0.32605} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.00968, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.93438, "top5_acc": 0.99875, "loss_cls": 0.37819, "loss": 0.37819, "time": 0.4914} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.00966, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91938, "top5_acc": 0.99688, "loss_cls": 0.42232, "loss": 0.42232, "time": 0.48949} +{"mode": "val", "epoch": 86, "iter": 533, "lr": 0.00965, "top1_acc": 0.84262, "top5_acc": 0.99002, "mean_class_accuracy": 0.78079} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.00963, "memory": 4083, "data_time": 0.18867, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.34857, "loss": 0.34857, "time": 0.79903} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.00961, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.935, "top5_acc": 0.99938, "loss_cls": 0.37048, "loss": 0.37048, "time": 0.49021} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.00959, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94, "top5_acc": 1.0, "loss_cls": 0.34773, "loss": 0.34773, "time": 0.49149} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.00957, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.32048, "loss": 0.32048, "time": 0.48992} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.00955, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9225, "top5_acc": 0.99938, "loss_cls": 0.38855, "loss": 0.38855, "time": 0.48843} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.00953, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93062, "top5_acc": 0.99812, "loss_cls": 0.36346, "loss": 0.36346, "time": 0.48914} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.00951, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93875, "top5_acc": 1.0, "loss_cls": 0.33974, "loss": 0.33974, "time": 0.49711} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.00949, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92562, "top5_acc": 1.0, "loss_cls": 0.39069, "loss": 0.39069, "time": 0.28616} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.00947, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92625, "top5_acc": 0.99938, "loss_cls": 0.39037, "loss": 0.39037, "time": 0.47542} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.00945, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94, "top5_acc": 0.99875, "loss_cls": 0.37094, "loss": 0.37094, "time": 0.3333} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.00943, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9225, "top5_acc": 0.9975, "loss_cls": 0.40887, "loss": 0.40887, "time": 0.49223} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.00941, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93, "top5_acc": 0.99875, "loss_cls": 0.38369, "loss": 0.38369, "time": 0.49101} +{"mode": "val", "epoch": 87, "iter": 533, "lr": 0.00939, "top1_acc": 0.87255, "top5_acc": 0.99237, "mean_class_accuracy": 0.8301} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.00937, "memory": 4083, "data_time": 0.19303, "top1_acc": 0.94625, "top5_acc": 1.0, "loss_cls": 0.33007, "loss": 0.33007, "time": 0.80221} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.00935, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.34021, "loss": 0.34021, "time": 0.49292} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.00933, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.30553, "loss": 0.30553, "time": 0.49456} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.00931, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93812, "top5_acc": 0.99812, "loss_cls": 0.37111, "loss": 0.37111, "time": 0.48922} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.00929, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.93312, "top5_acc": 0.9975, "loss_cls": 0.35806, "loss": 0.35806, "time": 0.4882} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.00927, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.39739, "loss": 0.39739, "time": 0.48912} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.00925, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94188, "top5_acc": 0.99812, "loss_cls": 0.34078, "loss": 0.34078, "time": 0.48937} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.00923, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9375, "top5_acc": 0.99875, "loss_cls": 0.36726, "loss": 0.36726, "time": 0.30338} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.00921, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94688, "top5_acc": 0.99938, "loss_cls": 0.32242, "loss": 0.32242, "time": 0.4478} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.00919, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93562, "top5_acc": 0.99812, "loss_cls": 0.35689, "loss": 0.35689, "time": 0.34554} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.00917, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92938, "top5_acc": 0.99938, "loss_cls": 0.36777, "loss": 0.36777, "time": 0.49515} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.00915, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94188, "top5_acc": 0.99938, "loss_cls": 0.33358, "loss": 0.33358, "time": 0.49238} +{"mode": "val", "epoch": 88, "iter": 533, "lr": 0.00914, "top1_acc": 0.87232, "top5_acc": 0.99214, "mean_class_accuracy": 0.82048} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.00912, "memory": 4083, "data_time": 0.18673, "top1_acc": 0.94062, "top5_acc": 0.99938, "loss_cls": 0.32303, "loss": 0.32303, "time": 0.79378} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0091, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.27561, "loss": 0.27561, "time": 0.49344} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.00908, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.32259, "loss": 0.32259, "time": 0.48889} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.00906, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.32913, "loss": 0.32913, "time": 0.49298} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.00904, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.93312, "top5_acc": 0.99875, "loss_cls": 0.35655, "loss": 0.35655, "time": 0.49122} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.00902, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.94625, "top5_acc": 0.99812, "loss_cls": 0.33943, "loss": 0.33943, "time": 0.49264} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.009, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.935, "top5_acc": 0.99938, "loss_cls": 0.37081, "loss": 0.37081, "time": 0.49103} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.00898, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.36849, "loss": 0.36849, "time": 0.3178} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.00896, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.94375, "top5_acc": 0.99812, "loss_cls": 0.32929, "loss": 0.32929, "time": 0.42591} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.00894, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92812, "top5_acc": 0.99875, "loss_cls": 0.36124, "loss": 0.36124, "time": 0.36145} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.00892, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.945, "top5_acc": 0.99812, "loss_cls": 0.33234, "loss": 0.33234, "time": 0.4892} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.0089, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92625, "top5_acc": 0.9975, "loss_cls": 0.41022, "loss": 0.41022, "time": 0.49023} +{"mode": "val", "epoch": 89, "iter": 533, "lr": 0.00889, "top1_acc": 0.8844, "top5_acc": 0.99343, "mean_class_accuracy": 0.83989} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.00887, "memory": 4083, "data_time": 0.19692, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.24024, "loss": 0.24024, "time": 0.81201} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.00885, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.27584, "loss": 0.27584, "time": 0.49563} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.00883, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.27854, "loss": 0.27854, "time": 0.49469} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.00881, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.27782, "loss": 0.27782, "time": 0.49201} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.00879, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.93938, "top5_acc": 1.0, "loss_cls": 0.33228, "loss": 0.33228, "time": 0.49027} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.00877, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.34135, "loss": 0.34135, "time": 0.48797} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.00875, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.945, "top5_acc": 0.99938, "loss_cls": 0.33126, "loss": 0.33126, "time": 0.46469} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.00873, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94, "top5_acc": 0.99875, "loss_cls": 0.33938, "loss": 0.33938, "time": 0.37649} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.00871, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94688, "top5_acc": 0.99938, "loss_cls": 0.33273, "loss": 0.33273, "time": 0.35772} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.00869, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.42733, "loss": 0.42733, "time": 0.38758} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.00867, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.34037, "loss": 0.34037, "time": 0.49478} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.00865, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93688, "top5_acc": 1.0, "loss_cls": 0.35961, "loss": 0.35961, "time": 0.4928} +{"mode": "val", "epoch": 90, "iter": 533, "lr": 0.00864, "top1_acc": 0.87783, "top5_acc": 0.99202, "mean_class_accuracy": 0.83161} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.00862, "memory": 4083, "data_time": 0.18933, "top1_acc": 0.96438, "top5_acc": 0.99875, "loss_cls": 0.25482, "loss": 0.25482, "time": 0.80158} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0086, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.30154, "loss": 0.30154, "time": 0.49066} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.00858, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.30448, "loss": 0.30448, "time": 0.49002} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.00856, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.30261, "loss": 0.30261, "time": 0.49392} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.00854, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94125, "top5_acc": 0.99812, "loss_cls": 0.315, "loss": 0.315, "time": 0.48546} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.00852, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.33431, "loss": 0.33431, "time": 0.48917} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.0085, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94375, "top5_acc": 1.0, "loss_cls": 0.32344, "loss": 0.32344, "time": 0.44978} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.00848, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.30357, "loss": 0.30357, "time": 0.40606} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.00846, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93438, "top5_acc": 0.99875, "loss_cls": 0.3453, "loss": 0.3453, "time": 0.32809} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.00844, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93562, "top5_acc": 0.99875, "loss_cls": 0.35781, "loss": 0.35781, "time": 0.38622} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.00842, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.32335, "loss": 0.32335, "time": 0.49023} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.0084, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9325, "top5_acc": 0.99875, "loss_cls": 0.37433, "loss": 0.37433, "time": 0.49062} +{"mode": "val", "epoch": 91, "iter": 533, "lr": 0.00839, "top1_acc": 0.87325, "top5_acc": 0.99167, "mean_class_accuracy": 0.82859} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.00837, "memory": 4083, "data_time": 0.18809, "top1_acc": 0.96375, "top5_acc": 0.99938, "loss_cls": 0.25001, "loss": 0.25001, "time": 0.80876} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.00835, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.28521, "loss": 0.28521, "time": 0.48977} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.00833, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.26584, "loss": 0.26584, "time": 0.48948} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.00831, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93812, "top5_acc": 0.99812, "loss_cls": 0.33704, "loss": 0.33704, "time": 0.49075} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.00829, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94938, "top5_acc": 0.99875, "loss_cls": 0.29985, "loss": 0.29985, "time": 0.49176} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.00827, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93188, "top5_acc": 0.99938, "loss_cls": 0.36533, "loss": 0.36533, "time": 0.48957} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.00825, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94062, "top5_acc": 0.99938, "loss_cls": 0.34983, "loss": 0.34983, "time": 0.44609} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.00824, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.29353, "loss": 0.29353, "time": 0.43368} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.00822, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.30827, "loss": 0.30827, "time": 0.29898} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.0082, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.31523, "loss": 0.31523, "time": 0.40882} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.00818, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.3484, "loss": 0.3484, "time": 0.49367} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.00816, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94188, "top5_acc": 1.0, "loss_cls": 0.33149, "loss": 0.33149, "time": 0.49195} +{"mode": "val", "epoch": 92, "iter": 533, "lr": 0.00814, "top1_acc": 0.86387, "top5_acc": 0.99038, "mean_class_accuracy": 0.80809} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.00812, "memory": 4083, "data_time": 0.19403, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.24146, "loss": 0.24146, "time": 0.79411} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.0081, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.26215, "loss": 0.26215, "time": 0.49055} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.00809, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94812, "top5_acc": 0.99875, "loss_cls": 0.29016, "loss": 0.29016, "time": 0.49309} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.00807, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.27345, "loss": 0.27345, "time": 0.49066} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.00805, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.29155, "loss": 0.29155, "time": 0.48695} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.00803, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.94188, "top5_acc": 0.99938, "loss_cls": 0.31865, "loss": 0.31865, "time": 0.48956} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.00801, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95062, "top5_acc": 0.99875, "loss_cls": 0.2729, "loss": 0.2729, "time": 0.423} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.00799, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.93938, "top5_acc": 0.99938, "loss_cls": 0.33089, "loss": 0.33089, "time": 0.46689} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.00797, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.30834, "loss": 0.30834, "time": 0.27096} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.00795, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94875, "top5_acc": 0.99875, "loss_cls": 0.31982, "loss": 0.31982, "time": 0.41821} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.00793, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.32024, "loss": 0.32024, "time": 0.4903} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.00791, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94812, "top5_acc": 1.0, "loss_cls": 0.29683, "loss": 0.29683, "time": 0.48632} +{"mode": "val", "epoch": 93, "iter": 533, "lr": 0.0079, "top1_acc": 0.86739, "top5_acc": 0.99272, "mean_class_accuracy": 0.80546} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.00788, "memory": 4083, "data_time": 0.18566, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.25479, "loss": 0.25479, "time": 0.79715} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.00786, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.29492, "loss": 0.29492, "time": 0.48841} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.00784, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.27874, "loss": 0.27874, "time": 0.48811} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.00782, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.2722, "loss": 0.2722, "time": 0.49202} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.0078, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.30758, "loss": 0.30758, "time": 0.49138} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.00778, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.29312, "loss": 0.29312, "time": 0.49063} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.00777, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94062, "top5_acc": 1.0, "loss_cls": 0.30992, "loss": 0.30992, "time": 0.41782} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.00775, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.30833, "loss": 0.30833, "time": 0.50629} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.00773, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94875, "top5_acc": 0.99812, "loss_cls": 0.29921, "loss": 0.29921, "time": 0.2385} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.00771, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.24616, "loss": 0.24616, "time": 0.44805} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.00769, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.27658, "loss": 0.27658, "time": 0.49159} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.00767, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94812, "top5_acc": 1.0, "loss_cls": 0.30549, "loss": 0.30549, "time": 0.48971} +{"mode": "val", "epoch": 94, "iter": 533, "lr": 0.00766, "top1_acc": 0.8675, "top5_acc": 0.99167, "mean_class_accuracy": 0.82622} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.00764, "memory": 4083, "data_time": 0.18738, "top1_acc": 0.96, "top5_acc": 0.99938, "loss_cls": 0.26504, "loss": 0.26504, "time": 0.80434} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.00762, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96375, "top5_acc": 0.99938, "loss_cls": 0.23062, "loss": 0.23062, "time": 0.49567} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.0076, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.96, "top5_acc": 0.99875, "loss_cls": 0.26358, "loss": 0.26358, "time": 0.48865} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.00758, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.27602, "loss": 0.27602, "time": 0.48922} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.00756, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95688, "top5_acc": 0.99938, "loss_cls": 0.2716, "loss": 0.2716, "time": 0.49104} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.00754, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94562, "top5_acc": 1.0, "loss_cls": 0.30568, "loss": 0.30568, "time": 0.49143} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.00752, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94812, "top5_acc": 1.0, "loss_cls": 0.33431, "loss": 0.33431, "time": 0.3742} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.00751, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.2829, "loss": 0.2829, "time": 0.50948} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.00749, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.26576, "loss": 0.26576, "time": 0.24689} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.00747, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94688, "top5_acc": 0.99938, "loss_cls": 0.33126, "loss": 0.33126, "time": 0.46448} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.00745, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94812, "top5_acc": 0.99812, "loss_cls": 0.32509, "loss": 0.32509, "time": 0.49445} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.00743, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.29963, "loss": 0.29963, "time": 0.49109} +{"mode": "val", "epoch": 95, "iter": 533, "lr": 0.00742, "top1_acc": 0.88933, "top5_acc": 0.99331, "mean_class_accuracy": 0.84699} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.0074, "memory": 4083, "data_time": 0.18351, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.24625, "loss": 0.24625, "time": 0.77903} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.00738, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.25249, "loss": 0.25249, "time": 0.48916} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.00736, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.965, "top5_acc": 0.99875, "loss_cls": 0.2389, "loss": 0.2389, "time": 0.49003} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.00734, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96812, "top5_acc": 0.99938, "loss_cls": 0.21609, "loss": 0.21609, "time": 0.49063} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.00732, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.26127, "loss": 0.26127, "time": 0.49165} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.0073, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.24239, "loss": 0.24239, "time": 0.48785} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.00729, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.27553, "loss": 0.27553, "time": 0.36464} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.00727, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95438, "top5_acc": 0.99875, "loss_cls": 0.25284, "loss": 0.25284, "time": 0.50965} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.00725, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.2701, "loss": 0.2701, "time": 0.24676} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.00723, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94938, "top5_acc": 0.99875, "loss_cls": 0.29583, "loss": 0.29583, "time": 0.46523} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.00721, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.955, "top5_acc": 0.99812, "loss_cls": 0.28537, "loss": 0.28537, "time": 0.49024} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.00719, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95062, "top5_acc": 1.0, "loss_cls": 0.28249, "loss": 0.28249, "time": 0.4886} +{"mode": "val", "epoch": 96, "iter": 533, "lr": 0.00718, "top1_acc": 0.89109, "top5_acc": 0.99296, "mean_class_accuracy": 0.84608} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.00716, "memory": 4083, "data_time": 0.18338, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.21625, "loss": 0.21625, "time": 0.80274} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.00714, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.2298, "loss": 0.2298, "time": 0.48567} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.00712, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.20809, "loss": 0.20809, "time": 0.4868} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.0071, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.24365, "loss": 0.24365, "time": 0.49056} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.00709, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.24478, "loss": 0.24478, "time": 0.48822} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.00707, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.24563, "loss": 0.24563, "time": 0.4912} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.00705, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.25392, "loss": 0.25392, "time": 0.34312} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.00703, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9425, "top5_acc": 1.0, "loss_cls": 0.30802, "loss": 0.30802, "time": 0.50816} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.00701, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.94875, "top5_acc": 0.99875, "loss_cls": 0.28563, "loss": 0.28563, "time": 0.25439} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.00699, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94688, "top5_acc": 0.99938, "loss_cls": 0.29727, "loss": 0.29727, "time": 0.48461} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.00698, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96, "top5_acc": 0.99938, "loss_cls": 0.26278, "loss": 0.26278, "time": 0.492} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.00696, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.25532, "loss": 0.25532, "time": 0.48822} +{"mode": "val", "epoch": 97, "iter": 533, "lr": 0.00694, "top1_acc": 0.8722, "top5_acc": 0.99002, "mean_class_accuracy": 0.81932} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.00692, "memory": 4083, "data_time": 0.18978, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.22404, "loss": 0.22404, "time": 0.79946} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.00691, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.21739, "loss": 0.21739, "time": 0.48812} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.00689, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.23238, "loss": 0.23238, "time": 0.49186} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.00687, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.20243, "loss": 0.20243, "time": 0.48746} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.00685, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.21045, "loss": 0.21045, "time": 0.48764} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.00683, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95562, "top5_acc": 1.0, "loss_cls": 0.23435, "loss": 0.23435, "time": 0.49352} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.00681, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.25051, "loss": 0.25051, "time": 0.32675} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.0068, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95188, "top5_acc": 0.99875, "loss_cls": 0.2707, "loss": 0.2707, "time": 0.50937} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.00678, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96625, "top5_acc": 0.99875, "loss_cls": 0.2299, "loss": 0.2299, "time": 0.25688} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.00676, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.26576, "loss": 0.26576, "time": 0.48828} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.00674, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9475, "top5_acc": 0.99875, "loss_cls": 0.28378, "loss": 0.28378, "time": 0.48711} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.00672, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.25191, "loss": 0.25191, "time": 0.49251} +{"mode": "val", "epoch": 98, "iter": 533, "lr": 0.00671, "top1_acc": 0.89039, "top5_acc": 0.99308, "mean_class_accuracy": 0.8447} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.00669, "memory": 4083, "data_time": 0.18583, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.19563, "loss": 0.19563, "time": 0.77522} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.00667, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.20437, "loss": 0.20437, "time": 0.49329} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.00665, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.23252, "loss": 0.23252, "time": 0.48855} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.00664, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.20493, "loss": 0.20493, "time": 0.49083} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.00662, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96125, "top5_acc": 0.99938, "loss_cls": 0.24222, "loss": 0.24222, "time": 0.4934} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.0066, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.2163, "loss": 0.2163, "time": 0.48777} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.00658, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93938, "top5_acc": 1.0, "loss_cls": 0.31147, "loss": 0.31147, "time": 0.33093} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.00656, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.95375, "top5_acc": 0.99875, "loss_cls": 0.26979, "loss": 0.26979, "time": 0.51102} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.00655, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.2336, "loss": 0.2336, "time": 0.26199} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.00653, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95, "top5_acc": 1.0, "loss_cls": 0.27856, "loss": 0.27856, "time": 0.49131} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.00651, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95562, "top5_acc": 0.99875, "loss_cls": 0.26877, "loss": 0.26877, "time": 0.49353} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.00649, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.25056, "loss": 0.25056, "time": 0.48808} +{"mode": "val", "epoch": 99, "iter": 533, "lr": 0.00648, "top1_acc": 0.89461, "top5_acc": 0.99355, "mean_class_accuracy": 0.84904} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.00646, "memory": 4083, "data_time": 0.183, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.22417, "loss": 0.22417, "time": 0.79285} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.00644, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96188, "top5_acc": 0.99875, "loss_cls": 0.24652, "loss": 0.24652, "time": 0.48924} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.00642, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.2139, "loss": 0.2139, "time": 0.48844} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.00641, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.21609, "loss": 0.21609, "time": 0.49043} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.00639, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.17169, "loss": 0.17169, "time": 0.48743} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.00637, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96375, "top5_acc": 0.99938, "loss_cls": 0.21323, "loss": 0.21323, "time": 0.48841} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.00635, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.23276, "loss": 0.23276, "time": 0.32162} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.00634, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.26362, "loss": 0.26362, "time": 0.51087} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.00632, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95562, "top5_acc": 1.0, "loss_cls": 0.25024, "loss": 0.25024, "time": 0.26117} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.0063, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95812, "top5_acc": 0.99938, "loss_cls": 0.23408, "loss": 0.23408, "time": 0.48938} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.00628, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.2542, "loss": 0.2542, "time": 0.48942} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.00626, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.28777, "loss": 0.28777, "time": 0.49025} +{"mode": "val", "epoch": 100, "iter": 533, "lr": 0.00625, "top1_acc": 0.87795, "top5_acc": 0.99214, "mean_class_accuracy": 0.82595} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.00623, "memory": 4083, "data_time": 0.18537, "top1_acc": 0.96125, "top5_acc": 0.99938, "loss_cls": 0.24404, "loss": 0.24404, "time": 0.79116} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.00621, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.20384, "loss": 0.20384, "time": 0.48937} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.0062, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.21484, "loss": 0.21484, "time": 0.48996} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.00618, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.2271, "loss": 0.2271, "time": 0.48711} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.00616, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.96, "top5_acc": 0.99875, "loss_cls": 0.23569, "loss": 0.23569, "time": 0.48771} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.00614, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.21758, "loss": 0.21758, "time": 0.48696} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.00613, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.2521, "loss": 0.2521, "time": 0.32118} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.00611, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.23855, "loss": 0.23855, "time": 0.50963} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.00609, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.20175, "loss": 0.20175, "time": 0.27024} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.00607, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.20691, "loss": 0.20691, "time": 0.4912} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.00606, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.20624, "loss": 0.20624, "time": 0.49258} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.00604, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.19781, "loss": 0.19781, "time": 0.48916} +{"mode": "val", "epoch": 101, "iter": 533, "lr": 0.00602, "top1_acc": 0.88018, "top5_acc": 0.99038, "mean_class_accuracy": 0.85302} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.00601, "memory": 4083, "data_time": 0.18575, "top1_acc": 0.97562, "top5_acc": 0.99938, "loss_cls": 0.1927, "loss": 0.1927, "time": 0.79001} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.00599, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.16293, "loss": 0.16293, "time": 0.49314} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.00597, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.19419, "loss": 0.19419, "time": 0.48868} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.00596, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.19626, "loss": 0.19626, "time": 0.49092} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.00594, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96562, "top5_acc": 0.99938, "loss_cls": 0.20795, "loss": 0.20795, "time": 0.48719} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.00592, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95688, "top5_acc": 0.99875, "loss_cls": 0.25401, "loss": 0.25401, "time": 0.48935} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.0059, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.24746, "loss": 0.24746, "time": 0.31754} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.00589, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.22507, "loss": 0.22507, "time": 0.51137} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.00587, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96125, "top5_acc": 0.99938, "loss_cls": 0.21963, "loss": 0.21963, "time": 0.28218} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.00585, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97188, "top5_acc": 0.99938, "loss_cls": 0.20694, "loss": 0.20694, "time": 0.48972} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.00583, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.22701, "loss": 0.22701, "time": 0.48895} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.00582, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96, "top5_acc": 0.99875, "loss_cls": 0.27042, "loss": 0.27042, "time": 0.48962} +{"mode": "val", "epoch": 102, "iter": 533, "lr": 0.0058, "top1_acc": 0.87466, "top5_acc": 0.99026, "mean_class_accuracy": 0.83175} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.00579, "memory": 4083, "data_time": 0.18426, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.21431, "loss": 0.21431, "time": 0.80175} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.00577, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.15454, "loss": 0.15454, "time": 0.49078} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.00575, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9725, "top5_acc": 0.99938, "loss_cls": 0.17719, "loss": 0.17719, "time": 0.49213} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.00573, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.20287, "loss": 0.20287, "time": 0.48598} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.00572, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.17374, "loss": 0.17374, "time": 0.48937} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.0057, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96688, "top5_acc": 0.99938, "loss_cls": 0.20675, "loss": 0.20675, "time": 0.48758} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.00568, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96188, "top5_acc": 0.99875, "loss_cls": 0.23431, "loss": 0.23431, "time": 0.27652} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.00566, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.22952, "loss": 0.22952, "time": 0.51059} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.00565, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.19869, "loss": 0.19869, "time": 0.31313} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.00563, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.20399, "loss": 0.20399, "time": 0.4863} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.00561, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.19058, "loss": 0.19058, "time": 0.4894} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.0056, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.17817, "loss": 0.17817, "time": 0.49052} +{"mode": "val", "epoch": 103, "iter": 533, "lr": 0.00558, "top1_acc": 0.89133, "top5_acc": 0.99355, "mean_class_accuracy": 0.84488} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.00557, "memory": 4083, "data_time": 0.18515, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.17046, "loss": 0.17046, "time": 0.79913} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.00555, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.98062, "top5_acc": 0.99938, "loss_cls": 0.15666, "loss": 0.15666, "time": 0.49489} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.00553, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.98188, "top5_acc": 0.99938, "loss_cls": 0.1641, "loss": 0.1641, "time": 0.48794} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.00551, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.23258, "loss": 0.23258, "time": 0.48883} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.0055, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.25904, "loss": 0.25904, "time": 0.48676} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.00548, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.20861, "loss": 0.20861, "time": 0.4913} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.00546, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.20767, "loss": 0.20767, "time": 0.31052} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.00545, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.18279, "loss": 0.18279, "time": 0.44807} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.00543, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.22161, "loss": 0.22161, "time": 0.33424} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.00541, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.19865, "loss": 0.19865, "time": 0.49059} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.0054, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.20525, "loss": 0.20525, "time": 0.48852} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.00538, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.96938, "top5_acc": 0.99938, "loss_cls": 0.18972, "loss": 0.18972, "time": 0.48951} +{"mode": "val", "epoch": 104, "iter": 533, "lr": 0.00537, "top1_acc": 0.89579, "top5_acc": 0.99179, "mean_class_accuracy": 0.85377} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.00535, "memory": 4083, "data_time": 0.18607, "top1_acc": 0.97438, "top5_acc": 0.99938, "loss_cls": 0.17868, "loss": 0.17868, "time": 0.79033} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.00533, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.18317, "loss": 0.18317, "time": 0.494} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.00532, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9825, "top5_acc": 0.99938, "loss_cls": 0.15208, "loss": 0.15208, "time": 0.48776} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.0053, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.16274, "loss": 0.16274, "time": 0.49042} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.00528, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9825, "top5_acc": 0.99938, "loss_cls": 0.13794, "loss": 0.13794, "time": 0.48675} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.00527, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.16967, "loss": 0.16967, "time": 0.48953} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.00525, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.15465, "loss": 0.15465, "time": 0.31876} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.00523, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.15269, "loss": 0.15269, "time": 0.43247} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.00522, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.18049, "loss": 0.18049, "time": 0.35466} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.0052, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96688, "top5_acc": 0.99938, "loss_cls": 0.18602, "loss": 0.18602, "time": 0.49148} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.00518, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.21673, "loss": 0.21673, "time": 0.48948} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.00517, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95562, "top5_acc": 1.0, "loss_cls": 0.22283, "loss": 0.22283, "time": 0.49053} +{"mode": "val", "epoch": 105, "iter": 533, "lr": 0.00515, "top1_acc": 0.88828, "top5_acc": 0.99096, "mean_class_accuracy": 0.85236} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.00514, "memory": 4083, "data_time": 0.1926, "top1_acc": 0.9775, "top5_acc": 0.99938, "loss_cls": 0.17614, "loss": 0.17614, "time": 0.79704} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.00512, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.14353, "loss": 0.14353, "time": 0.49034} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.0051, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.13586, "loss": 0.13586, "time": 0.48782} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.00509, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.17861, "loss": 0.17861, "time": 0.48976} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.00507, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.16963, "loss": 0.16963, "time": 0.4882} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.00505, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.18497, "loss": 0.18497, "time": 0.48257} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.00504, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97438, "top5_acc": 0.99938, "loss_cls": 0.16818, "loss": 0.16818, "time": 0.35305} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.00502, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96, "top5_acc": 0.99875, "loss_cls": 0.21508, "loss": 0.21508, "time": 0.3793} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.005, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.17125, "loss": 0.17125, "time": 0.37686} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.00499, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.15823, "loss": 0.15823, "time": 0.49137} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.00497, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.17918, "loss": 0.17918, "time": 0.49454} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.00496, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.15369, "loss": 0.15369, "time": 0.48724} +{"mode": "val", "epoch": 106, "iter": 533, "lr": 0.00494, "top1_acc": 0.88182, "top5_acc": 0.99343, "mean_class_accuracy": 0.83521} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.00493, "memory": 4083, "data_time": 0.18606, "top1_acc": 0.97938, "top5_acc": 0.99938, "loss_cls": 0.1583, "loss": 0.1583, "time": 0.78769} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.00491, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.15504, "loss": 0.15504, "time": 0.49141} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.00489, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.13509, "loss": 0.13509, "time": 0.4922} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.00488, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.12434, "loss": 0.12434, "time": 0.48722} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.00486, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.98688, "top5_acc": 0.99938, "loss_cls": 0.10842, "loss": 0.10842, "time": 0.48945} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.00485, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98375, "top5_acc": 0.99938, "loss_cls": 0.13285, "loss": 0.13285, "time": 0.47908} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.00483, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.14211, "loss": 0.14211, "time": 0.37092} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.00481, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.16919, "loss": 0.16919, "time": 0.36185} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.0048, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.17462, "loss": 0.17462, "time": 0.38223} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.00478, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.1756, "loss": 0.1756, "time": 0.48895} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.00476, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.17409, "loss": 0.17409, "time": 0.48996} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.00475, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14486, "loss": 0.14486, "time": 0.48876} +{"mode": "val", "epoch": 107, "iter": 533, "lr": 0.00474, "top1_acc": 0.8925, "top5_acc": 0.9919, "mean_class_accuracy": 0.85193} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.00472, "memory": 4083, "data_time": 0.18486, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.16323, "loss": 0.16323, "time": 0.79707} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0047, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97875, "top5_acc": 0.99938, "loss_cls": 0.14512, "loss": 0.14512, "time": 0.49444} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.00469, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.1503, "loss": 0.1503, "time": 0.49023} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.00467, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.12625, "loss": 0.12625, "time": 0.48833} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.00466, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.18521, "loss": 0.18521, "time": 0.48899} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.00464, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.17019, "loss": 0.17019, "time": 0.4632} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.00462, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.98125, "top5_acc": 0.99938, "loss_cls": 0.14607, "loss": 0.14607, "time": 0.3933} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.00461, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98062, "top5_acc": 0.99938, "loss_cls": 0.15703, "loss": 0.15703, "time": 0.34137} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.00459, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.11727, "loss": 0.11727, "time": 0.39704} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.00458, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.14287, "loss": 0.14287, "time": 0.49237} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.00456, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.17634, "loss": 0.17634, "time": 0.49102} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.00455, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.15165, "loss": 0.15165, "time": 0.49114} +{"mode": "val", "epoch": 108, "iter": 533, "lr": 0.00453, "top1_acc": 0.89344, "top5_acc": 0.99472, "mean_class_accuracy": 0.85062} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.00452, "memory": 4083, "data_time": 0.18635, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.16566, "loss": 0.16566, "time": 0.79701} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.0045, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97938, "top5_acc": 0.99938, "loss_cls": 0.1446, "loss": 0.1446, "time": 0.49034} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.00449, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98688, "top5_acc": 0.99938, "loss_cls": 0.12396, "loss": 0.12396, "time": 0.48593} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.00447, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.12097, "loss": 0.12097, "time": 0.4901} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.00445, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.12111, "loss": 0.12111, "time": 0.49135} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.00444, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.13215, "loss": 0.13215, "time": 0.45421} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.00442, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.13854, "loss": 0.13854, "time": 0.41719} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.00441, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.1404, "loss": 0.1404, "time": 0.31635} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.00439, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.10484, "loss": 0.10484, "time": 0.4101} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.00438, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.15398, "loss": 0.15398, "time": 0.49025} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.00436, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.16751, "loss": 0.16751, "time": 0.49219} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.00434, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.18876, "loss": 0.18876, "time": 0.48932} +{"mode": "val", "epoch": 109, "iter": 533, "lr": 0.00433, "top1_acc": 0.89262, "top5_acc": 0.99202, "mean_class_accuracy": 0.85184} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.00432, "memory": 4083, "data_time": 0.18789, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.14766, "loss": 0.14766, "time": 0.79519} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.0043, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.1298, "loss": 0.1298, "time": 0.48771} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.00429, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.11112, "loss": 0.11112, "time": 0.48699} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.00427, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.10834, "loss": 0.10834, "time": 0.4888} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.00426, "memory": 4083, "data_time": 0.00062, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.11818, "loss": 0.11818, "time": 0.48978} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.00424, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11334, "loss": 0.11334, "time": 0.43585} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.00422, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.13855, "loss": 0.13855, "time": 0.46467} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.00421, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13486, "loss": 0.13486, "time": 0.27152} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.00419, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14553, "loss": 0.14553, "time": 0.42133} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.00418, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.13509, "loss": 0.13509, "time": 0.49183} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.00416, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.14247, "loss": 0.14247, "time": 0.48702} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.00415, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.17528, "loss": 0.17528, "time": 0.49007} +{"mode": "val", "epoch": 110, "iter": 533, "lr": 0.00414, "top1_acc": 0.89942, "top5_acc": 0.99331, "mean_class_accuracy": 0.86575} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.00412, "memory": 4083, "data_time": 0.18917, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.17494, "loss": 0.17494, "time": 0.79712} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.00411, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12493, "loss": 0.12493, "time": 0.49362} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.00409, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.15864, "loss": 0.15864, "time": 0.48931} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.00408, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.12538, "loss": 0.12538, "time": 0.4867} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.00406, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.11088, "loss": 0.11088, "time": 0.48859} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.00405, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.16314, "loss": 0.16314, "time": 0.43082} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.00403, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.11594, "loss": 0.11594, "time": 0.45992} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.00402, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.13929, "loss": 0.13929, "time": 0.2793} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.004, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.1483, "loss": 0.1483, "time": 0.41459} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.00399, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.14098, "loss": 0.14098, "time": 0.49222} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.00397, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.12669, "loss": 0.12669, "time": 0.49363} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.00396, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97688, "top5_acc": 0.99938, "loss_cls": 0.16487, "loss": 0.16487, "time": 0.49163} +{"mode": "val", "epoch": 111, "iter": 533, "lr": 0.00394, "top1_acc": 0.89978, "top5_acc": 0.9946, "mean_class_accuracy": 0.85432} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.00393, "memory": 4083, "data_time": 0.18675, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.12374, "loss": 0.12374, "time": 0.78233} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.00391, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.1271, "loss": 0.1271, "time": 0.49102} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.0039, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.13938, "loss": 0.13938, "time": 0.48995} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.00388, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11993, "loss": 0.11993, "time": 0.4928} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.00387, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.09274, "loss": 0.09274, "time": 0.49016} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.00385, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.0995, "loss": 0.0995, "time": 0.43614} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.00384, "memory": 4083, "data_time": 0.00077, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13708, "loss": 0.13708, "time": 0.4398} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.00382, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14556, "loss": 0.14556, "time": 0.30054} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.00381, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.13516, "loss": 0.13516, "time": 0.41654} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.0038, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.12151, "loss": 0.12151, "time": 0.48809} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.00378, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.14374, "loss": 0.14374, "time": 0.48962} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.00377, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11168, "loss": 0.11168, "time": 0.49432} +{"mode": "val", "epoch": 112, "iter": 533, "lr": 0.00375, "top1_acc": 0.9094, "top5_acc": 0.99343, "mean_class_accuracy": 0.87916} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.00374, "memory": 4083, "data_time": 0.18617, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12287, "loss": 0.12287, "time": 0.80113} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.00373, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11818, "loss": 0.11818, "time": 0.49014} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.00371, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.0957, "loss": 0.0957, "time": 0.48852} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.0037, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.14158, "loss": 0.14158, "time": 0.48939} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.00368, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.09589, "loss": 0.09589, "time": 0.49175} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.00367, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.1061, "loss": 0.1061, "time": 0.4243} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.00365, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.11502, "loss": 0.11502, "time": 0.46019} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.00364, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.09381, "loss": 0.09381, "time": 0.27632} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.00362, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.11686, "loss": 0.11686, "time": 0.43455} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.00361, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13242, "loss": 0.13242, "time": 0.48784} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0036, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.1038, "loss": 0.1038, "time": 0.49363} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.00358, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.10841, "loss": 0.10841, "time": 0.49153} +{"mode": "val", "epoch": 113, "iter": 533, "lr": 0.00357, "top1_acc": 0.90694, "top5_acc": 0.99261, "mean_class_accuracy": 0.87499} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.00355, "memory": 4083, "data_time": 0.18465, "top1_acc": 0.99438, "top5_acc": 0.99938, "loss_cls": 0.06998, "loss": 0.06998, "time": 0.79196} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.00354, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.1021, "loss": 0.1021, "time": 0.49318} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.00353, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10435, "loss": 0.10435, "time": 0.48939} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.00351, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.08215, "loss": 0.08215, "time": 0.48612} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.0035, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.08182, "loss": 0.08182, "time": 0.48955} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.00348, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.11005, "loss": 0.11005, "time": 0.426} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.00347, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.11614, "loss": 0.11614, "time": 0.4785} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.00346, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.12564, "loss": 0.12564, "time": 0.26453} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.00344, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.12358, "loss": 0.12358, "time": 0.41808} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.00343, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.07579, "loss": 0.07579, "time": 0.48749} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.00341, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.10944, "loss": 0.10944, "time": 0.49252} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.0034, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11454, "loss": 0.11454, "time": 0.48956} +{"mode": "val", "epoch": 114, "iter": 533, "lr": 0.00339, "top1_acc": 0.90928, "top5_acc": 0.99355, "mean_class_accuracy": 0.87145} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.00337, "memory": 4083, "data_time": 0.18397, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06837, "loss": 0.06837, "time": 0.79311} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.00336, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.07412, "loss": 0.07412, "time": 0.49196} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.00335, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.09089, "loss": 0.09089, "time": 0.49526} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.00333, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.0768, "loss": 0.0768, "time": 0.49143} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.00332, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.06318, "loss": 0.06318, "time": 0.49002} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.0033, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07399, "loss": 0.07399, "time": 0.41313} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.00329, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.05205, "loss": 0.05205, "time": 0.50395} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.00328, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.05092, "loss": 0.05092, "time": 0.24247} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.00326, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.06039, "loss": 0.06039, "time": 0.44316} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.00325, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08445, "loss": 0.08445, "time": 0.49066} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.00324, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.09707, "loss": 0.09707, "time": 0.4913} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.00322, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.08967, "loss": 0.08967, "time": 0.49214} +{"mode": "val", "epoch": 115, "iter": 533, "lr": 0.00321, "top1_acc": 0.90518, "top5_acc": 0.99214, "mean_class_accuracy": 0.87057} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.0032, "memory": 4083, "data_time": 0.1854, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.07305, "loss": 0.07305, "time": 0.78278} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.00318, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.06659, "loss": 0.06659, "time": 0.4898} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.00317, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.06976, "loss": 0.06976, "time": 0.48914} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.00316, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07192, "loss": 0.07192, "time": 0.48723} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.00314, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.09851, "loss": 0.09851, "time": 0.48885} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.00313, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.11954, "loss": 0.11954, "time": 0.39789} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.00312, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.09135, "loss": 0.09135, "time": 0.51024} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.0031, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.0935, "loss": 0.0935, "time": 0.233} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.00309, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.07876, "loss": 0.07876, "time": 0.44174} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.00308, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.99062, "top5_acc": 0.99938, "loss_cls": 0.08146, "loss": 0.08146, "time": 0.49191} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.00306, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.07507, "loss": 0.07507, "time": 0.49081} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.00305, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08982, "loss": 0.08982, "time": 0.49385} +{"mode": "val", "epoch": 116, "iter": 533, "lr": 0.00304, "top1_acc": 0.91316, "top5_acc": 0.99401, "mean_class_accuracy": 0.87961} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.00302, "memory": 4083, "data_time": 0.18927, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06741, "loss": 0.06741, "time": 0.79601} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.00301, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.07086, "loss": 0.07086, "time": 0.48672} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.003, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.06926, "loss": 0.06926, "time": 0.48874} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.00298, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.06256, "loss": 0.06256, "time": 0.49034} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.00297, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05637, "loss": 0.05637, "time": 0.48823} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.00296, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06718, "loss": 0.06718, "time": 0.38958} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.00294, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07858, "loss": 0.07858, "time": 0.5116} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.00293, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99375, "top5_acc": 0.99938, "loss_cls": 0.07359, "loss": 0.07359, "time": 0.23895} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.00292, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.065, "loss": 0.065, "time": 0.44955} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.00291, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.06625, "loss": 0.06625, "time": 0.49143} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.00289, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.09116, "loss": 0.09116, "time": 0.4912} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.00288, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.06703, "loss": 0.06703, "time": 0.49179} +{"mode": "val", "epoch": 117, "iter": 533, "lr": 0.00287, "top1_acc": 0.91562, "top5_acc": 0.99437, "mean_class_accuracy": 0.88268} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.00286, "memory": 4083, "data_time": 0.18647, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.07095, "loss": 0.07095, "time": 0.78001} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.00284, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04879, "loss": 0.04879, "time": 0.48997} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.00283, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06642, "loss": 0.06642, "time": 0.49056} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.00282, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.05366, "loss": 0.05366, "time": 0.49494} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.0028, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.08342, "loss": 0.08342, "time": 0.49274} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.00279, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.06499, "loss": 0.06499, "time": 0.38754} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.00278, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07445, "loss": 0.07445, "time": 0.51002} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.00277, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.05392, "loss": 0.05392, "time": 0.24171} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.00275, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06395, "loss": 0.06395, "time": 0.46997} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.00274, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05865, "loss": 0.05865, "time": 0.48876} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.00273, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07203, "loss": 0.07203, "time": 0.49376} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.00271, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.06209, "loss": 0.06209, "time": 0.48919} +{"mode": "val", "epoch": 118, "iter": 533, "lr": 0.0027, "top1_acc": 0.91456, "top5_acc": 0.99284, "mean_class_accuracy": 0.88201} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.00269, "memory": 4083, "data_time": 0.18841, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07087, "loss": 0.07087, "time": 0.79111} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.00268, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.08029, "loss": 0.08029, "time": 0.48817} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.00267, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08737, "loss": 0.08737, "time": 0.4895} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.00265, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.06229, "loss": 0.06229, "time": 0.49265} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.00264, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.0709, "loss": 0.0709, "time": 0.49024} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.00263, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.07771, "loss": 0.07771, "time": 0.35055} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.00262, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99, "top5_acc": 0.99938, "loss_cls": 0.08719, "loss": 0.08719, "time": 0.50847} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.0026, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07627, "loss": 0.07627, "time": 0.24749} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.00259, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.995, "top5_acc": 0.99938, "loss_cls": 0.07718, "loss": 0.07718, "time": 0.46884} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.00258, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.05753, "loss": 0.05753, "time": 0.49033} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.00257, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06955, "loss": 0.06955, "time": 0.48678} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.00255, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.05172, "loss": 0.05172, "time": 0.49215} +{"mode": "val", "epoch": 119, "iter": 533, "lr": 0.00254, "top1_acc": 0.91245, "top5_acc": 0.99343, "mean_class_accuracy": 0.8779} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.00253, "memory": 4083, "data_time": 0.18789, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.05194, "loss": 0.05194, "time": 0.78854} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.00252, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.04878, "loss": 0.04878, "time": 0.48713} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.00251, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.03716, "loss": 0.03716, "time": 0.4896} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.00249, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03748, "loss": 0.03748, "time": 0.48839} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.00248, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04999, "loss": 0.04999, "time": 0.49254} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.00247, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99312, "top5_acc": 0.99938, "loss_cls": 0.0698, "loss": 0.0698, "time": 0.3527} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.00246, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05851, "loss": 0.05851, "time": 0.51061} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.00245, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.0613, "loss": 0.0613, "time": 0.25246} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.00243, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.06278, "loss": 0.06278, "time": 0.47885} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.00242, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06583, "loss": 0.06583, "time": 0.49068} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00241, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08437, "loss": 0.08437, "time": 0.48314} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.0024, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05845, "loss": 0.05845, "time": 0.49432} +{"mode": "val", "epoch": 120, "iter": 533, "lr": 0.00239, "top1_acc": 0.90611, "top5_acc": 0.99472, "mean_class_accuracy": 0.88252} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00238, "memory": 4083, "data_time": 0.18843, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.05707, "loss": 0.05707, "time": 0.78633} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00236, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.06038, "loss": 0.06038, "time": 0.48886} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.00235, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.04232, "loss": 0.04232, "time": 0.49422} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00234, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03851, "loss": 0.03851, "time": 0.49031} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00233, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04704, "loss": 0.04704, "time": 0.48943} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00232, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0502, "loss": 0.0502, "time": 0.34962} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.0023, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0406, "loss": 0.0406, "time": 0.50978} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00229, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03727, "loss": 0.03727, "time": 0.25027} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.00228, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04805, "loss": 0.04805, "time": 0.47249} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00227, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.043, "loss": 0.043, "time": 0.48956} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00226, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.0528, "loss": 0.0528, "time": 0.48994} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00225, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05511, "loss": 0.05511, "time": 0.49122} +{"mode": "val", "epoch": 121, "iter": 533, "lr": 0.00224, "top1_acc": 0.9148, "top5_acc": 0.9939, "mean_class_accuracy": 0.88016} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00222, "memory": 4083, "data_time": 0.18523, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.04419, "loss": 0.04419, "time": 0.78292} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00221, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.04125, "loss": 0.04125, "time": 0.48938} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.0022, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.05347, "loss": 0.05347, "time": 0.48961} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00219, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05396, "loss": 0.05396, "time": 0.48955} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00218, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04798, "loss": 0.04798, "time": 0.48889} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00217, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04176, "loss": 0.04176, "time": 0.35084} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00215, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04227, "loss": 0.04227, "time": 0.51077} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00214, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04051, "loss": 0.04051, "time": 0.25281} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.00213, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04376, "loss": 0.04376, "time": 0.48848} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00212, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06719, "loss": 0.06719, "time": 0.49204} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00211, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.04848, "loss": 0.04848, "time": 0.49355} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.0021, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04681, "loss": 0.04681, "time": 0.48603} +{"mode": "val", "epoch": 122, "iter": 533, "lr": 0.00209, "top1_acc": 0.91691, "top5_acc": 0.99484, "mean_class_accuracy": 0.88623} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00208, "memory": 4083, "data_time": 0.18357, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03543, "loss": 0.03543, "time": 0.78676} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00207, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04882, "loss": 0.04882, "time": 0.48735} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00205, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04547, "loss": 0.04547, "time": 0.49359} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00204, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.04123, "loss": 0.04123, "time": 0.49031} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00203, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03652, "loss": 0.03652, "time": 0.49079} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00202, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04043, "loss": 0.04043, "time": 0.33794} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00201, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03847, "loss": 0.03847, "time": 0.51047} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.002, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04439, "loss": 0.04439, "time": 0.25573} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00199, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06204, "loss": 0.06204, "time": 0.48738} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.00198, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.05443, "loss": 0.05443, "time": 0.49606} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00197, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.0554, "loss": 0.0554, "time": 0.49112} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00195, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05732, "loss": 0.05732, "time": 0.48623} +{"mode": "val", "epoch": 123, "iter": 533, "lr": 0.00195, "top1_acc": 0.91691, "top5_acc": 0.99507, "mean_class_accuracy": 0.88463} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00194, "memory": 4083, "data_time": 0.18557, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0442, "loss": 0.0442, "time": 0.79016} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00192, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03835, "loss": 0.03835, "time": 0.48865} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00191, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03284, "loss": 0.03284, "time": 0.48826} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.0019, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04517, "loss": 0.04517, "time": 0.49253} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00189, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05818, "loss": 0.05818, "time": 0.48832} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00188, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04604, "loss": 0.04604, "time": 0.32793} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00187, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.0494, "loss": 0.0494, "time": 0.51165} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00186, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03952, "loss": 0.03952, "time": 0.25572} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00185, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03618, "loss": 0.03618, "time": 0.48992} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00184, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.04161, "loss": 0.04161, "time": 0.49114} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00183, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03639, "loss": 0.03639, "time": 0.4907} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.00182, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.04132, "loss": 0.04132, "time": 0.49294} +{"mode": "val", "epoch": 124, "iter": 533, "lr": 0.00181, "top1_acc": 0.91738, "top5_acc": 0.99531, "mean_class_accuracy": 0.8877} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.0018, "memory": 4083, "data_time": 0.18914, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02993, "loss": 0.02993, "time": 0.78981} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.00179, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02986, "loss": 0.02986, "time": 0.49008} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00178, "memory": 4083, "data_time": 0.00043, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02973, "loss": 0.02973, "time": 0.49109} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00177, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03366, "loss": 0.03366, "time": 0.49103} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00176, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03413, "loss": 0.03413, "time": 0.49139} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00175, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02844, "loss": 0.02844, "time": 0.32084} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00173, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02911, "loss": 0.02911, "time": 0.50986} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00172, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03539, "loss": 0.03539, "time": 0.27154} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.00171, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03683, "loss": 0.03683, "time": 0.49082} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.0017, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03858, "loss": 0.03858, "time": 0.48861} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00169, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03131, "loss": 0.03131, "time": 0.49086} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00168, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04773, "loss": 0.04773, "time": 0.48988} +{"mode": "val", "epoch": 125, "iter": 533, "lr": 0.00167, "top1_acc": 0.92031, "top5_acc": 0.99437, "mean_class_accuracy": 0.88931} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00166, "memory": 4083, "data_time": 0.19038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.03175, "loss": 0.03175, "time": 0.79487} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00165, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02808, "loss": 0.02808, "time": 0.48932} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00164, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03046, "loss": 0.03046, "time": 0.49139} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00163, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.035, "loss": 0.035, "time": 0.48784} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00162, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03268, "loss": 0.03268, "time": 0.49108} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00161, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03308, "loss": 0.03308, "time": 0.29495} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0016, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03468, "loss": 0.03468, "time": 0.50964} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00159, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.03211, "loss": 0.03211, "time": 0.29874} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00158, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02939, "loss": 0.02939, "time": 0.49057} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00157, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.03137, "loss": 0.03137, "time": 0.48876} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00156, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03633, "loss": 0.03633, "time": 0.48958} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00155, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03137, "loss": 0.03137, "time": 0.48916} +{"mode": "val", "epoch": 126, "iter": 533, "lr": 0.00155, "top1_acc": 0.9202, "top5_acc": 0.99566, "mean_class_accuracy": 0.88775} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00154, "memory": 4083, "data_time": 0.18961, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03044, "loss": 0.03044, "time": 0.78789} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00153, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03041, "loss": 0.03041, "time": 0.49032} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00152, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03059, "loss": 0.03059, "time": 0.49104} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00151, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0268, "loss": 0.0268, "time": 0.48777} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.0015, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02931, "loss": 0.02931, "time": 0.48868} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.00149, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03049, "loss": 0.03049, "time": 0.28788} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00148, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02787, "loss": 0.02787, "time": 0.50972} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00147, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03125, "loss": 0.03125, "time": 0.30969} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00146, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02626, "loss": 0.02626, "time": 0.48836} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00145, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02999, "loss": 0.02999, "time": 0.49167} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00144, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0268, "loss": 0.0268, "time": 0.49178} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00143, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03265, "loss": 0.03265, "time": 0.48401} +{"mode": "val", "epoch": 127, "iter": 533, "lr": 0.00142, "top1_acc": 0.92489, "top5_acc": 0.99531, "mean_class_accuracy": 0.89678} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00141, "memory": 4083, "data_time": 0.19772, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02571, "loss": 0.02571, "time": 0.82105} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.0014, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02578, "loss": 0.02578, "time": 0.49162} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00139, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02593, "loss": 0.02593, "time": 0.48614} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00138, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02739, "loss": 0.02739, "time": 0.48915} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00138, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0257, "loss": 0.0257, "time": 0.48857} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00137, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02673, "loss": 0.02673, "time": 0.30111} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.00136, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03183, "loss": 0.03183, "time": 0.44489} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00135, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03217, "loss": 0.03217, "time": 0.33119} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00134, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02771, "loss": 0.02771, "time": 0.49253} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00133, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02697, "loss": 0.02697, "time": 0.49184} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00132, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02933, "loss": 0.02933, "time": 0.48789} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00131, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03047, "loss": 0.03047, "time": 0.48803} +{"mode": "val", "epoch": 128, "iter": 533, "lr": 0.0013, "top1_acc": 0.92489, "top5_acc": 0.99578, "mean_class_accuracy": 0.89655} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.00129, "memory": 4083, "data_time": 0.19197, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02867, "loss": 0.02867, "time": 0.80409} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00129, "memory": 4083, "data_time": 0.00042, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02553, "loss": 0.02553, "time": 0.48935} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00128, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02548, "loss": 0.02548, "time": 0.48992} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00127, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02515, "loss": 0.02515, "time": 0.49034} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00126, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02865, "loss": 0.02865, "time": 0.48769} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00125, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02776, "loss": 0.02776, "time": 0.32445} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00124, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02784, "loss": 0.02784, "time": 0.42176} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00123, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0244, "loss": 0.0244, "time": 0.35075} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.00122, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02659, "loss": 0.02659, "time": 0.48586} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00121, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02802, "loss": 0.02802, "time": 0.49116} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00121, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02642, "loss": 0.02642, "time": 0.48858} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.0012, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02634, "loss": 0.02634, "time": 0.49285} +{"mode": "val", "epoch": 129, "iter": 533, "lr": 0.00119, "top1_acc": 0.92524, "top5_acc": 0.99578, "mean_class_accuracy": 0.89624} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00118, "memory": 4083, "data_time": 0.18949, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02711, "loss": 0.02711, "time": 0.79899} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00117, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02499, "loss": 0.02499, "time": 0.49035} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00116, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02793, "loss": 0.02793, "time": 0.48738} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00116, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02862, "loss": 0.02862, "time": 0.49193} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.00115, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02714, "loss": 0.02714, "time": 0.48816} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00114, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02709, "loss": 0.02709, "time": 0.32971} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00113, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02641, "loss": 0.02641, "time": 0.40516} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00112, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02467, "loss": 0.02467, "time": 0.34682} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00111, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02725, "loss": 0.02725, "time": 0.49376} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.0011, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0246, "loss": 0.0246, "time": 0.48897} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.0011, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02517, "loss": 0.02517, "time": 0.48861} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00109, "memory": 4083, "data_time": 0.00041, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02639, "loss": 0.02639, "time": 0.48981} +{"mode": "val", "epoch": 130, "iter": 533, "lr": 0.00108, "top1_acc": 0.92325, "top5_acc": 0.99531, "mean_class_accuracy": 0.89034} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00107, "memory": 4083, "data_time": 0.19628, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02667, "loss": 0.02667, "time": 0.80928} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.00106, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02982, "loss": 0.02982, "time": 0.4901} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00106, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02955, "loss": 0.02955, "time": 0.48952} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00105, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03132, "loss": 0.03132, "time": 0.48839} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00104, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02844, "loss": 0.02844, "time": 0.48545} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00103, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03253, "loss": 0.03253, "time": 0.34915} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00102, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02581, "loss": 0.02581, "time": 0.38533} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00102, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0267, "loss": 0.0267, "time": 0.37231} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00101, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03417, "loss": 0.03417, "time": 0.48691} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.001, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02553, "loss": 0.02553, "time": 0.4897} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.00099, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0264, "loss": 0.0264, "time": 0.49039} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00098, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02765, "loss": 0.02765, "time": 0.4926} +{"mode": "val", "epoch": 131, "iter": 533, "lr": 0.00098, "top1_acc": 0.92513, "top5_acc": 0.99589, "mean_class_accuracy": 0.89744} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.00097, "memory": 4083, "data_time": 0.19698, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02551, "loss": 0.02551, "time": 0.81255} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00096, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02409, "loss": 0.02409, "time": 0.4925} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00095, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02479, "loss": 0.02479, "time": 0.49264} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00095, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0252, "loss": 0.0252, "time": 0.4882} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00094, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02547, "loss": 0.02547, "time": 0.45645} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00093, "memory": 4083, "data_time": 0.00044, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02603, "loss": 0.02603, "time": 0.39133} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00092, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02708, "loss": 0.02708, "time": 0.34251} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00091, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0318, "loss": 0.0318, "time": 0.37485} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00091, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02877, "loss": 0.02877, "time": 0.49074} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0009, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02686, "loss": 0.02686, "time": 0.4892} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00089, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0243, "loss": 0.0243, "time": 0.48714} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00088, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02639, "loss": 0.02639, "time": 0.4911} +{"mode": "val", "epoch": 132, "iter": 533, "lr": 0.00088, "top1_acc": 0.92665, "top5_acc": 0.99531, "mean_class_accuracy": 0.89835} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.00087, "memory": 4083, "data_time": 0.19124, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02479, "loss": 0.02479, "time": 0.81506} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00086, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02669, "loss": 0.02669, "time": 0.49013} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00086, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02631, "loss": 0.02631, "time": 0.48954} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00085, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02488, "loss": 0.02488, "time": 0.4924} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00084, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02583, "loss": 0.02583, "time": 0.45622} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00083, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0266, "loss": 0.0266, "time": 0.40119} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00083, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0239, "loss": 0.0239, "time": 0.33271} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00082, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.026, "loss": 0.026, "time": 0.3896} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00081, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02503, "loss": 0.02503, "time": 0.48964} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.0008, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02492, "loss": 0.02492, "time": 0.48399} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0008, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02444, "loss": 0.02444, "time": 0.48821} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00079, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02402, "loss": 0.02402, "time": 0.48743} +{"mode": "val", "epoch": 133, "iter": 533, "lr": 0.00078, "top1_acc": 0.92677, "top5_acc": 0.99542, "mean_class_accuracy": 0.89964} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00078, "memory": 4083, "data_time": 0.19325, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0247, "loss": 0.0247, "time": 0.82031} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00077, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02868, "loss": 0.02868, "time": 0.48811} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00076, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02922, "loss": 0.02922, "time": 0.49061} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.00076, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03167, "loss": 0.03167, "time": 0.49442} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00075, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02458, "loss": 0.02458, "time": 0.43006} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00074, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02529, "loss": 0.02529, "time": 0.46473} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00073, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02742, "loss": 0.02742, "time": 0.2758} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00073, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02489, "loss": 0.02489, "time": 0.42196} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00072, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02463, "loss": 0.02463, "time": 0.48638} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00071, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02538, "loss": 0.02538, "time": 0.48797} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00071, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02607, "loss": 0.02607, "time": 0.48754} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.0007, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02437, "loss": 0.02437, "time": 0.4888} +{"mode": "val", "epoch": 134, "iter": 533, "lr": 0.0007, "top1_acc": 0.92653, "top5_acc": 0.99542, "mean_class_accuracy": 0.89735} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00069, "memory": 4083, "data_time": 0.18958, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02447, "loss": 0.02447, "time": 0.816} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00068, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02458, "loss": 0.02458, "time": 0.49242} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00068, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02338, "loss": 0.02338, "time": 0.48797} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00067, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02621, "loss": 0.02621, "time": 0.48904} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00066, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02572, "loss": 0.02572, "time": 0.39654} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00066, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02723, "loss": 0.02723, "time": 0.51256} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00065, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02666, "loss": 0.02666, "time": 0.23646} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00064, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02435, "loss": 0.02435, "time": 0.44018} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.00064, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03184, "loss": 0.03184, "time": 0.48668} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00063, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02307, "loss": 0.02307, "time": 0.49113} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00062, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02527, "loss": 0.02527, "time": 0.4929} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00062, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02518, "loss": 0.02518, "time": 0.48793} +{"mode": "val", "epoch": 135, "iter": 533, "lr": 0.00061, "top1_acc": 0.92583, "top5_acc": 0.99542, "mean_class_accuracy": 0.89759} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00061, "memory": 4083, "data_time": 0.18933, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0233, "loss": 0.0233, "time": 0.80153} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.0006, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02809, "loss": 0.02809, "time": 0.48852} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00059, "memory": 4083, "data_time": 0.00054, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02385, "loss": 0.02385, "time": 0.48795} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00059, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.024, "loss": 0.024, "time": 0.49245} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.00058, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02704, "loss": 0.02704, "time": 0.38807} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.00057, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0274, "loss": 0.0274, "time": 0.51064} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00057, "memory": 4083, "data_time": 0.00043, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02494, "loss": 0.02494, "time": 0.23481} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00056, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02646, "loss": 0.02646, "time": 0.44213} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00056, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02634, "loss": 0.02634, "time": 0.48775} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00055, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0245, "loss": 0.0245, "time": 0.48939} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00054, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02565, "loss": 0.02565, "time": 0.48985} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00054, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02419, "loss": 0.02419, "time": 0.49006} +{"mode": "val", "epoch": 136, "iter": 533, "lr": 0.00053, "top1_acc": 0.92536, "top5_acc": 0.99578, "mean_class_accuracy": 0.89605} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00053, "memory": 4083, "data_time": 0.18691, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02526, "loss": 0.02526, "time": 0.79514} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00052, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0256, "loss": 0.0256, "time": 0.49182} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00052, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0243, "loss": 0.0243, "time": 0.48958} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.00051, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02424, "loss": 0.02424, "time": 0.48806} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.0005, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02341, "loss": 0.02341, "time": 0.40001} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.0005, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02319, "loss": 0.02319, "time": 0.50958} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00049, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02399, "loss": 0.02399, "time": 0.23642} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00049, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02421, "loss": 0.02421, "time": 0.45443} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00048, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02329, "loss": 0.02329, "time": 0.48833} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00048, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02627, "loss": 0.02627, "time": 0.48921} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00047, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02432, "loss": 0.02432, "time": 0.48984} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00046, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02468, "loss": 0.02468, "time": 0.49156} +{"mode": "val", "epoch": 137, "iter": 533, "lr": 0.00046, "top1_acc": 0.92665, "top5_acc": 0.99519, "mean_class_accuracy": 0.89656} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00046, "memory": 4083, "data_time": 0.18533, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02356, "loss": 0.02356, "time": 0.79045} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00045, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02608, "loss": 0.02608, "time": 0.49037} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00044, "memory": 4083, "data_time": 0.00061, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02659, "loss": 0.02659, "time": 0.48585} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00044, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02616, "loss": 0.02616, "time": 0.48913} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.00043, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02385, "loss": 0.02385, "time": 0.3821} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.00043, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02383, "loss": 0.02383, "time": 0.51094} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00042, "memory": 4083, "data_time": 0.00043, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02516, "loss": 0.02516, "time": 0.24334} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00042, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02254, "loss": 0.02254, "time": 0.46063} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00041, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02476, "loss": 0.02476, "time": 0.49087} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00041, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02367, "loss": 0.02367, "time": 0.48919} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.0004, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02553, "loss": 0.02553, "time": 0.49011} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.0004, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0247, "loss": 0.0247, "time": 0.48894} +{"mode": "val", "epoch": 138, "iter": 533, "lr": 0.00039, "top1_acc": 0.92712, "top5_acc": 0.99495, "mean_class_accuracy": 0.89802} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00039, "memory": 4083, "data_time": 0.18558, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02485, "loss": 0.02485, "time": 0.79596} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00038, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02486, "loss": 0.02486, "time": 0.48944} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00038, "memory": 4083, "data_time": 0.00046, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0241, "loss": 0.0241, "time": 0.49293} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00037, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02473, "loss": 0.02473, "time": 0.48821} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00037, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02376, "loss": 0.02376, "time": 0.35034} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00036, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02423, "loss": 0.02423, "time": 0.51034} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00036, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02489, "loss": 0.02489, "time": 0.24689} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00035, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02383, "loss": 0.02383, "time": 0.48312} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00035, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0255, "loss": 0.0255, "time": 0.48892} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.00034, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02438, "loss": 0.02438, "time": 0.4912} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.00034, "memory": 4083, "data_time": 0.00042, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02344, "loss": 0.02344, "time": 0.49035} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00033, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02266, "loss": 0.02266, "time": 0.48995} +{"mode": "val", "epoch": 139, "iter": 533, "lr": 0.00033, "top1_acc": 0.92665, "top5_acc": 0.99542, "mean_class_accuracy": 0.89706} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00033, "memory": 4083, "data_time": 0.18625, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02436, "loss": 0.02436, "time": 0.79121} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00032, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02481, "loss": 0.02481, "time": 0.49092} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.00032, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0249, "loss": 0.0249, "time": 0.48887} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.00031, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02269, "loss": 0.02269, "time": 0.49082} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00031, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02586, "loss": 0.02586, "time": 0.34018} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.0003, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02567, "loss": 0.02567, "time": 0.5087} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.0003, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02328, "loss": 0.02328, "time": 0.25339} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00029, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02421, "loss": 0.02421, "time": 0.48892} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00029, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02404, "loss": 0.02404, "time": 0.49406} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00029, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02362, "loss": 0.02362, "time": 0.48907} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00028, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02305, "loss": 0.02305, "time": 0.48885} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00028, "memory": 4083, "data_time": 0.00044, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02434, "loss": 0.02434, "time": 0.49162} +{"mode": "val", "epoch": 140, "iter": 533, "lr": 0.00027, "top1_acc": 0.92595, "top5_acc": 0.99601, "mean_class_accuracy": 0.89536} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00027, "memory": 4083, "data_time": 0.18699, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02472, "loss": 0.02472, "time": 0.78417} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00026, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02411, "loss": 0.02411, "time": 0.4854} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00026, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02315, "loss": 0.02315, "time": 0.48408} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00026, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02427, "loss": 0.02427, "time": 0.48996} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00025, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02297, "loss": 0.02297, "time": 0.33282} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00025, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0247, "loss": 0.0247, "time": 0.50888} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00024, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02519, "loss": 0.02519, "time": 0.26242} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00024, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02367, "loss": 0.02367, "time": 0.48914} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00024, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02358, "loss": 0.02358, "time": 0.48902} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00023, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02412, "loss": 0.02412, "time": 0.49314} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00023, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02392, "loss": 0.02392, "time": 0.49283} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00022, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0235, "loss": 0.0235, "time": 0.48884} +{"mode": "val", "epoch": 141, "iter": 533, "lr": 0.00022, "top1_acc": 0.92794, "top5_acc": 0.99566, "mean_class_accuracy": 0.89856} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00022, "memory": 4083, "data_time": 0.18268, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02388, "loss": 0.02388, "time": 0.77888} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00021, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02204, "loss": 0.02204, "time": 0.48748} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00021, "memory": 4083, "data_time": 0.00045, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02263, "loss": 0.02263, "time": 0.4868} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00021, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02279, "loss": 0.02279, "time": 0.48841} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.0002, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0234, "loss": 0.0234, "time": 0.32559} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.0002, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02307, "loss": 0.02307, "time": 0.50843} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.0002, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02435, "loss": 0.02435, "time": 0.25318} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00019, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0235, "loss": 0.0235, "time": 0.48811} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00019, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02515, "loss": 0.02515, "time": 0.49164} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00018, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02517, "loss": 0.02517, "time": 0.49298} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00018, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02491, "loss": 0.02491, "time": 0.49002} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00018, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0246, "loss": 0.0246, "time": 0.49076} +{"mode": "val", "epoch": 142, "iter": 533, "lr": 0.00018, "top1_acc": 0.92747, "top5_acc": 0.99578, "mean_class_accuracy": 0.89861} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.00017, "memory": 4083, "data_time": 0.1835, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02253, "loss": 0.02253, "time": 0.79389} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00017, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02543, "loss": 0.02543, "time": 0.49233} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00017, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0236, "loss": 0.0236, "time": 0.48844} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00016, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02446, "loss": 0.02446, "time": 0.49166} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00016, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02662, "loss": 0.02662, "time": 0.32028} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00016, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02563, "loss": 0.02563, "time": 0.50975} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00015, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.023, "loss": 0.023, "time": 0.25477} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00015, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02406, "loss": 0.02406, "time": 0.48247} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00015, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02395, "loss": 0.02395, "time": 0.48571} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00014, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02275, "loss": 0.02275, "time": 0.49078} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00014, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02656, "loss": 0.02656, "time": 0.48999} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00014, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02526, "loss": 0.02526, "time": 0.48912} +{"mode": "val", "epoch": 143, "iter": 533, "lr": 0.00013, "top1_acc": 0.92583, "top5_acc": 0.99589, "mean_class_accuracy": 0.89756} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00013, "memory": 4083, "data_time": 0.183, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02188, "loss": 0.02188, "time": 0.77231} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00013, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02295, "loss": 0.02295, "time": 0.49001} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00013, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02487, "loss": 0.02487, "time": 0.48962} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00012, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02397, "loss": 0.02397, "time": 0.48847} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00012, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0243, "loss": 0.0243, "time": 0.35084} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00012, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02349, "loss": 0.02349, "time": 0.50802} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00011, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02307, "loss": 0.02307, "time": 0.2386} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.00011, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02515, "loss": 0.02515, "time": 0.45929} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.00011, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02397, "loss": 0.02397, "time": 0.48882} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.00011, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02267, "loss": 0.02267, "time": 0.49254} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.0001, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02294, "loss": 0.02294, "time": 0.49171} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.0001, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02457, "loss": 0.02457, "time": 0.49287} +{"mode": "val", "epoch": 144, "iter": 533, "lr": 0.0001, "top1_acc": 0.92806, "top5_acc": 0.99554, "mean_class_accuracy": 0.90111} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.0001, "memory": 4083, "data_time": 0.18205, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02486, "loss": 0.02486, "time": 0.79943} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 9e-05, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0253, "loss": 0.0253, "time": 0.48949} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 9e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.024, "loss": 0.024, "time": 0.49052} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 9e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02352, "loss": 0.02352, "time": 0.49017} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 9e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02247, "loss": 0.02247, "time": 0.3709} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 8e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02364, "loss": 0.02364, "time": 0.50848} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 8e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02299, "loss": 0.02299, "time": 0.23847} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 8e-05, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02329, "loss": 0.02329, "time": 0.46309} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 8e-05, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02335, "loss": 0.02335, "time": 0.49127} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 7e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0235, "loss": 0.0235, "time": 0.48998} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 7e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02399, "loss": 0.02399, "time": 0.4907} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 7e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02526, "loss": 0.02526, "time": 0.49133} +{"mode": "val", "epoch": 145, "iter": 533, "lr": 7e-05, "top1_acc": 0.92618, "top5_acc": 0.99542, "mean_class_accuracy": 0.89605} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 7e-05, "memory": 4083, "data_time": 0.18136, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02454, "loss": 0.02454, "time": 0.79338} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 6e-05, "memory": 4083, "data_time": 0.00048, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02279, "loss": 0.02279, "time": 0.48874} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 6e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02433, "loss": 0.02433, "time": 0.4906} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 6e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0252, "loss": 0.0252, "time": 0.49036} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 6e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02367, "loss": 0.02367, "time": 0.36469} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 6e-05, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0246, "loss": 0.0246, "time": 0.5092} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 5e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02342, "loss": 0.02342, "time": 0.24328} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 5e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02298, "loss": 0.02298, "time": 0.46094} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 5e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0224, "loss": 0.0224, "time": 0.49464} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 5e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02524, "loss": 0.02524, "time": 0.4892} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 5e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0241, "loss": 0.0241, "time": 0.49305} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 5e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02235, "loss": 0.02235, "time": 0.48887} +{"mode": "val", "epoch": 146, "iter": 533, "lr": 4e-05, "top1_acc": 0.9263, "top5_acc": 0.99578, "mean_class_accuracy": 0.89648} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 4e-05, "memory": 4083, "data_time": 0.18884, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02355, "loss": 0.02355, "time": 0.80129} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 4e-05, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02287, "loss": 0.02287, "time": 0.48756} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 4e-05, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02299, "loss": 0.02299, "time": 0.49045} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 4e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02506, "loss": 0.02506, "time": 0.48554} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 4e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02269, "loss": 0.02269, "time": 0.36624} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 3e-05, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0239, "loss": 0.0239, "time": 0.50908} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 3e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02555, "loss": 0.02555, "time": 0.23425} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 3e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02472, "loss": 0.02472, "time": 0.44674} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 3e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02614, "loss": 0.02614, "time": 0.49326} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 3e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0256, "loss": 0.0256, "time": 0.4943} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 3e-05, "memory": 4083, "data_time": 0.00047, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02228, "loss": 0.02228, "time": 0.49242} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 3e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02311, "loss": 0.02311, "time": 0.4906} +{"mode": "val", "epoch": 147, "iter": 533, "lr": 2e-05, "top1_acc": 0.92759, "top5_acc": 0.99566, "mean_class_accuracy": 0.89928} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 2e-05, "memory": 4083, "data_time": 0.18456, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02272, "loss": 0.02272, "time": 0.80132} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 2e-05, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0245, "loss": 0.0245, "time": 0.48999} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 2e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0251, "loss": 0.0251, "time": 0.49049} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 2e-05, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02342, "loss": 0.02342, "time": 0.48988} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 2e-05, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02296, "loss": 0.02296, "time": 0.38013} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 2e-05, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02421, "loss": 0.02421, "time": 0.5106} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 2e-05, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02394, "loss": 0.02394, "time": 0.23528} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 2e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.024, "loss": 0.024, "time": 0.43163} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02438, "loss": 0.02438, "time": 0.49187} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 1e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02343, "loss": 0.02343, "time": 0.48802} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 1e-05, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02452, "loss": 0.02452, "time": 0.48872} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02508, "loss": 0.02508, "time": 0.49193} +{"mode": "val", "epoch": 148, "iter": 533, "lr": 1e-05, "top1_acc": 0.92689, "top5_acc": 0.99542, "mean_class_accuracy": 0.89768} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 1e-05, "memory": 4083, "data_time": 0.18463, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02397, "loss": 0.02397, "time": 0.7893} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02432, "loss": 0.02432, "time": 0.49073} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 1e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0237, "loss": 0.0237, "time": 0.49458} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 1e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02423, "loss": 0.02423, "time": 0.49312} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 1e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02536, "loss": 0.02536, "time": 0.41358} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 1e-05, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02397, "loss": 0.02397, "time": 0.47481} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02276, "loss": 0.02276, "time": 0.25668} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02313, "loss": 0.02313, "time": 0.42901} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02426, "loss": 0.02426, "time": 0.49155} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02278, "loss": 0.02278, "time": 0.49286} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02237, "loss": 0.02237, "time": 0.49276} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02376, "loss": 0.02376, "time": 0.4918} +{"mode": "val", "epoch": 149, "iter": 533, "lr": 0.0, "top1_acc": 0.92783, "top5_acc": 0.99566, "mean_class_accuracy": 0.89833} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 0.0, "memory": 4083, "data_time": 0.18401, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02212, "loss": 0.02212, "time": 0.80815} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 0.0, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0231, "loss": 0.0231, "time": 0.48863} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 0.0, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02421, "loss": 0.02421, "time": 0.49195} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02504, "loss": 0.02504, "time": 0.48941} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 0.0, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02269, "loss": 0.02269, "time": 0.40722} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02281, "loss": 0.02281, "time": 0.48215} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02437, "loss": 0.02437, "time": 0.25051} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 0.0, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0233, "loss": 0.0233, "time": 0.41906} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02194, "loss": 0.02194, "time": 0.48672} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02466, "loss": 0.02466, "time": 0.49027} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02286, "loss": 0.02286, "time": 0.49541} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02346, "loss": 0.02346, "time": 0.48934} +{"mode": "val", "epoch": 150, "iter": 533, "lr": 0.0, "top1_acc": 0.92853, "top5_acc": 0.99542, "mean_class_accuracy": 0.89895} diff --git a/finegym/j_2/best_pred.pkl b/finegym/j_2/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..30951247ea936841681f910af309272de34b18b8 --- /dev/null +++ b/finegym/j_2/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd20197d42a554c12bc0af8293b23f4a96c30bf6ca028d911b11074c11352432 +size 5257662 diff --git a/finegym/j_2/best_top1_acc_epoch_150.pth b/finegym/j_2/best_top1_acc_epoch_150.pth new file mode 100644 index 0000000000000000000000000000000000000000..33bad558bffec364cd91391badbd34df13ddd684 --- /dev/null +++ b/finegym/j_2/best_top1_acc_epoch_150.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bad9ebbd0bcf82a0c88cbab4ab8c28418e150df63fa0be4db2d67e03725efe35 +size 31999601 diff --git a/finegym/j_2/j_2.py b/finegym/j_2/j_2.py new file mode 100644 index 0000000000000000000000000000000000000000..f06b4272ce23bdb5c81bcd510f3c05a4ee4b90fa --- /dev/null +++ b/finegym/j_2/j_2.py @@ -0,0 +1,113 @@ +modality = 'j' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/j_2' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/finegym/j_3/20250624_084345.log b/finegym/j_3/20250624_084345.log new file mode 100644 index 0000000000000000000000000000000000000000..738519d11ec485975ac0d5bfdef5dae2b376245d --- /dev/null +++ b/finegym/j_3/20250624_084345.log @@ -0,0 +1,3510 @@ +2025-06-24 08:43:45,978 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2025-06-24 08:43:46,258 - pyskl - INFO - Config: modality = 'j' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/j_3' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2025-06-24 08:43:46,259 - pyskl - INFO - Set random seed to 1562067252, deterministic: False +2025-06-24 08:43:47,751 - pyskl - INFO - 20484 videos remain after valid thresholding +2025-06-24 08:43:51,939 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-24 08:43:51,939 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3 +2025-06-24 08:43:51,940 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2025-06-24 08:43:51,940 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2025-06-24 08:43:51,940 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3 by HardDiskBackend. +2025-06-24 08:44:32,072 - pyskl - INFO - Epoch [1][100/1281] lr: 2.500e-02, eta: 21:24:25, time: 0.401, data_time: 0.184, memory: 4082, top1_acc: 0.0481, top5_acc: 0.2094, loss_cls: 4.5701, loss: 4.5701 +2025-06-24 08:44:53,589 - pyskl - INFO - Epoch [1][200/1281] lr: 2.500e-02, eta: 16:26:03, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.0750, top5_acc: 0.3081, loss_cls: 4.6447, loss: 4.6447 +2025-06-24 08:45:15,092 - pyskl - INFO - Epoch [1][300/1281] lr: 2.500e-02, eta: 14:46:12, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.1100, top5_acc: 0.3419, loss_cls: 4.4468, loss: 4.4468 +2025-06-24 08:45:37,003 - pyskl - INFO - Epoch [1][400/1281] lr: 2.500e-02, eta: 13:59:21, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.1212, top5_acc: 0.4100, loss_cls: 4.1699, loss: 4.1699 +2025-06-24 08:45:58,764 - pyskl - INFO - Epoch [1][500/1281] lr: 2.500e-02, eta: 13:30:08, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.1575, top5_acc: 0.5012, loss_cls: 3.8703, loss: 3.8703 +2025-06-24 08:46:20,716 - pyskl - INFO - Epoch [1][600/1281] lr: 2.500e-02, eta: 13:11:34, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.1913, top5_acc: 0.5687, loss_cls: 3.5739, loss: 3.5739 +2025-06-24 08:46:42,452 - pyskl - INFO - Epoch [1][700/1281] lr: 2.500e-02, eta: 12:57:12, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.2450, top5_acc: 0.5975, loss_cls: 3.3258, loss: 3.3258 +2025-06-24 08:47:04,369 - pyskl - INFO - Epoch [1][800/1281] lr: 2.500e-02, eta: 12:47:04, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.2737, top5_acc: 0.6412, loss_cls: 3.1596, loss: 3.1596 +2025-06-24 08:47:26,219 - pyskl - INFO - Epoch [1][900/1281] lr: 2.500e-02, eta: 12:38:52, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.3031, top5_acc: 0.6994, loss_cls: 2.9544, loss: 2.9544 +2025-06-24 08:47:47,880 - pyskl - INFO - Epoch [1][1000/1281] lr: 2.500e-02, eta: 12:31:37, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.3294, top5_acc: 0.7100, loss_cls: 2.8390, loss: 2.8390 +2025-06-24 08:48:09,647 - pyskl - INFO - Epoch [1][1100/1281] lr: 2.500e-02, eta: 12:25:56, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.3531, top5_acc: 0.7269, loss_cls: 2.8031, loss: 2.8031 +2025-06-24 08:48:31,510 - pyskl - INFO - Epoch [1][1200/1281] lr: 2.500e-02, eta: 12:21:24, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.3731, top5_acc: 0.7775, loss_cls: 2.6199, loss: 2.6199 +2025-06-24 08:48:50,017 - pyskl - INFO - Saving checkpoint at 1 epochs +2025-06-24 08:49:33,271 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 08:49:33,340 - pyskl - INFO - +top1_acc 0.4055 +top5_acc 0.7757 +2025-06-24 08:49:33,340 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 08:49:33,347 - pyskl - INFO - +mean_acc 0.2065 +2025-06-24 08:49:33,536 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2025-06-24 08:49:33,536 - pyskl - INFO - Best top1_acc is 0.4055 at 1 epoch. +2025-06-24 08:49:33,539 - pyskl - INFO - Epoch(val) [1][533] top1_acc: 0.4055, top5_acc: 0.7757, mean_class_accuracy: 0.2065 +2025-06-24 08:50:13,836 - pyskl - INFO - Epoch [2][100/1281] lr: 2.500e-02, eta: 12:16:23, time: 0.403, data_time: 0.185, memory: 4082, top1_acc: 0.3762, top5_acc: 0.7781, loss_cls: 2.5608, loss: 2.5608 +2025-06-24 08:50:35,805 - pyskl - INFO - Epoch [2][200/1281] lr: 2.500e-02, eta: 12:13:26, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.4350, top5_acc: 0.8181, loss_cls: 2.4102, loss: 2.4102 +2025-06-24 08:50:57,851 - pyskl - INFO - Epoch [2][300/1281] lr: 2.500e-02, eta: 12:10:58, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.4425, top5_acc: 0.8219, loss_cls: 2.3340, loss: 2.3340 +2025-06-24 08:51:19,727 - pyskl - INFO - Epoch [2][400/1281] lr: 2.500e-02, eta: 12:08:26, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.4475, top5_acc: 0.8231, loss_cls: 2.2844, loss: 2.2844 +2025-06-24 08:51:41,610 - pyskl - INFO - Epoch [2][500/1281] lr: 2.499e-02, eta: 12:06:09, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.4725, top5_acc: 0.8469, loss_cls: 2.1704, loss: 2.1704 +2025-06-24 08:52:03,308 - pyskl - INFO - Epoch [2][600/1281] lr: 2.499e-02, eta: 12:03:46, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.4481, top5_acc: 0.8562, loss_cls: 2.1963, loss: 2.1963 +2025-06-24 08:52:25,063 - pyskl - INFO - Epoch [2][700/1281] lr: 2.499e-02, eta: 12:01:41, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.4738, top5_acc: 0.8562, loss_cls: 2.1393, loss: 2.1393 +2025-06-24 08:52:46,824 - pyskl - INFO - Epoch [2][800/1281] lr: 2.499e-02, eta: 11:59:46, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.4988, top5_acc: 0.8806, loss_cls: 2.0107, loss: 2.0107 +2025-06-24 08:53:08,616 - pyskl - INFO - Epoch [2][900/1281] lr: 2.499e-02, eta: 11:58:02, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.5025, top5_acc: 0.8638, loss_cls: 2.0205, loss: 2.0205 +2025-06-24 08:53:30,212 - pyskl - INFO - Epoch [2][1000/1281] lr: 2.499e-02, eta: 11:56:09, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.5181, top5_acc: 0.8775, loss_cls: 1.9561, loss: 1.9561 +2025-06-24 08:53:52,040 - pyskl - INFO - Epoch [2][1100/1281] lr: 2.499e-02, eta: 11:54:42, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.5144, top5_acc: 0.8950, loss_cls: 1.9483, loss: 1.9483 +2025-06-24 08:54:14,052 - pyskl - INFO - Epoch [2][1200/1281] lr: 2.499e-02, eta: 11:53:35, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5056, top5_acc: 0.8825, loss_cls: 1.9601, loss: 1.9601 +2025-06-24 08:54:32,513 - pyskl - INFO - Saving checkpoint at 2 epochs +2025-06-24 08:55:15,395 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 08:55:15,451 - pyskl - INFO - +top1_acc 0.5043 +top5_acc 0.8811 +2025-06-24 08:55:15,451 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 08:55:15,458 - pyskl - INFO - +mean_acc 0.2876 +2025-06-24 08:55:15,462 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_1.pth was removed +2025-06-24 08:55:15,649 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2025-06-24 08:55:15,649 - pyskl - INFO - Best top1_acc is 0.5043 at 2 epoch. +2025-06-24 08:55:15,652 - pyskl - INFO - Epoch(val) [2][533] top1_acc: 0.5043, top5_acc: 0.8811, mean_class_accuracy: 0.2876 +2025-06-24 08:55:56,110 - pyskl - INFO - Epoch [3][100/1281] lr: 2.499e-02, eta: 11:52:25, time: 0.405, data_time: 0.184, memory: 4082, top1_acc: 0.5431, top5_acc: 0.9050, loss_cls: 1.7942, loss: 1.7942 +2025-06-24 08:56:18,063 - pyskl - INFO - Epoch [3][200/1281] lr: 2.499e-02, eta: 11:51:21, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5556, top5_acc: 0.8988, loss_cls: 1.8330, loss: 1.8330 +2025-06-24 08:56:40,025 - pyskl - INFO - Epoch [3][300/1281] lr: 2.499e-02, eta: 11:50:21, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5450, top5_acc: 0.9006, loss_cls: 1.8062, loss: 1.8062 +2025-06-24 08:57:01,778 - pyskl - INFO - Epoch [3][400/1281] lr: 2.499e-02, eta: 11:49:09, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.5575, top5_acc: 0.9113, loss_cls: 1.7391, loss: 1.7391 +2025-06-24 08:57:23,632 - pyskl - INFO - Epoch [3][500/1281] lr: 2.498e-02, eta: 11:48:07, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.5694, top5_acc: 0.9119, loss_cls: 1.7590, loss: 1.7590 +2025-06-24 08:57:45,647 - pyskl - INFO - Epoch [3][600/1281] lr: 2.498e-02, eta: 11:47:18, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5494, top5_acc: 0.9206, loss_cls: 1.7890, loss: 1.7890 +2025-06-24 08:58:07,650 - pyskl - INFO - Epoch [3][700/1281] lr: 2.498e-02, eta: 11:46:29, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5581, top5_acc: 0.9213, loss_cls: 1.7464, loss: 1.7464 +2025-06-24 08:58:29,362 - pyskl - INFO - Epoch [3][800/1281] lr: 2.498e-02, eta: 11:45:25, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.5519, top5_acc: 0.9275, loss_cls: 1.7218, loss: 1.7218 +2025-06-24 08:58:51,112 - pyskl - INFO - Epoch [3][900/1281] lr: 2.498e-02, eta: 11:44:26, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.5563, top5_acc: 0.9069, loss_cls: 1.7678, loss: 1.7678 +2025-06-24 08:59:12,819 - pyskl - INFO - Epoch [3][1000/1281] lr: 2.498e-02, eta: 11:43:27, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.5769, top5_acc: 0.9244, loss_cls: 1.6812, loss: 1.6812 +2025-06-24 08:59:34,725 - pyskl - INFO - Epoch [3][1100/1281] lr: 2.498e-02, eta: 11:42:40, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.5950, top5_acc: 0.9294, loss_cls: 1.6191, loss: 1.6191 +2025-06-24 08:59:56,856 - pyskl - INFO - Epoch [3][1200/1281] lr: 2.498e-02, eta: 11:42:06, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.5725, top5_acc: 0.9200, loss_cls: 1.6828, loss: 1.6828 +2025-06-24 09:00:15,176 - pyskl - INFO - Saving checkpoint at 3 epochs +2025-06-24 09:00:58,669 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:00:58,740 - pyskl - INFO - +top1_acc 0.5643 +top5_acc 0.9060 +2025-06-24 09:00:58,740 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:00:58,748 - pyskl - INFO - +mean_acc 0.3926 +2025-06-24 09:00:58,753 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_2.pth was removed +2025-06-24 09:00:59,047 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2025-06-24 09:00:59,048 - pyskl - INFO - Best top1_acc is 0.5643 at 3 epoch. +2025-06-24 09:00:59,050 - pyskl - INFO - Epoch(val) [3][533] top1_acc: 0.5643, top5_acc: 0.9060, mean_class_accuracy: 0.3926 +2025-06-24 09:01:38,940 - pyskl - INFO - Epoch [4][100/1281] lr: 2.497e-02, eta: 11:40:58, time: 0.399, data_time: 0.182, memory: 4082, top1_acc: 0.5850, top5_acc: 0.9356, loss_cls: 1.5944, loss: 1.5944 +2025-06-24 09:02:00,550 - pyskl - INFO - Epoch [4][200/1281] lr: 2.497e-02, eta: 11:40:01, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6150, top5_acc: 0.9350, loss_cls: 1.5410, loss: 1.5410 +2025-06-24 09:02:22,147 - pyskl - INFO - Epoch [4][300/1281] lr: 2.497e-02, eta: 11:39:05, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.5969, top5_acc: 0.9387, loss_cls: 1.5897, loss: 1.5897 +2025-06-24 09:02:43,941 - pyskl - INFO - Epoch [4][400/1281] lr: 2.497e-02, eta: 11:38:20, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6069, top5_acc: 0.9337, loss_cls: 1.6188, loss: 1.6188 +2025-06-24 09:03:05,560 - pyskl - INFO - Epoch [4][500/1281] lr: 2.497e-02, eta: 11:37:28, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6225, top5_acc: 0.9369, loss_cls: 1.5508, loss: 1.5508 +2025-06-24 09:03:27,046 - pyskl - INFO - Epoch [4][600/1281] lr: 2.497e-02, eta: 11:36:32, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.6156, top5_acc: 0.9469, loss_cls: 1.4946, loss: 1.4946 +2025-06-24 09:03:48,939 - pyskl - INFO - Epoch [4][700/1281] lr: 2.497e-02, eta: 11:35:55, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6100, top5_acc: 0.9394, loss_cls: 1.5414, loss: 1.5414 +2025-06-24 09:04:10,536 - pyskl - INFO - Epoch [4][800/1281] lr: 2.496e-02, eta: 11:35:06, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6138, top5_acc: 0.9463, loss_cls: 1.4844, loss: 1.4844 +2025-06-24 09:04:32,119 - pyskl - INFO - Epoch [4][900/1281] lr: 2.496e-02, eta: 11:34:17, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6131, top5_acc: 0.9425, loss_cls: 1.5241, loss: 1.5241 +2025-06-24 09:04:53,852 - pyskl - INFO - Epoch [4][1000/1281] lr: 2.496e-02, eta: 11:33:36, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6412, top5_acc: 0.9563, loss_cls: 1.4403, loss: 1.4403 +2025-06-24 09:05:15,708 - pyskl - INFO - Epoch [4][1100/1281] lr: 2.496e-02, eta: 11:33:00, time: 0.219, data_time: 0.001, memory: 4082, top1_acc: 0.6250, top5_acc: 0.9425, loss_cls: 1.5077, loss: 1.5077 +2025-06-24 09:05:37,144 - pyskl - INFO - Epoch [4][1200/1281] lr: 2.496e-02, eta: 11:32:09, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.6362, top5_acc: 0.9494, loss_cls: 1.5025, loss: 1.5025 +2025-06-24 09:05:55,737 - pyskl - INFO - Saving checkpoint at 4 epochs +2025-06-24 09:06:39,261 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:06:39,323 - pyskl - INFO - +top1_acc 0.6142 +top5_acc 0.9337 +2025-06-24 09:06:39,323 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:06:39,330 - pyskl - INFO - +mean_acc 0.4420 +2025-06-24 09:06:39,334 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_3.pth was removed +2025-06-24 09:06:39,514 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2025-06-24 09:06:39,515 - pyskl - INFO - Best top1_acc is 0.6142 at 4 epoch. +2025-06-24 09:06:39,517 - pyskl - INFO - Epoch(val) [4][533] top1_acc: 0.6142, top5_acc: 0.9337, mean_class_accuracy: 0.4420 +2025-06-24 09:07:19,048 - pyskl - INFO - Epoch [5][100/1281] lr: 2.495e-02, eta: 11:31:06, time: 0.395, data_time: 0.178, memory: 4082, top1_acc: 0.6294, top5_acc: 0.9513, loss_cls: 1.4474, loss: 1.4474 +2025-06-24 09:07:41,097 - pyskl - INFO - Epoch [5][200/1281] lr: 2.495e-02, eta: 11:30:39, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6400, top5_acc: 0.9513, loss_cls: 1.4097, loss: 1.4097 +2025-06-24 09:08:02,902 - pyskl - INFO - Epoch [5][300/1281] lr: 2.495e-02, eta: 11:30:04, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6538, top5_acc: 0.9563, loss_cls: 1.3751, loss: 1.3751 +2025-06-24 09:08:24,635 - pyskl - INFO - Epoch [5][400/1281] lr: 2.495e-02, eta: 11:29:27, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6319, top5_acc: 0.9519, loss_cls: 1.4211, loss: 1.4211 +2025-06-24 09:08:46,291 - pyskl - INFO - Epoch [5][500/1281] lr: 2.495e-02, eta: 11:28:47, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6525, top5_acc: 0.9556, loss_cls: 1.3878, loss: 1.3878 +2025-06-24 09:09:07,830 - pyskl - INFO - Epoch [5][600/1281] lr: 2.495e-02, eta: 11:28:05, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.6581, top5_acc: 0.9513, loss_cls: 1.3689, loss: 1.3689 +2025-06-24 09:09:29,558 - pyskl - INFO - Epoch [5][700/1281] lr: 2.494e-02, eta: 11:27:30, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6619, top5_acc: 0.9525, loss_cls: 1.3966, loss: 1.3966 +2025-06-24 09:09:51,560 - pyskl - INFO - Epoch [5][800/1281] lr: 2.494e-02, eta: 11:27:03, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6556, top5_acc: 0.9550, loss_cls: 1.4045, loss: 1.4045 +2025-06-24 09:10:13,420 - pyskl - INFO - Epoch [5][900/1281] lr: 2.494e-02, eta: 11:26:32, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6713, top5_acc: 0.9544, loss_cls: 1.3445, loss: 1.3445 +2025-06-24 09:10:35,405 - pyskl - INFO - Epoch [5][1000/1281] lr: 2.494e-02, eta: 11:26:06, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6706, top5_acc: 0.9519, loss_cls: 1.3663, loss: 1.3663 +2025-06-24 09:10:57,401 - pyskl - INFO - Epoch [5][1100/1281] lr: 2.494e-02, eta: 11:25:40, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6444, top5_acc: 0.9556, loss_cls: 1.4250, loss: 1.4250 +2025-06-24 09:11:19,417 - pyskl - INFO - Epoch [5][1200/1281] lr: 2.493e-02, eta: 11:25:14, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6512, top5_acc: 0.9619, loss_cls: 1.4186, loss: 1.4186 +2025-06-24 09:11:37,998 - pyskl - INFO - Saving checkpoint at 5 epochs +2025-06-24 09:12:20,797 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:12:20,864 - pyskl - INFO - +top1_acc 0.6658 +top5_acc 0.9489 +2025-06-24 09:12:20,864 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:12:20,871 - pyskl - INFO - +mean_acc 0.5409 +2025-06-24 09:12:20,876 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_4.pth was removed +2025-06-24 09:12:21,070 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2025-06-24 09:12:21,070 - pyskl - INFO - Best top1_acc is 0.6658 at 5 epoch. +2025-06-24 09:12:21,073 - pyskl - INFO - Epoch(val) [5][533] top1_acc: 0.6658, top5_acc: 0.9489, mean_class_accuracy: 0.5409 +2025-06-24 09:13:01,559 - pyskl - INFO - Epoch [6][100/1281] lr: 2.493e-02, eta: 11:24:47, time: 0.405, data_time: 0.188, memory: 4082, top1_acc: 0.6769, top5_acc: 0.9581, loss_cls: 1.2951, loss: 1.2951 +2025-06-24 09:13:23,435 - pyskl - INFO - Epoch [6][200/1281] lr: 2.493e-02, eta: 11:24:17, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6719, top5_acc: 0.9625, loss_cls: 1.3166, loss: 1.3166 +2025-06-24 09:13:45,009 - pyskl - INFO - Epoch [6][300/1281] lr: 2.492e-02, eta: 11:23:40, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6700, top5_acc: 0.9556, loss_cls: 1.3587, loss: 1.3587 +2025-06-24 09:14:06,956 - pyskl - INFO - Epoch [6][400/1281] lr: 2.492e-02, eta: 11:23:13, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6744, top5_acc: 0.9613, loss_cls: 1.2863, loss: 1.2863 +2025-06-24 09:14:28,883 - pyskl - INFO - Epoch [6][500/1281] lr: 2.492e-02, eta: 11:22:46, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6606, top5_acc: 0.9606, loss_cls: 1.3772, loss: 1.3772 +2025-06-24 09:14:50,631 - pyskl - INFO - Epoch [6][600/1281] lr: 2.492e-02, eta: 11:22:14, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6444, top5_acc: 0.9581, loss_cls: 1.3702, loss: 1.3702 +2025-06-24 09:15:12,451 - pyskl - INFO - Epoch [6][700/1281] lr: 2.492e-02, eta: 11:21:44, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7037, top5_acc: 0.9712, loss_cls: 1.2067, loss: 1.2067 +2025-06-24 09:15:34,075 - pyskl - INFO - Epoch [6][800/1281] lr: 2.491e-02, eta: 11:21:10, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7050, top5_acc: 0.9681, loss_cls: 1.2496, loss: 1.2496 +2025-06-24 09:15:55,901 - pyskl - INFO - Epoch [6][900/1281] lr: 2.491e-02, eta: 11:20:41, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6956, top5_acc: 0.9706, loss_cls: 1.2511, loss: 1.2511 +2025-06-24 09:16:17,542 - pyskl - INFO - Epoch [6][1000/1281] lr: 2.491e-02, eta: 11:20:07, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7163, top5_acc: 0.9681, loss_cls: 1.1860, loss: 1.1860 +2025-06-24 09:16:39,250 - pyskl - INFO - Epoch [6][1100/1281] lr: 2.491e-02, eta: 11:19:36, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6669, top5_acc: 0.9569, loss_cls: 1.3157, loss: 1.3157 +2025-06-24 09:17:01,175 - pyskl - INFO - Epoch [6][1200/1281] lr: 2.490e-02, eta: 11:19:10, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6931, top5_acc: 0.9644, loss_cls: 1.2684, loss: 1.2684 +2025-06-24 09:17:19,739 - pyskl - INFO - Saving checkpoint at 6 epochs +2025-06-24 09:18:03,551 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:18:03,625 - pyskl - INFO - +top1_acc 0.6794 +top5_acc 0.9626 +2025-06-24 09:18:03,626 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:18:03,634 - pyskl - INFO - +mean_acc 0.5321 +2025-06-24 09:18:03,639 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_5.pth was removed +2025-06-24 09:18:03,819 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_6.pth. +2025-06-24 09:18:03,820 - pyskl - INFO - Best top1_acc is 0.6794 at 6 epoch. +2025-06-24 09:18:03,823 - pyskl - INFO - Epoch(val) [6][533] top1_acc: 0.6794, top5_acc: 0.9626, mean_class_accuracy: 0.5321 +2025-06-24 09:18:44,182 - pyskl - INFO - Epoch [7][100/1281] lr: 2.490e-02, eta: 11:18:39, time: 0.404, data_time: 0.187, memory: 4082, top1_acc: 0.7175, top5_acc: 0.9738, loss_cls: 1.1671, loss: 1.1671 +2025-06-24 09:19:05,815 - pyskl - INFO - Epoch [7][200/1281] lr: 2.490e-02, eta: 11:18:06, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.6869, top5_acc: 0.9681, loss_cls: 1.2487, loss: 1.2487 +2025-06-24 09:19:27,779 - pyskl - INFO - Epoch [7][300/1281] lr: 2.489e-02, eta: 11:17:42, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6887, top5_acc: 0.9675, loss_cls: 1.2553, loss: 1.2553 +2025-06-24 09:19:49,470 - pyskl - INFO - Epoch [7][400/1281] lr: 2.489e-02, eta: 11:17:11, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6863, top5_acc: 0.9750, loss_cls: 1.2195, loss: 1.2195 +2025-06-24 09:20:11,149 - pyskl - INFO - Epoch [7][500/1281] lr: 2.489e-02, eta: 11:16:40, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6994, top5_acc: 0.9663, loss_cls: 1.2224, loss: 1.2224 +2025-06-24 09:20:32,832 - pyskl - INFO - Epoch [7][600/1281] lr: 2.489e-02, eta: 11:16:09, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7050, top5_acc: 0.9719, loss_cls: 1.2054, loss: 1.2054 +2025-06-24 09:20:54,589 - pyskl - INFO - Epoch [7][700/1281] lr: 2.488e-02, eta: 11:15:40, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7019, top5_acc: 0.9706, loss_cls: 1.2379, loss: 1.2379 +2025-06-24 09:21:16,325 - pyskl - INFO - Epoch [7][800/1281] lr: 2.488e-02, eta: 11:15:11, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7106, top5_acc: 0.9706, loss_cls: 1.1706, loss: 1.1706 +2025-06-24 09:21:38,084 - pyskl - INFO - Epoch [7][900/1281] lr: 2.488e-02, eta: 11:14:43, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7006, top5_acc: 0.9625, loss_cls: 1.2394, loss: 1.2394 +2025-06-24 09:21:59,468 - pyskl - INFO - Epoch [7][1000/1281] lr: 2.487e-02, eta: 11:14:06, time: 0.214, data_time: 0.000, memory: 4082, top1_acc: 0.7081, top5_acc: 0.9663, loss_cls: 1.1906, loss: 1.1906 +2025-06-24 09:22:21,251 - pyskl - INFO - Epoch [7][1100/1281] lr: 2.487e-02, eta: 11:13:39, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6987, top5_acc: 0.9712, loss_cls: 1.1923, loss: 1.1923 +2025-06-24 09:22:43,281 - pyskl - INFO - Epoch [7][1200/1281] lr: 2.487e-02, eta: 11:13:16, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7225, top5_acc: 0.9681, loss_cls: 1.1873, loss: 1.1873 +2025-06-24 09:23:01,698 - pyskl - INFO - Saving checkpoint at 7 epochs +2025-06-24 09:23:45,127 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:23:45,180 - pyskl - INFO - +top1_acc 0.6468 +top5_acc 0.9495 +2025-06-24 09:23:45,181 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:23:45,187 - pyskl - INFO - +mean_acc 0.4955 +2025-06-24 09:23:45,188 - pyskl - INFO - Epoch(val) [7][533] top1_acc: 0.6468, top5_acc: 0.9495, mean_class_accuracy: 0.4955 +2025-06-24 09:24:25,757 - pyskl - INFO - Epoch [8][100/1281] lr: 2.486e-02, eta: 11:12:50, time: 0.406, data_time: 0.185, memory: 4082, top1_acc: 0.7250, top5_acc: 0.9694, loss_cls: 1.1535, loss: 1.1535 +2025-06-24 09:24:47,911 - pyskl - INFO - Epoch [8][200/1281] lr: 2.486e-02, eta: 11:12:30, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7006, top5_acc: 0.9725, loss_cls: 1.1758, loss: 1.1758 +2025-06-24 09:25:09,524 - pyskl - INFO - Epoch [8][300/1281] lr: 2.486e-02, eta: 11:11:59, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7100, top5_acc: 0.9631, loss_cls: 1.2033, loss: 1.2033 +2025-06-24 09:25:31,292 - pyskl - INFO - Epoch [8][400/1281] lr: 2.485e-02, eta: 11:11:32, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7206, top5_acc: 0.9694, loss_cls: 1.1482, loss: 1.1482 +2025-06-24 09:25:52,994 - pyskl - INFO - Epoch [8][500/1281] lr: 2.485e-02, eta: 11:11:03, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7281, top5_acc: 0.9731, loss_cls: 1.1504, loss: 1.1504 +2025-06-24 09:26:14,896 - pyskl - INFO - Epoch [8][600/1281] lr: 2.485e-02, eta: 11:10:38, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7269, top5_acc: 0.9712, loss_cls: 1.1216, loss: 1.1216 +2025-06-24 09:26:36,580 - pyskl - INFO - Epoch [8][700/1281] lr: 2.484e-02, eta: 11:10:10, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6850, top5_acc: 0.9688, loss_cls: 1.2523, loss: 1.2523 +2025-06-24 09:26:58,578 - pyskl - INFO - Epoch [8][800/1281] lr: 2.484e-02, eta: 11:09:47, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7225, top5_acc: 0.9694, loss_cls: 1.1646, loss: 1.1646 +2025-06-24 09:27:20,312 - pyskl - INFO - Epoch [8][900/1281] lr: 2.484e-02, eta: 11:09:19, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7044, top5_acc: 0.9681, loss_cls: 1.1931, loss: 1.1931 +2025-06-24 09:27:42,145 - pyskl - INFO - Epoch [8][1000/1281] lr: 2.483e-02, eta: 11:08:54, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7450, top5_acc: 0.9775, loss_cls: 1.0770, loss: 1.0770 +2025-06-24 09:28:03,975 - pyskl - INFO - Epoch [8][1100/1281] lr: 2.483e-02, eta: 11:08:28, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7081, top5_acc: 0.9669, loss_cls: 1.1703, loss: 1.1703 +2025-06-24 09:28:25,709 - pyskl - INFO - Epoch [8][1200/1281] lr: 2.483e-02, eta: 11:08:01, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7194, top5_acc: 0.9750, loss_cls: 1.1460, loss: 1.1460 +2025-06-24 09:28:43,922 - pyskl - INFO - Saving checkpoint at 8 epochs +2025-06-24 09:29:26,962 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:29:27,018 - pyskl - INFO - +top1_acc 0.7047 +top5_acc 0.9723 +2025-06-24 09:29:27,018 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:29:27,024 - pyskl - INFO - +mean_acc 0.5743 +2025-06-24 09:29:27,028 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_6.pth was removed +2025-06-24 09:29:27,196 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_8.pth. +2025-06-24 09:29:27,196 - pyskl - INFO - Best top1_acc is 0.7047 at 8 epoch. +2025-06-24 09:29:27,199 - pyskl - INFO - Epoch(val) [8][533] top1_acc: 0.7047, top5_acc: 0.9723, mean_class_accuracy: 0.5743 +2025-06-24 09:30:06,856 - pyskl - INFO - Epoch [9][100/1281] lr: 2.482e-02, eta: 11:07:17, time: 0.397, data_time: 0.179, memory: 4082, top1_acc: 0.7525, top5_acc: 0.9775, loss_cls: 1.0806, loss: 1.0806 +2025-06-24 09:30:28,761 - pyskl - INFO - Epoch [9][200/1281] lr: 2.482e-02, eta: 11:06:53, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7212, top5_acc: 0.9637, loss_cls: 1.1540, loss: 1.1540 +2025-06-24 09:30:50,533 - pyskl - INFO - Epoch [9][300/1281] lr: 2.481e-02, eta: 11:06:27, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7262, top5_acc: 0.9781, loss_cls: 1.1044, loss: 1.1044 +2025-06-24 09:31:12,238 - pyskl - INFO - Epoch [9][400/1281] lr: 2.481e-02, eta: 11:05:59, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7275, top5_acc: 0.9637, loss_cls: 1.1552, loss: 1.1552 +2025-06-24 09:31:34,091 - pyskl - INFO - Epoch [9][500/1281] lr: 2.481e-02, eta: 11:05:34, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7312, top5_acc: 0.9725, loss_cls: 1.1681, loss: 1.1681 +2025-06-24 09:31:55,732 - pyskl - INFO - Epoch [9][600/1281] lr: 2.480e-02, eta: 11:05:06, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7188, top5_acc: 0.9706, loss_cls: 1.1658, loss: 1.1658 +2025-06-24 09:32:17,449 - pyskl - INFO - Epoch [9][700/1281] lr: 2.480e-02, eta: 11:04:39, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7356, top5_acc: 0.9744, loss_cls: 1.1048, loss: 1.1048 +2025-06-24 09:32:39,719 - pyskl - INFO - Epoch [9][800/1281] lr: 2.480e-02, eta: 11:04:22, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.7475, top5_acc: 0.9775, loss_cls: 1.0463, loss: 1.0463 +2025-06-24 09:33:01,279 - pyskl - INFO - Epoch [9][900/1281] lr: 2.479e-02, eta: 11:03:52, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7431, top5_acc: 0.9744, loss_cls: 1.0907, loss: 1.0907 +2025-06-24 09:33:23,172 - pyskl - INFO - Epoch [9][1000/1281] lr: 2.479e-02, eta: 11:03:28, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7075, top5_acc: 0.9675, loss_cls: 1.1593, loss: 1.1593 +2025-06-24 09:33:45,066 - pyskl - INFO - Epoch [9][1100/1281] lr: 2.479e-02, eta: 11:03:05, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7388, top5_acc: 0.9831, loss_cls: 1.0454, loss: 1.0454 +2025-06-24 09:34:06,991 - pyskl - INFO - Epoch [9][1200/1281] lr: 2.478e-02, eta: 11:02:41, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7300, top5_acc: 0.9775, loss_cls: 1.0892, loss: 1.0892 +2025-06-24 09:34:25,354 - pyskl - INFO - Saving checkpoint at 9 epochs +2025-06-24 09:35:09,011 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:35:09,069 - pyskl - INFO - +top1_acc 0.6818 +top5_acc 0.9528 +2025-06-24 09:35:09,069 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:35:09,077 - pyskl - INFO - +mean_acc 0.5434 +2025-06-24 09:35:09,079 - pyskl - INFO - Epoch(val) [9][533] top1_acc: 0.6818, top5_acc: 0.9528, mean_class_accuracy: 0.5434 +2025-06-24 09:35:49,213 - pyskl - INFO - Epoch [10][100/1281] lr: 2.477e-02, eta: 11:02:06, time: 0.401, data_time: 0.183, memory: 4082, top1_acc: 0.7300, top5_acc: 0.9688, loss_cls: 1.1078, loss: 1.1078 +2025-06-24 09:36:11,077 - pyskl - INFO - Epoch [10][200/1281] lr: 2.477e-02, eta: 11:01:42, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7456, top5_acc: 0.9744, loss_cls: 1.0688, loss: 1.0688 +2025-06-24 09:36:32,790 - pyskl - INFO - Epoch [10][300/1281] lr: 2.477e-02, eta: 11:01:15, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7612, top5_acc: 0.9775, loss_cls: 1.0216, loss: 1.0216 +2025-06-24 09:36:54,412 - pyskl - INFO - Epoch [10][400/1281] lr: 2.476e-02, eta: 11:00:48, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7238, top5_acc: 0.9788, loss_cls: 1.0817, loss: 1.0817 +2025-06-24 09:37:16,120 - pyskl - INFO - Epoch [10][500/1281] lr: 2.476e-02, eta: 11:00:21, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7375, top5_acc: 0.9731, loss_cls: 1.0972, loss: 1.0972 +2025-06-24 09:37:37,989 - pyskl - INFO - Epoch [10][600/1281] lr: 2.476e-02, eta: 10:59:57, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7438, top5_acc: 0.9781, loss_cls: 1.0728, loss: 1.0728 +2025-06-24 09:37:59,484 - pyskl - INFO - Epoch [10][700/1281] lr: 2.475e-02, eta: 10:59:28, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7481, top5_acc: 0.9794, loss_cls: 1.0548, loss: 1.0548 +2025-06-24 09:38:21,265 - pyskl - INFO - Epoch [10][800/1281] lr: 2.475e-02, eta: 10:59:03, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7194, top5_acc: 0.9719, loss_cls: 1.1362, loss: 1.1362 +2025-06-24 09:38:42,961 - pyskl - INFO - Epoch [10][900/1281] lr: 2.474e-02, eta: 10:58:37, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7606, top5_acc: 0.9800, loss_cls: 1.0242, loss: 1.0242 +2025-06-24 09:39:04,947 - pyskl - INFO - Epoch [10][1000/1281] lr: 2.474e-02, eta: 10:58:15, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7562, top5_acc: 0.9756, loss_cls: 1.0616, loss: 1.0616 +2025-06-24 09:39:26,933 - pyskl - INFO - Epoch [10][1100/1281] lr: 2.473e-02, eta: 10:57:53, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7425, top5_acc: 0.9769, loss_cls: 1.0899, loss: 1.0899 +2025-06-24 09:39:48,842 - pyskl - INFO - Epoch [10][1200/1281] lr: 2.473e-02, eta: 10:57:30, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7369, top5_acc: 0.9681, loss_cls: 1.1191, loss: 1.1191 +2025-06-24 09:40:07,048 - pyskl - INFO - Saving checkpoint at 10 epochs +2025-06-24 09:40:50,638 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:40:50,696 - pyskl - INFO - +top1_acc 0.7249 +top5_acc 0.9768 +2025-06-24 09:40:50,696 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:40:50,703 - pyskl - INFO - +mean_acc 0.5902 +2025-06-24 09:40:50,708 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_8.pth was removed +2025-06-24 09:40:50,878 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_10.pth. +2025-06-24 09:40:50,878 - pyskl - INFO - Best top1_acc is 0.7249 at 10 epoch. +2025-06-24 09:40:50,881 - pyskl - INFO - Epoch(val) [10][533] top1_acc: 0.7249, top5_acc: 0.9768, mean_class_accuracy: 0.5902 +2025-06-24 09:41:31,541 - pyskl - INFO - Epoch [11][100/1281] lr: 2.472e-02, eta: 10:57:02, time: 0.407, data_time: 0.187, memory: 4082, top1_acc: 0.7581, top5_acc: 0.9806, loss_cls: 1.0316, loss: 1.0316 +2025-06-24 09:41:53,380 - pyskl - INFO - Epoch [11][200/1281] lr: 2.472e-02, eta: 10:56:38, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7625, top5_acc: 0.9800, loss_cls: 1.0077, loss: 1.0077 +2025-06-24 09:42:15,140 - pyskl - INFO - Epoch [11][300/1281] lr: 2.471e-02, eta: 10:56:12, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7356, top5_acc: 0.9850, loss_cls: 1.0735, loss: 1.0735 +2025-06-24 09:42:36,691 - pyskl - INFO - Epoch [11][400/1281] lr: 2.471e-02, eta: 10:55:44, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7450, top5_acc: 0.9750, loss_cls: 1.0758, loss: 1.0758 +2025-06-24 09:42:58,609 - pyskl - INFO - Epoch [11][500/1281] lr: 2.471e-02, eta: 10:55:21, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7538, top5_acc: 0.9862, loss_cls: 1.0199, loss: 1.0199 +2025-06-24 09:43:20,339 - pyskl - INFO - Epoch [11][600/1281] lr: 2.470e-02, eta: 10:54:56, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7481, top5_acc: 0.9825, loss_cls: 1.0227, loss: 1.0227 +2025-06-24 09:43:42,024 - pyskl - INFO - Epoch [11][700/1281] lr: 2.470e-02, eta: 10:54:30, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7456, top5_acc: 0.9775, loss_cls: 1.0706, loss: 1.0706 +2025-06-24 09:44:03,891 - pyskl - INFO - Epoch [11][800/1281] lr: 2.469e-02, eta: 10:54:07, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7588, top5_acc: 0.9806, loss_cls: 1.0122, loss: 1.0122 +2025-06-24 09:44:25,576 - pyskl - INFO - Epoch [11][900/1281] lr: 2.469e-02, eta: 10:53:41, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7719, top5_acc: 0.9812, loss_cls: 0.9842, loss: 0.9842 +2025-06-24 09:44:47,399 - pyskl - INFO - Epoch [11][1000/1281] lr: 2.468e-02, eta: 10:53:17, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7462, top5_acc: 0.9800, loss_cls: 0.9956, loss: 0.9956 +2025-06-24 09:45:08,894 - pyskl - INFO - Epoch [11][1100/1281] lr: 2.468e-02, eta: 10:52:48, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7619, top5_acc: 0.9819, loss_cls: 1.0040, loss: 1.0040 +2025-06-24 09:45:30,787 - pyskl - INFO - Epoch [11][1200/1281] lr: 2.467e-02, eta: 10:52:25, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7381, top5_acc: 0.9700, loss_cls: 1.0759, loss: 1.0759 +2025-06-24 09:45:48,820 - pyskl - INFO - Saving checkpoint at 11 epochs +2025-06-24 09:46:31,818 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:46:31,884 - pyskl - INFO - +top1_acc 0.7208 +top5_acc 0.9745 +2025-06-24 09:46:31,884 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:46:31,891 - pyskl - INFO - +mean_acc 0.6022 +2025-06-24 09:46:31,893 - pyskl - INFO - Epoch(val) [11][533] top1_acc: 0.7208, top5_acc: 0.9745, mean_class_accuracy: 0.6022 +2025-06-24 09:47:12,080 - pyskl - INFO - Epoch [12][100/1281] lr: 2.467e-02, eta: 10:51:51, time: 0.402, data_time: 0.182, memory: 4082, top1_acc: 0.7812, top5_acc: 0.9819, loss_cls: 0.9888, loss: 0.9888 +2025-06-24 09:47:34,033 - pyskl - INFO - Epoch [12][200/1281] lr: 2.466e-02, eta: 10:51:28, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7406, top5_acc: 0.9825, loss_cls: 1.0297, loss: 1.0297 +2025-06-24 09:47:55,910 - pyskl - INFO - Epoch [12][300/1281] lr: 2.466e-02, eta: 10:51:05, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7350, top5_acc: 0.9725, loss_cls: 1.0946, loss: 1.0946 +2025-06-24 09:48:17,587 - pyskl - INFO - Epoch [12][400/1281] lr: 2.465e-02, eta: 10:50:40, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7662, top5_acc: 0.9831, loss_cls: 0.9653, loss: 0.9653 +2025-06-24 09:48:39,307 - pyskl - INFO - Epoch [12][500/1281] lr: 2.465e-02, eta: 10:50:14, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7594, top5_acc: 0.9812, loss_cls: 1.0174, loss: 1.0174 +2025-06-24 09:49:01,041 - pyskl - INFO - Epoch [12][600/1281] lr: 2.464e-02, eta: 10:49:50, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7506, top5_acc: 0.9775, loss_cls: 1.0404, loss: 1.0404 +2025-06-24 09:49:22,743 - pyskl - INFO - Epoch [12][700/1281] lr: 2.464e-02, eta: 10:49:24, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7531, top5_acc: 0.9762, loss_cls: 1.0049, loss: 1.0049 +2025-06-24 09:49:44,681 - pyskl - INFO - Epoch [12][800/1281] lr: 2.463e-02, eta: 10:49:02, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7275, top5_acc: 0.9794, loss_cls: 1.0937, loss: 1.0937 +2025-06-24 09:50:06,171 - pyskl - INFO - Epoch [12][900/1281] lr: 2.463e-02, eta: 10:48:34, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7619, top5_acc: 0.9800, loss_cls: 1.0220, loss: 1.0220 +2025-06-24 09:50:28,069 - pyskl - INFO - Epoch [12][1000/1281] lr: 2.462e-02, eta: 10:48:12, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7556, top5_acc: 0.9844, loss_cls: 1.0195, loss: 1.0195 +2025-06-24 09:50:49,786 - pyskl - INFO - Epoch [12][1100/1281] lr: 2.462e-02, eta: 10:47:47, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7538, top5_acc: 0.9825, loss_cls: 1.0044, loss: 1.0044 +2025-06-24 09:51:11,690 - pyskl - INFO - Epoch [12][1200/1281] lr: 2.461e-02, eta: 10:47:24, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7506, top5_acc: 0.9862, loss_cls: 0.9951, loss: 0.9951 +2025-06-24 09:51:30,303 - pyskl - INFO - Saving checkpoint at 12 epochs +2025-06-24 09:52:13,193 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:52:13,247 - pyskl - INFO - +top1_acc 0.6665 +top5_acc 0.9623 +2025-06-24 09:52:13,247 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:52:13,253 - pyskl - INFO - +mean_acc 0.5639 +2025-06-24 09:52:13,255 - pyskl - INFO - Epoch(val) [12][533] top1_acc: 0.6665, top5_acc: 0.9623, mean_class_accuracy: 0.5639 +2025-06-24 09:52:53,320 - pyskl - INFO - Epoch [13][100/1281] lr: 2.460e-02, eta: 10:46:48, time: 0.401, data_time: 0.183, memory: 4082, top1_acc: 0.7762, top5_acc: 0.9806, loss_cls: 0.9615, loss: 0.9615 +2025-06-24 09:53:15,053 - pyskl - INFO - Epoch [13][200/1281] lr: 2.460e-02, eta: 10:46:23, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7644, top5_acc: 0.9762, loss_cls: 1.0036, loss: 1.0036 +2025-06-24 09:53:37,073 - pyskl - INFO - Epoch [13][300/1281] lr: 2.459e-02, eta: 10:46:02, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7394, top5_acc: 0.9744, loss_cls: 1.0414, loss: 1.0414 +2025-06-24 09:53:58,668 - pyskl - INFO - Epoch [13][400/1281] lr: 2.459e-02, eta: 10:45:36, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7531, top5_acc: 0.9856, loss_cls: 0.9807, loss: 0.9807 +2025-06-24 09:54:20,581 - pyskl - INFO - Epoch [13][500/1281] lr: 2.458e-02, eta: 10:45:13, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7575, top5_acc: 0.9800, loss_cls: 1.0021, loss: 1.0021 +2025-06-24 09:54:42,508 - pyskl - INFO - Epoch [13][600/1281] lr: 2.458e-02, eta: 10:44:51, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7619, top5_acc: 0.9850, loss_cls: 0.9934, loss: 0.9934 +2025-06-24 09:55:04,303 - pyskl - INFO - Epoch [13][700/1281] lr: 2.457e-02, eta: 10:44:27, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7612, top5_acc: 0.9856, loss_cls: 0.9818, loss: 0.9818 +2025-06-24 09:55:26,179 - pyskl - INFO - Epoch [13][800/1281] lr: 2.457e-02, eta: 10:44:04, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7450, top5_acc: 0.9744, loss_cls: 1.0680, loss: 1.0680 +2025-06-24 09:55:48,055 - pyskl - INFO - Epoch [13][900/1281] lr: 2.456e-02, eta: 10:43:41, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7675, top5_acc: 0.9819, loss_cls: 0.9743, loss: 0.9743 +2025-06-24 09:56:09,605 - pyskl - INFO - Epoch [13][1000/1281] lr: 2.455e-02, eta: 10:43:15, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7400, top5_acc: 0.9800, loss_cls: 1.0403, loss: 1.0403 +2025-06-24 09:56:31,604 - pyskl - INFO - Epoch [13][1100/1281] lr: 2.455e-02, eta: 10:42:53, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7619, top5_acc: 0.9862, loss_cls: 0.9958, loss: 0.9958 +2025-06-24 09:56:53,464 - pyskl - INFO - Epoch [13][1200/1281] lr: 2.454e-02, eta: 10:42:30, time: 0.219, data_time: 0.001, memory: 4082, top1_acc: 0.7500, top5_acc: 0.9806, loss_cls: 1.0114, loss: 1.0114 +2025-06-24 09:57:11,624 - pyskl - INFO - Saving checkpoint at 13 epochs +2025-06-24 09:57:54,900 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 09:57:54,954 - pyskl - INFO - +top1_acc 0.7135 +top5_acc 0.9731 +2025-06-24 09:57:54,954 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 09:57:54,961 - pyskl - INFO - +mean_acc 0.6199 +2025-06-24 09:57:54,962 - pyskl - INFO - Epoch(val) [13][533] top1_acc: 0.7135, top5_acc: 0.9731, mean_class_accuracy: 0.6199 +2025-06-24 09:58:35,630 - pyskl - INFO - Epoch [14][100/1281] lr: 2.453e-02, eta: 10:42:00, time: 0.407, data_time: 0.187, memory: 4082, top1_acc: 0.7806, top5_acc: 0.9825, loss_cls: 0.9280, loss: 0.9280 +2025-06-24 09:58:57,554 - pyskl - INFO - Epoch [14][200/1281] lr: 2.453e-02, eta: 10:41:38, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7631, top5_acc: 0.9769, loss_cls: 1.0215, loss: 1.0215 +2025-06-24 09:59:19,173 - pyskl - INFO - Epoch [14][300/1281] lr: 2.452e-02, eta: 10:41:12, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7731, top5_acc: 0.9894, loss_cls: 0.9434, loss: 0.9434 +2025-06-24 09:59:40,999 - pyskl - INFO - Epoch [14][400/1281] lr: 2.452e-02, eta: 10:40:49, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7825, top5_acc: 0.9775, loss_cls: 0.9529, loss: 0.9529 +2025-06-24 10:00:02,878 - pyskl - INFO - Epoch [14][500/1281] lr: 2.451e-02, eta: 10:40:26, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7512, top5_acc: 0.9825, loss_cls: 1.0303, loss: 1.0303 +2025-06-24 10:00:24,678 - pyskl - INFO - Epoch [14][600/1281] lr: 2.451e-02, eta: 10:40:03, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7656, top5_acc: 0.9812, loss_cls: 0.9730, loss: 0.9730 +2025-06-24 10:00:46,714 - pyskl - INFO - Epoch [14][700/1281] lr: 2.450e-02, eta: 10:39:42, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7762, top5_acc: 0.9819, loss_cls: 0.9658, loss: 0.9658 +2025-06-24 10:01:08,673 - pyskl - INFO - Epoch [14][800/1281] lr: 2.449e-02, eta: 10:39:20, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7462, top5_acc: 0.9819, loss_cls: 0.9999, loss: 0.9999 +2025-06-24 10:01:30,372 - pyskl - INFO - Epoch [14][900/1281] lr: 2.449e-02, eta: 10:38:55, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7662, top5_acc: 0.9844, loss_cls: 0.9796, loss: 0.9796 +2025-06-24 10:01:52,229 - pyskl - INFO - Epoch [14][1000/1281] lr: 2.448e-02, eta: 10:38:32, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7538, top5_acc: 0.9844, loss_cls: 1.0340, loss: 1.0340 +2025-06-24 10:02:14,155 - pyskl - INFO - Epoch [14][1100/1281] lr: 2.448e-02, eta: 10:38:10, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7788, top5_acc: 0.9819, loss_cls: 0.9571, loss: 0.9571 +2025-06-24 10:02:35,782 - pyskl - INFO - Epoch [14][1200/1281] lr: 2.447e-02, eta: 10:37:45, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7662, top5_acc: 0.9881, loss_cls: 0.9343, loss: 0.9343 +2025-06-24 10:02:54,040 - pyskl - INFO - Saving checkpoint at 14 epochs +2025-06-24 10:03:37,307 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:03:37,376 - pyskl - INFO - +top1_acc 0.7304 +top5_acc 0.9736 +2025-06-24 10:03:37,376 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:03:37,384 - pyskl - INFO - +mean_acc 0.6129 +2025-06-24 10:03:37,388 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_10.pth was removed +2025-06-24 10:03:37,592 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_14.pth. +2025-06-24 10:03:37,592 - pyskl - INFO - Best top1_acc is 0.7304 at 14 epoch. +2025-06-24 10:03:37,594 - pyskl - INFO - Epoch(val) [14][533] top1_acc: 0.7304, top5_acc: 0.9736, mean_class_accuracy: 0.6129 +2025-06-24 10:04:18,512 - pyskl - INFO - Epoch [15][100/1281] lr: 2.446e-02, eta: 10:37:16, time: 0.409, data_time: 0.191, memory: 4082, top1_acc: 0.7681, top5_acc: 0.9794, loss_cls: 1.0094, loss: 1.0094 +2025-06-24 10:04:40,178 - pyskl - INFO - Epoch [15][200/1281] lr: 2.445e-02, eta: 10:36:52, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7606, top5_acc: 0.9862, loss_cls: 0.9574, loss: 0.9574 +2025-06-24 10:05:01,947 - pyskl - INFO - Epoch [15][300/1281] lr: 2.445e-02, eta: 10:36:28, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7575, top5_acc: 0.9781, loss_cls: 0.9881, loss: 0.9881 +2025-06-24 10:05:23,568 - pyskl - INFO - Epoch [15][400/1281] lr: 2.444e-02, eta: 10:36:03, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7937, top5_acc: 0.9862, loss_cls: 0.9176, loss: 0.9176 +2025-06-24 10:05:45,305 - pyskl - INFO - Epoch [15][500/1281] lr: 2.444e-02, eta: 10:35:39, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7488, top5_acc: 0.9756, loss_cls: 1.0340, loss: 1.0340 +2025-06-24 10:06:07,010 - pyskl - INFO - Epoch [15][600/1281] lr: 2.443e-02, eta: 10:35:14, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7869, top5_acc: 0.9844, loss_cls: 0.8784, loss: 0.8784 +2025-06-24 10:06:28,660 - pyskl - INFO - Epoch [15][700/1281] lr: 2.442e-02, eta: 10:34:49, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7794, top5_acc: 0.9831, loss_cls: 0.9416, loss: 0.9416 +2025-06-24 10:06:50,474 - pyskl - INFO - Epoch [15][800/1281] lr: 2.442e-02, eta: 10:34:26, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7481, top5_acc: 0.9819, loss_cls: 1.0205, loss: 1.0205 +2025-06-24 10:07:12,143 - pyskl - INFO - Epoch [15][900/1281] lr: 2.441e-02, eta: 10:34:02, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7800, top5_acc: 0.9850, loss_cls: 0.9154, loss: 0.9154 +2025-06-24 10:07:34,140 - pyskl - INFO - Epoch [15][1000/1281] lr: 2.441e-02, eta: 10:33:40, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7731, top5_acc: 0.9862, loss_cls: 0.9509, loss: 0.9509 +2025-06-24 10:07:55,991 - pyskl - INFO - Epoch [15][1100/1281] lr: 2.440e-02, eta: 10:33:17, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7700, top5_acc: 0.9800, loss_cls: 0.9338, loss: 0.9338 +2025-06-24 10:08:17,764 - pyskl - INFO - Epoch [15][1200/1281] lr: 2.439e-02, eta: 10:32:54, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7831, top5_acc: 0.9862, loss_cls: 0.9280, loss: 0.9280 +2025-06-24 10:08:36,220 - pyskl - INFO - Saving checkpoint at 15 epochs +2025-06-24 10:09:19,805 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:09:19,859 - pyskl - INFO - +top1_acc 0.7242 +top5_acc 0.9709 +2025-06-24 10:09:19,859 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:09:19,865 - pyskl - INFO - +mean_acc 0.6372 +2025-06-24 10:09:19,866 - pyskl - INFO - Epoch(val) [15][533] top1_acc: 0.7242, top5_acc: 0.9709, mean_class_accuracy: 0.6372 +2025-06-24 10:10:00,640 - pyskl - INFO - Epoch [16][100/1281] lr: 2.438e-02, eta: 10:32:23, time: 0.408, data_time: 0.189, memory: 4082, top1_acc: 0.7831, top5_acc: 0.9838, loss_cls: 0.9363, loss: 0.9363 +2025-06-24 10:10:22,321 - pyskl - INFO - Epoch [16][200/1281] lr: 2.438e-02, eta: 10:31:59, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7975, top5_acc: 0.9919, loss_cls: 0.8607, loss: 0.8607 +2025-06-24 10:10:44,175 - pyskl - INFO - Epoch [16][300/1281] lr: 2.437e-02, eta: 10:31:36, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7837, top5_acc: 0.9800, loss_cls: 0.9410, loss: 0.9410 +2025-06-24 10:11:05,727 - pyskl - INFO - Epoch [16][400/1281] lr: 2.436e-02, eta: 10:31:11, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7812, top5_acc: 0.9856, loss_cls: 0.9125, loss: 0.9125 +2025-06-24 10:11:27,663 - pyskl - INFO - Epoch [16][500/1281] lr: 2.436e-02, eta: 10:30:49, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7694, top5_acc: 0.9838, loss_cls: 0.9793, loss: 0.9793 +2025-06-24 10:11:49,151 - pyskl - INFO - Epoch [16][600/1281] lr: 2.435e-02, eta: 10:30:23, time: 0.215, data_time: 0.000, memory: 4082, top1_acc: 0.7788, top5_acc: 0.9794, loss_cls: 0.9387, loss: 0.9387 +2025-06-24 10:12:11,004 - pyskl - INFO - Epoch [16][700/1281] lr: 2.434e-02, eta: 10:30:00, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7794, top5_acc: 0.9862, loss_cls: 0.9184, loss: 0.9184 +2025-06-24 10:12:33,014 - pyskl - INFO - Epoch [16][800/1281] lr: 2.434e-02, eta: 10:29:38, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7538, top5_acc: 0.9869, loss_cls: 1.0088, loss: 1.0088 +2025-06-24 10:12:54,952 - pyskl - INFO - Epoch [16][900/1281] lr: 2.433e-02, eta: 10:29:16, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7669, top5_acc: 0.9831, loss_cls: 0.9634, loss: 0.9634 +2025-06-24 10:13:17,241 - pyskl - INFO - Epoch [16][1000/1281] lr: 2.432e-02, eta: 10:28:57, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.7600, top5_acc: 0.9831, loss_cls: 0.9875, loss: 0.9875 +2025-06-24 10:13:39,420 - pyskl - INFO - Epoch [16][1100/1281] lr: 2.432e-02, eta: 10:28:37, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7906, top5_acc: 0.9856, loss_cls: 0.9115, loss: 0.9115 +2025-06-24 10:14:01,404 - pyskl - INFO - Epoch [16][1200/1281] lr: 2.431e-02, eta: 10:28:16, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7662, top5_acc: 0.9831, loss_cls: 0.9420, loss: 0.9420 +2025-06-24 10:14:20,155 - pyskl - INFO - Saving checkpoint at 16 epochs +2025-06-24 10:15:04,588 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:15:04,643 - pyskl - INFO - +top1_acc 0.7313 +top5_acc 0.9738 +2025-06-24 10:15:04,643 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:15:04,650 - pyskl - INFO - +mean_acc 0.6078 +2025-06-24 10:15:04,654 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_14.pth was removed +2025-06-24 10:15:04,825 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_16.pth. +2025-06-24 10:15:04,825 - pyskl - INFO - Best top1_acc is 0.7313 at 16 epoch. +2025-06-24 10:15:04,828 - pyskl - INFO - Epoch(val) [16][533] top1_acc: 0.7313, top5_acc: 0.9738, mean_class_accuracy: 0.6078 +2025-06-24 10:16:00,979 - pyskl - INFO - Epoch [17][100/1281] lr: 2.430e-02, eta: 10:29:53, time: 0.561, data_time: 0.189, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9888, loss_cls: 0.8509, loss: 0.8509 +2025-06-24 10:16:42,664 - pyskl - INFO - Epoch [17][200/1281] lr: 2.429e-02, eta: 10:32:13, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7825, top5_acc: 0.9825, loss_cls: 0.9163, loss: 0.9163 +2025-06-24 10:17:24,524 - pyskl - INFO - Epoch [17][300/1281] lr: 2.428e-02, eta: 10:34:34, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7981, top5_acc: 0.9831, loss_cls: 0.8639, loss: 0.8639 +2025-06-24 10:18:06,166 - pyskl - INFO - Epoch [17][400/1281] lr: 2.428e-02, eta: 10:36:51, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7794, top5_acc: 0.9862, loss_cls: 0.8774, loss: 0.8774 +2025-06-24 10:18:48,059 - pyskl - INFO - Epoch [17][500/1281] lr: 2.427e-02, eta: 10:39:08, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7731, top5_acc: 0.9838, loss_cls: 0.9487, loss: 0.9487 +2025-06-24 10:19:29,913 - pyskl - INFO - Epoch [17][600/1281] lr: 2.426e-02, eta: 10:41:23, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7738, top5_acc: 0.9869, loss_cls: 0.9320, loss: 0.9320 +2025-06-24 10:20:11,488 - pyskl - INFO - Epoch [17][700/1281] lr: 2.426e-02, eta: 10:43:35, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7662, top5_acc: 0.9875, loss_cls: 0.9679, loss: 0.9679 +2025-06-24 10:20:53,540 - pyskl - INFO - Epoch [17][800/1281] lr: 2.425e-02, eta: 10:45:48, time: 0.421, data_time: 0.000, memory: 4082, top1_acc: 0.7919, top5_acc: 0.9794, loss_cls: 0.9421, loss: 0.9421 +2025-06-24 10:21:35,323 - pyskl - INFO - Epoch [17][900/1281] lr: 2.424e-02, eta: 10:47:58, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7744, top5_acc: 0.9856, loss_cls: 0.9018, loss: 0.9018 +2025-06-24 10:22:17,128 - pyskl - INFO - Epoch [17][1000/1281] lr: 2.424e-02, eta: 10:50:06, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7638, top5_acc: 0.9812, loss_cls: 0.9799, loss: 0.9799 +2025-06-24 10:22:58,923 - pyskl - INFO - Epoch [17][1100/1281] lr: 2.423e-02, eta: 10:52:13, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7831, top5_acc: 0.9850, loss_cls: 0.9214, loss: 0.9214 +2025-06-24 10:23:41,948 - pyskl - INFO - Epoch [17][1200/1281] lr: 2.422e-02, eta: 10:54:28, time: 0.430, data_time: 0.001, memory: 4082, top1_acc: 0.7625, top5_acc: 0.9825, loss_cls: 0.9558, loss: 0.9558 +2025-06-24 10:24:17,006 - pyskl - INFO - Saving checkpoint at 17 epochs +2025-06-24 10:25:25,815 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:25:25,887 - pyskl - INFO - +top1_acc 0.7412 +top5_acc 0.9757 +2025-06-24 10:25:25,887 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:25:25,896 - pyskl - INFO - +mean_acc 0.6472 +2025-06-24 10:25:25,900 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_16.pth was removed +2025-06-24 10:25:26,098 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_17.pth. +2025-06-24 10:25:26,098 - pyskl - INFO - Best top1_acc is 0.7412 at 17 epoch. +2025-06-24 10:25:26,101 - pyskl - INFO - Epoch(val) [17][533] top1_acc: 0.7412, top5_acc: 0.9757, mean_class_accuracy: 0.6472 +2025-06-24 10:26:21,619 - pyskl - INFO - Epoch [18][100/1281] lr: 2.421e-02, eta: 10:55:34, time: 0.555, data_time: 0.191, memory: 4082, top1_acc: 0.7969, top5_acc: 0.9912, loss_cls: 0.8348, loss: 0.8348 +2025-06-24 10:27:04,532 - pyskl - INFO - Epoch [18][200/1281] lr: 2.420e-02, eta: 10:57:44, time: 0.429, data_time: 0.000, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9831, loss_cls: 0.8765, loss: 0.8765 +2025-06-24 10:27:46,214 - pyskl - INFO - Epoch [18][300/1281] lr: 2.419e-02, eta: 10:59:43, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7963, top5_acc: 0.9856, loss_cls: 0.8832, loss: 0.8832 +2025-06-24 10:28:27,903 - pyskl - INFO - Epoch [18][400/1281] lr: 2.419e-02, eta: 11:01:41, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7869, top5_acc: 0.9875, loss_cls: 0.8729, loss: 0.8729 +2025-06-24 10:29:09,625 - pyskl - INFO - Epoch [18][500/1281] lr: 2.418e-02, eta: 11:03:38, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9881, loss_cls: 0.8677, loss: 0.8677 +2025-06-24 10:29:51,310 - pyskl - INFO - Epoch [18][600/1281] lr: 2.417e-02, eta: 11:05:33, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7644, top5_acc: 0.9862, loss_cls: 0.9251, loss: 0.9251 +2025-06-24 10:30:32,865 - pyskl - INFO - Epoch [18][700/1281] lr: 2.417e-02, eta: 11:07:25, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7869, top5_acc: 0.9825, loss_cls: 0.9262, loss: 0.9262 +2025-06-24 10:31:14,471 - pyskl - INFO - Epoch [18][800/1281] lr: 2.416e-02, eta: 11:09:17, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8044, top5_acc: 0.9844, loss_cls: 0.8887, loss: 0.8887 +2025-06-24 10:31:56,265 - pyskl - INFO - Epoch [18][900/1281] lr: 2.415e-02, eta: 11:11:09, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7788, top5_acc: 0.9825, loss_cls: 0.9392, loss: 0.9392 +2025-06-24 10:32:37,981 - pyskl - INFO - Epoch [18][1000/1281] lr: 2.414e-02, eta: 11:12:59, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7956, top5_acc: 0.9856, loss_cls: 0.8727, loss: 0.8727 +2025-06-24 10:33:19,790 - pyskl - INFO - Epoch [18][1100/1281] lr: 2.414e-02, eta: 11:14:48, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7863, top5_acc: 0.9838, loss_cls: 0.9103, loss: 0.9103 +2025-06-24 10:34:01,525 - pyskl - INFO - Epoch [18][1200/1281] lr: 2.413e-02, eta: 11:16:35, time: 0.417, data_time: 0.001, memory: 4082, top1_acc: 0.7825, top5_acc: 0.9881, loss_cls: 0.9693, loss: 0.9693 +2025-06-24 10:34:35,987 - pyskl - INFO - Saving checkpoint at 18 epochs +2025-06-24 10:35:42,597 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:35:42,665 - pyskl - INFO - +top1_acc 0.7287 +top5_acc 0.9742 +2025-06-24 10:35:42,665 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:35:42,673 - pyskl - INFO - +mean_acc 0.6340 +2025-06-24 10:35:42,675 - pyskl - INFO - Epoch(val) [18][533] top1_acc: 0.7287, top5_acc: 0.9742, mean_class_accuracy: 0.6340 +2025-06-24 10:36:37,703 - pyskl - INFO - Epoch [19][100/1281] lr: 2.411e-02, eta: 11:17:16, time: 0.550, data_time: 0.196, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9919, loss_cls: 0.8641, loss: 0.8641 +2025-06-24 10:37:21,354 - pyskl - INFO - Epoch [19][200/1281] lr: 2.411e-02, eta: 11:19:14, time: 0.436, data_time: 0.000, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9881, loss_cls: 0.8689, loss: 0.8689 +2025-06-24 10:38:03,016 - pyskl - INFO - Epoch [19][300/1281] lr: 2.410e-02, eta: 11:20:57, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7975, top5_acc: 0.9844, loss_cls: 0.8675, loss: 0.8675 +2025-06-24 10:38:44,738 - pyskl - INFO - Epoch [19][400/1281] lr: 2.409e-02, eta: 11:22:39, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7975, top5_acc: 0.9850, loss_cls: 0.8806, loss: 0.8806 +2025-06-24 10:39:26,512 - pyskl - INFO - Epoch [19][500/1281] lr: 2.408e-02, eta: 11:24:20, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9862, loss_cls: 0.8432, loss: 0.8432 +2025-06-24 10:40:08,209 - pyskl - INFO - Epoch [19][600/1281] lr: 2.408e-02, eta: 11:25:59, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8044, top5_acc: 0.9850, loss_cls: 0.8508, loss: 0.8508 +2025-06-24 10:40:50,089 - pyskl - INFO - Epoch [19][700/1281] lr: 2.407e-02, eta: 11:27:38, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7844, top5_acc: 0.9831, loss_cls: 0.9206, loss: 0.9206 +2025-06-24 10:41:31,818 - pyskl - INFO - Epoch [19][800/1281] lr: 2.406e-02, eta: 11:29:15, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8013, top5_acc: 0.9825, loss_cls: 0.8903, loss: 0.8903 +2025-06-24 10:42:13,665 - pyskl - INFO - Epoch [19][900/1281] lr: 2.405e-02, eta: 11:30:52, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9850, loss_cls: 0.8730, loss: 0.8730 +2025-06-24 10:42:55,468 - pyskl - INFO - Epoch [19][1000/1281] lr: 2.405e-02, eta: 11:32:27, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7875, top5_acc: 0.9912, loss_cls: 0.8816, loss: 0.8816 +2025-06-24 10:43:37,391 - pyskl - INFO - Epoch [19][1100/1281] lr: 2.404e-02, eta: 11:34:02, time: 0.419, data_time: 0.001, memory: 4082, top1_acc: 0.8031, top5_acc: 0.9900, loss_cls: 0.8453, loss: 0.8453 +2025-06-24 10:44:19,131 - pyskl - INFO - Epoch [19][1200/1281] lr: 2.403e-02, eta: 11:35:34, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7788, top5_acc: 0.9844, loss_cls: 0.9350, loss: 0.9350 +2025-06-24 10:44:53,899 - pyskl - INFO - Saving checkpoint at 19 epochs +2025-06-24 10:46:00,642 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:46:00,706 - pyskl - INFO - +top1_acc 0.7548 +top5_acc 0.9757 +2025-06-24 10:46:00,707 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:46:00,714 - pyskl - INFO - +mean_acc 0.6235 +2025-06-24 10:46:00,718 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_17.pth was removed +2025-06-24 10:46:00,893 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_19.pth. +2025-06-24 10:46:00,894 - pyskl - INFO - Best top1_acc is 0.7548 at 19 epoch. +2025-06-24 10:46:00,897 - pyskl - INFO - Epoch(val) [19][533] top1_acc: 0.7548, top5_acc: 0.9757, mean_class_accuracy: 0.6235 +2025-06-24 10:46:55,320 - pyskl - INFO - Epoch [20][100/1281] lr: 2.402e-02, eta: 11:35:54, time: 0.544, data_time: 0.193, memory: 4082, top1_acc: 0.7950, top5_acc: 0.9881, loss_cls: 0.8913, loss: 0.8913 +2025-06-24 10:47:36,955 - pyskl - INFO - Epoch [20][200/1281] lr: 2.401e-02, eta: 11:37:23, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8025, top5_acc: 0.9881, loss_cls: 0.8483, loss: 0.8483 +2025-06-24 10:48:18,704 - pyskl - INFO - Epoch [20][300/1281] lr: 2.400e-02, eta: 11:38:53, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8056, top5_acc: 0.9894, loss_cls: 0.8538, loss: 0.8538 +2025-06-24 10:49:00,733 - pyskl - INFO - Epoch [20][400/1281] lr: 2.399e-02, eta: 11:40:23, time: 0.420, data_time: 0.000, memory: 4082, top1_acc: 0.7913, top5_acc: 0.9869, loss_cls: 0.8903, loss: 0.8903 +2025-06-24 10:49:42,508 - pyskl - INFO - Epoch [20][500/1281] lr: 2.398e-02, eta: 11:41:50, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8013, top5_acc: 0.9888, loss_cls: 0.8409, loss: 0.8409 +2025-06-24 10:50:24,529 - pyskl - INFO - Epoch [20][600/1281] lr: 2.398e-02, eta: 11:43:18, time: 0.420, data_time: 0.000, memory: 4082, top1_acc: 0.8031, top5_acc: 0.9894, loss_cls: 0.8552, loss: 0.8552 +2025-06-24 10:51:06,344 - pyskl - INFO - Epoch [20][700/1281] lr: 2.397e-02, eta: 11:44:43, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8013, top5_acc: 0.9850, loss_cls: 0.8934, loss: 0.8934 +2025-06-24 10:51:48,242 - pyskl - INFO - Epoch [20][800/1281] lr: 2.396e-02, eta: 11:46:08, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7844, top5_acc: 0.9869, loss_cls: 0.8840, loss: 0.8840 +2025-06-24 10:52:30,154 - pyskl - INFO - Epoch [20][900/1281] lr: 2.395e-02, eta: 11:47:32, time: 0.419, data_time: 0.001, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9850, loss_cls: 0.8544, loss: 0.8544 +2025-06-24 10:53:11,984 - pyskl - INFO - Epoch [20][1000/1281] lr: 2.394e-02, eta: 11:48:54, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7913, top5_acc: 0.9844, loss_cls: 0.8727, loss: 0.8727 +2025-06-24 10:53:53,789 - pyskl - INFO - Epoch [20][1100/1281] lr: 2.393e-02, eta: 11:50:16, time: 0.418, data_time: 0.001, memory: 4082, top1_acc: 0.7925, top5_acc: 0.9850, loss_cls: 0.8720, loss: 0.8720 +2025-06-24 10:54:35,621 - pyskl - INFO - Epoch [20][1200/1281] lr: 2.393e-02, eta: 11:51:36, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7931, top5_acc: 0.9869, loss_cls: 0.8867, loss: 0.8867 +2025-06-24 10:55:10,781 - pyskl - INFO - Saving checkpoint at 20 epochs +2025-06-24 10:56:16,903 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:56:16,958 - pyskl - INFO - +top1_acc 0.7491 +top5_acc 0.9799 +2025-06-24 10:56:16,958 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:56:16,971 - pyskl - INFO - +mean_acc 0.6408 +2025-06-24 10:56:16,974 - pyskl - INFO - Epoch(val) [20][533] top1_acc: 0.7491, top5_acc: 0.9799, mean_class_accuracy: 0.6408 +2025-06-24 10:57:10,357 - pyskl - INFO - Epoch [21][100/1281] lr: 2.391e-02, eta: 11:51:35, time: 0.534, data_time: 0.196, memory: 4082, top1_acc: 0.7913, top5_acc: 0.9850, loss_cls: 0.8699, loss: 0.8699 +2025-06-24 10:57:51,863 - pyskl - INFO - Epoch [21][200/1281] lr: 2.390e-02, eta: 11:52:52, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7975, top5_acc: 0.9900, loss_cls: 0.8140, loss: 0.8140 +2025-06-24 10:58:33,751 - pyskl - INFO - Epoch [21][300/1281] lr: 2.389e-02, eta: 11:54:10, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7981, top5_acc: 0.9900, loss_cls: 0.8674, loss: 0.8674 +2025-06-24 10:59:15,733 - pyskl - INFO - Epoch [21][400/1281] lr: 2.389e-02, eta: 11:55:27, time: 0.420, data_time: 0.000, memory: 4082, top1_acc: 0.8056, top5_acc: 0.9906, loss_cls: 0.8336, loss: 0.8336 +2025-06-24 10:59:57,640 - pyskl - INFO - Epoch [21][500/1281] lr: 2.388e-02, eta: 11:56:44, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.8025, top5_acc: 0.9881, loss_cls: 0.8819, loss: 0.8819 +2025-06-24 11:00:39,271 - pyskl - INFO - Epoch [21][600/1281] lr: 2.387e-02, eta: 11:57:57, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8087, top5_acc: 0.9900, loss_cls: 0.8191, loss: 0.8191 +2025-06-24 11:01:21,093 - pyskl - INFO - Epoch [21][700/1281] lr: 2.386e-02, eta: 11:59:11, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9881, loss_cls: 0.7946, loss: 0.7946 +2025-06-24 11:02:02,915 - pyskl - INFO - Epoch [21][800/1281] lr: 2.385e-02, eta: 12:00:24, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8013, top5_acc: 0.9838, loss_cls: 0.8613, loss: 0.8613 +2025-06-24 11:02:44,856 - pyskl - INFO - Epoch [21][900/1281] lr: 2.384e-02, eta: 12:01:37, time: 0.419, data_time: 0.001, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9875, loss_cls: 0.8858, loss: 0.8858 +2025-06-24 11:03:26,687 - pyskl - INFO - Epoch [21][1000/1281] lr: 2.383e-02, eta: 12:02:49, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8031, top5_acc: 0.9881, loss_cls: 0.8281, loss: 0.8281 +2025-06-24 11:04:10,606 - pyskl - INFO - Epoch [21][1100/1281] lr: 2.383e-02, eta: 12:04:12, time: 0.439, data_time: 0.000, memory: 4082, top1_acc: 0.8050, top5_acc: 0.9875, loss_cls: 0.8627, loss: 0.8627 +2025-06-24 11:04:54,493 - pyskl - INFO - Epoch [21][1200/1281] lr: 2.382e-02, eta: 12:05:35, time: 0.439, data_time: 0.000, memory: 4082, top1_acc: 0.7875, top5_acc: 0.9856, loss_cls: 0.9523, loss: 0.9523 +2025-06-24 11:05:29,636 - pyskl - INFO - Saving checkpoint at 21 epochs +2025-06-24 11:06:34,729 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:06:34,784 - pyskl - INFO - +top1_acc 0.7429 +top5_acc 0.9782 +2025-06-24 11:06:34,784 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:06:34,794 - pyskl - INFO - +mean_acc 0.6412 +2025-06-24 11:06:34,795 - pyskl - INFO - Epoch(val) [21][533] top1_acc: 0.7429, top5_acc: 0.9782, mean_class_accuracy: 0.6412 +2025-06-24 11:07:28,568 - pyskl - INFO - Epoch [22][100/1281] lr: 2.380e-02, eta: 12:05:24, time: 0.538, data_time: 0.194, memory: 4082, top1_acc: 0.7937, top5_acc: 0.9875, loss_cls: 0.8706, loss: 0.8706 +2025-06-24 11:08:10,286 - pyskl - INFO - Epoch [22][200/1281] lr: 2.379e-02, eta: 12:06:31, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9888, loss_cls: 0.7965, loss: 0.7965 +2025-06-24 11:08:52,130 - pyskl - INFO - Epoch [22][300/1281] lr: 2.378e-02, eta: 12:07:39, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9831, loss_cls: 0.8336, loss: 0.8336 +2025-06-24 11:09:33,782 - pyskl - INFO - Epoch [22][400/1281] lr: 2.378e-02, eta: 12:08:44, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8050, top5_acc: 0.9869, loss_cls: 0.8626, loss: 0.8626 +2025-06-24 11:10:15,688 - pyskl - INFO - Epoch [22][500/1281] lr: 2.377e-02, eta: 12:09:50, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.8213, top5_acc: 0.9906, loss_cls: 0.8119, loss: 0.8119 +2025-06-24 11:10:57,322 - pyskl - INFO - Epoch [22][600/1281] lr: 2.376e-02, eta: 12:10:53, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8087, top5_acc: 0.9850, loss_cls: 0.8276, loss: 0.8276 +2025-06-24 11:11:39,190 - pyskl - INFO - Epoch [22][700/1281] lr: 2.375e-02, eta: 12:11:57, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9844, loss_cls: 0.8374, loss: 0.8374 +2025-06-24 11:12:21,044 - pyskl - INFO - Epoch [22][800/1281] lr: 2.374e-02, eta: 12:13:01, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7987, top5_acc: 0.9875, loss_cls: 0.8566, loss: 0.8566 +2025-06-24 11:13:02,900 - pyskl - INFO - Epoch [22][900/1281] lr: 2.373e-02, eta: 12:14:03, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.8037, top5_acc: 0.9869, loss_cls: 0.8631, loss: 0.8631 +2025-06-24 11:13:44,715 - pyskl - INFO - Epoch [22][1000/1281] lr: 2.372e-02, eta: 12:15:05, time: 0.418, data_time: 0.001, memory: 4082, top1_acc: 0.8113, top5_acc: 0.9881, loss_cls: 0.8121, loss: 0.8121 +2025-06-24 11:14:27,627 - pyskl - INFO - Epoch [22][1100/1281] lr: 2.371e-02, eta: 12:16:12, time: 0.429, data_time: 0.001, memory: 4082, top1_acc: 0.8044, top5_acc: 0.9831, loss_cls: 0.8696, loss: 0.8696 +2025-06-24 11:15:11,551 - pyskl - INFO - Epoch [22][1200/1281] lr: 2.370e-02, eta: 12:17:25, time: 0.439, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9888, loss_cls: 0.8725, loss: 0.8725 +2025-06-24 11:15:46,392 - pyskl - INFO - Saving checkpoint at 22 epochs +2025-06-24 11:16:50,629 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:16:50,684 - pyskl - INFO - +top1_acc 0.7621 +top5_acc 0.9768 +2025-06-24 11:16:50,685 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:16:50,691 - pyskl - INFO - +mean_acc 0.6450 +2025-06-24 11:16:50,696 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_19.pth was removed +2025-06-24 11:16:50,877 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_22.pth. +2025-06-24 11:16:50,877 - pyskl - INFO - Best top1_acc is 0.7621 at 22 epoch. +2025-06-24 11:16:50,879 - pyskl - INFO - Epoch(val) [22][533] top1_acc: 0.7621, top5_acc: 0.9768, mean_class_accuracy: 0.6450 +2025-06-24 11:17:43,214 - pyskl - INFO - Epoch [23][100/1281] lr: 2.369e-02, eta: 12:16:56, time: 0.523, data_time: 0.196, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9875, loss_cls: 0.8264, loss: 0.8264 +2025-06-24 11:18:24,987 - pyskl - INFO - Epoch [23][200/1281] lr: 2.368e-02, eta: 12:17:55, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9862, loss_cls: 0.7876, loss: 0.7876 +2025-06-24 11:19:07,488 - pyskl - INFO - Epoch [23][300/1281] lr: 2.367e-02, eta: 12:18:56, time: 0.425, data_time: 0.000, memory: 4082, top1_acc: 0.8175, top5_acc: 0.9894, loss_cls: 0.8081, loss: 0.8081 +2025-06-24 11:19:49,137 - pyskl - INFO - Epoch [23][400/1281] lr: 2.366e-02, eta: 12:19:53, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9856, loss_cls: 0.8554, loss: 0.8554 +2025-06-24 11:20:30,847 - pyskl - INFO - Epoch [23][500/1281] lr: 2.365e-02, eta: 12:20:48, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7987, top5_acc: 0.9875, loss_cls: 0.8439, loss: 0.8439 +2025-06-24 11:21:12,423 - pyskl - INFO - Epoch [23][600/1281] lr: 2.364e-02, eta: 12:21:43, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7944, top5_acc: 0.9906, loss_cls: 0.8396, loss: 0.8396 +2025-06-24 11:21:54,086 - pyskl - INFO - Epoch [23][700/1281] lr: 2.363e-02, eta: 12:22:37, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9856, loss_cls: 0.8475, loss: 0.8475 +2025-06-24 11:22:35,859 - pyskl - INFO - Epoch [23][800/1281] lr: 2.362e-02, eta: 12:23:31, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9862, loss_cls: 0.8220, loss: 0.8220 +2025-06-24 11:23:17,866 - pyskl - INFO - Epoch [23][900/1281] lr: 2.361e-02, eta: 12:24:26, time: 0.420, data_time: 0.000, memory: 4082, top1_acc: 0.8069, top5_acc: 0.9869, loss_cls: 0.8537, loss: 0.8537 +2025-06-24 11:23:59,735 - pyskl - INFO - Epoch [23][1000/1281] lr: 2.360e-02, eta: 12:25:20, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9875, loss_cls: 0.8036, loss: 0.8036 +2025-06-24 11:24:41,517 - pyskl - INFO - Epoch [23][1100/1281] lr: 2.359e-02, eta: 12:26:12, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8037, top5_acc: 0.9844, loss_cls: 0.8492, loss: 0.8492 +2025-06-24 11:25:23,412 - pyskl - INFO - Epoch [23][1200/1281] lr: 2.359e-02, eta: 12:27:04, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7981, top5_acc: 0.9875, loss_cls: 0.8447, loss: 0.8447 +2025-06-24 11:25:57,887 - pyskl - INFO - Saving checkpoint at 23 epochs +2025-06-24 11:27:01,700 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:27:01,769 - pyskl - INFO - +top1_acc 0.7329 +top5_acc 0.9702 +2025-06-24 11:27:01,769 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:27:01,777 - pyskl - INFO - +mean_acc 0.6301 +2025-06-24 11:27:01,779 - pyskl - INFO - Epoch(val) [23][533] top1_acc: 0.7329, top5_acc: 0.9702, mean_class_accuracy: 0.6301 +2025-06-24 11:27:54,316 - pyskl - INFO - Epoch [24][100/1281] lr: 2.357e-02, eta: 12:26:29, time: 0.525, data_time: 0.200, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9844, loss_cls: 0.8137, loss: 0.8137 +2025-06-24 11:28:36,103 - pyskl - INFO - Epoch [24][200/1281] lr: 2.356e-02, eta: 12:27:20, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9850, loss_cls: 0.8651, loss: 0.8651 +2025-06-24 11:29:17,770 - pyskl - INFO - Epoch [24][300/1281] lr: 2.355e-02, eta: 12:28:09, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9881, loss_cls: 0.7736, loss: 0.7736 +2025-06-24 11:29:59,554 - pyskl - INFO - Epoch [24][400/1281] lr: 2.354e-02, eta: 12:28:58, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7981, top5_acc: 0.9825, loss_cls: 0.8365, loss: 0.8365 +2025-06-24 11:30:41,393 - pyskl - INFO - Epoch [24][500/1281] lr: 2.353e-02, eta: 12:29:47, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8106, top5_acc: 0.9869, loss_cls: 0.8216, loss: 0.8216 +2025-06-24 11:31:23,193 - pyskl - INFO - Epoch [24][600/1281] lr: 2.352e-02, eta: 12:30:35, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9875, loss_cls: 0.7859, loss: 0.7859 +2025-06-24 11:32:04,804 - pyskl - INFO - Epoch [24][700/1281] lr: 2.351e-02, eta: 12:31:22, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8037, top5_acc: 0.9850, loss_cls: 0.8448, loss: 0.8448 +2025-06-24 11:32:46,491 - pyskl - INFO - Epoch [24][800/1281] lr: 2.350e-02, eta: 12:32:08, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9894, loss_cls: 0.8247, loss: 0.8247 +2025-06-24 11:33:28,191 - pyskl - INFO - Epoch [24][900/1281] lr: 2.349e-02, eta: 12:32:54, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8125, top5_acc: 0.9919, loss_cls: 0.7858, loss: 0.7858 +2025-06-24 11:34:10,074 - pyskl - INFO - Epoch [24][1000/1281] lr: 2.348e-02, eta: 12:33:40, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.8087, top5_acc: 0.9900, loss_cls: 0.8174, loss: 0.8174 +2025-06-24 11:34:51,968 - pyskl - INFO - Epoch [24][1100/1281] lr: 2.347e-02, eta: 12:34:25, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9925, loss_cls: 0.7547, loss: 0.7547 +2025-06-24 11:35:33,678 - pyskl - INFO - Epoch [24][1200/1281] lr: 2.346e-02, eta: 12:35:10, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8113, top5_acc: 0.9894, loss_cls: 0.8686, loss: 0.8686 +2025-06-24 11:36:08,388 - pyskl - INFO - Saving checkpoint at 24 epochs +2025-06-24 11:37:11,073 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:37:11,142 - pyskl - INFO - +top1_acc 0.7531 +top5_acc 0.9795 +2025-06-24 11:37:11,142 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:37:11,150 - pyskl - INFO - +mean_acc 0.6626 +2025-06-24 11:37:11,153 - pyskl - INFO - Epoch(val) [24][533] top1_acc: 0.7531, top5_acc: 0.9795, mean_class_accuracy: 0.6626 +2025-06-24 11:38:03,144 - pyskl - INFO - Epoch [25][100/1281] lr: 2.344e-02, eta: 12:34:25, time: 0.520, data_time: 0.201, memory: 4082, top1_acc: 0.7987, top5_acc: 0.9925, loss_cls: 0.8009, loss: 0.8009 +2025-06-24 11:38:44,900 - pyskl - INFO - Epoch [25][200/1281] lr: 2.343e-02, eta: 12:35:08, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8213, top5_acc: 0.9912, loss_cls: 0.7514, loss: 0.7514 +2025-06-24 11:39:26,604 - pyskl - INFO - Epoch [25][300/1281] lr: 2.342e-02, eta: 12:35:51, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9906, loss_cls: 0.7318, loss: 0.7318 +2025-06-24 11:40:08,349 - pyskl - INFO - Epoch [25][400/1281] lr: 2.341e-02, eta: 12:36:33, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8075, top5_acc: 0.9869, loss_cls: 0.7940, loss: 0.7940 +2025-06-24 11:40:49,969 - pyskl - INFO - Epoch [25][500/1281] lr: 2.340e-02, eta: 12:37:14, time: 0.416, data_time: 0.001, memory: 4082, top1_acc: 0.8131, top5_acc: 0.9912, loss_cls: 0.7886, loss: 0.7886 +2025-06-24 11:41:31,553 - pyskl - INFO - Epoch [25][600/1281] lr: 2.339e-02, eta: 12:37:54, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9906, loss_cls: 0.7457, loss: 0.7457 +2025-06-24 11:42:13,159 - pyskl - INFO - Epoch [25][700/1281] lr: 2.338e-02, eta: 12:38:34, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9875, loss_cls: 0.7844, loss: 0.7844 +2025-06-24 11:42:55,093 - pyskl - INFO - Epoch [25][800/1281] lr: 2.337e-02, eta: 12:39:15, time: 0.419, data_time: 0.001, memory: 4082, top1_acc: 0.7987, top5_acc: 0.9844, loss_cls: 0.8658, loss: 0.8658 +2025-06-24 11:43:36,742 - pyskl - INFO - Epoch [25][900/1281] lr: 2.336e-02, eta: 12:39:54, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9881, loss_cls: 0.8248, loss: 0.8248 +2025-06-24 11:44:18,404 - pyskl - INFO - Epoch [25][1000/1281] lr: 2.335e-02, eta: 12:40:32, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8106, top5_acc: 0.9819, loss_cls: 0.8412, loss: 0.8412 +2025-06-24 11:45:00,121 - pyskl - INFO - Epoch [25][1100/1281] lr: 2.334e-02, eta: 12:41:11, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8106, top5_acc: 0.9875, loss_cls: 0.8222, loss: 0.8222 +2025-06-24 11:45:41,967 - pyskl - INFO - Epoch [25][1200/1281] lr: 2.333e-02, eta: 12:41:49, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9869, loss_cls: 0.8484, loss: 0.8484 +2025-06-24 11:46:16,526 - pyskl - INFO - Saving checkpoint at 25 epochs +2025-06-24 11:47:18,180 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:47:18,234 - pyskl - INFO - +top1_acc 0.7741 +top5_acc 0.9837 +2025-06-24 11:47:18,234 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:47:18,241 - pyskl - INFO - +mean_acc 0.6618 +2025-06-24 11:47:18,245 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_22.pth was removed +2025-06-24 11:47:18,480 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_25.pth. +2025-06-24 11:47:18,481 - pyskl - INFO - Best top1_acc is 0.7741 at 25 epoch. +2025-06-24 11:47:18,484 - pyskl - INFO - Epoch(val) [25][533] top1_acc: 0.7741, top5_acc: 0.9837, mean_class_accuracy: 0.6618 +2025-06-24 11:48:10,454 - pyskl - INFO - Epoch [26][100/1281] lr: 2.332e-02, eta: 12:40:59, time: 0.520, data_time: 0.195, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9906, loss_cls: 0.7379, loss: 0.7379 +2025-06-24 11:48:53,061 - pyskl - INFO - Epoch [26][200/1281] lr: 2.330e-02, eta: 12:41:41, time: 0.426, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9912, loss_cls: 0.7454, loss: 0.7454 +2025-06-24 11:49:34,734 - pyskl - INFO - Epoch [26][300/1281] lr: 2.329e-02, eta: 12:42:17, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9888, loss_cls: 0.7328, loss: 0.7328 +2025-06-24 11:50:16,359 - pyskl - INFO - Epoch [26][400/1281] lr: 2.328e-02, eta: 12:42:52, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9912, loss_cls: 0.7852, loss: 0.7852 +2025-06-24 11:50:58,022 - pyskl - INFO - Epoch [26][500/1281] lr: 2.327e-02, eta: 12:43:27, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8163, top5_acc: 0.9912, loss_cls: 0.7952, loss: 0.7952 +2025-06-24 11:51:39,847 - pyskl - INFO - Epoch [26][600/1281] lr: 2.326e-02, eta: 12:44:03, time: 0.418, data_time: 0.001, memory: 4082, top1_acc: 0.7944, top5_acc: 0.9850, loss_cls: 0.8554, loss: 0.8554 +2025-06-24 11:52:21,595 - pyskl - INFO - Epoch [26][700/1281] lr: 2.325e-02, eta: 12:44:37, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9931, loss_cls: 0.7538, loss: 0.7538 +2025-06-24 11:53:03,291 - pyskl - INFO - Epoch [26][800/1281] lr: 2.324e-02, eta: 12:45:11, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8025, top5_acc: 0.9912, loss_cls: 0.8257, loss: 0.8257 +2025-06-24 11:53:44,968 - pyskl - INFO - Epoch [26][900/1281] lr: 2.323e-02, eta: 12:45:45, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7937, top5_acc: 0.9850, loss_cls: 0.8865, loss: 0.8865 +2025-06-24 11:54:26,819 - pyskl - INFO - Epoch [26][1000/1281] lr: 2.322e-02, eta: 12:46:19, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9944, loss_cls: 0.7608, loss: 0.7608 +2025-06-24 11:55:08,648 - pyskl - INFO - Epoch [26][1100/1281] lr: 2.321e-02, eta: 12:46:52, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9875, loss_cls: 0.7751, loss: 0.7751 +2025-06-24 11:55:50,340 - pyskl - INFO - Epoch [26][1200/1281] lr: 2.320e-02, eta: 12:47:24, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8175, top5_acc: 0.9925, loss_cls: 0.7824, loss: 0.7824 +2025-06-24 11:56:24,572 - pyskl - INFO - Saving checkpoint at 26 epochs +2025-06-24 11:57:27,121 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:57:27,188 - pyskl - INFO - +top1_acc 0.7906 +top5_acc 0.9850 +2025-06-24 11:57:27,188 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:57:27,196 - pyskl - INFO - +mean_acc 0.7004 +2025-06-24 11:57:27,201 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_25.pth was removed +2025-06-24 11:57:27,392 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_26.pth. +2025-06-24 11:57:27,392 - pyskl - INFO - Best top1_acc is 0.7906 at 26 epoch. +2025-06-24 11:57:27,395 - pyskl - INFO - Epoch(val) [26][533] top1_acc: 0.7906, top5_acc: 0.9850, mean_class_accuracy: 0.7004 +2025-06-24 11:58:19,167 - pyskl - INFO - Epoch [27][100/1281] lr: 2.318e-02, eta: 12:46:28, time: 0.518, data_time: 0.198, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9900, loss_cls: 0.7718, loss: 0.7718 +2025-06-24 11:59:02,206 - pyskl - INFO - Epoch [27][200/1281] lr: 2.317e-02, eta: 12:47:06, time: 0.430, data_time: 0.000, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9906, loss_cls: 0.7000, loss: 0.7000 +2025-06-24 11:59:43,700 - pyskl - INFO - Epoch [27][300/1281] lr: 2.316e-02, eta: 12:47:36, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9938, loss_cls: 0.7685, loss: 0.7685 +2025-06-24 12:00:25,393 - pyskl - INFO - Epoch [27][400/1281] lr: 2.315e-02, eta: 12:48:06, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8175, top5_acc: 0.9881, loss_cls: 0.7921, loss: 0.7921 +2025-06-24 12:01:07,030 - pyskl - INFO - Epoch [27][500/1281] lr: 2.314e-02, eta: 12:48:36, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9906, loss_cls: 0.7198, loss: 0.7198 +2025-06-24 12:01:48,962 - pyskl - INFO - Epoch [27][600/1281] lr: 2.313e-02, eta: 12:49:06, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.8206, top5_acc: 0.9912, loss_cls: 0.7582, loss: 0.7582 +2025-06-24 12:02:30,620 - pyskl - INFO - Epoch [27][700/1281] lr: 2.312e-02, eta: 12:49:35, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8213, top5_acc: 0.9894, loss_cls: 0.7722, loss: 0.7722 +2025-06-24 12:03:12,402 - pyskl - INFO - Epoch [27][800/1281] lr: 2.311e-02, eta: 12:50:04, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8275, top5_acc: 0.9938, loss_cls: 0.7684, loss: 0.7684 +2025-06-24 12:03:54,157 - pyskl - INFO - Epoch [27][900/1281] lr: 2.310e-02, eta: 12:50:33, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7950, top5_acc: 0.9888, loss_cls: 0.8390, loss: 0.8390 +2025-06-24 12:04:35,928 - pyskl - INFO - Epoch [27][1000/1281] lr: 2.308e-02, eta: 12:51:01, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8237, top5_acc: 0.9906, loss_cls: 0.8104, loss: 0.8104 +2025-06-24 12:05:17,546 - pyskl - INFO - Epoch [27][1100/1281] lr: 2.307e-02, eta: 12:51:28, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9900, loss_cls: 0.7508, loss: 0.7508 +2025-06-24 12:05:59,333 - pyskl - INFO - Epoch [27][1200/1281] lr: 2.306e-02, eta: 12:51:56, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9906, loss_cls: 0.7813, loss: 0.7813 +2025-06-24 12:06:33,969 - pyskl - INFO - Saving checkpoint at 27 epochs +2025-06-24 12:07:34,430 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:07:34,496 - pyskl - INFO - +top1_acc 0.7614 +top5_acc 0.9756 +2025-06-24 12:07:34,496 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:07:34,504 - pyskl - INFO - +mean_acc 0.6781 +2025-06-24 12:07:34,507 - pyskl - INFO - Epoch(val) [27][533] top1_acc: 0.7614, top5_acc: 0.9756, mean_class_accuracy: 0.6781 +2025-06-24 12:08:24,668 - pyskl - INFO - Epoch [28][100/1281] lr: 2.304e-02, eta: 12:50:49, time: 0.502, data_time: 0.191, memory: 4082, top1_acc: 0.8400, top5_acc: 0.9944, loss_cls: 0.7364, loss: 0.7364 +2025-06-24 12:09:08,608 - pyskl - INFO - Epoch [28][200/1281] lr: 2.303e-02, eta: 12:51:25, time: 0.439, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9906, loss_cls: 0.7725, loss: 0.7725 +2025-06-24 12:09:50,956 - pyskl - INFO - Epoch [28][300/1281] lr: 2.302e-02, eta: 12:51:54, time: 0.423, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9894, loss_cls: 0.7541, loss: 0.7541 +2025-06-24 12:10:32,521 - pyskl - INFO - Epoch [28][400/1281] lr: 2.301e-02, eta: 12:52:19, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9919, loss_cls: 0.7345, loss: 0.7345 +2025-06-24 12:11:14,189 - pyskl - INFO - Epoch [28][500/1281] lr: 2.300e-02, eta: 12:52:44, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9888, loss_cls: 0.8141, loss: 0.8141 +2025-06-24 12:11:55,967 - pyskl - INFO - Epoch [28][600/1281] lr: 2.299e-02, eta: 12:53:10, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8050, top5_acc: 0.9919, loss_cls: 0.8057, loss: 0.8057 +2025-06-24 12:12:38,991 - pyskl - INFO - Epoch [28][700/1281] lr: 2.298e-02, eta: 12:53:40, time: 0.430, data_time: 0.000, memory: 4082, top1_acc: 0.8237, top5_acc: 0.9888, loss_cls: 0.7809, loss: 0.7809 +2025-06-24 12:13:22,237 - pyskl - INFO - Epoch [28][800/1281] lr: 2.297e-02, eta: 12:54:11, time: 0.432, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9944, loss_cls: 0.7260, loss: 0.7260 +2025-06-24 12:14:03,844 - pyskl - INFO - Epoch [28][900/1281] lr: 2.295e-02, eta: 12:54:34, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9894, loss_cls: 0.7578, loss: 0.7578 +2025-06-24 12:14:45,648 - pyskl - INFO - Epoch [28][1000/1281] lr: 2.294e-02, eta: 12:54:58, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9888, loss_cls: 0.8067, loss: 0.8067 +2025-06-24 12:15:27,585 - pyskl - INFO - Epoch [28][1100/1281] lr: 2.293e-02, eta: 12:55:22, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9881, loss_cls: 0.7447, loss: 0.7447 +2025-06-24 12:16:09,194 - pyskl - INFO - Epoch [28][1200/1281] lr: 2.292e-02, eta: 12:55:44, time: 0.416, data_time: 0.001, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9894, loss_cls: 0.7957, loss: 0.7957 +2025-06-24 12:16:43,590 - pyskl - INFO - Saving checkpoint at 28 epochs +2025-06-24 12:17:44,095 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:17:44,150 - pyskl - INFO - +top1_acc 0.7611 +top5_acc 0.9758 +2025-06-24 12:17:44,151 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:17:44,157 - pyskl - INFO - +mean_acc 0.6660 +2025-06-24 12:17:44,159 - pyskl - INFO - Epoch(val) [28][533] top1_acc: 0.7611, top5_acc: 0.9758, mean_class_accuracy: 0.6660 +2025-06-24 12:18:34,462 - pyskl - INFO - Epoch [29][100/1281] lr: 2.290e-02, eta: 12:54:34, time: 0.503, data_time: 0.196, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9925, loss_cls: 0.7449, loss: 0.7449 +2025-06-24 12:19:18,320 - pyskl - INFO - Epoch [29][200/1281] lr: 2.289e-02, eta: 12:55:06, time: 0.439, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9912, loss_cls: 0.7012, loss: 0.7012 +2025-06-24 12:20:02,016 - pyskl - INFO - Epoch [29][300/1281] lr: 2.288e-02, eta: 12:55:36, time: 0.437, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9875, loss_cls: 0.7595, loss: 0.7595 +2025-06-24 12:20:45,708 - pyskl - INFO - Epoch [29][400/1281] lr: 2.287e-02, eta: 12:56:06, time: 0.437, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9881, loss_cls: 0.7575, loss: 0.7575 +2025-06-24 12:21:28,994 - pyskl - INFO - Epoch [29][500/1281] lr: 2.285e-02, eta: 12:56:33, time: 0.433, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9919, loss_cls: 0.7131, loss: 0.7131 +2025-06-24 12:22:12,822 - pyskl - INFO - Epoch [29][600/1281] lr: 2.284e-02, eta: 12:57:03, time: 0.438, data_time: 0.000, memory: 4082, top1_acc: 0.8263, top5_acc: 0.9875, loss_cls: 0.7863, loss: 0.7863 +2025-06-24 12:22:56,250 - pyskl - INFO - Epoch [29][700/1281] lr: 2.283e-02, eta: 12:57:30, time: 0.434, data_time: 0.000, memory: 4082, top1_acc: 0.8375, top5_acc: 0.9906, loss_cls: 0.7329, loss: 0.7329 +2025-06-24 12:23:39,896 - pyskl - INFO - Epoch [29][800/1281] lr: 2.282e-02, eta: 12:57:58, time: 0.436, data_time: 0.000, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9881, loss_cls: 0.7867, loss: 0.7867 +2025-06-24 12:24:21,541 - pyskl - INFO - Epoch [29][900/1281] lr: 2.281e-02, eta: 12:58:17, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9912, loss_cls: 0.7564, loss: 0.7564 +2025-06-24 12:25:03,087 - pyskl - INFO - Epoch [29][1000/1281] lr: 2.280e-02, eta: 12:58:35, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9919, loss_cls: 0.7746, loss: 0.7746 +2025-06-24 12:25:44,950 - pyskl - INFO - Epoch [29][1100/1281] lr: 2.279e-02, eta: 12:58:55, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9850, loss_cls: 0.8458, loss: 0.8458 +2025-06-24 12:26:26,671 - pyskl - INFO - Epoch [29][1200/1281] lr: 2.277e-02, eta: 12:59:13, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9894, loss_cls: 0.7208, loss: 0.7208 +2025-06-24 12:27:01,158 - pyskl - INFO - Saving checkpoint at 29 epochs +2025-06-24 12:27:59,086 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:27:59,141 - pyskl - INFO - +top1_acc 0.7519 +top5_acc 0.9770 +2025-06-24 12:27:59,141 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:27:59,148 - pyskl - INFO - +mean_acc 0.6696 +2025-06-24 12:27:59,150 - pyskl - INFO - Epoch(val) [29][533] top1_acc: 0.7519, top5_acc: 0.9770, mean_class_accuracy: 0.6696 +2025-06-24 12:28:51,169 - pyskl - INFO - Epoch [30][100/1281] lr: 2.275e-02, eta: 12:58:08, time: 0.520, data_time: 0.192, memory: 4082, top1_acc: 0.8400, top5_acc: 0.9894, loss_cls: 0.7024, loss: 0.7024 +2025-06-24 12:29:42,666 - pyskl - INFO - Epoch [30][200/1281] lr: 2.274e-02, eta: 12:59:06, time: 0.515, data_time: 0.000, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9950, loss_cls: 0.7082, loss: 0.7082 +2025-06-24 12:30:35,213 - pyskl - INFO - Epoch [30][300/1281] lr: 2.273e-02, eta: 13:00:08, time: 0.525, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9950, loss_cls: 0.7071, loss: 0.7071 +2025-06-24 12:31:26,837 - pyskl - INFO - Epoch [30][400/1281] lr: 2.272e-02, eta: 13:01:06, time: 0.516, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9950, loss_cls: 0.7207, loss: 0.7207 +2025-06-24 12:32:18,754 - pyskl - INFO - Epoch [30][500/1281] lr: 2.271e-02, eta: 13:02:05, time: 0.519, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9862, loss_cls: 0.7353, loss: 0.7353 +2025-06-24 12:33:09,657 - pyskl - INFO - Epoch [30][600/1281] lr: 2.269e-02, eta: 13:02:58, time: 0.509, data_time: 0.000, memory: 4082, top1_acc: 0.8275, top5_acc: 0.9944, loss_cls: 0.7666, loss: 0.7666 +2025-06-24 12:34:00,475 - pyskl - INFO - Epoch [30][700/1281] lr: 2.268e-02, eta: 13:03:51, time: 0.508, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9900, loss_cls: 0.7269, loss: 0.7269 +2025-06-24 12:34:52,042 - pyskl - INFO - Epoch [30][800/1281] lr: 2.267e-02, eta: 13:04:46, time: 0.516, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9900, loss_cls: 0.7255, loss: 0.7255 +2025-06-24 12:35:43,171 - pyskl - INFO - Epoch [30][900/1281] lr: 2.266e-02, eta: 13:05:39, time: 0.511, data_time: 0.000, memory: 4082, top1_acc: 0.8237, top5_acc: 0.9925, loss_cls: 0.7375, loss: 0.7375 +2025-06-24 12:36:34,219 - pyskl - INFO - Epoch [30][1000/1281] lr: 2.265e-02, eta: 13:06:31, time: 0.510, data_time: 0.000, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9912, loss_cls: 0.8108, loss: 0.8108 +2025-06-24 12:37:05,103 - pyskl - INFO - Epoch [30][1100/1281] lr: 2.263e-02, eta: 13:06:01, time: 0.309, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9875, loss_cls: 0.7222, loss: 0.7222 +2025-06-24 12:37:56,332 - pyskl - INFO - Epoch [30][1200/1281] lr: 2.262e-02, eta: 13:06:53, time: 0.512, data_time: 0.000, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9875, loss_cls: 0.8322, loss: 0.8322 +2025-06-24 12:38:23,006 - pyskl - INFO - Saving checkpoint at 30 epochs +2025-06-24 12:39:34,827 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:39:34,889 - pyskl - INFO - +top1_acc 0.7807 +top5_acc 0.9810 +2025-06-24 12:39:34,889 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:39:34,899 - pyskl - INFO - +mean_acc 0.6612 +2025-06-24 12:39:34,901 - pyskl - INFO - Epoch(val) [30][533] top1_acc: 0.7807, top5_acc: 0.9810, mean_class_accuracy: 0.6612 +2025-06-24 12:41:06,608 - pyskl - INFO - Epoch [31][100/1281] lr: 2.260e-02, eta: 13:08:22, time: 0.917, data_time: 0.198, memory: 4083, top1_acc: 0.8375, top5_acc: 0.9912, loss_cls: 0.8957, loss: 0.8957 +2025-06-24 12:42:00,566 - pyskl - INFO - Epoch [31][200/1281] lr: 2.259e-02, eta: 13:09:23, time: 0.540, data_time: 0.000, memory: 4083, top1_acc: 0.8256, top5_acc: 0.9912, loss_cls: 0.8750, loss: 0.8750 +2025-06-24 12:42:53,338 - pyskl - INFO - Epoch [31][300/1281] lr: 2.258e-02, eta: 13:10:19, time: 0.528, data_time: 0.000, memory: 4083, top1_acc: 0.8350, top5_acc: 0.9931, loss_cls: 0.8636, loss: 0.8636 +2025-06-24 12:43:46,597 - pyskl - INFO - Epoch [31][400/1281] lr: 2.256e-02, eta: 13:11:16, time: 0.533, data_time: 0.000, memory: 4083, top1_acc: 0.8287, top5_acc: 0.9881, loss_cls: 0.9070, loss: 0.9070 +2025-06-24 12:44:40,000 - pyskl - INFO - Epoch [31][500/1281] lr: 2.255e-02, eta: 13:12:14, time: 0.534, data_time: 0.000, memory: 4083, top1_acc: 0.8331, top5_acc: 0.9912, loss_cls: 0.8991, loss: 0.8991 +2025-06-24 12:45:34,423 - pyskl - INFO - Epoch [31][600/1281] lr: 2.254e-02, eta: 13:13:14, time: 0.544, data_time: 0.000, memory: 4083, top1_acc: 0.8438, top5_acc: 0.9919, loss_cls: 0.8910, loss: 0.8910 +2025-06-24 12:46:03,932 - pyskl - INFO - Epoch [31][700/1281] lr: 2.253e-02, eta: 13:12:37, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.8319, top5_acc: 0.9900, loss_cls: 0.8895, loss: 0.8895 +2025-06-24 12:46:55,139 - pyskl - INFO - Epoch [31][800/1281] lr: 2.252e-02, eta: 13:13:24, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8263, top5_acc: 0.9912, loss_cls: 0.8980, loss: 0.8980 +2025-06-24 12:47:32,688 - pyskl - INFO - Epoch [31][900/1281] lr: 2.250e-02, eta: 13:13:18, time: 0.375, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9944, loss_cls: 0.8390, loss: 0.8390 +2025-06-24 12:48:26,248 - pyskl - INFO - Epoch [31][1000/1281] lr: 2.249e-02, eta: 13:14:14, time: 0.536, data_time: 0.000, memory: 4083, top1_acc: 0.8387, top5_acc: 0.9950, loss_cls: 0.8435, loss: 0.8435 +2025-06-24 12:49:19,605 - pyskl - INFO - Epoch [31][1100/1281] lr: 2.248e-02, eta: 13:15:08, time: 0.534, data_time: 0.000, memory: 4083, top1_acc: 0.8294, top5_acc: 0.9888, loss_cls: 0.9496, loss: 0.9496 +2025-06-24 12:50:12,708 - pyskl - INFO - Epoch [31][1200/1281] lr: 2.247e-02, eta: 13:16:01, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.8325, top5_acc: 0.9912, loss_cls: 0.8907, loss: 0.8907 +2025-06-24 12:50:57,139 - pyskl - INFO - Saving checkpoint at 31 epochs +2025-06-24 12:52:08,663 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:52:08,730 - pyskl - INFO - +top1_acc 0.7731 +top5_acc 0.9811 +2025-06-24 12:52:08,730 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:52:08,738 - pyskl - INFO - +mean_acc 0.6971 +2025-06-24 12:52:08,740 - pyskl - INFO - Epoch(val) [31][533] top1_acc: 0.7731, top5_acc: 0.9811, mean_class_accuracy: 0.6971 +2025-06-24 12:53:37,930 - pyskl - INFO - Epoch [32][100/1281] lr: 2.244e-02, eta: 13:17:09, time: 0.892, data_time: 0.199, memory: 4083, top1_acc: 0.8450, top5_acc: 0.9925, loss_cls: 0.7922, loss: 0.7922 +2025-06-24 12:54:31,670 - pyskl - INFO - Epoch [32][200/1281] lr: 2.243e-02, eta: 13:18:03, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.8194, top5_acc: 0.9912, loss_cls: 0.8518, loss: 0.8518 +2025-06-24 12:55:01,313 - pyskl - INFO - Epoch [32][300/1281] lr: 2.242e-02, eta: 13:17:24, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 0.8263, top5_acc: 0.9912, loss_cls: 0.8326, loss: 0.8326 +2025-06-24 12:55:52,450 - pyskl - INFO - Epoch [32][400/1281] lr: 2.241e-02, eta: 13:18:07, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8263, top5_acc: 0.9906, loss_cls: 0.8626, loss: 0.8626 +2025-06-24 12:56:31,215 - pyskl - INFO - Epoch [32][500/1281] lr: 2.239e-02, eta: 13:18:03, time: 0.388, data_time: 0.001, memory: 4083, top1_acc: 0.8406, top5_acc: 0.9919, loss_cls: 0.7796, loss: 0.7796 +2025-06-24 12:57:24,227 - pyskl - INFO - Epoch [32][600/1281] lr: 2.238e-02, eta: 13:18:53, time: 0.530, data_time: 0.000, memory: 4083, top1_acc: 0.8056, top5_acc: 0.9888, loss_cls: 0.9166, loss: 0.9166 +2025-06-24 12:58:17,691 - pyskl - INFO - Epoch [32][700/1281] lr: 2.237e-02, eta: 13:19:43, time: 0.535, data_time: 0.000, memory: 4083, top1_acc: 0.8381, top5_acc: 0.9912, loss_cls: 0.8250, loss: 0.8250 +2025-06-24 12:59:09,822 - pyskl - INFO - Epoch [32][800/1281] lr: 2.236e-02, eta: 13:20:29, time: 0.521, data_time: 0.000, memory: 4083, top1_acc: 0.8325, top5_acc: 0.9912, loss_cls: 0.7912, loss: 0.7912 +2025-06-24 13:00:02,587 - pyskl - INFO - Epoch [32][900/1281] lr: 2.234e-02, eta: 13:21:16, time: 0.528, data_time: 0.000, memory: 4083, top1_acc: 0.8150, top5_acc: 0.9894, loss_cls: 0.8941, loss: 0.8941 +2025-06-24 13:00:55,080 - pyskl - INFO - Epoch [32][1000/1281] lr: 2.233e-02, eta: 13:22:01, time: 0.525, data_time: 0.001, memory: 4083, top1_acc: 0.8431, top5_acc: 0.9938, loss_cls: 0.8004, loss: 0.8004 +2025-06-24 13:01:49,558 - pyskl - INFO - Epoch [32][1100/1281] lr: 2.232e-02, eta: 13:22:54, time: 0.545, data_time: 0.001, memory: 4083, top1_acc: 0.8306, top5_acc: 0.9931, loss_cls: 0.8045, loss: 0.8045 +2025-06-24 13:02:44,069 - pyskl - INFO - Epoch [32][1200/1281] lr: 2.231e-02, eta: 13:23:46, time: 0.545, data_time: 0.000, memory: 4083, top1_acc: 0.8319, top5_acc: 0.9919, loss_cls: 0.8525, loss: 0.8525 +2025-06-24 13:03:28,270 - pyskl - INFO - Saving checkpoint at 32 epochs +2025-06-24 13:04:19,517 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:04:19,594 - pyskl - INFO - +top1_acc 0.7909 +top5_acc 0.9805 +2025-06-24 13:04:19,594 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:04:19,603 - pyskl - INFO - +mean_acc 0.6958 +2025-06-24 13:04:19,607 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_26.pth was removed +2025-06-24 13:04:19,781 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_32.pth. +2025-06-24 13:04:19,781 - pyskl - INFO - Best top1_acc is 0.7909 at 32 epoch. +2025-06-24 13:04:19,784 - pyskl - INFO - Epoch(val) [32][533] top1_acc: 0.7909, top5_acc: 0.9805, mean_class_accuracy: 0.6958 +2025-06-24 13:05:12,369 - pyskl - INFO - Epoch [33][100/1281] lr: 2.228e-02, eta: 13:22:29, time: 0.526, data_time: 0.190, memory: 4083, top1_acc: 0.8394, top5_acc: 0.9931, loss_cls: 0.7660, loss: 0.7660 +2025-06-24 13:06:04,802 - pyskl - INFO - Epoch [33][200/1281] lr: 2.227e-02, eta: 13:23:12, time: 0.524, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9950, loss_cls: 0.7504, loss: 0.7504 +2025-06-24 13:06:59,095 - pyskl - INFO - Epoch [33][300/1281] lr: 2.226e-02, eta: 13:24:02, time: 0.543, data_time: 0.000, memory: 4083, top1_acc: 0.8369, top5_acc: 0.9912, loss_cls: 0.7797, loss: 0.7797 +2025-06-24 13:07:53,873 - pyskl - INFO - Epoch [33][400/1281] lr: 2.225e-02, eta: 13:24:53, time: 0.548, data_time: 0.000, memory: 4083, top1_acc: 0.8431, top5_acc: 0.9919, loss_cls: 0.7728, loss: 0.7728 +2025-06-24 13:08:47,878 - pyskl - INFO - Epoch [33][500/1281] lr: 2.223e-02, eta: 13:25:41, time: 0.540, data_time: 0.000, memory: 4083, top1_acc: 0.8231, top5_acc: 0.9919, loss_cls: 0.8416, loss: 0.8416 +2025-06-24 13:09:40,601 - pyskl - INFO - Epoch [33][600/1281] lr: 2.222e-02, eta: 13:26:24, time: 0.527, data_time: 0.000, memory: 4083, top1_acc: 0.8206, top5_acc: 0.9900, loss_cls: 0.8222, loss: 0.8222 +2025-06-24 13:10:34,234 - pyskl - INFO - Epoch [33][700/1281] lr: 2.221e-02, eta: 13:27:09, time: 0.536, data_time: 0.000, memory: 4083, top1_acc: 0.8350, top5_acc: 0.9881, loss_cls: 0.7835, loss: 0.7835 +2025-06-24 13:11:28,249 - pyskl - INFO - Epoch [33][800/1281] lr: 2.219e-02, eta: 13:27:55, time: 0.540, data_time: 0.000, memory: 4083, top1_acc: 0.8244, top5_acc: 0.9956, loss_cls: 0.8287, loss: 0.8287 +2025-06-24 13:12:21,774 - pyskl - INFO - Epoch [33][900/1281] lr: 2.218e-02, eta: 13:28:39, time: 0.535, data_time: 0.000, memory: 4083, top1_acc: 0.8488, top5_acc: 0.9869, loss_cls: 0.7537, loss: 0.7537 +2025-06-24 13:13:11,468 - pyskl - INFO - Epoch [33][1000/1281] lr: 2.217e-02, eta: 13:29:09, time: 0.497, data_time: 0.001, memory: 4083, top1_acc: 0.8331, top5_acc: 0.9912, loss_cls: 0.7973, loss: 0.7973 +2025-06-24 13:13:50,550 - pyskl - INFO - Epoch [33][1100/1281] lr: 2.216e-02, eta: 13:29:01, time: 0.391, data_time: 0.000, memory: 4083, top1_acc: 0.8156, top5_acc: 0.9919, loss_cls: 0.8464, loss: 0.8464 +2025-06-24 13:14:25,293 - pyskl - INFO - Epoch [33][1200/1281] lr: 2.214e-02, eta: 13:28:37, time: 0.347, data_time: 0.001, memory: 4083, top1_acc: 0.8287, top5_acc: 0.9931, loss_cls: 0.7970, loss: 0.7970 +2025-06-24 13:15:05,685 - pyskl - INFO - Saving checkpoint at 33 epochs +2025-06-24 13:16:17,244 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:16:17,303 - pyskl - INFO - +top1_acc 0.7803 +top5_acc 0.9811 +2025-06-24 13:16:17,303 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:16:17,310 - pyskl - INFO - +mean_acc 0.6874 +2025-06-24 13:16:17,312 - pyskl - INFO - Epoch(val) [33][533] top1_acc: 0.7803, top5_acc: 0.9811, mean_class_accuracy: 0.6874 +2025-06-24 13:17:44,388 - pyskl - INFO - Epoch [34][100/1281] lr: 2.212e-02, eta: 13:29:20, time: 0.871, data_time: 0.196, memory: 4083, top1_acc: 0.8400, top5_acc: 0.9912, loss_cls: 0.7732, loss: 0.7732 +2025-06-24 13:18:38,734 - pyskl - INFO - Epoch [34][200/1281] lr: 2.211e-02, eta: 13:30:04, time: 0.543, data_time: 0.000, memory: 4083, top1_acc: 0.8306, top5_acc: 0.9931, loss_cls: 0.8003, loss: 0.8003 +2025-06-24 13:19:31,867 - pyskl - INFO - Epoch [34][300/1281] lr: 2.209e-02, eta: 13:30:44, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9944, loss_cls: 0.7325, loss: 0.7325 +2025-06-24 13:20:26,027 - pyskl - INFO - Epoch [34][400/1281] lr: 2.208e-02, eta: 13:31:28, time: 0.542, data_time: 0.000, memory: 4083, top1_acc: 0.8475, top5_acc: 0.9956, loss_cls: 0.7474, loss: 0.7474 +2025-06-24 13:21:19,100 - pyskl - INFO - Epoch [34][500/1281] lr: 2.207e-02, eta: 13:32:07, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.8444, top5_acc: 0.9919, loss_cls: 0.7795, loss: 0.7795 +2025-06-24 13:22:08,835 - pyskl - INFO - Epoch [34][600/1281] lr: 2.205e-02, eta: 13:32:34, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.8413, top5_acc: 0.9931, loss_cls: 0.7727, loss: 0.7727 +2025-06-24 13:22:47,196 - pyskl - INFO - Epoch [34][700/1281] lr: 2.204e-02, eta: 13:32:21, time: 0.384, data_time: 0.000, memory: 4083, top1_acc: 0.8462, top5_acc: 0.9875, loss_cls: 0.7523, loss: 0.7523 +2025-06-24 13:23:22,981 - pyskl - INFO - Epoch [34][800/1281] lr: 2.203e-02, eta: 13:31:59, time: 0.358, data_time: 0.000, memory: 4083, top1_acc: 0.8331, top5_acc: 0.9919, loss_cls: 0.7686, loss: 0.7686 +2025-06-24 13:24:10,442 - pyskl - INFO - Epoch [34][900/1281] lr: 2.201e-02, eta: 13:32:17, time: 0.475, data_time: 0.000, memory: 4083, top1_acc: 0.8475, top5_acc: 0.9931, loss_cls: 0.7289, loss: 0.7289 +2025-06-24 13:25:04,114 - pyskl - INFO - Epoch [34][1000/1281] lr: 2.200e-02, eta: 13:32:57, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.8144, top5_acc: 0.9938, loss_cls: 0.8607, loss: 0.8607 +2025-06-24 13:25:57,901 - pyskl - INFO - Epoch [34][1100/1281] lr: 2.199e-02, eta: 13:33:36, time: 0.538, data_time: 0.001, memory: 4083, top1_acc: 0.8113, top5_acc: 0.9900, loss_cls: 0.8358, loss: 0.8358 +2025-06-24 13:26:51,218 - pyskl - INFO - Epoch [34][1200/1281] lr: 2.197e-02, eta: 13:34:13, time: 0.533, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9925, loss_cls: 0.7853, loss: 0.7853 +2025-06-24 13:27:35,244 - pyskl - INFO - Saving checkpoint at 34 epochs +2025-06-24 13:28:46,489 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:28:46,545 - pyskl - INFO - +top1_acc 0.7827 +top5_acc 0.9852 +2025-06-24 13:28:46,545 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:28:46,552 - pyskl - INFO - +mean_acc 0.7081 +2025-06-24 13:28:46,553 - pyskl - INFO - Epoch(val) [34][533] top1_acc: 0.7827, top5_acc: 0.9852, mean_class_accuracy: 0.7081 +2025-06-24 13:30:14,158 - pyskl - INFO - Epoch [35][100/1281] lr: 2.195e-02, eta: 13:34:49, time: 0.876, data_time: 0.194, memory: 4083, top1_acc: 0.8369, top5_acc: 0.9894, loss_cls: 0.7934, loss: 0.7934 +2025-06-24 13:31:06,617 - pyskl - INFO - Epoch [35][200/1281] lr: 2.194e-02, eta: 13:35:23, time: 0.525, data_time: 0.000, memory: 4083, top1_acc: 0.8419, top5_acc: 0.9962, loss_cls: 0.7476, loss: 0.7476 +2025-06-24 13:31:41,398 - pyskl - INFO - Epoch [35][300/1281] lr: 2.192e-02, eta: 13:34:56, time: 0.348, data_time: 0.000, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9906, loss_cls: 0.7301, loss: 0.7301 +2025-06-24 13:32:20,418 - pyskl - INFO - Epoch [35][400/1281] lr: 2.191e-02, eta: 13:34:43, time: 0.390, data_time: 0.000, memory: 4083, top1_acc: 0.8225, top5_acc: 0.9862, loss_cls: 0.8553, loss: 0.8553 +2025-06-24 13:33:05,642 - pyskl - INFO - Epoch [35][500/1281] lr: 2.190e-02, eta: 13:34:52, time: 0.452, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9925, loss_cls: 0.7741, loss: 0.7741 +2025-06-24 13:33:59,032 - pyskl - INFO - Epoch [35][600/1281] lr: 2.188e-02, eta: 13:35:27, time: 0.534, data_time: 0.000, memory: 4083, top1_acc: 0.8462, top5_acc: 0.9938, loss_cls: 0.7178, loss: 0.7178 +2025-06-24 13:34:52,548 - pyskl - INFO - Epoch [35][700/1281] lr: 2.187e-02, eta: 13:36:02, time: 0.535, data_time: 0.000, memory: 4083, top1_acc: 0.8450, top5_acc: 0.9912, loss_cls: 0.7413, loss: 0.7413 +2025-06-24 13:35:46,851 - pyskl - INFO - Epoch [35][800/1281] lr: 2.185e-02, eta: 13:36:40, time: 0.543, data_time: 0.000, memory: 4083, top1_acc: 0.8319, top5_acc: 0.9894, loss_cls: 0.7855, loss: 0.7855 +2025-06-24 13:36:40,688 - pyskl - INFO - Epoch [35][900/1281] lr: 2.184e-02, eta: 13:37:15, time: 0.538, data_time: 0.000, memory: 4083, top1_acc: 0.8413, top5_acc: 0.9938, loss_cls: 0.7858, loss: 0.7858 +2025-06-24 13:37:34,499 - pyskl - INFO - Epoch [35][1000/1281] lr: 2.183e-02, eta: 13:37:50, time: 0.538, data_time: 0.000, memory: 4083, top1_acc: 0.8394, top5_acc: 0.9906, loss_cls: 0.7578, loss: 0.7578 +2025-06-24 13:38:27,400 - pyskl - INFO - Epoch [35][1100/1281] lr: 2.181e-02, eta: 13:38:22, time: 0.529, data_time: 0.000, memory: 4083, top1_acc: 0.8475, top5_acc: 0.9912, loss_cls: 0.7411, loss: 0.7411 +2025-06-24 13:39:20,458 - pyskl - INFO - Epoch [35][1200/1281] lr: 2.180e-02, eta: 13:38:54, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.8488, top5_acc: 0.9944, loss_cls: 0.7466, loss: 0.7466 +2025-06-24 13:40:05,614 - pyskl - INFO - Saving checkpoint at 35 epochs +2025-06-24 13:41:04,784 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:41:04,852 - pyskl - INFO - +top1_acc 0.8086 +top5_acc 0.9871 +2025-06-24 13:41:04,853 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:41:04,861 - pyskl - INFO - +mean_acc 0.7238 +2025-06-24 13:41:04,866 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_32.pth was removed +2025-06-24 13:41:05,050 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_35.pth. +2025-06-24 13:41:05,050 - pyskl - INFO - Best top1_acc is 0.8086 at 35 epoch. +2025-06-24 13:41:05,053 - pyskl - INFO - Epoch(val) [35][533] top1_acc: 0.8086, top5_acc: 0.9871, mean_class_accuracy: 0.7238 +2025-06-24 13:42:02,011 - pyskl - INFO - Epoch [36][100/1281] lr: 2.178e-02, eta: 13:37:42, time: 0.570, data_time: 0.196, memory: 4083, top1_acc: 0.8419, top5_acc: 0.9944, loss_cls: 0.7643, loss: 0.7643 +2025-06-24 13:42:55,592 - pyskl - INFO - Epoch [36][200/1281] lr: 2.176e-02, eta: 13:38:15, time: 0.536, data_time: 0.000, memory: 4083, top1_acc: 0.8406, top5_acc: 0.9900, loss_cls: 0.7933, loss: 0.7933 +2025-06-24 13:43:48,428 - pyskl - INFO - Epoch [36][300/1281] lr: 2.175e-02, eta: 13:38:45, time: 0.528, data_time: 0.000, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9925, loss_cls: 0.7040, loss: 0.7040 +2025-06-24 13:44:42,514 - pyskl - INFO - Epoch [36][400/1281] lr: 2.173e-02, eta: 13:39:19, time: 0.541, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9931, loss_cls: 0.7023, loss: 0.7023 +2025-06-24 13:45:36,634 - pyskl - INFO - Epoch [36][500/1281] lr: 2.172e-02, eta: 13:39:52, time: 0.541, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9925, loss_cls: 0.7039, loss: 0.7039 +2025-06-24 13:46:30,768 - pyskl - INFO - Epoch [36][600/1281] lr: 2.171e-02, eta: 13:40:25, time: 0.541, data_time: 0.000, memory: 4083, top1_acc: 0.8325, top5_acc: 0.9912, loss_cls: 0.7821, loss: 0.7821 +2025-06-24 13:47:24,002 - pyskl - INFO - Epoch [36][700/1281] lr: 2.169e-02, eta: 13:40:55, time: 0.532, data_time: 0.000, memory: 4083, top1_acc: 0.8488, top5_acc: 0.9925, loss_cls: 0.7687, loss: 0.7687 +2025-06-24 13:48:18,165 - pyskl - INFO - Epoch [36][800/1281] lr: 2.168e-02, eta: 13:41:28, time: 0.542, data_time: 0.000, memory: 4083, top1_acc: 0.8263, top5_acc: 0.9881, loss_cls: 0.8401, loss: 0.8401 +2025-06-24 13:49:11,871 - pyskl - INFO - Epoch [36][900/1281] lr: 2.167e-02, eta: 13:41:58, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9956, loss_cls: 0.6975, loss: 0.6975 +2025-06-24 13:49:51,867 - pyskl - INFO - Epoch [36][1000/1281] lr: 2.165e-02, eta: 13:41:45, time: 0.400, data_time: 0.000, memory: 4083, top1_acc: 0.8256, top5_acc: 0.9944, loss_cls: 0.8221, loss: 0.8221 +2025-06-24 13:50:43,207 - pyskl - INFO - Epoch [36][1100/1281] lr: 2.164e-02, eta: 13:42:07, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9900, loss_cls: 0.7693, loss: 0.7693 +2025-06-24 13:51:11,624 - pyskl - INFO - Epoch [36][1200/1281] lr: 2.162e-02, eta: 13:41:17, time: 0.284, data_time: 0.000, memory: 4083, top1_acc: 0.8256, top5_acc: 0.9869, loss_cls: 0.8217, loss: 0.8217 +2025-06-24 13:51:55,985 - pyskl - INFO - Saving checkpoint at 36 epochs +2025-06-24 13:53:07,864 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:53:07,920 - pyskl - INFO - +top1_acc 0.7683 +top5_acc 0.9837 +2025-06-24 13:53:07,920 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:53:07,928 - pyskl - INFO - +mean_acc 0.6991 +2025-06-24 13:53:07,930 - pyskl - INFO - Epoch(val) [36][533] top1_acc: 0.7683, top5_acc: 0.9837, mean_class_accuracy: 0.6991 +2025-06-24 13:54:35,100 - pyskl - INFO - Epoch [37][100/1281] lr: 2.160e-02, eta: 13:41:38, time: 0.872, data_time: 0.201, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9950, loss_cls: 0.6259, loss: 0.6259 +2025-06-24 13:55:29,428 - pyskl - INFO - Epoch [37][200/1281] lr: 2.158e-02, eta: 13:42:09, time: 0.543, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9931, loss_cls: 0.6765, loss: 0.6765 +2025-06-24 13:56:24,359 - pyskl - INFO - Epoch [37][300/1281] lr: 2.157e-02, eta: 13:42:41, time: 0.549, data_time: 0.000, memory: 4083, top1_acc: 0.8325, top5_acc: 0.9919, loss_cls: 0.7858, loss: 0.7858 +2025-06-24 13:57:18,865 - pyskl - INFO - Epoch [37][400/1281] lr: 2.156e-02, eta: 13:43:12, time: 0.545, data_time: 0.000, memory: 4083, top1_acc: 0.8544, top5_acc: 0.9912, loss_cls: 0.7306, loss: 0.7306 +2025-06-24 13:58:13,268 - pyskl - INFO - Epoch [37][500/1281] lr: 2.154e-02, eta: 13:43:42, time: 0.544, data_time: 0.000, memory: 4083, top1_acc: 0.8225, top5_acc: 0.9888, loss_cls: 0.8144, loss: 0.8144 +2025-06-24 13:58:48,823 - pyskl - INFO - Epoch [37][600/1281] lr: 2.153e-02, eta: 13:43:13, time: 0.356, data_time: 0.000, memory: 4083, top1_acc: 0.8413, top5_acc: 0.9894, loss_cls: 0.7843, loss: 0.7843 +2025-06-24 13:59:40,046 - pyskl - INFO - Epoch [37][700/1281] lr: 2.151e-02, eta: 13:43:33, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8394, top5_acc: 0.9912, loss_cls: 0.7836, loss: 0.7836 +2025-06-24 14:00:13,004 - pyskl - INFO - Epoch [37][800/1281] lr: 2.150e-02, eta: 13:42:56, time: 0.330, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9944, loss_cls: 0.6849, loss: 0.6849 +2025-06-24 14:01:05,682 - pyskl - INFO - Epoch [37][900/1281] lr: 2.149e-02, eta: 13:43:19, time: 0.527, data_time: 0.000, memory: 4083, top1_acc: 0.8481, top5_acc: 0.9944, loss_cls: 0.7490, loss: 0.7490 +2025-06-24 14:01:58,777 - pyskl - INFO - Epoch [37][1000/1281] lr: 2.147e-02, eta: 13:43:44, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.8387, top5_acc: 0.9931, loss_cls: 0.7316, loss: 0.7316 +2025-06-24 14:02:52,525 - pyskl - INFO - Epoch [37][1100/1281] lr: 2.146e-02, eta: 13:44:10, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.8444, top5_acc: 0.9906, loss_cls: 0.7584, loss: 0.7584 +2025-06-24 14:03:45,670 - pyskl - INFO - Epoch [37][1200/1281] lr: 2.144e-02, eta: 13:44:34, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.8431, top5_acc: 0.9912, loss_cls: 0.7872, loss: 0.7872 +2025-06-24 14:04:29,554 - pyskl - INFO - Saving checkpoint at 37 epochs +2025-06-24 14:05:41,458 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:05:41,526 - pyskl - INFO - +top1_acc 0.7818 +top5_acc 0.9839 +2025-06-24 14:05:41,526 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:05:41,534 - pyskl - INFO - +mean_acc 0.6998 +2025-06-24 14:05:41,537 - pyskl - INFO - Epoch(val) [37][533] top1_acc: 0.7818, top5_acc: 0.9839, mean_class_accuracy: 0.6998 +2025-06-24 14:07:06,261 - pyskl - INFO - Epoch [38][100/1281] lr: 2.142e-02, eta: 13:44:42, time: 0.847, data_time: 0.194, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9931, loss_cls: 0.6765, loss: 0.6765 +2025-06-24 14:07:46,142 - pyskl - INFO - Epoch [38][200/1281] lr: 2.140e-02, eta: 13:44:25, time: 0.399, data_time: 0.000, memory: 4083, top1_acc: 0.8337, top5_acc: 0.9944, loss_cls: 0.7876, loss: 0.7876 +2025-06-24 14:08:37,538 - pyskl - INFO - Epoch [38][300/1281] lr: 2.139e-02, eta: 13:44:43, time: 0.514, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9931, loss_cls: 0.7299, loss: 0.7299 +2025-06-24 14:09:05,786 - pyskl - INFO - Epoch [38][400/1281] lr: 2.137e-02, eta: 13:43:51, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.8500, top5_acc: 0.9925, loss_cls: 0.7117, loss: 0.7117 +2025-06-24 14:09:59,589 - pyskl - INFO - Epoch [38][500/1281] lr: 2.136e-02, eta: 13:44:15, time: 0.538, data_time: 0.000, memory: 4083, top1_acc: 0.8325, top5_acc: 0.9912, loss_cls: 0.7598, loss: 0.7598 +2025-06-24 14:10:52,592 - pyskl - INFO - Epoch [38][600/1281] lr: 2.134e-02, eta: 13:44:37, time: 0.530, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9919, loss_cls: 0.6867, loss: 0.6867 +2025-06-24 14:11:45,948 - pyskl - INFO - Epoch [38][700/1281] lr: 2.133e-02, eta: 13:45:00, time: 0.534, data_time: 0.001, memory: 4083, top1_acc: 0.8413, top5_acc: 0.9906, loss_cls: 0.7711, loss: 0.7711 +2025-06-24 14:12:39,692 - pyskl - INFO - Epoch [38][800/1281] lr: 2.132e-02, eta: 13:45:23, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.8269, top5_acc: 0.9906, loss_cls: 0.8263, loss: 0.8263 +2025-06-24 14:13:33,075 - pyskl - INFO - Epoch [38][900/1281] lr: 2.130e-02, eta: 13:45:45, time: 0.534, data_time: 0.000, memory: 4083, top1_acc: 0.8419, top5_acc: 0.9912, loss_cls: 0.7719, loss: 0.7719 +2025-06-24 14:14:27,141 - pyskl - INFO - Epoch [38][1000/1281] lr: 2.129e-02, eta: 13:46:09, time: 0.541, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9912, loss_cls: 0.7919, loss: 0.7919 +2025-06-24 14:15:21,121 - pyskl - INFO - Epoch [38][1100/1281] lr: 2.127e-02, eta: 13:46:33, time: 0.540, data_time: 0.000, memory: 4083, top1_acc: 0.8469, top5_acc: 0.9912, loss_cls: 0.7316, loss: 0.7316 +2025-06-24 14:16:14,385 - pyskl - INFO - Epoch [38][1200/1281] lr: 2.126e-02, eta: 13:46:53, time: 0.533, data_time: 0.000, memory: 4083, top1_acc: 0.8544, top5_acc: 0.9944, loss_cls: 0.6800, loss: 0.6800 +2025-06-24 14:16:57,528 - pyskl - INFO - Saving checkpoint at 38 epochs +2025-06-24 14:18:11,235 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:18:11,291 - pyskl - INFO - +top1_acc 0.7998 +top5_acc 0.9853 +2025-06-24 14:18:11,291 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:18:11,298 - pyskl - INFO - +mean_acc 0.7164 +2025-06-24 14:18:11,299 - pyskl - INFO - Epoch(val) [38][533] top1_acc: 0.7998, top5_acc: 0.9853, mean_class_accuracy: 0.7164 +2025-06-24 14:19:23,945 - pyskl - INFO - Epoch [39][100/1281] lr: 2.123e-02, eta: 13:46:20, time: 0.726, data_time: 0.193, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9969, loss_cls: 0.6658, loss: 0.6658 +2025-06-24 14:20:17,893 - pyskl - INFO - Epoch [39][200/1281] lr: 2.122e-02, eta: 13:46:43, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9950, loss_cls: 0.6746, loss: 0.6746 +2025-06-24 14:21:11,608 - pyskl - INFO - Epoch [39][300/1281] lr: 2.120e-02, eta: 13:47:04, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9925, loss_cls: 0.7191, loss: 0.7191 +2025-06-24 14:22:06,241 - pyskl - INFO - Epoch [39][400/1281] lr: 2.119e-02, eta: 13:47:27, time: 0.546, data_time: 0.000, memory: 4083, top1_acc: 0.8375, top5_acc: 0.9944, loss_cls: 0.7752, loss: 0.7752 +2025-06-24 14:23:00,190 - pyskl - INFO - Epoch [39][500/1281] lr: 2.117e-02, eta: 13:47:49, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.8300, top5_acc: 0.9925, loss_cls: 0.7883, loss: 0.7883 +2025-06-24 14:23:53,788 - pyskl - INFO - Epoch [39][600/1281] lr: 2.116e-02, eta: 13:48:09, time: 0.536, data_time: 0.000, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9938, loss_cls: 0.7438, loss: 0.7438 +2025-06-24 14:24:48,183 - pyskl - INFO - Epoch [39][700/1281] lr: 2.114e-02, eta: 13:48:31, time: 0.544, data_time: 0.000, memory: 4083, top1_acc: 0.8500, top5_acc: 0.9925, loss_cls: 0.7305, loss: 0.7305 +2025-06-24 14:25:42,055 - pyskl - INFO - Epoch [39][800/1281] lr: 2.113e-02, eta: 13:48:51, time: 0.539, data_time: 0.000, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9938, loss_cls: 0.7317, loss: 0.7317 +2025-06-24 14:26:35,530 - pyskl - INFO - Epoch [39][900/1281] lr: 2.111e-02, eta: 13:49:09, time: 0.535, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9950, loss_cls: 0.6935, loss: 0.6935 +2025-06-24 14:27:07,425 - pyskl - INFO - Epoch [39][1000/1281] lr: 2.110e-02, eta: 13:48:26, time: 0.319, data_time: 0.001, memory: 4083, top1_acc: 0.8431, top5_acc: 0.9938, loss_cls: 0.7312, loss: 0.7312 +2025-06-24 14:27:49,348 - pyskl - INFO - Epoch [39][1100/1281] lr: 2.108e-02, eta: 13:48:11, time: 0.419, data_time: 0.000, memory: 4083, top1_acc: 0.8300, top5_acc: 0.9925, loss_cls: 0.8091, loss: 0.8091 +2025-06-24 14:28:35,280 - pyskl - INFO - Epoch [39][1200/1281] lr: 2.107e-02, eta: 13:48:08, time: 0.459, data_time: 0.000, memory: 4083, top1_acc: 0.8275, top5_acc: 0.9925, loss_cls: 0.8004, loss: 0.8004 +2025-06-24 14:29:20,117 - pyskl - INFO - Saving checkpoint at 39 epochs +2025-06-24 14:30:32,014 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:30:32,083 - pyskl - INFO - +top1_acc 0.8089 +top5_acc 0.9858 +2025-06-24 14:30:32,083 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:30:32,091 - pyskl - INFO - +mean_acc 0.7143 +2025-06-24 14:30:32,095 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_35.pth was removed +2025-06-24 14:30:32,292 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_39.pth. +2025-06-24 14:30:32,293 - pyskl - INFO - Best top1_acc is 0.8089 at 39 epoch. +2025-06-24 14:30:32,295 - pyskl - INFO - Epoch(val) [39][533] top1_acc: 0.8089, top5_acc: 0.9858, mean_class_accuracy: 0.7143 +2025-06-24 14:31:59,181 - pyskl - INFO - Epoch [40][100/1281] lr: 2.104e-02, eta: 13:48:12, time: 0.869, data_time: 0.193, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9938, loss_cls: 0.6749, loss: 0.6749 +2025-06-24 14:32:52,911 - pyskl - INFO - Epoch [40][200/1281] lr: 2.103e-02, eta: 13:48:30, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.8519, top5_acc: 0.9881, loss_cls: 0.7143, loss: 0.7143 +2025-06-24 14:33:46,614 - pyskl - INFO - Epoch [40][300/1281] lr: 2.101e-02, eta: 13:48:47, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9938, loss_cls: 0.6893, loss: 0.6893 +2025-06-24 14:34:40,020 - pyskl - INFO - Epoch [40][400/1281] lr: 2.100e-02, eta: 13:49:04, time: 0.534, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9925, loss_cls: 0.6569, loss: 0.6569 +2025-06-24 14:35:33,435 - pyskl - INFO - Epoch [40][500/1281] lr: 2.098e-02, eta: 13:49:21, time: 0.534, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9938, loss_cls: 0.6628, loss: 0.6628 +2025-06-24 14:36:05,833 - pyskl - INFO - Epoch [40][600/1281] lr: 2.097e-02, eta: 13:48:38, time: 0.324, data_time: 0.000, memory: 4083, top1_acc: 0.8519, top5_acc: 0.9931, loss_cls: 0.7290, loss: 0.7290 +2025-06-24 14:36:47,324 - pyskl - INFO - Epoch [40][700/1281] lr: 2.095e-02, eta: 13:48:20, time: 0.415, data_time: 0.001, memory: 4083, top1_acc: 0.8450, top5_acc: 0.9944, loss_cls: 0.7519, loss: 0.7519 +2025-06-24 14:37:31,755 - pyskl - INFO - Epoch [40][800/1281] lr: 2.094e-02, eta: 13:48:11, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 0.8394, top5_acc: 0.9906, loss_cls: 0.7305, loss: 0.7305 +2025-06-24 14:38:24,560 - pyskl - INFO - Epoch [40][900/1281] lr: 2.092e-02, eta: 13:48:25, time: 0.528, data_time: 0.000, memory: 4083, top1_acc: 0.8488, top5_acc: 0.9950, loss_cls: 0.7095, loss: 0.7095 +2025-06-24 14:39:19,339 - pyskl - INFO - Epoch [40][1000/1281] lr: 2.091e-02, eta: 13:48:44, time: 0.548, data_time: 0.000, memory: 4083, top1_acc: 0.8438, top5_acc: 0.9881, loss_cls: 0.7379, loss: 0.7379 +2025-06-24 14:40:13,140 - pyskl - INFO - Epoch [40][1100/1281] lr: 2.089e-02, eta: 13:49:00, time: 0.538, data_time: 0.001, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9938, loss_cls: 0.7404, loss: 0.7404 +2025-06-24 14:41:06,004 - pyskl - INFO - Epoch [40][1200/1281] lr: 2.088e-02, eta: 13:49:14, time: 0.529, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9931, loss_cls: 0.7196, loss: 0.7196 +2025-06-24 14:41:50,198 - pyskl - INFO - Saving checkpoint at 40 epochs +2025-06-24 14:43:02,047 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:43:02,105 - pyskl - INFO - +top1_acc 0.8165 +top5_acc 0.9883 +2025-06-24 14:43:02,105 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:43:02,112 - pyskl - INFO - +mean_acc 0.7303 +2025-06-24 14:43:02,116 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_39.pth was removed +2025-06-24 14:43:02,306 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_40.pth. +2025-06-24 14:43:02,307 - pyskl - INFO - Best top1_acc is 0.8165 at 40 epoch. +2025-06-24 14:43:02,310 - pyskl - INFO - Epoch(val) [40][533] top1_acc: 0.8165, top5_acc: 0.9883, mean_class_accuracy: 0.7303 +2025-06-24 14:44:29,009 - pyskl - INFO - Epoch [41][100/1281] lr: 2.085e-02, eta: 13:49:12, time: 0.867, data_time: 0.197, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9956, loss_cls: 0.6584, loss: 0.6584 +2025-06-24 14:44:59,283 - pyskl - INFO - Epoch [41][200/1281] lr: 2.083e-02, eta: 13:48:23, time: 0.303, data_time: 0.000, memory: 4083, top1_acc: 0.8475, top5_acc: 0.9906, loss_cls: 0.7045, loss: 0.7045 +2025-06-24 14:45:44,716 - pyskl - INFO - Epoch [41][300/1281] lr: 2.082e-02, eta: 13:48:15, time: 0.454, data_time: 0.000, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9925, loss_cls: 0.6991, loss: 0.6991 +2025-06-24 14:46:28,157 - pyskl - INFO - Epoch [41][400/1281] lr: 2.080e-02, eta: 13:48:02, time: 0.434, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9888, loss_cls: 0.7186, loss: 0.7186 +2025-06-24 14:47:21,468 - pyskl - INFO - Epoch [41][500/1281] lr: 2.079e-02, eta: 13:48:16, time: 0.533, data_time: 0.000, memory: 4083, top1_acc: 0.8506, top5_acc: 0.9900, loss_cls: 0.7563, loss: 0.7563 +2025-06-24 14:48:14,465 - pyskl - INFO - Epoch [41][600/1281] lr: 2.077e-02, eta: 13:48:28, time: 0.530, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9962, loss_cls: 0.6614, loss: 0.6614 +2025-06-24 14:49:08,193 - pyskl - INFO - Epoch [41][700/1281] lr: 2.076e-02, eta: 13:48:42, time: 0.537, data_time: 0.000, memory: 4083, top1_acc: 0.8375, top5_acc: 0.9906, loss_cls: 0.7938, loss: 0.7938 +2025-06-24 14:50:02,275 - pyskl - INFO - Epoch [41][800/1281] lr: 2.074e-02, eta: 13:48:56, time: 0.541, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9950, loss_cls: 0.6814, loss: 0.6814 +2025-06-24 14:50:57,042 - pyskl - INFO - Epoch [41][900/1281] lr: 2.073e-02, eta: 13:49:13, time: 0.548, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9950, loss_cls: 0.6769, loss: 0.6769 +2025-06-24 14:51:50,048 - pyskl - INFO - Epoch [41][1000/1281] lr: 2.071e-02, eta: 13:49:24, time: 0.530, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9881, loss_cls: 0.7562, loss: 0.7562 +2025-06-24 14:52:43,175 - pyskl - INFO - Epoch [41][1100/1281] lr: 2.070e-02, eta: 13:49:35, time: 0.531, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9956, loss_cls: 0.6699, loss: 0.6699 +2025-06-24 14:53:35,823 - pyskl - INFO - Epoch [41][1200/1281] lr: 2.068e-02, eta: 13:49:45, time: 0.526, data_time: 0.001, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9938, loss_cls: 0.6753, loss: 0.6753 +2025-06-24 14:54:06,896 - pyskl - INFO - Saving checkpoint at 41 epochs +2025-06-24 14:55:12,745 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:55:12,799 - pyskl - INFO - +top1_acc 0.8007 +top5_acc 0.9832 +2025-06-24 14:55:12,800 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:55:12,806 - pyskl - INFO - +mean_acc 0.7029 +2025-06-24 14:55:12,807 - pyskl - INFO - Epoch(val) [41][533] top1_acc: 0.8007, top5_acc: 0.9832, mean_class_accuracy: 0.7029 +2025-06-24 14:56:30,229 - pyskl - INFO - Epoch [42][100/1281] lr: 2.065e-02, eta: 13:49:15, time: 0.774, data_time: 0.195, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9938, loss_cls: 0.6269, loss: 0.6269 +2025-06-24 14:57:18,170 - pyskl - INFO - Epoch [42][200/1281] lr: 2.064e-02, eta: 13:49:12, time: 0.479, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9950, loss_cls: 0.6192, loss: 0.6192 +2025-06-24 14:58:06,209 - pyskl - INFO - Epoch [42][300/1281] lr: 2.062e-02, eta: 13:49:09, time: 0.480, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9919, loss_cls: 0.6988, loss: 0.6988 +2025-06-24 14:58:54,321 - pyskl - INFO - Epoch [42][400/1281] lr: 2.061e-02, eta: 13:49:05, time: 0.481, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9931, loss_cls: 0.7039, loss: 0.7039 +2025-06-24 14:59:42,265 - pyskl - INFO - Epoch [42][500/1281] lr: 2.059e-02, eta: 13:49:02, time: 0.479, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9944, loss_cls: 0.6861, loss: 0.6861 +2025-06-24 15:00:30,477 - pyskl - INFO - Epoch [42][600/1281] lr: 2.057e-02, eta: 13:48:59, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9956, loss_cls: 0.6421, loss: 0.6421 +2025-06-24 15:01:18,778 - pyskl - INFO - Epoch [42][700/1281] lr: 2.056e-02, eta: 13:48:56, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.8544, top5_acc: 0.9912, loss_cls: 0.7376, loss: 0.7376 +2025-06-24 15:02:07,182 - pyskl - INFO - Epoch [42][800/1281] lr: 2.054e-02, eta: 13:48:53, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.8325, top5_acc: 0.9869, loss_cls: 0.7676, loss: 0.7676 +2025-06-24 15:02:55,410 - pyskl - INFO - Epoch [42][900/1281] lr: 2.053e-02, eta: 13:48:49, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 0.8488, top5_acc: 0.9925, loss_cls: 0.7536, loss: 0.7536 +2025-06-24 15:03:43,684 - pyskl - INFO - Epoch [42][1000/1281] lr: 2.051e-02, eta: 13:48:45, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.8325, top5_acc: 0.9912, loss_cls: 0.7903, loss: 0.7903 +2025-06-24 15:04:31,673 - pyskl - INFO - Epoch [42][1100/1281] lr: 2.050e-02, eta: 13:48:41, time: 0.480, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9938, loss_cls: 0.7115, loss: 0.7115 +2025-06-24 15:05:19,697 - pyskl - INFO - Epoch [42][1200/1281] lr: 2.048e-02, eta: 13:48:36, time: 0.480, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9956, loss_cls: 0.7262, loss: 0.7262 +2025-06-24 15:05:41,572 - pyskl - INFO - Saving checkpoint at 42 epochs +2025-06-24 15:06:38,864 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:06:38,934 - pyskl - INFO - +top1_acc 0.8256 +top5_acc 0.9893 +2025-06-24 15:06:38,934 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:06:38,943 - pyskl - INFO - +mean_acc 0.7352 +2025-06-24 15:06:38,948 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_40.pth was removed +2025-06-24 15:06:39,153 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_42.pth. +2025-06-24 15:06:39,153 - pyskl - INFO - Best top1_acc is 0.8256 at 42 epoch. +2025-06-24 15:06:39,156 - pyskl - INFO - Epoch(val) [42][533] top1_acc: 0.8256, top5_acc: 0.9893, mean_class_accuracy: 0.7352 +2025-06-24 15:07:59,385 - pyskl - INFO - Epoch [43][100/1281] lr: 2.045e-02, eta: 13:48:10, time: 0.802, data_time: 0.194, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9950, loss_cls: 0.6652, loss: 0.6652 +2025-06-24 15:08:48,645 - pyskl - INFO - Epoch [43][200/1281] lr: 2.044e-02, eta: 13:48:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9931, loss_cls: 0.6144, loss: 0.6144 +2025-06-24 15:09:37,687 - pyskl - INFO - Epoch [43][300/1281] lr: 2.042e-02, eta: 13:48:06, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9938, loss_cls: 0.6844, loss: 0.6844 +2025-06-24 15:10:26,516 - pyskl - INFO - Epoch [43][400/1281] lr: 2.040e-02, eta: 13:48:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9931, loss_cls: 0.7117, loss: 0.7117 +2025-06-24 15:11:15,197 - pyskl - INFO - Epoch [43][500/1281] lr: 2.039e-02, eta: 13:47:58, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9931, loss_cls: 0.6850, loss: 0.6850 +2025-06-24 15:12:04,439 - pyskl - INFO - Epoch [43][600/1281] lr: 2.037e-02, eta: 13:47:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8525, top5_acc: 0.9931, loss_cls: 0.7028, loss: 0.7028 +2025-06-24 15:12:53,502 - pyskl - INFO - Epoch [43][700/1281] lr: 2.036e-02, eta: 13:47:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9919, loss_cls: 0.6776, loss: 0.6776 +2025-06-24 15:13:42,716 - pyskl - INFO - Epoch [43][800/1281] lr: 2.034e-02, eta: 13:47:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8438, top5_acc: 0.9925, loss_cls: 0.7186, loss: 0.7186 +2025-06-24 15:14:31,934 - pyskl - INFO - Epoch [43][900/1281] lr: 2.033e-02, eta: 13:47:46, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8394, top5_acc: 0.9956, loss_cls: 0.7373, loss: 0.7373 +2025-06-24 15:15:21,071 - pyskl - INFO - Epoch [43][1000/1281] lr: 2.031e-02, eta: 13:47:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8419, top5_acc: 0.9938, loss_cls: 0.7341, loss: 0.7341 +2025-06-24 15:16:10,202 - pyskl - INFO - Epoch [43][1100/1281] lr: 2.029e-02, eta: 13:47:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8475, top5_acc: 0.9931, loss_cls: 0.7276, loss: 0.7276 +2025-06-24 15:16:53,331 - pyskl - INFO - Epoch [43][1200/1281] lr: 2.028e-02, eta: 13:47:20, time: 0.431, data_time: 0.000, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9944, loss_cls: 0.6761, loss: 0.6761 +2025-06-24 15:17:21,849 - pyskl - INFO - Saving checkpoint at 43 epochs +2025-06-24 15:18:06,806 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:18:06,877 - pyskl - INFO - +top1_acc 0.7986 +top5_acc 0.9853 +2025-06-24 15:18:06,877 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:18:06,884 - pyskl - INFO - +mean_acc 0.7164 +2025-06-24 15:18:06,886 - pyskl - INFO - Epoch(val) [43][533] top1_acc: 0.7986, top5_acc: 0.9853, mean_class_accuracy: 0.7164 +2025-06-24 15:19:27,912 - pyskl - INFO - Epoch [44][100/1281] lr: 2.025e-02, eta: 13:46:53, time: 0.810, data_time: 0.195, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9931, loss_cls: 0.7174, loss: 0.7174 +2025-06-24 15:20:16,972 - pyskl - INFO - Epoch [44][200/1281] lr: 2.023e-02, eta: 13:46:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9919, loss_cls: 0.6565, loss: 0.6565 +2025-06-24 15:21:06,234 - pyskl - INFO - Epoch [44][300/1281] lr: 2.022e-02, eta: 13:46:45, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9981, loss_cls: 0.6188, loss: 0.6188 +2025-06-24 15:21:55,258 - pyskl - INFO - Epoch [44][400/1281] lr: 2.020e-02, eta: 13:46:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9931, loss_cls: 0.6498, loss: 0.6498 +2025-06-24 15:22:44,380 - pyskl - INFO - Epoch [44][500/1281] lr: 2.018e-02, eta: 13:46:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8462, top5_acc: 0.9975, loss_cls: 0.7427, loss: 0.7427 +2025-06-24 15:23:33,487 - pyskl - INFO - Epoch [44][600/1281] lr: 2.017e-02, eta: 13:46:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9938, loss_cls: 0.6995, loss: 0.6995 +2025-06-24 15:24:22,665 - pyskl - INFO - Epoch [44][700/1281] lr: 2.015e-02, eta: 13:46:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9962, loss_cls: 0.7002, loss: 0.7002 +2025-06-24 15:25:11,996 - pyskl - INFO - Epoch [44][800/1281] lr: 2.014e-02, eta: 13:46:20, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8300, top5_acc: 0.9894, loss_cls: 0.7720, loss: 0.7720 +2025-06-24 15:26:01,117 - pyskl - INFO - Epoch [44][900/1281] lr: 2.012e-02, eta: 13:46:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8250, top5_acc: 0.9925, loss_cls: 0.7957, loss: 0.7957 +2025-06-24 15:26:50,079 - pyskl - INFO - Epoch [44][1000/1281] lr: 2.010e-02, eta: 13:46:09, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.8506, top5_acc: 0.9938, loss_cls: 0.6817, loss: 0.6817 +2025-06-24 15:27:39,562 - pyskl - INFO - Epoch [44][1100/1281] lr: 2.009e-02, eta: 13:46:04, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8438, top5_acc: 0.9925, loss_cls: 0.6810, loss: 0.6810 +2025-06-24 15:28:16,485 - pyskl - INFO - Epoch [44][1200/1281] lr: 2.007e-02, eta: 13:45:29, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9944, loss_cls: 0.7274, loss: 0.7274 +2025-06-24 15:29:01,269 - pyskl - INFO - Saving checkpoint at 44 epochs +2025-06-24 15:29:49,762 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:29:49,820 - pyskl - INFO - +top1_acc 0.8120 +top5_acc 0.9856 +2025-06-24 15:29:49,820 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:29:49,831 - pyskl - INFO - +mean_acc 0.7308 +2025-06-24 15:29:49,834 - pyskl - INFO - Epoch(val) [44][533] top1_acc: 0.8120, top5_acc: 0.9856, mean_class_accuracy: 0.7308 +2025-06-24 15:31:09,729 - pyskl - INFO - Epoch [45][100/1281] lr: 2.004e-02, eta: 13:44:56, time: 0.799, data_time: 0.193, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9969, loss_cls: 0.6681, loss: 0.6681 +2025-06-24 15:31:58,974 - pyskl - INFO - Epoch [45][200/1281] lr: 2.003e-02, eta: 13:44:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9931, loss_cls: 0.6572, loss: 0.6572 +2025-06-24 15:32:47,975 - pyskl - INFO - Epoch [45][300/1281] lr: 2.001e-02, eta: 13:44:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9981, loss_cls: 0.6090, loss: 0.6090 +2025-06-24 15:33:37,569 - pyskl - INFO - Epoch [45][400/1281] lr: 1.999e-02, eta: 13:44:38, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9950, loss_cls: 0.6634, loss: 0.6634 +2025-06-24 15:34:26,691 - pyskl - INFO - Epoch [45][500/1281] lr: 1.998e-02, eta: 13:44:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9956, loss_cls: 0.6494, loss: 0.6494 +2025-06-24 15:35:16,111 - pyskl - INFO - Epoch [45][600/1281] lr: 1.996e-02, eta: 13:44:26, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8506, top5_acc: 0.9862, loss_cls: 0.7062, loss: 0.7062 +2025-06-24 15:36:05,907 - pyskl - INFO - Epoch [45][700/1281] lr: 1.994e-02, eta: 13:44:20, time: 0.498, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9925, loss_cls: 0.6671, loss: 0.6671 +2025-06-24 15:36:55,137 - pyskl - INFO - Epoch [45][800/1281] lr: 1.993e-02, eta: 13:44:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9944, loss_cls: 0.6996, loss: 0.6996 +2025-06-24 15:37:44,298 - pyskl - INFO - Epoch [45][900/1281] lr: 1.991e-02, eta: 13:44:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9962, loss_cls: 0.6026, loss: 0.6026 +2025-06-24 15:38:33,484 - pyskl - INFO - Epoch [45][1000/1281] lr: 1.989e-02, eta: 13:43:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9944, loss_cls: 0.6666, loss: 0.6666 +2025-06-24 15:39:22,680 - pyskl - INFO - Epoch [45][1100/1281] lr: 1.988e-02, eta: 13:43:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8500, top5_acc: 0.9931, loss_cls: 0.7402, loss: 0.7402 +2025-06-24 15:39:59,650 - pyskl - INFO - Epoch [45][1200/1281] lr: 1.986e-02, eta: 13:43:16, time: 0.370, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9900, loss_cls: 0.7232, loss: 0.7232 +2025-06-24 15:40:43,031 - pyskl - INFO - Saving checkpoint at 45 epochs +2025-06-24 15:41:31,645 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:41:31,699 - pyskl - INFO - +top1_acc 0.8075 +top5_acc 0.9833 +2025-06-24 15:41:31,700 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:41:31,707 - pyskl - INFO - +mean_acc 0.7296 +2025-06-24 15:41:31,709 - pyskl - INFO - Epoch(val) [45][533] top1_acc: 0.8075, top5_acc: 0.9833, mean_class_accuracy: 0.7296 +2025-06-24 15:42:51,349 - pyskl - INFO - Epoch [46][100/1281] lr: 1.983e-02, eta: 13:42:40, time: 0.796, data_time: 0.190, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9925, loss_cls: 0.7165, loss: 0.7165 +2025-06-24 15:43:40,410 - pyskl - INFO - Epoch [46][200/1281] lr: 1.981e-02, eta: 13:42:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9944, loss_cls: 0.6864, loss: 0.6864 +2025-06-24 15:44:29,500 - pyskl - INFO - Epoch [46][300/1281] lr: 1.980e-02, eta: 13:42:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9944, loss_cls: 0.6745, loss: 0.6745 +2025-06-24 15:45:18,697 - pyskl - INFO - Epoch [46][400/1281] lr: 1.978e-02, eta: 13:42:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8387, top5_acc: 0.9906, loss_cls: 0.7462, loss: 0.7462 +2025-06-24 15:46:07,917 - pyskl - INFO - Epoch [46][500/1281] lr: 1.976e-02, eta: 13:42:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9956, loss_cls: 0.6144, loss: 0.6144 +2025-06-24 15:46:57,102 - pyskl - INFO - Epoch [46][600/1281] lr: 1.975e-02, eta: 13:41:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8506, top5_acc: 0.9975, loss_cls: 0.7075, loss: 0.7075 +2025-06-24 15:47:46,542 - pyskl - INFO - Epoch [46][700/1281] lr: 1.973e-02, eta: 13:41:51, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9956, loss_cls: 0.6728, loss: 0.6728 +2025-06-24 15:48:35,837 - pyskl - INFO - Epoch [46][800/1281] lr: 1.971e-02, eta: 13:41:43, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9956, loss_cls: 0.6722, loss: 0.6722 +2025-06-24 15:49:24,746 - pyskl - INFO - Epoch [46][900/1281] lr: 1.970e-02, eta: 13:41:34, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9956, loss_cls: 0.6943, loss: 0.6943 +2025-06-24 15:50:14,197 - pyskl - INFO - Epoch [46][1000/1281] lr: 1.968e-02, eta: 13:41:25, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9944, loss_cls: 0.6447, loss: 0.6447 +2025-06-24 15:51:03,396 - pyskl - INFO - Epoch [46][1100/1281] lr: 1.966e-02, eta: 13:41:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9938, loss_cls: 0.6918, loss: 0.6918 +2025-06-24 15:51:41,470 - pyskl - INFO - Epoch [46][1200/1281] lr: 1.965e-02, eta: 13:40:42, time: 0.381, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9900, loss_cls: 0.7233, loss: 0.7233 +2025-06-24 15:52:22,578 - pyskl - INFO - Saving checkpoint at 46 epochs +2025-06-24 15:53:10,138 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:53:10,211 - pyskl - INFO - +top1_acc 0.8072 +top5_acc 0.9831 +2025-06-24 15:53:10,211 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:53:10,220 - pyskl - INFO - +mean_acc 0.7077 +2025-06-24 15:53:10,222 - pyskl - INFO - Epoch(val) [46][533] top1_acc: 0.8072, top5_acc: 0.9831, mean_class_accuracy: 0.7077 +2025-06-24 15:54:31,249 - pyskl - INFO - Epoch [47][100/1281] lr: 1.962e-02, eta: 13:40:07, time: 0.810, data_time: 0.195, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9944, loss_cls: 0.6295, loss: 0.6295 +2025-06-24 15:55:20,610 - pyskl - INFO - Epoch [47][200/1281] lr: 1.960e-02, eta: 13:39:58, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9931, loss_cls: 0.6091, loss: 0.6091 +2025-06-24 15:56:09,888 - pyskl - INFO - Epoch [47][300/1281] lr: 1.958e-02, eta: 13:39:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9969, loss_cls: 0.6000, loss: 0.6000 +2025-06-24 15:56:59,189 - pyskl - INFO - Epoch [47][400/1281] lr: 1.957e-02, eta: 13:39:39, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9931, loss_cls: 0.6718, loss: 0.6718 +2025-06-24 15:57:48,323 - pyskl - INFO - Epoch [47][500/1281] lr: 1.955e-02, eta: 13:39:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9925, loss_cls: 0.7120, loss: 0.7120 +2025-06-24 15:58:37,651 - pyskl - INFO - Epoch [47][600/1281] lr: 1.953e-02, eta: 13:39:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9931, loss_cls: 0.6587, loss: 0.6587 +2025-06-24 15:59:26,973 - pyskl - INFO - Epoch [47][700/1281] lr: 1.952e-02, eta: 13:39:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9931, loss_cls: 0.7054, loss: 0.7054 +2025-06-24 16:00:15,958 - pyskl - INFO - Epoch [47][800/1281] lr: 1.950e-02, eta: 13:38:59, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9912, loss_cls: 0.6947, loss: 0.6947 +2025-06-24 16:01:05,378 - pyskl - INFO - Epoch [47][900/1281] lr: 1.948e-02, eta: 13:38:49, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8538, top5_acc: 0.9950, loss_cls: 0.7086, loss: 0.7086 +2025-06-24 16:01:54,660 - pyskl - INFO - Epoch [47][1000/1281] lr: 1.947e-02, eta: 13:38:39, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9956, loss_cls: 0.6596, loss: 0.6596 +2025-06-24 16:02:43,597 - pyskl - INFO - Epoch [47][1100/1281] lr: 1.945e-02, eta: 13:38:27, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9938, loss_cls: 0.6866, loss: 0.6866 +2025-06-24 16:03:20,680 - pyskl - INFO - Epoch [47][1200/1281] lr: 1.943e-02, eta: 13:37:50, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9894, loss_cls: 0.6369, loss: 0.6369 +2025-06-24 16:04:03,501 - pyskl - INFO - Saving checkpoint at 47 epochs +2025-06-24 16:04:51,354 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:04:51,425 - pyskl - INFO - +top1_acc 0.8141 +top5_acc 0.9886 +2025-06-24 16:04:51,425 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:04:51,433 - pyskl - INFO - +mean_acc 0.7218 +2025-06-24 16:04:51,435 - pyskl - INFO - Epoch(val) [47][533] top1_acc: 0.8141, top5_acc: 0.9886, mean_class_accuracy: 0.7218 +2025-06-24 16:06:11,610 - pyskl - INFO - Epoch [48][100/1281] lr: 1.940e-02, eta: 13:37:11, time: 0.802, data_time: 0.192, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9919, loss_cls: 0.6223, loss: 0.6223 +2025-06-24 16:07:00,469 - pyskl - INFO - Epoch [48][200/1281] lr: 1.938e-02, eta: 13:36:59, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9931, loss_cls: 0.6632, loss: 0.6632 +2025-06-24 16:07:49,755 - pyskl - INFO - Epoch [48][300/1281] lr: 1.937e-02, eta: 13:36:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9969, loss_cls: 0.6393, loss: 0.6393 +2025-06-24 16:08:38,881 - pyskl - INFO - Epoch [48][400/1281] lr: 1.935e-02, eta: 13:36:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9925, loss_cls: 0.6455, loss: 0.6455 +2025-06-24 16:09:28,109 - pyskl - INFO - Epoch [48][500/1281] lr: 1.933e-02, eta: 13:36:26, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9938, loss_cls: 0.6956, loss: 0.6956 +2025-06-24 16:10:17,144 - pyskl - INFO - Epoch [48][600/1281] lr: 1.932e-02, eta: 13:36:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9919, loss_cls: 0.6513, loss: 0.6513 +2025-06-24 16:11:06,422 - pyskl - INFO - Epoch [48][700/1281] lr: 1.930e-02, eta: 13:36:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9919, loss_cls: 0.6998, loss: 0.6998 +2025-06-24 16:11:55,563 - pyskl - INFO - Epoch [48][800/1281] lr: 1.928e-02, eta: 13:35:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8538, top5_acc: 0.9931, loss_cls: 0.6841, loss: 0.6841 +2025-06-24 16:12:44,710 - pyskl - INFO - Epoch [48][900/1281] lr: 1.926e-02, eta: 13:35:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9944, loss_cls: 0.6880, loss: 0.6880 +2025-06-24 16:13:33,765 - pyskl - INFO - Epoch [48][1000/1281] lr: 1.925e-02, eta: 13:35:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9944, loss_cls: 0.6960, loss: 0.6960 +2025-06-24 16:14:23,044 - pyskl - INFO - Epoch [48][1100/1281] lr: 1.923e-02, eta: 13:35:14, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9938, loss_cls: 0.6948, loss: 0.6948 +2025-06-24 16:14:59,632 - pyskl - INFO - Epoch [48][1200/1281] lr: 1.921e-02, eta: 13:34:35, time: 0.366, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9981, loss_cls: 0.6493, loss: 0.6493 +2025-06-24 16:15:43,402 - pyskl - INFO - Saving checkpoint at 48 epochs +2025-06-24 16:16:31,662 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:16:31,733 - pyskl - INFO - +top1_acc 0.8371 +top5_acc 0.9898 +2025-06-24 16:16:31,733 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:16:31,740 - pyskl - INFO - +mean_acc 0.7534 +2025-06-24 16:16:31,744 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_42.pth was removed +2025-06-24 16:16:31,924 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_48.pth. +2025-06-24 16:16:31,924 - pyskl - INFO - Best top1_acc is 0.8371 at 48 epoch. +2025-06-24 16:16:31,927 - pyskl - INFO - Epoch(val) [48][533] top1_acc: 0.8371, top5_acc: 0.9898, mean_class_accuracy: 0.7534 +2025-06-24 16:17:51,755 - pyskl - INFO - Epoch [49][100/1281] lr: 1.918e-02, eta: 13:33:53, time: 0.798, data_time: 0.200, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9962, loss_cls: 0.6086, loss: 0.6086 +2025-06-24 16:18:41,096 - pyskl - INFO - Epoch [49][200/1281] lr: 1.916e-02, eta: 13:33:41, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9975, loss_cls: 0.5837, loss: 0.5837 +2025-06-24 16:19:30,132 - pyskl - INFO - Epoch [49][300/1281] lr: 1.915e-02, eta: 13:33:28, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9944, loss_cls: 0.6096, loss: 0.6096 +2025-06-24 16:20:19,345 - pyskl - INFO - Epoch [49][400/1281] lr: 1.913e-02, eta: 13:33:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9925, loss_cls: 0.6703, loss: 0.6703 +2025-06-24 16:21:08,468 - pyskl - INFO - Epoch [49][500/1281] lr: 1.911e-02, eta: 13:33:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9944, loss_cls: 0.6791, loss: 0.6791 +2025-06-24 16:21:57,709 - pyskl - INFO - Epoch [49][600/1281] lr: 1.909e-02, eta: 13:32:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9938, loss_cls: 0.6114, loss: 0.6114 +2025-06-24 16:22:47,010 - pyskl - INFO - Epoch [49][700/1281] lr: 1.908e-02, eta: 13:32:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9956, loss_cls: 0.6313, loss: 0.6313 +2025-06-24 16:23:36,394 - pyskl - INFO - Epoch [49][800/1281] lr: 1.906e-02, eta: 13:32:24, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9962, loss_cls: 0.6252, loss: 0.6252 +2025-06-24 16:24:25,713 - pyskl - INFO - Epoch [49][900/1281] lr: 1.904e-02, eta: 13:32:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8500, top5_acc: 0.9888, loss_cls: 0.7709, loss: 0.7709 +2025-06-24 16:25:14,693 - pyskl - INFO - Epoch [49][1000/1281] lr: 1.902e-02, eta: 13:31:57, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9925, loss_cls: 0.6947, loss: 0.6947 +2025-06-24 16:26:03,987 - pyskl - INFO - Epoch [49][1100/1281] lr: 1.901e-02, eta: 13:31:44, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9962, loss_cls: 0.6291, loss: 0.6291 +2025-06-24 16:26:40,781 - pyskl - INFO - Epoch [49][1200/1281] lr: 1.899e-02, eta: 13:31:05, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9925, loss_cls: 0.6399, loss: 0.6399 +2025-06-24 16:27:23,921 - pyskl - INFO - Saving checkpoint at 49 epochs +2025-06-24 16:28:12,265 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:28:12,319 - pyskl - INFO - +top1_acc 0.8010 +top5_acc 0.9883 +2025-06-24 16:28:12,320 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:28:12,326 - pyskl - INFO - +mean_acc 0.7182 +2025-06-24 16:28:12,328 - pyskl - INFO - Epoch(val) [49][533] top1_acc: 0.8010, top5_acc: 0.9883, mean_class_accuracy: 0.7182 +2025-06-24 16:29:32,766 - pyskl - INFO - Epoch [50][100/1281] lr: 1.896e-02, eta: 13:30:22, time: 0.804, data_time: 0.195, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9919, loss_cls: 0.6528, loss: 0.6528 +2025-06-24 16:30:21,734 - pyskl - INFO - Epoch [50][200/1281] lr: 1.894e-02, eta: 13:30:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9969, loss_cls: 0.5509, loss: 0.5509 +2025-06-24 16:31:11,107 - pyskl - INFO - Epoch [50][300/1281] lr: 1.892e-02, eta: 13:29:54, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9981, loss_cls: 0.6027, loss: 0.6027 +2025-06-24 16:32:00,241 - pyskl - INFO - Epoch [50][400/1281] lr: 1.891e-02, eta: 13:29:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9962, loss_cls: 0.6155, loss: 0.6155 +2025-06-24 16:32:49,506 - pyskl - INFO - Epoch [50][500/1281] lr: 1.889e-02, eta: 13:29:26, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9950, loss_cls: 0.6550, loss: 0.6550 +2025-06-24 16:33:39,138 - pyskl - INFO - Epoch [50][600/1281] lr: 1.887e-02, eta: 13:29:12, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9950, loss_cls: 0.6531, loss: 0.6531 +2025-06-24 16:34:28,522 - pyskl - INFO - Epoch [50][700/1281] lr: 1.885e-02, eta: 13:28:58, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9950, loss_cls: 0.6703, loss: 0.6703 +2025-06-24 16:35:17,752 - pyskl - INFO - Epoch [50][800/1281] lr: 1.884e-02, eta: 13:28:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9956, loss_cls: 0.6553, loss: 0.6553 +2025-06-24 16:36:06,975 - pyskl - INFO - Epoch [50][900/1281] lr: 1.882e-02, eta: 13:28:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9956, loss_cls: 0.6291, loss: 0.6291 +2025-06-24 16:36:56,228 - pyskl - INFO - Epoch [50][1000/1281] lr: 1.880e-02, eta: 13:28:15, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9944, loss_cls: 0.6396, loss: 0.6396 +2025-06-24 16:37:44,824 - pyskl - INFO - Epoch [50][1100/1281] lr: 1.878e-02, eta: 13:27:59, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.8512, top5_acc: 0.9956, loss_cls: 0.6853, loss: 0.6853 +2025-06-24 16:38:21,419 - pyskl - INFO - Epoch [50][1200/1281] lr: 1.876e-02, eta: 13:27:19, time: 0.366, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9956, loss_cls: 0.6732, loss: 0.6732 +2025-06-24 16:39:05,006 - pyskl - INFO - Saving checkpoint at 50 epochs +2025-06-24 16:39:53,164 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:39:53,232 - pyskl - INFO - +top1_acc 0.7925 +top5_acc 0.9854 +2025-06-24 16:39:53,232 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:39:53,241 - pyskl - INFO - +mean_acc 0.7237 +2025-06-24 16:39:53,243 - pyskl - INFO - Epoch(val) [50][533] top1_acc: 0.7925, top5_acc: 0.9854, mean_class_accuracy: 0.7237 +2025-06-24 16:41:11,293 - pyskl - INFO - Epoch [51][100/1281] lr: 1.873e-02, eta: 13:26:29, time: 0.780, data_time: 0.196, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9975, loss_cls: 0.6024, loss: 0.6024 +2025-06-24 16:42:00,541 - pyskl - INFO - Epoch [51][200/1281] lr: 1.871e-02, eta: 13:26:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9956, loss_cls: 0.5624, loss: 0.5624 +2025-06-24 16:42:49,817 - pyskl - INFO - Epoch [51][300/1281] lr: 1.870e-02, eta: 13:25:59, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9944, loss_cls: 0.6304, loss: 0.6304 +2025-06-24 16:43:39,238 - pyskl - INFO - Epoch [51][400/1281] lr: 1.868e-02, eta: 13:25:44, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9906, loss_cls: 0.6316, loss: 0.6316 +2025-06-24 16:44:27,912 - pyskl - INFO - Epoch [51][500/1281] lr: 1.866e-02, eta: 13:25:28, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9912, loss_cls: 0.6411, loss: 0.6411 +2025-06-24 16:45:16,719 - pyskl - INFO - Epoch [51][600/1281] lr: 1.864e-02, eta: 13:25:12, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9956, loss_cls: 0.6540, loss: 0.6540 +2025-06-24 16:46:05,744 - pyskl - INFO - Epoch [51][700/1281] lr: 1.863e-02, eta: 13:24:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9938, loss_cls: 0.6650, loss: 0.6650 +2025-06-24 16:46:54,795 - pyskl - INFO - Epoch [51][800/1281] lr: 1.861e-02, eta: 13:24:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9969, loss_cls: 0.6181, loss: 0.6181 +2025-06-24 16:47:43,793 - pyskl - INFO - Epoch [51][900/1281] lr: 1.859e-02, eta: 13:24:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9919, loss_cls: 0.6592, loss: 0.6592 +2025-06-24 16:48:33,327 - pyskl - INFO - Epoch [51][1000/1281] lr: 1.857e-02, eta: 13:24:08, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9956, loss_cls: 0.6581, loss: 0.6581 +2025-06-24 16:49:22,518 - pyskl - INFO - Epoch [51][1100/1281] lr: 1.855e-02, eta: 13:23:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9938, loss_cls: 0.6786, loss: 0.6786 +2025-06-24 16:50:00,595 - pyskl - INFO - Epoch [51][1200/1281] lr: 1.854e-02, eta: 13:23:14, time: 0.381, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9912, loss_cls: 0.6236, loss: 0.6236 +2025-06-24 16:50:40,904 - pyskl - INFO - Saving checkpoint at 51 epochs +2025-06-24 16:51:28,663 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:51:28,719 - pyskl - INFO - +top1_acc 0.8322 +top5_acc 0.9865 +2025-06-24 16:51:28,719 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:51:28,727 - pyskl - INFO - +mean_acc 0.7351 +2025-06-24 16:51:28,729 - pyskl - INFO - Epoch(val) [51][533] top1_acc: 0.8322, top5_acc: 0.9865, mean_class_accuracy: 0.7351 +2025-06-24 16:52:50,124 - pyskl - INFO - Epoch [52][100/1281] lr: 1.850e-02, eta: 13:22:30, time: 0.814, data_time: 0.205, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9994, loss_cls: 0.6051, loss: 0.6051 +2025-06-24 16:53:39,613 - pyskl - INFO - Epoch [52][200/1281] lr: 1.849e-02, eta: 13:22:14, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9931, loss_cls: 0.5958, loss: 0.5958 +2025-06-24 16:54:29,197 - pyskl - INFO - Epoch [52][300/1281] lr: 1.847e-02, eta: 13:21:58, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9944, loss_cls: 0.6598, loss: 0.6598 +2025-06-24 16:55:18,557 - pyskl - INFO - Epoch [52][400/1281] lr: 1.845e-02, eta: 13:21:42, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9944, loss_cls: 0.6054, loss: 0.6054 +2025-06-24 16:56:07,638 - pyskl - INFO - Epoch [52][500/1281] lr: 1.843e-02, eta: 13:21:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9938, loss_cls: 0.5751, loss: 0.5751 +2025-06-24 16:56:56,743 - pyskl - INFO - Epoch [52][600/1281] lr: 1.841e-02, eta: 13:21:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9938, loss_cls: 0.6440, loss: 0.6440 +2025-06-24 16:57:45,729 - pyskl - INFO - Epoch [52][700/1281] lr: 1.840e-02, eta: 13:20:51, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9938, loss_cls: 0.6049, loss: 0.6049 +2025-06-24 16:58:35,099 - pyskl - INFO - Epoch [52][800/1281] lr: 1.838e-02, eta: 13:20:35, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9969, loss_cls: 0.6480, loss: 0.6480 +2025-06-24 16:59:24,468 - pyskl - INFO - Epoch [52][900/1281] lr: 1.836e-02, eta: 13:20:18, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9925, loss_cls: 0.6071, loss: 0.6071 +2025-06-24 17:00:13,506 - pyskl - INFO - Epoch [52][1000/1281] lr: 1.834e-02, eta: 13:20:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9906, loss_cls: 0.6308, loss: 0.6308 +2025-06-24 17:01:02,649 - pyskl - INFO - Epoch [52][1100/1281] lr: 1.832e-02, eta: 13:19:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9956, loss_cls: 0.6520, loss: 0.6520 +2025-06-24 17:01:38,592 - pyskl - INFO - Epoch [52][1200/1281] lr: 1.831e-02, eta: 13:19:01, time: 0.359, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9931, loss_cls: 0.6819, loss: 0.6819 +2025-06-24 17:02:25,131 - pyskl - INFO - Saving checkpoint at 52 epochs +2025-06-24 17:03:13,990 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:03:14,046 - pyskl - INFO - +top1_acc 0.8227 +top5_acc 0.9903 +2025-06-24 17:03:14,046 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:03:14,054 - pyskl - INFO - +mean_acc 0.7573 +2025-06-24 17:03:14,056 - pyskl - INFO - Epoch(val) [52][533] top1_acc: 0.8227, top5_acc: 0.9903, mean_class_accuracy: 0.7573 +2025-06-24 17:04:34,073 - pyskl - INFO - Epoch [53][100/1281] lr: 1.827e-02, eta: 13:18:12, time: 0.800, data_time: 0.196, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9944, loss_cls: 0.6065, loss: 0.6065 +2025-06-24 17:05:23,204 - pyskl - INFO - Epoch [53][200/1281] lr: 1.826e-02, eta: 13:17:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9956, loss_cls: 0.5762, loss: 0.5762 +2025-06-24 17:06:12,190 - pyskl - INFO - Epoch [53][300/1281] lr: 1.824e-02, eta: 13:17:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9931, loss_cls: 0.6568, loss: 0.6568 +2025-06-24 17:07:01,837 - pyskl - INFO - Epoch [53][400/1281] lr: 1.822e-02, eta: 13:17:20, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9962, loss_cls: 0.5733, loss: 0.5733 +2025-06-24 17:07:50,991 - pyskl - INFO - Epoch [53][500/1281] lr: 1.820e-02, eta: 13:17:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9975, loss_cls: 0.6110, loss: 0.6110 +2025-06-24 17:08:39,870 - pyskl - INFO - Epoch [53][600/1281] lr: 1.818e-02, eta: 13:16:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9931, loss_cls: 0.6354, loss: 0.6354 +2025-06-24 17:09:29,131 - pyskl - INFO - Epoch [53][700/1281] lr: 1.816e-02, eta: 13:16:25, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9931, loss_cls: 0.6655, loss: 0.6655 +2025-06-24 17:10:18,740 - pyskl - INFO - Epoch [53][800/1281] lr: 1.815e-02, eta: 13:16:08, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9944, loss_cls: 0.6088, loss: 0.6088 +2025-06-24 17:11:08,001 - pyskl - INFO - Epoch [53][900/1281] lr: 1.813e-02, eta: 13:15:50, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9938, loss_cls: 0.6449, loss: 0.6449 +2025-06-24 17:11:57,153 - pyskl - INFO - Epoch [53][1000/1281] lr: 1.811e-02, eta: 13:15:32, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9944, loss_cls: 0.6200, loss: 0.6200 +2025-06-24 17:12:46,224 - pyskl - INFO - Epoch [53][1100/1281] lr: 1.809e-02, eta: 13:15:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9931, loss_cls: 0.6033, loss: 0.6033 +2025-06-24 17:13:22,360 - pyskl - INFO - Epoch [53][1200/1281] lr: 1.807e-02, eta: 13:14:31, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9969, loss_cls: 0.6238, loss: 0.6238 +2025-06-24 17:14:07,929 - pyskl - INFO - Saving checkpoint at 53 epochs +2025-06-24 17:14:57,784 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:14:57,851 - pyskl - INFO - +top1_acc 0.8214 +top5_acc 0.9891 +2025-06-24 17:14:57,851 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:14:57,859 - pyskl - INFO - +mean_acc 0.7515 +2025-06-24 17:14:57,861 - pyskl - INFO - Epoch(val) [53][533] top1_acc: 0.8214, top5_acc: 0.9891, mean_class_accuracy: 0.7515 +2025-06-24 17:16:18,542 - pyskl - INFO - Epoch [54][100/1281] lr: 1.804e-02, eta: 13:13:42, time: 0.807, data_time: 0.201, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9938, loss_cls: 0.5883, loss: 0.5883 +2025-06-24 17:17:07,672 - pyskl - INFO - Epoch [54][200/1281] lr: 1.802e-02, eta: 13:13:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9988, loss_cls: 0.5468, loss: 0.5468 +2025-06-24 17:17:57,186 - pyskl - INFO - Epoch [54][300/1281] lr: 1.800e-02, eta: 13:13:05, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9938, loss_cls: 0.6077, loss: 0.6077 +2025-06-24 17:18:46,704 - pyskl - INFO - Epoch [54][400/1281] lr: 1.798e-02, eta: 13:12:47, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9969, loss_cls: 0.5664, loss: 0.5664 +2025-06-24 17:19:36,155 - pyskl - INFO - Epoch [54][500/1281] lr: 1.797e-02, eta: 13:12:28, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9956, loss_cls: 0.5685, loss: 0.5685 +2025-06-24 17:20:25,417 - pyskl - INFO - Epoch [54][600/1281] lr: 1.795e-02, eta: 13:12:10, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9956, loss_cls: 0.6138, loss: 0.6138 +2025-06-24 17:21:14,669 - pyskl - INFO - Epoch [54][700/1281] lr: 1.793e-02, eta: 13:11:51, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9956, loss_cls: 0.6006, loss: 0.6006 +2025-06-24 17:22:03,944 - pyskl - INFO - Epoch [54][800/1281] lr: 1.791e-02, eta: 13:11:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9962, loss_cls: 0.6652, loss: 0.6652 +2025-06-24 17:22:53,124 - pyskl - INFO - Epoch [54][900/1281] lr: 1.789e-02, eta: 13:11:12, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9938, loss_cls: 0.6470, loss: 0.6470 +2025-06-24 17:23:42,660 - pyskl - INFO - Epoch [54][1000/1281] lr: 1.787e-02, eta: 13:10:54, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8544, top5_acc: 0.9919, loss_cls: 0.7128, loss: 0.7128 +2025-06-24 17:24:32,420 - pyskl - INFO - Epoch [54][1100/1281] lr: 1.786e-02, eta: 13:10:35, time: 0.498, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9931, loss_cls: 0.5706, loss: 0.5706 +2025-06-24 17:25:08,421 - pyskl - INFO - Epoch [54][1200/1281] lr: 1.784e-02, eta: 13:09:52, time: 0.360, data_time: 0.000, memory: 4083, top1_acc: 0.8544, top5_acc: 0.9931, loss_cls: 0.7089, loss: 0.7089 +2025-06-24 17:25:52,704 - pyskl - INFO - Saving checkpoint at 54 epochs +2025-06-24 17:26:41,259 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:26:41,319 - pyskl - INFO - +top1_acc 0.8267 +top5_acc 0.9906 +2025-06-24 17:26:41,320 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:26:41,329 - pyskl - INFO - +mean_acc 0.7765 +2025-06-24 17:26:41,332 - pyskl - INFO - Epoch(val) [54][533] top1_acc: 0.8267, top5_acc: 0.9906, mean_class_accuracy: 0.7765 +2025-06-24 17:28:02,819 - pyskl - INFO - Epoch [55][100/1281] lr: 1.780e-02, eta: 13:09:03, time: 0.815, data_time: 0.203, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9938, loss_cls: 0.5450, loss: 0.5450 +2025-06-24 17:28:51,840 - pyskl - INFO - Epoch [55][200/1281] lr: 1.779e-02, eta: 13:08:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9969, loss_cls: 0.5531, loss: 0.5531 +2025-06-24 17:29:40,956 - pyskl - INFO - Epoch [55][300/1281] lr: 1.777e-02, eta: 13:08:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9981, loss_cls: 0.5908, loss: 0.5908 +2025-06-24 17:30:30,067 - pyskl - INFO - Epoch [55][400/1281] lr: 1.775e-02, eta: 13:08:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9944, loss_cls: 0.6580, loss: 0.6580 +2025-06-24 17:31:19,225 - pyskl - INFO - Epoch [55][500/1281] lr: 1.773e-02, eta: 13:07:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9919, loss_cls: 0.6799, loss: 0.6799 +2025-06-24 17:32:08,294 - pyskl - INFO - Epoch [55][600/1281] lr: 1.771e-02, eta: 13:07:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9956, loss_cls: 0.5666, loss: 0.5666 +2025-06-24 17:32:57,595 - pyskl - INFO - Epoch [55][700/1281] lr: 1.769e-02, eta: 13:07:03, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9938, loss_cls: 0.6069, loss: 0.6069 +2025-06-24 17:33:46,579 - pyskl - INFO - Epoch [55][800/1281] lr: 1.767e-02, eta: 13:06:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9962, loss_cls: 0.6157, loss: 0.6157 +2025-06-24 17:34:35,534 - pyskl - INFO - Epoch [55][900/1281] lr: 1.766e-02, eta: 13:06:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9962, loss_cls: 0.6157, loss: 0.6157 +2025-06-24 17:35:24,562 - pyskl - INFO - Epoch [55][1000/1281] lr: 1.764e-02, eta: 13:06:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9969, loss_cls: 0.5888, loss: 0.5888 +2025-06-24 17:36:13,892 - pyskl - INFO - Epoch [55][1100/1281] lr: 1.762e-02, eta: 13:05:42, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9931, loss_cls: 0.6091, loss: 0.6091 +2025-06-24 17:36:49,706 - pyskl - INFO - Epoch [55][1200/1281] lr: 1.760e-02, eta: 13:04:58, time: 0.358, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9962, loss_cls: 0.6655, loss: 0.6655 +2025-06-24 17:37:33,388 - pyskl - INFO - Saving checkpoint at 55 epochs +2025-06-24 17:38:21,830 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:38:21,891 - pyskl - INFO - +top1_acc 0.8296 +top5_acc 0.9897 +2025-06-24 17:38:21,892 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:38:21,898 - pyskl - INFO - +mean_acc 0.7554 +2025-06-24 17:38:21,900 - pyskl - INFO - Epoch(val) [55][533] top1_acc: 0.8296, top5_acc: 0.9897, mean_class_accuracy: 0.7554 +2025-06-24 17:39:41,829 - pyskl - INFO - Epoch [56][100/1281] lr: 1.757e-02, eta: 13:04:05, time: 0.799, data_time: 0.196, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9950, loss_cls: 0.5213, loss: 0.5213 +2025-06-24 17:40:31,040 - pyskl - INFO - Epoch [56][200/1281] lr: 1.755e-02, eta: 13:03:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9950, loss_cls: 0.6027, loss: 0.6027 +2025-06-24 17:41:20,526 - pyskl - INFO - Epoch [56][300/1281] lr: 1.753e-02, eta: 13:03:24, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9969, loss_cls: 0.5317, loss: 0.5317 +2025-06-24 17:42:10,297 - pyskl - INFO - Epoch [56][400/1281] lr: 1.751e-02, eta: 13:03:04, time: 0.498, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9938, loss_cls: 0.6828, loss: 0.6828 +2025-06-24 17:42:59,506 - pyskl - INFO - Epoch [56][500/1281] lr: 1.749e-02, eta: 13:02:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9956, loss_cls: 0.5462, loss: 0.5462 +2025-06-24 17:43:48,480 - pyskl - INFO - Epoch [56][600/1281] lr: 1.747e-02, eta: 13:02:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9950, loss_cls: 0.6309, loss: 0.6309 +2025-06-24 17:44:37,576 - pyskl - INFO - Epoch [56][700/1281] lr: 1.745e-02, eta: 13:02:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9962, loss_cls: 0.6537, loss: 0.6537 +2025-06-24 17:45:26,590 - pyskl - INFO - Epoch [56][800/1281] lr: 1.743e-02, eta: 13:01:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9919, loss_cls: 0.6904, loss: 0.6904 +2025-06-24 17:46:15,574 - pyskl - INFO - Epoch [56][900/1281] lr: 1.742e-02, eta: 13:01:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9912, loss_cls: 0.6637, loss: 0.6637 +2025-06-24 17:47:04,531 - pyskl - INFO - Epoch [56][1000/1281] lr: 1.740e-02, eta: 13:00:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9938, loss_cls: 0.6129, loss: 0.6129 +2025-06-24 17:47:53,936 - pyskl - INFO - Epoch [56][1100/1281] lr: 1.738e-02, eta: 13:00:35, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9975, loss_cls: 0.6240, loss: 0.6240 +2025-06-24 17:48:31,190 - pyskl - INFO - Epoch [56][1200/1281] lr: 1.736e-02, eta: 12:59:54, time: 0.373, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9956, loss_cls: 0.6343, loss: 0.6343 +2025-06-24 17:49:14,416 - pyskl - INFO - Saving checkpoint at 56 epochs +2025-06-24 17:50:02,569 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:50:02,626 - pyskl - INFO - +top1_acc 0.8361 +top5_acc 0.9889 +2025-06-24 17:50:02,627 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:50:02,635 - pyskl - INFO - +mean_acc 0.7687 +2025-06-24 17:50:02,637 - pyskl - INFO - Epoch(val) [56][533] top1_acc: 0.8361, top5_acc: 0.9889, mean_class_accuracy: 0.7687 +2025-06-24 17:51:23,747 - pyskl - INFO - Epoch [57][100/1281] lr: 1.733e-02, eta: 12:59:02, time: 0.811, data_time: 0.195, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9944, loss_cls: 0.5954, loss: 0.5954 +2025-06-24 17:52:13,312 - pyskl - INFO - Epoch [57][200/1281] lr: 1.731e-02, eta: 12:58:41, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9975, loss_cls: 0.5530, loss: 0.5530 +2025-06-24 17:53:02,686 - pyskl - INFO - Epoch [57][300/1281] lr: 1.729e-02, eta: 12:58:19, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9969, loss_cls: 0.5378, loss: 0.5378 +2025-06-24 17:53:51,953 - pyskl - INFO - Epoch [57][400/1281] lr: 1.727e-02, eta: 12:57:58, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9981, loss_cls: 0.5262, loss: 0.5262 +2025-06-24 17:54:41,136 - pyskl - INFO - Epoch [57][500/1281] lr: 1.725e-02, eta: 12:57:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9950, loss_cls: 0.5553, loss: 0.5553 +2025-06-24 17:55:30,169 - pyskl - INFO - Epoch [57][600/1281] lr: 1.723e-02, eta: 12:57:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9956, loss_cls: 0.6057, loss: 0.6057 +2025-06-24 17:56:19,516 - pyskl - INFO - Epoch [57][700/1281] lr: 1.721e-02, eta: 12:56:52, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9950, loss_cls: 0.5704, loss: 0.5704 +2025-06-24 17:57:08,367 - pyskl - INFO - Epoch [57][800/1281] lr: 1.719e-02, eta: 12:56:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9906, loss_cls: 0.6162, loss: 0.6162 +2025-06-24 17:57:57,509 - pyskl - INFO - Epoch [57][900/1281] lr: 1.717e-02, eta: 12:56:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9938, loss_cls: 0.5819, loss: 0.5819 +2025-06-24 17:58:46,912 - pyskl - INFO - Epoch [57][1000/1281] lr: 1.716e-02, eta: 12:55:46, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9931, loss_cls: 0.6332, loss: 0.6332 +2025-06-24 17:59:36,006 - pyskl - INFO - Epoch [57][1100/1281] lr: 1.714e-02, eta: 12:55:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9962, loss_cls: 0.5986, loss: 0.5986 +2025-06-24 18:00:11,897 - pyskl - INFO - Epoch [57][1200/1281] lr: 1.712e-02, eta: 12:54:39, time: 0.359, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9956, loss_cls: 0.5951, loss: 0.5951 +2025-06-24 18:00:55,868 - pyskl - INFO - Saving checkpoint at 57 epochs +2025-06-24 18:01:44,397 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:01:44,453 - pyskl - INFO - +top1_acc 0.8180 +top5_acc 0.9870 +2025-06-24 18:01:44,453 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:01:44,459 - pyskl - INFO - +mean_acc 0.7677 +2025-06-24 18:01:44,461 - pyskl - INFO - Epoch(val) [57][533] top1_acc: 0.8180, top5_acc: 0.9870, mean_class_accuracy: 0.7677 +2025-06-24 18:03:06,224 - pyskl - INFO - Epoch [58][100/1281] lr: 1.708e-02, eta: 12:53:47, time: 0.818, data_time: 0.202, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9975, loss_cls: 0.6774, loss: 0.6774 +2025-06-24 18:03:55,368 - pyskl - INFO - Epoch [58][200/1281] lr: 1.706e-02, eta: 12:53:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9962, loss_cls: 0.6227, loss: 0.6227 +2025-06-24 18:04:44,753 - pyskl - INFO - Epoch [58][300/1281] lr: 1.704e-02, eta: 12:53:02, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9994, loss_cls: 0.5037, loss: 0.5037 +2025-06-24 18:05:34,120 - pyskl - INFO - Epoch [58][400/1281] lr: 1.703e-02, eta: 12:52:40, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9969, loss_cls: 0.5911, loss: 0.5911 +2025-06-24 18:06:23,282 - pyskl - INFO - Epoch [58][500/1281] lr: 1.701e-02, eta: 12:52:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9969, loss_cls: 0.5946, loss: 0.5946 +2025-06-24 18:07:12,592 - pyskl - INFO - Epoch [58][600/1281] lr: 1.699e-02, eta: 12:51:54, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9962, loss_cls: 0.5553, loss: 0.5553 +2025-06-24 18:08:01,694 - pyskl - INFO - Epoch [58][700/1281] lr: 1.697e-02, eta: 12:51:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9988, loss_cls: 0.6125, loss: 0.6125 +2025-06-24 18:08:50,919 - pyskl - INFO - Epoch [58][800/1281] lr: 1.695e-02, eta: 12:51:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9944, loss_cls: 0.5833, loss: 0.5833 +2025-06-24 18:09:40,340 - pyskl - INFO - Epoch [58][900/1281] lr: 1.693e-02, eta: 12:50:46, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9944, loss_cls: 0.6236, loss: 0.6236 +2025-06-24 18:10:29,219 - pyskl - INFO - Epoch [58][1000/1281] lr: 1.691e-02, eta: 12:50:22, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9956, loss_cls: 0.5924, loss: 0.5924 +2025-06-24 18:11:18,523 - pyskl - INFO - Epoch [58][1100/1281] lr: 1.689e-02, eta: 12:49:59, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9988, loss_cls: 0.5419, loss: 0.5419 +2025-06-24 18:11:53,203 - pyskl - INFO - Epoch [58][1200/1281] lr: 1.687e-02, eta: 12:49:13, time: 0.347, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9919, loss_cls: 0.6293, loss: 0.6293 +2025-06-24 18:12:40,950 - pyskl - INFO - Saving checkpoint at 58 epochs +2025-06-24 18:13:30,052 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:13:30,108 - pyskl - INFO - +top1_acc 0.8343 +top5_acc 0.9898 +2025-06-24 18:13:30,108 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:13:30,115 - pyskl - INFO - +mean_acc 0.7626 +2025-06-24 18:13:30,117 - pyskl - INFO - Epoch(val) [58][533] top1_acc: 0.8343, top5_acc: 0.9898, mean_class_accuracy: 0.7626 +2025-06-24 18:14:50,860 - pyskl - INFO - Epoch [59][100/1281] lr: 1.684e-02, eta: 12:48:18, time: 0.807, data_time: 0.196, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9962, loss_cls: 0.5781, loss: 0.5781 +2025-06-24 18:15:39,949 - pyskl - INFO - Epoch [59][200/1281] lr: 1.682e-02, eta: 12:47:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9969, loss_cls: 0.5244, loss: 0.5244 +2025-06-24 18:16:28,852 - pyskl - INFO - Epoch [59][300/1281] lr: 1.680e-02, eta: 12:47:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9981, loss_cls: 0.6318, loss: 0.6318 +2025-06-24 18:17:18,104 - pyskl - INFO - Epoch [59][400/1281] lr: 1.678e-02, eta: 12:47:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9962, loss_cls: 0.5290, loss: 0.5290 +2025-06-24 18:18:07,097 - pyskl - INFO - Epoch [59][500/1281] lr: 1.676e-02, eta: 12:46:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9931, loss_cls: 0.5951, loss: 0.5951 +2025-06-24 18:18:56,085 - pyskl - INFO - Epoch [59][600/1281] lr: 1.674e-02, eta: 12:46:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9956, loss_cls: 0.5750, loss: 0.5750 +2025-06-24 18:19:45,020 - pyskl - INFO - Epoch [59][700/1281] lr: 1.672e-02, eta: 12:45:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9956, loss_cls: 0.6068, loss: 0.6068 +2025-06-24 18:20:33,922 - pyskl - INFO - Epoch [59][800/1281] lr: 1.670e-02, eta: 12:45:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9962, loss_cls: 0.5954, loss: 0.5954 +2025-06-24 18:21:23,236 - pyskl - INFO - Epoch [59][900/1281] lr: 1.668e-02, eta: 12:45:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9944, loss_cls: 0.5911, loss: 0.5911 +2025-06-24 18:22:12,555 - pyskl - INFO - Epoch [59][1000/1281] lr: 1.667e-02, eta: 12:44:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9944, loss_cls: 0.6093, loss: 0.6093 +2025-06-24 18:23:01,603 - pyskl - INFO - Epoch [59][1100/1281] lr: 1.665e-02, eta: 12:44:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9950, loss_cls: 0.5846, loss: 0.5846 +2025-06-24 18:23:37,473 - pyskl - INFO - Epoch [59][1200/1281] lr: 1.663e-02, eta: 12:43:36, time: 0.359, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9944, loss_cls: 0.6420, loss: 0.6420 +2025-06-24 18:24:21,397 - pyskl - INFO - Saving checkpoint at 59 epochs +2025-06-24 18:25:09,926 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:25:09,982 - pyskl - INFO - +top1_acc 0.8263 +top5_acc 0.9897 +2025-06-24 18:25:09,983 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:25:09,990 - pyskl - INFO - +mean_acc 0.7699 +2025-06-24 18:25:09,992 - pyskl - INFO - Epoch(val) [59][533] top1_acc: 0.8263, top5_acc: 0.9897, mean_class_accuracy: 0.7699 +2025-06-24 18:26:30,606 - pyskl - INFO - Epoch [60][100/1281] lr: 1.659e-02, eta: 12:42:39, time: 0.806, data_time: 0.197, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9994, loss_cls: 0.5283, loss: 0.5283 +2025-06-24 18:27:19,455 - pyskl - INFO - Epoch [60][200/1281] lr: 1.657e-02, eta: 12:42:15, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9988, loss_cls: 0.5211, loss: 0.5211 +2025-06-24 18:28:08,837 - pyskl - INFO - Epoch [60][300/1281] lr: 1.655e-02, eta: 12:41:51, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9950, loss_cls: 0.5913, loss: 0.5913 +2025-06-24 18:28:58,081 - pyskl - INFO - Epoch [60][400/1281] lr: 1.653e-02, eta: 12:41:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9962, loss_cls: 0.5390, loss: 0.5390 +2025-06-24 18:29:47,474 - pyskl - INFO - Epoch [60][500/1281] lr: 1.651e-02, eta: 12:41:03, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9981, loss_cls: 0.5924, loss: 0.5924 +2025-06-24 18:30:36,183 - pyskl - INFO - Epoch [60][600/1281] lr: 1.650e-02, eta: 12:40:38, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9962, loss_cls: 0.5736, loss: 0.5736 +2025-06-24 18:31:25,312 - pyskl - INFO - Epoch [60][700/1281] lr: 1.648e-02, eta: 12:40:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9931, loss_cls: 0.5982, loss: 0.5982 +2025-06-24 18:32:14,812 - pyskl - INFO - Epoch [60][800/1281] lr: 1.646e-02, eta: 12:39:49, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9988, loss_cls: 0.5541, loss: 0.5541 +2025-06-24 18:33:04,056 - pyskl - INFO - Epoch [60][900/1281] lr: 1.644e-02, eta: 12:39:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9950, loss_cls: 0.6152, loss: 0.6152 +2025-06-24 18:33:53,238 - pyskl - INFO - Epoch [60][1000/1281] lr: 1.642e-02, eta: 12:39:00, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9962, loss_cls: 0.5229, loss: 0.5229 +2025-06-24 18:34:42,242 - pyskl - INFO - Epoch [60][1100/1281] lr: 1.640e-02, eta: 12:38:35, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9975, loss_cls: 0.5535, loss: 0.5535 +2025-06-24 18:35:18,850 - pyskl - INFO - Epoch [60][1200/1281] lr: 1.638e-02, eta: 12:37:51, time: 0.366, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9944, loss_cls: 0.6303, loss: 0.6303 +2025-06-24 18:36:02,234 - pyskl - INFO - Saving checkpoint at 60 epochs +2025-06-24 18:36:50,194 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:36:50,250 - pyskl - INFO - +top1_acc 0.8476 +top5_acc 0.9906 +2025-06-24 18:36:50,250 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:36:50,257 - pyskl - INFO - +mean_acc 0.7846 +2025-06-24 18:36:50,261 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_48.pth was removed +2025-06-24 18:36:50,451 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_60.pth. +2025-06-24 18:36:50,452 - pyskl - INFO - Best top1_acc is 0.8476 at 60 epoch. +2025-06-24 18:36:50,455 - pyskl - INFO - Epoch(val) [60][533] top1_acc: 0.8476, top5_acc: 0.9906, mean_class_accuracy: 0.7846 +2025-06-24 18:38:11,513 - pyskl - INFO - Epoch [61][100/1281] lr: 1.634e-02, eta: 12:36:55, time: 0.811, data_time: 0.192, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9969, loss_cls: 0.5264, loss: 0.5264 +2025-06-24 18:39:00,712 - pyskl - INFO - Epoch [61][200/1281] lr: 1.632e-02, eta: 12:36:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9988, loss_cls: 0.4968, loss: 0.4968 +2025-06-24 18:39:49,811 - pyskl - INFO - Epoch [61][300/1281] lr: 1.630e-02, eta: 12:36:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9969, loss_cls: 0.5356, loss: 0.5356 +2025-06-24 18:40:38,964 - pyskl - INFO - Epoch [61][400/1281] lr: 1.629e-02, eta: 12:35:40, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9956, loss_cls: 0.5405, loss: 0.5405 +2025-06-24 18:41:28,049 - pyskl - INFO - Epoch [61][500/1281] lr: 1.627e-02, eta: 12:35:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9994, loss_cls: 0.4919, loss: 0.4919 +2025-06-24 18:42:16,923 - pyskl - INFO - Epoch [61][600/1281] lr: 1.625e-02, eta: 12:34:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9975, loss_cls: 0.6100, loss: 0.6100 +2025-06-24 18:43:06,025 - pyskl - INFO - Epoch [61][700/1281] lr: 1.623e-02, eta: 12:34:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9956, loss_cls: 0.5495, loss: 0.5495 +2025-06-24 18:43:55,381 - pyskl - INFO - Epoch [61][800/1281] lr: 1.621e-02, eta: 12:33:59, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9956, loss_cls: 0.5580, loss: 0.5580 +2025-06-24 18:44:44,725 - pyskl - INFO - Epoch [61][900/1281] lr: 1.619e-02, eta: 12:33:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9956, loss_cls: 0.6288, loss: 0.6288 +2025-06-24 18:45:33,896 - pyskl - INFO - Epoch [61][1000/1281] lr: 1.617e-02, eta: 12:33:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9925, loss_cls: 0.6156, loss: 0.6156 +2025-06-24 18:46:23,063 - pyskl - INFO - Epoch [61][1100/1281] lr: 1.615e-02, eta: 12:32:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9969, loss_cls: 0.6203, loss: 0.6203 +2025-06-24 18:46:59,676 - pyskl - INFO - Epoch [61][1200/1281] lr: 1.613e-02, eta: 12:31:59, time: 0.366, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9944, loss_cls: 0.5686, loss: 0.5686 +2025-06-24 18:47:45,605 - pyskl - INFO - Saving checkpoint at 61 epochs +2025-06-24 18:48:34,526 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:48:34,582 - pyskl - INFO - +top1_acc 0.8407 +top5_acc 0.9910 +2025-06-24 18:48:34,582 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:48:34,589 - pyskl - INFO - +mean_acc 0.7958 +2025-06-24 18:48:34,591 - pyskl - INFO - Epoch(val) [61][533] top1_acc: 0.8407, top5_acc: 0.9910, mean_class_accuracy: 0.7958 +2025-06-24 18:49:54,679 - pyskl - INFO - Epoch [62][100/1281] lr: 1.609e-02, eta: 12:31:00, time: 0.801, data_time: 0.196, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9969, loss_cls: 0.5072, loss: 0.5072 +2025-06-24 18:50:43,734 - pyskl - INFO - Epoch [62][200/1281] lr: 1.607e-02, eta: 12:30:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9981, loss_cls: 0.5200, loss: 0.5200 +2025-06-24 18:51:32,546 - pyskl - INFO - Epoch [62][300/1281] lr: 1.605e-02, eta: 12:30:08, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9988, loss_cls: 0.4724, loss: 0.4724 +2025-06-24 18:52:21,604 - pyskl - INFO - Epoch [62][400/1281] lr: 1.603e-02, eta: 12:29:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9981, loss_cls: 0.5167, loss: 0.5167 +2025-06-24 18:53:10,650 - pyskl - INFO - Epoch [62][500/1281] lr: 1.602e-02, eta: 12:29:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9988, loss_cls: 0.5141, loss: 0.5141 +2025-06-24 18:53:59,815 - pyskl - INFO - Epoch [62][600/1281] lr: 1.600e-02, eta: 12:28:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9975, loss_cls: 0.5275, loss: 0.5275 +2025-06-24 18:54:49,009 - pyskl - INFO - Epoch [62][700/1281] lr: 1.598e-02, eta: 12:28:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9962, loss_cls: 0.5344, loss: 0.5344 +2025-06-24 18:55:38,236 - pyskl - INFO - Epoch [62][800/1281] lr: 1.596e-02, eta: 12:27:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9975, loss_cls: 0.5650, loss: 0.5650 +2025-06-24 18:56:27,543 - pyskl - INFO - Epoch [62][900/1281] lr: 1.594e-02, eta: 12:27:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9950, loss_cls: 0.6005, loss: 0.6005 +2025-06-24 18:57:16,589 - pyskl - INFO - Epoch [62][1000/1281] lr: 1.592e-02, eta: 12:27:06, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9956, loss_cls: 0.5445, loss: 0.5445 +2025-06-24 18:58:06,009 - pyskl - INFO - Epoch [62][1100/1281] lr: 1.590e-02, eta: 12:26:41, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9969, loss_cls: 0.5987, loss: 0.5987 +2025-06-24 18:58:41,863 - pyskl - INFO - Epoch [62][1200/1281] lr: 1.588e-02, eta: 12:25:56, time: 0.359, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9969, loss_cls: 0.5396, loss: 0.5396 +2025-06-24 18:59:27,352 - pyskl - INFO - Saving checkpoint at 62 epochs +2025-06-24 19:00:16,342 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:00:16,397 - pyskl - INFO - +top1_acc 0.8511 +top5_acc 0.9901 +2025-06-24 19:00:16,397 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:00:16,404 - pyskl - INFO - +mean_acc 0.7790 +2025-06-24 19:00:16,408 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_60.pth was removed +2025-06-24 19:00:16,752 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_62.pth. +2025-06-24 19:00:16,753 - pyskl - INFO - Best top1_acc is 0.8511 at 62 epoch. +2025-06-24 19:00:16,756 - pyskl - INFO - Epoch(val) [62][533] top1_acc: 0.8511, top5_acc: 0.9901, mean_class_accuracy: 0.7790 +2025-06-24 19:01:36,441 - pyskl - INFO - Epoch [63][100/1281] lr: 1.584e-02, eta: 12:24:55, time: 0.797, data_time: 0.195, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9962, loss_cls: 0.5245, loss: 0.5245 +2025-06-24 19:02:25,585 - pyskl - INFO - Epoch [63][200/1281] lr: 1.582e-02, eta: 12:24:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.5144, loss: 0.5144 +2025-06-24 19:03:14,838 - pyskl - INFO - Epoch [63][300/1281] lr: 1.580e-02, eta: 12:24:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9956, loss_cls: 0.5250, loss: 0.5250 +2025-06-24 19:04:04,300 - pyskl - INFO - Epoch [63][400/1281] lr: 1.578e-02, eta: 12:23:36, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9962, loss_cls: 0.5340, loss: 0.5340 +2025-06-24 19:04:53,633 - pyskl - INFO - Epoch [63][500/1281] lr: 1.576e-02, eta: 12:23:10, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9969, loss_cls: 0.5072, loss: 0.5072 +2025-06-24 19:05:42,686 - pyskl - INFO - Epoch [63][600/1281] lr: 1.574e-02, eta: 12:22:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9988, loss_cls: 0.4943, loss: 0.4943 +2025-06-24 19:06:32,001 - pyskl - INFO - Epoch [63][700/1281] lr: 1.572e-02, eta: 12:22:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.5078, loss: 0.5078 +2025-06-24 19:07:20,982 - pyskl - INFO - Epoch [63][800/1281] lr: 1.570e-02, eta: 12:21:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9969, loss_cls: 0.5496, loss: 0.5496 +2025-06-24 19:08:10,258 - pyskl - INFO - Epoch [63][900/1281] lr: 1.568e-02, eta: 12:21:24, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9956, loss_cls: 0.5873, loss: 0.5873 +2025-06-24 19:08:59,248 - pyskl - INFO - Epoch [63][1000/1281] lr: 1.566e-02, eta: 12:20:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9962, loss_cls: 0.5263, loss: 0.5263 +2025-06-24 19:09:48,699 - pyskl - INFO - Epoch [63][1100/1281] lr: 1.564e-02, eta: 12:20:30, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9969, loss_cls: 0.5491, loss: 0.5491 +2025-06-24 19:10:25,879 - pyskl - INFO - Epoch [63][1200/1281] lr: 1.562e-02, eta: 12:19:47, time: 0.372, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9956, loss_cls: 0.6082, loss: 0.6082 +2025-06-24 19:11:08,965 - pyskl - INFO - Saving checkpoint at 63 epochs +2025-06-24 19:11:57,002 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:11:57,058 - pyskl - INFO - +top1_acc 0.8379 +top5_acc 0.9885 +2025-06-24 19:11:57,059 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:11:57,065 - pyskl - INFO - +mean_acc 0.7617 +2025-06-24 19:11:57,066 - pyskl - INFO - Epoch(val) [63][533] top1_acc: 0.8379, top5_acc: 0.9885, mean_class_accuracy: 0.7617 +2025-06-24 19:13:18,654 - pyskl - INFO - Epoch [64][100/1281] lr: 1.559e-02, eta: 12:18:48, time: 0.816, data_time: 0.195, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9975, loss_cls: 0.5251, loss: 0.5251 +2025-06-24 19:14:07,529 - pyskl - INFO - Epoch [64][200/1281] lr: 1.557e-02, eta: 12:18:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9981, loss_cls: 0.4776, loss: 0.4776 +2025-06-24 19:14:56,991 - pyskl - INFO - Epoch [64][300/1281] lr: 1.555e-02, eta: 12:17:54, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9956, loss_cls: 0.5469, loss: 0.5469 +2025-06-24 19:15:46,511 - pyskl - INFO - Epoch [64][400/1281] lr: 1.553e-02, eta: 12:17:27, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9975, loss_cls: 0.5398, loss: 0.5398 +2025-06-24 19:16:35,845 - pyskl - INFO - Epoch [64][500/1281] lr: 1.551e-02, eta: 12:17:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9950, loss_cls: 0.6028, loss: 0.6028 +2025-06-24 19:17:24,989 - pyskl - INFO - Epoch [64][600/1281] lr: 1.549e-02, eta: 12:16:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9969, loss_cls: 0.5375, loss: 0.5375 +2025-06-24 19:18:14,126 - pyskl - INFO - Epoch [64][700/1281] lr: 1.547e-02, eta: 12:16:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9906, loss_cls: 0.5492, loss: 0.5492 +2025-06-24 19:19:03,008 - pyskl - INFO - Epoch [64][800/1281] lr: 1.545e-02, eta: 12:15:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9962, loss_cls: 0.5673, loss: 0.5673 +2025-06-24 19:19:52,154 - pyskl - INFO - Epoch [64][900/1281] lr: 1.543e-02, eta: 12:15:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9931, loss_cls: 0.5564, loss: 0.5564 +2025-06-24 19:20:41,439 - pyskl - INFO - Epoch [64][1000/1281] lr: 1.541e-02, eta: 12:14:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9944, loss_cls: 0.5700, loss: 0.5700 +2025-06-24 19:21:30,677 - pyskl - INFO - Epoch [64][1100/1281] lr: 1.539e-02, eta: 12:14:17, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9962, loss_cls: 0.5732, loss: 0.5732 +2025-06-24 19:22:06,482 - pyskl - INFO - Epoch [64][1200/1281] lr: 1.537e-02, eta: 12:13:31, time: 0.358, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9956, loss_cls: 0.5579, loss: 0.5579 +2025-06-24 19:22:51,940 - pyskl - INFO - Saving checkpoint at 64 epochs +2025-06-24 19:23:40,723 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:23:40,781 - pyskl - INFO - +top1_acc 0.8438 +top5_acc 0.9903 +2025-06-24 19:23:40,781 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:23:40,789 - pyskl - INFO - +mean_acc 0.7733 +2025-06-24 19:23:40,791 - pyskl - INFO - Epoch(val) [64][533] top1_acc: 0.8438, top5_acc: 0.9903, mean_class_accuracy: 0.7733 +2025-06-24 19:25:01,947 - pyskl - INFO - Epoch [65][100/1281] lr: 1.533e-02, eta: 12:12:31, time: 0.812, data_time: 0.192, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9944, loss_cls: 0.4966, loss: 0.4966 +2025-06-24 19:25:51,394 - pyskl - INFO - Epoch [65][200/1281] lr: 1.531e-02, eta: 12:12:04, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9969, loss_cls: 0.4731, loss: 0.4731 +2025-06-24 19:26:40,713 - pyskl - INFO - Epoch [65][300/1281] lr: 1.529e-02, eta: 12:11:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9969, loss_cls: 0.5803, loss: 0.5803 +2025-06-24 19:27:30,066 - pyskl - INFO - Epoch [65][400/1281] lr: 1.527e-02, eta: 12:11:09, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9994, loss_cls: 0.4917, loss: 0.4917 +2025-06-24 19:28:18,805 - pyskl - INFO - Epoch [65][500/1281] lr: 1.526e-02, eta: 12:10:40, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9975, loss_cls: 0.5362, loss: 0.5362 +2025-06-24 19:29:08,155 - pyskl - INFO - Epoch [65][600/1281] lr: 1.524e-02, eta: 12:10:13, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9969, loss_cls: 0.5608, loss: 0.5608 +2025-06-24 19:29:57,703 - pyskl - INFO - Epoch [65][700/1281] lr: 1.522e-02, eta: 12:09:46, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9969, loss_cls: 0.5047, loss: 0.5047 +2025-06-24 19:30:47,004 - pyskl - INFO - Epoch [65][800/1281] lr: 1.520e-02, eta: 12:09:18, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9975, loss_cls: 0.4973, loss: 0.4973 +2025-06-24 19:31:36,216 - pyskl - INFO - Epoch [65][900/1281] lr: 1.518e-02, eta: 12:08:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9956, loss_cls: 0.5313, loss: 0.5313 +2025-06-24 19:32:25,329 - pyskl - INFO - Epoch [65][1000/1281] lr: 1.516e-02, eta: 12:08:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9950, loss_cls: 0.6109, loss: 0.6109 +2025-06-24 19:33:14,675 - pyskl - INFO - Epoch [65][1100/1281] lr: 1.514e-02, eta: 12:07:54, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9962, loss_cls: 0.4905, loss: 0.4905 +2025-06-24 19:33:50,984 - pyskl - INFO - Epoch [65][1200/1281] lr: 1.512e-02, eta: 12:07:09, time: 0.363, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9962, loss_cls: 0.5387, loss: 0.5387 +2025-06-24 19:34:35,906 - pyskl - INFO - Saving checkpoint at 65 epochs +2025-06-24 19:35:24,720 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:35:24,776 - pyskl - INFO - +top1_acc 0.8384 +top5_acc 0.9873 +2025-06-24 19:35:24,777 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:35:24,784 - pyskl - INFO - +mean_acc 0.7852 +2025-06-24 19:35:24,786 - pyskl - INFO - Epoch(val) [65][533] top1_acc: 0.8384, top5_acc: 0.9873, mean_class_accuracy: 0.7852 +2025-06-24 19:36:43,209 - pyskl - INFO - Epoch [66][100/1281] lr: 1.508e-02, eta: 12:06:04, time: 0.784, data_time: 0.192, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9969, loss_cls: 0.5048, loss: 0.5048 +2025-06-24 19:37:32,622 - pyskl - INFO - Epoch [66][200/1281] lr: 1.506e-02, eta: 12:05:37, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9988, loss_cls: 0.4132, loss: 0.4132 +2025-06-24 19:38:21,802 - pyskl - INFO - Epoch [66][300/1281] lr: 1.504e-02, eta: 12:05:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9962, loss_cls: 0.5466, loss: 0.5466 +2025-06-24 19:39:10,702 - pyskl - INFO - Epoch [66][400/1281] lr: 1.502e-02, eta: 12:04:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9975, loss_cls: 0.4961, loss: 0.4961 +2025-06-24 19:39:59,788 - pyskl - INFO - Epoch [66][500/1281] lr: 1.500e-02, eta: 12:04:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9962, loss_cls: 0.4503, loss: 0.4503 +2025-06-24 19:40:48,832 - pyskl - INFO - Epoch [66][600/1281] lr: 1.498e-02, eta: 12:03:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9950, loss_cls: 0.5542, loss: 0.5542 +2025-06-24 19:41:38,450 - pyskl - INFO - Epoch [66][700/1281] lr: 1.496e-02, eta: 12:03:15, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9962, loss_cls: 0.5421, loss: 0.5421 +2025-06-24 19:42:27,599 - pyskl - INFO - Epoch [66][800/1281] lr: 1.494e-02, eta: 12:02:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9981, loss_cls: 0.5340, loss: 0.5340 +2025-06-24 19:43:16,657 - pyskl - INFO - Epoch [66][900/1281] lr: 1.492e-02, eta: 12:02:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9962, loss_cls: 0.5373, loss: 0.5373 +2025-06-24 19:44:06,142 - pyskl - INFO - Epoch [66][1000/1281] lr: 1.490e-02, eta: 12:01:50, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9975, loss_cls: 0.4507, loss: 0.4507 +2025-06-24 19:44:55,122 - pyskl - INFO - Epoch [66][1100/1281] lr: 1.488e-02, eta: 12:01:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9950, loss_cls: 0.5307, loss: 0.5307 +2025-06-24 19:45:33,134 - pyskl - INFO - Epoch [66][1200/1281] lr: 1.486e-02, eta: 12:00:38, time: 0.380, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9975, loss_cls: 0.5318, loss: 0.5318 +2025-06-24 19:46:12,395 - pyskl - INFO - Saving checkpoint at 66 epochs +2025-06-24 19:46:59,531 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:46:59,588 - pyskl - INFO - +top1_acc 0.8483 +top5_acc 0.9912 +2025-06-24 19:46:59,588 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:46:59,596 - pyskl - INFO - +mean_acc 0.7952 +2025-06-24 19:46:59,599 - pyskl - INFO - Epoch(val) [66][533] top1_acc: 0.8483, top5_acc: 0.9912, mean_class_accuracy: 0.7952 +2025-06-24 19:48:20,177 - pyskl - INFO - Epoch [67][100/1281] lr: 1.482e-02, eta: 11:59:35, time: 0.806, data_time: 0.190, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9969, loss_cls: 0.5038, loss: 0.5038 +2025-06-24 19:49:09,297 - pyskl - INFO - Epoch [67][200/1281] lr: 1.480e-02, eta: 11:59:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9975, loss_cls: 0.5139, loss: 0.5139 +2025-06-24 19:49:58,330 - pyskl - INFO - Epoch [67][300/1281] lr: 1.478e-02, eta: 11:58:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9981, loss_cls: 0.4975, loss: 0.4975 +2025-06-24 19:50:47,581 - pyskl - INFO - Epoch [67][400/1281] lr: 1.476e-02, eta: 11:58:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9956, loss_cls: 0.4915, loss: 0.4915 +2025-06-24 19:51:36,431 - pyskl - INFO - Epoch [67][500/1281] lr: 1.474e-02, eta: 11:57:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9969, loss_cls: 0.4918, loss: 0.4918 +2025-06-24 19:52:25,139 - pyskl - INFO - Epoch [67][600/1281] lr: 1.472e-02, eta: 11:57:10, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9981, loss_cls: 0.5423, loss: 0.5423 +2025-06-24 19:53:14,342 - pyskl - INFO - Epoch [67][700/1281] lr: 1.470e-02, eta: 11:56:41, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9962, loss_cls: 0.5448, loss: 0.5448 +2025-06-24 19:54:03,229 - pyskl - INFO - Epoch [67][800/1281] lr: 1.468e-02, eta: 11:56:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9956, loss_cls: 0.5161, loss: 0.5161 +2025-06-24 19:54:52,497 - pyskl - INFO - Epoch [67][900/1281] lr: 1.466e-02, eta: 11:55:43, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9938, loss_cls: 0.5272, loss: 0.5272 +2025-06-24 19:55:41,770 - pyskl - INFO - Epoch [67][1000/1281] lr: 1.464e-02, eta: 11:55:14, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9956, loss_cls: 0.5263, loss: 0.5263 +2025-06-24 19:56:31,269 - pyskl - INFO - Epoch [67][1100/1281] lr: 1.462e-02, eta: 11:54:45, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9962, loss_cls: 0.5116, loss: 0.5116 +2025-06-24 19:57:08,276 - pyskl - INFO - Epoch [67][1200/1281] lr: 1.460e-02, eta: 11:54:01, time: 0.370, data_time: 0.001, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9962, loss_cls: 0.5137, loss: 0.5137 +2025-06-24 19:57:50,973 - pyskl - INFO - Saving checkpoint at 67 epochs +2025-06-24 19:58:39,128 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:58:39,183 - pyskl - INFO - +top1_acc 0.8319 +top5_acc 0.9894 +2025-06-24 19:58:39,183 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:58:39,189 - pyskl - INFO - +mean_acc 0.7587 +2025-06-24 19:58:39,191 - pyskl - INFO - Epoch(val) [67][533] top1_acc: 0.8319, top5_acc: 0.9894, mean_class_accuracy: 0.7587 +2025-06-24 19:59:58,846 - pyskl - INFO - Epoch [68][100/1281] lr: 1.456e-02, eta: 11:52:56, time: 0.797, data_time: 0.197, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9975, loss_cls: 0.4780, loss: 0.4780 +2025-06-24 20:00:48,353 - pyskl - INFO - Epoch [68][200/1281] lr: 1.454e-02, eta: 11:52:27, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9944, loss_cls: 0.4949, loss: 0.4949 +2025-06-24 20:01:37,420 - pyskl - INFO - Epoch [68][300/1281] lr: 1.452e-02, eta: 11:51:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9988, loss_cls: 0.5107, loss: 0.5107 +2025-06-24 20:02:26,513 - pyskl - INFO - Epoch [68][400/1281] lr: 1.450e-02, eta: 11:51:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9962, loss_cls: 0.4719, loss: 0.4719 +2025-06-24 20:03:15,648 - pyskl - INFO - Epoch [68][500/1281] lr: 1.448e-02, eta: 11:50:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9962, loss_cls: 0.4849, loss: 0.4849 +2025-06-24 20:04:04,795 - pyskl - INFO - Epoch [68][600/1281] lr: 1.446e-02, eta: 11:50:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9994, loss_cls: 0.4545, loss: 0.4545 +2025-06-24 20:04:53,914 - pyskl - INFO - Epoch [68][700/1281] lr: 1.444e-02, eta: 11:50:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9962, loss_cls: 0.5120, loss: 0.5120 +2025-06-24 20:05:42,975 - pyskl - INFO - Epoch [68][800/1281] lr: 1.442e-02, eta: 11:49:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9956, loss_cls: 0.5082, loss: 0.5082 +2025-06-24 20:06:32,129 - pyskl - INFO - Epoch [68][900/1281] lr: 1.440e-02, eta: 11:49:00, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9962, loss_cls: 0.5214, loss: 0.5214 +2025-06-24 20:07:21,014 - pyskl - INFO - Epoch [68][1000/1281] lr: 1.438e-02, eta: 11:48:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9969, loss_cls: 0.4568, loss: 0.4568 +2025-06-24 20:08:10,280 - pyskl - INFO - Epoch [68][1100/1281] lr: 1.436e-02, eta: 11:48:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9981, loss_cls: 0.5022, loss: 0.5022 +2025-06-24 20:08:47,011 - pyskl - INFO - Epoch [68][1200/1281] lr: 1.434e-02, eta: 11:47:16, time: 0.367, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9975, loss_cls: 0.5093, loss: 0.5093 +2025-06-24 20:09:28,456 - pyskl - INFO - Saving checkpoint at 68 epochs +2025-06-24 20:10:16,437 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:10:16,494 - pyskl - INFO - +top1_acc 0.8373 +top5_acc 0.9832 +2025-06-24 20:10:16,494 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:10:16,501 - pyskl - INFO - +mean_acc 0.7831 +2025-06-24 20:10:16,504 - pyskl - INFO - Epoch(val) [68][533] top1_acc: 0.8373, top5_acc: 0.9832, mean_class_accuracy: 0.7831 +2025-06-24 20:11:35,146 - pyskl - INFO - Epoch [69][100/1281] lr: 1.431e-02, eta: 11:46:10, time: 0.786, data_time: 0.196, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9981, loss_cls: 0.4821, loss: 0.4821 +2025-06-24 20:12:24,333 - pyskl - INFO - Epoch [69][200/1281] lr: 1.429e-02, eta: 11:45:40, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9969, loss_cls: 0.4719, loss: 0.4719 +2025-06-24 20:13:13,756 - pyskl - INFO - Epoch [69][300/1281] lr: 1.427e-02, eta: 11:45:10, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9975, loss_cls: 0.4891, loss: 0.4891 +2025-06-24 20:14:02,544 - pyskl - INFO - Epoch [69][400/1281] lr: 1.425e-02, eta: 11:44:40, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9944, loss_cls: 0.5741, loss: 0.5741 +2025-06-24 20:14:51,636 - pyskl - INFO - Epoch [69][500/1281] lr: 1.423e-02, eta: 11:44:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9975, loss_cls: 0.5145, loss: 0.5145 +2025-06-24 20:15:40,599 - pyskl - INFO - Epoch [69][600/1281] lr: 1.420e-02, eta: 11:43:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9994, loss_cls: 0.5096, loss: 0.5096 +2025-06-24 20:16:29,763 - pyskl - INFO - Epoch [69][700/1281] lr: 1.418e-02, eta: 11:43:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9975, loss_cls: 0.5109, loss: 0.5109 +2025-06-24 20:17:19,019 - pyskl - INFO - Epoch [69][800/1281] lr: 1.416e-02, eta: 11:42:40, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9975, loss_cls: 0.5442, loss: 0.5442 +2025-06-24 20:18:08,194 - pyskl - INFO - Epoch [69][900/1281] lr: 1.414e-02, eta: 11:42:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9950, loss_cls: 0.5841, loss: 0.5841 +2025-06-24 20:18:57,312 - pyskl - INFO - Epoch [69][1000/1281] lr: 1.412e-02, eta: 11:41:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9981, loss_cls: 0.4739, loss: 0.4739 +2025-06-24 20:19:46,192 - pyskl - INFO - Epoch [69][1100/1281] lr: 1.410e-02, eta: 11:41:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9988, loss_cls: 0.4570, loss: 0.4570 +2025-06-24 20:20:24,782 - pyskl - INFO - Epoch [69][1200/1281] lr: 1.408e-02, eta: 11:40:26, time: 0.386, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9994, loss_cls: 0.4401, loss: 0.4401 +2025-06-24 20:21:03,568 - pyskl - INFO - Saving checkpoint at 69 epochs +2025-06-24 20:21:50,990 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:21:51,047 - pyskl - INFO - +top1_acc 0.8443 +top5_acc 0.9900 +2025-06-24 20:21:51,047 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:21:51,055 - pyskl - INFO - +mean_acc 0.7936 +2025-06-24 20:21:51,057 - pyskl - INFO - Epoch(val) [69][533] top1_acc: 0.8443, top5_acc: 0.9900, mean_class_accuracy: 0.7936 +2025-06-24 20:23:10,546 - pyskl - INFO - Epoch [70][100/1281] lr: 1.405e-02, eta: 11:39:20, time: 0.795, data_time: 0.193, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9962, loss_cls: 0.4357, loss: 0.4357 +2025-06-24 20:23:59,871 - pyskl - INFO - Epoch [70][200/1281] lr: 1.403e-02, eta: 11:38:50, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9988, loss_cls: 0.4665, loss: 0.4665 +2025-06-24 20:24:48,864 - pyskl - INFO - Epoch [70][300/1281] lr: 1.401e-02, eta: 11:38:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9988, loss_cls: 0.4503, loss: 0.4503 +2025-06-24 20:25:38,068 - pyskl - INFO - Epoch [70][400/1281] lr: 1.399e-02, eta: 11:37:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 1.0000, loss_cls: 0.4003, loss: 0.4003 +2025-06-24 20:26:27,441 - pyskl - INFO - Epoch [70][500/1281] lr: 1.397e-02, eta: 11:37:19, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9975, loss_cls: 0.4512, loss: 0.4512 +2025-06-24 20:27:16,638 - pyskl - INFO - Epoch [70][600/1281] lr: 1.395e-02, eta: 11:36:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9975, loss_cls: 0.4423, loss: 0.4423 +2025-06-24 20:28:05,814 - pyskl - INFO - Epoch [70][700/1281] lr: 1.392e-02, eta: 11:36:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9975, loss_cls: 0.4861, loss: 0.4861 +2025-06-24 20:28:55,174 - pyskl - INFO - Epoch [70][800/1281] lr: 1.390e-02, eta: 11:35:47, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9975, loss_cls: 0.4604, loss: 0.4604 +2025-06-24 20:29:44,517 - pyskl - INFO - Epoch [70][900/1281] lr: 1.388e-02, eta: 11:35:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9950, loss_cls: 0.4698, loss: 0.4698 +2025-06-24 20:30:33,623 - pyskl - INFO - Epoch [70][1000/1281] lr: 1.386e-02, eta: 11:34:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9969, loss_cls: 0.5603, loss: 0.5603 +2025-06-24 20:31:22,767 - pyskl - INFO - Epoch [70][1100/1281] lr: 1.384e-02, eta: 11:34:16, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9956, loss_cls: 0.5138, loss: 0.5138 +2025-06-24 20:31:59,819 - pyskl - INFO - Epoch [70][1200/1281] lr: 1.382e-02, eta: 11:33:31, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9975, loss_cls: 0.4962, loss: 0.4962 +2025-06-24 20:32:41,076 - pyskl - INFO - Saving checkpoint at 70 epochs +2025-06-24 20:33:28,901 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:33:28,979 - pyskl - INFO - +top1_acc 0.8412 +top5_acc 0.9898 +2025-06-24 20:33:28,980 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:33:28,990 - pyskl - INFO - +mean_acc 0.7806 +2025-06-24 20:33:28,993 - pyskl - INFO - Epoch(val) [70][533] top1_acc: 0.8412, top5_acc: 0.9898, mean_class_accuracy: 0.7806 +2025-06-24 20:34:50,352 - pyskl - INFO - Epoch [71][100/1281] lr: 1.379e-02, eta: 11:32:27, time: 0.814, data_time: 0.195, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9981, loss_cls: 0.4572, loss: 0.4572 +2025-06-24 20:35:39,616 - pyskl - INFO - Epoch [71][200/1281] lr: 1.377e-02, eta: 11:31:56, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9994, loss_cls: 0.4293, loss: 0.4293 +2025-06-24 20:36:28,678 - pyskl - INFO - Epoch [71][300/1281] lr: 1.375e-02, eta: 11:31:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9969, loss_cls: 0.4964, loss: 0.4964 +2025-06-24 20:37:17,854 - pyskl - INFO - Epoch [71][400/1281] lr: 1.373e-02, eta: 11:30:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9969, loss_cls: 0.4564, loss: 0.4564 +2025-06-24 20:38:07,025 - pyskl - INFO - Epoch [71][500/1281] lr: 1.371e-02, eta: 11:30:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9981, loss_cls: 0.4550, loss: 0.4550 +2025-06-24 20:38:56,434 - pyskl - INFO - Epoch [71][600/1281] lr: 1.368e-02, eta: 11:29:52, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9988, loss_cls: 0.4900, loss: 0.4900 +2025-06-24 20:39:45,812 - pyskl - INFO - Epoch [71][700/1281] lr: 1.366e-02, eta: 11:29:22, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9969, loss_cls: 0.4539, loss: 0.4539 +2025-06-24 20:40:35,035 - pyskl - INFO - Epoch [71][800/1281] lr: 1.364e-02, eta: 11:28:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9988, loss_cls: 0.4760, loss: 0.4760 +2025-06-24 20:41:24,213 - pyskl - INFO - Epoch [71][900/1281] lr: 1.362e-02, eta: 11:28:19, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9950, loss_cls: 0.4645, loss: 0.4645 +2025-06-24 20:42:13,504 - pyskl - INFO - Epoch [71][1000/1281] lr: 1.360e-02, eta: 11:27:49, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9950, loss_cls: 0.4755, loss: 0.4755 +2025-06-24 20:43:02,953 - pyskl - INFO - Epoch [71][1100/1281] lr: 1.358e-02, eta: 11:27:18, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9988, loss_cls: 0.4656, loss: 0.4656 +2025-06-24 20:43:38,387 - pyskl - INFO - Epoch [71][1200/1281] lr: 1.356e-02, eta: 11:26:31, time: 0.354, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9975, loss_cls: 0.5064, loss: 0.5064 +2025-06-24 20:44:23,280 - pyskl - INFO - Saving checkpoint at 71 epochs +2025-06-24 20:45:11,416 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:45:11,471 - pyskl - INFO - +top1_acc 0.7773 +top5_acc 0.9788 +2025-06-24 20:45:11,471 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:45:11,479 - pyskl - INFO - +mean_acc 0.6912 +2025-06-24 20:45:11,481 - pyskl - INFO - Epoch(val) [71][533] top1_acc: 0.7773, top5_acc: 0.9788, mean_class_accuracy: 0.6912 +2025-06-24 20:46:30,952 - pyskl - INFO - Epoch [72][100/1281] lr: 1.353e-02, eta: 11:25:24, time: 0.795, data_time: 0.191, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9994, loss_cls: 0.4655, loss: 0.4655 +2025-06-24 20:47:20,021 - pyskl - INFO - Epoch [72][200/1281] lr: 1.351e-02, eta: 11:24:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9988, loss_cls: 0.4482, loss: 0.4482 +2025-06-24 20:48:08,967 - pyskl - INFO - Epoch [72][300/1281] lr: 1.349e-02, eta: 11:24:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9988, loss_cls: 0.4337, loss: 0.4337 +2025-06-24 20:48:57,747 - pyskl - INFO - Epoch [72][400/1281] lr: 1.346e-02, eta: 11:23:49, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9988, loss_cls: 0.4969, loss: 0.4969 +2025-06-24 20:49:46,942 - pyskl - INFO - Epoch [72][500/1281] lr: 1.344e-02, eta: 11:23:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9975, loss_cls: 0.4499, loss: 0.4499 +2025-06-24 20:50:36,109 - pyskl - INFO - Epoch [72][600/1281] lr: 1.342e-02, eta: 11:22:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9969, loss_cls: 0.5038, loss: 0.5038 +2025-06-24 20:51:25,000 - pyskl - INFO - Epoch [72][700/1281] lr: 1.340e-02, eta: 11:22:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9975, loss_cls: 0.4578, loss: 0.4578 +2025-06-24 20:52:13,781 - pyskl - INFO - Epoch [72][800/1281] lr: 1.338e-02, eta: 11:21:43, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9975, loss_cls: 0.4804, loss: 0.4804 +2025-06-24 20:53:02,794 - pyskl - INFO - Epoch [72][900/1281] lr: 1.336e-02, eta: 11:21:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9975, loss_cls: 0.5061, loss: 0.5061 +2025-06-24 20:53:51,831 - pyskl - INFO - Epoch [72][1000/1281] lr: 1.334e-02, eta: 11:20:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9969, loss_cls: 0.5463, loss: 0.5463 +2025-06-24 20:54:40,733 - pyskl - INFO - Epoch [72][1100/1281] lr: 1.332e-02, eta: 11:20:07, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9988, loss_cls: 0.4337, loss: 0.4337 +2025-06-24 20:55:18,255 - pyskl - INFO - Epoch [72][1200/1281] lr: 1.330e-02, eta: 11:19:23, time: 0.375, data_time: 0.001, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9956, loss_cls: 0.5117, loss: 0.5117 +2025-06-24 20:56:00,815 - pyskl - INFO - Saving checkpoint at 72 epochs +2025-06-24 20:56:48,921 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:56:48,977 - pyskl - INFO - +top1_acc 0.8494 +top5_acc 0.9911 +2025-06-24 20:56:48,978 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:56:48,984 - pyskl - INFO - +mean_acc 0.7925 +2025-06-24 20:56:48,986 - pyskl - INFO - Epoch(val) [72][533] top1_acc: 0.8494, top5_acc: 0.9911, mean_class_accuracy: 0.7925 +2025-06-24 20:58:08,847 - pyskl - INFO - Epoch [73][100/1281] lr: 1.326e-02, eta: 11:18:16, time: 0.799, data_time: 0.192, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9981, loss_cls: 0.4738, loss: 0.4738 +2025-06-24 20:58:58,101 - pyskl - INFO - Epoch [73][200/1281] lr: 1.324e-02, eta: 11:17:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9975, loss_cls: 0.4204, loss: 0.4204 +2025-06-24 20:59:47,324 - pyskl - INFO - Epoch [73][300/1281] lr: 1.322e-02, eta: 11:17:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9969, loss_cls: 0.4562, loss: 0.4562 +2025-06-24 21:00:36,085 - pyskl - INFO - Epoch [73][400/1281] lr: 1.320e-02, eta: 11:16:40, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9994, loss_cls: 0.3999, loss: 0.3999 +2025-06-24 21:01:25,604 - pyskl - INFO - Epoch [73][500/1281] lr: 1.318e-02, eta: 11:16:09, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9981, loss_cls: 0.4001, loss: 0.4001 +2025-06-24 21:02:14,739 - pyskl - INFO - Epoch [73][600/1281] lr: 1.316e-02, eta: 11:15:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9969, loss_cls: 0.4507, loss: 0.4507 +2025-06-24 21:03:04,098 - pyskl - INFO - Epoch [73][700/1281] lr: 1.314e-02, eta: 11:15:05, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9994, loss_cls: 0.4990, loss: 0.4990 +2025-06-24 21:03:53,383 - pyskl - INFO - Epoch [73][800/1281] lr: 1.312e-02, eta: 11:14:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9981, loss_cls: 0.4437, loss: 0.4437 +2025-06-24 21:04:42,624 - pyskl - INFO - Epoch [73][900/1281] lr: 1.310e-02, eta: 11:14:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9962, loss_cls: 0.4919, loss: 0.4919 +2025-06-24 21:05:31,983 - pyskl - INFO - Epoch [73][1000/1281] lr: 1.308e-02, eta: 11:13:30, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9956, loss_cls: 0.5050, loss: 0.5050 +2025-06-24 21:06:21,501 - pyskl - INFO - Epoch [73][1100/1281] lr: 1.306e-02, eta: 11:12:58, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9994, loss_cls: 0.4321, loss: 0.4321 +2025-06-24 21:06:58,535 - pyskl - INFO - Epoch [73][1200/1281] lr: 1.304e-02, eta: 11:12:13, time: 0.370, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9975, loss_cls: 0.4919, loss: 0.4919 +2025-06-24 21:07:41,090 - pyskl - INFO - Saving checkpoint at 73 epochs +2025-06-24 21:08:28,998 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:08:29,055 - pyskl - INFO - +top1_acc 0.8574 +top5_acc 0.9917 +2025-06-24 21:08:29,056 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:08:29,075 - pyskl - INFO - +mean_acc 0.8026 +2025-06-24 21:08:29,080 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_62.pth was removed +2025-06-24 21:08:29,278 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_73.pth. +2025-06-24 21:08:29,278 - pyskl - INFO - Best top1_acc is 0.8574 at 73 epoch. +2025-06-24 21:08:29,281 - pyskl - INFO - Epoch(val) [73][533] top1_acc: 0.8574, top5_acc: 0.9917, mean_class_accuracy: 0.8026 +2025-06-24 21:09:50,136 - pyskl - INFO - Epoch [74][100/1281] lr: 1.300e-02, eta: 11:11:07, time: 0.808, data_time: 0.196, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9994, loss_cls: 0.4191, loss: 0.4191 +2025-06-24 21:10:39,024 - pyskl - INFO - Epoch [74][200/1281] lr: 1.298e-02, eta: 11:10:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9975, loss_cls: 0.3763, loss: 0.3763 +2025-06-24 21:11:28,074 - pyskl - INFO - Epoch [74][300/1281] lr: 1.296e-02, eta: 11:10:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9994, loss_cls: 0.3860, loss: 0.3860 +2025-06-24 21:12:17,303 - pyskl - INFO - Epoch [74][400/1281] lr: 1.294e-02, eta: 11:09:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9969, loss_cls: 0.3968, loss: 0.3968 +2025-06-24 21:13:06,185 - pyskl - INFO - Epoch [74][500/1281] lr: 1.292e-02, eta: 11:08:57, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9962, loss_cls: 0.4516, loss: 0.4516 +2025-06-24 21:13:55,503 - pyskl - INFO - Epoch [74][600/1281] lr: 1.290e-02, eta: 11:08:25, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9975, loss_cls: 0.4480, loss: 0.4480 +2025-06-24 21:14:44,582 - pyskl - INFO - Epoch [74][700/1281] lr: 1.288e-02, eta: 11:07:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9981, loss_cls: 0.4831, loss: 0.4831 +2025-06-24 21:15:33,976 - pyskl - INFO - Epoch [74][800/1281] lr: 1.286e-02, eta: 11:07:20, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9956, loss_cls: 0.4401, loss: 0.4401 +2025-06-24 21:16:23,706 - pyskl - INFO - Epoch [74][900/1281] lr: 1.284e-02, eta: 11:06:49, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9994, loss_cls: 0.4214, loss: 0.4214 +2025-06-24 21:17:12,815 - pyskl - INFO - Epoch [74][1000/1281] lr: 1.282e-02, eta: 11:06:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9988, loss_cls: 0.4114, loss: 0.4114 +2025-06-24 21:18:02,142 - pyskl - INFO - Epoch [74][1100/1281] lr: 1.280e-02, eta: 11:05:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9994, loss_cls: 0.4698, loss: 0.4698 +2025-06-24 21:18:38,073 - pyskl - INFO - Epoch [74][1200/1281] lr: 1.278e-02, eta: 11:04:58, time: 0.359, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9988, loss_cls: 0.4415, loss: 0.4415 +2025-06-24 21:19:21,489 - pyskl - INFO - Saving checkpoint at 74 epochs +2025-06-24 21:20:09,385 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:20:09,442 - pyskl - INFO - +top1_acc 0.8459 +top5_acc 0.9932 +2025-06-24 21:20:09,442 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:20:09,449 - pyskl - INFO - +mean_acc 0.7929 +2025-06-24 21:20:09,451 - pyskl - INFO - Epoch(val) [74][533] top1_acc: 0.8459, top5_acc: 0.9932, mean_class_accuracy: 0.7929 +2025-06-24 21:21:29,814 - pyskl - INFO - Epoch [75][100/1281] lr: 1.274e-02, eta: 11:03:50, time: 0.804, data_time: 0.195, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9969, loss_cls: 0.4236, loss: 0.4236 +2025-06-24 21:22:19,409 - pyskl - INFO - Epoch [75][200/1281] lr: 1.272e-02, eta: 11:03:18, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9988, loss_cls: 0.3588, loss: 0.3588 +2025-06-24 21:23:08,623 - pyskl - INFO - Epoch [75][300/1281] lr: 1.270e-02, eta: 11:02:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9975, loss_cls: 0.3939, loss: 0.3939 +2025-06-24 21:23:57,832 - pyskl - INFO - Epoch [75][400/1281] lr: 1.268e-02, eta: 11:02:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 1.0000, loss_cls: 0.4001, loss: 0.4001 +2025-06-24 21:24:46,846 - pyskl - INFO - Epoch [75][500/1281] lr: 1.266e-02, eta: 11:01:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9988, loss_cls: 0.4298, loss: 0.4298 +2025-06-24 21:25:35,865 - pyskl - INFO - Epoch [75][600/1281] lr: 1.264e-02, eta: 11:01:07, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9988, loss_cls: 0.4931, loss: 0.4931 +2025-06-24 21:26:24,712 - pyskl - INFO - Epoch [75][700/1281] lr: 1.262e-02, eta: 11:00:34, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9988, loss_cls: 0.5116, loss: 0.5116 +2025-06-24 21:27:13,762 - pyskl - INFO - Epoch [75][800/1281] lr: 1.260e-02, eta: 11:00:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9944, loss_cls: 0.5269, loss: 0.5269 +2025-06-24 21:28:03,125 - pyskl - INFO - Epoch [75][900/1281] lr: 1.258e-02, eta: 10:59:29, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9969, loss_cls: 0.4539, loss: 0.4539 +2025-06-24 21:28:52,705 - pyskl - INFO - Epoch [75][1000/1281] lr: 1.256e-02, eta: 10:58:56, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9988, loss_cls: 0.4640, loss: 0.4640 +2025-06-24 21:29:41,642 - pyskl - INFO - Epoch [75][1100/1281] lr: 1.254e-02, eta: 10:58:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9988, loss_cls: 0.4232, loss: 0.4232 +2025-06-24 21:30:18,522 - pyskl - INFO - Epoch [75][1200/1281] lr: 1.252e-02, eta: 10:57:38, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9994, loss_cls: 0.4308, loss: 0.4308 +2025-06-24 21:31:00,330 - pyskl - INFO - Saving checkpoint at 75 epochs +2025-06-24 21:31:48,017 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:31:48,073 - pyskl - INFO - +top1_acc 0.8434 +top5_acc 0.9891 +2025-06-24 21:31:48,073 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:31:48,079 - pyskl - INFO - +mean_acc 0.7881 +2025-06-24 21:31:48,081 - pyskl - INFO - Epoch(val) [75][533] top1_acc: 0.8434, top5_acc: 0.9891, mean_class_accuracy: 0.7881 +2025-06-24 21:33:08,716 - pyskl - INFO - Epoch [76][100/1281] lr: 1.248e-02, eta: 10:56:30, time: 0.806, data_time: 0.193, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9994, loss_cls: 0.4478, loss: 0.4478 +2025-06-24 21:33:57,780 - pyskl - INFO - Epoch [76][200/1281] lr: 1.246e-02, eta: 10:55:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9994, loss_cls: 0.3677, loss: 0.3677 +2025-06-24 21:34:46,642 - pyskl - INFO - Epoch [76][300/1281] lr: 1.244e-02, eta: 10:55:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9981, loss_cls: 0.4273, loss: 0.4273 +2025-06-24 21:35:35,620 - pyskl - INFO - Epoch [76][400/1281] lr: 1.242e-02, eta: 10:54:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9969, loss_cls: 0.4393, loss: 0.4393 +2025-06-24 21:36:24,676 - pyskl - INFO - Epoch [76][500/1281] lr: 1.240e-02, eta: 10:54:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9981, loss_cls: 0.4173, loss: 0.4173 +2025-06-24 21:37:13,678 - pyskl - INFO - Epoch [76][600/1281] lr: 1.238e-02, eta: 10:53:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9994, loss_cls: 0.4213, loss: 0.4213 +2025-06-24 21:38:02,532 - pyskl - INFO - Epoch [76][700/1281] lr: 1.236e-02, eta: 10:53:11, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9981, loss_cls: 0.3736, loss: 0.3736 +2025-06-24 21:38:51,751 - pyskl - INFO - Epoch [76][800/1281] lr: 1.234e-02, eta: 10:52:37, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9988, loss_cls: 0.4513, loss: 0.4513 +2025-06-24 21:39:40,881 - pyskl - INFO - Epoch [76][900/1281] lr: 1.232e-02, eta: 10:52:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9975, loss_cls: 0.4535, loss: 0.4535 +2025-06-24 21:40:29,959 - pyskl - INFO - Epoch [76][1000/1281] lr: 1.230e-02, eta: 10:51:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9988, loss_cls: 0.4464, loss: 0.4464 +2025-06-24 21:41:19,116 - pyskl - INFO - Epoch [76][1100/1281] lr: 1.228e-02, eta: 10:50:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9975, loss_cls: 0.4978, loss: 0.4978 +2025-06-24 21:41:55,067 - pyskl - INFO - Epoch [76][1200/1281] lr: 1.225e-02, eta: 10:50:11, time: 0.360, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9981, loss_cls: 0.4579, loss: 0.4579 +2025-06-24 21:42:38,119 - pyskl - INFO - Saving checkpoint at 76 epochs +2025-06-24 21:43:26,018 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:43:26,076 - pyskl - INFO - +top1_acc 0.8694 +top5_acc 0.9903 +2025-06-24 21:43:26,076 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:43:26,085 - pyskl - INFO - +mean_acc 0.8137 +2025-06-24 21:43:26,090 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_73.pth was removed +2025-06-24 21:43:26,272 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_76.pth. +2025-06-24 21:43:26,272 - pyskl - INFO - Best top1_acc is 0.8694 at 76 epoch. +2025-06-24 21:43:26,274 - pyskl - INFO - Epoch(val) [76][533] top1_acc: 0.8694, top5_acc: 0.9903, mean_class_accuracy: 0.8137 +2025-06-24 21:44:46,688 - pyskl - INFO - Epoch [77][100/1281] lr: 1.222e-02, eta: 10:49:03, time: 0.804, data_time: 0.192, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.3501, loss: 0.3501 +2025-06-24 21:45:35,764 - pyskl - INFO - Epoch [77][200/1281] lr: 1.220e-02, eta: 10:48:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9988, loss_cls: 0.3090, loss: 0.3090 +2025-06-24 21:46:24,901 - pyskl - INFO - Epoch [77][300/1281] lr: 1.218e-02, eta: 10:47:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9975, loss_cls: 0.4426, loss: 0.4426 +2025-06-24 21:47:13,903 - pyskl - INFO - Epoch [77][400/1281] lr: 1.216e-02, eta: 10:47:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9988, loss_cls: 0.3972, loss: 0.3972 +2025-06-24 21:48:03,142 - pyskl - INFO - Epoch [77][500/1281] lr: 1.214e-02, eta: 10:46:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9994, loss_cls: 0.3701, loss: 0.3701 +2025-06-24 21:48:52,261 - pyskl - INFO - Epoch [77][600/1281] lr: 1.212e-02, eta: 10:46:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9956, loss_cls: 0.4662, loss: 0.4662 +2025-06-24 21:49:41,768 - pyskl - INFO - Epoch [77][700/1281] lr: 1.210e-02, eta: 10:45:42, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9956, loss_cls: 0.4487, loss: 0.4487 +2025-06-24 21:50:30,739 - pyskl - INFO - Epoch [77][800/1281] lr: 1.207e-02, eta: 10:45:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9981, loss_cls: 0.4417, loss: 0.4417 +2025-06-24 21:51:20,117 - pyskl - INFO - Epoch [77][900/1281] lr: 1.205e-02, eta: 10:44:35, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9962, loss_cls: 0.4665, loss: 0.4665 +2025-06-24 21:52:09,312 - pyskl - INFO - Epoch [77][1000/1281] lr: 1.203e-02, eta: 10:44:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9975, loss_cls: 0.5143, loss: 0.5143 +2025-06-24 21:52:58,485 - pyskl - INFO - Epoch [77][1100/1281] lr: 1.201e-02, eta: 10:43:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9975, loss_cls: 0.4350, loss: 0.4350 +2025-06-24 21:53:35,123 - pyskl - INFO - Epoch [77][1200/1281] lr: 1.199e-02, eta: 10:42:43, time: 0.366, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9988, loss_cls: 0.3916, loss: 0.3916 +2025-06-24 21:54:17,525 - pyskl - INFO - Saving checkpoint at 77 epochs +2025-06-24 21:55:05,264 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:55:05,334 - pyskl - INFO - +top1_acc 0.8747 +top5_acc 0.9928 +2025-06-24 21:55:05,334 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:55:05,342 - pyskl - INFO - +mean_acc 0.8302 +2025-06-24 21:55:05,346 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_76.pth was removed +2025-06-24 21:55:05,534 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_77.pth. +2025-06-24 21:55:05,535 - pyskl - INFO - Best top1_acc is 0.8747 at 77 epoch. +2025-06-24 21:55:05,537 - pyskl - INFO - Epoch(val) [77][533] top1_acc: 0.8747, top5_acc: 0.9928, mean_class_accuracy: 0.8302 +2025-06-24 21:56:25,601 - pyskl - INFO - Epoch [78][100/1281] lr: 1.196e-02, eta: 10:41:33, time: 0.801, data_time: 0.195, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3598, loss: 0.3598 +2025-06-24 21:57:14,918 - pyskl - INFO - Epoch [78][200/1281] lr: 1.194e-02, eta: 10:41:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.3374, loss: 0.3374 +2025-06-24 21:58:03,913 - pyskl - INFO - Epoch [78][300/1281] lr: 1.192e-02, eta: 10:40:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9988, loss_cls: 0.3209, loss: 0.3209 +2025-06-24 21:58:52,959 - pyskl - INFO - Epoch [78][400/1281] lr: 1.190e-02, eta: 10:39:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9981, loss_cls: 0.4088, loss: 0.4088 +2025-06-24 21:59:42,444 - pyskl - INFO - Epoch [78][500/1281] lr: 1.187e-02, eta: 10:39:19, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9975, loss_cls: 0.4040, loss: 0.4040 +2025-06-24 22:00:31,525 - pyskl - INFO - Epoch [78][600/1281] lr: 1.185e-02, eta: 10:38:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9975, loss_cls: 0.4330, loss: 0.4330 +2025-06-24 22:01:20,527 - pyskl - INFO - Epoch [78][700/1281] lr: 1.183e-02, eta: 10:38:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.4123, loss: 0.4123 +2025-06-24 22:02:10,119 - pyskl - INFO - Epoch [78][800/1281] lr: 1.181e-02, eta: 10:37:37, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9988, loss_cls: 0.4141, loss: 0.4141 +2025-06-24 22:02:59,334 - pyskl - INFO - Epoch [78][900/1281] lr: 1.179e-02, eta: 10:37:03, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9956, loss_cls: 0.4642, loss: 0.4642 +2025-06-24 22:03:48,484 - pyskl - INFO - Epoch [78][1000/1281] lr: 1.177e-02, eta: 10:36:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9956, loss_cls: 0.4433, loss: 0.4433 +2025-06-24 22:04:37,498 - pyskl - INFO - Epoch [78][1100/1281] lr: 1.175e-02, eta: 10:35:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9994, loss_cls: 0.4219, loss: 0.4219 +2025-06-24 22:05:13,800 - pyskl - INFO - Epoch [78][1200/1281] lr: 1.173e-02, eta: 10:35:09, time: 0.363, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9981, loss_cls: 0.4178, loss: 0.4178 +2025-06-24 22:05:57,812 - pyskl - INFO - Saving checkpoint at 78 epochs +2025-06-24 22:06:45,949 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:06:46,011 - pyskl - INFO - +top1_acc 0.8619 +top5_acc 0.9928 +2025-06-24 22:06:46,011 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:06:46,019 - pyskl - INFO - +mean_acc 0.8152 +2025-06-24 22:06:46,021 - pyskl - INFO - Epoch(val) [78][533] top1_acc: 0.8619, top5_acc: 0.9928, mean_class_accuracy: 0.8152 +2025-06-24 22:08:06,241 - pyskl - INFO - Epoch [79][100/1281] lr: 1.169e-02, eta: 10:34:00, time: 0.802, data_time: 0.196, memory: 4083, top1_acc: 0.9294, top5_acc: 1.0000, loss_cls: 0.3551, loss: 0.3551 +2025-06-24 22:08:55,373 - pyskl - INFO - Epoch [79][200/1281] lr: 1.167e-02, eta: 10:33:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3460, loss: 0.3460 +2025-06-24 22:09:44,479 - pyskl - INFO - Epoch [79][300/1281] lr: 1.165e-02, eta: 10:32:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9975, loss_cls: 0.3965, loss: 0.3965 +2025-06-24 22:10:33,529 - pyskl - INFO - Epoch [79][400/1281] lr: 1.163e-02, eta: 10:32:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9969, loss_cls: 0.4292, loss: 0.4292 +2025-06-24 22:11:22,755 - pyskl - INFO - Epoch [79][500/1281] lr: 1.161e-02, eta: 10:31:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9975, loss_cls: 0.4316, loss: 0.4316 +2025-06-24 22:12:12,200 - pyskl - INFO - Epoch [79][600/1281] lr: 1.159e-02, eta: 10:31:09, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 1.0000, loss_cls: 0.3715, loss: 0.3715 +2025-06-24 22:13:01,553 - pyskl - INFO - Epoch [79][700/1281] lr: 1.157e-02, eta: 10:30:35, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 1.0000, loss_cls: 0.4275, loss: 0.4275 +2025-06-24 22:13:50,683 - pyskl - INFO - Epoch [79][800/1281] lr: 1.155e-02, eta: 10:30:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9956, loss_cls: 0.3742, loss: 0.3742 +2025-06-24 22:14:39,926 - pyskl - INFO - Epoch [79][900/1281] lr: 1.153e-02, eta: 10:29:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9994, loss_cls: 0.4251, loss: 0.4251 +2025-06-24 22:15:29,008 - pyskl - INFO - Epoch [79][1000/1281] lr: 1.151e-02, eta: 10:28:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9975, loss_cls: 0.4651, loss: 0.4651 +2025-06-24 22:16:18,587 - pyskl - INFO - Epoch [79][1100/1281] lr: 1.149e-02, eta: 10:28:18, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9994, loss_cls: 0.4745, loss: 0.4745 +2025-06-24 22:16:55,144 - pyskl - INFO - Epoch [79][1200/1281] lr: 1.147e-02, eta: 10:27:33, time: 0.366, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9988, loss_cls: 0.4672, loss: 0.4672 +2025-06-24 22:17:40,291 - pyskl - INFO - Saving checkpoint at 79 epochs +2025-06-24 22:18:28,669 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:18:28,737 - pyskl - INFO - +top1_acc 0.8464 +top5_acc 0.9899 +2025-06-24 22:18:28,737 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:18:28,745 - pyskl - INFO - +mean_acc 0.7915 +2025-06-24 22:18:28,747 - pyskl - INFO - Epoch(val) [79][533] top1_acc: 0.8464, top5_acc: 0.9899, mean_class_accuracy: 0.7915 +2025-06-24 22:19:48,985 - pyskl - INFO - Epoch [80][100/1281] lr: 1.143e-02, eta: 10:26:23, time: 0.802, data_time: 0.195, memory: 4083, top1_acc: 0.9406, top5_acc: 1.0000, loss_cls: 0.3391, loss: 0.3391 +2025-06-24 22:20:38,409 - pyskl - INFO - Epoch [80][200/1281] lr: 1.141e-02, eta: 10:25:49, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 1.0000, loss_cls: 0.3624, loss: 0.3624 +2025-06-24 22:21:27,446 - pyskl - INFO - Epoch [80][300/1281] lr: 1.139e-02, eta: 10:25:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9994, loss_cls: 0.3738, loss: 0.3738 +2025-06-24 22:22:16,783 - pyskl - INFO - Epoch [80][400/1281] lr: 1.137e-02, eta: 10:24:40, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3829, loss: 0.3829 +2025-06-24 22:23:05,668 - pyskl - INFO - Epoch [80][500/1281] lr: 1.135e-02, eta: 10:24:05, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.3268, loss: 0.3268 +2025-06-24 22:23:54,939 - pyskl - INFO - Epoch [80][600/1281] lr: 1.133e-02, eta: 10:23:31, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 1.0000, loss_cls: 0.3993, loss: 0.3993 +2025-06-24 22:24:43,894 - pyskl - INFO - Epoch [80][700/1281] lr: 1.131e-02, eta: 10:22:56, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9962, loss_cls: 0.4629, loss: 0.4629 +2025-06-24 22:25:33,218 - pyskl - INFO - Epoch [80][800/1281] lr: 1.129e-02, eta: 10:22:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4531, loss: 0.4531 +2025-06-24 22:26:22,254 - pyskl - INFO - Epoch [80][900/1281] lr: 1.127e-02, eta: 10:21:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9981, loss_cls: 0.3694, loss: 0.3694 +2025-06-24 22:27:11,291 - pyskl - INFO - Epoch [80][1000/1281] lr: 1.125e-02, eta: 10:21:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9962, loss_cls: 0.4835, loss: 0.4835 +2025-06-24 22:28:00,164 - pyskl - INFO - Epoch [80][1100/1281] lr: 1.123e-02, eta: 10:20:37, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9969, loss_cls: 0.4160, loss: 0.4160 +2025-06-24 22:28:36,243 - pyskl - INFO - Epoch [80][1200/1281] lr: 1.121e-02, eta: 10:19:51, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9988, loss_cls: 0.3693, loss: 0.3693 +2025-06-24 22:29:19,300 - pyskl - INFO - Saving checkpoint at 80 epochs +2025-06-24 22:30:07,470 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:30:07,532 - pyskl - INFO - +top1_acc 0.8662 +top5_acc 0.9914 +2025-06-24 22:30:07,532 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:30:07,539 - pyskl - INFO - +mean_acc 0.8196 +2025-06-24 22:30:07,541 - pyskl - INFO - Epoch(val) [80][533] top1_acc: 0.8662, top5_acc: 0.9914, mean_class_accuracy: 0.8196 +2025-06-24 22:31:27,775 - pyskl - INFO - Epoch [81][100/1281] lr: 1.117e-02, eta: 10:18:40, time: 0.802, data_time: 0.197, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3426, loss: 0.3426 +2025-06-24 22:32:16,850 - pyskl - INFO - Epoch [81][200/1281] lr: 1.115e-02, eta: 10:18:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9981, loss_cls: 0.3290, loss: 0.3290 +2025-06-24 22:33:06,183 - pyskl - INFO - Epoch [81][300/1281] lr: 1.113e-02, eta: 10:17:31, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9969, loss_cls: 0.3311, loss: 0.3311 +2025-06-24 22:33:55,308 - pyskl - INFO - Epoch [81][400/1281] lr: 1.111e-02, eta: 10:16:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9956, loss_cls: 0.3637, loss: 0.3637 +2025-06-24 22:34:44,337 - pyskl - INFO - Epoch [81][500/1281] lr: 1.109e-02, eta: 10:16:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9988, loss_cls: 0.3646, loss: 0.3646 +2025-06-24 22:35:33,790 - pyskl - INFO - Epoch [81][600/1281] lr: 1.107e-02, eta: 10:15:47, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9969, loss_cls: 0.4020, loss: 0.4020 +2025-06-24 22:36:22,835 - pyskl - INFO - Epoch [81][700/1281] lr: 1.105e-02, eta: 10:15:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9975, loss_cls: 0.3940, loss: 0.3940 +2025-06-24 22:37:12,083 - pyskl - INFO - Epoch [81][800/1281] lr: 1.103e-02, eta: 10:14:37, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9994, loss_cls: 0.3528, loss: 0.3528 +2025-06-24 22:38:01,169 - pyskl - INFO - Epoch [81][900/1281] lr: 1.101e-02, eta: 10:14:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4674, loss: 0.4674 +2025-06-24 22:38:50,337 - pyskl - INFO - Epoch [81][1000/1281] lr: 1.099e-02, eta: 10:13:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9956, loss_cls: 0.3914, loss: 0.3914 +2025-06-24 22:39:39,733 - pyskl - INFO - Epoch [81][1100/1281] lr: 1.097e-02, eta: 10:12:52, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9988, loss_cls: 0.4147, loss: 0.4147 +2025-06-24 22:40:16,693 - pyskl - INFO - Epoch [81][1200/1281] lr: 1.095e-02, eta: 10:12:07, time: 0.370, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3876, loss: 0.3876 +2025-06-24 22:40:58,913 - pyskl - INFO - Saving checkpoint at 81 epochs +2025-06-24 22:41:46,568 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:41:46,625 - pyskl - INFO - +top1_acc 0.8762 +top5_acc 0.9921 +2025-06-24 22:41:46,626 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:41:46,632 - pyskl - INFO - +mean_acc 0.8221 +2025-06-24 22:41:46,636 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_77.pth was removed +2025-06-24 22:41:46,815 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_81.pth. +2025-06-24 22:41:46,815 - pyskl - INFO - Best top1_acc is 0.8762 at 81 epoch. +2025-06-24 22:41:46,818 - pyskl - INFO - Epoch(val) [81][533] top1_acc: 0.8762, top5_acc: 0.9921, mean_class_accuracy: 0.8221 +2025-06-24 22:43:05,953 - pyskl - INFO - Epoch [82][100/1281] lr: 1.091e-02, eta: 10:10:55, time: 0.791, data_time: 0.191, memory: 4083, top1_acc: 0.9437, top5_acc: 1.0000, loss_cls: 0.3359, loss: 0.3359 +2025-06-24 22:43:54,908 - pyskl - INFO - Epoch [82][200/1281] lr: 1.089e-02, eta: 10:10:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9994, loss_cls: 0.3448, loss: 0.3448 +2025-06-24 22:44:44,004 - pyskl - INFO - Epoch [82][300/1281] lr: 1.087e-02, eta: 10:09:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9994, loss_cls: 0.3963, loss: 0.3963 +2025-06-24 22:45:33,167 - pyskl - INFO - Epoch [82][400/1281] lr: 1.085e-02, eta: 10:09:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9981, loss_cls: 0.3901, loss: 0.3901 +2025-06-24 22:46:22,725 - pyskl - INFO - Epoch [82][500/1281] lr: 1.083e-02, eta: 10:08:35, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9981, loss_cls: 0.3733, loss: 0.3733 +2025-06-24 22:47:12,001 - pyskl - INFO - Epoch [82][600/1281] lr: 1.081e-02, eta: 10:08:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.3054, loss: 0.3054 +2025-06-24 22:48:01,178 - pyskl - INFO - Epoch [82][700/1281] lr: 1.079e-02, eta: 10:07:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9981, loss_cls: 0.3736, loss: 0.3736 +2025-06-24 22:48:50,584 - pyskl - INFO - Epoch [82][800/1281] lr: 1.077e-02, eta: 10:06:50, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9988, loss_cls: 0.3335, loss: 0.3335 +2025-06-24 22:49:39,759 - pyskl - INFO - Epoch [82][900/1281] lr: 1.075e-02, eta: 10:06:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.4021, loss: 0.4021 +2025-06-24 22:50:28,812 - pyskl - INFO - Epoch [82][1000/1281] lr: 1.073e-02, eta: 10:05:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9975, loss_cls: 0.4128, loss: 0.4128 +2025-06-24 22:51:17,942 - pyskl - INFO - Epoch [82][1100/1281] lr: 1.071e-02, eta: 10:05:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9988, loss_cls: 0.4148, loss: 0.4148 +2025-06-24 22:51:55,624 - pyskl - INFO - Epoch [82][1200/1281] lr: 1.069e-02, eta: 10:04:19, time: 0.377, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9975, loss_cls: 0.3948, loss: 0.3948 +2025-06-24 22:52:38,832 - pyskl - INFO - Saving checkpoint at 82 epochs +2025-06-24 22:53:26,812 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:53:26,867 - pyskl - INFO - +top1_acc 0.8540 +top5_acc 0.9897 +2025-06-24 22:53:26,868 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:53:26,874 - pyskl - INFO - +mean_acc 0.7924 +2025-06-24 22:53:26,876 - pyskl - INFO - Epoch(val) [82][533] top1_acc: 0.8540, top5_acc: 0.9897, mean_class_accuracy: 0.7924 +2025-06-24 22:54:46,787 - pyskl - INFO - Epoch [83][100/1281] lr: 1.065e-02, eta: 10:03:08, time: 0.799, data_time: 0.191, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.3081, loss: 0.3081 +2025-06-24 22:55:36,099 - pyskl - INFO - Epoch [83][200/1281] lr: 1.063e-02, eta: 10:02:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9981, loss_cls: 0.3475, loss: 0.3475 +2025-06-24 22:56:25,332 - pyskl - INFO - Epoch [83][300/1281] lr: 1.061e-02, eta: 10:01:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 1.0000, loss_cls: 0.3120, loss: 0.3120 +2025-06-24 22:57:14,482 - pyskl - INFO - Epoch [83][400/1281] lr: 1.059e-02, eta: 10:01:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9994, loss_cls: 0.3659, loss: 0.3659 +2025-06-24 22:58:03,758 - pyskl - INFO - Epoch [83][500/1281] lr: 1.057e-02, eta: 10:00:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9975, loss_cls: 0.3594, loss: 0.3594 +2025-06-24 22:58:53,034 - pyskl - INFO - Epoch [83][600/1281] lr: 1.055e-02, eta: 10:00:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9994, loss_cls: 0.3565, loss: 0.3565 +2025-06-24 22:59:42,172 - pyskl - INFO - Epoch [83][700/1281] lr: 1.053e-02, eta: 9:59:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9969, loss_cls: 0.3737, loss: 0.3737 +2025-06-24 23:00:31,303 - pyskl - INFO - Epoch [83][800/1281] lr: 1.051e-02, eta: 9:59:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9981, loss_cls: 0.4346, loss: 0.4346 +2025-06-24 23:01:20,513 - pyskl - INFO - Epoch [83][900/1281] lr: 1.049e-02, eta: 9:58:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.4042, loss: 0.4042 +2025-06-24 23:02:09,820 - pyskl - INFO - Epoch [83][1000/1281] lr: 1.047e-02, eta: 9:57:49, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9975, loss_cls: 0.3550, loss: 0.3550 +2025-06-24 23:02:58,940 - pyskl - INFO - Epoch [83][1100/1281] lr: 1.045e-02, eta: 9:57:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 1.0000, loss_cls: 0.4074, loss: 0.4074 +2025-06-24 23:03:34,965 - pyskl - INFO - Epoch [83][1200/1281] lr: 1.043e-02, eta: 9:56:27, time: 0.360, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9981, loss_cls: 0.3561, loss: 0.3561 +2025-06-24 23:04:19,835 - pyskl - INFO - Saving checkpoint at 83 epochs +2025-06-24 23:05:08,047 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:05:08,101 - pyskl - INFO - +top1_acc 0.8754 +top5_acc 0.9939 +2025-06-24 23:05:08,101 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:05:08,108 - pyskl - INFO - +mean_acc 0.8281 +2025-06-24 23:05:08,109 - pyskl - INFO - Epoch(val) [83][533] top1_acc: 0.8754, top5_acc: 0.9939, mean_class_accuracy: 0.8281 +2025-06-24 23:06:27,249 - pyskl - INFO - Epoch [84][100/1281] lr: 1.040e-02, eta: 9:55:15, time: 0.791, data_time: 0.193, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9988, loss_cls: 0.3080, loss: 0.3080 +2025-06-24 23:07:16,262 - pyskl - INFO - Epoch [84][200/1281] lr: 1.038e-02, eta: 9:54:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9981, loss_cls: 0.3921, loss: 0.3921 +2025-06-24 23:08:05,480 - pyskl - INFO - Epoch [84][300/1281] lr: 1.036e-02, eta: 9:54:03, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9994, loss_cls: 0.3553, loss: 0.3553 +2025-06-24 23:08:54,474 - pyskl - INFO - Epoch [84][400/1281] lr: 1.034e-02, eta: 9:53:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9981, loss_cls: 0.4004, loss: 0.4004 +2025-06-24 23:09:43,921 - pyskl - INFO - Epoch [84][500/1281] lr: 1.031e-02, eta: 9:52:52, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9994, loss_cls: 0.3573, loss: 0.3573 +2025-06-24 23:10:33,292 - pyskl - INFO - Epoch [84][600/1281] lr: 1.029e-02, eta: 9:52:17, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9988, loss_cls: 0.3271, loss: 0.3271 +2025-06-24 23:11:22,621 - pyskl - INFO - Epoch [84][700/1281] lr: 1.027e-02, eta: 9:51:41, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9988, loss_cls: 0.3197, loss: 0.3197 +2025-06-24 23:12:11,736 - pyskl - INFO - Epoch [84][800/1281] lr: 1.025e-02, eta: 9:51:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9975, loss_cls: 0.4112, loss: 0.4112 +2025-06-24 23:13:00,955 - pyskl - INFO - Epoch [84][900/1281] lr: 1.023e-02, eta: 9:50:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9994, loss_cls: 0.4282, loss: 0.4282 +2025-06-24 23:13:50,125 - pyskl - INFO - Epoch [84][1000/1281] lr: 1.021e-02, eta: 9:49:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9988, loss_cls: 0.3947, loss: 0.3947 +2025-06-24 23:14:39,124 - pyskl - INFO - Epoch [84][1100/1281] lr: 1.019e-02, eta: 9:49:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9988, loss_cls: 0.3726, loss: 0.3726 +2025-06-24 23:15:17,003 - pyskl - INFO - Epoch [84][1200/1281] lr: 1.017e-02, eta: 9:48:33, time: 0.379, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 1.0000, loss_cls: 0.3440, loss: 0.3440 +2025-06-24 23:15:59,223 - pyskl - INFO - Saving checkpoint at 84 epochs +2025-06-24 23:16:47,126 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:16:47,195 - pyskl - INFO - +top1_acc 0.8709 +top5_acc 0.9921 +2025-06-24 23:16:47,195 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:16:47,202 - pyskl - INFO - +mean_acc 0.8288 +2025-06-24 23:16:47,204 - pyskl - INFO - Epoch(val) [84][533] top1_acc: 0.8709, top5_acc: 0.9921, mean_class_accuracy: 0.8288 +2025-06-24 23:18:08,077 - pyskl - INFO - Epoch [85][100/1281] lr: 1.014e-02, eta: 9:47:21, time: 0.809, data_time: 0.197, memory: 4083, top1_acc: 0.9456, top5_acc: 1.0000, loss_cls: 0.3175, loss: 0.3175 +2025-06-24 23:18:56,982 - pyskl - INFO - Epoch [85][200/1281] lr: 1.012e-02, eta: 9:46:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2878, loss: 0.2878 +2025-06-24 23:19:46,160 - pyskl - INFO - Epoch [85][300/1281] lr: 1.010e-02, eta: 9:46:09, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.2951, loss: 0.2951 +2025-06-24 23:20:35,130 - pyskl - INFO - Epoch [85][400/1281] lr: 1.008e-02, eta: 9:45:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9988, loss_cls: 0.3193, loss: 0.3193 +2025-06-24 23:21:24,106 - pyskl - INFO - Epoch [85][500/1281] lr: 1.006e-02, eta: 9:44:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.2797, loss: 0.2797 +2025-06-24 23:22:13,301 - pyskl - INFO - Epoch [85][600/1281] lr: 1.004e-02, eta: 9:44:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9994, loss_cls: 0.3741, loss: 0.3741 +2025-06-24 23:23:02,413 - pyskl - INFO - Epoch [85][700/1281] lr: 1.002e-02, eta: 9:43:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9988, loss_cls: 0.3430, loss: 0.3430 +2025-06-24 23:23:51,395 - pyskl - INFO - Epoch [85][800/1281] lr: 9.998e-03, eta: 9:43:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 1.0000, loss_cls: 0.3190, loss: 0.3190 +2025-06-24 23:24:41,070 - pyskl - INFO - Epoch [85][900/1281] lr: 9.978e-03, eta: 9:42:33, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3781, loss: 0.3781 +2025-06-24 23:25:30,507 - pyskl - INFO - Epoch [85][1000/1281] lr: 9.958e-03, eta: 9:41:57, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9975, loss_cls: 0.4004, loss: 0.4004 +2025-06-24 23:26:19,795 - pyskl - INFO - Epoch [85][1100/1281] lr: 9.937e-03, eta: 9:41:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.3215, loss: 0.3215 +2025-06-24 23:26:55,820 - pyskl - INFO - Epoch [85][1200/1281] lr: 9.917e-03, eta: 9:40:35, time: 0.360, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9969, loss_cls: 0.4346, loss: 0.4346 +2025-06-24 23:27:42,148 - pyskl - INFO - Saving checkpoint at 85 epochs +2025-06-24 23:28:30,666 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:28:30,724 - pyskl - INFO - +top1_acc 0.8656 +top5_acc 0.9927 +2025-06-24 23:28:30,724 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:28:30,732 - pyskl - INFO - +mean_acc 0.8227 +2025-06-24 23:28:30,734 - pyskl - INFO - Epoch(val) [85][533] top1_acc: 0.8656, top5_acc: 0.9927, mean_class_accuracy: 0.8227 +2025-06-24 23:29:50,787 - pyskl - INFO - Epoch [86][100/1281] lr: 9.881e-03, eta: 9:39:23, time: 0.800, data_time: 0.196, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2693, loss: 0.2693 +2025-06-24 23:30:39,635 - pyskl - INFO - Epoch [86][200/1281] lr: 9.861e-03, eta: 9:38:46, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.3152, loss: 0.3152 +2025-06-24 23:31:28,684 - pyskl - INFO - Epoch [86][300/1281] lr: 9.841e-03, eta: 9:38:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2875, loss: 0.2875 +2025-06-24 23:32:18,042 - pyskl - INFO - Epoch [86][400/1281] lr: 9.821e-03, eta: 9:37:34, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2900, loss: 0.2900 +2025-06-24 23:33:07,455 - pyskl - INFO - Epoch [86][500/1281] lr: 9.801e-03, eta: 9:36:58, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2829, loss: 0.2829 +2025-06-24 23:33:56,680 - pyskl - INFO - Epoch [86][600/1281] lr: 9.781e-03, eta: 9:36:22, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9975, loss_cls: 0.3497, loss: 0.3497 +2025-06-24 23:34:45,903 - pyskl - INFO - Epoch [86][700/1281] lr: 9.762e-03, eta: 9:35:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3342, loss: 0.3342 +2025-06-24 23:35:34,835 - pyskl - INFO - Epoch [86][800/1281] lr: 9.742e-03, eta: 9:35:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3229, loss: 0.3229 +2025-06-24 23:36:23,717 - pyskl - INFO - Epoch [86][900/1281] lr: 9.722e-03, eta: 9:34:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9975, loss_cls: 0.3910, loss: 0.3910 +2025-06-24 23:37:12,770 - pyskl - INFO - Epoch [86][1000/1281] lr: 9.702e-03, eta: 9:33:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9975, loss_cls: 0.3732, loss: 0.3732 +2025-06-24 23:38:02,061 - pyskl - INFO - Epoch [86][1100/1281] lr: 9.682e-03, eta: 9:33:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.3859, loss: 0.3859 +2025-06-24 23:38:38,814 - pyskl - INFO - Epoch [86][1200/1281] lr: 9.662e-03, eta: 9:32:34, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9975, loss_cls: 0.4358, loss: 0.4358 +2025-06-24 23:39:23,940 - pyskl - INFO - Saving checkpoint at 86 epochs +2025-06-24 23:40:11,616 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:40:11,678 - pyskl - INFO - +top1_acc 0.8790 +top5_acc 0.9933 +2025-06-24 23:40:11,678 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:40:11,687 - pyskl - INFO - +mean_acc 0.8366 +2025-06-24 23:40:11,691 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_81.pth was removed +2025-06-24 23:40:11,876 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_86.pth. +2025-06-24 23:40:11,876 - pyskl - INFO - Best top1_acc is 0.8790 at 86 epoch. +2025-06-24 23:40:11,879 - pyskl - INFO - Epoch(val) [86][533] top1_acc: 0.8790, top5_acc: 0.9933, mean_class_accuracy: 0.8366 +2025-06-24 23:41:31,814 - pyskl - INFO - Epoch [87][100/1281] lr: 9.626e-03, eta: 9:31:21, time: 0.799, data_time: 0.196, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2748, loss: 0.2748 +2025-06-24 23:42:20,871 - pyskl - INFO - Epoch [87][200/1281] lr: 9.606e-03, eta: 9:30:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.3273, loss: 0.3273 +2025-06-24 23:43:09,967 - pyskl - INFO - Epoch [87][300/1281] lr: 9.586e-03, eta: 9:30:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9988, loss_cls: 0.3384, loss: 0.3384 +2025-06-24 23:43:59,052 - pyskl - INFO - Epoch [87][400/1281] lr: 9.566e-03, eta: 9:29:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3194, loss: 0.3194 +2025-06-24 23:44:48,312 - pyskl - INFO - Epoch [87][500/1281] lr: 9.546e-03, eta: 9:28:55, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.3256, loss: 0.3256 +2025-06-24 23:45:36,975 - pyskl - INFO - Epoch [87][600/1281] lr: 9.527e-03, eta: 9:28:18, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9988, loss_cls: 0.3157, loss: 0.3157 +2025-06-24 23:46:26,234 - pyskl - INFO - Epoch [87][700/1281] lr: 9.507e-03, eta: 9:27:42, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9981, loss_cls: 0.3719, loss: 0.3719 +2025-06-24 23:47:15,409 - pyskl - INFO - Epoch [87][800/1281] lr: 9.487e-03, eta: 9:27:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9994, loss_cls: 0.3417, loss: 0.3417 +2025-06-24 23:48:04,579 - pyskl - INFO - Epoch [87][900/1281] lr: 9.467e-03, eta: 9:26:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2940, loss: 0.2940 +2025-06-24 23:48:53,731 - pyskl - INFO - Epoch [87][1000/1281] lr: 9.447e-03, eta: 9:25:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9994, loss_cls: 0.3210, loss: 0.3210 +2025-06-24 23:49:42,751 - pyskl - INFO - Epoch [87][1100/1281] lr: 9.427e-03, eta: 9:25:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.3260, loss: 0.3260 +2025-06-24 23:50:19,797 - pyskl - INFO - Epoch [87][1200/1281] lr: 9.408e-03, eta: 9:24:29, time: 0.370, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 1.0000, loss_cls: 0.3341, loss: 0.3341 +2025-06-24 23:51:04,398 - pyskl - INFO - Saving checkpoint at 87 epochs +2025-06-24 23:51:52,639 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:51:52,697 - pyskl - INFO - +top1_acc 0.8649 +top5_acc 0.9914 +2025-06-24 23:51:52,697 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:51:52,705 - pyskl - INFO - +mean_acc 0.8219 +2025-06-24 23:51:52,707 - pyskl - INFO - Epoch(val) [87][533] top1_acc: 0.8649, top5_acc: 0.9914, mean_class_accuracy: 0.8219 +2025-06-24 23:53:12,125 - pyskl - INFO - Epoch [88][100/1281] lr: 9.372e-03, eta: 9:23:16, time: 0.794, data_time: 0.189, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9975, loss_cls: 0.2594, loss: 0.2594 +2025-06-24 23:54:01,080 - pyskl - INFO - Epoch [88][200/1281] lr: 9.352e-03, eta: 9:22:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 1.0000, loss_cls: 0.2999, loss: 0.2999 +2025-06-24 23:54:50,558 - pyskl - INFO - Epoch [88][300/1281] lr: 9.332e-03, eta: 9:22:03, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 1.0000, loss_cls: 0.3434, loss: 0.3434 +2025-06-24 23:55:39,939 - pyskl - INFO - Epoch [88][400/1281] lr: 9.312e-03, eta: 9:21:26, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.3018, loss: 0.3018 +2025-06-24 23:56:29,152 - pyskl - INFO - Epoch [88][500/1281] lr: 9.293e-03, eta: 9:20:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 1.0000, loss_cls: 0.3104, loss: 0.3104 +2025-06-24 23:57:18,401 - pyskl - INFO - Epoch [88][600/1281] lr: 9.273e-03, eta: 9:20:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.3389, loss: 0.3389 +2025-06-24 23:58:07,305 - pyskl - INFO - Epoch [88][700/1281] lr: 9.253e-03, eta: 9:19:36, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9981, loss_cls: 0.3270, loss: 0.3270 +2025-06-24 23:58:56,802 - pyskl - INFO - Epoch [88][800/1281] lr: 9.233e-03, eta: 9:18:59, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9981, loss_cls: 0.3302, loss: 0.3302 +2025-06-24 23:59:46,058 - pyskl - INFO - Epoch [88][900/1281] lr: 9.214e-03, eta: 9:18:22, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.3203, loss: 0.3203 +2025-06-25 00:00:35,320 - pyskl - INFO - Epoch [88][1000/1281] lr: 9.194e-03, eta: 9:17:45, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9988, loss_cls: 0.3493, loss: 0.3493 +2025-06-25 00:01:24,481 - pyskl - INFO - Epoch [88][1100/1281] lr: 9.174e-03, eta: 9:17:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9994, loss_cls: 0.3817, loss: 0.3817 +2025-06-25 00:02:01,560 - pyskl - INFO - Epoch [88][1200/1281] lr: 9.155e-03, eta: 9:16:23, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9988, loss_cls: 0.3489, loss: 0.3489 +2025-06-25 00:02:43,909 - pyskl - INFO - Saving checkpoint at 88 epochs +2025-06-25 00:03:32,065 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:03:32,121 - pyskl - INFO - +top1_acc 0.8722 +top5_acc 0.9912 +2025-06-25 00:03:32,122 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:03:32,128 - pyskl - INFO - +mean_acc 0.8331 +2025-06-25 00:03:32,130 - pyskl - INFO - Epoch(val) [88][533] top1_acc: 0.8722, top5_acc: 0.9912, mean_class_accuracy: 0.8331 +2025-06-25 00:04:51,186 - pyskl - INFO - Epoch [89][100/1281] lr: 9.119e-03, eta: 9:15:09, time: 0.791, data_time: 0.193, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2731, loss: 0.2731 +2025-06-25 00:05:40,426 - pyskl - INFO - Epoch [89][200/1281] lr: 9.099e-03, eta: 9:14:32, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2637, loss: 0.2637 +2025-06-25 00:06:29,780 - pyskl - INFO - Epoch [89][300/1281] lr: 9.080e-03, eta: 9:13:55, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9969, loss_cls: 0.3190, loss: 0.3190 +2025-06-25 00:07:18,890 - pyskl - INFO - Epoch [89][400/1281] lr: 9.060e-03, eta: 9:13:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9981, loss_cls: 0.3787, loss: 0.3787 +2025-06-25 00:08:08,152 - pyskl - INFO - Epoch [89][500/1281] lr: 9.040e-03, eta: 9:12:41, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 1.0000, loss_cls: 0.3197, loss: 0.3197 +2025-06-25 00:08:57,061 - pyskl - INFO - Epoch [89][600/1281] lr: 9.021e-03, eta: 9:12:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9988, loss_cls: 0.3092, loss: 0.3092 +2025-06-25 00:09:46,063 - pyskl - INFO - Epoch [89][700/1281] lr: 9.001e-03, eta: 9:11:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3472, loss: 0.3472 +2025-06-25 00:10:35,146 - pyskl - INFO - Epoch [89][800/1281] lr: 8.982e-03, eta: 9:10:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3578, loss: 0.3578 +2025-06-25 00:11:24,324 - pyskl - INFO - Epoch [89][900/1281] lr: 8.962e-03, eta: 9:10:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9994, loss_cls: 0.3360, loss: 0.3360 +2025-06-25 00:12:13,258 - pyskl - INFO - Epoch [89][1000/1281] lr: 8.942e-03, eta: 9:09:35, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9981, loss_cls: 0.3780, loss: 0.3780 +2025-06-25 00:13:02,415 - pyskl - INFO - Epoch [89][1100/1281] lr: 8.923e-03, eta: 9:08:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9988, loss_cls: 0.3290, loss: 0.3290 +2025-06-25 00:13:39,815 - pyskl - INFO - Epoch [89][1200/1281] lr: 8.903e-03, eta: 9:08:13, time: 0.374, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3346, loss: 0.3346 +2025-06-25 00:14:19,012 - pyskl - INFO - Saving checkpoint at 89 epochs +2025-06-25 00:15:05,659 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:15:05,718 - pyskl - INFO - +top1_acc 0.8675 +top5_acc 0.9917 +2025-06-25 00:15:05,718 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:15:05,725 - pyskl - INFO - +mean_acc 0.8198 +2025-06-25 00:15:05,727 - pyskl - INFO - Epoch(val) [89][533] top1_acc: 0.8675, top5_acc: 0.9917, mean_class_accuracy: 0.8198 +2025-06-25 00:16:26,588 - pyskl - INFO - Epoch [90][100/1281] lr: 8.868e-03, eta: 9:07:00, time: 0.809, data_time: 0.195, memory: 4083, top1_acc: 0.9475, top5_acc: 1.0000, loss_cls: 0.3136, loss: 0.3136 +2025-06-25 00:17:15,346 - pyskl - INFO - Epoch [90][200/1281] lr: 8.848e-03, eta: 9:06:23, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9988, loss_cls: 0.3066, loss: 0.3066 +2025-06-25 00:18:04,359 - pyskl - INFO - Epoch [90][300/1281] lr: 8.829e-03, eta: 9:05:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2855, loss: 0.2855 +2025-06-25 00:18:53,382 - pyskl - INFO - Epoch [90][400/1281] lr: 8.809e-03, eta: 9:05:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9981, loss_cls: 0.3279, loss: 0.3279 +2025-06-25 00:19:42,492 - pyskl - INFO - Epoch [90][500/1281] lr: 8.790e-03, eta: 9:04:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2457, loss: 0.2457 +2025-06-25 00:20:31,482 - pyskl - INFO - Epoch [90][600/1281] lr: 8.770e-03, eta: 9:03:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9988, loss_cls: 0.3487, loss: 0.3487 +2025-06-25 00:21:20,492 - pyskl - INFO - Epoch [90][700/1281] lr: 8.751e-03, eta: 9:03:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3457, loss: 0.3457 +2025-06-25 00:22:09,809 - pyskl - INFO - Epoch [90][800/1281] lr: 8.731e-03, eta: 9:02:39, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9981, loss_cls: 0.3008, loss: 0.3008 +2025-06-25 00:22:58,861 - pyskl - INFO - Epoch [90][900/1281] lr: 8.712e-03, eta: 9:02:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9994, loss_cls: 0.3275, loss: 0.3275 +2025-06-25 00:23:48,132 - pyskl - INFO - Epoch [90][1000/1281] lr: 8.692e-03, eta: 9:01:24, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 1.0000, loss_cls: 0.3245, loss: 0.3245 +2025-06-25 00:24:37,540 - pyskl - INFO - Epoch [90][1100/1281] lr: 8.673e-03, eta: 9:00:47, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2845, loss: 0.2845 +2025-06-25 00:25:14,319 - pyskl - INFO - Epoch [90][1200/1281] lr: 8.653e-03, eta: 9:00:01, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9994, loss_cls: 0.3129, loss: 0.3129 +2025-06-25 00:25:57,225 - pyskl - INFO - Saving checkpoint at 90 epochs +2025-06-25 00:26:44,742 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:26:44,799 - pyskl - INFO - +top1_acc 0.8821 +top5_acc 0.9923 +2025-06-25 00:26:44,799 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:26:44,806 - pyskl - INFO - +mean_acc 0.8376 +2025-06-25 00:26:44,810 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_86.pth was removed +2025-06-25 00:26:44,980 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_90.pth. +2025-06-25 00:26:44,981 - pyskl - INFO - Best top1_acc is 0.8821 at 90 epoch. +2025-06-25 00:26:44,983 - pyskl - INFO - Epoch(val) [90][533] top1_acc: 0.8821, top5_acc: 0.9923, mean_class_accuracy: 0.8376 +2025-06-25 00:28:04,532 - pyskl - INFO - Epoch [91][100/1281] lr: 8.618e-03, eta: 8:58:47, time: 0.795, data_time: 0.192, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9981, loss_cls: 0.2772, loss: 0.2772 +2025-06-25 00:28:53,672 - pyskl - INFO - Epoch [91][200/1281] lr: 8.599e-03, eta: 8:58:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.2806, loss: 0.2806 +2025-06-25 00:29:43,077 - pyskl - INFO - Epoch [91][300/1281] lr: 8.579e-03, eta: 8:57:32, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2428, loss: 0.2428 +2025-06-25 00:30:32,324 - pyskl - INFO - Epoch [91][400/1281] lr: 8.560e-03, eta: 8:56:55, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9988, loss_cls: 0.3355, loss: 0.3355 +2025-06-25 00:31:21,491 - pyskl - INFO - Epoch [91][500/1281] lr: 8.540e-03, eta: 8:56:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.2987, loss: 0.2987 +2025-06-25 00:32:10,368 - pyskl - INFO - Epoch [91][600/1281] lr: 8.521e-03, eta: 8:55:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2986, loss: 0.2986 +2025-06-25 00:32:59,674 - pyskl - INFO - Epoch [91][700/1281] lr: 8.502e-03, eta: 8:55:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9988, loss_cls: 0.2666, loss: 0.2666 +2025-06-25 00:33:48,784 - pyskl - INFO - Epoch [91][800/1281] lr: 8.482e-03, eta: 8:54:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9994, loss_cls: 0.2951, loss: 0.2951 +2025-06-25 00:34:37,630 - pyskl - INFO - Epoch [91][900/1281] lr: 8.463e-03, eta: 8:53:47, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.3183, loss: 0.3183 +2025-06-25 00:35:26,619 - pyskl - INFO - Epoch [91][1000/1281] lr: 8.444e-03, eta: 8:53:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 1.0000, loss_cls: 0.2744, loss: 0.2744 +2025-06-25 00:36:15,536 - pyskl - INFO - Epoch [91][1100/1281] lr: 8.424e-03, eta: 8:52:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9988, loss_cls: 0.3168, loss: 0.3168 +2025-06-25 00:36:52,396 - pyskl - INFO - Epoch [91][1200/1281] lr: 8.405e-03, eta: 8:51:46, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9981, loss_cls: 0.2999, loss: 0.2999 +2025-06-25 00:37:33,399 - pyskl - INFO - Saving checkpoint at 91 epochs +2025-06-25 00:38:20,714 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:38:20,769 - pyskl - INFO - +top1_acc 0.8808 +top5_acc 0.9944 +2025-06-25 00:38:20,769 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:38:20,776 - pyskl - INFO - +mean_acc 0.8418 +2025-06-25 00:38:20,778 - pyskl - INFO - Epoch(val) [91][533] top1_acc: 0.8808, top5_acc: 0.9944, mean_class_accuracy: 0.8418 +2025-06-25 00:39:40,865 - pyskl - INFO - Epoch [92][100/1281] lr: 8.370e-03, eta: 8:50:32, time: 0.801, data_time: 0.192, memory: 4083, top1_acc: 0.9556, top5_acc: 1.0000, loss_cls: 0.2730, loss: 0.2730 +2025-06-25 00:40:29,859 - pyskl - INFO - Epoch [92][200/1281] lr: 8.351e-03, eta: 8:49:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 1.0000, loss_cls: 0.2989, loss: 0.2989 +2025-06-25 00:41:18,951 - pyskl - INFO - Epoch [92][300/1281] lr: 8.332e-03, eta: 8:49:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9988, loss_cls: 0.3032, loss: 0.3032 +2025-06-25 00:42:08,170 - pyskl - INFO - Epoch [92][400/1281] lr: 8.312e-03, eta: 8:48:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.2969, loss: 0.2969 +2025-06-25 00:42:57,272 - pyskl - INFO - Epoch [92][500/1281] lr: 8.293e-03, eta: 8:48:01, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.2982, loss: 0.2982 +2025-06-25 00:43:46,470 - pyskl - INFO - Epoch [92][600/1281] lr: 8.274e-03, eta: 8:47:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9988, loss_cls: 0.2919, loss: 0.2919 +2025-06-25 00:44:35,434 - pyskl - INFO - Epoch [92][700/1281] lr: 8.255e-03, eta: 8:46:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9988, loss_cls: 0.2894, loss: 0.2894 +2025-06-25 00:45:24,544 - pyskl - INFO - Epoch [92][800/1281] lr: 8.235e-03, eta: 8:46:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.3074, loss: 0.3074 +2025-06-25 00:46:13,659 - pyskl - INFO - Epoch [92][900/1281] lr: 8.216e-03, eta: 8:45:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3265, loss: 0.3265 +2025-06-25 00:47:02,700 - pyskl - INFO - Epoch [92][1000/1281] lr: 8.197e-03, eta: 8:44:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 1.0000, loss_cls: 0.2814, loss: 0.2814 +2025-06-25 00:47:52,076 - pyskl - INFO - Epoch [92][1100/1281] lr: 8.178e-03, eta: 8:44:14, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2680, loss: 0.2680 +2025-06-25 00:48:28,731 - pyskl - INFO - Epoch [92][1200/1281] lr: 8.159e-03, eta: 8:43:28, time: 0.367, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 1.0000, loss_cls: 0.3284, loss: 0.3284 +2025-06-25 00:49:12,360 - pyskl - INFO - Saving checkpoint at 92 epochs +2025-06-25 00:50:00,596 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:50:00,653 - pyskl - INFO - +top1_acc 0.8729 +top5_acc 0.9925 +2025-06-25 00:50:00,653 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:50:00,660 - pyskl - INFO - +mean_acc 0.8350 +2025-06-25 00:50:00,662 - pyskl - INFO - Epoch(val) [92][533] top1_acc: 0.8729, top5_acc: 0.9925, mean_class_accuracy: 0.8350 +2025-06-25 00:51:21,874 - pyskl - INFO - Epoch [93][100/1281] lr: 8.124e-03, eta: 8:42:15, time: 0.812, data_time: 0.193, memory: 4083, top1_acc: 0.9494, top5_acc: 1.0000, loss_cls: 0.2740, loss: 0.2740 +2025-06-25 00:52:10,900 - pyskl - INFO - Epoch [93][200/1281] lr: 8.105e-03, eta: 8:41:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2463, loss: 0.2463 +2025-06-25 00:52:59,800 - pyskl - INFO - Epoch [93][300/1281] lr: 8.086e-03, eta: 8:40:59, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2711, loss: 0.2711 +2025-06-25 00:53:48,855 - pyskl - INFO - Epoch [93][400/1281] lr: 8.067e-03, eta: 8:40:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2604, loss: 0.2604 +2025-06-25 00:54:37,904 - pyskl - INFO - Epoch [93][500/1281] lr: 8.047e-03, eta: 8:39:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2559, loss: 0.2559 +2025-06-25 00:55:27,286 - pyskl - INFO - Epoch [93][600/1281] lr: 8.028e-03, eta: 8:39:05, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2537, loss: 0.2537 +2025-06-25 00:56:16,566 - pyskl - INFO - Epoch [93][700/1281] lr: 8.009e-03, eta: 8:38:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2835, loss: 0.2835 +2025-06-25 00:57:05,624 - pyskl - INFO - Epoch [93][800/1281] lr: 7.990e-03, eta: 8:37:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2463, loss: 0.2463 +2025-06-25 00:57:55,193 - pyskl - INFO - Epoch [93][900/1281] lr: 7.971e-03, eta: 8:37:11, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2707, loss: 0.2707 +2025-06-25 00:58:44,303 - pyskl - INFO - Epoch [93][1000/1281] lr: 7.952e-03, eta: 8:36:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9988, loss_cls: 0.3511, loss: 0.3511 +2025-06-25 00:59:33,398 - pyskl - INFO - Epoch [93][1100/1281] lr: 7.933e-03, eta: 8:35:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9988, loss_cls: 0.3189, loss: 0.3189 +2025-06-25 01:00:09,368 - pyskl - INFO - Epoch [93][1200/1281] lr: 7.914e-03, eta: 8:35:09, time: 0.360, data_time: 0.001, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3230, loss: 0.3230 +2025-06-25 01:00:55,350 - pyskl - INFO - Saving checkpoint at 93 epochs +2025-06-25 01:01:43,790 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:01:43,858 - pyskl - INFO - +top1_acc 0.8622 +top5_acc 0.9896 +2025-06-25 01:01:43,858 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:01:43,867 - pyskl - INFO - +mean_acc 0.8159 +2025-06-25 01:01:43,870 - pyskl - INFO - Epoch(val) [93][533] top1_acc: 0.8622, top5_acc: 0.9896, mean_class_accuracy: 0.8159 +2025-06-25 01:03:05,146 - pyskl - INFO - Epoch [94][100/1281] lr: 7.880e-03, eta: 8:33:55, time: 0.813, data_time: 0.192, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2639, loss: 0.2639 +2025-06-25 01:03:54,223 - pyskl - INFO - Epoch [94][200/1281] lr: 7.861e-03, eta: 8:33:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2453, loss: 0.2453 +2025-06-25 01:04:43,429 - pyskl - INFO - Epoch [94][300/1281] lr: 7.842e-03, eta: 8:32:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2624, loss: 0.2624 +2025-06-25 01:05:32,465 - pyskl - INFO - Epoch [94][400/1281] lr: 7.823e-03, eta: 8:32:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9988, loss_cls: 0.2504, loss: 0.2504 +2025-06-25 01:06:21,411 - pyskl - INFO - Epoch [94][500/1281] lr: 7.804e-03, eta: 8:31:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2508, loss: 0.2508 +2025-06-25 01:07:10,294 - pyskl - INFO - Epoch [94][600/1281] lr: 7.785e-03, eta: 8:30:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9981, loss_cls: 0.2954, loss: 0.2954 +2025-06-25 01:07:59,087 - pyskl - INFO - Epoch [94][700/1281] lr: 7.766e-03, eta: 8:30:06, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9994, loss_cls: 0.2778, loss: 0.2778 +2025-06-25 01:08:48,109 - pyskl - INFO - Epoch [94][800/1281] lr: 7.747e-03, eta: 8:29:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.2703, loss: 0.2703 +2025-06-25 01:09:37,086 - pyskl - INFO - Epoch [94][900/1281] lr: 7.728e-03, eta: 8:28:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9994, loss_cls: 0.2458, loss: 0.2458 +2025-06-25 01:10:26,336 - pyskl - INFO - Epoch [94][1000/1281] lr: 7.709e-03, eta: 8:28:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2511, loss: 0.2511 +2025-06-25 01:11:15,514 - pyskl - INFO - Epoch [94][1100/1281] lr: 7.690e-03, eta: 8:27:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9994, loss_cls: 0.2551, loss: 0.2551 +2025-06-25 01:11:50,490 - pyskl - INFO - Epoch [94][1200/1281] lr: 7.672e-03, eta: 8:26:46, time: 0.350, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9981, loss_cls: 0.2685, loss: 0.2685 +2025-06-25 01:12:35,944 - pyskl - INFO - Saving checkpoint at 94 epochs +2025-06-25 01:13:23,777 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:13:23,832 - pyskl - INFO - +top1_acc 0.8853 +top5_acc 0.9933 +2025-06-25 01:13:23,832 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:13:23,839 - pyskl - INFO - +mean_acc 0.8442 +2025-06-25 01:13:23,843 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_90.pth was removed +2025-06-25 01:13:24,017 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_94.pth. +2025-06-25 01:13:24,017 - pyskl - INFO - Best top1_acc is 0.8853 at 94 epoch. +2025-06-25 01:13:24,020 - pyskl - INFO - Epoch(val) [94][533] top1_acc: 0.8853, top5_acc: 0.9933, mean_class_accuracy: 0.8442 +2025-06-25 01:14:44,150 - pyskl - INFO - Epoch [95][100/1281] lr: 7.637e-03, eta: 8:25:31, time: 0.801, data_time: 0.191, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2319, loss: 0.2319 +2025-06-25 01:15:33,137 - pyskl - INFO - Epoch [95][200/1281] lr: 7.619e-03, eta: 8:24:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2502, loss: 0.2502 +2025-06-25 01:16:21,973 - pyskl - INFO - Epoch [95][300/1281] lr: 7.600e-03, eta: 8:24:15, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1998, loss: 0.1998 +2025-06-25 01:17:10,905 - pyskl - INFO - Epoch [95][400/1281] lr: 7.581e-03, eta: 8:23:36, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2328, loss: 0.2328 +2025-06-25 01:18:00,074 - pyskl - INFO - Epoch [95][500/1281] lr: 7.562e-03, eta: 8:22:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9988, loss_cls: 0.2248, loss: 0.2248 +2025-06-25 01:18:48,878 - pyskl - INFO - Epoch [95][600/1281] lr: 7.543e-03, eta: 8:22:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.3211, loss: 0.3211 +2025-06-25 01:19:38,090 - pyskl - INFO - Epoch [95][700/1281] lr: 7.525e-03, eta: 8:21:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9994, loss_cls: 0.2598, loss: 0.2598 +2025-06-25 01:20:27,346 - pyskl - INFO - Epoch [95][800/1281] lr: 7.506e-03, eta: 8:21:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 1.0000, loss_cls: 0.3003, loss: 0.3003 +2025-06-25 01:21:16,621 - pyskl - INFO - Epoch [95][900/1281] lr: 7.487e-03, eta: 8:20:24, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9988, loss_cls: 0.2679, loss: 0.2679 +2025-06-25 01:22:05,410 - pyskl - INFO - Epoch [95][1000/1281] lr: 7.468e-03, eta: 8:19:45, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2721, loss: 0.2721 +2025-06-25 01:22:54,531 - pyskl - INFO - Epoch [95][1100/1281] lr: 7.450e-03, eta: 8:19:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2438, loss: 0.2438 +2025-06-25 01:23:30,697 - pyskl - INFO - Epoch [95][1200/1281] lr: 7.431e-03, eta: 8:18:21, time: 0.362, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.2876, loss: 0.2876 +2025-06-25 01:24:15,116 - pyskl - INFO - Saving checkpoint at 95 epochs +2025-06-25 01:25:02,859 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:25:02,915 - pyskl - INFO - +top1_acc 0.8899 +top5_acc 0.9938 +2025-06-25 01:25:02,915 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:25:02,922 - pyskl - INFO - +mean_acc 0.8417 +2025-06-25 01:25:02,926 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_94.pth was removed +2025-06-25 01:25:03,105 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_95.pth. +2025-06-25 01:25:03,106 - pyskl - INFO - Best top1_acc is 0.8899 at 95 epoch. +2025-06-25 01:25:03,108 - pyskl - INFO - Epoch(val) [95][533] top1_acc: 0.8899, top5_acc: 0.9938, mean_class_accuracy: 0.8417 +2025-06-25 01:26:23,301 - pyskl - INFO - Epoch [96][100/1281] lr: 7.397e-03, eta: 8:17:06, time: 0.802, data_time: 0.193, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2230, loss: 0.2230 +2025-06-25 01:27:12,462 - pyskl - INFO - Epoch [96][200/1281] lr: 7.379e-03, eta: 8:16:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1881, loss: 0.1881 +2025-06-25 01:28:01,271 - pyskl - INFO - Epoch [96][300/1281] lr: 7.360e-03, eta: 8:15:49, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2275, loss: 0.2275 +2025-06-25 01:28:50,610 - pyskl - INFO - Epoch [96][400/1281] lr: 7.341e-03, eta: 8:15:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9988, loss_cls: 0.2259, loss: 0.2259 +2025-06-25 01:29:39,750 - pyskl - INFO - Epoch [96][500/1281] lr: 7.323e-03, eta: 8:14:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9981, loss_cls: 0.2461, loss: 0.2461 +2025-06-25 01:30:28,352 - pyskl - INFO - Epoch [96][600/1281] lr: 7.304e-03, eta: 8:13:53, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2380, loss: 0.2380 +2025-06-25 01:31:17,170 - pyskl - INFO - Epoch [96][700/1281] lr: 7.286e-03, eta: 8:13:14, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.2801, loss: 0.2801 +2025-06-25 01:32:06,187 - pyskl - INFO - Epoch [96][800/1281] lr: 7.267e-03, eta: 8:12:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9994, loss_cls: 0.2339, loss: 0.2339 +2025-06-25 01:32:55,414 - pyskl - INFO - Epoch [96][900/1281] lr: 7.249e-03, eta: 8:11:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2441, loss: 0.2441 +2025-06-25 01:33:44,271 - pyskl - INFO - Epoch [96][1000/1281] lr: 7.230e-03, eta: 8:11:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9988, loss_cls: 0.3077, loss: 0.3077 +2025-06-25 01:34:33,035 - pyskl - INFO - Epoch [96][1100/1281] lr: 7.211e-03, eta: 8:10:39, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9988, loss_cls: 0.3489, loss: 0.3489 +2025-06-25 01:35:09,106 - pyskl - INFO - Epoch [96][1200/1281] lr: 7.193e-03, eta: 8:09:53, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2905, loss: 0.2905 +2025-06-25 01:35:53,754 - pyskl - INFO - Saving checkpoint at 96 epochs +2025-06-25 01:36:41,225 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:36:41,281 - pyskl - INFO - +top1_acc 0.8825 +top5_acc 0.9913 +2025-06-25 01:36:41,282 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:36:41,288 - pyskl - INFO - +mean_acc 0.8429 +2025-06-25 01:36:41,290 - pyskl - INFO - Epoch(val) [96][533] top1_acc: 0.8825, top5_acc: 0.9913, mean_class_accuracy: 0.8429 +2025-06-25 01:38:00,550 - pyskl - INFO - Epoch [97][100/1281] lr: 7.159e-03, eta: 8:08:38, time: 0.793, data_time: 0.191, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2367, loss: 0.2367 +2025-06-25 01:38:49,552 - pyskl - INFO - Epoch [97][200/1281] lr: 7.141e-03, eta: 8:07:59, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2284, loss: 0.2284 +2025-06-25 01:39:38,539 - pyskl - INFO - Epoch [97][300/1281] lr: 7.123e-03, eta: 8:07:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2662, loss: 0.2662 +2025-06-25 01:40:27,731 - pyskl - INFO - Epoch [97][400/1281] lr: 7.104e-03, eta: 8:06:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9988, loss_cls: 0.2640, loss: 0.2640 +2025-06-25 01:41:16,813 - pyskl - INFO - Epoch [97][500/1281] lr: 7.086e-03, eta: 8:06:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1963, loss: 0.1963 +2025-06-25 01:42:05,492 - pyskl - INFO - Epoch [97][600/1281] lr: 7.067e-03, eta: 8:05:24, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2123, loss: 0.2123 +2025-06-25 01:42:54,570 - pyskl - INFO - Epoch [97][700/1281] lr: 7.049e-03, eta: 8:04:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2376, loss: 0.2376 +2025-06-25 01:43:43,800 - pyskl - INFO - Epoch [97][800/1281] lr: 7.030e-03, eta: 8:04:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 1.0000, loss_cls: 0.2726, loss: 0.2726 +2025-06-25 01:44:32,745 - pyskl - INFO - Epoch [97][900/1281] lr: 7.012e-03, eta: 8:03:27, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9988, loss_cls: 0.2535, loss: 0.2535 +2025-06-25 01:45:21,713 - pyskl - INFO - Epoch [97][1000/1281] lr: 6.994e-03, eta: 8:02:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 1.0000, loss_cls: 0.2925, loss: 0.2925 +2025-06-25 01:46:10,831 - pyskl - INFO - Epoch [97][1100/1281] lr: 6.975e-03, eta: 8:02:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2687, loss: 0.2687 +2025-06-25 01:46:48,325 - pyskl - INFO - Epoch [97][1200/1281] lr: 6.957e-03, eta: 8:01:24, time: 0.375, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9975, loss_cls: 0.2882, loss: 0.2882 +2025-06-25 01:47:30,178 - pyskl - INFO - Saving checkpoint at 97 epochs +2025-06-25 01:48:17,534 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:48:17,604 - pyskl - INFO - +top1_acc 0.8730 +top5_acc 0.9900 +2025-06-25 01:48:17,604 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:48:17,612 - pyskl - INFO - +mean_acc 0.8386 +2025-06-25 01:48:17,614 - pyskl - INFO - Epoch(val) [97][533] top1_acc: 0.8730, top5_acc: 0.9900, mean_class_accuracy: 0.8386 +2025-06-25 01:49:37,126 - pyskl - INFO - Epoch [98][100/1281] lr: 6.924e-03, eta: 8:00:09, time: 0.795, data_time: 0.190, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2460, loss: 0.2460 +2025-06-25 01:50:26,111 - pyskl - INFO - Epoch [98][200/1281] lr: 6.906e-03, eta: 7:59:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2372, loss: 0.2372 +2025-06-25 01:51:14,851 - pyskl - INFO - Epoch [98][300/1281] lr: 6.887e-03, eta: 7:58:51, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2483, loss: 0.2483 +2025-06-25 01:52:03,698 - pyskl - INFO - Epoch [98][400/1281] lr: 6.869e-03, eta: 7:58:12, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2230, loss: 0.2230 +2025-06-25 01:52:52,817 - pyskl - INFO - Epoch [98][500/1281] lr: 6.851e-03, eta: 7:57:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2011, loss: 0.2011 +2025-06-25 01:53:42,083 - pyskl - INFO - Epoch [98][600/1281] lr: 6.833e-03, eta: 7:56:54, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2264, loss: 0.2264 +2025-06-25 01:54:31,503 - pyskl - INFO - Epoch [98][700/1281] lr: 6.814e-03, eta: 7:56:15, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2225, loss: 0.2225 +2025-06-25 01:55:20,473 - pyskl - INFO - Epoch [98][800/1281] lr: 6.796e-03, eta: 7:55:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2367, loss: 0.2367 +2025-06-25 01:56:09,231 - pyskl - INFO - Epoch [98][900/1281] lr: 6.778e-03, eta: 7:54:57, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9994, loss_cls: 0.2455, loss: 0.2455 +2025-06-25 01:56:58,124 - pyskl - INFO - Epoch [98][1000/1281] lr: 6.760e-03, eta: 7:54:18, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2026, loss: 0.2026 +2025-06-25 01:57:46,813 - pyskl - INFO - Epoch [98][1100/1281] lr: 6.742e-03, eta: 7:53:38, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2285, loss: 0.2285 +2025-06-25 01:58:24,018 - pyskl - INFO - Epoch [98][1200/1281] lr: 6.724e-03, eta: 7:52:53, time: 0.372, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2359, loss: 0.2359 +2025-06-25 01:59:06,279 - pyskl - INFO - Saving checkpoint at 98 epochs +2025-06-25 01:59:53,702 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:59:53,778 - pyskl - INFO - +top1_acc 0.8849 +top5_acc 0.9926 +2025-06-25 01:59:53,778 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:59:53,787 - pyskl - INFO - +mean_acc 0.8372 +2025-06-25 01:59:53,789 - pyskl - INFO - Epoch(val) [98][533] top1_acc: 0.8849, top5_acc: 0.9926, mean_class_accuracy: 0.8372 +2025-06-25 02:01:13,793 - pyskl - INFO - Epoch [99][100/1281] lr: 6.691e-03, eta: 7:51:38, time: 0.800, data_time: 0.188, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1929, loss: 0.1929 +2025-06-25 02:02:02,528 - pyskl - INFO - Epoch [99][200/1281] lr: 6.673e-03, eta: 7:50:58, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1629, loss: 0.1629 +2025-06-25 02:02:51,517 - pyskl - INFO - Epoch [99][300/1281] lr: 6.655e-03, eta: 7:50:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 0.9994, loss_cls: 0.1595, loss: 0.1595 +2025-06-25 02:03:40,537 - pyskl - INFO - Epoch [99][400/1281] lr: 6.637e-03, eta: 7:49:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 0.9994, loss_cls: 0.1774, loss: 0.1774 +2025-06-25 02:04:29,540 - pyskl - INFO - Epoch [99][500/1281] lr: 6.619e-03, eta: 7:49:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1630, loss: 0.1630 +2025-06-25 02:05:18,709 - pyskl - INFO - Epoch [99][600/1281] lr: 6.601e-03, eta: 7:48:22, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2241, loss: 0.2241 +2025-06-25 02:06:08,053 - pyskl - INFO - Epoch [99][700/1281] lr: 6.583e-03, eta: 7:47:43, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.2041, loss: 0.2041 +2025-06-25 02:06:56,904 - pyskl - INFO - Epoch [99][800/1281] lr: 6.565e-03, eta: 7:47:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1802, loss: 0.1802 +2025-06-25 02:07:46,137 - pyskl - INFO - Epoch [99][900/1281] lr: 6.547e-03, eta: 7:46:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2405, loss: 0.2405 +2025-06-25 02:08:34,869 - pyskl - INFO - Epoch [99][1000/1281] lr: 6.529e-03, eta: 7:45:45, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2186, loss: 0.2186 +2025-06-25 02:09:24,031 - pyskl - INFO - Epoch [99][1100/1281] lr: 6.511e-03, eta: 7:45:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2192, loss: 0.2192 +2025-06-25 02:10:00,351 - pyskl - INFO - Epoch [99][1200/1281] lr: 6.493e-03, eta: 7:44:20, time: 0.363, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2523, loss: 0.2523 +2025-06-25 02:10:45,631 - pyskl - INFO - Saving checkpoint at 99 epochs +2025-06-25 02:11:34,167 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:11:34,234 - pyskl - INFO - +top1_acc 0.8662 +top5_acc 0.9883 +2025-06-25 02:11:34,234 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:11:34,242 - pyskl - INFO - +mean_acc 0.8321 +2025-06-25 02:11:34,244 - pyskl - INFO - Epoch(val) [99][533] top1_acc: 0.8662, top5_acc: 0.9883, mean_class_accuracy: 0.8321 +2025-06-25 02:12:52,550 - pyskl - INFO - Epoch [100][100/1281] lr: 6.460e-03, eta: 7:43:03, time: 0.783, data_time: 0.193, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1702, loss: 0.1702 +2025-06-25 02:13:41,534 - pyskl - INFO - Epoch [100][200/1281] lr: 6.442e-03, eta: 7:42:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9994, loss_cls: 0.2035, loss: 0.2035 +2025-06-25 02:14:30,740 - pyskl - INFO - Epoch [100][300/1281] lr: 6.425e-03, eta: 7:41:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2132, loss: 0.2132 +2025-06-25 02:15:19,693 - pyskl - INFO - Epoch [100][400/1281] lr: 6.407e-03, eta: 7:41:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2198, loss: 0.2198 +2025-06-25 02:16:08,688 - pyskl - INFO - Epoch [100][500/1281] lr: 6.389e-03, eta: 7:40:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2422, loss: 0.2422 +2025-06-25 02:16:57,877 - pyskl - INFO - Epoch [100][600/1281] lr: 6.371e-03, eta: 7:39:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2316, loss: 0.2316 +2025-06-25 02:17:47,223 - pyskl - INFO - Epoch [100][700/1281] lr: 6.353e-03, eta: 7:39:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9988, loss_cls: 0.2424, loss: 0.2424 +2025-06-25 02:18:36,385 - pyskl - INFO - Epoch [100][800/1281] lr: 6.336e-03, eta: 7:38:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.1978, loss: 0.1978 +2025-06-25 02:19:25,649 - pyskl - INFO - Epoch [100][900/1281] lr: 6.318e-03, eta: 7:37:49, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1993, loss: 0.1993 +2025-06-25 02:20:15,051 - pyskl - INFO - Epoch [100][1000/1281] lr: 6.300e-03, eta: 7:37:10, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2399, loss: 0.2399 +2025-06-25 02:21:04,066 - pyskl - INFO - Epoch [100][1100/1281] lr: 6.282e-03, eta: 7:36:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9994, loss_cls: 0.2379, loss: 0.2379 +2025-06-25 02:21:42,129 - pyskl - INFO - Epoch [100][1200/1281] lr: 6.265e-03, eta: 7:35:45, time: 0.381, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2666, loss: 0.2666 +2025-06-25 02:22:23,571 - pyskl - INFO - Saving checkpoint at 100 epochs +2025-06-25 02:23:10,729 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:23:10,786 - pyskl - INFO - +top1_acc 0.8852 +top5_acc 0.9935 +2025-06-25 02:23:10,786 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:23:10,793 - pyskl - INFO - +mean_acc 0.8541 +2025-06-25 02:23:10,795 - pyskl - INFO - Epoch(val) [100][533] top1_acc: 0.8852, top5_acc: 0.9935, mean_class_accuracy: 0.8541 +2025-06-25 02:24:30,449 - pyskl - INFO - Epoch [101][100/1281] lr: 6.232e-03, eta: 7:34:29, time: 0.796, data_time: 0.192, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2186, loss: 0.2186 +2025-06-25 02:25:19,700 - pyskl - INFO - Epoch [101][200/1281] lr: 6.215e-03, eta: 7:33:50, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.2106, loss: 0.2106 +2025-06-25 02:26:09,006 - pyskl - INFO - Epoch [101][300/1281] lr: 6.197e-03, eta: 7:33:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1983, loss: 0.1983 +2025-06-25 02:26:58,004 - pyskl - INFO - Epoch [101][400/1281] lr: 6.180e-03, eta: 7:32:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2087, loss: 0.2087 +2025-06-25 02:27:47,218 - pyskl - INFO - Epoch [101][500/1281] lr: 6.162e-03, eta: 7:31:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1432, loss: 0.1432 +2025-06-25 02:28:36,062 - pyskl - INFO - Epoch [101][600/1281] lr: 6.144e-03, eta: 7:31:12, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1919, loss: 0.1919 +2025-06-25 02:29:25,314 - pyskl - INFO - Epoch [101][700/1281] lr: 6.127e-03, eta: 7:30:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.2735, loss: 0.2735 +2025-06-25 02:30:14,457 - pyskl - INFO - Epoch [101][800/1281] lr: 6.109e-03, eta: 7:29:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.2113, loss: 0.2113 +2025-06-25 02:31:03,514 - pyskl - INFO - Epoch [101][900/1281] lr: 6.092e-03, eta: 7:29:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2697, loss: 0.2697 +2025-06-25 02:31:52,523 - pyskl - INFO - Epoch [101][1000/1281] lr: 6.074e-03, eta: 7:28:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2141, loss: 0.2141 +2025-06-25 02:32:41,687 - pyskl - INFO - Epoch [101][1100/1281] lr: 6.057e-03, eta: 7:27:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2168, loss: 0.2168 +2025-06-25 02:33:18,619 - pyskl - INFO - Epoch [101][1200/1281] lr: 6.039e-03, eta: 7:27:09, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.2897, loss: 0.2897 +2025-06-25 02:34:01,530 - pyskl - INFO - Saving checkpoint at 101 epochs +2025-06-25 02:34:49,092 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:34:49,147 - pyskl - INFO - +top1_acc 0.8823 +top5_acc 0.9918 +2025-06-25 02:34:49,147 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:34:49,153 - pyskl - INFO - +mean_acc 0.8324 +2025-06-25 02:34:49,155 - pyskl - INFO - Epoch(val) [101][533] top1_acc: 0.8823, top5_acc: 0.9918, mean_class_accuracy: 0.8324 +2025-06-25 02:36:08,282 - pyskl - INFO - Epoch [102][100/1281] lr: 6.007e-03, eta: 7:25:53, time: 0.791, data_time: 0.183, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1888, loss: 0.1888 +2025-06-25 02:36:57,290 - pyskl - INFO - Epoch [102][200/1281] lr: 5.990e-03, eta: 7:25:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1876, loss: 0.1876 +2025-06-25 02:37:46,312 - pyskl - INFO - Epoch [102][300/1281] lr: 5.972e-03, eta: 7:24:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1912, loss: 0.1912 +2025-06-25 02:38:35,514 - pyskl - INFO - Epoch [102][400/1281] lr: 5.955e-03, eta: 7:23:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 0.9988, loss_cls: 0.2125, loss: 0.2125 +2025-06-25 02:39:24,538 - pyskl - INFO - Epoch [102][500/1281] lr: 5.938e-03, eta: 7:23:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9994, loss_cls: 0.2050, loss: 0.2050 +2025-06-25 02:40:13,645 - pyskl - INFO - Epoch [102][600/1281] lr: 5.920e-03, eta: 7:22:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.2324, loss: 0.2324 +2025-06-25 02:41:02,755 - pyskl - INFO - Epoch [102][700/1281] lr: 5.903e-03, eta: 7:21:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2121, loss: 0.2121 +2025-06-25 02:41:51,867 - pyskl - INFO - Epoch [102][800/1281] lr: 5.886e-03, eta: 7:21:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2132, loss: 0.2132 +2025-06-25 02:42:40,855 - pyskl - INFO - Epoch [102][900/1281] lr: 5.868e-03, eta: 7:20:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2577, loss: 0.2577 +2025-06-25 02:43:30,097 - pyskl - INFO - Epoch [102][1000/1281] lr: 5.851e-03, eta: 7:19:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2353, loss: 0.2353 +2025-06-25 02:44:19,462 - pyskl - INFO - Epoch [102][1100/1281] lr: 5.834e-03, eta: 7:19:16, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 0.9988, loss_cls: 0.1957, loss: 0.1957 +2025-06-25 02:44:57,483 - pyskl - INFO - Epoch [102][1200/1281] lr: 5.816e-03, eta: 7:18:31, time: 0.380, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1913, loss: 0.1913 +2025-06-25 02:45:41,221 - pyskl - INFO - Saving checkpoint at 102 epochs +2025-06-25 02:46:29,036 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:46:29,097 - pyskl - INFO - +top1_acc 0.8925 +top5_acc 0.9933 +2025-06-25 02:46:29,097 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:46:29,103 - pyskl - INFO - +mean_acc 0.8512 +2025-06-25 02:46:29,108 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_95.pth was removed +2025-06-25 02:46:29,274 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_102.pth. +2025-06-25 02:46:29,274 - pyskl - INFO - Best top1_acc is 0.8925 at 102 epoch. +2025-06-25 02:46:29,277 - pyskl - INFO - Epoch(val) [102][533] top1_acc: 0.8925, top5_acc: 0.9933, mean_class_accuracy: 0.8512 +2025-06-25 02:47:48,323 - pyskl - INFO - Epoch [103][100/1281] lr: 5.785e-03, eta: 7:17:15, time: 0.790, data_time: 0.182, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1996, loss: 0.1996 +2025-06-25 02:48:37,247 - pyskl - INFO - Epoch [103][200/1281] lr: 5.768e-03, eta: 7:16:35, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1571, loss: 0.1571 +2025-06-25 02:49:26,483 - pyskl - INFO - Epoch [103][300/1281] lr: 5.751e-03, eta: 7:15:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.1839, loss: 0.1839 +2025-06-25 02:50:15,337 - pyskl - INFO - Epoch [103][400/1281] lr: 5.733e-03, eta: 7:15:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1749, loss: 0.1749 +2025-06-25 02:51:04,705 - pyskl - INFO - Epoch [103][500/1281] lr: 5.716e-03, eta: 7:14:36, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1390, loss: 0.1390 +2025-06-25 02:51:53,833 - pyskl - INFO - Epoch [103][600/1281] lr: 5.699e-03, eta: 7:13:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1819, loss: 0.1819 +2025-06-25 02:52:42,783 - pyskl - INFO - Epoch [103][700/1281] lr: 5.682e-03, eta: 7:13:16, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9988, loss_cls: 0.1771, loss: 0.1771 +2025-06-25 02:53:31,754 - pyskl - INFO - Epoch [103][800/1281] lr: 5.665e-03, eta: 7:12:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1830, loss: 0.1830 +2025-06-25 02:54:20,929 - pyskl - INFO - Epoch [103][900/1281] lr: 5.648e-03, eta: 7:11:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9981, loss_cls: 0.1995, loss: 0.1995 +2025-06-25 02:55:10,188 - pyskl - INFO - Epoch [103][1000/1281] lr: 5.631e-03, eta: 7:11:16, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2082, loss: 0.2082 +2025-06-25 02:55:59,435 - pyskl - INFO - Epoch [103][1100/1281] lr: 5.614e-03, eta: 7:10:37, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1719, loss: 0.1719 +2025-06-25 02:56:36,876 - pyskl - INFO - Epoch [103][1200/1281] lr: 5.597e-03, eta: 7:09:51, time: 0.374, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1741, loss: 0.1741 +2025-06-25 02:57:20,315 - pyskl - INFO - Saving checkpoint at 103 epochs +2025-06-25 02:58:07,451 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:58:07,511 - pyskl - INFO - +top1_acc 0.8883 +top5_acc 0.9931 +2025-06-25 02:58:07,511 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:58:07,518 - pyskl - INFO - +mean_acc 0.8584 +2025-06-25 02:58:07,521 - pyskl - INFO - Epoch(val) [103][533] top1_acc: 0.8883, top5_acc: 0.9931, mean_class_accuracy: 0.8584 +2025-06-25 02:59:28,359 - pyskl - INFO - Epoch [104][100/1281] lr: 5.566e-03, eta: 7:08:35, time: 0.808, data_time: 0.195, memory: 4083, top1_acc: 0.9694, top5_acc: 0.9994, loss_cls: 0.1843, loss: 0.1843 +2025-06-25 03:00:17,831 - pyskl - INFO - Epoch [104][200/1281] lr: 5.549e-03, eta: 7:07:56, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1712, loss: 0.1712 +2025-06-25 03:01:06,951 - pyskl - INFO - Epoch [104][300/1281] lr: 5.532e-03, eta: 7:07:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1770, loss: 0.1770 +2025-06-25 03:01:56,479 - pyskl - INFO - Epoch [104][400/1281] lr: 5.515e-03, eta: 7:06:36, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1373, loss: 0.1373 +2025-06-25 03:02:46,033 - pyskl - INFO - Epoch [104][500/1281] lr: 5.498e-03, eta: 7:05:56, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1739, loss: 0.1739 +2025-06-25 03:03:35,200 - pyskl - INFO - Epoch [104][600/1281] lr: 5.481e-03, eta: 7:05:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1827, loss: 0.1827 +2025-06-25 03:04:24,161 - pyskl - INFO - Epoch [104][700/1281] lr: 5.464e-03, eta: 7:04:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1626, loss: 0.1626 +2025-06-25 03:05:13,373 - pyskl - INFO - Epoch [104][800/1281] lr: 5.447e-03, eta: 7:03:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9988, loss_cls: 0.2164, loss: 0.2164 +2025-06-25 03:06:02,225 - pyskl - INFO - Epoch [104][900/1281] lr: 5.430e-03, eta: 7:03:16, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9994, loss_cls: 0.2444, loss: 0.2444 +2025-06-25 03:06:51,483 - pyskl - INFO - Epoch [104][1000/1281] lr: 5.413e-03, eta: 7:02:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2237, loss: 0.2237 +2025-06-25 03:07:40,490 - pyskl - INFO - Epoch [104][1100/1281] lr: 5.397e-03, eta: 7:01:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1620, loss: 0.1620 +2025-06-25 03:08:16,753 - pyskl - INFO - Epoch [104][1200/1281] lr: 5.380e-03, eta: 7:01:10, time: 0.363, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9988, loss_cls: 0.1793, loss: 0.1793 +2025-06-25 03:09:03,826 - pyskl - INFO - Saving checkpoint at 104 epochs +2025-06-25 03:09:51,948 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:09:52,017 - pyskl - INFO - +top1_acc 0.8939 +top5_acc 0.9930 +2025-06-25 03:09:52,017 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:09:52,025 - pyskl - INFO - +mean_acc 0.8533 +2025-06-25 03:09:52,029 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_102.pth was removed +2025-06-25 03:09:52,197 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_104.pth. +2025-06-25 03:09:52,197 - pyskl - INFO - Best top1_acc is 0.8939 at 104 epoch. +2025-06-25 03:09:52,199 - pyskl - INFO - Epoch(val) [104][533] top1_acc: 0.8939, top5_acc: 0.9930, mean_class_accuracy: 0.8533 +2025-06-25 03:11:12,560 - pyskl - INFO - Epoch [105][100/1281] lr: 5.349e-03, eta: 6:59:54, time: 0.804, data_time: 0.183, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1207, loss: 0.1207 +2025-06-25 03:12:01,622 - pyskl - INFO - Epoch [105][200/1281] lr: 5.333e-03, eta: 6:59:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1618, loss: 0.1618 +2025-06-25 03:12:50,579 - pyskl - INFO - Epoch [105][300/1281] lr: 5.316e-03, eta: 6:58:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1340, loss: 0.1340 +2025-06-25 03:13:39,652 - pyskl - INFO - Epoch [105][400/1281] lr: 5.299e-03, eta: 6:57:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1349, loss: 0.1349 +2025-06-25 03:14:28,758 - pyskl - INFO - Epoch [105][500/1281] lr: 5.283e-03, eta: 6:57:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1684, loss: 0.1684 +2025-06-25 03:15:17,908 - pyskl - INFO - Epoch [105][600/1281] lr: 5.266e-03, eta: 6:56:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 0.9994, loss_cls: 0.1703, loss: 0.1703 +2025-06-25 03:16:06,879 - pyskl - INFO - Epoch [105][700/1281] lr: 5.249e-03, eta: 6:55:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1625, loss: 0.1625 +2025-06-25 03:16:56,066 - pyskl - INFO - Epoch [105][800/1281] lr: 5.233e-03, eta: 6:55:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.2104, loss: 0.2104 +2025-06-25 03:17:45,439 - pyskl - INFO - Epoch [105][900/1281] lr: 5.216e-03, eta: 6:54:33, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1518, loss: 0.1518 +2025-06-25 03:18:34,914 - pyskl - INFO - Epoch [105][1000/1281] lr: 5.199e-03, eta: 6:53:53, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.1978, loss: 0.1978 +2025-06-25 03:19:24,317 - pyskl - INFO - Epoch [105][1100/1281] lr: 5.183e-03, eta: 6:53:13, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1773, loss: 0.1773 +2025-06-25 03:20:00,441 - pyskl - INFO - Epoch [105][1200/1281] lr: 5.166e-03, eta: 6:52:27, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.1640, loss: 0.1640 +2025-06-25 03:20:46,368 - pyskl - INFO - Saving checkpoint at 105 epochs +2025-06-25 03:21:34,172 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:21:34,227 - pyskl - INFO - +top1_acc 0.9000 +top5_acc 0.9926 +2025-06-25 03:21:34,227 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:21:34,233 - pyskl - INFO - +mean_acc 0.8728 +2025-06-25 03:21:34,237 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_104.pth was removed +2025-06-25 03:21:34,425 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_105.pth. +2025-06-25 03:21:34,425 - pyskl - INFO - Best top1_acc is 0.9000 at 105 epoch. +2025-06-25 03:21:34,428 - pyskl - INFO - Epoch(val) [105][533] top1_acc: 0.9000, top5_acc: 0.9926, mean_class_accuracy: 0.8728 +2025-06-25 03:22:53,447 - pyskl - INFO - Epoch [106][100/1281] lr: 5.136e-03, eta: 6:51:10, time: 0.790, data_time: 0.186, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1503, loss: 0.1503 +2025-06-25 03:23:42,562 - pyskl - INFO - Epoch [106][200/1281] lr: 5.120e-03, eta: 6:50:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 0.9994, loss_cls: 0.1522, loss: 0.1522 +2025-06-25 03:24:32,250 - pyskl - INFO - Epoch [106][300/1281] lr: 5.103e-03, eta: 6:49:50, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1421, loss: 0.1421 +2025-06-25 03:25:21,480 - pyskl - INFO - Epoch [106][400/1281] lr: 5.087e-03, eta: 6:49:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1569, loss: 0.1569 +2025-06-25 03:26:10,279 - pyskl - INFO - Epoch [106][500/1281] lr: 5.070e-03, eta: 6:48:30, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1054, loss: 0.1054 +2025-06-25 03:26:59,419 - pyskl - INFO - Epoch [106][600/1281] lr: 5.054e-03, eta: 6:47:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1639, loss: 0.1639 +2025-06-25 03:27:48,628 - pyskl - INFO - Epoch [106][700/1281] lr: 5.038e-03, eta: 6:47:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1429, loss: 0.1429 +2025-06-25 03:28:37,929 - pyskl - INFO - Epoch [106][800/1281] lr: 5.021e-03, eta: 6:46:29, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1833, loss: 0.1833 +2025-06-25 03:29:27,055 - pyskl - INFO - Epoch [106][900/1281] lr: 5.005e-03, eta: 6:45:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1622, loss: 0.1622 +2025-06-25 03:30:16,285 - pyskl - INFO - Epoch [106][1000/1281] lr: 4.988e-03, eta: 6:45:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1964, loss: 0.1964 +2025-06-25 03:31:05,433 - pyskl - INFO - Epoch [106][1100/1281] lr: 4.972e-03, eta: 6:44:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1528, loss: 0.1528 +2025-06-25 03:31:42,555 - pyskl - INFO - Epoch [106][1200/1281] lr: 4.956e-03, eta: 6:43:43, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1731, loss: 0.1731 +2025-06-25 03:32:26,493 - pyskl - INFO - Saving checkpoint at 106 epochs +2025-06-25 03:33:13,867 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:33:13,922 - pyskl - INFO - +top1_acc 0.8900 +top5_acc 0.9937 +2025-06-25 03:33:13,922 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:33:13,928 - pyskl - INFO - +mean_acc 0.8456 +2025-06-25 03:33:13,930 - pyskl - INFO - Epoch(val) [106][533] top1_acc: 0.8900, top5_acc: 0.9937, mean_class_accuracy: 0.8456 +2025-06-25 03:34:34,498 - pyskl - INFO - Epoch [107][100/1281] lr: 4.926e-03, eta: 6:42:26, time: 0.806, data_time: 0.188, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1293, loss: 0.1293 +2025-06-25 03:35:23,689 - pyskl - INFO - Epoch [107][200/1281] lr: 4.910e-03, eta: 6:41:46, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1337, loss: 0.1337 +2025-06-25 03:36:13,056 - pyskl - INFO - Epoch [107][300/1281] lr: 4.894e-03, eta: 6:41:06, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1257, loss: 0.1257 +2025-06-25 03:37:02,294 - pyskl - INFO - Epoch [107][400/1281] lr: 4.878e-03, eta: 6:40:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1362, loss: 0.1362 +2025-06-25 03:37:51,518 - pyskl - INFO - Epoch [107][500/1281] lr: 4.862e-03, eta: 6:39:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1360, loss: 0.1360 +2025-06-25 03:38:40,625 - pyskl - INFO - Epoch [107][600/1281] lr: 4.845e-03, eta: 6:39:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1664, loss: 0.1664 +2025-06-25 03:39:30,127 - pyskl - INFO - Epoch [107][700/1281] lr: 4.829e-03, eta: 6:38:24, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1478, loss: 0.1478 +2025-06-25 03:40:19,235 - pyskl - INFO - Epoch [107][800/1281] lr: 4.813e-03, eta: 6:37:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1772, loss: 0.1772 +2025-06-25 03:41:08,154 - pyskl - INFO - Epoch [107][900/1281] lr: 4.797e-03, eta: 6:37:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.1843, loss: 0.1843 +2025-06-25 03:41:57,399 - pyskl - INFO - Epoch [107][1000/1281] lr: 4.781e-03, eta: 6:36:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1467, loss: 0.1467 +2025-06-25 03:42:46,345 - pyskl - INFO - Epoch [107][1100/1281] lr: 4.765e-03, eta: 6:35:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1521, loss: 0.1521 +2025-06-25 03:43:22,381 - pyskl - INFO - Epoch [107][1200/1281] lr: 4.749e-03, eta: 6:34:57, time: 0.360, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1739, loss: 0.1739 +2025-06-25 03:44:08,384 - pyskl - INFO - Saving checkpoint at 107 epochs +2025-06-25 03:44:55,978 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:44:56,047 - pyskl - INFO - +top1_acc 0.9007 +top5_acc 0.9933 +2025-06-25 03:44:56,048 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:44:56,059 - pyskl - INFO - +mean_acc 0.8607 +2025-06-25 03:44:56,064 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_105.pth was removed +2025-06-25 03:44:56,242 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_107.pth. +2025-06-25 03:44:56,242 - pyskl - INFO - Best top1_acc is 0.9007 at 107 epoch. +2025-06-25 03:44:56,245 - pyskl - INFO - Epoch(val) [107][533] top1_acc: 0.9007, top5_acc: 0.9933, mean_class_accuracy: 0.8607 +2025-06-25 03:46:14,889 - pyskl - INFO - Epoch [108][100/1281] lr: 4.720e-03, eta: 6:33:39, time: 0.786, data_time: 0.180, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1209, loss: 0.1209 +2025-06-25 03:47:04,335 - pyskl - INFO - Epoch [108][200/1281] lr: 4.704e-03, eta: 6:32:59, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.1034, loss: 0.1034 +2025-06-25 03:47:53,361 - pyskl - INFO - Epoch [108][300/1281] lr: 4.688e-03, eta: 6:32:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 0.9994, loss_cls: 0.1236, loss: 0.1236 +2025-06-25 03:48:42,311 - pyskl - INFO - Epoch [108][400/1281] lr: 4.672e-03, eta: 6:31:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0922, loss: 0.0922 +2025-06-25 03:49:31,555 - pyskl - INFO - Epoch [108][500/1281] lr: 4.656e-03, eta: 6:30:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1536, loss: 0.1536 +2025-06-25 03:50:20,736 - pyskl - INFO - Epoch [108][600/1281] lr: 4.640e-03, eta: 6:30:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1096, loss: 0.1096 +2025-06-25 03:51:09,729 - pyskl - INFO - Epoch [108][700/1281] lr: 4.624e-03, eta: 6:29:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1138, loss: 0.1138 +2025-06-25 03:51:59,098 - pyskl - INFO - Epoch [108][800/1281] lr: 4.608e-03, eta: 6:28:56, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0957, loss: 0.0957 +2025-06-25 03:52:48,304 - pyskl - INFO - Epoch [108][900/1281] lr: 4.593e-03, eta: 6:28:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 0.9994, loss_cls: 0.1588, loss: 0.1588 +2025-06-25 03:53:37,552 - pyskl - INFO - Epoch [108][1000/1281] lr: 4.577e-03, eta: 6:27:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1405, loss: 0.1405 +2025-06-25 03:54:27,007 - pyskl - INFO - Epoch [108][1100/1281] lr: 4.561e-03, eta: 6:26:54, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1227, loss: 0.1227 +2025-06-25 03:55:04,988 - pyskl - INFO - Epoch [108][1200/1281] lr: 4.545e-03, eta: 6:26:09, time: 0.380, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9988, loss_cls: 0.1905, loss: 0.1905 +2025-06-25 03:55:47,059 - pyskl - INFO - Saving checkpoint at 108 epochs +2025-06-25 03:56:35,279 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:56:35,334 - pyskl - INFO - +top1_acc 0.9000 +top5_acc 0.9934 +2025-06-25 03:56:35,334 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:56:35,341 - pyskl - INFO - +mean_acc 0.8679 +2025-06-25 03:56:35,343 - pyskl - INFO - Epoch(val) [108][533] top1_acc: 0.9000, top5_acc: 0.9934, mean_class_accuracy: 0.8679 +2025-06-25 03:57:54,215 - pyskl - INFO - Epoch [109][100/1281] lr: 4.517e-03, eta: 6:24:52, time: 0.789, data_time: 0.188, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1256, loss: 0.1256 +2025-06-25 03:58:43,505 - pyskl - INFO - Epoch [109][200/1281] lr: 4.501e-03, eta: 6:24:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1017, loss: 0.1017 +2025-06-25 03:59:33,127 - pyskl - INFO - Epoch [109][300/1281] lr: 4.485e-03, eta: 6:23:31, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0995, loss: 0.0995 +2025-06-25 04:00:22,656 - pyskl - INFO - Epoch [109][400/1281] lr: 4.470e-03, eta: 6:22:50, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1075, loss: 0.1075 +2025-06-25 04:01:11,757 - pyskl - INFO - Epoch [109][500/1281] lr: 4.454e-03, eta: 6:22:09, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0861, loss: 0.0861 +2025-06-25 04:02:00,654 - pyskl - INFO - Epoch [109][600/1281] lr: 4.438e-03, eta: 6:21:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0970, loss: 0.0970 +2025-06-25 04:02:49,657 - pyskl - INFO - Epoch [109][700/1281] lr: 4.423e-03, eta: 6:20:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 0.9994, loss_cls: 0.1356, loss: 0.1356 +2025-06-25 04:03:38,510 - pyskl - INFO - Epoch [109][800/1281] lr: 4.407e-03, eta: 6:20:07, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1339, loss: 0.1339 +2025-06-25 04:04:27,360 - pyskl - INFO - Epoch [109][900/1281] lr: 4.391e-03, eta: 6:19:26, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1320, loss: 0.1320 +2025-06-25 04:05:16,498 - pyskl - INFO - Epoch [109][1000/1281] lr: 4.376e-03, eta: 6:18:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1720, loss: 0.1720 +2025-06-25 04:06:05,888 - pyskl - INFO - Epoch [109][1100/1281] lr: 4.360e-03, eta: 6:18:05, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1225, loss: 0.1225 +2025-06-25 04:06:43,696 - pyskl - INFO - Epoch [109][1200/1281] lr: 4.345e-03, eta: 6:17:20, time: 0.378, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1393, loss: 0.1393 +2025-06-25 04:07:24,993 - pyskl - INFO - Saving checkpoint at 109 epochs +2025-06-25 04:08:12,335 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:08:12,403 - pyskl - INFO - +top1_acc 0.9005 +top5_acc 0.9933 +2025-06-25 04:08:12,403 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:08:12,410 - pyskl - INFO - +mean_acc 0.8661 +2025-06-25 04:08:12,411 - pyskl - INFO - Epoch(val) [109][533] top1_acc: 0.9005, top5_acc: 0.9933, mean_class_accuracy: 0.8661 +2025-06-25 04:09:32,237 - pyskl - INFO - Epoch [110][100/1281] lr: 4.317e-03, eta: 6:16:03, time: 0.798, data_time: 0.189, memory: 4083, top1_acc: 0.9869, top5_acc: 0.9994, loss_cls: 0.1089, loss: 0.1089 +2025-06-25 04:10:21,307 - pyskl - INFO - Epoch [110][200/1281] lr: 4.301e-03, eta: 6:15:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 0.9994, loss_cls: 0.1240, loss: 0.1240 +2025-06-25 04:11:10,523 - pyskl - INFO - Epoch [110][300/1281] lr: 4.286e-03, eta: 6:14:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1356, loss: 0.1356 +2025-06-25 04:11:59,815 - pyskl - INFO - Epoch [110][400/1281] lr: 4.271e-03, eta: 6:14:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 0.9994, loss_cls: 0.1469, loss: 0.1469 +2025-06-25 04:12:48,507 - pyskl - INFO - Epoch [110][500/1281] lr: 4.255e-03, eta: 6:13:20, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1266, loss: 0.1266 +2025-06-25 04:13:37,708 - pyskl - INFO - Epoch [110][600/1281] lr: 4.240e-03, eta: 6:12:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1385, loss: 0.1385 +2025-06-25 04:14:26,938 - pyskl - INFO - Epoch [110][700/1281] lr: 4.225e-03, eta: 6:11:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.1861, loss: 0.1861 +2025-06-25 04:15:15,945 - pyskl - INFO - Epoch [110][800/1281] lr: 4.209e-03, eta: 6:11:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1379, loss: 0.1379 +2025-06-25 04:16:05,023 - pyskl - INFO - Epoch [110][900/1281] lr: 4.194e-03, eta: 6:10:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1153, loss: 0.1153 +2025-06-25 04:16:54,340 - pyskl - INFO - Epoch [110][1000/1281] lr: 4.179e-03, eta: 6:09:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1134, loss: 0.1134 +2025-06-25 04:17:43,681 - pyskl - INFO - Epoch [110][1100/1281] lr: 4.164e-03, eta: 6:09:15, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 0.9994, loss_cls: 0.1203, loss: 0.1203 +2025-06-25 04:18:20,575 - pyskl - INFO - Epoch [110][1200/1281] lr: 4.148e-03, eta: 6:08:29, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1139, loss: 0.1139 +2025-06-25 04:19:04,251 - pyskl - INFO - Saving checkpoint at 110 epochs +2025-06-25 04:19:51,605 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:19:51,661 - pyskl - INFO - +top1_acc 0.9040 +top5_acc 0.9945 +2025-06-25 04:19:51,661 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:19:51,668 - pyskl - INFO - +mean_acc 0.8719 +2025-06-25 04:19:51,672 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_107.pth was removed +2025-06-25 04:19:51,992 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_110.pth. +2025-06-25 04:19:51,992 - pyskl - INFO - Best top1_acc is 0.9040 at 110 epoch. +2025-06-25 04:19:51,995 - pyskl - INFO - Epoch(val) [110][533] top1_acc: 0.9040, top5_acc: 0.9945, mean_class_accuracy: 0.8719 +2025-06-25 04:21:10,792 - pyskl - INFO - Epoch [111][100/1281] lr: 4.121e-03, eta: 6:07:12, time: 0.788, data_time: 0.179, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0753, loss: 0.0753 +2025-06-25 04:21:59,910 - pyskl - INFO - Epoch [111][200/1281] lr: 4.106e-03, eta: 6:06:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.1009, loss: 0.1009 +2025-06-25 04:22:48,659 - pyskl - INFO - Epoch [111][300/1281] lr: 4.091e-03, eta: 6:05:50, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1145, loss: 0.1145 +2025-06-25 04:23:37,568 - pyskl - INFO - Epoch [111][400/1281] lr: 4.075e-03, eta: 6:05:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1187, loss: 0.1187 +2025-06-25 04:24:26,574 - pyskl - INFO - Epoch [111][500/1281] lr: 4.060e-03, eta: 6:04:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1294, loss: 0.1294 +2025-06-25 04:25:15,641 - pyskl - INFO - Epoch [111][600/1281] lr: 4.045e-03, eta: 6:03:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 0.9994, loss_cls: 0.1138, loss: 0.1138 +2025-06-25 04:26:04,653 - pyskl - INFO - Epoch [111][700/1281] lr: 4.030e-03, eta: 6:03:06, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 0.9994, loss_cls: 0.1124, loss: 0.1124 +2025-06-25 04:26:54,034 - pyskl - INFO - Epoch [111][800/1281] lr: 4.015e-03, eta: 6:02:25, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1056, loss: 0.1056 +2025-06-25 04:27:43,150 - pyskl - INFO - Epoch [111][900/1281] lr: 4.000e-03, eta: 6:01:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1622, loss: 0.1622 +2025-06-25 04:28:32,193 - pyskl - INFO - Epoch [111][1000/1281] lr: 3.985e-03, eta: 6:01:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1317, loss: 0.1317 +2025-06-25 04:29:21,499 - pyskl - INFO - Epoch [111][1100/1281] lr: 3.970e-03, eta: 6:00:22, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1138, loss: 0.1138 +2025-06-25 04:29:59,396 - pyskl - INFO - Epoch [111][1200/1281] lr: 3.955e-03, eta: 5:59:37, time: 0.379, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1099, loss: 0.1099 +2025-06-25 04:30:41,579 - pyskl - INFO - Saving checkpoint at 111 epochs +2025-06-25 04:31:28,545 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:31:28,600 - pyskl - INFO - +top1_acc 0.9045 +top5_acc 0.9935 +2025-06-25 04:31:28,600 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:31:28,607 - pyskl - INFO - +mean_acc 0.8735 +2025-06-25 04:31:28,610 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_110.pth was removed +2025-06-25 04:31:28,782 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_111.pth. +2025-06-25 04:31:28,782 - pyskl - INFO - Best top1_acc is 0.9045 at 111 epoch. +2025-06-25 04:31:28,785 - pyskl - INFO - Epoch(val) [111][533] top1_acc: 0.9045, top5_acc: 0.9935, mean_class_accuracy: 0.8735 +2025-06-25 04:32:48,994 - pyskl - INFO - Epoch [112][100/1281] lr: 3.928e-03, eta: 5:58:20, time: 0.802, data_time: 0.186, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0882, loss: 0.0882 +2025-06-25 04:33:38,243 - pyskl - INFO - Epoch [112][200/1281] lr: 3.914e-03, eta: 5:57:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.0935, loss: 0.0935 +2025-06-25 04:34:27,498 - pyskl - INFO - Epoch [112][300/1281] lr: 3.899e-03, eta: 5:56:58, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 0.9994, loss_cls: 0.1590, loss: 0.1590 +2025-06-25 04:35:16,783 - pyskl - INFO - Epoch [112][400/1281] lr: 3.884e-03, eta: 5:56:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1536, loss: 0.1536 +2025-06-25 04:36:05,837 - pyskl - INFO - Epoch [112][500/1281] lr: 3.869e-03, eta: 5:55:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1193, loss: 0.1193 +2025-06-25 04:36:54,613 - pyskl - INFO - Epoch [112][600/1281] lr: 3.854e-03, eta: 5:54:54, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1130, loss: 0.1130 +2025-06-25 04:37:43,683 - pyskl - INFO - Epoch [112][700/1281] lr: 3.840e-03, eta: 5:54:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1334, loss: 0.1334 +2025-06-25 04:38:33,091 - pyskl - INFO - Epoch [112][800/1281] lr: 3.825e-03, eta: 5:53:32, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1319, loss: 0.1319 +2025-06-25 04:39:22,496 - pyskl - INFO - Epoch [112][900/1281] lr: 3.810e-03, eta: 5:52:51, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1098, loss: 0.1098 +2025-06-25 04:40:11,568 - pyskl - INFO - Epoch [112][1000/1281] lr: 3.795e-03, eta: 5:52:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1286, loss: 0.1286 +2025-06-25 04:41:00,850 - pyskl - INFO - Epoch [112][1100/1281] lr: 3.781e-03, eta: 5:51:29, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1316, loss: 0.1316 +2025-06-25 04:41:36,982 - pyskl - INFO - Epoch [112][1200/1281] lr: 3.766e-03, eta: 5:50:44, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0965, loss: 0.0965 +2025-06-25 04:42:24,168 - pyskl - INFO - Saving checkpoint at 112 epochs +2025-06-25 04:43:12,166 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:43:12,220 - pyskl - INFO - +top1_acc 0.9082 +top5_acc 0.9937 +2025-06-25 04:43:12,220 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:43:12,226 - pyskl - INFO - +mean_acc 0.8763 +2025-06-25 04:43:12,230 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_111.pth was removed +2025-06-25 04:43:12,402 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_112.pth. +2025-06-25 04:43:12,402 - pyskl - INFO - Best top1_acc is 0.9082 at 112 epoch. +2025-06-25 04:43:12,405 - pyskl - INFO - Epoch(val) [112][533] top1_acc: 0.9082, top5_acc: 0.9937, mean_class_accuracy: 0.8763 +2025-06-25 04:44:32,111 - pyskl - INFO - Epoch [113][100/1281] lr: 3.740e-03, eta: 5:49:26, time: 0.797, data_time: 0.186, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0842, loss: 0.0842 +2025-06-25 04:45:21,703 - pyskl - INFO - Epoch [113][200/1281] lr: 3.725e-03, eta: 5:48:45, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0732, loss: 0.0732 +2025-06-25 04:46:10,783 - pyskl - INFO - Epoch [113][300/1281] lr: 3.711e-03, eta: 5:48:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1058, loss: 0.1058 +2025-06-25 04:46:59,506 - pyskl - INFO - Epoch [113][400/1281] lr: 3.696e-03, eta: 5:47:23, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 0.9994, loss_cls: 0.0949, loss: 0.0949 +2025-06-25 04:47:48,630 - pyskl - INFO - Epoch [113][500/1281] lr: 3.682e-03, eta: 5:46:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0761, loss: 0.0761 +2025-06-25 04:48:37,975 - pyskl - INFO - Epoch [113][600/1281] lr: 3.667e-03, eta: 5:46:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0607, loss: 0.0607 +2025-06-25 04:49:26,863 - pyskl - INFO - Epoch [113][700/1281] lr: 3.653e-03, eta: 5:45:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0788, loss: 0.0788 +2025-06-25 04:50:15,889 - pyskl - INFO - Epoch [113][800/1281] lr: 3.638e-03, eta: 5:44:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0869, loss: 0.0869 +2025-06-25 04:51:04,780 - pyskl - INFO - Epoch [113][900/1281] lr: 3.624e-03, eta: 5:43:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0767, loss: 0.0767 +2025-06-25 04:51:53,924 - pyskl - INFO - Epoch [113][1000/1281] lr: 3.610e-03, eta: 5:43:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0653, loss: 0.0653 +2025-06-25 04:52:43,086 - pyskl - INFO - Epoch [113][1100/1281] lr: 3.595e-03, eta: 5:42:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0598, loss: 0.0598 +2025-06-25 04:53:19,814 - pyskl - INFO - Epoch [113][1200/1281] lr: 3.581e-03, eta: 5:41:49, time: 0.367, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1147, loss: 0.1147 +2025-06-25 04:54:05,096 - pyskl - INFO - Saving checkpoint at 113 epochs +2025-06-25 04:54:52,764 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:54:52,826 - pyskl - INFO - +top1_acc 0.9085 +top5_acc 0.9951 +2025-06-25 04:54:52,826 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:54:52,832 - pyskl - INFO - +mean_acc 0.8704 +2025-06-25 04:54:52,836 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_112.pth was removed +2025-06-25 04:54:53,000 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_113.pth. +2025-06-25 04:54:53,001 - pyskl - INFO - Best top1_acc is 0.9085 at 113 epoch. +2025-06-25 04:54:53,004 - pyskl - INFO - Epoch(val) [113][533] top1_acc: 0.9085, top5_acc: 0.9951, mean_class_accuracy: 0.8704 +2025-06-25 04:56:12,899 - pyskl - INFO - Epoch [114][100/1281] lr: 3.555e-03, eta: 5:40:31, time: 0.799, data_time: 0.183, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0938, loss: 0.0938 +2025-06-25 04:57:02,422 - pyskl - INFO - Epoch [114][200/1281] lr: 3.541e-03, eta: 5:39:50, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.1023, loss: 0.1023 +2025-06-25 04:57:51,467 - pyskl - INFO - Epoch [114][300/1281] lr: 3.526e-03, eta: 5:39:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0685, loss: 0.0685 +2025-06-25 04:58:40,704 - pyskl - INFO - Epoch [114][400/1281] lr: 3.512e-03, eta: 5:38:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0732, loss: 0.0732 +2025-06-25 04:59:29,629 - pyskl - INFO - Epoch [114][500/1281] lr: 3.498e-03, eta: 5:37:46, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1203, loss: 0.1203 +2025-06-25 05:00:18,744 - pyskl - INFO - Epoch [114][600/1281] lr: 3.484e-03, eta: 5:37:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1314, loss: 0.1314 +2025-06-25 05:01:07,855 - pyskl - INFO - Epoch [114][700/1281] lr: 3.470e-03, eta: 5:36:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1225, loss: 0.1225 +2025-06-25 05:01:56,270 - pyskl - INFO - Epoch [114][800/1281] lr: 3.456e-03, eta: 5:35:42, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.0919, loss: 0.0919 +2025-06-25 05:02:45,265 - pyskl - INFO - Epoch [114][900/1281] lr: 3.442e-03, eta: 5:35:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0650, loss: 0.0650 +2025-06-25 05:03:34,303 - pyskl - INFO - Epoch [114][1000/1281] lr: 3.427e-03, eta: 5:34:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0814, loss: 0.0814 +2025-06-25 05:04:23,478 - pyskl - INFO - Epoch [114][1100/1281] lr: 3.413e-03, eta: 5:33:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 0.9994, loss_cls: 0.1184, loss: 0.1184 +2025-06-25 05:05:00,259 - pyskl - INFO - Epoch [114][1200/1281] lr: 3.399e-03, eta: 5:32:52, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1083, loss: 0.1083 +2025-06-25 05:05:44,831 - pyskl - INFO - Saving checkpoint at 114 epochs +2025-06-25 05:06:32,080 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:06:32,134 - pyskl - INFO - +top1_acc 0.9114 +top5_acc 0.9957 +2025-06-25 05:06:32,134 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:06:32,140 - pyskl - INFO - +mean_acc 0.8763 +2025-06-25 05:06:32,144 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_113.pth was removed +2025-06-25 05:06:32,315 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_114.pth. +2025-06-25 05:06:32,315 - pyskl - INFO - Best top1_acc is 0.9114 at 114 epoch. +2025-06-25 05:06:32,318 - pyskl - INFO - Epoch(val) [114][533] top1_acc: 0.9114, top5_acc: 0.9957, mean_class_accuracy: 0.8763 +2025-06-25 05:07:53,552 - pyskl - INFO - Epoch [115][100/1281] lr: 3.374e-03, eta: 5:31:35, time: 0.812, data_time: 0.188, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0754, loss: 0.0754 +2025-06-25 05:08:42,975 - pyskl - INFO - Epoch [115][200/1281] lr: 3.360e-03, eta: 5:30:54, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0603, loss: 0.0603 +2025-06-25 05:09:32,211 - pyskl - INFO - Epoch [115][300/1281] lr: 3.346e-03, eta: 5:30:12, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0755, loss: 0.0755 +2025-06-25 05:10:21,475 - pyskl - INFO - Epoch [115][400/1281] lr: 3.332e-03, eta: 5:29:31, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0780, loss: 0.0780 +2025-06-25 05:11:10,829 - pyskl - INFO - Epoch [115][500/1281] lr: 3.318e-03, eta: 5:28:50, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0773, loss: 0.0773 +2025-06-25 05:12:00,025 - pyskl - INFO - Epoch [115][600/1281] lr: 3.305e-03, eta: 5:28:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0745, loss: 0.0745 +2025-06-25 05:12:49,030 - pyskl - INFO - Epoch [115][700/1281] lr: 3.291e-03, eta: 5:27:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1126, loss: 0.1126 +2025-06-25 05:13:38,526 - pyskl - INFO - Epoch [115][800/1281] lr: 3.277e-03, eta: 5:26:45, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0755, loss: 0.0755 +2025-06-25 05:14:27,666 - pyskl - INFO - Epoch [115][900/1281] lr: 3.263e-03, eta: 5:26:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0754, loss: 0.0754 +2025-06-25 05:15:16,696 - pyskl - INFO - Epoch [115][1000/1281] lr: 3.249e-03, eta: 5:25:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0914, loss: 0.0914 +2025-06-25 05:16:06,054 - pyskl - INFO - Epoch [115][1100/1281] lr: 3.236e-03, eta: 5:24:41, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1198, loss: 0.1198 +2025-06-25 05:16:40,768 - pyskl - INFO - Epoch [115][1200/1281] lr: 3.222e-03, eta: 5:23:55, time: 0.347, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0890, loss: 0.0890 +2025-06-25 05:17:28,438 - pyskl - INFO - Saving checkpoint at 115 epochs +2025-06-25 05:18:16,701 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:18:16,756 - pyskl - INFO - +top1_acc 0.9066 +top5_acc 0.9948 +2025-06-25 05:18:16,756 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:18:16,763 - pyskl - INFO - +mean_acc 0.8791 +2025-06-25 05:18:16,765 - pyskl - INFO - Epoch(val) [115][533] top1_acc: 0.9066, top5_acc: 0.9948, mean_class_accuracy: 0.8791 +2025-06-25 05:19:37,164 - pyskl - INFO - Epoch [116][100/1281] lr: 3.197e-03, eta: 5:22:37, time: 0.804, data_time: 0.186, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0768, loss: 0.0768 +2025-06-25 05:20:26,214 - pyskl - INFO - Epoch [116][200/1281] lr: 3.184e-03, eta: 5:21:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0585, loss: 0.0585 +2025-06-25 05:21:15,437 - pyskl - INFO - Epoch [116][300/1281] lr: 3.170e-03, eta: 5:21:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0623, loss: 0.0623 +2025-06-25 05:22:04,331 - pyskl - INFO - Epoch [116][400/1281] lr: 3.156e-03, eta: 5:20:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0640, loss: 0.0640 +2025-06-25 05:22:53,142 - pyskl - INFO - Epoch [116][500/1281] lr: 3.143e-03, eta: 5:19:51, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0648, loss: 0.0648 +2025-06-25 05:23:42,192 - pyskl - INFO - Epoch [116][600/1281] lr: 3.129e-03, eta: 5:19:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0671, loss: 0.0671 +2025-06-25 05:24:30,770 - pyskl - INFO - Epoch [116][700/1281] lr: 3.116e-03, eta: 5:18:28, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0547, loss: 0.0547 +2025-06-25 05:25:19,993 - pyskl - INFO - Epoch [116][800/1281] lr: 3.102e-03, eta: 5:17:46, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0517, loss: 0.0517 +2025-06-25 05:26:09,097 - pyskl - INFO - Epoch [116][900/1281] lr: 3.089e-03, eta: 5:17:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0887, loss: 0.0887 +2025-06-25 05:26:58,177 - pyskl - INFO - Epoch [116][1000/1281] lr: 3.075e-03, eta: 5:16:23, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0779, loss: 0.0779 +2025-06-25 05:27:47,057 - pyskl - INFO - Epoch [116][1100/1281] lr: 3.062e-03, eta: 5:15:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0836, loss: 0.0836 +2025-06-25 05:28:23,650 - pyskl - INFO - Epoch [116][1200/1281] lr: 3.049e-03, eta: 5:14:56, time: 0.366, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0770, loss: 0.0770 +2025-06-25 05:29:08,609 - pyskl - INFO - Saving checkpoint at 116 epochs +2025-06-25 05:29:56,146 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:29:56,205 - pyskl - INFO - +top1_acc 0.9027 +top5_acc 0.9923 +2025-06-25 05:29:56,205 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:29:56,212 - pyskl - INFO - +mean_acc 0.8628 +2025-06-25 05:29:56,214 - pyskl - INFO - Epoch(val) [116][533] top1_acc: 0.9027, top5_acc: 0.9923, mean_class_accuracy: 0.8628 +2025-06-25 05:31:16,031 - pyskl - INFO - Epoch [117][100/1281] lr: 3.024e-03, eta: 5:13:38, time: 0.798, data_time: 0.191, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0820, loss: 0.0820 +2025-06-25 05:32:05,189 - pyskl - INFO - Epoch [117][200/1281] lr: 3.011e-03, eta: 5:12:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0941, loss: 0.0941 +2025-06-25 05:32:53,973 - pyskl - INFO - Epoch [117][300/1281] lr: 2.998e-03, eta: 5:12:15, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0663, loss: 0.0663 +2025-06-25 05:33:43,328 - pyskl - INFO - Epoch [117][400/1281] lr: 2.984e-03, eta: 5:11:33, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0722, loss: 0.0722 +2025-06-25 05:34:32,566 - pyskl - INFO - Epoch [117][500/1281] lr: 2.971e-03, eta: 5:10:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 0.9994, loss_cls: 0.0615, loss: 0.0615 +2025-06-25 05:35:21,418 - pyskl - INFO - Epoch [117][600/1281] lr: 2.958e-03, eta: 5:10:10, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0779, loss: 0.0779 +2025-06-25 05:36:10,404 - pyskl - INFO - Epoch [117][700/1281] lr: 2.945e-03, eta: 5:09:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0799, loss: 0.0799 +2025-06-25 05:36:59,756 - pyskl - INFO - Epoch [117][800/1281] lr: 2.932e-03, eta: 5:08:47, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0582, loss: 0.0582 +2025-06-25 05:37:48,833 - pyskl - INFO - Epoch [117][900/1281] lr: 2.919e-03, eta: 5:08:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0577, loss: 0.0577 +2025-06-25 05:38:37,913 - pyskl - INFO - Epoch [117][1000/1281] lr: 2.905e-03, eta: 5:07:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0916, loss: 0.0916 +2025-06-25 05:39:27,201 - pyskl - INFO - Epoch [117][1100/1281] lr: 2.892e-03, eta: 5:06:42, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0611, loss: 0.0611 +2025-06-25 05:40:03,970 - pyskl - INFO - Epoch [117][1200/1281] lr: 2.879e-03, eta: 5:05:56, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0583, loss: 0.0583 +2025-06-25 05:40:49,166 - pyskl - INFO - Saving checkpoint at 117 epochs +2025-06-25 05:41:36,164 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:41:36,221 - pyskl - INFO - +top1_acc 0.9147 +top5_acc 0.9937 +2025-06-25 05:41:36,221 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:41:36,227 - pyskl - INFO - +mean_acc 0.8818 +2025-06-25 05:41:36,232 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_114.pth was removed +2025-06-25 05:41:36,407 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_117.pth. +2025-06-25 05:41:36,407 - pyskl - INFO - Best top1_acc is 0.9147 at 117 epoch. +2025-06-25 05:41:36,410 - pyskl - INFO - Epoch(val) [117][533] top1_acc: 0.9147, top5_acc: 0.9937, mean_class_accuracy: 0.8818 +2025-06-25 05:42:56,107 - pyskl - INFO - Epoch [118][100/1281] lr: 2.856e-03, eta: 5:04:38, time: 0.797, data_time: 0.183, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0560, loss: 0.0560 +2025-06-25 05:43:45,466 - pyskl - INFO - Epoch [118][200/1281] lr: 2.843e-03, eta: 5:03:57, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0546, loss: 0.0546 +2025-06-25 05:44:34,386 - pyskl - INFO - Epoch [118][300/1281] lr: 2.830e-03, eta: 5:03:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0638, loss: 0.0638 +2025-06-25 05:45:23,745 - pyskl - INFO - Epoch [118][400/1281] lr: 2.817e-03, eta: 5:02:33, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0569, loss: 0.0569 +2025-06-25 05:46:12,783 - pyskl - INFO - Epoch [118][500/1281] lr: 2.804e-03, eta: 5:01:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0458, loss: 0.0458 +2025-06-25 05:47:01,776 - pyskl - INFO - Epoch [118][600/1281] lr: 2.791e-03, eta: 5:01:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0510, loss: 0.0510 +2025-06-25 05:47:50,672 - pyskl - INFO - Epoch [118][700/1281] lr: 2.778e-03, eta: 5:00:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0423, loss: 0.0423 +2025-06-25 05:48:39,865 - pyskl - INFO - Epoch [118][800/1281] lr: 2.765e-03, eta: 4:59:46, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0575, loss: 0.0575 +2025-06-25 05:49:29,253 - pyskl - INFO - Epoch [118][900/1281] lr: 2.753e-03, eta: 4:59:04, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0515, loss: 0.0515 +2025-06-25 05:50:18,271 - pyskl - INFO - Epoch [118][1000/1281] lr: 2.740e-03, eta: 4:58:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0691, loss: 0.0691 +2025-06-25 05:51:07,758 - pyskl - INFO - Epoch [118][1100/1281] lr: 2.727e-03, eta: 4:57:41, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0529, loss: 0.0529 +2025-06-25 05:51:44,137 - pyskl - INFO - Epoch [118][1200/1281] lr: 2.714e-03, eta: 4:56:56, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0626, loss: 0.0626 +2025-06-25 05:52:29,479 - pyskl - INFO - Saving checkpoint at 118 epochs +2025-06-25 05:53:17,150 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:53:17,217 - pyskl - INFO - +top1_acc 0.9143 +top5_acc 0.9954 +2025-06-25 05:53:17,217 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:53:17,225 - pyskl - INFO - +mean_acc 0.8793 +2025-06-25 05:53:17,227 - pyskl - INFO - Epoch(val) [118][533] top1_acc: 0.9143, top5_acc: 0.9954, mean_class_accuracy: 0.8793 +2025-06-25 05:54:36,536 - pyskl - INFO - Epoch [119][100/1281] lr: 2.691e-03, eta: 4:55:37, time: 0.793, data_time: 0.187, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0470, loss: 0.0470 +2025-06-25 05:55:25,866 - pyskl - INFO - Epoch [119][200/1281] lr: 2.679e-03, eta: 4:54:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0392, loss: 0.0392 +2025-06-25 05:56:15,146 - pyskl - INFO - Epoch [119][300/1281] lr: 2.666e-03, eta: 4:54:14, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0588, loss: 0.0588 +2025-06-25 05:57:04,107 - pyskl - INFO - Epoch [119][400/1281] lr: 2.653e-03, eta: 4:53:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0435, loss: 0.0435 +2025-06-25 05:57:53,197 - pyskl - INFO - Epoch [119][500/1281] lr: 2.641e-03, eta: 4:52:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0429, loss: 0.0429 +2025-06-25 05:58:42,479 - pyskl - INFO - Epoch [119][600/1281] lr: 2.628e-03, eta: 4:52:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0307, loss: 0.0307 +2025-06-25 05:59:31,377 - pyskl - INFO - Epoch [119][700/1281] lr: 2.616e-03, eta: 4:51:26, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0304, loss: 0.0304 +2025-06-25 06:00:20,531 - pyskl - INFO - Epoch [119][800/1281] lr: 2.603e-03, eta: 4:50:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0375, loss: 0.0375 +2025-06-25 06:01:09,859 - pyskl - INFO - Epoch [119][900/1281] lr: 2.591e-03, eta: 4:50:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0444, loss: 0.0444 +2025-06-25 06:01:58,833 - pyskl - INFO - Epoch [119][1000/1281] lr: 2.578e-03, eta: 4:49:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0502, loss: 0.0502 +2025-06-25 06:02:48,400 - pyskl - INFO - Epoch [119][1100/1281] lr: 2.566e-03, eta: 4:48:39, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0487, loss: 0.0487 +2025-06-25 06:03:25,597 - pyskl - INFO - Epoch [119][1200/1281] lr: 2.554e-03, eta: 4:47:54, time: 0.372, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0496, loss: 0.0496 +2025-06-25 06:04:08,955 - pyskl - INFO - Saving checkpoint at 119 epochs +2025-06-25 06:04:56,572 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:04:56,627 - pyskl - INFO - +top1_acc 0.9195 +top5_acc 0.9947 +2025-06-25 06:04:56,627 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:04:56,634 - pyskl - INFO - +mean_acc 0.8877 +2025-06-25 06:04:56,638 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_117.pth was removed +2025-06-25 06:04:56,869 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_119.pth. +2025-06-25 06:04:56,869 - pyskl - INFO - Best top1_acc is 0.9195 at 119 epoch. +2025-06-25 06:04:56,872 - pyskl - INFO - Epoch(val) [119][533] top1_acc: 0.9195, top5_acc: 0.9947, mean_class_accuracy: 0.8877 +2025-06-25 06:06:16,857 - pyskl - INFO - Epoch [120][100/1281] lr: 2.531e-03, eta: 4:46:35, time: 0.800, data_time: 0.192, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0399, loss: 0.0399 +2025-06-25 06:07:05,867 - pyskl - INFO - Epoch [120][200/1281] lr: 2.519e-03, eta: 4:45:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0283, loss: 0.0283 +2025-06-25 06:07:55,223 - pyskl - INFO - Epoch [120][300/1281] lr: 2.507e-03, eta: 4:45:12, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0314, loss: 0.0314 +2025-06-25 06:08:44,289 - pyskl - INFO - Epoch [120][400/1281] lr: 2.494e-03, eta: 4:44:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0345, loss: 0.0345 +2025-06-25 06:09:33,843 - pyskl - INFO - Epoch [120][500/1281] lr: 2.482e-03, eta: 4:43:48, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0599, loss: 0.0599 +2025-06-25 06:10:23,021 - pyskl - INFO - Epoch [120][600/1281] lr: 2.470e-03, eta: 4:43:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0563, loss: 0.0563 +2025-06-25 06:11:12,003 - pyskl - INFO - Epoch [120][700/1281] lr: 2.458e-03, eta: 4:42:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0564, loss: 0.0564 +2025-06-25 06:12:01,477 - pyskl - INFO - Epoch [120][800/1281] lr: 2.446e-03, eta: 4:41:42, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0539, loss: 0.0539 +2025-06-25 06:12:50,622 - pyskl - INFO - Epoch [120][900/1281] lr: 2.433e-03, eta: 4:41:00, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0496, loss: 0.0496 +2025-06-25 06:13:39,504 - pyskl - INFO - Epoch [120][1000/1281] lr: 2.421e-03, eta: 4:40:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0508, loss: 0.0508 +2025-06-25 06:14:28,945 - pyskl - INFO - Epoch [120][1100/1281] lr: 2.409e-03, eta: 4:39:36, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0390, loss: 0.0390 +2025-06-25 06:15:04,488 - pyskl - INFO - Epoch [120][1200/1281] lr: 2.397e-03, eta: 4:38:50, time: 0.355, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0376, loss: 0.0376 +2025-06-25 06:15:51,847 - pyskl - INFO - Saving checkpoint at 120 epochs +2025-06-25 06:16:39,912 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:16:39,973 - pyskl - INFO - +top1_acc 0.9201 +top5_acc 0.9951 +2025-06-25 06:16:39,974 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:16:39,981 - pyskl - INFO - +mean_acc 0.8893 +2025-06-25 06:16:39,985 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_119.pth was removed +2025-06-25 06:16:40,156 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_120.pth. +2025-06-25 06:16:40,156 - pyskl - INFO - Best top1_acc is 0.9201 at 120 epoch. +2025-06-25 06:16:40,159 - pyskl - INFO - Epoch(val) [120][533] top1_acc: 0.9201, top5_acc: 0.9951, mean_class_accuracy: 0.8893 +2025-06-25 06:17:59,495 - pyskl - INFO - Epoch [121][100/1281] lr: 2.375e-03, eta: 4:37:32, time: 0.793, data_time: 0.187, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0345, loss: 0.0345 +2025-06-25 06:18:48,481 - pyskl - INFO - Epoch [121][200/1281] lr: 2.363e-03, eta: 4:36:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0390, loss: 0.0390 +2025-06-25 06:19:37,824 - pyskl - INFO - Epoch [121][300/1281] lr: 2.351e-03, eta: 4:36:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0327, loss: 0.0327 +2025-06-25 06:20:26,843 - pyskl - INFO - Epoch [121][400/1281] lr: 2.340e-03, eta: 4:35:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0326, loss: 0.0326 +2025-06-25 06:21:15,984 - pyskl - INFO - Epoch [121][500/1281] lr: 2.328e-03, eta: 4:34:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0434, loss: 0.0434 +2025-06-25 06:22:05,281 - pyskl - INFO - Epoch [121][600/1281] lr: 2.316e-03, eta: 4:34:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0365, loss: 0.0365 +2025-06-25 06:22:54,369 - pyskl - INFO - Epoch [121][700/1281] lr: 2.304e-03, eta: 4:33:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0355, loss: 0.0355 +2025-06-25 06:23:43,347 - pyskl - INFO - Epoch [121][800/1281] lr: 2.292e-03, eta: 4:32:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0352, loss: 0.0352 +2025-06-25 06:24:32,367 - pyskl - INFO - Epoch [121][900/1281] lr: 2.280e-03, eta: 4:31:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0449, loss: 0.0449 +2025-06-25 06:25:21,710 - pyskl - INFO - Epoch [121][1000/1281] lr: 2.269e-03, eta: 4:31:13, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0510, loss: 0.0510 +2025-06-25 06:26:10,824 - pyskl - INFO - Epoch [121][1100/1281] lr: 2.257e-03, eta: 4:30:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0364, loss: 0.0364 +2025-06-25 06:26:47,388 - pyskl - INFO - Epoch [121][1200/1281] lr: 2.245e-03, eta: 4:29:46, time: 0.366, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0467, loss: 0.0467 +2025-06-25 06:27:31,310 - pyskl - INFO - Saving checkpoint at 121 epochs +2025-06-25 06:28:19,120 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:28:19,175 - pyskl - INFO - +top1_acc 0.9166 +top5_acc 0.9953 +2025-06-25 06:28:19,175 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:28:19,181 - pyskl - INFO - +mean_acc 0.8817 +2025-06-25 06:28:19,183 - pyskl - INFO - Epoch(val) [121][533] top1_acc: 0.9166, top5_acc: 0.9953, mean_class_accuracy: 0.8817 +2025-06-25 06:29:37,552 - pyskl - INFO - Epoch [122][100/1281] lr: 2.224e-03, eta: 4:28:27, time: 0.784, data_time: 0.188, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0476, loss: 0.0476 +2025-06-25 06:30:26,611 - pyskl - INFO - Epoch [122][200/1281] lr: 2.212e-03, eta: 4:27:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0402, loss: 0.0402 +2025-06-25 06:31:15,650 - pyskl - INFO - Epoch [122][300/1281] lr: 2.201e-03, eta: 4:27:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0397, loss: 0.0397 +2025-06-25 06:32:04,914 - pyskl - INFO - Epoch [122][400/1281] lr: 2.189e-03, eta: 4:26:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0369, loss: 0.0369 +2025-06-25 06:32:54,375 - pyskl - INFO - Epoch [122][500/1281] lr: 2.178e-03, eta: 4:25:39, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0270, loss: 0.0270 +2025-06-25 06:33:43,651 - pyskl - INFO - Epoch [122][600/1281] lr: 2.166e-03, eta: 4:24:57, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0396, loss: 0.0396 +2025-06-25 06:34:32,731 - pyskl - INFO - Epoch [122][700/1281] lr: 2.155e-03, eta: 4:24:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0302, loss: 0.0302 +2025-06-25 06:35:21,808 - pyskl - INFO - Epoch [122][800/1281] lr: 2.143e-03, eta: 4:23:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0308, loss: 0.0308 +2025-06-25 06:36:10,765 - pyskl - INFO - Epoch [122][900/1281] lr: 2.132e-03, eta: 4:22:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0318, loss: 0.0318 +2025-06-25 06:37:00,083 - pyskl - INFO - Epoch [122][1000/1281] lr: 2.120e-03, eta: 4:22:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0306, loss: 0.0306 +2025-06-25 06:37:49,586 - pyskl - INFO - Epoch [122][1100/1281] lr: 2.109e-03, eta: 4:21:26, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0467, loss: 0.0467 +2025-06-25 06:38:28,118 - pyskl - INFO - Epoch [122][1200/1281] lr: 2.098e-03, eta: 4:20:41, time: 0.385, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0493, loss: 0.0493 +2025-06-25 06:39:09,534 - pyskl - INFO - Saving checkpoint at 122 epochs +2025-06-25 06:39:56,083 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:39:56,139 - pyskl - INFO - +top1_acc 0.9162 +top5_acc 0.9957 +2025-06-25 06:39:56,139 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:39:56,147 - pyskl - INFO - +mean_acc 0.8839 +2025-06-25 06:39:56,149 - pyskl - INFO - Epoch(val) [122][533] top1_acc: 0.9162, top5_acc: 0.9957, mean_class_accuracy: 0.8839 +2025-06-25 06:41:15,877 - pyskl - INFO - Epoch [123][100/1281] lr: 2.077e-03, eta: 4:19:23, time: 0.797, data_time: 0.190, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0309, loss: 0.0309 +2025-06-25 06:42:05,309 - pyskl - INFO - Epoch [123][200/1281] lr: 2.066e-03, eta: 4:18:40, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0317, loss: 0.0317 +2025-06-25 06:42:54,464 - pyskl - INFO - Epoch [123][300/1281] lr: 2.055e-03, eta: 4:17:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0497, loss: 0.0497 +2025-06-25 06:43:43,654 - pyskl - INFO - Epoch [123][400/1281] lr: 2.044e-03, eta: 4:17:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0392, loss: 0.0392 +2025-06-25 06:44:33,033 - pyskl - INFO - Epoch [123][500/1281] lr: 2.032e-03, eta: 4:16:34, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0264, loss: 0.0264 +2025-06-25 06:45:22,151 - pyskl - INFO - Epoch [123][600/1281] lr: 2.021e-03, eta: 4:15:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0246, loss: 0.0246 +2025-06-25 06:46:11,115 - pyskl - INFO - Epoch [123][700/1281] lr: 2.010e-03, eta: 4:15:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0251, loss: 0.0251 +2025-06-25 06:47:00,128 - pyskl - INFO - Epoch [123][800/1281] lr: 1.999e-03, eta: 4:14:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0258, loss: 0.0258 +2025-06-25 06:47:49,083 - pyskl - INFO - Epoch [123][900/1281] lr: 1.988e-03, eta: 4:13:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0293, loss: 0.0293 +2025-06-25 06:48:38,445 - pyskl - INFO - Epoch [123][1000/1281] lr: 1.977e-03, eta: 4:13:02, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 06:49:27,602 - pyskl - INFO - Epoch [123][1100/1281] lr: 1.966e-03, eta: 4:12:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0289, loss: 0.0289 +2025-06-25 06:50:04,976 - pyskl - INFO - Epoch [123][1200/1281] lr: 1.955e-03, eta: 4:11:35, time: 0.374, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0351, loss: 0.0351 +2025-06-25 06:50:47,226 - pyskl - INFO - Saving checkpoint at 123 epochs +2025-06-25 06:51:33,541 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:51:33,596 - pyskl - INFO - +top1_acc 0.9148 +top5_acc 0.9940 +2025-06-25 06:51:33,596 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:51:33,602 - pyskl - INFO - +mean_acc 0.8831 +2025-06-25 06:51:33,603 - pyskl - INFO - Epoch(val) [123][533] top1_acc: 0.9148, top5_acc: 0.9940, mean_class_accuracy: 0.8831 +2025-06-25 06:52:52,448 - pyskl - INFO - Epoch [124][100/1281] lr: 1.935e-03, eta: 4:10:16, time: 0.788, data_time: 0.181, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0323, loss: 0.0323 +2025-06-25 06:53:41,382 - pyskl - INFO - Epoch [124][200/1281] lr: 1.924e-03, eta: 4:09:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0538, loss: 0.0538 +2025-06-25 06:54:30,512 - pyskl - INFO - Epoch [124][300/1281] lr: 1.913e-03, eta: 4:08:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0548, loss: 0.0548 +2025-06-25 06:55:19,749 - pyskl - INFO - Epoch [124][400/1281] lr: 1.902e-03, eta: 4:08:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0502, loss: 0.0502 +2025-06-25 06:56:08,830 - pyskl - INFO - Epoch [124][500/1281] lr: 1.892e-03, eta: 4:07:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0448, loss: 0.0448 +2025-06-25 06:56:57,959 - pyskl - INFO - Epoch [124][600/1281] lr: 1.881e-03, eta: 4:06:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0434, loss: 0.0434 +2025-06-25 06:57:47,160 - pyskl - INFO - Epoch [124][700/1281] lr: 1.870e-03, eta: 4:06:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0414, loss: 0.0414 +2025-06-25 06:58:36,252 - pyskl - INFO - Epoch [124][800/1281] lr: 1.859e-03, eta: 4:05:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0280, loss: 0.0280 +2025-06-25 06:59:25,218 - pyskl - INFO - Epoch [124][900/1281] lr: 1.849e-03, eta: 4:04:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0307, loss: 0.0307 +2025-06-25 07:00:14,396 - pyskl - INFO - Epoch [124][1000/1281] lr: 1.838e-03, eta: 4:03:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0321, loss: 0.0321 +2025-06-25 07:01:03,605 - pyskl - INFO - Epoch [124][1100/1281] lr: 1.827e-03, eta: 4:03:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0425, loss: 0.0425 +2025-06-25 07:01:41,033 - pyskl - INFO - Epoch [124][1200/1281] lr: 1.817e-03, eta: 4:02:28, time: 0.374, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0328, loss: 0.0328 +2025-06-25 07:02:21,718 - pyskl - INFO - Saving checkpoint at 124 epochs +2025-06-25 07:03:08,440 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:03:08,508 - pyskl - INFO - +top1_acc 0.9162 +top5_acc 0.9948 +2025-06-25 07:03:08,509 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:03:08,516 - pyskl - INFO - +mean_acc 0.8858 +2025-06-25 07:03:08,518 - pyskl - INFO - Epoch(val) [124][533] top1_acc: 0.9162, top5_acc: 0.9948, mean_class_accuracy: 0.8858 +2025-06-25 07:04:27,636 - pyskl - INFO - Epoch [125][100/1281] lr: 1.797e-03, eta: 4:01:09, time: 0.791, data_time: 0.185, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0320, loss: 0.0320 +2025-06-25 07:05:16,452 - pyskl - INFO - Epoch [125][200/1281] lr: 1.787e-03, eta: 4:00:26, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0324, loss: 0.0324 +2025-06-25 07:06:06,001 - pyskl - INFO - Epoch [125][300/1281] lr: 1.776e-03, eta: 3:59:44, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0412, loss: 0.0412 +2025-06-25 07:06:54,855 - pyskl - INFO - Epoch [125][400/1281] lr: 1.766e-03, eta: 3:59:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0344, loss: 0.0344 +2025-06-25 07:07:43,859 - pyskl - INFO - Epoch [125][500/1281] lr: 1.755e-03, eta: 3:58:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0249, loss: 0.0249 +2025-06-25 07:08:32,819 - pyskl - INFO - Epoch [125][600/1281] lr: 1.745e-03, eta: 3:57:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0315, loss: 0.0315 +2025-06-25 07:09:21,786 - pyskl - INFO - Epoch [125][700/1281] lr: 1.735e-03, eta: 3:56:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0264, loss: 0.0264 +2025-06-25 07:10:10,760 - pyskl - INFO - Epoch [125][800/1281] lr: 1.724e-03, eta: 3:56:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0321, loss: 0.0321 +2025-06-25 07:10:59,960 - pyskl - INFO - Epoch [125][900/1281] lr: 1.714e-03, eta: 3:55:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0522, loss: 0.0522 +2025-06-25 07:11:49,268 - pyskl - INFO - Epoch [125][1000/1281] lr: 1.704e-03, eta: 3:54:47, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0339, loss: 0.0339 +2025-06-25 07:12:38,047 - pyskl - INFO - Epoch [125][1100/1281] lr: 1.693e-03, eta: 3:54:04, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0364, loss: 0.0364 +2025-06-25 07:13:15,712 - pyskl - INFO - Epoch [125][1200/1281] lr: 1.683e-03, eta: 3:53:19, time: 0.377, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0508, loss: 0.0508 +2025-06-25 07:13:58,234 - pyskl - INFO - Saving checkpoint at 125 epochs +2025-06-25 07:14:46,626 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:14:46,682 - pyskl - INFO - +top1_acc 0.9144 +top5_acc 0.9954 +2025-06-25 07:14:46,682 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:14:46,689 - pyskl - INFO - +mean_acc 0.8842 +2025-06-25 07:14:46,691 - pyskl - INFO - Epoch(val) [125][533] top1_acc: 0.9144, top5_acc: 0.9954, mean_class_accuracy: 0.8842 +2025-06-25 07:16:05,935 - pyskl - INFO - Epoch [126][100/1281] lr: 1.665e-03, eta: 3:52:01, time: 0.792, data_time: 0.186, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0328, loss: 0.0328 +2025-06-25 07:16:54,927 - pyskl - INFO - Epoch [126][200/1281] lr: 1.654e-03, eta: 3:51:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0331, loss: 0.0331 +2025-06-25 07:17:44,014 - pyskl - INFO - Epoch [126][300/1281] lr: 1.644e-03, eta: 3:50:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0289, loss: 0.0289 +2025-06-25 07:18:33,248 - pyskl - INFO - Epoch [126][400/1281] lr: 1.634e-03, eta: 3:49:53, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0317, loss: 0.0317 +2025-06-25 07:19:22,161 - pyskl - INFO - Epoch [126][500/1281] lr: 1.624e-03, eta: 3:49:10, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0432, loss: 0.0432 +2025-06-25 07:20:11,066 - pyskl - INFO - Epoch [126][600/1281] lr: 1.614e-03, eta: 3:48:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0335, loss: 0.0335 +2025-06-25 07:21:00,031 - pyskl - INFO - Epoch [126][700/1281] lr: 1.604e-03, eta: 3:47:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0307, loss: 0.0307 +2025-06-25 07:21:49,110 - pyskl - INFO - Epoch [126][800/1281] lr: 1.594e-03, eta: 3:47:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0296, loss: 0.0296 +2025-06-25 07:22:38,431 - pyskl - INFO - Epoch [126][900/1281] lr: 1.584e-03, eta: 3:46:20, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0331, loss: 0.0331 +2025-06-25 07:23:27,550 - pyskl - INFO - Epoch [126][1000/1281] lr: 1.574e-03, eta: 3:45:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 07:24:16,524 - pyskl - INFO - Epoch [126][1100/1281] lr: 1.564e-03, eta: 3:44:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 07:24:54,087 - pyskl - INFO - Epoch [126][1200/1281] lr: 1.554e-03, eta: 3:44:10, time: 0.376, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0411, loss: 0.0411 +2025-06-25 07:25:37,046 - pyskl - INFO - Saving checkpoint at 126 epochs +2025-06-25 07:26:23,804 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:26:23,861 - pyskl - INFO - +top1_acc 0.9125 +top5_acc 0.9952 +2025-06-25 07:26:23,861 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:26:23,869 - pyskl - INFO - +mean_acc 0.8764 +2025-06-25 07:26:23,871 - pyskl - INFO - Epoch(val) [126][533] top1_acc: 0.9125, top5_acc: 0.9952, mean_class_accuracy: 0.8764 +2025-06-25 07:27:44,560 - pyskl - INFO - Epoch [127][100/1281] lr: 1.536e-03, eta: 3:42:51, time: 0.807, data_time: 0.194, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0328, loss: 0.0328 +2025-06-25 07:28:33,843 - pyskl - INFO - Epoch [127][200/1281] lr: 1.527e-03, eta: 3:42:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0282, loss: 0.0282 +2025-06-25 07:29:23,195 - pyskl - INFO - Epoch [127][300/1281] lr: 1.517e-03, eta: 3:41:26, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0269, loss: 0.0269 +2025-06-25 07:30:12,312 - pyskl - INFO - Epoch [127][400/1281] lr: 1.507e-03, eta: 3:40:44, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0266, loss: 0.0266 +2025-06-25 07:31:01,663 - pyskl - INFO - Epoch [127][500/1281] lr: 1.497e-03, eta: 3:40:01, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 07:31:50,602 - pyskl - INFO - Epoch [127][600/1281] lr: 1.488e-03, eta: 3:39:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0261, loss: 0.0261 +2025-06-25 07:32:39,658 - pyskl - INFO - Epoch [127][700/1281] lr: 1.478e-03, eta: 3:38:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0292, loss: 0.0292 +2025-06-25 07:33:28,818 - pyskl - INFO - Epoch [127][800/1281] lr: 1.468e-03, eta: 3:37:53, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0294, loss: 0.0294 +2025-06-25 07:34:18,037 - pyskl - INFO - Epoch [127][900/1281] lr: 1.459e-03, eta: 3:37:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0286, loss: 0.0286 +2025-06-25 07:35:07,138 - pyskl - INFO - Epoch [127][1000/1281] lr: 1.449e-03, eta: 3:36:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0271, loss: 0.0271 +2025-06-25 07:35:56,074 - pyskl - INFO - Epoch [127][1100/1281] lr: 1.440e-03, eta: 3:35:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0357, loss: 0.0357 +2025-06-25 07:36:31,959 - pyskl - INFO - Epoch [127][1200/1281] lr: 1.430e-03, eta: 3:35:00, time: 0.359, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0297, loss: 0.0297 +2025-06-25 07:37:17,833 - pyskl - INFO - Saving checkpoint at 127 epochs +2025-06-25 07:38:06,438 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:38:06,493 - pyskl - INFO - +top1_acc 0.9177 +top5_acc 0.9957 +2025-06-25 07:38:06,493 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:38:06,500 - pyskl - INFO - +mean_acc 0.8869 +2025-06-25 07:38:06,502 - pyskl - INFO - Epoch(val) [127][533] top1_acc: 0.9177, top5_acc: 0.9957, mean_class_accuracy: 0.8869 +2025-06-25 07:39:25,713 - pyskl - INFO - Epoch [128][100/1281] lr: 1.413e-03, eta: 3:33:41, time: 0.792, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0264, loss: 0.0264 +2025-06-25 07:40:15,163 - pyskl - INFO - Epoch [128][200/1281] lr: 1.404e-03, eta: 3:32:58, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0356, loss: 0.0356 +2025-06-25 07:41:04,275 - pyskl - INFO - Epoch [128][300/1281] lr: 1.394e-03, eta: 3:32:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0274, loss: 0.0274 +2025-06-25 07:41:53,099 - pyskl - INFO - Epoch [128][400/1281] lr: 1.385e-03, eta: 3:31:33, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0257, loss: 0.0257 +2025-06-25 07:42:41,710 - pyskl - INFO - Epoch [128][500/1281] lr: 1.376e-03, eta: 3:30:50, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0256, loss: 0.0256 +2025-06-25 07:43:30,979 - pyskl - INFO - Epoch [128][600/1281] lr: 1.366e-03, eta: 3:30:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-25 07:44:20,153 - pyskl - INFO - Epoch [128][700/1281] lr: 1.357e-03, eta: 3:29:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0275, loss: 0.0275 +2025-06-25 07:45:09,162 - pyskl - INFO - Epoch [128][800/1281] lr: 1.348e-03, eta: 3:28:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0226, loss: 0.0226 +2025-06-25 07:45:58,013 - pyskl - INFO - Epoch [128][900/1281] lr: 1.339e-03, eta: 3:27:59, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0263, loss: 0.0263 +2025-06-25 07:46:47,393 - pyskl - INFO - Epoch [128][1000/1281] lr: 1.329e-03, eta: 3:27:17, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0218, loss: 0.0218 +2025-06-25 07:47:36,710 - pyskl - INFO - Epoch [128][1100/1281] lr: 1.320e-03, eta: 3:26:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 07:48:15,149 - pyskl - INFO - Epoch [128][1200/1281] lr: 1.311e-03, eta: 3:25:49, time: 0.384, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-25 07:48:58,053 - pyskl - INFO - Saving checkpoint at 128 epochs +2025-06-25 07:49:46,032 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:49:46,087 - pyskl - INFO - +top1_acc 0.9220 +top5_acc 0.9961 +2025-06-25 07:49:46,087 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:49:46,094 - pyskl - INFO - +mean_acc 0.8920 +2025-06-25 07:49:46,098 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_120.pth was removed +2025-06-25 07:49:46,286 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_128.pth. +2025-06-25 07:49:46,286 - pyskl - INFO - Best top1_acc is 0.9220 at 128 epoch. +2025-06-25 07:49:46,289 - pyskl - INFO - Epoch(val) [128][533] top1_acc: 0.9220, top5_acc: 0.9961, mean_class_accuracy: 0.8920 +2025-06-25 07:51:06,285 - pyskl - INFO - Epoch [129][100/1281] lr: 1.295e-03, eta: 3:24:30, time: 0.800, data_time: 0.185, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 07:51:55,591 - pyskl - INFO - Epoch [129][200/1281] lr: 1.286e-03, eta: 3:23:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-25 07:52:44,805 - pyskl - INFO - Epoch [129][300/1281] lr: 1.277e-03, eta: 3:23:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 07:53:33,938 - pyskl - INFO - Epoch [129][400/1281] lr: 1.268e-03, eta: 3:22:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 07:54:22,784 - pyskl - INFO - Epoch [129][500/1281] lr: 1.259e-03, eta: 3:21:39, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0237, loss: 0.0237 +2025-06-25 07:55:11,701 - pyskl - INFO - Epoch [129][600/1281] lr: 1.250e-03, eta: 3:20:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-25 07:56:00,740 - pyskl - INFO - Epoch [129][700/1281] lr: 1.241e-03, eta: 3:20:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 07:56:49,761 - pyskl - INFO - Epoch [129][800/1281] lr: 1.232e-03, eta: 3:19:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0277, loss: 0.0277 +2025-06-25 07:57:38,650 - pyskl - INFO - Epoch [129][900/1281] lr: 1.223e-03, eta: 3:18:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0226, loss: 0.0226 +2025-06-25 07:58:27,873 - pyskl - INFO - Epoch [129][1000/1281] lr: 1.214e-03, eta: 3:18:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0229, loss: 0.0229 +2025-06-25 07:59:17,091 - pyskl - INFO - Epoch [129][1100/1281] lr: 1.206e-03, eta: 3:17:22, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 07:59:53,890 - pyskl - INFO - Epoch [129][1200/1281] lr: 1.197e-03, eta: 3:16:37, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 08:00:37,048 - pyskl - INFO - Saving checkpoint at 129 epochs +2025-06-25 08:01:24,504 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:01:24,561 - pyskl - INFO - +top1_acc 0.9224 +top5_acc 0.9958 +2025-06-25 08:01:24,561 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:01:24,569 - pyskl - INFO - +mean_acc 0.8959 +2025-06-25 08:01:24,573 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_128.pth was removed +2025-06-25 08:01:24,749 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_129.pth. +2025-06-25 08:01:24,749 - pyskl - INFO - Best top1_acc is 0.9224 at 129 epoch. +2025-06-25 08:01:24,752 - pyskl - INFO - Epoch(val) [129][533] top1_acc: 0.9224, top5_acc: 0.9958, mean_class_accuracy: 0.8959 +2025-06-25 08:02:43,691 - pyskl - INFO - Epoch [130][100/1281] lr: 1.181e-03, eta: 3:15:18, time: 0.789, data_time: 0.188, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 08:03:33,156 - pyskl - INFO - Epoch [130][200/1281] lr: 1.172e-03, eta: 3:14:35, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 08:04:22,378 - pyskl - INFO - Epoch [130][300/1281] lr: 1.164e-03, eta: 3:13:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0249, loss: 0.0249 +2025-06-25 08:05:11,693 - pyskl - INFO - Epoch [130][400/1281] lr: 1.155e-03, eta: 3:13:10, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-25 08:06:00,742 - pyskl - INFO - Epoch [130][500/1281] lr: 1.147e-03, eta: 3:12:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0226, loss: 0.0226 +2025-06-25 08:06:49,965 - pyskl - INFO - Epoch [130][600/1281] lr: 1.138e-03, eta: 3:11:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 08:07:38,977 - pyskl - INFO - Epoch [130][700/1281] lr: 1.130e-03, eta: 3:11:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0222, loss: 0.0222 +2025-06-25 08:08:27,864 - pyskl - INFO - Epoch [130][800/1281] lr: 1.121e-03, eta: 3:10:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-25 08:09:16,886 - pyskl - INFO - Epoch [130][900/1281] lr: 1.113e-03, eta: 3:09:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 08:10:05,901 - pyskl - INFO - Epoch [130][1000/1281] lr: 1.104e-03, eta: 3:08:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 08:10:54,792 - pyskl - INFO - Epoch [130][1100/1281] lr: 1.096e-03, eta: 3:08:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 08:11:32,633 - pyskl - INFO - Epoch [130][1200/1281] lr: 1.088e-03, eta: 3:07:25, time: 0.378, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 08:12:14,243 - pyskl - INFO - Saving checkpoint at 130 epochs +2025-06-25 08:13:01,618 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:13:01,672 - pyskl - INFO - +top1_acc 0.9237 +top5_acc 0.9953 +2025-06-25 08:13:01,672 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:13:01,678 - pyskl - INFO - +mean_acc 0.8939 +2025-06-25 08:13:01,683 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_129.pth was removed +2025-06-25 08:13:01,858 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_130.pth. +2025-06-25 08:13:01,858 - pyskl - INFO - Best top1_acc is 0.9237 at 130 epoch. +2025-06-25 08:13:01,860 - pyskl - INFO - Epoch(val) [130][533] top1_acc: 0.9237, top5_acc: 0.9953, mean_class_accuracy: 0.8939 +2025-06-25 08:14:22,323 - pyskl - INFO - Epoch [131][100/1281] lr: 1.072e-03, eta: 3:06:06, time: 0.805, data_time: 0.190, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 08:15:11,470 - pyskl - INFO - Epoch [131][200/1281] lr: 1.064e-03, eta: 3:05:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 08:16:00,516 - pyskl - INFO - Epoch [131][300/1281] lr: 1.056e-03, eta: 3:04:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 08:16:49,642 - pyskl - INFO - Epoch [131][400/1281] lr: 1.048e-03, eta: 3:03:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 08:17:38,719 - pyskl - INFO - Epoch [131][500/1281] lr: 1.040e-03, eta: 3:03:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 08:18:27,464 - pyskl - INFO - Epoch [131][600/1281] lr: 1.031e-03, eta: 3:02:31, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 08:19:16,036 - pyskl - INFO - Epoch [131][700/1281] lr: 1.023e-03, eta: 3:01:48, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 08:20:04,973 - pyskl - INFO - Epoch [131][800/1281] lr: 1.015e-03, eta: 3:01:05, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0219, loss: 0.0219 +2025-06-25 08:20:54,015 - pyskl - INFO - Epoch [131][900/1281] lr: 1.007e-03, eta: 3:00:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 08:21:43,088 - pyskl - INFO - Epoch [131][1000/1281] lr: 9.992e-04, eta: 2:59:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 08:22:32,244 - pyskl - INFO - Epoch [131][1100/1281] lr: 9.912e-04, eta: 2:58:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 08:23:09,839 - pyskl - INFO - Epoch [131][1200/1281] lr: 9.832e-04, eta: 2:58:11, time: 0.376, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 08:23:52,769 - pyskl - INFO - Saving checkpoint at 131 epochs +2025-06-25 08:24:40,457 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:24:40,515 - pyskl - INFO - +top1_acc 0.9232 +top5_acc 0.9961 +2025-06-25 08:24:40,515 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:24:40,523 - pyskl - INFO - +mean_acc 0.8918 +2025-06-25 08:24:40,525 - pyskl - INFO - Epoch(val) [131][533] top1_acc: 0.9232, top5_acc: 0.9961, mean_class_accuracy: 0.8918 +2025-06-25 08:26:00,246 - pyskl - INFO - Epoch [132][100/1281] lr: 9.689e-04, eta: 2:56:52, time: 0.797, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 08:26:49,344 - pyskl - INFO - Epoch [132][200/1281] lr: 9.610e-04, eta: 2:56:09, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 08:27:38,222 - pyskl - INFO - Epoch [132][300/1281] lr: 9.532e-04, eta: 2:55:26, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-25 08:28:27,138 - pyskl - INFO - Epoch [132][400/1281] lr: 9.454e-04, eta: 2:54:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 08:29:16,081 - pyskl - INFO - Epoch [132][500/1281] lr: 9.376e-04, eta: 2:54:00, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 08:30:04,891 - pyskl - INFO - Epoch [132][600/1281] lr: 9.298e-04, eta: 2:53:17, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 08:30:54,129 - pyskl - INFO - Epoch [132][700/1281] lr: 9.221e-04, eta: 2:52:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 08:31:43,355 - pyskl - INFO - Epoch [132][800/1281] lr: 9.144e-04, eta: 2:51:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 08:32:32,415 - pyskl - INFO - Epoch [132][900/1281] lr: 9.068e-04, eta: 2:51:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 08:33:21,246 - pyskl - INFO - Epoch [132][1000/1281] lr: 8.991e-04, eta: 2:50:24, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 08:34:09,943 - pyskl - INFO - Epoch [132][1100/1281] lr: 8.915e-04, eta: 2:49:41, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 08:34:47,035 - pyskl - INFO - Epoch [132][1200/1281] lr: 8.840e-04, eta: 2:48:56, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 08:35:31,672 - pyskl - INFO - Saving checkpoint at 132 epochs +2025-06-25 08:36:19,999 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:36:20,055 - pyskl - INFO - +top1_acc 0.9227 +top5_acc 0.9965 +2025-06-25 08:36:20,055 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:36:20,061 - pyskl - INFO - +mean_acc 0.8933 +2025-06-25 08:36:20,063 - pyskl - INFO - Epoch(val) [132][533] top1_acc: 0.9227, top5_acc: 0.9965, mean_class_accuracy: 0.8933 +2025-06-25 08:37:39,872 - pyskl - INFO - Epoch [133][100/1281] lr: 8.704e-04, eta: 2:47:37, time: 0.798, data_time: 0.189, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 08:38:28,580 - pyskl - INFO - Epoch [133][200/1281] lr: 8.629e-04, eta: 2:46:54, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 08:39:17,456 - pyskl - INFO - Epoch [133][300/1281] lr: 8.554e-04, eta: 2:46:11, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 08:40:06,211 - pyskl - INFO - Epoch [133][400/1281] lr: 8.480e-04, eta: 2:45:28, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 08:40:55,472 - pyskl - INFO - Epoch [133][500/1281] lr: 8.406e-04, eta: 2:44:45, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 08:41:44,507 - pyskl - INFO - Epoch [133][600/1281] lr: 8.333e-04, eta: 2:44:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 08:42:33,737 - pyskl - INFO - Epoch [133][700/1281] lr: 8.260e-04, eta: 2:43:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 08:43:22,853 - pyskl - INFO - Epoch [133][800/1281] lr: 8.187e-04, eta: 2:42:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-25 08:44:12,263 - pyskl - INFO - Epoch [133][900/1281] lr: 8.114e-04, eta: 2:41:52, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-25 08:45:01,021 - pyskl - INFO - Epoch [133][1000/1281] lr: 8.042e-04, eta: 2:41:09, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 08:45:49,864 - pyskl - INFO - Epoch [133][1100/1281] lr: 7.970e-04, eta: 2:40:26, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 08:46:27,754 - pyskl - INFO - Epoch [133][1200/1281] lr: 7.898e-04, eta: 2:39:41, time: 0.379, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 08:47:10,277 - pyskl - INFO - Saving checkpoint at 133 epochs +2025-06-25 08:47:58,000 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:47:58,055 - pyskl - INFO - +top1_acc 0.9252 +top5_acc 0.9958 +2025-06-25 08:47:58,055 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:47:58,062 - pyskl - INFO - +mean_acc 0.8954 +2025-06-25 08:47:58,067 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_130.pth was removed +2025-06-25 08:47:58,249 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_133.pth. +2025-06-25 08:47:58,249 - pyskl - INFO - Best top1_acc is 0.9252 at 133 epoch. +2025-06-25 08:47:58,252 - pyskl - INFO - Epoch(val) [133][533] top1_acc: 0.9252, top5_acc: 0.9958, mean_class_accuracy: 0.8954 +2025-06-25 08:49:19,731 - pyskl - INFO - Epoch [134][100/1281] lr: 7.769e-04, eta: 2:38:22, time: 0.815, data_time: 0.193, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 08:50:08,890 - pyskl - INFO - Epoch [134][200/1281] lr: 7.699e-04, eta: 2:37:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 08:50:57,969 - pyskl - INFO - Epoch [134][300/1281] lr: 7.628e-04, eta: 2:36:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 08:51:47,202 - pyskl - INFO - Epoch [134][400/1281] lr: 7.558e-04, eta: 2:36:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 08:52:36,509 - pyskl - INFO - Epoch [134][500/1281] lr: 7.488e-04, eta: 2:35:29, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 08:53:25,546 - pyskl - INFO - Epoch [134][600/1281] lr: 7.419e-04, eta: 2:34:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 08:54:14,686 - pyskl - INFO - Epoch [134][700/1281] lr: 7.349e-04, eta: 2:34:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 08:55:04,253 - pyskl - INFO - Epoch [134][800/1281] lr: 7.281e-04, eta: 2:33:20, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 08:55:53,288 - pyskl - INFO - Epoch [134][900/1281] lr: 7.212e-04, eta: 2:32:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 08:56:42,458 - pyskl - INFO - Epoch [134][1000/1281] lr: 7.144e-04, eta: 2:31:53, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 08:57:31,367 - pyskl - INFO - Epoch [134][1100/1281] lr: 7.076e-04, eta: 2:31:10, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 08:58:06,928 - pyskl - INFO - Epoch [134][1200/1281] lr: 7.008e-04, eta: 2:30:25, time: 0.356, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 08:58:54,524 - pyskl - INFO - Saving checkpoint at 134 epochs +2025-06-25 08:59:43,157 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:59:43,213 - pyskl - INFO - +top1_acc 0.9243 +top5_acc 0.9958 +2025-06-25 08:59:43,214 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:59:43,220 - pyskl - INFO - +mean_acc 0.8954 +2025-06-25 08:59:43,222 - pyskl - INFO - Epoch(val) [134][533] top1_acc: 0.9243, top5_acc: 0.9958, mean_class_accuracy: 0.8954 +2025-06-25 09:01:01,187 - pyskl - INFO - Epoch [135][100/1281] lr: 6.887e-04, eta: 2:29:06, time: 0.780, data_time: 0.182, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 09:01:50,209 - pyskl - INFO - Epoch [135][200/1281] lr: 6.820e-04, eta: 2:28:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 09:02:39,156 - pyskl - INFO - Epoch [135][300/1281] lr: 6.753e-04, eta: 2:27:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 09:03:28,164 - pyskl - INFO - Epoch [135][400/1281] lr: 6.687e-04, eta: 2:26:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 09:04:17,186 - pyskl - INFO - Epoch [135][500/1281] lr: 6.622e-04, eta: 2:26:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 09:05:06,065 - pyskl - INFO - Epoch [135][600/1281] lr: 6.556e-04, eta: 2:25:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 09:05:55,315 - pyskl - INFO - Epoch [135][700/1281] lr: 6.491e-04, eta: 2:24:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 09:06:44,407 - pyskl - INFO - Epoch [135][800/1281] lr: 6.426e-04, eta: 2:24:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 09:07:33,443 - pyskl - INFO - Epoch [135][900/1281] lr: 6.362e-04, eta: 2:23:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 09:08:22,619 - pyskl - INFO - Epoch [135][1000/1281] lr: 6.297e-04, eta: 2:22:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 09:09:11,661 - pyskl - INFO - Epoch [135][1100/1281] lr: 6.233e-04, eta: 2:21:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 09:09:50,385 - pyskl - INFO - Epoch [135][1200/1281] lr: 6.170e-04, eta: 2:21:08, time: 0.387, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 09:10:30,526 - pyskl - INFO - Saving checkpoint at 135 epochs +2025-06-25 09:11:17,252 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:11:17,326 - pyskl - INFO - +top1_acc 0.9247 +top5_acc 0.9961 +2025-06-25 09:11:17,327 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:11:17,335 - pyskl - INFO - +mean_acc 0.8936 +2025-06-25 09:11:17,339 - pyskl - INFO - Epoch(val) [135][533] top1_acc: 0.9247, top5_acc: 0.9961, mean_class_accuracy: 0.8936 +2025-06-25 09:12:37,787 - pyskl - INFO - Epoch [136][100/1281] lr: 6.056e-04, eta: 2:19:49, time: 0.804, data_time: 0.196, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 09:13:26,622 - pyskl - INFO - Epoch [136][200/1281] lr: 5.993e-04, eta: 2:19:06, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 09:14:15,981 - pyskl - INFO - Epoch [136][300/1281] lr: 5.931e-04, eta: 2:18:22, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 09:15:05,136 - pyskl - INFO - Epoch [136][400/1281] lr: 5.868e-04, eta: 2:17:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 09:15:54,434 - pyskl - INFO - Epoch [136][500/1281] lr: 5.807e-04, eta: 2:16:56, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 09:16:43,581 - pyskl - INFO - Epoch [136][600/1281] lr: 5.745e-04, eta: 2:16:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 09:17:32,486 - pyskl - INFO - Epoch [136][700/1281] lr: 5.684e-04, eta: 2:15:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 09:18:21,631 - pyskl - INFO - Epoch [136][800/1281] lr: 5.623e-04, eta: 2:14:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 09:19:10,260 - pyskl - INFO - Epoch [136][900/1281] lr: 5.563e-04, eta: 2:14:02, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 09:19:59,341 - pyskl - INFO - Epoch [136][1000/1281] lr: 5.503e-04, eta: 2:13:19, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 09:20:48,723 - pyskl - INFO - Epoch [136][1100/1281] lr: 5.443e-04, eta: 2:12:36, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 09:21:25,528 - pyskl - INFO - Epoch [136][1200/1281] lr: 5.384e-04, eta: 2:11:51, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 09:22:07,980 - pyskl - INFO - Saving checkpoint at 136 epochs +2025-06-25 09:22:55,040 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:22:55,097 - pyskl - INFO - +top1_acc 0.9249 +top5_acc 0.9964 +2025-06-25 09:22:55,097 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:22:55,105 - pyskl - INFO - +mean_acc 0.8949 +2025-06-25 09:22:55,107 - pyskl - INFO - Epoch(val) [136][533] top1_acc: 0.9249, top5_acc: 0.9964, mean_class_accuracy: 0.8949 +2025-06-25 09:24:14,569 - pyskl - INFO - Epoch [137][100/1281] lr: 5.277e-04, eta: 2:10:31, time: 0.795, data_time: 0.189, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 09:25:03,602 - pyskl - INFO - Epoch [137][200/1281] lr: 5.218e-04, eta: 2:09:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 09:25:52,656 - pyskl - INFO - Epoch [137][300/1281] lr: 5.160e-04, eta: 2:09:05, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 09:26:41,341 - pyskl - INFO - Epoch [137][400/1281] lr: 5.102e-04, eta: 2:08:21, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 09:27:30,893 - pyskl - INFO - Epoch [137][500/1281] lr: 5.044e-04, eta: 2:07:38, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 09:28:20,283 - pyskl - INFO - Epoch [137][600/1281] lr: 4.987e-04, eta: 2:06:54, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 09:29:09,337 - pyskl - INFO - Epoch [137][700/1281] lr: 4.930e-04, eta: 2:06:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 09:29:58,761 - pyskl - INFO - Epoch [137][800/1281] lr: 4.873e-04, eta: 2:05:28, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 09:30:47,705 - pyskl - INFO - Epoch [137][900/1281] lr: 4.817e-04, eta: 2:04:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 09:31:36,829 - pyskl - INFO - Epoch [137][1000/1281] lr: 4.761e-04, eta: 2:04:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-25 09:32:25,975 - pyskl - INFO - Epoch [137][1100/1281] lr: 4.705e-04, eta: 2:03:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 09:33:03,775 - pyskl - INFO - Epoch [137][1200/1281] lr: 4.650e-04, eta: 2:02:33, time: 0.378, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 09:33:47,270 - pyskl - INFO - Saving checkpoint at 137 epochs +2025-06-25 09:34:34,783 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:34:34,850 - pyskl - INFO - +top1_acc 0.9240 +top5_acc 0.9964 +2025-06-25 09:34:34,850 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:34:34,857 - pyskl - INFO - +mean_acc 0.8952 +2025-06-25 09:34:34,858 - pyskl - INFO - Epoch(val) [137][533] top1_acc: 0.9240, top5_acc: 0.9964, mean_class_accuracy: 0.8952 +2025-06-25 09:35:53,543 - pyskl - INFO - Epoch [138][100/1281] lr: 4.550e-04, eta: 2:01:13, time: 0.787, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 09:36:42,437 - pyskl - INFO - Epoch [138][200/1281] lr: 4.496e-04, eta: 2:00:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 09:37:31,254 - pyskl - INFO - Epoch [138][300/1281] lr: 4.442e-04, eta: 1:59:46, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 09:38:20,423 - pyskl - INFO - Epoch [138][400/1281] lr: 4.388e-04, eta: 1:59:03, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 09:39:09,709 - pyskl - INFO - Epoch [138][500/1281] lr: 4.334e-04, eta: 1:58:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 09:39:59,021 - pyskl - INFO - Epoch [138][600/1281] lr: 4.281e-04, eta: 1:57:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 09:40:47,938 - pyskl - INFO - Epoch [138][700/1281] lr: 4.228e-04, eta: 1:56:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 09:41:37,298 - pyskl - INFO - Epoch [138][800/1281] lr: 4.176e-04, eta: 1:56:09, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 09:42:26,255 - pyskl - INFO - Epoch [138][900/1281] lr: 4.124e-04, eta: 1:55:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 09:43:15,397 - pyskl - INFO - Epoch [138][1000/1281] lr: 4.072e-04, eta: 1:54:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 09:44:04,432 - pyskl - INFO - Epoch [138][1100/1281] lr: 4.020e-04, eta: 1:53:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 09:44:41,916 - pyskl - INFO - Epoch [138][1200/1281] lr: 3.969e-04, eta: 1:53:14, time: 0.375, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 09:45:23,886 - pyskl - INFO - Saving checkpoint at 138 epochs +2025-06-25 09:46:11,077 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:46:11,143 - pyskl - INFO - +top1_acc 0.9245 +top5_acc 0.9962 +2025-06-25 09:46:11,143 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:46:11,150 - pyskl - INFO - +mean_acc 0.8959 +2025-06-25 09:46:11,152 - pyskl - INFO - Epoch(val) [138][533] top1_acc: 0.9245, top5_acc: 0.9962, mean_class_accuracy: 0.8959 +2025-06-25 09:47:30,697 - pyskl - INFO - Epoch [139][100/1281] lr: 3.877e-04, eta: 1:51:54, time: 0.795, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 09:48:19,539 - pyskl - INFO - Epoch [139][200/1281] lr: 3.827e-04, eta: 1:51:11, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 09:49:08,492 - pyskl - INFO - Epoch [139][300/1281] lr: 3.777e-04, eta: 1:50:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 09:49:57,565 - pyskl - INFO - Epoch [139][400/1281] lr: 3.727e-04, eta: 1:49:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 09:50:46,967 - pyskl - INFO - Epoch [139][500/1281] lr: 3.678e-04, eta: 1:49:00, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 09:51:36,258 - pyskl - INFO - Epoch [139][600/1281] lr: 3.628e-04, eta: 1:48:16, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 09:52:25,606 - pyskl - INFO - Epoch [139][700/1281] lr: 3.580e-04, eta: 1:47:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 09:53:14,350 - pyskl - INFO - Epoch [139][800/1281] lr: 3.531e-04, eta: 1:46:49, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 09:54:03,262 - pyskl - INFO - Epoch [139][900/1281] lr: 3.483e-04, eta: 1:46:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 09:54:52,425 - pyskl - INFO - Epoch [139][1000/1281] lr: 3.436e-04, eta: 1:45:22, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 09:55:41,796 - pyskl - INFO - Epoch [139][1100/1281] lr: 3.388e-04, eta: 1:44:39, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 09:56:19,214 - pyskl - INFO - Epoch [139][1200/1281] lr: 3.341e-04, eta: 1:43:54, time: 0.374, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 09:57:02,321 - pyskl - INFO - Saving checkpoint at 139 epochs +2025-06-25 09:57:49,726 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:57:49,782 - pyskl - INFO - +top1_acc 0.9227 +top5_acc 0.9961 +2025-06-25 09:57:49,782 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:57:49,788 - pyskl - INFO - +mean_acc 0.8923 +2025-06-25 09:57:49,790 - pyskl - INFO - Epoch(val) [139][533] top1_acc: 0.9227, top5_acc: 0.9961, mean_class_accuracy: 0.8923 +2025-06-25 09:59:08,676 - pyskl - INFO - Epoch [140][100/1281] lr: 3.257e-04, eta: 1:42:34, time: 0.789, data_time: 0.188, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 09:59:57,835 - pyskl - INFO - Epoch [140][200/1281] lr: 3.210e-04, eta: 1:41:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 10:00:46,774 - pyskl - INFO - Epoch [140][300/1281] lr: 3.165e-04, eta: 1:41:07, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 10:01:35,957 - pyskl - INFO - Epoch [140][400/1281] lr: 3.119e-04, eta: 1:40:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 10:02:24,924 - pyskl - INFO - Epoch [140][500/1281] lr: 3.074e-04, eta: 1:39:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 10:03:13,936 - pyskl - INFO - Epoch [140][600/1281] lr: 3.029e-04, eta: 1:38:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 10:04:03,070 - pyskl - INFO - Epoch [140][700/1281] lr: 2.984e-04, eta: 1:38:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 10:04:52,316 - pyskl - INFO - Epoch [140][800/1281] lr: 2.940e-04, eta: 1:37:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 10:05:41,452 - pyskl - INFO - Epoch [140][900/1281] lr: 2.896e-04, eta: 1:36:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 10:06:30,654 - pyskl - INFO - Epoch [140][1000/1281] lr: 2.853e-04, eta: 1:36:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 10:07:19,871 - pyskl - INFO - Epoch [140][1100/1281] lr: 2.809e-04, eta: 1:35:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 10:07:58,121 - pyskl - INFO - Epoch [140][1200/1281] lr: 2.767e-04, eta: 1:34:34, time: 0.383, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 10:08:39,465 - pyskl - INFO - Saving checkpoint at 140 epochs +2025-06-25 10:09:26,664 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:09:26,722 - pyskl - INFO - +top1_acc 0.9236 +top5_acc 0.9967 +2025-06-25 10:09:26,722 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:09:26,729 - pyskl - INFO - +mean_acc 0.8932 +2025-06-25 10:09:26,731 - pyskl - INFO - Epoch(val) [140][533] top1_acc: 0.9236, top5_acc: 0.9967, mean_class_accuracy: 0.8932 +2025-06-25 10:10:46,521 - pyskl - INFO - Epoch [141][100/1281] lr: 2.690e-04, eta: 1:33:14, time: 0.798, data_time: 0.186, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 10:11:35,699 - pyskl - INFO - Epoch [141][200/1281] lr: 2.648e-04, eta: 1:32:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 10:12:25,211 - pyskl - INFO - Epoch [141][300/1281] lr: 2.606e-04, eta: 1:31:47, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 10:13:14,528 - pyskl - INFO - Epoch [141][400/1281] lr: 2.565e-04, eta: 1:31:03, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 10:14:03,748 - pyskl - INFO - Epoch [141][500/1281] lr: 2.524e-04, eta: 1:30:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 10:14:52,980 - pyskl - INFO - Epoch [141][600/1281] lr: 2.483e-04, eta: 1:29:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 10:15:42,255 - pyskl - INFO - Epoch [141][700/1281] lr: 2.443e-04, eta: 1:28:52, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 10:16:31,330 - pyskl - INFO - Epoch [141][800/1281] lr: 2.402e-04, eta: 1:28:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 10:17:20,424 - pyskl - INFO - Epoch [141][900/1281] lr: 2.363e-04, eta: 1:27:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 10:18:09,416 - pyskl - INFO - Epoch [141][1000/1281] lr: 2.323e-04, eta: 1:26:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 10:18:58,379 - pyskl - INFO - Epoch [141][1100/1281] lr: 2.284e-04, eta: 1:25:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 10:19:35,730 - pyskl - INFO - Epoch [141][1200/1281] lr: 2.246e-04, eta: 1:25:13, time: 0.373, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 10:20:20,751 - pyskl - INFO - Saving checkpoint at 141 epochs +2025-06-25 10:21:09,307 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:21:09,366 - pyskl - INFO - +top1_acc 0.9229 +top5_acc 0.9969 +2025-06-25 10:21:09,366 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:21:09,374 - pyskl - INFO - +mean_acc 0.8943 +2025-06-25 10:21:09,377 - pyskl - INFO - Epoch(val) [141][533] top1_acc: 0.9229, top5_acc: 0.9969, mean_class_accuracy: 0.8943 +2025-06-25 10:22:27,855 - pyskl - INFO - Epoch [142][100/1281] lr: 2.176e-04, eta: 1:23:53, time: 0.785, data_time: 0.182, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0229, loss: 0.0229 +2025-06-25 10:23:17,296 - pyskl - INFO - Epoch [142][200/1281] lr: 2.139e-04, eta: 1:23:09, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 10:24:06,603 - pyskl - INFO - Epoch [142][300/1281] lr: 2.101e-04, eta: 1:22:26, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 10:24:55,856 - pyskl - INFO - Epoch [142][400/1281] lr: 2.064e-04, eta: 1:21:42, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 10:25:45,106 - pyskl - INFO - Epoch [142][500/1281] lr: 2.027e-04, eta: 1:20:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 10:26:34,268 - pyskl - INFO - Epoch [142][600/1281] lr: 1.991e-04, eta: 1:20:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 10:27:22,818 - pyskl - INFO - Epoch [142][700/1281] lr: 1.954e-04, eta: 1:19:31, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 10:28:11,879 - pyskl - INFO - Epoch [142][800/1281] lr: 1.919e-04, eta: 1:18:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 10:29:01,073 - pyskl - INFO - Epoch [142][900/1281] lr: 1.883e-04, eta: 1:18:03, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 10:29:50,249 - pyskl - INFO - Epoch [142][1000/1281] lr: 1.848e-04, eta: 1:17:19, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 10:30:39,360 - pyskl - INFO - Epoch [142][1100/1281] lr: 1.813e-04, eta: 1:16:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 10:31:17,507 - pyskl - INFO - Epoch [142][1200/1281] lr: 1.779e-04, eta: 1:15:51, time: 0.381, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 10:31:58,142 - pyskl - INFO - Saving checkpoint at 142 epochs +2025-06-25 10:32:45,322 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:32:45,378 - pyskl - INFO - +top1_acc 0.9235 +top5_acc 0.9967 +2025-06-25 10:32:45,378 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:32:45,385 - pyskl - INFO - +mean_acc 0.8945 +2025-06-25 10:32:45,387 - pyskl - INFO - Epoch(val) [142][533] top1_acc: 0.9235, top5_acc: 0.9967, mean_class_accuracy: 0.8945 +2025-06-25 10:34:05,960 - pyskl - INFO - Epoch [143][100/1281] lr: 1.717e-04, eta: 1:14:31, time: 0.806, data_time: 0.188, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 10:34:55,507 - pyskl - INFO - Epoch [143][200/1281] lr: 1.683e-04, eta: 1:13:48, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 10:35:44,721 - pyskl - INFO - Epoch [143][300/1281] lr: 1.650e-04, eta: 1:13:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 10:36:34,078 - pyskl - INFO - Epoch [143][400/1281] lr: 1.617e-04, eta: 1:12:20, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 10:37:23,138 - pyskl - INFO - Epoch [143][500/1281] lr: 1.585e-04, eta: 1:11:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 10:38:12,254 - pyskl - INFO - Epoch [143][600/1281] lr: 1.552e-04, eta: 1:10:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 10:39:01,517 - pyskl - INFO - Epoch [143][700/1281] lr: 1.520e-04, eta: 1:10:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 10:39:50,419 - pyskl - INFO - Epoch [143][800/1281] lr: 1.489e-04, eta: 1:09:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 10:40:39,629 - pyskl - INFO - Epoch [143][900/1281] lr: 1.457e-04, eta: 1:08:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 10:41:28,387 - pyskl - INFO - Epoch [143][1000/1281] lr: 1.426e-04, eta: 1:07:57, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 10:42:17,499 - pyskl - INFO - Epoch [143][1100/1281] lr: 1.396e-04, eta: 1:07:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 10:42:54,334 - pyskl - INFO - Epoch [143][1200/1281] lr: 1.366e-04, eta: 1:06:29, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 10:43:38,321 - pyskl - INFO - Saving checkpoint at 143 epochs +2025-06-25 10:44:26,178 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:44:26,233 - pyskl - INFO - +top1_acc 0.9254 +top5_acc 0.9967 +2025-06-25 10:44:26,233 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:44:26,240 - pyskl - INFO - +mean_acc 0.8958 +2025-06-25 10:44:26,244 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_133.pth was removed +2025-06-25 10:44:26,418 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_143.pth. +2025-06-25 10:44:26,418 - pyskl - INFO - Best top1_acc is 0.9254 at 143 epoch. +2025-06-25 10:44:26,421 - pyskl - INFO - Epoch(val) [143][533] top1_acc: 0.9254, top5_acc: 0.9967, mean_class_accuracy: 0.8958 +2025-06-25 10:45:46,667 - pyskl - INFO - Epoch [144][100/1281] lr: 1.312e-04, eta: 1:05:09, time: 0.802, data_time: 0.193, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 10:46:36,167 - pyskl - INFO - Epoch [144][200/1281] lr: 1.282e-04, eta: 1:04:25, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 10:47:25,503 - pyskl - INFO - Epoch [144][300/1281] lr: 1.253e-04, eta: 1:03:41, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 10:48:14,437 - pyskl - INFO - Epoch [144][400/1281] lr: 1.224e-04, eta: 1:02:58, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 10:49:03,658 - pyskl - INFO - Epoch [144][500/1281] lr: 1.196e-04, eta: 1:02:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 10:49:52,729 - pyskl - INFO - Epoch [144][600/1281] lr: 1.168e-04, eta: 1:01:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 10:50:42,029 - pyskl - INFO - Epoch [144][700/1281] lr: 1.140e-04, eta: 1:00:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 10:51:31,083 - pyskl - INFO - Epoch [144][800/1281] lr: 1.113e-04, eta: 1:00:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 10:52:20,208 - pyskl - INFO - Epoch [144][900/1281] lr: 1.086e-04, eta: 0:59:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 10:53:09,512 - pyskl - INFO - Epoch [144][1000/1281] lr: 1.059e-04, eta: 0:58:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 10:53:58,347 - pyskl - INFO - Epoch [144][1100/1281] lr: 1.033e-04, eta: 0:57:50, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 10:54:34,580 - pyskl - INFO - Epoch [144][1200/1281] lr: 1.007e-04, eta: 0:57:06, time: 0.362, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 10:55:19,696 - pyskl - INFO - Saving checkpoint at 144 epochs +2025-06-25 10:56:07,451 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:56:07,533 - pyskl - INFO - +top1_acc 0.9241 +top5_acc 0.9961 +2025-06-25 10:56:07,533 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:56:07,546 - pyskl - INFO - +mean_acc 0.8946 +2025-06-25 10:56:07,549 - pyskl - INFO - Epoch(val) [144][533] top1_acc: 0.9241, top5_acc: 0.9961, mean_class_accuracy: 0.8946 +2025-06-25 10:57:27,456 - pyskl - INFO - Epoch [145][100/1281] lr: 9.605e-05, eta: 0:55:46, time: 0.799, data_time: 0.189, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 10:58:16,806 - pyskl - INFO - Epoch [145][200/1281] lr: 9.353e-05, eta: 0:55:02, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 10:59:05,883 - pyskl - INFO - Epoch [145][300/1281] lr: 9.106e-05, eta: 0:54:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 10:59:54,888 - pyskl - INFO - Epoch [145][400/1281] lr: 8.861e-05, eta: 0:53:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 11:00:43,997 - pyskl - INFO - Epoch [145][500/1281] lr: 8.620e-05, eta: 0:52:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 11:01:33,378 - pyskl - INFO - Epoch [145][600/1281] lr: 8.382e-05, eta: 0:52:06, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 11:02:22,496 - pyskl - INFO - Epoch [145][700/1281] lr: 8.147e-05, eta: 0:51:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 11:03:11,364 - pyskl - INFO - Epoch [145][800/1281] lr: 7.916e-05, eta: 0:50:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 11:04:00,588 - pyskl - INFO - Epoch [145][900/1281] lr: 7.688e-05, eta: 0:49:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 11:04:50,280 - pyskl - INFO - Epoch [145][1000/1281] lr: 7.463e-05, eta: 0:49:11, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 11:05:39,229 - pyskl - INFO - Epoch [145][1100/1281] lr: 7.242e-05, eta: 0:48:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 11:06:16,531 - pyskl - INFO - Epoch [145][1200/1281] lr: 7.024e-05, eta: 0:47:42, time: 0.373, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 11:06:59,114 - pyskl - INFO - Saving checkpoint at 145 epochs +2025-06-25 11:07:46,468 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:07:46,535 - pyskl - INFO - +top1_acc 0.9255 +top5_acc 0.9959 +2025-06-25 11:07:46,536 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:07:46,543 - pyskl - INFO - +mean_acc 0.8964 +2025-06-25 11:07:46,547 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_143.pth was removed +2025-06-25 11:07:46,733 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_145.pth. +2025-06-25 11:07:46,734 - pyskl - INFO - Best top1_acc is 0.9255 at 145 epoch. +2025-06-25 11:07:46,736 - pyskl - INFO - Epoch(val) [145][533] top1_acc: 0.9255, top5_acc: 0.9959, mean_class_accuracy: 0.8964 +2025-06-25 11:09:05,867 - pyskl - INFO - Epoch [146][100/1281] lr: 6.638e-05, eta: 0:46:22, time: 0.791, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 11:09:55,414 - pyskl - INFO - Epoch [146][200/1281] lr: 6.429e-05, eta: 0:45:38, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 11:10:44,289 - pyskl - INFO - Epoch [146][300/1281] lr: 6.224e-05, eta: 0:44:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 11:11:33,523 - pyskl - INFO - Epoch [146][400/1281] lr: 6.022e-05, eta: 0:44:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 11:12:22,807 - pyskl - INFO - Epoch [146][500/1281] lr: 5.823e-05, eta: 0:43:26, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 11:13:11,787 - pyskl - INFO - Epoch [146][600/1281] lr: 5.628e-05, eta: 0:42:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 11:14:00,889 - pyskl - INFO - Epoch [146][700/1281] lr: 5.436e-05, eta: 0:41:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 11:14:50,314 - pyskl - INFO - Epoch [146][800/1281] lr: 5.247e-05, eta: 0:41:14, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 11:15:39,780 - pyskl - INFO - Epoch [146][900/1281] lr: 5.061e-05, eta: 0:40:30, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 11:16:29,092 - pyskl - INFO - Epoch [146][1000/1281] lr: 4.879e-05, eta: 0:39:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 11:17:18,147 - pyskl - INFO - Epoch [146][1100/1281] lr: 4.701e-05, eta: 0:39:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 11:17:56,168 - pyskl - INFO - Epoch [146][1200/1281] lr: 4.525e-05, eta: 0:38:18, time: 0.380, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 11:18:38,858 - pyskl - INFO - Saving checkpoint at 146 epochs +2025-06-25 11:19:26,513 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:19:26,573 - pyskl - INFO - +top1_acc 0.9256 +top5_acc 0.9957 +2025-06-25 11:19:26,573 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:19:26,580 - pyskl - INFO - +mean_acc 0.8962 +2025-06-25 11:19:26,584 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_145.pth was removed +2025-06-25 11:19:26,772 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_146.pth. +2025-06-25 11:19:26,772 - pyskl - INFO - Best top1_acc is 0.9256 at 146 epoch. +2025-06-25 11:19:26,776 - pyskl - INFO - Epoch(val) [146][533] top1_acc: 0.9256, top5_acc: 0.9957, mean_class_accuracy: 0.8962 +2025-06-25 11:20:45,633 - pyskl - INFO - Epoch [147][100/1281] lr: 4.216e-05, eta: 0:36:58, time: 0.789, data_time: 0.187, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 11:21:35,045 - pyskl - INFO - Epoch [147][200/1281] lr: 4.050e-05, eta: 0:36:14, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 11:22:24,356 - pyskl - INFO - Epoch [147][300/1281] lr: 3.887e-05, eta: 0:35:30, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 11:23:13,263 - pyskl - INFO - Epoch [147][400/1281] lr: 3.728e-05, eta: 0:34:46, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 11:24:02,408 - pyskl - INFO - Epoch [147][500/1281] lr: 3.572e-05, eta: 0:34:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 11:24:51,461 - pyskl - INFO - Epoch [147][600/1281] lr: 3.419e-05, eta: 0:33:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 11:25:40,608 - pyskl - INFO - Epoch [147][700/1281] lr: 3.270e-05, eta: 0:32:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 11:26:29,884 - pyskl - INFO - Epoch [147][800/1281] lr: 3.124e-05, eta: 0:31:50, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 11:27:18,914 - pyskl - INFO - Epoch [147][900/1281] lr: 2.981e-05, eta: 0:31:06, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 11:28:07,987 - pyskl - INFO - Epoch [147][1000/1281] lr: 2.842e-05, eta: 0:30:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 11:28:57,376 - pyskl - INFO - Epoch [147][1100/1281] lr: 2.706e-05, eta: 0:29:38, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 11:29:34,857 - pyskl - INFO - Epoch [147][1200/1281] lr: 2.573e-05, eta: 0:28:53, time: 0.375, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 11:30:19,319 - pyskl - INFO - Saving checkpoint at 147 epochs +2025-06-25 11:31:06,433 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:31:06,502 - pyskl - INFO - +top1_acc 0.9240 +top5_acc 0.9964 +2025-06-25 11:31:06,502 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:31:06,510 - pyskl - INFO - +mean_acc 0.8938 +2025-06-25 11:31:06,512 - pyskl - INFO - Epoch(val) [147][533] top1_acc: 0.9240, top5_acc: 0.9964, mean_class_accuracy: 0.8938 +2025-06-25 11:32:26,922 - pyskl - INFO - Epoch [148][100/1281] lr: 2.341e-05, eta: 0:27:33, time: 0.804, data_time: 0.184, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 11:33:15,834 - pyskl - INFO - Epoch [148][200/1281] lr: 2.218e-05, eta: 0:26:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 11:34:04,970 - pyskl - INFO - Epoch [148][300/1281] lr: 2.098e-05, eta: 0:26:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 11:34:53,946 - pyskl - INFO - Epoch [148][400/1281] lr: 1.981e-05, eta: 0:25:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 11:35:43,198 - pyskl - INFO - Epoch [148][500/1281] lr: 1.868e-05, eta: 0:24:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 11:36:32,190 - pyskl - INFO - Epoch [148][600/1281] lr: 1.758e-05, eta: 0:23:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 11:37:21,019 - pyskl - INFO - Epoch [148][700/1281] lr: 1.651e-05, eta: 0:23:09, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 11:38:09,942 - pyskl - INFO - Epoch [148][800/1281] lr: 1.548e-05, eta: 0:22:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 11:38:58,804 - pyskl - INFO - Epoch [148][900/1281] lr: 1.448e-05, eta: 0:21:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 11:39:47,861 - pyskl - INFO - Epoch [148][1000/1281] lr: 1.351e-05, eta: 0:20:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 11:40:36,978 - pyskl - INFO - Epoch [148][1100/1281] lr: 1.258e-05, eta: 0:20:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 11:41:12,406 - pyskl - INFO - Epoch [148][1200/1281] lr: 1.168e-05, eta: 0:19:28, time: 0.354, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 11:42:00,096 - pyskl - INFO - Saving checkpoint at 148 epochs +2025-06-25 11:42:48,031 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:42:48,085 - pyskl - INFO - +top1_acc 0.9241 +top5_acc 0.9959 +2025-06-25 11:42:48,085 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:42:48,091 - pyskl - INFO - +mean_acc 0.8949 +2025-06-25 11:42:48,093 - pyskl - INFO - Epoch(val) [148][533] top1_acc: 0.9241, top5_acc: 0.9959, mean_class_accuracy: 0.8949 +2025-06-25 11:44:07,809 - pyskl - INFO - Epoch [149][100/1281] lr: 1.013e-05, eta: 0:18:08, time: 0.797, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 11:44:56,791 - pyskl - INFO - Epoch [149][200/1281] lr: 9.328e-06, eta: 0:17:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 11:45:45,740 - pyskl - INFO - Epoch [149][300/1281] lr: 8.555e-06, eta: 0:16:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 11:46:34,367 - pyskl - INFO - Epoch [149][400/1281] lr: 7.816e-06, eta: 0:15:55, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 11:47:22,957 - pyskl - INFO - Epoch [149][500/1281] lr: 7.110e-06, eta: 0:15:11, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 11:48:11,805 - pyskl - INFO - Epoch [149][600/1281] lr: 6.437e-06, eta: 0:14:27, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 11:49:00,508 - pyskl - INFO - Epoch [149][700/1281] lr: 5.798e-06, eta: 0:13:43, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 11:49:49,500 - pyskl - INFO - Epoch [149][800/1281] lr: 5.192e-06, eta: 0:12:59, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 11:50:38,408 - pyskl - INFO - Epoch [149][900/1281] lr: 4.620e-06, eta: 0:12:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 11:51:27,264 - pyskl - INFO - Epoch [149][1000/1281] lr: 4.081e-06, eta: 0:11:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 11:52:16,092 - pyskl - INFO - Epoch [149][1100/1281] lr: 3.576e-06, eta: 0:10:46, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 11:52:53,409 - pyskl - INFO - Epoch [149][1200/1281] lr: 3.104e-06, eta: 0:10:02, time: 0.373, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 11:53:38,897 - pyskl - INFO - Saving checkpoint at 149 epochs +2025-06-25 11:54:26,344 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:54:26,412 - pyskl - INFO - +top1_acc 0.9251 +top5_acc 0.9965 +2025-06-25 11:54:26,413 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:54:26,421 - pyskl - INFO - +mean_acc 0.8953 +2025-06-25 11:54:26,422 - pyskl - INFO - Epoch(val) [149][533] top1_acc: 0.9251, top5_acc: 0.9965, mean_class_accuracy: 0.8953 +2025-06-25 11:55:46,019 - pyskl - INFO - Epoch [150][100/1281] lr: 2.334e-06, eta: 0:08:42, time: 0.796, data_time: 0.190, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 11:56:35,115 - pyskl - INFO - Epoch [150][200/1281] lr: 1.956e-06, eta: 0:07:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 11:57:23,866 - pyskl - INFO - Epoch [150][300/1281] lr: 1.611e-06, eta: 0:07:13, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 11:58:12,868 - pyskl - INFO - Epoch [150][400/1281] lr: 1.300e-06, eta: 0:06:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 11:59:01,695 - pyskl - INFO - Epoch [150][500/1281] lr: 1.022e-06, eta: 0:05:45, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 11:59:50,612 - pyskl - INFO - Epoch [150][600/1281] lr: 7.771e-07, eta: 0:05:01, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 12:00:39,508 - pyskl - INFO - Epoch [150][700/1281] lr: 5.659e-07, eta: 0:04:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 12:01:28,487 - pyskl - INFO - Epoch [150][800/1281] lr: 3.881e-07, eta: 0:03:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 12:02:17,434 - pyskl - INFO - Epoch [150][900/1281] lr: 2.438e-07, eta: 0:02:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 12:03:06,398 - pyskl - INFO - Epoch [150][1000/1281] lr: 1.329e-07, eta: 0:02:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 12:03:55,404 - pyskl - INFO - Epoch [150][1100/1281] lr: 5.534e-08, eta: 0:01:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 12:04:32,255 - pyskl - INFO - Epoch [150][1200/1281] lr: 1.123e-08, eta: 0:00:35, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 12:05:16,602 - pyskl - INFO - Saving checkpoint at 150 epochs +2025-06-25 12:06:04,129 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:06:04,196 - pyskl - INFO - +top1_acc 0.9237 +top5_acc 0.9964 +2025-06-25 12:06:04,197 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:06:04,204 - pyskl - INFO - +mean_acc 0.8932 +2025-06-25 12:06:04,206 - pyskl - INFO - Epoch(val) [150][533] top1_acc: 0.9237, top5_acc: 0.9964, mean_class_accuracy: 0.8932 +2025-06-25 12:06:08,676 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-25 12:13:45,791 - pyskl - INFO - Testing results of the last checkpoint +2025-06-25 12:13:45,792 - pyskl - INFO - top1_acc: 0.9289 +2025-06-25 12:13:45,792 - pyskl - INFO - top5_acc: 0.9969 +2025-06-25 12:13:45,792 - pyskl - INFO - mean_class_accuracy: 0.8982 +2025-06-25 12:13:45,792 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/finegym/j_3/best_top1_acc_epoch_146.pth +2025-06-25 12:21:27,191 - pyskl - INFO - Testing results of the best checkpoint +2025-06-25 12:21:27,192 - pyskl - INFO - top1_acc: 0.9283 +2025-06-25 12:21:27,192 - pyskl - INFO - top5_acc: 0.9971 +2025-06-25 12:21:27,192 - pyskl - INFO - mean_class_accuracy: 0.8998 diff --git a/finegym/j_3/20250624_084345.log.json b/finegym/j_3/20250624_084345.log.json new file mode 100644 index 0000000000000000000000000000000000000000..52162dfe84bcef4310b17c16bb4fe2b91a4a16e0 --- /dev/null +++ b/finegym/j_3/20250624_084345.log.json @@ -0,0 +1,1951 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 1562067252, "config_name": "j_3.py", "work_dir": "j_3", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.18354, "top1_acc": 0.04812, "top5_acc": 0.20938, "loss_cls": 4.57007, "loss": 4.57007, "time": 0.40128} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.075, "top5_acc": 0.30812, "loss_cls": 4.64466, "loss": 4.64466, "time": 0.21516} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.11, "top5_acc": 0.34188, "loss_cls": 4.44677, "loss": 4.44677, "time": 0.21502} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.12125, "top5_acc": 0.41, "loss_cls": 4.16991, "loss": 4.16991, "time": 0.2191} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.025, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.1575, "top5_acc": 0.50125, "loss_cls": 3.87032, "loss": 3.87032, "time": 0.2176} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.19125, "top5_acc": 0.56875, "loss_cls": 3.57394, "loss": 3.57394, "time": 0.21951} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.025, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.245, "top5_acc": 0.5975, "loss_cls": 3.32581, "loss": 3.32581, "time": 0.21735} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.025, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.27375, "top5_acc": 0.64125, "loss_cls": 3.15961, "loss": 3.15961, "time": 0.21916} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.025, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.30312, "top5_acc": 0.69938, "loss_cls": 2.9544, "loss": 2.9544, "time": 0.2185} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.32938, "top5_acc": 0.71, "loss_cls": 2.83895, "loss": 2.83895, "time": 0.21659} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.35312, "top5_acc": 0.72688, "loss_cls": 2.8031, "loss": 2.8031, "time": 0.21767} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.025, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.37312, "top5_acc": 0.7775, "loss_cls": 2.61987, "loss": 2.61987, "time": 0.21862} +{"mode": "val", "epoch": 1, "iter": 533, "lr": 0.025, "top1_acc": 0.40547, "top5_acc": 0.77573, "mean_class_accuracy": 0.20655} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.18461, "top1_acc": 0.37625, "top5_acc": 0.77812, "loss_cls": 2.56085, "loss": 2.56085, "time": 0.40293} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.435, "top5_acc": 0.81812, "loss_cls": 2.41022, "loss": 2.41022, "time": 0.21968} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.4425, "top5_acc": 0.82188, "loss_cls": 2.33402, "loss": 2.33402, "time": 0.22045} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.4475, "top5_acc": 0.82312, "loss_cls": 2.28445, "loss": 2.28445, "time": 0.21874} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.4725, "top5_acc": 0.84688, "loss_cls": 2.17035, "loss": 2.17035, "time": 0.21882} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.02499, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.44812, "top5_acc": 0.85625, "loss_cls": 2.19628, "loss": 2.19628, "time": 0.21698} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.47375, "top5_acc": 0.85625, "loss_cls": 2.13933, "loss": 2.13933, "time": 0.21754} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.02499, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.49875, "top5_acc": 0.88062, "loss_cls": 2.01067, "loss": 2.01067, "time": 0.2176} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.5025, "top5_acc": 0.86375, "loss_cls": 2.02046, "loss": 2.02046, "time": 0.21792} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.02499, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.51812, "top5_acc": 0.8775, "loss_cls": 1.95611, "loss": 1.95611, "time": 0.21595} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.02499, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.51438, "top5_acc": 0.895, "loss_cls": 1.94832, "loss": 1.94832, "time": 0.21828} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.02499, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.50562, "top5_acc": 0.8825, "loss_cls": 1.96014, "loss": 1.96014, "time": 0.22011} +{"mode": "val", "epoch": 2, "iter": 533, "lr": 0.02499, "top1_acc": 0.50428, "top5_acc": 0.88112, "mean_class_accuracy": 0.28758} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.02499, "memory": 4082, "data_time": 0.18406, "top1_acc": 0.54312, "top5_acc": 0.905, "loss_cls": 1.79425, "loss": 1.79425, "time": 0.40453} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.02499, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.55562, "top5_acc": 0.89875, "loss_cls": 1.83296, "loss": 1.83296, "time": 0.21953} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.02499, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.545, "top5_acc": 0.90062, "loss_cls": 1.80622, "loss": 1.80622, "time": 0.21961} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.5575, "top5_acc": 0.91125, "loss_cls": 1.73912, "loss": 1.73912, "time": 0.21752} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.02498, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.56938, "top5_acc": 0.91188, "loss_cls": 1.75895, "loss": 1.75895, "time": 0.21853} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.02498, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.54937, "top5_acc": 0.92062, "loss_cls": 1.789, "loss": 1.789, "time": 0.22014} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.02498, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.55812, "top5_acc": 0.92125, "loss_cls": 1.74636, "loss": 1.74636, "time": 0.22002} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.02498, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.55188, "top5_acc": 0.9275, "loss_cls": 1.72177, "loss": 1.72177, "time": 0.21711} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.02498, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.55625, "top5_acc": 0.90688, "loss_cls": 1.76779, "loss": 1.76779, "time": 0.21749} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.02498, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.57688, "top5_acc": 0.92438, "loss_cls": 1.68124, "loss": 1.68124, "time": 0.21707} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.02498, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.595, "top5_acc": 0.92938, "loss_cls": 1.6191, "loss": 1.6191, "time": 0.21905} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.02498, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.5725, "top5_acc": 0.92, "loss_cls": 1.68278, "loss": 1.68278, "time": 0.2213} +{"mode": "val", "epoch": 3, "iter": 533, "lr": 0.02498, "top1_acc": 0.56425, "top5_acc": 0.906, "mean_class_accuracy": 0.39262} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.02497, "memory": 4082, "data_time": 0.18158, "top1_acc": 0.585, "top5_acc": 0.93562, "loss_cls": 1.59436, "loss": 1.59436, "time": 0.39885} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.02497, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.615, "top5_acc": 0.935, "loss_cls": 1.54103, "loss": 1.54103, "time": 0.21609} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.02497, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.59688, "top5_acc": 0.93875, "loss_cls": 1.58971, "loss": 1.58971, "time": 0.21596} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.02497, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.60688, "top5_acc": 0.93375, "loss_cls": 1.61882, "loss": 1.61882, "time": 0.21794} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.02497, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.6225, "top5_acc": 0.93688, "loss_cls": 1.55079, "loss": 1.55079, "time": 0.21617} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.02497, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.61562, "top5_acc": 0.94688, "loss_cls": 1.4946, "loss": 1.4946, "time": 0.21485} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.02497, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.61, "top5_acc": 0.93938, "loss_cls": 1.54136, "loss": 1.54136, "time": 0.21892} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.02496, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.61375, "top5_acc": 0.94625, "loss_cls": 1.48445, "loss": 1.48445, "time": 0.21596} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.02496, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.61312, "top5_acc": 0.9425, "loss_cls": 1.52408, "loss": 1.52408, "time": 0.21583} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.02496, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.64125, "top5_acc": 0.95625, "loss_cls": 1.4403, "loss": 1.4403, "time": 0.21732} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.02496, "memory": 4082, "data_time": 0.00071, "top1_acc": 0.625, "top5_acc": 0.9425, "loss_cls": 1.50768, "loss": 1.50768, "time": 0.21855} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.02496, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.63625, "top5_acc": 0.94938, "loss_cls": 1.50249, "loss": 1.50249, "time": 0.21436} +{"mode": "val", "epoch": 4, "iter": 533, "lr": 0.02496, "top1_acc": 0.61425, "top5_acc": 0.93369, "mean_class_accuracy": 0.442} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.02495, "memory": 4082, "data_time": 0.17847, "top1_acc": 0.62938, "top5_acc": 0.95125, "loss_cls": 1.44736, "loss": 1.44736, "time": 0.39526} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.02495, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.64, "top5_acc": 0.95125, "loss_cls": 1.40967, "loss": 1.40967, "time": 0.22049} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.02495, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.65375, "top5_acc": 0.95625, "loss_cls": 1.37512, "loss": 1.37512, "time": 0.21804} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.02495, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.63188, "top5_acc": 0.95188, "loss_cls": 1.42113, "loss": 1.42113, "time": 0.21732} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.02495, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.6525, "top5_acc": 0.95562, "loss_cls": 1.38783, "loss": 1.38783, "time": 0.21656} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.02495, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.65812, "top5_acc": 0.95125, "loss_cls": 1.36886, "loss": 1.36886, "time": 0.21538} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.02494, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.66188, "top5_acc": 0.9525, "loss_cls": 1.39662, "loss": 1.39662, "time": 0.21727} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.02494, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.65562, "top5_acc": 0.955, "loss_cls": 1.40451, "loss": 1.40451, "time": 0.22001} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.02494, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.67125, "top5_acc": 0.95438, "loss_cls": 1.34445, "loss": 1.34445, "time": 0.21859} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.02494, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.67062, "top5_acc": 0.95188, "loss_cls": 1.36629, "loss": 1.36629, "time": 0.21984} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.02494, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.64438, "top5_acc": 0.95562, "loss_cls": 1.42502, "loss": 1.42502, "time": 0.21995} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.02493, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.65125, "top5_acc": 0.96188, "loss_cls": 1.41862, "loss": 1.41862, "time": 0.22015} +{"mode": "val", "epoch": 5, "iter": 533, "lr": 0.02493, "top1_acc": 0.66577, "top5_acc": 0.94895, "mean_class_accuracy": 0.54092} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.02493, "memory": 4082, "data_time": 0.18816, "top1_acc": 0.67688, "top5_acc": 0.95812, "loss_cls": 1.29506, "loss": 1.29506, "time": 0.4048} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.02493, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.67188, "top5_acc": 0.9625, "loss_cls": 1.31665, "loss": 1.31665, "time": 0.21876} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.02492, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.67, "top5_acc": 0.95562, "loss_cls": 1.3587, "loss": 1.3587, "time": 0.21573} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.02492, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.67438, "top5_acc": 0.96125, "loss_cls": 1.28629, "loss": 1.28629, "time": 0.21946} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.02492, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.66062, "top5_acc": 0.96062, "loss_cls": 1.37718, "loss": 1.37718, "time": 0.21926} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.02492, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.64438, "top5_acc": 0.95812, "loss_cls": 1.37017, "loss": 1.37017, "time": 0.21747} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.02492, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.70375, "top5_acc": 0.97125, "loss_cls": 1.20673, "loss": 1.20673, "time": 0.21819} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.02491, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.705, "top5_acc": 0.96812, "loss_cls": 1.24955, "loss": 1.24955, "time": 0.21623} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.02491, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.69562, "top5_acc": 0.97062, "loss_cls": 1.25109, "loss": 1.25109, "time": 0.21825} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.02491, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.71625, "top5_acc": 0.96812, "loss_cls": 1.18596, "loss": 1.18596, "time": 0.2164} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.02491, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.66688, "top5_acc": 0.95688, "loss_cls": 1.31568, "loss": 1.31568, "time": 0.21707} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.0249, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.69312, "top5_acc": 0.96438, "loss_cls": 1.26839, "loss": 1.26839, "time": 0.21924} +{"mode": "val", "epoch": 6, "iter": 533, "lr": 0.0249, "top1_acc": 0.67938, "top5_acc": 0.96256, "mean_class_accuracy": 0.53215} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0249, "memory": 4082, "data_time": 0.18688, "top1_acc": 0.7175, "top5_acc": 0.97375, "loss_cls": 1.16713, "loss": 1.16713, "time": 0.40355} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0249, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.68688, "top5_acc": 0.96812, "loss_cls": 1.24867, "loss": 1.24867, "time": 0.21632} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.02489, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.68875, "top5_acc": 0.9675, "loss_cls": 1.25531, "loss": 1.25531, "time": 0.21963} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.02489, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.68625, "top5_acc": 0.975, "loss_cls": 1.21952, "loss": 1.21952, "time": 0.2169} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.02489, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.69938, "top5_acc": 0.96625, "loss_cls": 1.22241, "loss": 1.22241, "time": 0.21678} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.02489, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.705, "top5_acc": 0.97188, "loss_cls": 1.20535, "loss": 1.20535, "time": 0.21682} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.02488, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.70188, "top5_acc": 0.97062, "loss_cls": 1.23788, "loss": 1.23788, "time": 0.21756} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.02488, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.71062, "top5_acc": 0.97062, "loss_cls": 1.17056, "loss": 1.17056, "time": 0.21736} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.02488, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.70062, "top5_acc": 0.9625, "loss_cls": 1.23938, "loss": 1.23938, "time": 0.21758} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.02487, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.70812, "top5_acc": 0.96625, "loss_cls": 1.19064, "loss": 1.19064, "time": 0.21383} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.02487, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.69875, "top5_acc": 0.97125, "loss_cls": 1.19233, "loss": 1.19233, "time": 0.21782} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.02487, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.7225, "top5_acc": 0.96812, "loss_cls": 1.1873, "loss": 1.1873, "time": 0.22029} +{"mode": "val", "epoch": 7, "iter": 533, "lr": 0.02487, "top1_acc": 0.64676, "top5_acc": 0.94954, "mean_class_accuracy": 0.49548} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.02486, "memory": 4082, "data_time": 0.18458, "top1_acc": 0.725, "top5_acc": 0.96938, "loss_cls": 1.15353, "loss": 1.15353, "time": 0.40564} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.02486, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.70062, "top5_acc": 0.9725, "loss_cls": 1.17576, "loss": 1.17576, "time": 0.22153} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.02486, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.71, "top5_acc": 0.96312, "loss_cls": 1.20332, "loss": 1.20332, "time": 0.21612} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.02485, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.72062, "top5_acc": 0.96938, "loss_cls": 1.14822, "loss": 1.14822, "time": 0.21768} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.02485, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.72812, "top5_acc": 0.97312, "loss_cls": 1.15041, "loss": 1.15041, "time": 0.21701} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.02485, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.72688, "top5_acc": 0.97125, "loss_cls": 1.12164, "loss": 1.12164, "time": 0.21901} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.02484, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.685, "top5_acc": 0.96875, "loss_cls": 1.25232, "loss": 1.25232, "time": 0.21683} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.02484, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.7225, "top5_acc": 0.96938, "loss_cls": 1.16462, "loss": 1.16462, "time": 0.21997} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.02484, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.70438, "top5_acc": 0.96812, "loss_cls": 1.19315, "loss": 1.19315, "time": 0.21733} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.02483, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.745, "top5_acc": 0.9775, "loss_cls": 1.07701, "loss": 1.07701, "time": 0.21832} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.02483, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.70812, "top5_acc": 0.96688, "loss_cls": 1.17031, "loss": 1.17031, "time": 0.2183} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.02483, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.71938, "top5_acc": 0.975, "loss_cls": 1.14599, "loss": 1.14599, "time": 0.21734} +{"mode": "val", "epoch": 8, "iter": 533, "lr": 0.02482, "top1_acc": 0.70473, "top5_acc": 0.9723, "mean_class_accuracy": 0.57435} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.02482, "memory": 4082, "data_time": 0.17896, "top1_acc": 0.7525, "top5_acc": 0.9775, "loss_cls": 1.08055, "loss": 1.08055, "time": 0.39653} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.02482, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.72125, "top5_acc": 0.96375, "loss_cls": 1.15402, "loss": 1.15402, "time": 0.21904} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.02481, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.72625, "top5_acc": 0.97812, "loss_cls": 1.10439, "loss": 1.10439, "time": 0.21771} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.02481, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.7275, "top5_acc": 0.96375, "loss_cls": 1.15517, "loss": 1.15517, "time": 0.21704} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.02481, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.73125, "top5_acc": 0.9725, "loss_cls": 1.16809, "loss": 1.16809, "time": 0.21853} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.0248, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.71875, "top5_acc": 0.97062, "loss_cls": 1.1658, "loss": 1.1658, "time": 0.21641} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.0248, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.73562, "top5_acc": 0.97438, "loss_cls": 1.10478, "loss": 1.10478, "time": 0.21715} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.0248, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7475, "top5_acc": 0.9775, "loss_cls": 1.0463, "loss": 1.0463, "time": 0.22269} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.02479, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.74312, "top5_acc": 0.97438, "loss_cls": 1.09073, "loss": 1.09073, "time": 0.2156} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.02479, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.7075, "top5_acc": 0.9675, "loss_cls": 1.15931, "loss": 1.15931, "time": 0.21892} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.02479, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.73875, "top5_acc": 0.98312, "loss_cls": 1.04544, "loss": 1.04544, "time": 0.21893} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.02478, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.73, "top5_acc": 0.9775, "loss_cls": 1.08915, "loss": 1.08915, "time": 0.21925} +{"mode": "val", "epoch": 9, "iter": 533, "lr": 0.02478, "top1_acc": 0.68184, "top5_acc": 0.95282, "mean_class_accuracy": 0.54341} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.02477, "memory": 4082, "data_time": 0.18306, "top1_acc": 0.73, "top5_acc": 0.96875, "loss_cls": 1.10781, "loss": 1.10781, "time": 0.40129} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.02477, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.74562, "top5_acc": 0.97438, "loss_cls": 1.06876, "loss": 1.06876, "time": 0.21863} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.02477, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.76125, "top5_acc": 0.9775, "loss_cls": 1.02157, "loss": 1.02157, "time": 0.21712} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.02476, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.72375, "top5_acc": 0.97875, "loss_cls": 1.08172, "loss": 1.08172, "time": 0.21621} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.02476, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7375, "top5_acc": 0.97312, "loss_cls": 1.09718, "loss": 1.09718, "time": 0.21707} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.02476, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.74375, "top5_acc": 0.97812, "loss_cls": 1.07275, "loss": 1.07275, "time": 0.21868} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.02475, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.74812, "top5_acc": 0.97938, "loss_cls": 1.05478, "loss": 1.05478, "time": 0.21494} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.02475, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.71938, "top5_acc": 0.97188, "loss_cls": 1.13621, "loss": 1.13621, "time": 0.2178} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.02474, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.76062, "top5_acc": 0.98, "loss_cls": 1.02424, "loss": 1.02424, "time": 0.21696} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.02474, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.75625, "top5_acc": 0.97562, "loss_cls": 1.0616, "loss": 1.0616, "time": 0.21985} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.02473, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.7425, "top5_acc": 0.97688, "loss_cls": 1.08991, "loss": 1.08991, "time": 0.21986} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.02473, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.73688, "top5_acc": 0.96812, "loss_cls": 1.11915, "loss": 1.11915, "time": 0.21908} +{"mode": "val", "epoch": 10, "iter": 533, "lr": 0.02473, "top1_acc": 0.72491, "top5_acc": 0.97676, "mean_class_accuracy": 0.59024} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.02472, "memory": 4082, "data_time": 0.18673, "top1_acc": 0.75812, "top5_acc": 0.98062, "loss_cls": 1.03164, "loss": 1.03164, "time": 0.40655} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.02472, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.7625, "top5_acc": 0.98, "loss_cls": 1.00766, "loss": 1.00766, "time": 0.21838} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.02471, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.73562, "top5_acc": 0.985, "loss_cls": 1.0735, "loss": 1.0735, "time": 0.21759} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.02471, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.745, "top5_acc": 0.975, "loss_cls": 1.07576, "loss": 1.07576, "time": 0.21551} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.02471, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.75375, "top5_acc": 0.98625, "loss_cls": 1.01987, "loss": 1.01987, "time": 0.21917} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.0247, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.74812, "top5_acc": 0.9825, "loss_cls": 1.02269, "loss": 1.02269, "time": 0.2173} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.0247, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.74562, "top5_acc": 0.9775, "loss_cls": 1.07064, "loss": 1.07064, "time": 0.21684} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.02469, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.75875, "top5_acc": 0.98062, "loss_cls": 1.01222, "loss": 1.01222, "time": 0.21867} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.02469, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.77188, "top5_acc": 0.98125, "loss_cls": 0.98422, "loss": 0.98422, "time": 0.21684} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.02468, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.74625, "top5_acc": 0.98, "loss_cls": 0.99564, "loss": 0.99564, "time": 0.21822} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.02468, "memory": 4082, "data_time": 0.00021, "top1_acc": 0.76188, "top5_acc": 0.98188, "loss_cls": 1.004, "loss": 1.004, "time": 0.21495} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.02467, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.73812, "top5_acc": 0.97, "loss_cls": 1.07588, "loss": 1.07588, "time": 0.21892} +{"mode": "val", "epoch": 11, "iter": 533, "lr": 0.02467, "top1_acc": 0.72081, "top5_acc": 0.97453, "mean_class_accuracy": 0.60216} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.02467, "memory": 4082, "data_time": 0.18212, "top1_acc": 0.78125, "top5_acc": 0.98188, "loss_cls": 0.98877, "loss": 0.98877, "time": 0.40182} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.02466, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.74062, "top5_acc": 0.9825, "loss_cls": 1.02975, "loss": 1.02975, "time": 0.21953} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.02466, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.735, "top5_acc": 0.9725, "loss_cls": 1.09459, "loss": 1.09459, "time": 0.21876} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.02465, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.76625, "top5_acc": 0.98312, "loss_cls": 0.96532, "loss": 0.96532, "time": 0.21676} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.02465, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.75938, "top5_acc": 0.98125, "loss_cls": 1.01744, "loss": 1.01744, "time": 0.2172} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.02464, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.75062, "top5_acc": 0.9775, "loss_cls": 1.04038, "loss": 1.04038, "time": 0.21733} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.02464, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.75312, "top5_acc": 0.97625, "loss_cls": 1.00493, "loss": 1.00493, "time": 0.217} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.02463, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.7275, "top5_acc": 0.97938, "loss_cls": 1.09367, "loss": 1.09367, "time": 0.21937} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.02463, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.76188, "top5_acc": 0.98, "loss_cls": 1.022, "loss": 1.022, "time": 0.21489} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.02462, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.75562, "top5_acc": 0.98438, "loss_cls": 1.01947, "loss": 1.01947, "time": 0.21897} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.02462, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.75375, "top5_acc": 0.9825, "loss_cls": 1.00439, "loss": 1.00439, "time": 0.21717} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.02461, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.75062, "top5_acc": 0.98625, "loss_cls": 0.99508, "loss": 0.99508, "time": 0.21902} +{"mode": "val", "epoch": 12, "iter": 533, "lr": 0.02461, "top1_acc": 0.66647, "top5_acc": 0.96233, "mean_class_accuracy": 0.56387} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.0246, "memory": 4082, "data_time": 0.18338, "top1_acc": 0.77625, "top5_acc": 0.98062, "loss_cls": 0.96148, "loss": 0.96148, "time": 0.4006} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.0246, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.76438, "top5_acc": 0.97625, "loss_cls": 1.00363, "loss": 1.00363, "time": 0.21732} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.02459, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.73938, "top5_acc": 0.97438, "loss_cls": 1.04138, "loss": 1.04138, "time": 0.22019} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.02459, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.75312, "top5_acc": 0.98562, "loss_cls": 0.98074, "loss": 0.98074, "time": 0.21593} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.02458, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7575, "top5_acc": 0.98, "loss_cls": 1.00213, "loss": 1.00213, "time": 0.21913} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.02458, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.76188, "top5_acc": 0.985, "loss_cls": 0.99342, "loss": 0.99342, "time": 0.21926} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.02457, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.76125, "top5_acc": 0.98562, "loss_cls": 0.98183, "loss": 0.98183, "time": 0.21794} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.02457, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.745, "top5_acc": 0.97438, "loss_cls": 1.06803, "loss": 1.06803, "time": 0.21875} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.02456, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7675, "top5_acc": 0.98188, "loss_cls": 0.97432, "loss": 0.97432, "time": 0.21875} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.02455, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.74, "top5_acc": 0.98, "loss_cls": 1.04033, "loss": 1.04033, "time": 0.21549} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.02455, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.76188, "top5_acc": 0.98625, "loss_cls": 0.99583, "loss": 0.99583, "time": 0.21998} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.02454, "memory": 4082, "data_time": 0.00057, "top1_acc": 0.75, "top5_acc": 0.98062, "loss_cls": 1.01139, "loss": 1.01139, "time": 0.2186} +{"mode": "val", "epoch": 13, "iter": 533, "lr": 0.02454, "top1_acc": 0.71353, "top5_acc": 0.97313, "mean_class_accuracy": 0.6199} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.02453, "memory": 4082, "data_time": 0.18692, "top1_acc": 0.78062, "top5_acc": 0.9825, "loss_cls": 0.92797, "loss": 0.92797, "time": 0.40663} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.02453, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.76312, "top5_acc": 0.97688, "loss_cls": 1.02151, "loss": 1.02151, "time": 0.21924} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.02452, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.77312, "top5_acc": 0.98938, "loss_cls": 0.94338, "loss": 0.94338, "time": 0.21617} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.02452, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7825, "top5_acc": 0.9775, "loss_cls": 0.9529, "loss": 0.9529, "time": 0.21825} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.02451, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.75125, "top5_acc": 0.9825, "loss_cls": 1.03026, "loss": 1.03026, "time": 0.21878} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.02451, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.76562, "top5_acc": 0.98125, "loss_cls": 0.97295, "loss": 0.97295, "time": 0.21799} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.0245, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.77625, "top5_acc": 0.98188, "loss_cls": 0.96581, "loss": 0.96581, "time": 0.22035} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.02449, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.74625, "top5_acc": 0.98188, "loss_cls": 0.9999, "loss": 0.9999, "time": 0.21958} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.02449, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.76625, "top5_acc": 0.98438, "loss_cls": 0.97957, "loss": 0.97957, "time": 0.21698} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.02448, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.75375, "top5_acc": 0.98438, "loss_cls": 1.03403, "loss": 1.03403, "time": 0.21856} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.02448, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.77875, "top5_acc": 0.98188, "loss_cls": 0.95708, "loss": 0.95708, "time": 0.21925} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.02447, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.76625, "top5_acc": 0.98812, "loss_cls": 0.93432, "loss": 0.93432, "time": 0.21626} +{"mode": "val", "epoch": 14, "iter": 533, "lr": 0.02447, "top1_acc": 0.73043, "top5_acc": 0.97359, "mean_class_accuracy": 0.61285} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.02446, "memory": 4082, "data_time": 0.19076, "top1_acc": 0.76812, "top5_acc": 0.97938, "loss_cls": 1.00936, "loss": 1.00936, "time": 0.40913} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.02445, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.76062, "top5_acc": 0.98625, "loss_cls": 0.95741, "loss": 0.95741, "time": 0.21665} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.02445, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7575, "top5_acc": 0.97812, "loss_cls": 0.98809, "loss": 0.98809, "time": 0.21767} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.02444, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79375, "top5_acc": 0.98625, "loss_cls": 0.91759, "loss": 0.91759, "time": 0.2162} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.02444, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.74875, "top5_acc": 0.97562, "loss_cls": 1.034, "loss": 1.034, "time": 0.21736} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.02443, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.78688, "top5_acc": 0.98438, "loss_cls": 0.87845, "loss": 0.87845, "time": 0.21705} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.02442, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.77938, "top5_acc": 0.98312, "loss_cls": 0.94162, "loss": 0.94162, "time": 0.2165} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.02442, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.74812, "top5_acc": 0.98188, "loss_cls": 1.02054, "loss": 1.02054, "time": 0.21813} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.02441, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.78, "top5_acc": 0.985, "loss_cls": 0.91542, "loss": 0.91542, "time": 0.21668} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.02441, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.77312, "top5_acc": 0.98625, "loss_cls": 0.9509, "loss": 0.9509, "time": 0.21997} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.0244, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.77, "top5_acc": 0.98, "loss_cls": 0.93375, "loss": 0.93375, "time": 0.2185} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.02439, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.78312, "top5_acc": 0.98625, "loss_cls": 0.928, "loss": 0.928, "time": 0.21772} +{"mode": "val", "epoch": 15, "iter": 533, "lr": 0.02439, "top1_acc": 0.72421, "top5_acc": 0.9709, "mean_class_accuracy": 0.63719} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.02438, "memory": 4082, "data_time": 0.18925, "top1_acc": 0.78312, "top5_acc": 0.98375, "loss_cls": 0.93631, "loss": 0.93631, "time": 0.40769} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.02438, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.7975, "top5_acc": 0.99188, "loss_cls": 0.86075, "loss": 0.86075, "time": 0.21681} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.02437, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78375, "top5_acc": 0.98, "loss_cls": 0.94101, "loss": 0.94101, "time": 0.21853} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.02436, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.78125, "top5_acc": 0.98562, "loss_cls": 0.91252, "loss": 0.91252, "time": 0.2155} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.02436, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.76938, "top5_acc": 0.98375, "loss_cls": 0.97929, "loss": 0.97929, "time": 0.21935} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.02435, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.77875, "top5_acc": 0.97938, "loss_cls": 0.93867, "loss": 0.93867, "time": 0.21488} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.02434, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.77938, "top5_acc": 0.98625, "loss_cls": 0.91841, "loss": 0.91841, "time": 0.21852} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.02434, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.75375, "top5_acc": 0.98688, "loss_cls": 1.00877, "loss": 1.00877, "time": 0.22008} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.02433, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.76688, "top5_acc": 0.98312, "loss_cls": 0.96337, "loss": 0.96337, "time": 0.21937} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.02432, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.76, "top5_acc": 0.98312, "loss_cls": 0.98751, "loss": 0.98751, "time": 0.22288} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.02432, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.79062, "top5_acc": 0.98562, "loss_cls": 0.91148, "loss": 0.91148, "time": 0.22178} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.02431, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.76625, "top5_acc": 0.98312, "loss_cls": 0.942, "loss": 0.942, "time": 0.21983} +{"mode": "val", "epoch": 16, "iter": 533, "lr": 0.0243, "top1_acc": 0.73125, "top5_acc": 0.97383, "mean_class_accuracy": 0.60776} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.0243, "memory": 4082, "data_time": 0.18887, "top1_acc": 0.80062, "top5_acc": 0.98875, "loss_cls": 0.85089, "loss": 0.85089, "time": 0.56145} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.02429, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.7825, "top5_acc": 0.9825, "loss_cls": 0.91634, "loss": 0.91634, "time": 0.41685} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.02428, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79812, "top5_acc": 0.98312, "loss_cls": 0.86387, "loss": 0.86387, "time": 0.41858} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.02428, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.77938, "top5_acc": 0.98625, "loss_cls": 0.87744, "loss": 0.87744, "time": 0.41643} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.02427, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.77312, "top5_acc": 0.98375, "loss_cls": 0.94871, "loss": 0.94871, "time": 0.41891} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.02426, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.77375, "top5_acc": 0.98688, "loss_cls": 0.93204, "loss": 0.93204, "time": 0.41852} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.02426, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.76625, "top5_acc": 0.9875, "loss_cls": 0.96791, "loss": 0.96791, "time": 0.41574} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.02425, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79188, "top5_acc": 0.97938, "loss_cls": 0.94207, "loss": 0.94207, "time": 0.42052} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.02424, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.77438, "top5_acc": 0.98562, "loss_cls": 0.90176, "loss": 0.90176, "time": 0.41782} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.02424, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.76375, "top5_acc": 0.98125, "loss_cls": 0.97994, "loss": 0.97994, "time": 0.41804} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.02423, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78312, "top5_acc": 0.985, "loss_cls": 0.92144, "loss": 0.92144, "time": 0.41795} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.02422, "memory": 4082, "data_time": 0.00054, "top1_acc": 0.7625, "top5_acc": 0.9825, "loss_cls": 0.95576, "loss": 0.95576, "time": 0.43024} +{"mode": "val", "epoch": 17, "iter": 533, "lr": 0.02422, "top1_acc": 0.74123, "top5_acc": 0.97571, "mean_class_accuracy": 0.64718} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.02421, "memory": 4082, "data_time": 0.19121, "top1_acc": 0.79688, "top5_acc": 0.99125, "loss_cls": 0.83476, "loss": 0.83476, "time": 0.55513} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.0242, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.78812, "top5_acc": 0.98312, "loss_cls": 0.8765, "loss": 0.8765, "time": 0.42911} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.02419, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.79625, "top5_acc": 0.98562, "loss_cls": 0.88322, "loss": 0.88322, "time": 0.41682} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.02419, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78688, "top5_acc": 0.9875, "loss_cls": 0.87286, "loss": 0.87286, "time": 0.41687} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.02418, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79938, "top5_acc": 0.98812, "loss_cls": 0.86769, "loss": 0.86769, "time": 0.41722} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.02417, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.76438, "top5_acc": 0.98625, "loss_cls": 0.92514, "loss": 0.92514, "time": 0.41683} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.02417, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78688, "top5_acc": 0.9825, "loss_cls": 0.92617, "loss": 0.92617, "time": 0.41553} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.02416, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.80438, "top5_acc": 0.98438, "loss_cls": 0.88873, "loss": 0.88873, "time": 0.41605} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.02415, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.77875, "top5_acc": 0.9825, "loss_cls": 0.93923, "loss": 0.93923, "time": 0.41793} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.02414, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.79562, "top5_acc": 0.98562, "loss_cls": 0.87272, "loss": 0.87272, "time": 0.41716} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.02414, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.78625, "top5_acc": 0.98375, "loss_cls": 0.91033, "loss": 0.91033, "time": 0.41809} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.02413, "memory": 4082, "data_time": 0.00064, "top1_acc": 0.7825, "top5_acc": 0.98812, "loss_cls": 0.96929, "loss": 0.96929, "time": 0.41735} +{"mode": "val", "epoch": 18, "iter": 533, "lr": 0.02412, "top1_acc": 0.72867, "top5_acc": 0.97418, "mean_class_accuracy": 0.63405} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.02411, "memory": 4082, "data_time": 0.19633, "top1_acc": 0.78812, "top5_acc": 0.99188, "loss_cls": 0.86408, "loss": 0.86408, "time": 0.55023} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.02411, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78812, "top5_acc": 0.98812, "loss_cls": 0.86887, "loss": 0.86887, "time": 0.43648} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.0241, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.7975, "top5_acc": 0.98438, "loss_cls": 0.86752, "loss": 0.86752, "time": 0.4166} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.02409, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7975, "top5_acc": 0.985, "loss_cls": 0.88063, "loss": 0.88063, "time": 0.41722} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.02408, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.80188, "top5_acc": 0.98625, "loss_cls": 0.84322, "loss": 0.84322, "time": 0.41774} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.02408, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80438, "top5_acc": 0.985, "loss_cls": 0.85077, "loss": 0.85077, "time": 0.41696} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.02407, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.78438, "top5_acc": 0.98312, "loss_cls": 0.92063, "loss": 0.92063, "time": 0.41878} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.02406, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80125, "top5_acc": 0.9825, "loss_cls": 0.89034, "loss": 0.89034, "time": 0.41728} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.02405, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.8, "top5_acc": 0.985, "loss_cls": 0.873, "loss": 0.873, "time": 0.41846} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.02405, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7875, "top5_acc": 0.99125, "loss_cls": 0.88158, "loss": 0.88158, "time": 0.41803} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.02404, "memory": 4082, "data_time": 0.00062, "top1_acc": 0.80312, "top5_acc": 0.99, "loss_cls": 0.8453, "loss": 0.8453, "time": 0.41923} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.02403, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77875, "top5_acc": 0.98438, "loss_cls": 0.93503, "loss": 0.93503, "time": 0.41737} +{"mode": "val", "epoch": 19, "iter": 533, "lr": 0.02402, "top1_acc": 0.75484, "top5_acc": 0.97571, "mean_class_accuracy": 0.6235} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.02402, "memory": 4082, "data_time": 0.19266, "top1_acc": 0.795, "top5_acc": 0.98812, "loss_cls": 0.8913, "loss": 0.8913, "time": 0.54416} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.02401, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8025, "top5_acc": 0.98812, "loss_cls": 0.84834, "loss": 0.84834, "time": 0.41634} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.024, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.80562, "top5_acc": 0.98938, "loss_cls": 0.85382, "loss": 0.85382, "time": 0.41748} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.02399, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79125, "top5_acc": 0.98688, "loss_cls": 0.89028, "loss": 0.89028, "time": 0.42027} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.02398, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.80125, "top5_acc": 0.98875, "loss_cls": 0.8409, "loss": 0.8409, "time": 0.41775} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.02398, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80312, "top5_acc": 0.98938, "loss_cls": 0.8552, "loss": 0.8552, "time": 0.42019} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.02397, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80125, "top5_acc": 0.985, "loss_cls": 0.89336, "loss": 0.89336, "time": 0.41814} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.02396, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.78438, "top5_acc": 0.98688, "loss_cls": 0.88397, "loss": 0.88397, "time": 0.41897} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.02395, "memory": 4082, "data_time": 0.00065, "top1_acc": 0.8, "top5_acc": 0.985, "loss_cls": 0.85442, "loss": 0.85442, "time": 0.41911} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.02394, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.79125, "top5_acc": 0.98438, "loss_cls": 0.87266, "loss": 0.87266, "time": 0.41831} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.02393, "memory": 4082, "data_time": 0.00065, "top1_acc": 0.7925, "top5_acc": 0.985, "loss_cls": 0.87197, "loss": 0.87197, "time": 0.41804} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.02393, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.79312, "top5_acc": 0.98688, "loss_cls": 0.88672, "loss": 0.88672, "time": 0.41831} +{"mode": "val", "epoch": 20, "iter": 533, "lr": 0.02392, "top1_acc": 0.74909, "top5_acc": 0.97993, "mean_class_accuracy": 0.64082} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.02391, "memory": 4082, "data_time": 0.19645, "top1_acc": 0.79125, "top5_acc": 0.985, "loss_cls": 0.86987, "loss": 0.86987, "time": 0.53375} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.0239, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.7975, "top5_acc": 0.99, "loss_cls": 0.81402, "loss": 0.81402, "time": 0.41505} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.02389, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.79812, "top5_acc": 0.99, "loss_cls": 0.86739, "loss": 0.86739, "time": 0.41887} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.02389, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80562, "top5_acc": 0.99062, "loss_cls": 0.83363, "loss": 0.83363, "time": 0.41982} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.02388, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8025, "top5_acc": 0.98812, "loss_cls": 0.88194, "loss": 0.88194, "time": 0.41904} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.02387, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.80875, "top5_acc": 0.99, "loss_cls": 0.81911, "loss": 0.81911, "time": 0.41632} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.02386, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.81688, "top5_acc": 0.98812, "loss_cls": 0.79461, "loss": 0.79461, "time": 0.41819} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.02385, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.80125, "top5_acc": 0.98375, "loss_cls": 0.86127, "loss": 0.86127, "time": 0.41822} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.02384, "memory": 4082, "data_time": 0.00076, "top1_acc": 0.8, "top5_acc": 0.9875, "loss_cls": 0.88582, "loss": 0.88582, "time": 0.41941} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.02383, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80312, "top5_acc": 0.98812, "loss_cls": 0.82809, "loss": 0.82809, "time": 0.4183} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.02383, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.805, "top5_acc": 0.9875, "loss_cls": 0.86273, "loss": 0.86273, "time": 0.43917} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.02382, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.7875, "top5_acc": 0.98562, "loss_cls": 0.95227, "loss": 0.95227, "time": 0.43887} +{"mode": "val", "epoch": 21, "iter": 533, "lr": 0.02381, "top1_acc": 0.74287, "top5_acc": 0.97817, "mean_class_accuracy": 0.64115} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.0238, "memory": 4082, "data_time": 0.19439, "top1_acc": 0.79375, "top5_acc": 0.9875, "loss_cls": 0.87062, "loss": 0.87062, "time": 0.53766} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.02379, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81688, "top5_acc": 0.98875, "loss_cls": 0.79649, "loss": 0.79649, "time": 0.41717} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.02378, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81875, "top5_acc": 0.98312, "loss_cls": 0.83365, "loss": 0.83365, "time": 0.41842} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.02378, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.805, "top5_acc": 0.98688, "loss_cls": 0.86257, "loss": 0.86257, "time": 0.41653} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.02377, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82125, "top5_acc": 0.99062, "loss_cls": 0.81188, "loss": 0.81188, "time": 0.41904} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.02376, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80875, "top5_acc": 0.985, "loss_cls": 0.82756, "loss": 0.82756, "time": 0.41632} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.02375, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.81375, "top5_acc": 0.98438, "loss_cls": 0.83741, "loss": 0.83741, "time": 0.41867} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.02374, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79875, "top5_acc": 0.9875, "loss_cls": 0.8566, "loss": 0.8566, "time": 0.41854} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.02373, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80375, "top5_acc": 0.98688, "loss_cls": 0.86312, "loss": 0.86312, "time": 0.41855} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.02372, "memory": 4082, "data_time": 0.00071, "top1_acc": 0.81125, "top5_acc": 0.98812, "loss_cls": 0.81209, "loss": 0.81209, "time": 0.41815} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.02371, "memory": 4082, "data_time": 0.00069, "top1_acc": 0.80438, "top5_acc": 0.98312, "loss_cls": 0.86962, "loss": 0.86962, "time": 0.42911} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0237, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8, "top5_acc": 0.98875, "loss_cls": 0.87253, "loss": 0.87253, "time": 0.43921} +{"mode": "val", "epoch": 22, "iter": 533, "lr": 0.0237, "top1_acc": 0.76212, "top5_acc": 0.97676, "mean_class_accuracy": 0.645} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.02369, "memory": 4082, "data_time": 0.19619, "top1_acc": 0.81375, "top5_acc": 0.9875, "loss_cls": 0.82636, "loss": 0.82636, "time": 0.52328} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.02368, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80938, "top5_acc": 0.98625, "loss_cls": 0.78765, "loss": 0.78765, "time": 0.41772} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.02367, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8175, "top5_acc": 0.98938, "loss_cls": 0.8081, "loss": 0.8081, "time": 0.425} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.02366, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80062, "top5_acc": 0.98562, "loss_cls": 0.8554, "loss": 0.8554, "time": 0.41648} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.02365, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.79875, "top5_acc": 0.9875, "loss_cls": 0.84386, "loss": 0.84386, "time": 0.4171} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.02364, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79438, "top5_acc": 0.99062, "loss_cls": 0.83964, "loss": 0.83964, "time": 0.41576} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.02363, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.81188, "top5_acc": 0.98562, "loss_cls": 0.84754, "loss": 0.84754, "time": 0.41663} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.02362, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81562, "top5_acc": 0.98625, "loss_cls": 0.82197, "loss": 0.82197, "time": 0.4177} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.02361, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.80688, "top5_acc": 0.98688, "loss_cls": 0.85374, "loss": 0.85374, "time": 0.42007} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.0236, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.81375, "top5_acc": 0.9875, "loss_cls": 0.80364, "loss": 0.80364, "time": 0.41869} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.02359, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80375, "top5_acc": 0.98438, "loss_cls": 0.84919, "loss": 0.84919, "time": 0.4178} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.02359, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.79812, "top5_acc": 0.9875, "loss_cls": 0.84472, "loss": 0.84472, "time": 0.41894} +{"mode": "val", "epoch": 23, "iter": 533, "lr": 0.02358, "top1_acc": 0.7329, "top5_acc": 0.97019, "mean_class_accuracy": 0.63012} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.02357, "memory": 4082, "data_time": 0.19998, "top1_acc": 0.81812, "top5_acc": 0.98438, "loss_cls": 0.81373, "loss": 0.81373, "time": 0.5253} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.02356, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.80812, "top5_acc": 0.985, "loss_cls": 0.86513, "loss": 0.86513, "time": 0.41786} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.02355, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82188, "top5_acc": 0.98812, "loss_cls": 0.77358, "loss": 0.77358, "time": 0.41668} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.02354, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.79812, "top5_acc": 0.9825, "loss_cls": 0.8365, "loss": 0.8365, "time": 0.41783} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.02353, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81062, "top5_acc": 0.98688, "loss_cls": 0.82155, "loss": 0.82155, "time": 0.41837} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.02352, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.8225, "top5_acc": 0.9875, "loss_cls": 0.78587, "loss": 0.78587, "time": 0.41799} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.02351, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.80375, "top5_acc": 0.985, "loss_cls": 0.8448, "loss": 0.8448, "time": 0.41609} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.0235, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.81875, "top5_acc": 0.98938, "loss_cls": 0.82473, "loss": 0.82473, "time": 0.41688} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.02349, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8125, "top5_acc": 0.99188, "loss_cls": 0.78583, "loss": 0.78583, "time": 0.41698} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.02348, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.80875, "top5_acc": 0.99, "loss_cls": 0.81737, "loss": 0.81737, "time": 0.41883} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.02347, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82938, "top5_acc": 0.9925, "loss_cls": 0.75465, "loss": 0.75465, "time": 0.41893} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.02346, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81125, "top5_acc": 0.98938, "loss_cls": 0.86864, "loss": 0.86864, "time": 0.41709} +{"mode": "val", "epoch": 24, "iter": 533, "lr": 0.02345, "top1_acc": 0.75308, "top5_acc": 0.97946, "mean_class_accuracy": 0.66259} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.02344, "memory": 4082, "data_time": 0.20053, "top1_acc": 0.79875, "top5_acc": 0.9925, "loss_cls": 0.80086, "loss": 0.80086, "time": 0.51985} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.02343, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82125, "top5_acc": 0.99125, "loss_cls": 0.75142, "loss": 0.75142, "time": 0.41753} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.02342, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82188, "top5_acc": 0.99062, "loss_cls": 0.73182, "loss": 0.73182, "time": 0.41706} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.02341, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8075, "top5_acc": 0.98688, "loss_cls": 0.79396, "loss": 0.79396, "time": 0.41743} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.0234, "memory": 4082, "data_time": 0.00051, "top1_acc": 0.81312, "top5_acc": 0.99125, "loss_cls": 0.78859, "loss": 0.78859, "time": 0.41619} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.02339, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83625, "top5_acc": 0.99062, "loss_cls": 0.74567, "loss": 0.74567, "time": 0.41583} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.02338, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81562, "top5_acc": 0.9875, "loss_cls": 0.78439, "loss": 0.78439, "time": 0.41606} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.02337, "memory": 4082, "data_time": 0.00051, "top1_acc": 0.79875, "top5_acc": 0.98438, "loss_cls": 0.86584, "loss": 0.86584, "time": 0.41932} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.02336, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.81375, "top5_acc": 0.98812, "loss_cls": 0.82483, "loss": 0.82483, "time": 0.41648} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.02335, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.81062, "top5_acc": 0.98188, "loss_cls": 0.84118, "loss": 0.84118, "time": 0.41661} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.02334, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81062, "top5_acc": 0.9875, "loss_cls": 0.82219, "loss": 0.82219, "time": 0.41717} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.02333, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.80938, "top5_acc": 0.98688, "loss_cls": 0.84845, "loss": 0.84845, "time": 0.41844} +{"mode": "val", "epoch": 25, "iter": 533, "lr": 0.02333, "top1_acc": 0.77409, "top5_acc": 0.98369, "mean_class_accuracy": 0.66177} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.02332, "memory": 4082, "data_time": 0.19521, "top1_acc": 0.825, "top5_acc": 0.99062, "loss_cls": 0.73787, "loss": 0.73787, "time": 0.51964} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.0233, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83, "top5_acc": 0.99125, "loss_cls": 0.74541, "loss": 0.74541, "time": 0.42605} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.02329, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.84188, "top5_acc": 0.98875, "loss_cls": 0.73279, "loss": 0.73279, "time": 0.41671} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.02328, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82875, "top5_acc": 0.99125, "loss_cls": 0.78523, "loss": 0.78523, "time": 0.41624} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.02327, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81625, "top5_acc": 0.99125, "loss_cls": 0.79522, "loss": 0.79522, "time": 0.41663} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.02326, "memory": 4082, "data_time": 0.00057, "top1_acc": 0.79438, "top5_acc": 0.985, "loss_cls": 0.85537, "loss": 0.85537, "time": 0.41824} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.02325, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82875, "top5_acc": 0.99312, "loss_cls": 0.75383, "loss": 0.75383, "time": 0.41747} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.02324, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8025, "top5_acc": 0.99125, "loss_cls": 0.82574, "loss": 0.82574, "time": 0.41696} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.02323, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79375, "top5_acc": 0.985, "loss_cls": 0.88654, "loss": 0.88654, "time": 0.41676} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.02322, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.815, "top5_acc": 0.99438, "loss_cls": 0.76083, "loss": 0.76083, "time": 0.41849} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.02321, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81812, "top5_acc": 0.9875, "loss_cls": 0.77509, "loss": 0.77509, "time": 0.41828} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.0232, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.8175, "top5_acc": 0.9925, "loss_cls": 0.78238, "loss": 0.78238, "time": 0.41692} +{"mode": "val", "epoch": 26, "iter": 533, "lr": 0.02319, "top1_acc": 0.79063, "top5_acc": 0.98498, "mean_class_accuracy": 0.70035} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.02318, "memory": 4082, "data_time": 0.19784, "top1_acc": 0.81812, "top5_acc": 0.99, "loss_cls": 0.77182, "loss": 0.77182, "time": 0.51767} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.02317, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85062, "top5_acc": 0.99062, "loss_cls": 0.70005, "loss": 0.70005, "time": 0.43038} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.02316, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.82438, "top5_acc": 0.99375, "loss_cls": 0.76852, "loss": 0.76852, "time": 0.41493} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.02315, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8175, "top5_acc": 0.98812, "loss_cls": 0.79206, "loss": 0.79206, "time": 0.41691} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.02314, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.8325, "top5_acc": 0.99062, "loss_cls": 0.71975, "loss": 0.71975, "time": 0.41637} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.02313, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.82062, "top5_acc": 0.99125, "loss_cls": 0.75819, "loss": 0.75819, "time": 0.4193} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.02312, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82125, "top5_acc": 0.98938, "loss_cls": 0.77222, "loss": 0.77222, "time": 0.41657} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.02311, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8275, "top5_acc": 0.99375, "loss_cls": 0.76841, "loss": 0.76841, "time": 0.41782} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.0231, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.795, "top5_acc": 0.98875, "loss_cls": 0.839, "loss": 0.839, "time": 0.4175} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.02308, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82375, "top5_acc": 0.99062, "loss_cls": 0.81042, "loss": 0.81042, "time": 0.41771} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.02307, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82688, "top5_acc": 0.99, "loss_cls": 0.75077, "loss": 0.75077, "time": 0.41618} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.02306, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80938, "top5_acc": 0.99062, "loss_cls": 0.78131, "loss": 0.78131, "time": 0.41786} +{"mode": "val", "epoch": 27, "iter": 533, "lr": 0.02305, "top1_acc": 0.76141, "top5_acc": 0.97559, "mean_class_accuracy": 0.67811} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.02304, "memory": 4082, "data_time": 0.19065, "top1_acc": 0.84, "top5_acc": 0.99438, "loss_cls": 0.73641, "loss": 0.73641, "time": 0.50154} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.02303, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.82188, "top5_acc": 0.99062, "loss_cls": 0.77253, "loss": 0.77253, "time": 0.4394} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.02302, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82188, "top5_acc": 0.98938, "loss_cls": 0.75411, "loss": 0.75411, "time": 0.42346} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.02301, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82875, "top5_acc": 0.99188, "loss_cls": 0.73454, "loss": 0.73454, "time": 0.41566} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.023, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80812, "top5_acc": 0.98875, "loss_cls": 0.81415, "loss": 0.81415, "time": 0.41665} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.02299, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.805, "top5_acc": 0.99188, "loss_cls": 0.80574, "loss": 0.80574, "time": 0.4178} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.02298, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.82375, "top5_acc": 0.98875, "loss_cls": 0.78092, "loss": 0.78092, "time": 0.43023} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.02297, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.82938, "top5_acc": 0.99438, "loss_cls": 0.72605, "loss": 0.72605, "time": 0.43244} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.02295, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.83312, "top5_acc": 0.98938, "loss_cls": 0.75781, "loss": 0.75781, "time": 0.41605} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.02294, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.825, "top5_acc": 0.98875, "loss_cls": 0.80672, "loss": 0.80672, "time": 0.41804} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.02293, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82562, "top5_acc": 0.98812, "loss_cls": 0.74473, "loss": 0.74473, "time": 0.41936} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.02292, "memory": 4082, "data_time": 0.00054, "top1_acc": 0.81188, "top5_acc": 0.98938, "loss_cls": 0.79568, "loss": 0.79568, "time": 0.41606} +{"mode": "val", "epoch": 28, "iter": 533, "lr": 0.02291, "top1_acc": 0.76106, "top5_acc": 0.97582, "mean_class_accuracy": 0.66597} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.0229, "memory": 4082, "data_time": 0.1958, "top1_acc": 0.825, "top5_acc": 0.9925, "loss_cls": 0.74495, "loss": 0.74495, "time": 0.50298} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.02289, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.84688, "top5_acc": 0.99125, "loss_cls": 0.7012, "loss": 0.7012, "time": 0.43856} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.02288, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.82875, "top5_acc": 0.9875, "loss_cls": 0.75951, "loss": 0.75951, "time": 0.43694} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.02287, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82688, "top5_acc": 0.98812, "loss_cls": 0.75748, "loss": 0.75748, "time": 0.43692} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.02285, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83375, "top5_acc": 0.99188, "loss_cls": 0.71306, "loss": 0.71306, "time": 0.43284} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.02284, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82625, "top5_acc": 0.9875, "loss_cls": 0.78626, "loss": 0.78626, "time": 0.43829} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.02283, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8375, "top5_acc": 0.99062, "loss_cls": 0.73288, "loss": 0.73288, "time": 0.43425} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.02282, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.81688, "top5_acc": 0.98812, "loss_cls": 0.78673, "loss": 0.78673, "time": 0.43645} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.02281, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82812, "top5_acc": 0.99125, "loss_cls": 0.75642, "loss": 0.75642, "time": 0.41645} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.0228, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.81688, "top5_acc": 0.99188, "loss_cls": 0.77458, "loss": 0.77458, "time": 0.41544} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.02279, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8, "top5_acc": 0.985, "loss_cls": 0.84581, "loss": 0.84581, "time": 0.41863} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.02277, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.83938, "top5_acc": 0.98938, "loss_cls": 0.72076, "loss": 0.72076, "time": 0.4172} +{"mode": "val", "epoch": 29, "iter": 533, "lr": 0.02276, "top1_acc": 0.75191, "top5_acc": 0.977, "mean_class_accuracy": 0.66959} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.02275, "memory": 4082, "data_time": 0.19188, "top1_acc": 0.84, "top5_acc": 0.98938, "loss_cls": 0.70237, "loss": 0.70237, "time": 0.52015} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.02274, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.845, "top5_acc": 0.995, "loss_cls": 0.70819, "loss": 0.70819, "time": 0.51494} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.02273, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83875, "top5_acc": 0.995, "loss_cls": 0.70713, "loss": 0.70713, "time": 0.52545} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.02272, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83938, "top5_acc": 0.995, "loss_cls": 0.72067, "loss": 0.72067, "time": 0.51625} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.02271, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.83375, "top5_acc": 0.98625, "loss_cls": 0.73533, "loss": 0.73533, "time": 0.51915} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.02269, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8275, "top5_acc": 0.99438, "loss_cls": 0.76663, "loss": 0.76663, "time": 0.50903} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.02268, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83562, "top5_acc": 0.99, "loss_cls": 0.72686, "loss": 0.72686, "time": 0.50818} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.02267, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.82875, "top5_acc": 0.99, "loss_cls": 0.7255, "loss": 0.7255, "time": 0.51565} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.02266, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.82375, "top5_acc": 0.9925, "loss_cls": 0.7375, "loss": 0.7375, "time": 0.51128} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.02265, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81688, "top5_acc": 0.99125, "loss_cls": 0.81082, "loss": 0.81082, "time": 0.51047} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.02263, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.83875, "top5_acc": 0.9875, "loss_cls": 0.72222, "loss": 0.72222, "time": 0.30883} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.02262, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.80062, "top5_acc": 0.9875, "loss_cls": 0.83224, "loss": 0.83224, "time": 0.51228} +{"mode": "val", "epoch": 30, "iter": 533, "lr": 0.02261, "top1_acc": 0.78066, "top5_acc": 0.98099, "mean_class_accuracy": 0.66116} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.0226, "memory": 4083, "data_time": 0.19784, "top1_acc": 0.8375, "top5_acc": 0.99125, "loss_cls": 0.89568, "loss": 0.89568, "time": 0.91699} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.02259, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.82562, "top5_acc": 0.99125, "loss_cls": 0.87496, "loss": 0.87496, "time": 0.53958} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.02258, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.835, "top5_acc": 0.99312, "loss_cls": 0.86356, "loss": 0.86356, "time": 0.52772} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.02256, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.82875, "top5_acc": 0.98812, "loss_cls": 0.907, "loss": 0.907, "time": 0.53257} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.02255, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.83312, "top5_acc": 0.99125, "loss_cls": 0.89915, "loss": 0.89915, "time": 0.53403} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.02254, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.84375, "top5_acc": 0.99188, "loss_cls": 0.89102, "loss": 0.89102, "time": 0.54423} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.02253, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.83188, "top5_acc": 0.99, "loss_cls": 0.88946, "loss": 0.88946, "time": 0.29508} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.02252, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.82625, "top5_acc": 0.99125, "loss_cls": 0.89803, "loss": 0.89803, "time": 0.51207} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0225, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8425, "top5_acc": 0.99438, "loss_cls": 0.83898, "loss": 0.83898, "time": 0.37548} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.02249, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.83875, "top5_acc": 0.995, "loss_cls": 0.84351, "loss": 0.84351, "time": 0.53557} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.02248, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.82938, "top5_acc": 0.98875, "loss_cls": 0.94963, "loss": 0.94963, "time": 0.53357} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.02247, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8325, "top5_acc": 0.99125, "loss_cls": 0.89071, "loss": 0.89071, "time": 0.53103} +{"mode": "val", "epoch": 31, "iter": 533, "lr": 0.02246, "top1_acc": 0.77315, "top5_acc": 0.98111, "mean_class_accuracy": 0.69707} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.02244, "memory": 4083, "data_time": 0.19852, "top1_acc": 0.845, "top5_acc": 0.9925, "loss_cls": 0.79219, "loss": 0.79219, "time": 0.89185} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.02243, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.81938, "top5_acc": 0.99125, "loss_cls": 0.85183, "loss": 0.85183, "time": 0.53738} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.02242, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.82625, "top5_acc": 0.99125, "loss_cls": 0.83263, "loss": 0.83263, "time": 0.29643} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.02241, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.82625, "top5_acc": 0.99062, "loss_cls": 0.86265, "loss": 0.86265, "time": 0.51137} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.02239, "memory": 4083, "data_time": 0.00078, "top1_acc": 0.84062, "top5_acc": 0.99188, "loss_cls": 0.77961, "loss": 0.77961, "time": 0.38764} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.02238, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.80562, "top5_acc": 0.98875, "loss_cls": 0.91663, "loss": 0.91663, "time": 0.53011} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.02237, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.83812, "top5_acc": 0.99125, "loss_cls": 0.82497, "loss": 0.82497, "time": 0.53462} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.02236, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.8325, "top5_acc": 0.99125, "loss_cls": 0.79124, "loss": 0.79124, "time": 0.52131} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.02234, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.815, "top5_acc": 0.98938, "loss_cls": 0.89407, "loss": 0.89407, "time": 0.52764} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.02233, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.84312, "top5_acc": 0.99375, "loss_cls": 0.80038, "loss": 0.80038, "time": 0.52483} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.02232, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.83062, "top5_acc": 0.99312, "loss_cls": 0.80453, "loss": 0.80453, "time": 0.54478} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.02231, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.83188, "top5_acc": 0.99188, "loss_cls": 0.85249, "loss": 0.85249, "time": 0.54509} +{"mode": "val", "epoch": 32, "iter": 533, "lr": 0.0223, "top1_acc": 0.79087, "top5_acc": 0.98052, "mean_class_accuracy": 0.69579} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.02228, "memory": 4083, "data_time": 0.1902, "top1_acc": 0.83938, "top5_acc": 0.99312, "loss_cls": 0.76603, "loss": 0.76603, "time": 0.52578} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.02227, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.8425, "top5_acc": 0.995, "loss_cls": 0.75036, "loss": 0.75036, "time": 0.52433} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.02226, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.83688, "top5_acc": 0.99125, "loss_cls": 0.77973, "loss": 0.77973, "time": 0.54292} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.02225, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84312, "top5_acc": 0.99188, "loss_cls": 0.77285, "loss": 0.77285, "time": 0.54778} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.02223, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.82312, "top5_acc": 0.99188, "loss_cls": 0.84165, "loss": 0.84165, "time": 0.54002} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.02222, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.82062, "top5_acc": 0.99, "loss_cls": 0.8222, "loss": 0.8222, "time": 0.52724} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.02221, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.835, "top5_acc": 0.98812, "loss_cls": 0.78346, "loss": 0.78346, "time": 0.5363} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.02219, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.82438, "top5_acc": 0.99562, "loss_cls": 0.82869, "loss": 0.82869, "time": 0.54016} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.02218, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.84875, "top5_acc": 0.98688, "loss_cls": 0.75368, "loss": 0.75368, "time": 0.53524} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.02217, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.83312, "top5_acc": 0.99125, "loss_cls": 0.79733, "loss": 0.79733, "time": 0.49693} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.02216, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.81562, "top5_acc": 0.99188, "loss_cls": 0.84645, "loss": 0.84645, "time": 0.3908} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.02214, "memory": 4083, "data_time": 0.00068, "top1_acc": 0.82875, "top5_acc": 0.99312, "loss_cls": 0.79696, "loss": 0.79696, "time": 0.34745} +{"mode": "val", "epoch": 33, "iter": 533, "lr": 0.02213, "top1_acc": 0.78031, "top5_acc": 0.98111, "mean_class_accuracy": 0.68745} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.02212, "memory": 4083, "data_time": 0.19607, "top1_acc": 0.84, "top5_acc": 0.99125, "loss_cls": 0.77317, "loss": 0.77317, "time": 0.87071} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.02211, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.83062, "top5_acc": 0.99312, "loss_cls": 0.80034, "loss": 0.80034, "time": 0.54345} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.02209, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84938, "top5_acc": 0.99438, "loss_cls": 0.73255, "loss": 0.73255, "time": 0.53133} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.02208, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8475, "top5_acc": 0.99562, "loss_cls": 0.74739, "loss": 0.74739, "time": 0.54157} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.02207, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.84438, "top5_acc": 0.99188, "loss_cls": 0.7795, "loss": 0.7795, "time": 0.53072} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.02205, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84125, "top5_acc": 0.99312, "loss_cls": 0.77268, "loss": 0.77268, "time": 0.49735} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.02204, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.84625, "top5_acc": 0.9875, "loss_cls": 0.75228, "loss": 0.75228, "time": 0.38361} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.02203, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.83312, "top5_acc": 0.99188, "loss_cls": 0.76859, "loss": 0.76859, "time": 0.35784} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.02201, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8475, "top5_acc": 0.99312, "loss_cls": 0.72889, "loss": 0.72889, "time": 0.47458} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.022, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.81438, "top5_acc": 0.99375, "loss_cls": 0.86067, "loss": 0.86067, "time": 0.53671} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.02199, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.81125, "top5_acc": 0.99, "loss_cls": 0.83584, "loss": 0.83584, "time": 0.53788} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.02197, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8425, "top5_acc": 0.9925, "loss_cls": 0.78533, "loss": 0.78533, "time": 0.53315} +{"mode": "val", "epoch": 34, "iter": 533, "lr": 0.02196, "top1_acc": 0.78265, "top5_acc": 0.98521, "mean_class_accuracy": 0.70812} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.02195, "memory": 4083, "data_time": 0.19436, "top1_acc": 0.83688, "top5_acc": 0.98938, "loss_cls": 0.79336, "loss": 0.79336, "time": 0.876} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.02194, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.84188, "top5_acc": 0.99625, "loss_cls": 0.74755, "loss": 0.74755, "time": 0.52457} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.02192, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.84938, "top5_acc": 0.99062, "loss_cls": 0.73006, "loss": 0.73006, "time": 0.34779} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.02191, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8225, "top5_acc": 0.98625, "loss_cls": 0.85529, "loss": 0.85529, "time": 0.39021} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.0219, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8425, "top5_acc": 0.9925, "loss_cls": 0.77412, "loss": 0.77412, "time": 0.45223} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.02188, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.84625, "top5_acc": 0.99375, "loss_cls": 0.71779, "loss": 0.71779, "time": 0.53388} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.02187, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.845, "top5_acc": 0.99125, "loss_cls": 0.74134, "loss": 0.74134, "time": 0.53516} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.02185, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.83188, "top5_acc": 0.98938, "loss_cls": 0.78546, "loss": 0.78546, "time": 0.54302} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.02184, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.84125, "top5_acc": 0.99375, "loss_cls": 0.78577, "loss": 0.78577, "time": 0.53836} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.02183, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.83938, "top5_acc": 0.99062, "loss_cls": 0.75778, "loss": 0.75778, "time": 0.5381} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.02181, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.8475, "top5_acc": 0.99125, "loss_cls": 0.74115, "loss": 0.74115, "time": 0.52901} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.0218, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.84875, "top5_acc": 0.99438, "loss_cls": 0.74664, "loss": 0.74664, "time": 0.53057} +{"mode": "val", "epoch": 35, "iter": 533, "lr": 0.02179, "top1_acc": 0.80859, "top5_acc": 0.98709, "mean_class_accuracy": 0.72383} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.02178, "memory": 4083, "data_time": 0.19603, "top1_acc": 0.84188, "top5_acc": 0.99438, "loss_cls": 0.76433, "loss": 0.76433, "time": 0.56953} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.02176, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.84062, "top5_acc": 0.99, "loss_cls": 0.79333, "loss": 0.79333, "time": 0.5358} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.02175, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.85688, "top5_acc": 0.9925, "loss_cls": 0.70398, "loss": 0.70398, "time": 0.52834} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.02173, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86562, "top5_acc": 0.99312, "loss_cls": 0.70231, "loss": 0.70231, "time": 0.54086} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.02172, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85312, "top5_acc": 0.9925, "loss_cls": 0.70393, "loss": 0.70393, "time": 0.54119} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.02171, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8325, "top5_acc": 0.99125, "loss_cls": 0.78214, "loss": 0.78214, "time": 0.54132} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.02169, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.84875, "top5_acc": 0.9925, "loss_cls": 0.76867, "loss": 0.76867, "time": 0.53234} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.02168, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.82625, "top5_acc": 0.98812, "loss_cls": 0.84009, "loss": 0.84009, "time": 0.54161} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.02167, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.85938, "top5_acc": 0.99562, "loss_cls": 0.69749, "loss": 0.69749, "time": 0.53706} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.02165, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.82562, "top5_acc": 0.99438, "loss_cls": 0.82213, "loss": 0.82213, "time": 0.39995} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.02164, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8425, "top5_acc": 0.99, "loss_cls": 0.76926, "loss": 0.76926, "time": 0.5134} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.02162, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.82562, "top5_acc": 0.98688, "loss_cls": 0.82173, "loss": 0.82173, "time": 0.28415} +{"mode": "val", "epoch": 36, "iter": 533, "lr": 0.02161, "top1_acc": 0.76834, "top5_acc": 0.98369, "mean_class_accuracy": 0.69907} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.0216, "memory": 4083, "data_time": 0.20057, "top1_acc": 0.87, "top5_acc": 0.995, "loss_cls": 0.62585, "loss": 0.62585, "time": 0.87163} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.02158, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.8625, "top5_acc": 0.99312, "loss_cls": 0.67647, "loss": 0.67647, "time": 0.54327} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.02157, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.8325, "top5_acc": 0.99188, "loss_cls": 0.78581, "loss": 0.78581, "time": 0.5493} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.02156, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.85438, "top5_acc": 0.99125, "loss_cls": 0.73058, "loss": 0.73058, "time": 0.54504} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.02154, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.8225, "top5_acc": 0.98875, "loss_cls": 0.81439, "loss": 0.81439, "time": 0.54403} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.02153, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.84125, "top5_acc": 0.98938, "loss_cls": 0.78427, "loss": 0.78427, "time": 0.35556} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.02151, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.83938, "top5_acc": 0.99125, "loss_cls": 0.78359, "loss": 0.78359, "time": 0.51223} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0215, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86312, "top5_acc": 0.99438, "loss_cls": 0.68489, "loss": 0.68489, "time": 0.32954} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.02149, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.84812, "top5_acc": 0.99438, "loss_cls": 0.74897, "loss": 0.74897, "time": 0.52679} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.02147, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.83875, "top5_acc": 0.99312, "loss_cls": 0.73164, "loss": 0.73164, "time": 0.53094} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.02146, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.84438, "top5_acc": 0.99062, "loss_cls": 0.75843, "loss": 0.75843, "time": 0.53747} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.02144, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.84312, "top5_acc": 0.99125, "loss_cls": 0.78718, "loss": 0.78718, "time": 0.53145} +{"mode": "val", "epoch": 37, "iter": 533, "lr": 0.02143, "top1_acc": 0.78183, "top5_acc": 0.98392, "mean_class_accuracy": 0.69985} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.02142, "memory": 4083, "data_time": 0.1939, "top1_acc": 0.86312, "top5_acc": 0.99312, "loss_cls": 0.67655, "loss": 0.67655, "time": 0.84719} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.0214, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.83375, "top5_acc": 0.99438, "loss_cls": 0.78759, "loss": 0.78759, "time": 0.39881} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.02139, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.85562, "top5_acc": 0.99312, "loss_cls": 0.72992, "loss": 0.72992, "time": 0.51396} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.02137, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.85, "top5_acc": 0.9925, "loss_cls": 0.7117, "loss": 0.7117, "time": 0.28246} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.02136, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.8325, "top5_acc": 0.99125, "loss_cls": 0.75982, "loss": 0.75982, "time": 0.53801} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.02134, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86188, "top5_acc": 0.99188, "loss_cls": 0.68667, "loss": 0.68667, "time": 0.53} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.02133, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.84125, "top5_acc": 0.99062, "loss_cls": 0.77113, "loss": 0.77113, "time": 0.53357} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.02132, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.82688, "top5_acc": 0.99062, "loss_cls": 0.82626, "loss": 0.82626, "time": 0.53743} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.0213, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.84188, "top5_acc": 0.99125, "loss_cls": 0.77189, "loss": 0.77189, "time": 0.53382} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.02129, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8425, "top5_acc": 0.99125, "loss_cls": 0.79193, "loss": 0.79193, "time": 0.54064} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.02127, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.84688, "top5_acc": 0.99125, "loss_cls": 0.73162, "loss": 0.73162, "time": 0.53981} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.02126, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.85438, "top5_acc": 0.99438, "loss_cls": 0.68, "loss": 0.68, "time": 0.53263} +{"mode": "val", "epoch": 38, "iter": 533, "lr": 0.02125, "top1_acc": 0.79979, "top5_acc": 0.98533, "mean_class_accuracy": 0.71639} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.02123, "memory": 4083, "data_time": 0.19288, "top1_acc": 0.86625, "top5_acc": 0.99688, "loss_cls": 0.66579, "loss": 0.66579, "time": 0.7264} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.02122, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87188, "top5_acc": 0.995, "loss_cls": 0.67462, "loss": 0.67462, "time": 0.53947} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.0212, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85938, "top5_acc": 0.9925, "loss_cls": 0.7191, "loss": 0.7191, "time": 0.53714} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.02119, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8375, "top5_acc": 0.99438, "loss_cls": 0.77524, "loss": 0.77524, "time": 0.54633} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.02117, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.83, "top5_acc": 0.9925, "loss_cls": 0.78828, "loss": 0.78828, "time": 0.53945} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.02116, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.84562, "top5_acc": 0.99375, "loss_cls": 0.7438, "loss": 0.7438, "time": 0.536} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.02114, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.85, "top5_acc": 0.9925, "loss_cls": 0.7305, "loss": 0.7305, "time": 0.54393} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.02113, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84562, "top5_acc": 0.99375, "loss_cls": 0.73169, "loss": 0.73169, "time": 0.53869} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.02111, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86, "top5_acc": 0.995, "loss_cls": 0.69353, "loss": 0.69353, "time": 0.53477} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.0211, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.84312, "top5_acc": 0.99375, "loss_cls": 0.73118, "loss": 0.73118, "time": 0.31891} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.02108, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.83, "top5_acc": 0.9925, "loss_cls": 0.80909, "loss": 0.80909, "time": 0.41925} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.02107, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8275, "top5_acc": 0.9925, "loss_cls": 0.80041, "loss": 0.80041, "time": 0.45929} +{"mode": "val", "epoch": 39, "iter": 533, "lr": 0.02106, "top1_acc": 0.80894, "top5_acc": 0.9858, "mean_class_accuracy": 0.71432} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.02104, "memory": 4083, "data_time": 0.19336, "top1_acc": 0.86375, "top5_acc": 0.99375, "loss_cls": 0.67487, "loss": 0.67487, "time": 0.8688} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.02103, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.85188, "top5_acc": 0.98812, "loss_cls": 0.7143, "loss": 0.7143, "time": 0.53729} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.02101, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85562, "top5_acc": 0.99375, "loss_cls": 0.68931, "loss": 0.68931, "time": 0.537} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.021, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86188, "top5_acc": 0.9925, "loss_cls": 0.65694, "loss": 0.65694, "time": 0.53406} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.02098, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.86188, "top5_acc": 0.99375, "loss_cls": 0.66277, "loss": 0.66277, "time": 0.53416} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.02097, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85188, "top5_acc": 0.99312, "loss_cls": 0.72903, "loss": 0.72903, "time": 0.32397} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.02095, "memory": 4083, "data_time": 0.00069, "top1_acc": 0.845, "top5_acc": 0.99438, "loss_cls": 0.75191, "loss": 0.75191, "time": 0.4149} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.02094, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.83938, "top5_acc": 0.99062, "loss_cls": 0.7305, "loss": 0.7305, "time": 0.44428} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.02092, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.84875, "top5_acc": 0.995, "loss_cls": 0.70948, "loss": 0.70948, "time": 0.52807} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.02091, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84375, "top5_acc": 0.98812, "loss_cls": 0.73792, "loss": 0.73792, "time": 0.54776} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.02089, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.84562, "top5_acc": 0.99375, "loss_cls": 0.74039, "loss": 0.74039, "time": 0.538} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.02088, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.85875, "top5_acc": 0.99312, "loss_cls": 0.71955, "loss": 0.71955, "time": 0.52863} +{"mode": "val", "epoch": 40, "iter": 533, "lr": 0.02086, "top1_acc": 0.81645, "top5_acc": 0.98826, "mean_class_accuracy": 0.73031} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.02085, "memory": 4083, "data_time": 0.19729, "top1_acc": 0.87, "top5_acc": 0.99562, "loss_cls": 0.65835, "loss": 0.65835, "time": 0.86692} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.02083, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.8475, "top5_acc": 0.99062, "loss_cls": 0.70453, "loss": 0.70453, "time": 0.30272} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.02082, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.84938, "top5_acc": 0.9925, "loss_cls": 0.69908, "loss": 0.69908, "time": 0.45434} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.0208, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.855, "top5_acc": 0.98875, "loss_cls": 0.71857, "loss": 0.71857, "time": 0.43438} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.02079, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.85062, "top5_acc": 0.99, "loss_cls": 0.75633, "loss": 0.75633, "time": 0.53312} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.02077, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.875, "top5_acc": 0.99625, "loss_cls": 0.66142, "loss": 0.66142, "time": 0.52996} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.02076, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8375, "top5_acc": 0.99062, "loss_cls": 0.79376, "loss": 0.79376, "time": 0.53727} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.02074, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85812, "top5_acc": 0.995, "loss_cls": 0.68141, "loss": 0.68141, "time": 0.54079} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.02073, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8575, "top5_acc": 0.995, "loss_cls": 0.6769, "loss": 0.6769, "time": 0.54767} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.02071, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.85812, "top5_acc": 0.98812, "loss_cls": 0.75622, "loss": 0.75622, "time": 0.53005} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.0207, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87, "top5_acc": 0.99562, "loss_cls": 0.66985, "loss": 0.66985, "time": 0.53125} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.02068, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.85938, "top5_acc": 0.99375, "loss_cls": 0.67534, "loss": 0.67534, "time": 0.52649} +{"mode": "val", "epoch": 41, "iter": 533, "lr": 0.02067, "top1_acc": 0.80073, "top5_acc": 0.98322, "mean_class_accuracy": 0.70291} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.02065, "memory": 4083, "data_time": 0.19506, "top1_acc": 0.87, "top5_acc": 0.99375, "loss_cls": 0.62694, "loss": 0.62694, "time": 0.77417} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.02064, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8775, "top5_acc": 0.995, "loss_cls": 0.61918, "loss": 0.61918, "time": 0.4794} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.02062, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.85625, "top5_acc": 0.99188, "loss_cls": 0.69876, "loss": 0.69876, "time": 0.48039} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.02061, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.855, "top5_acc": 0.99312, "loss_cls": 0.70393, "loss": 0.70393, "time": 0.48108} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.02059, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.86125, "top5_acc": 0.99438, "loss_cls": 0.68605, "loss": 0.68605, "time": 0.47946} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.02057, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86625, "top5_acc": 0.99562, "loss_cls": 0.64206, "loss": 0.64206, "time": 0.48208} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.02056, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85438, "top5_acc": 0.99125, "loss_cls": 0.73759, "loss": 0.73759, "time": 0.48301} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.02054, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8325, "top5_acc": 0.98688, "loss_cls": 0.76759, "loss": 0.76759, "time": 0.48405} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.02053, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84875, "top5_acc": 0.9925, "loss_cls": 0.75359, "loss": 0.75359, "time": 0.48227} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.02051, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.8325, "top5_acc": 0.99125, "loss_cls": 0.79025, "loss": 0.79025, "time": 0.48274} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.0205, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.855, "top5_acc": 0.99375, "loss_cls": 0.71151, "loss": 0.71151, "time": 0.47987} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.02048, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85875, "top5_acc": 0.99562, "loss_cls": 0.72617, "loss": 0.72617, "time": 0.48024} +{"mode": "val", "epoch": 42, "iter": 533, "lr": 0.02047, "top1_acc": 0.82561, "top5_acc": 0.98932, "mean_class_accuracy": 0.73518} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.02045, "memory": 4083, "data_time": 0.194, "top1_acc": 0.8625, "top5_acc": 0.995, "loss_cls": 0.66522, "loss": 0.66522, "time": 0.80225} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.02044, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.885, "top5_acc": 0.99312, "loss_cls": 0.61442, "loss": 0.61442, "time": 0.49258} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.02042, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.855, "top5_acc": 0.99375, "loss_cls": 0.68436, "loss": 0.68436, "time": 0.49041} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.0204, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.86312, "top5_acc": 0.99312, "loss_cls": 0.71165, "loss": 0.71165, "time": 0.48828} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.02039, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.84938, "top5_acc": 0.99312, "loss_cls": 0.68501, "loss": 0.68501, "time": 0.4868} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.02037, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8525, "top5_acc": 0.99312, "loss_cls": 0.70279, "loss": 0.70279, "time": 0.49241} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.02036, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85875, "top5_acc": 0.99188, "loss_cls": 0.67763, "loss": 0.67763, "time": 0.4906} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.02034, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.84375, "top5_acc": 0.9925, "loss_cls": 0.7186, "loss": 0.7186, "time": 0.49214} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.02033, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.83938, "top5_acc": 0.99562, "loss_cls": 0.73726, "loss": 0.73726, "time": 0.49218} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.02031, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.84188, "top5_acc": 0.99375, "loss_cls": 0.7341, "loss": 0.7341, "time": 0.49134} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.02029, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8475, "top5_acc": 0.99312, "loss_cls": 0.72755, "loss": 0.72755, "time": 0.49131} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.02028, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85688, "top5_acc": 0.99438, "loss_cls": 0.67606, "loss": 0.67606, "time": 0.43129} +{"mode": "val", "epoch": 43, "iter": 533, "lr": 0.02026, "top1_acc": 0.79862, "top5_acc": 0.98533, "mean_class_accuracy": 0.71636} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.02025, "memory": 4083, "data_time": 0.19474, "top1_acc": 0.855, "top5_acc": 0.99312, "loss_cls": 0.71741, "loss": 0.71741, "time": 0.81021} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.02023, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88125, "top5_acc": 0.99188, "loss_cls": 0.65649, "loss": 0.65649, "time": 0.49058} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.02022, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87375, "top5_acc": 0.99812, "loss_cls": 0.61878, "loss": 0.61878, "time": 0.49262} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.0202, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.87125, "top5_acc": 0.99312, "loss_cls": 0.64979, "loss": 0.64979, "time": 0.4902} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.02018, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.84625, "top5_acc": 0.9975, "loss_cls": 0.74266, "loss": 0.74266, "time": 0.49122} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.02017, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85938, "top5_acc": 0.99375, "loss_cls": 0.69947, "loss": 0.69947, "time": 0.49107} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.02015, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.855, "top5_acc": 0.99625, "loss_cls": 0.70019, "loss": 0.70019, "time": 0.49178} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.02014, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.83, "top5_acc": 0.98938, "loss_cls": 0.77195, "loss": 0.77195, "time": 0.4933} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.02012, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.825, "top5_acc": 0.9925, "loss_cls": 0.79572, "loss": 0.79572, "time": 0.49118} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.0201, "memory": 4083, "data_time": 0.00062, "top1_acc": 0.85062, "top5_acc": 0.99375, "loss_cls": 0.68172, "loss": 0.68172, "time": 0.4895} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.02009, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84375, "top5_acc": 0.9925, "loss_cls": 0.681, "loss": 0.681, "time": 0.49482} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.02007, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.84562, "top5_acc": 0.99438, "loss_cls": 0.7274, "loss": 0.7274, "time": 0.36924} +{"mode": "val", "epoch": 44, "iter": 533, "lr": 0.02006, "top1_acc": 0.81199, "top5_acc": 0.98557, "mean_class_accuracy": 0.73079} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.02004, "memory": 4083, "data_time": 0.19299, "top1_acc": 0.86688, "top5_acc": 0.99688, "loss_cls": 0.66808, "loss": 0.66808, "time": 0.79889} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.02003, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87062, "top5_acc": 0.99312, "loss_cls": 0.65716, "loss": 0.65716, "time": 0.49245} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.02001, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86812, "top5_acc": 0.99812, "loss_cls": 0.609, "loss": 0.609, "time": 0.48999} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.01999, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86562, "top5_acc": 0.995, "loss_cls": 0.66336, "loss": 0.66336, "time": 0.49594} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.01998, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86812, "top5_acc": 0.99562, "loss_cls": 0.64938, "loss": 0.64938, "time": 0.49121} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.01996, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.85062, "top5_acc": 0.98625, "loss_cls": 0.70622, "loss": 0.70622, "time": 0.49418} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.01994, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86188, "top5_acc": 0.9925, "loss_cls": 0.66713, "loss": 0.66713, "time": 0.49796} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.01993, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85688, "top5_acc": 0.99438, "loss_cls": 0.69965, "loss": 0.69965, "time": 0.4923} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.01991, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88875, "top5_acc": 0.99625, "loss_cls": 0.60258, "loss": 0.60258, "time": 0.4916} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.01989, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.86, "top5_acc": 0.99438, "loss_cls": 0.66657, "loss": 0.66657, "time": 0.49185} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.01988, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85, "top5_acc": 0.99312, "loss_cls": 0.74017, "loss": 0.74017, "time": 0.49196} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.01986, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85625, "top5_acc": 0.99, "loss_cls": 0.72322, "loss": 0.72322, "time": 0.36969} +{"mode": "val", "epoch": 45, "iter": 533, "lr": 0.01985, "top1_acc": 0.80753, "top5_acc": 0.98334, "mean_class_accuracy": 0.72963} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.01983, "memory": 4083, "data_time": 0.18959, "top1_acc": 0.85688, "top5_acc": 0.9925, "loss_cls": 0.71655, "loss": 0.71655, "time": 0.79633} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.01981, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.86, "top5_acc": 0.99438, "loss_cls": 0.68636, "loss": 0.68636, "time": 0.4906} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.0198, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86625, "top5_acc": 0.99438, "loss_cls": 0.67452, "loss": 0.67452, "time": 0.4909} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.01978, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.83875, "top5_acc": 0.99062, "loss_cls": 0.74623, "loss": 0.74623, "time": 0.49196} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.01976, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87562, "top5_acc": 0.99562, "loss_cls": 0.61436, "loss": 0.61436, "time": 0.49219} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.01975, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85062, "top5_acc": 0.9975, "loss_cls": 0.70748, "loss": 0.70748, "time": 0.49184} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.01973, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85312, "top5_acc": 0.99562, "loss_cls": 0.6728, "loss": 0.6728, "time": 0.4944} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.01971, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86812, "top5_acc": 0.99562, "loss_cls": 0.67221, "loss": 0.67221, "time": 0.49293} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.0197, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.8625, "top5_acc": 0.99562, "loss_cls": 0.69433, "loss": 0.69433, "time": 0.48907} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.01968, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86812, "top5_acc": 0.99438, "loss_cls": 0.64468, "loss": 0.64468, "time": 0.49452} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.01966, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.86562, "top5_acc": 0.99375, "loss_cls": 0.69183, "loss": 0.69183, "time": 0.49198} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.01965, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.8575, "top5_acc": 0.99, "loss_cls": 0.72333, "loss": 0.72333, "time": 0.38073} +{"mode": "val", "epoch": 46, "iter": 533, "lr": 0.01963, "top1_acc": 0.80718, "top5_acc": 0.9831, "mean_class_accuracy": 0.7077} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.01962, "memory": 4083, "data_time": 0.19527, "top1_acc": 0.87812, "top5_acc": 0.99438, "loss_cls": 0.62953, "loss": 0.62953, "time": 0.81022} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.0196, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.87438, "top5_acc": 0.99312, "loss_cls": 0.6091, "loss": 0.6091, "time": 0.4936} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.01958, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87438, "top5_acc": 0.99688, "loss_cls": 0.60002, "loss": 0.60002, "time": 0.49277} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.01957, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85562, "top5_acc": 0.99312, "loss_cls": 0.67176, "loss": 0.67176, "time": 0.493} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.01955, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.855, "top5_acc": 0.9925, "loss_cls": 0.71199, "loss": 0.71199, "time": 0.49133} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.01953, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86812, "top5_acc": 0.99312, "loss_cls": 0.65874, "loss": 0.65874, "time": 0.49325} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.01952, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85312, "top5_acc": 0.99312, "loss_cls": 0.70539, "loss": 0.70539, "time": 0.49322} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.0195, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.8625, "top5_acc": 0.99125, "loss_cls": 0.69469, "loss": 0.69469, "time": 0.48983} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.01948, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.85375, "top5_acc": 0.995, "loss_cls": 0.70859, "loss": 0.70859, "time": 0.49418} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.01947, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87188, "top5_acc": 0.99562, "loss_cls": 0.65964, "loss": 0.65964, "time": 0.49283} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.01945, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.86375, "top5_acc": 0.99375, "loss_cls": 0.6866, "loss": 0.6866, "time": 0.48935} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.01943, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.87125, "top5_acc": 0.98938, "loss_cls": 0.63686, "loss": 0.63686, "time": 0.37083} +{"mode": "val", "epoch": 47, "iter": 533, "lr": 0.01942, "top1_acc": 0.81411, "top5_acc": 0.98862, "mean_class_accuracy": 0.72183} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.0194, "memory": 4083, "data_time": 0.19212, "top1_acc": 0.8725, "top5_acc": 0.99188, "loss_cls": 0.62226, "loss": 0.62226, "time": 0.8017} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.01938, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.85625, "top5_acc": 0.99312, "loss_cls": 0.66321, "loss": 0.66321, "time": 0.48857} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.01937, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87625, "top5_acc": 0.99688, "loss_cls": 0.63929, "loss": 0.63929, "time": 0.49284} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.01935, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.87062, "top5_acc": 0.9925, "loss_cls": 0.64549, "loss": 0.64549, "time": 0.49127} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.01933, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85938, "top5_acc": 0.99375, "loss_cls": 0.69557, "loss": 0.69557, "time": 0.49226} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.01932, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8775, "top5_acc": 0.99188, "loss_cls": 0.65131, "loss": 0.65131, "time": 0.49035} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.0193, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86, "top5_acc": 0.99188, "loss_cls": 0.69979, "loss": 0.69979, "time": 0.49277} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.01928, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85375, "top5_acc": 0.99312, "loss_cls": 0.68413, "loss": 0.68413, "time": 0.4914} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.01926, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.85562, "top5_acc": 0.99438, "loss_cls": 0.68798, "loss": 0.68798, "time": 0.49146} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.01925, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86625, "top5_acc": 0.99438, "loss_cls": 0.69597, "loss": 0.69597, "time": 0.49055} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.01923, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.86125, "top5_acc": 0.99375, "loss_cls": 0.69484, "loss": 0.69484, "time": 0.49276} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.01921, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.86688, "top5_acc": 0.99812, "loss_cls": 0.64935, "loss": 0.64935, "time": 0.36589} +{"mode": "val", "epoch": 48, "iter": 533, "lr": 0.0192, "top1_acc": 0.83711, "top5_acc": 0.98979, "mean_class_accuracy": 0.75344} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.01918, "memory": 4083, "data_time": 0.19977, "top1_acc": 0.87812, "top5_acc": 0.99625, "loss_cls": 0.60864, "loss": 0.60864, "time": 0.79822} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.01916, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88375, "top5_acc": 0.9975, "loss_cls": 0.58371, "loss": 0.58371, "time": 0.49341} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.01915, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.88, "top5_acc": 0.99438, "loss_cls": 0.60956, "loss": 0.60956, "time": 0.49035} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.01913, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.85938, "top5_acc": 0.9925, "loss_cls": 0.67032, "loss": 0.67032, "time": 0.4921} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.01911, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.85875, "top5_acc": 0.99438, "loss_cls": 0.67912, "loss": 0.67912, "time": 0.49124} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.01909, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88125, "top5_acc": 0.99375, "loss_cls": 0.6114, "loss": 0.6114, "time": 0.4924} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.01908, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87062, "top5_acc": 0.99562, "loss_cls": 0.63133, "loss": 0.63133, "time": 0.49298} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.01906, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.875, "top5_acc": 0.99625, "loss_cls": 0.62522, "loss": 0.62522, "time": 0.49384} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.01904, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85, "top5_acc": 0.98875, "loss_cls": 0.77089, "loss": 0.77089, "time": 0.49319} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.01902, "memory": 4083, "data_time": 0.00071, "top1_acc": 0.86188, "top5_acc": 0.9925, "loss_cls": 0.6947, "loss": 0.6947, "time": 0.48979} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.01901, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.86938, "top5_acc": 0.99625, "loss_cls": 0.62908, "loss": 0.62908, "time": 0.49292} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.01899, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87188, "top5_acc": 0.9925, "loss_cls": 0.63987, "loss": 0.63987, "time": 0.36794} +{"mode": "val", "epoch": 49, "iter": 533, "lr": 0.01898, "top1_acc": 0.80096, "top5_acc": 0.98826, "mean_class_accuracy": 0.71824} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.01896, "memory": 4083, "data_time": 0.19487, "top1_acc": 0.86562, "top5_acc": 0.99188, "loss_cls": 0.65276, "loss": 0.65276, "time": 0.80433} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.01894, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.89188, "top5_acc": 0.99688, "loss_cls": 0.55092, "loss": 0.55092, "time": 0.48967} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.01892, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87562, "top5_acc": 0.99812, "loss_cls": 0.60274, "loss": 0.60274, "time": 0.49372} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.01891, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87562, "top5_acc": 0.99625, "loss_cls": 0.61546, "loss": 0.61546, "time": 0.49132} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.01889, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85625, "top5_acc": 0.995, "loss_cls": 0.65501, "loss": 0.65501, "time": 0.49264} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.01887, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86312, "top5_acc": 0.995, "loss_cls": 0.65314, "loss": 0.65314, "time": 0.49632} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.01885, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86, "top5_acc": 0.995, "loss_cls": 0.67031, "loss": 0.67031, "time": 0.49384} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.01884, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.86562, "top5_acc": 0.99562, "loss_cls": 0.65529, "loss": 0.65529, "time": 0.49229} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.01882, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87688, "top5_acc": 0.99562, "loss_cls": 0.62907, "loss": 0.62907, "time": 0.49221} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.0188, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.8675, "top5_acc": 0.99438, "loss_cls": 0.63958, "loss": 0.63958, "time": 0.49253} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.01878, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85125, "top5_acc": 0.99562, "loss_cls": 0.68527, "loss": 0.68527, "time": 0.48595} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.01876, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86062, "top5_acc": 0.99562, "loss_cls": 0.67323, "loss": 0.67323, "time": 0.36595} +{"mode": "val", "epoch": 50, "iter": 533, "lr": 0.01875, "top1_acc": 0.79251, "top5_acc": 0.98545, "mean_class_accuracy": 0.72368} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.01873, "memory": 4083, "data_time": 0.19572, "top1_acc": 0.87125, "top5_acc": 0.9975, "loss_cls": 0.60245, "loss": 0.60245, "time": 0.78044} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.01871, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88938, "top5_acc": 0.99562, "loss_cls": 0.56237, "loss": 0.56237, "time": 0.49246} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.0187, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.87375, "top5_acc": 0.99438, "loss_cls": 0.63043, "loss": 0.63043, "time": 0.49276} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.01868, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87688, "top5_acc": 0.99062, "loss_cls": 0.63156, "loss": 0.63156, "time": 0.4942} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.01866, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87125, "top5_acc": 0.99125, "loss_cls": 0.64112, "loss": 0.64112, "time": 0.48673} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.01864, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.865, "top5_acc": 0.99562, "loss_cls": 0.65401, "loss": 0.65401, "time": 0.48805} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.01863, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.85938, "top5_acc": 0.99375, "loss_cls": 0.66497, "loss": 0.66497, "time": 0.49024} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.01861, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87312, "top5_acc": 0.99688, "loss_cls": 0.61808, "loss": 0.61808, "time": 0.49049} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.01859, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.86188, "top5_acc": 0.99188, "loss_cls": 0.65924, "loss": 0.65924, "time": 0.48999} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.01857, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.8675, "top5_acc": 0.99562, "loss_cls": 0.65815, "loss": 0.65815, "time": 0.49533} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.01855, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85938, "top5_acc": 0.99375, "loss_cls": 0.67861, "loss": 0.67861, "time": 0.4919} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.01854, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87812, "top5_acc": 0.99125, "loss_cls": 0.62364, "loss": 0.62364, "time": 0.38076} +{"mode": "val", "epoch": 51, "iter": 533, "lr": 0.01852, "top1_acc": 0.83218, "top5_acc": 0.9865, "mean_class_accuracy": 0.7351} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.0185, "memory": 4083, "data_time": 0.20533, "top1_acc": 0.88375, "top5_acc": 0.99938, "loss_cls": 0.60513, "loss": 0.60513, "time": 0.81391} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.01849, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8775, "top5_acc": 0.99312, "loss_cls": 0.59578, "loss": 0.59578, "time": 0.49487} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.01847, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85312, "top5_acc": 0.99438, "loss_cls": 0.65984, "loss": 0.65984, "time": 0.49582} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.01845, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87625, "top5_acc": 0.99438, "loss_cls": 0.6054, "loss": 0.6054, "time": 0.49359} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.01843, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.885, "top5_acc": 0.99375, "loss_cls": 0.57513, "loss": 0.57513, "time": 0.4908} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.01841, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.86438, "top5_acc": 0.99375, "loss_cls": 0.64398, "loss": 0.64398, "time": 0.49105} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.0184, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87562, "top5_acc": 0.99375, "loss_cls": 0.60486, "loss": 0.60486, "time": 0.48984} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.01838, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86312, "top5_acc": 0.99688, "loss_cls": 0.64799, "loss": 0.64799, "time": 0.49369} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.01836, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.87938, "top5_acc": 0.9925, "loss_cls": 0.60708, "loss": 0.60708, "time": 0.49369} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.01834, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8725, "top5_acc": 0.99062, "loss_cls": 0.63084, "loss": 0.63084, "time": 0.49037} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.01832, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86062, "top5_acc": 0.99562, "loss_cls": 0.65203, "loss": 0.65203, "time": 0.49142} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.01831, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.86062, "top5_acc": 0.99312, "loss_cls": 0.68186, "loss": 0.68186, "time": 0.35942} +{"mode": "val", "epoch": 52, "iter": 533, "lr": 0.01829, "top1_acc": 0.82267, "top5_acc": 0.99026, "mean_class_accuracy": 0.7573} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.01827, "memory": 4083, "data_time": 0.19634, "top1_acc": 0.86938, "top5_acc": 0.99438, "loss_cls": 0.60646, "loss": 0.60646, "time": 0.80012} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.01826, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89125, "top5_acc": 0.99562, "loss_cls": 0.57616, "loss": 0.57616, "time": 0.49131} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.01824, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86562, "top5_acc": 0.99312, "loss_cls": 0.65679, "loss": 0.65679, "time": 0.48985} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.01822, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.885, "top5_acc": 0.99625, "loss_cls": 0.5733, "loss": 0.5733, "time": 0.49646} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.0182, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88188, "top5_acc": 0.9975, "loss_cls": 0.61101, "loss": 0.61101, "time": 0.49153} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.01818, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87438, "top5_acc": 0.99312, "loss_cls": 0.63537, "loss": 0.63537, "time": 0.48878} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.01816, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.865, "top5_acc": 0.99312, "loss_cls": 0.66549, "loss": 0.66549, "time": 0.49259} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.01815, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.87688, "top5_acc": 0.99438, "loss_cls": 0.60881, "loss": 0.60881, "time": 0.49607} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.01813, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.86938, "top5_acc": 0.99375, "loss_cls": 0.64488, "loss": 0.64488, "time": 0.49261} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.01811, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.86875, "top5_acc": 0.99438, "loss_cls": 0.62004, "loss": 0.62004, "time": 0.49151} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.01809, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88438, "top5_acc": 0.99312, "loss_cls": 0.60332, "loss": 0.60332, "time": 0.49071} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.01807, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86812, "top5_acc": 0.99688, "loss_cls": 0.62377, "loss": 0.62377, "time": 0.36135} +{"mode": "val", "epoch": 53, "iter": 533, "lr": 0.01806, "top1_acc": 0.82138, "top5_acc": 0.98909, "mean_class_accuracy": 0.75154} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.01804, "memory": 4083, "data_time": 0.20084, "top1_acc": 0.88438, "top5_acc": 0.99375, "loss_cls": 0.58829, "loss": 0.58829, "time": 0.80676} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.01802, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88375, "top5_acc": 0.99875, "loss_cls": 0.54677, "loss": 0.54677, "time": 0.49129} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.018, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.87875, "top5_acc": 0.99375, "loss_cls": 0.60772, "loss": 0.60772, "time": 0.49513} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.01798, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88938, "top5_acc": 0.99688, "loss_cls": 0.56637, "loss": 0.56637, "time": 0.49517} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.01797, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.89, "top5_acc": 0.99562, "loss_cls": 0.56848, "loss": 0.56848, "time": 0.49449} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.01795, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87875, "top5_acc": 0.99562, "loss_cls": 0.61378, "loss": 0.61378, "time": 0.4926} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.01793, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.885, "top5_acc": 0.99562, "loss_cls": 0.60061, "loss": 0.60061, "time": 0.49251} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.01791, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.85812, "top5_acc": 0.99625, "loss_cls": 0.66518, "loss": 0.66518, "time": 0.49274} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.01789, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.87812, "top5_acc": 0.99375, "loss_cls": 0.64702, "loss": 0.64702, "time": 0.49178} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.01787, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85438, "top5_acc": 0.99188, "loss_cls": 0.71279, "loss": 0.71279, "time": 0.49537} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.01786, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89, "top5_acc": 0.99312, "loss_cls": 0.57055, "loss": 0.57055, "time": 0.49758} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.01784, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85438, "top5_acc": 0.99312, "loss_cls": 0.70889, "loss": 0.70889, "time": 0.36} +{"mode": "val", "epoch": 54, "iter": 533, "lr": 0.01782, "top1_acc": 0.82666, "top5_acc": 0.99061, "mean_class_accuracy": 0.77648} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.0178, "memory": 4083, "data_time": 0.20314, "top1_acc": 0.89562, "top5_acc": 0.99375, "loss_cls": 0.545, "loss": 0.545, "time": 0.8148} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.01779, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90188, "top5_acc": 0.99688, "loss_cls": 0.55312, "loss": 0.55312, "time": 0.49019} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.01777, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88438, "top5_acc": 0.99812, "loss_cls": 0.59077, "loss": 0.59077, "time": 0.49116} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.01775, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86375, "top5_acc": 0.99438, "loss_cls": 0.65799, "loss": 0.65799, "time": 0.4911} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.01773, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.86688, "top5_acc": 0.99188, "loss_cls": 0.67991, "loss": 0.67991, "time": 0.49156} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.01771, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89125, "top5_acc": 0.99562, "loss_cls": 0.56656, "loss": 0.56656, "time": 0.49068} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.01769, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8775, "top5_acc": 0.99375, "loss_cls": 0.60694, "loss": 0.60694, "time": 0.49299} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.01767, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88188, "top5_acc": 0.99625, "loss_cls": 0.61575, "loss": 0.61575, "time": 0.48982} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.01766, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8725, "top5_acc": 0.99625, "loss_cls": 0.61567, "loss": 0.61567, "time": 0.48957} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.01764, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88312, "top5_acc": 0.99688, "loss_cls": 0.58877, "loss": 0.58877, "time": 0.49027} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.01762, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87875, "top5_acc": 0.99312, "loss_cls": 0.60908, "loss": 0.60908, "time": 0.49329} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.0176, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86125, "top5_acc": 0.99625, "loss_cls": 0.66554, "loss": 0.66554, "time": 0.35814} +{"mode": "val", "epoch": 55, "iter": 533, "lr": 0.01758, "top1_acc": 0.8296, "top5_acc": 0.98967, "mean_class_accuracy": 0.75535} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.01757, "memory": 4083, "data_time": 0.19615, "top1_acc": 0.90312, "top5_acc": 0.995, "loss_cls": 0.52132, "loss": 0.52132, "time": 0.79924} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.01755, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.88125, "top5_acc": 0.995, "loss_cls": 0.60267, "loss": 0.60267, "time": 0.49209} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.01753, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.88875, "top5_acc": 0.99688, "loss_cls": 0.53169, "loss": 0.53169, "time": 0.49484} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.01751, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86312, "top5_acc": 0.99375, "loss_cls": 0.68279, "loss": 0.68279, "time": 0.49772} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.01749, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.88625, "top5_acc": 0.99562, "loss_cls": 0.54623, "loss": 0.54623, "time": 0.49208} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.01747, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.87188, "top5_acc": 0.995, "loss_cls": 0.63089, "loss": 0.63089, "time": 0.48972} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.01745, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86188, "top5_acc": 0.99625, "loss_cls": 0.65373, "loss": 0.65373, "time": 0.49096} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.01743, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.86312, "top5_acc": 0.99188, "loss_cls": 0.69041, "loss": 0.69041, "time": 0.49012} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.01742, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.86688, "top5_acc": 0.99125, "loss_cls": 0.66371, "loss": 0.66371, "time": 0.48984} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.0174, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87625, "top5_acc": 0.99375, "loss_cls": 0.6129, "loss": 0.6129, "time": 0.48956} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.01738, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86688, "top5_acc": 0.9975, "loss_cls": 0.62401, "loss": 0.62401, "time": 0.49403} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.01736, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8775, "top5_acc": 0.99562, "loss_cls": 0.63432, "loss": 0.63432, "time": 0.37255} +{"mode": "val", "epoch": 56, "iter": 533, "lr": 0.01734, "top1_acc": 0.83605, "top5_acc": 0.98885, "mean_class_accuracy": 0.76874} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.01733, "memory": 4083, "data_time": 0.19507, "top1_acc": 0.88312, "top5_acc": 0.99438, "loss_cls": 0.59539, "loss": 0.59539, "time": 0.81105} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.01731, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88562, "top5_acc": 0.9975, "loss_cls": 0.55303, "loss": 0.55303, "time": 0.49561} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.01729, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.88188, "top5_acc": 0.99688, "loss_cls": 0.53781, "loss": 0.53781, "time": 0.49374} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.01727, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89938, "top5_acc": 0.99812, "loss_cls": 0.52624, "loss": 0.52624, "time": 0.49264} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.01725, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.88938, "top5_acc": 0.995, "loss_cls": 0.55533, "loss": 0.55533, "time": 0.49183} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.01723, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87375, "top5_acc": 0.99562, "loss_cls": 0.60566, "loss": 0.60566, "time": 0.49033} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.01721, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88438, "top5_acc": 0.995, "loss_cls": 0.57044, "loss": 0.57044, "time": 0.49346} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.01719, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87938, "top5_acc": 0.99062, "loss_cls": 0.61617, "loss": 0.61617, "time": 0.4885} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.01717, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88312, "top5_acc": 0.99375, "loss_cls": 0.58186, "loss": 0.58186, "time": 0.4914} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.01716, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87625, "top5_acc": 0.99312, "loss_cls": 0.63325, "loss": 0.63325, "time": 0.49401} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.01714, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8775, "top5_acc": 0.99625, "loss_cls": 0.5986, "loss": 0.5986, "time": 0.49095} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.01712, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.88188, "top5_acc": 0.99562, "loss_cls": 0.59512, "loss": 0.59512, "time": 0.3589} +{"mode": "val", "epoch": 57, "iter": 533, "lr": 0.0171, "top1_acc": 0.81798, "top5_acc": 0.98697, "mean_class_accuracy": 0.76769} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.01708, "memory": 4083, "data_time": 0.20159, "top1_acc": 0.86188, "top5_acc": 0.9975, "loss_cls": 0.67743, "loss": 0.67743, "time": 0.81756} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.01706, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.88125, "top5_acc": 0.99625, "loss_cls": 0.62275, "loss": 0.62275, "time": 0.49145} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.01704, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9, "top5_acc": 0.99938, "loss_cls": 0.50368, "loss": 0.50368, "time": 0.49384} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.01703, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87562, "top5_acc": 0.99688, "loss_cls": 0.59107, "loss": 0.59107, "time": 0.49367} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.01701, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8775, "top5_acc": 0.99688, "loss_cls": 0.59462, "loss": 0.59462, "time": 0.4916} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.01699, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89062, "top5_acc": 0.99625, "loss_cls": 0.55529, "loss": 0.55529, "time": 0.4931} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.01697, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.87188, "top5_acc": 0.99875, "loss_cls": 0.61252, "loss": 0.61252, "time": 0.491} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.01695, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8825, "top5_acc": 0.99438, "loss_cls": 0.58333, "loss": 0.58333, "time": 0.49225} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.01693, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88, "top5_acc": 0.99438, "loss_cls": 0.62363, "loss": 0.62363, "time": 0.49419} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.01691, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87562, "top5_acc": 0.99562, "loss_cls": 0.59239, "loss": 0.59239, "time": 0.48879} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.01689, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88438, "top5_acc": 0.99875, "loss_cls": 0.54188, "loss": 0.54188, "time": 0.49303} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.01687, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87312, "top5_acc": 0.99188, "loss_cls": 0.62928, "loss": 0.62928, "time": 0.3468} +{"mode": "val", "epoch": 58, "iter": 533, "lr": 0.01686, "top1_acc": 0.83429, "top5_acc": 0.98979, "mean_class_accuracy": 0.76257} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.01684, "memory": 4083, "data_time": 0.19602, "top1_acc": 0.89188, "top5_acc": 0.99625, "loss_cls": 0.5781, "loss": 0.5781, "time": 0.80738} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.01682, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89625, "top5_acc": 0.99688, "loss_cls": 0.52437, "loss": 0.52437, "time": 0.49087} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.0168, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.87438, "top5_acc": 0.99812, "loss_cls": 0.63176, "loss": 0.63176, "time": 0.48901} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.01678, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.90062, "top5_acc": 0.99625, "loss_cls": 0.52897, "loss": 0.52897, "time": 0.49252} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.01676, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87562, "top5_acc": 0.99312, "loss_cls": 0.59506, "loss": 0.59506, "time": 0.48992} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.01674, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.88875, "top5_acc": 0.99562, "loss_cls": 0.57498, "loss": 0.57498, "time": 0.48988} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.01672, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87812, "top5_acc": 0.99562, "loss_cls": 0.60681, "loss": 0.60681, "time": 0.48934} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.0167, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87938, "top5_acc": 0.99625, "loss_cls": 0.59541, "loss": 0.59541, "time": 0.48901} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.01668, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8775, "top5_acc": 0.99438, "loss_cls": 0.59113, "loss": 0.59113, "time": 0.49313} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.01667, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87875, "top5_acc": 0.99438, "loss_cls": 0.60929, "loss": 0.60929, "time": 0.49316} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.01665, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.88, "top5_acc": 0.995, "loss_cls": 0.58456, "loss": 0.58456, "time": 0.49049} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.01663, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87312, "top5_acc": 0.99438, "loss_cls": 0.64201, "loss": 0.64201, "time": 0.35869} +{"mode": "val", "epoch": 59, "iter": 533, "lr": 0.01661, "top1_acc": 0.82631, "top5_acc": 0.98967, "mean_class_accuracy": 0.76988} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.01659, "memory": 4083, "data_time": 0.19676, "top1_acc": 0.89625, "top5_acc": 0.99938, "loss_cls": 0.52833, "loss": 0.52833, "time": 0.80608} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.01657, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8975, "top5_acc": 0.99875, "loss_cls": 0.52111, "loss": 0.52111, "time": 0.48849} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.01655, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.875, "top5_acc": 0.995, "loss_cls": 0.59133, "loss": 0.59133, "time": 0.4938} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.01653, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.90125, "top5_acc": 0.99625, "loss_cls": 0.539, "loss": 0.539, "time": 0.49243} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.01651, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.875, "top5_acc": 0.99812, "loss_cls": 0.5924, "loss": 0.5924, "time": 0.49393} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.0165, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89312, "top5_acc": 0.99625, "loss_cls": 0.5736, "loss": 0.5736, "time": 0.48707} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.01648, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87312, "top5_acc": 0.99312, "loss_cls": 0.59825, "loss": 0.59825, "time": 0.4913} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.01646, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89, "top5_acc": 0.99875, "loss_cls": 0.55406, "loss": 0.55406, "time": 0.49499} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.01644, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8725, "top5_acc": 0.995, "loss_cls": 0.61521, "loss": 0.61521, "time": 0.49243} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.01642, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89312, "top5_acc": 0.99625, "loss_cls": 0.52295, "loss": 0.52295, "time": 0.4918} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.0164, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.89062, "top5_acc": 0.9975, "loss_cls": 0.55349, "loss": 0.55349, "time": 0.49002} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.01638, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.87688, "top5_acc": 0.99438, "loss_cls": 0.63026, "loss": 0.63026, "time": 0.36609} +{"mode": "val", "epoch": 60, "iter": 533, "lr": 0.01636, "top1_acc": 0.84755, "top5_acc": 0.99061, "mean_class_accuracy": 0.78463} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.01634, "memory": 4083, "data_time": 0.19212, "top1_acc": 0.88625, "top5_acc": 0.99688, "loss_cls": 0.52638, "loss": 0.52638, "time": 0.81051} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.01632, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90188, "top5_acc": 0.99875, "loss_cls": 0.49678, "loss": 0.49678, "time": 0.49197} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.0163, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.88625, "top5_acc": 0.99688, "loss_cls": 0.53564, "loss": 0.53564, "time": 0.49099} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.01629, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89125, "top5_acc": 0.99562, "loss_cls": 0.54054, "loss": 0.54054, "time": 0.49151} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.01627, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.90562, "top5_acc": 0.99938, "loss_cls": 0.49195, "loss": 0.49195, "time": 0.49084} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.01625, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86938, "top5_acc": 0.9975, "loss_cls": 0.60999, "loss": 0.60999, "time": 0.48873} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.01623, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.90062, "top5_acc": 0.99562, "loss_cls": 0.54947, "loss": 0.54947, "time": 0.49102} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.01621, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88875, "top5_acc": 0.99562, "loss_cls": 0.55804, "loss": 0.55804, "time": 0.49355} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.01619, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8725, "top5_acc": 0.99562, "loss_cls": 0.6288, "loss": 0.6288, "time": 0.49344} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.01617, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87125, "top5_acc": 0.9925, "loss_cls": 0.61564, "loss": 0.61564, "time": 0.4917} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.01615, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.86375, "top5_acc": 0.99688, "loss_cls": 0.62035, "loss": 0.62035, "time": 0.49163} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.01613, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.89188, "top5_acc": 0.99438, "loss_cls": 0.56858, "loss": 0.56858, "time": 0.36614} +{"mode": "val", "epoch": 61, "iter": 533, "lr": 0.01611, "top1_acc": 0.84075, "top5_acc": 0.99096, "mean_class_accuracy": 0.79578} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.01609, "memory": 4083, "data_time": 0.19555, "top1_acc": 0.905, "top5_acc": 0.99688, "loss_cls": 0.50722, "loss": 0.50722, "time": 0.80082} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.01607, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89688, "top5_acc": 0.99812, "loss_cls": 0.52004, "loss": 0.52004, "time": 0.49053} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.01605, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.905, "top5_acc": 0.99875, "loss_cls": 0.47244, "loss": 0.47244, "time": 0.48813} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.01603, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.895, "top5_acc": 0.99812, "loss_cls": 0.51666, "loss": 0.51666, "time": 0.49056} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.01602, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90188, "top5_acc": 0.99875, "loss_cls": 0.51408, "loss": 0.51408, "time": 0.49045} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.016, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88625, "top5_acc": 0.9975, "loss_cls": 0.52753, "loss": 0.52753, "time": 0.49162} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.01598, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88562, "top5_acc": 0.99625, "loss_cls": 0.53437, "loss": 0.53437, "time": 0.49194} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.01596, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87688, "top5_acc": 0.9975, "loss_cls": 0.56499, "loss": 0.56499, "time": 0.49225} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.01594, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.88438, "top5_acc": 0.995, "loss_cls": 0.60054, "loss": 0.60054, "time": 0.49308} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.01592, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8925, "top5_acc": 0.99562, "loss_cls": 0.54448, "loss": 0.54448, "time": 0.49043} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.0159, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8825, "top5_acc": 0.99688, "loss_cls": 0.59875, "loss": 0.59875, "time": 0.4942} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.01588, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8975, "top5_acc": 0.99688, "loss_cls": 0.53962, "loss": 0.53962, "time": 0.35853} +{"mode": "val", "epoch": 62, "iter": 533, "lr": 0.01586, "top1_acc": 0.85107, "top5_acc": 0.99014, "mean_class_accuracy": 0.77902} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.01584, "memory": 4083, "data_time": 0.19535, "top1_acc": 0.895, "top5_acc": 0.99625, "loss_cls": 0.52449, "loss": 0.52449, "time": 0.79679} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.01582, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.5144, "loss": 0.5144, "time": 0.49142} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.0158, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.89688, "top5_acc": 0.99562, "loss_cls": 0.52504, "loss": 0.52504, "time": 0.49253} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.01578, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89312, "top5_acc": 0.99625, "loss_cls": 0.53403, "loss": 0.53403, "time": 0.49459} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.01576, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.90688, "top5_acc": 0.99688, "loss_cls": 0.50719, "loss": 0.50719, "time": 0.49333} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.01574, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89688, "top5_acc": 0.99875, "loss_cls": 0.49434, "loss": 0.49434, "time": 0.49053} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.01572, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.50778, "loss": 0.50778, "time": 0.49312} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.0157, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89188, "top5_acc": 0.99688, "loss_cls": 0.54956, "loss": 0.54956, "time": 0.48981} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.01568, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.88, "top5_acc": 0.99562, "loss_cls": 0.5873, "loss": 0.5873, "time": 0.49275} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.01566, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.895, "top5_acc": 0.99625, "loss_cls": 0.52635, "loss": 0.52635, "time": 0.48989} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.01564, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88812, "top5_acc": 0.99688, "loss_cls": 0.54912, "loss": 0.54912, "time": 0.49449} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.01562, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88312, "top5_acc": 0.99562, "loss_cls": 0.60819, "loss": 0.60819, "time": 0.37182} +{"mode": "val", "epoch": 63, "iter": 533, "lr": 0.01561, "top1_acc": 0.83793, "top5_acc": 0.9885, "mean_class_accuracy": 0.76171} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.01559, "memory": 4083, "data_time": 0.1945, "top1_acc": 0.89625, "top5_acc": 0.9975, "loss_cls": 0.5251, "loss": 0.5251, "time": 0.81582} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.01557, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90438, "top5_acc": 0.99812, "loss_cls": 0.47756, "loss": 0.47756, "time": 0.48875} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.01555, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89812, "top5_acc": 0.99562, "loss_cls": 0.54695, "loss": 0.54695, "time": 0.4946} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.01553, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.89438, "top5_acc": 0.9975, "loss_cls": 0.53977, "loss": 0.53977, "time": 0.49519} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.01551, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87625, "top5_acc": 0.995, "loss_cls": 0.60285, "loss": 0.60285, "time": 0.49331} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.01549, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89562, "top5_acc": 0.99688, "loss_cls": 0.53749, "loss": 0.53749, "time": 0.49144} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.01547, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.90188, "top5_acc": 0.99062, "loss_cls": 0.54925, "loss": 0.54925, "time": 0.49136} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.01545, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88125, "top5_acc": 0.99625, "loss_cls": 0.56733, "loss": 0.56733, "time": 0.48882} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.01543, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89062, "top5_acc": 0.99312, "loss_cls": 0.55643, "loss": 0.55643, "time": 0.49143} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.01541, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8875, "top5_acc": 0.99438, "loss_cls": 0.57, "loss": 0.57, "time": 0.49285} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.01539, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.88188, "top5_acc": 0.99625, "loss_cls": 0.57321, "loss": 0.57321, "time": 0.49237} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.01537, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8925, "top5_acc": 0.99562, "loss_cls": 0.55785, "loss": 0.55785, "time": 0.35805} +{"mode": "val", "epoch": 64, "iter": 533, "lr": 0.01535, "top1_acc": 0.8438, "top5_acc": 0.99026, "mean_class_accuracy": 0.7733} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.01533, "memory": 4083, "data_time": 0.19211, "top1_acc": 0.89938, "top5_acc": 0.99438, "loss_cls": 0.49655, "loss": 0.49655, "time": 0.81151} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.01531, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90375, "top5_acc": 0.99688, "loss_cls": 0.47305, "loss": 0.47305, "time": 0.49446} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.01529, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89125, "top5_acc": 0.99688, "loss_cls": 0.58029, "loss": 0.58029, "time": 0.49317} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.01527, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9025, "top5_acc": 0.99938, "loss_cls": 0.49173, "loss": 0.49173, "time": 0.49352} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.01526, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89125, "top5_acc": 0.9975, "loss_cls": 0.53624, "loss": 0.53624, "time": 0.48737} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.01524, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89, "top5_acc": 0.99688, "loss_cls": 0.56083, "loss": 0.56083, "time": 0.4935} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.01522, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.90688, "top5_acc": 0.99688, "loss_cls": 0.50471, "loss": 0.50471, "time": 0.49546} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0152, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89812, "top5_acc": 0.9975, "loss_cls": 0.49734, "loss": 0.49734, "time": 0.493} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.01518, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.89188, "top5_acc": 0.99562, "loss_cls": 0.53127, "loss": 0.53127, "time": 0.49212} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.01516, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.88188, "top5_acc": 0.995, "loss_cls": 0.61086, "loss": 0.61086, "time": 0.49112} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.01514, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90938, "top5_acc": 0.99625, "loss_cls": 0.49054, "loss": 0.49054, "time": 0.49344} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.01512, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89188, "top5_acc": 0.99625, "loss_cls": 0.5387, "loss": 0.5387, "time": 0.36308} +{"mode": "val", "epoch": 65, "iter": 533, "lr": 0.0151, "top1_acc": 0.8384, "top5_acc": 0.98733, "mean_class_accuracy": 0.78516} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.01508, "memory": 4083, "data_time": 0.19234, "top1_acc": 0.90312, "top5_acc": 0.99688, "loss_cls": 0.5048, "loss": 0.5048, "time": 0.78418} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.01506, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92, "top5_acc": 0.99875, "loss_cls": 0.41319, "loss": 0.41319, "time": 0.49411} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.01504, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.8875, "top5_acc": 0.99625, "loss_cls": 0.54663, "loss": 0.54663, "time": 0.4918} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.01502, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89188, "top5_acc": 0.9975, "loss_cls": 0.49612, "loss": 0.49612, "time": 0.48898} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.015, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.91812, "top5_acc": 0.99625, "loss_cls": 0.4503, "loss": 0.4503, "time": 0.49086} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.01498, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.8975, "top5_acc": 0.995, "loss_cls": 0.55419, "loss": 0.55419, "time": 0.49043} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.01496, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8875, "top5_acc": 0.99625, "loss_cls": 0.54214, "loss": 0.54214, "time": 0.49618} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.01494, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89875, "top5_acc": 0.99812, "loss_cls": 0.53395, "loss": 0.53395, "time": 0.49147} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.01492, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.89125, "top5_acc": 0.99625, "loss_cls": 0.53729, "loss": 0.53729, "time": 0.49057} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.0149, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92125, "top5_acc": 0.9975, "loss_cls": 0.45068, "loss": 0.45068, "time": 0.49482} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.01488, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90312, "top5_acc": 0.995, "loss_cls": 0.53071, "loss": 0.53071, "time": 0.48981} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.01486, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88625, "top5_acc": 0.9975, "loss_cls": 0.53183, "loss": 0.53183, "time": 0.38012} +{"mode": "val", "epoch": 66, "iter": 533, "lr": 0.01484, "top1_acc": 0.84826, "top5_acc": 0.9912, "mean_class_accuracy": 0.79522} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.01482, "memory": 4083, "data_time": 0.19045, "top1_acc": 0.8975, "top5_acc": 0.99688, "loss_cls": 0.50381, "loss": 0.50381, "time": 0.80573} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.0148, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9, "top5_acc": 0.9975, "loss_cls": 0.51394, "loss": 0.51394, "time": 0.49116} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.01478, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.89938, "top5_acc": 0.99812, "loss_cls": 0.4975, "loss": 0.4975, "time": 0.49034} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.01476, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.90375, "top5_acc": 0.99562, "loss_cls": 0.49148, "loss": 0.49148, "time": 0.49249} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.01474, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.90062, "top5_acc": 0.99688, "loss_cls": 0.49182, "loss": 0.49182, "time": 0.4885} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.01472, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.8925, "top5_acc": 0.99812, "loss_cls": 0.54229, "loss": 0.54229, "time": 0.48707} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.0147, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.88875, "top5_acc": 0.99625, "loss_cls": 0.54484, "loss": 0.54484, "time": 0.49202} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.01468, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89875, "top5_acc": 0.99562, "loss_cls": 0.5161, "loss": 0.5161, "time": 0.48885} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.01466, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.89312, "top5_acc": 0.99375, "loss_cls": 0.52715, "loss": 0.52715, "time": 0.49268} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.01464, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89938, "top5_acc": 0.99562, "loss_cls": 0.52628, "loss": 0.52628, "time": 0.4927} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.01462, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89812, "top5_acc": 0.99625, "loss_cls": 0.51161, "loss": 0.51161, "time": 0.495} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.0146, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.9, "top5_acc": 0.99625, "loss_cls": 0.51374, "loss": 0.51374, "time": 0.37007} +{"mode": "val", "epoch": 67, "iter": 533, "lr": 0.01458, "top1_acc": 0.83194, "top5_acc": 0.98944, "mean_class_accuracy": 0.75865} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.01456, "memory": 4083, "data_time": 0.19715, "top1_acc": 0.90562, "top5_acc": 0.9975, "loss_cls": 0.47802, "loss": 0.47802, "time": 0.7965} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.01454, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9025, "top5_acc": 0.99438, "loss_cls": 0.49486, "loss": 0.49486, "time": 0.49505} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.01452, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90188, "top5_acc": 0.99875, "loss_cls": 0.51073, "loss": 0.51073, "time": 0.49064} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.0145, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9075, "top5_acc": 0.99625, "loss_cls": 0.47187, "loss": 0.47187, "time": 0.49094} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.01448, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90375, "top5_acc": 0.99625, "loss_cls": 0.48486, "loss": 0.48486, "time": 0.49134} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.01446, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9125, "top5_acc": 0.99938, "loss_cls": 0.45449, "loss": 0.45449, "time": 0.49145} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.01444, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9, "top5_acc": 0.99625, "loss_cls": 0.51197, "loss": 0.51197, "time": 0.4912} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.01442, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.90562, "top5_acc": 0.99562, "loss_cls": 0.50823, "loss": 0.50823, "time": 0.4906} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.0144, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90062, "top5_acc": 0.99625, "loss_cls": 0.52136, "loss": 0.52136, "time": 0.49152} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.01438, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.905, "top5_acc": 0.99688, "loss_cls": 0.45685, "loss": 0.45685, "time": 0.48884} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.01436, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.895, "top5_acc": 0.99812, "loss_cls": 0.50224, "loss": 0.50224, "time": 0.49266} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.01434, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.89812, "top5_acc": 0.9975, "loss_cls": 0.50927, "loss": 0.50927, "time": 0.36729} +{"mode": "val", "epoch": 68, "iter": 533, "lr": 0.01433, "top1_acc": 0.83734, "top5_acc": 0.98322, "mean_class_accuracy": 0.78311} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.01431, "memory": 4083, "data_time": 0.19621, "top1_acc": 0.9125, "top5_acc": 0.99812, "loss_cls": 0.48205, "loss": 0.48205, "time": 0.78637} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.01429, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92188, "top5_acc": 0.99688, "loss_cls": 0.4719, "loss": 0.4719, "time": 0.49186} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.01427, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9, "top5_acc": 0.9975, "loss_cls": 0.48913, "loss": 0.48913, "time": 0.4942} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.01425, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88875, "top5_acc": 0.99438, "loss_cls": 0.57407, "loss": 0.57407, "time": 0.48788} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.01423, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.89812, "top5_acc": 0.9975, "loss_cls": 0.51452, "loss": 0.51452, "time": 0.49093} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.0142, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89938, "top5_acc": 0.99938, "loss_cls": 0.50959, "loss": 0.50959, "time": 0.48962} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.01418, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.89812, "top5_acc": 0.9975, "loss_cls": 0.51085, "loss": 0.51085, "time": 0.49162} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.01416, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.89312, "top5_acc": 0.9975, "loss_cls": 0.54422, "loss": 0.54422, "time": 0.49256} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.01414, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.88125, "top5_acc": 0.995, "loss_cls": 0.58409, "loss": 0.58409, "time": 0.49174} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.01412, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9125, "top5_acc": 0.99812, "loss_cls": 0.47394, "loss": 0.47394, "time": 0.49115} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.0141, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91375, "top5_acc": 0.99875, "loss_cls": 0.45702, "loss": 0.45702, "time": 0.48882} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.01408, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.91688, "top5_acc": 0.99938, "loss_cls": 0.44013, "loss": 0.44013, "time": 0.38589} +{"mode": "val", "epoch": 69, "iter": 533, "lr": 0.01407, "top1_acc": 0.84427, "top5_acc": 0.99002, "mean_class_accuracy": 0.7936} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.01405, "memory": 4083, "data_time": 0.1933, "top1_acc": 0.92, "top5_acc": 0.99625, "loss_cls": 0.43566, "loss": 0.43566, "time": 0.79484} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.01403, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90625, "top5_acc": 0.99875, "loss_cls": 0.46647, "loss": 0.46647, "time": 0.49324} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.01401, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91125, "top5_acc": 0.99875, "loss_cls": 0.4503, "loss": 0.4503, "time": 0.48992} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.01399, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.925, "top5_acc": 1.0, "loss_cls": 0.40025, "loss": 0.40025, "time": 0.49201} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.01397, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90625, "top5_acc": 0.9975, "loss_cls": 0.45117, "loss": 0.45117, "time": 0.49374} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.01395, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.91125, "top5_acc": 0.9975, "loss_cls": 0.44232, "loss": 0.44232, "time": 0.49194} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.01392, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90688, "top5_acc": 0.9975, "loss_cls": 0.48613, "loss": 0.48613, "time": 0.49176} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.0139, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.91062, "top5_acc": 0.9975, "loss_cls": 0.46036, "loss": 0.46036, "time": 0.49359} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.01388, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90688, "top5_acc": 0.995, "loss_cls": 0.46976, "loss": 0.46976, "time": 0.4934} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.01386, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89125, "top5_acc": 0.99688, "loss_cls": 0.56032, "loss": 0.56032, "time": 0.49107} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.01384, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.90375, "top5_acc": 0.99562, "loss_cls": 0.51384, "loss": 0.51384, "time": 0.49144} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.01382, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89688, "top5_acc": 0.9975, "loss_cls": 0.4962, "loss": 0.4962, "time": 0.37051} +{"mode": "val", "epoch": 70, "iter": 533, "lr": 0.01381, "top1_acc": 0.84122, "top5_acc": 0.98979, "mean_class_accuracy": 0.78057} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.01379, "memory": 4083, "data_time": 0.1955, "top1_acc": 0.90875, "top5_acc": 0.99812, "loss_cls": 0.45718, "loss": 0.45718, "time": 0.81353} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.01377, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91312, "top5_acc": 0.99938, "loss_cls": 0.42935, "loss": 0.42935, "time": 0.49264} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.01375, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.8975, "top5_acc": 0.99688, "loss_cls": 0.49645, "loss": 0.49645, "time": 0.49061} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.01373, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91312, "top5_acc": 0.99688, "loss_cls": 0.45642, "loss": 0.45642, "time": 0.49174} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.01371, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91562, "top5_acc": 0.99812, "loss_cls": 0.45502, "loss": 0.45502, "time": 0.4917} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.01368, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90438, "top5_acc": 0.99875, "loss_cls": 0.49001, "loss": 0.49001, "time": 0.49408} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.01366, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91875, "top5_acc": 0.99688, "loss_cls": 0.45394, "loss": 0.45394, "time": 0.49372} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.01364, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.90625, "top5_acc": 0.99875, "loss_cls": 0.47598, "loss": 0.47598, "time": 0.49227} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.01362, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91062, "top5_acc": 0.995, "loss_cls": 0.46454, "loss": 0.46454, "time": 0.49178} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.0136, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90625, "top5_acc": 0.995, "loss_cls": 0.47553, "loss": 0.47553, "time": 0.49289} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.01358, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91125, "top5_acc": 0.99875, "loss_cls": 0.46558, "loss": 0.46558, "time": 0.49448} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.01356, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.89562, "top5_acc": 0.9975, "loss_cls": 0.50636, "loss": 0.50636, "time": 0.35435} +{"mode": "val", "epoch": 71, "iter": 533, "lr": 0.01355, "top1_acc": 0.77726, "top5_acc": 0.97876, "mean_class_accuracy": 0.69123} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.01353, "memory": 4083, "data_time": 0.19087, "top1_acc": 0.9025, "top5_acc": 0.99938, "loss_cls": 0.46552, "loss": 0.46552, "time": 0.79465} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.01351, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91625, "top5_acc": 0.99875, "loss_cls": 0.44816, "loss": 0.44816, "time": 0.49066} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.01349, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92, "top5_acc": 0.99875, "loss_cls": 0.43369, "loss": 0.43369, "time": 0.48947} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.01346, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90375, "top5_acc": 0.99875, "loss_cls": 0.49693, "loss": 0.49693, "time": 0.48779} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.01344, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.915, "top5_acc": 0.9975, "loss_cls": 0.44991, "loss": 0.44991, "time": 0.49195} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.01342, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90688, "top5_acc": 0.99688, "loss_cls": 0.50378, "loss": 0.50378, "time": 0.49165} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.0134, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.915, "top5_acc": 0.9975, "loss_cls": 0.4578, "loss": 0.4578, "time": 0.4889} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.01338, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.905, "top5_acc": 0.9975, "loss_cls": 0.48039, "loss": 0.48039, "time": 0.48779} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.01336, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89688, "top5_acc": 0.9975, "loss_cls": 0.50607, "loss": 0.50607, "time": 0.49011} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.01334, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89375, "top5_acc": 0.99688, "loss_cls": 0.54632, "loss": 0.54632, "time": 0.49038} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.01332, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91688, "top5_acc": 0.99875, "loss_cls": 0.43374, "loss": 0.43374, "time": 0.489} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.0133, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.90562, "top5_acc": 0.99562, "loss_cls": 0.51173, "loss": 0.51173, "time": 0.37523} +{"mode": "val", "epoch": 72, "iter": 533, "lr": 0.01329, "top1_acc": 0.84943, "top5_acc": 0.99108, "mean_class_accuracy": 0.79252} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.01326, "memory": 4083, "data_time": 0.19197, "top1_acc": 0.91, "top5_acc": 0.99812, "loss_cls": 0.4738, "loss": 0.4738, "time": 0.79856} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.01324, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9175, "top5_acc": 0.9975, "loss_cls": 0.42043, "loss": 0.42043, "time": 0.49253} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.01322, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91375, "top5_acc": 0.99688, "loss_cls": 0.45617, "loss": 0.45617, "time": 0.4922} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.0132, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92438, "top5_acc": 0.99938, "loss_cls": 0.39992, "loss": 0.39992, "time": 0.48762} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.01318, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92188, "top5_acc": 0.99812, "loss_cls": 0.40008, "loss": 0.40008, "time": 0.49515} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.01316, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91812, "top5_acc": 0.99688, "loss_cls": 0.45067, "loss": 0.45067, "time": 0.49136} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.01314, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.90125, "top5_acc": 0.99938, "loss_cls": 0.49896, "loss": 0.49896, "time": 0.49358} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.01312, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9125, "top5_acc": 0.99812, "loss_cls": 0.4437, "loss": 0.4437, "time": 0.49282} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.0131, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.90625, "top5_acc": 0.99625, "loss_cls": 0.49189, "loss": 0.49189, "time": 0.4924} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.01308, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9075, "top5_acc": 0.99562, "loss_cls": 0.50498, "loss": 0.50498, "time": 0.4936} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.01306, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91875, "top5_acc": 0.99938, "loss_cls": 0.43213, "loss": 0.43213, "time": 0.49516} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.01304, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.90125, "top5_acc": 0.9975, "loss_cls": 0.49186, "loss": 0.49186, "time": 0.37034} +{"mode": "val", "epoch": 73, "iter": 533, "lr": 0.01302, "top1_acc": 0.85741, "top5_acc": 0.99167, "mean_class_accuracy": 0.80255} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.013, "memory": 4083, "data_time": 0.19581, "top1_acc": 0.92125, "top5_acc": 0.99938, "loss_cls": 0.41907, "loss": 0.41907, "time": 0.8085} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.01298, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93438, "top5_acc": 0.9975, "loss_cls": 0.37627, "loss": 0.37627, "time": 0.48887} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.01296, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92688, "top5_acc": 0.99938, "loss_cls": 0.38601, "loss": 0.38601, "time": 0.49047} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.01294, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93375, "top5_acc": 0.99688, "loss_cls": 0.39682, "loss": 0.39682, "time": 0.49228} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.01292, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.9125, "top5_acc": 0.99625, "loss_cls": 0.45162, "loss": 0.45162, "time": 0.48881} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.0129, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.90688, "top5_acc": 0.9975, "loss_cls": 0.448, "loss": 0.448, "time": 0.49319} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.01288, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90875, "top5_acc": 0.99812, "loss_cls": 0.48313, "loss": 0.48313, "time": 0.49077} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.01286, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.91062, "top5_acc": 0.99562, "loss_cls": 0.44009, "loss": 0.44009, "time": 0.49394} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.01284, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91938, "top5_acc": 0.99938, "loss_cls": 0.42135, "loss": 0.42135, "time": 0.49729} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.01282, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.92, "top5_acc": 0.99875, "loss_cls": 0.41142, "loss": 0.41142, "time": 0.49107} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.0128, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.90625, "top5_acc": 0.99938, "loss_cls": 0.46981, "loss": 0.46981, "time": 0.49327} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.01278, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.9125, "top5_acc": 0.99875, "loss_cls": 0.44152, "loss": 0.44152, "time": 0.35931} +{"mode": "val", "epoch": 74, "iter": 533, "lr": 0.01276, "top1_acc": 0.84591, "top5_acc": 0.99319, "mean_class_accuracy": 0.79295} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.01274, "memory": 4083, "data_time": 0.19496, "top1_acc": 0.93062, "top5_acc": 0.99688, "loss_cls": 0.42359, "loss": 0.42359, "time": 0.80359} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.01272, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9275, "top5_acc": 0.99875, "loss_cls": 0.35884, "loss": 0.35884, "time": 0.49593} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.0127, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92688, "top5_acc": 0.9975, "loss_cls": 0.39395, "loss": 0.39395, "time": 0.49212} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.01268, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92312, "top5_acc": 1.0, "loss_cls": 0.40009, "loss": 0.40009, "time": 0.49208} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.01266, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9225, "top5_acc": 0.99875, "loss_cls": 0.42982, "loss": 0.42982, "time": 0.49012} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.01264, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9, "top5_acc": 0.99875, "loss_cls": 0.49311, "loss": 0.49311, "time": 0.49018} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.01262, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90188, "top5_acc": 0.99875, "loss_cls": 0.51159, "loss": 0.51159, "time": 0.48846} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.0126, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90188, "top5_acc": 0.99438, "loss_cls": 0.52692, "loss": 0.52692, "time": 0.49051} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.01258, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91188, "top5_acc": 0.99688, "loss_cls": 0.45389, "loss": 0.45389, "time": 0.49361} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.01256, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9075, "top5_acc": 0.99875, "loss_cls": 0.464, "loss": 0.464, "time": 0.4958} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.01254, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92562, "top5_acc": 0.99875, "loss_cls": 0.42324, "loss": 0.42324, "time": 0.48934} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.01252, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.91688, "top5_acc": 0.99938, "loss_cls": 0.4308, "loss": 0.4308, "time": 0.36881} +{"mode": "val", "epoch": 75, "iter": 533, "lr": 0.0125, "top1_acc": 0.84345, "top5_acc": 0.98909, "mean_class_accuracy": 0.7881} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.01248, "memory": 4083, "data_time": 0.19262, "top1_acc": 0.91312, "top5_acc": 0.99938, "loss_cls": 0.44782, "loss": 0.44782, "time": 0.80629} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.01246, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93312, "top5_acc": 0.99938, "loss_cls": 0.36773, "loss": 0.36773, "time": 0.49064} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.01244, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9175, "top5_acc": 0.99812, "loss_cls": 0.42732, "loss": 0.42732, "time": 0.4886} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.01242, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9175, "top5_acc": 0.99688, "loss_cls": 0.43931, "loss": 0.43931, "time": 0.48977} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.0124, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.9225, "top5_acc": 0.99812, "loss_cls": 0.41729, "loss": 0.41729, "time": 0.49055} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.01238, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92188, "top5_acc": 0.99938, "loss_cls": 0.42131, "loss": 0.42131, "time": 0.49001} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.01236, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92938, "top5_acc": 0.99812, "loss_cls": 0.37356, "loss": 0.37356, "time": 0.48854} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.01234, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92188, "top5_acc": 0.99875, "loss_cls": 0.45135, "loss": 0.45135, "time": 0.49219} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.01232, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91375, "top5_acc": 0.9975, "loss_cls": 0.45347, "loss": 0.45347, "time": 0.49126} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.0123, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9175, "top5_acc": 0.99875, "loss_cls": 0.44641, "loss": 0.44641, "time": 0.49078} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.01228, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.90062, "top5_acc": 0.9975, "loss_cls": 0.49781, "loss": 0.49781, "time": 0.49157} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.01225, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90875, "top5_acc": 0.99812, "loss_cls": 0.45785, "loss": 0.45785, "time": 0.35951} +{"mode": "val", "epoch": 76, "iter": 533, "lr": 0.01224, "top1_acc": 0.86938, "top5_acc": 0.99026, "mean_class_accuracy": 0.81373} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.01222, "memory": 4083, "data_time": 0.19226, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.35014, "loss": 0.35014, "time": 0.80407} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0122, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94812, "top5_acc": 0.99875, "loss_cls": 0.30901, "loss": 0.30901, "time": 0.49076} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.01218, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90625, "top5_acc": 0.9975, "loss_cls": 0.44261, "loss": 0.44261, "time": 0.49137} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.01216, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92562, "top5_acc": 0.99875, "loss_cls": 0.39724, "loss": 0.39724, "time": 0.49} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.01214, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93438, "top5_acc": 0.99938, "loss_cls": 0.37005, "loss": 0.37005, "time": 0.49239} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.01212, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90938, "top5_acc": 0.99562, "loss_cls": 0.46621, "loss": 0.46621, "time": 0.49119} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.0121, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9175, "top5_acc": 0.99562, "loss_cls": 0.44872, "loss": 0.44872, "time": 0.49506} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.01207, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91625, "top5_acc": 0.99812, "loss_cls": 0.44173, "loss": 0.44173, "time": 0.4897} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.01205, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.915, "top5_acc": 0.99625, "loss_cls": 0.46654, "loss": 0.46654, "time": 0.49375} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.01203, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89562, "top5_acc": 0.9975, "loss_cls": 0.51427, "loss": 0.51427, "time": 0.49196} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.01201, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92375, "top5_acc": 0.9975, "loss_cls": 0.43499, "loss": 0.43499, "time": 0.4917} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.01199, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93, "top5_acc": 0.99875, "loss_cls": 0.39158, "loss": 0.39158, "time": 0.36639} +{"mode": "val", "epoch": 77, "iter": 533, "lr": 0.01198, "top1_acc": 0.87466, "top5_acc": 0.99284, "mean_class_accuracy": 0.83024} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.01196, "memory": 4083, "data_time": 0.19466, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.35979, "loss": 0.35979, "time": 0.80059} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.01194, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.33744, "loss": 0.33744, "time": 0.49315} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.01192, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94812, "top5_acc": 0.99875, "loss_cls": 0.32086, "loss": 0.32086, "time": 0.48995} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.0119, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92188, "top5_acc": 0.99812, "loss_cls": 0.40878, "loss": 0.40878, "time": 0.49044} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.01187, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.92312, "top5_acc": 0.9975, "loss_cls": 0.40396, "loss": 0.40396, "time": 0.49483} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.01185, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9225, "top5_acc": 0.9975, "loss_cls": 0.43303, "loss": 0.43303, "time": 0.4908} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.01183, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.41233, "loss": 0.41233, "time": 0.49} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.01181, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91312, "top5_acc": 0.99875, "loss_cls": 0.41407, "loss": 0.41407, "time": 0.49592} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.01179, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90125, "top5_acc": 0.99562, "loss_cls": 0.4642, "loss": 0.4642, "time": 0.49212} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.01177, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91875, "top5_acc": 0.99562, "loss_cls": 0.44332, "loss": 0.44332, "time": 0.4915} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.01175, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90875, "top5_acc": 0.99938, "loss_cls": 0.42194, "loss": 0.42194, "time": 0.49012} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.01173, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92375, "top5_acc": 0.99812, "loss_cls": 0.41779, "loss": 0.41779, "time": 0.36302} +{"mode": "val", "epoch": 78, "iter": 533, "lr": 0.01172, "top1_acc": 0.86187, "top5_acc": 0.99284, "mean_class_accuracy": 0.81516} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.01169, "memory": 4083, "data_time": 0.19571, "top1_acc": 0.92938, "top5_acc": 1.0, "loss_cls": 0.35513, "loss": 0.35513, "time": 0.80215} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.01167, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.34599, "loss": 0.34599, "time": 0.49131} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.01165, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92438, "top5_acc": 0.9975, "loss_cls": 0.39648, "loss": 0.39648, "time": 0.49105} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.01163, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9225, "top5_acc": 0.99688, "loss_cls": 0.42922, "loss": 0.42922, "time": 0.49049} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.01161, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92312, "top5_acc": 0.9975, "loss_cls": 0.43159, "loss": 0.43159, "time": 0.49223} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.01159, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92938, "top5_acc": 1.0, "loss_cls": 0.37155, "loss": 0.37155, "time": 0.49446} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.01157, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.91375, "top5_acc": 1.0, "loss_cls": 0.42752, "loss": 0.42752, "time": 0.49349} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.01155, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93875, "top5_acc": 0.99562, "loss_cls": 0.37424, "loss": 0.37424, "time": 0.49132} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.01153, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91438, "top5_acc": 0.99938, "loss_cls": 0.42513, "loss": 0.42513, "time": 0.49242} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.01151, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9075, "top5_acc": 0.9975, "loss_cls": 0.46512, "loss": 0.46512, "time": 0.4908} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.01149, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90562, "top5_acc": 0.99938, "loss_cls": 0.47449, "loss": 0.47449, "time": 0.49578} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.01147, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.9, "top5_acc": 0.99875, "loss_cls": 0.46719, "loss": 0.46719, "time": 0.36559} +{"mode": "val", "epoch": 79, "iter": 533, "lr": 0.01145, "top1_acc": 0.84638, "top5_acc": 0.98991, "mean_class_accuracy": 0.7915} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.01143, "memory": 4083, "data_time": 0.19483, "top1_acc": 0.94062, "top5_acc": 1.0, "loss_cls": 0.33912, "loss": 0.33912, "time": 0.80233} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.01141, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.94188, "top5_acc": 1.0, "loss_cls": 0.36239, "loss": 0.36239, "time": 0.49421} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.01139, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92812, "top5_acc": 0.99938, "loss_cls": 0.37382, "loss": 0.37382, "time": 0.49037} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.01137, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.38286, "loss": 0.38286, "time": 0.49336} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.01135, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.32676, "loss": 0.32676, "time": 0.48884} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.01133, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92125, "top5_acc": 1.0, "loss_cls": 0.39934, "loss": 0.39934, "time": 0.49268} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.01131, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.91312, "top5_acc": 0.99625, "loss_cls": 0.46291, "loss": 0.46291, "time": 0.48955} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.01129, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.45314, "loss": 0.45314, "time": 0.49325} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.01127, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93812, "top5_acc": 0.99812, "loss_cls": 0.3694, "loss": 0.3694, "time": 0.49035} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.01125, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90062, "top5_acc": 0.99625, "loss_cls": 0.48354, "loss": 0.48354, "time": 0.49036} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.01123, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92562, "top5_acc": 0.99688, "loss_cls": 0.416, "loss": 0.416, "time": 0.48873} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.01121, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92312, "top5_acc": 0.99875, "loss_cls": 0.36934, "loss": 0.36934, "time": 0.36078} +{"mode": "val", "epoch": 80, "iter": 533, "lr": 0.01119, "top1_acc": 0.86621, "top5_acc": 0.99143, "mean_class_accuracy": 0.81963} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.01117, "memory": 4083, "data_time": 0.19747, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.34255, "loss": 0.34255, "time": 0.80229} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.01115, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94438, "top5_acc": 0.99812, "loss_cls": 0.32902, "loss": 0.32902, "time": 0.49073} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.01113, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94, "top5_acc": 0.99688, "loss_cls": 0.33113, "loss": 0.33113, "time": 0.49331} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.01111, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93375, "top5_acc": 0.99562, "loss_cls": 0.36375, "loss": 0.36375, "time": 0.49125} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.01109, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.93125, "top5_acc": 0.99875, "loss_cls": 0.36462, "loss": 0.36462, "time": 0.49028} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.01107, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92062, "top5_acc": 0.99688, "loss_cls": 0.40203, "loss": 0.40203, "time": 0.49452} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.01105, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.92688, "top5_acc": 0.9975, "loss_cls": 0.39403, "loss": 0.39403, "time": 0.49044} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.01103, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93812, "top5_acc": 0.99938, "loss_cls": 0.35279, "loss": 0.35279, "time": 0.49246} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.01101, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.46741, "loss": 0.46741, "time": 0.49086} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.01099, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.93125, "top5_acc": 0.99562, "loss_cls": 0.39138, "loss": 0.39138, "time": 0.49161} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.01097, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.92688, "top5_acc": 0.99875, "loss_cls": 0.41469, "loss": 0.41469, "time": 0.49401} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.01095, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.38759, "loss": 0.38759, "time": 0.36959} +{"mode": "val", "epoch": 81, "iter": 533, "lr": 0.01093, "top1_acc": 0.87619, "top5_acc": 0.99214, "mean_class_accuracy": 0.8221} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.01091, "memory": 4083, "data_time": 0.1912, "top1_acc": 0.94375, "top5_acc": 1.0, "loss_cls": 0.33585, "loss": 0.33585, "time": 0.7913} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.01089, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93438, "top5_acc": 0.99938, "loss_cls": 0.34485, "loss": 0.34485, "time": 0.48955} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.01087, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92688, "top5_acc": 0.99938, "loss_cls": 0.39627, "loss": 0.39627, "time": 0.49095} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.01085, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92562, "top5_acc": 0.99812, "loss_cls": 0.39006, "loss": 0.39006, "time": 0.49162} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.01083, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9275, "top5_acc": 0.99812, "loss_cls": 0.3733, "loss": 0.3733, "time": 0.49558} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.01081, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.30545, "loss": 0.30545, "time": 0.49275} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.01079, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9325, "top5_acc": 0.99812, "loss_cls": 0.37362, "loss": 0.37362, "time": 0.49174} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.01077, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.93938, "top5_acc": 0.99875, "loss_cls": 0.33349, "loss": 0.33349, "time": 0.49405} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.01075, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.40207, "loss": 0.40207, "time": 0.49176} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.01073, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9325, "top5_acc": 0.9975, "loss_cls": 0.41284, "loss": 0.41284, "time": 0.4905} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.01071, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91812, "top5_acc": 0.99875, "loss_cls": 0.41485, "loss": 0.41485, "time": 0.49131} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.01069, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92062, "top5_acc": 0.9975, "loss_cls": 0.39481, "loss": 0.39481, "time": 0.37682} +{"mode": "val", "epoch": 82, "iter": 533, "lr": 0.01067, "top1_acc": 0.85401, "top5_acc": 0.98967, "mean_class_accuracy": 0.79245} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.01065, "memory": 4083, "data_time": 0.19069, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.30806, "loss": 0.30806, "time": 0.79904} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.01063, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.935, "top5_acc": 0.99812, "loss_cls": 0.34753, "loss": 0.34753, "time": 0.4931} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.01061, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94, "top5_acc": 1.0, "loss_cls": 0.31202, "loss": 0.31202, "time": 0.49235} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.01059, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.93062, "top5_acc": 0.99938, "loss_cls": 0.36589, "loss": 0.36589, "time": 0.49149} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.01057, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94062, "top5_acc": 0.9975, "loss_cls": 0.35936, "loss": 0.35936, "time": 0.49274} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.01055, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93438, "top5_acc": 0.99938, "loss_cls": 0.35645, "loss": 0.35645, "time": 0.49273} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.01053, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.92938, "top5_acc": 0.99688, "loss_cls": 0.37367, "loss": 0.37367, "time": 0.4914} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.01051, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9125, "top5_acc": 0.99812, "loss_cls": 0.43455, "loss": 0.43455, "time": 0.4913} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.01049, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.40419, "loss": 0.40419, "time": 0.49209} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.01047, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93625, "top5_acc": 0.9975, "loss_cls": 0.35504, "loss": 0.35504, "time": 0.49306} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.01045, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.92062, "top5_acc": 1.0, "loss_cls": 0.40743, "loss": 0.40743, "time": 0.49118} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.01043, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93062, "top5_acc": 0.99812, "loss_cls": 0.35615, "loss": 0.35615, "time": 0.36025} +{"mode": "val", "epoch": 83, "iter": 533, "lr": 0.01042, "top1_acc": 0.87537, "top5_acc": 0.9939, "mean_class_accuracy": 0.82809} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.0104, "memory": 4083, "data_time": 0.19313, "top1_acc": 0.94812, "top5_acc": 0.99875, "loss_cls": 0.30802, "loss": 0.30802, "time": 0.79135} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.01038, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.92625, "top5_acc": 0.99812, "loss_cls": 0.39213, "loss": 0.39213, "time": 0.49012} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.01036, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93375, "top5_acc": 0.99938, "loss_cls": 0.35529, "loss": 0.35529, "time": 0.49217} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.01034, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92688, "top5_acc": 0.99812, "loss_cls": 0.40045, "loss": 0.40045, "time": 0.48994} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.01031, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93312, "top5_acc": 0.99938, "loss_cls": 0.3573, "loss": 0.3573, "time": 0.49445} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.01029, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9425, "top5_acc": 0.99875, "loss_cls": 0.32706, "loss": 0.32706, "time": 0.4937} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.01027, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94312, "top5_acc": 0.99875, "loss_cls": 0.31965, "loss": 0.31965, "time": 0.49329} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.01025, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.92312, "top5_acc": 0.9975, "loss_cls": 0.41116, "loss": 0.41116, "time": 0.49113} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.01023, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.92, "top5_acc": 0.99938, "loss_cls": 0.4282, "loss": 0.4282, "time": 0.49217} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.01021, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91875, "top5_acc": 0.99875, "loss_cls": 0.39474, "loss": 0.39474, "time": 0.49171} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.01019, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9325, "top5_acc": 0.99875, "loss_cls": 0.37258, "loss": 0.37258, "time": 0.48999} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.01017, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93812, "top5_acc": 1.0, "loss_cls": 0.34404, "loss": 0.34404, "time": 0.37877} +{"mode": "val", "epoch": 84, "iter": 533, "lr": 0.01016, "top1_acc": 0.87091, "top5_acc": 0.99214, "mean_class_accuracy": 0.82878} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.01014, "memory": 4083, "data_time": 0.19744, "top1_acc": 0.94562, "top5_acc": 1.0, "loss_cls": 0.31749, "loss": 0.31749, "time": 0.80868} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.01012, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.28785, "loss": 0.28785, "time": 0.48904} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.0101, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.29508, "loss": 0.29508, "time": 0.49177} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.01008, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94, "top5_acc": 0.99875, "loss_cls": 0.31927, "loss": 0.31927, "time": 0.48969} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.01006, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.27965, "loss": 0.27965, "time": 0.48975} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.01004, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.92688, "top5_acc": 0.99938, "loss_cls": 0.37414, "loss": 0.37414, "time": 0.49194} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.01002, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.93562, "top5_acc": 0.99875, "loss_cls": 0.34303, "loss": 0.34303, "time": 0.49112} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.01, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95, "top5_acc": 1.0, "loss_cls": 0.31898, "loss": 0.31898, "time": 0.48979} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.00998, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.37809, "loss": 0.37809, "time": 0.49675} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.00996, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92, "top5_acc": 0.9975, "loss_cls": 0.40038, "loss": 0.40038, "time": 0.49437} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.00994, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.32149, "loss": 0.32149, "time": 0.49288} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.00992, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91938, "top5_acc": 0.99688, "loss_cls": 0.43457, "loss": 0.43457, "time": 0.36024} +{"mode": "val", "epoch": 85, "iter": 533, "lr": 0.0099, "top1_acc": 0.86563, "top5_acc": 0.99272, "mean_class_accuracy": 0.82269} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.00988, "memory": 4083, "data_time": 0.19581, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.26932, "loss": 0.26932, "time": 0.80044} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.00986, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.31519, "loss": 0.31519, "time": 0.48849} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.00984, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.28749, "loss": 0.28749, "time": 0.49048} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.00982, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.28997, "loss": 0.28997, "time": 0.49357} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.0098, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.28291, "loss": 0.28291, "time": 0.49412} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.00978, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.93688, "top5_acc": 0.9975, "loss_cls": 0.34972, "loss": 0.34972, "time": 0.49224} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.00976, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.33423, "loss": 0.33423, "time": 0.49221} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.00974, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.32289, "loss": 0.32289, "time": 0.4893} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.00972, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.92625, "top5_acc": 0.9975, "loss_cls": 0.391, "loss": 0.391, "time": 0.4888} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.0097, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93625, "top5_acc": 0.9975, "loss_cls": 0.37318, "loss": 0.37318, "time": 0.49055} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.00968, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.38593, "loss": 0.38593, "time": 0.49288} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.00966, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91438, "top5_acc": 0.9975, "loss_cls": 0.43581, "loss": 0.43581, "time": 0.36754} +{"mode": "val", "epoch": 86, "iter": 533, "lr": 0.00965, "top1_acc": 0.879, "top5_acc": 0.99331, "mean_class_accuracy": 0.83658} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.00963, "memory": 4083, "data_time": 0.19564, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.2748, "loss": 0.2748, "time": 0.79929} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.00961, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.32731, "loss": 0.32731, "time": 0.49057} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.00959, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94312, "top5_acc": 0.99875, "loss_cls": 0.33842, "loss": 0.33842, "time": 0.49095} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.00957, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.31942, "loss": 0.31942, "time": 0.49085} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.00955, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.32558, "loss": 0.32558, "time": 0.49258} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.00953, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94625, "top5_acc": 0.99875, "loss_cls": 0.3157, "loss": 0.3157, "time": 0.48662} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.00951, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.92875, "top5_acc": 0.99812, "loss_cls": 0.37194, "loss": 0.37194, "time": 0.49256} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.00949, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9275, "top5_acc": 0.99938, "loss_cls": 0.3417, "loss": 0.3417, "time": 0.49178} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.00947, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.29402, "loss": 0.29402, "time": 0.49168} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.00945, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94, "top5_acc": 0.99938, "loss_cls": 0.32098, "loss": 0.32098, "time": 0.49151} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.00943, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.32595, "loss": 0.32595, "time": 0.49019} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.00941, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.93562, "top5_acc": 1.0, "loss_cls": 0.33408, "loss": 0.33408, "time": 0.37046} +{"mode": "val", "epoch": 87, "iter": 533, "lr": 0.00939, "top1_acc": 0.86492, "top5_acc": 0.99143, "mean_class_accuracy": 0.82189} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.00937, "memory": 4083, "data_time": 0.1891, "top1_acc": 0.95625, "top5_acc": 0.9975, "loss_cls": 0.25937, "loss": 0.25937, "time": 0.79411} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.00935, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94812, "top5_acc": 1.0, "loss_cls": 0.29994, "loss": 0.29994, "time": 0.48954} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.00933, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93688, "top5_acc": 1.0, "loss_cls": 0.34335, "loss": 0.34335, "time": 0.49478} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.00931, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.30175, "loss": 0.30175, "time": 0.49381} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.00929, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9425, "top5_acc": 1.0, "loss_cls": 0.31041, "loss": 0.31041, "time": 0.4921} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.00927, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.33891, "loss": 0.33891, "time": 0.4925} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.00925, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94625, "top5_acc": 0.99812, "loss_cls": 0.327, "loss": 0.327, "time": 0.48903} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.00923, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93812, "top5_acc": 0.99812, "loss_cls": 0.33017, "loss": 0.33017, "time": 0.49496} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.00921, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.32028, "loss": 0.32028, "time": 0.49255} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.00919, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94, "top5_acc": 0.99875, "loss_cls": 0.34935, "loss": 0.34935, "time": 0.49261} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.00917, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92812, "top5_acc": 0.99938, "loss_cls": 0.38167, "loss": 0.38167, "time": 0.4916} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.00915, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93938, "top5_acc": 0.99875, "loss_cls": 0.34885, "loss": 0.34885, "time": 0.3708} +{"mode": "val", "epoch": 88, "iter": 533, "lr": 0.00914, "top1_acc": 0.8722, "top5_acc": 0.9912, "mean_class_accuracy": 0.83306} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.00912, "memory": 4083, "data_time": 0.19343, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.27313, "loss": 0.27313, "time": 0.79051} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0091, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.26367, "loss": 0.26367, "time": 0.49239} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.00908, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94375, "top5_acc": 0.99688, "loss_cls": 0.31902, "loss": 0.31902, "time": 0.49351} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.00906, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92875, "top5_acc": 0.99812, "loss_cls": 0.37867, "loss": 0.37867, "time": 0.49111} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.00904, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.935, "top5_acc": 1.0, "loss_cls": 0.31967, "loss": 0.31967, "time": 0.49261} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.00902, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93812, "top5_acc": 0.99875, "loss_cls": 0.30925, "loss": 0.30925, "time": 0.48909} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.009, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.34724, "loss": 0.34724, "time": 0.48999} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.00898, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.35785, "loss": 0.35785, "time": 0.49083} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.00896, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93812, "top5_acc": 0.99938, "loss_cls": 0.336, "loss": 0.336, "time": 0.49178} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.00894, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9325, "top5_acc": 0.99812, "loss_cls": 0.37801, "loss": 0.37801, "time": 0.48931} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.00892, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94125, "top5_acc": 0.99875, "loss_cls": 0.32897, "loss": 0.32897, "time": 0.49156} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.0089, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.33456, "loss": 0.33456, "time": 0.37402} +{"mode": "val", "epoch": 89, "iter": 533, "lr": 0.00889, "top1_acc": 0.8675, "top5_acc": 0.99167, "mean_class_accuracy": 0.81977} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.00887, "memory": 4083, "data_time": 0.19482, "top1_acc": 0.9475, "top5_acc": 1.0, "loss_cls": 0.31361, "loss": 0.31361, "time": 0.80855} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.00885, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.94625, "top5_acc": 0.99875, "loss_cls": 0.30659, "loss": 0.30659, "time": 0.48757} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.00883, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.28549, "loss": 0.28549, "time": 0.49013} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.00881, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94188, "top5_acc": 0.99812, "loss_cls": 0.32794, "loss": 0.32794, "time": 0.49022} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.00879, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.24574, "loss": 0.24574, "time": 0.49109} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.00877, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.93375, "top5_acc": 0.99875, "loss_cls": 0.34871, "loss": 0.34871, "time": 0.48988} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.00875, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.34571, "loss": 0.34571, "time": 0.4901} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.00873, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94375, "top5_acc": 0.99812, "loss_cls": 0.30077, "loss": 0.30077, "time": 0.49315} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.00871, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94, "top5_acc": 0.99938, "loss_cls": 0.32748, "loss": 0.32748, "time": 0.49052} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.00869, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94312, "top5_acc": 1.0, "loss_cls": 0.32452, "loss": 0.32452, "time": 0.4927} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.00867, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.28454, "loss": 0.28454, "time": 0.49406} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.00865, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94, "top5_acc": 0.99938, "loss_cls": 0.31289, "loss": 0.31289, "time": 0.36779} +{"mode": "val", "epoch": 90, "iter": 533, "lr": 0.00864, "top1_acc": 0.88206, "top5_acc": 0.99225, "mean_class_accuracy": 0.8376} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.00862, "memory": 4083, "data_time": 0.19189, "top1_acc": 0.95312, "top5_acc": 0.99812, "loss_cls": 0.27718, "loss": 0.27718, "time": 0.79544} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0086, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.28056, "loss": 0.28056, "time": 0.49137} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.00858, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.24279, "loss": 0.24279, "time": 0.49405} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.00856, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.93688, "top5_acc": 0.99875, "loss_cls": 0.33554, "loss": 0.33554, "time": 0.49247} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.00854, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.29865, "loss": 0.29865, "time": 0.49166} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.00852, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.29857, "loss": 0.29857, "time": 0.48874} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.0085, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95, "top5_acc": 0.99875, "loss_cls": 0.2666, "loss": 0.2666, "time": 0.49306} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.00848, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93812, "top5_acc": 0.99938, "loss_cls": 0.29508, "loss": 0.29508, "time": 0.49108} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.00846, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.31825, "loss": 0.31825, "time": 0.48847} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.00844, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95, "top5_acc": 1.0, "loss_cls": 0.27436, "loss": 0.27436, "time": 0.48986} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.00842, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94125, "top5_acc": 0.99875, "loss_cls": 0.31681, "loss": 0.31681, "time": 0.48918} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.0084, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.95125, "top5_acc": 0.99812, "loss_cls": 0.29991, "loss": 0.29991, "time": 0.36859} +{"mode": "val", "epoch": 91, "iter": 533, "lr": 0.00839, "top1_acc": 0.88077, "top5_acc": 0.99437, "mean_class_accuracy": 0.8418} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.00837, "memory": 4083, "data_time": 0.19184, "top1_acc": 0.95562, "top5_acc": 1.0, "loss_cls": 0.27297, "loss": 0.27297, "time": 0.80082} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.00835, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9475, "top5_acc": 1.0, "loss_cls": 0.29893, "loss": 0.29893, "time": 0.48992} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.00833, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94438, "top5_acc": 0.99875, "loss_cls": 0.30316, "loss": 0.30316, "time": 0.49091} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.00831, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.29688, "loss": 0.29688, "time": 0.49218} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.00829, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.29824, "loss": 0.29824, "time": 0.49101} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.00827, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94812, "top5_acc": 0.99875, "loss_cls": 0.29192, "loss": 0.29192, "time": 0.49199} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.00825, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95125, "top5_acc": 0.99875, "loss_cls": 0.28939, "loss": 0.28939, "time": 0.48962} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.00824, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.30739, "loss": 0.30739, "time": 0.4911} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.00822, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.32652, "loss": 0.32652, "time": 0.49113} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.0082, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95188, "top5_acc": 1.0, "loss_cls": 0.28144, "loss": 0.28144, "time": 0.49041} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.00818, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.26797, "loss": 0.26797, "time": 0.49374} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.00816, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9375, "top5_acc": 1.0, "loss_cls": 0.32837, "loss": 0.32837, "time": 0.36656} +{"mode": "val", "epoch": 92, "iter": 533, "lr": 0.00814, "top1_acc": 0.8729, "top5_acc": 0.99249, "mean_class_accuracy": 0.835} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.00812, "memory": 4083, "data_time": 0.19297, "top1_acc": 0.94938, "top5_acc": 1.0, "loss_cls": 0.27405, "loss": 0.27405, "time": 0.81207} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.0081, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.24626, "loss": 0.24626, "time": 0.49024} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.00809, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.27107, "loss": 0.27107, "time": 0.489} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.00807, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.26037, "loss": 0.26037, "time": 0.49054} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.00805, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.25588, "loss": 0.25588, "time": 0.49047} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.00803, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.25371, "loss": 0.25371, "time": 0.4938} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.00801, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.28348, "loss": 0.28348, "time": 0.4928} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.00799, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.24625, "loss": 0.24625, "time": 0.49058} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.00797, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.27069, "loss": 0.27069, "time": 0.49567} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.00795, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9375, "top5_acc": 0.99875, "loss_cls": 0.35114, "loss": 0.35114, "time": 0.49108} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.00793, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94625, "top5_acc": 0.99875, "loss_cls": 0.31892, "loss": 0.31892, "time": 0.49096} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.00791, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.32303, "loss": 0.32303, "time": 0.35971} +{"mode": "val", "epoch": 93, "iter": 533, "lr": 0.0079, "top1_acc": 0.86222, "top5_acc": 0.98956, "mean_class_accuracy": 0.81591} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.00788, "memory": 4083, "data_time": 0.19213, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.26395, "loss": 0.26395, "time": 0.81271} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.00786, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.24526, "loss": 0.24526, "time": 0.49075} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.00784, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.26242, "loss": 0.26242, "time": 0.49206} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.00782, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9575, "top5_acc": 0.99875, "loss_cls": 0.25044, "loss": 0.25044, "time": 0.49036} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.0078, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.25077, "loss": 0.25077, "time": 0.48945} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.00778, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9475, "top5_acc": 0.99812, "loss_cls": 0.2954, "loss": 0.2954, "time": 0.4888} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.00777, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95438, "top5_acc": 0.99938, "loss_cls": 0.27782, "loss": 0.27782, "time": 0.48794} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.00775, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.27033, "loss": 0.27033, "time": 0.49022} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.00773, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95812, "top5_acc": 0.99938, "loss_cls": 0.24578, "loss": 0.24578, "time": 0.48976} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.00771, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.25114, "loss": 0.25114, "time": 0.4925} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.00769, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95688, "top5_acc": 0.99938, "loss_cls": 0.25508, "loss": 0.25508, "time": 0.49177} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.00767, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95438, "top5_acc": 0.99812, "loss_cls": 0.26851, "loss": 0.26851, "time": 0.34976} +{"mode": "val", "epoch": 94, "iter": 533, "lr": 0.00766, "top1_acc": 0.88534, "top5_acc": 0.99331, "mean_class_accuracy": 0.84422} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.00764, "memory": 4083, "data_time": 0.19106, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.23191, "loss": 0.23191, "time": 0.80125} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.00762, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.25022, "loss": 0.25022, "time": 0.48985} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.0076, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.19977, "loss": 0.19977, "time": 0.48835} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.00758, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.23282, "loss": 0.23282, "time": 0.48929} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.00756, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96312, "top5_acc": 0.99875, "loss_cls": 0.22475, "loss": 0.22475, "time": 0.49171} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.00754, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.32114, "loss": 0.32114, "time": 0.48803} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.00752, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95812, "top5_acc": 0.99938, "loss_cls": 0.25982, "loss": 0.25982, "time": 0.49211} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.00751, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.94438, "top5_acc": 1.0, "loss_cls": 0.30033, "loss": 0.30033, "time": 0.49255} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.00749, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95688, "top5_acc": 0.99875, "loss_cls": 0.26789, "loss": 0.26789, "time": 0.49273} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.00747, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.27207, "loss": 0.27207, "time": 0.48789} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.00745, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.24384, "loss": 0.24384, "time": 0.4912} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.00743, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.28761, "loss": 0.28761, "time": 0.36166} +{"mode": "val", "epoch": 95, "iter": 533, "lr": 0.00742, "top1_acc": 0.88992, "top5_acc": 0.99378, "mean_class_accuracy": 0.8417} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.0074, "memory": 4083, "data_time": 0.19295, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.22298, "loss": 0.22298, "time": 0.80188} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.00738, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.18808, "loss": 0.18808, "time": 0.4916} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.00736, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.22754, "loss": 0.22754, "time": 0.48807} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.00734, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96375, "top5_acc": 0.99875, "loss_cls": 0.22589, "loss": 0.22589, "time": 0.4934} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.00732, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96562, "top5_acc": 0.99812, "loss_cls": 0.24611, "loss": 0.24611, "time": 0.49139} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.0073, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.23801, "loss": 0.23801, "time": 0.486} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.00729, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.28006, "loss": 0.28006, "time": 0.48818} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.00727, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96, "top5_acc": 0.99938, "loss_cls": 0.23394, "loss": 0.23394, "time": 0.49016} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.00725, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.24408, "loss": 0.24408, "time": 0.49225} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.00723, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94312, "top5_acc": 0.99875, "loss_cls": 0.3077, "loss": 0.3077, "time": 0.48857} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.00721, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9325, "top5_acc": 0.99875, "loss_cls": 0.34885, "loss": 0.34885, "time": 0.48763} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.00719, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.29055, "loss": 0.29055, "time": 0.36071} +{"mode": "val", "epoch": 96, "iter": 533, "lr": 0.00718, "top1_acc": 0.88253, "top5_acc": 0.99132, "mean_class_accuracy": 0.84288} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.00716, "memory": 4083, "data_time": 0.19113, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.23667, "loss": 0.23667, "time": 0.79253} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.00714, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.22845, "loss": 0.22845, "time": 0.49004} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.00712, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.26625, "loss": 0.26625, "time": 0.48986} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.0071, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.95375, "top5_acc": 0.99875, "loss_cls": 0.26405, "loss": 0.26405, "time": 0.49191} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.00709, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.1963, "loss": 0.1963, "time": 0.49081} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.00707, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.21229, "loss": 0.21229, "time": 0.48679} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.00705, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.23756, "loss": 0.23756, "time": 0.49077} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.00703, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.95188, "top5_acc": 1.0, "loss_cls": 0.27263, "loss": 0.27263, "time": 0.49229} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.00701, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95875, "top5_acc": 0.99875, "loss_cls": 0.25346, "loss": 0.25346, "time": 0.48944} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.00699, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9475, "top5_acc": 1.0, "loss_cls": 0.29253, "loss": 0.29253, "time": 0.48966} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.00698, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.26868, "loss": 0.26868, "time": 0.49119} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.00696, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95, "top5_acc": 0.9975, "loss_cls": 0.28819, "loss": 0.28819, "time": 0.37492} +{"mode": "val", "epoch": 97, "iter": 533, "lr": 0.00694, "top1_acc": 0.87302, "top5_acc": 0.99002, "mean_class_accuracy": 0.83859} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.00692, "memory": 4083, "data_time": 0.1901, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.24599, "loss": 0.24599, "time": 0.79507} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.00691, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.23715, "loss": 0.23715, "time": 0.48983} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.00689, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.24826, "loss": 0.24826, "time": 0.4874} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.00687, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.22304, "loss": 0.22304, "time": 0.48845} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.00685, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.20111, "loss": 0.20111, "time": 0.49119} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.00683, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.22642, "loss": 0.22642, "time": 0.49266} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.00681, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.22247, "loss": 0.22247, "time": 0.49416} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.0068, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.2367, "loss": 0.2367, "time": 0.48971} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.00678, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96125, "top5_acc": 0.99938, "loss_cls": 0.24551, "loss": 0.24551, "time": 0.48758} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.00676, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.20261, "loss": 0.20261, "time": 0.48893} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.00674, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.22854, "loss": 0.22854, "time": 0.48687} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.00672, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.23586, "loss": 0.23586, "time": 0.37205} +{"mode": "val", "epoch": 98, "iter": 533, "lr": 0.00671, "top1_acc": 0.88487, "top5_acc": 0.99261, "mean_class_accuracy": 0.83721} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.00669, "memory": 4083, "data_time": 0.1883, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.19287, "loss": 0.19287, "time": 0.79998} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.00667, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.16295, "loss": 0.16295, "time": 0.48733} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.00665, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97625, "top5_acc": 0.99938, "loss_cls": 0.15952, "loss": 0.15952, "time": 0.48989} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.00664, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.97438, "top5_acc": 0.99938, "loss_cls": 0.17743, "loss": 0.17743, "time": 0.49018} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.00662, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.16295, "loss": 0.16295, "time": 0.49} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.0066, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.22406, "loss": 0.22406, "time": 0.4917} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.00658, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.20407, "loss": 0.20407, "time": 0.49344} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.00656, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.18017, "loss": 0.18017, "time": 0.4885} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.00655, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.24045, "loss": 0.24045, "time": 0.4923} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.00653, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.21855, "loss": 0.21855, "time": 0.48733} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.00651, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.21916, "loss": 0.21916, "time": 0.49161} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.00649, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.25235, "loss": 0.25235, "time": 0.36319} +{"mode": "val", "epoch": 99, "iter": 533, "lr": 0.00648, "top1_acc": 0.86621, "top5_acc": 0.98826, "mean_class_accuracy": 0.83212} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.00646, "memory": 4083, "data_time": 0.19272, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.17022, "loss": 0.17022, "time": 0.78299} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.00644, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96625, "top5_acc": 0.99938, "loss_cls": 0.20352, "loss": 0.20352, "time": 0.48983} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.00642, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.21317, "loss": 0.21317, "time": 0.49204} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.00641, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.21983, "loss": 0.21983, "time": 0.48954} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.00639, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.2422, "loss": 0.2422, "time": 0.48994} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.00637, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.23158, "loss": 0.23158, "time": 0.49187} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.00635, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95688, "top5_acc": 0.99875, "loss_cls": 0.24238, "loss": 0.24238, "time": 0.49348} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.00634, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.19778, "loss": 0.19778, "time": 0.4916} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.00632, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.19925, "loss": 0.19925, "time": 0.49264} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.0063, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.23994, "loss": 0.23994, "time": 0.49402} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.00628, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.96375, "top5_acc": 0.99938, "loss_cls": 0.23792, "loss": 0.23792, "time": 0.49013} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.00626, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.2666, "loss": 0.2666, "time": 0.38063} +{"mode": "val", "epoch": 100, "iter": 533, "lr": 0.00625, "top1_acc": 0.88522, "top5_acc": 0.99355, "mean_class_accuracy": 0.85414} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.00623, "memory": 4083, "data_time": 0.19213, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.21856, "loss": 0.21856, "time": 0.79649} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.00621, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.21056, "loss": 0.21056, "time": 0.4925} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.0062, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.19827, "loss": 0.19827, "time": 0.49304} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.00618, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.20869, "loss": 0.20869, "time": 0.48998} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.00616, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.14317, "loss": 0.14317, "time": 0.49212} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.00614, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.19188, "loss": 0.19188, "time": 0.48844} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.00613, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.27351, "loss": 0.27351, "time": 0.49252} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.00611, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.21132, "loss": 0.21132, "time": 0.49141} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.00609, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.26974, "loss": 0.26974, "time": 0.49056} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.00607, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.21407, "loss": 0.21407, "time": 0.49009} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.00606, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.21684, "loss": 0.21684, "time": 0.49162} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.00604, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.28966, "loss": 0.28966, "time": 0.36932} +{"mode": "val", "epoch": 101, "iter": 533, "lr": 0.00602, "top1_acc": 0.88229, "top5_acc": 0.99179, "mean_class_accuracy": 0.83239} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.00601, "memory": 4083, "data_time": 0.18305, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.18877, "loss": 0.18877, "time": 0.79122} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.00599, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.18765, "loss": 0.18765, "time": 0.49005} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.00597, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.19116, "loss": 0.19116, "time": 0.49022} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.00596, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96938, "top5_acc": 0.99875, "loss_cls": 0.21246, "loss": 0.21246, "time": 0.49202} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.00594, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96562, "top5_acc": 0.99938, "loss_cls": 0.20497, "loss": 0.20497, "time": 0.49021} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.00592, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.23241, "loss": 0.23241, "time": 0.49107} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.0059, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.21213, "loss": 0.21213, "time": 0.49108} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.00589, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.21317, "loss": 0.21317, "time": 0.49113} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.00587, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.25766, "loss": 0.25766, "time": 0.48989} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.00585, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.23534, "loss": 0.23534, "time": 0.49241} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.00583, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96938, "top5_acc": 0.99875, "loss_cls": 0.19565, "loss": 0.19565, "time": 0.49365} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.00582, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.19134, "loss": 0.19134, "time": 0.3802} +{"mode": "val", "epoch": 102, "iter": 533, "lr": 0.0058, "top1_acc": 0.8925, "top5_acc": 0.99331, "mean_class_accuracy": 0.85115} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.00579, "memory": 4083, "data_time": 0.18243, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.19959, "loss": 0.19959, "time": 0.79041} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.00577, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.15714, "loss": 0.15714, "time": 0.48923} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.00575, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.18388, "loss": 0.18388, "time": 0.49235} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.00573, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.17489, "loss": 0.17489, "time": 0.48853} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.00572, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.13905, "loss": 0.13905, "time": 0.49367} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.0057, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.18188, "loss": 0.18188, "time": 0.49127} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.00568, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.975, "top5_acc": 0.99875, "loss_cls": 0.17713, "loss": 0.17713, "time": 0.48948} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.00566, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.18303, "loss": 0.18303, "time": 0.48971} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.00565, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96688, "top5_acc": 0.99812, "loss_cls": 0.19951, "loss": 0.19951, "time": 0.49174} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.00563, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.20818, "loss": 0.20818, "time": 0.49258} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.00561, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.17191, "loss": 0.17191, "time": 0.49246} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.0056, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.17406, "loss": 0.17406, "time": 0.37441} +{"mode": "val", "epoch": 103, "iter": 533, "lr": 0.00558, "top1_acc": 0.88828, "top5_acc": 0.99308, "mean_class_accuracy": 0.85838} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.00557, "memory": 4083, "data_time": 0.19451, "top1_acc": 0.96938, "top5_acc": 0.99938, "loss_cls": 0.18426, "loss": 0.18426, "time": 0.80834} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.00555, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.17122, "loss": 0.17122, "time": 0.4947} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.00553, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.177, "loss": 0.177, "time": 0.4912} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.00551, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.13727, "loss": 0.13727, "time": 0.49527} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.0055, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.17387, "loss": 0.17387, "time": 0.49552} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.00548, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.18271, "loss": 0.18271, "time": 0.49166} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.00546, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.16255, "loss": 0.16255, "time": 0.48961} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.00545, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.96438, "top5_acc": 0.99875, "loss_cls": 0.2164, "loss": 0.2164, "time": 0.49209} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.00543, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95812, "top5_acc": 0.99938, "loss_cls": 0.24438, "loss": 0.24438, "time": 0.48853} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.00541, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.22374, "loss": 0.22374, "time": 0.49257} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.0054, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.16202, "loss": 0.16202, "time": 0.49006} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.00538, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97062, "top5_acc": 0.99875, "loss_cls": 0.17928, "loss": 0.17928, "time": 0.36262} +{"mode": "val", "epoch": 104, "iter": 533, "lr": 0.00537, "top1_acc": 0.89391, "top5_acc": 0.99296, "mean_class_accuracy": 0.85326} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.00535, "memory": 4083, "data_time": 0.18276, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12067, "loss": 0.12067, "time": 0.80355} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.00533, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.16179, "loss": 0.16179, "time": 0.49061} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.00532, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13403, "loss": 0.13403, "time": 0.48958} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.0053, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.13495, "loss": 0.13495, "time": 0.49072} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.00528, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.16845, "loss": 0.16845, "time": 0.49105} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.00527, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.97562, "top5_acc": 0.99938, "loss_cls": 0.17031, "loss": 0.17031, "time": 0.49148} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.00525, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.16252, "loss": 0.16252, "time": 0.48971} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.00523, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.21044, "loss": 0.21044, "time": 0.49184} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.00522, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.15178, "loss": 0.15178, "time": 0.49374} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.0052, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.19781, "loss": 0.19781, "time": 0.49474} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.00518, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.17729, "loss": 0.17729, "time": 0.49402} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.00517, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.16398, "loss": 0.16398, "time": 0.36122} +{"mode": "val", "epoch": 105, "iter": 533, "lr": 0.00515, "top1_acc": 0.90001, "top5_acc": 0.99261, "mean_class_accuracy": 0.87276} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.00514, "memory": 4083, "data_time": 0.186, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.15032, "loss": 0.15032, "time": 0.79014} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.00512, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97625, "top5_acc": 0.99938, "loss_cls": 0.15221, "loss": 0.15221, "time": 0.49114} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.0051, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.14215, "loss": 0.14215, "time": 0.49688} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.00509, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.15686, "loss": 0.15686, "time": 0.49229} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.00507, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10541, "loss": 0.10541, "time": 0.48796} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.00505, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.16392, "loss": 0.16392, "time": 0.49141} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.00504, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.14285, "loss": 0.14285, "time": 0.49208} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.00502, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.18334, "loss": 0.18334, "time": 0.49301} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.005, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.16218, "loss": 0.16218, "time": 0.49124} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.00499, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.19636, "loss": 0.19636, "time": 0.4923} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.00497, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.15277, "loss": 0.15277, "time": 0.49147} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.00496, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.17315, "loss": 0.17315, "time": 0.37121} +{"mode": "val", "epoch": 106, "iter": 533, "lr": 0.00494, "top1_acc": 0.89004, "top5_acc": 0.99366, "mean_class_accuracy": 0.8456} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.00493, "memory": 4083, "data_time": 0.18817, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12929, "loss": 0.12929, "time": 0.80564} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.00491, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.1337, "loss": 0.1337, "time": 0.4919} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.00489, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.12567, "loss": 0.12567, "time": 0.49366} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.00488, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13618, "loss": 0.13618, "time": 0.49237} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.00486, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.13596, "loss": 0.13596, "time": 0.49223} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.00485, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.16642, "loss": 0.16642, "time": 0.49106} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.00483, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.14781, "loss": 0.14781, "time": 0.49501} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.00481, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.17716, "loss": 0.17716, "time": 0.49107} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.0048, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.18431, "loss": 0.18431, "time": 0.48918} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.00478, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14671, "loss": 0.14671, "time": 0.49244} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.00476, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.15212, "loss": 0.15212, "time": 0.48945} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.00475, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.17389, "loss": 0.17389, "time": 0.36036} +{"mode": "val", "epoch": 107, "iter": 533, "lr": 0.00474, "top1_acc": 0.90072, "top5_acc": 0.99331, "mean_class_accuracy": 0.8607} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.00472, "memory": 4083, "data_time": 0.18006, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12086, "loss": 0.12086, "time": 0.78639} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0047, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.10337, "loss": 0.10337, "time": 0.49444} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.00469, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98, "top5_acc": 0.99938, "loss_cls": 0.12356, "loss": 0.12356, "time": 0.49026} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.00467, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.09217, "loss": 0.09217, "time": 0.48949} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.00466, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.15357, "loss": 0.15357, "time": 0.49241} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.00464, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.10957, "loss": 0.10957, "time": 0.49182} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.00462, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11376, "loss": 0.11376, "time": 0.48992} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.00461, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.09568, "loss": 0.09568, "time": 0.49369} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.00459, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97312, "top5_acc": 0.99938, "loss_cls": 0.15883, "loss": 0.15883, "time": 0.49205} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.00458, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.14049, "loss": 0.14049, "time": 0.49246} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.00456, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12274, "loss": 0.12274, "time": 0.49451} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.00455, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97125, "top5_acc": 0.99875, "loss_cls": 0.19052, "loss": 0.19052, "time": 0.37983} +{"mode": "val", "epoch": 108, "iter": 533, "lr": 0.00453, "top1_acc": 0.90001, "top5_acc": 0.99343, "mean_class_accuracy": 0.86794} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.00452, "memory": 4083, "data_time": 0.18838, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.12559, "loss": 0.12559, "time": 0.78865} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.0045, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10167, "loss": 0.10167, "time": 0.49291} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.00449, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.09948, "loss": 0.09948, "time": 0.49621} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.00447, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.10754, "loss": 0.10754, "time": 0.49527} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.00445, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.08613, "loss": 0.08613, "time": 0.491} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.00444, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.09698, "loss": 0.09698, "time": 0.48896} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.00442, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.98188, "top5_acc": 0.99938, "loss_cls": 0.13561, "loss": 0.13561, "time": 0.49003} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.00441, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.1339, "loss": 0.1339, "time": 0.48853} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.00439, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13204, "loss": 0.13204, "time": 0.48849} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.00438, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.17198, "loss": 0.17198, "time": 0.49136} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.00436, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.12249, "loss": 0.12249, "time": 0.49391} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.00434, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.13926, "loss": 0.13926, "time": 0.37807} +{"mode": "val", "epoch": 109, "iter": 533, "lr": 0.00433, "top1_acc": 0.90048, "top5_acc": 0.99331, "mean_class_accuracy": 0.86614} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.00432, "memory": 4083, "data_time": 0.18879, "top1_acc": 0.98688, "top5_acc": 0.99938, "loss_cls": 0.10889, "loss": 0.10889, "time": 0.7982} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.0043, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98438, "top5_acc": 0.99938, "loss_cls": 0.12399, "loss": 0.12399, "time": 0.49068} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.00429, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.13557, "loss": 0.13557, "time": 0.49216} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.00427, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9825, "top5_acc": 0.99938, "loss_cls": 0.14693, "loss": 0.14693, "time": 0.49292} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.00426, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.12665, "loss": 0.12665, "time": 0.4869} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.00424, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13851, "loss": 0.13851, "time": 0.49201} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.00422, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.18614, "loss": 0.18614, "time": 0.49228} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.00421, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13786, "loss": 0.13786, "time": 0.49008} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.00419, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11529, "loss": 0.11529, "time": 0.49077} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.00418, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.11335, "loss": 0.11335, "time": 0.49316} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.00416, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98, "top5_acc": 0.99938, "loss_cls": 0.12035, "loss": 0.12035, "time": 0.49339} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.00415, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11389, "loss": 0.11389, "time": 0.36894} +{"mode": "val", "epoch": 110, "iter": 533, "lr": 0.00414, "top1_acc": 0.904, "top5_acc": 0.99448, "mean_class_accuracy": 0.87195} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.00412, "memory": 4083, "data_time": 0.17917, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.07527, "loss": 0.07527, "time": 0.7879} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.00411, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.10089, "loss": 0.10089, "time": 0.4912} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.00409, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.1145, "loss": 0.1145, "time": 0.48748} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.00408, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.11865, "loss": 0.11865, "time": 0.48908} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.00406, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.12941, "loss": 0.12941, "time": 0.49005} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.00405, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98438, "top5_acc": 0.99938, "loss_cls": 0.11381, "loss": 0.11381, "time": 0.49065} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.00403, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98625, "top5_acc": 0.99938, "loss_cls": 0.11241, "loss": 0.11241, "time": 0.49013} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.00402, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.1056, "loss": 0.1056, "time": 0.4938} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.004, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.1622, "loss": 0.1622, "time": 0.49115} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.00399, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13167, "loss": 0.13167, "time": 0.49042} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.00397, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.11384, "loss": 0.11384, "time": 0.49306} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.00396, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.10987, "loss": 0.10987, "time": 0.37897} +{"mode": "val", "epoch": 111, "iter": 533, "lr": 0.00394, "top1_acc": 0.90447, "top5_acc": 0.99355, "mean_class_accuracy": 0.87345} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.00393, "memory": 4083, "data_time": 0.18585, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08825, "loss": 0.08825, "time": 0.80204} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.00391, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.09346, "loss": 0.09346, "time": 0.49248} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.0039, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97312, "top5_acc": 0.99938, "loss_cls": 0.15898, "loss": 0.15898, "time": 0.49255} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.00388, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.1536, "loss": 0.1536, "time": 0.49284} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.00387, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.11927, "loss": 0.11927, "time": 0.49053} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.00385, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.11299, "loss": 0.11299, "time": 0.48775} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.00384, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.13339, "loss": 0.13339, "time": 0.4907} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.00382, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.13192, "loss": 0.13192, "time": 0.49408} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.00381, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.10978, "loss": 0.10978, "time": 0.49404} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.0038, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.12863, "loss": 0.12863, "time": 0.49071} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.00378, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.13155, "loss": 0.13155, "time": 0.49282} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.00377, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.0965, "loss": 0.0965, "time": 0.36131} +{"mode": "val", "epoch": 112, "iter": 533, "lr": 0.00375, "top1_acc": 0.90823, "top5_acc": 0.99366, "mean_class_accuracy": 0.87634} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.00374, "memory": 4083, "data_time": 0.18617, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08418, "loss": 0.08418, "time": 0.797} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.00373, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.07323, "loss": 0.07323, "time": 0.49593} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.00371, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.10584, "loss": 0.10584, "time": 0.49079} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.0037, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.98938, "top5_acc": 0.99938, "loss_cls": 0.09491, "loss": 0.09491, "time": 0.48723} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.00368, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.07611, "loss": 0.07611, "time": 0.49123} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.00367, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06068, "loss": 0.06068, "time": 0.49342} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.00365, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.07877, "loss": 0.07877, "time": 0.48889} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.00364, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.08687, "loss": 0.08687, "time": 0.49025} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.00362, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.0767, "loss": 0.0767, "time": 0.4889} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.00361, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06532, "loss": 0.06532, "time": 0.49144} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0036, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05982, "loss": 0.05982, "time": 0.4916} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.00358, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.1147, "loss": 0.1147, "time": 0.36728} +{"mode": "val", "epoch": 113, "iter": 533, "lr": 0.00357, "top1_acc": 0.90846, "top5_acc": 0.99507, "mean_class_accuracy": 0.87041} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.00355, "memory": 4083, "data_time": 0.18328, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.09378, "loss": 0.09378, "time": 0.7989} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.00354, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.10227, "loss": 0.10227, "time": 0.49522} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.00353, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.06853, "loss": 0.06853, "time": 0.49044} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.00351, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07317, "loss": 0.07317, "time": 0.49236} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.0035, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12035, "loss": 0.12035, "time": 0.48922} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.00348, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13139, "loss": 0.13139, "time": 0.49115} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.00347, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.12246, "loss": 0.12246, "time": 0.49108} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.00346, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.09186, "loss": 0.09186, "time": 0.48415} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.00344, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.065, "loss": 0.065, "time": 0.48995} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.00343, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08136, "loss": 0.08136, "time": 0.49036} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.00341, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98312, "top5_acc": 0.99938, "loss_cls": 0.11843, "loss": 0.11843, "time": 0.49177} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.0034, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.10833, "loss": 0.10833, "time": 0.3678} +{"mode": "val", "epoch": 114, "iter": 533, "lr": 0.00339, "top1_acc": 0.9114, "top5_acc": 0.99566, "mean_class_accuracy": 0.87633} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.00337, "memory": 4083, "data_time": 0.18762, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07539, "loss": 0.07539, "time": 0.8123} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.00336, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.06029, "loss": 0.06029, "time": 0.49422} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.00335, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.0755, "loss": 0.0755, "time": 0.49235} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.00333, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.07797, "loss": 0.07797, "time": 0.49261} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.00332, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.07726, "loss": 0.07726, "time": 0.49354} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.0033, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.07448, "loss": 0.07448, "time": 0.49196} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.00329, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11264, "loss": 0.11264, "time": 0.49004} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.00328, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.07546, "loss": 0.07546, "time": 0.49494} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.00326, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.07536, "loss": 0.07536, "time": 0.49139} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.00325, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.09137, "loss": 0.09137, "time": 0.49032} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.00324, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.11985, "loss": 0.11985, "time": 0.49355} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.00322, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08897, "loss": 0.08897, "time": 0.34715} +{"mode": "val", "epoch": 115, "iter": 533, "lr": 0.00321, "top1_acc": 0.90658, "top5_acc": 0.99484, "mean_class_accuracy": 0.87909} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.0032, "memory": 4083, "data_time": 0.18577, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.0768, "loss": 0.0768, "time": 0.80394} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.00318, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.05851, "loss": 0.05851, "time": 0.49049} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.00317, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.06234, "loss": 0.06234, "time": 0.49222} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.00316, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06404, "loss": 0.06404, "time": 0.48892} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.00314, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06483, "loss": 0.06483, "time": 0.48812} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.00313, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06706, "loss": 0.06706, "time": 0.49049} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.00312, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.0547, "loss": 0.0547, "time": 0.48576} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.0031, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.05168, "loss": 0.05168, "time": 0.49221} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.00309, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.0887, "loss": 0.0887, "time": 0.49103} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.00308, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07793, "loss": 0.07793, "time": 0.4908} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.00306, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.0836, "loss": 0.0836, "time": 0.4888} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.00305, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07697, "loss": 0.07697, "time": 0.36593} +{"mode": "val", "epoch": 116, "iter": 533, "lr": 0.00304, "top1_acc": 0.90271, "top5_acc": 0.99225, "mean_class_accuracy": 0.86277} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.00302, "memory": 4083, "data_time": 0.19092, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08195, "loss": 0.08195, "time": 0.79812} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.00301, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09414, "loss": 0.09414, "time": 0.49157} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.003, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.06629, "loss": 0.06629, "time": 0.48784} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.00298, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07217, "loss": 0.07217, "time": 0.49354} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.00297, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99312, "top5_acc": 0.99938, "loss_cls": 0.06153, "loss": 0.06153, "time": 0.49237} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.00296, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07793, "loss": 0.07793, "time": 0.4885} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.00294, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.07994, "loss": 0.07994, "time": 0.48986} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.00293, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05816, "loss": 0.05816, "time": 0.49351} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.00292, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05775, "loss": 0.05775, "time": 0.49077} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.00291, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.09164, "loss": 0.09164, "time": 0.49079} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.00289, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.06111, "loss": 0.06111, "time": 0.49286} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.00288, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05829, "loss": 0.05829, "time": 0.3677} +{"mode": "val", "epoch": 117, "iter": 533, "lr": 0.00287, "top1_acc": 0.91468, "top5_acc": 0.99366, "mean_class_accuracy": 0.88184} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.00286, "memory": 4083, "data_time": 0.1833, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.05605, "loss": 0.05605, "time": 0.79693} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.00284, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05463, "loss": 0.05463, "time": 0.49357} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.00283, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06381, "loss": 0.06381, "time": 0.48921} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.00282, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05685, "loss": 0.05685, "time": 0.49358} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.0028, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04579, "loss": 0.04579, "time": 0.49035} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.00279, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05096, "loss": 0.05096, "time": 0.48994} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.00278, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04231, "loss": 0.04231, "time": 0.48895} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.00277, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05753, "loss": 0.05753, "time": 0.49189} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.00275, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05151, "loss": 0.05151, "time": 0.4939} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.00274, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06907, "loss": 0.06907, "time": 0.49018} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.00273, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.05291, "loss": 0.05291, "time": 0.49485} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.00271, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06263, "loss": 0.06263, "time": 0.36379} +{"mode": "val", "epoch": 118, "iter": 533, "lr": 0.0027, "top1_acc": 0.91433, "top5_acc": 0.99542, "mean_class_accuracy": 0.8793} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.00269, "memory": 4083, "data_time": 0.18718, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04696, "loss": 0.04696, "time": 0.79304} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.00268, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03917, "loss": 0.03917, "time": 0.4933} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.00267, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05883, "loss": 0.05883, "time": 0.49279} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.00265, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04354, "loss": 0.04354, "time": 0.4896} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.00264, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.04289, "loss": 0.04289, "time": 0.49089} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.00263, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03074, "loss": 0.03074, "time": 0.4928} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.00262, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0304, "loss": 0.0304, "time": 0.48898} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.0026, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03751, "loss": 0.03751, "time": 0.49152} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.00259, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04435, "loss": 0.04435, "time": 0.49327} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.00258, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.05019, "loss": 0.05019, "time": 0.48973} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.00257, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.04873, "loss": 0.04873, "time": 0.49567} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.00255, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04958, "loss": 0.04958, "time": 0.37196} +{"mode": "val", "epoch": 119, "iter": 533, "lr": 0.00254, "top1_acc": 0.91949, "top5_acc": 0.99472, "mean_class_accuracy": 0.8877} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.00253, "memory": 4083, "data_time": 0.1916, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.0399, "loss": 0.0399, "time": 0.79981} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.00252, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02829, "loss": 0.02829, "time": 0.49009} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.00251, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03137, "loss": 0.03137, "time": 0.49354} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.00249, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03454, "loss": 0.03454, "time": 0.49065} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.00248, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05988, "loss": 0.05988, "time": 0.49554} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.00247, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05633, "loss": 0.05633, "time": 0.49175} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.00246, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05635, "loss": 0.05635, "time": 0.48981} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.00245, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.0539, "loss": 0.0539, "time": 0.49465} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.00243, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.0496, "loss": 0.0496, "time": 0.49154} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.00242, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05085, "loss": 0.05085, "time": 0.48881} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00241, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03898, "loss": 0.03898, "time": 0.49439} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.0024, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03763, "loss": 0.03763, "time": 0.35545} +{"mode": "val", "epoch": 120, "iter": 533, "lr": 0.00239, "top1_acc": 0.92008, "top5_acc": 0.99507, "mean_class_accuracy": 0.88928} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00238, "memory": 4083, "data_time": 0.18674, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03446, "loss": 0.03446, "time": 0.79329} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00236, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.03895, "loss": 0.03895, "time": 0.48985} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.00235, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03275, "loss": 0.03275, "time": 0.49342} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00234, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03258, "loss": 0.03258, "time": 0.49019} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00233, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04343, "loss": 0.04343, "time": 0.49138} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00232, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03648, "loss": 0.03648, "time": 0.49296} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.0023, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03549, "loss": 0.03549, "time": 0.49088} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00229, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03522, "loss": 0.03522, "time": 0.48978} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.00228, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04487, "loss": 0.04487, "time": 0.4902} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00227, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05105, "loss": 0.05105, "time": 0.49342} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00226, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03636, "loss": 0.03636, "time": 0.49112} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00225, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04668, "loss": 0.04668, "time": 0.36565} +{"mode": "val", "epoch": 121, "iter": 533, "lr": 0.00224, "top1_acc": 0.91656, "top5_acc": 0.99531, "mean_class_accuracy": 0.88167} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00222, "memory": 4083, "data_time": 0.18817, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04759, "loss": 0.04759, "time": 0.78364} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00221, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.04024, "loss": 0.04024, "time": 0.49058} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.0022, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03971, "loss": 0.03971, "time": 0.49036} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00219, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03691, "loss": 0.03691, "time": 0.49265} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00218, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02698, "loss": 0.02698, "time": 0.4946} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00217, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03955, "loss": 0.03955, "time": 0.49275} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00215, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03017, "loss": 0.03017, "time": 0.49078} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00214, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03081, "loss": 0.03081, "time": 0.49075} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.00213, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03184, "loss": 0.03184, "time": 0.48958} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00212, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03059, "loss": 0.03059, "time": 0.49318} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00211, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04673, "loss": 0.04673, "time": 0.49503} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.0021, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04928, "loss": 0.04928, "time": 0.38532} +{"mode": "val", "epoch": 122, "iter": 533, "lr": 0.00209, "top1_acc": 0.91621, "top5_acc": 0.99566, "mean_class_accuracy": 0.88394} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00208, "memory": 4083, "data_time": 0.1901, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03091, "loss": 0.03091, "time": 0.79721} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00207, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03166, "loss": 0.03166, "time": 0.49432} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00205, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.0497, "loss": 0.0497, "time": 0.49155} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00204, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0392, "loss": 0.0392, "time": 0.49186} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00203, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02644, "loss": 0.02644, "time": 0.49378} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00202, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02456, "loss": 0.02456, "time": 0.49119} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00201, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02508, "loss": 0.02508, "time": 0.4896} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.002, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0258, "loss": 0.0258, "time": 0.49014} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00199, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02926, "loss": 0.02926, "time": 0.48954} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.00198, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02468, "loss": 0.02468, "time": 0.49362} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00197, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0289, "loss": 0.0289, "time": 0.49156} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00195, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.0351, "loss": 0.0351, "time": 0.37373} +{"mode": "val", "epoch": 123, "iter": 533, "lr": 0.00195, "top1_acc": 0.9148, "top5_acc": 0.99401, "mean_class_accuracy": 0.88308} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00194, "memory": 4083, "data_time": 0.18127, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03228, "loss": 0.03228, "time": 0.78841} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00192, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05383, "loss": 0.05383, "time": 0.48932} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00191, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.0548, "loss": 0.0548, "time": 0.49127} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.0019, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05015, "loss": 0.05015, "time": 0.49237} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00189, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04482, "loss": 0.04482, "time": 0.49079} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00188, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.0434, "loss": 0.0434, "time": 0.4913} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00187, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0414, "loss": 0.0414, "time": 0.492} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00186, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.028, "loss": 0.028, "time": 0.49092} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00185, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03071, "loss": 0.03071, "time": 0.48965} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00184, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.0321, "loss": 0.0321, "time": 0.49177} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00183, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04249, "loss": 0.04249, "time": 0.49208} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.00182, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03281, "loss": 0.03281, "time": 0.37427} +{"mode": "val", "epoch": 124, "iter": 533, "lr": 0.00181, "top1_acc": 0.91621, "top5_acc": 0.99484, "mean_class_accuracy": 0.88581} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.0018, "memory": 4083, "data_time": 0.18489, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03203, "loss": 0.03203, "time": 0.79112} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.00179, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.0324, "loss": 0.0324, "time": 0.48816} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00178, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04116, "loss": 0.04116, "time": 0.49548} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00177, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03441, "loss": 0.03441, "time": 0.48853} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00176, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0249, "loss": 0.0249, "time": 0.49003} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00175, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.0315, "loss": 0.0315, "time": 0.48959} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00173, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02637, "loss": 0.02637, "time": 0.48966} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00172, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03205, "loss": 0.03205, "time": 0.48973} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.00171, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.05217, "loss": 0.05217, "time": 0.492} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.0017, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03393, "loss": 0.03393, "time": 0.49306} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00169, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03643, "loss": 0.03643, "time": 0.48778} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00168, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05083, "loss": 0.05083, "time": 0.37665} +{"mode": "val", "epoch": 125, "iter": 533, "lr": 0.00167, "top1_acc": 0.91445, "top5_acc": 0.99542, "mean_class_accuracy": 0.88423} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00166, "memory": 4083, "data_time": 0.1858, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0328, "loss": 0.0328, "time": 0.7924} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00165, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0331, "loss": 0.0331, "time": 0.4899} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00164, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02886, "loss": 0.02886, "time": 0.49087} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00163, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03171, "loss": 0.03171, "time": 0.49231} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00162, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04316, "loss": 0.04316, "time": 0.48912} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00161, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03351, "loss": 0.03351, "time": 0.48905} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0016, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03069, "loss": 0.03069, "time": 0.48964} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00159, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02959, "loss": 0.02959, "time": 0.49079} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00158, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03313, "loss": 0.03313, "time": 0.4932} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00157, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02247, "loss": 0.02247, "time": 0.49117} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00156, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02471, "loss": 0.02471, "time": 0.48972} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00155, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04105, "loss": 0.04105, "time": 0.37564} +{"mode": "val", "epoch": 126, "iter": 533, "lr": 0.00155, "top1_acc": 0.91245, "top5_acc": 0.99519, "mean_class_accuracy": 0.87642} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00154, "memory": 4083, "data_time": 0.19359, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03283, "loss": 0.03283, "time": 0.80684} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00153, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02824, "loss": 0.02824, "time": 0.49283} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00152, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0269, "loss": 0.0269, "time": 0.49349} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00151, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0266, "loss": 0.0266, "time": 0.49115} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.0015, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0243, "loss": 0.0243, "time": 0.49352} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.00149, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02611, "loss": 0.02611, "time": 0.48937} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00148, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.0292, "loss": 0.0292, "time": 0.49055} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00147, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02944, "loss": 0.02944, "time": 0.4916} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00146, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02864, "loss": 0.02864, "time": 0.49219} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00145, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02714, "loss": 0.02714, "time": 0.49098} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00144, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03572, "loss": 0.03572, "time": 0.48938} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00143, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02969, "loss": 0.02969, "time": 0.35884} +{"mode": "val", "epoch": 127, "iter": 533, "lr": 0.00142, "top1_acc": 0.91773, "top5_acc": 0.99566, "mean_class_accuracy": 0.88689} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00141, "memory": 4083, "data_time": 0.18565, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02639, "loss": 0.02639, "time": 0.79207} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.0014, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03564, "loss": 0.03564, "time": 0.49449} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00139, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0274, "loss": 0.0274, "time": 0.49111} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00138, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02571, "loss": 0.02571, "time": 0.48823} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00138, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02561, "loss": 0.02561, "time": 0.48611} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00137, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02378, "loss": 0.02378, "time": 0.49265} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.00136, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02749, "loss": 0.02749, "time": 0.49176} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00135, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02261, "loss": 0.02261, "time": 0.49008} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00134, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02631, "loss": 0.02631, "time": 0.48851} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00133, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02176, "loss": 0.02176, "time": 0.49377} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00132, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01978, "loss": 0.01978, "time": 0.49318} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00131, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02214, "loss": 0.02214, "time": 0.38438} +{"mode": "val", "epoch": 128, "iter": 533, "lr": 0.0013, "top1_acc": 0.92196, "top5_acc": 0.99613, "mean_class_accuracy": 0.892} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.00129, "memory": 4083, "data_time": 0.18514, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02023, "loss": 0.02023, "time": 0.79991} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00129, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02114, "loss": 0.02114, "time": 0.49306} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00128, "memory": 4083, "data_time": 0.0004, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01978, "loss": 0.01978, "time": 0.49212} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00127, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01985, "loss": 0.01985, "time": 0.49133} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00126, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02366, "loss": 0.02366, "time": 0.48846} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00125, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02375, "loss": 0.02375, "time": 0.48914} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00124, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01961, "loss": 0.01961, "time": 0.4904} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00123, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02769, "loss": 0.02769, "time": 0.4902} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.00122, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02262, "loss": 0.02262, "time": 0.48887} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00121, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02289, "loss": 0.02289, "time": 0.49223} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00121, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02398, "loss": 0.02398, "time": 0.49218} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.0012, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02132, "loss": 0.02132, "time": 0.36799} +{"mode": "val", "epoch": 129, "iter": 533, "lr": 0.00119, "top1_acc": 0.92243, "top5_acc": 0.99578, "mean_class_accuracy": 0.89592} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00118, "memory": 4083, "data_time": 0.18817, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0202, "loss": 0.0202, "time": 0.78932} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00117, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02133, "loss": 0.02133, "time": 0.49464} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00116, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02491, "loss": 0.02491, "time": 0.49223} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00116, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02329, "loss": 0.02329, "time": 0.49315} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.00115, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02258, "loss": 0.02258, "time": 0.49047} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00114, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02238, "loss": 0.02238, "time": 0.49223} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00113, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02217, "loss": 0.02217, "time": 0.49011} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00112, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02146, "loss": 0.02146, "time": 0.48887} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00111, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02163, "loss": 0.02163, "time": 0.4902} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.0011, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01964, "loss": 0.01964, "time": 0.49015} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.0011, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02062, "loss": 0.02062, "time": 0.48891} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00109, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01989, "loss": 0.01989, "time": 0.3784} +{"mode": "val", "epoch": 130, "iter": 533, "lr": 0.00108, "top1_acc": 0.92372, "top5_acc": 0.99531, "mean_class_accuracy": 0.89393} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00107, "memory": 4083, "data_time": 0.18993, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01911, "loss": 0.01911, "time": 0.80458} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.00106, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02011, "loss": 0.02011, "time": 0.49146} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00106, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01749, "loss": 0.01749, "time": 0.49034} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00105, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01845, "loss": 0.01845, "time": 0.49135} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00104, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02037, "loss": 0.02037, "time": 0.49074} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00103, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02167, "loss": 0.02167, "time": 0.48747} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00102, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0225, "loss": 0.0225, "time": 0.48571} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00102, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02187, "loss": 0.02187, "time": 0.48934} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00101, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01887, "loss": 0.01887, "time": 0.49041} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.001, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01957, "loss": 0.01957, "time": 0.49074} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.00099, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02342, "loss": 0.02342, "time": 0.49154} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00098, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02084, "loss": 0.02084, "time": 0.37594} +{"mode": "val", "epoch": 131, "iter": 533, "lr": 0.00098, "top1_acc": 0.92325, "top5_acc": 0.99613, "mean_class_accuracy": 0.89178} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.00097, "memory": 4083, "data_time": 0.18537, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01966, "loss": 0.01966, "time": 0.79716} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00096, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01779, "loss": 0.01779, "time": 0.49097} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00095, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02109, "loss": 0.02109, "time": 0.48877} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00095, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01981, "loss": 0.01981, "time": 0.48914} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00094, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01999, "loss": 0.01999, "time": 0.48941} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00093, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01761, "loss": 0.01761, "time": 0.48811} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00092, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02137, "loss": 0.02137, "time": 0.49237} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00091, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01967, "loss": 0.01967, "time": 0.49225} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00091, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01865, "loss": 0.01865, "time": 0.4906} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0009, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01821, "loss": 0.01821, "time": 0.48831} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00089, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02132, "loss": 0.02132, "time": 0.48696} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00088, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02075, "loss": 0.02075, "time": 0.37092} +{"mode": "val", "epoch": 132, "iter": 533, "lr": 0.00088, "top1_acc": 0.92266, "top5_acc": 0.99648, "mean_class_accuracy": 0.89329} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.00087, "memory": 4083, "data_time": 0.1892, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01782, "loss": 0.01782, "time": 0.79804} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00086, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01951, "loss": 0.01951, "time": 0.48707} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00086, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02072, "loss": 0.02072, "time": 0.48873} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00085, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01921, "loss": 0.01921, "time": 0.48756} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00084, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02032, "loss": 0.02032, "time": 0.4926} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00083, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01944, "loss": 0.01944, "time": 0.49035} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00083, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02062, "loss": 0.02062, "time": 0.49226} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00082, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02359, "loss": 0.02359, "time": 0.49118} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00081, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02086, "loss": 0.02086, "time": 0.4941} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.0008, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01912, "loss": 0.01912, "time": 0.48756} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0008, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01794, "loss": 0.01794, "time": 0.48843} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00079, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01931, "loss": 0.01931, "time": 0.37888} +{"mode": "val", "epoch": 133, "iter": 533, "lr": 0.00078, "top1_acc": 0.92524, "top5_acc": 0.99578, "mean_class_accuracy": 0.89543} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00078, "memory": 4083, "data_time": 0.19303, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01887, "loss": 0.01887, "time": 0.81473} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00077, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01842, "loss": 0.01842, "time": 0.49159} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00076, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02011, "loss": 0.02011, "time": 0.49079} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.00076, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01846, "loss": 0.01846, "time": 0.49232} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00075, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01739, "loss": 0.01739, "time": 0.49306} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00074, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01778, "loss": 0.01778, "time": 0.49036} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00073, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02129, "loss": 0.02129, "time": 0.49139} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00073, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01883, "loss": 0.01883, "time": 0.49565} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00072, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01915, "loss": 0.01915, "time": 0.49033} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00071, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01948, "loss": 0.01948, "time": 0.49171} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00071, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0201, "loss": 0.0201, "time": 0.48908} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.0007, "memory": 4083, "data_time": 0.00041, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01943, "loss": 0.01943, "time": 0.3556} +{"mode": "val", "epoch": 134, "iter": 533, "lr": 0.0007, "top1_acc": 0.9243, "top5_acc": 0.99578, "mean_class_accuracy": 0.89544} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00069, "memory": 4083, "data_time": 0.18216, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01784, "loss": 0.01784, "time": 0.77961} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00068, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02076, "loss": 0.02076, "time": 0.49021} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00068, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01883, "loss": 0.01883, "time": 0.48946} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00067, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01899, "loss": 0.01899, "time": 0.49007} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00066, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02064, "loss": 0.02064, "time": 0.49022} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00066, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01764, "loss": 0.01764, "time": 0.48876} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00065, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0177, "loss": 0.0177, "time": 0.49251} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00064, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02014, "loss": 0.02014, "time": 0.49091} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.00064, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01719, "loss": 0.01719, "time": 0.49035} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00063, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01979, "loss": 0.01979, "time": 0.49176} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00062, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02063, "loss": 0.02063, "time": 0.49042} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00062, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01761, "loss": 0.01761, "time": 0.38723} +{"mode": "val", "epoch": 135, "iter": 533, "lr": 0.00061, "top1_acc": 0.92466, "top5_acc": 0.99613, "mean_class_accuracy": 0.89358} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00061, "memory": 4083, "data_time": 0.19603, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02008, "loss": 0.02008, "time": 0.80442} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.0006, "memory": 4083, "data_time": 0.00048, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01739, "loss": 0.01739, "time": 0.48832} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00059, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01702, "loss": 0.01702, "time": 0.49359} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00059, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01798, "loss": 0.01798, "time": 0.49155} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.00058, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01913, "loss": 0.01913, "time": 0.49296} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.00057, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01867, "loss": 0.01867, "time": 0.49147} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00057, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01789, "loss": 0.01789, "time": 0.48904} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00056, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01806, "loss": 0.01806, "time": 0.49144} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00056, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01813, "loss": 0.01813, "time": 0.48628} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00055, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0188, "loss": 0.0188, "time": 0.49079} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00054, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01817, "loss": 0.01817, "time": 0.49382} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00054, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01653, "loss": 0.01653, "time": 0.36805} +{"mode": "val", "epoch": 136, "iter": 533, "lr": 0.00053, "top1_acc": 0.92489, "top5_acc": 0.99636, "mean_class_accuracy": 0.89486} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00053, "memory": 4083, "data_time": 0.1894, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01858, "loss": 0.01858, "time": 0.79457} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00052, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01791, "loss": 0.01791, "time": 0.49032} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00052, "memory": 4083, "data_time": 0.00055, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01727, "loss": 0.01727, "time": 0.49053} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.00051, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01685, "loss": 0.01685, "time": 0.48684} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.0005, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01705, "loss": 0.01705, "time": 0.49551} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.0005, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0178, "loss": 0.0178, "time": 0.4939} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00049, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01725, "loss": 0.01725, "time": 0.49053} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00049, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01741, "loss": 0.01741, "time": 0.49423} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00048, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01993, "loss": 0.01993, "time": 0.48944} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00048, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02149, "loss": 0.02149, "time": 0.49122} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00047, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02063, "loss": 0.02063, "time": 0.49147} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00046, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01922, "loss": 0.01922, "time": 0.37799} +{"mode": "val", "epoch": 137, "iter": 533, "lr": 0.00046, "top1_acc": 0.92395, "top5_acc": 0.99636, "mean_class_accuracy": 0.89517} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00046, "memory": 4083, "data_time": 0.18457, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01588, "loss": 0.01588, "time": 0.7868} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00045, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01867, "loss": 0.01867, "time": 0.48893} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00044, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01882, "loss": 0.01882, "time": 0.48816} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00044, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01878, "loss": 0.01878, "time": 0.49167} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.00043, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02099, "loss": 0.02099, "time": 0.49284} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.00043, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01842, "loss": 0.01842, "time": 0.49312} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00042, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01857, "loss": 0.01857, "time": 0.48917} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00042, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01716, "loss": 0.01716, "time": 0.49359} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00041, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01829, "loss": 0.01829, "time": 0.48957} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00041, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01733, "loss": 0.01733, "time": 0.4914} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.0004, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02118, "loss": 0.02118, "time": 0.49034} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.0004, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01632, "loss": 0.01632, "time": 0.37484} +{"mode": "val", "epoch": 138, "iter": 533, "lr": 0.00039, "top1_acc": 0.92454, "top5_acc": 0.99624, "mean_class_accuracy": 0.89593} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00039, "memory": 4083, "data_time": 0.18493, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01763, "loss": 0.01763, "time": 0.79539} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00038, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01872, "loss": 0.01872, "time": 0.48841} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00038, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01564, "loss": 0.01564, "time": 0.48952} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00037, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01758, "loss": 0.01758, "time": 0.49073} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00037, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01861, "loss": 0.01861, "time": 0.49399} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00036, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01765, "loss": 0.01765, "time": 0.4929} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00036, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01837, "loss": 0.01837, "time": 0.49349} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00035, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0178, "loss": 0.0178, "time": 0.48741} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00035, "memory": 4083, "data_time": 0.00042, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0178, "loss": 0.0178, "time": 0.48913} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.00034, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01755, "loss": 0.01755, "time": 0.49162} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.00034, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01739, "loss": 0.01739, "time": 0.49369} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00033, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01716, "loss": 0.01716, "time": 0.37418} +{"mode": "val", "epoch": 139, "iter": 533, "lr": 0.00033, "top1_acc": 0.92266, "top5_acc": 0.99613, "mean_class_accuracy": 0.89226} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00033, "memory": 4083, "data_time": 0.18783, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01893, "loss": 0.01893, "time": 0.7888} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00032, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01858, "loss": 0.01858, "time": 0.49159} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.00032, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01754, "loss": 0.01754, "time": 0.48938} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.00031, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01753, "loss": 0.01753, "time": 0.4918} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00031, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01858, "loss": 0.01858, "time": 0.48968} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.0003, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01913, "loss": 0.01913, "time": 0.49011} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.0003, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01686, "loss": 0.01686, "time": 0.49134} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00029, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02017, "loss": 0.02017, "time": 0.49245} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00029, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01747, "loss": 0.01747, "time": 0.49135} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00029, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01854, "loss": 0.01854, "time": 0.49202} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00028, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01786, "loss": 0.01786, "time": 0.49215} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00028, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01652, "loss": 0.01652, "time": 0.3825} +{"mode": "val", "epoch": 140, "iter": 533, "lr": 0.00027, "top1_acc": 0.9236, "top5_acc": 0.99671, "mean_class_accuracy": 0.89317} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00027, "memory": 4083, "data_time": 0.18553, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01962, "loss": 0.01962, "time": 0.79784} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00026, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01772, "loss": 0.01772, "time": 0.49178} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00026, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01748, "loss": 0.01748, "time": 0.49511} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00026, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0176, "loss": 0.0176, "time": 0.49314} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00025, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01904, "loss": 0.01904, "time": 0.4922} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00025, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01812, "loss": 0.01812, "time": 0.49233} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00024, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01919, "loss": 0.01919, "time": 0.49272} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00024, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01654, "loss": 0.01654, "time": 0.49075} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00024, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01776, "loss": 0.01776, "time": 0.49093} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00023, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0172, "loss": 0.0172, "time": 0.48992} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00023, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01891, "loss": 0.01891, "time": 0.48962} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00022, "memory": 4083, "data_time": 0.00041, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01774, "loss": 0.01774, "time": 0.3735} +{"mode": "val", "epoch": 141, "iter": 533, "lr": 0.00022, "top1_acc": 0.9229, "top5_acc": 0.99695, "mean_class_accuracy": 0.89427} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00022, "memory": 4083, "data_time": 0.18211, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02293, "loss": 0.02293, "time": 0.78474} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00021, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0188, "loss": 0.0188, "time": 0.4944} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00021, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01872, "loss": 0.01872, "time": 0.49306} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00021, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01716, "loss": 0.01716, "time": 0.49252} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.0002, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01526, "loss": 0.01526, "time": 0.4925} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.0002, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0179, "loss": 0.0179, "time": 0.49161} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.0002, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01841, "loss": 0.01841, "time": 0.48548} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00019, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01847, "loss": 0.01847, "time": 0.4906} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00019, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01837, "loss": 0.01837, "time": 0.49191} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00018, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01792, "loss": 0.01792, "time": 0.49177} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00018, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01891, "loss": 0.01891, "time": 0.49111} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00018, "memory": 4083, "data_time": 0.00048, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01724, "loss": 0.01724, "time": 0.38147} +{"mode": "val", "epoch": 142, "iter": 533, "lr": 0.00018, "top1_acc": 0.92348, "top5_acc": 0.99671, "mean_class_accuracy": 0.89451} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.00017, "memory": 4083, "data_time": 0.18799, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0178, "loss": 0.0178, "time": 0.80568} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00017, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0172, "loss": 0.0172, "time": 0.49546} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00017, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01685, "loss": 0.01685, "time": 0.49212} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00016, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01792, "loss": 0.01792, "time": 0.49358} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00016, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0168, "loss": 0.0168, "time": 0.49059} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00016, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01752, "loss": 0.01752, "time": 0.49115} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00015, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01796, "loss": 0.01796, "time": 0.49262} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00015, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01738, "loss": 0.01738, "time": 0.48901} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00015, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01649, "loss": 0.01649, "time": 0.4921} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00014, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01849, "loss": 0.01849, "time": 0.48758} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00014, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01674, "loss": 0.01674, "time": 0.49112} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00014, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01891, "loss": 0.01891, "time": 0.36834} +{"mode": "val", "epoch": 143, "iter": 533, "lr": 0.00013, "top1_acc": 0.92536, "top5_acc": 0.99671, "mean_class_accuracy": 0.89585} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00013, "memory": 4083, "data_time": 0.19272, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01731, "loss": 0.01731, "time": 0.8024} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00013, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01831, "loss": 0.01831, "time": 0.495} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00013, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01678, "loss": 0.01678, "time": 0.49335} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00012, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01746, "loss": 0.01746, "time": 0.48933} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00012, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01798, "loss": 0.01798, "time": 0.49221} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00012, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01928, "loss": 0.01928, "time": 0.49071} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00011, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01709, "loss": 0.01709, "time": 0.49298} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.00011, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01621, "loss": 0.01621, "time": 0.49054} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.00011, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01743, "loss": 0.01743, "time": 0.49125} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.00011, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01802, "loss": 0.01802, "time": 0.49302} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.0001, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01827, "loss": 0.01827, "time": 0.48835} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.0001, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0163, "loss": 0.0163, "time": 0.36233} +{"mode": "val", "epoch": 144, "iter": 533, "lr": 0.0001, "top1_acc": 0.92407, "top5_acc": 0.99613, "mean_class_accuracy": 0.89463} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.0001, "memory": 4083, "data_time": 0.18869, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01765, "loss": 0.01765, "time": 0.79902} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 9e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01553, "loss": 0.01553, "time": 0.4935} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 9e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01829, "loss": 0.01829, "time": 0.49074} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 9e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01687, "loss": 0.01687, "time": 0.49005} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 9e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01749, "loss": 0.01749, "time": 0.49108} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 8e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01654, "loss": 0.01654, "time": 0.49381} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 8e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01634, "loss": 0.01634, "time": 0.49117} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 8e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01873, "loss": 0.01873, "time": 0.48865} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 8e-05, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01902, "loss": 0.01902, "time": 0.49225} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 7e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01731, "loss": 0.01731, "time": 0.4969} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 7e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01721, "loss": 0.01721, "time": 0.4895} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 7e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01763, "loss": 0.01763, "time": 0.37301} +{"mode": "val", "epoch": 145, "iter": 533, "lr": 7e-05, "top1_acc": 0.92548, "top5_acc": 0.99589, "mean_class_accuracy": 0.89641} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 7e-05, "memory": 4083, "data_time": 0.18452, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01636, "loss": 0.01636, "time": 0.79124} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 6e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01824, "loss": 0.01824, "time": 0.49549} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 6e-05, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01677, "loss": 0.01677, "time": 0.48873} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 6e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0171, "loss": 0.0171, "time": 0.49234} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 6e-05, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01838, "loss": 0.01838, "time": 0.49283} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 6e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0171, "loss": 0.0171, "time": 0.48978} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 5e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01773, "loss": 0.01773, "time": 0.49102} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 5e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01623, "loss": 0.01623, "time": 0.49425} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 5e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01659, "loss": 0.01659, "time": 0.49465} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 5e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0184, "loss": 0.0184, "time": 0.49311} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 5e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01799, "loss": 0.01799, "time": 0.49053} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 5e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01802, "loss": 0.01802, "time": 0.38021} +{"mode": "val", "epoch": 146, "iter": 533, "lr": 4e-05, "top1_acc": 0.9256, "top5_acc": 0.99566, "mean_class_accuracy": 0.8962} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 4e-05, "memory": 4083, "data_time": 0.18652, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01825, "loss": 0.01825, "time": 0.78851} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 4e-05, "memory": 4083, "data_time": 0.00044, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01706, "loss": 0.01706, "time": 0.49411} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 4e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01895, "loss": 0.01895, "time": 0.49311} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 4e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01732, "loss": 0.01732, "time": 0.48905} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 4e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0173, "loss": 0.0173, "time": 0.49145} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 3e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01719, "loss": 0.01719, "time": 0.49051} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 3e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01673, "loss": 0.01673, "time": 0.49147} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 3e-05, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0173, "loss": 0.0173, "time": 0.49276} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 3e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01902, "loss": 0.01902, "time": 0.49028} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 3e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01719, "loss": 0.01719, "time": 0.49072} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 3e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01757, "loss": 0.01757, "time": 0.49387} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 3e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01707, "loss": 0.01707, "time": 0.37482} +{"mode": "val", "epoch": 147, "iter": 533, "lr": 2e-05, "top1_acc": 0.92395, "top5_acc": 0.99636, "mean_class_accuracy": 0.89376} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 2e-05, "memory": 4083, "data_time": 0.18409, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0177, "loss": 0.0177, "time": 0.80403} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 2e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01843, "loss": 0.01843, "time": 0.48913} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 2e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01609, "loss": 0.01609, "time": 0.49133} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 2e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01795, "loss": 0.01795, "time": 0.48976} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 2e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0178, "loss": 0.0178, "time": 0.49252} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 2e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01645, "loss": 0.01645, "time": 0.4899} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 2e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01703, "loss": 0.01703, "time": 0.48829} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 2e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01791, "loss": 0.01791, "time": 0.48922} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 1e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01894, "loss": 0.01894, "time": 0.4886} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 1e-05, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02073, "loss": 0.02073, "time": 0.49056} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 1e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01592, "loss": 0.01592, "time": 0.49117} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01891, "loss": 0.01891, "time": 0.35428} +{"mode": "val", "epoch": 148, "iter": 533, "lr": 1e-05, "top1_acc": 0.92407, "top5_acc": 0.99589, "mean_class_accuracy": 0.89489} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 1e-05, "memory": 4083, "data_time": 0.18539, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01653, "loss": 0.01653, "time": 0.79712} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 1e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0166, "loss": 0.0166, "time": 0.48981} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 1e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01801, "loss": 0.01801, "time": 0.48948} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01806, "loss": 0.01806, "time": 0.48626} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 1e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0179, "loss": 0.0179, "time": 0.48589} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 1e-05, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01774, "loss": 0.01774, "time": 0.48847} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 1e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02018, "loss": 0.02018, "time": 0.48703} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 1e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01769, "loss": 0.01769, "time": 0.48991} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01747, "loss": 0.01747, "time": 0.48906} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0166, "loss": 0.0166, "time": 0.48856} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01767, "loss": 0.01767, "time": 0.48828} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0161, "loss": 0.0161, "time": 0.37316} +{"mode": "val", "epoch": 149, "iter": 533, "lr": 0.0, "top1_acc": 0.92513, "top5_acc": 0.99648, "mean_class_accuracy": 0.89525} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 0.0, "memory": 4083, "data_time": 0.1897, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01782, "loss": 0.01782, "time": 0.79591} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 0.0, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01683, "loss": 0.01683, "time": 0.49094} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 0.0, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01783, "loss": 0.01783, "time": 0.4875} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 0.0, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01736, "loss": 0.01736, "time": 0.49} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 0.0, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02067, "loss": 0.02067, "time": 0.48827} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 0.0, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01735, "loss": 0.01735, "time": 0.48915} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 0.0, "memory": 4083, "data_time": 0.00048, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01739, "loss": 0.01739, "time": 0.48897} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01744, "loss": 0.01744, "time": 0.48977} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01917, "loss": 0.01917, "time": 0.48947} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01688, "loss": 0.01688, "time": 0.48963} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01693, "loss": 0.01693, "time": 0.49006} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01585, "loss": 0.01585, "time": 0.3685} +{"mode": "val", "epoch": 150, "iter": 533, "lr": 0.0, "top1_acc": 0.92372, "top5_acc": 0.99636, "mean_class_accuracy": 0.8932} diff --git a/finegym/j_3/best_pred.pkl b/finegym/j_3/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..7a0d1d7b221151b4022cf48db32fe8050fae3077 --- /dev/null +++ b/finegym/j_3/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3b7f5f79a0963b9578a46e210f6d17c92b75b738349250975086528b5b7f987a +size 5255370 diff --git a/finegym/j_3/best_top1_acc_epoch_150.pth b/finegym/j_3/best_top1_acc_epoch_150.pth new file mode 100644 index 0000000000000000000000000000000000000000..42fab91599980db8226d982141ce47a8854e21d4 --- /dev/null +++ b/finegym/j_3/best_top1_acc_epoch_150.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4b6490fedb1736242b388dbce225ae00b1d62fe536f276a106e4baa1472103fd +size 16118201 diff --git a/finegym/j_3/j_3.py b/finegym/j_3/j_3.py new file mode 100644 index 0000000000000000000000000000000000000000..1a5243d5823bd38f33b0ff7cf0d4037211f01a74 --- /dev/null +++ b/finegym/j_3/j_3.py @@ -0,0 +1,113 @@ +modality = 'j' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/j_3' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/finegym/jm/20250624_101434.log b/finegym/jm/20250624_101434.log new file mode 100644 index 0000000000000000000000000000000000000000..9a77c24ef849281e2bc6cc2bda4ec0ec84f92f5c --- /dev/null +++ b/finegym/jm/20250624_101434.log @@ -0,0 +1,3486 @@ +2025-06-24 10:14:34,378 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2025-06-24 10:14:34,679 - pyskl - INFO - Config: modality = 'jm' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/jm' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2025-06-24 10:14:34,680 - pyskl - INFO - Set random seed to 1178020540, deterministic: False +2025-06-24 10:14:36,228 - pyskl - INFO - 20484 videos remain after valid thresholding +2025-06-24 10:14:42,100 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-24 10:14:42,101 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm +2025-06-24 10:14:42,101 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2025-06-24 10:14:42,101 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2025-06-24 10:14:42,101 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm by HardDiskBackend. +2025-06-24 10:15:44,403 - pyskl - INFO - Epoch [1][100/1281] lr: 2.500e-02, eta: 1 day, 9:14:00, time: 0.623, data_time: 0.199, memory: 4082, top1_acc: 0.0606, top5_acc: 0.1988, loss_cls: 4.6087, loss: 4.6087 +2025-06-24 10:16:26,118 - pyskl - INFO - Epoch [1][200/1281] lr: 2.500e-02, eta: 1 day, 3:43:43, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.0688, top5_acc: 0.2838, loss_cls: 4.6863, loss: 4.6863 +2025-06-24 10:17:07,794 - pyskl - INFO - Epoch [1][300/1281] lr: 2.500e-02, eta: 1 day, 1:52:45, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.1100, top5_acc: 0.3262, loss_cls: 4.5120, loss: 4.5120 +2025-06-24 10:17:49,265 - pyskl - INFO - Epoch [1][400/1281] lr: 2.500e-02, eta: 1 day, 0:55:18, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.1031, top5_acc: 0.3650, loss_cls: 4.3571, loss: 4.3571 +2025-06-24 10:18:30,728 - pyskl - INFO - Epoch [1][500/1281] lr: 2.500e-02, eta: 1 day, 0:20:28, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.1494, top5_acc: 0.3906, loss_cls: 4.2050, loss: 4.2050 +2025-06-24 10:19:12,294 - pyskl - INFO - Epoch [1][600/1281] lr: 2.500e-02, eta: 23:57:35, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.1575, top5_acc: 0.4531, loss_cls: 4.0568, loss: 4.0568 +2025-06-24 10:19:54,019 - pyskl - INFO - Epoch [1][700/1281] lr: 2.500e-02, eta: 23:41:46, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.2100, top5_acc: 0.5238, loss_cls: 3.7875, loss: 3.7875 +2025-06-24 10:20:35,514 - pyskl - INFO - Epoch [1][800/1281] lr: 2.500e-02, eta: 23:28:48, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.2375, top5_acc: 0.5994, loss_cls: 3.4488, loss: 3.4488 +2025-06-24 10:21:17,024 - pyskl - INFO - Epoch [1][900/1281] lr: 2.500e-02, eta: 23:18:38, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.2712, top5_acc: 0.6194, loss_cls: 3.2879, loss: 3.2879 +2025-06-24 10:21:58,619 - pyskl - INFO - Epoch [1][1000/1281] lr: 2.500e-02, eta: 23:10:37, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.2712, top5_acc: 0.6475, loss_cls: 3.1791, loss: 3.1791 +2025-06-24 10:22:40,239 - pyskl - INFO - Epoch [1][1100/1281] lr: 2.500e-02, eta: 23:04:01, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.3350, top5_acc: 0.7037, loss_cls: 2.9595, loss: 2.9595 +2025-06-24 10:23:05,888 - pyskl - INFO - Epoch [1][1200/1281] lr: 2.500e-02, eta: 22:16:02, time: 0.256, data_time: 0.000, memory: 4082, top1_acc: 0.3369, top5_acc: 0.7113, loss_cls: 2.9039, loss: 2.9039 +2025-06-24 10:23:43,396 - pyskl - INFO - Saving checkpoint at 1 epochs +2025-06-24 10:24:47,207 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:24:47,263 - pyskl - INFO - +top1_acc 0.2720 +top5_acc 0.6755 +2025-06-24 10:24:47,263 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:24:47,270 - pyskl - INFO - +mean_acc 0.1362 +2025-06-24 10:24:47,437 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2025-06-24 10:24:47,437 - pyskl - INFO - Best top1_acc is 0.2720 at 1 epoch. +2025-06-24 10:24:47,440 - pyskl - INFO - Epoch(val) [1][533] top1_acc: 0.2720, top5_acc: 0.6755, mean_class_accuracy: 0.1362 +2025-06-24 10:25:48,656 - pyskl - INFO - Epoch [2][100/1281] lr: 2.500e-02, eta: 21:40:45, time: 0.612, data_time: 0.195, memory: 4082, top1_acc: 0.3831, top5_acc: 0.7706, loss_cls: 2.6356, loss: 2.6356 +2025-06-24 10:26:30,333 - pyskl - INFO - Epoch [2][200/1281] lr: 2.500e-02, eta: 21:41:43, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.4138, top5_acc: 0.7900, loss_cls: 2.4917, loss: 2.4917 +2025-06-24 10:27:11,919 - pyskl - INFO - Epoch [2][300/1281] lr: 2.500e-02, eta: 21:42:17, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.3944, top5_acc: 0.7950, loss_cls: 2.4871, loss: 2.4871 +2025-06-24 10:27:53,525 - pyskl - INFO - Epoch [2][400/1281] lr: 2.500e-02, eta: 21:42:44, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.4575, top5_acc: 0.8244, loss_cls: 2.3178, loss: 2.3178 +2025-06-24 10:28:35,189 - pyskl - INFO - Epoch [2][500/1281] lr: 2.499e-02, eta: 21:43:10, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.4288, top5_acc: 0.8387, loss_cls: 2.3096, loss: 2.3096 +2025-06-24 10:29:17,059 - pyskl - INFO - Epoch [2][600/1281] lr: 2.499e-02, eta: 21:43:49, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.4631, top5_acc: 0.8581, loss_cls: 2.1788, loss: 2.1788 +2025-06-24 10:29:58,869 - pyskl - INFO - Epoch [2][700/1281] lr: 2.499e-02, eta: 21:44:15, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.4225, top5_acc: 0.8450, loss_cls: 2.3018, loss: 2.3018 +2025-06-24 10:30:40,524 - pyskl - INFO - Epoch [2][800/1281] lr: 2.499e-02, eta: 21:44:20, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.4694, top5_acc: 0.8875, loss_cls: 2.0834, loss: 2.0834 +2025-06-24 10:31:22,999 - pyskl - INFO - Epoch [2][900/1281] lr: 2.499e-02, eta: 21:45:32, time: 0.425, data_time: 0.000, memory: 4082, top1_acc: 0.4775, top5_acc: 0.8706, loss_cls: 2.0662, loss: 2.0662 +2025-06-24 10:32:06,323 - pyskl - INFO - Epoch [2][1000/1281] lr: 2.499e-02, eta: 21:47:44, time: 0.433, data_time: 0.000, memory: 4082, top1_acc: 0.4900, top5_acc: 0.8956, loss_cls: 2.0584, loss: 2.0584 +2025-06-24 10:32:48,720 - pyskl - INFO - Epoch [2][1100/1281] lr: 2.499e-02, eta: 21:48:28, time: 0.424, data_time: 0.000, memory: 4082, top1_acc: 0.4956, top5_acc: 0.8888, loss_cls: 2.0153, loss: 2.0153 +2025-06-24 10:33:14,911 - pyskl - INFO - Epoch [2][1200/1281] lr: 2.499e-02, eta: 21:28:26, time: 0.262, data_time: 0.000, memory: 4082, top1_acc: 0.5050, top5_acc: 0.8875, loss_cls: 1.9377, loss: 1.9377 +2025-06-24 10:33:52,621 - pyskl - INFO - Saving checkpoint at 2 epochs +2025-06-24 10:34:57,024 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:34:57,081 - pyskl - INFO - +top1_acc 0.4342 +top5_acc 0.8507 +2025-06-24 10:34:57,081 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:34:57,088 - pyskl - INFO - +mean_acc 0.2392 +2025-06-24 10:34:57,092 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_1.pth was removed +2025-06-24 10:34:57,293 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2025-06-24 10:34:57,294 - pyskl - INFO - Best top1_acc is 0.4342 at 2 epoch. +2025-06-24 10:34:57,297 - pyskl - INFO - Epoch(val) [2][533] top1_acc: 0.4342, top5_acc: 0.8507, mean_class_accuracy: 0.2392 +2025-06-24 10:36:00,816 - pyskl - INFO - Epoch [3][100/1281] lr: 2.499e-02, eta: 21:15:02, time: 0.635, data_time: 0.198, memory: 4082, top1_acc: 0.4875, top5_acc: 0.8894, loss_cls: 1.9883, loss: 1.9883 +2025-06-24 10:36:42,708 - pyskl - INFO - Epoch [3][200/1281] lr: 2.499e-02, eta: 21:16:06, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.5150, top5_acc: 0.8981, loss_cls: 1.9194, loss: 1.9194 +2025-06-24 10:37:24,303 - pyskl - INFO - Epoch [3][300/1281] lr: 2.499e-02, eta: 21:16:42, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.5225, top5_acc: 0.9244, loss_cls: 1.8270, loss: 1.8270 +2025-06-24 10:38:05,676 - pyskl - INFO - Epoch [3][400/1281] lr: 2.499e-02, eta: 21:16:59, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.5594, top5_acc: 0.8988, loss_cls: 1.8624, loss: 1.8624 +2025-06-24 10:38:47,292 - pyskl - INFO - Epoch [3][500/1281] lr: 2.498e-02, eta: 21:17:28, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.5387, top5_acc: 0.9206, loss_cls: 1.7888, loss: 1.7888 +2025-06-24 10:39:28,781 - pyskl - INFO - Epoch [3][600/1281] lr: 2.498e-02, eta: 21:17:44, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.5406, top5_acc: 0.9419, loss_cls: 1.7638, loss: 1.7638 +2025-06-24 10:40:10,242 - pyskl - INFO - Epoch [3][700/1281] lr: 2.498e-02, eta: 21:17:55, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.5619, top5_acc: 0.9144, loss_cls: 1.7412, loss: 1.7412 +2025-06-24 10:40:51,698 - pyskl - INFO - Epoch [3][800/1281] lr: 2.498e-02, eta: 21:18:03, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.5825, top5_acc: 0.9363, loss_cls: 1.6820, loss: 1.6820 +2025-06-24 10:41:33,206 - pyskl - INFO - Epoch [3][900/1281] lr: 2.498e-02, eta: 21:18:11, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.5594, top5_acc: 0.9287, loss_cls: 1.7365, loss: 1.7365 +2025-06-24 10:42:15,433 - pyskl - INFO - Epoch [3][1000/1281] lr: 2.498e-02, eta: 21:18:54, time: 0.422, data_time: 0.000, memory: 4082, top1_acc: 0.5787, top5_acc: 0.9300, loss_cls: 1.6970, loss: 1.6970 +2025-06-24 10:42:56,804 - pyskl - INFO - Epoch [3][1100/1281] lr: 2.498e-02, eta: 21:18:49, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.5625, top5_acc: 0.9256, loss_cls: 1.7353, loss: 1.7353 +2025-06-24 10:43:23,676 - pyskl - INFO - Epoch [3][1200/1281] lr: 2.498e-02, eta: 21:06:35, time: 0.269, data_time: 0.000, memory: 4082, top1_acc: 0.5863, top5_acc: 0.9350, loss_cls: 1.6415, loss: 1.6415 +2025-06-24 10:44:00,960 - pyskl - INFO - Saving checkpoint at 3 epochs +2025-06-24 10:45:06,321 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:45:06,390 - pyskl - INFO - +top1_acc 0.5509 +top5_acc 0.9161 +2025-06-24 10:45:06,391 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:45:06,399 - pyskl - INFO - +mean_acc 0.3698 +2025-06-24 10:45:06,404 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_2.pth was removed +2025-06-24 10:45:06,612 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2025-06-24 10:45:06,612 - pyskl - INFO - Best top1_acc is 0.5509 at 3 epoch. +2025-06-24 10:45:06,615 - pyskl - INFO - Epoch(val) [3][533] top1_acc: 0.5509, top5_acc: 0.9161, mean_class_accuracy: 0.3698 +2025-06-24 10:46:08,245 - pyskl - INFO - Epoch [4][100/1281] lr: 2.497e-02, eta: 20:56:18, time: 0.616, data_time: 0.201, memory: 4082, top1_acc: 0.5944, top5_acc: 0.9481, loss_cls: 1.6106, loss: 1.6106 +2025-06-24 10:46:49,707 - pyskl - INFO - Epoch [4][200/1281] lr: 2.497e-02, eta: 20:56:44, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.5894, top5_acc: 0.9463, loss_cls: 1.5935, loss: 1.5935 +2025-06-24 10:47:31,245 - pyskl - INFO - Epoch [4][300/1281] lr: 2.497e-02, eta: 20:57:09, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.5962, top5_acc: 0.9369, loss_cls: 1.5912, loss: 1.5912 +2025-06-24 10:48:12,755 - pyskl - INFO - Epoch [4][400/1281] lr: 2.497e-02, eta: 20:57:31, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6081, top5_acc: 0.9563, loss_cls: 1.4982, loss: 1.4982 +2025-06-24 10:48:54,333 - pyskl - INFO - Epoch [4][500/1281] lr: 2.497e-02, eta: 20:57:52, time: 0.416, data_time: 0.001, memory: 4082, top1_acc: 0.6044, top5_acc: 0.9513, loss_cls: 1.5454, loss: 1.5454 +2025-06-24 10:49:35,831 - pyskl - INFO - Epoch [4][600/1281] lr: 2.497e-02, eta: 20:58:07, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6088, top5_acc: 0.9463, loss_cls: 1.5437, loss: 1.5437 +2025-06-24 10:50:17,384 - pyskl - INFO - Epoch [4][700/1281] lr: 2.497e-02, eta: 20:58:22, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.5938, top5_acc: 0.9500, loss_cls: 1.5752, loss: 1.5752 +2025-06-24 10:50:58,907 - pyskl - INFO - Epoch [4][800/1281] lr: 2.496e-02, eta: 20:58:34, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6281, top5_acc: 0.9494, loss_cls: 1.4871, loss: 1.4871 +2025-06-24 10:51:40,479 - pyskl - INFO - Epoch [4][900/1281] lr: 2.496e-02, eta: 20:58:45, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6169, top5_acc: 0.9563, loss_cls: 1.5108, loss: 1.5108 +2025-06-24 10:52:21,992 - pyskl - INFO - Epoch [4][1000/1281] lr: 2.496e-02, eta: 20:58:51, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6162, top5_acc: 0.9506, loss_cls: 1.5131, loss: 1.5131 +2025-06-24 10:53:02,029 - pyskl - INFO - Epoch [4][1100/1281] lr: 2.496e-02, eta: 20:58:00, time: 0.400, data_time: 0.000, memory: 4082, top1_acc: 0.6081, top5_acc: 0.9556, loss_cls: 1.4998, loss: 1.4998 +2025-06-24 10:53:30,258 - pyskl - INFO - Epoch [4][1200/1281] lr: 2.496e-02, eta: 20:49:51, time: 0.282, data_time: 0.001, memory: 4082, top1_acc: 0.6138, top5_acc: 0.9600, loss_cls: 1.4727, loss: 1.4727 +2025-06-24 10:54:07,690 - pyskl - INFO - Saving checkpoint at 4 epochs +2025-06-24 10:55:16,334 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:55:16,400 - pyskl - INFO - +top1_acc 0.5601 +top5_acc 0.9313 +2025-06-24 10:55:16,400 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:55:16,408 - pyskl - INFO - +mean_acc 0.4340 +2025-06-24 10:55:16,413 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_3.pth was removed +2025-06-24 10:55:16,667 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2025-06-24 10:55:16,667 - pyskl - INFO - Best top1_acc is 0.5601 at 4 epoch. +2025-06-24 10:55:16,670 - pyskl - INFO - Epoch(val) [4][533] top1_acc: 0.5601, top5_acc: 0.9313, mean_class_accuracy: 0.4340 +2025-06-24 10:56:20,751 - pyskl - INFO - Epoch [5][100/1281] lr: 2.495e-02, eta: 20:43:35, time: 0.641, data_time: 0.202, memory: 4082, top1_acc: 0.6169, top5_acc: 0.9594, loss_cls: 1.4467, loss: 1.4467 +2025-06-24 10:57:02,550 - pyskl - INFO - Epoch [5][200/1281] lr: 2.495e-02, eta: 20:44:02, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.6394, top5_acc: 0.9619, loss_cls: 1.3867, loss: 1.3867 +2025-06-24 10:57:43,959 - pyskl - INFO - Epoch [5][300/1281] lr: 2.495e-02, eta: 20:44:12, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6356, top5_acc: 0.9619, loss_cls: 1.4081, loss: 1.4081 +2025-06-24 10:58:25,327 - pyskl - INFO - Epoch [5][400/1281] lr: 2.495e-02, eta: 20:44:19, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6562, top5_acc: 0.9600, loss_cls: 1.3816, loss: 1.3816 +2025-06-24 10:59:06,758 - pyskl - INFO - Epoch [5][500/1281] lr: 2.495e-02, eta: 20:44:26, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6462, top5_acc: 0.9675, loss_cls: 1.3707, loss: 1.3707 +2025-06-24 10:59:48,195 - pyskl - INFO - Epoch [5][600/1281] lr: 2.495e-02, eta: 20:44:32, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6519, top5_acc: 0.9644, loss_cls: 1.4061, loss: 1.4061 +2025-06-24 11:00:29,626 - pyskl - INFO - Epoch [5][700/1281] lr: 2.494e-02, eta: 20:44:36, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6300, top5_acc: 0.9631, loss_cls: 1.4142, loss: 1.4142 +2025-06-24 11:01:11,096 - pyskl - INFO - Epoch [5][800/1281] lr: 2.494e-02, eta: 20:44:39, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6544, top5_acc: 0.9613, loss_cls: 1.3797, loss: 1.3797 +2025-06-24 11:01:52,682 - pyskl - INFO - Epoch [5][900/1281] lr: 2.494e-02, eta: 20:44:45, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6706, top5_acc: 0.9669, loss_cls: 1.3090, loss: 1.3090 +2025-06-24 11:02:34,061 - pyskl - INFO - Epoch [5][1000/1281] lr: 2.494e-02, eta: 20:44:43, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6575, top5_acc: 0.9631, loss_cls: 1.3704, loss: 1.3704 +2025-06-24 11:03:12,407 - pyskl - INFO - Epoch [5][1100/1281] lr: 2.494e-02, eta: 20:43:09, time: 0.383, data_time: 0.001, memory: 4082, top1_acc: 0.6406, top5_acc: 0.9594, loss_cls: 1.4377, loss: 1.4377 +2025-06-24 11:03:42,221 - pyskl - INFO - Epoch [5][1200/1281] lr: 2.493e-02, eta: 20:37:26, time: 0.298, data_time: 0.000, memory: 4082, top1_acc: 0.6594, top5_acc: 0.9637, loss_cls: 1.3650, loss: 1.3650 +2025-06-24 11:04:19,660 - pyskl - INFO - Saving checkpoint at 5 epochs +2025-06-24 11:05:28,856 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:05:28,925 - pyskl - INFO - +top1_acc 0.6533 +top5_acc 0.9586 +2025-06-24 11:05:28,925 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:05:28,933 - pyskl - INFO - +mean_acc 0.5117 +2025-06-24 11:05:28,937 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_4.pth was removed +2025-06-24 11:05:29,162 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2025-06-24 11:05:29,162 - pyskl - INFO - Best top1_acc is 0.6533 at 5 epoch. +2025-06-24 11:05:29,166 - pyskl - INFO - Epoch(val) [5][533] top1_acc: 0.6533, top5_acc: 0.9586, mean_class_accuracy: 0.5117 +2025-06-24 11:06:30,077 - pyskl - INFO - Epoch [6][100/1281] lr: 2.493e-02, eta: 20:30:48, time: 0.609, data_time: 0.193, memory: 4082, top1_acc: 0.6900, top5_acc: 0.9688, loss_cls: 1.2917, loss: 1.2917 +2025-06-24 11:07:11,651 - pyskl - INFO - Epoch [6][200/1281] lr: 2.493e-02, eta: 20:30:59, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6837, top5_acc: 0.9656, loss_cls: 1.2952, loss: 1.2952 +2025-06-24 11:07:53,195 - pyskl - INFO - Epoch [6][300/1281] lr: 2.492e-02, eta: 20:31:07, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6881, top5_acc: 0.9681, loss_cls: 1.2876, loss: 1.2876 +2025-06-24 11:08:34,585 - pyskl - INFO - Epoch [6][400/1281] lr: 2.492e-02, eta: 20:31:09, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6956, top5_acc: 0.9681, loss_cls: 1.2804, loss: 1.2804 +2025-06-24 11:09:16,254 - pyskl - INFO - Epoch [6][500/1281] lr: 2.492e-02, eta: 20:31:18, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.6756, top5_acc: 0.9669, loss_cls: 1.3109, loss: 1.3109 +2025-06-24 11:09:57,849 - pyskl - INFO - Epoch [6][600/1281] lr: 2.492e-02, eta: 20:31:23, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6562, top5_acc: 0.9656, loss_cls: 1.3498, loss: 1.3498 +2025-06-24 11:10:39,314 - pyskl - INFO - Epoch [6][700/1281] lr: 2.492e-02, eta: 20:31:24, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6819, top5_acc: 0.9694, loss_cls: 1.2829, loss: 1.2829 +2025-06-24 11:11:20,794 - pyskl - INFO - Epoch [6][800/1281] lr: 2.491e-02, eta: 20:31:24, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6781, top5_acc: 0.9744, loss_cls: 1.3209, loss: 1.3209 +2025-06-24 11:12:04,256 - pyskl - INFO - Epoch [6][900/1281] lr: 2.491e-02, eta: 20:32:13, time: 0.435, data_time: 0.000, memory: 4082, top1_acc: 0.6937, top5_acc: 0.9706, loss_cls: 1.2598, loss: 1.2598 +2025-06-24 11:12:46,728 - pyskl - INFO - Epoch [6][1000/1281] lr: 2.491e-02, eta: 20:32:34, time: 0.425, data_time: 0.001, memory: 4082, top1_acc: 0.7119, top5_acc: 0.9788, loss_cls: 1.1968, loss: 1.1968 +2025-06-24 11:13:24,864 - pyskl - INFO - Epoch [6][1100/1281] lr: 2.491e-02, eta: 20:31:08, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.6944, top5_acc: 0.9719, loss_cls: 1.2408, loss: 1.2408 +2025-06-24 11:13:54,785 - pyskl - INFO - Epoch [6][1200/1281] lr: 2.490e-02, eta: 20:26:23, time: 0.299, data_time: 0.000, memory: 4082, top1_acc: 0.6894, top5_acc: 0.9762, loss_cls: 1.2676, loss: 1.2676 +2025-06-24 11:14:32,232 - pyskl - INFO - Saving checkpoint at 6 epochs +2025-06-24 11:15:40,830 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:15:40,898 - pyskl - INFO - +top1_acc 0.6506 +top5_acc 0.9589 +2025-06-24 11:15:40,899 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:15:40,906 - pyskl - INFO - +mean_acc 0.5182 +2025-06-24 11:15:40,908 - pyskl - INFO - Epoch(val) [6][533] top1_acc: 0.6506, top5_acc: 0.9589, mean_class_accuracy: 0.5182 +2025-06-24 11:16:42,511 - pyskl - INFO - Epoch [7][100/1281] lr: 2.490e-02, eta: 20:21:01, time: 0.616, data_time: 0.203, memory: 4082, top1_acc: 0.7069, top5_acc: 0.9719, loss_cls: 1.2502, loss: 1.2502 +2025-06-24 11:17:23,965 - pyskl - INFO - Epoch [7][200/1281] lr: 2.490e-02, eta: 20:21:01, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6963, top5_acc: 0.9769, loss_cls: 1.2100, loss: 1.2100 +2025-06-24 11:18:05,298 - pyskl - INFO - Epoch [7][300/1281] lr: 2.489e-02, eta: 20:20:58, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7150, top5_acc: 0.9838, loss_cls: 1.1585, loss: 1.1585 +2025-06-24 11:18:46,611 - pyskl - INFO - Epoch [7][400/1281] lr: 2.489e-02, eta: 20:20:53, time: 0.413, data_time: 0.001, memory: 4082, top1_acc: 0.6937, top5_acc: 0.9781, loss_cls: 1.2047, loss: 1.2047 +2025-06-24 11:19:28,381 - pyskl - INFO - Epoch [7][500/1281] lr: 2.489e-02, eta: 20:20:57, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7200, top5_acc: 0.9788, loss_cls: 1.1765, loss: 1.1765 +2025-06-24 11:20:09,862 - pyskl - INFO - Epoch [7][600/1281] lr: 2.489e-02, eta: 20:20:54, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6737, top5_acc: 0.9719, loss_cls: 1.2674, loss: 1.2674 +2025-06-24 11:20:51,231 - pyskl - INFO - Epoch [7][700/1281] lr: 2.488e-02, eta: 20:20:48, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7069, top5_acc: 0.9781, loss_cls: 1.1891, loss: 1.1891 +2025-06-24 11:21:32,769 - pyskl - INFO - Epoch [7][800/1281] lr: 2.488e-02, eta: 20:20:44, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7044, top5_acc: 0.9725, loss_cls: 1.2247, loss: 1.2247 +2025-06-24 11:22:14,146 - pyskl - INFO - Epoch [7][900/1281] lr: 2.488e-02, eta: 20:20:36, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7106, top5_acc: 0.9731, loss_cls: 1.2044, loss: 1.2044 +2025-06-24 11:22:55,703 - pyskl - INFO - Epoch [7][1000/1281] lr: 2.487e-02, eta: 20:20:31, time: 0.416, data_time: 0.001, memory: 4082, top1_acc: 0.7113, top5_acc: 0.9831, loss_cls: 1.1664, loss: 1.1664 +2025-06-24 11:23:33,539 - pyskl - INFO - Epoch [7][1100/1281] lr: 2.487e-02, eta: 20:19:08, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.7056, top5_acc: 0.9775, loss_cls: 1.2015, loss: 1.2015 +2025-06-24 11:24:04,338 - pyskl - INFO - Epoch [7][1200/1281] lr: 2.487e-02, eta: 20:15:21, time: 0.308, data_time: 0.000, memory: 4082, top1_acc: 0.7037, top5_acc: 0.9731, loss_cls: 1.2317, loss: 1.2317 +2025-06-24 11:24:41,651 - pyskl - INFO - Saving checkpoint at 7 epochs +2025-06-24 11:25:50,637 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:25:50,715 - pyskl - INFO - +top1_acc 0.6890 +top5_acc 0.9700 +2025-06-24 11:25:50,715 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:25:50,722 - pyskl - INFO - +mean_acc 0.5179 +2025-06-24 11:25:50,726 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_5.pth was removed +2025-06-24 11:25:50,954 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2025-06-24 11:25:50,954 - pyskl - INFO - Best top1_acc is 0.6890 at 7 epoch. +2025-06-24 11:25:50,957 - pyskl - INFO - Epoch(val) [7][533] top1_acc: 0.6890, top5_acc: 0.9700, mean_class_accuracy: 0.5179 +2025-06-24 11:26:52,861 - pyskl - INFO - Epoch [8][100/1281] lr: 2.486e-02, eta: 20:10:44, time: 0.619, data_time: 0.205, memory: 4082, top1_acc: 0.7250, top5_acc: 0.9794, loss_cls: 1.1057, loss: 1.1057 +2025-06-24 11:27:34,333 - pyskl - INFO - Epoch [8][200/1281] lr: 2.486e-02, eta: 20:10:40, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7331, top5_acc: 0.9806, loss_cls: 1.1107, loss: 1.1107 +2025-06-24 11:28:15,866 - pyskl - INFO - Epoch [8][300/1281] lr: 2.486e-02, eta: 20:10:37, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7188, top5_acc: 0.9750, loss_cls: 1.1826, loss: 1.1826 +2025-06-24 11:28:57,230 - pyskl - INFO - Epoch [8][400/1281] lr: 2.485e-02, eta: 20:10:29, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7163, top5_acc: 0.9762, loss_cls: 1.2037, loss: 1.2037 +2025-06-24 11:29:38,868 - pyskl - INFO - Epoch [8][500/1281] lr: 2.485e-02, eta: 20:10:26, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7338, top5_acc: 0.9775, loss_cls: 1.1203, loss: 1.1203 +2025-06-24 11:30:20,535 - pyskl - INFO - Epoch [8][600/1281] lr: 2.485e-02, eta: 20:10:23, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7331, top5_acc: 0.9844, loss_cls: 1.0974, loss: 1.0974 +2025-06-24 11:31:02,066 - pyskl - INFO - Epoch [8][700/1281] lr: 2.484e-02, eta: 20:10:16, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7119, top5_acc: 0.9812, loss_cls: 1.1360, loss: 1.1360 +2025-06-24 11:31:43,716 - pyskl - INFO - Epoch [8][800/1281] lr: 2.484e-02, eta: 20:10:11, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7150, top5_acc: 0.9788, loss_cls: 1.1247, loss: 1.1247 +2025-06-24 11:32:25,360 - pyskl - INFO - Epoch [8][900/1281] lr: 2.484e-02, eta: 20:10:05, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7325, top5_acc: 0.9788, loss_cls: 1.1183, loss: 1.1183 +2025-06-24 11:33:07,166 - pyskl - INFO - Epoch [8][1000/1281] lr: 2.483e-02, eta: 20:10:01, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7194, top5_acc: 0.9775, loss_cls: 1.1597, loss: 1.1597 +2025-06-24 11:33:45,533 - pyskl - INFO - Epoch [8][1100/1281] lr: 2.483e-02, eta: 20:08:55, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.7281, top5_acc: 0.9744, loss_cls: 1.1282, loss: 1.1282 +2025-06-24 11:34:17,385 - pyskl - INFO - Epoch [8][1200/1281] lr: 2.483e-02, eta: 20:05:52, time: 0.319, data_time: 0.001, memory: 4082, top1_acc: 0.7163, top5_acc: 0.9769, loss_cls: 1.1643, loss: 1.1643 +2025-06-24 11:34:53,659 - pyskl - INFO - Saving checkpoint at 8 epochs +2025-06-24 11:36:03,745 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:36:03,801 - pyskl - INFO - +top1_acc 0.6540 +top5_acc 0.9568 +2025-06-24 11:36:03,801 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:36:03,808 - pyskl - INFO - +mean_acc 0.5454 +2025-06-24 11:36:03,811 - pyskl - INFO - Epoch(val) [8][533] top1_acc: 0.6540, top5_acc: 0.9568, mean_class_accuracy: 0.5454 +2025-06-24 11:37:05,275 - pyskl - INFO - Epoch [9][100/1281] lr: 2.482e-02, eta: 20:01:35, time: 0.615, data_time: 0.200, memory: 4082, top1_acc: 0.7500, top5_acc: 0.9856, loss_cls: 1.0595, loss: 1.0595 +2025-06-24 11:37:46,725 - pyskl - INFO - Epoch [9][200/1281] lr: 2.482e-02, eta: 20:01:27, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7350, top5_acc: 0.9844, loss_cls: 1.0677, loss: 1.0677 +2025-06-24 11:38:28,219 - pyskl - INFO - Epoch [9][300/1281] lr: 2.481e-02, eta: 20:01:19, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7300, top5_acc: 0.9888, loss_cls: 1.0861, loss: 1.0861 +2025-06-24 11:39:09,710 - pyskl - INFO - Epoch [9][400/1281] lr: 2.481e-02, eta: 20:01:10, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7288, top5_acc: 0.9769, loss_cls: 1.1041, loss: 1.1041 +2025-06-24 11:39:51,246 - pyskl - INFO - Epoch [9][500/1281] lr: 2.481e-02, eta: 20:01:01, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7388, top5_acc: 0.9831, loss_cls: 1.0833, loss: 1.0833 +2025-06-24 11:40:32,838 - pyskl - INFO - Epoch [9][600/1281] lr: 2.480e-02, eta: 20:00:52, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7319, top5_acc: 0.9806, loss_cls: 1.0828, loss: 1.0828 +2025-06-24 11:41:14,198 - pyskl - INFO - Epoch [9][700/1281] lr: 2.480e-02, eta: 20:00:39, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7269, top5_acc: 0.9794, loss_cls: 1.1082, loss: 1.1082 +2025-06-24 11:41:55,533 - pyskl - INFO - Epoch [9][800/1281] lr: 2.480e-02, eta: 20:00:25, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7506, top5_acc: 0.9869, loss_cls: 1.0261, loss: 1.0261 +2025-06-24 11:42:36,910 - pyskl - INFO - Epoch [9][900/1281] lr: 2.479e-02, eta: 20:00:12, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7531, top5_acc: 0.9788, loss_cls: 1.0407, loss: 1.0407 +2025-06-24 11:43:18,916 - pyskl - INFO - Epoch [9][1000/1281] lr: 2.479e-02, eta: 20:00:07, time: 0.420, data_time: 0.000, memory: 4082, top1_acc: 0.7369, top5_acc: 0.9788, loss_cls: 1.1042, loss: 1.1042 +2025-06-24 11:43:55,859 - pyskl - INFO - Epoch [9][1100/1281] lr: 2.479e-02, eta: 19:58:42, time: 0.369, data_time: 0.000, memory: 4082, top1_acc: 0.7412, top5_acc: 0.9788, loss_cls: 1.0980, loss: 1.0980 +2025-06-24 11:44:26,561 - pyskl - INFO - Epoch [9][1200/1281] lr: 2.478e-02, eta: 19:55:39, time: 0.307, data_time: 0.001, memory: 4082, top1_acc: 0.7275, top5_acc: 0.9806, loss_cls: 1.1116, loss: 1.1116 +2025-06-24 11:45:03,877 - pyskl - INFO - Saving checkpoint at 9 epochs +2025-06-24 11:46:14,262 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:46:14,337 - pyskl - INFO - +top1_acc 0.6633 +top5_acc 0.9565 +2025-06-24 11:46:14,337 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:46:14,346 - pyskl - INFO - +mean_acc 0.5738 +2025-06-24 11:46:14,348 - pyskl - INFO - Epoch(val) [9][533] top1_acc: 0.6633, top5_acc: 0.9565, mean_class_accuracy: 0.5738 +2025-06-24 11:47:16,133 - pyskl - INFO - Epoch [10][100/1281] lr: 2.477e-02, eta: 19:51:51, time: 0.618, data_time: 0.203, memory: 4082, top1_acc: 0.7600, top5_acc: 0.9838, loss_cls: 1.0180, loss: 1.0180 +2025-06-24 11:47:57,735 - pyskl - INFO - Epoch [10][200/1281] lr: 2.477e-02, eta: 19:51:42, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7700, top5_acc: 0.9812, loss_cls: 1.0235, loss: 1.0235 +2025-06-24 11:48:39,160 - pyskl - INFO - Epoch [10][300/1281] lr: 2.477e-02, eta: 19:51:29, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7631, top5_acc: 0.9888, loss_cls: 0.9847, loss: 0.9847 +2025-06-24 11:49:20,600 - pyskl - INFO - Epoch [10][400/1281] lr: 2.476e-02, eta: 19:51:17, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7525, top5_acc: 0.9794, loss_cls: 1.0588, loss: 1.0588 +2025-06-24 11:50:01,991 - pyskl - INFO - Epoch [10][500/1281] lr: 2.476e-02, eta: 19:51:03, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7438, top5_acc: 0.9781, loss_cls: 1.0803, loss: 1.0803 +2025-06-24 11:50:43,493 - pyskl - INFO - Epoch [10][600/1281] lr: 2.476e-02, eta: 19:50:50, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7550, top5_acc: 0.9838, loss_cls: 1.0275, loss: 1.0275 +2025-06-24 11:51:24,920 - pyskl - INFO - Epoch [10][700/1281] lr: 2.475e-02, eta: 19:50:36, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7631, top5_acc: 0.9856, loss_cls: 0.9857, loss: 0.9857 +2025-06-24 11:52:06,428 - pyskl - INFO - Epoch [10][800/1281] lr: 2.475e-02, eta: 19:50:23, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7544, top5_acc: 0.9869, loss_cls: 1.0814, loss: 1.0814 +2025-06-24 11:52:47,972 - pyskl - INFO - Epoch [10][900/1281] lr: 2.474e-02, eta: 19:50:09, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7562, top5_acc: 0.9825, loss_cls: 1.0014, loss: 1.0014 +2025-06-24 11:53:29,528 - pyskl - INFO - Epoch [10][1000/1281] lr: 2.474e-02, eta: 19:49:56, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7425, top5_acc: 0.9838, loss_cls: 1.0386, loss: 1.0386 +2025-06-24 11:54:06,395 - pyskl - INFO - Epoch [10][1100/1281] lr: 2.473e-02, eta: 19:48:35, time: 0.369, data_time: 0.001, memory: 4082, top1_acc: 0.7250, top5_acc: 0.9719, loss_cls: 1.1340, loss: 1.1340 +2025-06-24 11:54:36,946 - pyskl - INFO - Epoch [10][1200/1281] lr: 2.473e-02, eta: 19:45:46, time: 0.305, data_time: 0.000, memory: 4082, top1_acc: 0.7362, top5_acc: 0.9844, loss_cls: 1.0696, loss: 1.0696 +2025-06-24 11:55:14,345 - pyskl - INFO - Saving checkpoint at 10 epochs +2025-06-24 11:56:24,580 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:56:24,650 - pyskl - INFO - +top1_acc 0.7185 +top5_acc 0.9735 +2025-06-24 11:56:24,650 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:56:24,658 - pyskl - INFO - +mean_acc 0.6015 +2025-06-24 11:56:24,663 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_7.pth was removed +2025-06-24 11:56:24,867 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_10.pth. +2025-06-24 11:56:24,868 - pyskl - INFO - Best top1_acc is 0.7185 at 10 epoch. +2025-06-24 11:56:24,870 - pyskl - INFO - Epoch(val) [10][533] top1_acc: 0.7185, top5_acc: 0.9735, mean_class_accuracy: 0.6015 +2025-06-24 11:57:26,159 - pyskl - INFO - Epoch [11][100/1281] lr: 2.472e-02, eta: 19:42:09, time: 0.613, data_time: 0.200, memory: 4082, top1_acc: 0.7856, top5_acc: 0.9838, loss_cls: 0.9238, loss: 0.9238 +2025-06-24 11:58:07,772 - pyskl - INFO - Epoch [11][200/1281] lr: 2.472e-02, eta: 19:41:57, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7644, top5_acc: 0.9831, loss_cls: 0.9367, loss: 0.9367 +2025-06-24 11:58:49,255 - pyskl - INFO - Epoch [11][300/1281] lr: 2.471e-02, eta: 19:41:43, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7556, top5_acc: 0.9869, loss_cls: 1.0295, loss: 1.0295 +2025-06-24 11:59:30,830 - pyskl - INFO - Epoch [11][400/1281] lr: 2.471e-02, eta: 19:41:31, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7844, top5_acc: 0.9869, loss_cls: 0.9485, loss: 0.9485 +2025-06-24 12:00:12,613 - pyskl - INFO - Epoch [11][500/1281] lr: 2.471e-02, eta: 19:41:20, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7581, top5_acc: 0.9825, loss_cls: 1.0113, loss: 1.0113 +2025-06-24 12:00:55,867 - pyskl - INFO - Epoch [11][600/1281] lr: 2.470e-02, eta: 19:41:29, time: 0.433, data_time: 0.000, memory: 4082, top1_acc: 0.7694, top5_acc: 0.9881, loss_cls: 0.9721, loss: 0.9721 +2025-06-24 12:01:39,560 - pyskl - INFO - Epoch [11][700/1281] lr: 2.470e-02, eta: 19:41:42, time: 0.437, data_time: 0.000, memory: 4082, top1_acc: 0.7638, top5_acc: 0.9869, loss_cls: 0.9841, loss: 0.9841 +2025-06-24 12:02:21,244 - pyskl - INFO - Epoch [11][800/1281] lr: 2.469e-02, eta: 19:41:29, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7750, top5_acc: 0.9844, loss_cls: 0.9516, loss: 0.9516 +2025-06-24 12:03:02,902 - pyskl - INFO - Epoch [11][900/1281] lr: 2.469e-02, eta: 19:41:14, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7819, top5_acc: 0.9831, loss_cls: 0.9699, loss: 0.9699 +2025-06-24 12:03:44,331 - pyskl - INFO - Epoch [11][1000/1281] lr: 2.468e-02, eta: 19:40:57, time: 0.414, data_time: 0.001, memory: 4082, top1_acc: 0.7831, top5_acc: 0.9850, loss_cls: 0.9745, loss: 0.9745 +2025-06-24 12:04:21,395 - pyskl - INFO - Epoch [11][1100/1281] lr: 2.468e-02, eta: 19:39:43, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.7812, top5_acc: 0.9869, loss_cls: 0.9409, loss: 0.9409 +2025-06-24 12:04:52,840 - pyskl - INFO - Epoch [11][1200/1281] lr: 2.467e-02, eta: 19:37:18, time: 0.314, data_time: 0.000, memory: 4082, top1_acc: 0.7525, top5_acc: 0.9838, loss_cls: 1.0304, loss: 1.0304 +2025-06-24 12:05:29,311 - pyskl - INFO - Saving checkpoint at 11 epochs +2025-06-24 12:06:39,188 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:06:39,261 - pyskl - INFO - +top1_acc 0.7531 +top5_acc 0.9798 +2025-06-24 12:06:39,261 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:06:39,269 - pyskl - INFO - +mean_acc 0.6502 +2025-06-24 12:06:39,274 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_10.pth was removed +2025-06-24 12:06:39,616 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_11.pth. +2025-06-24 12:06:39,617 - pyskl - INFO - Best top1_acc is 0.7531 at 11 epoch. +2025-06-24 12:06:39,619 - pyskl - INFO - Epoch(val) [11][533] top1_acc: 0.7531, top5_acc: 0.9798, mean_class_accuracy: 0.6502 +2025-06-24 12:07:42,426 - pyskl - INFO - Epoch [12][100/1281] lr: 2.467e-02, eta: 19:34:14, time: 0.628, data_time: 0.194, memory: 4082, top1_acc: 0.7731, top5_acc: 0.9900, loss_cls: 0.9400, loss: 0.9400 +2025-06-24 12:08:23,884 - pyskl - INFO - Epoch [12][200/1281] lr: 2.466e-02, eta: 19:33:57, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7788, top5_acc: 0.9856, loss_cls: 0.9830, loss: 0.9830 +2025-06-24 12:09:05,162 - pyskl - INFO - Epoch [12][300/1281] lr: 2.466e-02, eta: 19:33:38, time: 0.413, data_time: 0.001, memory: 4082, top1_acc: 0.7731, top5_acc: 0.9925, loss_cls: 0.9285, loss: 0.9285 +2025-06-24 12:09:46,599 - pyskl - INFO - Epoch [12][400/1281] lr: 2.465e-02, eta: 19:33:21, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7800, top5_acc: 0.9850, loss_cls: 0.9648, loss: 0.9648 +2025-06-24 12:10:27,953 - pyskl - INFO - Epoch [12][500/1281] lr: 2.465e-02, eta: 19:33:03, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7631, top5_acc: 0.9831, loss_cls: 0.9792, loss: 0.9792 +2025-06-24 12:11:09,407 - pyskl - INFO - Epoch [12][600/1281] lr: 2.464e-02, eta: 19:32:45, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7438, top5_acc: 0.9844, loss_cls: 1.0035, loss: 1.0035 +2025-06-24 12:11:51,121 - pyskl - INFO - Epoch [12][700/1281] lr: 2.464e-02, eta: 19:32:30, time: 0.417, data_time: 0.001, memory: 4082, top1_acc: 0.7800, top5_acc: 0.9862, loss_cls: 0.8916, loss: 0.8916 +2025-06-24 12:12:32,781 - pyskl - INFO - Epoch [12][800/1281] lr: 2.463e-02, eta: 19:32:14, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7738, top5_acc: 0.9838, loss_cls: 1.0051, loss: 1.0051 +2025-06-24 12:13:14,261 - pyskl - INFO - Epoch [12][900/1281] lr: 2.463e-02, eta: 19:31:56, time: 0.415, data_time: 0.001, memory: 4082, top1_acc: 0.7863, top5_acc: 0.9881, loss_cls: 0.9071, loss: 0.9071 +2025-06-24 12:13:55,725 - pyskl - INFO - Epoch [12][1000/1281] lr: 2.462e-02, eta: 19:31:37, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7575, top5_acc: 0.9850, loss_cls: 1.0189, loss: 1.0189 +2025-06-24 12:14:33,026 - pyskl - INFO - Epoch [12][1100/1281] lr: 2.462e-02, eta: 19:30:29, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.7725, top5_acc: 0.9825, loss_cls: 0.9789, loss: 0.9789 +2025-06-24 12:15:04,972 - pyskl - INFO - Epoch [12][1200/1281] lr: 2.461e-02, eta: 19:28:20, time: 0.319, data_time: 0.000, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9819, loss_cls: 0.9501, loss: 0.9501 +2025-06-24 12:15:40,783 - pyskl - INFO - Saving checkpoint at 12 epochs +2025-06-24 12:16:51,811 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:16:51,868 - pyskl - INFO - +top1_acc 0.7068 +top5_acc 0.9749 +2025-06-24 12:16:51,868 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:16:51,875 - pyskl - INFO - +mean_acc 0.6042 +2025-06-24 12:16:51,878 - pyskl - INFO - Epoch(val) [12][533] top1_acc: 0.7068, top5_acc: 0.9749, mean_class_accuracy: 0.6042 +2025-06-24 12:17:52,937 - pyskl - INFO - Epoch [13][100/1281] lr: 2.460e-02, eta: 19:25:06, time: 0.611, data_time: 0.196, memory: 4082, top1_acc: 0.7869, top5_acc: 0.9938, loss_cls: 0.9066, loss: 0.9066 +2025-06-24 12:18:34,523 - pyskl - INFO - Epoch [13][200/1281] lr: 2.460e-02, eta: 19:24:49, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7875, top5_acc: 0.9906, loss_cls: 0.9399, loss: 0.9399 +2025-06-24 12:19:16,091 - pyskl - INFO - Epoch [13][300/1281] lr: 2.459e-02, eta: 19:24:32, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7750, top5_acc: 0.9850, loss_cls: 0.9212, loss: 0.9212 +2025-06-24 12:19:57,612 - pyskl - INFO - Epoch [13][400/1281] lr: 2.459e-02, eta: 19:24:14, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7681, top5_acc: 0.9869, loss_cls: 0.9673, loss: 0.9673 +2025-06-24 12:20:39,072 - pyskl - INFO - Epoch [13][500/1281] lr: 2.458e-02, eta: 19:23:55, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9844, loss_cls: 0.9220, loss: 0.9220 +2025-06-24 12:21:21,160 - pyskl - INFO - Epoch [13][600/1281] lr: 2.458e-02, eta: 19:23:43, time: 0.421, data_time: 0.000, memory: 4082, top1_acc: 0.7887, top5_acc: 0.9888, loss_cls: 0.9226, loss: 0.9226 +2025-06-24 12:22:02,642 - pyskl - INFO - Epoch [13][700/1281] lr: 2.457e-02, eta: 19:23:23, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7913, top5_acc: 0.9912, loss_cls: 0.9008, loss: 0.9008 +2025-06-24 12:22:44,152 - pyskl - INFO - Epoch [13][800/1281] lr: 2.457e-02, eta: 19:23:04, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7831, top5_acc: 0.9800, loss_cls: 0.9654, loss: 0.9654 +2025-06-24 12:23:25,561 - pyskl - INFO - Epoch [13][900/1281] lr: 2.456e-02, eta: 19:22:43, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7825, top5_acc: 0.9800, loss_cls: 0.9686, loss: 0.9686 +2025-06-24 12:24:07,109 - pyskl - INFO - Epoch [13][1000/1281] lr: 2.455e-02, eta: 19:22:24, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9844, loss_cls: 0.9263, loss: 0.9263 +2025-06-24 12:24:43,526 - pyskl - INFO - Epoch [13][1100/1281] lr: 2.455e-02, eta: 19:21:09, time: 0.364, data_time: 0.000, memory: 4082, top1_acc: 0.7819, top5_acc: 0.9862, loss_cls: 0.9400, loss: 0.9400 +2025-06-24 12:25:15,014 - pyskl - INFO - Epoch [13][1200/1281] lr: 2.454e-02, eta: 19:19:03, time: 0.315, data_time: 0.000, memory: 4082, top1_acc: 0.7769, top5_acc: 0.9838, loss_cls: 0.9623, loss: 0.9623 +2025-06-24 12:25:51,250 - pyskl - INFO - Saving checkpoint at 13 epochs +2025-06-24 12:26:50,857 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:26:50,912 - pyskl - INFO - +top1_acc 0.7432 +top5_acc 0.9757 +2025-06-24 12:26:50,912 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:26:50,919 - pyskl - INFO - +mean_acc 0.6460 +2025-06-24 12:26:50,920 - pyskl - INFO - Epoch(val) [13][533] top1_acc: 0.7432, top5_acc: 0.9757, mean_class_accuracy: 0.6460 +2025-06-24 12:27:50,477 - pyskl - INFO - Epoch [14][100/1281] lr: 2.453e-02, eta: 19:15:44, time: 0.596, data_time: 0.198, memory: 4082, top1_acc: 0.7937, top5_acc: 0.9906, loss_cls: 0.8664, loss: 0.8664 +2025-06-24 12:28:30,391 - pyskl - INFO - Epoch [14][200/1281] lr: 2.453e-02, eta: 19:15:09, time: 0.399, data_time: 0.000, memory: 4082, top1_acc: 0.7812, top5_acc: 0.9919, loss_cls: 0.9032, loss: 0.9032 +2025-06-24 12:29:09,707 - pyskl - INFO - Epoch [14][300/1281] lr: 2.452e-02, eta: 19:14:27, time: 0.393, data_time: 0.000, memory: 4082, top1_acc: 0.7975, top5_acc: 0.9906, loss_cls: 0.8643, loss: 0.8643 +2025-06-24 12:29:49,601 - pyskl - INFO - Epoch [14][400/1281] lr: 2.452e-02, eta: 19:13:51, time: 0.399, data_time: 0.000, memory: 4082, top1_acc: 0.7856, top5_acc: 0.9856, loss_cls: 0.9461, loss: 0.9461 +2025-06-24 12:30:29,133 - pyskl - INFO - Epoch [14][500/1281] lr: 2.451e-02, eta: 19:13:11, time: 0.395, data_time: 0.000, memory: 4082, top1_acc: 0.7944, top5_acc: 0.9881, loss_cls: 0.8868, loss: 0.8868 +2025-06-24 12:31:09,892 - pyskl - INFO - Epoch [14][600/1281] lr: 2.451e-02, eta: 19:12:44, time: 0.408, data_time: 0.000, memory: 4082, top1_acc: 0.7856, top5_acc: 0.9900, loss_cls: 0.8963, loss: 0.8963 +2025-06-24 12:31:50,764 - pyskl - INFO - Epoch [14][700/1281] lr: 2.450e-02, eta: 19:12:18, time: 0.409, data_time: 0.000, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9850, loss_cls: 0.8946, loss: 0.8946 +2025-06-24 12:32:30,872 - pyskl - INFO - Epoch [14][800/1281] lr: 2.449e-02, eta: 19:11:44, time: 0.401, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9850, loss_cls: 0.8874, loss: 0.8874 +2025-06-24 12:33:10,570 - pyskl - INFO - Epoch [14][900/1281] lr: 2.449e-02, eta: 19:11:06, time: 0.397, data_time: 0.000, memory: 4082, top1_acc: 0.7837, top5_acc: 0.9856, loss_cls: 0.9210, loss: 0.9210 +2025-06-24 12:33:49,785 - pyskl - INFO - Epoch [14][1000/1281] lr: 2.448e-02, eta: 19:10:23, time: 0.392, data_time: 0.000, memory: 4082, top1_acc: 0.7900, top5_acc: 0.9875, loss_cls: 0.9274, loss: 0.9274 +2025-06-24 12:34:29,949 - pyskl - INFO - Epoch [14][1100/1281] lr: 2.448e-02, eta: 19:09:49, time: 0.402, data_time: 0.001, memory: 4082, top1_acc: 0.8106, top5_acc: 0.9850, loss_cls: 0.8691, loss: 0.8691 +2025-06-24 12:35:09,432 - pyskl - INFO - Epoch [14][1200/1281] lr: 2.447e-02, eta: 19:09:09, time: 0.395, data_time: 0.001, memory: 4082, top1_acc: 0.7837, top5_acc: 0.9850, loss_cls: 0.9152, loss: 0.9152 +2025-06-24 12:35:42,100 - pyskl - INFO - Saving checkpoint at 14 epochs +2025-06-24 12:36:33,955 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:36:34,026 - pyskl - INFO - +top1_acc 0.7586 +top5_acc 0.9758 +2025-06-24 12:36:34,027 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:36:34,038 - pyskl - INFO - +mean_acc 0.6565 +2025-06-24 12:36:34,044 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_11.pth was removed +2025-06-24 12:36:34,264 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_14.pth. +2025-06-24 12:36:34,264 - pyskl - INFO - Best top1_acc is 0.7586 at 14 epoch. +2025-06-24 12:36:34,267 - pyskl - INFO - Epoch(val) [14][533] top1_acc: 0.7586, top5_acc: 0.9758, mean_class_accuracy: 0.6565 +2025-06-24 12:37:16,258 - pyskl - INFO - Epoch [15][100/1281] lr: 2.446e-02, eta: 19:03:11, time: 0.420, data_time: 0.189, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9906, loss_cls: 0.8763, loss: 0.8763 +2025-06-24 12:37:49,144 - pyskl - INFO - Epoch [15][200/1281] lr: 2.445e-02, eta: 19:01:29, time: 0.329, data_time: 0.000, memory: 4082, top1_acc: 0.7950, top5_acc: 0.9881, loss_cls: 0.9266, loss: 0.9266 +2025-06-24 12:38:27,736 - pyskl - INFO - Epoch [15][300/1281] lr: 2.445e-02, eta: 19:00:43, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8044, top5_acc: 0.9869, loss_cls: 0.9004, loss: 0.9004 +2025-06-24 12:39:06,118 - pyskl - INFO - Epoch [15][400/1281] lr: 2.444e-02, eta: 18:59:54, time: 0.384, data_time: 0.001, memory: 4082, top1_acc: 0.7963, top5_acc: 0.9862, loss_cls: 0.8693, loss: 0.8693 +2025-06-24 12:39:43,695 - pyskl - INFO - Epoch [15][500/1281] lr: 2.444e-02, eta: 18:58:58, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9881, loss_cls: 0.8854, loss: 0.8854 +2025-06-24 12:40:22,543 - pyskl - INFO - Epoch [15][600/1281] lr: 2.443e-02, eta: 18:58:14, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.8037, top5_acc: 0.9875, loss_cls: 0.8634, loss: 0.8634 +2025-06-24 12:41:01,823 - pyskl - INFO - Epoch [15][700/1281] lr: 2.442e-02, eta: 18:57:34, time: 0.393, data_time: 0.000, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9862, loss_cls: 0.9298, loss: 0.9298 +2025-06-24 12:41:41,540 - pyskl - INFO - Epoch [15][800/1281] lr: 2.442e-02, eta: 18:56:59, time: 0.397, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9881, loss_cls: 0.8826, loss: 0.8826 +2025-06-24 12:42:20,433 - pyskl - INFO - Epoch [15][900/1281] lr: 2.441e-02, eta: 18:56:15, time: 0.389, data_time: 0.000, memory: 4082, top1_acc: 0.7950, top5_acc: 0.9838, loss_cls: 0.8968, loss: 0.8968 +2025-06-24 12:42:58,907 - pyskl - INFO - Epoch [15][1000/1281] lr: 2.441e-02, eta: 18:55:28, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9888, loss_cls: 0.8611, loss: 0.8611 +2025-06-24 12:43:38,320 - pyskl - INFO - Epoch [15][1100/1281] lr: 2.440e-02, eta: 18:54:49, time: 0.394, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9894, loss_cls: 0.8498, loss: 0.8498 +2025-06-24 12:44:16,469 - pyskl - INFO - Epoch [15][1200/1281] lr: 2.439e-02, eta: 18:53:59, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9881, loss_cls: 0.8350, loss: 0.8350 +2025-06-24 12:44:48,377 - pyskl - INFO - Saving checkpoint at 15 epochs +2025-06-24 12:45:48,240 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:45:48,298 - pyskl - INFO - +top1_acc 0.7552 +top5_acc 0.9820 +2025-06-24 12:45:48,298 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:45:48,305 - pyskl - INFO - +mean_acc 0.6584 +2025-06-24 12:45:48,307 - pyskl - INFO - Epoch(val) [15][533] top1_acc: 0.7552, top5_acc: 0.9820, mean_class_accuracy: 0.6584 +2025-06-24 12:46:47,701 - pyskl - INFO - Epoch [16][100/1281] lr: 2.438e-02, eta: 18:51:03, time: 0.594, data_time: 0.199, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9900, loss_cls: 0.8038, loss: 0.8038 +2025-06-24 12:47:26,390 - pyskl - INFO - Epoch [16][200/1281] lr: 2.438e-02, eta: 18:50:18, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9888, loss_cls: 0.8510, loss: 0.8510 +2025-06-24 12:47:54,981 - pyskl - INFO - Epoch [16][300/1281] lr: 2.437e-02, eta: 18:48:04, time: 0.286, data_time: 0.000, memory: 4082, top1_acc: 0.7913, top5_acc: 0.9838, loss_cls: 0.9279, loss: 0.9279 +2025-06-24 12:48:34,213 - pyskl - INFO - Epoch [16][400/1281] lr: 2.436e-02, eta: 18:47:25, time: 0.392, data_time: 0.000, memory: 4082, top1_acc: 0.8231, top5_acc: 0.9888, loss_cls: 0.7820, loss: 0.7820 +2025-06-24 12:49:06,129 - pyskl - INFO - Epoch [16][500/1281] lr: 2.436e-02, eta: 18:45:42, time: 0.319, data_time: 0.000, memory: 4082, top1_acc: 0.7850, top5_acc: 0.9856, loss_cls: 0.9150, loss: 0.9150 +2025-06-24 12:49:31,331 - pyskl - INFO - Epoch [16][600/1281] lr: 2.435e-02, eta: 18:43:02, time: 0.252, data_time: 0.000, memory: 4082, top1_acc: 0.7969, top5_acc: 0.9900, loss_cls: 0.9077, loss: 0.9077 +2025-06-24 12:50:09,878 - pyskl - INFO - Epoch [16][700/1281] lr: 2.434e-02, eta: 18:42:18, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.7969, top5_acc: 0.9900, loss_cls: 0.8727, loss: 0.8727 +2025-06-24 12:50:48,092 - pyskl - INFO - Epoch [16][800/1281] lr: 2.434e-02, eta: 18:41:31, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9912, loss_cls: 0.7954, loss: 0.7954 +2025-06-24 12:51:26,824 - pyskl - INFO - Epoch [16][900/1281] lr: 2.433e-02, eta: 18:40:49, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.8031, top5_acc: 0.9881, loss_cls: 0.8417, loss: 0.8417 +2025-06-24 12:52:05,846 - pyskl - INFO - Epoch [16][1000/1281] lr: 2.432e-02, eta: 18:40:09, time: 0.390, data_time: 0.000, memory: 4082, top1_acc: 0.8025, top5_acc: 0.9931, loss_cls: 0.8393, loss: 0.8393 +2025-06-24 12:52:45,056 - pyskl - INFO - Epoch [16][1100/1281] lr: 2.432e-02, eta: 18:39:31, time: 0.392, data_time: 0.000, memory: 4082, top1_acc: 0.8056, top5_acc: 0.9825, loss_cls: 0.8591, loss: 0.8591 +2025-06-24 12:53:23,664 - pyskl - INFO - Epoch [16][1200/1281] lr: 2.431e-02, eta: 18:38:48, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8144, top5_acc: 0.9931, loss_cls: 0.7984, loss: 0.7984 +2025-06-24 12:53:55,511 - pyskl - INFO - Saving checkpoint at 16 epochs +2025-06-24 12:54:54,874 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:54:54,930 - pyskl - INFO - +top1_acc 0.7731 +top5_acc 0.9778 +2025-06-24 12:54:54,930 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:54:54,937 - pyskl - INFO - +mean_acc 0.7008 +2025-06-24 12:54:54,941 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_14.pth was removed +2025-06-24 12:54:55,119 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_16.pth. +2025-06-24 12:54:55,119 - pyskl - INFO - Best top1_acc is 0.7731 at 16 epoch. +2025-06-24 12:54:55,122 - pyskl - INFO - Epoch(val) [16][533] top1_acc: 0.7731, top5_acc: 0.9778, mean_class_accuracy: 0.7008 +2025-06-24 12:55:53,351 - pyskl - INFO - Epoch [17][100/1281] lr: 2.430e-02, eta: 18:35:53, time: 0.582, data_time: 0.195, memory: 4082, top1_acc: 0.8037, top5_acc: 0.9931, loss_cls: 0.8498, loss: 0.8498 +2025-06-24 12:56:31,389 - pyskl - INFO - Epoch [17][200/1281] lr: 2.429e-02, eta: 18:35:06, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9900, loss_cls: 0.7757, loss: 0.7757 +2025-06-24 12:57:10,532 - pyskl - INFO - Epoch [17][300/1281] lr: 2.428e-02, eta: 18:34:28, time: 0.391, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9900, loss_cls: 0.7807, loss: 0.7807 +2025-06-24 12:57:48,659 - pyskl - INFO - Epoch [17][400/1281] lr: 2.428e-02, eta: 18:33:41, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9894, loss_cls: 0.7662, loss: 0.7662 +2025-06-24 12:58:27,581 - pyskl - INFO - Epoch [17][500/1281] lr: 2.427e-02, eta: 18:33:02, time: 0.389, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9875, loss_cls: 0.8347, loss: 0.8347 +2025-06-24 12:59:05,382 - pyskl - INFO - Epoch [17][600/1281] lr: 2.426e-02, eta: 18:32:13, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8063, top5_acc: 0.9912, loss_cls: 0.8783, loss: 0.8783 +2025-06-24 12:59:43,560 - pyskl - INFO - Epoch [17][700/1281] lr: 2.426e-02, eta: 18:31:27, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8194, top5_acc: 0.9906, loss_cls: 0.8202, loss: 0.8202 +2025-06-24 13:00:08,803 - pyskl - INFO - Epoch [17][800/1281] lr: 2.425e-02, eta: 18:28:57, time: 0.252, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9944, loss_cls: 0.7552, loss: 0.7552 +2025-06-24 13:00:54,153 - pyskl - INFO - Epoch [17][900/1281] lr: 2.424e-02, eta: 18:29:10, time: 0.453, data_time: 0.000, memory: 4082, top1_acc: 0.8069, top5_acc: 0.9900, loss_cls: 0.8306, loss: 0.8306 +2025-06-24 13:01:17,768 - pyskl - INFO - Epoch [17][1000/1281] lr: 2.424e-02, eta: 18:26:29, time: 0.236, data_time: 0.001, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9912, loss_cls: 0.8004, loss: 0.8004 +2025-06-24 13:01:51,188 - pyskl - INFO - Epoch [17][1100/1281] lr: 2.423e-02, eta: 18:25:06, time: 0.334, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9906, loss_cls: 0.8179, loss: 0.8179 +2025-06-24 13:02:29,534 - pyskl - INFO - Epoch [17][1200/1281] lr: 2.422e-02, eta: 18:24:23, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.7887, top5_acc: 0.9881, loss_cls: 0.9059, loss: 0.9059 +2025-06-24 13:03:01,717 - pyskl - INFO - Saving checkpoint at 17 epochs +2025-06-24 13:04:01,149 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:04:01,218 - pyskl - INFO - +top1_acc 0.7778 +top5_acc 0.9838 +2025-06-24 13:04:01,218 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:04:01,227 - pyskl - INFO - +mean_acc 0.6760 +2025-06-24 13:04:01,232 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_16.pth was removed +2025-06-24 13:04:01,410 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_17.pth. +2025-06-24 13:04:01,410 - pyskl - INFO - Best top1_acc is 0.7778 at 17 epoch. +2025-06-24 13:04:01,413 - pyskl - INFO - Epoch(val) [17][533] top1_acc: 0.7778, top5_acc: 0.9838, mean_class_accuracy: 0.6760 +2025-06-24 13:04:59,488 - pyskl - INFO - Epoch [18][100/1281] lr: 2.421e-02, eta: 18:21:37, time: 0.581, data_time: 0.196, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9844, loss_cls: 0.8274, loss: 0.8274 +2025-06-24 13:05:38,196 - pyskl - INFO - Epoch [18][200/1281] lr: 2.420e-02, eta: 18:20:58, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9931, loss_cls: 0.7543, loss: 0.7543 +2025-06-24 13:06:16,846 - pyskl - INFO - Epoch [18][300/1281] lr: 2.419e-02, eta: 18:20:18, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.7944, top5_acc: 0.9894, loss_cls: 0.8378, loss: 0.8378 +2025-06-24 13:06:55,184 - pyskl - INFO - Epoch [18][400/1281] lr: 2.419e-02, eta: 18:19:35, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9894, loss_cls: 0.7895, loss: 0.7895 +2025-06-24 13:07:34,324 - pyskl - INFO - Epoch [18][500/1281] lr: 2.418e-02, eta: 18:18:59, time: 0.391, data_time: 0.000, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9912, loss_cls: 0.8133, loss: 0.8133 +2025-06-24 13:08:13,799 - pyskl - INFO - Epoch [18][600/1281] lr: 2.417e-02, eta: 18:18:25, time: 0.395, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9894, loss_cls: 0.7973, loss: 0.7973 +2025-06-24 13:08:52,507 - pyskl - INFO - Epoch [18][700/1281] lr: 2.417e-02, eta: 18:17:45, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.8237, top5_acc: 0.9900, loss_cls: 0.7832, loss: 0.7832 +2025-06-24 13:09:30,686 - pyskl - INFO - Epoch [18][800/1281] lr: 2.416e-02, eta: 18:17:02, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9906, loss_cls: 0.8195, loss: 0.8195 +2025-06-24 13:10:08,966 - pyskl - INFO - Epoch [18][900/1281] lr: 2.415e-02, eta: 18:16:19, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8025, top5_acc: 0.9881, loss_cls: 0.8634, loss: 0.8634 +2025-06-24 13:10:47,914 - pyskl - INFO - Epoch [18][1000/1281] lr: 2.414e-02, eta: 18:15:41, time: 0.389, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9906, loss_cls: 0.7859, loss: 0.7859 +2025-06-24 13:11:27,726 - pyskl - INFO - Epoch [18][1100/1281] lr: 2.414e-02, eta: 18:15:09, time: 0.398, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9925, loss_cls: 0.7380, loss: 0.7380 +2025-06-24 13:11:55,288 - pyskl - INFO - Epoch [18][1200/1281] lr: 2.413e-02, eta: 18:13:08, time: 0.276, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9900, loss_cls: 0.8620, loss: 0.8620 +2025-06-24 13:12:30,035 - pyskl - INFO - Saving checkpoint at 18 epochs +2025-06-24 13:13:16,764 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:13:16,834 - pyskl - INFO - +top1_acc 0.7645 +top5_acc 0.9840 +2025-06-24 13:13:16,835 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:13:16,845 - pyskl - INFO - +mean_acc 0.6910 +2025-06-24 13:13:16,848 - pyskl - INFO - Epoch(val) [18][533] top1_acc: 0.7645, top5_acc: 0.9840, mean_class_accuracy: 0.6910 +2025-06-24 13:14:16,122 - pyskl - INFO - Epoch [19][100/1281] lr: 2.411e-02, eta: 18:10:38, time: 0.593, data_time: 0.196, memory: 4082, top1_acc: 0.7963, top5_acc: 0.9919, loss_cls: 0.8468, loss: 0.8468 +2025-06-24 13:14:54,489 - pyskl - INFO - Epoch [19][200/1281] lr: 2.411e-02, eta: 18:09:57, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9906, loss_cls: 0.7984, loss: 0.7984 +2025-06-24 13:15:32,557 - pyskl - INFO - Epoch [19][300/1281] lr: 2.410e-02, eta: 18:09:13, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8231, top5_acc: 0.9906, loss_cls: 0.7982, loss: 0.7982 +2025-06-24 13:16:10,605 - pyskl - INFO - Epoch [19][400/1281] lr: 2.409e-02, eta: 18:08:30, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9894, loss_cls: 0.7604, loss: 0.7604 +2025-06-24 13:16:49,279 - pyskl - INFO - Epoch [19][500/1281] lr: 2.408e-02, eta: 18:07:51, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9931, loss_cls: 0.7905, loss: 0.7905 +2025-06-24 13:17:28,244 - pyskl - INFO - Epoch [19][600/1281] lr: 2.408e-02, eta: 18:07:14, time: 0.390, data_time: 0.000, memory: 4082, top1_acc: 0.8275, top5_acc: 0.9931, loss_cls: 0.7789, loss: 0.7789 +2025-06-24 13:18:06,920 - pyskl - INFO - Epoch [19][700/1281] lr: 2.407e-02, eta: 18:06:35, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.8106, top5_acc: 0.9919, loss_cls: 0.8201, loss: 0.8201 +2025-06-24 13:18:45,795 - pyskl - INFO - Epoch [19][800/1281] lr: 2.406e-02, eta: 18:05:57, time: 0.389, data_time: 0.000, memory: 4082, top1_acc: 0.8175, top5_acc: 0.9925, loss_cls: 0.7819, loss: 0.7819 +2025-06-24 13:19:24,358 - pyskl - INFO - Epoch [19][900/1281] lr: 2.405e-02, eta: 18:05:17, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8144, top5_acc: 0.9931, loss_cls: 0.7894, loss: 0.7894 +2025-06-24 13:20:03,100 - pyskl - INFO - Epoch [19][1000/1281] lr: 2.405e-02, eta: 18:04:39, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.8194, top5_acc: 0.9900, loss_cls: 0.7955, loss: 0.7955 +2025-06-24 13:20:41,670 - pyskl - INFO - Epoch [19][1100/1281] lr: 2.404e-02, eta: 18:03:59, time: 0.386, data_time: 0.001, memory: 4082, top1_acc: 0.8231, top5_acc: 0.9881, loss_cls: 0.8236, loss: 0.8236 +2025-06-24 13:21:19,968 - pyskl - INFO - Epoch [19][1200/1281] lr: 2.403e-02, eta: 18:03:17, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9938, loss_cls: 0.7383, loss: 0.7383 +2025-06-24 13:21:51,980 - pyskl - INFO - Saving checkpoint at 19 epochs +2025-06-24 13:22:51,617 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:22:51,687 - pyskl - INFO - +top1_acc 0.7511 +top5_acc 0.9755 +2025-06-24 13:22:51,687 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:22:51,696 - pyskl - INFO - +mean_acc 0.6504 +2025-06-24 13:22:51,698 - pyskl - INFO - Epoch(val) [19][533] top1_acc: 0.7511, top5_acc: 0.9755, mean_class_accuracy: 0.6504 +2025-06-24 13:23:37,065 - pyskl - INFO - Epoch [20][100/1281] lr: 2.402e-02, eta: 17:59:18, time: 0.454, data_time: 0.191, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9925, loss_cls: 0.7515, loss: 0.7515 +2025-06-24 13:24:22,085 - pyskl - INFO - Epoch [20][200/1281] lr: 2.401e-02, eta: 17:59:23, time: 0.450, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9938, loss_cls: 0.7437, loss: 0.7437 +2025-06-24 13:24:44,593 - pyskl - INFO - Epoch [20][300/1281] lr: 2.400e-02, eta: 17:56:55, time: 0.225, data_time: 0.000, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9912, loss_cls: 0.7946, loss: 0.7946 +2025-06-24 13:25:17,930 - pyskl - INFO - Epoch [20][400/1281] lr: 2.399e-02, eta: 17:55:41, time: 0.333, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9925, loss_cls: 0.7970, loss: 0.7970 +2025-06-24 13:25:56,168 - pyskl - INFO - Epoch [20][500/1281] lr: 2.398e-02, eta: 17:55:00, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9900, loss_cls: 0.7613, loss: 0.7613 +2025-06-24 13:26:33,566 - pyskl - INFO - Epoch [20][600/1281] lr: 2.398e-02, eta: 17:54:14, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8163, top5_acc: 0.9919, loss_cls: 0.8164, loss: 0.8164 +2025-06-24 13:27:12,365 - pyskl - INFO - Epoch [20][700/1281] lr: 2.397e-02, eta: 17:53:37, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.8237, top5_acc: 0.9894, loss_cls: 0.8109, loss: 0.8109 +2025-06-24 13:27:50,416 - pyskl - INFO - Epoch [20][800/1281] lr: 2.396e-02, eta: 17:52:55, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8069, top5_acc: 0.9912, loss_cls: 0.7956, loss: 0.7956 +2025-06-24 13:28:28,512 - pyskl - INFO - Epoch [20][900/1281] lr: 2.395e-02, eta: 17:52:13, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9900, loss_cls: 0.7912, loss: 0.7912 +2025-06-24 13:29:07,339 - pyskl - INFO - Epoch [20][1000/1281] lr: 2.394e-02, eta: 17:51:37, time: 0.388, data_time: 0.001, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9875, loss_cls: 0.8105, loss: 0.8105 +2025-06-24 13:29:45,391 - pyskl - INFO - Epoch [20][1100/1281] lr: 2.393e-02, eta: 17:50:55, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8131, top5_acc: 0.9919, loss_cls: 0.7963, loss: 0.7963 +2025-06-24 13:30:23,580 - pyskl - INFO - Epoch [20][1200/1281] lr: 2.393e-02, eta: 17:50:14, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8200, top5_acc: 0.9881, loss_cls: 0.7832, loss: 0.7832 +2025-06-24 13:30:55,294 - pyskl - INFO - Saving checkpoint at 20 epochs +2025-06-24 13:31:54,626 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:31:54,705 - pyskl - INFO - +top1_acc 0.8013 +top5_acc 0.9878 +2025-06-24 13:31:54,705 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:31:54,715 - pyskl - INFO - +mean_acc 0.7120 +2025-06-24 13:31:54,721 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_17.pth was removed +2025-06-24 13:31:54,896 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_20.pth. +2025-06-24 13:31:54,896 - pyskl - INFO - Best top1_acc is 0.8013 at 20 epoch. +2025-06-24 13:31:54,900 - pyskl - INFO - Epoch(val) [20][533] top1_acc: 0.8013, top5_acc: 0.9878, mean_class_accuracy: 0.7120 +2025-06-24 13:32:53,178 - pyskl - INFO - Epoch [21][100/1281] lr: 2.391e-02, eta: 17:47:50, time: 0.583, data_time: 0.196, memory: 4082, top1_acc: 0.8344, top5_acc: 0.9944, loss_cls: 0.7240, loss: 0.7240 +2025-06-24 13:33:31,304 - pyskl - INFO - Epoch [21][200/1281] lr: 2.390e-02, eta: 17:47:09, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9925, loss_cls: 0.7389, loss: 0.7389 +2025-06-24 13:34:09,333 - pyskl - INFO - Epoch [21][300/1281] lr: 2.389e-02, eta: 17:46:28, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8494, top5_acc: 0.9919, loss_cls: 0.7057, loss: 0.7057 +2025-06-24 13:34:46,942 - pyskl - INFO - Epoch [21][400/1281] lr: 2.389e-02, eta: 17:45:43, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.7963, top5_acc: 0.9894, loss_cls: 0.8048, loss: 0.8048 +2025-06-24 13:35:23,216 - pyskl - INFO - Epoch [21][500/1281] lr: 2.388e-02, eta: 17:44:51, time: 0.363, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9931, loss_cls: 0.7566, loss: 0.7566 +2025-06-24 13:35:52,327 - pyskl - INFO - Epoch [21][600/1281] lr: 2.387e-02, eta: 17:43:13, time: 0.291, data_time: 0.000, memory: 4082, top1_acc: 0.8406, top5_acc: 0.9950, loss_cls: 0.7032, loss: 0.7032 +2025-06-24 13:36:34,544 - pyskl - INFO - Epoch [21][700/1281] lr: 2.386e-02, eta: 17:42:58, time: 0.422, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9912, loss_cls: 0.8116, loss: 0.8116 +2025-06-24 13:36:57,358 - pyskl - INFO - Epoch [21][800/1281] lr: 2.385e-02, eta: 17:40:42, time: 0.228, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9906, loss_cls: 0.7607, loss: 0.7607 +2025-06-24 13:37:31,630 - pyskl - INFO - Epoch [21][900/1281] lr: 2.384e-02, eta: 17:39:38, time: 0.343, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9894, loss_cls: 0.7842, loss: 0.7842 +2025-06-24 13:38:09,616 - pyskl - INFO - Epoch [21][1000/1281] lr: 2.383e-02, eta: 17:38:57, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9856, loss_cls: 0.8296, loss: 0.8296 +2025-06-24 13:38:47,882 - pyskl - INFO - Epoch [21][1100/1281] lr: 2.383e-02, eta: 17:38:18, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9906, loss_cls: 0.7680, loss: 0.7680 +2025-06-24 13:39:25,980 - pyskl - INFO - Epoch [21][1200/1281] lr: 2.382e-02, eta: 17:37:37, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9900, loss_cls: 0.7526, loss: 0.7526 +2025-06-24 13:39:58,011 - pyskl - INFO - Saving checkpoint at 21 epochs +2025-06-24 13:40:57,807 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:40:57,864 - pyskl - INFO - +top1_acc 0.7715 +top5_acc 0.9805 +2025-06-24 13:40:57,864 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:40:57,872 - pyskl - INFO - +mean_acc 0.6570 +2025-06-24 13:40:57,874 - pyskl - INFO - Epoch(val) [21][533] top1_acc: 0.7715, top5_acc: 0.9805, mean_class_accuracy: 0.6570 +2025-06-24 13:41:56,823 - pyskl - INFO - Epoch [22][100/1281] lr: 2.380e-02, eta: 17:35:24, time: 0.589, data_time: 0.196, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9944, loss_cls: 0.7342, loss: 0.7342 +2025-06-24 13:42:35,551 - pyskl - INFO - Epoch [22][200/1281] lr: 2.379e-02, eta: 17:34:47, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.8344, top5_acc: 0.9906, loss_cls: 0.7298, loss: 0.7298 +2025-06-24 13:43:14,514 - pyskl - INFO - Epoch [22][300/1281] lr: 2.378e-02, eta: 17:34:13, time: 0.390, data_time: 0.000, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9919, loss_cls: 0.7250, loss: 0.7250 +2025-06-24 13:43:52,882 - pyskl - INFO - Epoch [22][400/1281] lr: 2.378e-02, eta: 17:33:35, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9944, loss_cls: 0.6808, loss: 0.6808 +2025-06-24 13:44:30,927 - pyskl - INFO - Epoch [22][500/1281] lr: 2.377e-02, eta: 17:32:54, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8144, top5_acc: 0.9925, loss_cls: 0.7766, loss: 0.7766 +2025-06-24 13:45:09,249 - pyskl - INFO - Epoch [22][600/1281] lr: 2.376e-02, eta: 17:32:16, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9856, loss_cls: 0.7868, loss: 0.7868 +2025-06-24 13:45:48,068 - pyskl - INFO - Epoch [22][700/1281] lr: 2.375e-02, eta: 17:31:40, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9931, loss_cls: 0.7771, loss: 0.7771 +2025-06-24 13:46:26,847 - pyskl - INFO - Epoch [22][800/1281] lr: 2.374e-02, eta: 17:31:05, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.8263, top5_acc: 0.9919, loss_cls: 0.7640, loss: 0.7640 +2025-06-24 13:47:06,031 - pyskl - INFO - Epoch [22][900/1281] lr: 2.373e-02, eta: 17:30:31, time: 0.392, data_time: 0.000, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9931, loss_cls: 0.7299, loss: 0.7299 +2025-06-24 13:47:35,033 - pyskl - INFO - Epoch [22][1000/1281] lr: 2.372e-02, eta: 17:28:58, time: 0.290, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9912, loss_cls: 0.7041, loss: 0.7041 +2025-06-24 13:48:13,835 - pyskl - INFO - Epoch [22][1100/1281] lr: 2.371e-02, eta: 17:28:22, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.8063, top5_acc: 0.9919, loss_cls: 0.8226, loss: 0.8226 +2025-06-24 13:48:46,291 - pyskl - INFO - Epoch [22][1200/1281] lr: 2.370e-02, eta: 17:27:10, time: 0.325, data_time: 0.000, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9888, loss_cls: 0.7781, loss: 0.7781 +2025-06-24 13:49:06,340 - pyskl - INFO - Saving checkpoint at 22 epochs +2025-06-24 13:50:05,952 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:50:06,026 - pyskl - INFO - +top1_acc 0.7693 +top5_acc 0.9786 +2025-06-24 13:50:06,026 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:50:06,036 - pyskl - INFO - +mean_acc 0.7044 +2025-06-24 13:50:06,039 - pyskl - INFO - Epoch(val) [22][533] top1_acc: 0.7693, top5_acc: 0.9786, mean_class_accuracy: 0.7044 +2025-06-24 13:51:04,314 - pyskl - INFO - Epoch [23][100/1281] lr: 2.369e-02, eta: 17:24:56, time: 0.583, data_time: 0.195, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9931, loss_cls: 0.7030, loss: 0.7030 +2025-06-24 13:51:42,463 - pyskl - INFO - Epoch [23][200/1281] lr: 2.368e-02, eta: 17:24:17, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8425, top5_acc: 0.9925, loss_cls: 0.6803, loss: 0.6803 +2025-06-24 13:52:20,988 - pyskl - INFO - Epoch [23][300/1281] lr: 2.367e-02, eta: 17:23:41, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9938, loss_cls: 0.7372, loss: 0.7372 +2025-06-24 13:52:58,188 - pyskl - INFO - Epoch [23][400/1281] lr: 2.366e-02, eta: 17:22:56, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9950, loss_cls: 0.7332, loss: 0.7332 +2025-06-24 13:53:36,400 - pyskl - INFO - Epoch [23][500/1281] lr: 2.365e-02, eta: 17:22:18, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8231, top5_acc: 0.9906, loss_cls: 0.7841, loss: 0.7841 +2025-06-24 13:54:14,595 - pyskl - INFO - Epoch [23][600/1281] lr: 2.364e-02, eta: 17:21:39, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9944, loss_cls: 0.7445, loss: 0.7445 +2025-06-24 13:54:53,010 - pyskl - INFO - Epoch [23][700/1281] lr: 2.363e-02, eta: 17:21:02, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9869, loss_cls: 0.7820, loss: 0.7820 +2025-06-24 13:55:31,330 - pyskl - INFO - Epoch [23][800/1281] lr: 2.362e-02, eta: 17:20:24, time: 0.383, data_time: 0.001, memory: 4082, top1_acc: 0.8075, top5_acc: 0.9894, loss_cls: 0.8107, loss: 0.8107 +2025-06-24 13:56:10,360 - pyskl - INFO - Epoch [23][900/1281] lr: 2.361e-02, eta: 17:19:50, time: 0.390, data_time: 0.000, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9925, loss_cls: 0.7441, loss: 0.7441 +2025-06-24 13:56:48,173 - pyskl - INFO - Epoch [23][1000/1281] lr: 2.360e-02, eta: 17:19:09, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9919, loss_cls: 0.7483, loss: 0.7483 +2025-06-24 13:57:26,544 - pyskl - INFO - Epoch [23][1100/1281] lr: 2.359e-02, eta: 17:18:32, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9856, loss_cls: 0.7751, loss: 0.7751 +2025-06-24 13:58:04,742 - pyskl - INFO - Epoch [23][1200/1281] lr: 2.359e-02, eta: 17:17:53, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9894, loss_cls: 0.7777, loss: 0.7777 +2025-06-24 13:58:36,534 - pyskl - INFO - Saving checkpoint at 23 epochs +2025-06-24 13:59:26,753 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:59:26,809 - pyskl - INFO - +top1_acc 0.8064 +top5_acc 0.9887 +2025-06-24 13:59:26,809 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:59:26,816 - pyskl - INFO - +mean_acc 0.7265 +2025-06-24 13:59:26,821 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_20.pth was removed +2025-06-24 13:59:27,000 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_23.pth. +2025-06-24 13:59:27,000 - pyskl - INFO - Best top1_acc is 0.8064 at 23 epoch. +2025-06-24 13:59:27,003 - pyskl - INFO - Epoch(val) [23][533] top1_acc: 0.8064, top5_acc: 0.9887, mean_class_accuracy: 0.7265 +2025-06-24 14:00:15,430 - pyskl - INFO - Epoch [24][100/1281] lr: 2.357e-02, eta: 17:14:49, time: 0.484, data_time: 0.201, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9931, loss_cls: 0.6464, loss: 0.6464 +2025-06-24 14:00:44,066 - pyskl - INFO - Epoch [24][200/1281] lr: 2.356e-02, eta: 17:13:19, time: 0.286, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9938, loss_cls: 0.6927, loss: 0.6927 +2025-06-24 14:01:22,949 - pyskl - INFO - Epoch [24][300/1281] lr: 2.355e-02, eta: 17:12:45, time: 0.389, data_time: 0.000, memory: 4082, top1_acc: 0.8406, top5_acc: 0.9894, loss_cls: 0.7261, loss: 0.7261 +2025-06-24 14:02:00,933 - pyskl - INFO - Epoch [24][400/1281] lr: 2.354e-02, eta: 17:12:05, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9950, loss_cls: 0.7077, loss: 0.7077 +2025-06-24 14:02:39,805 - pyskl - INFO - Epoch [24][500/1281] lr: 2.353e-02, eta: 17:11:31, time: 0.389, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9931, loss_cls: 0.6881, loss: 0.6881 +2025-06-24 14:03:18,083 - pyskl - INFO - Epoch [24][600/1281] lr: 2.352e-02, eta: 17:10:54, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9931, loss_cls: 0.7471, loss: 0.7471 +2025-06-24 14:03:57,779 - pyskl - INFO - Epoch [24][700/1281] lr: 2.351e-02, eta: 17:10:24, time: 0.397, data_time: 0.000, memory: 4082, top1_acc: 0.8263, top5_acc: 0.9944, loss_cls: 0.7313, loss: 0.7313 +2025-06-24 14:04:36,545 - pyskl - INFO - Epoch [24][800/1281] lr: 2.350e-02, eta: 17:09:49, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.8406, top5_acc: 0.9906, loss_cls: 0.7248, loss: 0.7248 +2025-06-24 14:05:14,726 - pyskl - INFO - Epoch [24][900/1281] lr: 2.349e-02, eta: 17:09:11, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9900, loss_cls: 0.8056, loss: 0.8056 +2025-06-24 14:05:52,089 - pyskl - INFO - Epoch [24][1000/1281] lr: 2.348e-02, eta: 17:08:28, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9912, loss_cls: 0.7282, loss: 0.7282 +2025-06-24 14:06:29,962 - pyskl - INFO - Epoch [24][1100/1281] lr: 2.347e-02, eta: 17:07:48, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9938, loss_cls: 0.7163, loss: 0.7163 +2025-06-24 14:07:08,377 - pyskl - INFO - Epoch [24][1200/1281] lr: 2.346e-02, eta: 17:07:12, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9919, loss_cls: 0.7390, loss: 0.7390 +2025-06-24 14:07:39,780 - pyskl - INFO - Saving checkpoint at 24 epochs +2025-06-24 14:08:39,185 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:08:39,252 - pyskl - INFO - +top1_acc 0.7717 +top5_acc 0.9799 +2025-06-24 14:08:39,252 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:08:39,260 - pyskl - INFO - +mean_acc 0.7182 +2025-06-24 14:08:39,261 - pyskl - INFO - Epoch(val) [24][533] top1_acc: 0.7717, top5_acc: 0.9799, mean_class_accuracy: 0.7182 +2025-06-24 14:09:37,827 - pyskl - INFO - Epoch [25][100/1281] lr: 2.344e-02, eta: 17:05:08, time: 0.586, data_time: 0.198, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9938, loss_cls: 0.7071, loss: 0.7071 +2025-06-24 14:10:16,276 - pyskl - INFO - Epoch [25][200/1281] lr: 2.343e-02, eta: 17:04:31, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9962, loss_cls: 0.7075, loss: 0.7075 +2025-06-24 14:10:53,561 - pyskl - INFO - Epoch [25][300/1281] lr: 2.342e-02, eta: 17:03:48, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8313, top5_acc: 0.9925, loss_cls: 0.7319, loss: 0.7319 +2025-06-24 14:11:20,840 - pyskl - INFO - Epoch [25][400/1281] lr: 2.341e-02, eta: 17:02:14, time: 0.273, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9962, loss_cls: 0.6952, loss: 0.6952 +2025-06-24 14:12:04,690 - pyskl - INFO - Epoch [25][500/1281] lr: 2.340e-02, eta: 17:02:06, time: 0.439, data_time: 0.000, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9931, loss_cls: 0.6763, loss: 0.6763 +2025-06-24 14:12:27,063 - pyskl - INFO - Epoch [25][600/1281] lr: 2.339e-02, eta: 17:00:07, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.8381, top5_acc: 0.9906, loss_cls: 0.7392, loss: 0.7392 +2025-06-24 14:13:00,876 - pyskl - INFO - Epoch [25][700/1281] lr: 2.338e-02, eta: 16:59:07, time: 0.338, data_time: 0.000, memory: 4082, top1_acc: 0.8263, top5_acc: 0.9919, loss_cls: 0.7653, loss: 0.7653 +2025-06-24 14:13:39,392 - pyskl - INFO - Epoch [25][800/1281] lr: 2.337e-02, eta: 16:58:31, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9956, loss_cls: 0.6336, loss: 0.6336 +2025-06-24 14:14:17,543 - pyskl - INFO - Epoch [25][900/1281] lr: 2.336e-02, eta: 16:57:54, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9900, loss_cls: 0.7721, loss: 0.7721 +2025-06-24 14:14:56,204 - pyskl - INFO - Epoch [25][1000/1281] lr: 2.335e-02, eta: 16:57:19, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9950, loss_cls: 0.7280, loss: 0.7280 +2025-06-24 14:15:34,382 - pyskl - INFO - Epoch [25][1100/1281] lr: 2.334e-02, eta: 16:56:42, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9931, loss_cls: 0.7249, loss: 0.7249 +2025-06-24 14:16:13,180 - pyskl - INFO - Epoch [25][1200/1281] lr: 2.333e-02, eta: 16:56:07, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9869, loss_cls: 0.7631, loss: 0.7631 +2025-06-24 14:16:44,661 - pyskl - INFO - Saving checkpoint at 25 epochs +2025-06-24 14:17:44,133 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:17:44,188 - pyskl - INFO - +top1_acc 0.8062 +top5_acc 0.9877 +2025-06-24 14:17:44,188 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:17:44,196 - pyskl - INFO - +mean_acc 0.7263 +2025-06-24 14:17:44,198 - pyskl - INFO - Epoch(val) [25][533] top1_acc: 0.8062, top5_acc: 0.9877, mean_class_accuracy: 0.7263 +2025-06-24 14:18:41,812 - pyskl - INFO - Epoch [26][100/1281] lr: 2.332e-02, eta: 16:54:02, time: 0.576, data_time: 0.190, memory: 4082, top1_acc: 0.8556, top5_acc: 0.9962, loss_cls: 0.6353, loss: 0.6353 +2025-06-24 14:19:20,197 - pyskl - INFO - Epoch [26][200/1281] lr: 2.330e-02, eta: 16:53:26, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9931, loss_cls: 0.7212, loss: 0.7212 +2025-06-24 14:19:58,667 - pyskl - INFO - Epoch [26][300/1281] lr: 2.329e-02, eta: 16:52:50, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9919, loss_cls: 0.7138, loss: 0.7138 +2025-06-24 14:20:37,278 - pyskl - INFO - Epoch [26][400/1281] lr: 2.328e-02, eta: 16:52:15, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8569, top5_acc: 0.9925, loss_cls: 0.6873, loss: 0.6873 +2025-06-24 14:21:16,262 - pyskl - INFO - Epoch [26][500/1281] lr: 2.327e-02, eta: 16:51:42, time: 0.390, data_time: 0.000, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9931, loss_cls: 0.7345, loss: 0.7345 +2025-06-24 14:21:54,989 - pyskl - INFO - Epoch [26][600/1281] lr: 2.326e-02, eta: 16:51:07, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9944, loss_cls: 0.6592, loss: 0.6592 +2025-06-24 14:22:33,149 - pyskl - INFO - Epoch [26][700/1281] lr: 2.325e-02, eta: 16:50:30, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8612, top5_acc: 0.9919, loss_cls: 0.6807, loss: 0.6807 +2025-06-24 14:23:04,623 - pyskl - INFO - Epoch [26][800/1281] lr: 2.324e-02, eta: 16:49:20, time: 0.315, data_time: 0.000, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9925, loss_cls: 0.7329, loss: 0.7329 +2025-06-24 14:23:41,162 - pyskl - INFO - Epoch [26][900/1281] lr: 2.323e-02, eta: 16:48:35, time: 0.365, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9900, loss_cls: 0.7554, loss: 0.7554 +2025-06-24 14:24:15,619 - pyskl - INFO - Epoch [26][1000/1281] lr: 2.322e-02, eta: 16:47:40, time: 0.345, data_time: 0.000, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9912, loss_cls: 0.7007, loss: 0.7007 +2025-06-24 14:24:41,085 - pyskl - INFO - Epoch [26][1100/1281] lr: 2.321e-02, eta: 16:46:01, time: 0.255, data_time: 0.001, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9956, loss_cls: 0.7421, loss: 0.7421 +2025-06-24 14:25:19,613 - pyskl - INFO - Epoch [26][1200/1281] lr: 2.320e-02, eta: 16:45:26, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8494, top5_acc: 0.9919, loss_cls: 0.6783, loss: 0.6783 +2025-06-24 14:25:50,862 - pyskl - INFO - Saving checkpoint at 26 epochs +2025-06-24 14:26:50,486 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:26:50,556 - pyskl - INFO - +top1_acc 0.8040 +top5_acc 0.9839 +2025-06-24 14:26:50,556 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:26:50,564 - pyskl - INFO - +mean_acc 0.7119 +2025-06-24 14:26:50,567 - pyskl - INFO - Epoch(val) [26][533] top1_acc: 0.8040, top5_acc: 0.9839, mean_class_accuracy: 0.7119 +2025-06-24 14:27:49,299 - pyskl - INFO - Epoch [27][100/1281] lr: 2.318e-02, eta: 16:43:30, time: 0.587, data_time: 0.193, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9944, loss_cls: 0.7032, loss: 0.7032 +2025-06-24 14:28:27,905 - pyskl - INFO - Epoch [27][200/1281] lr: 2.317e-02, eta: 16:42:55, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8581, top5_acc: 0.9919, loss_cls: 0.6528, loss: 0.6528 +2025-06-24 14:29:05,864 - pyskl - INFO - Epoch [27][300/1281] lr: 2.316e-02, eta: 16:42:18, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9925, loss_cls: 0.7137, loss: 0.7137 +2025-06-24 14:29:44,613 - pyskl - INFO - Epoch [27][400/1281] lr: 2.315e-02, eta: 16:41:43, time: 0.387, data_time: 0.001, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9956, loss_cls: 0.6844, loss: 0.6844 +2025-06-24 14:30:23,127 - pyskl - INFO - Epoch [27][500/1281] lr: 2.314e-02, eta: 16:41:08, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8481, top5_acc: 0.9888, loss_cls: 0.6829, loss: 0.6829 +2025-06-24 14:31:01,613 - pyskl - INFO - Epoch [27][600/1281] lr: 2.313e-02, eta: 16:40:33, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9956, loss_cls: 0.7164, loss: 0.7164 +2025-06-24 14:31:40,095 - pyskl - INFO - Epoch [27][700/1281] lr: 2.312e-02, eta: 16:39:57, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9969, loss_cls: 0.7049, loss: 0.7049 +2025-06-24 14:32:18,536 - pyskl - INFO - Epoch [27][800/1281] lr: 2.311e-02, eta: 16:39:22, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8594, top5_acc: 0.9956, loss_cls: 0.6561, loss: 0.6561 +2025-06-24 14:32:57,565 - pyskl - INFO - Epoch [27][900/1281] lr: 2.310e-02, eta: 16:38:49, time: 0.390, data_time: 0.000, memory: 4082, top1_acc: 0.8556, top5_acc: 0.9962, loss_cls: 0.6613, loss: 0.6613 +2025-06-24 14:33:35,651 - pyskl - INFO - Epoch [27][1000/1281] lr: 2.308e-02, eta: 16:38:12, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9944, loss_cls: 0.7131, loss: 0.7131 +2025-06-24 14:34:13,858 - pyskl - INFO - Epoch [27][1100/1281] lr: 2.307e-02, eta: 16:37:35, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8575, top5_acc: 0.9906, loss_cls: 0.6840, loss: 0.6840 +2025-06-24 14:34:52,629 - pyskl - INFO - Epoch [27][1200/1281] lr: 2.306e-02, eta: 16:37:01, time: 0.388, data_time: 0.001, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9894, loss_cls: 0.7519, loss: 0.7519 +2025-06-24 14:35:12,236 - pyskl - INFO - Saving checkpoint at 27 epochs +2025-06-24 14:36:07,497 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:36:07,567 - pyskl - INFO - +top1_acc 0.7776 +top5_acc 0.9738 +2025-06-24 14:36:07,567 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:36:07,577 - pyskl - INFO - +mean_acc 0.6715 +2025-06-24 14:36:07,579 - pyskl - INFO - Epoch(val) [27][533] top1_acc: 0.7776, top5_acc: 0.9738, mean_class_accuracy: 0.6715 +2025-06-24 14:37:05,553 - pyskl - INFO - Epoch [28][100/1281] lr: 2.304e-02, eta: 16:35:03, time: 0.580, data_time: 0.191, memory: 4082, top1_acc: 0.8644, top5_acc: 0.9956, loss_cls: 0.6247, loss: 0.6247 +2025-06-24 14:37:43,699 - pyskl - INFO - Epoch [28][200/1281] lr: 2.303e-02, eta: 16:34:26, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9975, loss_cls: 0.6563, loss: 0.6563 +2025-06-24 14:38:22,278 - pyskl - INFO - Epoch [28][300/1281] lr: 2.302e-02, eta: 16:33:52, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8625, top5_acc: 0.9931, loss_cls: 0.6247, loss: 0.6247 +2025-06-24 14:39:00,029 - pyskl - INFO - Epoch [28][400/1281] lr: 2.301e-02, eta: 16:33:13, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8525, top5_acc: 0.9888, loss_cls: 0.6727, loss: 0.6727 +2025-06-24 14:39:37,932 - pyskl - INFO - Epoch [28][500/1281] lr: 2.300e-02, eta: 16:32:35, time: 0.379, data_time: 0.001, memory: 4082, top1_acc: 0.8194, top5_acc: 0.9906, loss_cls: 0.7907, loss: 0.7907 +2025-06-24 14:40:16,288 - pyskl - INFO - Epoch [28][600/1281] lr: 2.299e-02, eta: 16:31:59, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9912, loss_cls: 0.6613, loss: 0.6613 +2025-06-24 14:40:54,657 - pyskl - INFO - Epoch [28][700/1281] lr: 2.298e-02, eta: 16:31:23, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9938, loss_cls: 0.7177, loss: 0.7177 +2025-06-24 14:41:32,798 - pyskl - INFO - Epoch [28][800/1281] lr: 2.297e-02, eta: 16:30:46, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8381, top5_acc: 0.9919, loss_cls: 0.7407, loss: 0.7407 +2025-06-24 14:42:11,054 - pyskl - INFO - Epoch [28][900/1281] lr: 2.295e-02, eta: 16:30:10, time: 0.383, data_time: 0.001, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9944, loss_cls: 0.7168, loss: 0.7168 +2025-06-24 14:42:49,521 - pyskl - INFO - Epoch [28][1000/1281] lr: 2.294e-02, eta: 16:29:34, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9888, loss_cls: 0.7583, loss: 0.7583 +2025-06-24 14:43:27,409 - pyskl - INFO - Epoch [28][1100/1281] lr: 2.293e-02, eta: 16:28:56, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8381, top5_acc: 0.9956, loss_cls: 0.7060, loss: 0.7060 +2025-06-24 14:44:05,295 - pyskl - INFO - Epoch [28][1200/1281] lr: 2.292e-02, eta: 16:28:18, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9894, loss_cls: 0.7048, loss: 0.7048 +2025-06-24 14:44:36,916 - pyskl - INFO - Saving checkpoint at 28 epochs +2025-06-24 14:45:35,910 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:45:35,968 - pyskl - INFO - +top1_acc 0.7937 +top5_acc 0.9844 +2025-06-24 14:45:35,968 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:45:35,976 - pyskl - INFO - +mean_acc 0.7228 +2025-06-24 14:45:35,978 - pyskl - INFO - Epoch(val) [28][533] top1_acc: 0.7937, top5_acc: 0.9844, mean_class_accuracy: 0.7228 +2025-06-24 14:46:25,988 - pyskl - INFO - Epoch [29][100/1281] lr: 2.290e-02, eta: 16:25:48, time: 0.500, data_time: 0.195, memory: 4082, top1_acc: 0.8481, top5_acc: 0.9944, loss_cls: 0.6490, loss: 0.6490 +2025-06-24 14:47:03,139 - pyskl - INFO - Epoch [29][200/1281] lr: 2.289e-02, eta: 16:25:07, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8531, top5_acc: 0.9944, loss_cls: 0.6291, loss: 0.6291 +2025-06-24 14:47:37,002 - pyskl - INFO - Epoch [29][300/1281] lr: 2.288e-02, eta: 16:24:12, time: 0.339, data_time: 0.000, memory: 4082, top1_acc: 0.8538, top5_acc: 0.9962, loss_cls: 0.6609, loss: 0.6609 +2025-06-24 14:48:00,863 - pyskl - INFO - Epoch [29][400/1281] lr: 2.287e-02, eta: 16:22:34, time: 0.239, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9938, loss_cls: 0.6386, loss: 0.6386 +2025-06-24 14:48:39,967 - pyskl - INFO - Epoch [29][500/1281] lr: 2.285e-02, eta: 16:22:02, time: 0.391, data_time: 0.000, memory: 4082, top1_acc: 0.8581, top5_acc: 0.9931, loss_cls: 0.6252, loss: 0.6252 +2025-06-24 14:49:18,335 - pyskl - INFO - Epoch [29][600/1281] lr: 2.284e-02, eta: 16:21:26, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9925, loss_cls: 0.6168, loss: 0.6168 +2025-06-24 14:49:56,460 - pyskl - INFO - Epoch [29][700/1281] lr: 2.283e-02, eta: 16:20:50, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8425, top5_acc: 0.9944, loss_cls: 0.6735, loss: 0.6735 +2025-06-24 14:50:34,421 - pyskl - INFO - Epoch [29][800/1281] lr: 2.282e-02, eta: 16:20:12, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8562, top5_acc: 0.9906, loss_cls: 0.6873, loss: 0.6873 +2025-06-24 14:51:11,963 - pyskl - INFO - Epoch [29][900/1281] lr: 2.281e-02, eta: 16:19:33, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8594, top5_acc: 0.9962, loss_cls: 0.6746, loss: 0.6746 +2025-06-24 14:51:51,023 - pyskl - INFO - Epoch [29][1000/1281] lr: 2.280e-02, eta: 16:19:01, time: 0.391, data_time: 0.000, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9919, loss_cls: 0.6751, loss: 0.6751 +2025-06-24 14:52:29,745 - pyskl - INFO - Epoch [29][1100/1281] lr: 2.279e-02, eta: 16:18:27, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9931, loss_cls: 0.7163, loss: 0.7163 +2025-06-24 14:53:07,811 - pyskl - INFO - Epoch [29][1200/1281] lr: 2.277e-02, eta: 16:17:50, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9931, loss_cls: 0.6666, loss: 0.6666 +2025-06-24 14:53:39,977 - pyskl - INFO - Saving checkpoint at 29 epochs +2025-06-24 14:54:40,101 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:54:40,166 - pyskl - INFO - +top1_acc 0.7954 +top5_acc 0.9851 +2025-06-24 14:54:40,166 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:54:40,173 - pyskl - INFO - +mean_acc 0.7026 +2025-06-24 14:54:40,176 - pyskl - INFO - Epoch(val) [29][533] top1_acc: 0.7954, top5_acc: 0.9851, mean_class_accuracy: 0.7026 +2025-06-24 14:55:47,698 - pyskl - INFO - Epoch [30][100/1281] lr: 2.275e-02, eta: 16:16:37, time: 0.675, data_time: 0.189, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9938, loss_cls: 0.6790, loss: 0.6790 +2025-06-24 14:56:35,746 - pyskl - INFO - Epoch [30][200/1281] lr: 2.274e-02, eta: 16:16:42, time: 0.480, data_time: 0.000, memory: 4082, top1_acc: 0.8675, top5_acc: 0.9944, loss_cls: 0.6282, loss: 0.6282 +2025-06-24 14:57:23,902 - pyskl - INFO - Epoch [30][300/1281] lr: 2.273e-02, eta: 16:16:47, time: 0.482, data_time: 0.000, memory: 4082, top1_acc: 0.8656, top5_acc: 0.9944, loss_cls: 0.6035, loss: 0.6035 +2025-06-24 14:58:09,165 - pyskl - INFO - Epoch [30][400/1281] lr: 2.272e-02, eta: 16:16:39, time: 0.453, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9938, loss_cls: 0.6967, loss: 0.6967 +2025-06-24 14:58:46,788 - pyskl - INFO - Epoch [30][500/1281] lr: 2.271e-02, eta: 16:16:00, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9881, loss_cls: 0.7332, loss: 0.7332 +2025-06-24 14:59:23,060 - pyskl - INFO - Epoch [30][600/1281] lr: 2.269e-02, eta: 16:15:15, time: 0.363, data_time: 0.001, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9931, loss_cls: 0.7225, loss: 0.7225 +2025-06-24 15:00:00,388 - pyskl - INFO - Epoch [30][700/1281] lr: 2.268e-02, eta: 16:14:35, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9925, loss_cls: 0.7285, loss: 0.7285 +2025-06-24 15:00:48,834 - pyskl - INFO - Epoch [30][800/1281] lr: 2.267e-02, eta: 16:14:40, time: 0.484, data_time: 0.000, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9969, loss_cls: 0.6158, loss: 0.6158 +2025-06-24 15:01:37,063 - pyskl - INFO - Epoch [30][900/1281] lr: 2.266e-02, eta: 16:14:44, time: 0.482, data_time: 0.000, memory: 4082, top1_acc: 0.8538, top5_acc: 0.9938, loss_cls: 0.6783, loss: 0.6783 +2025-06-24 15:02:25,398 - pyskl - INFO - Epoch [30][1000/1281] lr: 2.265e-02, eta: 16:14:48, time: 0.483, data_time: 0.000, memory: 4082, top1_acc: 0.8556, top5_acc: 0.9906, loss_cls: 0.6608, loss: 0.6608 +2025-06-24 15:03:13,512 - pyskl - INFO - Epoch [30][1100/1281] lr: 2.263e-02, eta: 16:14:51, time: 0.481, data_time: 0.000, memory: 4082, top1_acc: 0.8456, top5_acc: 0.9938, loss_cls: 0.6944, loss: 0.6944 +2025-06-24 15:04:01,862 - pyskl - INFO - Epoch [30][1200/1281] lr: 2.262e-02, eta: 16:14:54, time: 0.484, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9938, loss_cls: 0.7179, loss: 0.7179 +2025-06-24 15:04:41,725 - pyskl - INFO - Saving checkpoint at 30 epochs +2025-06-24 15:05:41,358 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:05:41,423 - pyskl - INFO - +top1_acc 0.7967 +top5_acc 0.9862 +2025-06-24 15:05:41,423 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:05:41,431 - pyskl - INFO - +mean_acc 0.7230 +2025-06-24 15:05:41,434 - pyskl - INFO - Epoch(val) [30][533] top1_acc: 0.7967, top5_acc: 0.9862, mean_class_accuracy: 0.7230 +2025-06-24 15:07:07,820 - pyskl - INFO - Epoch [31][100/1281] lr: 2.260e-02, eta: 16:14:55, time: 0.864, data_time: 0.196, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9931, loss_cls: 0.7820, loss: 0.7820 +2025-06-24 15:07:56,784 - pyskl - INFO - Epoch [31][200/1281] lr: 2.259e-02, eta: 16:15:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9944, loss_cls: 0.7714, loss: 0.7714 +2025-06-24 15:08:46,227 - pyskl - INFO - Epoch [31][300/1281] lr: 2.258e-02, eta: 16:15:07, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9956, loss_cls: 0.7954, loss: 0.7954 +2025-06-24 15:09:26,343 - pyskl - INFO - Epoch [31][400/1281] lr: 2.256e-02, eta: 16:14:37, time: 0.401, data_time: 0.000, memory: 4083, top1_acc: 0.8413, top5_acc: 0.9912, loss_cls: 0.8339, loss: 0.8339 +2025-06-24 15:10:16,168 - pyskl - INFO - Epoch [31][500/1281] lr: 2.255e-02, eta: 16:14:45, time: 0.498, data_time: 0.000, memory: 4083, top1_acc: 0.8475, top5_acc: 0.9900, loss_cls: 0.8336, loss: 0.8336 +2025-06-24 15:10:40,597 - pyskl - INFO - Epoch [31][600/1281] lr: 2.254e-02, eta: 16:13:13, time: 0.244, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9969, loss_cls: 0.7817, loss: 0.7817 +2025-06-24 15:11:22,624 - pyskl - INFO - Epoch [31][700/1281] lr: 2.253e-02, eta: 16:12:50, time: 0.420, data_time: 0.000, memory: 4083, top1_acc: 0.8538, top5_acc: 0.9925, loss_cls: 0.8310, loss: 0.8310 +2025-06-24 15:12:11,612 - pyskl - INFO - Epoch [31][800/1281] lr: 2.252e-02, eta: 16:12:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9925, loss_cls: 0.7877, loss: 0.7877 +2025-06-24 15:13:00,771 - pyskl - INFO - Epoch [31][900/1281] lr: 2.250e-02, eta: 16:12:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9969, loss_cls: 0.7510, loss: 0.7510 +2025-06-24 15:13:50,024 - pyskl - INFO - Epoch [31][1000/1281] lr: 2.249e-02, eta: 16:13:03, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9938, loss_cls: 0.8398, loss: 0.8398 +2025-06-24 15:14:38,923 - pyskl - INFO - Epoch [31][1100/1281] lr: 2.248e-02, eta: 16:13:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8313, top5_acc: 0.9912, loss_cls: 0.8799, loss: 0.8799 +2025-06-24 15:15:27,897 - pyskl - INFO - Epoch [31][1200/1281] lr: 2.247e-02, eta: 16:13:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9931, loss_cls: 0.7771, loss: 0.7771 +2025-06-24 15:16:08,223 - pyskl - INFO - Saving checkpoint at 31 epochs +2025-06-24 15:17:07,785 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:17:07,840 - pyskl - INFO - +top1_acc 0.8001 +top5_acc 0.9853 +2025-06-24 15:17:07,840 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:17:07,847 - pyskl - INFO - +mean_acc 0.7506 +2025-06-24 15:17:07,849 - pyskl - INFO - Epoch(val) [31][533] top1_acc: 0.8001, top5_acc: 0.9853, mean_class_accuracy: 0.7506 +2025-06-24 15:18:28,019 - pyskl - INFO - Epoch [32][100/1281] lr: 2.244e-02, eta: 16:12:41, time: 0.802, data_time: 0.192, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9938, loss_cls: 0.7428, loss: 0.7428 +2025-06-24 15:19:16,911 - pyskl - INFO - Epoch [32][200/1281] lr: 2.243e-02, eta: 16:12:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9950, loss_cls: 0.6915, loss: 0.6915 +2025-06-24 15:20:05,651 - pyskl - INFO - Epoch [32][300/1281] lr: 2.242e-02, eta: 16:12:45, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8512, top5_acc: 0.9944, loss_cls: 0.7425, loss: 0.7425 +2025-06-24 15:20:46,555 - pyskl - INFO - Epoch [32][400/1281] lr: 2.241e-02, eta: 16:12:16, time: 0.409, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9925, loss_cls: 0.7446, loss: 0.7446 +2025-06-24 15:21:36,119 - pyskl - INFO - Epoch [32][500/1281] lr: 2.239e-02, eta: 16:12:20, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8450, top5_acc: 0.9950, loss_cls: 0.7738, loss: 0.7738 +2025-06-24 15:22:01,018 - pyskl - INFO - Epoch [32][600/1281] lr: 2.238e-02, eta: 16:10:51, time: 0.249, data_time: 0.000, memory: 4083, top1_acc: 0.8544, top5_acc: 0.9925, loss_cls: 0.7475, loss: 0.7475 +2025-06-24 15:22:43,338 - pyskl - INFO - Epoch [32][700/1281] lr: 2.237e-02, eta: 16:10:27, time: 0.423, data_time: 0.000, memory: 4083, top1_acc: 0.8462, top5_acc: 0.9956, loss_cls: 0.7684, loss: 0.7684 +2025-06-24 15:23:32,487 - pyskl - INFO - Epoch [32][800/1281] lr: 2.236e-02, eta: 16:10:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9956, loss_cls: 0.6845, loss: 0.6845 +2025-06-24 15:24:21,641 - pyskl - INFO - Epoch [32][900/1281] lr: 2.234e-02, eta: 16:10:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9962, loss_cls: 0.7016, loss: 0.7016 +2025-06-24 15:25:10,991 - pyskl - INFO - Epoch [32][1000/1281] lr: 2.233e-02, eta: 16:10:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9944, loss_cls: 0.7463, loss: 0.7463 +2025-06-24 15:26:00,112 - pyskl - INFO - Epoch [32][1100/1281] lr: 2.232e-02, eta: 16:10:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9956, loss_cls: 0.7000, loss: 0.7000 +2025-06-24 15:26:48,996 - pyskl - INFO - Epoch [32][1200/1281] lr: 2.231e-02, eta: 16:10:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8387, top5_acc: 0.9944, loss_cls: 0.7497, loss: 0.7497 +2025-06-24 15:27:29,082 - pyskl - INFO - Saving checkpoint at 32 epochs +2025-06-24 15:28:27,898 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:28:27,964 - pyskl - INFO - +top1_acc 0.8253 +top5_acc 0.9889 +2025-06-24 15:28:27,964 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:28:27,974 - pyskl - INFO - +mean_acc 0.7624 +2025-06-24 15:28:27,979 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_23.pth was removed +2025-06-24 15:28:28,169 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_32.pth. +2025-06-24 15:28:28,169 - pyskl - INFO - Best top1_acc is 0.8253 at 32 epoch. +2025-06-24 15:28:28,172 - pyskl - INFO - Epoch(val) [32][533] top1_acc: 0.8253, top5_acc: 0.9889, mean_class_accuracy: 0.7624 +2025-06-24 15:29:48,619 - pyskl - INFO - Epoch [33][100/1281] lr: 2.228e-02, eta: 16:10:04, time: 0.804, data_time: 0.191, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9944, loss_cls: 0.6815, loss: 0.6815 +2025-06-24 15:30:37,750 - pyskl - INFO - Epoch [33][200/1281] lr: 2.227e-02, eta: 16:10:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8519, top5_acc: 0.9944, loss_cls: 0.7240, loss: 0.7240 +2025-06-24 15:31:26,939 - pyskl - INFO - Epoch [33][300/1281] lr: 2.226e-02, eta: 16:10:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9912, loss_cls: 0.7351, loss: 0.7351 +2025-06-24 15:32:07,646 - pyskl - INFO - Epoch [33][400/1281] lr: 2.225e-02, eta: 16:09:34, time: 0.407, data_time: 0.000, memory: 4083, top1_acc: 0.8369, top5_acc: 0.9919, loss_cls: 0.7760, loss: 0.7760 +2025-06-24 15:32:57,042 - pyskl - INFO - Epoch [33][500/1281] lr: 2.223e-02, eta: 16:09:34, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9925, loss_cls: 0.6931, loss: 0.6931 +2025-06-24 15:33:21,769 - pyskl - INFO - Epoch [33][600/1281] lr: 2.222e-02, eta: 16:08:05, time: 0.247, data_time: 0.000, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9962, loss_cls: 0.7523, loss: 0.7523 +2025-06-24 15:34:04,073 - pyskl - INFO - Epoch [33][700/1281] lr: 2.221e-02, eta: 16:07:40, time: 0.423, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9956, loss_cls: 0.6806, loss: 0.6806 +2025-06-24 15:34:53,016 - pyskl - INFO - Epoch [33][800/1281] lr: 2.219e-02, eta: 16:07:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9906, loss_cls: 0.7973, loss: 0.7973 +2025-06-24 15:35:42,300 - pyskl - INFO - Epoch [33][900/1281] lr: 2.218e-02, eta: 16:07:38, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8544, top5_acc: 0.9962, loss_cls: 0.7191, loss: 0.7191 +2025-06-24 15:36:32,033 - pyskl - INFO - Epoch [33][1000/1281] lr: 2.217e-02, eta: 16:07:40, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.8481, top5_acc: 0.9925, loss_cls: 0.7115, loss: 0.7115 +2025-06-24 15:37:21,228 - pyskl - INFO - Epoch [33][1100/1281] lr: 2.216e-02, eta: 16:07:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9912, loss_cls: 0.7268, loss: 0.7268 +2025-06-24 15:38:10,178 - pyskl - INFO - Epoch [33][1200/1281] lr: 2.214e-02, eta: 16:07:36, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9944, loss_cls: 0.7021, loss: 0.7021 +2025-06-24 15:38:50,612 - pyskl - INFO - Saving checkpoint at 33 epochs +2025-06-24 15:39:49,267 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:39:49,334 - pyskl - INFO - +top1_acc 0.8001 +top5_acc 0.9836 +2025-06-24 15:39:49,334 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:39:49,342 - pyskl - INFO - +mean_acc 0.7396 +2025-06-24 15:39:49,344 - pyskl - INFO - Epoch(val) [33][533] top1_acc: 0.8001, top5_acc: 0.9836, mean_class_accuracy: 0.7396 +2025-06-24 15:41:10,407 - pyskl - INFO - Epoch [34][100/1281] lr: 2.212e-02, eta: 16:07:05, time: 0.811, data_time: 0.198, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9956, loss_cls: 0.6792, loss: 0.6792 +2025-06-24 15:41:59,547 - pyskl - INFO - Epoch [34][200/1281] lr: 2.211e-02, eta: 16:07:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9956, loss_cls: 0.6509, loss: 0.6509 +2025-06-24 15:42:48,722 - pyskl - INFO - Epoch [34][300/1281] lr: 2.209e-02, eta: 16:07:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9956, loss_cls: 0.6802, loss: 0.6802 +2025-06-24 15:43:30,644 - pyskl - INFO - Epoch [34][400/1281] lr: 2.208e-02, eta: 16:06:33, time: 0.419, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9944, loss_cls: 0.7134, loss: 0.7134 +2025-06-24 15:44:17,457 - pyskl - INFO - Epoch [34][500/1281] lr: 2.207e-02, eta: 16:06:22, time: 0.468, data_time: 0.001, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9912, loss_cls: 0.6763, loss: 0.6763 +2025-06-24 15:44:44,496 - pyskl - INFO - Epoch [34][600/1281] lr: 2.205e-02, eta: 16:05:02, time: 0.270, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9956, loss_cls: 0.7046, loss: 0.7046 +2025-06-24 15:45:26,763 - pyskl - INFO - Epoch [34][700/1281] lr: 2.204e-02, eta: 16:04:35, time: 0.423, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9950, loss_cls: 0.7101, loss: 0.7101 +2025-06-24 15:46:15,806 - pyskl - INFO - Epoch [34][800/1281] lr: 2.203e-02, eta: 16:04:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9931, loss_cls: 0.7011, loss: 0.7011 +2025-06-24 15:47:04,819 - pyskl - INFO - Epoch [34][900/1281] lr: 2.201e-02, eta: 16:04:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9944, loss_cls: 0.7300, loss: 0.7300 +2025-06-24 15:47:54,560 - pyskl - INFO - Epoch [34][1000/1281] lr: 2.200e-02, eta: 16:04:27, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.8525, top5_acc: 0.9906, loss_cls: 0.7107, loss: 0.7107 +2025-06-24 15:48:43,617 - pyskl - INFO - Epoch [34][1100/1281] lr: 2.199e-02, eta: 16:04:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9950, loss_cls: 0.6931, loss: 0.6931 +2025-06-24 15:49:32,190 - pyskl - INFO - Epoch [34][1200/1281] lr: 2.197e-02, eta: 16:04:17, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9925, loss_cls: 0.7235, loss: 0.7235 +2025-06-24 15:50:12,867 - pyskl - INFO - Saving checkpoint at 34 epochs +2025-06-24 15:51:12,481 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:51:12,539 - pyskl - INFO - +top1_acc 0.7862 +top5_acc 0.9854 +2025-06-24 15:51:12,540 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:51:12,549 - pyskl - INFO - +mean_acc 0.7245 +2025-06-24 15:51:12,552 - pyskl - INFO - Epoch(val) [34][533] top1_acc: 0.7862, top5_acc: 0.9854, mean_class_accuracy: 0.7245 +2025-06-24 15:52:32,088 - pyskl - INFO - Epoch [35][100/1281] lr: 2.195e-02, eta: 16:03:38, time: 0.795, data_time: 0.194, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9969, loss_cls: 0.6823, loss: 0.6823 +2025-06-24 15:53:21,098 - pyskl - INFO - Epoch [35][200/1281] lr: 2.194e-02, eta: 16:03:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9950, loss_cls: 0.6154, loss: 0.6154 +2025-06-24 15:54:10,394 - pyskl - INFO - Epoch [35][300/1281] lr: 2.192e-02, eta: 16:03:29, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9956, loss_cls: 0.5960, loss: 0.5960 +2025-06-24 15:54:52,601 - pyskl - INFO - Epoch [35][400/1281] lr: 2.191e-02, eta: 16:03:01, time: 0.422, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9944, loss_cls: 0.6273, loss: 0.6273 +2025-06-24 15:55:39,703 - pyskl - INFO - Epoch [35][500/1281] lr: 2.190e-02, eta: 16:02:49, time: 0.471, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9938, loss_cls: 0.6612, loss: 0.6612 +2025-06-24 15:56:06,270 - pyskl - INFO - Epoch [35][600/1281] lr: 2.188e-02, eta: 16:01:29, time: 0.266, data_time: 0.000, memory: 4083, top1_acc: 0.8387, top5_acc: 0.9894, loss_cls: 0.7711, loss: 0.7711 +2025-06-24 15:56:48,128 - pyskl - INFO - Epoch [35][700/1281] lr: 2.187e-02, eta: 16:00:59, time: 0.419, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9931, loss_cls: 0.7314, loss: 0.7314 +2025-06-24 15:57:37,460 - pyskl - INFO - Epoch [35][800/1281] lr: 2.185e-02, eta: 16:00:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9925, loss_cls: 0.7464, loss: 0.7464 +2025-06-24 15:58:26,758 - pyskl - INFO - Epoch [35][900/1281] lr: 2.184e-02, eta: 16:00:50, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9956, loss_cls: 0.7106, loss: 0.7106 +2025-06-24 15:59:16,192 - pyskl - INFO - Epoch [35][1000/1281] lr: 2.183e-02, eta: 16:00:45, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9944, loss_cls: 0.7115, loss: 0.7115 +2025-06-24 16:00:05,005 - pyskl - INFO - Epoch [35][1100/1281] lr: 2.181e-02, eta: 16:00:38, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9956, loss_cls: 0.6897, loss: 0.6897 +2025-06-24 16:00:54,044 - pyskl - INFO - Epoch [35][1200/1281] lr: 2.180e-02, eta: 16:00:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9962, loss_cls: 0.6774, loss: 0.6774 +2025-06-24 16:01:34,553 - pyskl - INFO - Saving checkpoint at 35 epochs +2025-06-24 16:02:33,625 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:02:33,685 - pyskl - INFO - +top1_acc 0.8104 +top5_acc 0.9859 +2025-06-24 16:02:33,685 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:02:33,693 - pyskl - INFO - +mean_acc 0.7451 +2025-06-24 16:02:33,695 - pyskl - INFO - Epoch(val) [35][533] top1_acc: 0.8104, top5_acc: 0.9859, mean_class_accuracy: 0.7451 +2025-06-24 16:03:54,301 - pyskl - INFO - Epoch [36][100/1281] lr: 2.178e-02, eta: 15:59:54, time: 0.806, data_time: 0.194, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9950, loss_cls: 0.6085, loss: 0.6085 +2025-06-24 16:04:43,340 - pyskl - INFO - Epoch [36][200/1281] lr: 2.176e-02, eta: 15:59:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9950, loss_cls: 0.6485, loss: 0.6485 +2025-06-24 16:05:32,385 - pyskl - INFO - Epoch [36][300/1281] lr: 2.175e-02, eta: 15:59:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9962, loss_cls: 0.6270, loss: 0.6270 +2025-06-24 16:06:13,542 - pyskl - INFO - Epoch [36][400/1281] lr: 2.173e-02, eta: 15:59:07, time: 0.412, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9956, loss_cls: 0.6223, loss: 0.6223 +2025-06-24 16:07:03,279 - pyskl - INFO - Epoch [36][500/1281] lr: 2.172e-02, eta: 15:59:02, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.8481, top5_acc: 0.9938, loss_cls: 0.7251, loss: 0.7251 +2025-06-24 16:07:27,860 - pyskl - INFO - Epoch [36][600/1281] lr: 2.171e-02, eta: 15:57:36, time: 0.246, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9906, loss_cls: 0.7055, loss: 0.7055 +2025-06-24 16:08:10,981 - pyskl - INFO - Epoch [36][700/1281] lr: 2.169e-02, eta: 15:57:09, time: 0.431, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9944, loss_cls: 0.6655, loss: 0.6655 +2025-06-24 16:09:00,014 - pyskl - INFO - Epoch [36][800/1281] lr: 2.168e-02, eta: 15:57:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9950, loss_cls: 0.6706, loss: 0.6706 +2025-06-24 16:09:48,797 - pyskl - INFO - Epoch [36][900/1281] lr: 2.167e-02, eta: 15:56:53, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9956, loss_cls: 0.6368, loss: 0.6368 +2025-06-24 16:10:38,070 - pyskl - INFO - Epoch [36][1000/1281] lr: 2.165e-02, eta: 15:56:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8331, top5_acc: 0.9938, loss_cls: 0.7684, loss: 0.7684 +2025-06-24 16:11:26,980 - pyskl - INFO - Epoch [36][1100/1281] lr: 2.164e-02, eta: 15:56:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9956, loss_cls: 0.7364, loss: 0.7364 +2025-06-24 16:12:16,048 - pyskl - INFO - Epoch [36][1200/1281] lr: 2.162e-02, eta: 15:56:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9938, loss_cls: 0.6780, loss: 0.6780 +2025-06-24 16:12:56,560 - pyskl - INFO - Saving checkpoint at 36 epochs +2025-06-24 16:13:56,136 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:13:56,207 - pyskl - INFO - +top1_acc 0.8058 +top5_acc 0.9840 +2025-06-24 16:13:56,208 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:13:56,215 - pyskl - INFO - +mean_acc 0.7386 +2025-06-24 16:13:56,217 - pyskl - INFO - Epoch(val) [36][533] top1_acc: 0.8058, top5_acc: 0.9840, mean_class_accuracy: 0.7386 +2025-06-24 16:15:16,207 - pyskl - INFO - Epoch [37][100/1281] lr: 2.160e-02, eta: 15:55:47, time: 0.800, data_time: 0.197, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9988, loss_cls: 0.6081, loss: 0.6081 +2025-06-24 16:16:05,683 - pyskl - INFO - Epoch [37][200/1281] lr: 2.158e-02, eta: 15:55:40, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9969, loss_cls: 0.5496, loss: 0.5496 +2025-06-24 16:16:54,957 - pyskl - INFO - Epoch [37][300/1281] lr: 2.157e-02, eta: 15:55:31, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9988, loss_cls: 0.6249, loss: 0.6249 +2025-06-24 16:17:35,220 - pyskl - INFO - Epoch [37][400/1281] lr: 2.156e-02, eta: 15:54:55, time: 0.403, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9919, loss_cls: 0.7072, loss: 0.7072 +2025-06-24 16:18:25,837 - pyskl - INFO - Epoch [37][500/1281] lr: 2.154e-02, eta: 15:54:51, time: 0.506, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9931, loss_cls: 0.6375, loss: 0.6375 +2025-06-24 16:18:50,112 - pyskl - INFO - Epoch [37][600/1281] lr: 2.153e-02, eta: 15:53:25, time: 0.243, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9938, loss_cls: 0.6408, loss: 0.6408 +2025-06-24 16:19:34,194 - pyskl - INFO - Epoch [37][700/1281] lr: 2.151e-02, eta: 15:53:00, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9981, loss_cls: 0.6842, loss: 0.6842 +2025-06-24 16:20:23,171 - pyskl - INFO - Epoch [37][800/1281] lr: 2.150e-02, eta: 15:52:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9931, loss_cls: 0.7346, loss: 0.7346 +2025-06-24 16:21:12,481 - pyskl - INFO - Epoch [37][900/1281] lr: 2.149e-02, eta: 15:52:42, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9962, loss_cls: 0.6640, loss: 0.6640 +2025-06-24 16:22:01,619 - pyskl - INFO - Epoch [37][1000/1281] lr: 2.147e-02, eta: 15:52:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9938, loss_cls: 0.6601, loss: 0.6601 +2025-06-24 16:22:50,703 - pyskl - INFO - Epoch [37][1100/1281] lr: 2.146e-02, eta: 15:52:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9919, loss_cls: 0.7210, loss: 0.7210 +2025-06-24 16:23:39,747 - pyskl - INFO - Epoch [37][1200/1281] lr: 2.144e-02, eta: 15:52:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9956, loss_cls: 0.7032, loss: 0.7032 +2025-06-24 16:24:20,397 - pyskl - INFO - Saving checkpoint at 37 epochs +2025-06-24 16:25:19,902 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:25:19,957 - pyskl - INFO - +top1_acc 0.8303 +top5_acc 0.9896 +2025-06-24 16:25:19,957 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:25:19,965 - pyskl - INFO - +mean_acc 0.7729 +2025-06-24 16:25:19,969 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_32.pth was removed +2025-06-24 16:25:20,145 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_37.pth. +2025-06-24 16:25:20,145 - pyskl - INFO - Best top1_acc is 0.8303 at 37 epoch. +2025-06-24 16:25:20,148 - pyskl - INFO - Epoch(val) [37][533] top1_acc: 0.8303, top5_acc: 0.9896, mean_class_accuracy: 0.7729 +2025-06-24 16:26:40,673 - pyskl - INFO - Epoch [38][100/1281] lr: 2.142e-02, eta: 15:51:29, time: 0.805, data_time: 0.195, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9975, loss_cls: 0.6514, loss: 0.6514 +2025-06-24 16:27:29,610 - pyskl - INFO - Epoch [38][200/1281] lr: 2.140e-02, eta: 15:51:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9938, loss_cls: 0.6174, loss: 0.6174 +2025-06-24 16:28:18,573 - pyskl - INFO - Epoch [38][300/1281] lr: 2.139e-02, eta: 15:51:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9950, loss_cls: 0.5936, loss: 0.5936 +2025-06-24 16:28:55,405 - pyskl - INFO - Epoch [38][400/1281] lr: 2.137e-02, eta: 15:50:20, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9938, loss_cls: 0.6419, loss: 0.6419 +2025-06-24 16:29:46,373 - pyskl - INFO - Epoch [38][500/1281] lr: 2.136e-02, eta: 15:50:15, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9950, loss_cls: 0.6682, loss: 0.6682 +2025-06-24 16:30:10,747 - pyskl - INFO - Epoch [38][600/1281] lr: 2.134e-02, eta: 15:48:50, time: 0.244, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9931, loss_cls: 0.6872, loss: 0.6872 +2025-06-24 16:30:57,198 - pyskl - INFO - Epoch [38][700/1281] lr: 2.133e-02, eta: 15:48:31, time: 0.464, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9950, loss_cls: 0.6267, loss: 0.6267 +2025-06-24 16:31:46,467 - pyskl - INFO - Epoch [38][800/1281] lr: 2.132e-02, eta: 15:48:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9906, loss_cls: 0.6297, loss: 0.6297 +2025-06-24 16:32:35,214 - pyskl - INFO - Epoch [38][900/1281] lr: 2.130e-02, eta: 15:48:09, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9938, loss_cls: 0.6522, loss: 0.6522 +2025-06-24 16:33:24,229 - pyskl - INFO - Epoch [38][1000/1281] lr: 2.129e-02, eta: 15:47:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9956, loss_cls: 0.6196, loss: 0.6196 +2025-06-24 16:34:13,295 - pyskl - INFO - Epoch [38][1100/1281] lr: 2.127e-02, eta: 15:47:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9938, loss_cls: 0.6697, loss: 0.6697 +2025-06-24 16:35:02,591 - pyskl - INFO - Epoch [38][1200/1281] lr: 2.126e-02, eta: 15:47:35, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8469, top5_acc: 0.9900, loss_cls: 0.7539, loss: 0.7539 +2025-06-24 16:35:42,620 - pyskl - INFO - Saving checkpoint at 38 epochs +2025-06-24 16:36:41,425 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:36:41,489 - pyskl - INFO - +top1_acc 0.7809 +top5_acc 0.9835 +2025-06-24 16:36:41,489 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:36:41,498 - pyskl - INFO - +mean_acc 0.7355 +2025-06-24 16:36:41,500 - pyskl - INFO - Epoch(val) [38][533] top1_acc: 0.7809, top5_acc: 0.9835, mean_class_accuracy: 0.7355 +2025-06-24 16:38:01,103 - pyskl - INFO - Epoch [39][100/1281] lr: 2.123e-02, eta: 15:46:47, time: 0.796, data_time: 0.192, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9981, loss_cls: 0.6611, loss: 0.6611 +2025-06-24 16:38:50,322 - pyskl - INFO - Epoch [39][200/1281] lr: 2.122e-02, eta: 15:46:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9938, loss_cls: 0.7161, loss: 0.7161 +2025-06-24 16:39:39,187 - pyskl - INFO - Epoch [39][300/1281] lr: 2.120e-02, eta: 15:46:22, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9956, loss_cls: 0.6271, loss: 0.6271 +2025-06-24 16:40:16,525 - pyskl - INFO - Epoch [39][400/1281] lr: 2.119e-02, eta: 15:45:36, time: 0.373, data_time: 0.001, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9956, loss_cls: 0.6678, loss: 0.6678 +2025-06-24 16:41:07,756 - pyskl - INFO - Epoch [39][500/1281] lr: 2.117e-02, eta: 15:45:30, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9950, loss_cls: 0.7005, loss: 0.7005 +2025-06-24 16:41:32,871 - pyskl - INFO - Epoch [39][600/1281] lr: 2.116e-02, eta: 15:44:08, time: 0.251, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9975, loss_cls: 0.6019, loss: 0.6019 +2025-06-24 16:42:20,269 - pyskl - INFO - Epoch [39][700/1281] lr: 2.114e-02, eta: 15:43:51, time: 0.474, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9962, loss_cls: 0.6497, loss: 0.6497 +2025-06-24 16:43:08,980 - pyskl - INFO - Epoch [39][800/1281] lr: 2.113e-02, eta: 15:43:37, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9919, loss_cls: 0.6514, loss: 0.6514 +2025-06-24 16:43:58,072 - pyskl - INFO - Epoch [39][900/1281] lr: 2.111e-02, eta: 15:43:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9962, loss_cls: 0.6846, loss: 0.6846 +2025-06-24 16:44:47,023 - pyskl - INFO - Epoch [39][1000/1281] lr: 2.110e-02, eta: 15:43:11, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9956, loss_cls: 0.6450, loss: 0.6450 +2025-06-24 16:45:35,904 - pyskl - INFO - Epoch [39][1100/1281] lr: 2.108e-02, eta: 15:42:58, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9925, loss_cls: 0.6705, loss: 0.6705 +2025-06-24 16:46:24,678 - pyskl - INFO - Epoch [39][1200/1281] lr: 2.107e-02, eta: 15:42:44, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8469, top5_acc: 0.9931, loss_cls: 0.7185, loss: 0.7185 +2025-06-24 16:47:04,836 - pyskl - INFO - Saving checkpoint at 39 epochs +2025-06-24 16:48:03,180 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:48:03,275 - pyskl - INFO - +top1_acc 0.8187 +top5_acc 0.9878 +2025-06-24 16:48:03,275 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:48:03,285 - pyskl - INFO - +mean_acc 0.7664 +2025-06-24 16:48:03,287 - pyskl - INFO - Epoch(val) [39][533] top1_acc: 0.8187, top5_acc: 0.9878, mean_class_accuracy: 0.7664 +2025-06-24 16:49:22,673 - pyskl - INFO - Epoch [40][100/1281] lr: 2.104e-02, eta: 15:41:53, time: 0.794, data_time: 0.190, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9950, loss_cls: 0.5685, loss: 0.5685 +2025-06-24 16:50:11,500 - pyskl - INFO - Epoch [40][200/1281] lr: 2.103e-02, eta: 15:41:39, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9969, loss_cls: 0.5829, loss: 0.5829 +2025-06-24 16:51:00,757 - pyskl - INFO - Epoch [40][300/1281] lr: 2.101e-02, eta: 15:41:26, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9944, loss_cls: 0.6365, loss: 0.6365 +2025-06-24 16:51:37,060 - pyskl - INFO - Epoch [40][400/1281] lr: 2.100e-02, eta: 15:40:36, time: 0.363, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9962, loss_cls: 0.5819, loss: 0.5819 +2025-06-24 16:52:28,408 - pyskl - INFO - Epoch [40][500/1281] lr: 2.098e-02, eta: 15:40:29, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9950, loss_cls: 0.6390, loss: 0.6390 +2025-06-24 16:52:53,231 - pyskl - INFO - Epoch [40][600/1281] lr: 2.097e-02, eta: 15:39:07, time: 0.248, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9919, loss_cls: 0.6880, loss: 0.6880 +2025-06-24 16:53:40,109 - pyskl - INFO - Epoch [40][700/1281] lr: 2.095e-02, eta: 15:38:47, time: 0.469, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9956, loss_cls: 0.6208, loss: 0.6208 +2025-06-24 16:54:29,559 - pyskl - INFO - Epoch [40][800/1281] lr: 2.094e-02, eta: 15:38:34, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9969, loss_cls: 0.6719, loss: 0.6719 +2025-06-24 16:55:19,061 - pyskl - INFO - Epoch [40][900/1281] lr: 2.092e-02, eta: 15:38:21, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9988, loss_cls: 0.6354, loss: 0.6354 +2025-06-24 16:56:08,269 - pyskl - INFO - Epoch [40][1000/1281] lr: 2.091e-02, eta: 15:38:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9944, loss_cls: 0.6697, loss: 0.6697 +2025-06-24 16:56:57,145 - pyskl - INFO - Epoch [40][1100/1281] lr: 2.089e-02, eta: 15:37:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9950, loss_cls: 0.6797, loss: 0.6797 +2025-06-24 16:57:46,273 - pyskl - INFO - Epoch [40][1200/1281] lr: 2.088e-02, eta: 15:37:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9925, loss_cls: 0.6512, loss: 0.6512 +2025-06-24 16:58:26,753 - pyskl - INFO - Saving checkpoint at 40 epochs +2025-06-24 16:59:26,489 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:59:26,558 - pyskl - INFO - +top1_acc 0.8331 +top5_acc 0.9894 +2025-06-24 16:59:26,558 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:59:26,566 - pyskl - INFO - +mean_acc 0.7708 +2025-06-24 16:59:26,570 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_37.pth was removed +2025-06-24 16:59:26,752 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_40.pth. +2025-06-24 16:59:26,753 - pyskl - INFO - Best top1_acc is 0.8331 at 40 epoch. +2025-06-24 16:59:26,755 - pyskl - INFO - Epoch(val) [40][533] top1_acc: 0.8331, top5_acc: 0.9894, mean_class_accuracy: 0.7708 +2025-06-24 17:00:46,110 - pyskl - INFO - Epoch [41][100/1281] lr: 2.085e-02, eta: 15:36:45, time: 0.793, data_time: 0.193, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9944, loss_cls: 0.6197, loss: 0.6197 +2025-06-24 17:01:35,223 - pyskl - INFO - Epoch [41][200/1281] lr: 2.083e-02, eta: 15:36:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9956, loss_cls: 0.6447, loss: 0.6447 +2025-06-24 17:02:24,233 - pyskl - INFO - Epoch [41][300/1281] lr: 2.082e-02, eta: 15:36:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9962, loss_cls: 0.6288, loss: 0.6288 +2025-06-24 17:02:59,086 - pyskl - INFO - Epoch [41][400/1281] lr: 2.080e-02, eta: 15:35:21, time: 0.349, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9950, loss_cls: 0.6250, loss: 0.6250 +2025-06-24 17:03:50,260 - pyskl - INFO - Epoch [41][500/1281] lr: 2.079e-02, eta: 15:35:12, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9969, loss_cls: 0.6504, loss: 0.6504 +2025-06-24 17:04:15,285 - pyskl - INFO - Epoch [41][600/1281] lr: 2.077e-02, eta: 15:33:51, time: 0.250, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9981, loss_cls: 0.6448, loss: 0.6448 +2025-06-24 17:05:02,893 - pyskl - INFO - Epoch [41][700/1281] lr: 2.076e-02, eta: 15:33:32, time: 0.476, data_time: 0.000, memory: 4083, top1_acc: 0.8544, top5_acc: 0.9962, loss_cls: 0.7119, loss: 0.7119 +2025-06-24 17:05:51,943 - pyskl - INFO - Epoch [41][800/1281] lr: 2.074e-02, eta: 15:33:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9944, loss_cls: 0.6748, loss: 0.6748 +2025-06-24 17:06:41,433 - pyskl - INFO - Epoch [41][900/1281] lr: 2.073e-02, eta: 15:33:02, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9944, loss_cls: 0.6268, loss: 0.6268 +2025-06-24 17:07:30,812 - pyskl - INFO - Epoch [41][1000/1281] lr: 2.071e-02, eta: 15:32:48, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9962, loss_cls: 0.6088, loss: 0.6088 +2025-06-24 17:08:20,124 - pyskl - INFO - Epoch [41][1100/1281] lr: 2.070e-02, eta: 15:32:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9962, loss_cls: 0.6278, loss: 0.6278 +2025-06-24 17:09:09,123 - pyskl - INFO - Epoch [41][1200/1281] lr: 2.068e-02, eta: 15:32:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9950, loss_cls: 0.6205, loss: 0.6205 +2025-06-24 17:09:49,501 - pyskl - INFO - Saving checkpoint at 41 epochs +2025-06-24 17:10:48,780 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:10:48,841 - pyskl - INFO - +top1_acc 0.8207 +top5_acc 0.9837 +2025-06-24 17:10:48,842 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:10:48,851 - pyskl - INFO - +mean_acc 0.7509 +2025-06-24 17:10:48,853 - pyskl - INFO - Epoch(val) [41][533] top1_acc: 0.8207, top5_acc: 0.9837, mean_class_accuracy: 0.7509 +2025-06-24 17:12:08,764 - pyskl - INFO - Epoch [42][100/1281] lr: 2.065e-02, eta: 15:31:24, time: 0.799, data_time: 0.193, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9988, loss_cls: 0.5453, loss: 0.5453 +2025-06-24 17:12:58,152 - pyskl - INFO - Epoch [42][200/1281] lr: 2.064e-02, eta: 15:31:08, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9950, loss_cls: 0.6132, loss: 0.6132 +2025-06-24 17:13:47,275 - pyskl - INFO - Epoch [42][300/1281] lr: 2.062e-02, eta: 15:30:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9938, loss_cls: 0.7126, loss: 0.7126 +2025-06-24 17:14:20,209 - pyskl - INFO - Epoch [42][400/1281] lr: 2.061e-02, eta: 15:29:53, time: 0.329, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 1.0000, loss_cls: 0.6172, loss: 0.6172 +2025-06-24 17:15:11,513 - pyskl - INFO - Epoch [42][500/1281] lr: 2.059e-02, eta: 15:29:42, time: 0.513, data_time: 0.001, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9950, loss_cls: 0.6493, loss: 0.6493 +2025-06-24 17:15:37,383 - pyskl - INFO - Epoch [42][600/1281] lr: 2.057e-02, eta: 15:28:25, time: 0.259, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9956, loss_cls: 0.5995, loss: 0.5995 +2025-06-24 17:16:26,316 - pyskl - INFO - Epoch [42][700/1281] lr: 2.056e-02, eta: 15:28:08, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9988, loss_cls: 0.6119, loss: 0.6119 +2025-06-24 17:17:15,238 - pyskl - INFO - Epoch [42][800/1281] lr: 2.054e-02, eta: 15:27:51, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9938, loss_cls: 0.6643, loss: 0.6643 +2025-06-24 17:18:04,562 - pyskl - INFO - Epoch [42][900/1281] lr: 2.053e-02, eta: 15:27:35, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9969, loss_cls: 0.6728, loss: 0.6728 +2025-06-24 17:18:53,928 - pyskl - INFO - Epoch [42][1000/1281] lr: 2.051e-02, eta: 15:27:19, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9981, loss_cls: 0.5839, loss: 0.5839 +2025-06-24 17:19:42,885 - pyskl - INFO - Epoch [42][1100/1281] lr: 2.050e-02, eta: 15:27:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9931, loss_cls: 0.6976, loss: 0.6976 +2025-06-24 17:20:31,667 - pyskl - INFO - Epoch [42][1200/1281] lr: 2.048e-02, eta: 15:26:44, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9962, loss_cls: 0.6646, loss: 0.6646 +2025-06-24 17:21:11,964 - pyskl - INFO - Saving checkpoint at 42 epochs +2025-06-24 17:22:11,335 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:22:11,390 - pyskl - INFO - +top1_acc 0.8113 +top5_acc 0.9846 +2025-06-24 17:22:11,390 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:22:11,398 - pyskl - INFO - +mean_acc 0.7349 +2025-06-24 17:22:11,399 - pyskl - INFO - Epoch(val) [42][533] top1_acc: 0.8113, top5_acc: 0.9846, mean_class_accuracy: 0.7349 +2025-06-24 17:23:30,896 - pyskl - INFO - Epoch [43][100/1281] lr: 2.045e-02, eta: 15:25:48, time: 0.795, data_time: 0.193, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9962, loss_cls: 0.5772, loss: 0.5772 +2025-06-24 17:24:20,378 - pyskl - INFO - Epoch [43][200/1281] lr: 2.044e-02, eta: 15:25:32, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9969, loss_cls: 0.5788, loss: 0.5788 +2025-06-24 17:25:09,301 - pyskl - INFO - Epoch [43][300/1281] lr: 2.042e-02, eta: 15:25:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9962, loss_cls: 0.5820, loss: 0.5820 +2025-06-24 17:25:41,594 - pyskl - INFO - Epoch [43][400/1281] lr: 2.040e-02, eta: 15:24:14, time: 0.323, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9962, loss_cls: 0.6005, loss: 0.6005 +2025-06-24 17:26:32,651 - pyskl - INFO - Epoch [43][500/1281] lr: 2.039e-02, eta: 15:24:01, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9938, loss_cls: 0.6602, loss: 0.6602 +2025-06-24 17:27:00,509 - pyskl - INFO - Epoch [43][600/1281] lr: 2.037e-02, eta: 15:22:49, time: 0.279, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9925, loss_cls: 0.6651, loss: 0.6651 +2025-06-24 17:27:49,269 - pyskl - INFO - Epoch [43][700/1281] lr: 2.036e-02, eta: 15:22:31, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9962, loss_cls: 0.6332, loss: 0.6332 +2025-06-24 17:28:38,392 - pyskl - INFO - Epoch [43][800/1281] lr: 2.034e-02, eta: 15:22:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9938, loss_cls: 0.6244, loss: 0.6244 +2025-06-24 17:29:27,505 - pyskl - INFO - Epoch [43][900/1281] lr: 2.033e-02, eta: 15:21:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9969, loss_cls: 0.5377, loss: 0.5377 +2025-06-24 17:30:16,770 - pyskl - INFO - Epoch [43][1000/1281] lr: 2.031e-02, eta: 15:21:38, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9975, loss_cls: 0.6294, loss: 0.6294 +2025-06-24 17:31:05,955 - pyskl - INFO - Epoch [43][1100/1281] lr: 2.029e-02, eta: 15:21:20, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9931, loss_cls: 0.6404, loss: 0.6404 +2025-06-24 17:31:55,067 - pyskl - INFO - Epoch [43][1200/1281] lr: 2.028e-02, eta: 15:21:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9988, loss_cls: 0.6581, loss: 0.6581 +2025-06-24 17:32:35,287 - pyskl - INFO - Saving checkpoint at 43 epochs +2025-06-24 17:33:34,967 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:33:35,023 - pyskl - INFO - +top1_acc 0.8425 +top5_acc 0.9872 +2025-06-24 17:33:35,023 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:33:35,032 - pyskl - INFO - +mean_acc 0.7756 +2025-06-24 17:33:35,036 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_40.pth was removed +2025-06-24 17:33:35,210 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_43.pth. +2025-06-24 17:33:35,210 - pyskl - INFO - Best top1_acc is 0.8425 at 43 epoch. +2025-06-24 17:33:35,213 - pyskl - INFO - Epoch(val) [43][533] top1_acc: 0.8425, top5_acc: 0.9872, mean_class_accuracy: 0.7756 +2025-06-24 17:34:56,174 - pyskl - INFO - Epoch [44][100/1281] lr: 2.025e-02, eta: 15:20:08, time: 0.810, data_time: 0.201, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9969, loss_cls: 0.5587, loss: 0.5587 +2025-06-24 17:35:45,304 - pyskl - INFO - Epoch [44][200/1281] lr: 2.023e-02, eta: 15:19:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9944, loss_cls: 0.5478, loss: 0.5478 +2025-06-24 17:36:34,524 - pyskl - INFO - Epoch [44][300/1281] lr: 2.022e-02, eta: 15:19:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9969, loss_cls: 0.5412, loss: 0.5412 +2025-06-24 17:37:02,637 - pyskl - INFO - Epoch [44][400/1281] lr: 2.020e-02, eta: 15:18:21, time: 0.281, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9956, loss_cls: 0.5898, loss: 0.5898 +2025-06-24 17:37:53,887 - pyskl - INFO - Epoch [44][500/1281] lr: 2.018e-02, eta: 15:18:08, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9962, loss_cls: 0.6071, loss: 0.6071 +2025-06-24 17:38:24,183 - pyskl - INFO - Epoch [44][600/1281] lr: 2.017e-02, eta: 15:17:03, time: 0.303, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9981, loss_cls: 0.5987, loss: 0.5987 +2025-06-24 17:39:13,186 - pyskl - INFO - Epoch [44][700/1281] lr: 2.015e-02, eta: 15:16:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9944, loss_cls: 0.5934, loss: 0.5934 +2025-06-24 17:40:02,302 - pyskl - INFO - Epoch [44][800/1281] lr: 2.014e-02, eta: 15:16:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9912, loss_cls: 0.6000, loss: 0.6000 +2025-06-24 17:40:51,202 - pyskl - INFO - Epoch [44][900/1281] lr: 2.012e-02, eta: 15:16:05, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9975, loss_cls: 0.6000, loss: 0.6000 +2025-06-24 17:41:40,291 - pyskl - INFO - Epoch [44][1000/1281] lr: 2.010e-02, eta: 15:15:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9975, loss_cls: 0.6067, loss: 0.6067 +2025-06-24 17:42:29,898 - pyskl - INFO - Epoch [44][1100/1281] lr: 2.009e-02, eta: 15:15:28, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9925, loss_cls: 0.5967, loss: 0.5967 +2025-06-24 17:43:18,830 - pyskl - INFO - Epoch [44][1200/1281] lr: 2.007e-02, eta: 15:15:08, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9944, loss_cls: 0.6774, loss: 0.6774 +2025-06-24 17:43:59,221 - pyskl - INFO - Saving checkpoint at 44 epochs +2025-06-24 17:44:58,404 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:44:58,465 - pyskl - INFO - +top1_acc 0.8118 +top5_acc 0.9886 +2025-06-24 17:44:58,466 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:44:58,472 - pyskl - INFO - +mean_acc 0.7470 +2025-06-24 17:44:58,474 - pyskl - INFO - Epoch(val) [44][533] top1_acc: 0.8118, top5_acc: 0.9886, mean_class_accuracy: 0.7470 +2025-06-24 17:46:17,654 - pyskl - INFO - Epoch [45][100/1281] lr: 2.004e-02, eta: 15:14:09, time: 0.792, data_time: 0.191, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9956, loss_cls: 0.6263, loss: 0.6263 +2025-06-24 17:47:06,606 - pyskl - INFO - Epoch [45][200/1281] lr: 2.003e-02, eta: 15:13:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9962, loss_cls: 0.5588, loss: 0.5588 +2025-06-24 17:47:55,911 - pyskl - INFO - Epoch [45][300/1281] lr: 2.001e-02, eta: 15:13:30, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.5009, loss: 0.5009 +2025-06-24 17:48:24,228 - pyskl - INFO - Epoch [45][400/1281] lr: 1.999e-02, eta: 15:12:21, time: 0.283, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9975, loss_cls: 0.5869, loss: 0.5869 +2025-06-24 17:49:15,246 - pyskl - INFO - Epoch [45][500/1281] lr: 1.998e-02, eta: 15:12:05, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9981, loss_cls: 0.5512, loss: 0.5512 +2025-06-24 17:49:46,526 - pyskl - INFO - Epoch [45][600/1281] lr: 1.996e-02, eta: 15:11:03, time: 0.313, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9944, loss_cls: 0.5901, loss: 0.5901 +2025-06-24 17:50:35,783 - pyskl - INFO - Epoch [45][700/1281] lr: 1.994e-02, eta: 15:10:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9938, loss_cls: 0.6374, loss: 0.6374 +2025-06-24 17:51:25,013 - pyskl - INFO - Epoch [45][800/1281] lr: 1.993e-02, eta: 15:10:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9950, loss_cls: 0.6596, loss: 0.6596 +2025-06-24 17:52:14,713 - pyskl - INFO - Epoch [45][900/1281] lr: 1.991e-02, eta: 15:10:05, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9950, loss_cls: 0.5793, loss: 0.5793 +2025-06-24 17:53:03,790 - pyskl - INFO - Epoch [45][1000/1281] lr: 1.989e-02, eta: 15:09:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9950, loss_cls: 0.6470, loss: 0.6470 +2025-06-24 17:53:52,918 - pyskl - INFO - Epoch [45][1100/1281] lr: 1.988e-02, eta: 15:09:25, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9944, loss_cls: 0.6537, loss: 0.6537 +2025-06-24 17:54:42,093 - pyskl - INFO - Epoch [45][1200/1281] lr: 1.986e-02, eta: 15:09:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9975, loss_cls: 0.6156, loss: 0.6156 +2025-06-24 17:55:22,287 - pyskl - INFO - Saving checkpoint at 45 epochs +2025-06-24 17:56:21,616 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:56:21,712 - pyskl - INFO - +top1_acc 0.8405 +top5_acc 0.9887 +2025-06-24 17:56:21,712 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:56:21,732 - pyskl - INFO - +mean_acc 0.7759 +2025-06-24 17:56:21,735 - pyskl - INFO - Epoch(val) [45][533] top1_acc: 0.8405, top5_acc: 0.9887, mean_class_accuracy: 0.7759 +2025-06-24 17:57:42,416 - pyskl - INFO - Epoch [46][100/1281] lr: 1.983e-02, eta: 15:08:08, time: 0.807, data_time: 0.198, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9956, loss_cls: 0.6165, loss: 0.6165 +2025-06-24 17:58:31,560 - pyskl - INFO - Epoch [46][200/1281] lr: 1.981e-02, eta: 15:07:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9956, loss_cls: 0.5359, loss: 0.5359 +2025-06-24 17:59:21,089 - pyskl - INFO - Epoch [46][300/1281] lr: 1.980e-02, eta: 15:07:28, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9950, loss_cls: 0.6650, loss: 0.6650 +2025-06-24 17:59:50,824 - pyskl - INFO - Epoch [46][400/1281] lr: 1.978e-02, eta: 15:06:22, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9975, loss_cls: 0.6455, loss: 0.6455 +2025-06-24 18:00:37,288 - pyskl - INFO - Epoch [46][500/1281] lr: 1.976e-02, eta: 15:05:55, time: 0.465, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9994, loss_cls: 0.5861, loss: 0.5861 +2025-06-24 18:01:09,340 - pyskl - INFO - Epoch [46][600/1281] lr: 1.975e-02, eta: 15:04:55, time: 0.320, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9975, loss_cls: 0.5642, loss: 0.5642 +2025-06-24 18:01:58,515 - pyskl - INFO - Epoch [46][700/1281] lr: 1.973e-02, eta: 15:04:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9988, loss_cls: 0.5148, loss: 0.5148 +2025-06-24 18:02:47,281 - pyskl - INFO - Epoch [46][800/1281] lr: 1.971e-02, eta: 15:04:12, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9962, loss_cls: 0.5813, loss: 0.5813 +2025-06-24 18:03:36,437 - pyskl - INFO - Epoch [46][900/1281] lr: 1.970e-02, eta: 15:03:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9962, loss_cls: 0.6275, loss: 0.6275 +2025-06-24 18:04:25,880 - pyskl - INFO - Epoch [46][1000/1281] lr: 1.968e-02, eta: 15:03:31, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9975, loss_cls: 0.6286, loss: 0.6286 +2025-06-24 18:05:14,919 - pyskl - INFO - Epoch [46][1100/1281] lr: 1.966e-02, eta: 15:03:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9956, loss_cls: 0.6279, loss: 0.6279 +2025-06-24 18:06:03,881 - pyskl - INFO - Epoch [46][1200/1281] lr: 1.965e-02, eta: 15:02:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9950, loss_cls: 0.6084, loss: 0.6084 +2025-06-24 18:06:43,941 - pyskl - INFO - Saving checkpoint at 46 epochs +2025-06-24 18:07:43,196 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:07:43,252 - pyskl - INFO - +top1_acc 0.7766 +top5_acc 0.9838 +2025-06-24 18:07:43,252 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:07:43,259 - pyskl - INFO - +mean_acc 0.7093 +2025-06-24 18:07:43,261 - pyskl - INFO - Epoch(val) [46][533] top1_acc: 0.7766, top5_acc: 0.9838, mean_class_accuracy: 0.7093 +2025-06-24 18:09:03,116 - pyskl - INFO - Epoch [47][100/1281] lr: 1.962e-02, eta: 15:01:48, time: 0.798, data_time: 0.197, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9975, loss_cls: 0.5566, loss: 0.5566 +2025-06-24 18:09:52,466 - pyskl - INFO - Epoch [47][200/1281] lr: 1.960e-02, eta: 15:01:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9975, loss_cls: 0.4896, loss: 0.4896 +2025-06-24 18:10:41,753 - pyskl - INFO - Epoch [47][300/1281] lr: 1.958e-02, eta: 15:01:06, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9975, loss_cls: 0.5704, loss: 0.5704 +2025-06-24 18:11:10,628 - pyskl - INFO - Epoch [47][400/1281] lr: 1.957e-02, eta: 14:59:59, time: 0.289, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9975, loss_cls: 0.5586, loss: 0.5586 +2025-06-24 18:11:58,907 - pyskl - INFO - Epoch [47][500/1281] lr: 1.955e-02, eta: 14:59:35, time: 0.483, data_time: 0.001, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9925, loss_cls: 0.6016, loss: 0.6016 +2025-06-24 18:12:31,693 - pyskl - INFO - Epoch [47][600/1281] lr: 1.953e-02, eta: 14:58:37, time: 0.328, data_time: 0.001, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9975, loss_cls: 0.5490, loss: 0.5490 +2025-06-24 18:13:20,649 - pyskl - INFO - Epoch [47][700/1281] lr: 1.952e-02, eta: 14:58:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9944, loss_cls: 0.6840, loss: 0.6840 +2025-06-24 18:14:09,406 - pyskl - INFO - Epoch [47][800/1281] lr: 1.950e-02, eta: 14:57:52, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9962, loss_cls: 0.6317, loss: 0.6317 +2025-06-24 18:14:58,487 - pyskl - INFO - Epoch [47][900/1281] lr: 1.948e-02, eta: 14:57:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9944, loss_cls: 0.6140, loss: 0.6140 +2025-06-24 18:15:47,615 - pyskl - INFO - Epoch [47][1000/1281] lr: 1.947e-02, eta: 14:57:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9962, loss_cls: 0.6748, loss: 0.6748 +2025-06-24 18:16:36,331 - pyskl - INFO - Epoch [47][1100/1281] lr: 1.945e-02, eta: 14:56:45, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9975, loss_cls: 0.5759, loss: 0.5759 +2025-06-24 18:17:25,326 - pyskl - INFO - Epoch [47][1200/1281] lr: 1.943e-02, eta: 14:56:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9956, loss_cls: 0.6423, loss: 0.6423 +2025-06-24 18:18:05,561 - pyskl - INFO - Saving checkpoint at 47 epochs +2025-06-24 18:19:05,003 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:19:05,076 - pyskl - INFO - +top1_acc 0.8150 +top5_acc 0.9843 +2025-06-24 18:19:05,076 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:19:05,085 - pyskl - INFO - +mean_acc 0.7581 +2025-06-24 18:19:05,088 - pyskl - INFO - Epoch(val) [47][533] top1_acc: 0.8150, top5_acc: 0.9843, mean_class_accuracy: 0.7581 +2025-06-24 18:20:23,923 - pyskl - INFO - Epoch [48][100/1281] lr: 1.940e-02, eta: 14:55:20, time: 0.788, data_time: 0.194, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9975, loss_cls: 0.6071, loss: 0.6071 +2025-06-24 18:21:12,992 - pyskl - INFO - Epoch [48][200/1281] lr: 1.938e-02, eta: 14:54:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9956, loss_cls: 0.5317, loss: 0.5317 +2025-06-24 18:22:02,333 - pyskl - INFO - Epoch [48][300/1281] lr: 1.937e-02, eta: 14:54:35, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9962, loss_cls: 0.5683, loss: 0.5683 +2025-06-24 18:22:31,302 - pyskl - INFO - Epoch [48][400/1281] lr: 1.935e-02, eta: 14:53:29, time: 0.290, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9956, loss_cls: 0.6047, loss: 0.6047 +2025-06-24 18:23:19,533 - pyskl - INFO - Epoch [48][500/1281] lr: 1.933e-02, eta: 14:53:04, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9944, loss_cls: 0.5972, loss: 0.5972 +2025-06-24 18:23:52,294 - pyskl - INFO - Epoch [48][600/1281] lr: 1.932e-02, eta: 14:52:06, time: 0.328, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9988, loss_cls: 0.5600, loss: 0.5600 +2025-06-24 18:24:41,345 - pyskl - INFO - Epoch [48][700/1281] lr: 1.930e-02, eta: 14:51:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9956, loss_cls: 0.6229, loss: 0.6229 +2025-06-24 18:25:30,664 - pyskl - INFO - Epoch [48][800/1281] lr: 1.928e-02, eta: 14:51:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9975, loss_cls: 0.5673, loss: 0.5673 +2025-06-24 18:26:19,540 - pyskl - INFO - Epoch [48][900/1281] lr: 1.926e-02, eta: 14:50:57, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9975, loss_cls: 0.5822, loss: 0.5822 +2025-06-24 18:27:08,142 - pyskl - INFO - Epoch [48][1000/1281] lr: 1.925e-02, eta: 14:50:33, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9962, loss_cls: 0.5515, loss: 0.5515 +2025-06-24 18:27:56,867 - pyskl - INFO - Epoch [48][1100/1281] lr: 1.923e-02, eta: 14:50:09, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9950, loss_cls: 0.5974, loss: 0.5974 +2025-06-24 18:28:45,655 - pyskl - INFO - Epoch [48][1200/1281] lr: 1.921e-02, eta: 14:49:46, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9919, loss_cls: 0.6691, loss: 0.6691 +2025-06-24 18:29:26,491 - pyskl - INFO - Saving checkpoint at 48 epochs +2025-06-24 18:30:25,203 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:30:25,274 - pyskl - INFO - +top1_acc 0.8431 +top5_acc 0.9907 +2025-06-24 18:30:25,275 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:30:25,285 - pyskl - INFO - +mean_acc 0.7678 +2025-06-24 18:30:25,291 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_43.pth was removed +2025-06-24 18:30:25,466 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_48.pth. +2025-06-24 18:30:25,467 - pyskl - INFO - Best top1_acc is 0.8431 at 48 epoch. +2025-06-24 18:30:25,469 - pyskl - INFO - Epoch(val) [48][533] top1_acc: 0.8431, top5_acc: 0.9907, mean_class_accuracy: 0.7678 +2025-06-24 18:31:45,105 - pyskl - INFO - Epoch [49][100/1281] lr: 1.918e-02, eta: 14:48:44, time: 0.796, data_time: 0.199, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9994, loss_cls: 0.5404, loss: 0.5404 +2025-06-24 18:32:34,296 - pyskl - INFO - Epoch [49][200/1281] lr: 1.916e-02, eta: 14:48:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9962, loss_cls: 0.5547, loss: 0.5547 +2025-06-24 18:33:23,528 - pyskl - INFO - Epoch [49][300/1281] lr: 1.915e-02, eta: 14:47:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9975, loss_cls: 0.5404, loss: 0.5404 +2025-06-24 18:33:54,722 - pyskl - INFO - Epoch [49][400/1281] lr: 1.913e-02, eta: 14:46:56, time: 0.312, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9969, loss_cls: 0.5583, loss: 0.5583 +2025-06-24 18:34:39,258 - pyskl - INFO - Epoch [49][500/1281] lr: 1.911e-02, eta: 14:46:23, time: 0.445, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9956, loss_cls: 0.6038, loss: 0.6038 +2025-06-24 18:35:12,892 - pyskl - INFO - Epoch [49][600/1281] lr: 1.909e-02, eta: 14:45:27, time: 0.336, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9956, loss_cls: 0.5986, loss: 0.5986 +2025-06-24 18:36:01,970 - pyskl - INFO - Epoch [49][700/1281] lr: 1.908e-02, eta: 14:45:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9956, loss_cls: 0.5382, loss: 0.5382 +2025-06-24 18:36:50,753 - pyskl - INFO - Epoch [49][800/1281] lr: 1.906e-02, eta: 14:44:39, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9950, loss_cls: 0.5724, loss: 0.5724 +2025-06-24 18:37:39,983 - pyskl - INFO - Epoch [49][900/1281] lr: 1.904e-02, eta: 14:44:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9994, loss_cls: 0.5607, loss: 0.5607 +2025-06-24 18:38:29,734 - pyskl - INFO - Epoch [49][1000/1281] lr: 1.902e-02, eta: 14:43:53, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9944, loss_cls: 0.5933, loss: 0.5933 +2025-06-24 18:39:19,096 - pyskl - INFO - Epoch [49][1100/1281] lr: 1.901e-02, eta: 14:43:30, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9988, loss_cls: 0.5772, loss: 0.5772 +2025-06-24 18:40:07,702 - pyskl - INFO - Epoch [49][1200/1281] lr: 1.899e-02, eta: 14:43:05, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9962, loss_cls: 0.5925, loss: 0.5925 +2025-06-24 18:40:48,266 - pyskl - INFO - Saving checkpoint at 49 epochs +2025-06-24 18:41:47,775 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:41:47,851 - pyskl - INFO - +top1_acc 0.8031 +top5_acc 0.9846 +2025-06-24 18:41:47,851 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:41:47,860 - pyskl - INFO - +mean_acc 0.7190 +2025-06-24 18:41:47,864 - pyskl - INFO - Epoch(val) [49][533] top1_acc: 0.8031, top5_acc: 0.9846, mean_class_accuracy: 0.7190 +2025-06-24 18:43:08,569 - pyskl - INFO - Epoch [50][100/1281] lr: 1.896e-02, eta: 14:42:04, time: 0.807, data_time: 0.201, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9956, loss_cls: 0.6131, loss: 0.6131 +2025-06-24 18:43:57,822 - pyskl - INFO - Epoch [50][200/1281] lr: 1.894e-02, eta: 14:41:40, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9956, loss_cls: 0.5704, loss: 0.5704 +2025-06-24 18:44:47,123 - pyskl - INFO - Epoch [50][300/1281] lr: 1.892e-02, eta: 14:41:16, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9956, loss_cls: 0.5126, loss: 0.5126 +2025-06-24 18:45:18,604 - pyskl - INFO - Epoch [50][400/1281] lr: 1.891e-02, eta: 14:40:16, time: 0.315, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9962, loss_cls: 0.5371, loss: 0.5371 +2025-06-24 18:46:00,929 - pyskl - INFO - Epoch [50][500/1281] lr: 1.889e-02, eta: 14:39:38, time: 0.423, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9944, loss_cls: 0.6030, loss: 0.6030 +2025-06-24 18:46:36,306 - pyskl - INFO - Epoch [50][600/1281] lr: 1.887e-02, eta: 14:38:46, time: 0.354, data_time: 0.001, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9956, loss_cls: 0.5019, loss: 0.5019 +2025-06-24 18:47:25,588 - pyskl - INFO - Epoch [50][700/1281] lr: 1.885e-02, eta: 14:38:22, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9944, loss_cls: 0.6119, loss: 0.6119 +2025-06-24 18:48:14,633 - pyskl - INFO - Epoch [50][800/1281] lr: 1.884e-02, eta: 14:37:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9944, loss_cls: 0.6524, loss: 0.6524 +2025-06-24 18:49:03,862 - pyskl - INFO - Epoch [50][900/1281] lr: 1.882e-02, eta: 14:37:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9962, loss_cls: 0.5780, loss: 0.5780 +2025-06-24 18:49:53,096 - pyskl - INFO - Epoch [50][1000/1281] lr: 1.880e-02, eta: 14:37:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9962, loss_cls: 0.5603, loss: 0.5603 +2025-06-24 18:50:41,957 - pyskl - INFO - Epoch [50][1100/1281] lr: 1.878e-02, eta: 14:36:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9975, loss_cls: 0.5948, loss: 0.5948 +2025-06-24 18:51:30,921 - pyskl - INFO - Epoch [50][1200/1281] lr: 1.876e-02, eta: 14:36:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9975, loss_cls: 0.5768, loss: 0.5768 +2025-06-24 18:52:11,069 - pyskl - INFO - Saving checkpoint at 50 epochs +2025-06-24 18:53:09,121 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:53:09,178 - pyskl - INFO - +top1_acc 0.8366 +top5_acc 0.9898 +2025-06-24 18:53:09,179 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:53:09,186 - pyskl - INFO - +mean_acc 0.7778 +2025-06-24 18:53:09,188 - pyskl - INFO - Epoch(val) [50][533] top1_acc: 0.8366, top5_acc: 0.9898, mean_class_accuracy: 0.7778 +2025-06-24 18:54:29,274 - pyskl - INFO - Epoch [51][100/1281] lr: 1.873e-02, eta: 14:35:15, time: 0.801, data_time: 0.194, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9962, loss_cls: 0.5472, loss: 0.5472 +2025-06-24 18:55:18,652 - pyskl - INFO - Epoch [51][200/1281] lr: 1.871e-02, eta: 14:34:51, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9981, loss_cls: 0.5182, loss: 0.5182 +2025-06-24 18:56:07,777 - pyskl - INFO - Epoch [51][300/1281] lr: 1.870e-02, eta: 14:34:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9988, loss_cls: 0.5005, loss: 0.5005 +2025-06-24 18:56:37,347 - pyskl - INFO - Epoch [51][400/1281] lr: 1.868e-02, eta: 14:33:22, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9975, loss_cls: 0.5208, loss: 0.5208 +2025-06-24 18:57:22,918 - pyskl - INFO - Epoch [51][500/1281] lr: 1.866e-02, eta: 14:32:50, time: 0.456, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9975, loss_cls: 0.5250, loss: 0.5250 +2025-06-24 18:57:56,161 - pyskl - INFO - Epoch [51][600/1281] lr: 1.864e-02, eta: 14:31:54, time: 0.332, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9962, loss_cls: 0.5334, loss: 0.5334 +2025-06-24 18:58:45,379 - pyskl - INFO - Epoch [51][700/1281] lr: 1.863e-02, eta: 14:31:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9975, loss_cls: 0.5147, loss: 0.5147 +2025-06-24 18:59:34,680 - pyskl - INFO - Epoch [51][800/1281] lr: 1.861e-02, eta: 14:31:04, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9969, loss_cls: 0.5923, loss: 0.5923 +2025-06-24 19:00:23,884 - pyskl - INFO - Epoch [51][900/1281] lr: 1.859e-02, eta: 14:30:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9956, loss_cls: 0.5666, loss: 0.5666 +2025-06-24 19:01:12,839 - pyskl - INFO - Epoch [51][1000/1281] lr: 1.857e-02, eta: 14:30:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9956, loss_cls: 0.5669, loss: 0.5669 +2025-06-24 19:02:01,920 - pyskl - INFO - Epoch [51][1100/1281] lr: 1.855e-02, eta: 14:29:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9919, loss_cls: 0.6121, loss: 0.6121 +2025-06-24 19:02:51,109 - pyskl - INFO - Epoch [51][1200/1281] lr: 1.854e-02, eta: 14:29:22, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9938, loss_cls: 0.5731, loss: 0.5731 +2025-06-24 19:03:31,712 - pyskl - INFO - Saving checkpoint at 51 epochs +2025-06-24 19:04:31,197 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:04:31,263 - pyskl - INFO - +top1_acc 0.8114 +top5_acc 0.9854 +2025-06-24 19:04:31,263 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:04:31,270 - pyskl - INFO - +mean_acc 0.7535 +2025-06-24 19:04:31,272 - pyskl - INFO - Epoch(val) [51][533] top1_acc: 0.8114, top5_acc: 0.9854, mean_class_accuracy: 0.7535 +2025-06-24 19:05:50,915 - pyskl - INFO - Epoch [52][100/1281] lr: 1.850e-02, eta: 14:28:18, time: 0.796, data_time: 0.196, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9969, loss_cls: 0.5778, loss: 0.5778 +2025-06-24 19:06:40,010 - pyskl - INFO - Epoch [52][200/1281] lr: 1.849e-02, eta: 14:27:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9969, loss_cls: 0.5581, loss: 0.5581 +2025-06-24 19:07:29,173 - pyskl - INFO - Epoch [52][300/1281] lr: 1.847e-02, eta: 14:27:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9962, loss_cls: 0.5422, loss: 0.5422 +2025-06-24 19:07:57,521 - pyskl - INFO - Epoch [52][400/1281] lr: 1.845e-02, eta: 14:26:21, time: 0.283, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9962, loss_cls: 0.5166, loss: 0.5166 +2025-06-24 19:08:44,872 - pyskl - INFO - Epoch [52][500/1281] lr: 1.843e-02, eta: 14:25:52, time: 0.474, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9956, loss_cls: 0.5580, loss: 0.5580 +2025-06-24 19:09:17,521 - pyskl - INFO - Epoch [52][600/1281] lr: 1.841e-02, eta: 14:24:55, time: 0.326, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9962, loss_cls: 0.5861, loss: 0.5861 +2025-06-24 19:10:07,022 - pyskl - INFO - Epoch [52][700/1281] lr: 1.840e-02, eta: 14:24:29, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9981, loss_cls: 0.5821, loss: 0.5821 +2025-06-24 19:10:56,495 - pyskl - INFO - Epoch [52][800/1281] lr: 1.838e-02, eta: 14:24:04, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9981, loss_cls: 0.5742, loss: 0.5742 +2025-06-24 19:11:45,564 - pyskl - INFO - Epoch [52][900/1281] lr: 1.836e-02, eta: 14:23:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9962, loss_cls: 0.5686, loss: 0.5686 +2025-06-24 19:12:34,759 - pyskl - INFO - Epoch [52][1000/1281] lr: 1.834e-02, eta: 14:23:12, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9975, loss_cls: 0.5946, loss: 0.5946 +2025-06-24 19:13:24,001 - pyskl - INFO - Epoch [52][1100/1281] lr: 1.832e-02, eta: 14:22:46, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9894, loss_cls: 0.6932, loss: 0.6932 +2025-06-24 19:14:12,913 - pyskl - INFO - Epoch [52][1200/1281] lr: 1.831e-02, eta: 14:22:20, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9962, loss_cls: 0.5629, loss: 0.5629 +2025-06-24 19:14:53,333 - pyskl - INFO - Saving checkpoint at 52 epochs +2025-06-24 19:15:53,137 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:15:53,192 - pyskl - INFO - +top1_acc 0.8274 +top5_acc 0.9866 +2025-06-24 19:15:53,192 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:15:53,199 - pyskl - INFO - +mean_acc 0.7607 +2025-06-24 19:15:53,201 - pyskl - INFO - Epoch(val) [52][533] top1_acc: 0.8274, top5_acc: 0.9866, mean_class_accuracy: 0.7607 +2025-06-24 19:17:11,819 - pyskl - INFO - Epoch [53][100/1281] lr: 1.827e-02, eta: 14:21:13, time: 0.786, data_time: 0.191, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9938, loss_cls: 0.5336, loss: 0.5336 +2025-06-24 19:18:00,975 - pyskl - INFO - Epoch [53][200/1281] lr: 1.826e-02, eta: 14:20:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9994, loss_cls: 0.5128, loss: 0.5128 +2025-06-24 19:18:50,598 - pyskl - INFO - Epoch [53][300/1281] lr: 1.824e-02, eta: 14:20:21, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9994, loss_cls: 0.5257, loss: 0.5257 +2025-06-24 19:19:18,315 - pyskl - INFO - Epoch [53][400/1281] lr: 1.822e-02, eta: 14:19:15, time: 0.277, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9956, loss_cls: 0.5551, loss: 0.5551 +2025-06-24 19:20:07,713 - pyskl - INFO - Epoch [53][500/1281] lr: 1.820e-02, eta: 14:18:49, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9938, loss_cls: 0.5778, loss: 0.5778 +2025-06-24 19:20:40,735 - pyskl - INFO - Epoch [53][600/1281] lr: 1.818e-02, eta: 14:17:52, time: 0.330, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9956, loss_cls: 0.5450, loss: 0.5450 +2025-06-24 19:21:29,318 - pyskl - INFO - Epoch [53][700/1281] lr: 1.816e-02, eta: 14:17:25, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9931, loss_cls: 0.6078, loss: 0.6078 +2025-06-24 19:22:18,704 - pyskl - INFO - Epoch [53][800/1281] lr: 1.815e-02, eta: 14:16:59, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9975, loss_cls: 0.5247, loss: 0.5247 +2025-06-24 19:23:08,117 - pyskl - INFO - Epoch [53][900/1281] lr: 1.813e-02, eta: 14:16:33, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9981, loss_cls: 0.5323, loss: 0.5323 +2025-06-24 19:23:57,612 - pyskl - INFO - Epoch [53][1000/1281] lr: 1.811e-02, eta: 14:16:07, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9962, loss_cls: 0.5455, loss: 0.5455 +2025-06-24 19:24:46,455 - pyskl - INFO - Epoch [53][1100/1281] lr: 1.809e-02, eta: 14:15:39, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9975, loss_cls: 0.5229, loss: 0.5229 +2025-06-24 19:25:35,421 - pyskl - INFO - Epoch [53][1200/1281] lr: 1.807e-02, eta: 14:15:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9975, loss_cls: 0.5623, loss: 0.5623 +2025-06-24 19:26:15,987 - pyskl - INFO - Saving checkpoint at 53 epochs +2025-06-24 19:27:15,198 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:27:15,269 - pyskl - INFO - +top1_acc 0.8415 +top5_acc 0.9889 +2025-06-24 19:27:15,269 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:27:15,278 - pyskl - INFO - +mean_acc 0.7918 +2025-06-24 19:27:15,281 - pyskl - INFO - Epoch(val) [53][533] top1_acc: 0.8415, top5_acc: 0.9889, mean_class_accuracy: 0.7918 +2025-06-24 19:28:35,090 - pyskl - INFO - Epoch [54][100/1281] lr: 1.804e-02, eta: 14:14:07, time: 0.798, data_time: 0.195, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9994, loss_cls: 0.4649, loss: 0.4649 +2025-06-24 19:29:24,258 - pyskl - INFO - Epoch [54][200/1281] lr: 1.802e-02, eta: 14:13:40, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9962, loss_cls: 0.5496, loss: 0.5496 +2025-06-24 19:30:13,424 - pyskl - INFO - Epoch [54][300/1281] lr: 1.800e-02, eta: 14:13:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9969, loss_cls: 0.5192, loss: 0.5192 +2025-06-24 19:30:43,583 - pyskl - INFO - Epoch [54][400/1281] lr: 1.798e-02, eta: 14:12:12, time: 0.302, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9981, loss_cls: 0.4819, loss: 0.4819 +2025-06-24 19:31:29,132 - pyskl - INFO - Epoch [54][500/1281] lr: 1.797e-02, eta: 14:11:38, time: 0.455, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9969, loss_cls: 0.5753, loss: 0.5753 +2025-06-24 19:32:03,928 - pyskl - INFO - Epoch [54][600/1281] lr: 1.795e-02, eta: 14:10:45, time: 0.348, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9962, loss_cls: 0.5680, loss: 0.5680 +2025-06-24 19:32:53,091 - pyskl - INFO - Epoch [54][700/1281] lr: 1.793e-02, eta: 14:10:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9962, loss_cls: 0.5766, loss: 0.5766 +2025-06-24 19:33:41,817 - pyskl - INFO - Epoch [54][800/1281] lr: 1.791e-02, eta: 14:09:50, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9975, loss_cls: 0.5860, loss: 0.5860 +2025-06-24 19:34:31,094 - pyskl - INFO - Epoch [54][900/1281] lr: 1.789e-02, eta: 14:09:23, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9962, loss_cls: 0.5657, loss: 0.5657 +2025-06-24 19:35:19,934 - pyskl - INFO - Epoch [54][1000/1281] lr: 1.787e-02, eta: 14:08:55, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9975, loss_cls: 0.5407, loss: 0.5407 +2025-06-24 19:36:08,967 - pyskl - INFO - Epoch [54][1100/1281] lr: 1.786e-02, eta: 14:08:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9938, loss_cls: 0.6032, loss: 0.6032 +2025-06-24 19:36:58,114 - pyskl - INFO - Epoch [54][1200/1281] lr: 1.784e-02, eta: 14:08:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9956, loss_cls: 0.5804, loss: 0.5804 +2025-06-24 19:37:38,467 - pyskl - INFO - Saving checkpoint at 54 epochs +2025-06-24 19:38:38,033 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:38:38,090 - pyskl - INFO - +top1_acc 0.8610 +top5_acc 0.9926 +2025-06-24 19:38:38,090 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:38:38,096 - pyskl - INFO - +mean_acc 0.8049 +2025-06-24 19:38:38,101 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_48.pth was removed +2025-06-24 19:38:38,277 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_54.pth. +2025-06-24 19:38:38,278 - pyskl - INFO - Best top1_acc is 0.8610 at 54 epoch. +2025-06-24 19:38:38,281 - pyskl - INFO - Epoch(val) [54][533] top1_acc: 0.8610, top5_acc: 0.9926, mean_class_accuracy: 0.8049 +2025-06-24 19:39:58,195 - pyskl - INFO - Epoch [55][100/1281] lr: 1.780e-02, eta: 14:06:54, time: 0.799, data_time: 0.199, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9981, loss_cls: 0.4932, loss: 0.4932 +2025-06-24 19:40:47,149 - pyskl - INFO - Epoch [55][200/1281] lr: 1.779e-02, eta: 14:06:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9975, loss_cls: 0.5345, loss: 0.5345 +2025-06-24 19:41:35,618 - pyskl - INFO - Epoch [55][300/1281] lr: 1.777e-02, eta: 14:05:57, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9962, loss_cls: 0.5348, loss: 0.5348 +2025-06-24 19:42:10,652 - pyskl - INFO - Epoch [55][400/1281] lr: 1.775e-02, eta: 14:05:05, time: 0.350, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9981, loss_cls: 0.5454, loss: 0.5454 +2025-06-24 19:42:49,149 - pyskl - INFO - Epoch [55][500/1281] lr: 1.773e-02, eta: 14:04:19, time: 0.385, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9956, loss_cls: 0.5308, loss: 0.5308 +2025-06-24 19:43:26,725 - pyskl - INFO - Epoch [55][600/1281] lr: 1.771e-02, eta: 14:03:31, time: 0.376, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9988, loss_cls: 0.5256, loss: 0.5256 +2025-06-24 19:44:16,081 - pyskl - INFO - Epoch [55][700/1281] lr: 1.769e-02, eta: 14:03:03, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9944, loss_cls: 0.5651, loss: 0.5651 +2025-06-24 19:45:04,824 - pyskl - INFO - Epoch [55][800/1281] lr: 1.767e-02, eta: 14:02:35, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9956, loss_cls: 0.5350, loss: 0.5350 +2025-06-24 19:45:54,415 - pyskl - INFO - Epoch [55][900/1281] lr: 1.766e-02, eta: 14:02:08, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9975, loss_cls: 0.5154, loss: 0.5154 +2025-06-24 19:46:43,812 - pyskl - INFO - Epoch [55][1000/1281] lr: 1.764e-02, eta: 14:01:40, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9950, loss_cls: 0.5286, loss: 0.5286 +2025-06-24 19:47:32,485 - pyskl - INFO - Epoch [55][1100/1281] lr: 1.762e-02, eta: 14:01:11, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9975, loss_cls: 0.5837, loss: 0.5837 +2025-06-24 19:48:21,485 - pyskl - INFO - Epoch [55][1200/1281] lr: 1.760e-02, eta: 14:00:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9962, loss_cls: 0.5187, loss: 0.5187 +2025-06-24 19:49:02,004 - pyskl - INFO - Saving checkpoint at 55 epochs +2025-06-24 19:50:01,122 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:50:01,194 - pyskl - INFO - +top1_acc 0.8345 +top5_acc 0.9873 +2025-06-24 19:50:01,194 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:50:01,212 - pyskl - INFO - +mean_acc 0.7548 +2025-06-24 19:50:01,217 - pyskl - INFO - Epoch(val) [55][533] top1_acc: 0.8345, top5_acc: 0.9873, mean_class_accuracy: 0.7548 +2025-06-24 19:51:20,733 - pyskl - INFO - Epoch [56][100/1281] lr: 1.757e-02, eta: 13:59:36, time: 0.795, data_time: 0.191, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9969, loss_cls: 0.5136, loss: 0.5136 +2025-06-24 19:52:09,733 - pyskl - INFO - Epoch [56][200/1281] lr: 1.755e-02, eta: 13:59:07, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9975, loss_cls: 0.5431, loss: 0.5431 +2025-06-24 19:52:56,451 - pyskl - INFO - Epoch [56][300/1281] lr: 1.753e-02, eta: 13:58:35, time: 0.467, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9950, loss_cls: 0.5043, loss: 0.5043 +2025-06-24 19:53:32,829 - pyskl - INFO - Epoch [56][400/1281] lr: 1.751e-02, eta: 13:57:45, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9975, loss_cls: 0.4907, loss: 0.4907 +2025-06-24 19:54:10,109 - pyskl - INFO - Epoch [56][500/1281] lr: 1.749e-02, eta: 13:56:56, time: 0.373, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9962, loss_cls: 0.5240, loss: 0.5240 +2025-06-24 19:54:46,786 - pyskl - INFO - Epoch [56][600/1281] lr: 1.747e-02, eta: 13:56:07, time: 0.367, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9981, loss_cls: 0.4986, loss: 0.4986 +2025-06-24 19:55:35,844 - pyskl - INFO - Epoch [56][700/1281] lr: 1.745e-02, eta: 13:55:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9969, loss_cls: 0.5302, loss: 0.5302 +2025-06-24 19:56:25,135 - pyskl - INFO - Epoch [56][800/1281] lr: 1.743e-02, eta: 13:55:10, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9969, loss_cls: 0.5113, loss: 0.5113 +2025-06-24 19:57:14,723 - pyskl - INFO - Epoch [56][900/1281] lr: 1.742e-02, eta: 13:54:43, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9969, loss_cls: 0.5396, loss: 0.5396 +2025-06-24 19:58:03,822 - pyskl - INFO - Epoch [56][1000/1281] lr: 1.740e-02, eta: 13:54:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9956, loss_cls: 0.5463, loss: 0.5463 +2025-06-24 19:58:52,572 - pyskl - INFO - Epoch [56][1100/1281] lr: 1.738e-02, eta: 13:53:45, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9938, loss_cls: 0.5236, loss: 0.5236 +2025-06-24 19:59:41,370 - pyskl - INFO - Epoch [56][1200/1281] lr: 1.736e-02, eta: 13:53:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9988, loss_cls: 0.5055, loss: 0.5055 +2025-06-24 20:00:21,541 - pyskl - INFO - Saving checkpoint at 56 epochs +2025-06-24 20:01:20,331 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:01:20,398 - pyskl - INFO - +top1_acc 0.8613 +top5_acc 0.9908 +2025-06-24 20:01:20,398 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:01:20,406 - pyskl - INFO - +mean_acc 0.8021 +2025-06-24 20:01:20,411 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_54.pth was removed +2025-06-24 20:01:20,609 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_56.pth. +2025-06-24 20:01:20,609 - pyskl - INFO - Best top1_acc is 0.8613 at 56 epoch. +2025-06-24 20:01:20,613 - pyskl - INFO - Epoch(val) [56][533] top1_acc: 0.8613, top5_acc: 0.9908, mean_class_accuracy: 0.8021 +2025-06-24 20:02:40,496 - pyskl - INFO - Epoch [57][100/1281] lr: 1.733e-02, eta: 13:52:09, time: 0.799, data_time: 0.195, memory: 4083, top1_acc: 0.9113, top5_acc: 1.0000, loss_cls: 0.4793, loss: 0.4793 +2025-06-24 20:03:29,861 - pyskl - INFO - Epoch [57][200/1281] lr: 1.731e-02, eta: 13:51:40, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9981, loss_cls: 0.5104, loss: 0.5104 +2025-06-24 20:04:17,682 - pyskl - INFO - Epoch [57][300/1281] lr: 1.729e-02, eta: 13:51:09, time: 0.478, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9981, loss_cls: 0.4956, loss: 0.4956 +2025-06-24 20:04:52,339 - pyskl - INFO - Epoch [57][400/1281] lr: 1.727e-02, eta: 13:50:16, time: 0.347, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9950, loss_cls: 0.5430, loss: 0.5430 +2025-06-24 20:05:31,374 - pyskl - INFO - Epoch [57][500/1281] lr: 1.725e-02, eta: 13:49:31, time: 0.390, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9944, loss_cls: 0.5319, loss: 0.5319 +2025-06-24 20:06:08,483 - pyskl - INFO - Epoch [57][600/1281] lr: 1.723e-02, eta: 13:48:42, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9956, loss_cls: 0.5692, loss: 0.5692 +2025-06-24 20:06:57,059 - pyskl - INFO - Epoch [57][700/1281] lr: 1.721e-02, eta: 13:48:12, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9969, loss_cls: 0.5448, loss: 0.5448 +2025-06-24 20:07:46,305 - pyskl - INFO - Epoch [57][800/1281] lr: 1.719e-02, eta: 13:47:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9969, loss_cls: 0.5340, loss: 0.5340 +2025-06-24 20:08:35,736 - pyskl - INFO - Epoch [57][900/1281] lr: 1.717e-02, eta: 13:47:15, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9962, loss_cls: 0.5333, loss: 0.5333 +2025-06-24 20:09:25,024 - pyskl - INFO - Epoch [57][1000/1281] lr: 1.716e-02, eta: 13:46:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9944, loss_cls: 0.5960, loss: 0.5960 +2025-06-24 20:10:13,726 - pyskl - INFO - Epoch [57][1100/1281] lr: 1.714e-02, eta: 13:46:16, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9944, loss_cls: 0.6182, loss: 0.6182 +2025-06-24 20:11:02,734 - pyskl - INFO - Epoch [57][1200/1281] lr: 1.712e-02, eta: 13:45:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9969, loss_cls: 0.5308, loss: 0.5308 +2025-06-24 20:11:43,135 - pyskl - INFO - Saving checkpoint at 57 epochs +2025-06-24 20:12:42,050 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:12:42,118 - pyskl - INFO - +top1_acc 0.8135 +top5_acc 0.9829 +2025-06-24 20:12:42,118 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:12:42,126 - pyskl - INFO - +mean_acc 0.7860 +2025-06-24 20:12:42,129 - pyskl - INFO - Epoch(val) [57][533] top1_acc: 0.8135, top5_acc: 0.9829, mean_class_accuracy: 0.7860 +2025-06-24 20:14:01,307 - pyskl - INFO - Epoch [58][100/1281] lr: 1.708e-02, eta: 13:44:38, time: 0.792, data_time: 0.190, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9969, loss_cls: 0.4849, loss: 0.4849 +2025-06-24 20:14:50,632 - pyskl - INFO - Epoch [58][200/1281] lr: 1.706e-02, eta: 13:44:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9988, loss_cls: 0.5368, loss: 0.5368 +2025-06-24 20:15:39,183 - pyskl - INFO - Epoch [58][300/1281] lr: 1.704e-02, eta: 13:43:39, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9944, loss_cls: 0.5084, loss: 0.5084 +2025-06-24 20:16:11,754 - pyskl - INFO - Epoch [58][400/1281] lr: 1.703e-02, eta: 13:42:43, time: 0.326, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9969, loss_cls: 0.5080, loss: 0.5080 +2025-06-24 20:16:52,764 - pyskl - INFO - Epoch [58][500/1281] lr: 1.701e-02, eta: 13:42:00, time: 0.410, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9975, loss_cls: 0.4957, loss: 0.4957 +2025-06-24 20:17:29,142 - pyskl - INFO - Epoch [58][600/1281] lr: 1.699e-02, eta: 13:41:10, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9956, loss_cls: 0.4706, loss: 0.4706 +2025-06-24 20:18:18,419 - pyskl - INFO - Epoch [58][700/1281] lr: 1.697e-02, eta: 13:40:41, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.5060, loss: 0.5060 +2025-06-24 20:19:07,602 - pyskl - INFO - Epoch [58][800/1281] lr: 1.695e-02, eta: 13:40:12, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9981, loss_cls: 0.5458, loss: 0.5458 +2025-06-24 20:19:56,599 - pyskl - INFO - Epoch [58][900/1281] lr: 1.693e-02, eta: 13:39:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9975, loss_cls: 0.5214, loss: 0.5214 +2025-06-24 20:20:45,499 - pyskl - INFO - Epoch [58][1000/1281] lr: 1.691e-02, eta: 13:39:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9938, loss_cls: 0.5202, loss: 0.5202 +2025-06-24 20:21:34,430 - pyskl - INFO - Epoch [58][1100/1281] lr: 1.689e-02, eta: 13:38:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9956, loss_cls: 0.5360, loss: 0.5360 +2025-06-24 20:22:23,301 - pyskl - INFO - Epoch [58][1200/1281] lr: 1.687e-02, eta: 13:38:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9969, loss_cls: 0.5452, loss: 0.5452 +2025-06-24 20:23:03,695 - pyskl - INFO - Saving checkpoint at 58 epochs +2025-06-24 20:24:02,364 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:24:02,419 - pyskl - INFO - +top1_acc 0.8535 +top5_acc 0.9921 +2025-06-24 20:24:02,420 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:24:02,426 - pyskl - INFO - +mean_acc 0.8037 +2025-06-24 20:24:02,428 - pyskl - INFO - Epoch(val) [58][533] top1_acc: 0.8535, top5_acc: 0.9921, mean_class_accuracy: 0.8037 +2025-06-24 20:25:21,806 - pyskl - INFO - Epoch [59][100/1281] lr: 1.684e-02, eta: 13:37:03, time: 0.794, data_time: 0.189, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9988, loss_cls: 0.4859, loss: 0.4859 +2025-06-24 20:26:10,702 - pyskl - INFO - Epoch [59][200/1281] lr: 1.682e-02, eta: 13:36:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9988, loss_cls: 0.5181, loss: 0.5181 +2025-06-24 20:26:59,623 - pyskl - INFO - Epoch [59][300/1281] lr: 1.680e-02, eta: 13:36:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9975, loss_cls: 0.4805, loss: 0.4805 +2025-06-24 20:27:31,656 - pyskl - INFO - Epoch [59][400/1281] lr: 1.678e-02, eta: 13:35:06, time: 0.320, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9988, loss_cls: 0.5130, loss: 0.5130 +2025-06-24 20:28:13,131 - pyskl - INFO - Epoch [59][500/1281] lr: 1.676e-02, eta: 13:34:24, time: 0.415, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9956, loss_cls: 0.5243, loss: 0.5243 +2025-06-24 20:28:49,984 - pyskl - INFO - Epoch [59][600/1281] lr: 1.674e-02, eta: 13:33:35, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9950, loss_cls: 0.5340, loss: 0.5340 +2025-06-24 20:29:39,045 - pyskl - INFO - Epoch [59][700/1281] lr: 1.672e-02, eta: 13:33:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9975, loss_cls: 0.4873, loss: 0.4873 +2025-06-24 20:30:28,194 - pyskl - INFO - Epoch [59][800/1281] lr: 1.670e-02, eta: 13:32:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9981, loss_cls: 0.4826, loss: 0.4826 +2025-06-24 20:31:17,312 - pyskl - INFO - Epoch [59][900/1281] lr: 1.668e-02, eta: 13:32:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9962, loss_cls: 0.4792, loss: 0.4792 +2025-06-24 20:32:05,920 - pyskl - INFO - Epoch [59][1000/1281] lr: 1.667e-02, eta: 13:31:34, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9975, loss_cls: 0.5840, loss: 0.5840 +2025-06-24 20:32:54,655 - pyskl - INFO - Epoch [59][1100/1281] lr: 1.665e-02, eta: 13:31:03, time: 0.487, data_time: 0.001, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9975, loss_cls: 0.5609, loss: 0.5609 +2025-06-24 20:33:43,802 - pyskl - INFO - Epoch [59][1200/1281] lr: 1.663e-02, eta: 13:30:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9988, loss_cls: 0.5477, loss: 0.5477 +2025-06-24 20:34:24,320 - pyskl - INFO - Saving checkpoint at 59 epochs +2025-06-24 20:35:23,432 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:35:23,505 - pyskl - INFO - +top1_acc 0.8607 +top5_acc 0.9910 +2025-06-24 20:35:23,505 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:35:23,514 - pyskl - INFO - +mean_acc 0.8194 +2025-06-24 20:35:23,517 - pyskl - INFO - Epoch(val) [59][533] top1_acc: 0.8607, top5_acc: 0.9910, mean_class_accuracy: 0.8194 +2025-06-24 20:36:43,507 - pyskl - INFO - Epoch [60][100/1281] lr: 1.659e-02, eta: 13:29:24, time: 0.800, data_time: 0.199, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9950, loss_cls: 0.5383, loss: 0.5383 +2025-06-24 20:37:32,534 - pyskl - INFO - Epoch [60][200/1281] lr: 1.657e-02, eta: 13:28:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9994, loss_cls: 0.5185, loss: 0.5185 +2025-06-24 20:38:19,887 - pyskl - INFO - Epoch [60][300/1281] lr: 1.655e-02, eta: 13:28:21, time: 0.474, data_time: 0.001, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9969, loss_cls: 0.5243, loss: 0.5243 +2025-06-24 20:38:55,833 - pyskl - INFO - Epoch [60][400/1281] lr: 1.653e-02, eta: 13:27:30, time: 0.359, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.5312, loss: 0.5312 +2025-06-24 20:39:33,653 - pyskl - INFO - Epoch [60][500/1281] lr: 1.651e-02, eta: 13:26:43, time: 0.378, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9962, loss_cls: 0.5727, loss: 0.5727 +2025-06-24 20:40:10,811 - pyskl - INFO - Epoch [60][600/1281] lr: 1.650e-02, eta: 13:25:54, time: 0.372, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9969, loss_cls: 0.4592, loss: 0.4592 +2025-06-24 20:40:59,711 - pyskl - INFO - Epoch [60][700/1281] lr: 1.648e-02, eta: 13:25:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9994, loss_cls: 0.4334, loss: 0.4334 +2025-06-24 20:41:48,896 - pyskl - INFO - Epoch [60][800/1281] lr: 1.646e-02, eta: 13:24:53, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9962, loss_cls: 0.5273, loss: 0.5273 +2025-06-24 20:42:38,090 - pyskl - INFO - Epoch [60][900/1281] lr: 1.644e-02, eta: 13:24:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9950, loss_cls: 0.5104, loss: 0.5104 +2025-06-24 20:43:27,331 - pyskl - INFO - Epoch [60][1000/1281] lr: 1.642e-02, eta: 13:23:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9969, loss_cls: 0.5199, loss: 0.5199 +2025-06-24 20:44:16,139 - pyskl - INFO - Epoch [60][1100/1281] lr: 1.640e-02, eta: 13:23:21, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9969, loss_cls: 0.5433, loss: 0.5433 +2025-06-24 20:45:05,059 - pyskl - INFO - Epoch [60][1200/1281] lr: 1.638e-02, eta: 13:22:50, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9969, loss_cls: 0.5030, loss: 0.5030 +2025-06-24 20:45:45,316 - pyskl - INFO - Saving checkpoint at 60 epochs +2025-06-24 20:46:44,411 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:46:44,476 - pyskl - INFO - +top1_acc 0.8484 +top5_acc 0.9901 +2025-06-24 20:46:44,477 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:46:44,484 - pyskl - INFO - +mean_acc 0.7961 +2025-06-24 20:46:44,486 - pyskl - INFO - Epoch(val) [60][533] top1_acc: 0.8484, top5_acc: 0.9901, mean_class_accuracy: 0.7961 +2025-06-24 20:48:04,563 - pyskl - INFO - Epoch [61][100/1281] lr: 1.634e-02, eta: 13:21:41, time: 0.801, data_time: 0.196, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9969, loss_cls: 0.4959, loss: 0.4959 +2025-06-24 20:48:53,605 - pyskl - INFO - Epoch [61][200/1281] lr: 1.632e-02, eta: 13:21:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9981, loss_cls: 0.4874, loss: 0.4874 +2025-06-24 20:49:40,206 - pyskl - INFO - Epoch [61][300/1281] lr: 1.630e-02, eta: 13:20:36, time: 0.466, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9981, loss_cls: 0.4795, loss: 0.4795 +2025-06-24 20:50:17,710 - pyskl - INFO - Epoch [61][400/1281] lr: 1.629e-02, eta: 13:19:48, time: 0.375, data_time: 0.001, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9969, loss_cls: 0.5036, loss: 0.5036 +2025-06-24 20:50:53,818 - pyskl - INFO - Epoch [61][500/1281] lr: 1.627e-02, eta: 13:18:58, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9962, loss_cls: 0.5809, loss: 0.5809 +2025-06-24 20:51:31,036 - pyskl - INFO - Epoch [61][600/1281] lr: 1.625e-02, eta: 13:18:09, time: 0.372, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9994, loss_cls: 0.4661, loss: 0.4661 +2025-06-24 20:52:19,915 - pyskl - INFO - Epoch [61][700/1281] lr: 1.623e-02, eta: 13:17:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9975, loss_cls: 0.4920, loss: 0.4920 +2025-06-24 20:53:08,889 - pyskl - INFO - Epoch [61][800/1281] lr: 1.621e-02, eta: 13:17:07, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9988, loss_cls: 0.4512, loss: 0.4512 +2025-06-24 20:53:57,883 - pyskl - INFO - Epoch [61][900/1281] lr: 1.619e-02, eta: 13:16:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9962, loss_cls: 0.5145, loss: 0.5145 +2025-06-24 20:54:46,924 - pyskl - INFO - Epoch [61][1000/1281] lr: 1.617e-02, eta: 13:16:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9981, loss_cls: 0.5116, loss: 0.5116 +2025-06-24 20:55:36,232 - pyskl - INFO - Epoch [61][1100/1281] lr: 1.615e-02, eta: 13:15:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9956, loss_cls: 0.5189, loss: 0.5189 +2025-06-24 20:56:24,849 - pyskl - INFO - Epoch [61][1200/1281] lr: 1.613e-02, eta: 13:15:02, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9962, loss_cls: 0.5074, loss: 0.5074 +2025-06-24 20:57:05,551 - pyskl - INFO - Saving checkpoint at 61 epochs +2025-06-24 20:58:05,234 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:58:05,293 - pyskl - INFO - +top1_acc 0.8407 +top5_acc 0.9894 +2025-06-24 20:58:05,293 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:58:05,300 - pyskl - INFO - +mean_acc 0.7668 +2025-06-24 20:58:05,301 - pyskl - INFO - Epoch(val) [61][533] top1_acc: 0.8407, top5_acc: 0.9894, mean_class_accuracy: 0.7668 +2025-06-24 20:59:24,643 - pyskl - INFO - Epoch [62][100/1281] lr: 1.609e-02, eta: 13:13:52, time: 0.793, data_time: 0.194, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.4349, loss: 0.4349 +2025-06-24 21:00:13,742 - pyskl - INFO - Epoch [62][200/1281] lr: 1.607e-02, eta: 13:13:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9962, loss_cls: 0.4096, loss: 0.4096 +2025-06-24 21:01:01,498 - pyskl - INFO - Epoch [62][300/1281] lr: 1.605e-02, eta: 13:12:47, time: 0.478, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9969, loss_cls: 0.4378, loss: 0.4378 +2025-06-24 21:01:37,698 - pyskl - INFO - Epoch [62][400/1281] lr: 1.603e-02, eta: 13:11:57, time: 0.362, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9988, loss_cls: 0.4334, loss: 0.4334 +2025-06-24 21:02:15,186 - pyskl - INFO - Epoch [62][500/1281] lr: 1.602e-02, eta: 13:11:09, time: 0.375, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9981, loss_cls: 0.4929, loss: 0.4929 +2025-06-24 21:02:53,223 - pyskl - INFO - Epoch [62][600/1281] lr: 1.600e-02, eta: 13:10:22, time: 0.380, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9969, loss_cls: 0.4898, loss: 0.4898 +2025-06-24 21:03:42,455 - pyskl - INFO - Epoch [62][700/1281] lr: 1.598e-02, eta: 13:09:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9988, loss_cls: 0.4969, loss: 0.4969 +2025-06-24 21:04:31,320 - pyskl - INFO - Epoch [62][800/1281] lr: 1.596e-02, eta: 13:09:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9988, loss_cls: 0.5024, loss: 0.5024 +2025-06-24 21:05:20,561 - pyskl - INFO - Epoch [62][900/1281] lr: 1.594e-02, eta: 13:08:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9956, loss_cls: 0.5599, loss: 0.5599 +2025-06-24 21:06:10,101 - pyskl - INFO - Epoch [62][1000/1281] lr: 1.592e-02, eta: 13:08:17, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9988, loss_cls: 0.4675, loss: 0.4675 +2025-06-24 21:06:58,751 - pyskl - INFO - Epoch [62][1100/1281] lr: 1.590e-02, eta: 13:07:45, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9988, loss_cls: 0.5881, loss: 0.5881 +2025-06-24 21:07:47,966 - pyskl - INFO - Epoch [62][1200/1281] lr: 1.588e-02, eta: 13:07:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9956, loss_cls: 0.5617, loss: 0.5617 +2025-06-24 21:08:28,612 - pyskl - INFO - Saving checkpoint at 62 epochs +2025-06-24 21:09:28,237 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:09:28,293 - pyskl - INFO - +top1_acc 0.8559 +top5_acc 0.9921 +2025-06-24 21:09:28,294 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:09:28,300 - pyskl - INFO - +mean_acc 0.8185 +2025-06-24 21:09:28,302 - pyskl - INFO - Epoch(val) [62][533] top1_acc: 0.8559, top5_acc: 0.9921, mean_class_accuracy: 0.8185 +2025-06-24 21:10:48,078 - pyskl - INFO - Epoch [63][100/1281] lr: 1.584e-02, eta: 13:06:03, time: 0.798, data_time: 0.190, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9975, loss_cls: 0.4129, loss: 0.4129 +2025-06-24 21:11:37,430 - pyskl - INFO - Epoch [63][200/1281] lr: 1.582e-02, eta: 13:05:32, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9981, loss_cls: 0.4577, loss: 0.4577 +2025-06-24 21:12:24,017 - pyskl - INFO - Epoch [63][300/1281] lr: 1.580e-02, eta: 13:04:56, time: 0.466, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9994, loss_cls: 0.4976, loss: 0.4976 +2025-06-24 21:13:00,460 - pyskl - INFO - Epoch [63][400/1281] lr: 1.578e-02, eta: 13:04:07, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9956, loss_cls: 0.4696, loss: 0.4696 +2025-06-24 21:13:37,318 - pyskl - INFO - Epoch [63][500/1281] lr: 1.576e-02, eta: 13:03:18, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9969, loss_cls: 0.4321, loss: 0.4321 +2025-06-24 21:14:14,900 - pyskl - INFO - Epoch [63][600/1281] lr: 1.574e-02, eta: 13:02:30, time: 0.376, data_time: 0.001, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9988, loss_cls: 0.4310, loss: 0.4310 +2025-06-24 21:15:03,890 - pyskl - INFO - Epoch [63][700/1281] lr: 1.572e-02, eta: 13:01:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4778, loss: 0.4778 +2025-06-24 21:15:53,278 - pyskl - INFO - Epoch [63][800/1281] lr: 1.570e-02, eta: 13:01:27, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9994, loss_cls: 0.4446, loss: 0.4446 +2025-06-24 21:16:42,758 - pyskl - INFO - Epoch [63][900/1281] lr: 1.568e-02, eta: 13:00:55, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9981, loss_cls: 0.5629, loss: 0.5629 +2025-06-24 21:17:32,138 - pyskl - INFO - Epoch [63][1000/1281] lr: 1.566e-02, eta: 13:00:24, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9950, loss_cls: 0.4928, loss: 0.4928 +2025-06-24 21:18:21,035 - pyskl - INFO - Epoch [63][1100/1281] lr: 1.564e-02, eta: 12:59:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9944, loss_cls: 0.5149, loss: 0.5149 +2025-06-24 21:19:10,283 - pyskl - INFO - Epoch [63][1200/1281] lr: 1.562e-02, eta: 12:59:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9950, loss_cls: 0.4840, loss: 0.4840 +2025-06-24 21:19:51,107 - pyskl - INFO - Saving checkpoint at 63 epochs +2025-06-24 21:20:50,285 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:20:50,339 - pyskl - INFO - +top1_acc 0.8515 +top5_acc 0.9910 +2025-06-24 21:20:50,340 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:20:50,347 - pyskl - INFO - +mean_acc 0.8012 +2025-06-24 21:20:50,349 - pyskl - INFO - Epoch(val) [63][533] top1_acc: 0.8515, top5_acc: 0.9910, mean_class_accuracy: 0.8012 +2025-06-24 21:22:08,886 - pyskl - INFO - Epoch [64][100/1281] lr: 1.559e-02, eta: 12:58:08, time: 0.785, data_time: 0.191, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9994, loss_cls: 0.4616, loss: 0.4616 +2025-06-24 21:22:58,101 - pyskl - INFO - Epoch [64][200/1281] lr: 1.557e-02, eta: 12:57:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9969, loss_cls: 0.4365, loss: 0.4365 +2025-06-24 21:23:46,161 - pyskl - INFO - Epoch [64][300/1281] lr: 1.555e-02, eta: 12:57:02, time: 0.481, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9981, loss_cls: 0.4670, loss: 0.4670 +2025-06-24 21:24:20,545 - pyskl - INFO - Epoch [64][400/1281] lr: 1.553e-02, eta: 12:56:10, time: 0.344, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9988, loss_cls: 0.4696, loss: 0.4696 +2025-06-24 21:24:59,662 - pyskl - INFO - Epoch [64][500/1281] lr: 1.551e-02, eta: 12:55:24, time: 0.391, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9988, loss_cls: 0.4411, loss: 0.4411 +2025-06-24 21:25:36,896 - pyskl - INFO - Epoch [64][600/1281] lr: 1.549e-02, eta: 12:54:36, time: 0.372, data_time: 0.001, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9988, loss_cls: 0.4693, loss: 0.4693 +2025-06-24 21:26:26,048 - pyskl - INFO - Epoch [64][700/1281] lr: 1.547e-02, eta: 12:54:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4291, loss: 0.4291 +2025-06-24 21:27:15,248 - pyskl - INFO - Epoch [64][800/1281] lr: 1.545e-02, eta: 12:53:32, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9975, loss_cls: 0.4758, loss: 0.4758 +2025-06-24 21:28:04,379 - pyskl - INFO - Epoch [64][900/1281] lr: 1.543e-02, eta: 12:52:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9944, loss_cls: 0.5259, loss: 0.5259 +2025-06-24 21:28:53,485 - pyskl - INFO - Epoch [64][1000/1281] lr: 1.541e-02, eta: 12:52:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9975, loss_cls: 0.5033, loss: 0.5033 +2025-06-24 21:29:42,364 - pyskl - INFO - Epoch [64][1100/1281] lr: 1.539e-02, eta: 12:51:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9981, loss_cls: 0.5349, loss: 0.5349 +2025-06-24 21:30:31,337 - pyskl - INFO - Epoch [64][1200/1281] lr: 1.537e-02, eta: 12:51:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9981, loss_cls: 0.4747, loss: 0.4747 +2025-06-24 21:31:11,446 - pyskl - INFO - Saving checkpoint at 64 epochs +2025-06-24 21:32:10,634 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:32:10,688 - pyskl - INFO - +top1_acc 0.8540 +top5_acc 0.9879 +2025-06-24 21:32:10,689 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:32:10,695 - pyskl - INFO - +mean_acc 0.7913 +2025-06-24 21:32:10,697 - pyskl - INFO - Epoch(val) [64][533] top1_acc: 0.8540, top5_acc: 0.9879, mean_class_accuracy: 0.7913 +2025-06-24 21:33:31,533 - pyskl - INFO - Epoch [65][100/1281] lr: 1.533e-02, eta: 12:50:12, time: 0.808, data_time: 0.201, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9975, loss_cls: 0.4843, loss: 0.4843 +2025-06-24 21:34:20,848 - pyskl - INFO - Epoch [65][200/1281] lr: 1.531e-02, eta: 12:49:40, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9994, loss_cls: 0.4278, loss: 0.4278 +2025-06-24 21:35:07,096 - pyskl - INFO - Epoch [65][300/1281] lr: 1.529e-02, eta: 12:49:04, time: 0.462, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9981, loss_cls: 0.4339, loss: 0.4339 +2025-06-24 21:35:44,656 - pyskl - INFO - Epoch [65][400/1281] lr: 1.527e-02, eta: 12:48:16, time: 0.376, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9969, loss_cls: 0.5034, loss: 0.5034 +2025-06-24 21:36:20,565 - pyskl - INFO - Epoch [65][500/1281] lr: 1.526e-02, eta: 12:47:26, time: 0.359, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9988, loss_cls: 0.4435, loss: 0.4435 +2025-06-24 21:36:58,884 - pyskl - INFO - Epoch [65][600/1281] lr: 1.524e-02, eta: 12:46:39, time: 0.383, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9956, loss_cls: 0.4878, loss: 0.4878 +2025-06-24 21:37:47,757 - pyskl - INFO - Epoch [65][700/1281] lr: 1.522e-02, eta: 12:46:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9944, loss_cls: 0.4698, loss: 0.4698 +2025-06-24 21:38:37,133 - pyskl - INFO - Epoch [65][800/1281] lr: 1.520e-02, eta: 12:45:34, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9962, loss_cls: 0.4775, loss: 0.4775 +2025-06-24 21:39:26,023 - pyskl - INFO - Epoch [65][900/1281] lr: 1.518e-02, eta: 12:45:01, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 1.0000, loss_cls: 0.4439, loss: 0.4439 +2025-06-24 21:40:15,187 - pyskl - INFO - Epoch [65][1000/1281] lr: 1.516e-02, eta: 12:44:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9969, loss_cls: 0.5052, loss: 0.5052 +2025-06-24 21:41:04,371 - pyskl - INFO - Epoch [65][1100/1281] lr: 1.514e-02, eta: 12:43:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9975, loss_cls: 0.4398, loss: 0.4398 +2025-06-24 21:41:53,293 - pyskl - INFO - Epoch [65][1200/1281] lr: 1.512e-02, eta: 12:43:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9962, loss_cls: 0.4698, loss: 0.4698 +2025-06-24 21:42:33,773 - pyskl - INFO - Saving checkpoint at 65 epochs +2025-06-24 21:43:33,045 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:43:33,100 - pyskl - INFO - +top1_acc 0.8480 +top5_acc 0.9892 +2025-06-24 21:43:33,100 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:43:33,107 - pyskl - INFO - +mean_acc 0.8049 +2025-06-24 21:43:33,109 - pyskl - INFO - Epoch(val) [65][533] top1_acc: 0.8480, top5_acc: 0.9892, mean_class_accuracy: 0.8049 +2025-06-24 21:44:52,304 - pyskl - INFO - Epoch [66][100/1281] lr: 1.508e-02, eta: 12:42:11, time: 0.792, data_time: 0.199, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9962, loss_cls: 0.4788, loss: 0.4788 +2025-06-24 21:45:41,548 - pyskl - INFO - Epoch [66][200/1281] lr: 1.506e-02, eta: 12:41:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9969, loss_cls: 0.4484, loss: 0.4484 +2025-06-24 21:46:27,868 - pyskl - INFO - Epoch [66][300/1281] lr: 1.504e-02, eta: 12:41:02, time: 0.463, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9994, loss_cls: 0.4581, loss: 0.4581 +2025-06-24 21:47:04,659 - pyskl - INFO - Epoch [66][400/1281] lr: 1.502e-02, eta: 12:40:13, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9988, loss_cls: 0.4027, loss: 0.4027 +2025-06-24 21:47:41,565 - pyskl - INFO - Epoch [66][500/1281] lr: 1.500e-02, eta: 12:39:24, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9988, loss_cls: 0.4218, loss: 0.4218 +2025-06-24 21:48:19,365 - pyskl - INFO - Epoch [66][600/1281] lr: 1.498e-02, eta: 12:38:37, time: 0.378, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9975, loss_cls: 0.3988, loss: 0.3988 +2025-06-24 21:49:08,610 - pyskl - INFO - Epoch [66][700/1281] lr: 1.496e-02, eta: 12:38:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9962, loss_cls: 0.4699, loss: 0.4699 +2025-06-24 21:49:57,974 - pyskl - INFO - Epoch [66][800/1281] lr: 1.494e-02, eta: 12:37:31, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9962, loss_cls: 0.4403, loss: 0.4403 +2025-06-24 21:50:47,036 - pyskl - INFO - Epoch [66][900/1281] lr: 1.492e-02, eta: 12:36:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9981, loss_cls: 0.4350, loss: 0.4350 +2025-06-24 21:51:36,299 - pyskl - INFO - Epoch [66][1000/1281] lr: 1.490e-02, eta: 12:36:25, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9969, loss_cls: 0.5008, loss: 0.5008 +2025-06-24 21:52:25,386 - pyskl - INFO - Epoch [66][1100/1281] lr: 1.488e-02, eta: 12:35:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9975, loss_cls: 0.4293, loss: 0.4293 +2025-06-24 21:53:14,674 - pyskl - INFO - Epoch [66][1200/1281] lr: 1.486e-02, eta: 12:35:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9988, loss_cls: 0.5476, loss: 0.5476 +2025-06-24 21:53:54,797 - pyskl - INFO - Saving checkpoint at 66 epochs +2025-06-24 21:54:53,781 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:54:53,836 - pyskl - INFO - +top1_acc 0.8443 +top5_acc 0.9907 +2025-06-24 21:54:53,836 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:54:53,844 - pyskl - INFO - +mean_acc 0.7832 +2025-06-24 21:54:53,846 - pyskl - INFO - Epoch(val) [66][533] top1_acc: 0.8443, top5_acc: 0.9907, mean_class_accuracy: 0.7832 +2025-06-24 21:56:13,090 - pyskl - INFO - Epoch [67][100/1281] lr: 1.482e-02, eta: 12:34:07, time: 0.792, data_time: 0.188, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9981, loss_cls: 0.4689, loss: 0.4689 +2025-06-24 21:57:02,223 - pyskl - INFO - Epoch [67][200/1281] lr: 1.480e-02, eta: 12:33:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9988, loss_cls: 0.4168, loss: 0.4168 +2025-06-24 21:57:49,914 - pyskl - INFO - Epoch [67][300/1281] lr: 1.478e-02, eta: 12:32:59, time: 0.477, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 1.0000, loss_cls: 0.4386, loss: 0.4386 +2025-06-24 21:58:25,879 - pyskl - INFO - Epoch [67][400/1281] lr: 1.476e-02, eta: 12:32:09, time: 0.360, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9975, loss_cls: 0.4561, loss: 0.4561 +2025-06-24 21:59:03,516 - pyskl - INFO - Epoch [67][500/1281] lr: 1.474e-02, eta: 12:31:21, time: 0.376, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9988, loss_cls: 0.3982, loss: 0.3982 +2025-06-24 21:59:39,928 - pyskl - INFO - Epoch [67][600/1281] lr: 1.472e-02, eta: 12:30:32, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9962, loss_cls: 0.4578, loss: 0.4578 +2025-06-24 22:00:29,046 - pyskl - INFO - Epoch [67][700/1281] lr: 1.470e-02, eta: 12:29:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9956, loss_cls: 0.4369, loss: 0.4369 +2025-06-24 22:01:18,053 - pyskl - INFO - Epoch [67][800/1281] lr: 1.468e-02, eta: 12:29:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9981, loss_cls: 0.4327, loss: 0.4327 +2025-06-24 22:02:07,665 - pyskl - INFO - Epoch [67][900/1281] lr: 1.466e-02, eta: 12:28:53, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9962, loss_cls: 0.4715, loss: 0.4715 +2025-06-24 22:02:56,710 - pyskl - INFO - Epoch [67][1000/1281] lr: 1.464e-02, eta: 12:28:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9969, loss_cls: 0.5079, loss: 0.5079 +2025-06-24 22:03:45,691 - pyskl - INFO - Epoch [67][1100/1281] lr: 1.462e-02, eta: 12:27:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9969, loss_cls: 0.4988, loss: 0.4988 +2025-06-24 22:04:34,928 - pyskl - INFO - Epoch [67][1200/1281] lr: 1.460e-02, eta: 12:27:12, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 1.0000, loss_cls: 0.4378, loss: 0.4378 +2025-06-24 22:05:15,498 - pyskl - INFO - Saving checkpoint at 67 epochs +2025-06-24 22:06:14,301 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:06:14,358 - pyskl - INFO - +top1_acc 0.8625 +top5_acc 0.9893 +2025-06-24 22:06:14,358 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:06:14,367 - pyskl - INFO - +mean_acc 0.7984 +2025-06-24 22:06:14,371 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_56.pth was removed +2025-06-24 22:06:14,542 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_67.pth. +2025-06-24 22:06:14,542 - pyskl - INFO - Best top1_acc is 0.8625 at 67 epoch. +2025-06-24 22:06:14,545 - pyskl - INFO - Epoch(val) [67][533] top1_acc: 0.8625, top5_acc: 0.9893, mean_class_accuracy: 0.7984 +2025-06-24 22:07:34,621 - pyskl - INFO - Epoch [68][100/1281] lr: 1.456e-02, eta: 12:26:01, time: 0.801, data_time: 0.190, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9994, loss_cls: 0.4286, loss: 0.4286 +2025-06-24 22:08:23,344 - pyskl - INFO - Epoch [68][200/1281] lr: 1.454e-02, eta: 12:25:27, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9981, loss_cls: 0.4390, loss: 0.4390 +2025-06-24 22:09:11,357 - pyskl - INFO - Epoch [68][300/1281] lr: 1.452e-02, eta: 12:24:52, time: 0.480, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9975, loss_cls: 0.4447, loss: 0.4447 +2025-06-24 22:09:44,309 - pyskl - INFO - Epoch [68][400/1281] lr: 1.450e-02, eta: 12:23:58, time: 0.330, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9975, loss_cls: 0.4433, loss: 0.4433 +2025-06-24 22:10:24,984 - pyskl - INFO - Epoch [68][500/1281] lr: 1.448e-02, eta: 12:23:14, time: 0.407, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9962, loss_cls: 0.4688, loss: 0.4688 +2025-06-24 22:11:01,259 - pyskl - INFO - Epoch [68][600/1281] lr: 1.446e-02, eta: 12:22:25, time: 0.363, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9975, loss_cls: 0.4295, loss: 0.4295 +2025-06-24 22:11:50,496 - pyskl - INFO - Epoch [68][700/1281] lr: 1.444e-02, eta: 12:21:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9988, loss_cls: 0.4581, loss: 0.4581 +2025-06-24 22:12:39,938 - pyskl - INFO - Epoch [68][800/1281] lr: 1.442e-02, eta: 12:21:18, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.4022, loss: 0.4022 +2025-06-24 22:13:28,913 - pyskl - INFO - Epoch [68][900/1281] lr: 1.440e-02, eta: 12:20:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9969, loss_cls: 0.4527, loss: 0.4527 +2025-06-24 22:14:18,072 - pyskl - INFO - Epoch [68][1000/1281] lr: 1.438e-02, eta: 12:20:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9981, loss_cls: 0.4627, loss: 0.4627 +2025-06-24 22:15:06,869 - pyskl - INFO - Epoch [68][1100/1281] lr: 1.436e-02, eta: 12:19:37, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9962, loss_cls: 0.5223, loss: 0.5223 +2025-06-24 22:15:55,779 - pyskl - INFO - Epoch [68][1200/1281] lr: 1.434e-02, eta: 12:19:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9975, loss_cls: 0.4435, loss: 0.4435 +2025-06-24 22:16:36,099 - pyskl - INFO - Saving checkpoint at 68 epochs +2025-06-24 22:17:35,630 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:17:35,688 - pyskl - INFO - +top1_acc 0.8418 +top5_acc 0.9852 +2025-06-24 22:17:35,688 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:17:35,695 - pyskl - INFO - +mean_acc 0.8044 +2025-06-24 22:17:35,697 - pyskl - INFO - Epoch(val) [68][533] top1_acc: 0.8418, top5_acc: 0.9852, mean_class_accuracy: 0.8044 +2025-06-24 22:18:55,268 - pyskl - INFO - Epoch [69][100/1281] lr: 1.431e-02, eta: 12:17:50, time: 0.796, data_time: 0.189, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9988, loss_cls: 0.4551, loss: 0.4551 +2025-06-24 22:19:44,355 - pyskl - INFO - Epoch [69][200/1281] lr: 1.429e-02, eta: 12:17:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9988, loss_cls: 0.3474, loss: 0.3474 +2025-06-24 22:20:32,692 - pyskl - INFO - Epoch [69][300/1281] lr: 1.427e-02, eta: 12:16:41, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9969, loss_cls: 0.4037, loss: 0.4037 +2025-06-24 22:21:06,405 - pyskl - INFO - Epoch [69][400/1281] lr: 1.425e-02, eta: 12:15:49, time: 0.337, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9962, loss_cls: 0.4297, loss: 0.4297 +2025-06-24 22:21:46,183 - pyskl - INFO - Epoch [69][500/1281] lr: 1.423e-02, eta: 12:15:04, time: 0.398, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9988, loss_cls: 0.4284, loss: 0.4284 +2025-06-24 22:22:23,044 - pyskl - INFO - Epoch [69][600/1281] lr: 1.420e-02, eta: 12:14:15, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9944, loss_cls: 0.4557, loss: 0.4557 +2025-06-24 22:23:12,019 - pyskl - INFO - Epoch [69][700/1281] lr: 1.418e-02, eta: 12:13:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9981, loss_cls: 0.4560, loss: 0.4560 +2025-06-24 22:24:01,283 - pyskl - INFO - Epoch [69][800/1281] lr: 1.416e-02, eta: 12:13:07, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9994, loss_cls: 0.4375, loss: 0.4375 +2025-06-24 22:24:50,159 - pyskl - INFO - Epoch [69][900/1281] lr: 1.414e-02, eta: 12:12:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9975, loss_cls: 0.4600, loss: 0.4600 +2025-06-24 22:25:39,604 - pyskl - INFO - Epoch [69][1000/1281] lr: 1.412e-02, eta: 12:12:00, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9969, loss_cls: 0.4538, loss: 0.4538 +2025-06-24 22:26:28,372 - pyskl - INFO - Epoch [69][1100/1281] lr: 1.410e-02, eta: 12:11:25, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9975, loss_cls: 0.5118, loss: 0.5118 +2025-06-24 22:27:17,568 - pyskl - INFO - Epoch [69][1200/1281] lr: 1.408e-02, eta: 12:10:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9988, loss_cls: 0.4616, loss: 0.4616 +2025-06-24 22:27:57,850 - pyskl - INFO - Saving checkpoint at 69 epochs +2025-06-24 22:28:56,245 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:28:56,300 - pyskl - INFO - +top1_acc 0.8628 +top5_acc 0.9910 +2025-06-24 22:28:56,301 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:28:56,308 - pyskl - INFO - +mean_acc 0.7979 +2025-06-24 22:28:56,312 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_67.pth was removed +2025-06-24 22:28:56,486 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_69.pth. +2025-06-24 22:28:56,487 - pyskl - INFO - Best top1_acc is 0.8628 at 69 epoch. +2025-06-24 22:28:56,492 - pyskl - INFO - Epoch(val) [69][533] top1_acc: 0.8628, top5_acc: 0.9910, mean_class_accuracy: 0.7979 +2025-06-24 22:30:15,394 - pyskl - INFO - Epoch [70][100/1281] lr: 1.405e-02, eta: 12:09:37, time: 0.789, data_time: 0.195, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9988, loss_cls: 0.4495, loss: 0.4495 +2025-06-24 22:31:04,345 - pyskl - INFO - Epoch [70][200/1281] lr: 1.403e-02, eta: 12:09:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9975, loss_cls: 0.3377, loss: 0.3377 +2025-06-24 22:31:53,165 - pyskl - INFO - Epoch [70][300/1281] lr: 1.401e-02, eta: 12:08:29, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4467, loss: 0.4467 +2025-06-24 22:32:23,232 - pyskl - INFO - Epoch [70][400/1281] lr: 1.399e-02, eta: 12:07:32, time: 0.301, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9988, loss_cls: 0.3870, loss: 0.3870 +2025-06-24 22:33:07,370 - pyskl - INFO - Epoch [70][500/1281] lr: 1.397e-02, eta: 12:06:52, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9988, loss_cls: 0.4073, loss: 0.4073 +2025-06-24 22:33:40,348 - pyskl - INFO - Epoch [70][600/1281] lr: 1.395e-02, eta: 12:05:59, time: 0.330, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9975, loss_cls: 0.4358, loss: 0.4358 +2025-06-24 22:34:29,526 - pyskl - INFO - Epoch [70][700/1281] lr: 1.392e-02, eta: 12:05:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.3758, loss: 0.3758 +2025-06-24 22:35:19,036 - pyskl - INFO - Epoch [70][800/1281] lr: 1.390e-02, eta: 12:04:51, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9962, loss_cls: 0.4677, loss: 0.4677 +2025-06-24 22:36:08,431 - pyskl - INFO - Epoch [70][900/1281] lr: 1.388e-02, eta: 12:04:17, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9975, loss_cls: 0.5411, loss: 0.5411 +2025-06-24 22:36:57,640 - pyskl - INFO - Epoch [70][1000/1281] lr: 1.386e-02, eta: 12:03:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9956, loss_cls: 0.5205, loss: 0.5205 +2025-06-24 22:37:46,843 - pyskl - INFO - Epoch [70][1100/1281] lr: 1.384e-02, eta: 12:03:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.4261, loss: 0.4261 +2025-06-24 22:38:35,860 - pyskl - INFO - Epoch [70][1200/1281] lr: 1.382e-02, eta: 12:02:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9956, loss_cls: 0.4448, loss: 0.4448 +2025-06-24 22:39:16,533 - pyskl - INFO - Saving checkpoint at 70 epochs +2025-06-24 22:40:15,802 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:40:15,869 - pyskl - INFO - +top1_acc 0.8505 +top5_acc 0.9900 +2025-06-24 22:40:15,869 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:40:15,877 - pyskl - INFO - +mean_acc 0.8115 +2025-06-24 22:40:15,879 - pyskl - INFO - Epoch(val) [70][533] top1_acc: 0.8505, top5_acc: 0.9900, mean_class_accuracy: 0.8115 +2025-06-24 22:41:34,954 - pyskl - INFO - Epoch [71][100/1281] lr: 1.379e-02, eta: 12:01:20, time: 0.791, data_time: 0.191, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.4056, loss: 0.4056 +2025-06-24 22:42:24,259 - pyskl - INFO - Epoch [71][200/1281] lr: 1.377e-02, eta: 12:00:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 1.0000, loss_cls: 0.3480, loss: 0.3480 +2025-06-24 22:43:13,241 - pyskl - INFO - Epoch [71][300/1281] lr: 1.375e-02, eta: 12:00:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.3714, loss: 0.3714 +2025-06-24 22:43:42,177 - pyskl - INFO - Epoch [71][400/1281] lr: 1.373e-02, eta: 11:59:14, time: 0.289, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9994, loss_cls: 0.4141, loss: 0.4141 +2025-06-24 22:44:29,590 - pyskl - INFO - Epoch [71][500/1281] lr: 1.371e-02, eta: 11:58:38, time: 0.474, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9975, loss_cls: 0.4359, loss: 0.4359 +2025-06-24 22:45:02,462 - pyskl - INFO - Epoch [71][600/1281] lr: 1.368e-02, eta: 11:57:45, time: 0.329, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9981, loss_cls: 0.3816, loss: 0.3816 +2025-06-24 22:45:51,543 - pyskl - INFO - Epoch [71][700/1281] lr: 1.366e-02, eta: 11:57:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9988, loss_cls: 0.4854, loss: 0.4854 +2025-06-24 22:46:41,093 - pyskl - INFO - Epoch [71][800/1281] lr: 1.364e-02, eta: 11:56:36, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9969, loss_cls: 0.5222, loss: 0.5222 +2025-06-24 22:47:30,115 - pyskl - INFO - Epoch [71][900/1281] lr: 1.362e-02, eta: 11:56:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9969, loss_cls: 0.4931, loss: 0.4931 +2025-06-24 22:48:19,603 - pyskl - INFO - Epoch [71][1000/1281] lr: 1.360e-02, eta: 11:55:27, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9962, loss_cls: 0.4433, loss: 0.4433 +2025-06-24 22:49:08,811 - pyskl - INFO - Epoch [71][1100/1281] lr: 1.358e-02, eta: 11:54:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3535, loss: 0.3535 +2025-06-24 22:49:57,955 - pyskl - INFO - Epoch [71][1200/1281] lr: 1.356e-02, eta: 11:54:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9975, loss_cls: 0.4103, loss: 0.4103 +2025-06-24 22:50:38,217 - pyskl - INFO - Saving checkpoint at 71 epochs +2025-06-24 22:51:37,008 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:51:37,076 - pyskl - INFO - +top1_acc 0.8608 +top5_acc 0.9907 +2025-06-24 22:51:37,076 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:51:37,084 - pyskl - INFO - +mean_acc 0.8246 +2025-06-24 22:51:37,086 - pyskl - INFO - Epoch(val) [71][533] top1_acc: 0.8608, top5_acc: 0.9907, mean_class_accuracy: 0.8246 +2025-06-24 22:52:57,097 - pyskl - INFO - Epoch [72][100/1281] lr: 1.353e-02, eta: 11:53:05, time: 0.800, data_time: 0.190, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9981, loss_cls: 0.4173, loss: 0.4173 +2025-06-24 22:53:46,680 - pyskl - INFO - Epoch [72][200/1281] lr: 1.351e-02, eta: 11:52:31, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9969, loss_cls: 0.4040, loss: 0.4040 +2025-06-24 22:54:35,897 - pyskl - INFO - Epoch [72][300/1281] lr: 1.349e-02, eta: 11:51:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3416, loss: 0.3416 +2025-06-24 22:55:05,977 - pyskl - INFO - Epoch [72][400/1281] lr: 1.346e-02, eta: 11:51:00, time: 0.301, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 1.0000, loss_cls: 0.3392, loss: 0.3392 +2025-06-24 22:55:51,564 - pyskl - INFO - Epoch [72][500/1281] lr: 1.344e-02, eta: 11:50:21, time: 0.456, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4598, loss: 0.4598 +2025-06-24 22:56:23,115 - pyskl - INFO - Epoch [72][600/1281] lr: 1.342e-02, eta: 11:49:27, time: 0.315, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9975, loss_cls: 0.4390, loss: 0.4390 +2025-06-24 22:57:12,300 - pyskl - INFO - Epoch [72][700/1281] lr: 1.340e-02, eta: 11:48:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4157, loss: 0.4157 +2025-06-24 22:58:01,801 - pyskl - INFO - Epoch [72][800/1281] lr: 1.338e-02, eta: 11:48:18, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9981, loss_cls: 0.4454, loss: 0.4454 +2025-06-24 22:58:51,029 - pyskl - INFO - Epoch [72][900/1281] lr: 1.336e-02, eta: 11:47:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9981, loss_cls: 0.4755, loss: 0.4755 +2025-06-24 22:59:40,216 - pyskl - INFO - Epoch [72][1000/1281] lr: 1.334e-02, eta: 11:47:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9988, loss_cls: 0.4668, loss: 0.4668 +2025-06-24 23:00:29,326 - pyskl - INFO - Epoch [72][1100/1281] lr: 1.332e-02, eta: 11:46:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9988, loss_cls: 0.4745, loss: 0.4745 +2025-06-24 23:01:18,516 - pyskl - INFO - Epoch [72][1200/1281] lr: 1.330e-02, eta: 11:45:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9956, loss_cls: 0.4398, loss: 0.4398 +2025-06-24 23:01:58,909 - pyskl - INFO - Saving checkpoint at 72 epochs +2025-06-24 23:02:57,555 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:02:57,613 - pyskl - INFO - +top1_acc 0.8661 +top5_acc 0.9912 +2025-06-24 23:02:57,613 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:02:57,619 - pyskl - INFO - +mean_acc 0.8170 +2025-06-24 23:02:57,623 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_69.pth was removed +2025-06-24 23:02:57,793 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_72.pth. +2025-06-24 23:02:57,793 - pyskl - INFO - Best top1_acc is 0.8661 at 72 epoch. +2025-06-24 23:02:57,796 - pyskl - INFO - Epoch(val) [72][533] top1_acc: 0.8661, top5_acc: 0.9912, mean_class_accuracy: 0.8170 +2025-06-24 23:04:16,871 - pyskl - INFO - Epoch [73][100/1281] lr: 1.326e-02, eta: 11:44:44, time: 0.791, data_time: 0.195, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9994, loss_cls: 0.3899, loss: 0.3899 +2025-06-24 23:05:06,140 - pyskl - INFO - Epoch [73][200/1281] lr: 1.324e-02, eta: 11:44:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9975, loss_cls: 0.4112, loss: 0.4112 +2025-06-24 23:05:55,022 - pyskl - INFO - Epoch [73][300/1281] lr: 1.322e-02, eta: 11:43:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 1.0000, loss_cls: 0.3876, loss: 0.3876 +2025-06-24 23:06:22,597 - pyskl - INFO - Epoch [73][400/1281] lr: 1.320e-02, eta: 11:42:36, time: 0.276, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 1.0000, loss_cls: 0.4183, loss: 0.4183 +2025-06-24 23:07:13,658 - pyskl - INFO - Epoch [73][500/1281] lr: 1.318e-02, eta: 11:42:03, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 1.0000, loss_cls: 0.3956, loss: 0.3956 +2025-06-24 23:07:45,625 - pyskl - INFO - Epoch [73][600/1281] lr: 1.316e-02, eta: 11:41:09, time: 0.320, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4381, loss: 0.4381 +2025-06-24 23:08:35,020 - pyskl - INFO - Epoch [73][700/1281] lr: 1.314e-02, eta: 11:40:34, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9981, loss_cls: 0.4231, loss: 0.4231 +2025-06-24 23:09:24,107 - pyskl - INFO - Epoch [73][800/1281] lr: 1.312e-02, eta: 11:39:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9988, loss_cls: 0.4239, loss: 0.4239 +2025-06-24 23:10:13,610 - pyskl - INFO - Epoch [73][900/1281] lr: 1.310e-02, eta: 11:39:24, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9994, loss_cls: 0.3900, loss: 0.3900 +2025-06-24 23:11:02,835 - pyskl - INFO - Epoch [73][1000/1281] lr: 1.308e-02, eta: 11:38:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9962, loss_cls: 0.4486, loss: 0.4486 +2025-06-24 23:11:52,395 - pyskl - INFO - Epoch [73][1100/1281] lr: 1.306e-02, eta: 11:38:14, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9981, loss_cls: 0.3865, loss: 0.3865 +2025-06-24 23:12:41,538 - pyskl - INFO - Epoch [73][1200/1281] lr: 1.304e-02, eta: 11:37:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9994, loss_cls: 0.4157, loss: 0.4157 +2025-06-24 23:13:21,902 - pyskl - INFO - Saving checkpoint at 73 epochs +2025-06-24 23:14:21,187 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:14:21,242 - pyskl - INFO - +top1_acc 0.8851 +top5_acc 0.9945 +2025-06-24 23:14:21,242 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:14:21,249 - pyskl - INFO - +mean_acc 0.8492 +2025-06-24 23:14:21,253 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_72.pth was removed +2025-06-24 23:14:21,445 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_73.pth. +2025-06-24 23:14:21,446 - pyskl - INFO - Best top1_acc is 0.8851 at 73 epoch. +2025-06-24 23:14:21,448 - pyskl - INFO - Epoch(val) [73][533] top1_acc: 0.8851, top5_acc: 0.9945, mean_class_accuracy: 0.8492 +2025-06-24 23:15:42,001 - pyskl - INFO - Epoch [74][100/1281] lr: 1.300e-02, eta: 11:36:26, time: 0.805, data_time: 0.194, memory: 4083, top1_acc: 0.9337, top5_acc: 1.0000, loss_cls: 0.3670, loss: 0.3670 +2025-06-24 23:16:31,101 - pyskl - INFO - Epoch [74][200/1281] lr: 1.298e-02, eta: 11:35:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9988, loss_cls: 0.4139, loss: 0.4139 +2025-06-24 23:17:20,090 - pyskl - INFO - Epoch [74][300/1281] lr: 1.296e-02, eta: 11:35:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.3892, loss: 0.3892 +2025-06-24 23:17:50,348 - pyskl - INFO - Epoch [74][400/1281] lr: 1.294e-02, eta: 11:34:20, time: 0.303, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9975, loss_cls: 0.3838, loss: 0.3838 +2025-06-24 23:18:35,541 - pyskl - INFO - Epoch [74][500/1281] lr: 1.292e-02, eta: 11:33:41, time: 0.452, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 1.0000, loss_cls: 0.3680, loss: 0.3680 +2025-06-24 23:19:09,264 - pyskl - INFO - Epoch [74][600/1281] lr: 1.290e-02, eta: 11:32:49, time: 0.337, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9981, loss_cls: 0.3737, loss: 0.3737 +2025-06-24 23:19:58,197 - pyskl - INFO - Epoch [74][700/1281] lr: 1.288e-02, eta: 11:32:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4435, loss: 0.4435 +2025-06-24 23:20:47,192 - pyskl - INFO - Epoch [74][800/1281] lr: 1.286e-02, eta: 11:31:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9981, loss_cls: 0.4144, loss: 0.4144 +2025-06-24 23:21:36,387 - pyskl - INFO - Epoch [74][900/1281] lr: 1.284e-02, eta: 11:31:03, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9975, loss_cls: 0.4595, loss: 0.4595 +2025-06-24 23:22:25,383 - pyskl - INFO - Epoch [74][1000/1281] lr: 1.282e-02, eta: 11:30:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.4200, loss: 0.4200 +2025-06-24 23:23:14,149 - pyskl - INFO - Epoch [74][1100/1281] lr: 1.280e-02, eta: 11:29:51, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9981, loss_cls: 0.4487, loss: 0.4487 +2025-06-24 23:24:03,330 - pyskl - INFO - Epoch [74][1200/1281] lr: 1.278e-02, eta: 11:29:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9988, loss_cls: 0.4366, loss: 0.4366 +2025-06-24 23:24:44,117 - pyskl - INFO - Saving checkpoint at 74 epochs +2025-06-24 23:25:42,962 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:25:43,017 - pyskl - INFO - +top1_acc 0.8715 +top5_acc 0.9914 +2025-06-24 23:25:43,017 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:25:43,024 - pyskl - INFO - +mean_acc 0.8280 +2025-06-24 23:25:43,026 - pyskl - INFO - Epoch(val) [74][533] top1_acc: 0.8715, top5_acc: 0.9914, mean_class_accuracy: 0.8280 +2025-06-24 23:27:02,131 - pyskl - INFO - Epoch [75][100/1281] lr: 1.274e-02, eta: 11:28:01, time: 0.791, data_time: 0.187, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9975, loss_cls: 0.3588, loss: 0.3588 +2025-06-24 23:27:51,091 - pyskl - INFO - Epoch [75][200/1281] lr: 1.272e-02, eta: 11:27:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9981, loss_cls: 0.3935, loss: 0.3935 +2025-06-24 23:28:40,062 - pyskl - INFO - Epoch [75][300/1281] lr: 1.270e-02, eta: 11:26:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.3905, loss: 0.3905 +2025-06-24 23:29:09,366 - pyskl - INFO - Epoch [75][400/1281] lr: 1.268e-02, eta: 11:25:54, time: 0.293, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9975, loss_cls: 0.4140, loss: 0.4140 +2025-06-24 23:29:56,588 - pyskl - INFO - Epoch [75][500/1281] lr: 1.266e-02, eta: 11:25:16, time: 0.472, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9994, loss_cls: 0.4780, loss: 0.4780 +2025-06-24 23:30:28,949 - pyskl - INFO - Epoch [75][600/1281] lr: 1.264e-02, eta: 11:24:24, time: 0.324, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9981, loss_cls: 0.4055, loss: 0.4055 +2025-06-24 23:31:18,243 - pyskl - INFO - Epoch [75][700/1281] lr: 1.262e-02, eta: 11:23:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.3637, loss: 0.3637 +2025-06-24 23:32:07,734 - pyskl - INFO - Epoch [75][800/1281] lr: 1.260e-02, eta: 11:23:13, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 1.0000, loss_cls: 0.3518, loss: 0.3518 +2025-06-24 23:32:57,052 - pyskl - INFO - Epoch [75][900/1281] lr: 1.258e-02, eta: 11:22:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9975, loss_cls: 0.3951, loss: 0.3951 +2025-06-24 23:33:45,816 - pyskl - INFO - Epoch [75][1000/1281] lr: 1.256e-02, eta: 11:22:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9981, loss_cls: 0.3811, loss: 0.3811 +2025-06-24 23:34:34,783 - pyskl - INFO - Epoch [75][1100/1281] lr: 1.254e-02, eta: 11:21:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9988, loss_cls: 0.4161, loss: 0.4161 +2025-06-24 23:35:23,646 - pyskl - INFO - Epoch [75][1200/1281] lr: 1.252e-02, eta: 11:20:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9981, loss_cls: 0.4312, loss: 0.4312 +2025-06-24 23:36:03,916 - pyskl - INFO - Saving checkpoint at 75 epochs +2025-06-24 23:37:02,525 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:37:02,592 - pyskl - INFO - +top1_acc 0.8767 +top5_acc 0.9930 +2025-06-24 23:37:02,592 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:37:02,601 - pyskl - INFO - +mean_acc 0.8332 +2025-06-24 23:37:02,603 - pyskl - INFO - Epoch(val) [75][533] top1_acc: 0.8767, top5_acc: 0.9930, mean_class_accuracy: 0.8332 +2025-06-24 23:38:22,331 - pyskl - INFO - Epoch [76][100/1281] lr: 1.248e-02, eta: 11:19:35, time: 0.797, data_time: 0.189, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9994, loss_cls: 0.3629, loss: 0.3629 +2025-06-24 23:39:11,506 - pyskl - INFO - Epoch [76][200/1281] lr: 1.246e-02, eta: 11:18:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9994, loss_cls: 0.3411, loss: 0.3411 +2025-06-24 23:40:01,028 - pyskl - INFO - Epoch [76][300/1281] lr: 1.244e-02, eta: 11:18:24, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9994, loss_cls: 0.3643, loss: 0.3643 +2025-06-24 23:40:29,656 - pyskl - INFO - Epoch [76][400/1281] lr: 1.242e-02, eta: 11:17:27, time: 0.286, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9962, loss_cls: 0.4496, loss: 0.4496 +2025-06-24 23:41:18,234 - pyskl - INFO - Epoch [76][500/1281] lr: 1.240e-02, eta: 11:16:51, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9994, loss_cls: 0.4023, loss: 0.4023 +2025-06-24 23:41:50,437 - pyskl - INFO - Epoch [76][600/1281] lr: 1.238e-02, eta: 11:15:58, time: 0.322, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9988, loss_cls: 0.4117, loss: 0.4117 +2025-06-24 23:42:39,493 - pyskl - INFO - Epoch [76][700/1281] lr: 1.236e-02, eta: 11:15:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9981, loss_cls: 0.3533, loss: 0.3533 +2025-06-24 23:43:28,325 - pyskl - INFO - Epoch [76][800/1281] lr: 1.234e-02, eta: 11:14:46, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9962, loss_cls: 0.4103, loss: 0.4103 +2025-06-24 23:44:17,297 - pyskl - INFO - Epoch [76][900/1281] lr: 1.232e-02, eta: 11:14:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9981, loss_cls: 0.4089, loss: 0.4089 +2025-06-24 23:45:06,593 - pyskl - INFO - Epoch [76][1000/1281] lr: 1.230e-02, eta: 11:13:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9988, loss_cls: 0.3921, loss: 0.3921 +2025-06-24 23:45:55,687 - pyskl - INFO - Epoch [76][1100/1281] lr: 1.228e-02, eta: 11:12:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.4180, loss: 0.4180 +2025-06-24 23:46:44,904 - pyskl - INFO - Epoch [76][1200/1281] lr: 1.225e-02, eta: 11:12:22, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9962, loss_cls: 0.4287, loss: 0.4287 +2025-06-24 23:47:25,197 - pyskl - INFO - Saving checkpoint at 76 epochs +2025-06-24 23:48:23,303 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:48:23,357 - pyskl - INFO - +top1_acc 0.8668 +top5_acc 0.9914 +2025-06-24 23:48:23,357 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:48:23,364 - pyskl - INFO - +mean_acc 0.8338 +2025-06-24 23:48:23,366 - pyskl - INFO - Epoch(val) [76][533] top1_acc: 0.8668, top5_acc: 0.9914, mean_class_accuracy: 0.8338 +2025-06-24 23:49:42,059 - pyskl - INFO - Epoch [77][100/1281] lr: 1.222e-02, eta: 11:11:07, time: 0.787, data_time: 0.184, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9969, loss_cls: 0.3440, loss: 0.3440 +2025-06-24 23:50:31,113 - pyskl - INFO - Epoch [77][200/1281] lr: 1.220e-02, eta: 11:10:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9975, loss_cls: 0.3362, loss: 0.3362 +2025-06-24 23:51:20,227 - pyskl - INFO - Epoch [77][300/1281] lr: 1.218e-02, eta: 11:09:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 1.0000, loss_cls: 0.3627, loss: 0.3627 +2025-06-24 23:51:47,720 - pyskl - INFO - Epoch [77][400/1281] lr: 1.216e-02, eta: 11:08:57, time: 0.275, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3757, loss: 0.3757 +2025-06-24 23:52:38,549 - pyskl - INFO - Epoch [77][500/1281] lr: 1.214e-02, eta: 11:08:23, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9994, loss_cls: 0.3925, loss: 0.3925 +2025-06-24 23:53:06,454 - pyskl - INFO - Epoch [77][600/1281] lr: 1.212e-02, eta: 11:07:26, time: 0.279, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3976, loss: 0.3976 +2025-06-24 23:53:55,479 - pyskl - INFO - Epoch [77][700/1281] lr: 1.210e-02, eta: 11:06:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9994, loss_cls: 0.4126, loss: 0.4126 +2025-06-24 23:54:45,087 - pyskl - INFO - Epoch [77][800/1281] lr: 1.207e-02, eta: 11:06:14, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9994, loss_cls: 0.4013, loss: 0.4013 +2025-06-24 23:55:34,544 - pyskl - INFO - Epoch [77][900/1281] lr: 1.205e-02, eta: 11:05:38, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9988, loss_cls: 0.3737, loss: 0.3737 +2025-06-24 23:56:23,727 - pyskl - INFO - Epoch [77][1000/1281] lr: 1.203e-02, eta: 11:05:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9988, loss_cls: 0.3933, loss: 0.3933 +2025-06-24 23:57:12,734 - pyskl - INFO - Epoch [77][1100/1281] lr: 1.201e-02, eta: 11:04:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.3871, loss: 0.3871 +2025-06-24 23:58:01,573 - pyskl - INFO - Epoch [77][1200/1281] lr: 1.199e-02, eta: 11:03:49, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9969, loss_cls: 0.4095, loss: 0.4095 +2025-06-24 23:58:42,042 - pyskl - INFO - Saving checkpoint at 77 epochs +2025-06-24 23:59:40,724 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:59:40,779 - pyskl - INFO - +top1_acc 0.8873 +top5_acc 0.9928 +2025-06-24 23:59:40,779 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:59:40,785 - pyskl - INFO - +mean_acc 0.8440 +2025-06-24 23:59:40,788 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_73.pth was removed +2025-06-24 23:59:40,966 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_77.pth. +2025-06-24 23:59:40,967 - pyskl - INFO - Best top1_acc is 0.8873 at 77 epoch. +2025-06-24 23:59:40,969 - pyskl - INFO - Epoch(val) [77][533] top1_acc: 0.8873, top5_acc: 0.9928, mean_class_accuracy: 0.8440 +2025-06-25 00:01:00,540 - pyskl - INFO - Epoch [78][100/1281] lr: 1.196e-02, eta: 11:02:34, time: 0.796, data_time: 0.186, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9994, loss_cls: 0.3105, loss: 0.3105 +2025-06-25 00:01:49,724 - pyskl - INFO - Epoch [78][200/1281] lr: 1.194e-02, eta: 11:01:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9981, loss_cls: 0.3415, loss: 0.3415 +2025-06-25 00:02:39,069 - pyskl - INFO - Epoch [78][300/1281] lr: 1.192e-02, eta: 11:01:22, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9975, loss_cls: 0.3428, loss: 0.3428 +2025-06-25 00:03:09,635 - pyskl - INFO - Epoch [78][400/1281] lr: 1.190e-02, eta: 11:00:28, time: 0.306, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3160, loss: 0.3160 +2025-06-25 00:04:00,761 - pyskl - INFO - Epoch [78][500/1281] lr: 1.187e-02, eta: 10:59:54, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9969, loss_cls: 0.3498, loss: 0.3498 +2025-06-25 00:04:27,520 - pyskl - INFO - Epoch [78][600/1281] lr: 1.185e-02, eta: 10:58:56, time: 0.268, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9981, loss_cls: 0.3899, loss: 0.3899 +2025-06-25 00:05:16,672 - pyskl - INFO - Epoch [78][700/1281] lr: 1.183e-02, eta: 10:58:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9994, loss_cls: 0.3479, loss: 0.3479 +2025-06-25 00:06:05,759 - pyskl - INFO - Epoch [78][800/1281] lr: 1.181e-02, eta: 10:57:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.3841, loss: 0.3841 +2025-06-25 00:06:54,737 - pyskl - INFO - Epoch [78][900/1281] lr: 1.179e-02, eta: 10:57:07, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3406, loss: 0.3406 +2025-06-25 00:07:43,600 - pyskl - INFO - Epoch [78][1000/1281] lr: 1.177e-02, eta: 10:56:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9975, loss_cls: 0.4295, loss: 0.4295 +2025-06-25 00:08:32,645 - pyskl - INFO - Epoch [78][1100/1281] lr: 1.175e-02, eta: 10:55:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9962, loss_cls: 0.3959, loss: 0.3959 +2025-06-25 00:09:21,556 - pyskl - INFO - Epoch [78][1200/1281] lr: 1.173e-02, eta: 10:55:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9981, loss_cls: 0.3751, loss: 0.3751 +2025-06-25 00:10:01,688 - pyskl - INFO - Saving checkpoint at 78 epochs +2025-06-25 00:11:00,234 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:11:00,289 - pyskl - INFO - +top1_acc 0.8856 +top5_acc 0.9927 +2025-06-25 00:11:00,289 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:11:00,296 - pyskl - INFO - +mean_acc 0.8430 +2025-06-25 00:11:00,298 - pyskl - INFO - Epoch(val) [78][533] top1_acc: 0.8856, top5_acc: 0.9927, mean_class_accuracy: 0.8430 +2025-06-25 00:12:20,616 - pyskl - INFO - Epoch [79][100/1281] lr: 1.169e-02, eta: 10:54:03, time: 0.803, data_time: 0.194, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9994, loss_cls: 0.3670, loss: 0.3670 +2025-06-25 00:13:09,752 - pyskl - INFO - Epoch [79][200/1281] lr: 1.167e-02, eta: 10:53:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 1.0000, loss_cls: 0.3601, loss: 0.3601 +2025-06-25 00:13:58,956 - pyskl - INFO - Epoch [79][300/1281] lr: 1.165e-02, eta: 10:52:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9981, loss_cls: 0.2939, loss: 0.2939 +2025-06-25 00:14:29,098 - pyskl - INFO - Epoch [79][400/1281] lr: 1.163e-02, eta: 10:51:55, time: 0.301, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 1.0000, loss_cls: 0.3229, loss: 0.3229 +2025-06-25 00:15:20,097 - pyskl - INFO - Epoch [79][500/1281] lr: 1.161e-02, eta: 10:51:20, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 1.0000, loss_cls: 0.3718, loss: 0.3718 +2025-06-25 00:15:46,491 - pyskl - INFO - Epoch [79][600/1281] lr: 1.159e-02, eta: 10:50:23, time: 0.264, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9994, loss_cls: 0.3422, loss: 0.3422 +2025-06-25 00:16:35,671 - pyskl - INFO - Epoch [79][700/1281] lr: 1.157e-02, eta: 10:49:46, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9981, loss_cls: 0.3118, loss: 0.3118 +2025-06-25 00:17:24,618 - pyskl - INFO - Epoch [79][800/1281] lr: 1.155e-02, eta: 10:49:10, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.3209, loss: 0.3209 +2025-06-25 00:18:13,526 - pyskl - INFO - Epoch [79][900/1281] lr: 1.153e-02, eta: 10:48:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9969, loss_cls: 0.4042, loss: 0.4042 +2025-06-25 00:19:02,654 - pyskl - INFO - Epoch [79][1000/1281] lr: 1.151e-02, eta: 10:47:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9994, loss_cls: 0.4076, loss: 0.4076 +2025-06-25 00:19:51,856 - pyskl - INFO - Epoch [79][1100/1281] lr: 1.149e-02, eta: 10:47:19, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9350, top5_acc: 1.0000, loss_cls: 0.3509, loss: 0.3509 +2025-06-25 00:20:40,797 - pyskl - INFO - Epoch [79][1200/1281] lr: 1.147e-02, eta: 10:46:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.3971, loss: 0.3971 +2025-06-25 00:21:20,957 - pyskl - INFO - Saving checkpoint at 79 epochs +2025-06-25 00:22:18,851 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:22:18,906 - pyskl - INFO - +top1_acc 0.8422 +top5_acc 0.9891 +2025-06-25 00:22:18,906 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:22:18,912 - pyskl - INFO - +mean_acc 0.8079 +2025-06-25 00:22:18,914 - pyskl - INFO - Epoch(val) [79][533] top1_acc: 0.8422, top5_acc: 0.9891, mean_class_accuracy: 0.8079 +2025-06-25 00:23:39,362 - pyskl - INFO - Epoch [80][100/1281] lr: 1.143e-02, eta: 10:45:28, time: 0.804, data_time: 0.188, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9988, loss_cls: 0.3493, loss: 0.3493 +2025-06-25 00:24:28,730 - pyskl - INFO - Epoch [80][200/1281] lr: 1.141e-02, eta: 10:44:52, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 1.0000, loss_cls: 0.3393, loss: 0.3393 +2025-06-25 00:25:17,737 - pyskl - INFO - Epoch [80][300/1281] lr: 1.139e-02, eta: 10:44:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9988, loss_cls: 0.3323, loss: 0.3323 +2025-06-25 00:25:50,030 - pyskl - INFO - Epoch [80][400/1281] lr: 1.137e-02, eta: 10:43:23, time: 0.323, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9988, loss_cls: 0.3125, loss: 0.3125 +2025-06-25 00:26:40,992 - pyskl - INFO - Epoch [80][500/1281] lr: 1.135e-02, eta: 10:42:48, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 1.0000, loss_cls: 0.3418, loss: 0.3418 +2025-06-25 00:27:06,040 - pyskl - INFO - Epoch [80][600/1281] lr: 1.133e-02, eta: 10:41:49, time: 0.250, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3674, loss: 0.3674 +2025-06-25 00:27:53,390 - pyskl - INFO - Epoch [80][700/1281] lr: 1.131e-02, eta: 10:41:11, time: 0.473, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9994, loss_cls: 0.4228, loss: 0.4228 +2025-06-25 00:28:42,404 - pyskl - INFO - Epoch [80][800/1281] lr: 1.129e-02, eta: 10:40:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9988, loss_cls: 0.4190, loss: 0.4190 +2025-06-25 00:29:31,787 - pyskl - INFO - Epoch [80][900/1281] lr: 1.127e-02, eta: 10:39:57, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9981, loss_cls: 0.3962, loss: 0.3962 +2025-06-25 00:30:20,800 - pyskl - INFO - Epoch [80][1000/1281] lr: 1.125e-02, eta: 10:39:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9981, loss_cls: 0.2911, loss: 0.2911 +2025-06-25 00:31:09,502 - pyskl - INFO - Epoch [80][1100/1281] lr: 1.123e-02, eta: 10:38:43, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9994, loss_cls: 0.3603, loss: 0.3603 +2025-06-25 00:31:58,430 - pyskl - INFO - Epoch [80][1200/1281] lr: 1.121e-02, eta: 10:38:06, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9981, loss_cls: 0.3474, loss: 0.3474 +2025-06-25 00:32:39,024 - pyskl - INFO - Saving checkpoint at 80 epochs +2025-06-25 00:33:37,828 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:33:37,891 - pyskl - INFO - +top1_acc 0.8648 +top5_acc 0.9908 +2025-06-25 00:33:37,892 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:33:37,901 - pyskl - INFO - +mean_acc 0.8328 +2025-06-25 00:33:37,903 - pyskl - INFO - Epoch(val) [80][533] top1_acc: 0.8648, top5_acc: 0.9908, mean_class_accuracy: 0.8328 +2025-06-25 00:34:57,910 - pyskl - INFO - Epoch [81][100/1281] lr: 1.117e-02, eta: 10:36:51, time: 0.800, data_time: 0.189, memory: 4083, top1_acc: 0.9381, top5_acc: 1.0000, loss_cls: 0.3554, loss: 0.3554 +2025-06-25 00:35:47,125 - pyskl - INFO - Epoch [81][200/1281] lr: 1.115e-02, eta: 10:36:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 1.0000, loss_cls: 0.3077, loss: 0.3077 +2025-06-25 00:36:36,113 - pyskl - INFO - Epoch [81][300/1281] lr: 1.113e-02, eta: 10:35:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9981, loss_cls: 0.3997, loss: 0.3997 +2025-06-25 00:37:09,890 - pyskl - INFO - Epoch [81][400/1281] lr: 1.111e-02, eta: 10:34:46, time: 0.338, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 1.0000, loss_cls: 0.3043, loss: 0.3043 +2025-06-25 00:38:00,727 - pyskl - INFO - Epoch [81][500/1281] lr: 1.109e-02, eta: 10:34:11, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9969, loss_cls: 0.4025, loss: 0.4025 +2025-06-25 00:38:25,437 - pyskl - INFO - Epoch [81][600/1281] lr: 1.107e-02, eta: 10:33:13, time: 0.247, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 1.0000, loss_cls: 0.3565, loss: 0.3565 +2025-06-25 00:39:12,787 - pyskl - INFO - Epoch [81][700/1281] lr: 1.105e-02, eta: 10:32:34, time: 0.473, data_time: 0.001, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9981, loss_cls: 0.3368, loss: 0.3368 +2025-06-25 00:40:01,942 - pyskl - INFO - Epoch [81][800/1281] lr: 1.103e-02, eta: 10:31:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9981, loss_cls: 0.3664, loss: 0.3664 +2025-06-25 00:40:50,999 - pyskl - INFO - Epoch [81][900/1281] lr: 1.101e-02, eta: 10:31:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9969, loss_cls: 0.3617, loss: 0.3617 +2025-06-25 00:41:40,119 - pyskl - INFO - Epoch [81][1000/1281] lr: 1.099e-02, eta: 10:30:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9988, loss_cls: 0.4033, loss: 0.4033 +2025-06-25 00:42:29,221 - pyskl - INFO - Epoch [81][1100/1281] lr: 1.097e-02, eta: 10:30:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9975, loss_cls: 0.4167, loss: 0.4167 +2025-06-25 00:43:18,377 - pyskl - INFO - Epoch [81][1200/1281] lr: 1.095e-02, eta: 10:29:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9975, loss_cls: 0.4207, loss: 0.4207 +2025-06-25 00:43:58,543 - pyskl - INFO - Saving checkpoint at 81 epochs +2025-06-25 00:44:56,420 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:44:56,490 - pyskl - INFO - +top1_acc 0.8797 +top5_acc 0.9928 +2025-06-25 00:44:56,490 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:44:56,499 - pyskl - INFO - +mean_acc 0.8329 +2025-06-25 00:44:56,501 - pyskl - INFO - Epoch(val) [81][533] top1_acc: 0.8797, top5_acc: 0.9928, mean_class_accuracy: 0.8329 +2025-06-25 00:46:14,207 - pyskl - INFO - Epoch [82][100/1281] lr: 1.091e-02, eta: 10:28:11, time: 0.777, data_time: 0.182, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9994, loss_cls: 0.3414, loss: 0.3414 +2025-06-25 00:47:03,208 - pyskl - INFO - Epoch [82][200/1281] lr: 1.089e-02, eta: 10:27:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 1.0000, loss_cls: 0.3386, loss: 0.3386 +2025-06-25 00:47:52,352 - pyskl - INFO - Epoch [82][300/1281] lr: 1.087e-02, eta: 10:26:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.3115, loss: 0.3115 +2025-06-25 00:48:30,391 - pyskl - INFO - Epoch [82][400/1281] lr: 1.085e-02, eta: 10:26:10, time: 0.380, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.2998, loss: 0.2998 +2025-06-25 00:49:21,523 - pyskl - INFO - Epoch [82][500/1281] lr: 1.083e-02, eta: 10:25:35, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2794, loss: 0.2794 +2025-06-25 00:49:45,259 - pyskl - INFO - Epoch [82][600/1281] lr: 1.081e-02, eta: 10:24:36, time: 0.237, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.3031, loss: 0.3031 +2025-06-25 00:50:29,462 - pyskl - INFO - Epoch [82][700/1281] lr: 1.079e-02, eta: 10:23:55, time: 0.442, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9988, loss_cls: 0.3992, loss: 0.3992 +2025-06-25 00:51:18,856 - pyskl - INFO - Epoch [82][800/1281] lr: 1.077e-02, eta: 10:23:17, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3506, loss: 0.3506 +2025-06-25 00:52:07,871 - pyskl - INFO - Epoch [82][900/1281] lr: 1.075e-02, eta: 10:22:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9994, loss_cls: 0.3995, loss: 0.3995 +2025-06-25 00:52:56,845 - pyskl - INFO - Epoch [82][1000/1281] lr: 1.073e-02, eta: 10:22:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9981, loss_cls: 0.3585, loss: 0.3585 +2025-06-25 00:53:45,884 - pyskl - INFO - Epoch [82][1100/1281] lr: 1.071e-02, eta: 10:21:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9988, loss_cls: 0.3264, loss: 0.3264 +2025-06-25 00:54:34,877 - pyskl - INFO - Epoch [82][1200/1281] lr: 1.069e-02, eta: 10:20:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9975, loss_cls: 0.3658, loss: 0.3658 +2025-06-25 00:55:15,430 - pyskl - INFO - Saving checkpoint at 82 epochs +2025-06-25 00:56:13,415 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:56:13,471 - pyskl - INFO - +top1_acc 0.8843 +top5_acc 0.9926 +2025-06-25 00:56:13,471 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:56:13,478 - pyskl - INFO - +mean_acc 0.8508 +2025-06-25 00:56:13,480 - pyskl - INFO - Epoch(val) [82][533] top1_acc: 0.8843, top5_acc: 0.9926, mean_class_accuracy: 0.8508 +2025-06-25 00:57:33,736 - pyskl - INFO - Epoch [83][100/1281] lr: 1.065e-02, eta: 10:19:33, time: 0.803, data_time: 0.182, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.2863, loss: 0.2863 +2025-06-25 00:58:23,067 - pyskl - INFO - Epoch [83][200/1281] lr: 1.063e-02, eta: 10:18:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9981, loss_cls: 0.3243, loss: 0.3243 +2025-06-25 00:59:12,225 - pyskl - INFO - Epoch [83][300/1281] lr: 1.061e-02, eta: 10:18:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 1.0000, loss_cls: 0.3182, loss: 0.3182 +2025-06-25 00:59:52,271 - pyskl - INFO - Epoch [83][400/1281] lr: 1.059e-02, eta: 10:17:33, time: 0.400, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2701, loss: 0.2701 +2025-06-25 01:00:42,217 - pyskl - INFO - Epoch [83][500/1281] lr: 1.057e-02, eta: 10:16:56, time: 0.499, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 1.0000, loss_cls: 0.3334, loss: 0.3334 +2025-06-25 01:01:05,778 - pyskl - INFO - Epoch [83][600/1281] lr: 1.055e-02, eta: 10:15:58, time: 0.236, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9981, loss_cls: 0.2985, loss: 0.2985 +2025-06-25 01:01:49,458 - pyskl - INFO - Epoch [83][700/1281] lr: 1.053e-02, eta: 10:15:16, time: 0.437, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9994, loss_cls: 0.3759, loss: 0.3759 +2025-06-25 01:02:38,237 - pyskl - INFO - Epoch [83][800/1281] lr: 1.051e-02, eta: 10:14:38, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3470, loss: 0.3470 +2025-06-25 01:03:27,227 - pyskl - INFO - Epoch [83][900/1281] lr: 1.049e-02, eta: 10:14:00, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9962, loss_cls: 0.3735, loss: 0.3735 +2025-06-25 01:04:16,148 - pyskl - INFO - Epoch [83][1000/1281] lr: 1.047e-02, eta: 10:13:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9988, loss_cls: 0.3379, loss: 0.3379 +2025-06-25 01:05:05,564 - pyskl - INFO - Epoch [83][1100/1281] lr: 1.045e-02, eta: 10:12:45, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.2995, loss: 0.2995 +2025-06-25 01:05:54,227 - pyskl - INFO - Epoch [83][1200/1281] lr: 1.043e-02, eta: 10:12:07, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9994, loss_cls: 0.3757, loss: 0.3757 +2025-06-25 01:06:34,504 - pyskl - INFO - Saving checkpoint at 83 epochs +2025-06-25 01:07:32,207 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:07:32,261 - pyskl - INFO - +top1_acc 0.8859 +top5_acc 0.9912 +2025-06-25 01:07:32,261 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:07:32,267 - pyskl - INFO - +mean_acc 0.8593 +2025-06-25 01:07:32,269 - pyskl - INFO - Epoch(val) [83][533] top1_acc: 0.8859, top5_acc: 0.9912, mean_class_accuracy: 0.8593 +2025-06-25 01:08:51,949 - pyskl - INFO - Epoch [84][100/1281] lr: 1.040e-02, eta: 10:10:52, time: 0.797, data_time: 0.182, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2771, loss: 0.2771 +2025-06-25 01:09:41,006 - pyskl - INFO - Epoch [84][200/1281] lr: 1.038e-02, eta: 10:10:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9988, loss_cls: 0.2680, loss: 0.2680 +2025-06-25 01:10:29,854 - pyskl - INFO - Epoch [84][300/1281] lr: 1.036e-02, eta: 10:09:36, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 1.0000, loss_cls: 0.2925, loss: 0.2925 +2025-06-25 01:11:10,373 - pyskl - INFO - Epoch [84][400/1281] lr: 1.034e-02, eta: 10:08:52, time: 0.405, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9994, loss_cls: 0.2904, loss: 0.2904 +2025-06-25 01:11:59,793 - pyskl - INFO - Epoch [84][500/1281] lr: 1.031e-02, eta: 10:08:14, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9981, loss_cls: 0.3201, loss: 0.3201 +2025-06-25 01:12:24,167 - pyskl - INFO - Epoch [84][600/1281] lr: 1.029e-02, eta: 10:07:17, time: 0.244, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9975, loss_cls: 0.3719, loss: 0.3719 +2025-06-25 01:13:06,364 - pyskl - INFO - Epoch [84][700/1281] lr: 1.027e-02, eta: 10:06:34, time: 0.422, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9981, loss_cls: 0.3296, loss: 0.3296 +2025-06-25 01:13:54,804 - pyskl - INFO - Epoch [84][800/1281] lr: 1.025e-02, eta: 10:05:55, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9981, loss_cls: 0.3581, loss: 0.3581 +2025-06-25 01:14:43,634 - pyskl - INFO - Epoch [84][900/1281] lr: 1.023e-02, eta: 10:05:17, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9994, loss_cls: 0.3283, loss: 0.3283 +2025-06-25 01:15:32,634 - pyskl - INFO - Epoch [84][1000/1281] lr: 1.021e-02, eta: 10:04:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9988, loss_cls: 0.3585, loss: 0.3585 +2025-06-25 01:16:21,492 - pyskl - INFO - Epoch [84][1100/1281] lr: 1.019e-02, eta: 10:04:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9994, loss_cls: 0.3546, loss: 0.3546 +2025-06-25 01:17:10,421 - pyskl - INFO - Epoch [84][1200/1281] lr: 1.017e-02, eta: 10:03:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9981, loss_cls: 0.3918, loss: 0.3918 +2025-06-25 01:17:50,881 - pyskl - INFO - Saving checkpoint at 84 epochs +2025-06-25 01:18:48,421 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:18:48,479 - pyskl - INFO - +top1_acc 0.8764 +top5_acc 0.9919 +2025-06-25 01:18:48,479 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:18:48,488 - pyskl - INFO - +mean_acc 0.8455 +2025-06-25 01:18:48,490 - pyskl - INFO - Epoch(val) [84][533] top1_acc: 0.8764, top5_acc: 0.9919, mean_class_accuracy: 0.8455 +2025-06-25 01:20:07,134 - pyskl - INFO - Epoch [85][100/1281] lr: 1.014e-02, eta: 10:02:07, time: 0.786, data_time: 0.184, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2823, loss: 0.2823 +2025-06-25 01:20:56,368 - pyskl - INFO - Epoch [85][200/1281] lr: 1.012e-02, eta: 10:01:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.2978, loss: 0.2978 +2025-06-25 01:21:45,663 - pyskl - INFO - Epoch [85][300/1281] lr: 1.010e-02, eta: 10:00:52, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 1.0000, loss_cls: 0.2787, loss: 0.2787 +2025-06-25 01:22:28,733 - pyskl - INFO - Epoch [85][400/1281] lr: 1.008e-02, eta: 10:00:09, time: 0.431, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.2980, loss: 0.2980 +2025-06-25 01:23:13,032 - pyskl - INFO - Epoch [85][500/1281] lr: 1.006e-02, eta: 9:59:28, time: 0.443, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9994, loss_cls: 0.3529, loss: 0.3529 +2025-06-25 01:23:41,659 - pyskl - INFO - Epoch [85][600/1281] lr: 1.004e-02, eta: 9:58:34, time: 0.286, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9988, loss_cls: 0.2760, loss: 0.2760 +2025-06-25 01:24:22,691 - pyskl - INFO - Epoch [85][700/1281] lr: 1.002e-02, eta: 9:57:50, time: 0.410, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 1.0000, loss_cls: 0.3570, loss: 0.3570 +2025-06-25 01:25:11,540 - pyskl - INFO - Epoch [85][800/1281] lr: 9.998e-03, eta: 9:57:12, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9975, loss_cls: 0.3414, loss: 0.3414 +2025-06-25 01:26:00,262 - pyskl - INFO - Epoch [85][900/1281] lr: 9.978e-03, eta: 9:56:33, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3310, loss: 0.3310 +2025-06-25 01:26:49,451 - pyskl - INFO - Epoch [85][1000/1281] lr: 9.958e-03, eta: 9:55:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3282, loss: 0.3282 +2025-06-25 01:27:38,342 - pyskl - INFO - Epoch [85][1100/1281] lr: 9.937e-03, eta: 9:55:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3143, loss: 0.3143 +2025-06-25 01:28:27,627 - pyskl - INFO - Epoch [85][1200/1281] lr: 9.917e-03, eta: 9:54:40, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.2978, loss: 0.2978 +2025-06-25 01:29:07,622 - pyskl - INFO - Saving checkpoint at 85 epochs +2025-06-25 01:30:05,613 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:30:05,677 - pyskl - INFO - +top1_acc 0.8803 +top5_acc 0.9930 +2025-06-25 01:30:05,677 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:30:05,686 - pyskl - INFO - +mean_acc 0.8376 +2025-06-25 01:30:05,690 - pyskl - INFO - Epoch(val) [85][533] top1_acc: 0.8803, top5_acc: 0.9930, mean_class_accuracy: 0.8376 +2025-06-25 01:31:25,320 - pyskl - INFO - Epoch [86][100/1281] lr: 9.881e-03, eta: 9:53:24, time: 0.796, data_time: 0.184, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2429, loss: 0.2429 +2025-06-25 01:32:14,059 - pyskl - INFO - Epoch [86][200/1281] lr: 9.861e-03, eta: 9:52:45, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 1.0000, loss_cls: 0.2957, loss: 0.2957 +2025-06-25 01:33:03,287 - pyskl - INFO - Epoch [86][300/1281] lr: 9.841e-03, eta: 9:52:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2623, loss: 0.2623 +2025-06-25 01:33:47,076 - pyskl - INFO - Epoch [86][400/1281] lr: 9.821e-03, eta: 9:51:25, time: 0.438, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2789, loss: 0.2789 +2025-06-25 01:34:28,542 - pyskl - INFO - Epoch [86][500/1281] lr: 9.801e-03, eta: 9:50:42, time: 0.415, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2737, loss: 0.2737 +2025-06-25 01:34:59,985 - pyskl - INFO - Epoch [86][600/1281] lr: 9.781e-03, eta: 9:49:50, time: 0.314, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2551, loss: 0.2551 +2025-06-25 01:35:41,116 - pyskl - INFO - Epoch [86][700/1281] lr: 9.762e-03, eta: 9:49:06, time: 0.411, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9988, loss_cls: 0.3402, loss: 0.3402 +2025-06-25 01:36:30,314 - pyskl - INFO - Epoch [86][800/1281] lr: 9.742e-03, eta: 9:48:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9969, loss_cls: 0.3594, loss: 0.3594 +2025-06-25 01:37:19,345 - pyskl - INFO - Epoch [86][900/1281] lr: 9.722e-03, eta: 9:47:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9975, loss_cls: 0.3847, loss: 0.3847 +2025-06-25 01:38:08,183 - pyskl - INFO - Epoch [86][1000/1281] lr: 9.702e-03, eta: 9:47:12, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9994, loss_cls: 0.3573, loss: 0.3573 +2025-06-25 01:38:57,427 - pyskl - INFO - Epoch [86][1100/1281] lr: 9.682e-03, eta: 9:46:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.2691, loss: 0.2691 +2025-06-25 01:39:46,093 - pyskl - INFO - Epoch [86][1200/1281] lr: 9.662e-03, eta: 9:45:55, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.3026, loss: 0.3026 +2025-06-25 01:40:26,255 - pyskl - INFO - Saving checkpoint at 86 epochs +2025-06-25 01:41:24,147 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:41:24,204 - pyskl - INFO - +top1_acc 0.8669 +top5_acc 0.9918 +2025-06-25 01:41:24,204 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:41:24,211 - pyskl - INFO - +mean_acc 0.8130 +2025-06-25 01:41:24,213 - pyskl - INFO - Epoch(val) [86][533] top1_acc: 0.8669, top5_acc: 0.9918, mean_class_accuracy: 0.8130 +2025-06-25 01:42:43,604 - pyskl - INFO - Epoch [87][100/1281] lr: 9.626e-03, eta: 9:44:39, time: 0.794, data_time: 0.187, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9994, loss_cls: 0.3510, loss: 0.3510 +2025-06-25 01:43:33,031 - pyskl - INFO - Epoch [87][200/1281] lr: 9.606e-03, eta: 9:44:01, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.2878, loss: 0.2878 +2025-06-25 01:44:21,846 - pyskl - INFO - Epoch [87][300/1281] lr: 9.586e-03, eta: 9:43:22, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.2986, loss: 0.2986 +2025-06-25 01:45:06,415 - pyskl - INFO - Epoch [87][400/1281] lr: 9.566e-03, eta: 9:42:41, time: 0.446, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2597, loss: 0.2597 +2025-06-25 01:45:48,440 - pyskl - INFO - Epoch [87][500/1281] lr: 9.546e-03, eta: 9:41:57, time: 0.420, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.2645, loss: 0.2645 +2025-06-25 01:46:19,417 - pyskl - INFO - Epoch [87][600/1281] lr: 9.527e-03, eta: 9:41:06, time: 0.310, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.2878, loss: 0.2878 +2025-06-25 01:47:00,068 - pyskl - INFO - Epoch [87][700/1281] lr: 9.507e-03, eta: 9:40:21, time: 0.407, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3106, loss: 0.3106 +2025-06-25 01:47:49,023 - pyskl - INFO - Epoch [87][800/1281] lr: 9.487e-03, eta: 9:39:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9981, loss_cls: 0.2702, loss: 0.2702 +2025-06-25 01:48:37,805 - pyskl - INFO - Epoch [87][900/1281] lr: 9.467e-03, eta: 9:39:04, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 1.0000, loss_cls: 0.3117, loss: 0.3117 +2025-06-25 01:49:26,829 - pyskl - INFO - Epoch [87][1000/1281] lr: 9.447e-03, eta: 9:38:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2613, loss: 0.2613 +2025-06-25 01:50:15,647 - pyskl - INFO - Epoch [87][1100/1281] lr: 9.427e-03, eta: 9:37:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9994, loss_cls: 0.3099, loss: 0.3099 +2025-06-25 01:51:04,475 - pyskl - INFO - Epoch [87][1200/1281] lr: 9.408e-03, eta: 9:37:09, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9988, loss_cls: 0.3472, loss: 0.3472 +2025-06-25 01:51:44,723 - pyskl - INFO - Saving checkpoint at 87 epochs +2025-06-25 01:52:42,205 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:52:42,272 - pyskl - INFO - +top1_acc 0.8676 +top5_acc 0.9930 +2025-06-25 01:52:42,272 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:52:42,280 - pyskl - INFO - +mean_acc 0.8221 +2025-06-25 01:52:42,282 - pyskl - INFO - Epoch(val) [87][533] top1_acc: 0.8676, top5_acc: 0.9930, mean_class_accuracy: 0.8221 +2025-06-25 01:54:02,815 - pyskl - INFO - Epoch [88][100/1281] lr: 9.372e-03, eta: 9:35:54, time: 0.805, data_time: 0.185, memory: 4083, top1_acc: 0.9450, top5_acc: 1.0000, loss_cls: 0.2864, loss: 0.2864 +2025-06-25 01:54:51,768 - pyskl - INFO - Epoch [88][200/1281] lr: 9.352e-03, eta: 9:35:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3197, loss: 0.3197 +2025-06-25 01:55:40,828 - pyskl - INFO - Epoch [88][300/1281] lr: 9.332e-03, eta: 9:34:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2876, loss: 0.2876 +2025-06-25 01:56:24,964 - pyskl - INFO - Epoch [88][400/1281] lr: 9.312e-03, eta: 9:33:55, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 1.0000, loss_cls: 0.2909, loss: 0.2909 +2025-06-25 01:57:06,684 - pyskl - INFO - Epoch [88][500/1281] lr: 9.293e-03, eta: 9:33:11, time: 0.417, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3514, loss: 0.3514 +2025-06-25 01:57:38,385 - pyskl - INFO - Epoch [88][600/1281] lr: 9.273e-03, eta: 9:32:20, time: 0.317, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 1.0000, loss_cls: 0.3649, loss: 0.3649 +2025-06-25 01:58:18,821 - pyskl - INFO - Epoch [88][700/1281] lr: 9.253e-03, eta: 9:31:36, time: 0.404, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 1.0000, loss_cls: 0.3004, loss: 0.3004 +2025-06-25 01:59:07,673 - pyskl - INFO - Epoch [88][800/1281] lr: 9.233e-03, eta: 9:30:57, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9988, loss_cls: 0.2638, loss: 0.2638 +2025-06-25 01:59:56,597 - pyskl - INFO - Epoch [88][900/1281] lr: 9.214e-03, eta: 9:30:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.3055, loss: 0.3055 +2025-06-25 02:00:45,388 - pyskl - INFO - Epoch [88][1000/1281] lr: 9.194e-03, eta: 9:29:40, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9988, loss_cls: 0.3156, loss: 0.3156 +2025-06-25 02:01:34,200 - pyskl - INFO - Epoch [88][1100/1281] lr: 9.174e-03, eta: 9:29:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.3052, loss: 0.3052 +2025-06-25 02:02:22,994 - pyskl - INFO - Epoch [88][1200/1281] lr: 9.155e-03, eta: 9:28:22, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9994, loss_cls: 0.3479, loss: 0.3479 +2025-06-25 02:03:03,038 - pyskl - INFO - Saving checkpoint at 88 epochs +2025-06-25 02:04:00,086 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:04:00,140 - pyskl - INFO - +top1_acc 0.8871 +top5_acc 0.9927 +2025-06-25 02:04:00,140 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:04:00,146 - pyskl - INFO - +mean_acc 0.8517 +2025-06-25 02:04:00,147 - pyskl - INFO - Epoch(val) [88][533] top1_acc: 0.8871, top5_acc: 0.9927, mean_class_accuracy: 0.8517 +2025-06-25 02:05:20,059 - pyskl - INFO - Epoch [89][100/1281] lr: 9.119e-03, eta: 9:27:06, time: 0.799, data_time: 0.188, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2800, loss: 0.2800 +2025-06-25 02:06:09,430 - pyskl - INFO - Epoch [89][200/1281] lr: 9.099e-03, eta: 9:26:28, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 1.0000, loss_cls: 0.2574, loss: 0.2574 +2025-06-25 02:06:58,302 - pyskl - INFO - Epoch [89][300/1281] lr: 9.080e-03, eta: 9:25:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.2989, loss: 0.2989 +2025-06-25 02:07:43,808 - pyskl - INFO - Epoch [89][400/1281] lr: 9.060e-03, eta: 9:25:08, time: 0.455, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9981, loss_cls: 0.2936, loss: 0.2936 +2025-06-25 02:08:24,338 - pyskl - INFO - Epoch [89][500/1281] lr: 9.040e-03, eta: 9:24:23, time: 0.405, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9994, loss_cls: 0.3079, loss: 0.3079 +2025-06-25 02:08:56,721 - pyskl - INFO - Epoch [89][600/1281] lr: 9.021e-03, eta: 9:23:33, time: 0.324, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9988, loss_cls: 0.2584, loss: 0.2584 +2025-06-25 02:09:36,315 - pyskl - INFO - Epoch [89][700/1281] lr: 9.001e-03, eta: 9:22:48, time: 0.396, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 1.0000, loss_cls: 0.2823, loss: 0.2823 +2025-06-25 02:10:25,324 - pyskl - INFO - Epoch [89][800/1281] lr: 8.982e-03, eta: 9:22:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2850, loss: 0.2850 +2025-06-25 02:11:14,520 - pyskl - INFO - Epoch [89][900/1281] lr: 8.962e-03, eta: 9:21:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.2989, loss: 0.2989 +2025-06-25 02:12:03,448 - pyskl - INFO - Epoch [89][1000/1281] lr: 8.942e-03, eta: 9:20:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 1.0000, loss_cls: 0.3018, loss: 0.3018 +2025-06-25 02:12:52,542 - pyskl - INFO - Epoch [89][1100/1281] lr: 8.923e-03, eta: 9:20:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.3351, loss: 0.3351 +2025-06-25 02:13:41,722 - pyskl - INFO - Epoch [89][1200/1281] lr: 8.903e-03, eta: 9:19:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.3066, loss: 0.3066 +2025-06-25 02:14:21,964 - pyskl - INFO - Saving checkpoint at 89 epochs +2025-06-25 02:15:18,968 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:15:19,024 - pyskl - INFO - +top1_acc 0.8899 +top5_acc 0.9927 +2025-06-25 02:15:19,024 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:15:19,030 - pyskl - INFO - +mean_acc 0.8518 +2025-06-25 02:15:19,034 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_77.pth was removed +2025-06-25 02:15:19,197 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_89.pth. +2025-06-25 02:15:19,198 - pyskl - INFO - Best top1_acc is 0.8899 at 89 epoch. +2025-06-25 02:15:19,201 - pyskl - INFO - Epoch(val) [89][533] top1_acc: 0.8899, top5_acc: 0.9927, mean_class_accuracy: 0.8518 +2025-06-25 02:16:38,356 - pyskl - INFO - Epoch [90][100/1281] lr: 8.868e-03, eta: 9:18:18, time: 0.792, data_time: 0.188, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9988, loss_cls: 0.3083, loss: 0.3083 +2025-06-25 02:17:27,329 - pyskl - INFO - Epoch [90][200/1281] lr: 8.848e-03, eta: 9:17:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.2720, loss: 0.2720 +2025-06-25 02:18:16,197 - pyskl - INFO - Epoch [90][300/1281] lr: 8.829e-03, eta: 9:17:00, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2497, loss: 0.2497 +2025-06-25 02:19:03,135 - pyskl - INFO - Epoch [90][400/1281] lr: 8.809e-03, eta: 9:16:20, time: 0.469, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.2498, loss: 0.2498 +2025-06-25 02:19:38,536 - pyskl - INFO - Epoch [90][500/1281] lr: 8.790e-03, eta: 9:15:32, time: 0.354, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.2923, loss: 0.2923 +2025-06-25 02:20:16,128 - pyskl - INFO - Epoch [90][600/1281] lr: 8.770e-03, eta: 9:14:46, time: 0.376, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2525, loss: 0.2525 +2025-06-25 02:20:52,573 - pyskl - INFO - Epoch [90][700/1281] lr: 8.751e-03, eta: 9:13:58, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2531, loss: 0.2531 +2025-06-25 02:21:41,262 - pyskl - INFO - Epoch [90][800/1281] lr: 8.731e-03, eta: 9:13:19, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.2871, loss: 0.2871 +2025-06-25 02:22:30,443 - pyskl - INFO - Epoch [90][900/1281] lr: 8.712e-03, eta: 9:12:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9988, loss_cls: 0.3523, loss: 0.3523 +2025-06-25 02:23:18,712 - pyskl - INFO - Epoch [90][1000/1281] lr: 8.692e-03, eta: 9:12:01, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2663, loss: 0.2663 +2025-06-25 02:24:07,417 - pyskl - INFO - Epoch [90][1100/1281] lr: 8.673e-03, eta: 9:11:22, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3206, loss: 0.3206 +2025-06-25 02:24:56,490 - pyskl - INFO - Epoch [90][1200/1281] lr: 8.653e-03, eta: 9:10:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3285, loss: 0.3285 +2025-06-25 02:25:36,571 - pyskl - INFO - Saving checkpoint at 90 epochs +2025-06-25 02:26:34,523 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:26:34,597 - pyskl - INFO - +top1_acc 0.8886 +top5_acc 0.9927 +2025-06-25 02:26:34,597 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:26:34,604 - pyskl - INFO - +mean_acc 0.8509 +2025-06-25 02:26:34,606 - pyskl - INFO - Epoch(val) [90][533] top1_acc: 0.8886, top5_acc: 0.9927, mean_class_accuracy: 0.8509 +2025-06-25 02:27:54,514 - pyskl - INFO - Epoch [91][100/1281] lr: 8.618e-03, eta: 9:09:27, time: 0.799, data_time: 0.184, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2582, loss: 0.2582 +2025-06-25 02:28:43,519 - pyskl - INFO - Epoch [91][200/1281] lr: 8.599e-03, eta: 9:08:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9981, loss_cls: 0.2779, loss: 0.2779 +2025-06-25 02:29:32,498 - pyskl - INFO - Epoch [91][300/1281] lr: 8.579e-03, eta: 9:08:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9988, loss_cls: 0.2783, loss: 0.2783 +2025-06-25 02:30:21,369 - pyskl - INFO - Epoch [91][400/1281] lr: 8.560e-03, eta: 9:07:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2936, loss: 0.2936 +2025-06-25 02:30:52,048 - pyskl - INFO - Epoch [91][500/1281] lr: 8.540e-03, eta: 9:06:39, time: 0.307, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9988, loss_cls: 0.3279, loss: 0.3279 +2025-06-25 02:31:34,692 - pyskl - INFO - Epoch [91][600/1281] lr: 8.521e-03, eta: 9:05:56, time: 0.426, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9994, loss_cls: 0.2733, loss: 0.2733 +2025-06-25 02:32:08,951 - pyskl - INFO - Epoch [91][700/1281] lr: 8.502e-03, eta: 9:05:07, time: 0.343, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2750, loss: 0.2750 +2025-06-25 02:32:58,056 - pyskl - INFO - Epoch [91][800/1281] lr: 8.482e-03, eta: 9:04:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2664, loss: 0.2664 +2025-06-25 02:33:47,349 - pyskl - INFO - Epoch [91][900/1281] lr: 8.463e-03, eta: 9:03:49, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2696, loss: 0.2696 +2025-06-25 02:34:36,000 - pyskl - INFO - Epoch [91][1000/1281] lr: 8.444e-03, eta: 9:03:10, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.2898, loss: 0.2898 +2025-06-25 02:35:24,553 - pyskl - INFO - Epoch [91][1100/1281] lr: 8.424e-03, eta: 9:02:31, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2811, loss: 0.2811 +2025-06-25 02:36:13,226 - pyskl - INFO - Epoch [91][1200/1281] lr: 8.405e-03, eta: 9:01:52, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2793, loss: 0.2793 +2025-06-25 02:36:52,715 - pyskl - INFO - Saving checkpoint at 91 epochs +2025-06-25 02:37:50,183 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:37:50,238 - pyskl - INFO - +top1_acc 0.9034 +top5_acc 0.9939 +2025-06-25 02:37:50,238 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:37:50,244 - pyskl - INFO - +mean_acc 0.8737 +2025-06-25 02:37:50,248 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_89.pth was removed +2025-06-25 02:37:50,416 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_91.pth. +2025-06-25 02:37:50,417 - pyskl - INFO - Best top1_acc is 0.9034 at 91 epoch. +2025-06-25 02:37:50,420 - pyskl - INFO - Epoch(val) [91][533] top1_acc: 0.9034, top5_acc: 0.9939, mean_class_accuracy: 0.8737 +2025-06-25 02:39:10,432 - pyskl - INFO - Epoch [92][100/1281] lr: 8.370e-03, eta: 9:00:35, time: 0.800, data_time: 0.184, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.1910, loss: 0.1910 +2025-06-25 02:39:59,281 - pyskl - INFO - Epoch [92][200/1281] lr: 8.351e-03, eta: 8:59:56, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9994, loss_cls: 0.2096, loss: 0.2096 +2025-06-25 02:40:48,194 - pyskl - INFO - Epoch [92][300/1281] lr: 8.332e-03, eta: 8:59:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2676, loss: 0.2676 +2025-06-25 02:41:37,415 - pyskl - INFO - Epoch [92][400/1281] lr: 8.312e-03, eta: 8:58:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2484, loss: 0.2484 +2025-06-25 02:42:05,590 - pyskl - INFO - Epoch [92][500/1281] lr: 8.293e-03, eta: 8:57:45, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2788, loss: 0.2788 +2025-06-25 02:42:53,325 - pyskl - INFO - Epoch [92][600/1281] lr: 8.274e-03, eta: 8:57:06, time: 0.477, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9981, loss_cls: 0.2577, loss: 0.2577 +2025-06-25 02:43:26,102 - pyskl - INFO - Epoch [92][700/1281] lr: 8.255e-03, eta: 8:56:16, time: 0.328, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2292, loss: 0.2292 +2025-06-25 02:44:14,918 - pyskl - INFO - Epoch [92][800/1281] lr: 8.235e-03, eta: 8:55:37, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9988, loss_cls: 0.2759, loss: 0.2759 +2025-06-25 02:45:04,080 - pyskl - INFO - Epoch [92][900/1281] lr: 8.216e-03, eta: 8:54:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2484, loss: 0.2484 +2025-06-25 02:45:52,865 - pyskl - INFO - Epoch [92][1000/1281] lr: 8.197e-03, eta: 8:54:18, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9988, loss_cls: 0.3298, loss: 0.3298 +2025-06-25 02:46:41,861 - pyskl - INFO - Epoch [92][1100/1281] lr: 8.178e-03, eta: 8:53:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3380, loss: 0.3380 +2025-06-25 02:47:30,681 - pyskl - INFO - Epoch [92][1200/1281] lr: 8.159e-03, eta: 8:53:00, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 1.0000, loss_cls: 0.2963, loss: 0.2963 +2025-06-25 02:48:10,872 - pyskl - INFO - Saving checkpoint at 92 epochs +2025-06-25 02:49:08,288 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:49:08,343 - pyskl - INFO - +top1_acc 0.8841 +top5_acc 0.9925 +2025-06-25 02:49:08,343 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:49:08,350 - pyskl - INFO - +mean_acc 0.8346 +2025-06-25 02:49:08,352 - pyskl - INFO - Epoch(val) [92][533] top1_acc: 0.8841, top5_acc: 0.9925, mean_class_accuracy: 0.8346 +2025-06-25 02:50:27,434 - pyskl - INFO - Epoch [93][100/1281] lr: 8.124e-03, eta: 8:51:43, time: 0.791, data_time: 0.186, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2877, loss: 0.2877 +2025-06-25 02:51:16,536 - pyskl - INFO - Epoch [93][200/1281] lr: 8.105e-03, eta: 8:51:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.2863, loss: 0.2863 +2025-06-25 02:52:05,660 - pyskl - INFO - Epoch [93][300/1281] lr: 8.086e-03, eta: 8:50:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9988, loss_cls: 0.2664, loss: 0.2664 +2025-06-25 02:52:54,428 - pyskl - INFO - Epoch [93][400/1281] lr: 8.067e-03, eta: 8:49:45, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2337, loss: 0.2337 +2025-06-25 02:53:21,632 - pyskl - INFO - Epoch [93][500/1281] lr: 8.047e-03, eta: 8:48:52, time: 0.272, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9988, loss_cls: 0.2662, loss: 0.2662 +2025-06-25 02:54:12,269 - pyskl - INFO - Epoch [93][600/1281] lr: 8.028e-03, eta: 8:48:14, time: 0.506, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2501, loss: 0.2501 +2025-06-25 02:54:42,491 - pyskl - INFO - Epoch [93][700/1281] lr: 8.009e-03, eta: 8:47:23, time: 0.302, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2158, loss: 0.2158 +2025-06-25 02:55:31,445 - pyskl - INFO - Epoch [93][800/1281] lr: 7.990e-03, eta: 8:46:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9988, loss_cls: 0.2731, loss: 0.2731 +2025-06-25 02:56:20,346 - pyskl - INFO - Epoch [93][900/1281] lr: 7.971e-03, eta: 8:46:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9988, loss_cls: 0.2525, loss: 0.2525 +2025-06-25 02:57:08,667 - pyskl - INFO - Epoch [93][1000/1281] lr: 7.952e-03, eta: 8:45:25, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9988, loss_cls: 0.2379, loss: 0.2379 +2025-06-25 02:57:57,716 - pyskl - INFO - Epoch [93][1100/1281] lr: 7.933e-03, eta: 8:44:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2458, loss: 0.2458 +2025-06-25 02:58:46,473 - pyskl - INFO - Epoch [93][1200/1281] lr: 7.914e-03, eta: 8:44:06, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.2655, loss: 0.2655 +2025-06-25 02:59:26,618 - pyskl - INFO - Saving checkpoint at 93 epochs +2025-06-25 03:00:24,555 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:00:24,612 - pyskl - INFO - +top1_acc 0.8950 +top5_acc 0.9912 +2025-06-25 03:00:24,613 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:00:24,619 - pyskl - INFO - +mean_acc 0.8645 +2025-06-25 03:00:24,620 - pyskl - INFO - Epoch(val) [93][533] top1_acc: 0.8950, top5_acc: 0.9912, mean_class_accuracy: 0.8645 +2025-06-25 03:01:44,383 - pyskl - INFO - Epoch [94][100/1281] lr: 7.880e-03, eta: 8:42:49, time: 0.798, data_time: 0.183, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9988, loss_cls: 0.2454, loss: 0.2454 +2025-06-25 03:02:33,674 - pyskl - INFO - Epoch [94][200/1281] lr: 7.861e-03, eta: 8:42:10, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9994, loss_cls: 0.2548, loss: 0.2548 +2025-06-25 03:03:22,873 - pyskl - INFO - Epoch [94][300/1281] lr: 7.842e-03, eta: 8:41:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1963, loss: 0.1963 +2025-06-25 03:04:11,517 - pyskl - INFO - Epoch [94][400/1281] lr: 7.823e-03, eta: 8:40:51, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9988, loss_cls: 0.2687, loss: 0.2687 +2025-06-25 03:04:40,236 - pyskl - INFO - Epoch [94][500/1281] lr: 7.804e-03, eta: 8:39:59, time: 0.287, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.2908, loss: 0.2908 +2025-06-25 03:05:31,076 - pyskl - INFO - Epoch [94][600/1281] lr: 7.785e-03, eta: 8:39:21, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9988, loss_cls: 0.2510, loss: 0.2510 +2025-06-25 03:06:00,408 - pyskl - INFO - Epoch [94][700/1281] lr: 7.766e-03, eta: 8:38:30, time: 0.293, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2491, loss: 0.2491 +2025-06-25 03:06:49,512 - pyskl - INFO - Epoch [94][800/1281] lr: 7.747e-03, eta: 8:37:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2358, loss: 0.2358 +2025-06-25 03:07:38,051 - pyskl - INFO - Epoch [94][900/1281] lr: 7.728e-03, eta: 8:37:11, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 0.9994, loss_cls: 0.1893, loss: 0.1893 +2025-06-25 03:08:27,266 - pyskl - INFO - Epoch [94][1000/1281] lr: 7.709e-03, eta: 8:36:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2740, loss: 0.2740 +2025-06-25 03:09:16,038 - pyskl - INFO - Epoch [94][1100/1281] lr: 7.690e-03, eta: 8:35:52, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2842, loss: 0.2842 +2025-06-25 03:10:05,087 - pyskl - INFO - Epoch [94][1200/1281] lr: 7.672e-03, eta: 8:35:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9994, loss_cls: 0.3101, loss: 0.3101 +2025-06-25 03:10:45,466 - pyskl - INFO - Saving checkpoint at 94 epochs +2025-06-25 03:11:42,925 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:11:42,980 - pyskl - INFO - +top1_acc 0.8997 +top5_acc 0.9946 +2025-06-25 03:11:42,980 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:11:42,987 - pyskl - INFO - +mean_acc 0.8621 +2025-06-25 03:11:42,989 - pyskl - INFO - Epoch(val) [94][533] top1_acc: 0.8997, top5_acc: 0.9946, mean_class_accuracy: 0.8621 +2025-06-25 03:13:03,077 - pyskl - INFO - Epoch [95][100/1281] lr: 7.637e-03, eta: 8:33:56, time: 0.801, data_time: 0.179, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.2036, loss: 0.2036 +2025-06-25 03:13:51,872 - pyskl - INFO - Epoch [95][200/1281] lr: 7.619e-03, eta: 8:33:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2114, loss: 0.2114 +2025-06-25 03:14:40,882 - pyskl - INFO - Epoch [95][300/1281] lr: 7.600e-03, eta: 8:32:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2132, loss: 0.2132 +2025-06-25 03:15:29,957 - pyskl - INFO - Epoch [95][400/1281] lr: 7.581e-03, eta: 8:31:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2160, loss: 0.2160 +2025-06-25 03:15:59,467 - pyskl - INFO - Epoch [95][500/1281] lr: 7.562e-03, eta: 8:31:06, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.2547, loss: 0.2547 +2025-06-25 03:16:50,274 - pyskl - INFO - Epoch [95][600/1281] lr: 7.543e-03, eta: 8:30:28, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 1.0000, loss_cls: 0.2679, loss: 0.2679 +2025-06-25 03:17:18,523 - pyskl - INFO - Epoch [95][700/1281] lr: 7.525e-03, eta: 8:29:36, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2623, loss: 0.2623 +2025-06-25 03:18:07,239 - pyskl - INFO - Epoch [95][800/1281] lr: 7.506e-03, eta: 8:28:56, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2151, loss: 0.2151 +2025-06-25 03:18:56,775 - pyskl - INFO - Epoch [95][900/1281] lr: 7.487e-03, eta: 8:28:17, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9988, loss_cls: 0.2614, loss: 0.2614 +2025-06-25 03:19:45,673 - pyskl - INFO - Epoch [95][1000/1281] lr: 7.468e-03, eta: 8:27:37, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 1.0000, loss_cls: 0.2571, loss: 0.2571 +2025-06-25 03:20:34,510 - pyskl - INFO - Epoch [95][1100/1281] lr: 7.450e-03, eta: 8:26:57, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2691, loss: 0.2691 +2025-06-25 03:21:23,419 - pyskl - INFO - Epoch [95][1200/1281] lr: 7.431e-03, eta: 8:26:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9988, loss_cls: 0.3083, loss: 0.3083 +2025-06-25 03:22:03,630 - pyskl - INFO - Saving checkpoint at 95 epochs +2025-06-25 03:23:01,472 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:23:01,544 - pyskl - INFO - +top1_acc 0.8814 +top5_acc 0.9926 +2025-06-25 03:23:01,544 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:23:01,555 - pyskl - INFO - +mean_acc 0.8476 +2025-06-25 03:23:01,557 - pyskl - INFO - Epoch(val) [95][533] top1_acc: 0.8814, top5_acc: 0.9926, mean_class_accuracy: 0.8476 +2025-06-25 03:24:21,687 - pyskl - INFO - Epoch [96][100/1281] lr: 7.397e-03, eta: 8:25:01, time: 0.801, data_time: 0.183, memory: 4083, top1_acc: 0.9556, top5_acc: 1.0000, loss_cls: 0.2489, loss: 0.2489 +2025-06-25 03:25:10,942 - pyskl - INFO - Epoch [96][200/1281] lr: 7.379e-03, eta: 8:24:22, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.1953, loss: 0.1953 +2025-06-25 03:25:59,884 - pyskl - INFO - Epoch [96][300/1281] lr: 7.360e-03, eta: 8:23:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1978, loss: 0.1978 +2025-06-25 03:26:48,944 - pyskl - INFO - Epoch [96][400/1281] lr: 7.341e-03, eta: 8:23:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2274, loss: 0.2274 +2025-06-25 03:27:20,075 - pyskl - INFO - Epoch [96][500/1281] lr: 7.323e-03, eta: 8:22:12, time: 0.311, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2439, loss: 0.2439 +2025-06-25 03:28:10,979 - pyskl - INFO - Epoch [96][600/1281] lr: 7.304e-03, eta: 8:21:34, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2226, loss: 0.2226 +2025-06-25 03:28:38,262 - pyskl - INFO - Epoch [96][700/1281] lr: 7.286e-03, eta: 8:20:42, time: 0.273, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 1.0000, loss_cls: 0.2726, loss: 0.2726 +2025-06-25 03:29:27,357 - pyskl - INFO - Epoch [96][800/1281] lr: 7.267e-03, eta: 8:20:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2585, loss: 0.2585 +2025-06-25 03:30:16,317 - pyskl - INFO - Epoch [96][900/1281] lr: 7.249e-03, eta: 8:19:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.2170, loss: 0.2170 +2025-06-25 03:31:04,964 - pyskl - INFO - Epoch [96][1000/1281] lr: 7.230e-03, eta: 8:18:42, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2735, loss: 0.2735 +2025-06-25 03:31:53,905 - pyskl - INFO - Epoch [96][1100/1281] lr: 7.211e-03, eta: 8:18:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2189, loss: 0.2189 +2025-06-25 03:32:42,614 - pyskl - INFO - Epoch [96][1200/1281] lr: 7.193e-03, eta: 8:17:23, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.2899, loss: 0.2899 +2025-06-25 03:33:22,883 - pyskl - INFO - Saving checkpoint at 96 epochs +2025-06-25 03:34:20,629 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:34:20,684 - pyskl - INFO - +top1_acc 0.9004 +top5_acc 0.9952 +2025-06-25 03:34:20,684 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:34:20,690 - pyskl - INFO - +mean_acc 0.8692 +2025-06-25 03:34:20,691 - pyskl - INFO - Epoch(val) [96][533] top1_acc: 0.9004, top5_acc: 0.9952, mean_class_accuracy: 0.8692 +2025-06-25 03:35:39,480 - pyskl - INFO - Epoch [97][100/1281] lr: 7.159e-03, eta: 8:16:05, time: 0.788, data_time: 0.181, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.2054, loss: 0.2054 +2025-06-25 03:36:28,716 - pyskl - INFO - Epoch [97][200/1281] lr: 7.141e-03, eta: 8:15:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2384, loss: 0.2384 +2025-06-25 03:37:17,808 - pyskl - INFO - Epoch [97][300/1281] lr: 7.123e-03, eta: 8:14:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9994, loss_cls: 0.2181, loss: 0.2181 +2025-06-25 03:38:06,692 - pyskl - INFO - Epoch [97][400/1281] lr: 7.104e-03, eta: 8:14:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2399, loss: 0.2399 +2025-06-25 03:38:39,501 - pyskl - INFO - Epoch [97][500/1281] lr: 7.086e-03, eta: 8:13:17, time: 0.328, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9988, loss_cls: 0.2187, loss: 0.2187 +2025-06-25 03:39:30,280 - pyskl - INFO - Epoch [97][600/1281] lr: 7.067e-03, eta: 8:12:38, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2582, loss: 0.2582 +2025-06-25 03:39:55,681 - pyskl - INFO - Epoch [97][700/1281] lr: 7.049e-03, eta: 8:11:45, time: 0.254, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9988, loss_cls: 0.2498, loss: 0.2498 +2025-06-25 03:40:43,936 - pyskl - INFO - Epoch [97][800/1281] lr: 7.030e-03, eta: 8:11:05, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1951, loss: 0.1951 +2025-06-25 03:41:32,682 - pyskl - INFO - Epoch [97][900/1281] lr: 7.012e-03, eta: 8:10:25, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2228, loss: 0.2228 +2025-06-25 03:42:21,428 - pyskl - INFO - Epoch [97][1000/1281] lr: 6.994e-03, eta: 8:09:45, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9994, loss_cls: 0.2154, loss: 0.2154 +2025-06-25 03:43:10,284 - pyskl - INFO - Epoch [97][1100/1281] lr: 6.975e-03, eta: 8:09:05, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2233, loss: 0.2233 +2025-06-25 03:43:59,264 - pyskl - INFO - Epoch [97][1200/1281] lr: 6.957e-03, eta: 8:08:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9988, loss_cls: 0.2435, loss: 0.2435 +2025-06-25 03:44:39,849 - pyskl - INFO - Saving checkpoint at 97 epochs +2025-06-25 03:45:37,415 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:45:37,469 - pyskl - INFO - +top1_acc 0.8925 +top5_acc 0.9911 +2025-06-25 03:45:37,469 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:45:37,475 - pyskl - INFO - +mean_acc 0.8634 +2025-06-25 03:45:37,476 - pyskl - INFO - Epoch(val) [97][533] top1_acc: 0.8925, top5_acc: 0.9911, mean_class_accuracy: 0.8634 +2025-06-25 03:46:56,887 - pyskl - INFO - Epoch [98][100/1281] lr: 6.924e-03, eta: 8:07:08, time: 0.794, data_time: 0.185, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2289, loss: 0.2289 +2025-06-25 03:47:45,809 - pyskl - INFO - Epoch [98][200/1281] lr: 6.906e-03, eta: 8:06:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 0.9994, loss_cls: 0.1787, loss: 0.1787 +2025-06-25 03:48:34,688 - pyskl - INFO - Epoch [98][300/1281] lr: 6.887e-03, eta: 8:05:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9994, loss_cls: 0.1890, loss: 0.1890 +2025-06-25 03:49:23,715 - pyskl - INFO - Epoch [98][400/1281] lr: 6.869e-03, eta: 8:05:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2456, loss: 0.2456 +2025-06-25 03:49:58,428 - pyskl - INFO - Epoch [98][500/1281] lr: 6.851e-03, eta: 8:04:20, time: 0.347, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2209, loss: 0.2209 +2025-06-25 03:50:49,176 - pyskl - INFO - Epoch [98][600/1281] lr: 6.833e-03, eta: 8:03:41, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1792, loss: 0.1792 +2025-06-25 03:51:14,703 - pyskl - INFO - Epoch [98][700/1281] lr: 6.814e-03, eta: 8:02:49, time: 0.255, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1710, loss: 0.1710 +2025-06-25 03:52:03,428 - pyskl - INFO - Epoch [98][800/1281] lr: 6.796e-03, eta: 8:02:09, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2314, loss: 0.2314 +2025-06-25 03:52:52,744 - pyskl - INFO - Epoch [98][900/1281] lr: 6.778e-03, eta: 8:01:29, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2181, loss: 0.2181 +2025-06-25 03:53:41,747 - pyskl - INFO - Epoch [98][1000/1281] lr: 6.760e-03, eta: 8:00:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2410, loss: 0.2410 +2025-06-25 03:54:30,464 - pyskl - INFO - Epoch [98][1100/1281] lr: 6.742e-03, eta: 8:00:09, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9988, loss_cls: 0.2607, loss: 0.2607 +2025-06-25 03:55:19,273 - pyskl - INFO - Epoch [98][1200/1281] lr: 6.724e-03, eta: 7:59:29, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1945, loss: 0.1945 +2025-06-25 03:55:59,416 - pyskl - INFO - Saving checkpoint at 98 epochs +2025-06-25 03:56:57,946 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:56:58,002 - pyskl - INFO - +top1_acc 0.9032 +top5_acc 0.9939 +2025-06-25 03:56:58,002 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:56:58,008 - pyskl - INFO - +mean_acc 0.8735 +2025-06-25 03:56:58,009 - pyskl - INFO - Epoch(val) [98][533] top1_acc: 0.9032, top5_acc: 0.9939, mean_class_accuracy: 0.8735 +2025-06-25 03:58:17,890 - pyskl - INFO - Epoch [99][100/1281] lr: 6.691e-03, eta: 7:58:11, time: 0.799, data_time: 0.183, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1533, loss: 0.1533 +2025-06-25 03:59:07,292 - pyskl - INFO - Epoch [99][200/1281] lr: 6.673e-03, eta: 7:57:32, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1982, loss: 0.1982 +2025-06-25 03:59:56,368 - pyskl - INFO - Epoch [99][300/1281] lr: 6.655e-03, eta: 7:56:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.1658, loss: 0.1658 +2025-06-25 04:00:45,771 - pyskl - INFO - Epoch [99][400/1281] lr: 6.637e-03, eta: 7:56:12, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1649, loss: 0.1649 +2025-06-25 04:01:19,190 - pyskl - INFO - Epoch [99][500/1281] lr: 6.619e-03, eta: 7:55:23, time: 0.334, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2156, loss: 0.2156 +2025-06-25 04:02:10,154 - pyskl - INFO - Epoch [99][600/1281] lr: 6.601e-03, eta: 7:54:44, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9988, loss_cls: 0.2117, loss: 0.2117 +2025-06-25 04:02:35,460 - pyskl - INFO - Epoch [99][700/1281] lr: 6.583e-03, eta: 7:53:52, time: 0.253, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.2031, loss: 0.2031 +2025-06-25 04:03:24,212 - pyskl - INFO - Epoch [99][800/1281] lr: 6.565e-03, eta: 7:53:12, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2016, loss: 0.2016 +2025-06-25 04:04:13,329 - pyskl - INFO - Epoch [99][900/1281] lr: 6.547e-03, eta: 7:52:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2275, loss: 0.2275 +2025-06-25 04:05:02,326 - pyskl - INFO - Epoch [99][1000/1281] lr: 6.529e-03, eta: 7:51:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9988, loss_cls: 0.2432, loss: 0.2432 +2025-06-25 04:05:51,586 - pyskl - INFO - Epoch [99][1100/1281] lr: 6.511e-03, eta: 7:51:12, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.9681, top5_acc: 0.9994, loss_cls: 0.2104, loss: 0.2104 +2025-06-25 04:06:40,611 - pyskl - INFO - Epoch [99][1200/1281] lr: 6.493e-03, eta: 7:50:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2164, loss: 0.2164 +2025-06-25 04:07:20,706 - pyskl - INFO - Saving checkpoint at 99 epochs +2025-06-25 04:08:19,404 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:08:19,472 - pyskl - INFO - +top1_acc 0.8887 +top5_acc 0.9925 +2025-06-25 04:08:19,472 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:08:19,479 - pyskl - INFO - +mean_acc 0.8523 +2025-06-25 04:08:19,481 - pyskl - INFO - Epoch(val) [99][533] top1_acc: 0.8887, top5_acc: 0.9925, mean_class_accuracy: 0.8523 +2025-06-25 04:09:39,390 - pyskl - INFO - Epoch [100][100/1281] lr: 6.460e-03, eta: 7:49:14, time: 0.799, data_time: 0.191, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2243, loss: 0.2243 +2025-06-25 04:10:28,281 - pyskl - INFO - Epoch [100][200/1281] lr: 6.442e-03, eta: 7:48:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 0.9988, loss_cls: 0.1927, loss: 0.1927 +2025-06-25 04:11:17,596 - pyskl - INFO - Epoch [100][300/1281] lr: 6.425e-03, eta: 7:47:54, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.1950, loss: 0.1950 +2025-06-25 04:12:06,706 - pyskl - INFO - Epoch [100][400/1281] lr: 6.407e-03, eta: 7:47:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2103, loss: 0.2103 +2025-06-25 04:12:39,895 - pyskl - INFO - Epoch [100][500/1281] lr: 6.389e-03, eta: 7:46:26, time: 0.332, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1842, loss: 0.1842 +2025-06-25 04:13:30,698 - pyskl - INFO - Epoch [100][600/1281] lr: 6.371e-03, eta: 7:45:46, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.1768, loss: 0.1768 +2025-06-25 04:13:56,141 - pyskl - INFO - Epoch [100][700/1281] lr: 6.353e-03, eta: 7:44:54, time: 0.254, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1712, loss: 0.1712 +2025-06-25 04:14:44,284 - pyskl - INFO - Epoch [100][800/1281] lr: 6.336e-03, eta: 7:44:14, time: 0.481, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1907, loss: 0.1907 +2025-06-25 04:15:33,305 - pyskl - INFO - Epoch [100][900/1281] lr: 6.318e-03, eta: 7:43:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2215, loss: 0.2215 +2025-06-25 04:16:22,170 - pyskl - INFO - Epoch [100][1000/1281] lr: 6.300e-03, eta: 7:42:53, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9988, loss_cls: 0.2294, loss: 0.2294 +2025-06-25 04:17:11,106 - pyskl - INFO - Epoch [100][1100/1281] lr: 6.282e-03, eta: 7:42:13, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2071, loss: 0.2071 +2025-06-25 04:18:00,281 - pyskl - INFO - Epoch [100][1200/1281] lr: 6.265e-03, eta: 7:41:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2280, loss: 0.2280 +2025-06-25 04:18:40,206 - pyskl - INFO - Saving checkpoint at 100 epochs +2025-06-25 04:19:38,595 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:19:38,650 - pyskl - INFO - +top1_acc 0.8974 +top5_acc 0.9948 +2025-06-25 04:19:38,650 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:19:38,657 - pyskl - INFO - +mean_acc 0.8673 +2025-06-25 04:19:38,659 - pyskl - INFO - Epoch(val) [100][533] top1_acc: 0.8974, top5_acc: 0.9948, mean_class_accuracy: 0.8673 +2025-06-25 04:20:57,028 - pyskl - INFO - Epoch [101][100/1281] lr: 6.232e-03, eta: 7:40:15, time: 0.784, data_time: 0.182, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2165, loss: 0.2165 +2025-06-25 04:21:45,852 - pyskl - INFO - Epoch [101][200/1281] lr: 6.215e-03, eta: 7:39:34, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1743, loss: 0.1743 +2025-06-25 04:22:35,166 - pyskl - INFO - Epoch [101][300/1281] lr: 6.197e-03, eta: 7:38:54, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1651, loss: 0.1651 +2025-06-25 04:23:24,310 - pyskl - INFO - Epoch [101][400/1281] lr: 6.180e-03, eta: 7:38:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.2113, loss: 0.2113 +2025-06-25 04:24:00,006 - pyskl - INFO - Epoch [101][500/1281] lr: 6.162e-03, eta: 7:37:27, time: 0.357, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2265, loss: 0.2265 +2025-06-25 04:24:50,971 - pyskl - INFO - Epoch [101][600/1281] lr: 6.144e-03, eta: 7:36:48, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2300, loss: 0.2300 +2025-06-25 04:25:15,985 - pyskl - INFO - Epoch [101][700/1281] lr: 6.127e-03, eta: 7:35:55, time: 0.250, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2296, loss: 0.2296 +2025-06-25 04:26:03,765 - pyskl - INFO - Epoch [101][800/1281] lr: 6.109e-03, eta: 7:35:14, time: 0.478, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2269, loss: 0.2269 +2025-06-25 04:26:53,043 - pyskl - INFO - Epoch [101][900/1281] lr: 6.092e-03, eta: 7:34:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2028, loss: 0.2028 +2025-06-25 04:27:42,000 - pyskl - INFO - Epoch [101][1000/1281] lr: 6.074e-03, eta: 7:33:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1717, loss: 0.1717 +2025-06-25 04:28:31,090 - pyskl - INFO - Epoch [101][1100/1281] lr: 6.057e-03, eta: 7:33:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9994, loss_cls: 0.2017, loss: 0.2017 +2025-06-25 04:29:20,019 - pyskl - INFO - Epoch [101][1200/1281] lr: 6.039e-03, eta: 7:32:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.1930, loss: 0.1930 +2025-06-25 04:30:00,395 - pyskl - INFO - Saving checkpoint at 101 epochs +2025-06-25 04:30:58,619 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:30:58,673 - pyskl - INFO - +top1_acc 0.8974 +top5_acc 0.9947 +2025-06-25 04:30:58,674 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:30:58,680 - pyskl - INFO - +mean_acc 0.8579 +2025-06-25 04:30:58,682 - pyskl - INFO - Epoch(val) [101][533] top1_acc: 0.8974, top5_acc: 0.9947, mean_class_accuracy: 0.8579 +2025-06-25 04:32:18,300 - pyskl - INFO - Epoch [102][100/1281] lr: 6.007e-03, eta: 7:31:16, time: 0.796, data_time: 0.178, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9994, loss_cls: 0.2163, loss: 0.2163 +2025-06-25 04:33:07,093 - pyskl - INFO - Epoch [102][200/1281] lr: 5.990e-03, eta: 7:30:35, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1858, loss: 0.1858 +2025-06-25 04:33:56,341 - pyskl - INFO - Epoch [102][300/1281] lr: 5.972e-03, eta: 7:29:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1584, loss: 0.1584 +2025-06-25 04:34:45,323 - pyskl - INFO - Epoch [102][400/1281] lr: 5.955e-03, eta: 7:29:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1663, loss: 0.1663 +2025-06-25 04:35:19,767 - pyskl - INFO - Epoch [102][500/1281] lr: 5.938e-03, eta: 7:28:27, time: 0.344, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1470, loss: 0.1470 +2025-06-25 04:36:10,459 - pyskl - INFO - Epoch [102][600/1281] lr: 5.920e-03, eta: 7:27:47, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1856, loss: 0.1856 +2025-06-25 04:36:35,399 - pyskl - INFO - Epoch [102][700/1281] lr: 5.903e-03, eta: 7:26:55, time: 0.249, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1688, loss: 0.1688 +2025-06-25 04:37:22,444 - pyskl - INFO - Epoch [102][800/1281] lr: 5.886e-03, eta: 7:26:14, time: 0.470, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1961, loss: 0.1961 +2025-06-25 04:38:11,627 - pyskl - INFO - Epoch [102][900/1281] lr: 5.868e-03, eta: 7:25:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9988, loss_cls: 0.2172, loss: 0.2172 +2025-06-25 04:39:01,126 - pyskl - INFO - Epoch [102][1000/1281] lr: 5.851e-03, eta: 7:24:53, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9994, loss_cls: 0.2020, loss: 0.2020 +2025-06-25 04:39:50,402 - pyskl - INFO - Epoch [102][1100/1281] lr: 5.834e-03, eta: 7:24:13, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1803, loss: 0.1803 +2025-06-25 04:40:39,370 - pyskl - INFO - Epoch [102][1200/1281] lr: 5.816e-03, eta: 7:23:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.1804, loss: 0.1804 +2025-06-25 04:41:19,479 - pyskl - INFO - Saving checkpoint at 102 epochs +2025-06-25 04:42:17,538 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:42:17,595 - pyskl - INFO - +top1_acc 0.8977 +top5_acc 0.9927 +2025-06-25 04:42:17,595 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:42:17,601 - pyskl - INFO - +mean_acc 0.8709 +2025-06-25 04:42:17,603 - pyskl - INFO - Epoch(val) [102][533] top1_acc: 0.8977, top5_acc: 0.9927, mean_class_accuracy: 0.8709 +2025-06-25 04:43:36,016 - pyskl - INFO - Epoch [103][100/1281] lr: 5.785e-03, eta: 7:22:14, time: 0.784, data_time: 0.183, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1683, loss: 0.1683 +2025-06-25 04:44:24,910 - pyskl - INFO - Epoch [103][200/1281] lr: 5.768e-03, eta: 7:21:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1675, loss: 0.1675 +2025-06-25 04:45:14,320 - pyskl - INFO - Epoch [103][300/1281] lr: 5.751e-03, eta: 7:20:53, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1525, loss: 0.1525 +2025-06-25 04:46:03,234 - pyskl - INFO - Epoch [103][400/1281] lr: 5.733e-03, eta: 7:20:13, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2063, loss: 0.2063 +2025-06-25 04:46:40,532 - pyskl - INFO - Epoch [103][500/1281] lr: 5.716e-03, eta: 7:19:27, time: 0.373, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1754, loss: 0.1754 +2025-06-25 04:47:31,345 - pyskl - INFO - Epoch [103][600/1281] lr: 5.699e-03, eta: 7:18:47, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.1991, loss: 0.1991 +2025-06-25 04:47:55,145 - pyskl - INFO - Epoch [103][700/1281] lr: 5.682e-03, eta: 7:17:55, time: 0.238, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2064, loss: 0.2064 +2025-06-25 04:48:41,353 - pyskl - INFO - Epoch [103][800/1281] lr: 5.665e-03, eta: 7:17:13, time: 0.462, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 0.9994, loss_cls: 0.1768, loss: 0.1768 +2025-06-25 04:49:30,303 - pyskl - INFO - Epoch [103][900/1281] lr: 5.648e-03, eta: 7:16:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.1708, loss: 0.1708 +2025-06-25 04:50:19,227 - pyskl - INFO - Epoch [103][1000/1281] lr: 5.631e-03, eta: 7:15:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2222, loss: 0.2222 +2025-06-25 04:51:07,943 - pyskl - INFO - Epoch [103][1100/1281] lr: 5.614e-03, eta: 7:15:11, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 0.9994, loss_cls: 0.1866, loss: 0.1866 +2025-06-25 04:51:56,848 - pyskl - INFO - Epoch [103][1200/1281] lr: 5.597e-03, eta: 7:14:30, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2323, loss: 0.2323 +2025-06-25 04:52:37,097 - pyskl - INFO - Saving checkpoint at 103 epochs +2025-06-25 04:53:36,051 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:53:36,119 - pyskl - INFO - +top1_acc 0.8936 +top5_acc 0.9945 +2025-06-25 04:53:36,119 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:53:36,127 - pyskl - INFO - +mean_acc 0.8650 +2025-06-25 04:53:36,129 - pyskl - INFO - Epoch(val) [103][533] top1_acc: 0.8936, top5_acc: 0.9945, mean_class_accuracy: 0.8650 +2025-06-25 04:54:54,760 - pyskl - INFO - Epoch [104][100/1281] lr: 5.566e-03, eta: 7:13:12, time: 0.786, data_time: 0.186, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.1925, loss: 0.1925 +2025-06-25 04:55:43,809 - pyskl - INFO - Epoch [104][200/1281] lr: 5.549e-03, eta: 7:12:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.1737, loss: 0.1737 +2025-06-25 04:56:33,125 - pyskl - INFO - Epoch [104][300/1281] lr: 5.532e-03, eta: 7:11:51, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1587, loss: 0.1587 +2025-06-25 04:57:22,365 - pyskl - INFO - Epoch [104][400/1281] lr: 5.515e-03, eta: 7:11:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1546, loss: 0.1546 +2025-06-25 04:57:59,681 - pyskl - INFO - Epoch [104][500/1281] lr: 5.498e-03, eta: 7:10:25, time: 0.373, data_time: 0.001, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1364, loss: 0.1364 +2025-06-25 04:58:50,484 - pyskl - INFO - Epoch [104][600/1281] lr: 5.481e-03, eta: 7:09:45, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1146, loss: 0.1146 +2025-06-25 04:59:14,259 - pyskl - INFO - Epoch [104][700/1281] lr: 5.464e-03, eta: 7:08:53, time: 0.238, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 0.9994, loss_cls: 0.1630, loss: 0.1630 +2025-06-25 04:59:59,646 - pyskl - INFO - Epoch [104][800/1281] lr: 5.447e-03, eta: 7:08:11, time: 0.454, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1456, loss: 0.1456 +2025-06-25 05:00:48,470 - pyskl - INFO - Epoch [104][900/1281] lr: 5.430e-03, eta: 7:07:30, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1769, loss: 0.1769 +2025-06-25 05:01:37,522 - pyskl - INFO - Epoch [104][1000/1281] lr: 5.413e-03, eta: 7:06:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1873, loss: 0.1873 +2025-06-25 05:02:26,631 - pyskl - INFO - Epoch [104][1100/1281] lr: 5.397e-03, eta: 7:06:09, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1743, loss: 0.1743 +2025-06-25 05:03:15,475 - pyskl - INFO - Epoch [104][1200/1281] lr: 5.380e-03, eta: 7:05:28, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1516, loss: 0.1516 +2025-06-25 05:03:55,459 - pyskl - INFO - Saving checkpoint at 104 epochs +2025-06-25 05:04:53,611 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:04:53,667 - pyskl - INFO - +top1_acc 0.8899 +top5_acc 0.9937 +2025-06-25 05:04:53,667 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:04:53,673 - pyskl - INFO - +mean_acc 0.8531 +2025-06-25 05:04:53,674 - pyskl - INFO - Epoch(val) [104][533] top1_acc: 0.8899, top5_acc: 0.9937, mean_class_accuracy: 0.8531 +2025-06-25 05:06:13,427 - pyskl - INFO - Epoch [105][100/1281] lr: 5.349e-03, eta: 7:04:10, time: 0.797, data_time: 0.190, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9994, loss_cls: 0.1810, loss: 0.1810 +2025-06-25 05:07:02,577 - pyskl - INFO - Epoch [105][200/1281] lr: 5.333e-03, eta: 7:03:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1379, loss: 0.1379 +2025-06-25 05:07:51,664 - pyskl - INFO - Epoch [105][300/1281] lr: 5.316e-03, eta: 7:02:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 0.9994, loss_cls: 0.1409, loss: 0.1409 +2025-06-25 05:08:40,994 - pyskl - INFO - Epoch [105][400/1281] lr: 5.299e-03, eta: 7:02:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.1583, loss: 0.1583 +2025-06-25 05:09:19,280 - pyskl - INFO - Epoch [105][500/1281] lr: 5.283e-03, eta: 7:01:23, time: 0.383, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1585, loss: 0.1585 +2025-06-25 05:10:09,887 - pyskl - INFO - Epoch [105][600/1281] lr: 5.266e-03, eta: 7:00:43, time: 0.506, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2087, loss: 0.2087 +2025-06-25 05:10:33,532 - pyskl - INFO - Epoch [105][700/1281] lr: 5.249e-03, eta: 6:59:51, time: 0.236, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1268, loss: 0.1268 +2025-06-25 05:11:19,255 - pyskl - INFO - Epoch [105][800/1281] lr: 5.233e-03, eta: 6:59:09, time: 0.457, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1750, loss: 0.1750 +2025-06-25 05:12:08,449 - pyskl - INFO - Epoch [105][900/1281] lr: 5.216e-03, eta: 6:58:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1427, loss: 0.1427 +2025-06-25 05:12:57,444 - pyskl - INFO - Epoch [105][1000/1281] lr: 5.199e-03, eta: 6:57:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1902, loss: 0.1902 +2025-06-25 05:13:46,843 - pyskl - INFO - Epoch [105][1100/1281] lr: 5.183e-03, eta: 6:57:06, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 0.9994, loss_cls: 0.1710, loss: 0.1710 +2025-06-25 05:14:35,984 - pyskl - INFO - Epoch [105][1200/1281] lr: 5.166e-03, eta: 6:56:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1386, loss: 0.1386 +2025-06-25 05:15:15,723 - pyskl - INFO - Saving checkpoint at 105 epochs +2025-06-25 05:16:13,615 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:16:13,671 - pyskl - INFO - +top1_acc 0.8981 +top5_acc 0.9947 +2025-06-25 05:16:13,671 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:16:13,678 - pyskl - INFO - +mean_acc 0.8702 +2025-06-25 05:16:13,679 - pyskl - INFO - Epoch(val) [105][533] top1_acc: 0.8981, top5_acc: 0.9947, mean_class_accuracy: 0.8702 +2025-06-25 05:17:30,771 - pyskl - INFO - Epoch [106][100/1281] lr: 5.136e-03, eta: 6:55:07, time: 0.771, data_time: 0.181, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1262, loss: 0.1262 +2025-06-25 05:18:19,730 - pyskl - INFO - Epoch [106][200/1281] lr: 5.120e-03, eta: 6:54:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1182, loss: 0.1182 +2025-06-25 05:19:09,089 - pyskl - INFO - Epoch [106][300/1281] lr: 5.103e-03, eta: 6:53:45, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1432, loss: 0.1432 +2025-06-25 05:19:57,946 - pyskl - INFO - Epoch [106][400/1281] lr: 5.087e-03, eta: 6:53:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1701, loss: 0.1701 +2025-06-25 05:20:38,758 - pyskl - INFO - Epoch [106][500/1281] lr: 5.070e-03, eta: 6:52:20, time: 0.408, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1711, loss: 0.1711 +2025-06-25 05:21:27,737 - pyskl - INFO - Epoch [106][600/1281] lr: 5.054e-03, eta: 6:51:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1700, loss: 0.1700 +2025-06-25 05:21:52,310 - pyskl - INFO - Epoch [106][700/1281] lr: 5.038e-03, eta: 6:50:48, time: 0.246, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1364, loss: 0.1364 +2025-06-25 05:22:34,913 - pyskl - INFO - Epoch [106][800/1281] lr: 5.021e-03, eta: 6:50:04, time: 0.426, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1408, loss: 0.1408 +2025-06-25 05:23:23,806 - pyskl - INFO - Epoch [106][900/1281] lr: 5.005e-03, eta: 6:49:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1447, loss: 0.1447 +2025-06-25 05:24:12,612 - pyskl - INFO - Epoch [106][1000/1281] lr: 4.988e-03, eta: 6:48:42, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1139, loss: 0.1139 +2025-06-25 05:25:01,768 - pyskl - INFO - Epoch [106][1100/1281] lr: 4.972e-03, eta: 6:48:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 0.9988, loss_cls: 0.1713, loss: 0.1713 +2025-06-25 05:25:50,662 - pyskl - INFO - Epoch [106][1200/1281] lr: 4.956e-03, eta: 6:47:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1346, loss: 0.1346 +2025-06-25 05:26:31,154 - pyskl - INFO - Saving checkpoint at 106 epochs +2025-06-25 05:27:29,118 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:27:29,182 - pyskl - INFO - +top1_acc 0.9085 +top5_acc 0.9953 +2025-06-25 05:27:29,182 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:27:29,189 - pyskl - INFO - +mean_acc 0.8809 +2025-06-25 05:27:29,193 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_91.pth was removed +2025-06-25 05:27:29,357 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_106.pth. +2025-06-25 05:27:29,357 - pyskl - INFO - Best top1_acc is 0.9085 at 106 epoch. +2025-06-25 05:27:29,360 - pyskl - INFO - Epoch(val) [106][533] top1_acc: 0.9085, top5_acc: 0.9953, mean_class_accuracy: 0.8809 +2025-06-25 05:28:49,030 - pyskl - INFO - Epoch [107][100/1281] lr: 4.926e-03, eta: 6:46:03, time: 0.797, data_time: 0.185, memory: 4083, top1_acc: 0.9825, top5_acc: 0.9994, loss_cls: 0.1159, loss: 0.1159 +2025-06-25 05:29:38,090 - pyskl - INFO - Epoch [107][200/1281] lr: 4.910e-03, eta: 6:45:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1057, loss: 0.1057 +2025-06-25 05:30:27,252 - pyskl - INFO - Epoch [107][300/1281] lr: 4.894e-03, eta: 6:44:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1164, loss: 0.1164 +2025-06-25 05:31:16,530 - pyskl - INFO - Epoch [107][400/1281] lr: 4.878e-03, eta: 6:44:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1255, loss: 0.1255 +2025-06-25 05:31:57,426 - pyskl - INFO - Epoch [107][500/1281] lr: 4.862e-03, eta: 6:43:16, time: 0.409, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1223, loss: 0.1223 +2025-06-25 05:32:44,933 - pyskl - INFO - Epoch [107][600/1281] lr: 4.845e-03, eta: 6:42:34, time: 0.475, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1209, loss: 0.1209 +2025-06-25 05:33:10,847 - pyskl - INFO - Epoch [107][700/1281] lr: 4.829e-03, eta: 6:41:44, time: 0.259, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.1708, loss: 0.1708 +2025-06-25 05:33:53,946 - pyskl - INFO - Epoch [107][800/1281] lr: 4.813e-03, eta: 6:41:00, time: 0.431, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1333, loss: 0.1333 +2025-06-25 05:34:42,634 - pyskl - INFO - Epoch [107][900/1281] lr: 4.797e-03, eta: 6:40:19, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1185, loss: 0.1185 +2025-06-25 05:35:31,721 - pyskl - INFO - Epoch [107][1000/1281] lr: 4.781e-03, eta: 6:39:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 0.9988, loss_cls: 0.1569, loss: 0.1569 +2025-06-25 05:36:20,413 - pyskl - INFO - Epoch [107][1100/1281] lr: 4.765e-03, eta: 6:38:57, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1525, loss: 0.1525 +2025-06-25 05:37:09,517 - pyskl - INFO - Epoch [107][1200/1281] lr: 4.749e-03, eta: 6:38:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1505, loss: 0.1505 +2025-06-25 05:37:50,080 - pyskl - INFO - Saving checkpoint at 107 epochs +2025-06-25 05:38:47,440 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:38:47,507 - pyskl - INFO - +top1_acc 0.9013 +top5_acc 0.9917 +2025-06-25 05:38:47,507 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:38:47,514 - pyskl - INFO - +mean_acc 0.8658 +2025-06-25 05:38:47,516 - pyskl - INFO - Epoch(val) [107][533] top1_acc: 0.9013, top5_acc: 0.9917, mean_class_accuracy: 0.8658 +2025-06-25 05:40:07,215 - pyskl - INFO - Epoch [108][100/1281] lr: 4.720e-03, eta: 6:36:58, time: 0.797, data_time: 0.188, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1559, loss: 0.1559 +2025-06-25 05:40:56,419 - pyskl - INFO - Epoch [108][200/1281] lr: 4.704e-03, eta: 6:36:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1409, loss: 0.1409 +2025-06-25 05:41:45,573 - pyskl - INFO - Epoch [108][300/1281] lr: 4.688e-03, eta: 6:35:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1250, loss: 0.1250 +2025-06-25 05:42:34,892 - pyskl - INFO - Epoch [108][400/1281] lr: 4.672e-03, eta: 6:34:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1347, loss: 0.1347 +2025-06-25 05:43:16,917 - pyskl - INFO - Epoch [108][500/1281] lr: 4.656e-03, eta: 6:34:11, time: 0.420, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 0.9994, loss_cls: 0.1111, loss: 0.1111 +2025-06-25 05:44:02,447 - pyskl - INFO - Epoch [108][600/1281] lr: 4.640e-03, eta: 6:33:29, time: 0.455, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 0.9988, loss_cls: 0.1385, loss: 0.1385 +2025-06-25 05:44:30,466 - pyskl - INFO - Epoch [108][700/1281] lr: 4.624e-03, eta: 6:32:40, time: 0.280, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1872, loss: 0.1872 +2025-06-25 05:45:13,287 - pyskl - INFO - Epoch [108][800/1281] lr: 4.608e-03, eta: 6:31:56, time: 0.428, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.2006, loss: 0.2006 +2025-06-25 05:46:02,185 - pyskl - INFO - Epoch [108][900/1281] lr: 4.593e-03, eta: 6:31:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1702, loss: 0.1702 +2025-06-25 05:46:51,424 - pyskl - INFO - Epoch [108][1000/1281] lr: 4.577e-03, eta: 6:30:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.1059, loss: 0.1059 +2025-06-25 05:47:40,547 - pyskl - INFO - Epoch [108][1100/1281] lr: 4.561e-03, eta: 6:29:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 0.9994, loss_cls: 0.1651, loss: 0.1651 +2025-06-25 05:48:29,531 - pyskl - INFO - Epoch [108][1200/1281] lr: 4.545e-03, eta: 6:29:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1662, loss: 0.1662 +2025-06-25 05:49:10,102 - pyskl - INFO - Saving checkpoint at 108 epochs +2025-06-25 05:50:07,962 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:50:08,022 - pyskl - INFO - +top1_acc 0.8986 +top5_acc 0.9911 +2025-06-25 05:50:08,022 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:50:08,029 - pyskl - INFO - +mean_acc 0.8603 +2025-06-25 05:50:08,031 - pyskl - INFO - Epoch(val) [108][533] top1_acc: 0.8986, top5_acc: 0.9911, mean_class_accuracy: 0.8603 +2025-06-25 05:51:28,343 - pyskl - INFO - Epoch [109][100/1281] lr: 4.517e-03, eta: 6:27:54, time: 0.803, data_time: 0.187, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1132, loss: 0.1132 +2025-06-25 05:52:17,286 - pyskl - INFO - Epoch [109][200/1281] lr: 4.501e-03, eta: 6:27:13, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1152, loss: 0.1152 +2025-06-25 05:53:06,300 - pyskl - INFO - Epoch [109][300/1281] lr: 4.485e-03, eta: 6:26:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1382, loss: 0.1382 +2025-06-25 05:53:55,350 - pyskl - INFO - Epoch [109][400/1281] lr: 4.470e-03, eta: 6:25:51, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1281, loss: 0.1281 +2025-06-25 05:54:36,891 - pyskl - INFO - Epoch [109][500/1281] lr: 4.454e-03, eta: 6:25:06, time: 0.415, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1123, loss: 0.1123 +2025-06-25 05:55:23,484 - pyskl - INFO - Epoch [109][600/1281] lr: 4.438e-03, eta: 6:24:24, time: 0.466, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.1556, loss: 0.1556 +2025-06-25 05:55:50,475 - pyskl - INFO - Epoch [109][700/1281] lr: 4.423e-03, eta: 6:23:35, time: 0.270, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1260, loss: 0.1260 +2025-06-25 05:56:33,215 - pyskl - INFO - Epoch [109][800/1281] lr: 4.407e-03, eta: 6:22:51, time: 0.427, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1247, loss: 0.1247 +2025-06-25 05:57:22,158 - pyskl - INFO - Epoch [109][900/1281] lr: 4.391e-03, eta: 6:22:10, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 0.9994, loss_cls: 0.1502, loss: 0.1502 +2025-06-25 05:58:11,093 - pyskl - INFO - Epoch [109][1000/1281] lr: 4.376e-03, eta: 6:21:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1346, loss: 0.1346 +2025-06-25 05:59:00,083 - pyskl - INFO - Epoch [109][1100/1281] lr: 4.360e-03, eta: 6:20:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1060, loss: 0.1060 +2025-06-25 05:59:48,980 - pyskl - INFO - Epoch [109][1200/1281] lr: 4.345e-03, eta: 6:20:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1298, loss: 0.1298 +2025-06-25 06:00:29,163 - pyskl - INFO - Saving checkpoint at 109 epochs +2025-06-25 06:01:26,475 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:01:26,541 - pyskl - INFO - +top1_acc 0.9128 +top5_acc 0.9948 +2025-06-25 06:01:26,542 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:01:26,555 - pyskl - INFO - +mean_acc 0.8804 +2025-06-25 06:01:26,561 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_106.pth was removed +2025-06-25 06:01:26,751 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_109.pth. +2025-06-25 06:01:26,751 - pyskl - INFO - Best top1_acc is 0.9128 at 109 epoch. +2025-06-25 06:01:26,755 - pyskl - INFO - Epoch(val) [109][533] top1_acc: 0.9128, top5_acc: 0.9948, mean_class_accuracy: 0.8804 +2025-06-25 06:02:45,946 - pyskl - INFO - Epoch [110][100/1281] lr: 4.317e-03, eta: 6:18:48, time: 0.792, data_time: 0.183, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1393, loss: 0.1393 +2025-06-25 06:03:35,117 - pyskl - INFO - Epoch [110][200/1281] lr: 4.301e-03, eta: 6:18:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1494, loss: 0.1494 +2025-06-25 06:04:24,448 - pyskl - INFO - Epoch [110][300/1281] lr: 4.286e-03, eta: 6:17:26, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1401, loss: 0.1401 +2025-06-25 06:05:13,561 - pyskl - INFO - Epoch [110][400/1281] lr: 4.271e-03, eta: 6:16:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1329, loss: 0.1329 +2025-06-25 06:05:56,520 - pyskl - INFO - Epoch [110][500/1281] lr: 4.255e-03, eta: 6:16:01, time: 0.430, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1177, loss: 0.1177 +2025-06-25 06:06:41,549 - pyskl - INFO - Epoch [110][600/1281] lr: 4.240e-03, eta: 6:15:18, time: 0.450, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1417, loss: 0.1417 +2025-06-25 06:07:09,731 - pyskl - INFO - Epoch [110][700/1281] lr: 4.225e-03, eta: 6:14:29, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1140, loss: 0.1140 +2025-06-25 06:07:51,532 - pyskl - INFO - Epoch [110][800/1281] lr: 4.209e-03, eta: 6:13:45, time: 0.418, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1525, loss: 0.1525 +2025-06-25 06:08:40,499 - pyskl - INFO - Epoch [110][900/1281] lr: 4.194e-03, eta: 6:13:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1309, loss: 0.1309 +2025-06-25 06:09:29,700 - pyskl - INFO - Epoch [110][1000/1281] lr: 4.179e-03, eta: 6:12:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1337, loss: 0.1337 +2025-06-25 06:10:18,571 - pyskl - INFO - Epoch [110][1100/1281] lr: 4.164e-03, eta: 6:11:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1325, loss: 0.1325 +2025-06-25 06:11:07,560 - pyskl - INFO - Epoch [110][1200/1281] lr: 4.148e-03, eta: 6:11:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1298, loss: 0.1298 +2025-06-25 06:11:48,008 - pyskl - INFO - Saving checkpoint at 110 epochs +2025-06-25 06:12:45,515 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:12:45,570 - pyskl - INFO - +top1_acc 0.9142 +top5_acc 0.9950 +2025-06-25 06:12:45,570 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:12:45,576 - pyskl - INFO - +mean_acc 0.8803 +2025-06-25 06:12:45,580 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_109.pth was removed +2025-06-25 06:12:45,804 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_110.pth. +2025-06-25 06:12:45,804 - pyskl - INFO - Best top1_acc is 0.9142 at 110 epoch. +2025-06-25 06:12:45,807 - pyskl - INFO - Epoch(val) [110][533] top1_acc: 0.9142, top5_acc: 0.9950, mean_class_accuracy: 0.8803 +2025-06-25 06:14:06,026 - pyskl - INFO - Epoch [111][100/1281] lr: 4.121e-03, eta: 6:09:42, time: 0.802, data_time: 0.187, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1191, loss: 0.1191 +2025-06-25 06:14:55,699 - pyskl - INFO - Epoch [111][200/1281] lr: 4.106e-03, eta: 6:09:01, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 0.9994, loss_cls: 0.1280, loss: 0.1280 +2025-06-25 06:15:44,738 - pyskl - INFO - Epoch [111][300/1281] lr: 4.091e-03, eta: 6:08:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0837, loss: 0.0837 +2025-06-25 06:16:33,376 - pyskl - INFO - Epoch [111][400/1281] lr: 4.075e-03, eta: 6:07:38, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 0.9994, loss_cls: 0.0898, loss: 0.0898 +2025-06-25 06:17:16,409 - pyskl - INFO - Epoch [111][500/1281] lr: 4.060e-03, eta: 6:06:55, time: 0.430, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1040, loss: 0.1040 +2025-06-25 06:17:59,881 - pyskl - INFO - Epoch [111][600/1281] lr: 4.045e-03, eta: 6:06:11, time: 0.435, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0966, loss: 0.0966 +2025-06-25 06:18:29,275 - pyskl - INFO - Epoch [111][700/1281] lr: 4.030e-03, eta: 6:05:23, time: 0.294, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1146, loss: 0.1146 +2025-06-25 06:19:10,585 - pyskl - INFO - Epoch [111][800/1281] lr: 4.015e-03, eta: 6:04:39, time: 0.413, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1078, loss: 0.1078 +2025-06-25 06:19:59,799 - pyskl - INFO - Epoch [111][900/1281] lr: 4.000e-03, eta: 6:03:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1386, loss: 0.1386 +2025-06-25 06:20:48,558 - pyskl - INFO - Epoch [111][1000/1281] lr: 3.985e-03, eta: 6:03:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1194, loss: 0.1194 +2025-06-25 06:21:37,597 - pyskl - INFO - Epoch [111][1100/1281] lr: 3.970e-03, eta: 6:02:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1138, loss: 0.1138 +2025-06-25 06:22:26,699 - pyskl - INFO - Epoch [111][1200/1281] lr: 3.955e-03, eta: 6:01:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1019, loss: 0.1019 +2025-06-25 06:23:06,626 - pyskl - INFO - Saving checkpoint at 111 epochs +2025-06-25 06:24:04,390 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:24:04,445 - pyskl - INFO - +top1_acc 0.9058 +top5_acc 0.9945 +2025-06-25 06:24:04,445 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:24:04,452 - pyskl - INFO - +mean_acc 0.8787 +2025-06-25 06:24:04,453 - pyskl - INFO - Epoch(val) [111][533] top1_acc: 0.9058, top5_acc: 0.9945, mean_class_accuracy: 0.8787 +2025-06-25 06:25:25,219 - pyskl - INFO - Epoch [112][100/1281] lr: 3.928e-03, eta: 6:00:36, time: 0.808, data_time: 0.185, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0862, loss: 0.0862 +2025-06-25 06:26:14,332 - pyskl - INFO - Epoch [112][200/1281] lr: 3.914e-03, eta: 5:59:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0928, loss: 0.0928 +2025-06-25 06:27:03,404 - pyskl - INFO - Epoch [112][300/1281] lr: 3.899e-03, eta: 5:59:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1065, loss: 0.1065 +2025-06-25 06:27:52,703 - pyskl - INFO - Epoch [112][400/1281] lr: 3.884e-03, eta: 5:58:31, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0812, loss: 0.0812 +2025-06-25 06:28:36,169 - pyskl - INFO - Epoch [112][500/1281] lr: 3.869e-03, eta: 5:57:48, time: 0.435, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1129, loss: 0.1129 +2025-06-25 06:29:19,304 - pyskl - INFO - Epoch [112][600/1281] lr: 3.854e-03, eta: 5:57:05, time: 0.431, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.1085, loss: 0.1085 +2025-06-25 06:29:49,492 - pyskl - INFO - Epoch [112][700/1281] lr: 3.840e-03, eta: 5:56:17, time: 0.302, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.0999, loss: 0.0999 +2025-06-25 06:30:29,928 - pyskl - INFO - Epoch [112][800/1281] lr: 3.825e-03, eta: 5:55:32, time: 0.404, data_time: 0.001, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1071, loss: 0.1071 +2025-06-25 06:31:18,620 - pyskl - INFO - Epoch [112][900/1281] lr: 3.810e-03, eta: 5:54:51, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1278, loss: 0.1278 +2025-06-25 06:32:07,850 - pyskl - INFO - Epoch [112][1000/1281] lr: 3.795e-03, eta: 5:54:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1036, loss: 0.1036 +2025-06-25 06:32:57,238 - pyskl - INFO - Epoch [112][1100/1281] lr: 3.781e-03, eta: 5:53:28, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1322, loss: 0.1322 +2025-06-25 06:33:46,101 - pyskl - INFO - Epoch [112][1200/1281] lr: 3.766e-03, eta: 5:52:46, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 0.9994, loss_cls: 0.1460, loss: 0.1460 +2025-06-25 06:34:26,377 - pyskl - INFO - Saving checkpoint at 112 epochs +2025-06-25 06:35:24,636 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:35:24,691 - pyskl - INFO - +top1_acc 0.8995 +top5_acc 0.9939 +2025-06-25 06:35:24,691 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:35:24,698 - pyskl - INFO - +mean_acc 0.8662 +2025-06-25 06:35:24,700 - pyskl - INFO - Epoch(val) [112][533] top1_acc: 0.8995, top5_acc: 0.9939, mean_class_accuracy: 0.8662 +2025-06-25 06:36:43,191 - pyskl - INFO - Epoch [113][100/1281] lr: 3.740e-03, eta: 5:51:28, time: 0.785, data_time: 0.181, memory: 4083, top1_acc: 0.9844, top5_acc: 0.9994, loss_cls: 0.1266, loss: 0.1266 +2025-06-25 06:37:32,014 - pyskl - INFO - Epoch [113][200/1281] lr: 3.725e-03, eta: 5:50:46, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.0941, loss: 0.0941 +2025-06-25 06:38:21,154 - pyskl - INFO - Epoch [113][300/1281] lr: 3.711e-03, eta: 5:50:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0951, loss: 0.0951 +2025-06-25 06:39:10,069 - pyskl - INFO - Epoch [113][400/1281] lr: 3.696e-03, eta: 5:49:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0783, loss: 0.0783 +2025-06-25 06:39:55,518 - pyskl - INFO - Epoch [113][500/1281] lr: 3.682e-03, eta: 5:48:40, time: 0.454, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0731, loss: 0.0731 +2025-06-25 06:40:32,447 - pyskl - INFO - Epoch [113][600/1281] lr: 3.667e-03, eta: 5:47:55, time: 0.369, data_time: 0.001, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0774, loss: 0.0774 +2025-06-25 06:41:08,873 - pyskl - INFO - Epoch [113][700/1281] lr: 3.653e-03, eta: 5:47:09, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1088, loss: 0.1088 +2025-06-25 06:41:46,546 - pyskl - INFO - Epoch [113][800/1281] lr: 3.638e-03, eta: 5:46:24, time: 0.377, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1067, loss: 0.1067 +2025-06-25 06:42:35,334 - pyskl - INFO - Epoch [113][900/1281] lr: 3.624e-03, eta: 5:45:42, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0733, loss: 0.0733 +2025-06-25 06:43:24,268 - pyskl - INFO - Epoch [113][1000/1281] lr: 3.610e-03, eta: 5:45:00, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0695, loss: 0.0695 +2025-06-25 06:44:13,321 - pyskl - INFO - Epoch [113][1100/1281] lr: 3.595e-03, eta: 5:44:19, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0868, loss: 0.0868 +2025-06-25 06:45:02,276 - pyskl - INFO - Epoch [113][1200/1281] lr: 3.581e-03, eta: 5:43:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0917, loss: 0.0917 +2025-06-25 06:45:42,848 - pyskl - INFO - Saving checkpoint at 113 epochs +2025-06-25 06:46:40,482 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:46:40,550 - pyskl - INFO - +top1_acc 0.9100 +top5_acc 0.9939 +2025-06-25 06:46:40,550 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:46:40,557 - pyskl - INFO - +mean_acc 0.8827 +2025-06-25 06:46:40,559 - pyskl - INFO - Epoch(val) [113][533] top1_acc: 0.9100, top5_acc: 0.9939, mean_class_accuracy: 0.8827 +2025-06-25 06:47:59,887 - pyskl - INFO - Epoch [114][100/1281] lr: 3.555e-03, eta: 5:42:19, time: 0.793, data_time: 0.183, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0597, loss: 0.0597 +2025-06-25 06:48:48,790 - pyskl - INFO - Epoch [114][200/1281] lr: 3.541e-03, eta: 5:41:37, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0766, loss: 0.0766 +2025-06-25 06:49:37,803 - pyskl - INFO - Epoch [114][300/1281] lr: 3.526e-03, eta: 5:40:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0778, loss: 0.0778 +2025-06-25 06:50:26,696 - pyskl - INFO - Epoch [114][400/1281] lr: 3.512e-03, eta: 5:40:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 0.9994, loss_cls: 0.1320, loss: 0.1320 +2025-06-25 06:51:14,763 - pyskl - INFO - Epoch [114][500/1281] lr: 3.498e-03, eta: 5:39:32, time: 0.481, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1551, loss: 0.1551 +2025-06-25 06:51:48,481 - pyskl - INFO - Epoch [114][600/1281] lr: 3.484e-03, eta: 5:38:45, time: 0.337, data_time: 0.001, memory: 4083, top1_acc: 0.9775, top5_acc: 0.9994, loss_cls: 0.1539, loss: 0.1539 +2025-06-25 06:52:28,077 - pyskl - INFO - Epoch [114][700/1281] lr: 3.470e-03, eta: 5:38:01, time: 0.396, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1173, loss: 0.1173 +2025-06-25 06:53:04,760 - pyskl - INFO - Epoch [114][800/1281] lr: 3.456e-03, eta: 5:37:15, time: 0.367, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 0.9994, loss_cls: 0.1334, loss: 0.1334 +2025-06-25 06:53:53,924 - pyskl - INFO - Epoch [114][900/1281] lr: 3.442e-03, eta: 5:36:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1136, loss: 0.1136 +2025-06-25 06:54:42,790 - pyskl - INFO - Epoch [114][1000/1281] lr: 3.427e-03, eta: 5:35:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0885, loss: 0.0885 +2025-06-25 06:55:31,799 - pyskl - INFO - Epoch [114][1100/1281] lr: 3.413e-03, eta: 5:35:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1192, loss: 0.1192 +2025-06-25 06:56:20,719 - pyskl - INFO - Epoch [114][1200/1281] lr: 3.399e-03, eta: 5:34:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0944, loss: 0.0944 +2025-06-25 06:57:00,894 - pyskl - INFO - Saving checkpoint at 114 epochs +2025-06-25 06:57:58,263 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:57:58,331 - pyskl - INFO - +top1_acc 0.9048 +top5_acc 0.9933 +2025-06-25 06:57:58,331 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:57:58,339 - pyskl - INFO - +mean_acc 0.8788 +2025-06-25 06:57:58,341 - pyskl - INFO - Epoch(val) [114][533] top1_acc: 0.9048, top5_acc: 0.9933, mean_class_accuracy: 0.8788 +2025-06-25 06:59:17,481 - pyskl - INFO - Epoch [115][100/1281] lr: 3.374e-03, eta: 5:33:10, time: 0.791, data_time: 0.184, memory: 4083, top1_acc: 0.9756, top5_acc: 0.9988, loss_cls: 0.1618, loss: 0.1618 +2025-06-25 07:00:06,545 - pyskl - INFO - Epoch [115][200/1281] lr: 3.360e-03, eta: 5:32:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0868, loss: 0.0868 +2025-06-25 07:00:55,714 - pyskl - INFO - Epoch [115][300/1281] lr: 3.346e-03, eta: 5:31:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0810, loss: 0.0810 +2025-06-25 07:01:44,508 - pyskl - INFO - Epoch [115][400/1281] lr: 3.332e-03, eta: 5:31:05, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1106, loss: 0.1106 +2025-06-25 07:02:33,272 - pyskl - INFO - Epoch [115][500/1281] lr: 3.318e-03, eta: 5:30:23, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0848, loss: 0.0848 +2025-06-25 07:03:05,500 - pyskl - INFO - Epoch [115][600/1281] lr: 3.305e-03, eta: 5:29:36, time: 0.322, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1230, loss: 0.1230 +2025-06-25 07:03:47,157 - pyskl - INFO - Epoch [115][700/1281] lr: 3.291e-03, eta: 5:28:52, time: 0.417, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.1001, loss: 0.1001 +2025-06-25 07:04:19,535 - pyskl - INFO - Epoch [115][800/1281] lr: 3.277e-03, eta: 5:28:05, time: 0.324, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0743, loss: 0.0743 +2025-06-25 07:05:08,353 - pyskl - INFO - Epoch [115][900/1281] lr: 3.263e-03, eta: 5:27:23, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0837, loss: 0.0837 +2025-06-25 07:05:57,505 - pyskl - INFO - Epoch [115][1000/1281] lr: 3.249e-03, eta: 5:26:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1244, loss: 0.1244 +2025-06-25 07:06:46,261 - pyskl - INFO - Epoch [115][1100/1281] lr: 3.236e-03, eta: 5:26:00, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0819, loss: 0.0819 +2025-06-25 07:07:35,270 - pyskl - INFO - Epoch [115][1200/1281] lr: 3.222e-03, eta: 5:25:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0985, loss: 0.0985 +2025-06-25 07:08:15,213 - pyskl - INFO - Saving checkpoint at 115 epochs +2025-06-25 07:09:14,171 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:09:14,249 - pyskl - INFO - +top1_acc 0.9101 +top5_acc 0.9938 +2025-06-25 07:09:14,249 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:09:14,259 - pyskl - INFO - +mean_acc 0.8855 +2025-06-25 07:09:14,262 - pyskl - INFO - Epoch(val) [115][533] top1_acc: 0.9101, top5_acc: 0.9938, mean_class_accuracy: 0.8855 +2025-06-25 07:10:34,554 - pyskl - INFO - Epoch [116][100/1281] lr: 3.197e-03, eta: 5:24:00, time: 0.803, data_time: 0.189, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0999, loss: 0.0999 +2025-06-25 07:11:23,420 - pyskl - INFO - Epoch [116][200/1281] lr: 3.184e-03, eta: 5:23:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0783, loss: 0.0783 +2025-06-25 07:12:12,622 - pyskl - INFO - Epoch [116][300/1281] lr: 3.170e-03, eta: 5:22:37, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0665, loss: 0.0665 +2025-06-25 07:13:01,387 - pyskl - INFO - Epoch [116][400/1281] lr: 3.156e-03, eta: 5:21:55, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0536, loss: 0.0536 +2025-06-25 07:13:50,162 - pyskl - INFO - Epoch [116][500/1281] lr: 3.143e-03, eta: 5:21:13, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0773, loss: 0.0773 +2025-06-25 07:14:17,715 - pyskl - INFO - Epoch [116][600/1281] lr: 3.129e-03, eta: 5:20:25, time: 0.276, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 0.9994, loss_cls: 0.0717, loss: 0.0717 +2025-06-25 07:15:05,986 - pyskl - INFO - Epoch [116][700/1281] lr: 3.116e-03, eta: 5:19:42, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0768, loss: 0.0768 +2025-06-25 07:15:37,780 - pyskl - INFO - Epoch [116][800/1281] lr: 3.102e-03, eta: 5:18:56, time: 0.318, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0792, loss: 0.0792 +2025-06-25 07:16:26,664 - pyskl - INFO - Epoch [116][900/1281] lr: 3.089e-03, eta: 5:18:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0609, loss: 0.0609 +2025-06-25 07:17:15,629 - pyskl - INFO - Epoch [116][1000/1281] lr: 3.075e-03, eta: 5:17:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0603, loss: 0.0603 +2025-06-25 07:18:04,616 - pyskl - INFO - Epoch [116][1100/1281] lr: 3.062e-03, eta: 5:16:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0716, loss: 0.0716 +2025-06-25 07:18:53,510 - pyskl - INFO - Epoch [116][1200/1281] lr: 3.049e-03, eta: 5:16:08, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0811, loss: 0.0811 +2025-06-25 07:19:33,606 - pyskl - INFO - Saving checkpoint at 116 epochs +2025-06-25 07:20:31,929 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:20:31,984 - pyskl - INFO - +top1_acc 0.9202 +top5_acc 0.9953 +2025-06-25 07:20:31,984 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:20:31,991 - pyskl - INFO - +mean_acc 0.8895 +2025-06-25 07:20:31,995 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_110.pth was removed +2025-06-25 07:20:32,371 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_116.pth. +2025-06-25 07:20:32,372 - pyskl - INFO - Best top1_acc is 0.9202 at 116 epoch. +2025-06-25 07:20:32,374 - pyskl - INFO - Epoch(val) [116][533] top1_acc: 0.9202, top5_acc: 0.9953, mean_class_accuracy: 0.8895 +2025-06-25 07:21:52,555 - pyskl - INFO - Epoch [117][100/1281] lr: 3.024e-03, eta: 5:14:50, time: 0.802, data_time: 0.186, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0769, loss: 0.0769 +2025-06-25 07:22:41,389 - pyskl - INFO - Epoch [117][200/1281] lr: 3.011e-03, eta: 5:14:08, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0763, loss: 0.0763 +2025-06-25 07:23:30,497 - pyskl - INFO - Epoch [117][300/1281] lr: 2.998e-03, eta: 5:13:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0765, loss: 0.0765 +2025-06-25 07:24:19,533 - pyskl - INFO - Epoch [117][400/1281] lr: 2.984e-03, eta: 5:12:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0756, loss: 0.0756 +2025-06-25 07:25:08,304 - pyskl - INFO - Epoch [117][500/1281] lr: 2.971e-03, eta: 5:12:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0638, loss: 0.0638 +2025-06-25 07:25:35,651 - pyskl - INFO - Epoch [117][600/1281] lr: 2.958e-03, eta: 5:11:14, time: 0.273, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0582, loss: 0.0582 +2025-06-25 07:26:25,795 - pyskl - INFO - Epoch [117][700/1281] lr: 2.945e-03, eta: 5:10:33, time: 0.501, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0636, loss: 0.0636 +2025-06-25 07:26:56,238 - pyskl - INFO - Epoch [117][800/1281] lr: 2.932e-03, eta: 5:09:45, time: 0.304, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0574, loss: 0.0574 +2025-06-25 07:27:44,711 - pyskl - INFO - Epoch [117][900/1281] lr: 2.919e-03, eta: 5:09:03, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0541, loss: 0.0541 +2025-06-25 07:28:33,878 - pyskl - INFO - Epoch [117][1000/1281] lr: 2.905e-03, eta: 5:08:22, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0573, loss: 0.0573 +2025-06-25 07:29:22,690 - pyskl - INFO - Epoch [117][1100/1281] lr: 2.892e-03, eta: 5:07:40, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0610, loss: 0.0610 +2025-06-25 07:30:11,802 - pyskl - INFO - Epoch [117][1200/1281] lr: 2.879e-03, eta: 5:06:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 0.9994, loss_cls: 0.0655, loss: 0.0655 +2025-06-25 07:30:52,291 - pyskl - INFO - Saving checkpoint at 117 epochs +2025-06-25 07:31:50,728 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:31:50,790 - pyskl - INFO - +top1_acc 0.9218 +top5_acc 0.9957 +2025-06-25 07:31:50,790 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:31:50,798 - pyskl - INFO - +mean_acc 0.8947 +2025-06-25 07:31:50,802 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_116.pth was removed +2025-06-25 07:31:50,976 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_117.pth. +2025-06-25 07:31:50,976 - pyskl - INFO - Best top1_acc is 0.9218 at 117 epoch. +2025-06-25 07:31:50,979 - pyskl - INFO - Epoch(val) [117][533] top1_acc: 0.9218, top5_acc: 0.9957, mean_class_accuracy: 0.8947 +2025-06-25 07:33:12,442 - pyskl - INFO - Epoch [118][100/1281] lr: 2.856e-03, eta: 5:05:40, time: 0.815, data_time: 0.190, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0479, loss: 0.0479 +2025-06-25 07:34:01,499 - pyskl - INFO - Epoch [118][200/1281] lr: 2.843e-03, eta: 5:04:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0518, loss: 0.0518 +2025-06-25 07:34:50,540 - pyskl - INFO - Epoch [118][300/1281] lr: 2.830e-03, eta: 5:04:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0638, loss: 0.0638 +2025-06-25 07:35:39,406 - pyskl - INFO - Epoch [118][400/1281] lr: 2.817e-03, eta: 5:03:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0563, loss: 0.0563 +2025-06-25 07:36:28,192 - pyskl - INFO - Epoch [118][500/1281] lr: 2.804e-03, eta: 5:02:52, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0531, loss: 0.0531 +2025-06-25 07:36:56,335 - pyskl - INFO - Epoch [118][600/1281] lr: 2.791e-03, eta: 5:02:04, time: 0.281, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0753, loss: 0.0753 +2025-06-25 07:37:45,549 - pyskl - INFO - Epoch [118][700/1281] lr: 2.778e-03, eta: 5:01:22, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0476, loss: 0.0476 +2025-06-25 07:38:16,301 - pyskl - INFO - Epoch [118][800/1281] lr: 2.765e-03, eta: 5:00:35, time: 0.308, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0506, loss: 0.0506 +2025-06-25 07:39:05,083 - pyskl - INFO - Epoch [118][900/1281] lr: 2.753e-03, eta: 4:59:53, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0723, loss: 0.0723 +2025-06-25 07:39:54,094 - pyskl - INFO - Epoch [118][1000/1281] lr: 2.740e-03, eta: 4:59:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0455, loss: 0.0455 +2025-06-25 07:40:43,104 - pyskl - INFO - Epoch [118][1100/1281] lr: 2.727e-03, eta: 4:58:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.0886, loss: 0.0886 +2025-06-25 07:41:31,856 - pyskl - INFO - Epoch [118][1200/1281] lr: 2.714e-03, eta: 4:57:47, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0616, loss: 0.0616 +2025-06-25 07:42:11,850 - pyskl - INFO - Saving checkpoint at 118 epochs +2025-06-25 07:43:09,598 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:43:09,653 - pyskl - INFO - +top1_acc 0.9242 +top5_acc 0.9950 +2025-06-25 07:43:09,653 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:43:09,660 - pyskl - INFO - +mean_acc 0.8947 +2025-06-25 07:43:09,664 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_117.pth was removed +2025-06-25 07:43:10,051 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2025-06-25 07:43:10,051 - pyskl - INFO - Best top1_acc is 0.9242 at 118 epoch. +2025-06-25 07:43:10,054 - pyskl - INFO - Epoch(val) [118][533] top1_acc: 0.9242, top5_acc: 0.9950, mean_class_accuracy: 0.8947 +2025-06-25 07:44:29,176 - pyskl - INFO - Epoch [119][100/1281] lr: 2.691e-03, eta: 4:56:28, time: 0.791, data_time: 0.185, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0735, loss: 0.0735 +2025-06-25 07:45:18,023 - pyskl - INFO - Epoch [119][200/1281] lr: 2.679e-03, eta: 4:55:46, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0678, loss: 0.0678 +2025-06-25 07:46:06,962 - pyskl - INFO - Epoch [119][300/1281] lr: 2.666e-03, eta: 4:55:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0442, loss: 0.0442 +2025-06-25 07:46:56,431 - pyskl - INFO - Epoch [119][400/1281] lr: 2.653e-03, eta: 4:54:22, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0534, loss: 0.0534 +2025-06-25 07:47:45,044 - pyskl - INFO - Epoch [119][500/1281] lr: 2.641e-03, eta: 4:53:40, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0739, loss: 0.0739 +2025-06-25 07:48:14,163 - pyskl - INFO - Epoch [119][600/1281] lr: 2.628e-03, eta: 4:52:53, time: 0.291, data_time: 0.001, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0725, loss: 0.0725 +2025-06-25 07:49:05,193 - pyskl - INFO - Epoch [119][700/1281] lr: 2.616e-03, eta: 4:52:11, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0687, loss: 0.0687 +2025-06-25 07:49:33,131 - pyskl - INFO - Epoch [119][800/1281] lr: 2.603e-03, eta: 4:51:24, time: 0.279, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0657, loss: 0.0657 +2025-06-25 07:50:21,935 - pyskl - INFO - Epoch [119][900/1281] lr: 2.591e-03, eta: 4:50:42, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0756, loss: 0.0756 +2025-06-25 07:51:11,277 - pyskl - INFO - Epoch [119][1000/1281] lr: 2.578e-03, eta: 4:50:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0833, loss: 0.0833 +2025-06-25 07:51:59,976 - pyskl - INFO - Epoch [119][1100/1281] lr: 2.566e-03, eta: 4:49:18, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0801, loss: 0.0801 +2025-06-25 07:52:48,764 - pyskl - INFO - Epoch [119][1200/1281] lr: 2.554e-03, eta: 4:48:35, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0793, loss: 0.0793 +2025-06-25 07:53:29,061 - pyskl - INFO - Saving checkpoint at 119 epochs +2025-06-25 07:54:27,728 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:54:27,785 - pyskl - INFO - +top1_acc 0.9176 +top5_acc 0.9950 +2025-06-25 07:54:27,785 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:54:27,792 - pyskl - INFO - +mean_acc 0.8855 +2025-06-25 07:54:27,794 - pyskl - INFO - Epoch(val) [119][533] top1_acc: 0.9176, top5_acc: 0.9950, mean_class_accuracy: 0.8855 +2025-06-25 07:55:48,508 - pyskl - INFO - Epoch [120][100/1281] lr: 2.531e-03, eta: 4:47:17, time: 0.807, data_time: 0.193, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0767, loss: 0.0767 +2025-06-25 07:56:37,619 - pyskl - INFO - Epoch [120][200/1281] lr: 2.519e-03, eta: 4:46:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0459, loss: 0.0459 +2025-06-25 07:57:26,175 - pyskl - INFO - Epoch [120][300/1281] lr: 2.507e-03, eta: 4:45:53, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0539, loss: 0.0539 +2025-06-25 07:58:14,947 - pyskl - INFO - Epoch [120][400/1281] lr: 2.494e-03, eta: 4:45:11, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0540, loss: 0.0540 +2025-06-25 07:59:03,940 - pyskl - INFO - Epoch [120][500/1281] lr: 2.482e-03, eta: 4:44:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0429, loss: 0.0429 +2025-06-25 07:59:33,374 - pyskl - INFO - Epoch [120][600/1281] lr: 2.470e-03, eta: 4:43:41, time: 0.294, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0383, loss: 0.0383 +2025-06-25 08:00:24,425 - pyskl - INFO - Epoch [120][700/1281] lr: 2.458e-03, eta: 4:43:00, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0404, loss: 0.0404 +2025-06-25 08:00:51,780 - pyskl - INFO - Epoch [120][800/1281] lr: 2.446e-03, eta: 4:42:12, time: 0.274, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0657, loss: 0.0657 +2025-06-25 08:01:40,838 - pyskl - INFO - Epoch [120][900/1281] lr: 2.433e-03, eta: 4:41:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0524, loss: 0.0524 +2025-06-25 08:02:29,870 - pyskl - INFO - Epoch [120][1000/1281] lr: 2.421e-03, eta: 4:40:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0505, loss: 0.0505 +2025-06-25 08:03:18,837 - pyskl - INFO - Epoch [120][1100/1281] lr: 2.409e-03, eta: 4:40:06, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0447, loss: 0.0447 +2025-06-25 08:04:08,079 - pyskl - INFO - Epoch [120][1200/1281] lr: 2.397e-03, eta: 4:39:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0522, loss: 0.0522 +2025-06-25 08:04:48,514 - pyskl - INFO - Saving checkpoint at 120 epochs +2025-06-25 08:05:47,019 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:05:47,086 - pyskl - INFO - +top1_acc 0.9230 +top5_acc 0.9945 +2025-06-25 08:05:47,086 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:05:47,096 - pyskl - INFO - +mean_acc 0.8936 +2025-06-25 08:05:47,099 - pyskl - INFO - Epoch(val) [120][533] top1_acc: 0.9230, top5_acc: 0.9945, mean_class_accuracy: 0.8936 +2025-06-25 08:07:07,649 - pyskl - INFO - Epoch [121][100/1281] lr: 2.375e-03, eta: 4:38:05, time: 0.805, data_time: 0.191, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0457, loss: 0.0457 +2025-06-25 08:07:56,447 - pyskl - INFO - Epoch [121][200/1281] lr: 2.363e-03, eta: 4:37:23, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0343, loss: 0.0343 +2025-06-25 08:08:45,298 - pyskl - INFO - Epoch [121][300/1281] lr: 2.351e-03, eta: 4:36:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0362, loss: 0.0362 +2025-06-25 08:09:34,159 - pyskl - INFO - Epoch [121][400/1281] lr: 2.340e-03, eta: 4:35:59, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0316, loss: 0.0316 +2025-06-25 08:10:23,004 - pyskl - INFO - Epoch [121][500/1281] lr: 2.328e-03, eta: 4:35:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0374, loss: 0.0374 +2025-06-25 08:10:53,112 - pyskl - INFO - Epoch [121][600/1281] lr: 2.316e-03, eta: 4:34:30, time: 0.301, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0281, loss: 0.0281 +2025-06-25 08:11:44,242 - pyskl - INFO - Epoch [121][700/1281] lr: 2.304e-03, eta: 4:33:48, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0492, loss: 0.0492 +2025-06-25 08:12:09,865 - pyskl - INFO - Epoch [121][800/1281] lr: 2.292e-03, eta: 4:33:00, time: 0.256, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0258, loss: 0.0258 +2025-06-25 08:12:58,200 - pyskl - INFO - Epoch [121][900/1281] lr: 2.280e-03, eta: 4:32:18, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0338, loss: 0.0338 +2025-06-25 08:13:46,996 - pyskl - INFO - Epoch [121][1000/1281] lr: 2.269e-03, eta: 4:31:35, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0290, loss: 0.0290 +2025-06-25 08:14:36,053 - pyskl - INFO - Epoch [121][1100/1281] lr: 2.257e-03, eta: 4:30:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0383, loss: 0.0383 +2025-06-25 08:15:25,483 - pyskl - INFO - Epoch [121][1200/1281] lr: 2.245e-03, eta: 4:30:11, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0430, loss: 0.0430 +2025-06-25 08:16:05,541 - pyskl - INFO - Saving checkpoint at 121 epochs +2025-06-25 08:17:03,150 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:17:03,208 - pyskl - INFO - +top1_acc 0.9234 +top5_acc 0.9953 +2025-06-25 08:17:03,208 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:17:03,216 - pyskl - INFO - +mean_acc 0.8953 +2025-06-25 08:17:03,218 - pyskl - INFO - Epoch(val) [121][533] top1_acc: 0.9234, top5_acc: 0.9953, mean_class_accuracy: 0.8953 +2025-06-25 08:18:22,815 - pyskl - INFO - Epoch [122][100/1281] lr: 2.224e-03, eta: 4:28:52, time: 0.796, data_time: 0.190, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0386, loss: 0.0386 +2025-06-25 08:19:11,353 - pyskl - INFO - Epoch [122][200/1281] lr: 2.212e-03, eta: 4:28:10, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0326, loss: 0.0326 +2025-06-25 08:20:00,283 - pyskl - INFO - Epoch [122][300/1281] lr: 2.201e-03, eta: 4:27:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0310, loss: 0.0310 +2025-06-25 08:20:49,124 - pyskl - INFO - Epoch [122][400/1281] lr: 2.189e-03, eta: 4:26:46, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0318, loss: 0.0318 +2025-06-25 08:21:38,073 - pyskl - INFO - Epoch [122][500/1281] lr: 2.178e-03, eta: 4:26:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0350, loss: 0.0350 +2025-06-25 08:22:12,334 - pyskl - INFO - Epoch [122][600/1281] lr: 2.166e-03, eta: 4:25:17, time: 0.343, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0269, loss: 0.0269 +2025-06-25 08:23:03,499 - pyskl - INFO - Epoch [122][700/1281] lr: 2.155e-03, eta: 4:24:36, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0280, loss: 0.0280 +2025-06-25 08:23:28,495 - pyskl - INFO - Epoch [122][800/1281] lr: 2.143e-03, eta: 4:23:48, time: 0.250, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0286, loss: 0.0286 +2025-06-25 08:24:14,761 - pyskl - INFO - Epoch [122][900/1281] lr: 2.132e-03, eta: 4:23:05, time: 0.463, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0325, loss: 0.0325 +2025-06-25 08:25:04,015 - pyskl - INFO - Epoch [122][1000/1281] lr: 2.120e-03, eta: 4:22:23, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0324, loss: 0.0324 +2025-06-25 08:25:52,873 - pyskl - INFO - Epoch [122][1100/1281] lr: 2.109e-03, eta: 4:21:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0372, loss: 0.0372 +2025-06-25 08:26:41,623 - pyskl - INFO - Epoch [122][1200/1281] lr: 2.098e-03, eta: 4:20:58, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0360, loss: 0.0360 +2025-06-25 08:27:21,547 - pyskl - INFO - Saving checkpoint at 122 epochs +2025-06-25 08:28:19,467 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:28:19,523 - pyskl - INFO - +top1_acc 0.9245 +top5_acc 0.9957 +2025-06-25 08:28:19,523 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:28:19,530 - pyskl - INFO - +mean_acc 0.8995 +2025-06-25 08:28:19,534 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_118.pth was removed +2025-06-25 08:28:19,709 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_122.pth. +2025-06-25 08:28:19,709 - pyskl - INFO - Best top1_acc is 0.9245 at 122 epoch. +2025-06-25 08:28:19,712 - pyskl - INFO - Epoch(val) [122][533] top1_acc: 0.9245, top5_acc: 0.9957, mean_class_accuracy: 0.8995 +2025-06-25 08:29:39,317 - pyskl - INFO - Epoch [123][100/1281] lr: 2.077e-03, eta: 4:19:39, time: 0.796, data_time: 0.192, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0549, loss: 0.0549 +2025-06-25 08:30:27,997 - pyskl - INFO - Epoch [123][200/1281] lr: 2.066e-03, eta: 4:18:57, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0419, loss: 0.0419 +2025-06-25 08:31:17,283 - pyskl - INFO - Epoch [123][300/1281] lr: 2.055e-03, eta: 4:18:15, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0337, loss: 0.0337 +2025-06-25 08:32:06,438 - pyskl - INFO - Epoch [123][400/1281] lr: 2.044e-03, eta: 4:17:32, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0394, loss: 0.0394 +2025-06-25 08:32:55,132 - pyskl - INFO - Epoch [123][500/1281] lr: 2.032e-03, eta: 4:16:50, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0283, loss: 0.0283 +2025-06-25 08:33:32,038 - pyskl - INFO - Epoch [123][600/1281] lr: 2.021e-03, eta: 4:16:05, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 0.9994, loss_cls: 0.0417, loss: 0.0417 +2025-06-25 08:34:23,169 - pyskl - INFO - Epoch [123][700/1281] lr: 2.010e-03, eta: 4:15:23, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0321, loss: 0.0321 +2025-06-25 08:34:47,664 - pyskl - INFO - Epoch [123][800/1281] lr: 1.999e-03, eta: 4:14:35, time: 0.245, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0500, loss: 0.0500 +2025-06-25 08:35:33,067 - pyskl - INFO - Epoch [123][900/1281] lr: 1.988e-03, eta: 4:13:52, time: 0.454, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0548, loss: 0.0548 +2025-06-25 08:36:21,912 - pyskl - INFO - Epoch [123][1000/1281] lr: 1.977e-03, eta: 4:13:10, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0516, loss: 0.0516 +2025-06-25 08:37:10,669 - pyskl - INFO - Epoch [123][1100/1281] lr: 1.966e-03, eta: 4:12:27, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0520, loss: 0.0520 +2025-06-25 08:37:59,869 - pyskl - INFO - Epoch [123][1200/1281] lr: 1.955e-03, eta: 4:11:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0546, loss: 0.0546 +2025-06-25 08:38:39,988 - pyskl - INFO - Saving checkpoint at 123 epochs +2025-06-25 08:39:38,672 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:39:38,736 - pyskl - INFO - +top1_acc 0.9220 +top5_acc 0.9948 +2025-06-25 08:39:38,737 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:39:38,746 - pyskl - INFO - +mean_acc 0.8957 +2025-06-25 08:39:38,749 - pyskl - INFO - Epoch(val) [123][533] top1_acc: 0.9220, top5_acc: 0.9948, mean_class_accuracy: 0.8957 +2025-06-25 08:40:58,377 - pyskl - INFO - Epoch [124][100/1281] lr: 1.935e-03, eta: 4:10:26, time: 0.796, data_time: 0.193, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0441, loss: 0.0441 +2025-06-25 08:41:47,311 - pyskl - INFO - Epoch [124][200/1281] lr: 1.924e-03, eta: 4:09:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0418, loss: 0.0418 +2025-06-25 08:42:36,309 - pyskl - INFO - Epoch [124][300/1281] lr: 1.913e-03, eta: 4:09:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0407, loss: 0.0407 +2025-06-25 08:43:25,268 - pyskl - INFO - Epoch [124][400/1281] lr: 1.902e-03, eta: 4:08:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0352, loss: 0.0352 +2025-06-25 08:44:14,185 - pyskl - INFO - Epoch [124][500/1281] lr: 1.892e-03, eta: 4:07:37, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0410, loss: 0.0410 +2025-06-25 08:44:51,210 - pyskl - INFO - Epoch [124][600/1281] lr: 1.881e-03, eta: 4:06:52, time: 0.370, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0438, loss: 0.0438 +2025-06-25 08:45:42,262 - pyskl - INFO - Epoch [124][700/1281] lr: 1.870e-03, eta: 4:06:10, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0302, loss: 0.0302 +2025-06-25 08:46:06,560 - pyskl - INFO - Epoch [124][800/1281] lr: 1.859e-03, eta: 4:05:22, time: 0.243, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0281, loss: 0.0281 +2025-06-25 08:46:52,940 - pyskl - INFO - Epoch [124][900/1281] lr: 1.849e-03, eta: 4:04:39, time: 0.464, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0312, loss: 0.0312 +2025-06-25 08:47:42,249 - pyskl - INFO - Epoch [124][1000/1281] lr: 1.838e-03, eta: 4:03:57, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0346, loss: 0.0346 +2025-06-25 08:48:31,229 - pyskl - INFO - Epoch [124][1100/1281] lr: 1.827e-03, eta: 4:03:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0227, loss: 0.0227 +2025-06-25 08:49:19,934 - pyskl - INFO - Epoch [124][1200/1281] lr: 1.817e-03, eta: 4:02:32, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0296, loss: 0.0296 +2025-06-25 08:50:00,509 - pyskl - INFO - Saving checkpoint at 124 epochs +2025-06-25 08:50:58,564 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:50:58,618 - pyskl - INFO - +top1_acc 0.9271 +top5_acc 0.9947 +2025-06-25 08:50:58,619 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:50:58,626 - pyskl - INFO - +mean_acc 0.9020 +2025-06-25 08:50:58,631 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_122.pth was removed +2025-06-25 08:50:58,802 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_124.pth. +2025-06-25 08:50:58,802 - pyskl - INFO - Best top1_acc is 0.9271 at 124 epoch. +2025-06-25 08:50:58,805 - pyskl - INFO - Epoch(val) [124][533] top1_acc: 0.9271, top5_acc: 0.9947, mean_class_accuracy: 0.9020 +2025-06-25 08:52:17,256 - pyskl - INFO - Epoch [125][100/1281] lr: 1.797e-03, eta: 4:01:13, time: 0.784, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 08:53:06,022 - pyskl - INFO - Epoch [125][200/1281] lr: 1.787e-03, eta: 4:00:30, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0348, loss: 0.0348 +2025-06-25 08:53:54,882 - pyskl - INFO - Epoch [125][300/1281] lr: 1.776e-03, eta: 3:59:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0263, loss: 0.0263 +2025-06-25 08:54:44,014 - pyskl - INFO - Epoch [125][400/1281] lr: 1.766e-03, eta: 3:59:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0279, loss: 0.0279 +2025-06-25 08:55:33,139 - pyskl - INFO - Epoch [125][500/1281] lr: 1.755e-03, eta: 3:58:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0298, loss: 0.0298 +2025-06-25 08:56:11,639 - pyskl - INFO - Epoch [125][600/1281] lr: 1.745e-03, eta: 3:57:38, time: 0.385, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0302, loss: 0.0302 +2025-06-25 08:57:02,463 - pyskl - INFO - Epoch [125][700/1281] lr: 1.735e-03, eta: 3:56:56, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0355, loss: 0.0355 +2025-06-25 08:57:26,200 - pyskl - INFO - Epoch [125][800/1281] lr: 1.724e-03, eta: 3:56:08, time: 0.237, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 08:58:10,834 - pyskl - INFO - Epoch [125][900/1281] lr: 1.714e-03, eta: 3:55:25, time: 0.446, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0255, loss: 0.0255 +2025-06-25 08:58:59,872 - pyskl - INFO - Epoch [125][1000/1281] lr: 1.704e-03, eta: 3:54:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0272, loss: 0.0272 +2025-06-25 08:59:49,013 - pyskl - INFO - Epoch [125][1100/1281] lr: 1.693e-03, eta: 3:54:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0305, loss: 0.0305 +2025-06-25 09:00:37,847 - pyskl - INFO - Epoch [125][1200/1281] lr: 1.683e-03, eta: 3:53:17, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0289, loss: 0.0289 +2025-06-25 09:01:18,004 - pyskl - INFO - Saving checkpoint at 125 epochs +2025-06-25 09:02:15,637 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:02:15,692 - pyskl - INFO - +top1_acc 0.9211 +top5_acc 0.9955 +2025-06-25 09:02:15,692 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:02:15,698 - pyskl - INFO - +mean_acc 0.8958 +2025-06-25 09:02:15,700 - pyskl - INFO - Epoch(val) [125][533] top1_acc: 0.9211, top5_acc: 0.9955, mean_class_accuracy: 0.8958 +2025-06-25 09:03:34,162 - pyskl - INFO - Epoch [126][100/1281] lr: 1.665e-03, eta: 3:51:58, time: 0.785, data_time: 0.186, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0326, loss: 0.0326 +2025-06-25 09:04:23,354 - pyskl - INFO - Epoch [126][200/1281] lr: 1.654e-03, eta: 3:51:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0263, loss: 0.0263 +2025-06-25 09:05:12,181 - pyskl - INFO - Epoch [126][300/1281] lr: 1.644e-03, eta: 3:50:33, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 09:06:01,198 - pyskl - INFO - Epoch [126][400/1281] lr: 1.634e-03, eta: 3:49:51, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 09:06:50,081 - pyskl - INFO - Epoch [126][500/1281] lr: 1.624e-03, eta: 3:49:08, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0262, loss: 0.0262 +2025-06-25 09:07:30,210 - pyskl - INFO - Epoch [126][600/1281] lr: 1.614e-03, eta: 3:48:24, time: 0.401, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0266, loss: 0.0266 +2025-06-25 09:08:19,451 - pyskl - INFO - Epoch [126][700/1281] lr: 1.604e-03, eta: 3:47:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-25 09:08:43,812 - pyskl - INFO - Epoch [126][800/1281] lr: 1.594e-03, eta: 3:46:54, time: 0.244, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 09:09:26,888 - pyskl - INFO - Epoch [126][900/1281] lr: 1.584e-03, eta: 3:46:10, time: 0.431, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 09:10:16,183 - pyskl - INFO - Epoch [126][1000/1281] lr: 1.574e-03, eta: 3:45:28, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0232, loss: 0.0232 +2025-06-25 09:11:05,292 - pyskl - INFO - Epoch [126][1100/1281] lr: 1.564e-03, eta: 3:44:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0227, loss: 0.0227 +2025-06-25 09:11:54,196 - pyskl - INFO - Epoch [126][1200/1281] lr: 1.554e-03, eta: 3:44:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 09:12:34,306 - pyskl - INFO - Saving checkpoint at 126 epochs +2025-06-25 09:13:32,775 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:13:32,833 - pyskl - INFO - +top1_acc 0.9283 +top5_acc 0.9960 +2025-06-25 09:13:32,833 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:13:32,839 - pyskl - INFO - +mean_acc 0.9022 +2025-06-25 09:13:32,843 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_124.pth was removed +2025-06-25 09:13:33,005 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_126.pth. +2025-06-25 09:13:33,005 - pyskl - INFO - Best top1_acc is 0.9283 at 126 epoch. +2025-06-25 09:13:33,008 - pyskl - INFO - Epoch(val) [126][533] top1_acc: 0.9283, top5_acc: 0.9960, mean_class_accuracy: 0.9022 +2025-06-25 09:14:51,294 - pyskl - INFO - Epoch [127][100/1281] lr: 1.536e-03, eta: 3:42:44, time: 0.783, data_time: 0.183, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 09:15:40,278 - pyskl - INFO - Epoch [127][200/1281] lr: 1.527e-03, eta: 3:42:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 09:16:29,411 - pyskl - INFO - Epoch [127][300/1281] lr: 1.517e-03, eta: 3:41:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-25 09:17:18,421 - pyskl - INFO - Epoch [127][400/1281] lr: 1.507e-03, eta: 3:40:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 09:18:07,387 - pyskl - INFO - Epoch [127][500/1281] lr: 1.497e-03, eta: 3:39:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0293, loss: 0.0293 +2025-06-25 09:18:49,909 - pyskl - INFO - Epoch [127][600/1281] lr: 1.488e-03, eta: 3:39:09, time: 0.425, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0226, loss: 0.0226 +2025-06-25 09:19:33,689 - pyskl - INFO - Epoch [127][700/1281] lr: 1.478e-03, eta: 3:38:26, time: 0.438, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0272, loss: 0.0272 +2025-06-25 09:20:03,092 - pyskl - INFO - Epoch [127][800/1281] lr: 1.468e-03, eta: 3:37:40, time: 0.294, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0294, loss: 0.0294 +2025-06-25 09:20:45,226 - pyskl - INFO - Epoch [127][900/1281] lr: 1.459e-03, eta: 3:36:56, time: 0.421, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0291, loss: 0.0291 +2025-06-25 09:21:34,307 - pyskl - INFO - Epoch [127][1000/1281] lr: 1.449e-03, eta: 3:36:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0288, loss: 0.0288 +2025-06-25 09:22:23,366 - pyskl - INFO - Epoch [127][1100/1281] lr: 1.440e-03, eta: 3:35:30, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-25 09:23:12,413 - pyskl - INFO - Epoch [127][1200/1281] lr: 1.430e-03, eta: 3:34:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-25 09:23:52,909 - pyskl - INFO - Saving checkpoint at 127 epochs +2025-06-25 09:24:50,705 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:24:50,761 - pyskl - INFO - +top1_acc 0.9299 +top5_acc 0.9955 +2025-06-25 09:24:50,761 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:24:50,767 - pyskl - INFO - +mean_acc 0.9036 +2025-06-25 09:24:50,771 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_126.pth was removed +2025-06-25 09:24:50,933 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_127.pth. +2025-06-25 09:24:50,933 - pyskl - INFO - Best top1_acc is 0.9299 at 127 epoch. +2025-06-25 09:24:50,936 - pyskl - INFO - Epoch(val) [127][533] top1_acc: 0.9299, top5_acc: 0.9955, mean_class_accuracy: 0.9036 +2025-06-25 09:26:10,125 - pyskl - INFO - Epoch [128][100/1281] lr: 1.413e-03, eta: 3:33:29, time: 0.792, data_time: 0.182, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0245, loss: 0.0245 +2025-06-25 09:26:59,541 - pyskl - INFO - Epoch [128][200/1281] lr: 1.404e-03, eta: 3:32:46, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 09:27:48,439 - pyskl - INFO - Epoch [128][300/1281] lr: 1.394e-03, eta: 3:32:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0383, loss: 0.0383 +2025-06-25 09:28:37,330 - pyskl - INFO - Epoch [128][400/1281] lr: 1.385e-03, eta: 3:31:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 09:29:25,989 - pyskl - INFO - Epoch [128][500/1281] lr: 1.376e-03, eta: 3:30:38, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 09:30:10,360 - pyskl - INFO - Epoch [128][600/1281] lr: 1.366e-03, eta: 3:29:55, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 09:30:52,196 - pyskl - INFO - Epoch [128][700/1281] lr: 1.357e-03, eta: 3:29:11, time: 0.418, data_time: 0.001, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 09:31:23,224 - pyskl - INFO - Epoch [128][800/1281] lr: 1.348e-03, eta: 3:28:25, time: 0.310, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 09:32:03,785 - pyskl - INFO - Epoch [128][900/1281] lr: 1.339e-03, eta: 3:27:41, time: 0.406, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0235, loss: 0.0235 +2025-06-25 09:32:52,497 - pyskl - INFO - Epoch [128][1000/1281] lr: 1.329e-03, eta: 3:26:58, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 09:33:41,810 - pyskl - INFO - Epoch [128][1100/1281] lr: 1.320e-03, eta: 3:26:15, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 09:34:31,151 - pyskl - INFO - Epoch [128][1200/1281] lr: 1.311e-03, eta: 3:25:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0278, loss: 0.0278 +2025-06-25 09:35:11,193 - pyskl - INFO - Saving checkpoint at 128 epochs +2025-06-25 09:36:09,386 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:36:09,441 - pyskl - INFO - +top1_acc 0.9292 +top5_acc 0.9967 +2025-06-25 09:36:09,441 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:36:09,447 - pyskl - INFO - +mean_acc 0.9012 +2025-06-25 09:36:09,449 - pyskl - INFO - Epoch(val) [128][533] top1_acc: 0.9292, top5_acc: 0.9967, mean_class_accuracy: 0.9012 +2025-06-25 09:37:27,594 - pyskl - INFO - Epoch [129][100/1281] lr: 1.295e-03, eta: 3:24:14, time: 0.781, data_time: 0.183, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0251, loss: 0.0251 +2025-06-25 09:38:16,434 - pyskl - INFO - Epoch [129][200/1281] lr: 1.286e-03, eta: 3:23:31, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 09:39:05,801 - pyskl - INFO - Epoch [129][300/1281] lr: 1.277e-03, eta: 3:22:48, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 09:39:55,072 - pyskl - INFO - Epoch [129][400/1281] lr: 1.268e-03, eta: 3:22:05, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-25 09:40:43,903 - pyskl - INFO - Epoch [129][500/1281] lr: 1.259e-03, eta: 3:21:23, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0251, loss: 0.0251 +2025-06-25 09:41:29,057 - pyskl - INFO - Epoch [129][600/1281] lr: 1.250e-03, eta: 3:20:39, time: 0.452, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0320, loss: 0.0320 +2025-06-25 09:42:09,991 - pyskl - INFO - Epoch [129][700/1281] lr: 1.241e-03, eta: 3:19:55, time: 0.409, data_time: 0.001, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-25 09:42:42,358 - pyskl - INFO - Epoch [129][800/1281] lr: 1.232e-03, eta: 3:19:10, time: 0.324, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0252, loss: 0.0252 +2025-06-25 09:43:23,020 - pyskl - INFO - Epoch [129][900/1281] lr: 1.223e-03, eta: 3:18:26, time: 0.407, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 09:44:11,770 - pyskl - INFO - Epoch [129][1000/1281] lr: 1.214e-03, eta: 3:17:43, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 09:45:00,482 - pyskl - INFO - Epoch [129][1100/1281] lr: 1.206e-03, eta: 3:17:00, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 09:45:49,290 - pyskl - INFO - Epoch [129][1200/1281] lr: 1.197e-03, eta: 3:16:17, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 09:46:29,581 - pyskl - INFO - Saving checkpoint at 129 epochs +2025-06-25 09:47:27,879 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:47:27,936 - pyskl - INFO - +top1_acc 0.9346 +top5_acc 0.9965 +2025-06-25 09:47:27,936 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:47:27,943 - pyskl - INFO - +mean_acc 0.9099 +2025-06-25 09:47:27,948 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_127.pth was removed +2025-06-25 09:47:28,124 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_129.pth. +2025-06-25 09:47:28,124 - pyskl - INFO - Best top1_acc is 0.9346 at 129 epoch. +2025-06-25 09:47:28,127 - pyskl - INFO - Epoch(val) [129][533] top1_acc: 0.9346, top5_acc: 0.9965, mean_class_accuracy: 0.9099 +2025-06-25 09:48:47,220 - pyskl - INFO - Epoch [130][100/1281] lr: 1.181e-03, eta: 3:14:58, time: 0.791, data_time: 0.183, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 09:49:36,385 - pyskl - INFO - Epoch [130][200/1281] lr: 1.172e-03, eta: 3:14:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 09:50:25,689 - pyskl - INFO - Epoch [130][300/1281] lr: 1.164e-03, eta: 3:13:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 09:51:14,696 - pyskl - INFO - Epoch [130][400/1281] lr: 1.155e-03, eta: 3:12:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 09:52:03,777 - pyskl - INFO - Epoch [130][500/1281] lr: 1.147e-03, eta: 3:12:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0275, loss: 0.0275 +2025-06-25 09:52:47,568 - pyskl - INFO - Epoch [130][600/1281] lr: 1.138e-03, eta: 3:11:23, time: 0.438, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 09:53:30,116 - pyskl - INFO - Epoch [130][700/1281] lr: 1.130e-03, eta: 3:10:40, time: 0.425, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 09:54:00,597 - pyskl - INFO - Epoch [130][800/1281] lr: 1.121e-03, eta: 3:09:54, time: 0.305, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 09:54:41,292 - pyskl - INFO - Epoch [130][900/1281] lr: 1.113e-03, eta: 3:09:10, time: 0.407, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 09:55:30,037 - pyskl - INFO - Epoch [130][1000/1281] lr: 1.104e-03, eta: 3:08:27, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 09:56:18,867 - pyskl - INFO - Epoch [130][1100/1281] lr: 1.096e-03, eta: 3:07:44, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0227, loss: 0.0227 +2025-06-25 09:57:07,980 - pyskl - INFO - Epoch [130][1200/1281] lr: 1.088e-03, eta: 3:07:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 09:57:48,128 - pyskl - INFO - Saving checkpoint at 130 epochs +2025-06-25 09:58:47,189 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:58:47,246 - pyskl - INFO - +top1_acc 0.9316 +top5_acc 0.9958 +2025-06-25 09:58:47,246 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:58:47,253 - pyskl - INFO - +mean_acc 0.9036 +2025-06-25 09:58:47,255 - pyskl - INFO - Epoch(val) [130][533] top1_acc: 0.9316, top5_acc: 0.9958, mean_class_accuracy: 0.9036 +2025-06-25 10:00:07,726 - pyskl - INFO - Epoch [131][100/1281] lr: 1.072e-03, eta: 3:05:42, time: 0.805, data_time: 0.187, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 10:00:56,316 - pyskl - INFO - Epoch [131][200/1281] lr: 1.064e-03, eta: 3:04:59, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 10:01:45,378 - pyskl - INFO - Epoch [131][300/1281] lr: 1.056e-03, eta: 3:04:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0226, loss: 0.0226 +2025-06-25 10:02:34,382 - pyskl - INFO - Epoch [131][400/1281] lr: 1.048e-03, eta: 3:03:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 10:03:23,297 - pyskl - INFO - Epoch [131][500/1281] lr: 1.040e-03, eta: 3:02:51, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0235, loss: 0.0235 +2025-06-25 10:04:06,023 - pyskl - INFO - Epoch [131][600/1281] lr: 1.031e-03, eta: 3:02:07, time: 0.427, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 10:04:50,089 - pyskl - INFO - Epoch [131][700/1281] lr: 1.023e-03, eta: 3:01:23, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 10:05:19,116 - pyskl - INFO - Epoch [131][800/1281] lr: 1.015e-03, eta: 3:00:38, time: 0.290, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 10:06:02,306 - pyskl - INFO - Epoch [131][900/1281] lr: 1.007e-03, eta: 2:59:54, time: 0.432, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 10:06:51,319 - pyskl - INFO - Epoch [131][1000/1281] lr: 9.992e-04, eta: 2:59:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 10:07:40,443 - pyskl - INFO - Epoch [131][1100/1281] lr: 9.912e-04, eta: 2:58:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 10:08:29,137 - pyskl - INFO - Epoch [131][1200/1281] lr: 9.832e-04, eta: 2:57:45, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 10:09:09,357 - pyskl - INFO - Saving checkpoint at 131 epochs +2025-06-25 10:10:08,707 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:10:08,764 - pyskl - INFO - +top1_acc 0.9347 +top5_acc 0.9961 +2025-06-25 10:10:08,764 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:10:08,777 - pyskl - INFO - +mean_acc 0.9092 +2025-06-25 10:10:08,782 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_129.pth was removed +2025-06-25 10:10:08,988 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_131.pth. +2025-06-25 10:10:08,989 - pyskl - INFO - Best top1_acc is 0.9347 at 131 epoch. +2025-06-25 10:10:08,992 - pyskl - INFO - Epoch(val) [131][533] top1_acc: 0.9347, top5_acc: 0.9961, mean_class_accuracy: 0.9092 +2025-06-25 10:11:27,851 - pyskl - INFO - Epoch [132][100/1281] lr: 9.689e-04, eta: 2:56:26, time: 0.789, data_time: 0.188, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 10:12:16,753 - pyskl - INFO - Epoch [132][200/1281] lr: 9.610e-04, eta: 2:55:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 10:13:06,064 - pyskl - INFO - Epoch [132][300/1281] lr: 9.532e-04, eta: 2:55:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 10:13:55,233 - pyskl - INFO - Epoch [132][400/1281] lr: 9.454e-04, eta: 2:54:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 10:14:44,425 - pyskl - INFO - Epoch [132][500/1281] lr: 9.376e-04, eta: 2:53:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0264, loss: 0.0264 +2025-06-25 10:15:24,027 - pyskl - INFO - Epoch [132][600/1281] lr: 9.298e-04, eta: 2:52:50, time: 0.396, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 10:16:15,021 - pyskl - INFO - Epoch [132][700/1281] lr: 9.221e-04, eta: 2:52:07, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 10:16:38,672 - pyskl - INFO - Epoch [132][800/1281] lr: 9.144e-04, eta: 2:51:21, time: 0.237, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 10:17:22,843 - pyskl - INFO - Epoch [132][900/1281] lr: 9.068e-04, eta: 2:50:37, time: 0.442, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 10:18:11,619 - pyskl - INFO - Epoch [132][1000/1281] lr: 8.991e-04, eta: 2:49:54, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-25 10:19:00,759 - pyskl - INFO - Epoch [132][1100/1281] lr: 8.915e-04, eta: 2:49:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 10:19:49,778 - pyskl - INFO - Epoch [132][1200/1281] lr: 8.840e-04, eta: 2:48:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 10:20:30,016 - pyskl - INFO - Saving checkpoint at 132 epochs +2025-06-25 10:21:28,365 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:21:28,421 - pyskl - INFO - +top1_acc 0.9302 +top5_acc 0.9960 +2025-06-25 10:21:28,421 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:21:28,430 - pyskl - INFO - +mean_acc 0.9027 +2025-06-25 10:21:28,433 - pyskl - INFO - Epoch(val) [132][533] top1_acc: 0.9302, top5_acc: 0.9960, mean_class_accuracy: 0.9027 +2025-06-25 10:22:47,958 - pyskl - INFO - Epoch [133][100/1281] lr: 8.704e-04, eta: 2:47:10, time: 0.795, data_time: 0.188, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 10:23:37,055 - pyskl - INFO - Epoch [133][200/1281] lr: 8.629e-04, eta: 2:46:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 10:24:26,004 - pyskl - INFO - Epoch [133][300/1281] lr: 8.554e-04, eta: 2:45:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 10:25:15,004 - pyskl - INFO - Epoch [133][400/1281] lr: 8.480e-04, eta: 2:45:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 10:26:04,322 - pyskl - INFO - Epoch [133][500/1281] lr: 8.406e-04, eta: 2:44:18, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 10:26:43,901 - pyskl - INFO - Epoch [133][600/1281] lr: 8.333e-04, eta: 2:43:33, time: 0.396, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0219, loss: 0.0219 +2025-06-25 10:27:34,701 - pyskl - INFO - Epoch [133][700/1281] lr: 8.260e-04, eta: 2:42:51, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 10:27:57,873 - pyskl - INFO - Epoch [133][800/1281] lr: 8.187e-04, eta: 2:42:04, time: 0.232, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 10:28:41,629 - pyskl - INFO - Epoch [133][900/1281] lr: 8.114e-04, eta: 2:41:21, time: 0.438, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 10:29:30,224 - pyskl - INFO - Epoch [133][1000/1281] lr: 8.042e-04, eta: 2:40:37, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 10:30:19,400 - pyskl - INFO - Epoch [133][1100/1281] lr: 7.970e-04, eta: 2:39:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 10:31:08,197 - pyskl - INFO - Epoch [133][1200/1281] lr: 7.898e-04, eta: 2:39:11, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 10:31:48,581 - pyskl - INFO - Saving checkpoint at 133 epochs +2025-06-25 10:32:47,143 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:32:47,205 - pyskl - INFO - +top1_acc 0.9344 +top5_acc 0.9962 +2025-06-25 10:32:47,205 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:32:47,211 - pyskl - INFO - +mean_acc 0.9090 +2025-06-25 10:32:47,213 - pyskl - INFO - Epoch(val) [133][533] top1_acc: 0.9344, top5_acc: 0.9962, mean_class_accuracy: 0.9090 +2025-06-25 10:34:06,977 - pyskl - INFO - Epoch [134][100/1281] lr: 7.769e-04, eta: 2:37:52, time: 0.798, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 10:34:56,306 - pyskl - INFO - Epoch [134][200/1281] lr: 7.699e-04, eta: 2:37:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 10:35:45,216 - pyskl - INFO - Epoch [134][300/1281] lr: 7.628e-04, eta: 2:36:26, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 10:36:34,362 - pyskl - INFO - Epoch [134][400/1281] lr: 7.558e-04, eta: 2:35:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 10:37:23,385 - pyskl - INFO - Epoch [134][500/1281] lr: 7.488e-04, eta: 2:35:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 10:38:03,254 - pyskl - INFO - Epoch [134][600/1281] lr: 7.419e-04, eta: 2:34:16, time: 0.399, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 10:38:54,431 - pyskl - INFO - Epoch [134][700/1281] lr: 7.349e-04, eta: 2:33:33, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 10:39:18,023 - pyskl - INFO - Epoch [134][800/1281] lr: 7.281e-04, eta: 2:32:47, time: 0.236, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 10:40:01,877 - pyskl - INFO - Epoch [134][900/1281] lr: 7.212e-04, eta: 2:32:03, time: 0.439, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 10:40:50,666 - pyskl - INFO - Epoch [134][1000/1281] lr: 7.144e-04, eta: 2:31:20, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 10:41:39,695 - pyskl - INFO - Epoch [134][1100/1281] lr: 7.076e-04, eta: 2:30:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 10:42:28,435 - pyskl - INFO - Epoch [134][1200/1281] lr: 7.008e-04, eta: 2:29:54, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 10:43:08,526 - pyskl - INFO - Saving checkpoint at 134 epochs +2025-06-25 10:44:07,375 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:44:07,433 - pyskl - INFO - +top1_acc 0.9346 +top5_acc 0.9961 +2025-06-25 10:44:07,433 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:44:07,444 - pyskl - INFO - +mean_acc 0.9090 +2025-06-25 10:44:07,447 - pyskl - INFO - Epoch(val) [134][533] top1_acc: 0.9346, top5_acc: 0.9961, mean_class_accuracy: 0.9090 +2025-06-25 10:45:26,539 - pyskl - INFO - Epoch [135][100/1281] lr: 6.887e-04, eta: 2:28:35, time: 0.791, data_time: 0.182, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-25 10:46:15,929 - pyskl - INFO - Epoch [135][200/1281] lr: 6.820e-04, eta: 2:27:52, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 10:47:05,285 - pyskl - INFO - Epoch [135][300/1281] lr: 6.753e-04, eta: 2:27:09, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 10:47:54,358 - pyskl - INFO - Epoch [135][400/1281] lr: 6.687e-04, eta: 2:26:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 10:48:43,553 - pyskl - INFO - Epoch [135][500/1281] lr: 6.622e-04, eta: 2:25:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 10:49:21,969 - pyskl - INFO - Epoch [135][600/1281] lr: 6.556e-04, eta: 2:24:58, time: 0.384, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 10:50:12,882 - pyskl - INFO - Epoch [135][700/1281] lr: 6.491e-04, eta: 2:24:16, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 10:50:36,795 - pyskl - INFO - Epoch [135][800/1281] lr: 6.426e-04, eta: 2:23:30, time: 0.239, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 10:51:21,530 - pyskl - INFO - Epoch [135][900/1281] lr: 6.362e-04, eta: 2:22:46, time: 0.447, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 10:52:10,589 - pyskl - INFO - Epoch [135][1000/1281] lr: 6.297e-04, eta: 2:22:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 10:52:59,621 - pyskl - INFO - Epoch [135][1100/1281] lr: 6.233e-04, eta: 2:21:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 10:53:48,462 - pyskl - INFO - Epoch [135][1200/1281] lr: 6.170e-04, eta: 2:20:37, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 10:54:28,698 - pyskl - INFO - Saving checkpoint at 135 epochs +2025-06-25 10:55:27,554 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:55:27,609 - pyskl - INFO - +top1_acc 0.9356 +top5_acc 0.9966 +2025-06-25 10:55:27,609 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:55:27,616 - pyskl - INFO - +mean_acc 0.9120 +2025-06-25 10:55:27,620 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_131.pth was removed +2025-06-25 10:55:27,804 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_135.pth. +2025-06-25 10:55:27,804 - pyskl - INFO - Best top1_acc is 0.9356 at 135 epoch. +2025-06-25 10:55:27,807 - pyskl - INFO - Epoch(val) [135][533] top1_acc: 0.9356, top5_acc: 0.9966, mean_class_accuracy: 0.9120 +2025-06-25 10:56:49,908 - pyskl - INFO - Epoch [136][100/1281] lr: 6.056e-04, eta: 2:19:18, time: 0.821, data_time: 0.190, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0253, loss: 0.0253 +2025-06-25 10:57:38,984 - pyskl - INFO - Epoch [136][200/1281] lr: 5.993e-04, eta: 2:18:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 10:58:27,797 - pyskl - INFO - Epoch [136][300/1281] lr: 5.931e-04, eta: 2:17:51, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 10:59:16,809 - pyskl - INFO - Epoch [136][400/1281] lr: 5.868e-04, eta: 2:17:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-25 11:00:05,938 - pyskl - INFO - Epoch [136][500/1281] lr: 5.807e-04, eta: 2:16:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 11:00:41,733 - pyskl - INFO - Epoch [136][600/1281] lr: 5.745e-04, eta: 2:15:41, time: 0.358, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 11:01:32,602 - pyskl - INFO - Epoch [136][700/1281] lr: 5.684e-04, eta: 2:14:58, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 11:01:57,461 - pyskl - INFO - Epoch [136][800/1281] lr: 5.623e-04, eta: 2:14:12, time: 0.249, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 11:02:44,869 - pyskl - INFO - Epoch [136][900/1281] lr: 5.563e-04, eta: 2:13:29, time: 0.474, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 11:03:33,905 - pyskl - INFO - Epoch [136][1000/1281] lr: 5.503e-04, eta: 2:12:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 11:04:23,318 - pyskl - INFO - Epoch [136][1100/1281] lr: 5.443e-04, eta: 2:12:02, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 11:05:12,763 - pyskl - INFO - Epoch [136][1200/1281] lr: 5.384e-04, eta: 2:11:19, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 11:05:52,671 - pyskl - INFO - Saving checkpoint at 136 epochs +2025-06-25 11:06:51,024 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:06:51,086 - pyskl - INFO - +top1_acc 0.9331 +top5_acc 0.9961 +2025-06-25 11:06:51,086 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:06:51,093 - pyskl - INFO - +mean_acc 0.9075 +2025-06-25 11:06:51,095 - pyskl - INFO - Epoch(val) [136][533] top1_acc: 0.9331, top5_acc: 0.9961, mean_class_accuracy: 0.9075 +2025-06-25 11:08:10,392 - pyskl - INFO - Epoch [137][100/1281] lr: 5.277e-04, eta: 2:10:00, time: 0.793, data_time: 0.189, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 11:08:59,450 - pyskl - INFO - Epoch [137][200/1281] lr: 5.218e-04, eta: 2:09:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 11:09:48,906 - pyskl - INFO - Epoch [137][300/1281] lr: 5.160e-04, eta: 2:08:34, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 11:10:37,931 - pyskl - INFO - Epoch [137][400/1281] lr: 5.102e-04, eta: 2:07:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 11:11:27,214 - pyskl - INFO - Epoch [137][500/1281] lr: 5.044e-04, eta: 2:07:07, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 11:12:02,087 - pyskl - INFO - Epoch [137][600/1281] lr: 4.987e-04, eta: 2:06:23, time: 0.349, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 11:12:53,032 - pyskl - INFO - Epoch [137][700/1281] lr: 4.930e-04, eta: 2:05:39, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 11:13:17,708 - pyskl - INFO - Epoch [137][800/1281] lr: 4.873e-04, eta: 2:04:54, time: 0.247, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 11:14:04,838 - pyskl - INFO - Epoch [137][900/1281] lr: 4.817e-04, eta: 2:04:10, time: 0.471, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 11:14:53,814 - pyskl - INFO - Epoch [137][1000/1281] lr: 4.761e-04, eta: 2:03:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 11:15:43,098 - pyskl - INFO - Epoch [137][1100/1281] lr: 4.705e-04, eta: 2:02:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 11:16:32,053 - pyskl - INFO - Epoch [137][1200/1281] lr: 4.650e-04, eta: 2:02:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 11:17:12,221 - pyskl - INFO - Saving checkpoint at 137 epochs +2025-06-25 11:18:10,922 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:18:10,988 - pyskl - INFO - +top1_acc 0.9356 +top5_acc 0.9962 +2025-06-25 11:18:10,989 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:18:10,995 - pyskl - INFO - +mean_acc 0.9093 +2025-06-25 11:18:10,997 - pyskl - INFO - Epoch(val) [137][533] top1_acc: 0.9356, top5_acc: 0.9962, mean_class_accuracy: 0.9093 +2025-06-25 11:19:30,992 - pyskl - INFO - Epoch [138][100/1281] lr: 4.550e-04, eta: 2:00:42, time: 0.800, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 11:20:20,128 - pyskl - INFO - Epoch [138][200/1281] lr: 4.496e-04, eta: 1:59:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 11:21:09,380 - pyskl - INFO - Epoch [138][300/1281] lr: 4.442e-04, eta: 1:59:15, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 11:21:58,634 - pyskl - INFO - Epoch [138][400/1281] lr: 4.388e-04, eta: 1:58:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 11:22:47,619 - pyskl - INFO - Epoch [138][500/1281] lr: 4.334e-04, eta: 1:57:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 11:23:21,284 - pyskl - INFO - Epoch [138][600/1281] lr: 4.281e-04, eta: 1:57:04, time: 0.337, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 11:24:12,203 - pyskl - INFO - Epoch [138][700/1281] lr: 4.228e-04, eta: 1:56:21, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 11:24:47,099 - pyskl - INFO - Epoch [138][800/1281] lr: 4.176e-04, eta: 1:55:36, time: 0.349, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 11:25:57,038 - pyskl - INFO - Epoch [138][900/1281] lr: 4.124e-04, eta: 1:54:55, time: 0.699, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 11:27:08,033 - pyskl - INFO - Epoch [138][1000/1281] lr: 4.072e-04, eta: 1:54:13, time: 0.710, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 11:28:19,432 - pyskl - INFO - Epoch [138][1100/1281] lr: 4.020e-04, eta: 1:53:32, time: 0.714, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0142, loss: 0.0142 +2025-06-25 11:29:29,524 - pyskl - INFO - Epoch [138][1200/1281] lr: 3.969e-04, eta: 1:52:51, time: 0.701, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 11:30:24,806 - pyskl - INFO - Saving checkpoint at 138 epochs +2025-06-25 11:31:39,271 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:31:39,328 - pyskl - INFO - +top1_acc 0.9353 +top5_acc 0.9961 +2025-06-25 11:31:39,328 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:31:39,335 - pyskl - INFO - +mean_acc 0.9104 +2025-06-25 11:31:39,337 - pyskl - INFO - Epoch(val) [138][533] top1_acc: 0.9353, top5_acc: 0.9961, mean_class_accuracy: 0.9104 +2025-06-25 11:32:44,531 - pyskl - INFO - Epoch [139][100/1281] lr: 3.877e-04, eta: 1:51:30, time: 0.652, data_time: 0.182, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 11:33:55,834 - pyskl - INFO - Epoch [139][200/1281] lr: 3.827e-04, eta: 1:50:49, time: 0.713, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 11:35:06,284 - pyskl - INFO - Epoch [139][300/1281] lr: 3.777e-04, eta: 1:50:07, time: 0.705, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 11:36:16,783 - pyskl - INFO - Epoch [139][400/1281] lr: 3.727e-04, eta: 1:49:26, time: 0.705, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 11:37:27,763 - pyskl - INFO - Epoch [139][500/1281] lr: 3.678e-04, eta: 1:48:44, time: 0.710, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 11:38:37,195 - pyskl - INFO - Epoch [139][600/1281] lr: 3.628e-04, eta: 1:48:02, time: 0.694, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 11:39:37,983 - pyskl - INFO - Epoch [139][700/1281] lr: 3.580e-04, eta: 1:47:20, time: 0.608, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 11:39:59,965 - pyskl - INFO - Epoch [139][800/1281] lr: 3.531e-04, eta: 1:46:34, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 11:40:22,094 - pyskl - INFO - Epoch [139][900/1281] lr: 3.483e-04, eta: 1:45:48, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 11:40:44,179 - pyskl - INFO - Epoch [139][1000/1281] lr: 3.436e-04, eta: 1:45:03, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 11:41:06,355 - pyskl - INFO - Epoch [139][1100/1281] lr: 3.388e-04, eta: 1:44:17, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 11:41:28,489 - pyskl - INFO - Epoch [139][1200/1281] lr: 3.341e-04, eta: 1:43:32, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 11:41:46,722 - pyskl - INFO - Saving checkpoint at 139 epochs +2025-06-25 11:42:29,556 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:42:29,613 - pyskl - INFO - +top1_acc 0.9330 +top5_acc 0.9971 +2025-06-25 11:42:29,613 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:42:29,620 - pyskl - INFO - +mean_acc 0.9053 +2025-06-25 11:42:29,622 - pyskl - INFO - Epoch(val) [139][533] top1_acc: 0.9330, top5_acc: 0.9971, mean_class_accuracy: 0.9053 +2025-06-25 11:43:11,378 - pyskl - INFO - Epoch [140][100/1281] lr: 3.257e-04, eta: 1:42:09, time: 0.418, data_time: 0.181, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 11:43:33,379 - pyskl - INFO - Epoch [140][200/1281] lr: 3.210e-04, eta: 1:41:24, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 11:43:55,489 - pyskl - INFO - Epoch [140][300/1281] lr: 3.165e-04, eta: 1:40:38, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 11:44:17,280 - pyskl - INFO - Epoch [140][400/1281] lr: 3.119e-04, eta: 1:39:53, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 11:44:39,076 - pyskl - INFO - Epoch [140][500/1281] lr: 3.074e-04, eta: 1:39:07, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 11:45:00,931 - pyskl - INFO - Epoch [140][600/1281] lr: 3.029e-04, eta: 1:38:22, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 11:45:22,781 - pyskl - INFO - Epoch [140][700/1281] lr: 2.984e-04, eta: 1:37:37, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 11:45:44,412 - pyskl - INFO - Epoch [140][800/1281] lr: 2.940e-04, eta: 1:36:51, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 11:46:06,433 - pyskl - INFO - Epoch [140][900/1281] lr: 2.896e-04, eta: 1:36:06, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 11:46:28,035 - pyskl - INFO - Epoch [140][1000/1281] lr: 2.853e-04, eta: 1:35:20, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 11:46:49,510 - pyskl - INFO - Epoch [140][1100/1281] lr: 2.809e-04, eta: 1:34:35, time: 0.215, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 11:47:11,388 - pyskl - INFO - Epoch [140][1200/1281] lr: 2.767e-04, eta: 1:33:50, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 11:47:29,624 - pyskl - INFO - Saving checkpoint at 140 epochs +2025-06-25 11:48:12,516 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:48:12,570 - pyskl - INFO - +top1_acc 0.9353 +top5_acc 0.9962 +2025-06-25 11:48:12,571 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:48:12,577 - pyskl - INFO - +mean_acc 0.9068 +2025-06-25 11:48:12,578 - pyskl - INFO - Epoch(val) [140][533] top1_acc: 0.9353, top5_acc: 0.9962, mean_class_accuracy: 0.9068 +2025-06-25 11:48:53,439 - pyskl - INFO - Epoch [141][100/1281] lr: 2.690e-04, eta: 1:32:28, time: 0.409, data_time: 0.180, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 11:49:15,344 - pyskl - INFO - Epoch [141][200/1281] lr: 2.648e-04, eta: 1:31:43, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 11:49:37,381 - pyskl - INFO - Epoch [141][300/1281] lr: 2.606e-04, eta: 1:30:58, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 11:49:59,206 - pyskl - INFO - Epoch [141][400/1281] lr: 2.565e-04, eta: 1:30:13, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 11:50:20,857 - pyskl - INFO - Epoch [141][500/1281] lr: 2.524e-04, eta: 1:29:28, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 11:50:42,846 - pyskl - INFO - Epoch [141][600/1281] lr: 2.483e-04, eta: 1:28:42, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 11:51:04,762 - pyskl - INFO - Epoch [141][700/1281] lr: 2.443e-04, eta: 1:27:57, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 11:51:26,603 - pyskl - INFO - Epoch [141][800/1281] lr: 2.402e-04, eta: 1:27:12, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 11:51:48,646 - pyskl - INFO - Epoch [141][900/1281] lr: 2.363e-04, eta: 1:26:27, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 11:52:10,735 - pyskl - INFO - Epoch [141][1000/1281] lr: 2.323e-04, eta: 1:25:42, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 11:52:32,632 - pyskl - INFO - Epoch [141][1100/1281] lr: 2.284e-04, eta: 1:24:57, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 11:52:55,097 - pyskl - INFO - Epoch [141][1200/1281] lr: 2.246e-04, eta: 1:24:13, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 11:53:13,667 - pyskl - INFO - Saving checkpoint at 141 epochs +2025-06-25 11:53:56,682 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:53:56,754 - pyskl - INFO - +top1_acc 0.9336 +top5_acc 0.9969 +2025-06-25 11:53:56,754 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:53:56,762 - pyskl - INFO - +mean_acc 0.9064 +2025-06-25 11:53:56,765 - pyskl - INFO - Epoch(val) [141][533] top1_acc: 0.9336, top5_acc: 0.9969, mean_class_accuracy: 0.9064 +2025-06-25 11:54:38,577 - pyskl - INFO - Epoch [142][100/1281] lr: 2.176e-04, eta: 1:22:51, time: 0.418, data_time: 0.184, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 11:55:00,665 - pyskl - INFO - Epoch [142][200/1281] lr: 2.139e-04, eta: 1:22:07, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 11:55:22,323 - pyskl - INFO - Epoch [142][300/1281] lr: 2.101e-04, eta: 1:21:22, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 11:55:44,264 - pyskl - INFO - Epoch [142][400/1281] lr: 2.064e-04, eta: 1:20:37, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 11:56:06,307 - pyskl - INFO - Epoch [142][500/1281] lr: 2.027e-04, eta: 1:19:52, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 11:56:28,323 - pyskl - INFO - Epoch [142][600/1281] lr: 1.991e-04, eta: 1:19:07, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 11:56:50,302 - pyskl - INFO - Epoch [142][700/1281] lr: 1.954e-04, eta: 1:18:23, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 11:57:12,238 - pyskl - INFO - Epoch [142][800/1281] lr: 1.919e-04, eta: 1:17:38, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 11:57:34,719 - pyskl - INFO - Epoch [142][900/1281] lr: 1.883e-04, eta: 1:16:53, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 11:57:56,753 - pyskl - INFO - Epoch [142][1000/1281] lr: 1.848e-04, eta: 1:16:09, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 11:58:18,517 - pyskl - INFO - Epoch [142][1100/1281] lr: 1.813e-04, eta: 1:15:24, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 11:58:40,634 - pyskl - INFO - Epoch [142][1200/1281] lr: 1.779e-04, eta: 1:14:40, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 11:58:59,030 - pyskl - INFO - Saving checkpoint at 142 epochs +2025-06-25 11:59:42,262 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:59:42,318 - pyskl - INFO - +top1_acc 0.9335 +top5_acc 0.9966 +2025-06-25 11:59:42,318 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:59:42,324 - pyskl - INFO - +mean_acc 0.9060 +2025-06-25 11:59:42,326 - pyskl - INFO - Epoch(val) [142][533] top1_acc: 0.9335, top5_acc: 0.9966, mean_class_accuracy: 0.9060 +2025-06-25 12:00:23,647 - pyskl - INFO - Epoch [143][100/1281] lr: 1.717e-04, eta: 1:13:19, time: 0.413, data_time: 0.181, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 12:00:45,647 - pyskl - INFO - Epoch [143][200/1281] lr: 1.683e-04, eta: 1:12:34, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:01:07,598 - pyskl - INFO - Epoch [143][300/1281] lr: 1.650e-04, eta: 1:11:50, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:01:29,558 - pyskl - INFO - Epoch [143][400/1281] lr: 1.617e-04, eta: 1:11:05, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 12:01:51,531 - pyskl - INFO - Epoch [143][500/1281] lr: 1.585e-04, eta: 1:10:21, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:02:13,537 - pyskl - INFO - Epoch [143][600/1281] lr: 1.552e-04, eta: 1:09:37, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:02:35,747 - pyskl - INFO - Epoch [143][700/1281] lr: 1.520e-04, eta: 1:08:52, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0142, loss: 0.0142 +2025-06-25 12:02:57,610 - pyskl - INFO - Epoch [143][800/1281] lr: 1.489e-04, eta: 1:08:08, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:03:19,930 - pyskl - INFO - Epoch [143][900/1281] lr: 1.457e-04, eta: 1:07:23, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 12:03:42,206 - pyskl - INFO - Epoch [143][1000/1281] lr: 1.426e-04, eta: 1:06:39, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 12:04:04,173 - pyskl - INFO - Epoch [143][1100/1281] lr: 1.396e-04, eta: 1:05:55, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 12:04:26,513 - pyskl - INFO - Epoch [143][1200/1281] lr: 1.366e-04, eta: 1:05:10, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 12:04:45,153 - pyskl - INFO - Saving checkpoint at 143 epochs +2025-06-25 12:05:27,848 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:05:27,915 - pyskl - INFO - +top1_acc 0.9356 +top5_acc 0.9966 +2025-06-25 12:05:27,915 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:05:27,925 - pyskl - INFO - +mean_acc 0.9105 +2025-06-25 12:05:27,927 - pyskl - INFO - Epoch(val) [143][533] top1_acc: 0.9356, top5_acc: 0.9966, mean_class_accuracy: 0.9105 +2025-06-25 12:06:09,762 - pyskl - INFO - Epoch [144][100/1281] lr: 1.312e-04, eta: 1:03:50, time: 0.418, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:06:32,068 - pyskl - INFO - Epoch [144][200/1281] lr: 1.282e-04, eta: 1:03:06, time: 0.223, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 12:06:53,963 - pyskl - INFO - Epoch [144][300/1281] lr: 1.253e-04, eta: 1:02:22, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 12:07:15,892 - pyskl - INFO - Epoch [144][400/1281] lr: 1.224e-04, eta: 1:01:38, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0141, loss: 0.0141 +2025-06-25 12:07:38,029 - pyskl - INFO - Epoch [144][500/1281] lr: 1.196e-04, eta: 1:00:54, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 12:07:59,955 - pyskl - INFO - Epoch [144][600/1281] lr: 1.168e-04, eta: 1:00:10, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 12:08:21,913 - pyskl - INFO - Epoch [144][700/1281] lr: 1.140e-04, eta: 0:59:26, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 12:08:43,762 - pyskl - INFO - Epoch [144][800/1281] lr: 1.113e-04, eta: 0:58:41, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 12:09:06,137 - pyskl - INFO - Epoch [144][900/1281] lr: 1.086e-04, eta: 0:57:57, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 12:09:28,114 - pyskl - INFO - Epoch [144][1000/1281] lr: 1.059e-04, eta: 0:57:13, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 12:09:50,132 - pyskl - INFO - Epoch [144][1100/1281] lr: 1.033e-04, eta: 0:56:29, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0142, loss: 0.0142 +2025-06-25 12:10:12,004 - pyskl - INFO - Epoch [144][1200/1281] lr: 1.007e-04, eta: 0:55:45, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 12:10:30,575 - pyskl - INFO - Saving checkpoint at 144 epochs +2025-06-25 12:11:13,162 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:11:13,216 - pyskl - INFO - +top1_acc 0.9343 +top5_acc 0.9961 +2025-06-25 12:11:13,216 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:11:13,223 - pyskl - INFO - +mean_acc 0.9087 +2025-06-25 12:11:13,224 - pyskl - INFO - Epoch(val) [144][533] top1_acc: 0.9343, top5_acc: 0.9961, mean_class_accuracy: 0.9087 +2025-06-25 12:11:54,223 - pyskl - INFO - Epoch [145][100/1281] lr: 9.605e-05, eta: 0:54:26, time: 0.410, data_time: 0.180, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 12:12:16,331 - pyskl - INFO - Epoch [145][200/1281] lr: 9.353e-05, eta: 0:53:42, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 12:12:38,453 - pyskl - INFO - Epoch [145][300/1281] lr: 9.106e-05, eta: 0:52:58, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 12:13:00,480 - pyskl - INFO - Epoch [145][400/1281] lr: 8.861e-05, eta: 0:52:14, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 12:13:22,269 - pyskl - INFO - Epoch [145][500/1281] lr: 8.620e-05, eta: 0:51:30, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 12:13:44,259 - pyskl - INFO - Epoch [145][600/1281] lr: 8.382e-05, eta: 0:50:47, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 12:14:06,535 - pyskl - INFO - Epoch [145][700/1281] lr: 8.147e-05, eta: 0:50:03, time: 0.223, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 12:14:28,464 - pyskl - INFO - Epoch [145][800/1281] lr: 7.916e-05, eta: 0:49:19, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:14:50,465 - pyskl - INFO - Epoch [145][900/1281] lr: 7.688e-05, eta: 0:48:35, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0142, loss: 0.0142 +2025-06-25 12:15:12,529 - pyskl - INFO - Epoch [145][1000/1281] lr: 7.463e-05, eta: 0:47:52, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:15:34,502 - pyskl - INFO - Epoch [145][1100/1281] lr: 7.242e-05, eta: 0:47:08, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 12:15:56,549 - pyskl - INFO - Epoch [145][1200/1281] lr: 7.024e-05, eta: 0:46:24, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:16:15,115 - pyskl - INFO - Saving checkpoint at 145 epochs +2025-06-25 12:16:58,000 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:16:58,056 - pyskl - INFO - +top1_acc 0.9363 +top5_acc 0.9966 +2025-06-25 12:16:58,056 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:16:58,063 - pyskl - INFO - +mean_acc 0.9108 +2025-06-25 12:16:58,067 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_135.pth was removed +2025-06-25 12:16:58,230 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_145.pth. +2025-06-25 12:16:58,231 - pyskl - INFO - Best top1_acc is 0.9363 at 145 epoch. +2025-06-25 12:16:58,234 - pyskl - INFO - Epoch(val) [145][533] top1_acc: 0.9363, top5_acc: 0.9966, mean_class_accuracy: 0.9108 +2025-06-25 12:17:39,614 - pyskl - INFO - Epoch [146][100/1281] lr: 6.638e-05, eta: 0:45:05, time: 0.414, data_time: 0.183, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0146, loss: 0.0146 +2025-06-25 12:18:01,726 - pyskl - INFO - Epoch [146][200/1281] lr: 6.429e-05, eta: 0:44:22, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 12:18:23,831 - pyskl - INFO - Epoch [146][300/1281] lr: 6.224e-05, eta: 0:43:38, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 12:18:45,628 - pyskl - INFO - Epoch [146][400/1281] lr: 6.022e-05, eta: 0:42:54, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 12:19:07,563 - pyskl - INFO - Epoch [146][500/1281] lr: 5.823e-05, eta: 0:42:11, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0144, loss: 0.0144 +2025-06-25 12:19:29,390 - pyskl - INFO - Epoch [146][600/1281] lr: 5.628e-05, eta: 0:41:27, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0138, loss: 0.0138 +2025-06-25 12:19:51,499 - pyskl - INFO - Epoch [146][700/1281] lr: 5.436e-05, eta: 0:40:44, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 12:20:13,512 - pyskl - INFO - Epoch [146][800/1281] lr: 5.247e-05, eta: 0:40:00, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 12:20:35,502 - pyskl - INFO - Epoch [146][900/1281] lr: 5.061e-05, eta: 0:39:17, time: 0.220, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0144, loss: 0.0144 +2025-06-25 12:20:57,386 - pyskl - INFO - Epoch [146][1000/1281] lr: 4.879e-05, eta: 0:38:34, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 12:21:19,295 - pyskl - INFO - Epoch [146][1100/1281] lr: 4.701e-05, eta: 0:37:50, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 12:21:41,442 - pyskl - INFO - Epoch [146][1200/1281] lr: 4.525e-05, eta: 0:37:07, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 12:21:59,877 - pyskl - INFO - Saving checkpoint at 146 epochs +2025-06-25 12:22:42,399 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:22:42,466 - pyskl - INFO - +top1_acc 0.9358 +top5_acc 0.9965 +2025-06-25 12:22:42,467 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:22:42,475 - pyskl - INFO - +mean_acc 0.9107 +2025-06-25 12:22:42,477 - pyskl - INFO - Epoch(val) [146][533] top1_acc: 0.9358, top5_acc: 0.9965, mean_class_accuracy: 0.9107 +2025-06-25 12:23:23,655 - pyskl - INFO - Epoch [147][100/1281] lr: 4.216e-05, eta: 0:35:48, time: 0.412, data_time: 0.182, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0145, loss: 0.0145 +2025-06-25 12:23:45,829 - pyskl - INFO - Epoch [147][200/1281] lr: 4.050e-05, eta: 0:35:05, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:24:08,087 - pyskl - INFO - Epoch [147][300/1281] lr: 3.887e-05, eta: 0:34:22, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 12:24:30,098 - pyskl - INFO - Epoch [147][400/1281] lr: 3.728e-05, eta: 0:33:38, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 12:24:52,085 - pyskl - INFO - Epoch [147][500/1281] lr: 3.572e-05, eta: 0:32:55, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 12:25:14,064 - pyskl - INFO - Epoch [147][600/1281] lr: 3.419e-05, eta: 0:32:12, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 12:25:36,226 - pyskl - INFO - Epoch [147][700/1281] lr: 3.270e-05, eta: 0:31:29, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:25:58,281 - pyskl - INFO - Epoch [147][800/1281] lr: 3.124e-05, eta: 0:30:46, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0146, loss: 0.0146 +2025-06-25 12:26:20,205 - pyskl - INFO - Epoch [147][900/1281] lr: 2.981e-05, eta: 0:30:02, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0132, loss: 0.0132 +2025-06-25 12:26:42,134 - pyskl - INFO - Epoch [147][1000/1281] lr: 2.842e-05, eta: 0:29:19, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0142, loss: 0.0142 +2025-06-25 12:27:03,896 - pyskl - INFO - Epoch [147][1100/1281] lr: 2.706e-05, eta: 0:28:36, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 12:27:25,995 - pyskl - INFO - Epoch [147][1200/1281] lr: 2.573e-05, eta: 0:27:53, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:27:44,500 - pyskl - INFO - Saving checkpoint at 147 epochs +2025-06-25 12:28:27,612 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:28:27,679 - pyskl - INFO - +top1_acc 0.9364 +top5_acc 0.9966 +2025-06-25 12:28:27,679 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:28:27,687 - pyskl - INFO - +mean_acc 0.9111 +2025-06-25 12:28:27,692 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_145.pth was removed +2025-06-25 12:28:27,863 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_147.pth. +2025-06-25 12:28:27,864 - pyskl - INFO - Best top1_acc is 0.9364 at 147 epoch. +2025-06-25 12:28:27,866 - pyskl - INFO - Epoch(val) [147][533] top1_acc: 0.9364, top5_acc: 0.9966, mean_class_accuracy: 0.9111 +2025-06-25 12:29:09,671 - pyskl - INFO - Epoch [148][100/1281] lr: 2.341e-05, eta: 0:26:35, time: 0.418, data_time: 0.183, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 12:29:31,602 - pyskl - INFO - Epoch [148][200/1281] lr: 2.218e-05, eta: 0:25:52, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 12:29:53,584 - pyskl - INFO - Epoch [148][300/1281] lr: 2.098e-05, eta: 0:25:09, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 12:30:15,651 - pyskl - INFO - Epoch [148][400/1281] lr: 1.981e-05, eta: 0:24:26, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 12:30:37,607 - pyskl - INFO - Epoch [148][500/1281] lr: 1.868e-05, eta: 0:23:43, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 12:30:59,464 - pyskl - INFO - Epoch [148][600/1281] lr: 1.758e-05, eta: 0:23:00, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 12:31:21,396 - pyskl - INFO - Epoch [148][700/1281] lr: 1.651e-05, eta: 0:22:17, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 12:31:43,212 - pyskl - INFO - Epoch [148][800/1281] lr: 1.548e-05, eta: 0:21:34, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 12:32:05,377 - pyskl - INFO - Epoch [148][900/1281] lr: 1.448e-05, eta: 0:20:52, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 12:32:27,476 - pyskl - INFO - Epoch [148][1000/1281] lr: 1.351e-05, eta: 0:20:09, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0145, loss: 0.0145 +2025-06-25 12:32:49,113 - pyskl - INFO - Epoch [148][1100/1281] lr: 1.258e-05, eta: 0:19:26, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 12:33:11,083 - pyskl - INFO - Epoch [148][1200/1281] lr: 1.168e-05, eta: 0:18:43, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 12:33:29,622 - pyskl - INFO - Saving checkpoint at 148 epochs +2025-06-25 12:34:12,739 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:34:12,826 - pyskl - INFO - +top1_acc 0.9360 +top5_acc 0.9965 +2025-06-25 12:34:12,826 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:34:12,835 - pyskl - INFO - +mean_acc 0.9103 +2025-06-25 12:34:12,838 - pyskl - INFO - Epoch(val) [148][533] top1_acc: 0.9360, top5_acc: 0.9965, mean_class_accuracy: 0.9103 +2025-06-25 12:34:54,659 - pyskl - INFO - Epoch [149][100/1281] lr: 1.013e-05, eta: 0:17:26, time: 0.418, data_time: 0.188, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 12:35:16,839 - pyskl - INFO - Epoch [149][200/1281] lr: 9.328e-06, eta: 0:16:43, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 12:35:38,966 - pyskl - INFO - Epoch [149][300/1281] lr: 8.555e-06, eta: 0:16:00, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 12:36:01,016 - pyskl - INFO - Epoch [149][400/1281] lr: 7.816e-06, eta: 0:15:18, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0141, loss: 0.0141 +2025-06-25 12:36:22,947 - pyskl - INFO - Epoch [149][500/1281] lr: 7.110e-06, eta: 0:14:35, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 12:36:44,921 - pyskl - INFO - Epoch [149][600/1281] lr: 6.437e-06, eta: 0:13:52, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 12:37:07,003 - pyskl - INFO - Epoch [149][700/1281] lr: 5.798e-06, eta: 0:13:10, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 12:37:28,802 - pyskl - INFO - Epoch [149][800/1281] lr: 5.192e-06, eta: 0:12:27, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 12:37:50,811 - pyskl - INFO - Epoch [149][900/1281] lr: 4.620e-06, eta: 0:11:44, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 12:38:12,841 - pyskl - INFO - Epoch [149][1000/1281] lr: 4.081e-06, eta: 0:11:02, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0145, loss: 0.0145 +2025-06-25 12:38:34,747 - pyskl - INFO - Epoch [149][1100/1281] lr: 3.576e-06, eta: 0:10:19, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 12:38:57,393 - pyskl - INFO - Epoch [149][1200/1281] lr: 3.104e-06, eta: 0:09:37, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 12:39:15,862 - pyskl - INFO - Saving checkpoint at 149 epochs +2025-06-25 12:39:58,102 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:39:58,154 - pyskl - INFO - +top1_acc 0.9345 +top5_acc 0.9961 +2025-06-25 12:39:58,154 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:39:58,160 - pyskl - INFO - +mean_acc 0.9087 +2025-06-25 12:39:58,162 - pyskl - INFO - Epoch(val) [149][533] top1_acc: 0.9345, top5_acc: 0.9961, mean_class_accuracy: 0.9087 +2025-06-25 12:40:39,905 - pyskl - INFO - Epoch [150][100/1281] lr: 2.334e-06, eta: 0:08:20, time: 0.417, data_time: 0.186, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 12:41:02,171 - pyskl - INFO - Epoch [150][200/1281] lr: 1.956e-06, eta: 0:07:37, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 12:41:24,088 - pyskl - INFO - Epoch [150][300/1281] lr: 1.611e-06, eta: 0:06:55, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:41:46,273 - pyskl - INFO - Epoch [150][400/1281] lr: 1.300e-06, eta: 0:06:12, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 12:42:08,426 - pyskl - INFO - Epoch [150][500/1281] lr: 1.022e-06, eta: 0:05:30, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 12:42:30,626 - pyskl - INFO - Epoch [150][600/1281] lr: 7.771e-07, eta: 0:04:48, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:42:52,886 - pyskl - INFO - Epoch [150][700/1281] lr: 5.659e-07, eta: 0:04:05, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 12:43:14,866 - pyskl - INFO - Epoch [150][800/1281] lr: 3.881e-07, eta: 0:03:23, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 12:43:37,098 - pyskl - INFO - Epoch [150][900/1281] lr: 2.438e-07, eta: 0:02:41, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 12:43:59,298 - pyskl - INFO - Epoch [150][1000/1281] lr: 1.329e-07, eta: 0:01:58, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 12:44:21,002 - pyskl - INFO - Epoch [150][1100/1281] lr: 5.534e-08, eta: 0:01:16, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 12:44:42,769 - pyskl - INFO - Epoch [150][1200/1281] lr: 1.123e-08, eta: 0:00:34, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 12:45:01,236 - pyskl - INFO - Saving checkpoint at 150 epochs +2025-06-25 12:45:44,103 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:45:44,154 - pyskl - INFO - +top1_acc 0.9357 +top5_acc 0.9966 +2025-06-25 12:45:44,155 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:45:44,160 - pyskl - INFO - +mean_acc 0.9104 +2025-06-25 12:45:44,162 - pyskl - INFO - Epoch(val) [150][533] top1_acc: 0.9357, top5_acc: 0.9966, mean_class_accuracy: 0.9104 +2025-06-25 12:45:48,645 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-25 12:50:58,023 - pyskl - INFO - Testing results of the last checkpoint +2025-06-25 12:50:58,023 - pyskl - INFO - top1_acc: 0.9367 +2025-06-25 12:50:58,024 - pyskl - INFO - top5_acc: 0.9971 +2025-06-25 12:50:58,024 - pyskl - INFO - mean_class_accuracy: 0.9123 +2025-06-25 12:50:58,024 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/finegym/jm/best_top1_acc_epoch_147.pth +2025-06-25 12:56:04,612 - pyskl - INFO - Testing results of the best checkpoint +2025-06-25 12:56:04,612 - pyskl - INFO - top1_acc: 0.9390 +2025-06-25 12:56:04,612 - pyskl - INFO - top5_acc: 0.9975 +2025-06-25 12:56:04,612 - pyskl - INFO - mean_class_accuracy: 0.9149 diff --git a/finegym/jm/20250624_101434.log.json b/finegym/jm/20250624_101434.log.json new file mode 100644 index 0000000000000000000000000000000000000000..a63204268f6f92b160c637cc708d3bcc39ab26e9 --- /dev/null +++ b/finegym/jm/20250624_101434.log.json @@ -0,0 +1,1951 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 1178020540, "config_name": "jm.py", "work_dir": "jm", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.19935, "top1_acc": 0.06062, "top5_acc": 0.19875, "loss_cls": 4.60869, "loss": 4.60869, "time": 0.62297} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.06875, "top5_acc": 0.28375, "loss_cls": 4.68633, "loss": 4.68633, "time": 0.41713} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.11, "top5_acc": 0.32625, "loss_cls": 4.51201, "loss": 4.51201, "time": 0.41676} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.10312, "top5_acc": 0.365, "loss_cls": 4.35712, "loss": 4.35712, "time": 0.41471} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.025, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.14938, "top5_acc": 0.39062, "loss_cls": 4.20497, "loss": 4.20497, "time": 0.41461} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.1575, "top5_acc": 0.45312, "loss_cls": 4.05678, "loss": 4.05678, "time": 0.41565} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.21, "top5_acc": 0.52375, "loss_cls": 3.7875, "loss": 3.7875, "time": 0.41724} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.2375, "top5_acc": 0.59938, "loss_cls": 3.4488, "loss": 3.4488, "time": 0.41494} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.025, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.27125, "top5_acc": 0.61938, "loss_cls": 3.28789, "loss": 3.28789, "time": 0.4151} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.27125, "top5_acc": 0.6475, "loss_cls": 3.17907, "loss": 3.17907, "time": 0.41593} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.025, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.335, "top5_acc": 0.70375, "loss_cls": 2.95948, "loss": 2.95948, "time": 0.4162} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.33688, "top5_acc": 0.71125, "loss_cls": 2.90389, "loss": 2.90389, "time": 0.25647} +{"mode": "val", "epoch": 1, "iter": 533, "lr": 0.025, "top1_acc": 0.27203, "top5_acc": 0.67551, "mean_class_accuracy": 0.13621} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.19513, "top1_acc": 0.38312, "top5_acc": 0.77062, "loss_cls": 2.63557, "loss": 2.63557, "time": 0.61211} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.41375, "top5_acc": 0.79, "loss_cls": 2.49169, "loss": 2.49169, "time": 0.41675} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.39438, "top5_acc": 0.795, "loss_cls": 2.48708, "loss": 2.48708, "time": 0.41586} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.4575, "top5_acc": 0.82438, "loss_cls": 2.31782, "loss": 2.31782, "time": 0.41604} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.02499, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.42875, "top5_acc": 0.83875, "loss_cls": 2.30958, "loss": 2.30958, "time": 0.41664} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.02499, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.46312, "top5_acc": 0.85812, "loss_cls": 2.17877, "loss": 2.17877, "time": 0.41868} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.02499, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.4225, "top5_acc": 0.845, "loss_cls": 2.30179, "loss": 2.30179, "time": 0.41809} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.02499, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.46938, "top5_acc": 0.8875, "loss_cls": 2.08337, "loss": 2.08337, "time": 0.41655} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.02499, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.4775, "top5_acc": 0.87062, "loss_cls": 2.0662, "loss": 2.0662, "time": 0.42474} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.02499, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.49, "top5_acc": 0.89562, "loss_cls": 2.05835, "loss": 2.05835, "time": 0.43322} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.02499, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.49562, "top5_acc": 0.88875, "loss_cls": 2.01534, "loss": 2.01534, "time": 0.42397} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.02499, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.505, "top5_acc": 0.8875, "loss_cls": 1.93773, "loss": 1.93773, "time": 0.26188} +{"mode": "val", "epoch": 2, "iter": 533, "lr": 0.02499, "top1_acc": 0.43422, "top5_acc": 0.85072, "mean_class_accuracy": 0.23917} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.02499, "memory": 4082, "data_time": 0.1977, "top1_acc": 0.4875, "top5_acc": 0.88938, "loss_cls": 1.98834, "loss": 1.98834, "time": 0.63511} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.02499, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.515, "top5_acc": 0.89812, "loss_cls": 1.9194, "loss": 1.9194, "time": 0.41892} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.02499, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.5225, "top5_acc": 0.92438, "loss_cls": 1.82699, "loss": 1.82699, "time": 0.41594} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.02499, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.55937, "top5_acc": 0.89875, "loss_cls": 1.86241, "loss": 1.86241, "time": 0.41371} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.02498, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.53875, "top5_acc": 0.92062, "loss_cls": 1.78879, "loss": 1.78879, "time": 0.41616} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.02498, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.54062, "top5_acc": 0.94188, "loss_cls": 1.76383, "loss": 1.76383, "time": 0.41487} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.02498, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.56188, "top5_acc": 0.91438, "loss_cls": 1.74117, "loss": 1.74117, "time": 0.4146} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.02498, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.5825, "top5_acc": 0.93625, "loss_cls": 1.68199, "loss": 1.68199, "time": 0.41456} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.02498, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.55937, "top5_acc": 0.92875, "loss_cls": 1.73655, "loss": 1.73655, "time": 0.41508} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.02498, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.57875, "top5_acc": 0.93, "loss_cls": 1.69698, "loss": 1.69698, "time": 0.42226} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.02498, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.5625, "top5_acc": 0.92562, "loss_cls": 1.7353, "loss": 1.7353, "time": 0.41372} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.02498, "memory": 4082, "data_time": 0.00049, "top1_acc": 0.58625, "top5_acc": 0.935, "loss_cls": 1.64154, "loss": 1.64154, "time": 0.26869} +{"mode": "val", "epoch": 3, "iter": 533, "lr": 0.02498, "top1_acc": 0.55087, "top5_acc": 0.91609, "mean_class_accuracy": 0.36982} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.02497, "memory": 4082, "data_time": 0.20052, "top1_acc": 0.59438, "top5_acc": 0.94812, "loss_cls": 1.61063, "loss": 1.61063, "time": 0.61624} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.02497, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.58938, "top5_acc": 0.94625, "loss_cls": 1.59351, "loss": 1.59351, "time": 0.41461} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.02497, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.59625, "top5_acc": 0.93688, "loss_cls": 1.59124, "loss": 1.59124, "time": 0.41538} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.02497, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.60812, "top5_acc": 0.95625, "loss_cls": 1.49821, "loss": 1.49821, "time": 0.4151} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.02497, "memory": 4082, "data_time": 0.00053, "top1_acc": 0.60438, "top5_acc": 0.95125, "loss_cls": 1.54537, "loss": 1.54537, "time": 0.41576} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.02497, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.60875, "top5_acc": 0.94625, "loss_cls": 1.5437, "loss": 1.5437, "time": 0.41498} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.02497, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.59375, "top5_acc": 0.95, "loss_cls": 1.57521, "loss": 1.57521, "time": 0.4155} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.02496, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.62813, "top5_acc": 0.94938, "loss_cls": 1.48713, "loss": 1.48713, "time": 0.41522} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.02496, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.61687, "top5_acc": 0.95625, "loss_cls": 1.51084, "loss": 1.51084, "time": 0.41573} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.02496, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.61625, "top5_acc": 0.95062, "loss_cls": 1.51309, "loss": 1.51309, "time": 0.41512} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.02496, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.60812, "top5_acc": 0.95562, "loss_cls": 1.49979, "loss": 1.49979, "time": 0.40035} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.02496, "memory": 4082, "data_time": 0.00051, "top1_acc": 0.61375, "top5_acc": 0.96, "loss_cls": 1.47272, "loss": 1.47272, "time": 0.28227} +{"mode": "val", "epoch": 4, "iter": 533, "lr": 0.02496, "top1_acc": 0.56015, "top5_acc": 0.93135, "mean_class_accuracy": 0.43396} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.02495, "memory": 4082, "data_time": 0.20221, "top1_acc": 0.61687, "top5_acc": 0.95938, "loss_cls": 1.44666, "loss": 1.44666, "time": 0.64076} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.02495, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.63938, "top5_acc": 0.96188, "loss_cls": 1.38673, "loss": 1.38673, "time": 0.41797} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.02495, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.63562, "top5_acc": 0.96188, "loss_cls": 1.40812, "loss": 1.40812, "time": 0.41406} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.02495, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.65625, "top5_acc": 0.96, "loss_cls": 1.38156, "loss": 1.38156, "time": 0.41369} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.02495, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.64625, "top5_acc": 0.9675, "loss_cls": 1.37071, "loss": 1.37071, "time": 0.41431} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.02495, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.65188, "top5_acc": 0.96438, "loss_cls": 1.40607, "loss": 1.40607, "time": 0.41435} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.02494, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.63, "top5_acc": 0.96312, "loss_cls": 1.41417, "loss": 1.41417, "time": 0.41431} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.02494, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.65438, "top5_acc": 0.96125, "loss_cls": 1.37969, "loss": 1.37969, "time": 0.4147} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.02494, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.67062, "top5_acc": 0.96688, "loss_cls": 1.30896, "loss": 1.30896, "time": 0.41586} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.02494, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.6575, "top5_acc": 0.96312, "loss_cls": 1.3704, "loss": 1.3704, "time": 0.41378} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.02494, "memory": 4082, "data_time": 0.00058, "top1_acc": 0.64062, "top5_acc": 0.95938, "loss_cls": 1.4377, "loss": 1.4377, "time": 0.38346} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.02493, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.65938, "top5_acc": 0.96375, "loss_cls": 1.36504, "loss": 1.36504, "time": 0.29811} +{"mode": "val", "epoch": 5, "iter": 533, "lr": 0.02493, "top1_acc": 0.65333, "top5_acc": 0.95857, "mean_class_accuracy": 0.51172} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.02493, "memory": 4082, "data_time": 0.19341, "top1_acc": 0.69, "top5_acc": 0.96875, "loss_cls": 1.29166, "loss": 1.29166, "time": 0.60904} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.02493, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.68375, "top5_acc": 0.96562, "loss_cls": 1.29522, "loss": 1.29522, "time": 0.41574} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.02492, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.68812, "top5_acc": 0.96812, "loss_cls": 1.28764, "loss": 1.28764, "time": 0.41543} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.02492, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.69562, "top5_acc": 0.96812, "loss_cls": 1.28036, "loss": 1.28036, "time": 0.41389} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.02492, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.67562, "top5_acc": 0.96688, "loss_cls": 1.31095, "loss": 1.31095, "time": 0.41667} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.02492, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.65625, "top5_acc": 0.96562, "loss_cls": 1.34983, "loss": 1.34983, "time": 0.41595} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.02492, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.68188, "top5_acc": 0.96938, "loss_cls": 1.28293, "loss": 1.28293, "time": 0.41464} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.02491, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.67812, "top5_acc": 0.97438, "loss_cls": 1.3209, "loss": 1.3209, "time": 0.41479} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.02491, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.69375, "top5_acc": 0.97062, "loss_cls": 1.25981, "loss": 1.25981, "time": 0.43462} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.02491, "memory": 4082, "data_time": 0.00055, "top1_acc": 0.71188, "top5_acc": 0.97875, "loss_cls": 1.19682, "loss": 1.19682, "time": 0.42463} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.02491, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.69438, "top5_acc": 0.97188, "loss_cls": 1.24084, "loss": 1.24084, "time": 0.38145} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.0249, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.68938, "top5_acc": 0.97625, "loss_cls": 1.26762, "loss": 1.26762, "time": 0.29919} +{"mode": "val", "epoch": 6, "iter": 533, "lr": 0.0249, "top1_acc": 0.65063, "top5_acc": 0.95893, "mean_class_accuracy": 0.51823} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0249, "memory": 4082, "data_time": 0.2029, "top1_acc": 0.70688, "top5_acc": 0.97188, "loss_cls": 1.25018, "loss": 1.25018, "time": 0.61597} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0249, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.69625, "top5_acc": 0.97688, "loss_cls": 1.20997, "loss": 1.20997, "time": 0.41453} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.02489, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.715, "top5_acc": 0.98375, "loss_cls": 1.15854, "loss": 1.15854, "time": 0.41332} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.02489, "memory": 4082, "data_time": 0.00053, "top1_acc": 0.69375, "top5_acc": 0.97812, "loss_cls": 1.20472, "loss": 1.20472, "time": 0.41311} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.02489, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.72, "top5_acc": 0.97875, "loss_cls": 1.17649, "loss": 1.17649, "time": 0.41769} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.02489, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.67375, "top5_acc": 0.97188, "loss_cls": 1.26738, "loss": 1.26738, "time": 0.41481} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.02488, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.70688, "top5_acc": 0.97812, "loss_cls": 1.18911, "loss": 1.18911, "time": 0.41367} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.02488, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.70438, "top5_acc": 0.9725, "loss_cls": 1.22469, "loss": 1.22469, "time": 0.41535} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.02488, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.71062, "top5_acc": 0.97312, "loss_cls": 1.20438, "loss": 1.20438, "time": 0.41376} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.02487, "memory": 4082, "data_time": 0.00076, "top1_acc": 0.71125, "top5_acc": 0.98312, "loss_cls": 1.16639, "loss": 1.16639, "time": 0.41557} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.02487, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.70562, "top5_acc": 0.9775, "loss_cls": 1.2015, "loss": 1.2015, "time": 0.37837} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.02487, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.70375, "top5_acc": 0.97312, "loss_cls": 1.23169, "loss": 1.23169, "time": 0.30797} +{"mode": "val", "epoch": 7, "iter": 533, "lr": 0.02487, "top1_acc": 0.689, "top5_acc": 0.96996, "mean_class_accuracy": 0.51786} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.02486, "memory": 4082, "data_time": 0.20494, "top1_acc": 0.725, "top5_acc": 0.97938, "loss_cls": 1.10568, "loss": 1.10568, "time": 0.61899} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.02486, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.73312, "top5_acc": 0.98062, "loss_cls": 1.11072, "loss": 1.11072, "time": 0.41471} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.02486, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.71875, "top5_acc": 0.975, "loss_cls": 1.18261, "loss": 1.18261, "time": 0.41531} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.02485, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.71625, "top5_acc": 0.97625, "loss_cls": 1.20373, "loss": 1.20373, "time": 0.41364} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.02485, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.73375, "top5_acc": 0.9775, "loss_cls": 1.12028, "loss": 1.12028, "time": 0.41636} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.02485, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.73312, "top5_acc": 0.98438, "loss_cls": 1.09744, "loss": 1.09744, "time": 0.41667} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.02484, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.71188, "top5_acc": 0.98125, "loss_cls": 1.13595, "loss": 1.13595, "time": 0.4153} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.02484, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.715, "top5_acc": 0.97875, "loss_cls": 1.12472, "loss": 1.12472, "time": 0.41647} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.02484, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.7325, "top5_acc": 0.97875, "loss_cls": 1.11828, "loss": 1.11828, "time": 0.41643} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.02483, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.71938, "top5_acc": 0.9775, "loss_cls": 1.15966, "loss": 1.15966, "time": 0.41804} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.02483, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.72812, "top5_acc": 0.97438, "loss_cls": 1.12818, "loss": 1.12818, "time": 0.38369} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.02483, "memory": 4082, "data_time": 0.00059, "top1_acc": 0.71625, "top5_acc": 0.97688, "loss_cls": 1.16429, "loss": 1.16429, "time": 0.31851} +{"mode": "val", "epoch": 8, "iter": 533, "lr": 0.02482, "top1_acc": 0.65403, "top5_acc": 0.95681, "mean_class_accuracy": 0.5454} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.02482, "memory": 4082, "data_time": 0.20047, "top1_acc": 0.75, "top5_acc": 0.98562, "loss_cls": 1.05951, "loss": 1.05951, "time": 0.61458} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.02482, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.735, "top5_acc": 0.98438, "loss_cls": 1.06772, "loss": 1.06772, "time": 0.4145} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.02481, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.73, "top5_acc": 0.98875, "loss_cls": 1.08611, "loss": 1.08611, "time": 0.41493} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.02481, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.72875, "top5_acc": 0.97688, "loss_cls": 1.10411, "loss": 1.10411, "time": 0.4149} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.02481, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.73875, "top5_acc": 0.98312, "loss_cls": 1.0833, "loss": 1.0833, "time": 0.41534} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.0248, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.73188, "top5_acc": 0.98062, "loss_cls": 1.08277, "loss": 1.08277, "time": 0.41591} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.0248, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.72688, "top5_acc": 0.97938, "loss_cls": 1.10822, "loss": 1.10822, "time": 0.4136} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.0248, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.75062, "top5_acc": 0.98688, "loss_cls": 1.02615, "loss": 1.02615, "time": 0.41334} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.02479, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.75312, "top5_acc": 0.97875, "loss_cls": 1.0407, "loss": 1.0407, "time": 0.41377} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.02479, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.73688, "top5_acc": 0.97875, "loss_cls": 1.10417, "loss": 1.10417, "time": 0.42004} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.02479, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.74125, "top5_acc": 0.97875, "loss_cls": 1.09804, "loss": 1.09804, "time": 0.36943} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.02478, "memory": 4082, "data_time": 0.00068, "top1_acc": 0.7275, "top5_acc": 0.98062, "loss_cls": 1.11164, "loss": 1.11164, "time": 0.307} +{"mode": "val", "epoch": 9, "iter": 533, "lr": 0.02478, "top1_acc": 0.6633, "top5_acc": 0.95646, "mean_class_accuracy": 0.57376} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.02477, "memory": 4082, "data_time": 0.20327, "top1_acc": 0.76, "top5_acc": 0.98375, "loss_cls": 1.01799, "loss": 1.01799, "time": 0.61778} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.02477, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.77, "top5_acc": 0.98125, "loss_cls": 1.02346, "loss": 1.02346, "time": 0.41601} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.02477, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.76312, "top5_acc": 0.98875, "loss_cls": 0.98466, "loss": 0.98466, "time": 0.41425} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.02476, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.7525, "top5_acc": 0.97938, "loss_cls": 1.05881, "loss": 1.05881, "time": 0.41438} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.02476, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.74375, "top5_acc": 0.97812, "loss_cls": 1.08029, "loss": 1.08029, "time": 0.4139} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.02476, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.755, "top5_acc": 0.98375, "loss_cls": 1.02754, "loss": 1.02754, "time": 0.41503} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.02475, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.76312, "top5_acc": 0.98562, "loss_cls": 0.98569, "loss": 0.98569, "time": 0.41425} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.02475, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.75438, "top5_acc": 0.98688, "loss_cls": 1.08144, "loss": 1.08144, "time": 0.41506} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.02474, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.75625, "top5_acc": 0.9825, "loss_cls": 1.00145, "loss": 1.00145, "time": 0.41543} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.02474, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.7425, "top5_acc": 0.98375, "loss_cls": 1.03861, "loss": 1.03861, "time": 0.41555} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.02473, "memory": 4082, "data_time": 0.00061, "top1_acc": 0.725, "top5_acc": 0.97188, "loss_cls": 1.13401, "loss": 1.13401, "time": 0.36868} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.02473, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.73625, "top5_acc": 0.98438, "loss_cls": 1.0696, "loss": 1.0696, "time": 0.30548} +{"mode": "val", "epoch": 10, "iter": 533, "lr": 0.02473, "top1_acc": 0.71846, "top5_acc": 0.97348, "mean_class_accuracy": 0.60152} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.02472, "memory": 4082, "data_time": 0.1999, "top1_acc": 0.78562, "top5_acc": 0.98375, "loss_cls": 0.92377, "loss": 0.92377, "time": 0.61283} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.02472, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.76438, "top5_acc": 0.98312, "loss_cls": 0.93672, "loss": 0.93672, "time": 0.41613} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.02471, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.75562, "top5_acc": 0.98688, "loss_cls": 1.0295, "loss": 1.0295, "time": 0.4148} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.02471, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.78438, "top5_acc": 0.98688, "loss_cls": 0.94848, "loss": 0.94848, "time": 0.41575} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.02471, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.75812, "top5_acc": 0.9825, "loss_cls": 1.01129, "loss": 1.01129, "time": 0.41782} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.0247, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.76938, "top5_acc": 0.98812, "loss_cls": 0.97208, "loss": 0.97208, "time": 0.43252} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.0247, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.76375, "top5_acc": 0.98688, "loss_cls": 0.98415, "loss": 0.98415, "time": 0.43692} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.02469, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.775, "top5_acc": 0.98438, "loss_cls": 0.9516, "loss": 0.9516, "time": 0.41683} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.02469, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.78188, "top5_acc": 0.98312, "loss_cls": 0.96987, "loss": 0.96987, "time": 0.41657} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.02468, "memory": 4082, "data_time": 0.00051, "top1_acc": 0.78312, "top5_acc": 0.985, "loss_cls": 0.97454, "loss": 0.97454, "time": 0.41429} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.02468, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.78125, "top5_acc": 0.98688, "loss_cls": 0.94094, "loss": 0.94094, "time": 0.37064} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.02467, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.7525, "top5_acc": 0.98375, "loss_cls": 1.03042, "loss": 1.03042, "time": 0.31442} +{"mode": "val", "epoch": 11, "iter": 533, "lr": 0.02467, "top1_acc": 0.75308, "top5_acc": 0.97981, "mean_class_accuracy": 0.65022} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.02467, "memory": 4082, "data_time": 0.19426, "top1_acc": 0.77312, "top5_acc": 0.99, "loss_cls": 0.94, "loss": 0.94, "time": 0.628} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.02466, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.77875, "top5_acc": 0.98562, "loss_cls": 0.98301, "loss": 0.98301, "time": 0.41458} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.02466, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.77312, "top5_acc": 0.9925, "loss_cls": 0.92851, "loss": 0.92851, "time": 0.41278} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.02465, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.78, "top5_acc": 0.985, "loss_cls": 0.96485, "loss": 0.96485, "time": 0.41434} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.02465, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.76312, "top5_acc": 0.98312, "loss_cls": 0.97922, "loss": 0.97922, "time": 0.41355} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.02464, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.74375, "top5_acc": 0.98438, "loss_cls": 1.00353, "loss": 1.00353, "time": 0.41454} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.02464, "memory": 4082, "data_time": 0.00054, "top1_acc": 0.78, "top5_acc": 0.98625, "loss_cls": 0.89155, "loss": 0.89155, "time": 0.41711} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.02463, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.77375, "top5_acc": 0.98375, "loss_cls": 1.00506, "loss": 1.00506, "time": 0.4166} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.02463, "memory": 4082, "data_time": 0.00072, "top1_acc": 0.78625, "top5_acc": 0.98812, "loss_cls": 0.90711, "loss": 0.90711, "time": 0.41478} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.02462, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7575, "top5_acc": 0.985, "loss_cls": 1.01892, "loss": 1.01892, "time": 0.41463} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.02462, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.7725, "top5_acc": 0.9825, "loss_cls": 0.97885, "loss": 0.97885, "time": 0.37303} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.02461, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78938, "top5_acc": 0.98188, "loss_cls": 0.95013, "loss": 0.95013, "time": 0.31942} +{"mode": "val", "epoch": 12, "iter": 533, "lr": 0.02461, "top1_acc": 0.70684, "top5_acc": 0.97489, "mean_class_accuracy": 0.60418} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.0246, "memory": 4082, "data_time": 0.19633, "top1_acc": 0.78688, "top5_acc": 0.99375, "loss_cls": 0.90659, "loss": 0.90659, "time": 0.61053} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.0246, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.7875, "top5_acc": 0.99062, "loss_cls": 0.93989, "loss": 0.93989, "time": 0.41585} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.02459, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.775, "top5_acc": 0.985, "loss_cls": 0.92116, "loss": 0.92116, "time": 0.41567} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.02459, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.76812, "top5_acc": 0.98688, "loss_cls": 0.96729, "loss": 0.96729, "time": 0.41518} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.02458, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78812, "top5_acc": 0.98438, "loss_cls": 0.92203, "loss": 0.92203, "time": 0.41459} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.02458, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.78875, "top5_acc": 0.98875, "loss_cls": 0.92257, "loss": 0.92257, "time": 0.42088} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.02457, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79125, "top5_acc": 0.99125, "loss_cls": 0.90082, "loss": 0.90082, "time": 0.41482} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.02457, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.78312, "top5_acc": 0.98, "loss_cls": 0.96536, "loss": 0.96536, "time": 0.41509} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.02456, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.7825, "top5_acc": 0.98, "loss_cls": 0.96855, "loss": 0.96855, "time": 0.41407} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.02455, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.78938, "top5_acc": 0.98438, "loss_cls": 0.92625, "loss": 0.92625, "time": 0.41547} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.02455, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.78188, "top5_acc": 0.98625, "loss_cls": 0.94002, "loss": 0.94002, "time": 0.36418} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.02454, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.77688, "top5_acc": 0.98375, "loss_cls": 0.96228, "loss": 0.96228, "time": 0.31487} +{"mode": "val", "epoch": 13, "iter": 533, "lr": 0.02454, "top1_acc": 0.74322, "top5_acc": 0.97571, "mean_class_accuracy": 0.64597} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.02453, "memory": 4082, "data_time": 0.198, "top1_acc": 0.79375, "top5_acc": 0.99062, "loss_cls": 0.86643, "loss": 0.86643, "time": 0.59552} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.02453, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.78125, "top5_acc": 0.99188, "loss_cls": 0.90316, "loss": 0.90316, "time": 0.39913} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.02452, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.7975, "top5_acc": 0.99062, "loss_cls": 0.86431, "loss": 0.86431, "time": 0.39315} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.02452, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.78562, "top5_acc": 0.98562, "loss_cls": 0.94614, "loss": 0.94614, "time": 0.39893} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.02451, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.79438, "top5_acc": 0.98812, "loss_cls": 0.88685, "loss": 0.88685, "time": 0.39531} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.02451, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.78562, "top5_acc": 0.99, "loss_cls": 0.89627, "loss": 0.89627, "time": 0.40757} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.0245, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.78812, "top5_acc": 0.985, "loss_cls": 0.89463, "loss": 0.89463, "time": 0.40872} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.02449, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.79938, "top5_acc": 0.985, "loss_cls": 0.88741, "loss": 0.88741, "time": 0.40108} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.02449, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.78375, "top5_acc": 0.98562, "loss_cls": 0.92104, "loss": 0.92104, "time": 0.39697} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.02448, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.79, "top5_acc": 0.9875, "loss_cls": 0.92737, "loss": 0.92737, "time": 0.39214} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.02448, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.81062, "top5_acc": 0.985, "loss_cls": 0.8691, "loss": 0.8691, "time": 0.40162} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.02447, "memory": 4082, "data_time": 0.00054, "top1_acc": 0.78375, "top5_acc": 0.985, "loss_cls": 0.91523, "loss": 0.91523, "time": 0.39482} +{"mode": "val", "epoch": 14, "iter": 533, "lr": 0.02447, "top1_acc": 0.7586, "top5_acc": 0.97582, "mean_class_accuracy": 0.65653} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.02446, "memory": 4082, "data_time": 0.18899, "top1_acc": 0.80188, "top5_acc": 0.99062, "loss_cls": 0.87629, "loss": 0.87629, "time": 0.41986} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.02445, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.795, "top5_acc": 0.98812, "loss_cls": 0.92657, "loss": 0.92657, "time": 0.32885} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.02445, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.80438, "top5_acc": 0.98688, "loss_cls": 0.90036, "loss": 0.90036, "time": 0.38591} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.02444, "memory": 4082, "data_time": 0.00053, "top1_acc": 0.79625, "top5_acc": 0.98625, "loss_cls": 0.86926, "loss": 0.86926, "time": 0.38381} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.02444, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80188, "top5_acc": 0.98812, "loss_cls": 0.88537, "loss": 0.88537, "time": 0.37576} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.02443, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.80375, "top5_acc": 0.9875, "loss_cls": 0.86339, "loss": 0.86339, "time": 0.38847} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.02442, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78938, "top5_acc": 0.98625, "loss_cls": 0.92976, "loss": 0.92976, "time": 0.39279} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.02442, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.79938, "top5_acc": 0.98812, "loss_cls": 0.88264, "loss": 0.88264, "time": 0.39716} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.02441, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.795, "top5_acc": 0.98375, "loss_cls": 0.89679, "loss": 0.89679, "time": 0.38892} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.02441, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8, "top5_acc": 0.98875, "loss_cls": 0.86115, "loss": 0.86115, "time": 0.38473} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.0244, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.8, "top5_acc": 0.98938, "loss_cls": 0.84979, "loss": 0.84979, "time": 0.3941} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.02439, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.815, "top5_acc": 0.98812, "loss_cls": 0.83496, "loss": 0.83496, "time": 0.38149} +{"mode": "val", "epoch": 15, "iter": 533, "lr": 0.02439, "top1_acc": 0.75519, "top5_acc": 0.98204, "mean_class_accuracy": 0.65843} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.02438, "memory": 4082, "data_time": 0.19943, "top1_acc": 0.81375, "top5_acc": 0.99, "loss_cls": 0.80379, "loss": 0.80379, "time": 0.59389} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.02438, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.80812, "top5_acc": 0.98875, "loss_cls": 0.85104, "loss": 0.85104, "time": 0.38686} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.02437, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.79125, "top5_acc": 0.98375, "loss_cls": 0.92785, "loss": 0.92785, "time": 0.28591} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.02436, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82312, "top5_acc": 0.98875, "loss_cls": 0.78203, "loss": 0.78203, "time": 0.39229} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.02436, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.785, "top5_acc": 0.98562, "loss_cls": 0.91496, "loss": 0.91496, "time": 0.31917} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.02435, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79688, "top5_acc": 0.99, "loss_cls": 0.90765, "loss": 0.90765, "time": 0.25201} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.02434, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.79688, "top5_acc": 0.99, "loss_cls": 0.87269, "loss": 0.87269, "time": 0.38546} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.02434, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81188, "top5_acc": 0.99125, "loss_cls": 0.79544, "loss": 0.79544, "time": 0.38214} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.02433, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80312, "top5_acc": 0.98812, "loss_cls": 0.84167, "loss": 0.84167, "time": 0.38731} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.02432, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.8025, "top5_acc": 0.99312, "loss_cls": 0.83934, "loss": 0.83934, "time": 0.39021} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.02432, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.80562, "top5_acc": 0.9825, "loss_cls": 0.85915, "loss": 0.85915, "time": 0.39209} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.02431, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81438, "top5_acc": 0.99312, "loss_cls": 0.79844, "loss": 0.79844, "time": 0.38606} +{"mode": "val", "epoch": 16, "iter": 533, "lr": 0.0243, "top1_acc": 0.77315, "top5_acc": 0.97782, "mean_class_accuracy": 0.70085} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.0243, "memory": 4082, "data_time": 0.19474, "top1_acc": 0.80375, "top5_acc": 0.99312, "loss_cls": 0.84982, "loss": 0.84982, "time": 0.58223} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.02429, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81812, "top5_acc": 0.99, "loss_cls": 0.77568, "loss": 0.77568, "time": 0.38038} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.02428, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81812, "top5_acc": 0.99, "loss_cls": 0.78074, "loss": 0.78074, "time": 0.39142} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.02428, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.82938, "top5_acc": 0.98938, "loss_cls": 0.76618, "loss": 0.76618, "time": 0.38125} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.02427, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.8, "top5_acc": 0.9875, "loss_cls": 0.83469, "loss": 0.83469, "time": 0.38922} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.02426, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80625, "top5_acc": 0.99125, "loss_cls": 0.87826, "loss": 0.87826, "time": 0.378} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.02426, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.81938, "top5_acc": 0.99062, "loss_cls": 0.8202, "loss": 0.8202, "time": 0.38177} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.02425, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.83188, "top5_acc": 0.99438, "loss_cls": 0.75517, "loss": 0.75517, "time": 0.25241} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.02424, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80688, "top5_acc": 0.99, "loss_cls": 0.83062, "loss": 0.83062, "time": 0.45349} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.02424, "memory": 4082, "data_time": 0.00061, "top1_acc": 0.825, "top5_acc": 0.99125, "loss_cls": 0.80041, "loss": 0.80041, "time": 0.23614} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.02423, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.80938, "top5_acc": 0.99062, "loss_cls": 0.81793, "loss": 0.81793, "time": 0.33418} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.02422, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.78875, "top5_acc": 0.98812, "loss_cls": 0.90593, "loss": 0.90593, "time": 0.38346} +{"mode": "val", "epoch": 17, "iter": 533, "lr": 0.02422, "top1_acc": 0.77784, "top5_acc": 0.9838, "mean_class_accuracy": 0.67603} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.02421, "memory": 4082, "data_time": 0.19562, "top1_acc": 0.81875, "top5_acc": 0.98438, "loss_cls": 0.8274, "loss": 0.8274, "time": 0.58071} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.0242, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.83, "top5_acc": 0.99312, "loss_cls": 0.75433, "loss": 0.75433, "time": 0.38706} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.02419, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.79438, "top5_acc": 0.98938, "loss_cls": 0.83777, "loss": 0.83777, "time": 0.38648} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.02419, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82562, "top5_acc": 0.98938, "loss_cls": 0.78951, "loss": 0.78951, "time": 0.38337} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.02418, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.81, "top5_acc": 0.99125, "loss_cls": 0.8133, "loss": 0.8133, "time": 0.3914} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.02417, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82188, "top5_acc": 0.98938, "loss_cls": 0.79726, "loss": 0.79726, "time": 0.39473} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.02417, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82375, "top5_acc": 0.99, "loss_cls": 0.78325, "loss": 0.78325, "time": 0.38708} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.02416, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81812, "top5_acc": 0.99062, "loss_cls": 0.81953, "loss": 0.81953, "time": 0.38177} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.02415, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8025, "top5_acc": 0.98812, "loss_cls": 0.86337, "loss": 0.86337, "time": 0.3828} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.02414, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.8225, "top5_acc": 0.99062, "loss_cls": 0.7859, "loss": 0.7859, "time": 0.38946} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.02414, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.84188, "top5_acc": 0.9925, "loss_cls": 0.738, "loss": 0.738, "time": 0.39812} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.02413, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.79938, "top5_acc": 0.99, "loss_cls": 0.86199, "loss": 0.86199, "time": 0.27562} +{"mode": "val", "epoch": 18, "iter": 533, "lr": 0.02412, "top1_acc": 0.76446, "top5_acc": 0.98404, "mean_class_accuracy": 0.69102} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.02411, "memory": 4082, "data_time": 0.19585, "top1_acc": 0.79625, "top5_acc": 0.99188, "loss_cls": 0.84681, "loss": 0.84681, "time": 0.59268} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.02411, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.83062, "top5_acc": 0.99062, "loss_cls": 0.79838, "loss": 0.79838, "time": 0.38364} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.0241, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.82312, "top5_acc": 0.99062, "loss_cls": 0.79816, "loss": 0.79816, "time": 0.38069} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.02409, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83062, "top5_acc": 0.98938, "loss_cls": 0.76043, "loss": 0.76043, "time": 0.38046} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.02408, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.81188, "top5_acc": 0.99312, "loss_cls": 0.79048, "loss": 0.79048, "time": 0.38673} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.02408, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8275, "top5_acc": 0.99312, "loss_cls": 0.77887, "loss": 0.77887, "time": 0.38965} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.02407, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.81062, "top5_acc": 0.99188, "loss_cls": 0.82009, "loss": 0.82009, "time": 0.38673} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.02406, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.8175, "top5_acc": 0.9925, "loss_cls": 0.78187, "loss": 0.78187, "time": 0.38876} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.02405, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.81438, "top5_acc": 0.99312, "loss_cls": 0.78939, "loss": 0.78939, "time": 0.38559} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.02405, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.81938, "top5_acc": 0.99, "loss_cls": 0.79554, "loss": 0.79554, "time": 0.38744} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.02404, "memory": 4082, "data_time": 0.0008, "top1_acc": 0.82312, "top5_acc": 0.98812, "loss_cls": 0.8236, "loss": 0.8236, "time": 0.38569} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.02403, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.83875, "top5_acc": 0.99375, "loss_cls": 0.73828, "loss": 0.73828, "time": 0.38296} +{"mode": "val", "epoch": 19, "iter": 533, "lr": 0.02402, "top1_acc": 0.75109, "top5_acc": 0.97547, "mean_class_accuracy": 0.65035} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.02402, "memory": 4082, "data_time": 0.19065, "top1_acc": 0.82812, "top5_acc": 0.9925, "loss_cls": 0.75152, "loss": 0.75152, "time": 0.45359} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.02401, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.83188, "top5_acc": 0.99375, "loss_cls": 0.7437, "loss": 0.7437, "time": 0.45021} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.024, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.81688, "top5_acc": 0.99125, "loss_cls": 0.79464, "loss": 0.79464, "time": 0.22507} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.02399, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.8225, "top5_acc": 0.9925, "loss_cls": 0.797, "loss": 0.797, "time": 0.33336} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.02398, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83188, "top5_acc": 0.99, "loss_cls": 0.76132, "loss": 0.76132, "time": 0.38238} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.02398, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81625, "top5_acc": 0.99188, "loss_cls": 0.81637, "loss": 0.81637, "time": 0.37397} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.02397, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82375, "top5_acc": 0.98938, "loss_cls": 0.81094, "loss": 0.81094, "time": 0.38797} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.02396, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.80688, "top5_acc": 0.99125, "loss_cls": 0.79556, "loss": 0.79556, "time": 0.38052} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.02395, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.825, "top5_acc": 0.99, "loss_cls": 0.79116, "loss": 0.79116, "time": 0.38093} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.02394, "memory": 4082, "data_time": 0.00051, "top1_acc": 0.82188, "top5_acc": 0.9875, "loss_cls": 0.81046, "loss": 0.81046, "time": 0.38827} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.02393, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.81312, "top5_acc": 0.99188, "loss_cls": 0.79628, "loss": 0.79628, "time": 0.38052} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.02393, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.82, "top5_acc": 0.98812, "loss_cls": 0.7832, "loss": 0.7832, "time": 0.38187} +{"mode": "val", "epoch": 20, "iter": 533, "lr": 0.02392, "top1_acc": 0.80131, "top5_acc": 0.98779, "mean_class_accuracy": 0.71199} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.02391, "memory": 4082, "data_time": 0.19565, "top1_acc": 0.83438, "top5_acc": 0.99438, "loss_cls": 0.72399, "loss": 0.72399, "time": 0.58272} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.0239, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.835, "top5_acc": 0.9925, "loss_cls": 0.73894, "loss": 0.73894, "time": 0.38125} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.02389, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.84938, "top5_acc": 0.99188, "loss_cls": 0.70574, "loss": 0.70574, "time": 0.38027} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.02389, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.79625, "top5_acc": 0.98938, "loss_cls": 0.8048, "loss": 0.8048, "time": 0.37608} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.02388, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83188, "top5_acc": 0.99312, "loss_cls": 0.75656, "loss": 0.75656, "time": 0.36273} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.02387, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.84062, "top5_acc": 0.995, "loss_cls": 0.70315, "loss": 0.70315, "time": 0.29103} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.02386, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.815, "top5_acc": 0.99125, "loss_cls": 0.81158, "loss": 0.81158, "time": 0.42223} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.02385, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83875, "top5_acc": 0.99062, "loss_cls": 0.76068, "loss": 0.76068, "time": 0.22812} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.02384, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82688, "top5_acc": 0.98938, "loss_cls": 0.78424, "loss": 0.78424, "time": 0.34272} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.02383, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.80938, "top5_acc": 0.98562, "loss_cls": 0.82964, "loss": 0.82964, "time": 0.37983} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.02383, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.82438, "top5_acc": 0.99062, "loss_cls": 0.76795, "loss": 0.76795, "time": 0.38266} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.02382, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.83188, "top5_acc": 0.99, "loss_cls": 0.75264, "loss": 0.75264, "time": 0.38098} +{"mode": "val", "epoch": 21, "iter": 533, "lr": 0.02381, "top1_acc": 0.77151, "top5_acc": 0.98052, "mean_class_accuracy": 0.65704} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.0238, "memory": 4082, "data_time": 0.19647, "top1_acc": 0.8325, "top5_acc": 0.99438, "loss_cls": 0.73424, "loss": 0.73424, "time": 0.58944} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.02379, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83438, "top5_acc": 0.99062, "loss_cls": 0.72976, "loss": 0.72976, "time": 0.38726} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.02378, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.84875, "top5_acc": 0.99188, "loss_cls": 0.72497, "loss": 0.72497, "time": 0.38963} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.02378, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86, "top5_acc": 0.99438, "loss_cls": 0.68084, "loss": 0.68084, "time": 0.38365} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.02377, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81438, "top5_acc": 0.9925, "loss_cls": 0.77664, "loss": 0.77664, "time": 0.38043} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.02376, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83, "top5_acc": 0.98562, "loss_cls": 0.78685, "loss": 0.78685, "time": 0.38322} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.02375, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.82875, "top5_acc": 0.99312, "loss_cls": 0.77714, "loss": 0.77714, "time": 0.38818} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.02374, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82625, "top5_acc": 0.99188, "loss_cls": 0.76404, "loss": 0.76404, "time": 0.38779} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.02373, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83625, "top5_acc": 0.99312, "loss_cls": 0.72993, "loss": 0.72993, "time": 0.39183} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.02372, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.84438, "top5_acc": 0.99125, "loss_cls": 0.70411, "loss": 0.70411, "time": 0.29001} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.02371, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.80625, "top5_acc": 0.99188, "loss_cls": 0.82258, "loss": 0.82258, "time": 0.38801} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0237, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.82562, "top5_acc": 0.98875, "loss_cls": 0.77806, "loss": 0.77806, "time": 0.32455} +{"mode": "val", "epoch": 22, "iter": 533, "lr": 0.0237, "top1_acc": 0.76928, "top5_acc": 0.97864, "mean_class_accuracy": 0.70439} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.02369, "memory": 4082, "data_time": 0.19548, "top1_acc": 0.845, "top5_acc": 0.99312, "loss_cls": 0.70296, "loss": 0.70296, "time": 0.58268} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.02368, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.8425, "top5_acc": 0.9925, "loss_cls": 0.68031, "loss": 0.68031, "time": 0.38146} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.02367, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.83375, "top5_acc": 0.99375, "loss_cls": 0.73719, "loss": 0.73719, "time": 0.38524} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.02366, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.845, "top5_acc": 0.995, "loss_cls": 0.73318, "loss": 0.73318, "time": 0.37202} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.02365, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82312, "top5_acc": 0.99062, "loss_cls": 0.78407, "loss": 0.78407, "time": 0.38211} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.02364, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83562, "top5_acc": 0.99438, "loss_cls": 0.74454, "loss": 0.74454, "time": 0.38193} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.02363, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.83688, "top5_acc": 0.98688, "loss_cls": 0.78202, "loss": 0.78202, "time": 0.38414} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.02362, "memory": 4082, "data_time": 0.00065, "top1_acc": 0.8075, "top5_acc": 0.98938, "loss_cls": 0.81071, "loss": 0.81071, "time": 0.38321} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.02361, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.83625, "top5_acc": 0.9925, "loss_cls": 0.74415, "loss": 0.74415, "time": 0.39027} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.0236, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83312, "top5_acc": 0.99188, "loss_cls": 0.74831, "loss": 0.74831, "time": 0.37813} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.02359, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.825, "top5_acc": 0.98562, "loss_cls": 0.77508, "loss": 0.77508, "time": 0.38371} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.02359, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81688, "top5_acc": 0.98938, "loss_cls": 0.77772, "loss": 0.77772, "time": 0.38197} +{"mode": "val", "epoch": 23, "iter": 533, "lr": 0.02358, "top1_acc": 0.80636, "top5_acc": 0.98873, "mean_class_accuracy": 0.72651} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.02357, "memory": 4082, "data_time": 0.20059, "top1_acc": 0.86062, "top5_acc": 0.99312, "loss_cls": 0.6464, "loss": 0.6464, "time": 0.48423} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.02356, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84375, "top5_acc": 0.99375, "loss_cls": 0.69274, "loss": 0.69274, "time": 0.28635} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.02355, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84062, "top5_acc": 0.98938, "loss_cls": 0.72611, "loss": 0.72611, "time": 0.38881} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.02354, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.8325, "top5_acc": 0.995, "loss_cls": 0.70767, "loss": 0.70767, "time": 0.37984} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.02353, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.85188, "top5_acc": 0.99312, "loss_cls": 0.68814, "loss": 0.68814, "time": 0.3887} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.02352, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82562, "top5_acc": 0.99312, "loss_cls": 0.74708, "loss": 0.74708, "time": 0.38276} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.02351, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.82625, "top5_acc": 0.99438, "loss_cls": 0.73127, "loss": 0.73127, "time": 0.39696} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.0235, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.84062, "top5_acc": 0.99062, "loss_cls": 0.72478, "loss": 0.72478, "time": 0.38763} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.02349, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.81375, "top5_acc": 0.99, "loss_cls": 0.8056, "loss": 0.8056, "time": 0.38183} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.02348, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82875, "top5_acc": 0.99125, "loss_cls": 0.72822, "loss": 0.72822, "time": 0.37362} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.02347, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.8475, "top5_acc": 0.99375, "loss_cls": 0.71626, "loss": 0.71626, "time": 0.37871} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.02346, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82938, "top5_acc": 0.99188, "loss_cls": 0.73904, "loss": 0.73904, "time": 0.38414} +{"mode": "val", "epoch": 24, "iter": 533, "lr": 0.02345, "top1_acc": 0.77174, "top5_acc": 0.97993, "mean_class_accuracy": 0.71816} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.02344, "memory": 4082, "data_time": 0.19802, "top1_acc": 0.84438, "top5_acc": 0.99375, "loss_cls": 0.70712, "loss": 0.70712, "time": 0.58561} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.02343, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83312, "top5_acc": 0.99625, "loss_cls": 0.7075, "loss": 0.7075, "time": 0.38447} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.02342, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83125, "top5_acc": 0.9925, "loss_cls": 0.73187, "loss": 0.73187, "time": 0.37286} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.02341, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83938, "top5_acc": 0.99625, "loss_cls": 0.69515, "loss": 0.69515, "time": 0.27276} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.0234, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.84875, "top5_acc": 0.99312, "loss_cls": 0.67626, "loss": 0.67626, "time": 0.43851} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.02339, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83812, "top5_acc": 0.99062, "loss_cls": 0.73924, "loss": 0.73924, "time": 0.22372} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.02338, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82625, "top5_acc": 0.99188, "loss_cls": 0.76529, "loss": 0.76529, "time": 0.3381} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.02337, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.86375, "top5_acc": 0.99562, "loss_cls": 0.63362, "loss": 0.63362, "time": 0.38518} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.02336, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82688, "top5_acc": 0.99, "loss_cls": 0.77212, "loss": 0.77212, "time": 0.3815} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.02335, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.82812, "top5_acc": 0.995, "loss_cls": 0.72802, "loss": 0.72802, "time": 0.38659} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.02334, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.83688, "top5_acc": 0.99312, "loss_cls": 0.72491, "loss": 0.72491, "time": 0.38178} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.02333, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83562, "top5_acc": 0.98688, "loss_cls": 0.76306, "loss": 0.76306, "time": 0.38797} +{"mode": "val", "epoch": 25, "iter": 533, "lr": 0.02333, "top1_acc": 0.80624, "top5_acc": 0.98768, "mean_class_accuracy": 0.72629} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.02332, "memory": 4082, "data_time": 0.18955, "top1_acc": 0.85562, "top5_acc": 0.99625, "loss_cls": 0.63531, "loss": 0.63531, "time": 0.57607} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.0233, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.8325, "top5_acc": 0.99312, "loss_cls": 0.72117, "loss": 0.72117, "time": 0.38384} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.02329, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.8325, "top5_acc": 0.99188, "loss_cls": 0.71381, "loss": 0.71381, "time": 0.3847} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.02328, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85688, "top5_acc": 0.9925, "loss_cls": 0.68728, "loss": 0.68728, "time": 0.38611} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.02327, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83688, "top5_acc": 0.99312, "loss_cls": 0.73447, "loss": 0.73447, "time": 0.38981} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.02326, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.845, "top5_acc": 0.99438, "loss_cls": 0.65917, "loss": 0.65917, "time": 0.38726} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.02325, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.86125, "top5_acc": 0.99188, "loss_cls": 0.6807, "loss": 0.6807, "time": 0.38161} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.02324, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83688, "top5_acc": 0.9925, "loss_cls": 0.73293, "loss": 0.73293, "time": 0.31473} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.02323, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82938, "top5_acc": 0.99, "loss_cls": 0.75536, "loss": 0.75536, "time": 0.36537} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.02322, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.85, "top5_acc": 0.99125, "loss_cls": 0.70065, "loss": 0.70065, "time": 0.34458} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.02321, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.8325, "top5_acc": 0.99562, "loss_cls": 0.74206, "loss": 0.74206, "time": 0.25464} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.0232, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.84938, "top5_acc": 0.99188, "loss_cls": 0.6783, "loss": 0.6783, "time": 0.38528} +{"mode": "val", "epoch": 26, "iter": 533, "lr": 0.02319, "top1_acc": 0.80401, "top5_acc": 0.98392, "mean_class_accuracy": 0.71193} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.02318, "memory": 4082, "data_time": 0.19315, "top1_acc": 0.83938, "top5_acc": 0.99438, "loss_cls": 0.70324, "loss": 0.70324, "time": 0.58727} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.02317, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85812, "top5_acc": 0.99188, "loss_cls": 0.65282, "loss": 0.65282, "time": 0.38605} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.02316, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.84312, "top5_acc": 0.9925, "loss_cls": 0.71366, "loss": 0.71366, "time": 0.37957} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.02315, "memory": 4082, "data_time": 0.00065, "top1_acc": 0.84375, "top5_acc": 0.99562, "loss_cls": 0.68444, "loss": 0.68444, "time": 0.38746} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.02314, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84812, "top5_acc": 0.98875, "loss_cls": 0.68293, "loss": 0.68293, "time": 0.38516} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.02313, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83375, "top5_acc": 0.99562, "loss_cls": 0.71641, "loss": 0.71641, "time": 0.38484} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.02312, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83875, "top5_acc": 0.99688, "loss_cls": 0.70495, "loss": 0.70495, "time": 0.38479} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.02311, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.85938, "top5_acc": 0.99562, "loss_cls": 0.65612, "loss": 0.65612, "time": 0.38443} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.0231, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85562, "top5_acc": 0.99625, "loss_cls": 0.66125, "loss": 0.66125, "time": 0.39027} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.02308, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.83688, "top5_acc": 0.99438, "loss_cls": 0.71312, "loss": 0.71312, "time": 0.38082} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.02307, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.8575, "top5_acc": 0.99062, "loss_cls": 0.68404, "loss": 0.68404, "time": 0.38209} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.02306, "memory": 4082, "data_time": 0.00059, "top1_acc": 0.835, "top5_acc": 0.98938, "loss_cls": 0.75194, "loss": 0.75194, "time": 0.3877} +{"mode": "val", "epoch": 27, "iter": 533, "lr": 0.02305, "top1_acc": 0.77761, "top5_acc": 0.97383, "mean_class_accuracy": 0.67152} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.02304, "memory": 4082, "data_time": 0.19114, "top1_acc": 0.86438, "top5_acc": 0.99562, "loss_cls": 0.62472, "loss": 0.62472, "time": 0.57968} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.02303, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.84438, "top5_acc": 0.9975, "loss_cls": 0.65626, "loss": 0.65626, "time": 0.38146} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.02302, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8625, "top5_acc": 0.99312, "loss_cls": 0.62472, "loss": 0.62472, "time": 0.38578} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.02301, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.8525, "top5_acc": 0.98875, "loss_cls": 0.67275, "loss": 0.67275, "time": 0.3775} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.023, "memory": 4082, "data_time": 0.0006, "top1_acc": 0.81938, "top5_acc": 0.99062, "loss_cls": 0.79072, "loss": 0.79072, "time": 0.37902} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.02299, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86375, "top5_acc": 0.99125, "loss_cls": 0.6613, "loss": 0.6613, "time": 0.38353} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.02298, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.835, "top5_acc": 0.99375, "loss_cls": 0.71773, "loss": 0.71773, "time": 0.38368} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.02297, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.83812, "top5_acc": 0.99188, "loss_cls": 0.7407, "loss": 0.7407, "time": 0.38142} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.02295, "memory": 4082, "data_time": 0.00075, "top1_acc": 0.84438, "top5_acc": 0.99438, "loss_cls": 0.71682, "loss": 0.71682, "time": 0.38255} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.02294, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83688, "top5_acc": 0.98875, "loss_cls": 0.75829, "loss": 0.75829, "time": 0.38464} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.02293, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83812, "top5_acc": 0.99562, "loss_cls": 0.70603, "loss": 0.70603, "time": 0.37887} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.02292, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.84625, "top5_acc": 0.98938, "loss_cls": 0.70485, "loss": 0.70485, "time": 0.37886} +{"mode": "val", "epoch": 28, "iter": 533, "lr": 0.02291, "top1_acc": 0.79369, "top5_acc": 0.98439, "mean_class_accuracy": 0.72278} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.0229, "memory": 4082, "data_time": 0.19547, "top1_acc": 0.84812, "top5_acc": 0.99438, "loss_cls": 0.64898, "loss": 0.64898, "time": 0.50005} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.02289, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.85312, "top5_acc": 0.99438, "loss_cls": 0.62913, "loss": 0.62913, "time": 0.37148} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.02288, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.85375, "top5_acc": 0.99625, "loss_cls": 0.66094, "loss": 0.66094, "time": 0.33864} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.02287, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85875, "top5_acc": 0.99375, "loss_cls": 0.63861, "loss": 0.63861, "time": 0.2386} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.02285, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85812, "top5_acc": 0.99312, "loss_cls": 0.62519, "loss": 0.62519, "time": 0.39101} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.02284, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.85875, "top5_acc": 0.9925, "loss_cls": 0.61677, "loss": 0.61677, "time": 0.38368} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.02283, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8425, "top5_acc": 0.99438, "loss_cls": 0.67349, "loss": 0.67349, "time": 0.38123} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.02282, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.85625, "top5_acc": 0.99062, "loss_cls": 0.68727, "loss": 0.68727, "time": 0.37962} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.02281, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85938, "top5_acc": 0.99625, "loss_cls": 0.67463, "loss": 0.67463, "time": 0.37541} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.0228, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.8475, "top5_acc": 0.99188, "loss_cls": 0.67506, "loss": 0.67506, "time": 0.39059} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.02279, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83, "top5_acc": 0.99312, "loss_cls": 0.71628, "loss": 0.71628, "time": 0.38722} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.02277, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85, "top5_acc": 0.99312, "loss_cls": 0.66662, "loss": 0.66662, "time": 0.38065} +{"mode": "val", "epoch": 29, "iter": 533, "lr": 0.02276, "top1_acc": 0.79545, "top5_acc": 0.9851, "mean_class_accuracy": 0.70263} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.02275, "memory": 4082, "data_time": 0.18884, "top1_acc": 0.85062, "top5_acc": 0.99375, "loss_cls": 0.67905, "loss": 0.67905, "time": 0.67517} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.02274, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8675, "top5_acc": 0.99438, "loss_cls": 0.6282, "loss": 0.6282, "time": 0.48047} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.02273, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.86562, "top5_acc": 0.99438, "loss_cls": 0.60353, "loss": 0.60353, "time": 0.48155} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.02272, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.84438, "top5_acc": 0.99375, "loss_cls": 0.69671, "loss": 0.69671, "time": 0.45262} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.02271, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84375, "top5_acc": 0.98812, "loss_cls": 0.73321, "loss": 0.73321, "time": 0.37621} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.02269, "memory": 4082, "data_time": 0.00064, "top1_acc": 0.83375, "top5_acc": 0.99312, "loss_cls": 0.72247, "loss": 0.72247, "time": 0.36271} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.02268, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.84188, "top5_acc": 0.9925, "loss_cls": 0.72851, "loss": 0.72851, "time": 0.37325} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.02267, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86375, "top5_acc": 0.99688, "loss_cls": 0.61576, "loss": 0.61576, "time": 0.48446} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.02266, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85375, "top5_acc": 0.99375, "loss_cls": 0.67826, "loss": 0.67826, "time": 0.4823} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.02265, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85562, "top5_acc": 0.99062, "loss_cls": 0.6608, "loss": 0.6608, "time": 0.48333} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.02263, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.84562, "top5_acc": 0.99375, "loss_cls": 0.69438, "loss": 0.69438, "time": 0.48112} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.02262, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.84188, "top5_acc": 0.99375, "loss_cls": 0.71785, "loss": 0.71785, "time": 0.48351} +{"mode": "val", "epoch": 30, "iter": 533, "lr": 0.02261, "top1_acc": 0.79674, "top5_acc": 0.98615, "mean_class_accuracy": 0.72295} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.0226, "memory": 4083, "data_time": 0.19597, "top1_acc": 0.86062, "top5_acc": 0.99312, "loss_cls": 0.78202, "loss": 0.78202, "time": 0.8638} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.02259, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8575, "top5_acc": 0.99438, "loss_cls": 0.7714, "loss": 0.7714, "time": 0.48959} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.02258, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86, "top5_acc": 0.99562, "loss_cls": 0.79545, "loss": 0.79545, "time": 0.49446} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.02256, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.84125, "top5_acc": 0.99125, "loss_cls": 0.83395, "loss": 0.83395, "time": 0.40115} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.02255, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.8475, "top5_acc": 0.99, "loss_cls": 0.83358, "loss": 0.83358, "time": 0.49823} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.02254, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.86188, "top5_acc": 0.99688, "loss_cls": 0.7817, "loss": 0.7817, "time": 0.24429} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.02253, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85375, "top5_acc": 0.9925, "loss_cls": 0.83095, "loss": 0.83095, "time": 0.42026} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.02252, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8575, "top5_acc": 0.9925, "loss_cls": 0.78774, "loss": 0.78774, "time": 0.48986} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0225, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86438, "top5_acc": 0.99688, "loss_cls": 0.75096, "loss": 0.75096, "time": 0.49157} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.02249, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.84562, "top5_acc": 0.99375, "loss_cls": 0.83983, "loss": 0.83983, "time": 0.49253} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.02248, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.83125, "top5_acc": 0.99125, "loss_cls": 0.87992, "loss": 0.87992, "time": 0.48898} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.02247, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.86125, "top5_acc": 0.99312, "loss_cls": 0.77709, "loss": 0.77709, "time": 0.48973} +{"mode": "val", "epoch": 31, "iter": 533, "lr": 0.02246, "top1_acc": 0.80014, "top5_acc": 0.98533, "mean_class_accuracy": 0.75058} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.02244, "memory": 4083, "data_time": 0.1923, "top1_acc": 0.85688, "top5_acc": 0.99375, "loss_cls": 0.74277, "loss": 0.74277, "time": 0.80163} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.02243, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87438, "top5_acc": 0.995, "loss_cls": 0.6915, "loss": 0.6915, "time": 0.48893} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.02242, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85125, "top5_acc": 0.99438, "loss_cls": 0.74248, "loss": 0.74248, "time": 0.48736} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.02241, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85625, "top5_acc": 0.9925, "loss_cls": 0.74463, "loss": 0.74463, "time": 0.40903} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.02239, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.845, "top5_acc": 0.995, "loss_cls": 0.77381, "loss": 0.77381, "time": 0.49562} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.02238, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.85438, "top5_acc": 0.9925, "loss_cls": 0.74749, "loss": 0.74749, "time": 0.24899} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.02237, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.84625, "top5_acc": 0.99562, "loss_cls": 0.76845, "loss": 0.76845, "time": 0.42319} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.02236, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86438, "top5_acc": 0.99562, "loss_cls": 0.6845, "loss": 0.6845, "time": 0.49147} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.02234, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.85938, "top5_acc": 0.99625, "loss_cls": 0.70157, "loss": 0.70157, "time": 0.49155} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.02233, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85312, "top5_acc": 0.99438, "loss_cls": 0.7463, "loss": 0.7463, "time": 0.49349} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.02232, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85875, "top5_acc": 0.99562, "loss_cls": 0.69995, "loss": 0.69995, "time": 0.4912} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.02231, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.83875, "top5_acc": 0.99438, "loss_cls": 0.74969, "loss": 0.74969, "time": 0.48881} +{"mode": "val", "epoch": 32, "iter": 533, "lr": 0.0223, "top1_acc": 0.82526, "top5_acc": 0.98885, "mean_class_accuracy": 0.76245} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.02228, "memory": 4083, "data_time": 0.19087, "top1_acc": 0.85688, "top5_acc": 0.99438, "loss_cls": 0.68152, "loss": 0.68152, "time": 0.80442} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.02227, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85188, "top5_acc": 0.99438, "loss_cls": 0.72402, "loss": 0.72402, "time": 0.49128} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.02226, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86, "top5_acc": 0.99125, "loss_cls": 0.7351, "loss": 0.7351, "time": 0.4919} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.02225, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.83688, "top5_acc": 0.99188, "loss_cls": 0.776, "loss": 0.776, "time": 0.40706} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.02223, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86, "top5_acc": 0.9925, "loss_cls": 0.69309, "loss": 0.69309, "time": 0.49396} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.02222, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.84938, "top5_acc": 0.99625, "loss_cls": 0.75234, "loss": 0.75234, "time": 0.24726} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.02221, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86625, "top5_acc": 0.99562, "loss_cls": 0.68058, "loss": 0.68058, "time": 0.42302} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.02219, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.84562, "top5_acc": 0.99062, "loss_cls": 0.79732, "loss": 0.79732, "time": 0.48942} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.02218, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.85438, "top5_acc": 0.99625, "loss_cls": 0.71908, "loss": 0.71908, "time": 0.49282} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.02217, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84812, "top5_acc": 0.9925, "loss_cls": 0.71152, "loss": 0.71152, "time": 0.49732} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.02216, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.85312, "top5_acc": 0.99125, "loss_cls": 0.72676, "loss": 0.72676, "time": 0.49193} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.02214, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.85875, "top5_acc": 0.99438, "loss_cls": 0.70213, "loss": 0.70213, "time": 0.48951} +{"mode": "val", "epoch": 33, "iter": 533, "lr": 0.02213, "top1_acc": 0.80014, "top5_acc": 0.98357, "mean_class_accuracy": 0.7396} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.02212, "memory": 4083, "data_time": 0.1984, "top1_acc": 0.85875, "top5_acc": 0.99562, "loss_cls": 0.67918, "loss": 0.67918, "time": 0.81056} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.02211, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.865, "top5_acc": 0.99562, "loss_cls": 0.65085, "loss": 0.65085, "time": 0.49141} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.02209, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85812, "top5_acc": 0.99562, "loss_cls": 0.68024, "loss": 0.68024, "time": 0.49172} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.02208, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85812, "top5_acc": 0.99438, "loss_cls": 0.71341, "loss": 0.71341, "time": 0.41923} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.02207, "memory": 4083, "data_time": 0.00067, "top1_acc": 0.86625, "top5_acc": 0.99125, "loss_cls": 0.67632, "loss": 0.67632, "time": 0.46811} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.02205, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8625, "top5_acc": 0.99562, "loss_cls": 0.70464, "loss": 0.70464, "time": 0.27039} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.02204, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85625, "top5_acc": 0.995, "loss_cls": 0.71013, "loss": 0.71013, "time": 0.42264} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.02203, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85312, "top5_acc": 0.99312, "loss_cls": 0.70112, "loss": 0.70112, "time": 0.49044} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.02201, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84938, "top5_acc": 0.99438, "loss_cls": 0.73002, "loss": 0.73002, "time": 0.49013} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.022, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8525, "top5_acc": 0.99062, "loss_cls": 0.71067, "loss": 0.71067, "time": 0.4974} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.02199, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86062, "top5_acc": 0.995, "loss_cls": 0.69312, "loss": 0.69312, "time": 0.49057} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.02197, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85562, "top5_acc": 0.9925, "loss_cls": 0.72352, "loss": 0.72352, "time": 0.48572} +{"mode": "val", "epoch": 34, "iter": 533, "lr": 0.02196, "top1_acc": 0.78618, "top5_acc": 0.98545, "mean_class_accuracy": 0.72449} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.02195, "memory": 4083, "data_time": 0.19387, "top1_acc": 0.86312, "top5_acc": 0.99688, "loss_cls": 0.68232, "loss": 0.68232, "time": 0.79531} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.02194, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88062, "top5_acc": 0.995, "loss_cls": 0.61536, "loss": 0.61536, "time": 0.49007} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.02192, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87562, "top5_acc": 0.99562, "loss_cls": 0.59601, "loss": 0.59601, "time": 0.49293} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.02191, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8825, "top5_acc": 0.99438, "loss_cls": 0.62732, "loss": 0.62732, "time": 0.4221} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.0219, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8725, "top5_acc": 0.99375, "loss_cls": 0.66124, "loss": 0.66124, "time": 0.47098} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.02188, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.83875, "top5_acc": 0.98938, "loss_cls": 0.77115, "loss": 0.77115, "time": 0.26569} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.02187, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85562, "top5_acc": 0.99312, "loss_cls": 0.73143, "loss": 0.73143, "time": 0.41856} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.02185, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8425, "top5_acc": 0.9925, "loss_cls": 0.74642, "loss": 0.74642, "time": 0.4933} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.02184, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.85562, "top5_acc": 0.99562, "loss_cls": 0.71061, "loss": 0.71061, "time": 0.49299} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.02183, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.855, "top5_acc": 0.99438, "loss_cls": 0.71152, "loss": 0.71152, "time": 0.49433} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.02181, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.865, "top5_acc": 0.99562, "loss_cls": 0.6897, "loss": 0.6897, "time": 0.48813} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.0218, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86562, "top5_acc": 0.99625, "loss_cls": 0.6774, "loss": 0.6774, "time": 0.49037} +{"mode": "val", "epoch": 35, "iter": 533, "lr": 0.02179, "top1_acc": 0.81035, "top5_acc": 0.98592, "mean_class_accuracy": 0.7451} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.02178, "memory": 4083, "data_time": 0.19438, "top1_acc": 0.88438, "top5_acc": 0.995, "loss_cls": 0.60852, "loss": 0.60852, "time": 0.80601} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.02176, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87562, "top5_acc": 0.995, "loss_cls": 0.64848, "loss": 0.64848, "time": 0.49038} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.02175, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.875, "top5_acc": 0.99625, "loss_cls": 0.62705, "loss": 0.62705, "time": 0.49044} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.02173, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8825, "top5_acc": 0.99562, "loss_cls": 0.6223, "loss": 0.6223, "time": 0.41156} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.02172, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.84812, "top5_acc": 0.99375, "loss_cls": 0.72515, "loss": 0.72515, "time": 0.49736} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.02171, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86062, "top5_acc": 0.99062, "loss_cls": 0.70555, "loss": 0.70555, "time": 0.2458} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.02169, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87, "top5_acc": 0.99438, "loss_cls": 0.66549, "loss": 0.66549, "time": 0.4312} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.02168, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8675, "top5_acc": 0.995, "loss_cls": 0.67061, "loss": 0.67061, "time": 0.49032} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.02167, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.88062, "top5_acc": 0.99562, "loss_cls": 0.63677, "loss": 0.63677, "time": 0.48782} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.02165, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.83312, "top5_acc": 0.99375, "loss_cls": 0.76844, "loss": 0.76844, "time": 0.49272} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.02164, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85625, "top5_acc": 0.99562, "loss_cls": 0.73645, "loss": 0.73645, "time": 0.48908} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.02162, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.86125, "top5_acc": 0.99375, "loss_cls": 0.67799, "loss": 0.67799, "time": 0.49069} +{"mode": "val", "epoch": 36, "iter": 533, "lr": 0.02161, "top1_acc": 0.80577, "top5_acc": 0.98404, "mean_class_accuracy": 0.73858} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.0216, "memory": 4083, "data_time": 0.19733, "top1_acc": 0.87688, "top5_acc": 0.99875, "loss_cls": 0.60809, "loss": 0.60809, "time": 0.79985} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.02158, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88562, "top5_acc": 0.99688, "loss_cls": 0.54958, "loss": 0.54958, "time": 0.49475} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.02157, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.87625, "top5_acc": 0.99875, "loss_cls": 0.62487, "loss": 0.62487, "time": 0.49273} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.02156, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85812, "top5_acc": 0.99188, "loss_cls": 0.7072, "loss": 0.7072, "time": 0.40262} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.02154, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87625, "top5_acc": 0.99312, "loss_cls": 0.63746, "loss": 0.63746, "time": 0.50615} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.02153, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88062, "top5_acc": 0.99375, "loss_cls": 0.64085, "loss": 0.64085, "time": 0.24275} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.02151, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86312, "top5_acc": 0.99812, "loss_cls": 0.68417, "loss": 0.68417, "time": 0.44081} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0215, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.84562, "top5_acc": 0.99312, "loss_cls": 0.73458, "loss": 0.73458, "time": 0.48976} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.02149, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86438, "top5_acc": 0.99625, "loss_cls": 0.66396, "loss": 0.66396, "time": 0.49309} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.02147, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86938, "top5_acc": 0.99375, "loss_cls": 0.66007, "loss": 0.66007, "time": 0.49137} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.02146, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85625, "top5_acc": 0.99188, "loss_cls": 0.721, "loss": 0.721, "time": 0.49083} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.02144, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.86, "top5_acc": 0.99562, "loss_cls": 0.70322, "loss": 0.70322, "time": 0.49042} +{"mode": "val", "epoch": 37, "iter": 533, "lr": 0.02143, "top1_acc": 0.8303, "top5_acc": 0.98956, "mean_class_accuracy": 0.77291} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.02142, "memory": 4083, "data_time": 0.19533, "top1_acc": 0.86875, "top5_acc": 0.9975, "loss_cls": 0.65142, "loss": 0.65142, "time": 0.80521} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.0214, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.87625, "top5_acc": 0.99375, "loss_cls": 0.61744, "loss": 0.61744, "time": 0.48935} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.02139, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89062, "top5_acc": 0.995, "loss_cls": 0.59363, "loss": 0.59363, "time": 0.48962} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.02137, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.87125, "top5_acc": 0.99375, "loss_cls": 0.64193, "loss": 0.64193, "time": 0.36831} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.02136, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86125, "top5_acc": 0.995, "loss_cls": 0.66819, "loss": 0.66819, "time": 0.50967} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.02134, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.86125, "top5_acc": 0.99312, "loss_cls": 0.68722, "loss": 0.68722, "time": 0.24374} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.02133, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.88125, "top5_acc": 0.995, "loss_cls": 0.62672, "loss": 0.62672, "time": 0.46449} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.02132, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87438, "top5_acc": 0.99062, "loss_cls": 0.62966, "loss": 0.62966, "time": 0.49268} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.0213, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86562, "top5_acc": 0.99375, "loss_cls": 0.65215, "loss": 0.65215, "time": 0.48746} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.02129, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8725, "top5_acc": 0.99562, "loss_cls": 0.61958, "loss": 0.61958, "time": 0.49014} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.02127, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.85875, "top5_acc": 0.99375, "loss_cls": 0.66971, "loss": 0.66971, "time": 0.49063} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.02126, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.84688, "top5_acc": 0.99, "loss_cls": 0.75387, "loss": 0.75387, "time": 0.49297} +{"mode": "val", "epoch": 38, "iter": 533, "lr": 0.02125, "top1_acc": 0.78089, "top5_acc": 0.98345, "mean_class_accuracy": 0.7355} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.02123, "memory": 4083, "data_time": 0.19193, "top1_acc": 0.86938, "top5_acc": 0.99812, "loss_cls": 0.66106, "loss": 0.66106, "time": 0.79598} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.02122, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.85812, "top5_acc": 0.99375, "loss_cls": 0.71608, "loss": 0.71608, "time": 0.49218} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.0212, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87938, "top5_acc": 0.99562, "loss_cls": 0.62713, "loss": 0.62713, "time": 0.48863} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.02119, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.8675, "top5_acc": 0.99562, "loss_cls": 0.66783, "loss": 0.66783, "time": 0.37339} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.02117, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.85312, "top5_acc": 0.995, "loss_cls": 0.70052, "loss": 0.70052, "time": 0.5123} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.02116, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.88312, "top5_acc": 0.9975, "loss_cls": 0.60186, "loss": 0.60186, "time": 0.25113} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.02114, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86938, "top5_acc": 0.99625, "loss_cls": 0.64971, "loss": 0.64971, "time": 0.47397} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.02113, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86875, "top5_acc": 0.99188, "loss_cls": 0.65139, "loss": 0.65139, "time": 0.48709} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.02111, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.86, "top5_acc": 0.99625, "loss_cls": 0.68457, "loss": 0.68457, "time": 0.49093} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.0211, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.8625, "top5_acc": 0.99562, "loss_cls": 0.64504, "loss": 0.64504, "time": 0.48948} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.02108, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86875, "top5_acc": 0.9925, "loss_cls": 0.67046, "loss": 0.67046, "time": 0.48881} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.02107, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.84688, "top5_acc": 0.99312, "loss_cls": 0.71846, "loss": 0.71846, "time": 0.48773} +{"mode": "val", "epoch": 39, "iter": 533, "lr": 0.02106, "top1_acc": 0.81868, "top5_acc": 0.98779, "mean_class_accuracy": 0.76636} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.02104, "memory": 4083, "data_time": 0.19046, "top1_acc": 0.89375, "top5_acc": 0.995, "loss_cls": 0.56849, "loss": 0.56849, "time": 0.79379} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.02103, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88312, "top5_acc": 0.99688, "loss_cls": 0.58288, "loss": 0.58288, "time": 0.48828} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.02101, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87312, "top5_acc": 0.99438, "loss_cls": 0.63651, "loss": 0.63651, "time": 0.49254} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.021, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.87875, "top5_acc": 0.99625, "loss_cls": 0.58193, "loss": 0.58193, "time": 0.36304} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.02098, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.86812, "top5_acc": 0.995, "loss_cls": 0.63903, "loss": 0.63903, "time": 0.51347} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.02097, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86312, "top5_acc": 0.99188, "loss_cls": 0.68803, "loss": 0.68803, "time": 0.24823} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.02095, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.875, "top5_acc": 0.99562, "loss_cls": 0.62081, "loss": 0.62081, "time": 0.46875} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.02094, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86625, "top5_acc": 0.99688, "loss_cls": 0.67195, "loss": 0.67195, "time": 0.49451} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.02092, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87188, "top5_acc": 0.99875, "loss_cls": 0.6354, "loss": 0.6354, "time": 0.495} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.02091, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86312, "top5_acc": 0.99438, "loss_cls": 0.6697, "loss": 0.6697, "time": 0.49208} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.02089, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85875, "top5_acc": 0.995, "loss_cls": 0.67966, "loss": 0.67966, "time": 0.48874} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.02088, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87062, "top5_acc": 0.9925, "loss_cls": 0.65122, "loss": 0.65122, "time": 0.49129} +{"mode": "val", "epoch": 40, "iter": 533, "lr": 0.02086, "top1_acc": 0.83312, "top5_acc": 0.98944, "mean_class_accuracy": 0.77076} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.02085, "memory": 4083, "data_time": 0.19339, "top1_acc": 0.88, "top5_acc": 0.99438, "loss_cls": 0.61967, "loss": 0.61967, "time": 0.79349} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.02083, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86875, "top5_acc": 0.99562, "loss_cls": 0.64471, "loss": 0.64471, "time": 0.49113} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.02082, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88, "top5_acc": 0.99625, "loss_cls": 0.62884, "loss": 0.62884, "time": 0.49008} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.0208, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.88, "top5_acc": 0.995, "loss_cls": 0.62499, "loss": 0.62499, "time": 0.34852} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.02079, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.87312, "top5_acc": 0.99688, "loss_cls": 0.65037, "loss": 0.65037, "time": 0.51173} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.02077, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.86125, "top5_acc": 0.99812, "loss_cls": 0.64476, "loss": 0.64476, "time": 0.25025} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.02076, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85438, "top5_acc": 0.99625, "loss_cls": 0.71185, "loss": 0.71185, "time": 0.47605} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.02074, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86188, "top5_acc": 0.99438, "loss_cls": 0.6748, "loss": 0.6748, "time": 0.4905} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.02073, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8725, "top5_acc": 0.99438, "loss_cls": 0.6268, "loss": 0.6268, "time": 0.49488} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.02071, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.88688, "top5_acc": 0.99625, "loss_cls": 0.60883, "loss": 0.60883, "time": 0.49379} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.0207, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.87812, "top5_acc": 0.99625, "loss_cls": 0.62777, "loss": 0.62777, "time": 0.49312} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.02068, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.87812, "top5_acc": 0.995, "loss_cls": 0.62047, "loss": 0.62047, "time": 0.48998} +{"mode": "val", "epoch": 41, "iter": 533, "lr": 0.02067, "top1_acc": 0.82068, "top5_acc": 0.98369, "mean_class_accuracy": 0.75092} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.02065, "memory": 4083, "data_time": 0.19274, "top1_acc": 0.88625, "top5_acc": 0.99875, "loss_cls": 0.54526, "loss": 0.54526, "time": 0.79906} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.02064, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.88688, "top5_acc": 0.995, "loss_cls": 0.61315, "loss": 0.61315, "time": 0.49386} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.02062, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.85875, "top5_acc": 0.99375, "loss_cls": 0.71259, "loss": 0.71259, "time": 0.4912} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.02061, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.875, "top5_acc": 1.0, "loss_cls": 0.61724, "loss": 0.61724, "time": 0.32935} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.02059, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.87562, "top5_acc": 0.995, "loss_cls": 0.6493, "loss": 0.6493, "time": 0.51302} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.02057, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.88125, "top5_acc": 0.99562, "loss_cls": 0.59952, "loss": 0.59952, "time": 0.25869} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.02056, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88125, "top5_acc": 0.99875, "loss_cls": 0.61187, "loss": 0.61187, "time": 0.48932} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.02054, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87625, "top5_acc": 0.99375, "loss_cls": 0.6643, "loss": 0.6643, "time": 0.48921} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.02053, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.865, "top5_acc": 0.99688, "loss_cls": 0.67279, "loss": 0.67279, "time": 0.49321} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.02051, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.89062, "top5_acc": 0.99812, "loss_cls": 0.58389, "loss": 0.58389, "time": 0.49367} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.0205, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8575, "top5_acc": 0.99312, "loss_cls": 0.69764, "loss": 0.69764, "time": 0.48956} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.02048, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.865, "top5_acc": 0.99625, "loss_cls": 0.66463, "loss": 0.66463, "time": 0.48781} +{"mode": "val", "epoch": 42, "iter": 533, "lr": 0.02047, "top1_acc": 0.81129, "top5_acc": 0.98463, "mean_class_accuracy": 0.73492} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.02045, "memory": 4083, "data_time": 0.19331, "top1_acc": 0.89688, "top5_acc": 0.99625, "loss_cls": 0.57719, "loss": 0.57719, "time": 0.7949} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.02044, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.8725, "top5_acc": 0.99688, "loss_cls": 0.57876, "loss": 0.57876, "time": 0.49482} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.02042, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8875, "top5_acc": 0.99625, "loss_cls": 0.58198, "loss": 0.58198, "time": 0.48922} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.0204, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87312, "top5_acc": 0.99625, "loss_cls": 0.6005, "loss": 0.6005, "time": 0.32292} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.02039, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.87125, "top5_acc": 0.99375, "loss_cls": 0.66024, "loss": 0.66024, "time": 0.51056} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.02037, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87188, "top5_acc": 0.9925, "loss_cls": 0.66509, "loss": 0.66509, "time": 0.27856} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.02036, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87438, "top5_acc": 0.99625, "loss_cls": 0.63324, "loss": 0.63324, "time": 0.4876} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.02034, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87812, "top5_acc": 0.99375, "loss_cls": 0.62435, "loss": 0.62435, "time": 0.49122} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.02033, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89812, "top5_acc": 0.99688, "loss_cls": 0.53771, "loss": 0.53771, "time": 0.49111} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.02031, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87062, "top5_acc": 0.9975, "loss_cls": 0.62938, "loss": 0.62938, "time": 0.49264} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.02029, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.87312, "top5_acc": 0.99312, "loss_cls": 0.64043, "loss": 0.64043, "time": 0.49182} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.02028, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86875, "top5_acc": 0.99875, "loss_cls": 0.65806, "loss": 0.65806, "time": 0.49113} +{"mode": "val", "epoch": 43, "iter": 533, "lr": 0.02026, "top1_acc": 0.84251, "top5_acc": 0.98721, "mean_class_accuracy": 0.77561} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.02025, "memory": 4083, "data_time": 0.20056, "top1_acc": 0.89375, "top5_acc": 0.99688, "loss_cls": 0.55872, "loss": 0.55872, "time": 0.80955} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.02023, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.89438, "top5_acc": 0.99438, "loss_cls": 0.54779, "loss": 0.54779, "time": 0.49129} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.02022, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89, "top5_acc": 0.99688, "loss_cls": 0.54116, "loss": 0.54116, "time": 0.49217} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.0202, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.88375, "top5_acc": 0.99562, "loss_cls": 0.58984, "loss": 0.58984, "time": 0.28113} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.02018, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88812, "top5_acc": 0.99625, "loss_cls": 0.60706, "loss": 0.60706, "time": 0.51251} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.02017, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88625, "top5_acc": 0.99812, "loss_cls": 0.59866, "loss": 0.59866, "time": 0.30294} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.02015, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.87812, "top5_acc": 0.99438, "loss_cls": 0.59345, "loss": 0.59345, "time": 0.49} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.02014, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.8825, "top5_acc": 0.99125, "loss_cls": 0.59999, "loss": 0.59999, "time": 0.49116} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.02012, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.885, "top5_acc": 0.9975, "loss_cls": 0.6, "loss": 0.6, "time": 0.48899} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.0201, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.875, "top5_acc": 0.9975, "loss_cls": 0.60665, "loss": 0.60665, "time": 0.49085} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.02009, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88, "top5_acc": 0.9925, "loss_cls": 0.59674, "loss": 0.59674, "time": 0.49609} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.02007, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.865, "top5_acc": 0.99438, "loss_cls": 0.6774, "loss": 0.6774, "time": 0.48931} +{"mode": "val", "epoch": 44, "iter": 533, "lr": 0.02006, "top1_acc": 0.81176, "top5_acc": 0.98862, "mean_class_accuracy": 0.747} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.02004, "memory": 4083, "data_time": 0.19052, "top1_acc": 0.87375, "top5_acc": 0.99562, "loss_cls": 0.62633, "loss": 0.62633, "time": 0.79176} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.02003, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89688, "top5_acc": 0.99625, "loss_cls": 0.55876, "loss": 0.55876, "time": 0.4895} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.02001, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.50093, "loss": 0.50093, "time": 0.49303} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.01999, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88312, "top5_acc": 0.9975, "loss_cls": 0.58686, "loss": 0.58686, "time": 0.28317} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.01998, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89812, "top5_acc": 0.99812, "loss_cls": 0.55117, "loss": 0.55117, "time": 0.51017} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.01996, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88188, "top5_acc": 0.99438, "loss_cls": 0.59013, "loss": 0.59013, "time": 0.31277} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.01994, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.86375, "top5_acc": 0.99375, "loss_cls": 0.63738, "loss": 0.63738, "time": 0.49255} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.01993, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86312, "top5_acc": 0.995, "loss_cls": 0.65957, "loss": 0.65957, "time": 0.49229} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.01991, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.8925, "top5_acc": 0.995, "loss_cls": 0.5793, "loss": 0.5793, "time": 0.497} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.01989, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86938, "top5_acc": 0.995, "loss_cls": 0.64705, "loss": 0.64705, "time": 0.49077} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.01988, "memory": 4083, "data_time": 0.00065, "top1_acc": 0.87312, "top5_acc": 0.99438, "loss_cls": 0.65372, "loss": 0.65372, "time": 0.49126} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.01986, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.86938, "top5_acc": 0.9975, "loss_cls": 0.61562, "loss": 0.61562, "time": 0.49175} +{"mode": "val", "epoch": 45, "iter": 533, "lr": 0.01985, "top1_acc": 0.84051, "top5_acc": 0.98873, "mean_class_accuracy": 0.77587} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.01983, "memory": 4083, "data_time": 0.19792, "top1_acc": 0.87812, "top5_acc": 0.99562, "loss_cls": 0.61648, "loss": 0.61648, "time": 0.80673} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.01981, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89375, "top5_acc": 0.99562, "loss_cls": 0.53591, "loss": 0.53591, "time": 0.49145} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.0198, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.85938, "top5_acc": 0.995, "loss_cls": 0.66504, "loss": 0.66504, "time": 0.49529} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.01978, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.87688, "top5_acc": 0.9975, "loss_cls": 0.64547, "loss": 0.64547, "time": 0.29734} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.01976, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.8725, "top5_acc": 0.99938, "loss_cls": 0.58607, "loss": 0.58607, "time": 0.46463} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.01975, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.88312, "top5_acc": 0.9975, "loss_cls": 0.56419, "loss": 0.56419, "time": 0.32048} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.01973, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90812, "top5_acc": 0.99875, "loss_cls": 0.51478, "loss": 0.51478, "time": 0.49174} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.01971, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8875, "top5_acc": 0.99625, "loss_cls": 0.58133, "loss": 0.58133, "time": 0.48766} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.0197, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.87125, "top5_acc": 0.99625, "loss_cls": 0.62754, "loss": 0.62754, "time": 0.49155} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.01968, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.87438, "top5_acc": 0.9975, "loss_cls": 0.62859, "loss": 0.62859, "time": 0.49442} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.01966, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88125, "top5_acc": 0.99562, "loss_cls": 0.62793, "loss": 0.62793, "time": 0.49037} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.01965, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87875, "top5_acc": 0.995, "loss_cls": 0.6084, "loss": 0.6084, "time": 0.48961} +{"mode": "val", "epoch": 46, "iter": 533, "lr": 0.01963, "top1_acc": 0.77655, "top5_acc": 0.9838, "mean_class_accuracy": 0.70935} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.01962, "memory": 4083, "data_time": 0.19724, "top1_acc": 0.89312, "top5_acc": 0.9975, "loss_cls": 0.55663, "loss": 0.55663, "time": 0.79848} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.0196, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91438, "top5_acc": 0.9975, "loss_cls": 0.48962, "loss": 0.48962, "time": 0.49348} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.01958, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.885, "top5_acc": 0.9975, "loss_cls": 0.57042, "loss": 0.57042, "time": 0.49288} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.01957, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8875, "top5_acc": 0.9975, "loss_cls": 0.55865, "loss": 0.55865, "time": 0.28873} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.01955, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.88375, "top5_acc": 0.9925, "loss_cls": 0.60162, "loss": 0.60162, "time": 0.48279} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.01953, "memory": 4083, "data_time": 0.00072, "top1_acc": 0.89188, "top5_acc": 0.9975, "loss_cls": 0.54897, "loss": 0.54897, "time": 0.32785} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.01952, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.86, "top5_acc": 0.99438, "loss_cls": 0.68395, "loss": 0.68395, "time": 0.48955} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.0195, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.87875, "top5_acc": 0.99625, "loss_cls": 0.63172, "loss": 0.63172, "time": 0.48756} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.01948, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87875, "top5_acc": 0.99438, "loss_cls": 0.61399, "loss": 0.61399, "time": 0.49078} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.01947, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.8675, "top5_acc": 0.99625, "loss_cls": 0.67475, "loss": 0.67475, "time": 0.49127} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.01945, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88062, "top5_acc": 0.9975, "loss_cls": 0.57587, "loss": 0.57587, "time": 0.48717} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.01943, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.875, "top5_acc": 0.99562, "loss_cls": 0.64231, "loss": 0.64231, "time": 0.48994} +{"mode": "val", "epoch": 47, "iter": 533, "lr": 0.01942, "top1_acc": 0.81505, "top5_acc": 0.98427, "mean_class_accuracy": 0.75809} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.0194, "memory": 4083, "data_time": 0.19447, "top1_acc": 0.86938, "top5_acc": 0.9975, "loss_cls": 0.60712, "loss": 0.60712, "time": 0.78827} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.01938, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90938, "top5_acc": 0.99562, "loss_cls": 0.53171, "loss": 0.53171, "time": 0.49068} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.01937, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89438, "top5_acc": 0.99625, "loss_cls": 0.56828, "loss": 0.56828, "time": 0.49339} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.01935, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88, "top5_acc": 0.99562, "loss_cls": 0.60472, "loss": 0.60472, "time": 0.28968} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.01933, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88188, "top5_acc": 0.99438, "loss_cls": 0.59716, "loss": 0.59716, "time": 0.48231} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.01932, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.88062, "top5_acc": 0.99875, "loss_cls": 0.56002, "loss": 0.56002, "time": 0.3276} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.0193, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87812, "top5_acc": 0.99562, "loss_cls": 0.62286, "loss": 0.62286, "time": 0.49047} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.01928, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88938, "top5_acc": 0.9975, "loss_cls": 0.56733, "loss": 0.56733, "time": 0.49319} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.01926, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88438, "top5_acc": 0.9975, "loss_cls": 0.58224, "loss": 0.58224, "time": 0.48876} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.01925, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89062, "top5_acc": 0.99625, "loss_cls": 0.55147, "loss": 0.55147, "time": 0.486} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.01923, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88062, "top5_acc": 0.995, "loss_cls": 0.59743, "loss": 0.59743, "time": 0.48724} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.01921, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87, "top5_acc": 0.99188, "loss_cls": 0.66914, "loss": 0.66914, "time": 0.48788} +{"mode": "val", "epoch": 48, "iter": 533, "lr": 0.0192, "top1_acc": 0.84309, "top5_acc": 0.99073, "mean_class_accuracy": 0.76784} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.01918, "memory": 4083, "data_time": 0.19858, "top1_acc": 0.9025, "top5_acc": 0.99938, "loss_cls": 0.54037, "loss": 0.54037, "time": 0.7963} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.01916, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.88688, "top5_acc": 0.99625, "loss_cls": 0.55473, "loss": 0.55473, "time": 0.49191} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.01915, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89562, "top5_acc": 0.9975, "loss_cls": 0.54043, "loss": 0.54043, "time": 0.4923} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.01913, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89688, "top5_acc": 0.99688, "loss_cls": 0.55829, "loss": 0.55829, "time": 0.31194} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.01911, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.88438, "top5_acc": 0.99562, "loss_cls": 0.60377, "loss": 0.60377, "time": 0.44535} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.01909, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89125, "top5_acc": 0.99562, "loss_cls": 0.59857, "loss": 0.59857, "time": 0.33632} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.01908, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90062, "top5_acc": 0.99562, "loss_cls": 0.53822, "loss": 0.53822, "time": 0.49077} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.01906, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87688, "top5_acc": 0.995, "loss_cls": 0.57239, "loss": 0.57239, "time": 0.48783} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.01904, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89, "top5_acc": 0.99938, "loss_cls": 0.56069, "loss": 0.56069, "time": 0.49229} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.01902, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.87938, "top5_acc": 0.99438, "loss_cls": 0.59333, "loss": 0.59333, "time": 0.4975} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.01901, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88938, "top5_acc": 0.99875, "loss_cls": 0.5772, "loss": 0.5772, "time": 0.4936} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.01899, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88812, "top5_acc": 0.99625, "loss_cls": 0.59255, "loss": 0.59255, "time": 0.48606} +{"mode": "val", "epoch": 49, "iter": 533, "lr": 0.01898, "top1_acc": 0.80307, "top5_acc": 0.98463, "mean_class_accuracy": 0.71897} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.01896, "memory": 4083, "data_time": 0.20074, "top1_acc": 0.88, "top5_acc": 0.99562, "loss_cls": 0.61311, "loss": 0.61311, "time": 0.80697} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.01894, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88375, "top5_acc": 0.99562, "loss_cls": 0.57042, "loss": 0.57042, "time": 0.49254} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.01892, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.90312, "top5_acc": 0.99562, "loss_cls": 0.51261, "loss": 0.51261, "time": 0.493} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.01891, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.89812, "top5_acc": 0.99625, "loss_cls": 0.53709, "loss": 0.53709, "time": 0.31478} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.01889, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.87125, "top5_acc": 0.99438, "loss_cls": 0.60304, "loss": 0.60304, "time": 0.42327} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.01887, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.91062, "top5_acc": 0.99562, "loss_cls": 0.50187, "loss": 0.50187, "time": 0.35375} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.01885, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.88375, "top5_acc": 0.99438, "loss_cls": 0.61195, "loss": 0.61195, "time": 0.49282} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.01884, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.87375, "top5_acc": 0.99438, "loss_cls": 0.65244, "loss": 0.65244, "time": 0.49044} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.01882, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87812, "top5_acc": 0.99625, "loss_cls": 0.57802, "loss": 0.57802, "time": 0.49228} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.0188, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.89812, "top5_acc": 0.99625, "loss_cls": 0.5603, "loss": 0.5603, "time": 0.49234} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.01878, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8775, "top5_acc": 0.9975, "loss_cls": 0.59476, "loss": 0.59476, "time": 0.48858} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.01876, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.88438, "top5_acc": 0.9975, "loss_cls": 0.57675, "loss": 0.57675, "time": 0.48965} +{"mode": "val", "epoch": 50, "iter": 533, "lr": 0.01875, "top1_acc": 0.83664, "top5_acc": 0.98979, "mean_class_accuracy": 0.77777} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.01873, "memory": 4083, "data_time": 0.19395, "top1_acc": 0.89062, "top5_acc": 0.99625, "loss_cls": 0.54718, "loss": 0.54718, "time": 0.80081} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.01871, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9025, "top5_acc": 0.99812, "loss_cls": 0.51819, "loss": 0.51819, "time": 0.49377} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.0187, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90312, "top5_acc": 0.99875, "loss_cls": 0.50054, "loss": 0.50054, "time": 0.49123} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.01868, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.9025, "top5_acc": 0.9975, "loss_cls": 0.52076, "loss": 0.52076, "time": 0.29566} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.01866, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89375, "top5_acc": 0.9975, "loss_cls": 0.52501, "loss": 0.52501, "time": 0.45571} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.01864, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.90375, "top5_acc": 0.99625, "loss_cls": 0.5334, "loss": 0.5334, "time": 0.33242} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.01863, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90562, "top5_acc": 0.9975, "loss_cls": 0.51474, "loss": 0.51474, "time": 0.49216} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.01861, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.87375, "top5_acc": 0.99688, "loss_cls": 0.59233, "loss": 0.59233, "time": 0.49299} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.01859, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88875, "top5_acc": 0.99562, "loss_cls": 0.56657, "loss": 0.56657, "time": 0.49206} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.01857, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.88875, "top5_acc": 0.99562, "loss_cls": 0.56685, "loss": 0.56685, "time": 0.48954} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.01855, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88312, "top5_acc": 0.99188, "loss_cls": 0.61211, "loss": 0.61211, "time": 0.49079} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.01854, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89, "top5_acc": 0.99375, "loss_cls": 0.57306, "loss": 0.57306, "time": 0.49189} +{"mode": "val", "epoch": 51, "iter": 533, "lr": 0.01852, "top1_acc": 0.81141, "top5_acc": 0.98545, "mean_class_accuracy": 0.75348} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.0185, "memory": 4083, "data_time": 0.19623, "top1_acc": 0.88688, "top5_acc": 0.99688, "loss_cls": 0.57777, "loss": 0.57777, "time": 0.79637} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.01849, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89375, "top5_acc": 0.99688, "loss_cls": 0.55809, "loss": 0.55809, "time": 0.49093} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.01847, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90062, "top5_acc": 0.99625, "loss_cls": 0.54222, "loss": 0.54222, "time": 0.49162} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.01845, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89438, "top5_acc": 0.99625, "loss_cls": 0.51662, "loss": 0.51662, "time": 0.28337} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.01843, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.89312, "top5_acc": 0.99562, "loss_cls": 0.55796, "loss": 0.55796, "time": 0.47351} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.01841, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.89438, "top5_acc": 0.99625, "loss_cls": 0.58615, "loss": 0.58615, "time": 0.32649} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.0184, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89125, "top5_acc": 0.99812, "loss_cls": 0.58208, "loss": 0.58208, "time": 0.495} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.01838, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87812, "top5_acc": 0.99812, "loss_cls": 0.57421, "loss": 0.57421, "time": 0.49472} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.01836, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89, "top5_acc": 0.99625, "loss_cls": 0.56856, "loss": 0.56856, "time": 0.49068} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.01834, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88125, "top5_acc": 0.9975, "loss_cls": 0.59464, "loss": 0.59464, "time": 0.49193} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.01832, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87, "top5_acc": 0.98938, "loss_cls": 0.69321, "loss": 0.69321, "time": 0.49241} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.01831, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89938, "top5_acc": 0.99625, "loss_cls": 0.56292, "loss": 0.56292, "time": 0.48913} +{"mode": "val", "epoch": 52, "iter": 533, "lr": 0.01829, "top1_acc": 0.82737, "top5_acc": 0.98662, "mean_class_accuracy": 0.76072} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.01827, "memory": 4083, "data_time": 0.19063, "top1_acc": 0.89688, "top5_acc": 0.99375, "loss_cls": 0.53357, "loss": 0.53357, "time": 0.78611} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.01826, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89625, "top5_acc": 0.99938, "loss_cls": 0.51283, "loss": 0.51283, "time": 0.49157} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.01824, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90062, "top5_acc": 0.99938, "loss_cls": 0.52567, "loss": 0.52567, "time": 0.49621} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.01822, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88375, "top5_acc": 0.99562, "loss_cls": 0.55508, "loss": 0.55508, "time": 0.27715} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.0182, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.87688, "top5_acc": 0.99375, "loss_cls": 0.57785, "loss": 0.57785, "time": 0.49399} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.01818, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90188, "top5_acc": 0.99562, "loss_cls": 0.54502, "loss": 0.54502, "time": 0.33018} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.01816, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87938, "top5_acc": 0.99312, "loss_cls": 0.60782, "loss": 0.60782, "time": 0.48583} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.01815, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89312, "top5_acc": 0.9975, "loss_cls": 0.52474, "loss": 0.52474, "time": 0.49384} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.01813, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89625, "top5_acc": 0.99812, "loss_cls": 0.5323, "loss": 0.5323, "time": 0.49412} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.01811, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89188, "top5_acc": 0.99625, "loss_cls": 0.54546, "loss": 0.54546, "time": 0.49494} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.01809, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90375, "top5_acc": 0.9975, "loss_cls": 0.5229, "loss": 0.5229, "time": 0.48845} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.01807, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89062, "top5_acc": 0.9975, "loss_cls": 0.56232, "loss": 0.56232, "time": 0.48964} +{"mode": "val", "epoch": 53, "iter": 533, "lr": 0.01806, "top1_acc": 0.84145, "top5_acc": 0.98885, "mean_class_accuracy": 0.79179} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.01804, "memory": 4083, "data_time": 0.19466, "top1_acc": 0.91188, "top5_acc": 0.99938, "loss_cls": 0.46492, "loss": 0.46492, "time": 0.79805} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.01802, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89125, "top5_acc": 0.99625, "loss_cls": 0.54958, "loss": 0.54958, "time": 0.49167} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.018, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.895, "top5_acc": 0.99688, "loss_cls": 0.51924, "loss": 0.51924, "time": 0.49165} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.01798, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90625, "top5_acc": 0.99812, "loss_cls": 0.48189, "loss": 0.48189, "time": 0.30158} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.01797, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8925, "top5_acc": 0.99688, "loss_cls": 0.57525, "loss": 0.57525, "time": 0.45548} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.01795, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90062, "top5_acc": 0.99625, "loss_cls": 0.56795, "loss": 0.56795, "time": 0.34795} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.01793, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8925, "top5_acc": 0.99625, "loss_cls": 0.57663, "loss": 0.57663, "time": 0.49162} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.01791, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.88062, "top5_acc": 0.9975, "loss_cls": 0.58596, "loss": 0.58596, "time": 0.48725} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.01789, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89, "top5_acc": 0.99625, "loss_cls": 0.56569, "loss": 0.56569, "time": 0.49276} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.01787, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89375, "top5_acc": 0.9975, "loss_cls": 0.54073, "loss": 0.54073, "time": 0.4884} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.01786, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.87188, "top5_acc": 0.99375, "loss_cls": 0.60317, "loss": 0.60317, "time": 0.49032} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.01784, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.89125, "top5_acc": 0.99562, "loss_cls": 0.58037, "loss": 0.58037, "time": 0.49144} +{"mode": "val", "epoch": 54, "iter": 533, "lr": 0.01782, "top1_acc": 0.86105, "top5_acc": 0.99261, "mean_class_accuracy": 0.80491} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.0178, "memory": 4083, "data_time": 0.19901, "top1_acc": 0.90688, "top5_acc": 0.99812, "loss_cls": 0.49318, "loss": 0.49318, "time": 0.79909} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.01779, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89562, "top5_acc": 0.9975, "loss_cls": 0.53449, "loss": 0.53449, "time": 0.48952} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.01777, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89375, "top5_acc": 0.99625, "loss_cls": 0.53483, "loss": 0.53483, "time": 0.4847} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.01775, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8925, "top5_acc": 0.99812, "loss_cls": 0.54538, "loss": 0.54538, "time": 0.35033} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.01773, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.91062, "top5_acc": 0.99562, "loss_cls": 0.53076, "loss": 0.53076, "time": 0.38496} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.01771, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89625, "top5_acc": 0.99875, "loss_cls": 0.52561, "loss": 0.52561, "time": 0.37574} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.01769, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.89438, "top5_acc": 0.99438, "loss_cls": 0.56508, "loss": 0.56508, "time": 0.49354} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.01767, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89875, "top5_acc": 0.99562, "loss_cls": 0.535, "loss": 0.535, "time": 0.48742} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.01766, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9, "top5_acc": 0.9975, "loss_cls": 0.51543, "loss": 0.51543, "time": 0.4959} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.01764, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.8925, "top5_acc": 0.995, "loss_cls": 0.52865, "loss": 0.52865, "time": 0.49396} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.01762, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.87625, "top5_acc": 0.9975, "loss_cls": 0.58369, "loss": 0.58369, "time": 0.48672} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.0176, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9025, "top5_acc": 0.99625, "loss_cls": 0.51875, "loss": 0.51875, "time": 0.48999} +{"mode": "val", "epoch": 55, "iter": 533, "lr": 0.01758, "top1_acc": 0.83453, "top5_acc": 0.98733, "mean_class_accuracy": 0.75478} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.01757, "memory": 4083, "data_time": 0.19107, "top1_acc": 0.8975, "top5_acc": 0.99688, "loss_cls": 0.5136, "loss": 0.5136, "time": 0.79508} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.01755, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.88688, "top5_acc": 0.9975, "loss_cls": 0.54311, "loss": 0.54311, "time": 0.49} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.01753, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90062, "top5_acc": 0.995, "loss_cls": 0.50435, "loss": 0.50435, "time": 0.46718} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.01751, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9075, "top5_acc": 0.9975, "loss_cls": 0.49069, "loss": 0.49069, "time": 0.36375} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.01749, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90188, "top5_acc": 0.99625, "loss_cls": 0.52403, "loss": 0.52403, "time": 0.37281} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.01747, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.905, "top5_acc": 0.99812, "loss_cls": 0.49862, "loss": 0.49862, "time": 0.36676} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.01745, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.905, "top5_acc": 0.99688, "loss_cls": 0.53021, "loss": 0.53021, "time": 0.49056} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.01743, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88875, "top5_acc": 0.99688, "loss_cls": 0.51134, "loss": 0.51134, "time": 0.4929} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.01742, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.88562, "top5_acc": 0.99688, "loss_cls": 0.5396, "loss": 0.5396, "time": 0.49588} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.0174, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.895, "top5_acc": 0.99562, "loss_cls": 0.54629, "loss": 0.54629, "time": 0.49097} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.01738, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90062, "top5_acc": 0.99375, "loss_cls": 0.52363, "loss": 0.52363, "time": 0.48749} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.01736, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.905, "top5_acc": 0.99875, "loss_cls": 0.50552, "loss": 0.50552, "time": 0.48796} +{"mode": "val", "epoch": 56, "iter": 533, "lr": 0.01734, "top1_acc": 0.86128, "top5_acc": 0.99085, "mean_class_accuracy": 0.8021} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.01733, "memory": 4083, "data_time": 0.19506, "top1_acc": 0.91125, "top5_acc": 1.0, "loss_cls": 0.47928, "loss": 0.47928, "time": 0.79878} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.01731, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8925, "top5_acc": 0.99812, "loss_cls": 0.51043, "loss": 0.51043, "time": 0.49363} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.01729, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90062, "top5_acc": 0.99812, "loss_cls": 0.4956, "loss": 0.4956, "time": 0.4782} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.01727, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89188, "top5_acc": 0.995, "loss_cls": 0.54303, "loss": 0.54303, "time": 0.34655} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.01725, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90625, "top5_acc": 0.99438, "loss_cls": 0.5319, "loss": 0.5319, "time": 0.39036} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.01723, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89562, "top5_acc": 0.99562, "loss_cls": 0.5692, "loss": 0.5692, "time": 0.37108} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.01721, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88875, "top5_acc": 0.99688, "loss_cls": 0.5448, "loss": 0.5448, "time": 0.48574} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.01719, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8925, "top5_acc": 0.99688, "loss_cls": 0.53403, "loss": 0.53403, "time": 0.49246} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.01717, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.905, "top5_acc": 0.99625, "loss_cls": 0.53334, "loss": 0.53334, "time": 0.49429} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.01716, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.8825, "top5_acc": 0.99438, "loss_cls": 0.596, "loss": 0.596, "time": 0.49289} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.01714, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8825, "top5_acc": 0.99438, "loss_cls": 0.61819, "loss": 0.61819, "time": 0.48701} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.01712, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90312, "top5_acc": 0.99688, "loss_cls": 0.53076, "loss": 0.53076, "time": 0.49007} +{"mode": "val", "epoch": 57, "iter": 533, "lr": 0.0171, "top1_acc": 0.81352, "top5_acc": 0.98287, "mean_class_accuracy": 0.78602} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.01708, "memory": 4083, "data_time": 0.18961, "top1_acc": 0.91125, "top5_acc": 0.99688, "loss_cls": 0.48485, "loss": 0.48485, "time": 0.79173} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.01706, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90125, "top5_acc": 0.99875, "loss_cls": 0.5368, "loss": 0.5368, "time": 0.49325} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.01704, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.91125, "top5_acc": 0.99438, "loss_cls": 0.50844, "loss": 0.50844, "time": 0.48549} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.01703, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90625, "top5_acc": 0.99688, "loss_cls": 0.50803, "loss": 0.50803, "time": 0.32564} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.01701, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9, "top5_acc": 0.9975, "loss_cls": 0.49568, "loss": 0.49568, "time": 0.41015} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.01699, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9025, "top5_acc": 0.99562, "loss_cls": 0.47064, "loss": 0.47064, "time": 0.36376} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.01697, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.50598, "loss": 0.50598, "time": 0.49277} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.01695, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.895, "top5_acc": 0.99812, "loss_cls": 0.54584, "loss": 0.54584, "time": 0.4918} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.01693, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8925, "top5_acc": 0.9975, "loss_cls": 0.52143, "loss": 0.52143, "time": 0.48997} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.01691, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90312, "top5_acc": 0.99375, "loss_cls": 0.52022, "loss": 0.52022, "time": 0.48898} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.01689, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89125, "top5_acc": 0.99562, "loss_cls": 0.53604, "loss": 0.53604, "time": 0.48932} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.01687, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89688, "top5_acc": 0.99688, "loss_cls": 0.54518, "loss": 0.54518, "time": 0.48869} +{"mode": "val", "epoch": 58, "iter": 533, "lr": 0.01686, "top1_acc": 0.85354, "top5_acc": 0.99214, "mean_class_accuracy": 0.80367} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.01684, "memory": 4083, "data_time": 0.18863, "top1_acc": 0.91062, "top5_acc": 0.99875, "loss_cls": 0.48592, "loss": 0.48592, "time": 0.79372} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.01682, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89562, "top5_acc": 0.99875, "loss_cls": 0.51812, "loss": 0.51812, "time": 0.48895} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.0168, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.9075, "top5_acc": 0.9975, "loss_cls": 0.48046, "loss": 0.48046, "time": 0.48921} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.01678, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.89688, "top5_acc": 0.99875, "loss_cls": 0.51303, "loss": 0.51303, "time": 0.32032} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.01676, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90062, "top5_acc": 0.99562, "loss_cls": 0.52433, "loss": 0.52433, "time": 0.41474} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.01674, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90438, "top5_acc": 0.995, "loss_cls": 0.53401, "loss": 0.53401, "time": 0.36853} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.01672, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9125, "top5_acc": 0.9975, "loss_cls": 0.48731, "loss": 0.48731, "time": 0.49058} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.0167, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.905, "top5_acc": 0.99812, "loss_cls": 0.48262, "loss": 0.48262, "time": 0.49149} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.01668, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90375, "top5_acc": 0.99625, "loss_cls": 0.47921, "loss": 0.47921, "time": 0.49118} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.01667, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89438, "top5_acc": 0.9975, "loss_cls": 0.58399, "loss": 0.58399, "time": 0.48595} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.01665, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.89062, "top5_acc": 0.9975, "loss_cls": 0.56087, "loss": 0.56087, "time": 0.48746} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.01663, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88062, "top5_acc": 0.99875, "loss_cls": 0.54765, "loss": 0.54765, "time": 0.49147} +{"mode": "val", "epoch": 59, "iter": 533, "lr": 0.01661, "top1_acc": 0.8607, "top5_acc": 0.99096, "mean_class_accuracy": 0.81941} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.01659, "memory": 4083, "data_time": 0.19892, "top1_acc": 0.8975, "top5_acc": 0.995, "loss_cls": 0.53829, "loss": 0.53829, "time": 0.79985} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.01657, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.90188, "top5_acc": 0.99938, "loss_cls": 0.51849, "loss": 0.51849, "time": 0.49026} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.01655, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.905, "top5_acc": 0.99688, "loss_cls": 0.52428, "loss": 0.52428, "time": 0.47352} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.01653, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.53121, "loss": 0.53121, "time": 0.35944} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.01651, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.875, "top5_acc": 0.99625, "loss_cls": 0.57268, "loss": 0.57268, "time": 0.3782} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.0165, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91, "top5_acc": 0.99688, "loss_cls": 0.45916, "loss": 0.45916, "time": 0.37157} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.01648, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9175, "top5_acc": 0.99938, "loss_cls": 0.43345, "loss": 0.43345, "time": 0.48897} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.01646, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90188, "top5_acc": 0.99625, "loss_cls": 0.52729, "loss": 0.52729, "time": 0.49184} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.01644, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9025, "top5_acc": 0.995, "loss_cls": 0.51037, "loss": 0.51037, "time": 0.49194} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.01642, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.90688, "top5_acc": 0.99688, "loss_cls": 0.51991, "loss": 0.51991, "time": 0.49241} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.0164, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88312, "top5_acc": 0.99688, "loss_cls": 0.54327, "loss": 0.54327, "time": 0.48807} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.01638, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9025, "top5_acc": 0.99688, "loss_cls": 0.50303, "loss": 0.50303, "time": 0.48917} +{"mode": "val", "epoch": 60, "iter": 533, "lr": 0.01636, "top1_acc": 0.84837, "top5_acc": 0.99014, "mean_class_accuracy": 0.79606} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.01634, "memory": 4083, "data_time": 0.19562, "top1_acc": 0.9125, "top5_acc": 0.99688, "loss_cls": 0.49594, "loss": 0.49594, "time": 0.80072} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.01632, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9075, "top5_acc": 0.99812, "loss_cls": 0.4874, "loss": 0.4874, "time": 0.49041} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.0163, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.90938, "top5_acc": 0.99812, "loss_cls": 0.47948, "loss": 0.47948, "time": 0.46601} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.01629, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.90375, "top5_acc": 0.99688, "loss_cls": 0.50355, "loss": 0.50355, "time": 0.37502} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.01627, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88188, "top5_acc": 0.99625, "loss_cls": 0.58094, "loss": 0.58094, "time": 0.36108} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.01625, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.91, "top5_acc": 0.99938, "loss_cls": 0.4661, "loss": 0.4661, "time": 0.37214} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.01623, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.90812, "top5_acc": 0.9975, "loss_cls": 0.49202, "loss": 0.49202, "time": 0.4888} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.01621, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91562, "top5_acc": 0.99875, "loss_cls": 0.45123, "loss": 0.45123, "time": 0.48971} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.01619, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89938, "top5_acc": 0.99625, "loss_cls": 0.51452, "loss": 0.51452, "time": 0.48996} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.01617, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9, "top5_acc": 0.99812, "loss_cls": 0.51157, "loss": 0.51157, "time": 0.4904} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.01615, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.89875, "top5_acc": 0.99562, "loss_cls": 0.51894, "loss": 0.51894, "time": 0.49306} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.01613, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.90062, "top5_acc": 0.99625, "loss_cls": 0.5074, "loss": 0.5074, "time": 0.48616} +{"mode": "val", "epoch": 61, "iter": 533, "lr": 0.01611, "top1_acc": 0.84075, "top5_acc": 0.98944, "mean_class_accuracy": 0.76683} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.01609, "memory": 4083, "data_time": 0.19401, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.43493, "loss": 0.43493, "time": 0.79337} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.01607, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9325, "top5_acc": 0.99625, "loss_cls": 0.40957, "loss": 0.40957, "time": 0.49098} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.01605, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91938, "top5_acc": 0.99688, "loss_cls": 0.43781, "loss": 0.43781, "time": 0.47756} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.01603, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91688, "top5_acc": 0.99875, "loss_cls": 0.4334, "loss": 0.4334, "time": 0.36197} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.01602, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.90625, "top5_acc": 0.99812, "loss_cls": 0.49292, "loss": 0.49292, "time": 0.37489} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.016, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.90812, "top5_acc": 0.99688, "loss_cls": 0.48982, "loss": 0.48982, "time": 0.38034} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.01598, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89625, "top5_acc": 0.99875, "loss_cls": 0.49685, "loss": 0.49685, "time": 0.49231} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.01596, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89938, "top5_acc": 0.99875, "loss_cls": 0.50235, "loss": 0.50235, "time": 0.48864} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.01594, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.8875, "top5_acc": 0.99562, "loss_cls": 0.55985, "loss": 0.55985, "time": 0.49242} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.01592, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.90812, "top5_acc": 0.99875, "loss_cls": 0.46755, "loss": 0.46755, "time": 0.49538} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.0159, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88, "top5_acc": 0.99875, "loss_cls": 0.58812, "loss": 0.58812, "time": 0.4865} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.01588, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.8875, "top5_acc": 0.99562, "loss_cls": 0.5617, "loss": 0.5617, "time": 0.49212} +{"mode": "val", "epoch": 62, "iter": 533, "lr": 0.01586, "top1_acc": 0.85589, "top5_acc": 0.99214, "mean_class_accuracy": 0.81845} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.01584, "memory": 4083, "data_time": 0.18997, "top1_acc": 0.92938, "top5_acc": 0.9975, "loss_cls": 0.41292, "loss": 0.41292, "time": 0.79771} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.01582, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91562, "top5_acc": 0.99812, "loss_cls": 0.45767, "loss": 0.45767, "time": 0.4935} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.0158, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8975, "top5_acc": 0.99938, "loss_cls": 0.49759, "loss": 0.49759, "time": 0.46586} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.01578, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91375, "top5_acc": 0.99562, "loss_cls": 0.46955, "loss": 0.46955, "time": 0.36441} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.01576, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91625, "top5_acc": 0.99688, "loss_cls": 0.43211, "loss": 0.43211, "time": 0.36857} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.01574, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.92125, "top5_acc": 0.99875, "loss_cls": 0.43101, "loss": 0.43101, "time": 0.37582} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.01572, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.47776, "loss": 0.47776, "time": 0.48988} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.0157, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.91375, "top5_acc": 0.99938, "loss_cls": 0.4446, "loss": 0.4446, "time": 0.49385} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.01568, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8875, "top5_acc": 0.99812, "loss_cls": 0.56287, "loss": 0.56287, "time": 0.4948} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.01566, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90688, "top5_acc": 0.995, "loss_cls": 0.49276, "loss": 0.49276, "time": 0.4938} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.01564, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90562, "top5_acc": 0.99438, "loss_cls": 0.51488, "loss": 0.51488, "time": 0.48897} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.01562, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.90625, "top5_acc": 0.995, "loss_cls": 0.48399, "loss": 0.48399, "time": 0.49245} +{"mode": "val", "epoch": 63, "iter": 533, "lr": 0.01561, "top1_acc": 0.85154, "top5_acc": 0.99096, "mean_class_accuracy": 0.80118} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.01559, "memory": 4083, "data_time": 0.1907, "top1_acc": 0.91375, "top5_acc": 0.99938, "loss_cls": 0.46155, "loss": 0.46155, "time": 0.78531} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.01557, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91812, "top5_acc": 0.99688, "loss_cls": 0.43652, "loss": 0.43652, "time": 0.49215} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.01555, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91, "top5_acc": 0.99812, "loss_cls": 0.46696, "loss": 0.46696, "time": 0.48059} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.01553, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91062, "top5_acc": 0.99875, "loss_cls": 0.46957, "loss": 0.46957, "time": 0.34381} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.01551, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.91312, "top5_acc": 0.99875, "loss_cls": 0.44108, "loss": 0.44108, "time": 0.39112} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.01549, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.91188, "top5_acc": 0.99875, "loss_cls": 0.46928, "loss": 0.46928, "time": 0.37237} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.01547, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.42912, "loss": 0.42912, "time": 0.49152} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.01545, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9025, "top5_acc": 0.9975, "loss_cls": 0.47578, "loss": 0.47578, "time": 0.49198} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.01543, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89938, "top5_acc": 0.99438, "loss_cls": 0.52593, "loss": 0.52593, "time": 0.49132} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.01541, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.90062, "top5_acc": 0.9975, "loss_cls": 0.50333, "loss": 0.50333, "time": 0.49105} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.01539, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.88875, "top5_acc": 0.99812, "loss_cls": 0.53485, "loss": 0.53485, "time": 0.48879} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.01537, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.91062, "top5_acc": 0.99812, "loss_cls": 0.4747, "loss": 0.4747, "time": 0.48971} +{"mode": "val", "epoch": 64, "iter": 533, "lr": 0.01535, "top1_acc": 0.85401, "top5_acc": 0.98791, "mean_class_accuracy": 0.79132} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.01533, "memory": 4083, "data_time": 0.2006, "top1_acc": 0.90812, "top5_acc": 0.9975, "loss_cls": 0.48428, "loss": 0.48428, "time": 0.8083} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.01531, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92312, "top5_acc": 0.99938, "loss_cls": 0.42778, "loss": 0.42778, "time": 0.49313} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.01529, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91312, "top5_acc": 0.99812, "loss_cls": 0.43391, "loss": 0.43391, "time": 0.46249} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.01527, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.89562, "top5_acc": 0.99688, "loss_cls": 0.50344, "loss": 0.50344, "time": 0.37558} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.01526, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.915, "top5_acc": 0.99875, "loss_cls": 0.44349, "loss": 0.44349, "time": 0.35909} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.01524, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.9075, "top5_acc": 0.99562, "loss_cls": 0.48785, "loss": 0.48785, "time": 0.38318} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.01522, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.91625, "top5_acc": 0.99438, "loss_cls": 0.46977, "loss": 0.46977, "time": 0.4887} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0152, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9125, "top5_acc": 0.99625, "loss_cls": 0.4775, "loss": 0.4775, "time": 0.49376} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.01518, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91688, "top5_acc": 1.0, "loss_cls": 0.44385, "loss": 0.44385, "time": 0.4889} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.01516, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.895, "top5_acc": 0.99688, "loss_cls": 0.50523, "loss": 0.50523, "time": 0.49163} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.01514, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.91125, "top5_acc": 0.9975, "loss_cls": 0.43978, "loss": 0.43978, "time": 0.49183} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.01512, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91062, "top5_acc": 0.99625, "loss_cls": 0.46984, "loss": 0.46984, "time": 0.48921} +{"mode": "val", "epoch": 65, "iter": 533, "lr": 0.0151, "top1_acc": 0.84802, "top5_acc": 0.9892, "mean_class_accuracy": 0.80492} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.01508, "memory": 4083, "data_time": 0.19908, "top1_acc": 0.91062, "top5_acc": 0.99625, "loss_cls": 0.47883, "loss": 0.47883, "time": 0.79191} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.01506, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91812, "top5_acc": 0.99688, "loss_cls": 0.44842, "loss": 0.44842, "time": 0.49242} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.01504, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91062, "top5_acc": 0.99938, "loss_cls": 0.45809, "loss": 0.45809, "time": 0.4632} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.01502, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92875, "top5_acc": 0.99875, "loss_cls": 0.40272, "loss": 0.40272, "time": 0.36788} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.015, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.92312, "top5_acc": 0.99875, "loss_cls": 0.42178, "loss": 0.42178, "time": 0.36906} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.01498, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.92125, "top5_acc": 0.9975, "loss_cls": 0.39881, "loss": 0.39881, "time": 0.37798} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.01496, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90312, "top5_acc": 0.99625, "loss_cls": 0.46989, "loss": 0.46989, "time": 0.49245} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.01494, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.92438, "top5_acc": 0.99625, "loss_cls": 0.44029, "loss": 0.44029, "time": 0.49364} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.01492, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91625, "top5_acc": 0.99812, "loss_cls": 0.43504, "loss": 0.43504, "time": 0.49061} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.0149, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90312, "top5_acc": 0.99688, "loss_cls": 0.50077, "loss": 0.50077, "time": 0.49262} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.01488, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91812, "top5_acc": 0.9975, "loss_cls": 0.42929, "loss": 0.42929, "time": 0.49086} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.01486, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89062, "top5_acc": 0.99875, "loss_cls": 0.54756, "loss": 0.54756, "time": 0.49285} +{"mode": "val", "epoch": 66, "iter": 533, "lr": 0.01484, "top1_acc": 0.84427, "top5_acc": 0.99073, "mean_class_accuracy": 0.7832} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.01482, "memory": 4083, "data_time": 0.1876, "top1_acc": 0.91, "top5_acc": 0.99812, "loss_cls": 0.46885, "loss": 0.46885, "time": 0.79239} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.0148, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92188, "top5_acc": 0.99875, "loss_cls": 0.41684, "loss": 0.41684, "time": 0.49132} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.01478, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91875, "top5_acc": 1.0, "loss_cls": 0.43858, "loss": 0.43858, "time": 0.4769} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.01476, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92125, "top5_acc": 0.9975, "loss_cls": 0.45612, "loss": 0.45612, "time": 0.35963} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.01474, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92812, "top5_acc": 0.99875, "loss_cls": 0.39824, "loss": 0.39824, "time": 0.37638} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.01472, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91438, "top5_acc": 0.99625, "loss_cls": 0.45777, "loss": 0.45777, "time": 0.36411} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.0147, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91875, "top5_acc": 0.99562, "loss_cls": 0.43687, "loss": 0.43687, "time": 0.49117} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.01468, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92125, "top5_acc": 0.99812, "loss_cls": 0.43272, "loss": 0.43272, "time": 0.49003} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.01466, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.915, "top5_acc": 0.99625, "loss_cls": 0.47149, "loss": 0.47149, "time": 0.49611} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.01464, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9025, "top5_acc": 0.99688, "loss_cls": 0.50792, "loss": 0.50792, "time": 0.49045} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.01462, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9025, "top5_acc": 0.99688, "loss_cls": 0.49881, "loss": 0.49881, "time": 0.4898} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.0146, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91625, "top5_acc": 1.0, "loss_cls": 0.4378, "loss": 0.4378, "time": 0.49237} +{"mode": "val", "epoch": 67, "iter": 533, "lr": 0.01458, "top1_acc": 0.86246, "top5_acc": 0.98932, "mean_class_accuracy": 0.79842} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.01456, "memory": 4083, "data_time": 0.18964, "top1_acc": 0.915, "top5_acc": 0.99938, "loss_cls": 0.42861, "loss": 0.42861, "time": 0.80071} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.01454, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91938, "top5_acc": 0.99812, "loss_cls": 0.43901, "loss": 0.43901, "time": 0.48721} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.01452, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90188, "top5_acc": 0.9975, "loss_cls": 0.44465, "loss": 0.44465, "time": 0.48013} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.0145, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.915, "top5_acc": 0.9975, "loss_cls": 0.44326, "loss": 0.44326, "time": 0.32951} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.01448, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91312, "top5_acc": 0.99625, "loss_cls": 0.46879, "loss": 0.46879, "time": 0.40675} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.01446, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92375, "top5_acc": 0.9975, "loss_cls": 0.42952, "loss": 0.42952, "time": 0.36273} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.01444, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.905, "top5_acc": 0.99875, "loss_cls": 0.45814, "loss": 0.45814, "time": 0.49233} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.01442, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.40222, "loss": 0.40222, "time": 0.49444} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.0144, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.90875, "top5_acc": 0.99688, "loss_cls": 0.45271, "loss": 0.45271, "time": 0.48974} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.01438, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.905, "top5_acc": 0.99812, "loss_cls": 0.46269, "loss": 0.46269, "time": 0.49158} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.01436, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8975, "top5_acc": 0.99625, "loss_cls": 0.5223, "loss": 0.5223, "time": 0.48796} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.01434, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.91188, "top5_acc": 0.9975, "loss_cls": 0.44354, "loss": 0.44354, "time": 0.48908} +{"mode": "val", "epoch": 68, "iter": 533, "lr": 0.01433, "top1_acc": 0.8418, "top5_acc": 0.98521, "mean_class_accuracy": 0.80441} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.01431, "memory": 4083, "data_time": 0.18883, "top1_acc": 0.91812, "top5_acc": 0.99875, "loss_cls": 0.4551, "loss": 0.4551, "time": 0.79565} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.01429, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93688, "top5_acc": 0.99875, "loss_cls": 0.34739, "loss": 0.34739, "time": 0.49087} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.01427, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9225, "top5_acc": 0.99688, "loss_cls": 0.40367, "loss": 0.40367, "time": 0.48336} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.01425, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91812, "top5_acc": 0.99625, "loss_cls": 0.4297, "loss": 0.4297, "time": 0.3371} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.01423, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91938, "top5_acc": 0.99875, "loss_cls": 0.42836, "loss": 0.42836, "time": 0.39779} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.0142, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92, "top5_acc": 0.99438, "loss_cls": 0.45568, "loss": 0.45568, "time": 0.36858} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.01418, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91188, "top5_acc": 0.99812, "loss_cls": 0.45601, "loss": 0.45601, "time": 0.48974} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.01416, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91, "top5_acc": 0.99938, "loss_cls": 0.43748, "loss": 0.43748, "time": 0.49265} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.01414, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.90812, "top5_acc": 0.9975, "loss_cls": 0.45995, "loss": 0.45995, "time": 0.48875} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.01412, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91062, "top5_acc": 0.99688, "loss_cls": 0.45377, "loss": 0.45377, "time": 0.49443} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.0141, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90188, "top5_acc": 0.9975, "loss_cls": 0.51177, "loss": 0.51177, "time": 0.48769} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.01408, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89938, "top5_acc": 0.99875, "loss_cls": 0.4616, "loss": 0.4616, "time": 0.49196} +{"mode": "val", "epoch": 69, "iter": 533, "lr": 0.01407, "top1_acc": 0.86281, "top5_acc": 0.99096, "mean_class_accuracy": 0.79788} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.01405, "memory": 4083, "data_time": 0.19529, "top1_acc": 0.91562, "top5_acc": 0.99875, "loss_cls": 0.44951, "loss": 0.44951, "time": 0.78897} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.01403, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.94312, "top5_acc": 0.9975, "loss_cls": 0.33775, "loss": 0.33775, "time": 0.48949} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.01401, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.44672, "loss": 0.44672, "time": 0.48817} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.01399, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9275, "top5_acc": 0.99875, "loss_cls": 0.38698, "loss": 0.38698, "time": 0.30063} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.01397, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92312, "top5_acc": 0.99875, "loss_cls": 0.40731, "loss": 0.40731, "time": 0.44138} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.01395, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.91562, "top5_acc": 0.9975, "loss_cls": 0.43584, "loss": 0.43584, "time": 0.32976} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.01392, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.37578, "loss": 0.37578, "time": 0.49177} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.0139, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90312, "top5_acc": 0.99625, "loss_cls": 0.46771, "loss": 0.46771, "time": 0.49509} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.01388, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89625, "top5_acc": 0.9975, "loss_cls": 0.54114, "loss": 0.54114, "time": 0.49396} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.01386, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90438, "top5_acc": 0.99562, "loss_cls": 0.52045, "loss": 0.52045, "time": 0.49207} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.01384, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.42615, "loss": 0.42615, "time": 0.49202} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.01382, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91688, "top5_acc": 0.99562, "loss_cls": 0.44481, "loss": 0.44481, "time": 0.49016} +{"mode": "val", "epoch": 70, "iter": 533, "lr": 0.01381, "top1_acc": 0.85049, "top5_acc": 0.99002, "mean_class_accuracy": 0.81152} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.01379, "memory": 4083, "data_time": 0.19053, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.40559, "loss": 0.40559, "time": 0.7907} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.01377, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93625, "top5_acc": 1.0, "loss_cls": 0.34802, "loss": 0.34802, "time": 0.49303} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.01375, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.37139, "loss": 0.37139, "time": 0.48979} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.01373, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.92188, "top5_acc": 0.99938, "loss_cls": 0.41415, "loss": 0.41415, "time": 0.28935} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.01371, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.92375, "top5_acc": 0.9975, "loss_cls": 0.43592, "loss": 0.43592, "time": 0.47414} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.01368, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92688, "top5_acc": 0.99812, "loss_cls": 0.38157, "loss": 0.38157, "time": 0.3287} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.01366, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.90625, "top5_acc": 0.99875, "loss_cls": 0.48543, "loss": 0.48543, "time": 0.4908} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.01364, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90438, "top5_acc": 0.99688, "loss_cls": 0.52223, "loss": 0.52223, "time": 0.49548} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.01362, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.90812, "top5_acc": 0.99688, "loss_cls": 0.49309, "loss": 0.49309, "time": 0.49021} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.0136, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91188, "top5_acc": 0.99625, "loss_cls": 0.4433, "loss": 0.4433, "time": 0.49488} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.01358, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.35348, "loss": 0.35348, "time": 0.49207} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.01356, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.91438, "top5_acc": 0.9975, "loss_cls": 0.41034, "loss": 0.41034, "time": 0.49142} +{"mode": "val", "epoch": 71, "iter": 533, "lr": 0.01355, "top1_acc": 0.86081, "top5_acc": 0.99073, "mean_class_accuracy": 0.82457} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.01353, "memory": 4083, "data_time": 0.19035, "top1_acc": 0.92562, "top5_acc": 0.99812, "loss_cls": 0.41733, "loss": 0.41733, "time": 0.80004} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.01351, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.93125, "top5_acc": 0.99688, "loss_cls": 0.404, "loss": 0.404, "time": 0.49582} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.01349, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.34158, "loss": 0.34158, "time": 0.49216} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.01346, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.9375, "top5_acc": 1.0, "loss_cls": 0.33918, "loss": 0.33918, "time": 0.30079} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.01344, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.4598, "loss": 0.4598, "time": 0.45588} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.01342, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.91938, "top5_acc": 0.9975, "loss_cls": 0.43903, "loss": 0.43903, "time": 0.3155} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.0134, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.41569, "loss": 0.41569, "time": 0.49182} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.01338, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91062, "top5_acc": 0.99812, "loss_cls": 0.44535, "loss": 0.44535, "time": 0.49501} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.01336, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91062, "top5_acc": 0.99812, "loss_cls": 0.4755, "loss": 0.4755, "time": 0.49228} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.01334, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90938, "top5_acc": 0.99875, "loss_cls": 0.46679, "loss": 0.46679, "time": 0.49187} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.01332, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9075, "top5_acc": 0.99875, "loss_cls": 0.47454, "loss": 0.47454, "time": 0.4911} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.0133, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9275, "top5_acc": 0.99562, "loss_cls": 0.43983, "loss": 0.43983, "time": 0.49188} +{"mode": "val", "epoch": 72, "iter": 533, "lr": 0.01329, "top1_acc": 0.8661, "top5_acc": 0.9912, "mean_class_accuracy": 0.81702} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.01326, "memory": 4083, "data_time": 0.19457, "top1_acc": 0.92938, "top5_acc": 0.99938, "loss_cls": 0.3899, "loss": 0.3899, "time": 0.7907} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.01324, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91875, "top5_acc": 0.9975, "loss_cls": 0.41118, "loss": 0.41118, "time": 0.49268} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.01322, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.92938, "top5_acc": 1.0, "loss_cls": 0.38763, "loss": 0.38763, "time": 0.48881} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.0132, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92188, "top5_acc": 1.0, "loss_cls": 0.41828, "loss": 0.41828, "time": 0.27574} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.01318, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92438, "top5_acc": 1.0, "loss_cls": 0.39563, "loss": 0.39563, "time": 0.5106} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.01316, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.43809, "loss": 0.43809, "time": 0.31966} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.01314, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92, "top5_acc": 0.99812, "loss_cls": 0.42309, "loss": 0.42309, "time": 0.49392} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.01312, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91812, "top5_acc": 0.99875, "loss_cls": 0.42394, "loss": 0.42394, "time": 0.49088} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.0131, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.9325, "top5_acc": 0.99938, "loss_cls": 0.38997, "loss": 0.38997, "time": 0.49501} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.01308, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.92, "top5_acc": 0.99625, "loss_cls": 0.4486, "loss": 0.4486, "time": 0.49223} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.01306, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.93312, "top5_acc": 0.99812, "loss_cls": 0.3865, "loss": 0.3865, "time": 0.4956} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.01304, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91688, "top5_acc": 0.99938, "loss_cls": 0.41571, "loss": 0.41571, "time": 0.49143} +{"mode": "val", "epoch": 73, "iter": 533, "lr": 0.01302, "top1_acc": 0.88511, "top5_acc": 0.99448, "mean_class_accuracy": 0.84924} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.013, "memory": 4083, "data_time": 0.19386, "top1_acc": 0.93375, "top5_acc": 1.0, "loss_cls": 0.36704, "loss": 0.36704, "time": 0.80546} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.01298, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92562, "top5_acc": 0.99875, "loss_cls": 0.41387, "loss": 0.41387, "time": 0.49099} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.01296, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.38921, "loss": 0.38921, "time": 0.48989} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.01294, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9275, "top5_acc": 0.9975, "loss_cls": 0.38377, "loss": 0.38377, "time": 0.30254} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.01292, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93625, "top5_acc": 1.0, "loss_cls": 0.36797, "loss": 0.36797, "time": 0.45195} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.0129, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92938, "top5_acc": 0.99812, "loss_cls": 0.37366, "loss": 0.37366, "time": 0.33723} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.01288, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.44355, "loss": 0.44355, "time": 0.48932} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.01286, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92375, "top5_acc": 0.99812, "loss_cls": 0.41439, "loss": 0.41439, "time": 0.48993} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.01284, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.91188, "top5_acc": 0.9975, "loss_cls": 0.45949, "loss": 0.45949, "time": 0.49194} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.01282, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.41999, "loss": 0.41999, "time": 0.48995} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.0128, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91938, "top5_acc": 0.99812, "loss_cls": 0.4487, "loss": 0.4487, "time": 0.48762} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.01278, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9125, "top5_acc": 0.99875, "loss_cls": 0.43656, "loss": 0.43656, "time": 0.49181} +{"mode": "val", "epoch": 74, "iter": 533, "lr": 0.01276, "top1_acc": 0.87149, "top5_acc": 0.99143, "mean_class_accuracy": 0.82805} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.01274, "memory": 4083, "data_time": 0.18666, "top1_acc": 0.9375, "top5_acc": 0.9975, "loss_cls": 0.35878, "loss": 0.35878, "time": 0.79099} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.01272, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.92125, "top5_acc": 0.99812, "loss_cls": 0.3935, "loss": 0.3935, "time": 0.4896} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.0127, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.39054, "loss": 0.39054, "time": 0.4897} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.01268, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.92312, "top5_acc": 0.9975, "loss_cls": 0.414, "loss": 0.414, "time": 0.293} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.01266, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.90812, "top5_acc": 0.99938, "loss_cls": 0.478, "loss": 0.478, "time": 0.47223} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.01264, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93625, "top5_acc": 0.99812, "loss_cls": 0.40548, "loss": 0.40548, "time": 0.3236} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.01262, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.36365, "loss": 0.36365, "time": 0.49291} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.0126, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94, "top5_acc": 1.0, "loss_cls": 0.35184, "loss": 0.35184, "time": 0.49492} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.01258, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92625, "top5_acc": 0.9975, "loss_cls": 0.39513, "loss": 0.39513, "time": 0.49318} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.01256, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92312, "top5_acc": 0.99812, "loss_cls": 0.38112, "loss": 0.38112, "time": 0.48763} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.01254, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.92562, "top5_acc": 0.99875, "loss_cls": 0.41611, "loss": 0.41611, "time": 0.48967} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.01252, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91938, "top5_acc": 0.99812, "loss_cls": 0.43121, "loss": 0.43121, "time": 0.48862} +{"mode": "val", "epoch": 75, "iter": 533, "lr": 0.0125, "top1_acc": 0.87666, "top5_acc": 0.99296, "mean_class_accuracy": 0.83318} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.01248, "memory": 4083, "data_time": 0.18878, "top1_acc": 0.93375, "top5_acc": 0.99938, "loss_cls": 0.3629, "loss": 0.3629, "time": 0.79722} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.01246, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93375, "top5_acc": 0.99938, "loss_cls": 0.34112, "loss": 0.34112, "time": 0.49173} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.01244, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93625, "top5_acc": 0.99938, "loss_cls": 0.36434, "loss": 0.36434, "time": 0.49521} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.01242, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92375, "top5_acc": 0.99625, "loss_cls": 0.44956, "loss": 0.44956, "time": 0.28627} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.0124, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92938, "top5_acc": 0.99938, "loss_cls": 0.40229, "loss": 0.40229, "time": 0.48579} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.01238, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91562, "top5_acc": 0.99875, "loss_cls": 0.41171, "loss": 0.41171, "time": 0.32202} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.01236, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94125, "top5_acc": 0.99812, "loss_cls": 0.35334, "loss": 0.35334, "time": 0.49054} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.01234, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93, "top5_acc": 0.99625, "loss_cls": 0.41026, "loss": 0.41026, "time": 0.4883} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.01232, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92062, "top5_acc": 0.99812, "loss_cls": 0.40885, "loss": 0.40885, "time": 0.48971} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.0123, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92688, "top5_acc": 0.99875, "loss_cls": 0.39212, "loss": 0.39212, "time": 0.49295} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.01228, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.41802, "loss": 0.41802, "time": 0.49094} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.01225, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9175, "top5_acc": 0.99625, "loss_cls": 0.42866, "loss": 0.42866, "time": 0.49217} +{"mode": "val", "epoch": 76, "iter": 533, "lr": 0.01224, "top1_acc": 0.8668, "top5_acc": 0.99143, "mean_class_accuracy": 0.83383} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.01222, "memory": 4083, "data_time": 0.18445, "top1_acc": 0.93875, "top5_acc": 0.99688, "loss_cls": 0.34405, "loss": 0.34405, "time": 0.78689} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0122, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93375, "top5_acc": 0.9975, "loss_cls": 0.33615, "loss": 0.33615, "time": 0.4905} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.01218, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.94, "top5_acc": 1.0, "loss_cls": 0.36266, "loss": 0.36266, "time": 0.49115} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.01216, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.3757, "loss": 0.3757, "time": 0.27491} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.01214, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92375, "top5_acc": 0.99938, "loss_cls": 0.39249, "loss": 0.39249, "time": 0.50829} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.01212, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.39764, "loss": 0.39764, "time": 0.27904} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.0121, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91938, "top5_acc": 0.99938, "loss_cls": 0.4126, "loss": 0.4126, "time": 0.49024} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.01207, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93375, "top5_acc": 0.99938, "loss_cls": 0.40125, "loss": 0.40125, "time": 0.49607} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.01205, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.92875, "top5_acc": 0.99875, "loss_cls": 0.37366, "loss": 0.37366, "time": 0.49457} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.01203, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92438, "top5_acc": 0.99875, "loss_cls": 0.39325, "loss": 0.39325, "time": 0.49179} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.01201, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.38711, "loss": 0.38711, "time": 0.49008} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.01199, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92062, "top5_acc": 0.99688, "loss_cls": 0.40948, "loss": 0.40948, "time": 0.48839} +{"mode": "val", "epoch": 77, "iter": 533, "lr": 0.01198, "top1_acc": 0.88734, "top5_acc": 0.99284, "mean_class_accuracy": 0.84404} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.01196, "memory": 4083, "data_time": 0.18552, "top1_acc": 0.93812, "top5_acc": 0.99938, "loss_cls": 0.31052, "loss": 0.31052, "time": 0.79566} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.01194, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94, "top5_acc": 0.99812, "loss_cls": 0.34151, "loss": 0.34151, "time": 0.49181} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.01192, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.94, "top5_acc": 0.9975, "loss_cls": 0.34282, "loss": 0.34282, "time": 0.49346} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.0119, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.31603, "loss": 0.31603, "time": 0.30561} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.01187, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94062, "top5_acc": 0.99688, "loss_cls": 0.34981, "loss": 0.34981, "time": 0.51125} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.01185, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93188, "top5_acc": 0.99812, "loss_cls": 0.38992, "loss": 0.38992, "time": 0.26758} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.01183, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.93438, "top5_acc": 0.99938, "loss_cls": 0.3479, "loss": 0.3479, "time": 0.49149} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.01181, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.38409, "loss": 0.38409, "time": 0.49088} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.01179, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.34065, "loss": 0.34065, "time": 0.48976} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.01177, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9175, "top5_acc": 0.9975, "loss_cls": 0.42949, "loss": 0.42949, "time": 0.48863} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.01175, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92625, "top5_acc": 0.99625, "loss_cls": 0.39587, "loss": 0.39587, "time": 0.49043} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.01173, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92938, "top5_acc": 0.99812, "loss_cls": 0.37511, "loss": 0.37511, "time": 0.48911} +{"mode": "val", "epoch": 78, "iter": 533, "lr": 0.01172, "top1_acc": 0.88558, "top5_acc": 0.99272, "mean_class_accuracy": 0.84301} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.01169, "memory": 4083, "data_time": 0.19441, "top1_acc": 0.92688, "top5_acc": 0.99938, "loss_cls": 0.36695, "loss": 0.36695, "time": 0.80312} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.01167, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93188, "top5_acc": 1.0, "loss_cls": 0.36005, "loss": 0.36005, "time": 0.49136} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.01165, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95062, "top5_acc": 0.99812, "loss_cls": 0.29393, "loss": 0.29393, "time": 0.49203} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.01163, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.94, "top5_acc": 1.0, "loss_cls": 0.32291, "loss": 0.32291, "time": 0.3014} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.01161, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.93625, "top5_acc": 1.0, "loss_cls": 0.37183, "loss": 0.37183, "time": 0.50999} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.01159, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93625, "top5_acc": 0.99938, "loss_cls": 0.34218, "loss": 0.34218, "time": 0.26393} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.01157, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94875, "top5_acc": 0.99812, "loss_cls": 0.31177, "loss": 0.31177, "time": 0.49179} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.01155, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.32092, "loss": 0.32092, "time": 0.48946} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.01153, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92688, "top5_acc": 0.99688, "loss_cls": 0.40418, "loss": 0.40418, "time": 0.48907} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.01151, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.92062, "top5_acc": 0.99938, "loss_cls": 0.40756, "loss": 0.40756, "time": 0.49126} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.01149, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.935, "top5_acc": 1.0, "loss_cls": 0.35087, "loss": 0.35087, "time": 0.49195} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.01147, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.39708, "loss": 0.39708, "time": 0.48948} +{"mode": "val", "epoch": 79, "iter": 533, "lr": 0.01145, "top1_acc": 0.84215, "top5_acc": 0.98909, "mean_class_accuracy": 0.8079} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.01143, "memory": 4083, "data_time": 0.18752, "top1_acc": 0.93625, "top5_acc": 0.99875, "loss_cls": 0.34929, "loss": 0.34929, "time": 0.8044} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.01141, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94188, "top5_acc": 1.0, "loss_cls": 0.33933, "loss": 0.33933, "time": 0.49363} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.01139, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94188, "top5_acc": 0.99875, "loss_cls": 0.33227, "loss": 0.33227, "time": 0.49009} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.01137, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94625, "top5_acc": 0.99875, "loss_cls": 0.31254, "loss": 0.31254, "time": 0.32294} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.01135, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9425, "top5_acc": 1.0, "loss_cls": 0.34182, "loss": 0.34182, "time": 0.5096} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.01133, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.3674, "loss": 0.3674, "time": 0.25048} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.01131, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92375, "top5_acc": 0.99938, "loss_cls": 0.42283, "loss": 0.42283, "time": 0.47349} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.01129, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91812, "top5_acc": 0.99875, "loss_cls": 0.41904, "loss": 0.41904, "time": 0.49014} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.01127, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93062, "top5_acc": 0.99812, "loss_cls": 0.39621, "loss": 0.39621, "time": 0.49381} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.01125, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94812, "top5_acc": 0.99812, "loss_cls": 0.29112, "loss": 0.29112, "time": 0.49013} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.01123, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93375, "top5_acc": 0.99938, "loss_cls": 0.36027, "loss": 0.36027, "time": 0.48701} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.01121, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.93062, "top5_acc": 0.99812, "loss_cls": 0.34738, "loss": 0.34738, "time": 0.48925} +{"mode": "val", "epoch": 80, "iter": 533, "lr": 0.01119, "top1_acc": 0.8648, "top5_acc": 0.99085, "mean_class_accuracy": 0.83278} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.01117, "memory": 4083, "data_time": 0.18912, "top1_acc": 0.93812, "top5_acc": 1.0, "loss_cls": 0.35543, "loss": 0.35543, "time": 0.80001} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.01115, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94438, "top5_acc": 1.0, "loss_cls": 0.30774, "loss": 0.30774, "time": 0.49214} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.01113, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92688, "top5_acc": 0.99812, "loss_cls": 0.39974, "loss": 0.39974, "time": 0.48986} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.01111, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9475, "top5_acc": 1.0, "loss_cls": 0.30432, "loss": 0.30432, "time": 0.33777} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.01109, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92438, "top5_acc": 0.99688, "loss_cls": 0.40246, "loss": 0.40246, "time": 0.50836} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.01107, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.93562, "top5_acc": 1.0, "loss_cls": 0.35649, "loss": 0.35649, "time": 0.24709} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.01105, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.93562, "top5_acc": 0.99812, "loss_cls": 0.33676, "loss": 0.33676, "time": 0.47347} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.01103, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93, "top5_acc": 0.99812, "loss_cls": 0.3664, "loss": 0.3664, "time": 0.49156} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.01101, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.93312, "top5_acc": 0.99688, "loss_cls": 0.36169, "loss": 0.36169, "time": 0.49055} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.01099, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93125, "top5_acc": 0.99875, "loss_cls": 0.40328, "loss": 0.40328, "time": 0.49121} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.01097, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92312, "top5_acc": 0.9975, "loss_cls": 0.41671, "loss": 0.41671, "time": 0.49101} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.01095, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92688, "top5_acc": 0.9975, "loss_cls": 0.42073, "loss": 0.42073, "time": 0.49155} +{"mode": "val", "epoch": 81, "iter": 533, "lr": 0.01093, "top1_acc": 0.87971, "top5_acc": 0.99284, "mean_class_accuracy": 0.83286} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.01091, "memory": 4083, "data_time": 0.18247, "top1_acc": 0.93688, "top5_acc": 0.99938, "loss_cls": 0.34135, "loss": 0.34135, "time": 0.777} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.01089, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93312, "top5_acc": 1.0, "loss_cls": 0.33863, "loss": 0.33863, "time": 0.49001} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.01087, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.31149, "loss": 0.31149, "time": 0.49143} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.01085, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.2998, "loss": 0.2998, "time": 0.38039} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.01083, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.27936, "loss": 0.27936, "time": 0.51131} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.01081, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.30312, "loss": 0.30312, "time": 0.23736} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.01079, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93375, "top5_acc": 0.99875, "loss_cls": 0.39918, "loss": 0.39918, "time": 0.44202} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.01077, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.35058, "loss": 0.35058, "time": 0.49393} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.01075, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.92375, "top5_acc": 0.99938, "loss_cls": 0.39951, "loss": 0.39951, "time": 0.49014} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.01073, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9275, "top5_acc": 0.99812, "loss_cls": 0.35855, "loss": 0.35855, "time": 0.48974} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.01071, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94438, "top5_acc": 0.99875, "loss_cls": 0.32636, "loss": 0.32636, "time": 0.49035} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.01069, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93438, "top5_acc": 0.9975, "loss_cls": 0.36585, "loss": 0.36585, "time": 0.48993} +{"mode": "val", "epoch": 82, "iter": 533, "lr": 0.01067, "top1_acc": 0.88429, "top5_acc": 0.99261, "mean_class_accuracy": 0.85083} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.01065, "memory": 4083, "data_time": 0.1824, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.2863, "loss": 0.2863, "time": 0.80252} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.01063, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94562, "top5_acc": 0.99812, "loss_cls": 0.32429, "loss": 0.32429, "time": 0.4933} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.01061, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94188, "top5_acc": 1.0, "loss_cls": 0.31823, "loss": 0.31823, "time": 0.49156} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.01059, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.2701, "loss": 0.2701, "time": 0.40046} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.01057, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94, "top5_acc": 1.0, "loss_cls": 0.33343, "loss": 0.33343, "time": 0.49943} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.01055, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9475, "top5_acc": 0.99812, "loss_cls": 0.29855, "loss": 0.29855, "time": 0.23562} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.01053, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93188, "top5_acc": 0.99938, "loss_cls": 0.37593, "loss": 0.37593, "time": 0.43677} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.01051, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.34695, "loss": 0.34695, "time": 0.4878} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.01049, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.92562, "top5_acc": 0.99625, "loss_cls": 0.37346, "loss": 0.37346, "time": 0.4899} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.01047, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94125, "top5_acc": 0.99875, "loss_cls": 0.33786, "loss": 0.33786, "time": 0.48919} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.01045, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.29945, "loss": 0.29945, "time": 0.49416} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.01043, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93312, "top5_acc": 0.99938, "loss_cls": 0.37574, "loss": 0.37574, "time": 0.48663} +{"mode": "val", "epoch": 83, "iter": 533, "lr": 0.01042, "top1_acc": 0.88593, "top5_acc": 0.9912, "mean_class_accuracy": 0.85934} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.0104, "memory": 4083, "data_time": 0.18172, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.27714, "loss": 0.27714, "time": 0.79674} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.01038, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95438, "top5_acc": 0.99875, "loss_cls": 0.26804, "loss": 0.26804, "time": 0.49057} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.01036, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94375, "top5_acc": 1.0, "loss_cls": 0.29249, "loss": 0.29249, "time": 0.48846} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.01034, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95438, "top5_acc": 0.99938, "loss_cls": 0.29035, "loss": 0.29035, "time": 0.40519} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.01031, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9375, "top5_acc": 0.99812, "loss_cls": 0.3201, "loss": 0.3201, "time": 0.49417} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.01029, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93188, "top5_acc": 0.9975, "loss_cls": 0.3719, "loss": 0.3719, "time": 0.24375} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.01027, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94188, "top5_acc": 0.99812, "loss_cls": 0.32959, "loss": 0.32959, "time": 0.42193} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.01025, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92938, "top5_acc": 0.99812, "loss_cls": 0.35809, "loss": 0.35809, "time": 0.48441} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.01023, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93625, "top5_acc": 0.99938, "loss_cls": 0.32832, "loss": 0.32832, "time": 0.48831} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.01021, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93062, "top5_acc": 0.99875, "loss_cls": 0.35853, "loss": 0.35853, "time": 0.48998} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.01019, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.93812, "top5_acc": 0.99938, "loss_cls": 0.35459, "loss": 0.35459, "time": 0.48857} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.01017, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92312, "top5_acc": 0.99812, "loss_cls": 0.39177, "loss": 0.39177, "time": 0.48928} +{"mode": "val", "epoch": 84, "iter": 533, "lr": 0.01016, "top1_acc": 0.87642, "top5_acc": 0.9919, "mean_class_accuracy": 0.84548} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.01014, "memory": 4083, "data_time": 0.18441, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.28233, "loss": 0.28233, "time": 0.78637} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.01012, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.29781, "loss": 0.29781, "time": 0.49232} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.0101, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.95062, "top5_acc": 1.0, "loss_cls": 0.27872, "loss": 0.27872, "time": 0.49295} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.01008, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.298, "loss": 0.298, "time": 0.43071} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.01006, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94062, "top5_acc": 0.99938, "loss_cls": 0.35289, "loss": 0.35289, "time": 0.44298} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.01004, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95312, "top5_acc": 0.99875, "loss_cls": 0.27598, "loss": 0.27598, "time": 0.28627} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.01002, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93188, "top5_acc": 1.0, "loss_cls": 0.35696, "loss": 0.35696, "time": 0.4103} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.01, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94125, "top5_acc": 0.9975, "loss_cls": 0.3414, "loss": 0.3414, "time": 0.48849} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.00998, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.33097, "loss": 0.33097, "time": 0.48721} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.00996, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.32819, "loss": 0.32819, "time": 0.49188} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.00994, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.31428, "loss": 0.31428, "time": 0.4889} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.00992, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.29784, "loss": 0.29784, "time": 0.49284} +{"mode": "val", "epoch": 85, "iter": 533, "lr": 0.0099, "top1_acc": 0.8803, "top5_acc": 0.99296, "mean_class_accuracy": 0.83758} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.00988, "memory": 4083, "data_time": 0.18402, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.24288, "loss": 0.24288, "time": 0.79624} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.00986, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94812, "top5_acc": 1.0, "loss_cls": 0.29574, "loss": 0.29574, "time": 0.48739} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.00984, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.26234, "loss": 0.26234, "time": 0.49227} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.00982, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.27891, "loss": 0.27891, "time": 0.43789} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.0098, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.2737, "loss": 0.2737, "time": 0.41462} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.00978, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.25508, "loss": 0.25508, "time": 0.31444} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.00976, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.93938, "top5_acc": 0.99875, "loss_cls": 0.34021, "loss": 0.34021, "time": 0.4113} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.00974, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94, "top5_acc": 0.99688, "loss_cls": 0.35937, "loss": 0.35937, "time": 0.49197} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.00972, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93, "top5_acc": 0.9975, "loss_cls": 0.38469, "loss": 0.38469, "time": 0.4903} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.0097, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93375, "top5_acc": 0.99938, "loss_cls": 0.35735, "loss": 0.35735, "time": 0.48835} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.00968, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.26912, "loss": 0.26912, "time": 0.49245} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.00966, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.30259, "loss": 0.30259, "time": 0.48664} +{"mode": "val", "epoch": 86, "iter": 533, "lr": 0.00965, "top1_acc": 0.86692, "top5_acc": 0.99179, "mean_class_accuracy": 0.81303} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.00963, "memory": 4083, "data_time": 0.18701, "top1_acc": 0.935, "top5_acc": 0.99938, "loss_cls": 0.35103, "loss": 0.35103, "time": 0.79387} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.00961, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.28778, "loss": 0.28778, "time": 0.49425} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.00959, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.29859, "loss": 0.29859, "time": 0.48814} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.00957, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.25969, "loss": 0.25969, "time": 0.4457} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.00955, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.26453, "loss": 0.26453, "time": 0.42023} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.00953, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.28784, "loss": 0.28784, "time": 0.30977} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.00951, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.31063, "loss": 0.31063, "time": 0.4065} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.00949, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95625, "top5_acc": 0.99812, "loss_cls": 0.27024, "loss": 0.27024, "time": 0.48954} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.00947, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94188, "top5_acc": 1.0, "loss_cls": 0.31172, "loss": 0.31172, "time": 0.48781} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.00945, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.26131, "loss": 0.26131, "time": 0.49023} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.00943, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.94, "top5_acc": 0.99938, "loss_cls": 0.30988, "loss": 0.30988, "time": 0.48817} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.00941, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93688, "top5_acc": 0.99875, "loss_cls": 0.34717, "loss": 0.34717, "time": 0.48828} +{"mode": "val", "epoch": 87, "iter": 533, "lr": 0.00939, "top1_acc": 0.86762, "top5_acc": 0.99296, "mean_class_accuracy": 0.82205} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.00937, "memory": 4083, "data_time": 0.18481, "top1_acc": 0.945, "top5_acc": 1.0, "loss_cls": 0.28637, "loss": 0.28637, "time": 0.80528} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.00935, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.31969, "loss": 0.31969, "time": 0.48952} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.00933, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.28763, "loss": 0.28763, "time": 0.49058} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.00931, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94312, "top5_acc": 1.0, "loss_cls": 0.2909, "loss": 0.2909, "time": 0.44136} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.00929, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.35143, "loss": 0.35143, "time": 0.41717} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.00927, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.92625, "top5_acc": 1.0, "loss_cls": 0.3649, "loss": 0.3649, "time": 0.31702} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.00925, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94938, "top5_acc": 1.0, "loss_cls": 0.30042, "loss": 0.30042, "time": 0.40434} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.00923, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.955, "top5_acc": 0.99875, "loss_cls": 0.26381, "loss": 0.26381, "time": 0.48852} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.00921, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.30551, "loss": 0.30551, "time": 0.48922} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.00919, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93688, "top5_acc": 0.99875, "loss_cls": 0.31557, "loss": 0.31557, "time": 0.48791} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.00917, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.30525, "loss": 0.30525, "time": 0.48809} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.00915, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9325, "top5_acc": 0.99938, "loss_cls": 0.34791, "loss": 0.34791, "time": 0.48795} +{"mode": "val", "epoch": 88, "iter": 533, "lr": 0.00914, "top1_acc": 0.8871, "top5_acc": 0.99272, "mean_class_accuracy": 0.85166} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.00912, "memory": 4083, "data_time": 0.18772, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.27997, "loss": 0.27997, "time": 0.79906} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0091, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95062, "top5_acc": 1.0, "loss_cls": 0.25744, "loss": 0.25744, "time": 0.49371} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.00908, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.29892, "loss": 0.29892, "time": 0.48869} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.00906, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95875, "top5_acc": 0.99812, "loss_cls": 0.29361, "loss": 0.29361, "time": 0.45508} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.00904, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94188, "top5_acc": 0.99938, "loss_cls": 0.30788, "loss": 0.30788, "time": 0.40528} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.00902, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.95812, "top5_acc": 0.99875, "loss_cls": 0.25836, "loss": 0.25836, "time": 0.32383} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.009, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94625, "top5_acc": 1.0, "loss_cls": 0.28232, "loss": 0.28232, "time": 0.39591} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.00898, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.28499, "loss": 0.28499, "time": 0.4901} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.00896, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.2989, "loss": 0.2989, "time": 0.49194} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.00894, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94438, "top5_acc": 1.0, "loss_cls": 0.30184, "loss": 0.30184, "time": 0.48928} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.00892, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.33511, "loss": 0.33511, "time": 0.49093} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.0089, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.30663, "loss": 0.30663, "time": 0.4918} +{"mode": "val", "epoch": 89, "iter": 533, "lr": 0.00889, "top1_acc": 0.88992, "top5_acc": 0.99272, "mean_class_accuracy": 0.85184} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.00887, "memory": 4083, "data_time": 0.18838, "top1_acc": 0.94188, "top5_acc": 0.99875, "loss_cls": 0.30826, "loss": 0.30826, "time": 0.7915} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.00885, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.27195, "loss": 0.27195, "time": 0.48972} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.00883, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.24969, "loss": 0.24969, "time": 0.48868} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.00881, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.24978, "loss": 0.24978, "time": 0.46937} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.00879, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.2923, "loss": 0.2923, "time": 0.35398} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.00877, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.25246, "loss": 0.25246, "time": 0.37593} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.00875, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.25308, "loss": 0.25308, "time": 0.36444} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.00873, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.28711, "loss": 0.28711, "time": 0.48685} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.00871, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93562, "top5_acc": 0.99875, "loss_cls": 0.35227, "loss": 0.35227, "time": 0.4918} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.00869, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.26626, "loss": 0.26626, "time": 0.48271} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.00867, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.32056, "loss": 0.32056, "time": 0.48703} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.00865, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.32847, "loss": 0.32847, "time": 0.49072} +{"mode": "val", "epoch": 90, "iter": 533, "lr": 0.00864, "top1_acc": 0.88863, "top5_acc": 0.99272, "mean_class_accuracy": 0.85092} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.00862, "memory": 4083, "data_time": 0.18425, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.2582, "loss": 0.2582, "time": 0.79903} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0086, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95, "top5_acc": 0.99812, "loss_cls": 0.27787, "loss": 0.27787, "time": 0.49003} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.00858, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95625, "top5_acc": 0.99875, "loss_cls": 0.27833, "loss": 0.27833, "time": 0.48978} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.00856, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.29355, "loss": 0.29355, "time": 0.4887} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.00854, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94125, "top5_acc": 0.99875, "loss_cls": 0.32789, "loss": 0.32789, "time": 0.30678} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.00852, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94688, "top5_acc": 0.99938, "loss_cls": 0.27335, "loss": 0.27335, "time": 0.42646} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.0085, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.27499, "loss": 0.27499, "time": 0.34256} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.00848, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.26636, "loss": 0.26636, "time": 0.49106} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.00846, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.2696, "loss": 0.2696, "time": 0.49292} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.00844, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.28976, "loss": 0.28976, "time": 0.4865} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.00842, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.2811, "loss": 0.2811, "time": 0.48552} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.0084, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.27929, "loss": 0.27929, "time": 0.48672} +{"mode": "val", "epoch": 91, "iter": 533, "lr": 0.00839, "top1_acc": 0.90342, "top5_acc": 0.9939, "mean_class_accuracy": 0.87373} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.00837, "memory": 4083, "data_time": 0.1837, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.19102, "loss": 0.19102, "time": 0.80007} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.00835, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96688, "top5_acc": 0.99938, "loss_cls": 0.20957, "loss": 0.20957, "time": 0.48845} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.00833, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.2676, "loss": 0.2676, "time": 0.48913} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.00831, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.24835, "loss": 0.24835, "time": 0.49221} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.00829, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.27882, "loss": 0.27882, "time": 0.28173} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.00827, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95875, "top5_acc": 0.99812, "loss_cls": 0.25769, "loss": 0.25769, "time": 0.47735} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.00825, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.22922, "loss": 0.22922, "time": 0.32775} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.00824, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95312, "top5_acc": 0.99875, "loss_cls": 0.27592, "loss": 0.27592, "time": 0.48817} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.00822, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.24839, "loss": 0.24839, "time": 0.49161} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.0082, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.94438, "top5_acc": 0.99875, "loss_cls": 0.32979, "loss": 0.32979, "time": 0.48783} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.00818, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.33803, "loss": 0.33803, "time": 0.48995} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.00816, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.94812, "top5_acc": 1.0, "loss_cls": 0.29632, "loss": 0.29632, "time": 0.48819} +{"mode": "val", "epoch": 92, "iter": 533, "lr": 0.00814, "top1_acc": 0.88405, "top5_acc": 0.99249, "mean_class_accuracy": 0.83457} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.00812, "memory": 4083, "data_time": 0.18575, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.28767, "loss": 0.28767, "time": 0.79077} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.0081, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.28634, "loss": 0.28634, "time": 0.49102} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.00809, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95625, "top5_acc": 0.99875, "loss_cls": 0.2664, "loss": 0.2664, "time": 0.49121} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.00807, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.23372, "loss": 0.23372, "time": 0.48767} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.00805, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9525, "top5_acc": 0.99875, "loss_cls": 0.26625, "loss": 0.26625, "time": 0.27204} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.00803, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.25007, "loss": 0.25007, "time": 0.50636} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.00801, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.21576, "loss": 0.21576, "time": 0.3022} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.00799, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94625, "top5_acc": 0.99875, "loss_cls": 0.27314, "loss": 0.27314, "time": 0.48954} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.00797, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95375, "top5_acc": 0.99875, "loss_cls": 0.25253, "loss": 0.25253, "time": 0.48901} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.00795, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96438, "top5_acc": 0.99875, "loss_cls": 0.23786, "loss": 0.23786, "time": 0.4832} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.00793, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.24578, "loss": 0.24578, "time": 0.49047} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.00791, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.26554, "loss": 0.26554, "time": 0.48757} +{"mode": "val", "epoch": 93, "iter": 533, "lr": 0.0079, "top1_acc": 0.89497, "top5_acc": 0.9912, "mean_class_accuracy": 0.86447} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.00788, "memory": 4083, "data_time": 0.18317, "top1_acc": 0.96, "top5_acc": 0.99875, "loss_cls": 0.24539, "loss": 0.24539, "time": 0.79758} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.00786, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95438, "top5_acc": 0.99938, "loss_cls": 0.25484, "loss": 0.25484, "time": 0.49289} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.00784, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.1963, "loss": 0.1963, "time": 0.49199} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.00782, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9525, "top5_acc": 0.99875, "loss_cls": 0.26872, "loss": 0.26872, "time": 0.48643} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.0078, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.29078, "loss": 0.29078, "time": 0.28719} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.00778, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95562, "top5_acc": 0.99875, "loss_cls": 0.25095, "loss": 0.25095, "time": 0.50839} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.00777, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.24907, "loss": 0.24907, "time": 0.29329} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.00775, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.23575, "loss": 0.23575, "time": 0.49105} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.00773, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97438, "top5_acc": 0.99938, "loss_cls": 0.18927, "loss": 0.18927, "time": 0.48538} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.00771, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.27404, "loss": 0.27404, "time": 0.49214} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.00769, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.28421, "loss": 0.28421, "time": 0.48772} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.00767, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.945, "top5_acc": 0.99938, "loss_cls": 0.31006, "loss": 0.31006, "time": 0.49047} +{"mode": "val", "epoch": 94, "iter": 533, "lr": 0.00766, "top1_acc": 0.89966, "top5_acc": 0.9946, "mean_class_accuracy": 0.86209} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.00764, "memory": 4083, "data_time": 0.17855, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.20358, "loss": 0.20358, "time": 0.80082} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.00762, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.21137, "loss": 0.21137, "time": 0.48792} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.0076, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.21317, "loss": 0.21317, "time": 0.49012} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.00758, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.21602, "loss": 0.21602, "time": 0.49073} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.00756, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.25466, "loss": 0.25466, "time": 0.29511} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.00754, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.94438, "top5_acc": 1.0, "loss_cls": 0.26786, "loss": 0.26786, "time": 0.50806} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.00752, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.26231, "loss": 0.26231, "time": 0.28248} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.00751, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.21507, "loss": 0.21507, "time": 0.48714} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.00749, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95562, "top5_acc": 0.99875, "loss_cls": 0.26138, "loss": 0.26138, "time": 0.49536} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.00747, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95188, "top5_acc": 1.0, "loss_cls": 0.25709, "loss": 0.25709, "time": 0.48895} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.00745, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.26908, "loss": 0.26908, "time": 0.48837} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.00743, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.94562, "top5_acc": 0.99875, "loss_cls": 0.30826, "loss": 0.30826, "time": 0.4891} +{"mode": "val", "epoch": 95, "iter": 533, "lr": 0.00742, "top1_acc": 0.88135, "top5_acc": 0.99261, "mean_class_accuracy": 0.84763} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.0074, "memory": 4083, "data_time": 0.18329, "top1_acc": 0.95562, "top5_acc": 1.0, "loss_cls": 0.24886, "loss": 0.24886, "time": 0.80125} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.00738, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.19529, "loss": 0.19529, "time": 0.49254} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.00736, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.19783, "loss": 0.19783, "time": 0.48941} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.00734, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.22738, "loss": 0.22738, "time": 0.49058} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.00732, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.24391, "loss": 0.24391, "time": 0.31131} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.0073, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.22262, "loss": 0.22262, "time": 0.50902} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.00729, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.95188, "top5_acc": 1.0, "loss_cls": 0.27261, "loss": 0.27261, "time": 0.27281} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.00727, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.25854, "loss": 0.25854, "time": 0.49095} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.00725, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.21698, "loss": 0.21698, "time": 0.4896} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.00723, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.27353, "loss": 0.27353, "time": 0.48645} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.00721, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.21888, "loss": 0.21888, "time": 0.48939} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.00719, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.28991, "loss": 0.28991, "time": 0.48708} +{"mode": "val", "epoch": 96, "iter": 533, "lr": 0.00718, "top1_acc": 0.90036, "top5_acc": 0.99519, "mean_class_accuracy": 0.86922} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.00716, "memory": 4083, "data_time": 0.18132, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.20539, "loss": 0.20539, "time": 0.78783} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.00714, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.23839, "loss": 0.23839, "time": 0.49235} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.00712, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.96625, "top5_acc": 0.99938, "loss_cls": 0.21813, "loss": 0.21813, "time": 0.49091} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.0071, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.23988, "loss": 0.23988, "time": 0.48882} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.00709, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96375, "top5_acc": 0.99875, "loss_cls": 0.21868, "loss": 0.21868, "time": 0.3281} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.00707, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.25816, "loss": 0.25816, "time": 0.50778} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.00705, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95938, "top5_acc": 0.99875, "loss_cls": 0.24976, "loss": 0.24976, "time": 0.25399} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.00703, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.1951, "loss": 0.1951, "time": 0.48254} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.00701, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.22276, "loss": 0.22276, "time": 0.48745} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.00699, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96125, "top5_acc": 0.99938, "loss_cls": 0.21539, "loss": 0.21539, "time": 0.48745} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.00698, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.22332, "loss": 0.22332, "time": 0.48854} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.00696, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95625, "top5_acc": 0.99875, "loss_cls": 0.24349, "loss": 0.24349, "time": 0.48979} +{"mode": "val", "epoch": 97, "iter": 533, "lr": 0.00694, "top1_acc": 0.8925, "top5_acc": 0.99108, "mean_class_accuracy": 0.86336} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.00692, "memory": 4083, "data_time": 0.18465, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.22886, "loss": 0.22886, "time": 0.79406} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.00691, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9725, "top5_acc": 0.99938, "loss_cls": 0.17867, "loss": 0.17867, "time": 0.48919} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.00689, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96688, "top5_acc": 0.99938, "loss_cls": 0.18903, "loss": 0.18903, "time": 0.4888} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.00687, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.24555, "loss": 0.24555, "time": 0.49025} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.00685, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.22086, "loss": 0.22086, "time": 0.34714} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.00683, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.17921, "loss": 0.17921, "time": 0.50747} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.00681, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.17095, "loss": 0.17095, "time": 0.25526} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.0068, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.23138, "loss": 0.23138, "time": 0.48724} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.00678, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.21813, "loss": 0.21813, "time": 0.49315} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.00676, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.241, "loss": 0.241, "time": 0.49001} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.00674, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95062, "top5_acc": 0.99875, "loss_cls": 0.2607, "loss": 0.2607, "time": 0.48715} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.00672, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.19448, "loss": 0.19448, "time": 0.48807} +{"mode": "val", "epoch": 98, "iter": 533, "lr": 0.00671, "top1_acc": 0.90318, "top5_acc": 0.9939, "mean_class_accuracy": 0.8735} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.00669, "memory": 4083, "data_time": 0.18306, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.15326, "loss": 0.15326, "time": 0.79876} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.00667, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.19821, "loss": 0.19821, "time": 0.49402} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.00665, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.16581, "loss": 0.16581, "time": 0.49073} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.00664, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.1649, "loss": 0.1649, "time": 0.49403} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.00662, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.21559, "loss": 0.21559, "time": 0.33418} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.0066, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9675, "top5_acc": 0.99875, "loss_cls": 0.21166, "loss": 0.21166, "time": 0.50963} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.00658, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.20309, "loss": 0.20309, "time": 0.25304} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.00656, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.20155, "loss": 0.20155, "time": 0.48752} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.00655, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.22751, "loss": 0.22751, "time": 0.49115} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.00653, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96062, "top5_acc": 0.99875, "loss_cls": 0.24315, "loss": 0.24315, "time": 0.48996} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.00651, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.96812, "top5_acc": 0.99938, "loss_cls": 0.21044, "loss": 0.21044, "time": 0.49261} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.00649, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.21642, "loss": 0.21642, "time": 0.49024} +{"mode": "val", "epoch": 99, "iter": 533, "lr": 0.00648, "top1_acc": 0.88875, "top5_acc": 0.99249, "mean_class_accuracy": 0.85228} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.00646, "memory": 4083, "data_time": 0.19057, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.2243, "loss": 0.2243, "time": 0.79902} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.00644, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96938, "top5_acc": 0.99875, "loss_cls": 0.19268, "loss": 0.19268, "time": 0.48891} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.00642, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.19496, "loss": 0.19496, "time": 0.49315} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.00641, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.21034, "loss": 0.21034, "time": 0.4911} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.00639, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.18419, "loss": 0.18419, "time": 0.33188} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.00637, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.17682, "loss": 0.17682, "time": 0.50802} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.00635, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.17122, "loss": 0.17122, "time": 0.25441} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.00634, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.1907, "loss": 0.1907, "time": 0.48143} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.00632, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.22155, "loss": 0.22155, "time": 0.4902} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.0063, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95938, "top5_acc": 0.99875, "loss_cls": 0.2294, "loss": 0.2294, "time": 0.48863} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.00628, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.2071, "loss": 0.2071, "time": 0.48935} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.00626, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.22804, "loss": 0.22804, "time": 0.49175} +{"mode": "val", "epoch": 100, "iter": 533, "lr": 0.00625, "top1_acc": 0.89743, "top5_acc": 0.99484, "mean_class_accuracy": 0.86734} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.00623, "memory": 4083, "data_time": 0.18196, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.21649, "loss": 0.21649, "time": 0.78363} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.00621, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.17425, "loss": 0.17425, "time": 0.48824} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.0062, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.1651, "loss": 0.1651, "time": 0.49313} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.00618, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.21131, "loss": 0.21131, "time": 0.49143} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.00616, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.22647, "loss": 0.22647, "time": 0.35696} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.00614, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.22998, "loss": 0.22998, "time": 0.50964} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.00613, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.22956, "loss": 0.22956, "time": 0.25013} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.00611, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.22686, "loss": 0.22686, "time": 0.47779} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.00609, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.2028, "loss": 0.2028, "time": 0.49277} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.00607, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.17174, "loss": 0.17174, "time": 0.48957} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.00606, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96688, "top5_acc": 0.99938, "loss_cls": 0.20175, "loss": 0.20175, "time": 0.49089} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.00604, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.19299, "loss": 0.19299, "time": 0.48927} +{"mode": "val", "epoch": 101, "iter": 533, "lr": 0.00602, "top1_acc": 0.89743, "top5_acc": 0.99472, "mean_class_accuracy": 0.85794} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.00601, "memory": 4083, "data_time": 0.17815, "top1_acc": 0.96375, "top5_acc": 0.99938, "loss_cls": 0.21625, "loss": 0.21625, "time": 0.79614} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.00599, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.18581, "loss": 0.18581, "time": 0.48791} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.00597, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.15838, "loss": 0.15838, "time": 0.49247} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.00596, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.16634, "loss": 0.16634, "time": 0.48979} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.00594, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.14697, "loss": 0.14697, "time": 0.34446} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.00592, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.18557, "loss": 0.18557, "time": 0.50691} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.0059, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.16877, "loss": 0.16877, "time": 0.24939} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.00589, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.19609, "loss": 0.19609, "time": 0.47044} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.00587, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96562, "top5_acc": 0.99875, "loss_cls": 0.21721, "loss": 0.21721, "time": 0.49182} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.00585, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96625, "top5_acc": 0.99938, "loss_cls": 0.20202, "loss": 0.20202, "time": 0.49498} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.00583, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.18031, "loss": 0.18031, "time": 0.49275} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.00582, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.1804, "loss": 0.1804, "time": 0.48967} +{"mode": "val", "epoch": 102, "iter": 533, "lr": 0.0058, "top1_acc": 0.89766, "top5_acc": 0.99272, "mean_class_accuracy": 0.87095} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.00579, "memory": 4083, "data_time": 0.18301, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.16825, "loss": 0.16825, "time": 0.78408} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.00577, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.16754, "loss": 0.16754, "time": 0.48892} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.00575, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.15246, "loss": 0.15246, "time": 0.49409} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.00573, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.20626, "loss": 0.20626, "time": 0.48915} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.00572, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.17537, "loss": 0.17537, "time": 0.37297} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.0057, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.19915, "loss": 0.19915, "time": 0.50812} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.00568, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.20635, "loss": 0.20635, "time": 0.23799} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.00566, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97312, "top5_acc": 0.99938, "loss_cls": 0.17679, "loss": 0.17679, "time": 0.46206} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.00565, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.17079, "loss": 0.17079, "time": 0.4895} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.00563, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.22219, "loss": 0.22219, "time": 0.4892} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.00561, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96938, "top5_acc": 0.99938, "loss_cls": 0.18659, "loss": 0.18659, "time": 0.48718} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.0056, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.23229, "loss": 0.23229, "time": 0.48902} +{"mode": "val", "epoch": 103, "iter": 533, "lr": 0.00558, "top1_acc": 0.89356, "top5_acc": 0.99448, "mean_class_accuracy": 0.86505} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.00557, "memory": 4083, "data_time": 0.18553, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.19246, "loss": 0.19246, "time": 0.78626} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.00555, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.17366, "loss": 0.17366, "time": 0.49047} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.00553, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.15875, "loss": 0.15875, "time": 0.49317} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.00551, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.15458, "loss": 0.15458, "time": 0.49237} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.0055, "memory": 4083, "data_time": 0.00062, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.13641, "loss": 0.13641, "time": 0.37318} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.00548, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.11465, "loss": 0.11465, "time": 0.50801} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.00546, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97625, "top5_acc": 0.99938, "loss_cls": 0.16302, "loss": 0.16302, "time": 0.23774} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.00545, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.14564, "loss": 0.14564, "time": 0.45385} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.00543, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.17689, "loss": 0.17689, "time": 0.48824} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.00541, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.18733, "loss": 0.18733, "time": 0.49052} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.0054, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.17428, "loss": 0.17428, "time": 0.49106} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.00538, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.15164, "loss": 0.15164, "time": 0.48844} +{"mode": "val", "epoch": 104, "iter": 533, "lr": 0.00537, "top1_acc": 0.88992, "top5_acc": 0.99366, "mean_class_accuracy": 0.85313} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.00535, "memory": 4083, "data_time": 0.18973, "top1_acc": 0.96688, "top5_acc": 0.99938, "loss_cls": 0.18105, "loss": 0.18105, "time": 0.79747} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.00533, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13795, "loss": 0.13795, "time": 0.49149} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.00532, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97875, "top5_acc": 0.99938, "loss_cls": 0.14092, "loss": 0.14092, "time": 0.49087} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.0053, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.15827, "loss": 0.15827, "time": 0.49328} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.00528, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.15851, "loss": 0.15851, "time": 0.38285} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.00527, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.20868, "loss": 0.20868, "time": 0.50606} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.00525, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12682, "loss": 0.12682, "time": 0.23644} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.00523, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.17504, "loss": 0.17504, "time": 0.45722} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.00522, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14269, "loss": 0.14269, "time": 0.49193} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.0052, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.19021, "loss": 0.19021, "time": 0.48995} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.00518, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97812, "top5_acc": 0.99938, "loss_cls": 0.17103, "loss": 0.17103, "time": 0.49398} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.00517, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.13857, "loss": 0.13857, "time": 0.49141} +{"mode": "val", "epoch": 105, "iter": 533, "lr": 0.00515, "top1_acc": 0.89813, "top5_acc": 0.99472, "mean_class_accuracy": 0.87021} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.00514, "memory": 4083, "data_time": 0.18132, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.12622, "loss": 0.12622, "time": 0.77086} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.00512, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.11817, "loss": 0.11817, "time": 0.48957} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.0051, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.14318, "loss": 0.14318, "time": 0.49359} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.00509, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.17006, "loss": 0.17006, "time": 0.48855} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.00507, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.17113, "loss": 0.17113, "time": 0.40812} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.00505, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.16995, "loss": 0.16995, "time": 0.48977} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.00504, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.13645, "loss": 0.13645, "time": 0.24574} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.00502, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.14083, "loss": 0.14083, "time": 0.42599} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.005, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.14465, "loss": 0.14465, "time": 0.48892} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.00499, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.11386, "loss": 0.11386, "time": 0.48807} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.00497, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.96938, "top5_acc": 0.99875, "loss_cls": 0.17132, "loss": 0.17132, "time": 0.49156} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.00496, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13458, "loss": 0.13458, "time": 0.48892} +{"mode": "val", "epoch": 106, "iter": 533, "lr": 0.00494, "top1_acc": 0.90846, "top5_acc": 0.99531, "mean_class_accuracy": 0.88086} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.00493, "memory": 4083, "data_time": 0.18491, "top1_acc": 0.9825, "top5_acc": 0.99938, "loss_cls": 0.1159, "loss": 0.1159, "time": 0.79665} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.00491, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.10572, "loss": 0.10572, "time": 0.4906} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.00489, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.11642, "loss": 0.11642, "time": 0.49161} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.00488, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.12548, "loss": 0.12548, "time": 0.49271} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.00486, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.12226, "loss": 0.12226, "time": 0.40901} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.00485, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12093, "loss": 0.12093, "time": 0.47504} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.00483, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.17082, "loss": 0.17082, "time": 0.25915} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.00481, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.13329, "loss": 0.13329, "time": 0.43097} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.0048, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.11851, "loss": 0.11851, "time": 0.48688} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.00478, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97438, "top5_acc": 0.99875, "loss_cls": 0.15695, "loss": 0.15695, "time": 0.49085} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.00476, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.15245, "loss": 0.15245, "time": 0.48691} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.00475, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.15047, "loss": 0.15047, "time": 0.49102} +{"mode": "val", "epoch": 107, "iter": 533, "lr": 0.00474, "top1_acc": 0.9013, "top5_acc": 0.99167, "mean_class_accuracy": 0.86581} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.00472, "memory": 4083, "data_time": 0.18764, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.15588, "loss": 0.15588, "time": 0.79694} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0047, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.14093, "loss": 0.14093, "time": 0.49204} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.00469, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.12497, "loss": 0.12497, "time": 0.49152} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.00467, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13472, "loss": 0.13472, "time": 0.49316} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.00466, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98688, "top5_acc": 0.99938, "loss_cls": 0.11113, "loss": 0.11113, "time": 0.42026} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.00464, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.97938, "top5_acc": 0.99875, "loss_cls": 0.13846, "loss": 0.13846, "time": 0.45529} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.00462, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.18722, "loss": 0.18722, "time": 0.28019} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.00461, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.20064, "loss": 0.20064, "time": 0.42817} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.00459, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.17022, "loss": 0.17022, "time": 0.48899} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.00458, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.10586, "loss": 0.10586, "time": 0.49236} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.00456, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97625, "top5_acc": 0.99938, "loss_cls": 0.16514, "loss": 0.16514, "time": 0.49126} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.00455, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.16625, "loss": 0.16625, "time": 0.48982} +{"mode": "val", "epoch": 108, "iter": 533, "lr": 0.00453, "top1_acc": 0.8986, "top5_acc": 0.99108, "mean_class_accuracy": 0.86035} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.00452, "memory": 4083, "data_time": 0.1872, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.11316, "loss": 0.11316, "time": 0.80308} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.0045, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.11519, "loss": 0.11519, "time": 0.48941} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.00449, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.13817, "loss": 0.13817, "time": 0.49012} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.00447, "memory": 4083, "data_time": 0.00073, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.12814, "loss": 0.12814, "time": 0.49051} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.00445, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.11226, "loss": 0.11226, "time": 0.41541} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.00444, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.15562, "loss": 0.15562, "time": 0.46589} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.00442, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.12599, "loss": 0.12599, "time": 0.26993} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.00441, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.12466, "loss": 0.12466, "time": 0.4274} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.00439, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.97875, "top5_acc": 0.99938, "loss_cls": 0.15017, "loss": 0.15017, "time": 0.48939} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.00438, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.13461, "loss": 0.13461, "time": 0.48936} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.00436, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.10595, "loss": 0.10595, "time": 0.48987} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.00434, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.12976, "loss": 0.12976, "time": 0.48898} +{"mode": "val", "epoch": 109, "iter": 533, "lr": 0.00433, "top1_acc": 0.9128, "top5_acc": 0.99484, "mean_class_accuracy": 0.88036} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.00432, "memory": 4083, "data_time": 0.18293, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.1393, "loss": 0.1393, "time": 0.79184} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.0043, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.14936, "loss": 0.14936, "time": 0.49171} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.00429, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.14008, "loss": 0.14008, "time": 0.49328} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.00427, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.13292, "loss": 0.13292, "time": 0.49114} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.00426, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.11767, "loss": 0.11767, "time": 0.42958} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.00424, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.14172, "loss": 0.14172, "time": 0.45027} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.00422, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.11403, "loss": 0.11403, "time": 0.28182} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.00421, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.15247, "loss": 0.15247, "time": 0.41799} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.00419, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.13095, "loss": 0.13095, "time": 0.48965} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.00418, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.13372, "loss": 0.13372, "time": 0.492} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.00416, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13247, "loss": 0.13247, "time": 0.48872} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.00415, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.12979, "loss": 0.12979, "time": 0.48986} +{"mode": "val", "epoch": 110, "iter": 533, "lr": 0.00414, "top1_acc": 0.91421, "top5_acc": 0.99495, "mean_class_accuracy": 0.88034} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.00412, "memory": 4083, "data_time": 0.1872, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.11906, "loss": 0.11906, "time": 0.80215} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.00411, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98375, "top5_acc": 0.99938, "loss_cls": 0.12798, "loss": 0.12798, "time": 0.4967} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.00409, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.08374, "loss": 0.08374, "time": 0.4904} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.00408, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.98812, "top5_acc": 0.99938, "loss_cls": 0.08982, "loss": 0.08982, "time": 0.48637} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.00406, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10397, "loss": 0.10397, "time": 0.43033} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.00405, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.0966, "loss": 0.0966, "time": 0.4347} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.00403, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11458, "loss": 0.11458, "time": 0.29394} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.00402, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.1078, "loss": 0.1078, "time": 0.41309} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.004, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.13864, "loss": 0.13864, "time": 0.49205} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.00399, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.1194, "loss": 0.1194, "time": 0.48767} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.00397, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11377, "loss": 0.11377, "time": 0.49038} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.00396, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.1019, "loss": 0.1019, "time": 0.49102} +{"mode": "val", "epoch": 111, "iter": 533, "lr": 0.00394, "top1_acc": 0.90576, "top5_acc": 0.99448, "mean_class_accuracy": 0.87869} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.00393, "memory": 4083, "data_time": 0.18544, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08618, "loss": 0.08618, "time": 0.80761} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.00391, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09283, "loss": 0.09283, "time": 0.49112} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.0039, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.10646, "loss": 0.10646, "time": 0.4907} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.00388, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08118, "loss": 0.08118, "time": 0.493} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.00387, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.11285, "loss": 0.11285, "time": 0.43466} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.00385, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.10847, "loss": 0.10847, "time": 0.43132} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.00384, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.09992, "loss": 0.09992, "time": 0.30189} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.00382, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.1071, "loss": 0.1071, "time": 0.40435} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.00381, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.12778, "loss": 0.12778, "time": 0.48689} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.0038, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.1036, "loss": 0.1036, "time": 0.49232} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.00378, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.13219, "loss": 0.13219, "time": 0.49387} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.00377, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97625, "top5_acc": 0.99938, "loss_cls": 0.14603, "loss": 0.14603, "time": 0.48862} +{"mode": "val", "epoch": 112, "iter": 533, "lr": 0.00375, "top1_acc": 0.89954, "top5_acc": 0.9939, "mean_class_accuracy": 0.86625} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.00374, "memory": 4083, "data_time": 0.1814, "top1_acc": 0.98438, "top5_acc": 0.99938, "loss_cls": 0.12656, "loss": 0.12656, "time": 0.78485} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.00373, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.0941, "loss": 0.0941, "time": 0.48824} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.00371, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.09508, "loss": 0.09508, "time": 0.49137} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.0037, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.07834, "loss": 0.07834, "time": 0.48915} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.00368, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07311, "loss": 0.07311, "time": 0.45449} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.00367, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07739, "loss": 0.07739, "time": 0.36927} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.00365, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.1088, "loss": 0.1088, "time": 0.36426} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.00364, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.10674, "loss": 0.10674, "time": 0.37671} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.00362, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07329, "loss": 0.07329, "time": 0.48788} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.00361, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.0695, "loss": 0.0695, "time": 0.48932} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0036, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08681, "loss": 0.08681, "time": 0.49053} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.00358, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.09166, "loss": 0.09166, "time": 0.48955} +{"mode": "val", "epoch": 113, "iter": 533, "lr": 0.00357, "top1_acc": 0.90999, "top5_acc": 0.9939, "mean_class_accuracy": 0.88269} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.00355, "memory": 4083, "data_time": 0.18332, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05968, "loss": 0.05968, "time": 0.79323} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.00354, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.07662, "loss": 0.07662, "time": 0.48901} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.00353, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.0778, "loss": 0.0778, "time": 0.49013} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.00351, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97812, "top5_acc": 0.99938, "loss_cls": 0.13203, "loss": 0.13203, "time": 0.48892} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.0035, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.15506, "loss": 0.15506, "time": 0.48066} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.00348, "memory": 4083, "data_time": 0.00074, "top1_acc": 0.9775, "top5_acc": 0.99938, "loss_cls": 0.15394, "loss": 0.15394, "time": 0.33716} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.00347, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.11725, "loss": 0.11725, "time": 0.39597} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.00346, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97812, "top5_acc": 0.99938, "loss_cls": 0.13344, "loss": 0.13344, "time": 0.36682} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.00344, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11359, "loss": 0.11359, "time": 0.49162} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.00343, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.08845, "loss": 0.08845, "time": 0.48865} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.00341, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.1192, "loss": 0.1192, "time": 0.49009} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.0034, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.09435, "loss": 0.09435, "time": 0.48918} +{"mode": "val", "epoch": 114, "iter": 533, "lr": 0.00339, "top1_acc": 0.90482, "top5_acc": 0.99331, "mean_class_accuracy": 0.87877} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.00337, "memory": 4083, "data_time": 0.18434, "top1_acc": 0.97562, "top5_acc": 0.99875, "loss_cls": 0.16176, "loss": 0.16176, "time": 0.79135} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.00336, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.08681, "loss": 0.08681, "time": 0.49061} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.00335, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.08098, "loss": 0.08098, "time": 0.49168} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.00333, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11057, "loss": 0.11057, "time": 0.48794} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.00332, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.08484, "loss": 0.08484, "time": 0.48761} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.0033, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.12301, "loss": 0.12301, "time": 0.32228} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.00329, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.10007, "loss": 0.10007, "time": 0.41658} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.00328, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07426, "loss": 0.07426, "time": 0.32376} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.00326, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08371, "loss": 0.08371, "time": 0.48817} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.00325, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.12441, "loss": 0.12441, "time": 0.49151} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.00324, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.0819, "loss": 0.0819, "time": 0.48753} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.00322, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.09849, "loss": 0.09849, "time": 0.49008} +{"mode": "val", "epoch": 115, "iter": 533, "lr": 0.00321, "top1_acc": 0.9101, "top5_acc": 0.99378, "mean_class_accuracy": 0.88553} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.0032, "memory": 4083, "data_time": 0.18852, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.09989, "loss": 0.09989, "time": 0.80287} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.00318, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07834, "loss": 0.07834, "time": 0.48865} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.00317, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.0665, "loss": 0.0665, "time": 0.49199} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.00316, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05361, "loss": 0.05361, "time": 0.48766} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.00314, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.07728, "loss": 0.07728, "time": 0.48774} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.00313, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99438, "top5_acc": 0.99938, "loss_cls": 0.07174, "loss": 0.07174, "time": 0.27552} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.00312, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.07685, "loss": 0.07685, "time": 0.4827} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.0031, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07923, "loss": 0.07923, "time": 0.31792} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.00309, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06087, "loss": 0.06087, "time": 0.48884} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.00308, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06029, "loss": 0.06029, "time": 0.48963} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.00306, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.07156, "loss": 0.07156, "time": 0.48987} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.00305, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08114, "loss": 0.08114, "time": 0.48892} +{"mode": "val", "epoch": 116, "iter": 533, "lr": 0.00304, "top1_acc": 0.9202, "top5_acc": 0.99531, "mean_class_accuracy": 0.88951} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.00302, "memory": 4083, "data_time": 0.18623, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.07688, "loss": 0.07688, "time": 0.80174} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.00301, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.07634, "loss": 0.07634, "time": 0.48835} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.003, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07654, "loss": 0.07654, "time": 0.49107} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.00298, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.07557, "loss": 0.07557, "time": 0.49035} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.00297, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06375, "loss": 0.06375, "time": 0.48771} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.00296, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05822, "loss": 0.05822, "time": 0.27343} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.00294, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06357, "loss": 0.06357, "time": 0.50146} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.00293, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05742, "loss": 0.05742, "time": 0.30442} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.00292, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05408, "loss": 0.05408, "time": 0.48473} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.00291, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05727, "loss": 0.05727, "time": 0.49163} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.00289, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06098, "loss": 0.06098, "time": 0.48814} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.00288, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9925, "top5_acc": 0.99938, "loss_cls": 0.06548, "loss": 0.06548, "time": 0.4911} +{"mode": "val", "epoch": 117, "iter": 533, "lr": 0.00287, "top1_acc": 0.92184, "top5_acc": 0.99566, "mean_class_accuracy": 0.89468} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.00286, "memory": 4083, "data_time": 0.18976, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04786, "loss": 0.04786, "time": 0.81458} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.00284, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05175, "loss": 0.05175, "time": 0.49056} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.00283, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06375, "loss": 0.06375, "time": 0.4904} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.00282, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.05631, "loss": 0.05631, "time": 0.48864} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.0028, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05313, "loss": 0.05313, "time": 0.48786} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.00279, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.0753, "loss": 0.0753, "time": 0.28141} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.00278, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04759, "loss": 0.04759, "time": 0.49214} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.00277, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05058, "loss": 0.05058, "time": 0.30751} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.00275, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07234, "loss": 0.07234, "time": 0.48782} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.00274, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.04547, "loss": 0.04547, "time": 0.49008} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.00273, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.08858, "loss": 0.08858, "time": 0.49012} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.00271, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.06155, "loss": 0.06155, "time": 0.4875} +{"mode": "val", "epoch": 118, "iter": 533, "lr": 0.0027, "top1_acc": 0.92419, "top5_acc": 0.99495, "mean_class_accuracy": 0.8947} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.00269, "memory": 4083, "data_time": 0.18458, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.07352, "loss": 0.07352, "time": 0.79118} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.00268, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06783, "loss": 0.06783, "time": 0.48846} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.00267, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04419, "loss": 0.04419, "time": 0.48938} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.00265, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.05339, "loss": 0.05339, "time": 0.49466} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.00264, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.07392, "loss": 0.07392, "time": 0.48613} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.00263, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07254, "loss": 0.07254, "time": 0.29118} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.00262, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06874, "loss": 0.06874, "time": 0.5103} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.0026, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.06574, "loss": 0.06574, "time": 0.27937} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.00259, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.0756, "loss": 0.0756, "time": 0.48803} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.00258, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.08328, "loss": 0.08328, "time": 0.49339} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.00257, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08009, "loss": 0.08009, "time": 0.48699} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.00255, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.07933, "loss": 0.07933, "time": 0.48786} +{"mode": "val", "epoch": 119, "iter": 533, "lr": 0.00254, "top1_acc": 0.91762, "top5_acc": 0.99495, "mean_class_accuracy": 0.88548} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.00253, "memory": 4083, "data_time": 0.19311, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07669, "loss": 0.07669, "time": 0.80707} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.00252, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.0459, "loss": 0.0459, "time": 0.49112} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.00251, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.05389, "loss": 0.05389, "time": 0.48556} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.00249, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05398, "loss": 0.05398, "time": 0.48771} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.00248, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04288, "loss": 0.04288, "time": 0.48991} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.00247, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03831, "loss": 0.03831, "time": 0.29434} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.00246, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.04045, "loss": 0.04045, "time": 0.5105} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.00245, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06573, "loss": 0.06573, "time": 0.27353} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.00243, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.05239, "loss": 0.05239, "time": 0.49058} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.00242, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05049, "loss": 0.05049, "time": 0.4903} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00241, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04473, "loss": 0.04473, "time": 0.48967} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.0024, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05222, "loss": 0.05222, "time": 0.49241} +{"mode": "val", "epoch": 120, "iter": 533, "lr": 0.00239, "top1_acc": 0.92301, "top5_acc": 0.99448, "mean_class_accuracy": 0.89365} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00238, "memory": 4083, "data_time": 0.19086, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.04565, "loss": 0.04565, "time": 0.80543} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00236, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03434, "loss": 0.03434, "time": 0.48798} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.00235, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03617, "loss": 0.03617, "time": 0.4885} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00234, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03159, "loss": 0.03159, "time": 0.48857} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00233, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03744, "loss": 0.03744, "time": 0.48847} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00232, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02805, "loss": 0.02805, "time": 0.30107} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.0023, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04915, "loss": 0.04915, "time": 0.51129} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00229, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02582, "loss": 0.02582, "time": 0.25623} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.00228, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03382, "loss": 0.03382, "time": 0.48334} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00227, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02897, "loss": 0.02897, "time": 0.48795} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00226, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03835, "loss": 0.03835, "time": 0.49054} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00225, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.043, "loss": 0.043, "time": 0.4943} +{"mode": "val", "epoch": 121, "iter": 533, "lr": 0.00224, "top1_acc": 0.92337, "top5_acc": 0.99531, "mean_class_accuracy": 0.89527} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00222, "memory": 4083, "data_time": 0.1903, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03859, "loss": 0.03859, "time": 0.7959} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00221, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03259, "loss": 0.03259, "time": 0.4854} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.0022, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03098, "loss": 0.03098, "time": 0.48929} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00219, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03179, "loss": 0.03179, "time": 0.4884} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00218, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03499, "loss": 0.03499, "time": 0.48947} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00217, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02687, "loss": 0.02687, "time": 0.3426} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00215, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.028, "loss": 0.028, "time": 0.51164} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00214, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0286, "loss": 0.0286, "time": 0.24996} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.00213, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03247, "loss": 0.03247, "time": 0.46264} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00212, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03244, "loss": 0.03244, "time": 0.49254} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00211, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03717, "loss": 0.03717, "time": 0.48857} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.0021, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03601, "loss": 0.03601, "time": 0.48746} +{"mode": "val", "epoch": 122, "iter": 533, "lr": 0.00209, "top1_acc": 0.92454, "top5_acc": 0.99566, "mean_class_accuracy": 0.8995} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00208, "memory": 4083, "data_time": 0.19162, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05491, "loss": 0.05491, "time": 0.796} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00207, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04192, "loss": 0.04192, "time": 0.48679} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00205, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03367, "loss": 0.03367, "time": 0.49285} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00204, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03944, "loss": 0.03944, "time": 0.49154} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00203, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02833, "loss": 0.02833, "time": 0.48693} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00202, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 0.99938, "loss_cls": 0.0417, "loss": 0.0417, "time": 0.36905} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00201, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03211, "loss": 0.03211, "time": 0.5113} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.002, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05004, "loss": 0.05004, "time": 0.24495} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00199, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.0548, "loss": 0.0548, "time": 0.45401} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.00198, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.05162, "loss": 0.05162, "time": 0.48846} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00197, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05196, "loss": 0.05196, "time": 0.48753} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00195, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.0546, "loss": 0.0546, "time": 0.49197} +{"mode": "val", "epoch": 123, "iter": 533, "lr": 0.00195, "top1_acc": 0.92196, "top5_acc": 0.99484, "mean_class_accuracy": 0.89569} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00194, "memory": 4083, "data_time": 0.19255, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04406, "loss": 0.04406, "time": 0.79621} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00192, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04179, "loss": 0.04179, "time": 0.48933} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00191, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04068, "loss": 0.04068, "time": 0.48995} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.0019, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03524, "loss": 0.03524, "time": 0.48958} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00189, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04103, "loss": 0.04103, "time": 0.48917} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00188, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04375, "loss": 0.04375, "time": 0.37024} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00187, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03015, "loss": 0.03015, "time": 0.51051} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00186, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02814, "loss": 0.02814, "time": 0.24296} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00185, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03122, "loss": 0.03122, "time": 0.46377} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00184, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03462, "loss": 0.03462, "time": 0.4931} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00183, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0227, "loss": 0.0227, "time": 0.4898} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.00182, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.02959, "loss": 0.02959, "time": 0.48704} +{"mode": "val", "epoch": 124, "iter": 533, "lr": 0.00181, "top1_acc": 0.92712, "top5_acc": 0.99472, "mean_class_accuracy": 0.90195} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.0018, "memory": 4083, "data_time": 0.18489, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02237, "loss": 0.02237, "time": 0.78445} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.00179, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.03477, "loss": 0.03477, "time": 0.48767} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00178, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02628, "loss": 0.02628, "time": 0.48848} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00177, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.0279, "loss": 0.0279, "time": 0.49141} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00176, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02983, "loss": 0.02983, "time": 0.49123} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00175, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03016, "loss": 0.03016, "time": 0.385} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00173, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03549, "loss": 0.03549, "time": 0.50824} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00172, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02425, "loss": 0.02425, "time": 0.23736} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.00171, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02553, "loss": 0.02553, "time": 0.44633} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.0017, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02725, "loss": 0.02725, "time": 0.49036} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00169, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03053, "loss": 0.03053, "time": 0.4914} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00168, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02887, "loss": 0.02887, "time": 0.48835} +{"mode": "val", "epoch": 125, "iter": 533, "lr": 0.00167, "top1_acc": 0.92114, "top5_acc": 0.99554, "mean_class_accuracy": 0.89584} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00166, "memory": 4083, "data_time": 0.18578, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03259, "loss": 0.03259, "time": 0.78458} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00165, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02628, "loss": 0.02628, "time": 0.4919} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00164, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02467, "loss": 0.02467, "time": 0.48827} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00163, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02466, "loss": 0.02466, "time": 0.49015} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00162, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02615, "loss": 0.02615, "time": 0.48883} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00161, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02658, "loss": 0.02658, "time": 0.40129} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0016, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02364, "loss": 0.02364, "time": 0.49238} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00159, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02247, "loss": 0.02247, "time": 0.24361} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00158, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02313, "loss": 0.02313, "time": 0.43073} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00157, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02324, "loss": 0.02324, "time": 0.49294} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00156, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02268, "loss": 0.02268, "time": 0.49108} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00155, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02239, "loss": 0.02239, "time": 0.48904} +{"mode": "val", "epoch": 126, "iter": 533, "lr": 0.00155, "top1_acc": 0.92829, "top5_acc": 0.99601, "mean_class_accuracy": 0.90216} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00154, "memory": 4083, "data_time": 0.18349, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02468, "loss": 0.02468, "time": 0.78282} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00153, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02129, "loss": 0.02129, "time": 0.48981} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00152, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0238, "loss": 0.0238, "time": 0.49133} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00151, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0224, "loss": 0.0224, "time": 0.49009} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.0015, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02929, "loss": 0.02929, "time": 0.48966} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.00149, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0226, "loss": 0.0226, "time": 0.42521} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00148, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02721, "loss": 0.02721, "time": 0.43777} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00147, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.0294, "loss": 0.0294, "time": 0.29404} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00146, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02905, "loss": 0.02905, "time": 0.42131} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00145, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02881, "loss": 0.02881, "time": 0.49081} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00144, "memory": 4083, "data_time": 0.00051, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02197, "loss": 0.02197, "time": 0.49057} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00143, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02155, "loss": 0.02155, "time": 0.49048} +{"mode": "val", "epoch": 127, "iter": 533, "lr": 0.00142, "top1_acc": 0.92994, "top5_acc": 0.99554, "mean_class_accuracy": 0.9036} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00141, "memory": 4083, "data_time": 0.18172, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0245, "loss": 0.0245, "time": 0.79184} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.0014, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02475, "loss": 0.02475, "time": 0.49416} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00139, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03827, "loss": 0.03827, "time": 0.48897} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00138, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02435, "loss": 0.02435, "time": 0.4889} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00138, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0187, "loss": 0.0187, "time": 0.48658} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00137, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01753, "loss": 0.01753, "time": 0.4437} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.00136, "memory": 4083, "data_time": 0.00076, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02083, "loss": 0.02083, "time": 0.41833} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00135, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01957, "loss": 0.01957, "time": 0.31029} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00134, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02352, "loss": 0.02352, "time": 0.4056} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00133, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01941, "loss": 0.01941, "time": 0.48711} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00132, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02315, "loss": 0.02315, "time": 0.49312} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00131, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0278, "loss": 0.0278, "time": 0.49341} +{"mode": "val", "epoch": 128, "iter": 533, "lr": 0.0013, "top1_acc": 0.92923, "top5_acc": 0.99671, "mean_class_accuracy": 0.90119} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.00129, "memory": 4083, "data_time": 0.18321, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02508, "loss": 0.02508, "time": 0.7814} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00129, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01957, "loss": 0.01957, "time": 0.48839} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00128, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02059, "loss": 0.02059, "time": 0.49367} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00127, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02053, "loss": 0.02053, "time": 0.4927} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00126, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02508, "loss": 0.02508, "time": 0.48831} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00125, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.032, "loss": 0.032, "time": 0.45153} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00124, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02362, "loss": 0.02362, "time": 0.40931} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00123, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02517, "loss": 0.02517, "time": 0.32367} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.00122, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02064, "loss": 0.02064, "time": 0.4066} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00121, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01941, "loss": 0.01941, "time": 0.48749} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00121, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02162, "loss": 0.02162, "time": 0.48712} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.0012, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0203, "loss": 0.0203, "time": 0.48805} +{"mode": "val", "epoch": 129, "iter": 533, "lr": 0.00119, "top1_acc": 0.93463, "top5_acc": 0.99648, "mean_class_accuracy": 0.90989} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00118, "memory": 4083, "data_time": 0.18278, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02022, "loss": 0.02022, "time": 0.79089} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00117, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0186, "loss": 0.0186, "time": 0.49163} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00116, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01921, "loss": 0.01921, "time": 0.49304} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00116, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0217, "loss": 0.0217, "time": 0.49006} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.00115, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.0275, "loss": 0.0275, "time": 0.49078} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00114, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01971, "loss": 0.01971, "time": 0.43792} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00113, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0174, "loss": 0.0174, "time": 0.42545} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00112, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01861, "loss": 0.01861, "time": 0.30483} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00111, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01951, "loss": 0.01951, "time": 0.40693} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.0011, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01771, "loss": 0.01771, "time": 0.48744} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.0011, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02273, "loss": 0.02273, "time": 0.4883} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00109, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01936, "loss": 0.01936, "time": 0.4911} +{"mode": "val", "epoch": 130, "iter": 533, "lr": 0.00108, "top1_acc": 0.93158, "top5_acc": 0.99578, "mean_class_accuracy": 0.9036} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00107, "memory": 4083, "data_time": 0.18736, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02399, "loss": 0.02399, "time": 0.8046} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.00106, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0186, "loss": 0.0186, "time": 0.48591} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00106, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02264, "loss": 0.02264, "time": 0.4906} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00105, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02076, "loss": 0.02076, "time": 0.49004} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00104, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02353, "loss": 0.02353, "time": 0.48912} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00103, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01868, "loss": 0.01868, "time": 0.42727} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00102, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01864, "loss": 0.01864, "time": 0.44064} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00102, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01832, "loss": 0.01832, "time": 0.29027} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00101, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01744, "loss": 0.01744, "time": 0.43187} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.001, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01943, "loss": 0.01943, "time": 0.49013} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.00099, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01731, "loss": 0.01731, "time": 0.49123} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00098, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01764, "loss": 0.01764, "time": 0.48693} +{"mode": "val", "epoch": 131, "iter": 533, "lr": 0.00098, "top1_acc": 0.93475, "top5_acc": 0.99613, "mean_class_accuracy": 0.90916} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.00097, "memory": 4083, "data_time": 0.18806, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01651, "loss": 0.01651, "time": 0.78853} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00096, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01762, "loss": 0.01762, "time": 0.48902} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00095, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01868, "loss": 0.01868, "time": 0.49308} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00095, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02115, "loss": 0.02115, "time": 0.4917} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00094, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02636, "loss": 0.02636, "time": 0.49191} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00093, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01814, "loss": 0.01814, "time": 0.39602} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00092, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01826, "loss": 0.01826, "time": 0.50993} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00091, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01595, "loss": 0.01595, "time": 0.2365} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00091, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01548, "loss": 0.01548, "time": 0.44169} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0009, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02113, "loss": 0.02113, "time": 0.48776} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00089, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02117, "loss": 0.02117, "time": 0.49138} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00088, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01963, "loss": 0.01963, "time": 0.49018} +{"mode": "val", "epoch": 132, "iter": 533, "lr": 0.00088, "top1_acc": 0.93017, "top5_acc": 0.99601, "mean_class_accuracy": 0.90273} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.00087, "memory": 4083, "data_time": 0.18821, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01926, "loss": 0.01926, "time": 0.7952} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00086, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0177, "loss": 0.0177, "time": 0.49093} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00086, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01871, "loss": 0.01871, "time": 0.4895} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00085, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01994, "loss": 0.01994, "time": 0.48999} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00084, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01765, "loss": 0.01765, "time": 0.49317} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00083, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.0219, "loss": 0.0219, "time": 0.39579} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00083, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02253, "loss": 0.02253, "time": 0.50798} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00082, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02066, "loss": 0.02066, "time": 0.23172} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00081, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01756, "loss": 0.01756, "time": 0.43753} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.0008, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02013, "loss": 0.02013, "time": 0.48594} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0008, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01749, "loss": 0.01749, "time": 0.49175} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00079, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02157, "loss": 0.02157, "time": 0.48796} +{"mode": "val", "epoch": 133, "iter": 533, "lr": 0.00078, "top1_acc": 0.9344, "top5_acc": 0.99624, "mean_class_accuracy": 0.90899} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00078, "memory": 4083, "data_time": 0.18577, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01929, "loss": 0.01929, "time": 0.79759} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00077, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01598, "loss": 0.01598, "time": 0.49328} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00076, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01804, "loss": 0.01804, "time": 0.48908} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.00076, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0181, "loss": 0.0181, "time": 0.49146} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00075, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01986, "loss": 0.01986, "time": 0.49022} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00074, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0168, "loss": 0.0168, "time": 0.39868} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00073, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0183, "loss": 0.0183, "time": 0.51176} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00073, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01775, "loss": 0.01775, "time": 0.23592} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00072, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01797, "loss": 0.01797, "time": 0.43852} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00071, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0166, "loss": 0.0166, "time": 0.48788} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00071, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01736, "loss": 0.01736, "time": 0.49029} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.0007, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01716, "loss": 0.01716, "time": 0.48737} +{"mode": "val", "epoch": 134, "iter": 533, "lr": 0.0007, "top1_acc": 0.93463, "top5_acc": 0.99613, "mean_class_accuracy": 0.90903} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00069, "memory": 4083, "data_time": 0.18185, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02151, "loss": 0.02151, "time": 0.79084} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00068, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01704, "loss": 0.01704, "time": 0.49391} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00068, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01698, "loss": 0.01698, "time": 0.49356} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00067, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01727, "loss": 0.01727, "time": 0.49072} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00066, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01683, "loss": 0.01683, "time": 0.49194} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00066, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01702, "loss": 0.01702, "time": 0.38415} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00065, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01591, "loss": 0.01591, "time": 0.50913} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00064, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01622, "loss": 0.01622, "time": 0.23911} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.00064, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02164, "loss": 0.02164, "time": 0.44735} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00063, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0181, "loss": 0.0181, "time": 0.49057} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00062, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0165, "loss": 0.0165, "time": 0.49032} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00062, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01522, "loss": 0.01522, "time": 0.48839} +{"mode": "val", "epoch": 135, "iter": 533, "lr": 0.00061, "top1_acc": 0.93557, "top5_acc": 0.9966, "mean_class_accuracy": 0.912} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00061, "memory": 4083, "data_time": 0.1903, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0253, "loss": 0.0253, "time": 0.82096} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.0006, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01833, "loss": 0.01833, "time": 0.49074} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00059, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01525, "loss": 0.01525, "time": 0.48813} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00059, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02207, "loss": 0.02207, "time": 0.49012} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.00058, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01898, "loss": 0.01898, "time": 0.49127} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.00057, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01579, "loss": 0.01579, "time": 0.35795} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00057, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01681, "loss": 0.01681, "time": 0.50868} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00056, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01641, "loss": 0.01641, "time": 0.24859} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00056, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01817, "loss": 0.01817, "time": 0.47407} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00055, "memory": 4083, "data_time": 0.00043, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01552, "loss": 0.01552, "time": 0.49035} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00054, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01664, "loss": 0.01664, "time": 0.49411} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00054, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01643, "loss": 0.01643, "time": 0.49445} +{"mode": "val", "epoch": 136, "iter": 533, "lr": 0.00053, "top1_acc": 0.93311, "top5_acc": 0.99613, "mean_class_accuracy": 0.90752} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00053, "memory": 4083, "data_time": 0.18907, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02006, "loss": 0.02006, "time": 0.79291} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00052, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01774, "loss": 0.01774, "time": 0.49057} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00052, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01618, "loss": 0.01618, "time": 0.49456} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.00051, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02026, "loss": 0.02026, "time": 0.49025} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.0005, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0163, "loss": 0.0163, "time": 0.4928} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.0005, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01617, "loss": 0.01617, "time": 0.34873} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00049, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01482, "loss": 0.01482, "time": 0.50944} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00049, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01784, "loss": 0.01784, "time": 0.24675} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00048, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01585, "loss": 0.01585, "time": 0.47129} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00048, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01672, "loss": 0.01672, "time": 0.48973} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00047, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01678, "loss": 0.01678, "time": 0.49283} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00046, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01526, "loss": 0.01526, "time": 0.48955} +{"mode": "val", "epoch": 137, "iter": 533, "lr": 0.00046, "top1_acc": 0.93557, "top5_acc": 0.99624, "mean_class_accuracy": 0.90928} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00046, "memory": 4083, "data_time": 0.18522, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01517, "loss": 0.01517, "time": 0.79987} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00045, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01525, "loss": 0.01525, "time": 0.49138} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00044, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01639, "loss": 0.01639, "time": 0.49251} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00044, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01877, "loss": 0.01877, "time": 0.49253} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.00043, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01585, "loss": 0.01585, "time": 0.48984} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.00043, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01524, "loss": 0.01524, "time": 0.33664} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00042, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01708, "loss": 0.01708, "time": 0.50918} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00042, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01744, "loss": 0.01744, "time": 0.34895} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00041, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01471, "loss": 0.01471, "time": 0.69936} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00041, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01569, "loss": 0.01569, "time": 0.70997} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.0004, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01421, "loss": 0.01421, "time": 0.71396} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.0004, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0157, "loss": 0.0157, "time": 0.70093} +{"mode": "val", "epoch": 138, "iter": 533, "lr": 0.00039, "top1_acc": 0.93534, "top5_acc": 0.99613, "mean_class_accuracy": 0.91041} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00039, "memory": 4083, "data_time": 0.18196, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01472, "loss": 0.01472, "time": 0.65189} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00038, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01866, "loss": 0.01866, "time": 0.713} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00038, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01531, "loss": 0.01531, "time": 0.7045} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00037, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01575, "loss": 0.01575, "time": 0.70499} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00037, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01553, "loss": 0.01553, "time": 0.70978} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00036, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01619, "loss": 0.01619, "time": 0.69432} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00036, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01497, "loss": 0.01497, "time": 0.60788} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00035, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01487, "loss": 0.01487, "time": 0.21981} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00035, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01564, "loss": 0.01564, "time": 0.22127} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.00034, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01863, "loss": 0.01863, "time": 0.22084} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.00034, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01502, "loss": 0.01502, "time": 0.22176} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00033, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01745, "loss": 0.01745, "time": 0.22132} +{"mode": "val", "epoch": 139, "iter": 533, "lr": 0.00033, "top1_acc": 0.93299, "top5_acc": 0.99707, "mean_class_accuracy": 0.90533} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00033, "memory": 4083, "data_time": 0.18095, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01538, "loss": 0.01538, "time": 0.41751} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00032, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01733, "loss": 0.01733, "time": 0.22} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.00032, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01511, "loss": 0.01511, "time": 0.22109} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.00031, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01746, "loss": 0.01746, "time": 0.2179} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00031, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01602, "loss": 0.01602, "time": 0.21795} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.0003, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01752, "loss": 0.01752, "time": 0.21854} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.0003, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0163, "loss": 0.0163, "time": 0.21849} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00029, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01553, "loss": 0.01553, "time": 0.2163} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00029, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01534, "loss": 0.01534, "time": 0.22021} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00029, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01942, "loss": 0.01942, "time": 0.21601} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00028, "memory": 4083, "data_time": 0.00019, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01616, "loss": 0.01616, "time": 0.21474} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00028, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01652, "loss": 0.01652, "time": 0.21878} +{"mode": "val", "epoch": 140, "iter": 533, "lr": 0.00027, "top1_acc": 0.93534, "top5_acc": 0.99624, "mean_class_accuracy": 0.90677} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00027, "memory": 4083, "data_time": 0.17971, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01524, "loss": 0.01524, "time": 0.40856} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00026, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01538, "loss": 0.01538, "time": 0.21904} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00026, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01835, "loss": 0.01835, "time": 0.22036} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00026, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01627, "loss": 0.01627, "time": 0.21824} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00025, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02036, "loss": 0.02036, "time": 0.2165} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00025, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01571, "loss": 0.01571, "time": 0.21988} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00024, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01654, "loss": 0.01654, "time": 0.21915} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00024, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0159, "loss": 0.0159, "time": 0.2184} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00024, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01669, "loss": 0.01669, "time": 0.22042} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00023, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01548, "loss": 0.01548, "time": 0.22088} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00023, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01949, "loss": 0.01949, "time": 0.21897} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00022, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01685, "loss": 0.01685, "time": 0.22464} +{"mode": "val", "epoch": 141, "iter": 533, "lr": 0.00022, "top1_acc": 0.93358, "top5_acc": 0.99695, "mean_class_accuracy": 0.90643} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00022, "memory": 4083, "data_time": 0.18384, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01876, "loss": 0.01876, "time": 0.41807} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00021, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01466, "loss": 0.01466, "time": 0.22087} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00021, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01584, "loss": 0.01584, "time": 0.21657} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00021, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01683, "loss": 0.01683, "time": 0.21941} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.0002, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01668, "loss": 0.01668, "time": 0.22042} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.0002, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01685, "loss": 0.01685, "time": 0.22015} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.0002, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01503, "loss": 0.01503, "time": 0.21978} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00019, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01712, "loss": 0.01712, "time": 0.21935} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00019, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01756, "loss": 0.01756, "time": 0.2248} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00018, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01476, "loss": 0.01476, "time": 0.22034} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00018, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01563, "loss": 0.01563, "time": 0.21763} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00018, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01475, "loss": 0.01475, "time": 0.22115} +{"mode": "val", "epoch": 142, "iter": 533, "lr": 0.00018, "top1_acc": 0.93346, "top5_acc": 0.9966, "mean_class_accuracy": 0.90601} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.00017, "memory": 4083, "data_time": 0.18101, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01991, "loss": 0.01991, "time": 0.41316} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00017, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01542, "loss": 0.01542, "time": 0.21999} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00017, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01531, "loss": 0.01531, "time": 0.21951} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00016, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01685, "loss": 0.01685, "time": 0.21959} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00016, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01539, "loss": 0.01539, "time": 0.21972} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00016, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01533, "loss": 0.01533, "time": 0.22004} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00015, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01419, "loss": 0.01419, "time": 0.22209} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00015, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01528, "loss": 0.01528, "time": 0.21863} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00015, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01699, "loss": 0.01699, "time": 0.22318} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00014, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0158, "loss": 0.0158, "time": 0.22276} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00014, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01659, "loss": 0.01659, "time": 0.21966} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00014, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01577, "loss": 0.01577, "time": 0.22339} +{"mode": "val", "epoch": 143, "iter": 533, "lr": 0.00013, "top1_acc": 0.93557, "top5_acc": 0.9966, "mean_class_accuracy": 0.91054} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00013, "memory": 4083, "data_time": 0.18581, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01621, "loss": 0.01621, "time": 0.41823} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00013, "memory": 4083, "data_time": 0.00057, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01594, "loss": 0.01594, "time": 0.22305} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00013, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01514, "loss": 0.01514, "time": 0.21894} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00012, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01414, "loss": 0.01414, "time": 0.21928} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00012, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01934, "loss": 0.01934, "time": 0.22135} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00012, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01555, "loss": 0.01555, "time": 0.21925} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00011, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01515, "loss": 0.01515, "time": 0.21957} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.00011, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01839, "loss": 0.01839, "time": 0.21849} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.00011, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01468, "loss": 0.01468, "time": 0.22373} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.00011, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01497, "loss": 0.01497, "time": 0.21976} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.0001, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01417, "loss": 0.01417, "time": 0.22018} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.0001, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01701, "loss": 0.01701, "time": 0.21871} +{"mode": "val", "epoch": 144, "iter": 533, "lr": 0.0001, "top1_acc": 0.93428, "top5_acc": 0.99613, "mean_class_accuracy": 0.90873} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.0001, "memory": 4083, "data_time": 0.17992, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01507, "loss": 0.01507, "time": 0.40994} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 9e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01434, "loss": 0.01434, "time": 0.22107} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 9e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01735, "loss": 0.01735, "time": 0.22121} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 9e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01707, "loss": 0.01707, "time": 0.22026} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 9e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01566, "loss": 0.01566, "time": 0.21788} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 8e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.018, "loss": 0.018, "time": 0.21989} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 8e-05, "memory": 4083, "data_time": 0.00057, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01646, "loss": 0.01646, "time": 0.22276} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 8e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01528, "loss": 0.01528, "time": 0.21927} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 8e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0142, "loss": 0.0142, "time": 0.22} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 7e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01543, "loss": 0.01543, "time": 0.22063} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 7e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01592, "loss": 0.01592, "time": 0.21973} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 7e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01617, "loss": 0.01617, "time": 0.22046} +{"mode": "val", "epoch": 145, "iter": 533, "lr": 7e-05, "top1_acc": 0.93628, "top5_acc": 0.9966, "mean_class_accuracy": 0.91079} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 7e-05, "memory": 4083, "data_time": 0.18255, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01462, "loss": 0.01462, "time": 0.41376} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 6e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01565, "loss": 0.01565, "time": 0.22111} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 6e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01627, "loss": 0.01627, "time": 0.22104} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 6e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01648, "loss": 0.01648, "time": 0.21796} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 6e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01442, "loss": 0.01442, "time": 0.21933} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 6e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01379, "loss": 0.01379, "time": 0.21827} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 5e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01433, "loss": 0.01433, "time": 0.22107} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 5e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0167, "loss": 0.0167, "time": 0.22013} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 5e-05, "memory": 4083, "data_time": 0.00063, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0144, "loss": 0.0144, "time": 0.21989} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 5e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01595, "loss": 0.01595, "time": 0.21883} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 5e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01787, "loss": 0.01787, "time": 0.21908} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 5e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01469, "loss": 0.01469, "time": 0.22146} +{"mode": "val", "epoch": 146, "iter": 533, "lr": 4e-05, "top1_acc": 0.93581, "top5_acc": 0.99648, "mean_class_accuracy": 0.9107} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 4e-05, "memory": 4083, "data_time": 0.18185, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01452, "loss": 0.01452, "time": 0.41174} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 4e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01537, "loss": 0.01537, "time": 0.22173} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 4e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01656, "loss": 0.01656, "time": 0.22257} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 4e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01587, "loss": 0.01587, "time": 0.22011} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 4e-05, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01644, "loss": 0.01644, "time": 0.21986} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 3e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01595, "loss": 0.01595, "time": 0.21978} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 3e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0153, "loss": 0.0153, "time": 0.22161} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 3e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01459, "loss": 0.01459, "time": 0.22054} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 3e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01324, "loss": 0.01324, "time": 0.21923} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 3e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01417, "loss": 0.01417, "time": 0.21929} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 3e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01639, "loss": 0.01639, "time": 0.21762} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 3e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0162, "loss": 0.0162, "time": 0.22098} +{"mode": "val", "epoch": 147, "iter": 533, "lr": 2e-05, "top1_acc": 0.93639, "top5_acc": 0.9966, "mean_class_accuracy": 0.91108} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 2e-05, "memory": 4083, "data_time": 0.18346, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01478, "loss": 0.01478, "time": 0.41801} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 2e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01614, "loss": 0.01614, "time": 0.21929} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 2e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0164, "loss": 0.0164, "time": 0.21981} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 2e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01488, "loss": 0.01488, "time": 0.22067} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 2e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01523, "loss": 0.01523, "time": 0.21955} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 2e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01969, "loss": 0.01969, "time": 0.21856} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 2e-05, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01667, "loss": 0.01667, "time": 0.21931} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 2e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01513, "loss": 0.01513, "time": 0.21815} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 1e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02004, "loss": 0.02004, "time": 0.22165} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 1e-05, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01445, "loss": 0.01445, "time": 0.22098} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 1e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01647, "loss": 0.01647, "time": 0.21636} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 1e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01595, "loss": 0.01595, "time": 0.21969} +{"mode": "val", "epoch": 148, "iter": 533, "lr": 1e-05, "top1_acc": 0.93604, "top5_acc": 0.99648, "mean_class_accuracy": 0.91028} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 1e-05, "memory": 4083, "data_time": 0.18751, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0143, "loss": 0.0143, "time": 0.41816} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 1e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01503, "loss": 0.01503, "time": 0.22179} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 1e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0156, "loss": 0.0156, "time": 0.22127} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 1e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01411, "loss": 0.01411, "time": 0.22049} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 1e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01593, "loss": 0.01593, "time": 0.2193} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 1e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01553, "loss": 0.01553, "time": 0.21973} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 1e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01558, "loss": 0.01558, "time": 0.22082} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 1e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01501, "loss": 0.01501, "time": 0.21798} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01518, "loss": 0.01518, "time": 0.22008} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01453, "loss": 0.01453, "time": 0.2203} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01663, "loss": 0.01663, "time": 0.21906} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01683, "loss": 0.01683, "time": 0.22644} +{"mode": "val", "epoch": 149, "iter": 533, "lr": 0.0, "top1_acc": 0.93451, "top5_acc": 0.99613, "mean_class_accuracy": 0.90869} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 0.0, "memory": 4083, "data_time": 0.18555, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01753, "loss": 0.01753, "time": 0.41738} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 0.0, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01579, "loss": 0.01579, "time": 0.22266} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01531, "loss": 0.01531, "time": 0.21915} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01685, "loss": 0.01685, "time": 0.22184} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 0.0, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01498, "loss": 0.01498, "time": 0.22152} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 0.0, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01527, "loss": 0.01527, "time": 0.22198} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 0.0, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01431, "loss": 0.01431, "time": 0.22259} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01768, "loss": 0.01768, "time": 0.2198} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01591, "loss": 0.01591, "time": 0.22231} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01758, "loss": 0.01758, "time": 0.22199} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01513, "loss": 0.01513, "time": 0.21704} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01562, "loss": 0.01562, "time": 0.21766} +{"mode": "val", "epoch": 150, "iter": 533, "lr": 0.0, "top1_acc": 0.93569, "top5_acc": 0.9966, "mean_class_accuracy": 0.91037} diff --git a/finegym/jm/best_pred.pkl b/finegym/jm/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..6d37a4cad89f7bc2a455a3cc58bafa5dc063398b --- /dev/null +++ b/finegym/jm/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f2e4dd79eca7185518373b4ee6b273b476f724304ec175ce47a725dc6f3d92d2 +size 5255302 diff --git a/finegym/jm/best_top1_acc_epoch_147.pth b/finegym/jm/best_top1_acc_epoch_147.pth new file mode 100644 index 0000000000000000000000000000000000000000..2785900263101ff0f280d63145862b159f1ad1a8 --- /dev/null +++ b/finegym/jm/best_top1_acc_epoch_147.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:151266b00eca2713ffb4366fcc1348a209ec738956a8390e3fd416af288b2383 +size 31999601 diff --git a/finegym/jm/jm.py b/finegym/jm/jm.py new file mode 100644 index 0000000000000000000000000000000000000000..6817f19401fe8581e5e1c9c4b387290681933e84 --- /dev/null +++ b/finegym/jm/jm.py @@ -0,0 +1,113 @@ +modality = 'jm' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/jm' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/finegym/k_1/20250624_101323.log b/finegym/k_1/20250624_101323.log new file mode 100644 index 0000000000000000000000000000000000000000..001aca7c00024c8b060cf5faa9fd0a2ae903921e --- /dev/null +++ b/finegym/k_1/20250624_101323.log @@ -0,0 +1,3480 @@ +2025-06-24 10:13:23,969 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2025-06-24 10:13:24,226 - pyskl - INFO - Config: modality = 'k' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/k_1' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2025-06-24 10:13:24,226 - pyskl - INFO - Set random seed to 1914909370, deterministic: False +2025-06-24 10:13:25,802 - pyskl - INFO - 20484 videos remain after valid thresholding +2025-06-24 10:13:30,147 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-24 10:13:30,148 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1 +2025-06-24 10:13:30,149 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2025-06-24 10:13:30,149 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2025-06-24 10:13:30,149 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1 by HardDiskBackend. +2025-06-24 10:14:10,416 - pyskl - INFO - Epoch [1][100/1281] lr: 2.500e-02, eta: 21:28:44, time: 0.403, data_time: 0.184, memory: 4082, top1_acc: 0.0631, top5_acc: 0.2506, loss_cls: 4.5008, loss: 4.5008 +2025-06-24 10:14:33,000 - pyskl - INFO - Epoch [1][200/1281] lr: 2.500e-02, eta: 16:45:15, time: 0.226, data_time: 0.001, memory: 4082, top1_acc: 0.0887, top5_acc: 0.3175, loss_cls: 4.5462, loss: 4.5462 +2025-06-24 10:14:55,385 - pyskl - INFO - Epoch [1][300/1281] lr: 2.500e-02, eta: 15:08:23, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.0931, top5_acc: 0.4019, loss_cls: 4.2312, loss: 4.2312 +2025-06-24 10:15:17,459 - pyskl - INFO - Epoch [1][400/1281] lr: 2.500e-02, eta: 14:17:17, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.1219, top5_acc: 0.4219, loss_cls: 4.1323, loss: 4.1323 +2025-06-24 10:15:39,586 - pyskl - INFO - Epoch [1][500/1281] lr: 2.500e-02, eta: 13:46:49, time: 0.221, data_time: 0.001, memory: 4082, top1_acc: 0.1363, top5_acc: 0.4437, loss_cls: 3.9138, loss: 3.9138 +2025-06-24 10:16:01,498 - pyskl - INFO - Epoch [1][600/1281] lr: 2.500e-02, eta: 13:25:15, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.1675, top5_acc: 0.5006, loss_cls: 3.7379, loss: 3.7379 +2025-06-24 10:16:23,485 - pyskl - INFO - Epoch [1][700/1281] lr: 2.500e-02, eta: 13:10:04, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.1825, top5_acc: 0.5275, loss_cls: 3.6024, loss: 3.6024 +2025-06-24 10:16:45,378 - pyskl - INFO - Epoch [1][800/1281] lr: 2.500e-02, eta: 12:58:13, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.2156, top5_acc: 0.5700, loss_cls: 3.5022, loss: 3.5022 +2025-06-24 10:17:07,297 - pyskl - INFO - Epoch [1][900/1281] lr: 2.500e-02, eta: 12:49:01, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.2400, top5_acc: 0.6356, loss_cls: 3.2933, loss: 3.2933 +2025-06-24 10:17:29,389 - pyskl - INFO - Epoch [1][1000/1281] lr: 2.500e-02, eta: 12:42:08, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.2406, top5_acc: 0.6375, loss_cls: 3.2172, loss: 3.2172 +2025-06-24 10:17:51,107 - pyskl - INFO - Epoch [1][1100/1281] lr: 2.500e-02, eta: 12:35:21, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.2469, top5_acc: 0.6756, loss_cls: 3.0984, loss: 3.0984 +2025-06-24 10:18:13,084 - pyskl - INFO - Epoch [1][1200/1281] lr: 2.500e-02, eta: 12:30:19, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.2694, top5_acc: 0.6744, loss_cls: 3.0506, loss: 3.0506 +2025-06-24 10:18:31,324 - pyskl - INFO - Saving checkpoint at 1 epochs +2025-06-24 10:19:15,147 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:19:15,213 - pyskl - INFO - +top1_acc 0.3211 +top5_acc 0.7315 +2025-06-24 10:19:15,213 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:19:15,221 - pyskl - INFO - +mean_acc 0.1662 +2025-06-24 10:19:15,406 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2025-06-24 10:19:15,406 - pyskl - INFO - Best top1_acc is 0.3211 at 1 epoch. +2025-06-24 10:19:15,409 - pyskl - INFO - Epoch(val) [1][533] top1_acc: 0.3211, top5_acc: 0.7315, mean_class_accuracy: 0.1662 +2025-06-24 10:19:56,409 - pyskl - INFO - Epoch [2][100/1281] lr: 2.500e-02, eta: 12:25:45, time: 0.410, data_time: 0.191, memory: 4082, top1_acc: 0.3394, top5_acc: 0.7494, loss_cls: 2.8379, loss: 2.8379 +2025-06-24 10:20:18,137 - pyskl - INFO - Epoch [2][200/1281] lr: 2.500e-02, eta: 12:21:39, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.3438, top5_acc: 0.7913, loss_cls: 2.7019, loss: 2.7019 +2025-06-24 10:20:39,805 - pyskl - INFO - Epoch [2][300/1281] lr: 2.500e-02, eta: 12:17:54, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.3725, top5_acc: 0.7969, loss_cls: 2.6425, loss: 2.6425 +2025-06-24 10:21:01,617 - pyskl - INFO - Epoch [2][400/1281] lr: 2.500e-02, eta: 12:14:50, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.4106, top5_acc: 0.7994, loss_cls: 2.5375, loss: 2.5375 +2025-06-24 10:21:23,317 - pyskl - INFO - Epoch [2][500/1281] lr: 2.499e-02, eta: 12:11:52, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.3931, top5_acc: 0.8106, loss_cls: 2.5224, loss: 2.5224 +2025-06-24 10:21:45,263 - pyskl - INFO - Epoch [2][600/1281] lr: 2.499e-02, eta: 12:09:35, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.4269, top5_acc: 0.8256, loss_cls: 2.3698, loss: 2.3698 +2025-06-24 10:22:07,243 - pyskl - INFO - Epoch [2][700/1281] lr: 2.499e-02, eta: 12:07:33, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.4288, top5_acc: 0.8419, loss_cls: 2.3224, loss: 2.3224 +2025-06-24 10:22:29,000 - pyskl - INFO - Epoch [2][800/1281] lr: 2.499e-02, eta: 12:05:21, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.4525, top5_acc: 0.8456, loss_cls: 2.2585, loss: 2.2585 +2025-06-24 10:22:51,297 - pyskl - INFO - Epoch [2][900/1281] lr: 2.499e-02, eta: 12:04:06, time: 0.223, data_time: 0.001, memory: 4082, top1_acc: 0.4612, top5_acc: 0.8562, loss_cls: 2.2000, loss: 2.2000 +2025-06-24 10:23:13,487 - pyskl - INFO - Epoch [2][1000/1281] lr: 2.499e-02, eta: 12:02:46, time: 0.222, data_time: 0.001, memory: 4082, top1_acc: 0.4819, top5_acc: 0.8700, loss_cls: 2.1313, loss: 2.1313 +2025-06-24 10:23:35,646 - pyskl - INFO - Epoch [2][1100/1281] lr: 2.499e-02, eta: 12:01:29, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.4856, top5_acc: 0.8812, loss_cls: 2.0934, loss: 2.0934 +2025-06-24 10:23:57,620 - pyskl - INFO - Epoch [2][1200/1281] lr: 2.499e-02, eta: 12:00:02, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5206, top5_acc: 0.8975, loss_cls: 2.0120, loss: 2.0120 +2025-06-24 10:24:15,919 - pyskl - INFO - Saving checkpoint at 2 epochs +2025-06-24 10:25:00,907 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:25:00,966 - pyskl - INFO - +top1_acc 0.4860 +top5_acc 0.8806 +2025-06-24 10:25:00,966 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:25:00,973 - pyskl - INFO - +mean_acc 0.3341 +2025-06-24 10:25:00,977 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_1.pth was removed +2025-06-24 10:25:01,176 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2025-06-24 10:25:01,176 - pyskl - INFO - Best top1_acc is 0.4860 at 2 epoch. +2025-06-24 10:25:01,179 - pyskl - INFO - Epoch(val) [2][533] top1_acc: 0.4860, top5_acc: 0.8806, mean_class_accuracy: 0.3341 +2025-06-24 10:25:42,297 - pyskl - INFO - Epoch [3][100/1281] lr: 2.499e-02, eta: 11:59:12, time: 0.411, data_time: 0.191, memory: 4082, top1_acc: 0.5200, top5_acc: 0.8950, loss_cls: 1.9662, loss: 1.9662 +2025-06-24 10:26:04,200 - pyskl - INFO - Epoch [3][200/1281] lr: 2.499e-02, eta: 11:57:50, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.5337, top5_acc: 0.9131, loss_cls: 1.9240, loss: 1.9240 +2025-06-24 10:26:26,183 - pyskl - INFO - Epoch [3][300/1281] lr: 2.499e-02, eta: 11:56:37, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5450, top5_acc: 0.9050, loss_cls: 1.8935, loss: 1.8935 +2025-06-24 10:26:48,180 - pyskl - INFO - Epoch [3][400/1281] lr: 2.499e-02, eta: 11:55:28, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5569, top5_acc: 0.9181, loss_cls: 1.8250, loss: 1.8250 +2025-06-24 10:27:09,980 - pyskl - INFO - Epoch [3][500/1281] lr: 2.498e-02, eta: 11:54:11, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.5625, top5_acc: 0.9194, loss_cls: 1.7692, loss: 1.7692 +2025-06-24 10:27:32,061 - pyskl - INFO - Epoch [3][600/1281] lr: 2.498e-02, eta: 11:53:13, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.5506, top5_acc: 0.9150, loss_cls: 1.8507, loss: 1.8507 +2025-06-24 10:27:54,018 - pyskl - INFO - Epoch [3][700/1281] lr: 2.498e-02, eta: 11:52:11, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5481, top5_acc: 0.9031, loss_cls: 1.8963, loss: 1.8963 +2025-06-24 10:28:15,872 - pyskl - INFO - Epoch [3][800/1281] lr: 2.498e-02, eta: 11:51:05, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.5775, top5_acc: 0.9213, loss_cls: 1.7492, loss: 1.7492 +2025-06-24 10:28:37,845 - pyskl - INFO - Epoch [3][900/1281] lr: 2.498e-02, eta: 11:50:08, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.5713, top5_acc: 0.9331, loss_cls: 1.7428, loss: 1.7428 +2025-06-24 10:28:59,709 - pyskl - INFO - Epoch [3][1000/1281] lr: 2.498e-02, eta: 11:49:07, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.5756, top5_acc: 0.9219, loss_cls: 1.7473, loss: 1.7473 +2025-06-24 10:29:21,643 - pyskl - INFO - Epoch [3][1100/1281] lr: 2.498e-02, eta: 11:48:12, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6075, top5_acc: 0.9375, loss_cls: 1.6352, loss: 1.6352 +2025-06-24 10:29:43,499 - pyskl - INFO - Epoch [3][1200/1281] lr: 2.498e-02, eta: 11:47:15, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.6206, top5_acc: 0.9400, loss_cls: 1.6018, loss: 1.6018 +2025-06-24 10:30:02,205 - pyskl - INFO - Saving checkpoint at 3 epochs +2025-06-24 10:30:46,441 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:30:46,505 - pyskl - INFO - +top1_acc 0.5796 +top5_acc 0.9283 +2025-06-24 10:30:46,505 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:30:46,512 - pyskl - INFO - +mean_acc 0.4355 +2025-06-24 10:30:46,516 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_2.pth was removed +2025-06-24 10:30:46,704 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2025-06-24 10:30:46,705 - pyskl - INFO - Best top1_acc is 0.5796 at 3 epoch. +2025-06-24 10:30:46,708 - pyskl - INFO - Epoch(val) [3][533] top1_acc: 0.5796, top5_acc: 0.9283, mean_class_accuracy: 0.4355 +2025-06-24 10:31:27,775 - pyskl - INFO - Epoch [4][100/1281] lr: 2.497e-02, eta: 11:46:48, time: 0.411, data_time: 0.190, memory: 4082, top1_acc: 0.5956, top5_acc: 0.9487, loss_cls: 1.6217, loss: 1.6217 +2025-06-24 10:31:49,931 - pyskl - INFO - Epoch [4][200/1281] lr: 2.497e-02, eta: 11:46:08, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.6094, top5_acc: 0.9419, loss_cls: 1.5966, loss: 1.5966 +2025-06-24 10:32:11,928 - pyskl - INFO - Epoch [4][300/1281] lr: 2.497e-02, eta: 11:45:22, time: 0.220, data_time: 0.001, memory: 4082, top1_acc: 0.6169, top5_acc: 0.9437, loss_cls: 1.5618, loss: 1.5618 +2025-06-24 10:32:33,929 - pyskl - INFO - Epoch [4][400/1281] lr: 2.497e-02, eta: 11:44:37, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6275, top5_acc: 0.9419, loss_cls: 1.5616, loss: 1.5616 +2025-06-24 10:32:56,099 - pyskl - INFO - Epoch [4][500/1281] lr: 2.497e-02, eta: 11:44:00, time: 0.222, data_time: 0.001, memory: 4082, top1_acc: 0.6325, top5_acc: 0.9406, loss_cls: 1.5510, loss: 1.5510 +2025-06-24 10:33:18,549 - pyskl - INFO - Epoch [4][600/1281] lr: 2.497e-02, eta: 11:43:36, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.6169, top5_acc: 0.9375, loss_cls: 1.5528, loss: 1.5528 +2025-06-24 10:33:40,782 - pyskl - INFO - Epoch [4][700/1281] lr: 2.497e-02, eta: 11:43:02, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.6156, top5_acc: 0.9531, loss_cls: 1.5376, loss: 1.5376 +2025-06-24 10:34:02,919 - pyskl - INFO - Epoch [4][800/1281] lr: 2.496e-02, eta: 11:42:26, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.6438, top5_acc: 0.9587, loss_cls: 1.4327, loss: 1.4327 +2025-06-24 10:34:24,966 - pyskl - INFO - Epoch [4][900/1281] lr: 2.496e-02, eta: 11:41:46, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6400, top5_acc: 0.9463, loss_cls: 1.4947, loss: 1.4947 +2025-06-24 10:34:47,215 - pyskl - INFO - Epoch [4][1000/1281] lr: 2.496e-02, eta: 11:41:15, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.6388, top5_acc: 0.9481, loss_cls: 1.5111, loss: 1.5111 +2025-06-24 10:35:09,179 - pyskl - INFO - Epoch [4][1100/1281] lr: 2.496e-02, eta: 11:40:34, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6250, top5_acc: 0.9431, loss_cls: 1.5259, loss: 1.5259 +2025-06-24 10:35:30,932 - pyskl - INFO - Epoch [4][1200/1281] lr: 2.496e-02, eta: 11:39:46, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6438, top5_acc: 0.9550, loss_cls: 1.4622, loss: 1.4622 +2025-06-24 10:35:49,403 - pyskl - INFO - Saving checkpoint at 4 epochs +2025-06-24 10:36:33,994 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:36:34,048 - pyskl - INFO - +top1_acc 0.6268 +top5_acc 0.9522 +2025-06-24 10:36:34,048 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:36:34,057 - pyskl - INFO - +mean_acc 0.4958 +2025-06-24 10:36:34,063 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_3.pth was removed +2025-06-24 10:36:34,277 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2025-06-24 10:36:34,277 - pyskl - INFO - Best top1_acc is 0.6268 at 4 epoch. +2025-06-24 10:36:34,280 - pyskl - INFO - Epoch(val) [4][533] top1_acc: 0.6268, top5_acc: 0.9522, mean_class_accuracy: 0.4958 +2025-06-24 10:37:15,044 - pyskl - INFO - Epoch [5][100/1281] lr: 2.495e-02, eta: 11:39:10, time: 0.408, data_time: 0.189, memory: 4082, top1_acc: 0.6806, top5_acc: 0.9619, loss_cls: 1.3558, loss: 1.3558 +2025-06-24 10:37:36,817 - pyskl - INFO - Epoch [5][200/1281] lr: 2.495e-02, eta: 11:38:24, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6750, top5_acc: 0.9531, loss_cls: 1.4216, loss: 1.4216 +2025-06-24 10:37:58,869 - pyskl - INFO - Epoch [5][300/1281] lr: 2.495e-02, eta: 11:37:49, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.6687, top5_acc: 0.9613, loss_cls: 1.3580, loss: 1.3580 +2025-06-24 10:38:20,701 - pyskl - INFO - Epoch [5][400/1281] lr: 2.495e-02, eta: 11:37:06, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6619, top5_acc: 0.9619, loss_cls: 1.3694, loss: 1.3694 +2025-06-24 10:38:42,848 - pyskl - INFO - Epoch [5][500/1281] lr: 2.495e-02, eta: 11:36:35, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.6544, top5_acc: 0.9600, loss_cls: 1.4321, loss: 1.4321 +2025-06-24 10:39:05,023 - pyskl - INFO - Epoch [5][600/1281] lr: 2.495e-02, eta: 11:36:05, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.6675, top5_acc: 0.9625, loss_cls: 1.3814, loss: 1.3814 +2025-06-24 10:39:27,032 - pyskl - INFO - Epoch [5][700/1281] lr: 2.494e-02, eta: 11:35:30, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6694, top5_acc: 0.9650, loss_cls: 1.3168, loss: 1.3168 +2025-06-24 10:39:48,846 - pyskl - INFO - Epoch [5][800/1281] lr: 2.494e-02, eta: 11:34:49, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6500, top5_acc: 0.9650, loss_cls: 1.3709, loss: 1.3709 +2025-06-24 10:40:10,551 - pyskl - INFO - Epoch [5][900/1281] lr: 2.494e-02, eta: 11:34:06, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6750, top5_acc: 0.9625, loss_cls: 1.3212, loss: 1.3212 +2025-06-24 10:40:32,220 - pyskl - INFO - Epoch [5][1000/1281] lr: 2.494e-02, eta: 11:33:22, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.6963, top5_acc: 0.9706, loss_cls: 1.2577, loss: 1.2577 +2025-06-24 10:40:54,205 - pyskl - INFO - Epoch [5][1100/1281] lr: 2.494e-02, eta: 11:32:48, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.6694, top5_acc: 0.9675, loss_cls: 1.3099, loss: 1.3099 +2025-06-24 10:41:16,015 - pyskl - INFO - Epoch [5][1200/1281] lr: 2.493e-02, eta: 11:32:10, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.6819, top5_acc: 0.9594, loss_cls: 1.3229, loss: 1.3229 +2025-06-24 10:41:34,442 - pyskl - INFO - Saving checkpoint at 5 epochs +2025-06-24 10:42:19,144 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:42:19,213 - pyskl - INFO - +top1_acc 0.6517 +top5_acc 0.9605 +2025-06-24 10:42:19,214 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:42:19,222 - pyskl - INFO - +mean_acc 0.5148 +2025-06-24 10:42:19,227 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_4.pth was removed +2025-06-24 10:42:19,429 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2025-06-24 10:42:19,430 - pyskl - INFO - Best top1_acc is 0.6517 at 5 epoch. +2025-06-24 10:42:19,433 - pyskl - INFO - Epoch(val) [5][533] top1_acc: 0.6517, top5_acc: 0.9605, mean_class_accuracy: 0.5148 +2025-06-24 10:43:00,912 - pyskl - INFO - Epoch [6][100/1281] lr: 2.493e-02, eta: 11:31:58, time: 0.415, data_time: 0.194, memory: 4082, top1_acc: 0.6956, top5_acc: 0.9606, loss_cls: 1.2932, loss: 1.2932 +2025-06-24 10:43:22,987 - pyskl - INFO - Epoch [6][200/1281] lr: 2.493e-02, eta: 11:31:28, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.6919, top5_acc: 0.9700, loss_cls: 1.2503, loss: 1.2503 +2025-06-24 10:43:45,292 - pyskl - INFO - Epoch [6][300/1281] lr: 2.492e-02, eta: 11:31:04, time: 0.223, data_time: 0.001, memory: 4082, top1_acc: 0.7087, top5_acc: 0.9731, loss_cls: 1.2092, loss: 1.2092 +2025-06-24 10:44:07,443 - pyskl - INFO - Epoch [6][400/1281] lr: 2.492e-02, eta: 11:30:36, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.6800, top5_acc: 0.9669, loss_cls: 1.3038, loss: 1.3038 +2025-06-24 10:44:29,336 - pyskl - INFO - Epoch [6][500/1281] lr: 2.492e-02, eta: 11:30:01, time: 0.219, data_time: 0.001, memory: 4082, top1_acc: 0.7119, top5_acc: 0.9706, loss_cls: 1.2702, loss: 1.2702 +2025-06-24 10:44:51,510 - pyskl - INFO - Epoch [6][600/1281] lr: 2.492e-02, eta: 11:29:34, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.6850, top5_acc: 0.9663, loss_cls: 1.2600, loss: 1.2600 +2025-06-24 10:45:13,902 - pyskl - INFO - Epoch [6][700/1281] lr: 2.492e-02, eta: 11:29:13, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.7225, top5_acc: 0.9700, loss_cls: 1.2067, loss: 1.2067 +2025-06-24 10:45:36,281 - pyskl - INFO - Epoch [6][800/1281] lr: 2.491e-02, eta: 11:28:51, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.7100, top5_acc: 0.9700, loss_cls: 1.2105, loss: 1.2105 +2025-06-24 10:45:58,093 - pyskl - INFO - Epoch [6][900/1281] lr: 2.491e-02, eta: 11:28:15, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7131, top5_acc: 0.9775, loss_cls: 1.2169, loss: 1.2169 +2025-06-24 10:46:20,299 - pyskl - INFO - Epoch [6][1000/1281] lr: 2.491e-02, eta: 11:27:50, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7031, top5_acc: 0.9706, loss_cls: 1.2175, loss: 1.2175 +2025-06-24 10:46:42,386 - pyskl - INFO - Epoch [6][1100/1281] lr: 2.491e-02, eta: 11:27:21, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7037, top5_acc: 0.9656, loss_cls: 1.2397, loss: 1.2397 +2025-06-24 10:47:04,246 - pyskl - INFO - Epoch [6][1200/1281] lr: 2.490e-02, eta: 11:26:47, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7262, top5_acc: 0.9694, loss_cls: 1.1857, loss: 1.1857 +2025-06-24 10:47:22,870 - pyskl - INFO - Saving checkpoint at 6 epochs +2025-06-24 10:48:07,560 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:48:07,633 - pyskl - INFO - +top1_acc 0.6871 +top5_acc 0.9669 +2025-06-24 10:48:07,633 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:48:07,641 - pyskl - INFO - +mean_acc 0.5599 +2025-06-24 10:48:07,645 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_5.pth was removed +2025-06-24 10:48:07,861 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_6.pth. +2025-06-24 10:48:07,862 - pyskl - INFO - Best top1_acc is 0.6871 at 6 epoch. +2025-06-24 10:48:07,865 - pyskl - INFO - Epoch(val) [6][533] top1_acc: 0.6871, top5_acc: 0.9669, mean_class_accuracy: 0.5599 +2025-06-24 10:48:49,330 - pyskl - INFO - Epoch [7][100/1281] lr: 2.490e-02, eta: 11:26:31, time: 0.415, data_time: 0.192, memory: 4082, top1_acc: 0.6981, top5_acc: 0.9781, loss_cls: 1.1861, loss: 1.1861 +2025-06-24 10:49:11,300 - pyskl - INFO - Epoch [7][200/1281] lr: 2.490e-02, eta: 11:26:00, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7175, top5_acc: 0.9712, loss_cls: 1.1658, loss: 1.1658 +2025-06-24 10:49:33,444 - pyskl - INFO - Epoch [7][300/1281] lr: 2.489e-02, eta: 11:25:34, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7119, top5_acc: 0.9744, loss_cls: 1.1490, loss: 1.1490 +2025-06-24 10:49:55,449 - pyskl - INFO - Epoch [7][400/1281] lr: 2.489e-02, eta: 11:25:04, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7094, top5_acc: 0.9750, loss_cls: 1.2133, loss: 1.2133 +2025-06-24 10:50:17,428 - pyskl - INFO - Epoch [7][500/1281] lr: 2.489e-02, eta: 11:24:33, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7288, top5_acc: 0.9731, loss_cls: 1.1335, loss: 1.1335 +2025-06-24 10:50:39,520 - pyskl - INFO - Epoch [7][600/1281] lr: 2.489e-02, eta: 11:24:06, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7131, top5_acc: 0.9762, loss_cls: 1.1873, loss: 1.1873 +2025-06-24 10:51:01,602 - pyskl - INFO - Epoch [7][700/1281] lr: 2.488e-02, eta: 11:23:38, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7069, top5_acc: 0.9725, loss_cls: 1.1958, loss: 1.1958 +2025-06-24 10:51:23,582 - pyskl - INFO - Epoch [7][800/1281] lr: 2.488e-02, eta: 11:23:08, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7225, top5_acc: 0.9788, loss_cls: 1.1835, loss: 1.1835 +2025-06-24 10:51:45,595 - pyskl - INFO - Epoch [7][900/1281] lr: 2.488e-02, eta: 11:22:40, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7250, top5_acc: 0.9744, loss_cls: 1.1802, loss: 1.1802 +2025-06-24 10:52:07,962 - pyskl - INFO - Epoch [7][1000/1281] lr: 2.487e-02, eta: 11:22:18, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.7181, top5_acc: 0.9762, loss_cls: 1.1264, loss: 1.1264 +2025-06-24 10:52:30,136 - pyskl - INFO - Epoch [7][1100/1281] lr: 2.487e-02, eta: 11:21:53, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7275, top5_acc: 0.9788, loss_cls: 1.1281, loss: 1.1281 +2025-06-24 10:52:52,203 - pyskl - INFO - Epoch [7][1200/1281] lr: 2.487e-02, eta: 11:21:26, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7269, top5_acc: 0.9694, loss_cls: 1.1339, loss: 1.1339 +2025-06-24 10:53:10,878 - pyskl - INFO - Saving checkpoint at 7 epochs +2025-06-24 10:53:55,108 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:53:55,170 - pyskl - INFO - +top1_acc 0.6877 +top5_acc 0.9642 +2025-06-24 10:53:55,170 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:53:55,177 - pyskl - INFO - +mean_acc 0.5723 +2025-06-24 10:53:55,181 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_6.pth was removed +2025-06-24 10:53:55,364 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2025-06-24 10:53:55,364 - pyskl - INFO - Best top1_acc is 0.6877 at 7 epoch. +2025-06-24 10:53:55,368 - pyskl - INFO - Epoch(val) [7][533] top1_acc: 0.6877, top5_acc: 0.9642, mean_class_accuracy: 0.5723 +2025-06-24 10:54:36,365 - pyskl - INFO - Epoch [8][100/1281] lr: 2.486e-02, eta: 11:20:58, time: 0.410, data_time: 0.188, memory: 4082, top1_acc: 0.7444, top5_acc: 0.9781, loss_cls: 1.0802, loss: 1.0802 +2025-06-24 10:54:58,638 - pyskl - INFO - Epoch [8][200/1281] lr: 2.486e-02, eta: 11:20:34, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.7381, top5_acc: 0.9781, loss_cls: 1.0777, loss: 1.0777 +2025-06-24 10:55:20,844 - pyskl - INFO - Epoch [8][300/1281] lr: 2.486e-02, eta: 11:20:10, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7119, top5_acc: 0.9756, loss_cls: 1.1746, loss: 1.1746 +2025-06-24 10:55:43,202 - pyskl - INFO - Epoch [8][400/1281] lr: 2.485e-02, eta: 11:19:48, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.7400, top5_acc: 0.9725, loss_cls: 1.1369, loss: 1.1369 +2025-06-24 10:56:05,284 - pyskl - INFO - Epoch [8][500/1281] lr: 2.485e-02, eta: 11:19:22, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7288, top5_acc: 0.9750, loss_cls: 1.1199, loss: 1.1199 +2025-06-24 10:56:27,258 - pyskl - INFO - Epoch [8][600/1281] lr: 2.485e-02, eta: 11:18:53, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7550, top5_acc: 0.9781, loss_cls: 1.0827, loss: 1.0827 +2025-06-24 10:56:49,551 - pyskl - INFO - Epoch [8][700/1281] lr: 2.484e-02, eta: 11:18:30, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.7300, top5_acc: 0.9700, loss_cls: 1.1230, loss: 1.1230 +2025-06-24 10:57:11,283 - pyskl - INFO - Epoch [8][800/1281] lr: 2.484e-02, eta: 11:17:57, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7556, top5_acc: 0.9756, loss_cls: 1.0714, loss: 1.0714 +2025-06-24 10:57:33,053 - pyskl - INFO - Epoch [8][900/1281] lr: 2.484e-02, eta: 11:17:25, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7450, top5_acc: 0.9744, loss_cls: 1.0892, loss: 1.0892 +2025-06-24 10:57:54,737 - pyskl - INFO - Epoch [8][1000/1281] lr: 2.483e-02, eta: 11:16:51, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7219, top5_acc: 0.9731, loss_cls: 1.1541, loss: 1.1541 +2025-06-24 10:58:16,703 - pyskl - INFO - Epoch [8][1100/1281] lr: 2.483e-02, eta: 11:16:23, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7475, top5_acc: 0.9794, loss_cls: 1.0647, loss: 1.0647 +2025-06-24 10:58:38,780 - pyskl - INFO - Epoch [8][1200/1281] lr: 2.483e-02, eta: 11:15:57, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7419, top5_acc: 0.9750, loss_cls: 1.0674, loss: 1.0674 +2025-06-24 10:58:57,292 - pyskl - INFO - Saving checkpoint at 8 epochs +2025-06-24 10:59:41,911 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:59:41,972 - pyskl - INFO - +top1_acc 0.7132 +top5_acc 0.9741 +2025-06-24 10:59:41,972 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:59:41,979 - pyskl - INFO - +mean_acc 0.6029 +2025-06-24 10:59:41,983 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_7.pth was removed +2025-06-24 10:59:42,318 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_8.pth. +2025-06-24 10:59:42,318 - pyskl - INFO - Best top1_acc is 0.7132 at 8 epoch. +2025-06-24 10:59:42,321 - pyskl - INFO - Epoch(val) [8][533] top1_acc: 0.7132, top5_acc: 0.9741, mean_class_accuracy: 0.6029 +2025-06-24 11:00:23,917 - pyskl - INFO - Epoch [9][100/1281] lr: 2.482e-02, eta: 11:15:39, time: 0.416, data_time: 0.195, memory: 4082, top1_acc: 0.7538, top5_acc: 0.9806, loss_cls: 1.0702, loss: 1.0702 +2025-06-24 11:00:45,762 - pyskl - INFO - Epoch [9][200/1281] lr: 2.482e-02, eta: 11:15:08, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7506, top5_acc: 0.9794, loss_cls: 1.0238, loss: 1.0238 +2025-06-24 11:01:07,900 - pyskl - INFO - Epoch [9][300/1281] lr: 2.481e-02, eta: 11:14:43, time: 0.221, data_time: 0.001, memory: 4082, top1_acc: 0.7550, top5_acc: 0.9838, loss_cls: 1.0149, loss: 1.0149 +2025-06-24 11:01:30,119 - pyskl - INFO - Epoch [9][400/1281] lr: 2.481e-02, eta: 11:14:20, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7419, top5_acc: 0.9856, loss_cls: 1.0638, loss: 1.0638 +2025-06-24 11:01:52,193 - pyskl - INFO - Epoch [9][500/1281] lr: 2.481e-02, eta: 11:13:54, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7331, top5_acc: 0.9806, loss_cls: 1.0700, loss: 1.0700 +2025-06-24 11:02:14,396 - pyskl - INFO - Epoch [9][600/1281] lr: 2.480e-02, eta: 11:13:30, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7394, top5_acc: 0.9812, loss_cls: 1.0898, loss: 1.0898 +2025-06-24 11:02:36,584 - pyskl - INFO - Epoch [9][700/1281] lr: 2.480e-02, eta: 11:13:06, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7619, top5_acc: 0.9762, loss_cls: 1.0234, loss: 1.0234 +2025-06-24 11:02:58,969 - pyskl - INFO - Epoch [9][800/1281] lr: 2.480e-02, eta: 11:12:45, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.7381, top5_acc: 0.9825, loss_cls: 1.0539, loss: 1.0539 +2025-06-24 11:03:21,602 - pyskl - INFO - Epoch [9][900/1281] lr: 2.479e-02, eta: 11:12:29, time: 0.226, data_time: 0.000, memory: 4082, top1_acc: 0.7444, top5_acc: 0.9819, loss_cls: 1.0650, loss: 1.0650 +2025-06-24 11:03:43,971 - pyskl - INFO - Epoch [9][1000/1281] lr: 2.479e-02, eta: 11:12:08, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.7544, top5_acc: 0.9769, loss_cls: 1.0763, loss: 1.0763 +2025-06-24 11:04:06,484 - pyskl - INFO - Epoch [9][1100/1281] lr: 2.479e-02, eta: 11:11:49, time: 0.225, data_time: 0.000, memory: 4082, top1_acc: 0.7362, top5_acc: 0.9712, loss_cls: 1.1349, loss: 1.1349 +2025-06-24 11:04:28,532 - pyskl - INFO - Epoch [9][1200/1281] lr: 2.478e-02, eta: 11:11:23, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7588, top5_acc: 0.9781, loss_cls: 1.0684, loss: 1.0684 +2025-06-24 11:04:47,097 - pyskl - INFO - Saving checkpoint at 9 epochs +2025-06-24 11:05:31,235 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:05:31,295 - pyskl - INFO - +top1_acc 0.7420 +top5_acc 0.9724 +2025-06-24 11:05:31,295 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:05:31,303 - pyskl - INFO - +mean_acc 0.6104 +2025-06-24 11:05:31,308 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_8.pth was removed +2025-06-24 11:05:31,483 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_9.pth. +2025-06-24 11:05:31,484 - pyskl - INFO - Best top1_acc is 0.7420 at 9 epoch. +2025-06-24 11:05:31,487 - pyskl - INFO - Epoch(val) [9][533] top1_acc: 0.7420, top5_acc: 0.9724, mean_class_accuracy: 0.6104 +2025-06-24 11:06:13,125 - pyskl - INFO - Epoch [10][100/1281] lr: 2.477e-02, eta: 11:11:02, time: 0.416, data_time: 0.196, memory: 4082, top1_acc: 0.7588, top5_acc: 0.9831, loss_cls: 1.0103, loss: 1.0103 +2025-06-24 11:06:34,967 - pyskl - INFO - Epoch [10][200/1281] lr: 2.477e-02, eta: 11:10:33, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7612, top5_acc: 0.9850, loss_cls: 1.0139, loss: 1.0139 +2025-06-24 11:06:57,318 - pyskl - INFO - Epoch [10][300/1281] lr: 2.477e-02, eta: 11:10:11, time: 0.223, data_time: 0.001, memory: 4082, top1_acc: 0.7688, top5_acc: 0.9875, loss_cls: 0.9883, loss: 0.9883 +2025-06-24 11:07:19,358 - pyskl - INFO - Epoch [10][400/1281] lr: 2.476e-02, eta: 11:09:45, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7681, top5_acc: 0.9819, loss_cls: 1.0024, loss: 1.0024 +2025-06-24 11:07:41,362 - pyskl - INFO - Epoch [10][500/1281] lr: 2.476e-02, eta: 11:09:18, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7588, top5_acc: 0.9862, loss_cls: 0.9866, loss: 0.9866 +2025-06-24 11:08:03,290 - pyskl - INFO - Epoch [10][600/1281] lr: 2.476e-02, eta: 11:08:51, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7412, top5_acc: 0.9812, loss_cls: 1.0533, loss: 1.0533 +2025-06-24 11:08:25,363 - pyskl - INFO - Epoch [10][700/1281] lr: 2.475e-02, eta: 11:08:25, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7638, top5_acc: 0.9819, loss_cls: 1.0247, loss: 1.0247 +2025-06-24 11:08:47,497 - pyskl - INFO - Epoch [10][800/1281] lr: 2.475e-02, eta: 11:08:00, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7612, top5_acc: 0.9800, loss_cls: 0.9959, loss: 0.9959 +2025-06-24 11:09:09,427 - pyskl - INFO - Epoch [10][900/1281] lr: 2.474e-02, eta: 11:07:33, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7694, top5_acc: 0.9819, loss_cls: 0.9878, loss: 0.9878 +2025-06-24 11:09:31,427 - pyskl - INFO - Epoch [10][1000/1281] lr: 2.474e-02, eta: 11:07:07, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7506, top5_acc: 0.9775, loss_cls: 1.1042, loss: 1.1042 +2025-06-24 11:09:53,345 - pyskl - INFO - Epoch [10][1100/1281] lr: 2.473e-02, eta: 11:06:39, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7550, top5_acc: 0.9769, loss_cls: 1.0369, loss: 1.0369 +2025-06-24 11:10:15,179 - pyskl - INFO - Epoch [10][1200/1281] lr: 2.473e-02, eta: 11:06:10, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7631, top5_acc: 0.9869, loss_cls: 1.0297, loss: 1.0297 +2025-06-24 11:10:33,573 - pyskl - INFO - Saving checkpoint at 10 epochs +2025-06-24 11:11:17,773 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:11:17,831 - pyskl - INFO - +top1_acc 0.7239 +top5_acc 0.9745 +2025-06-24 11:11:17,831 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:11:17,837 - pyskl - INFO - +mean_acc 0.6346 +2025-06-24 11:11:17,839 - pyskl - INFO - Epoch(val) [10][533] top1_acc: 0.7239, top5_acc: 0.9745, mean_class_accuracy: 0.6346 +2025-06-24 11:11:59,385 - pyskl - INFO - Epoch [11][100/1281] lr: 2.472e-02, eta: 11:05:47, time: 0.415, data_time: 0.193, memory: 4082, top1_acc: 0.7594, top5_acc: 0.9806, loss_cls: 0.9893, loss: 0.9893 +2025-06-24 11:12:21,495 - pyskl - INFO - Epoch [11][200/1281] lr: 2.472e-02, eta: 11:05:22, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7913, top5_acc: 0.9869, loss_cls: 0.9209, loss: 0.9209 +2025-06-24 11:12:43,803 - pyskl - INFO - Epoch [11][300/1281] lr: 2.471e-02, eta: 11:05:00, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.7444, top5_acc: 0.9800, loss_cls: 1.0719, loss: 1.0719 +2025-06-24 11:13:05,894 - pyskl - INFO - Epoch [11][400/1281] lr: 2.471e-02, eta: 11:04:35, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7806, top5_acc: 0.9838, loss_cls: 0.9809, loss: 0.9809 +2025-06-24 11:13:27,989 - pyskl - INFO - Epoch [11][500/1281] lr: 2.471e-02, eta: 11:04:11, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7731, top5_acc: 0.9869, loss_cls: 0.9930, loss: 0.9930 +2025-06-24 11:13:50,500 - pyskl - INFO - Epoch [11][600/1281] lr: 2.470e-02, eta: 11:03:51, time: 0.225, data_time: 0.000, memory: 4082, top1_acc: 0.7588, top5_acc: 0.9831, loss_cls: 1.0136, loss: 1.0136 +2025-06-24 11:14:12,886 - pyskl - INFO - Epoch [11][700/1281] lr: 2.470e-02, eta: 11:03:30, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.7769, top5_acc: 0.9825, loss_cls: 0.9735, loss: 0.9735 +2025-06-24 11:14:35,022 - pyskl - INFO - Epoch [11][800/1281] lr: 2.469e-02, eta: 11:03:06, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7612, top5_acc: 0.9869, loss_cls: 0.9702, loss: 0.9702 +2025-06-24 11:14:57,251 - pyskl - INFO - Epoch [11][900/1281] lr: 2.469e-02, eta: 11:02:43, time: 0.222, data_time: 0.001, memory: 4082, top1_acc: 0.7775, top5_acc: 0.9838, loss_cls: 0.9585, loss: 0.9585 +2025-06-24 11:15:19,523 - pyskl - INFO - Epoch [11][1000/1281] lr: 2.468e-02, eta: 11:02:21, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.7512, top5_acc: 0.9819, loss_cls: 1.0237, loss: 1.0237 +2025-06-24 11:15:41,569 - pyskl - INFO - Epoch [11][1100/1281] lr: 2.468e-02, eta: 11:01:55, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7788, top5_acc: 0.9812, loss_cls: 0.9749, loss: 0.9749 +2025-06-24 11:16:03,720 - pyskl - INFO - Epoch [11][1200/1281] lr: 2.467e-02, eta: 11:01:31, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7444, top5_acc: 0.9838, loss_cls: 1.0454, loss: 1.0454 +2025-06-24 11:16:22,451 - pyskl - INFO - Saving checkpoint at 11 epochs +2025-06-24 11:17:05,839 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:17:05,899 - pyskl - INFO - +top1_acc 0.7462 +top5_acc 0.9800 +2025-06-24 11:17:05,899 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:17:05,907 - pyskl - INFO - +mean_acc 0.6549 +2025-06-24 11:17:05,911 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_9.pth was removed +2025-06-24 11:17:06,081 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_11.pth. +2025-06-24 11:17:06,081 - pyskl - INFO - Best top1_acc is 0.7462 at 11 epoch. +2025-06-24 11:17:06,084 - pyskl - INFO - Epoch(val) [11][533] top1_acc: 0.7462, top5_acc: 0.9800, mean_class_accuracy: 0.6549 +2025-06-24 11:17:47,576 - pyskl - INFO - Epoch [12][100/1281] lr: 2.467e-02, eta: 11:01:05, time: 0.415, data_time: 0.196, memory: 4082, top1_acc: 0.7800, top5_acc: 0.9869, loss_cls: 0.9396, loss: 0.9396 +2025-06-24 11:18:09,435 - pyskl - INFO - Epoch [12][200/1281] lr: 2.466e-02, eta: 11:00:38, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7750, top5_acc: 0.9869, loss_cls: 0.9469, loss: 0.9469 +2025-06-24 11:18:31,504 - pyskl - INFO - Epoch [12][300/1281] lr: 2.466e-02, eta: 11:00:13, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9819, loss_cls: 0.9414, loss: 0.9414 +2025-06-24 11:18:53,327 - pyskl - INFO - Epoch [12][400/1281] lr: 2.465e-02, eta: 10:59:45, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7825, top5_acc: 0.9844, loss_cls: 0.9291, loss: 0.9291 +2025-06-24 11:19:15,043 - pyskl - INFO - Epoch [12][500/1281] lr: 2.465e-02, eta: 10:59:16, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7788, top5_acc: 0.9856, loss_cls: 0.9481, loss: 0.9481 +2025-06-24 11:19:36,862 - pyskl - INFO - Epoch [12][600/1281] lr: 2.464e-02, eta: 10:58:48, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7937, top5_acc: 0.9831, loss_cls: 0.9233, loss: 0.9233 +2025-06-24 11:19:58,815 - pyskl - INFO - Epoch [12][700/1281] lr: 2.464e-02, eta: 10:58:22, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7919, top5_acc: 0.9856, loss_cls: 0.9388, loss: 0.9388 +2025-06-24 11:20:20,874 - pyskl - INFO - Epoch [12][800/1281] lr: 2.463e-02, eta: 10:57:57, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7612, top5_acc: 0.9825, loss_cls: 1.0287, loss: 1.0287 +2025-06-24 11:20:42,787 - pyskl - INFO - Epoch [12][900/1281] lr: 2.463e-02, eta: 10:57:30, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7744, top5_acc: 0.9869, loss_cls: 0.9343, loss: 0.9343 +2025-06-24 11:21:04,558 - pyskl - INFO - Epoch [12][1000/1281] lr: 2.462e-02, eta: 10:57:02, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7863, top5_acc: 0.9869, loss_cls: 0.9402, loss: 0.9402 +2025-06-24 11:21:26,953 - pyskl - INFO - Epoch [12][1100/1281] lr: 2.462e-02, eta: 10:56:41, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.7887, top5_acc: 0.9825, loss_cls: 0.9259, loss: 0.9259 +2025-06-24 11:21:48,860 - pyskl - INFO - Epoch [12][1200/1281] lr: 2.461e-02, eta: 10:56:15, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7850, top5_acc: 0.9850, loss_cls: 0.9345, loss: 0.9345 +2025-06-24 11:22:07,260 - pyskl - INFO - Saving checkpoint at 12 epochs +2025-06-24 11:22:51,825 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:22:51,882 - pyskl - INFO - +top1_acc 0.7102 +top5_acc 0.9669 +2025-06-24 11:22:51,882 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:22:51,889 - pyskl - INFO - +mean_acc 0.6373 +2025-06-24 11:22:51,890 - pyskl - INFO - Epoch(val) [12][533] top1_acc: 0.7102, top5_acc: 0.9669, mean_class_accuracy: 0.6373 +2025-06-24 11:23:33,168 - pyskl - INFO - Epoch [13][100/1281] lr: 2.460e-02, eta: 10:55:46, time: 0.413, data_time: 0.194, memory: 4082, top1_acc: 0.7825, top5_acc: 0.9881, loss_cls: 0.8999, loss: 0.8999 +2025-06-24 11:23:55,793 - pyskl - INFO - Epoch [13][200/1281] lr: 2.460e-02, eta: 10:55:28, time: 0.226, data_time: 0.000, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9888, loss_cls: 0.8728, loss: 0.8728 +2025-06-24 11:24:18,038 - pyskl - INFO - Epoch [13][300/1281] lr: 2.459e-02, eta: 10:55:05, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.7937, top5_acc: 0.9831, loss_cls: 0.8925, loss: 0.8925 +2025-06-24 11:24:40,028 - pyskl - INFO - Epoch [13][400/1281] lr: 2.459e-02, eta: 10:54:40, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7850, top5_acc: 0.9881, loss_cls: 0.9280, loss: 0.9280 +2025-06-24 11:25:02,467 - pyskl - INFO - Epoch [13][500/1281] lr: 2.458e-02, eta: 10:54:19, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.7781, top5_acc: 0.9888, loss_cls: 0.9185, loss: 0.9185 +2025-06-24 11:25:24,522 - pyskl - INFO - Epoch [13][600/1281] lr: 2.458e-02, eta: 10:53:55, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7925, top5_acc: 0.9869, loss_cls: 0.9135, loss: 0.9135 +2025-06-24 11:25:46,580 - pyskl - INFO - Epoch [13][700/1281] lr: 2.457e-02, eta: 10:53:30, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7756, top5_acc: 0.9819, loss_cls: 0.9596, loss: 0.9596 +2025-06-24 11:26:08,588 - pyskl - INFO - Epoch [13][800/1281] lr: 2.457e-02, eta: 10:53:05, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8037, top5_acc: 0.9856, loss_cls: 0.8730, loss: 0.8730 +2025-06-24 11:26:30,609 - pyskl - INFO - Epoch [13][900/1281] lr: 2.456e-02, eta: 10:52:40, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7944, top5_acc: 0.9844, loss_cls: 0.9052, loss: 0.9052 +2025-06-24 11:26:52,443 - pyskl - INFO - Epoch [13][1000/1281] lr: 2.455e-02, eta: 10:52:13, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7831, top5_acc: 0.9812, loss_cls: 0.9445, loss: 0.9445 +2025-06-24 11:27:14,368 - pyskl - INFO - Epoch [13][1100/1281] lr: 2.455e-02, eta: 10:51:47, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7656, top5_acc: 0.9756, loss_cls: 0.9963, loss: 0.9963 +2025-06-24 11:27:36,409 - pyskl - INFO - Epoch [13][1200/1281] lr: 2.454e-02, eta: 10:51:22, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9875, loss_cls: 0.8912, loss: 0.8912 +2025-06-24 11:27:55,019 - pyskl - INFO - Saving checkpoint at 13 epochs +2025-06-24 11:28:39,360 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:28:39,433 - pyskl - INFO - +top1_acc 0.7397 +top5_acc 0.9728 +2025-06-24 11:28:39,434 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:28:39,443 - pyskl - INFO - +mean_acc 0.6672 +2025-06-24 11:28:39,446 - pyskl - INFO - Epoch(val) [13][533] top1_acc: 0.7397, top5_acc: 0.9728, mean_class_accuracy: 0.6672 +2025-06-24 11:29:21,334 - pyskl - INFO - Epoch [14][100/1281] lr: 2.453e-02, eta: 10:50:59, time: 0.419, data_time: 0.200, memory: 4082, top1_acc: 0.7900, top5_acc: 0.9850, loss_cls: 0.9143, loss: 0.9143 +2025-06-24 11:29:43,270 - pyskl - INFO - Epoch [14][200/1281] lr: 2.453e-02, eta: 10:50:33, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8056, top5_acc: 0.9900, loss_cls: 0.8437, loss: 0.8437 +2025-06-24 11:30:05,278 - pyskl - INFO - Epoch [14][300/1281] lr: 2.452e-02, eta: 10:50:08, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7844, top5_acc: 0.9869, loss_cls: 0.9306, loss: 0.9306 +2025-06-24 11:30:26,833 - pyskl - INFO - Epoch [14][400/1281] lr: 2.452e-02, eta: 10:49:39, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.7831, top5_acc: 0.9894, loss_cls: 0.9122, loss: 0.9122 +2025-06-24 11:30:48,573 - pyskl - INFO - Epoch [14][500/1281] lr: 2.451e-02, eta: 10:49:11, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7806, top5_acc: 0.9806, loss_cls: 0.9382, loss: 0.9382 +2025-06-24 11:31:10,351 - pyskl - INFO - Epoch [14][600/1281] lr: 2.451e-02, eta: 10:48:44, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8013, top5_acc: 0.9875, loss_cls: 0.8944, loss: 0.8944 +2025-06-24 11:31:32,205 - pyskl - INFO - Epoch [14][700/1281] lr: 2.450e-02, eta: 10:48:18, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8031, top5_acc: 0.9850, loss_cls: 0.8608, loss: 0.8608 +2025-06-24 11:31:54,134 - pyskl - INFO - Epoch [14][800/1281] lr: 2.449e-02, eta: 10:47:52, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7937, top5_acc: 0.9931, loss_cls: 0.8677, loss: 0.8677 +2025-06-24 11:32:15,877 - pyskl - INFO - Epoch [14][900/1281] lr: 2.449e-02, eta: 10:47:25, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7981, top5_acc: 0.9900, loss_cls: 0.9016, loss: 0.9016 +2025-06-24 11:32:38,172 - pyskl - INFO - Epoch [14][1000/1281] lr: 2.448e-02, eta: 10:47:03, time: 0.223, data_time: 0.001, memory: 4082, top1_acc: 0.7769, top5_acc: 0.9825, loss_cls: 0.9231, loss: 0.9231 +2025-06-24 11:33:00,186 - pyskl - INFO - Epoch [14][1100/1281] lr: 2.448e-02, eta: 10:46:38, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.7850, top5_acc: 0.9794, loss_cls: 0.9723, loss: 0.9723 +2025-06-24 11:33:22,123 - pyskl - INFO - Epoch [14][1200/1281] lr: 2.447e-02, eta: 10:46:13, time: 0.219, data_time: 0.001, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9888, loss_cls: 0.8397, loss: 0.8397 +2025-06-24 11:33:40,527 - pyskl - INFO - Saving checkpoint at 14 epochs +2025-06-24 11:34:25,295 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:34:25,351 - pyskl - INFO - +top1_acc 0.7450 +top5_acc 0.9795 +2025-06-24 11:34:25,352 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:34:25,359 - pyskl - INFO - +mean_acc 0.6396 +2025-06-24 11:34:25,361 - pyskl - INFO - Epoch(val) [14][533] top1_acc: 0.7450, top5_acc: 0.9795, mean_class_accuracy: 0.6396 +2025-06-24 11:35:06,389 - pyskl - INFO - Epoch [15][100/1281] lr: 2.446e-02, eta: 10:45:40, time: 0.410, data_time: 0.189, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9894, loss_cls: 0.8244, loss: 0.8244 +2025-06-24 11:35:28,462 - pyskl - INFO - Epoch [15][200/1281] lr: 2.445e-02, eta: 10:45:16, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9894, loss_cls: 0.8222, loss: 0.8222 +2025-06-24 11:35:50,701 - pyskl - INFO - Epoch [15][300/1281] lr: 2.445e-02, eta: 10:44:54, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9888, loss_cls: 0.8365, loss: 0.8365 +2025-06-24 11:36:12,679 - pyskl - INFO - Epoch [15][400/1281] lr: 2.444e-02, eta: 10:44:29, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8069, top5_acc: 0.9856, loss_cls: 0.8532, loss: 0.8532 +2025-06-24 11:36:34,884 - pyskl - INFO - Epoch [15][500/1281] lr: 2.444e-02, eta: 10:44:06, time: 0.222, data_time: 0.001, memory: 4082, top1_acc: 0.8113, top5_acc: 0.9906, loss_cls: 0.8264, loss: 0.8264 +2025-06-24 11:36:56,685 - pyskl - INFO - Epoch [15][600/1281] lr: 2.443e-02, eta: 10:43:40, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.7919, top5_acc: 0.9875, loss_cls: 0.9042, loss: 0.9042 +2025-06-24 11:37:18,618 - pyskl - INFO - Epoch [15][700/1281] lr: 2.442e-02, eta: 10:43:15, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.7931, top5_acc: 0.9869, loss_cls: 0.8832, loss: 0.8832 +2025-06-24 11:37:40,764 - pyskl - INFO - Epoch [15][800/1281] lr: 2.442e-02, eta: 10:42:52, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.7887, top5_acc: 0.9869, loss_cls: 0.9223, loss: 0.9223 +2025-06-24 11:38:02,687 - pyskl - INFO - Epoch [15][900/1281] lr: 2.441e-02, eta: 10:42:26, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9888, loss_cls: 0.8700, loss: 0.8700 +2025-06-24 11:38:24,576 - pyskl - INFO - Epoch [15][1000/1281] lr: 2.441e-02, eta: 10:42:01, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8075, top5_acc: 0.9888, loss_cls: 0.8552, loss: 0.8552 +2025-06-24 11:38:46,323 - pyskl - INFO - Epoch [15][1100/1281] lr: 2.440e-02, eta: 10:41:34, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8175, top5_acc: 0.9844, loss_cls: 0.8572, loss: 0.8572 +2025-06-24 11:39:08,017 - pyskl - INFO - Epoch [15][1200/1281] lr: 2.439e-02, eta: 10:41:07, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.7837, top5_acc: 0.9838, loss_cls: 0.9098, loss: 0.9098 +2025-06-24 11:39:26,454 - pyskl - INFO - Saving checkpoint at 15 epochs +2025-06-24 11:40:10,687 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:40:10,754 - pyskl - INFO - +top1_acc 0.7778 +top5_acc 0.9812 +2025-06-24 11:40:10,755 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:40:10,763 - pyskl - INFO - +mean_acc 0.6808 +2025-06-24 11:40:10,767 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_11.pth was removed +2025-06-24 11:40:10,971 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_15.pth. +2025-06-24 11:40:10,971 - pyskl - INFO - Best top1_acc is 0.7778 at 15 epoch. +2025-06-24 11:40:10,974 - pyskl - INFO - Epoch(val) [15][533] top1_acc: 0.7778, top5_acc: 0.9812, mean_class_accuracy: 0.6808 +2025-06-24 11:40:52,621 - pyskl - INFO - Epoch [16][100/1281] lr: 2.438e-02, eta: 10:40:39, time: 0.416, data_time: 0.195, memory: 4082, top1_acc: 0.7869, top5_acc: 0.9906, loss_cls: 0.8576, loss: 0.8576 +2025-06-24 11:41:14,503 - pyskl - INFO - Epoch [16][200/1281] lr: 2.438e-02, eta: 10:40:14, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8125, top5_acc: 0.9912, loss_cls: 0.8083, loss: 0.8083 +2025-06-24 11:41:36,486 - pyskl - INFO - Epoch [16][300/1281] lr: 2.437e-02, eta: 10:39:49, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9838, loss_cls: 0.8508, loss: 0.8508 +2025-06-24 11:41:58,575 - pyskl - INFO - Epoch [16][400/1281] lr: 2.436e-02, eta: 10:39:26, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9906, loss_cls: 0.8142, loss: 0.8142 +2025-06-24 11:42:20,529 - pyskl - INFO - Epoch [16][500/1281] lr: 2.436e-02, eta: 10:39:01, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9875, loss_cls: 0.8365, loss: 0.8365 +2025-06-24 11:42:42,843 - pyskl - INFO - Epoch [16][600/1281] lr: 2.435e-02, eta: 10:38:40, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.8106, top5_acc: 0.9862, loss_cls: 0.8563, loss: 0.8563 +2025-06-24 11:43:04,926 - pyskl - INFO - Epoch [16][700/1281] lr: 2.434e-02, eta: 10:38:16, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9862, loss_cls: 0.8427, loss: 0.8427 +2025-06-24 11:43:26,998 - pyskl - INFO - Epoch [16][800/1281] lr: 2.434e-02, eta: 10:37:52, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8131, top5_acc: 0.9856, loss_cls: 0.8663, loss: 0.8663 +2025-06-24 11:43:48,870 - pyskl - INFO - Epoch [16][900/1281] lr: 2.433e-02, eta: 10:37:27, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8144, top5_acc: 0.9925, loss_cls: 0.8332, loss: 0.8332 +2025-06-24 11:44:11,080 - pyskl - INFO - Epoch [16][1000/1281] lr: 2.432e-02, eta: 10:37:05, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9875, loss_cls: 0.8264, loss: 0.8264 +2025-06-24 11:44:33,249 - pyskl - INFO - Epoch [16][1100/1281] lr: 2.432e-02, eta: 10:36:42, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9900, loss_cls: 0.8550, loss: 0.8550 +2025-06-24 11:44:54,853 - pyskl - INFO - Epoch [16][1200/1281] lr: 2.431e-02, eta: 10:36:14, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8200, top5_acc: 0.9925, loss_cls: 0.8081, loss: 0.8081 +2025-06-24 11:45:13,364 - pyskl - INFO - Saving checkpoint at 16 epochs +2025-06-24 11:45:58,063 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:45:58,120 - pyskl - INFO - +top1_acc 0.7988 +top5_acc 0.9847 +2025-06-24 11:45:58,120 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:45:58,127 - pyskl - INFO - +mean_acc 0.6886 +2025-06-24 11:45:58,131 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_15.pth was removed +2025-06-24 11:45:58,308 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_16.pth. +2025-06-24 11:45:58,308 - pyskl - INFO - Best top1_acc is 0.7988 at 16 epoch. +2025-06-24 11:45:58,311 - pyskl - INFO - Epoch(val) [16][533] top1_acc: 0.7988, top5_acc: 0.9847, mean_class_accuracy: 0.6886 +2025-06-24 11:46:40,082 - pyskl - INFO - Epoch [17][100/1281] lr: 2.430e-02, eta: 10:35:47, time: 0.418, data_time: 0.195, memory: 4082, top1_acc: 0.8231, top5_acc: 0.9912, loss_cls: 0.7861, loss: 0.7861 +2025-06-24 11:47:02,218 - pyskl - INFO - Epoch [17][200/1281] lr: 2.429e-02, eta: 10:35:24, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9881, loss_cls: 0.7825, loss: 0.7825 +2025-06-24 11:47:24,080 - pyskl - INFO - Epoch [17][300/1281] lr: 2.428e-02, eta: 10:34:58, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9844, loss_cls: 0.7876, loss: 0.7876 +2025-06-24 11:47:46,529 - pyskl - INFO - Epoch [17][400/1281] lr: 2.428e-02, eta: 10:34:38, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9900, loss_cls: 0.7412, loss: 0.7412 +2025-06-24 11:48:08,899 - pyskl - INFO - Epoch [17][500/1281] lr: 2.427e-02, eta: 10:34:17, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9919, loss_cls: 0.7959, loss: 0.7959 +2025-06-24 11:48:30,821 - pyskl - INFO - Epoch [17][600/1281] lr: 2.426e-02, eta: 10:33:52, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9888, loss_cls: 0.7926, loss: 0.7926 +2025-06-24 11:48:52,926 - pyskl - INFO - Epoch [17][700/1281] lr: 2.426e-02, eta: 10:33:29, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9888, loss_cls: 0.8050, loss: 0.8050 +2025-06-24 11:49:14,761 - pyskl - INFO - Epoch [17][800/1281] lr: 2.425e-02, eta: 10:33:03, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9888, loss_cls: 0.7842, loss: 0.7842 +2025-06-24 11:49:36,780 - pyskl - INFO - Epoch [17][900/1281] lr: 2.424e-02, eta: 10:32:39, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9900, loss_cls: 0.8177, loss: 0.8177 +2025-06-24 11:49:58,578 - pyskl - INFO - Epoch [17][1000/1281] lr: 2.424e-02, eta: 10:32:14, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8025, top5_acc: 0.9900, loss_cls: 0.8226, loss: 0.8226 +2025-06-24 11:50:20,520 - pyskl - INFO - Epoch [17][1100/1281] lr: 2.423e-02, eta: 10:31:49, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9875, loss_cls: 0.8441, loss: 0.8441 +2025-06-24 11:50:42,374 - pyskl - INFO - Epoch [17][1200/1281] lr: 2.422e-02, eta: 10:31:24, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9900, loss_cls: 0.8301, loss: 0.8301 +2025-06-24 11:51:00,926 - pyskl - INFO - Saving checkpoint at 17 epochs +2025-06-24 11:51:44,923 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:51:44,979 - pyskl - INFO - +top1_acc 0.7837 +top5_acc 0.9850 +2025-06-24 11:51:44,979 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:51:44,986 - pyskl - INFO - +mean_acc 0.7031 +2025-06-24 11:51:44,988 - pyskl - INFO - Epoch(val) [17][533] top1_acc: 0.7837, top5_acc: 0.9850, mean_class_accuracy: 0.7031 +2025-06-24 11:52:26,536 - pyskl - INFO - Epoch [18][100/1281] lr: 2.421e-02, eta: 10:30:54, time: 0.415, data_time: 0.196, memory: 4082, top1_acc: 0.8194, top5_acc: 0.9888, loss_cls: 0.8309, loss: 0.8309 +2025-06-24 11:52:48,680 - pyskl - INFO - Epoch [18][200/1281] lr: 2.420e-02, eta: 10:30:31, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9888, loss_cls: 0.8395, loss: 0.8395 +2025-06-24 11:53:10,760 - pyskl - INFO - Epoch [18][300/1281] lr: 2.419e-02, eta: 10:30:08, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9900, loss_cls: 0.8393, loss: 0.8393 +2025-06-24 11:53:32,569 - pyskl - INFO - Epoch [18][400/1281] lr: 2.419e-02, eta: 10:29:42, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9875, loss_cls: 0.7647, loss: 0.7647 +2025-06-24 11:53:54,602 - pyskl - INFO - Epoch [18][500/1281] lr: 2.418e-02, eta: 10:29:18, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9900, loss_cls: 0.7780, loss: 0.7780 +2025-06-24 11:54:16,866 - pyskl - INFO - Epoch [18][600/1281] lr: 2.417e-02, eta: 10:28:57, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9925, loss_cls: 0.7544, loss: 0.7544 +2025-06-24 11:54:38,927 - pyskl - INFO - Epoch [18][700/1281] lr: 2.417e-02, eta: 10:28:33, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9894, loss_cls: 0.8089, loss: 0.8089 +2025-06-24 11:55:00,934 - pyskl - INFO - Epoch [18][800/1281] lr: 2.416e-02, eta: 10:28:09, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9919, loss_cls: 0.7859, loss: 0.7859 +2025-06-24 11:55:22,911 - pyskl - INFO - Epoch [18][900/1281] lr: 2.415e-02, eta: 10:27:45, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9925, loss_cls: 0.7413, loss: 0.7413 +2025-06-24 11:55:44,930 - pyskl - INFO - Epoch [18][1000/1281] lr: 2.414e-02, eta: 10:27:21, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8087, top5_acc: 0.9919, loss_cls: 0.8111, loss: 0.8111 +2025-06-24 11:56:06,960 - pyskl - INFO - Epoch [18][1100/1281] lr: 2.414e-02, eta: 10:26:58, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9869, loss_cls: 0.8428, loss: 0.8428 +2025-06-24 11:56:28,990 - pyskl - INFO - Epoch [18][1200/1281] lr: 2.413e-02, eta: 10:26:34, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9894, loss_cls: 0.7576, loss: 0.7576 +2025-06-24 11:56:47,838 - pyskl - INFO - Saving checkpoint at 18 epochs +2025-06-24 11:57:31,541 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:57:31,595 - pyskl - INFO - +top1_acc 0.7886 +top5_acc 0.9856 +2025-06-24 11:57:31,595 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:57:31,602 - pyskl - INFO - +mean_acc 0.7094 +2025-06-24 11:57:31,604 - pyskl - INFO - Epoch(val) [18][533] top1_acc: 0.7886, top5_acc: 0.9856, mean_class_accuracy: 0.7094 +2025-06-24 11:58:13,064 - pyskl - INFO - Epoch [19][100/1281] lr: 2.411e-02, eta: 10:26:03, time: 0.415, data_time: 0.194, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9950, loss_cls: 0.7544, loss: 0.7544 +2025-06-24 11:58:35,192 - pyskl - INFO - Epoch [19][200/1281] lr: 2.411e-02, eta: 10:25:40, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9825, loss_cls: 0.7810, loss: 0.7810 +2025-06-24 11:58:56,971 - pyskl - INFO - Epoch [19][300/1281] lr: 2.410e-02, eta: 10:25:14, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9906, loss_cls: 0.7261, loss: 0.7261 +2025-06-24 11:59:18,731 - pyskl - INFO - Epoch [19][400/1281] lr: 2.409e-02, eta: 10:24:49, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9938, loss_cls: 0.7888, loss: 0.7888 +2025-06-24 11:59:40,749 - pyskl - INFO - Epoch [19][500/1281] lr: 2.408e-02, eta: 10:24:25, time: 0.220, data_time: 0.001, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9931, loss_cls: 0.7445, loss: 0.7445 +2025-06-24 12:00:02,692 - pyskl - INFO - Epoch [19][600/1281] lr: 2.408e-02, eta: 10:24:01, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9919, loss_cls: 0.7946, loss: 0.7946 +2025-06-24 12:00:24,594 - pyskl - INFO - Epoch [19][700/1281] lr: 2.407e-02, eta: 10:23:36, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8113, top5_acc: 0.9919, loss_cls: 0.8581, loss: 0.8581 +2025-06-24 12:00:46,486 - pyskl - INFO - Epoch [19][800/1281] lr: 2.406e-02, eta: 10:23:12, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9925, loss_cls: 0.7799, loss: 0.7799 +2025-06-24 12:01:08,249 - pyskl - INFO - Epoch [19][900/1281] lr: 2.405e-02, eta: 10:22:46, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8313, top5_acc: 0.9900, loss_cls: 0.7752, loss: 0.7752 +2025-06-24 12:01:30,136 - pyskl - INFO - Epoch [19][1000/1281] lr: 2.405e-02, eta: 10:22:22, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9912, loss_cls: 0.8043, loss: 0.8043 +2025-06-24 12:01:52,054 - pyskl - INFO - Epoch [19][1100/1281] lr: 2.404e-02, eta: 10:21:58, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9888, loss_cls: 0.8664, loss: 0.8664 +2025-06-24 12:02:13,886 - pyskl - INFO - Epoch [19][1200/1281] lr: 2.403e-02, eta: 10:21:33, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8125, top5_acc: 0.9912, loss_cls: 0.8310, loss: 0.8310 +2025-06-24 12:02:32,425 - pyskl - INFO - Saving checkpoint at 19 epochs +2025-06-24 12:03:15,989 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:03:16,045 - pyskl - INFO - +top1_acc 0.7615 +top5_acc 0.9744 +2025-06-24 12:03:16,045 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:03:16,051 - pyskl - INFO - +mean_acc 0.6812 +2025-06-24 12:03:16,053 - pyskl - INFO - Epoch(val) [19][533] top1_acc: 0.7615, top5_acc: 0.9744, mean_class_accuracy: 0.6812 +2025-06-24 12:03:56,878 - pyskl - INFO - Epoch [20][100/1281] lr: 2.402e-02, eta: 10:20:57, time: 0.408, data_time: 0.189, memory: 4082, top1_acc: 0.8263, top5_acc: 0.9925, loss_cls: 0.7758, loss: 0.7758 +2025-06-24 12:04:19,081 - pyskl - INFO - Epoch [20][200/1281] lr: 2.401e-02, eta: 10:20:34, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8481, top5_acc: 0.9962, loss_cls: 0.7083, loss: 0.7083 +2025-06-24 12:04:41,435 - pyskl - INFO - Epoch [20][300/1281] lr: 2.400e-02, eta: 10:20:13, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9938, loss_cls: 0.7620, loss: 0.7620 +2025-06-24 12:05:03,532 - pyskl - INFO - Epoch [20][400/1281] lr: 2.399e-02, eta: 10:19:50, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9944, loss_cls: 0.7402, loss: 0.7402 +2025-06-24 12:05:25,358 - pyskl - INFO - Epoch [20][500/1281] lr: 2.398e-02, eta: 10:19:25, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9894, loss_cls: 0.7696, loss: 0.7696 +2025-06-24 12:05:47,588 - pyskl - INFO - Epoch [20][600/1281] lr: 2.398e-02, eta: 10:19:03, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9869, loss_cls: 0.7990, loss: 0.7990 +2025-06-24 12:06:09,536 - pyskl - INFO - Epoch [20][700/1281] lr: 2.397e-02, eta: 10:18:39, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8213, top5_acc: 0.9925, loss_cls: 0.7745, loss: 0.7745 +2025-06-24 12:06:31,424 - pyskl - INFO - Epoch [20][800/1281] lr: 2.396e-02, eta: 10:18:15, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9956, loss_cls: 0.6976, loss: 0.6976 +2025-06-24 12:06:53,651 - pyskl - INFO - Epoch [20][900/1281] lr: 2.395e-02, eta: 10:17:53, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9938, loss_cls: 0.8165, loss: 0.8165 +2025-06-24 12:07:15,742 - pyskl - INFO - Epoch [20][1000/1281] lr: 2.394e-02, eta: 10:17:30, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9919, loss_cls: 0.7637, loss: 0.7637 +2025-06-24 12:07:37,401 - pyskl - INFO - Epoch [20][1100/1281] lr: 2.393e-02, eta: 10:17:04, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8512, top5_acc: 0.9931, loss_cls: 0.7310, loss: 0.7310 +2025-06-24 12:07:59,337 - pyskl - INFO - Epoch [20][1200/1281] lr: 2.393e-02, eta: 10:16:40, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8400, top5_acc: 0.9925, loss_cls: 0.7352, loss: 0.7352 +2025-06-24 12:08:17,736 - pyskl - INFO - Saving checkpoint at 20 epochs +2025-06-24 12:09:01,990 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:09:02,076 - pyskl - INFO - +top1_acc 0.8121 +top5_acc 0.9865 +2025-06-24 12:09:02,076 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:09:02,084 - pyskl - INFO - +mean_acc 0.7378 +2025-06-24 12:09:02,089 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_16.pth was removed +2025-06-24 12:09:02,294 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_20.pth. +2025-06-24 12:09:02,295 - pyskl - INFO - Best top1_acc is 0.8121 at 20 epoch. +2025-06-24 12:09:02,298 - pyskl - INFO - Epoch(val) [20][533] top1_acc: 0.8121, top5_acc: 0.9865, mean_class_accuracy: 0.7378 +2025-06-24 12:09:44,162 - pyskl - INFO - Epoch [21][100/1281] lr: 2.391e-02, eta: 10:16:10, time: 0.419, data_time: 0.198, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9944, loss_cls: 0.6961, loss: 0.6961 +2025-06-24 12:10:06,253 - pyskl - INFO - Epoch [21][200/1281] lr: 2.390e-02, eta: 10:15:47, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9925, loss_cls: 0.7153, loss: 0.7153 +2025-06-24 12:10:28,292 - pyskl - INFO - Epoch [21][300/1281] lr: 2.389e-02, eta: 10:15:24, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8113, top5_acc: 0.9888, loss_cls: 0.8054, loss: 0.8054 +2025-06-24 12:10:50,289 - pyskl - INFO - Epoch [21][400/1281] lr: 2.389e-02, eta: 10:15:00, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9900, loss_cls: 0.7766, loss: 0.7766 +2025-06-24 12:11:12,141 - pyskl - INFO - Epoch [21][500/1281] lr: 2.388e-02, eta: 10:14:36, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8644, top5_acc: 0.9931, loss_cls: 0.6652, loss: 0.6652 +2025-06-24 12:11:34,213 - pyskl - INFO - Epoch [21][600/1281] lr: 2.387e-02, eta: 10:14:13, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9919, loss_cls: 0.7310, loss: 0.7310 +2025-06-24 12:11:56,185 - pyskl - INFO - Epoch [21][700/1281] lr: 2.386e-02, eta: 10:13:49, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9906, loss_cls: 0.7461, loss: 0.7461 +2025-06-24 12:12:18,019 - pyskl - INFO - Epoch [21][800/1281] lr: 2.385e-02, eta: 10:13:25, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9925, loss_cls: 0.8039, loss: 0.8039 +2025-06-24 12:12:39,781 - pyskl - INFO - Epoch [21][900/1281] lr: 2.384e-02, eta: 10:13:00, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9919, loss_cls: 0.7380, loss: 0.7380 +2025-06-24 12:13:01,888 - pyskl - INFO - Epoch [21][1000/1281] lr: 2.383e-02, eta: 10:12:37, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9944, loss_cls: 0.7547, loss: 0.7547 +2025-06-24 12:13:23,914 - pyskl - INFO - Epoch [21][1100/1281] lr: 2.383e-02, eta: 10:12:13, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9906, loss_cls: 0.8003, loss: 0.8003 +2025-06-24 12:13:45,727 - pyskl - INFO - Epoch [21][1200/1281] lr: 2.382e-02, eta: 10:11:49, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8425, top5_acc: 0.9931, loss_cls: 0.7273, loss: 0.7273 +2025-06-24 12:14:04,440 - pyskl - INFO - Saving checkpoint at 21 epochs +2025-06-24 12:14:48,412 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:14:48,469 - pyskl - INFO - +top1_acc 0.8302 +top5_acc 0.9883 +2025-06-24 12:14:48,470 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:14:48,476 - pyskl - INFO - +mean_acc 0.7640 +2025-06-24 12:14:48,480 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_20.pth was removed +2025-06-24 12:14:48,659 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_21.pth. +2025-06-24 12:14:48,659 - pyskl - INFO - Best top1_acc is 0.8302 at 21 epoch. +2025-06-24 12:14:48,661 - pyskl - INFO - Epoch(val) [21][533] top1_acc: 0.8302, top5_acc: 0.9883, mean_class_accuracy: 0.7640 +2025-06-24 12:15:29,890 - pyskl - INFO - Epoch [22][100/1281] lr: 2.380e-02, eta: 10:11:15, time: 0.412, data_time: 0.189, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9944, loss_cls: 0.6878, loss: 0.6878 +2025-06-24 12:15:52,306 - pyskl - INFO - Epoch [22][200/1281] lr: 2.379e-02, eta: 10:10:54, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9931, loss_cls: 0.6682, loss: 0.6682 +2025-06-24 12:16:14,481 - pyskl - INFO - Epoch [22][300/1281] lr: 2.378e-02, eta: 10:10:32, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9894, loss_cls: 0.7626, loss: 0.7626 +2025-06-24 12:16:36,644 - pyskl - INFO - Epoch [22][400/1281] lr: 2.378e-02, eta: 10:10:09, time: 0.222, data_time: 0.001, memory: 4082, top1_acc: 0.8400, top5_acc: 0.9912, loss_cls: 0.7660, loss: 0.7660 +2025-06-24 12:16:58,710 - pyskl - INFO - Epoch [22][500/1281] lr: 2.377e-02, eta: 10:09:46, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9931, loss_cls: 0.6812, loss: 0.6812 +2025-06-24 12:17:20,928 - pyskl - INFO - Epoch [22][600/1281] lr: 2.376e-02, eta: 10:09:24, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9881, loss_cls: 0.7594, loss: 0.7594 +2025-06-24 12:17:42,656 - pyskl - INFO - Epoch [22][700/1281] lr: 2.375e-02, eta: 10:08:59, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9912, loss_cls: 0.6736, loss: 0.6736 +2025-06-24 12:18:04,770 - pyskl - INFO - Epoch [22][800/1281] lr: 2.374e-02, eta: 10:08:36, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9919, loss_cls: 0.7439, loss: 0.7439 +2025-06-24 12:18:26,885 - pyskl - INFO - Epoch [22][900/1281] lr: 2.373e-02, eta: 10:08:13, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8375, top5_acc: 0.9894, loss_cls: 0.7508, loss: 0.7508 +2025-06-24 12:18:48,819 - pyskl - INFO - Epoch [22][1000/1281] lr: 2.372e-02, eta: 10:07:49, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9912, loss_cls: 0.7606, loss: 0.7606 +2025-06-24 12:19:10,685 - pyskl - INFO - Epoch [22][1100/1281] lr: 2.371e-02, eta: 10:07:25, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8275, top5_acc: 0.9931, loss_cls: 0.7529, loss: 0.7529 +2025-06-24 12:19:32,439 - pyskl - INFO - Epoch [22][1200/1281] lr: 2.370e-02, eta: 10:07:00, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8237, top5_acc: 0.9944, loss_cls: 0.7372, loss: 0.7372 +2025-06-24 12:19:50,927 - pyskl - INFO - Saving checkpoint at 22 epochs +2025-06-24 12:20:34,666 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:20:34,745 - pyskl - INFO - +top1_acc 0.8226 +top5_acc 0.9878 +2025-06-24 12:20:34,746 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:20:34,757 - pyskl - INFO - +mean_acc 0.7498 +2025-06-24 12:20:34,759 - pyskl - INFO - Epoch(val) [22][533] top1_acc: 0.8226, top5_acc: 0.9878, mean_class_accuracy: 0.7498 +2025-06-24 12:21:15,815 - pyskl - INFO - Epoch [23][100/1281] lr: 2.369e-02, eta: 10:06:25, time: 0.410, data_time: 0.190, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9931, loss_cls: 0.7422, loss: 0.7422 +2025-06-24 12:21:37,710 - pyskl - INFO - Epoch [23][200/1281] lr: 2.368e-02, eta: 10:06:01, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8569, top5_acc: 0.9931, loss_cls: 0.6580, loss: 0.6580 +2025-06-24 12:21:59,848 - pyskl - INFO - Epoch [23][300/1281] lr: 2.367e-02, eta: 10:05:39, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9900, loss_cls: 0.7645, loss: 0.7645 +2025-06-24 12:22:21,772 - pyskl - INFO - Epoch [23][400/1281] lr: 2.366e-02, eta: 10:05:15, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9931, loss_cls: 0.6496, loss: 0.6496 +2025-06-24 12:22:43,567 - pyskl - INFO - Epoch [23][500/1281] lr: 2.365e-02, eta: 10:04:50, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9938, loss_cls: 0.7402, loss: 0.7402 +2025-06-24 12:23:05,697 - pyskl - INFO - Epoch [23][600/1281] lr: 2.364e-02, eta: 10:04:28, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9912, loss_cls: 0.7174, loss: 0.7174 +2025-06-24 12:23:27,751 - pyskl - INFO - Epoch [23][700/1281] lr: 2.363e-02, eta: 10:04:05, time: 0.221, data_time: 0.001, memory: 4082, top1_acc: 0.8494, top5_acc: 0.9894, loss_cls: 0.7087, loss: 0.7087 +2025-06-24 12:23:49,380 - pyskl - INFO - Epoch [23][800/1281] lr: 2.362e-02, eta: 10:03:39, time: 0.216, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9900, loss_cls: 0.7368, loss: 0.7368 +2025-06-24 12:24:11,651 - pyskl - INFO - Epoch [23][900/1281] lr: 2.361e-02, eta: 10:03:17, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9938, loss_cls: 0.7105, loss: 0.7105 +2025-06-24 12:24:33,864 - pyskl - INFO - Epoch [23][1000/1281] lr: 2.360e-02, eta: 10:02:55, time: 0.222, data_time: 0.001, memory: 4082, top1_acc: 0.8413, top5_acc: 0.9888, loss_cls: 0.7310, loss: 0.7310 +2025-06-24 12:24:55,766 - pyskl - INFO - Epoch [23][1100/1281] lr: 2.359e-02, eta: 10:02:32, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8556, top5_acc: 0.9931, loss_cls: 0.6896, loss: 0.6896 +2025-06-24 12:25:17,434 - pyskl - INFO - Epoch [23][1200/1281] lr: 2.359e-02, eta: 10:02:06, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9900, loss_cls: 0.7895, loss: 0.7895 +2025-06-24 12:25:36,244 - pyskl - INFO - Saving checkpoint at 23 epochs +2025-06-24 12:26:20,556 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:26:20,623 - pyskl - INFO - +top1_acc 0.8223 +top5_acc 0.9876 +2025-06-24 12:26:20,624 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:26:20,632 - pyskl - INFO - +mean_acc 0.7329 +2025-06-24 12:26:20,634 - pyskl - INFO - Epoch(val) [23][533] top1_acc: 0.8223, top5_acc: 0.9876, mean_class_accuracy: 0.7329 +2025-06-24 12:27:01,936 - pyskl - INFO - Epoch [24][100/1281] lr: 2.357e-02, eta: 10:01:32, time: 0.413, data_time: 0.189, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9956, loss_cls: 0.6792, loss: 0.6792 +2025-06-24 12:27:24,379 - pyskl - INFO - Epoch [24][200/1281] lr: 2.356e-02, eta: 10:01:12, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9925, loss_cls: 0.7045, loss: 0.7045 +2025-06-24 12:27:46,243 - pyskl - INFO - Epoch [24][300/1281] lr: 2.355e-02, eta: 10:00:47, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9919, loss_cls: 0.7194, loss: 0.7194 +2025-06-24 12:28:08,225 - pyskl - INFO - Epoch [24][400/1281] lr: 2.354e-02, eta: 10:00:24, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8644, top5_acc: 0.9938, loss_cls: 0.6378, loss: 0.6378 +2025-06-24 12:28:30,280 - pyskl - INFO - Epoch [24][500/1281] lr: 2.353e-02, eta: 10:00:01, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9869, loss_cls: 0.7653, loss: 0.7653 +2025-06-24 12:28:52,340 - pyskl - INFO - Epoch [24][600/1281] lr: 2.352e-02, eta: 9:59:38, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8425, top5_acc: 0.9950, loss_cls: 0.7127, loss: 0.7127 +2025-06-24 12:29:14,197 - pyskl - INFO - Epoch [24][700/1281] lr: 2.351e-02, eta: 9:59:14, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8525, top5_acc: 0.9931, loss_cls: 0.6653, loss: 0.6653 +2025-06-24 12:29:36,376 - pyskl - INFO - Epoch [24][800/1281] lr: 2.350e-02, eta: 9:58:52, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9919, loss_cls: 0.7283, loss: 0.7283 +2025-06-24 12:29:58,387 - pyskl - INFO - Epoch [24][900/1281] lr: 2.349e-02, eta: 9:58:29, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9950, loss_cls: 0.7472, loss: 0.7472 +2025-06-24 12:30:20,279 - pyskl - INFO - Epoch [24][1000/1281] lr: 2.348e-02, eta: 9:58:05, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8525, top5_acc: 0.9894, loss_cls: 0.6923, loss: 0.6923 +2025-06-24 12:30:42,245 - pyskl - INFO - Epoch [24][1100/1281] lr: 2.347e-02, eta: 9:57:41, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9950, loss_cls: 0.6875, loss: 0.6875 +2025-06-24 12:31:04,057 - pyskl - INFO - Epoch [24][1200/1281] lr: 2.346e-02, eta: 9:57:17, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9919, loss_cls: 0.7128, loss: 0.7128 +2025-06-24 12:31:22,486 - pyskl - INFO - Saving checkpoint at 24 epochs +2025-06-24 12:32:06,826 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:32:06,893 - pyskl - INFO - +top1_acc 0.7951 +top5_acc 0.9863 +2025-06-24 12:32:06,893 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:32:06,901 - pyskl - INFO - +mean_acc 0.7257 +2025-06-24 12:32:06,903 - pyskl - INFO - Epoch(val) [24][533] top1_acc: 0.7951, top5_acc: 0.9863, mean_class_accuracy: 0.7257 +2025-06-24 12:32:48,078 - pyskl - INFO - Epoch [25][100/1281] lr: 2.344e-02, eta: 9:56:42, time: 0.412, data_time: 0.191, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9925, loss_cls: 0.7476, loss: 0.7476 +2025-06-24 12:33:10,356 - pyskl - INFO - Epoch [25][200/1281] lr: 2.343e-02, eta: 9:56:21, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.8381, top5_acc: 0.9900, loss_cls: 0.7377, loss: 0.7377 +2025-06-24 12:33:32,307 - pyskl - INFO - Epoch [25][300/1281] lr: 2.342e-02, eta: 9:55:57, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9938, loss_cls: 0.6808, loss: 0.6808 +2025-06-24 12:33:54,376 - pyskl - INFO - Epoch [25][400/1281] lr: 2.341e-02, eta: 9:55:34, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9931, loss_cls: 0.7189, loss: 0.7189 +2025-06-24 12:34:16,920 - pyskl - INFO - Epoch [25][500/1281] lr: 2.340e-02, eta: 9:55:14, time: 0.225, data_time: 0.000, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9931, loss_cls: 0.6695, loss: 0.6695 +2025-06-24 12:34:38,910 - pyskl - INFO - Epoch [25][600/1281] lr: 2.339e-02, eta: 9:54:51, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8569, top5_acc: 0.9925, loss_cls: 0.6861, loss: 0.6861 +2025-06-24 12:35:00,967 - pyskl - INFO - Epoch [25][700/1281] lr: 2.338e-02, eta: 9:54:28, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9938, loss_cls: 0.7089, loss: 0.7089 +2025-06-24 12:35:23,225 - pyskl - INFO - Epoch [25][800/1281] lr: 2.337e-02, eta: 9:54:06, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9925, loss_cls: 0.7025, loss: 0.7025 +2025-06-24 12:35:45,240 - pyskl - INFO - Epoch [25][900/1281] lr: 2.336e-02, eta: 9:53:43, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8694, top5_acc: 0.9888, loss_cls: 0.6588, loss: 0.6588 +2025-06-24 12:36:07,455 - pyskl - INFO - Epoch [25][1000/1281] lr: 2.335e-02, eta: 9:53:21, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9931, loss_cls: 0.6931, loss: 0.6931 +2025-06-24 12:36:29,548 - pyskl - INFO - Epoch [25][1100/1281] lr: 2.334e-02, eta: 9:52:58, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8581, top5_acc: 0.9912, loss_cls: 0.6854, loss: 0.6854 +2025-06-24 12:36:51,481 - pyskl - INFO - Epoch [25][1200/1281] lr: 2.333e-02, eta: 9:52:34, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8544, top5_acc: 0.9912, loss_cls: 0.6795, loss: 0.6795 +2025-06-24 12:37:10,407 - pyskl - INFO - Saving checkpoint at 25 epochs +2025-06-24 12:37:55,108 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:37:55,164 - pyskl - INFO - +top1_acc 0.7487 +top5_acc 0.9690 +2025-06-24 12:37:55,164 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:37:55,172 - pyskl - INFO - +mean_acc 0.6778 +2025-06-24 12:37:55,173 - pyskl - INFO - Epoch(val) [25][533] top1_acc: 0.7487, top5_acc: 0.9690, mean_class_accuracy: 0.6778 +2025-06-24 12:38:36,844 - pyskl - INFO - Epoch [26][100/1281] lr: 2.332e-02, eta: 9:52:02, time: 0.417, data_time: 0.193, memory: 4082, top1_acc: 0.8406, top5_acc: 0.9950, loss_cls: 0.7070, loss: 0.7070 +2025-06-24 12:38:58,720 - pyskl - INFO - Epoch [26][200/1281] lr: 2.330e-02, eta: 9:51:38, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8562, top5_acc: 0.9906, loss_cls: 0.6380, loss: 0.6380 +2025-06-24 12:39:20,745 - pyskl - INFO - Epoch [26][300/1281] lr: 2.329e-02, eta: 9:51:15, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9938, loss_cls: 0.6810, loss: 0.6810 +2025-06-24 12:39:42,725 - pyskl - INFO - Epoch [26][400/1281] lr: 2.328e-02, eta: 9:50:52, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8688, top5_acc: 0.9931, loss_cls: 0.6374, loss: 0.6374 +2025-06-24 12:40:04,872 - pyskl - INFO - Epoch [26][500/1281] lr: 2.327e-02, eta: 9:50:29, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8512, top5_acc: 0.9912, loss_cls: 0.6942, loss: 0.6942 +2025-06-24 12:40:26,923 - pyskl - INFO - Epoch [26][600/1281] lr: 2.326e-02, eta: 9:50:06, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9938, loss_cls: 0.7080, loss: 0.7080 +2025-06-24 12:40:48,877 - pyskl - INFO - Epoch [26][700/1281] lr: 2.325e-02, eta: 9:49:43, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8381, top5_acc: 0.9925, loss_cls: 0.7158, loss: 0.7158 +2025-06-24 12:41:11,045 - pyskl - INFO - Epoch [26][800/1281] lr: 2.324e-02, eta: 9:49:20, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9919, loss_cls: 0.7619, loss: 0.7619 +2025-06-24 12:41:32,878 - pyskl - INFO - Epoch [26][900/1281] lr: 2.323e-02, eta: 9:48:57, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8569, top5_acc: 0.9912, loss_cls: 0.6925, loss: 0.6925 +2025-06-24 12:41:54,991 - pyskl - INFO - Epoch [26][1000/1281] lr: 2.322e-02, eta: 9:48:34, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9906, loss_cls: 0.7250, loss: 0.7250 +2025-06-24 12:42:17,051 - pyskl - INFO - Epoch [26][1100/1281] lr: 2.321e-02, eta: 9:48:11, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8481, top5_acc: 0.9950, loss_cls: 0.6920, loss: 0.6920 +2025-06-24 12:42:38,987 - pyskl - INFO - Epoch [26][1200/1281] lr: 2.320e-02, eta: 9:47:48, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9931, loss_cls: 0.7263, loss: 0.7263 +2025-06-24 12:42:57,541 - pyskl - INFO - Saving checkpoint at 26 epochs +2025-06-24 12:43:41,985 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:43:42,041 - pyskl - INFO - +top1_acc 0.8341 +top5_acc 0.9869 +2025-06-24 12:43:42,041 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:43:42,048 - pyskl - INFO - +mean_acc 0.7574 +2025-06-24 12:43:42,052 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_21.pth was removed +2025-06-24 12:43:42,218 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_26.pth. +2025-06-24 12:43:42,218 - pyskl - INFO - Best top1_acc is 0.8341 at 26 epoch. +2025-06-24 12:43:42,220 - pyskl - INFO - Epoch(val) [26][533] top1_acc: 0.8341, top5_acc: 0.9869, mean_class_accuracy: 0.7574 +2025-06-24 12:44:23,360 - pyskl - INFO - Epoch [27][100/1281] lr: 2.318e-02, eta: 9:47:12, time: 0.411, data_time: 0.193, memory: 4082, top1_acc: 0.8831, top5_acc: 0.9969, loss_cls: 0.5609, loss: 0.5609 +2025-06-24 12:44:45,613 - pyskl - INFO - Epoch [27][200/1281] lr: 2.317e-02, eta: 9:46:50, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9956, loss_cls: 0.7135, loss: 0.7135 +2025-06-24 12:45:07,706 - pyskl - INFO - Epoch [27][300/1281] lr: 2.316e-02, eta: 9:46:28, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9925, loss_cls: 0.6599, loss: 0.6599 +2025-06-24 12:45:29,515 - pyskl - INFO - Epoch [27][400/1281] lr: 2.315e-02, eta: 9:46:03, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9888, loss_cls: 0.7025, loss: 0.7025 +2025-06-24 12:45:51,667 - pyskl - INFO - Epoch [27][500/1281] lr: 2.314e-02, eta: 9:45:41, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8525, top5_acc: 0.9925, loss_cls: 0.6687, loss: 0.6687 +2025-06-24 12:46:13,782 - pyskl - INFO - Epoch [27][600/1281] lr: 2.313e-02, eta: 9:45:19, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9931, loss_cls: 0.6620, loss: 0.6620 +2025-06-24 12:46:35,712 - pyskl - INFO - Epoch [27][700/1281] lr: 2.312e-02, eta: 9:44:55, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9956, loss_cls: 0.6561, loss: 0.6561 +2025-06-24 12:46:57,740 - pyskl - INFO - Epoch [27][800/1281] lr: 2.311e-02, eta: 9:44:32, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8531, top5_acc: 0.9938, loss_cls: 0.7002, loss: 0.7002 +2025-06-24 12:47:19,855 - pyskl - INFO - Epoch [27][900/1281] lr: 2.310e-02, eta: 9:44:10, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9906, loss_cls: 0.7257, loss: 0.7257 +2025-06-24 12:47:42,122 - pyskl - INFO - Epoch [27][1000/1281] lr: 2.308e-02, eta: 9:43:48, time: 0.223, data_time: 0.000, memory: 4082, top1_acc: 0.8650, top5_acc: 0.9944, loss_cls: 0.6333, loss: 0.6333 +2025-06-24 12:48:04,185 - pyskl - INFO - Epoch [27][1100/1281] lr: 2.307e-02, eta: 9:43:25, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9925, loss_cls: 0.6565, loss: 0.6565 +2025-06-24 12:48:26,082 - pyskl - INFO - Epoch [27][1200/1281] lr: 2.306e-02, eta: 9:43:01, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9919, loss_cls: 0.6742, loss: 0.6742 +2025-06-24 12:48:44,685 - pyskl - INFO - Saving checkpoint at 27 epochs +2025-06-24 12:49:29,033 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:49:29,101 - pyskl - INFO - +top1_acc 0.8262 +top5_acc 0.9879 +2025-06-24 12:49:29,101 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:49:29,109 - pyskl - INFO - +mean_acc 0.7527 +2025-06-24 12:49:29,111 - pyskl - INFO - Epoch(val) [27][533] top1_acc: 0.8262, top5_acc: 0.9879, mean_class_accuracy: 0.7527 +2025-06-24 12:50:10,747 - pyskl - INFO - Epoch [28][100/1281] lr: 2.304e-02, eta: 9:42:28, time: 0.416, data_time: 0.192, memory: 4082, top1_acc: 0.8712, top5_acc: 0.9931, loss_cls: 0.6521, loss: 0.6521 +2025-06-24 12:50:32,689 - pyskl - INFO - Epoch [28][200/1281] lr: 2.303e-02, eta: 9:42:05, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9944, loss_cls: 0.6538, loss: 0.6538 +2025-06-24 12:50:54,576 - pyskl - INFO - Epoch [28][300/1281] lr: 2.302e-02, eta: 9:41:41, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8750, top5_acc: 0.9944, loss_cls: 0.6245, loss: 0.6245 +2025-06-24 12:51:16,703 - pyskl - INFO - Epoch [28][400/1281] lr: 2.301e-02, eta: 9:41:19, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9894, loss_cls: 0.6849, loss: 0.6849 +2025-06-24 12:51:38,566 - pyskl - INFO - Epoch [28][500/1281] lr: 2.300e-02, eta: 9:40:55, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8612, top5_acc: 0.9962, loss_cls: 0.6439, loss: 0.6439 +2025-06-24 12:52:00,670 - pyskl - INFO - Epoch [28][600/1281] lr: 2.299e-02, eta: 9:40:32, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8694, top5_acc: 0.9944, loss_cls: 0.6468, loss: 0.6468 +2025-06-24 12:52:22,646 - pyskl - INFO - Epoch [28][700/1281] lr: 2.298e-02, eta: 9:40:09, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8594, top5_acc: 0.9962, loss_cls: 0.6379, loss: 0.6379 +2025-06-24 12:52:44,723 - pyskl - INFO - Epoch [28][800/1281] lr: 2.297e-02, eta: 9:39:46, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8562, top5_acc: 0.9956, loss_cls: 0.6359, loss: 0.6359 +2025-06-24 12:53:07,077 - pyskl - INFO - Epoch [28][900/1281] lr: 2.295e-02, eta: 9:39:25, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.8725, top5_acc: 0.9962, loss_cls: 0.6234, loss: 0.6234 +2025-06-24 12:53:29,174 - pyskl - INFO - Epoch [28][1000/1281] lr: 2.294e-02, eta: 9:39:02, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8694, top5_acc: 0.9906, loss_cls: 0.6810, loss: 0.6810 +2025-06-24 12:53:51,363 - pyskl - INFO - Epoch [28][1100/1281] lr: 2.293e-02, eta: 9:38:40, time: 0.222, data_time: 0.000, memory: 4082, top1_acc: 0.8625, top5_acc: 0.9906, loss_cls: 0.6924, loss: 0.6924 +2025-06-24 12:54:13,474 - pyskl - INFO - Epoch [28][1200/1281] lr: 2.292e-02, eta: 9:38:18, time: 0.221, data_time: 0.000, memory: 4082, top1_acc: 0.8456, top5_acc: 0.9919, loss_cls: 0.7075, loss: 0.7075 +2025-06-24 12:54:31,946 - pyskl - INFO - Saving checkpoint at 28 epochs +2025-06-24 12:55:15,643 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:55:15,697 - pyskl - INFO - +top1_acc 0.7223 +top5_acc 0.9572 +2025-06-24 12:55:15,697 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:55:15,704 - pyskl - INFO - +mean_acc 0.6452 +2025-06-24 12:55:15,705 - pyskl - INFO - Epoch(val) [28][533] top1_acc: 0.7223, top5_acc: 0.9572, mean_class_accuracy: 0.6452 +2025-06-24 12:55:57,017 - pyskl - INFO - Epoch [29][100/1281] lr: 2.290e-02, eta: 9:37:42, time: 0.413, data_time: 0.191, memory: 4082, top1_acc: 0.8725, top5_acc: 0.9956, loss_cls: 0.5847, loss: 0.5847 +2025-06-24 12:56:19,013 - pyskl - INFO - Epoch [29][200/1281] lr: 2.289e-02, eta: 9:37:19, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9925, loss_cls: 0.6585, loss: 0.6585 +2025-06-24 12:56:40,759 - pyskl - INFO - Epoch [29][300/1281] lr: 2.288e-02, eta: 9:36:55, time: 0.217, data_time: 0.000, memory: 4082, top1_acc: 0.8788, top5_acc: 0.9925, loss_cls: 0.5911, loss: 0.5911 +2025-06-24 12:57:02,767 - pyskl - INFO - Epoch [29][400/1281] lr: 2.287e-02, eta: 9:36:32, time: 0.220, data_time: 0.000, memory: 4082, top1_acc: 0.8912, top5_acc: 0.9931, loss_cls: 0.5529, loss: 0.5529 +2025-06-24 12:57:24,662 - pyskl - INFO - Epoch [29][500/1281] lr: 2.285e-02, eta: 9:36:09, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8694, top5_acc: 0.9938, loss_cls: 0.6036, loss: 0.6036 +2025-06-24 12:57:46,533 - pyskl - INFO - Epoch [29][600/1281] lr: 2.284e-02, eta: 9:35:45, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8531, top5_acc: 0.9919, loss_cls: 0.7115, loss: 0.7115 +2025-06-24 12:58:08,403 - pyskl - INFO - Epoch [29][700/1281] lr: 2.283e-02, eta: 9:35:22, time: 0.219, data_time: 0.000, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9956, loss_cls: 0.6395, loss: 0.6395 +2025-06-24 12:58:30,238 - pyskl - INFO - Epoch [29][800/1281] lr: 2.282e-02, eta: 9:34:58, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9931, loss_cls: 0.6332, loss: 0.6332 +2025-06-24 12:58:52,457 - pyskl - INFO - Epoch [29][900/1281] lr: 2.281e-02, eta: 9:34:36, time: 0.222, data_time: 0.001, memory: 4082, top1_acc: 0.8656, top5_acc: 0.9900, loss_cls: 0.6541, loss: 0.6541 +2025-06-24 12:59:14,258 - pyskl - INFO - Epoch [29][1000/1281] lr: 2.280e-02, eta: 9:34:12, time: 0.218, data_time: 0.000, memory: 4082, top1_acc: 0.8719, top5_acc: 0.9938, loss_cls: 0.6295, loss: 0.6295 +2025-06-24 12:59:36,311 - pyskl - INFO - Epoch [29][1100/1281] lr: 2.279e-02, eta: 9:33:49, time: 0.221, data_time: 0.001, memory: 4082, top1_acc: 0.8494, top5_acc: 0.9944, loss_cls: 0.7016, loss: 0.7016 +2025-06-24 12:59:58,416 - pyskl - INFO - Epoch [29][1200/1281] lr: 2.277e-02, eta: 9:33:27, time: 0.221, data_time: 0.001, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9912, loss_cls: 0.6963, loss: 0.6963 +2025-06-24 13:00:16,981 - pyskl - INFO - Saving checkpoint at 29 epochs +2025-06-24 13:01:01,817 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:01:01,890 - pyskl - INFO - +top1_acc 0.8257 +top5_acc 0.9859 +2025-06-24 13:01:01,890 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:01:01,898 - pyskl - INFO - +mean_acc 0.7710 +2025-06-24 13:01:01,900 - pyskl - INFO - Epoch(val) [29][533] top1_acc: 0.8257, top5_acc: 0.9859, mean_class_accuracy: 0.7710 +2025-06-24 13:01:45,155 - pyskl - INFO - Epoch [30][100/1281] lr: 2.275e-02, eta: 9:32:59, time: 0.433, data_time: 0.204, memory: 4082, top1_acc: 0.8738, top5_acc: 0.9969, loss_cls: 0.5899, loss: 0.5899 +2025-06-24 13:02:07,751 - pyskl - INFO - Epoch [30][200/1281] lr: 2.274e-02, eta: 9:32:39, time: 0.226, data_time: 0.000, memory: 4082, top1_acc: 0.8594, top5_acc: 0.9950, loss_cls: 0.6354, loss: 0.6354 +2025-06-24 13:02:30,708 - pyskl - INFO - Epoch [30][300/1281] lr: 2.273e-02, eta: 9:32:20, time: 0.230, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9912, loss_cls: 0.6431, loss: 0.6431 +2025-06-24 13:02:53,387 - pyskl - INFO - Epoch [30][400/1281] lr: 2.272e-02, eta: 9:32:00, time: 0.227, data_time: 0.000, memory: 4082, top1_acc: 0.8675, top5_acc: 0.9938, loss_cls: 0.6450, loss: 0.6450 +2025-06-24 13:03:16,237 - pyskl - INFO - Epoch [30][500/1281] lr: 2.271e-02, eta: 9:31:40, time: 0.228, data_time: 0.000, memory: 4082, top1_acc: 0.8662, top5_acc: 0.9975, loss_cls: 0.6110, loss: 0.6110 +2025-06-24 13:03:38,932 - pyskl - INFO - Epoch [30][600/1281] lr: 2.269e-02, eta: 9:31:20, time: 0.227, data_time: 0.000, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9925, loss_cls: 0.6482, loss: 0.6482 +2025-06-24 13:04:01,741 - pyskl - INFO - Epoch [30][700/1281] lr: 2.268e-02, eta: 9:31:00, time: 0.228, data_time: 0.000, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9925, loss_cls: 0.7003, loss: 0.7003 +2025-06-24 13:04:24,605 - pyskl - INFO - Epoch [30][800/1281] lr: 2.267e-02, eta: 9:30:41, time: 0.229, data_time: 0.000, memory: 4082, top1_acc: 0.8662, top5_acc: 0.9925, loss_cls: 0.6403, loss: 0.6403 +2025-06-24 13:04:47,518 - pyskl - INFO - Epoch [30][900/1281] lr: 2.266e-02, eta: 9:30:21, time: 0.229, data_time: 0.000, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9912, loss_cls: 0.6235, loss: 0.6235 +2025-06-24 13:05:10,460 - pyskl - INFO - Epoch [30][1000/1281] lr: 2.265e-02, eta: 9:30:02, time: 0.229, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9950, loss_cls: 0.6843, loss: 0.6843 +2025-06-24 13:05:33,247 - pyskl - INFO - Epoch [30][1100/1281] lr: 2.263e-02, eta: 9:29:42, time: 0.228, data_time: 0.000, memory: 4082, top1_acc: 0.8612, top5_acc: 0.9900, loss_cls: 0.6327, loss: 0.6327 +2025-06-24 13:05:55,970 - pyskl - INFO - Epoch [30][1200/1281] lr: 2.262e-02, eta: 9:29:22, time: 0.227, data_time: 0.000, memory: 4082, top1_acc: 0.8531, top5_acc: 0.9906, loss_cls: 0.6743, loss: 0.6743 +2025-06-24 13:06:15,193 - pyskl - INFO - Saving checkpoint at 30 epochs +2025-06-24 13:06:59,164 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:06:59,233 - pyskl - INFO - +top1_acc 0.8235 +top5_acc 0.9866 +2025-06-24 13:06:59,234 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:06:59,241 - pyskl - INFO - +mean_acc 0.7588 +2025-06-24 13:06:59,243 - pyskl - INFO - Epoch(val) [30][533] top1_acc: 0.8235, top5_acc: 0.9866, mean_class_accuracy: 0.7588 +2025-06-24 13:07:42,315 - pyskl - INFO - Epoch [31][100/1281] lr: 2.260e-02, eta: 9:28:53, time: 0.431, data_time: 0.191, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9962, loss_cls: 0.7513, loss: 0.7513 +2025-06-24 13:08:04,480 - pyskl - INFO - Epoch [31][200/1281] lr: 2.259e-02, eta: 9:28:31, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9944, loss_cls: 0.8013, loss: 0.8013 +2025-06-24 13:08:26,855 - pyskl - INFO - Epoch [31][300/1281] lr: 2.258e-02, eta: 9:28:09, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9925, loss_cls: 0.8026, loss: 0.8026 +2025-06-24 13:08:49,092 - pyskl - INFO - Epoch [31][400/1281] lr: 2.256e-02, eta: 9:27:47, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9931, loss_cls: 0.8147, loss: 0.8147 +2025-06-24 13:09:11,563 - pyskl - INFO - Epoch [31][500/1281] lr: 2.255e-02, eta: 9:27:26, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9925, loss_cls: 0.8401, loss: 0.8401 +2025-06-24 13:09:33,696 - pyskl - INFO - Epoch [31][600/1281] lr: 2.254e-02, eta: 9:27:03, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8462, top5_acc: 0.9888, loss_cls: 0.8723, loss: 0.8723 +2025-06-24 13:09:56,002 - pyskl - INFO - Epoch [31][700/1281] lr: 2.253e-02, eta: 9:26:41, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8506, top5_acc: 0.9925, loss_cls: 0.8384, loss: 0.8384 +2025-06-24 13:10:18,347 - pyskl - INFO - Epoch [31][800/1281] lr: 2.252e-02, eta: 9:26:20, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8538, top5_acc: 0.9956, loss_cls: 0.8297, loss: 0.8297 +2025-06-24 13:10:41,115 - pyskl - INFO - Epoch [31][900/1281] lr: 2.250e-02, eta: 9:26:00, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8400, top5_acc: 0.9944, loss_cls: 0.8793, loss: 0.8793 +2025-06-24 13:11:03,629 - pyskl - INFO - Epoch [31][1000/1281] lr: 2.249e-02, eta: 9:25:38, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9962, loss_cls: 0.7534, loss: 0.7534 +2025-06-24 13:11:26,182 - pyskl - INFO - Epoch [31][1100/1281] lr: 2.248e-02, eta: 9:25:17, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9931, loss_cls: 0.8083, loss: 0.8083 +2025-06-24 13:11:48,713 - pyskl - INFO - Epoch [31][1200/1281] lr: 2.247e-02, eta: 9:24:56, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9925, loss_cls: 0.8489, loss: 0.8489 +2025-06-24 13:12:07,769 - pyskl - INFO - Saving checkpoint at 31 epochs +2025-06-24 13:12:51,687 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:12:51,762 - pyskl - INFO - +top1_acc 0.8120 +top5_acc 0.9842 +2025-06-24 13:12:51,762 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:12:51,770 - pyskl - INFO - +mean_acc 0.7507 +2025-06-24 13:12:51,773 - pyskl - INFO - Epoch(val) [31][533] top1_acc: 0.8120, top5_acc: 0.9842, mean_class_accuracy: 0.7507 +2025-06-24 13:13:35,863 - pyskl - INFO - Epoch [32][100/1281] lr: 2.244e-02, eta: 9:24:31, time: 0.441, data_time: 0.197, memory: 4083, top1_acc: 0.8475, top5_acc: 0.9944, loss_cls: 0.7899, loss: 0.7899 +2025-06-24 13:13:58,245 - pyskl - INFO - Epoch [32][200/1281] lr: 2.243e-02, eta: 9:24:09, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9950, loss_cls: 0.7267, loss: 0.7267 +2025-06-24 13:14:20,513 - pyskl - INFO - Epoch [32][300/1281] lr: 2.242e-02, eta: 9:23:47, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9944, loss_cls: 0.6895, loss: 0.6895 +2025-06-24 13:14:43,147 - pyskl - INFO - Epoch [32][400/1281] lr: 2.241e-02, eta: 9:23:26, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9912, loss_cls: 0.7638, loss: 0.7638 +2025-06-24 13:15:05,829 - pyskl - INFO - Epoch [32][500/1281] lr: 2.239e-02, eta: 9:23:06, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8544, top5_acc: 0.9944, loss_cls: 0.7828, loss: 0.7828 +2025-06-24 13:15:28,685 - pyskl - INFO - Epoch [32][600/1281] lr: 2.238e-02, eta: 9:22:46, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9962, loss_cls: 0.7065, loss: 0.7065 +2025-06-24 13:15:51,461 - pyskl - INFO - Epoch [32][700/1281] lr: 2.237e-02, eta: 9:22:26, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9931, loss_cls: 0.7422, loss: 0.7422 +2025-06-24 13:16:13,894 - pyskl - INFO - Epoch [32][800/1281] lr: 2.236e-02, eta: 9:22:04, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9938, loss_cls: 0.7293, loss: 0.7293 +2025-06-24 13:16:37,030 - pyskl - INFO - Epoch [32][900/1281] lr: 2.234e-02, eta: 9:21:45, time: 0.231, data_time: 0.001, memory: 4083, top1_acc: 0.8475, top5_acc: 0.9950, loss_cls: 0.7891, loss: 0.7891 +2025-06-24 13:16:59,643 - pyskl - INFO - Epoch [32][1000/1281] lr: 2.233e-02, eta: 9:21:24, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9925, loss_cls: 0.7694, loss: 0.7694 +2025-06-24 13:17:22,386 - pyskl - INFO - Epoch [32][1100/1281] lr: 2.232e-02, eta: 9:21:04, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8544, top5_acc: 0.9950, loss_cls: 0.7536, loss: 0.7536 +2025-06-24 13:17:45,015 - pyskl - INFO - Epoch [32][1200/1281] lr: 2.231e-02, eta: 9:20:43, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9894, loss_cls: 0.7170, loss: 0.7170 +2025-06-24 13:18:04,453 - pyskl - INFO - Saving checkpoint at 32 epochs +2025-06-24 13:18:48,501 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:18:48,582 - pyskl - INFO - +top1_acc 0.8186 +top5_acc 0.9881 +2025-06-24 13:18:48,582 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:18:48,593 - pyskl - INFO - +mean_acc 0.7759 +2025-06-24 13:18:48,596 - pyskl - INFO - Epoch(val) [32][533] top1_acc: 0.8186, top5_acc: 0.9881, mean_class_accuracy: 0.7759 +2025-06-24 13:19:31,979 - pyskl - INFO - Epoch [33][100/1281] lr: 2.228e-02, eta: 9:20:14, time: 0.434, data_time: 0.194, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9944, loss_cls: 0.6149, loss: 0.6149 +2025-06-24 13:19:54,752 - pyskl - INFO - Epoch [33][200/1281] lr: 2.227e-02, eta: 9:19:54, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9938, loss_cls: 0.7076, loss: 0.7076 +2025-06-24 13:20:17,147 - pyskl - INFO - Epoch [33][300/1281] lr: 2.226e-02, eta: 9:19:32, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9931, loss_cls: 0.7429, loss: 0.7429 +2025-06-24 13:20:39,994 - pyskl - INFO - Epoch [33][400/1281] lr: 2.225e-02, eta: 9:19:12, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9956, loss_cls: 0.7157, loss: 0.7157 +2025-06-24 13:21:02,346 - pyskl - INFO - Epoch [33][500/1281] lr: 2.223e-02, eta: 9:18:50, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9981, loss_cls: 0.6444, loss: 0.6444 +2025-06-24 13:21:24,654 - pyskl - INFO - Epoch [33][600/1281] lr: 2.222e-02, eta: 9:18:28, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9919, loss_cls: 0.6718, loss: 0.6718 +2025-06-24 13:21:47,229 - pyskl - INFO - Epoch [33][700/1281] lr: 2.221e-02, eta: 9:18:07, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8356, top5_acc: 0.9919, loss_cls: 0.7920, loss: 0.7920 +2025-06-24 13:22:09,952 - pyskl - INFO - Epoch [33][800/1281] lr: 2.219e-02, eta: 9:17:47, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9975, loss_cls: 0.6509, loss: 0.6509 +2025-06-24 13:22:32,375 - pyskl - INFO - Epoch [33][900/1281] lr: 2.218e-02, eta: 9:17:25, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9950, loss_cls: 0.6666, loss: 0.6666 +2025-06-24 13:22:54,639 - pyskl - INFO - Epoch [33][1000/1281] lr: 2.217e-02, eta: 9:17:03, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9931, loss_cls: 0.6365, loss: 0.6365 +2025-06-24 13:23:17,119 - pyskl - INFO - Epoch [33][1100/1281] lr: 2.216e-02, eta: 9:16:41, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9925, loss_cls: 0.6998, loss: 0.6998 +2025-06-24 13:23:39,685 - pyskl - INFO - Epoch [33][1200/1281] lr: 2.214e-02, eta: 9:16:20, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9931, loss_cls: 0.6981, loss: 0.6981 +2025-06-24 13:23:58,882 - pyskl - INFO - Saving checkpoint at 33 epochs +2025-06-24 13:24:43,313 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:24:43,379 - pyskl - INFO - +top1_acc 0.7937 +top5_acc 0.9809 +2025-06-24 13:24:43,379 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:24:43,387 - pyskl - INFO - +mean_acc 0.7293 +2025-06-24 13:24:43,389 - pyskl - INFO - Epoch(val) [33][533] top1_acc: 0.7937, top5_acc: 0.9809, mean_class_accuracy: 0.7293 +2025-06-24 13:25:26,193 - pyskl - INFO - Epoch [34][100/1281] lr: 2.212e-02, eta: 9:15:49, time: 0.428, data_time: 0.193, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9956, loss_cls: 0.6350, loss: 0.6350 +2025-06-24 13:25:48,598 - pyskl - INFO - Epoch [34][200/1281] lr: 2.211e-02, eta: 9:15:27, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9919, loss_cls: 0.6850, loss: 0.6850 +2025-06-24 13:26:10,856 - pyskl - INFO - Epoch [34][300/1281] lr: 2.209e-02, eta: 9:15:04, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9950, loss_cls: 0.6616, loss: 0.6616 +2025-06-24 13:26:33,036 - pyskl - INFO - Epoch [34][400/1281] lr: 2.208e-02, eta: 9:14:42, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9912, loss_cls: 0.6931, loss: 0.6931 +2025-06-24 13:26:55,454 - pyskl - INFO - Epoch [34][500/1281] lr: 2.207e-02, eta: 9:14:20, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9944, loss_cls: 0.6552, loss: 0.6552 +2025-06-24 13:27:17,932 - pyskl - INFO - Epoch [34][600/1281] lr: 2.205e-02, eta: 9:13:59, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9925, loss_cls: 0.6362, loss: 0.6362 +2025-06-24 13:27:40,324 - pyskl - INFO - Epoch [34][700/1281] lr: 2.204e-02, eta: 9:13:37, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9938, loss_cls: 0.6545, loss: 0.6545 +2025-06-24 13:28:02,490 - pyskl - INFO - Epoch [34][800/1281] lr: 2.203e-02, eta: 9:13:14, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8462, top5_acc: 0.9900, loss_cls: 0.7330, loss: 0.7330 +2025-06-24 13:28:24,908 - pyskl - INFO - Epoch [34][900/1281] lr: 2.201e-02, eta: 9:12:52, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9962, loss_cls: 0.6426, loss: 0.6426 +2025-06-24 13:28:47,508 - pyskl - INFO - Epoch [34][1000/1281] lr: 2.200e-02, eta: 9:12:31, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9950, loss_cls: 0.6840, loss: 0.6840 +2025-06-24 13:29:10,252 - pyskl - INFO - Epoch [34][1100/1281] lr: 2.199e-02, eta: 9:12:11, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9962, loss_cls: 0.6736, loss: 0.6736 +2025-06-24 13:29:32,612 - pyskl - INFO - Epoch [34][1200/1281] lr: 2.197e-02, eta: 9:11:49, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9912, loss_cls: 0.6709, loss: 0.6709 +2025-06-24 13:29:51,710 - pyskl - INFO - Saving checkpoint at 34 epochs +2025-06-24 13:30:35,629 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:30:35,686 - pyskl - INFO - +top1_acc 0.8060 +top5_acc 0.9876 +2025-06-24 13:30:35,686 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:30:35,693 - pyskl - INFO - +mean_acc 0.7292 +2025-06-24 13:30:35,696 - pyskl - INFO - Epoch(val) [34][533] top1_acc: 0.8060, top5_acc: 0.9876, mean_class_accuracy: 0.7292 +2025-06-24 13:31:19,536 - pyskl - INFO - Epoch [35][100/1281] lr: 2.195e-02, eta: 9:11:20, time: 0.438, data_time: 0.197, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9962, loss_cls: 0.6219, loss: 0.6219 +2025-06-24 13:31:41,764 - pyskl - INFO - Epoch [35][200/1281] lr: 2.194e-02, eta: 9:10:58, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9962, loss_cls: 0.6346, loss: 0.6346 +2025-06-24 13:32:04,157 - pyskl - INFO - Epoch [35][300/1281] lr: 2.192e-02, eta: 9:10:36, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9962, loss_cls: 0.6689, loss: 0.6689 +2025-06-24 13:32:26,765 - pyskl - INFO - Epoch [35][400/1281] lr: 2.191e-02, eta: 9:10:15, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9950, loss_cls: 0.5887, loss: 0.5887 +2025-06-24 13:32:49,213 - pyskl - INFO - Epoch [35][500/1281] lr: 2.190e-02, eta: 9:09:53, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9931, loss_cls: 0.6600, loss: 0.6600 +2025-06-24 13:33:11,471 - pyskl - INFO - Epoch [35][600/1281] lr: 2.188e-02, eta: 9:09:31, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9950, loss_cls: 0.6519, loss: 0.6519 +2025-06-24 13:33:33,827 - pyskl - INFO - Epoch [35][700/1281] lr: 2.187e-02, eta: 9:09:09, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9975, loss_cls: 0.5819, loss: 0.5819 +2025-06-24 13:33:56,253 - pyskl - INFO - Epoch [35][800/1281] lr: 2.185e-02, eta: 9:08:47, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9931, loss_cls: 0.6755, loss: 0.6755 +2025-06-24 13:34:18,407 - pyskl - INFO - Epoch [35][900/1281] lr: 2.184e-02, eta: 9:08:24, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9938, loss_cls: 0.6611, loss: 0.6611 +2025-06-24 13:34:40,981 - pyskl - INFO - Epoch [35][1000/1281] lr: 2.183e-02, eta: 9:08:03, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9950, loss_cls: 0.6285, loss: 0.6285 +2025-06-24 13:35:03,773 - pyskl - INFO - Epoch [35][1100/1281] lr: 2.181e-02, eta: 9:07:42, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9944, loss_cls: 0.6658, loss: 0.6658 +2025-06-24 13:35:26,209 - pyskl - INFO - Epoch [35][1200/1281] lr: 2.180e-02, eta: 9:07:21, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9938, loss_cls: 0.5984, loss: 0.5984 +2025-06-24 13:35:44,934 - pyskl - INFO - Saving checkpoint at 35 epochs +2025-06-24 13:36:29,189 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:36:29,257 - pyskl - INFO - +top1_acc 0.8147 +top5_acc 0.9827 +2025-06-24 13:36:29,258 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:36:29,266 - pyskl - INFO - +mean_acc 0.7520 +2025-06-24 13:36:29,268 - pyskl - INFO - Epoch(val) [35][533] top1_acc: 0.8147, top5_acc: 0.9827, mean_class_accuracy: 0.7520 +2025-06-24 13:37:11,980 - pyskl - INFO - Epoch [36][100/1281] lr: 2.178e-02, eta: 9:06:48, time: 0.427, data_time: 0.192, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9938, loss_cls: 0.7351, loss: 0.7351 +2025-06-24 13:37:34,574 - pyskl - INFO - Epoch [36][200/1281] lr: 2.176e-02, eta: 9:06:27, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9956, loss_cls: 0.6246, loss: 0.6246 +2025-06-24 13:37:57,070 - pyskl - INFO - Epoch [36][300/1281] lr: 2.175e-02, eta: 9:06:05, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9950, loss_cls: 0.6674, loss: 0.6674 +2025-06-24 13:38:19,570 - pyskl - INFO - Epoch [36][400/1281] lr: 2.173e-02, eta: 9:05:44, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9944, loss_cls: 0.6265, loss: 0.6265 +2025-06-24 13:38:41,957 - pyskl - INFO - Epoch [36][500/1281] lr: 2.172e-02, eta: 9:05:22, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9962, loss_cls: 0.5460, loss: 0.5460 +2025-06-24 13:39:04,364 - pyskl - INFO - Epoch [36][600/1281] lr: 2.171e-02, eta: 9:05:00, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9950, loss_cls: 0.6542, loss: 0.6542 +2025-06-24 13:39:26,622 - pyskl - INFO - Epoch [36][700/1281] lr: 2.169e-02, eta: 9:04:37, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9912, loss_cls: 0.7020, loss: 0.7020 +2025-06-24 13:39:49,334 - pyskl - INFO - Epoch [36][800/1281] lr: 2.168e-02, eta: 9:04:16, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9944, loss_cls: 0.6636, loss: 0.6636 +2025-06-24 13:40:12,068 - pyskl - INFO - Epoch [36][900/1281] lr: 2.167e-02, eta: 9:03:56, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9950, loss_cls: 0.6216, loss: 0.6216 +2025-06-24 13:40:34,494 - pyskl - INFO - Epoch [36][1000/1281] lr: 2.165e-02, eta: 9:03:34, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9962, loss_cls: 0.6236, loss: 0.6236 +2025-06-24 13:40:56,665 - pyskl - INFO - Epoch [36][1100/1281] lr: 2.164e-02, eta: 9:03:11, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9931, loss_cls: 0.6221, loss: 0.6221 +2025-06-24 13:41:19,589 - pyskl - INFO - Epoch [36][1200/1281] lr: 2.162e-02, eta: 9:02:51, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9950, loss_cls: 0.6320, loss: 0.6320 +2025-06-24 13:41:38,626 - pyskl - INFO - Saving checkpoint at 36 epochs +2025-06-24 13:42:22,763 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:42:22,824 - pyskl - INFO - +top1_acc 0.8263 +top5_acc 0.9887 +2025-06-24 13:42:22,824 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:42:22,831 - pyskl - INFO - +mean_acc 0.7746 +2025-06-24 13:42:22,833 - pyskl - INFO - Epoch(val) [36][533] top1_acc: 0.8263, top5_acc: 0.9887, mean_class_accuracy: 0.7746 +2025-06-24 13:43:05,821 - pyskl - INFO - Epoch [37][100/1281] lr: 2.160e-02, eta: 9:02:19, time: 0.430, data_time: 0.194, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9931, loss_cls: 0.6624, loss: 0.6624 +2025-06-24 13:43:28,357 - pyskl - INFO - Epoch [37][200/1281] lr: 2.158e-02, eta: 9:01:57, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9956, loss_cls: 0.6163, loss: 0.6163 +2025-06-24 13:43:50,787 - pyskl - INFO - Epoch [37][300/1281] lr: 2.157e-02, eta: 9:01:35, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9962, loss_cls: 0.5922, loss: 0.5922 +2025-06-24 13:44:13,217 - pyskl - INFO - Epoch [37][400/1281] lr: 2.156e-02, eta: 9:01:13, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9925, loss_cls: 0.6627, loss: 0.6627 +2025-06-24 13:44:35,566 - pyskl - INFO - Epoch [37][500/1281] lr: 2.154e-02, eta: 9:00:51, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9944, loss_cls: 0.6507, loss: 0.6507 +2025-06-24 13:44:57,866 - pyskl - INFO - Epoch [37][600/1281] lr: 2.153e-02, eta: 9:00:29, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9956, loss_cls: 0.6412, loss: 0.6412 +2025-06-24 13:45:20,161 - pyskl - INFO - Epoch [37][700/1281] lr: 2.151e-02, eta: 9:00:07, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9931, loss_cls: 0.6164, loss: 0.6164 +2025-06-24 13:45:42,590 - pyskl - INFO - Epoch [37][800/1281] lr: 2.150e-02, eta: 8:59:45, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9925, loss_cls: 0.6716, loss: 0.6716 +2025-06-24 13:46:05,030 - pyskl - INFO - Epoch [37][900/1281] lr: 2.149e-02, eta: 8:59:23, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9931, loss_cls: 0.7001, loss: 0.7001 +2025-06-24 13:46:27,744 - pyskl - INFO - Epoch [37][1000/1281] lr: 2.147e-02, eta: 8:59:02, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9944, loss_cls: 0.6427, loss: 0.6427 +2025-06-24 13:46:50,166 - pyskl - INFO - Epoch [37][1100/1281] lr: 2.146e-02, eta: 8:58:40, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9931, loss_cls: 0.6044, loss: 0.6044 +2025-06-24 13:47:12,719 - pyskl - INFO - Epoch [37][1200/1281] lr: 2.144e-02, eta: 8:58:18, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9950, loss_cls: 0.6586, loss: 0.6586 +2025-06-24 13:47:31,652 - pyskl - INFO - Saving checkpoint at 37 epochs +2025-06-24 13:48:15,533 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:48:15,590 - pyskl - INFO - +top1_acc 0.8317 +top5_acc 0.9885 +2025-06-24 13:48:15,590 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:48:15,597 - pyskl - INFO - +mean_acc 0.7644 +2025-06-24 13:48:15,600 - pyskl - INFO - Epoch(val) [37][533] top1_acc: 0.8317, top5_acc: 0.9885, mean_class_accuracy: 0.7644 +2025-06-24 13:48:58,890 - pyskl - INFO - Epoch [38][100/1281] lr: 2.142e-02, eta: 8:57:47, time: 0.433, data_time: 0.195, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9944, loss_cls: 0.6097, loss: 0.6097 +2025-06-24 13:49:21,403 - pyskl - INFO - Epoch [38][200/1281] lr: 2.140e-02, eta: 8:57:25, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9962, loss_cls: 0.6323, loss: 0.6323 +2025-06-24 13:49:43,567 - pyskl - INFO - Epoch [38][300/1281] lr: 2.139e-02, eta: 8:57:03, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9950, loss_cls: 0.6007, loss: 0.6007 +2025-06-24 13:50:05,971 - pyskl - INFO - Epoch [38][400/1281] lr: 2.137e-02, eta: 8:56:41, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9956, loss_cls: 0.5811, loss: 0.5811 +2025-06-24 13:50:28,449 - pyskl - INFO - Epoch [38][500/1281] lr: 2.136e-02, eta: 8:56:19, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9950, loss_cls: 0.6426, loss: 0.6426 +2025-06-24 13:50:50,763 - pyskl - INFO - Epoch [38][600/1281] lr: 2.134e-02, eta: 8:55:56, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9962, loss_cls: 0.6662, loss: 0.6662 +2025-06-24 13:51:13,077 - pyskl - INFO - Epoch [38][700/1281] lr: 2.133e-02, eta: 8:55:34, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9950, loss_cls: 0.6469, loss: 0.6469 +2025-06-24 13:51:35,734 - pyskl - INFO - Epoch [38][800/1281] lr: 2.132e-02, eta: 8:55:13, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9906, loss_cls: 0.6118, loss: 0.6118 +2025-06-24 13:51:58,038 - pyskl - INFO - Epoch [38][900/1281] lr: 2.130e-02, eta: 8:54:51, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9950, loss_cls: 0.6718, loss: 0.6718 +2025-06-24 13:52:20,395 - pyskl - INFO - Epoch [38][1000/1281] lr: 2.129e-02, eta: 8:54:28, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9962, loss_cls: 0.6224, loss: 0.6224 +2025-06-24 13:52:42,953 - pyskl - INFO - Epoch [38][1100/1281] lr: 2.127e-02, eta: 8:54:07, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9950, loss_cls: 0.6319, loss: 0.6319 +2025-06-24 13:53:05,206 - pyskl - INFO - Epoch [38][1200/1281] lr: 2.126e-02, eta: 8:53:44, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9919, loss_cls: 0.6291, loss: 0.6291 +2025-06-24 13:53:24,150 - pyskl - INFO - Saving checkpoint at 38 epochs +2025-06-24 13:54:07,757 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:54:07,812 - pyskl - INFO - +top1_acc 0.8459 +top5_acc 0.9864 +2025-06-24 13:54:07,812 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:54:07,821 - pyskl - INFO - +mean_acc 0.7645 +2025-06-24 13:54:07,825 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_26.pth was removed +2025-06-24 13:54:08,018 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_38.pth. +2025-06-24 13:54:08,018 - pyskl - INFO - Best top1_acc is 0.8459 at 38 epoch. +2025-06-24 13:54:08,021 - pyskl - INFO - Epoch(val) [38][533] top1_acc: 0.8459, top5_acc: 0.9864, mean_class_accuracy: 0.7645 +2025-06-24 13:54:50,992 - pyskl - INFO - Epoch [39][100/1281] lr: 2.123e-02, eta: 8:53:12, time: 0.430, data_time: 0.195, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9981, loss_cls: 0.5782, loss: 0.5782 +2025-06-24 13:55:13,247 - pyskl - INFO - Epoch [39][200/1281] lr: 2.122e-02, eta: 8:52:49, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9938, loss_cls: 0.6455, loss: 0.6455 +2025-06-24 13:55:35,799 - pyskl - INFO - Epoch [39][300/1281] lr: 2.120e-02, eta: 8:52:27, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9944, loss_cls: 0.6266, loss: 0.6266 +2025-06-24 13:55:58,247 - pyskl - INFO - Epoch [39][400/1281] lr: 2.119e-02, eta: 8:52:06, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9956, loss_cls: 0.6225, loss: 0.6225 +2025-06-24 13:56:20,365 - pyskl - INFO - Epoch [39][500/1281] lr: 2.117e-02, eta: 8:51:43, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9950, loss_cls: 0.5824, loss: 0.5824 +2025-06-24 13:56:42,522 - pyskl - INFO - Epoch [39][600/1281] lr: 2.116e-02, eta: 8:51:20, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9944, loss_cls: 0.6234, loss: 0.6234 +2025-06-24 13:57:04,965 - pyskl - INFO - Epoch [39][700/1281] lr: 2.114e-02, eta: 8:50:58, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9944, loss_cls: 0.6103, loss: 0.6103 +2025-06-24 13:57:27,345 - pyskl - INFO - Epoch [39][800/1281] lr: 2.113e-02, eta: 8:50:36, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9950, loss_cls: 0.6524, loss: 0.6524 +2025-06-24 13:57:49,841 - pyskl - INFO - Epoch [39][900/1281] lr: 2.111e-02, eta: 8:50:14, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9938, loss_cls: 0.6715, loss: 0.6715 +2025-06-24 13:58:12,129 - pyskl - INFO - Epoch [39][1000/1281] lr: 2.110e-02, eta: 8:49:52, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9956, loss_cls: 0.6402, loss: 0.6402 +2025-06-24 13:58:34,603 - pyskl - INFO - Epoch [39][1100/1281] lr: 2.108e-02, eta: 8:49:30, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9950, loss_cls: 0.6124, loss: 0.6124 +2025-06-24 13:58:57,295 - pyskl - INFO - Epoch [39][1200/1281] lr: 2.107e-02, eta: 8:49:08, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9956, loss_cls: 0.6282, loss: 0.6282 +2025-06-24 13:59:15,921 - pyskl - INFO - Saving checkpoint at 39 epochs +2025-06-24 14:00:00,259 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:00:00,323 - pyskl - INFO - +top1_acc 0.8530 +top5_acc 0.9887 +2025-06-24 14:00:00,323 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:00:00,330 - pyskl - INFO - +mean_acc 0.7994 +2025-06-24 14:00:00,334 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_38.pth was removed +2025-06-24 14:00:00,525 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_39.pth. +2025-06-24 14:00:00,525 - pyskl - INFO - Best top1_acc is 0.8530 at 39 epoch. +2025-06-24 14:00:00,528 - pyskl - INFO - Epoch(val) [39][533] top1_acc: 0.8530, top5_acc: 0.9887, mean_class_accuracy: 0.7994 +2025-06-24 14:00:42,921 - pyskl - INFO - Epoch [40][100/1281] lr: 2.104e-02, eta: 8:48:34, time: 0.424, data_time: 0.192, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9950, loss_cls: 0.6073, loss: 0.6073 +2025-06-24 14:01:05,287 - pyskl - INFO - Epoch [40][200/1281] lr: 2.103e-02, eta: 8:48:12, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9981, loss_cls: 0.5861, loss: 0.5861 +2025-06-24 14:01:27,624 - pyskl - INFO - Epoch [40][300/1281] lr: 2.101e-02, eta: 8:47:49, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9969, loss_cls: 0.5703, loss: 0.5703 +2025-06-24 14:01:49,918 - pyskl - INFO - Epoch [40][400/1281] lr: 2.100e-02, eta: 8:47:27, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9969, loss_cls: 0.6144, loss: 0.6144 +2025-06-24 14:02:12,275 - pyskl - INFO - Epoch [40][500/1281] lr: 2.098e-02, eta: 8:47:05, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9988, loss_cls: 0.5718, loss: 0.5718 +2025-06-24 14:02:34,462 - pyskl - INFO - Epoch [40][600/1281] lr: 2.097e-02, eta: 8:46:42, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9962, loss_cls: 0.5839, loss: 0.5839 +2025-06-24 14:02:56,816 - pyskl - INFO - Epoch [40][700/1281] lr: 2.095e-02, eta: 8:46:20, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9950, loss_cls: 0.6392, loss: 0.6392 +2025-06-24 14:03:18,955 - pyskl - INFO - Epoch [40][800/1281] lr: 2.094e-02, eta: 8:45:57, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9962, loss_cls: 0.5611, loss: 0.5611 +2025-06-24 14:03:41,497 - pyskl - INFO - Epoch [40][900/1281] lr: 2.092e-02, eta: 8:45:35, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9962, loss_cls: 0.6155, loss: 0.6155 +2025-06-24 14:04:03,958 - pyskl - INFO - Epoch [40][1000/1281] lr: 2.091e-02, eta: 8:45:13, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9938, loss_cls: 0.6070, loss: 0.6070 +2025-06-24 14:04:26,463 - pyskl - INFO - Epoch [40][1100/1281] lr: 2.089e-02, eta: 8:44:51, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9931, loss_cls: 0.6722, loss: 0.6722 +2025-06-24 14:04:49,075 - pyskl - INFO - Epoch [40][1200/1281] lr: 2.088e-02, eta: 8:44:30, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9962, loss_cls: 0.6781, loss: 0.6781 +2025-06-24 14:05:07,880 - pyskl - INFO - Saving checkpoint at 40 epochs +2025-06-24 14:05:51,476 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:05:51,543 - pyskl - INFO - +top1_acc 0.8265 +top5_acc 0.9862 +2025-06-24 14:05:51,543 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:05:51,551 - pyskl - INFO - +mean_acc 0.7463 +2025-06-24 14:05:51,553 - pyskl - INFO - Epoch(val) [40][533] top1_acc: 0.8265, top5_acc: 0.9862, mean_class_accuracy: 0.7463 +2025-06-24 14:06:34,430 - pyskl - INFO - Epoch [41][100/1281] lr: 2.085e-02, eta: 8:43:56, time: 0.429, data_time: 0.194, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9956, loss_cls: 0.5562, loss: 0.5562 +2025-06-24 14:06:56,704 - pyskl - INFO - Epoch [41][200/1281] lr: 2.083e-02, eta: 8:43:34, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9969, loss_cls: 0.5729, loss: 0.5729 +2025-06-24 14:07:19,263 - pyskl - INFO - Epoch [41][300/1281] lr: 2.082e-02, eta: 8:43:12, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9956, loss_cls: 0.5890, loss: 0.5890 +2025-06-24 14:07:41,619 - pyskl - INFO - Epoch [41][400/1281] lr: 2.080e-02, eta: 8:42:50, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9962, loss_cls: 0.6000, loss: 0.6000 +2025-06-24 14:08:04,087 - pyskl - INFO - Epoch [41][500/1281] lr: 2.079e-02, eta: 8:42:28, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9962, loss_cls: 0.5762, loss: 0.5762 +2025-06-24 14:08:26,424 - pyskl - INFO - Epoch [41][600/1281] lr: 2.077e-02, eta: 8:42:06, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9931, loss_cls: 0.6264, loss: 0.6264 +2025-06-24 14:08:48,648 - pyskl - INFO - Epoch [41][700/1281] lr: 2.076e-02, eta: 8:41:43, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9969, loss_cls: 0.6429, loss: 0.6429 +2025-06-24 14:09:10,943 - pyskl - INFO - Epoch [41][800/1281] lr: 2.074e-02, eta: 8:41:21, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9969, loss_cls: 0.5844, loss: 0.5844 +2025-06-24 14:09:33,226 - pyskl - INFO - Epoch [41][900/1281] lr: 2.073e-02, eta: 8:40:58, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9962, loss_cls: 0.6176, loss: 0.6176 +2025-06-24 14:09:55,422 - pyskl - INFO - Epoch [41][1000/1281] lr: 2.071e-02, eta: 8:40:36, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9975, loss_cls: 0.6441, loss: 0.6441 +2025-06-24 14:10:17,718 - pyskl - INFO - Epoch [41][1100/1281] lr: 2.070e-02, eta: 8:40:13, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9956, loss_cls: 0.6210, loss: 0.6210 +2025-06-24 14:10:39,972 - pyskl - INFO - Epoch [41][1200/1281] lr: 2.068e-02, eta: 8:39:51, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9962, loss_cls: 0.5901, loss: 0.5901 +2025-06-24 14:10:59,200 - pyskl - INFO - Saving checkpoint at 41 epochs +2025-06-24 14:11:42,605 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:11:42,660 - pyskl - INFO - +top1_acc 0.8490 +top5_acc 0.9901 +2025-06-24 14:11:42,660 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:11:42,667 - pyskl - INFO - +mean_acc 0.7873 +2025-06-24 14:11:42,669 - pyskl - INFO - Epoch(val) [41][533] top1_acc: 0.8490, top5_acc: 0.9901, mean_class_accuracy: 0.7873 +2025-06-24 14:12:25,067 - pyskl - INFO - Epoch [42][100/1281] lr: 2.065e-02, eta: 8:39:15, time: 0.424, data_time: 0.188, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9975, loss_cls: 0.5454, loss: 0.5454 +2025-06-24 14:12:47,597 - pyskl - INFO - Epoch [42][200/1281] lr: 2.064e-02, eta: 8:38:54, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9975, loss_cls: 0.5744, loss: 0.5744 +2025-06-24 14:13:09,733 - pyskl - INFO - Epoch [42][300/1281] lr: 2.062e-02, eta: 8:38:31, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9975, loss_cls: 0.6123, loss: 0.6123 +2025-06-24 14:13:32,154 - pyskl - INFO - Epoch [42][400/1281] lr: 2.061e-02, eta: 8:38:09, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9962, loss_cls: 0.5988, loss: 0.5988 +2025-06-24 14:13:54,514 - pyskl - INFO - Epoch [42][500/1281] lr: 2.059e-02, eta: 8:37:46, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9925, loss_cls: 0.5662, loss: 0.5662 +2025-06-24 14:14:16,872 - pyskl - INFO - Epoch [42][600/1281] lr: 2.057e-02, eta: 8:37:24, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9956, loss_cls: 0.5435, loss: 0.5435 +2025-06-24 14:14:39,372 - pyskl - INFO - Epoch [42][700/1281] lr: 2.056e-02, eta: 8:37:02, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9969, loss_cls: 0.6261, loss: 0.6261 +2025-06-24 14:15:01,694 - pyskl - INFO - Epoch [42][800/1281] lr: 2.054e-02, eta: 8:36:40, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9956, loss_cls: 0.6491, loss: 0.6491 +2025-06-24 14:15:24,119 - pyskl - INFO - Epoch [42][900/1281] lr: 2.053e-02, eta: 8:36:18, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9975, loss_cls: 0.6312, loss: 0.6312 +2025-06-24 14:15:46,533 - pyskl - INFO - Epoch [42][1000/1281] lr: 2.051e-02, eta: 8:35:56, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9950, loss_cls: 0.5671, loss: 0.5671 +2025-06-24 14:16:09,046 - pyskl - INFO - Epoch [42][1100/1281] lr: 2.050e-02, eta: 8:35:34, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9938, loss_cls: 0.6483, loss: 0.6483 +2025-06-24 14:16:31,341 - pyskl - INFO - Epoch [42][1200/1281] lr: 2.048e-02, eta: 8:35:12, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9956, loss_cls: 0.6226, loss: 0.6226 +2025-06-24 14:16:50,329 - pyskl - INFO - Saving checkpoint at 42 epochs +2025-06-24 14:17:35,026 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:17:35,080 - pyskl - INFO - +top1_acc 0.8453 +top5_acc 0.9899 +2025-06-24 14:17:35,080 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:17:35,087 - pyskl - INFO - +mean_acc 0.7844 +2025-06-24 14:17:35,089 - pyskl - INFO - Epoch(val) [42][533] top1_acc: 0.8453, top5_acc: 0.9899, mean_class_accuracy: 0.7844 +2025-06-24 14:18:17,960 - pyskl - INFO - Epoch [43][100/1281] lr: 2.045e-02, eta: 8:34:37, time: 0.429, data_time: 0.193, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9981, loss_cls: 0.5291, loss: 0.5291 +2025-06-24 14:18:40,742 - pyskl - INFO - Epoch [43][200/1281] lr: 2.044e-02, eta: 8:34:16, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9981, loss_cls: 0.5132, loss: 0.5132 +2025-06-24 14:19:02,937 - pyskl - INFO - Epoch [43][300/1281] lr: 2.042e-02, eta: 8:33:53, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9969, loss_cls: 0.4999, loss: 0.4999 +2025-06-24 14:19:25,344 - pyskl - INFO - Epoch [43][400/1281] lr: 2.040e-02, eta: 8:33:31, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9944, loss_cls: 0.5875, loss: 0.5875 +2025-06-24 14:19:47,548 - pyskl - INFO - Epoch [43][500/1281] lr: 2.039e-02, eta: 8:33:09, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9962, loss_cls: 0.6346, loss: 0.6346 +2025-06-24 14:20:09,750 - pyskl - INFO - Epoch [43][600/1281] lr: 2.037e-02, eta: 8:32:46, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9931, loss_cls: 0.6065, loss: 0.6065 +2025-06-24 14:20:32,100 - pyskl - INFO - Epoch [43][700/1281] lr: 2.036e-02, eta: 8:32:24, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9950, loss_cls: 0.5629, loss: 0.5629 +2025-06-24 14:20:54,261 - pyskl - INFO - Epoch [43][800/1281] lr: 2.034e-02, eta: 8:32:01, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9969, loss_cls: 0.5923, loss: 0.5923 +2025-06-24 14:21:16,772 - pyskl - INFO - Epoch [43][900/1281] lr: 2.033e-02, eta: 8:31:39, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9956, loss_cls: 0.5597, loss: 0.5597 +2025-06-24 14:21:38,909 - pyskl - INFO - Epoch [43][1000/1281] lr: 2.031e-02, eta: 8:31:16, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9938, loss_cls: 0.6324, loss: 0.6324 +2025-06-24 14:22:01,187 - pyskl - INFO - Epoch [43][1100/1281] lr: 2.029e-02, eta: 8:30:54, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9944, loss_cls: 0.5970, loss: 0.5970 +2025-06-24 14:22:23,601 - pyskl - INFO - Epoch [43][1200/1281] lr: 2.028e-02, eta: 8:30:32, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9975, loss_cls: 0.5989, loss: 0.5989 +2025-06-24 14:22:42,412 - pyskl - INFO - Saving checkpoint at 43 epochs +2025-06-24 14:23:26,525 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:23:26,589 - pyskl - INFO - +top1_acc 0.8478 +top5_acc 0.9904 +2025-06-24 14:23:26,589 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:23:26,599 - pyskl - INFO - +mean_acc 0.7866 +2025-06-24 14:23:26,602 - pyskl - INFO - Epoch(val) [43][533] top1_acc: 0.8478, top5_acc: 0.9904, mean_class_accuracy: 0.7866 +2025-06-24 14:24:08,707 - pyskl - INFO - Epoch [44][100/1281] lr: 2.025e-02, eta: 8:29:55, time: 0.421, data_time: 0.186, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9962, loss_cls: 0.6412, loss: 0.6412 +2025-06-24 14:24:31,581 - pyskl - INFO - Epoch [44][200/1281] lr: 2.023e-02, eta: 8:29:34, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9969, loss_cls: 0.5588, loss: 0.5588 +2025-06-24 14:24:54,088 - pyskl - INFO - Epoch [44][300/1281] lr: 2.022e-02, eta: 8:29:12, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9931, loss_cls: 0.5655, loss: 0.5655 +2025-06-24 14:25:16,168 - pyskl - INFO - Epoch [44][400/1281] lr: 2.020e-02, eta: 8:28:49, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9962, loss_cls: 0.5734, loss: 0.5734 +2025-06-24 14:25:38,778 - pyskl - INFO - Epoch [44][500/1281] lr: 2.018e-02, eta: 8:28:28, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9919, loss_cls: 0.6746, loss: 0.6746 +2025-06-24 14:26:01,182 - pyskl - INFO - Epoch [44][600/1281] lr: 2.017e-02, eta: 8:28:05, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9938, loss_cls: 0.5500, loss: 0.5500 +2025-06-24 14:26:23,599 - pyskl - INFO - Epoch [44][700/1281] lr: 2.015e-02, eta: 8:27:43, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9988, loss_cls: 0.5810, loss: 0.5810 +2025-06-24 14:26:45,728 - pyskl - INFO - Epoch [44][800/1281] lr: 2.014e-02, eta: 8:27:20, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9912, loss_cls: 0.6150, loss: 0.6150 +2025-06-24 14:27:08,540 - pyskl - INFO - Epoch [44][900/1281] lr: 2.012e-02, eta: 8:26:59, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9962, loss_cls: 0.6072, loss: 0.6072 +2025-06-24 14:27:30,382 - pyskl - INFO - Epoch [44][1000/1281] lr: 2.010e-02, eta: 8:26:36, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9962, loss_cls: 0.5636, loss: 0.5636 +2025-06-24 14:27:53,011 - pyskl - INFO - Epoch [44][1100/1281] lr: 2.009e-02, eta: 8:26:14, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9975, loss_cls: 0.5534, loss: 0.5534 +2025-06-24 14:28:15,291 - pyskl - INFO - Epoch [44][1200/1281] lr: 2.007e-02, eta: 8:25:52, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9969, loss_cls: 0.5412, loss: 0.5412 +2025-06-24 14:28:33,976 - pyskl - INFO - Saving checkpoint at 44 epochs +2025-06-24 14:29:18,603 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:29:18,657 - pyskl - INFO - +top1_acc 0.8380 +top5_acc 0.9893 +2025-06-24 14:29:18,657 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:29:18,664 - pyskl - INFO - +mean_acc 0.7745 +2025-06-24 14:29:18,666 - pyskl - INFO - Epoch(val) [44][533] top1_acc: 0.8380, top5_acc: 0.9893, mean_class_accuracy: 0.7745 +2025-06-24 14:30:02,205 - pyskl - INFO - Epoch [45][100/1281] lr: 2.004e-02, eta: 8:25:19, time: 0.435, data_time: 0.197, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9988, loss_cls: 0.4760, loss: 0.4760 +2025-06-24 14:30:24,564 - pyskl - INFO - Epoch [45][200/1281] lr: 2.003e-02, eta: 8:24:56, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9962, loss_cls: 0.5366, loss: 0.5366 +2025-06-24 14:30:46,807 - pyskl - INFO - Epoch [45][300/1281] lr: 2.001e-02, eta: 8:24:34, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9962, loss_cls: 0.5631, loss: 0.5631 +2025-06-24 14:31:09,427 - pyskl - INFO - Epoch [45][400/1281] lr: 1.999e-02, eta: 8:24:12, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9950, loss_cls: 0.5301, loss: 0.5301 +2025-06-24 14:31:31,953 - pyskl - INFO - Epoch [45][500/1281] lr: 1.998e-02, eta: 8:23:50, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9962, loss_cls: 0.6163, loss: 0.6163 +2025-06-24 14:31:54,300 - pyskl - INFO - Epoch [45][600/1281] lr: 1.996e-02, eta: 8:23:28, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9944, loss_cls: 0.6257, loss: 0.6257 +2025-06-24 14:32:16,521 - pyskl - INFO - Epoch [45][700/1281] lr: 1.994e-02, eta: 8:23:05, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9956, loss_cls: 0.5774, loss: 0.5774 +2025-06-24 14:32:38,893 - pyskl - INFO - Epoch [45][800/1281] lr: 1.993e-02, eta: 8:22:43, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9969, loss_cls: 0.5715, loss: 0.5715 +2025-06-24 14:33:01,278 - pyskl - INFO - Epoch [45][900/1281] lr: 1.991e-02, eta: 8:22:21, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9962, loss_cls: 0.6235, loss: 0.6235 +2025-06-24 14:33:23,571 - pyskl - INFO - Epoch [45][1000/1281] lr: 1.989e-02, eta: 8:21:58, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9944, loss_cls: 0.5873, loss: 0.5873 +2025-06-24 14:33:46,026 - pyskl - INFO - Epoch [45][1100/1281] lr: 1.988e-02, eta: 8:21:36, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9969, loss_cls: 0.5534, loss: 0.5534 +2025-06-24 14:34:08,452 - pyskl - INFO - Epoch [45][1200/1281] lr: 1.986e-02, eta: 8:21:14, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9912, loss_cls: 0.6110, loss: 0.6110 +2025-06-24 14:34:27,574 - pyskl - INFO - Saving checkpoint at 45 epochs +2025-06-24 14:35:11,877 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:35:11,934 - pyskl - INFO - +top1_acc 0.8429 +top5_acc 0.9897 +2025-06-24 14:35:11,934 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:35:11,942 - pyskl - INFO - +mean_acc 0.8080 +2025-06-24 14:35:11,944 - pyskl - INFO - Epoch(val) [45][533] top1_acc: 0.8429, top5_acc: 0.9897, mean_class_accuracy: 0.8080 +2025-06-24 14:35:54,588 - pyskl - INFO - Epoch [46][100/1281] lr: 1.983e-02, eta: 8:20:39, time: 0.426, data_time: 0.193, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9975, loss_cls: 0.5598, loss: 0.5598 +2025-06-24 14:36:17,012 - pyskl - INFO - Epoch [46][200/1281] lr: 1.981e-02, eta: 8:20:16, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9938, loss_cls: 0.5364, loss: 0.5364 +2025-06-24 14:36:39,331 - pyskl - INFO - Epoch [46][300/1281] lr: 1.980e-02, eta: 8:19:54, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9969, loss_cls: 0.5885, loss: 0.5885 +2025-06-24 14:37:01,860 - pyskl - INFO - Epoch [46][400/1281] lr: 1.978e-02, eta: 8:19:32, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9962, loss_cls: 0.5425, loss: 0.5425 +2025-06-24 14:37:24,033 - pyskl - INFO - Epoch [46][500/1281] lr: 1.976e-02, eta: 8:19:09, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9956, loss_cls: 0.5765, loss: 0.5765 +2025-06-24 14:37:46,441 - pyskl - INFO - Epoch [46][600/1281] lr: 1.975e-02, eta: 8:18:47, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9938, loss_cls: 0.5890, loss: 0.5890 +2025-06-24 14:38:08,480 - pyskl - INFO - Epoch [46][700/1281] lr: 1.973e-02, eta: 8:18:24, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9975, loss_cls: 0.5433, loss: 0.5433 +2025-06-24 14:38:30,666 - pyskl - INFO - Epoch [46][800/1281] lr: 1.971e-02, eta: 8:18:01, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9950, loss_cls: 0.5536, loss: 0.5536 +2025-06-24 14:38:53,051 - pyskl - INFO - Epoch [46][900/1281] lr: 1.970e-02, eta: 8:17:39, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9950, loss_cls: 0.5794, loss: 0.5794 +2025-06-24 14:39:15,254 - pyskl - INFO - Epoch [46][1000/1281] lr: 1.968e-02, eta: 8:17:16, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9962, loss_cls: 0.5614, loss: 0.5614 +2025-06-24 14:39:37,725 - pyskl - INFO - Epoch [46][1100/1281] lr: 1.966e-02, eta: 8:16:54, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9969, loss_cls: 0.5635, loss: 0.5635 +2025-06-24 14:39:59,989 - pyskl - INFO - Epoch [46][1200/1281] lr: 1.965e-02, eta: 8:16:32, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9956, loss_cls: 0.5799, loss: 0.5799 +2025-06-24 14:40:18,744 - pyskl - INFO - Saving checkpoint at 46 epochs +2025-06-24 14:41:02,630 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:41:02,698 - pyskl - INFO - +top1_acc 0.8413 +top5_acc 0.9885 +2025-06-24 14:41:02,698 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:41:02,707 - pyskl - INFO - +mean_acc 0.7930 +2025-06-24 14:41:02,709 - pyskl - INFO - Epoch(val) [46][533] top1_acc: 0.8413, top5_acc: 0.9885, mean_class_accuracy: 0.7930 +2025-06-24 14:41:46,013 - pyskl - INFO - Epoch [47][100/1281] lr: 1.962e-02, eta: 8:15:58, time: 0.433, data_time: 0.198, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9962, loss_cls: 0.5596, loss: 0.5596 +2025-06-24 14:42:08,779 - pyskl - INFO - Epoch [47][200/1281] lr: 1.960e-02, eta: 8:15:36, time: 0.228, data_time: 0.001, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9962, loss_cls: 0.5106, loss: 0.5106 +2025-06-24 14:42:31,273 - pyskl - INFO - Epoch [47][300/1281] lr: 1.958e-02, eta: 8:15:14, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9956, loss_cls: 0.5954, loss: 0.5954 +2025-06-24 14:42:53,501 - pyskl - INFO - Epoch [47][400/1281] lr: 1.957e-02, eta: 8:14:52, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9969, loss_cls: 0.4644, loss: 0.4644 +2025-06-24 14:43:16,456 - pyskl - INFO - Epoch [47][500/1281] lr: 1.955e-02, eta: 8:14:30, time: 0.230, data_time: 0.001, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9956, loss_cls: 0.5542, loss: 0.5542 +2025-06-24 14:43:38,739 - pyskl - INFO - Epoch [47][600/1281] lr: 1.953e-02, eta: 8:14:08, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9950, loss_cls: 0.5724, loss: 0.5724 +2025-06-24 14:44:01,081 - pyskl - INFO - Epoch [47][700/1281] lr: 1.952e-02, eta: 8:13:46, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9962, loss_cls: 0.5504, loss: 0.5504 +2025-06-24 14:44:23,403 - pyskl - INFO - Epoch [47][800/1281] lr: 1.950e-02, eta: 8:13:23, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9962, loss_cls: 0.5561, loss: 0.5561 +2025-06-24 14:44:46,056 - pyskl - INFO - Epoch [47][900/1281] lr: 1.948e-02, eta: 8:13:01, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9962, loss_cls: 0.5274, loss: 0.5274 +2025-06-24 14:45:08,154 - pyskl - INFO - Epoch [47][1000/1281] lr: 1.947e-02, eta: 8:12:39, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9969, loss_cls: 0.5441, loss: 0.5441 +2025-06-24 14:45:30,366 - pyskl - INFO - Epoch [47][1100/1281] lr: 1.945e-02, eta: 8:12:16, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9962, loss_cls: 0.5785, loss: 0.5785 +2025-06-24 14:45:52,913 - pyskl - INFO - Epoch [47][1200/1281] lr: 1.943e-02, eta: 8:11:54, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9975, loss_cls: 0.6418, loss: 0.6418 +2025-06-24 14:46:11,712 - pyskl - INFO - Saving checkpoint at 47 epochs +2025-06-24 14:46:55,393 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:46:55,450 - pyskl - INFO - +top1_acc 0.8474 +top5_acc 0.9900 +2025-06-24 14:46:55,450 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:46:55,457 - pyskl - INFO - +mean_acc 0.8101 +2025-06-24 14:46:55,459 - pyskl - INFO - Epoch(val) [47][533] top1_acc: 0.8474, top5_acc: 0.9900, mean_class_accuracy: 0.8101 +2025-06-24 14:47:37,867 - pyskl - INFO - Epoch [48][100/1281] lr: 1.940e-02, eta: 8:11:18, time: 0.424, data_time: 0.187, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9975, loss_cls: 0.5731, loss: 0.5731 +2025-06-24 14:48:00,336 - pyskl - INFO - Epoch [48][200/1281] lr: 1.938e-02, eta: 8:10:56, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9975, loss_cls: 0.5413, loss: 0.5413 +2025-06-24 14:48:22,637 - pyskl - INFO - Epoch [48][300/1281] lr: 1.937e-02, eta: 8:10:33, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9981, loss_cls: 0.5360, loss: 0.5360 +2025-06-24 14:48:44,902 - pyskl - INFO - Epoch [48][400/1281] lr: 1.935e-02, eta: 8:10:11, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9950, loss_cls: 0.5827, loss: 0.5827 +2025-06-24 14:49:07,263 - pyskl - INFO - Epoch [48][500/1281] lr: 1.933e-02, eta: 8:09:48, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9944, loss_cls: 0.5598, loss: 0.5598 +2025-06-24 14:49:29,488 - pyskl - INFO - Epoch [48][600/1281] lr: 1.932e-02, eta: 8:09:26, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9981, loss_cls: 0.5781, loss: 0.5781 +2025-06-24 14:49:51,459 - pyskl - INFO - Epoch [48][700/1281] lr: 1.930e-02, eta: 8:09:02, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9950, loss_cls: 0.6338, loss: 0.6338 +2025-06-24 14:50:13,716 - pyskl - INFO - Epoch [48][800/1281] lr: 1.928e-02, eta: 8:08:40, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9975, loss_cls: 0.4969, loss: 0.4969 +2025-06-24 14:50:36,142 - pyskl - INFO - Epoch [48][900/1281] lr: 1.926e-02, eta: 8:08:18, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9962, loss_cls: 0.5830, loss: 0.5830 +2025-06-24 14:50:58,403 - pyskl - INFO - Epoch [48][1000/1281] lr: 1.925e-02, eta: 8:07:55, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9962, loss_cls: 0.5167, loss: 0.5167 +2025-06-24 14:51:20,808 - pyskl - INFO - Epoch [48][1100/1281] lr: 1.923e-02, eta: 8:07:33, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9962, loss_cls: 0.5093, loss: 0.5093 +2025-06-24 14:51:43,392 - pyskl - INFO - Epoch [48][1200/1281] lr: 1.921e-02, eta: 8:07:11, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9956, loss_cls: 0.5503, loss: 0.5503 +2025-06-24 14:52:02,287 - pyskl - INFO - Saving checkpoint at 48 epochs +2025-06-24 14:52:46,242 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:52:46,297 - pyskl - INFO - +top1_acc 0.8161 +top5_acc 0.9867 +2025-06-24 14:52:46,297 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:52:46,304 - pyskl - INFO - +mean_acc 0.7379 +2025-06-24 14:52:46,305 - pyskl - INFO - Epoch(val) [48][533] top1_acc: 0.8161, top5_acc: 0.9867, mean_class_accuracy: 0.7379 +2025-06-24 14:53:28,845 - pyskl - INFO - Epoch [49][100/1281] lr: 1.918e-02, eta: 8:06:35, time: 0.425, data_time: 0.191, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9950, loss_cls: 0.4887, loss: 0.4887 +2025-06-24 14:53:51,224 - pyskl - INFO - Epoch [49][200/1281] lr: 1.916e-02, eta: 8:06:12, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9988, loss_cls: 0.4752, loss: 0.4752 +2025-06-24 14:54:13,906 - pyskl - INFO - Epoch [49][300/1281] lr: 1.915e-02, eta: 8:05:51, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9950, loss_cls: 0.5230, loss: 0.5230 +2025-06-24 14:54:36,051 - pyskl - INFO - Epoch [49][400/1281] lr: 1.913e-02, eta: 8:05:28, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9938, loss_cls: 0.6009, loss: 0.6009 +2025-06-24 14:54:58,411 - pyskl - INFO - Epoch [49][500/1281] lr: 1.911e-02, eta: 8:05:06, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9969, loss_cls: 0.5823, loss: 0.5823 +2025-06-24 14:55:20,854 - pyskl - INFO - Epoch [49][600/1281] lr: 1.909e-02, eta: 8:04:43, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9956, loss_cls: 0.5070, loss: 0.5070 +2025-06-24 14:55:43,369 - pyskl - INFO - Epoch [49][700/1281] lr: 1.908e-02, eta: 8:04:21, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9962, loss_cls: 0.5851, loss: 0.5851 +2025-06-24 14:56:05,574 - pyskl - INFO - Epoch [49][800/1281] lr: 1.906e-02, eta: 8:03:59, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9938, loss_cls: 0.5374, loss: 0.5374 +2025-06-24 14:56:27,894 - pyskl - INFO - Epoch [49][900/1281] lr: 1.904e-02, eta: 8:03:36, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9962, loss_cls: 0.5610, loss: 0.5610 +2025-06-24 14:56:49,968 - pyskl - INFO - Epoch [49][1000/1281] lr: 1.902e-02, eta: 8:03:13, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9962, loss_cls: 0.5706, loss: 0.5706 +2025-06-24 14:57:12,318 - pyskl - INFO - Epoch [49][1100/1281] lr: 1.901e-02, eta: 8:02:51, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9962, loss_cls: 0.5660, loss: 0.5660 +2025-06-24 14:57:34,686 - pyskl - INFO - Epoch [49][1200/1281] lr: 1.899e-02, eta: 8:02:28, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9962, loss_cls: 0.5772, loss: 0.5772 +2025-06-24 14:57:53,510 - pyskl - INFO - Saving checkpoint at 49 epochs +2025-06-24 14:58:37,503 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:58:37,569 - pyskl - INFO - +top1_acc 0.8693 +top5_acc 0.9916 +2025-06-24 14:58:37,570 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:58:37,577 - pyskl - INFO - +mean_acc 0.8096 +2025-06-24 14:58:37,582 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_39.pth was removed +2025-06-24 14:58:37,769 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_49.pth. +2025-06-24 14:58:37,770 - pyskl - INFO - Best top1_acc is 0.8693 at 49 epoch. +2025-06-24 14:58:37,772 - pyskl - INFO - Epoch(val) [49][533] top1_acc: 0.8693, top5_acc: 0.9916, mean_class_accuracy: 0.8096 +2025-06-24 14:59:20,090 - pyskl - INFO - Epoch [50][100/1281] lr: 1.896e-02, eta: 8:01:52, time: 0.423, data_time: 0.187, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9988, loss_cls: 0.4953, loss: 0.4953 +2025-06-24 14:59:42,407 - pyskl - INFO - Epoch [50][200/1281] lr: 1.894e-02, eta: 8:01:29, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9975, loss_cls: 0.5103, loss: 0.5103 +2025-06-24 15:00:04,622 - pyskl - INFO - Epoch [50][300/1281] lr: 1.892e-02, eta: 8:01:07, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9950, loss_cls: 0.5467, loss: 0.5467 +2025-06-24 15:00:26,758 - pyskl - INFO - Epoch [50][400/1281] lr: 1.891e-02, eta: 8:00:44, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9975, loss_cls: 0.5358, loss: 0.5358 +2025-06-24 15:00:49,050 - pyskl - INFO - Epoch [50][500/1281] lr: 1.889e-02, eta: 8:00:21, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9975, loss_cls: 0.4994, loss: 0.4994 +2025-06-24 15:01:11,716 - pyskl - INFO - Epoch [50][600/1281] lr: 1.887e-02, eta: 8:00:00, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9925, loss_cls: 0.5429, loss: 0.5429 +2025-06-24 15:01:33,755 - pyskl - INFO - Epoch [50][700/1281] lr: 1.885e-02, eta: 7:59:37, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9962, loss_cls: 0.5888, loss: 0.5888 +2025-06-24 15:01:55,941 - pyskl - INFO - Epoch [50][800/1281] lr: 1.884e-02, eta: 7:59:14, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9962, loss_cls: 0.6081, loss: 0.6081 +2025-06-24 15:02:18,360 - pyskl - INFO - Epoch [50][900/1281] lr: 1.882e-02, eta: 7:58:52, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 1.0000, loss_cls: 0.5081, loss: 0.5081 +2025-06-24 15:02:40,636 - pyskl - INFO - Epoch [50][1000/1281] lr: 1.880e-02, eta: 7:58:29, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9969, loss_cls: 0.6095, loss: 0.6095 +2025-06-24 15:03:03,062 - pyskl - INFO - Epoch [50][1100/1281] lr: 1.878e-02, eta: 7:58:07, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9956, loss_cls: 0.5890, loss: 0.5890 +2025-06-24 15:03:25,683 - pyskl - INFO - Epoch [50][1200/1281] lr: 1.876e-02, eta: 7:57:45, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9969, loss_cls: 0.5268, loss: 0.5268 +2025-06-24 15:03:44,290 - pyskl - INFO - Saving checkpoint at 50 epochs +2025-06-24 15:04:28,688 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:04:28,751 - pyskl - INFO - +top1_acc 0.8630 +top5_acc 0.9910 +2025-06-24 15:04:28,751 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:04:28,759 - pyskl - INFO - +mean_acc 0.8071 +2025-06-24 15:04:28,761 - pyskl - INFO - Epoch(val) [50][533] top1_acc: 0.8630, top5_acc: 0.9910, mean_class_accuracy: 0.8071 +2025-06-24 15:05:11,276 - pyskl - INFO - Epoch [51][100/1281] lr: 1.873e-02, eta: 7:57:09, time: 0.425, data_time: 0.190, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9981, loss_cls: 0.4727, loss: 0.4727 +2025-06-24 15:05:33,738 - pyskl - INFO - Epoch [51][200/1281] lr: 1.871e-02, eta: 7:56:46, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9962, loss_cls: 0.5475, loss: 0.5475 +2025-06-24 15:05:56,534 - pyskl - INFO - Epoch [51][300/1281] lr: 1.870e-02, eta: 7:56:25, time: 0.228, data_time: 0.001, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.5307, loss: 0.5307 +2025-06-24 15:06:18,782 - pyskl - INFO - Epoch [51][400/1281] lr: 1.868e-02, eta: 7:56:02, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9975, loss_cls: 0.5764, loss: 0.5764 +2025-06-24 15:06:41,220 - pyskl - INFO - Epoch [51][500/1281] lr: 1.866e-02, eta: 7:55:40, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9969, loss_cls: 0.5163, loss: 0.5163 +2025-06-24 15:07:03,819 - pyskl - INFO - Epoch [51][600/1281] lr: 1.864e-02, eta: 7:55:18, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9944, loss_cls: 0.5361, loss: 0.5361 +2025-06-24 15:07:26,112 - pyskl - INFO - Epoch [51][700/1281] lr: 1.863e-02, eta: 7:54:56, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9950, loss_cls: 0.5482, loss: 0.5482 +2025-06-24 15:07:48,576 - pyskl - INFO - Epoch [51][800/1281] lr: 1.861e-02, eta: 7:54:33, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9950, loss_cls: 0.4855, loss: 0.4855 +2025-06-24 15:08:10,696 - pyskl - INFO - Epoch [51][900/1281] lr: 1.859e-02, eta: 7:54:11, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9950, loss_cls: 0.5178, loss: 0.5178 +2025-06-24 15:08:33,074 - pyskl - INFO - Epoch [51][1000/1281] lr: 1.857e-02, eta: 7:53:48, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9988, loss_cls: 0.4941, loss: 0.4941 +2025-06-24 15:08:55,389 - pyskl - INFO - Epoch [51][1100/1281] lr: 1.855e-02, eta: 7:53:26, time: 0.223, data_time: 0.001, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9969, loss_cls: 0.5584, loss: 0.5584 +2025-06-24 15:09:17,931 - pyskl - INFO - Epoch [51][1200/1281] lr: 1.854e-02, eta: 7:53:04, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9956, loss_cls: 0.5447, loss: 0.5447 +2025-06-24 15:09:37,177 - pyskl - INFO - Saving checkpoint at 51 epochs +2025-06-24 15:10:20,592 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:10:20,647 - pyskl - INFO - +top1_acc 0.8524 +top5_acc 0.9925 +2025-06-24 15:10:20,647 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:10:20,654 - pyskl - INFO - +mean_acc 0.7978 +2025-06-24 15:10:20,656 - pyskl - INFO - Epoch(val) [51][533] top1_acc: 0.8524, top5_acc: 0.9925, mean_class_accuracy: 0.7978 +2025-06-24 15:11:03,047 - pyskl - INFO - Epoch [52][100/1281] lr: 1.850e-02, eta: 7:52:27, time: 0.424, data_time: 0.192, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9962, loss_cls: 0.5237, loss: 0.5237 +2025-06-24 15:11:25,411 - pyskl - INFO - Epoch [52][200/1281] lr: 1.849e-02, eta: 7:52:04, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9950, loss_cls: 0.5193, loss: 0.5193 +2025-06-24 15:11:47,821 - pyskl - INFO - Epoch [52][300/1281] lr: 1.847e-02, eta: 7:51:42, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9975, loss_cls: 0.5299, loss: 0.5299 +2025-06-24 15:12:10,259 - pyskl - INFO - Epoch [52][400/1281] lr: 1.845e-02, eta: 7:51:20, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9975, loss_cls: 0.4469, loss: 0.4469 +2025-06-24 15:12:32,630 - pyskl - INFO - Epoch [52][500/1281] lr: 1.843e-02, eta: 7:50:58, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9956, loss_cls: 0.5485, loss: 0.5485 +2025-06-24 15:12:54,793 - pyskl - INFO - Epoch [52][600/1281] lr: 1.841e-02, eta: 7:50:35, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9969, loss_cls: 0.4839, loss: 0.4839 +2025-06-24 15:13:17,086 - pyskl - INFO - Epoch [52][700/1281] lr: 1.840e-02, eta: 7:50:12, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9950, loss_cls: 0.5240, loss: 0.5240 +2025-06-24 15:13:39,569 - pyskl - INFO - Epoch [52][800/1281] lr: 1.838e-02, eta: 7:49:50, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9962, loss_cls: 0.5560, loss: 0.5560 +2025-06-24 15:14:01,996 - pyskl - INFO - Epoch [52][900/1281] lr: 1.836e-02, eta: 7:49:28, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9969, loss_cls: 0.5070, loss: 0.5070 +2025-06-24 15:14:24,237 - pyskl - INFO - Epoch [52][1000/1281] lr: 1.834e-02, eta: 7:49:05, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9981, loss_cls: 0.5306, loss: 0.5306 +2025-06-24 15:14:46,373 - pyskl - INFO - Epoch [52][1100/1281] lr: 1.832e-02, eta: 7:48:42, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9944, loss_cls: 0.5889, loss: 0.5889 +2025-06-24 15:15:08,758 - pyskl - INFO - Epoch [52][1200/1281] lr: 1.831e-02, eta: 7:48:20, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9962, loss_cls: 0.5406, loss: 0.5406 +2025-06-24 15:15:28,084 - pyskl - INFO - Saving checkpoint at 52 epochs +2025-06-24 15:16:11,859 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:16:11,922 - pyskl - INFO - +top1_acc 0.8737 +top5_acc 0.9896 +2025-06-24 15:16:11,922 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:16:11,929 - pyskl - INFO - +mean_acc 0.8362 +2025-06-24 15:16:11,933 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_49.pth was removed +2025-06-24 15:16:12,113 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_52.pth. +2025-06-24 15:16:12,113 - pyskl - INFO - Best top1_acc is 0.8737 at 52 epoch. +2025-06-24 15:16:12,116 - pyskl - INFO - Epoch(val) [52][533] top1_acc: 0.8737, top5_acc: 0.9896, mean_class_accuracy: 0.8362 +2025-06-24 15:16:54,880 - pyskl - INFO - Epoch [53][100/1281] lr: 1.827e-02, eta: 7:47:44, time: 0.428, data_time: 0.191, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9969, loss_cls: 0.5357, loss: 0.5357 +2025-06-24 15:17:17,585 - pyskl - INFO - Epoch [53][200/1281] lr: 1.826e-02, eta: 7:47:22, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9994, loss_cls: 0.4927, loss: 0.4927 +2025-06-24 15:17:39,836 - pyskl - INFO - Epoch [53][300/1281] lr: 1.824e-02, eta: 7:47:00, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9950, loss_cls: 0.4795, loss: 0.4795 +2025-06-24 15:18:02,211 - pyskl - INFO - Epoch [53][400/1281] lr: 1.822e-02, eta: 7:46:37, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9994, loss_cls: 0.4704, loss: 0.4704 +2025-06-24 15:18:24,791 - pyskl - INFO - Epoch [53][500/1281] lr: 1.820e-02, eta: 7:46:15, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9981, loss_cls: 0.5175, loss: 0.5175 +2025-06-24 15:18:46,984 - pyskl - INFO - Epoch [53][600/1281] lr: 1.818e-02, eta: 7:45:52, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9981, loss_cls: 0.5343, loss: 0.5343 +2025-06-24 15:19:09,537 - pyskl - INFO - Epoch [53][700/1281] lr: 1.816e-02, eta: 7:45:30, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9975, loss_cls: 0.5276, loss: 0.5276 +2025-06-24 15:19:32,221 - pyskl - INFO - Epoch [53][800/1281] lr: 1.815e-02, eta: 7:45:09, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9975, loss_cls: 0.5573, loss: 0.5573 +2025-06-24 15:19:54,727 - pyskl - INFO - Epoch [53][900/1281] lr: 1.813e-02, eta: 7:44:46, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9994, loss_cls: 0.4982, loss: 0.4982 +2025-06-24 15:20:17,172 - pyskl - INFO - Epoch [53][1000/1281] lr: 1.811e-02, eta: 7:44:24, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9944, loss_cls: 0.5895, loss: 0.5895 +2025-06-24 15:20:39,630 - pyskl - INFO - Epoch [53][1100/1281] lr: 1.809e-02, eta: 7:44:02, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9981, loss_cls: 0.5073, loss: 0.5073 +2025-06-24 15:21:02,409 - pyskl - INFO - Epoch [53][1200/1281] lr: 1.807e-02, eta: 7:43:40, time: 0.228, data_time: 0.001, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9944, loss_cls: 0.5606, loss: 0.5606 +2025-06-24 15:21:21,137 - pyskl - INFO - Saving checkpoint at 53 epochs +2025-06-24 15:22:04,651 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:22:04,706 - pyskl - INFO - +top1_acc 0.8358 +top5_acc 0.9842 +2025-06-24 15:22:04,707 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:22:04,713 - pyskl - INFO - +mean_acc 0.7899 +2025-06-24 15:22:04,715 - pyskl - INFO - Epoch(val) [53][533] top1_acc: 0.8358, top5_acc: 0.9842, mean_class_accuracy: 0.7899 +2025-06-24 15:22:47,846 - pyskl - INFO - Epoch [54][100/1281] lr: 1.804e-02, eta: 7:43:05, time: 0.431, data_time: 0.193, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9975, loss_cls: 0.4968, loss: 0.4968 +2025-06-24 15:23:10,169 - pyskl - INFO - Epoch [54][200/1281] lr: 1.802e-02, eta: 7:42:42, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.5271, loss: 0.5271 +2025-06-24 15:23:32,520 - pyskl - INFO - Epoch [54][300/1281] lr: 1.800e-02, eta: 7:42:20, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9962, loss_cls: 0.5935, loss: 0.5935 +2025-06-24 15:23:54,991 - pyskl - INFO - Epoch [54][400/1281] lr: 1.798e-02, eta: 7:41:58, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9956, loss_cls: 0.5190, loss: 0.5190 +2025-06-24 15:24:17,429 - pyskl - INFO - Epoch [54][500/1281] lr: 1.797e-02, eta: 7:41:35, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9975, loss_cls: 0.4497, loss: 0.4497 +2025-06-24 15:24:39,640 - pyskl - INFO - Epoch [54][600/1281] lr: 1.795e-02, eta: 7:41:13, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9969, loss_cls: 0.5256, loss: 0.5256 +2025-06-24 15:25:02,005 - pyskl - INFO - Epoch [54][700/1281] lr: 1.793e-02, eta: 7:40:50, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9988, loss_cls: 0.4699, loss: 0.4699 +2025-06-24 15:25:24,832 - pyskl - INFO - Epoch [54][800/1281] lr: 1.791e-02, eta: 7:40:29, time: 0.228, data_time: 0.001, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9944, loss_cls: 0.4906, loss: 0.4906 +2025-06-24 15:25:47,580 - pyskl - INFO - Epoch [54][900/1281] lr: 1.789e-02, eta: 7:40:07, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9962, loss_cls: 0.5395, loss: 0.5395 +2025-06-24 15:26:10,258 - pyskl - INFO - Epoch [54][1000/1281] lr: 1.787e-02, eta: 7:39:45, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.5081, loss: 0.5081 +2025-06-24 15:26:32,882 - pyskl - INFO - Epoch [54][1100/1281] lr: 1.786e-02, eta: 7:39:23, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9962, loss_cls: 0.4910, loss: 0.4910 +2025-06-24 15:26:55,631 - pyskl - INFO - Epoch [54][1200/1281] lr: 1.784e-02, eta: 7:39:01, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9975, loss_cls: 0.4834, loss: 0.4834 +2025-06-24 15:27:14,569 - pyskl - INFO - Saving checkpoint at 54 epochs +2025-06-24 15:27:59,297 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:27:59,365 - pyskl - INFO - +top1_acc 0.8729 +top5_acc 0.9910 +2025-06-24 15:27:59,365 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:27:59,374 - pyskl - INFO - +mean_acc 0.8289 +2025-06-24 15:27:59,376 - pyskl - INFO - Epoch(val) [54][533] top1_acc: 0.8729, top5_acc: 0.9910, mean_class_accuracy: 0.8289 +2025-06-24 15:28:43,009 - pyskl - INFO - Epoch [55][100/1281] lr: 1.780e-02, eta: 7:38:26, time: 0.436, data_time: 0.197, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9975, loss_cls: 0.4942, loss: 0.4942 +2025-06-24 15:29:05,212 - pyskl - INFO - Epoch [55][200/1281] lr: 1.779e-02, eta: 7:38:04, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9975, loss_cls: 0.4683, loss: 0.4683 +2025-06-24 15:29:27,739 - pyskl - INFO - Epoch [55][300/1281] lr: 1.777e-02, eta: 7:37:42, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9969, loss_cls: 0.4950, loss: 0.4950 +2025-06-24 15:29:50,069 - pyskl - INFO - Epoch [55][400/1281] lr: 1.775e-02, eta: 7:37:19, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9956, loss_cls: 0.4812, loss: 0.4812 +2025-06-24 15:30:12,549 - pyskl - INFO - Epoch [55][500/1281] lr: 1.773e-02, eta: 7:36:57, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9956, loss_cls: 0.5756, loss: 0.5756 +2025-06-24 15:30:35,007 - pyskl - INFO - Epoch [55][600/1281] lr: 1.771e-02, eta: 7:36:35, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9975, loss_cls: 0.5534, loss: 0.5534 +2025-06-24 15:30:57,576 - pyskl - INFO - Epoch [55][700/1281] lr: 1.769e-02, eta: 7:36:12, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9969, loss_cls: 0.4956, loss: 0.4956 +2025-06-24 15:31:20,229 - pyskl - INFO - Epoch [55][800/1281] lr: 1.767e-02, eta: 7:35:51, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9994, loss_cls: 0.4856, loss: 0.4856 +2025-06-24 15:31:42,810 - pyskl - INFO - Epoch [55][900/1281] lr: 1.766e-02, eta: 7:35:29, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9944, loss_cls: 0.5144, loss: 0.5144 +2025-06-24 15:32:05,330 - pyskl - INFO - Epoch [55][1000/1281] lr: 1.764e-02, eta: 7:35:06, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9975, loss_cls: 0.4812, loss: 0.4812 +2025-06-24 15:32:28,024 - pyskl - INFO - Epoch [55][1100/1281] lr: 1.762e-02, eta: 7:34:44, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9969, loss_cls: 0.5348, loss: 0.5348 +2025-06-24 15:32:50,190 - pyskl - INFO - Epoch [55][1200/1281] lr: 1.760e-02, eta: 7:34:22, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9950, loss_cls: 0.5632, loss: 0.5632 +2025-06-24 15:33:09,255 - pyskl - INFO - Saving checkpoint at 55 epochs +2025-06-24 15:33:53,377 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:33:53,432 - pyskl - INFO - +top1_acc 0.8710 +top5_acc 0.9923 +2025-06-24 15:33:53,432 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:33:53,439 - pyskl - INFO - +mean_acc 0.8254 +2025-06-24 15:33:53,441 - pyskl - INFO - Epoch(val) [55][533] top1_acc: 0.8710, top5_acc: 0.9923, mean_class_accuracy: 0.8254 +2025-06-24 15:34:36,033 - pyskl - INFO - Epoch [56][100/1281] lr: 1.757e-02, eta: 7:33:45, time: 0.426, data_time: 0.190, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9969, loss_cls: 0.5207, loss: 0.5207 +2025-06-24 15:34:57,990 - pyskl - INFO - Epoch [56][200/1281] lr: 1.755e-02, eta: 7:33:22, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9994, loss_cls: 0.4379, loss: 0.4379 +2025-06-24 15:35:20,406 - pyskl - INFO - Epoch [56][300/1281] lr: 1.753e-02, eta: 7:32:59, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9981, loss_cls: 0.4660, loss: 0.4660 +2025-06-24 15:35:42,740 - pyskl - INFO - Epoch [56][400/1281] lr: 1.751e-02, eta: 7:32:37, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9956, loss_cls: 0.5790, loss: 0.5790 +2025-06-24 15:36:05,331 - pyskl - INFO - Epoch [56][500/1281] lr: 1.749e-02, eta: 7:32:15, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9962, loss_cls: 0.5137, loss: 0.5137 +2025-06-24 15:36:27,687 - pyskl - INFO - Epoch [56][600/1281] lr: 1.747e-02, eta: 7:31:52, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9956, loss_cls: 0.4468, loss: 0.4468 +2025-06-24 15:36:50,477 - pyskl - INFO - Epoch [56][700/1281] lr: 1.745e-02, eta: 7:31:31, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9988, loss_cls: 0.4963, loss: 0.4963 +2025-06-24 15:37:12,986 - pyskl - INFO - Epoch [56][800/1281] lr: 1.743e-02, eta: 7:31:08, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9969, loss_cls: 0.5148, loss: 0.5148 +2025-06-24 15:37:35,318 - pyskl - INFO - Epoch [56][900/1281] lr: 1.742e-02, eta: 7:30:46, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9975, loss_cls: 0.5123, loss: 0.5123 +2025-06-24 15:37:57,821 - pyskl - INFO - Epoch [56][1000/1281] lr: 1.740e-02, eta: 7:30:24, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9969, loss_cls: 0.4996, loss: 0.4996 +2025-06-24 15:38:20,187 - pyskl - INFO - Epoch [56][1100/1281] lr: 1.738e-02, eta: 7:30:01, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9975, loss_cls: 0.4768, loss: 0.4768 +2025-06-24 15:38:42,583 - pyskl - INFO - Epoch [56][1200/1281] lr: 1.736e-02, eta: 7:29:39, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9944, loss_cls: 0.5558, loss: 0.5558 +2025-06-24 15:39:01,390 - pyskl - INFO - Saving checkpoint at 56 epochs +2025-06-24 15:39:45,443 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:39:45,513 - pyskl - INFO - +top1_acc 0.8032 +top5_acc 0.9783 +2025-06-24 15:39:45,513 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:39:45,521 - pyskl - INFO - +mean_acc 0.7416 +2025-06-24 15:39:45,523 - pyskl - INFO - Epoch(val) [56][533] top1_acc: 0.8032, top5_acc: 0.9783, mean_class_accuracy: 0.7416 +2025-06-24 15:40:28,517 - pyskl - INFO - Epoch [57][100/1281] lr: 1.733e-02, eta: 7:29:02, time: 0.430, data_time: 0.196, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9956, loss_cls: 0.5154, loss: 0.5154 +2025-06-24 15:40:51,132 - pyskl - INFO - Epoch [57][200/1281] lr: 1.731e-02, eta: 7:28:40, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9988, loss_cls: 0.4715, loss: 0.4715 +2025-06-24 15:41:13,700 - pyskl - INFO - Epoch [57][300/1281] lr: 1.729e-02, eta: 7:28:18, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9956, loss_cls: 0.4938, loss: 0.4938 +2025-06-24 15:41:35,947 - pyskl - INFO - Epoch [57][400/1281] lr: 1.727e-02, eta: 7:27:56, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9969, loss_cls: 0.5466, loss: 0.5466 +2025-06-24 15:41:58,385 - pyskl - INFO - Epoch [57][500/1281] lr: 1.725e-02, eta: 7:27:33, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9994, loss_cls: 0.4865, loss: 0.4865 +2025-06-24 15:42:20,800 - pyskl - INFO - Epoch [57][600/1281] lr: 1.723e-02, eta: 7:27:11, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9975, loss_cls: 0.4628, loss: 0.4628 +2025-06-24 15:42:43,282 - pyskl - INFO - Epoch [57][700/1281] lr: 1.721e-02, eta: 7:26:49, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9944, loss_cls: 0.5383, loss: 0.5383 +2025-06-24 15:43:05,618 - pyskl - INFO - Epoch [57][800/1281] lr: 1.719e-02, eta: 7:26:26, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9981, loss_cls: 0.4982, loss: 0.4982 +2025-06-24 15:43:27,946 - pyskl - INFO - Epoch [57][900/1281] lr: 1.717e-02, eta: 7:26:04, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9950, loss_cls: 0.5086, loss: 0.5086 +2025-06-24 15:43:50,427 - pyskl - INFO - Epoch [57][1000/1281] lr: 1.716e-02, eta: 7:25:42, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9969, loss_cls: 0.5666, loss: 0.5666 +2025-06-24 15:44:12,791 - pyskl - INFO - Epoch [57][1100/1281] lr: 1.714e-02, eta: 7:25:19, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9956, loss_cls: 0.5271, loss: 0.5271 +2025-06-24 15:44:35,507 - pyskl - INFO - Epoch [57][1200/1281] lr: 1.712e-02, eta: 7:24:57, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9962, loss_cls: 0.5155, loss: 0.5155 +2025-06-24 15:44:54,451 - pyskl - INFO - Saving checkpoint at 57 epochs +2025-06-24 15:45:38,171 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:45:38,247 - pyskl - INFO - +top1_acc 0.8551 +top5_acc 0.9887 +2025-06-24 15:45:38,247 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:45:38,254 - pyskl - INFO - +mean_acc 0.7997 +2025-06-24 15:45:38,256 - pyskl - INFO - Epoch(val) [57][533] top1_acc: 0.8551, top5_acc: 0.9887, mean_class_accuracy: 0.7997 +2025-06-24 15:46:20,479 - pyskl - INFO - Epoch [58][100/1281] lr: 1.708e-02, eta: 7:24:19, time: 0.422, data_time: 0.189, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9981, loss_cls: 0.4412, loss: 0.4412 +2025-06-24 15:46:43,063 - pyskl - INFO - Epoch [58][200/1281] lr: 1.706e-02, eta: 7:23:57, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9988, loss_cls: 0.4558, loss: 0.4558 +2025-06-24 15:47:05,388 - pyskl - INFO - Epoch [58][300/1281] lr: 1.704e-02, eta: 7:23:35, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9956, loss_cls: 0.4804, loss: 0.4804 +2025-06-24 15:47:27,547 - pyskl - INFO - Epoch [58][400/1281] lr: 1.703e-02, eta: 7:23:12, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9956, loss_cls: 0.5330, loss: 0.5330 +2025-06-24 15:47:49,923 - pyskl - INFO - Epoch [58][500/1281] lr: 1.701e-02, eta: 7:22:50, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9981, loss_cls: 0.4827, loss: 0.4827 +2025-06-24 15:48:12,748 - pyskl - INFO - Epoch [58][600/1281] lr: 1.699e-02, eta: 7:22:28, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9981, loss_cls: 0.3746, loss: 0.3746 +2025-06-24 15:48:35,141 - pyskl - INFO - Epoch [58][700/1281] lr: 1.697e-02, eta: 7:22:05, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9981, loss_cls: 0.5154, loss: 0.5154 +2025-06-24 15:48:57,051 - pyskl - INFO - Epoch [58][800/1281] lr: 1.695e-02, eta: 7:21:42, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9981, loss_cls: 0.4625, loss: 0.4625 +2025-06-24 15:49:19,449 - pyskl - INFO - Epoch [58][900/1281] lr: 1.693e-02, eta: 7:21:20, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9975, loss_cls: 0.5676, loss: 0.5676 +2025-06-24 15:49:41,972 - pyskl - INFO - Epoch [58][1000/1281] lr: 1.691e-02, eta: 7:20:58, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9988, loss_cls: 0.4582, loss: 0.4582 +2025-06-24 15:50:04,270 - pyskl - INFO - Epoch [58][1100/1281] lr: 1.689e-02, eta: 7:20:35, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9988, loss_cls: 0.4983, loss: 0.4983 +2025-06-24 15:50:26,857 - pyskl - INFO - Epoch [58][1200/1281] lr: 1.687e-02, eta: 7:20:13, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9962, loss_cls: 0.4809, loss: 0.4809 +2025-06-24 15:50:45,670 - pyskl - INFO - Saving checkpoint at 58 epochs +2025-06-24 15:51:29,351 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:51:29,409 - pyskl - INFO - +top1_acc 0.8607 +top5_acc 0.9908 +2025-06-24 15:51:29,409 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:51:29,416 - pyskl - INFO - +mean_acc 0.8199 +2025-06-24 15:51:29,417 - pyskl - INFO - Epoch(val) [58][533] top1_acc: 0.8607, top5_acc: 0.9908, mean_class_accuracy: 0.8199 +2025-06-24 15:52:11,797 - pyskl - INFO - Epoch [59][100/1281] lr: 1.684e-02, eta: 7:19:35, time: 0.424, data_time: 0.186, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9988, loss_cls: 0.4725, loss: 0.4725 +2025-06-24 15:52:34,448 - pyskl - INFO - Epoch [59][200/1281] lr: 1.682e-02, eta: 7:19:13, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9962, loss_cls: 0.4440, loss: 0.4440 +2025-06-24 15:52:56,840 - pyskl - INFO - Epoch [59][300/1281] lr: 1.680e-02, eta: 7:18:51, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9994, loss_cls: 0.4974, loss: 0.4974 +2025-06-24 15:53:18,870 - pyskl - INFO - Epoch [59][400/1281] lr: 1.678e-02, eta: 7:18:28, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9981, loss_cls: 0.4070, loss: 0.4070 +2025-06-24 15:53:41,417 - pyskl - INFO - Epoch [59][500/1281] lr: 1.676e-02, eta: 7:18:06, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9981, loss_cls: 0.4506, loss: 0.4506 +2025-06-24 15:54:03,459 - pyskl - INFO - Epoch [59][600/1281] lr: 1.674e-02, eta: 7:17:43, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9988, loss_cls: 0.4732, loss: 0.4732 +2025-06-24 15:54:25,782 - pyskl - INFO - Epoch [59][700/1281] lr: 1.672e-02, eta: 7:17:20, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9962, loss_cls: 0.4657, loss: 0.4657 +2025-06-24 15:54:48,099 - pyskl - INFO - Epoch [59][800/1281] lr: 1.670e-02, eta: 7:16:58, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9931, loss_cls: 0.5683, loss: 0.5683 +2025-06-24 15:55:10,755 - pyskl - INFO - Epoch [59][900/1281] lr: 1.668e-02, eta: 7:16:36, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9969, loss_cls: 0.4916, loss: 0.4916 +2025-06-24 15:55:33,154 - pyskl - INFO - Epoch [59][1000/1281] lr: 1.667e-02, eta: 7:16:13, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9981, loss_cls: 0.5059, loss: 0.5059 +2025-06-24 15:55:55,397 - pyskl - INFO - Epoch [59][1100/1281] lr: 1.665e-02, eta: 7:15:51, time: 0.222, data_time: 0.001, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9944, loss_cls: 0.5462, loss: 0.5462 +2025-06-24 15:56:17,809 - pyskl - INFO - Epoch [59][1200/1281] lr: 1.663e-02, eta: 7:15:28, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9988, loss_cls: 0.4381, loss: 0.4381 +2025-06-24 15:56:36,568 - pyskl - INFO - Saving checkpoint at 59 epochs +2025-06-24 15:57:19,663 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:57:19,734 - pyskl - INFO - +top1_acc 0.8436 +top5_acc 0.9892 +2025-06-24 15:57:19,734 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:57:19,742 - pyskl - INFO - +mean_acc 0.7901 +2025-06-24 15:57:19,743 - pyskl - INFO - Epoch(val) [59][533] top1_acc: 0.8436, top5_acc: 0.9892, mean_class_accuracy: 0.7901 +2025-06-24 15:58:02,091 - pyskl - INFO - Epoch [60][100/1281] lr: 1.659e-02, eta: 7:14:51, time: 0.423, data_time: 0.190, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9975, loss_cls: 0.4717, loss: 0.4717 +2025-06-24 15:58:24,308 - pyskl - INFO - Epoch [60][200/1281] lr: 1.657e-02, eta: 7:14:28, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9975, loss_cls: 0.4882, loss: 0.4882 +2025-06-24 15:58:46,929 - pyskl - INFO - Epoch [60][300/1281] lr: 1.655e-02, eta: 7:14:06, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9975, loss_cls: 0.4339, loss: 0.4339 +2025-06-24 15:59:09,247 - pyskl - INFO - Epoch [60][400/1281] lr: 1.653e-02, eta: 7:13:43, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9988, loss_cls: 0.5030, loss: 0.5030 +2025-06-24 15:59:31,990 - pyskl - INFO - Epoch [60][500/1281] lr: 1.651e-02, eta: 7:13:21, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9950, loss_cls: 0.5070, loss: 0.5070 +2025-06-24 15:59:54,264 - pyskl - INFO - Epoch [60][600/1281] lr: 1.650e-02, eta: 7:12:59, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9975, loss_cls: 0.4766, loss: 0.4766 +2025-06-24 16:00:16,512 - pyskl - INFO - Epoch [60][700/1281] lr: 1.648e-02, eta: 7:12:36, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9956, loss_cls: 0.5374, loss: 0.5374 +2025-06-24 16:00:38,947 - pyskl - INFO - Epoch [60][800/1281] lr: 1.646e-02, eta: 7:12:14, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9956, loss_cls: 0.4684, loss: 0.4684 +2025-06-24 16:01:01,499 - pyskl - INFO - Epoch [60][900/1281] lr: 1.644e-02, eta: 7:11:52, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9950, loss_cls: 0.5025, loss: 0.5025 +2025-06-24 16:01:24,007 - pyskl - INFO - Epoch [60][1000/1281] lr: 1.642e-02, eta: 7:11:29, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9969, loss_cls: 0.5120, loss: 0.5120 +2025-06-24 16:01:46,573 - pyskl - INFO - Epoch [60][1100/1281] lr: 1.640e-02, eta: 7:11:07, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9938, loss_cls: 0.5311, loss: 0.5311 +2025-06-24 16:02:08,760 - pyskl - INFO - Epoch [60][1200/1281] lr: 1.638e-02, eta: 7:10:45, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4579, loss: 0.4579 +2025-06-24 16:02:27,442 - pyskl - INFO - Saving checkpoint at 60 epochs +2025-06-24 16:03:11,689 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:03:11,746 - pyskl - INFO - +top1_acc 0.8782 +top5_acc 0.9926 +2025-06-24 16:03:11,746 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:03:11,754 - pyskl - INFO - +mean_acc 0.8300 +2025-06-24 16:03:11,758 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_52.pth was removed +2025-06-24 16:03:11,924 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_60.pth. +2025-06-24 16:03:11,925 - pyskl - INFO - Best top1_acc is 0.8782 at 60 epoch. +2025-06-24 16:03:11,927 - pyskl - INFO - Epoch(val) [60][533] top1_acc: 0.8782, top5_acc: 0.9926, mean_class_accuracy: 0.8300 +2025-06-24 16:03:54,515 - pyskl - INFO - Epoch [61][100/1281] lr: 1.634e-02, eta: 7:10:07, time: 0.426, data_time: 0.191, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9988, loss_cls: 0.4517, loss: 0.4517 +2025-06-24 16:04:16,874 - pyskl - INFO - Epoch [61][200/1281] lr: 1.632e-02, eta: 7:09:45, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9994, loss_cls: 0.4257, loss: 0.4257 +2025-06-24 16:04:39,022 - pyskl - INFO - Epoch [61][300/1281] lr: 1.630e-02, eta: 7:09:22, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9981, loss_cls: 0.4318, loss: 0.4318 +2025-06-24 16:05:01,321 - pyskl - INFO - Epoch [61][400/1281] lr: 1.629e-02, eta: 7:08:59, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9956, loss_cls: 0.5027, loss: 0.5027 +2025-06-24 16:05:23,403 - pyskl - INFO - Epoch [61][500/1281] lr: 1.627e-02, eta: 7:08:36, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9950, loss_cls: 0.4252, loss: 0.4252 +2025-06-24 16:05:45,617 - pyskl - INFO - Epoch [61][600/1281] lr: 1.625e-02, eta: 7:08:14, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9969, loss_cls: 0.4031, loss: 0.4031 +2025-06-24 16:06:08,051 - pyskl - INFO - Epoch [61][700/1281] lr: 1.623e-02, eta: 7:07:51, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9950, loss_cls: 0.5040, loss: 0.5040 +2025-06-24 16:06:30,646 - pyskl - INFO - Epoch [61][800/1281] lr: 1.621e-02, eta: 7:07:29, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9962, loss_cls: 0.4638, loss: 0.4638 +2025-06-24 16:06:52,599 - pyskl - INFO - Epoch [61][900/1281] lr: 1.619e-02, eta: 7:07:06, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9969, loss_cls: 0.4910, loss: 0.4910 +2025-06-24 16:07:15,228 - pyskl - INFO - Epoch [61][1000/1281] lr: 1.617e-02, eta: 7:06:44, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9975, loss_cls: 0.5466, loss: 0.5466 +2025-06-24 16:07:37,426 - pyskl - INFO - Epoch [61][1100/1281] lr: 1.615e-02, eta: 7:06:21, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9962, loss_cls: 0.4313, loss: 0.4313 +2025-06-24 16:07:59,819 - pyskl - INFO - Epoch [61][1200/1281] lr: 1.613e-02, eta: 7:05:59, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9988, loss_cls: 0.4809, loss: 0.4809 +2025-06-24 16:08:18,937 - pyskl - INFO - Saving checkpoint at 61 epochs +2025-06-24 16:09:02,261 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:09:02,328 - pyskl - INFO - +top1_acc 0.8632 +top5_acc 0.9916 +2025-06-24 16:09:02,329 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:09:02,337 - pyskl - INFO - +mean_acc 0.8136 +2025-06-24 16:09:02,339 - pyskl - INFO - Epoch(val) [61][533] top1_acc: 0.8632, top5_acc: 0.9916, mean_class_accuracy: 0.8136 +2025-06-24 16:09:43,806 - pyskl - INFO - Epoch [62][100/1281] lr: 1.609e-02, eta: 7:05:20, time: 0.415, data_time: 0.184, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9975, loss_cls: 0.4997, loss: 0.4997 +2025-06-24 16:10:06,115 - pyskl - INFO - Epoch [62][200/1281] lr: 1.607e-02, eta: 7:04:57, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9988, loss_cls: 0.4248, loss: 0.4248 +2025-06-24 16:10:28,250 - pyskl - INFO - Epoch [62][300/1281] lr: 1.605e-02, eta: 7:04:34, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9981, loss_cls: 0.4365, loss: 0.4365 +2025-06-24 16:10:50,927 - pyskl - INFO - Epoch [62][400/1281] lr: 1.603e-02, eta: 7:04:12, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9988, loss_cls: 0.4964, loss: 0.4964 +2025-06-24 16:11:13,717 - pyskl - INFO - Epoch [62][500/1281] lr: 1.602e-02, eta: 7:03:51, time: 0.228, data_time: 0.001, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9975, loss_cls: 0.4635, loss: 0.4635 +2025-06-24 16:11:35,872 - pyskl - INFO - Epoch [62][600/1281] lr: 1.600e-02, eta: 7:03:28, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9975, loss_cls: 0.4423, loss: 0.4423 +2025-06-24 16:11:58,422 - pyskl - INFO - Epoch [62][700/1281] lr: 1.598e-02, eta: 7:03:06, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9950, loss_cls: 0.4704, loss: 0.4704 +2025-06-24 16:12:21,064 - pyskl - INFO - Epoch [62][800/1281] lr: 1.596e-02, eta: 7:02:44, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9981, loss_cls: 0.4213, loss: 0.4213 +2025-06-24 16:12:43,724 - pyskl - INFO - Epoch [62][900/1281] lr: 1.594e-02, eta: 7:02:21, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9956, loss_cls: 0.4812, loss: 0.4812 +2025-06-24 16:13:06,085 - pyskl - INFO - Epoch [62][1000/1281] lr: 1.592e-02, eta: 7:01:59, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9962, loss_cls: 0.4947, loss: 0.4947 +2025-06-24 16:13:28,086 - pyskl - INFO - Epoch [62][1100/1281] lr: 1.590e-02, eta: 7:01:36, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9975, loss_cls: 0.4729, loss: 0.4729 +2025-06-24 16:13:50,192 - pyskl - INFO - Epoch [62][1200/1281] lr: 1.588e-02, eta: 7:01:13, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9969, loss_cls: 0.4637, loss: 0.4637 +2025-06-24 16:14:09,237 - pyskl - INFO - Saving checkpoint at 62 epochs +2025-06-24 16:14:52,970 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:14:53,025 - pyskl - INFO - +top1_acc 0.8811 +top5_acc 0.9942 +2025-06-24 16:14:53,025 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:14:53,033 - pyskl - INFO - +mean_acc 0.8442 +2025-06-24 16:14:53,040 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_60.pth was removed +2025-06-24 16:14:53,213 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_62.pth. +2025-06-24 16:14:53,214 - pyskl - INFO - Best top1_acc is 0.8811 at 62 epoch. +2025-06-24 16:14:53,216 - pyskl - INFO - Epoch(val) [62][533] top1_acc: 0.8811, top5_acc: 0.9942, mean_class_accuracy: 0.8442 +2025-06-24 16:15:35,854 - pyskl - INFO - Epoch [63][100/1281] lr: 1.584e-02, eta: 7:00:36, time: 0.426, data_time: 0.196, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9994, loss_cls: 0.3827, loss: 0.3827 +2025-06-24 16:15:58,337 - pyskl - INFO - Epoch [63][200/1281] lr: 1.582e-02, eta: 7:00:13, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9981, loss_cls: 0.4016, loss: 0.4016 +2025-06-24 16:16:20,639 - pyskl - INFO - Epoch [63][300/1281] lr: 1.580e-02, eta: 6:59:51, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9969, loss_cls: 0.4529, loss: 0.4529 +2025-06-24 16:16:43,170 - pyskl - INFO - Epoch [63][400/1281] lr: 1.578e-02, eta: 6:59:29, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9981, loss_cls: 0.4490, loss: 0.4490 +2025-06-24 16:17:05,506 - pyskl - INFO - Epoch [63][500/1281] lr: 1.576e-02, eta: 6:59:06, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9981, loss_cls: 0.4368, loss: 0.4368 +2025-06-24 16:17:27,822 - pyskl - INFO - Epoch [63][600/1281] lr: 1.574e-02, eta: 6:58:43, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9981, loss_cls: 0.4770, loss: 0.4770 +2025-06-24 16:17:50,440 - pyskl - INFO - Epoch [63][700/1281] lr: 1.572e-02, eta: 6:58:21, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9962, loss_cls: 0.4999, loss: 0.4999 +2025-06-24 16:18:12,835 - pyskl - INFO - Epoch [63][800/1281] lr: 1.570e-02, eta: 6:57:59, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9975, loss_cls: 0.4215, loss: 0.4215 +2025-06-24 16:18:35,254 - pyskl - INFO - Epoch [63][900/1281] lr: 1.568e-02, eta: 6:57:37, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9975, loss_cls: 0.4642, loss: 0.4642 +2025-06-24 16:18:57,847 - pyskl - INFO - Epoch [63][1000/1281] lr: 1.566e-02, eta: 6:57:14, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9962, loss_cls: 0.4846, loss: 0.4846 +2025-06-24 16:19:19,978 - pyskl - INFO - Epoch [63][1100/1281] lr: 1.564e-02, eta: 6:56:52, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9969, loss_cls: 0.4365, loss: 0.4365 +2025-06-24 16:19:42,218 - pyskl - INFO - Epoch [63][1200/1281] lr: 1.562e-02, eta: 6:56:29, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9975, loss_cls: 0.5009, loss: 0.5009 +2025-06-24 16:20:01,005 - pyskl - INFO - Saving checkpoint at 63 epochs +2025-06-24 16:20:44,832 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:20:44,899 - pyskl - INFO - +top1_acc 0.8659 +top5_acc 0.9923 +2025-06-24 16:20:44,900 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:20:44,908 - pyskl - INFO - +mean_acc 0.8138 +2025-06-24 16:20:44,911 - pyskl - INFO - Epoch(val) [63][533] top1_acc: 0.8659, top5_acc: 0.9923, mean_class_accuracy: 0.8138 +2025-06-24 16:21:27,828 - pyskl - INFO - Epoch [64][100/1281] lr: 1.559e-02, eta: 6:55:52, time: 0.429, data_time: 0.195, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9988, loss_cls: 0.4479, loss: 0.4479 +2025-06-24 16:21:50,063 - pyskl - INFO - Epoch [64][200/1281] lr: 1.557e-02, eta: 6:55:29, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9988, loss_cls: 0.4757, loss: 0.4757 +2025-06-24 16:22:12,619 - pyskl - INFO - Epoch [64][300/1281] lr: 1.555e-02, eta: 6:55:07, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 1.0000, loss_cls: 0.4878, loss: 0.4878 +2025-06-24 16:22:35,309 - pyskl - INFO - Epoch [64][400/1281] lr: 1.553e-02, eta: 6:54:45, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9988, loss_cls: 0.4463, loss: 0.4463 +2025-06-24 16:22:57,573 - pyskl - INFO - Epoch [64][500/1281] lr: 1.551e-02, eta: 6:54:22, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 1.0000, loss_cls: 0.4280, loss: 0.4280 +2025-06-24 16:23:20,145 - pyskl - INFO - Epoch [64][600/1281] lr: 1.549e-02, eta: 6:54:00, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9969, loss_cls: 0.4576, loss: 0.4576 +2025-06-24 16:23:42,730 - pyskl - INFO - Epoch [64][700/1281] lr: 1.547e-02, eta: 6:53:38, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9981, loss_cls: 0.4997, loss: 0.4997 +2025-06-24 16:24:04,866 - pyskl - INFO - Epoch [64][800/1281] lr: 1.545e-02, eta: 6:53:15, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9988, loss_cls: 0.4129, loss: 0.4129 +2025-06-24 16:24:27,480 - pyskl - INFO - Epoch [64][900/1281] lr: 1.543e-02, eta: 6:52:53, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9956, loss_cls: 0.5240, loss: 0.5240 +2025-06-24 16:24:50,136 - pyskl - INFO - Epoch [64][1000/1281] lr: 1.541e-02, eta: 6:52:31, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9994, loss_cls: 0.4715, loss: 0.4715 +2025-06-24 16:25:12,241 - pyskl - INFO - Epoch [64][1100/1281] lr: 1.539e-02, eta: 6:52:08, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9975, loss_cls: 0.4708, loss: 0.4708 +2025-06-24 16:25:34,840 - pyskl - INFO - Epoch [64][1200/1281] lr: 1.537e-02, eta: 6:51:46, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9956, loss_cls: 0.4777, loss: 0.4777 +2025-06-24 16:25:53,526 - pyskl - INFO - Saving checkpoint at 64 epochs +2025-06-24 16:26:37,523 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:26:37,579 - pyskl - INFO - +top1_acc 0.8764 +top5_acc 0.9921 +2025-06-24 16:26:37,579 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:26:37,585 - pyskl - INFO - +mean_acc 0.8279 +2025-06-24 16:26:37,586 - pyskl - INFO - Epoch(val) [64][533] top1_acc: 0.8764, top5_acc: 0.9921, mean_class_accuracy: 0.8279 +2025-06-24 16:27:20,189 - pyskl - INFO - Epoch [65][100/1281] lr: 1.533e-02, eta: 6:51:08, time: 0.426, data_time: 0.194, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9981, loss_cls: 0.4058, loss: 0.4058 +2025-06-24 16:27:42,713 - pyskl - INFO - Epoch [65][200/1281] lr: 1.531e-02, eta: 6:50:46, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9988, loss_cls: 0.4272, loss: 0.4272 +2025-06-24 16:28:05,076 - pyskl - INFO - Epoch [65][300/1281] lr: 1.529e-02, eta: 6:50:23, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9981, loss_cls: 0.4581, loss: 0.4581 +2025-06-24 16:28:27,401 - pyskl - INFO - Epoch [65][400/1281] lr: 1.527e-02, eta: 6:50:01, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9975, loss_cls: 0.5184, loss: 0.5184 +2025-06-24 16:28:49,691 - pyskl - INFO - Epoch [65][500/1281] lr: 1.526e-02, eta: 6:49:38, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9981, loss_cls: 0.4534, loss: 0.4534 +2025-06-24 16:29:12,194 - pyskl - INFO - Epoch [65][600/1281] lr: 1.524e-02, eta: 6:49:16, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9981, loss_cls: 0.4170, loss: 0.4170 +2025-06-24 16:29:34,468 - pyskl - INFO - Epoch [65][700/1281] lr: 1.522e-02, eta: 6:48:53, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9969, loss_cls: 0.5371, loss: 0.5371 +2025-06-24 16:29:56,960 - pyskl - INFO - Epoch [65][800/1281] lr: 1.520e-02, eta: 6:48:31, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9962, loss_cls: 0.4145, loss: 0.4145 +2025-06-24 16:30:19,476 - pyskl - INFO - Epoch [65][900/1281] lr: 1.518e-02, eta: 6:48:09, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9969, loss_cls: 0.5070, loss: 0.5070 +2025-06-24 16:30:41,662 - pyskl - INFO - Epoch [65][1000/1281] lr: 1.516e-02, eta: 6:47:46, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9975, loss_cls: 0.4580, loss: 0.4580 +2025-06-24 16:31:04,020 - pyskl - INFO - Epoch [65][1100/1281] lr: 1.514e-02, eta: 6:47:23, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9981, loss_cls: 0.4424, loss: 0.4424 +2025-06-24 16:31:26,230 - pyskl - INFO - Epoch [65][1200/1281] lr: 1.512e-02, eta: 6:47:01, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9962, loss_cls: 0.4751, loss: 0.4751 +2025-06-24 16:31:44,890 - pyskl - INFO - Saving checkpoint at 65 epochs +2025-06-24 16:32:28,300 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:32:28,355 - pyskl - INFO - +top1_acc 0.8618 +top5_acc 0.9928 +2025-06-24 16:32:28,355 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:32:28,366 - pyskl - INFO - +mean_acc 0.8199 +2025-06-24 16:32:28,369 - pyskl - INFO - Epoch(val) [65][533] top1_acc: 0.8618, top5_acc: 0.9928, mean_class_accuracy: 0.8199 +2025-06-24 16:33:10,412 - pyskl - INFO - Epoch [66][100/1281] lr: 1.508e-02, eta: 6:46:22, time: 0.420, data_time: 0.188, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9981, loss_cls: 0.4654, loss: 0.4654 +2025-06-24 16:33:32,827 - pyskl - INFO - Epoch [66][200/1281] lr: 1.506e-02, eta: 6:46:00, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9994, loss_cls: 0.3742, loss: 0.3742 +2025-06-24 16:33:55,627 - pyskl - INFO - Epoch [66][300/1281] lr: 1.504e-02, eta: 6:45:38, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9975, loss_cls: 0.4342, loss: 0.4342 +2025-06-24 16:34:18,057 - pyskl - INFO - Epoch [66][400/1281] lr: 1.502e-02, eta: 6:45:15, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 1.0000, loss_cls: 0.4106, loss: 0.4106 +2025-06-24 16:34:40,342 - pyskl - INFO - Epoch [66][500/1281] lr: 1.500e-02, eta: 6:44:53, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9988, loss_cls: 0.4714, loss: 0.4714 +2025-06-24 16:35:02,838 - pyskl - INFO - Epoch [66][600/1281] lr: 1.498e-02, eta: 6:44:31, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9956, loss_cls: 0.4600, loss: 0.4600 +2025-06-24 16:35:25,057 - pyskl - INFO - Epoch [66][700/1281] lr: 1.496e-02, eta: 6:44:08, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9994, loss_cls: 0.4612, loss: 0.4612 +2025-06-24 16:35:47,267 - pyskl - INFO - Epoch [66][800/1281] lr: 1.494e-02, eta: 6:43:45, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9988, loss_cls: 0.4515, loss: 0.4515 +2025-06-24 16:36:09,614 - pyskl - INFO - Epoch [66][900/1281] lr: 1.492e-02, eta: 6:43:23, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9994, loss_cls: 0.4215, loss: 0.4215 +2025-06-24 16:36:31,612 - pyskl - INFO - Epoch [66][1000/1281] lr: 1.490e-02, eta: 6:43:00, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9969, loss_cls: 0.4567, loss: 0.4567 +2025-06-24 16:36:54,066 - pyskl - INFO - Epoch [66][1100/1281] lr: 1.488e-02, eta: 6:42:37, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9944, loss_cls: 0.4798, loss: 0.4798 +2025-06-24 16:37:16,336 - pyskl - INFO - Epoch [66][1200/1281] lr: 1.486e-02, eta: 6:42:15, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9981, loss_cls: 0.4499, loss: 0.4499 +2025-06-24 16:37:35,191 - pyskl - INFO - Saving checkpoint at 66 epochs +2025-06-24 16:38:19,702 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:38:19,757 - pyskl - INFO - +top1_acc 0.8682 +top5_acc 0.9891 +2025-06-24 16:38:19,757 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:38:19,764 - pyskl - INFO - +mean_acc 0.8287 +2025-06-24 16:38:19,769 - pyskl - INFO - Epoch(val) [66][533] top1_acc: 0.8682, top5_acc: 0.9891, mean_class_accuracy: 0.8287 +2025-06-24 16:39:01,765 - pyskl - INFO - Epoch [67][100/1281] lr: 1.482e-02, eta: 6:41:36, time: 0.420, data_time: 0.189, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9981, loss_cls: 0.4272, loss: 0.4272 +2025-06-24 16:39:24,178 - pyskl - INFO - Epoch [67][200/1281] lr: 1.480e-02, eta: 6:41:14, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9962, loss_cls: 0.4008, loss: 0.4008 +2025-06-24 16:39:46,394 - pyskl - INFO - Epoch [67][300/1281] lr: 1.478e-02, eta: 6:40:51, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9988, loss_cls: 0.4185, loss: 0.4185 +2025-06-24 16:40:08,567 - pyskl - INFO - Epoch [67][400/1281] lr: 1.476e-02, eta: 6:40:28, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9975, loss_cls: 0.3816, loss: 0.3816 +2025-06-24 16:40:31,272 - pyskl - INFO - Epoch [67][500/1281] lr: 1.474e-02, eta: 6:40:06, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9969, loss_cls: 0.3948, loss: 0.3948 +2025-06-24 16:40:53,416 - pyskl - INFO - Epoch [67][600/1281] lr: 1.472e-02, eta: 6:39:43, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9988, loss_cls: 0.4424, loss: 0.4424 +2025-06-24 16:41:15,562 - pyskl - INFO - Epoch [67][700/1281] lr: 1.470e-02, eta: 6:39:21, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9988, loss_cls: 0.3796, loss: 0.3796 +2025-06-24 16:41:38,034 - pyskl - INFO - Epoch [67][800/1281] lr: 1.468e-02, eta: 6:38:58, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9988, loss_cls: 0.4054, loss: 0.4054 +2025-06-24 16:42:00,349 - pyskl - INFO - Epoch [67][900/1281] lr: 1.466e-02, eta: 6:38:36, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9981, loss_cls: 0.4188, loss: 0.4188 +2025-06-24 16:42:22,818 - pyskl - INFO - Epoch [67][1000/1281] lr: 1.464e-02, eta: 6:38:13, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9988, loss_cls: 0.3797, loss: 0.3797 +2025-06-24 16:42:44,892 - pyskl - INFO - Epoch [67][1100/1281] lr: 1.462e-02, eta: 6:37:51, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9962, loss_cls: 0.3963, loss: 0.3963 +2025-06-24 16:43:06,995 - pyskl - INFO - Epoch [67][1200/1281] lr: 1.460e-02, eta: 6:37:28, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9981, loss_cls: 0.3953, loss: 0.3953 +2025-06-24 16:43:25,670 - pyskl - INFO - Saving checkpoint at 67 epochs +2025-06-24 16:44:09,288 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:44:09,345 - pyskl - INFO - +top1_acc 0.8736 +top5_acc 0.9904 +2025-06-24 16:44:09,345 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:44:09,352 - pyskl - INFO - +mean_acc 0.8332 +2025-06-24 16:44:09,353 - pyskl - INFO - Epoch(val) [67][533] top1_acc: 0.8736, top5_acc: 0.9904, mean_class_accuracy: 0.8332 +2025-06-24 16:44:52,005 - pyskl - INFO - Epoch [68][100/1281] lr: 1.456e-02, eta: 6:36:50, time: 0.426, data_time: 0.189, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9969, loss_cls: 0.4716, loss: 0.4716 +2025-06-24 16:45:14,845 - pyskl - INFO - Epoch [68][200/1281] lr: 1.454e-02, eta: 6:36:28, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9994, loss_cls: 0.4375, loss: 0.4375 +2025-06-24 16:45:36,972 - pyskl - INFO - Epoch [68][300/1281] lr: 1.452e-02, eta: 6:36:05, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9975, loss_cls: 0.4687, loss: 0.4687 +2025-06-24 16:45:59,121 - pyskl - INFO - Epoch [68][400/1281] lr: 1.450e-02, eta: 6:35:42, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9981, loss_cls: 0.4327, loss: 0.4327 +2025-06-24 16:46:21,756 - pyskl - INFO - Epoch [68][500/1281] lr: 1.448e-02, eta: 6:35:20, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9969, loss_cls: 0.4768, loss: 0.4768 +2025-06-24 16:46:43,855 - pyskl - INFO - Epoch [68][600/1281] lr: 1.446e-02, eta: 6:34:57, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9988, loss_cls: 0.4124, loss: 0.4124 +2025-06-24 16:47:05,912 - pyskl - INFO - Epoch [68][700/1281] lr: 1.444e-02, eta: 6:34:35, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9956, loss_cls: 0.4431, loss: 0.4431 +2025-06-24 16:47:28,336 - pyskl - INFO - Epoch [68][800/1281] lr: 1.442e-02, eta: 6:34:12, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9962, loss_cls: 0.5027, loss: 0.5027 +2025-06-24 16:47:50,513 - pyskl - INFO - Epoch [68][900/1281] lr: 1.440e-02, eta: 6:33:49, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9969, loss_cls: 0.4605, loss: 0.4605 +2025-06-24 16:48:13,238 - pyskl - INFO - Epoch [68][1000/1281] lr: 1.438e-02, eta: 6:33:27, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9975, loss_cls: 0.4495, loss: 0.4495 +2025-06-24 16:48:35,733 - pyskl - INFO - Epoch [68][1100/1281] lr: 1.436e-02, eta: 6:33:05, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9969, loss_cls: 0.3992, loss: 0.3992 +2025-06-24 16:48:57,890 - pyskl - INFO - Epoch [68][1200/1281] lr: 1.434e-02, eta: 6:32:42, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9981, loss_cls: 0.5003, loss: 0.5003 +2025-06-24 16:49:16,939 - pyskl - INFO - Saving checkpoint at 68 epochs +2025-06-24 16:50:00,689 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:50:00,746 - pyskl - INFO - +top1_acc 0.8737 +top5_acc 0.9923 +2025-06-24 16:50:00,746 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:50:00,754 - pyskl - INFO - +mean_acc 0.8330 +2025-06-24 16:50:00,756 - pyskl - INFO - Epoch(val) [68][533] top1_acc: 0.8737, top5_acc: 0.9923, mean_class_accuracy: 0.8330 +2025-06-24 16:50:43,140 - pyskl - INFO - Epoch [69][100/1281] lr: 1.431e-02, eta: 6:32:04, time: 0.424, data_time: 0.191, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9981, loss_cls: 0.4335, loss: 0.4335 +2025-06-24 16:51:05,343 - pyskl - INFO - Epoch [69][200/1281] lr: 1.429e-02, eta: 6:31:41, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9975, loss_cls: 0.4010, loss: 0.4010 +2025-06-24 16:51:27,521 - pyskl - INFO - Epoch [69][300/1281] lr: 1.427e-02, eta: 6:31:19, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9988, loss_cls: 0.4278, loss: 0.4278 +2025-06-24 16:51:50,196 - pyskl - INFO - Epoch [69][400/1281] lr: 1.425e-02, eta: 6:30:57, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9981, loss_cls: 0.4107, loss: 0.4107 +2025-06-24 16:52:12,420 - pyskl - INFO - Epoch [69][500/1281] lr: 1.423e-02, eta: 6:30:34, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.3998, loss: 0.3998 +2025-06-24 16:52:34,910 - pyskl - INFO - Epoch [69][600/1281] lr: 1.420e-02, eta: 6:30:12, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9975, loss_cls: 0.4780, loss: 0.4780 +2025-06-24 16:52:57,037 - pyskl - INFO - Epoch [69][700/1281] lr: 1.418e-02, eta: 6:29:49, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9969, loss_cls: 0.3813, loss: 0.3813 +2025-06-24 16:53:19,353 - pyskl - INFO - Epoch [69][800/1281] lr: 1.416e-02, eta: 6:29:26, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9962, loss_cls: 0.4257, loss: 0.4257 +2025-06-24 16:53:41,594 - pyskl - INFO - Epoch [69][900/1281] lr: 1.414e-02, eta: 6:29:04, time: 0.222, data_time: 0.001, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9981, loss_cls: 0.4159, loss: 0.4159 +2025-06-24 16:54:03,842 - pyskl - INFO - Epoch [69][1000/1281] lr: 1.412e-02, eta: 6:28:41, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9975, loss_cls: 0.4714, loss: 0.4714 +2025-06-24 16:54:26,164 - pyskl - INFO - Epoch [69][1100/1281] lr: 1.410e-02, eta: 6:28:18, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9975, loss_cls: 0.4200, loss: 0.4200 +2025-06-24 16:54:48,404 - pyskl - INFO - Epoch [69][1200/1281] lr: 1.408e-02, eta: 6:27:56, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9975, loss_cls: 0.4431, loss: 0.4431 +2025-06-24 16:55:07,090 - pyskl - INFO - Saving checkpoint at 69 epochs +2025-06-24 16:55:50,489 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:55:50,544 - pyskl - INFO - +top1_acc 0.8917 +top5_acc 0.9955 +2025-06-24 16:55:50,544 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:55:50,550 - pyskl - INFO - +mean_acc 0.8478 +2025-06-24 16:55:50,554 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_62.pth was removed +2025-06-24 16:55:50,733 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_69.pth. +2025-06-24 16:55:50,733 - pyskl - INFO - Best top1_acc is 0.8917 at 69 epoch. +2025-06-24 16:55:50,736 - pyskl - INFO - Epoch(val) [69][533] top1_acc: 0.8917, top5_acc: 0.9955, mean_class_accuracy: 0.8478 +2025-06-24 16:56:33,377 - pyskl - INFO - Epoch [70][100/1281] lr: 1.405e-02, eta: 6:27:18, time: 0.426, data_time: 0.191, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9994, loss_cls: 0.4025, loss: 0.4025 +2025-06-24 16:56:55,848 - pyskl - INFO - Epoch [70][200/1281] lr: 1.403e-02, eta: 6:26:55, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9988, loss_cls: 0.4215, loss: 0.4215 +2025-06-24 16:57:18,084 - pyskl - INFO - Epoch [70][300/1281] lr: 1.401e-02, eta: 6:26:33, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9981, loss_cls: 0.3861, loss: 0.3861 +2025-06-24 16:57:40,444 - pyskl - INFO - Epoch [70][400/1281] lr: 1.399e-02, eta: 6:26:10, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9994, loss_cls: 0.3257, loss: 0.3257 +2025-06-24 16:58:02,754 - pyskl - INFO - Epoch [70][500/1281] lr: 1.397e-02, eta: 6:25:48, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3547, loss: 0.3547 +2025-06-24 16:58:25,317 - pyskl - INFO - Epoch [70][600/1281] lr: 1.395e-02, eta: 6:25:25, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9988, loss_cls: 0.3389, loss: 0.3389 +2025-06-24 16:58:47,836 - pyskl - INFO - Epoch [70][700/1281] lr: 1.392e-02, eta: 6:25:03, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9956, loss_cls: 0.4215, loss: 0.4215 +2025-06-24 16:59:10,080 - pyskl - INFO - Epoch [70][800/1281] lr: 1.390e-02, eta: 6:24:41, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 1.0000, loss_cls: 0.3599, loss: 0.3599 +2025-06-24 16:59:32,558 - pyskl - INFO - Epoch [70][900/1281] lr: 1.388e-02, eta: 6:24:18, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4068, loss: 0.4068 +2025-06-24 16:59:55,132 - pyskl - INFO - Epoch [70][1000/1281] lr: 1.386e-02, eta: 6:23:56, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9969, loss_cls: 0.4289, loss: 0.4289 +2025-06-24 17:00:17,089 - pyskl - INFO - Epoch [70][1100/1281] lr: 1.384e-02, eta: 6:23:33, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9981, loss_cls: 0.4641, loss: 0.4641 +2025-06-24 17:00:39,532 - pyskl - INFO - Epoch [70][1200/1281] lr: 1.382e-02, eta: 6:23:11, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4724, loss: 0.4724 +2025-06-24 17:00:58,154 - pyskl - INFO - Saving checkpoint at 70 epochs +2025-06-24 17:01:41,895 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:01:41,949 - pyskl - INFO - +top1_acc 0.8799 +top5_acc 0.9941 +2025-06-24 17:01:41,949 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:01:41,956 - pyskl - INFO - +mean_acc 0.8402 +2025-06-24 17:01:41,957 - pyskl - INFO - Epoch(val) [70][533] top1_acc: 0.8799, top5_acc: 0.9941, mean_class_accuracy: 0.8402 +2025-06-24 17:02:23,937 - pyskl - INFO - Epoch [71][100/1281] lr: 1.379e-02, eta: 6:22:32, time: 0.420, data_time: 0.186, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9988, loss_cls: 0.3717, loss: 0.3717 +2025-06-24 17:02:46,240 - pyskl - INFO - Epoch [71][200/1281] lr: 1.377e-02, eta: 6:22:09, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9994, loss_cls: 0.3525, loss: 0.3525 +2025-06-24 17:03:09,011 - pyskl - INFO - Epoch [71][300/1281] lr: 1.375e-02, eta: 6:21:47, time: 0.228, data_time: 0.001, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.3894, loss: 0.3894 +2025-06-24 17:03:31,486 - pyskl - INFO - Epoch [71][400/1281] lr: 1.373e-02, eta: 6:21:25, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 1.0000, loss_cls: 0.3580, loss: 0.3580 +2025-06-24 17:03:53,913 - pyskl - INFO - Epoch [71][500/1281] lr: 1.371e-02, eta: 6:21:02, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9994, loss_cls: 0.4239, loss: 0.4239 +2025-06-24 17:04:16,574 - pyskl - INFO - Epoch [71][600/1281] lr: 1.368e-02, eta: 6:20:40, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9969, loss_cls: 0.3996, loss: 0.3996 +2025-06-24 17:04:38,656 - pyskl - INFO - Epoch [71][700/1281] lr: 1.366e-02, eta: 6:20:17, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9944, loss_cls: 0.4488, loss: 0.4488 +2025-06-24 17:05:01,174 - pyskl - INFO - Epoch [71][800/1281] lr: 1.364e-02, eta: 6:19:55, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9988, loss_cls: 0.3903, loss: 0.3903 +2025-06-24 17:05:23,356 - pyskl - INFO - Epoch [71][900/1281] lr: 1.362e-02, eta: 6:19:32, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9981, loss_cls: 0.4366, loss: 0.4366 +2025-06-24 17:05:45,374 - pyskl - INFO - Epoch [71][1000/1281] lr: 1.360e-02, eta: 6:19:09, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3896, loss: 0.3896 +2025-06-24 17:06:07,640 - pyskl - INFO - Epoch [71][1100/1281] lr: 1.358e-02, eta: 6:18:47, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9994, loss_cls: 0.4347, loss: 0.4347 +2025-06-24 17:06:29,690 - pyskl - INFO - Epoch [71][1200/1281] lr: 1.356e-02, eta: 6:18:24, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9975, loss_cls: 0.3906, loss: 0.3906 +2025-06-24 17:06:48,575 - pyskl - INFO - Saving checkpoint at 71 epochs +2025-06-24 17:07:32,174 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:07:32,229 - pyskl - INFO - +top1_acc 0.8823 +top5_acc 0.9924 +2025-06-24 17:07:32,229 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:07:32,236 - pyskl - INFO - +mean_acc 0.8262 +2025-06-24 17:07:32,238 - pyskl - INFO - Epoch(val) [71][533] top1_acc: 0.8823, top5_acc: 0.9924, mean_class_accuracy: 0.8262 +2025-06-24 17:08:15,617 - pyskl - INFO - Epoch [72][100/1281] lr: 1.353e-02, eta: 6:17:47, time: 0.434, data_time: 0.194, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9988, loss_cls: 0.3497, loss: 0.3497 +2025-06-24 17:08:38,077 - pyskl - INFO - Epoch [72][200/1281] lr: 1.351e-02, eta: 6:17:24, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9994, loss_cls: 0.3639, loss: 0.3639 +2025-06-24 17:09:00,834 - pyskl - INFO - Epoch [72][300/1281] lr: 1.349e-02, eta: 6:17:02, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9981, loss_cls: 0.4071, loss: 0.4071 +2025-06-24 17:09:23,158 - pyskl - INFO - Epoch [72][400/1281] lr: 1.346e-02, eta: 6:16:40, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9988, loss_cls: 0.3448, loss: 0.3448 +2025-06-24 17:09:45,383 - pyskl - INFO - Epoch [72][500/1281] lr: 1.344e-02, eta: 6:16:17, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9994, loss_cls: 0.3719, loss: 0.3719 +2025-06-24 17:10:07,843 - pyskl - INFO - Epoch [72][600/1281] lr: 1.342e-02, eta: 6:15:55, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9994, loss_cls: 0.4540, loss: 0.4540 +2025-06-24 17:10:30,157 - pyskl - INFO - Epoch [72][700/1281] lr: 1.340e-02, eta: 6:15:32, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9994, loss_cls: 0.3367, loss: 0.3367 +2025-06-24 17:10:52,672 - pyskl - INFO - Epoch [72][800/1281] lr: 1.338e-02, eta: 6:15:10, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9981, loss_cls: 0.4222, loss: 0.4222 +2025-06-24 17:11:15,002 - pyskl - INFO - Epoch [72][900/1281] lr: 1.336e-02, eta: 6:14:47, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9988, loss_cls: 0.4180, loss: 0.4180 +2025-06-24 17:11:37,225 - pyskl - INFO - Epoch [72][1000/1281] lr: 1.334e-02, eta: 6:14:25, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9956, loss_cls: 0.3989, loss: 0.3989 +2025-06-24 17:11:59,855 - pyskl - INFO - Epoch [72][1100/1281] lr: 1.332e-02, eta: 6:14:02, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9975, loss_cls: 0.4064, loss: 0.4064 +2025-06-24 17:12:22,115 - pyskl - INFO - Epoch [72][1200/1281] lr: 1.330e-02, eta: 6:13:40, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9969, loss_cls: 0.4119, loss: 0.4119 +2025-06-24 17:12:41,075 - pyskl - INFO - Saving checkpoint at 72 epochs +2025-06-24 17:13:24,681 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:13:24,747 - pyskl - INFO - +top1_acc 0.8906 +top5_acc 0.9954 +2025-06-24 17:13:24,747 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:13:24,754 - pyskl - INFO - +mean_acc 0.8519 +2025-06-24 17:13:24,755 - pyskl - INFO - Epoch(val) [72][533] top1_acc: 0.8906, top5_acc: 0.9954, mean_class_accuracy: 0.8519 +2025-06-24 17:14:06,466 - pyskl - INFO - Epoch [73][100/1281] lr: 1.326e-02, eta: 6:13:00, time: 0.417, data_time: 0.185, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9988, loss_cls: 0.3902, loss: 0.3902 +2025-06-24 17:14:29,420 - pyskl - INFO - Epoch [73][200/1281] lr: 1.324e-02, eta: 6:12:39, time: 0.230, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9988, loss_cls: 0.4227, loss: 0.4227 +2025-06-24 17:14:51,633 - pyskl - INFO - Epoch [73][300/1281] lr: 1.322e-02, eta: 6:12:16, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9962, loss_cls: 0.4228, loss: 0.4228 +2025-06-24 17:15:13,820 - pyskl - INFO - Epoch [73][400/1281] lr: 1.320e-02, eta: 6:11:53, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9994, loss_cls: 0.3828, loss: 0.3828 +2025-06-24 17:15:36,471 - pyskl - INFO - Epoch [73][500/1281] lr: 1.318e-02, eta: 6:11:31, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9988, loss_cls: 0.3889, loss: 0.3889 +2025-06-24 17:15:58,591 - pyskl - INFO - Epoch [73][600/1281] lr: 1.316e-02, eta: 6:11:08, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9969, loss_cls: 0.4352, loss: 0.4352 +2025-06-24 17:16:21,241 - pyskl - INFO - Epoch [73][700/1281] lr: 1.314e-02, eta: 6:10:46, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9988, loss_cls: 0.4106, loss: 0.4106 +2025-06-24 17:16:43,431 - pyskl - INFO - Epoch [73][800/1281] lr: 1.312e-02, eta: 6:10:23, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 1.0000, loss_cls: 0.3846, loss: 0.3846 +2025-06-24 17:17:05,605 - pyskl - INFO - Epoch [73][900/1281] lr: 1.310e-02, eta: 6:10:01, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9994, loss_cls: 0.3778, loss: 0.3778 +2025-06-24 17:17:28,001 - pyskl - INFO - Epoch [73][1000/1281] lr: 1.308e-02, eta: 6:09:38, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9969, loss_cls: 0.3889, loss: 0.3889 +2025-06-24 17:17:50,178 - pyskl - INFO - Epoch [73][1100/1281] lr: 1.306e-02, eta: 6:09:16, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9969, loss_cls: 0.4918, loss: 0.4918 +2025-06-24 17:18:12,415 - pyskl - INFO - Epoch [73][1200/1281] lr: 1.304e-02, eta: 6:08:53, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9994, loss_cls: 0.4296, loss: 0.4296 +2025-06-24 17:18:31,161 - pyskl - INFO - Saving checkpoint at 73 epochs +2025-06-24 17:19:14,801 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:19:14,856 - pyskl - INFO - +top1_acc 0.8801 +top5_acc 0.9944 +2025-06-24 17:19:14,856 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:19:14,862 - pyskl - INFO - +mean_acc 0.8378 +2025-06-24 17:19:14,864 - pyskl - INFO - Epoch(val) [73][533] top1_acc: 0.8801, top5_acc: 0.9944, mean_class_accuracy: 0.8378 +2025-06-24 17:19:57,625 - pyskl - INFO - Epoch [74][100/1281] lr: 1.300e-02, eta: 6:08:15, time: 0.428, data_time: 0.195, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9988, loss_cls: 0.3400, loss: 0.3400 +2025-06-24 17:20:20,060 - pyskl - INFO - Epoch [74][200/1281] lr: 1.298e-02, eta: 6:07:52, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9994, loss_cls: 0.4058, loss: 0.4058 +2025-06-24 17:20:42,479 - pyskl - INFO - Epoch [74][300/1281] lr: 1.296e-02, eta: 6:07:30, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9988, loss_cls: 0.4219, loss: 0.4219 +2025-06-24 17:21:04,795 - pyskl - INFO - Epoch [74][400/1281] lr: 1.294e-02, eta: 6:07:07, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9988, loss_cls: 0.3496, loss: 0.3496 +2025-06-24 17:21:27,250 - pyskl - INFO - Epoch [74][500/1281] lr: 1.292e-02, eta: 6:06:45, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.3385, loss: 0.3385 +2025-06-24 17:21:49,514 - pyskl - INFO - Epoch [74][600/1281] lr: 1.290e-02, eta: 6:06:22, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.3930, loss: 0.3930 +2025-06-24 17:22:11,946 - pyskl - INFO - Epoch [74][700/1281] lr: 1.288e-02, eta: 6:06:00, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9975, loss_cls: 0.3655, loss: 0.3655 +2025-06-24 17:22:34,703 - pyskl - INFO - Epoch [74][800/1281] lr: 1.286e-02, eta: 6:05:38, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 1.0000, loss_cls: 0.2999, loss: 0.2999 +2025-06-24 17:22:57,136 - pyskl - INFO - Epoch [74][900/1281] lr: 1.284e-02, eta: 6:05:15, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9988, loss_cls: 0.3983, loss: 0.3983 +2025-06-24 17:23:19,544 - pyskl - INFO - Epoch [74][1000/1281] lr: 1.282e-02, eta: 6:04:53, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9988, loss_cls: 0.4042, loss: 0.4042 +2025-06-24 17:23:41,968 - pyskl - INFO - Epoch [74][1100/1281] lr: 1.280e-02, eta: 6:04:31, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9981, loss_cls: 0.4592, loss: 0.4592 +2025-06-24 17:24:04,417 - pyskl - INFO - Epoch [74][1200/1281] lr: 1.278e-02, eta: 6:04:08, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9975, loss_cls: 0.3993, loss: 0.3993 +2025-06-24 17:24:23,175 - pyskl - INFO - Saving checkpoint at 74 epochs +2025-06-24 17:25:06,615 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:25:06,669 - pyskl - INFO - +top1_acc 0.8515 +top5_acc 0.9912 +2025-06-24 17:25:06,670 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:25:06,677 - pyskl - INFO - +mean_acc 0.8028 +2025-06-24 17:25:06,679 - pyskl - INFO - Epoch(val) [74][533] top1_acc: 0.8515, top5_acc: 0.9912, mean_class_accuracy: 0.8028 +2025-06-24 17:25:49,138 - pyskl - INFO - Epoch [75][100/1281] lr: 1.274e-02, eta: 6:03:30, time: 0.425, data_time: 0.188, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9994, loss_cls: 0.3701, loss: 0.3701 +2025-06-24 17:26:11,347 - pyskl - INFO - Epoch [75][200/1281] lr: 1.272e-02, eta: 6:03:07, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9988, loss_cls: 0.3347, loss: 0.3347 +2025-06-24 17:26:33,306 - pyskl - INFO - Epoch [75][300/1281] lr: 1.270e-02, eta: 6:02:44, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3282, loss: 0.3282 +2025-06-24 17:26:55,949 - pyskl - INFO - Epoch [75][400/1281] lr: 1.268e-02, eta: 6:02:22, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9994, loss_cls: 0.3671, loss: 0.3671 +2025-06-24 17:27:18,050 - pyskl - INFO - Epoch [75][500/1281] lr: 1.266e-02, eta: 6:01:59, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9988, loss_cls: 0.4015, loss: 0.4015 +2025-06-24 17:27:40,349 - pyskl - INFO - Epoch [75][600/1281] lr: 1.264e-02, eta: 6:01:36, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9969, loss_cls: 0.3989, loss: 0.3989 +2025-06-24 17:28:02,813 - pyskl - INFO - Epoch [75][700/1281] lr: 1.262e-02, eta: 6:01:14, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9988, loss_cls: 0.4112, loss: 0.4112 +2025-06-24 17:28:25,137 - pyskl - INFO - Epoch [75][800/1281] lr: 1.260e-02, eta: 6:00:52, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9981, loss_cls: 0.3155, loss: 0.3155 +2025-06-24 17:28:47,683 - pyskl - INFO - Epoch [75][900/1281] lr: 1.258e-02, eta: 6:00:29, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9988, loss_cls: 0.3510, loss: 0.3510 +2025-06-24 17:29:09,733 - pyskl - INFO - Epoch [75][1000/1281] lr: 1.256e-02, eta: 6:00:06, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9994, loss_cls: 0.3829, loss: 0.3829 +2025-06-24 17:29:31,723 - pyskl - INFO - Epoch [75][1100/1281] lr: 1.254e-02, eta: 5:59:44, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9975, loss_cls: 0.3894, loss: 0.3894 +2025-06-24 17:29:54,028 - pyskl - INFO - Epoch [75][1200/1281] lr: 1.252e-02, eta: 5:59:21, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9962, loss_cls: 0.3928, loss: 0.3928 +2025-06-24 17:30:12,887 - pyskl - INFO - Saving checkpoint at 75 epochs +2025-06-24 17:30:56,579 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:30:56,636 - pyskl - INFO - +top1_acc 0.8783 +top5_acc 0.9927 +2025-06-24 17:30:56,636 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:30:56,643 - pyskl - INFO - +mean_acc 0.8439 +2025-06-24 17:30:56,646 - pyskl - INFO - Epoch(val) [75][533] top1_acc: 0.8783, top5_acc: 0.9927, mean_class_accuracy: 0.8439 +2025-06-24 17:31:38,554 - pyskl - INFO - Epoch [76][100/1281] lr: 1.248e-02, eta: 5:58:42, time: 0.419, data_time: 0.182, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9988, loss_cls: 0.3751, loss: 0.3751 +2025-06-24 17:32:01,078 - pyskl - INFO - Epoch [76][200/1281] lr: 1.246e-02, eta: 5:58:19, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 1.0000, loss_cls: 0.3648, loss: 0.3648 +2025-06-24 17:32:23,027 - pyskl - INFO - Epoch [76][300/1281] lr: 1.244e-02, eta: 5:57:57, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9988, loss_cls: 0.3465, loss: 0.3465 +2025-06-24 17:32:45,559 - pyskl - INFO - Epoch [76][400/1281] lr: 1.242e-02, eta: 5:57:34, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9994, loss_cls: 0.3816, loss: 0.3816 +2025-06-24 17:33:08,052 - pyskl - INFO - Epoch [76][500/1281] lr: 1.240e-02, eta: 5:57:12, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9994, loss_cls: 0.4053, loss: 0.4053 +2025-06-24 17:33:30,151 - pyskl - INFO - Epoch [76][600/1281] lr: 1.238e-02, eta: 5:56:49, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9975, loss_cls: 0.3956, loss: 0.3956 +2025-06-24 17:33:52,978 - pyskl - INFO - Epoch [76][700/1281] lr: 1.236e-02, eta: 5:56:27, time: 0.228, data_time: 0.001, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.3305, loss: 0.3305 +2025-06-24 17:34:15,099 - pyskl - INFO - Epoch [76][800/1281] lr: 1.234e-02, eta: 5:56:04, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 1.0000, loss_cls: 0.4020, loss: 0.4020 +2025-06-24 17:34:37,606 - pyskl - INFO - Epoch [76][900/1281] lr: 1.232e-02, eta: 5:55:42, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9969, loss_cls: 0.4089, loss: 0.4089 +2025-06-24 17:35:00,093 - pyskl - INFO - Epoch [76][1000/1281] lr: 1.230e-02, eta: 5:55:20, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9969, loss_cls: 0.4070, loss: 0.4070 +2025-06-24 17:35:22,429 - pyskl - INFO - Epoch [76][1100/1281] lr: 1.228e-02, eta: 5:54:57, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9975, loss_cls: 0.4572, loss: 0.4572 +2025-06-24 17:35:44,686 - pyskl - INFO - Epoch [76][1200/1281] lr: 1.225e-02, eta: 5:54:35, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9969, loss_cls: 0.4055, loss: 0.4055 +2025-06-24 17:36:03,467 - pyskl - INFO - Saving checkpoint at 76 epochs +2025-06-24 17:36:46,906 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:36:46,961 - pyskl - INFO - +top1_acc 0.8831 +top5_acc 0.9935 +2025-06-24 17:36:46,961 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:36:46,968 - pyskl - INFO - +mean_acc 0.8471 +2025-06-24 17:36:46,971 - pyskl - INFO - Epoch(val) [76][533] top1_acc: 0.8831, top5_acc: 0.9935, mean_class_accuracy: 0.8471 +2025-06-24 17:37:29,555 - pyskl - INFO - Epoch [77][100/1281] lr: 1.222e-02, eta: 5:53:56, time: 0.426, data_time: 0.193, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9988, loss_cls: 0.3530, loss: 0.3530 +2025-06-24 17:37:52,089 - pyskl - INFO - Epoch [77][200/1281] lr: 1.220e-02, eta: 5:53:34, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3394, loss: 0.3394 +2025-06-24 17:38:14,727 - pyskl - INFO - Epoch [77][300/1281] lr: 1.218e-02, eta: 5:53:11, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.3617, loss: 0.3617 +2025-06-24 17:38:37,002 - pyskl - INFO - Epoch [77][400/1281] lr: 1.216e-02, eta: 5:52:49, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9981, loss_cls: 0.3424, loss: 0.3424 +2025-06-24 17:38:59,367 - pyskl - INFO - Epoch [77][500/1281] lr: 1.214e-02, eta: 5:52:26, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 1.0000, loss_cls: 0.3409, loss: 0.3409 +2025-06-24 17:39:21,513 - pyskl - INFO - Epoch [77][600/1281] lr: 1.212e-02, eta: 5:52:04, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9988, loss_cls: 0.3896, loss: 0.3896 +2025-06-24 17:39:43,837 - pyskl - INFO - Epoch [77][700/1281] lr: 1.210e-02, eta: 5:51:41, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9988, loss_cls: 0.3710, loss: 0.3710 +2025-06-24 17:40:06,153 - pyskl - INFO - Epoch [77][800/1281] lr: 1.207e-02, eta: 5:51:19, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9981, loss_cls: 0.3923, loss: 0.3923 +2025-06-24 17:40:28,256 - pyskl - INFO - Epoch [77][900/1281] lr: 1.205e-02, eta: 5:50:56, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9975, loss_cls: 0.3922, loss: 0.3922 +2025-06-24 17:40:50,590 - pyskl - INFO - Epoch [77][1000/1281] lr: 1.203e-02, eta: 5:50:33, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9956, loss_cls: 0.4410, loss: 0.4410 +2025-06-24 17:41:12,610 - pyskl - INFO - Epoch [77][1100/1281] lr: 1.201e-02, eta: 5:50:11, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9969, loss_cls: 0.4266, loss: 0.4266 +2025-06-24 17:41:35,276 - pyskl - INFO - Epoch [77][1200/1281] lr: 1.199e-02, eta: 5:49:48, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9981, loss_cls: 0.4422, loss: 0.4422 +2025-06-24 17:41:54,213 - pyskl - INFO - Saving checkpoint at 77 epochs +2025-06-24 17:42:38,784 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:42:38,841 - pyskl - INFO - +top1_acc 0.8863 +top5_acc 0.9941 +2025-06-24 17:42:38,841 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:42:38,847 - pyskl - INFO - +mean_acc 0.8443 +2025-06-24 17:42:38,849 - pyskl - INFO - Epoch(val) [77][533] top1_acc: 0.8863, top5_acc: 0.9941, mean_class_accuracy: 0.8443 +2025-06-24 17:43:22,195 - pyskl - INFO - Epoch [78][100/1281] lr: 1.196e-02, eta: 5:49:10, time: 0.433, data_time: 0.194, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.3231, loss: 0.3231 +2025-06-24 17:43:44,396 - pyskl - INFO - Epoch [78][200/1281] lr: 1.194e-02, eta: 5:48:48, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9994, loss_cls: 0.2514, loss: 0.2514 +2025-06-24 17:44:06,628 - pyskl - INFO - Epoch [78][300/1281] lr: 1.192e-02, eta: 5:48:25, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2943, loss: 0.2943 +2025-06-24 17:44:28,874 - pyskl - INFO - Epoch [78][400/1281] lr: 1.190e-02, eta: 5:48:02, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9981, loss_cls: 0.3257, loss: 0.3257 +2025-06-24 17:44:50,862 - pyskl - INFO - Epoch [78][500/1281] lr: 1.187e-02, eta: 5:47:40, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.3336, loss: 0.3336 +2025-06-24 17:45:13,565 - pyskl - INFO - Epoch [78][600/1281] lr: 1.185e-02, eta: 5:47:17, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9994, loss_cls: 0.3781, loss: 0.3781 +2025-06-24 17:45:35,756 - pyskl - INFO - Epoch [78][700/1281] lr: 1.183e-02, eta: 5:46:55, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9975, loss_cls: 0.3626, loss: 0.3626 +2025-06-24 17:45:57,968 - pyskl - INFO - Epoch [78][800/1281] lr: 1.181e-02, eta: 5:46:32, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9969, loss_cls: 0.4378, loss: 0.4378 +2025-06-24 17:46:20,408 - pyskl - INFO - Epoch [78][900/1281] lr: 1.179e-02, eta: 5:46:10, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 1.0000, loss_cls: 0.4295, loss: 0.4295 +2025-06-24 17:46:42,553 - pyskl - INFO - Epoch [78][1000/1281] lr: 1.177e-02, eta: 5:45:47, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9994, loss_cls: 0.4191, loss: 0.4191 +2025-06-24 17:47:04,828 - pyskl - INFO - Epoch [78][1100/1281] lr: 1.175e-02, eta: 5:45:25, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9975, loss_cls: 0.3605, loss: 0.3605 +2025-06-24 17:47:27,079 - pyskl - INFO - Epoch [78][1200/1281] lr: 1.173e-02, eta: 5:45:02, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9981, loss_cls: 0.3850, loss: 0.3850 +2025-06-24 17:47:46,023 - pyskl - INFO - Saving checkpoint at 78 epochs +2025-06-24 17:48:29,597 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:48:29,654 - pyskl - INFO - +top1_acc 0.8788 +top5_acc 0.9905 +2025-06-24 17:48:29,654 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:48:29,662 - pyskl - INFO - +mean_acc 0.8291 +2025-06-24 17:48:29,664 - pyskl - INFO - Epoch(val) [78][533] top1_acc: 0.8788, top5_acc: 0.9905, mean_class_accuracy: 0.8291 +2025-06-24 17:49:11,500 - pyskl - INFO - Epoch [79][100/1281] lr: 1.169e-02, eta: 5:44:22, time: 0.418, data_time: 0.185, memory: 4083, top1_acc: 0.9387, top5_acc: 1.0000, loss_cls: 0.3483, loss: 0.3483 +2025-06-24 17:49:34,071 - pyskl - INFO - Epoch [79][200/1281] lr: 1.167e-02, eta: 5:44:00, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 1.0000, loss_cls: 0.3417, loss: 0.3417 +2025-06-24 17:49:56,242 - pyskl - INFO - Epoch [79][300/1281] lr: 1.165e-02, eta: 5:43:38, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.3116, loss: 0.3116 +2025-06-24 17:50:18,734 - pyskl - INFO - Epoch [79][400/1281] lr: 1.163e-02, eta: 5:43:15, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 1.0000, loss_cls: 0.3340, loss: 0.3340 +2025-06-24 17:50:40,950 - pyskl - INFO - Epoch [79][500/1281] lr: 1.161e-02, eta: 5:42:53, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 1.0000, loss_cls: 0.3211, loss: 0.3211 +2025-06-24 17:51:03,021 - pyskl - INFO - Epoch [79][600/1281] lr: 1.159e-02, eta: 5:42:30, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9988, loss_cls: 0.3572, loss: 0.3572 +2025-06-24 17:51:25,366 - pyskl - INFO - Epoch [79][700/1281] lr: 1.157e-02, eta: 5:42:07, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9981, loss_cls: 0.3535, loss: 0.3535 +2025-06-24 17:51:47,596 - pyskl - INFO - Epoch [79][800/1281] lr: 1.155e-02, eta: 5:41:45, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9981, loss_cls: 0.3204, loss: 0.3204 +2025-06-24 17:52:09,960 - pyskl - INFO - Epoch [79][900/1281] lr: 1.153e-02, eta: 5:41:22, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9994, loss_cls: 0.3677, loss: 0.3677 +2025-06-24 17:52:32,261 - pyskl - INFO - Epoch [79][1000/1281] lr: 1.151e-02, eta: 5:41:00, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9988, loss_cls: 0.3650, loss: 0.3650 +2025-06-24 17:52:54,707 - pyskl - INFO - Epoch [79][1100/1281] lr: 1.149e-02, eta: 5:40:37, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9994, loss_cls: 0.3377, loss: 0.3377 +2025-06-24 17:53:16,906 - pyskl - INFO - Epoch [79][1200/1281] lr: 1.147e-02, eta: 5:40:15, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9975, loss_cls: 0.3406, loss: 0.3406 +2025-06-24 17:53:35,629 - pyskl - INFO - Saving checkpoint at 79 epochs +2025-06-24 17:54:19,138 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:54:19,212 - pyskl - INFO - +top1_acc 0.8912 +top5_acc 0.9916 +2025-06-24 17:54:19,212 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:54:19,223 - pyskl - INFO - +mean_acc 0.8589 +2025-06-24 17:54:19,226 - pyskl - INFO - Epoch(val) [79][533] top1_acc: 0.8912, top5_acc: 0.9916, mean_class_accuracy: 0.8589 +2025-06-24 17:55:02,652 - pyskl - INFO - Epoch [80][100/1281] lr: 1.143e-02, eta: 5:39:37, time: 0.434, data_time: 0.196, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9994, loss_cls: 0.3183, loss: 0.3183 +2025-06-24 17:55:24,892 - pyskl - INFO - Epoch [80][200/1281] lr: 1.141e-02, eta: 5:39:14, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.3234, loss: 0.3234 +2025-06-24 17:55:47,400 - pyskl - INFO - Epoch [80][300/1281] lr: 1.139e-02, eta: 5:38:52, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9981, loss_cls: 0.3664, loss: 0.3664 +2025-06-24 17:56:09,750 - pyskl - INFO - Epoch [80][400/1281] lr: 1.137e-02, eta: 5:38:29, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9994, loss_cls: 0.3682, loss: 0.3682 +2025-06-24 17:56:32,301 - pyskl - INFO - Epoch [80][500/1281] lr: 1.135e-02, eta: 5:38:07, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9988, loss_cls: 0.3668, loss: 0.3668 +2025-06-24 17:56:54,723 - pyskl - INFO - Epoch [80][600/1281] lr: 1.133e-02, eta: 5:37:44, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3486, loss: 0.3486 +2025-06-24 17:57:16,912 - pyskl - INFO - Epoch [80][700/1281] lr: 1.131e-02, eta: 5:37:22, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9981, loss_cls: 0.3782, loss: 0.3782 +2025-06-24 17:57:39,495 - pyskl - INFO - Epoch [80][800/1281] lr: 1.129e-02, eta: 5:36:59, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9994, loss_cls: 0.3739, loss: 0.3739 +2025-06-24 17:58:01,773 - pyskl - INFO - Epoch [80][900/1281] lr: 1.127e-02, eta: 5:36:37, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9988, loss_cls: 0.3520, loss: 0.3520 +2025-06-24 17:58:24,134 - pyskl - INFO - Epoch [80][1000/1281] lr: 1.125e-02, eta: 5:36:14, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9975, loss_cls: 0.3469, loss: 0.3469 +2025-06-24 17:58:46,281 - pyskl - INFO - Epoch [80][1100/1281] lr: 1.123e-02, eta: 5:35:52, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.3705, loss: 0.3705 +2025-06-24 17:59:08,613 - pyskl - INFO - Epoch [80][1200/1281] lr: 1.121e-02, eta: 5:35:29, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9956, loss_cls: 0.3650, loss: 0.3650 +2025-06-24 17:59:27,319 - pyskl - INFO - Saving checkpoint at 80 epochs +2025-06-24 18:00:11,702 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:00:11,770 - pyskl - INFO - +top1_acc 0.8998 +top5_acc 0.9926 +2025-06-24 18:00:11,770 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:00:11,781 - pyskl - INFO - +mean_acc 0.8686 +2025-06-24 18:00:11,787 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_69.pth was removed +2025-06-24 18:00:11,986 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_80.pth. +2025-06-24 18:00:11,986 - pyskl - INFO - Best top1_acc is 0.8998 at 80 epoch. +2025-06-24 18:00:11,990 - pyskl - INFO - Epoch(val) [80][533] top1_acc: 0.8998, top5_acc: 0.9926, mean_class_accuracy: 0.8686 +2025-06-24 18:00:54,109 - pyskl - INFO - Epoch [81][100/1281] lr: 1.117e-02, eta: 5:34:50, time: 0.421, data_time: 0.185, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2864, loss: 0.2864 +2025-06-24 18:01:16,886 - pyskl - INFO - Epoch [81][200/1281] lr: 1.115e-02, eta: 5:34:28, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2966, loss: 0.2966 +2025-06-24 18:01:39,016 - pyskl - INFO - Epoch [81][300/1281] lr: 1.113e-02, eta: 5:34:05, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9988, loss_cls: 0.3102, loss: 0.3102 +2025-06-24 18:02:01,342 - pyskl - INFO - Epoch [81][400/1281] lr: 1.111e-02, eta: 5:33:43, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9975, loss_cls: 0.3579, loss: 0.3579 +2025-06-24 18:02:23,981 - pyskl - INFO - Epoch [81][500/1281] lr: 1.109e-02, eta: 5:33:20, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9988, loss_cls: 0.3394, loss: 0.3394 +2025-06-24 18:02:46,762 - pyskl - INFO - Epoch [81][600/1281] lr: 1.107e-02, eta: 5:32:58, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9994, loss_cls: 0.3486, loss: 0.3486 +2025-06-24 18:03:09,262 - pyskl - INFO - Epoch [81][700/1281] lr: 1.105e-02, eta: 5:32:36, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9988, loss_cls: 0.3477, loss: 0.3477 +2025-06-24 18:03:31,539 - pyskl - INFO - Epoch [81][800/1281] lr: 1.103e-02, eta: 5:32:13, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3141, loss: 0.3141 +2025-06-24 18:03:54,326 - pyskl - INFO - Epoch [81][900/1281] lr: 1.101e-02, eta: 5:31:51, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9975, loss_cls: 0.3452, loss: 0.3452 +2025-06-24 18:04:16,791 - pyskl - INFO - Epoch [81][1000/1281] lr: 1.099e-02, eta: 5:31:29, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9988, loss_cls: 0.4084, loss: 0.4084 +2025-06-24 18:04:39,208 - pyskl - INFO - Epoch [81][1100/1281] lr: 1.097e-02, eta: 5:31:06, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9969, loss_cls: 0.3882, loss: 0.3882 +2025-06-24 18:05:01,853 - pyskl - INFO - Epoch [81][1200/1281] lr: 1.095e-02, eta: 5:30:44, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.9456, top5_acc: 1.0000, loss_cls: 0.3311, loss: 0.3311 +2025-06-24 18:05:20,530 - pyskl - INFO - Saving checkpoint at 81 epochs +2025-06-24 18:06:04,120 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:06:04,175 - pyskl - INFO - +top1_acc 0.8826 +top5_acc 0.9919 +2025-06-24 18:06:04,175 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:06:04,181 - pyskl - INFO - +mean_acc 0.8606 +2025-06-24 18:06:04,183 - pyskl - INFO - Epoch(val) [81][533] top1_acc: 0.8826, top5_acc: 0.9919, mean_class_accuracy: 0.8606 +2025-06-24 18:06:47,048 - pyskl - INFO - Epoch [82][100/1281] lr: 1.091e-02, eta: 5:30:05, time: 0.429, data_time: 0.194, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9981, loss_cls: 0.3232, loss: 0.3232 +2025-06-24 18:07:09,577 - pyskl - INFO - Epoch [82][200/1281] lr: 1.089e-02, eta: 5:29:43, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9981, loss_cls: 0.2790, loss: 0.2790 +2025-06-24 18:07:31,813 - pyskl - INFO - Epoch [82][300/1281] lr: 1.087e-02, eta: 5:29:20, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9988, loss_cls: 0.3027, loss: 0.3027 +2025-06-24 18:07:54,147 - pyskl - INFO - Epoch [82][400/1281] lr: 1.085e-02, eta: 5:28:58, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9994, loss_cls: 0.3383, loss: 0.3383 +2025-06-24 18:08:16,495 - pyskl - INFO - Epoch [82][500/1281] lr: 1.083e-02, eta: 5:28:35, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9975, loss_cls: 0.3694, loss: 0.3694 +2025-06-24 18:08:38,470 - pyskl - INFO - Epoch [82][600/1281] lr: 1.081e-02, eta: 5:28:13, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9988, loss_cls: 0.3274, loss: 0.3274 +2025-06-24 18:09:00,891 - pyskl - INFO - Epoch [82][700/1281] lr: 1.079e-02, eta: 5:27:50, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.3069, loss: 0.3069 +2025-06-24 18:09:22,937 - pyskl - INFO - Epoch [82][800/1281] lr: 1.077e-02, eta: 5:27:27, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9956, loss_cls: 0.3970, loss: 0.3970 +2025-06-24 18:09:44,896 - pyskl - INFO - Epoch [82][900/1281] lr: 1.075e-02, eta: 5:27:05, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9981, loss_cls: 0.3519, loss: 0.3519 +2025-06-24 18:10:06,872 - pyskl - INFO - Epoch [82][1000/1281] lr: 1.073e-02, eta: 5:26:42, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3319, loss: 0.3319 +2025-06-24 18:10:29,274 - pyskl - INFO - Epoch [82][1100/1281] lr: 1.071e-02, eta: 5:26:19, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 1.0000, loss_cls: 0.3901, loss: 0.3901 +2025-06-24 18:10:51,678 - pyskl - INFO - Epoch [82][1200/1281] lr: 1.069e-02, eta: 5:25:57, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9994, loss_cls: 0.3825, loss: 0.3825 +2025-06-24 18:11:10,743 - pyskl - INFO - Saving checkpoint at 82 epochs +2025-06-24 18:11:54,524 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:11:54,599 - pyskl - INFO - +top1_acc 0.8886 +top5_acc 0.9932 +2025-06-24 18:11:54,600 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:11:54,609 - pyskl - INFO - +mean_acc 0.8553 +2025-06-24 18:11:54,611 - pyskl - INFO - Epoch(val) [82][533] top1_acc: 0.8886, top5_acc: 0.9932, mean_class_accuracy: 0.8553 +2025-06-24 18:12:37,705 - pyskl - INFO - Epoch [83][100/1281] lr: 1.065e-02, eta: 5:25:18, time: 0.431, data_time: 0.194, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9981, loss_cls: 0.3169, loss: 0.3169 +2025-06-24 18:13:00,161 - pyskl - INFO - Epoch [83][200/1281] lr: 1.063e-02, eta: 5:24:56, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9988, loss_cls: 0.3034, loss: 0.3034 +2025-06-24 18:13:22,205 - pyskl - INFO - Epoch [83][300/1281] lr: 1.061e-02, eta: 5:24:33, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9988, loss_cls: 0.2868, loss: 0.2868 +2025-06-24 18:13:44,514 - pyskl - INFO - Epoch [83][400/1281] lr: 1.059e-02, eta: 5:24:11, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.3052, loss: 0.3052 +2025-06-24 18:14:07,092 - pyskl - INFO - Epoch [83][500/1281] lr: 1.057e-02, eta: 5:23:48, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.2996, loss: 0.2996 +2025-06-24 18:14:29,251 - pyskl - INFO - Epoch [83][600/1281] lr: 1.055e-02, eta: 5:23:26, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9988, loss_cls: 0.3539, loss: 0.3539 +2025-06-24 18:14:51,706 - pyskl - INFO - Epoch [83][700/1281] lr: 1.053e-02, eta: 5:23:03, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9975, loss_cls: 0.3405, loss: 0.3405 +2025-06-24 18:15:14,015 - pyskl - INFO - Epoch [83][800/1281] lr: 1.051e-02, eta: 5:22:41, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9988, loss_cls: 0.3373, loss: 0.3373 +2025-06-24 18:15:36,154 - pyskl - INFO - Epoch [83][900/1281] lr: 1.049e-02, eta: 5:22:18, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9975, loss_cls: 0.3206, loss: 0.3206 +2025-06-24 18:15:58,465 - pyskl - INFO - Epoch [83][1000/1281] lr: 1.047e-02, eta: 5:21:55, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9994, loss_cls: 0.3630, loss: 0.3630 +2025-06-24 18:16:20,960 - pyskl - INFO - Epoch [83][1100/1281] lr: 1.045e-02, eta: 5:21:33, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.3402, loss: 0.3402 +2025-06-24 18:16:43,188 - pyskl - INFO - Epoch [83][1200/1281] lr: 1.043e-02, eta: 5:21:10, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3209, loss: 0.3209 +2025-06-24 18:17:02,086 - pyskl - INFO - Saving checkpoint at 83 epochs +2025-06-24 18:17:46,518 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:17:46,590 - pyskl - INFO - +top1_acc 0.8943 +top5_acc 0.9940 +2025-06-24 18:17:46,590 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:17:46,598 - pyskl - INFO - +mean_acc 0.8474 +2025-06-24 18:17:46,600 - pyskl - INFO - Epoch(val) [83][533] top1_acc: 0.8943, top5_acc: 0.9940, mean_class_accuracy: 0.8474 +2025-06-24 18:18:29,231 - pyskl - INFO - Epoch [84][100/1281] lr: 1.040e-02, eta: 5:20:31, time: 0.426, data_time: 0.190, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3155, loss: 0.3155 +2025-06-24 18:18:51,493 - pyskl - INFO - Epoch [84][200/1281] lr: 1.038e-02, eta: 5:20:09, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9988, loss_cls: 0.2970, loss: 0.2970 +2025-06-24 18:19:14,285 - pyskl - INFO - Epoch [84][300/1281] lr: 1.036e-02, eta: 5:19:47, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.3233, loss: 0.3233 +2025-06-24 18:19:36,799 - pyskl - INFO - Epoch [84][400/1281] lr: 1.034e-02, eta: 5:19:24, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.3056, loss: 0.3056 +2025-06-24 18:19:59,150 - pyskl - INFO - Epoch [84][500/1281] lr: 1.031e-02, eta: 5:19:02, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9962, loss_cls: 0.3484, loss: 0.3484 +2025-06-24 18:20:21,512 - pyskl - INFO - Epoch [84][600/1281] lr: 1.029e-02, eta: 5:18:39, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.3475, loss: 0.3475 +2025-06-24 18:20:44,026 - pyskl - INFO - Epoch [84][700/1281] lr: 1.027e-02, eta: 5:18:17, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9994, loss_cls: 0.3345, loss: 0.3345 +2025-06-24 18:21:06,201 - pyskl - INFO - Epoch [84][800/1281] lr: 1.025e-02, eta: 5:17:54, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9994, loss_cls: 0.3324, loss: 0.3324 +2025-06-24 18:21:28,406 - pyskl - INFO - Epoch [84][900/1281] lr: 1.023e-02, eta: 5:17:32, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.3727, loss: 0.3727 +2025-06-24 18:21:50,697 - pyskl - INFO - Epoch [84][1000/1281] lr: 1.021e-02, eta: 5:17:09, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 1.0000, loss_cls: 0.3219, loss: 0.3219 +2025-06-24 18:22:12,996 - pyskl - INFO - Epoch [84][1100/1281] lr: 1.019e-02, eta: 5:16:47, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9988, loss_cls: 0.3534, loss: 0.3534 +2025-06-24 18:22:35,508 - pyskl - INFO - Epoch [84][1200/1281] lr: 1.017e-02, eta: 5:16:24, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3409, loss: 0.3409 +2025-06-24 18:22:53,947 - pyskl - INFO - Saving checkpoint at 84 epochs +2025-06-24 18:23:37,678 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:23:37,747 - pyskl - INFO - +top1_acc 0.8606 +top5_acc 0.9885 +2025-06-24 18:23:37,748 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:23:37,758 - pyskl - INFO - +mean_acc 0.8305 +2025-06-24 18:23:37,760 - pyskl - INFO - Epoch(val) [84][533] top1_acc: 0.8606, top5_acc: 0.9885, mean_class_accuracy: 0.8305 +2025-06-24 18:24:19,848 - pyskl - INFO - Epoch [85][100/1281] lr: 1.014e-02, eta: 5:15:45, time: 0.421, data_time: 0.185, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9994, loss_cls: 0.3385, loss: 0.3385 +2025-06-24 18:24:42,493 - pyskl - INFO - Epoch [85][200/1281] lr: 1.012e-02, eta: 5:15:23, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9975, loss_cls: 0.3044, loss: 0.3044 +2025-06-24 18:25:04,606 - pyskl - INFO - Epoch [85][300/1281] lr: 1.010e-02, eta: 5:15:00, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.2778, loss: 0.2778 +2025-06-24 18:25:26,961 - pyskl - INFO - Epoch [85][400/1281] lr: 1.008e-02, eta: 5:14:37, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 1.0000, loss_cls: 0.3066, loss: 0.3066 +2025-06-24 18:25:49,309 - pyskl - INFO - Epoch [85][500/1281] lr: 1.006e-02, eta: 5:14:15, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2795, loss: 0.2795 +2025-06-24 18:26:11,299 - pyskl - INFO - Epoch [85][600/1281] lr: 1.004e-02, eta: 5:13:52, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.3111, loss: 0.3111 +2025-06-24 18:26:33,675 - pyskl - INFO - Epoch [85][700/1281] lr: 1.002e-02, eta: 5:13:30, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3000, loss: 0.3000 +2025-06-24 18:26:55,853 - pyskl - INFO - Epoch [85][800/1281] lr: 9.998e-03, eta: 5:13:07, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 1.0000, loss_cls: 0.3192, loss: 0.3192 +2025-06-24 18:27:17,945 - pyskl - INFO - Epoch [85][900/1281] lr: 9.978e-03, eta: 5:12:44, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 1.0000, loss_cls: 0.3450, loss: 0.3450 +2025-06-24 18:27:40,440 - pyskl - INFO - Epoch [85][1000/1281] lr: 9.958e-03, eta: 5:12:22, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 1.0000, loss_cls: 0.2972, loss: 0.2972 +2025-06-24 18:28:02,868 - pyskl - INFO - Epoch [85][1100/1281] lr: 9.937e-03, eta: 5:11:59, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 1.0000, loss_cls: 0.3361, loss: 0.3361 +2025-06-24 18:28:25,223 - pyskl - INFO - Epoch [85][1200/1281] lr: 9.917e-03, eta: 5:11:37, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9988, loss_cls: 0.3076, loss: 0.3076 +2025-06-24 18:28:44,301 - pyskl - INFO - Saving checkpoint at 85 epochs +2025-06-24 18:29:28,291 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:29:28,346 - pyskl - INFO - +top1_acc 0.8795 +top5_acc 0.9920 +2025-06-24 18:29:28,346 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:29:28,353 - pyskl - INFO - +mean_acc 0.8530 +2025-06-24 18:29:28,355 - pyskl - INFO - Epoch(val) [85][533] top1_acc: 0.8795, top5_acc: 0.9920, mean_class_accuracy: 0.8530 +2025-06-24 18:30:10,435 - pyskl - INFO - Epoch [86][100/1281] lr: 9.881e-03, eta: 5:10:57, time: 0.421, data_time: 0.187, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9988, loss_cls: 0.2935, loss: 0.2935 +2025-06-24 18:30:32,847 - pyskl - INFO - Epoch [86][200/1281] lr: 9.861e-03, eta: 5:10:35, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 0.9556, top5_acc: 1.0000, loss_cls: 0.2643, loss: 0.2643 +2025-06-24 18:30:55,358 - pyskl - INFO - Epoch [86][300/1281] lr: 9.841e-03, eta: 5:10:13, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2398, loss: 0.2398 +2025-06-24 18:31:17,650 - pyskl - INFO - Epoch [86][400/1281] lr: 9.821e-03, eta: 5:09:50, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9988, loss_cls: 0.2459, loss: 0.2459 +2025-06-24 18:31:39,958 - pyskl - INFO - Epoch [86][500/1281] lr: 9.801e-03, eta: 5:09:28, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.3012, loss: 0.3012 +2025-06-24 18:32:02,095 - pyskl - INFO - Epoch [86][600/1281] lr: 9.781e-03, eta: 5:09:05, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2719, loss: 0.2719 +2025-06-24 18:32:24,246 - pyskl - INFO - Epoch [86][700/1281] lr: 9.762e-03, eta: 5:08:42, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.3146, loss: 0.3146 +2025-06-24 18:32:46,526 - pyskl - INFO - Epoch [86][800/1281] lr: 9.742e-03, eta: 5:08:20, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3127, loss: 0.3127 +2025-06-24 18:33:08,964 - pyskl - INFO - Epoch [86][900/1281] lr: 9.722e-03, eta: 5:07:57, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.3007, loss: 0.3007 +2025-06-24 18:33:31,341 - pyskl - INFO - Epoch [86][1000/1281] lr: 9.702e-03, eta: 5:07:35, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3360, loss: 0.3360 +2025-06-24 18:33:53,828 - pyskl - INFO - Epoch [86][1100/1281] lr: 9.682e-03, eta: 5:07:12, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9988, loss_cls: 0.2930, loss: 0.2930 +2025-06-24 18:34:16,363 - pyskl - INFO - Epoch [86][1200/1281] lr: 9.662e-03, eta: 5:06:50, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9975, loss_cls: 0.3329, loss: 0.3329 +2025-06-24 18:34:35,100 - pyskl - INFO - Saving checkpoint at 86 epochs +2025-06-24 18:35:19,202 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:35:19,260 - pyskl - INFO - +top1_acc 0.8943 +top5_acc 0.9950 +2025-06-24 18:35:19,260 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:35:19,269 - pyskl - INFO - +mean_acc 0.8524 +2025-06-24 18:35:19,271 - pyskl - INFO - Epoch(val) [86][533] top1_acc: 0.8943, top5_acc: 0.9950, mean_class_accuracy: 0.8524 +2025-06-24 18:36:02,466 - pyskl - INFO - Epoch [87][100/1281] lr: 9.626e-03, eta: 5:06:11, time: 0.432, data_time: 0.195, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2601, loss: 0.2601 +2025-06-24 18:36:25,202 - pyskl - INFO - Epoch [87][200/1281] lr: 9.606e-03, eta: 5:05:49, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2548, loss: 0.2548 +2025-06-24 18:36:47,364 - pyskl - INFO - Epoch [87][300/1281] lr: 9.586e-03, eta: 5:05:27, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2188, loss: 0.2188 +2025-06-24 18:37:09,702 - pyskl - INFO - Epoch [87][400/1281] lr: 9.566e-03, eta: 5:05:04, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9994, loss_cls: 0.2312, loss: 0.2312 +2025-06-24 18:37:31,783 - pyskl - INFO - Epoch [87][500/1281] lr: 9.546e-03, eta: 5:04:41, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9988, loss_cls: 0.3089, loss: 0.3089 +2025-06-24 18:37:54,021 - pyskl - INFO - Epoch [87][600/1281] lr: 9.527e-03, eta: 5:04:19, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9981, loss_cls: 0.2813, loss: 0.2813 +2025-06-24 18:38:16,536 - pyskl - INFO - Epoch [87][700/1281] lr: 9.507e-03, eta: 5:03:56, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9994, loss_cls: 0.3110, loss: 0.3110 +2025-06-24 18:38:38,811 - pyskl - INFO - Epoch [87][800/1281] lr: 9.487e-03, eta: 5:03:34, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.2982, loss: 0.2982 +2025-06-24 18:39:01,156 - pyskl - INFO - Epoch [87][900/1281] lr: 9.467e-03, eta: 5:03:11, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 1.0000, loss_cls: 0.3627, loss: 0.3627 +2025-06-24 18:39:23,588 - pyskl - INFO - Epoch [87][1000/1281] lr: 9.447e-03, eta: 5:02:49, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3044, loss: 0.3044 +2025-06-24 18:39:45,876 - pyskl - INFO - Epoch [87][1100/1281] lr: 9.427e-03, eta: 5:02:26, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9994, loss_cls: 0.2917, loss: 0.2917 +2025-06-24 18:40:08,373 - pyskl - INFO - Epoch [87][1200/1281] lr: 9.408e-03, eta: 5:02:04, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 1.0000, loss_cls: 0.3389, loss: 0.3389 +2025-06-24 18:40:27,362 - pyskl - INFO - Saving checkpoint at 87 epochs +2025-06-24 18:41:11,513 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:41:11,570 - pyskl - INFO - +top1_acc 0.9022 +top5_acc 0.9946 +2025-06-24 18:41:11,571 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:41:11,577 - pyskl - INFO - +mean_acc 0.8732 +2025-06-24 18:41:11,581 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_80.pth was removed +2025-06-24 18:41:11,748 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_87.pth. +2025-06-24 18:41:11,749 - pyskl - INFO - Best top1_acc is 0.9022 at 87 epoch. +2025-06-24 18:41:11,751 - pyskl - INFO - Epoch(val) [87][533] top1_acc: 0.9022, top5_acc: 0.9946, mean_class_accuracy: 0.8732 +2025-06-24 18:41:53,996 - pyskl - INFO - Epoch [88][100/1281] lr: 9.372e-03, eta: 5:01:24, time: 0.422, data_time: 0.187, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2401, loss: 0.2401 +2025-06-24 18:42:17,013 - pyskl - INFO - Epoch [88][200/1281] lr: 9.352e-03, eta: 5:01:02, time: 0.230, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9981, loss_cls: 0.2940, loss: 0.2940 +2025-06-24 18:42:39,193 - pyskl - INFO - Epoch [88][300/1281] lr: 9.332e-03, eta: 5:00:40, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9981, loss_cls: 0.2724, loss: 0.2724 +2025-06-24 18:43:01,459 - pyskl - INFO - Epoch [88][400/1281] lr: 9.312e-03, eta: 5:00:17, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9988, loss_cls: 0.3087, loss: 0.3087 +2025-06-24 18:43:23,796 - pyskl - INFO - Epoch [88][500/1281] lr: 9.293e-03, eta: 4:59:55, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2706, loss: 0.2706 +2025-06-24 18:43:45,895 - pyskl - INFO - Epoch [88][600/1281] lr: 9.273e-03, eta: 4:59:32, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2591, loss: 0.2591 +2025-06-24 18:44:07,885 - pyskl - INFO - Epoch [88][700/1281] lr: 9.253e-03, eta: 4:59:09, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9981, loss_cls: 0.2688, loss: 0.2688 +2025-06-24 18:44:30,359 - pyskl - INFO - Epoch [88][800/1281] lr: 9.233e-03, eta: 4:58:47, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2826, loss: 0.2826 +2025-06-24 18:44:52,505 - pyskl - INFO - Epoch [88][900/1281] lr: 9.214e-03, eta: 4:58:24, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9988, loss_cls: 0.2712, loss: 0.2712 +2025-06-24 18:45:14,934 - pyskl - INFO - Epoch [88][1000/1281] lr: 9.194e-03, eta: 4:58:02, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9988, loss_cls: 0.3316, loss: 0.3316 +2025-06-24 18:45:37,233 - pyskl - INFO - Epoch [88][1100/1281] lr: 9.174e-03, eta: 4:57:39, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9988, loss_cls: 0.2834, loss: 0.2834 +2025-06-24 18:45:59,478 - pyskl - INFO - Epoch [88][1200/1281] lr: 9.155e-03, eta: 4:57:17, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3364, loss: 0.3364 +2025-06-24 18:46:18,486 - pyskl - INFO - Saving checkpoint at 88 epochs +2025-06-24 18:47:02,261 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:47:02,317 - pyskl - INFO - +top1_acc 0.8760 +top5_acc 0.9891 +2025-06-24 18:47:02,317 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:47:02,324 - pyskl - INFO - +mean_acc 0.8528 +2025-06-24 18:47:02,326 - pyskl - INFO - Epoch(val) [88][533] top1_acc: 0.8760, top5_acc: 0.9891, mean_class_accuracy: 0.8528 +2025-06-24 18:47:44,073 - pyskl - INFO - Epoch [89][100/1281] lr: 9.119e-03, eta: 4:56:37, time: 0.417, data_time: 0.184, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2428, loss: 0.2428 +2025-06-24 18:48:06,857 - pyskl - INFO - Epoch [89][200/1281] lr: 9.099e-03, eta: 4:56:15, time: 0.228, data_time: 0.001, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2213, loss: 0.2213 +2025-06-24 18:48:28,958 - pyskl - INFO - Epoch [89][300/1281] lr: 9.080e-03, eta: 4:55:52, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2829, loss: 0.2829 +2025-06-24 18:48:51,314 - pyskl - INFO - Epoch [89][400/1281] lr: 9.060e-03, eta: 4:55:30, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9988, loss_cls: 0.2846, loss: 0.2846 +2025-06-24 18:49:13,595 - pyskl - INFO - Epoch [89][500/1281] lr: 9.040e-03, eta: 4:55:07, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2674, loss: 0.2674 +2025-06-24 18:49:36,032 - pyskl - INFO - Epoch [89][600/1281] lr: 9.021e-03, eta: 4:54:45, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.3016, loss: 0.3016 +2025-06-24 18:49:58,384 - pyskl - INFO - Epoch [89][700/1281] lr: 9.001e-03, eta: 4:54:22, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9988, loss_cls: 0.3380, loss: 0.3380 +2025-06-24 18:50:20,741 - pyskl - INFO - Epoch [89][800/1281] lr: 8.982e-03, eta: 4:54:00, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9988, loss_cls: 0.3364, loss: 0.3364 +2025-06-24 18:50:43,249 - pyskl - INFO - Epoch [89][900/1281] lr: 8.962e-03, eta: 4:53:37, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9994, loss_cls: 0.3221, loss: 0.3221 +2025-06-24 18:51:05,333 - pyskl - INFO - Epoch [89][1000/1281] lr: 8.942e-03, eta: 4:53:15, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 1.0000, loss_cls: 0.3098, loss: 0.3098 +2025-06-24 18:51:27,743 - pyskl - INFO - Epoch [89][1100/1281] lr: 8.923e-03, eta: 4:52:52, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 1.0000, loss_cls: 0.3161, loss: 0.3161 +2025-06-24 18:51:50,070 - pyskl - INFO - Epoch [89][1200/1281] lr: 8.903e-03, eta: 4:52:30, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9988, loss_cls: 0.3126, loss: 0.3126 +2025-06-24 18:52:08,954 - pyskl - INFO - Saving checkpoint at 89 epochs +2025-06-24 18:52:52,771 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:52:52,826 - pyskl - INFO - +top1_acc 0.8749 +top5_acc 0.9920 +2025-06-24 18:52:52,826 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:52:52,833 - pyskl - INFO - +mean_acc 0.8486 +2025-06-24 18:52:52,835 - pyskl - INFO - Epoch(val) [89][533] top1_acc: 0.8749, top5_acc: 0.9920, mean_class_accuracy: 0.8486 +2025-06-24 18:53:35,265 - pyskl - INFO - Epoch [90][100/1281] lr: 8.868e-03, eta: 4:51:50, time: 0.424, data_time: 0.186, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2415, loss: 0.2415 +2025-06-24 18:53:57,774 - pyskl - INFO - Epoch [90][200/1281] lr: 8.848e-03, eta: 4:51:28, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9988, loss_cls: 0.2665, loss: 0.2665 +2025-06-24 18:54:20,034 - pyskl - INFO - Epoch [90][300/1281] lr: 8.829e-03, eta: 4:51:05, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 1.0000, loss_cls: 0.3152, loss: 0.3152 +2025-06-24 18:54:42,533 - pyskl - INFO - Epoch [90][400/1281] lr: 8.809e-03, eta: 4:50:43, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9981, loss_cls: 0.3267, loss: 0.3267 +2025-06-24 18:55:04,800 - pyskl - INFO - Epoch [90][500/1281] lr: 8.790e-03, eta: 4:50:20, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9988, loss_cls: 0.2843, loss: 0.2843 +2025-06-24 18:55:27,271 - pyskl - INFO - Epoch [90][600/1281] lr: 8.770e-03, eta: 4:49:58, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2785, loss: 0.2785 +2025-06-24 18:55:49,570 - pyskl - INFO - Epoch [90][700/1281] lr: 8.751e-03, eta: 4:49:35, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2701, loss: 0.2701 +2025-06-24 18:56:11,980 - pyskl - INFO - Epoch [90][800/1281] lr: 8.731e-03, eta: 4:49:13, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 1.0000, loss_cls: 0.3259, loss: 0.3259 +2025-06-24 18:56:34,623 - pyskl - INFO - Epoch [90][900/1281] lr: 8.712e-03, eta: 4:48:51, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.2937, loss: 0.2937 +2025-06-24 18:56:56,908 - pyskl - INFO - Epoch [90][1000/1281] lr: 8.692e-03, eta: 4:48:28, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2616, loss: 0.2616 +2025-06-24 18:57:19,185 - pyskl - INFO - Epoch [90][1100/1281] lr: 8.673e-03, eta: 4:48:05, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9988, loss_cls: 0.2808, loss: 0.2808 +2025-06-24 18:57:41,706 - pyskl - INFO - Epoch [90][1200/1281] lr: 8.653e-03, eta: 4:47:43, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9994, loss_cls: 0.2583, loss: 0.2583 +2025-06-24 18:58:00,792 - pyskl - INFO - Saving checkpoint at 90 epochs +2025-06-24 18:58:44,500 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:58:44,567 - pyskl - INFO - +top1_acc 0.8958 +top5_acc 0.9933 +2025-06-24 18:58:44,568 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:58:44,582 - pyskl - INFO - +mean_acc 0.8621 +2025-06-24 18:58:44,585 - pyskl - INFO - Epoch(val) [90][533] top1_acc: 0.8958, top5_acc: 0.9933, mean_class_accuracy: 0.8621 +2025-06-24 18:59:27,083 - pyskl - INFO - Epoch [91][100/1281] lr: 8.618e-03, eta: 4:47:04, time: 0.425, data_time: 0.191, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2545, loss: 0.2545 +2025-06-24 18:59:49,483 - pyskl - INFO - Epoch [91][200/1281] lr: 8.599e-03, eta: 4:46:41, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2459, loss: 0.2459 +2025-06-24 19:00:11,586 - pyskl - INFO - Epoch [91][300/1281] lr: 8.579e-03, eta: 4:46:19, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9994, loss_cls: 0.2654, loss: 0.2654 +2025-06-24 19:00:34,048 - pyskl - INFO - Epoch [91][400/1281] lr: 8.560e-03, eta: 4:45:56, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2385, loss: 0.2385 +2025-06-24 19:00:56,438 - pyskl - INFO - Epoch [91][500/1281] lr: 8.540e-03, eta: 4:45:34, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2274, loss: 0.2274 +2025-06-24 19:01:18,494 - pyskl - INFO - Epoch [91][600/1281] lr: 8.521e-03, eta: 4:45:11, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2782, loss: 0.2782 +2025-06-24 19:01:40,816 - pyskl - INFO - Epoch [91][700/1281] lr: 8.502e-03, eta: 4:44:49, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 1.0000, loss_cls: 0.2945, loss: 0.2945 +2025-06-24 19:02:03,261 - pyskl - INFO - Epoch [91][800/1281] lr: 8.482e-03, eta: 4:44:26, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 1.0000, loss_cls: 0.3003, loss: 0.3003 +2025-06-24 19:02:25,890 - pyskl - INFO - Epoch [91][900/1281] lr: 8.463e-03, eta: 4:44:04, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9981, loss_cls: 0.3041, loss: 0.3041 +2025-06-24 19:02:48,626 - pyskl - INFO - Epoch [91][1000/1281] lr: 8.444e-03, eta: 4:43:42, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2830, loss: 0.2830 +2025-06-24 19:03:11,039 - pyskl - INFO - Epoch [91][1100/1281] lr: 8.424e-03, eta: 4:43:19, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 1.0000, loss_cls: 0.2659, loss: 0.2659 +2025-06-24 19:03:33,261 - pyskl - INFO - Epoch [91][1200/1281] lr: 8.405e-03, eta: 4:42:56, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9988, loss_cls: 0.2800, loss: 0.2800 +2025-06-24 19:03:52,535 - pyskl - INFO - Saving checkpoint at 91 epochs +2025-06-24 19:04:36,460 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:04:36,519 - pyskl - INFO - +top1_acc 0.8947 +top5_acc 0.9926 +2025-06-24 19:04:36,519 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:04:36,526 - pyskl - INFO - +mean_acc 0.8674 +2025-06-24 19:04:36,528 - pyskl - INFO - Epoch(val) [91][533] top1_acc: 0.8947, top5_acc: 0.9926, mean_class_accuracy: 0.8674 +2025-06-24 19:05:19,170 - pyskl - INFO - Epoch [92][100/1281] lr: 8.370e-03, eta: 4:42:17, time: 0.426, data_time: 0.191, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2614, loss: 0.2614 +2025-06-24 19:05:41,587 - pyskl - INFO - Epoch [92][200/1281] lr: 8.351e-03, eta: 4:41:55, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2132, loss: 0.2132 +2025-06-24 19:06:04,173 - pyskl - INFO - Epoch [92][300/1281] lr: 8.332e-03, eta: 4:41:32, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2425, loss: 0.2425 +2025-06-24 19:06:26,407 - pyskl - INFO - Epoch [92][400/1281] lr: 8.312e-03, eta: 4:41:10, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2823, loss: 0.2823 +2025-06-24 19:06:49,108 - pyskl - INFO - Epoch [92][500/1281] lr: 8.293e-03, eta: 4:40:48, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9988, loss_cls: 0.2808, loss: 0.2808 +2025-06-24 19:07:11,654 - pyskl - INFO - Epoch [92][600/1281] lr: 8.274e-03, eta: 4:40:25, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9988, loss_cls: 0.2798, loss: 0.2798 +2025-06-24 19:07:34,018 - pyskl - INFO - Epoch [92][700/1281] lr: 8.255e-03, eta: 4:40:03, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2556, loss: 0.2556 +2025-06-24 19:07:56,505 - pyskl - INFO - Epoch [92][800/1281] lr: 8.235e-03, eta: 4:39:40, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2207, loss: 0.2207 +2025-06-24 19:08:18,984 - pyskl - INFO - Epoch [92][900/1281] lr: 8.216e-03, eta: 4:39:18, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2439, loss: 0.2439 +2025-06-24 19:08:41,300 - pyskl - INFO - Epoch [92][1000/1281] lr: 8.197e-03, eta: 4:38:55, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.2742, loss: 0.2742 +2025-06-24 19:09:03,839 - pyskl - INFO - Epoch [92][1100/1281] lr: 8.178e-03, eta: 4:38:33, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2747, loss: 0.2747 +2025-06-24 19:09:26,290 - pyskl - INFO - Epoch [92][1200/1281] lr: 8.159e-03, eta: 4:38:11, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2748, loss: 0.2748 +2025-06-24 19:09:45,329 - pyskl - INFO - Saving checkpoint at 92 epochs +2025-06-24 19:10:28,590 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:10:28,645 - pyskl - INFO - +top1_acc 0.8979 +top5_acc 0.9938 +2025-06-24 19:10:28,645 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:10:28,652 - pyskl - INFO - +mean_acc 0.8622 +2025-06-24 19:10:28,653 - pyskl - INFO - Epoch(val) [92][533] top1_acc: 0.8979, top5_acc: 0.9938, mean_class_accuracy: 0.8622 +2025-06-24 19:11:11,701 - pyskl - INFO - Epoch [93][100/1281] lr: 8.124e-03, eta: 4:37:31, time: 0.430, data_time: 0.191, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9975, loss_cls: 0.2921, loss: 0.2921 +2025-06-24 19:11:34,306 - pyskl - INFO - Epoch [93][200/1281] lr: 8.105e-03, eta: 4:37:09, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2180, loss: 0.2180 +2025-06-24 19:11:56,524 - pyskl - INFO - Epoch [93][300/1281] lr: 8.086e-03, eta: 4:36:46, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9988, loss_cls: 0.2376, loss: 0.2376 +2025-06-24 19:12:18,951 - pyskl - INFO - Epoch [93][400/1281] lr: 8.067e-03, eta: 4:36:24, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2450, loss: 0.2450 +2025-06-24 19:12:41,110 - pyskl - INFO - Epoch [93][500/1281] lr: 8.047e-03, eta: 4:36:01, time: 0.222, data_time: 0.001, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2317, loss: 0.2317 +2025-06-24 19:13:03,494 - pyskl - INFO - Epoch [93][600/1281] lr: 8.028e-03, eta: 4:35:39, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 1.0000, loss_cls: 0.2589, loss: 0.2589 +2025-06-24 19:13:25,838 - pyskl - INFO - Epoch [93][700/1281] lr: 8.009e-03, eta: 4:35:16, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9988, loss_cls: 0.2475, loss: 0.2475 +2025-06-24 19:13:48,113 - pyskl - INFO - Epoch [93][800/1281] lr: 7.990e-03, eta: 4:34:54, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9994, loss_cls: 0.3119, loss: 0.3119 +2025-06-24 19:14:10,471 - pyskl - INFO - Epoch [93][900/1281] lr: 7.971e-03, eta: 4:34:31, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3091, loss: 0.3091 +2025-06-24 19:14:32,853 - pyskl - INFO - Epoch [93][1000/1281] lr: 7.952e-03, eta: 4:34:09, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.3010, loss: 0.3010 +2025-06-24 19:14:55,109 - pyskl - INFO - Epoch [93][1100/1281] lr: 7.933e-03, eta: 4:33:46, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9975, loss_cls: 0.3025, loss: 0.3025 +2025-06-24 19:15:17,606 - pyskl - INFO - Epoch [93][1200/1281] lr: 7.914e-03, eta: 4:33:24, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9981, loss_cls: 0.2743, loss: 0.2743 +2025-06-24 19:15:36,517 - pyskl - INFO - Saving checkpoint at 93 epochs +2025-06-24 19:16:19,903 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:16:19,966 - pyskl - INFO - +top1_acc 0.8871 +top5_acc 0.9933 +2025-06-24 19:16:19,966 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:16:19,974 - pyskl - INFO - +mean_acc 0.8465 +2025-06-24 19:16:19,976 - pyskl - INFO - Epoch(val) [93][533] top1_acc: 0.8871, top5_acc: 0.9933, mean_class_accuracy: 0.8465 +2025-06-24 19:17:01,780 - pyskl - INFO - Epoch [94][100/1281] lr: 7.880e-03, eta: 4:32:44, time: 0.418, data_time: 0.186, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2565, loss: 0.2565 +2025-06-24 19:17:24,456 - pyskl - INFO - Epoch [94][200/1281] lr: 7.861e-03, eta: 4:32:22, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2256, loss: 0.2256 +2025-06-24 19:17:46,814 - pyskl - INFO - Epoch [94][300/1281] lr: 7.842e-03, eta: 4:31:59, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.2211, loss: 0.2211 +2025-06-24 19:18:09,325 - pyskl - INFO - Epoch [94][400/1281] lr: 7.823e-03, eta: 4:31:37, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.2742, loss: 0.2742 +2025-06-24 19:18:31,604 - pyskl - INFO - Epoch [94][500/1281] lr: 7.804e-03, eta: 4:31:14, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9994, loss_cls: 0.2667, loss: 0.2667 +2025-06-24 19:18:53,756 - pyskl - INFO - Epoch [94][600/1281] lr: 7.785e-03, eta: 4:30:52, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.2857, loss: 0.2857 +2025-06-24 19:19:16,163 - pyskl - INFO - Epoch [94][700/1281] lr: 7.766e-03, eta: 4:30:29, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2733, loss: 0.2733 +2025-06-24 19:19:38,419 - pyskl - INFO - Epoch [94][800/1281] lr: 7.747e-03, eta: 4:30:07, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9988, loss_cls: 0.2730, loss: 0.2730 +2025-06-24 19:20:00,688 - pyskl - INFO - Epoch [94][900/1281] lr: 7.728e-03, eta: 4:29:44, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 1.0000, loss_cls: 0.2856, loss: 0.2856 +2025-06-24 19:20:23,122 - pyskl - INFO - Epoch [94][1000/1281] lr: 7.709e-03, eta: 4:29:22, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.3076, loss: 0.3076 +2025-06-24 19:20:45,537 - pyskl - INFO - Epoch [94][1100/1281] lr: 7.690e-03, eta: 4:28:59, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9988, loss_cls: 0.2480, loss: 0.2480 +2025-06-24 19:21:08,274 - pyskl - INFO - Epoch [94][1200/1281] lr: 7.672e-03, eta: 4:28:37, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2469, loss: 0.2469 +2025-06-24 19:21:26,996 - pyskl - INFO - Saving checkpoint at 94 epochs +2025-06-24 19:22:10,691 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:22:10,748 - pyskl - INFO - +top1_acc 0.8978 +top5_acc 0.9957 +2025-06-24 19:22:10,748 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:22:10,755 - pyskl - INFO - +mean_acc 0.8610 +2025-06-24 19:22:10,757 - pyskl - INFO - Epoch(val) [94][533] top1_acc: 0.8978, top5_acc: 0.9957, mean_class_accuracy: 0.8610 +2025-06-24 19:22:52,647 - pyskl - INFO - Epoch [95][100/1281] lr: 7.637e-03, eta: 4:27:57, time: 0.419, data_time: 0.186, memory: 4083, top1_acc: 0.9500, top5_acc: 1.0000, loss_cls: 0.2776, loss: 0.2776 +2025-06-24 19:23:15,012 - pyskl - INFO - Epoch [95][200/1281] lr: 7.619e-03, eta: 4:27:35, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.2154, loss: 0.2154 +2025-06-24 19:23:37,343 - pyskl - INFO - Epoch [95][300/1281] lr: 7.600e-03, eta: 4:27:12, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9994, loss_cls: 0.1974, loss: 0.1974 +2025-06-24 19:23:59,859 - pyskl - INFO - Epoch [95][400/1281] lr: 7.581e-03, eta: 4:26:50, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2311, loss: 0.2311 +2025-06-24 19:24:22,260 - pyskl - INFO - Epoch [95][500/1281] lr: 7.562e-03, eta: 4:26:27, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2355, loss: 0.2355 +2025-06-24 19:24:44,626 - pyskl - INFO - Epoch [95][600/1281] lr: 7.543e-03, eta: 4:26:05, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9988, loss_cls: 0.2459, loss: 0.2459 +2025-06-24 19:25:06,942 - pyskl - INFO - Epoch [95][700/1281] lr: 7.525e-03, eta: 4:25:42, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2320, loss: 0.2320 +2025-06-24 19:25:29,220 - pyskl - INFO - Epoch [95][800/1281] lr: 7.506e-03, eta: 4:25:20, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2168, loss: 0.2168 +2025-06-24 19:25:51,859 - pyskl - INFO - Epoch [95][900/1281] lr: 7.487e-03, eta: 4:24:57, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2350, loss: 0.2350 +2025-06-24 19:26:14,588 - pyskl - INFO - Epoch [95][1000/1281] lr: 7.468e-03, eta: 4:24:35, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9988, loss_cls: 0.2770, loss: 0.2770 +2025-06-24 19:26:37,172 - pyskl - INFO - Epoch [95][1100/1281] lr: 7.450e-03, eta: 4:24:13, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2813, loss: 0.2813 +2025-06-24 19:26:59,255 - pyskl - INFO - Epoch [95][1200/1281] lr: 7.431e-03, eta: 4:23:50, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9994, loss_cls: 0.2202, loss: 0.2202 +2025-06-24 19:27:18,023 - pyskl - INFO - Saving checkpoint at 95 epochs +2025-06-24 19:28:02,064 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:28:02,145 - pyskl - INFO - +top1_acc 0.9114 +top5_acc 0.9946 +2025-06-24 19:28:02,145 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:28:02,153 - pyskl - INFO - +mean_acc 0.8755 +2025-06-24 19:28:02,157 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_87.pth was removed +2025-06-24 19:28:02,367 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_95.pth. +2025-06-24 19:28:02,368 - pyskl - INFO - Best top1_acc is 0.9114 at 95 epoch. +2025-06-24 19:28:02,370 - pyskl - INFO - Epoch(val) [95][533] top1_acc: 0.9114, top5_acc: 0.9946, mean_class_accuracy: 0.8755 +2025-06-24 19:28:44,604 - pyskl - INFO - Epoch [96][100/1281] lr: 7.397e-03, eta: 4:23:10, time: 0.422, data_time: 0.188, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2031, loss: 0.2031 +2025-06-24 19:29:06,921 - pyskl - INFO - Epoch [96][200/1281] lr: 7.379e-03, eta: 4:22:48, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2430, loss: 0.2430 +2025-06-24 19:29:29,602 - pyskl - INFO - Epoch [96][300/1281] lr: 7.360e-03, eta: 4:22:26, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2195, loss: 0.2195 +2025-06-24 19:29:51,953 - pyskl - INFO - Epoch [96][400/1281] lr: 7.341e-03, eta: 4:22:03, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9988, loss_cls: 0.2345, loss: 0.2345 +2025-06-24 19:30:14,160 - pyskl - INFO - Epoch [96][500/1281] lr: 7.323e-03, eta: 4:21:40, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.2834, loss: 0.2834 +2025-06-24 19:30:36,713 - pyskl - INFO - Epoch [96][600/1281] lr: 7.304e-03, eta: 4:21:18, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2421, loss: 0.2421 +2025-06-24 19:30:59,219 - pyskl - INFO - Epoch [96][700/1281] lr: 7.286e-03, eta: 4:20:56, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2451, loss: 0.2451 +2025-06-24 19:31:21,312 - pyskl - INFO - Epoch [96][800/1281] lr: 7.267e-03, eta: 4:20:33, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2262, loss: 0.2262 +2025-06-24 19:31:43,832 - pyskl - INFO - Epoch [96][900/1281] lr: 7.249e-03, eta: 4:20:11, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.2096, loss: 0.2096 +2025-06-24 19:32:06,150 - pyskl - INFO - Epoch [96][1000/1281] lr: 7.230e-03, eta: 4:19:48, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.1939, loss: 0.1939 +2025-06-24 19:32:28,707 - pyskl - INFO - Epoch [96][1100/1281] lr: 7.211e-03, eta: 4:19:26, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1939, loss: 0.1939 +2025-06-24 19:32:51,156 - pyskl - INFO - Epoch [96][1200/1281] lr: 7.193e-03, eta: 4:19:03, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9988, loss_cls: 0.2488, loss: 0.2488 +2025-06-24 19:33:10,161 - pyskl - INFO - Saving checkpoint at 96 epochs +2025-06-24 19:33:54,218 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:33:54,279 - pyskl - INFO - +top1_acc 0.9060 +top5_acc 0.9951 +2025-06-24 19:33:54,279 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:33:54,286 - pyskl - INFO - +mean_acc 0.8729 +2025-06-24 19:33:54,289 - pyskl - INFO - Epoch(val) [96][533] top1_acc: 0.9060, top5_acc: 0.9951, mean_class_accuracy: 0.8729 +2025-06-24 19:34:36,712 - pyskl - INFO - Epoch [97][100/1281] lr: 7.159e-03, eta: 4:18:24, time: 0.424, data_time: 0.192, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2068, loss: 0.2068 +2025-06-24 19:34:59,433 - pyskl - INFO - Epoch [97][200/1281] lr: 7.141e-03, eta: 4:18:01, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 0.9994, loss_cls: 0.1890, loss: 0.1890 +2025-06-24 19:35:21,653 - pyskl - INFO - Epoch [97][300/1281] lr: 7.123e-03, eta: 4:17:39, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2277, loss: 0.2277 +2025-06-24 19:35:44,268 - pyskl - INFO - Epoch [97][400/1281] lr: 7.104e-03, eta: 4:17:16, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2217, loss: 0.2217 +2025-06-24 19:36:06,548 - pyskl - INFO - Epoch [97][500/1281] lr: 7.086e-03, eta: 4:16:54, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2592, loss: 0.2592 +2025-06-24 19:36:28,799 - pyskl - INFO - Epoch [97][600/1281] lr: 7.067e-03, eta: 4:16:31, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2197, loss: 0.2197 +2025-06-24 19:36:50,978 - pyskl - INFO - Epoch [97][700/1281] lr: 7.049e-03, eta: 4:16:09, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.2340, loss: 0.2340 +2025-06-24 19:37:13,444 - pyskl - INFO - Epoch [97][800/1281] lr: 7.030e-03, eta: 4:15:46, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2287, loss: 0.2287 +2025-06-24 19:37:35,936 - pyskl - INFO - Epoch [97][900/1281] lr: 7.012e-03, eta: 4:15:24, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2251, loss: 0.2251 +2025-06-24 19:37:58,306 - pyskl - INFO - Epoch [97][1000/1281] lr: 6.994e-03, eta: 4:15:01, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2476, loss: 0.2476 +2025-06-24 19:38:20,336 - pyskl - INFO - Epoch [97][1100/1281] lr: 6.975e-03, eta: 4:14:39, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2130, loss: 0.2130 +2025-06-24 19:38:42,537 - pyskl - INFO - Epoch [97][1200/1281] lr: 6.957e-03, eta: 4:14:16, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 1.0000, loss_cls: 0.2689, loss: 0.2689 +2025-06-24 19:39:01,394 - pyskl - INFO - Saving checkpoint at 97 epochs +2025-06-24 19:39:45,264 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:39:45,322 - pyskl - INFO - +top1_acc 0.9119 +top5_acc 0.9942 +2025-06-24 19:39:45,322 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:39:45,329 - pyskl - INFO - +mean_acc 0.8753 +2025-06-24 19:39:45,333 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_95.pth was removed +2025-06-24 19:39:45,514 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_97.pth. +2025-06-24 19:39:45,515 - pyskl - INFO - Best top1_acc is 0.9119 at 97 epoch. +2025-06-24 19:39:45,518 - pyskl - INFO - Epoch(val) [97][533] top1_acc: 0.9119, top5_acc: 0.9942, mean_class_accuracy: 0.8753 +2025-06-24 19:40:28,045 - pyskl - INFO - Epoch [98][100/1281] lr: 6.924e-03, eta: 4:13:37, time: 0.425, data_time: 0.186, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9994, loss_cls: 0.2329, loss: 0.2329 +2025-06-24 19:40:50,346 - pyskl - INFO - Epoch [98][200/1281] lr: 6.906e-03, eta: 4:13:14, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1562, loss: 0.1562 +2025-06-24 19:41:12,921 - pyskl - INFO - Epoch [98][300/1281] lr: 6.887e-03, eta: 4:12:52, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9994, loss_cls: 0.2369, loss: 0.2369 +2025-06-24 19:41:35,156 - pyskl - INFO - Epoch [98][400/1281] lr: 6.869e-03, eta: 4:12:29, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1718, loss: 0.1718 +2025-06-24 19:41:57,844 - pyskl - INFO - Epoch [98][500/1281] lr: 6.851e-03, eta: 4:12:07, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2454, loss: 0.2454 +2025-06-24 19:42:20,269 - pyskl - INFO - Epoch [98][600/1281] lr: 6.833e-03, eta: 4:11:44, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 1.0000, loss_cls: 0.2392, loss: 0.2392 +2025-06-24 19:42:42,568 - pyskl - INFO - Epoch [98][700/1281] lr: 6.814e-03, eta: 4:11:22, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2220, loss: 0.2220 +2025-06-24 19:43:05,156 - pyskl - INFO - Epoch [98][800/1281] lr: 6.796e-03, eta: 4:10:59, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2146, loss: 0.2146 +2025-06-24 19:43:27,591 - pyskl - INFO - Epoch [98][900/1281] lr: 6.778e-03, eta: 4:10:37, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2704, loss: 0.2704 +2025-06-24 19:43:50,232 - pyskl - INFO - Epoch [98][1000/1281] lr: 6.760e-03, eta: 4:10:15, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1978, loss: 0.1978 +2025-06-24 19:44:12,468 - pyskl - INFO - Epoch [98][1100/1281] lr: 6.742e-03, eta: 4:09:52, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2284, loss: 0.2284 +2025-06-24 19:44:34,660 - pyskl - INFO - Epoch [98][1200/1281] lr: 6.724e-03, eta: 4:09:29, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9988, loss_cls: 0.2053, loss: 0.2053 +2025-06-24 19:44:53,643 - pyskl - INFO - Saving checkpoint at 98 epochs +2025-06-24 19:45:37,490 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:45:37,546 - pyskl - INFO - +top1_acc 0.9078 +top5_acc 0.9924 +2025-06-24 19:45:37,547 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:45:37,553 - pyskl - INFO - +mean_acc 0.8861 +2025-06-24 19:45:37,555 - pyskl - INFO - Epoch(val) [98][533] top1_acc: 0.9078, top5_acc: 0.9924, mean_class_accuracy: 0.8861 +2025-06-24 19:46:19,608 - pyskl - INFO - Epoch [99][100/1281] lr: 6.691e-03, eta: 4:08:50, time: 0.420, data_time: 0.187, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1660, loss: 0.1660 +2025-06-24 19:46:42,226 - pyskl - INFO - Epoch [99][200/1281] lr: 6.673e-03, eta: 4:08:27, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2146, loss: 0.2146 +2025-06-24 19:47:04,734 - pyskl - INFO - Epoch [99][300/1281] lr: 6.655e-03, eta: 4:08:05, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2221, loss: 0.2221 +2025-06-24 19:47:26,733 - pyskl - INFO - Epoch [99][400/1281] lr: 6.637e-03, eta: 4:07:42, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2247, loss: 0.2247 +2025-06-24 19:47:49,249 - pyskl - INFO - Epoch [99][500/1281] lr: 6.619e-03, eta: 4:07:20, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2503, loss: 0.2503 +2025-06-24 19:48:11,606 - pyskl - INFO - Epoch [99][600/1281] lr: 6.601e-03, eta: 4:06:57, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.1995, loss: 0.1995 +2025-06-24 19:48:33,864 - pyskl - INFO - Epoch [99][700/1281] lr: 6.583e-03, eta: 4:06:35, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2393, loss: 0.2393 +2025-06-24 19:48:55,888 - pyskl - INFO - Epoch [99][800/1281] lr: 6.565e-03, eta: 4:06:12, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9994, loss_cls: 0.1891, loss: 0.1891 +2025-06-24 19:49:18,206 - pyskl - INFO - Epoch [99][900/1281] lr: 6.547e-03, eta: 4:05:50, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9988, loss_cls: 0.2025, loss: 0.2025 +2025-06-24 19:49:40,422 - pyskl - INFO - Epoch [99][1000/1281] lr: 6.529e-03, eta: 4:05:27, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.1914, loss: 0.1914 +2025-06-24 19:50:02,672 - pyskl - INFO - Epoch [99][1100/1281] lr: 6.511e-03, eta: 4:05:04, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9988, loss_cls: 0.2376, loss: 0.2376 +2025-06-24 19:50:25,035 - pyskl - INFO - Epoch [99][1200/1281] lr: 6.493e-03, eta: 4:04:42, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2536, loss: 0.2536 +2025-06-24 19:50:43,893 - pyskl - INFO - Saving checkpoint at 99 epochs +2025-06-24 19:51:27,414 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:51:27,469 - pyskl - INFO - +top1_acc 0.9053 +top5_acc 0.9937 +2025-06-24 19:51:27,469 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:51:27,476 - pyskl - INFO - +mean_acc 0.8859 +2025-06-24 19:51:27,478 - pyskl - INFO - Epoch(val) [99][533] top1_acc: 0.9053, top5_acc: 0.9937, mean_class_accuracy: 0.8859 +2025-06-24 19:52:10,461 - pyskl - INFO - Epoch [100][100/1281] lr: 6.460e-03, eta: 4:04:02, time: 0.430, data_time: 0.191, memory: 4083, top1_acc: 0.9775, top5_acc: 0.9994, loss_cls: 0.1634, loss: 0.1634 +2025-06-24 19:52:32,870 - pyskl - INFO - Epoch [100][200/1281] lr: 6.442e-03, eta: 4:03:40, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.2060, loss: 0.2060 +2025-06-24 19:52:55,216 - pyskl - INFO - Epoch [100][300/1281] lr: 6.425e-03, eta: 4:03:18, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1874, loss: 0.1874 +2025-06-24 19:53:17,939 - pyskl - INFO - Epoch [100][400/1281] lr: 6.407e-03, eta: 4:02:55, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1933, loss: 0.1933 +2025-06-24 19:53:40,168 - pyskl - INFO - Epoch [100][500/1281] lr: 6.389e-03, eta: 4:02:33, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1688, loss: 0.1688 +2025-06-24 19:54:02,559 - pyskl - INFO - Epoch [100][600/1281] lr: 6.371e-03, eta: 4:02:10, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2110, loss: 0.2110 +2025-06-24 19:54:25,098 - pyskl - INFO - Epoch [100][700/1281] lr: 6.353e-03, eta: 4:01:48, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9994, loss_cls: 0.2508, loss: 0.2508 +2025-06-24 19:54:47,680 - pyskl - INFO - Epoch [100][800/1281] lr: 6.336e-03, eta: 4:01:25, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2550, loss: 0.2550 +2025-06-24 19:55:10,145 - pyskl - INFO - Epoch [100][900/1281] lr: 6.318e-03, eta: 4:01:03, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.2057, loss: 0.2057 +2025-06-24 19:55:32,241 - pyskl - INFO - Epoch [100][1000/1281] lr: 6.300e-03, eta: 4:00:40, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2393, loss: 0.2393 +2025-06-24 19:55:54,348 - pyskl - INFO - Epoch [100][1100/1281] lr: 6.282e-03, eta: 4:00:18, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2096, loss: 0.2096 +2025-06-24 19:56:16,930 - pyskl - INFO - Epoch [100][1200/1281] lr: 6.265e-03, eta: 3:59:55, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9994, loss_cls: 0.2393, loss: 0.2393 +2025-06-24 19:56:35,908 - pyskl - INFO - Saving checkpoint at 100 epochs +2025-06-24 19:57:19,679 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:57:19,735 - pyskl - INFO - +top1_acc 0.9083 +top5_acc 0.9931 +2025-06-24 19:57:19,735 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:57:19,744 - pyskl - INFO - +mean_acc 0.8723 +2025-06-24 19:57:19,747 - pyskl - INFO - Epoch(val) [100][533] top1_acc: 0.9083, top5_acc: 0.9931, mean_class_accuracy: 0.8723 +2025-06-24 19:58:02,286 - pyskl - INFO - Epoch [101][100/1281] lr: 6.232e-03, eta: 3:59:16, time: 0.425, data_time: 0.190, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2408, loss: 0.2408 +2025-06-24 19:58:25,027 - pyskl - INFO - Epoch [101][200/1281] lr: 6.215e-03, eta: 3:58:53, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1959, loss: 0.1959 +2025-06-24 19:58:47,363 - pyskl - INFO - Epoch [101][300/1281] lr: 6.197e-03, eta: 3:58:31, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9988, loss_cls: 0.1853, loss: 0.1853 +2025-06-24 19:59:09,901 - pyskl - INFO - Epoch [101][400/1281] lr: 6.180e-03, eta: 3:58:08, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.1662, loss: 0.1662 +2025-06-24 19:59:32,393 - pyskl - INFO - Epoch [101][500/1281] lr: 6.162e-03, eta: 3:57:46, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2050, loss: 0.2050 +2025-06-24 19:59:54,709 - pyskl - INFO - Epoch [101][600/1281] lr: 6.144e-03, eta: 3:57:23, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 0.9988, loss_cls: 0.1710, loss: 0.1710 +2025-06-24 20:00:17,317 - pyskl - INFO - Epoch [101][700/1281] lr: 6.127e-03, eta: 3:57:01, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1811, loss: 0.1811 +2025-06-24 20:00:39,867 - pyskl - INFO - Epoch [101][800/1281] lr: 6.109e-03, eta: 3:56:39, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1930, loss: 0.1930 +2025-06-24 20:01:02,204 - pyskl - INFO - Epoch [101][900/1281] lr: 6.092e-03, eta: 3:56:16, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2134, loss: 0.2134 +2025-06-24 20:01:24,469 - pyskl - INFO - Epoch [101][1000/1281] lr: 6.074e-03, eta: 3:55:54, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1895, loss: 0.1895 +2025-06-24 20:01:46,938 - pyskl - INFO - Epoch [101][1100/1281] lr: 6.057e-03, eta: 3:55:31, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2169, loss: 0.2169 +2025-06-24 20:02:09,317 - pyskl - INFO - Epoch [101][1200/1281] lr: 6.039e-03, eta: 3:55:09, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9994, loss_cls: 0.2176, loss: 0.2176 +2025-06-24 20:02:28,218 - pyskl - INFO - Saving checkpoint at 101 epochs +2025-06-24 20:03:12,159 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:03:12,217 - pyskl - INFO - +top1_acc 0.9101 +top5_acc 0.9941 +2025-06-24 20:03:12,217 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:03:12,225 - pyskl - INFO - +mean_acc 0.8854 +2025-06-24 20:03:12,227 - pyskl - INFO - Epoch(val) [101][533] top1_acc: 0.9101, top5_acc: 0.9941, mean_class_accuracy: 0.8854 +2025-06-24 20:03:54,675 - pyskl - INFO - Epoch [102][100/1281] lr: 6.007e-03, eta: 3:54:29, time: 0.424, data_time: 0.189, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9988, loss_cls: 0.2003, loss: 0.2003 +2025-06-24 20:04:17,069 - pyskl - INFO - Epoch [102][200/1281] lr: 5.990e-03, eta: 3:54:07, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1828, loss: 0.1828 +2025-06-24 20:04:39,559 - pyskl - INFO - Epoch [102][300/1281] lr: 5.972e-03, eta: 3:53:44, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.1882, loss: 0.1882 +2025-06-24 20:05:02,085 - pyskl - INFO - Epoch [102][400/1281] lr: 5.955e-03, eta: 3:53:22, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9994, loss_cls: 0.2113, loss: 0.2113 +2025-06-24 20:05:24,180 - pyskl - INFO - Epoch [102][500/1281] lr: 5.938e-03, eta: 3:52:59, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1910, loss: 0.1910 +2025-06-24 20:05:46,663 - pyskl - INFO - Epoch [102][600/1281] lr: 5.920e-03, eta: 3:52:37, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1657, loss: 0.1657 +2025-06-24 20:06:08,983 - pyskl - INFO - Epoch [102][700/1281] lr: 5.903e-03, eta: 3:52:14, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1743, loss: 0.1743 +2025-06-24 20:06:31,380 - pyskl - INFO - Epoch [102][800/1281] lr: 5.886e-03, eta: 3:51:52, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1506, loss: 0.1506 +2025-06-24 20:06:53,629 - pyskl - INFO - Epoch [102][900/1281] lr: 5.868e-03, eta: 3:51:29, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1368, loss: 0.1368 +2025-06-24 20:07:15,862 - pyskl - INFO - Epoch [102][1000/1281] lr: 5.851e-03, eta: 3:51:07, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1736, loss: 0.1736 +2025-06-24 20:07:38,231 - pyskl - INFO - Epoch [102][1100/1281] lr: 5.834e-03, eta: 3:50:44, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.1882, loss: 0.1882 +2025-06-24 20:08:00,688 - pyskl - INFO - Epoch [102][1200/1281] lr: 5.816e-03, eta: 3:50:22, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2276, loss: 0.2276 +2025-06-24 20:08:19,506 - pyskl - INFO - Saving checkpoint at 102 epochs +2025-06-24 20:09:03,108 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:09:03,177 - pyskl - INFO - +top1_acc 0.8934 +top5_acc 0.9911 +2025-06-24 20:09:03,178 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:09:03,188 - pyskl - INFO - +mean_acc 0.8597 +2025-06-24 20:09:03,191 - pyskl - INFO - Epoch(val) [102][533] top1_acc: 0.8934, top5_acc: 0.9911, mean_class_accuracy: 0.8597 +2025-06-24 20:09:45,854 - pyskl - INFO - Epoch [103][100/1281] lr: 5.785e-03, eta: 3:49:42, time: 0.427, data_time: 0.190, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1806, loss: 0.1806 +2025-06-24 20:10:08,100 - pyskl - INFO - Epoch [103][200/1281] lr: 5.768e-03, eta: 3:49:19, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1573, loss: 0.1573 +2025-06-24 20:10:30,544 - pyskl - INFO - Epoch [103][300/1281] lr: 5.751e-03, eta: 3:48:57, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1864, loss: 0.1864 +2025-06-24 20:10:52,908 - pyskl - INFO - Epoch [103][400/1281] lr: 5.733e-03, eta: 3:48:34, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1663, loss: 0.1663 +2025-06-24 20:11:15,400 - pyskl - INFO - Epoch [103][500/1281] lr: 5.716e-03, eta: 3:48:12, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 0.9994, loss_cls: 0.1915, loss: 0.1915 +2025-06-24 20:11:37,858 - pyskl - INFO - Epoch [103][600/1281] lr: 5.699e-03, eta: 3:47:50, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1816, loss: 0.1816 +2025-06-24 20:12:00,402 - pyskl - INFO - Epoch [103][700/1281] lr: 5.682e-03, eta: 3:47:27, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1868, loss: 0.1868 +2025-06-24 20:12:22,713 - pyskl - INFO - Epoch [103][800/1281] lr: 5.665e-03, eta: 3:47:05, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 0.9994, loss_cls: 0.1807, loss: 0.1807 +2025-06-24 20:12:45,490 - pyskl - INFO - Epoch [103][900/1281] lr: 5.648e-03, eta: 3:46:42, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1737, loss: 0.1737 +2025-06-24 20:13:07,963 - pyskl - INFO - Epoch [103][1000/1281] lr: 5.631e-03, eta: 3:46:20, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.2052, loss: 0.2052 +2025-06-24 20:13:30,590 - pyskl - INFO - Epoch [103][1100/1281] lr: 5.614e-03, eta: 3:45:57, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9994, loss_cls: 0.1856, loss: 0.1856 +2025-06-24 20:13:52,966 - pyskl - INFO - Epoch [103][1200/1281] lr: 5.597e-03, eta: 3:45:35, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9988, loss_cls: 0.1837, loss: 0.1837 +2025-06-24 20:14:11,754 - pyskl - INFO - Saving checkpoint at 103 epochs +2025-06-24 20:14:55,113 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:14:55,171 - pyskl - INFO - +top1_acc 0.9065 +top5_acc 0.9948 +2025-06-24 20:14:55,171 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:14:55,177 - pyskl - INFO - +mean_acc 0.8738 +2025-06-24 20:14:55,179 - pyskl - INFO - Epoch(val) [103][533] top1_acc: 0.9065, top5_acc: 0.9948, mean_class_accuracy: 0.8738 +2025-06-24 20:15:37,770 - pyskl - INFO - Epoch [104][100/1281] lr: 5.566e-03, eta: 3:44:55, time: 0.426, data_time: 0.190, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1575, loss: 0.1575 +2025-06-24 20:16:00,156 - pyskl - INFO - Epoch [104][200/1281] lr: 5.549e-03, eta: 3:44:33, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1392, loss: 0.1392 +2025-06-24 20:16:22,534 - pyskl - INFO - Epoch [104][300/1281] lr: 5.532e-03, eta: 3:44:10, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1652, loss: 0.1652 +2025-06-24 20:16:44,957 - pyskl - INFO - Epoch [104][400/1281] lr: 5.515e-03, eta: 3:43:48, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.1971, loss: 0.1971 +2025-06-24 20:17:07,747 - pyskl - INFO - Epoch [104][500/1281] lr: 5.498e-03, eta: 3:43:26, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1452, loss: 0.1452 +2025-06-24 20:17:30,323 - pyskl - INFO - Epoch [104][600/1281] lr: 5.481e-03, eta: 3:43:03, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2060, loss: 0.2060 +2025-06-24 20:17:52,909 - pyskl - INFO - Epoch [104][700/1281] lr: 5.464e-03, eta: 3:42:41, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1706, loss: 0.1706 +2025-06-24 20:18:15,420 - pyskl - INFO - Epoch [104][800/1281] lr: 5.447e-03, eta: 3:42:18, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1810, loss: 0.1810 +2025-06-24 20:18:37,460 - pyskl - INFO - Epoch [104][900/1281] lr: 5.430e-03, eta: 3:41:56, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.1914, loss: 0.1914 +2025-06-24 20:19:00,182 - pyskl - INFO - Epoch [104][1000/1281] lr: 5.413e-03, eta: 3:41:33, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 0.9994, loss_cls: 0.1930, loss: 0.1930 +2025-06-24 20:19:22,644 - pyskl - INFO - Epoch [104][1100/1281] lr: 5.397e-03, eta: 3:41:11, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1804, loss: 0.1804 +2025-06-24 20:19:45,168 - pyskl - INFO - Epoch [104][1200/1281] lr: 5.380e-03, eta: 3:40:48, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2018, loss: 0.2018 +2025-06-24 20:20:04,235 - pyskl - INFO - Saving checkpoint at 104 epochs +2025-06-24 20:20:47,583 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:20:47,638 - pyskl - INFO - +top1_acc 0.8995 +top5_acc 0.9931 +2025-06-24 20:20:47,638 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:20:47,645 - pyskl - INFO - +mean_acc 0.8618 +2025-06-24 20:20:47,648 - pyskl - INFO - Epoch(val) [104][533] top1_acc: 0.8995, top5_acc: 0.9931, mean_class_accuracy: 0.8618 +2025-06-24 20:21:30,645 - pyskl - INFO - Epoch [105][100/1281] lr: 5.349e-03, eta: 3:40:09, time: 0.430, data_time: 0.192, memory: 4083, top1_acc: 0.9756, top5_acc: 0.9994, loss_cls: 0.1789, loss: 0.1789 +2025-06-24 20:21:53,101 - pyskl - INFO - Epoch [105][200/1281] lr: 5.333e-03, eta: 3:39:46, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1767, loss: 0.1767 +2025-06-24 20:22:15,566 - pyskl - INFO - Epoch [105][300/1281] lr: 5.316e-03, eta: 3:39:24, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 0.9994, loss_cls: 0.1564, loss: 0.1564 +2025-06-24 20:22:37,902 - pyskl - INFO - Epoch [105][400/1281] lr: 5.299e-03, eta: 3:39:01, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1853, loss: 0.1853 +2025-06-24 20:23:00,318 - pyskl - INFO - Epoch [105][500/1281] lr: 5.283e-03, eta: 3:38:39, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9994, loss_cls: 0.1842, loss: 0.1842 +2025-06-24 20:23:23,072 - pyskl - INFO - Epoch [105][600/1281] lr: 5.266e-03, eta: 3:38:17, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 0.9994, loss_cls: 0.1580, loss: 0.1580 +2025-06-24 20:23:45,210 - pyskl - INFO - Epoch [105][700/1281] lr: 5.249e-03, eta: 3:37:54, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1564, loss: 0.1564 +2025-06-24 20:24:07,489 - pyskl - INFO - Epoch [105][800/1281] lr: 5.233e-03, eta: 3:37:32, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1781, loss: 0.1781 +2025-06-24 20:24:29,878 - pyskl - INFO - Epoch [105][900/1281] lr: 5.216e-03, eta: 3:37:09, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 0.9994, loss_cls: 0.2079, loss: 0.2079 +2025-06-24 20:24:52,291 - pyskl - INFO - Epoch [105][1000/1281] lr: 5.199e-03, eta: 3:36:47, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1903, loss: 0.1903 +2025-06-24 20:25:14,584 - pyskl - INFO - Epoch [105][1100/1281] lr: 5.183e-03, eta: 3:36:24, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1801, loss: 0.1801 +2025-06-24 20:25:36,840 - pyskl - INFO - Epoch [105][1200/1281] lr: 5.166e-03, eta: 3:36:01, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1537, loss: 0.1537 +2025-06-24 20:25:55,994 - pyskl - INFO - Saving checkpoint at 105 epochs +2025-06-24 20:26:39,705 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:26:39,774 - pyskl - INFO - +top1_acc 0.9143 +top5_acc 0.9960 +2025-06-24 20:26:39,774 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:26:39,782 - pyskl - INFO - +mean_acc 0.8903 +2025-06-24 20:26:39,786 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_97.pth was removed +2025-06-24 20:26:39,985 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_105.pth. +2025-06-24 20:26:39,986 - pyskl - INFO - Best top1_acc is 0.9143 at 105 epoch. +2025-06-24 20:26:39,989 - pyskl - INFO - Epoch(val) [105][533] top1_acc: 0.9143, top5_acc: 0.9960, mean_class_accuracy: 0.8903 +2025-06-24 20:27:22,443 - pyskl - INFO - Epoch [106][100/1281] lr: 5.136e-03, eta: 3:35:22, time: 0.424, data_time: 0.189, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1876, loss: 0.1876 +2025-06-24 20:27:44,945 - pyskl - INFO - Epoch [106][200/1281] lr: 5.120e-03, eta: 3:34:59, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1601, loss: 0.1601 +2025-06-24 20:28:07,324 - pyskl - INFO - Epoch [106][300/1281] lr: 5.103e-03, eta: 3:34:37, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1608, loss: 0.1608 +2025-06-24 20:28:29,863 - pyskl - INFO - Epoch [106][400/1281] lr: 5.087e-03, eta: 3:34:14, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1381, loss: 0.1381 +2025-06-24 20:28:52,267 - pyskl - INFO - Epoch [106][500/1281] lr: 5.070e-03, eta: 3:33:52, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1476, loss: 0.1476 +2025-06-24 20:29:14,504 - pyskl - INFO - Epoch [106][600/1281] lr: 5.054e-03, eta: 3:33:29, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1767, loss: 0.1767 +2025-06-24 20:29:37,044 - pyskl - INFO - Epoch [106][700/1281] lr: 5.038e-03, eta: 3:33:07, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1414, loss: 0.1414 +2025-06-24 20:29:59,364 - pyskl - INFO - Epoch [106][800/1281] lr: 5.021e-03, eta: 3:32:44, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1468, loss: 0.1468 +2025-06-24 20:30:21,870 - pyskl - INFO - Epoch [106][900/1281] lr: 5.005e-03, eta: 3:32:22, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1554, loss: 0.1554 +2025-06-24 20:30:44,119 - pyskl - INFO - Epoch [106][1000/1281] lr: 4.988e-03, eta: 3:31:59, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1770, loss: 0.1770 +2025-06-24 20:31:06,349 - pyskl - INFO - Epoch [106][1100/1281] lr: 4.972e-03, eta: 3:31:37, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 0.9994, loss_cls: 0.1606, loss: 0.1606 +2025-06-24 20:31:28,582 - pyskl - INFO - Epoch [106][1200/1281] lr: 4.956e-03, eta: 3:31:14, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1697, loss: 0.1697 +2025-06-24 20:31:47,384 - pyskl - INFO - Saving checkpoint at 106 epochs +2025-06-24 20:32:31,348 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:32:31,404 - pyskl - INFO - +top1_acc 0.9195 +top5_acc 0.9955 +2025-06-24 20:32:31,404 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:32:31,411 - pyskl - INFO - +mean_acc 0.8951 +2025-06-24 20:32:31,415 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_105.pth was removed +2025-06-24 20:32:31,599 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_106.pth. +2025-06-24 20:32:31,599 - pyskl - INFO - Best top1_acc is 0.9195 at 106 epoch. +2025-06-24 20:32:31,603 - pyskl - INFO - Epoch(val) [106][533] top1_acc: 0.9195, top5_acc: 0.9955, mean_class_accuracy: 0.8951 +2025-06-24 20:33:14,457 - pyskl - INFO - Epoch [107][100/1281] lr: 4.926e-03, eta: 3:30:35, time: 0.428, data_time: 0.190, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1388, loss: 0.1388 +2025-06-24 20:33:36,927 - pyskl - INFO - Epoch [107][200/1281] lr: 4.910e-03, eta: 3:30:12, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 0.9994, loss_cls: 0.1235, loss: 0.1235 +2025-06-24 20:33:59,512 - pyskl - INFO - Epoch [107][300/1281] lr: 4.894e-03, eta: 3:29:50, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1610, loss: 0.1610 +2025-06-24 20:34:21,790 - pyskl - INFO - Epoch [107][400/1281] lr: 4.878e-03, eta: 3:29:27, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.1692, loss: 0.1692 +2025-06-24 20:34:44,166 - pyskl - INFO - Epoch [107][500/1281] lr: 4.862e-03, eta: 3:29:05, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1784, loss: 0.1784 +2025-06-24 20:35:06,332 - pyskl - INFO - Epoch [107][600/1281] lr: 4.845e-03, eta: 3:28:42, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1474, loss: 0.1474 +2025-06-24 20:35:28,562 - pyskl - INFO - Epoch [107][700/1281] lr: 4.829e-03, eta: 3:28:20, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1748, loss: 0.1748 +2025-06-24 20:35:50,829 - pyskl - INFO - Epoch [107][800/1281] lr: 4.813e-03, eta: 3:27:57, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1925, loss: 0.1925 +2025-06-24 20:36:13,052 - pyskl - INFO - Epoch [107][900/1281] lr: 4.797e-03, eta: 3:27:34, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1580, loss: 0.1580 +2025-06-24 20:36:35,395 - pyskl - INFO - Epoch [107][1000/1281] lr: 4.781e-03, eta: 3:27:12, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1732, loss: 0.1732 +2025-06-24 20:36:57,594 - pyskl - INFO - Epoch [107][1100/1281] lr: 4.765e-03, eta: 3:26:49, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1608, loss: 0.1608 +2025-06-24 20:37:20,182 - pyskl - INFO - Epoch [107][1200/1281] lr: 4.749e-03, eta: 3:26:27, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1263, loss: 0.1263 +2025-06-24 20:37:39,152 - pyskl - INFO - Saving checkpoint at 107 epochs +2025-06-24 20:38:22,502 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:38:22,561 - pyskl - INFO - +top1_acc 0.9173 +top5_acc 0.9957 +2025-06-24 20:38:22,561 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:38:22,569 - pyskl - INFO - +mean_acc 0.8859 +2025-06-24 20:38:22,571 - pyskl - INFO - Epoch(val) [107][533] top1_acc: 0.9173, top5_acc: 0.9957, mean_class_accuracy: 0.8859 +2025-06-24 20:39:05,394 - pyskl - INFO - Epoch [108][100/1281] lr: 4.720e-03, eta: 3:25:47, time: 0.428, data_time: 0.192, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1385, loss: 0.1385 +2025-06-24 20:39:27,872 - pyskl - INFO - Epoch [108][200/1281] lr: 4.704e-03, eta: 3:25:25, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1168, loss: 0.1168 +2025-06-24 20:39:50,413 - pyskl - INFO - Epoch [108][300/1281] lr: 4.688e-03, eta: 3:25:02, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1317, loss: 0.1317 +2025-06-24 20:40:12,935 - pyskl - INFO - Epoch [108][400/1281] lr: 4.672e-03, eta: 3:24:40, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1336, loss: 0.1336 +2025-06-24 20:40:35,240 - pyskl - INFO - Epoch [108][500/1281] lr: 4.656e-03, eta: 3:24:17, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1496, loss: 0.1496 +2025-06-24 20:40:57,483 - pyskl - INFO - Epoch [108][600/1281] lr: 4.640e-03, eta: 3:23:55, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1359, loss: 0.1359 +2025-06-24 20:41:19,796 - pyskl - INFO - Epoch [108][700/1281] lr: 4.624e-03, eta: 3:23:32, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 0.9994, loss_cls: 0.1486, loss: 0.1486 +2025-06-24 20:41:42,493 - pyskl - INFO - Epoch [108][800/1281] lr: 4.608e-03, eta: 3:23:10, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1629, loss: 0.1629 +2025-06-24 20:42:04,674 - pyskl - INFO - Epoch [108][900/1281] lr: 4.593e-03, eta: 3:22:47, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 0.9994, loss_cls: 0.1533, loss: 0.1533 +2025-06-24 20:42:26,902 - pyskl - INFO - Epoch [108][1000/1281] lr: 4.577e-03, eta: 3:22:25, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1442, loss: 0.1442 +2025-06-24 20:42:49,615 - pyskl - INFO - Epoch [108][1100/1281] lr: 4.561e-03, eta: 3:22:02, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1826, loss: 0.1826 +2025-06-24 20:43:11,800 - pyskl - INFO - Epoch [108][1200/1281] lr: 4.545e-03, eta: 3:21:40, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1548, loss: 0.1548 +2025-06-24 20:43:30,846 - pyskl - INFO - Saving checkpoint at 108 epochs +2025-06-24 20:44:14,474 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:44:14,542 - pyskl - INFO - +top1_acc 0.9218 +top5_acc 0.9957 +2025-06-24 20:44:14,542 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:44:14,550 - pyskl - INFO - +mean_acc 0.8881 +2025-06-24 20:44:14,555 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_106.pth was removed +2025-06-24 20:44:14,750 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_108.pth. +2025-06-24 20:44:14,751 - pyskl - INFO - Best top1_acc is 0.9218 at 108 epoch. +2025-06-24 20:44:14,754 - pyskl - INFO - Epoch(val) [108][533] top1_acc: 0.9218, top5_acc: 0.9957, mean_class_accuracy: 0.8881 +2025-06-24 20:44:58,059 - pyskl - INFO - Epoch [109][100/1281] lr: 4.517e-03, eta: 3:21:00, time: 0.433, data_time: 0.195, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1026, loss: 0.1026 +2025-06-24 20:45:20,873 - pyskl - INFO - Epoch [109][200/1281] lr: 4.501e-03, eta: 3:20:38, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0863, loss: 0.0863 +2025-06-24 20:45:43,473 - pyskl - INFO - Epoch [109][300/1281] lr: 4.485e-03, eta: 3:20:16, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1151, loss: 0.1151 +2025-06-24 20:46:06,185 - pyskl - INFO - Epoch [109][400/1281] lr: 4.470e-03, eta: 3:19:53, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1474, loss: 0.1474 +2025-06-24 20:46:28,445 - pyskl - INFO - Epoch [109][500/1281] lr: 4.454e-03, eta: 3:19:31, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1234, loss: 0.1234 +2025-06-24 20:46:50,966 - pyskl - INFO - Epoch [109][600/1281] lr: 4.438e-03, eta: 3:19:08, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1233, loss: 0.1233 +2025-06-24 20:47:13,596 - pyskl - INFO - Epoch [109][700/1281] lr: 4.423e-03, eta: 3:18:46, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1125, loss: 0.1125 +2025-06-24 20:47:36,030 - pyskl - INFO - Epoch [109][800/1281] lr: 4.407e-03, eta: 3:18:23, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1387, loss: 0.1387 +2025-06-24 20:47:58,362 - pyskl - INFO - Epoch [109][900/1281] lr: 4.391e-03, eta: 3:18:01, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1452, loss: 0.1452 +2025-06-24 20:48:20,529 - pyskl - INFO - Epoch [109][1000/1281] lr: 4.376e-03, eta: 3:17:38, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 0.9981, loss_cls: 0.1727, loss: 0.1727 +2025-06-24 20:48:42,781 - pyskl - INFO - Epoch [109][1100/1281] lr: 4.360e-03, eta: 3:17:16, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1555, loss: 0.1555 +2025-06-24 20:49:05,101 - pyskl - INFO - Epoch [109][1200/1281] lr: 4.345e-03, eta: 3:16:53, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1221, loss: 0.1221 +2025-06-24 20:49:24,201 - pyskl - INFO - Saving checkpoint at 109 epochs +2025-06-24 20:50:08,078 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:50:08,136 - pyskl - INFO - +top1_acc 0.9173 +top5_acc 0.9948 +2025-06-24 20:50:08,136 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:50:08,143 - pyskl - INFO - +mean_acc 0.8851 +2025-06-24 20:50:08,145 - pyskl - INFO - Epoch(val) [109][533] top1_acc: 0.9173, top5_acc: 0.9948, mean_class_accuracy: 0.8851 +2025-06-24 20:50:50,908 - pyskl - INFO - Epoch [110][100/1281] lr: 4.317e-03, eta: 3:16:13, time: 0.428, data_time: 0.192, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1219, loss: 0.1219 +2025-06-24 20:51:13,392 - pyskl - INFO - Epoch [110][200/1281] lr: 4.301e-03, eta: 3:15:51, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1069, loss: 0.1069 +2025-06-24 20:51:35,995 - pyskl - INFO - Epoch [110][300/1281] lr: 4.286e-03, eta: 3:15:29, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1001, loss: 0.1001 +2025-06-24 20:51:58,354 - pyskl - INFO - Epoch [110][400/1281] lr: 4.271e-03, eta: 3:15:06, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1467, loss: 0.1467 +2025-06-24 20:52:20,711 - pyskl - INFO - Epoch [110][500/1281] lr: 4.255e-03, eta: 3:14:44, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1376, loss: 0.1376 +2025-06-24 20:52:42,873 - pyskl - INFO - Epoch [110][600/1281] lr: 4.240e-03, eta: 3:14:21, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1334, loss: 0.1334 +2025-06-24 20:53:05,426 - pyskl - INFO - Epoch [110][700/1281] lr: 4.225e-03, eta: 3:13:59, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1250, loss: 0.1250 +2025-06-24 20:53:27,793 - pyskl - INFO - Epoch [110][800/1281] lr: 4.209e-03, eta: 3:13:36, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1602, loss: 0.1602 +2025-06-24 20:53:50,477 - pyskl - INFO - Epoch [110][900/1281] lr: 4.194e-03, eta: 3:13:14, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1304, loss: 0.1304 +2025-06-24 20:54:12,859 - pyskl - INFO - Epoch [110][1000/1281] lr: 4.179e-03, eta: 3:12:51, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1537, loss: 0.1537 +2025-06-24 20:54:35,259 - pyskl - INFO - Epoch [110][1100/1281] lr: 4.164e-03, eta: 3:12:29, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 0.9994, loss_cls: 0.1265, loss: 0.1265 +2025-06-24 20:54:57,855 - pyskl - INFO - Epoch [110][1200/1281] lr: 4.148e-03, eta: 3:12:06, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1487, loss: 0.1487 +2025-06-24 20:55:16,641 - pyskl - INFO - Saving checkpoint at 110 epochs +2025-06-24 20:56:00,457 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:56:00,517 - pyskl - INFO - +top1_acc 0.9234 +top5_acc 0.9959 +2025-06-24 20:56:00,517 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:56:00,524 - pyskl - INFO - +mean_acc 0.8966 +2025-06-24 20:56:00,528 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_108.pth was removed +2025-06-24 20:56:00,700 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_110.pth. +2025-06-24 20:56:00,701 - pyskl - INFO - Best top1_acc is 0.9234 at 110 epoch. +2025-06-24 20:56:00,703 - pyskl - INFO - Epoch(val) [110][533] top1_acc: 0.9234, top5_acc: 0.9959, mean_class_accuracy: 0.8966 +2025-06-24 20:56:43,307 - pyskl - INFO - Epoch [111][100/1281] lr: 4.121e-03, eta: 3:11:26, time: 0.426, data_time: 0.192, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1109, loss: 0.1109 +2025-06-24 20:57:05,763 - pyskl - INFO - Epoch [111][200/1281] lr: 4.106e-03, eta: 3:11:04, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1083, loss: 0.1083 +2025-06-24 20:57:28,214 - pyskl - INFO - Epoch [111][300/1281] lr: 4.091e-03, eta: 3:10:41, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0901, loss: 0.0901 +2025-06-24 20:57:50,530 - pyskl - INFO - Epoch [111][400/1281] lr: 4.075e-03, eta: 3:10:19, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.1003, loss: 0.1003 +2025-06-24 20:58:12,529 - pyskl - INFO - Epoch [111][500/1281] lr: 4.060e-03, eta: 3:09:56, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0984, loss: 0.0984 +2025-06-24 20:58:34,856 - pyskl - INFO - Epoch [111][600/1281] lr: 4.045e-03, eta: 3:09:34, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1163, loss: 0.1163 +2025-06-24 20:58:57,064 - pyskl - INFO - Epoch [111][700/1281] lr: 4.030e-03, eta: 3:09:11, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1687, loss: 0.1687 +2025-06-24 20:59:19,683 - pyskl - INFO - Epoch [111][800/1281] lr: 4.015e-03, eta: 3:08:49, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1213, loss: 0.1213 +2025-06-24 20:59:42,093 - pyskl - INFO - Epoch [111][900/1281] lr: 4.000e-03, eta: 3:08:26, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1388, loss: 0.1388 +2025-06-24 21:00:04,653 - pyskl - INFO - Epoch [111][1000/1281] lr: 3.985e-03, eta: 3:08:04, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 0.9994, loss_cls: 0.1212, loss: 0.1212 +2025-06-24 21:00:27,267 - pyskl - INFO - Epoch [111][1100/1281] lr: 3.970e-03, eta: 3:07:41, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1324, loss: 0.1324 +2025-06-24 21:00:49,965 - pyskl - INFO - Epoch [111][1200/1281] lr: 3.955e-03, eta: 3:07:19, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 0.9994, loss_cls: 0.1190, loss: 0.1190 +2025-06-24 21:01:08,883 - pyskl - INFO - Saving checkpoint at 111 epochs +2025-06-24 21:01:52,060 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:01:52,117 - pyskl - INFO - +top1_acc 0.9231 +top5_acc 0.9942 +2025-06-24 21:01:52,117 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:01:52,124 - pyskl - INFO - +mean_acc 0.8982 +2025-06-24 21:01:52,126 - pyskl - INFO - Epoch(val) [111][533] top1_acc: 0.9231, top5_acc: 0.9942, mean_class_accuracy: 0.8982 +2025-06-24 21:02:34,223 - pyskl - INFO - Epoch [112][100/1281] lr: 3.928e-03, eta: 3:06:39, time: 0.421, data_time: 0.185, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1046, loss: 0.1046 +2025-06-24 21:02:56,607 - pyskl - INFO - Epoch [112][200/1281] lr: 3.914e-03, eta: 3:06:16, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1284, loss: 0.1284 +2025-06-24 21:03:19,039 - pyskl - INFO - Epoch [112][300/1281] lr: 3.899e-03, eta: 3:05:54, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 0.9994, loss_cls: 0.1132, loss: 0.1132 +2025-06-24 21:03:41,526 - pyskl - INFO - Epoch [112][400/1281] lr: 3.884e-03, eta: 3:05:32, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1619, loss: 0.1619 +2025-06-24 21:04:03,935 - pyskl - INFO - Epoch [112][500/1281] lr: 3.869e-03, eta: 3:05:09, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1335, loss: 0.1335 +2025-06-24 21:04:26,300 - pyskl - INFO - Epoch [112][600/1281] lr: 3.854e-03, eta: 3:04:47, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1135, loss: 0.1135 +2025-06-24 21:04:48,736 - pyskl - INFO - Epoch [112][700/1281] lr: 3.840e-03, eta: 3:04:24, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1275, loss: 0.1275 +2025-06-24 21:05:11,100 - pyskl - INFO - Epoch [112][800/1281] lr: 3.825e-03, eta: 3:04:02, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1325, loss: 0.1325 +2025-06-24 21:05:33,311 - pyskl - INFO - Epoch [112][900/1281] lr: 3.810e-03, eta: 3:03:39, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1398, loss: 0.1398 +2025-06-24 21:05:55,453 - pyskl - INFO - Epoch [112][1000/1281] lr: 3.795e-03, eta: 3:03:16, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1174, loss: 0.1174 +2025-06-24 21:06:17,853 - pyskl - INFO - Epoch [112][1100/1281] lr: 3.781e-03, eta: 3:02:54, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1235, loss: 0.1235 +2025-06-24 21:06:40,058 - pyskl - INFO - Epoch [112][1200/1281] lr: 3.766e-03, eta: 3:02:31, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1418, loss: 0.1418 +2025-06-24 21:06:58,795 - pyskl - INFO - Saving checkpoint at 112 epochs +2025-06-24 21:07:42,947 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:07:43,004 - pyskl - INFO - +top1_acc 0.9182 +top5_acc 0.9948 +2025-06-24 21:07:43,004 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:07:43,012 - pyskl - INFO - +mean_acc 0.8920 +2025-06-24 21:07:43,014 - pyskl - INFO - Epoch(val) [112][533] top1_acc: 0.9182, top5_acc: 0.9948, mean_class_accuracy: 0.8920 +2025-06-24 21:08:25,250 - pyskl - INFO - Epoch [113][100/1281] lr: 3.740e-03, eta: 3:01:51, time: 0.422, data_time: 0.189, memory: 4083, top1_acc: 0.9925, top5_acc: 0.9994, loss_cls: 0.0867, loss: 0.0867 +2025-06-24 21:08:47,814 - pyskl - INFO - Epoch [113][200/1281] lr: 3.725e-03, eta: 3:01:29, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0737, loss: 0.0737 +2025-06-24 21:09:10,262 - pyskl - INFO - Epoch [113][300/1281] lr: 3.711e-03, eta: 3:01:06, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0827, loss: 0.0827 +2025-06-24 21:09:32,831 - pyskl - INFO - Epoch [113][400/1281] lr: 3.696e-03, eta: 3:00:44, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1148, loss: 0.1148 +2025-06-24 21:09:55,447 - pyskl - INFO - Epoch [113][500/1281] lr: 3.682e-03, eta: 3:00:22, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0963, loss: 0.0963 +2025-06-24 21:10:17,841 - pyskl - INFO - Epoch [113][600/1281] lr: 3.667e-03, eta: 2:59:59, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0884, loss: 0.0884 +2025-06-24 21:10:40,538 - pyskl - INFO - Epoch [113][700/1281] lr: 3.653e-03, eta: 2:59:37, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1008, loss: 0.1008 +2025-06-24 21:11:02,987 - pyskl - INFO - Epoch [113][800/1281] lr: 3.638e-03, eta: 2:59:14, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 0.9994, loss_cls: 0.1056, loss: 0.1056 +2025-06-24 21:11:25,666 - pyskl - INFO - Epoch [113][900/1281] lr: 3.624e-03, eta: 2:58:52, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0823, loss: 0.0823 +2025-06-24 21:11:48,323 - pyskl - INFO - Epoch [113][1000/1281] lr: 3.610e-03, eta: 2:58:29, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0882, loss: 0.0882 +2025-06-24 21:12:10,653 - pyskl - INFO - Epoch [113][1100/1281] lr: 3.595e-03, eta: 2:58:07, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0844, loss: 0.0844 +2025-06-24 21:12:33,261 - pyskl - INFO - Epoch [113][1200/1281] lr: 3.581e-03, eta: 2:57:45, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0850, loss: 0.0850 +2025-06-24 21:12:51,946 - pyskl - INFO - Saving checkpoint at 113 epochs +2025-06-24 21:13:36,231 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:13:36,289 - pyskl - INFO - +top1_acc 0.9196 +top5_acc 0.9954 +2025-06-24 21:13:36,289 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:13:36,298 - pyskl - INFO - +mean_acc 0.8919 +2025-06-24 21:13:36,301 - pyskl - INFO - Epoch(val) [113][533] top1_acc: 0.9196, top5_acc: 0.9954, mean_class_accuracy: 0.8919 +2025-06-24 21:14:18,664 - pyskl - INFO - Epoch [114][100/1281] lr: 3.555e-03, eta: 2:57:04, time: 0.424, data_time: 0.187, memory: 4083, top1_acc: 0.9869, top5_acc: 0.9994, loss_cls: 0.1121, loss: 0.1121 +2025-06-24 21:14:41,115 - pyskl - INFO - Epoch [114][200/1281] lr: 3.541e-03, eta: 2:56:42, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0936, loss: 0.0936 +2025-06-24 21:15:03,304 - pyskl - INFO - Epoch [114][300/1281] lr: 3.526e-03, eta: 2:56:19, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0777, loss: 0.0777 +2025-06-24 21:15:25,867 - pyskl - INFO - Epoch [114][400/1281] lr: 3.512e-03, eta: 2:55:57, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1022, loss: 0.1022 +2025-06-24 21:15:48,217 - pyskl - INFO - Epoch [114][500/1281] lr: 3.498e-03, eta: 2:55:34, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 0.9994, loss_cls: 0.1073, loss: 0.1073 +2025-06-24 21:16:10,516 - pyskl - INFO - Epoch [114][600/1281] lr: 3.484e-03, eta: 2:55:12, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0826, loss: 0.0826 +2025-06-24 21:16:33,201 - pyskl - INFO - Epoch [114][700/1281] lr: 3.470e-03, eta: 2:54:50, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1072, loss: 0.1072 +2025-06-24 21:16:55,571 - pyskl - INFO - Epoch [114][800/1281] lr: 3.456e-03, eta: 2:54:27, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0935, loss: 0.0935 +2025-06-24 21:17:17,704 - pyskl - INFO - Epoch [114][900/1281] lr: 3.442e-03, eta: 2:54:04, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0971, loss: 0.0971 +2025-06-24 21:17:40,181 - pyskl - INFO - Epoch [114][1000/1281] lr: 3.427e-03, eta: 2:53:42, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0998, loss: 0.0998 +2025-06-24 21:18:02,389 - pyskl - INFO - Epoch [114][1100/1281] lr: 3.413e-03, eta: 2:53:19, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0854, loss: 0.0854 +2025-06-24 21:18:24,628 - pyskl - INFO - Epoch [114][1200/1281] lr: 3.399e-03, eta: 2:52:57, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1072, loss: 0.1072 +2025-06-24 21:18:43,275 - pyskl - INFO - Saving checkpoint at 114 epochs +2025-06-24 21:19:27,244 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:19:27,305 - pyskl - INFO - +top1_acc 0.9168 +top5_acc 0.9939 +2025-06-24 21:19:27,305 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:19:27,312 - pyskl - INFO - +mean_acc 0.8802 +2025-06-24 21:19:27,314 - pyskl - INFO - Epoch(val) [114][533] top1_acc: 0.9168, top5_acc: 0.9939, mean_class_accuracy: 0.8802 +2025-06-24 21:20:09,570 - pyskl - INFO - Epoch [115][100/1281] lr: 3.374e-03, eta: 2:52:17, time: 0.423, data_time: 0.189, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1071, loss: 0.1071 +2025-06-24 21:20:31,866 - pyskl - INFO - Epoch [115][200/1281] lr: 3.360e-03, eta: 2:51:54, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1197, loss: 0.1197 +2025-06-24 21:20:53,909 - pyskl - INFO - Epoch [115][300/1281] lr: 3.346e-03, eta: 2:51:32, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1100, loss: 0.1100 +2025-06-24 21:21:16,365 - pyskl - INFO - Epoch [115][400/1281] lr: 3.332e-03, eta: 2:51:09, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0841, loss: 0.0841 +2025-06-24 21:21:38,846 - pyskl - INFO - Epoch [115][500/1281] lr: 3.318e-03, eta: 2:50:47, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0831, loss: 0.0831 +2025-06-24 21:22:01,396 - pyskl - INFO - Epoch [115][600/1281] lr: 3.305e-03, eta: 2:50:24, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0626, loss: 0.0626 +2025-06-24 21:22:23,761 - pyskl - INFO - Epoch [115][700/1281] lr: 3.291e-03, eta: 2:50:02, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1049, loss: 0.1049 +2025-06-24 21:22:45,942 - pyskl - INFO - Epoch [115][800/1281] lr: 3.277e-03, eta: 2:49:39, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1063, loss: 0.1063 +2025-06-24 21:23:08,250 - pyskl - INFO - Epoch [115][900/1281] lr: 3.263e-03, eta: 2:49:17, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0781, loss: 0.0781 +2025-06-24 21:23:30,575 - pyskl - INFO - Epoch [115][1000/1281] lr: 3.249e-03, eta: 2:48:54, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 0.9994, loss_cls: 0.0817, loss: 0.0817 +2025-06-24 21:23:52,813 - pyskl - INFO - Epoch [115][1100/1281] lr: 3.236e-03, eta: 2:48:32, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1076, loss: 0.1076 +2025-06-24 21:24:15,147 - pyskl - INFO - Epoch [115][1200/1281] lr: 3.222e-03, eta: 2:48:09, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.1035, loss: 0.1035 +2025-06-24 21:24:34,150 - pyskl - INFO - Saving checkpoint at 115 epochs +2025-06-24 21:25:17,619 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:25:17,676 - pyskl - INFO - +top1_acc 0.9143 +top5_acc 0.9946 +2025-06-24 21:25:17,676 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:25:17,683 - pyskl - INFO - +mean_acc 0.8911 +2025-06-24 21:25:17,685 - pyskl - INFO - Epoch(val) [115][533] top1_acc: 0.9143, top5_acc: 0.9946, mean_class_accuracy: 0.8911 +2025-06-24 21:26:00,517 - pyskl - INFO - Epoch [116][100/1281] lr: 3.197e-03, eta: 2:47:29, time: 0.428, data_time: 0.191, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0827, loss: 0.0827 +2025-06-24 21:26:22,980 - pyskl - INFO - Epoch [116][200/1281] lr: 3.184e-03, eta: 2:47:07, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0734, loss: 0.0734 +2025-06-24 21:26:45,140 - pyskl - INFO - Epoch [116][300/1281] lr: 3.170e-03, eta: 2:46:44, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0765, loss: 0.0765 +2025-06-24 21:27:07,380 - pyskl - INFO - Epoch [116][400/1281] lr: 3.156e-03, eta: 2:46:22, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0817, loss: 0.0817 +2025-06-24 21:27:29,595 - pyskl - INFO - Epoch [116][500/1281] lr: 3.143e-03, eta: 2:45:59, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0963, loss: 0.0963 +2025-06-24 21:27:51,998 - pyskl - INFO - Epoch [116][600/1281] lr: 3.129e-03, eta: 2:45:37, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0935, loss: 0.0935 +2025-06-24 21:28:14,509 - pyskl - INFO - Epoch [116][700/1281] lr: 3.116e-03, eta: 2:45:14, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0964, loss: 0.0964 +2025-06-24 21:28:36,647 - pyskl - INFO - Epoch [116][800/1281] lr: 3.102e-03, eta: 2:44:52, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0687, loss: 0.0687 +2025-06-24 21:28:59,161 - pyskl - INFO - Epoch [116][900/1281] lr: 3.089e-03, eta: 2:44:29, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0618, loss: 0.0618 +2025-06-24 21:29:21,481 - pyskl - INFO - Epoch [116][1000/1281] lr: 3.075e-03, eta: 2:44:07, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0733, loss: 0.0733 +2025-06-24 21:29:43,850 - pyskl - INFO - Epoch [116][1100/1281] lr: 3.062e-03, eta: 2:43:44, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0845, loss: 0.0845 +2025-06-24 21:30:06,099 - pyskl - INFO - Epoch [116][1200/1281] lr: 3.049e-03, eta: 2:43:22, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0872, loss: 0.0872 +2025-06-24 21:30:25,126 - pyskl - INFO - Saving checkpoint at 116 epochs +2025-06-24 21:31:08,849 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:31:08,903 - pyskl - INFO - +top1_acc 0.9229 +top5_acc 0.9953 +2025-06-24 21:31:08,904 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:31:08,910 - pyskl - INFO - +mean_acc 0.8995 +2025-06-24 21:31:08,912 - pyskl - INFO - Epoch(val) [116][533] top1_acc: 0.9229, top5_acc: 0.9953, mean_class_accuracy: 0.8995 +2025-06-24 21:31:51,669 - pyskl - INFO - Epoch [117][100/1281] lr: 3.024e-03, eta: 2:42:42, time: 0.427, data_time: 0.193, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0670, loss: 0.0670 +2025-06-24 21:32:14,071 - pyskl - INFO - Epoch [117][200/1281] lr: 3.011e-03, eta: 2:42:19, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0759, loss: 0.0759 +2025-06-24 21:32:36,602 - pyskl - INFO - Epoch [117][300/1281] lr: 2.998e-03, eta: 2:41:57, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0658, loss: 0.0658 +2025-06-24 21:32:58,994 - pyskl - INFO - Epoch [117][400/1281] lr: 2.984e-03, eta: 2:41:34, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0622, loss: 0.0622 +2025-06-24 21:33:21,344 - pyskl - INFO - Epoch [117][500/1281] lr: 2.971e-03, eta: 2:41:12, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0681, loss: 0.0681 +2025-06-24 21:33:43,781 - pyskl - INFO - Epoch [117][600/1281] lr: 2.958e-03, eta: 2:40:49, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0845, loss: 0.0845 +2025-06-24 21:34:05,888 - pyskl - INFO - Epoch [117][700/1281] lr: 2.945e-03, eta: 2:40:27, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0619, loss: 0.0619 +2025-06-24 21:34:28,325 - pyskl - INFO - Epoch [117][800/1281] lr: 2.932e-03, eta: 2:40:04, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0864, loss: 0.0864 +2025-06-24 21:34:50,726 - pyskl - INFO - Epoch [117][900/1281] lr: 2.919e-03, eta: 2:39:42, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0822, loss: 0.0822 +2025-06-24 21:35:13,077 - pyskl - INFO - Epoch [117][1000/1281] lr: 2.905e-03, eta: 2:39:19, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0766, loss: 0.0766 +2025-06-24 21:35:35,781 - pyskl - INFO - Epoch [117][1100/1281] lr: 2.892e-03, eta: 2:38:57, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0642, loss: 0.0642 +2025-06-24 21:35:58,058 - pyskl - INFO - Epoch [117][1200/1281] lr: 2.879e-03, eta: 2:38:34, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0702, loss: 0.0702 +2025-06-24 21:36:16,988 - pyskl - INFO - Saving checkpoint at 117 epochs +2025-06-24 21:37:01,416 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:37:01,477 - pyskl - INFO - +top1_acc 0.9275 +top5_acc 0.9958 +2025-06-24 21:37:01,477 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:37:01,483 - pyskl - INFO - +mean_acc 0.8993 +2025-06-24 21:37:01,487 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_110.pth was removed +2025-06-24 21:37:01,698 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_117.pth. +2025-06-24 21:37:01,698 - pyskl - INFO - Best top1_acc is 0.9275 at 117 epoch. +2025-06-24 21:37:01,700 - pyskl - INFO - Epoch(val) [117][533] top1_acc: 0.9275, top5_acc: 0.9958, mean_class_accuracy: 0.8993 +2025-06-24 21:37:44,145 - pyskl - INFO - Epoch [118][100/1281] lr: 2.856e-03, eta: 2:37:54, time: 0.424, data_time: 0.187, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0827, loss: 0.0827 +2025-06-24 21:38:06,403 - pyskl - INFO - Epoch [118][200/1281] lr: 2.843e-03, eta: 2:37:32, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0557, loss: 0.0557 +2025-06-24 21:38:28,569 - pyskl - INFO - Epoch [118][300/1281] lr: 2.830e-03, eta: 2:37:09, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0689, loss: 0.0689 +2025-06-24 21:38:50,781 - pyskl - INFO - Epoch [118][400/1281] lr: 2.817e-03, eta: 2:36:46, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0648, loss: 0.0648 +2025-06-24 21:39:13,195 - pyskl - INFO - Epoch [118][500/1281] lr: 2.804e-03, eta: 2:36:24, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0532, loss: 0.0532 +2025-06-24 21:39:35,619 - pyskl - INFO - Epoch [118][600/1281] lr: 2.791e-03, eta: 2:36:02, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0641, loss: 0.0641 +2025-06-24 21:39:57,981 - pyskl - INFO - Epoch [118][700/1281] lr: 2.778e-03, eta: 2:35:39, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0549, loss: 0.0549 +2025-06-24 21:40:20,665 - pyskl - INFO - Epoch [118][800/1281] lr: 2.765e-03, eta: 2:35:17, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0759, loss: 0.0759 +2025-06-24 21:40:43,105 - pyskl - INFO - Epoch [118][900/1281] lr: 2.753e-03, eta: 2:34:54, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0802, loss: 0.0802 +2025-06-24 21:41:05,670 - pyskl - INFO - Epoch [118][1000/1281] lr: 2.740e-03, eta: 2:34:32, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1028, loss: 0.1028 +2025-06-24 21:41:27,829 - pyskl - INFO - Epoch [118][1100/1281] lr: 2.727e-03, eta: 2:34:09, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 0.9994, loss_cls: 0.0979, loss: 0.0979 +2025-06-24 21:41:50,088 - pyskl - INFO - Epoch [118][1200/1281] lr: 2.714e-03, eta: 2:33:47, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0876, loss: 0.0876 +2025-06-24 21:42:08,853 - pyskl - INFO - Saving checkpoint at 118 epochs +2025-06-24 21:42:52,550 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:42:52,608 - pyskl - INFO - +top1_acc 0.9270 +top5_acc 0.9946 +2025-06-24 21:42:52,608 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:42:52,616 - pyskl - INFO - +mean_acc 0.9010 +2025-06-24 21:42:52,618 - pyskl - INFO - Epoch(val) [118][533] top1_acc: 0.9270, top5_acc: 0.9946, mean_class_accuracy: 0.9010 +2025-06-24 21:43:34,482 - pyskl - INFO - Epoch [119][100/1281] lr: 2.691e-03, eta: 2:33:06, time: 0.419, data_time: 0.185, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0591, loss: 0.0591 +2025-06-24 21:43:57,190 - pyskl - INFO - Epoch [119][200/1281] lr: 2.679e-03, eta: 2:32:44, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0507, loss: 0.0507 +2025-06-24 21:44:19,606 - pyskl - INFO - Epoch [119][300/1281] lr: 2.666e-03, eta: 2:32:21, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0467, loss: 0.0467 +2025-06-24 21:44:41,869 - pyskl - INFO - Epoch [119][400/1281] lr: 2.653e-03, eta: 2:31:59, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 0.9994, loss_cls: 0.0746, loss: 0.0746 +2025-06-24 21:45:04,334 - pyskl - INFO - Epoch [119][500/1281] lr: 2.641e-03, eta: 2:31:36, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0641, loss: 0.0641 +2025-06-24 21:45:26,454 - pyskl - INFO - Epoch [119][600/1281] lr: 2.628e-03, eta: 2:31:14, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0581, loss: 0.0581 +2025-06-24 21:45:48,667 - pyskl - INFO - Epoch [119][700/1281] lr: 2.616e-03, eta: 2:30:51, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0544, loss: 0.0544 +2025-06-24 21:46:10,931 - pyskl - INFO - Epoch [119][800/1281] lr: 2.603e-03, eta: 2:30:29, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0582, loss: 0.0582 +2025-06-24 21:46:33,016 - pyskl - INFO - Epoch [119][900/1281] lr: 2.591e-03, eta: 2:30:06, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0591, loss: 0.0591 +2025-06-24 21:46:55,123 - pyskl - INFO - Epoch [119][1000/1281] lr: 2.578e-03, eta: 2:29:44, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0785, loss: 0.0785 +2025-06-24 21:47:17,544 - pyskl - INFO - Epoch [119][1100/1281] lr: 2.566e-03, eta: 2:29:21, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0634, loss: 0.0634 +2025-06-24 21:47:39,943 - pyskl - INFO - Epoch [119][1200/1281] lr: 2.554e-03, eta: 2:28:59, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0771, loss: 0.0771 +2025-06-24 21:47:58,652 - pyskl - INFO - Saving checkpoint at 119 epochs +2025-06-24 21:48:42,842 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:48:42,910 - pyskl - INFO - +top1_acc 0.9312 +top5_acc 0.9950 +2025-06-24 21:48:42,910 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:48:42,919 - pyskl - INFO - +mean_acc 0.9033 +2025-06-24 21:48:42,923 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_117.pth was removed +2025-06-24 21:48:43,118 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_119.pth. +2025-06-24 21:48:43,118 - pyskl - INFO - Best top1_acc is 0.9312 at 119 epoch. +2025-06-24 21:48:43,124 - pyskl - INFO - Epoch(val) [119][533] top1_acc: 0.9312, top5_acc: 0.9950, mean_class_accuracy: 0.9033 +2025-06-24 21:49:25,077 - pyskl - INFO - Epoch [120][100/1281] lr: 2.531e-03, eta: 2:28:18, time: 0.419, data_time: 0.186, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0688, loss: 0.0688 +2025-06-24 21:49:47,581 - pyskl - INFO - Epoch [120][200/1281] lr: 2.519e-03, eta: 2:27:56, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 0.9994, loss_cls: 0.0604, loss: 0.0604 +2025-06-24 21:50:09,999 - pyskl - INFO - Epoch [120][300/1281] lr: 2.507e-03, eta: 2:27:33, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0546, loss: 0.0546 +2025-06-24 21:50:32,558 - pyskl - INFO - Epoch [120][400/1281] lr: 2.494e-03, eta: 2:27:11, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0605, loss: 0.0605 +2025-06-24 21:50:54,833 - pyskl - INFO - Epoch [120][500/1281] lr: 2.482e-03, eta: 2:26:49, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0544, loss: 0.0544 +2025-06-24 21:51:17,134 - pyskl - INFO - Epoch [120][600/1281] lr: 2.470e-03, eta: 2:26:26, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0558, loss: 0.0558 +2025-06-24 21:51:39,814 - pyskl - INFO - Epoch [120][700/1281] lr: 2.458e-03, eta: 2:26:04, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0722, loss: 0.0722 +2025-06-24 21:52:02,240 - pyskl - INFO - Epoch [120][800/1281] lr: 2.446e-03, eta: 2:25:41, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0537, loss: 0.0537 +2025-06-24 21:52:24,777 - pyskl - INFO - Epoch [120][900/1281] lr: 2.433e-03, eta: 2:25:19, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0707, loss: 0.0707 +2025-06-24 21:52:47,168 - pyskl - INFO - Epoch [120][1000/1281] lr: 2.421e-03, eta: 2:24:56, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0612, loss: 0.0612 +2025-06-24 21:53:10,068 - pyskl - INFO - Epoch [120][1100/1281] lr: 2.409e-03, eta: 2:24:34, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0588, loss: 0.0588 +2025-06-24 21:53:32,456 - pyskl - INFO - Epoch [120][1200/1281] lr: 2.397e-03, eta: 2:24:11, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0512, loss: 0.0512 +2025-06-24 21:53:51,747 - pyskl - INFO - Saving checkpoint at 120 epochs +2025-06-24 21:54:35,850 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:54:35,922 - pyskl - INFO - +top1_acc 0.9279 +top5_acc 0.9954 +2025-06-24 21:54:35,922 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:54:35,932 - pyskl - INFO - +mean_acc 0.8957 +2025-06-24 21:54:35,936 - pyskl - INFO - Epoch(val) [120][533] top1_acc: 0.9279, top5_acc: 0.9954, mean_class_accuracy: 0.8957 +2025-06-24 21:55:17,985 - pyskl - INFO - Epoch [121][100/1281] lr: 2.375e-03, eta: 2:23:31, time: 0.420, data_time: 0.185, memory: 4083, top1_acc: 0.9931, top5_acc: 0.9994, loss_cls: 0.0612, loss: 0.0612 +2025-06-24 21:55:40,366 - pyskl - INFO - Epoch [121][200/1281] lr: 2.363e-03, eta: 2:23:09, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0604, loss: 0.0604 +2025-06-24 21:56:02,899 - pyskl - INFO - Epoch [121][300/1281] lr: 2.351e-03, eta: 2:22:46, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0443, loss: 0.0443 +2025-06-24 21:56:25,730 - pyskl - INFO - Epoch [121][400/1281] lr: 2.340e-03, eta: 2:22:24, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0455, loss: 0.0455 +2025-06-24 21:56:48,179 - pyskl - INFO - Epoch [121][500/1281] lr: 2.328e-03, eta: 2:22:01, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0637, loss: 0.0637 +2025-06-24 21:57:10,603 - pyskl - INFO - Epoch [121][600/1281] lr: 2.316e-03, eta: 2:21:39, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0682, loss: 0.0682 +2025-06-24 21:57:33,113 - pyskl - INFO - Epoch [121][700/1281] lr: 2.304e-03, eta: 2:21:16, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0779, loss: 0.0779 +2025-06-24 21:57:55,437 - pyskl - INFO - Epoch [121][800/1281] lr: 2.292e-03, eta: 2:20:54, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0718, loss: 0.0718 +2025-06-24 21:58:17,970 - pyskl - INFO - Epoch [121][900/1281] lr: 2.280e-03, eta: 2:20:31, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0649, loss: 0.0649 +2025-06-24 21:58:40,201 - pyskl - INFO - Epoch [121][1000/1281] lr: 2.269e-03, eta: 2:20:09, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0836, loss: 0.0836 +2025-06-24 21:59:02,381 - pyskl - INFO - Epoch [121][1100/1281] lr: 2.257e-03, eta: 2:19:46, time: 0.222, data_time: 0.001, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0641, loss: 0.0641 +2025-06-24 21:59:24,603 - pyskl - INFO - Epoch [121][1200/1281] lr: 2.245e-03, eta: 2:19:24, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0496, loss: 0.0496 +2025-06-24 21:59:43,611 - pyskl - INFO - Saving checkpoint at 121 epochs +2025-06-24 22:00:27,521 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:00:27,576 - pyskl - INFO - +top1_acc 0.9283 +top5_acc 0.9955 +2025-06-24 22:00:27,576 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:00:27,583 - pyskl - INFO - +mean_acc 0.9015 +2025-06-24 22:00:27,585 - pyskl - INFO - Epoch(val) [121][533] top1_acc: 0.9283, top5_acc: 0.9955, mean_class_accuracy: 0.9015 +2025-06-24 22:01:09,608 - pyskl - INFO - Epoch [122][100/1281] lr: 2.224e-03, eta: 2:18:43, time: 0.420, data_time: 0.187, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0548, loss: 0.0548 +2025-06-24 22:01:32,021 - pyskl - INFO - Epoch [122][200/1281] lr: 2.212e-03, eta: 2:18:21, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0556, loss: 0.0556 +2025-06-24 22:01:54,558 - pyskl - INFO - Epoch [122][300/1281] lr: 2.201e-03, eta: 2:17:59, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0672, loss: 0.0672 +2025-06-24 22:02:17,025 - pyskl - INFO - Epoch [122][400/1281] lr: 2.189e-03, eta: 2:17:36, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0546, loss: 0.0546 +2025-06-24 22:02:39,302 - pyskl - INFO - Epoch [122][500/1281] lr: 2.178e-03, eta: 2:17:14, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0439, loss: 0.0439 +2025-06-24 22:03:01,651 - pyskl - INFO - Epoch [122][600/1281] lr: 2.166e-03, eta: 2:16:51, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0388, loss: 0.0388 +2025-06-24 22:03:23,848 - pyskl - INFO - Epoch [122][700/1281] lr: 2.155e-03, eta: 2:16:28, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0391, loss: 0.0391 +2025-06-24 22:03:46,075 - pyskl - INFO - Epoch [122][800/1281] lr: 2.143e-03, eta: 2:16:06, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0586, loss: 0.0586 +2025-06-24 22:04:08,407 - pyskl - INFO - Epoch [122][900/1281] lr: 2.132e-03, eta: 2:15:43, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0746, loss: 0.0746 +2025-06-24 22:04:30,827 - pyskl - INFO - Epoch [122][1000/1281] lr: 2.120e-03, eta: 2:15:21, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0563, loss: 0.0563 +2025-06-24 22:04:53,396 - pyskl - INFO - Epoch [122][1100/1281] lr: 2.109e-03, eta: 2:14:59, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0493, loss: 0.0493 +2025-06-24 22:05:15,938 - pyskl - INFO - Epoch [122][1200/1281] lr: 2.098e-03, eta: 2:14:36, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0610, loss: 0.0610 +2025-06-24 22:05:34,693 - pyskl - INFO - Saving checkpoint at 122 epochs +2025-06-24 22:06:18,101 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:06:18,162 - pyskl - INFO - +top1_acc 0.9288 +top5_acc 0.9952 +2025-06-24 22:06:18,162 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:06:18,169 - pyskl - INFO - +mean_acc 0.9050 +2025-06-24 22:06:18,171 - pyskl - INFO - Epoch(val) [122][533] top1_acc: 0.9288, top5_acc: 0.9952, mean_class_accuracy: 0.9050 +2025-06-24 22:07:00,221 - pyskl - INFO - Epoch [123][100/1281] lr: 2.077e-03, eta: 2:13:56, time: 0.420, data_time: 0.185, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0376, loss: 0.0376 +2025-06-24 22:07:22,418 - pyskl - INFO - Epoch [123][200/1281] lr: 2.066e-03, eta: 2:13:33, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0346, loss: 0.0346 +2025-06-24 22:07:44,771 - pyskl - INFO - Epoch [123][300/1281] lr: 2.055e-03, eta: 2:13:11, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0509, loss: 0.0509 +2025-06-24 22:08:07,144 - pyskl - INFO - Epoch [123][400/1281] lr: 2.044e-03, eta: 2:12:48, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0484, loss: 0.0484 +2025-06-24 22:08:29,591 - pyskl - INFO - Epoch [123][500/1281] lr: 2.032e-03, eta: 2:12:26, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0644, loss: 0.0644 +2025-06-24 22:08:52,004 - pyskl - INFO - Epoch [123][600/1281] lr: 2.021e-03, eta: 2:12:03, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0477, loss: 0.0477 +2025-06-24 22:09:14,479 - pyskl - INFO - Epoch [123][700/1281] lr: 2.010e-03, eta: 2:11:41, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 0.9994, loss_cls: 0.0459, loss: 0.0459 +2025-06-24 22:09:37,249 - pyskl - INFO - Epoch [123][800/1281] lr: 1.999e-03, eta: 2:11:18, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0538, loss: 0.0538 +2025-06-24 22:09:59,617 - pyskl - INFO - Epoch [123][900/1281] lr: 1.988e-03, eta: 2:10:56, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0545, loss: 0.0545 +2025-06-24 22:10:22,312 - pyskl - INFO - Epoch [123][1000/1281] lr: 1.977e-03, eta: 2:10:34, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0490, loss: 0.0490 +2025-06-24 22:10:44,632 - pyskl - INFO - Epoch [123][1100/1281] lr: 1.966e-03, eta: 2:10:11, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0333, loss: 0.0333 +2025-06-24 22:11:06,989 - pyskl - INFO - Epoch [123][1200/1281] lr: 1.955e-03, eta: 2:09:49, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0500, loss: 0.0500 +2025-06-24 22:11:25,908 - pyskl - INFO - Saving checkpoint at 123 epochs +2025-06-24 22:12:10,323 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:12:10,379 - pyskl - INFO - +top1_acc 0.9338 +top5_acc 0.9953 +2025-06-24 22:12:10,379 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:12:10,387 - pyskl - INFO - +mean_acc 0.9064 +2025-06-24 22:12:10,392 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_119.pth was removed +2025-06-24 22:12:10,638 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_123.pth. +2025-06-24 22:12:10,638 - pyskl - INFO - Best top1_acc is 0.9338 at 123 epoch. +2025-06-24 22:12:10,642 - pyskl - INFO - Epoch(val) [123][533] top1_acc: 0.9338, top5_acc: 0.9953, mean_class_accuracy: 0.9064 +2025-06-24 22:12:52,605 - pyskl - INFO - Epoch [124][100/1281] lr: 1.935e-03, eta: 2:09:08, time: 0.420, data_time: 0.183, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0372, loss: 0.0372 +2025-06-24 22:13:15,223 - pyskl - INFO - Epoch [124][200/1281] lr: 1.924e-03, eta: 2:08:46, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0348, loss: 0.0348 +2025-06-24 22:13:37,424 - pyskl - INFO - Epoch [124][300/1281] lr: 1.913e-03, eta: 2:08:23, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0395, loss: 0.0395 +2025-06-24 22:13:59,840 - pyskl - INFO - Epoch [124][400/1281] lr: 1.902e-03, eta: 2:08:01, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0447, loss: 0.0447 +2025-06-24 22:14:22,172 - pyskl - INFO - Epoch [124][500/1281] lr: 1.892e-03, eta: 2:07:38, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0472, loss: 0.0472 +2025-06-24 22:14:44,461 - pyskl - INFO - Epoch [124][600/1281] lr: 1.881e-03, eta: 2:07:16, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0545, loss: 0.0545 +2025-06-24 22:15:06,761 - pyskl - INFO - Epoch [124][700/1281] lr: 1.870e-03, eta: 2:06:53, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0558, loss: 0.0558 +2025-06-24 22:15:28,924 - pyskl - INFO - Epoch [124][800/1281] lr: 1.859e-03, eta: 2:06:31, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0543, loss: 0.0543 +2025-06-24 22:15:51,267 - pyskl - INFO - Epoch [124][900/1281] lr: 1.849e-03, eta: 2:06:08, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0745, loss: 0.0745 +2025-06-24 22:16:13,554 - pyskl - INFO - Epoch [124][1000/1281] lr: 1.838e-03, eta: 2:05:46, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0621, loss: 0.0621 +2025-06-24 22:16:36,112 - pyskl - INFO - Epoch [124][1100/1281] lr: 1.827e-03, eta: 2:05:23, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0414, loss: 0.0414 +2025-06-24 22:16:58,543 - pyskl - INFO - Epoch [124][1200/1281] lr: 1.817e-03, eta: 2:05:01, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0468, loss: 0.0468 +2025-06-24 22:17:17,521 - pyskl - INFO - Saving checkpoint at 124 epochs +2025-06-24 22:18:01,318 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:18:01,373 - pyskl - INFO - +top1_acc 0.9313 +top5_acc 0.9951 +2025-06-24 22:18:01,374 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:18:01,380 - pyskl - INFO - +mean_acc 0.9063 +2025-06-24 22:18:01,382 - pyskl - INFO - Epoch(val) [124][533] top1_acc: 0.9313, top5_acc: 0.9951, mean_class_accuracy: 0.9063 +2025-06-24 22:18:42,949 - pyskl - INFO - Epoch [125][100/1281] lr: 1.797e-03, eta: 2:04:20, time: 0.416, data_time: 0.183, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0358, loss: 0.0358 +2025-06-24 22:19:05,537 - pyskl - INFO - Epoch [125][200/1281] lr: 1.787e-03, eta: 2:03:58, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0306, loss: 0.0306 +2025-06-24 22:19:27,548 - pyskl - INFO - Epoch [125][300/1281] lr: 1.776e-03, eta: 2:03:35, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0283, loss: 0.0283 +2025-06-24 22:19:49,739 - pyskl - INFO - Epoch [125][400/1281] lr: 1.766e-03, eta: 2:03:13, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0414, loss: 0.0414 +2025-06-24 22:20:11,809 - pyskl - INFO - Epoch [125][500/1281] lr: 1.755e-03, eta: 2:02:50, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0333, loss: 0.0333 +2025-06-24 22:20:33,960 - pyskl - INFO - Epoch [125][600/1281] lr: 1.745e-03, eta: 2:02:28, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0327, loss: 0.0327 +2025-06-24 22:20:56,212 - pyskl - INFO - Epoch [125][700/1281] lr: 1.735e-03, eta: 2:02:05, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0319, loss: 0.0319 +2025-06-24 22:21:18,520 - pyskl - INFO - Epoch [125][800/1281] lr: 1.724e-03, eta: 2:01:43, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0323, loss: 0.0323 +2025-06-24 22:21:40,863 - pyskl - INFO - Epoch [125][900/1281] lr: 1.714e-03, eta: 2:01:20, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0371, loss: 0.0371 +2025-06-24 22:22:03,243 - pyskl - INFO - Epoch [125][1000/1281] lr: 1.704e-03, eta: 2:00:58, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0518, loss: 0.0518 +2025-06-24 22:22:25,558 - pyskl - INFO - Epoch [125][1100/1281] lr: 1.693e-03, eta: 2:00:35, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0363, loss: 0.0363 +2025-06-24 22:22:47,735 - pyskl - INFO - Epoch [125][1200/1281] lr: 1.683e-03, eta: 2:00:13, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0429, loss: 0.0429 +2025-06-24 22:23:06,414 - pyskl - INFO - Saving checkpoint at 125 epochs +2025-06-24 22:23:50,580 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:23:50,642 - pyskl - INFO - +top1_acc 0.9330 +top5_acc 0.9965 +2025-06-24 22:23:50,642 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:23:50,650 - pyskl - INFO - +mean_acc 0.9079 +2025-06-24 22:23:50,652 - pyskl - INFO - Epoch(val) [125][533] top1_acc: 0.9330, top5_acc: 0.9965, mean_class_accuracy: 0.9079 +2025-06-24 22:24:33,347 - pyskl - INFO - Epoch [126][100/1281] lr: 1.665e-03, eta: 1:59:32, time: 0.427, data_time: 0.189, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0320, loss: 0.0320 +2025-06-24 22:24:55,745 - pyskl - INFO - Epoch [126][200/1281] lr: 1.654e-03, eta: 1:59:10, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0387, loss: 0.0387 +2025-06-24 22:25:17,858 - pyskl - INFO - Epoch [126][300/1281] lr: 1.644e-03, eta: 1:58:47, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0358, loss: 0.0358 +2025-06-24 22:25:40,385 - pyskl - INFO - Epoch [126][400/1281] lr: 1.634e-03, eta: 1:58:25, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0369, loss: 0.0369 +2025-06-24 22:26:02,788 - pyskl - INFO - Epoch [126][500/1281] lr: 1.624e-03, eta: 1:58:02, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0403, loss: 0.0403 +2025-06-24 22:26:25,143 - pyskl - INFO - Epoch [126][600/1281] lr: 1.614e-03, eta: 1:57:40, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0343, loss: 0.0343 +2025-06-24 22:26:47,401 - pyskl - INFO - Epoch [126][700/1281] lr: 1.604e-03, eta: 1:57:17, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0407, loss: 0.0407 +2025-06-24 22:27:10,062 - pyskl - INFO - Epoch [126][800/1281] lr: 1.594e-03, eta: 1:56:55, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0398, loss: 0.0398 +2025-06-24 22:27:32,385 - pyskl - INFO - Epoch [126][900/1281] lr: 1.584e-03, eta: 1:56:32, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0456, loss: 0.0456 +2025-06-24 22:27:54,853 - pyskl - INFO - Epoch [126][1000/1281] lr: 1.574e-03, eta: 1:56:10, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0330, loss: 0.0330 +2025-06-24 22:28:17,027 - pyskl - INFO - Epoch [126][1100/1281] lr: 1.564e-03, eta: 1:55:47, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0357, loss: 0.0357 +2025-06-24 22:28:39,092 - pyskl - INFO - Epoch [126][1200/1281] lr: 1.554e-03, eta: 1:55:25, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0441, loss: 0.0441 +2025-06-24 22:28:57,646 - pyskl - INFO - Saving checkpoint at 126 epochs +2025-06-24 22:29:41,456 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:29:41,525 - pyskl - INFO - +top1_acc 0.9370 +top5_acc 0.9965 +2025-06-24 22:29:41,525 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:29:41,534 - pyskl - INFO - +mean_acc 0.9129 +2025-06-24 22:29:41,539 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_123.pth was removed +2025-06-24 22:29:41,731 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_126.pth. +2025-06-24 22:29:41,731 - pyskl - INFO - Best top1_acc is 0.9370 at 126 epoch. +2025-06-24 22:29:41,735 - pyskl - INFO - Epoch(val) [126][533] top1_acc: 0.9370, top5_acc: 0.9965, mean_class_accuracy: 0.9129 +2025-06-24 22:30:24,260 - pyskl - INFO - Epoch [127][100/1281] lr: 1.536e-03, eta: 1:54:45, time: 0.425, data_time: 0.186, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0401, loss: 0.0401 +2025-06-24 22:30:46,599 - pyskl - INFO - Epoch [127][200/1281] lr: 1.527e-03, eta: 1:54:22, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0281, loss: 0.0281 +2025-06-24 22:31:08,642 - pyskl - INFO - Epoch [127][300/1281] lr: 1.517e-03, eta: 1:54:00, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0377, loss: 0.0377 +2025-06-24 22:31:31,178 - pyskl - INFO - Epoch [127][400/1281] lr: 1.507e-03, eta: 1:53:37, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0385, loss: 0.0385 +2025-06-24 22:31:53,434 - pyskl - INFO - Epoch [127][500/1281] lr: 1.497e-03, eta: 1:53:15, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0285, loss: 0.0285 +2025-06-24 22:32:15,752 - pyskl - INFO - Epoch [127][600/1281] lr: 1.488e-03, eta: 1:52:52, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0283, loss: 0.0283 +2025-06-24 22:32:37,819 - pyskl - INFO - Epoch [127][700/1281] lr: 1.478e-03, eta: 1:52:30, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0389, loss: 0.0389 +2025-06-24 22:33:00,138 - pyskl - INFO - Epoch [127][800/1281] lr: 1.468e-03, eta: 1:52:07, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0282, loss: 0.0282 +2025-06-24 22:33:22,578 - pyskl - INFO - Epoch [127][900/1281] lr: 1.459e-03, eta: 1:51:45, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0269, loss: 0.0269 +2025-06-24 22:33:44,851 - pyskl - INFO - Epoch [127][1000/1281] lr: 1.449e-03, eta: 1:51:22, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0321, loss: 0.0321 +2025-06-24 22:34:07,077 - pyskl - INFO - Epoch [127][1100/1281] lr: 1.440e-03, eta: 1:51:00, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0284, loss: 0.0284 +2025-06-24 22:34:29,553 - pyskl - INFO - Epoch [127][1200/1281] lr: 1.430e-03, eta: 1:50:37, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0338, loss: 0.0338 +2025-06-24 22:34:48,351 - pyskl - INFO - Saving checkpoint at 127 epochs +2025-06-24 22:35:31,983 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:35:32,043 - pyskl - INFO - +top1_acc 0.9378 +top5_acc 0.9968 +2025-06-24 22:35:32,043 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:35:32,051 - pyskl - INFO - +mean_acc 0.9140 +2025-06-24 22:35:32,055 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_126.pth was removed +2025-06-24 22:35:32,220 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_127.pth. +2025-06-24 22:35:32,220 - pyskl - INFO - Best top1_acc is 0.9378 at 127 epoch. +2025-06-24 22:35:32,223 - pyskl - INFO - Epoch(val) [127][533] top1_acc: 0.9378, top5_acc: 0.9968, mean_class_accuracy: 0.9140 +2025-06-24 22:36:14,546 - pyskl - INFO - Epoch [128][100/1281] lr: 1.413e-03, eta: 1:49:57, time: 0.423, data_time: 0.191, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0332, loss: 0.0332 +2025-06-24 22:36:37,030 - pyskl - INFO - Epoch [128][200/1281] lr: 1.404e-03, eta: 1:49:34, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0280, loss: 0.0280 +2025-06-24 22:36:59,499 - pyskl - INFO - Epoch [128][300/1281] lr: 1.394e-03, eta: 1:49:12, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-24 22:37:21,874 - pyskl - INFO - Epoch [128][400/1281] lr: 1.385e-03, eta: 1:48:49, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0279, loss: 0.0279 +2025-06-24 22:37:44,681 - pyskl - INFO - Epoch [128][500/1281] lr: 1.376e-03, eta: 1:48:27, time: 0.228, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0292, loss: 0.0292 +2025-06-24 22:38:06,902 - pyskl - INFO - Epoch [128][600/1281] lr: 1.366e-03, eta: 1:48:04, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0250, loss: 0.0250 +2025-06-24 22:38:29,290 - pyskl - INFO - Epoch [128][700/1281] lr: 1.357e-03, eta: 1:47:42, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0271, loss: 0.0271 +2025-06-24 22:38:51,464 - pyskl - INFO - Epoch [128][800/1281] lr: 1.348e-03, eta: 1:47:19, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0279, loss: 0.0279 +2025-06-24 22:39:13,854 - pyskl - INFO - Epoch [128][900/1281] lr: 1.339e-03, eta: 1:46:57, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0372, loss: 0.0372 +2025-06-24 22:39:36,555 - pyskl - INFO - Epoch [128][1000/1281] lr: 1.329e-03, eta: 1:46:35, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0398, loss: 0.0398 +2025-06-24 22:39:58,549 - pyskl - INFO - Epoch [128][1100/1281] lr: 1.320e-03, eta: 1:46:12, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0260, loss: 0.0260 +2025-06-24 22:40:21,066 - pyskl - INFO - Epoch [128][1200/1281] lr: 1.311e-03, eta: 1:45:50, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0323, loss: 0.0323 +2025-06-24 22:40:40,203 - pyskl - INFO - Saving checkpoint at 128 epochs +2025-06-24 22:41:24,022 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:41:24,078 - pyskl - INFO - +top1_acc 0.9352 +top5_acc 0.9961 +2025-06-24 22:41:24,078 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:41:24,087 - pyskl - INFO - +mean_acc 0.9080 +2025-06-24 22:41:24,089 - pyskl - INFO - Epoch(val) [128][533] top1_acc: 0.9352, top5_acc: 0.9961, mean_class_accuracy: 0.9080 +2025-06-24 22:42:06,625 - pyskl - INFO - Epoch [129][100/1281] lr: 1.295e-03, eta: 1:45:09, time: 0.425, data_time: 0.187, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-24 22:42:28,910 - pyskl - INFO - Epoch [129][200/1281] lr: 1.286e-03, eta: 1:44:47, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-24 22:42:51,519 - pyskl - INFO - Epoch [129][300/1281] lr: 1.277e-03, eta: 1:44:24, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0389, loss: 0.0389 +2025-06-24 22:43:13,941 - pyskl - INFO - Epoch [129][400/1281] lr: 1.268e-03, eta: 1:44:02, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0294, loss: 0.0294 +2025-06-24 22:43:36,460 - pyskl - INFO - Epoch [129][500/1281] lr: 1.259e-03, eta: 1:43:39, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0332, loss: 0.0332 +2025-06-24 22:43:58,753 - pyskl - INFO - Epoch [129][600/1281] lr: 1.250e-03, eta: 1:43:17, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0255, loss: 0.0255 +2025-06-24 22:44:21,351 - pyskl - INFO - Epoch [129][700/1281] lr: 1.241e-03, eta: 1:42:54, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0328, loss: 0.0328 +2025-06-24 22:44:43,879 - pyskl - INFO - Epoch [129][800/1281] lr: 1.232e-03, eta: 1:42:32, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0314, loss: 0.0314 +2025-06-24 22:45:06,570 - pyskl - INFO - Epoch [129][900/1281] lr: 1.223e-03, eta: 1:42:09, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0340, loss: 0.0340 +2025-06-24 22:45:29,058 - pyskl - INFO - Epoch [129][1000/1281] lr: 1.214e-03, eta: 1:41:47, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0291, loss: 0.0291 +2025-06-24 22:45:51,635 - pyskl - INFO - Epoch [129][1100/1281] lr: 1.206e-03, eta: 1:41:25, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-24 22:46:14,140 - pyskl - INFO - Epoch [129][1200/1281] lr: 1.197e-03, eta: 1:41:02, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0301, loss: 0.0301 +2025-06-24 22:46:33,328 - pyskl - INFO - Saving checkpoint at 129 epochs +2025-06-24 22:47:17,159 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:47:17,226 - pyskl - INFO - +top1_acc 0.9369 +top5_acc 0.9969 +2025-06-24 22:47:17,227 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:47:17,234 - pyskl - INFO - +mean_acc 0.9085 +2025-06-24 22:47:17,236 - pyskl - INFO - Epoch(val) [129][533] top1_acc: 0.9369, top5_acc: 0.9969, mean_class_accuracy: 0.9085 +2025-06-24 22:47:59,059 - pyskl - INFO - Epoch [130][100/1281] lr: 1.181e-03, eta: 1:40:22, time: 0.418, data_time: 0.182, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0405, loss: 0.0405 +2025-06-24 22:48:21,567 - pyskl - INFO - Epoch [130][200/1281] lr: 1.172e-03, eta: 1:39:59, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0338, loss: 0.0338 +2025-06-24 22:48:43,848 - pyskl - INFO - Epoch [130][300/1281] lr: 1.164e-03, eta: 1:39:37, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0248, loss: 0.0248 +2025-06-24 22:49:06,213 - pyskl - INFO - Epoch [130][400/1281] lr: 1.155e-03, eta: 1:39:14, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-24 22:49:28,700 - pyskl - INFO - Epoch [130][500/1281] lr: 1.147e-03, eta: 1:38:52, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-24 22:49:51,076 - pyskl - INFO - Epoch [130][600/1281] lr: 1.138e-03, eta: 1:38:29, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-24 22:50:13,491 - pyskl - INFO - Epoch [130][700/1281] lr: 1.130e-03, eta: 1:38:07, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0292, loss: 0.0292 +2025-06-24 22:50:35,760 - pyskl - INFO - Epoch [130][800/1281] lr: 1.121e-03, eta: 1:37:44, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0280, loss: 0.0280 +2025-06-24 22:50:58,238 - pyskl - INFO - Epoch [130][900/1281] lr: 1.113e-03, eta: 1:37:22, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0350, loss: 0.0350 +2025-06-24 22:51:20,310 - pyskl - INFO - Epoch [130][1000/1281] lr: 1.104e-03, eta: 1:36:59, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0264, loss: 0.0264 +2025-06-24 22:51:42,578 - pyskl - INFO - Epoch [130][1100/1281] lr: 1.096e-03, eta: 1:36:37, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0260, loss: 0.0260 +2025-06-24 22:52:05,122 - pyskl - INFO - Epoch [130][1200/1281] lr: 1.088e-03, eta: 1:36:14, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0327, loss: 0.0327 +2025-06-24 22:52:23,869 - pyskl - INFO - Saving checkpoint at 130 epochs +2025-06-24 22:53:07,631 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:53:07,695 - pyskl - INFO - +top1_acc 0.9399 +top5_acc 0.9965 +2025-06-24 22:53:07,695 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:53:07,702 - pyskl - INFO - +mean_acc 0.9124 +2025-06-24 22:53:07,706 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_127.pth was removed +2025-06-24 22:53:07,868 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_130.pth. +2025-06-24 22:53:07,868 - pyskl - INFO - Best top1_acc is 0.9399 at 130 epoch. +2025-06-24 22:53:07,871 - pyskl - INFO - Epoch(val) [130][533] top1_acc: 0.9399, top5_acc: 0.9965, mean_class_accuracy: 0.9124 +2025-06-24 22:53:49,667 - pyskl - INFO - Epoch [131][100/1281] lr: 1.072e-03, eta: 1:35:34, time: 0.418, data_time: 0.183, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-24 22:54:12,309 - pyskl - INFO - Epoch [131][200/1281] lr: 1.064e-03, eta: 1:35:11, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0276, loss: 0.0276 +2025-06-24 22:54:34,549 - pyskl - INFO - Epoch [131][300/1281] lr: 1.056e-03, eta: 1:34:49, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-24 22:54:56,962 - pyskl - INFO - Epoch [131][400/1281] lr: 1.048e-03, eta: 1:34:26, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0268, loss: 0.0268 +2025-06-24 22:55:19,041 - pyskl - INFO - Epoch [131][500/1281] lr: 1.040e-03, eta: 1:34:04, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0329, loss: 0.0329 +2025-06-24 22:55:41,187 - pyskl - INFO - Epoch [131][600/1281] lr: 1.031e-03, eta: 1:33:41, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0287, loss: 0.0287 +2025-06-24 22:56:03,578 - pyskl - INFO - Epoch [131][700/1281] lr: 1.023e-03, eta: 1:33:19, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0226, loss: 0.0226 +2025-06-24 22:56:26,078 - pyskl - INFO - Epoch [131][800/1281] lr: 1.015e-03, eta: 1:32:56, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0261, loss: 0.0261 +2025-06-24 22:56:48,485 - pyskl - INFO - Epoch [131][900/1281] lr: 1.007e-03, eta: 1:32:34, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0246, loss: 0.0246 +2025-06-24 22:57:11,016 - pyskl - INFO - Epoch [131][1000/1281] lr: 9.992e-04, eta: 1:32:11, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0323, loss: 0.0323 +2025-06-24 22:57:33,304 - pyskl - INFO - Epoch [131][1100/1281] lr: 9.912e-04, eta: 1:31:49, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0250, loss: 0.0250 +2025-06-24 22:57:55,671 - pyskl - INFO - Epoch [131][1200/1281] lr: 9.832e-04, eta: 1:31:26, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0229, loss: 0.0229 +2025-06-24 22:58:14,619 - pyskl - INFO - Saving checkpoint at 131 epochs +2025-06-24 22:58:58,647 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:58:58,743 - pyskl - INFO - +top1_acc 0.9367 +top5_acc 0.9966 +2025-06-24 22:58:58,743 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:58:58,755 - pyskl - INFO - +mean_acc 0.9112 +2025-06-24 22:58:58,757 - pyskl - INFO - Epoch(val) [131][533] top1_acc: 0.9367, top5_acc: 0.9966, mean_class_accuracy: 0.9112 +2025-06-24 22:59:41,002 - pyskl - INFO - Epoch [132][100/1281] lr: 9.689e-04, eta: 1:30:46, time: 0.422, data_time: 0.187, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-24 23:00:03,529 - pyskl - INFO - Epoch [132][200/1281] lr: 9.610e-04, eta: 1:30:24, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-24 23:00:25,889 - pyskl - INFO - Epoch [132][300/1281] lr: 9.532e-04, eta: 1:30:01, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-24 23:00:48,000 - pyskl - INFO - Epoch [132][400/1281] lr: 9.454e-04, eta: 1:29:39, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0228, loss: 0.0228 +2025-06-24 23:01:10,192 - pyskl - INFO - Epoch [132][500/1281] lr: 9.376e-04, eta: 1:29:16, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0251, loss: 0.0251 +2025-06-24 23:01:32,604 - pyskl - INFO - Epoch [132][600/1281] lr: 9.298e-04, eta: 1:28:54, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0253, loss: 0.0253 +2025-06-24 23:01:55,020 - pyskl - INFO - Epoch [132][700/1281] lr: 9.221e-04, eta: 1:28:31, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0219, loss: 0.0219 +2025-06-24 23:02:17,559 - pyskl - INFO - Epoch [132][800/1281] lr: 9.144e-04, eta: 1:28:09, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0261, loss: 0.0261 +2025-06-24 23:02:39,808 - pyskl - INFO - Epoch [132][900/1281] lr: 9.068e-04, eta: 1:27:46, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-24 23:03:02,160 - pyskl - INFO - Epoch [132][1000/1281] lr: 8.991e-04, eta: 1:27:24, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-24 23:03:24,809 - pyskl - INFO - Epoch [132][1100/1281] lr: 8.915e-04, eta: 1:27:01, time: 0.226, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0228, loss: 0.0228 +2025-06-24 23:03:47,123 - pyskl - INFO - Epoch [132][1200/1281] lr: 8.840e-04, eta: 1:26:39, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0253, loss: 0.0253 +2025-06-24 23:04:05,668 - pyskl - INFO - Saving checkpoint at 132 epochs +2025-06-24 23:04:49,591 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:04:49,647 - pyskl - INFO - +top1_acc 0.9386 +top5_acc 0.9965 +2025-06-24 23:04:49,647 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:04:49,654 - pyskl - INFO - +mean_acc 0.9127 +2025-06-24 23:04:49,656 - pyskl - INFO - Epoch(val) [132][533] top1_acc: 0.9386, top5_acc: 0.9965, mean_class_accuracy: 0.9127 +2025-06-24 23:05:31,943 - pyskl - INFO - Epoch [133][100/1281] lr: 8.704e-04, eta: 1:25:58, time: 0.423, data_time: 0.190, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-24 23:05:54,490 - pyskl - INFO - Epoch [133][200/1281] lr: 8.629e-04, eta: 1:25:36, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-24 23:06:16,826 - pyskl - INFO - Epoch [133][300/1281] lr: 8.554e-04, eta: 1:25:13, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0218, loss: 0.0218 +2025-06-24 23:06:38,934 - pyskl - INFO - Epoch [133][400/1281] lr: 8.480e-04, eta: 1:24:51, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0252, loss: 0.0252 +2025-06-24 23:07:01,505 - pyskl - INFO - Epoch [133][500/1281] lr: 8.406e-04, eta: 1:24:28, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0254, loss: 0.0254 +2025-06-24 23:07:24,071 - pyskl - INFO - Epoch [133][600/1281] lr: 8.333e-04, eta: 1:24:06, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0270, loss: 0.0270 +2025-06-24 23:07:46,211 - pyskl - INFO - Epoch [133][700/1281] lr: 8.260e-04, eta: 1:23:43, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-24 23:08:08,785 - pyskl - INFO - Epoch [133][800/1281] lr: 8.187e-04, eta: 1:23:21, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0291, loss: 0.0291 +2025-06-24 23:08:31,173 - pyskl - INFO - Epoch [133][900/1281] lr: 8.114e-04, eta: 1:22:58, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-24 23:08:53,241 - pyskl - INFO - Epoch [133][1000/1281] lr: 8.042e-04, eta: 1:22:36, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-24 23:09:15,545 - pyskl - INFO - Epoch [133][1100/1281] lr: 7.970e-04, eta: 1:22:13, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-24 23:09:37,923 - pyskl - INFO - Epoch [133][1200/1281] lr: 7.898e-04, eta: 1:21:51, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-24 23:09:56,633 - pyskl - INFO - Saving checkpoint at 133 epochs +2025-06-24 23:10:40,032 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:10:40,093 - pyskl - INFO - +top1_acc 0.9389 +top5_acc 0.9964 +2025-06-24 23:10:40,093 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:10:40,103 - pyskl - INFO - +mean_acc 0.9120 +2025-06-24 23:10:40,106 - pyskl - INFO - Epoch(val) [133][533] top1_acc: 0.9389, top5_acc: 0.9964, mean_class_accuracy: 0.9120 +2025-06-24 23:11:22,629 - pyskl - INFO - Epoch [134][100/1281] lr: 7.769e-04, eta: 1:21:10, time: 0.425, data_time: 0.191, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-24 23:11:45,259 - pyskl - INFO - Epoch [134][200/1281] lr: 7.699e-04, eta: 1:20:48, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-24 23:12:07,607 - pyskl - INFO - Epoch [134][300/1281] lr: 7.628e-04, eta: 1:20:26, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0227, loss: 0.0227 +2025-06-24 23:12:30,000 - pyskl - INFO - Epoch [134][400/1281] lr: 7.558e-04, eta: 1:20:03, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0277, loss: 0.0277 +2025-06-24 23:12:52,375 - pyskl - INFO - Epoch [134][500/1281] lr: 7.488e-04, eta: 1:19:41, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-24 23:13:14,474 - pyskl - INFO - Epoch [134][600/1281] lr: 7.419e-04, eta: 1:19:18, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-24 23:13:37,060 - pyskl - INFO - Epoch [134][700/1281] lr: 7.349e-04, eta: 1:18:56, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-24 23:13:59,291 - pyskl - INFO - Epoch [134][800/1281] lr: 7.281e-04, eta: 1:18:33, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-24 23:14:21,476 - pyskl - INFO - Epoch [134][900/1281] lr: 7.212e-04, eta: 1:18:11, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0232, loss: 0.0232 +2025-06-24 23:14:43,575 - pyskl - INFO - Epoch [134][1000/1281] lr: 7.144e-04, eta: 1:17:48, time: 0.221, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-24 23:15:05,899 - pyskl - INFO - Epoch [134][1100/1281] lr: 7.076e-04, eta: 1:17:26, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0259, loss: 0.0259 +2025-06-24 23:15:28,679 - pyskl - INFO - Epoch [134][1200/1281] lr: 7.008e-04, eta: 1:17:03, time: 0.228, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-24 23:15:47,241 - pyskl - INFO - Saving checkpoint at 134 epochs +2025-06-24 23:16:31,105 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:16:31,163 - pyskl - INFO - +top1_acc 0.9390 +top5_acc 0.9969 +2025-06-24 23:16:31,163 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:16:31,170 - pyskl - INFO - +mean_acc 0.9145 +2025-06-24 23:16:31,172 - pyskl - INFO - Epoch(val) [134][533] top1_acc: 0.9390, top5_acc: 0.9969, mean_class_accuracy: 0.9145 +2025-06-24 23:17:14,191 - pyskl - INFO - Epoch [135][100/1281] lr: 6.887e-04, eta: 1:16:23, time: 0.430, data_time: 0.192, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0232, loss: 0.0232 +2025-06-24 23:17:36,765 - pyskl - INFO - Epoch [135][200/1281] lr: 6.820e-04, eta: 1:16:00, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-24 23:17:59,029 - pyskl - INFO - Epoch [135][300/1281] lr: 6.753e-04, eta: 1:15:38, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-24 23:18:21,371 - pyskl - INFO - Epoch [135][400/1281] lr: 6.687e-04, eta: 1:15:15, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-24 23:18:43,987 - pyskl - INFO - Epoch [135][500/1281] lr: 6.622e-04, eta: 1:14:53, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0223, loss: 0.0223 +2025-06-24 23:19:06,429 - pyskl - INFO - Epoch [135][600/1281] lr: 6.556e-04, eta: 1:14:30, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-24 23:19:28,806 - pyskl - INFO - Epoch [135][700/1281] lr: 6.491e-04, eta: 1:14:08, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-24 23:19:51,438 - pyskl - INFO - Epoch [135][800/1281] lr: 6.426e-04, eta: 1:13:45, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0279, loss: 0.0279 +2025-06-24 23:20:13,709 - pyskl - INFO - Epoch [135][900/1281] lr: 6.362e-04, eta: 1:13:23, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0228, loss: 0.0228 +2025-06-24 23:20:36,144 - pyskl - INFO - Epoch [135][1000/1281] lr: 6.297e-04, eta: 1:13:00, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0218, loss: 0.0218 +2025-06-24 23:20:58,670 - pyskl - INFO - Epoch [135][1100/1281] lr: 6.233e-04, eta: 1:12:38, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-24 23:21:21,355 - pyskl - INFO - Epoch [135][1200/1281] lr: 6.170e-04, eta: 1:12:16, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-24 23:21:40,380 - pyskl - INFO - Saving checkpoint at 135 epochs +2025-06-24 23:22:24,407 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:22:24,462 - pyskl - INFO - +top1_acc 0.9384 +top5_acc 0.9964 +2025-06-24 23:22:24,462 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:22:24,469 - pyskl - INFO - +mean_acc 0.9131 +2025-06-24 23:22:24,470 - pyskl - INFO - Epoch(val) [135][533] top1_acc: 0.9384, top5_acc: 0.9964, mean_class_accuracy: 0.9131 +2025-06-24 23:23:07,386 - pyskl - INFO - Epoch [136][100/1281] lr: 6.056e-04, eta: 1:11:35, time: 0.429, data_time: 0.194, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-24 23:23:29,839 - pyskl - INFO - Epoch [136][200/1281] lr: 5.993e-04, eta: 1:11:13, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-24 23:23:52,040 - pyskl - INFO - Epoch [136][300/1281] lr: 5.931e-04, eta: 1:10:50, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-24 23:24:14,312 - pyskl - INFO - Epoch [136][400/1281] lr: 5.868e-04, eta: 1:10:28, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-24 23:24:36,486 - pyskl - INFO - Epoch [136][500/1281] lr: 5.807e-04, eta: 1:10:05, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-24 23:24:58,798 - pyskl - INFO - Epoch [136][600/1281] lr: 5.745e-04, eta: 1:09:43, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-24 23:25:21,179 - pyskl - INFO - Epoch [136][700/1281] lr: 5.684e-04, eta: 1:09:20, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-24 23:25:43,530 - pyskl - INFO - Epoch [136][800/1281] lr: 5.623e-04, eta: 1:08:58, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-24 23:26:05,952 - pyskl - INFO - Epoch [136][900/1281] lr: 5.563e-04, eta: 1:08:35, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-24 23:26:27,936 - pyskl - INFO - Epoch [136][1000/1281] lr: 5.503e-04, eta: 1:08:13, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-24 23:26:50,195 - pyskl - INFO - Epoch [136][1100/1281] lr: 5.443e-04, eta: 1:07:50, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-24 23:27:12,615 - pyskl - INFO - Epoch [136][1200/1281] lr: 5.384e-04, eta: 1:07:28, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-24 23:27:31,428 - pyskl - INFO - Saving checkpoint at 136 epochs +2025-06-24 23:28:14,887 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:28:14,962 - pyskl - INFO - +top1_acc 0.9390 +top5_acc 0.9962 +2025-06-24 23:28:14,962 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:28:14,974 - pyskl - INFO - +mean_acc 0.9123 +2025-06-24 23:28:14,978 - pyskl - INFO - Epoch(val) [136][533] top1_acc: 0.9390, top5_acc: 0.9962, mean_class_accuracy: 0.9123 +2025-06-24 23:28:58,047 - pyskl - INFO - Epoch [137][100/1281] lr: 5.277e-04, eta: 1:06:47, time: 0.431, data_time: 0.195, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0218, loss: 0.0218 +2025-06-24 23:29:20,439 - pyskl - INFO - Epoch [137][200/1281] lr: 5.218e-04, eta: 1:06:25, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-24 23:29:42,857 - pyskl - INFO - Epoch [137][300/1281] lr: 5.160e-04, eta: 1:06:02, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-24 23:30:05,112 - pyskl - INFO - Epoch [137][400/1281] lr: 5.102e-04, eta: 1:05:40, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-24 23:30:27,429 - pyskl - INFO - Epoch [137][500/1281] lr: 5.044e-04, eta: 1:05:17, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0248, loss: 0.0248 +2025-06-24 23:30:50,131 - pyskl - INFO - Epoch [137][600/1281] lr: 4.987e-04, eta: 1:04:55, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-24 23:31:12,549 - pyskl - INFO - Epoch [137][700/1281] lr: 4.930e-04, eta: 1:04:32, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0263, loss: 0.0263 +2025-06-24 23:31:34,848 - pyskl - INFO - Epoch [137][800/1281] lr: 4.873e-04, eta: 1:04:10, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-24 23:31:56,925 - pyskl - INFO - Epoch [137][900/1281] lr: 4.817e-04, eta: 1:03:47, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-24 23:32:19,414 - pyskl - INFO - Epoch [137][1000/1281] lr: 4.761e-04, eta: 1:03:25, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-24 23:32:41,881 - pyskl - INFO - Epoch [137][1100/1281] lr: 4.705e-04, eta: 1:03:02, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0223, loss: 0.0223 +2025-06-24 23:33:04,181 - pyskl - INFO - Epoch [137][1200/1281] lr: 4.650e-04, eta: 1:02:40, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-24 23:33:23,031 - pyskl - INFO - Saving checkpoint at 137 epochs +2025-06-24 23:34:07,155 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:34:07,215 - pyskl - INFO - +top1_acc 0.9414 +top5_acc 0.9967 +2025-06-24 23:34:07,215 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:34:07,223 - pyskl - INFO - +mean_acc 0.9182 +2025-06-24 23:34:07,227 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_130.pth was removed +2025-06-24 23:34:07,439 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_137.pth. +2025-06-24 23:34:07,439 - pyskl - INFO - Best top1_acc is 0.9414 at 137 epoch. +2025-06-24 23:34:07,443 - pyskl - INFO - Epoch(val) [137][533] top1_acc: 0.9414, top5_acc: 0.9967, mean_class_accuracy: 0.9182 +2025-06-24 23:34:49,350 - pyskl - INFO - Epoch [138][100/1281] lr: 4.550e-04, eta: 1:01:59, time: 0.419, data_time: 0.188, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-24 23:35:11,708 - pyskl - INFO - Epoch [138][200/1281] lr: 4.496e-04, eta: 1:01:37, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-24 23:35:33,824 - pyskl - INFO - Epoch [138][300/1281] lr: 4.442e-04, eta: 1:01:14, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-24 23:35:56,139 - pyskl - INFO - Epoch [138][400/1281] lr: 4.388e-04, eta: 1:00:52, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-24 23:36:18,543 - pyskl - INFO - Epoch [138][500/1281] lr: 4.334e-04, eta: 1:00:29, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-24 23:36:40,617 - pyskl - INFO - Epoch [138][600/1281] lr: 4.281e-04, eta: 1:00:07, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-24 23:37:02,702 - pyskl - INFO - Epoch [138][700/1281] lr: 4.228e-04, eta: 0:59:44, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-24 23:37:25,373 - pyskl - INFO - Epoch [138][800/1281] lr: 4.176e-04, eta: 0:59:22, time: 0.227, data_time: 0.001, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0222, loss: 0.0222 +2025-06-24 23:37:47,255 - pyskl - INFO - Epoch [138][900/1281] lr: 4.124e-04, eta: 0:58:59, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-24 23:38:09,435 - pyskl - INFO - Epoch [138][1000/1281] lr: 4.072e-04, eta: 0:58:37, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-24 23:38:31,426 - pyskl - INFO - Epoch [138][1100/1281] lr: 4.020e-04, eta: 0:58:14, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-24 23:38:53,770 - pyskl - INFO - Epoch [138][1200/1281] lr: 3.969e-04, eta: 0:57:52, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-24 23:39:12,344 - pyskl - INFO - Saving checkpoint at 138 epochs +2025-06-24 23:39:55,180 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:39:55,235 - pyskl - INFO - +top1_acc 0.9392 +top5_acc 0.9965 +2025-06-24 23:39:55,235 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:39:55,242 - pyskl - INFO - +mean_acc 0.9146 +2025-06-24 23:39:55,244 - pyskl - INFO - Epoch(val) [138][533] top1_acc: 0.9392, top5_acc: 0.9965, mean_class_accuracy: 0.9146 +2025-06-24 23:40:36,915 - pyskl - INFO - Epoch [139][100/1281] lr: 3.877e-04, eta: 0:57:11, time: 0.417, data_time: 0.182, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-24 23:40:58,914 - pyskl - INFO - Epoch [139][200/1281] lr: 3.827e-04, eta: 0:56:49, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0280, loss: 0.0280 +2025-06-24 23:41:21,245 - pyskl - INFO - Epoch [139][300/1281] lr: 3.777e-04, eta: 0:56:26, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-24 23:41:43,552 - pyskl - INFO - Epoch [139][400/1281] lr: 3.727e-04, eta: 0:56:04, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-24 23:42:05,628 - pyskl - INFO - Epoch [139][500/1281] lr: 3.678e-04, eta: 0:55:41, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-24 23:42:27,856 - pyskl - INFO - Epoch [139][600/1281] lr: 3.628e-04, eta: 0:55:19, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-24 23:42:49,929 - pyskl - INFO - Epoch [139][700/1281] lr: 3.580e-04, eta: 0:54:56, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-24 23:43:12,221 - pyskl - INFO - Epoch [139][800/1281] lr: 3.531e-04, eta: 0:54:34, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-24 23:43:34,604 - pyskl - INFO - Epoch [139][900/1281] lr: 3.483e-04, eta: 0:54:11, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-24 23:43:57,302 - pyskl - INFO - Epoch [139][1000/1281] lr: 3.436e-04, eta: 0:53:49, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-24 23:44:19,519 - pyskl - INFO - Epoch [139][1100/1281] lr: 3.388e-04, eta: 0:53:27, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-24 23:44:41,812 - pyskl - INFO - Epoch [139][1200/1281] lr: 3.341e-04, eta: 0:53:04, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-24 23:45:00,806 - pyskl - INFO - Saving checkpoint at 139 epochs +2025-06-24 23:45:44,113 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:45:44,189 - pyskl - INFO - +top1_acc 0.9404 +top5_acc 0.9964 +2025-06-24 23:45:44,189 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:45:44,196 - pyskl - INFO - +mean_acc 0.9166 +2025-06-24 23:45:44,198 - pyskl - INFO - Epoch(val) [139][533] top1_acc: 0.9404, top5_acc: 0.9964, mean_class_accuracy: 0.9166 +2025-06-24 23:46:26,084 - pyskl - INFO - Epoch [140][100/1281] lr: 3.257e-04, eta: 0:52:23, time: 0.419, data_time: 0.184, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-24 23:46:48,182 - pyskl - INFO - Epoch [140][200/1281] lr: 3.210e-04, eta: 0:52:01, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-24 23:47:10,401 - pyskl - INFO - Epoch [140][300/1281] lr: 3.165e-04, eta: 0:51:38, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-24 23:47:32,495 - pyskl - INFO - Epoch [140][400/1281] lr: 3.119e-04, eta: 0:51:16, time: 0.221, data_time: 0.001, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-24 23:47:55,052 - pyskl - INFO - Epoch [140][500/1281] lr: 3.074e-04, eta: 0:50:54, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0219, loss: 0.0219 +2025-06-24 23:48:17,268 - pyskl - INFO - Epoch [140][600/1281] lr: 3.029e-04, eta: 0:50:31, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-24 23:48:39,658 - pyskl - INFO - Epoch [140][700/1281] lr: 2.984e-04, eta: 0:50:09, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-24 23:49:01,953 - pyskl - INFO - Epoch [140][800/1281] lr: 2.940e-04, eta: 0:49:46, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-24 23:49:24,111 - pyskl - INFO - Epoch [140][900/1281] lr: 2.896e-04, eta: 0:49:24, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0226, loss: 0.0226 +2025-06-24 23:49:46,349 - pyskl - INFO - Epoch [140][1000/1281] lr: 2.853e-04, eta: 0:49:01, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-24 23:50:08,352 - pyskl - INFO - Epoch [140][1100/1281] lr: 2.809e-04, eta: 0:48:39, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-24 23:50:31,215 - pyskl - INFO - Epoch [140][1200/1281] lr: 2.767e-04, eta: 0:48:16, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-24 23:50:49,727 - pyskl - INFO - Saving checkpoint at 140 epochs +2025-06-24 23:51:32,961 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:51:33,037 - pyskl - INFO - +top1_acc 0.9393 +top5_acc 0.9965 +2025-06-24 23:51:33,037 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:51:33,045 - pyskl - INFO - +mean_acc 0.9155 +2025-06-24 23:51:33,047 - pyskl - INFO - Epoch(val) [140][533] top1_acc: 0.9393, top5_acc: 0.9965, mean_class_accuracy: 0.9155 +2025-06-24 23:52:14,875 - pyskl - INFO - Epoch [141][100/1281] lr: 2.690e-04, eta: 0:47:36, time: 0.418, data_time: 0.185, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-24 23:52:37,120 - pyskl - INFO - Epoch [141][200/1281] lr: 2.648e-04, eta: 0:47:13, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-24 23:52:59,229 - pyskl - INFO - Epoch [141][300/1281] lr: 2.606e-04, eta: 0:46:51, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-24 23:53:21,253 - pyskl - INFO - Epoch [141][400/1281] lr: 2.565e-04, eta: 0:46:28, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-24 23:53:43,618 - pyskl - INFO - Epoch [141][500/1281] lr: 2.524e-04, eta: 0:46:06, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-24 23:54:06,122 - pyskl - INFO - Epoch [141][600/1281] lr: 2.483e-04, eta: 0:45:43, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-24 23:54:28,287 - pyskl - INFO - Epoch [141][700/1281] lr: 2.443e-04, eta: 0:45:21, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-24 23:54:50,775 - pyskl - INFO - Epoch [141][800/1281] lr: 2.402e-04, eta: 0:44:58, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-24 23:55:13,235 - pyskl - INFO - Epoch [141][900/1281] lr: 2.363e-04, eta: 0:44:36, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-24 23:55:35,086 - pyskl - INFO - Epoch [141][1000/1281] lr: 2.323e-04, eta: 0:44:13, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-24 23:55:57,099 - pyskl - INFO - Epoch [141][1100/1281] lr: 2.284e-04, eta: 0:43:51, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-24 23:56:19,578 - pyskl - INFO - Epoch [141][1200/1281] lr: 2.246e-04, eta: 0:43:28, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-24 23:56:38,142 - pyskl - INFO - Saving checkpoint at 141 epochs +2025-06-24 23:57:22,078 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:57:22,133 - pyskl - INFO - +top1_acc 0.9413 +top5_acc 0.9969 +2025-06-24 23:57:22,133 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:57:22,139 - pyskl - INFO - +mean_acc 0.9166 +2025-06-24 23:57:22,140 - pyskl - INFO - Epoch(val) [141][533] top1_acc: 0.9413, top5_acc: 0.9969, mean_class_accuracy: 0.9166 +2025-06-24 23:58:04,593 - pyskl - INFO - Epoch [142][100/1281] lr: 2.176e-04, eta: 0:42:48, time: 0.424, data_time: 0.191, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-24 23:58:26,487 - pyskl - INFO - Epoch [142][200/1281] lr: 2.139e-04, eta: 0:42:25, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-24 23:58:48,380 - pyskl - INFO - Epoch [142][300/1281] lr: 2.101e-04, eta: 0:42:03, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-24 23:59:10,759 - pyskl - INFO - Epoch [142][400/1281] lr: 2.064e-04, eta: 0:41:40, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-24 23:59:32,852 - pyskl - INFO - Epoch [142][500/1281] lr: 2.027e-04, eta: 0:41:18, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0275, loss: 0.0275 +2025-06-24 23:59:55,111 - pyskl - INFO - Epoch [142][600/1281] lr: 1.991e-04, eta: 0:40:55, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-25 00:00:17,112 - pyskl - INFO - Epoch [142][700/1281] lr: 1.954e-04, eta: 0:40:33, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 00:00:39,071 - pyskl - INFO - Epoch [142][800/1281] lr: 1.919e-04, eta: 0:40:10, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 00:01:01,413 - pyskl - INFO - Epoch [142][900/1281] lr: 1.883e-04, eta: 0:39:48, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 00:01:23,703 - pyskl - INFO - Epoch [142][1000/1281] lr: 1.848e-04, eta: 0:39:25, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 00:01:45,807 - pyskl - INFO - Epoch [142][1100/1281] lr: 1.813e-04, eta: 0:39:03, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 00:02:08,025 - pyskl - INFO - Epoch [142][1200/1281] lr: 1.779e-04, eta: 0:38:40, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 00:02:26,859 - pyskl - INFO - Saving checkpoint at 142 epochs +2025-06-25 00:03:10,402 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:03:10,470 - pyskl - INFO - +top1_acc 0.9403 +top5_acc 0.9959 +2025-06-25 00:03:10,470 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:03:10,478 - pyskl - INFO - +mean_acc 0.9173 +2025-06-25 00:03:10,480 - pyskl - INFO - Epoch(val) [142][533] top1_acc: 0.9403, top5_acc: 0.9959, mean_class_accuracy: 0.9173 +2025-06-25 00:03:52,471 - pyskl - INFO - Epoch [143][100/1281] lr: 1.717e-04, eta: 0:38:00, time: 0.420, data_time: 0.184, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 00:04:15,054 - pyskl - INFO - Epoch [143][200/1281] lr: 1.683e-04, eta: 0:37:37, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 00:04:37,189 - pyskl - INFO - Epoch [143][300/1281] lr: 1.650e-04, eta: 0:37:15, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 00:04:59,516 - pyskl - INFO - Epoch [143][400/1281] lr: 1.617e-04, eta: 0:36:52, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 00:05:22,201 - pyskl - INFO - Epoch [143][500/1281] lr: 1.585e-04, eta: 0:36:30, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 00:05:44,533 - pyskl - INFO - Epoch [143][600/1281] lr: 1.552e-04, eta: 0:36:07, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 00:06:07,009 - pyskl - INFO - Epoch [143][700/1281] lr: 1.520e-04, eta: 0:35:45, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 00:06:29,226 - pyskl - INFO - Epoch [143][800/1281] lr: 1.489e-04, eta: 0:35:22, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 00:06:51,463 - pyskl - INFO - Epoch [143][900/1281] lr: 1.457e-04, eta: 0:35:00, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 00:07:13,422 - pyskl - INFO - Epoch [143][1000/1281] lr: 1.426e-04, eta: 0:34:37, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 00:07:36,004 - pyskl - INFO - Epoch [143][1100/1281] lr: 1.396e-04, eta: 0:34:15, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 00:07:57,969 - pyskl - INFO - Epoch [143][1200/1281] lr: 1.366e-04, eta: 0:33:52, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 00:08:16,847 - pyskl - INFO - Saving checkpoint at 143 epochs +2025-06-25 00:08:59,676 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:08:59,741 - pyskl - INFO - +top1_acc 0.9414 +top5_acc 0.9965 +2025-06-25 00:08:59,741 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:08:59,749 - pyskl - INFO - +mean_acc 0.9173 +2025-06-25 00:08:59,751 - pyskl - INFO - Epoch(val) [143][533] top1_acc: 0.9414, top5_acc: 0.9965, mean_class_accuracy: 0.9173 +2025-06-25 00:09:42,005 - pyskl - INFO - Epoch [144][100/1281] lr: 1.312e-04, eta: 0:33:12, time: 0.422, data_time: 0.188, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-25 00:10:04,193 - pyskl - INFO - Epoch [144][200/1281] lr: 1.282e-04, eta: 0:32:49, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 00:10:26,324 - pyskl - INFO - Epoch [144][300/1281] lr: 1.253e-04, eta: 0:32:27, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 00:10:48,584 - pyskl - INFO - Epoch [144][400/1281] lr: 1.224e-04, eta: 0:32:04, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 00:11:10,731 - pyskl - INFO - Epoch [144][500/1281] lr: 1.196e-04, eta: 0:31:42, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 00:11:32,891 - pyskl - INFO - Epoch [144][600/1281] lr: 1.168e-04, eta: 0:31:19, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 00:11:55,113 - pyskl - INFO - Epoch [144][700/1281] lr: 1.140e-04, eta: 0:30:57, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 00:12:17,233 - pyskl - INFO - Epoch [144][800/1281] lr: 1.113e-04, eta: 0:30:34, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 00:12:39,685 - pyskl - INFO - Epoch [144][900/1281] lr: 1.086e-04, eta: 0:30:12, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-25 00:13:02,067 - pyskl - INFO - Epoch [144][1000/1281] lr: 1.059e-04, eta: 0:29:50, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 00:13:24,099 - pyskl - INFO - Epoch [144][1100/1281] lr: 1.033e-04, eta: 0:29:27, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0227, loss: 0.0227 +2025-06-25 00:13:46,259 - pyskl - INFO - Epoch [144][1200/1281] lr: 1.007e-04, eta: 0:29:05, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 00:14:04,875 - pyskl - INFO - Saving checkpoint at 144 epochs +2025-06-25 00:14:48,376 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:14:48,432 - pyskl - INFO - +top1_acc 0.9411 +top5_acc 0.9964 +2025-06-25 00:14:48,432 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:14:48,438 - pyskl - INFO - +mean_acc 0.9177 +2025-06-25 00:14:48,440 - pyskl - INFO - Epoch(val) [144][533] top1_acc: 0.9411, top5_acc: 0.9964, mean_class_accuracy: 0.9177 +2025-06-25 00:15:30,767 - pyskl - INFO - Epoch [145][100/1281] lr: 9.605e-05, eta: 0:28:24, time: 0.423, data_time: 0.187, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 00:15:53,043 - pyskl - INFO - Epoch [145][200/1281] lr: 9.353e-05, eta: 0:28:01, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-25 00:16:15,557 - pyskl - INFO - Epoch [145][300/1281] lr: 9.106e-05, eta: 0:27:39, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 00:16:37,928 - pyskl - INFO - Epoch [145][400/1281] lr: 8.861e-05, eta: 0:27:17, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 00:16:59,952 - pyskl - INFO - Epoch [145][500/1281] lr: 8.620e-05, eta: 0:26:54, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 00:17:22,201 - pyskl - INFO - Epoch [145][600/1281] lr: 8.382e-05, eta: 0:26:32, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 00:17:44,328 - pyskl - INFO - Epoch [145][700/1281] lr: 8.147e-05, eta: 0:26:09, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 00:18:06,629 - pyskl - INFO - Epoch [145][800/1281] lr: 7.916e-05, eta: 0:25:47, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 00:18:28,771 - pyskl - INFO - Epoch [145][900/1281] lr: 7.688e-05, eta: 0:25:24, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 00:18:51,094 - pyskl - INFO - Epoch [145][1000/1281] lr: 7.463e-05, eta: 0:25:02, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 00:19:13,167 - pyskl - INFO - Epoch [145][1100/1281] lr: 7.242e-05, eta: 0:24:39, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 00:19:35,764 - pyskl - INFO - Epoch [145][1200/1281] lr: 7.024e-05, eta: 0:24:17, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 00:19:54,269 - pyskl - INFO - Saving checkpoint at 145 epochs +2025-06-25 00:20:37,216 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:20:37,270 - pyskl - INFO - +top1_acc 0.9407 +top5_acc 0.9968 +2025-06-25 00:20:37,270 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:20:37,277 - pyskl - INFO - +mean_acc 0.9161 +2025-06-25 00:20:37,279 - pyskl - INFO - Epoch(val) [145][533] top1_acc: 0.9407, top5_acc: 0.9968, mean_class_accuracy: 0.9161 +2025-06-25 00:21:19,001 - pyskl - INFO - Epoch [146][100/1281] lr: 6.638e-05, eta: 0:23:36, time: 0.417, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 00:21:41,408 - pyskl - INFO - Epoch [146][200/1281] lr: 6.429e-05, eta: 0:23:14, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 00:22:03,574 - pyskl - INFO - Epoch [146][300/1281] lr: 6.224e-05, eta: 0:22:51, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 00:22:25,808 - pyskl - INFO - Epoch [146][400/1281] lr: 6.022e-05, eta: 0:22:29, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 00:22:48,105 - pyskl - INFO - Epoch [146][500/1281] lr: 5.823e-05, eta: 0:22:06, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 00:23:09,967 - pyskl - INFO - Epoch [146][600/1281] lr: 5.628e-05, eta: 0:21:44, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 00:23:31,995 - pyskl - INFO - Epoch [146][700/1281] lr: 5.436e-05, eta: 0:21:21, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 00:23:54,036 - pyskl - INFO - Epoch [146][800/1281] lr: 5.247e-05, eta: 0:20:59, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 00:24:16,230 - pyskl - INFO - Epoch [146][900/1281] lr: 5.061e-05, eta: 0:20:36, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 00:24:38,668 - pyskl - INFO - Epoch [146][1000/1281] lr: 4.879e-05, eta: 0:20:14, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 00:25:00,669 - pyskl - INFO - Epoch [146][1100/1281] lr: 4.701e-05, eta: 0:19:51, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 00:25:23,188 - pyskl - INFO - Epoch [146][1200/1281] lr: 4.525e-05, eta: 0:19:29, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 00:25:41,894 - pyskl - INFO - Saving checkpoint at 146 epochs +2025-06-25 00:26:25,470 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:26:25,525 - pyskl - INFO - +top1_acc 0.9412 +top5_acc 0.9967 +2025-06-25 00:26:25,526 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:26:25,533 - pyskl - INFO - +mean_acc 0.9166 +2025-06-25 00:26:25,535 - pyskl - INFO - Epoch(val) [146][533] top1_acc: 0.9412, top5_acc: 0.9967, mean_class_accuracy: 0.9166 +2025-06-25 00:27:07,453 - pyskl - INFO - Epoch [147][100/1281] lr: 4.216e-05, eta: 0:18:48, time: 0.419, data_time: 0.183, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 00:27:29,948 - pyskl - INFO - Epoch [147][200/1281] lr: 4.050e-05, eta: 0:18:26, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-25 00:27:52,526 - pyskl - INFO - Epoch [147][300/1281] lr: 3.887e-05, eta: 0:18:03, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 00:28:14,718 - pyskl - INFO - Epoch [147][400/1281] lr: 3.728e-05, eta: 0:17:41, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 00:28:36,916 - pyskl - INFO - Epoch [147][500/1281] lr: 3.572e-05, eta: 0:17:18, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 00:28:59,309 - pyskl - INFO - Epoch [147][600/1281] lr: 3.419e-05, eta: 0:16:56, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 00:29:21,806 - pyskl - INFO - Epoch [147][700/1281] lr: 3.270e-05, eta: 0:16:33, time: 0.225, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 00:29:43,871 - pyskl - INFO - Epoch [147][800/1281] lr: 3.124e-05, eta: 0:16:11, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 00:30:06,236 - pyskl - INFO - Epoch [147][900/1281] lr: 2.981e-05, eta: 0:15:48, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 00:30:28,533 - pyskl - INFO - Epoch [147][1000/1281] lr: 2.842e-05, eta: 0:15:26, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 00:30:50,888 - pyskl - INFO - Epoch [147][1100/1281] lr: 2.706e-05, eta: 0:15:04, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 00:31:13,286 - pyskl - INFO - Epoch [147][1200/1281] lr: 2.573e-05, eta: 0:14:41, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 00:31:31,854 - pyskl - INFO - Saving checkpoint at 147 epochs +2025-06-25 00:32:15,636 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:32:15,692 - pyskl - INFO - +top1_acc 0.9424 +top5_acc 0.9967 +2025-06-25 00:32:15,692 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:32:15,698 - pyskl - INFO - +mean_acc 0.9196 +2025-06-25 00:32:15,702 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_137.pth was removed +2025-06-25 00:32:15,875 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_147.pth. +2025-06-25 00:32:15,875 - pyskl - INFO - Best top1_acc is 0.9424 at 147 epoch. +2025-06-25 00:32:15,877 - pyskl - INFO - Epoch(val) [147][533] top1_acc: 0.9424, top5_acc: 0.9967, mean_class_accuracy: 0.9196 +2025-06-25 00:32:57,374 - pyskl - INFO - Epoch [148][100/1281] lr: 2.341e-05, eta: 0:14:00, time: 0.415, data_time: 0.183, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 00:33:19,472 - pyskl - INFO - Epoch [148][200/1281] lr: 2.218e-05, eta: 0:13:38, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 00:33:41,718 - pyskl - INFO - Epoch [148][300/1281] lr: 2.098e-05, eta: 0:13:15, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 00:34:03,931 - pyskl - INFO - Epoch [148][400/1281] lr: 1.981e-05, eta: 0:12:53, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 00:34:25,861 - pyskl - INFO - Epoch [148][500/1281] lr: 1.868e-05, eta: 0:12:31, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 00:34:47,893 - pyskl - INFO - Epoch [148][600/1281] lr: 1.758e-05, eta: 0:12:08, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 00:35:09,778 - pyskl - INFO - Epoch [148][700/1281] lr: 1.651e-05, eta: 0:11:46, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 00:35:31,783 - pyskl - INFO - Epoch [148][800/1281] lr: 1.548e-05, eta: 0:11:23, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 00:35:54,059 - pyskl - INFO - Epoch [148][900/1281] lr: 1.448e-05, eta: 0:11:01, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 00:36:16,143 - pyskl - INFO - Epoch [148][1000/1281] lr: 1.351e-05, eta: 0:10:38, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 00:36:38,271 - pyskl - INFO - Epoch [148][1100/1281] lr: 1.258e-05, eta: 0:10:16, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 00:37:00,882 - pyskl - INFO - Epoch [148][1200/1281] lr: 1.168e-05, eta: 0:09:53, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-25 00:37:19,798 - pyskl - INFO - Saving checkpoint at 148 epochs +2025-06-25 00:38:02,882 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:38:02,938 - pyskl - INFO - +top1_acc 0.9425 +top5_acc 0.9967 +2025-06-25 00:38:02,938 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:38:02,945 - pyskl - INFO - +mean_acc 0.9189 +2025-06-25 00:38:02,950 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_147.pth was removed +2025-06-25 00:38:03,119 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_148.pth. +2025-06-25 00:38:03,119 - pyskl - INFO - Best top1_acc is 0.9425 at 148 epoch. +2025-06-25 00:38:03,122 - pyskl - INFO - Epoch(val) [148][533] top1_acc: 0.9425, top5_acc: 0.9967, mean_class_accuracy: 0.9189 +2025-06-25 00:38:45,406 - pyskl - INFO - Epoch [149][100/1281] lr: 1.013e-05, eta: 0:09:13, time: 0.423, data_time: 0.187, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 00:39:07,863 - pyskl - INFO - Epoch [149][200/1281] lr: 9.328e-06, eta: 0:08:50, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 00:39:30,086 - pyskl - INFO - Epoch [149][300/1281] lr: 8.555e-06, eta: 0:08:28, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 00:39:52,311 - pyskl - INFO - Epoch [149][400/1281] lr: 7.816e-06, eta: 0:08:05, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 00:40:14,696 - pyskl - INFO - Epoch [149][500/1281] lr: 7.110e-06, eta: 0:07:43, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 00:40:37,082 - pyskl - INFO - Epoch [149][600/1281] lr: 6.437e-06, eta: 0:07:20, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 00:40:59,255 - pyskl - INFO - Epoch [149][700/1281] lr: 5.798e-06, eta: 0:06:58, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-25 00:41:21,394 - pyskl - INFO - Epoch [149][800/1281] lr: 5.192e-06, eta: 0:06:35, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 00:41:43,754 - pyskl - INFO - Epoch [149][900/1281] lr: 4.620e-06, eta: 0:06:13, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 00:42:05,684 - pyskl - INFO - Epoch [149][1000/1281] lr: 4.081e-06, eta: 0:05:50, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 00:42:28,409 - pyskl - INFO - Epoch [149][1100/1281] lr: 3.576e-06, eta: 0:05:28, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 00:42:50,460 - pyskl - INFO - Epoch [149][1200/1281] lr: 3.104e-06, eta: 0:05:05, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 00:43:09,140 - pyskl - INFO - Saving checkpoint at 149 epochs +2025-06-25 00:43:52,974 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:43:53,030 - pyskl - INFO - +top1_acc 0.9421 +top5_acc 0.9965 +2025-06-25 00:43:53,030 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:43:53,037 - pyskl - INFO - +mean_acc 0.9183 +2025-06-25 00:43:53,039 - pyskl - INFO - Epoch(val) [149][533] top1_acc: 0.9421, top5_acc: 0.9965, mean_class_accuracy: 0.9183 +2025-06-25 00:44:35,332 - pyskl - INFO - Epoch [150][100/1281] lr: 2.334e-06, eta: 0:04:25, time: 0.423, data_time: 0.190, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 00:44:57,529 - pyskl - INFO - Epoch [150][200/1281] lr: 1.956e-06, eta: 0:04:02, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 00:45:19,884 - pyskl - INFO - Epoch [150][300/1281] lr: 1.611e-06, eta: 0:03:40, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 00:45:42,020 - pyskl - INFO - Epoch [150][400/1281] lr: 1.300e-06, eta: 0:03:17, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 00:46:04,368 - pyskl - INFO - Epoch [150][500/1281] lr: 1.022e-06, eta: 0:02:55, time: 0.223, data_time: 0.001, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0258, loss: 0.0258 +2025-06-25 00:46:26,424 - pyskl - INFO - Epoch [150][600/1281] lr: 7.771e-07, eta: 0:02:32, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-25 00:46:48,371 - pyskl - INFO - Epoch [150][700/1281] lr: 5.659e-07, eta: 0:02:10, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 00:47:10,784 - pyskl - INFO - Epoch [150][800/1281] lr: 3.881e-07, eta: 0:01:48, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 00:47:32,698 - pyskl - INFO - Epoch [150][900/1281] lr: 2.438e-07, eta: 0:01:25, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 00:47:54,744 - pyskl - INFO - Epoch [150][1000/1281] lr: 1.329e-07, eta: 0:01:03, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 00:48:17,257 - pyskl - INFO - Epoch [150][1100/1281] lr: 5.534e-08, eta: 0:00:40, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 00:48:39,690 - pyskl - INFO - Epoch [150][1200/1281] lr: 1.123e-08, eta: 0:00:18, time: 0.224, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 00:48:58,185 - pyskl - INFO - Saving checkpoint at 150 epochs +2025-06-25 00:49:41,397 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:49:41,452 - pyskl - INFO - +top1_acc 0.9426 +top5_acc 0.9966 +2025-06-25 00:49:41,452 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:49:41,459 - pyskl - INFO - +mean_acc 0.9193 +2025-06-25 00:49:41,463 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_148.pth was removed +2025-06-25 00:49:41,629 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_150.pth. +2025-06-25 00:49:41,629 - pyskl - INFO - Best top1_acc is 0.9426 at 150 epoch. +2025-06-25 00:49:41,632 - pyskl - INFO - Epoch(val) [150][533] top1_acc: 0.9426, top5_acc: 0.9966, mean_class_accuracy: 0.9193 +2025-06-25 00:49:46,044 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-25 00:55:01,544 - pyskl - INFO - Testing results of the last checkpoint +2025-06-25 00:55:01,545 - pyskl - INFO - top1_acc: 0.9444 +2025-06-25 00:55:01,545 - pyskl - INFO - top5_acc: 0.9969 +2025-06-25 00:55:01,545 - pyskl - INFO - mean_class_accuracy: 0.9227 +2025-06-25 00:55:01,545 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/finegym/k_1/best_top1_acc_epoch_150.pth +2025-06-25 01:00:16,611 - pyskl - INFO - Testing results of the best checkpoint +2025-06-25 01:00:16,612 - pyskl - INFO - top1_acc: 0.9444 +2025-06-25 01:00:16,612 - pyskl - INFO - top5_acc: 0.9969 +2025-06-25 01:00:16,612 - pyskl - INFO - mean_class_accuracy: 0.9227 diff --git a/finegym/k_1/20250624_101323.log.json b/finegym/k_1/20250624_101323.log.json new file mode 100644 index 0000000000000000000000000000000000000000..23bd47738ad52534548fa765f75200787612bc73 --- /dev/null +++ b/finegym/k_1/20250624_101323.log.json @@ -0,0 +1,1951 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 1914909370, "config_name": "k_1.py", "work_dir": "k_1", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.18446, "top1_acc": 0.06312, "top5_acc": 0.25062, "loss_cls": 4.50075, "loss": 4.50075, "time": 0.40263} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00064, "top1_acc": 0.08875, "top5_acc": 0.3175, "loss_cls": 4.54621, "loss": 4.54621, "time": 0.22583} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.09312, "top5_acc": 0.40188, "loss_cls": 4.23115, "loss": 4.23115, "time": 0.22383} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.12188, "top5_acc": 0.42188, "loss_cls": 4.13226, "loss": 4.13226, "time": 0.22073} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.025, "memory": 4082, "data_time": 0.00056, "top1_acc": 0.13625, "top5_acc": 0.44375, "loss_cls": 3.91379, "loss": 3.91379, "time": 0.22126} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.1675, "top5_acc": 0.50062, "loss_cls": 3.73787, "loss": 3.73787, "time": 0.21911} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.1825, "top5_acc": 0.5275, "loss_cls": 3.60235, "loss": 3.60235, "time": 0.21986} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.025, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.21562, "top5_acc": 0.57, "loss_cls": 3.50219, "loss": 3.50219, "time": 0.21892} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.24, "top5_acc": 0.63562, "loss_cls": 3.29327, "loss": 3.29327, "time": 0.21917} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.025, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.24062, "top5_acc": 0.6375, "loss_cls": 3.21724, "loss": 3.21724, "time": 0.22091} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.24688, "top5_acc": 0.67562, "loss_cls": 3.09841, "loss": 3.09841, "time": 0.21718} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.26937, "top5_acc": 0.67438, "loss_cls": 3.05056, "loss": 3.05056, "time": 0.21976} +{"mode": "val", "epoch": 1, "iter": 533, "lr": 0.025, "top1_acc": 0.32109, "top5_acc": 0.73149, "mean_class_accuracy": 0.16622} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.19076, "top1_acc": 0.33938, "top5_acc": 0.74938, "loss_cls": 2.8379, "loss": 2.8379, "time": 0.40996} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.34375, "top5_acc": 0.79125, "loss_cls": 2.70185, "loss": 2.70185, "time": 0.21727} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.3725, "top5_acc": 0.79688, "loss_cls": 2.64251, "loss": 2.64251, "time": 0.21666} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.41062, "top5_acc": 0.79938, "loss_cls": 2.53747, "loss": 2.53747, "time": 0.21811} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.02499, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.39312, "top5_acc": 0.81062, "loss_cls": 2.52237, "loss": 2.52237, "time": 0.21699} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.02499, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.42688, "top5_acc": 0.82562, "loss_cls": 2.36983, "loss": 2.36983, "time": 0.21945} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.02499, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.42875, "top5_acc": 0.84188, "loss_cls": 2.32236, "loss": 2.32236, "time": 0.21979} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.02499, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.4525, "top5_acc": 0.84562, "loss_cls": 2.25846, "loss": 2.25846, "time": 0.21756} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.02499, "memory": 4082, "data_time": 0.00078, "top1_acc": 0.46125, "top5_acc": 0.85625, "loss_cls": 2.2, "loss": 2.2, "time": 0.22296} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.02499, "memory": 4082, "data_time": 0.00056, "top1_acc": 0.48188, "top5_acc": 0.87, "loss_cls": 2.13127, "loss": 2.13127, "time": 0.22189} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.02499, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.48562, "top5_acc": 0.88125, "loss_cls": 2.09336, "loss": 2.09336, "time": 0.22158} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.02499, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.52062, "top5_acc": 0.8975, "loss_cls": 2.01202, "loss": 2.01202, "time": 0.21972} +{"mode": "val", "epoch": 2, "iter": 533, "lr": 0.02499, "top1_acc": 0.48598, "top5_acc": 0.88065, "mean_class_accuracy": 0.33413} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.02499, "memory": 4082, "data_time": 0.1906, "top1_acc": 0.52, "top5_acc": 0.895, "loss_cls": 1.96622, "loss": 1.96622, "time": 0.41114} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.02499, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.53375, "top5_acc": 0.91312, "loss_cls": 1.92396, "loss": 1.92396, "time": 0.21902} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.02499, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.545, "top5_acc": 0.905, "loss_cls": 1.89346, "loss": 1.89346, "time": 0.21982} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.02499, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.55688, "top5_acc": 0.91812, "loss_cls": 1.82495, "loss": 1.82495, "time": 0.21996} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.02498, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.5625, "top5_acc": 0.91938, "loss_cls": 1.7692, "loss": 1.7692, "time": 0.21799} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.02498, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.55062, "top5_acc": 0.915, "loss_cls": 1.85073, "loss": 1.85073, "time": 0.22079} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.02498, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.54812, "top5_acc": 0.90312, "loss_cls": 1.89627, "loss": 1.89627, "time": 0.21957} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.02498, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.5775, "top5_acc": 0.92125, "loss_cls": 1.7492, "loss": 1.7492, "time": 0.21853} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.02498, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.57125, "top5_acc": 0.93312, "loss_cls": 1.74275, "loss": 1.74275, "time": 0.21972} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.02498, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.57563, "top5_acc": 0.92188, "loss_cls": 1.74732, "loss": 1.74732, "time": 0.21863} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.02498, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.6075, "top5_acc": 0.9375, "loss_cls": 1.6352, "loss": 1.6352, "time": 0.21933} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.02498, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.62062, "top5_acc": 0.94, "loss_cls": 1.60185, "loss": 1.60185, "time": 0.21855} +{"mode": "val", "epoch": 3, "iter": 533, "lr": 0.02498, "top1_acc": 0.57963, "top5_acc": 0.92829, "mean_class_accuracy": 0.43554} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.02497, "memory": 4082, "data_time": 0.18952, "top1_acc": 0.59562, "top5_acc": 0.94875, "loss_cls": 1.62172, "loss": 1.62172, "time": 0.41062} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.02497, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.60938, "top5_acc": 0.94188, "loss_cls": 1.59657, "loss": 1.59657, "time": 0.22155} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.02497, "memory": 4082, "data_time": 0.00056, "top1_acc": 0.61687, "top5_acc": 0.94375, "loss_cls": 1.56183, "loss": 1.56183, "time": 0.21996} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.02497, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.6275, "top5_acc": 0.94188, "loss_cls": 1.56165, "loss": 1.56165, "time": 0.22001} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.02497, "memory": 4082, "data_time": 0.00066, "top1_acc": 0.6325, "top5_acc": 0.94062, "loss_cls": 1.55099, "loss": 1.55099, "time": 0.22168} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.02497, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.61687, "top5_acc": 0.9375, "loss_cls": 1.55283, "loss": 1.55283, "time": 0.22449} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.02497, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.61562, "top5_acc": 0.95312, "loss_cls": 1.53756, "loss": 1.53756, "time": 0.22232} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.02496, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.64375, "top5_acc": 0.95875, "loss_cls": 1.43269, "loss": 1.43269, "time": 0.22137} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.02496, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.64, "top5_acc": 0.94625, "loss_cls": 1.49467, "loss": 1.49467, "time": 0.22046} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.02496, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.63875, "top5_acc": 0.94812, "loss_cls": 1.51109, "loss": 1.51109, "time": 0.22248} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.02496, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.625, "top5_acc": 0.94312, "loss_cls": 1.52592, "loss": 1.52592, "time": 0.21963} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.02496, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.64375, "top5_acc": 0.955, "loss_cls": 1.46222, "loss": 1.46222, "time": 0.21752} +{"mode": "val", "epoch": 4, "iter": 533, "lr": 0.02496, "top1_acc": 0.6268, "top5_acc": 0.95224, "mean_class_accuracy": 0.4958} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.02495, "memory": 4082, "data_time": 0.18876, "top1_acc": 0.68062, "top5_acc": 0.96188, "loss_cls": 1.35585, "loss": 1.35585, "time": 0.40759} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.02495, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.675, "top5_acc": 0.95312, "loss_cls": 1.42159, "loss": 1.42159, "time": 0.21773} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.02495, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.66875, "top5_acc": 0.96125, "loss_cls": 1.35795, "loss": 1.35795, "time": 0.2205} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.02495, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.66188, "top5_acc": 0.96188, "loss_cls": 1.36939, "loss": 1.36939, "time": 0.21832} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.02495, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.65438, "top5_acc": 0.96, "loss_cls": 1.43215, "loss": 1.43215, "time": 0.22145} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.02495, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.6675, "top5_acc": 0.9625, "loss_cls": 1.38145, "loss": 1.38145, "time": 0.22174} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.02494, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.66938, "top5_acc": 0.965, "loss_cls": 1.31683, "loss": 1.31683, "time": 0.22008} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.02494, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.65, "top5_acc": 0.965, "loss_cls": 1.3709, "loss": 1.3709, "time": 0.21813} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.02494, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.675, "top5_acc": 0.9625, "loss_cls": 1.32123, "loss": 1.32123, "time": 0.21705} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.02494, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.69625, "top5_acc": 0.97062, "loss_cls": 1.25768, "loss": 1.25768, "time": 0.21668} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.02494, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.66938, "top5_acc": 0.9675, "loss_cls": 1.30994, "loss": 1.30994, "time": 0.21984} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.02493, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.68188, "top5_acc": 0.95938, "loss_cls": 1.32288, "loss": 1.32288, "time": 0.21809} +{"mode": "val", "epoch": 5, "iter": 533, "lr": 0.02493, "top1_acc": 0.65168, "top5_acc": 0.96045, "mean_class_accuracy": 0.51478} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.02493, "memory": 4082, "data_time": 0.19383, "top1_acc": 0.69562, "top5_acc": 0.96062, "loss_cls": 1.29323, "loss": 1.29323, "time": 0.41474} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.02493, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.69188, "top5_acc": 0.97, "loss_cls": 1.25028, "loss": 1.25028, "time": 0.22074} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.02492, "memory": 4082, "data_time": 0.00074, "top1_acc": 0.70875, "top5_acc": 0.97312, "loss_cls": 1.20918, "loss": 1.20918, "time": 0.22304} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.02492, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.68, "top5_acc": 0.96688, "loss_cls": 1.30378, "loss": 1.30378, "time": 0.2215} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.02492, "memory": 4082, "data_time": 0.00071, "top1_acc": 0.71188, "top5_acc": 0.97062, "loss_cls": 1.27023, "loss": 1.27023, "time": 0.21892} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.02492, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.685, "top5_acc": 0.96625, "loss_cls": 1.26001, "loss": 1.26001, "time": 0.22172} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.02492, "memory": 4082, "data_time": 0.00049, "top1_acc": 0.7225, "top5_acc": 0.97, "loss_cls": 1.20666, "loss": 1.20666, "time": 0.22391} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.02491, "memory": 4082, "data_time": 0.00049, "top1_acc": 0.71, "top5_acc": 0.97, "loss_cls": 1.21054, "loss": 1.21054, "time": 0.22378} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.02491, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.71312, "top5_acc": 0.9775, "loss_cls": 1.21691, "loss": 1.21691, "time": 0.21811} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.02491, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.70312, "top5_acc": 0.97062, "loss_cls": 1.21755, "loss": 1.21755, "time": 0.22205} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.02491, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.70375, "top5_acc": 0.96562, "loss_cls": 1.23965, "loss": 1.23965, "time": 0.22086} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.0249, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.72625, "top5_acc": 0.96938, "loss_cls": 1.18567, "loss": 1.18567, "time": 0.21859} +{"mode": "val", "epoch": 6, "iter": 533, "lr": 0.0249, "top1_acc": 0.68713, "top5_acc": 0.96691, "mean_class_accuracy": 0.55988} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0249, "memory": 4082, "data_time": 0.19194, "top1_acc": 0.69812, "top5_acc": 0.97812, "loss_cls": 1.1861, "loss": 1.1861, "time": 0.4146} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0249, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7175, "top5_acc": 0.97125, "loss_cls": 1.16579, "loss": 1.16579, "time": 0.21968} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.02489, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.71188, "top5_acc": 0.97438, "loss_cls": 1.149, "loss": 1.149, "time": 0.22143} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.02489, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.70938, "top5_acc": 0.975, "loss_cls": 1.21325, "loss": 1.21325, "time": 0.22004} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.02489, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.72875, "top5_acc": 0.97312, "loss_cls": 1.13354, "loss": 1.13354, "time": 0.21978} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.02489, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.71312, "top5_acc": 0.97625, "loss_cls": 1.18734, "loss": 1.18734, "time": 0.22091} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.02488, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.70688, "top5_acc": 0.9725, "loss_cls": 1.19582, "loss": 1.19582, "time": 0.2208} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.02488, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.7225, "top5_acc": 0.97875, "loss_cls": 1.18352, "loss": 1.18352, "time": 0.21979} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.02488, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.725, "top5_acc": 0.97438, "loss_cls": 1.18018, "loss": 1.18018, "time": 0.22013} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.02487, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.71812, "top5_acc": 0.97625, "loss_cls": 1.12643, "loss": 1.12643, "time": 0.22366} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.02487, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.7275, "top5_acc": 0.97875, "loss_cls": 1.12814, "loss": 1.12814, "time": 0.22173} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.02487, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.72688, "top5_acc": 0.96938, "loss_cls": 1.13389, "loss": 1.13389, "time": 0.22066} +{"mode": "val", "epoch": 7, "iter": 533, "lr": 0.02487, "top1_acc": 0.68771, "top5_acc": 0.96421, "mean_class_accuracy": 0.57229} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.02486, "memory": 4082, "data_time": 0.1879, "top1_acc": 0.74438, "top5_acc": 0.97812, "loss_cls": 1.08023, "loss": 1.08023, "time": 0.40991} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.02486, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.73812, "top5_acc": 0.97812, "loss_cls": 1.07775, "loss": 1.07775, "time": 0.22272} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.02486, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.71188, "top5_acc": 0.97562, "loss_cls": 1.17456, "loss": 1.17456, "time": 0.22204} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.02485, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.74, "top5_acc": 0.9725, "loss_cls": 1.13691, "loss": 1.13691, "time": 0.22357} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.02485, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.72875, "top5_acc": 0.975, "loss_cls": 1.11986, "loss": 1.11986, "time": 0.22081} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.02485, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.755, "top5_acc": 0.97812, "loss_cls": 1.08273, "loss": 1.08273, "time": 0.21973} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.02484, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.73, "top5_acc": 0.97, "loss_cls": 1.12299, "loss": 1.12299, "time": 0.22291} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.02484, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.75562, "top5_acc": 0.97562, "loss_cls": 1.07144, "loss": 1.07144, "time": 0.21732} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.02484, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.745, "top5_acc": 0.97438, "loss_cls": 1.08922, "loss": 1.08922, "time": 0.21769} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.02483, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.72188, "top5_acc": 0.97312, "loss_cls": 1.15414, "loss": 1.15414, "time": 0.21683} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.02483, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7475, "top5_acc": 0.97938, "loss_cls": 1.06469, "loss": 1.06469, "time": 0.21965} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.02483, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.74188, "top5_acc": 0.975, "loss_cls": 1.06737, "loss": 1.06737, "time": 0.22076} +{"mode": "val", "epoch": 8, "iter": 533, "lr": 0.02482, "top1_acc": 0.71318, "top5_acc": 0.97406, "mean_class_accuracy": 0.60292} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.02482, "memory": 4082, "data_time": 0.19459, "top1_acc": 0.75375, "top5_acc": 0.98062, "loss_cls": 1.07022, "loss": 1.07022, "time": 0.41592} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.02482, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.75062, "top5_acc": 0.97938, "loss_cls": 1.02381, "loss": 1.02381, "time": 0.21843} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.02481, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.755, "top5_acc": 0.98375, "loss_cls": 1.0149, "loss": 1.0149, "time": 0.22137} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.02481, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.74188, "top5_acc": 0.98562, "loss_cls": 1.06384, "loss": 1.06384, "time": 0.22218} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.02481, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.73312, "top5_acc": 0.98062, "loss_cls": 1.07003, "loss": 1.07003, "time": 0.22073} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.0248, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.73938, "top5_acc": 0.98125, "loss_cls": 1.08983, "loss": 1.08983, "time": 0.22202} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.0248, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.76188, "top5_acc": 0.97625, "loss_cls": 1.02337, "loss": 1.02337, "time": 0.22187} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.0248, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.73812, "top5_acc": 0.9825, "loss_cls": 1.05389, "loss": 1.05389, "time": 0.22384} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.02479, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.74438, "top5_acc": 0.98188, "loss_cls": 1.06503, "loss": 1.06503, "time": 0.22632} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.02479, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.75438, "top5_acc": 0.97688, "loss_cls": 1.07631, "loss": 1.07631, "time": 0.22367} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.02479, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.73625, "top5_acc": 0.97125, "loss_cls": 1.13489, "loss": 1.13489, "time": 0.22512} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.02478, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.75875, "top5_acc": 0.97812, "loss_cls": 1.06844, "loss": 1.06844, "time": 0.22047} +{"mode": "val", "epoch": 9, "iter": 533, "lr": 0.02478, "top1_acc": 0.74205, "top5_acc": 0.97242, "mean_class_accuracy": 0.61035} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.02477, "memory": 4082, "data_time": 0.19618, "top1_acc": 0.75875, "top5_acc": 0.98312, "loss_cls": 1.01026, "loss": 1.01026, "time": 0.41634} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.02477, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.76125, "top5_acc": 0.985, "loss_cls": 1.01387, "loss": 1.01387, "time": 0.21841} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.02477, "memory": 4082, "data_time": 0.00057, "top1_acc": 0.76875, "top5_acc": 0.9875, "loss_cls": 0.98833, "loss": 0.98833, "time": 0.2235} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.02476, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.76812, "top5_acc": 0.98188, "loss_cls": 1.00242, "loss": 1.00242, "time": 0.2204} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.02476, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.75875, "top5_acc": 0.98625, "loss_cls": 0.98657, "loss": 0.98657, "time": 0.22003} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.02476, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.74125, "top5_acc": 0.98125, "loss_cls": 1.05334, "loss": 1.05334, "time": 0.21927} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.02475, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.76375, "top5_acc": 0.98188, "loss_cls": 1.02471, "loss": 1.02471, "time": 0.22072} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.02475, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.76125, "top5_acc": 0.98, "loss_cls": 0.9959, "loss": 0.9959, "time": 0.22133} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.02474, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.76938, "top5_acc": 0.98188, "loss_cls": 0.98776, "loss": 0.98776, "time": 0.21929} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.02474, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.75062, "top5_acc": 0.9775, "loss_cls": 1.10423, "loss": 1.10423, "time": 0.21999} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.02473, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.755, "top5_acc": 0.97688, "loss_cls": 1.03692, "loss": 1.03692, "time": 0.21917} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.02473, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.76312, "top5_acc": 0.98688, "loss_cls": 1.02972, "loss": 1.02972, "time": 0.21833} +{"mode": "val", "epoch": 10, "iter": 533, "lr": 0.02473, "top1_acc": 0.72386, "top5_acc": 0.97453, "mean_class_accuracy": 0.63455} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.02472, "memory": 4082, "data_time": 0.19281, "top1_acc": 0.75938, "top5_acc": 0.98062, "loss_cls": 0.98934, "loss": 0.98934, "time": 0.41542} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.02472, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.79125, "top5_acc": 0.98688, "loss_cls": 0.9209, "loss": 0.9209, "time": 0.22109} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.02471, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.74438, "top5_acc": 0.98, "loss_cls": 1.07189, "loss": 1.07189, "time": 0.22307} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.02471, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.78062, "top5_acc": 0.98375, "loss_cls": 0.98089, "loss": 0.98089, "time": 0.2209} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.02471, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.77312, "top5_acc": 0.98688, "loss_cls": 0.99302, "loss": 0.99302, "time": 0.22093} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.0247, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.75875, "top5_acc": 0.98312, "loss_cls": 1.01364, "loss": 1.01364, "time": 0.2251} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.0247, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77688, "top5_acc": 0.9825, "loss_cls": 0.9735, "loss": 0.9735, "time": 0.22385} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.02469, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.76125, "top5_acc": 0.98688, "loss_cls": 0.97017, "loss": 0.97017, "time": 0.22135} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.02469, "memory": 4082, "data_time": 0.00059, "top1_acc": 0.7775, "top5_acc": 0.98375, "loss_cls": 0.95847, "loss": 0.95847, "time": 0.22227} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.02468, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.75125, "top5_acc": 0.98188, "loss_cls": 1.02374, "loss": 1.02374, "time": 0.22271} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.02468, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.77875, "top5_acc": 0.98125, "loss_cls": 0.9749, "loss": 0.9749, "time": 0.22046} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.02467, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.74438, "top5_acc": 0.98375, "loss_cls": 1.04537, "loss": 1.04537, "time": 0.22151} +{"mode": "val", "epoch": 11, "iter": 533, "lr": 0.02467, "top1_acc": 0.74616, "top5_acc": 0.98005, "mean_class_accuracy": 0.65488} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.02467, "memory": 4082, "data_time": 0.19599, "top1_acc": 0.78, "top5_acc": 0.98688, "loss_cls": 0.93958, "loss": 0.93958, "time": 0.41488} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.02466, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.775, "top5_acc": 0.98688, "loss_cls": 0.94687, "loss": 0.94687, "time": 0.21858} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.02466, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.78812, "top5_acc": 0.98188, "loss_cls": 0.94142, "loss": 0.94142, "time": 0.22067} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.02465, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.7825, "top5_acc": 0.98438, "loss_cls": 0.92907, "loss": 0.92907, "time": 0.21823} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.02465, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.77875, "top5_acc": 0.98562, "loss_cls": 0.94806, "loss": 0.94806, "time": 0.21714} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.02464, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.79375, "top5_acc": 0.98312, "loss_cls": 0.92326, "loss": 0.92326, "time": 0.21818} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.02464, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.79188, "top5_acc": 0.98562, "loss_cls": 0.93879, "loss": 0.93879, "time": 0.21951} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.02463, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.76125, "top5_acc": 0.9825, "loss_cls": 1.02867, "loss": 1.02867, "time": 0.22059} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.02463, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.77438, "top5_acc": 0.98688, "loss_cls": 0.93427, "loss": 0.93427, "time": 0.21912} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.02462, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78625, "top5_acc": 0.98688, "loss_cls": 0.94017, "loss": 0.94017, "time": 0.2177} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.02462, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78875, "top5_acc": 0.9825, "loss_cls": 0.92592, "loss": 0.92592, "time": 0.22394} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.02461, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.785, "top5_acc": 0.985, "loss_cls": 0.93452, "loss": 0.93452, "time": 0.21907} +{"mode": "val", "epoch": 12, "iter": 533, "lr": 0.02461, "top1_acc": 0.71025, "top5_acc": 0.96691, "mean_class_accuracy": 0.6373} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.0246, "memory": 4082, "data_time": 0.19422, "top1_acc": 0.7825, "top5_acc": 0.98812, "loss_cls": 0.8999, "loss": 0.8999, "time": 0.41273} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.0246, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.78812, "top5_acc": 0.98875, "loss_cls": 0.87284, "loss": 0.87284, "time": 0.22624} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.02459, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.79375, "top5_acc": 0.98312, "loss_cls": 0.89254, "loss": 0.89254, "time": 0.22243} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.02459, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.785, "top5_acc": 0.98812, "loss_cls": 0.92804, "loss": 0.92804, "time": 0.21989} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.02458, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.77812, "top5_acc": 0.98875, "loss_cls": 0.91854, "loss": 0.91854, "time": 0.22439} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.02458, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.7925, "top5_acc": 0.98688, "loss_cls": 0.91347, "loss": 0.91347, "time": 0.22054} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.02457, "memory": 4082, "data_time": 0.00049, "top1_acc": 0.77562, "top5_acc": 0.98188, "loss_cls": 0.95963, "loss": 0.95963, "time": 0.22057} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.02457, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.80375, "top5_acc": 0.98562, "loss_cls": 0.87296, "loss": 0.87296, "time": 0.22006} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.02456, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.79438, "top5_acc": 0.98438, "loss_cls": 0.90523, "loss": 0.90523, "time": 0.22021} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.02455, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78312, "top5_acc": 0.98125, "loss_cls": 0.94452, "loss": 0.94452, "time": 0.21833} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.02455, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.76562, "top5_acc": 0.97562, "loss_cls": 0.9963, "loss": 0.9963, "time": 0.21923} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.02454, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80188, "top5_acc": 0.9875, "loss_cls": 0.89119, "loss": 0.89119, "time": 0.22041} +{"mode": "val", "epoch": 13, "iter": 533, "lr": 0.02454, "top1_acc": 0.7397, "top5_acc": 0.97277, "mean_class_accuracy": 0.66719} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.02453, "memory": 4082, "data_time": 0.19965, "top1_acc": 0.79, "top5_acc": 0.985, "loss_cls": 0.91426, "loss": 0.91426, "time": 0.41882} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.02453, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80562, "top5_acc": 0.99, "loss_cls": 0.84366, "loss": 0.84366, "time": 0.21934} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.02452, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.78438, "top5_acc": 0.98688, "loss_cls": 0.93061, "loss": 0.93061, "time": 0.22008} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.02452, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.78312, "top5_acc": 0.98938, "loss_cls": 0.91216, "loss": 0.91216, "time": 0.21554} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.02451, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.78062, "top5_acc": 0.98062, "loss_cls": 0.93816, "loss": 0.93816, "time": 0.21739} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.02451, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80125, "top5_acc": 0.9875, "loss_cls": 0.89439, "loss": 0.89439, "time": 0.21777} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.0245, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80312, "top5_acc": 0.985, "loss_cls": 0.86078, "loss": 0.86078, "time": 0.21853} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.02449, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79375, "top5_acc": 0.99312, "loss_cls": 0.86774, "loss": 0.86774, "time": 0.21928} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.02449, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.79812, "top5_acc": 0.99, "loss_cls": 0.90158, "loss": 0.90158, "time": 0.21742} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.02448, "memory": 4082, "data_time": 0.00056, "top1_acc": 0.77688, "top5_acc": 0.9825, "loss_cls": 0.92311, "loss": 0.92311, "time": 0.22294} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.02448, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.785, "top5_acc": 0.97938, "loss_cls": 0.97231, "loss": 0.97231, "time": 0.22013} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.02447, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.81875, "top5_acc": 0.98875, "loss_cls": 0.8397, "loss": 0.8397, "time": 0.21937} +{"mode": "val", "epoch": 14, "iter": 533, "lr": 0.02447, "top1_acc": 0.74498, "top5_acc": 0.97946, "mean_class_accuracy": 0.63959} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.02446, "memory": 4082, "data_time": 0.18877, "top1_acc": 0.80812, "top5_acc": 0.98938, "loss_cls": 0.82435, "loss": 0.82435, "time": 0.41024} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.02445, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81188, "top5_acc": 0.98938, "loss_cls": 0.82222, "loss": 0.82222, "time": 0.22072} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.02445, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.80938, "top5_acc": 0.98875, "loss_cls": 0.83649, "loss": 0.83649, "time": 0.22238} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.02444, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.80688, "top5_acc": 0.98562, "loss_cls": 0.85317, "loss": 0.85317, "time": 0.21977} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.02444, "memory": 4082, "data_time": 0.00059, "top1_acc": 0.81125, "top5_acc": 0.99062, "loss_cls": 0.82641, "loss": 0.82641, "time": 0.22204} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.02443, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79188, "top5_acc": 0.9875, "loss_cls": 0.9042, "loss": 0.9042, "time": 0.218} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.02442, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79312, "top5_acc": 0.98688, "loss_cls": 0.8832, "loss": 0.8832, "time": 0.21932} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.02442, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.78875, "top5_acc": 0.98688, "loss_cls": 0.92233, "loss": 0.92233, "time": 0.22145} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.02441, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8, "top5_acc": 0.98875, "loss_cls": 0.86996, "loss": 0.86996, "time": 0.21922} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.02441, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8075, "top5_acc": 0.98875, "loss_cls": 0.85515, "loss": 0.85515, "time": 0.21887} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.0244, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.8175, "top5_acc": 0.98438, "loss_cls": 0.85719, "loss": 0.85719, "time": 0.21747} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.02439, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.78375, "top5_acc": 0.98375, "loss_cls": 0.90979, "loss": 0.90979, "time": 0.21693} +{"mode": "val", "epoch": 15, "iter": 533, "lr": 0.02439, "top1_acc": 0.77784, "top5_acc": 0.98122, "mean_class_accuracy": 0.68083} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.02438, "memory": 4082, "data_time": 0.19521, "top1_acc": 0.78688, "top5_acc": 0.99062, "loss_cls": 0.85759, "loss": 0.85759, "time": 0.41643} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.02438, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8125, "top5_acc": 0.99125, "loss_cls": 0.80828, "loss": 0.80828, "time": 0.2188} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.02437, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81188, "top5_acc": 0.98375, "loss_cls": 0.85079, "loss": 0.85079, "time": 0.21982} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.02436, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.825, "top5_acc": 0.99062, "loss_cls": 0.8142, "loss": 0.8142, "time": 0.22088} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.02436, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81375, "top5_acc": 0.9875, "loss_cls": 0.83651, "loss": 0.83651, "time": 0.21952} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.02435, "memory": 4082, "data_time": 0.00049, "top1_acc": 0.81062, "top5_acc": 0.98625, "loss_cls": 0.85632, "loss": 0.85632, "time": 0.22313} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.02434, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81812, "top5_acc": 0.98625, "loss_cls": 0.84275, "loss": 0.84275, "time": 0.22082} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.02434, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81312, "top5_acc": 0.98562, "loss_cls": 0.86634, "loss": 0.86634, "time": 0.22071} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.02433, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81438, "top5_acc": 0.9925, "loss_cls": 0.83323, "loss": 0.83323, "time": 0.21871} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.02432, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.80938, "top5_acc": 0.9875, "loss_cls": 0.82644, "loss": 0.82644, "time": 0.22209} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.02432, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.81, "top5_acc": 0.99, "loss_cls": 0.85502, "loss": 0.85502, "time": 0.22169} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.02431, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.82, "top5_acc": 0.9925, "loss_cls": 0.80808, "loss": 0.80808, "time": 0.21603} +{"mode": "val", "epoch": 16, "iter": 533, "lr": 0.0243, "top1_acc": 0.79885, "top5_acc": 0.98474, "mean_class_accuracy": 0.68865} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.0243, "memory": 4082, "data_time": 0.1945, "top1_acc": 0.82312, "top5_acc": 0.99125, "loss_cls": 0.78612, "loss": 0.78612, "time": 0.41765} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.02429, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.82812, "top5_acc": 0.98812, "loss_cls": 0.78245, "loss": 0.78245, "time": 0.22136} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.02428, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82812, "top5_acc": 0.98438, "loss_cls": 0.78761, "loss": 0.78761, "time": 0.2186} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.02428, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.83875, "top5_acc": 0.99, "loss_cls": 0.74117, "loss": 0.74117, "time": 0.22448} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.02427, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.815, "top5_acc": 0.99188, "loss_cls": 0.7959, "loss": 0.7959, "time": 0.22369} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.02426, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83625, "top5_acc": 0.98875, "loss_cls": 0.79261, "loss": 0.79261, "time": 0.21922} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.02426, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81812, "top5_acc": 0.98875, "loss_cls": 0.80504, "loss": 0.80504, "time": 0.22103} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.02425, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.835, "top5_acc": 0.98875, "loss_cls": 0.78417, "loss": 0.78417, "time": 0.21835} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.02424, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81375, "top5_acc": 0.99, "loss_cls": 0.81769, "loss": 0.81769, "time": 0.22018} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.02424, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8025, "top5_acc": 0.99, "loss_cls": 0.82263, "loss": 0.82263, "time": 0.21797} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.02423, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80062, "top5_acc": 0.9875, "loss_cls": 0.84408, "loss": 0.84408, "time": 0.21941} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.02422, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81375, "top5_acc": 0.99, "loss_cls": 0.8301, "loss": 0.8301, "time": 0.21853} +{"mode": "val", "epoch": 17, "iter": 533, "lr": 0.02422, "top1_acc": 0.78371, "top5_acc": 0.98498, "mean_class_accuracy": 0.70309} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.02421, "memory": 4082, "data_time": 0.19573, "top1_acc": 0.81938, "top5_acc": 0.98875, "loss_cls": 0.83088, "loss": 0.83088, "time": 0.41543} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.0242, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.80812, "top5_acc": 0.98875, "loss_cls": 0.83948, "loss": 0.83948, "time": 0.22143} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.02419, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81, "top5_acc": 0.99, "loss_cls": 0.83933, "loss": 0.83933, "time": 0.22079} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.02419, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83375, "top5_acc": 0.9875, "loss_cls": 0.76469, "loss": 0.76469, "time": 0.21808} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.02418, "memory": 4082, "data_time": 0.00049, "top1_acc": 0.825, "top5_acc": 0.99, "loss_cls": 0.77798, "loss": 0.77798, "time": 0.22033} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.02417, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.81562, "top5_acc": 0.9925, "loss_cls": 0.75438, "loss": 0.75438, "time": 0.22263} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.02417, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81188, "top5_acc": 0.98938, "loss_cls": 0.80894, "loss": 0.80894, "time": 0.22059} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.02416, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.82438, "top5_acc": 0.99188, "loss_cls": 0.78587, "loss": 0.78587, "time": 0.22007} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.02415, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.83312, "top5_acc": 0.9925, "loss_cls": 0.7413, "loss": 0.7413, "time": 0.21976} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.02414, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80875, "top5_acc": 0.99188, "loss_cls": 0.81113, "loss": 0.81113, "time": 0.22018} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.02414, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.80938, "top5_acc": 0.98688, "loss_cls": 0.84282, "loss": 0.84282, "time": 0.2203} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.02413, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.82875, "top5_acc": 0.98938, "loss_cls": 0.7576, "loss": 0.7576, "time": 0.22028} +{"mode": "val", "epoch": 18, "iter": 533, "lr": 0.02412, "top1_acc": 0.78864, "top5_acc": 0.98557, "mean_class_accuracy": 0.70938} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.02411, "memory": 4082, "data_time": 0.19374, "top1_acc": 0.82875, "top5_acc": 0.995, "loss_cls": 0.75435, "loss": 0.75435, "time": 0.41455} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.02411, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83062, "top5_acc": 0.9825, "loss_cls": 0.781, "loss": 0.781, "time": 0.22127} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.0241, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84438, "top5_acc": 0.99062, "loss_cls": 0.72609, "loss": 0.72609, "time": 0.21778} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.02409, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.83, "top5_acc": 0.99375, "loss_cls": 0.78884, "loss": 0.78884, "time": 0.21759} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.02408, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.845, "top5_acc": 0.99312, "loss_cls": 0.74453, "loss": 0.74453, "time": 0.22017} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.02408, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.82812, "top5_acc": 0.99188, "loss_cls": 0.79456, "loss": 0.79456, "time": 0.21943} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.02407, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.81125, "top5_acc": 0.99188, "loss_cls": 0.85807, "loss": 0.85807, "time": 0.219} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.02406, "memory": 4082, "data_time": 0.00046, "top1_acc": 0.83688, "top5_acc": 0.9925, "loss_cls": 0.7799, "loss": 0.7799, "time": 0.21891} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.02405, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.83125, "top5_acc": 0.99, "loss_cls": 0.77516, "loss": 0.77516, "time": 0.21763} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.02405, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81875, "top5_acc": 0.99125, "loss_cls": 0.80431, "loss": 0.80431, "time": 0.21886} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.02404, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80812, "top5_acc": 0.98875, "loss_cls": 0.86644, "loss": 0.86644, "time": 0.21918} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.02403, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8125, "top5_acc": 0.99125, "loss_cls": 0.83103, "loss": 0.83103, "time": 0.2183} +{"mode": "val", "epoch": 19, "iter": 533, "lr": 0.02402, "top1_acc": 0.76153, "top5_acc": 0.97442, "mean_class_accuracy": 0.68115} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.02402, "memory": 4082, "data_time": 0.18918, "top1_acc": 0.82625, "top5_acc": 0.9925, "loss_cls": 0.77579, "loss": 0.77579, "time": 0.4082} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.02401, "memory": 4082, "data_time": 0.00049, "top1_acc": 0.84812, "top5_acc": 0.99625, "loss_cls": 0.7083, "loss": 0.7083, "time": 0.22202} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.024, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.82188, "top5_acc": 0.99375, "loss_cls": 0.76199, "loss": 0.76199, "time": 0.22353} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.02399, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.835, "top5_acc": 0.99438, "loss_cls": 0.74023, "loss": 0.74023, "time": 0.22097} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.02398, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83, "top5_acc": 0.98938, "loss_cls": 0.76958, "loss": 0.76958, "time": 0.21824} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.02398, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82188, "top5_acc": 0.98688, "loss_cls": 0.79896, "loss": 0.79896, "time": 0.2223} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.02397, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.82125, "top5_acc": 0.9925, "loss_cls": 0.77446, "loss": 0.77446, "time": 0.21947} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.02396, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.85062, "top5_acc": 0.99562, "loss_cls": 0.69757, "loss": 0.69757, "time": 0.21887} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.02395, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.83, "top5_acc": 0.99375, "loss_cls": 0.81645, "loss": 0.81645, "time": 0.22226} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.02394, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.83062, "top5_acc": 0.99188, "loss_cls": 0.76373, "loss": 0.76373, "time": 0.2209} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.02393, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.85125, "top5_acc": 0.99312, "loss_cls": 0.73099, "loss": 0.73099, "time": 0.21658} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.02393, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84, "top5_acc": 0.9925, "loss_cls": 0.73521, "loss": 0.73521, "time": 0.21935} +{"mode": "val", "epoch": 20, "iter": 533, "lr": 0.02392, "top1_acc": 0.81211, "top5_acc": 0.9865, "mean_class_accuracy": 0.73778} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.02391, "memory": 4082, "data_time": 0.1976, "top1_acc": 0.85062, "top5_acc": 0.99438, "loss_cls": 0.69609, "loss": 0.69609, "time": 0.41857} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.0239, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.845, "top5_acc": 0.9925, "loss_cls": 0.7153, "loss": 0.7153, "time": 0.2209} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.02389, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.81125, "top5_acc": 0.98875, "loss_cls": 0.80543, "loss": 0.80543, "time": 0.22038} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.02389, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.82875, "top5_acc": 0.99, "loss_cls": 0.77665, "loss": 0.77665, "time": 0.21996} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.02388, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86438, "top5_acc": 0.99312, "loss_cls": 0.66517, "loss": 0.66517, "time": 0.21851} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.02387, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.8325, "top5_acc": 0.99188, "loss_cls": 0.73097, "loss": 0.73097, "time": 0.22071} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.02386, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.83875, "top5_acc": 0.99062, "loss_cls": 0.74612, "loss": 0.74612, "time": 0.2197} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.02385, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82188, "top5_acc": 0.9925, "loss_cls": 0.80393, "loss": 0.80393, "time": 0.21833} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.02384, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.8475, "top5_acc": 0.99188, "loss_cls": 0.73804, "loss": 0.73804, "time": 0.21762} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.02383, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8325, "top5_acc": 0.99438, "loss_cls": 0.75467, "loss": 0.75467, "time": 0.22105} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.02383, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81875, "top5_acc": 0.99062, "loss_cls": 0.80031, "loss": 0.80031, "time": 0.22026} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.02382, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8425, "top5_acc": 0.99312, "loss_cls": 0.72734, "loss": 0.72734, "time": 0.21812} +{"mode": "val", "epoch": 21, "iter": 533, "lr": 0.02381, "top1_acc": 0.83018, "top5_acc": 0.98826, "mean_class_accuracy": 0.76402} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.0238, "memory": 4082, "data_time": 0.18869, "top1_acc": 0.85062, "top5_acc": 0.99438, "loss_cls": 0.68776, "loss": 0.68776, "time": 0.41224} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.02379, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85875, "top5_acc": 0.99312, "loss_cls": 0.6682, "loss": 0.6682, "time": 0.22415} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.02378, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83312, "top5_acc": 0.98938, "loss_cls": 0.76258, "loss": 0.76258, "time": 0.22174} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.02378, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.84, "top5_acc": 0.99125, "loss_cls": 0.76603, "loss": 0.76603, "time": 0.22162} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.02377, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85, "top5_acc": 0.99312, "loss_cls": 0.68124, "loss": 0.68124, "time": 0.22065} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.02376, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.83562, "top5_acc": 0.98812, "loss_cls": 0.75941, "loss": 0.75941, "time": 0.22217} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.02375, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.83938, "top5_acc": 0.99125, "loss_cls": 0.67363, "loss": 0.67363, "time": 0.21726} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.02374, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.83875, "top5_acc": 0.99188, "loss_cls": 0.74393, "loss": 0.74393, "time": 0.22113} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.02373, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.8375, "top5_acc": 0.98938, "loss_cls": 0.75083, "loss": 0.75083, "time": 0.22114} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.02372, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8325, "top5_acc": 0.99125, "loss_cls": 0.76061, "loss": 0.76061, "time": 0.21933} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.02371, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.8275, "top5_acc": 0.99312, "loss_cls": 0.75295, "loss": 0.75295, "time": 0.21865} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0237, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.82375, "top5_acc": 0.99438, "loss_cls": 0.73721, "loss": 0.73721, "time": 0.21753} +{"mode": "val", "epoch": 22, "iter": 533, "lr": 0.0237, "top1_acc": 0.82256, "top5_acc": 0.98779, "mean_class_accuracy": 0.74979} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.02369, "memory": 4082, "data_time": 0.19019, "top1_acc": 0.83, "top5_acc": 0.99312, "loss_cls": 0.74224, "loss": 0.74224, "time": 0.41049} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.02368, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85688, "top5_acc": 0.99312, "loss_cls": 0.65797, "loss": 0.65797, "time": 0.21893} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.02367, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.81812, "top5_acc": 0.99, "loss_cls": 0.76447, "loss": 0.76447, "time": 0.22137} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.02366, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.86062, "top5_acc": 0.99312, "loss_cls": 0.64961, "loss": 0.64961, "time": 0.21923} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.02365, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.835, "top5_acc": 0.99375, "loss_cls": 0.74019, "loss": 0.74019, "time": 0.21794} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.02364, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.84688, "top5_acc": 0.99125, "loss_cls": 0.7174, "loss": 0.7174, "time": 0.22129} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.02363, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.84938, "top5_acc": 0.98938, "loss_cls": 0.70872, "loss": 0.70872, "time": 0.22053} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.02362, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.84375, "top5_acc": 0.99, "loss_cls": 0.73682, "loss": 0.73682, "time": 0.21628} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.02361, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.8475, "top5_acc": 0.99375, "loss_cls": 0.71052, "loss": 0.71052, "time": 0.22271} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.0236, "memory": 4082, "data_time": 0.00073, "top1_acc": 0.84125, "top5_acc": 0.98875, "loss_cls": 0.73098, "loss": 0.73098, "time": 0.22211} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.02359, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.85562, "top5_acc": 0.99312, "loss_cls": 0.6896, "loss": 0.6896, "time": 0.21901} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.02359, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.8325, "top5_acc": 0.99, "loss_cls": 0.78949, "loss": 0.78949, "time": 0.21667} +{"mode": "val", "epoch": 23, "iter": 533, "lr": 0.02358, "top1_acc": 0.82232, "top5_acc": 0.98756, "mean_class_accuracy": 0.73294} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.02357, "memory": 4082, "data_time": 0.18928, "top1_acc": 0.85, "top5_acc": 0.99562, "loss_cls": 0.67918, "loss": 0.67918, "time": 0.41297} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.02356, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.86062, "top5_acc": 0.9925, "loss_cls": 0.70448, "loss": 0.70448, "time": 0.22442} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.02355, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.85188, "top5_acc": 0.99188, "loss_cls": 0.71937, "loss": 0.71937, "time": 0.21863} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.02354, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.86438, "top5_acc": 0.99375, "loss_cls": 0.63775, "loss": 0.63775, "time": 0.2198} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.02353, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82938, "top5_acc": 0.98688, "loss_cls": 0.7653, "loss": 0.7653, "time": 0.22055} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.02352, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8425, "top5_acc": 0.995, "loss_cls": 0.71269, "loss": 0.71269, "time": 0.22058} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.02351, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.8525, "top5_acc": 0.99312, "loss_cls": 0.66527, "loss": 0.66527, "time": 0.21857} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.0235, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83688, "top5_acc": 0.99188, "loss_cls": 0.72827, "loss": 0.72827, "time": 0.22178} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.02349, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.84688, "top5_acc": 0.995, "loss_cls": 0.74719, "loss": 0.74719, "time": 0.2201} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.02348, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.8525, "top5_acc": 0.98938, "loss_cls": 0.69227, "loss": 0.69227, "time": 0.21891} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.02347, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.84438, "top5_acc": 0.995, "loss_cls": 0.68751, "loss": 0.68751, "time": 0.21965} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.02346, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84438, "top5_acc": 0.99188, "loss_cls": 0.71284, "loss": 0.71284, "time": 0.21812} +{"mode": "val", "epoch": 24, "iter": 533, "lr": 0.02345, "top1_acc": 0.79509, "top5_acc": 0.98627, "mean_class_accuracy": 0.72573} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.02344, "memory": 4082, "data_time": 0.1915, "top1_acc": 0.82562, "top5_acc": 0.9925, "loss_cls": 0.74756, "loss": 0.74756, "time": 0.4117} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.02343, "memory": 4082, "data_time": 0.00046, "top1_acc": 0.83812, "top5_acc": 0.99, "loss_cls": 0.73774, "loss": 0.73774, "time": 0.22276} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.02342, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.85, "top5_acc": 0.99375, "loss_cls": 0.68077, "loss": 0.68077, "time": 0.21949} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.02341, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.84375, "top5_acc": 0.99312, "loss_cls": 0.71888, "loss": 0.71888, "time": 0.22069} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.0234, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.855, "top5_acc": 0.99312, "loss_cls": 0.66951, "loss": 0.66951, "time": 0.22542} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.02339, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.85688, "top5_acc": 0.9925, "loss_cls": 0.68608, "loss": 0.68608, "time": 0.2199} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.02338, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.84688, "top5_acc": 0.99375, "loss_cls": 0.70893, "loss": 0.70893, "time": 0.22056} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.02337, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.84688, "top5_acc": 0.9925, "loss_cls": 0.70246, "loss": 0.70246, "time": 0.22257} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.02336, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.86938, "top5_acc": 0.98875, "loss_cls": 0.65879, "loss": 0.65879, "time": 0.22015} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.02335, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.84375, "top5_acc": 0.99312, "loss_cls": 0.69313, "loss": 0.69313, "time": 0.22214} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.02334, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85812, "top5_acc": 0.99125, "loss_cls": 0.68542, "loss": 0.68542, "time": 0.22092} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.02333, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.85438, "top5_acc": 0.99125, "loss_cls": 0.6795, "loss": 0.6795, "time": 0.21933} +{"mode": "val", "epoch": 25, "iter": 533, "lr": 0.02333, "top1_acc": 0.74874, "top5_acc": 0.96902, "mean_class_accuracy": 0.67782} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.02332, "memory": 4082, "data_time": 0.19344, "top1_acc": 0.84062, "top5_acc": 0.995, "loss_cls": 0.70697, "loss": 0.70697, "time": 0.41666} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.0233, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.85625, "top5_acc": 0.99062, "loss_cls": 0.63802, "loss": 0.63802, "time": 0.21875} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.02329, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84875, "top5_acc": 0.99375, "loss_cls": 0.68103, "loss": 0.68103, "time": 0.22024} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.02328, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.86875, "top5_acc": 0.99312, "loss_cls": 0.63739, "loss": 0.63739, "time": 0.2198} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.02327, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85125, "top5_acc": 0.99125, "loss_cls": 0.69416, "loss": 0.69416, "time": 0.22145} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.02326, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84188, "top5_acc": 0.99375, "loss_cls": 0.70801, "loss": 0.70801, "time": 0.2205} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.02325, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83812, "top5_acc": 0.9925, "loss_cls": 0.71584, "loss": 0.71584, "time": 0.21952} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.02324, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82875, "top5_acc": 0.99188, "loss_cls": 0.76189, "loss": 0.76189, "time": 0.22167} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.02323, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85688, "top5_acc": 0.99125, "loss_cls": 0.69252, "loss": 0.69252, "time": 0.21832} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.02322, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84438, "top5_acc": 0.99062, "loss_cls": 0.725, "loss": 0.725, "time": 0.22112} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.02321, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84812, "top5_acc": 0.995, "loss_cls": 0.69203, "loss": 0.69203, "time": 0.22059} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.0232, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.83188, "top5_acc": 0.99312, "loss_cls": 0.72629, "loss": 0.72629, "time": 0.21936} +{"mode": "val", "epoch": 26, "iter": 533, "lr": 0.02319, "top1_acc": 0.83406, "top5_acc": 0.98686, "mean_class_accuracy": 0.75742} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.02318, "memory": 4082, "data_time": 0.19265, "top1_acc": 0.88312, "top5_acc": 0.99688, "loss_cls": 0.56094, "loss": 0.56094, "time": 0.41135} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.02317, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.83688, "top5_acc": 0.99562, "loss_cls": 0.71346, "loss": 0.71346, "time": 0.22251} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.02316, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86, "top5_acc": 0.9925, "loss_cls": 0.6599, "loss": 0.6599, "time": 0.22092} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.02315, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.85062, "top5_acc": 0.98875, "loss_cls": 0.70247, "loss": 0.70247, "time": 0.21808} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.02314, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8525, "top5_acc": 0.9925, "loss_cls": 0.6687, "loss": 0.6687, "time": 0.22152} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.02313, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85188, "top5_acc": 0.99312, "loss_cls": 0.66197, "loss": 0.66197, "time": 0.22114} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.02312, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.85188, "top5_acc": 0.99562, "loss_cls": 0.65607, "loss": 0.65607, "time": 0.21929} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.02311, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.85312, "top5_acc": 0.99375, "loss_cls": 0.70022, "loss": 0.70022, "time": 0.22027} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.0231, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83625, "top5_acc": 0.99062, "loss_cls": 0.72566, "loss": 0.72566, "time": 0.22113} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.02308, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.865, "top5_acc": 0.99438, "loss_cls": 0.63333, "loss": 0.63333, "time": 0.22266} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.02307, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.855, "top5_acc": 0.9925, "loss_cls": 0.65651, "loss": 0.65651, "time": 0.22062} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.02306, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.86375, "top5_acc": 0.99188, "loss_cls": 0.67418, "loss": 0.67418, "time": 0.21897} +{"mode": "val", "epoch": 27, "iter": 533, "lr": 0.02305, "top1_acc": 0.82619, "top5_acc": 0.98791, "mean_class_accuracy": 0.75265} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.02304, "memory": 4082, "data_time": 0.19155, "top1_acc": 0.87125, "top5_acc": 0.99312, "loss_cls": 0.65214, "loss": 0.65214, "time": 0.41631} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.02303, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.85188, "top5_acc": 0.99438, "loss_cls": 0.65378, "loss": 0.65378, "time": 0.21941} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.02302, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.875, "top5_acc": 0.99438, "loss_cls": 0.62445, "loss": 0.62445, "time": 0.21886} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.02301, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85188, "top5_acc": 0.98938, "loss_cls": 0.68494, "loss": 0.68494, "time": 0.22125} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.023, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.86125, "top5_acc": 0.99625, "loss_cls": 0.64391, "loss": 0.64391, "time": 0.21863} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.02299, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86938, "top5_acc": 0.99438, "loss_cls": 0.64678, "loss": 0.64678, "time": 0.22102} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.02298, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.85938, "top5_acc": 0.99625, "loss_cls": 0.63795, "loss": 0.63795, "time": 0.21975} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.02297, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.85625, "top5_acc": 0.99562, "loss_cls": 0.63587, "loss": 0.63587, "time": 0.22076} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.02295, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.8725, "top5_acc": 0.99625, "loss_cls": 0.6234, "loss": 0.6234, "time": 0.22353} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.02294, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.86938, "top5_acc": 0.99062, "loss_cls": 0.681, "loss": 0.681, "time": 0.22095} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.02293, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.8625, "top5_acc": 0.99062, "loss_cls": 0.69241, "loss": 0.69241, "time": 0.22189} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.02292, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84562, "top5_acc": 0.99188, "loss_cls": 0.7075, "loss": 0.7075, "time": 0.2211} +{"mode": "val", "epoch": 28, "iter": 533, "lr": 0.02291, "top1_acc": 0.72233, "top5_acc": 0.95716, "mean_class_accuracy": 0.64524} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.0229, "memory": 4082, "data_time": 0.19052, "top1_acc": 0.8725, "top5_acc": 0.99562, "loss_cls": 0.58473, "loss": 0.58473, "time": 0.41307} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.02289, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.86, "top5_acc": 0.9925, "loss_cls": 0.65845, "loss": 0.65845, "time": 0.21995} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.02288, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.87875, "top5_acc": 0.9925, "loss_cls": 0.5911, "loss": 0.5911, "time": 0.21745} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.02287, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.89125, "top5_acc": 0.99312, "loss_cls": 0.55285, "loss": 0.55285, "time": 0.22007} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.02285, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.86938, "top5_acc": 0.99375, "loss_cls": 0.60362, "loss": 0.60362, "time": 0.21895} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.02284, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85312, "top5_acc": 0.99188, "loss_cls": 0.71146, "loss": 0.71146, "time": 0.2187} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.02283, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.86375, "top5_acc": 0.99562, "loss_cls": 0.6395, "loss": 0.6395, "time": 0.21869} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.02282, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86062, "top5_acc": 0.99312, "loss_cls": 0.63322, "loss": 0.63322, "time": 0.21834} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.02281, "memory": 4082, "data_time": 0.00081, "top1_acc": 0.86562, "top5_acc": 0.99, "loss_cls": 0.65408, "loss": 0.65408, "time": 0.22218} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.0228, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.87188, "top5_acc": 0.99375, "loss_cls": 0.62951, "loss": 0.62951, "time": 0.21801} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.02279, "memory": 4082, "data_time": 0.00051, "top1_acc": 0.84938, "top5_acc": 0.99438, "loss_cls": 0.70157, "loss": 0.70157, "time": 0.22052} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.02277, "memory": 4082, "data_time": 0.00069, "top1_acc": 0.84875, "top5_acc": 0.99125, "loss_cls": 0.69632, "loss": 0.69632, "time": 0.22104} +{"mode": "val", "epoch": 29, "iter": 533, "lr": 0.02276, "top1_acc": 0.82572, "top5_acc": 0.98592, "mean_class_accuracy": 0.77095} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.02275, "memory": 4082, "data_time": 0.20444, "top1_acc": 0.87375, "top5_acc": 0.99688, "loss_cls": 0.58994, "loss": 0.58994, "time": 0.4325} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.02274, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85938, "top5_acc": 0.995, "loss_cls": 0.63543, "loss": 0.63543, "time": 0.22595} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.02273, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85875, "top5_acc": 0.99125, "loss_cls": 0.6431, "loss": 0.6431, "time": 0.22956} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.02272, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8675, "top5_acc": 0.99375, "loss_cls": 0.64497, "loss": 0.64497, "time": 0.22677} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.02271, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.86625, "top5_acc": 0.9975, "loss_cls": 0.61096, "loss": 0.61096, "time": 0.22849} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.02269, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.86062, "top5_acc": 0.9925, "loss_cls": 0.64817, "loss": 0.64817, "time": 0.22694} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.02268, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8475, "top5_acc": 0.9925, "loss_cls": 0.7003, "loss": 0.7003, "time": 0.22808} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.02267, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.86625, "top5_acc": 0.9925, "loss_cls": 0.64028, "loss": 0.64028, "time": 0.22862} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.02266, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.86375, "top5_acc": 0.99125, "loss_cls": 0.62351, "loss": 0.62351, "time": 0.22912} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.02265, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.85188, "top5_acc": 0.995, "loss_cls": 0.68426, "loss": 0.68426, "time": 0.22941} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.02263, "memory": 4082, "data_time": 0.00046, "top1_acc": 0.86125, "top5_acc": 0.99, "loss_cls": 0.63266, "loss": 0.63266, "time": 0.22786} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.02262, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.85312, "top5_acc": 0.99062, "loss_cls": 0.67426, "loss": 0.67426, "time": 0.22723} +{"mode": "val", "epoch": 30, "iter": 533, "lr": 0.02261, "top1_acc": 0.82349, "top5_acc": 0.98662, "mean_class_accuracy": 0.75879} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.0226, "memory": 4083, "data_time": 0.19106, "top1_acc": 0.87938, "top5_acc": 0.99625, "loss_cls": 0.75127, "loss": 0.75127, "time": 0.43067} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.02259, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.85875, "top5_acc": 0.99438, "loss_cls": 0.80126, "loss": 0.80126, "time": 0.22165} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.02258, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8575, "top5_acc": 0.9925, "loss_cls": 0.80255, "loss": 0.80255, "time": 0.22373} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.02256, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8625, "top5_acc": 0.99312, "loss_cls": 0.81471, "loss": 0.81471, "time": 0.22236} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.02255, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86188, "top5_acc": 0.9925, "loss_cls": 0.8401, "loss": 0.8401, "time": 0.2247} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.02254, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.84625, "top5_acc": 0.98875, "loss_cls": 0.87235, "loss": 0.87235, "time": 0.22132} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.02253, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85062, "top5_acc": 0.9925, "loss_cls": 0.83838, "loss": 0.83838, "time": 0.22306} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.02252, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85375, "top5_acc": 0.99562, "loss_cls": 0.8297, "loss": 0.8297, "time": 0.22344} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0225, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84, "top5_acc": 0.99438, "loss_cls": 0.87929, "loss": 0.87929, "time": 0.22767} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.02249, "memory": 4083, "data_time": 0.00096, "top1_acc": 0.875, "top5_acc": 0.99625, "loss_cls": 0.75343, "loss": 0.75343, "time": 0.22513} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.02248, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85562, "top5_acc": 0.99312, "loss_cls": 0.80835, "loss": 0.80835, "time": 0.22552} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.02247, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85812, "top5_acc": 0.9925, "loss_cls": 0.84886, "loss": 0.84886, "time": 0.2253} +{"mode": "val", "epoch": 31, "iter": 533, "lr": 0.02246, "top1_acc": 0.81199, "top5_acc": 0.98416, "mean_class_accuracy": 0.75068} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.02244, "memory": 4083, "data_time": 0.1974, "top1_acc": 0.8475, "top5_acc": 0.99438, "loss_cls": 0.78991, "loss": 0.78991, "time": 0.44085} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.02243, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8675, "top5_acc": 0.995, "loss_cls": 0.72666, "loss": 0.72666, "time": 0.22381} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.02242, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87812, "top5_acc": 0.99438, "loss_cls": 0.68946, "loss": 0.68946, "time": 0.22266} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.02241, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.84938, "top5_acc": 0.99125, "loss_cls": 0.76378, "loss": 0.76378, "time": 0.22634} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.02239, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.85438, "top5_acc": 0.99438, "loss_cls": 0.78278, "loss": 0.78278, "time": 0.22681} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.02238, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.87375, "top5_acc": 0.99625, "loss_cls": 0.70654, "loss": 0.70654, "time": 0.22854} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.02237, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86125, "top5_acc": 0.99312, "loss_cls": 0.74216, "loss": 0.74216, "time": 0.22775} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.02236, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86812, "top5_acc": 0.99375, "loss_cls": 0.72934, "loss": 0.72934, "time": 0.22432} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.02234, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.8475, "top5_acc": 0.995, "loss_cls": 0.78911, "loss": 0.78911, "time": 0.23135} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.02233, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.855, "top5_acc": 0.9925, "loss_cls": 0.76939, "loss": 0.76939, "time": 0.22612} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.02232, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85438, "top5_acc": 0.995, "loss_cls": 0.75363, "loss": 0.75363, "time": 0.22741} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.02231, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86938, "top5_acc": 0.98938, "loss_cls": 0.717, "loss": 0.717, "time": 0.22629} +{"mode": "val", "epoch": 32, "iter": 533, "lr": 0.0223, "top1_acc": 0.81857, "top5_acc": 0.98815, "mean_class_accuracy": 0.77593} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.02228, "memory": 4083, "data_time": 0.19356, "top1_acc": 0.88625, "top5_acc": 0.99438, "loss_cls": 0.61492, "loss": 0.61492, "time": 0.43377} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.02227, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85875, "top5_acc": 0.99375, "loss_cls": 0.7076, "loss": 0.7076, "time": 0.22772} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.02226, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85688, "top5_acc": 0.99312, "loss_cls": 0.74294, "loss": 0.74294, "time": 0.22394} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.02225, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.85625, "top5_acc": 0.99562, "loss_cls": 0.71569, "loss": 0.71569, "time": 0.22846} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.02223, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.875, "top5_acc": 0.99812, "loss_cls": 0.64444, "loss": 0.64444, "time": 0.22351} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.02222, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86938, "top5_acc": 0.99188, "loss_cls": 0.67178, "loss": 0.67178, "time": 0.22307} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.02221, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.83562, "top5_acc": 0.99188, "loss_cls": 0.79203, "loss": 0.79203, "time": 0.22574} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.02219, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.86625, "top5_acc": 0.9975, "loss_cls": 0.65093, "loss": 0.65093, "time": 0.22723} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.02218, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.87062, "top5_acc": 0.995, "loss_cls": 0.66658, "loss": 0.66658, "time": 0.22422} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.02217, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87312, "top5_acc": 0.99312, "loss_cls": 0.63653, "loss": 0.63653, "time": 0.22262} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.02216, "memory": 4083, "data_time": 0.00072, "top1_acc": 0.87, "top5_acc": 0.9925, "loss_cls": 0.69978, "loss": 0.69978, "time": 0.22479} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.02214, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86375, "top5_acc": 0.99312, "loss_cls": 0.69811, "loss": 0.69811, "time": 0.22566} +{"mode": "val", "epoch": 33, "iter": 533, "lr": 0.02213, "top1_acc": 0.79369, "top5_acc": 0.98087, "mean_class_accuracy": 0.72925} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.02212, "memory": 4083, "data_time": 0.19273, "top1_acc": 0.87562, "top5_acc": 0.99562, "loss_cls": 0.63501, "loss": 0.63501, "time": 0.428} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.02211, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.86188, "top5_acc": 0.99188, "loss_cls": 0.68497, "loss": 0.68497, "time": 0.22404} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.02209, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87562, "top5_acc": 0.995, "loss_cls": 0.66164, "loss": 0.66164, "time": 0.22257} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.02208, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85562, "top5_acc": 0.99125, "loss_cls": 0.69305, "loss": 0.69305, "time": 0.22178} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.02207, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86938, "top5_acc": 0.99438, "loss_cls": 0.65519, "loss": 0.65519, "time": 0.22417} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.02205, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88312, "top5_acc": 0.9925, "loss_cls": 0.63621, "loss": 0.63621, "time": 0.22477} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.02204, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87375, "top5_acc": 0.99375, "loss_cls": 0.65453, "loss": 0.65453, "time": 0.22392} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.02203, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.84625, "top5_acc": 0.99, "loss_cls": 0.73299, "loss": 0.73299, "time": 0.22165} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.02201, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87875, "top5_acc": 0.99625, "loss_cls": 0.64262, "loss": 0.64262, "time": 0.22417} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.022, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.86875, "top5_acc": 0.995, "loss_cls": 0.68397, "loss": 0.68397, "time": 0.22599} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.02199, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86562, "top5_acc": 0.99625, "loss_cls": 0.67364, "loss": 0.67364, "time": 0.22743} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.02197, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.86875, "top5_acc": 0.99125, "loss_cls": 0.67089, "loss": 0.67089, "time": 0.2236} +{"mode": "val", "epoch": 34, "iter": 533, "lr": 0.02196, "top1_acc": 0.80601, "top5_acc": 0.98756, "mean_class_accuracy": 0.72915} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.02195, "memory": 4083, "data_time": 0.19723, "top1_acc": 0.88375, "top5_acc": 0.99625, "loss_cls": 0.6219, "loss": 0.6219, "time": 0.43835} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.02194, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87125, "top5_acc": 0.99625, "loss_cls": 0.63459, "loss": 0.63459, "time": 0.22226} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.02192, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86562, "top5_acc": 0.99625, "loss_cls": 0.66895, "loss": 0.66895, "time": 0.22392} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.02191, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89562, "top5_acc": 0.995, "loss_cls": 0.58871, "loss": 0.58871, "time": 0.22607} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.0219, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.875, "top5_acc": 0.99312, "loss_cls": 0.66002, "loss": 0.66002, "time": 0.22447} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.02188, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88062, "top5_acc": 0.995, "loss_cls": 0.65192, "loss": 0.65192, "time": 0.22254} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.02187, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88375, "top5_acc": 0.9975, "loss_cls": 0.5819, "loss": 0.5819, "time": 0.22356} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.02185, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86062, "top5_acc": 0.99312, "loss_cls": 0.67549, "loss": 0.67549, "time": 0.22425} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.02184, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87812, "top5_acc": 0.99375, "loss_cls": 0.66111, "loss": 0.66111, "time": 0.22153} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.02183, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.87812, "top5_acc": 0.995, "loss_cls": 0.62852, "loss": 0.62852, "time": 0.22573} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.02181, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87562, "top5_acc": 0.99438, "loss_cls": 0.66577, "loss": 0.66577, "time": 0.22791} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.0218, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88875, "top5_acc": 0.99375, "loss_cls": 0.59842, "loss": 0.59842, "time": 0.22436} +{"mode": "val", "epoch": 35, "iter": 533, "lr": 0.02179, "top1_acc": 0.81469, "top5_acc": 0.98275, "mean_class_accuracy": 0.75203} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.02178, "memory": 4083, "data_time": 0.19165, "top1_acc": 0.84938, "top5_acc": 0.99375, "loss_cls": 0.73512, "loss": 0.73512, "time": 0.42706} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.02176, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88812, "top5_acc": 0.99562, "loss_cls": 0.62459, "loss": 0.62459, "time": 0.22594} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.02175, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8675, "top5_acc": 0.995, "loss_cls": 0.66743, "loss": 0.66743, "time": 0.22495} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.02173, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8775, "top5_acc": 0.99438, "loss_cls": 0.62651, "loss": 0.62651, "time": 0.22499} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.02172, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90438, "top5_acc": 0.99625, "loss_cls": 0.54597, "loss": 0.54597, "time": 0.22386} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.02171, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.86062, "top5_acc": 0.995, "loss_cls": 0.65419, "loss": 0.65419, "time": 0.22406} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.02169, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86625, "top5_acc": 0.99125, "loss_cls": 0.70204, "loss": 0.70204, "time": 0.22257} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.02168, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88062, "top5_acc": 0.99438, "loss_cls": 0.66363, "loss": 0.66363, "time": 0.22711} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.02167, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87562, "top5_acc": 0.995, "loss_cls": 0.62161, "loss": 0.62161, "time": 0.22733} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.02165, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.88125, "top5_acc": 0.99625, "loss_cls": 0.62363, "loss": 0.62363, "time": 0.22425} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.02164, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.885, "top5_acc": 0.99312, "loss_cls": 0.62209, "loss": 0.62209, "time": 0.22171} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.02162, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.87, "top5_acc": 0.995, "loss_cls": 0.63201, "loss": 0.63201, "time": 0.22923} +{"mode": "val", "epoch": 36, "iter": 533, "lr": 0.02161, "top1_acc": 0.82631, "top5_acc": 0.98873, "mean_class_accuracy": 0.77464} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.0216, "memory": 4083, "data_time": 0.19412, "top1_acc": 0.8775, "top5_acc": 0.99312, "loss_cls": 0.66241, "loss": 0.66241, "time": 0.42984} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.02158, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88438, "top5_acc": 0.99562, "loss_cls": 0.61635, "loss": 0.61635, "time": 0.22535} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.02157, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89, "top5_acc": 0.99625, "loss_cls": 0.59221, "loss": 0.59221, "time": 0.22428} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.02156, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87, "top5_acc": 0.9925, "loss_cls": 0.66266, "loss": 0.66266, "time": 0.22429} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.02154, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87562, "top5_acc": 0.99438, "loss_cls": 0.65071, "loss": 0.65071, "time": 0.22348} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.02153, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87188, "top5_acc": 0.99562, "loss_cls": 0.64117, "loss": 0.64117, "time": 0.223} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.02151, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87812, "top5_acc": 0.99312, "loss_cls": 0.61643, "loss": 0.61643, "time": 0.22292} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0215, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.86625, "top5_acc": 0.9925, "loss_cls": 0.67164, "loss": 0.67164, "time": 0.2243} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.02149, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85812, "top5_acc": 0.99312, "loss_cls": 0.70009, "loss": 0.70009, "time": 0.22439} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.02147, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88125, "top5_acc": 0.99438, "loss_cls": 0.64269, "loss": 0.64269, "time": 0.22713} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.02146, "memory": 4083, "data_time": 0.00061, "top1_acc": 0.87688, "top5_acc": 0.99312, "loss_cls": 0.60445, "loss": 0.60445, "time": 0.22421} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.02144, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.87438, "top5_acc": 0.995, "loss_cls": 0.65864, "loss": 0.65864, "time": 0.22552} +{"mode": "val", "epoch": 37, "iter": 533, "lr": 0.02143, "top1_acc": 0.83171, "top5_acc": 0.9885, "mean_class_accuracy": 0.76444} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.02142, "memory": 4083, "data_time": 0.19466, "top1_acc": 0.88438, "top5_acc": 0.99438, "loss_cls": 0.6097, "loss": 0.6097, "time": 0.43285} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.0214, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87625, "top5_acc": 0.99625, "loss_cls": 0.63225, "loss": 0.63225, "time": 0.22513} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.02139, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88938, "top5_acc": 0.995, "loss_cls": 0.6007, "loss": 0.6007, "time": 0.22163} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.02137, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89438, "top5_acc": 0.99562, "loss_cls": 0.58112, "loss": 0.58112, "time": 0.22402} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.02136, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8825, "top5_acc": 0.995, "loss_cls": 0.64255, "loss": 0.64255, "time": 0.22477} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.02134, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86875, "top5_acc": 0.99625, "loss_cls": 0.66622, "loss": 0.66622, "time": 0.22314} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.02133, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.87438, "top5_acc": 0.995, "loss_cls": 0.64689, "loss": 0.64689, "time": 0.22309} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.02132, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.8875, "top5_acc": 0.99062, "loss_cls": 0.61182, "loss": 0.61182, "time": 0.22657} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.0213, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85562, "top5_acc": 0.995, "loss_cls": 0.67179, "loss": 0.67179, "time": 0.22303} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.02129, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88375, "top5_acc": 0.99625, "loss_cls": 0.62241, "loss": 0.62241, "time": 0.22357} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.02127, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.87438, "top5_acc": 0.995, "loss_cls": 0.63195, "loss": 0.63195, "time": 0.22557} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.02126, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88125, "top5_acc": 0.99188, "loss_cls": 0.62913, "loss": 0.62913, "time": 0.22252} +{"mode": "val", "epoch": 38, "iter": 533, "lr": 0.02125, "top1_acc": 0.84591, "top5_acc": 0.98639, "mean_class_accuracy": 0.7645} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.02123, "memory": 4083, "data_time": 0.19543, "top1_acc": 0.89062, "top5_acc": 0.99812, "loss_cls": 0.57818, "loss": 0.57818, "time": 0.42964} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.02122, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87625, "top5_acc": 0.99375, "loss_cls": 0.64552, "loss": 0.64552, "time": 0.22254} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.0212, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.88375, "top5_acc": 0.99438, "loss_cls": 0.62656, "loss": 0.62656, "time": 0.22551} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.02119, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.8725, "top5_acc": 0.99562, "loss_cls": 0.62247, "loss": 0.62247, "time": 0.22447} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.02117, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88312, "top5_acc": 0.995, "loss_cls": 0.58235, "loss": 0.58235, "time": 0.22117} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.02116, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.87812, "top5_acc": 0.99438, "loss_cls": 0.62336, "loss": 0.62336, "time": 0.22156} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.02114, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.88812, "top5_acc": 0.99438, "loss_cls": 0.61029, "loss": 0.61029, "time": 0.22442} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.02113, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87188, "top5_acc": 0.995, "loss_cls": 0.65245, "loss": 0.65245, "time": 0.2238} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.02111, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.865, "top5_acc": 0.99375, "loss_cls": 0.67152, "loss": 0.67152, "time": 0.22495} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.0211, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.87125, "top5_acc": 0.99562, "loss_cls": 0.64016, "loss": 0.64016, "time": 0.22287} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.02108, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88688, "top5_acc": 0.995, "loss_cls": 0.61245, "loss": 0.61245, "time": 0.22474} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.02107, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.86875, "top5_acc": 0.99562, "loss_cls": 0.62817, "loss": 0.62817, "time": 0.22691} +{"mode": "val", "epoch": 39, "iter": 533, "lr": 0.02106, "top1_acc": 0.85295, "top5_acc": 0.98873, "mean_class_accuracy": 0.79938} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.02104, "memory": 4083, "data_time": 0.19213, "top1_acc": 0.88562, "top5_acc": 0.995, "loss_cls": 0.60728, "loss": 0.60728, "time": 0.42389} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.02103, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8825, "top5_acc": 0.99812, "loss_cls": 0.58608, "loss": 0.58608, "time": 0.22364} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.02101, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88938, "top5_acc": 0.99688, "loss_cls": 0.57033, "loss": 0.57033, "time": 0.22337} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.021, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8825, "top5_acc": 0.99688, "loss_cls": 0.61442, "loss": 0.61442, "time": 0.22293} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.02098, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89375, "top5_acc": 0.99875, "loss_cls": 0.57182, "loss": 0.57182, "time": 0.22356} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.02097, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88625, "top5_acc": 0.99625, "loss_cls": 0.58385, "loss": 0.58385, "time": 0.22186} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.02095, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88, "top5_acc": 0.995, "loss_cls": 0.63917, "loss": 0.63917, "time": 0.22353} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.02094, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88938, "top5_acc": 0.99625, "loss_cls": 0.5611, "loss": 0.5611, "time": 0.22138} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.02092, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.88188, "top5_acc": 0.99625, "loss_cls": 0.61546, "loss": 0.61546, "time": 0.22541} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.02091, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.87875, "top5_acc": 0.99375, "loss_cls": 0.60703, "loss": 0.60703, "time": 0.2246} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.02089, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.86938, "top5_acc": 0.99312, "loss_cls": 0.67223, "loss": 0.67223, "time": 0.22505} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.02088, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.875, "top5_acc": 0.99625, "loss_cls": 0.67815, "loss": 0.67815, "time": 0.22611} +{"mode": "val", "epoch": 40, "iter": 533, "lr": 0.02086, "top1_acc": 0.82655, "top5_acc": 0.98615, "mean_class_accuracy": 0.74629} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.02085, "memory": 4083, "data_time": 0.19355, "top1_acc": 0.895, "top5_acc": 0.99562, "loss_cls": 0.55623, "loss": 0.55623, "time": 0.42872} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.02083, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88688, "top5_acc": 0.99688, "loss_cls": 0.57289, "loss": 0.57289, "time": 0.22272} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.02082, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88188, "top5_acc": 0.99562, "loss_cls": 0.58897, "loss": 0.58897, "time": 0.22558} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.0208, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88, "top5_acc": 0.99625, "loss_cls": 0.6, "loss": 0.6, "time": 0.22356} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.02079, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.885, "top5_acc": 0.99625, "loss_cls": 0.57619, "loss": 0.57619, "time": 0.22467} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.02077, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88, "top5_acc": 0.99312, "loss_cls": 0.62643, "loss": 0.62643, "time": 0.22337} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.02076, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86312, "top5_acc": 0.99688, "loss_cls": 0.64293, "loss": 0.64293, "time": 0.22222} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.02074, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.88438, "top5_acc": 0.99688, "loss_cls": 0.58445, "loss": 0.58445, "time": 0.22295} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.02073, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87438, "top5_acc": 0.99625, "loss_cls": 0.61758, "loss": 0.61758, "time": 0.22282} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.02071, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87562, "top5_acc": 0.9975, "loss_cls": 0.64414, "loss": 0.64414, "time": 0.22196} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.0207, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.87, "top5_acc": 0.99562, "loss_cls": 0.62099, "loss": 0.62099, "time": 0.22295} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.02068, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88812, "top5_acc": 0.99625, "loss_cls": 0.59011, "loss": 0.59011, "time": 0.22254} +{"mode": "val", "epoch": 41, "iter": 533, "lr": 0.02067, "top1_acc": 0.84896, "top5_acc": 0.99014, "mean_class_accuracy": 0.78729} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.02065, "memory": 4083, "data_time": 0.18847, "top1_acc": 0.8925, "top5_acc": 0.9975, "loss_cls": 0.54537, "loss": 0.54537, "time": 0.42393} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.02064, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88125, "top5_acc": 0.9975, "loss_cls": 0.57439, "loss": 0.57439, "time": 0.2253} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.02062, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87625, "top5_acc": 0.9975, "loss_cls": 0.6123, "loss": 0.6123, "time": 0.22135} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.02061, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89, "top5_acc": 0.99625, "loss_cls": 0.5988, "loss": 0.5988, "time": 0.2242} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.02059, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.895, "top5_acc": 0.9925, "loss_cls": 0.56622, "loss": 0.56622, "time": 0.22359} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.02057, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90125, "top5_acc": 0.99562, "loss_cls": 0.54351, "loss": 0.54351, "time": 0.22356} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.02056, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.87875, "top5_acc": 0.99688, "loss_cls": 0.62614, "loss": 0.62614, "time": 0.22499} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.02054, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87188, "top5_acc": 0.99562, "loss_cls": 0.64907, "loss": 0.64907, "time": 0.22322} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.02053, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87188, "top5_acc": 0.9975, "loss_cls": 0.63118, "loss": 0.63118, "time": 0.22424} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.02051, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.89312, "top5_acc": 0.995, "loss_cls": 0.56713, "loss": 0.56713, "time": 0.22412} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.0205, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.86438, "top5_acc": 0.99375, "loss_cls": 0.64834, "loss": 0.64834, "time": 0.22512} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.02048, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88062, "top5_acc": 0.99562, "loss_cls": 0.62255, "loss": 0.62255, "time": 0.22294} +{"mode": "val", "epoch": 42, "iter": 533, "lr": 0.02047, "top1_acc": 0.84532, "top5_acc": 0.98991, "mean_class_accuracy": 0.78439} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.02045, "memory": 4083, "data_time": 0.19334, "top1_acc": 0.89938, "top5_acc": 0.99812, "loss_cls": 0.52911, "loss": 0.52911, "time": 0.42866} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.02044, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91312, "top5_acc": 0.99812, "loss_cls": 0.51325, "loss": 0.51325, "time": 0.22781} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.02042, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.91125, "top5_acc": 0.99688, "loss_cls": 0.49994, "loss": 0.49994, "time": 0.22195} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.0204, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89188, "top5_acc": 0.99438, "loss_cls": 0.58748, "loss": 0.58748, "time": 0.22406} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.02039, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.875, "top5_acc": 0.99625, "loss_cls": 0.63464, "loss": 0.63464, "time": 0.22204} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.02037, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.88438, "top5_acc": 0.99312, "loss_cls": 0.60645, "loss": 0.60645, "time": 0.222} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.02036, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89188, "top5_acc": 0.995, "loss_cls": 0.56285, "loss": 0.56285, "time": 0.22349} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.02034, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87875, "top5_acc": 0.99688, "loss_cls": 0.59228, "loss": 0.59228, "time": 0.2216} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.02033, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.895, "top5_acc": 0.99562, "loss_cls": 0.55973, "loss": 0.55973, "time": 0.2251} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.02031, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8825, "top5_acc": 0.99375, "loss_cls": 0.63235, "loss": 0.63235, "time": 0.22136} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.02029, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87375, "top5_acc": 0.99438, "loss_cls": 0.59703, "loss": 0.59703, "time": 0.22277} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.02028, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87875, "top5_acc": 0.9975, "loss_cls": 0.59895, "loss": 0.59895, "time": 0.22413} +{"mode": "val", "epoch": 43, "iter": 533, "lr": 0.02026, "top1_acc": 0.84779, "top5_acc": 0.99038, "mean_class_accuracy": 0.78657} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.02025, "memory": 4083, "data_time": 0.18592, "top1_acc": 0.87375, "top5_acc": 0.99625, "loss_cls": 0.64119, "loss": 0.64119, "time": 0.42099} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.02023, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.89312, "top5_acc": 0.99688, "loss_cls": 0.55883, "loss": 0.55883, "time": 0.22874} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.02022, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90125, "top5_acc": 0.99312, "loss_cls": 0.56554, "loss": 0.56554, "time": 0.22506} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.0202, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.88562, "top5_acc": 0.99625, "loss_cls": 0.57343, "loss": 0.57343, "time": 0.22079} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.02018, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.86688, "top5_acc": 0.99188, "loss_cls": 0.67457, "loss": 0.67457, "time": 0.22609} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.02017, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88562, "top5_acc": 0.99375, "loss_cls": 0.55004, "loss": 0.55004, "time": 0.22403} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.02015, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.88688, "top5_acc": 0.99875, "loss_cls": 0.58097, "loss": 0.58097, "time": 0.22416} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.02014, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8825, "top5_acc": 0.99125, "loss_cls": 0.61497, "loss": 0.61497, "time": 0.22128} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.02012, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88312, "top5_acc": 0.99625, "loss_cls": 0.60715, "loss": 0.60715, "time": 0.22811} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.0201, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.895, "top5_acc": 0.99625, "loss_cls": 0.56362, "loss": 0.56362, "time": 0.21841} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.02009, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.89, "top5_acc": 0.9975, "loss_cls": 0.55342, "loss": 0.55342, "time": 0.22628} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.02007, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89062, "top5_acc": 0.99688, "loss_cls": 0.5412, "loss": 0.5412, "time": 0.22279} +{"mode": "val", "epoch": 44, "iter": 533, "lr": 0.02006, "top1_acc": 0.83805, "top5_acc": 0.98932, "mean_class_accuracy": 0.77445} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.02004, "memory": 4083, "data_time": 0.19662, "top1_acc": 0.91938, "top5_acc": 0.99875, "loss_cls": 0.47602, "loss": 0.47602, "time": 0.43535} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.02003, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89625, "top5_acc": 0.99625, "loss_cls": 0.5366, "loss": 0.5366, "time": 0.22357} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.02001, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88875, "top5_acc": 0.99625, "loss_cls": 0.56309, "loss": 0.56309, "time": 0.22242} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.01999, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.89625, "top5_acc": 0.995, "loss_cls": 0.53014, "loss": 0.53014, "time": 0.2262} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.01998, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.88375, "top5_acc": 0.99625, "loss_cls": 0.61633, "loss": 0.61633, "time": 0.22525} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.01996, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88, "top5_acc": 0.99438, "loss_cls": 0.62566, "loss": 0.62566, "time": 0.22346} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.01994, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89062, "top5_acc": 0.99562, "loss_cls": 0.57742, "loss": 0.57742, "time": 0.2222} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.01993, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8875, "top5_acc": 0.99688, "loss_cls": 0.57148, "loss": 0.57148, "time": 0.2237} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.01991, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89125, "top5_acc": 0.99625, "loss_cls": 0.62351, "loss": 0.62351, "time": 0.22385} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.01989, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88562, "top5_acc": 0.99438, "loss_cls": 0.58731, "loss": 0.58731, "time": 0.22292} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.01988, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89812, "top5_acc": 0.99688, "loss_cls": 0.55337, "loss": 0.55337, "time": 0.22454} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.01986, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88562, "top5_acc": 0.99125, "loss_cls": 0.61105, "loss": 0.61105, "time": 0.22425} +{"mode": "val", "epoch": 45, "iter": 533, "lr": 0.01985, "top1_acc": 0.84286, "top5_acc": 0.98967, "mean_class_accuracy": 0.80804} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.01983, "memory": 4083, "data_time": 0.19271, "top1_acc": 0.89562, "top5_acc": 0.9975, "loss_cls": 0.55982, "loss": 0.55982, "time": 0.4264} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.01981, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.89, "top5_acc": 0.99375, "loss_cls": 0.53635, "loss": 0.53635, "time": 0.22422} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.0198, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89062, "top5_acc": 0.99688, "loss_cls": 0.5885, "loss": 0.5885, "time": 0.22318} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.01978, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89625, "top5_acc": 0.99625, "loss_cls": 0.54254, "loss": 0.54254, "time": 0.22528} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.01976, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88188, "top5_acc": 0.99562, "loss_cls": 0.57652, "loss": 0.57652, "time": 0.22172} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.01975, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88812, "top5_acc": 0.99375, "loss_cls": 0.58896, "loss": 0.58896, "time": 0.22407} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.01973, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8975, "top5_acc": 0.9975, "loss_cls": 0.5433, "loss": 0.5433, "time": 0.22038} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.01971, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90188, "top5_acc": 0.995, "loss_cls": 0.5536, "loss": 0.5536, "time": 0.22185} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.0197, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89062, "top5_acc": 0.995, "loss_cls": 0.57936, "loss": 0.57936, "time": 0.22384} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.01968, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89625, "top5_acc": 0.99625, "loss_cls": 0.56144, "loss": 0.56144, "time": 0.22203} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.01966, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.89, "top5_acc": 0.99688, "loss_cls": 0.5635, "loss": 0.5635, "time": 0.22469} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.01965, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89, "top5_acc": 0.99562, "loss_cls": 0.57986, "loss": 0.57986, "time": 0.22263} +{"mode": "val", "epoch": 46, "iter": 533, "lr": 0.01963, "top1_acc": 0.84133, "top5_acc": 0.9885, "mean_class_accuracy": 0.79303} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.01962, "memory": 4083, "data_time": 0.19761, "top1_acc": 0.89375, "top5_acc": 0.99625, "loss_cls": 0.55956, "loss": 0.55956, "time": 0.43298} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.0196, "memory": 4083, "data_time": 0.00066, "top1_acc": 0.90875, "top5_acc": 0.99625, "loss_cls": 0.51057, "loss": 0.51057, "time": 0.22764} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.01958, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87938, "top5_acc": 0.99562, "loss_cls": 0.59535, "loss": 0.59535, "time": 0.22494} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.01957, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9125, "top5_acc": 0.99688, "loss_cls": 0.46438, "loss": 0.46438, "time": 0.22227} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.01955, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.89125, "top5_acc": 0.99562, "loss_cls": 0.55421, "loss": 0.55421, "time": 0.22954} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.01953, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88562, "top5_acc": 0.995, "loss_cls": 0.57245, "loss": 0.57245, "time": 0.22282} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.01952, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89188, "top5_acc": 0.99625, "loss_cls": 0.5504, "loss": 0.5504, "time": 0.2234} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.0195, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88938, "top5_acc": 0.99625, "loss_cls": 0.55607, "loss": 0.55607, "time": 0.22322} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.01948, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.89938, "top5_acc": 0.99625, "loss_cls": 0.52741, "loss": 0.52741, "time": 0.22652} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.01947, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.89625, "top5_acc": 0.99688, "loss_cls": 0.54413, "loss": 0.54413, "time": 0.22097} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.01945, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89688, "top5_acc": 0.99625, "loss_cls": 0.57849, "loss": 0.57849, "time": 0.22211} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.01943, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.87375, "top5_acc": 0.9975, "loss_cls": 0.64184, "loss": 0.64184, "time": 0.22546} +{"mode": "val", "epoch": 47, "iter": 533, "lr": 0.01942, "top1_acc": 0.84744, "top5_acc": 0.99002, "mean_class_accuracy": 0.81014} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.0194, "memory": 4083, "data_time": 0.18746, "top1_acc": 0.88625, "top5_acc": 0.9975, "loss_cls": 0.57313, "loss": 0.57313, "time": 0.42403} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.01938, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89438, "top5_acc": 0.9975, "loss_cls": 0.54129, "loss": 0.54129, "time": 0.22468} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.01937, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89438, "top5_acc": 0.99812, "loss_cls": 0.53603, "loss": 0.53603, "time": 0.22301} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.01935, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88938, "top5_acc": 0.995, "loss_cls": 0.5827, "loss": 0.5827, "time": 0.22264} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.01933, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89562, "top5_acc": 0.99438, "loss_cls": 0.5598, "loss": 0.5598, "time": 0.2236} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.01932, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89, "top5_acc": 0.99812, "loss_cls": 0.57811, "loss": 0.57811, "time": 0.22224} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.0193, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.88, "top5_acc": 0.995, "loss_cls": 0.63382, "loss": 0.63382, "time": 0.21971} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.01928, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.915, "top5_acc": 0.9975, "loss_cls": 0.49694, "loss": 0.49694, "time": 0.22256} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.01926, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88812, "top5_acc": 0.99625, "loss_cls": 0.58305, "loss": 0.58305, "time": 0.22425} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.01925, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89688, "top5_acc": 0.99625, "loss_cls": 0.51672, "loss": 0.51672, "time": 0.22259} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.01923, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91125, "top5_acc": 0.99625, "loss_cls": 0.50926, "loss": 0.50926, "time": 0.22405} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.01921, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89438, "top5_acc": 0.99562, "loss_cls": 0.55032, "loss": 0.55032, "time": 0.22583} +{"mode": "val", "epoch": 48, "iter": 533, "lr": 0.0192, "top1_acc": 0.8161, "top5_acc": 0.98674, "mean_class_accuracy": 0.73794} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.01918, "memory": 4083, "data_time": 0.19116, "top1_acc": 0.91562, "top5_acc": 0.995, "loss_cls": 0.48874, "loss": 0.48874, "time": 0.42535} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.01916, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91, "top5_acc": 0.99875, "loss_cls": 0.47519, "loss": 0.47519, "time": 0.22378} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.01915, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.89375, "top5_acc": 0.995, "loss_cls": 0.52301, "loss": 0.52301, "time": 0.22682} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.01913, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89125, "top5_acc": 0.99375, "loss_cls": 0.60088, "loss": 0.60088, "time": 0.22143} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.01911, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.89812, "top5_acc": 0.99688, "loss_cls": 0.58227, "loss": 0.58227, "time": 0.22359} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.01909, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90625, "top5_acc": 0.99562, "loss_cls": 0.50702, "loss": 0.50702, "time": 0.22441} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.01908, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.88812, "top5_acc": 0.99625, "loss_cls": 0.58506, "loss": 0.58506, "time": 0.22515} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.01906, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9, "top5_acc": 0.99375, "loss_cls": 0.53744, "loss": 0.53744, "time": 0.22204} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.01904, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.885, "top5_acc": 0.99625, "loss_cls": 0.56097, "loss": 0.56097, "time": 0.22319} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.01902, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89062, "top5_acc": 0.99625, "loss_cls": 0.57063, "loss": 0.57063, "time": 0.22073} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.01901, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89125, "top5_acc": 0.99625, "loss_cls": 0.56601, "loss": 0.56601, "time": 0.22349} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.01899, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88938, "top5_acc": 0.99625, "loss_cls": 0.57719, "loss": 0.57719, "time": 0.22367} +{"mode": "val", "epoch": 49, "iter": 533, "lr": 0.01898, "top1_acc": 0.86926, "top5_acc": 0.99155, "mean_class_accuracy": 0.80964} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.01896, "memory": 4083, "data_time": 0.18692, "top1_acc": 0.90812, "top5_acc": 0.99875, "loss_cls": 0.49529, "loss": 0.49529, "time": 0.42313} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.01894, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.905, "top5_acc": 0.9975, "loss_cls": 0.51031, "loss": 0.51031, "time": 0.22316} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.01892, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.895, "top5_acc": 0.995, "loss_cls": 0.5467, "loss": 0.5467, "time": 0.22214} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.01891, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88625, "top5_acc": 0.9975, "loss_cls": 0.53582, "loss": 0.53582, "time": 0.22135} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.01889, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89938, "top5_acc": 0.9975, "loss_cls": 0.4994, "loss": 0.4994, "time": 0.22291} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.01887, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90312, "top5_acc": 0.9925, "loss_cls": 0.5429, "loss": 0.5429, "time": 0.22665} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.01885, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.885, "top5_acc": 0.99625, "loss_cls": 0.58882, "loss": 0.58882, "time": 0.22037} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.01884, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.875, "top5_acc": 0.99625, "loss_cls": 0.60805, "loss": 0.60805, "time": 0.22186} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.01882, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.905, "top5_acc": 1.0, "loss_cls": 0.50808, "loss": 0.50808, "time": 0.22419} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.0188, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.8775, "top5_acc": 0.99688, "loss_cls": 0.60946, "loss": 0.60946, "time": 0.22275} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.01878, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89562, "top5_acc": 0.99562, "loss_cls": 0.58897, "loss": 0.58897, "time": 0.22425} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.01876, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89188, "top5_acc": 0.99688, "loss_cls": 0.52683, "loss": 0.52683, "time": 0.22621} +{"mode": "val", "epoch": 50, "iter": 533, "lr": 0.01875, "top1_acc": 0.86304, "top5_acc": 0.99096, "mean_class_accuracy": 0.80705} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.01873, "memory": 4083, "data_time": 0.18996, "top1_acc": 0.91188, "top5_acc": 0.99812, "loss_cls": 0.47265, "loss": 0.47265, "time": 0.42505} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.01871, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.8975, "top5_acc": 0.99625, "loss_cls": 0.54747, "loss": 0.54747, "time": 0.22466} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.0187, "memory": 4083, "data_time": 0.00065, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.53067, "loss": 0.53067, "time": 0.22796} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.01868, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.895, "top5_acc": 0.9975, "loss_cls": 0.57636, "loss": 0.57636, "time": 0.22247} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.01866, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90312, "top5_acc": 0.99688, "loss_cls": 0.51629, "loss": 0.51629, "time": 0.22437} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.01864, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89875, "top5_acc": 0.99438, "loss_cls": 0.53605, "loss": 0.53605, "time": 0.22598} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.01863, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9, "top5_acc": 0.995, "loss_cls": 0.54823, "loss": 0.54823, "time": 0.22293} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.01861, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90375, "top5_acc": 0.995, "loss_cls": 0.48549, "loss": 0.48549, "time": 0.22463} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.01859, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.8925, "top5_acc": 0.995, "loss_cls": 0.51782, "loss": 0.51782, "time": 0.22119} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.01857, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9125, "top5_acc": 0.99875, "loss_cls": 0.49413, "loss": 0.49413, "time": 0.22373} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.01855, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.88562, "top5_acc": 0.99688, "loss_cls": 0.55838, "loss": 0.55838, "time": 0.22315} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.01854, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.90188, "top5_acc": 0.99562, "loss_cls": 0.54466, "loss": 0.54466, "time": 0.2254} +{"mode": "val", "epoch": 51, "iter": 533, "lr": 0.01852, "top1_acc": 0.85236, "top5_acc": 0.99249, "mean_class_accuracy": 0.79777} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.0185, "memory": 4083, "data_time": 0.19206, "top1_acc": 0.89375, "top5_acc": 0.99625, "loss_cls": 0.52365, "loss": 0.52365, "time": 0.42386} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.01849, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90062, "top5_acc": 0.995, "loss_cls": 0.51926, "loss": 0.51926, "time": 0.22362} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.01847, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.89938, "top5_acc": 0.9975, "loss_cls": 0.52985, "loss": 0.52985, "time": 0.22409} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.01845, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91812, "top5_acc": 0.9975, "loss_cls": 0.44688, "loss": 0.44688, "time": 0.22438} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.01843, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89688, "top5_acc": 0.99562, "loss_cls": 0.54852, "loss": 0.54852, "time": 0.2237} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.01841, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.905, "top5_acc": 0.99688, "loss_cls": 0.48394, "loss": 0.48394, "time": 0.22161} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.0184, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9, "top5_acc": 0.995, "loss_cls": 0.52398, "loss": 0.52398, "time": 0.22293} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.01838, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.895, "top5_acc": 0.99625, "loss_cls": 0.55604, "loss": 0.55604, "time": 0.22482} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.01836, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.91062, "top5_acc": 0.99688, "loss_cls": 0.507, "loss": 0.507, "time": 0.22425} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.01834, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89438, "top5_acc": 0.99812, "loss_cls": 0.5306, "loss": 0.5306, "time": 0.22241} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.01832, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88188, "top5_acc": 0.99438, "loss_cls": 0.58887, "loss": 0.58887, "time": 0.22135} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.01831, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.89562, "top5_acc": 0.99625, "loss_cls": 0.54064, "loss": 0.54064, "time": 0.22385} +{"mode": "val", "epoch": 52, "iter": 533, "lr": 0.01829, "top1_acc": 0.87372, "top5_acc": 0.98956, "mean_class_accuracy": 0.83615} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.01827, "memory": 4083, "data_time": 0.19102, "top1_acc": 0.89938, "top5_acc": 0.99688, "loss_cls": 0.53565, "loss": 0.53565, "time": 0.42759} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.01826, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9075, "top5_acc": 0.99938, "loss_cls": 0.49267, "loss": 0.49267, "time": 0.22704} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.01824, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.91688, "top5_acc": 0.995, "loss_cls": 0.47951, "loss": 0.47951, "time": 0.2225} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.01822, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90875, "top5_acc": 0.99938, "loss_cls": 0.47041, "loss": 0.47041, "time": 0.22374} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.0182, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89375, "top5_acc": 0.99812, "loss_cls": 0.51753, "loss": 0.51753, "time": 0.22579} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.01818, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90188, "top5_acc": 0.99812, "loss_cls": 0.53435, "loss": 0.53435, "time": 0.22192} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.01816, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.895, "top5_acc": 0.9975, "loss_cls": 0.5276, "loss": 0.5276, "time": 0.22553} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.01815, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88188, "top5_acc": 0.9975, "loss_cls": 0.55727, "loss": 0.55727, "time": 0.22683} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.01813, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.90188, "top5_acc": 0.99938, "loss_cls": 0.49817, "loss": 0.49817, "time": 0.22505} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.01811, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.87625, "top5_acc": 0.99438, "loss_cls": 0.58947, "loss": 0.58947, "time": 0.2244} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.01809, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90125, "top5_acc": 0.99812, "loss_cls": 0.5073, "loss": 0.5073, "time": 0.22457} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.01807, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.89312, "top5_acc": 0.99438, "loss_cls": 0.56058, "loss": 0.56058, "time": 0.22778} +{"mode": "val", "epoch": 53, "iter": 533, "lr": 0.01806, "top1_acc": 0.83582, "top5_acc": 0.98416, "mean_class_accuracy": 0.78995} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.01804, "memory": 4083, "data_time": 0.19255, "top1_acc": 0.91562, "top5_acc": 0.9975, "loss_cls": 0.49684, "loss": 0.49684, "time": 0.43126} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.01802, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.52714, "loss": 0.52714, "time": 0.22322} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.018, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87938, "top5_acc": 0.99625, "loss_cls": 0.59346, "loss": 0.59346, "time": 0.2235} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.01798, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90188, "top5_acc": 0.99562, "loss_cls": 0.51901, "loss": 0.51901, "time": 0.22471} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.01797, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92938, "top5_acc": 0.9975, "loss_cls": 0.44972, "loss": 0.44972, "time": 0.22436} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.01795, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89312, "top5_acc": 0.99688, "loss_cls": 0.52556, "loss": 0.52556, "time": 0.2221} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.01793, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.915, "top5_acc": 0.99875, "loss_cls": 0.46991, "loss": 0.46991, "time": 0.22364} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.01791, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.90688, "top5_acc": 0.99438, "loss_cls": 0.49059, "loss": 0.49059, "time": 0.22826} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.01789, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9, "top5_acc": 0.99625, "loss_cls": 0.53946, "loss": 0.53946, "time": 0.22747} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.01787, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.50811, "loss": 0.50811, "time": 0.22677} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.01786, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.915, "top5_acc": 0.99625, "loss_cls": 0.49105, "loss": 0.49105, "time": 0.22623} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.01784, "memory": 4083, "data_time": 0.00061, "top1_acc": 0.90312, "top5_acc": 0.9975, "loss_cls": 0.48339, "loss": 0.48339, "time": 0.22748} +{"mode": "val", "epoch": 54, "iter": 533, "lr": 0.01782, "top1_acc": 0.8729, "top5_acc": 0.99096, "mean_class_accuracy": 0.8289} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.0178, "memory": 4083, "data_time": 0.19744, "top1_acc": 0.90875, "top5_acc": 0.9975, "loss_cls": 0.4942, "loss": 0.4942, "time": 0.43626} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.01779, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90938, "top5_acc": 0.9975, "loss_cls": 0.46827, "loss": 0.46827, "time": 0.22202} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.01777, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.91438, "top5_acc": 0.99688, "loss_cls": 0.49497, "loss": 0.49497, "time": 0.22526} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.01775, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90688, "top5_acc": 0.99562, "loss_cls": 0.48118, "loss": 0.48118, "time": 0.22328} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.01773, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8925, "top5_acc": 0.99562, "loss_cls": 0.57556, "loss": 0.57556, "time": 0.22479} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.01771, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89188, "top5_acc": 0.9975, "loss_cls": 0.55344, "loss": 0.55344, "time": 0.22457} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.01769, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.90625, "top5_acc": 0.99688, "loss_cls": 0.49561, "loss": 0.49561, "time": 0.22568} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.01767, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91375, "top5_acc": 0.99938, "loss_cls": 0.48555, "loss": 0.48555, "time": 0.22652} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.01766, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90375, "top5_acc": 0.99438, "loss_cls": 0.51437, "loss": 0.51437, "time": 0.2258} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.01764, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.90875, "top5_acc": 0.9975, "loss_cls": 0.48117, "loss": 0.48117, "time": 0.22519} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.01762, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8925, "top5_acc": 0.99688, "loss_cls": 0.53482, "loss": 0.53482, "time": 0.22694} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.0176, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89562, "top5_acc": 0.995, "loss_cls": 0.56319, "loss": 0.56319, "time": 0.22166} +{"mode": "val", "epoch": 55, "iter": 533, "lr": 0.01758, "top1_acc": 0.87102, "top5_acc": 0.99225, "mean_class_accuracy": 0.82536} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.01757, "memory": 4083, "data_time": 0.19013, "top1_acc": 0.90562, "top5_acc": 0.99688, "loss_cls": 0.52074, "loss": 0.52074, "time": 0.42588} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.01755, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9075, "top5_acc": 0.99938, "loss_cls": 0.43789, "loss": 0.43789, "time": 0.21952} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.01753, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90688, "top5_acc": 0.99812, "loss_cls": 0.46597, "loss": 0.46597, "time": 0.22414} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.01751, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.89625, "top5_acc": 0.99562, "loss_cls": 0.57904, "loss": 0.57904, "time": 0.22333} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.01749, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90688, "top5_acc": 0.99625, "loss_cls": 0.5137, "loss": 0.5137, "time": 0.2259} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.01747, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92688, "top5_acc": 0.99562, "loss_cls": 0.4468, "loss": 0.4468, "time": 0.22355} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.01745, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90375, "top5_acc": 0.99875, "loss_cls": 0.49634, "loss": 0.49634, "time": 0.22789} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.01743, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.90375, "top5_acc": 0.99688, "loss_cls": 0.51481, "loss": 0.51481, "time": 0.22508} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.01742, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.9, "top5_acc": 0.9975, "loss_cls": 0.51228, "loss": 0.51228, "time": 0.22331} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.0174, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.905, "top5_acc": 0.99688, "loss_cls": 0.49957, "loss": 0.49957, "time": 0.22501} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.01738, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.91188, "top5_acc": 0.9975, "loss_cls": 0.47677, "loss": 0.47677, "time": 0.22366} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.01736, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.89562, "top5_acc": 0.99438, "loss_cls": 0.55582, "loss": 0.55582, "time": 0.22395} +{"mode": "val", "epoch": 56, "iter": 533, "lr": 0.01734, "top1_acc": 0.80319, "top5_acc": 0.97829, "mean_class_accuracy": 0.74158} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.01733, "memory": 4083, "data_time": 0.19599, "top1_acc": 0.90062, "top5_acc": 0.99562, "loss_cls": 0.5154, "loss": 0.5154, "time": 0.42989} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.01731, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91125, "top5_acc": 0.99875, "loss_cls": 0.4715, "loss": 0.4715, "time": 0.22613} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.01729, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90562, "top5_acc": 0.99562, "loss_cls": 0.49377, "loss": 0.49377, "time": 0.22568} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.01727, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89375, "top5_acc": 0.99688, "loss_cls": 0.54658, "loss": 0.54658, "time": 0.22245} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.01725, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90062, "top5_acc": 0.99938, "loss_cls": 0.48647, "loss": 0.48647, "time": 0.22437} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.01723, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.915, "top5_acc": 0.9975, "loss_cls": 0.46278, "loss": 0.46278, "time": 0.22414} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.01721, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89875, "top5_acc": 0.99438, "loss_cls": 0.53825, "loss": 0.53825, "time": 0.22481} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.01719, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90625, "top5_acc": 0.99812, "loss_cls": 0.49815, "loss": 0.49815, "time": 0.22336} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.01717, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.90375, "top5_acc": 0.995, "loss_cls": 0.50863, "loss": 0.50863, "time": 0.22326} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.01716, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89062, "top5_acc": 0.99688, "loss_cls": 0.56663, "loss": 0.56663, "time": 0.2248} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.01714, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90438, "top5_acc": 0.99562, "loss_cls": 0.52715, "loss": 0.52715, "time": 0.22364} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.01712, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90562, "top5_acc": 0.99625, "loss_cls": 0.51548, "loss": 0.51548, "time": 0.22714} +{"mode": "val", "epoch": 57, "iter": 533, "lr": 0.0171, "top1_acc": 0.85506, "top5_acc": 0.98873, "mean_class_accuracy": 0.79967} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.01708, "memory": 4083, "data_time": 0.18865, "top1_acc": 0.9225, "top5_acc": 0.99812, "loss_cls": 0.44116, "loss": 0.44116, "time": 0.42219} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.01706, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.91938, "top5_acc": 0.99875, "loss_cls": 0.45576, "loss": 0.45576, "time": 0.22583} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.01704, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90562, "top5_acc": 0.99562, "loss_cls": 0.48039, "loss": 0.48039, "time": 0.22324} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.01703, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.90125, "top5_acc": 0.99562, "loss_cls": 0.53299, "loss": 0.53299, "time": 0.22158} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.01701, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.90688, "top5_acc": 0.99812, "loss_cls": 0.48267, "loss": 0.48267, "time": 0.22372} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.01699, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.9275, "top5_acc": 0.99812, "loss_cls": 0.37462, "loss": 0.37462, "time": 0.22826} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.01697, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.89625, "top5_acc": 0.99812, "loss_cls": 0.51541, "loss": 0.51541, "time": 0.22393} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.01695, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.91125, "top5_acc": 0.99812, "loss_cls": 0.46251, "loss": 0.46251, "time": 0.21909} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.01693, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.88812, "top5_acc": 0.9975, "loss_cls": 0.56763, "loss": 0.56763, "time": 0.22396} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.01691, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91562, "top5_acc": 0.99875, "loss_cls": 0.45817, "loss": 0.45817, "time": 0.22523} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.01689, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89625, "top5_acc": 0.99875, "loss_cls": 0.4983, "loss": 0.4983, "time": 0.22297} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.01687, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90625, "top5_acc": 0.99625, "loss_cls": 0.48092, "loss": 0.48092, "time": 0.22586} +{"mode": "val", "epoch": 58, "iter": 533, "lr": 0.01686, "top1_acc": 0.8607, "top5_acc": 0.99085, "mean_class_accuracy": 0.81985} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.01684, "memory": 4083, "data_time": 0.18599, "top1_acc": 0.90438, "top5_acc": 0.99875, "loss_cls": 0.47251, "loss": 0.47251, "time": 0.42375} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.01682, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91375, "top5_acc": 0.99625, "loss_cls": 0.44402, "loss": 0.44402, "time": 0.22649} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.0168, "memory": 4083, "data_time": 0.00063, "top1_acc": 0.905, "top5_acc": 0.99938, "loss_cls": 0.49742, "loss": 0.49742, "time": 0.22392} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.01678, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9325, "top5_acc": 0.99812, "loss_cls": 0.40699, "loss": 0.40699, "time": 0.22028} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.01676, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.915, "top5_acc": 0.99812, "loss_cls": 0.45057, "loss": 0.45057, "time": 0.22547} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.01674, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90625, "top5_acc": 0.99875, "loss_cls": 0.47315, "loss": 0.47315, "time": 0.2204} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.01672, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91, "top5_acc": 0.99625, "loss_cls": 0.46572, "loss": 0.46572, "time": 0.22323} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.0167, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89812, "top5_acc": 0.99312, "loss_cls": 0.56829, "loss": 0.56829, "time": 0.22316} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.01668, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90875, "top5_acc": 0.99688, "loss_cls": 0.4916, "loss": 0.4916, "time": 0.22655} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.01667, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90125, "top5_acc": 0.99812, "loss_cls": 0.50589, "loss": 0.50589, "time": 0.22398} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.01665, "memory": 4083, "data_time": 0.00065, "top1_acc": 0.90188, "top5_acc": 0.99438, "loss_cls": 0.5462, "loss": 0.5462, "time": 0.22242} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.01663, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.91312, "top5_acc": 0.99875, "loss_cls": 0.43814, "loss": 0.43814, "time": 0.22412} +{"mode": "val", "epoch": 59, "iter": 533, "lr": 0.01661, "top1_acc": 0.84356, "top5_acc": 0.9892, "mean_class_accuracy": 0.79011} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.01659, "memory": 4083, "data_time": 0.18955, "top1_acc": 0.91562, "top5_acc": 0.9975, "loss_cls": 0.47174, "loss": 0.47174, "time": 0.42342} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.01657, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.90938, "top5_acc": 0.9975, "loss_cls": 0.48818, "loss": 0.48818, "time": 0.22216} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.01655, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92312, "top5_acc": 0.9975, "loss_cls": 0.43393, "loss": 0.43393, "time": 0.22621} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.01653, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91062, "top5_acc": 0.99875, "loss_cls": 0.50304, "loss": 0.50304, "time": 0.22316} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.01651, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.90938, "top5_acc": 0.995, "loss_cls": 0.50698, "loss": 0.50698, "time": 0.22742} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.0165, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90938, "top5_acc": 0.9975, "loss_cls": 0.4766, "loss": 0.4766, "time": 0.22273} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.01648, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88688, "top5_acc": 0.99562, "loss_cls": 0.53745, "loss": 0.53745, "time": 0.22247} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.01646, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91875, "top5_acc": 0.99562, "loss_cls": 0.46845, "loss": 0.46845, "time": 0.22432} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.01644, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9, "top5_acc": 0.995, "loss_cls": 0.50253, "loss": 0.50253, "time": 0.22553} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.01642, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8975, "top5_acc": 0.99688, "loss_cls": 0.51202, "loss": 0.51202, "time": 0.22507} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.0164, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89875, "top5_acc": 0.99375, "loss_cls": 0.53112, "loss": 0.53112, "time": 0.22565} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.01638, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.45792, "loss": 0.45792, "time": 0.22185} +{"mode": "val", "epoch": 60, "iter": 533, "lr": 0.01636, "top1_acc": 0.87818, "top5_acc": 0.99261, "mean_class_accuracy": 0.83004} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.01634, "memory": 4083, "data_time": 0.19102, "top1_acc": 0.91875, "top5_acc": 0.99875, "loss_cls": 0.45174, "loss": 0.45174, "time": 0.42583} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.01632, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9225, "top5_acc": 0.99938, "loss_cls": 0.42575, "loss": 0.42575, "time": 0.22358} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.0163, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91938, "top5_acc": 0.99812, "loss_cls": 0.43176, "loss": 0.43176, "time": 0.22147} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.01629, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90562, "top5_acc": 0.99562, "loss_cls": 0.50275, "loss": 0.50275, "time": 0.22298} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.01627, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92562, "top5_acc": 0.995, "loss_cls": 0.4252, "loss": 0.4252, "time": 0.22082} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.01625, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92875, "top5_acc": 0.99688, "loss_cls": 0.40305, "loss": 0.40305, "time": 0.22213} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.01623, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91125, "top5_acc": 0.995, "loss_cls": 0.50397, "loss": 0.50397, "time": 0.22432} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.01621, "memory": 4083, "data_time": 0.00069, "top1_acc": 0.90375, "top5_acc": 0.99625, "loss_cls": 0.46378, "loss": 0.46378, "time": 0.22594} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.01619, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.905, "top5_acc": 0.99688, "loss_cls": 0.49102, "loss": 0.49102, "time": 0.21953} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.01617, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.90188, "top5_acc": 0.9975, "loss_cls": 0.54655, "loss": 0.54655, "time": 0.22629} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.01615, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.92188, "top5_acc": 0.99625, "loss_cls": 0.43131, "loss": 0.43131, "time": 0.22197} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.01613, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90625, "top5_acc": 0.99875, "loss_cls": 0.48089, "loss": 0.48089, "time": 0.22392} +{"mode": "val", "epoch": 61, "iter": 533, "lr": 0.01611, "top1_acc": 0.86316, "top5_acc": 0.99155, "mean_class_accuracy": 0.81358} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.01609, "memory": 4083, "data_time": 0.18391, "top1_acc": 0.905, "top5_acc": 0.9975, "loss_cls": 0.49969, "loss": 0.49969, "time": 0.41462} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.01607, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9225, "top5_acc": 0.99875, "loss_cls": 0.42478, "loss": 0.42478, "time": 0.22308} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.01605, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.9175, "top5_acc": 0.99812, "loss_cls": 0.43646, "loss": 0.43646, "time": 0.22134} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.01603, "memory": 4083, "data_time": 0.00064, "top1_acc": 0.89875, "top5_acc": 0.99875, "loss_cls": 0.49645, "loss": 0.49645, "time": 0.22675} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.01602, "memory": 4083, "data_time": 0.00078, "top1_acc": 0.91125, "top5_acc": 0.9975, "loss_cls": 0.4635, "loss": 0.4635, "time": 0.2279} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.016, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.915, "top5_acc": 0.9975, "loss_cls": 0.44234, "loss": 0.44234, "time": 0.22154} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.01598, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90875, "top5_acc": 0.995, "loss_cls": 0.47039, "loss": 0.47039, "time": 0.2255} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.01596, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.925, "top5_acc": 0.99812, "loss_cls": 0.42132, "loss": 0.42132, "time": 0.22641} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.01594, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.90625, "top5_acc": 0.99562, "loss_cls": 0.48125, "loss": 0.48125, "time": 0.22659} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.01592, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90562, "top5_acc": 0.99625, "loss_cls": 0.4947, "loss": 0.4947, "time": 0.2236} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.0159, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91625, "top5_acc": 0.9975, "loss_cls": 0.47288, "loss": 0.47288, "time": 0.22001} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.01588, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.90875, "top5_acc": 0.99688, "loss_cls": 0.46369, "loss": 0.46369, "time": 0.22104} +{"mode": "val", "epoch": 62, "iter": 533, "lr": 0.01586, "top1_acc": 0.88112, "top5_acc": 0.99425, "mean_class_accuracy": 0.84422} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.01584, "memory": 4083, "data_time": 0.19554, "top1_acc": 0.93188, "top5_acc": 0.99938, "loss_cls": 0.3827, "loss": 0.3827, "time": 0.42633} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.01582, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92188, "top5_acc": 0.99812, "loss_cls": 0.40161, "loss": 0.40161, "time": 0.22478} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.0158, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91375, "top5_acc": 0.99688, "loss_cls": 0.45288, "loss": 0.45288, "time": 0.22305} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.01578, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91, "top5_acc": 0.99812, "loss_cls": 0.44899, "loss": 0.44899, "time": 0.2253} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.01576, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91875, "top5_acc": 0.99812, "loss_cls": 0.43683, "loss": 0.43683, "time": 0.22335} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.01574, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90688, "top5_acc": 0.99812, "loss_cls": 0.47702, "loss": 0.47702, "time": 0.22315} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.01572, "memory": 4083, "data_time": 0.0007, "top1_acc": 0.89625, "top5_acc": 0.99625, "loss_cls": 0.49988, "loss": 0.49988, "time": 0.22617} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.0157, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91812, "top5_acc": 0.9975, "loss_cls": 0.42151, "loss": 0.42151, "time": 0.22394} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.01568, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9025, "top5_acc": 0.9975, "loss_cls": 0.46417, "loss": 0.46417, "time": 0.22418} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.01566, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90125, "top5_acc": 0.99625, "loss_cls": 0.48463, "loss": 0.48463, "time": 0.22591} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.01564, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91625, "top5_acc": 0.99688, "loss_cls": 0.43648, "loss": 0.43648, "time": 0.2213} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.01562, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.90625, "top5_acc": 0.9975, "loss_cls": 0.50088, "loss": 0.50088, "time": 0.22239} +{"mode": "val", "epoch": 63, "iter": 533, "lr": 0.01561, "top1_acc": 0.86586, "top5_acc": 0.99225, "mean_class_accuracy": 0.81376} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.01559, "memory": 4083, "data_time": 0.19469, "top1_acc": 0.915, "top5_acc": 0.99875, "loss_cls": 0.44793, "loss": 0.44793, "time": 0.42909} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.01557, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90688, "top5_acc": 0.99875, "loss_cls": 0.47575, "loss": 0.47575, "time": 0.22235} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.01555, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9, "top5_acc": 1.0, "loss_cls": 0.48778, "loss": 0.48778, "time": 0.22554} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.01553, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91125, "top5_acc": 0.99875, "loss_cls": 0.4463, "loss": 0.4463, "time": 0.22689} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.01551, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92375, "top5_acc": 1.0, "loss_cls": 0.42798, "loss": 0.42798, "time": 0.22263} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.01549, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90625, "top5_acc": 0.99688, "loss_cls": 0.4576, "loss": 0.4576, "time": 0.22572} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.01547, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9075, "top5_acc": 0.99812, "loss_cls": 0.4997, "loss": 0.4997, "time": 0.22583} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.01545, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92062, "top5_acc": 0.99875, "loss_cls": 0.41285, "loss": 0.41285, "time": 0.22136} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.01543, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89688, "top5_acc": 0.99562, "loss_cls": 0.52404, "loss": 0.52404, "time": 0.22612} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.01541, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9125, "top5_acc": 0.99938, "loss_cls": 0.47154, "loss": 0.47154, "time": 0.22656} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.01539, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91375, "top5_acc": 0.9975, "loss_cls": 0.47076, "loss": 0.47076, "time": 0.22105} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.01537, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.91125, "top5_acc": 0.99562, "loss_cls": 0.47765, "loss": 0.47765, "time": 0.22598} +{"mode": "val", "epoch": 64, "iter": 533, "lr": 0.01535, "top1_acc": 0.87642, "top5_acc": 0.99214, "mean_class_accuracy": 0.82786} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.01533, "memory": 4083, "data_time": 0.19359, "top1_acc": 0.925, "top5_acc": 0.99812, "loss_cls": 0.40575, "loss": 0.40575, "time": 0.42598} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.01531, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.91875, "top5_acc": 0.99875, "loss_cls": 0.42717, "loss": 0.42717, "time": 0.22522} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.01529, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91438, "top5_acc": 0.99812, "loss_cls": 0.45809, "loss": 0.45809, "time": 0.22362} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.01527, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.89625, "top5_acc": 0.9975, "loss_cls": 0.51839, "loss": 0.51839, "time": 0.22323} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.01526, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92, "top5_acc": 0.99812, "loss_cls": 0.4534, "loss": 0.4534, "time": 0.2229} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.01524, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92, "top5_acc": 0.99812, "loss_cls": 0.41698, "loss": 0.41698, "time": 0.22502} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.01522, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88938, "top5_acc": 0.99688, "loss_cls": 0.53709, "loss": 0.53709, "time": 0.22273} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0152, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92375, "top5_acc": 0.99625, "loss_cls": 0.41452, "loss": 0.41452, "time": 0.2249} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.01518, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.90375, "top5_acc": 0.99688, "loss_cls": 0.50702, "loss": 0.50702, "time": 0.22516} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.01516, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.91562, "top5_acc": 0.9975, "loss_cls": 0.45803, "loss": 0.45803, "time": 0.22185} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.01514, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.92062, "top5_acc": 0.99812, "loss_cls": 0.44239, "loss": 0.44239, "time": 0.22357} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.01512, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91312, "top5_acc": 0.99625, "loss_cls": 0.4751, "loss": 0.4751, "time": 0.22209} +{"mode": "val", "epoch": 65, "iter": 533, "lr": 0.0151, "top1_acc": 0.86175, "top5_acc": 0.99284, "mean_class_accuracy": 0.81991} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.01508, "memory": 4083, "data_time": 0.18782, "top1_acc": 0.91688, "top5_acc": 0.99812, "loss_cls": 0.46544, "loss": 0.46544, "time": 0.42036} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.01506, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.935, "top5_acc": 0.99938, "loss_cls": 0.3742, "loss": 0.3742, "time": 0.22414} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.01504, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92125, "top5_acc": 0.9975, "loss_cls": 0.43421, "loss": 0.43421, "time": 0.228} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.01502, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91938, "top5_acc": 1.0, "loss_cls": 0.41058, "loss": 0.41058, "time": 0.22428} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.015, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.91188, "top5_acc": 0.99875, "loss_cls": 0.47143, "loss": 0.47143, "time": 0.22284} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.01498, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.91, "top5_acc": 0.99562, "loss_cls": 0.46004, "loss": 0.46004, "time": 0.22496} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.01496, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.90938, "top5_acc": 0.99938, "loss_cls": 0.46119, "loss": 0.46119, "time": 0.22218} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.01494, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91125, "top5_acc": 0.99875, "loss_cls": 0.45148, "loss": 0.45148, "time": 0.2221} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.01492, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92438, "top5_acc": 0.99938, "loss_cls": 0.42145, "loss": 0.42145, "time": 0.22346} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.0149, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.90875, "top5_acc": 0.99688, "loss_cls": 0.4567, "loss": 0.4567, "time": 0.21997} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.01488, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91, "top5_acc": 0.99438, "loss_cls": 0.47982, "loss": 0.47982, "time": 0.22452} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.01486, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90938, "top5_acc": 0.99812, "loss_cls": 0.44992, "loss": 0.44992, "time": 0.22269} +{"mode": "val", "epoch": 66, "iter": 533, "lr": 0.01484, "top1_acc": 0.86821, "top5_acc": 0.98909, "mean_class_accuracy": 0.82874} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.01482, "memory": 4083, "data_time": 0.18895, "top1_acc": 0.91812, "top5_acc": 0.99812, "loss_cls": 0.42716, "loss": 0.42716, "time": 0.41989} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.0148, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9325, "top5_acc": 0.99625, "loss_cls": 0.40078, "loss": 0.40078, "time": 0.22412} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.01478, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9175, "top5_acc": 0.99875, "loss_cls": 0.41847, "loss": 0.41847, "time": 0.22215} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.01476, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93438, "top5_acc": 0.9975, "loss_cls": 0.3816, "loss": 0.3816, "time": 0.22171} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.01474, "memory": 4083, "data_time": 0.00071, "top1_acc": 0.92562, "top5_acc": 0.99688, "loss_cls": 0.39484, "loss": 0.39484, "time": 0.22704} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.01472, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91938, "top5_acc": 0.99875, "loss_cls": 0.44236, "loss": 0.44236, "time": 0.22144} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.0147, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93812, "top5_acc": 0.99875, "loss_cls": 0.37959, "loss": 0.37959, "time": 0.22145} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.01468, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.9175, "top5_acc": 0.99875, "loss_cls": 0.40535, "loss": 0.40535, "time": 0.22471} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.01466, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92375, "top5_acc": 0.99812, "loss_cls": 0.41876, "loss": 0.41876, "time": 0.22313} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.01464, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.92875, "top5_acc": 0.99875, "loss_cls": 0.37973, "loss": 0.37973, "time": 0.22469} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.01462, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92688, "top5_acc": 0.99625, "loss_cls": 0.39634, "loss": 0.39634, "time": 0.22073} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.0146, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92812, "top5_acc": 0.99812, "loss_cls": 0.39527, "loss": 0.39527, "time": 0.22102} +{"mode": "val", "epoch": 67, "iter": 533, "lr": 0.01458, "top1_acc": 0.87361, "top5_acc": 0.99038, "mean_class_accuracy": 0.83321} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.01456, "memory": 4083, "data_time": 0.18941, "top1_acc": 0.90625, "top5_acc": 0.99688, "loss_cls": 0.4716, "loss": 0.4716, "time": 0.42647} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.01454, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9175, "top5_acc": 0.99938, "loss_cls": 0.43748, "loss": 0.43748, "time": 0.22839} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.01452, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90562, "top5_acc": 0.9975, "loss_cls": 0.46873, "loss": 0.46873, "time": 0.22125} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.0145, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9275, "top5_acc": 0.99812, "loss_cls": 0.43274, "loss": 0.43274, "time": 0.22149} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.01448, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.90938, "top5_acc": 0.99688, "loss_cls": 0.47682, "loss": 0.47682, "time": 0.22634} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.01446, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.91312, "top5_acc": 0.99875, "loss_cls": 0.41244, "loss": 0.41244, "time": 0.22098} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.01444, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9175, "top5_acc": 0.99562, "loss_cls": 0.44313, "loss": 0.44313, "time": 0.22056} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.01442, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9125, "top5_acc": 0.99625, "loss_cls": 0.50275, "loss": 0.50275, "time": 0.22424} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.0144, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.91812, "top5_acc": 0.99688, "loss_cls": 0.4605, "loss": 0.4605, "time": 0.22175} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.01438, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.915, "top5_acc": 0.9975, "loss_cls": 0.44955, "loss": 0.44955, "time": 0.22724} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.01436, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92188, "top5_acc": 0.99688, "loss_cls": 0.39918, "loss": 0.39918, "time": 0.22494} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.01434, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89938, "top5_acc": 0.99812, "loss_cls": 0.50029, "loss": 0.50029, "time": 0.22156} +{"mode": "val", "epoch": 68, "iter": 533, "lr": 0.01433, "top1_acc": 0.87372, "top5_acc": 0.99225, "mean_class_accuracy": 0.83301} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.01431, "memory": 4083, "data_time": 0.19088, "top1_acc": 0.91812, "top5_acc": 0.99812, "loss_cls": 0.43346, "loss": 0.43346, "time": 0.4238} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.01429, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93062, "top5_acc": 0.9975, "loss_cls": 0.40098, "loss": 0.40098, "time": 0.22202} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.01427, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92188, "top5_acc": 0.99875, "loss_cls": 0.42784, "loss": 0.42784, "time": 0.22177} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.01425, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.92062, "top5_acc": 0.99812, "loss_cls": 0.41072, "loss": 0.41072, "time": 0.22674} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.01423, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.39981, "loss": 0.39981, "time": 0.22223} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.0142, "memory": 4083, "data_time": 0.0007, "top1_acc": 0.90125, "top5_acc": 0.9975, "loss_cls": 0.47801, "loss": 0.47801, "time": 0.22489} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.01418, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.935, "top5_acc": 0.99688, "loss_cls": 0.38135, "loss": 0.38135, "time": 0.22126} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.01416, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92688, "top5_acc": 0.99625, "loss_cls": 0.42573, "loss": 0.42573, "time": 0.22315} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.01414, "memory": 4083, "data_time": 0.00061, "top1_acc": 0.92125, "top5_acc": 0.99812, "loss_cls": 0.41589, "loss": 0.41589, "time": 0.22241} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.01412, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91188, "top5_acc": 0.9975, "loss_cls": 0.47141, "loss": 0.47141, "time": 0.22246} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.0141, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92, "top5_acc": 0.9975, "loss_cls": 0.42001, "loss": 0.42001, "time": 0.22322} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.01408, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.925, "top5_acc": 0.9975, "loss_cls": 0.44315, "loss": 0.44315, "time": 0.2224} +{"mode": "val", "epoch": 69, "iter": 533, "lr": 0.01407, "top1_acc": 0.89168, "top5_acc": 0.99554, "mean_class_accuracy": 0.8478} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.01405, "memory": 4083, "data_time": 0.19066, "top1_acc": 0.92188, "top5_acc": 0.99938, "loss_cls": 0.40253, "loss": 0.40253, "time": 0.42637} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.01403, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.91812, "top5_acc": 0.99875, "loss_cls": 0.42154, "loss": 0.42154, "time": 0.2247} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.01401, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93, "top5_acc": 0.99812, "loss_cls": 0.3861, "loss": 0.3861, "time": 0.22235} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.01399, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.93625, "top5_acc": 0.99938, "loss_cls": 0.32573, "loss": 0.32573, "time": 0.22359} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.01397, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.35466, "loss": 0.35466, "time": 0.22309} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.01395, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94312, "top5_acc": 0.99875, "loss_cls": 0.33891, "loss": 0.33891, "time": 0.22561} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.01392, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92062, "top5_acc": 0.99562, "loss_cls": 0.42147, "loss": 0.42147, "time": 0.22519} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.0139, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93188, "top5_acc": 1.0, "loss_cls": 0.35987, "loss": 0.35987, "time": 0.22243} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.01388, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.40684, "loss": 0.40684, "time": 0.22476} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.01386, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.925, "top5_acc": 0.99688, "loss_cls": 0.42886, "loss": 0.42886, "time": 0.22573} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.01384, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.90938, "top5_acc": 0.99812, "loss_cls": 0.46406, "loss": 0.46406, "time": 0.21957} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.01382, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.47243, "loss": 0.47243, "time": 0.22442} +{"mode": "val", "epoch": 70, "iter": 533, "lr": 0.01381, "top1_acc": 0.87994, "top5_acc": 0.99413, "mean_class_accuracy": 0.84023} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.01379, "memory": 4083, "data_time": 0.18577, "top1_acc": 0.93562, "top5_acc": 0.99875, "loss_cls": 0.3717, "loss": 0.3717, "time": 0.41974} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.01377, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93688, "top5_acc": 0.99938, "loss_cls": 0.35254, "loss": 0.35254, "time": 0.22302} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.01375, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.38943, "loss": 0.38943, "time": 0.22771} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.01373, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.92875, "top5_acc": 1.0, "loss_cls": 0.35801, "loss": 0.35801, "time": 0.22474} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.01371, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91812, "top5_acc": 0.99938, "loss_cls": 0.4239, "loss": 0.4239, "time": 0.22426} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.01368, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92312, "top5_acc": 0.99688, "loss_cls": 0.39961, "loss": 0.39961, "time": 0.2266} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.01366, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9175, "top5_acc": 0.99438, "loss_cls": 0.44882, "loss": 0.44882, "time": 0.22076} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.01364, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.92812, "top5_acc": 0.99875, "loss_cls": 0.39026, "loss": 0.39026, "time": 0.22521} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.01362, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92312, "top5_acc": 0.99812, "loss_cls": 0.43663, "loss": 0.43663, "time": 0.22182} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.0136, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.38957, "loss": 0.38957, "time": 0.22016} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.01358, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.91312, "top5_acc": 0.99938, "loss_cls": 0.43475, "loss": 0.43475, "time": 0.22266} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.01356, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93125, "top5_acc": 0.9975, "loss_cls": 0.39058, "loss": 0.39058, "time": 0.22049} +{"mode": "val", "epoch": 71, "iter": 533, "lr": 0.01355, "top1_acc": 0.88229, "top5_acc": 0.99237, "mean_class_accuracy": 0.82624} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.01353, "memory": 4083, "data_time": 0.19424, "top1_acc": 0.93812, "top5_acc": 0.99875, "loss_cls": 0.34973, "loss": 0.34973, "time": 0.43374} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.01351, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93312, "top5_acc": 0.99938, "loss_cls": 0.36388, "loss": 0.36388, "time": 0.22459} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.01349, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.91625, "top5_acc": 0.99812, "loss_cls": 0.40711, "loss": 0.40711, "time": 0.22756} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.01346, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94188, "top5_acc": 0.99875, "loss_cls": 0.34482, "loss": 0.34482, "time": 0.22323} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.01344, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93125, "top5_acc": 0.99938, "loss_cls": 0.37194, "loss": 0.37194, "time": 0.22224} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.01342, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90688, "top5_acc": 0.99938, "loss_cls": 0.45403, "loss": 0.45403, "time": 0.22459} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.0134, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94, "top5_acc": 0.99938, "loss_cls": 0.33674, "loss": 0.33674, "time": 0.22313} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.01338, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92312, "top5_acc": 0.99812, "loss_cls": 0.42222, "loss": 0.42222, "time": 0.22514} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.01336, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9175, "top5_acc": 0.99875, "loss_cls": 0.41797, "loss": 0.41797, "time": 0.22329} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.01334, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93625, "top5_acc": 0.99562, "loss_cls": 0.39887, "loss": 0.39887, "time": 0.22222} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.01332, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.92938, "top5_acc": 0.9975, "loss_cls": 0.40645, "loss": 0.40645, "time": 0.22629} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.0133, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.92375, "top5_acc": 0.99688, "loss_cls": 0.41191, "loss": 0.41191, "time": 0.2226} +{"mode": "val", "epoch": 72, "iter": 533, "lr": 0.01329, "top1_acc": 0.89062, "top5_acc": 0.99542, "mean_class_accuracy": 0.85191} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.01326, "memory": 4083, "data_time": 0.18515, "top1_acc": 0.92875, "top5_acc": 0.99875, "loss_cls": 0.39022, "loss": 0.39022, "time": 0.41706} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.01324, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.91875, "top5_acc": 0.99875, "loss_cls": 0.42272, "loss": 0.42272, "time": 0.22954} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.01322, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92438, "top5_acc": 0.99625, "loss_cls": 0.42283, "loss": 0.42283, "time": 0.22211} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.0132, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.92438, "top5_acc": 0.99938, "loss_cls": 0.38285, "loss": 0.38285, "time": 0.22186} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.01318, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.9275, "top5_acc": 0.99875, "loss_cls": 0.38895, "loss": 0.38895, "time": 0.2265} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.01316, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91562, "top5_acc": 0.99688, "loss_cls": 0.43518, "loss": 0.43518, "time": 0.22119} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.01314, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92312, "top5_acc": 0.99875, "loss_cls": 0.41063, "loss": 0.41063, "time": 0.22649} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.01312, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93375, "top5_acc": 1.0, "loss_cls": 0.38462, "loss": 0.38462, "time": 0.22189} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.0131, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93188, "top5_acc": 0.99938, "loss_cls": 0.37777, "loss": 0.37777, "time": 0.22173} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.01308, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92938, "top5_acc": 0.99688, "loss_cls": 0.3889, "loss": 0.3889, "time": 0.22396} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.01306, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9025, "top5_acc": 0.99688, "loss_cls": 0.49179, "loss": 0.49179, "time": 0.22176} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.01304, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9225, "top5_acc": 0.99938, "loss_cls": 0.42958, "loss": 0.42958, "time": 0.22236} +{"mode": "val", "epoch": 73, "iter": 533, "lr": 0.01302, "top1_acc": 0.88006, "top5_acc": 0.99437, "mean_class_accuracy": 0.83779} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.013, "memory": 4083, "data_time": 0.19544, "top1_acc": 0.93375, "top5_acc": 0.99875, "loss_cls": 0.34, "loss": 0.34, "time": 0.42755} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.01298, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.925, "top5_acc": 0.99938, "loss_cls": 0.40581, "loss": 0.40581, "time": 0.22433} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.01296, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.92188, "top5_acc": 0.99875, "loss_cls": 0.42185, "loss": 0.42185, "time": 0.22419} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.01294, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93625, "top5_acc": 0.99875, "loss_cls": 0.34958, "loss": 0.34958, "time": 0.22315} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.01292, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.33854, "loss": 0.33854, "time": 0.22454} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.0129, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.39295, "loss": 0.39295, "time": 0.22264} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.01288, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9325, "top5_acc": 0.9975, "loss_cls": 0.36552, "loss": 0.36552, "time": 0.22431} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.01286, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.9475, "top5_acc": 1.0, "loss_cls": 0.29988, "loss": 0.29988, "time": 0.22756} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.01284, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93, "top5_acc": 0.99875, "loss_cls": 0.39832, "loss": 0.39832, "time": 0.22432} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.01282, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92188, "top5_acc": 0.99875, "loss_cls": 0.40422, "loss": 0.40422, "time": 0.22407} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.0128, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90812, "top5_acc": 0.99812, "loss_cls": 0.45916, "loss": 0.45916, "time": 0.22423} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.01278, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93, "top5_acc": 0.9975, "loss_cls": 0.39925, "loss": 0.39925, "time": 0.22448} +{"mode": "val", "epoch": 74, "iter": 533, "lr": 0.01276, "top1_acc": 0.85154, "top5_acc": 0.9912, "mean_class_accuracy": 0.80278} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.01274, "memory": 4083, "data_time": 0.18815, "top1_acc": 0.93062, "top5_acc": 0.99938, "loss_cls": 0.37006, "loss": 0.37006, "time": 0.42454} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.01272, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.94, "top5_acc": 0.99875, "loss_cls": 0.33465, "loss": 0.33465, "time": 0.22209} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.0127, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.32818, "loss": 0.32818, "time": 0.21957} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.01268, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.92938, "top5_acc": 0.99938, "loss_cls": 0.3671, "loss": 0.3671, "time": 0.22643} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.01266, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93, "top5_acc": 0.99875, "loss_cls": 0.40153, "loss": 0.40153, "time": 0.221} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.01264, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92625, "top5_acc": 0.99688, "loss_cls": 0.39894, "loss": 0.39894, "time": 0.22298} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.01262, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92312, "top5_acc": 0.99875, "loss_cls": 0.41119, "loss": 0.41119, "time": 0.22463} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.0126, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94188, "top5_acc": 0.99812, "loss_cls": 0.31547, "loss": 0.31547, "time": 0.22323} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.01258, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9375, "top5_acc": 0.99875, "loss_cls": 0.351, "loss": 0.351, "time": 0.22545} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.01256, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9325, "top5_acc": 0.99938, "loss_cls": 0.38291, "loss": 0.38291, "time": 0.22049} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.01254, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93125, "top5_acc": 0.9975, "loss_cls": 0.3894, "loss": 0.3894, "time": 0.2199} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.01252, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9275, "top5_acc": 0.99625, "loss_cls": 0.39279, "loss": 0.39279, "time": 0.22303} +{"mode": "val", "epoch": 75, "iter": 533, "lr": 0.0125, "top1_acc": 0.8783, "top5_acc": 0.99272, "mean_class_accuracy": 0.8439} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.01248, "memory": 4083, "data_time": 0.18233, "top1_acc": 0.9325, "top5_acc": 0.99875, "loss_cls": 0.3751, "loss": 0.3751, "time": 0.41904} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.01246, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.935, "top5_acc": 1.0, "loss_cls": 0.36483, "loss": 0.36483, "time": 0.22523} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.01244, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93812, "top5_acc": 0.99875, "loss_cls": 0.34649, "loss": 0.34649, "time": 0.21948} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.01242, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92562, "top5_acc": 0.99938, "loss_cls": 0.38164, "loss": 0.38164, "time": 0.22531} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.0124, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.92812, "top5_acc": 0.99938, "loss_cls": 0.40527, "loss": 0.40527, "time": 0.22492} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.01238, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92438, "top5_acc": 0.9975, "loss_cls": 0.39556, "loss": 0.39556, "time": 0.22098} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.01236, "memory": 4083, "data_time": 0.00073, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.33051, "loss": 0.33051, "time": 0.22826} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.01234, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92188, "top5_acc": 1.0, "loss_cls": 0.402, "loss": 0.402, "time": 0.2212} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.01232, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92688, "top5_acc": 0.99688, "loss_cls": 0.40889, "loss": 0.40889, "time": 0.22506} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.0123, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.915, "top5_acc": 0.99688, "loss_cls": 0.40701, "loss": 0.40701, "time": 0.22486} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.01228, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9125, "top5_acc": 0.9975, "loss_cls": 0.45724, "loss": 0.45724, "time": 0.22335} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.01225, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9275, "top5_acc": 0.99688, "loss_cls": 0.40548, "loss": 0.40548, "time": 0.22256} +{"mode": "val", "epoch": 76, "iter": 533, "lr": 0.01224, "top1_acc": 0.88311, "top5_acc": 0.99355, "mean_class_accuracy": 0.84713} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.01222, "memory": 4083, "data_time": 0.19251, "top1_acc": 0.9325, "top5_acc": 0.99875, "loss_cls": 0.35302, "loss": 0.35302, "time": 0.4258} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0122, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.33938, "loss": 0.33938, "time": 0.22533} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.01218, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.36166, "loss": 0.36166, "time": 0.22636} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.01216, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94, "top5_acc": 0.99812, "loss_cls": 0.34239, "loss": 0.34239, "time": 0.22274} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.01214, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9375, "top5_acc": 1.0, "loss_cls": 0.34095, "loss": 0.34095, "time": 0.22364} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.01212, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.92562, "top5_acc": 0.99875, "loss_cls": 0.38963, "loss": 0.38963, "time": 0.22146} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.0121, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9325, "top5_acc": 0.99875, "loss_cls": 0.37096, "loss": 0.37096, "time": 0.22323} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.01207, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92062, "top5_acc": 0.99812, "loss_cls": 0.39234, "loss": 0.39234, "time": 0.22315} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.01205, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92875, "top5_acc": 0.9975, "loss_cls": 0.39222, "loss": 0.39222, "time": 0.22101} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.01203, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.925, "top5_acc": 0.99562, "loss_cls": 0.44098, "loss": 0.44098, "time": 0.22334} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.01201, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9275, "top5_acc": 0.99688, "loss_cls": 0.42661, "loss": 0.42661, "time": 0.22019} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.01199, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91938, "top5_acc": 0.99812, "loss_cls": 0.44222, "loss": 0.44222, "time": 0.22665} +{"mode": "val", "epoch": 77, "iter": 533, "lr": 0.01198, "top1_acc": 0.88628, "top5_acc": 0.99413, "mean_class_accuracy": 0.84432} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.01196, "memory": 4083, "data_time": 0.19403, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.32307, "loss": 0.32307, "time": 0.43341} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.01194, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95812, "top5_acc": 0.99938, "loss_cls": 0.25142, "loss": 0.25142, "time": 0.222} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.01192, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.29429, "loss": 0.29429, "time": 0.22231} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.0119, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94125, "top5_acc": 0.99812, "loss_cls": 0.32569, "loss": 0.32569, "time": 0.22245} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.01187, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.33361, "loss": 0.33361, "time": 0.21987} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.01185, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.92562, "top5_acc": 0.99938, "loss_cls": 0.3781, "loss": 0.3781, "time": 0.22702} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.01183, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93375, "top5_acc": 0.9975, "loss_cls": 0.36256, "loss": 0.36256, "time": 0.2219} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.01181, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9225, "top5_acc": 0.99688, "loss_cls": 0.43782, "loss": 0.43782, "time": 0.22211} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.01179, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.9225, "top5_acc": 1.0, "loss_cls": 0.42948, "loss": 0.42948, "time": 0.22439} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.01177, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.91875, "top5_acc": 0.99938, "loss_cls": 0.41906, "loss": 0.41906, "time": 0.22144} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.01175, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93812, "top5_acc": 0.9975, "loss_cls": 0.36046, "loss": 0.36046, "time": 0.22275} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.01173, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.92375, "top5_acc": 0.99812, "loss_cls": 0.38497, "loss": 0.38497, "time": 0.2225} +{"mode": "val", "epoch": 78, "iter": 533, "lr": 0.01172, "top1_acc": 0.87877, "top5_acc": 0.99049, "mean_class_accuracy": 0.82914} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.01169, "memory": 4083, "data_time": 0.18513, "top1_acc": 0.93875, "top5_acc": 1.0, "loss_cls": 0.34828, "loss": 0.34828, "time": 0.41831} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.01167, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9375, "top5_acc": 1.0, "loss_cls": 0.34169, "loss": 0.34169, "time": 0.22569} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.01165, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.31158, "loss": 0.31158, "time": 0.2217} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.01163, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9375, "top5_acc": 1.0, "loss_cls": 0.33397, "loss": 0.33397, "time": 0.22491} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.01161, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94, "top5_acc": 1.0, "loss_cls": 0.32108, "loss": 0.32108, "time": 0.22216} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.01159, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93375, "top5_acc": 0.99875, "loss_cls": 0.35719, "loss": 0.35719, "time": 0.2207} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.01157, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93625, "top5_acc": 0.99812, "loss_cls": 0.35349, "loss": 0.35349, "time": 0.22345} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.01155, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93812, "top5_acc": 0.99812, "loss_cls": 0.3204, "loss": 0.3204, "time": 0.22229} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.01153, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93312, "top5_acc": 0.99938, "loss_cls": 0.36768, "loss": 0.36768, "time": 0.22363} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.01151, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93812, "top5_acc": 0.99875, "loss_cls": 0.36502, "loss": 0.36502, "time": 0.223} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.01149, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93938, "top5_acc": 0.99938, "loss_cls": 0.33771, "loss": 0.33771, "time": 0.22445} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.01147, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.93875, "top5_acc": 0.9975, "loss_cls": 0.34056, "loss": 0.34056, "time": 0.22198} +{"mode": "val", "epoch": 79, "iter": 533, "lr": 0.01145, "top1_acc": 0.89121, "top5_acc": 0.99155, "mean_class_accuracy": 0.85891} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.01143, "memory": 4083, "data_time": 0.19645, "top1_acc": 0.93938, "top5_acc": 0.99938, "loss_cls": 0.3183, "loss": 0.3183, "time": 0.4342} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.01141, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.32341, "loss": 0.32341, "time": 0.2224} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.01139, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93062, "top5_acc": 0.99812, "loss_cls": 0.36638, "loss": 0.36638, "time": 0.22506} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.01137, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93062, "top5_acc": 0.99938, "loss_cls": 0.36821, "loss": 0.36821, "time": 0.2235} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.01135, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9275, "top5_acc": 0.99875, "loss_cls": 0.36677, "loss": 0.36677, "time": 0.22549} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.01133, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.34857, "loss": 0.34857, "time": 0.22421} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.01131, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.935, "top5_acc": 0.99812, "loss_cls": 0.37821, "loss": 0.37821, "time": 0.22188} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.01129, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93312, "top5_acc": 0.99938, "loss_cls": 0.37391, "loss": 0.37391, "time": 0.22582} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.01127, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93312, "top5_acc": 0.99875, "loss_cls": 0.35201, "loss": 0.35201, "time": 0.22277} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.01125, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94312, "top5_acc": 0.9975, "loss_cls": 0.34686, "loss": 0.34686, "time": 0.2236} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.01123, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.37052, "loss": 0.37052, "time": 0.22146} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.01121, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93188, "top5_acc": 0.99562, "loss_cls": 0.36497, "loss": 0.36497, "time": 0.22331} +{"mode": "val", "epoch": 80, "iter": 533, "lr": 0.01119, "top1_acc": 0.89978, "top5_acc": 0.99261, "mean_class_accuracy": 0.86864} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.01117, "memory": 4083, "data_time": 0.18485, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.28643, "loss": 0.28643, "time": 0.42113} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.01115, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.29661, "loss": 0.29661, "time": 0.22776} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.01113, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94562, "top5_acc": 0.99875, "loss_cls": 0.31022, "loss": 0.31022, "time": 0.22129} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.01111, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.935, "top5_acc": 0.9975, "loss_cls": 0.35795, "loss": 0.35795, "time": 0.22325} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.01109, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9375, "top5_acc": 0.99875, "loss_cls": 0.33944, "loss": 0.33944, "time": 0.22638} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.01107, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93125, "top5_acc": 0.99938, "loss_cls": 0.34856, "loss": 0.34856, "time": 0.2278} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.01105, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93562, "top5_acc": 0.99875, "loss_cls": 0.34771, "loss": 0.34771, "time": 0.22499} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.01103, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.31411, "loss": 0.31411, "time": 0.22276} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.01101, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9375, "top5_acc": 0.9975, "loss_cls": 0.3452, "loss": 0.3452, "time": 0.22786} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.01099, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92688, "top5_acc": 0.99875, "loss_cls": 0.40842, "loss": 0.40842, "time": 0.22464} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.01097, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92688, "top5_acc": 0.99688, "loss_cls": 0.38817, "loss": 0.38817, "time": 0.22416} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.01095, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.94562, "top5_acc": 1.0, "loss_cls": 0.33111, "loss": 0.33111, "time": 0.22644} +{"mode": "val", "epoch": 81, "iter": 533, "lr": 0.01093, "top1_acc": 0.88264, "top5_acc": 0.9919, "mean_class_accuracy": 0.86062} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.01091, "memory": 4083, "data_time": 0.19426, "top1_acc": 0.945, "top5_acc": 0.99812, "loss_cls": 0.32319, "loss": 0.32319, "time": 0.4286} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.01089, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.95375, "top5_acc": 0.99812, "loss_cls": 0.27905, "loss": 0.27905, "time": 0.22528} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.01087, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94938, "top5_acc": 0.99875, "loss_cls": 0.30267, "loss": 0.30267, "time": 0.22236} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.01085, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93688, "top5_acc": 0.99938, "loss_cls": 0.33827, "loss": 0.33827, "time": 0.22333} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.01083, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93312, "top5_acc": 0.9975, "loss_cls": 0.3694, "loss": 0.3694, "time": 0.22347} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.01081, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93625, "top5_acc": 0.99875, "loss_cls": 0.32744, "loss": 0.32744, "time": 0.21969} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.01079, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.30687, "loss": 0.30687, "time": 0.2242} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.01077, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9225, "top5_acc": 0.99562, "loss_cls": 0.39698, "loss": 0.39698, "time": 0.22045} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.01075, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9425, "top5_acc": 0.99812, "loss_cls": 0.35192, "loss": 0.35192, "time": 0.21959} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.01073, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.33195, "loss": 0.33195, "time": 0.21976} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.01071, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92312, "top5_acc": 1.0, "loss_cls": 0.39006, "loss": 0.39006, "time": 0.22401} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.01069, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92875, "top5_acc": 0.99938, "loss_cls": 0.38252, "loss": 0.38252, "time": 0.22403} +{"mode": "val", "epoch": 82, "iter": 533, "lr": 0.01067, "top1_acc": 0.88863, "top5_acc": 0.99319, "mean_class_accuracy": 0.8553} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.01065, "memory": 4083, "data_time": 0.1943, "top1_acc": 0.9425, "top5_acc": 0.99812, "loss_cls": 0.31688, "loss": 0.31688, "time": 0.43089} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.01063, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95125, "top5_acc": 0.99875, "loss_cls": 0.3034, "loss": 0.3034, "time": 0.22455} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.01061, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94812, "top5_acc": 0.99875, "loss_cls": 0.28675, "loss": 0.28675, "time": 0.22043} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.01059, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.30516, "loss": 0.30516, "time": 0.22308} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.01057, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.29956, "loss": 0.29956, "time": 0.22577} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.01055, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93562, "top5_acc": 0.99875, "loss_cls": 0.35389, "loss": 0.35389, "time": 0.22159} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.01053, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94125, "top5_acc": 0.9975, "loss_cls": 0.34051, "loss": 0.34051, "time": 0.22453} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.01051, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93312, "top5_acc": 0.99875, "loss_cls": 0.33732, "loss": 0.33732, "time": 0.22309} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.01049, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94688, "top5_acc": 0.9975, "loss_cls": 0.32063, "loss": 0.32063, "time": 0.22138} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.01047, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.93125, "top5_acc": 0.99938, "loss_cls": 0.36295, "loss": 0.36295, "time": 0.22311} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.01045, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.34023, "loss": 0.34023, "time": 0.22494} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.01043, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.32086, "loss": 0.32086, "time": 0.22227} +{"mode": "val", "epoch": 83, "iter": 533, "lr": 0.01042, "top1_acc": 0.89426, "top5_acc": 0.99401, "mean_class_accuracy": 0.84737} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.0104, "memory": 4083, "data_time": 0.18967, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.31554, "loss": 0.31554, "time": 0.42627} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.01038, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94812, "top5_acc": 0.99875, "loss_cls": 0.29699, "loss": 0.29699, "time": 0.2226} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.01036, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.32327, "loss": 0.32327, "time": 0.22791} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.01034, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.3056, "loss": 0.3056, "time": 0.22513} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.01031, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93562, "top5_acc": 0.99625, "loss_cls": 0.34843, "loss": 0.34843, "time": 0.2235} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.01029, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.34748, "loss": 0.34748, "time": 0.22362} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.01027, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93938, "top5_acc": 0.99938, "loss_cls": 0.33451, "loss": 0.33451, "time": 0.22512} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.01025, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.935, "top5_acc": 0.99938, "loss_cls": 0.33244, "loss": 0.33244, "time": 0.22174} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.01023, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.37274, "loss": 0.37274, "time": 0.22205} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.01021, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.945, "top5_acc": 1.0, "loss_cls": 0.32192, "loss": 0.32192, "time": 0.2229} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.01019, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9325, "top5_acc": 0.99875, "loss_cls": 0.35344, "loss": 0.35344, "time": 0.22298} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.01017, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.34087, "loss": 0.34087, "time": 0.22511} +{"mode": "val", "epoch": 84, "iter": 533, "lr": 0.01016, "top1_acc": 0.86058, "top5_acc": 0.9885, "mean_class_accuracy": 0.83046} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.01014, "memory": 4083, "data_time": 0.18479, "top1_acc": 0.94188, "top5_acc": 0.99938, "loss_cls": 0.33847, "loss": 0.33847, "time": 0.42083} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.01012, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93812, "top5_acc": 0.9975, "loss_cls": 0.30436, "loss": 0.30436, "time": 0.22644} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.0101, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.27778, "loss": 0.27778, "time": 0.22112} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.01008, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94375, "top5_acc": 1.0, "loss_cls": 0.30655, "loss": 0.30655, "time": 0.22349} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.01006, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.27951, "loss": 0.27951, "time": 0.22352} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.01004, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.31114, "loss": 0.31114, "time": 0.21989} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.01002, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.29996, "loss": 0.29996, "time": 0.22375} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.01, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93875, "top5_acc": 1.0, "loss_cls": 0.31922, "loss": 0.31922, "time": 0.22178} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.00998, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93062, "top5_acc": 1.0, "loss_cls": 0.34497, "loss": 0.34497, "time": 0.22091} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.00996, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.94562, "top5_acc": 1.0, "loss_cls": 0.29723, "loss": 0.29723, "time": 0.22494} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.00994, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.935, "top5_acc": 1.0, "loss_cls": 0.33606, "loss": 0.33606, "time": 0.22427} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.00992, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94938, "top5_acc": 0.99875, "loss_cls": 0.30758, "loss": 0.30758, "time": 0.22354} +{"mode": "val", "epoch": 85, "iter": 533, "lr": 0.0099, "top1_acc": 0.87947, "top5_acc": 0.99202, "mean_class_accuracy": 0.853} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.00988, "memory": 4083, "data_time": 0.18741, "top1_acc": 0.95188, "top5_acc": 0.99875, "loss_cls": 0.29347, "loss": 0.29347, "time": 0.42075} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.00986, "memory": 4083, "data_time": 0.00068, "top1_acc": 0.95562, "top5_acc": 1.0, "loss_cls": 0.26429, "loss": 0.26429, "time": 0.22412} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.00984, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.23983, "loss": 0.23983, "time": 0.2251} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.00982, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9575, "top5_acc": 0.99875, "loss_cls": 0.24591, "loss": 0.24591, "time": 0.22291} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.0098, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.30122, "loss": 0.30122, "time": 0.22307} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.00978, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.27186, "loss": 0.27186, "time": 0.22136} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.00976, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.31458, "loss": 0.31458, "time": 0.2215} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.00974, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.31267, "loss": 0.31267, "time": 0.22279} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.00972, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.30067, "loss": 0.30067, "time": 0.22437} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.0097, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.33597, "loss": 0.33597, "time": 0.22376} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.00968, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94438, "top5_acc": 0.99875, "loss_cls": 0.29305, "loss": 0.29305, "time": 0.22486} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.00966, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93562, "top5_acc": 0.9975, "loss_cls": 0.33287, "loss": 0.33287, "time": 0.22534} +{"mode": "val", "epoch": 86, "iter": 533, "lr": 0.00965, "top1_acc": 0.89426, "top5_acc": 0.99495, "mean_class_accuracy": 0.85235} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.00963, "memory": 4083, "data_time": 0.1953, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.26008, "loss": 0.26008, "time": 0.4319} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.00961, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.2548, "loss": 0.2548, "time": 0.22735} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.00959, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.21878, "loss": 0.21878, "time": 0.22162} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.00957, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96, "top5_acc": 0.99938, "loss_cls": 0.23118, "loss": 0.23118, "time": 0.22337} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.00955, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.95, "top5_acc": 0.99875, "loss_cls": 0.30891, "loss": 0.30891, "time": 0.2208} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.00953, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95438, "top5_acc": 0.99812, "loss_cls": 0.28126, "loss": 0.28126, "time": 0.22237} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.00951, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93625, "top5_acc": 0.99938, "loss_cls": 0.31097, "loss": 0.31097, "time": 0.22515} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.00949, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.29822, "loss": 0.29822, "time": 0.22274} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.00947, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93812, "top5_acc": 1.0, "loss_cls": 0.3627, "loss": 0.3627, "time": 0.22344} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.00945, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.30445, "loss": 0.30445, "time": 0.2243} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.00943, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95812, "top5_acc": 0.99938, "loss_cls": 0.29174, "loss": 0.29174, "time": 0.22288} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.00941, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93625, "top5_acc": 1.0, "loss_cls": 0.3389, "loss": 0.3389, "time": 0.22496} +{"mode": "val", "epoch": 87, "iter": 533, "lr": 0.00939, "top1_acc": 0.90224, "top5_acc": 0.9946, "mean_class_accuracy": 0.87318} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.00937, "memory": 4083, "data_time": 0.18659, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.24011, "loss": 0.24011, "time": 0.4224} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.00935, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94812, "top5_acc": 0.99812, "loss_cls": 0.29403, "loss": 0.29403, "time": 0.23016} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.00933, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94938, "top5_acc": 0.99812, "loss_cls": 0.27236, "loss": 0.27236, "time": 0.22179} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.00931, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93938, "top5_acc": 0.99875, "loss_cls": 0.30866, "loss": 0.30866, "time": 0.22265} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.00929, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.27059, "loss": 0.27059, "time": 0.22336} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.00927, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.25908, "loss": 0.25908, "time": 0.22098} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.00925, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.95375, "top5_acc": 0.99812, "loss_cls": 0.26877, "loss": 0.26877, "time": 0.2199} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.00923, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.28264, "loss": 0.28264, "time": 0.22473} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.00921, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95875, "top5_acc": 0.99875, "loss_cls": 0.27123, "loss": 0.27123, "time": 0.22145} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.00919, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93938, "top5_acc": 0.99875, "loss_cls": 0.33155, "loss": 0.33155, "time": 0.22427} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.00917, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95125, "top5_acc": 0.99875, "loss_cls": 0.28336, "loss": 0.28336, "time": 0.22299} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.00915, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.33643, "loss": 0.33643, "time": 0.22244} +{"mode": "val", "epoch": 88, "iter": 533, "lr": 0.00914, "top1_acc": 0.87595, "top5_acc": 0.98909, "mean_class_accuracy": 0.85281} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.00912, "memory": 4083, "data_time": 0.18393, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.24278, "loss": 0.24278, "time": 0.41742} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0091, "memory": 4083, "data_time": 0.00072, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.22134, "loss": 0.22134, "time": 0.22784} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.00908, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.28293, "loss": 0.28293, "time": 0.221} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.00906, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9475, "top5_acc": 0.99875, "loss_cls": 0.28458, "loss": 0.28458, "time": 0.2235} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.00904, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.26742, "loss": 0.26742, "time": 0.22285} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.00902, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.30156, "loss": 0.30156, "time": 0.22436} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.009, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93375, "top5_acc": 0.99875, "loss_cls": 0.33799, "loss": 0.33799, "time": 0.22351} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.00898, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9425, "top5_acc": 0.99875, "loss_cls": 0.3364, "loss": 0.3364, "time": 0.22356} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.00896, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93625, "top5_acc": 0.99938, "loss_cls": 0.32212, "loss": 0.32212, "time": 0.22507} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.00894, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94562, "top5_acc": 1.0, "loss_cls": 0.30982, "loss": 0.30982, "time": 0.22083} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.00892, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94312, "top5_acc": 1.0, "loss_cls": 0.31608, "loss": 0.31608, "time": 0.22409} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.0089, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9475, "top5_acc": 0.99875, "loss_cls": 0.31262, "loss": 0.31262, "time": 0.22327} +{"mode": "val", "epoch": 89, "iter": 533, "lr": 0.00889, "top1_acc": 0.8749, "top5_acc": 0.99202, "mean_class_accuracy": 0.84859} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.00887, "memory": 4083, "data_time": 0.18589, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.24147, "loss": 0.24147, "time": 0.42426} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.00885, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95375, "top5_acc": 0.99875, "loss_cls": 0.26655, "loss": 0.26655, "time": 0.22508} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.00883, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94062, "top5_acc": 1.0, "loss_cls": 0.31522, "loss": 0.31522, "time": 0.22259} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.00881, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94062, "top5_acc": 0.99812, "loss_cls": 0.32669, "loss": 0.32669, "time": 0.22498} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.00879, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.95562, "top5_acc": 0.99875, "loss_cls": 0.28425, "loss": 0.28425, "time": 0.22266} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.00877, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.27852, "loss": 0.27852, "time": 0.2247} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.00875, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.27015, "loss": 0.27015, "time": 0.22298} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.00873, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94312, "top5_acc": 1.0, "loss_cls": 0.32593, "loss": 0.32593, "time": 0.22409} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.00871, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.29368, "loss": 0.29368, "time": 0.22642} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.00869, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.26161, "loss": 0.26161, "time": 0.22283} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.00867, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9525, "top5_acc": 0.99875, "loss_cls": 0.28079, "loss": 0.28079, "time": 0.22277} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.00865, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95688, "top5_acc": 0.99938, "loss_cls": 0.25833, "loss": 0.25833, "time": 0.2252} +{"mode": "val", "epoch": 90, "iter": 533, "lr": 0.00864, "top1_acc": 0.89579, "top5_acc": 0.99331, "mean_class_accuracy": 0.8621} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.00862, "memory": 4083, "data_time": 0.1913, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.25447, "loss": 0.25447, "time": 0.42492} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0086, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.24587, "loss": 0.24587, "time": 0.22399} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.00858, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96, "top5_acc": 0.99938, "loss_cls": 0.2654, "loss": 0.2654, "time": 0.22102} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.00856, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.23848, "loss": 0.23848, "time": 0.22461} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.00854, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.22744, "loss": 0.22744, "time": 0.22389} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.00852, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.27825, "loss": 0.27825, "time": 0.22056} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.0085, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95188, "top5_acc": 1.0, "loss_cls": 0.29451, "loss": 0.29451, "time": 0.2232} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.00848, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.94625, "top5_acc": 1.0, "loss_cls": 0.30034, "loss": 0.30034, "time": 0.22444} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.00846, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.95, "top5_acc": 0.99812, "loss_cls": 0.30408, "loss": 0.30408, "time": 0.22629} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.00844, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.28303, "loss": 0.28303, "time": 0.22735} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.00842, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95562, "top5_acc": 1.0, "loss_cls": 0.26587, "loss": 0.26587, "time": 0.22413} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.0084, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94812, "top5_acc": 0.99875, "loss_cls": 0.28, "loss": 0.28, "time": 0.22221} +{"mode": "val", "epoch": 91, "iter": 533, "lr": 0.00839, "top1_acc": 0.89473, "top5_acc": 0.99261, "mean_class_accuracy": 0.86745} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.00837, "memory": 4083, "data_time": 0.19108, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.26141, "loss": 0.26141, "time": 0.42638} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.00835, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.21321, "loss": 0.21321, "time": 0.22416} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.00833, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.24248, "loss": 0.24248, "time": 0.22585} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.00831, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.28227, "loss": 0.28227, "time": 0.22232} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.00829, "memory": 4083, "data_time": 0.00062, "top1_acc": 0.95062, "top5_acc": 0.99875, "loss_cls": 0.28075, "loss": 0.28075, "time": 0.22701} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.00827, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.95125, "top5_acc": 0.99875, "loss_cls": 0.2798, "loss": 0.2798, "time": 0.22545} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.00825, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.2556, "loss": 0.2556, "time": 0.22363} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.00824, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.22073, "loss": 0.22073, "time": 0.22485} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.00822, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.24391, "loss": 0.24391, "time": 0.22479} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.0082, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.27424, "loss": 0.27424, "time": 0.22314} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.00818, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.27473, "loss": 0.27473, "time": 0.22538} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.00816, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.27484, "loss": 0.27484, "time": 0.22451} +{"mode": "val", "epoch": 92, "iter": 533, "lr": 0.00814, "top1_acc": 0.8979, "top5_acc": 0.99378, "mean_class_accuracy": 0.86222} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.00812, "memory": 4083, "data_time": 0.19085, "top1_acc": 0.945, "top5_acc": 0.9975, "loss_cls": 0.29209, "loss": 0.29209, "time": 0.43042} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.0081, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.21796, "loss": 0.21796, "time": 0.22605} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.00809, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96125, "top5_acc": 0.99875, "loss_cls": 0.23761, "loss": 0.23761, "time": 0.22216} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.00807, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.24497, "loss": 0.24497, "time": 0.22426} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.00805, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.23167, "loss": 0.23167, "time": 0.22158} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.00803, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.95562, "top5_acc": 1.0, "loss_cls": 0.25887, "loss": 0.25887, "time": 0.22383} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.00801, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.95938, "top5_acc": 0.99875, "loss_cls": 0.2475, "loss": 0.2475, "time": 0.22343} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.00799, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.94062, "top5_acc": 0.99938, "loss_cls": 0.31195, "loss": 0.31195, "time": 0.22273} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.00797, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.30912, "loss": 0.30912, "time": 0.22358} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.00795, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.30098, "loss": 0.30098, "time": 0.22381} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.00793, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9475, "top5_acc": 0.9975, "loss_cls": 0.30249, "loss": 0.30249, "time": 0.22255} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.00791, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.95062, "top5_acc": 0.99812, "loss_cls": 0.27427, "loss": 0.27427, "time": 0.22497} +{"mode": "val", "epoch": 93, "iter": 533, "lr": 0.0079, "top1_acc": 0.8871, "top5_acc": 0.99331, "mean_class_accuracy": 0.84649} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.00788, "memory": 4083, "data_time": 0.18638, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.25651, "loss": 0.25651, "time": 0.41797} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.00786, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.22565, "loss": 0.22565, "time": 0.22675} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.00784, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.2211, "loss": 0.2211, "time": 0.22357} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.00782, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.27421, "loss": 0.27421, "time": 0.2251} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.0078, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95812, "top5_acc": 0.99938, "loss_cls": 0.26672, "loss": 0.26672, "time": 0.22278} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.00778, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.28573, "loss": 0.28573, "time": 0.22151} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.00777, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.2733, "loss": 0.2733, "time": 0.22406} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.00775, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95562, "top5_acc": 0.99875, "loss_cls": 0.27296, "loss": 0.27296, "time": 0.22255} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.00773, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94812, "top5_acc": 1.0, "loss_cls": 0.28559, "loss": 0.28559, "time": 0.22268} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.00771, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.30759, "loss": 0.30759, "time": 0.22433} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.00769, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96, "top5_acc": 0.99875, "loss_cls": 0.24804, "loss": 0.24804, "time": 0.22414} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.00767, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.24693, "loss": 0.24693, "time": 0.22737} +{"mode": "val", "epoch": 94, "iter": 533, "lr": 0.00766, "top1_acc": 0.89778, "top5_acc": 0.99566, "mean_class_accuracy": 0.86103} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.00764, "memory": 4083, "data_time": 0.18623, "top1_acc": 0.95, "top5_acc": 1.0, "loss_cls": 0.27763, "loss": 0.27763, "time": 0.41885} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.00762, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.21543, "loss": 0.21543, "time": 0.22364} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.0076, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96688, "top5_acc": 0.99938, "loss_cls": 0.19736, "loss": 0.19736, "time": 0.2233} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.00758, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.23114, "loss": 0.23114, "time": 0.22515} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.00756, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.23549, "loss": 0.23549, "time": 0.22395} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.00754, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.95375, "top5_acc": 0.99875, "loss_cls": 0.24587, "loss": 0.24587, "time": 0.2237} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.00752, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.23202, "loss": 0.23202, "time": 0.22315} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.00751, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.21684, "loss": 0.21684, "time": 0.22278} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.00749, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.23498, "loss": 0.23498, "time": 0.22637} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.00747, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.95125, "top5_acc": 0.99875, "loss_cls": 0.27702, "loss": 0.27702, "time": 0.22729} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.00745, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.28126, "loss": 0.28126, "time": 0.22583} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.00743, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96562, "top5_acc": 0.99938, "loss_cls": 0.22016, "loss": 0.22016, "time": 0.22082} +{"mode": "val", "epoch": 95, "iter": 533, "lr": 0.00742, "top1_acc": 0.9114, "top5_acc": 0.9946, "mean_class_accuracy": 0.87549} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.0074, "memory": 4083, "data_time": 0.18808, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.20314, "loss": 0.20314, "time": 0.4223} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.00738, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.24301, "loss": 0.24301, "time": 0.22315} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.00736, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.21946, "loss": 0.21946, "time": 0.2268} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.00734, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96, "top5_acc": 0.99875, "loss_cls": 0.23447, "loss": 0.23447, "time": 0.2235} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.00732, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.28344, "loss": 0.28344, "time": 0.22207} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.0073, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.24213, "loss": 0.24213, "time": 0.22551} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.00729, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.24509, "loss": 0.24509, "time": 0.22505} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.00727, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.22625, "loss": 0.22625, "time": 0.22087} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.00725, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.20955, "loss": 0.20955, "time": 0.22518} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.00723, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.19392, "loss": 0.19392, "time": 0.22319} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.00721, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.19395, "loss": 0.19395, "time": 0.22555} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.00719, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9575, "top5_acc": 0.99875, "loss_cls": 0.24875, "loss": 0.24875, "time": 0.22448} +{"mode": "val", "epoch": 96, "iter": 533, "lr": 0.00718, "top1_acc": 0.906, "top5_acc": 0.99507, "mean_class_accuracy": 0.87294} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.00716, "memory": 4083, "data_time": 0.19196, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.20677, "loss": 0.20677, "time": 0.42418} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.00714, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.97188, "top5_acc": 0.99938, "loss_cls": 0.189, "loss": 0.189, "time": 0.2272} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.00712, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.22771, "loss": 0.22771, "time": 0.22219} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.0071, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.22169, "loss": 0.22169, "time": 0.22614} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.00709, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.2592, "loss": 0.2592, "time": 0.22279} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.00707, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.21969, "loss": 0.21969, "time": 0.2225} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.00705, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.234, "loss": 0.234, "time": 0.22178} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.00703, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.22872, "loss": 0.22872, "time": 0.22466} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.00701, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.22512, "loss": 0.22512, "time": 0.22491} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.00699, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.24764, "loss": 0.24764, "time": 0.22369} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.00698, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.21295, "loss": 0.21295, "time": 0.22029} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.00696, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94812, "top5_acc": 1.0, "loss_cls": 0.26888, "loss": 0.26888, "time": 0.222} +{"mode": "val", "epoch": 97, "iter": 533, "lr": 0.00694, "top1_acc": 0.91186, "top5_acc": 0.99425, "mean_class_accuracy": 0.8753} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.00692, "memory": 4083, "data_time": 0.1862, "top1_acc": 0.9575, "top5_acc": 0.99938, "loss_cls": 0.23289, "loss": 0.23289, "time": 0.42522} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.00691, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.15622, "loss": 0.15622, "time": 0.223} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.00689, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9575, "top5_acc": 0.99938, "loss_cls": 0.2369, "loss": 0.2369, "time": 0.22574} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.00687, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.17176, "loss": 0.17176, "time": 0.22234} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.00685, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.24544, "loss": 0.24544, "time": 0.22687} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.00683, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95562, "top5_acc": 1.0, "loss_cls": 0.23921, "loss": 0.23921, "time": 0.22424} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.00681, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.22205, "loss": 0.22205, "time": 0.22298} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.0068, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.21456, "loss": 0.21456, "time": 0.22586} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.00678, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.27039, "loss": 0.27039, "time": 0.22435} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.00676, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.19779, "loss": 0.19779, "time": 0.2264} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.00674, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.22838, "loss": 0.22838, "time": 0.22234} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.00672, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9675, "top5_acc": 0.99875, "loss_cls": 0.20528, "loss": 0.20528, "time": 0.22192} +{"mode": "val", "epoch": 98, "iter": 533, "lr": 0.00671, "top1_acc": 0.90776, "top5_acc": 0.99237, "mean_class_accuracy": 0.88614} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.00669, "memory": 4083, "data_time": 0.18704, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.16604, "loss": 0.16604, "time": 0.42049} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.00667, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.21464, "loss": 0.21464, "time": 0.22617} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.00665, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.22214, "loss": 0.22214, "time": 0.22506} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.00664, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.22467, "loss": 0.22467, "time": 0.21999} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.00662, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.25029, "loss": 0.25029, "time": 0.22515} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.0066, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.19948, "loss": 0.19948, "time": 0.22356} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.00658, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.23932, "loss": 0.23932, "time": 0.22257} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.00656, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97062, "top5_acc": 0.99938, "loss_cls": 0.18914, "loss": 0.18914, "time": 0.22023} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.00655, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96875, "top5_acc": 0.99875, "loss_cls": 0.20247, "loss": 0.20247, "time": 0.22317} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.00653, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.19141, "loss": 0.19141, "time": 0.22216} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.00651, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95625, "top5_acc": 0.99875, "loss_cls": 0.23763, "loss": 0.23763, "time": 0.22249} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.00649, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.25365, "loss": 0.25365, "time": 0.22362} +{"mode": "val", "epoch": 99, "iter": 533, "lr": 0.00648, "top1_acc": 0.90529, "top5_acc": 0.99366, "mean_class_accuracy": 0.88592} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.00646, "memory": 4083, "data_time": 0.19144, "top1_acc": 0.9775, "top5_acc": 0.99938, "loss_cls": 0.16337, "loss": 0.16337, "time": 0.42978} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.00644, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.20601, "loss": 0.20601, "time": 0.22408} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.00642, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.18743, "loss": 0.18743, "time": 0.22345} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.00641, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.1933, "loss": 0.1933, "time": 0.22708} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.00639, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.1688, "loss": 0.1688, "time": 0.22242} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.00637, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.21101, "loss": 0.21101, "time": 0.2239} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.00635, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95688, "top5_acc": 0.99938, "loss_cls": 0.25081, "loss": 0.25081, "time": 0.22538} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.00634, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.25496, "loss": 0.25496, "time": 0.22581} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.00632, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.20565, "loss": 0.20565, "time": 0.22444} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.0063, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.23929, "loss": 0.23929, "time": 0.22115} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.00628, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.20959, "loss": 0.20959, "time": 0.22106} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.00626, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96, "top5_acc": 0.99938, "loss_cls": 0.2393, "loss": 0.2393, "time": 0.22582} +{"mode": "val", "epoch": 100, "iter": 533, "lr": 0.00625, "top1_acc": 0.90834, "top5_acc": 0.99308, "mean_class_accuracy": 0.87229} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.00623, "memory": 4083, "data_time": 0.1896, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.24076, "loss": 0.24076, "time": 0.42533} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.00621, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.19595, "loss": 0.19595, "time": 0.2274} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.0062, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97, "top5_acc": 0.99875, "loss_cls": 0.18531, "loss": 0.18531, "time": 0.22335} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.00618, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.16617, "loss": 0.16617, "time": 0.22536} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.00616, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.20503, "loss": 0.20503, "time": 0.22492} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.00614, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97312, "top5_acc": 0.99875, "loss_cls": 0.17101, "loss": 0.17101, "time": 0.22315} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.00613, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.18114, "loss": 0.18114, "time": 0.22606} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.00611, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.19302, "loss": 0.19302, "time": 0.2255} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.00609, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.21342, "loss": 0.21342, "time": 0.22336} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.00607, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.18949, "loss": 0.18949, "time": 0.22264} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.00606, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.21689, "loss": 0.21689, "time": 0.22465} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.00604, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96562, "top5_acc": 0.99938, "loss_cls": 0.21756, "loss": 0.21756, "time": 0.22381} +{"mode": "val", "epoch": 101, "iter": 533, "lr": 0.00602, "top1_acc": 0.9101, "top5_acc": 0.99413, "mean_class_accuracy": 0.88541} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.00601, "memory": 4083, "data_time": 0.18895, "top1_acc": 0.96875, "top5_acc": 0.99875, "loss_cls": 0.2003, "loss": 0.2003, "time": 0.42443} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.00599, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.18277, "loss": 0.18277, "time": 0.22392} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.00597, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.18819, "loss": 0.18819, "time": 0.22489} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.00596, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96625, "top5_acc": 0.99938, "loss_cls": 0.21127, "loss": 0.21127, "time": 0.22525} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.00594, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.19101, "loss": 0.19101, "time": 0.22094} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.00592, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.16568, "loss": 0.16568, "time": 0.22482} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.0059, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.17429, "loss": 0.17429, "time": 0.2232} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.00589, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.15065, "loss": 0.15065, "time": 0.22395} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.00587, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13679, "loss": 0.13679, "time": 0.22248} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.00585, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.17356, "loss": 0.17356, "time": 0.22232} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.00583, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.18816, "loss": 0.18816, "time": 0.22369} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.00582, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.22762, "loss": 0.22762, "time": 0.22456} +{"mode": "val", "epoch": 102, "iter": 533, "lr": 0.0058, "top1_acc": 0.89344, "top5_acc": 0.99108, "mean_class_accuracy": 0.85972} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.00579, "memory": 4083, "data_time": 0.19015, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.18061, "loss": 0.18061, "time": 0.42656} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.00577, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.15733, "loss": 0.15733, "time": 0.22246} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.00575, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.18641, "loss": 0.18641, "time": 0.22443} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.00573, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.16627, "loss": 0.16627, "time": 0.22363} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.00572, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96938, "top5_acc": 0.99938, "loss_cls": 0.19152, "loss": 0.19152, "time": 0.22491} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.0057, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.18156, "loss": 0.18156, "time": 0.22457} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.00568, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.18681, "loss": 0.18681, "time": 0.22543} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.00566, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97188, "top5_acc": 0.99938, "loss_cls": 0.18071, "loss": 0.18071, "time": 0.22311} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.00565, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.17368, "loss": 0.17368, "time": 0.22775} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.00563, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.20524, "loss": 0.20524, "time": 0.22472} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.00561, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97062, "top5_acc": 0.99938, "loss_cls": 0.1856, "loss": 0.1856, "time": 0.22627} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.0056, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97, "top5_acc": 0.99875, "loss_cls": 0.18371, "loss": 0.18371, "time": 0.22375} +{"mode": "val", "epoch": 103, "iter": 533, "lr": 0.00558, "top1_acc": 0.90647, "top5_acc": 0.99484, "mean_class_accuracy": 0.8738} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.00557, "memory": 4083, "data_time": 0.19042, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.15752, "loss": 0.15752, "time": 0.42587} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.00555, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.13923, "loss": 0.13923, "time": 0.22384} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.00553, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.16516, "loss": 0.16516, "time": 0.22378} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.00551, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.19714, "loss": 0.19714, "time": 0.22422} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.0055, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.14518, "loss": 0.14518, "time": 0.22789} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.00548, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.20597, "loss": 0.20597, "time": 0.22575} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.00546, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.17062, "loss": 0.17062, "time": 0.22586} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.00545, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.18095, "loss": 0.18095, "time": 0.2251} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.00543, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.19144, "loss": 0.19144, "time": 0.22039} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.00541, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96938, "top5_acc": 0.99938, "loss_cls": 0.19297, "loss": 0.19297, "time": 0.22722} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.0054, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.18036, "loss": 0.18036, "time": 0.2246} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.00538, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.20176, "loss": 0.20176, "time": 0.22523} +{"mode": "val", "epoch": 104, "iter": 533, "lr": 0.00537, "top1_acc": 0.89954, "top5_acc": 0.99308, "mean_class_accuracy": 0.86182} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.00535, "memory": 4083, "data_time": 0.19202, "top1_acc": 0.97562, "top5_acc": 0.99938, "loss_cls": 0.17895, "loss": 0.17895, "time": 0.42991} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.00533, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.17666, "loss": 0.17666, "time": 0.22456} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.00532, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97562, "top5_acc": 0.99938, "loss_cls": 0.15643, "loss": 0.15643, "time": 0.22464} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.0053, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.18528, "loss": 0.18528, "time": 0.22336} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.00528, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96875, "top5_acc": 0.99938, "loss_cls": 0.18423, "loss": 0.18423, "time": 0.22415} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.00527, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97625, "top5_acc": 0.99938, "loss_cls": 0.15798, "loss": 0.15798, "time": 0.22752} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.00525, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.15644, "loss": 0.15644, "time": 0.22137} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.00523, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.17813, "loss": 0.17813, "time": 0.22279} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.00522, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96812, "top5_acc": 0.99938, "loss_cls": 0.20792, "loss": 0.20792, "time": 0.22388} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.0052, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.19029, "loss": 0.19029, "time": 0.22412} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.00518, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.18011, "loss": 0.18011, "time": 0.22293} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.00517, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.1537, "loss": 0.1537, "time": 0.22255} +{"mode": "val", "epoch": 105, "iter": 533, "lr": 0.00515, "top1_acc": 0.91433, "top5_acc": 0.99601, "mean_class_accuracy": 0.89028} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.00514, "memory": 4083, "data_time": 0.1893, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.18762, "loss": 0.18762, "time": 0.42447} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.00512, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.16007, "loss": 0.16007, "time": 0.22501} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.0051, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.16077, "loss": 0.16077, "time": 0.22378} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.00509, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.13806, "loss": 0.13806, "time": 0.22538} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.00507, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14759, "loss": 0.14759, "time": 0.22404} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.00505, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.17667, "loss": 0.17667, "time": 0.22236} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.00504, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.14138, "loss": 0.14138, "time": 0.22539} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.00502, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14684, "loss": 0.14684, "time": 0.22319} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.005, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.15538, "loss": 0.15538, "time": 0.22505} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.00499, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.17705, "loss": 0.17705, "time": 0.22248} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.00497, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97812, "top5_acc": 0.99938, "loss_cls": 0.16061, "loss": 0.16061, "time": 0.22229} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.00496, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.1697, "loss": 0.1697, "time": 0.22232} +{"mode": "val", "epoch": 106, "iter": 533, "lr": 0.00494, "top1_acc": 0.91949, "top5_acc": 0.99554, "mean_class_accuracy": 0.8951} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.00493, "memory": 4083, "data_time": 0.18993, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.13877, "loss": 0.13877, "time": 0.42848} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.00491, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98438, "top5_acc": 0.99938, "loss_cls": 0.12345, "loss": 0.12345, "time": 0.22469} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.00489, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.16104, "loss": 0.16104, "time": 0.22583} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.00488, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.16918, "loss": 0.16918, "time": 0.22278} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.00486, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.17835, "loss": 0.17835, "time": 0.22375} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.00485, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.14742, "loss": 0.14742, "time": 0.22165} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.00483, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.17476, "loss": 0.17476, "time": 0.22229} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.00481, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.19247, "loss": 0.19247, "time": 0.22266} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.0048, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.15797, "loss": 0.15797, "time": 0.22222} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.00478, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.17316, "loss": 0.17316, "time": 0.22342} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.00476, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.16085, "loss": 0.16085, "time": 0.22199} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.00475, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.12631, "loss": 0.12631, "time": 0.22587} +{"mode": "val", "epoch": 107, "iter": 533, "lr": 0.00474, "top1_acc": 0.91726, "top5_acc": 0.99566, "mean_class_accuracy": 0.88592} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.00472, "memory": 4083, "data_time": 0.19229, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.1385, "loss": 0.1385, "time": 0.42818} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0047, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.11684, "loss": 0.11684, "time": 0.22477} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.00469, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.13166, "loss": 0.13166, "time": 0.2254} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.00467, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13362, "loss": 0.13362, "time": 0.22521} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.00466, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.14959, "loss": 0.14959, "time": 0.22304} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.00464, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.13588, "loss": 0.13588, "time": 0.22241} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.00462, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97938, "top5_acc": 0.99938, "loss_cls": 0.14861, "loss": 0.14861, "time": 0.22313} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.00461, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.16287, "loss": 0.16287, "time": 0.22696} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.00459, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9775, "top5_acc": 0.99938, "loss_cls": 0.15329, "loss": 0.15329, "time": 0.22179} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.00458, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.14416, "loss": 0.14416, "time": 0.22228} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.00456, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.18262, "loss": 0.18262, "time": 0.22712} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.00455, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.15482, "loss": 0.15482, "time": 0.22184} +{"mode": "val", "epoch": 108, "iter": 533, "lr": 0.00453, "top1_acc": 0.92184, "top5_acc": 0.99566, "mean_class_accuracy": 0.88814} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.00452, "memory": 4083, "data_time": 0.19495, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10264, "loss": 0.10264, "time": 0.433} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.0045, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.0863, "loss": 0.0863, "time": 0.22812} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.00449, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11512, "loss": 0.11512, "time": 0.226} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.00447, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.14741, "loss": 0.14741, "time": 0.22711} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.00445, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.12337, "loss": 0.12337, "time": 0.22259} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.00444, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.12325, "loss": 0.12325, "time": 0.2252} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.00442, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.1125, "loss": 0.1125, "time": 0.22629} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.00441, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.13871, "loss": 0.13871, "time": 0.22433} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.00439, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.14517, "loss": 0.14517, "time": 0.22331} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.00438, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97312, "top5_acc": 0.99812, "loss_cls": 0.1727, "loss": 0.1727, "time": 0.22166} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.00436, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.1555, "loss": 0.1555, "time": 0.22252} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.00434, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.12209, "loss": 0.12209, "time": 0.22318} +{"mode": "val", "epoch": 109, "iter": 533, "lr": 0.00433, "top1_acc": 0.91726, "top5_acc": 0.99484, "mean_class_accuracy": 0.88508} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.00432, "memory": 4083, "data_time": 0.19177, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12187, "loss": 0.12187, "time": 0.42757} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.0043, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10694, "loss": 0.10694, "time": 0.22484} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.00429, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10006, "loss": 0.10006, "time": 0.22602} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.00427, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.14668, "loss": 0.14668, "time": 0.22358} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.00426, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13763, "loss": 0.13763, "time": 0.22356} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.00424, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.13338, "loss": 0.13338, "time": 0.22162} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.00422, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.12504, "loss": 0.12504, "time": 0.22551} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.00421, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.16016, "loss": 0.16016, "time": 0.22366} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.00419, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.13037, "loss": 0.13037, "time": 0.22683} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.00418, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.15372, "loss": 0.15372, "time": 0.2238} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.00416, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98125, "top5_acc": 0.99938, "loss_cls": 0.12653, "loss": 0.12653, "time": 0.22399} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.00415, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.14875, "loss": 0.14875, "time": 0.22595} +{"mode": "val", "epoch": 110, "iter": 533, "lr": 0.00414, "top1_acc": 0.92337, "top5_acc": 0.99589, "mean_class_accuracy": 0.89661} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.00412, "memory": 4083, "data_time": 0.19247, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11088, "loss": 0.11088, "time": 0.42599} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.00411, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.1083, "loss": 0.1083, "time": 0.22455} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.00409, "memory": 4083, "data_time": 0.00066, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.09011, "loss": 0.09011, "time": 0.2245} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.00408, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.10031, "loss": 0.10031, "time": 0.22315} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.00406, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.09843, "loss": 0.09843, "time": 0.21998} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.00405, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11627, "loss": 0.11627, "time": 0.22326} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.00403, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.1687, "loss": 0.1687, "time": 0.22207} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.00402, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.12127, "loss": 0.12127, "time": 0.22618} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.004, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13883, "loss": 0.13883, "time": 0.2241} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.00399, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98, "top5_acc": 0.99938, "loss_cls": 0.12116, "loss": 0.12116, "time": 0.22559} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.00397, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13238, "loss": 0.13238, "time": 0.22613} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.00396, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9825, "top5_acc": 0.99938, "loss_cls": 0.11901, "loss": 0.11901, "time": 0.22697} +{"mode": "val", "epoch": 111, "iter": 533, "lr": 0.00394, "top1_acc": 0.92313, "top5_acc": 0.99425, "mean_class_accuracy": 0.8982} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.00393, "memory": 4083, "data_time": 0.18484, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.10456, "loss": 0.10456, "time": 0.42092} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.00391, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.12835, "loss": 0.12835, "time": 0.22383} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.0039, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.98125, "top5_acc": 0.99938, "loss_cls": 0.11319, "loss": 0.11319, "time": 0.2243} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.00388, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.16187, "loss": 0.16187, "time": 0.22487} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.00387, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13346, "loss": 0.13346, "time": 0.22408} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.00385, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.11349, "loss": 0.11349, "time": 0.22364} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.00384, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12748, "loss": 0.12748, "time": 0.22436} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.00382, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.13249, "loss": 0.13249, "time": 0.22363} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.00381, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.1398, "loss": 0.1398, "time": 0.2221} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.0038, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.11744, "loss": 0.11744, "time": 0.22141} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.00378, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12346, "loss": 0.12346, "time": 0.224} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.00377, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.14179, "loss": 0.14179, "time": 0.22204} +{"mode": "val", "epoch": 112, "iter": 533, "lr": 0.00375, "top1_acc": 0.9182, "top5_acc": 0.99484, "mean_class_accuracy": 0.89195} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.00374, "memory": 4083, "data_time": 0.1889, "top1_acc": 0.9925, "top5_acc": 0.99938, "loss_cls": 0.08672, "loss": 0.08672, "time": 0.42232} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.00373, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07373, "loss": 0.07373, "time": 0.22562} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.00371, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08275, "loss": 0.08275, "time": 0.22446} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.0037, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11479, "loss": 0.11479, "time": 0.22569} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.00368, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.09628, "loss": 0.09628, "time": 0.22615} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.00367, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08845, "loss": 0.08845, "time": 0.22393} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.00365, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10079, "loss": 0.10079, "time": 0.22695} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.00364, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.985, "top5_acc": 0.99938, "loss_cls": 0.1056, "loss": 0.1056, "time": 0.22448} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.00362, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08234, "loss": 0.08234, "time": 0.22678} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.00361, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08821, "loss": 0.08821, "time": 0.22656} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0036, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.08443, "loss": 0.08443, "time": 0.22329} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.00358, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08502, "loss": 0.08502, "time": 0.22607} +{"mode": "val", "epoch": 113, "iter": 533, "lr": 0.00357, "top1_acc": 0.91961, "top5_acc": 0.99542, "mean_class_accuracy": 0.89185} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.00355, "memory": 4083, "data_time": 0.18717, "top1_acc": 0.98688, "top5_acc": 0.99938, "loss_cls": 0.11212, "loss": 0.11212, "time": 0.42357} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.00354, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.09355, "loss": 0.09355, "time": 0.2245} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.00353, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.07773, "loss": 0.07773, "time": 0.22188} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.00351, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.1022, "loss": 0.1022, "time": 0.22562} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.0035, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.985, "top5_acc": 0.99938, "loss_cls": 0.10732, "loss": 0.10732, "time": 0.22349} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.00348, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08259, "loss": 0.08259, "time": 0.22298} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.00347, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.10716, "loss": 0.10716, "time": 0.22684} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.00346, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.09349, "loss": 0.09349, "time": 0.2237} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.00344, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09705, "loss": 0.09705, "time": 0.22131} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.00343, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.09984, "loss": 0.09984, "time": 0.22476} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.00341, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08542, "loss": 0.08542, "time": 0.22208} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.0034, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10724, "loss": 0.10724, "time": 0.22238} +{"mode": "val", "epoch": 114, "iter": 533, "lr": 0.00339, "top1_acc": 0.91679, "top5_acc": 0.9939, "mean_class_accuracy": 0.88016} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.00337, "memory": 4083, "data_time": 0.18862, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.10712, "loss": 0.10712, "time": 0.42251} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.00336, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.11967, "loss": 0.11967, "time": 0.22295} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.00335, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.10997, "loss": 0.10997, "time": 0.22043} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.00333, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.08411, "loss": 0.08411, "time": 0.22454} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.00332, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.08306, "loss": 0.08306, "time": 0.2248} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.0033, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.06258, "loss": 0.06258, "time": 0.22548} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.00329, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10491, "loss": 0.10491, "time": 0.22364} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.00328, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.10633, "loss": 0.10633, "time": 0.2218} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.00326, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07812, "loss": 0.07812, "time": 0.22307} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.00325, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99125, "top5_acc": 0.99938, "loss_cls": 0.08168, "loss": 0.08168, "time": 0.22325} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.00324, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.1076, "loss": 0.1076, "time": 0.22237} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.00322, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.10351, "loss": 0.10351, "time": 0.22333} +{"mode": "val", "epoch": 115, "iter": 533, "lr": 0.00321, "top1_acc": 0.91433, "top5_acc": 0.9946, "mean_class_accuracy": 0.89113} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.0032, "memory": 4083, "data_time": 0.19115, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08265, "loss": 0.08265, "time": 0.42828} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.00318, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.0734, "loss": 0.0734, "time": 0.22461} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.00317, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.07651, "loss": 0.07651, "time": 0.22159} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.00316, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08166, "loss": 0.08166, "time": 0.22239} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.00314, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.09635, "loss": 0.09635, "time": 0.22214} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.00313, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09355, "loss": 0.09355, "time": 0.22402} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.00312, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.09635, "loss": 0.09635, "time": 0.2251} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.0031, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06875, "loss": 0.06875, "time": 0.22137} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.00309, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06177, "loss": 0.06177, "time": 0.22513} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.00308, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.0733, "loss": 0.0733, "time": 0.22319} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.00306, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.08451, "loss": 0.08451, "time": 0.22368} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.00305, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.08722, "loss": 0.08722, "time": 0.22248} +{"mode": "val", "epoch": 116, "iter": 533, "lr": 0.00304, "top1_acc": 0.9229, "top5_acc": 0.99531, "mean_class_accuracy": 0.89952} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.00302, "memory": 4083, "data_time": 0.19299, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06703, "loss": 0.06703, "time": 0.4275} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.00301, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07593, "loss": 0.07593, "time": 0.224} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.003, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.0658, "loss": 0.0658, "time": 0.22531} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.00298, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06215, "loss": 0.06215, "time": 0.22391} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.00297, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.0681, "loss": 0.0681, "time": 0.22349} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.00296, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08448, "loss": 0.08448, "time": 0.22436} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.00294, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06188, "loss": 0.06188, "time": 0.22106} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.00293, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.08636, "loss": 0.08636, "time": 0.22436} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.00292, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08215, "loss": 0.08215, "time": 0.224} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.00291, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.0766, "loss": 0.0766, "time": 0.22351} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.00289, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.06423, "loss": 0.06423, "time": 0.22703} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.00288, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.07021, "loss": 0.07021, "time": 0.22276} +{"mode": "val", "epoch": 117, "iter": 533, "lr": 0.00287, "top1_acc": 0.92747, "top5_acc": 0.99578, "mean_class_accuracy": 0.89933} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.00286, "memory": 4083, "data_time": 0.18683, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08273, "loss": 0.08273, "time": 0.4244} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.00284, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05568, "loss": 0.05568, "time": 0.22257} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.00283, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06891, "loss": 0.06891, "time": 0.22164} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.00282, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06478, "loss": 0.06478, "time": 0.22212} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.0028, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.05322, "loss": 0.05322, "time": 0.22413} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.00279, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06405, "loss": 0.06405, "time": 0.22423} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.00278, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05488, "loss": 0.05488, "time": 0.22361} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.00277, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07586, "loss": 0.07586, "time": 0.22682} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.00275, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08018, "loss": 0.08018, "time": 0.2244} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.00274, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.10278, "loss": 0.10278, "time": 0.22563} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.00273, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98688, "top5_acc": 0.99938, "loss_cls": 0.09794, "loss": 0.09794, "time": 0.22159} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.00271, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.08756, "loss": 0.08756, "time": 0.22258} +{"mode": "val", "epoch": 118, "iter": 533, "lr": 0.0027, "top1_acc": 0.927, "top5_acc": 0.9946, "mean_class_accuracy": 0.90102} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.00269, "memory": 4083, "data_time": 0.18506, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05909, "loss": 0.05909, "time": 0.41859} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.00268, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05067, "loss": 0.05067, "time": 0.22707} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.00267, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04666, "loss": 0.04666, "time": 0.22415} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.00265, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99188, "top5_acc": 0.99938, "loss_cls": 0.0746, "loss": 0.0746, "time": 0.22262} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.00264, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06412, "loss": 0.06412, "time": 0.22463} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.00263, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05806, "loss": 0.05806, "time": 0.2212} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.00262, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05442, "loss": 0.05442, "time": 0.22212} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.0026, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.05817, "loss": 0.05817, "time": 0.22263} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.00259, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.0591, "loss": 0.0591, "time": 0.22084} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.00258, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.0785, "loss": 0.0785, "time": 0.22106} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.00257, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06337, "loss": 0.06337, "time": 0.2242} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.00255, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07711, "loss": 0.07711, "time": 0.22398} +{"mode": "val", "epoch": 119, "iter": 533, "lr": 0.00254, "top1_acc": 0.93123, "top5_acc": 0.99495, "mean_class_accuracy": 0.9033} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.00253, "memory": 4083, "data_time": 0.18551, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06881, "loss": 0.06881, "time": 0.41944} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.00252, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.99312, "top5_acc": 0.99938, "loss_cls": 0.06044, "loss": 0.06044, "time": 0.22502} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.00251, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05461, "loss": 0.05461, "time": 0.22417} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.00249, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.0605, "loss": 0.0605, "time": 0.22557} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.00248, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05442, "loss": 0.05442, "time": 0.22274} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.00247, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.05582, "loss": 0.05582, "time": 0.22301} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.00246, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.0722, "loss": 0.0722, "time": 0.2268} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.00245, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05375, "loss": 0.05375, "time": 0.22425} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.00243, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07065, "loss": 0.07065, "time": 0.22536} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.00242, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.06116, "loss": 0.06116, "time": 0.22389} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00241, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.0588, "loss": 0.0588, "time": 0.22899} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.0024, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05121, "loss": 0.05121, "time": 0.22388} +{"mode": "val", "epoch": 120, "iter": 533, "lr": 0.00239, "top1_acc": 0.92794, "top5_acc": 0.99542, "mean_class_accuracy": 0.89569} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00238, "memory": 4083, "data_time": 0.18541, "top1_acc": 0.99312, "top5_acc": 0.99938, "loss_cls": 0.06124, "loss": 0.06124, "time": 0.42044} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00236, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06041, "loss": 0.06041, "time": 0.22379} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.00235, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04429, "loss": 0.04429, "time": 0.22532} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00234, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04551, "loss": 0.04551, "time": 0.2283} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00233, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.06373, "loss": 0.06373, "time": 0.22449} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00232, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06823, "loss": 0.06823, "time": 0.22422} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.0023, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.07794, "loss": 0.07794, "time": 0.22509} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00229, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07177, "loss": 0.07177, "time": 0.22324} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.00228, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.06491, "loss": 0.06491, "time": 0.22532} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00227, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.08359, "loss": 0.08359, "time": 0.22224} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00226, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.06415, "loss": 0.06415, "time": 0.22185} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00225, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04964, "loss": 0.04964, "time": 0.22221} +{"mode": "val", "epoch": 121, "iter": 533, "lr": 0.00224, "top1_acc": 0.92829, "top5_acc": 0.99554, "mean_class_accuracy": 0.90145} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00222, "memory": 4083, "data_time": 0.18728, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05479, "loss": 0.05479, "time": 0.42018} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00221, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05555, "loss": 0.05555, "time": 0.22413} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.0022, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.0672, "loss": 0.0672, "time": 0.22535} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00219, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05464, "loss": 0.05464, "time": 0.22467} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00218, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04394, "loss": 0.04394, "time": 0.22275} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00217, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03875, "loss": 0.03875, "time": 0.22349} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00215, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03908, "loss": 0.03908, "time": 0.22195} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00214, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.05862, "loss": 0.05862, "time": 0.22227} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.00213, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07458, "loss": 0.07458, "time": 0.22331} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00212, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05633, "loss": 0.05633, "time": 0.22419} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00211, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04929, "loss": 0.04929, "time": 0.22568} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.0021, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.061, "loss": 0.061, "time": 0.22541} +{"mode": "val", "epoch": 122, "iter": 533, "lr": 0.00209, "top1_acc": 0.92876, "top5_acc": 0.99519, "mean_class_accuracy": 0.90497} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00208, "memory": 4083, "data_time": 0.18525, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0376, "loss": 0.0376, "time": 0.42045} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00207, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03463, "loss": 0.03463, "time": 0.22196} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00205, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05095, "loss": 0.05095, "time": 0.22352} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00204, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04839, "loss": 0.04839, "time": 0.22372} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00203, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.06436, "loss": 0.06436, "time": 0.22446} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00202, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.04771, "loss": 0.04771, "time": 0.22412} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00201, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99688, "top5_acc": 0.99938, "loss_cls": 0.04589, "loss": 0.04589, "time": 0.22474} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.002, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05385, "loss": 0.05385, "time": 0.22769} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00199, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05453, "loss": 0.05453, "time": 0.22367} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.00198, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04899, "loss": 0.04899, "time": 0.22694} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00197, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03327, "loss": 0.03327, "time": 0.22319} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00195, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05005, "loss": 0.05005, "time": 0.22356} +{"mode": "val", "epoch": 123, "iter": 533, "lr": 0.00195, "top1_acc": 0.93381, "top5_acc": 0.99531, "mean_class_accuracy": 0.9064} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00194, "memory": 4083, "data_time": 0.18344, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03717, "loss": 0.03717, "time": 0.41959} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00192, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.0348, "loss": 0.0348, "time": 0.22616} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00191, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03951, "loss": 0.03951, "time": 0.22201} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.0019, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04472, "loss": 0.04472, "time": 0.22415} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00189, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04719, "loss": 0.04719, "time": 0.22331} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00188, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05454, "loss": 0.05454, "time": 0.22288} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00187, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.05584, "loss": 0.05584, "time": 0.22299} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00186, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05427, "loss": 0.05427, "time": 0.22162} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00185, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07451, "loss": 0.07451, "time": 0.22342} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00184, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06213, "loss": 0.06213, "time": 0.22286} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00183, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04144, "loss": 0.04144, "time": 0.22557} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.00182, "memory": 4083, "data_time": 0.00066, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04684, "loss": 0.04684, "time": 0.2243} +{"mode": "val", "epoch": 124, "iter": 533, "lr": 0.00181, "top1_acc": 0.93135, "top5_acc": 0.99507, "mean_class_accuracy": 0.9063} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.0018, "memory": 4083, "data_time": 0.1832, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03585, "loss": 0.03585, "time": 0.41562} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.00179, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03062, "loss": 0.03062, "time": 0.22587} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00178, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02825, "loss": 0.02825, "time": 0.2201} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00177, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04139, "loss": 0.04139, "time": 0.2219} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00176, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03328, "loss": 0.03328, "time": 0.2207} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00175, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03272, "loss": 0.03272, "time": 0.2215} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00173, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03193, "loss": 0.03193, "time": 0.2225} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00172, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03225, "loss": 0.03225, "time": 0.22307} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.00171, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03711, "loss": 0.03711, "time": 0.22342} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.0017, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05178, "loss": 0.05178, "time": 0.22378} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00169, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03635, "loss": 0.03635, "time": 0.22314} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00168, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04289, "loss": 0.04289, "time": 0.22177} +{"mode": "val", "epoch": 125, "iter": 533, "lr": 0.00167, "top1_acc": 0.93299, "top5_acc": 0.99648, "mean_class_accuracy": 0.90793} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00166, "memory": 4083, "data_time": 0.18901, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.032, "loss": 0.032, "time": 0.42691} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00165, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0387, "loss": 0.0387, "time": 0.22392} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00164, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.0358, "loss": 0.0358, "time": 0.22117} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00163, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03692, "loss": 0.03692, "time": 0.22525} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00162, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04026, "loss": 0.04026, "time": 0.22402} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00161, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03434, "loss": 0.03434, "time": 0.22354} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0016, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04068, "loss": 0.04068, "time": 0.22258} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00159, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03983, "loss": 0.03983, "time": 0.2266} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00158, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04559, "loss": 0.04559, "time": 0.22322} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00157, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03297, "loss": 0.03297, "time": 0.22468} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00156, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03566, "loss": 0.03566, "time": 0.22173} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00155, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04413, "loss": 0.04413, "time": 0.22064} +{"mode": "val", "epoch": 126, "iter": 533, "lr": 0.00155, "top1_acc": 0.93698, "top5_acc": 0.99648, "mean_class_accuracy": 0.91288} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00154, "memory": 4083, "data_time": 0.1857, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04012, "loss": 0.04012, "time": 0.4252} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00153, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02814, "loss": 0.02814, "time": 0.22338} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00152, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03769, "loss": 0.03769, "time": 0.22042} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00151, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03855, "loss": 0.03855, "time": 0.22534} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.0015, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02853, "loss": 0.02853, "time": 0.22256} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.00149, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02833, "loss": 0.02833, "time": 0.22317} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00148, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03886, "loss": 0.03886, "time": 0.22066} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00147, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0282, "loss": 0.0282, "time": 0.22318} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00146, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02688, "loss": 0.02688, "time": 0.22438} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00145, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03212, "loss": 0.03212, "time": 0.22273} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00144, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02838, "loss": 0.02838, "time": 0.22225} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00143, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03384, "loss": 0.03384, "time": 0.22475} +{"mode": "val", "epoch": 127, "iter": 533, "lr": 0.00142, "top1_acc": 0.9378, "top5_acc": 0.99683, "mean_class_accuracy": 0.91399} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00141, "memory": 4083, "data_time": 0.19063, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03322, "loss": 0.03322, "time": 0.42319} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.0014, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.028, "loss": 0.028, "time": 0.22483} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00139, "memory": 4083, "data_time": 0.00065, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02422, "loss": 0.02422, "time": 0.22468} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00138, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02787, "loss": 0.02787, "time": 0.22374} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00138, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02921, "loss": 0.02921, "time": 0.22805} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00137, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02496, "loss": 0.02496, "time": 0.2222} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.00136, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02708, "loss": 0.02708, "time": 0.22388} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00135, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02786, "loss": 0.02786, "time": 0.22172} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00134, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03725, "loss": 0.03725, "time": 0.2239} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00133, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03984, "loss": 0.03984, "time": 0.227} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00132, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02603, "loss": 0.02603, "time": 0.21993} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00131, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03233, "loss": 0.03233, "time": 0.22517} +{"mode": "val", "epoch": 128, "iter": 533, "lr": 0.0013, "top1_acc": 0.93522, "top5_acc": 0.99613, "mean_class_accuracy": 0.90804} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.00129, "memory": 4083, "data_time": 0.18697, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02327, "loss": 0.02327, "time": 0.42531} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00129, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02359, "loss": 0.02359, "time": 0.22284} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00128, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.03885, "loss": 0.03885, "time": 0.22608} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00127, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02939, "loss": 0.02939, "time": 0.22421} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00126, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03325, "loss": 0.03325, "time": 0.22518} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00125, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02551, "loss": 0.02551, "time": 0.22292} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00124, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03277, "loss": 0.03277, "time": 0.22598} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00123, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03144, "loss": 0.03144, "time": 0.22527} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.00122, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03396, "loss": 0.03396, "time": 0.2269} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00121, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02909, "loss": 0.02909, "time": 0.22487} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00121, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02357, "loss": 0.02357, "time": 0.22576} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.0012, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03014, "loss": 0.03014, "time": 0.22505} +{"mode": "val", "epoch": 129, "iter": 533, "lr": 0.00119, "top1_acc": 0.93686, "top5_acc": 0.99695, "mean_class_accuracy": 0.90849} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00118, "memory": 4083, "data_time": 0.18224, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04045, "loss": 0.04045, "time": 0.41819} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00117, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03383, "loss": 0.03383, "time": 0.22507} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00116, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02476, "loss": 0.02476, "time": 0.22279} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00116, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02314, "loss": 0.02314, "time": 0.22364} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.00115, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0238, "loss": 0.0238, "time": 0.22486} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00114, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02382, "loss": 0.02382, "time": 0.22375} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00113, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02923, "loss": 0.02923, "time": 0.22414} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00112, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02798, "loss": 0.02798, "time": 0.22268} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00111, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03495, "loss": 0.03495, "time": 0.22477} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.0011, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02635, "loss": 0.02635, "time": 0.22071} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.0011, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02597, "loss": 0.02597, "time": 0.22268} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00109, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03271, "loss": 0.03271, "time": 0.22543} +{"mode": "val", "epoch": 130, "iter": 533, "lr": 0.00108, "top1_acc": 0.93991, "top5_acc": 0.99648, "mean_class_accuracy": 0.91245} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00107, "memory": 4083, "data_time": 0.18257, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0212, "loss": 0.0212, "time": 0.41787} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.00106, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02764, "loss": 0.02764, "time": 0.22643} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00106, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02442, "loss": 0.02442, "time": 0.22242} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00105, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02676, "loss": 0.02676, "time": 0.22412} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00104, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03286, "loss": 0.03286, "time": 0.22077} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00103, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.02866, "loss": 0.02866, "time": 0.22146} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00102, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02255, "loss": 0.02255, "time": 0.2239} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00102, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02613, "loss": 0.02613, "time": 0.22499} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00101, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02457, "loss": 0.02457, "time": 0.22405} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.001, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03227, "loss": 0.03227, "time": 0.2253} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.00099, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02502, "loss": 0.02502, "time": 0.22287} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00098, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02287, "loss": 0.02287, "time": 0.22366} +{"mode": "val", "epoch": 131, "iter": 533, "lr": 0.00098, "top1_acc": 0.93674, "top5_acc": 0.9966, "mean_class_accuracy": 0.91124} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.00097, "memory": 4083, "data_time": 0.18728, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02215, "loss": 0.02215, "time": 0.42239} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00096, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02059, "loss": 0.02059, "time": 0.22527} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00095, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02364, "loss": 0.02364, "time": 0.22359} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00095, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02282, "loss": 0.02282, "time": 0.2211} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00094, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02512, "loss": 0.02512, "time": 0.22191} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00093, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02528, "loss": 0.02528, "time": 0.22411} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00092, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02185, "loss": 0.02185, "time": 0.22415} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00091, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02605, "loss": 0.02605, "time": 0.22538} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00091, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02143, "loss": 0.02143, "time": 0.22248} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0009, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02108, "loss": 0.02108, "time": 0.22351} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00089, "memory": 4083, "data_time": 0.00056, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02277, "loss": 0.02277, "time": 0.22648} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00088, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02531, "loss": 0.02531, "time": 0.22313} +{"mode": "val", "epoch": 132, "iter": 533, "lr": 0.00088, "top1_acc": 0.93862, "top5_acc": 0.99648, "mean_class_accuracy": 0.91271} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.00087, "memory": 4083, "data_time": 0.18956, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02122, "loss": 0.02122, "time": 0.42282} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00086, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02124, "loss": 0.02124, "time": 0.22546} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00086, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02179, "loss": 0.02179, "time": 0.22335} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00085, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02518, "loss": 0.02518, "time": 0.22108} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00084, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02544, "loss": 0.02544, "time": 0.2257} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00083, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02701, "loss": 0.02701, "time": 0.22564} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00083, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02363, "loss": 0.02363, "time": 0.22139} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00082, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02913, "loss": 0.02913, "time": 0.22573} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00081, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02056, "loss": 0.02056, "time": 0.22387} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.0008, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02253, "loss": 0.02253, "time": 0.22066} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0008, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02361, "loss": 0.02361, "time": 0.22304} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00079, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02422, "loss": 0.02422, "time": 0.22377} +{"mode": "val", "epoch": 133, "iter": 533, "lr": 0.00078, "top1_acc": 0.93886, "top5_acc": 0.99636, "mean_class_accuracy": 0.91201} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00078, "memory": 4083, "data_time": 0.19134, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02056, "loss": 0.02056, "time": 0.42517} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00077, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02396, "loss": 0.02396, "time": 0.22628} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00076, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02267, "loss": 0.02267, "time": 0.22347} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.00076, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02775, "loss": 0.02775, "time": 0.22392} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00075, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02088, "loss": 0.02088, "time": 0.22373} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00074, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01972, "loss": 0.01972, "time": 0.22099} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00073, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02017, "loss": 0.02017, "time": 0.22585} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00073, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02096, "loss": 0.02096, "time": 0.2223} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00072, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02323, "loss": 0.02323, "time": 0.22184} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00071, "memory": 4083, "data_time": 0.00052, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02005, "loss": 0.02005, "time": 0.22098} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00071, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02587, "loss": 0.02587, "time": 0.22323} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.0007, "memory": 4083, "data_time": 0.00065, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02056, "loss": 0.02056, "time": 0.22779} +{"mode": "val", "epoch": 134, "iter": 533, "lr": 0.0007, "top1_acc": 0.93897, "top5_acc": 0.99695, "mean_class_accuracy": 0.91453} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00069, "memory": 4083, "data_time": 0.19229, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02315, "loss": 0.02315, "time": 0.43015} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00068, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01912, "loss": 0.01912, "time": 0.22573} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00068, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02006, "loss": 0.02006, "time": 0.22262} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00067, "memory": 4083, "data_time": 0.00041, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01937, "loss": 0.01937, "time": 0.22342} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00066, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02229, "loss": 0.02229, "time": 0.22615} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00066, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02252, "loss": 0.02252, "time": 0.22441} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00065, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01947, "loss": 0.01947, "time": 0.22376} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00064, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02788, "loss": 0.02788, "time": 0.22631} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.00064, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02278, "loss": 0.02278, "time": 0.2227} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00063, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02183, "loss": 0.02183, "time": 0.22434} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00062, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01914, "loss": 0.01914, "time": 0.22526} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00062, "memory": 4083, "data_time": 0.0005, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02006, "loss": 0.02006, "time": 0.22683} +{"mode": "val", "epoch": 135, "iter": 533, "lr": 0.00061, "top1_acc": 0.93839, "top5_acc": 0.99636, "mean_class_accuracy": 0.91314} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00061, "memory": 4083, "data_time": 0.19403, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01935, "loss": 0.01935, "time": 0.42911} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.0006, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02133, "loss": 0.02133, "time": 0.22452} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00059, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01859, "loss": 0.01859, "time": 0.222} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00059, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01903, "loss": 0.01903, "time": 0.22271} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.00058, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01991, "loss": 0.01991, "time": 0.22174} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.00057, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02172, "loss": 0.02172, "time": 0.22311} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00057, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01983, "loss": 0.01983, "time": 0.2238} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00056, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01848, "loss": 0.01848, "time": 0.2235} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00056, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02122, "loss": 0.02122, "time": 0.22422} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00055, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01884, "loss": 0.01884, "time": 0.21984} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00054, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02082, "loss": 0.02082, "time": 0.22258} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00054, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02396, "loss": 0.02396, "time": 0.22419} +{"mode": "val", "epoch": 136, "iter": 533, "lr": 0.00053, "top1_acc": 0.93897, "top5_acc": 0.99624, "mean_class_accuracy": 0.91226} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00053, "memory": 4083, "data_time": 0.19463, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02184, "loss": 0.02184, "time": 0.43063} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00052, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02161, "loss": 0.02161, "time": 0.2239} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00052, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01967, "loss": 0.01967, "time": 0.22417} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.00051, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01997, "loss": 0.01997, "time": 0.22255} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.0005, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02478, "loss": 0.02478, "time": 0.22316} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.0005, "memory": 4083, "data_time": 0.00063, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02068, "loss": 0.02068, "time": 0.22701} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00049, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.0263, "loss": 0.0263, "time": 0.22417} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00049, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01953, "loss": 0.01953, "time": 0.22299} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00048, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02043, "loss": 0.02043, "time": 0.22076} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00048, "memory": 4083, "data_time": 0.00045, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02072, "loss": 0.02072, "time": 0.22488} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00047, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02232, "loss": 0.02232, "time": 0.22465} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00046, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01837, "loss": 0.01837, "time": 0.223} +{"mode": "val", "epoch": 137, "iter": 533, "lr": 0.00046, "top1_acc": 0.94144, "top5_acc": 0.99671, "mean_class_accuracy": 0.91823} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00046, "memory": 4083, "data_time": 0.18841, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02241, "loss": 0.02241, "time": 0.41902} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00045, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02248, "loss": 0.02248, "time": 0.22356} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00044, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01963, "loss": 0.01963, "time": 0.22115} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00044, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02111, "loss": 0.02111, "time": 0.2231} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.00043, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01903, "loss": 0.01903, "time": 0.22407} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.00043, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01974, "loss": 0.01974, "time": 0.22073} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00042, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01781, "loss": 0.01781, "time": 0.22085} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00042, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0222, "loss": 0.0222, "time": 0.2267} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00041, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01795, "loss": 0.01795, "time": 0.21881} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00041, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01959, "loss": 0.01959, "time": 0.22178} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.0004, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01947, "loss": 0.01947, "time": 0.2199} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.0004, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01902, "loss": 0.01902, "time": 0.22343} +{"mode": "val", "epoch": 138, "iter": 533, "lr": 0.00039, "top1_acc": 0.93921, "top5_acc": 0.99648, "mean_class_accuracy": 0.91459} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00039, "memory": 4083, "data_time": 0.1817, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02338, "loss": 0.02338, "time": 0.41667} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00038, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02798, "loss": 0.02798, "time": 0.21998} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00038, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01872, "loss": 0.01872, "time": 0.2233} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00037, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02311, "loss": 0.02311, "time": 0.22306} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00037, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01963, "loss": 0.01963, "time": 0.22075} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00036, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02088, "loss": 0.02088, "time": 0.22226} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00036, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01848, "loss": 0.01848, "time": 0.22073} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00035, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01684, "loss": 0.01684, "time": 0.22291} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00035, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02087, "loss": 0.02087, "time": 0.22381} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.00034, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02097, "loss": 0.02097, "time": 0.22697} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.00034, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01978, "loss": 0.01978, "time": 0.22217} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00033, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0185, "loss": 0.0185, "time": 0.22292} +{"mode": "val", "epoch": 139, "iter": 533, "lr": 0.00033, "top1_acc": 0.94038, "top5_acc": 0.99636, "mean_class_accuracy": 0.91665} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00033, "memory": 4083, "data_time": 0.1839, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02396, "loss": 0.02396, "time": 0.41881} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00032, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01971, "loss": 0.01971, "time": 0.22097} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.00032, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01786, "loss": 0.01786, "time": 0.22219} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.00031, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02029, "loss": 0.02029, "time": 0.22092} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00031, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02192, "loss": 0.02192, "time": 0.22556} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.0003, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01981, "loss": 0.01981, "time": 0.22215} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.0003, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01663, "loss": 0.01663, "time": 0.22389} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00029, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01956, "loss": 0.01956, "time": 0.22294} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00029, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02258, "loss": 0.02258, "time": 0.22157} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00029, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02113, "loss": 0.02113, "time": 0.22237} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00028, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02028, "loss": 0.02028, "time": 0.22003} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00028, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02058, "loss": 0.02058, "time": 0.22861} +{"mode": "val", "epoch": 140, "iter": 533, "lr": 0.00027, "top1_acc": 0.93933, "top5_acc": 0.99648, "mean_class_accuracy": 0.91553} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00027, "memory": 4083, "data_time": 0.18526, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02129, "loss": 0.02129, "time": 0.41823} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00026, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01968, "loss": 0.01968, "time": 0.22243} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00026, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02061, "loss": 0.02061, "time": 0.22109} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00026, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02162, "loss": 0.02162, "time": 0.22023} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00025, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01883, "loss": 0.01883, "time": 0.22364} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00025, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01809, "loss": 0.01809, "time": 0.22502} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00024, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0205, "loss": 0.0205, "time": 0.22164} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00024, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02087, "loss": 0.02087, "time": 0.22488} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00024, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01897, "loss": 0.01897, "time": 0.2246} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00023, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02406, "loss": 0.02406, "time": 0.21849} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00023, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01971, "loss": 0.01971, "time": 0.22013} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00022, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01667, "loss": 0.01667, "time": 0.22477} +{"mode": "val", "epoch": 141, "iter": 533, "lr": 0.00022, "top1_acc": 0.94132, "top5_acc": 0.99695, "mean_class_accuracy": 0.91664} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00022, "memory": 4083, "data_time": 0.19104, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02095, "loss": 0.02095, "time": 0.42449} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00021, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01768, "loss": 0.01768, "time": 0.21893} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00021, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01782, "loss": 0.01782, "time": 0.21893} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00021, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02211, "loss": 0.02211, "time": 0.22377} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.0002, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02746, "loss": 0.02746, "time": 0.22092} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.0002, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02053, "loss": 0.02053, "time": 0.22258} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.0002, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01973, "loss": 0.01973, "time": 0.22001} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00019, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01752, "loss": 0.01752, "time": 0.21958} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00019, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01812, "loss": 0.01812, "time": 0.22341} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00018, "memory": 4083, "data_time": 0.0005, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01752, "loss": 0.01752, "time": 0.22289} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00018, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02101, "loss": 0.02101, "time": 0.22103} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00018, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01938, "loss": 0.01938, "time": 0.22217} +{"mode": "val", "epoch": 142, "iter": 533, "lr": 0.00018, "top1_acc": 0.94027, "top5_acc": 0.99589, "mean_class_accuracy": 0.91732} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.00017, "memory": 4083, "data_time": 0.18357, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01682, "loss": 0.01682, "time": 0.41986} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00017, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01945, "loss": 0.01945, "time": 0.22583} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00017, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01731, "loss": 0.01731, "time": 0.22134} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00016, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01918, "loss": 0.01918, "time": 0.22326} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00016, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01927, "loss": 0.01927, "time": 0.22684} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00016, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01722, "loss": 0.01722, "time": 0.2233} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00015, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01775, "loss": 0.01775, "time": 0.22475} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00015, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02301, "loss": 0.02301, "time": 0.22217} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00015, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02025, "loss": 0.02025, "time": 0.22235} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00014, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02121, "loss": 0.02121, "time": 0.21959} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00014, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01817, "loss": 0.01817, "time": 0.22581} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00014, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01751, "loss": 0.01751, "time": 0.21965} +{"mode": "val", "epoch": 143, "iter": 533, "lr": 0.00013, "top1_acc": 0.94144, "top5_acc": 0.99648, "mean_class_accuracy": 0.91725} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00013, "memory": 4083, "data_time": 0.18846, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02196, "loss": 0.02196, "time": 0.42249} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00013, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01752, "loss": 0.01752, "time": 0.22187} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00013, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01929, "loss": 0.01929, "time": 0.22131} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00012, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01886, "loss": 0.01886, "time": 0.22259} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00012, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02081, "loss": 0.02081, "time": 0.22146} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00012, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02467, "loss": 0.02467, "time": 0.22159} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00011, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02044, "loss": 0.02044, "time": 0.22222} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.00011, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01987, "loss": 0.01987, "time": 0.22119} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.00011, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02108, "loss": 0.02108, "time": 0.22451} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.00011, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01825, "loss": 0.01825, "time": 0.22381} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.0001, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02271, "loss": 0.02271, "time": 0.22032} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.0001, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02007, "loss": 0.02007, "time": 0.22158} +{"mode": "val", "epoch": 144, "iter": 533, "lr": 0.0001, "top1_acc": 0.94109, "top5_acc": 0.99636, "mean_class_accuracy": 0.91774} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.0001, "memory": 4083, "data_time": 0.18744, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01799, "loss": 0.01799, "time": 0.42322} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 9e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02205, "loss": 0.02205, "time": 0.22276} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 9e-05, "memory": 4083, "data_time": 0.00052, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01886, "loss": 0.01886, "time": 0.22513} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 9e-05, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01943, "loss": 0.01943, "time": 0.2237} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 9e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01729, "loss": 0.01729, "time": 0.22023} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 8e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01974, "loss": 0.01974, "time": 0.22248} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 8e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01827, "loss": 0.01827, "time": 0.22126} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 8e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01764, "loss": 0.01764, "time": 0.223} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 8e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01984, "loss": 0.01984, "time": 0.22141} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 7e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0169, "loss": 0.0169, "time": 0.22322} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 7e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01769, "loss": 0.01769, "time": 0.22073} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 7e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02018, "loss": 0.02018, "time": 0.22596} +{"mode": "val", "epoch": 145, "iter": 533, "lr": 7e-05, "top1_acc": 0.94073, "top5_acc": 0.99683, "mean_class_accuracy": 0.91606} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 7e-05, "memory": 4083, "data_time": 0.1859, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01772, "loss": 0.01772, "time": 0.41718} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 6e-05, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01727, "loss": 0.01727, "time": 0.22405} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 6e-05, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01889, "loss": 0.01889, "time": 0.22165} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 6e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01852, "loss": 0.01852, "time": 0.22234} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 6e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02237, "loss": 0.02237, "time": 0.22296} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 6e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01941, "loss": 0.01941, "time": 0.21861} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 5e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01788, "loss": 0.01788, "time": 0.22027} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 5e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01704, "loss": 0.01704, "time": 0.2204} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 5e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01948, "loss": 0.01948, "time": 0.22193} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 5e-05, "memory": 4083, "data_time": 0.00063, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0212, "loss": 0.0212, "time": 0.22438} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 5e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01974, "loss": 0.01974, "time": 0.21999} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 5e-05, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.019, "loss": 0.019, "time": 0.22518} +{"mode": "val", "epoch": 146, "iter": 533, "lr": 4e-05, "top1_acc": 0.9412, "top5_acc": 0.99671, "mean_class_accuracy": 0.91661} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 4e-05, "memory": 4083, "data_time": 0.18334, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02017, "loss": 0.02017, "time": 0.41913} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 4e-05, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02201, "loss": 0.02201, "time": 0.22494} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 4e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01849, "loss": 0.01849, "time": 0.22578} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 4e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0178, "loss": 0.0178, "time": 0.22191} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 4e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01887, "loss": 0.01887, "time": 0.22197} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 3e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02162, "loss": 0.02162, "time": 0.22391} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 3e-05, "memory": 4083, "data_time": 0.00052, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01828, "loss": 0.01828, "time": 0.22496} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 3e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01803, "loss": 0.01803, "time": 0.22065} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 3e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01966, "loss": 0.01966, "time": 0.22364} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 3e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01732, "loss": 0.01732, "time": 0.22296} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 3e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02065, "loss": 0.02065, "time": 0.22354} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 3e-05, "memory": 4083, "data_time": 0.00048, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01912, "loss": 0.01912, "time": 0.22396} +{"mode": "val", "epoch": 147, "iter": 533, "lr": 2e-05, "top1_acc": 0.94238, "top5_acc": 0.99671, "mean_class_accuracy": 0.91956} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 2e-05, "memory": 4083, "data_time": 0.18267, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01727, "loss": 0.01727, "time": 0.41493} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 2e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01814, "loss": 0.01814, "time": 0.22097} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 2e-05, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01773, "loss": 0.01773, "time": 0.22245} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 2e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01727, "loss": 0.01727, "time": 0.22213} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 2e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01974, "loss": 0.01974, "time": 0.21929} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 2e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01805, "loss": 0.01805, "time": 0.22031} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 2e-05, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01881, "loss": 0.01881, "time": 0.21885} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 2e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01711, "loss": 0.01711, "time": 0.22003} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 1e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02058, "loss": 0.02058, "time": 0.22276} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 1e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01909, "loss": 0.01909, "time": 0.22083} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01918, "loss": 0.01918, "time": 0.22127} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 1e-05, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02333, "loss": 0.02333, "time": 0.22611} +{"mode": "val", "epoch": 148, "iter": 533, "lr": 1e-05, "top1_acc": 0.9425, "top5_acc": 0.99671, "mean_class_accuracy": 0.91889} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 1e-05, "memory": 4083, "data_time": 0.18708, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02057, "loss": 0.02057, "time": 0.42279} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 1e-05, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02075, "loss": 0.02075, "time": 0.22457} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 1e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01829, "loss": 0.01829, "time": 0.22222} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 1e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01742, "loss": 0.01742, "time": 0.22224} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 1e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01897, "loss": 0.01897, "time": 0.22384} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 1e-05, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01899, "loss": 0.01899, "time": 0.22385} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 1e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02053, "loss": 0.02053, "time": 0.22172} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 1e-05, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01729, "loss": 0.01729, "time": 0.22139} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02099, "loss": 0.02099, "time": 0.22359} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01891, "loss": 0.01891, "time": 0.21929} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01789, "loss": 0.01789, "time": 0.22724} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01889, "loss": 0.01889, "time": 0.22051} +{"mode": "val", "epoch": 149, "iter": 533, "lr": 0.0, "top1_acc": 0.94214, "top5_acc": 0.99648, "mean_class_accuracy": 0.91826} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 0.0, "memory": 4083, "data_time": 0.19016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0176, "loss": 0.0176, "time": 0.42289} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02039, "loss": 0.02039, "time": 0.22196} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 0.0, "memory": 4083, "data_time": 0.00044, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01922, "loss": 0.01922, "time": 0.22354} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01786, "loss": 0.01786, "time": 0.22135} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 0.0, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02576, "loss": 0.02576, "time": 0.22347} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02047, "loss": 0.02047, "time": 0.22055} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01833, "loss": 0.01833, "time": 0.21946} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 0.0, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01904, "loss": 0.01904, "time": 0.22413} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01747, "loss": 0.01747, "time": 0.21913} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01837, "loss": 0.01837, "time": 0.22045} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01831, "loss": 0.01831, "time": 0.22512} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00052, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01771, "loss": 0.01771, "time": 0.22432} +{"mode": "val", "epoch": 150, "iter": 533, "lr": 0.0, "top1_acc": 0.94261, "top5_acc": 0.9966, "mean_class_accuracy": 0.91928} diff --git a/finegym/k_1/best_pred.pkl b/finegym/k_1/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..2e178e332ebec4d0ddcff03ffb44fe56b673001f --- /dev/null +++ b/finegym/k_1/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3c06926547b6652db4986b1a7d59f41d80ae7834c149eb0689a0317f5f4e848a +size 5257632 diff --git a/finegym/k_1/best_top1_acc_epoch_150.pth b/finegym/k_1/best_top1_acc_epoch_150.pth new file mode 100644 index 0000000000000000000000000000000000000000..bd4d141fc24d48c2de490338bafa26c3cee7b96a --- /dev/null +++ b/finegym/k_1/best_top1_acc_epoch_150.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:487f7f480cc3b6c91ab42dfbef04731c5dfc3c9628e1af31eb58e87f5aca765c +size 31999601 diff --git a/finegym/k_1/k_1.py b/finegym/k_1/k_1.py new file mode 100644 index 0000000000000000000000000000000000000000..9ee44729d4c6704bae43bda0c0ec2268a8f23335 --- /dev/null +++ b/finegym/k_1/k_1.py @@ -0,0 +1,113 @@ +modality = 'k' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/k_1' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/finegym/k_2/20250624_101213.log b/finegym/k_2/20250624_101213.log new file mode 100644 index 0000000000000000000000000000000000000000..9b2779e246a10f0a644cad23d90476f017a9c092 --- /dev/null +++ b/finegym/k_2/20250624_101213.log @@ -0,0 +1,3498 @@ +2025-06-24 10:12:13,796 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2025-06-24 10:12:14,054 - pyskl - INFO - Config: modality = 'k' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/k_2' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2025-06-24 10:12:14,055 - pyskl - INFO - Set random seed to 171654347, deterministic: False +2025-06-24 10:12:15,600 - pyskl - INFO - 20484 videos remain after valid thresholding +2025-06-24 10:12:21,206 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-24 10:12:21,207 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2 +2025-06-24 10:12:21,207 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2025-06-24 10:12:21,208 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2025-06-24 10:12:21,208 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2 by HardDiskBackend. +2025-06-24 10:13:26,244 - pyskl - INFO - Epoch [1][100/1281] lr: 2.500e-02, eta: 1 day, 10:41:33, time: 0.650, data_time: 0.194, memory: 4082, top1_acc: 0.0650, top5_acc: 0.2256, loss_cls: 4.5640, loss: 4.5640 +2025-06-24 10:13:50,359 - pyskl - INFO - Epoch [1][200/1281] lr: 2.500e-02, eta: 23:45:55, time: 0.241, data_time: 0.000, memory: 4082, top1_acc: 0.1013, top5_acc: 0.3319, loss_cls: 4.5377, loss: 4.5377 +2025-06-24 10:14:31,871 - pyskl - INFO - Epoch [1][300/1281] lr: 2.500e-02, eta: 23:12:33, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.1169, top5_acc: 0.3619, loss_cls: 4.3175, loss: 4.3175 +2025-06-24 10:15:13,209 - pyskl - INFO - Epoch [1][400/1281] lr: 2.500e-02, eta: 22:54:08, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.1256, top5_acc: 0.4188, loss_cls: 4.0323, loss: 4.0323 +2025-06-24 10:15:54,699 - pyskl - INFO - Epoch [1][500/1281] lr: 2.500e-02, eta: 22:43:47, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.1437, top5_acc: 0.5081, loss_cls: 3.7829, loss: 3.7829 +2025-06-24 10:16:36,060 - pyskl - INFO - Epoch [1][600/1281] lr: 2.500e-02, eta: 22:35:58, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.1688, top5_acc: 0.5106, loss_cls: 3.6910, loss: 3.6910 +2025-06-24 10:17:17,705 - pyskl - INFO - Epoch [1][700/1281] lr: 2.500e-02, eta: 22:31:29, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.1787, top5_acc: 0.5306, loss_cls: 3.6085, loss: 3.6085 +2025-06-24 10:17:59,212 - pyskl - INFO - Epoch [1][800/1281] lr: 2.500e-02, eta: 22:27:23, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.2169, top5_acc: 0.5913, loss_cls: 3.3913, loss: 3.3913 +2025-06-24 10:18:40,700 - pyskl - INFO - Epoch [1][900/1281] lr: 2.500e-02, eta: 22:23:59, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.2544, top5_acc: 0.6050, loss_cls: 3.2616, loss: 3.2616 +2025-06-24 10:19:21,961 - pyskl - INFO - Epoch [1][1000/1281] lr: 2.500e-02, eta: 22:20:24, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.2825, top5_acc: 0.6512, loss_cls: 3.0926, loss: 3.0926 +2025-06-24 10:20:03,423 - pyskl - INFO - Epoch [1][1100/1281] lr: 2.500e-02, eta: 22:17:55, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.3050, top5_acc: 0.7119, loss_cls: 2.9579, loss: 2.9579 +2025-06-24 10:20:44,751 - pyskl - INFO - Epoch [1][1200/1281] lr: 2.500e-02, eta: 22:15:23, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.3269, top5_acc: 0.7275, loss_cls: 2.8641, loss: 2.8641 +2025-06-24 10:21:20,319 - pyskl - INFO - Saving checkpoint at 1 epochs +2025-06-24 10:22:29,526 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:22:29,582 - pyskl - INFO - +top1_acc 0.2853 +top5_acc 0.6909 +2025-06-24 10:22:29,582 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:22:29,591 - pyskl - INFO - +mean_acc 0.1552 +2025-06-24 10:22:29,782 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2025-06-24 10:22:29,782 - pyskl - INFO - Best top1_acc is 0.2853 at 1 epoch. +2025-06-24 10:22:29,785 - pyskl - INFO - Epoch(val) [1][533] top1_acc: 0.2853, top5_acc: 0.6909, mean_class_accuracy: 0.1552 +2025-06-24 10:23:33,870 - pyskl - INFO - Epoch [2][100/1281] lr: 2.500e-02, eta: 21:46:48, time: 0.641, data_time: 0.190, memory: 4082, top1_acc: 0.3431, top5_acc: 0.7494, loss_cls: 2.7588, loss: 2.7588 +2025-06-24 10:23:56,665 - pyskl - INFO - Epoch [2][200/1281] lr: 2.500e-02, eta: 21:06:49, time: 0.228, data_time: 0.000, memory: 4082, top1_acc: 0.3606, top5_acc: 0.7819, loss_cls: 2.6062, loss: 2.6062 +2025-06-24 10:24:38,360 - pyskl - INFO - Epoch [2][300/1281] lr: 2.500e-02, eta: 21:09:50, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.3912, top5_acc: 0.8169, loss_cls: 2.4801, loss: 2.4801 +2025-06-24 10:25:19,967 - pyskl - INFO - Epoch [2][400/1281] lr: 2.500e-02, eta: 21:12:14, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.4025, top5_acc: 0.8025, loss_cls: 2.4226, loss: 2.4226 +2025-06-24 10:26:01,760 - pyskl - INFO - Epoch [2][500/1281] lr: 2.499e-02, eta: 21:14:37, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.4306, top5_acc: 0.8163, loss_cls: 2.3769, loss: 2.3769 +2025-06-24 10:26:43,138 - pyskl - INFO - Epoch [2][600/1281] lr: 2.499e-02, eta: 21:15:59, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.4531, top5_acc: 0.8363, loss_cls: 2.2740, loss: 2.2740 +2025-06-24 10:27:24,699 - pyskl - INFO - Epoch [2][700/1281] lr: 2.499e-02, eta: 21:17:26, time: 0.416, data_time: 0.001, memory: 4082, top1_acc: 0.4437, top5_acc: 0.8581, loss_cls: 2.2071, loss: 2.2071 +2025-06-24 10:28:08,194 - pyskl - INFO - Epoch [2][800/1281] lr: 2.499e-02, eta: 21:21:37, time: 0.435, data_time: 0.000, memory: 4082, top1_acc: 0.4700, top5_acc: 0.8569, loss_cls: 2.1740, loss: 2.1740 +2025-06-24 10:28:49,478 - pyskl - INFO - Epoch [2][900/1281] lr: 2.499e-02, eta: 21:22:08, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.4775, top5_acc: 0.8719, loss_cls: 2.1156, loss: 2.1156 +2025-06-24 10:29:30,860 - pyskl - INFO - Epoch [2][1000/1281] lr: 2.499e-02, eta: 21:22:41, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.4700, top5_acc: 0.8444, loss_cls: 2.1963, loss: 2.1963 +2025-06-24 10:30:12,147 - pyskl - INFO - Epoch [2][1100/1281] lr: 2.499e-02, eta: 21:23:01, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.4963, top5_acc: 0.8788, loss_cls: 2.0291, loss: 2.0291 +2025-06-24 10:30:53,559 - pyskl - INFO - Epoch [2][1200/1281] lr: 2.499e-02, eta: 21:23:25, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.4913, top5_acc: 0.8712, loss_cls: 2.0970, loss: 2.0970 +2025-06-24 10:31:27,960 - pyskl - INFO - Saving checkpoint at 2 epochs +2025-06-24 10:32:37,739 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:32:37,793 - pyskl - INFO - +top1_acc 0.4982 +top5_acc 0.8781 +2025-06-24 10:32:37,793 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:32:37,800 - pyskl - INFO - +mean_acc 0.3199 +2025-06-24 10:32:37,804 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_1.pth was removed +2025-06-24 10:32:37,988 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2025-06-24 10:32:37,989 - pyskl - INFO - Best top1_acc is 0.4982 at 2 epoch. +2025-06-24 10:32:37,991 - pyskl - INFO - Epoch(val) [2][533] top1_acc: 0.4982, top5_acc: 0.8781, mean_class_accuracy: 0.3199 +2025-06-24 10:33:42,320 - pyskl - INFO - Epoch [3][100/1281] lr: 2.499e-02, eta: 21:11:19, time: 0.643, data_time: 0.189, memory: 4082, top1_acc: 0.5069, top5_acc: 0.8838, loss_cls: 1.9956, loss: 1.9956 +2025-06-24 10:34:06,750 - pyskl - INFO - Epoch [3][200/1281] lr: 2.499e-02, eta: 20:52:34, time: 0.244, data_time: 0.001, memory: 4082, top1_acc: 0.5406, top5_acc: 0.9100, loss_cls: 1.8627, loss: 1.8627 +2025-06-24 10:34:47,866 - pyskl - INFO - Epoch [3][300/1281] lr: 2.499e-02, eta: 20:53:29, time: 0.411, data_time: 0.001, memory: 4082, top1_acc: 0.5506, top5_acc: 0.9106, loss_cls: 1.8351, loss: 1.8351 +2025-06-24 10:35:30,134 - pyskl - INFO - Epoch [3][400/1281] lr: 2.499e-02, eta: 20:55:31, time: 0.423, data_time: 0.001, memory: 4082, top1_acc: 0.5406, top5_acc: 0.9006, loss_cls: 1.8638, loss: 1.8638 +2025-06-24 10:36:11,567 - pyskl - INFO - Epoch [3][500/1281] lr: 2.498e-02, eta: 20:56:31, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.5531, top5_acc: 0.9187, loss_cls: 1.8045, loss: 1.8045 +2025-06-24 10:36:53,019 - pyskl - INFO - Epoch [3][600/1281] lr: 2.498e-02, eta: 20:57:25, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.5794, top5_acc: 0.9181, loss_cls: 1.7233, loss: 1.7233 +2025-06-24 10:37:34,527 - pyskl - INFO - Epoch [3][700/1281] lr: 2.498e-02, eta: 20:58:17, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.5787, top5_acc: 0.9081, loss_cls: 1.7783, loss: 1.7783 +2025-06-24 10:38:15,875 - pyskl - INFO - Epoch [3][800/1281] lr: 2.498e-02, eta: 20:58:54, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.5819, top5_acc: 0.9281, loss_cls: 1.7140, loss: 1.7140 +2025-06-24 10:38:57,323 - pyskl - INFO - Epoch [3][900/1281] lr: 2.498e-02, eta: 20:59:33, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.5625, top5_acc: 0.9250, loss_cls: 1.7824, loss: 1.7824 +2025-06-24 10:39:38,727 - pyskl - INFO - Epoch [3][1000/1281] lr: 2.498e-02, eta: 21:00:04, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.5869, top5_acc: 0.9287, loss_cls: 1.6625, loss: 1.6625 +2025-06-24 10:40:20,216 - pyskl - INFO - Epoch [3][1100/1281] lr: 2.498e-02, eta: 21:00:36, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6144, top5_acc: 0.9387, loss_cls: 1.5963, loss: 1.5963 +2025-06-24 10:41:01,769 - pyskl - INFO - Epoch [3][1200/1281] lr: 2.498e-02, eta: 21:01:07, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.5931, top5_acc: 0.9287, loss_cls: 1.6715, loss: 1.6715 +2025-06-24 10:41:36,025 - pyskl - INFO - Saving checkpoint at 3 epochs +2025-06-24 10:42:47,208 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:42:47,275 - pyskl - INFO - +top1_acc 0.5999 +top5_acc 0.9356 +2025-06-24 10:42:47,276 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:42:47,285 - pyskl - INFO - +mean_acc 0.4576 +2025-06-24 10:42:47,291 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_2.pth was removed +2025-06-24 10:42:47,492 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2025-06-24 10:42:47,492 - pyskl - INFO - Best top1_acc is 0.5999 at 3 epoch. +2025-06-24 10:42:47,495 - pyskl - INFO - Epoch(val) [3][533] top1_acc: 0.5999, top5_acc: 0.9356, mean_class_accuracy: 0.4576 +2025-06-24 10:43:52,093 - pyskl - INFO - Epoch [4][100/1281] lr: 2.497e-02, eta: 20:53:28, time: 0.646, data_time: 0.191, memory: 4082, top1_acc: 0.6000, top5_acc: 0.9450, loss_cls: 1.6151, loss: 1.6151 +2025-06-24 10:44:16,015 - pyskl - INFO - Epoch [4][200/1281] lr: 2.497e-02, eta: 20:40:21, time: 0.239, data_time: 0.000, memory: 4082, top1_acc: 0.6081, top5_acc: 0.9406, loss_cls: 1.5600, loss: 1.5600 +2025-06-24 10:44:57,581 - pyskl - INFO - Epoch [4][300/1281] lr: 2.497e-02, eta: 20:41:13, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6238, top5_acc: 0.9575, loss_cls: 1.5345, loss: 1.5345 +2025-06-24 10:45:38,960 - pyskl - INFO - Epoch [4][400/1281] lr: 2.497e-02, eta: 20:41:51, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.5962, top5_acc: 0.9394, loss_cls: 1.5874, loss: 1.5874 +2025-06-24 10:46:20,503 - pyskl - INFO - Epoch [4][500/1281] lr: 2.497e-02, eta: 20:42:33, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6306, top5_acc: 0.9550, loss_cls: 1.5343, loss: 1.5343 +2025-06-24 10:47:01,828 - pyskl - INFO - Epoch [4][600/1281] lr: 2.497e-02, eta: 20:43:02, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.6100, top5_acc: 0.9525, loss_cls: 1.5264, loss: 1.5264 +2025-06-24 10:47:43,187 - pyskl - INFO - Epoch [4][700/1281] lr: 2.497e-02, eta: 20:43:30, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6312, top5_acc: 0.9581, loss_cls: 1.4938, loss: 1.4938 +2025-06-24 10:48:24,685 - pyskl - INFO - Epoch [4][800/1281] lr: 2.496e-02, eta: 20:44:00, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6275, top5_acc: 0.9463, loss_cls: 1.5357, loss: 1.5357 +2025-06-24 10:49:06,087 - pyskl - INFO - Epoch [4][900/1281] lr: 2.496e-02, eta: 20:44:23, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6250, top5_acc: 0.9487, loss_cls: 1.4759, loss: 1.4759 +2025-06-24 10:49:47,518 - pyskl - INFO - Epoch [4][1000/1281] lr: 2.496e-02, eta: 20:44:44, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6538, top5_acc: 0.9487, loss_cls: 1.4489, loss: 1.4489 +2025-06-24 10:50:29,080 - pyskl - INFO - Epoch [4][1100/1281] lr: 2.496e-02, eta: 20:45:08, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6500, top5_acc: 0.9556, loss_cls: 1.4622, loss: 1.4622 +2025-06-24 10:51:10,301 - pyskl - INFO - Epoch [4][1200/1281] lr: 2.496e-02, eta: 20:45:17, time: 0.412, data_time: 0.000, memory: 4082, top1_acc: 0.6481, top5_acc: 0.9481, loss_cls: 1.4411, loss: 1.4411 +2025-06-24 10:51:44,517 - pyskl - INFO - Saving checkpoint at 4 epochs +2025-06-24 10:52:55,510 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:52:55,581 - pyskl - INFO - +top1_acc 0.6476 +top5_acc 0.9506 +2025-06-24 10:52:55,581 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:52:55,589 - pyskl - INFO - +mean_acc 0.5083 +2025-06-24 10:52:55,593 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_3.pth was removed +2025-06-24 10:52:55,792 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2025-06-24 10:52:55,792 - pyskl - INFO - Best top1_acc is 0.6476 at 4 epoch. +2025-06-24 10:52:55,795 - pyskl - INFO - Epoch(val) [4][533] top1_acc: 0.6476, top5_acc: 0.9506, mean_class_accuracy: 0.5083 +2025-06-24 10:54:00,474 - pyskl - INFO - Epoch [5][100/1281] lr: 2.495e-02, eta: 20:39:33, time: 0.647, data_time: 0.195, memory: 4082, top1_acc: 0.6569, top5_acc: 0.9550, loss_cls: 1.4052, loss: 1.4052 +2025-06-24 10:54:24,061 - pyskl - INFO - Epoch [5][200/1281] lr: 2.495e-02, eta: 20:29:25, time: 0.236, data_time: 0.001, memory: 4082, top1_acc: 0.6581, top5_acc: 0.9544, loss_cls: 1.4174, loss: 1.4174 +2025-06-24 10:55:07,244 - pyskl - INFO - Epoch [5][300/1281] lr: 2.495e-02, eta: 20:30:52, time: 0.432, data_time: 0.001, memory: 4082, top1_acc: 0.6669, top5_acc: 0.9700, loss_cls: 1.3546, loss: 1.3546 +2025-06-24 10:55:48,742 - pyskl - INFO - Epoch [5][400/1281] lr: 2.495e-02, eta: 20:31:19, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6531, top5_acc: 0.9581, loss_cls: 1.3921, loss: 1.3921 +2025-06-24 10:56:30,127 - pyskl - INFO - Epoch [5][500/1281] lr: 2.495e-02, eta: 20:31:39, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6669, top5_acc: 0.9587, loss_cls: 1.3584, loss: 1.3584 +2025-06-24 10:57:11,481 - pyskl - INFO - Epoch [5][600/1281] lr: 2.495e-02, eta: 20:31:55, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6787, top5_acc: 0.9656, loss_cls: 1.3346, loss: 1.3346 +2025-06-24 10:57:52,922 - pyskl - INFO - Epoch [5][700/1281] lr: 2.494e-02, eta: 20:32:13, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6656, top5_acc: 0.9544, loss_cls: 1.3797, loss: 1.3797 +2025-06-24 10:58:34,163 - pyskl - INFO - Epoch [5][800/1281] lr: 2.494e-02, eta: 20:32:22, time: 0.412, data_time: 0.000, memory: 4082, top1_acc: 0.6906, top5_acc: 0.9681, loss_cls: 1.3028, loss: 1.3028 +2025-06-24 10:59:15,445 - pyskl - INFO - Epoch [5][900/1281] lr: 2.494e-02, eta: 20:32:31, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.6763, top5_acc: 0.9594, loss_cls: 1.3596, loss: 1.3596 +2025-06-24 10:59:56,924 - pyskl - INFO - Epoch [5][1000/1281] lr: 2.494e-02, eta: 20:32:45, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6562, top5_acc: 0.9613, loss_cls: 1.3721, loss: 1.3721 +2025-06-24 11:00:38,317 - pyskl - INFO - Epoch [5][1100/1281] lr: 2.494e-02, eta: 20:32:54, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6887, top5_acc: 0.9656, loss_cls: 1.2939, loss: 1.2939 +2025-06-24 11:01:19,670 - pyskl - INFO - Epoch [5][1200/1281] lr: 2.493e-02, eta: 20:33:00, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6794, top5_acc: 0.9656, loss_cls: 1.2901, loss: 1.2901 +2025-06-24 11:01:53,827 - pyskl - INFO - Saving checkpoint at 5 epochs +2025-06-24 11:03:04,526 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:03:04,582 - pyskl - INFO - +top1_acc 0.6551 +top5_acc 0.9532 +2025-06-24 11:03:04,583 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:03:04,589 - pyskl - INFO - +mean_acc 0.5088 +2025-06-24 11:03:04,593 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_4.pth was removed +2025-06-24 11:03:04,771 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2025-06-24 11:03:04,771 - pyskl - INFO - Best top1_acc is 0.6551 at 5 epoch. +2025-06-24 11:03:04,773 - pyskl - INFO - Epoch(val) [5][533] top1_acc: 0.6551, top5_acc: 0.9532, mean_class_accuracy: 0.5088 +2025-06-24 11:04:09,050 - pyskl - INFO - Epoch [6][100/1281] lr: 2.493e-02, eta: 20:28:06, time: 0.643, data_time: 0.191, memory: 4082, top1_acc: 0.6631, top5_acc: 0.9694, loss_cls: 1.3271, loss: 1.3271 +2025-06-24 11:04:34,176 - pyskl - INFO - Epoch [6][200/1281] lr: 2.493e-02, eta: 20:20:37, time: 0.251, data_time: 0.000, memory: 4082, top1_acc: 0.6731, top5_acc: 0.9719, loss_cls: 1.3092, loss: 1.3092 +2025-06-24 11:05:16,070 - pyskl - INFO - Epoch [6][300/1281] lr: 2.492e-02, eta: 20:21:04, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.6713, top5_acc: 0.9688, loss_cls: 1.3249, loss: 1.3249 +2025-06-24 11:05:57,767 - pyskl - INFO - Epoch [6][400/1281] lr: 2.492e-02, eta: 20:21:24, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.6825, top5_acc: 0.9756, loss_cls: 1.2646, loss: 1.2646 +2025-06-24 11:06:40,924 - pyskl - INFO - Epoch [6][500/1281] lr: 2.492e-02, eta: 20:22:22, time: 0.432, data_time: 0.000, memory: 4082, top1_acc: 0.6750, top5_acc: 0.9669, loss_cls: 1.3306, loss: 1.3306 +2025-06-24 11:07:24,602 - pyskl - INFO - Epoch [6][600/1281] lr: 2.492e-02, eta: 20:23:30, time: 0.437, data_time: 0.000, memory: 4082, top1_acc: 0.7037, top5_acc: 0.9644, loss_cls: 1.2401, loss: 1.2401 +2025-06-24 11:08:06,434 - pyskl - INFO - Epoch [6][700/1281] lr: 2.492e-02, eta: 20:23:47, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.6987, top5_acc: 0.9694, loss_cls: 1.2656, loss: 1.2656 +2025-06-24 11:08:47,706 - pyskl - INFO - Epoch [6][800/1281] lr: 2.491e-02, eta: 20:23:48, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7119, top5_acc: 0.9706, loss_cls: 1.2132, loss: 1.2132 +2025-06-24 11:09:29,069 - pyskl - INFO - Epoch [6][900/1281] lr: 2.491e-02, eta: 20:23:51, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7119, top5_acc: 0.9669, loss_cls: 1.2502, loss: 1.2502 +2025-06-24 11:10:10,432 - pyskl - INFO - Epoch [6][1000/1281] lr: 2.491e-02, eta: 20:23:52, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7069, top5_acc: 0.9681, loss_cls: 1.2362, loss: 1.2362 +2025-06-24 11:10:51,744 - pyskl - INFO - Epoch [6][1100/1281] lr: 2.491e-02, eta: 20:23:50, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7137, top5_acc: 0.9762, loss_cls: 1.1851, loss: 1.1851 +2025-06-24 11:11:33,090 - pyskl - INFO - Epoch [6][1200/1281] lr: 2.490e-02, eta: 20:23:49, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7031, top5_acc: 0.9688, loss_cls: 1.2328, loss: 1.2328 +2025-06-24 11:12:07,221 - pyskl - INFO - Saving checkpoint at 6 epochs +2025-06-24 11:13:19,622 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:13:19,679 - pyskl - INFO - +top1_acc 0.6734 +top5_acc 0.9643 +2025-06-24 11:13:19,680 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:13:19,687 - pyskl - INFO - +mean_acc 0.5452 +2025-06-24 11:13:19,691 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_5.pth was removed +2025-06-24 11:13:19,887 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_6.pth. +2025-06-24 11:13:19,888 - pyskl - INFO - Best top1_acc is 0.6734 at 6 epoch. +2025-06-24 11:13:19,891 - pyskl - INFO - Epoch(val) [6][533] top1_acc: 0.6734, top5_acc: 0.9643, mean_class_accuracy: 0.5452 +2025-06-24 11:14:24,791 - pyskl - INFO - Epoch [7][100/1281] lr: 2.490e-02, eta: 20:19:48, time: 0.649, data_time: 0.195, memory: 4082, top1_acc: 0.6950, top5_acc: 0.9744, loss_cls: 1.2433, loss: 1.2433 +2025-06-24 11:14:49,711 - pyskl - INFO - Epoch [7][200/1281] lr: 2.490e-02, eta: 20:13:23, time: 0.249, data_time: 0.000, memory: 4082, top1_acc: 0.7256, top5_acc: 0.9788, loss_cls: 1.1495, loss: 1.1495 +2025-06-24 11:15:30,084 - pyskl - INFO - Epoch [7][300/1281] lr: 2.489e-02, eta: 20:13:04, time: 0.404, data_time: 0.000, memory: 4082, top1_acc: 0.7163, top5_acc: 0.9700, loss_cls: 1.1772, loss: 1.1772 +2025-06-24 11:16:11,646 - pyskl - INFO - Epoch [7][400/1281] lr: 2.489e-02, eta: 20:13:10, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7050, top5_acc: 0.9656, loss_cls: 1.2565, loss: 1.2565 +2025-06-24 11:16:53,087 - pyskl - INFO - Epoch [7][500/1281] lr: 2.489e-02, eta: 20:13:13, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7163, top5_acc: 0.9738, loss_cls: 1.1872, loss: 1.1872 +2025-06-24 11:17:34,678 - pyskl - INFO - Epoch [7][600/1281] lr: 2.489e-02, eta: 20:13:19, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7244, top5_acc: 0.9781, loss_cls: 1.1352, loss: 1.1352 +2025-06-24 11:18:15,982 - pyskl - INFO - Epoch [7][700/1281] lr: 2.488e-02, eta: 20:13:17, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7325, top5_acc: 0.9750, loss_cls: 1.1102, loss: 1.1102 +2025-06-24 11:18:57,520 - pyskl - INFO - Epoch [7][800/1281] lr: 2.488e-02, eta: 20:13:19, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7181, top5_acc: 0.9725, loss_cls: 1.1983, loss: 1.1983 +2025-06-24 11:19:38,863 - pyskl - INFO - Epoch [7][900/1281] lr: 2.488e-02, eta: 20:13:15, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7238, top5_acc: 0.9756, loss_cls: 1.1553, loss: 1.1553 +2025-06-24 11:20:20,426 - pyskl - INFO - Epoch [7][1000/1281] lr: 2.487e-02, eta: 20:13:16, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7281, top5_acc: 0.9744, loss_cls: 1.1420, loss: 1.1420 +2025-06-24 11:21:01,793 - pyskl - INFO - Epoch [7][1100/1281] lr: 2.487e-02, eta: 20:13:11, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7394, top5_acc: 0.9731, loss_cls: 1.1135, loss: 1.1135 +2025-06-24 11:21:43,011 - pyskl - INFO - Epoch [7][1200/1281] lr: 2.487e-02, eta: 20:13:03, time: 0.412, data_time: 0.000, memory: 4082, top1_acc: 0.7087, top5_acc: 0.9644, loss_cls: 1.1789, loss: 1.1789 +2025-06-24 11:22:17,335 - pyskl - INFO - Saving checkpoint at 7 epochs +2025-06-24 11:23:28,805 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:23:28,874 - pyskl - INFO - +top1_acc 0.7070 +top5_acc 0.9685 +2025-06-24 11:23:28,874 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:23:28,882 - pyskl - INFO - +mean_acc 0.5866 +2025-06-24 11:23:28,886 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_6.pth was removed +2025-06-24 11:23:29,096 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2025-06-24 11:23:29,097 - pyskl - INFO - Best top1_acc is 0.7070 at 7 epoch. +2025-06-24 11:23:29,099 - pyskl - INFO - Epoch(val) [7][533] top1_acc: 0.7070, top5_acc: 0.9685, mean_class_accuracy: 0.5866 +2025-06-24 11:24:34,176 - pyskl - INFO - Epoch [8][100/1281] lr: 2.486e-02, eta: 20:09:34, time: 0.651, data_time: 0.198, memory: 4082, top1_acc: 0.7506, top5_acc: 0.9725, loss_cls: 1.0678, loss: 1.0678 +2025-06-24 11:24:59,061 - pyskl - INFO - Epoch [8][200/1281] lr: 2.486e-02, eta: 20:03:59, time: 0.249, data_time: 0.000, memory: 4082, top1_acc: 0.7331, top5_acc: 0.9769, loss_cls: 1.1138, loss: 1.1138 +2025-06-24 11:25:39,540 - pyskl - INFO - Epoch [8][300/1281] lr: 2.486e-02, eta: 20:03:40, time: 0.405, data_time: 0.000, memory: 4082, top1_acc: 0.7512, top5_acc: 0.9825, loss_cls: 1.0610, loss: 1.0610 +2025-06-24 11:26:21,075 - pyskl - INFO - Epoch [8][400/1281] lr: 2.485e-02, eta: 20:03:40, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7188, top5_acc: 0.9769, loss_cls: 1.1135, loss: 1.1135 +2025-06-24 11:27:02,513 - pyskl - INFO - Epoch [8][500/1281] lr: 2.485e-02, eta: 20:03:38, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7381, top5_acc: 0.9750, loss_cls: 1.1554, loss: 1.1554 +2025-06-24 11:27:44,085 - pyskl - INFO - Epoch [8][600/1281] lr: 2.485e-02, eta: 20:03:37, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7269, top5_acc: 0.9788, loss_cls: 1.1314, loss: 1.1314 +2025-06-24 11:28:25,572 - pyskl - INFO - Epoch [8][700/1281] lr: 2.484e-02, eta: 20:03:34, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7444, top5_acc: 0.9794, loss_cls: 1.0748, loss: 1.0748 +2025-06-24 11:29:06,976 - pyskl - INFO - Epoch [8][800/1281] lr: 2.484e-02, eta: 20:03:29, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7450, top5_acc: 0.9775, loss_cls: 1.0676, loss: 1.0676 +2025-06-24 11:29:48,225 - pyskl - INFO - Epoch [8][900/1281] lr: 2.484e-02, eta: 20:03:20, time: 0.412, data_time: 0.000, memory: 4082, top1_acc: 0.7412, top5_acc: 0.9775, loss_cls: 1.0961, loss: 1.0961 +2025-06-24 11:30:29,617 - pyskl - INFO - Epoch [8][1000/1281] lr: 2.483e-02, eta: 20:03:13, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7469, top5_acc: 0.9762, loss_cls: 1.0737, loss: 1.0737 +2025-06-24 11:31:11,151 - pyskl - INFO - Epoch [8][1100/1281] lr: 2.483e-02, eta: 20:03:07, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7256, top5_acc: 0.9675, loss_cls: 1.1882, loss: 1.1882 +2025-06-24 11:31:52,568 - pyskl - INFO - Epoch [8][1200/1281] lr: 2.483e-02, eta: 20:03:00, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7550, top5_acc: 0.9838, loss_cls: 1.0749, loss: 1.0749 +2025-06-24 11:32:26,671 - pyskl - INFO - Saving checkpoint at 8 epochs +2025-06-24 11:33:38,685 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:33:38,741 - pyskl - INFO - +top1_acc 0.7147 +top5_acc 0.9731 +2025-06-24 11:33:38,741 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:33:38,749 - pyskl - INFO - +mean_acc 0.6143 +2025-06-24 11:33:38,753 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_7.pth was removed +2025-06-24 11:33:38,936 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_8.pth. +2025-06-24 11:33:38,936 - pyskl - INFO - Best top1_acc is 0.7147 at 8 epoch. +2025-06-24 11:33:38,939 - pyskl - INFO - Epoch(val) [8][533] top1_acc: 0.7147, top5_acc: 0.9731, mean_class_accuracy: 0.6143 +2025-06-24 11:34:44,305 - pyskl - INFO - Epoch [9][100/1281] lr: 2.482e-02, eta: 19:59:55, time: 0.654, data_time: 0.201, memory: 4082, top1_acc: 0.7544, top5_acc: 0.9844, loss_cls: 1.0294, loss: 1.0294 +2025-06-24 11:35:08,799 - pyskl - INFO - Epoch [9][200/1281] lr: 2.482e-02, eta: 19:54:52, time: 0.245, data_time: 0.000, memory: 4082, top1_acc: 0.7381, top5_acc: 0.9800, loss_cls: 1.0753, loss: 1.0753 +2025-06-24 11:35:50,340 - pyskl - INFO - Epoch [9][300/1281] lr: 2.481e-02, eta: 19:54:49, time: 0.415, data_time: 0.001, memory: 4082, top1_acc: 0.7669, top5_acc: 0.9838, loss_cls: 1.0214, loss: 1.0214 +2025-06-24 11:36:31,887 - pyskl - INFO - Epoch [9][400/1281] lr: 2.481e-02, eta: 19:54:45, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7519, top5_acc: 0.9862, loss_cls: 1.0431, loss: 1.0431 +2025-06-24 11:37:13,360 - pyskl - INFO - Epoch [9][500/1281] lr: 2.481e-02, eta: 19:54:39, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7231, top5_acc: 0.9800, loss_cls: 1.1292, loss: 1.1292 +2025-06-24 11:37:54,745 - pyskl - INFO - Epoch [9][600/1281] lr: 2.480e-02, eta: 19:54:30, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7438, top5_acc: 0.9762, loss_cls: 1.0553, loss: 1.0553 +2025-06-24 11:38:36,130 - pyskl - INFO - Epoch [9][700/1281] lr: 2.480e-02, eta: 19:54:21, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7419, top5_acc: 0.9800, loss_cls: 1.1073, loss: 1.1073 +2025-06-24 11:39:17,524 - pyskl - INFO - Epoch [9][800/1281] lr: 2.480e-02, eta: 19:54:12, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7531, top5_acc: 0.9769, loss_cls: 1.0577, loss: 1.0577 +2025-06-24 11:39:59,087 - pyskl - INFO - Epoch [9][900/1281] lr: 2.479e-02, eta: 19:54:05, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7581, top5_acc: 0.9794, loss_cls: 1.0302, loss: 1.0302 +2025-06-24 11:40:40,577 - pyskl - INFO - Epoch [9][1000/1281] lr: 2.479e-02, eta: 19:53:56, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7375, top5_acc: 0.9788, loss_cls: 1.0875, loss: 1.0875 +2025-06-24 11:41:22,005 - pyskl - INFO - Epoch [9][1100/1281] lr: 2.479e-02, eta: 19:53:46, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7525, top5_acc: 0.9794, loss_cls: 1.0411, loss: 1.0411 +2025-06-24 11:42:03,555 - pyskl - INFO - Epoch [9][1200/1281] lr: 2.478e-02, eta: 19:53:36, time: 0.415, data_time: 0.001, memory: 4082, top1_acc: 0.7575, top5_acc: 0.9800, loss_cls: 1.0099, loss: 1.0099 +2025-06-24 11:42:37,751 - pyskl - INFO - Saving checkpoint at 9 epochs +2025-06-24 11:43:49,121 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:43:49,183 - pyskl - INFO - +top1_acc 0.7093 +top5_acc 0.9661 +2025-06-24 11:43:49,183 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:43:49,193 - pyskl - INFO - +mean_acc 0.6012 +2025-06-24 11:43:49,195 - pyskl - INFO - Epoch(val) [9][533] top1_acc: 0.7093, top5_acc: 0.9661, mean_class_accuracy: 0.6012 +2025-06-24 11:44:53,833 - pyskl - INFO - Epoch [10][100/1281] lr: 2.477e-02, eta: 19:50:34, time: 0.646, data_time: 0.194, memory: 4082, top1_acc: 0.7419, top5_acc: 0.9731, loss_cls: 1.0984, loss: 1.0984 +2025-06-24 11:45:17,877 - pyskl - INFO - Epoch [10][200/1281] lr: 2.477e-02, eta: 19:45:56, time: 0.240, data_time: 0.000, memory: 4082, top1_acc: 0.7481, top5_acc: 0.9812, loss_cls: 1.0484, loss: 1.0484 +2025-06-24 11:45:58,900 - pyskl - INFO - Epoch [10][300/1281] lr: 2.477e-02, eta: 19:45:41, time: 0.410, data_time: 0.000, memory: 4082, top1_acc: 0.7575, top5_acc: 0.9788, loss_cls: 1.0274, loss: 1.0274 +2025-06-24 11:46:40,297 - pyskl - INFO - Epoch [10][400/1281] lr: 2.476e-02, eta: 19:45:31, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7694, top5_acc: 0.9856, loss_cls: 0.9837, loss: 0.9837 +2025-06-24 11:47:21,656 - pyskl - INFO - Epoch [10][500/1281] lr: 2.476e-02, eta: 19:45:19, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7488, top5_acc: 0.9856, loss_cls: 1.0522, loss: 1.0522 +2025-06-24 11:48:03,040 - pyskl - INFO - Epoch [10][600/1281] lr: 2.476e-02, eta: 19:45:08, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7762, top5_acc: 0.9800, loss_cls: 1.0106, loss: 1.0106 +2025-06-24 11:48:44,482 - pyskl - INFO - Epoch [10][700/1281] lr: 2.475e-02, eta: 19:44:57, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7550, top5_acc: 0.9838, loss_cls: 1.0260, loss: 1.0260 +2025-06-24 11:49:25,776 - pyskl - INFO - Epoch [10][800/1281] lr: 2.475e-02, eta: 19:44:44, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7688, top5_acc: 0.9806, loss_cls: 0.9931, loss: 0.9931 +2025-06-24 11:50:07,116 - pyskl - INFO - Epoch [10][900/1281] lr: 2.474e-02, eta: 19:44:30, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7594, top5_acc: 0.9812, loss_cls: 1.0438, loss: 1.0438 +2025-06-24 11:50:48,464 - pyskl - INFO - Epoch [10][1000/1281] lr: 2.474e-02, eta: 19:44:16, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7450, top5_acc: 0.9788, loss_cls: 1.0577, loss: 1.0577 +2025-06-24 11:51:29,762 - pyskl - INFO - Epoch [10][1100/1281] lr: 2.473e-02, eta: 19:44:02, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7512, top5_acc: 0.9869, loss_cls: 1.0210, loss: 1.0210 +2025-06-24 11:52:11,133 - pyskl - INFO - Epoch [10][1200/1281] lr: 2.473e-02, eta: 19:43:47, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7812, top5_acc: 0.9869, loss_cls: 0.9579, loss: 0.9579 +2025-06-24 11:52:45,266 - pyskl - INFO - Saving checkpoint at 10 epochs +2025-06-24 11:53:56,656 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:53:56,725 - pyskl - INFO - +top1_acc 0.7518 +top5_acc 0.9784 +2025-06-24 11:53:56,725 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:53:56,734 - pyskl - INFO - +mean_acc 0.6477 +2025-06-24 11:53:56,738 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_8.pth was removed +2025-06-24 11:53:56,975 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_10.pth. +2025-06-24 11:53:56,975 - pyskl - INFO - Best top1_acc is 0.7518 at 10 epoch. +2025-06-24 11:53:56,978 - pyskl - INFO - Epoch(val) [10][533] top1_acc: 0.7518, top5_acc: 0.9784, mean_class_accuracy: 0.6477 +2025-06-24 11:55:02,166 - pyskl - INFO - Epoch [11][100/1281] lr: 2.472e-02, eta: 19:41:06, time: 0.652, data_time: 0.200, memory: 4082, top1_acc: 0.7762, top5_acc: 0.9875, loss_cls: 0.9286, loss: 0.9286 +2025-06-24 11:55:26,925 - pyskl - INFO - Epoch [11][200/1281] lr: 2.472e-02, eta: 19:37:03, time: 0.248, data_time: 0.000, memory: 4082, top1_acc: 0.7662, top5_acc: 0.9850, loss_cls: 1.0071, loss: 1.0071 +2025-06-24 11:56:07,956 - pyskl - INFO - Epoch [11][300/1281] lr: 2.471e-02, eta: 19:36:45, time: 0.410, data_time: 0.000, memory: 4082, top1_acc: 0.7675, top5_acc: 0.9756, loss_cls: 1.0258, loss: 1.0258 +2025-06-24 11:56:49,404 - pyskl - INFO - Epoch [11][400/1281] lr: 2.471e-02, eta: 19:36:33, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7575, top5_acc: 0.9781, loss_cls: 1.0094, loss: 1.0094 +2025-06-24 11:57:30,837 - pyskl - INFO - Epoch [11][500/1281] lr: 2.471e-02, eta: 19:36:20, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7725, top5_acc: 0.9806, loss_cls: 0.9585, loss: 0.9585 +2025-06-24 11:58:12,284 - pyskl - INFO - Epoch [11][600/1281] lr: 2.470e-02, eta: 19:36:07, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7869, top5_acc: 0.9881, loss_cls: 0.9400, loss: 0.9400 +2025-06-24 11:58:53,746 - pyskl - INFO - Epoch [11][700/1281] lr: 2.470e-02, eta: 19:35:54, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7831, top5_acc: 0.9825, loss_cls: 0.9530, loss: 0.9530 +2025-06-24 11:59:35,151 - pyskl - INFO - Epoch [11][800/1281] lr: 2.469e-02, eta: 19:35:39, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7619, top5_acc: 0.9831, loss_cls: 0.9811, loss: 0.9811 +2025-06-24 12:00:16,575 - pyskl - INFO - Epoch [11][900/1281] lr: 2.469e-02, eta: 19:35:25, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7594, top5_acc: 0.9794, loss_cls: 1.0015, loss: 1.0015 +2025-06-24 12:00:58,039 - pyskl - INFO - Epoch [11][1000/1281] lr: 2.468e-02, eta: 19:35:10, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7750, top5_acc: 0.9869, loss_cls: 0.9507, loss: 0.9507 +2025-06-24 12:01:39,426 - pyskl - INFO - Epoch [11][1100/1281] lr: 2.468e-02, eta: 19:34:55, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7519, top5_acc: 0.9831, loss_cls: 1.0129, loss: 1.0129 +2025-06-24 12:02:20,865 - pyskl - INFO - Epoch [11][1200/1281] lr: 2.467e-02, eta: 19:34:39, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7812, top5_acc: 0.9881, loss_cls: 0.9790, loss: 0.9790 +2025-06-24 12:02:55,267 - pyskl - INFO - Saving checkpoint at 11 epochs +2025-06-24 12:04:06,932 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:04:06,989 - pyskl - INFO - +top1_acc 0.7219 +top5_acc 0.9707 +2025-06-24 12:04:06,989 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:04:06,997 - pyskl - INFO - +mean_acc 0.6283 +2025-06-24 12:04:07,000 - pyskl - INFO - Epoch(val) [11][533] top1_acc: 0.7219, top5_acc: 0.9707, mean_class_accuracy: 0.6283 +2025-06-24 12:05:12,322 - pyskl - INFO - Epoch [12][100/1281] lr: 2.467e-02, eta: 19:32:08, time: 0.653, data_time: 0.198, memory: 4082, top1_acc: 0.7519, top5_acc: 0.9856, loss_cls: 1.0172, loss: 1.0172 +2025-06-24 12:05:37,057 - pyskl - INFO - Epoch [12][200/1281] lr: 2.466e-02, eta: 19:28:25, time: 0.247, data_time: 0.001, memory: 4082, top1_acc: 0.7569, top5_acc: 0.9794, loss_cls: 1.0099, loss: 1.0099 +2025-06-24 12:06:17,121 - pyskl - INFO - Epoch [12][300/1281] lr: 2.466e-02, eta: 19:27:53, time: 0.401, data_time: 0.000, memory: 4082, top1_acc: 0.7794, top5_acc: 0.9869, loss_cls: 0.9321, loss: 0.9321 +2025-06-24 12:06:58,559 - pyskl - INFO - Epoch [12][400/1281] lr: 2.465e-02, eta: 19:27:39, time: 0.414, data_time: 0.001, memory: 4082, top1_acc: 0.7738, top5_acc: 0.9862, loss_cls: 0.9593, loss: 0.9593 +2025-06-24 12:07:40,036 - pyskl - INFO - Epoch [12][500/1281] lr: 2.465e-02, eta: 19:27:24, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9856, loss_cls: 0.9175, loss: 0.9175 +2025-06-24 12:08:21,581 - pyskl - INFO - Epoch [12][600/1281] lr: 2.464e-02, eta: 19:27:10, time: 0.415, data_time: 0.001, memory: 4082, top1_acc: 0.7956, top5_acc: 0.9862, loss_cls: 0.8926, loss: 0.8926 +2025-06-24 12:09:02,847 - pyskl - INFO - Epoch [12][700/1281] lr: 2.464e-02, eta: 19:26:52, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7731, top5_acc: 0.9838, loss_cls: 0.9881, loss: 0.9881 +2025-06-24 12:09:44,281 - pyskl - INFO - Epoch [12][800/1281] lr: 2.463e-02, eta: 19:26:36, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7913, top5_acc: 0.9869, loss_cls: 0.9261, loss: 0.9261 +2025-06-24 12:10:25,690 - pyskl - INFO - Epoch [12][900/1281] lr: 2.463e-02, eta: 19:26:19, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7900, top5_acc: 0.9800, loss_cls: 0.9650, loss: 0.9650 +2025-06-24 12:11:07,122 - pyskl - INFO - Epoch [12][1000/1281] lr: 2.462e-02, eta: 19:26:02, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7738, top5_acc: 0.9831, loss_cls: 0.9761, loss: 0.9761 +2025-06-24 12:11:48,514 - pyskl - INFO - Epoch [12][1100/1281] lr: 2.462e-02, eta: 19:25:45, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7706, top5_acc: 0.9831, loss_cls: 0.9585, loss: 0.9585 +2025-06-24 12:12:29,899 - pyskl - INFO - Epoch [12][1200/1281] lr: 2.461e-02, eta: 19:25:27, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7519, top5_acc: 0.9794, loss_cls: 1.0291, loss: 1.0291 +2025-06-24 12:13:04,011 - pyskl - INFO - Saving checkpoint at 12 epochs +2025-06-24 12:14:15,641 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:14:15,698 - pyskl - INFO - +top1_acc 0.7406 +top5_acc 0.9776 +2025-06-24 12:14:15,698 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:14:15,706 - pyskl - INFO - +mean_acc 0.6405 +2025-06-24 12:14:15,709 - pyskl - INFO - Epoch(val) [12][533] top1_acc: 0.7406, top5_acc: 0.9776, mean_class_accuracy: 0.6405 +2025-06-24 12:15:20,814 - pyskl - INFO - Epoch [13][100/1281] lr: 2.460e-02, eta: 19:23:01, time: 0.651, data_time: 0.195, memory: 4082, top1_acc: 0.7900, top5_acc: 0.9894, loss_cls: 0.9182, loss: 0.9182 +2025-06-24 12:15:46,291 - pyskl - INFO - Epoch [13][200/1281] lr: 2.460e-02, eta: 19:19:43, time: 0.255, data_time: 0.000, memory: 4082, top1_acc: 0.7931, top5_acc: 0.9931, loss_cls: 0.8881, loss: 0.8881 +2025-06-24 12:16:25,906 - pyskl - INFO - Epoch [13][300/1281] lr: 2.459e-02, eta: 19:19:06, time: 0.396, data_time: 0.000, memory: 4082, top1_acc: 0.7688, top5_acc: 0.9806, loss_cls: 0.9877, loss: 0.9877 +2025-06-24 12:17:07,235 - pyskl - INFO - Epoch [13][400/1281] lr: 2.459e-02, eta: 19:18:48, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7913, top5_acc: 0.9850, loss_cls: 0.9271, loss: 0.9271 +2025-06-24 12:17:48,761 - pyskl - INFO - Epoch [13][500/1281] lr: 2.458e-02, eta: 19:18:32, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7856, top5_acc: 0.9856, loss_cls: 0.9237, loss: 0.9237 +2025-06-24 12:18:30,220 - pyskl - INFO - Epoch [13][600/1281] lr: 2.458e-02, eta: 19:18:15, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7819, top5_acc: 0.9812, loss_cls: 0.9337, loss: 0.9337 +2025-06-24 12:19:11,549 - pyskl - INFO - Epoch [13][700/1281] lr: 2.457e-02, eta: 19:17:56, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7669, top5_acc: 0.9819, loss_cls: 0.9981, loss: 0.9981 +2025-06-24 12:19:53,031 - pyskl - INFO - Epoch [13][800/1281] lr: 2.457e-02, eta: 19:17:38, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7756, top5_acc: 0.9812, loss_cls: 0.9223, loss: 0.9223 +2025-06-24 12:20:34,376 - pyskl - INFO - Epoch [13][900/1281] lr: 2.456e-02, eta: 19:17:19, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7688, top5_acc: 0.9869, loss_cls: 0.9616, loss: 0.9616 +2025-06-24 12:21:15,981 - pyskl - INFO - Epoch [13][1000/1281] lr: 2.455e-02, eta: 19:17:02, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7762, top5_acc: 0.9850, loss_cls: 0.9421, loss: 0.9421 +2025-06-24 12:21:57,325 - pyskl - INFO - Epoch [13][1100/1281] lr: 2.455e-02, eta: 19:16:43, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7837, top5_acc: 0.9856, loss_cls: 0.9308, loss: 0.9308 +2025-06-24 12:22:38,662 - pyskl - INFO - Epoch [13][1200/1281] lr: 2.454e-02, eta: 19:16:23, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9850, loss_cls: 0.9165, loss: 0.9165 +2025-06-24 12:23:13,188 - pyskl - INFO - Saving checkpoint at 13 epochs +2025-06-24 12:24:24,555 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:24:24,625 - pyskl - INFO - +top1_acc 0.7469 +top5_acc 0.9781 +2025-06-24 12:24:24,625 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:24:24,633 - pyskl - INFO - +mean_acc 0.6524 +2025-06-24 12:24:24,635 - pyskl - INFO - Epoch(val) [13][533] top1_acc: 0.7469, top5_acc: 0.9781, mean_class_accuracy: 0.6524 +2025-06-24 12:25:28,199 - pyskl - INFO - Epoch [14][100/1281] lr: 2.453e-02, eta: 19:13:48, time: 0.636, data_time: 0.191, memory: 4082, top1_acc: 0.7906, top5_acc: 0.9869, loss_cls: 0.9018, loss: 0.9018 +2025-06-24 12:25:54,985 - pyskl - INFO - Epoch [14][200/1281] lr: 2.453e-02, eta: 19:10:56, time: 0.268, data_time: 0.001, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9906, loss_cls: 0.9011, loss: 0.9011 +2025-06-24 12:26:31,826 - pyskl - INFO - Epoch [14][300/1281] lr: 2.452e-02, eta: 19:09:50, time: 0.368, data_time: 0.000, memory: 4082, top1_acc: 0.8131, top5_acc: 0.9850, loss_cls: 0.8589, loss: 0.8589 +2025-06-24 12:27:11,254 - pyskl - INFO - Epoch [14][400/1281] lr: 2.452e-02, eta: 19:09:12, time: 0.394, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9888, loss_cls: 0.8679, loss: 0.8679 +2025-06-24 12:27:50,833 - pyskl - INFO - Epoch [14][500/1281] lr: 2.451e-02, eta: 19:08:34, time: 0.396, data_time: 0.000, memory: 4082, top1_acc: 0.7887, top5_acc: 0.9881, loss_cls: 0.8794, loss: 0.8794 +2025-06-24 12:28:30,846 - pyskl - INFO - Epoch [14][600/1281] lr: 2.451e-02, eta: 19:08:01, time: 0.400, data_time: 0.000, memory: 4082, top1_acc: 0.7963, top5_acc: 0.9844, loss_cls: 0.8647, loss: 0.8647 +2025-06-24 12:29:09,661 - pyskl - INFO - Epoch [14][700/1281] lr: 2.450e-02, eta: 19:07:16, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9894, loss_cls: 0.9120, loss: 0.9120 +2025-06-24 12:29:48,238 - pyskl - INFO - Epoch [14][800/1281] lr: 2.449e-02, eta: 19:06:29, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.7844, top5_acc: 0.9838, loss_cls: 0.9137, loss: 0.9137 +2025-06-24 12:30:26,841 - pyskl - INFO - Epoch [14][900/1281] lr: 2.449e-02, eta: 19:05:42, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9881, loss_cls: 0.8299, loss: 0.8299 +2025-06-24 12:31:05,360 - pyskl - INFO - Epoch [14][1000/1281] lr: 2.448e-02, eta: 19:04:54, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9900, loss_cls: 0.8835, loss: 0.8835 +2025-06-24 12:31:44,051 - pyskl - INFO - Epoch [14][1100/1281] lr: 2.448e-02, eta: 19:04:08, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.7856, top5_acc: 0.9800, loss_cls: 0.9340, loss: 0.9340 +2025-06-24 12:32:21,663 - pyskl - INFO - Epoch [14][1200/1281] lr: 2.447e-02, eta: 19:03:11, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.7887, top5_acc: 0.9838, loss_cls: 0.9196, loss: 0.9196 +2025-06-24 12:32:53,509 - pyskl - INFO - Saving checkpoint at 14 epochs +2025-06-24 12:33:54,342 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:33:54,401 - pyskl - INFO - +top1_acc 0.7607 +top5_acc 0.9816 +2025-06-24 12:33:54,401 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:33:54,408 - pyskl - INFO - +mean_acc 0.6446 +2025-06-24 12:33:54,412 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_10.pth was removed +2025-06-24 12:33:54,587 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_14.pth. +2025-06-24 12:33:54,587 - pyskl - INFO - Best top1_acc is 0.7607 at 14 epoch. +2025-06-24 12:33:54,590 - pyskl - INFO - Epoch(val) [14][533] top1_acc: 0.7607, top5_acc: 0.9816, mean_class_accuracy: 0.6446 +2025-06-24 12:34:54,399 - pyskl - INFO - Epoch [15][100/1281] lr: 2.446e-02, eta: 19:00:10, time: 0.598, data_time: 0.197, memory: 4082, top1_acc: 0.8175, top5_acc: 0.9881, loss_cls: 0.8056, loss: 0.8056 +2025-06-24 12:35:32,393 - pyskl - INFO - Epoch [15][200/1281] lr: 2.445e-02, eta: 18:59:18, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9931, loss_cls: 0.8539, loss: 0.8539 +2025-06-24 12:36:08,538 - pyskl - INFO - Epoch [15][300/1281] lr: 2.445e-02, eta: 18:58:09, time: 0.361, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9869, loss_cls: 0.8853, loss: 0.8853 +2025-06-24 12:36:37,670 - pyskl - INFO - Epoch [15][400/1281] lr: 2.444e-02, eta: 18:55:53, time: 0.291, data_time: 0.001, memory: 4082, top1_acc: 0.7694, top5_acc: 0.9881, loss_cls: 0.9600, loss: 0.9600 +2025-06-24 12:37:19,837 - pyskl - INFO - Epoch [15][500/1281] lr: 2.444e-02, eta: 18:55:42, time: 0.422, data_time: 0.001, memory: 4082, top1_acc: 0.8037, top5_acc: 0.9881, loss_cls: 0.8377, loss: 0.8377 +2025-06-24 12:37:42,468 - pyskl - INFO - Epoch [15][600/1281] lr: 2.443e-02, eta: 18:52:27, time: 0.226, data_time: 0.000, memory: 4082, top1_acc: 0.7844, top5_acc: 0.9888, loss_cls: 0.8955, loss: 0.8955 +2025-06-24 12:38:11,311 - pyskl - INFO - Epoch [15][700/1281] lr: 2.442e-02, eta: 18:50:12, time: 0.288, data_time: 0.000, memory: 4082, top1_acc: 0.7906, top5_acc: 0.9862, loss_cls: 0.9004, loss: 0.9004 +2025-06-24 12:38:49,369 - pyskl - INFO - Epoch [15][800/1281] lr: 2.442e-02, eta: 18:49:24, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.7781, top5_acc: 0.9788, loss_cls: 0.9531, loss: 0.9531 +2025-06-24 12:39:26,880 - pyskl - INFO - Epoch [15][900/1281] lr: 2.441e-02, eta: 18:48:30, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8131, top5_acc: 0.9875, loss_cls: 0.8499, loss: 0.8499 +2025-06-24 12:40:03,687 - pyskl - INFO - Epoch [15][1000/1281] lr: 2.441e-02, eta: 18:47:31, time: 0.368, data_time: 0.000, memory: 4082, top1_acc: 0.8050, top5_acc: 0.9875, loss_cls: 0.8773, loss: 0.8773 +2025-06-24 12:40:41,337 - pyskl - INFO - Epoch [15][1100/1281] lr: 2.440e-02, eta: 18:46:39, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.7906, top5_acc: 0.9931, loss_cls: 0.8825, loss: 0.8825 +2025-06-24 12:41:19,495 - pyskl - INFO - Epoch [15][1200/1281] lr: 2.439e-02, eta: 18:45:52, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.7837, top5_acc: 0.9844, loss_cls: 0.9505, loss: 0.9505 +2025-06-24 12:41:51,297 - pyskl - INFO - Saving checkpoint at 15 epochs +2025-06-24 12:42:50,061 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:42:50,135 - pyskl - INFO - +top1_acc 0.7504 +top5_acc 0.9770 +2025-06-24 12:42:50,135 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:42:50,147 - pyskl - INFO - +mean_acc 0.6553 +2025-06-24 12:42:50,152 - pyskl - INFO - Epoch(val) [15][533] top1_acc: 0.7504, top5_acc: 0.9770, mean_class_accuracy: 0.6553 +2025-06-24 12:43:47,669 - pyskl - INFO - Epoch [16][100/1281] lr: 2.438e-02, eta: 18:42:43, time: 0.575, data_time: 0.198, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9850, loss_cls: 0.8695, loss: 0.8695 +2025-06-24 12:44:25,377 - pyskl - INFO - Epoch [16][200/1281] lr: 2.438e-02, eta: 18:41:53, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9856, loss_cls: 0.8753, loss: 0.8753 +2025-06-24 12:45:03,629 - pyskl - INFO - Epoch [16][300/1281] lr: 2.437e-02, eta: 18:41:08, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9881, loss_cls: 0.8067, loss: 0.8067 +2025-06-24 12:45:40,708 - pyskl - INFO - Epoch [16][400/1281] lr: 2.436e-02, eta: 18:40:12, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.7969, top5_acc: 0.9888, loss_cls: 0.8666, loss: 0.8666 +2025-06-24 12:46:19,233 - pyskl - INFO - Epoch [16][500/1281] lr: 2.436e-02, eta: 18:39:29, time: 0.385, data_time: 0.001, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9881, loss_cls: 0.9068, loss: 0.9068 +2025-06-24 12:46:58,049 - pyskl - INFO - Epoch [16][600/1281] lr: 2.435e-02, eta: 18:38:49, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.7987, top5_acc: 0.9819, loss_cls: 0.8605, loss: 0.8605 +2025-06-24 12:47:36,284 - pyskl - INFO - Epoch [16][700/1281] lr: 2.434e-02, eta: 18:38:04, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9894, loss_cls: 0.7753, loss: 0.7753 +2025-06-24 12:48:14,103 - pyskl - INFO - Epoch [16][800/1281] lr: 2.434e-02, eta: 18:37:15, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8075, top5_acc: 0.9869, loss_cls: 0.8514, loss: 0.8514 +2025-06-24 12:48:43,969 - pyskl - INFO - Epoch [16][900/1281] lr: 2.433e-02, eta: 18:35:19, time: 0.299, data_time: 0.000, memory: 4082, top1_acc: 0.8163, top5_acc: 0.9856, loss_cls: 0.8197, loss: 0.8197 +2025-06-24 12:49:20,536 - pyskl - INFO - Epoch [16][1000/1281] lr: 2.432e-02, eta: 18:34:20, time: 0.366, data_time: 0.000, memory: 4082, top1_acc: 0.8069, top5_acc: 0.9925, loss_cls: 0.8360, loss: 0.8360 +2025-06-24 12:49:54,903 - pyskl - INFO - Epoch [16][1100/1281] lr: 2.432e-02, eta: 18:33:03, time: 0.344, data_time: 0.000, memory: 4082, top1_acc: 0.7975, top5_acc: 0.9869, loss_cls: 0.8897, loss: 0.8897 +2025-06-24 12:50:18,606 - pyskl - INFO - Epoch [16][1200/1281] lr: 2.431e-02, eta: 18:30:16, time: 0.237, data_time: 0.000, memory: 4082, top1_acc: 0.8044, top5_acc: 0.9850, loss_cls: 0.8525, loss: 0.8525 +2025-06-24 12:50:47,240 - pyskl - INFO - Saving checkpoint at 16 epochs +2025-06-24 12:51:46,664 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:51:46,722 - pyskl - INFO - +top1_acc 0.7861 +top5_acc 0.9817 +2025-06-24 12:51:46,722 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:51:46,729 - pyskl - INFO - +mean_acc 0.6957 +2025-06-24 12:51:46,733 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_14.pth was removed +2025-06-24 12:51:46,911 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_16.pth. +2025-06-24 12:51:46,912 - pyskl - INFO - Best top1_acc is 0.7861 at 16 epoch. +2025-06-24 12:51:46,914 - pyskl - INFO - Epoch(val) [16][533] top1_acc: 0.7861, top5_acc: 0.9817, mean_class_accuracy: 0.6957 +2025-06-24 12:52:44,155 - pyskl - INFO - Epoch [17][100/1281] lr: 2.430e-02, eta: 18:27:18, time: 0.572, data_time: 0.194, memory: 4082, top1_acc: 0.8175, top5_acc: 0.9900, loss_cls: 0.8028, loss: 0.8028 +2025-06-24 12:53:21,516 - pyskl - INFO - Epoch [17][200/1281] lr: 2.429e-02, eta: 18:26:28, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.7981, top5_acc: 0.9869, loss_cls: 0.8851, loss: 0.8851 +2025-06-24 12:53:59,293 - pyskl - INFO - Epoch [17][300/1281] lr: 2.428e-02, eta: 18:25:42, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9875, loss_cls: 0.8479, loss: 0.8479 +2025-06-24 12:54:36,005 - pyskl - INFO - Epoch [17][400/1281] lr: 2.428e-02, eta: 18:24:46, time: 0.367, data_time: 0.000, memory: 4082, top1_acc: 0.8163, top5_acc: 0.9919, loss_cls: 0.7800, loss: 0.7800 +2025-06-24 12:55:13,393 - pyskl - INFO - Epoch [17][500/1281] lr: 2.427e-02, eta: 18:23:57, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9875, loss_cls: 0.8200, loss: 0.8200 +2025-06-24 12:55:50,690 - pyskl - INFO - Epoch [17][600/1281] lr: 2.426e-02, eta: 18:23:07, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9856, loss_cls: 0.8586, loss: 0.8586 +2025-06-24 12:56:27,990 - pyskl - INFO - Epoch [17][700/1281] lr: 2.426e-02, eta: 18:22:17, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8075, top5_acc: 0.9869, loss_cls: 0.8580, loss: 0.8580 +2025-06-24 12:57:05,421 - pyskl - INFO - Epoch [17][800/1281] lr: 2.425e-02, eta: 18:21:28, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9919, loss_cls: 0.8022, loss: 0.8022 +2025-06-24 12:57:42,576 - pyskl - INFO - Epoch [17][900/1281] lr: 2.424e-02, eta: 18:20:37, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9869, loss_cls: 0.8487, loss: 0.8487 +2025-06-24 12:58:19,896 - pyskl - INFO - Epoch [17][1000/1281] lr: 2.424e-02, eta: 18:19:48, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9906, loss_cls: 0.7489, loss: 0.7489 +2025-06-24 12:58:57,169 - pyskl - INFO - Epoch [17][1100/1281] lr: 2.423e-02, eta: 18:18:58, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9956, loss_cls: 0.7336, loss: 0.7336 +2025-06-24 12:59:34,255 - pyskl - INFO - Epoch [17][1200/1281] lr: 2.422e-02, eta: 18:18:07, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9869, loss_cls: 0.7857, loss: 0.7857 +2025-06-24 13:00:05,177 - pyskl - INFO - Saving checkpoint at 17 epochs +2025-06-24 13:00:59,134 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:00:59,189 - pyskl - INFO - +top1_acc 0.7430 +top5_acc 0.9678 +2025-06-24 13:00:59,189 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:00:59,196 - pyskl - INFO - +mean_acc 0.6584 +2025-06-24 13:00:59,198 - pyskl - INFO - Epoch(val) [17][533] top1_acc: 0.7430, top5_acc: 0.9678, mean_class_accuracy: 0.6584 +2025-06-24 13:01:57,054 - pyskl - INFO - Epoch [18][100/1281] lr: 2.421e-02, eta: 18:15:23, time: 0.579, data_time: 0.197, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9906, loss_cls: 0.8453, loss: 0.8453 +2025-06-24 13:02:20,304 - pyskl - INFO - Epoch [18][200/1281] lr: 2.420e-02, eta: 18:12:45, time: 0.232, data_time: 0.000, memory: 4082, top1_acc: 0.8175, top5_acc: 0.9900, loss_cls: 0.8066, loss: 0.8066 +2025-06-24 13:02:55,015 - pyskl - INFO - Epoch [18][300/1281] lr: 2.419e-02, eta: 18:11:37, time: 0.347, data_time: 0.000, memory: 4082, top1_acc: 0.8313, top5_acc: 0.9931, loss_cls: 0.7236, loss: 0.7236 +2025-06-24 13:03:31,874 - pyskl - INFO - Epoch [18][400/1281] lr: 2.419e-02, eta: 18:10:46, time: 0.369, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9944, loss_cls: 0.7246, loss: 0.7246 +2025-06-24 13:04:09,392 - pyskl - INFO - Epoch [18][500/1281] lr: 2.418e-02, eta: 18:10:00, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9938, loss_cls: 0.7575, loss: 0.7575 +2025-06-24 13:04:46,756 - pyskl - INFO - Epoch [18][600/1281] lr: 2.417e-02, eta: 18:09:13, time: 0.374, data_time: 0.001, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9938, loss_cls: 0.7711, loss: 0.7711 +2025-06-24 13:05:24,105 - pyskl - INFO - Epoch [18][700/1281] lr: 2.417e-02, eta: 18:08:26, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8275, top5_acc: 0.9925, loss_cls: 0.7650, loss: 0.7650 +2025-06-24 13:06:01,581 - pyskl - INFO - Epoch [18][800/1281] lr: 2.416e-02, eta: 18:07:40, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9881, loss_cls: 0.7873, loss: 0.7873 +2025-06-24 13:06:38,988 - pyskl - INFO - Epoch [18][900/1281] lr: 2.415e-02, eta: 18:06:53, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9906, loss_cls: 0.8075, loss: 0.8075 +2025-06-24 13:07:15,784 - pyskl - INFO - Epoch [18][1000/1281] lr: 2.414e-02, eta: 18:06:02, time: 0.368, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9869, loss_cls: 0.8038, loss: 0.8038 +2025-06-24 13:07:52,908 - pyskl - INFO - Epoch [18][1100/1281] lr: 2.414e-02, eta: 18:05:14, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8113, top5_acc: 0.9881, loss_cls: 0.8085, loss: 0.8085 +2025-06-24 13:08:29,843 - pyskl - INFO - Epoch [18][1200/1281] lr: 2.413e-02, eta: 18:04:24, time: 0.369, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9912, loss_cls: 0.8146, loss: 0.8146 +2025-06-24 13:09:00,336 - pyskl - INFO - Saving checkpoint at 18 epochs +2025-06-24 13:09:58,816 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:09:58,870 - pyskl - INFO - +top1_acc 0.8027 +top5_acc 0.9837 +2025-06-24 13:09:58,870 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:09:58,877 - pyskl - INFO - +mean_acc 0.7198 +2025-06-24 13:09:58,881 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_16.pth was removed +2025-06-24 13:09:59,055 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_18.pth. +2025-06-24 13:09:59,055 - pyskl - INFO - Best top1_acc is 0.8027 at 18 epoch. +2025-06-24 13:09:59,059 - pyskl - INFO - Epoch(val) [18][533] top1_acc: 0.8027, top5_acc: 0.9837, mean_class_accuracy: 0.7198 +2025-06-24 13:10:56,061 - pyskl - INFO - Epoch [19][100/1281] lr: 2.411e-02, eta: 18:01:42, time: 0.570, data_time: 0.196, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9875, loss_cls: 0.7711, loss: 0.7711 +2025-06-24 13:11:32,995 - pyskl - INFO - Epoch [19][200/1281] lr: 2.411e-02, eta: 18:00:53, time: 0.369, data_time: 0.000, memory: 4082, top1_acc: 0.8213, top5_acc: 0.9931, loss_cls: 0.7448, loss: 0.7448 +2025-06-24 13:12:09,858 - pyskl - INFO - Epoch [19][300/1281] lr: 2.410e-02, eta: 18:00:04, time: 0.369, data_time: 0.000, memory: 4082, top1_acc: 0.8275, top5_acc: 0.9912, loss_cls: 0.7676, loss: 0.7676 +2025-06-24 13:12:47,345 - pyskl - INFO - Epoch [19][400/1281] lr: 2.409e-02, eta: 17:59:19, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8200, top5_acc: 0.9894, loss_cls: 0.8079, loss: 0.8079 +2025-06-24 13:13:19,393 - pyskl - INFO - Epoch [19][500/1281] lr: 2.408e-02, eta: 17:57:55, time: 0.320, data_time: 0.000, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9838, loss_cls: 0.8439, loss: 0.8439 +2025-06-24 13:13:53,205 - pyskl - INFO - Epoch [19][600/1281] lr: 2.408e-02, eta: 17:56:44, time: 0.338, data_time: 0.000, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9912, loss_cls: 0.8034, loss: 0.8034 +2025-06-24 13:14:30,535 - pyskl - INFO - Epoch [19][700/1281] lr: 2.407e-02, eta: 17:55:59, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9881, loss_cls: 0.7973, loss: 0.7973 +2025-06-24 13:14:53,694 - pyskl - INFO - Epoch [19][800/1281] lr: 2.406e-02, eta: 17:53:33, time: 0.232, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9919, loss_cls: 0.7535, loss: 0.7535 +2025-06-24 13:15:28,852 - pyskl - INFO - Epoch [19][900/1281] lr: 2.405e-02, eta: 17:52:33, time: 0.352, data_time: 0.000, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9869, loss_cls: 0.8272, loss: 0.8272 +2025-06-24 13:16:05,908 - pyskl - INFO - Epoch [19][1000/1281] lr: 2.405e-02, eta: 17:51:46, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9856, loss_cls: 0.8201, loss: 0.8201 +2025-06-24 13:16:43,765 - pyskl - INFO - Epoch [19][1100/1281] lr: 2.404e-02, eta: 17:51:05, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9925, loss_cls: 0.7244, loss: 0.7244 +2025-06-24 13:17:20,880 - pyskl - INFO - Epoch [19][1200/1281] lr: 2.403e-02, eta: 17:50:19, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9906, loss_cls: 0.7613, loss: 0.7613 +2025-06-24 13:17:51,529 - pyskl - INFO - Saving checkpoint at 19 epochs +2025-06-24 13:18:51,122 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:18:51,191 - pyskl - INFO - +top1_acc 0.7756 +top5_acc 0.9824 +2025-06-24 13:18:51,191 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:18:51,200 - pyskl - INFO - +mean_acc 0.6773 +2025-06-24 13:18:51,203 - pyskl - INFO - Epoch(val) [19][533] top1_acc: 0.7756, top5_acc: 0.9824, mean_class_accuracy: 0.6773 +2025-06-24 13:19:48,435 - pyskl - INFO - Epoch [20][100/1281] lr: 2.402e-02, eta: 17:47:48, time: 0.572, data_time: 0.197, memory: 4082, top1_acc: 0.8381, top5_acc: 0.9906, loss_cls: 0.7451, loss: 0.7451 +2025-06-24 13:20:25,056 - pyskl - INFO - Epoch [20][200/1281] lr: 2.401e-02, eta: 17:46:59, time: 0.366, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9894, loss_cls: 0.7478, loss: 0.7478 +2025-06-24 13:21:02,674 - pyskl - INFO - Epoch [20][300/1281] lr: 2.400e-02, eta: 17:46:17, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9869, loss_cls: 0.7364, loss: 0.7364 +2025-06-24 13:21:40,056 - pyskl - INFO - Epoch [20][400/1281] lr: 2.399e-02, eta: 17:45:33, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9925, loss_cls: 0.7551, loss: 0.7551 +2025-06-24 13:22:17,230 - pyskl - INFO - Epoch [20][500/1281] lr: 2.398e-02, eta: 17:44:48, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9950, loss_cls: 0.7494, loss: 0.7494 +2025-06-24 13:22:54,865 - pyskl - INFO - Epoch [20][600/1281] lr: 2.398e-02, eta: 17:44:06, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9906, loss_cls: 0.7593, loss: 0.7593 +2025-06-24 13:23:32,058 - pyskl - INFO - Epoch [20][700/1281] lr: 2.397e-02, eta: 17:43:21, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9931, loss_cls: 0.6887, loss: 0.6887 +2025-06-24 13:24:08,993 - pyskl - INFO - Epoch [20][800/1281] lr: 2.396e-02, eta: 17:42:35, time: 0.369, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9906, loss_cls: 0.7927, loss: 0.7927 +2025-06-24 13:24:46,298 - pyskl - INFO - Epoch [20][900/1281] lr: 2.395e-02, eta: 17:41:51, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9931, loss_cls: 0.7572, loss: 0.7572 +2025-06-24 13:25:24,328 - pyskl - INFO - Epoch [20][1000/1281] lr: 2.394e-02, eta: 17:41:12, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9894, loss_cls: 0.7431, loss: 0.7431 +2025-06-24 13:25:52,791 - pyskl - INFO - Epoch [20][1100/1281] lr: 2.393e-02, eta: 17:39:30, time: 0.285, data_time: 0.000, memory: 4082, top1_acc: 0.8425, top5_acc: 0.9944, loss_cls: 0.7390, loss: 0.7390 +2025-06-24 13:26:31,385 - pyskl - INFO - Epoch [20][1200/1281] lr: 2.393e-02, eta: 17:38:55, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9944, loss_cls: 0.7428, loss: 0.7428 +2025-06-24 13:27:00,115 - pyskl - INFO - Saving checkpoint at 20 epochs +2025-06-24 13:27:59,687 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:27:59,755 - pyskl - INFO - +top1_acc 0.7884 +top5_acc 0.9853 +2025-06-24 13:27:59,756 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:27:59,764 - pyskl - INFO - +mean_acc 0.7175 +2025-06-24 13:27:59,766 - pyskl - INFO - Epoch(val) [20][533] top1_acc: 0.7884, top5_acc: 0.9853, mean_class_accuracy: 0.7175 +2025-06-24 13:28:56,876 - pyskl - INFO - Epoch [21][100/1281] lr: 2.391e-02, eta: 17:36:29, time: 0.571, data_time: 0.193, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9956, loss_cls: 0.7237, loss: 0.7237 +2025-06-24 13:29:34,517 - pyskl - INFO - Epoch [21][200/1281] lr: 2.390e-02, eta: 17:35:48, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8413, top5_acc: 0.9906, loss_cls: 0.7202, loss: 0.7202 +2025-06-24 13:30:11,643 - pyskl - INFO - Epoch [21][300/1281] lr: 2.389e-02, eta: 17:35:03, time: 0.371, data_time: 0.001, memory: 4082, top1_acc: 0.8406, top5_acc: 0.9944, loss_cls: 0.7102, loss: 0.7102 +2025-06-24 13:30:49,002 - pyskl - INFO - Epoch [21][400/1281] lr: 2.389e-02, eta: 17:34:21, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9931, loss_cls: 0.7582, loss: 0.7582 +2025-06-24 13:31:26,666 - pyskl - INFO - Epoch [21][500/1281] lr: 2.388e-02, eta: 17:33:40, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9912, loss_cls: 0.7323, loss: 0.7323 +2025-06-24 13:32:03,733 - pyskl - INFO - Epoch [21][600/1281] lr: 2.387e-02, eta: 17:32:55, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9956, loss_cls: 0.8237, loss: 0.8237 +2025-06-24 13:32:40,651 - pyskl - INFO - Epoch [21][700/1281] lr: 2.386e-02, eta: 17:32:10, time: 0.369, data_time: 0.000, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9919, loss_cls: 0.7387, loss: 0.7387 +2025-06-24 13:33:17,534 - pyskl - INFO - Epoch [21][800/1281] lr: 2.385e-02, eta: 17:31:25, time: 0.369, data_time: 0.000, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9956, loss_cls: 0.6822, loss: 0.6822 +2025-06-24 13:33:54,892 - pyskl - INFO - Epoch [21][900/1281] lr: 2.384e-02, eta: 17:30:42, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9919, loss_cls: 0.6920, loss: 0.6920 +2025-06-24 13:34:32,078 - pyskl - INFO - Epoch [21][1000/1281] lr: 2.383e-02, eta: 17:29:59, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9888, loss_cls: 0.7237, loss: 0.7237 +2025-06-24 13:35:09,672 - pyskl - INFO - Epoch [21][1100/1281] lr: 2.383e-02, eta: 17:29:18, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9900, loss_cls: 0.8116, loss: 0.8116 +2025-06-24 13:35:47,085 - pyskl - INFO - Epoch [21][1200/1281] lr: 2.382e-02, eta: 17:28:36, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8375, top5_acc: 0.9869, loss_cls: 0.7216, loss: 0.7216 +2025-06-24 13:36:18,107 - pyskl - INFO - Saving checkpoint at 21 epochs +2025-06-24 13:37:17,083 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:37:17,141 - pyskl - INFO - +top1_acc 0.8241 +top5_acc 0.9856 +2025-06-24 13:37:17,142 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:37:17,148 - pyskl - INFO - +mean_acc 0.7363 +2025-06-24 13:37:17,152 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_18.pth was removed +2025-06-24 13:37:17,328 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_21.pth. +2025-06-24 13:37:17,329 - pyskl - INFO - Best top1_acc is 0.8241 at 21 epoch. +2025-06-24 13:37:17,331 - pyskl - INFO - Epoch(val) [21][533] top1_acc: 0.8241, top5_acc: 0.9856, mean_class_accuracy: 0.7363 +2025-06-24 13:38:03,863 - pyskl - INFO - Epoch [22][100/1281] lr: 2.380e-02, eta: 17:25:10, time: 0.465, data_time: 0.198, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9950, loss_cls: 0.6453, loss: 0.6453 +2025-06-24 13:38:48,018 - pyskl - INFO - Epoch [22][200/1281] lr: 2.379e-02, eta: 17:25:10, time: 0.442, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9919, loss_cls: 0.7219, loss: 0.7219 +2025-06-24 13:39:10,464 - pyskl - INFO - Epoch [22][300/1281] lr: 2.378e-02, eta: 17:22:57, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.8344, top5_acc: 0.9912, loss_cls: 0.7656, loss: 0.7656 +2025-06-24 13:39:42,594 - pyskl - INFO - Epoch [22][400/1281] lr: 2.378e-02, eta: 17:21:44, time: 0.321, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9944, loss_cls: 0.7235, loss: 0.7235 +2025-06-24 13:40:20,840 - pyskl - INFO - Epoch [22][500/1281] lr: 2.377e-02, eta: 17:21:08, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9894, loss_cls: 0.7449, loss: 0.7449 +2025-06-24 13:40:57,840 - pyskl - INFO - Epoch [22][600/1281] lr: 2.376e-02, eta: 17:20:25, time: 0.370, data_time: 0.000, memory: 4082, top1_acc: 0.8375, top5_acc: 0.9900, loss_cls: 0.7511, loss: 0.7511 +2025-06-24 13:41:35,591 - pyskl - INFO - Epoch [22][700/1281] lr: 2.375e-02, eta: 17:19:46, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9912, loss_cls: 0.7160, loss: 0.7160 +2025-06-24 13:42:13,244 - pyskl - INFO - Epoch [22][800/1281] lr: 2.374e-02, eta: 17:19:07, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9962, loss_cls: 0.6768, loss: 0.6768 +2025-06-24 13:42:49,964 - pyskl - INFO - Epoch [22][900/1281] lr: 2.373e-02, eta: 17:18:22, time: 0.367, data_time: 0.000, memory: 4082, top1_acc: 0.8400, top5_acc: 0.9912, loss_cls: 0.7417, loss: 0.7417 +2025-06-24 13:43:26,994 - pyskl - INFO - Epoch [22][1000/1281] lr: 2.372e-02, eta: 17:17:39, time: 0.370, data_time: 0.000, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9925, loss_cls: 0.7022, loss: 0.7022 +2025-06-24 13:44:04,529 - pyskl - INFO - Epoch [22][1100/1281] lr: 2.371e-02, eta: 17:16:59, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9919, loss_cls: 0.7053, loss: 0.7053 +2025-06-24 13:44:41,472 - pyskl - INFO - Epoch [22][1200/1281] lr: 2.370e-02, eta: 17:16:15, time: 0.369, data_time: 0.000, memory: 4082, top1_acc: 0.8206, top5_acc: 0.9900, loss_cls: 0.7662, loss: 0.7662 +2025-06-24 13:45:12,388 - pyskl - INFO - Saving checkpoint at 22 epochs +2025-06-24 13:46:11,311 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:46:11,366 - pyskl - INFO - +top1_acc 0.8028 +top5_acc 0.9805 +2025-06-24 13:46:11,366 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:46:11,373 - pyskl - INFO - +mean_acc 0.7410 +2025-06-24 13:46:11,375 - pyskl - INFO - Epoch(val) [22][533] top1_acc: 0.8028, top5_acc: 0.9805, mean_class_accuracy: 0.7410 +2025-06-24 13:47:08,843 - pyskl - INFO - Epoch [23][100/1281] lr: 2.369e-02, eta: 17:14:02, time: 0.575, data_time: 0.196, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9944, loss_cls: 0.6986, loss: 0.6986 +2025-06-24 13:47:46,792 - pyskl - INFO - Epoch [23][200/1281] lr: 2.368e-02, eta: 17:13:24, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8669, top5_acc: 0.9925, loss_cls: 0.6409, loss: 0.6409 +2025-06-24 13:48:26,379 - pyskl - INFO - Epoch [23][300/1281] lr: 2.367e-02, eta: 17:12:57, time: 0.396, data_time: 0.000, memory: 4082, top1_acc: 0.8381, top5_acc: 0.9925, loss_cls: 0.7473, loss: 0.7473 +2025-06-24 13:49:06,210 - pyskl - INFO - Epoch [23][400/1281] lr: 2.366e-02, eta: 17:12:30, time: 0.398, data_time: 0.000, memory: 4082, top1_acc: 0.8544, top5_acc: 0.9938, loss_cls: 0.6727, loss: 0.6727 +2025-06-24 13:49:43,857 - pyskl - INFO - Epoch [23][500/1281] lr: 2.365e-02, eta: 17:11:51, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9931, loss_cls: 0.6834, loss: 0.6834 +2025-06-24 13:50:10,533 - pyskl - INFO - Epoch [23][600/1281] lr: 2.364e-02, eta: 17:10:09, time: 0.267, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9900, loss_cls: 0.7224, loss: 0.7224 +2025-06-24 13:50:51,520 - pyskl - INFO - Epoch [23][700/1281] lr: 2.363e-02, eta: 17:09:49, time: 0.410, data_time: 0.000, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9931, loss_cls: 0.7207, loss: 0.7207 +2025-06-24 13:51:21,382 - pyskl - INFO - Epoch [23][800/1281] lr: 2.362e-02, eta: 17:08:26, time: 0.299, data_time: 0.000, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9944, loss_cls: 0.6850, loss: 0.6850 +2025-06-24 13:51:48,080 - pyskl - INFO - Epoch [23][900/1281] lr: 2.361e-02, eta: 17:06:46, time: 0.267, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9925, loss_cls: 0.7441, loss: 0.7441 +2025-06-24 13:52:24,931 - pyskl - INFO - Epoch [23][1000/1281] lr: 2.360e-02, eta: 17:06:03, time: 0.368, data_time: 0.000, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9950, loss_cls: 0.7539, loss: 0.7539 +2025-06-24 13:53:01,797 - pyskl - INFO - Epoch [23][1100/1281] lr: 2.359e-02, eta: 17:05:20, time: 0.369, data_time: 0.000, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9888, loss_cls: 0.7399, loss: 0.7399 +2025-06-24 13:53:39,086 - pyskl - INFO - Epoch [23][1200/1281] lr: 2.359e-02, eta: 17:04:40, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8406, top5_acc: 0.9938, loss_cls: 0.7234, loss: 0.7234 +2025-06-24 13:54:09,793 - pyskl - INFO - Saving checkpoint at 23 epochs +2025-06-24 13:55:08,727 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:55:08,784 - pyskl - INFO - +top1_acc 0.8091 +top5_acc 0.9839 +2025-06-24 13:55:08,784 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:55:08,791 - pyskl - INFO - +mean_acc 0.7320 +2025-06-24 13:55:08,793 - pyskl - INFO - Epoch(val) [23][533] top1_acc: 0.8091, top5_acc: 0.9839, mean_class_accuracy: 0.7320 +2025-06-24 13:56:06,358 - pyskl - INFO - Epoch [24][100/1281] lr: 2.357e-02, eta: 17:02:32, time: 0.576, data_time: 0.197, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9894, loss_cls: 0.7622, loss: 0.7622 +2025-06-24 13:56:43,362 - pyskl - INFO - Epoch [24][200/1281] lr: 2.356e-02, eta: 17:01:50, time: 0.370, data_time: 0.000, memory: 4082, top1_acc: 0.8400, top5_acc: 0.9919, loss_cls: 0.6985, loss: 0.6985 +2025-06-24 13:57:21,114 - pyskl - INFO - Epoch [24][300/1281] lr: 2.355e-02, eta: 17:01:13, time: 0.378, data_time: 0.001, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9919, loss_cls: 0.6578, loss: 0.6578 +2025-06-24 13:57:58,238 - pyskl - INFO - Epoch [24][400/1281] lr: 2.354e-02, eta: 17:00:32, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9894, loss_cls: 0.7289, loss: 0.7289 +2025-06-24 13:58:35,421 - pyskl - INFO - Epoch [24][500/1281] lr: 2.353e-02, eta: 16:59:51, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9944, loss_cls: 0.6386, loss: 0.6386 +2025-06-24 13:59:12,971 - pyskl - INFO - Epoch [24][600/1281] lr: 2.352e-02, eta: 16:59:12, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8581, top5_acc: 0.9938, loss_cls: 0.6577, loss: 0.6577 +2025-06-24 13:59:50,256 - pyskl - INFO - Epoch [24][700/1281] lr: 2.351e-02, eta: 16:58:32, time: 0.373, data_time: 0.001, memory: 4082, top1_acc: 0.8625, top5_acc: 0.9938, loss_cls: 0.6931, loss: 0.6931 +2025-06-24 14:00:27,302 - pyskl - INFO - Epoch [24][800/1281] lr: 2.350e-02, eta: 16:57:51, time: 0.370, data_time: 0.000, memory: 4082, top1_acc: 0.8456, top5_acc: 0.9925, loss_cls: 0.6850, loss: 0.6850 +2025-06-24 14:01:04,236 - pyskl - INFO - Epoch [24][900/1281] lr: 2.349e-02, eta: 16:57:09, time: 0.369, data_time: 0.000, memory: 4082, top1_acc: 0.8612, top5_acc: 0.9975, loss_cls: 0.6219, loss: 0.6219 +2025-06-24 14:01:41,400 - pyskl - INFO - Epoch [24][1000/1281] lr: 2.348e-02, eta: 16:56:28, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8544, top5_acc: 0.9938, loss_cls: 0.6612, loss: 0.6612 +2025-06-24 14:02:19,018 - pyskl - INFO - Epoch [24][1100/1281] lr: 2.347e-02, eta: 16:55:50, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8375, top5_acc: 0.9906, loss_cls: 0.7461, loss: 0.7461 +2025-06-24 14:02:43,921 - pyskl - INFO - Epoch [24][1200/1281] lr: 2.346e-02, eta: 16:54:05, time: 0.249, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9925, loss_cls: 0.7243, loss: 0.7243 +2025-06-24 14:03:18,611 - pyskl - INFO - Saving checkpoint at 24 epochs +2025-06-24 14:04:05,502 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:04:05,559 - pyskl - INFO - +top1_acc 0.7607 +top5_acc 0.9743 +2025-06-24 14:04:05,560 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:04:05,567 - pyskl - INFO - +mean_acc 0.7015 +2025-06-24 14:04:05,570 - pyskl - INFO - Epoch(val) [24][533] top1_acc: 0.7607, top5_acc: 0.9743, mean_class_accuracy: 0.7015 +2025-06-24 14:05:03,303 - pyskl - INFO - Epoch [25][100/1281] lr: 2.344e-02, eta: 16:52:02, time: 0.577, data_time: 0.196, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9944, loss_cls: 0.7090, loss: 0.7090 +2025-06-24 14:05:40,837 - pyskl - INFO - Epoch [25][200/1281] lr: 2.343e-02, eta: 16:51:24, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8375, top5_acc: 0.9925, loss_cls: 0.7048, loss: 0.7048 +2025-06-24 14:06:18,138 - pyskl - INFO - Epoch [25][300/1281] lr: 2.342e-02, eta: 16:50:44, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9919, loss_cls: 0.7260, loss: 0.7260 +2025-06-24 14:06:55,789 - pyskl - INFO - Epoch [25][400/1281] lr: 2.341e-02, eta: 16:50:07, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9931, loss_cls: 0.6974, loss: 0.6974 +2025-06-24 14:07:32,967 - pyskl - INFO - Epoch [25][500/1281] lr: 2.340e-02, eta: 16:49:26, time: 0.372, data_time: 0.001, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9950, loss_cls: 0.6553, loss: 0.6553 +2025-06-24 14:08:10,073 - pyskl - INFO - Epoch [25][600/1281] lr: 2.339e-02, eta: 16:48:46, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8562, top5_acc: 0.9950, loss_cls: 0.6698, loss: 0.6698 +2025-06-24 14:08:47,169 - pyskl - INFO - Epoch [25][700/1281] lr: 2.338e-02, eta: 16:48:06, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8681, top5_acc: 0.9956, loss_cls: 0.6310, loss: 0.6310 +2025-06-24 14:09:24,746 - pyskl - INFO - Epoch [25][800/1281] lr: 2.337e-02, eta: 16:47:28, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9938, loss_cls: 0.6994, loss: 0.6994 +2025-06-24 14:10:02,543 - pyskl - INFO - Epoch [25][900/1281] lr: 2.336e-02, eta: 16:46:51, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9931, loss_cls: 0.6914, loss: 0.6914 +2025-06-24 14:10:40,062 - pyskl - INFO - Epoch [25][1000/1281] lr: 2.335e-02, eta: 16:46:13, time: 0.375, data_time: 0.001, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9919, loss_cls: 0.6962, loss: 0.6962 +2025-06-24 14:11:17,497 - pyskl - INFO - Epoch [25][1100/1281] lr: 2.334e-02, eta: 16:45:34, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8425, top5_acc: 0.9912, loss_cls: 0.7074, loss: 0.7074 +2025-06-24 14:11:54,465 - pyskl - INFO - Epoch [25][1200/1281] lr: 2.333e-02, eta: 16:44:53, time: 0.370, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9925, loss_cls: 0.6877, loss: 0.6877 +2025-06-24 14:12:24,832 - pyskl - INFO - Saving checkpoint at 25 epochs +2025-06-24 14:13:24,113 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:13:24,168 - pyskl - INFO - +top1_acc 0.8146 +top5_acc 0.9873 +2025-06-24 14:13:24,168 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:13:24,174 - pyskl - INFO - +mean_acc 0.7526 +2025-06-24 14:13:24,176 - pyskl - INFO - Epoch(val) [25][533] top1_acc: 0.8146, top5_acc: 0.9873, mean_class_accuracy: 0.7526 +2025-06-24 14:14:20,012 - pyskl - INFO - Epoch [26][100/1281] lr: 2.332e-02, eta: 16:42:44, time: 0.558, data_time: 0.185, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9919, loss_cls: 0.6870, loss: 0.6870 +2025-06-24 14:14:44,391 - pyskl - INFO - Epoch [26][200/1281] lr: 2.330e-02, eta: 16:41:00, time: 0.244, data_time: 0.001, memory: 4082, top1_acc: 0.8644, top5_acc: 0.9938, loss_cls: 0.6319, loss: 0.6319 +2025-06-24 14:15:29,586 - pyskl - INFO - Epoch [26][300/1281] lr: 2.329e-02, eta: 16:41:01, time: 0.452, data_time: 0.000, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9906, loss_cls: 0.6794, loss: 0.6794 +2025-06-24 14:15:53,557 - pyskl - INFO - Epoch [26][400/1281] lr: 2.328e-02, eta: 16:39:16, time: 0.240, data_time: 0.000, memory: 4082, top1_acc: 0.8669, top5_acc: 0.9950, loss_cls: 0.6433, loss: 0.6433 +2025-06-24 14:16:24,109 - pyskl - INFO - Epoch [26][500/1281] lr: 2.327e-02, eta: 16:38:04, time: 0.306, data_time: 0.000, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9962, loss_cls: 0.6742, loss: 0.6742 +2025-06-24 14:17:01,566 - pyskl - INFO - Epoch [26][600/1281] lr: 2.326e-02, eta: 16:37:26, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8375, top5_acc: 0.9925, loss_cls: 0.7080, loss: 0.7080 +2025-06-24 14:17:38,931 - pyskl - INFO - Epoch [26][700/1281] lr: 2.325e-02, eta: 16:36:48, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9906, loss_cls: 0.7372, loss: 0.7372 +2025-06-24 14:18:16,879 - pyskl - INFO - Epoch [26][800/1281] lr: 2.324e-02, eta: 16:36:13, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8425, top5_acc: 0.9906, loss_cls: 0.7059, loss: 0.7059 +2025-06-24 14:18:54,752 - pyskl - INFO - Epoch [26][900/1281] lr: 2.323e-02, eta: 16:35:37, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9931, loss_cls: 0.6876, loss: 0.6876 +2025-06-24 14:19:31,964 - pyskl - INFO - Epoch [26][1000/1281] lr: 2.322e-02, eta: 16:34:58, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8619, top5_acc: 0.9962, loss_cls: 0.6267, loss: 0.6267 +2025-06-24 14:20:09,222 - pyskl - INFO - Epoch [26][1100/1281] lr: 2.321e-02, eta: 16:34:19, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8612, top5_acc: 0.9900, loss_cls: 0.6717, loss: 0.6717 +2025-06-24 14:20:46,203 - pyskl - INFO - Epoch [26][1200/1281] lr: 2.320e-02, eta: 16:33:39, time: 0.370, data_time: 0.000, memory: 4082, top1_acc: 0.8675, top5_acc: 0.9900, loss_cls: 0.6373, loss: 0.6373 +2025-06-24 14:21:16,732 - pyskl - INFO - Saving checkpoint at 26 epochs +2025-06-24 14:22:15,656 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:22:15,712 - pyskl - INFO - +top1_acc 0.8255 +top5_acc 0.9839 +2025-06-24 14:22:15,712 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:22:15,719 - pyskl - INFO - +mean_acc 0.7689 +2025-06-24 14:22:15,724 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_21.pth was removed +2025-06-24 14:22:15,907 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_26.pth. +2025-06-24 14:22:15,908 - pyskl - INFO - Best top1_acc is 0.8255 at 26 epoch. +2025-06-24 14:22:15,910 - pyskl - INFO - Epoch(val) [26][533] top1_acc: 0.8255, top5_acc: 0.9839, mean_class_accuracy: 0.7689 +2025-06-24 14:23:13,022 - pyskl - INFO - Epoch [27][100/1281] lr: 2.318e-02, eta: 16:31:40, time: 0.571, data_time: 0.194, memory: 4082, top1_acc: 0.8631, top5_acc: 0.9944, loss_cls: 0.6262, loss: 0.6262 +2025-06-24 14:23:50,552 - pyskl - INFO - Epoch [27][200/1281] lr: 2.317e-02, eta: 16:31:03, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9919, loss_cls: 0.6519, loss: 0.6519 +2025-06-24 14:24:28,202 - pyskl - INFO - Epoch [27][300/1281] lr: 2.316e-02, eta: 16:30:26, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9906, loss_cls: 0.6623, loss: 0.6623 +2025-06-24 14:25:06,189 - pyskl - INFO - Epoch [27][400/1281] lr: 2.315e-02, eta: 16:29:51, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8494, top5_acc: 0.9962, loss_cls: 0.6736, loss: 0.6736 +2025-06-24 14:25:43,674 - pyskl - INFO - Epoch [27][500/1281] lr: 2.314e-02, eta: 16:29:13, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8544, top5_acc: 0.9906, loss_cls: 0.6748, loss: 0.6748 +2025-06-24 14:26:21,123 - pyskl - INFO - Epoch [27][600/1281] lr: 2.313e-02, eta: 16:28:36, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8594, top5_acc: 0.9956, loss_cls: 0.6376, loss: 0.6376 +2025-06-24 14:26:54,419 - pyskl - INFO - Epoch [27][700/1281] lr: 2.312e-02, eta: 16:27:39, time: 0.333, data_time: 0.000, memory: 4082, top1_acc: 0.8400, top5_acc: 0.9888, loss_cls: 0.7085, loss: 0.7085 +2025-06-24 14:27:26,627 - pyskl - INFO - Epoch [27][800/1281] lr: 2.311e-02, eta: 16:26:37, time: 0.322, data_time: 0.000, memory: 4082, top1_acc: 0.8594, top5_acc: 0.9925, loss_cls: 0.6509, loss: 0.6509 +2025-06-24 14:28:05,566 - pyskl - INFO - Epoch [27][900/1281] lr: 2.310e-02, eta: 16:26:06, time: 0.389, data_time: 0.000, memory: 4082, top1_acc: 0.8900, top5_acc: 0.9925, loss_cls: 0.5661, loss: 0.5661 +2025-06-24 14:28:28,382 - pyskl - INFO - Epoch [27][1000/1281] lr: 2.308e-02, eta: 16:24:21, time: 0.228, data_time: 0.000, memory: 4082, top1_acc: 0.8413, top5_acc: 0.9912, loss_cls: 0.7155, loss: 0.7155 +2025-06-24 14:29:02,886 - pyskl - INFO - Epoch [27][1100/1281] lr: 2.307e-02, eta: 16:23:31, time: 0.345, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9925, loss_cls: 0.7166, loss: 0.7166 +2025-06-24 14:29:40,082 - pyskl - INFO - Epoch [27][1200/1281] lr: 2.306e-02, eta: 16:22:52, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8481, top5_acc: 0.9912, loss_cls: 0.7259, loss: 0.7259 +2025-06-24 14:30:10,853 - pyskl - INFO - Saving checkpoint at 27 epochs +2025-06-24 14:31:10,022 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:31:10,077 - pyskl - INFO - +top1_acc 0.7875 +top5_acc 0.9813 +2025-06-24 14:31:10,077 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:31:10,083 - pyskl - INFO - +mean_acc 0.7163 +2025-06-24 14:31:10,085 - pyskl - INFO - Epoch(val) [27][533] top1_acc: 0.7875, top5_acc: 0.9813, mean_class_accuracy: 0.7163 +2025-06-24 14:32:07,304 - pyskl - INFO - Epoch [28][100/1281] lr: 2.304e-02, eta: 16:20:57, time: 0.572, data_time: 0.195, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9938, loss_cls: 0.6755, loss: 0.6755 +2025-06-24 14:32:44,613 - pyskl - INFO - Epoch [28][200/1281] lr: 2.303e-02, eta: 16:20:19, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8556, top5_acc: 0.9925, loss_cls: 0.6556, loss: 0.6556 +2025-06-24 14:33:21,670 - pyskl - INFO - Epoch [28][300/1281] lr: 2.302e-02, eta: 16:19:40, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8675, top5_acc: 0.9900, loss_cls: 0.6267, loss: 0.6267 +2025-06-24 14:33:59,067 - pyskl - INFO - Epoch [28][400/1281] lr: 2.301e-02, eta: 16:19:03, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8856, top5_acc: 0.9975, loss_cls: 0.5831, loss: 0.5831 +2025-06-24 14:34:35,978 - pyskl - INFO - Epoch [28][500/1281] lr: 2.300e-02, eta: 16:18:24, time: 0.369, data_time: 0.000, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9931, loss_cls: 0.6562, loss: 0.6562 +2025-06-24 14:35:13,181 - pyskl - INFO - Epoch [28][600/1281] lr: 2.299e-02, eta: 16:17:45, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8625, top5_acc: 0.9888, loss_cls: 0.6847, loss: 0.6847 +2025-06-24 14:35:50,907 - pyskl - INFO - Epoch [28][700/1281] lr: 2.298e-02, eta: 16:17:10, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8594, top5_acc: 0.9975, loss_cls: 0.6362, loss: 0.6362 +2025-06-24 14:36:28,267 - pyskl - INFO - Epoch [28][800/1281] lr: 2.297e-02, eta: 16:16:32, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9931, loss_cls: 0.6948, loss: 0.6948 +2025-06-24 14:37:05,362 - pyskl - INFO - Epoch [28][900/1281] lr: 2.295e-02, eta: 16:15:54, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9925, loss_cls: 0.6726, loss: 0.6726 +2025-06-24 14:37:42,261 - pyskl - INFO - Epoch [28][1000/1281] lr: 2.294e-02, eta: 16:15:14, time: 0.369, data_time: 0.000, memory: 4082, top1_acc: 0.8644, top5_acc: 0.9950, loss_cls: 0.6265, loss: 0.6265 +2025-06-24 14:38:19,169 - pyskl - INFO - Epoch [28][1100/1281] lr: 2.293e-02, eta: 16:14:35, time: 0.369, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9931, loss_cls: 0.6828, loss: 0.6828 +2025-06-24 14:38:55,728 - pyskl - INFO - Epoch [28][1200/1281] lr: 2.292e-02, eta: 16:13:54, time: 0.366, data_time: 0.000, memory: 4082, top1_acc: 0.8706, top5_acc: 0.9925, loss_cls: 0.6060, loss: 0.6060 +2025-06-24 14:39:24,223 - pyskl - INFO - Saving checkpoint at 28 epochs +2025-06-24 14:40:36,839 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:40:36,897 - pyskl - INFO - +top1_acc 0.8391 +top5_acc 0.9867 +2025-06-24 14:40:36,897 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:40:36,905 - pyskl - INFO - +mean_acc 0.7712 +2025-06-24 14:40:36,909 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_26.pth was removed +2025-06-24 14:40:37,084 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_28.pth. +2025-06-24 14:40:37,084 - pyskl - INFO - Best top1_acc is 0.8391 at 28 epoch. +2025-06-24 14:40:37,087 - pyskl - INFO - Epoch(val) [28][533] top1_acc: 0.8391, top5_acc: 0.9867, mean_class_accuracy: 0.7712 +2025-06-24 14:41:33,857 - pyskl - INFO - Epoch [29][100/1281] lr: 2.290e-02, eta: 16:11:59, time: 0.568, data_time: 0.194, memory: 4082, top1_acc: 0.8788, top5_acc: 0.9950, loss_cls: 0.5870, loss: 0.5870 +2025-06-24 14:42:11,039 - pyskl - INFO - Epoch [29][200/1281] lr: 2.289e-02, eta: 16:11:21, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8650, top5_acc: 0.9900, loss_cls: 0.6467, loss: 0.6467 +2025-06-24 14:42:47,858 - pyskl - INFO - Epoch [29][300/1281] lr: 2.288e-02, eta: 16:10:41, time: 0.368, data_time: 0.000, memory: 4082, top1_acc: 0.8644, top5_acc: 0.9938, loss_cls: 0.6254, loss: 0.6254 +2025-06-24 14:43:25,792 - pyskl - INFO - Epoch [29][400/1281] lr: 2.287e-02, eta: 16:10:06, time: 0.379, data_time: 0.001, memory: 4082, top1_acc: 0.8631, top5_acc: 0.9956, loss_cls: 0.6180, loss: 0.6180 +2025-06-24 14:44:03,150 - pyskl - INFO - Epoch [29][500/1281] lr: 2.285e-02, eta: 16:09:29, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8706, top5_acc: 0.9969, loss_cls: 0.6217, loss: 0.6217 +2025-06-24 14:44:40,573 - pyskl - INFO - Epoch [29][600/1281] lr: 2.284e-02, eta: 16:08:52, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9925, loss_cls: 0.6830, loss: 0.6830 +2025-06-24 14:45:17,375 - pyskl - INFO - Epoch [29][700/1281] lr: 2.283e-02, eta: 16:08:13, time: 0.368, data_time: 0.000, memory: 4082, top1_acc: 0.8719, top5_acc: 0.9962, loss_cls: 0.6266, loss: 0.6266 +2025-06-24 14:45:54,915 - pyskl - INFO - Epoch [29][800/1281] lr: 2.282e-02, eta: 16:07:36, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9919, loss_cls: 0.6460, loss: 0.6460 +2025-06-24 14:46:32,734 - pyskl - INFO - Epoch [29][900/1281] lr: 2.281e-02, eta: 16:07:01, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8506, top5_acc: 0.9950, loss_cls: 0.6715, loss: 0.6715 +2025-06-24 14:47:09,829 - pyskl - INFO - Epoch [29][1000/1281] lr: 2.280e-02, eta: 16:06:22, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9944, loss_cls: 0.7143, loss: 0.7143 +2025-06-24 14:47:47,005 - pyskl - INFO - Epoch [29][1100/1281] lr: 2.279e-02, eta: 16:05:44, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8662, top5_acc: 0.9906, loss_cls: 0.6171, loss: 0.6171 +2025-06-24 14:48:24,434 - pyskl - INFO - Epoch [29][1200/1281] lr: 2.277e-02, eta: 16:05:07, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9919, loss_cls: 0.6617, loss: 0.6617 +2025-06-24 14:48:55,122 - pyskl - INFO - Saving checkpoint at 29 epochs +2025-06-24 14:49:54,609 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:49:54,679 - pyskl - INFO - +top1_acc 0.8177 +top5_acc 0.9880 +2025-06-24 14:49:54,679 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:49:54,687 - pyskl - INFO - +mean_acc 0.7528 +2025-06-24 14:49:54,689 - pyskl - INFO - Epoch(val) [29][533] top1_acc: 0.8177, top5_acc: 0.9880, mean_class_accuracy: 0.7528 +2025-06-24 14:51:02,106 - pyskl - INFO - Epoch [30][100/1281] lr: 2.275e-02, eta: 16:03:59, time: 0.674, data_time: 0.197, memory: 4082, top1_acc: 0.8594, top5_acc: 0.9938, loss_cls: 0.6353, loss: 0.6353 +2025-06-24 14:51:37,536 - pyskl - INFO - Epoch [30][200/1281] lr: 2.274e-02, eta: 16:03:14, time: 0.354, data_time: 0.001, memory: 4082, top1_acc: 0.8694, top5_acc: 0.9956, loss_cls: 0.6096, loss: 0.6096 +2025-06-24 14:52:28,348 - pyskl - INFO - Epoch [30][300/1281] lr: 2.273e-02, eta: 16:03:32, time: 0.508, data_time: 0.000, memory: 4082, top1_acc: 0.8762, top5_acc: 0.9950, loss_cls: 0.6190, loss: 0.6190 +2025-06-24 14:52:52,318 - pyskl - INFO - Epoch [30][400/1281] lr: 2.272e-02, eta: 16:02:00, time: 0.240, data_time: 0.000, memory: 4082, top1_acc: 0.8819, top5_acc: 0.9931, loss_cls: 0.5930, loss: 0.5930 +2025-06-24 14:53:36,795 - pyskl - INFO - Epoch [30][500/1281] lr: 2.271e-02, eta: 16:01:52, time: 0.445, data_time: 0.000, memory: 4082, top1_acc: 0.8738, top5_acc: 0.9931, loss_cls: 0.6351, loss: 0.6351 +2025-06-24 14:54:24,547 - pyskl - INFO - Epoch [30][600/1281] lr: 2.269e-02, eta: 16:01:57, time: 0.478, data_time: 0.000, memory: 4082, top1_acc: 0.8675, top5_acc: 0.9956, loss_cls: 0.6043, loss: 0.6043 +2025-06-24 14:55:12,393 - pyskl - INFO - Epoch [30][700/1281] lr: 2.268e-02, eta: 16:02:02, time: 0.478, data_time: 0.000, memory: 4082, top1_acc: 0.8612, top5_acc: 0.9938, loss_cls: 0.6557, loss: 0.6557 +2025-06-24 14:56:00,475 - pyskl - INFO - Epoch [30][800/1281] lr: 2.267e-02, eta: 16:02:08, time: 0.481, data_time: 0.000, memory: 4082, top1_acc: 0.8644, top5_acc: 0.9931, loss_cls: 0.6444, loss: 0.6444 +2025-06-24 14:56:48,434 - pyskl - INFO - Epoch [30][900/1281] lr: 2.266e-02, eta: 16:02:13, time: 0.480, data_time: 0.000, memory: 4082, top1_acc: 0.8675, top5_acc: 0.9956, loss_cls: 0.6344, loss: 0.6344 +2025-06-24 14:57:36,200 - pyskl - INFO - Epoch [30][1000/1281] lr: 2.265e-02, eta: 16:02:17, time: 0.478, data_time: 0.000, memory: 4082, top1_acc: 0.8413, top5_acc: 0.9900, loss_cls: 0.6960, loss: 0.6960 +2025-06-24 14:58:23,902 - pyskl - INFO - Epoch [30][1100/1281] lr: 2.263e-02, eta: 16:02:21, time: 0.477, data_time: 0.000, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9919, loss_cls: 0.6526, loss: 0.6526 +2025-06-24 14:59:11,587 - pyskl - INFO - Epoch [30][1200/1281] lr: 2.262e-02, eta: 16:02:24, time: 0.477, data_time: 0.000, memory: 4082, top1_acc: 0.8706, top5_acc: 0.9950, loss_cls: 0.5983, loss: 0.5983 +2025-06-24 14:59:50,812 - pyskl - INFO - Saving checkpoint at 30 epochs +2025-06-24 15:00:50,063 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:00:50,117 - pyskl - INFO - +top1_acc 0.7970 +top5_acc 0.9847 +2025-06-24 15:00:50,117 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:00:50,124 - pyskl - INFO - +mean_acc 0.7464 +2025-06-24 15:00:50,125 - pyskl - INFO - Epoch(val) [30][533] top1_acc: 0.7970, top5_acc: 0.9847, mean_class_accuracy: 0.7464 +2025-06-24 15:02:15,337 - pyskl - INFO - Epoch [31][100/1281] lr: 2.260e-02, eta: 16:02:25, time: 0.852, data_time: 0.194, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9969, loss_cls: 0.7454, loss: 0.7454 +2025-06-24 15:02:47,598 - pyskl - INFO - Epoch [31][200/1281] lr: 2.259e-02, eta: 16:01:26, time: 0.323, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9925, loss_cls: 0.7511, loss: 0.7511 +2025-06-24 15:03:38,624 - pyskl - INFO - Epoch [31][300/1281] lr: 2.258e-02, eta: 16:01:42, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9950, loss_cls: 0.6750, loss: 0.6750 +2025-06-24 15:04:03,681 - pyskl - INFO - Epoch [31][400/1281] lr: 2.256e-02, eta: 16:00:15, time: 0.251, data_time: 0.000, memory: 4083, top1_acc: 0.8450, top5_acc: 0.9894, loss_cls: 0.8642, loss: 0.8642 +2025-06-24 15:04:51,265 - pyskl - INFO - Epoch [31][500/1281] lr: 2.255e-02, eta: 16:00:16, time: 0.476, data_time: 0.000, memory: 4083, top1_acc: 0.8544, top5_acc: 0.9931, loss_cls: 0.7715, loss: 0.7715 +2025-06-24 15:05:40,220 - pyskl - INFO - Epoch [31][600/1281] lr: 2.254e-02, eta: 16:00:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9938, loss_cls: 0.7139, loss: 0.7139 +2025-06-24 15:06:29,055 - pyskl - INFO - Epoch [31][700/1281] lr: 2.253e-02, eta: 16:00:30, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9944, loss_cls: 0.7844, loss: 0.7844 +2025-06-24 15:07:18,029 - pyskl - INFO - Epoch [31][800/1281] lr: 2.252e-02, eta: 16:00:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9925, loss_cls: 0.7729, loss: 0.7729 +2025-06-24 15:08:07,054 - pyskl - INFO - Epoch [31][900/1281] lr: 2.250e-02, eta: 16:00:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9969, loss_cls: 0.7085, loss: 0.7085 +2025-06-24 15:08:56,057 - pyskl - INFO - Epoch [31][1000/1281] lr: 2.249e-02, eta: 16:00:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9931, loss_cls: 0.7983, loss: 0.7983 +2025-06-24 15:09:45,039 - pyskl - INFO - Epoch [31][1100/1281] lr: 2.248e-02, eta: 16:00:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9944, loss_cls: 0.7481, loss: 0.7481 +2025-06-24 15:10:33,788 - pyskl - INFO - Epoch [31][1200/1281] lr: 2.247e-02, eta: 16:00:58, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8544, top5_acc: 0.9962, loss_cls: 0.7836, loss: 0.7836 +2025-06-24 15:11:13,694 - pyskl - INFO - Saving checkpoint at 31 epochs +2025-06-24 15:12:13,437 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:12:13,495 - pyskl - INFO - +top1_acc 0.8309 +top5_acc 0.9874 +2025-06-24 15:12:13,495 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:12:13,504 - pyskl - INFO - +mean_acc 0.7757 +2025-06-24 15:12:13,506 - pyskl - INFO - Epoch(val) [31][533] top1_acc: 0.8309, top5_acc: 0.9874, mean_class_accuracy: 0.7757 +2025-06-24 15:13:34,844 - pyskl - INFO - Epoch [32][100/1281] lr: 2.244e-02, eta: 16:00:39, time: 0.813, data_time: 0.196, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9950, loss_cls: 0.7035, loss: 0.7035 +2025-06-24 15:14:06,219 - pyskl - INFO - Epoch [32][200/1281] lr: 2.243e-02, eta: 15:59:37, time: 0.314, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9969, loss_cls: 0.6864, loss: 0.6864 +2025-06-24 15:14:57,601 - pyskl - INFO - Epoch [32][300/1281] lr: 2.242e-02, eta: 15:59:51, time: 0.514, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9988, loss_cls: 0.6567, loss: 0.6567 +2025-06-24 15:15:23,263 - pyskl - INFO - Epoch [32][400/1281] lr: 2.241e-02, eta: 15:58:26, time: 0.257, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9944, loss_cls: 0.6291, loss: 0.6291 +2025-06-24 15:16:12,181 - pyskl - INFO - Epoch [32][500/1281] lr: 2.239e-02, eta: 15:58:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9900, loss_cls: 0.6832, loss: 0.6832 +2025-06-24 15:17:00,983 - pyskl - INFO - Epoch [32][600/1281] lr: 2.238e-02, eta: 15:58:34, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9919, loss_cls: 0.7317, loss: 0.7317 +2025-06-24 15:17:49,839 - pyskl - INFO - Epoch [32][700/1281] lr: 2.237e-02, eta: 15:58:37, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9919, loss_cls: 0.7470, loss: 0.7470 +2025-06-24 15:18:38,701 - pyskl - INFO - Epoch [32][800/1281] lr: 2.236e-02, eta: 15:58:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9950, loss_cls: 0.7103, loss: 0.7103 +2025-06-24 15:19:27,910 - pyskl - INFO - Epoch [32][900/1281] lr: 2.234e-02, eta: 15:58:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9919, loss_cls: 0.7323, loss: 0.7323 +2025-06-24 15:20:16,954 - pyskl - INFO - Epoch [32][1000/1281] lr: 2.233e-02, eta: 15:58:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9938, loss_cls: 0.7654, loss: 0.7654 +2025-06-24 15:21:06,138 - pyskl - INFO - Epoch [32][1100/1281] lr: 2.232e-02, eta: 15:58:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9931, loss_cls: 0.7023, loss: 0.7023 +2025-06-24 15:21:55,056 - pyskl - INFO - Epoch [32][1200/1281] lr: 2.231e-02, eta: 15:58:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9969, loss_cls: 0.6642, loss: 0.6642 +2025-06-24 15:22:35,134 - pyskl - INFO - Saving checkpoint at 32 epochs +2025-06-24 15:23:34,132 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:23:34,206 - pyskl - INFO - +top1_acc 0.8183 +top5_acc 0.9829 +2025-06-24 15:23:34,207 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:23:34,217 - pyskl - INFO - +mean_acc 0.7552 +2025-06-24 15:23:34,220 - pyskl - INFO - Epoch(val) [32][533] top1_acc: 0.8183, top5_acc: 0.9829, mean_class_accuracy: 0.7552 +2025-06-24 15:24:54,662 - pyskl - INFO - Epoch [33][100/1281] lr: 2.228e-02, eta: 15:58:27, time: 0.804, data_time: 0.193, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9950, loss_cls: 0.6291, loss: 0.6291 +2025-06-24 15:25:25,952 - pyskl - INFO - Epoch [33][200/1281] lr: 2.227e-02, eta: 15:57:24, time: 0.313, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9956, loss_cls: 0.6838, loss: 0.6838 +2025-06-24 15:26:16,977 - pyskl - INFO - Epoch [33][300/1281] lr: 2.226e-02, eta: 15:57:34, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9931, loss_cls: 0.7028, loss: 0.7028 +2025-06-24 15:26:43,265 - pyskl - INFO - Epoch [33][400/1281] lr: 2.225e-02, eta: 15:56:13, time: 0.263, data_time: 0.000, memory: 4083, top1_acc: 0.8488, top5_acc: 0.9969, loss_cls: 0.7143, loss: 0.7143 +2025-06-24 15:27:31,564 - pyskl - INFO - Epoch [33][500/1281] lr: 2.223e-02, eta: 15:56:12, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9931, loss_cls: 0.6651, loss: 0.6651 +2025-06-24 15:28:20,901 - pyskl - INFO - Epoch [33][600/1281] lr: 2.222e-02, eta: 15:56:14, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9944, loss_cls: 0.6341, loss: 0.6341 +2025-06-24 15:29:09,462 - pyskl - INFO - Epoch [33][700/1281] lr: 2.221e-02, eta: 15:56:14, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9938, loss_cls: 0.6476, loss: 0.6476 +2025-06-24 15:29:58,415 - pyskl - INFO - Epoch [33][800/1281] lr: 2.219e-02, eta: 15:56:15, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9912, loss_cls: 0.6560, loss: 0.6560 +2025-06-24 15:30:47,077 - pyskl - INFO - Epoch [33][900/1281] lr: 2.218e-02, eta: 15:56:14, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9900, loss_cls: 0.6527, loss: 0.6527 +2025-06-24 15:31:36,315 - pyskl - INFO - Epoch [33][1000/1281] lr: 2.217e-02, eta: 15:56:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9931, loss_cls: 0.6996, loss: 0.6996 +2025-06-24 15:32:25,248 - pyskl - INFO - Epoch [33][1100/1281] lr: 2.216e-02, eta: 15:56:16, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9931, loss_cls: 0.6891, loss: 0.6891 +2025-06-24 15:33:13,594 - pyskl - INFO - Epoch [33][1200/1281] lr: 2.214e-02, eta: 15:56:13, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9944, loss_cls: 0.6827, loss: 0.6827 +2025-06-24 15:33:53,675 - pyskl - INFO - Saving checkpoint at 33 epochs +2025-06-24 15:34:51,791 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:34:51,847 - pyskl - INFO - +top1_acc 0.8119 +top5_acc 0.9830 +2025-06-24 15:34:51,847 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:34:51,854 - pyskl - INFO - +mean_acc 0.7632 +2025-06-24 15:34:51,856 - pyskl - INFO - Epoch(val) [33][533] top1_acc: 0.8119, top5_acc: 0.9830, mean_class_accuracy: 0.7632 +2025-06-24 15:36:12,881 - pyskl - INFO - Epoch [34][100/1281] lr: 2.212e-02, eta: 15:55:46, time: 0.810, data_time: 0.195, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9950, loss_cls: 0.6047, loss: 0.6047 +2025-06-24 15:36:44,508 - pyskl - INFO - Epoch [34][200/1281] lr: 2.211e-02, eta: 15:54:44, time: 0.316, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9900, loss_cls: 0.6247, loss: 0.6247 +2025-06-24 15:37:35,513 - pyskl - INFO - Epoch [34][300/1281] lr: 2.209e-02, eta: 15:54:50, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9925, loss_cls: 0.6303, loss: 0.6303 +2025-06-24 15:38:00,881 - pyskl - INFO - Epoch [34][400/1281] lr: 2.208e-02, eta: 15:53:27, time: 0.254, data_time: 0.001, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9956, loss_cls: 0.6193, loss: 0.6193 +2025-06-24 15:38:49,625 - pyskl - INFO - Epoch [34][500/1281] lr: 2.207e-02, eta: 15:53:25, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9938, loss_cls: 0.6672, loss: 0.6672 +2025-06-24 15:39:38,352 - pyskl - INFO - Epoch [34][600/1281] lr: 2.205e-02, eta: 15:53:23, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9969, loss_cls: 0.6511, loss: 0.6511 +2025-06-24 15:40:27,089 - pyskl - INFO - Epoch [34][700/1281] lr: 2.204e-02, eta: 15:53:21, time: 0.487, data_time: 0.001, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9950, loss_cls: 0.6664, loss: 0.6664 +2025-06-24 15:41:16,352 - pyskl - INFO - Epoch [34][800/1281] lr: 2.203e-02, eta: 15:53:20, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9956, loss_cls: 0.6199, loss: 0.6199 +2025-06-24 15:42:05,725 - pyskl - INFO - Epoch [34][900/1281] lr: 2.201e-02, eta: 15:53:20, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9962, loss_cls: 0.6497, loss: 0.6497 +2025-06-24 15:42:54,760 - pyskl - INFO - Epoch [34][1000/1281] lr: 2.200e-02, eta: 15:53:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9956, loss_cls: 0.6729, loss: 0.6729 +2025-06-24 15:43:43,743 - pyskl - INFO - Epoch [34][1100/1281] lr: 2.199e-02, eta: 15:53:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9925, loss_cls: 0.6757, loss: 0.6757 +2025-06-24 15:44:32,196 - pyskl - INFO - Epoch [34][1200/1281] lr: 2.197e-02, eta: 15:53:12, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9888, loss_cls: 0.7417, loss: 0.7417 +2025-06-24 15:45:12,402 - pyskl - INFO - Saving checkpoint at 34 epochs +2025-06-24 15:46:11,171 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:46:11,227 - pyskl - INFO - +top1_acc 0.8192 +top5_acc 0.9845 +2025-06-24 15:46:11,227 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:46:11,234 - pyskl - INFO - +mean_acc 0.7626 +2025-06-24 15:46:11,236 - pyskl - INFO - Epoch(val) [34][533] top1_acc: 0.8192, top5_acc: 0.9845, mean_class_accuracy: 0.7626 +2025-06-24 15:47:31,115 - pyskl - INFO - Epoch [35][100/1281] lr: 2.195e-02, eta: 15:52:37, time: 0.799, data_time: 0.198, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9981, loss_cls: 0.5869, loss: 0.5869 +2025-06-24 15:48:04,310 - pyskl - INFO - Epoch [35][200/1281] lr: 2.194e-02, eta: 15:51:41, time: 0.332, data_time: 0.001, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9956, loss_cls: 0.6476, loss: 0.6476 +2025-06-24 15:48:55,449 - pyskl - INFO - Epoch [35][300/1281] lr: 2.192e-02, eta: 15:51:45, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9956, loss_cls: 0.6053, loss: 0.6053 +2025-06-24 15:49:21,017 - pyskl - INFO - Epoch [35][400/1281] lr: 2.191e-02, eta: 15:50:23, time: 0.256, data_time: 0.001, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9938, loss_cls: 0.6544, loss: 0.6544 +2025-06-24 15:50:08,719 - pyskl - INFO - Epoch [35][500/1281] lr: 2.190e-02, eta: 15:50:15, time: 0.477, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9912, loss_cls: 0.6973, loss: 0.6973 +2025-06-24 15:50:57,619 - pyskl - INFO - Epoch [35][600/1281] lr: 2.188e-02, eta: 15:50:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9950, loss_cls: 0.6543, loss: 0.6543 +2025-06-24 15:51:46,353 - pyskl - INFO - Epoch [35][700/1281] lr: 2.187e-02, eta: 15:50:07, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8469, top5_acc: 0.9925, loss_cls: 0.7201, loss: 0.7201 +2025-06-24 15:52:34,783 - pyskl - INFO - Epoch [35][800/1281] lr: 2.185e-02, eta: 15:50:02, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9912, loss_cls: 0.6936, loss: 0.6936 +2025-06-24 15:53:23,780 - pyskl - INFO - Epoch [35][900/1281] lr: 2.184e-02, eta: 15:49:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9944, loss_cls: 0.6672, loss: 0.6672 +2025-06-24 15:54:12,856 - pyskl - INFO - Epoch [35][1000/1281] lr: 2.183e-02, eta: 15:49:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9912, loss_cls: 0.6716, loss: 0.6716 +2025-06-24 15:55:02,365 - pyskl - INFO - Epoch [35][1100/1281] lr: 2.181e-02, eta: 15:49:51, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9950, loss_cls: 0.6641, loss: 0.6641 +2025-06-24 15:55:51,599 - pyskl - INFO - Epoch [35][1200/1281] lr: 2.180e-02, eta: 15:49:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9950, loss_cls: 0.7074, loss: 0.7074 +2025-06-24 15:56:32,328 - pyskl - INFO - Saving checkpoint at 35 epochs +2025-06-24 15:57:32,177 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:57:32,242 - pyskl - INFO - +top1_acc 0.8386 +top5_acc 0.9846 +2025-06-24 15:57:32,242 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:57:32,253 - pyskl - INFO - +mean_acc 0.7744 +2025-06-24 15:57:32,256 - pyskl - INFO - Epoch(val) [35][533] top1_acc: 0.8386, top5_acc: 0.9846, mean_class_accuracy: 0.7744 +2025-06-24 15:58:52,957 - pyskl - INFO - Epoch [36][100/1281] lr: 2.178e-02, eta: 15:49:13, time: 0.807, data_time: 0.195, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9938, loss_cls: 0.5779, loss: 0.5779 +2025-06-24 15:59:24,891 - pyskl - INFO - Epoch [36][200/1281] lr: 2.176e-02, eta: 15:48:12, time: 0.319, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9962, loss_cls: 0.6029, loss: 0.6029 +2025-06-24 16:00:15,955 - pyskl - INFO - Epoch [36][300/1281] lr: 2.175e-02, eta: 15:48:14, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9900, loss_cls: 0.6205, loss: 0.6205 +2025-06-24 16:00:41,773 - pyskl - INFO - Epoch [36][400/1281] lr: 2.173e-02, eta: 15:46:53, time: 0.258, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9950, loss_cls: 0.6228, loss: 0.6228 +2025-06-24 16:01:31,049 - pyskl - INFO - Epoch [36][500/1281] lr: 2.172e-02, eta: 15:46:49, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9962, loss_cls: 0.5577, loss: 0.5577 +2025-06-24 16:02:20,206 - pyskl - INFO - Epoch [36][600/1281] lr: 2.171e-02, eta: 15:46:44, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9931, loss_cls: 0.6341, loss: 0.6341 +2025-06-24 16:03:09,099 - pyskl - INFO - Epoch [36][700/1281] lr: 2.169e-02, eta: 15:46:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9962, loss_cls: 0.6233, loss: 0.6233 +2025-06-24 16:03:57,971 - pyskl - INFO - Epoch [36][800/1281] lr: 2.168e-02, eta: 15:46:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9956, loss_cls: 0.6521, loss: 0.6521 +2025-06-24 16:04:47,124 - pyskl - INFO - Epoch [36][900/1281] lr: 2.167e-02, eta: 15:46:26, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9925, loss_cls: 0.7200, loss: 0.7200 +2025-06-24 16:05:35,935 - pyskl - INFO - Epoch [36][1000/1281] lr: 2.165e-02, eta: 15:46:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9912, loss_cls: 0.7166, loss: 0.7166 +2025-06-24 16:06:24,697 - pyskl - INFO - Epoch [36][1100/1281] lr: 2.164e-02, eta: 15:46:12, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9944, loss_cls: 0.6947, loss: 0.6947 +2025-06-24 16:07:13,818 - pyskl - INFO - Epoch [36][1200/1281] lr: 2.162e-02, eta: 15:46:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9912, loss_cls: 0.6735, loss: 0.6735 +2025-06-24 16:07:54,137 - pyskl - INFO - Saving checkpoint at 36 epochs +2025-06-24 16:08:53,083 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:08:53,139 - pyskl - INFO - +top1_acc 0.8492 +top5_acc 0.9884 +2025-06-24 16:08:53,139 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:08:53,146 - pyskl - INFO - +mean_acc 0.8043 +2025-06-24 16:08:53,151 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_28.pth was removed +2025-06-24 16:08:53,500 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_36.pth. +2025-06-24 16:08:53,500 - pyskl - INFO - Best top1_acc is 0.8492 at 36 epoch. +2025-06-24 16:08:53,503 - pyskl - INFO - Epoch(val) [36][533] top1_acc: 0.8492, top5_acc: 0.9884, mean_class_accuracy: 0.8043 +2025-06-24 16:10:14,423 - pyskl - INFO - Epoch [37][100/1281] lr: 2.160e-02, eta: 15:45:29, time: 0.809, data_time: 0.196, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9956, loss_cls: 0.6498, loss: 0.6498 +2025-06-24 16:10:44,289 - pyskl - INFO - Epoch [37][200/1281] lr: 2.158e-02, eta: 15:44:22, time: 0.299, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9962, loss_cls: 0.6395, loss: 0.6395 +2025-06-24 16:11:35,269 - pyskl - INFO - Epoch [37][300/1281] lr: 2.157e-02, eta: 15:44:21, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9938, loss_cls: 0.6307, loss: 0.6307 +2025-06-24 16:12:04,182 - pyskl - INFO - Epoch [37][400/1281] lr: 2.156e-02, eta: 15:43:11, time: 0.289, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9950, loss_cls: 0.6353, loss: 0.6353 +2025-06-24 16:12:53,695 - pyskl - INFO - Epoch [37][500/1281] lr: 2.154e-02, eta: 15:43:05, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9975, loss_cls: 0.6130, loss: 0.6130 +2025-06-24 16:13:42,751 - pyskl - INFO - Epoch [37][600/1281] lr: 2.153e-02, eta: 15:42:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9950, loss_cls: 0.6238, loss: 0.6238 +2025-06-24 16:14:31,956 - pyskl - INFO - Epoch [37][700/1281] lr: 2.151e-02, eta: 15:42:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9950, loss_cls: 0.6439, loss: 0.6439 +2025-06-24 16:15:21,226 - pyskl - INFO - Epoch [37][800/1281] lr: 2.150e-02, eta: 15:42:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9975, loss_cls: 0.6230, loss: 0.6230 +2025-06-24 16:16:10,363 - pyskl - INFO - Epoch [37][900/1281] lr: 2.149e-02, eta: 15:42:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9894, loss_cls: 0.6940, loss: 0.6940 +2025-06-24 16:16:59,416 - pyskl - INFO - Epoch [37][1000/1281] lr: 2.147e-02, eta: 15:42:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9925, loss_cls: 0.6726, loss: 0.6726 +2025-06-24 16:17:48,481 - pyskl - INFO - Epoch [37][1100/1281] lr: 2.146e-02, eta: 15:42:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9944, loss_cls: 0.6430, loss: 0.6430 +2025-06-24 16:18:37,566 - pyskl - INFO - Epoch [37][1200/1281] lr: 2.144e-02, eta: 15:42:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9938, loss_cls: 0.6800, loss: 0.6800 +2025-06-24 16:19:17,751 - pyskl - INFO - Saving checkpoint at 37 epochs +2025-06-24 16:20:17,017 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:20:17,076 - pyskl - INFO - +top1_acc 0.8446 +top5_acc 0.9881 +2025-06-24 16:20:17,076 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:20:17,083 - pyskl - INFO - +mean_acc 0.7635 +2025-06-24 16:20:17,085 - pyskl - INFO - Epoch(val) [37][533] top1_acc: 0.8446, top5_acc: 0.9881, mean_class_accuracy: 0.7635 +2025-06-24 16:21:37,438 - pyskl - INFO - Epoch [38][100/1281] lr: 2.142e-02, eta: 15:41:32, time: 0.803, data_time: 0.190, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9938, loss_cls: 0.5802, loss: 0.5802 +2025-06-24 16:22:05,546 - pyskl - INFO - Epoch [38][200/1281] lr: 2.140e-02, eta: 15:40:19, time: 0.281, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9975, loss_cls: 0.5994, loss: 0.5994 +2025-06-24 16:22:56,473 - pyskl - INFO - Epoch [38][300/1281] lr: 2.139e-02, eta: 15:40:16, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9950, loss_cls: 0.5969, loss: 0.5969 +2025-06-24 16:23:26,087 - pyskl - INFO - Epoch [38][400/1281] lr: 2.137e-02, eta: 15:39:09, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9944, loss_cls: 0.6242, loss: 0.6242 +2025-06-24 16:24:15,296 - pyskl - INFO - Epoch [38][500/1281] lr: 2.136e-02, eta: 15:39:00, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9919, loss_cls: 0.5940, loss: 0.5940 +2025-06-24 16:25:04,096 - pyskl - INFO - Epoch [38][600/1281] lr: 2.134e-02, eta: 15:38:51, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9944, loss_cls: 0.6681, loss: 0.6681 +2025-06-24 16:25:52,970 - pyskl - INFO - Epoch [38][700/1281] lr: 2.133e-02, eta: 15:38:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9944, loss_cls: 0.6242, loss: 0.6242 +2025-06-24 16:26:41,665 - pyskl - INFO - Epoch [38][800/1281] lr: 2.132e-02, eta: 15:38:30, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9931, loss_cls: 0.5997, loss: 0.5997 +2025-06-24 16:27:30,585 - pyskl - INFO - Epoch [38][900/1281] lr: 2.130e-02, eta: 15:38:20, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9950, loss_cls: 0.6364, loss: 0.6364 +2025-06-24 16:28:19,169 - pyskl - INFO - Epoch [38][1000/1281] lr: 2.129e-02, eta: 15:38:09, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9919, loss_cls: 0.6772, loss: 0.6772 +2025-06-24 16:29:08,126 - pyskl - INFO - Epoch [38][1100/1281] lr: 2.127e-02, eta: 15:37:59, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9969, loss_cls: 0.6243, loss: 0.6243 +2025-06-24 16:29:57,130 - pyskl - INFO - Epoch [38][1200/1281] lr: 2.126e-02, eta: 15:37:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9900, loss_cls: 0.6292, loss: 0.6292 +2025-06-24 16:30:37,469 - pyskl - INFO - Saving checkpoint at 38 epochs +2025-06-24 16:31:36,652 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:31:36,719 - pyskl - INFO - +top1_acc 0.8655 +top5_acc 0.9932 +2025-06-24 16:31:36,720 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:31:36,729 - pyskl - INFO - +mean_acc 0.8093 +2025-06-24 16:31:36,735 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_36.pth was removed +2025-06-24 16:31:36,916 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_38.pth. +2025-06-24 16:31:36,917 - pyskl - INFO - Best top1_acc is 0.8655 at 38 epoch. +2025-06-24 16:31:36,920 - pyskl - INFO - Epoch(val) [38][533] top1_acc: 0.8655, top5_acc: 0.9932, mean_class_accuracy: 0.8093 +2025-06-24 16:32:57,113 - pyskl - INFO - Epoch [39][100/1281] lr: 2.123e-02, eta: 15:37:05, time: 0.802, data_time: 0.199, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9950, loss_cls: 0.5727, loss: 0.5727 +2025-06-24 16:33:25,348 - pyskl - INFO - Epoch [39][200/1281] lr: 2.122e-02, eta: 15:35:54, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9956, loss_cls: 0.6117, loss: 0.6117 +2025-06-24 16:34:15,416 - pyskl - INFO - Epoch [39][300/1281] lr: 2.120e-02, eta: 15:35:47, time: 0.501, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9962, loss_cls: 0.5330, loss: 0.5330 +2025-06-24 16:34:47,488 - pyskl - INFO - Epoch [39][400/1281] lr: 2.119e-02, eta: 15:34:46, time: 0.321, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9950, loss_cls: 0.5608, loss: 0.5608 +2025-06-24 16:35:36,293 - pyskl - INFO - Epoch [39][500/1281] lr: 2.117e-02, eta: 15:34:35, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9975, loss_cls: 0.5883, loss: 0.5883 +2025-06-24 16:36:25,712 - pyskl - INFO - Epoch [39][600/1281] lr: 2.116e-02, eta: 15:34:26, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9962, loss_cls: 0.5631, loss: 0.5631 +2025-06-24 16:37:14,684 - pyskl - INFO - Epoch [39][700/1281] lr: 2.114e-02, eta: 15:34:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9950, loss_cls: 0.5788, loss: 0.5788 +2025-06-24 16:38:03,617 - pyskl - INFO - Epoch [39][800/1281] lr: 2.113e-02, eta: 15:34:03, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9919, loss_cls: 0.6362, loss: 0.6362 +2025-06-24 16:38:52,585 - pyskl - INFO - Epoch [39][900/1281] lr: 2.111e-02, eta: 15:33:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9906, loss_cls: 0.6778, loss: 0.6778 +2025-06-24 16:39:41,642 - pyskl - INFO - Epoch [39][1000/1281] lr: 2.110e-02, eta: 15:33:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9950, loss_cls: 0.6050, loss: 0.6050 +2025-06-24 16:40:30,199 - pyskl - INFO - Epoch [39][1100/1281] lr: 2.108e-02, eta: 15:33:27, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9925, loss_cls: 0.6781, loss: 0.6781 +2025-06-24 16:41:19,150 - pyskl - INFO - Epoch [39][1200/1281] lr: 2.107e-02, eta: 15:33:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9969, loss_cls: 0.6012, loss: 0.6012 +2025-06-24 16:41:59,582 - pyskl - INFO - Saving checkpoint at 39 epochs +2025-06-24 16:42:58,895 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:42:58,958 - pyskl - INFO - +top1_acc 0.8277 +top5_acc 0.9872 +2025-06-24 16:42:58,958 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:42:58,965 - pyskl - INFO - +mean_acc 0.7742 +2025-06-24 16:42:58,968 - pyskl - INFO - Epoch(val) [39][533] top1_acc: 0.8277, top5_acc: 0.9872, mean_class_accuracy: 0.7742 +2025-06-24 16:44:18,715 - pyskl - INFO - Epoch [40][100/1281] lr: 2.104e-02, eta: 15:32:28, time: 0.797, data_time: 0.192, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9962, loss_cls: 0.6259, loss: 0.6259 +2025-06-24 16:44:44,939 - pyskl - INFO - Epoch [40][200/1281] lr: 2.103e-02, eta: 15:31:12, time: 0.262, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9969, loss_cls: 0.5868, loss: 0.5868 +2025-06-24 16:45:36,135 - pyskl - INFO - Epoch [40][300/1281] lr: 2.101e-02, eta: 15:31:06, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9938, loss_cls: 0.6546, loss: 0.6546 +2025-06-24 16:46:06,722 - pyskl - INFO - Epoch [40][400/1281] lr: 2.100e-02, eta: 15:30:02, time: 0.306, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9944, loss_cls: 0.5467, loss: 0.5467 +2025-06-24 16:46:55,721 - pyskl - INFO - Epoch [40][500/1281] lr: 2.098e-02, eta: 15:29:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9981, loss_cls: 0.5932, loss: 0.5932 +2025-06-24 16:47:44,508 - pyskl - INFO - Epoch [40][600/1281] lr: 2.097e-02, eta: 15:29:36, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9938, loss_cls: 0.6397, loss: 0.6397 +2025-06-24 16:48:33,330 - pyskl - INFO - Epoch [40][700/1281] lr: 2.095e-02, eta: 15:29:23, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9969, loss_cls: 0.5352, loss: 0.5352 +2025-06-24 16:49:22,493 - pyskl - INFO - Epoch [40][800/1281] lr: 2.094e-02, eta: 15:29:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9969, loss_cls: 0.5700, loss: 0.5700 +2025-06-24 16:50:11,427 - pyskl - INFO - Epoch [40][900/1281] lr: 2.092e-02, eta: 15:28:58, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9950, loss_cls: 0.6426, loss: 0.6426 +2025-06-24 16:51:00,783 - pyskl - INFO - Epoch [40][1000/1281] lr: 2.091e-02, eta: 15:28:46, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9912, loss_cls: 0.6682, loss: 0.6682 +2025-06-24 16:51:49,875 - pyskl - INFO - Epoch [40][1100/1281] lr: 2.089e-02, eta: 15:28:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9956, loss_cls: 0.6505, loss: 0.6505 +2025-06-24 16:52:38,565 - pyskl - INFO - Epoch [40][1200/1281] lr: 2.088e-02, eta: 15:28:19, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9919, loss_cls: 0.6042, loss: 0.6042 +2025-06-24 16:53:18,944 - pyskl - INFO - Saving checkpoint at 40 epochs +2025-06-24 16:54:18,751 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:54:18,806 - pyskl - INFO - +top1_acc 0.8569 +top5_acc 0.9901 +2025-06-24 16:54:18,806 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:54:18,813 - pyskl - INFO - +mean_acc 0.8022 +2025-06-24 16:54:18,815 - pyskl - INFO - Epoch(val) [40][533] top1_acc: 0.8569, top5_acc: 0.9901, mean_class_accuracy: 0.8022 +2025-06-24 16:55:39,242 - pyskl - INFO - Epoch [41][100/1281] lr: 2.085e-02, eta: 15:27:32, time: 0.804, data_time: 0.190, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9981, loss_cls: 0.5242, loss: 0.5242 +2025-06-24 16:56:08,079 - pyskl - INFO - Epoch [41][200/1281] lr: 2.083e-02, eta: 15:26:23, time: 0.288, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9988, loss_cls: 0.5005, loss: 0.5005 +2025-06-24 16:56:56,080 - pyskl - INFO - Epoch [41][300/1281] lr: 2.082e-02, eta: 15:26:07, time: 0.480, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9950, loss_cls: 0.6172, loss: 0.6172 +2025-06-24 16:57:29,301 - pyskl - INFO - Epoch [41][400/1281] lr: 2.080e-02, eta: 15:25:10, time: 0.332, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9969, loss_cls: 0.4915, loss: 0.4915 +2025-06-24 16:58:18,159 - pyskl - INFO - Epoch [41][500/1281] lr: 2.079e-02, eta: 15:24:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9944, loss_cls: 0.5718, loss: 0.5718 +2025-06-24 16:59:06,967 - pyskl - INFO - Epoch [41][600/1281] lr: 2.077e-02, eta: 15:24:42, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9938, loss_cls: 0.5774, loss: 0.5774 +2025-06-24 16:59:55,923 - pyskl - INFO - Epoch [41][700/1281] lr: 2.076e-02, eta: 15:24:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9969, loss_cls: 0.5228, loss: 0.5228 +2025-06-24 17:00:44,875 - pyskl - INFO - Epoch [41][800/1281] lr: 2.074e-02, eta: 15:24:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9975, loss_cls: 0.5946, loss: 0.5946 +2025-06-24 17:01:33,759 - pyskl - INFO - Epoch [41][900/1281] lr: 2.073e-02, eta: 15:23:59, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9950, loss_cls: 0.6315, loss: 0.6315 +2025-06-24 17:02:22,748 - pyskl - INFO - Epoch [41][1000/1281] lr: 2.071e-02, eta: 15:23:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9919, loss_cls: 0.6865, loss: 0.6865 +2025-06-24 17:03:11,657 - pyskl - INFO - Epoch [41][1100/1281] lr: 2.070e-02, eta: 15:23:29, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9956, loss_cls: 0.5867, loss: 0.5867 +2025-06-24 17:04:00,663 - pyskl - INFO - Epoch [41][1200/1281] lr: 2.068e-02, eta: 15:23:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9931, loss_cls: 0.6062, loss: 0.6062 +2025-06-24 17:04:41,098 - pyskl - INFO - Saving checkpoint at 41 epochs +2025-06-24 17:05:39,942 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:05:40,003 - pyskl - INFO - +top1_acc 0.8460 +top5_acc 0.9883 +2025-06-24 17:05:40,003 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:05:40,010 - pyskl - INFO - +mean_acc 0.7701 +2025-06-24 17:05:40,012 - pyskl - INFO - Epoch(val) [41][533] top1_acc: 0.8460, top5_acc: 0.9883, mean_class_accuracy: 0.7701 +2025-06-24 17:06:59,221 - pyskl - INFO - Epoch [42][100/1281] lr: 2.065e-02, eta: 15:22:23, time: 0.792, data_time: 0.196, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9950, loss_cls: 0.5498, loss: 0.5498 +2025-06-24 17:07:28,156 - pyskl - INFO - Epoch [42][200/1281] lr: 2.064e-02, eta: 15:21:15, time: 0.289, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9981, loss_cls: 0.6064, loss: 0.6064 +2025-06-24 17:08:16,034 - pyskl - INFO - Epoch [42][300/1281] lr: 2.062e-02, eta: 15:20:57, time: 0.479, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9988, loss_cls: 0.5126, loss: 0.5126 +2025-06-24 17:08:47,862 - pyskl - INFO - Epoch [42][400/1281] lr: 2.061e-02, eta: 15:19:57, time: 0.318, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9962, loss_cls: 0.5375, loss: 0.5375 +2025-06-24 17:09:37,224 - pyskl - INFO - Epoch [42][500/1281] lr: 2.059e-02, eta: 15:19:42, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9969, loss_cls: 0.5222, loss: 0.5222 +2025-06-24 17:10:26,137 - pyskl - INFO - Epoch [42][600/1281] lr: 2.057e-02, eta: 15:19:27, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9931, loss_cls: 0.6166, loss: 0.6166 +2025-06-24 17:11:15,137 - pyskl - INFO - Epoch [42][700/1281] lr: 2.056e-02, eta: 15:19:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9944, loss_cls: 0.5994, loss: 0.5994 +2025-06-24 17:12:03,980 - pyskl - INFO - Epoch [42][800/1281] lr: 2.054e-02, eta: 15:18:56, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9969, loss_cls: 0.6284, loss: 0.6284 +2025-06-24 17:12:53,087 - pyskl - INFO - Epoch [42][900/1281] lr: 2.053e-02, eta: 15:18:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9950, loss_cls: 0.6550, loss: 0.6550 +2025-06-24 17:13:41,887 - pyskl - INFO - Epoch [42][1000/1281] lr: 2.051e-02, eta: 15:18:24, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9906, loss_cls: 0.6605, loss: 0.6605 +2025-06-24 17:14:30,567 - pyskl - INFO - Epoch [42][1100/1281] lr: 2.050e-02, eta: 15:18:07, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9975, loss_cls: 0.6478, loss: 0.6478 +2025-06-24 17:15:19,509 - pyskl - INFO - Epoch [42][1200/1281] lr: 2.048e-02, eta: 15:17:51, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9906, loss_cls: 0.6322, loss: 0.6322 +2025-06-24 17:15:59,799 - pyskl - INFO - Saving checkpoint at 42 epochs +2025-06-24 17:16:59,074 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:16:59,135 - pyskl - INFO - +top1_acc 0.8323 +top5_acc 0.9897 +2025-06-24 17:16:59,135 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:16:59,142 - pyskl - INFO - +mean_acc 0.7826 +2025-06-24 17:16:59,144 - pyskl - INFO - Epoch(val) [42][533] top1_acc: 0.8323, top5_acc: 0.9897, mean_class_accuracy: 0.7826 +2025-06-24 17:18:18,828 - pyskl - INFO - Epoch [43][100/1281] lr: 2.045e-02, eta: 15:16:59, time: 0.797, data_time: 0.192, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9981, loss_cls: 0.5271, loss: 0.5271 +2025-06-24 17:18:46,885 - pyskl - INFO - Epoch [43][200/1281] lr: 2.044e-02, eta: 15:15:49, time: 0.281, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9969, loss_cls: 0.6052, loss: 0.6052 +2025-06-24 17:19:36,612 - pyskl - INFO - Epoch [43][300/1281] lr: 2.042e-02, eta: 15:15:35, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9975, loss_cls: 0.5877, loss: 0.5877 +2025-06-24 17:20:08,056 - pyskl - INFO - Epoch [43][400/1281] lr: 2.040e-02, eta: 15:14:34, time: 0.314, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9931, loss_cls: 0.5826, loss: 0.5826 +2025-06-24 17:20:57,180 - pyskl - INFO - Epoch [43][500/1281] lr: 2.039e-02, eta: 15:14:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9950, loss_cls: 0.6000, loss: 0.6000 +2025-06-24 17:21:46,256 - pyskl - INFO - Epoch [43][600/1281] lr: 2.037e-02, eta: 15:14:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9969, loss_cls: 0.5527, loss: 0.5527 +2025-06-24 17:22:35,286 - pyskl - INFO - Epoch [43][700/1281] lr: 2.036e-02, eta: 15:13:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9938, loss_cls: 0.6325, loss: 0.6325 +2025-06-24 17:23:24,804 - pyskl - INFO - Epoch [43][800/1281] lr: 2.034e-02, eta: 15:13:29, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9938, loss_cls: 0.5726, loss: 0.5726 +2025-06-24 17:24:13,935 - pyskl - INFO - Epoch [43][900/1281] lr: 2.033e-02, eta: 15:13:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9944, loss_cls: 0.6515, loss: 0.6515 +2025-06-24 17:25:03,668 - pyskl - INFO - Epoch [43][1000/1281] lr: 2.031e-02, eta: 15:12:58, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9975, loss_cls: 0.5851, loss: 0.5851 +2025-06-24 17:25:52,682 - pyskl - INFO - Epoch [43][1100/1281] lr: 2.029e-02, eta: 15:12:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9938, loss_cls: 0.6129, loss: 0.6129 +2025-06-24 17:26:41,208 - pyskl - INFO - Epoch [43][1200/1281] lr: 2.028e-02, eta: 15:12:22, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9975, loss_cls: 0.5801, loss: 0.5801 +2025-06-24 17:27:21,509 - pyskl - INFO - Saving checkpoint at 43 epochs +2025-06-24 17:28:20,046 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:28:20,102 - pyskl - INFO - +top1_acc 0.8609 +top5_acc 0.9932 +2025-06-24 17:28:20,102 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:28:20,109 - pyskl - INFO - +mean_acc 0.8004 +2025-06-24 17:28:20,111 - pyskl - INFO - Epoch(val) [43][533] top1_acc: 0.8609, top5_acc: 0.9932, mean_class_accuracy: 0.8004 +2025-06-24 17:29:40,409 - pyskl - INFO - Epoch [44][100/1281] lr: 2.025e-02, eta: 15:11:30, time: 0.803, data_time: 0.192, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9969, loss_cls: 0.5129, loss: 0.5129 +2025-06-24 17:30:10,255 - pyskl - INFO - Epoch [44][200/1281] lr: 2.023e-02, eta: 15:10:25, time: 0.298, data_time: 0.001, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9981, loss_cls: 0.5329, loss: 0.5329 +2025-06-24 17:30:57,617 - pyskl - INFO - Epoch [44][300/1281] lr: 2.022e-02, eta: 15:10:04, time: 0.474, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9988, loss_cls: 0.5098, loss: 0.5098 +2025-06-24 17:31:29,539 - pyskl - INFO - Epoch [44][400/1281] lr: 2.020e-02, eta: 15:09:04, time: 0.319, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9944, loss_cls: 0.5701, loss: 0.5701 +2025-06-24 17:32:18,476 - pyskl - INFO - Epoch [44][500/1281] lr: 2.018e-02, eta: 15:08:46, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9962, loss_cls: 0.5991, loss: 0.5991 +2025-06-24 17:33:07,661 - pyskl - INFO - Epoch [44][600/1281] lr: 2.017e-02, eta: 15:08:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9931, loss_cls: 0.6321, loss: 0.6321 +2025-06-24 17:33:56,512 - pyskl - INFO - Epoch [44][700/1281] lr: 2.015e-02, eta: 15:08:11, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9969, loss_cls: 0.6267, loss: 0.6267 +2025-06-24 17:34:45,337 - pyskl - INFO - Epoch [44][800/1281] lr: 2.014e-02, eta: 15:07:52, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9962, loss_cls: 0.5625, loss: 0.5625 +2025-06-24 17:35:34,477 - pyskl - INFO - Epoch [44][900/1281] lr: 2.012e-02, eta: 15:07:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9981, loss_cls: 0.5202, loss: 0.5202 +2025-06-24 17:36:23,159 - pyskl - INFO - Epoch [44][1000/1281] lr: 2.010e-02, eta: 15:07:16, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9938, loss_cls: 0.5932, loss: 0.5932 +2025-06-24 17:37:12,081 - pyskl - INFO - Epoch [44][1100/1281] lr: 2.009e-02, eta: 15:06:57, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9931, loss_cls: 0.4879, loss: 0.4879 +2025-06-24 17:38:00,906 - pyskl - INFO - Epoch [44][1200/1281] lr: 2.007e-02, eta: 15:06:39, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9938, loss_cls: 0.5856, loss: 0.5856 +2025-06-24 17:38:41,030 - pyskl - INFO - Saving checkpoint at 44 epochs +2025-06-24 17:39:39,924 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:39:39,979 - pyskl - INFO - +top1_acc 0.8573 +top5_acc 0.9859 +2025-06-24 17:39:39,979 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:39:39,986 - pyskl - INFO - +mean_acc 0.7963 +2025-06-24 17:39:39,987 - pyskl - INFO - Epoch(val) [44][533] top1_acc: 0.8573, top5_acc: 0.9859, mean_class_accuracy: 0.7963 +2025-06-24 17:41:00,706 - pyskl - INFO - Epoch [45][100/1281] lr: 2.004e-02, eta: 15:05:46, time: 0.807, data_time: 0.197, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9969, loss_cls: 0.5690, loss: 0.5690 +2025-06-24 17:41:28,973 - pyskl - INFO - Epoch [45][200/1281] lr: 2.003e-02, eta: 15:04:38, time: 0.283, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9969, loss_cls: 0.5513, loss: 0.5513 +2025-06-24 17:42:17,722 - pyskl - INFO - Epoch [45][300/1281] lr: 2.001e-02, eta: 15:04:19, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9956, loss_cls: 0.5686, loss: 0.5686 +2025-06-24 17:42:49,699 - pyskl - INFO - Epoch [45][400/1281] lr: 1.999e-02, eta: 15:03:19, time: 0.320, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9962, loss_cls: 0.5994, loss: 0.5994 +2025-06-24 17:43:38,622 - pyskl - INFO - Epoch [45][500/1281] lr: 1.998e-02, eta: 15:03:00, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9950, loss_cls: 0.5524, loss: 0.5524 +2025-06-24 17:44:27,470 - pyskl - INFO - Epoch [45][600/1281] lr: 1.996e-02, eta: 15:02:41, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9962, loss_cls: 0.5440, loss: 0.5440 +2025-06-24 17:45:16,595 - pyskl - INFO - Epoch [45][700/1281] lr: 1.994e-02, eta: 15:02:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9975, loss_cls: 0.6293, loss: 0.6293 +2025-06-24 17:46:05,574 - pyskl - INFO - Epoch [45][800/1281] lr: 1.993e-02, eta: 15:02:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9969, loss_cls: 0.5607, loss: 0.5607 +2025-06-24 17:46:54,591 - pyskl - INFO - Epoch [45][900/1281] lr: 1.991e-02, eta: 15:01:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9962, loss_cls: 0.5618, loss: 0.5618 +2025-06-24 17:47:44,054 - pyskl - INFO - Epoch [45][1000/1281] lr: 1.989e-02, eta: 15:01:26, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9925, loss_cls: 0.6414, loss: 0.6414 +2025-06-24 17:48:33,321 - pyskl - INFO - Epoch [45][1100/1281] lr: 1.988e-02, eta: 15:01:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9956, loss_cls: 0.5464, loss: 0.5464 +2025-06-24 17:49:22,446 - pyskl - INFO - Epoch [45][1200/1281] lr: 1.986e-02, eta: 15:00:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9975, loss_cls: 0.5123, loss: 0.5123 +2025-06-24 17:50:02,475 - pyskl - INFO - Saving checkpoint at 45 epochs +2025-06-24 17:51:02,055 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:51:02,128 - pyskl - INFO - +top1_acc 0.8426 +top5_acc 0.9899 +2025-06-24 17:51:02,129 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:51:02,138 - pyskl - INFO - +mean_acc 0.7927 +2025-06-24 17:51:02,141 - pyskl - INFO - Epoch(val) [45][533] top1_acc: 0.8426, top5_acc: 0.9899, mean_class_accuracy: 0.7927 +2025-06-24 17:52:22,845 - pyskl - INFO - Epoch [46][100/1281] lr: 1.983e-02, eta: 14:59:54, time: 0.807, data_time: 0.195, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9962, loss_cls: 0.5134, loss: 0.5134 +2025-06-24 17:52:52,963 - pyskl - INFO - Epoch [46][200/1281] lr: 1.981e-02, eta: 14:58:51, time: 0.301, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9969, loss_cls: 0.5328, loss: 0.5328 +2025-06-24 17:53:38,289 - pyskl - INFO - Epoch [46][300/1281] lr: 1.980e-02, eta: 14:58:23, time: 0.453, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9994, loss_cls: 0.5286, loss: 0.5286 +2025-06-24 17:54:12,638 - pyskl - INFO - Epoch [46][400/1281] lr: 1.978e-02, eta: 14:57:29, time: 0.343, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9962, loss_cls: 0.6129, loss: 0.6129 +2025-06-24 17:55:01,864 - pyskl - INFO - Epoch [46][500/1281] lr: 1.976e-02, eta: 14:57:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9969, loss_cls: 0.6118, loss: 0.6118 +2025-06-24 17:55:50,867 - pyskl - INFO - Epoch [46][600/1281] lr: 1.975e-02, eta: 14:56:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9956, loss_cls: 0.5638, loss: 0.5638 +2025-06-24 17:56:39,736 - pyskl - INFO - Epoch [46][700/1281] lr: 1.973e-02, eta: 14:56:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9956, loss_cls: 0.5208, loss: 0.5208 +2025-06-24 17:57:28,715 - pyskl - INFO - Epoch [46][800/1281] lr: 1.971e-02, eta: 14:56:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9950, loss_cls: 0.5832, loss: 0.5832 +2025-06-24 17:58:17,637 - pyskl - INFO - Epoch [46][900/1281] lr: 1.970e-02, eta: 14:55:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9981, loss_cls: 0.5448, loss: 0.5448 +2025-06-24 17:59:06,429 - pyskl - INFO - Epoch [46][1000/1281] lr: 1.968e-02, eta: 14:55:28, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9950, loss_cls: 0.5438, loss: 0.5438 +2025-06-24 17:59:55,143 - pyskl - INFO - Epoch [46][1100/1281] lr: 1.966e-02, eta: 14:55:07, time: 0.487, data_time: 0.001, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9919, loss_cls: 0.6375, loss: 0.6375 +2025-06-24 18:00:44,317 - pyskl - INFO - Epoch [46][1200/1281] lr: 1.965e-02, eta: 14:54:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9962, loss_cls: 0.5393, loss: 0.5393 +2025-06-24 18:01:24,324 - pyskl - INFO - Saving checkpoint at 46 epochs +2025-06-24 18:02:23,461 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:02:23,528 - pyskl - INFO - +top1_acc 0.8603 +top5_acc 0.9916 +2025-06-24 18:02:23,528 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:02:23,537 - pyskl - INFO - +mean_acc 0.8004 +2025-06-24 18:02:23,539 - pyskl - INFO - Epoch(val) [46][533] top1_acc: 0.8603, top5_acc: 0.9916, mean_class_accuracy: 0.8004 +2025-06-24 18:03:43,545 - pyskl - INFO - Epoch [47][100/1281] lr: 1.962e-02, eta: 14:53:50, time: 0.800, data_time: 0.197, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9975, loss_cls: 0.5792, loss: 0.5792 +2025-06-24 18:04:13,889 - pyskl - INFO - Epoch [47][200/1281] lr: 1.960e-02, eta: 14:52:48, time: 0.303, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9981, loss_cls: 0.5354, loss: 0.5354 +2025-06-24 18:04:57,819 - pyskl - INFO - Epoch [47][300/1281] lr: 1.958e-02, eta: 14:52:16, time: 0.439, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9981, loss_cls: 0.5457, loss: 0.5457 +2025-06-24 18:05:32,139 - pyskl - INFO - Epoch [47][400/1281] lr: 1.957e-02, eta: 14:51:22, time: 0.343, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9969, loss_cls: 0.5208, loss: 0.5208 +2025-06-24 18:06:20,803 - pyskl - INFO - Epoch [47][500/1281] lr: 1.955e-02, eta: 14:51:01, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9981, loss_cls: 0.5458, loss: 0.5458 +2025-06-24 18:07:09,872 - pyskl - INFO - Epoch [47][600/1281] lr: 1.953e-02, eta: 14:50:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9950, loss_cls: 0.6136, loss: 0.6136 +2025-06-24 18:07:58,915 - pyskl - INFO - Epoch [47][700/1281] lr: 1.952e-02, eta: 14:50:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9950, loss_cls: 0.6257, loss: 0.6257 +2025-06-24 18:08:47,879 - pyskl - INFO - Epoch [47][800/1281] lr: 1.950e-02, eta: 14:49:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9975, loss_cls: 0.5483, loss: 0.5483 +2025-06-24 18:09:37,440 - pyskl - INFO - Epoch [47][900/1281] lr: 1.948e-02, eta: 14:49:38, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9950, loss_cls: 0.5917, loss: 0.5917 +2025-06-24 18:10:26,276 - pyskl - INFO - Epoch [47][1000/1281] lr: 1.947e-02, eta: 14:49:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9956, loss_cls: 0.5405, loss: 0.5405 +2025-06-24 18:11:15,269 - pyskl - INFO - Epoch [47][1100/1281] lr: 1.945e-02, eta: 14:48:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9956, loss_cls: 0.5721, loss: 0.5721 +2025-06-24 18:12:04,208 - pyskl - INFO - Epoch [47][1200/1281] lr: 1.943e-02, eta: 14:48:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9962, loss_cls: 0.5395, loss: 0.5395 +2025-06-24 18:12:44,268 - pyskl - INFO - Saving checkpoint at 47 epochs +2025-06-24 18:13:43,411 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:13:43,483 - pyskl - INFO - +top1_acc 0.8411 +top5_acc 0.9894 +2025-06-24 18:13:43,483 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:13:43,492 - pyskl - INFO - +mean_acc 0.7996 +2025-06-24 18:13:43,494 - pyskl - INFO - Epoch(val) [47][533] top1_acc: 0.8411, top5_acc: 0.9894, mean_class_accuracy: 0.7996 +2025-06-24 18:15:02,668 - pyskl - INFO - Epoch [48][100/1281] lr: 1.940e-02, eta: 14:47:34, time: 0.792, data_time: 0.188, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9944, loss_cls: 0.7125, loss: 0.7125 +2025-06-24 18:15:32,826 - pyskl - INFO - Epoch [48][200/1281] lr: 1.938e-02, eta: 14:46:31, time: 0.302, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9988, loss_cls: 0.5891, loss: 0.5891 +2025-06-24 18:16:18,500 - pyskl - INFO - Epoch [48][300/1281] lr: 1.937e-02, eta: 14:46:02, time: 0.457, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9962, loss_cls: 0.5287, loss: 0.5287 +2025-06-24 18:16:53,310 - pyskl - INFO - Epoch [48][400/1281] lr: 1.935e-02, eta: 14:45:10, time: 0.348, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9969, loss_cls: 0.4726, loss: 0.4726 +2025-06-24 18:17:42,430 - pyskl - INFO - Epoch [48][500/1281] lr: 1.933e-02, eta: 14:44:48, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9975, loss_cls: 0.5845, loss: 0.5845 +2025-06-24 18:18:31,604 - pyskl - INFO - Epoch [48][600/1281] lr: 1.932e-02, eta: 14:44:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9950, loss_cls: 0.5667, loss: 0.5667 +2025-06-24 18:19:20,652 - pyskl - INFO - Epoch [48][700/1281] lr: 1.930e-02, eta: 14:44:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9975, loss_cls: 0.5347, loss: 0.5347 +2025-06-24 18:20:09,912 - pyskl - INFO - Epoch [48][800/1281] lr: 1.928e-02, eta: 14:43:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9962, loss_cls: 0.5017, loss: 0.5017 +2025-06-24 18:20:58,900 - pyskl - INFO - Epoch [48][900/1281] lr: 1.926e-02, eta: 14:43:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9981, loss_cls: 0.4829, loss: 0.4829 +2025-06-24 18:21:48,462 - pyskl - INFO - Epoch [48][1000/1281] lr: 1.925e-02, eta: 14:43:01, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9969, loss_cls: 0.5523, loss: 0.5523 +2025-06-24 18:22:37,595 - pyskl - INFO - Epoch [48][1100/1281] lr: 1.923e-02, eta: 14:42:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9938, loss_cls: 0.6877, loss: 0.6877 +2025-06-24 18:23:26,478 - pyskl - INFO - Epoch [48][1200/1281] lr: 1.921e-02, eta: 14:42:16, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9944, loss_cls: 0.6150, loss: 0.6150 +2025-06-24 18:24:06,379 - pyskl - INFO - Saving checkpoint at 48 epochs +2025-06-24 18:25:05,527 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:25:05,581 - pyskl - INFO - +top1_acc 0.8391 +top5_acc 0.9893 +2025-06-24 18:25:05,582 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:25:05,590 - pyskl - INFO - +mean_acc 0.7894 +2025-06-24 18:25:05,592 - pyskl - INFO - Epoch(val) [48][533] top1_acc: 0.8391, top5_acc: 0.9893, mean_class_accuracy: 0.7894 +2025-06-24 18:26:24,987 - pyskl - INFO - Epoch [49][100/1281] lr: 1.918e-02, eta: 14:41:16, time: 0.794, data_time: 0.192, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9950, loss_cls: 0.5273, loss: 0.5273 +2025-06-24 18:26:55,501 - pyskl - INFO - Epoch [49][200/1281] lr: 1.916e-02, eta: 14:40:14, time: 0.305, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9944, loss_cls: 0.5150, loss: 0.5150 +2025-06-24 18:27:39,971 - pyskl - INFO - Epoch [49][300/1281] lr: 1.915e-02, eta: 14:39:42, time: 0.445, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9975, loss_cls: 0.5485, loss: 0.5485 +2025-06-24 18:28:13,341 - pyskl - INFO - Epoch [49][400/1281] lr: 1.913e-02, eta: 14:38:47, time: 0.334, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9956, loss_cls: 0.5329, loss: 0.5329 +2025-06-24 18:29:02,521 - pyskl - INFO - Epoch [49][500/1281] lr: 1.911e-02, eta: 14:38:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9956, loss_cls: 0.5509, loss: 0.5509 +2025-06-24 18:29:51,785 - pyskl - INFO - Epoch [49][600/1281] lr: 1.909e-02, eta: 14:38:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9962, loss_cls: 0.5520, loss: 0.5520 +2025-06-24 18:30:40,735 - pyskl - INFO - Epoch [49][700/1281] lr: 1.908e-02, eta: 14:37:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.4809, loss: 0.4809 +2025-06-24 18:31:29,783 - pyskl - INFO - Epoch [49][800/1281] lr: 1.906e-02, eta: 14:37:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9956, loss_cls: 0.5711, loss: 0.5711 +2025-06-24 18:32:18,662 - pyskl - INFO - Epoch [49][900/1281] lr: 1.904e-02, eta: 14:36:53, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9950, loss_cls: 0.5108, loss: 0.5108 +2025-06-24 18:33:07,781 - pyskl - INFO - Epoch [49][1000/1281] lr: 1.902e-02, eta: 14:36:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9956, loss_cls: 0.5173, loss: 0.5173 +2025-06-24 18:33:56,675 - pyskl - INFO - Epoch [49][1100/1281] lr: 1.901e-02, eta: 14:36:07, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9950, loss_cls: 0.5509, loss: 0.5509 +2025-06-24 18:34:45,386 - pyskl - INFO - Epoch [49][1200/1281] lr: 1.899e-02, eta: 14:35:44, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9975, loss_cls: 0.5254, loss: 0.5254 +2025-06-24 18:35:25,492 - pyskl - INFO - Saving checkpoint at 49 epochs +2025-06-24 18:36:25,246 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:36:25,301 - pyskl - INFO - +top1_acc 0.8616 +top5_acc 0.9906 +2025-06-24 18:36:25,301 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:36:25,308 - pyskl - INFO - +mean_acc 0.8042 +2025-06-24 18:36:25,310 - pyskl - INFO - Epoch(val) [49][533] top1_acc: 0.8616, top5_acc: 0.9906, mean_class_accuracy: 0.8042 +2025-06-24 18:37:44,827 - pyskl - INFO - Epoch [50][100/1281] lr: 1.896e-02, eta: 14:34:43, time: 0.795, data_time: 0.183, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9994, loss_cls: 0.4765, loss: 0.4765 +2025-06-24 18:38:14,440 - pyskl - INFO - Epoch [50][200/1281] lr: 1.894e-02, eta: 14:33:40, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9975, loss_cls: 0.4683, loss: 0.4683 +2025-06-24 18:39:00,691 - pyskl - INFO - Epoch [50][300/1281] lr: 1.892e-02, eta: 14:33:11, time: 0.463, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9988, loss_cls: 0.5349, loss: 0.5349 +2025-06-24 18:39:34,142 - pyskl - INFO - Epoch [50][400/1281] lr: 1.891e-02, eta: 14:32:15, time: 0.335, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9956, loss_cls: 0.5486, loss: 0.5486 +2025-06-24 18:40:23,159 - pyskl - INFO - Epoch [50][500/1281] lr: 1.889e-02, eta: 14:31:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9938, loss_cls: 0.5759, loss: 0.5759 +2025-06-24 18:41:12,241 - pyskl - INFO - Epoch [50][600/1281] lr: 1.887e-02, eta: 14:31:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9962, loss_cls: 0.5450, loss: 0.5450 +2025-06-24 18:42:01,302 - pyskl - INFO - Epoch [50][700/1281] lr: 1.885e-02, eta: 14:31:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9988, loss_cls: 0.5270, loss: 0.5270 +2025-06-24 18:42:49,952 - pyskl - INFO - Epoch [50][800/1281] lr: 1.884e-02, eta: 14:30:41, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9950, loss_cls: 0.5650, loss: 0.5650 +2025-06-24 18:43:39,159 - pyskl - INFO - Epoch [50][900/1281] lr: 1.882e-02, eta: 14:30:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9975, loss_cls: 0.5393, loss: 0.5393 +2025-06-24 18:44:28,011 - pyskl - INFO - Epoch [50][1000/1281] lr: 1.880e-02, eta: 14:29:53, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9969, loss_cls: 0.5641, loss: 0.5641 +2025-06-24 18:45:16,933 - pyskl - INFO - Epoch [50][1100/1281] lr: 1.878e-02, eta: 14:29:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9938, loss_cls: 0.5977, loss: 0.5977 +2025-06-24 18:46:06,085 - pyskl - INFO - Epoch [50][1200/1281] lr: 1.876e-02, eta: 14:29:06, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9962, loss_cls: 0.5399, loss: 0.5399 +2025-06-24 18:46:46,271 - pyskl - INFO - Saving checkpoint at 50 epochs +2025-06-24 18:47:44,861 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:47:44,915 - pyskl - INFO - +top1_acc 0.8630 +top5_acc 0.9916 +2025-06-24 18:47:44,916 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:47:44,923 - pyskl - INFO - +mean_acc 0.8200 +2025-06-24 18:47:44,925 - pyskl - INFO - Epoch(val) [50][533] top1_acc: 0.8630, top5_acc: 0.9916, mean_class_accuracy: 0.8200 +2025-06-24 18:49:06,557 - pyskl - INFO - Epoch [51][100/1281] lr: 1.873e-02, eta: 14:28:08, time: 0.816, data_time: 0.200, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9975, loss_cls: 0.4697, loss: 0.4697 +2025-06-24 18:49:38,634 - pyskl - INFO - Epoch [51][200/1281] lr: 1.871e-02, eta: 14:27:10, time: 0.321, data_time: 0.001, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9944, loss_cls: 0.5484, loss: 0.5484 +2025-06-24 18:50:21,439 - pyskl - INFO - Epoch [51][300/1281] lr: 1.870e-02, eta: 14:26:34, time: 0.428, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9981, loss_cls: 0.4861, loss: 0.4861 +2025-06-24 18:50:55,419 - pyskl - INFO - Epoch [51][400/1281] lr: 1.868e-02, eta: 14:25:40, time: 0.340, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9981, loss_cls: 0.4811, loss: 0.4811 +2025-06-24 18:51:44,212 - pyskl - INFO - Epoch [51][500/1281] lr: 1.866e-02, eta: 14:25:15, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9981, loss_cls: 0.4783, loss: 0.4783 +2025-06-24 18:52:33,163 - pyskl - INFO - Epoch [51][600/1281] lr: 1.864e-02, eta: 14:24:51, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9981, loss_cls: 0.4804, loss: 0.4804 +2025-06-24 18:53:22,136 - pyskl - INFO - Epoch [51][700/1281] lr: 1.863e-02, eta: 14:24:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9969, loss_cls: 0.5462, loss: 0.5462 +2025-06-24 18:54:11,071 - pyskl - INFO - Epoch [51][800/1281] lr: 1.861e-02, eta: 14:24:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9950, loss_cls: 0.6125, loss: 0.6125 +2025-06-24 18:55:00,428 - pyskl - INFO - Epoch [51][900/1281] lr: 1.859e-02, eta: 14:23:38, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9981, loss_cls: 0.5848, loss: 0.5848 +2025-06-24 18:55:49,359 - pyskl - INFO - Epoch [51][1000/1281] lr: 1.857e-02, eta: 14:23:13, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9962, loss_cls: 0.5139, loss: 0.5139 +2025-06-24 18:56:38,230 - pyskl - INFO - Epoch [51][1100/1281] lr: 1.855e-02, eta: 14:22:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9975, loss_cls: 0.4771, loss: 0.4771 +2025-06-24 18:57:27,027 - pyskl - INFO - Epoch [51][1200/1281] lr: 1.854e-02, eta: 14:22:23, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9938, loss_cls: 0.6088, loss: 0.6088 +2025-06-24 18:58:07,579 - pyskl - INFO - Saving checkpoint at 51 epochs +2025-06-24 18:59:07,345 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:59:07,429 - pyskl - INFO - +top1_acc 0.8645 +top5_acc 0.9917 +2025-06-24 18:59:07,429 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:59:07,437 - pyskl - INFO - +mean_acc 0.8304 +2025-06-24 18:59:07,440 - pyskl - INFO - Epoch(val) [51][533] top1_acc: 0.8645, top5_acc: 0.9917, mean_class_accuracy: 0.8304 +2025-06-24 19:00:26,081 - pyskl - INFO - Epoch [52][100/1281] lr: 1.850e-02, eta: 14:21:19, time: 0.786, data_time: 0.192, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9962, loss_cls: 0.4978, loss: 0.4978 +2025-06-24 19:00:55,883 - pyskl - INFO - Epoch [52][200/1281] lr: 1.849e-02, eta: 14:20:17, time: 0.298, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9956, loss_cls: 0.4808, loss: 0.4808 +2025-06-24 19:01:40,680 - pyskl - INFO - Epoch [52][300/1281] lr: 1.847e-02, eta: 14:19:44, time: 0.448, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9969, loss_cls: 0.5271, loss: 0.5271 +2025-06-24 19:02:14,420 - pyskl - INFO - Epoch [52][400/1281] lr: 1.845e-02, eta: 14:18:49, time: 0.337, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9981, loss_cls: 0.5041, loss: 0.5041 +2025-06-24 19:03:03,516 - pyskl - INFO - Epoch [52][500/1281] lr: 1.843e-02, eta: 14:18:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9988, loss_cls: 0.5085, loss: 0.5085 +2025-06-24 19:03:52,957 - pyskl - INFO - Epoch [52][600/1281] lr: 1.841e-02, eta: 14:18:00, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9975, loss_cls: 0.4873, loss: 0.4873 +2025-06-24 19:04:42,359 - pyskl - INFO - Epoch [52][700/1281] lr: 1.840e-02, eta: 14:17:36, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9950, loss_cls: 0.5618, loss: 0.5618 +2025-06-24 19:05:31,356 - pyskl - INFO - Epoch [52][800/1281] lr: 1.838e-02, eta: 14:17:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9950, loss_cls: 0.5763, loss: 0.5763 +2025-06-24 19:06:20,133 - pyskl - INFO - Epoch [52][900/1281] lr: 1.836e-02, eta: 14:16:45, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9950, loss_cls: 0.5491, loss: 0.5491 +2025-06-24 19:07:09,443 - pyskl - INFO - Epoch [52][1000/1281] lr: 1.834e-02, eta: 14:16:20, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9962, loss_cls: 0.5954, loss: 0.5954 +2025-06-24 19:07:58,697 - pyskl - INFO - Epoch [52][1100/1281] lr: 1.832e-02, eta: 14:15:56, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9962, loss_cls: 0.6238, loss: 0.6238 +2025-06-24 19:08:47,548 - pyskl - INFO - Epoch [52][1200/1281] lr: 1.831e-02, eta: 14:15:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9988, loss_cls: 0.5371, loss: 0.5371 +2025-06-24 19:09:28,045 - pyskl - INFO - Saving checkpoint at 52 epochs +2025-06-24 19:10:27,455 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:10:27,531 - pyskl - INFO - +top1_acc 0.8677 +top5_acc 0.9928 +2025-06-24 19:10:27,531 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:10:27,540 - pyskl - INFO - +mean_acc 0.8312 +2025-06-24 19:10:27,545 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_38.pth was removed +2025-06-24 19:10:27,778 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_52.pth. +2025-06-24 19:10:27,778 - pyskl - INFO - Best top1_acc is 0.8677 at 52 epoch. +2025-06-24 19:10:27,781 - pyskl - INFO - Epoch(val) [52][533] top1_acc: 0.8677, top5_acc: 0.9928, mean_class_accuracy: 0.8312 +2025-06-24 19:11:46,282 - pyskl - INFO - Epoch [53][100/1281] lr: 1.827e-02, eta: 14:14:24, time: 0.785, data_time: 0.189, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9975, loss_cls: 0.5100, loss: 0.5100 +2025-06-24 19:12:15,680 - pyskl - INFO - Epoch [53][200/1281] lr: 1.826e-02, eta: 14:13:22, time: 0.294, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9981, loss_cls: 0.4438, loss: 0.4438 +2025-06-24 19:13:01,900 - pyskl - INFO - Epoch [53][300/1281] lr: 1.824e-02, eta: 14:12:51, time: 0.462, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9962, loss_cls: 0.4712, loss: 0.4712 +2025-06-24 19:13:35,086 - pyskl - INFO - Epoch [53][400/1281] lr: 1.822e-02, eta: 14:11:56, time: 0.332, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9969, loss_cls: 0.4844, loss: 0.4844 +2025-06-24 19:14:24,290 - pyskl - INFO - Epoch [53][500/1281] lr: 1.820e-02, eta: 14:11:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9950, loss_cls: 0.5245, loss: 0.5245 +2025-06-24 19:15:13,824 - pyskl - INFO - Epoch [53][600/1281] lr: 1.818e-02, eta: 14:11:06, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9975, loss_cls: 0.5296, loss: 0.5296 +2025-06-24 19:16:03,072 - pyskl - INFO - Epoch [53][700/1281] lr: 1.816e-02, eta: 14:10:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9956, loss_cls: 0.5550, loss: 0.5550 +2025-06-24 19:16:52,503 - pyskl - INFO - Epoch [53][800/1281] lr: 1.815e-02, eta: 14:10:16, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9962, loss_cls: 0.5818, loss: 0.5818 +2025-06-24 19:17:41,823 - pyskl - INFO - Epoch [53][900/1281] lr: 1.813e-02, eta: 14:09:50, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9938, loss_cls: 0.5480, loss: 0.5480 +2025-06-24 19:18:31,250 - pyskl - INFO - Epoch [53][1000/1281] lr: 1.811e-02, eta: 14:09:25, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9950, loss_cls: 0.4827, loss: 0.4827 +2025-06-24 19:19:20,318 - pyskl - INFO - Epoch [53][1100/1281] lr: 1.809e-02, eta: 14:08:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9938, loss_cls: 0.5176, loss: 0.5176 +2025-06-24 19:20:09,210 - pyskl - INFO - Epoch [53][1200/1281] lr: 1.807e-02, eta: 14:08:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9975, loss_cls: 0.4902, loss: 0.4902 +2025-06-24 19:20:49,555 - pyskl - INFO - Saving checkpoint at 53 epochs +2025-06-24 19:21:48,898 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:21:48,954 - pyskl - INFO - +top1_acc 0.8630 +top5_acc 0.9900 +2025-06-24 19:21:48,954 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:21:48,962 - pyskl - INFO - +mean_acc 0.8054 +2025-06-24 19:21:48,965 - pyskl - INFO - Epoch(val) [53][533] top1_acc: 0.8630, top5_acc: 0.9900, mean_class_accuracy: 0.8054 +2025-06-24 19:23:08,234 - pyskl - INFO - Epoch [54][100/1281] lr: 1.804e-02, eta: 14:07:28, time: 0.793, data_time: 0.192, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9975, loss_cls: 0.4869, loss: 0.4869 +2025-06-24 19:23:41,452 - pyskl - INFO - Epoch [54][200/1281] lr: 1.802e-02, eta: 14:06:33, time: 0.332, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9981, loss_cls: 0.4953, loss: 0.4953 +2025-06-24 19:24:24,114 - pyskl - INFO - Epoch [54][300/1281] lr: 1.800e-02, eta: 14:05:55, time: 0.427, data_time: 0.001, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9962, loss_cls: 0.5068, loss: 0.5068 +2025-06-24 19:24:57,832 - pyskl - INFO - Epoch [54][400/1281] lr: 1.798e-02, eta: 14:05:01, time: 0.337, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9981, loss_cls: 0.4802, loss: 0.4802 +2025-06-24 19:25:47,056 - pyskl - INFO - Epoch [54][500/1281] lr: 1.797e-02, eta: 14:04:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9950, loss_cls: 0.4853, loss: 0.4853 +2025-06-24 19:26:36,053 - pyskl - INFO - Epoch [54][600/1281] lr: 1.795e-02, eta: 14:04:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9969, loss_cls: 0.4918, loss: 0.4918 +2025-06-24 19:27:24,777 - pyskl - INFO - Epoch [54][700/1281] lr: 1.793e-02, eta: 14:03:42, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9981, loss_cls: 0.5238, loss: 0.5238 +2025-06-24 19:28:13,836 - pyskl - INFO - Epoch [54][800/1281] lr: 1.791e-02, eta: 14:03:15, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9969, loss_cls: 0.5274, loss: 0.5274 +2025-06-24 19:29:03,175 - pyskl - INFO - Epoch [54][900/1281] lr: 1.789e-02, eta: 14:02:49, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9956, loss_cls: 0.5488, loss: 0.5488 +2025-06-24 19:29:52,726 - pyskl - INFO - Epoch [54][1000/1281] lr: 1.787e-02, eta: 14:02:24, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9975, loss_cls: 0.4990, loss: 0.4990 +2025-06-24 19:30:41,418 - pyskl - INFO - Epoch [54][1100/1281] lr: 1.786e-02, eta: 14:01:56, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9975, loss_cls: 0.5134, loss: 0.5134 +2025-06-24 19:31:30,335 - pyskl - INFO - Epoch [54][1200/1281] lr: 1.784e-02, eta: 14:01:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9950, loss_cls: 0.5951, loss: 0.5951 +2025-06-24 19:32:10,645 - pyskl - INFO - Saving checkpoint at 54 epochs +2025-06-24 19:33:09,709 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:33:09,767 - pyskl - INFO - +top1_acc 0.8498 +top5_acc 0.9912 +2025-06-24 19:33:09,767 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:33:09,775 - pyskl - INFO - +mean_acc 0.7928 +2025-06-24 19:33:09,778 - pyskl - INFO - Epoch(val) [54][533] top1_acc: 0.8498, top5_acc: 0.9912, mean_class_accuracy: 0.7928 +2025-06-24 19:34:30,291 - pyskl - INFO - Epoch [55][100/1281] lr: 1.780e-02, eta: 14:00:26, time: 0.805, data_time: 0.189, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9969, loss_cls: 0.4664, loss: 0.4664 +2025-06-24 19:35:02,194 - pyskl - INFO - Epoch [55][200/1281] lr: 1.779e-02, eta: 13:59:29, time: 0.319, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9975, loss_cls: 0.4997, loss: 0.4997 +2025-06-24 19:35:44,954 - pyskl - INFO - Epoch [55][300/1281] lr: 1.777e-02, eta: 13:58:51, time: 0.428, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9950, loss_cls: 0.5360, loss: 0.5360 +2025-06-24 19:36:20,480 - pyskl - INFO - Epoch [55][400/1281] lr: 1.775e-02, eta: 13:58:00, time: 0.355, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9975, loss_cls: 0.5155, loss: 0.5155 +2025-06-24 19:37:09,446 - pyskl - INFO - Epoch [55][500/1281] lr: 1.773e-02, eta: 13:57:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9969, loss_cls: 0.5479, loss: 0.5479 +2025-06-24 19:37:58,695 - pyskl - INFO - Epoch [55][600/1281] lr: 1.771e-02, eta: 13:57:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9969, loss_cls: 0.5166, loss: 0.5166 +2025-06-24 19:38:47,654 - pyskl - INFO - Epoch [55][700/1281] lr: 1.769e-02, eta: 13:56:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9962, loss_cls: 0.5313, loss: 0.5313 +2025-06-24 19:39:36,446 - pyskl - INFO - Epoch [55][800/1281] lr: 1.767e-02, eta: 13:56:12, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9975, loss_cls: 0.5840, loss: 0.5840 +2025-06-24 19:40:25,623 - pyskl - INFO - Epoch [55][900/1281] lr: 1.766e-02, eta: 13:55:45, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9962, loss_cls: 0.4727, loss: 0.4727 +2025-06-24 19:41:15,106 - pyskl - INFO - Epoch [55][1000/1281] lr: 1.764e-02, eta: 13:55:18, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9975, loss_cls: 0.5113, loss: 0.5113 +2025-06-24 19:42:04,424 - pyskl - INFO - Epoch [55][1100/1281] lr: 1.762e-02, eta: 13:54:52, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9956, loss_cls: 0.5373, loss: 0.5373 +2025-06-24 19:42:53,497 - pyskl - INFO - Epoch [55][1200/1281] lr: 1.760e-02, eta: 13:54:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9969, loss_cls: 0.4824, loss: 0.4824 +2025-06-24 19:43:33,670 - pyskl - INFO - Saving checkpoint at 55 epochs +2025-06-24 19:44:33,001 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:44:33,068 - pyskl - INFO - +top1_acc 0.8539 +top5_acc 0.9874 +2025-06-24 19:44:33,068 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:44:33,076 - pyskl - INFO - +mean_acc 0.8122 +2025-06-24 19:44:33,077 - pyskl - INFO - Epoch(val) [55][533] top1_acc: 0.8539, top5_acc: 0.9874, mean_class_accuracy: 0.8122 +2025-06-24 19:45:51,486 - pyskl - INFO - Epoch [56][100/1281] lr: 1.757e-02, eta: 13:53:17, time: 0.784, data_time: 0.192, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9950, loss_cls: 0.4615, loss: 0.4615 +2025-06-24 19:46:27,268 - pyskl - INFO - Epoch [56][200/1281] lr: 1.755e-02, eta: 13:52:26, time: 0.358, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.4291, loss: 0.4291 +2025-06-24 19:47:05,535 - pyskl - INFO - Epoch [56][300/1281] lr: 1.753e-02, eta: 13:51:40, time: 0.383, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9981, loss_cls: 0.5077, loss: 0.5077 +2025-06-24 19:47:42,462 - pyskl - INFO - Epoch [56][400/1281] lr: 1.751e-02, eta: 13:50:52, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9975, loss_cls: 0.4532, loss: 0.4532 +2025-06-24 19:48:31,379 - pyskl - INFO - Epoch [56][500/1281] lr: 1.749e-02, eta: 13:50:24, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9969, loss_cls: 0.4482, loss: 0.4482 +2025-06-24 19:49:20,558 - pyskl - INFO - Epoch [56][600/1281] lr: 1.747e-02, eta: 13:49:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9994, loss_cls: 0.4814, loss: 0.4814 +2025-06-24 19:50:09,557 - pyskl - INFO - Epoch [56][700/1281] lr: 1.745e-02, eta: 13:49:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9969, loss_cls: 0.5479, loss: 0.5479 +2025-06-24 19:50:58,884 - pyskl - INFO - Epoch [56][800/1281] lr: 1.743e-02, eta: 13:49:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9969, loss_cls: 0.4589, loss: 0.4589 +2025-06-24 19:51:47,611 - pyskl - INFO - Epoch [56][900/1281] lr: 1.742e-02, eta: 13:48:34, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9950, loss_cls: 0.5162, loss: 0.5162 +2025-06-24 19:52:36,740 - pyskl - INFO - Epoch [56][1000/1281] lr: 1.740e-02, eta: 13:48:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9981, loss_cls: 0.4970, loss: 0.4970 +2025-06-24 19:53:26,172 - pyskl - INFO - Epoch [56][1100/1281] lr: 1.738e-02, eta: 13:47:39, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9956, loss_cls: 0.5277, loss: 0.5277 +2025-06-24 19:54:15,067 - pyskl - INFO - Epoch [56][1200/1281] lr: 1.736e-02, eta: 13:47:11, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9950, loss_cls: 0.5422, loss: 0.5422 +2025-06-24 19:54:55,515 - pyskl - INFO - Saving checkpoint at 56 epochs +2025-06-24 19:55:55,396 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:55:55,451 - pyskl - INFO - +top1_acc 0.8743 +top5_acc 0.9939 +2025-06-24 19:55:55,451 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:55:55,457 - pyskl - INFO - +mean_acc 0.8299 +2025-06-24 19:55:55,461 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_52.pth was removed +2025-06-24 19:55:55,641 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_56.pth. +2025-06-24 19:55:55,641 - pyskl - INFO - Best top1_acc is 0.8743 at 56 epoch. +2025-06-24 19:55:55,645 - pyskl - INFO - Epoch(val) [56][533] top1_acc: 0.8743, top5_acc: 0.9939, mean_class_accuracy: 0.8299 +2025-06-24 19:57:12,562 - pyskl - INFO - Epoch [57][100/1281] lr: 1.733e-02, eta: 13:46:00, time: 0.769, data_time: 0.196, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9962, loss_cls: 0.4918, loss: 0.4918 +2025-06-24 19:57:50,789 - pyskl - INFO - Epoch [57][200/1281] lr: 1.731e-02, eta: 13:45:14, time: 0.382, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9981, loss_cls: 0.4284, loss: 0.4284 +2025-06-24 19:58:26,157 - pyskl - INFO - Epoch [57][300/1281] lr: 1.729e-02, eta: 13:44:23, time: 0.354, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9981, loss_cls: 0.5339, loss: 0.5339 +2025-06-24 19:59:03,698 - pyskl - INFO - Epoch [57][400/1281] lr: 1.727e-02, eta: 13:43:36, time: 0.375, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9962, loss_cls: 0.5147, loss: 0.5147 +2025-06-24 19:59:52,501 - pyskl - INFO - Epoch [57][500/1281] lr: 1.725e-02, eta: 13:43:07, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9981, loss_cls: 0.4664, loss: 0.4664 +2025-06-24 20:00:41,513 - pyskl - INFO - Epoch [57][600/1281] lr: 1.723e-02, eta: 13:42:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9962, loss_cls: 0.5079, loss: 0.5079 +2025-06-24 20:01:30,463 - pyskl - INFO - Epoch [57][700/1281] lr: 1.721e-02, eta: 13:42:11, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9950, loss_cls: 0.4435, loss: 0.4435 +2025-06-24 20:02:19,427 - pyskl - INFO - Epoch [57][800/1281] lr: 1.719e-02, eta: 13:41:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9938, loss_cls: 0.5425, loss: 0.5425 +2025-06-24 20:03:08,543 - pyskl - INFO - Epoch [57][900/1281] lr: 1.717e-02, eta: 13:41:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9994, loss_cls: 0.4636, loss: 0.4636 +2025-06-24 20:03:57,692 - pyskl - INFO - Epoch [57][1000/1281] lr: 1.716e-02, eta: 13:40:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9962, loss_cls: 0.5570, loss: 0.5570 +2025-06-24 20:04:46,505 - pyskl - INFO - Epoch [57][1100/1281] lr: 1.714e-02, eta: 13:40:17, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9981, loss_cls: 0.5137, loss: 0.5137 +2025-06-24 20:05:35,492 - pyskl - INFO - Epoch [57][1200/1281] lr: 1.712e-02, eta: 13:39:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9969, loss_cls: 0.5324, loss: 0.5324 +2025-06-24 20:06:15,940 - pyskl - INFO - Saving checkpoint at 57 epochs +2025-06-24 20:07:15,978 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:07:16,043 - pyskl - INFO - +top1_acc 0.8835 +top5_acc 0.9938 +2025-06-24 20:07:16,043 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:07:16,050 - pyskl - INFO - +mean_acc 0.8314 +2025-06-24 20:07:16,054 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_56.pth was removed +2025-06-24 20:07:16,245 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_57.pth. +2025-06-24 20:07:16,245 - pyskl - INFO - Best top1_acc is 0.8835 at 57 epoch. +2025-06-24 20:07:16,248 - pyskl - INFO - Epoch(val) [57][533] top1_acc: 0.8835, top5_acc: 0.9938, mean_class_accuracy: 0.8314 +2025-06-24 20:08:32,400 - pyskl - INFO - Epoch [58][100/1281] lr: 1.708e-02, eta: 13:38:36, time: 0.761, data_time: 0.189, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9994, loss_cls: 0.3995, loss: 0.3995 +2025-06-24 20:09:13,472 - pyskl - INFO - Epoch [58][200/1281] lr: 1.706e-02, eta: 13:37:54, time: 0.411, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9981, loss_cls: 0.4438, loss: 0.4438 +2025-06-24 20:09:46,561 - pyskl - INFO - Epoch [58][300/1281] lr: 1.704e-02, eta: 13:37:00, time: 0.331, data_time: 0.001, memory: 4083, top1_acc: 0.9137, top5_acc: 1.0000, loss_cls: 0.4514, loss: 0.4514 +2025-06-24 20:10:27,422 - pyskl - INFO - Epoch [58][400/1281] lr: 1.703e-02, eta: 13:36:18, time: 0.409, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9975, loss_cls: 0.4977, loss: 0.4977 +2025-06-24 20:11:16,433 - pyskl - INFO - Epoch [58][500/1281] lr: 1.701e-02, eta: 13:35:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9962, loss_cls: 0.4604, loss: 0.4604 +2025-06-24 20:12:05,779 - pyskl - INFO - Epoch [58][600/1281] lr: 1.699e-02, eta: 13:35:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9988, loss_cls: 0.4556, loss: 0.4556 +2025-06-24 20:12:54,821 - pyskl - INFO - Epoch [58][700/1281] lr: 1.697e-02, eta: 13:34:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9969, loss_cls: 0.5238, loss: 0.5238 +2025-06-24 20:13:44,072 - pyskl - INFO - Epoch [58][800/1281] lr: 1.695e-02, eta: 13:34:24, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9969, loss_cls: 0.5153, loss: 0.5153 +2025-06-24 20:14:33,254 - pyskl - INFO - Epoch [58][900/1281] lr: 1.693e-02, eta: 13:33:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9988, loss_cls: 0.5085, loss: 0.5085 +2025-06-24 20:15:22,362 - pyskl - INFO - Epoch [58][1000/1281] lr: 1.691e-02, eta: 13:33:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9988, loss_cls: 0.4834, loss: 0.4834 +2025-06-24 20:16:11,265 - pyskl - INFO - Epoch [58][1100/1281] lr: 1.689e-02, eta: 13:32:57, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9988, loss_cls: 0.4698, loss: 0.4698 +2025-06-24 20:17:00,184 - pyskl - INFO - Epoch [58][1200/1281] lr: 1.687e-02, eta: 13:32:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9956, loss_cls: 0.5563, loss: 0.5563 +2025-06-24 20:17:40,544 - pyskl - INFO - Saving checkpoint at 58 epochs +2025-06-24 20:18:39,609 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:18:39,669 - pyskl - INFO - +top1_acc 0.8620 +top5_acc 0.9921 +2025-06-24 20:18:39,669 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:18:39,676 - pyskl - INFO - +mean_acc 0.8129 +2025-06-24 20:18:39,678 - pyskl - INFO - Epoch(val) [58][533] top1_acc: 0.8620, top5_acc: 0.9921, mean_class_accuracy: 0.8129 +2025-06-24 20:19:53,391 - pyskl - INFO - Epoch [59][100/1281] lr: 1.684e-02, eta: 13:31:11, time: 0.737, data_time: 0.196, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9994, loss_cls: 0.4732, loss: 0.4732 +2025-06-24 20:20:38,608 - pyskl - INFO - Epoch [59][200/1281] lr: 1.682e-02, eta: 13:30:36, time: 0.452, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9981, loss_cls: 0.4258, loss: 0.4258 +2025-06-24 20:21:07,360 - pyskl - INFO - Epoch [59][300/1281] lr: 1.680e-02, eta: 13:29:35, time: 0.288, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9981, loss_cls: 0.4724, loss: 0.4724 +2025-06-24 20:21:50,113 - pyskl - INFO - Epoch [59][400/1281] lr: 1.678e-02, eta: 13:28:56, time: 0.427, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9969, loss_cls: 0.4609, loss: 0.4609 +2025-06-24 20:22:39,030 - pyskl - INFO - Epoch [59][500/1281] lr: 1.676e-02, eta: 13:28:26, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9962, loss_cls: 0.4792, loss: 0.4792 +2025-06-24 20:23:28,186 - pyskl - INFO - Epoch [59][600/1281] lr: 1.674e-02, eta: 13:27:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9994, loss_cls: 0.4767, loss: 0.4767 +2025-06-24 20:24:17,302 - pyskl - INFO - Epoch [59][700/1281] lr: 1.672e-02, eta: 13:27:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9962, loss_cls: 0.4664, loss: 0.4664 +2025-06-24 20:25:06,307 - pyskl - INFO - Epoch [59][800/1281] lr: 1.670e-02, eta: 13:26:59, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9931, loss_cls: 0.5369, loss: 0.5369 +2025-06-24 20:25:55,204 - pyskl - INFO - Epoch [59][900/1281] lr: 1.668e-02, eta: 13:26:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9962, loss_cls: 0.5439, loss: 0.5439 +2025-06-24 20:26:44,135 - pyskl - INFO - Epoch [59][1000/1281] lr: 1.667e-02, eta: 13:25:59, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9969, loss_cls: 0.5033, loss: 0.5033 +2025-06-24 20:27:33,214 - pyskl - INFO - Epoch [59][1100/1281] lr: 1.665e-02, eta: 13:25:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9975, loss_cls: 0.4700, loss: 0.4700 +2025-06-24 20:28:22,619 - pyskl - INFO - Epoch [59][1200/1281] lr: 1.663e-02, eta: 13:25:01, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.5161, loss: 0.5161 +2025-06-24 20:29:03,148 - pyskl - INFO - Saving checkpoint at 59 epochs +2025-06-24 20:30:02,288 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:30:02,364 - pyskl - INFO - +top1_acc 0.8701 +top5_acc 0.9911 +2025-06-24 20:30:02,365 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:30:02,374 - pyskl - INFO - +mean_acc 0.8128 +2025-06-24 20:30:02,377 - pyskl - INFO - Epoch(val) [59][533] top1_acc: 0.8701, top5_acc: 0.9911, mean_class_accuracy: 0.8128 +2025-06-24 20:31:13,358 - pyskl - INFO - Epoch [60][100/1281] lr: 1.659e-02, eta: 13:23:40, time: 0.710, data_time: 0.199, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9956, loss_cls: 0.4639, loss: 0.4639 +2025-06-24 20:32:04,592 - pyskl - INFO - Epoch [60][200/1281] lr: 1.657e-02, eta: 13:23:13, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9975, loss_cls: 0.4615, loss: 0.4615 +2025-06-24 20:32:28,088 - pyskl - INFO - Epoch [60][300/1281] lr: 1.655e-02, eta: 13:22:05, time: 0.235, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9988, loss_cls: 0.4598, loss: 0.4598 +2025-06-24 20:33:12,069 - pyskl - INFO - Epoch [60][400/1281] lr: 1.653e-02, eta: 13:21:27, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9994, loss_cls: 0.5203, loss: 0.5203 +2025-06-24 20:34:01,244 - pyskl - INFO - Epoch [60][500/1281] lr: 1.651e-02, eta: 13:20:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9988, loss_cls: 0.4565, loss: 0.4565 +2025-06-24 20:34:50,203 - pyskl - INFO - Epoch [60][600/1281] lr: 1.650e-02, eta: 13:20:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9988, loss_cls: 0.4487, loss: 0.4487 +2025-06-24 20:35:39,381 - pyskl - INFO - Epoch [60][700/1281] lr: 1.648e-02, eta: 13:19:58, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9969, loss_cls: 0.4427, loss: 0.4427 +2025-06-24 20:36:28,163 - pyskl - INFO - Epoch [60][800/1281] lr: 1.646e-02, eta: 13:19:28, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9969, loss_cls: 0.4716, loss: 0.4716 +2025-06-24 20:37:17,407 - pyskl - INFO - Epoch [60][900/1281] lr: 1.644e-02, eta: 13:18:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9956, loss_cls: 0.4881, loss: 0.4881 +2025-06-24 20:38:06,505 - pyskl - INFO - Epoch [60][1000/1281] lr: 1.642e-02, eta: 13:18:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9944, loss_cls: 0.4725, loss: 0.4725 +2025-06-24 20:38:55,645 - pyskl - INFO - Epoch [60][1100/1281] lr: 1.640e-02, eta: 13:17:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9950, loss_cls: 0.5089, loss: 0.5089 +2025-06-24 20:39:44,646 - pyskl - INFO - Epoch [60][1200/1281] lr: 1.638e-02, eta: 13:17:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.5003, loss: 0.5003 +2025-06-24 20:40:24,714 - pyskl - INFO - Saving checkpoint at 60 epochs +2025-06-24 20:41:24,349 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:41:24,411 - pyskl - INFO - +top1_acc 0.8785 +top5_acc 0.9923 +2025-06-24 20:41:24,411 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:41:24,419 - pyskl - INFO - +mean_acc 0.8324 +2025-06-24 20:41:24,421 - pyskl - INFO - Epoch(val) [60][533] top1_acc: 0.8785, top5_acc: 0.9923, mean_class_accuracy: 0.8324 +2025-06-24 20:42:33,579 - pyskl - INFO - Epoch [61][100/1281] lr: 1.634e-02, eta: 13:16:04, time: 0.692, data_time: 0.193, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9994, loss_cls: 0.4504, loss: 0.4504 +2025-06-24 20:43:24,814 - pyskl - INFO - Epoch [61][200/1281] lr: 1.632e-02, eta: 13:15:37, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9988, loss_cls: 0.4259, loss: 0.4259 +2025-06-24 20:43:49,250 - pyskl - INFO - Epoch [61][300/1281] lr: 1.630e-02, eta: 13:14:31, time: 0.244, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9981, loss_cls: 0.4308, loss: 0.4308 +2025-06-24 20:44:35,227 - pyskl - INFO - Epoch [61][400/1281] lr: 1.629e-02, eta: 13:13:56, time: 0.460, data_time: 0.001, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9975, loss_cls: 0.4859, loss: 0.4859 +2025-06-24 20:45:24,069 - pyskl - INFO - Epoch [61][500/1281] lr: 1.627e-02, eta: 13:13:25, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9981, loss_cls: 0.4474, loss: 0.4474 +2025-06-24 20:46:13,080 - pyskl - INFO - Epoch [61][600/1281] lr: 1.625e-02, eta: 13:12:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9988, loss_cls: 0.4944, loss: 0.4944 +2025-06-24 20:47:02,071 - pyskl - INFO - Epoch [61][700/1281] lr: 1.623e-02, eta: 13:12:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9969, loss_cls: 0.4828, loss: 0.4828 +2025-06-24 20:47:51,085 - pyskl - INFO - Epoch [61][800/1281] lr: 1.621e-02, eta: 13:11:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9994, loss_cls: 0.4578, loss: 0.4578 +2025-06-24 20:48:40,060 - pyskl - INFO - Epoch [61][900/1281] lr: 1.619e-02, eta: 13:11:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9969, loss_cls: 0.4654, loss: 0.4654 +2025-06-24 20:49:29,021 - pyskl - INFO - Epoch [61][1000/1281] lr: 1.617e-02, eta: 13:10:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9981, loss_cls: 0.4976, loss: 0.4976 +2025-06-24 20:50:18,204 - pyskl - INFO - Epoch [61][1100/1281] lr: 1.615e-02, eta: 13:10:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9956, loss_cls: 0.4844, loss: 0.4844 +2025-06-24 20:51:07,210 - pyskl - INFO - Epoch [61][1200/1281] lr: 1.613e-02, eta: 13:09:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9969, loss_cls: 0.4804, loss: 0.4804 +2025-06-24 20:51:47,436 - pyskl - INFO - Saving checkpoint at 61 epochs +2025-06-24 20:52:46,899 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:52:46,954 - pyskl - INFO - +top1_acc 0.8606 +top5_acc 0.9893 +2025-06-24 20:52:46,954 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:52:46,961 - pyskl - INFO - +mean_acc 0.8063 +2025-06-24 20:52:46,963 - pyskl - INFO - Epoch(val) [61][533] top1_acc: 0.8606, top5_acc: 0.9893, mean_class_accuracy: 0.8063 +2025-06-24 20:53:53,418 - pyskl - INFO - Epoch [62][100/1281] lr: 1.609e-02, eta: 13:08:24, time: 0.665, data_time: 0.202, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9962, loss_cls: 0.4595, loss: 0.4595 +2025-06-24 20:54:44,739 - pyskl - INFO - Epoch [62][200/1281] lr: 1.607e-02, eta: 13:07:57, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9981, loss_cls: 0.4288, loss: 0.4288 +2025-06-24 20:55:10,190 - pyskl - INFO - Epoch [62][300/1281] lr: 1.605e-02, eta: 13:06:52, time: 0.254, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9956, loss_cls: 0.4437, loss: 0.4437 +2025-06-24 20:55:58,276 - pyskl - INFO - Epoch [62][400/1281] lr: 1.603e-02, eta: 13:06:20, time: 0.481, data_time: 0.001, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9969, loss_cls: 0.5055, loss: 0.5055 +2025-06-24 20:56:47,625 - pyskl - INFO - Epoch [62][500/1281] lr: 1.602e-02, eta: 13:05:50, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9938, loss_cls: 0.4698, loss: 0.4698 +2025-06-24 20:57:36,705 - pyskl - INFO - Epoch [62][600/1281] lr: 1.600e-02, eta: 13:05:19, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9975, loss_cls: 0.4365, loss: 0.4365 +2025-06-24 20:58:25,812 - pyskl - INFO - Epoch [62][700/1281] lr: 1.598e-02, eta: 13:04:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9988, loss_cls: 0.4612, loss: 0.4612 +2025-06-24 20:59:15,207 - pyskl - INFO - Epoch [62][800/1281] lr: 1.596e-02, eta: 13:04:18, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9988, loss_cls: 0.4583, loss: 0.4583 +2025-06-24 21:00:04,544 - pyskl - INFO - Epoch [62][900/1281] lr: 1.594e-02, eta: 13:03:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9944, loss_cls: 0.5375, loss: 0.5375 +2025-06-24 21:00:53,692 - pyskl - INFO - Epoch [62][1000/1281] lr: 1.592e-02, eta: 13:03:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9975, loss_cls: 0.4594, loss: 0.4594 +2025-06-24 21:01:43,104 - pyskl - INFO - Epoch [62][1100/1281] lr: 1.590e-02, eta: 13:02:47, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9988, loss_cls: 0.4805, loss: 0.4805 +2025-06-24 21:02:32,022 - pyskl - INFO - Epoch [62][1200/1281] lr: 1.588e-02, eta: 13:02:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9981, loss_cls: 0.4612, loss: 0.4612 +2025-06-24 21:03:12,449 - pyskl - INFO - Saving checkpoint at 62 epochs +2025-06-24 21:04:11,366 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:04:11,421 - pyskl - INFO - +top1_acc 0.8821 +top5_acc 0.9939 +2025-06-24 21:04:11,421 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:04:11,429 - pyskl - INFO - +mean_acc 0.8378 +2025-06-24 21:04:11,431 - pyskl - INFO - Epoch(val) [62][533] top1_acc: 0.8821, top5_acc: 0.9939, mean_class_accuracy: 0.8378 +2025-06-24 21:05:15,508 - pyskl - INFO - Epoch [63][100/1281] lr: 1.584e-02, eta: 13:00:44, time: 0.641, data_time: 0.196, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9994, loss_cls: 0.4530, loss: 0.4530 +2025-06-24 21:06:06,819 - pyskl - INFO - Epoch [63][200/1281] lr: 1.582e-02, eta: 13:00:16, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9981, loss_cls: 0.3945, loss: 0.3945 +2025-06-24 21:06:32,573 - pyskl - INFO - Epoch [63][300/1281] lr: 1.580e-02, eta: 12:59:12, time: 0.258, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9994, loss_cls: 0.4290, loss: 0.4290 +2025-06-24 21:07:20,338 - pyskl - INFO - Epoch [63][400/1281] lr: 1.578e-02, eta: 12:58:39, time: 0.478, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9988, loss_cls: 0.4127, loss: 0.4127 +2025-06-24 21:08:09,563 - pyskl - INFO - Epoch [63][500/1281] lr: 1.576e-02, eta: 12:58:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9975, loss_cls: 0.4960, loss: 0.4960 +2025-06-24 21:08:58,399 - pyskl - INFO - Epoch [63][600/1281] lr: 1.574e-02, eta: 12:57:37, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9994, loss_cls: 0.4784, loss: 0.4784 +2025-06-24 21:09:47,696 - pyskl - INFO - Epoch [63][700/1281] lr: 1.572e-02, eta: 12:57:06, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9956, loss_cls: 0.4331, loss: 0.4331 +2025-06-24 21:10:36,814 - pyskl - INFO - Epoch [63][800/1281] lr: 1.570e-02, eta: 12:56:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9962, loss_cls: 0.4319, loss: 0.4319 +2025-06-24 21:11:26,122 - pyskl - INFO - Epoch [63][900/1281] lr: 1.568e-02, eta: 12:56:04, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9962, loss_cls: 0.4539, loss: 0.4539 +2025-06-24 21:12:15,260 - pyskl - INFO - Epoch [63][1000/1281] lr: 1.566e-02, eta: 12:55:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9950, loss_cls: 0.4919, loss: 0.4919 +2025-06-24 21:13:04,277 - pyskl - INFO - Epoch [63][1100/1281] lr: 1.564e-02, eta: 12:55:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9981, loss_cls: 0.4893, loss: 0.4893 +2025-06-24 21:13:53,354 - pyskl - INFO - Epoch [63][1200/1281] lr: 1.562e-02, eta: 12:54:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9938, loss_cls: 0.4607, loss: 0.4607 +2025-06-24 21:14:33,458 - pyskl - INFO - Saving checkpoint at 63 epochs +2025-06-24 21:15:33,178 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:15:33,267 - pyskl - INFO - +top1_acc 0.8802 +top5_acc 0.9889 +2025-06-24 21:15:33,267 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:15:33,276 - pyskl - INFO - +mean_acc 0.8403 +2025-06-24 21:15:33,278 - pyskl - INFO - Epoch(val) [63][533] top1_acc: 0.8802, top5_acc: 0.9889, mean_class_accuracy: 0.8403 +2025-06-24 21:16:36,304 - pyskl - INFO - Epoch [64][100/1281] lr: 1.559e-02, eta: 12:52:58, time: 0.630, data_time: 0.188, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9988, loss_cls: 0.4032, loss: 0.4032 +2025-06-24 21:17:27,634 - pyskl - INFO - Epoch [64][200/1281] lr: 1.557e-02, eta: 12:52:29, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3795, loss: 0.3795 +2025-06-24 21:17:53,641 - pyskl - INFO - Epoch [64][300/1281] lr: 1.555e-02, eta: 12:51:26, time: 0.260, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9994, loss_cls: 0.3507, loss: 0.3507 +2025-06-24 21:18:42,604 - pyskl - INFO - Epoch [64][400/1281] lr: 1.553e-02, eta: 12:50:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9975, loss_cls: 0.4609, loss: 0.4609 +2025-06-24 21:19:31,999 - pyskl - INFO - Epoch [64][500/1281] lr: 1.551e-02, eta: 12:50:23, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9981, loss_cls: 0.5062, loss: 0.5062 +2025-06-24 21:20:21,154 - pyskl - INFO - Epoch [64][600/1281] lr: 1.549e-02, eta: 12:49:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9988, loss_cls: 0.4053, loss: 0.4053 +2025-06-24 21:21:10,062 - pyskl - INFO - Epoch [64][700/1281] lr: 1.547e-02, eta: 12:49:20, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9962, loss_cls: 0.4465, loss: 0.4465 +2025-06-24 21:21:59,571 - pyskl - INFO - Epoch [64][800/1281] lr: 1.545e-02, eta: 12:48:49, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9981, loss_cls: 0.4126, loss: 0.4126 +2025-06-24 21:22:48,723 - pyskl - INFO - Epoch [64][900/1281] lr: 1.543e-02, eta: 12:48:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9981, loss_cls: 0.4701, loss: 0.4701 +2025-06-24 21:23:37,611 - pyskl - INFO - Epoch [64][1000/1281] lr: 1.541e-02, eta: 12:47:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9956, loss_cls: 0.4392, loss: 0.4392 +2025-06-24 21:24:26,699 - pyskl - INFO - Epoch [64][1100/1281] lr: 1.539e-02, eta: 12:47:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9938, loss_cls: 0.4399, loss: 0.4399 +2025-06-24 21:25:15,449 - pyskl - INFO - Epoch [64][1200/1281] lr: 1.537e-02, eta: 12:46:41, time: 0.487, data_time: 0.001, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9969, loss_cls: 0.4586, loss: 0.4586 +2025-06-24 21:25:55,562 - pyskl - INFO - Saving checkpoint at 64 epochs +2025-06-24 21:26:54,942 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:26:54,999 - pyskl - INFO - +top1_acc 0.8762 +top5_acc 0.9901 +2025-06-24 21:26:55,000 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:26:55,008 - pyskl - INFO - +mean_acc 0.8261 +2025-06-24 21:26:55,011 - pyskl - INFO - Epoch(val) [64][533] top1_acc: 0.8762, top5_acc: 0.9901, mean_class_accuracy: 0.8261 +2025-06-24 21:27:57,870 - pyskl - INFO - Epoch [65][100/1281] lr: 1.533e-02, eta: 12:45:09, time: 0.629, data_time: 0.195, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9969, loss_cls: 0.4484, loss: 0.4484 +2025-06-24 21:28:48,987 - pyskl - INFO - Epoch [65][200/1281] lr: 1.531e-02, eta: 12:44:40, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9988, loss_cls: 0.3923, loss: 0.3923 +2025-06-24 21:29:15,646 - pyskl - INFO - Epoch [65][300/1281] lr: 1.529e-02, eta: 12:43:38, time: 0.267, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9975, loss_cls: 0.3940, loss: 0.3940 +2025-06-24 21:30:04,391 - pyskl - INFO - Epoch [65][400/1281] lr: 1.527e-02, eta: 12:43:06, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9994, loss_cls: 0.4317, loss: 0.4317 +2025-06-24 21:30:53,359 - pyskl - INFO - Epoch [65][500/1281] lr: 1.526e-02, eta: 12:42:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9981, loss_cls: 0.4730, loss: 0.4730 +2025-06-24 21:31:42,031 - pyskl - INFO - Epoch [65][600/1281] lr: 1.524e-02, eta: 12:42:01, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9975, loss_cls: 0.4788, loss: 0.4788 +2025-06-24 21:32:31,112 - pyskl - INFO - Epoch [65][700/1281] lr: 1.522e-02, eta: 12:41:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9969, loss_cls: 0.4503, loss: 0.4503 +2025-06-24 21:33:20,220 - pyskl - INFO - Epoch [65][800/1281] lr: 1.520e-02, eta: 12:40:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9975, loss_cls: 0.4726, loss: 0.4726 +2025-06-24 21:34:09,498 - pyskl - INFO - Epoch [65][900/1281] lr: 1.518e-02, eta: 12:40:25, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9975, loss_cls: 0.4022, loss: 0.4022 +2025-06-24 21:34:58,894 - pyskl - INFO - Epoch [65][1000/1281] lr: 1.516e-02, eta: 12:39:54, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9975, loss_cls: 0.4387, loss: 0.4387 +2025-06-24 21:35:47,889 - pyskl - INFO - Epoch [65][1100/1281] lr: 1.514e-02, eta: 12:39:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9988, loss_cls: 0.4296, loss: 0.4296 +2025-06-24 21:36:36,871 - pyskl - INFO - Epoch [65][1200/1281] lr: 1.512e-02, eta: 12:38:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9956, loss_cls: 0.5104, loss: 0.5104 +2025-06-24 21:37:17,109 - pyskl - INFO - Saving checkpoint at 65 epochs +2025-06-24 21:38:15,934 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:38:15,990 - pyskl - INFO - +top1_acc 0.8729 +top5_acc 0.9924 +2025-06-24 21:38:15,990 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:38:15,996 - pyskl - INFO - +mean_acc 0.8293 +2025-06-24 21:38:15,998 - pyskl - INFO - Epoch(val) [65][533] top1_acc: 0.8729, top5_acc: 0.9924, mean_class_accuracy: 0.8293 +2025-06-24 21:39:17,544 - pyskl - INFO - Epoch [66][100/1281] lr: 1.508e-02, eta: 12:37:15, time: 0.615, data_time: 0.192, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9956, loss_cls: 0.4037, loss: 0.4037 +2025-06-24 21:40:08,676 - pyskl - INFO - Epoch [66][200/1281] lr: 1.506e-02, eta: 12:36:45, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3565, loss: 0.3565 +2025-06-24 21:40:37,411 - pyskl - INFO - Epoch [66][300/1281] lr: 1.504e-02, eta: 12:35:47, time: 0.287, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9962, loss_cls: 0.4294, loss: 0.4294 +2025-06-24 21:41:26,479 - pyskl - INFO - Epoch [66][400/1281] lr: 1.502e-02, eta: 12:35:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9931, loss_cls: 0.4782, loss: 0.4782 +2025-06-24 21:42:15,584 - pyskl - INFO - Epoch [66][500/1281] lr: 1.500e-02, eta: 12:34:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9981, loss_cls: 0.4250, loss: 0.4250 +2025-06-24 21:43:04,957 - pyskl - INFO - Epoch [66][600/1281] lr: 1.498e-02, eta: 12:34:10, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9988, loss_cls: 0.4597, loss: 0.4597 +2025-06-24 21:43:54,368 - pyskl - INFO - Epoch [66][700/1281] lr: 1.496e-02, eta: 12:33:38, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9956, loss_cls: 0.4619, loss: 0.4619 +2025-06-24 21:44:43,684 - pyskl - INFO - Epoch [66][800/1281] lr: 1.494e-02, eta: 12:33:06, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9969, loss_cls: 0.4704, loss: 0.4704 +2025-06-24 21:45:32,677 - pyskl - INFO - Epoch [66][900/1281] lr: 1.492e-02, eta: 12:32:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.4341, loss: 0.4341 +2025-06-24 21:46:21,954 - pyskl - INFO - Epoch [66][1000/1281] lr: 1.490e-02, eta: 12:32:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9969, loss_cls: 0.4402, loss: 0.4402 +2025-06-24 21:47:10,953 - pyskl - INFO - Epoch [66][1100/1281] lr: 1.488e-02, eta: 12:31:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9994, loss_cls: 0.4200, loss: 0.4200 +2025-06-24 21:48:00,210 - pyskl - INFO - Epoch [66][1200/1281] lr: 1.486e-02, eta: 12:30:56, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9950, loss_cls: 0.4828, loss: 0.4828 +2025-06-24 21:48:40,501 - pyskl - INFO - Saving checkpoint at 66 epochs +2025-06-24 21:49:40,173 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:49:40,242 - pyskl - INFO - +top1_acc 0.8534 +top5_acc 0.9886 +2025-06-24 21:49:40,242 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:49:40,250 - pyskl - INFO - +mean_acc 0.8114 +2025-06-24 21:49:40,252 - pyskl - INFO - Epoch(val) [66][533] top1_acc: 0.8534, top5_acc: 0.9886, mean_class_accuracy: 0.8114 +2025-06-24 21:50:38,723 - pyskl - INFO - Epoch [67][100/1281] lr: 1.482e-02, eta: 12:29:18, time: 0.585, data_time: 0.196, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3942, loss: 0.3942 +2025-06-24 21:51:30,068 - pyskl - INFO - Epoch [67][200/1281] lr: 1.480e-02, eta: 12:28:49, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3749, loss: 0.3749 +2025-06-24 21:52:00,983 - pyskl - INFO - Epoch [67][300/1281] lr: 1.478e-02, eta: 12:27:53, time: 0.309, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9988, loss_cls: 0.4176, loss: 0.4176 +2025-06-24 21:52:50,053 - pyskl - INFO - Epoch [67][400/1281] lr: 1.476e-02, eta: 12:27:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9988, loss_cls: 0.3795, loss: 0.3795 +2025-06-24 21:53:39,113 - pyskl - INFO - Epoch [67][500/1281] lr: 1.474e-02, eta: 12:26:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9950, loss_cls: 0.4798, loss: 0.4798 +2025-06-24 21:54:28,077 - pyskl - INFO - Epoch [67][600/1281] lr: 1.472e-02, eta: 12:26:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9975, loss_cls: 0.5187, loss: 0.5187 +2025-06-24 21:55:17,413 - pyskl - INFO - Epoch [67][700/1281] lr: 1.470e-02, eta: 12:25:42, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9981, loss_cls: 0.4001, loss: 0.4001 +2025-06-24 21:56:06,401 - pyskl - INFO - Epoch [67][800/1281] lr: 1.468e-02, eta: 12:25:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9975, loss_cls: 0.4475, loss: 0.4475 +2025-06-24 21:56:54,920 - pyskl - INFO - Epoch [67][900/1281] lr: 1.466e-02, eta: 12:24:36, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9969, loss_cls: 0.4422, loss: 0.4422 +2025-06-24 21:57:43,758 - pyskl - INFO - Epoch [67][1000/1281] lr: 1.464e-02, eta: 12:24:03, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9969, loss_cls: 0.4631, loss: 0.4631 +2025-06-24 21:58:32,727 - pyskl - INFO - Epoch [67][1100/1281] lr: 1.462e-02, eta: 12:23:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9981, loss_cls: 0.4420, loss: 0.4420 +2025-06-24 21:59:22,065 - pyskl - INFO - Epoch [67][1200/1281] lr: 1.460e-02, eta: 12:22:57, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9988, loss_cls: 0.4505, loss: 0.4505 +2025-06-24 22:00:02,398 - pyskl - INFO - Saving checkpoint at 67 epochs +2025-06-24 22:01:01,955 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:01:02,010 - pyskl - INFO - +top1_acc 0.8859 +top5_acc 0.9927 +2025-06-24 22:01:02,011 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:01:02,017 - pyskl - INFO - +mean_acc 0.8380 +2025-06-24 22:01:02,021 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_57.pth was removed +2025-06-24 22:01:02,200 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_67.pth. +2025-06-24 22:01:02,201 - pyskl - INFO - Best top1_acc is 0.8859 at 67 epoch. +2025-06-24 22:01:02,203 - pyskl - INFO - Epoch(val) [67][533] top1_acc: 0.8859, top5_acc: 0.9927, mean_class_accuracy: 0.8380 +2025-06-24 22:02:06,658 - pyskl - INFO - Epoch [68][100/1281] lr: 1.456e-02, eta: 12:21:27, time: 0.645, data_time: 0.191, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9994, loss_cls: 0.3870, loss: 0.3870 +2025-06-24 22:02:49,907 - pyskl - INFO - Epoch [68][200/1281] lr: 1.454e-02, eta: 12:20:47, time: 0.432, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9994, loss_cls: 0.3706, loss: 0.3706 +2025-06-24 22:03:25,674 - pyskl - INFO - Epoch [68][300/1281] lr: 1.452e-02, eta: 12:19:57, time: 0.358, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9988, loss_cls: 0.3886, loss: 0.3886 +2025-06-24 22:04:14,726 - pyskl - INFO - Epoch [68][400/1281] lr: 1.450e-02, eta: 12:19:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9988, loss_cls: 0.3659, loss: 0.3659 +2025-06-24 22:05:04,098 - pyskl - INFO - Epoch [68][500/1281] lr: 1.448e-02, eta: 12:18:52, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9975, loss_cls: 0.4001, loss: 0.4001 +2025-06-24 22:05:52,485 - pyskl - INFO - Epoch [68][600/1281] lr: 1.446e-02, eta: 12:18:18, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9994, loss_cls: 0.4032, loss: 0.4032 +2025-06-24 22:06:41,410 - pyskl - INFO - Epoch [68][700/1281] lr: 1.444e-02, eta: 12:17:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9975, loss_cls: 0.4383, loss: 0.4383 +2025-06-24 22:07:30,609 - pyskl - INFO - Epoch [68][800/1281] lr: 1.442e-02, eta: 12:17:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9981, loss_cls: 0.4804, loss: 0.4804 +2025-06-24 22:08:19,409 - pyskl - INFO - Epoch [68][900/1281] lr: 1.440e-02, eta: 12:16:38, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9994, loss_cls: 0.4739, loss: 0.4739 +2025-06-24 22:09:08,668 - pyskl - INFO - Epoch [68][1000/1281] lr: 1.438e-02, eta: 12:16:05, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.4332, loss: 0.4332 +2025-06-24 22:09:57,696 - pyskl - INFO - Epoch [68][1100/1281] lr: 1.436e-02, eta: 12:15:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9988, loss_cls: 0.4465, loss: 0.4465 +2025-06-24 22:10:46,815 - pyskl - INFO - Epoch [68][1200/1281] lr: 1.434e-02, eta: 12:14:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9956, loss_cls: 0.4796, loss: 0.4796 +2025-06-24 22:11:26,956 - pyskl - INFO - Saving checkpoint at 68 epochs +2025-06-24 22:12:26,784 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:12:26,840 - pyskl - INFO - +top1_acc 0.8878 +top5_acc 0.9925 +2025-06-24 22:12:26,840 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:12:26,846 - pyskl - INFO - +mean_acc 0.8538 +2025-06-24 22:12:26,850 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_67.pth was removed +2025-06-24 22:12:27,171 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_68.pth. +2025-06-24 22:12:27,171 - pyskl - INFO - Best top1_acc is 0.8878 at 68 epoch. +2025-06-24 22:12:27,174 - pyskl - INFO - Epoch(val) [68][533] top1_acc: 0.8878, top5_acc: 0.9925, mean_class_accuracy: 0.8538 +2025-06-24 22:13:29,025 - pyskl - INFO - Epoch [69][100/1281] lr: 1.431e-02, eta: 12:13:25, time: 0.618, data_time: 0.186, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9969, loss_cls: 0.4110, loss: 0.4110 +2025-06-24 22:14:09,968 - pyskl - INFO - Epoch [69][200/1281] lr: 1.429e-02, eta: 12:12:42, time: 0.409, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9994, loss_cls: 0.4094, loss: 0.4094 +2025-06-24 22:14:45,207 - pyskl - INFO - Epoch [69][300/1281] lr: 1.427e-02, eta: 12:11:52, time: 0.352, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9994, loss_cls: 0.3464, loss: 0.3464 +2025-06-24 22:15:34,223 - pyskl - INFO - Epoch [69][400/1281] lr: 1.425e-02, eta: 12:11:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9969, loss_cls: 0.4089, loss: 0.4089 +2025-06-24 22:16:23,268 - pyskl - INFO - Epoch [69][500/1281] lr: 1.423e-02, eta: 12:10:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9969, loss_cls: 0.4322, loss: 0.4322 +2025-06-24 22:17:12,204 - pyskl - INFO - Epoch [69][600/1281] lr: 1.420e-02, eta: 12:10:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9988, loss_cls: 0.4439, loss: 0.4439 +2025-06-24 22:18:01,224 - pyskl - INFO - Epoch [69][700/1281] lr: 1.418e-02, eta: 12:09:38, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9981, loss_cls: 0.4082, loss: 0.4082 +2025-06-24 22:18:50,567 - pyskl - INFO - Epoch [69][800/1281] lr: 1.416e-02, eta: 12:09:05, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9988, loss_cls: 0.4177, loss: 0.4177 +2025-06-24 22:19:39,635 - pyskl - INFO - Epoch [69][900/1281] lr: 1.414e-02, eta: 12:08:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9988, loss_cls: 0.4484, loss: 0.4484 +2025-06-24 22:20:29,028 - pyskl - INFO - Epoch [69][1000/1281] lr: 1.412e-02, eta: 12:07:58, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9962, loss_cls: 0.4683, loss: 0.4683 +2025-06-24 22:21:18,040 - pyskl - INFO - Epoch [69][1100/1281] lr: 1.410e-02, eta: 12:07:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9975, loss_cls: 0.4237, loss: 0.4237 +2025-06-24 22:22:07,180 - pyskl - INFO - Epoch [69][1200/1281] lr: 1.408e-02, eta: 12:06:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9975, loss_cls: 0.4350, loss: 0.4350 +2025-06-24 22:22:47,562 - pyskl - INFO - Saving checkpoint at 69 epochs +2025-06-24 22:23:47,443 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:23:47,523 - pyskl - INFO - +top1_acc 0.8877 +top5_acc 0.9928 +2025-06-24 22:23:47,523 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:23:47,531 - pyskl - INFO - +mean_acc 0.8425 +2025-06-24 22:23:47,533 - pyskl - INFO - Epoch(val) [69][533] top1_acc: 0.8877, top5_acc: 0.9928, mean_class_accuracy: 0.8425 +2025-06-24 22:24:51,465 - pyskl - INFO - Epoch [70][100/1281] lr: 1.405e-02, eta: 12:05:21, time: 0.639, data_time: 0.192, memory: 4083, top1_acc: 0.9306, top5_acc: 1.0000, loss_cls: 0.3709, loss: 0.3709 +2025-06-24 22:25:31,884 - pyskl - INFO - Epoch [70][200/1281] lr: 1.403e-02, eta: 12:04:37, time: 0.404, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9988, loss_cls: 0.3860, loss: 0.3860 +2025-06-24 22:26:09,201 - pyskl - INFO - Epoch [70][300/1281] lr: 1.401e-02, eta: 12:03:50, time: 0.373, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9988, loss_cls: 0.4087, loss: 0.4087 +2025-06-24 22:26:58,557 - pyskl - INFO - Epoch [70][400/1281] lr: 1.399e-02, eta: 12:03:16, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3796, loss: 0.3796 +2025-06-24 22:27:47,495 - pyskl - INFO - Epoch [70][500/1281] lr: 1.397e-02, eta: 12:02:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.4362, loss: 0.4362 +2025-06-24 22:28:36,491 - pyskl - INFO - Epoch [70][600/1281] lr: 1.395e-02, eta: 12:02:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9988, loss_cls: 0.3645, loss: 0.3645 +2025-06-24 22:29:25,503 - pyskl - INFO - Epoch [70][700/1281] lr: 1.392e-02, eta: 12:01:34, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9969, loss_cls: 0.3904, loss: 0.3904 +2025-06-24 22:30:14,563 - pyskl - INFO - Epoch [70][800/1281] lr: 1.390e-02, eta: 12:01:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9981, loss_cls: 0.3946, loss: 0.3946 +2025-06-24 22:31:03,753 - pyskl - INFO - Epoch [70][900/1281] lr: 1.388e-02, eta: 12:00:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9975, loss_cls: 0.3798, loss: 0.3798 +2025-06-24 22:31:52,656 - pyskl - INFO - Epoch [70][1000/1281] lr: 1.386e-02, eta: 11:59:53, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9994, loss_cls: 0.4313, loss: 0.4313 +2025-06-24 22:32:42,059 - pyskl - INFO - Epoch [70][1100/1281] lr: 1.384e-02, eta: 11:59:19, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9975, loss_cls: 0.4244, loss: 0.4244 +2025-06-24 22:33:30,954 - pyskl - INFO - Epoch [70][1200/1281] lr: 1.382e-02, eta: 11:58:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9975, loss_cls: 0.4703, loss: 0.4703 +2025-06-24 22:34:11,433 - pyskl - INFO - Saving checkpoint at 70 epochs +2025-06-24 22:35:10,695 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:35:10,755 - pyskl - INFO - +top1_acc 0.8828 +top5_acc 0.9919 +2025-06-24 22:35:10,755 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:35:10,762 - pyskl - INFO - +mean_acc 0.8559 +2025-06-24 22:35:10,763 - pyskl - INFO - Epoch(val) [70][533] top1_acc: 0.8828, top5_acc: 0.9919, mean_class_accuracy: 0.8559 +2025-06-24 22:36:13,263 - pyskl - INFO - Epoch [71][100/1281] lr: 1.379e-02, eta: 11:57:13, time: 0.625, data_time: 0.190, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9981, loss_cls: 0.4118, loss: 0.4118 +2025-06-24 22:36:51,954 - pyskl - INFO - Epoch [71][200/1281] lr: 1.377e-02, eta: 11:56:27, time: 0.387, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.3834, loss: 0.3834 +2025-06-24 22:37:28,705 - pyskl - INFO - Epoch [71][300/1281] lr: 1.375e-02, eta: 11:55:39, time: 0.367, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 1.0000, loss_cls: 0.3428, loss: 0.3428 +2025-06-24 22:38:17,751 - pyskl - INFO - Epoch [71][400/1281] lr: 1.373e-02, eta: 11:55:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.4030, loss: 0.4030 +2025-06-24 22:39:06,924 - pyskl - INFO - Epoch [71][500/1281] lr: 1.371e-02, eta: 11:54:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9981, loss_cls: 0.3684, loss: 0.3684 +2025-06-24 22:39:55,956 - pyskl - INFO - Epoch [71][600/1281] lr: 1.368e-02, eta: 11:53:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9969, loss_cls: 0.4441, loss: 0.4441 +2025-06-24 22:40:45,333 - pyskl - INFO - Epoch [71][700/1281] lr: 1.366e-02, eta: 11:53:23, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9988, loss_cls: 0.4235, loss: 0.4235 +2025-06-24 22:41:34,567 - pyskl - INFO - Epoch [71][800/1281] lr: 1.364e-02, eta: 11:52:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9956, loss_cls: 0.4521, loss: 0.4521 +2025-06-24 22:42:23,579 - pyskl - INFO - Epoch [71][900/1281] lr: 1.362e-02, eta: 11:52:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9988, loss_cls: 0.4591, loss: 0.4591 +2025-06-24 22:43:12,343 - pyskl - INFO - Epoch [71][1000/1281] lr: 1.360e-02, eta: 11:51:41, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3756, loss: 0.3756 +2025-06-24 22:44:01,187 - pyskl - INFO - Epoch [71][1100/1281] lr: 1.358e-02, eta: 11:51:06, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9994, loss_cls: 0.3797, loss: 0.3797 +2025-06-24 22:44:50,078 - pyskl - INFO - Epoch [71][1200/1281] lr: 1.356e-02, eta: 11:50:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9981, loss_cls: 0.4782, loss: 0.4782 +2025-06-24 22:45:30,543 - pyskl - INFO - Saving checkpoint at 71 epochs +2025-06-24 22:46:29,890 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:46:29,958 - pyskl - INFO - +top1_acc 0.8841 +top5_acc 0.9934 +2025-06-24 22:46:29,958 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:46:29,968 - pyskl - INFO - +mean_acc 0.8373 +2025-06-24 22:46:29,971 - pyskl - INFO - Epoch(val) [71][533] top1_acc: 0.8841, top5_acc: 0.9934, mean_class_accuracy: 0.8373 +2025-06-24 22:47:32,959 - pyskl - INFO - Epoch [72][100/1281] lr: 1.353e-02, eta: 11:49:01, time: 0.630, data_time: 0.196, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9981, loss_cls: 0.3544, loss: 0.3544 +2025-06-24 22:48:13,036 - pyskl - INFO - Epoch [72][200/1281] lr: 1.351e-02, eta: 11:48:16, time: 0.401, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9969, loss_cls: 0.3787, loss: 0.3787 +2025-06-24 22:48:50,969 - pyskl - INFO - Epoch [72][300/1281] lr: 1.349e-02, eta: 11:47:30, time: 0.379, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9975, loss_cls: 0.4059, loss: 0.4059 +2025-06-24 22:49:39,863 - pyskl - INFO - Epoch [72][400/1281] lr: 1.346e-02, eta: 11:46:55, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9981, loss_cls: 0.3600, loss: 0.3600 +2025-06-24 22:50:28,739 - pyskl - INFO - Epoch [72][500/1281] lr: 1.344e-02, eta: 11:46:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9994, loss_cls: 0.4272, loss: 0.4272 +2025-06-24 22:51:17,853 - pyskl - INFO - Epoch [72][600/1281] lr: 1.342e-02, eta: 11:45:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9988, loss_cls: 0.4086, loss: 0.4086 +2025-06-24 22:52:06,773 - pyskl - INFO - Epoch [72][700/1281] lr: 1.340e-02, eta: 11:45:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9981, loss_cls: 0.3880, loss: 0.3880 +2025-06-24 22:52:56,146 - pyskl - INFO - Epoch [72][800/1281] lr: 1.338e-02, eta: 11:44:38, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9981, loss_cls: 0.3548, loss: 0.3548 +2025-06-24 22:53:45,126 - pyskl - INFO - Epoch [72][900/1281] lr: 1.336e-02, eta: 11:44:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9969, loss_cls: 0.4175, loss: 0.4175 +2025-06-24 22:54:34,093 - pyskl - INFO - Epoch [72][1000/1281] lr: 1.334e-02, eta: 11:43:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9994, loss_cls: 0.4341, loss: 0.4341 +2025-06-24 22:55:23,229 - pyskl - INFO - Epoch [72][1100/1281] lr: 1.332e-02, eta: 11:42:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9988, loss_cls: 0.4531, loss: 0.4531 +2025-06-24 22:56:11,959 - pyskl - INFO - Epoch [72][1200/1281] lr: 1.330e-02, eta: 11:42:19, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9969, loss_cls: 0.4323, loss: 0.4323 +2025-06-24 22:56:52,288 - pyskl - INFO - Saving checkpoint at 72 epochs +2025-06-24 22:57:51,739 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:57:51,797 - pyskl - INFO - +top1_acc 0.8818 +top5_acc 0.9919 +2025-06-24 22:57:51,797 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:57:51,804 - pyskl - INFO - +mean_acc 0.8328 +2025-06-24 22:57:51,806 - pyskl - INFO - Epoch(val) [72][533] top1_acc: 0.8818, top5_acc: 0.9919, mean_class_accuracy: 0.8328 +2025-06-24 22:58:53,622 - pyskl - INFO - Epoch [73][100/1281] lr: 1.326e-02, eta: 11:40:47, time: 0.618, data_time: 0.197, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9994, loss_cls: 0.4038, loss: 0.4038 +2025-06-24 22:59:33,079 - pyskl - INFO - Epoch [73][200/1281] lr: 1.324e-02, eta: 11:40:02, time: 0.395, data_time: 0.001, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9981, loss_cls: 0.3954, loss: 0.3954 +2025-06-24 23:00:10,839 - pyskl - INFO - Epoch [73][300/1281] lr: 1.322e-02, eta: 11:39:15, time: 0.378, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.3291, loss: 0.3291 +2025-06-24 23:01:00,162 - pyskl - INFO - Epoch [73][400/1281] lr: 1.320e-02, eta: 11:38:41, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.3210, loss: 0.3210 +2025-06-24 23:01:49,296 - pyskl - INFO - Epoch [73][500/1281] lr: 1.318e-02, eta: 11:38:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 1.0000, loss_cls: 0.3390, loss: 0.3390 +2025-06-24 23:02:38,646 - pyskl - INFO - Epoch [73][600/1281] lr: 1.316e-02, eta: 11:37:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9988, loss_cls: 0.3954, loss: 0.3954 +2025-06-24 23:03:27,656 - pyskl - INFO - Epoch [73][700/1281] lr: 1.314e-02, eta: 11:36:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9969, loss_cls: 0.4229, loss: 0.4229 +2025-06-24 23:04:16,848 - pyskl - INFO - Epoch [73][800/1281] lr: 1.312e-02, eta: 11:36:22, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.4178, loss: 0.4178 +2025-06-24 23:05:06,165 - pyskl - INFO - Epoch [73][900/1281] lr: 1.310e-02, eta: 11:35:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9981, loss_cls: 0.4293, loss: 0.4293 +2025-06-24 23:05:55,062 - pyskl - INFO - Epoch [73][1000/1281] lr: 1.308e-02, eta: 11:35:13, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9975, loss_cls: 0.3480, loss: 0.3480 +2025-06-24 23:06:43,889 - pyskl - INFO - Epoch [73][1100/1281] lr: 1.306e-02, eta: 11:34:38, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9981, loss_cls: 0.3799, loss: 0.3799 +2025-06-24 23:07:32,771 - pyskl - INFO - Epoch [73][1200/1281] lr: 1.304e-02, eta: 11:34:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9981, loss_cls: 0.4059, loss: 0.4059 +2025-06-24 23:08:13,045 - pyskl - INFO - Saving checkpoint at 73 epochs +2025-06-24 23:09:11,967 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:09:12,030 - pyskl - INFO - +top1_acc 0.8789 +top5_acc 0.9926 +2025-06-24 23:09:12,030 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:09:12,037 - pyskl - INFO - +mean_acc 0.8395 +2025-06-24 23:09:12,039 - pyskl - INFO - Epoch(val) [73][533] top1_acc: 0.8789, top5_acc: 0.9926, mean_class_accuracy: 0.8395 +2025-06-24 23:10:13,385 - pyskl - INFO - Epoch [74][100/1281] lr: 1.300e-02, eta: 11:32:30, time: 0.613, data_time: 0.194, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9981, loss_cls: 0.3700, loss: 0.3700 +2025-06-24 23:10:53,819 - pyskl - INFO - Epoch [74][200/1281] lr: 1.298e-02, eta: 11:31:46, time: 0.404, data_time: 0.001, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3286, loss: 0.3286 +2025-06-24 23:11:31,324 - pyskl - INFO - Epoch [74][300/1281] lr: 1.296e-02, eta: 11:30:59, time: 0.375, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9994, loss_cls: 0.3551, loss: 0.3551 +2025-06-24 23:12:20,426 - pyskl - INFO - Epoch [74][400/1281] lr: 1.294e-02, eta: 11:30:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9994, loss_cls: 0.3629, loss: 0.3629 +2025-06-24 23:13:09,612 - pyskl - INFO - Epoch [74][500/1281] lr: 1.292e-02, eta: 11:29:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9981, loss_cls: 0.3664, loss: 0.3664 +2025-06-24 23:13:58,494 - pyskl - INFO - Epoch [74][600/1281] lr: 1.290e-02, eta: 11:29:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9975, loss_cls: 0.3564, loss: 0.3564 +2025-06-24 23:14:47,533 - pyskl - INFO - Epoch [74][700/1281] lr: 1.288e-02, eta: 11:28:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9988, loss_cls: 0.3828, loss: 0.3828 +2025-06-24 23:15:36,593 - pyskl - INFO - Epoch [74][800/1281] lr: 1.286e-02, eta: 11:28:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4474, loss: 0.4474 +2025-06-24 23:16:25,524 - pyskl - INFO - Epoch [74][900/1281] lr: 1.284e-02, eta: 11:27:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 1.0000, loss_cls: 0.3561, loss: 0.3561 +2025-06-24 23:17:14,694 - pyskl - INFO - Epoch [74][1000/1281] lr: 1.282e-02, eta: 11:26:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9969, loss_cls: 0.3541, loss: 0.3541 +2025-06-24 23:18:03,674 - pyskl - INFO - Epoch [74][1100/1281] lr: 1.280e-02, eta: 11:26:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9981, loss_cls: 0.3807, loss: 0.3807 +2025-06-24 23:18:52,510 - pyskl - INFO - Epoch [74][1200/1281] lr: 1.278e-02, eta: 11:25:43, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9975, loss_cls: 0.3874, loss: 0.3874 +2025-06-24 23:19:32,945 - pyskl - INFO - Saving checkpoint at 74 epochs +2025-06-24 23:20:32,630 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:20:32,695 - pyskl - INFO - +top1_acc 0.8911 +top5_acc 0.9930 +2025-06-24 23:20:32,696 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:20:32,703 - pyskl - INFO - +mean_acc 0.8508 +2025-06-24 23:20:32,707 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_68.pth was removed +2025-06-24 23:20:32,881 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_74.pth. +2025-06-24 23:20:32,882 - pyskl - INFO - Best top1_acc is 0.8911 at 74 epoch. +2025-06-24 23:20:32,884 - pyskl - INFO - Epoch(val) [74][533] top1_acc: 0.8911, top5_acc: 0.9930, mean_class_accuracy: 0.8508 +2025-06-24 23:21:33,127 - pyskl - INFO - Epoch [75][100/1281] lr: 1.274e-02, eta: 11:24:10, time: 0.602, data_time: 0.189, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9969, loss_cls: 0.4095, loss: 0.4095 +2025-06-24 23:22:13,705 - pyskl - INFO - Epoch [75][200/1281] lr: 1.272e-02, eta: 11:23:26, time: 0.406, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9988, loss_cls: 0.3554, loss: 0.3554 +2025-06-24 23:22:50,203 - pyskl - INFO - Epoch [75][300/1281] lr: 1.270e-02, eta: 11:22:38, time: 0.365, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9981, loss_cls: 0.3914, loss: 0.3914 +2025-06-24 23:23:39,050 - pyskl - INFO - Epoch [75][400/1281] lr: 1.268e-02, eta: 11:22:03, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.3502, loss: 0.3502 +2025-06-24 23:24:28,159 - pyskl - INFO - Epoch [75][500/1281] lr: 1.266e-02, eta: 11:21:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9969, loss_cls: 0.4051, loss: 0.4051 +2025-06-24 23:25:17,743 - pyskl - INFO - Epoch [75][600/1281] lr: 1.264e-02, eta: 11:20:53, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3607, loss: 0.3607 +2025-06-24 23:26:07,063 - pyskl - INFO - Epoch [75][700/1281] lr: 1.262e-02, eta: 11:20:18, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9969, loss_cls: 0.4215, loss: 0.4215 +2025-06-24 23:26:56,239 - pyskl - INFO - Epoch [75][800/1281] lr: 1.260e-02, eta: 11:19:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.3918, loss: 0.3918 +2025-06-24 23:27:45,221 - pyskl - INFO - Epoch [75][900/1281] lr: 1.258e-02, eta: 11:19:07, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3698, loss: 0.3698 +2025-06-24 23:28:34,159 - pyskl - INFO - Epoch [75][1000/1281] lr: 1.256e-02, eta: 11:18:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9975, loss_cls: 0.3778, loss: 0.3778 +2025-06-24 23:29:23,071 - pyskl - INFO - Epoch [75][1100/1281] lr: 1.254e-02, eta: 11:17:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9981, loss_cls: 0.4125, loss: 0.4125 +2025-06-24 23:30:12,208 - pyskl - INFO - Epoch [75][1200/1281] lr: 1.252e-02, eta: 11:17:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9975, loss_cls: 0.4904, loss: 0.4904 +2025-06-24 23:30:52,701 - pyskl - INFO - Saving checkpoint at 75 epochs +2025-06-24 23:31:51,377 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:31:51,433 - pyskl - INFO - +top1_acc 0.8810 +top5_acc 0.9910 +2025-06-24 23:31:51,433 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:31:51,440 - pyskl - INFO - +mean_acc 0.8452 +2025-06-24 23:31:51,442 - pyskl - INFO - Epoch(val) [75][533] top1_acc: 0.8810, top5_acc: 0.9910, mean_class_accuracy: 0.8452 +2025-06-24 23:32:54,828 - pyskl - INFO - Epoch [76][100/1281] lr: 1.248e-02, eta: 11:15:51, time: 0.634, data_time: 0.194, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9994, loss_cls: 0.3968, loss: 0.3968 +2025-06-24 23:33:35,218 - pyskl - INFO - Epoch [76][200/1281] lr: 1.246e-02, eta: 11:15:07, time: 0.404, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9981, loss_cls: 0.3649, loss: 0.3649 +2025-06-24 23:34:10,972 - pyskl - INFO - Epoch [76][300/1281] lr: 1.244e-02, eta: 11:14:18, time: 0.358, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9969, loss_cls: 0.4143, loss: 0.4143 +2025-06-24 23:34:59,624 - pyskl - INFO - Epoch [76][400/1281] lr: 1.242e-02, eta: 11:13:43, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9994, loss_cls: 0.3218, loss: 0.3218 +2025-06-24 23:35:48,559 - pyskl - INFO - Epoch [76][500/1281] lr: 1.240e-02, eta: 11:13:07, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 1.0000, loss_cls: 0.3384, loss: 0.3384 +2025-06-24 23:36:37,328 - pyskl - INFO - Epoch [76][600/1281] lr: 1.238e-02, eta: 11:12:31, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9975, loss_cls: 0.3924, loss: 0.3924 +2025-06-24 23:37:26,275 - pyskl - INFO - Epoch [76][700/1281] lr: 1.236e-02, eta: 11:11:55, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9975, loss_cls: 0.3293, loss: 0.3293 +2025-06-24 23:38:15,665 - pyskl - INFO - Epoch [76][800/1281] lr: 1.234e-02, eta: 11:11:20, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9981, loss_cls: 0.3723, loss: 0.3723 +2025-06-24 23:39:04,559 - pyskl - INFO - Epoch [76][900/1281] lr: 1.232e-02, eta: 11:10:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9988, loss_cls: 0.3786, loss: 0.3786 +2025-06-24 23:39:54,040 - pyskl - INFO - Epoch [76][1000/1281] lr: 1.230e-02, eta: 11:10:09, time: 0.495, data_time: 0.001, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9994, loss_cls: 0.3805, loss: 0.3805 +2025-06-24 23:40:43,127 - pyskl - INFO - Epoch [76][1100/1281] lr: 1.228e-02, eta: 11:09:33, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9981, loss_cls: 0.4748, loss: 0.4748 +2025-06-24 23:41:31,582 - pyskl - INFO - Epoch [76][1200/1281] lr: 1.225e-02, eta: 11:08:57, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9981, loss_cls: 0.4262, loss: 0.4262 +2025-06-24 23:42:11,814 - pyskl - INFO - Saving checkpoint at 76 epochs +2025-06-24 23:43:09,678 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:43:09,735 - pyskl - INFO - +top1_acc 0.8876 +top5_acc 0.9934 +2025-06-24 23:43:09,735 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:43:09,741 - pyskl - INFO - +mean_acc 0.8519 +2025-06-24 23:43:09,743 - pyskl - INFO - Epoch(val) [76][533] top1_acc: 0.8876, top5_acc: 0.9934, mean_class_accuracy: 0.8519 +2025-06-24 23:44:12,612 - pyskl - INFO - Epoch [77][100/1281] lr: 1.222e-02, eta: 11:07:27, time: 0.629, data_time: 0.186, memory: 4083, top1_acc: 0.9313, top5_acc: 1.0000, loss_cls: 0.3591, loss: 0.3591 +2025-06-24 23:44:55,167 - pyskl - INFO - Epoch [77][200/1281] lr: 1.220e-02, eta: 11:06:45, time: 0.426, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3338, loss: 0.3338 +2025-06-24 23:45:30,911 - pyskl - INFO - Epoch [77][300/1281] lr: 1.218e-02, eta: 11:05:57, time: 0.357, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9975, loss_cls: 0.3096, loss: 0.3096 +2025-06-24 23:46:19,745 - pyskl - INFO - Epoch [77][400/1281] lr: 1.216e-02, eta: 11:05:21, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 1.0000, loss_cls: 0.3641, loss: 0.3641 +2025-06-24 23:47:08,813 - pyskl - INFO - Epoch [77][500/1281] lr: 1.214e-02, eta: 11:04:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 1.0000, loss_cls: 0.3700, loss: 0.3700 +2025-06-24 23:47:57,679 - pyskl - INFO - Epoch [77][600/1281] lr: 1.212e-02, eta: 11:04:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 1.0000, loss_cls: 0.3545, loss: 0.3545 +2025-06-24 23:48:46,943 - pyskl - INFO - Epoch [77][700/1281] lr: 1.210e-02, eta: 11:03:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9981, loss_cls: 0.3318, loss: 0.3318 +2025-06-24 23:49:35,807 - pyskl - INFO - Epoch [77][800/1281] lr: 1.207e-02, eta: 11:02:57, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9981, loss_cls: 0.3921, loss: 0.3921 +2025-06-24 23:50:24,975 - pyskl - INFO - Epoch [77][900/1281] lr: 1.205e-02, eta: 11:02:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9988, loss_cls: 0.3569, loss: 0.3569 +2025-06-24 23:51:14,272 - pyskl - INFO - Epoch [77][1000/1281] lr: 1.203e-02, eta: 11:01:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 1.0000, loss_cls: 0.3767, loss: 0.3767 +2025-06-24 23:52:03,318 - pyskl - INFO - Epoch [77][1100/1281] lr: 1.201e-02, eta: 11:01:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9975, loss_cls: 0.4152, loss: 0.4152 +2025-06-24 23:52:52,085 - pyskl - INFO - Epoch [77][1200/1281] lr: 1.199e-02, eta: 11:00:34, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9994, loss_cls: 0.3876, loss: 0.3876 +2025-06-24 23:53:32,381 - pyskl - INFO - Saving checkpoint at 77 epochs +2025-06-24 23:54:30,800 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:54:30,856 - pyskl - INFO - +top1_acc 0.9017 +top5_acc 0.9937 +2025-06-24 23:54:30,856 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:54:30,870 - pyskl - INFO - +mean_acc 0.8680 +2025-06-24 23:54:30,875 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_74.pth was removed +2025-06-24 23:54:31,084 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_77.pth. +2025-06-24 23:54:31,084 - pyskl - INFO - Best top1_acc is 0.9017 at 77 epoch. +2025-06-24 23:54:31,087 - pyskl - INFO - Epoch(val) [77][533] top1_acc: 0.9017, top5_acc: 0.9937, mean_class_accuracy: 0.8680 +2025-06-24 23:55:33,602 - pyskl - INFO - Epoch [78][100/1281] lr: 1.196e-02, eta: 10:59:04, time: 0.625, data_time: 0.187, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9994, loss_cls: 0.3579, loss: 0.3579 +2025-06-24 23:56:14,513 - pyskl - INFO - Epoch [78][200/1281] lr: 1.194e-02, eta: 10:58:20, time: 0.409, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 1.0000, loss_cls: 0.3614, loss: 0.3614 +2025-06-24 23:56:50,804 - pyskl - INFO - Epoch [78][300/1281] lr: 1.192e-02, eta: 10:57:32, time: 0.363, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.3065, loss: 0.3065 +2025-06-24 23:57:39,291 - pyskl - INFO - Epoch [78][400/1281] lr: 1.190e-02, eta: 10:56:55, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9975, loss_cls: 0.3326, loss: 0.3326 +2025-06-24 23:58:28,633 - pyskl - INFO - Epoch [78][500/1281] lr: 1.187e-02, eta: 10:56:20, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9994, loss_cls: 0.3448, loss: 0.3448 +2025-06-24 23:59:17,922 - pyskl - INFO - Epoch [78][600/1281] lr: 1.185e-02, eta: 10:55:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9988, loss_cls: 0.3878, loss: 0.3878 +2025-06-25 00:00:06,698 - pyskl - INFO - Epoch [78][700/1281] lr: 1.183e-02, eta: 10:55:07, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9988, loss_cls: 0.3767, loss: 0.3767 +2025-06-25 00:00:56,027 - pyskl - INFO - Epoch [78][800/1281] lr: 1.181e-02, eta: 10:54:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3559, loss: 0.3559 +2025-06-25 00:01:45,266 - pyskl - INFO - Epoch [78][900/1281] lr: 1.179e-02, eta: 10:53:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 1.0000, loss_cls: 0.3294, loss: 0.3294 +2025-06-25 00:02:34,605 - pyskl - INFO - Epoch [78][1000/1281] lr: 1.177e-02, eta: 10:53:20, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9994, loss_cls: 0.3529, loss: 0.3529 +2025-06-25 00:03:23,636 - pyskl - INFO - Epoch [78][1100/1281] lr: 1.175e-02, eta: 10:52:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9988, loss_cls: 0.3728, loss: 0.3728 +2025-06-25 00:04:12,726 - pyskl - INFO - Epoch [78][1200/1281] lr: 1.173e-02, eta: 10:52:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9988, loss_cls: 0.3552, loss: 0.3552 +2025-06-25 00:04:52,703 - pyskl - INFO - Saving checkpoint at 78 epochs +2025-06-25 00:05:51,173 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:05:51,227 - pyskl - INFO - +top1_acc 0.8903 +top5_acc 0.9934 +2025-06-25 00:05:51,227 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:05:51,233 - pyskl - INFO - +mean_acc 0.8673 +2025-06-25 00:05:51,235 - pyskl - INFO - Epoch(val) [78][533] top1_acc: 0.8903, top5_acc: 0.9934, mean_class_accuracy: 0.8673 +2025-06-25 00:06:55,402 - pyskl - INFO - Epoch [79][100/1281] lr: 1.169e-02, eta: 10:50:39, time: 0.642, data_time: 0.189, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2945, loss: 0.2945 +2025-06-25 00:07:35,501 - pyskl - INFO - Epoch [79][200/1281] lr: 1.167e-02, eta: 10:49:55, time: 0.401, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9981, loss_cls: 0.3727, loss: 0.3727 +2025-06-25 00:08:13,538 - pyskl - INFO - Epoch [79][300/1281] lr: 1.165e-02, eta: 10:49:08, time: 0.380, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9988, loss_cls: 0.3572, loss: 0.3572 +2025-06-25 00:09:02,441 - pyskl - INFO - Epoch [79][400/1281] lr: 1.163e-02, eta: 10:48:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9994, loss_cls: 0.3616, loss: 0.3616 +2025-06-25 00:09:51,308 - pyskl - INFO - Epoch [79][500/1281] lr: 1.161e-02, eta: 10:47:55, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 1.0000, loss_cls: 0.3429, loss: 0.3429 +2025-06-25 00:10:40,175 - pyskl - INFO - Epoch [79][600/1281] lr: 1.159e-02, eta: 10:47:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 1.0000, loss_cls: 0.3206, loss: 0.3206 +2025-06-25 00:11:29,118 - pyskl - INFO - Epoch [79][700/1281] lr: 1.157e-02, eta: 10:46:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9988, loss_cls: 0.3612, loss: 0.3612 +2025-06-25 00:12:18,003 - pyskl - INFO - Epoch [79][800/1281] lr: 1.155e-02, eta: 10:46:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3793, loss: 0.3793 +2025-06-25 00:13:06,918 - pyskl - INFO - Epoch [79][900/1281] lr: 1.153e-02, eta: 10:45:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9981, loss_cls: 0.3697, loss: 0.3697 +2025-06-25 00:13:55,747 - pyskl - INFO - Epoch [79][1000/1281] lr: 1.151e-02, eta: 10:44:53, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3932, loss: 0.3932 +2025-06-25 00:14:44,675 - pyskl - INFO - Epoch [79][1100/1281] lr: 1.149e-02, eta: 10:44:16, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.3371, loss: 0.3371 +2025-06-25 00:15:33,681 - pyskl - INFO - Epoch [79][1200/1281] lr: 1.147e-02, eta: 10:43:40, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9981, loss_cls: 0.3905, loss: 0.3905 +2025-06-25 00:16:13,774 - pyskl - INFO - Saving checkpoint at 79 epochs +2025-06-25 00:17:12,326 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:17:12,381 - pyskl - INFO - +top1_acc 0.8875 +top5_acc 0.9924 +2025-06-25 00:17:12,381 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:17:12,387 - pyskl - INFO - +mean_acc 0.8340 +2025-06-25 00:17:12,389 - pyskl - INFO - Epoch(val) [79][533] top1_acc: 0.8875, top5_acc: 0.9924, mean_class_accuracy: 0.8340 +2025-06-25 00:18:13,679 - pyskl - INFO - Epoch [80][100/1281] lr: 1.143e-02, eta: 10:42:09, time: 0.613, data_time: 0.191, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9994, loss_cls: 0.3218, loss: 0.3218 +2025-06-25 00:18:54,262 - pyskl - INFO - Epoch [80][200/1281] lr: 1.141e-02, eta: 10:41:25, time: 0.406, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 1.0000, loss_cls: 0.2932, loss: 0.2932 +2025-06-25 00:19:29,683 - pyskl - INFO - Epoch [80][300/1281] lr: 1.139e-02, eta: 10:40:37, time: 0.354, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9981, loss_cls: 0.3265, loss: 0.3265 +2025-06-25 00:20:18,695 - pyskl - INFO - Epoch [80][400/1281] lr: 1.137e-02, eta: 10:40:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9981, loss_cls: 0.3368, loss: 0.3368 +2025-06-25 00:21:07,661 - pyskl - INFO - Epoch [80][500/1281] lr: 1.135e-02, eta: 10:39:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9975, loss_cls: 0.3787, loss: 0.3787 +2025-06-25 00:21:56,896 - pyskl - INFO - Epoch [80][600/1281] lr: 1.133e-02, eta: 10:38:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.3539, loss: 0.3539 +2025-06-25 00:22:46,103 - pyskl - INFO - Epoch [80][700/1281] lr: 1.131e-02, eta: 10:38:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.3252, loss: 0.3252 +2025-06-25 00:23:35,401 - pyskl - INFO - Epoch [80][800/1281] lr: 1.129e-02, eta: 10:37:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.3942, loss: 0.3942 +2025-06-25 00:24:24,376 - pyskl - INFO - Epoch [80][900/1281] lr: 1.127e-02, eta: 10:36:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9969, loss_cls: 0.4147, loss: 0.4147 +2025-06-25 00:25:13,329 - pyskl - INFO - Epoch [80][1000/1281] lr: 1.125e-02, eta: 10:36:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9994, loss_cls: 0.3533, loss: 0.3533 +2025-06-25 00:26:02,395 - pyskl - INFO - Epoch [80][1100/1281] lr: 1.123e-02, eta: 10:35:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9981, loss_cls: 0.3999, loss: 0.3999 +2025-06-25 00:26:51,453 - pyskl - INFO - Epoch [80][1200/1281] lr: 1.121e-02, eta: 10:35:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3484, loss: 0.3484 +2025-06-25 00:27:31,329 - pyskl - INFO - Saving checkpoint at 80 epochs +2025-06-25 00:28:29,812 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:28:29,880 - pyskl - INFO - +top1_acc 0.8883 +top5_acc 0.9937 +2025-06-25 00:28:29,880 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:28:29,887 - pyskl - INFO - +mean_acc 0.8609 +2025-06-25 00:28:29,889 - pyskl - INFO - Epoch(val) [80][533] top1_acc: 0.8883, top5_acc: 0.9937, mean_class_accuracy: 0.8609 +2025-06-25 00:29:33,314 - pyskl - INFO - Epoch [81][100/1281] lr: 1.117e-02, eta: 10:33:39, time: 0.634, data_time: 0.188, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3329, loss: 0.3329 +2025-06-25 00:30:14,195 - pyskl - INFO - Epoch [81][200/1281] lr: 1.115e-02, eta: 10:32:55, time: 0.409, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9994, loss_cls: 0.3080, loss: 0.3080 +2025-06-25 00:30:49,552 - pyskl - INFO - Epoch [81][300/1281] lr: 1.113e-02, eta: 10:32:07, time: 0.354, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.3013, loss: 0.3013 +2025-06-25 00:31:38,466 - pyskl - INFO - Epoch [81][400/1281] lr: 1.111e-02, eta: 10:31:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9969, loss_cls: 0.2978, loss: 0.2978 +2025-06-25 00:32:27,591 - pyskl - INFO - Epoch [81][500/1281] lr: 1.109e-02, eta: 10:30:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9981, loss_cls: 0.3226, loss: 0.3226 +2025-06-25 00:33:16,938 - pyskl - INFO - Epoch [81][600/1281] lr: 1.107e-02, eta: 10:30:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.3151, loss: 0.3151 +2025-06-25 00:34:05,757 - pyskl - INFO - Epoch [81][700/1281] lr: 1.105e-02, eta: 10:29:40, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9981, loss_cls: 0.3648, loss: 0.3648 +2025-06-25 00:34:54,342 - pyskl - INFO - Epoch [81][800/1281] lr: 1.103e-02, eta: 10:29:02, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9969, loss_cls: 0.3084, loss: 0.3084 +2025-06-25 00:35:43,033 - pyskl - INFO - Epoch [81][900/1281] lr: 1.101e-02, eta: 10:28:25, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3530, loss: 0.3530 +2025-06-25 00:36:31,816 - pyskl - INFO - Epoch [81][1000/1281] lr: 1.099e-02, eta: 10:27:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9988, loss_cls: 0.3427, loss: 0.3427 +2025-06-25 00:37:20,813 - pyskl - INFO - Epoch [81][1100/1281] lr: 1.097e-02, eta: 10:27:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9962, loss_cls: 0.3793, loss: 0.3793 +2025-06-25 00:38:09,526 - pyskl - INFO - Epoch [81][1200/1281] lr: 1.095e-02, eta: 10:26:34, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9975, loss_cls: 0.4225, loss: 0.4225 +2025-06-25 00:38:49,745 - pyskl - INFO - Saving checkpoint at 81 epochs +2025-06-25 00:39:49,071 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:39:49,138 - pyskl - INFO - +top1_acc 0.9027 +top5_acc 0.9942 +2025-06-25 00:39:49,139 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:39:49,146 - pyskl - INFO - +mean_acc 0.8632 +2025-06-25 00:39:49,151 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_77.pth was removed +2025-06-25 00:39:49,359 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_81.pth. +2025-06-25 00:39:49,360 - pyskl - INFO - Best top1_acc is 0.9027 at 81 epoch. +2025-06-25 00:39:49,363 - pyskl - INFO - Epoch(val) [81][533] top1_acc: 0.9027, top5_acc: 0.9942, mean_class_accuracy: 0.8632 +2025-06-25 00:40:53,307 - pyskl - INFO - Epoch [82][100/1281] lr: 1.091e-02, eta: 10:25:06, time: 0.639, data_time: 0.189, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2500, loss: 0.2500 +2025-06-25 00:41:33,471 - pyskl - INFO - Epoch [82][200/1281] lr: 1.089e-02, eta: 10:24:22, time: 0.402, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2998, loss: 0.2998 +2025-06-25 00:42:10,424 - pyskl - INFO - Epoch [82][300/1281] lr: 1.087e-02, eta: 10:23:35, time: 0.370, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9988, loss_cls: 0.3470, loss: 0.3470 +2025-06-25 00:42:59,691 - pyskl - INFO - Epoch [82][400/1281] lr: 1.085e-02, eta: 10:22:58, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3338, loss: 0.3338 +2025-06-25 00:43:48,859 - pyskl - INFO - Epoch [82][500/1281] lr: 1.083e-02, eta: 10:22:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9988, loss_cls: 0.2807, loss: 0.2807 +2025-06-25 00:44:38,179 - pyskl - INFO - Epoch [82][600/1281] lr: 1.081e-02, eta: 10:21:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3239, loss: 0.3239 +2025-06-25 00:45:27,361 - pyskl - INFO - Epoch [82][700/1281] lr: 1.079e-02, eta: 10:21:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9994, loss_cls: 0.3877, loss: 0.3877 +2025-06-25 00:46:16,671 - pyskl - INFO - Epoch [82][800/1281] lr: 1.077e-02, eta: 10:20:31, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9994, loss_cls: 0.4073, loss: 0.4073 +2025-06-25 00:47:05,737 - pyskl - INFO - Epoch [82][900/1281] lr: 1.075e-02, eta: 10:19:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9988, loss_cls: 0.3489, loss: 0.3489 +2025-06-25 00:47:54,726 - pyskl - INFO - Epoch [82][1000/1281] lr: 1.073e-02, eta: 10:19:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.3277, loss: 0.3277 +2025-06-25 00:48:43,394 - pyskl - INFO - Epoch [82][1100/1281] lr: 1.071e-02, eta: 10:18:39, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9981, loss_cls: 0.3494, loss: 0.3494 +2025-06-25 00:49:32,325 - pyskl - INFO - Epoch [82][1200/1281] lr: 1.069e-02, eta: 10:18:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 1.0000, loss_cls: 0.3129, loss: 0.3129 +2025-06-25 00:50:12,275 - pyskl - INFO - Saving checkpoint at 82 epochs +2025-06-25 00:51:09,794 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:51:09,849 - pyskl - INFO - +top1_acc 0.8830 +top5_acc 0.9935 +2025-06-25 00:51:09,849 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:51:09,856 - pyskl - INFO - +mean_acc 0.8695 +2025-06-25 00:51:09,858 - pyskl - INFO - Epoch(val) [82][533] top1_acc: 0.8830, top5_acc: 0.9935, mean_class_accuracy: 0.8695 +2025-06-25 00:52:15,524 - pyskl - INFO - Epoch [83][100/1281] lr: 1.065e-02, eta: 10:16:36, time: 0.657, data_time: 0.184, memory: 4083, top1_acc: 0.9375, top5_acc: 1.0000, loss_cls: 0.3394, loss: 0.3394 +2025-06-25 00:52:54,051 - pyskl - INFO - Epoch [83][200/1281] lr: 1.063e-02, eta: 10:15:50, time: 0.385, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9988, loss_cls: 0.3304, loss: 0.3304 +2025-06-25 00:53:32,051 - pyskl - INFO - Epoch [83][300/1281] lr: 1.061e-02, eta: 10:15:03, time: 0.380, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.3060, loss: 0.3060 +2025-06-25 00:54:21,028 - pyskl - INFO - Epoch [83][400/1281] lr: 1.059e-02, eta: 10:14:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.3113, loss: 0.3113 +2025-06-25 00:55:10,424 - pyskl - INFO - Epoch [83][500/1281] lr: 1.057e-02, eta: 10:13:49, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 1.0000, loss_cls: 0.3120, loss: 0.3120 +2025-06-25 00:55:59,663 - pyskl - INFO - Epoch [83][600/1281] lr: 1.055e-02, eta: 10:13:12, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9988, loss_cls: 0.3551, loss: 0.3551 +2025-06-25 00:56:48,877 - pyskl - INFO - Epoch [83][700/1281] lr: 1.053e-02, eta: 10:12:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 1.0000, loss_cls: 0.2999, loss: 0.2999 +2025-06-25 00:57:38,136 - pyskl - INFO - Epoch [83][800/1281] lr: 1.051e-02, eta: 10:11:58, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9975, loss_cls: 0.3458, loss: 0.3458 +2025-06-25 00:58:27,065 - pyskl - INFO - Epoch [83][900/1281] lr: 1.049e-02, eta: 10:11:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3557, loss: 0.3557 +2025-06-25 00:59:16,140 - pyskl - INFO - Epoch [83][1000/1281] lr: 1.047e-02, eta: 10:10:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9994, loss_cls: 0.3235, loss: 0.3235 +2025-06-25 01:00:05,415 - pyskl - INFO - Epoch [83][1100/1281] lr: 1.045e-02, eta: 10:10:07, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9981, loss_cls: 0.3809, loss: 0.3809 +2025-06-25 01:00:54,284 - pyskl - INFO - Epoch [83][1200/1281] lr: 1.043e-02, eta: 10:09:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9994, loss_cls: 0.3273, loss: 0.3273 +2025-06-25 01:01:34,335 - pyskl - INFO - Saving checkpoint at 83 epochs +2025-06-25 01:02:32,574 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:02:32,629 - pyskl - INFO - +top1_acc 0.8808 +top5_acc 0.9896 +2025-06-25 01:02:32,630 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:02:32,637 - pyskl - INFO - +mean_acc 0.8364 +2025-06-25 01:02:32,639 - pyskl - INFO - Epoch(val) [83][533] top1_acc: 0.8808, top5_acc: 0.9896, mean_class_accuracy: 0.8364 +2025-06-25 01:03:33,623 - pyskl - INFO - Epoch [84][100/1281] lr: 1.040e-02, eta: 10:07:59, time: 0.610, data_time: 0.184, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2950, loss: 0.2950 +2025-06-25 01:04:14,309 - pyskl - INFO - Epoch [84][200/1281] lr: 1.038e-02, eta: 10:07:15, time: 0.407, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 1.0000, loss_cls: 0.3049, loss: 0.3049 +2025-06-25 01:04:49,325 - pyskl - INFO - Epoch [84][300/1281] lr: 1.036e-02, eta: 10:06:26, time: 0.350, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3276, loss: 0.3276 +2025-06-25 01:05:38,203 - pyskl - INFO - Epoch [84][400/1281] lr: 1.034e-02, eta: 10:05:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3569, loss: 0.3569 +2025-06-25 01:06:27,142 - pyskl - INFO - Epoch [84][500/1281] lr: 1.031e-02, eta: 10:05:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3281, loss: 0.3281 +2025-06-25 01:07:15,737 - pyskl - INFO - Epoch [84][600/1281] lr: 1.029e-02, eta: 10:04:34, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9981, loss_cls: 0.3299, loss: 0.3299 +2025-06-25 01:08:04,621 - pyskl - INFO - Epoch [84][700/1281] lr: 1.027e-02, eta: 10:03:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.2986, loss: 0.2986 +2025-06-25 01:08:53,423 - pyskl - INFO - Epoch [84][800/1281] lr: 1.025e-02, eta: 10:03:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3465, loss: 0.3465 +2025-06-25 01:09:42,517 - pyskl - INFO - Epoch [84][900/1281] lr: 1.023e-02, eta: 10:02:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2889, loss: 0.2889 +2025-06-25 01:10:31,161 - pyskl - INFO - Epoch [84][1000/1281] lr: 1.021e-02, eta: 10:02:03, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9988, loss_cls: 0.3080, loss: 0.3080 +2025-06-25 01:11:20,336 - pyskl - INFO - Epoch [84][1100/1281] lr: 1.019e-02, eta: 10:01:26, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9988, loss_cls: 0.3355, loss: 0.3355 +2025-06-25 01:12:09,174 - pyskl - INFO - Epoch [84][1200/1281] lr: 1.017e-02, eta: 10:00:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9975, loss_cls: 0.3928, loss: 0.3928 +2025-06-25 01:12:49,419 - pyskl - INFO - Saving checkpoint at 84 epochs +2025-06-25 01:13:47,354 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:13:47,410 - pyskl - INFO - +top1_acc 0.8846 +top5_acc 0.9951 +2025-06-25 01:13:47,410 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:13:47,418 - pyskl - INFO - +mean_acc 0.8588 +2025-06-25 01:13:47,419 - pyskl - INFO - Epoch(val) [84][533] top1_acc: 0.8846, top5_acc: 0.9951, mean_class_accuracy: 0.8588 +2025-06-25 01:14:51,279 - pyskl - INFO - Epoch [85][100/1281] lr: 1.014e-02, eta: 9:59:21, time: 0.639, data_time: 0.188, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3106, loss: 0.3106 +2025-06-25 01:15:33,176 - pyskl - INFO - Epoch [85][200/1281] lr: 1.012e-02, eta: 9:58:38, time: 0.419, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3258, loss: 0.3258 +2025-06-25 01:16:10,123 - pyskl - INFO - Epoch [85][300/1281] lr: 1.010e-02, eta: 9:57:51, time: 0.369, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.3171, loss: 0.3171 +2025-06-25 01:16:59,212 - pyskl - INFO - Epoch [85][400/1281] lr: 1.008e-02, eta: 9:57:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9981, loss_cls: 0.2970, loss: 0.2970 +2025-06-25 01:17:48,182 - pyskl - INFO - Epoch [85][500/1281] lr: 1.006e-02, eta: 9:56:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.3093, loss: 0.3093 +2025-06-25 01:18:36,804 - pyskl - INFO - Epoch [85][600/1281] lr: 1.004e-02, eta: 9:55:58, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9988, loss_cls: 0.2852, loss: 0.2852 +2025-06-25 01:19:25,691 - pyskl - INFO - Epoch [85][700/1281] lr: 1.002e-02, eta: 9:55:20, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9988, loss_cls: 0.3699, loss: 0.3699 +2025-06-25 01:20:14,641 - pyskl - INFO - Epoch [85][800/1281] lr: 9.998e-03, eta: 9:54:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9981, loss_cls: 0.3323, loss: 0.3323 +2025-06-25 01:21:03,466 - pyskl - INFO - Epoch [85][900/1281] lr: 9.978e-03, eta: 9:54:04, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3132, loss: 0.3132 +2025-06-25 01:21:52,200 - pyskl - INFO - Epoch [85][1000/1281] lr: 9.958e-03, eta: 9:53:27, time: 0.487, data_time: 0.001, memory: 4083, top1_acc: 0.9556, top5_acc: 1.0000, loss_cls: 0.2658, loss: 0.2658 +2025-06-25 01:22:41,100 - pyskl - INFO - Epoch [85][1100/1281] lr: 9.937e-03, eta: 9:52:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9988, loss_cls: 0.3207, loss: 0.3207 +2025-06-25 01:23:30,127 - pyskl - INFO - Epoch [85][1200/1281] lr: 9.917e-03, eta: 9:52:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9975, loss_cls: 0.3481, loss: 0.3481 +2025-06-25 01:24:10,029 - pyskl - INFO - Saving checkpoint at 85 epochs +2025-06-25 01:25:08,589 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:25:08,646 - pyskl - INFO - +top1_acc 0.8926 +top5_acc 0.9925 +2025-06-25 01:25:08,646 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:25:08,652 - pyskl - INFO - +mean_acc 0.8587 +2025-06-25 01:25:08,654 - pyskl - INFO - Epoch(val) [85][533] top1_acc: 0.8926, top5_acc: 0.9925, mean_class_accuracy: 0.8587 +2025-06-25 01:26:10,306 - pyskl - INFO - Epoch [86][100/1281] lr: 9.881e-03, eta: 9:50:42, time: 0.616, data_time: 0.187, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2556, loss: 0.2556 +2025-06-25 01:26:51,232 - pyskl - INFO - Epoch [86][200/1281] lr: 9.861e-03, eta: 9:49:58, time: 0.409, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9988, loss_cls: 0.2994, loss: 0.2994 +2025-06-25 01:27:26,055 - pyskl - INFO - Epoch [86][300/1281] lr: 9.841e-03, eta: 9:49:10, time: 0.348, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9988, loss_cls: 0.2953, loss: 0.2953 +2025-06-25 01:28:15,315 - pyskl - INFO - Epoch [86][400/1281] lr: 9.821e-03, eta: 9:48:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9988, loss_cls: 0.3168, loss: 0.3168 +2025-06-25 01:29:04,040 - pyskl - INFO - Epoch [86][500/1281] lr: 9.801e-03, eta: 9:47:54, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3523, loss: 0.3523 +2025-06-25 01:29:52,951 - pyskl - INFO - Epoch [86][600/1281] lr: 9.781e-03, eta: 9:47:16, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9988, loss_cls: 0.2946, loss: 0.2946 +2025-06-25 01:30:41,679 - pyskl - INFO - Epoch [86][700/1281] lr: 9.762e-03, eta: 9:46:38, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9988, loss_cls: 0.2898, loss: 0.2898 +2025-06-25 01:31:30,487 - pyskl - INFO - Epoch [86][800/1281] lr: 9.742e-03, eta: 9:46:00, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 1.0000, loss_cls: 0.3387, loss: 0.3387 +2025-06-25 01:32:19,411 - pyskl - INFO - Epoch [86][900/1281] lr: 9.722e-03, eta: 9:45:22, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9981, loss_cls: 0.3393, loss: 0.3393 +2025-06-25 01:33:08,535 - pyskl - INFO - Epoch [86][1000/1281] lr: 9.702e-03, eta: 9:44:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9988, loss_cls: 0.3534, loss: 0.3534 +2025-06-25 01:33:57,524 - pyskl - INFO - Epoch [86][1100/1281] lr: 9.682e-03, eta: 9:44:07, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9981, loss_cls: 0.3178, loss: 0.3178 +2025-06-25 01:34:46,040 - pyskl - INFO - Epoch [86][1200/1281] lr: 9.662e-03, eta: 9:43:28, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9975, loss_cls: 0.2918, loss: 0.2918 +2025-06-25 01:35:26,156 - pyskl - INFO - Saving checkpoint at 86 epochs +2025-06-25 01:36:23,741 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:36:23,796 - pyskl - INFO - +top1_acc 0.8966 +top5_acc 0.9917 +2025-06-25 01:36:23,797 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:36:23,803 - pyskl - INFO - +mean_acc 0.8612 +2025-06-25 01:36:23,804 - pyskl - INFO - Epoch(val) [86][533] top1_acc: 0.8966, top5_acc: 0.9917, mean_class_accuracy: 0.8612 +2025-06-25 01:37:22,454 - pyskl - INFO - Epoch [87][100/1281] lr: 9.626e-03, eta: 9:41:57, time: 0.586, data_time: 0.178, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2639, loss: 0.2639 +2025-06-25 01:38:10,159 - pyskl - INFO - Epoch [87][200/1281] lr: 9.606e-03, eta: 9:41:18, time: 0.477, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2787, loss: 0.2787 +2025-06-25 01:38:43,294 - pyskl - INFO - Epoch [87][300/1281] lr: 9.586e-03, eta: 9:40:29, time: 0.331, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 1.0000, loss_cls: 0.2985, loss: 0.2985 +2025-06-25 01:39:32,185 - pyskl - INFO - Epoch [87][400/1281] lr: 9.566e-03, eta: 9:39:51, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2755, loss: 0.2755 +2025-06-25 01:40:21,182 - pyskl - INFO - Epoch [87][500/1281] lr: 9.546e-03, eta: 9:39:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9994, loss_cls: 0.3138, loss: 0.3138 +2025-06-25 01:41:10,033 - pyskl - INFO - Epoch [87][600/1281] lr: 9.527e-03, eta: 9:38:35, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2671, loss: 0.2671 +2025-06-25 01:41:59,013 - pyskl - INFO - Epoch [87][700/1281] lr: 9.507e-03, eta: 9:37:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9981, loss_cls: 0.2857, loss: 0.2857 +2025-06-25 01:42:48,050 - pyskl - INFO - Epoch [87][800/1281] lr: 9.487e-03, eta: 9:37:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9981, loss_cls: 0.2833, loss: 0.2833 +2025-06-25 01:43:37,426 - pyskl - INFO - Epoch [87][900/1281] lr: 9.467e-03, eta: 9:36:41, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.3070, loss: 0.3070 +2025-06-25 01:44:26,142 - pyskl - INFO - Epoch [87][1000/1281] lr: 9.447e-03, eta: 9:36:03, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9988, loss_cls: 0.2997, loss: 0.2997 +2025-06-25 01:45:15,118 - pyskl - INFO - Epoch [87][1100/1281] lr: 9.427e-03, eta: 9:35:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.3215, loss: 0.3215 +2025-06-25 01:46:03,903 - pyskl - INFO - Epoch [87][1200/1281] lr: 9.408e-03, eta: 9:34:46, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9988, loss_cls: 0.3446, loss: 0.3446 +2025-06-25 01:46:44,049 - pyskl - INFO - Saving checkpoint at 87 epochs +2025-06-25 01:47:42,025 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:47:42,081 - pyskl - INFO - +top1_acc 0.8952 +top5_acc 0.9926 +2025-06-25 01:47:42,081 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:47:42,087 - pyskl - INFO - +mean_acc 0.8512 +2025-06-25 01:47:42,089 - pyskl - INFO - Epoch(val) [87][533] top1_acc: 0.8952, top5_acc: 0.9926, mean_class_accuracy: 0.8512 +2025-06-25 01:48:40,187 - pyskl - INFO - Epoch [88][100/1281] lr: 9.372e-03, eta: 9:33:15, time: 0.581, data_time: 0.182, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2709, loss: 0.2709 +2025-06-25 01:49:30,126 - pyskl - INFO - Epoch [88][200/1281] lr: 9.352e-03, eta: 9:32:38, time: 0.499, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2507, loss: 0.2507 +2025-06-25 01:50:02,268 - pyskl - INFO - Epoch [88][300/1281] lr: 9.332e-03, eta: 9:31:47, time: 0.321, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2606, loss: 0.2606 +2025-06-25 01:50:51,024 - pyskl - INFO - Epoch [88][400/1281] lr: 9.312e-03, eta: 9:31:09, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2565, loss: 0.2565 +2025-06-25 01:51:39,677 - pyskl - INFO - Epoch [88][500/1281] lr: 9.293e-03, eta: 9:30:31, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2501, loss: 0.2501 +2025-06-25 01:52:28,496 - pyskl - INFO - Epoch [88][600/1281] lr: 9.273e-03, eta: 9:29:52, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3009, loss: 0.3009 +2025-06-25 01:53:17,548 - pyskl - INFO - Epoch [88][700/1281] lr: 9.253e-03, eta: 9:29:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9975, loss_cls: 0.3016, loss: 0.3016 +2025-06-25 01:54:06,315 - pyskl - INFO - Epoch [88][800/1281] lr: 9.233e-03, eta: 9:28:36, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.2949, loss: 0.2949 +2025-06-25 01:54:55,256 - pyskl - INFO - Epoch [88][900/1281] lr: 9.214e-03, eta: 9:27:58, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9981, loss_cls: 0.2630, loss: 0.2630 +2025-06-25 01:55:43,976 - pyskl - INFO - Epoch [88][1000/1281] lr: 9.194e-03, eta: 9:27:19, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9988, loss_cls: 0.3411, loss: 0.3411 +2025-06-25 01:56:32,920 - pyskl - INFO - Epoch [88][1100/1281] lr: 9.174e-03, eta: 9:26:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2557, loss: 0.2557 +2025-06-25 01:57:21,896 - pyskl - INFO - Epoch [88][1200/1281] lr: 9.155e-03, eta: 9:26:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9981, loss_cls: 0.2826, loss: 0.2826 +2025-06-25 01:58:02,021 - pyskl - INFO - Saving checkpoint at 88 epochs +2025-06-25 01:59:00,058 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:59:00,116 - pyskl - INFO - +top1_acc 0.8848 +top5_acc 0.9937 +2025-06-25 01:59:00,116 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:59:00,123 - pyskl - INFO - +mean_acc 0.8534 +2025-06-25 01:59:00,124 - pyskl - INFO - Epoch(val) [88][533] top1_acc: 0.8848, top5_acc: 0.9937, mean_class_accuracy: 0.8534 +2025-06-25 01:59:57,664 - pyskl - INFO - Epoch [89][100/1281] lr: 9.119e-03, eta: 9:24:31, time: 0.575, data_time: 0.185, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9988, loss_cls: 0.2843, loss: 0.2843 +2025-06-25 02:00:47,472 - pyskl - INFO - Epoch [89][200/1281] lr: 9.099e-03, eta: 9:23:54, time: 0.498, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 1.0000, loss_cls: 0.2390, loss: 0.2390 +2025-06-25 02:01:19,101 - pyskl - INFO - Epoch [89][300/1281] lr: 9.080e-03, eta: 9:23:03, time: 0.316, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 1.0000, loss_cls: 0.3080, loss: 0.3080 +2025-06-25 02:02:07,428 - pyskl - INFO - Epoch [89][400/1281] lr: 9.060e-03, eta: 9:22:24, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.3328, loss: 0.3328 +2025-06-25 02:02:56,563 - pyskl - INFO - Epoch [89][500/1281] lr: 9.040e-03, eta: 9:21:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9988, loss_cls: 0.3035, loss: 0.3035 +2025-06-25 02:03:45,679 - pyskl - INFO - Epoch [89][600/1281] lr: 9.021e-03, eta: 9:21:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2943, loss: 0.2943 +2025-06-25 02:04:34,373 - pyskl - INFO - Epoch [89][700/1281] lr: 9.001e-03, eta: 9:20:29, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9994, loss_cls: 0.2545, loss: 0.2545 +2025-06-25 02:05:23,101 - pyskl - INFO - Epoch [89][800/1281] lr: 8.982e-03, eta: 9:19:51, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 1.0000, loss_cls: 0.2623, loss: 0.2623 +2025-06-25 02:06:11,976 - pyskl - INFO - Epoch [89][900/1281] lr: 8.962e-03, eta: 9:19:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9994, loss_cls: 0.3147, loss: 0.3147 +2025-06-25 02:07:00,790 - pyskl - INFO - Epoch [89][1000/1281] lr: 8.942e-03, eta: 9:18:34, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9994, loss_cls: 0.2729, loss: 0.2729 +2025-06-25 02:07:49,979 - pyskl - INFO - Epoch [89][1100/1281] lr: 8.923e-03, eta: 9:17:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9988, loss_cls: 0.2716, loss: 0.2716 +2025-06-25 02:08:38,604 - pyskl - INFO - Epoch [89][1200/1281] lr: 8.903e-03, eta: 9:17:17, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9981, loss_cls: 0.3280, loss: 0.3280 +2025-06-25 02:09:18,683 - pyskl - INFO - Saving checkpoint at 89 epochs +2025-06-25 02:10:16,789 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:10:16,844 - pyskl - INFO - +top1_acc 0.9075 +top5_acc 0.9938 +2025-06-25 02:10:16,844 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:10:16,850 - pyskl - INFO - +mean_acc 0.8673 +2025-06-25 02:10:16,854 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_81.pth was removed +2025-06-25 02:10:17,031 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_89.pth. +2025-06-25 02:10:17,031 - pyskl - INFO - Best top1_acc is 0.9075 at 89 epoch. +2025-06-25 02:10:17,034 - pyskl - INFO - Epoch(val) [89][533] top1_acc: 0.9075, top5_acc: 0.9938, mean_class_accuracy: 0.8673 +2025-06-25 02:11:15,842 - pyskl - INFO - Epoch [90][100/1281] lr: 8.868e-03, eta: 9:15:47, time: 0.588, data_time: 0.186, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2321, loss: 0.2321 +2025-06-25 02:12:05,339 - pyskl - INFO - Epoch [90][200/1281] lr: 8.848e-03, eta: 9:15:09, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 0.9994, loss_cls: 0.1912, loss: 0.1912 +2025-06-25 02:12:36,851 - pyskl - INFO - Epoch [90][300/1281] lr: 8.829e-03, eta: 9:14:18, time: 0.315, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2177, loss: 0.2177 +2025-06-25 02:13:25,505 - pyskl - INFO - Epoch [90][400/1281] lr: 8.809e-03, eta: 9:13:39, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2355, loss: 0.2355 +2025-06-25 02:14:14,405 - pyskl - INFO - Epoch [90][500/1281] lr: 8.790e-03, eta: 9:13:01, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2144, loss: 0.2144 +2025-06-25 02:15:03,507 - pyskl - INFO - Epoch [90][600/1281] lr: 8.770e-03, eta: 9:12:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9975, loss_cls: 0.3112, loss: 0.3112 +2025-06-25 02:15:52,449 - pyskl - INFO - Epoch [90][700/1281] lr: 8.751e-03, eta: 9:11:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.3140, loss: 0.3140 +2025-06-25 02:16:41,314 - pyskl - INFO - Epoch [90][800/1281] lr: 8.731e-03, eta: 9:11:05, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 1.0000, loss_cls: 0.3076, loss: 0.3076 +2025-06-25 02:17:30,110 - pyskl - INFO - Epoch [90][900/1281] lr: 8.712e-03, eta: 9:10:27, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3022, loss: 0.3022 +2025-06-25 02:18:19,115 - pyskl - INFO - Epoch [90][1000/1281] lr: 8.692e-03, eta: 9:09:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2727, loss: 0.2727 +2025-06-25 02:19:07,964 - pyskl - INFO - Epoch [90][1100/1281] lr: 8.673e-03, eta: 9:09:09, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9981, loss_cls: 0.2887, loss: 0.2887 +2025-06-25 02:19:57,249 - pyskl - INFO - Epoch [90][1200/1281] lr: 8.653e-03, eta: 9:08:31, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9981, loss_cls: 0.3354, loss: 0.3354 +2025-06-25 02:20:37,540 - pyskl - INFO - Saving checkpoint at 90 epochs +2025-06-25 02:21:35,341 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:21:35,398 - pyskl - INFO - +top1_acc 0.9017 +top5_acc 0.9932 +2025-06-25 02:21:35,398 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:21:35,405 - pyskl - INFO - +mean_acc 0.8661 +2025-06-25 02:21:35,406 - pyskl - INFO - Epoch(val) [90][533] top1_acc: 0.9017, top5_acc: 0.9932, mean_class_accuracy: 0.8661 +2025-06-25 02:22:36,580 - pyskl - INFO - Epoch [91][100/1281] lr: 8.618e-03, eta: 9:07:03, time: 0.612, data_time: 0.190, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2451, loss: 0.2451 +2025-06-25 02:23:25,062 - pyskl - INFO - Epoch [91][200/1281] lr: 8.599e-03, eta: 9:06:24, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2511, loss: 0.2511 +2025-06-25 02:23:58,616 - pyskl - INFO - Epoch [91][300/1281] lr: 8.579e-03, eta: 9:05:35, time: 0.336, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9994, loss_cls: 0.2555, loss: 0.2555 +2025-06-25 02:24:47,807 - pyskl - INFO - Epoch [91][400/1281] lr: 8.560e-03, eta: 9:04:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2397, loss: 0.2397 +2025-06-25 02:25:36,919 - pyskl - INFO - Epoch [91][500/1281] lr: 8.540e-03, eta: 9:04:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 1.0000, loss_cls: 0.3057, loss: 0.3057 +2025-06-25 02:26:26,112 - pyskl - INFO - Epoch [91][600/1281] lr: 8.521e-03, eta: 9:03:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9994, loss_cls: 0.2586, loss: 0.2586 +2025-06-25 02:27:15,181 - pyskl - INFO - Epoch [91][700/1281] lr: 8.502e-03, eta: 9:03:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9981, loss_cls: 0.3391, loss: 0.3391 +2025-06-25 02:28:03,732 - pyskl - INFO - Epoch [91][800/1281] lr: 8.482e-03, eta: 9:02:22, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3342, loss: 0.3342 +2025-06-25 02:28:52,603 - pyskl - INFO - Epoch [91][900/1281] lr: 8.463e-03, eta: 9:01:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9988, loss_cls: 0.2729, loss: 0.2729 +2025-06-25 02:29:41,819 - pyskl - INFO - Epoch [91][1000/1281] lr: 8.444e-03, eta: 9:01:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9981, loss_cls: 0.2818, loss: 0.2818 +2025-06-25 02:30:30,505 - pyskl - INFO - Epoch [91][1100/1281] lr: 8.424e-03, eta: 9:00:25, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9981, loss_cls: 0.2678, loss: 0.2678 +2025-06-25 02:31:19,262 - pyskl - INFO - Epoch [91][1200/1281] lr: 8.405e-03, eta: 8:59:46, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9981, loss_cls: 0.3028, loss: 0.3028 +2025-06-25 02:31:59,397 - pyskl - INFO - Saving checkpoint at 91 epochs +2025-06-25 02:32:57,011 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:32:57,072 - pyskl - INFO - +top1_acc 0.8873 +top5_acc 0.9942 +2025-06-25 02:32:57,072 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:32:57,079 - pyskl - INFO - +mean_acc 0.8593 +2025-06-25 02:32:57,081 - pyskl - INFO - Epoch(val) [91][533] top1_acc: 0.8873, top5_acc: 0.9942, mean_class_accuracy: 0.8593 +2025-06-25 02:33:54,930 - pyskl - INFO - Epoch [92][100/1281] lr: 8.370e-03, eta: 8:58:16, time: 0.578, data_time: 0.186, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9988, loss_cls: 0.2468, loss: 0.2468 +2025-06-25 02:34:44,201 - pyskl - INFO - Epoch [92][200/1281] lr: 8.351e-03, eta: 8:57:38, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2811, loss: 0.2811 +2025-06-25 02:35:15,607 - pyskl - INFO - Epoch [92][300/1281] lr: 8.332e-03, eta: 8:56:47, time: 0.314, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 1.0000, loss_cls: 0.2819, loss: 0.2819 +2025-06-25 02:36:04,452 - pyskl - INFO - Epoch [92][400/1281] lr: 8.312e-03, eta: 8:56:08, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9988, loss_cls: 0.2855, loss: 0.2855 +2025-06-25 02:36:53,183 - pyskl - INFO - Epoch [92][500/1281] lr: 8.293e-03, eta: 8:55:30, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9981, loss_cls: 0.2466, loss: 0.2466 +2025-06-25 02:37:42,035 - pyskl - INFO - Epoch [92][600/1281] lr: 8.274e-03, eta: 8:54:51, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2485, loss: 0.2485 +2025-06-25 02:38:31,052 - pyskl - INFO - Epoch [92][700/1281] lr: 8.255e-03, eta: 8:54:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2123, loss: 0.2123 +2025-06-25 02:39:19,974 - pyskl - INFO - Epoch [92][800/1281] lr: 8.235e-03, eta: 8:53:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 1.0000, loss_cls: 0.2673, loss: 0.2673 +2025-06-25 02:40:09,258 - pyskl - INFO - Epoch [92][900/1281] lr: 8.216e-03, eta: 8:52:54, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9994, loss_cls: 0.2847, loss: 0.2847 +2025-06-25 02:40:58,253 - pyskl - INFO - Epoch [92][1000/1281] lr: 8.197e-03, eta: 8:52:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9981, loss_cls: 0.3061, loss: 0.3061 +2025-06-25 02:41:47,451 - pyskl - INFO - Epoch [92][1100/1281] lr: 8.178e-03, eta: 8:51:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2568, loss: 0.2568 +2025-06-25 02:42:36,429 - pyskl - INFO - Epoch [92][1200/1281] lr: 8.159e-03, eta: 8:50:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9988, loss_cls: 0.2350, loss: 0.2350 +2025-06-25 02:43:16,779 - pyskl - INFO - Saving checkpoint at 92 epochs +2025-06-25 02:44:14,932 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:44:14,988 - pyskl - INFO - +top1_acc 0.9039 +top5_acc 0.9946 +2025-06-25 02:44:14,988 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:44:14,995 - pyskl - INFO - +mean_acc 0.8778 +2025-06-25 02:44:14,997 - pyskl - INFO - Epoch(val) [92][533] top1_acc: 0.9039, top5_acc: 0.9946, mean_class_accuracy: 0.8778 +2025-06-25 02:45:13,514 - pyskl - INFO - Epoch [93][100/1281] lr: 8.124e-03, eta: 8:49:28, time: 0.585, data_time: 0.185, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2482, loss: 0.2482 +2025-06-25 02:46:03,971 - pyskl - INFO - Epoch [93][200/1281] lr: 8.105e-03, eta: 8:48:50, time: 0.505, data_time: 0.001, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.2033, loss: 0.2033 +2025-06-25 02:46:36,509 - pyskl - INFO - Epoch [93][300/1281] lr: 8.086e-03, eta: 8:48:01, time: 0.325, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.2067, loss: 0.2067 +2025-06-25 02:47:25,291 - pyskl - INFO - Epoch [93][400/1281] lr: 8.067e-03, eta: 8:47:22, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.2135, loss: 0.2135 +2025-06-25 02:48:14,206 - pyskl - INFO - Epoch [93][500/1281] lr: 8.047e-03, eta: 8:46:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9988, loss_cls: 0.2670, loss: 0.2670 +2025-06-25 02:49:03,186 - pyskl - INFO - Epoch [93][600/1281] lr: 8.028e-03, eta: 8:46:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.2632, loss: 0.2632 +2025-06-25 02:49:52,101 - pyskl - INFO - Epoch [93][700/1281] lr: 8.009e-03, eta: 8:45:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.2964, loss: 0.2964 +2025-06-25 02:50:41,217 - pyskl - INFO - Epoch [93][800/1281] lr: 7.990e-03, eta: 8:44:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9981, loss_cls: 0.2428, loss: 0.2428 +2025-06-25 02:51:30,395 - pyskl - INFO - Epoch [93][900/1281] lr: 7.971e-03, eta: 8:44:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2568, loss: 0.2568 +2025-06-25 02:52:19,319 - pyskl - INFO - Epoch [93][1000/1281] lr: 7.952e-03, eta: 8:43:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2149, loss: 0.2149 +2025-06-25 02:53:08,099 - pyskl - INFO - Epoch [93][1100/1281] lr: 7.933e-03, eta: 8:42:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2837, loss: 0.2837 +2025-06-25 02:53:56,925 - pyskl - INFO - Epoch [93][1200/1281] lr: 7.914e-03, eta: 8:42:09, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2745, loss: 0.2745 +2025-06-25 02:54:37,217 - pyskl - INFO - Saving checkpoint at 93 epochs +2025-06-25 02:55:34,493 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:55:34,568 - pyskl - INFO - +top1_acc 0.9076 +top5_acc 0.9938 +2025-06-25 02:55:34,568 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:55:34,576 - pyskl - INFO - +mean_acc 0.8864 +2025-06-25 02:55:34,580 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_89.pth was removed +2025-06-25 02:55:34,746 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_93.pth. +2025-06-25 02:55:34,746 - pyskl - INFO - Best top1_acc is 0.9076 at 93 epoch. +2025-06-25 02:55:34,749 - pyskl - INFO - Epoch(val) [93][533] top1_acc: 0.9076, top5_acc: 0.9938, mean_class_accuracy: 0.8864 +2025-06-25 02:56:32,333 - pyskl - INFO - Epoch [94][100/1281] lr: 7.880e-03, eta: 8:40:39, time: 0.576, data_time: 0.178, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9994, loss_cls: 0.2511, loss: 0.2511 +2025-06-25 02:57:23,001 - pyskl - INFO - Epoch [94][200/1281] lr: 7.861e-03, eta: 8:40:01, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2168, loss: 0.2168 +2025-06-25 02:57:56,223 - pyskl - INFO - Epoch [94][300/1281] lr: 7.842e-03, eta: 8:39:13, time: 0.332, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9988, loss_cls: 0.2548, loss: 0.2548 +2025-06-25 02:58:45,041 - pyskl - INFO - Epoch [94][400/1281] lr: 7.823e-03, eta: 8:38:33, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2217, loss: 0.2217 +2025-06-25 02:59:34,023 - pyskl - INFO - Epoch [94][500/1281] lr: 7.804e-03, eta: 8:37:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2128, loss: 0.2128 +2025-06-25 03:00:23,245 - pyskl - INFO - Epoch [94][600/1281] lr: 7.785e-03, eta: 8:37:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2734, loss: 0.2734 +2025-06-25 03:01:12,535 - pyskl - INFO - Epoch [94][700/1281] lr: 7.766e-03, eta: 8:36:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9994, loss_cls: 0.2512, loss: 0.2512 +2025-06-25 03:02:01,568 - pyskl - INFO - Epoch [94][800/1281] lr: 7.747e-03, eta: 8:35:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2862, loss: 0.2862 +2025-06-25 03:02:50,671 - pyskl - INFO - Epoch [94][900/1281] lr: 7.728e-03, eta: 8:35:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9988, loss_cls: 0.2594, loss: 0.2594 +2025-06-25 03:03:39,481 - pyskl - INFO - Epoch [94][1000/1281] lr: 7.709e-03, eta: 8:34:39, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2639, loss: 0.2639 +2025-06-25 03:04:28,541 - pyskl - INFO - Epoch [94][1100/1281] lr: 7.690e-03, eta: 8:34:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 1.0000, loss_cls: 0.2830, loss: 0.2830 +2025-06-25 03:05:17,686 - pyskl - INFO - Epoch [94][1200/1281] lr: 7.672e-03, eta: 8:33:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9988, loss_cls: 0.2839, loss: 0.2839 +2025-06-25 03:05:57,904 - pyskl - INFO - Saving checkpoint at 94 epochs +2025-06-25 03:06:55,818 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:06:55,885 - pyskl - INFO - +top1_acc 0.9044 +top5_acc 0.9930 +2025-06-25 03:06:55,885 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:06:55,894 - pyskl - INFO - +mean_acc 0.8688 +2025-06-25 03:06:55,897 - pyskl - INFO - Epoch(val) [94][533] top1_acc: 0.9044, top5_acc: 0.9930, mean_class_accuracy: 0.8688 +2025-06-25 03:07:55,128 - pyskl - INFO - Epoch [95][100/1281] lr: 7.637e-03, eta: 8:31:52, time: 0.592, data_time: 0.188, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2444, loss: 0.2444 +2025-06-25 03:08:43,358 - pyskl - INFO - Epoch [95][200/1281] lr: 7.619e-03, eta: 8:31:12, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.2001, loss: 0.2001 +2025-06-25 03:09:15,701 - pyskl - INFO - Epoch [95][300/1281] lr: 7.600e-03, eta: 8:30:23, time: 0.323, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.2256, loss: 0.2256 +2025-06-25 03:10:04,581 - pyskl - INFO - Epoch [95][400/1281] lr: 7.581e-03, eta: 8:29:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9988, loss_cls: 0.2091, loss: 0.2091 +2025-06-25 03:10:53,496 - pyskl - INFO - Epoch [95][500/1281] lr: 7.562e-03, eta: 8:29:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2172, loss: 0.2172 +2025-06-25 03:11:42,522 - pyskl - INFO - Epoch [95][600/1281] lr: 7.543e-03, eta: 8:28:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9988, loss_cls: 0.2188, loss: 0.2188 +2025-06-25 03:12:31,437 - pyskl - INFO - Epoch [95][700/1281] lr: 7.525e-03, eta: 8:27:46, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2406, loss: 0.2406 +2025-06-25 03:13:20,100 - pyskl - INFO - Epoch [95][800/1281] lr: 7.506e-03, eta: 8:27:06, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2415, loss: 0.2415 +2025-06-25 03:14:09,347 - pyskl - INFO - Epoch [95][900/1281] lr: 7.487e-03, eta: 8:26:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9994, loss_cls: 0.2840, loss: 0.2840 +2025-06-25 03:14:58,005 - pyskl - INFO - Epoch [95][1000/1281] lr: 7.468e-03, eta: 8:25:48, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2284, loss: 0.2284 +2025-06-25 03:15:47,032 - pyskl - INFO - Epoch [95][1100/1281] lr: 7.450e-03, eta: 8:25:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2639, loss: 0.2639 +2025-06-25 03:16:35,628 - pyskl - INFO - Epoch [95][1200/1281] lr: 7.431e-03, eta: 8:24:29, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2336, loss: 0.2336 +2025-06-25 03:17:15,763 - pyskl - INFO - Saving checkpoint at 95 epochs +2025-06-25 03:18:14,447 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:18:14,502 - pyskl - INFO - +top1_acc 0.9051 +top5_acc 0.9951 +2025-06-25 03:18:14,502 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:18:14,508 - pyskl - INFO - +mean_acc 0.8692 +2025-06-25 03:18:14,510 - pyskl - INFO - Epoch(val) [95][533] top1_acc: 0.9051, top5_acc: 0.9951, mean_class_accuracy: 0.8692 +2025-06-25 03:19:14,381 - pyskl - INFO - Epoch [96][100/1281] lr: 7.397e-03, eta: 8:23:01, time: 0.599, data_time: 0.186, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2145, loss: 0.2145 +2025-06-25 03:20:01,611 - pyskl - INFO - Epoch [96][200/1281] lr: 7.379e-03, eta: 8:22:20, time: 0.472, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1854, loss: 0.1854 +2025-06-25 03:20:34,950 - pyskl - INFO - Epoch [96][300/1281] lr: 7.360e-03, eta: 8:21:32, time: 0.333, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1874, loss: 0.1874 +2025-06-25 03:21:23,931 - pyskl - INFO - Epoch [96][400/1281] lr: 7.341e-03, eta: 8:20:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9994, loss_cls: 0.2106, loss: 0.2106 +2025-06-25 03:22:12,813 - pyskl - INFO - Epoch [96][500/1281] lr: 7.323e-03, eta: 8:20:13, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1898, loss: 0.1898 +2025-06-25 03:23:01,694 - pyskl - INFO - Epoch [96][600/1281] lr: 7.304e-03, eta: 8:19:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2415, loss: 0.2415 +2025-06-25 03:23:50,659 - pyskl - INFO - Epoch [96][700/1281] lr: 7.286e-03, eta: 8:18:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2392, loss: 0.2392 +2025-06-25 03:24:39,699 - pyskl - INFO - Epoch [96][800/1281] lr: 7.267e-03, eta: 8:18:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2291, loss: 0.2291 +2025-06-25 03:25:28,772 - pyskl - INFO - Epoch [96][900/1281] lr: 7.249e-03, eta: 8:17:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2379, loss: 0.2379 +2025-06-25 03:26:17,675 - pyskl - INFO - Epoch [96][1000/1281] lr: 7.230e-03, eta: 8:16:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2554, loss: 0.2554 +2025-06-25 03:27:06,574 - pyskl - INFO - Epoch [96][1100/1281] lr: 7.211e-03, eta: 8:16:16, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2488, loss: 0.2488 +2025-06-25 03:27:55,452 - pyskl - INFO - Epoch [96][1200/1281] lr: 7.193e-03, eta: 8:15:37, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9981, loss_cls: 0.2791, loss: 0.2791 +2025-06-25 03:28:35,738 - pyskl - INFO - Saving checkpoint at 96 epochs +2025-06-25 03:29:33,361 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:29:33,429 - pyskl - INFO - +top1_acc 0.8991 +top5_acc 0.9941 +2025-06-25 03:29:33,429 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:29:33,437 - pyskl - INFO - +mean_acc 0.8636 +2025-06-25 03:29:33,438 - pyskl - INFO - Epoch(val) [96][533] top1_acc: 0.8991, top5_acc: 0.9941, mean_class_accuracy: 0.8636 +2025-06-25 03:30:34,705 - pyskl - INFO - Epoch [97][100/1281] lr: 7.159e-03, eta: 8:14:10, time: 0.613, data_time: 0.186, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.1883, loss: 0.1883 +2025-06-25 03:31:20,682 - pyskl - INFO - Epoch [97][200/1281] lr: 7.141e-03, eta: 8:13:28, time: 0.460, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1945, loss: 0.1945 +2025-06-25 03:31:54,362 - pyskl - INFO - Epoch [97][300/1281] lr: 7.123e-03, eta: 8:12:40, time: 0.337, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9994, loss_cls: 0.1980, loss: 0.1980 +2025-06-25 03:32:43,264 - pyskl - INFO - Epoch [97][400/1281] lr: 7.104e-03, eta: 8:12:01, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2080, loss: 0.2080 +2025-06-25 03:33:32,093 - pyskl - INFO - Epoch [97][500/1281] lr: 7.086e-03, eta: 8:11:21, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 0.9994, loss_cls: 0.2066, loss: 0.2066 +2025-06-25 03:34:21,030 - pyskl - INFO - Epoch [97][600/1281] lr: 7.067e-03, eta: 8:10:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9988, loss_cls: 0.2196, loss: 0.2196 +2025-06-25 03:35:10,172 - pyskl - INFO - Epoch [97][700/1281] lr: 7.049e-03, eta: 8:10:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1965, loss: 0.1965 +2025-06-25 03:35:59,587 - pyskl - INFO - Epoch [97][800/1281] lr: 7.030e-03, eta: 8:09:23, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9988, loss_cls: 0.2350, loss: 0.2350 +2025-06-25 03:36:48,892 - pyskl - INFO - Epoch [97][900/1281] lr: 7.012e-03, eta: 8:08:43, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.1994, loss: 0.1994 +2025-06-25 03:37:38,259 - pyskl - INFO - Epoch [97][1000/1281] lr: 6.994e-03, eta: 8:08:04, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2331, loss: 0.2331 +2025-06-25 03:38:26,884 - pyskl - INFO - Epoch [97][1100/1281] lr: 6.975e-03, eta: 8:07:24, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1939, loss: 0.1939 +2025-06-25 03:39:16,205 - pyskl - INFO - Epoch [97][1200/1281] lr: 6.957e-03, eta: 8:06:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.2012, loss: 0.2012 +2025-06-25 03:39:56,734 - pyskl - INFO - Saving checkpoint at 97 epochs +2025-06-25 03:40:54,879 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:40:54,948 - pyskl - INFO - +top1_acc 0.9105 +top5_acc 0.9950 +2025-06-25 03:40:54,949 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:40:54,957 - pyskl - INFO - +mean_acc 0.8889 +2025-06-25 03:40:54,961 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_93.pth was removed +2025-06-25 03:40:55,141 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_97.pth. +2025-06-25 03:40:55,141 - pyskl - INFO - Best top1_acc is 0.9105 at 97 epoch. +2025-06-25 03:40:55,144 - pyskl - INFO - Epoch(val) [97][533] top1_acc: 0.9105, top5_acc: 0.9950, mean_class_accuracy: 0.8889 +2025-06-25 03:41:59,925 - pyskl - INFO - Epoch [98][100/1281] lr: 6.924e-03, eta: 8:05:20, time: 0.648, data_time: 0.191, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1992, loss: 0.1992 +2025-06-25 03:42:40,430 - pyskl - INFO - Epoch [98][200/1281] lr: 6.906e-03, eta: 8:04:35, time: 0.405, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.1900, loss: 0.1900 +2025-06-25 03:43:15,730 - pyskl - INFO - Epoch [98][300/1281] lr: 6.887e-03, eta: 8:03:48, time: 0.353, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9994, loss_cls: 0.2065, loss: 0.2065 +2025-06-25 03:44:04,690 - pyskl - INFO - Epoch [98][400/1281] lr: 6.869e-03, eta: 8:03:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2224, loss: 0.2224 +2025-06-25 03:44:53,866 - pyskl - INFO - Epoch [98][500/1281] lr: 6.851e-03, eta: 8:02:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2257, loss: 0.2257 +2025-06-25 03:45:42,761 - pyskl - INFO - Epoch [98][600/1281] lr: 6.833e-03, eta: 8:01:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.1815, loss: 0.1815 +2025-06-25 03:46:31,672 - pyskl - INFO - Epoch [98][700/1281] lr: 6.814e-03, eta: 8:01:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2056, loss: 0.2056 +2025-06-25 03:47:20,943 - pyskl - INFO - Epoch [98][800/1281] lr: 6.796e-03, eta: 8:00:30, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.1998, loss: 0.1998 +2025-06-25 03:48:10,093 - pyskl - INFO - Epoch [98][900/1281] lr: 6.778e-03, eta: 7:59:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2526, loss: 0.2526 +2025-06-25 03:48:59,084 - pyskl - INFO - Epoch [98][1000/1281] lr: 6.760e-03, eta: 7:59:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9994, loss_cls: 0.2206, loss: 0.2206 +2025-06-25 03:49:48,229 - pyskl - INFO - Epoch [98][1100/1281] lr: 6.742e-03, eta: 7:58:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2443, loss: 0.2443 +2025-06-25 03:50:37,151 - pyskl - INFO - Epoch [98][1200/1281] lr: 6.724e-03, eta: 7:57:51, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2473, loss: 0.2473 +2025-06-25 03:51:17,113 - pyskl - INFO - Saving checkpoint at 98 epochs +2025-06-25 03:52:15,461 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:52:15,516 - pyskl - INFO - +top1_acc 0.9147 +top5_acc 0.9948 +2025-06-25 03:52:15,517 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:52:15,524 - pyskl - INFO - +mean_acc 0.8844 +2025-06-25 03:52:15,528 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_97.pth was removed +2025-06-25 03:52:15,726 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_98.pth. +2025-06-25 03:52:15,726 - pyskl - INFO - Best top1_acc is 0.9147 at 98 epoch. +2025-06-25 03:52:15,729 - pyskl - INFO - Epoch(val) [98][533] top1_acc: 0.9147, top5_acc: 0.9948, mean_class_accuracy: 0.8844 +2025-06-25 03:53:19,976 - pyskl - INFO - Epoch [99][100/1281] lr: 6.691e-03, eta: 7:56:26, time: 0.642, data_time: 0.185, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.1829, loss: 0.1829 +2025-06-25 03:54:00,222 - pyskl - INFO - Epoch [99][200/1281] lr: 6.673e-03, eta: 7:55:41, time: 0.402, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9988, loss_cls: 0.2033, loss: 0.2033 +2025-06-25 03:54:36,534 - pyskl - INFO - Epoch [99][300/1281] lr: 6.655e-03, eta: 7:54:55, time: 0.363, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1994, loss: 0.1994 +2025-06-25 03:55:25,318 - pyskl - INFO - Epoch [99][400/1281] lr: 6.637e-03, eta: 7:54:15, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 0.9994, loss_cls: 0.1273, loss: 0.1273 +2025-06-25 03:56:13,528 - pyskl - INFO - Epoch [99][500/1281] lr: 6.619e-03, eta: 7:53:35, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1733, loss: 0.1733 +2025-06-25 03:57:02,088 - pyskl - INFO - Epoch [99][600/1281] lr: 6.601e-03, eta: 7:52:55, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.1931, loss: 0.1931 +2025-06-25 03:57:50,772 - pyskl - INFO - Epoch [99][700/1281] lr: 6.583e-03, eta: 7:52:15, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1960, loss: 0.1960 +2025-06-25 03:58:39,995 - pyskl - INFO - Epoch [99][800/1281] lr: 6.565e-03, eta: 7:51:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.2020, loss: 0.2020 +2025-06-25 03:59:29,691 - pyskl - INFO - Epoch [99][900/1281] lr: 6.547e-03, eta: 7:50:55, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2340, loss: 0.2340 +2025-06-25 04:00:19,156 - pyskl - INFO - Epoch [99][1000/1281] lr: 6.529e-03, eta: 7:50:16, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2663, loss: 0.2663 +2025-06-25 04:01:08,270 - pyskl - INFO - Epoch [99][1100/1281] lr: 6.511e-03, eta: 7:49:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9988, loss_cls: 0.2375, loss: 0.2375 +2025-06-25 04:01:57,007 - pyskl - INFO - Epoch [99][1200/1281] lr: 6.493e-03, eta: 7:48:56, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2657, loss: 0.2657 +2025-06-25 04:02:37,189 - pyskl - INFO - Saving checkpoint at 99 epochs +2025-06-25 04:03:35,315 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:03:35,375 - pyskl - INFO - +top1_acc 0.9032 +top5_acc 0.9921 +2025-06-25 04:03:35,375 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:03:35,381 - pyskl - INFO - +mean_acc 0.8675 +2025-06-25 04:03:35,383 - pyskl - INFO - Epoch(val) [99][533] top1_acc: 0.9032, top5_acc: 0.9921, mean_class_accuracy: 0.8675 +2025-06-25 04:04:39,446 - pyskl - INFO - Epoch [100][100/1281] lr: 6.460e-03, eta: 7:47:31, time: 0.641, data_time: 0.186, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1950, loss: 0.1950 +2025-06-25 04:05:19,129 - pyskl - INFO - Epoch [100][200/1281] lr: 6.442e-03, eta: 7:46:46, time: 0.397, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 0.9994, loss_cls: 0.1589, loss: 0.1589 +2025-06-25 04:05:56,623 - pyskl - INFO - Epoch [100][300/1281] lr: 6.425e-03, eta: 7:46:00, time: 0.375, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.1663, loss: 0.1663 +2025-06-25 04:06:45,438 - pyskl - INFO - Epoch [100][400/1281] lr: 6.407e-03, eta: 7:45:20, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.2024, loss: 0.2024 +2025-06-25 04:07:34,396 - pyskl - INFO - Epoch [100][500/1281] lr: 6.389e-03, eta: 7:44:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1834, loss: 0.1834 +2025-06-25 04:08:23,036 - pyskl - INFO - Epoch [100][600/1281] lr: 6.371e-03, eta: 7:44:00, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2151, loss: 0.2151 +2025-06-25 04:09:11,963 - pyskl - INFO - Epoch [100][700/1281] lr: 6.353e-03, eta: 7:43:20, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.2016, loss: 0.2016 +2025-06-25 04:10:00,872 - pyskl - INFO - Epoch [100][800/1281] lr: 6.336e-03, eta: 7:42:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2124, loss: 0.2124 +2025-06-25 04:10:49,848 - pyskl - INFO - Epoch [100][900/1281] lr: 6.318e-03, eta: 7:42:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9988, loss_cls: 0.2375, loss: 0.2375 +2025-06-25 04:11:39,342 - pyskl - INFO - Epoch [100][1000/1281] lr: 6.300e-03, eta: 7:41:20, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2157, loss: 0.2157 +2025-06-25 04:12:28,295 - pyskl - INFO - Epoch [100][1100/1281] lr: 6.282e-03, eta: 7:40:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.1972, loss: 0.1972 +2025-06-25 04:13:16,938 - pyskl - INFO - Epoch [100][1200/1281] lr: 6.265e-03, eta: 7:40:00, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9988, loss_cls: 0.2000, loss: 0.2000 +2025-06-25 04:13:57,253 - pyskl - INFO - Saving checkpoint at 100 epochs +2025-06-25 04:14:56,183 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:14:56,238 - pyskl - INFO - +top1_acc 0.9028 +top5_acc 0.9946 +2025-06-25 04:14:56,238 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:14:56,246 - pyskl - INFO - +mean_acc 0.8810 +2025-06-25 04:14:56,249 - pyskl - INFO - Epoch(val) [100][533] top1_acc: 0.9028, top5_acc: 0.9946, mean_class_accuracy: 0.8810 +2025-06-25 04:15:56,269 - pyskl - INFO - Epoch [101][100/1281] lr: 6.232e-03, eta: 7:38:33, time: 0.600, data_time: 0.185, memory: 4083, top1_acc: 0.9769, top5_acc: 0.9994, loss_cls: 0.1828, loss: 0.1828 +2025-06-25 04:16:37,553 - pyskl - INFO - Epoch [101][200/1281] lr: 6.215e-03, eta: 7:37:49, time: 0.413, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1884, loss: 0.1884 +2025-06-25 04:17:14,157 - pyskl - INFO - Epoch [101][300/1281] lr: 6.197e-03, eta: 7:37:03, time: 0.366, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1597, loss: 0.1597 +2025-06-25 04:18:03,188 - pyskl - INFO - Epoch [101][400/1281] lr: 6.180e-03, eta: 7:36:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.2208, loss: 0.2208 +2025-06-25 04:18:51,649 - pyskl - INFO - Epoch [101][500/1281] lr: 6.162e-03, eta: 7:35:43, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2124, loss: 0.2124 +2025-06-25 04:19:40,656 - pyskl - INFO - Epoch [101][600/1281] lr: 6.144e-03, eta: 7:35:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2083, loss: 0.2083 +2025-06-25 04:20:29,814 - pyskl - INFO - Epoch [101][700/1281] lr: 6.127e-03, eta: 7:34:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2175, loss: 0.2175 +2025-06-25 04:21:18,975 - pyskl - INFO - Epoch [101][800/1281] lr: 6.109e-03, eta: 7:33:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 0.9994, loss_cls: 0.1984, loss: 0.1984 +2025-06-25 04:22:08,009 - pyskl - INFO - Epoch [101][900/1281] lr: 6.092e-03, eta: 7:33:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1995, loss: 0.1995 +2025-06-25 04:22:56,911 - pyskl - INFO - Epoch [101][1000/1281] lr: 6.074e-03, eta: 7:32:22, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1917, loss: 0.1917 +2025-06-25 04:23:45,850 - pyskl - INFO - Epoch [101][1100/1281] lr: 6.057e-03, eta: 7:31:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1963, loss: 0.1963 +2025-06-25 04:24:34,844 - pyskl - INFO - Epoch [101][1200/1281] lr: 6.039e-03, eta: 7:31:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2111, loss: 0.2111 +2025-06-25 04:25:15,339 - pyskl - INFO - Saving checkpoint at 101 epochs +2025-06-25 04:26:13,049 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:26:13,104 - pyskl - INFO - +top1_acc 0.9213 +top5_acc 0.9952 +2025-06-25 04:26:13,104 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:26:13,110 - pyskl - INFO - +mean_acc 0.8889 +2025-06-25 04:26:13,114 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_98.pth was removed +2025-06-25 04:26:13,345 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_101.pth. +2025-06-25 04:26:13,346 - pyskl - INFO - Best top1_acc is 0.9213 at 101 epoch. +2025-06-25 04:26:13,348 - pyskl - INFO - Epoch(val) [101][533] top1_acc: 0.9213, top5_acc: 0.9952, mean_class_accuracy: 0.8889 +2025-06-25 04:27:17,709 - pyskl - INFO - Epoch [102][100/1281] lr: 6.007e-03, eta: 7:29:38, time: 0.644, data_time: 0.184, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1780, loss: 0.1780 +2025-06-25 04:27:57,110 - pyskl - INFO - Epoch [102][200/1281] lr: 5.990e-03, eta: 7:28:53, time: 0.394, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2046, loss: 0.2046 +2025-06-25 04:28:33,467 - pyskl - INFO - Epoch [102][300/1281] lr: 5.972e-03, eta: 7:28:07, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1875, loss: 0.1875 +2025-06-25 04:29:22,514 - pyskl - INFO - Epoch [102][400/1281] lr: 5.955e-03, eta: 7:27:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1924, loss: 0.1924 +2025-06-25 04:30:11,613 - pyskl - INFO - Epoch [102][500/1281] lr: 5.938e-03, eta: 7:26:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2294, loss: 0.2294 +2025-06-25 04:31:00,164 - pyskl - INFO - Epoch [102][600/1281] lr: 5.920e-03, eta: 7:26:06, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 0.9994, loss_cls: 0.1715, loss: 0.1715 +2025-06-25 04:31:49,341 - pyskl - INFO - Epoch [102][700/1281] lr: 5.903e-03, eta: 7:25:26, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1547, loss: 0.1547 +2025-06-25 04:32:38,109 - pyskl - INFO - Epoch [102][800/1281] lr: 5.886e-03, eta: 7:24:45, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1450, loss: 0.1450 +2025-06-25 04:33:27,152 - pyskl - INFO - Epoch [102][900/1281] lr: 5.868e-03, eta: 7:24:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1632, loss: 0.1632 +2025-06-25 04:34:16,217 - pyskl - INFO - Epoch [102][1000/1281] lr: 5.851e-03, eta: 7:23:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9994, loss_cls: 0.2114, loss: 0.2114 +2025-06-25 04:35:05,286 - pyskl - INFO - Epoch [102][1100/1281] lr: 5.834e-03, eta: 7:22:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.2222, loss: 0.2222 +2025-06-25 04:35:54,020 - pyskl - INFO - Epoch [102][1200/1281] lr: 5.816e-03, eta: 7:22:04, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1798, loss: 0.1798 +2025-06-25 04:36:34,077 - pyskl - INFO - Saving checkpoint at 102 epochs +2025-06-25 04:37:31,759 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:37:31,813 - pyskl - INFO - +top1_acc 0.9166 +top5_acc 0.9944 +2025-06-25 04:37:31,813 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:37:31,819 - pyskl - INFO - +mean_acc 0.8888 +2025-06-25 04:37:31,821 - pyskl - INFO - Epoch(val) [102][533] top1_acc: 0.9166, top5_acc: 0.9944, mean_class_accuracy: 0.8888 +2025-06-25 04:38:35,997 - pyskl - INFO - Epoch [103][100/1281] lr: 5.785e-03, eta: 7:20:40, time: 0.642, data_time: 0.184, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1804, loss: 0.1804 +2025-06-25 04:39:15,867 - pyskl - INFO - Epoch [103][200/1281] lr: 5.768e-03, eta: 7:19:55, time: 0.399, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1664, loss: 0.1664 +2025-06-25 04:39:53,295 - pyskl - INFO - Epoch [103][300/1281] lr: 5.751e-03, eta: 7:19:09, time: 0.374, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.1582, loss: 0.1582 +2025-06-25 04:40:42,158 - pyskl - INFO - Epoch [103][400/1281] lr: 5.733e-03, eta: 7:18:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1946, loss: 0.1946 +2025-06-25 04:41:31,068 - pyskl - INFO - Epoch [103][500/1281] lr: 5.716e-03, eta: 7:17:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1600, loss: 0.1600 +2025-06-25 04:42:20,136 - pyskl - INFO - Epoch [103][600/1281] lr: 5.699e-03, eta: 7:17:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1466, loss: 0.1466 +2025-06-25 04:43:08,874 - pyskl - INFO - Epoch [103][700/1281] lr: 5.682e-03, eta: 7:16:28, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1637, loss: 0.1637 +2025-06-25 04:43:57,967 - pyskl - INFO - Epoch [103][800/1281] lr: 5.665e-03, eta: 7:15:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1831, loss: 0.1831 +2025-06-25 04:44:46,621 - pyskl - INFO - Epoch [103][900/1281] lr: 5.648e-03, eta: 7:15:07, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.2028, loss: 0.2028 +2025-06-25 04:45:36,097 - pyskl - INFO - Epoch [103][1000/1281] lr: 5.631e-03, eta: 7:14:27, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1663, loss: 0.1663 +2025-06-25 04:46:24,891 - pyskl - INFO - Epoch [103][1100/1281] lr: 5.614e-03, eta: 7:13:47, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1691, loss: 0.1691 +2025-06-25 04:47:13,593 - pyskl - INFO - Epoch [103][1200/1281] lr: 5.597e-03, eta: 7:13:06, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1933, loss: 0.1933 +2025-06-25 04:47:53,123 - pyskl - INFO - Saving checkpoint at 103 epochs +2025-06-25 04:48:50,951 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:48:51,008 - pyskl - INFO - +top1_acc 0.9116 +top5_acc 0.9947 +2025-06-25 04:48:51,008 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:48:51,015 - pyskl - INFO - +mean_acc 0.8839 +2025-06-25 04:48:51,016 - pyskl - INFO - Epoch(val) [103][533] top1_acc: 0.9116, top5_acc: 0.9947, mean_class_accuracy: 0.8839 +2025-06-25 04:49:53,228 - pyskl - INFO - Epoch [104][100/1281] lr: 5.566e-03, eta: 7:11:41, time: 0.622, data_time: 0.184, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1697, loss: 0.1697 +2025-06-25 04:50:34,296 - pyskl - INFO - Epoch [104][200/1281] lr: 5.549e-03, eta: 7:10:57, time: 0.411, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1463, loss: 0.1463 +2025-06-25 04:51:09,185 - pyskl - INFO - Epoch [104][300/1281] lr: 5.532e-03, eta: 7:10:10, time: 0.349, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 0.9994, loss_cls: 0.1664, loss: 0.1664 +2025-06-25 04:51:58,332 - pyskl - INFO - Epoch [104][400/1281] lr: 5.515e-03, eta: 7:09:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2011, loss: 0.2011 +2025-06-25 04:52:47,525 - pyskl - INFO - Epoch [104][500/1281] lr: 5.498e-03, eta: 7:08:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 0.9994, loss_cls: 0.1604, loss: 0.1604 +2025-06-25 04:53:36,345 - pyskl - INFO - Epoch [104][600/1281] lr: 5.481e-03, eta: 7:08:09, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 0.9994, loss_cls: 0.1959, loss: 0.1959 +2025-06-25 04:54:25,162 - pyskl - INFO - Epoch [104][700/1281] lr: 5.464e-03, eta: 7:07:28, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9988, loss_cls: 0.1952, loss: 0.1952 +2025-06-25 04:55:14,017 - pyskl - INFO - Epoch [104][800/1281] lr: 5.447e-03, eta: 7:06:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1571, loss: 0.1571 +2025-06-25 04:56:02,720 - pyskl - INFO - Epoch [104][900/1281] lr: 5.430e-03, eta: 7:06:07, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1322, loss: 0.1322 +2025-06-25 04:56:51,977 - pyskl - INFO - Epoch [104][1000/1281] lr: 5.413e-03, eta: 7:05:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1271, loss: 0.1271 +2025-06-25 04:57:40,994 - pyskl - INFO - Epoch [104][1100/1281] lr: 5.397e-03, eta: 7:04:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1574, loss: 0.1574 +2025-06-25 04:58:29,857 - pyskl - INFO - Epoch [104][1200/1281] lr: 5.380e-03, eta: 7:04:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 0.9994, loss_cls: 0.1827, loss: 0.1827 +2025-06-25 04:59:10,020 - pyskl - INFO - Saving checkpoint at 104 epochs +2025-06-25 05:00:08,012 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:00:08,079 - pyskl - INFO - +top1_acc 0.9160 +top5_acc 0.9950 +2025-06-25 05:00:08,079 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:00:08,086 - pyskl - INFO - +mean_acc 0.8906 +2025-06-25 05:00:08,087 - pyskl - INFO - Epoch(val) [104][533] top1_acc: 0.9160, top5_acc: 0.9950, mean_class_accuracy: 0.8906 +2025-06-25 05:01:12,238 - pyskl - INFO - Epoch [105][100/1281] lr: 5.349e-03, eta: 7:02:41, time: 0.641, data_time: 0.189, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1778, loss: 0.1778 +2025-06-25 05:01:53,471 - pyskl - INFO - Epoch [105][200/1281] lr: 5.333e-03, eta: 7:01:58, time: 0.412, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1744, loss: 0.1744 +2025-06-25 05:02:29,617 - pyskl - INFO - Epoch [105][300/1281] lr: 5.316e-03, eta: 7:01:11, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1430, loss: 0.1430 +2025-06-25 05:03:18,499 - pyskl - INFO - Epoch [105][400/1281] lr: 5.299e-03, eta: 7:00:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1456, loss: 0.1456 +2025-06-25 05:04:07,727 - pyskl - INFO - Epoch [105][500/1281] lr: 5.283e-03, eta: 6:59:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 0.9994, loss_cls: 0.1605, loss: 0.1605 +2025-06-25 05:04:56,778 - pyskl - INFO - Epoch [105][600/1281] lr: 5.266e-03, eta: 6:59:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1655, loss: 0.1655 +2025-06-25 05:05:45,852 - pyskl - INFO - Epoch [105][700/1281] lr: 5.249e-03, eta: 6:58:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1857, loss: 0.1857 +2025-06-25 05:06:34,654 - pyskl - INFO - Epoch [105][800/1281] lr: 5.233e-03, eta: 6:57:49, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1926, loss: 0.1926 +2025-06-25 05:07:23,880 - pyskl - INFO - Epoch [105][900/1281] lr: 5.216e-03, eta: 6:57:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1468, loss: 0.1468 +2025-06-25 05:08:12,947 - pyskl - INFO - Epoch [105][1000/1281] lr: 5.199e-03, eta: 6:56:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1809, loss: 0.1809 +2025-06-25 05:09:01,744 - pyskl - INFO - Epoch [105][1100/1281] lr: 5.183e-03, eta: 6:55:47, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1792, loss: 0.1792 +2025-06-25 05:09:50,551 - pyskl - INFO - Epoch [105][1200/1281] lr: 5.166e-03, eta: 6:55:06, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 0.9988, loss_cls: 0.1767, loss: 0.1767 +2025-06-25 05:10:30,876 - pyskl - INFO - Saving checkpoint at 105 epochs +2025-06-25 05:11:28,749 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:11:28,805 - pyskl - INFO - +top1_acc 0.9196 +top5_acc 0.9950 +2025-06-25 05:11:28,805 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:11:28,812 - pyskl - INFO - +mean_acc 0.8989 +2025-06-25 05:11:28,814 - pyskl - INFO - Epoch(val) [105][533] top1_acc: 0.9196, top5_acc: 0.9950, mean_class_accuracy: 0.8989 +2025-06-25 05:12:33,591 - pyskl - INFO - Epoch [106][100/1281] lr: 5.136e-03, eta: 6:53:42, time: 0.648, data_time: 0.187, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1558, loss: 0.1558 +2025-06-25 05:13:13,061 - pyskl - INFO - Epoch [106][200/1281] lr: 5.120e-03, eta: 6:52:58, time: 0.395, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1074, loss: 0.1074 +2025-06-25 05:13:49,809 - pyskl - INFO - Epoch [106][300/1281] lr: 5.103e-03, eta: 6:52:12, time: 0.367, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1373, loss: 0.1373 +2025-06-25 05:14:38,793 - pyskl - INFO - Epoch [106][400/1281] lr: 5.087e-03, eta: 6:51:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1208, loss: 0.1208 +2025-06-25 05:15:27,796 - pyskl - INFO - Epoch [106][500/1281] lr: 5.070e-03, eta: 6:50:51, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1203, loss: 0.1203 +2025-06-25 05:16:16,776 - pyskl - INFO - Epoch [106][600/1281] lr: 5.054e-03, eta: 6:50:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1270, loss: 0.1270 +2025-06-25 05:17:05,746 - pyskl - INFO - Epoch [106][700/1281] lr: 5.038e-03, eta: 6:49:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1508, loss: 0.1508 +2025-06-25 05:17:54,550 - pyskl - INFO - Epoch [106][800/1281] lr: 5.021e-03, eta: 6:48:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1496, loss: 0.1496 +2025-06-25 05:18:43,588 - pyskl - INFO - Epoch [106][900/1281] lr: 5.005e-03, eta: 6:48:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1781, loss: 0.1781 +2025-06-25 05:19:32,834 - pyskl - INFO - Epoch [106][1000/1281] lr: 4.988e-03, eta: 6:47:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1377, loss: 0.1377 +2025-06-25 05:20:21,724 - pyskl - INFO - Epoch [106][1100/1281] lr: 4.972e-03, eta: 6:46:46, time: 0.489, data_time: 0.001, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1714, loss: 0.1714 +2025-06-25 05:21:10,435 - pyskl - INFO - Epoch [106][1200/1281] lr: 4.956e-03, eta: 6:46:05, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 0.9994, loss_cls: 0.1487, loss: 0.1487 +2025-06-25 05:21:50,557 - pyskl - INFO - Saving checkpoint at 106 epochs +2025-06-25 05:22:47,864 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:22:47,923 - pyskl - INFO - +top1_acc 0.9115 +top5_acc 0.9951 +2025-06-25 05:22:47,923 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:22:47,930 - pyskl - INFO - +mean_acc 0.8798 +2025-06-25 05:22:47,932 - pyskl - INFO - Epoch(val) [106][533] top1_acc: 0.9115, top5_acc: 0.9951, mean_class_accuracy: 0.8798 +2025-06-25 05:23:52,288 - pyskl - INFO - Epoch [107][100/1281] lr: 4.926e-03, eta: 6:44:42, time: 0.643, data_time: 0.185, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1668, loss: 0.1668 +2025-06-25 05:24:31,903 - pyskl - INFO - Epoch [107][200/1281] lr: 4.910e-03, eta: 6:43:57, time: 0.396, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1435, loss: 0.1435 +2025-06-25 05:25:08,033 - pyskl - INFO - Epoch [107][300/1281] lr: 4.894e-03, eta: 6:43:11, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 0.9994, loss_cls: 0.1292, loss: 0.1292 +2025-06-25 05:25:57,072 - pyskl - INFO - Epoch [107][400/1281] lr: 4.878e-03, eta: 6:42:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1202, loss: 0.1202 +2025-06-25 05:26:46,050 - pyskl - INFO - Epoch [107][500/1281] lr: 4.862e-03, eta: 6:41:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1293, loss: 0.1293 +2025-06-25 05:27:34,831 - pyskl - INFO - Epoch [107][600/1281] lr: 4.845e-03, eta: 6:41:09, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1782, loss: 0.1782 +2025-06-25 05:28:23,762 - pyskl - INFO - Epoch [107][700/1281] lr: 4.829e-03, eta: 6:40:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.1827, loss: 0.1827 +2025-06-25 05:29:12,480 - pyskl - INFO - Epoch [107][800/1281] lr: 4.813e-03, eta: 6:39:47, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1522, loss: 0.1522 +2025-06-25 05:30:01,617 - pyskl - INFO - Epoch [107][900/1281] lr: 4.797e-03, eta: 6:39:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1563, loss: 0.1563 +2025-06-25 05:30:50,457 - pyskl - INFO - Epoch [107][1000/1281] lr: 4.781e-03, eta: 6:38:25, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1499, loss: 0.1499 +2025-06-25 05:31:39,698 - pyskl - INFO - Epoch [107][1100/1281] lr: 4.765e-03, eta: 6:37:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1740, loss: 0.1740 +2025-06-25 05:32:28,476 - pyskl - INFO - Epoch [107][1200/1281] lr: 4.749e-03, eta: 6:37:04, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 0.9994, loss_cls: 0.1756, loss: 0.1756 +2025-06-25 05:33:08,700 - pyskl - INFO - Saving checkpoint at 107 epochs +2025-06-25 05:34:06,302 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:34:06,358 - pyskl - INFO - +top1_acc 0.9129 +top5_acc 0.9938 +2025-06-25 05:34:06,358 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:34:06,365 - pyskl - INFO - +mean_acc 0.8920 +2025-06-25 05:34:06,366 - pyskl - INFO - Epoch(val) [107][533] top1_acc: 0.9129, top5_acc: 0.9938, mean_class_accuracy: 0.8920 +2025-06-25 05:35:10,914 - pyskl - INFO - Epoch [108][100/1281] lr: 4.720e-03, eta: 6:35:40, time: 0.645, data_time: 0.183, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1751, loss: 0.1751 +2025-06-25 05:35:51,215 - pyskl - INFO - Epoch [108][200/1281] lr: 4.704e-03, eta: 6:34:56, time: 0.403, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1257, loss: 0.1257 +2025-06-25 05:36:27,221 - pyskl - INFO - Epoch [108][300/1281] lr: 4.688e-03, eta: 6:34:10, time: 0.360, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 0.9994, loss_cls: 0.1229, loss: 0.1229 +2025-06-25 05:37:15,883 - pyskl - INFO - Epoch [108][400/1281] lr: 4.672e-03, eta: 6:33:29, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.1101, loss: 0.1101 +2025-06-25 05:38:04,944 - pyskl - INFO - Epoch [108][500/1281] lr: 4.656e-03, eta: 6:32:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0992, loss: 0.0992 +2025-06-25 05:38:54,038 - pyskl - INFO - Epoch [108][600/1281] lr: 4.640e-03, eta: 6:32:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1092, loss: 0.1092 +2025-06-25 05:39:42,839 - pyskl - INFO - Epoch [108][700/1281] lr: 4.624e-03, eta: 6:31:26, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 0.9994, loss_cls: 0.1342, loss: 0.1342 +2025-06-25 05:40:32,116 - pyskl - INFO - Epoch [108][800/1281] lr: 4.608e-03, eta: 6:30:45, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 0.9994, loss_cls: 0.1180, loss: 0.1180 +2025-06-25 05:41:21,135 - pyskl - INFO - Epoch [108][900/1281] lr: 4.593e-03, eta: 6:30:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1131, loss: 0.1131 +2025-06-25 05:42:09,971 - pyskl - INFO - Epoch [108][1000/1281] lr: 4.577e-03, eta: 6:29:23, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 0.9994, loss_cls: 0.1476, loss: 0.1476 +2025-06-25 05:42:59,098 - pyskl - INFO - Epoch [108][1100/1281] lr: 4.561e-03, eta: 6:28:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1551, loss: 0.1551 +2025-06-25 05:43:47,894 - pyskl - INFO - Epoch [108][1200/1281] lr: 4.545e-03, eta: 6:28:01, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1664, loss: 0.1664 +2025-06-25 05:44:27,919 - pyskl - INFO - Saving checkpoint at 108 epochs +2025-06-25 05:45:25,458 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:45:25,512 - pyskl - INFO - +top1_acc 0.9164 +top5_acc 0.9954 +2025-06-25 05:45:25,513 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:45:25,519 - pyskl - INFO - +mean_acc 0.8887 +2025-06-25 05:45:25,521 - pyskl - INFO - Epoch(val) [108][533] top1_acc: 0.9164, top5_acc: 0.9954, mean_class_accuracy: 0.8887 +2025-06-25 05:46:30,726 - pyskl - INFO - Epoch [109][100/1281] lr: 4.517e-03, eta: 6:26:38, time: 0.652, data_time: 0.187, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1142, loss: 0.1142 +2025-06-25 05:47:10,533 - pyskl - INFO - Epoch [109][200/1281] lr: 4.501e-03, eta: 6:25:54, time: 0.398, data_time: 0.001, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1240, loss: 0.1240 +2025-06-25 05:47:47,922 - pyskl - INFO - Epoch [109][300/1281] lr: 4.485e-03, eta: 6:25:08, time: 0.374, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1171, loss: 0.1171 +2025-06-25 05:48:36,792 - pyskl - INFO - Epoch [109][400/1281] lr: 4.470e-03, eta: 6:24:27, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 0.9994, loss_cls: 0.1384, loss: 0.1384 +2025-06-25 05:49:26,160 - pyskl - INFO - Epoch [109][500/1281] lr: 4.454e-03, eta: 6:23:46, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1563, loss: 0.1563 +2025-06-25 05:50:14,978 - pyskl - INFO - Epoch [109][600/1281] lr: 4.438e-03, eta: 6:23:05, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1507, loss: 0.1507 +2025-06-25 05:51:04,030 - pyskl - INFO - Epoch [109][700/1281] lr: 4.423e-03, eta: 6:22:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1119, loss: 0.1119 +2025-06-25 05:51:52,905 - pyskl - INFO - Epoch [109][800/1281] lr: 4.407e-03, eta: 6:21:43, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1336, loss: 0.1336 +2025-06-25 05:52:41,989 - pyskl - INFO - Epoch [109][900/1281] lr: 4.391e-03, eta: 6:21:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1505, loss: 0.1505 +2025-06-25 05:53:30,953 - pyskl - INFO - Epoch [109][1000/1281] lr: 4.376e-03, eta: 6:20:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1323, loss: 0.1323 +2025-06-25 05:54:19,829 - pyskl - INFO - Epoch [109][1100/1281] lr: 4.360e-03, eta: 6:19:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1431, loss: 0.1431 +2025-06-25 05:55:09,081 - pyskl - INFO - Epoch [109][1200/1281] lr: 4.345e-03, eta: 6:18:59, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.9794, top5_acc: 0.9994, loss_cls: 0.1462, loss: 0.1462 +2025-06-25 05:55:49,375 - pyskl - INFO - Saving checkpoint at 109 epochs +2025-06-25 05:56:47,305 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:56:47,376 - pyskl - INFO - +top1_acc 0.9202 +top5_acc 0.9955 +2025-06-25 05:56:47,377 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:56:47,384 - pyskl - INFO - +mean_acc 0.8880 +2025-06-25 05:56:47,386 - pyskl - INFO - Epoch(val) [109][533] top1_acc: 0.9202, top5_acc: 0.9955, mean_class_accuracy: 0.8880 +2025-06-25 05:57:50,002 - pyskl - INFO - Epoch [110][100/1281] lr: 4.317e-03, eta: 6:17:35, time: 0.626, data_time: 0.183, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.1093, loss: 0.1093 +2025-06-25 05:58:30,181 - pyskl - INFO - Epoch [110][200/1281] lr: 4.301e-03, eta: 6:16:51, time: 0.402, data_time: 0.001, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0941, loss: 0.0941 +2025-06-25 05:59:06,703 - pyskl - INFO - Epoch [110][300/1281] lr: 4.286e-03, eta: 6:16:05, time: 0.365, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1061, loss: 0.1061 +2025-06-25 05:59:55,534 - pyskl - INFO - Epoch [110][400/1281] lr: 4.271e-03, eta: 6:15:24, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0916, loss: 0.0916 +2025-06-25 06:00:44,629 - pyskl - INFO - Epoch [110][500/1281] lr: 4.255e-03, eta: 6:14:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1183, loss: 0.1183 +2025-06-25 06:01:33,762 - pyskl - INFO - Epoch [110][600/1281] lr: 4.240e-03, eta: 6:14:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.1014, loss: 0.1014 +2025-06-25 06:02:22,617 - pyskl - INFO - Epoch [110][700/1281] lr: 4.225e-03, eta: 6:13:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1270, loss: 0.1270 +2025-06-25 06:03:12,039 - pyskl - INFO - Epoch [110][800/1281] lr: 4.209e-03, eta: 6:12:40, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1198, loss: 0.1198 +2025-06-25 06:04:01,240 - pyskl - INFO - Epoch [110][900/1281] lr: 4.194e-03, eta: 6:11:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1445, loss: 0.1445 +2025-06-25 06:04:50,307 - pyskl - INFO - Epoch [110][1000/1281] lr: 4.179e-03, eta: 6:11:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1192, loss: 0.1192 +2025-06-25 06:05:39,112 - pyskl - INFO - Epoch [110][1100/1281] lr: 4.164e-03, eta: 6:10:36, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1420, loss: 0.1420 +2025-06-25 06:06:27,628 - pyskl - INFO - Epoch [110][1200/1281] lr: 4.148e-03, eta: 6:09:55, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1237, loss: 0.1237 +2025-06-25 06:07:07,887 - pyskl - INFO - Saving checkpoint at 110 epochs +2025-06-25 06:08:06,450 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:08:06,506 - pyskl - INFO - +top1_acc 0.9202 +top5_acc 0.9952 +2025-06-25 06:08:06,506 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:08:06,513 - pyskl - INFO - +mean_acc 0.8907 +2025-06-25 06:08:06,515 - pyskl - INFO - Epoch(val) [110][533] top1_acc: 0.9202, top5_acc: 0.9952, mean_class_accuracy: 0.8907 +2025-06-25 06:09:08,993 - pyskl - INFO - Epoch [111][100/1281] lr: 4.121e-03, eta: 6:08:31, time: 0.625, data_time: 0.189, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1272, loss: 0.1272 +2025-06-25 06:09:49,260 - pyskl - INFO - Epoch [111][200/1281] lr: 4.106e-03, eta: 6:07:47, time: 0.403, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1134, loss: 0.1134 +2025-06-25 06:10:26,029 - pyskl - INFO - Epoch [111][300/1281] lr: 4.091e-03, eta: 6:07:01, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1003, loss: 0.1003 +2025-06-25 06:11:14,938 - pyskl - INFO - Epoch [111][400/1281] lr: 4.075e-03, eta: 6:06:20, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1205, loss: 0.1205 +2025-06-25 06:12:03,946 - pyskl - INFO - Epoch [111][500/1281] lr: 4.060e-03, eta: 6:05:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0844, loss: 0.0844 +2025-06-25 06:12:52,894 - pyskl - INFO - Epoch [111][600/1281] lr: 4.045e-03, eta: 6:04:58, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1079, loss: 0.1079 +2025-06-25 06:13:42,037 - pyskl - INFO - Epoch [111][700/1281] lr: 4.030e-03, eta: 6:04:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0973, loss: 0.0973 +2025-06-25 06:14:31,272 - pyskl - INFO - Epoch [111][800/1281] lr: 4.015e-03, eta: 6:03:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1103, loss: 0.1103 +2025-06-25 06:15:20,286 - pyskl - INFO - Epoch [111][900/1281] lr: 4.000e-03, eta: 6:02:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1017, loss: 0.1017 +2025-06-25 06:16:08,984 - pyskl - INFO - Epoch [111][1000/1281] lr: 3.985e-03, eta: 6:02:13, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1155, loss: 0.1155 +2025-06-25 06:16:57,770 - pyskl - INFO - Epoch [111][1100/1281] lr: 3.970e-03, eta: 6:01:32, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1284, loss: 0.1284 +2025-06-25 06:17:46,740 - pyskl - INFO - Epoch [111][1200/1281] lr: 3.955e-03, eta: 6:00:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1342, loss: 0.1342 +2025-06-25 06:18:26,975 - pyskl - INFO - Saving checkpoint at 111 epochs +2025-06-25 06:19:25,236 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:19:25,291 - pyskl - INFO - +top1_acc 0.9142 +top5_acc 0.9938 +2025-06-25 06:19:25,291 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:19:25,297 - pyskl - INFO - +mean_acc 0.8856 +2025-06-25 06:19:25,299 - pyskl - INFO - Epoch(val) [111][533] top1_acc: 0.9142, top5_acc: 0.9938, mean_class_accuracy: 0.8856 +2025-06-25 06:20:28,496 - pyskl - INFO - Epoch [112][100/1281] lr: 3.928e-03, eta: 5:59:27, time: 0.632, data_time: 0.184, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1165, loss: 0.1165 +2025-06-25 06:21:08,280 - pyskl - INFO - Epoch [112][200/1281] lr: 3.914e-03, eta: 5:58:42, time: 0.398, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1075, loss: 0.1075 +2025-06-25 06:21:45,990 - pyskl - INFO - Epoch [112][300/1281] lr: 3.899e-03, eta: 5:57:57, time: 0.377, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1261, loss: 0.1261 +2025-06-25 06:22:34,747 - pyskl - INFO - Epoch [112][400/1281] lr: 3.884e-03, eta: 5:57:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 0.9994, loss_cls: 0.1208, loss: 0.1208 +2025-06-25 06:23:23,590 - pyskl - INFO - Epoch [112][500/1281] lr: 3.869e-03, eta: 5:56:34, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1178, loss: 0.1178 +2025-06-25 06:24:12,745 - pyskl - INFO - Epoch [112][600/1281] lr: 3.854e-03, eta: 5:55:53, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1197, loss: 0.1197 +2025-06-25 06:25:01,638 - pyskl - INFO - Epoch [112][700/1281] lr: 3.840e-03, eta: 5:55:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1454, loss: 0.1454 +2025-06-25 06:25:50,506 - pyskl - INFO - Epoch [112][800/1281] lr: 3.825e-03, eta: 5:54:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1068, loss: 0.1068 +2025-06-25 06:26:39,328 - pyskl - INFO - Epoch [112][900/1281] lr: 3.810e-03, eta: 5:53:49, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1089, loss: 0.1089 +2025-06-25 06:27:28,707 - pyskl - INFO - Epoch [112][1000/1281] lr: 3.795e-03, eta: 5:53:08, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1153, loss: 0.1153 +2025-06-25 06:28:17,808 - pyskl - INFO - Epoch [112][1100/1281] lr: 3.781e-03, eta: 5:52:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1538, loss: 0.1538 +2025-06-25 06:29:06,546 - pyskl - INFO - Epoch [112][1200/1281] lr: 3.766e-03, eta: 5:51:45, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1460, loss: 0.1460 +2025-06-25 06:29:46,852 - pyskl - INFO - Saving checkpoint at 112 epochs +2025-06-25 06:30:45,004 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:30:45,060 - pyskl - INFO - +top1_acc 0.9198 +top5_acc 0.9955 +2025-06-25 06:30:45,060 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:30:45,067 - pyskl - INFO - +mean_acc 0.8967 +2025-06-25 06:30:45,069 - pyskl - INFO - Epoch(val) [112][533] top1_acc: 0.9198, top5_acc: 0.9955, mean_class_accuracy: 0.8967 +2025-06-25 06:31:46,958 - pyskl - INFO - Epoch [113][100/1281] lr: 3.740e-03, eta: 5:50:21, time: 0.619, data_time: 0.185, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1402, loss: 0.1402 +2025-06-25 06:32:27,013 - pyskl - INFO - Epoch [113][200/1281] lr: 3.725e-03, eta: 5:49:37, time: 0.401, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1105, loss: 0.1105 +2025-06-25 06:33:05,166 - pyskl - INFO - Epoch [113][300/1281] lr: 3.711e-03, eta: 5:48:52, time: 0.382, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0773, loss: 0.0773 +2025-06-25 06:33:54,620 - pyskl - INFO - Epoch [113][400/1281] lr: 3.696e-03, eta: 5:48:11, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0999, loss: 0.0999 +2025-06-25 06:34:43,326 - pyskl - INFO - Epoch [113][500/1281] lr: 3.682e-03, eta: 5:47:29, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1064, loss: 0.1064 +2025-06-25 06:35:32,204 - pyskl - INFO - Epoch [113][600/1281] lr: 3.667e-03, eta: 5:46:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 0.9994, loss_cls: 0.1140, loss: 0.1140 +2025-06-25 06:36:21,047 - pyskl - INFO - Epoch [113][700/1281] lr: 3.653e-03, eta: 5:46:06, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1169, loss: 0.1169 +2025-06-25 06:37:10,577 - pyskl - INFO - Epoch [113][800/1281] lr: 3.638e-03, eta: 5:45:25, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0759, loss: 0.0759 +2025-06-25 06:37:59,785 - pyskl - INFO - Epoch [113][900/1281] lr: 3.624e-03, eta: 5:44:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0809, loss: 0.0809 +2025-06-25 06:38:48,847 - pyskl - INFO - Epoch [113][1000/1281] lr: 3.610e-03, eta: 5:44:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1056, loss: 0.1056 +2025-06-25 06:39:37,920 - pyskl - INFO - Epoch [113][1100/1281] lr: 3.595e-03, eta: 5:43:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1041, loss: 0.1041 +2025-06-25 06:40:26,977 - pyskl - INFO - Epoch [113][1200/1281] lr: 3.581e-03, eta: 5:42:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1059, loss: 0.1059 +2025-06-25 06:41:07,225 - pyskl - INFO - Saving checkpoint at 113 epochs +2025-06-25 06:42:05,164 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:42:05,219 - pyskl - INFO - +top1_acc 0.9228 +top5_acc 0.9944 +2025-06-25 06:42:05,219 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:42:05,225 - pyskl - INFO - +mean_acc 0.9029 +2025-06-25 06:42:05,229 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_101.pth was removed +2025-06-25 06:42:05,425 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_113.pth. +2025-06-25 06:42:05,425 - pyskl - INFO - Best top1_acc is 0.9228 at 113 epoch. +2025-06-25 06:42:05,428 - pyskl - INFO - Epoch(val) [113][533] top1_acc: 0.9228, top5_acc: 0.9944, mean_class_accuracy: 0.9029 +2025-06-25 06:43:08,357 - pyskl - INFO - Epoch [114][100/1281] lr: 3.555e-03, eta: 5:41:16, time: 0.629, data_time: 0.182, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1068, loss: 0.1068 +2025-06-25 06:43:46,428 - pyskl - INFO - Epoch [114][200/1281] lr: 3.541e-03, eta: 5:40:31, time: 0.381, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1063, loss: 0.1063 +2025-06-25 06:44:24,005 - pyskl - INFO - Epoch [114][300/1281] lr: 3.526e-03, eta: 5:39:46, time: 0.376, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.1017, loss: 0.1017 +2025-06-25 06:45:13,030 - pyskl - INFO - Epoch [114][400/1281] lr: 3.512e-03, eta: 5:39:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0830, loss: 0.0830 +2025-06-25 06:46:02,297 - pyskl - INFO - Epoch [114][500/1281] lr: 3.498e-03, eta: 5:38:23, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0910, loss: 0.0910 +2025-06-25 06:46:51,112 - pyskl - INFO - Epoch [114][600/1281] lr: 3.484e-03, eta: 5:37:42, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0820, loss: 0.0820 +2025-06-25 06:47:39,970 - pyskl - INFO - Epoch [114][700/1281] lr: 3.470e-03, eta: 5:37:00, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0998, loss: 0.0998 +2025-06-25 06:48:29,078 - pyskl - INFO - Epoch [114][800/1281] lr: 3.456e-03, eta: 5:36:19, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1091, loss: 0.1091 +2025-06-25 06:49:18,046 - pyskl - INFO - Epoch [114][900/1281] lr: 3.442e-03, eta: 5:35:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1007, loss: 0.1007 +2025-06-25 06:50:06,841 - pyskl - INFO - Epoch [114][1000/1281] lr: 3.427e-03, eta: 5:34:56, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1362, loss: 0.1362 +2025-06-25 06:50:55,688 - pyskl - INFO - Epoch [114][1100/1281] lr: 3.413e-03, eta: 5:34:14, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1286, loss: 0.1286 +2025-06-25 06:51:44,503 - pyskl - INFO - Epoch [114][1200/1281] lr: 3.399e-03, eta: 5:33:33, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1210, loss: 0.1210 +2025-06-25 06:52:24,493 - pyskl - INFO - Saving checkpoint at 114 epochs +2025-06-25 06:53:22,847 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:53:22,906 - pyskl - INFO - +top1_acc 0.9129 +top5_acc 0.9935 +2025-06-25 06:53:22,907 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:53:22,914 - pyskl - INFO - +mean_acc 0.8848 +2025-06-25 06:53:22,916 - pyskl - INFO - Epoch(val) [114][533] top1_acc: 0.9129, top5_acc: 0.9935, mean_class_accuracy: 0.8848 +2025-06-25 06:54:24,647 - pyskl - INFO - Epoch [115][100/1281] lr: 3.374e-03, eta: 5:32:09, time: 0.617, data_time: 0.182, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0885, loss: 0.0885 +2025-06-25 06:55:04,349 - pyskl - INFO - Epoch [115][200/1281] lr: 3.360e-03, eta: 5:31:25, time: 0.397, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0628, loss: 0.0628 +2025-06-25 06:55:42,040 - pyskl - INFO - Epoch [115][300/1281] lr: 3.346e-03, eta: 5:30:39, time: 0.377, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0740, loss: 0.0740 +2025-06-25 06:56:31,011 - pyskl - INFO - Epoch [115][400/1281] lr: 3.332e-03, eta: 5:29:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0953, loss: 0.0953 +2025-06-25 06:57:19,760 - pyskl - INFO - Epoch [115][500/1281] lr: 3.318e-03, eta: 5:29:16, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0802, loss: 0.0802 +2025-06-25 06:58:08,693 - pyskl - INFO - Epoch [115][600/1281] lr: 3.305e-03, eta: 5:28:35, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0980, loss: 0.0980 +2025-06-25 06:58:57,697 - pyskl - INFO - Epoch [115][700/1281] lr: 3.291e-03, eta: 5:27:53, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0876, loss: 0.0876 +2025-06-25 06:59:46,633 - pyskl - INFO - Epoch [115][800/1281] lr: 3.277e-03, eta: 5:27:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0749, loss: 0.0749 +2025-06-25 07:00:35,507 - pyskl - INFO - Epoch [115][900/1281] lr: 3.263e-03, eta: 5:26:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0754, loss: 0.0754 +2025-06-25 07:01:24,571 - pyskl - INFO - Epoch [115][1000/1281] lr: 3.249e-03, eta: 5:25:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0873, loss: 0.0873 +2025-06-25 07:02:13,197 - pyskl - INFO - Epoch [115][1100/1281] lr: 3.236e-03, eta: 5:25:07, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0982, loss: 0.0982 +2025-06-25 07:03:01,743 - pyskl - INFO - Epoch [115][1200/1281] lr: 3.222e-03, eta: 5:24:25, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0870, loss: 0.0870 +2025-06-25 07:03:42,083 - pyskl - INFO - Saving checkpoint at 115 epochs +2025-06-25 07:04:39,803 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:04:39,861 - pyskl - INFO - +top1_acc 0.9268 +top5_acc 0.9952 +2025-06-25 07:04:39,861 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:04:39,871 - pyskl - INFO - +mean_acc 0.9013 +2025-06-25 07:04:39,876 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_113.pth was removed +2025-06-25 07:04:40,081 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_115.pth. +2025-06-25 07:04:40,081 - pyskl - INFO - Best top1_acc is 0.9268 at 115 epoch. +2025-06-25 07:04:40,085 - pyskl - INFO - Epoch(val) [115][533] top1_acc: 0.9268, top5_acc: 0.9952, mean_class_accuracy: 0.9013 +2025-06-25 07:05:44,154 - pyskl - INFO - Epoch [116][100/1281] lr: 3.197e-03, eta: 5:23:02, time: 0.641, data_time: 0.185, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0696, loss: 0.0696 +2025-06-25 07:06:22,810 - pyskl - INFO - Epoch [116][200/1281] lr: 3.184e-03, eta: 5:22:17, time: 0.387, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0635, loss: 0.0635 +2025-06-25 07:06:59,559 - pyskl - INFO - Epoch [116][300/1281] lr: 3.170e-03, eta: 5:21:32, time: 0.367, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1012, loss: 0.1012 +2025-06-25 07:07:48,399 - pyskl - INFO - Epoch [116][400/1281] lr: 3.156e-03, eta: 5:20:50, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0805, loss: 0.0805 +2025-06-25 07:08:36,996 - pyskl - INFO - Epoch [116][500/1281] lr: 3.143e-03, eta: 5:20:09, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0850, loss: 0.0850 +2025-06-25 07:09:25,790 - pyskl - INFO - Epoch [116][600/1281] lr: 3.129e-03, eta: 5:19:27, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 0.9994, loss_cls: 0.0846, loss: 0.0846 +2025-06-25 07:10:14,668 - pyskl - INFO - Epoch [116][700/1281] lr: 3.116e-03, eta: 5:18:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1061, loss: 0.1061 +2025-06-25 07:11:03,799 - pyskl - INFO - Epoch [116][800/1281] lr: 3.102e-03, eta: 5:18:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0742, loss: 0.0742 +2025-06-25 07:11:52,929 - pyskl - INFO - Epoch [116][900/1281] lr: 3.089e-03, eta: 5:17:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0729, loss: 0.0729 +2025-06-25 07:12:41,717 - pyskl - INFO - Epoch [116][1000/1281] lr: 3.075e-03, eta: 5:16:40, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0764, loss: 0.0764 +2025-06-25 07:13:30,475 - pyskl - INFO - Epoch [116][1100/1281] lr: 3.062e-03, eta: 5:15:59, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 0.9994, loss_cls: 0.0799, loss: 0.0799 +2025-06-25 07:14:19,350 - pyskl - INFO - Epoch [116][1200/1281] lr: 3.049e-03, eta: 5:15:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0869, loss: 0.0869 +2025-06-25 07:14:59,419 - pyskl - INFO - Saving checkpoint at 116 epochs +2025-06-25 07:15:56,988 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:15:57,043 - pyskl - INFO - +top1_acc 0.9261 +top5_acc 0.9966 +2025-06-25 07:15:57,044 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:15:57,050 - pyskl - INFO - +mean_acc 0.9024 +2025-06-25 07:15:57,052 - pyskl - INFO - Epoch(val) [116][533] top1_acc: 0.9261, top5_acc: 0.9966, mean_class_accuracy: 0.9024 +2025-06-25 07:16:59,772 - pyskl - INFO - Epoch [117][100/1281] lr: 3.024e-03, eta: 5:13:54, time: 0.627, data_time: 0.184, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0794, loss: 0.0794 +2025-06-25 07:17:41,338 - pyskl - INFO - Epoch [117][200/1281] lr: 3.011e-03, eta: 5:13:10, time: 0.416, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0617, loss: 0.0617 +2025-06-25 07:18:17,141 - pyskl - INFO - Epoch [117][300/1281] lr: 2.998e-03, eta: 5:12:24, time: 0.358, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0712, loss: 0.0712 +2025-06-25 07:19:05,944 - pyskl - INFO - Epoch [117][400/1281] lr: 2.984e-03, eta: 5:11:43, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0626, loss: 0.0626 +2025-06-25 07:19:55,065 - pyskl - INFO - Epoch [117][500/1281] lr: 2.971e-03, eta: 5:11:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0638, loss: 0.0638 +2025-06-25 07:20:43,959 - pyskl - INFO - Epoch [117][600/1281] lr: 2.958e-03, eta: 5:10:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0784, loss: 0.0784 +2025-06-25 07:21:33,057 - pyskl - INFO - Epoch [117][700/1281] lr: 2.945e-03, eta: 5:09:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0713, loss: 0.0713 +2025-06-25 07:22:22,110 - pyskl - INFO - Epoch [117][800/1281] lr: 2.932e-03, eta: 5:08:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0741, loss: 0.0741 +2025-06-25 07:23:11,298 - pyskl - INFO - Epoch [117][900/1281] lr: 2.919e-03, eta: 5:08:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0912, loss: 0.0912 +2025-06-25 07:24:00,147 - pyskl - INFO - Epoch [117][1000/1281] lr: 2.905e-03, eta: 5:07:32, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0728, loss: 0.0728 +2025-06-25 07:24:49,306 - pyskl - INFO - Epoch [117][1100/1281] lr: 2.892e-03, eta: 5:06:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0788, loss: 0.0788 +2025-06-25 07:25:38,089 - pyskl - INFO - Epoch [117][1200/1281] lr: 2.879e-03, eta: 5:06:09, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0940, loss: 0.0940 +2025-06-25 07:26:18,302 - pyskl - INFO - Saving checkpoint at 117 epochs +2025-06-25 07:27:16,413 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:27:16,467 - pyskl - INFO - +top1_acc 0.9257 +top5_acc 0.9941 +2025-06-25 07:27:16,467 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:27:16,474 - pyskl - INFO - +mean_acc 0.9052 +2025-06-25 07:27:16,475 - pyskl - INFO - Epoch(val) [117][533] top1_acc: 0.9257, top5_acc: 0.9941, mean_class_accuracy: 0.9052 +2025-06-25 07:28:20,121 - pyskl - INFO - Epoch [118][100/1281] lr: 2.856e-03, eta: 5:04:46, time: 0.636, data_time: 0.187, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0710, loss: 0.0710 +2025-06-25 07:29:01,378 - pyskl - INFO - Epoch [118][200/1281] lr: 2.843e-03, eta: 5:04:02, time: 0.413, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0686, loss: 0.0686 +2025-06-25 07:29:37,094 - pyskl - INFO - Epoch [118][300/1281] lr: 2.830e-03, eta: 5:03:17, time: 0.357, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0481, loss: 0.0481 +2025-06-25 07:30:26,170 - pyskl - INFO - Epoch [118][400/1281] lr: 2.817e-03, eta: 5:02:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0565, loss: 0.0565 +2025-06-25 07:31:15,126 - pyskl - INFO - Epoch [118][500/1281] lr: 2.804e-03, eta: 5:01:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0710, loss: 0.0710 +2025-06-25 07:32:03,914 - pyskl - INFO - Epoch [118][600/1281] lr: 2.791e-03, eta: 5:01:11, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 0.9994, loss_cls: 0.0983, loss: 0.0983 +2025-06-25 07:32:52,564 - pyskl - INFO - Epoch [118][700/1281] lr: 2.778e-03, eta: 5:00:29, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0673, loss: 0.0673 +2025-06-25 07:33:41,721 - pyskl - INFO - Epoch [118][800/1281] lr: 2.765e-03, eta: 4:59:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0734, loss: 0.0734 +2025-06-25 07:34:30,609 - pyskl - INFO - Epoch [118][900/1281] lr: 2.753e-03, eta: 4:59:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0849, loss: 0.0849 +2025-06-25 07:35:19,096 - pyskl - INFO - Epoch [118][1000/1281] lr: 2.740e-03, eta: 4:58:24, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.0837, loss: 0.0837 +2025-06-25 07:36:07,734 - pyskl - INFO - Epoch [118][1100/1281] lr: 2.727e-03, eta: 4:57:42, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 0.9994, loss_cls: 0.0868, loss: 0.0868 +2025-06-25 07:36:56,683 - pyskl - INFO - Epoch [118][1200/1281] lr: 2.714e-03, eta: 4:57:00, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0875, loss: 0.0875 +2025-06-25 07:37:36,627 - pyskl - INFO - Saving checkpoint at 118 epochs +2025-06-25 07:38:34,995 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:38:35,052 - pyskl - INFO - +top1_acc 0.9261 +top5_acc 0.9959 +2025-06-25 07:38:35,052 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:38:35,059 - pyskl - INFO - +mean_acc 0.8997 +2025-06-25 07:38:35,061 - pyskl - INFO - Epoch(val) [118][533] top1_acc: 0.9261, top5_acc: 0.9959, mean_class_accuracy: 0.8997 +2025-06-25 07:39:38,922 - pyskl - INFO - Epoch [119][100/1281] lr: 2.691e-03, eta: 4:55:37, time: 0.639, data_time: 0.188, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0583, loss: 0.0583 +2025-06-25 07:40:19,550 - pyskl - INFO - Epoch [119][200/1281] lr: 2.679e-03, eta: 4:54:53, time: 0.406, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0709, loss: 0.0709 +2025-06-25 07:40:56,630 - pyskl - INFO - Epoch [119][300/1281] lr: 2.666e-03, eta: 4:54:08, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1027, loss: 0.1027 +2025-06-25 07:41:45,390 - pyskl - INFO - Epoch [119][400/1281] lr: 2.653e-03, eta: 4:53:26, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0529, loss: 0.0529 +2025-06-25 07:42:34,419 - pyskl - INFO - Epoch [119][500/1281] lr: 2.641e-03, eta: 4:52:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0804, loss: 0.0804 +2025-06-25 07:43:23,300 - pyskl - INFO - Epoch [119][600/1281] lr: 2.628e-03, eta: 4:52:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0809, loss: 0.0809 +2025-06-25 07:44:12,265 - pyskl - INFO - Epoch [119][700/1281] lr: 2.616e-03, eta: 4:51:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0615, loss: 0.0615 +2025-06-25 07:45:01,204 - pyskl - INFO - Epoch [119][800/1281] lr: 2.603e-03, eta: 4:50:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0628, loss: 0.0628 +2025-06-25 07:45:50,107 - pyskl - INFO - Epoch [119][900/1281] lr: 2.591e-03, eta: 4:49:57, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0747, loss: 0.0747 +2025-06-25 07:46:39,362 - pyskl - INFO - Epoch [119][1000/1281] lr: 2.578e-03, eta: 4:49:15, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0663, loss: 0.0663 +2025-06-25 07:47:28,435 - pyskl - INFO - Epoch [119][1100/1281] lr: 2.566e-03, eta: 4:48:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0723, loss: 0.0723 +2025-06-25 07:48:17,149 - pyskl - INFO - Epoch [119][1200/1281] lr: 2.554e-03, eta: 4:47:51, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0856, loss: 0.0856 +2025-06-25 07:48:57,426 - pyskl - INFO - Saving checkpoint at 119 epochs +2025-06-25 07:49:56,277 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:49:56,353 - pyskl - INFO - +top1_acc 0.9283 +top5_acc 0.9961 +2025-06-25 07:49:56,353 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:49:56,362 - pyskl - INFO - +mean_acc 0.9035 +2025-06-25 07:49:56,366 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_115.pth was removed +2025-06-25 07:49:56,560 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_119.pth. +2025-06-25 07:49:56,561 - pyskl - INFO - Best top1_acc is 0.9283 at 119 epoch. +2025-06-25 07:49:56,566 - pyskl - INFO - Epoch(val) [119][533] top1_acc: 0.9283, top5_acc: 0.9961, mean_class_accuracy: 0.9035 +2025-06-25 07:51:01,109 - pyskl - INFO - Epoch [120][100/1281] lr: 2.531e-03, eta: 4:46:29, time: 0.645, data_time: 0.185, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0475, loss: 0.0475 +2025-06-25 07:51:39,226 - pyskl - INFO - Epoch [120][200/1281] lr: 2.519e-03, eta: 4:45:44, time: 0.381, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0420, loss: 0.0420 +2025-06-25 07:52:15,694 - pyskl - INFO - Epoch [120][300/1281] lr: 2.507e-03, eta: 4:44:59, time: 0.365, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0540, loss: 0.0540 +2025-06-25 07:53:04,395 - pyskl - INFO - Epoch [120][400/1281] lr: 2.494e-03, eta: 4:44:17, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0577, loss: 0.0577 +2025-06-25 07:53:53,232 - pyskl - INFO - Epoch [120][500/1281] lr: 2.482e-03, eta: 4:43:35, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0840, loss: 0.0840 +2025-06-25 07:54:42,179 - pyskl - INFO - Epoch [120][600/1281] lr: 2.470e-03, eta: 4:42:53, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0752, loss: 0.0752 +2025-06-25 07:55:31,321 - pyskl - INFO - Epoch [120][700/1281] lr: 2.458e-03, eta: 4:42:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0535, loss: 0.0535 +2025-06-25 07:56:20,307 - pyskl - INFO - Epoch [120][800/1281] lr: 2.446e-03, eta: 4:41:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0879, loss: 0.0879 +2025-06-25 07:57:09,194 - pyskl - INFO - Epoch [120][900/1281] lr: 2.433e-03, eta: 4:40:47, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0559, loss: 0.0559 +2025-06-25 07:57:58,137 - pyskl - INFO - Epoch [120][1000/1281] lr: 2.421e-03, eta: 4:40:05, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0688, loss: 0.0688 +2025-06-25 07:58:46,874 - pyskl - INFO - Epoch [120][1100/1281] lr: 2.409e-03, eta: 4:39:23, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0579, loss: 0.0579 +2025-06-25 07:59:35,648 - pyskl - INFO - Epoch [120][1200/1281] lr: 2.397e-03, eta: 4:38:41, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0623, loss: 0.0623 +2025-06-25 08:00:15,951 - pyskl - INFO - Saving checkpoint at 120 epochs +2025-06-25 08:01:14,685 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:01:14,740 - pyskl - INFO - +top1_acc 0.9309 +top5_acc 0.9957 +2025-06-25 08:01:14,740 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:01:14,747 - pyskl - INFO - +mean_acc 0.9078 +2025-06-25 08:01:14,750 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_119.pth was removed +2025-06-25 08:01:14,918 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_120.pth. +2025-06-25 08:01:14,918 - pyskl - INFO - Best top1_acc is 0.9309 at 120 epoch. +2025-06-25 08:01:14,921 - pyskl - INFO - Epoch(val) [120][533] top1_acc: 0.9309, top5_acc: 0.9957, mean_class_accuracy: 0.9078 +2025-06-25 08:02:17,401 - pyskl - INFO - Epoch [121][100/1281] lr: 2.375e-03, eta: 4:37:18, time: 0.625, data_time: 0.184, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0367, loss: 0.0367 +2025-06-25 08:02:57,064 - pyskl - INFO - Epoch [121][200/1281] lr: 2.363e-03, eta: 4:36:34, time: 0.397, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0324, loss: 0.0324 +2025-06-25 08:03:32,940 - pyskl - INFO - Epoch [121][300/1281] lr: 2.351e-03, eta: 4:35:49, time: 0.359, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0407, loss: 0.0407 +2025-06-25 08:04:21,923 - pyskl - INFO - Epoch [121][400/1281] lr: 2.340e-03, eta: 4:35:07, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 0.9994, loss_cls: 0.0672, loss: 0.0672 +2025-06-25 08:05:11,011 - pyskl - INFO - Epoch [121][500/1281] lr: 2.328e-03, eta: 4:34:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0811, loss: 0.0811 +2025-06-25 08:05:59,989 - pyskl - INFO - Epoch [121][600/1281] lr: 2.316e-03, eta: 4:33:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0676, loss: 0.0676 +2025-06-25 08:06:49,019 - pyskl - INFO - Epoch [121][700/1281] lr: 2.304e-03, eta: 4:33:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0576, loss: 0.0576 +2025-06-25 08:07:37,949 - pyskl - INFO - Epoch [121][800/1281] lr: 2.292e-03, eta: 4:32:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0783, loss: 0.0783 +2025-06-25 08:08:26,469 - pyskl - INFO - Epoch [121][900/1281] lr: 2.280e-03, eta: 4:31:36, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0513, loss: 0.0513 +2025-06-25 08:09:15,392 - pyskl - INFO - Epoch [121][1000/1281] lr: 2.269e-03, eta: 4:30:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0530, loss: 0.0530 +2025-06-25 08:10:04,428 - pyskl - INFO - Epoch [121][1100/1281] lr: 2.257e-03, eta: 4:30:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0560, loss: 0.0560 +2025-06-25 08:10:53,331 - pyskl - INFO - Epoch [121][1200/1281] lr: 2.245e-03, eta: 4:29:30, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0591, loss: 0.0591 +2025-06-25 08:11:33,572 - pyskl - INFO - Saving checkpoint at 121 epochs +2025-06-25 08:12:32,641 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:12:32,711 - pyskl - INFO - +top1_acc 0.9303 +top5_acc 0.9960 +2025-06-25 08:12:32,712 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:12:32,720 - pyskl - INFO - +mean_acc 0.9036 +2025-06-25 08:12:32,722 - pyskl - INFO - Epoch(val) [121][533] top1_acc: 0.9303, top5_acc: 0.9960, mean_class_accuracy: 0.9036 +2025-06-25 08:13:36,389 - pyskl - INFO - Epoch [122][100/1281] lr: 2.224e-03, eta: 4:28:08, time: 0.637, data_time: 0.184, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0602, loss: 0.0602 +2025-06-25 08:14:15,945 - pyskl - INFO - Epoch [122][200/1281] lr: 2.212e-03, eta: 4:27:23, time: 0.396, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0436, loss: 0.0436 +2025-06-25 08:14:53,392 - pyskl - INFO - Epoch [122][300/1281] lr: 2.201e-03, eta: 4:26:39, time: 0.374, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0426, loss: 0.0426 +2025-06-25 08:15:42,298 - pyskl - INFO - Epoch [122][400/1281] lr: 2.189e-03, eta: 4:25:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0523, loss: 0.0523 +2025-06-25 08:16:31,094 - pyskl - INFO - Epoch [122][500/1281] lr: 2.178e-03, eta: 4:25:14, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0521, loss: 0.0521 +2025-06-25 08:17:20,014 - pyskl - INFO - Epoch [122][600/1281] lr: 2.166e-03, eta: 4:24:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0499, loss: 0.0499 +2025-06-25 08:18:09,002 - pyskl - INFO - Epoch [122][700/1281] lr: 2.155e-03, eta: 4:23:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0477, loss: 0.0477 +2025-06-25 08:18:57,734 - pyskl - INFO - Epoch [122][800/1281] lr: 2.143e-03, eta: 4:23:08, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0424, loss: 0.0424 +2025-06-25 08:19:46,550 - pyskl - INFO - Epoch [122][900/1281] lr: 2.132e-03, eta: 4:22:26, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0439, loss: 0.0439 +2025-06-25 08:20:35,260 - pyskl - INFO - Epoch [122][1000/1281] lr: 2.120e-03, eta: 4:21:43, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0596, loss: 0.0596 +2025-06-25 08:21:24,323 - pyskl - INFO - Epoch [122][1100/1281] lr: 2.109e-03, eta: 4:21:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0619, loss: 0.0619 +2025-06-25 08:22:13,116 - pyskl - INFO - Epoch [122][1200/1281] lr: 2.098e-03, eta: 4:20:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0548, loss: 0.0548 +2025-06-25 08:22:53,252 - pyskl - INFO - Saving checkpoint at 122 epochs +2025-06-25 08:23:52,165 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:23:52,224 - pyskl - INFO - +top1_acc 0.9332 +top5_acc 0.9961 +2025-06-25 08:23:52,224 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:23:52,233 - pyskl - INFO - +mean_acc 0.9067 +2025-06-25 08:23:52,237 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_120.pth was removed +2025-06-25 08:23:52,598 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_122.pth. +2025-06-25 08:23:52,599 - pyskl - INFO - Best top1_acc is 0.9332 at 122 epoch. +2025-06-25 08:23:52,601 - pyskl - INFO - Epoch(val) [122][533] top1_acc: 0.9332, top5_acc: 0.9961, mean_class_accuracy: 0.9067 +2025-06-25 08:24:56,176 - pyskl - INFO - Epoch [123][100/1281] lr: 2.077e-03, eta: 4:18:57, time: 0.636, data_time: 0.189, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0446, loss: 0.0446 +2025-06-25 08:25:33,996 - pyskl - INFO - Epoch [123][200/1281] lr: 2.066e-03, eta: 4:18:12, time: 0.378, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0432, loss: 0.0432 +2025-06-25 08:26:10,778 - pyskl - INFO - Epoch [123][300/1281] lr: 2.055e-03, eta: 4:17:27, time: 0.368, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0387, loss: 0.0387 +2025-06-25 08:26:59,525 - pyskl - INFO - Epoch [123][400/1281] lr: 2.044e-03, eta: 4:16:45, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0289, loss: 0.0289 +2025-06-25 08:27:48,404 - pyskl - INFO - Epoch [123][500/1281] lr: 2.032e-03, eta: 4:16:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0521, loss: 0.0521 +2025-06-25 08:28:37,451 - pyskl - INFO - Epoch [123][600/1281] lr: 2.021e-03, eta: 4:15:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0614, loss: 0.0614 +2025-06-25 08:29:26,322 - pyskl - INFO - Epoch [123][700/1281] lr: 2.010e-03, eta: 4:14:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0602, loss: 0.0602 +2025-06-25 08:30:15,079 - pyskl - INFO - Epoch [123][800/1281] lr: 1.999e-03, eta: 4:13:56, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0401, loss: 0.0401 +2025-06-25 08:31:04,326 - pyskl - INFO - Epoch [123][900/1281] lr: 1.988e-03, eta: 4:13:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0409, loss: 0.0409 +2025-06-25 08:31:53,136 - pyskl - INFO - Epoch [123][1000/1281] lr: 1.977e-03, eta: 4:12:32, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0434, loss: 0.0434 +2025-06-25 08:32:41,930 - pyskl - INFO - Epoch [123][1100/1281] lr: 1.966e-03, eta: 4:11:49, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0370, loss: 0.0370 +2025-06-25 08:33:30,482 - pyskl - INFO - Epoch [123][1200/1281] lr: 1.955e-03, eta: 4:11:07, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0427, loss: 0.0427 +2025-06-25 08:34:10,394 - pyskl - INFO - Saving checkpoint at 123 epochs +2025-06-25 08:35:08,875 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:35:08,931 - pyskl - INFO - +top1_acc 0.9338 +top5_acc 0.9953 +2025-06-25 08:35:08,932 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:35:08,939 - pyskl - INFO - +mean_acc 0.9115 +2025-06-25 08:35:08,945 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_122.pth was removed +2025-06-25 08:35:09,122 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_123.pth. +2025-06-25 08:35:09,122 - pyskl - INFO - Best top1_acc is 0.9338 at 123 epoch. +2025-06-25 08:35:09,125 - pyskl - INFO - Epoch(val) [123][533] top1_acc: 0.9338, top5_acc: 0.9953, mean_class_accuracy: 0.9115 +2025-06-25 08:36:13,185 - pyskl - INFO - Epoch [124][100/1281] lr: 1.935e-03, eta: 4:09:45, time: 0.641, data_time: 0.183, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0444, loss: 0.0444 +2025-06-25 08:36:52,640 - pyskl - INFO - Epoch [124][200/1281] lr: 1.924e-03, eta: 4:09:01, time: 0.395, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0318, loss: 0.0318 +2025-06-25 08:37:29,818 - pyskl - INFO - Epoch [124][300/1281] lr: 1.913e-03, eta: 4:08:16, time: 0.372, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0497, loss: 0.0497 +2025-06-25 08:38:18,988 - pyskl - INFO - Epoch [124][400/1281] lr: 1.902e-03, eta: 4:07:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0455, loss: 0.0455 +2025-06-25 08:39:07,936 - pyskl - INFO - Epoch [124][500/1281] lr: 1.892e-03, eta: 4:06:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0373, loss: 0.0373 +2025-06-25 08:39:56,798 - pyskl - INFO - Epoch [124][600/1281] lr: 1.881e-03, eta: 4:06:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0376, loss: 0.0376 +2025-06-25 08:40:45,839 - pyskl - INFO - Epoch [124][700/1281] lr: 1.870e-03, eta: 4:05:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0309, loss: 0.0309 +2025-06-25 08:41:34,839 - pyskl - INFO - Epoch [124][800/1281] lr: 1.859e-03, eta: 4:04:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0384, loss: 0.0384 +2025-06-25 08:42:23,460 - pyskl - INFO - Epoch [124][900/1281] lr: 1.849e-03, eta: 4:04:02, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0451, loss: 0.0451 +2025-06-25 08:43:12,333 - pyskl - INFO - Epoch [124][1000/1281] lr: 1.838e-03, eta: 4:03:20, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0369, loss: 0.0369 +2025-06-25 08:44:01,361 - pyskl - INFO - Epoch [124][1100/1281] lr: 1.827e-03, eta: 4:02:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0416, loss: 0.0416 +2025-06-25 08:44:50,193 - pyskl - INFO - Epoch [124][1200/1281] lr: 1.817e-03, eta: 4:01:55, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0318, loss: 0.0318 +2025-06-25 08:45:30,398 - pyskl - INFO - Saving checkpoint at 124 epochs +2025-06-25 08:46:28,639 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:46:28,695 - pyskl - INFO - +top1_acc 0.9312 +top5_acc 0.9966 +2025-06-25 08:46:28,695 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:46:28,703 - pyskl - INFO - +mean_acc 0.9096 +2025-06-25 08:46:28,705 - pyskl - INFO - Epoch(val) [124][533] top1_acc: 0.9312, top5_acc: 0.9966, mean_class_accuracy: 0.9096 +2025-06-25 08:47:31,932 - pyskl - INFO - Epoch [125][100/1281] lr: 1.797e-03, eta: 4:00:34, time: 0.632, data_time: 0.188, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0362, loss: 0.0362 +2025-06-25 08:48:11,836 - pyskl - INFO - Epoch [125][200/1281] lr: 1.787e-03, eta: 3:59:49, time: 0.399, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0331, loss: 0.0331 +2025-06-25 08:48:49,234 - pyskl - INFO - Epoch [125][300/1281] lr: 1.776e-03, eta: 3:59:05, time: 0.374, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0311, loss: 0.0311 +2025-06-25 08:49:37,783 - pyskl - INFO - Epoch [125][400/1281] lr: 1.766e-03, eta: 3:58:22, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0289, loss: 0.0289 +2025-06-25 08:50:26,888 - pyskl - INFO - Epoch [125][500/1281] lr: 1.755e-03, eta: 3:57:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0309, loss: 0.0309 +2025-06-25 08:51:15,888 - pyskl - INFO - Epoch [125][600/1281] lr: 1.745e-03, eta: 3:56:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0316, loss: 0.0316 +2025-06-25 08:52:05,372 - pyskl - INFO - Epoch [125][700/1281] lr: 1.735e-03, eta: 3:56:15, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0317, loss: 0.0317 +2025-06-25 08:52:54,536 - pyskl - INFO - Epoch [125][800/1281] lr: 1.724e-03, eta: 3:55:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0287, loss: 0.0287 +2025-06-25 08:53:43,618 - pyskl - INFO - Epoch [125][900/1281] lr: 1.714e-03, eta: 3:54:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0396, loss: 0.0396 +2025-06-25 08:54:32,587 - pyskl - INFO - Epoch [125][1000/1281] lr: 1.704e-03, eta: 3:54:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0352, loss: 0.0352 +2025-06-25 08:55:21,337 - pyskl - INFO - Epoch [125][1100/1281] lr: 1.693e-03, eta: 3:53:26, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0337, loss: 0.0337 +2025-06-25 08:56:10,460 - pyskl - INFO - Epoch [125][1200/1281] lr: 1.683e-03, eta: 3:52:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0346, loss: 0.0346 +2025-06-25 08:56:50,930 - pyskl - INFO - Saving checkpoint at 125 epochs +2025-06-25 08:57:48,491 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:57:48,547 - pyskl - INFO - +top1_acc 0.9392 +top5_acc 0.9973 +2025-06-25 08:57:48,547 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:57:48,553 - pyskl - INFO - +mean_acc 0.9162 +2025-06-25 08:57:48,557 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_123.pth was removed +2025-06-25 08:57:48,744 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_125.pth. +2025-06-25 08:57:48,744 - pyskl - INFO - Best top1_acc is 0.9392 at 125 epoch. +2025-06-25 08:57:48,747 - pyskl - INFO - Epoch(val) [125][533] top1_acc: 0.9392, top5_acc: 0.9973, mean_class_accuracy: 0.9162 +2025-06-25 08:58:52,049 - pyskl - INFO - Epoch [126][100/1281] lr: 1.665e-03, eta: 3:51:22, time: 0.633, data_time: 0.186, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0365, loss: 0.0365 +2025-06-25 08:59:31,868 - pyskl - INFO - Epoch [126][200/1281] lr: 1.654e-03, eta: 3:50:37, time: 0.398, data_time: 0.001, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0355, loss: 0.0355 +2025-06-25 09:00:09,007 - pyskl - INFO - Epoch [126][300/1281] lr: 1.644e-03, eta: 3:49:53, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0327, loss: 0.0327 +2025-06-25 09:00:57,739 - pyskl - INFO - Epoch [126][400/1281] lr: 1.634e-03, eta: 3:49:10, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0321, loss: 0.0321 +2025-06-25 09:01:46,409 - pyskl - INFO - Epoch [126][500/1281] lr: 1.624e-03, eta: 3:48:28, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0258, loss: 0.0258 +2025-06-25 09:02:35,232 - pyskl - INFO - Epoch [126][600/1281] lr: 1.614e-03, eta: 3:47:45, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0432, loss: 0.0432 +2025-06-25 09:03:24,168 - pyskl - INFO - Epoch [126][700/1281] lr: 1.604e-03, eta: 3:47:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0271, loss: 0.0271 +2025-06-25 09:04:12,933 - pyskl - INFO - Epoch [126][800/1281] lr: 1.594e-03, eta: 3:46:21, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0308, loss: 0.0308 +2025-06-25 09:05:01,683 - pyskl - INFO - Epoch [126][900/1281] lr: 1.584e-03, eta: 3:45:38, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0332, loss: 0.0332 +2025-06-25 09:05:50,827 - pyskl - INFO - Epoch [126][1000/1281] lr: 1.574e-03, eta: 3:44:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0412, loss: 0.0412 +2025-06-25 09:06:40,018 - pyskl - INFO - Epoch [126][1100/1281] lr: 1.564e-03, eta: 3:44:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0295, loss: 0.0295 +2025-06-25 09:07:28,721 - pyskl - INFO - Epoch [126][1200/1281] lr: 1.554e-03, eta: 3:43:31, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0410, loss: 0.0410 +2025-06-25 09:08:09,045 - pyskl - INFO - Saving checkpoint at 126 epochs +2025-06-25 09:09:06,768 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:09:06,825 - pyskl - INFO - +top1_acc 0.9362 +top5_acc 0.9971 +2025-06-25 09:09:06,825 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:09:06,831 - pyskl - INFO - +mean_acc 0.9163 +2025-06-25 09:09:06,833 - pyskl - INFO - Epoch(val) [126][533] top1_acc: 0.9362, top5_acc: 0.9971, mean_class_accuracy: 0.9163 +2025-06-25 09:10:10,219 - pyskl - INFO - Epoch [127][100/1281] lr: 1.536e-03, eta: 3:42:09, time: 0.634, data_time: 0.188, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0339, loss: 0.0339 +2025-06-25 09:10:50,256 - pyskl - INFO - Epoch [127][200/1281] lr: 1.527e-03, eta: 3:41:25, time: 0.400, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0267, loss: 0.0267 +2025-06-25 09:11:27,601 - pyskl - INFO - Epoch [127][300/1281] lr: 1.517e-03, eta: 3:40:40, time: 0.373, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0303, loss: 0.0303 +2025-06-25 09:12:16,663 - pyskl - INFO - Epoch [127][400/1281] lr: 1.507e-03, eta: 3:39:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0232, loss: 0.0232 +2025-06-25 09:13:05,368 - pyskl - INFO - Epoch [127][500/1281] lr: 1.497e-03, eta: 3:39:15, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0253, loss: 0.0253 +2025-06-25 09:13:54,325 - pyskl - INFO - Epoch [127][600/1281] lr: 1.488e-03, eta: 3:38:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0261, loss: 0.0261 +2025-06-25 09:14:43,398 - pyskl - INFO - Epoch [127][700/1281] lr: 1.478e-03, eta: 3:37:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0309, loss: 0.0309 +2025-06-25 09:15:32,400 - pyskl - INFO - Epoch [127][800/1281] lr: 1.468e-03, eta: 3:37:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0310, loss: 0.0310 +2025-06-25 09:16:21,315 - pyskl - INFO - Epoch [127][900/1281] lr: 1.459e-03, eta: 3:36:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 09:17:10,427 - pyskl - INFO - Epoch [127][1000/1281] lr: 1.449e-03, eta: 3:35:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-25 09:17:59,297 - pyskl - INFO - Epoch [127][1100/1281] lr: 1.440e-03, eta: 3:35:00, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0280, loss: 0.0280 +2025-06-25 09:18:48,280 - pyskl - INFO - Epoch [127][1200/1281] lr: 1.430e-03, eta: 3:34:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0340, loss: 0.0340 +2025-06-25 09:19:28,617 - pyskl - INFO - Saving checkpoint at 127 epochs +2025-06-25 09:20:27,278 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:20:27,334 - pyskl - INFO - +top1_acc 0.9367 +top5_acc 0.9975 +2025-06-25 09:20:27,334 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:20:27,341 - pyskl - INFO - +mean_acc 0.9164 +2025-06-25 09:20:27,343 - pyskl - INFO - Epoch(val) [127][533] top1_acc: 0.9367, top5_acc: 0.9975, mean_class_accuracy: 0.9164 +2025-06-25 09:21:30,469 - pyskl - INFO - Epoch [128][100/1281] lr: 1.413e-03, eta: 3:32:56, time: 0.631, data_time: 0.185, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0286, loss: 0.0286 +2025-06-25 09:22:09,645 - pyskl - INFO - Epoch [128][200/1281] lr: 1.404e-03, eta: 3:32:12, time: 0.392, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0263, loss: 0.0263 +2025-06-25 09:22:48,505 - pyskl - INFO - Epoch [128][300/1281] lr: 1.394e-03, eta: 3:31:28, time: 0.389, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0305, loss: 0.0305 +2025-06-25 09:23:37,452 - pyskl - INFO - Epoch [128][400/1281] lr: 1.385e-03, eta: 3:30:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0273, loss: 0.0273 +2025-06-25 09:24:26,321 - pyskl - INFO - Epoch [128][500/1281] lr: 1.376e-03, eta: 3:30:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0245, loss: 0.0245 +2025-06-25 09:25:15,578 - pyskl - INFO - Epoch [128][600/1281] lr: 1.366e-03, eta: 3:29:20, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0253, loss: 0.0253 +2025-06-25 09:26:04,259 - pyskl - INFO - Epoch [128][700/1281] lr: 1.357e-03, eta: 3:28:37, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0280, loss: 0.0280 +2025-06-25 09:26:53,800 - pyskl - INFO - Epoch [128][800/1281] lr: 1.348e-03, eta: 3:27:55, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-25 09:27:42,801 - pyskl - INFO - Epoch [128][900/1281] lr: 1.339e-03, eta: 3:27:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0239, loss: 0.0239 +2025-06-25 09:28:32,181 - pyskl - INFO - Epoch [128][1000/1281] lr: 1.329e-03, eta: 3:26:30, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0245, loss: 0.0245 +2025-06-25 09:29:21,091 - pyskl - INFO - Epoch [128][1100/1281] lr: 1.320e-03, eta: 3:25:47, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0313, loss: 0.0313 +2025-06-25 09:30:10,504 - pyskl - INFO - Epoch [128][1200/1281] lr: 1.311e-03, eta: 3:25:05, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0270, loss: 0.0270 +2025-06-25 09:30:50,761 - pyskl - INFO - Saving checkpoint at 128 epochs +2025-06-25 09:31:49,270 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:31:49,332 - pyskl - INFO - +top1_acc 0.9364 +top5_acc 0.9968 +2025-06-25 09:31:49,332 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:31:49,339 - pyskl - INFO - +mean_acc 0.9140 +2025-06-25 09:31:49,340 - pyskl - INFO - Epoch(val) [128][533] top1_acc: 0.9364, top5_acc: 0.9968, mean_class_accuracy: 0.9140 +2025-06-25 09:32:51,269 - pyskl - INFO - Epoch [129][100/1281] lr: 1.295e-03, eta: 3:23:43, time: 0.619, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 09:33:29,363 - pyskl - INFO - Epoch [129][200/1281] lr: 1.286e-03, eta: 3:22:59, time: 0.381, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0235, loss: 0.0235 +2025-06-25 09:34:05,626 - pyskl - INFO - Epoch [129][300/1281] lr: 1.277e-03, eta: 3:22:14, time: 0.363, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0266, loss: 0.0266 +2025-06-25 09:34:54,592 - pyskl - INFO - Epoch [129][400/1281] lr: 1.268e-03, eta: 3:21:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 09:35:43,365 - pyskl - INFO - Epoch [129][500/1281] lr: 1.259e-03, eta: 3:20:49, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0262, loss: 0.0262 +2025-06-25 09:36:32,134 - pyskl - INFO - Epoch [129][600/1281] lr: 1.250e-03, eta: 3:20:06, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-25 09:37:21,020 - pyskl - INFO - Epoch [129][700/1281] lr: 1.241e-03, eta: 3:19:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0235, loss: 0.0235 +2025-06-25 09:38:09,871 - pyskl - INFO - Epoch [129][800/1281] lr: 1.232e-03, eta: 3:18:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0257, loss: 0.0257 +2025-06-25 09:38:58,874 - pyskl - INFO - Epoch [129][900/1281] lr: 1.223e-03, eta: 3:17:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0299, loss: 0.0299 +2025-06-25 09:39:47,756 - pyskl - INFO - Epoch [129][1000/1281] lr: 1.214e-03, eta: 3:17:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0286, loss: 0.0286 +2025-06-25 09:40:36,640 - pyskl - INFO - Epoch [129][1100/1281] lr: 1.206e-03, eta: 3:16:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 09:41:25,734 - pyskl - INFO - Epoch [129][1200/1281] lr: 1.197e-03, eta: 3:15:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-25 09:42:05,999 - pyskl - INFO - Saving checkpoint at 129 epochs +2025-06-25 09:43:03,936 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:43:03,992 - pyskl - INFO - +top1_acc 0.9394 +top5_acc 0.9974 +2025-06-25 09:43:03,992 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:43:03,998 - pyskl - INFO - +mean_acc 0.9157 +2025-06-25 09:43:04,002 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_125.pth was removed +2025-06-25 09:43:04,174 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_129.pth. +2025-06-25 09:43:04,174 - pyskl - INFO - Best top1_acc is 0.9394 at 129 epoch. +2025-06-25 09:43:04,177 - pyskl - INFO - Epoch(val) [129][533] top1_acc: 0.9394, top5_acc: 0.9974, mean_class_accuracy: 0.9157 +2025-06-25 09:44:07,931 - pyskl - INFO - Epoch [130][100/1281] lr: 1.181e-03, eta: 3:14:29, time: 0.638, data_time: 0.186, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0250, loss: 0.0250 +2025-06-25 09:44:47,801 - pyskl - INFO - Epoch [130][200/1281] lr: 1.172e-03, eta: 3:13:45, time: 0.399, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-25 09:45:23,773 - pyskl - INFO - Epoch [130][300/1281] lr: 1.164e-03, eta: 3:13:00, time: 0.360, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 09:46:12,675 - pyskl - INFO - Epoch [130][400/1281] lr: 1.155e-03, eta: 3:12:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 09:47:01,111 - pyskl - INFO - Epoch [130][500/1281] lr: 1.147e-03, eta: 3:11:34, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0232, loss: 0.0232 +2025-06-25 09:47:49,670 - pyskl - INFO - Epoch [130][600/1281] lr: 1.138e-03, eta: 3:10:52, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-25 09:48:38,612 - pyskl - INFO - Epoch [130][700/1281] lr: 1.130e-03, eta: 3:10:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 09:49:27,530 - pyskl - INFO - Epoch [130][800/1281] lr: 1.121e-03, eta: 3:09:26, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 09:50:16,917 - pyskl - INFO - Epoch [130][900/1281] lr: 1.113e-03, eta: 3:08:44, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0232, loss: 0.0232 +2025-06-25 09:51:06,099 - pyskl - INFO - Epoch [130][1000/1281] lr: 1.104e-03, eta: 3:08:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0228, loss: 0.0228 +2025-06-25 09:51:55,072 - pyskl - INFO - Epoch [130][1100/1281] lr: 1.096e-03, eta: 3:07:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-25 09:52:44,115 - pyskl - INFO - Epoch [130][1200/1281] lr: 1.088e-03, eta: 3:06:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0223, loss: 0.0223 +2025-06-25 09:53:23,996 - pyskl - INFO - Saving checkpoint at 130 epochs +2025-06-25 09:54:22,364 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:54:22,418 - pyskl - INFO - +top1_acc 0.9392 +top5_acc 0.9977 +2025-06-25 09:54:22,418 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:54:22,424 - pyskl - INFO - +mean_acc 0.9176 +2025-06-25 09:54:22,426 - pyskl - INFO - Epoch(val) [130][533] top1_acc: 0.9392, top5_acc: 0.9977, mean_class_accuracy: 0.9176 +2025-06-25 09:55:24,924 - pyskl - INFO - Epoch [131][100/1281] lr: 1.072e-03, eta: 3:05:14, time: 0.625, data_time: 0.184, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 09:56:06,370 - pyskl - INFO - Epoch [131][200/1281] lr: 1.064e-03, eta: 3:04:30, time: 0.414, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 09:56:42,507 - pyskl - INFO - Epoch [131][300/1281] lr: 1.056e-03, eta: 3:03:46, time: 0.361, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 09:57:31,512 - pyskl - INFO - Epoch [131][400/1281] lr: 1.048e-03, eta: 3:03:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0316, loss: 0.0316 +2025-06-25 09:58:20,127 - pyskl - INFO - Epoch [131][500/1281] lr: 1.040e-03, eta: 3:02:20, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-25 09:59:09,113 - pyskl - INFO - Epoch [131][600/1281] lr: 1.031e-03, eta: 3:01:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0222, loss: 0.0222 +2025-06-25 09:59:58,320 - pyskl - INFO - Epoch [131][700/1281] lr: 1.023e-03, eta: 3:00:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0436, loss: 0.0436 +2025-06-25 10:00:47,301 - pyskl - INFO - Epoch [131][800/1281] lr: 1.015e-03, eta: 3:00:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0285, loss: 0.0285 +2025-06-25 10:01:36,379 - pyskl - INFO - Epoch [131][900/1281] lr: 1.007e-03, eta: 2:59:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 10:02:25,419 - pyskl - INFO - Epoch [131][1000/1281] lr: 9.992e-04, eta: 2:58:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 10:03:14,068 - pyskl - INFO - Epoch [131][1100/1281] lr: 9.912e-04, eta: 2:58:04, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 10:04:02,983 - pyskl - INFO - Epoch [131][1200/1281] lr: 9.832e-04, eta: 2:57:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-25 10:04:43,441 - pyskl - INFO - Saving checkpoint at 131 epochs +2025-06-25 10:05:41,490 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:05:41,545 - pyskl - INFO - +top1_acc 0.9386 +top5_acc 0.9969 +2025-06-25 10:05:41,545 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:05:41,551 - pyskl - INFO - +mean_acc 0.9163 +2025-06-25 10:05:41,552 - pyskl - INFO - Epoch(val) [131][533] top1_acc: 0.9386, top5_acc: 0.9969, mean_class_accuracy: 0.9163 +2025-06-25 10:06:43,850 - pyskl - INFO - Epoch [132][100/1281] lr: 9.689e-04, eta: 2:55:59, time: 0.623, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-25 10:07:24,925 - pyskl - INFO - Epoch [132][200/1281] lr: 9.610e-04, eta: 2:55:16, time: 0.411, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0218, loss: 0.0218 +2025-06-25 10:08:00,711 - pyskl - INFO - Epoch [132][300/1281] lr: 9.532e-04, eta: 2:54:31, time: 0.358, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 10:08:49,521 - pyskl - INFO - Epoch [132][400/1281] lr: 9.454e-04, eta: 2:53:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 10:09:38,279 - pyskl - INFO - Epoch [132][500/1281] lr: 9.376e-04, eta: 2:53:05, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0220, loss: 0.0220 +2025-06-25 10:10:27,207 - pyskl - INFO - Epoch [132][600/1281] lr: 9.298e-04, eta: 2:52:22, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 10:11:16,028 - pyskl - INFO - Epoch [132][700/1281] lr: 9.221e-04, eta: 2:51:40, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-25 10:12:05,477 - pyskl - INFO - Epoch [132][800/1281] lr: 9.144e-04, eta: 2:50:57, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 10:12:54,697 - pyskl - INFO - Epoch [132][900/1281] lr: 9.068e-04, eta: 2:50:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 10:13:43,854 - pyskl - INFO - Epoch [132][1000/1281] lr: 8.991e-04, eta: 2:49:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0259, loss: 0.0259 +2025-06-25 10:14:32,983 - pyskl - INFO - Epoch [132][1100/1281] lr: 8.915e-04, eta: 2:48:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 10:15:22,083 - pyskl - INFO - Epoch [132][1200/1281] lr: 8.840e-04, eta: 2:48:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 10:16:02,353 - pyskl - INFO - Saving checkpoint at 132 epochs +2025-06-25 10:17:00,797 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:17:00,864 - pyskl - INFO - +top1_acc 0.9377 +top5_acc 0.9972 +2025-06-25 10:17:00,864 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:17:00,871 - pyskl - INFO - +mean_acc 0.9163 +2025-06-25 10:17:00,872 - pyskl - INFO - Epoch(val) [132][533] top1_acc: 0.9377, top5_acc: 0.9972, mean_class_accuracy: 0.9163 +2025-06-25 10:18:03,933 - pyskl - INFO - Epoch [133][100/1281] lr: 8.704e-04, eta: 2:46:45, time: 0.631, data_time: 0.188, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 10:18:44,271 - pyskl - INFO - Epoch [133][200/1281] lr: 8.629e-04, eta: 2:46:01, time: 0.403, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-25 10:19:22,052 - pyskl - INFO - Epoch [133][300/1281] lr: 8.554e-04, eta: 2:45:16, time: 0.378, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 10:20:11,158 - pyskl - INFO - Epoch [133][400/1281] lr: 8.480e-04, eta: 2:44:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-25 10:21:00,286 - pyskl - INFO - Epoch [133][500/1281] lr: 8.406e-04, eta: 2:43:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 10:21:49,350 - pyskl - INFO - Epoch [133][600/1281] lr: 8.333e-04, eta: 2:43:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 10:22:38,319 - pyskl - INFO - Epoch [133][700/1281] lr: 8.260e-04, eta: 2:42:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 10:23:27,252 - pyskl - INFO - Epoch [133][800/1281] lr: 8.187e-04, eta: 2:41:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 10:24:15,935 - pyskl - INFO - Epoch [133][900/1281] lr: 8.114e-04, eta: 2:40:59, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 10:25:05,188 - pyskl - INFO - Epoch [133][1000/1281] lr: 8.042e-04, eta: 2:40:16, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 10:25:53,949 - pyskl - INFO - Epoch [133][1100/1281] lr: 7.970e-04, eta: 2:39:33, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 10:26:42,864 - pyskl - INFO - Epoch [133][1200/1281] lr: 7.898e-04, eta: 2:38:50, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 10:27:23,298 - pyskl - INFO - Saving checkpoint at 133 epochs +2025-06-25 10:28:20,817 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:28:20,871 - pyskl - INFO - +top1_acc 0.9400 +top5_acc 0.9975 +2025-06-25 10:28:20,871 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:28:20,877 - pyskl - INFO - +mean_acc 0.9186 +2025-06-25 10:28:20,881 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_129.pth was removed +2025-06-25 10:28:21,054 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_133.pth. +2025-06-25 10:28:21,055 - pyskl - INFO - Best top1_acc is 0.9400 at 133 epoch. +2025-06-25 10:28:21,057 - pyskl - INFO - Epoch(val) [133][533] top1_acc: 0.9400, top5_acc: 0.9975, mean_class_accuracy: 0.9186 +2025-06-25 10:29:23,729 - pyskl - INFO - Epoch [134][100/1281] lr: 7.769e-04, eta: 2:37:29, time: 0.627, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 10:30:03,443 - pyskl - INFO - Epoch [134][200/1281] lr: 7.699e-04, eta: 2:36:45, time: 0.397, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 10:30:40,874 - pyskl - INFO - Epoch [134][300/1281] lr: 7.628e-04, eta: 2:36:01, time: 0.374, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 10:31:29,982 - pyskl - INFO - Epoch [134][400/1281] lr: 7.558e-04, eta: 2:35:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 10:32:18,819 - pyskl - INFO - Epoch [134][500/1281] lr: 7.488e-04, eta: 2:34:35, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 10:33:08,029 - pyskl - INFO - Epoch [134][600/1281] lr: 7.419e-04, eta: 2:33:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0283, loss: 0.0283 +2025-06-25 10:33:56,958 - pyskl - INFO - Epoch [134][700/1281] lr: 7.349e-04, eta: 2:33:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 10:34:46,656 - pyskl - INFO - Epoch [134][800/1281] lr: 7.281e-04, eta: 2:32:26, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 10:35:35,314 - pyskl - INFO - Epoch [134][900/1281] lr: 7.212e-04, eta: 2:31:43, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0237, loss: 0.0237 +2025-06-25 10:36:24,620 - pyskl - INFO - Epoch [134][1000/1281] lr: 7.144e-04, eta: 2:31:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 10:37:13,564 - pyskl - INFO - Epoch [134][1100/1281] lr: 7.076e-04, eta: 2:30:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 10:38:02,705 - pyskl - INFO - Epoch [134][1200/1281] lr: 7.008e-04, eta: 2:29:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 10:38:42,757 - pyskl - INFO - Saving checkpoint at 134 epochs +2025-06-25 10:39:41,210 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:39:41,274 - pyskl - INFO - +top1_acc 0.9377 +top5_acc 0.9971 +2025-06-25 10:39:41,274 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:39:41,282 - pyskl - INFO - +mean_acc 0.9172 +2025-06-25 10:39:41,284 - pyskl - INFO - Epoch(val) [134][533] top1_acc: 0.9377, top5_acc: 0.9971, mean_class_accuracy: 0.9172 +2025-06-25 10:40:42,911 - pyskl - INFO - Epoch [135][100/1281] lr: 6.887e-04, eta: 2:28:13, time: 0.616, data_time: 0.193, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 10:41:23,301 - pyskl - INFO - Epoch [135][200/1281] lr: 6.820e-04, eta: 2:27:29, time: 0.404, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 10:41:59,997 - pyskl - INFO - Epoch [135][300/1281] lr: 6.753e-04, eta: 2:26:45, time: 0.367, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 10:42:48,778 - pyskl - INFO - Epoch [135][400/1281] lr: 6.687e-04, eta: 2:26:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 10:43:37,447 - pyskl - INFO - Epoch [135][500/1281] lr: 6.622e-04, eta: 2:25:19, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 10:44:26,282 - pyskl - INFO - Epoch [135][600/1281] lr: 6.556e-04, eta: 2:24:36, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 10:45:14,819 - pyskl - INFO - Epoch [135][700/1281] lr: 6.491e-04, eta: 2:23:53, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 10:46:03,982 - pyskl - INFO - Epoch [135][800/1281] lr: 6.426e-04, eta: 2:23:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0211, loss: 0.0211 +2025-06-25 10:46:53,388 - pyskl - INFO - Epoch [135][900/1281] lr: 6.362e-04, eta: 2:22:27, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 10:47:42,581 - pyskl - INFO - Epoch [135][1000/1281] lr: 6.297e-04, eta: 2:21:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 10:48:31,657 - pyskl - INFO - Epoch [135][1100/1281] lr: 6.233e-04, eta: 2:21:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 10:49:20,843 - pyskl - INFO - Epoch [135][1200/1281] lr: 6.170e-04, eta: 2:20:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 10:50:00,878 - pyskl - INFO - Saving checkpoint at 135 epochs +2025-06-25 10:50:59,335 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:50:59,390 - pyskl - INFO - +top1_acc 0.9411 +top5_acc 0.9974 +2025-06-25 10:50:59,390 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:50:59,396 - pyskl - INFO - +mean_acc 0.9221 +2025-06-25 10:50:59,401 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_133.pth was removed +2025-06-25 10:50:59,576 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_135.pth. +2025-06-25 10:50:59,576 - pyskl - INFO - Best top1_acc is 0.9411 at 135 epoch. +2025-06-25 10:50:59,579 - pyskl - INFO - Epoch(val) [135][533] top1_acc: 0.9411, top5_acc: 0.9974, mean_class_accuracy: 0.9221 +2025-06-25 10:52:02,107 - pyskl - INFO - Epoch [136][100/1281] lr: 6.056e-04, eta: 2:18:57, time: 0.625, data_time: 0.185, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0228, loss: 0.0228 +2025-06-25 10:52:42,674 - pyskl - INFO - Epoch [136][200/1281] lr: 5.993e-04, eta: 2:18:13, time: 0.406, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 10:53:19,952 - pyskl - INFO - Epoch [136][300/1281] lr: 5.931e-04, eta: 2:17:29, time: 0.373, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 10:54:08,924 - pyskl - INFO - Epoch [136][400/1281] lr: 5.868e-04, eta: 2:16:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 10:54:57,632 - pyskl - INFO - Epoch [136][500/1281] lr: 5.807e-04, eta: 2:16:03, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 10:55:46,853 - pyskl - INFO - Epoch [136][600/1281] lr: 5.745e-04, eta: 2:15:20, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 10:56:35,620 - pyskl - INFO - Epoch [136][700/1281] lr: 5.684e-04, eta: 2:14:37, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 10:57:24,768 - pyskl - INFO - Epoch [136][800/1281] lr: 5.623e-04, eta: 2:13:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 10:58:13,739 - pyskl - INFO - Epoch [136][900/1281] lr: 5.563e-04, eta: 2:13:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 10:59:02,675 - pyskl - INFO - Epoch [136][1000/1281] lr: 5.503e-04, eta: 2:12:28, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 10:59:51,605 - pyskl - INFO - Epoch [136][1100/1281] lr: 5.443e-04, eta: 2:11:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 11:00:40,708 - pyskl - INFO - Epoch [136][1200/1281] lr: 5.384e-04, eta: 2:11:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0228, loss: 0.0228 +2025-06-25 11:01:21,062 - pyskl - INFO - Saving checkpoint at 136 epochs +2025-06-25 11:02:19,089 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:02:19,167 - pyskl - INFO - +top1_acc 0.9397 +top5_acc 0.9973 +2025-06-25 11:02:19,167 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:02:19,176 - pyskl - INFO - +mean_acc 0.9211 +2025-06-25 11:02:19,179 - pyskl - INFO - Epoch(val) [136][533] top1_acc: 0.9397, top5_acc: 0.9973, mean_class_accuracy: 0.9211 +2025-06-25 11:03:21,541 - pyskl - INFO - Epoch [137][100/1281] lr: 5.277e-04, eta: 2:09:41, time: 0.624, data_time: 0.185, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 11:04:01,609 - pyskl - INFO - Epoch [137][200/1281] lr: 5.218e-04, eta: 2:08:57, time: 0.401, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 11:04:38,743 - pyskl - INFO - Epoch [137][300/1281] lr: 5.160e-04, eta: 2:08:13, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 11:05:27,614 - pyskl - INFO - Epoch [137][400/1281] lr: 5.102e-04, eta: 2:07:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 11:06:16,693 - pyskl - INFO - Epoch [137][500/1281] lr: 5.044e-04, eta: 2:06:46, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 11:07:05,554 - pyskl - INFO - Epoch [137][600/1281] lr: 4.987e-04, eta: 2:06:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 11:07:54,527 - pyskl - INFO - Epoch [137][700/1281] lr: 4.930e-04, eta: 2:05:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 11:08:43,605 - pyskl - INFO - Epoch [137][800/1281] lr: 4.873e-04, eta: 2:04:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0216, loss: 0.0216 +2025-06-25 11:09:32,989 - pyskl - INFO - Epoch [137][900/1281] lr: 4.817e-04, eta: 2:03:54, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 11:10:22,204 - pyskl - INFO - Epoch [137][1000/1281] lr: 4.761e-04, eta: 2:03:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 11:11:11,396 - pyskl - INFO - Epoch [137][1100/1281] lr: 4.705e-04, eta: 2:02:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 11:12:00,589 - pyskl - INFO - Epoch [137][1200/1281] lr: 4.650e-04, eta: 2:01:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 11:12:40,898 - pyskl - INFO - Saving checkpoint at 137 epochs +2025-06-25 11:13:39,042 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:13:39,099 - pyskl - INFO - +top1_acc 0.9387 +top5_acc 0.9975 +2025-06-25 11:13:39,100 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:13:39,106 - pyskl - INFO - +mean_acc 0.9192 +2025-06-25 11:13:39,108 - pyskl - INFO - Epoch(val) [137][533] top1_acc: 0.9387, top5_acc: 0.9975, mean_class_accuracy: 0.9192 +2025-06-25 11:14:42,245 - pyskl - INFO - Epoch [138][100/1281] lr: 4.550e-04, eta: 2:00:24, time: 0.631, data_time: 0.184, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0232, loss: 0.0232 +2025-06-25 11:15:21,667 - pyskl - INFO - Epoch [138][200/1281] lr: 4.496e-04, eta: 1:59:40, time: 0.394, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 11:15:57,642 - pyskl - INFO - Epoch [138][300/1281] lr: 4.442e-04, eta: 1:58:56, time: 0.360, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 11:16:46,444 - pyskl - INFO - Epoch [138][400/1281] lr: 4.388e-04, eta: 1:58:13, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 11:17:35,593 - pyskl - INFO - Epoch [138][500/1281] lr: 4.334e-04, eta: 1:57:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 11:18:24,464 - pyskl - INFO - Epoch [138][600/1281] lr: 4.281e-04, eta: 1:56:46, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 11:19:13,297 - pyskl - INFO - Epoch [138][700/1281] lr: 4.228e-04, eta: 1:56:03, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 11:20:02,410 - pyskl - INFO - Epoch [138][800/1281] lr: 4.176e-04, eta: 1:55:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 11:20:51,492 - pyskl - INFO - Epoch [138][900/1281] lr: 4.124e-04, eta: 1:54:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 11:21:40,634 - pyskl - INFO - Epoch [138][1000/1281] lr: 4.072e-04, eta: 1:53:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 11:22:29,497 - pyskl - INFO - Epoch [138][1100/1281] lr: 4.020e-04, eta: 1:53:10, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 11:23:18,286 - pyskl - INFO - Epoch [138][1200/1281] lr: 3.969e-04, eta: 1:52:27, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 11:23:58,270 - pyskl - INFO - Saving checkpoint at 138 epochs +2025-06-25 11:24:56,288 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:24:56,343 - pyskl - INFO - +top1_acc 0.9417 +top5_acc 0.9974 +2025-06-25 11:24:56,343 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:24:56,350 - pyskl - INFO - +mean_acc 0.9208 +2025-06-25 11:24:56,354 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_135.pth was removed +2025-06-25 11:24:56,526 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_138.pth. +2025-06-25 11:24:56,526 - pyskl - INFO - Best top1_acc is 0.9417 at 138 epoch. +2025-06-25 11:24:56,529 - pyskl - INFO - Epoch(val) [138][533] top1_acc: 0.9417, top5_acc: 0.9974, mean_class_accuracy: 0.9208 +2025-06-25 11:25:59,982 - pyskl - INFO - Epoch [139][100/1281] lr: 3.877e-04, eta: 1:51:07, time: 0.634, data_time: 0.179, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 11:26:41,290 - pyskl - INFO - Epoch [139][200/1281] lr: 3.827e-04, eta: 1:50:23, time: 0.413, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 11:27:36,178 - pyskl - INFO - Epoch [139][300/1281] lr: 3.777e-04, eta: 1:49:40, time: 0.549, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 11:28:46,451 - pyskl - INFO - Epoch [139][400/1281] lr: 3.727e-04, eta: 1:48:59, time: 0.703, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 11:29:55,497 - pyskl - INFO - Epoch [139][500/1281] lr: 3.678e-04, eta: 1:48:17, time: 0.690, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 11:31:03,538 - pyskl - INFO - Epoch [139][600/1281] lr: 3.628e-04, eta: 1:47:36, time: 0.680, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 11:32:10,996 - pyskl - INFO - Epoch [139][700/1281] lr: 3.580e-04, eta: 1:46:54, time: 0.675, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 11:33:20,342 - pyskl - INFO - Epoch [139][800/1281] lr: 3.531e-04, eta: 1:46:12, time: 0.693, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 11:34:20,985 - pyskl - INFO - Epoch [139][900/1281] lr: 3.483e-04, eta: 1:45:30, time: 0.606, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 11:35:00,035 - pyskl - INFO - Epoch [139][1000/1281] lr: 3.436e-04, eta: 1:44:46, time: 0.390, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 11:36:10,193 - pyskl - INFO - Epoch [139][1100/1281] lr: 3.388e-04, eta: 1:44:04, time: 0.702, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 11:37:20,690 - pyskl - INFO - Epoch [139][1200/1281] lr: 3.341e-04, eta: 1:43:22, time: 0.705, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 11:38:19,691 - pyskl - INFO - Saving checkpoint at 139 epochs +2025-06-25 11:39:34,164 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:39:34,235 - pyskl - INFO - +top1_acc 0.9417 +top5_acc 0.9971 +2025-06-25 11:39:34,235 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:39:34,243 - pyskl - INFO - +mean_acc 0.9208 +2025-06-25 11:39:34,245 - pyskl - INFO - Epoch(val) [139][533] top1_acc: 0.9417, top5_acc: 0.9971, mean_class_accuracy: 0.9208 +2025-06-25 11:41:31,132 - pyskl - INFO - Epoch [140][100/1281] lr: 3.257e-04, eta: 1:42:06, time: 1.169, data_time: 0.183, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 11:42:02,085 - pyskl - INFO - Epoch [140][200/1281] lr: 3.210e-04, eta: 1:41:21, time: 0.310, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 11:42:24,555 - pyskl - INFO - Epoch [140][300/1281] lr: 3.165e-04, eta: 1:40:36, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 11:42:46,708 - pyskl - INFO - Epoch [140][400/1281] lr: 3.119e-04, eta: 1:39:51, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 11:43:09,261 - pyskl - INFO - Epoch [140][500/1281] lr: 3.074e-04, eta: 1:39:05, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0228, loss: 0.0228 +2025-06-25 11:43:31,379 - pyskl - INFO - Epoch [140][600/1281] lr: 3.029e-04, eta: 1:38:20, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 11:43:53,647 - pyskl - INFO - Epoch [140][700/1281] lr: 2.984e-04, eta: 1:37:34, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 11:44:15,731 - pyskl - INFO - Epoch [140][800/1281] lr: 2.940e-04, eta: 1:36:49, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 11:44:37,466 - pyskl - INFO - Epoch [140][900/1281] lr: 2.896e-04, eta: 1:36:04, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 11:44:59,318 - pyskl - INFO - Epoch [140][1000/1281] lr: 2.853e-04, eta: 1:35:18, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 11:45:21,551 - pyskl - INFO - Epoch [140][1100/1281] lr: 2.809e-04, eta: 1:34:33, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 11:45:43,745 - pyskl - INFO - Epoch [140][1200/1281] lr: 2.767e-04, eta: 1:33:48, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 11:46:02,693 - pyskl - INFO - Saving checkpoint at 140 epochs +2025-06-25 11:46:45,218 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:46:45,272 - pyskl - INFO - +top1_acc 0.9420 +top5_acc 0.9974 +2025-06-25 11:46:45,272 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:46:45,279 - pyskl - INFO - +mean_acc 0.9224 +2025-06-25 11:46:45,283 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_138.pth was removed +2025-06-25 11:46:45,451 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_140.pth. +2025-06-25 11:46:45,451 - pyskl - INFO - Best top1_acc is 0.9420 at 140 epoch. +2025-06-25 11:46:45,454 - pyskl - INFO - Epoch(val) [140][533] top1_acc: 0.9420, top5_acc: 0.9974, mean_class_accuracy: 0.9224 +2025-06-25 11:47:27,240 - pyskl - INFO - Epoch [141][100/1281] lr: 2.690e-04, eta: 1:32:26, time: 0.418, data_time: 0.181, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 11:47:49,887 - pyskl - INFO - Epoch [141][200/1281] lr: 2.648e-04, eta: 1:31:41, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 11:48:11,884 - pyskl - INFO - Epoch [141][300/1281] lr: 2.606e-04, eta: 1:30:56, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 11:48:34,210 - pyskl - INFO - Epoch [141][400/1281] lr: 2.565e-04, eta: 1:30:11, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 11:48:56,347 - pyskl - INFO - Epoch [141][500/1281] lr: 2.524e-04, eta: 1:29:26, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 11:49:18,238 - pyskl - INFO - Epoch [141][600/1281] lr: 2.483e-04, eta: 1:28:41, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 11:49:40,379 - pyskl - INFO - Epoch [141][700/1281] lr: 2.443e-04, eta: 1:27:56, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0249, loss: 0.0249 +2025-06-25 11:50:02,349 - pyskl - INFO - Epoch [141][800/1281] lr: 2.402e-04, eta: 1:27:11, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 11:50:24,291 - pyskl - INFO - Epoch [141][900/1281] lr: 2.363e-04, eta: 1:26:26, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 11:50:46,472 - pyskl - INFO - Epoch [141][1000/1281] lr: 2.323e-04, eta: 1:25:41, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 11:51:08,778 - pyskl - INFO - Epoch [141][1100/1281] lr: 2.284e-04, eta: 1:24:56, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 11:51:31,075 - pyskl - INFO - Epoch [141][1200/1281] lr: 2.246e-04, eta: 1:24:11, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 11:51:49,583 - pyskl - INFO - Saving checkpoint at 141 epochs +2025-06-25 11:52:31,653 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:52:31,721 - pyskl - INFO - +top1_acc 0.9423 +top5_acc 0.9978 +2025-06-25 11:52:31,721 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:52:31,728 - pyskl - INFO - +mean_acc 0.9212 +2025-06-25 11:52:31,733 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_140.pth was removed +2025-06-25 11:52:31,900 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_141.pth. +2025-06-25 11:52:31,901 - pyskl - INFO - Best top1_acc is 0.9423 at 141 epoch. +2025-06-25 11:52:31,903 - pyskl - INFO - Epoch(val) [141][533] top1_acc: 0.9423, top5_acc: 0.9978, mean_class_accuracy: 0.9212 +2025-06-25 11:53:13,212 - pyskl - INFO - Epoch [142][100/1281] lr: 2.176e-04, eta: 1:22:50, time: 0.413, data_time: 0.180, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 11:53:35,364 - pyskl - INFO - Epoch [142][200/1281] lr: 2.139e-04, eta: 1:22:05, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 11:53:58,064 - pyskl - INFO - Epoch [142][300/1281] lr: 2.101e-04, eta: 1:21:20, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 11:54:20,208 - pyskl - INFO - Epoch [142][400/1281] lr: 2.064e-04, eta: 1:20:36, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 11:54:42,478 - pyskl - INFO - Epoch [142][500/1281] lr: 2.027e-04, eta: 1:19:51, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 11:55:04,881 - pyskl - INFO - Epoch [142][600/1281] lr: 1.991e-04, eta: 1:19:06, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 11:55:26,754 - pyskl - INFO - Epoch [142][700/1281] lr: 1.954e-04, eta: 1:18:21, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 11:55:49,005 - pyskl - INFO - Epoch [142][800/1281] lr: 1.919e-04, eta: 1:17:37, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0218, loss: 0.0218 +2025-06-25 11:56:11,217 - pyskl - INFO - Epoch [142][900/1281] lr: 1.883e-04, eta: 1:16:52, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 11:56:33,272 - pyskl - INFO - Epoch [142][1000/1281] lr: 1.848e-04, eta: 1:16:07, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 11:56:55,886 - pyskl - INFO - Epoch [142][1100/1281] lr: 1.813e-04, eta: 1:15:23, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 11:57:18,008 - pyskl - INFO - Epoch [142][1200/1281] lr: 1.779e-04, eta: 1:14:38, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 11:57:36,683 - pyskl - INFO - Saving checkpoint at 142 epochs +2025-06-25 11:58:19,458 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:58:19,513 - pyskl - INFO - +top1_acc 0.9425 +top5_acc 0.9974 +2025-06-25 11:58:19,513 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:58:19,519 - pyskl - INFO - +mean_acc 0.9240 +2025-06-25 11:58:19,523 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_141.pth was removed +2025-06-25 11:58:19,689 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_142.pth. +2025-06-25 11:58:19,690 - pyskl - INFO - Best top1_acc is 0.9425 at 142 epoch. +2025-06-25 11:58:19,692 - pyskl - INFO - Epoch(val) [142][533] top1_acc: 0.9425, top5_acc: 0.9974, mean_class_accuracy: 0.9240 +2025-06-25 11:59:01,396 - pyskl - INFO - Epoch [143][100/1281] lr: 1.717e-04, eta: 1:13:18, time: 0.417, data_time: 0.183, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 11:59:23,620 - pyskl - INFO - Epoch [143][200/1281] lr: 1.683e-04, eta: 1:12:33, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 11:59:45,652 - pyskl - INFO - Epoch [143][300/1281] lr: 1.650e-04, eta: 1:11:49, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 12:00:07,757 - pyskl - INFO - Epoch [143][400/1281] lr: 1.617e-04, eta: 1:11:04, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 12:00:29,886 - pyskl - INFO - Epoch [143][500/1281] lr: 1.585e-04, eta: 1:10:20, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 12:00:52,090 - pyskl - INFO - Epoch [143][600/1281] lr: 1.552e-04, eta: 1:09:35, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 12:01:13,963 - pyskl - INFO - Epoch [143][700/1281] lr: 1.520e-04, eta: 1:08:51, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 12:01:36,187 - pyskl - INFO - Epoch [143][800/1281] lr: 1.489e-04, eta: 1:08:07, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 12:01:58,275 - pyskl - INFO - Epoch [143][900/1281] lr: 1.457e-04, eta: 1:07:22, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 12:02:20,906 - pyskl - INFO - Epoch [143][1000/1281] lr: 1.426e-04, eta: 1:06:38, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 12:02:43,079 - pyskl - INFO - Epoch [143][1100/1281] lr: 1.396e-04, eta: 1:05:54, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 12:03:05,066 - pyskl - INFO - Epoch [143][1200/1281] lr: 1.366e-04, eta: 1:05:10, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 12:03:24,515 - pyskl - INFO - Saving checkpoint at 143 epochs +2025-06-25 12:04:06,743 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:04:06,802 - pyskl - INFO - +top1_acc 0.9418 +top5_acc 0.9969 +2025-06-25 12:04:06,802 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:04:06,808 - pyskl - INFO - +mean_acc 0.9219 +2025-06-25 12:04:06,810 - pyskl - INFO - Epoch(val) [143][533] top1_acc: 0.9418, top5_acc: 0.9969, mean_class_accuracy: 0.9219 +2025-06-25 12:04:47,909 - pyskl - INFO - Epoch [144][100/1281] lr: 1.312e-04, eta: 1:03:50, time: 0.411, data_time: 0.178, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 12:05:10,180 - pyskl - INFO - Epoch [144][200/1281] lr: 1.282e-04, eta: 1:03:05, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0224, loss: 0.0224 +2025-06-25 12:05:32,420 - pyskl - INFO - Epoch [144][300/1281] lr: 1.253e-04, eta: 1:02:21, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 12:05:54,875 - pyskl - INFO - Epoch [144][400/1281] lr: 1.224e-04, eta: 1:01:37, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 12:06:17,094 - pyskl - INFO - Epoch [144][500/1281] lr: 1.196e-04, eta: 1:00:53, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 12:06:39,442 - pyskl - INFO - Epoch [144][600/1281] lr: 1.168e-04, eta: 1:00:09, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 12:07:01,503 - pyskl - INFO - Epoch [144][700/1281] lr: 1.140e-04, eta: 0:59:25, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0222, loss: 0.0222 +2025-06-25 12:07:23,448 - pyskl - INFO - Epoch [144][800/1281] lr: 1.113e-04, eta: 0:58:41, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 12:07:45,497 - pyskl - INFO - Epoch [144][900/1281] lr: 1.086e-04, eta: 0:57:57, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 12:08:07,601 - pyskl - INFO - Epoch [144][1000/1281] lr: 1.059e-04, eta: 0:57:13, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 12:08:29,829 - pyskl - INFO - Epoch [144][1100/1281] lr: 1.033e-04, eta: 0:56:29, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 12:08:51,785 - pyskl - INFO - Epoch [144][1200/1281] lr: 1.007e-04, eta: 0:55:45, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 12:09:10,328 - pyskl - INFO - Saving checkpoint at 144 epochs +2025-06-25 12:09:52,611 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:09:52,666 - pyskl - INFO - +top1_acc 0.9426 +top5_acc 0.9980 +2025-06-25 12:09:52,666 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:09:52,673 - pyskl - INFO - +mean_acc 0.9218 +2025-06-25 12:09:52,676 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_142.pth was removed +2025-06-25 12:09:52,858 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_144.pth. +2025-06-25 12:09:52,858 - pyskl - INFO - Best top1_acc is 0.9426 at 144 epoch. +2025-06-25 12:09:52,861 - pyskl - INFO - Epoch(val) [144][533] top1_acc: 0.9426, top5_acc: 0.9980, mean_class_accuracy: 0.9218 +2025-06-25 12:10:33,895 - pyskl - INFO - Epoch [145][100/1281] lr: 9.605e-05, eta: 0:54:25, time: 0.410, data_time: 0.174, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 12:10:56,260 - pyskl - INFO - Epoch [145][200/1281] lr: 9.353e-05, eta: 0:53:41, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 12:11:18,134 - pyskl - INFO - Epoch [145][300/1281] lr: 9.106e-05, eta: 0:52:57, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 12:11:40,095 - pyskl - INFO - Epoch [145][400/1281] lr: 8.861e-05, eta: 0:52:14, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 12:12:02,351 - pyskl - INFO - Epoch [145][500/1281] lr: 8.620e-05, eta: 0:51:30, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 12:12:24,370 - pyskl - INFO - Epoch [145][600/1281] lr: 8.382e-05, eta: 0:50:46, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 12:12:46,278 - pyskl - INFO - Epoch [145][700/1281] lr: 8.147e-05, eta: 0:50:02, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 12:13:08,403 - pyskl - INFO - Epoch [145][800/1281] lr: 7.916e-05, eta: 0:49:18, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 12:13:30,416 - pyskl - INFO - Epoch [145][900/1281] lr: 7.688e-05, eta: 0:48:35, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 12:13:52,604 - pyskl - INFO - Epoch [145][1000/1281] lr: 7.463e-05, eta: 0:47:51, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 12:14:14,961 - pyskl - INFO - Epoch [145][1100/1281] lr: 7.242e-05, eta: 0:47:07, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 12:14:37,066 - pyskl - INFO - Epoch [145][1200/1281] lr: 7.024e-05, eta: 0:46:24, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 12:14:55,952 - pyskl - INFO - Saving checkpoint at 145 epochs +2025-06-25 12:15:38,158 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:15:38,214 - pyskl - INFO - +top1_acc 0.9423 +top5_acc 0.9974 +2025-06-25 12:15:38,214 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:15:38,221 - pyskl - INFO - +mean_acc 0.9193 +2025-06-25 12:15:38,222 - pyskl - INFO - Epoch(val) [145][533] top1_acc: 0.9423, top5_acc: 0.9974, mean_class_accuracy: 0.9193 +2025-06-25 12:16:18,908 - pyskl - INFO - Epoch [146][100/1281] lr: 6.638e-05, eta: 0:45:05, time: 0.407, data_time: 0.177, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0193, loss: 0.0193 +2025-06-25 12:16:40,921 - pyskl - INFO - Epoch [146][200/1281] lr: 6.429e-05, eta: 0:44:21, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 12:17:03,141 - pyskl - INFO - Epoch [146][300/1281] lr: 6.224e-05, eta: 0:43:37, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 12:17:25,352 - pyskl - INFO - Epoch [146][400/1281] lr: 6.022e-05, eta: 0:42:54, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 12:17:47,558 - pyskl - INFO - Epoch [146][500/1281] lr: 5.823e-05, eta: 0:42:10, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 12:18:09,654 - pyskl - INFO - Epoch [146][600/1281] lr: 5.628e-05, eta: 0:41:27, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 12:18:31,779 - pyskl - INFO - Epoch [146][700/1281] lr: 5.436e-05, eta: 0:40:43, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 12:18:53,603 - pyskl - INFO - Epoch [146][800/1281] lr: 5.247e-05, eta: 0:40:00, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 12:19:15,637 - pyskl - INFO - Epoch [146][900/1281] lr: 5.061e-05, eta: 0:39:16, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 12:19:37,688 - pyskl - INFO - Epoch [146][1000/1281] lr: 4.879e-05, eta: 0:38:33, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 12:19:59,948 - pyskl - INFO - Epoch [146][1100/1281] lr: 4.701e-05, eta: 0:37:50, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 12:20:22,223 - pyskl - INFO - Epoch [146][1200/1281] lr: 4.525e-05, eta: 0:37:06, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0188, loss: 0.0188 +2025-06-25 12:20:40,640 - pyskl - INFO - Saving checkpoint at 146 epochs +2025-06-25 12:21:23,183 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:21:23,237 - pyskl - INFO - +top1_acc 0.9418 +top5_acc 0.9973 +2025-06-25 12:21:23,237 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:21:23,244 - pyskl - INFO - +mean_acc 0.9189 +2025-06-25 12:21:23,245 - pyskl - INFO - Epoch(val) [146][533] top1_acc: 0.9418, top5_acc: 0.9973, mean_class_accuracy: 0.9189 +2025-06-25 12:22:04,584 - pyskl - INFO - Epoch [147][100/1281] lr: 4.216e-05, eta: 0:35:48, time: 0.413, data_time: 0.180, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 12:22:27,045 - pyskl - INFO - Epoch [147][200/1281] lr: 4.050e-05, eta: 0:35:05, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 12:22:49,121 - pyskl - INFO - Epoch [147][300/1281] lr: 3.887e-05, eta: 0:34:21, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 12:23:11,296 - pyskl - INFO - Epoch [147][400/1281] lr: 3.728e-05, eta: 0:33:38, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 12:23:33,382 - pyskl - INFO - Epoch [147][500/1281] lr: 3.572e-05, eta: 0:32:55, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 12:23:55,334 - pyskl - INFO - Epoch [147][600/1281] lr: 3.419e-05, eta: 0:32:12, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 12:24:17,432 - pyskl - INFO - Epoch [147][700/1281] lr: 3.270e-05, eta: 0:31:28, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 12:24:39,572 - pyskl - INFO - Epoch [147][800/1281] lr: 3.124e-05, eta: 0:30:45, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 12:25:01,720 - pyskl - INFO - Epoch [147][900/1281] lr: 2.981e-05, eta: 0:30:02, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 12:25:23,496 - pyskl - INFO - Epoch [147][1000/1281] lr: 2.842e-05, eta: 0:29:19, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 12:25:45,460 - pyskl - INFO - Epoch [147][1100/1281] lr: 2.706e-05, eta: 0:28:36, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-25 12:26:07,562 - pyskl - INFO - Epoch [147][1200/1281] lr: 2.573e-05, eta: 0:27:53, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 12:26:26,135 - pyskl - INFO - Saving checkpoint at 147 epochs +2025-06-25 12:27:08,723 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:27:08,779 - pyskl - INFO - +top1_acc 0.9421 +top5_acc 0.9972 +2025-06-25 12:27:08,780 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:27:08,786 - pyskl - INFO - +mean_acc 0.9215 +2025-06-25 12:27:08,788 - pyskl - INFO - Epoch(val) [147][533] top1_acc: 0.9421, top5_acc: 0.9972, mean_class_accuracy: 0.9215 +2025-06-25 12:27:49,289 - pyskl - INFO - Epoch [148][100/1281] lr: 2.341e-05, eta: 0:26:35, time: 0.405, data_time: 0.174, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 12:28:11,462 - pyskl - INFO - Epoch [148][200/1281] lr: 2.218e-05, eta: 0:25:52, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 12:28:33,489 - pyskl - INFO - Epoch [148][300/1281] lr: 2.098e-05, eta: 0:25:09, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 12:28:55,904 - pyskl - INFO - Epoch [148][400/1281] lr: 1.981e-05, eta: 0:24:26, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 12:29:18,082 - pyskl - INFO - Epoch [148][500/1281] lr: 1.868e-05, eta: 0:23:43, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 12:29:40,400 - pyskl - INFO - Epoch [148][600/1281] lr: 1.758e-05, eta: 0:23:00, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 12:30:02,629 - pyskl - INFO - Epoch [148][700/1281] lr: 1.651e-05, eta: 0:22:17, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 12:30:24,439 - pyskl - INFO - Epoch [148][800/1281] lr: 1.548e-05, eta: 0:21:34, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 12:30:46,272 - pyskl - INFO - Epoch [148][900/1281] lr: 1.448e-05, eta: 0:20:51, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 12:31:08,505 - pyskl - INFO - Epoch [148][1000/1281] lr: 1.351e-05, eta: 0:20:08, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 12:31:30,461 - pyskl - INFO - Epoch [148][1100/1281] lr: 1.258e-05, eta: 0:19:26, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 12:31:52,475 - pyskl - INFO - Epoch [148][1200/1281] lr: 1.168e-05, eta: 0:18:43, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 12:32:11,297 - pyskl - INFO - Saving checkpoint at 148 epochs +2025-06-25 12:32:53,579 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:32:53,632 - pyskl - INFO - +top1_acc 0.9424 +top5_acc 0.9973 +2025-06-25 12:32:53,633 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:32:53,639 - pyskl - INFO - +mean_acc 0.9216 +2025-06-25 12:32:53,640 - pyskl - INFO - Epoch(val) [148][533] top1_acc: 0.9424, top5_acc: 0.9973, mean_class_accuracy: 0.9216 +2025-06-25 12:33:35,425 - pyskl - INFO - Epoch [149][100/1281] lr: 1.013e-05, eta: 0:17:25, time: 0.418, data_time: 0.183, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 12:33:57,542 - pyskl - INFO - Epoch [149][200/1281] lr: 9.328e-06, eta: 0:16:43, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 12:34:19,913 - pyskl - INFO - Epoch [149][300/1281] lr: 8.555e-06, eta: 0:16:00, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 12:34:41,975 - pyskl - INFO - Epoch [149][400/1281] lr: 7.816e-06, eta: 0:15:17, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-25 12:35:03,978 - pyskl - INFO - Epoch [149][500/1281] lr: 7.110e-06, eta: 0:14:35, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 12:35:26,298 - pyskl - INFO - Epoch [149][600/1281] lr: 6.437e-06, eta: 0:13:52, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 12:35:48,470 - pyskl - INFO - Epoch [149][700/1281] lr: 5.798e-06, eta: 0:13:09, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 12:36:10,440 - pyskl - INFO - Epoch [149][800/1281] lr: 5.192e-06, eta: 0:12:27, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 12:36:32,540 - pyskl - INFO - Epoch [149][900/1281] lr: 4.620e-06, eta: 0:11:44, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 12:36:54,780 - pyskl - INFO - Epoch [149][1000/1281] lr: 4.081e-06, eta: 0:11:02, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 12:37:16,805 - pyskl - INFO - Epoch [149][1100/1281] lr: 3.576e-06, eta: 0:10:19, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 12:37:38,819 - pyskl - INFO - Epoch [149][1200/1281] lr: 3.104e-06, eta: 0:09:37, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 12:37:57,563 - pyskl - INFO - Saving checkpoint at 149 epochs +2025-06-25 12:38:40,516 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:38:40,569 - pyskl - INFO - +top1_acc 0.9418 +top5_acc 0.9973 +2025-06-25 12:38:40,569 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:38:40,575 - pyskl - INFO - +mean_acc 0.9224 +2025-06-25 12:38:40,577 - pyskl - INFO - Epoch(val) [149][533] top1_acc: 0.9418, top5_acc: 0.9973, mean_class_accuracy: 0.9224 +2025-06-25 12:39:21,733 - pyskl - INFO - Epoch [150][100/1281] lr: 2.334e-06, eta: 0:08:20, time: 0.412, data_time: 0.179, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 12:39:44,016 - pyskl - INFO - Epoch [150][200/1281] lr: 1.956e-06, eta: 0:07:37, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 12:40:06,329 - pyskl - INFO - Epoch [150][300/1281] lr: 1.611e-06, eta: 0:06:55, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 12:40:28,462 - pyskl - INFO - Epoch [150][400/1281] lr: 1.300e-06, eta: 0:06:12, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 12:40:50,597 - pyskl - INFO - Epoch [150][500/1281] lr: 1.022e-06, eta: 0:05:30, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 12:41:12,755 - pyskl - INFO - Epoch [150][600/1281] lr: 7.771e-07, eta: 0:04:48, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 12:41:34,726 - pyskl - INFO - Epoch [150][700/1281] lr: 5.659e-07, eta: 0:04:05, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 12:41:57,051 - pyskl - INFO - Epoch [150][800/1281] lr: 3.881e-07, eta: 0:03:23, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 12:42:19,492 - pyskl - INFO - Epoch [150][900/1281] lr: 2.438e-07, eta: 0:02:41, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 12:42:42,000 - pyskl - INFO - Epoch [150][1000/1281] lr: 1.329e-07, eta: 0:01:58, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 12:43:04,698 - pyskl - INFO - Epoch [150][1100/1281] lr: 5.534e-08, eta: 0:01:16, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 12:43:26,986 - pyskl - INFO - Epoch [150][1200/1281] lr: 1.123e-08, eta: 0:00:34, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 12:43:45,718 - pyskl - INFO - Saving checkpoint at 150 epochs +2025-06-25 12:44:27,608 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:44:27,662 - pyskl - INFO - +top1_acc 0.9437 +top5_acc 0.9973 +2025-06-25 12:44:27,662 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:44:27,669 - pyskl - INFO - +mean_acc 0.9233 +2025-06-25 12:44:27,672 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_144.pth was removed +2025-06-25 12:44:27,831 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_150.pth. +2025-06-25 12:44:27,832 - pyskl - INFO - Best top1_acc is 0.9437 at 150 epoch. +2025-06-25 12:44:27,834 - pyskl - INFO - Epoch(val) [150][533] top1_acc: 0.9437, top5_acc: 0.9973, mean_class_accuracy: 0.9233 +2025-06-25 12:44:32,229 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-25 12:49:42,729 - pyskl - INFO - Testing results of the last checkpoint +2025-06-25 12:49:42,729 - pyskl - INFO - top1_acc: 0.9438 +2025-06-25 12:49:42,729 - pyskl - INFO - top5_acc: 0.9979 +2025-06-25 12:49:42,729 - pyskl - INFO - mean_class_accuracy: 0.9248 +2025-06-25 12:49:42,730 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/finegym/k_2/best_top1_acc_epoch_150.pth +2025-06-25 12:54:50,057 - pyskl - INFO - Testing results of the best checkpoint +2025-06-25 12:54:50,057 - pyskl - INFO - top1_acc: 0.9438 +2025-06-25 12:54:50,057 - pyskl - INFO - top5_acc: 0.9979 +2025-06-25 12:54:50,057 - pyskl - INFO - mean_class_accuracy: 0.9248 diff --git a/finegym/k_2/20250624_101213.log.json b/finegym/k_2/20250624_101213.log.json new file mode 100644 index 0000000000000000000000000000000000000000..b401f79593d1e08f56b4912c9b938df062661508 --- /dev/null +++ b/finegym/k_2/20250624_101213.log.json @@ -0,0 +1,1951 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 171654347, "config_name": "k_2.py", "work_dir": "k_2", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.19385, "top1_acc": 0.065, "top5_acc": 0.22562, "loss_cls": 4.56401, "loss": 4.56401, "time": 0.65032} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.10125, "top5_acc": 0.33188, "loss_cls": 4.53775, "loss": 4.53775, "time": 0.24112} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.11688, "top5_acc": 0.36188, "loss_cls": 4.31755, "loss": 4.31755, "time": 0.41511} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.12562, "top5_acc": 0.41875, "loss_cls": 4.03233, "loss": 4.03233, "time": 0.41338} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.14375, "top5_acc": 0.50813, "loss_cls": 3.78292, "loss": 3.78292, "time": 0.4149} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.16875, "top5_acc": 0.51062, "loss_cls": 3.69096, "loss": 3.69096, "time": 0.41358} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.025, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.17875, "top5_acc": 0.53062, "loss_cls": 3.60852, "loss": 3.60852, "time": 0.41646} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.025, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.21688, "top5_acc": 0.59125, "loss_cls": 3.39126, "loss": 3.39126, "time": 0.41505} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.025, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.25438, "top5_acc": 0.605, "loss_cls": 3.2616, "loss": 3.2616, "time": 0.41486} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.2825, "top5_acc": 0.65125, "loss_cls": 3.09262, "loss": 3.09262, "time": 0.4126} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.305, "top5_acc": 0.71188, "loss_cls": 2.95794, "loss": 2.95794, "time": 0.41462} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.32688, "top5_acc": 0.7275, "loss_cls": 2.86411, "loss": 2.86411, "time": 0.41325} +{"mode": "val", "epoch": 1, "iter": 533, "lr": 0.025, "top1_acc": 0.2853, "top5_acc": 0.69088, "mean_class_accuracy": 0.15518} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.19044, "top1_acc": 0.34312, "top5_acc": 0.74938, "loss_cls": 2.75885, "loss": 2.75885, "time": 0.6408} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.36062, "top5_acc": 0.78188, "loss_cls": 2.60621, "loss": 2.60621, "time": 0.22792} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.39125, "top5_acc": 0.81688, "loss_cls": 2.48014, "loss": 2.48014, "time": 0.41694} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.4025, "top5_acc": 0.8025, "loss_cls": 2.42263, "loss": 2.42263, "time": 0.41605} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.02499, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.43062, "top5_acc": 0.81625, "loss_cls": 2.37694, "loss": 2.37694, "time": 0.41793} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.02499, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.45312, "top5_acc": 0.83625, "loss_cls": 2.27403, "loss": 2.27403, "time": 0.41375} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.02499, "memory": 4082, "data_time": 0.00053, "top1_acc": 0.44375, "top5_acc": 0.85812, "loss_cls": 2.20712, "loss": 2.20712, "time": 0.41562} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.02499, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.47, "top5_acc": 0.85688, "loss_cls": 2.17398, "loss": 2.17398, "time": 0.43494} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.02499, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.4775, "top5_acc": 0.87188, "loss_cls": 2.11558, "loss": 2.11558, "time": 0.41282} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.02499, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.47, "top5_acc": 0.84438, "loss_cls": 2.19631, "loss": 2.19631, "time": 0.41381} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.02499, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.49625, "top5_acc": 0.87875, "loss_cls": 2.0291, "loss": 2.0291, "time": 0.41287} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.02499, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.49125, "top5_acc": 0.87125, "loss_cls": 2.09698, "loss": 2.09698, "time": 0.41411} +{"mode": "val", "epoch": 2, "iter": 533, "lr": 0.02499, "top1_acc": 0.49818, "top5_acc": 0.87807, "mean_class_accuracy": 0.31987} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.02499, "memory": 4082, "data_time": 0.18863, "top1_acc": 0.50688, "top5_acc": 0.88375, "loss_cls": 1.99561, "loss": 1.99561, "time": 0.64323} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.02499, "memory": 4082, "data_time": 0.00059, "top1_acc": 0.54062, "top5_acc": 0.91, "loss_cls": 1.86266, "loss": 1.86266, "time": 0.2443} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.02499, "memory": 4082, "data_time": 0.00104, "top1_acc": 0.55062, "top5_acc": 0.91062, "loss_cls": 1.83512, "loss": 1.83512, "time": 0.41113} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.02499, "memory": 4082, "data_time": 0.00053, "top1_acc": 0.54062, "top5_acc": 0.90062, "loss_cls": 1.86382, "loss": 1.86382, "time": 0.42266} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.02498, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.55312, "top5_acc": 0.91875, "loss_cls": 1.80452, "loss": 1.80452, "time": 0.41433} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.02498, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.57938, "top5_acc": 0.91812, "loss_cls": 1.72329, "loss": 1.72329, "time": 0.41449} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.02498, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.57875, "top5_acc": 0.90812, "loss_cls": 1.77831, "loss": 1.77831, "time": 0.41508} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.02498, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.58188, "top5_acc": 0.92812, "loss_cls": 1.71397, "loss": 1.71397, "time": 0.41346} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.02498, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.5625, "top5_acc": 0.925, "loss_cls": 1.78236, "loss": 1.78236, "time": 0.41447} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.02498, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.58688, "top5_acc": 0.92875, "loss_cls": 1.66253, "loss": 1.66253, "time": 0.41405} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.02498, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.61438, "top5_acc": 0.93875, "loss_cls": 1.5963, "loss": 1.5963, "time": 0.41486} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.02498, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.59312, "top5_acc": 0.92875, "loss_cls": 1.67152, "loss": 1.67152, "time": 0.41551} +{"mode": "val", "epoch": 3, "iter": 533, "lr": 0.02498, "top1_acc": 0.59993, "top5_acc": 0.93557, "mean_class_accuracy": 0.45757} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.02497, "memory": 4082, "data_time": 0.19057, "top1_acc": 0.6, "top5_acc": 0.945, "loss_cls": 1.6151, "loss": 1.6151, "time": 0.64591} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.02497, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.60812, "top5_acc": 0.94062, "loss_cls": 1.56, "loss": 1.56, "time": 0.23921} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.02497, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.62375, "top5_acc": 0.9575, "loss_cls": 1.53451, "loss": 1.53451, "time": 0.41565} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.02497, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.59625, "top5_acc": 0.93938, "loss_cls": 1.58739, "loss": 1.58739, "time": 0.41378} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.02497, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.63062, "top5_acc": 0.955, "loss_cls": 1.53427, "loss": 1.53427, "time": 0.41542} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.02497, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.61, "top5_acc": 0.9525, "loss_cls": 1.52638, "loss": 1.52638, "time": 0.41325} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.02497, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.63125, "top5_acc": 0.95812, "loss_cls": 1.49383, "loss": 1.49383, "time": 0.41358} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.02496, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.6275, "top5_acc": 0.94625, "loss_cls": 1.53567, "loss": 1.53567, "time": 0.41496} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.02496, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.625, "top5_acc": 0.94875, "loss_cls": 1.47588, "loss": 1.47588, "time": 0.414} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.02496, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.65375, "top5_acc": 0.94875, "loss_cls": 1.44888, "loss": 1.44888, "time": 0.41432} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.02496, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.65, "top5_acc": 0.95562, "loss_cls": 1.46216, "loss": 1.46216, "time": 0.4156} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.02496, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.64812, "top5_acc": 0.94812, "loss_cls": 1.44109, "loss": 1.44109, "time": 0.4122} +{"mode": "val", "epoch": 4, "iter": 533, "lr": 0.02496, "top1_acc": 0.64758, "top5_acc": 0.95059, "mean_class_accuracy": 0.50831} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.02495, "memory": 4082, "data_time": 0.19517, "top1_acc": 0.65688, "top5_acc": 0.955, "loss_cls": 1.40519, "loss": 1.40519, "time": 0.64674} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.02495, "memory": 4082, "data_time": 0.00068, "top1_acc": 0.65812, "top5_acc": 0.95438, "loss_cls": 1.4174, "loss": 1.4174, "time": 0.23587} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.02495, "memory": 4082, "data_time": 0.00054, "top1_acc": 0.66688, "top5_acc": 0.97, "loss_cls": 1.35461, "loss": 1.35461, "time": 0.4318} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.02495, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.65312, "top5_acc": 0.95812, "loss_cls": 1.39212, "loss": 1.39212, "time": 0.41498} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.02495, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.66688, "top5_acc": 0.95875, "loss_cls": 1.35843, "loss": 1.35843, "time": 0.41383} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.02495, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.67875, "top5_acc": 0.96562, "loss_cls": 1.33461, "loss": 1.33461, "time": 0.41352} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.02494, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.66562, "top5_acc": 0.95438, "loss_cls": 1.3797, "loss": 1.3797, "time": 0.41441} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.02494, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.69062, "top5_acc": 0.96812, "loss_cls": 1.30285, "loss": 1.30285, "time": 0.4124} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.02494, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.67625, "top5_acc": 0.95938, "loss_cls": 1.35959, "loss": 1.35959, "time": 0.41282} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.02494, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.65625, "top5_acc": 0.96125, "loss_cls": 1.3721, "loss": 1.3721, "time": 0.41478} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.02494, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.68875, "top5_acc": 0.96562, "loss_cls": 1.29389, "loss": 1.29389, "time": 0.4139} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.02493, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.67938, "top5_acc": 0.96562, "loss_cls": 1.29006, "loss": 1.29006, "time": 0.41353} +{"mode": "val", "epoch": 5, "iter": 533, "lr": 0.02493, "top1_acc": 0.65509, "top5_acc": 0.95317, "mean_class_accuracy": 0.50877} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.02493, "memory": 4082, "data_time": 0.19144, "top1_acc": 0.66312, "top5_acc": 0.96938, "loss_cls": 1.32706, "loss": 1.32706, "time": 0.64271} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.02493, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.67312, "top5_acc": 0.97188, "loss_cls": 1.30923, "loss": 1.30923, "time": 0.25126} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.02492, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.67125, "top5_acc": 0.96875, "loss_cls": 1.3249, "loss": 1.3249, "time": 0.41893} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.02492, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.6825, "top5_acc": 0.97562, "loss_cls": 1.26459, "loss": 1.26459, "time": 0.41696} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.02492, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.675, "top5_acc": 0.96688, "loss_cls": 1.33061, "loss": 1.33061, "time": 0.43156} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.02492, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.70375, "top5_acc": 0.96438, "loss_cls": 1.24015, "loss": 1.24015, "time": 0.43677} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.02492, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.69875, "top5_acc": 0.96938, "loss_cls": 1.26558, "loss": 1.26558, "time": 0.4183} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.02491, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.71188, "top5_acc": 0.97062, "loss_cls": 1.21322, "loss": 1.21322, "time": 0.41271} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.02491, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.71188, "top5_acc": 0.96688, "loss_cls": 1.25024, "loss": 1.25024, "time": 0.41362} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.02491, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.70688, "top5_acc": 0.96812, "loss_cls": 1.23619, "loss": 1.23619, "time": 0.41361} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.02491, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.71375, "top5_acc": 0.97625, "loss_cls": 1.18512, "loss": 1.18512, "time": 0.41311} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.0249, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.70312, "top5_acc": 0.96875, "loss_cls": 1.23277, "loss": 1.23277, "time": 0.41345} +{"mode": "val", "epoch": 6, "iter": 533, "lr": 0.0249, "top1_acc": 0.6734, "top5_acc": 0.96432, "mean_class_accuracy": 0.54521} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0249, "memory": 4082, "data_time": 0.19506, "top1_acc": 0.695, "top5_acc": 0.97438, "loss_cls": 1.24334, "loss": 1.24334, "time": 0.64895} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0249, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.72562, "top5_acc": 0.97875, "loss_cls": 1.14951, "loss": 1.14951, "time": 0.24919} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.02489, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.71625, "top5_acc": 0.97, "loss_cls": 1.17716, "loss": 1.17716, "time": 0.40371} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.02489, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.705, "top5_acc": 0.96562, "loss_cls": 1.2565, "loss": 1.2565, "time": 0.4156} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.02489, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.71625, "top5_acc": 0.97375, "loss_cls": 1.18718, "loss": 1.18718, "time": 0.41441} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.02489, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.72438, "top5_acc": 0.97812, "loss_cls": 1.13522, "loss": 1.13522, "time": 0.4159} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.02488, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.7325, "top5_acc": 0.975, "loss_cls": 1.1102, "loss": 1.1102, "time": 0.41303} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.02488, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.71812, "top5_acc": 0.9725, "loss_cls": 1.1983, "loss": 1.1983, "time": 0.41535} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.02488, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.72375, "top5_acc": 0.97562, "loss_cls": 1.15532, "loss": 1.15532, "time": 0.41344} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.02487, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.72812, "top5_acc": 0.97438, "loss_cls": 1.14196, "loss": 1.14196, "time": 0.41562} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.02487, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.73938, "top5_acc": 0.97312, "loss_cls": 1.11348, "loss": 1.11348, "time": 0.41365} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.02487, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.70875, "top5_acc": 0.96438, "loss_cls": 1.17889, "loss": 1.17889, "time": 0.41218} +{"mode": "val", "epoch": 7, "iter": 533, "lr": 0.02487, "top1_acc": 0.70696, "top5_acc": 0.96855, "mean_class_accuracy": 0.58659} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.02486, "memory": 4082, "data_time": 0.19759, "top1_acc": 0.75062, "top5_acc": 0.9725, "loss_cls": 1.06777, "loss": 1.06777, "time": 0.65072} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.02486, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.73312, "top5_acc": 0.97688, "loss_cls": 1.11378, "loss": 1.11378, "time": 0.24884} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.02486, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.75125, "top5_acc": 0.9825, "loss_cls": 1.06099, "loss": 1.06099, "time": 0.40477} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.02485, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.71875, "top5_acc": 0.97688, "loss_cls": 1.11351, "loss": 1.11351, "time": 0.41535} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.02485, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.73812, "top5_acc": 0.975, "loss_cls": 1.15541, "loss": 1.15541, "time": 0.41437} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.02485, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.72688, "top5_acc": 0.97875, "loss_cls": 1.13138, "loss": 1.13138, "time": 0.4157} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.02484, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.74438, "top5_acc": 0.97938, "loss_cls": 1.07476, "loss": 1.07476, "time": 0.41487} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.02484, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.745, "top5_acc": 0.9775, "loss_cls": 1.06756, "loss": 1.06756, "time": 0.41402} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.02484, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.74125, "top5_acc": 0.9775, "loss_cls": 1.09605, "loss": 1.09605, "time": 0.4125} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.02483, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.74688, "top5_acc": 0.97625, "loss_cls": 1.07371, "loss": 1.07371, "time": 0.41389} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.02483, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.72562, "top5_acc": 0.9675, "loss_cls": 1.1882, "loss": 1.1882, "time": 0.41533} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.02483, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.755, "top5_acc": 0.98375, "loss_cls": 1.07487, "loss": 1.07487, "time": 0.41417} +{"mode": "val", "epoch": 8, "iter": 533, "lr": 0.02482, "top1_acc": 0.7147, "top5_acc": 0.97313, "mean_class_accuracy": 0.61425} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.02482, "memory": 4082, "data_time": 0.20135, "top1_acc": 0.75438, "top5_acc": 0.98438, "loss_cls": 1.02938, "loss": 1.02938, "time": 0.65361} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.02482, "memory": 4082, "data_time": 0.00046, "top1_acc": 0.73812, "top5_acc": 0.98, "loss_cls": 1.07531, "loss": 1.07531, "time": 0.24492} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.02481, "memory": 4082, "data_time": 0.00054, "top1_acc": 0.76688, "top5_acc": 0.98375, "loss_cls": 1.02141, "loss": 1.02141, "time": 0.4154} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.02481, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.75188, "top5_acc": 0.98625, "loss_cls": 1.04305, "loss": 1.04305, "time": 0.41544} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.02481, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.72312, "top5_acc": 0.98, "loss_cls": 1.12916, "loss": 1.12916, "time": 0.41473} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.0248, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.74375, "top5_acc": 0.97625, "loss_cls": 1.05529, "loss": 1.05529, "time": 0.41384} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.0248, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.74188, "top5_acc": 0.98, "loss_cls": 1.10727, "loss": 1.10727, "time": 0.41385} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.0248, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.75312, "top5_acc": 0.97688, "loss_cls": 1.05769, "loss": 1.05769, "time": 0.41392} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.02479, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.75812, "top5_acc": 0.97938, "loss_cls": 1.03016, "loss": 1.03016, "time": 0.41562} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.02479, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.7375, "top5_acc": 0.97875, "loss_cls": 1.08749, "loss": 1.08749, "time": 0.41488} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.02479, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7525, "top5_acc": 0.97938, "loss_cls": 1.04111, "loss": 1.04111, "time": 0.41426} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.02478, "memory": 4082, "data_time": 0.00062, "top1_acc": 0.7575, "top5_acc": 0.98, "loss_cls": 1.00991, "loss": 1.00991, "time": 0.4155} +{"mode": "val", "epoch": 9, "iter": 533, "lr": 0.02478, "top1_acc": 0.70931, "top5_acc": 0.96608, "mean_class_accuracy": 0.60123} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.02477, "memory": 4082, "data_time": 0.19437, "top1_acc": 0.74188, "top5_acc": 0.97312, "loss_cls": 1.0984, "loss": 1.0984, "time": 0.64629} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.02477, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.74812, "top5_acc": 0.98125, "loss_cls": 1.04844, "loss": 1.04844, "time": 0.24045} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.02477, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7575, "top5_acc": 0.97875, "loss_cls": 1.02736, "loss": 1.02736, "time": 0.41021} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.02476, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.76938, "top5_acc": 0.98562, "loss_cls": 0.98374, "loss": 0.98374, "time": 0.41396} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.02476, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.74875, "top5_acc": 0.98562, "loss_cls": 1.05224, "loss": 1.05224, "time": 0.41357} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.02476, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.77625, "top5_acc": 0.98, "loss_cls": 1.01057, "loss": 1.01057, "time": 0.41385} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.02475, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.755, "top5_acc": 0.98375, "loss_cls": 1.02596, "loss": 1.02596, "time": 0.4144} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.02475, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.76875, "top5_acc": 0.98062, "loss_cls": 0.9931, "loss": 0.9931, "time": 0.41293} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.02474, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.75938, "top5_acc": 0.98125, "loss_cls": 1.04385, "loss": 1.04385, "time": 0.4134} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.02474, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.745, "top5_acc": 0.97875, "loss_cls": 1.05775, "loss": 1.05775, "time": 0.41347} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.02473, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.75125, "top5_acc": 0.98688, "loss_cls": 1.02097, "loss": 1.02097, "time": 0.41296} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.02473, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.78125, "top5_acc": 0.98688, "loss_cls": 0.95793, "loss": 0.95793, "time": 0.4137} +{"mode": "val", "epoch": 10, "iter": 533, "lr": 0.02473, "top1_acc": 0.75179, "top5_acc": 0.97841, "mean_class_accuracy": 0.64773} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.02472, "memory": 4082, "data_time": 0.20006, "top1_acc": 0.77625, "top5_acc": 0.9875, "loss_cls": 0.92859, "loss": 0.92859, "time": 0.65182} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.02472, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.76625, "top5_acc": 0.985, "loss_cls": 1.00714, "loss": 1.00714, "time": 0.24759} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.02471, "memory": 4082, "data_time": 0.00046, "top1_acc": 0.7675, "top5_acc": 0.97562, "loss_cls": 1.02581, "loss": 1.02581, "time": 0.41029} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.02471, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7575, "top5_acc": 0.97812, "loss_cls": 1.00938, "loss": 1.00938, "time": 0.41448} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.02471, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7725, "top5_acc": 0.98062, "loss_cls": 0.95852, "loss": 0.95852, "time": 0.41431} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.0247, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.78688, "top5_acc": 0.98812, "loss_cls": 0.93997, "loss": 0.93997, "time": 0.41445} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.0247, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.78312, "top5_acc": 0.9825, "loss_cls": 0.95296, "loss": 0.95296, "time": 0.41462} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.02469, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.76188, "top5_acc": 0.98312, "loss_cls": 0.98106, "loss": 0.98106, "time": 0.41404} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.02469, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.75938, "top5_acc": 0.97938, "loss_cls": 1.00146, "loss": 1.00146, "time": 0.41422} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.02468, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.775, "top5_acc": 0.98688, "loss_cls": 0.95069, "loss": 0.95069, "time": 0.41463} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.02468, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.75188, "top5_acc": 0.98312, "loss_cls": 1.01291, "loss": 1.01291, "time": 0.41386} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.02467, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.78125, "top5_acc": 0.98812, "loss_cls": 0.979, "loss": 0.979, "time": 0.41439} +{"mode": "val", "epoch": 11, "iter": 533, "lr": 0.02467, "top1_acc": 0.72186, "top5_acc": 0.97066, "mean_class_accuracy": 0.62833} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.02467, "memory": 4082, "data_time": 0.19796, "top1_acc": 0.75188, "top5_acc": 0.98562, "loss_cls": 1.01718, "loss": 1.01718, "time": 0.65317} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.02466, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.75688, "top5_acc": 0.97938, "loss_cls": 1.00991, "loss": 1.00991, "time": 0.24734} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.02466, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.77938, "top5_acc": 0.98688, "loss_cls": 0.93207, "loss": 0.93207, "time": 0.40061} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.02465, "memory": 4082, "data_time": 0.00053, "top1_acc": 0.77375, "top5_acc": 0.98625, "loss_cls": 0.95926, "loss": 0.95926, "time": 0.41438} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.02465, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.8, "top5_acc": 0.98562, "loss_cls": 0.91753, "loss": 0.91753, "time": 0.41475} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.02464, "memory": 4082, "data_time": 0.00057, "top1_acc": 0.79562, "top5_acc": 0.98625, "loss_cls": 0.89265, "loss": 0.89265, "time": 0.41546} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.02464, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77312, "top5_acc": 0.98375, "loss_cls": 0.98812, "loss": 0.98812, "time": 0.41264} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.02463, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79125, "top5_acc": 0.98688, "loss_cls": 0.92609, "loss": 0.92609, "time": 0.41434} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.02463, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.79, "top5_acc": 0.98, "loss_cls": 0.96501, "loss": 0.96501, "time": 0.41409} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.02462, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.77375, "top5_acc": 0.98312, "loss_cls": 0.97612, "loss": 0.97612, "time": 0.4143} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.02462, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77062, "top5_acc": 0.98312, "loss_cls": 0.9585, "loss": 0.9585, "time": 0.4139} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.02461, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.75188, "top5_acc": 0.97938, "loss_cls": 1.02905, "loss": 1.02905, "time": 0.41383} +{"mode": "val", "epoch": 12, "iter": 533, "lr": 0.02461, "top1_acc": 0.74064, "top5_acc": 0.97758, "mean_class_accuracy": 0.64053} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.0246, "memory": 4082, "data_time": 0.19539, "top1_acc": 0.79, "top5_acc": 0.98938, "loss_cls": 0.9182, "loss": 0.9182, "time": 0.65099} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.0246, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.79312, "top5_acc": 0.99312, "loss_cls": 0.88811, "loss": 0.88811, "time": 0.25477} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.02459, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.76875, "top5_acc": 0.98062, "loss_cls": 0.98771, "loss": 0.98771, "time": 0.39613} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.02459, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.79125, "top5_acc": 0.985, "loss_cls": 0.92711, "loss": 0.92711, "time": 0.41329} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.02458, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78562, "top5_acc": 0.98562, "loss_cls": 0.92374, "loss": 0.92374, "time": 0.41524} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.02458, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.78188, "top5_acc": 0.98125, "loss_cls": 0.93372, "loss": 0.93372, "time": 0.41458} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.02457, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.76688, "top5_acc": 0.98188, "loss_cls": 0.99808, "loss": 0.99808, "time": 0.41328} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.02457, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.77562, "top5_acc": 0.98125, "loss_cls": 0.92232, "loss": 0.92232, "time": 0.41483} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.02456, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.76875, "top5_acc": 0.98688, "loss_cls": 0.96157, "loss": 0.96157, "time": 0.41343} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.02455, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.77625, "top5_acc": 0.985, "loss_cls": 0.94208, "loss": 0.94208, "time": 0.41604} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.02455, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78375, "top5_acc": 0.98562, "loss_cls": 0.9308, "loss": 0.9308, "time": 0.41343} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.02454, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80062, "top5_acc": 0.985, "loss_cls": 0.91652, "loss": 0.91652, "time": 0.41336} +{"mode": "val", "epoch": 13, "iter": 533, "lr": 0.02454, "top1_acc": 0.74686, "top5_acc": 0.97805, "mean_class_accuracy": 0.65244} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.02453, "memory": 4082, "data_time": 0.19141, "top1_acc": 0.79062, "top5_acc": 0.98688, "loss_cls": 0.90177, "loss": 0.90177, "time": 0.63559} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.02453, "memory": 4082, "data_time": 0.00064, "top1_acc": 0.78938, "top5_acc": 0.99062, "loss_cls": 0.90107, "loss": 0.90107, "time": 0.26785} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.02452, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.81312, "top5_acc": 0.985, "loss_cls": 0.85888, "loss": 0.85888, "time": 0.3684} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.02452, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.80188, "top5_acc": 0.98875, "loss_cls": 0.86791, "loss": 0.86791, "time": 0.39424} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.02451, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.78875, "top5_acc": 0.98812, "loss_cls": 0.87945, "loss": 0.87945, "time": 0.3958} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.02451, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.79625, "top5_acc": 0.98438, "loss_cls": 0.86472, "loss": 0.86472, "time": 0.40012} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.0245, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.78938, "top5_acc": 0.98938, "loss_cls": 0.91197, "loss": 0.91197, "time": 0.38813} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.02449, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.78438, "top5_acc": 0.98375, "loss_cls": 0.91367, "loss": 0.91367, "time": 0.38576} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.02449, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.81375, "top5_acc": 0.98812, "loss_cls": 0.82985, "loss": 0.82985, "time": 0.38602} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.02448, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.78812, "top5_acc": 0.99, "loss_cls": 0.88347, "loss": 0.88347, "time": 0.38518} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.02448, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.78562, "top5_acc": 0.98, "loss_cls": 0.93398, "loss": 0.93398, "time": 0.3869} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.02447, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.78875, "top5_acc": 0.98375, "loss_cls": 0.91962, "loss": 0.91962, "time": 0.37611} +{"mode": "val", "epoch": 14, "iter": 533, "lr": 0.02447, "top1_acc": 0.76071, "top5_acc": 0.98157, "mean_class_accuracy": 0.64455} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.02446, "memory": 4082, "data_time": 0.19653, "top1_acc": 0.8175, "top5_acc": 0.98812, "loss_cls": 0.80563, "loss": 0.80563, "time": 0.59804} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.02445, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.80188, "top5_acc": 0.99312, "loss_cls": 0.85391, "loss": 0.85391, "time": 0.37994} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.02445, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.80188, "top5_acc": 0.98688, "loss_cls": 0.88533, "loss": 0.88533, "time": 0.36143} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.02444, "memory": 4082, "data_time": 0.00054, "top1_acc": 0.76938, "top5_acc": 0.98812, "loss_cls": 0.95998, "loss": 0.95998, "time": 0.29132} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.02444, "memory": 4082, "data_time": 0.00054, "top1_acc": 0.80375, "top5_acc": 0.98812, "loss_cls": 0.83773, "loss": 0.83773, "time": 0.42166} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.02443, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.78438, "top5_acc": 0.98875, "loss_cls": 0.89546, "loss": 0.89546, "time": 0.2263} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.02442, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.79062, "top5_acc": 0.98625, "loss_cls": 0.90035, "loss": 0.90035, "time": 0.28842} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.02442, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.77812, "top5_acc": 0.97875, "loss_cls": 0.95314, "loss": 0.95314, "time": 0.38057} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.02441, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81312, "top5_acc": 0.9875, "loss_cls": 0.84987, "loss": 0.84987, "time": 0.3751} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.02441, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.805, "top5_acc": 0.9875, "loss_cls": 0.87731, "loss": 0.87731, "time": 0.36804} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.0244, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.79062, "top5_acc": 0.99312, "loss_cls": 0.88249, "loss": 0.88249, "time": 0.3765} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.02439, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.78375, "top5_acc": 0.98438, "loss_cls": 0.95053, "loss": 0.95053, "time": 0.38157} +{"mode": "val", "epoch": 15, "iter": 533, "lr": 0.02439, "top1_acc": 0.75038, "top5_acc": 0.977, "mean_class_accuracy": 0.6553} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.02438, "memory": 4082, "data_time": 0.19801, "top1_acc": 0.80812, "top5_acc": 0.985, "loss_cls": 0.86954, "loss": 0.86954, "time": 0.5751} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.02438, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.80062, "top5_acc": 0.98562, "loss_cls": 0.87533, "loss": 0.87533, "time": 0.37708} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.02437, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81875, "top5_acc": 0.98812, "loss_cls": 0.80666, "loss": 0.80666, "time": 0.38251} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.02436, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.79688, "top5_acc": 0.98875, "loss_cls": 0.86663, "loss": 0.86663, "time": 0.37078} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.02436, "memory": 4082, "data_time": 0.00055, "top1_acc": 0.78812, "top5_acc": 0.98812, "loss_cls": 0.9068, "loss": 0.9068, "time": 0.38524} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.02435, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.79875, "top5_acc": 0.98188, "loss_cls": 0.86049, "loss": 0.86049, "time": 0.38814} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.02434, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82688, "top5_acc": 0.98938, "loss_cls": 0.77531, "loss": 0.77531, "time": 0.38234} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.02434, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8075, "top5_acc": 0.98688, "loss_cls": 0.85144, "loss": 0.85144, "time": 0.37818} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.02433, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81625, "top5_acc": 0.98562, "loss_cls": 0.81973, "loss": 0.81973, "time": 0.29865} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.02432, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.80688, "top5_acc": 0.9925, "loss_cls": 0.83599, "loss": 0.83599, "time": 0.36564} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.02432, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.7975, "top5_acc": 0.98688, "loss_cls": 0.88969, "loss": 0.88969, "time": 0.34369} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.02431, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.80438, "top5_acc": 0.985, "loss_cls": 0.85247, "loss": 0.85247, "time": 0.23702} +{"mode": "val", "epoch": 16, "iter": 533, "lr": 0.0243, "top1_acc": 0.78606, "top5_acc": 0.98169, "mean_class_accuracy": 0.69571} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.0243, "memory": 4082, "data_time": 0.1938, "top1_acc": 0.8175, "top5_acc": 0.99, "loss_cls": 0.80278, "loss": 0.80278, "time": 0.57236} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.02429, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79812, "top5_acc": 0.98688, "loss_cls": 0.88515, "loss": 0.88515, "time": 0.37358} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.02428, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.81562, "top5_acc": 0.9875, "loss_cls": 0.84786, "loss": 0.84786, "time": 0.37778} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.02428, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.81625, "top5_acc": 0.99188, "loss_cls": 0.77996, "loss": 0.77996, "time": 0.3671} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.02427, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81, "top5_acc": 0.9875, "loss_cls": 0.82003, "loss": 0.82003, "time": 0.37388} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.02426, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.80812, "top5_acc": 0.98562, "loss_cls": 0.85865, "loss": 0.85865, "time": 0.37296} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.02426, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8075, "top5_acc": 0.98688, "loss_cls": 0.85796, "loss": 0.85796, "time": 0.37299} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.02425, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.815, "top5_acc": 0.99188, "loss_cls": 0.80224, "loss": 0.80224, "time": 0.37428} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.02424, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81375, "top5_acc": 0.98688, "loss_cls": 0.84875, "loss": 0.84875, "time": 0.37155} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.02424, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83062, "top5_acc": 0.99062, "loss_cls": 0.74885, "loss": 0.74885, "time": 0.37319} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.02423, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.83562, "top5_acc": 0.99562, "loss_cls": 0.73363, "loss": 0.73363, "time": 0.37272} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.02422, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.81812, "top5_acc": 0.98688, "loss_cls": 0.78568, "loss": 0.78568, "time": 0.37086} +{"mode": "val", "epoch": 17, "iter": 533, "lr": 0.02422, "top1_acc": 0.74299, "top5_acc": 0.96784, "mean_class_accuracy": 0.65841} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.02421, "memory": 4082, "data_time": 0.19671, "top1_acc": 0.80938, "top5_acc": 0.99062, "loss_cls": 0.84534, "loss": 0.84534, "time": 0.57851} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.0242, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8175, "top5_acc": 0.99, "loss_cls": 0.80656, "loss": 0.80656, "time": 0.23249} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.02419, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83125, "top5_acc": 0.99312, "loss_cls": 0.72364, "loss": 0.72364, "time": 0.3471} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.02419, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83938, "top5_acc": 0.99438, "loss_cls": 0.72457, "loss": 0.72457, "time": 0.36858} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.02418, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.835, "top5_acc": 0.99375, "loss_cls": 0.75748, "loss": 0.75748, "time": 0.37518} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.02417, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.82188, "top5_acc": 0.99375, "loss_cls": 0.77113, "loss": 0.77113, "time": 0.37363} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.02417, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.8275, "top5_acc": 0.9925, "loss_cls": 0.76502, "loss": 0.76502, "time": 0.37347} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.02416, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82562, "top5_acc": 0.98812, "loss_cls": 0.78729, "loss": 0.78729, "time": 0.37474} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.02415, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81812, "top5_acc": 0.99062, "loss_cls": 0.80752, "loss": 0.80752, "time": 0.37405} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.02414, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83375, "top5_acc": 0.98688, "loss_cls": 0.80378, "loss": 0.80378, "time": 0.36797} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.02414, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81125, "top5_acc": 0.98812, "loss_cls": 0.80854, "loss": 0.80854, "time": 0.37123} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.02413, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.80812, "top5_acc": 0.99125, "loss_cls": 0.81455, "loss": 0.81455, "time": 0.36934} +{"mode": "val", "epoch": 18, "iter": 533, "lr": 0.02412, "top1_acc": 0.80272, "top5_acc": 0.98369, "mean_class_accuracy": 0.71984} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.02411, "memory": 4082, "data_time": 0.19584, "top1_acc": 0.825, "top5_acc": 0.9875, "loss_cls": 0.7711, "loss": 0.7711, "time": 0.56993} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.02411, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82125, "top5_acc": 0.99312, "loss_cls": 0.74482, "loss": 0.74482, "time": 0.36934} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.0241, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.8275, "top5_acc": 0.99125, "loss_cls": 0.76763, "loss": 0.76763, "time": 0.36864} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.02409, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82, "top5_acc": 0.98938, "loss_cls": 0.80788, "loss": 0.80788, "time": 0.37485} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.02408, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81188, "top5_acc": 0.98375, "loss_cls": 0.84393, "loss": 0.84393, "time": 0.32048} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.02408, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.82562, "top5_acc": 0.99125, "loss_cls": 0.80344, "loss": 0.80344, "time": 0.3381} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.02407, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.82188, "top5_acc": 0.98812, "loss_cls": 0.79734, "loss": 0.79734, "time": 0.3733} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.02406, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82438, "top5_acc": 0.99188, "loss_cls": 0.75353, "loss": 0.75353, "time": 0.23158} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.02405, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.825, "top5_acc": 0.98688, "loss_cls": 0.82719, "loss": 0.82719, "time": 0.35157} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.02405, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82562, "top5_acc": 0.98562, "loss_cls": 0.8201, "loss": 0.8201, "time": 0.37054} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.02404, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.8475, "top5_acc": 0.9925, "loss_cls": 0.72439, "loss": 0.72439, "time": 0.37857} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.02403, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.81812, "top5_acc": 0.99062, "loss_cls": 0.76128, "loss": 0.76128, "time": 0.37113} +{"mode": "val", "epoch": 19, "iter": 533, "lr": 0.02402, "top1_acc": 0.77561, "top5_acc": 0.9824, "mean_class_accuracy": 0.67728} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.02402, "memory": 4082, "data_time": 0.19744, "top1_acc": 0.83812, "top5_acc": 0.99062, "loss_cls": 0.74507, "loss": 0.74507, "time": 0.57227} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.02401, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82875, "top5_acc": 0.98938, "loss_cls": 0.74777, "loss": 0.74777, "time": 0.36621} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.024, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.84688, "top5_acc": 0.98688, "loss_cls": 0.73638, "loss": 0.73638, "time": 0.37616} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.02399, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83, "top5_acc": 0.9925, "loss_cls": 0.75505, "loss": 0.75505, "time": 0.3738} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.02398, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84438, "top5_acc": 0.995, "loss_cls": 0.74938, "loss": 0.74938, "time": 0.37174} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.02398, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.83062, "top5_acc": 0.99062, "loss_cls": 0.75935, "loss": 0.75935, "time": 0.37634} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.02397, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.86, "top5_acc": 0.99312, "loss_cls": 0.68868, "loss": 0.68868, "time": 0.37192} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.02396, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8225, "top5_acc": 0.99062, "loss_cls": 0.79274, "loss": 0.79274, "time": 0.36935} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.02395, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84188, "top5_acc": 0.99312, "loss_cls": 0.75723, "loss": 0.75723, "time": 0.37303} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.02394, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.83, "top5_acc": 0.98938, "loss_cls": 0.74315, "loss": 0.74315, "time": 0.38029} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.02393, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8425, "top5_acc": 0.99438, "loss_cls": 0.73898, "loss": 0.73898, "time": 0.28463} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.02393, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8225, "top5_acc": 0.99438, "loss_cls": 0.74276, "loss": 0.74276, "time": 0.38591} +{"mode": "val", "epoch": 20, "iter": 533, "lr": 0.02392, "top1_acc": 0.78841, "top5_acc": 0.98533, "mean_class_accuracy": 0.71752} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.02391, "memory": 4082, "data_time": 0.19328, "top1_acc": 0.83875, "top5_acc": 0.99562, "loss_cls": 0.72371, "loss": 0.72371, "time": 0.57105} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.0239, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.84125, "top5_acc": 0.99062, "loss_cls": 0.72022, "loss": 0.72022, "time": 0.3764} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.02389, "memory": 4082, "data_time": 0.00055, "top1_acc": 0.84062, "top5_acc": 0.99438, "loss_cls": 0.71023, "loss": 0.71023, "time": 0.37125} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.02389, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.83312, "top5_acc": 0.99312, "loss_cls": 0.7582, "loss": 0.7582, "time": 0.37358} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.02388, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.845, "top5_acc": 0.99125, "loss_cls": 0.73228, "loss": 0.73228, "time": 0.37664} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.02387, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.815, "top5_acc": 0.99562, "loss_cls": 0.82367, "loss": 0.82367, "time": 0.37066} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.02386, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83625, "top5_acc": 0.99188, "loss_cls": 0.73873, "loss": 0.73873, "time": 0.36916} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.02385, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.845, "top5_acc": 0.99562, "loss_cls": 0.68217, "loss": 0.68217, "time": 0.36883} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.02384, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85062, "top5_acc": 0.99188, "loss_cls": 0.69197, "loss": 0.69197, "time": 0.37357} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.02383, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.84688, "top5_acc": 0.98875, "loss_cls": 0.72374, "loss": 0.72374, "time": 0.37186} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.02383, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82438, "top5_acc": 0.99, "loss_cls": 0.8116, "loss": 0.8116, "time": 0.3759} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.02382, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8375, "top5_acc": 0.98688, "loss_cls": 0.7216, "loss": 0.7216, "time": 0.37415} +{"mode": "val", "epoch": 21, "iter": 533, "lr": 0.02381, "top1_acc": 0.82408, "top5_acc": 0.98557, "mean_class_accuracy": 0.73631} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.0238, "memory": 4082, "data_time": 0.19798, "top1_acc": 0.86062, "top5_acc": 0.995, "loss_cls": 0.64529, "loss": 0.64529, "time": 0.46527} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.02379, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83188, "top5_acc": 0.99188, "loss_cls": 0.72191, "loss": 0.72191, "time": 0.44154} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.02378, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.83438, "top5_acc": 0.99125, "loss_cls": 0.76563, "loss": 0.76563, "time": 0.22445} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.02378, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.84438, "top5_acc": 0.99438, "loss_cls": 0.72348, "loss": 0.72348, "time": 0.32129} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.02377, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.83312, "top5_acc": 0.98938, "loss_cls": 0.74491, "loss": 0.74491, "time": 0.38245} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.02376, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.8375, "top5_acc": 0.99, "loss_cls": 0.75114, "loss": 0.75114, "time": 0.36998} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.02375, "memory": 4082, "data_time": 0.00049, "top1_acc": 0.83625, "top5_acc": 0.99125, "loss_cls": 0.716, "loss": 0.716, "time": 0.37751} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.02374, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.85062, "top5_acc": 0.99625, "loss_cls": 0.67683, "loss": 0.67683, "time": 0.37651} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.02373, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.84, "top5_acc": 0.99125, "loss_cls": 0.74167, "loss": 0.74167, "time": 0.36721} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.02372, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.845, "top5_acc": 0.9925, "loss_cls": 0.70223, "loss": 0.70223, "time": 0.3703} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.02371, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.84188, "top5_acc": 0.99188, "loss_cls": 0.70532, "loss": 0.70532, "time": 0.37533} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0237, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82062, "top5_acc": 0.99, "loss_cls": 0.76619, "loss": 0.76619, "time": 0.36941} +{"mode": "val", "epoch": 22, "iter": 533, "lr": 0.0237, "top1_acc": 0.80284, "top5_acc": 0.98052, "mean_class_accuracy": 0.74103} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.02369, "memory": 4082, "data_time": 0.19588, "top1_acc": 0.845, "top5_acc": 0.99438, "loss_cls": 0.6986, "loss": 0.6986, "time": 0.5746} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.02368, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.86688, "top5_acc": 0.9925, "loss_cls": 0.64085, "loss": 0.64085, "time": 0.37946} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.02367, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.83812, "top5_acc": 0.9925, "loss_cls": 0.7473, "loss": 0.7473, "time": 0.39586} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.02366, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.85438, "top5_acc": 0.99375, "loss_cls": 0.67266, "loss": 0.67266, "time": 0.39829} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.02365, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.84438, "top5_acc": 0.99312, "loss_cls": 0.68344, "loss": 0.68344, "time": 0.37648} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.02364, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.84375, "top5_acc": 0.99, "loss_cls": 0.72243, "loss": 0.72243, "time": 0.26675} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.02363, "memory": 4082, "data_time": 0.00046, "top1_acc": 0.84312, "top5_acc": 0.99312, "loss_cls": 0.72071, "loss": 0.72071, "time": 0.40984} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.02362, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.84625, "top5_acc": 0.99438, "loss_cls": 0.68496, "loss": 0.68496, "time": 0.29863} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.02361, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.83562, "top5_acc": 0.9925, "loss_cls": 0.74411, "loss": 0.74411, "time": 0.26697} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.0236, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.83688, "top5_acc": 0.995, "loss_cls": 0.75392, "loss": 0.75392, "time": 0.3685} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.02359, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.84875, "top5_acc": 0.98875, "loss_cls": 0.73994, "loss": 0.73994, "time": 0.36865} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.02359, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.84062, "top5_acc": 0.99375, "loss_cls": 0.72336, "loss": 0.72336, "time": 0.37287} +{"mode": "val", "epoch": 23, "iter": 533, "lr": 0.02358, "top1_acc": 0.80906, "top5_acc": 0.98392, "mean_class_accuracy": 0.732} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.02357, "memory": 4082, "data_time": 0.19738, "top1_acc": 0.83188, "top5_acc": 0.98938, "loss_cls": 0.76224, "loss": 0.76224, "time": 0.5756} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.02356, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84, "top5_acc": 0.99188, "loss_cls": 0.69853, "loss": 0.69853, "time": 0.37002} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.02355, "memory": 4082, "data_time": 0.00063, "top1_acc": 0.85062, "top5_acc": 0.99188, "loss_cls": 0.65778, "loss": 0.65778, "time": 0.37752} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.02354, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.83875, "top5_acc": 0.98938, "loss_cls": 0.72889, "loss": 0.72889, "time": 0.37123} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.02353, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86, "top5_acc": 0.99438, "loss_cls": 0.63861, "loss": 0.63861, "time": 0.37181} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.02352, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.85812, "top5_acc": 0.99375, "loss_cls": 0.65771, "loss": 0.65771, "time": 0.37551} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.02351, "memory": 4082, "data_time": 0.00051, "top1_acc": 0.8625, "top5_acc": 0.99375, "loss_cls": 0.69315, "loss": 0.69315, "time": 0.37284} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.0235, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.84562, "top5_acc": 0.9925, "loss_cls": 0.68501, "loss": 0.68501, "time": 0.37045} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.02349, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.86125, "top5_acc": 0.9975, "loss_cls": 0.62192, "loss": 0.62192, "time": 0.36934} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.02348, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.85438, "top5_acc": 0.99375, "loss_cls": 0.66118, "loss": 0.66118, "time": 0.37162} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.02347, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8375, "top5_acc": 0.99062, "loss_cls": 0.74612, "loss": 0.74612, "time": 0.37618} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.02346, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84688, "top5_acc": 0.9925, "loss_cls": 0.72428, "loss": 0.72428, "time": 0.24902} +{"mode": "val", "epoch": 24, "iter": 533, "lr": 0.02345, "top1_acc": 0.76071, "top5_acc": 0.9743, "mean_class_accuracy": 0.70151} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.02344, "memory": 4082, "data_time": 0.19593, "top1_acc": 0.8475, "top5_acc": 0.99438, "loss_cls": 0.70901, "loss": 0.70901, "time": 0.57729} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.02343, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8375, "top5_acc": 0.9925, "loss_cls": 0.70478, "loss": 0.70478, "time": 0.3753} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.02342, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.83938, "top5_acc": 0.99188, "loss_cls": 0.72597, "loss": 0.72597, "time": 0.37303} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.02341, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.83938, "top5_acc": 0.99312, "loss_cls": 0.6974, "loss": 0.6974, "time": 0.3765} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.0234, "memory": 4082, "data_time": 0.00071, "top1_acc": 0.85875, "top5_acc": 0.995, "loss_cls": 0.65526, "loss": 0.65526, "time": 0.37177} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.02339, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.85625, "top5_acc": 0.995, "loss_cls": 0.66983, "loss": 0.66983, "time": 0.37103} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.02338, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.86812, "top5_acc": 0.99562, "loss_cls": 0.63101, "loss": 0.63101, "time": 0.37097} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.02337, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.845, "top5_acc": 0.99375, "loss_cls": 0.69941, "loss": 0.69941, "time": 0.37576} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.02336, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83312, "top5_acc": 0.99312, "loss_cls": 0.69136, "loss": 0.69136, "time": 0.37795} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.02335, "memory": 4082, "data_time": 0.00057, "top1_acc": 0.84312, "top5_acc": 0.99188, "loss_cls": 0.69624, "loss": 0.69624, "time": 0.3752} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.02334, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.8425, "top5_acc": 0.99125, "loss_cls": 0.70735, "loss": 0.70735, "time": 0.37434} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.02333, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.86, "top5_acc": 0.9925, "loss_cls": 0.68774, "loss": 0.68774, "time": 0.36967} +{"mode": "val", "epoch": 25, "iter": 533, "lr": 0.02333, "top1_acc": 0.81458, "top5_acc": 0.98733, "mean_class_accuracy": 0.75255} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.02332, "memory": 4082, "data_time": 0.18461, "top1_acc": 0.84625, "top5_acc": 0.99188, "loss_cls": 0.68701, "loss": 0.68701, "time": 0.55831} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.0233, "memory": 4082, "data_time": 0.00051, "top1_acc": 0.86438, "top5_acc": 0.99375, "loss_cls": 0.63194, "loss": 0.63194, "time": 0.24377} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.02329, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.85062, "top5_acc": 0.99062, "loss_cls": 0.67941, "loss": 0.67941, "time": 0.45194} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.02328, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.86688, "top5_acc": 0.995, "loss_cls": 0.64331, "loss": 0.64331, "time": 0.23971} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.02327, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.85062, "top5_acc": 0.99625, "loss_cls": 0.67423, "loss": 0.67423, "time": 0.30551} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.02326, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.8375, "top5_acc": 0.9925, "loss_cls": 0.70803, "loss": 0.70803, "time": 0.37456} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.02325, "memory": 4082, "data_time": 0.00046, "top1_acc": 0.84312, "top5_acc": 0.99062, "loss_cls": 0.73719, "loss": 0.73719, "time": 0.37364} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.02324, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.8425, "top5_acc": 0.99062, "loss_cls": 0.70591, "loss": 0.70591, "time": 0.37947} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.02323, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.84875, "top5_acc": 0.99312, "loss_cls": 0.68761, "loss": 0.68761, "time": 0.37872} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.02322, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.86188, "top5_acc": 0.99625, "loss_cls": 0.62674, "loss": 0.62674, "time": 0.37211} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.02321, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86125, "top5_acc": 0.99, "loss_cls": 0.6717, "loss": 0.6717, "time": 0.37256} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.0232, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8675, "top5_acc": 0.99, "loss_cls": 0.6373, "loss": 0.6373, "time": 0.3698} +{"mode": "val", "epoch": 26, "iter": 533, "lr": 0.02319, "top1_acc": 0.82549, "top5_acc": 0.98392, "mean_class_accuracy": 0.76895} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.02318, "memory": 4082, "data_time": 0.19431, "top1_acc": 0.86312, "top5_acc": 0.99438, "loss_cls": 0.62618, "loss": 0.62618, "time": 0.57106} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.02317, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86375, "top5_acc": 0.99188, "loss_cls": 0.65191, "loss": 0.65191, "time": 0.37529} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.02316, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.855, "top5_acc": 0.99062, "loss_cls": 0.66229, "loss": 0.66229, "time": 0.37651} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.02315, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84938, "top5_acc": 0.99625, "loss_cls": 0.67361, "loss": 0.67361, "time": 0.37985} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.02314, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85438, "top5_acc": 0.99062, "loss_cls": 0.67478, "loss": 0.67478, "time": 0.37485} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.02313, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.85938, "top5_acc": 0.99562, "loss_cls": 0.63765, "loss": 0.63765, "time": 0.37447} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.02312, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84, "top5_acc": 0.98875, "loss_cls": 0.70851, "loss": 0.70851, "time": 0.33296} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.02311, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.85938, "top5_acc": 0.9925, "loss_cls": 0.65091, "loss": 0.65091, "time": 0.32205} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.0231, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.89, "top5_acc": 0.9925, "loss_cls": 0.56613, "loss": 0.56613, "time": 0.38939} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.02308, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.84125, "top5_acc": 0.99125, "loss_cls": 0.7155, "loss": 0.7155, "time": 0.22815} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.02307, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.85188, "top5_acc": 0.9925, "loss_cls": 0.71657, "loss": 0.71657, "time": 0.34504} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.02306, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.84812, "top5_acc": 0.99125, "loss_cls": 0.72587, "loss": 0.72587, "time": 0.37193} +{"mode": "val", "epoch": 27, "iter": 533, "lr": 0.02305, "top1_acc": 0.78747, "top5_acc": 0.98134, "mean_class_accuracy": 0.71633} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.02304, "memory": 4082, "data_time": 0.19486, "top1_acc": 0.86, "top5_acc": 0.99375, "loss_cls": 0.67549, "loss": 0.67549, "time": 0.57215} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.02303, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.85562, "top5_acc": 0.9925, "loss_cls": 0.65563, "loss": 0.65563, "time": 0.37306} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.02302, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.8675, "top5_acc": 0.99, "loss_cls": 0.62674, "loss": 0.62674, "time": 0.37056} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.02301, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.88562, "top5_acc": 0.9975, "loss_cls": 0.5831, "loss": 0.5831, "time": 0.37398} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.023, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85062, "top5_acc": 0.99312, "loss_cls": 0.65618, "loss": 0.65618, "time": 0.36909} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.02299, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8625, "top5_acc": 0.98875, "loss_cls": 0.68475, "loss": 0.68475, "time": 0.37202} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.02298, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85938, "top5_acc": 0.9975, "loss_cls": 0.63617, "loss": 0.63617, "time": 0.37724} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.02297, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.84688, "top5_acc": 0.99312, "loss_cls": 0.6948, "loss": 0.6948, "time": 0.37361} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.02295, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.85875, "top5_acc": 0.9925, "loss_cls": 0.67264, "loss": 0.67264, "time": 0.37092} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.02294, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.86438, "top5_acc": 0.995, "loss_cls": 0.62648, "loss": 0.62648, "time": 0.36899} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.02293, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.85188, "top5_acc": 0.99312, "loss_cls": 0.68279, "loss": 0.68279, "time": 0.36908} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.02292, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.87062, "top5_acc": 0.9925, "loss_cls": 0.60602, "loss": 0.60602, "time": 0.36558} +{"mode": "val", "epoch": 28, "iter": 533, "lr": 0.02291, "top1_acc": 0.8391, "top5_acc": 0.98674, "mean_class_accuracy": 0.77119} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.0229, "memory": 4082, "data_time": 0.19377, "top1_acc": 0.87875, "top5_acc": 0.995, "loss_cls": 0.58702, "loss": 0.58702, "time": 0.56765} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.02289, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.865, "top5_acc": 0.99, "loss_cls": 0.64666, "loss": 0.64666, "time": 0.37181} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.02288, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86438, "top5_acc": 0.99375, "loss_cls": 0.62544, "loss": 0.62544, "time": 0.36817} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.02287, "memory": 4082, "data_time": 0.00067, "top1_acc": 0.86312, "top5_acc": 0.99562, "loss_cls": 0.618, "loss": 0.618, "time": 0.37933} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.02285, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.87062, "top5_acc": 0.99688, "loss_cls": 0.62166, "loss": 0.62166, "time": 0.37357} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.02284, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.84875, "top5_acc": 0.9925, "loss_cls": 0.683, "loss": 0.683, "time": 0.37421} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.02283, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.87188, "top5_acc": 0.99625, "loss_cls": 0.62657, "loss": 0.62657, "time": 0.36802} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.02282, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.86062, "top5_acc": 0.99188, "loss_cls": 0.64601, "loss": 0.64601, "time": 0.37539} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.02281, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.85062, "top5_acc": 0.995, "loss_cls": 0.67153, "loss": 0.67153, "time": 0.37817} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.0228, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84625, "top5_acc": 0.99438, "loss_cls": 0.71433, "loss": 0.71433, "time": 0.37095} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.02279, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.86625, "top5_acc": 0.99062, "loss_cls": 0.61715, "loss": 0.61715, "time": 0.37176} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.02277, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85188, "top5_acc": 0.99188, "loss_cls": 0.66167, "loss": 0.66167, "time": 0.37427} +{"mode": "val", "epoch": 29, "iter": 533, "lr": 0.02276, "top1_acc": 0.81774, "top5_acc": 0.98803, "mean_class_accuracy": 0.75282} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.02275, "memory": 4082, "data_time": 0.19706, "top1_acc": 0.85938, "top5_acc": 0.99375, "loss_cls": 0.63527, "loss": 0.63527, "time": 0.67412} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.02274, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.86938, "top5_acc": 0.99562, "loss_cls": 0.60961, "loss": 0.60961, "time": 0.35429} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.02273, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.87625, "top5_acc": 0.995, "loss_cls": 0.61899, "loss": 0.61899, "time": 0.50811} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.02272, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.88188, "top5_acc": 0.99312, "loss_cls": 0.59296, "loss": 0.59296, "time": 0.23968} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.02271, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87375, "top5_acc": 0.99312, "loss_cls": 0.63509, "loss": 0.63509, "time": 0.44477} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.02269, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.8675, "top5_acc": 0.99562, "loss_cls": 0.60427, "loss": 0.60427, "time": 0.4775} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.02268, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86125, "top5_acc": 0.99375, "loss_cls": 0.6557, "loss": 0.6557, "time": 0.47845} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.02267, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.86438, "top5_acc": 0.99312, "loss_cls": 0.64443, "loss": 0.64443, "time": 0.48082} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.02266, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8675, "top5_acc": 0.99562, "loss_cls": 0.63445, "loss": 0.63445, "time": 0.47958} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.02265, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.84125, "top5_acc": 0.99, "loss_cls": 0.69602, "loss": 0.69602, "time": 0.47764} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.02263, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.86375, "top5_acc": 0.99188, "loss_cls": 0.65263, "loss": 0.65263, "time": 0.477} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.02262, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87062, "top5_acc": 0.995, "loss_cls": 0.59829, "loss": 0.59829, "time": 0.47685} +{"mode": "val", "epoch": 30, "iter": 533, "lr": 0.02261, "top1_acc": 0.79697, "top5_acc": 0.98474, "mean_class_accuracy": 0.74637} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.0226, "memory": 4083, "data_time": 0.19364, "top1_acc": 0.86812, "top5_acc": 0.99688, "loss_cls": 0.74544, "loss": 0.74544, "time": 0.85206} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.02259, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86688, "top5_acc": 0.9925, "loss_cls": 0.75108, "loss": 0.75108, "time": 0.32261} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.02258, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86625, "top5_acc": 0.995, "loss_cls": 0.67503, "loss": 0.67503, "time": 0.51025} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.02256, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.845, "top5_acc": 0.98938, "loss_cls": 0.86424, "loss": 0.86424, "time": 0.25056} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.02255, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85438, "top5_acc": 0.99312, "loss_cls": 0.77149, "loss": 0.77149, "time": 0.47582} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.02254, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87125, "top5_acc": 0.99375, "loss_cls": 0.71389, "loss": 0.71389, "time": 0.48954} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.02253, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.86438, "top5_acc": 0.99438, "loss_cls": 0.78436, "loss": 0.78436, "time": 0.48834} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.02252, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.85812, "top5_acc": 0.9925, "loss_cls": 0.77289, "loss": 0.77289, "time": 0.48972} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0225, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87188, "top5_acc": 0.99688, "loss_cls": 0.70849, "loss": 0.70849, "time": 0.49023} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.02249, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85625, "top5_acc": 0.99312, "loss_cls": 0.79831, "loss": 0.79831, "time": 0.49005} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.02248, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86625, "top5_acc": 0.99438, "loss_cls": 0.74814, "loss": 0.74814, "time": 0.48981} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.02247, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85438, "top5_acc": 0.99625, "loss_cls": 0.78365, "loss": 0.78365, "time": 0.48749} +{"mode": "val", "epoch": 31, "iter": 533, "lr": 0.02246, "top1_acc": 0.83089, "top5_acc": 0.98744, "mean_class_accuracy": 0.7757} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.02244, "memory": 4083, "data_time": 0.19554, "top1_acc": 0.86312, "top5_acc": 0.995, "loss_cls": 0.7035, "loss": 0.7035, "time": 0.81332} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.02243, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.86625, "top5_acc": 0.99688, "loss_cls": 0.68637, "loss": 0.68637, "time": 0.31375} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.02242, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8725, "top5_acc": 0.99875, "loss_cls": 0.6567, "loss": 0.6567, "time": 0.51381} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.02241, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89125, "top5_acc": 0.99438, "loss_cls": 0.62915, "loss": 0.62915, "time": 0.25659} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.02239, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.8875, "top5_acc": 0.99, "loss_cls": 0.68316, "loss": 0.68316, "time": 0.48919} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.02238, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.85625, "top5_acc": 0.99188, "loss_cls": 0.73168, "loss": 0.73168, "time": 0.48801} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.02237, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.865, "top5_acc": 0.99188, "loss_cls": 0.74697, "loss": 0.74697, "time": 0.48853} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.02236, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85562, "top5_acc": 0.995, "loss_cls": 0.71029, "loss": 0.71029, "time": 0.48863} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.02234, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8625, "top5_acc": 0.99188, "loss_cls": 0.73226, "loss": 0.73226, "time": 0.49207} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.02233, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86312, "top5_acc": 0.99375, "loss_cls": 0.7654, "loss": 0.7654, "time": 0.49044} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.02232, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87125, "top5_acc": 0.99312, "loss_cls": 0.70233, "loss": 0.70233, "time": 0.49183} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.02231, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.8725, "top5_acc": 0.99688, "loss_cls": 0.66422, "loss": 0.66422, "time": 0.48916} +{"mode": "val", "epoch": 32, "iter": 533, "lr": 0.0223, "top1_acc": 0.81833, "top5_acc": 0.98287, "mean_class_accuracy": 0.75518} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.02228, "memory": 4083, "data_time": 0.19338, "top1_acc": 0.89, "top5_acc": 0.995, "loss_cls": 0.62914, "loss": 0.62914, "time": 0.80435} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.02227, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86938, "top5_acc": 0.99562, "loss_cls": 0.68382, "loss": 0.68382, "time": 0.31291} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.02226, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85625, "top5_acc": 0.99312, "loss_cls": 0.70278, "loss": 0.70278, "time": 0.51024} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.02225, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84875, "top5_acc": 0.99688, "loss_cls": 0.71428, "loss": 0.71428, "time": 0.26286} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.02223, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87188, "top5_acc": 0.99312, "loss_cls": 0.66515, "loss": 0.66515, "time": 0.48297} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.02222, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.87688, "top5_acc": 0.99438, "loss_cls": 0.63409, "loss": 0.63409, "time": 0.49337} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.02221, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.87062, "top5_acc": 0.99375, "loss_cls": 0.6476, "loss": 0.6476, "time": 0.4856} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.02219, "memory": 4083, "data_time": 0.0007, "top1_acc": 0.87125, "top5_acc": 0.99125, "loss_cls": 0.65602, "loss": 0.65602, "time": 0.48951} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.02218, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87938, "top5_acc": 0.99, "loss_cls": 0.65273, "loss": 0.65273, "time": 0.48661} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.02217, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86, "top5_acc": 0.99312, "loss_cls": 0.69955, "loss": 0.69955, "time": 0.49238} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.02216, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85938, "top5_acc": 0.99312, "loss_cls": 0.68913, "loss": 0.68913, "time": 0.48932} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.02214, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86875, "top5_acc": 0.99438, "loss_cls": 0.68266, "loss": 0.68266, "time": 0.48345} +{"mode": "val", "epoch": 33, "iter": 533, "lr": 0.02213, "top1_acc": 0.81188, "top5_acc": 0.98298, "mean_class_accuracy": 0.76317} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.02212, "memory": 4083, "data_time": 0.19548, "top1_acc": 0.88562, "top5_acc": 0.995, "loss_cls": 0.60468, "loss": 0.60468, "time": 0.8102} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.02211, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.88062, "top5_acc": 0.99, "loss_cls": 0.62469, "loss": 0.62469, "time": 0.31626} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.02209, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88562, "top5_acc": 0.9925, "loss_cls": 0.63031, "loss": 0.63031, "time": 0.51004} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.02208, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.88688, "top5_acc": 0.99562, "loss_cls": 0.61933, "loss": 0.61933, "time": 0.25367} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.02207, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86688, "top5_acc": 0.99375, "loss_cls": 0.66718, "loss": 0.66718, "time": 0.48743} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.02205, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87, "top5_acc": 0.99688, "loss_cls": 0.65111, "loss": 0.65111, "time": 0.48726} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.02204, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.86875, "top5_acc": 0.995, "loss_cls": 0.6664, "loss": 0.6664, "time": 0.48735} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.02203, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.87812, "top5_acc": 0.99562, "loss_cls": 0.61988, "loss": 0.61988, "time": 0.49263} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.02201, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87375, "top5_acc": 0.99625, "loss_cls": 0.6497, "loss": 0.6497, "time": 0.49371} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.022, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.86312, "top5_acc": 0.99562, "loss_cls": 0.67292, "loss": 0.67292, "time": 0.49034} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.02199, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.87688, "top5_acc": 0.9925, "loss_cls": 0.67571, "loss": 0.67571, "time": 0.48982} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.02197, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86062, "top5_acc": 0.98875, "loss_cls": 0.74174, "loss": 0.74174, "time": 0.48452} +{"mode": "val", "epoch": 34, "iter": 533, "lr": 0.02196, "top1_acc": 0.81915, "top5_acc": 0.98451, "mean_class_accuracy": 0.7626} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.02195, "memory": 4083, "data_time": 0.19811, "top1_acc": 0.88, "top5_acc": 0.99812, "loss_cls": 0.5869, "loss": 0.5869, "time": 0.79874} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.02194, "memory": 4083, "data_time": 0.00067, "top1_acc": 0.8625, "top5_acc": 0.99562, "loss_cls": 0.64756, "loss": 0.64756, "time": 0.33195} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.02192, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88438, "top5_acc": 0.99562, "loss_cls": 0.60532, "loss": 0.60532, "time": 0.51137} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.02191, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.86875, "top5_acc": 0.99375, "loss_cls": 0.65436, "loss": 0.65436, "time": 0.25567} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.0219, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86312, "top5_acc": 0.99125, "loss_cls": 0.6973, "loss": 0.6973, "time": 0.47701} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.02188, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87625, "top5_acc": 0.995, "loss_cls": 0.65425, "loss": 0.65425, "time": 0.48899} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.02187, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.84688, "top5_acc": 0.9925, "loss_cls": 0.72015, "loss": 0.72015, "time": 0.48733} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.02185, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85625, "top5_acc": 0.99125, "loss_cls": 0.69362, "loss": 0.69362, "time": 0.48429} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.02184, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87062, "top5_acc": 0.99438, "loss_cls": 0.66721, "loss": 0.66721, "time": 0.48995} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.02183, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87625, "top5_acc": 0.99125, "loss_cls": 0.67157, "loss": 0.67157, "time": 0.49074} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.02181, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.86562, "top5_acc": 0.995, "loss_cls": 0.66407, "loss": 0.66407, "time": 0.4951} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.0218, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.86062, "top5_acc": 0.995, "loss_cls": 0.70737, "loss": 0.70737, "time": 0.49234} +{"mode": "val", "epoch": 35, "iter": 533, "lr": 0.02179, "top1_acc": 0.83863, "top5_acc": 0.98463, "mean_class_accuracy": 0.7744} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.02178, "memory": 4083, "data_time": 0.19475, "top1_acc": 0.9025, "top5_acc": 0.99375, "loss_cls": 0.5779, "loss": 0.5779, "time": 0.80695} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.02176, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87625, "top5_acc": 0.99625, "loss_cls": 0.6029, "loss": 0.6029, "time": 0.31932} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.02175, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87625, "top5_acc": 0.99, "loss_cls": 0.62052, "loss": 0.62052, "time": 0.51063} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.02173, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8775, "top5_acc": 0.995, "loss_cls": 0.6228, "loss": 0.6228, "time": 0.25817} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.02172, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89188, "top5_acc": 0.99625, "loss_cls": 0.55765, "loss": 0.55765, "time": 0.49275} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.02171, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.87625, "top5_acc": 0.99312, "loss_cls": 0.63405, "loss": 0.63405, "time": 0.49155} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.02169, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.8725, "top5_acc": 0.99625, "loss_cls": 0.62327, "loss": 0.62327, "time": 0.48894} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.02168, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.86688, "top5_acc": 0.99562, "loss_cls": 0.65208, "loss": 0.65208, "time": 0.4887} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.02167, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.85688, "top5_acc": 0.9925, "loss_cls": 0.72002, "loss": 0.72002, "time": 0.49151} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.02165, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.8575, "top5_acc": 0.99125, "loss_cls": 0.71662, "loss": 0.71662, "time": 0.48811} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.02164, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.86938, "top5_acc": 0.99438, "loss_cls": 0.69467, "loss": 0.69467, "time": 0.48759} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.02162, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86312, "top5_acc": 0.99125, "loss_cls": 0.67346, "loss": 0.67346, "time": 0.49122} +{"mode": "val", "epoch": 36, "iter": 533, "lr": 0.02161, "top1_acc": 0.8492, "top5_acc": 0.98838, "mean_class_accuracy": 0.80428} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.0216, "memory": 4083, "data_time": 0.19591, "top1_acc": 0.86125, "top5_acc": 0.99562, "loss_cls": 0.64977, "loss": 0.64977, "time": 0.80916} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.02158, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88438, "top5_acc": 0.99625, "loss_cls": 0.63951, "loss": 0.63951, "time": 0.29865} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.02157, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.875, "top5_acc": 0.99375, "loss_cls": 0.63074, "loss": 0.63074, "time": 0.50979} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.02156, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.87688, "top5_acc": 0.995, "loss_cls": 0.63534, "loss": 0.63534, "time": 0.28909} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.02154, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87812, "top5_acc": 0.9975, "loss_cls": 0.61303, "loss": 0.61303, "time": 0.49513} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.02153, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.885, "top5_acc": 0.995, "loss_cls": 0.62375, "loss": 0.62375, "time": 0.49055} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.02151, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.85812, "top5_acc": 0.995, "loss_cls": 0.64387, "loss": 0.64387, "time": 0.49205} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0215, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.8825, "top5_acc": 0.9975, "loss_cls": 0.62303, "loss": 0.62303, "time": 0.49269} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.02149, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.865, "top5_acc": 0.98938, "loss_cls": 0.69404, "loss": 0.69404, "time": 0.49134} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.02147, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87188, "top5_acc": 0.9925, "loss_cls": 0.67256, "loss": 0.67256, "time": 0.49054} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.02146, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.87062, "top5_acc": 0.99438, "loss_cls": 0.64297, "loss": 0.64297, "time": 0.49065} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.02144, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.86188, "top5_acc": 0.99375, "loss_cls": 0.67996, "loss": 0.67996, "time": 0.49084} +{"mode": "val", "epoch": 37, "iter": 533, "lr": 0.02143, "top1_acc": 0.84462, "top5_acc": 0.98815, "mean_class_accuracy": 0.76347} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.02142, "memory": 4083, "data_time": 0.19016, "top1_acc": 0.89375, "top5_acc": 0.99375, "loss_cls": 0.58016, "loss": 0.58016, "time": 0.80348} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.0214, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88188, "top5_acc": 0.9975, "loss_cls": 0.59939, "loss": 0.59939, "time": 0.28107} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.02139, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.885, "top5_acc": 0.995, "loss_cls": 0.59686, "loss": 0.59686, "time": 0.50925} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.02137, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87562, "top5_acc": 0.99438, "loss_cls": 0.62422, "loss": 0.62422, "time": 0.29614} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.02136, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8975, "top5_acc": 0.99188, "loss_cls": 0.59403, "loss": 0.59403, "time": 0.49207} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.02134, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87438, "top5_acc": 0.99438, "loss_cls": 0.66807, "loss": 0.66807, "time": 0.488} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.02133, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88, "top5_acc": 0.99438, "loss_cls": 0.62417, "loss": 0.62417, "time": 0.48873} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.02132, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8875, "top5_acc": 0.99312, "loss_cls": 0.59967, "loss": 0.59967, "time": 0.48692} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.0213, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.87438, "top5_acc": 0.995, "loss_cls": 0.6364, "loss": 0.6364, "time": 0.48921} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.02129, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8725, "top5_acc": 0.99188, "loss_cls": 0.67718, "loss": 0.67718, "time": 0.48583} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.02127, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87062, "top5_acc": 0.99688, "loss_cls": 0.62428, "loss": 0.62428, "time": 0.48956} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.02126, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88375, "top5_acc": 0.99, "loss_cls": 0.62923, "loss": 0.62923, "time": 0.49004} +{"mode": "val", "epoch": 38, "iter": 533, "lr": 0.02125, "top1_acc": 0.86551, "top5_acc": 0.99319, "mean_class_accuracy": 0.80925} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.02123, "memory": 4083, "data_time": 0.19899, "top1_acc": 0.88562, "top5_acc": 0.995, "loss_cls": 0.57265, "loss": 0.57265, "time": 0.80187} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.02122, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.88062, "top5_acc": 0.99562, "loss_cls": 0.61167, "loss": 0.61167, "time": 0.28234} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.0212, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.895, "top5_acc": 0.99625, "loss_cls": 0.53302, "loss": 0.53302, "time": 0.50067} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.02119, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8975, "top5_acc": 0.995, "loss_cls": 0.56076, "loss": 0.56076, "time": 0.32071} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.02117, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88375, "top5_acc": 0.9975, "loss_cls": 0.58835, "loss": 0.58835, "time": 0.48803} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.02116, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88875, "top5_acc": 0.99625, "loss_cls": 0.56315, "loss": 0.56315, "time": 0.49419} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.02114, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89625, "top5_acc": 0.995, "loss_cls": 0.57882, "loss": 0.57882, "time": 0.48971} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.02113, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.87625, "top5_acc": 0.99188, "loss_cls": 0.63618, "loss": 0.63618, "time": 0.48931} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.02111, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86312, "top5_acc": 0.99062, "loss_cls": 0.67776, "loss": 0.67776, "time": 0.48969} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.0211, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8875, "top5_acc": 0.995, "loss_cls": 0.60498, "loss": 0.60498, "time": 0.49056} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.02108, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.86312, "top5_acc": 0.9925, "loss_cls": 0.67815, "loss": 0.67815, "time": 0.48556} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.02107, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88625, "top5_acc": 0.99688, "loss_cls": 0.60124, "loss": 0.60124, "time": 0.4895} +{"mode": "val", "epoch": 39, "iter": 533, "lr": 0.02106, "top1_acc": 0.82772, "top5_acc": 0.98721, "mean_class_accuracy": 0.77419} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.02104, "memory": 4083, "data_time": 0.19191, "top1_acc": 0.87812, "top5_acc": 0.99625, "loss_cls": 0.6259, "loss": 0.6259, "time": 0.79742} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.02103, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.8925, "top5_acc": 0.99688, "loss_cls": 0.58676, "loss": 0.58676, "time": 0.26223} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.02101, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.87062, "top5_acc": 0.99375, "loss_cls": 0.6546, "loss": 0.6546, "time": 0.51195} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.021, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89625, "top5_acc": 0.99438, "loss_cls": 0.54668, "loss": 0.54668, "time": 0.30586} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.02098, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88938, "top5_acc": 0.99812, "loss_cls": 0.5932, "loss": 0.5932, "time": 0.48998} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.02097, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87438, "top5_acc": 0.99375, "loss_cls": 0.63971, "loss": 0.63971, "time": 0.48786} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.02095, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89562, "top5_acc": 0.99688, "loss_cls": 0.53521, "loss": 0.53521, "time": 0.48819} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.02094, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.87812, "top5_acc": 0.99688, "loss_cls": 0.56998, "loss": 0.56998, "time": 0.49164} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.02092, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.87125, "top5_acc": 0.995, "loss_cls": 0.64261, "loss": 0.64261, "time": 0.48933} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.02091, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.86125, "top5_acc": 0.99125, "loss_cls": 0.66822, "loss": 0.66822, "time": 0.49353} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.02089, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86938, "top5_acc": 0.99562, "loss_cls": 0.6505, "loss": 0.6505, "time": 0.49092} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.02088, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87688, "top5_acc": 0.99188, "loss_cls": 0.60425, "loss": 0.60425, "time": 0.48689} +{"mode": "val", "epoch": 40, "iter": 533, "lr": 0.02086, "top1_acc": 0.85694, "top5_acc": 0.99014, "mean_class_accuracy": 0.80221} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.02085, "memory": 4083, "data_time": 0.18991, "top1_acc": 0.90188, "top5_acc": 0.99812, "loss_cls": 0.52425, "loss": 0.52425, "time": 0.80421} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.02083, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.89875, "top5_acc": 0.99875, "loss_cls": 0.50054, "loss": 0.50054, "time": 0.28837} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.02082, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88125, "top5_acc": 0.995, "loss_cls": 0.61719, "loss": 0.61719, "time": 0.47999} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.0208, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91562, "top5_acc": 0.99688, "loss_cls": 0.49155, "loss": 0.49155, "time": 0.3322} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.02079, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88438, "top5_acc": 0.99438, "loss_cls": 0.57184, "loss": 0.57184, "time": 0.48855} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.02077, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8875, "top5_acc": 0.99375, "loss_cls": 0.57737, "loss": 0.57737, "time": 0.48809} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.02076, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90438, "top5_acc": 0.99688, "loss_cls": 0.52284, "loss": 0.52284, "time": 0.48955} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.02074, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88062, "top5_acc": 0.9975, "loss_cls": 0.59457, "loss": 0.59457, "time": 0.48951} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.02073, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87875, "top5_acc": 0.995, "loss_cls": 0.63154, "loss": 0.63154, "time": 0.48883} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.02071, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.86375, "top5_acc": 0.99188, "loss_cls": 0.68653, "loss": 0.68653, "time": 0.48988} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.0207, "memory": 4083, "data_time": 0.00075, "top1_acc": 0.88312, "top5_acc": 0.99562, "loss_cls": 0.58665, "loss": 0.58665, "time": 0.48907} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.02068, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88312, "top5_acc": 0.99312, "loss_cls": 0.6062, "loss": 0.6062, "time": 0.49006} +{"mode": "val", "epoch": 41, "iter": 533, "lr": 0.02067, "top1_acc": 0.84603, "top5_acc": 0.98826, "mean_class_accuracy": 0.77007} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.02065, "memory": 4083, "data_time": 0.19598, "top1_acc": 0.89375, "top5_acc": 0.995, "loss_cls": 0.54985, "loss": 0.54985, "time": 0.79204} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.02064, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88625, "top5_acc": 0.99812, "loss_cls": 0.60641, "loss": 0.60641, "time": 0.28934} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.02062, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9, "top5_acc": 0.99875, "loss_cls": 0.5126, "loss": 0.5126, "time": 0.47877} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.02061, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89375, "top5_acc": 0.99625, "loss_cls": 0.53752, "loss": 0.53752, "time": 0.31826} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.02059, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89375, "top5_acc": 0.99688, "loss_cls": 0.52218, "loss": 0.52218, "time": 0.49361} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.02057, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87375, "top5_acc": 0.99312, "loss_cls": 0.61658, "loss": 0.61658, "time": 0.48912} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.02056, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8775, "top5_acc": 0.99438, "loss_cls": 0.59938, "loss": 0.59938, "time": 0.48999} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.02054, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87125, "top5_acc": 0.99688, "loss_cls": 0.6284, "loss": 0.6284, "time": 0.48842} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.02053, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.86938, "top5_acc": 0.995, "loss_cls": 0.65502, "loss": 0.65502, "time": 0.49104} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.02051, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.87562, "top5_acc": 0.99062, "loss_cls": 0.66045, "loss": 0.66045, "time": 0.48799} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.0205, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8575, "top5_acc": 0.9975, "loss_cls": 0.64778, "loss": 0.64778, "time": 0.48681} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.02048, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88188, "top5_acc": 0.99062, "loss_cls": 0.63219, "loss": 0.63219, "time": 0.48941} +{"mode": "val", "epoch": 42, "iter": 533, "lr": 0.02047, "top1_acc": 0.8323, "top5_acc": 0.98967, "mean_class_accuracy": 0.78258} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.02045, "memory": 4083, "data_time": 0.1918, "top1_acc": 0.90562, "top5_acc": 0.99812, "loss_cls": 0.52712, "loss": 0.52712, "time": 0.79679} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.02044, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.88375, "top5_acc": 0.99688, "loss_cls": 0.60523, "loss": 0.60523, "time": 0.28054} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.02042, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.88188, "top5_acc": 0.9975, "loss_cls": 0.58768, "loss": 0.58768, "time": 0.49728} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.0204, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89062, "top5_acc": 0.99312, "loss_cls": 0.58261, "loss": 0.58261, "time": 0.31442} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.02039, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.885, "top5_acc": 0.995, "loss_cls": 0.60002, "loss": 0.60002, "time": 0.49121} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.02037, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89438, "top5_acc": 0.99688, "loss_cls": 0.55268, "loss": 0.55268, "time": 0.49078} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.02036, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88188, "top5_acc": 0.99375, "loss_cls": 0.63247, "loss": 0.63247, "time": 0.49029} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.02034, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89312, "top5_acc": 0.99375, "loss_cls": 0.57262, "loss": 0.57262, "time": 0.49516} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.02033, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8725, "top5_acc": 0.99438, "loss_cls": 0.65151, "loss": 0.65151, "time": 0.4913} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.02031, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89188, "top5_acc": 0.9975, "loss_cls": 0.58515, "loss": 0.58515, "time": 0.49732} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.02029, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.8675, "top5_acc": 0.99375, "loss_cls": 0.61295, "loss": 0.61295, "time": 0.49011} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.02028, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.88062, "top5_acc": 0.9975, "loss_cls": 0.58011, "loss": 0.58011, "time": 0.48526} +{"mode": "val", "epoch": 43, "iter": 533, "lr": 0.02026, "top1_acc": 0.86093, "top5_acc": 0.99319, "mean_class_accuracy": 0.8004} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.02025, "memory": 4083, "data_time": 0.19158, "top1_acc": 0.90062, "top5_acc": 0.99688, "loss_cls": 0.51286, "loss": 0.51286, "time": 0.80294} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.02023, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.90062, "top5_acc": 0.99812, "loss_cls": 0.5329, "loss": 0.5329, "time": 0.29842} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.02022, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89938, "top5_acc": 0.99875, "loss_cls": 0.50985, "loss": 0.50985, "time": 0.47364} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.0202, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88875, "top5_acc": 0.99438, "loss_cls": 0.57008, "loss": 0.57008, "time": 0.31921} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.02018, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88312, "top5_acc": 0.99625, "loss_cls": 0.59909, "loss": 0.59909, "time": 0.48936} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.02017, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88312, "top5_acc": 0.99312, "loss_cls": 0.63205, "loss": 0.63205, "time": 0.49182} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.02015, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.87812, "top5_acc": 0.99688, "loss_cls": 0.62667, "loss": 0.62667, "time": 0.48853} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.02014, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89125, "top5_acc": 0.99625, "loss_cls": 0.56252, "loss": 0.56252, "time": 0.48824} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.02012, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89812, "top5_acc": 0.99812, "loss_cls": 0.52019, "loss": 0.52019, "time": 0.4914} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.0201, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89, "top5_acc": 0.99375, "loss_cls": 0.59321, "loss": 0.59321, "time": 0.4868} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.02009, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.92125, "top5_acc": 0.99312, "loss_cls": 0.48792, "loss": 0.48792, "time": 0.48921} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.02007, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89, "top5_acc": 0.99375, "loss_cls": 0.58561, "loss": 0.58561, "time": 0.48823} +{"mode": "val", "epoch": 44, "iter": 533, "lr": 0.02006, "top1_acc": 0.85729, "top5_acc": 0.98592, "mean_class_accuracy": 0.79627} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.02004, "memory": 4083, "data_time": 0.19681, "top1_acc": 0.89875, "top5_acc": 0.99688, "loss_cls": 0.56902, "loss": 0.56902, "time": 0.80712} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.02003, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8925, "top5_acc": 0.99688, "loss_cls": 0.5513, "loss": 0.5513, "time": 0.28268} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.02001, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88688, "top5_acc": 0.99562, "loss_cls": 0.56859, "loss": 0.56859, "time": 0.48748} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.01999, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88625, "top5_acc": 0.99625, "loss_cls": 0.59938, "loss": 0.59938, "time": 0.31976} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.01998, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.90188, "top5_acc": 0.995, "loss_cls": 0.55242, "loss": 0.55242, "time": 0.48921} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.01996, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89625, "top5_acc": 0.99625, "loss_cls": 0.544, "loss": 0.544, "time": 0.48848} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.01994, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87562, "top5_acc": 0.9975, "loss_cls": 0.62925, "loss": 0.62925, "time": 0.49124} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.01993, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.895, "top5_acc": 0.99688, "loss_cls": 0.56073, "loss": 0.56073, "time": 0.48978} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.01991, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88938, "top5_acc": 0.99625, "loss_cls": 0.56183, "loss": 0.56183, "time": 0.49017} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.01989, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88125, "top5_acc": 0.9925, "loss_cls": 0.64139, "loss": 0.64139, "time": 0.49461} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.01988, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89375, "top5_acc": 0.99562, "loss_cls": 0.54643, "loss": 0.54643, "time": 0.49267} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.01986, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91, "top5_acc": 0.9975, "loss_cls": 0.51226, "loss": 0.51226, "time": 0.49124} +{"mode": "val", "epoch": 45, "iter": 533, "lr": 0.01985, "top1_acc": 0.84262, "top5_acc": 0.98991, "mean_class_accuracy": 0.79267} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.01983, "memory": 4083, "data_time": 0.19458, "top1_acc": 0.89812, "top5_acc": 0.99625, "loss_cls": 0.51342, "loss": 0.51342, "time": 0.80699} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.01981, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89625, "top5_acc": 0.99688, "loss_cls": 0.53281, "loss": 0.53281, "time": 0.30115} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.0198, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88875, "top5_acc": 0.99938, "loss_cls": 0.52864, "loss": 0.52864, "time": 0.45327} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.01978, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.88125, "top5_acc": 0.99625, "loss_cls": 0.61287, "loss": 0.61287, "time": 0.34348} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.01976, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.87625, "top5_acc": 0.99688, "loss_cls": 0.61185, "loss": 0.61185, "time": 0.49225} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.01975, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88938, "top5_acc": 0.99562, "loss_cls": 0.56375, "loss": 0.56375, "time": 0.49002} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.01973, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.90375, "top5_acc": 0.99562, "loss_cls": 0.52083, "loss": 0.52083, "time": 0.48869} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.01971, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88938, "top5_acc": 0.995, "loss_cls": 0.58322, "loss": 0.58322, "time": 0.48978} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.0197, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89188, "top5_acc": 0.99812, "loss_cls": 0.54483, "loss": 0.54483, "time": 0.48921} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.01968, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.895, "top5_acc": 0.995, "loss_cls": 0.54383, "loss": 0.54383, "time": 0.48789} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.01966, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.88062, "top5_acc": 0.99188, "loss_cls": 0.6375, "loss": 0.6375, "time": 0.48714} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.01965, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89562, "top5_acc": 0.99625, "loss_cls": 0.53927, "loss": 0.53927, "time": 0.49173} +{"mode": "val", "epoch": 46, "iter": 533, "lr": 0.01963, "top1_acc": 0.86035, "top5_acc": 0.99155, "mean_class_accuracy": 0.80043} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.01962, "memory": 4083, "data_time": 0.1974, "top1_acc": 0.88125, "top5_acc": 0.9975, "loss_cls": 0.57917, "loss": 0.57917, "time": 0.80001} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.0196, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89938, "top5_acc": 0.99812, "loss_cls": 0.53538, "loss": 0.53538, "time": 0.30343} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.01958, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89312, "top5_acc": 0.99812, "loss_cls": 0.54573, "loss": 0.54573, "time": 0.4393} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.01957, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90188, "top5_acc": 0.99688, "loss_cls": 0.5208, "loss": 0.5208, "time": 0.34317} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.01955, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88812, "top5_acc": 0.99812, "loss_cls": 0.54578, "loss": 0.54578, "time": 0.48663} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.01953, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89125, "top5_acc": 0.995, "loss_cls": 0.61356, "loss": 0.61356, "time": 0.49067} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.01952, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.8775, "top5_acc": 0.995, "loss_cls": 0.62569, "loss": 0.62569, "time": 0.49043} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.0195, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89062, "top5_acc": 0.9975, "loss_cls": 0.54834, "loss": 0.54834, "time": 0.48962} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.01948, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88375, "top5_acc": 0.995, "loss_cls": 0.59167, "loss": 0.59167, "time": 0.49562} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.01947, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89438, "top5_acc": 0.99562, "loss_cls": 0.54049, "loss": 0.54049, "time": 0.48835} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.01945, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88938, "top5_acc": 0.99562, "loss_cls": 0.57205, "loss": 0.57205, "time": 0.48993} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.01943, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89875, "top5_acc": 0.99625, "loss_cls": 0.53951, "loss": 0.53951, "time": 0.48938} +{"mode": "val", "epoch": 47, "iter": 533, "lr": 0.01942, "top1_acc": 0.8411, "top5_acc": 0.98944, "mean_class_accuracy": 0.79959} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.0194, "memory": 4083, "data_time": 0.18751, "top1_acc": 0.85625, "top5_acc": 0.99438, "loss_cls": 0.71252, "loss": 0.71252, "time": 0.79169} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.01938, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88188, "top5_acc": 0.99875, "loss_cls": 0.58909, "loss": 0.58909, "time": 0.30156} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.01937, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91188, "top5_acc": 0.99625, "loss_cls": 0.5287, "loss": 0.5287, "time": 0.45674} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.01935, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.91875, "top5_acc": 0.99688, "loss_cls": 0.47262, "loss": 0.47262, "time": 0.34808} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.01933, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.88125, "top5_acc": 0.9975, "loss_cls": 0.58446, "loss": 0.58446, "time": 0.49118} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.01932, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89, "top5_acc": 0.995, "loss_cls": 0.56672, "loss": 0.56672, "time": 0.49173} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.0193, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89188, "top5_acc": 0.9975, "loss_cls": 0.53467, "loss": 0.53467, "time": 0.49048} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.01928, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.90375, "top5_acc": 0.99625, "loss_cls": 0.50174, "loss": 0.50174, "time": 0.4926} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.01926, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91188, "top5_acc": 0.99812, "loss_cls": 0.48291, "loss": 0.48291, "time": 0.48986} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.01925, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88938, "top5_acc": 0.99688, "loss_cls": 0.55232, "loss": 0.55232, "time": 0.4956} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.01923, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87312, "top5_acc": 0.99375, "loss_cls": 0.6877, "loss": 0.6877, "time": 0.49134} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.01921, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88438, "top5_acc": 0.99438, "loss_cls": 0.61497, "loss": 0.61497, "time": 0.48882} +{"mode": "val", "epoch": 48, "iter": 533, "lr": 0.0192, "top1_acc": 0.8391, "top5_acc": 0.98932, "mean_class_accuracy": 0.7894} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.01918, "memory": 4083, "data_time": 0.19156, "top1_acc": 0.905, "top5_acc": 0.995, "loss_cls": 0.52733, "loss": 0.52733, "time": 0.79388} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.01916, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.8925, "top5_acc": 0.99438, "loss_cls": 0.51499, "loss": 0.51499, "time": 0.30514} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.01915, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89188, "top5_acc": 0.9975, "loss_cls": 0.54848, "loss": 0.54848, "time": 0.4447} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.01913, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89938, "top5_acc": 0.99562, "loss_cls": 0.53289, "loss": 0.53289, "time": 0.33369} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.01911, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.89938, "top5_acc": 0.99562, "loss_cls": 0.55094, "loss": 0.55094, "time": 0.49178} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.01909, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.895, "top5_acc": 0.99625, "loss_cls": 0.55199, "loss": 0.55199, "time": 0.49265} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.01908, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.4809, "loss": 0.4809, "time": 0.48948} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.01906, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87875, "top5_acc": 0.99562, "loss_cls": 0.5711, "loss": 0.5711, "time": 0.49047} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.01904, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9125, "top5_acc": 0.995, "loss_cls": 0.51079, "loss": 0.51079, "time": 0.48876} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.01902, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90438, "top5_acc": 0.99562, "loss_cls": 0.51735, "loss": 0.51735, "time": 0.49121} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.01901, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89375, "top5_acc": 0.995, "loss_cls": 0.55086, "loss": 0.55086, "time": 0.4889} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.01899, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.8925, "top5_acc": 0.9975, "loss_cls": 0.52539, "loss": 0.52539, "time": 0.48711} +{"mode": "val", "epoch": 49, "iter": 533, "lr": 0.01898, "top1_acc": 0.86164, "top5_acc": 0.99061, "mean_class_accuracy": 0.80418} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.01896, "memory": 4083, "data_time": 0.1834, "top1_acc": 0.9075, "top5_acc": 0.99938, "loss_cls": 0.47655, "loss": 0.47655, "time": 0.79512} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.01894, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9075, "top5_acc": 0.9975, "loss_cls": 0.46828, "loss": 0.46828, "time": 0.29612} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.01892, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89375, "top5_acc": 0.99875, "loss_cls": 0.53487, "loss": 0.53487, "time": 0.4625} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.01891, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89562, "top5_acc": 0.99562, "loss_cls": 0.54856, "loss": 0.54856, "time": 0.3345} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.01889, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88875, "top5_acc": 0.99375, "loss_cls": 0.57587, "loss": 0.57587, "time": 0.49015} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.01887, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89562, "top5_acc": 0.99625, "loss_cls": 0.54502, "loss": 0.54502, "time": 0.49081} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.01885, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90375, "top5_acc": 0.99875, "loss_cls": 0.527, "loss": 0.527, "time": 0.49058} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.01884, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.88938, "top5_acc": 0.995, "loss_cls": 0.56504, "loss": 0.56504, "time": 0.48651} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.01882, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.89688, "top5_acc": 0.9975, "loss_cls": 0.53935, "loss": 0.53935, "time": 0.49207} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.0188, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88562, "top5_acc": 0.99688, "loss_cls": 0.56413, "loss": 0.56413, "time": 0.48851} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.01878, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88562, "top5_acc": 0.99375, "loss_cls": 0.59772, "loss": 0.59772, "time": 0.48922} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.01876, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.89438, "top5_acc": 0.99625, "loss_cls": 0.53988, "loss": 0.53988, "time": 0.49148} +{"mode": "val", "epoch": 50, "iter": 533, "lr": 0.01875, "top1_acc": 0.86304, "top5_acc": 0.99155, "mean_class_accuracy": 0.81999} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.01873, "memory": 4083, "data_time": 0.19954, "top1_acc": 0.91188, "top5_acc": 0.9975, "loss_cls": 0.46966, "loss": 0.46966, "time": 0.81627} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.01871, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.88562, "top5_acc": 0.99438, "loss_cls": 0.54845, "loss": 0.54845, "time": 0.32076} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.0187, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.90312, "top5_acc": 0.99812, "loss_cls": 0.48613, "loss": 0.48613, "time": 0.42804} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.01868, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90438, "top5_acc": 0.99812, "loss_cls": 0.48112, "loss": 0.48112, "time": 0.33977} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.01866, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.90438, "top5_acc": 0.99812, "loss_cls": 0.47825, "loss": 0.47825, "time": 0.48793} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.01864, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.91188, "top5_acc": 0.99812, "loss_cls": 0.48044, "loss": 0.48044, "time": 0.4895} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.01863, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.90375, "top5_acc": 0.99688, "loss_cls": 0.54619, "loss": 0.54619, "time": 0.48972} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.01861, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.87562, "top5_acc": 0.995, "loss_cls": 0.61249, "loss": 0.61249, "time": 0.48935} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.01859, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88562, "top5_acc": 0.99812, "loss_cls": 0.58481, "loss": 0.58481, "time": 0.49356} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.01857, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90562, "top5_acc": 0.99625, "loss_cls": 0.51386, "loss": 0.51386, "time": 0.4893} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.01855, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91, "top5_acc": 0.9975, "loss_cls": 0.47714, "loss": 0.47714, "time": 0.48869} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.01854, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.88312, "top5_acc": 0.99375, "loss_cls": 0.60883, "loss": 0.60883, "time": 0.48797} +{"mode": "val", "epoch": 51, "iter": 533, "lr": 0.01852, "top1_acc": 0.86445, "top5_acc": 0.99167, "mean_class_accuracy": 0.83036} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.0185, "memory": 4083, "data_time": 0.19208, "top1_acc": 0.90625, "top5_acc": 0.99625, "loss_cls": 0.49784, "loss": 0.49784, "time": 0.78635} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.01849, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.9125, "top5_acc": 0.99562, "loss_cls": 0.48079, "loss": 0.48079, "time": 0.29801} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.01847, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89562, "top5_acc": 0.99688, "loss_cls": 0.52711, "loss": 0.52711, "time": 0.44798} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.01845, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91062, "top5_acc": 0.99812, "loss_cls": 0.5041, "loss": 0.5041, "time": 0.33738} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.01843, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9075, "top5_acc": 0.99875, "loss_cls": 0.50847, "loss": 0.50847, "time": 0.49095} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.01841, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.905, "top5_acc": 0.9975, "loss_cls": 0.48731, "loss": 0.48731, "time": 0.4944} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.0184, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.89562, "top5_acc": 0.995, "loss_cls": 0.56176, "loss": 0.56176, "time": 0.49399} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.01838, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89, "top5_acc": 0.995, "loss_cls": 0.57634, "loss": 0.57634, "time": 0.48999} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.01836, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89312, "top5_acc": 0.995, "loss_cls": 0.54911, "loss": 0.54911, "time": 0.48775} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.01834, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88, "top5_acc": 0.99625, "loss_cls": 0.59537, "loss": 0.59537, "time": 0.49308} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.01832, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.88188, "top5_acc": 0.99625, "loss_cls": 0.62384, "loss": 0.62384, "time": 0.49255} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.01831, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89938, "top5_acc": 0.99875, "loss_cls": 0.5371, "loss": 0.5371, "time": 0.48851} +{"mode": "val", "epoch": 52, "iter": 533, "lr": 0.01829, "top1_acc": 0.86774, "top5_acc": 0.99284, "mean_class_accuracy": 0.83116} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.01827, "memory": 4083, "data_time": 0.18865, "top1_acc": 0.90562, "top5_acc": 0.9975, "loss_cls": 0.50997, "loss": 0.50997, "time": 0.78496} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.01826, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91188, "top5_acc": 0.99812, "loss_cls": 0.44383, "loss": 0.44383, "time": 0.29397} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.01824, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90812, "top5_acc": 0.99625, "loss_cls": 0.4712, "loss": 0.4712, "time": 0.46219} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.01822, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.905, "top5_acc": 0.99688, "loss_cls": 0.48438, "loss": 0.48438, "time": 0.33184} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.0182, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.90062, "top5_acc": 0.995, "loss_cls": 0.5245, "loss": 0.5245, "time": 0.49203} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.01818, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90125, "top5_acc": 0.9975, "loss_cls": 0.52956, "loss": 0.52956, "time": 0.49532} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.01816, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.88625, "top5_acc": 0.99562, "loss_cls": 0.55498, "loss": 0.55498, "time": 0.49248} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.01815, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88312, "top5_acc": 0.99625, "loss_cls": 0.58176, "loss": 0.58176, "time": 0.49429} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.01813, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89438, "top5_acc": 0.99375, "loss_cls": 0.54804, "loss": 0.54804, "time": 0.49319} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.01811, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91, "top5_acc": 0.995, "loss_cls": 0.48272, "loss": 0.48272, "time": 0.49425} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.01809, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89, "top5_acc": 0.99375, "loss_cls": 0.51764, "loss": 0.51764, "time": 0.49068} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.01807, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9, "top5_acc": 0.9975, "loss_cls": 0.49025, "loss": 0.49025, "time": 0.48891} +{"mode": "val", "epoch": 53, "iter": 533, "lr": 0.01806, "top1_acc": 0.86304, "top5_acc": 0.99002, "mean_class_accuracy": 0.80539} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.01804, "memory": 4083, "data_time": 0.19187, "top1_acc": 0.9075, "top5_acc": 0.9975, "loss_cls": 0.48686, "loss": 0.48686, "time": 0.79264} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.01802, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90312, "top5_acc": 0.99812, "loss_cls": 0.49528, "loss": 0.49528, "time": 0.33216} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.018, "memory": 4083, "data_time": 0.00065, "top1_acc": 0.9075, "top5_acc": 0.99625, "loss_cls": 0.50683, "loss": 0.50683, "time": 0.42663} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.01798, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90938, "top5_acc": 0.99812, "loss_cls": 0.48024, "loss": 0.48024, "time": 0.33716} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.01797, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90688, "top5_acc": 0.995, "loss_cls": 0.48526, "loss": 0.48526, "time": 0.49224} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.01795, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.905, "top5_acc": 0.99688, "loss_cls": 0.49177, "loss": 0.49177, "time": 0.48994} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.01793, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9, "top5_acc": 0.99812, "loss_cls": 0.52383, "loss": 0.52383, "time": 0.48725} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.01791, "memory": 4083, "data_time": 0.00069, "top1_acc": 0.90312, "top5_acc": 0.99688, "loss_cls": 0.52741, "loss": 0.52741, "time": 0.49056} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.01789, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.88875, "top5_acc": 0.99562, "loss_cls": 0.54878, "loss": 0.54878, "time": 0.4934} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.01787, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9075, "top5_acc": 0.9975, "loss_cls": 0.49901, "loss": 0.49901, "time": 0.49549} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.01786, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89625, "top5_acc": 0.9975, "loss_cls": 0.51338, "loss": 0.51338, "time": 0.48692} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.01784, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.875, "top5_acc": 0.995, "loss_cls": 0.59514, "loss": 0.59514, "time": 0.48915} +{"mode": "val", "epoch": 54, "iter": 533, "lr": 0.01782, "top1_acc": 0.84978, "top5_acc": 0.9912, "mean_class_accuracy": 0.79279} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.0178, "memory": 4083, "data_time": 0.189, "top1_acc": 0.91625, "top5_acc": 0.99688, "loss_cls": 0.46643, "loss": 0.46643, "time": 0.80505} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.01779, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.9075, "top5_acc": 0.9975, "loss_cls": 0.49972, "loss": 0.49972, "time": 0.31903} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.01777, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.89875, "top5_acc": 0.995, "loss_cls": 0.53599, "loss": 0.53599, "time": 0.42761} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.01775, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9, "top5_acc": 0.9975, "loss_cls": 0.51551, "loss": 0.51551, "time": 0.35524} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.01773, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.895, "top5_acc": 0.99688, "loss_cls": 0.54793, "loss": 0.54793, "time": 0.48965} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.01771, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9, "top5_acc": 0.99688, "loss_cls": 0.51662, "loss": 0.51662, "time": 0.49248} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.01769, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89312, "top5_acc": 0.99625, "loss_cls": 0.53134, "loss": 0.53134, "time": 0.48957} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.01767, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87562, "top5_acc": 0.9975, "loss_cls": 0.58403, "loss": 0.58403, "time": 0.48791} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.01766, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.91188, "top5_acc": 0.99625, "loss_cls": 0.47265, "loss": 0.47265, "time": 0.49177} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.01764, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8975, "top5_acc": 0.9975, "loss_cls": 0.51128, "loss": 0.51128, "time": 0.49483} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.01762, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89438, "top5_acc": 0.99562, "loss_cls": 0.53725, "loss": 0.53725, "time": 0.49301} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.0176, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.91375, "top5_acc": 0.99688, "loss_cls": 0.48237, "loss": 0.48237, "time": 0.49088} +{"mode": "val", "epoch": 55, "iter": 533, "lr": 0.01758, "top1_acc": 0.85389, "top5_acc": 0.98744, "mean_class_accuracy": 0.81216} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.01757, "memory": 4083, "data_time": 0.19174, "top1_acc": 0.91438, "top5_acc": 0.995, "loss_cls": 0.4615, "loss": 0.4615, "time": 0.78403} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.01755, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.42914, "loss": 0.42914, "time": 0.35779} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.01753, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89875, "top5_acc": 0.99812, "loss_cls": 0.50771, "loss": 0.50771, "time": 0.38269} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.01751, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91625, "top5_acc": 0.9975, "loss_cls": 0.45317, "loss": 0.45317, "time": 0.36924} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.01749, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91625, "top5_acc": 0.99688, "loss_cls": 0.44823, "loss": 0.44823, "time": 0.48915} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.01747, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92, "top5_acc": 0.99938, "loss_cls": 0.48141, "loss": 0.48141, "time": 0.4918} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.01745, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88938, "top5_acc": 0.99688, "loss_cls": 0.54785, "loss": 0.54785, "time": 0.48997} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.01743, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.91562, "top5_acc": 0.99688, "loss_cls": 0.45889, "loss": 0.45889, "time": 0.49326} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.01742, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.90125, "top5_acc": 0.995, "loss_cls": 0.51623, "loss": 0.51623, "time": 0.48726} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.0174, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8975, "top5_acc": 0.99812, "loss_cls": 0.497, "loss": 0.497, "time": 0.49127} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.01738, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9, "top5_acc": 0.99562, "loss_cls": 0.52773, "loss": 0.52773, "time": 0.49432} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.01736, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9025, "top5_acc": 0.995, "loss_cls": 0.54217, "loss": 0.54217, "time": 0.48895} +{"mode": "val", "epoch": 56, "iter": 533, "lr": 0.01734, "top1_acc": 0.87431, "top5_acc": 0.9939, "mean_class_accuracy": 0.82994} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.01733, "memory": 4083, "data_time": 0.19641, "top1_acc": 0.91, "top5_acc": 0.99625, "loss_cls": 0.49176, "loss": 0.49176, "time": 0.76913} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.01731, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92312, "top5_acc": 0.99812, "loss_cls": 0.42844, "loss": 0.42844, "time": 0.38224} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.01729, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.90562, "top5_acc": 0.99812, "loss_cls": 0.53393, "loss": 0.53393, "time": 0.35367} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.01727, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9025, "top5_acc": 0.99625, "loss_cls": 0.51474, "loss": 0.51474, "time": 0.3754} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.01725, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91188, "top5_acc": 0.99812, "loss_cls": 0.46642, "loss": 0.46642, "time": 0.48802} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.01723, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91375, "top5_acc": 0.99625, "loss_cls": 0.5079, "loss": 0.5079, "time": 0.49011} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.01721, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9175, "top5_acc": 0.995, "loss_cls": 0.44345, "loss": 0.44345, "time": 0.4895} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.01719, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.8925, "top5_acc": 0.99375, "loss_cls": 0.54253, "loss": 0.54253, "time": 0.48961} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.01717, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91312, "top5_acc": 0.99938, "loss_cls": 0.46359, "loss": 0.46359, "time": 0.49115} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.01716, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89, "top5_acc": 0.99625, "loss_cls": 0.55703, "loss": 0.55703, "time": 0.49148} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.01714, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89938, "top5_acc": 0.99812, "loss_cls": 0.51365, "loss": 0.51365, "time": 0.48814} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.01712, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.89688, "top5_acc": 0.99688, "loss_cls": 0.53245, "loss": 0.53245, "time": 0.48986} +{"mode": "val", "epoch": 57, "iter": 533, "lr": 0.0171, "top1_acc": 0.88346, "top5_acc": 0.99378, "mean_class_accuracy": 0.83144} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.01708, "memory": 4083, "data_time": 0.1893, "top1_acc": 0.92312, "top5_acc": 0.99938, "loss_cls": 0.39948, "loss": 0.39948, "time": 0.76148} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.01706, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9225, "top5_acc": 0.99812, "loss_cls": 0.44383, "loss": 0.44383, "time": 0.4107} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.01704, "memory": 4083, "data_time": 0.00072, "top1_acc": 0.91375, "top5_acc": 1.0, "loss_cls": 0.45137, "loss": 0.45137, "time": 0.33088} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.01703, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91062, "top5_acc": 0.9975, "loss_cls": 0.49769, "loss": 0.49769, "time": 0.40857} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.01701, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91438, "top5_acc": 0.99625, "loss_cls": 0.46036, "loss": 0.46036, "time": 0.49011} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.01699, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91312, "top5_acc": 0.99875, "loss_cls": 0.45556, "loss": 0.45556, "time": 0.49344} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.01697, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89625, "top5_acc": 0.99688, "loss_cls": 0.52382, "loss": 0.52382, "time": 0.4904} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.01695, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89688, "top5_acc": 0.99688, "loss_cls": 0.51532, "loss": 0.51532, "time": 0.49252} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.01693, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89938, "top5_acc": 0.99875, "loss_cls": 0.50846, "loss": 0.50846, "time": 0.49179} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.01691, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91438, "top5_acc": 0.99875, "loss_cls": 0.48341, "loss": 0.48341, "time": 0.49109} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.01689, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.91, "top5_acc": 0.99875, "loss_cls": 0.46977, "loss": 0.46977, "time": 0.48902} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.01687, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8825, "top5_acc": 0.99562, "loss_cls": 0.55632, "loss": 0.55632, "time": 0.48918} +{"mode": "val", "epoch": 58, "iter": 533, "lr": 0.01686, "top1_acc": 0.86199, "top5_acc": 0.99214, "mean_class_accuracy": 0.81293} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.01684, "memory": 4083, "data_time": 0.19591, "top1_acc": 0.90312, "top5_acc": 0.99938, "loss_cls": 0.47316, "loss": 0.47316, "time": 0.73709} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.01682, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.92188, "top5_acc": 0.99812, "loss_cls": 0.42581, "loss": 0.42581, "time": 0.45213} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.0168, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90625, "top5_acc": 0.99812, "loss_cls": 0.47244, "loss": 0.47244, "time": 0.28753} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.01678, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.91562, "top5_acc": 0.99688, "loss_cls": 0.46091, "loss": 0.46091, "time": 0.4275} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.01676, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91062, "top5_acc": 0.99625, "loss_cls": 0.47918, "loss": 0.47918, "time": 0.48918} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.01674, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.90188, "top5_acc": 0.99938, "loss_cls": 0.47668, "loss": 0.47668, "time": 0.49155} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.01672, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91438, "top5_acc": 0.99625, "loss_cls": 0.46636, "loss": 0.46636, "time": 0.49115} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.0167, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.89688, "top5_acc": 0.99312, "loss_cls": 0.53688, "loss": 0.53688, "time": 0.49004} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.01668, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89062, "top5_acc": 0.99625, "loss_cls": 0.54393, "loss": 0.54393, "time": 0.48896} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.01667, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90312, "top5_acc": 0.99688, "loss_cls": 0.50327, "loss": 0.50327, "time": 0.4893} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.01665, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.91188, "top5_acc": 0.9975, "loss_cls": 0.46999, "loss": 0.46999, "time": 0.49078} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.01663, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.51608, "loss": 0.51608, "time": 0.49405} +{"mode": "val", "epoch": 59, "iter": 533, "lr": 0.01661, "top1_acc": 0.87009, "top5_acc": 0.99108, "mean_class_accuracy": 0.81281} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.01659, "memory": 4083, "data_time": 0.19911, "top1_acc": 0.9125, "top5_acc": 0.99562, "loss_cls": 0.46388, "loss": 0.46388, "time": 0.70975} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.01657, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.91125, "top5_acc": 0.9975, "loss_cls": 0.46149, "loss": 0.46149, "time": 0.51233} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.01655, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9125, "top5_acc": 0.99875, "loss_cls": 0.45985, "loss": 0.45985, "time": 0.23495} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.01653, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89938, "top5_acc": 0.99938, "loss_cls": 0.52035, "loss": 0.52035, "time": 0.4398} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.01651, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91438, "top5_acc": 0.99875, "loss_cls": 0.4565, "loss": 0.4565, "time": 0.49174} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.0165, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91062, "top5_acc": 0.99875, "loss_cls": 0.44872, "loss": 0.44872, "time": 0.48958} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.01648, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.91938, "top5_acc": 0.99688, "loss_cls": 0.44273, "loss": 0.44273, "time": 0.49177} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.01646, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.91188, "top5_acc": 0.99688, "loss_cls": 0.47156, "loss": 0.47156, "time": 0.48782} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.01644, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91062, "top5_acc": 0.99562, "loss_cls": 0.48805, "loss": 0.48805, "time": 0.49242} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.01642, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91062, "top5_acc": 0.99438, "loss_cls": 0.47252, "loss": 0.47252, "time": 0.49096} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.0164, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8975, "top5_acc": 0.995, "loss_cls": 0.50891, "loss": 0.50891, "time": 0.4914} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.01638, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.50028, "loss": 0.50028, "time": 0.49} +{"mode": "val", "epoch": 60, "iter": 533, "lr": 0.01636, "top1_acc": 0.87854, "top5_acc": 0.99225, "mean_class_accuracy": 0.83243} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.01634, "memory": 4083, "data_time": 0.19296, "top1_acc": 0.90875, "top5_acc": 0.99938, "loss_cls": 0.45039, "loss": 0.45039, "time": 0.69154} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.01632, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91375, "top5_acc": 0.99875, "loss_cls": 0.42586, "loss": 0.42586, "time": 0.51234} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.0163, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.9225, "top5_acc": 0.99812, "loss_cls": 0.43083, "loss": 0.43083, "time": 0.24435} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.01629, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.90562, "top5_acc": 0.9975, "loss_cls": 0.48595, "loss": 0.48595, "time": 0.45975} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.01627, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.90562, "top5_acc": 0.99812, "loss_cls": 0.44738, "loss": 0.44738, "time": 0.48842} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.01625, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.90312, "top5_acc": 0.99875, "loss_cls": 0.4944, "loss": 0.4944, "time": 0.49009} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.01623, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90625, "top5_acc": 0.99688, "loss_cls": 0.48275, "loss": 0.48275, "time": 0.4899} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.01621, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9125, "top5_acc": 0.99938, "loss_cls": 0.45782, "loss": 0.45782, "time": 0.49013} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.01619, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91, "top5_acc": 0.99688, "loss_cls": 0.46539, "loss": 0.46539, "time": 0.48973} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.01617, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90312, "top5_acc": 0.99812, "loss_cls": 0.4976, "loss": 0.4976, "time": 0.48959} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.01615, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90625, "top5_acc": 0.99562, "loss_cls": 0.48437, "loss": 0.48437, "time": 0.49182} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.01613, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90562, "top5_acc": 0.99688, "loss_cls": 0.48041, "loss": 0.48041, "time": 0.49006} +{"mode": "val", "epoch": 61, "iter": 533, "lr": 0.01611, "top1_acc": 0.86058, "top5_acc": 0.98932, "mean_class_accuracy": 0.80626} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.01609, "memory": 4083, "data_time": 0.20171, "top1_acc": 0.915, "top5_acc": 0.99625, "loss_cls": 0.45954, "loss": 0.45954, "time": 0.66451} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.01607, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92312, "top5_acc": 0.99812, "loss_cls": 0.42884, "loss": 0.42884, "time": 0.5132} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.01605, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91625, "top5_acc": 0.99562, "loss_cls": 0.44367, "loss": 0.44367, "time": 0.2545} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.01603, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.90375, "top5_acc": 0.99688, "loss_cls": 0.50545, "loss": 0.50545, "time": 0.48084} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.01602, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91625, "top5_acc": 0.99375, "loss_cls": 0.46978, "loss": 0.46978, "time": 0.49348} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.016, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91938, "top5_acc": 0.9975, "loss_cls": 0.43655, "loss": 0.43655, "time": 0.49079} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.01598, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91375, "top5_acc": 0.99875, "loss_cls": 0.46116, "loss": 0.46116, "time": 0.49106} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.01596, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91188, "top5_acc": 0.99875, "loss_cls": 0.4583, "loss": 0.4583, "time": 0.49394} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.01594, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89562, "top5_acc": 0.99438, "loss_cls": 0.53747, "loss": 0.53747, "time": 0.49336} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.01592, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91312, "top5_acc": 0.9975, "loss_cls": 0.45943, "loss": 0.45943, "time": 0.49146} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.0159, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.91125, "top5_acc": 0.99875, "loss_cls": 0.48053, "loss": 0.48053, "time": 0.49413} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.01588, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9175, "top5_acc": 0.99812, "loss_cls": 0.46121, "loss": 0.46121, "time": 0.48918} +{"mode": "val", "epoch": 62, "iter": 533, "lr": 0.01586, "top1_acc": 0.88206, "top5_acc": 0.9939, "mean_class_accuracy": 0.83779} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.01584, "memory": 4083, "data_time": 0.19635, "top1_acc": 0.90875, "top5_acc": 0.99938, "loss_cls": 0.45299, "loss": 0.45299, "time": 0.64072} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.01582, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93812, "top5_acc": 0.99812, "loss_cls": 0.39452, "loss": 0.39452, "time": 0.5131} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.0158, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.925, "top5_acc": 0.99938, "loss_cls": 0.429, "loss": 0.429, "time": 0.25753} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.01578, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92562, "top5_acc": 0.99875, "loss_cls": 0.41266, "loss": 0.41266, "time": 0.47764} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.01576, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.90688, "top5_acc": 0.9975, "loss_cls": 0.49601, "loss": 0.49601, "time": 0.49224} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.01574, "memory": 4083, "data_time": 0.00064, "top1_acc": 0.91375, "top5_acc": 0.99938, "loss_cls": 0.47845, "loss": 0.47845, "time": 0.48835} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.01572, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92125, "top5_acc": 0.99562, "loss_cls": 0.43315, "loss": 0.43315, "time": 0.49296} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.0157, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92062, "top5_acc": 0.99625, "loss_cls": 0.43191, "loss": 0.43191, "time": 0.49117} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.01568, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91625, "top5_acc": 0.99625, "loss_cls": 0.45386, "loss": 0.45386, "time": 0.49307} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.01566, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90625, "top5_acc": 0.995, "loss_cls": 0.49188, "loss": 0.49188, "time": 0.49136} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.01564, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90062, "top5_acc": 0.99812, "loss_cls": 0.48929, "loss": 0.48929, "time": 0.49015} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.01562, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.91938, "top5_acc": 0.99375, "loss_cls": 0.46067, "loss": 0.46067, "time": 0.49077} +{"mode": "val", "epoch": 63, "iter": 533, "lr": 0.01561, "top1_acc": 0.88018, "top5_acc": 0.98885, "mean_class_accuracy": 0.84028} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.01559, "memory": 4083, "data_time": 0.1883, "top1_acc": 0.9325, "top5_acc": 0.99875, "loss_cls": 0.40322, "loss": 0.40322, "time": 0.63021} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.01557, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.37952, "loss": 0.37952, "time": 0.51329} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.01555, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93688, "top5_acc": 0.99938, "loss_cls": 0.35067, "loss": 0.35067, "time": 0.26006} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.01553, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90562, "top5_acc": 0.9975, "loss_cls": 0.46086, "loss": 0.46086, "time": 0.48962} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.01551, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.89938, "top5_acc": 0.99812, "loss_cls": 0.5062, "loss": 0.5062, "time": 0.49393} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.01549, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.92125, "top5_acc": 0.99875, "loss_cls": 0.40531, "loss": 0.40531, "time": 0.49156} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.01547, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91812, "top5_acc": 0.99625, "loss_cls": 0.44647, "loss": 0.44647, "time": 0.48904} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.01545, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92562, "top5_acc": 0.99812, "loss_cls": 0.41265, "loss": 0.41265, "time": 0.49509} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.01543, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.91375, "top5_acc": 0.99812, "loss_cls": 0.47009, "loss": 0.47009, "time": 0.49153} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.01541, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.925, "top5_acc": 0.99562, "loss_cls": 0.43922, "loss": 0.43922, "time": 0.48886} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.01539, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91562, "top5_acc": 0.99375, "loss_cls": 0.43995, "loss": 0.43995, "time": 0.49088} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.01537, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.91188, "top5_acc": 0.99688, "loss_cls": 0.45858, "loss": 0.45858, "time": 0.48749} +{"mode": "val", "epoch": 64, "iter": 533, "lr": 0.01535, "top1_acc": 0.87619, "top5_acc": 0.99014, "mean_class_accuracy": 0.82612} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.01533, "memory": 4083, "data_time": 0.19514, "top1_acc": 0.9175, "top5_acc": 0.99688, "loss_cls": 0.44837, "loss": 0.44837, "time": 0.62854} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.01531, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93125, "top5_acc": 0.99875, "loss_cls": 0.39232, "loss": 0.39232, "time": 0.51116} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.01529, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93, "top5_acc": 0.9975, "loss_cls": 0.39395, "loss": 0.39395, "time": 0.26656} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.01527, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92062, "top5_acc": 0.99938, "loss_cls": 0.43165, "loss": 0.43165, "time": 0.48746} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.01526, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.91375, "top5_acc": 0.99812, "loss_cls": 0.47303, "loss": 0.47303, "time": 0.48966} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.01524, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90375, "top5_acc": 0.9975, "loss_cls": 0.4788, "loss": 0.4788, "time": 0.48672} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.01522, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9175, "top5_acc": 0.99688, "loss_cls": 0.45033, "loss": 0.45033, "time": 0.4908} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0152, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9125, "top5_acc": 0.9975, "loss_cls": 0.47259, "loss": 0.47259, "time": 0.49106} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.01518, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9375, "top5_acc": 0.9975, "loss_cls": 0.40224, "loss": 0.40224, "time": 0.49278} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.01516, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91062, "top5_acc": 0.9975, "loss_cls": 0.43874, "loss": 0.43874, "time": 0.49395} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.01514, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92, "top5_acc": 0.99875, "loss_cls": 0.42956, "loss": 0.42956, "time": 0.48994} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.01512, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90312, "top5_acc": 0.99562, "loss_cls": 0.51045, "loss": 0.51045, "time": 0.48982} +{"mode": "val", "epoch": 65, "iter": 533, "lr": 0.0151, "top1_acc": 0.8729, "top5_acc": 0.99237, "mean_class_accuracy": 0.82929} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.01508, "memory": 4083, "data_time": 0.19183, "top1_acc": 0.93188, "top5_acc": 0.99562, "loss_cls": 0.40366, "loss": 0.40366, "time": 0.61541} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.01506, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.35646, "loss": 0.35646, "time": 0.51131} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.01504, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92188, "top5_acc": 0.99625, "loss_cls": 0.42939, "loss": 0.42939, "time": 0.28734} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.01502, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9125, "top5_acc": 0.99312, "loss_cls": 0.47815, "loss": 0.47815, "time": 0.49067} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.015, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91938, "top5_acc": 0.99812, "loss_cls": 0.42496, "loss": 0.42496, "time": 0.49103} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.01498, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.91625, "top5_acc": 0.99875, "loss_cls": 0.45967, "loss": 0.45967, "time": 0.49373} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.01496, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9225, "top5_acc": 0.99562, "loss_cls": 0.46188, "loss": 0.46188, "time": 0.4941} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.01494, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.91688, "top5_acc": 0.99688, "loss_cls": 0.47041, "loss": 0.47041, "time": 0.49315} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.01492, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.43409, "loss": 0.43409, "time": 0.48991} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.0149, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91875, "top5_acc": 0.99688, "loss_cls": 0.44019, "loss": 0.44019, "time": 0.49277} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.01488, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91375, "top5_acc": 0.99938, "loss_cls": 0.41998, "loss": 0.41998, "time": 0.48998} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.01486, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91625, "top5_acc": 0.995, "loss_cls": 0.48279, "loss": 0.48279, "time": 0.49255} +{"mode": "val", "epoch": 66, "iter": 533, "lr": 0.01484, "top1_acc": 0.85342, "top5_acc": 0.98862, "mean_class_accuracy": 0.81137} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.01482, "memory": 4083, "data_time": 0.19574, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.39422, "loss": 0.39422, "time": 0.58466} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.0148, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.37491, "loss": 0.37491, "time": 0.51343} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.01478, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.92, "top5_acc": 0.99875, "loss_cls": 0.41758, "loss": 0.41758, "time": 0.30912} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.01476, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.93312, "top5_acc": 0.99875, "loss_cls": 0.37955, "loss": 0.37955, "time": 0.49071} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.01474, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91375, "top5_acc": 0.995, "loss_cls": 0.47978, "loss": 0.47978, "time": 0.49059} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.01472, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9, "top5_acc": 0.9975, "loss_cls": 0.51869, "loss": 0.51869, "time": 0.48963} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.0147, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.92812, "top5_acc": 0.99812, "loss_cls": 0.40006, "loss": 0.40006, "time": 0.49333} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.01468, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9125, "top5_acc": 0.9975, "loss_cls": 0.44748, "loss": 0.44748, "time": 0.48989} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.01466, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92438, "top5_acc": 0.99688, "loss_cls": 0.44221, "loss": 0.44221, "time": 0.48518} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.01464, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90812, "top5_acc": 0.99688, "loss_cls": 0.46309, "loss": 0.46309, "time": 0.48837} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.01462, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9175, "top5_acc": 0.99812, "loss_cls": 0.44199, "loss": 0.44199, "time": 0.48968} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.0146, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.915, "top5_acc": 0.99875, "loss_cls": 0.45047, "loss": 0.45047, "time": 0.49337} +{"mode": "val", "epoch": 67, "iter": 533, "lr": 0.01458, "top1_acc": 0.88593, "top5_acc": 0.99272, "mean_class_accuracy": 0.83796} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.01456, "memory": 4083, "data_time": 0.1913, "top1_acc": 0.93188, "top5_acc": 0.99938, "loss_cls": 0.38695, "loss": 0.38695, "time": 0.64451} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.01454, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93125, "top5_acc": 0.99938, "loss_cls": 0.37056, "loss": 0.37056, "time": 0.43247} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.01452, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9275, "top5_acc": 0.99875, "loss_cls": 0.38863, "loss": 0.38863, "time": 0.35766} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.0145, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93312, "top5_acc": 0.99875, "loss_cls": 0.36593, "loss": 0.36593, "time": 0.4905} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.01448, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.92438, "top5_acc": 0.9975, "loss_cls": 0.40006, "loss": 0.40006, "time": 0.49371} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.01446, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.925, "top5_acc": 0.99938, "loss_cls": 0.40323, "loss": 0.40323, "time": 0.48387} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.01444, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.91812, "top5_acc": 0.9975, "loss_cls": 0.43832, "loss": 0.43832, "time": 0.48923} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.01442, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9, "top5_acc": 0.99812, "loss_cls": 0.48038, "loss": 0.48038, "time": 0.49199} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.0144, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91375, "top5_acc": 0.99938, "loss_cls": 0.47392, "loss": 0.47392, "time": 0.48799} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.01438, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.43322, "loss": 0.43322, "time": 0.49258} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.01436, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91062, "top5_acc": 0.99875, "loss_cls": 0.44647, "loss": 0.44647, "time": 0.49028} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.01434, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.905, "top5_acc": 0.99562, "loss_cls": 0.47964, "loss": 0.47964, "time": 0.49117} +{"mode": "val", "epoch": 68, "iter": 533, "lr": 0.01433, "top1_acc": 0.88781, "top5_acc": 0.99249, "mean_class_accuracy": 0.85381} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.01431, "memory": 4083, "data_time": 0.18606, "top1_acc": 0.9175, "top5_acc": 0.99688, "loss_cls": 0.41096, "loss": 0.41096, "time": 0.61844} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.01429, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92375, "top5_acc": 0.99938, "loss_cls": 0.40935, "loss": 0.40935, "time": 0.40944} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.01427, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94, "top5_acc": 0.99938, "loss_cls": 0.34643, "loss": 0.34643, "time": 0.35238} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.01425, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.935, "top5_acc": 0.99688, "loss_cls": 0.40894, "loss": 0.40894, "time": 0.49014} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.01423, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92188, "top5_acc": 0.99688, "loss_cls": 0.43224, "loss": 0.43224, "time": 0.49045} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.0142, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92, "top5_acc": 0.99875, "loss_cls": 0.44389, "loss": 0.44389, "time": 0.48935} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.01418, "memory": 4083, "data_time": 0.00062, "top1_acc": 0.92812, "top5_acc": 0.99812, "loss_cls": 0.40824, "loss": 0.40824, "time": 0.49019} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.01416, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92062, "top5_acc": 0.99875, "loss_cls": 0.41774, "loss": 0.41774, "time": 0.49343} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.01414, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91812, "top5_acc": 0.99875, "loss_cls": 0.44842, "loss": 0.44842, "time": 0.49065} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.01412, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91438, "top5_acc": 0.99625, "loss_cls": 0.46829, "loss": 0.46829, "time": 0.49393} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.0141, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92062, "top5_acc": 0.9975, "loss_cls": 0.42367, "loss": 0.42367, "time": 0.49011} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.01408, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.92, "top5_acc": 0.9975, "loss_cls": 0.43501, "loss": 0.43501, "time": 0.4914} +{"mode": "val", "epoch": 69, "iter": 533, "lr": 0.01407, "top1_acc": 0.88769, "top5_acc": 0.99284, "mean_class_accuracy": 0.84255} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.01405, "memory": 4083, "data_time": 0.19217, "top1_acc": 0.93062, "top5_acc": 1.0, "loss_cls": 0.37093, "loss": 0.37093, "time": 0.63925} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.01403, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92688, "top5_acc": 0.99875, "loss_cls": 0.38596, "loss": 0.38596, "time": 0.4042} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.01401, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.9225, "top5_acc": 0.99875, "loss_cls": 0.40869, "loss": 0.40869, "time": 0.37315} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.01399, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.37957, "loss": 0.37957, "time": 0.49355} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.01397, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.43622, "loss": 0.43622, "time": 0.48938} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.01395, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93312, "top5_acc": 0.99875, "loss_cls": 0.36451, "loss": 0.36451, "time": 0.48995} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.01392, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93312, "top5_acc": 0.99688, "loss_cls": 0.39045, "loss": 0.39045, "time": 0.4901} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.0139, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9275, "top5_acc": 0.99812, "loss_cls": 0.39465, "loss": 0.39465, "time": 0.4906} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.01388, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93562, "top5_acc": 0.9975, "loss_cls": 0.37981, "loss": 0.37981, "time": 0.49188} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.01386, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9225, "top5_acc": 0.99938, "loss_cls": 0.43134, "loss": 0.43134, "time": 0.48903} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.01384, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92375, "top5_acc": 0.9975, "loss_cls": 0.42436, "loss": 0.42436, "time": 0.49403} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.01382, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90938, "top5_acc": 0.9975, "loss_cls": 0.4703, "loss": 0.4703, "time": 0.48895} +{"mode": "val", "epoch": 70, "iter": 533, "lr": 0.01381, "top1_acc": 0.88276, "top5_acc": 0.9919, "mean_class_accuracy": 0.85586} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.01379, "memory": 4083, "data_time": 0.19005, "top1_acc": 0.92062, "top5_acc": 0.99812, "loss_cls": 0.41176, "loss": 0.41176, "time": 0.62494} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.01377, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.38343, "loss": 0.38343, "time": 0.38691} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.01375, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93625, "top5_acc": 1.0, "loss_cls": 0.34278, "loss": 0.34278, "time": 0.36748} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.01373, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.40304, "loss": 0.40304, "time": 0.49046} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.01371, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93625, "top5_acc": 0.99812, "loss_cls": 0.36843, "loss": 0.36843, "time": 0.49172} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.01368, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9125, "top5_acc": 0.99688, "loss_cls": 0.44409, "loss": 0.44409, "time": 0.49032} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.01366, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.92562, "top5_acc": 0.99875, "loss_cls": 0.42349, "loss": 0.42349, "time": 0.49375} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.01364, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91375, "top5_acc": 0.99562, "loss_cls": 0.45212, "loss": 0.45212, "time": 0.49232} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.01362, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.91, "top5_acc": 0.99875, "loss_cls": 0.45912, "loss": 0.45912, "time": 0.49013} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.0136, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.37565, "loss": 0.37565, "time": 0.48761} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.01358, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.93188, "top5_acc": 0.99938, "loss_cls": 0.37969, "loss": 0.37969, "time": 0.48844} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.01356, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.91875, "top5_acc": 0.99812, "loss_cls": 0.47818, "loss": 0.47818, "time": 0.48889} +{"mode": "val", "epoch": 71, "iter": 533, "lr": 0.01355, "top1_acc": 0.88405, "top5_acc": 0.99343, "mean_class_accuracy": 0.83729} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.01353, "memory": 4083, "data_time": 0.19609, "top1_acc": 0.94062, "top5_acc": 0.99812, "loss_cls": 0.3544, "loss": 0.3544, "time": 0.62981} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.01351, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.92812, "top5_acc": 0.99688, "loss_cls": 0.3787, "loss": 0.3787, "time": 0.40078} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.01349, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92562, "top5_acc": 0.9975, "loss_cls": 0.40592, "loss": 0.40592, "time": 0.3793} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.01346, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93438, "top5_acc": 0.99812, "loss_cls": 0.36003, "loss": 0.36003, "time": 0.48893} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.01344, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92562, "top5_acc": 0.99938, "loss_cls": 0.42723, "loss": 0.42723, "time": 0.48875} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.01342, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93125, "top5_acc": 0.99875, "loss_cls": 0.40861, "loss": 0.40861, "time": 0.49112} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.0134, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92688, "top5_acc": 0.99812, "loss_cls": 0.38801, "loss": 0.38801, "time": 0.48922} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.01338, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93812, "top5_acc": 0.99812, "loss_cls": 0.35482, "loss": 0.35482, "time": 0.49371} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.01336, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91625, "top5_acc": 0.99688, "loss_cls": 0.41753, "loss": 0.41753, "time": 0.48979} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.01334, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92688, "top5_acc": 0.99938, "loss_cls": 0.43415, "loss": 0.43415, "time": 0.48968} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.01332, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9175, "top5_acc": 0.99875, "loss_cls": 0.45306, "loss": 0.45306, "time": 0.49134} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.0133, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.91438, "top5_acc": 0.99688, "loss_cls": 0.43231, "loss": 0.43231, "time": 0.48728} +{"mode": "val", "epoch": 72, "iter": 533, "lr": 0.01329, "top1_acc": 0.88182, "top5_acc": 0.9919, "mean_class_accuracy": 0.83276} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.01326, "memory": 4083, "data_time": 0.19686, "top1_acc": 0.92, "top5_acc": 0.99938, "loss_cls": 0.40378, "loss": 0.40378, "time": 0.6181} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.01324, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.93, "top5_acc": 0.99812, "loss_cls": 0.39538, "loss": 0.39538, "time": 0.39457} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.01322, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.3291, "loss": 0.3291, "time": 0.37758} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.0132, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.32101, "loss": 0.32101, "time": 0.49323} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.01318, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94, "top5_acc": 1.0, "loss_cls": 0.33898, "loss": 0.33898, "time": 0.49133} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.01316, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92438, "top5_acc": 0.99875, "loss_cls": 0.39542, "loss": 0.39542, "time": 0.49349} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.01314, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.92312, "top5_acc": 0.99688, "loss_cls": 0.42288, "loss": 0.42288, "time": 0.4901} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.01312, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.41779, "loss": 0.41779, "time": 0.4919} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.0131, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.91938, "top5_acc": 0.99812, "loss_cls": 0.42933, "loss": 0.42933, "time": 0.49317} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.01308, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.94125, "top5_acc": 0.9975, "loss_cls": 0.34804, "loss": 0.34804, "time": 0.48896} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.01306, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.93062, "top5_acc": 0.99812, "loss_cls": 0.37985, "loss": 0.37985, "time": 0.48827} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.01304, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.92312, "top5_acc": 0.99812, "loss_cls": 0.40588, "loss": 0.40588, "time": 0.48881} +{"mode": "val", "epoch": 73, "iter": 533, "lr": 0.01302, "top1_acc": 0.87889, "top5_acc": 0.99261, "mean_class_accuracy": 0.83954} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.013, "memory": 4083, "data_time": 0.19353, "top1_acc": 0.93375, "top5_acc": 0.99812, "loss_cls": 0.36998, "loss": 0.36998, "time": 0.61338} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.01298, "memory": 4083, "data_time": 0.00085, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.32858, "loss": 0.32858, "time": 0.40435} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.01296, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9325, "top5_acc": 0.99938, "loss_cls": 0.35506, "loss": 0.35506, "time": 0.37504} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.01294, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93125, "top5_acc": 0.99938, "loss_cls": 0.36292, "loss": 0.36292, "time": 0.491} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.01292, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93438, "top5_acc": 0.99812, "loss_cls": 0.36638, "loss": 0.36638, "time": 0.49184} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.0129, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93875, "top5_acc": 0.9975, "loss_cls": 0.35638, "loss": 0.35638, "time": 0.48881} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.01288, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9325, "top5_acc": 0.99875, "loss_cls": 0.38284, "loss": 0.38284, "time": 0.49037} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.01286, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.44738, "loss": 0.44738, "time": 0.49062} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.01284, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.93812, "top5_acc": 1.0, "loss_cls": 0.3561, "loss": 0.3561, "time": 0.48928} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.01282, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.94188, "top5_acc": 0.99688, "loss_cls": 0.35406, "loss": 0.35406, "time": 0.49171} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.0128, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92062, "top5_acc": 0.99812, "loss_cls": 0.38066, "loss": 0.38066, "time": 0.48979} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.01278, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93062, "top5_acc": 0.9975, "loss_cls": 0.38743, "loss": 0.38743, "time": 0.48835} +{"mode": "val", "epoch": 74, "iter": 533, "lr": 0.01276, "top1_acc": 0.89109, "top5_acc": 0.99296, "mean_class_accuracy": 0.85085} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.01274, "memory": 4083, "data_time": 0.18902, "top1_acc": 0.92438, "top5_acc": 0.99688, "loss_cls": 0.40952, "loss": 0.40952, "time": 0.60238} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.01272, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93125, "top5_acc": 0.99875, "loss_cls": 0.35545, "loss": 0.35545, "time": 0.40577} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.0127, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.93188, "top5_acc": 0.99812, "loss_cls": 0.39139, "loss": 0.39139, "time": 0.36496} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.01268, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.35023, "loss": 0.35023, "time": 0.48847} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.01266, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.92312, "top5_acc": 0.99688, "loss_cls": 0.4051, "loss": 0.4051, "time": 0.49106} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.01264, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.36069, "loss": 0.36069, "time": 0.49583} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.01262, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92812, "top5_acc": 0.99688, "loss_cls": 0.42147, "loss": 0.42147, "time": 0.49321} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.0126, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.39179, "loss": 0.39179, "time": 0.49175} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.01258, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.36984, "loss": 0.36984, "time": 0.48979} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.01256, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93125, "top5_acc": 0.9975, "loss_cls": 0.37781, "loss": 0.37781, "time": 0.48938} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.01254, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9225, "top5_acc": 0.99812, "loss_cls": 0.41253, "loss": 0.41253, "time": 0.48912} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.01252, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.90562, "top5_acc": 0.9975, "loss_cls": 0.4904, "loss": 0.4904, "time": 0.49136} +{"mode": "val", "epoch": 75, "iter": 533, "lr": 0.0125, "top1_acc": 0.881, "top5_acc": 0.99096, "mean_class_accuracy": 0.84518} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.01248, "memory": 4083, "data_time": 0.19384, "top1_acc": 0.92688, "top5_acc": 0.99938, "loss_cls": 0.39681, "loss": 0.39681, "time": 0.6338} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.01246, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.93438, "top5_acc": 0.99812, "loss_cls": 0.36488, "loss": 0.36488, "time": 0.4039} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.01244, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.915, "top5_acc": 0.99688, "loss_cls": 0.41428, "loss": 0.41428, "time": 0.35753} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.01242, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93625, "top5_acc": 0.99938, "loss_cls": 0.32183, "loss": 0.32183, "time": 0.48651} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.0124, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93875, "top5_acc": 1.0, "loss_cls": 0.33842, "loss": 0.33842, "time": 0.48933} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.01238, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.925, "top5_acc": 0.9975, "loss_cls": 0.3924, "loss": 0.3924, "time": 0.48768} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.01236, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.94562, "top5_acc": 0.9975, "loss_cls": 0.32927, "loss": 0.32927, "time": 0.48947} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.01234, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93625, "top5_acc": 0.99812, "loss_cls": 0.37232, "loss": 0.37232, "time": 0.49388} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.01232, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.93312, "top5_acc": 0.99875, "loss_cls": 0.37859, "loss": 0.37859, "time": 0.48894} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.0123, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.92188, "top5_acc": 0.99938, "loss_cls": 0.38047, "loss": 0.38047, "time": 0.49481} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.01228, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.9175, "top5_acc": 0.99812, "loss_cls": 0.47478, "loss": 0.47478, "time": 0.49085} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.01225, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9175, "top5_acc": 0.99812, "loss_cls": 0.42616, "loss": 0.42616, "time": 0.48455} +{"mode": "val", "epoch": 76, "iter": 533, "lr": 0.01224, "top1_acc": 0.88757, "top5_acc": 0.99343, "mean_class_accuracy": 0.8519} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.01222, "memory": 4083, "data_time": 0.18606, "top1_acc": 0.93125, "top5_acc": 1.0, "loss_cls": 0.35907, "loss": 0.35907, "time": 0.62864} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0122, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.33383, "loss": 0.33383, "time": 0.42554} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.01218, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94812, "top5_acc": 0.9975, "loss_cls": 0.3096, "loss": 0.3096, "time": 0.35742} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.01216, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93875, "top5_acc": 1.0, "loss_cls": 0.36415, "loss": 0.36415, "time": 0.48833} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.01214, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93062, "top5_acc": 1.0, "loss_cls": 0.37001, "loss": 0.37001, "time": 0.49068} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.01212, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.93188, "top5_acc": 1.0, "loss_cls": 0.35448, "loss": 0.35448, "time": 0.48863} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.0121, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9425, "top5_acc": 0.99812, "loss_cls": 0.33185, "loss": 0.33185, "time": 0.49264} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.01207, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92938, "top5_acc": 0.99812, "loss_cls": 0.39214, "loss": 0.39214, "time": 0.48864} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.01205, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93312, "top5_acc": 0.99875, "loss_cls": 0.35687, "loss": 0.35687, "time": 0.49167} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.01203, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92875, "top5_acc": 1.0, "loss_cls": 0.37669, "loss": 0.37669, "time": 0.49296} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.01201, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9325, "top5_acc": 0.9975, "loss_cls": 0.41521, "loss": 0.41521, "time": 0.49043} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.01199, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92938, "top5_acc": 0.99938, "loss_cls": 0.38761, "loss": 0.38761, "time": 0.48768} +{"mode": "val", "epoch": 77, "iter": 533, "lr": 0.01198, "top1_acc": 0.90165, "top5_acc": 0.99366, "mean_class_accuracy": 0.868} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.01196, "memory": 4083, "data_time": 0.18656, "top1_acc": 0.93812, "top5_acc": 0.99938, "loss_cls": 0.35793, "loss": 0.35793, "time": 0.62511} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.01194, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94062, "top5_acc": 1.0, "loss_cls": 0.36136, "loss": 0.36136, "time": 0.4091} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.01192, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.30646, "loss": 0.30646, "time": 0.3629} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.0119, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9425, "top5_acc": 0.9975, "loss_cls": 0.33255, "loss": 0.33255, "time": 0.48485} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.01187, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94188, "top5_acc": 0.99938, "loss_cls": 0.3448, "loss": 0.3448, "time": 0.49342} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.01185, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.92562, "top5_acc": 0.99875, "loss_cls": 0.38776, "loss": 0.38776, "time": 0.49288} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.01183, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93438, "top5_acc": 0.99875, "loss_cls": 0.37666, "loss": 0.37666, "time": 0.48775} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.01181, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.35589, "loss": 0.35589, "time": 0.49327} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.01179, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.945, "top5_acc": 1.0, "loss_cls": 0.32942, "loss": 0.32942, "time": 0.4924} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.01177, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93438, "top5_acc": 0.99938, "loss_cls": 0.35285, "loss": 0.35285, "time": 0.49338} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.01175, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92875, "top5_acc": 0.99875, "loss_cls": 0.37283, "loss": 0.37283, "time": 0.4903} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.01173, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.93062, "top5_acc": 0.99875, "loss_cls": 0.35523, "loss": 0.35523, "time": 0.49088} +{"mode": "val", "epoch": 78, "iter": 533, "lr": 0.01172, "top1_acc": 0.89027, "top5_acc": 0.99343, "mean_class_accuracy": 0.86732} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.01169, "memory": 4083, "data_time": 0.18908, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.29446, "loss": 0.29446, "time": 0.64162} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.01167, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93312, "top5_acc": 0.99812, "loss_cls": 0.37273, "loss": 0.37273, "time": 0.40098} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.01165, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94, "top5_acc": 0.99875, "loss_cls": 0.35717, "loss": 0.35717, "time": 0.38036} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.01163, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.935, "top5_acc": 0.99938, "loss_cls": 0.36165, "loss": 0.36165, "time": 0.48901} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.01161, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.93375, "top5_acc": 1.0, "loss_cls": 0.34286, "loss": 0.34286, "time": 0.48868} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.01159, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93938, "top5_acc": 1.0, "loss_cls": 0.32063, "loss": 0.32063, "time": 0.48865} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.01157, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92812, "top5_acc": 0.99875, "loss_cls": 0.3612, "loss": 0.3612, "time": 0.48943} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.01155, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.37935, "loss": 0.37935, "time": 0.48884} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.01153, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93, "top5_acc": 0.99812, "loss_cls": 0.36969, "loss": 0.36969, "time": 0.48914} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.01151, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.39321, "loss": 0.39321, "time": 0.48828} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.01149, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.33706, "loss": 0.33706, "time": 0.48927} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.01147, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.92812, "top5_acc": 0.99812, "loss_cls": 0.39052, "loss": 0.39052, "time": 0.49004} +{"mode": "val", "epoch": 79, "iter": 533, "lr": 0.01145, "top1_acc": 0.88745, "top5_acc": 0.99237, "mean_class_accuracy": 0.83396} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.01143, "memory": 4083, "data_time": 0.19137, "top1_acc": 0.94062, "top5_acc": 0.99938, "loss_cls": 0.32185, "loss": 0.32185, "time": 0.61285} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.01141, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94625, "top5_acc": 1.0, "loss_cls": 0.29324, "loss": 0.29324, "time": 0.40581} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.01139, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94625, "top5_acc": 0.99812, "loss_cls": 0.32652, "loss": 0.32652, "time": 0.3542} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.01137, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9425, "top5_acc": 0.99812, "loss_cls": 0.33677, "loss": 0.33677, "time": 0.49009} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.01135, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.93375, "top5_acc": 0.9975, "loss_cls": 0.37866, "loss": 0.37866, "time": 0.48967} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.01133, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.35388, "loss": 0.35388, "time": 0.49233} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.01131, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.32518, "loss": 0.32518, "time": 0.49207} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.01129, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.39422, "loss": 0.39422, "time": 0.49297} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.01127, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9275, "top5_acc": 0.99688, "loss_cls": 0.41472, "loss": 0.41472, "time": 0.48975} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.01125, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93688, "top5_acc": 0.99938, "loss_cls": 0.35329, "loss": 0.35329, "time": 0.48952} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.01123, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.9325, "top5_acc": 0.99812, "loss_cls": 0.39992, "loss": 0.39992, "time": 0.49065} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.01121, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.3484, "loss": 0.3484, "time": 0.49057} +{"mode": "val", "epoch": 80, "iter": 533, "lr": 0.01119, "top1_acc": 0.88828, "top5_acc": 0.99366, "mean_class_accuracy": 0.86089} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.01117, "memory": 4083, "data_time": 0.18841, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.3329, "loss": 0.3329, "time": 0.63419} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.01115, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94688, "top5_acc": 0.99938, "loss_cls": 0.308, "loss": 0.308, "time": 0.40881} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.01113, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.30134, "loss": 0.30134, "time": 0.35357} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.01111, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94812, "top5_acc": 0.99688, "loss_cls": 0.29778, "loss": 0.29778, "time": 0.48913} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.01109, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.94312, "top5_acc": 0.99812, "loss_cls": 0.32257, "loss": 0.32257, "time": 0.49124} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.01107, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.31513, "loss": 0.31513, "time": 0.49346} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.01105, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93312, "top5_acc": 0.99812, "loss_cls": 0.36483, "loss": 0.36483, "time": 0.48818} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.01103, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95, "top5_acc": 0.99688, "loss_cls": 0.3084, "loss": 0.3084, "time": 0.48584} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.01101, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.35297, "loss": 0.35297, "time": 0.48689} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.01099, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93375, "top5_acc": 0.99875, "loss_cls": 0.3427, "loss": 0.3427, "time": 0.48784} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.01097, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93312, "top5_acc": 0.99625, "loss_cls": 0.37931, "loss": 0.37931, "time": 0.48996} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.01095, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.9175, "top5_acc": 0.9975, "loss_cls": 0.42246, "loss": 0.42246, "time": 0.48711} +{"mode": "val", "epoch": 81, "iter": 533, "lr": 0.01093, "top1_acc": 0.90271, "top5_acc": 0.99425, "mean_class_accuracy": 0.86325} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.01091, "memory": 4083, "data_time": 0.18942, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.24996, "loss": 0.24996, "time": 0.63938} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.01089, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.29984, "loss": 0.29984, "time": 0.40165} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.01087, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93938, "top5_acc": 0.99875, "loss_cls": 0.34696, "loss": 0.34696, "time": 0.36951} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.01085, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.33384, "loss": 0.33384, "time": 0.49267} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.01083, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95062, "top5_acc": 0.99875, "loss_cls": 0.28067, "loss": 0.28067, "time": 0.49167} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.01081, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.32393, "loss": 0.32393, "time": 0.49319} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.01079, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92938, "top5_acc": 0.99938, "loss_cls": 0.38771, "loss": 0.38771, "time": 0.49181} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.01077, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92688, "top5_acc": 0.99938, "loss_cls": 0.40729, "loss": 0.40729, "time": 0.4931} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.01075, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93625, "top5_acc": 0.99875, "loss_cls": 0.34892, "loss": 0.34892, "time": 0.49064} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.01073, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.32774, "loss": 0.32774, "time": 0.4899} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.01071, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94, "top5_acc": 0.99812, "loss_cls": 0.34942, "loss": 0.34942, "time": 0.48667} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.01069, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94938, "top5_acc": 1.0, "loss_cls": 0.31294, "loss": 0.31294, "time": 0.48928} +{"mode": "val", "epoch": 82, "iter": 533, "lr": 0.01067, "top1_acc": 0.88299, "top5_acc": 0.99355, "mean_class_accuracy": 0.86947} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.01065, "memory": 4083, "data_time": 0.18353, "top1_acc": 0.9375, "top5_acc": 1.0, "loss_cls": 0.33936, "loss": 0.33936, "time": 0.65661} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.01063, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93812, "top5_acc": 0.99875, "loss_cls": 0.33039, "loss": 0.33039, "time": 0.38526} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.01061, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.30602, "loss": 0.30602, "time": 0.37998} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.01059, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.31126, "loss": 0.31126, "time": 0.48975} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.01057, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9425, "top5_acc": 1.0, "loss_cls": 0.312, "loss": 0.312, "time": 0.49397} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.01055, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93312, "top5_acc": 0.99875, "loss_cls": 0.35509, "loss": 0.35509, "time": 0.49236} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.01053, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.94562, "top5_acc": 1.0, "loss_cls": 0.29992, "loss": 0.29992, "time": 0.49216} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.01051, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.935, "top5_acc": 0.9975, "loss_cls": 0.34576, "loss": 0.34576, "time": 0.49257} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.01049, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.35567, "loss": 0.35567, "time": 0.48927} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.01047, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93625, "top5_acc": 0.99938, "loss_cls": 0.32352, "loss": 0.32352, "time": 0.49076} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.01045, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9325, "top5_acc": 0.99812, "loss_cls": 0.38089, "loss": 0.38089, "time": 0.49274} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.01043, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.93625, "top5_acc": 0.99938, "loss_cls": 0.32731, "loss": 0.32731, "time": 0.48868} +{"mode": "val", "epoch": 83, "iter": 533, "lr": 0.01042, "top1_acc": 0.88077, "top5_acc": 0.98956, "mean_class_accuracy": 0.83642} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.0104, "memory": 4083, "data_time": 0.18439, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.29499, "loss": 0.29499, "time": 0.60978} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.01038, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9475, "top5_acc": 1.0, "loss_cls": 0.30486, "loss": 0.30486, "time": 0.40687} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.01036, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.32762, "loss": 0.32762, "time": 0.35015} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.01034, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.35687, "loss": 0.35687, "time": 0.48877} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.01031, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.32811, "loss": 0.32811, "time": 0.48937} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.01029, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94312, "top5_acc": 0.99812, "loss_cls": 0.32992, "loss": 0.32992, "time": 0.48594} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.01027, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.2986, "loss": 0.2986, "time": 0.48883} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.01025, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.34652, "loss": 0.34652, "time": 0.48803} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.01023, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.28887, "loss": 0.28887, "time": 0.49093} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.01021, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94812, "top5_acc": 0.99875, "loss_cls": 0.30804, "loss": 0.30804, "time": 0.48643} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.01019, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93812, "top5_acc": 0.99875, "loss_cls": 0.33553, "loss": 0.33553, "time": 0.49175} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.01017, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92312, "top5_acc": 0.9975, "loss_cls": 0.39285, "loss": 0.39285, "time": 0.48837} +{"mode": "val", "epoch": 84, "iter": 533, "lr": 0.01016, "top1_acc": 0.88464, "top5_acc": 0.99507, "mean_class_accuracy": 0.85884} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.01014, "memory": 4083, "data_time": 0.18757, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.31065, "loss": 0.31065, "time": 0.63854} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.01012, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.32579, "loss": 0.32579, "time": 0.41896} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.0101, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.31708, "loss": 0.31708, "time": 0.36946} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.01008, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.94812, "top5_acc": 0.99812, "loss_cls": 0.29698, "loss": 0.29698, "time": 0.49088} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.01006, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.30927, "loss": 0.30927, "time": 0.48968} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.01004, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.95625, "top5_acc": 0.99875, "loss_cls": 0.28518, "loss": 0.28518, "time": 0.48622} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.01002, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9375, "top5_acc": 0.99875, "loss_cls": 0.36986, "loss": 0.36986, "time": 0.48885} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.01, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94125, "top5_acc": 0.99812, "loss_cls": 0.33228, "loss": 0.33228, "time": 0.48949} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.00998, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.31318, "loss": 0.31318, "time": 0.48826} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.00996, "memory": 4083, "data_time": 0.00063, "top1_acc": 0.95562, "top5_acc": 1.0, "loss_cls": 0.26579, "loss": 0.26579, "time": 0.48732} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.00994, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94, "top5_acc": 0.99875, "loss_cls": 0.32074, "loss": 0.32074, "time": 0.48899} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.00992, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93812, "top5_acc": 0.9975, "loss_cls": 0.34812, "loss": 0.34812, "time": 0.49026} +{"mode": "val", "epoch": 85, "iter": 533, "lr": 0.0099, "top1_acc": 0.89262, "top5_acc": 0.99249, "mean_class_accuracy": 0.85871} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.00988, "memory": 4083, "data_time": 0.18682, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.25556, "loss": 0.25556, "time": 0.61645} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.00986, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.94562, "top5_acc": 0.99875, "loss_cls": 0.2994, "loss": 0.2994, "time": 0.40927} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.00984, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95, "top5_acc": 0.99875, "loss_cls": 0.29533, "loss": 0.29533, "time": 0.34822} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.00982, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9475, "top5_acc": 0.99875, "loss_cls": 0.31676, "loss": 0.31676, "time": 0.4926} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.0098, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.35228, "loss": 0.35228, "time": 0.48724} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.00978, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.95375, "top5_acc": 0.99875, "loss_cls": 0.29463, "loss": 0.29463, "time": 0.48908} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.00976, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.94562, "top5_acc": 0.99875, "loss_cls": 0.28984, "loss": 0.28984, "time": 0.48729} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.00974, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93938, "top5_acc": 1.0, "loss_cls": 0.33867, "loss": 0.33867, "time": 0.48806} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.00972, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94125, "top5_acc": 0.99812, "loss_cls": 0.33932, "loss": 0.33932, "time": 0.48923} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.0097, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9325, "top5_acc": 0.99875, "loss_cls": 0.35337, "loss": 0.35337, "time": 0.49125} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.00968, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93812, "top5_acc": 0.99812, "loss_cls": 0.31779, "loss": 0.31779, "time": 0.48986} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.00966, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95562, "top5_acc": 0.9975, "loss_cls": 0.29181, "loss": 0.29181, "time": 0.48515} +{"mode": "val", "epoch": 86, "iter": 533, "lr": 0.00965, "top1_acc": 0.89661, "top5_acc": 0.99167, "mean_class_accuracy": 0.86118} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.00963, "memory": 4083, "data_time": 0.17769, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.26389, "loss": 0.26389, "time": 0.58638} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.00961, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.27871, "loss": 0.27871, "time": 0.47711} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.00959, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94812, "top5_acc": 1.0, "loss_cls": 0.29851, "loss": 0.29851, "time": 0.33134} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.00957, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.27548, "loss": 0.27548, "time": 0.48889} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.00955, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94688, "top5_acc": 0.99938, "loss_cls": 0.31383, "loss": 0.31383, "time": 0.48997} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.00953, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.26708, "loss": 0.26708, "time": 0.48848} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.00951, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94562, "top5_acc": 0.99812, "loss_cls": 0.2857, "loss": 0.2857, "time": 0.4898} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.00949, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95375, "top5_acc": 0.99812, "loss_cls": 0.28333, "loss": 0.28333, "time": 0.49036} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.00947, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.307, "loss": 0.307, "time": 0.49377} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.00945, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94938, "top5_acc": 0.99875, "loss_cls": 0.29966, "loss": 0.29966, "time": 0.48714} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.00943, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.32155, "loss": 0.32155, "time": 0.48975} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.00941, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93812, "top5_acc": 0.99875, "loss_cls": 0.34458, "loss": 0.34458, "time": 0.48785} +{"mode": "val", "epoch": 87, "iter": 533, "lr": 0.00939, "top1_acc": 0.8952, "top5_acc": 0.99261, "mean_class_accuracy": 0.85116} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.00937, "memory": 4083, "data_time": 0.1824, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.27095, "loss": 0.27095, "time": 0.58093} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.00935, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.25067, "loss": 0.25067, "time": 0.49939} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.00933, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.26062, "loss": 0.26062, "time": 0.3214} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.00931, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.25648, "loss": 0.25648, "time": 0.48752} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.00929, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.25014, "loss": 0.25014, "time": 0.48654} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.00927, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.30091, "loss": 0.30091, "time": 0.48817} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.00925, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94812, "top5_acc": 0.9975, "loss_cls": 0.30159, "loss": 0.30159, "time": 0.49052} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.00923, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.29486, "loss": 0.29486, "time": 0.48765} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.00921, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95375, "top5_acc": 0.99812, "loss_cls": 0.26304, "loss": 0.26304, "time": 0.48941} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.00919, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93625, "top5_acc": 0.99875, "loss_cls": 0.34111, "loss": 0.34111, "time": 0.48719} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.00917, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.25566, "loss": 0.25566, "time": 0.48941} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.00915, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95, "top5_acc": 0.99812, "loss_cls": 0.28262, "loss": 0.28262, "time": 0.48978} +{"mode": "val", "epoch": 88, "iter": 533, "lr": 0.00914, "top1_acc": 0.88476, "top5_acc": 0.99366, "mean_class_accuracy": 0.85336} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.00912, "memory": 4083, "data_time": 0.18518, "top1_acc": 0.95312, "top5_acc": 0.99875, "loss_cls": 0.28433, "loss": 0.28433, "time": 0.57535} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0091, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.95562, "top5_acc": 1.0, "loss_cls": 0.23905, "loss": 0.23905, "time": 0.49808} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.00908, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94188, "top5_acc": 1.0, "loss_cls": 0.30801, "loss": 0.30801, "time": 0.31627} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.00906, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.33279, "loss": 0.33279, "time": 0.48326} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.00904, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94938, "top5_acc": 0.99875, "loss_cls": 0.30349, "loss": 0.30349, "time": 0.49133} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.00902, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.29431, "loss": 0.29431, "time": 0.49117} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.009, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.95438, "top5_acc": 0.99938, "loss_cls": 0.25446, "loss": 0.25446, "time": 0.48693} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.00898, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95188, "top5_acc": 1.0, "loss_cls": 0.26234, "loss": 0.26234, "time": 0.48727} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.00896, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.94688, "top5_acc": 0.99938, "loss_cls": 0.3147, "loss": 0.3147, "time": 0.48874} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.00894, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.95438, "top5_acc": 0.99938, "loss_cls": 0.27295, "loss": 0.27295, "time": 0.48813} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.00892, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95625, "top5_acc": 0.99875, "loss_cls": 0.27164, "loss": 0.27164, "time": 0.49188} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.0089, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94375, "top5_acc": 0.99812, "loss_cls": 0.32803, "loss": 0.32803, "time": 0.48624} +{"mode": "val", "epoch": 89, "iter": 533, "lr": 0.00889, "top1_acc": 0.90752, "top5_acc": 0.99378, "mean_class_accuracy": 0.86733} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.00887, "memory": 4083, "data_time": 0.18586, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.23209, "loss": 0.23209, "time": 0.58803} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.00885, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.97312, "top5_acc": 0.99938, "loss_cls": 0.19125, "loss": 0.19125, "time": 0.49497} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.00883, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.21769, "loss": 0.21769, "time": 0.31511} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.00881, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.23549, "loss": 0.23549, "time": 0.48652} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.00879, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.21441, "loss": 0.21441, "time": 0.48897} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.00877, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95062, "top5_acc": 0.9975, "loss_cls": 0.31117, "loss": 0.31117, "time": 0.49104} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.00875, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.31399, "loss": 0.31399, "time": 0.4894} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.00873, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94312, "top5_acc": 1.0, "loss_cls": 0.30763, "loss": 0.30763, "time": 0.48863} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.00871, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.30217, "loss": 0.30217, "time": 0.48795} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.00869, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.27269, "loss": 0.27269, "time": 0.49005} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.00867, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9525, "top5_acc": 0.99812, "loss_cls": 0.28873, "loss": 0.28873, "time": 0.48849} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.00865, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94, "top5_acc": 0.99812, "loss_cls": 0.33536, "loss": 0.33536, "time": 0.49283} +{"mode": "val", "epoch": 90, "iter": 533, "lr": 0.00864, "top1_acc": 0.90165, "top5_acc": 0.99319, "mean_class_accuracy": 0.86607} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.00862, "memory": 4083, "data_time": 0.18957, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.24514, "loss": 0.24514, "time": 0.61169} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0086, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.25109, "loss": 0.25109, "time": 0.48481} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.00858, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95125, "top5_acc": 0.99938, "loss_cls": 0.25545, "loss": 0.25545, "time": 0.33551} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.00856, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.23975, "loss": 0.23975, "time": 0.49189} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.00854, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94812, "top5_acc": 1.0, "loss_cls": 0.30569, "loss": 0.30569, "time": 0.49113} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.00852, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96, "top5_acc": 0.99938, "loss_cls": 0.25856, "loss": 0.25856, "time": 0.49192} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.0085, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.94188, "top5_acc": 0.99812, "loss_cls": 0.33912, "loss": 0.33912, "time": 0.49069} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.00848, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.3342, "loss": 0.3342, "time": 0.4855} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.00846, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.955, "top5_acc": 0.99875, "loss_cls": 0.27291, "loss": 0.27291, "time": 0.48869} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.00844, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95375, "top5_acc": 0.99812, "loss_cls": 0.28184, "loss": 0.28184, "time": 0.49216} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.00842, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.955, "top5_acc": 0.99812, "loss_cls": 0.26775, "loss": 0.26775, "time": 0.48685} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.0084, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94562, "top5_acc": 0.99812, "loss_cls": 0.30279, "loss": 0.30279, "time": 0.48754} +{"mode": "val", "epoch": 91, "iter": 533, "lr": 0.00839, "top1_acc": 0.88734, "top5_acc": 0.99425, "mean_class_accuracy": 0.85925} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.00837, "memory": 4083, "data_time": 0.1859, "top1_acc": 0.95812, "top5_acc": 0.99875, "loss_cls": 0.24682, "loss": 0.24682, "time": 0.57843} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.00835, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.28109, "loss": 0.28109, "time": 0.49272} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.00833, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.95, "top5_acc": 1.0, "loss_cls": 0.2819, "loss": 0.2819, "time": 0.31405} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.00831, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95438, "top5_acc": 0.99875, "loss_cls": 0.28548, "loss": 0.28548, "time": 0.48844} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.00829, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96, "top5_acc": 0.99812, "loss_cls": 0.24664, "loss": 0.24664, "time": 0.48729} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.00827, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.24851, "loss": 0.24851, "time": 0.48852} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.00825, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.21228, "loss": 0.21228, "time": 0.49016} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.00824, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95188, "top5_acc": 1.0, "loss_cls": 0.26735, "loss": 0.26735, "time": 0.48921} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.00822, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95688, "top5_acc": 0.99938, "loss_cls": 0.28467, "loss": 0.28467, "time": 0.49284} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.0082, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.945, "top5_acc": 0.99812, "loss_cls": 0.30607, "loss": 0.30607, "time": 0.48992} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.00818, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.25677, "loss": 0.25677, "time": 0.492} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.00816, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96312, "top5_acc": 0.99875, "loss_cls": 0.23504, "loss": 0.23504, "time": 0.48976} +{"mode": "val", "epoch": 92, "iter": 533, "lr": 0.00814, "top1_acc": 0.90388, "top5_acc": 0.9946, "mean_class_accuracy": 0.87784} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.00812, "memory": 4083, "data_time": 0.18514, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.24819, "loss": 0.24819, "time": 0.58512} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.0081, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.20325, "loss": 0.20325, "time": 0.50456} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.00809, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.20667, "loss": 0.20667, "time": 0.32537} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.00807, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.21349, "loss": 0.21349, "time": 0.48781} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.00805, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.955, "top5_acc": 0.99875, "loss_cls": 0.26701, "loss": 0.26701, "time": 0.48914} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.00803, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.26323, "loss": 0.26323, "time": 0.48979} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.00801, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.29639, "loss": 0.29639, "time": 0.48913} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.00799, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96375, "top5_acc": 0.99812, "loss_cls": 0.24282, "loss": 0.24282, "time": 0.49115} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.00797, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.25677, "loss": 0.25677, "time": 0.49179} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.00795, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.21489, "loss": 0.21489, "time": 0.48923} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.00793, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.28366, "loss": 0.28366, "time": 0.4878} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.00791, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.27453, "loss": 0.27453, "time": 0.48824} +{"mode": "val", "epoch": 93, "iter": 533, "lr": 0.0079, "top1_acc": 0.90764, "top5_acc": 0.99378, "mean_class_accuracy": 0.8864} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.00788, "memory": 4083, "data_time": 0.17787, "top1_acc": 0.9575, "top5_acc": 0.99938, "loss_cls": 0.25114, "loss": 0.25114, "time": 0.57578} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.00786, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.21676, "loss": 0.21676, "time": 0.50669} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.00784, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.955, "top5_acc": 0.99875, "loss_cls": 0.25483, "loss": 0.25483, "time": 0.33219} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.00782, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.22166, "loss": 0.22166, "time": 0.48819} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.0078, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.2128, "loss": 0.2128, "time": 0.4898} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.00778, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.27335, "loss": 0.27335, "time": 0.49221} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.00777, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.9575, "top5_acc": 0.99938, "loss_cls": 0.25117, "loss": 0.25117, "time": 0.49289} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.00775, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.28624, "loss": 0.28624, "time": 0.49033} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.00773, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9575, "top5_acc": 0.99875, "loss_cls": 0.25944, "loss": 0.25944, "time": 0.49102} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.00771, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.26389, "loss": 0.26389, "time": 0.48809} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.00769, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95188, "top5_acc": 1.0, "loss_cls": 0.28298, "loss": 0.28298, "time": 0.49059} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.00767, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94938, "top5_acc": 0.99875, "loss_cls": 0.28386, "loss": 0.28386, "time": 0.49145} +{"mode": "val", "epoch": 94, "iter": 533, "lr": 0.00766, "top1_acc": 0.90435, "top5_acc": 0.99296, "mean_class_accuracy": 0.86883} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.00764, "memory": 4083, "data_time": 0.18778, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.24438, "loss": 0.24438, "time": 0.59224} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.00762, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.20008, "loss": 0.20008, "time": 0.4823} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.0076, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.22564, "loss": 0.22564, "time": 0.32341} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.00758, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9675, "top5_acc": 0.99875, "loss_cls": 0.2091, "loss": 0.2091, "time": 0.48879} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.00756, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.21719, "loss": 0.21719, "time": 0.48913} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.00754, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96125, "top5_acc": 0.99875, "loss_cls": 0.21883, "loss": 0.21883, "time": 0.49025} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.00752, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.24059, "loss": 0.24059, "time": 0.48916} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.00751, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.24154, "loss": 0.24154, "time": 0.48662} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.00749, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95062, "top5_acc": 0.99938, "loss_cls": 0.28401, "loss": 0.28401, "time": 0.49246} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.00747, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.22842, "loss": 0.22842, "time": 0.48655} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.00745, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.26387, "loss": 0.26387, "time": 0.49028} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.00743, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.23363, "loss": 0.23363, "time": 0.48595} +{"mode": "val", "epoch": 95, "iter": 533, "lr": 0.00742, "top1_acc": 0.90506, "top5_acc": 0.99507, "mean_class_accuracy": 0.86919} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.0074, "memory": 4083, "data_time": 0.1863, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.21445, "loss": 0.21445, "time": 0.59866} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.00738, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.18544, "loss": 0.18544, "time": 0.47229} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.00736, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.1874, "loss": 0.1874, "time": 0.33337} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.00734, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96688, "top5_acc": 0.99938, "loss_cls": 0.21061, "loss": 0.21061, "time": 0.48979} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.00732, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.18977, "loss": 0.18977, "time": 0.48883} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.0073, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.24146, "loss": 0.24146, "time": 0.4888} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.00729, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.23916, "loss": 0.23916, "time": 0.48964} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.00727, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.22909, "loss": 0.22909, "time": 0.49039} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.00725, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.23786, "loss": 0.23786, "time": 0.4907} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.00723, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.2554, "loss": 0.2554, "time": 0.48903} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.00721, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.24882, "loss": 0.24882, "time": 0.48899} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.00719, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.95812, "top5_acc": 0.99812, "loss_cls": 0.27907, "loss": 0.27907, "time": 0.48878} +{"mode": "val", "epoch": 96, "iter": 533, "lr": 0.00718, "top1_acc": 0.89907, "top5_acc": 0.99413, "mean_class_accuracy": 0.86364} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.00716, "memory": 4083, "data_time": 0.18582, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.18832, "loss": 0.18832, "time": 0.61259} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.00714, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.19445, "loss": 0.19445, "time": 0.45978} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.00712, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.97062, "top5_acc": 0.99938, "loss_cls": 0.19804, "loss": 0.19804, "time": 0.33679} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.0071, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.20801, "loss": 0.20801, "time": 0.489} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.00709, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96812, "top5_acc": 0.99938, "loss_cls": 0.20664, "loss": 0.20664, "time": 0.48829} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.00707, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96312, "top5_acc": 0.99875, "loss_cls": 0.21961, "loss": 0.21961, "time": 0.48935} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.00705, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.1965, "loss": 0.1965, "time": 0.49141} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.00703, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96, "top5_acc": 0.99875, "loss_cls": 0.23504, "loss": 0.23504, "time": 0.49413} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.00701, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.1994, "loss": 0.1994, "time": 0.49306} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.00699, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.23314, "loss": 0.23314, "time": 0.49365} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.00698, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.19395, "loss": 0.19395, "time": 0.48625} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.00696, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.20121, "loss": 0.20121, "time": 0.49318} +{"mode": "val", "epoch": 97, "iter": 533, "lr": 0.00694, "top1_acc": 0.91046, "top5_acc": 0.99495, "mean_class_accuracy": 0.8889} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.00692, "memory": 4083, "data_time": 0.19081, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.19919, "loss": 0.19919, "time": 0.64776} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.00691, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.19002, "loss": 0.19002, "time": 0.40504} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.00689, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96375, "top5_acc": 0.99938, "loss_cls": 0.20653, "loss": 0.20653, "time": 0.35298} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.00687, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.22242, "loss": 0.22242, "time": 0.48959} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.00685, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.22574, "loss": 0.22574, "time": 0.49175} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.00683, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.18146, "loss": 0.18146, "time": 0.48893} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.00681, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.20564, "loss": 0.20564, "time": 0.48911} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.0068, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.19977, "loss": 0.19977, "time": 0.4927} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.00678, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.25259, "loss": 0.25259, "time": 0.4915} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.00676, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.96562, "top5_acc": 0.99938, "loss_cls": 0.22061, "loss": 0.22061, "time": 0.4899} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.00674, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.24435, "loss": 0.24435, "time": 0.49144} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.00672, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.24732, "loss": 0.24732, "time": 0.48921} +{"mode": "val", "epoch": 98, "iter": 533, "lr": 0.00671, "top1_acc": 0.91468, "top5_acc": 0.99484, "mean_class_accuracy": 0.88438} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.00669, "memory": 4083, "data_time": 0.18491, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.1829, "loss": 0.1829, "time": 0.64242} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.00667, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96438, "top5_acc": 0.99875, "loss_cls": 0.20334, "loss": 0.20334, "time": 0.40245} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.00665, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.19942, "loss": 0.19942, "time": 0.36311} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.00664, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.98562, "top5_acc": 0.99938, "loss_cls": 0.12732, "loss": 0.12732, "time": 0.48783} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.00662, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.17329, "loss": 0.17329, "time": 0.48207} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.0066, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.19309, "loss": 0.19309, "time": 0.4856} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.00658, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.19603, "loss": 0.19603, "time": 0.48683} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.00656, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.20197, "loss": 0.20197, "time": 0.49223} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.00655, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.23404, "loss": 0.23404, "time": 0.49693} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.00653, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.26633, "loss": 0.26633, "time": 0.49467} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.00651, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.95938, "top5_acc": 0.99875, "loss_cls": 0.23751, "loss": 0.23751, "time": 0.49113} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.00649, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.26572, "loss": 0.26572, "time": 0.48736} +{"mode": "val", "epoch": 99, "iter": 533, "lr": 0.00648, "top1_acc": 0.90318, "top5_acc": 0.99214, "mean_class_accuracy": 0.86745} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.00646, "memory": 4083, "data_time": 0.18562, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.19501, "loss": 0.19501, "time": 0.64056} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.00644, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97562, "top5_acc": 0.99938, "loss_cls": 0.1589, "loss": 0.1589, "time": 0.39684} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.00642, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.16635, "loss": 0.16635, "time": 0.37493} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.00641, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.2024, "loss": 0.2024, "time": 0.48812} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.00639, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.18344, "loss": 0.18344, "time": 0.48959} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.00637, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.21514, "loss": 0.21514, "time": 0.48639} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.00635, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.20164, "loss": 0.20164, "time": 0.48927} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.00634, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.2124, "loss": 0.2124, "time": 0.48907} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.00632, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95312, "top5_acc": 0.99875, "loss_cls": 0.2375, "loss": 0.2375, "time": 0.48974} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.0063, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.21575, "loss": 0.21575, "time": 0.49495} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.00628, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.19725, "loss": 0.19725, "time": 0.48952} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.00626, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96875, "top5_acc": 0.99875, "loss_cls": 0.20003, "loss": 0.20003, "time": 0.48642} +{"mode": "val", "epoch": 100, "iter": 533, "lr": 0.00625, "top1_acc": 0.90283, "top5_acc": 0.9946, "mean_class_accuracy": 0.88101} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.00623, "memory": 4083, "data_time": 0.18501, "top1_acc": 0.97688, "top5_acc": 0.99938, "loss_cls": 0.18281, "loss": 0.18281, "time": 0.60016} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.00621, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.18845, "loss": 0.18845, "time": 0.41283} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.0062, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.15973, "loss": 0.15973, "time": 0.36602} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.00618, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.22083, "loss": 0.22083, "time": 0.49029} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.00616, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.21241, "loss": 0.21241, "time": 0.48463} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.00614, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.20827, "loss": 0.20827, "time": 0.49005} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.00613, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.21749, "loss": 0.21749, "time": 0.49158} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.00611, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96938, "top5_acc": 0.99938, "loss_cls": 0.19839, "loss": 0.19839, "time": 0.4916} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.00609, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.19952, "loss": 0.19952, "time": 0.49032} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.00607, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.19167, "loss": 0.19167, "time": 0.48901} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.00606, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.19626, "loss": 0.19626, "time": 0.48937} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.00604, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.21112, "loss": 0.21112, "time": 0.48994} +{"mode": "val", "epoch": 101, "iter": 533, "lr": 0.00602, "top1_acc": 0.92125, "top5_acc": 0.99519, "mean_class_accuracy": 0.88891} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.00601, "memory": 4083, "data_time": 0.1839, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.17796, "loss": 0.17796, "time": 0.64355} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.00599, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.20455, "loss": 0.20455, "time": 0.39401} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.00597, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.18746, "loss": 0.18746, "time": 0.36355} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.00596, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.19239, "loss": 0.19239, "time": 0.49046} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.00594, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.2294, "loss": 0.2294, "time": 0.49096} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.00592, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97812, "top5_acc": 0.99938, "loss_cls": 0.1715, "loss": 0.1715, "time": 0.48551} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.0059, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.15474, "loss": 0.15474, "time": 0.49177} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.00589, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.145, "loss": 0.145, "time": 0.48766} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.00587, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.16318, "loss": 0.16318, "time": 0.49044} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.00585, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96562, "top5_acc": 0.99938, "loss_cls": 0.21141, "loss": 0.21141, "time": 0.49063} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.00583, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.2222, "loss": 0.2222, "time": 0.49067} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.00582, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.17975, "loss": 0.17975, "time": 0.48733} +{"mode": "val", "epoch": 102, "iter": 533, "lr": 0.0058, "top1_acc": 0.91656, "top5_acc": 0.99437, "mean_class_accuracy": 0.88876} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.00579, "memory": 4083, "data_time": 0.18438, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.18045, "loss": 0.18045, "time": 0.6417} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.00577, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.16645, "loss": 0.16645, "time": 0.39871} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.00575, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.15824, "loss": 0.15824, "time": 0.37427} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.00573, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.1946, "loss": 0.1946, "time": 0.4886} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.00572, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.16004, "loss": 0.16004, "time": 0.4891} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.0057, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.14661, "loss": 0.14661, "time": 0.49066} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.00568, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.16369, "loss": 0.16369, "time": 0.48739} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.00566, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.18307, "loss": 0.18307, "time": 0.4909} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.00565, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.20284, "loss": 0.20284, "time": 0.48653} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.00563, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.16631, "loss": 0.16631, "time": 0.49475} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.00561, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.16914, "loss": 0.16914, "time": 0.48795} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.0056, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.1933, "loss": 0.1933, "time": 0.48701} +{"mode": "val", "epoch": 103, "iter": 533, "lr": 0.00558, "top1_acc": 0.91163, "top5_acc": 0.99472, "mean_class_accuracy": 0.88386} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.00557, "memory": 4083, "data_time": 0.18375, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.1697, "loss": 0.1697, "time": 0.62206} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.00555, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.14628, "loss": 0.14628, "time": 0.41068} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.00553, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97188, "top5_acc": 0.99938, "loss_cls": 0.16644, "loss": 0.16644, "time": 0.34885} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.00551, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.20106, "loss": 0.20106, "time": 0.49147} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.0055, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97688, "top5_acc": 0.99938, "loss_cls": 0.16042, "loss": 0.16042, "time": 0.49193} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.00548, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9725, "top5_acc": 0.99938, "loss_cls": 0.19592, "loss": 0.19592, "time": 0.48819} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.00546, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97062, "top5_acc": 0.99875, "loss_cls": 0.19515, "loss": 0.19515, "time": 0.48817} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.00545, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.15711, "loss": 0.15711, "time": 0.48855} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.00543, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.13217, "loss": 0.13217, "time": 0.48702} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.00541, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12712, "loss": 0.12712, "time": 0.49255} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.0054, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.15738, "loss": 0.15738, "time": 0.49016} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.00538, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97625, "top5_acc": 0.99938, "loss_cls": 0.18269, "loss": 0.18269, "time": 0.48862} +{"mode": "val", "epoch": 104, "iter": 533, "lr": 0.00537, "top1_acc": 0.91597, "top5_acc": 0.99495, "mean_class_accuracy": 0.89058} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.00535, "memory": 4083, "data_time": 0.18908, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.17782, "loss": 0.17782, "time": 0.64146} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.00533, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.17442, "loss": 0.17442, "time": 0.41232} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.00532, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.14303, "loss": 0.14303, "time": 0.36144} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.0053, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.14555, "loss": 0.14555, "time": 0.48881} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.00528, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97562, "top5_acc": 0.99938, "loss_cls": 0.16049, "loss": 0.16049, "time": 0.49227} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.00527, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.16553, "loss": 0.16553, "time": 0.4905} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.00525, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.18565, "loss": 0.18565, "time": 0.49073} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.00523, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.19262, "loss": 0.19262, "time": 0.48801} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.00522, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.14682, "loss": 0.14682, "time": 0.49224} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.0052, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.18095, "loss": 0.18095, "time": 0.49066} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.00518, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.17921, "loss": 0.17921, "time": 0.48797} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.00517, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.97188, "top5_acc": 0.99875, "loss_cls": 0.17673, "loss": 0.17673, "time": 0.48807} +{"mode": "val", "epoch": 105, "iter": 533, "lr": 0.00515, "top1_acc": 0.91961, "top5_acc": 0.99495, "mean_class_accuracy": 0.89889} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.00514, "memory": 4083, "data_time": 0.18729, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.15584, "loss": 0.15584, "time": 0.64771} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.00512, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.10742, "loss": 0.10742, "time": 0.39471} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.0051, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.13729, "loss": 0.13729, "time": 0.36746} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.00509, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12084, "loss": 0.12084, "time": 0.48983} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.00507, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12028, "loss": 0.12028, "time": 0.49002} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.00505, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.12699, "loss": 0.12699, "time": 0.48979} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.00504, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.15076, "loss": 0.15076, "time": 0.48968} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.00502, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.14957, "loss": 0.14957, "time": 0.48803} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.005, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.17814, "loss": 0.17814, "time": 0.49038} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.00499, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13772, "loss": 0.13772, "time": 0.49243} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.00497, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.17143, "loss": 0.17143, "time": 0.4889} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.00496, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97688, "top5_acc": 0.99938, "loss_cls": 0.14873, "loss": 0.14873, "time": 0.4871} +{"mode": "val", "epoch": 106, "iter": 533, "lr": 0.00494, "top1_acc": 0.91151, "top5_acc": 0.99507, "mean_class_accuracy": 0.87975} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.00493, "memory": 4083, "data_time": 0.1849, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.1668, "loss": 0.1668, "time": 0.6435} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.00491, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.14353, "loss": 0.14353, "time": 0.39615} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.00489, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98375, "top5_acc": 0.99938, "loss_cls": 0.12917, "loss": 0.12917, "time": 0.36128} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.00488, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.12024, "loss": 0.12024, "time": 0.49038} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.00486, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.12928, "loss": 0.12928, "time": 0.48977} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.00485, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.17816, "loss": 0.17816, "time": 0.48781} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.00483, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.18271, "loss": 0.18271, "time": 0.4893} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.00481, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.15217, "loss": 0.15217, "time": 0.48717} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.0048, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.15634, "loss": 0.15634, "time": 0.49135} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.00478, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.14991, "loss": 0.14991, "time": 0.4884} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.00476, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.17397, "loss": 0.17397, "time": 0.49241} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.00475, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97188, "top5_acc": 0.99938, "loss_cls": 0.17561, "loss": 0.17561, "time": 0.48775} +{"mode": "val", "epoch": 107, "iter": 533, "lr": 0.00474, "top1_acc": 0.91292, "top5_acc": 0.99378, "mean_class_accuracy": 0.89198} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.00472, "memory": 4083, "data_time": 0.18287, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.17514, "loss": 0.17514, "time": 0.64542} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0047, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.12573, "loss": 0.12573, "time": 0.40301} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.00469, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98312, "top5_acc": 0.99938, "loss_cls": 0.1229, "loss": 0.1229, "time": 0.36004} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.00467, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.11006, "loss": 0.11006, "time": 0.48662} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.00466, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.09922, "loss": 0.09922, "time": 0.49059} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.00464, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.10924, "loss": 0.10924, "time": 0.49092} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.00462, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9825, "top5_acc": 0.99938, "loss_cls": 0.13417, "loss": 0.13417, "time": 0.48802} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.00461, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.985, "top5_acc": 0.99938, "loss_cls": 0.11802, "loss": 0.11802, "time": 0.49276} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.00459, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.1131, "loss": 0.1131, "time": 0.49016} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.00458, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9775, "top5_acc": 0.99938, "loss_cls": 0.14759, "loss": 0.14759, "time": 0.48838} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.00456, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.15509, "loss": 0.15509, "time": 0.49124} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.00455, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.16635, "loss": 0.16635, "time": 0.48796} +{"mode": "val", "epoch": 108, "iter": 533, "lr": 0.00453, "top1_acc": 0.91644, "top5_acc": 0.99542, "mean_class_accuracy": 0.88871} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.00452, "memory": 4083, "data_time": 0.18694, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11417, "loss": 0.11417, "time": 0.652} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.0045, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.12402, "loss": 0.12402, "time": 0.39807} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.00449, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.1171, "loss": 0.1171, "time": 0.37387} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.00447, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98062, "top5_acc": 0.99938, "loss_cls": 0.13836, "loss": 0.13836, "time": 0.48869} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.00445, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.15632, "loss": 0.15632, "time": 0.49368} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.00444, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.15065, "loss": 0.15065, "time": 0.48817} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.00442, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.11194, "loss": 0.11194, "time": 0.49049} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.00441, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.13362, "loss": 0.13362, "time": 0.48876} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.00439, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.1505, "loss": 0.1505, "time": 0.49083} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.00438, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13227, "loss": 0.13227, "time": 0.48964} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.00436, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.14313, "loss": 0.14313, "time": 0.48875} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.00434, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.97938, "top5_acc": 0.99938, "loss_cls": 0.14616, "loss": 0.14616, "time": 0.49252} +{"mode": "val", "epoch": 109, "iter": 533, "lr": 0.00433, "top1_acc": 0.9202, "top5_acc": 0.99554, "mean_class_accuracy": 0.88804} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.00432, "memory": 4083, "data_time": 0.18267, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.10925, "loss": 0.10925, "time": 0.62609} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.0043, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.09406, "loss": 0.09406, "time": 0.4018} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.00429, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.10607, "loss": 0.10607, "time": 0.3652} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.00427, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.09155, "loss": 0.09155, "time": 0.4883} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.00426, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.11834, "loss": 0.11834, "time": 0.49095} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.00424, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.10139, "loss": 0.10139, "time": 0.49131} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.00422, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.12697, "loss": 0.12697, "time": 0.48855} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.00421, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.11984, "loss": 0.11984, "time": 0.49421} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.00419, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.1445, "loss": 0.1445, "time": 0.49201} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.00418, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11923, "loss": 0.11923, "time": 0.49065} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.00416, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.14203, "loss": 0.14203, "time": 0.48806} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.00415, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.12374, "loss": 0.12374, "time": 0.48514} +{"mode": "val", "epoch": 110, "iter": 533, "lr": 0.00414, "top1_acc": 0.9202, "top5_acc": 0.99519, "mean_class_accuracy": 0.89068} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.00412, "memory": 4083, "data_time": 0.18888, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.12715, "loss": 0.12715, "time": 0.62472} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.00411, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.11343, "loss": 0.11343, "time": 0.40268} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.00409, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.10028, "loss": 0.10028, "time": 0.36768} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.00408, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.12047, "loss": 0.12047, "time": 0.48908} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.00406, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08444, "loss": 0.08444, "time": 0.49007} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.00405, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.10792, "loss": 0.10792, "time": 0.48947} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.00403, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.09732, "loss": 0.09732, "time": 0.49142} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.00402, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11028, "loss": 0.11028, "time": 0.49235} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.004, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10169, "loss": 0.10169, "time": 0.4901} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.00399, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.11547, "loss": 0.11547, "time": 0.487} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.00397, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.12838, "loss": 0.12838, "time": 0.48785} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.00396, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.13417, "loss": 0.13417, "time": 0.48969} +{"mode": "val", "epoch": 111, "iter": 533, "lr": 0.00394, "top1_acc": 0.91421, "top5_acc": 0.99378, "mean_class_accuracy": 0.88558} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.00393, "memory": 4083, "data_time": 0.18415, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11646, "loss": 0.11646, "time": 0.63191} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.00391, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.10747, "loss": 0.10747, "time": 0.39785} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.0039, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.12609, "loss": 0.12609, "time": 0.37709} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.00388, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98438, "top5_acc": 0.99938, "loss_cls": 0.12076, "loss": 0.12076, "time": 0.48756} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.00387, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.11776, "loss": 0.11776, "time": 0.48842} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.00385, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.11973, "loss": 0.11973, "time": 0.49154} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.00384, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.14539, "loss": 0.14539, "time": 0.48893} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.00382, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10679, "loss": 0.10679, "time": 0.48867} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.00381, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.10893, "loss": 0.10893, "time": 0.48819} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.0038, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11533, "loss": 0.11533, "time": 0.49379} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.00378, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.15379, "loss": 0.15379, "time": 0.49099} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.00377, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.146, "loss": 0.146, "time": 0.48738} +{"mode": "val", "epoch": 112, "iter": 533, "lr": 0.00375, "top1_acc": 0.91985, "top5_acc": 0.99554, "mean_class_accuracy": 0.89669} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.00374, "memory": 4083, "data_time": 0.1854, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.1402, "loss": 0.1402, "time": 0.61884} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.00373, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11055, "loss": 0.11055, "time": 0.40055} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.00371, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.0773, "loss": 0.0773, "time": 0.38151} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.0037, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.09988, "loss": 0.09988, "time": 0.49452} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.00368, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.10637, "loss": 0.10637, "time": 0.48707} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.00367, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98688, "top5_acc": 0.99938, "loss_cls": 0.11396, "loss": 0.11396, "time": 0.48877} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.00365, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11691, "loss": 0.11691, "time": 0.48841} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.00364, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.07595, "loss": 0.07595, "time": 0.4953} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.00362, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.08089, "loss": 0.08089, "time": 0.49206} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.00361, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.10565, "loss": 0.10565, "time": 0.49063} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0036, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.10415, "loss": 0.10415, "time": 0.49072} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.00358, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10589, "loss": 0.10589, "time": 0.49057} +{"mode": "val", "epoch": 113, "iter": 533, "lr": 0.00357, "top1_acc": 0.92278, "top5_acc": 0.99437, "mean_class_accuracy": 0.90292} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.00355, "memory": 4083, "data_time": 0.18185, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10681, "loss": 0.10681, "time": 0.62925} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.00354, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.10625, "loss": 0.10625, "time": 0.3807} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.00353, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.10166, "loss": 0.10166, "time": 0.37574} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.00351, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.083, "loss": 0.083, "time": 0.49026} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.0035, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.09096, "loss": 0.09096, "time": 0.49264} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.00348, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08198, "loss": 0.08198, "time": 0.48815} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.00347, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.09976, "loss": 0.09976, "time": 0.48857} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.00346, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.10907, "loss": 0.10907, "time": 0.49108} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.00344, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10068, "loss": 0.10068, "time": 0.48967} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.00343, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.13617, "loss": 0.13617, "time": 0.48795} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.00341, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.12858, "loss": 0.12858, "time": 0.48845} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.0034, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.121, "loss": 0.121, "time": 0.48814} +{"mode": "val", "epoch": 114, "iter": 533, "lr": 0.00339, "top1_acc": 0.91292, "top5_acc": 0.99355, "mean_class_accuracy": 0.88481} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.00337, "memory": 4083, "data_time": 0.18203, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.08849, "loss": 0.08849, "time": 0.61726} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.00336, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.06283, "loss": 0.06283, "time": 0.39701} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.00335, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.07396, "loss": 0.07396, "time": 0.37689} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.00333, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09531, "loss": 0.09531, "time": 0.48971} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.00332, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.08021, "loss": 0.08021, "time": 0.48748} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.0033, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.09799, "loss": 0.09799, "time": 0.48931} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.00329, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08759, "loss": 0.08759, "time": 0.49003} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.00328, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07493, "loss": 0.07493, "time": 0.48936} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.00326, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07542, "loss": 0.07542, "time": 0.48873} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.00325, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08735, "loss": 0.08735, "time": 0.49063} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.00324, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.09818, "loss": 0.09818, "time": 0.48626} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.00322, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08699, "loss": 0.08699, "time": 0.48545} +{"mode": "val", "epoch": 115, "iter": 533, "lr": 0.00321, "top1_acc": 0.92677, "top5_acc": 0.99519, "mean_class_accuracy": 0.90133} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.0032, "memory": 4083, "data_time": 0.18455, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06957, "loss": 0.06957, "time": 0.64064} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.00318, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.06351, "loss": 0.06351, "time": 0.38654} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.00317, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.10118, "loss": 0.10118, "time": 0.36749} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.00316, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08052, "loss": 0.08052, "time": 0.48839} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.00314, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.085, "loss": 0.085, "time": 0.48597} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.00313, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98938, "top5_acc": 0.99938, "loss_cls": 0.08463, "loss": 0.08463, "time": 0.48792} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.00312, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.10609, "loss": 0.10609, "time": 0.48878} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.0031, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.07418, "loss": 0.07418, "time": 0.49128} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.00309, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07289, "loss": 0.07289, "time": 0.49131} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.00308, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07645, "loss": 0.07645, "time": 0.48787} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.00306, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.98875, "top5_acc": 0.99938, "loss_cls": 0.07995, "loss": 0.07995, "time": 0.48756} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.00305, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08694, "loss": 0.08694, "time": 0.48875} +{"mode": "val", "epoch": 116, "iter": 533, "lr": 0.00304, "top1_acc": 0.92607, "top5_acc": 0.9966, "mean_class_accuracy": 0.90241} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.00302, "memory": 4083, "data_time": 0.18397, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07943, "loss": 0.07943, "time": 0.62716} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.00301, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.0617, "loss": 0.0617, "time": 0.41566} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.003, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07116, "loss": 0.07116, "time": 0.35801} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.00298, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06259, "loss": 0.06259, "time": 0.48803} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.00297, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.0638, "loss": 0.0638, "time": 0.4912} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.00296, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07836, "loss": 0.07836, "time": 0.48892} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.00294, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07131, "loss": 0.07131, "time": 0.49097} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.00293, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.0741, "loss": 0.0741, "time": 0.49053} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.00292, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09119, "loss": 0.09119, "time": 0.49187} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.00291, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.07278, "loss": 0.07278, "time": 0.48848} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.00289, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07885, "loss": 0.07885, "time": 0.49157} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.00288, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.09402, "loss": 0.09402, "time": 0.48781} +{"mode": "val", "epoch": 117, "iter": 533, "lr": 0.00287, "top1_acc": 0.92571, "top5_acc": 0.99413, "mean_class_accuracy": 0.90524} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.00286, "memory": 4083, "data_time": 0.18741, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07103, "loss": 0.07103, "time": 0.63638} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.00284, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06862, "loss": 0.06862, "time": 0.41258} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.00283, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04808, "loss": 0.04808, "time": 0.35715} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.00282, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.05654, "loss": 0.05654, "time": 0.49075} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.0028, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.07098, "loss": 0.07098, "time": 0.48955} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.00279, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98625, "top5_acc": 0.99938, "loss_cls": 0.09829, "loss": 0.09829, "time": 0.48785} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.00278, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06729, "loss": 0.06729, "time": 0.48652} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.00277, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.07342, "loss": 0.07342, "time": 0.49156} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.00275, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08492, "loss": 0.08492, "time": 0.48887} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.00274, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.08371, "loss": 0.08371, "time": 0.48485} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.00273, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98938, "top5_acc": 0.99938, "loss_cls": 0.08683, "loss": 0.08683, "time": 0.48638} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.00271, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08747, "loss": 0.08747, "time": 0.48946} +{"mode": "val", "epoch": 118, "iter": 533, "lr": 0.0027, "top1_acc": 0.92607, "top5_acc": 0.99589, "mean_class_accuracy": 0.89967} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.00269, "memory": 4083, "data_time": 0.18827, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.0583, "loss": 0.0583, "time": 0.63856} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.00268, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07094, "loss": 0.07094, "time": 0.40627} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.00267, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.10269, "loss": 0.10269, "time": 0.37078} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.00265, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05294, "loss": 0.05294, "time": 0.4876} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.00264, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08037, "loss": 0.08037, "time": 0.49028} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.00263, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.08094, "loss": 0.08094, "time": 0.48881} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.00262, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.06147, "loss": 0.06147, "time": 0.48964} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.0026, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06283, "loss": 0.06283, "time": 0.48936} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.00259, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.0747, "loss": 0.0747, "time": 0.48904} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.00258, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.06628, "loss": 0.06628, "time": 0.49254} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.00257, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07231, "loss": 0.07231, "time": 0.49073} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.00255, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08559, "loss": 0.08559, "time": 0.4871} +{"mode": "val", "epoch": 119, "iter": 533, "lr": 0.00254, "top1_acc": 0.92829, "top5_acc": 0.99613, "mean_class_accuracy": 0.90347} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.00253, "memory": 4083, "data_time": 0.18503, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.04749, "loss": 0.04749, "time": 0.64537} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.00252, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04196, "loss": 0.04196, "time": 0.38116} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.00251, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05399, "loss": 0.05399, "time": 0.36467} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.00249, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05773, "loss": 0.05773, "time": 0.487} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.00248, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08398, "loss": 0.08398, "time": 0.48834} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.00247, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07518, "loss": 0.07518, "time": 0.48948} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.00246, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05349, "loss": 0.05349, "time": 0.49141} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.00245, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08792, "loss": 0.08792, "time": 0.48984} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.00243, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.05592, "loss": 0.05592, "time": 0.48887} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.00242, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.06879, "loss": 0.06879, "time": 0.48942} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00241, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05786, "loss": 0.05786, "time": 0.48736} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.0024, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06229, "loss": 0.06229, "time": 0.48774} +{"mode": "val", "epoch": 120, "iter": 533, "lr": 0.00239, "top1_acc": 0.93088, "top5_acc": 0.99566, "mean_class_accuracy": 0.90777} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00238, "memory": 4083, "data_time": 0.18381, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03665, "loss": 0.03665, "time": 0.62473} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00236, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03243, "loss": 0.03243, "time": 0.39665} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.00235, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04069, "loss": 0.04069, "time": 0.35875} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00234, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9925, "top5_acc": 0.99938, "loss_cls": 0.06722, "loss": 0.06722, "time": 0.4898} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00233, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08114, "loss": 0.08114, "time": 0.49089} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00232, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.0676, "loss": 0.0676, "time": 0.48976} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.0023, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.05757, "loss": 0.05757, "time": 0.4903} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00229, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07833, "loss": 0.07833, "time": 0.4893} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.00228, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.05128, "loss": 0.05128, "time": 0.48518} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00227, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05301, "loss": 0.05301, "time": 0.48923} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00226, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05604, "loss": 0.05604, "time": 0.49034} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00225, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.05912, "loss": 0.05912, "time": 0.48901} +{"mode": "val", "epoch": 121, "iter": 533, "lr": 0.00224, "top1_acc": 0.93029, "top5_acc": 0.99601, "mean_class_accuracy": 0.90364} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00222, "memory": 4083, "data_time": 0.18387, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06018, "loss": 0.06018, "time": 0.63661} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00221, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0436, "loss": 0.0436, "time": 0.39556} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.0022, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04255, "loss": 0.04255, "time": 0.37445} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00219, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.05225, "loss": 0.05225, "time": 0.48906} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00218, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05212, "loss": 0.05212, "time": 0.48794} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00217, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04987, "loss": 0.04987, "time": 0.4892} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00215, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04773, "loss": 0.04773, "time": 0.48986} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00214, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04242, "loss": 0.04242, "time": 0.48731} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.00213, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04386, "loss": 0.04386, "time": 0.48815} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00212, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.05957, "loss": 0.05957, "time": 0.4871} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00211, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06187, "loss": 0.06187, "time": 0.49062} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.0021, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05484, "loss": 0.05484, "time": 0.48793} +{"mode": "val", "epoch": 122, "iter": 533, "lr": 0.00209, "top1_acc": 0.93322, "top5_acc": 0.99613, "mean_class_accuracy": 0.90668} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00208, "memory": 4083, "data_time": 0.18909, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.0446, "loss": 0.0446, "time": 0.63569} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00207, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04321, "loss": 0.04321, "time": 0.3782} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00205, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03867, "loss": 0.03867, "time": 0.36781} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00204, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02888, "loss": 0.02888, "time": 0.48745} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00203, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05215, "loss": 0.05215, "time": 0.48879} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00202, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.0614, "loss": 0.0614, "time": 0.49044} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00201, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.06016, "loss": 0.06016, "time": 0.48872} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.002, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04008, "loss": 0.04008, "time": 0.48756} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00199, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0409, "loss": 0.0409, "time": 0.49247} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.00198, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04338, "loss": 0.04338, "time": 0.48807} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00197, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03698, "loss": 0.03698, "time": 0.48792} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00195, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04266, "loss": 0.04266, "time": 0.48554} +{"mode": "val", "epoch": 123, "iter": 533, "lr": 0.00195, "top1_acc": 0.93381, "top5_acc": 0.99531, "mean_class_accuracy": 0.91152} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00194, "memory": 4083, "data_time": 0.18345, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04443, "loss": 0.04443, "time": 0.64056} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00192, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03179, "loss": 0.03179, "time": 0.39454} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00191, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04965, "loss": 0.04965, "time": 0.37176} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.0019, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.04551, "loss": 0.04551, "time": 0.4917} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00189, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03725, "loss": 0.03725, "time": 0.48946} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00188, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03755, "loss": 0.03755, "time": 0.48862} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00187, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0309, "loss": 0.0309, "time": 0.4904} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00186, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03836, "loss": 0.03836, "time": 0.48999} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00185, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.04512, "loss": 0.04512, "time": 0.48619} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00184, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03693, "loss": 0.03693, "time": 0.48873} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00183, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04164, "loss": 0.04164, "time": 0.49025} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.00182, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03183, "loss": 0.03183, "time": 0.48833} +{"mode": "val", "epoch": 124, "iter": 533, "lr": 0.00181, "top1_acc": 0.93123, "top5_acc": 0.9966, "mean_class_accuracy": 0.90962} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.0018, "memory": 4083, "data_time": 0.1876, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03618, "loss": 0.03618, "time": 0.63222} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.00179, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03311, "loss": 0.03311, "time": 0.39904} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00178, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03115, "loss": 0.03115, "time": 0.37397} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00177, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02891, "loss": 0.02891, "time": 0.48547} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00176, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03089, "loss": 0.03089, "time": 0.49105} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00175, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03159, "loss": 0.03159, "time": 0.48998} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00173, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03169, "loss": 0.03169, "time": 0.49484} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00172, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02874, "loss": 0.02874, "time": 0.49162} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.00171, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.03959, "loss": 0.03959, "time": 0.49082} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.0017, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03518, "loss": 0.03518, "time": 0.48967} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00169, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.03374, "loss": 0.03374, "time": 0.4875} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00168, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03459, "loss": 0.03459, "time": 0.49121} +{"mode": "val", "epoch": 125, "iter": 533, "lr": 0.00167, "top1_acc": 0.93921, "top5_acc": 0.9973, "mean_class_accuracy": 0.9162} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00166, "memory": 4083, "data_time": 0.1862, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03651, "loss": 0.03651, "time": 0.63295} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00165, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03552, "loss": 0.03552, "time": 0.3982} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00164, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03272, "loss": 0.03272, "time": 0.37138} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00163, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03213, "loss": 0.03213, "time": 0.48732} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00162, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02582, "loss": 0.02582, "time": 0.48668} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00161, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04321, "loss": 0.04321, "time": 0.48822} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0016, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02715, "loss": 0.02715, "time": 0.48937} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00159, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0308, "loss": 0.0308, "time": 0.48763} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00158, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03318, "loss": 0.03318, "time": 0.4875} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00157, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04117, "loss": 0.04117, "time": 0.49141} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00156, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02946, "loss": 0.02946, "time": 0.49192} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00155, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04099, "loss": 0.04099, "time": 0.48701} +{"mode": "val", "epoch": 126, "iter": 533, "lr": 0.00155, "top1_acc": 0.93616, "top5_acc": 0.99707, "mean_class_accuracy": 0.91632} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00154, "memory": 4083, "data_time": 0.18791, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03388, "loss": 0.03388, "time": 0.63381} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00153, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02668, "loss": 0.02668, "time": 0.40037} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00152, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03034, "loss": 0.03034, "time": 0.37343} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00151, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02321, "loss": 0.02321, "time": 0.49061} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.0015, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02534, "loss": 0.02534, "time": 0.48703} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.00149, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02614, "loss": 0.02614, "time": 0.48958} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00148, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03088, "loss": 0.03088, "time": 0.49072} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00147, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03104, "loss": 0.03104, "time": 0.49001} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00146, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02469, "loss": 0.02469, "time": 0.48914} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00145, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02408, "loss": 0.02408, "time": 0.49109} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00144, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.028, "loss": 0.028, "time": 0.48868} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00143, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.034, "loss": 0.034, "time": 0.48985} +{"mode": "val", "epoch": 127, "iter": 533, "lr": 0.00142, "top1_acc": 0.93674, "top5_acc": 0.99754, "mean_class_accuracy": 0.91639} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00141, "memory": 4083, "data_time": 0.18497, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02858, "loss": 0.02858, "time": 0.63119} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.0014, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02633, "loss": 0.02633, "time": 0.39177} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00139, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03048, "loss": 0.03048, "time": 0.3886} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00138, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02734, "loss": 0.02734, "time": 0.48945} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00138, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02452, "loss": 0.02452, "time": 0.48867} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00137, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02529, "loss": 0.02529, "time": 0.49257} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.00136, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.028, "loss": 0.028, "time": 0.48679} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00135, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02215, "loss": 0.02215, "time": 0.49541} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00134, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02394, "loss": 0.02394, "time": 0.48999} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00133, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02446, "loss": 0.02446, "time": 0.49378} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00132, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0313, "loss": 0.0313, "time": 0.4891} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00131, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02699, "loss": 0.02699, "time": 0.49411} +{"mode": "val", "epoch": 128, "iter": 533, "lr": 0.0013, "top1_acc": 0.93639, "top5_acc": 0.99683, "mean_class_accuracy": 0.91398} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.00129, "memory": 4083, "data_time": 0.18559, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02443, "loss": 0.02443, "time": 0.61924} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00129, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02352, "loss": 0.02352, "time": 0.38094} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00128, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02663, "loss": 0.02663, "time": 0.36262} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00127, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02427, "loss": 0.02427, "time": 0.48965} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00126, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0262, "loss": 0.0262, "time": 0.48769} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00125, "memory": 4083, "data_time": 0.00054, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02414, "loss": 0.02414, "time": 0.48771} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00124, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02348, "loss": 0.02348, "time": 0.48885} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00123, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02572, "loss": 0.02572, "time": 0.4885} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.00122, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0299, "loss": 0.0299, "time": 0.49} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00121, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02859, "loss": 0.02859, "time": 0.48882} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00121, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02426, "loss": 0.02426, "time": 0.48884} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.0012, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0242, "loss": 0.0242, "time": 0.49092} +{"mode": "val", "epoch": 129, "iter": 533, "lr": 0.00119, "top1_acc": 0.93944, "top5_acc": 0.99742, "mean_class_accuracy": 0.9157} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00118, "memory": 4083, "data_time": 0.18648, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.025, "loss": 0.025, "time": 0.6375} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00117, "memory": 4083, "data_time": 0.00043, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02093, "loss": 0.02093, "time": 0.39869} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00116, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02245, "loss": 0.02245, "time": 0.35971} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00116, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02236, "loss": 0.02236, "time": 0.48899} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.00115, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02323, "loss": 0.02323, "time": 0.48437} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00114, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02329, "loss": 0.02329, "time": 0.48558} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00113, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02007, "loss": 0.02007, "time": 0.48941} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00112, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02076, "loss": 0.02076, "time": 0.48917} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00111, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02318, "loss": 0.02318, "time": 0.49386} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.0011, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02277, "loss": 0.02277, "time": 0.49182} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.0011, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02378, "loss": 0.02378, "time": 0.48972} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00109, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02231, "loss": 0.02231, "time": 0.49041} +{"mode": "val", "epoch": 130, "iter": 533, "lr": 0.00108, "top1_acc": 0.93921, "top5_acc": 0.99765, "mean_class_accuracy": 0.91763} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00107, "memory": 4083, "data_time": 0.18394, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02253, "loss": 0.02253, "time": 0.62492} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.00106, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02006, "loss": 0.02006, "time": 0.41447} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00106, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02072, "loss": 0.02072, "time": 0.36133} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00105, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03163, "loss": 0.03163, "time": 0.49006} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00104, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02378, "loss": 0.02378, "time": 0.48615} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00103, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02222, "loss": 0.02222, "time": 0.48985} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00102, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04355, "loss": 0.04355, "time": 0.49206} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00102, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0285, "loss": 0.0285, "time": 0.4898} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00101, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02248, "loss": 0.02248, "time": 0.49077} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.001, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02139, "loss": 0.02139, "time": 0.49039} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.00099, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02235, "loss": 0.02235, "time": 0.48648} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00098, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02094, "loss": 0.02094, "time": 0.48915} +{"mode": "val", "epoch": 131, "iter": 533, "lr": 0.00098, "top1_acc": 0.93862, "top5_acc": 0.99695, "mean_class_accuracy": 0.91633} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.00097, "memory": 4083, "data_time": 0.18476, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02047, "loss": 0.02047, "time": 0.62292} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00096, "memory": 4083, "data_time": 0.0004, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02179, "loss": 0.02179, "time": 0.41075} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00095, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0199, "loss": 0.0199, "time": 0.35784} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00095, "memory": 4083, "data_time": 0.00043, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02131, "loss": 0.02131, "time": 0.48811} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00094, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02197, "loss": 0.02197, "time": 0.48754} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00093, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02236, "loss": 0.02236, "time": 0.4893} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00092, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02207, "loss": 0.02207, "time": 0.4882} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00091, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0201, "loss": 0.0201, "time": 0.49448} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00091, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02075, "loss": 0.02075, "time": 0.49219} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0009, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02593, "loss": 0.02593, "time": 0.49155} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00089, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02252, "loss": 0.02252, "time": 0.49128} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00088, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02044, "loss": 0.02044, "time": 0.491} +{"mode": "val", "epoch": 132, "iter": 533, "lr": 0.00088, "top1_acc": 0.93768, "top5_acc": 0.99718, "mean_class_accuracy": 0.91627} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.00087, "memory": 4083, "data_time": 0.18771, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02241, "loss": 0.02241, "time": 0.63056} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00086, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02107, "loss": 0.02107, "time": 0.40337} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00086, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02156, "loss": 0.02156, "time": 0.37779} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00085, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02212, "loss": 0.02212, "time": 0.49106} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00084, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02, "loss": 0.02, "time": 0.49128} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00083, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02164, "loss": 0.02164, "time": 0.49063} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00083, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02059, "loss": 0.02059, "time": 0.48968} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00082, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02249, "loss": 0.02249, "time": 0.4893} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00081, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01954, "loss": 0.01954, "time": 0.48685} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.0008, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01762, "loss": 0.01762, "time": 0.49251} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0008, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02084, "loss": 0.02084, "time": 0.4876} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00079, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02307, "loss": 0.02307, "time": 0.48915} +{"mode": "val", "epoch": 133, "iter": 533, "lr": 0.00078, "top1_acc": 0.94003, "top5_acc": 0.99754, "mean_class_accuracy": 0.91861} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00078, "memory": 4083, "data_time": 0.1861, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01867, "loss": 0.01867, "time": 0.62667} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00077, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01885, "loss": 0.01885, "time": 0.39714} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00076, "memory": 4083, "data_time": 0.00041, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01965, "loss": 0.01965, "time": 0.3743} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.00076, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01976, "loss": 0.01976, "time": 0.49105} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00075, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01962, "loss": 0.01962, "time": 0.48836} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00074, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02834, "loss": 0.02834, "time": 0.4921} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00073, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01979, "loss": 0.01979, "time": 0.48928} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00073, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01949, "loss": 0.01949, "time": 0.49697} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00072, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02367, "loss": 0.02367, "time": 0.48656} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00071, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01966, "loss": 0.01966, "time": 0.49306} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00071, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01863, "loss": 0.01863, "time": 0.48944} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.0007, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02031, "loss": 0.02031, "time": 0.49139} +{"mode": "val", "epoch": 134, "iter": 533, "lr": 0.0007, "top1_acc": 0.93768, "top5_acc": 0.99707, "mean_class_accuracy": 0.91724} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00069, "memory": 4083, "data_time": 0.19266, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0178, "loss": 0.0178, "time": 0.61619} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00068, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01856, "loss": 0.01856, "time": 0.40391} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00068, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01924, "loss": 0.01924, "time": 0.36694} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00067, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01998, "loss": 0.01998, "time": 0.48778} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00066, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01808, "loss": 0.01808, "time": 0.48668} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00066, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01852, "loss": 0.01852, "time": 0.48836} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00065, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02253, "loss": 0.02253, "time": 0.48536} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00064, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02108, "loss": 0.02108, "time": 0.49162} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.00064, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02097, "loss": 0.02097, "time": 0.49404} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00063, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01804, "loss": 0.01804, "time": 0.49193} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00062, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01851, "loss": 0.01851, "time": 0.49074} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00062, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01877, "loss": 0.01877, "time": 0.49187} +{"mode": "val", "epoch": 135, "iter": 533, "lr": 0.00061, "top1_acc": 0.94109, "top5_acc": 0.99742, "mean_class_accuracy": 0.92208} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00061, "memory": 4083, "data_time": 0.18451, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02279, "loss": 0.02279, "time": 0.62524} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.0006, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01843, "loss": 0.01843, "time": 0.40566} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00059, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01863, "loss": 0.01863, "time": 0.37277} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00059, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01811, "loss": 0.01811, "time": 0.48971} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.00058, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02077, "loss": 0.02077, "time": 0.48707} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.00057, "memory": 4083, "data_time": 0.00068, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02315, "loss": 0.02315, "time": 0.4922} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00057, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01796, "loss": 0.01796, "time": 0.48765} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00056, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01922, "loss": 0.01922, "time": 0.49149} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00056, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01969, "loss": 0.01969, "time": 0.48969} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00055, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01717, "loss": 0.01717, "time": 0.48936} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00054, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01762, "loss": 0.01762, "time": 0.48927} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00054, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02276, "loss": 0.02276, "time": 0.49104} +{"mode": "val", "epoch": 136, "iter": 533, "lr": 0.00053, "top1_acc": 0.93968, "top5_acc": 0.9973, "mean_class_accuracy": 0.92112} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00053, "memory": 4083, "data_time": 0.18498, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02077, "loss": 0.02077, "time": 0.62357} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00052, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01922, "loss": 0.01922, "time": 0.40068} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00052, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01878, "loss": 0.01878, "time": 0.37132} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.00051, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02039, "loss": 0.02039, "time": 0.48869} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.0005, "memory": 4083, "data_time": 0.00052, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01802, "loss": 0.01802, "time": 0.49078} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.0005, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01893, "loss": 0.01893, "time": 0.48862} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00049, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02025, "loss": 0.02025, "time": 0.48971} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00049, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02156, "loss": 0.02156, "time": 0.49077} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00048, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01975, "loss": 0.01975, "time": 0.49383} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00048, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02073, "loss": 0.02073, "time": 0.49214} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00047, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02037, "loss": 0.02037, "time": 0.49192} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00046, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01888, "loss": 0.01888, "time": 0.49193} +{"mode": "val", "epoch": 137, "iter": 533, "lr": 0.00046, "top1_acc": 0.93874, "top5_acc": 0.99754, "mean_class_accuracy": 0.91921} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00046, "memory": 4083, "data_time": 0.18422, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02319, "loss": 0.02319, "time": 0.63132} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00045, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02305, "loss": 0.02305, "time": 0.39422} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00044, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01808, "loss": 0.01808, "time": 0.35975} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00044, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02018, "loss": 0.02018, "time": 0.48799} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.00043, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01892, "loss": 0.01892, "time": 0.49149} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.00043, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01816, "loss": 0.01816, "time": 0.4887} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00042, "memory": 4083, "data_time": 0.00053, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02001, "loss": 0.02001, "time": 0.48833} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00042, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02042, "loss": 0.02042, "time": 0.49112} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00041, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01919, "loss": 0.01919, "time": 0.4908} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00041, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02248, "loss": 0.02248, "time": 0.49141} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.0004, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02042, "loss": 0.02042, "time": 0.48862} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.0004, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02138, "loss": 0.02138, "time": 0.4879} +{"mode": "val", "epoch": 138, "iter": 533, "lr": 0.00039, "top1_acc": 0.94167, "top5_acc": 0.99742, "mean_class_accuracy": 0.92081} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00039, "memory": 4083, "data_time": 0.17939, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01976, "loss": 0.01976, "time": 0.63447} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00038, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01993, "loss": 0.01993, "time": 0.41307} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00038, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02143, "loss": 0.02143, "time": 0.54888} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00037, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01873, "loss": 0.01873, "time": 0.70272} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00037, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01668, "loss": 0.01668, "time": 0.69043} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00036, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02003, "loss": 0.02003, "time": 0.68041} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00036, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01893, "loss": 0.01893, "time": 0.67458} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00035, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01754, "loss": 0.01754, "time": 0.69344} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00035, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01879, "loss": 0.01879, "time": 0.60644} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.00034, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01892, "loss": 0.01892, "time": 0.39049} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.00034, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01945, "loss": 0.01945, "time": 0.70158} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00033, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01719, "loss": 0.01719, "time": 0.70495} +{"mode": "val", "epoch": 139, "iter": 533, "lr": 0.00033, "top1_acc": 0.94167, "top5_acc": 0.99707, "mean_class_accuracy": 0.92078} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00033, "memory": 4083, "data_time": 0.18292, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01586, "loss": 0.01586, "time": 1.16882} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00032, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01728, "loss": 0.01728, "time": 0.30953} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.00032, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01711, "loss": 0.01711, "time": 0.22468} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.00031, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01872, "loss": 0.01872, "time": 0.22152} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00031, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02279, "loss": 0.02279, "time": 0.22552} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.0003, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01813, "loss": 0.01813, "time": 0.22117} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.0003, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01906, "loss": 0.01906, "time": 0.22268} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00029, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.023, "loss": 0.023, "time": 0.22084} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00029, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01906, "loss": 0.01906, "time": 0.21733} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00029, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01905, "loss": 0.01905, "time": 0.21852} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00028, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01815, "loss": 0.01815, "time": 0.22232} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00028, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0187, "loss": 0.0187, "time": 0.22192} +{"mode": "val", "epoch": 140, "iter": 533, "lr": 0.00027, "top1_acc": 0.94203, "top5_acc": 0.99742, "mean_class_accuracy": 0.92239} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00027, "memory": 4083, "data_time": 0.18112, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01931, "loss": 0.01931, "time": 0.41781} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00026, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01835, "loss": 0.01835, "time": 0.22646} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00026, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01864, "loss": 0.01864, "time": 0.21996} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00026, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01896, "loss": 0.01896, "time": 0.22325} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00025, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01697, "loss": 0.01697, "time": 0.22137} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00025, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01961, "loss": 0.01961, "time": 0.2189} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00024, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0249, "loss": 0.0249, "time": 0.2214} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00024, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01934, "loss": 0.01934, "time": 0.21969} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00024, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01858, "loss": 0.01858, "time": 0.21941} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00023, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01939, "loss": 0.01939, "time": 0.2218} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00023, "memory": 4083, "data_time": 0.00032, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0175, "loss": 0.0175, "time": 0.22305} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00022, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01782, "loss": 0.01782, "time": 0.22297} +{"mode": "val", "epoch": 141, "iter": 533, "lr": 0.00022, "top1_acc": 0.94226, "top5_acc": 0.99777, "mean_class_accuracy": 0.92117} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00022, "memory": 4083, "data_time": 0.17996, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01721, "loss": 0.01721, "time": 0.41304} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00021, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01886, "loss": 0.01886, "time": 0.2215} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00021, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01662, "loss": 0.01662, "time": 0.227} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00021, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01842, "loss": 0.01842, "time": 0.22143} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.0002, "memory": 4083, "data_time": 0.00046, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01853, "loss": 0.01853, "time": 0.22269} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.0002, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01996, "loss": 0.01996, "time": 0.22402} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.0002, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01698, "loss": 0.01698, "time": 0.21873} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00019, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02182, "loss": 0.02182, "time": 0.2225} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00019, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0201, "loss": 0.0201, "time": 0.22211} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00018, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01817, "loss": 0.01817, "time": 0.22054} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00018, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01925, "loss": 0.01925, "time": 0.22614} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00018, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01848, "loss": 0.01848, "time": 0.2212} +{"mode": "val", "epoch": 142, "iter": 533, "lr": 0.00018, "top1_acc": 0.9425, "top5_acc": 0.99742, "mean_class_accuracy": 0.92403} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.00017, "memory": 4083, "data_time": 0.18314, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01978, "loss": 0.01978, "time": 0.41699} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00017, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01872, "loss": 0.01872, "time": 0.22223} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00017, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02018, "loss": 0.02018, "time": 0.22032} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00016, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01862, "loss": 0.01862, "time": 0.22104} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00016, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02309, "loss": 0.02309, "time": 0.22128} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00016, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02173, "loss": 0.02173, "time": 0.22203} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00015, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01688, "loss": 0.01688, "time": 0.21872} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00015, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01932, "loss": 0.01932, "time": 0.22223} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00015, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01702, "loss": 0.01702, "time": 0.22088} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00014, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01669, "loss": 0.01669, "time": 0.2263} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00014, "memory": 4083, "data_time": 0.0005, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0169, "loss": 0.0169, "time": 0.22172} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00014, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01794, "loss": 0.01794, "time": 0.21987} +{"mode": "val", "epoch": 143, "iter": 533, "lr": 0.00013, "top1_acc": 0.94179, "top5_acc": 0.99695, "mean_class_accuracy": 0.92192} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00013, "memory": 4083, "data_time": 0.17841, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01874, "loss": 0.01874, "time": 0.41095} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00013, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02242, "loss": 0.02242, "time": 0.22269} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00013, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0182, "loss": 0.0182, "time": 0.22227} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00012, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01917, "loss": 0.01917, "time": 0.22467} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00012, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01888, "loss": 0.01888, "time": 0.22218} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00012, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0169, "loss": 0.0169, "time": 0.22347} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00011, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02223, "loss": 0.02223, "time": 0.2206} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.00011, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01834, "loss": 0.01834, "time": 0.21943} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.00011, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01844, "loss": 0.01844, "time": 0.22049} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.00011, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01857, "loss": 0.01857, "time": 0.22103} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.0001, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01797, "loss": 0.01797, "time": 0.22228} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.0001, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02172, "loss": 0.02172, "time": 0.21955} +{"mode": "val", "epoch": 144, "iter": 533, "lr": 0.0001, "top1_acc": 0.94261, "top5_acc": 0.998, "mean_class_accuracy": 0.92177} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.0001, "memory": 4083, "data_time": 0.17393, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01886, "loss": 0.01886, "time": 0.41031} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 9e-05, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01868, "loss": 0.01868, "time": 0.22364} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 9e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01718, "loss": 0.01718, "time": 0.21873} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 9e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01796, "loss": 0.01796, "time": 0.2196} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 9e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01694, "loss": 0.01694, "time": 0.22255} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 8e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01881, "loss": 0.01881, "time": 0.22018} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 8e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01654, "loss": 0.01654, "time": 0.21908} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 8e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0176, "loss": 0.0176, "time": 0.22124} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 8e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01704, "loss": 0.01704, "time": 0.22012} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 7e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02083, "loss": 0.02083, "time": 0.22187} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 7e-05, "memory": 4083, "data_time": 0.00039, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01916, "loss": 0.01916, "time": 0.22356} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 7e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01862, "loss": 0.01862, "time": 0.22104} +{"mode": "val", "epoch": 145, "iter": 533, "lr": 7e-05, "top1_acc": 0.94226, "top5_acc": 0.99742, "mean_class_accuracy": 0.91931} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 7e-05, "memory": 4083, "data_time": 0.17667, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01927, "loss": 0.01927, "time": 0.40681} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 6e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01871, "loss": 0.01871, "time": 0.22013} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 6e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01786, "loss": 0.01786, "time": 0.22218} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 6e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01793, "loss": 0.01793, "time": 0.2221} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 6e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02141, "loss": 0.02141, "time": 0.22204} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 6e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0189, "loss": 0.0189, "time": 0.22096} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 5e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01741, "loss": 0.01741, "time": 0.22124} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 5e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01678, "loss": 0.01678, "time": 0.21824} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 5e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0173, "loss": 0.0173, "time": 0.22033} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 5e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01672, "loss": 0.01672, "time": 0.2205} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 5e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01633, "loss": 0.01633, "time": 0.22259} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 5e-05, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01882, "loss": 0.01882, "time": 0.22274} +{"mode": "val", "epoch": 146, "iter": 533, "lr": 4e-05, "top1_acc": 0.94179, "top5_acc": 0.9973, "mean_class_accuracy": 0.9189} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 4e-05, "memory": 4083, "data_time": 0.17977, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01805, "loss": 0.01805, "time": 0.41334} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 4e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02078, "loss": 0.02078, "time": 0.22461} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 4e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01745, "loss": 0.01745, "time": 0.22075} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 4e-05, "memory": 4083, "data_time": 0.00044, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01789, "loss": 0.01789, "time": 0.22174} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 4e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01892, "loss": 0.01892, "time": 0.22085} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 3e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01704, "loss": 0.01704, "time": 0.21951} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 3e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01761, "loss": 0.01761, "time": 0.22097} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 3e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01809, "loss": 0.01809, "time": 0.2214} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 3e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0173, "loss": 0.0173, "time": 0.22147} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 3e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01736, "loss": 0.01736, "time": 0.21775} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 3e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02153, "loss": 0.02153, "time": 0.21963} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 3e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01728, "loss": 0.01728, "time": 0.22102} +{"mode": "val", "epoch": 147, "iter": 533, "lr": 2e-05, "top1_acc": 0.94214, "top5_acc": 0.99718, "mean_class_accuracy": 0.9215} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 2e-05, "memory": 4083, "data_time": 0.17388, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01807, "loss": 0.01807, "time": 0.40497} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 2e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01948, "loss": 0.01948, "time": 0.22172} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 2e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01809, "loss": 0.01809, "time": 0.22027} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 2e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01814, "loss": 0.01814, "time": 0.22414} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 2e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01919, "loss": 0.01919, "time": 0.22177} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 2e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01965, "loss": 0.01965, "time": 0.22317} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 2e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01742, "loss": 0.01742, "time": 0.22229} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 2e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01703, "loss": 0.01703, "time": 0.21809} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 1e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01925, "loss": 0.01925, "time": 0.21832} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 1e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0186, "loss": 0.0186, "time": 0.22232} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 1e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01949, "loss": 0.01949, "time": 0.21955} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 1e-05, "memory": 4083, "data_time": 0.00045, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01763, "loss": 0.01763, "time": 0.22014} +{"mode": "val", "epoch": 148, "iter": 533, "lr": 1e-05, "top1_acc": 0.94238, "top5_acc": 0.9973, "mean_class_accuracy": 0.92163} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 1e-05, "memory": 4083, "data_time": 0.18327, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01744, "loss": 0.01744, "time": 0.4178} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 1e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01789, "loss": 0.01789, "time": 0.22116} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 1e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0172, "loss": 0.0172, "time": 0.2237} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0205, "loss": 0.0205, "time": 0.22061} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 1e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0185, "loss": 0.0185, "time": 0.22002} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01835, "loss": 0.01835, "time": 0.2232} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 1e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01691, "loss": 0.01691, "time": 0.22171} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 1e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0167, "loss": 0.0167, "time": 0.21968} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01953, "loss": 0.01953, "time": 0.22099} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01667, "loss": 0.01667, "time": 0.22239} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0174, "loss": 0.0174, "time": 0.22025} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01916, "loss": 0.01916, "time": 0.22013} +{"mode": "val", "epoch": 149, "iter": 533, "lr": 0.0, "top1_acc": 0.94179, "top5_acc": 0.9973, "mean_class_accuracy": 0.92238} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 0.0, "memory": 4083, "data_time": 0.17929, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01742, "loss": 0.01742, "time": 0.41152} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 0.0, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0169, "loss": 0.0169, "time": 0.22282} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0181, "loss": 0.0181, "time": 0.22311} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01783, "loss": 0.01783, "time": 0.22133} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01792, "loss": 0.01792, "time": 0.22134} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01761, "loss": 0.01761, "time": 0.22157} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01894, "loss": 0.01894, "time": 0.2197} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 0.0, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01809, "loss": 0.01809, "time": 0.22323} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01658, "loss": 0.01658, "time": 0.22441} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01818, "loss": 0.01818, "time": 0.22506} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01758, "loss": 0.01758, "time": 0.22698} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01771, "loss": 0.01771, "time": 0.22287} +{"mode": "val", "epoch": 150, "iter": 533, "lr": 0.0, "top1_acc": 0.94367, "top5_acc": 0.9973, "mean_class_accuracy": 0.92334} diff --git a/finegym/k_2/best_pred.pkl b/finegym/k_2/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..944e25b5a87e3a7c2bb3b56d7f02314b4f87c5e6 --- /dev/null +++ b/finegym/k_2/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5752510808b3d0082f81c86c622811a49bcc15d463858c6f764052114cf58ad2 +size 5256300 diff --git a/finegym/k_2/best_top1_acc_epoch_150.pth b/finegym/k_2/best_top1_acc_epoch_150.pth new file mode 100644 index 0000000000000000000000000000000000000000..9d8e1147494b97774078c7907a209ef85a636beb --- /dev/null +++ b/finegym/k_2/best_top1_acc_epoch_150.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d6560b85bcb87466c359d8a46b7f652c8063378ca94483039eb4bbeed21c0584 +size 31999601 diff --git a/finegym/k_2/k_2.py b/finegym/k_2/k_2.py new file mode 100644 index 0000000000000000000000000000000000000000..07a0e1a9a5e118f25bccfcd83bee9b787688be84 --- /dev/null +++ b/finegym/k_2/k_2.py @@ -0,0 +1,113 @@ +modality = 'k' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/k_2' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/finegym/k_3/20250624_101238.log b/finegym/k_3/20250624_101238.log new file mode 100644 index 0000000000000000000000000000000000000000..6d0c0a91624fd163b6b0cd58077e29e7188d75f6 --- /dev/null +++ b/finegym/k_3/20250624_101238.log @@ -0,0 +1,3498 @@ +2025-06-24 10:12:38,503 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2025-06-24 10:12:38,765 - pyskl - INFO - Config: modality = 'k' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/k_3' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2025-06-24 10:12:38,765 - pyskl - INFO - Set random seed to 1966239539, deterministic: False +2025-06-24 10:12:40,446 - pyskl - INFO - 20484 videos remain after valid thresholding +2025-06-24 10:12:46,321 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-24 10:12:46,322 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3 +2025-06-24 10:12:46,322 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2025-06-24 10:12:46,322 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2025-06-24 10:12:46,322 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3 by HardDiskBackend. +2025-06-24 10:13:49,123 - pyskl - INFO - Epoch [1][100/1281] lr: 2.500e-02, eta: 1 day, 9:29:58, time: 0.628, data_time: 0.190, memory: 4082, top1_acc: 0.0512, top5_acc: 0.1894, loss_cls: 4.6418, loss: 4.6418 +2025-06-24 10:14:16,974 - pyskl - INFO - Epoch [1][200/1281] lr: 2.500e-02, eta: 1 day, 0:09:57, time: 0.279, data_time: 0.000, memory: 4082, top1_acc: 0.0919, top5_acc: 0.3331, loss_cls: 4.6129, loss: 4.6129 +2025-06-24 10:14:57,167 - pyskl - INFO - Epoch [1][300/1281] lr: 2.500e-02, eta: 23:14:30, time: 0.402, data_time: 0.000, memory: 4082, top1_acc: 0.1050, top5_acc: 0.3887, loss_cls: 4.3222, loss: 4.3222 +2025-06-24 10:15:38,899 - pyskl - INFO - Epoch [1][400/1281] lr: 2.500e-02, eta: 22:58:44, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.1144, top5_acc: 0.4119, loss_cls: 4.1384, loss: 4.1384 +2025-06-24 10:16:20,348 - pyskl - INFO - Epoch [1][500/1281] lr: 2.500e-02, eta: 22:47:12, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.1456, top5_acc: 0.4713, loss_cls: 3.9131, loss: 3.9131 +2025-06-24 10:17:01,789 - pyskl - INFO - Epoch [1][600/1281] lr: 2.500e-02, eta: 22:39:14, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.1825, top5_acc: 0.4975, loss_cls: 3.7642, loss: 3.7642 +2025-06-24 10:17:43,249 - pyskl - INFO - Epoch [1][700/1281] lr: 2.500e-02, eta: 22:33:26, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.2013, top5_acc: 0.5575, loss_cls: 3.5615, loss: 3.5615 +2025-06-24 10:18:24,575 - pyskl - INFO - Epoch [1][800/1281] lr: 2.500e-02, eta: 22:28:22, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.2131, top5_acc: 0.5831, loss_cls: 3.4072, loss: 3.4072 +2025-06-24 10:19:06,156 - pyskl - INFO - Epoch [1][900/1281] lr: 2.500e-02, eta: 22:25:11, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.2681, top5_acc: 0.6312, loss_cls: 3.1789, loss: 3.1789 +2025-06-24 10:19:47,703 - pyskl - INFO - Epoch [1][1000/1281] lr: 2.500e-02, eta: 22:22:24, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.3038, top5_acc: 0.6850, loss_cls: 3.0885, loss: 3.0885 +2025-06-24 10:20:29,318 - pyskl - INFO - Epoch [1][1100/1281] lr: 2.500e-02, eta: 22:20:11, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.2856, top5_acc: 0.6937, loss_cls: 3.0204, loss: 3.0204 +2025-06-24 10:21:10,800 - pyskl - INFO - Epoch [1][1200/1281] lr: 2.500e-02, eta: 22:17:52, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.3144, top5_acc: 0.7169, loss_cls: 2.8868, loss: 2.8868 +2025-06-24 10:21:45,103 - pyskl - INFO - Saving checkpoint at 1 epochs +2025-06-24 10:22:56,878 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:22:56,945 - pyskl - INFO - +top1_acc 0.3099 +top5_acc 0.7287 +2025-06-24 10:22:56,945 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:22:56,955 - pyskl - INFO - +mean_acc 0.1617 +2025-06-24 10:22:57,185 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2025-06-24 10:22:57,185 - pyskl - INFO - Best top1_acc is 0.3099 at 1 epoch. +2025-06-24 10:22:57,190 - pyskl - INFO - Epoch(val) [1][533] top1_acc: 0.3099, top5_acc: 0.7287, mean_class_accuracy: 0.1617 +2025-06-24 10:24:01,881 - pyskl - INFO - Epoch [2][100/1281] lr: 2.500e-02, eta: 21:50:20, time: 0.647, data_time: 0.199, memory: 4082, top1_acc: 0.3563, top5_acc: 0.7619, loss_cls: 2.6939, loss: 2.6939 +2025-06-24 10:24:28,484 - pyskl - INFO - Epoch [2][200/1281] lr: 2.500e-02, eta: 21:18:18, time: 0.266, data_time: 0.000, memory: 4082, top1_acc: 0.3731, top5_acc: 0.7850, loss_cls: 2.5618, loss: 2.5618 +2025-06-24 10:25:07,802 - pyskl - INFO - Epoch [2][300/1281] lr: 2.500e-02, eta: 21:15:48, time: 0.393, data_time: 0.001, memory: 4082, top1_acc: 0.3931, top5_acc: 0.7919, loss_cls: 2.5156, loss: 2.5156 +2025-06-24 10:25:49,340 - pyskl - INFO - Epoch [2][400/1281] lr: 2.500e-02, eta: 21:17:43, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.4194, top5_acc: 0.8300, loss_cls: 2.3574, loss: 2.3574 +2025-06-24 10:26:30,955 - pyskl - INFO - Epoch [2][500/1281] lr: 2.499e-02, eta: 21:19:28, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.4031, top5_acc: 0.8219, loss_cls: 2.4284, loss: 2.4284 +2025-06-24 10:27:12,578 - pyskl - INFO - Epoch [2][600/1281] lr: 2.499e-02, eta: 21:20:59, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.4387, top5_acc: 0.8450, loss_cls: 2.2542, loss: 2.2542 +2025-06-24 10:27:54,114 - pyskl - INFO - Epoch [2][700/1281] lr: 2.499e-02, eta: 21:22:08, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.4356, top5_acc: 0.8431, loss_cls: 2.2427, loss: 2.2427 +2025-06-24 10:28:35,598 - pyskl - INFO - Epoch [2][800/1281] lr: 2.499e-02, eta: 21:23:02, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.4644, top5_acc: 0.8469, loss_cls: 2.2423, loss: 2.2423 +2025-06-24 10:29:17,011 - pyskl - INFO - Epoch [2][900/1281] lr: 2.499e-02, eta: 21:23:41, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.4637, top5_acc: 0.8625, loss_cls: 2.1537, loss: 2.1537 +2025-06-24 10:29:58,639 - pyskl - INFO - Epoch [2][1000/1281] lr: 2.499e-02, eta: 21:24:30, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.4688, top5_acc: 0.8550, loss_cls: 2.1729, loss: 2.1729 +2025-06-24 10:30:40,094 - pyskl - INFO - Epoch [2][1100/1281] lr: 2.499e-02, eta: 21:24:58, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.4969, top5_acc: 0.8719, loss_cls: 2.0655, loss: 2.0655 +2025-06-24 10:31:21,659 - pyskl - INFO - Epoch [2][1200/1281] lr: 2.499e-02, eta: 21:25:29, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.4825, top5_acc: 0.8806, loss_cls: 2.0421, loss: 2.0421 +2025-06-24 10:31:56,117 - pyskl - INFO - Saving checkpoint at 2 epochs +2025-06-24 10:33:07,630 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:33:07,696 - pyskl - INFO - +top1_acc 0.5028 +top5_acc 0.8848 +2025-06-24 10:33:07,696 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:33:07,704 - pyskl - INFO - +mean_acc 0.2922 +2025-06-24 10:33:07,709 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_1.pth was removed +2025-06-24 10:33:07,905 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2025-06-24 10:33:07,906 - pyskl - INFO - Best top1_acc is 0.5028 at 2 epoch. +2025-06-24 10:33:07,908 - pyskl - INFO - Epoch(val) [2][533] top1_acc: 0.5028, top5_acc: 0.8848, mean_class_accuracy: 0.2922 +2025-06-24 10:34:12,561 - pyskl - INFO - Epoch [3][100/1281] lr: 2.499e-02, eta: 21:13:38, time: 0.646, data_time: 0.200, memory: 4082, top1_acc: 0.5419, top5_acc: 0.9106, loss_cls: 1.9097, loss: 1.9097 +2025-06-24 10:34:38,988 - pyskl - INFO - Epoch [3][200/1281] lr: 2.499e-02, eta: 20:57:05, time: 0.264, data_time: 0.000, memory: 4082, top1_acc: 0.5212, top5_acc: 0.9094, loss_cls: 1.8872, loss: 1.8872 +2025-06-24 10:35:18,052 - pyskl - INFO - Epoch [3][300/1281] lr: 2.499e-02, eta: 20:55:34, time: 0.391, data_time: 0.000, memory: 4082, top1_acc: 0.5413, top5_acc: 0.9137, loss_cls: 1.8449, loss: 1.8449 +2025-06-24 10:35:59,526 - pyskl - INFO - Epoch [3][400/1281] lr: 2.499e-02, eta: 20:56:41, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.5387, top5_acc: 0.9113, loss_cls: 1.8669, loss: 1.8669 +2025-06-24 10:36:41,148 - pyskl - INFO - Epoch [3][500/1281] lr: 2.498e-02, eta: 20:57:50, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.5656, top5_acc: 0.9294, loss_cls: 1.7411, loss: 1.7411 +2025-06-24 10:37:23,102 - pyskl - INFO - Epoch [3][600/1281] lr: 2.498e-02, eta: 20:59:13, time: 0.420, data_time: 0.000, memory: 4082, top1_acc: 0.5381, top5_acc: 0.9175, loss_cls: 1.8388, loss: 1.8388 +2025-06-24 10:38:04,601 - pyskl - INFO - Epoch [3][700/1281] lr: 2.498e-02, eta: 21:00:01, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.5813, top5_acc: 0.9369, loss_cls: 1.6922, loss: 1.6922 +2025-06-24 10:38:46,408 - pyskl - INFO - Epoch [3][800/1281] lr: 2.498e-02, eta: 21:01:01, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.5613, top5_acc: 0.9237, loss_cls: 1.7371, loss: 1.7371 +2025-06-24 10:39:30,063 - pyskl - INFO - Epoch [3][900/1281] lr: 2.498e-02, eta: 21:03:35, time: 0.437, data_time: 0.000, memory: 4082, top1_acc: 0.5706, top5_acc: 0.9381, loss_cls: 1.7166, loss: 1.7166 +2025-06-24 10:40:11,713 - pyskl - INFO - Epoch [3][1000/1281] lr: 2.498e-02, eta: 21:04:13, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.5950, top5_acc: 0.9394, loss_cls: 1.6378, loss: 1.6378 +2025-06-24 10:40:53,228 - pyskl - INFO - Epoch [3][1100/1281] lr: 2.498e-02, eta: 21:04:39, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.5975, top5_acc: 0.9463, loss_cls: 1.6006, loss: 1.6006 +2025-06-24 10:41:34,615 - pyskl - INFO - Epoch [3][1200/1281] lr: 2.498e-02, eta: 21:04:55, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6031, top5_acc: 0.9419, loss_cls: 1.6141, loss: 1.6141 +2025-06-24 10:42:08,882 - pyskl - INFO - Saving checkpoint at 3 epochs +2025-06-24 10:43:20,742 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:43:20,798 - pyskl - INFO - +top1_acc 0.6106 +top5_acc 0.9340 +2025-06-24 10:43:20,799 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:43:20,805 - pyskl - INFO - +mean_acc 0.4485 +2025-06-24 10:43:20,809 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_2.pth was removed +2025-06-24 10:43:21,022 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2025-06-24 10:43:21,023 - pyskl - INFO - Best top1_acc is 0.6106 at 3 epoch. +2025-06-24 10:43:21,026 - pyskl - INFO - Epoch(val) [3][533] top1_acc: 0.6106, top5_acc: 0.9340, mean_class_accuracy: 0.4485 +2025-06-24 10:44:24,865 - pyskl - INFO - Epoch [4][100/1281] lr: 2.497e-02, eta: 20:56:29, time: 0.638, data_time: 0.195, memory: 4082, top1_acc: 0.6212, top5_acc: 0.9444, loss_cls: 1.5515, loss: 1.5515 +2025-06-24 10:44:51,948 - pyskl - INFO - Epoch [4][200/1281] lr: 2.497e-02, eta: 20:45:45, time: 0.271, data_time: 0.000, memory: 4082, top1_acc: 0.6112, top5_acc: 0.9500, loss_cls: 1.5375, loss: 1.5375 +2025-06-24 10:45:30,519 - pyskl - INFO - Epoch [4][300/1281] lr: 2.497e-02, eta: 20:44:12, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.6206, top5_acc: 0.9431, loss_cls: 1.5455, loss: 1.5455 +2025-06-24 10:46:12,072 - pyskl - INFO - Epoch [4][400/1281] lr: 2.497e-02, eta: 20:44:54, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6125, top5_acc: 0.9481, loss_cls: 1.5579, loss: 1.5579 +2025-06-24 10:46:53,566 - pyskl - INFO - Epoch [4][500/1281] lr: 2.497e-02, eta: 20:45:30, time: 0.415, data_time: 0.001, memory: 4082, top1_acc: 0.6069, top5_acc: 0.9544, loss_cls: 1.5258, loss: 1.5258 +2025-06-24 10:47:35,176 - pyskl - INFO - Epoch [4][600/1281] lr: 2.497e-02, eta: 20:46:07, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6306, top5_acc: 0.9500, loss_cls: 1.4998, loss: 1.4998 +2025-06-24 10:48:16,791 - pyskl - INFO - Epoch [4][700/1281] lr: 2.497e-02, eta: 20:46:41, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6494, top5_acc: 0.9500, loss_cls: 1.4604, loss: 1.4604 +2025-06-24 10:48:58,326 - pyskl - INFO - Epoch [4][800/1281] lr: 2.496e-02, eta: 20:47:08, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6394, top5_acc: 0.9569, loss_cls: 1.4405, loss: 1.4405 +2025-06-24 10:49:39,857 - pyskl - INFO - Epoch [4][900/1281] lr: 2.496e-02, eta: 20:47:32, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6225, top5_acc: 0.9581, loss_cls: 1.5178, loss: 1.5178 +2025-06-24 10:50:21,489 - pyskl - INFO - Epoch [4][1000/1281] lr: 2.496e-02, eta: 20:47:58, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6481, top5_acc: 0.9556, loss_cls: 1.4297, loss: 1.4297 +2025-06-24 10:51:03,108 - pyskl - INFO - Epoch [4][1100/1281] lr: 2.496e-02, eta: 20:48:20, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6544, top5_acc: 0.9631, loss_cls: 1.3951, loss: 1.3951 +2025-06-24 10:51:44,798 - pyskl - INFO - Epoch [4][1200/1281] lr: 2.496e-02, eta: 20:48:42, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.6675, top5_acc: 0.9675, loss_cls: 1.3818, loss: 1.3818 +2025-06-24 10:52:19,253 - pyskl - INFO - Saving checkpoint at 4 epochs +2025-06-24 10:53:30,860 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:53:30,918 - pyskl - INFO - +top1_acc 0.6376 +top5_acc 0.9458 +2025-06-24 10:53:30,918 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:53:30,925 - pyskl - INFO - +mean_acc 0.5059 +2025-06-24 10:53:30,929 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_3.pth was removed +2025-06-24 10:53:31,128 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2025-06-24 10:53:31,128 - pyskl - INFO - Best top1_acc is 0.6376 at 4 epoch. +2025-06-24 10:53:31,131 - pyskl - INFO - Epoch(val) [4][533] top1_acc: 0.6376, top5_acc: 0.9458, mean_class_accuracy: 0.5059 +2025-06-24 10:54:35,567 - pyskl - INFO - Epoch [5][100/1281] lr: 2.495e-02, eta: 20:42:42, time: 0.644, data_time: 0.197, memory: 4082, top1_acc: 0.6625, top5_acc: 0.9556, loss_cls: 1.3694, loss: 1.3694 +2025-06-24 10:55:02,320 - pyskl - INFO - Epoch [5][200/1281] lr: 2.495e-02, eta: 20:34:21, time: 0.268, data_time: 0.000, memory: 4082, top1_acc: 0.6675, top5_acc: 0.9606, loss_cls: 1.3712, loss: 1.3712 +2025-06-24 10:55:42,464 - pyskl - INFO - Epoch [5][300/1281] lr: 2.495e-02, eta: 20:33:59, time: 0.401, data_time: 0.000, memory: 4082, top1_acc: 0.6981, top5_acc: 0.9569, loss_cls: 1.3286, loss: 1.3286 +2025-06-24 10:56:24,777 - pyskl - INFO - Epoch [5][400/1281] lr: 2.495e-02, eta: 20:34:49, time: 0.423, data_time: 0.000, memory: 4082, top1_acc: 0.6769, top5_acc: 0.9663, loss_cls: 1.3489, loss: 1.3489 +2025-06-24 10:57:06,267 - pyskl - INFO - Epoch [5][500/1281] lr: 2.495e-02, eta: 20:35:08, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6925, top5_acc: 0.9606, loss_cls: 1.3249, loss: 1.3249 +2025-06-24 10:57:47,833 - pyskl - INFO - Epoch [5][600/1281] lr: 2.495e-02, eta: 20:35:28, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6794, top5_acc: 0.9637, loss_cls: 1.3513, loss: 1.3513 +2025-06-24 10:58:29,411 - pyskl - INFO - Epoch [5][700/1281] lr: 2.494e-02, eta: 20:35:47, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6613, top5_acc: 0.9675, loss_cls: 1.3580, loss: 1.3580 +2025-06-24 10:59:10,961 - pyskl - INFO - Epoch [5][800/1281] lr: 2.494e-02, eta: 20:36:02, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6850, top5_acc: 0.9581, loss_cls: 1.3362, loss: 1.3362 +2025-06-24 10:59:52,486 - pyskl - INFO - Epoch [5][900/1281] lr: 2.494e-02, eta: 20:36:15, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6906, top5_acc: 0.9637, loss_cls: 1.2821, loss: 1.2821 +2025-06-24 11:00:34,014 - pyskl - INFO - Epoch [5][1000/1281] lr: 2.494e-02, eta: 20:36:26, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6669, top5_acc: 0.9744, loss_cls: 1.3145, loss: 1.3145 +2025-06-24 11:01:15,457 - pyskl - INFO - Epoch [5][1100/1281] lr: 2.494e-02, eta: 20:36:33, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6825, top5_acc: 0.9625, loss_cls: 1.3088, loss: 1.3088 +2025-06-24 11:01:56,970 - pyskl - INFO - Epoch [5][1200/1281] lr: 2.493e-02, eta: 20:36:40, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6750, top5_acc: 0.9694, loss_cls: 1.2876, loss: 1.2876 +2025-06-24 11:02:31,609 - pyskl - INFO - Saving checkpoint at 5 epochs +2025-06-24 11:03:43,456 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:03:43,526 - pyskl - INFO - +top1_acc 0.6783 +top5_acc 0.9634 +2025-06-24 11:03:43,527 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:03:43,535 - pyskl - INFO - +mean_acc 0.5465 +2025-06-24 11:03:43,540 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_4.pth was removed +2025-06-24 11:03:43,734 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2025-06-24 11:03:43,735 - pyskl - INFO - Best top1_acc is 0.6783 at 5 epoch. +2025-06-24 11:03:43,738 - pyskl - INFO - Epoch(val) [5][533] top1_acc: 0.6783, top5_acc: 0.9634, mean_class_accuracy: 0.5465 +2025-06-24 11:04:49,049 - pyskl - INFO - Epoch [6][100/1281] lr: 2.493e-02, eta: 20:32:09, time: 0.653, data_time: 0.196, memory: 4082, top1_acc: 0.6813, top5_acc: 0.9663, loss_cls: 1.2980, loss: 1.2980 +2025-06-24 11:05:14,825 - pyskl - INFO - Epoch [6][200/1281] lr: 2.493e-02, eta: 20:24:54, time: 0.258, data_time: 0.000, memory: 4082, top1_acc: 0.7131, top5_acc: 0.9781, loss_cls: 1.2220, loss: 1.2220 +2025-06-24 11:05:54,954 - pyskl - INFO - Epoch [6][300/1281] lr: 2.492e-02, eta: 20:24:29, time: 0.401, data_time: 0.000, memory: 4082, top1_acc: 0.6887, top5_acc: 0.9669, loss_cls: 1.2833, loss: 1.2833 +2025-06-24 11:06:36,471 - pyskl - INFO - Epoch [6][400/1281] lr: 2.492e-02, eta: 20:24:41, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.6994, top5_acc: 0.9750, loss_cls: 1.2476, loss: 1.2476 +2025-06-24 11:07:18,039 - pyskl - INFO - Epoch [6][500/1281] lr: 2.492e-02, eta: 20:24:53, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7163, top5_acc: 0.9762, loss_cls: 1.2015, loss: 1.2015 +2025-06-24 11:07:59,414 - pyskl - INFO - Epoch [6][600/1281] lr: 2.492e-02, eta: 20:24:58, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.6913, top5_acc: 0.9675, loss_cls: 1.2602, loss: 1.2602 +2025-06-24 11:08:40,787 - pyskl - INFO - Epoch [6][700/1281] lr: 2.492e-02, eta: 20:25:02, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7094, top5_acc: 0.9769, loss_cls: 1.2111, loss: 1.2111 +2025-06-24 11:09:22,128 - pyskl - INFO - Epoch [6][800/1281] lr: 2.491e-02, eta: 20:25:04, time: 0.413, data_time: 0.000, memory: 4082, top1_acc: 0.7231, top5_acc: 0.9681, loss_cls: 1.2003, loss: 1.2003 +2025-06-24 11:10:03,735 - pyskl - INFO - Epoch [6][900/1281] lr: 2.491e-02, eta: 20:25:11, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7000, top5_acc: 0.9731, loss_cls: 1.2256, loss: 1.2256 +2025-06-24 11:10:45,255 - pyskl - INFO - Epoch [6][1000/1281] lr: 2.491e-02, eta: 20:25:15, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7087, top5_acc: 0.9719, loss_cls: 1.2410, loss: 1.2410 +2025-06-24 11:11:26,676 - pyskl - INFO - Epoch [6][1100/1281] lr: 2.491e-02, eta: 20:25:15, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7094, top5_acc: 0.9681, loss_cls: 1.2409, loss: 1.2409 +2025-06-24 11:12:08,172 - pyskl - INFO - Epoch [6][1200/1281] lr: 2.490e-02, eta: 20:25:16, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7281, top5_acc: 0.9806, loss_cls: 1.1422, loss: 1.1422 +2025-06-24 11:12:42,397 - pyskl - INFO - Saving checkpoint at 6 epochs +2025-06-24 11:13:53,796 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:13:53,852 - pyskl - INFO - +top1_acc 0.6708 +top5_acc 0.9593 +2025-06-24 11:13:53,852 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:13:53,858 - pyskl - INFO - +mean_acc 0.5413 +2025-06-24 11:13:53,860 - pyskl - INFO - Epoch(val) [6][533] top1_acc: 0.6708, top5_acc: 0.9593, mean_class_accuracy: 0.5413 +2025-06-24 11:14:59,255 - pyskl - INFO - Epoch [7][100/1281] lr: 2.490e-02, eta: 20:21:25, time: 0.654, data_time: 0.198, memory: 4082, top1_acc: 0.7094, top5_acc: 0.9744, loss_cls: 1.1805, loss: 1.1805 +2025-06-24 11:15:24,534 - pyskl - INFO - Epoch [7][200/1281] lr: 2.490e-02, eta: 20:15:07, time: 0.253, data_time: 0.000, memory: 4082, top1_acc: 0.7319, top5_acc: 0.9788, loss_cls: 1.1081, loss: 1.1081 +2025-06-24 11:16:04,798 - pyskl - INFO - Epoch [7][300/1281] lr: 2.489e-02, eta: 20:14:44, time: 0.403, data_time: 0.000, memory: 4082, top1_acc: 0.7262, top5_acc: 0.9712, loss_cls: 1.1632, loss: 1.1632 +2025-06-24 11:16:46,475 - pyskl - INFO - Epoch [7][400/1281] lr: 2.489e-02, eta: 20:14:52, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7056, top5_acc: 0.9700, loss_cls: 1.1943, loss: 1.1943 +2025-06-24 11:17:28,079 - pyskl - INFO - Epoch [7][500/1281] lr: 2.489e-02, eta: 20:14:57, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7462, top5_acc: 0.9800, loss_cls: 1.1226, loss: 1.1226 +2025-06-24 11:18:09,648 - pyskl - INFO - Epoch [7][600/1281] lr: 2.489e-02, eta: 20:15:01, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7444, top5_acc: 0.9769, loss_cls: 1.0844, loss: 1.0844 +2025-06-24 11:18:51,282 - pyskl - INFO - Epoch [7][700/1281] lr: 2.488e-02, eta: 20:15:05, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7300, top5_acc: 0.9744, loss_cls: 1.1006, loss: 1.1006 +2025-06-24 11:19:32,912 - pyskl - INFO - Epoch [7][800/1281] lr: 2.488e-02, eta: 20:15:07, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7125, top5_acc: 0.9750, loss_cls: 1.1873, loss: 1.1873 +2025-06-24 11:20:14,469 - pyskl - INFO - Epoch [7][900/1281] lr: 2.488e-02, eta: 20:15:07, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7469, top5_acc: 0.9794, loss_cls: 1.1132, loss: 1.1132 +2025-06-24 11:20:56,168 - pyskl - INFO - Epoch [7][1000/1281] lr: 2.487e-02, eta: 20:15:10, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7281, top5_acc: 0.9738, loss_cls: 1.1437, loss: 1.1437 +2025-06-24 11:21:37,666 - pyskl - INFO - Epoch [7][1100/1281] lr: 2.487e-02, eta: 20:15:06, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7325, top5_acc: 0.9794, loss_cls: 1.0896, loss: 1.0896 +2025-06-24 11:22:19,337 - pyskl - INFO - Epoch [7][1200/1281] lr: 2.487e-02, eta: 20:15:06, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7113, top5_acc: 0.9669, loss_cls: 1.2173, loss: 1.2173 +2025-06-24 11:22:53,716 - pyskl - INFO - Saving checkpoint at 7 epochs +2025-06-24 11:24:05,088 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:24:05,157 - pyskl - INFO - +top1_acc 0.7132 +top5_acc 0.9734 +2025-06-24 11:24:05,157 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:24:05,165 - pyskl - INFO - +mean_acc 0.6024 +2025-06-24 11:24:05,169 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_5.pth was removed +2025-06-24 11:24:05,357 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2025-06-24 11:24:05,357 - pyskl - INFO - Best top1_acc is 0.7132 at 7 epoch. +2025-06-24 11:24:05,360 - pyskl - INFO - Epoch(val) [7][533] top1_acc: 0.7132, top5_acc: 0.9734, mean_class_accuracy: 0.6024 +2025-06-24 11:25:10,199 - pyskl - INFO - Epoch [8][100/1281] lr: 2.486e-02, eta: 20:11:29, time: 0.648, data_time: 0.193, memory: 4082, top1_acc: 0.7562, top5_acc: 0.9850, loss_cls: 1.0343, loss: 1.0343 +2025-06-24 11:25:35,890 - pyskl - INFO - Epoch [8][200/1281] lr: 2.486e-02, eta: 20:06:10, time: 0.257, data_time: 0.000, memory: 4082, top1_acc: 0.7281, top5_acc: 0.9700, loss_cls: 1.1837, loss: 1.1837 +2025-06-24 11:26:16,793 - pyskl - INFO - Epoch [8][300/1281] lr: 2.486e-02, eta: 20:05:57, time: 0.409, data_time: 0.000, memory: 4082, top1_acc: 0.7481, top5_acc: 0.9806, loss_cls: 1.0693, loss: 1.0693 +2025-06-24 11:26:58,352 - pyskl - INFO - Epoch [8][400/1281] lr: 2.485e-02, eta: 20:05:56, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7450, top5_acc: 0.9800, loss_cls: 1.0697, loss: 1.0697 +2025-06-24 11:27:39,980 - pyskl - INFO - Epoch [8][500/1281] lr: 2.485e-02, eta: 20:05:56, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7562, top5_acc: 0.9806, loss_cls: 1.0526, loss: 1.0526 +2025-06-24 11:28:21,619 - pyskl - INFO - Epoch [8][600/1281] lr: 2.485e-02, eta: 20:05:55, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7506, top5_acc: 0.9838, loss_cls: 1.0536, loss: 1.0536 +2025-06-24 11:29:03,149 - pyskl - INFO - Epoch [8][700/1281] lr: 2.484e-02, eta: 20:05:51, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7419, top5_acc: 0.9838, loss_cls: 1.0629, loss: 1.0629 +2025-06-24 11:29:44,818 - pyskl - INFO - Epoch [8][800/1281] lr: 2.484e-02, eta: 20:05:49, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7569, top5_acc: 0.9788, loss_cls: 1.0429, loss: 1.0429 +2025-06-24 11:30:26,568 - pyskl - INFO - Epoch [8][900/1281] lr: 2.484e-02, eta: 20:05:48, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7362, top5_acc: 0.9775, loss_cls: 1.0547, loss: 1.0547 +2025-06-24 11:31:08,139 - pyskl - INFO - Epoch [8][1000/1281] lr: 2.483e-02, eta: 20:05:43, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7488, top5_acc: 0.9819, loss_cls: 1.0831, loss: 1.0831 +2025-06-24 11:31:49,767 - pyskl - INFO - Epoch [8][1100/1281] lr: 2.483e-02, eta: 20:05:38, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7344, top5_acc: 0.9756, loss_cls: 1.1246, loss: 1.1246 +2025-06-24 11:32:31,468 - pyskl - INFO - Epoch [8][1200/1281] lr: 2.483e-02, eta: 20:05:33, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7381, top5_acc: 0.9712, loss_cls: 1.1026, loss: 1.1026 +2025-06-24 11:33:05,935 - pyskl - INFO - Saving checkpoint at 8 epochs +2025-06-24 11:34:17,203 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:34:17,270 - pyskl - INFO - +top1_acc 0.7061 +top5_acc 0.9615 +2025-06-24 11:34:17,270 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:34:17,281 - pyskl - INFO - +mean_acc 0.6239 +2025-06-24 11:34:17,284 - pyskl - INFO - Epoch(val) [8][533] top1_acc: 0.7061, top5_acc: 0.9615, mean_class_accuracy: 0.6239 +2025-06-24 11:35:21,456 - pyskl - INFO - Epoch [9][100/1281] lr: 2.482e-02, eta: 20:02:05, time: 0.642, data_time: 0.187, memory: 4082, top1_acc: 0.7612, top5_acc: 0.9869, loss_cls: 1.0384, loss: 1.0384 +2025-06-24 11:35:47,545 - pyskl - INFO - Epoch [9][200/1281] lr: 2.482e-02, eta: 19:57:29, time: 0.261, data_time: 0.000, memory: 4082, top1_acc: 0.7462, top5_acc: 0.9825, loss_cls: 1.0454, loss: 1.0454 +2025-06-24 11:36:27,948 - pyskl - INFO - Epoch [9][300/1281] lr: 2.481e-02, eta: 19:57:04, time: 0.404, data_time: 0.000, memory: 4082, top1_acc: 0.7475, top5_acc: 0.9838, loss_cls: 1.0515, loss: 1.0515 +2025-06-24 11:37:09,315 - pyskl - INFO - Epoch [9][400/1281] lr: 2.481e-02, eta: 19:56:56, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7738, top5_acc: 0.9781, loss_cls: 1.0357, loss: 1.0357 +2025-06-24 11:37:50,980 - pyskl - INFO - Epoch [9][500/1281] lr: 2.481e-02, eta: 19:56:51, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7450, top5_acc: 0.9788, loss_cls: 1.0645, loss: 1.0645 +2025-06-24 11:38:32,586 - pyskl - INFO - Epoch [9][600/1281] lr: 2.480e-02, eta: 19:56:46, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7531, top5_acc: 0.9812, loss_cls: 1.0308, loss: 1.0308 +2025-06-24 11:39:14,011 - pyskl - INFO - Epoch [9][700/1281] lr: 2.480e-02, eta: 19:56:36, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7625, top5_acc: 0.9850, loss_cls: 1.0034, loss: 1.0034 +2025-06-24 11:39:55,531 - pyskl - INFO - Epoch [9][800/1281] lr: 2.480e-02, eta: 19:56:28, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7544, top5_acc: 0.9788, loss_cls: 1.0305, loss: 1.0305 +2025-06-24 11:40:36,982 - pyskl - INFO - Epoch [9][900/1281] lr: 2.479e-02, eta: 19:56:17, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7375, top5_acc: 0.9812, loss_cls: 1.0626, loss: 1.0626 +2025-06-24 11:41:18,499 - pyskl - INFO - Epoch [9][1000/1281] lr: 2.479e-02, eta: 19:56:07, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7469, top5_acc: 0.9731, loss_cls: 1.0787, loss: 1.0787 +2025-06-24 11:41:59,939 - pyskl - INFO - Epoch [9][1100/1281] lr: 2.479e-02, eta: 19:55:56, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7506, top5_acc: 0.9788, loss_cls: 1.0465, loss: 1.0465 +2025-06-24 11:42:41,479 - pyskl - INFO - Epoch [9][1200/1281] lr: 2.478e-02, eta: 19:55:46, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7594, top5_acc: 0.9875, loss_cls: 1.0050, loss: 1.0050 +2025-06-24 11:43:15,672 - pyskl - INFO - Saving checkpoint at 9 epochs +2025-06-24 11:44:27,056 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:44:27,125 - pyskl - INFO - +top1_acc 0.7127 +top5_acc 0.9690 +2025-06-24 11:44:27,126 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:44:27,134 - pyskl - INFO - +mean_acc 0.6024 +2025-06-24 11:44:27,136 - pyskl - INFO - Epoch(val) [9][533] top1_acc: 0.7127, top5_acc: 0.9690, mean_class_accuracy: 0.6024 +2025-06-24 11:45:32,257 - pyskl - INFO - Epoch [10][100/1281] lr: 2.477e-02, eta: 19:52:49, time: 0.651, data_time: 0.192, memory: 4082, top1_acc: 0.7538, top5_acc: 0.9838, loss_cls: 1.0211, loss: 1.0211 +2025-06-24 11:45:56,629 - pyskl - INFO - Epoch [10][200/1281] lr: 2.477e-02, eta: 19:48:14, time: 0.244, data_time: 0.000, memory: 4082, top1_acc: 0.7750, top5_acc: 0.9844, loss_cls: 0.9892, loss: 0.9892 +2025-06-24 11:46:39,862 - pyskl - INFO - Epoch [10][300/1281] lr: 2.477e-02, eta: 19:48:31, time: 0.432, data_time: 0.000, memory: 4082, top1_acc: 0.7669, top5_acc: 0.9850, loss_cls: 0.9954, loss: 0.9954 +2025-06-24 11:47:21,465 - pyskl - INFO - Epoch [10][400/1281] lr: 2.476e-02, eta: 19:48:23, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7731, top5_acc: 0.9856, loss_cls: 0.9709, loss: 0.9709 +2025-06-24 11:48:03,176 - pyskl - INFO - Epoch [10][500/1281] lr: 2.476e-02, eta: 19:48:15, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7550, top5_acc: 0.9850, loss_cls: 0.9970, loss: 0.9970 +2025-06-24 11:48:44,781 - pyskl - INFO - Epoch [10][600/1281] lr: 2.476e-02, eta: 19:48:06, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7694, top5_acc: 0.9888, loss_cls: 0.9598, loss: 0.9598 +2025-06-24 11:49:26,282 - pyskl - INFO - Epoch [10][700/1281] lr: 2.475e-02, eta: 19:47:54, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7594, top5_acc: 0.9800, loss_cls: 1.0094, loss: 1.0094 +2025-06-24 11:50:07,827 - pyskl - INFO - Epoch [10][800/1281] lr: 2.475e-02, eta: 19:47:43, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7619, top5_acc: 0.9881, loss_cls: 1.0220, loss: 1.0220 +2025-06-24 11:50:49,495 - pyskl - INFO - Epoch [10][900/1281] lr: 2.474e-02, eta: 19:47:33, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7712, top5_acc: 0.9781, loss_cls: 1.0048, loss: 1.0048 +2025-06-24 11:51:30,972 - pyskl - INFO - Epoch [10][1000/1281] lr: 2.474e-02, eta: 19:47:19, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7612, top5_acc: 0.9819, loss_cls: 1.0109, loss: 1.0109 +2025-06-24 11:52:12,437 - pyskl - INFO - Epoch [10][1100/1281] lr: 2.473e-02, eta: 19:47:05, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7556, top5_acc: 0.9762, loss_cls: 1.0368, loss: 1.0368 +2025-06-24 11:52:53,993 - pyskl - INFO - Epoch [10][1200/1281] lr: 2.473e-02, eta: 19:46:52, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7481, top5_acc: 0.9781, loss_cls: 1.0441, loss: 1.0441 +2025-06-24 11:53:28,313 - pyskl - INFO - Saving checkpoint at 10 epochs +2025-06-24 11:54:39,441 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:54:39,508 - pyskl - INFO - +top1_acc 0.7352 +top5_acc 0.9755 +2025-06-24 11:54:39,508 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:54:39,516 - pyskl - INFO - +mean_acc 0.6271 +2025-06-24 11:54:39,520 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_7.pth was removed +2025-06-24 11:54:39,749 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_10.pth. +2025-06-24 11:54:39,749 - pyskl - INFO - Best top1_acc is 0.7352 at 10 epoch. +2025-06-24 11:54:39,752 - pyskl - INFO - Epoch(val) [10][533] top1_acc: 0.7352, top5_acc: 0.9755, mean_class_accuracy: 0.6271 +2025-06-24 11:55:45,110 - pyskl - INFO - Epoch [11][100/1281] lr: 2.472e-02, eta: 19:44:10, time: 0.654, data_time: 0.195, memory: 4082, top1_acc: 0.7569, top5_acc: 0.9838, loss_cls: 0.9871, loss: 0.9871 +2025-06-24 11:56:09,170 - pyskl - INFO - Epoch [11][200/1281] lr: 2.472e-02, eta: 19:39:56, time: 0.241, data_time: 0.000, memory: 4082, top1_acc: 0.7688, top5_acc: 0.9806, loss_cls: 0.9949, loss: 0.9949 +2025-06-24 11:56:50,629 - pyskl - INFO - Epoch [11][300/1281] lr: 2.471e-02, eta: 19:39:43, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7612, top5_acc: 0.9806, loss_cls: 0.9992, loss: 0.9992 +2025-06-24 11:57:32,158 - pyskl - INFO - Epoch [11][400/1281] lr: 2.471e-02, eta: 19:39:30, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7631, top5_acc: 0.9825, loss_cls: 0.9854, loss: 0.9854 +2025-06-24 11:58:13,659 - pyskl - INFO - Epoch [11][500/1281] lr: 2.471e-02, eta: 19:39:17, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7750, top5_acc: 0.9888, loss_cls: 0.9492, loss: 0.9492 +2025-06-24 11:58:55,261 - pyskl - INFO - Epoch [11][600/1281] lr: 2.470e-02, eta: 19:39:05, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7538, top5_acc: 0.9794, loss_cls: 1.0367, loss: 1.0367 +2025-06-24 11:59:36,799 - pyskl - INFO - Epoch [11][700/1281] lr: 2.470e-02, eta: 19:38:51, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7788, top5_acc: 0.9862, loss_cls: 0.9528, loss: 0.9528 +2025-06-24 12:00:18,308 - pyskl - INFO - Epoch [11][800/1281] lr: 2.469e-02, eta: 19:38:36, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7688, top5_acc: 0.9850, loss_cls: 0.9630, loss: 0.9630 +2025-06-24 12:01:01,227 - pyskl - INFO - Epoch [11][900/1281] lr: 2.469e-02, eta: 19:38:40, time: 0.429, data_time: 0.000, memory: 4082, top1_acc: 0.7519, top5_acc: 0.9862, loss_cls: 0.9891, loss: 0.9891 +2025-06-24 12:01:45,298 - pyskl - INFO - Epoch [11][1000/1281] lr: 2.468e-02, eta: 19:38:57, time: 0.441, data_time: 0.000, memory: 4082, top1_acc: 0.7925, top5_acc: 0.9850, loss_cls: 0.9132, loss: 0.9132 +2025-06-24 12:02:28,077 - pyskl - INFO - Epoch [11][1100/1281] lr: 2.468e-02, eta: 19:38:58, time: 0.428, data_time: 0.000, memory: 4082, top1_acc: 0.7669, top5_acc: 0.9856, loss_cls: 0.9704, loss: 0.9704 +2025-06-24 12:03:09,599 - pyskl - INFO - Epoch [11][1200/1281] lr: 2.467e-02, eta: 19:38:41, time: 0.415, data_time: 0.001, memory: 4082, top1_acc: 0.7700, top5_acc: 0.9875, loss_cls: 0.9346, loss: 0.9346 +2025-06-24 12:03:44,648 - pyskl - INFO - Saving checkpoint at 11 epochs +2025-06-24 12:04:55,617 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:04:55,682 - pyskl - INFO - +top1_acc 0.7399 +top5_acc 0.9737 +2025-06-24 12:04:55,683 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:04:55,690 - pyskl - INFO - +mean_acc 0.6614 +2025-06-24 12:04:55,695 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_10.pth was removed +2025-06-24 12:04:56,010 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_11.pth. +2025-06-24 12:04:56,011 - pyskl - INFO - Best top1_acc is 0.7399 at 11 epoch. +2025-06-24 12:04:56,013 - pyskl - INFO - Epoch(val) [11][533] top1_acc: 0.7399, top5_acc: 0.9737, mean_class_accuracy: 0.6614 +2025-06-24 12:06:01,317 - pyskl - INFO - Epoch [12][100/1281] lr: 2.467e-02, eta: 19:36:07, time: 0.653, data_time: 0.195, memory: 4082, top1_acc: 0.7819, top5_acc: 0.9856, loss_cls: 0.9256, loss: 0.9256 +2025-06-24 12:06:24,602 - pyskl - INFO - Epoch [12][200/1281] lr: 2.466e-02, eta: 19:32:04, time: 0.233, data_time: 0.001, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9888, loss_cls: 0.8873, loss: 0.8873 +2025-06-24 12:07:06,456 - pyskl - INFO - Epoch [12][300/1281] lr: 2.466e-02, eta: 19:31:53, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7762, top5_acc: 0.9844, loss_cls: 0.9385, loss: 0.9385 +2025-06-24 12:07:47,964 - pyskl - INFO - Epoch [12][400/1281] lr: 2.465e-02, eta: 19:31:37, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7775, top5_acc: 0.9788, loss_cls: 0.9780, loss: 0.9780 +2025-06-24 12:08:29,629 - pyskl - INFO - Epoch [12][500/1281] lr: 2.465e-02, eta: 19:31:23, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7800, top5_acc: 0.9875, loss_cls: 0.9500, loss: 0.9500 +2025-06-24 12:09:11,199 - pyskl - INFO - Epoch [12][600/1281] lr: 2.464e-02, eta: 19:31:08, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9869, loss_cls: 0.9033, loss: 0.9033 +2025-06-24 12:09:52,785 - pyskl - INFO - Epoch [12][700/1281] lr: 2.464e-02, eta: 19:30:52, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7656, top5_acc: 0.9900, loss_cls: 0.9554, loss: 0.9554 +2025-06-24 12:10:34,194 - pyskl - INFO - Epoch [12][800/1281] lr: 2.463e-02, eta: 19:30:34, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7913, top5_acc: 0.9869, loss_cls: 0.8878, loss: 0.8878 +2025-06-24 12:11:15,694 - pyskl - INFO - Epoch [12][900/1281] lr: 2.463e-02, eta: 19:30:16, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9844, loss_cls: 0.8918, loss: 0.8918 +2025-06-24 12:11:57,274 - pyskl - INFO - Epoch [12][1000/1281] lr: 2.462e-02, eta: 19:29:59, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7756, top5_acc: 0.9856, loss_cls: 0.9666, loss: 0.9666 +2025-06-24 12:12:38,853 - pyskl - INFO - Epoch [12][1100/1281] lr: 2.462e-02, eta: 19:29:42, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7781, top5_acc: 0.9850, loss_cls: 0.9520, loss: 0.9520 +2025-06-24 12:13:20,513 - pyskl - INFO - Epoch [12][1200/1281] lr: 2.461e-02, eta: 19:29:26, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7806, top5_acc: 0.9794, loss_cls: 0.9759, loss: 0.9759 +2025-06-24 12:13:55,183 - pyskl - INFO - Saving checkpoint at 12 epochs +2025-06-24 12:15:05,391 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:15:05,455 - pyskl - INFO - +top1_acc 0.7452 +top5_acc 0.9763 +2025-06-24 12:15:05,456 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:15:05,463 - pyskl - INFO - +mean_acc 0.6463 +2025-06-24 12:15:05,467 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_11.pth was removed +2025-06-24 12:15:05,697 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_12.pth. +2025-06-24 12:15:05,697 - pyskl - INFO - Best top1_acc is 0.7452 at 12 epoch. +2025-06-24 12:15:05,700 - pyskl - INFO - Epoch(val) [12][533] top1_acc: 0.7452, top5_acc: 0.9763, mean_class_accuracy: 0.6463 +2025-06-24 12:16:10,562 - pyskl - INFO - Epoch [13][100/1281] lr: 2.460e-02, eta: 19:26:54, time: 0.649, data_time: 0.190, memory: 4082, top1_acc: 0.8025, top5_acc: 0.9875, loss_cls: 0.8608, loss: 0.8608 +2025-06-24 12:16:33,959 - pyskl - INFO - Epoch [13][200/1281] lr: 2.460e-02, eta: 19:23:11, time: 0.234, data_time: 0.001, memory: 4082, top1_acc: 0.7913, top5_acc: 0.9875, loss_cls: 0.9208, loss: 0.9208 +2025-06-24 12:17:15,559 - pyskl - INFO - Epoch [13][300/1281] lr: 2.459e-02, eta: 19:22:55, time: 0.416, data_time: 0.001, memory: 4082, top1_acc: 0.7844, top5_acc: 0.9888, loss_cls: 0.8997, loss: 0.8997 +2025-06-24 12:17:57,342 - pyskl - INFO - Epoch [13][400/1281] lr: 2.459e-02, eta: 19:22:40, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7812, top5_acc: 0.9862, loss_cls: 0.9411, loss: 0.9411 +2025-06-24 12:18:41,373 - pyskl - INFO - Epoch [13][500/1281] lr: 2.458e-02, eta: 19:22:50, time: 0.440, data_time: 0.000, memory: 4082, top1_acc: 0.7887, top5_acc: 0.9856, loss_cls: 0.9166, loss: 0.9166 +2025-06-24 12:19:25,140 - pyskl - INFO - Epoch [13][600/1281] lr: 2.458e-02, eta: 19:22:57, time: 0.438, data_time: 0.000, memory: 4082, top1_acc: 0.7788, top5_acc: 0.9844, loss_cls: 0.9206, loss: 0.9206 +2025-06-24 12:20:08,637 - pyskl - INFO - Epoch [13][700/1281] lr: 2.457e-02, eta: 19:23:00, time: 0.435, data_time: 0.000, memory: 4082, top1_acc: 0.7712, top5_acc: 0.9806, loss_cls: 0.9389, loss: 0.9389 +2025-06-24 12:20:51,139 - pyskl - INFO - Epoch [13][800/1281] lr: 2.457e-02, eta: 19:22:52, time: 0.425, data_time: 0.000, memory: 4082, top1_acc: 0.7937, top5_acc: 0.9894, loss_cls: 0.8890, loss: 0.8890 +2025-06-24 12:21:32,563 - pyskl - INFO - Epoch [13][900/1281] lr: 2.456e-02, eta: 19:22:31, time: 0.414, data_time: 0.000, memory: 4082, top1_acc: 0.7844, top5_acc: 0.9769, loss_cls: 0.9423, loss: 0.9423 +2025-06-24 12:22:14,065 - pyskl - INFO - Epoch [13][1000/1281] lr: 2.455e-02, eta: 19:22:11, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7825, top5_acc: 0.9881, loss_cls: 0.9247, loss: 0.9247 +2025-06-24 12:22:55,557 - pyskl - INFO - Epoch [13][1100/1281] lr: 2.455e-02, eta: 19:21:51, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9888, loss_cls: 0.8746, loss: 0.8746 +2025-06-24 12:23:37,072 - pyskl - INFO - Epoch [13][1200/1281] lr: 2.454e-02, eta: 19:21:31, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.8063, top5_acc: 0.9869, loss_cls: 0.8334, loss: 0.8334 +2025-06-24 12:24:11,343 - pyskl - INFO - Saving checkpoint at 13 epochs +2025-06-24 12:25:22,333 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:25:22,392 - pyskl - INFO - +top1_acc 0.7385 +top5_acc 0.9744 +2025-06-24 12:25:22,392 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:25:22,400 - pyskl - INFO - +mean_acc 0.6423 +2025-06-24 12:25:22,402 - pyskl - INFO - Epoch(val) [13][533] top1_acc: 0.7385, top5_acc: 0.9744, mean_class_accuracy: 0.6423 +2025-06-24 12:26:28,362 - pyskl - INFO - Epoch [14][100/1281] lr: 2.453e-02, eta: 19:19:17, time: 0.660, data_time: 0.200, memory: 4082, top1_acc: 0.7919, top5_acc: 0.9888, loss_cls: 0.8743, loss: 0.8743 +2025-06-24 12:26:52,395 - pyskl - INFO - Epoch [14][200/1281] lr: 2.453e-02, eta: 19:15:55, time: 0.240, data_time: 0.000, memory: 4082, top1_acc: 0.7863, top5_acc: 0.9925, loss_cls: 0.8971, loss: 0.8971 +2025-06-24 12:27:31,201 - pyskl - INFO - Epoch [14][300/1281] lr: 2.452e-02, eta: 19:15:08, time: 0.388, data_time: 0.000, memory: 4082, top1_acc: 0.7869, top5_acc: 0.9844, loss_cls: 0.9237, loss: 0.9237 +2025-06-24 12:28:10,601 - pyskl - INFO - Epoch [14][400/1281] lr: 2.452e-02, eta: 19:14:26, time: 0.394, data_time: 0.000, memory: 4082, top1_acc: 0.7869, top5_acc: 0.9856, loss_cls: 0.8906, loss: 0.8906 +2025-06-24 12:28:50,001 - pyskl - INFO - Epoch [14][500/1281] lr: 2.451e-02, eta: 19:13:45, time: 0.394, data_time: 0.000, memory: 4082, top1_acc: 0.7869, top5_acc: 0.9869, loss_cls: 0.9064, loss: 0.9064 +2025-06-24 12:29:28,924 - pyskl - INFO - Epoch [14][600/1281] lr: 2.451e-02, eta: 19:12:59, time: 0.389, data_time: 0.000, memory: 4082, top1_acc: 0.7894, top5_acc: 0.9862, loss_cls: 0.9023, loss: 0.9023 +2025-06-24 12:30:07,549 - pyskl - INFO - Epoch [14][700/1281] lr: 2.450e-02, eta: 19:12:10, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8075, top5_acc: 0.9906, loss_cls: 0.8371, loss: 0.8371 +2025-06-24 12:30:46,187 - pyskl - INFO - Epoch [14][800/1281] lr: 2.449e-02, eta: 19:11:22, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9862, loss_cls: 0.8581, loss: 0.8581 +2025-06-24 12:31:24,450 - pyskl - INFO - Epoch [14][900/1281] lr: 2.449e-02, eta: 19:10:29, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.7925, top5_acc: 0.9838, loss_cls: 0.8872, loss: 0.8872 +2025-06-24 12:32:02,973 - pyskl - INFO - Epoch [14][1000/1281] lr: 2.448e-02, eta: 19:09:40, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.7956, top5_acc: 0.9856, loss_cls: 0.8742, loss: 0.8742 +2025-06-24 12:32:41,872 - pyskl - INFO - Epoch [14][1100/1281] lr: 2.448e-02, eta: 19:08:54, time: 0.389, data_time: 0.000, memory: 4082, top1_acc: 0.7844, top5_acc: 0.9825, loss_cls: 0.8993, loss: 0.8993 +2025-06-24 12:33:20,838 - pyskl - INFO - Epoch [14][1200/1281] lr: 2.447e-02, eta: 19:08:09, time: 0.390, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9825, loss_cls: 0.9091, loss: 0.9091 +2025-06-24 12:33:52,496 - pyskl - INFO - Saving checkpoint at 14 epochs +2025-06-24 12:34:53,782 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:34:53,851 - pyskl - INFO - +top1_acc 0.7200 +top5_acc 0.9716 +2025-06-24 12:34:53,851 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:34:53,859 - pyskl - INFO - +mean_acc 0.6311 +2025-06-24 12:34:53,861 - pyskl - INFO - Epoch(val) [14][533] top1_acc: 0.7200, top5_acc: 0.9716, mean_class_accuracy: 0.6311 +2025-06-24 12:35:53,192 - pyskl - INFO - Epoch [15][100/1281] lr: 2.446e-02, eta: 19:04:59, time: 0.593, data_time: 0.195, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9881, loss_cls: 0.8237, loss: 0.8237 +2025-06-24 12:36:32,121 - pyskl - INFO - Epoch [15][200/1281] lr: 2.445e-02, eta: 19:04:15, time: 0.389, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9925, loss_cls: 0.8224, loss: 0.8224 +2025-06-24 12:37:09,650 - pyskl - INFO - Epoch [15][300/1281] lr: 2.445e-02, eta: 19:03:17, time: 0.375, data_time: 0.001, memory: 4082, top1_acc: 0.8031, top5_acc: 0.9906, loss_cls: 0.8403, loss: 0.8403 +2025-06-24 12:37:37,220 - pyskl - INFO - Epoch [15][400/1281] lr: 2.444e-02, eta: 19:00:45, time: 0.276, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9894, loss_cls: 0.8707, loss: 0.8707 +2025-06-24 12:38:21,229 - pyskl - INFO - Epoch [15][500/1281] lr: 2.444e-02, eta: 19:00:49, time: 0.440, data_time: 0.000, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9919, loss_cls: 0.8532, loss: 0.8532 +2025-06-24 12:38:44,205 - pyskl - INFO - Epoch [15][600/1281] lr: 2.443e-02, eta: 18:57:36, time: 0.230, data_time: 0.001, memory: 4082, top1_acc: 0.7969, top5_acc: 0.9894, loss_cls: 0.8755, loss: 0.8755 +2025-06-24 12:39:12,351 - pyskl - INFO - Epoch [15][700/1281] lr: 2.442e-02, eta: 18:55:13, time: 0.281, data_time: 0.000, memory: 4082, top1_acc: 0.7937, top5_acc: 0.9888, loss_cls: 0.8493, loss: 0.8493 +2025-06-24 12:39:50,516 - pyskl - INFO - Epoch [15][800/1281] lr: 2.442e-02, eta: 18:54:23, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8113, top5_acc: 0.9894, loss_cls: 0.8346, loss: 0.8346 +2025-06-24 12:40:28,736 - pyskl - INFO - Epoch [15][900/1281] lr: 2.441e-02, eta: 18:53:35, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9881, loss_cls: 0.8553, loss: 0.8553 +2025-06-24 12:41:06,089 - pyskl - INFO - Epoch [15][1000/1281] lr: 2.441e-02, eta: 18:52:38, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9900, loss_cls: 0.8201, loss: 0.8201 +2025-06-24 12:41:44,077 - pyskl - INFO - Epoch [15][1100/1281] lr: 2.440e-02, eta: 18:51:48, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.7975, top5_acc: 0.9850, loss_cls: 0.8699, loss: 0.8699 +2025-06-24 12:42:22,325 - pyskl - INFO - Epoch [15][1200/1281] lr: 2.439e-02, eta: 18:50:59, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.7987, top5_acc: 0.9831, loss_cls: 0.8910, loss: 0.8910 +2025-06-24 12:42:53,736 - pyskl - INFO - Saving checkpoint at 15 epochs +2025-06-24 12:43:53,745 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:43:53,805 - pyskl - INFO - +top1_acc 0.7675 +top5_acc 0.9781 +2025-06-24 12:43:53,805 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:43:53,813 - pyskl - INFO - +mean_acc 0.6848 +2025-06-24 12:43:53,817 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_12.pth was removed +2025-06-24 12:43:53,999 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_15.pth. +2025-06-24 12:43:53,999 - pyskl - INFO - Best top1_acc is 0.7675 at 15 epoch. +2025-06-24 12:43:54,002 - pyskl - INFO - Epoch(val) [15][533] top1_acc: 0.7675, top5_acc: 0.9781, mean_class_accuracy: 0.6848 +2025-06-24 12:44:50,915 - pyskl - INFO - Epoch [16][100/1281] lr: 2.438e-02, eta: 18:47:42, time: 0.569, data_time: 0.194, memory: 4082, top1_acc: 0.8275, top5_acc: 0.9906, loss_cls: 0.7391, loss: 0.7391 +2025-06-24 12:45:28,729 - pyskl - INFO - Epoch [16][200/1281] lr: 2.438e-02, eta: 18:46:51, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9925, loss_cls: 0.7732, loss: 0.7732 +2025-06-24 12:46:07,293 - pyskl - INFO - Epoch [16][300/1281] lr: 2.437e-02, eta: 18:46:07, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.7931, top5_acc: 0.9869, loss_cls: 0.8809, loss: 0.8809 +2025-06-24 12:46:44,850 - pyskl - INFO - Epoch [16][400/1281] lr: 2.436e-02, eta: 18:45:14, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8175, top5_acc: 0.9881, loss_cls: 0.8246, loss: 0.8246 +2025-06-24 12:47:22,829 - pyskl - INFO - Epoch [16][500/1281] lr: 2.436e-02, eta: 18:44:25, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8044, top5_acc: 0.9900, loss_cls: 0.8298, loss: 0.8298 +2025-06-24 12:48:00,968 - pyskl - INFO - Epoch [16][600/1281] lr: 2.435e-02, eta: 18:43:37, time: 0.381, data_time: 0.001, memory: 4082, top1_acc: 0.7806, top5_acc: 0.9856, loss_cls: 0.9096, loss: 0.9096 +2025-06-24 12:48:38,114 - pyskl - INFO - Epoch [16][700/1281] lr: 2.434e-02, eta: 18:42:41, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9888, loss_cls: 0.8027, loss: 0.8027 +2025-06-24 12:49:15,923 - pyskl - INFO - Epoch [16][800/1281] lr: 2.434e-02, eta: 18:41:51, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8213, top5_acc: 0.9906, loss_cls: 0.7847, loss: 0.7847 +2025-06-24 12:49:51,012 - pyskl - INFO - Epoch [16][900/1281] lr: 2.433e-02, eta: 18:40:37, time: 0.351, data_time: 0.000, memory: 4082, top1_acc: 0.8131, top5_acc: 0.9875, loss_cls: 0.8067, loss: 0.8067 +2025-06-24 12:50:19,418 - pyskl - INFO - Epoch [16][1000/1281] lr: 2.432e-02, eta: 18:38:27, time: 0.284, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9912, loss_cls: 0.7543, loss: 0.7543 +2025-06-24 12:51:02,296 - pyskl - INFO - Epoch [16][1100/1281] lr: 2.432e-02, eta: 18:38:21, time: 0.429, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9875, loss_cls: 0.8450, loss: 0.8450 +2025-06-24 12:51:24,834 - pyskl - INFO - Epoch [16][1200/1281] lr: 2.431e-02, eta: 18:35:23, time: 0.225, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9881, loss_cls: 0.8575, loss: 0.8575 +2025-06-24 12:51:48,712 - pyskl - INFO - Saving checkpoint at 16 epochs +2025-06-24 12:52:49,156 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:52:49,212 - pyskl - INFO - +top1_acc 0.7598 +top5_acc 0.9822 +2025-06-24 12:52:49,212 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:52:49,219 - pyskl - INFO - +mean_acc 0.6705 +2025-06-24 12:52:49,221 - pyskl - INFO - Epoch(val) [16][533] top1_acc: 0.7598, top5_acc: 0.9822, mean_class_accuracy: 0.6705 +2025-06-24 12:53:46,733 - pyskl - INFO - Epoch [17][100/1281] lr: 2.430e-02, eta: 18:32:24, time: 0.575, data_time: 0.193, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9912, loss_cls: 0.8376, loss: 0.8376 +2025-06-24 12:54:24,970 - pyskl - INFO - Epoch [17][200/1281] lr: 2.429e-02, eta: 18:31:39, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9931, loss_cls: 0.8112, loss: 0.8112 +2025-06-24 12:55:02,655 - pyskl - INFO - Epoch [17][300/1281] lr: 2.428e-02, eta: 18:30:50, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8200, top5_acc: 0.9881, loss_cls: 0.8054, loss: 0.8054 +2025-06-24 12:55:40,481 - pyskl - INFO - Epoch [17][400/1281] lr: 2.428e-02, eta: 18:30:03, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9912, loss_cls: 0.8765, loss: 0.8765 +2025-06-24 12:56:18,237 - pyskl - INFO - Epoch [17][500/1281] lr: 2.427e-02, eta: 18:29:15, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8125, top5_acc: 0.9881, loss_cls: 0.8251, loss: 0.8251 +2025-06-24 12:56:55,683 - pyskl - INFO - Epoch [17][600/1281] lr: 2.426e-02, eta: 18:28:24, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9862, loss_cls: 0.8443, loss: 0.8443 +2025-06-24 12:57:33,254 - pyskl - INFO - Epoch [17][700/1281] lr: 2.426e-02, eta: 18:27:35, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8113, top5_acc: 0.9919, loss_cls: 0.8149, loss: 0.8149 +2025-06-24 12:58:10,570 - pyskl - INFO - Epoch [17][800/1281] lr: 2.425e-02, eta: 18:26:43, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9925, loss_cls: 0.7649, loss: 0.7649 +2025-06-24 12:58:48,448 - pyskl - INFO - Epoch [17][900/1281] lr: 2.424e-02, eta: 18:25:56, time: 0.379, data_time: 0.001, memory: 4082, top1_acc: 0.8200, top5_acc: 0.9888, loss_cls: 0.7806, loss: 0.7806 +2025-06-24 12:59:25,851 - pyskl - INFO - Epoch [17][1000/1281] lr: 2.424e-02, eta: 18:25:06, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9931, loss_cls: 0.8007, loss: 0.8007 +2025-06-24 13:00:03,750 - pyskl - INFO - Epoch [17][1100/1281] lr: 2.423e-02, eta: 18:24:20, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9869, loss_cls: 0.8046, loss: 0.8046 +2025-06-24 13:00:40,929 - pyskl - INFO - Epoch [17][1200/1281] lr: 2.422e-02, eta: 18:23:28, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9875, loss_cls: 0.8157, loss: 0.8157 +2025-06-24 13:01:12,064 - pyskl - INFO - Saving checkpoint at 17 epochs +2025-06-24 13:02:11,435 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:02:11,490 - pyskl - INFO - +top1_acc 0.7891 +top5_acc 0.9846 +2025-06-24 13:02:11,490 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:02:11,496 - pyskl - INFO - +mean_acc 0.6959 +2025-06-24 13:02:11,500 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_15.pth was removed +2025-06-24 13:02:11,686 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_17.pth. +2025-06-24 13:02:11,686 - pyskl - INFO - Best top1_acc is 0.7891 at 17 epoch. +2025-06-24 13:02:11,690 - pyskl - INFO - Epoch(val) [17][533] top1_acc: 0.7891, top5_acc: 0.9846, mean_class_accuracy: 0.6959 +2025-06-24 13:03:16,771 - pyskl - INFO - Epoch [18][100/1281] lr: 2.421e-02, eta: 18:21:37, time: 0.651, data_time: 0.196, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9912, loss_cls: 0.7064, loss: 0.7064 +2025-06-24 13:03:42,500 - pyskl - INFO - Epoch [18][200/1281] lr: 2.420e-02, eta: 18:19:16, time: 0.257, data_time: 0.001, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9906, loss_cls: 0.7575, loss: 0.7575 +2025-06-24 13:04:11,204 - pyskl - INFO - Epoch [18][300/1281] lr: 2.419e-02, eta: 18:17:20, time: 0.287, data_time: 0.000, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9850, loss_cls: 0.8058, loss: 0.8058 +2025-06-24 13:04:48,670 - pyskl - INFO - Epoch [18][400/1281] lr: 2.419e-02, eta: 18:16:32, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8125, top5_acc: 0.9900, loss_cls: 0.8209, loss: 0.8209 +2025-06-24 13:05:26,277 - pyskl - INFO - Epoch [18][500/1281] lr: 2.418e-02, eta: 18:15:45, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8063, top5_acc: 0.9881, loss_cls: 0.8289, loss: 0.8289 +2025-06-24 13:06:03,965 - pyskl - INFO - Epoch [18][600/1281] lr: 2.417e-02, eta: 18:14:58, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8144, top5_acc: 0.9919, loss_cls: 0.7846, loss: 0.7846 +2025-06-24 13:06:41,412 - pyskl - INFO - Epoch [18][700/1281] lr: 2.417e-02, eta: 18:14:10, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8275, top5_acc: 0.9856, loss_cls: 0.7905, loss: 0.7905 +2025-06-24 13:07:19,184 - pyskl - INFO - Epoch [18][800/1281] lr: 2.416e-02, eta: 18:13:25, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9875, loss_cls: 0.8095, loss: 0.8095 +2025-06-24 13:07:56,541 - pyskl - INFO - Epoch [18][900/1281] lr: 2.415e-02, eta: 18:12:36, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8275, top5_acc: 0.9906, loss_cls: 0.7480, loss: 0.7480 +2025-06-24 13:08:33,661 - pyskl - INFO - Epoch [18][1000/1281] lr: 2.414e-02, eta: 18:11:46, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9888, loss_cls: 0.7854, loss: 0.7854 +2025-06-24 13:09:11,004 - pyskl - INFO - Epoch [18][1100/1281] lr: 2.414e-02, eta: 18:10:57, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.7987, top5_acc: 0.9856, loss_cls: 0.8531, loss: 0.8531 +2025-06-24 13:09:48,289 - pyskl - INFO - Epoch [18][1200/1281] lr: 2.413e-02, eta: 18:10:08, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9912, loss_cls: 0.7880, loss: 0.7880 +2025-06-24 13:10:19,356 - pyskl - INFO - Saving checkpoint at 18 epochs +2025-06-24 13:11:18,930 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:11:18,987 - pyskl - INFO - +top1_acc 0.8030 +top5_acc 0.9844 +2025-06-24 13:11:18,987 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:11:18,995 - pyskl - INFO - +mean_acc 0.7193 +2025-06-24 13:11:19,000 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_17.pth was removed +2025-06-24 13:11:19,170 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_18.pth. +2025-06-24 13:11:19,171 - pyskl - INFO - Best top1_acc is 0.8030 at 18 epoch. +2025-06-24 13:11:19,173 - pyskl - INFO - Epoch(val) [18][533] top1_acc: 0.8030, top5_acc: 0.9844, mean_class_accuracy: 0.7193 +2025-06-24 13:12:16,043 - pyskl - INFO - Epoch [19][100/1281] lr: 2.411e-02, eta: 18:07:23, time: 0.569, data_time: 0.192, memory: 4082, top1_acc: 0.8231, top5_acc: 0.9906, loss_cls: 0.7617, loss: 0.7617 +2025-06-24 13:12:53,404 - pyskl - INFO - Epoch [19][200/1281] lr: 2.411e-02, eta: 18:06:35, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8213, top5_acc: 0.9906, loss_cls: 0.7979, loss: 0.7979 +2025-06-24 13:13:31,823 - pyskl - INFO - Epoch [19][300/1281] lr: 2.410e-02, eta: 18:05:55, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9912, loss_cls: 0.7485, loss: 0.7485 +2025-06-24 13:14:09,274 - pyskl - INFO - Epoch [19][400/1281] lr: 2.409e-02, eta: 18:05:08, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9894, loss_cls: 0.7700, loss: 0.7700 +2025-06-24 13:14:46,906 - pyskl - INFO - Epoch [19][500/1281] lr: 2.408e-02, eta: 18:04:23, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9881, loss_cls: 0.6792, loss: 0.6792 +2025-06-24 13:15:11,243 - pyskl - INFO - Epoch [19][600/1281] lr: 2.408e-02, eta: 18:02:02, time: 0.243, data_time: 0.000, memory: 4082, top1_acc: 0.8369, top5_acc: 0.9950, loss_cls: 0.7305, loss: 0.7305 +2025-06-24 13:15:56,421 - pyskl - INFO - Epoch [19][700/1281] lr: 2.407e-02, eta: 18:02:11, time: 0.452, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9900, loss_cls: 0.7831, loss: 0.7831 +2025-06-24 13:16:22,434 - pyskl - INFO - Epoch [19][800/1281] lr: 2.406e-02, eta: 18:00:04, time: 0.260, data_time: 0.001, memory: 4082, top1_acc: 0.8425, top5_acc: 0.9944, loss_cls: 0.7107, loss: 0.7107 +2025-06-24 13:16:52,256 - pyskl - INFO - Epoch [19][900/1281] lr: 2.405e-02, eta: 17:58:24, time: 0.298, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9881, loss_cls: 0.7764, loss: 0.7764 +2025-06-24 13:17:29,546 - pyskl - INFO - Epoch [19][1000/1281] lr: 2.405e-02, eta: 17:57:38, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8237, top5_acc: 0.9881, loss_cls: 0.8123, loss: 0.8123 +2025-06-24 13:18:07,556 - pyskl - INFO - Epoch [19][1100/1281] lr: 2.404e-02, eta: 17:56:56, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9912, loss_cls: 0.7598, loss: 0.7598 +2025-06-24 13:18:44,820 - pyskl - INFO - Epoch [19][1200/1281] lr: 2.403e-02, eta: 17:56:09, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9881, loss_cls: 0.8028, loss: 0.8028 +2025-06-24 13:19:15,818 - pyskl - INFO - Saving checkpoint at 19 epochs +2025-06-24 13:20:15,820 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:20:15,877 - pyskl - INFO - +top1_acc 0.7571 +top5_acc 0.9769 +2025-06-24 13:20:15,877 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:20:15,884 - pyskl - INFO - +mean_acc 0.7006 +2025-06-24 13:20:15,886 - pyskl - INFO - Epoch(val) [19][533] top1_acc: 0.7571, top5_acc: 0.9769, mean_class_accuracy: 0.7006 +2025-06-24 13:21:14,129 - pyskl - INFO - Epoch [20][100/1281] lr: 2.402e-02, eta: 17:53:42, time: 0.582, data_time: 0.201, memory: 4082, top1_acc: 0.8381, top5_acc: 0.9938, loss_cls: 0.7191, loss: 0.7191 +2025-06-24 13:21:52,634 - pyskl - INFO - Epoch [20][200/1281] lr: 2.401e-02, eta: 17:53:04, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9906, loss_cls: 0.7106, loss: 0.7106 +2025-06-24 13:22:30,795 - pyskl - INFO - Epoch [20][300/1281] lr: 2.400e-02, eta: 17:52:24, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9925, loss_cls: 0.6900, loss: 0.6900 +2025-06-24 13:23:08,307 - pyskl - INFO - Epoch [20][400/1281] lr: 2.399e-02, eta: 17:51:39, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8575, top5_acc: 0.9925, loss_cls: 0.6912, loss: 0.6912 +2025-06-24 13:23:45,667 - pyskl - INFO - Epoch [20][500/1281] lr: 2.398e-02, eta: 17:50:54, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8525, top5_acc: 0.9931, loss_cls: 0.6906, loss: 0.6906 +2025-06-24 13:24:23,474 - pyskl - INFO - Epoch [20][600/1281] lr: 2.398e-02, eta: 17:50:11, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9894, loss_cls: 0.7443, loss: 0.7443 +2025-06-24 13:25:01,195 - pyskl - INFO - Epoch [20][700/1281] lr: 2.397e-02, eta: 17:49:28, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8250, top5_acc: 0.9869, loss_cls: 0.8107, loss: 0.8107 +2025-06-24 13:25:38,662 - pyskl - INFO - Epoch [20][800/1281] lr: 2.396e-02, eta: 17:48:44, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9888, loss_cls: 0.7649, loss: 0.7649 +2025-06-24 13:26:16,340 - pyskl - INFO - Epoch [20][900/1281] lr: 2.395e-02, eta: 17:48:01, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9906, loss_cls: 0.7392, loss: 0.7392 +2025-06-24 13:26:53,626 - pyskl - INFO - Epoch [20][1000/1281] lr: 2.394e-02, eta: 17:47:15, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9869, loss_cls: 0.7864, loss: 0.7864 +2025-06-24 13:27:27,286 - pyskl - INFO - Epoch [20][1100/1281] lr: 2.393e-02, eta: 17:46:06, time: 0.337, data_time: 0.000, memory: 4082, top1_acc: 0.8225, top5_acc: 0.9875, loss_cls: 0.7912, loss: 0.7912 +2025-06-24 13:27:58,739 - pyskl - INFO - Epoch [20][1200/1281] lr: 2.393e-02, eta: 17:44:42, time: 0.315, data_time: 0.000, memory: 4082, top1_acc: 0.8237, top5_acc: 0.9900, loss_cls: 0.7749, loss: 0.7749 +2025-06-24 13:28:35,269 - pyskl - INFO - Saving checkpoint at 20 epochs +2025-06-24 13:29:24,748 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:29:24,817 - pyskl - INFO - +top1_acc 0.8028 +top5_acc 0.9827 +2025-06-24 13:29:24,817 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:29:24,827 - pyskl - INFO - +mean_acc 0.7359 +2025-06-24 13:29:24,831 - pyskl - INFO - Epoch(val) [20][533] top1_acc: 0.8028, top5_acc: 0.9827, mean_class_accuracy: 0.7359 +2025-06-24 13:30:22,453 - pyskl - INFO - Epoch [21][100/1281] lr: 2.391e-02, eta: 17:42:16, time: 0.576, data_time: 0.201, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9912, loss_cls: 0.7642, loss: 0.7642 +2025-06-24 13:31:00,966 - pyskl - INFO - Epoch [21][200/1281] lr: 2.390e-02, eta: 17:41:40, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9912, loss_cls: 0.7280, loss: 0.7280 +2025-06-24 13:31:38,364 - pyskl - INFO - Epoch [21][300/1281] lr: 2.389e-02, eta: 17:40:55, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9925, loss_cls: 0.7772, loss: 0.7772 +2025-06-24 13:32:15,858 - pyskl - INFO - Epoch [21][400/1281] lr: 2.389e-02, eta: 17:40:12, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9888, loss_cls: 0.7085, loss: 0.7085 +2025-06-24 13:32:53,072 - pyskl - INFO - Epoch [21][500/1281] lr: 2.388e-02, eta: 17:39:27, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9919, loss_cls: 0.7679, loss: 0.7679 +2025-06-24 13:33:30,903 - pyskl - INFO - Epoch [21][600/1281] lr: 2.387e-02, eta: 17:38:46, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8544, top5_acc: 0.9944, loss_cls: 0.6798, loss: 0.6798 +2025-06-24 13:34:08,500 - pyskl - INFO - Epoch [21][700/1281] lr: 2.386e-02, eta: 17:38:03, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8313, top5_acc: 0.9906, loss_cls: 0.7636, loss: 0.7636 +2025-06-24 13:34:45,642 - pyskl - INFO - Epoch [21][800/1281] lr: 2.385e-02, eta: 17:37:18, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8544, top5_acc: 0.9944, loss_cls: 0.6850, loss: 0.6850 +2025-06-24 13:35:23,121 - pyskl - INFO - Epoch [21][900/1281] lr: 2.384e-02, eta: 17:36:34, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8313, top5_acc: 0.9931, loss_cls: 0.7222, loss: 0.7222 +2025-06-24 13:36:00,641 - pyskl - INFO - Epoch [21][1000/1281] lr: 2.383e-02, eta: 17:35:51, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9938, loss_cls: 0.7100, loss: 0.7100 +2025-06-24 13:36:38,138 - pyskl - INFO - Epoch [21][1100/1281] lr: 2.383e-02, eta: 17:35:08, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9894, loss_cls: 0.7673, loss: 0.7673 +2025-06-24 13:37:15,943 - pyskl - INFO - Epoch [21][1200/1281] lr: 2.382e-02, eta: 17:34:27, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9900, loss_cls: 0.7692, loss: 0.7692 +2025-06-24 13:37:47,206 - pyskl - INFO - Saving checkpoint at 21 epochs +2025-06-24 13:38:46,760 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:38:46,822 - pyskl - INFO - +top1_acc 0.8142 +top5_acc 0.9879 +2025-06-24 13:38:46,822 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:38:46,830 - pyskl - INFO - +mean_acc 0.7232 +2025-06-24 13:38:46,834 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_18.pth was removed +2025-06-24 13:38:47,051 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_21.pth. +2025-06-24 13:38:47,052 - pyskl - INFO - Best top1_acc is 0.8142 at 21 epoch. +2025-06-24 13:38:47,055 - pyskl - INFO - Epoch(val) [21][533] top1_acc: 0.8142, top5_acc: 0.9879, mean_class_accuracy: 0.7232 +2025-06-24 13:39:44,812 - pyskl - INFO - Epoch [22][100/1281] lr: 2.380e-02, eta: 17:32:08, time: 0.578, data_time: 0.202, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9950, loss_cls: 0.6738, loss: 0.6738 +2025-06-24 13:40:11,106 - pyskl - INFO - Epoch [22][200/1281] lr: 2.379e-02, eta: 17:30:17, time: 0.263, data_time: 0.000, memory: 4082, top1_acc: 0.8481, top5_acc: 0.9931, loss_cls: 0.6728, loss: 0.6728 +2025-06-24 13:40:56,342 - pyskl - INFO - Epoch [22][300/1281] lr: 2.378e-02, eta: 17:30:21, time: 0.452, data_time: 0.000, memory: 4082, top1_acc: 0.8619, top5_acc: 0.9950, loss_cls: 0.6703, loss: 0.6703 +2025-06-24 13:41:18,815 - pyskl - INFO - Epoch [22][400/1281] lr: 2.378e-02, eta: 17:28:08, time: 0.225, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9894, loss_cls: 0.7207, loss: 0.7207 +2025-06-24 13:41:50,526 - pyskl - INFO - Epoch [22][500/1281] lr: 2.377e-02, eta: 17:26:51, time: 0.317, data_time: 0.000, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9912, loss_cls: 0.7086, loss: 0.7086 +2025-06-24 13:42:28,782 - pyskl - INFO - Epoch [22][600/1281] lr: 2.376e-02, eta: 17:26:14, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9912, loss_cls: 0.6990, loss: 0.6990 +2025-06-24 13:43:05,923 - pyskl - INFO - Epoch [22][700/1281] lr: 2.375e-02, eta: 17:25:30, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8675, top5_acc: 0.9912, loss_cls: 0.6413, loss: 0.6413 +2025-06-24 13:43:44,044 - pyskl - INFO - Epoch [22][800/1281] lr: 2.374e-02, eta: 17:24:52, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9894, loss_cls: 0.7062, loss: 0.7062 +2025-06-24 13:44:21,105 - pyskl - INFO - Epoch [22][900/1281] lr: 2.373e-02, eta: 17:24:07, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9900, loss_cls: 0.7680, loss: 0.7680 +2025-06-24 13:44:58,176 - pyskl - INFO - Epoch [22][1000/1281] lr: 2.372e-02, eta: 17:23:23, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9850, loss_cls: 0.7900, loss: 0.7900 +2025-06-24 13:45:35,466 - pyskl - INFO - Epoch [22][1100/1281] lr: 2.371e-02, eta: 17:22:40, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9875, loss_cls: 0.7387, loss: 0.7387 +2025-06-24 13:46:13,210 - pyskl - INFO - Epoch [22][1200/1281] lr: 2.370e-02, eta: 17:22:00, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8406, top5_acc: 0.9944, loss_cls: 0.7311, loss: 0.7311 +2025-06-24 13:46:44,177 - pyskl - INFO - Saving checkpoint at 22 epochs +2025-06-24 13:47:44,560 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:47:44,630 - pyskl - INFO - +top1_acc 0.7777 +top5_acc 0.9811 +2025-06-24 13:47:44,630 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:47:44,639 - pyskl - INFO - +mean_acc 0.7175 +2025-06-24 13:47:44,642 - pyskl - INFO - Epoch(val) [22][533] top1_acc: 0.7777, top5_acc: 0.9811, mean_class_accuracy: 0.7175 +2025-06-24 13:48:42,753 - pyskl - INFO - Epoch [23][100/1281] lr: 2.369e-02, eta: 17:19:48, time: 0.581, data_time: 0.198, memory: 4082, top1_acc: 0.8525, top5_acc: 0.9912, loss_cls: 0.6604, loss: 0.6604 +2025-06-24 13:49:20,129 - pyskl - INFO - Epoch [23][200/1281] lr: 2.368e-02, eta: 17:19:06, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9925, loss_cls: 0.7140, loss: 0.7140 +2025-06-24 13:49:57,708 - pyskl - INFO - Epoch [23][300/1281] lr: 2.367e-02, eta: 17:18:25, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8656, top5_acc: 0.9912, loss_cls: 0.6384, loss: 0.6384 +2025-06-24 13:50:35,430 - pyskl - INFO - Epoch [23][400/1281] lr: 2.366e-02, eta: 17:17:45, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8544, top5_acc: 0.9944, loss_cls: 0.6613, loss: 0.6613 +2025-06-24 13:51:12,848 - pyskl - INFO - Epoch [23][500/1281] lr: 2.365e-02, eta: 17:17:03, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9900, loss_cls: 0.7605, loss: 0.7605 +2025-06-24 13:51:49,994 - pyskl - INFO - Epoch [23][600/1281] lr: 2.364e-02, eta: 17:16:20, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8313, top5_acc: 0.9912, loss_cls: 0.7526, loss: 0.7526 +2025-06-24 13:52:24,224 - pyskl - INFO - Epoch [23][700/1281] lr: 2.363e-02, eta: 17:15:20, time: 0.342, data_time: 0.001, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9950, loss_cls: 0.6904, loss: 0.6904 +2025-06-24 13:52:54,734 - pyskl - INFO - Epoch [23][800/1281] lr: 2.362e-02, eta: 17:13:59, time: 0.305, data_time: 0.000, memory: 4082, top1_acc: 0.8456, top5_acc: 0.9969, loss_cls: 0.6829, loss: 0.6829 +2025-06-24 13:53:35,448 - pyskl - INFO - Epoch [23][900/1281] lr: 2.361e-02, eta: 17:13:36, time: 0.407, data_time: 0.001, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9950, loss_cls: 0.7121, loss: 0.7121 +2025-06-24 13:53:58,454 - pyskl - INFO - Epoch [23][1000/1281] lr: 2.360e-02, eta: 17:11:35, time: 0.230, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9925, loss_cls: 0.6896, loss: 0.6896 +2025-06-24 13:54:31,851 - pyskl - INFO - Epoch [23][1100/1281] lr: 2.359e-02, eta: 17:10:31, time: 0.334, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9912, loss_cls: 0.7624, loss: 0.7624 +2025-06-24 13:55:09,079 - pyskl - INFO - Epoch [23][1200/1281] lr: 2.359e-02, eta: 17:09:49, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8425, top5_acc: 0.9900, loss_cls: 0.6994, loss: 0.6994 +2025-06-24 13:55:40,640 - pyskl - INFO - Saving checkpoint at 23 epochs +2025-06-24 13:56:40,016 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:56:40,088 - pyskl - INFO - +top1_acc 0.8173 +top5_acc 0.9853 +2025-06-24 13:56:40,088 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:56:40,096 - pyskl - INFO - +mean_acc 0.7442 +2025-06-24 13:56:40,101 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_21.pth was removed +2025-06-24 13:56:40,299 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_23.pth. +2025-06-24 13:56:40,299 - pyskl - INFO - Best top1_acc is 0.8173 at 23 epoch. +2025-06-24 13:56:40,302 - pyskl - INFO - Epoch(val) [23][533] top1_acc: 0.8173, top5_acc: 0.9853, mean_class_accuracy: 0.7442 +2025-06-24 13:57:38,457 - pyskl - INFO - Epoch [24][100/1281] lr: 2.357e-02, eta: 17:07:42, time: 0.582, data_time: 0.198, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9931, loss_cls: 0.7206, loss: 0.7206 +2025-06-24 13:58:16,231 - pyskl - INFO - Epoch [24][200/1281] lr: 2.356e-02, eta: 17:07:03, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8512, top5_acc: 0.9912, loss_cls: 0.7182, loss: 0.7182 +2025-06-24 13:58:54,356 - pyskl - INFO - Epoch [24][300/1281] lr: 2.355e-02, eta: 17:06:27, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8575, top5_acc: 0.9919, loss_cls: 0.6539, loss: 0.6539 +2025-06-24 13:59:32,093 - pyskl - INFO - Epoch [24][400/1281] lr: 2.354e-02, eta: 17:05:48, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9894, loss_cls: 0.6878, loss: 0.6878 +2025-06-24 14:00:10,523 - pyskl - INFO - Epoch [24][500/1281] lr: 2.353e-02, eta: 17:05:12, time: 0.384, data_time: 0.001, memory: 4082, top1_acc: 0.8444, top5_acc: 0.9944, loss_cls: 0.6922, loss: 0.6922 +2025-06-24 14:00:48,423 - pyskl - INFO - Epoch [24][600/1281] lr: 2.352e-02, eta: 17:04:34, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9912, loss_cls: 0.6606, loss: 0.6606 +2025-06-24 14:01:26,120 - pyskl - INFO - Epoch [24][700/1281] lr: 2.351e-02, eta: 17:03:55, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8481, top5_acc: 0.9938, loss_cls: 0.6730, loss: 0.6730 +2025-06-24 14:02:03,294 - pyskl - INFO - Epoch [24][800/1281] lr: 2.350e-02, eta: 17:03:13, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8413, top5_acc: 0.9950, loss_cls: 0.6872, loss: 0.6872 +2025-06-24 14:02:40,647 - pyskl - INFO - Epoch [24][900/1281] lr: 2.349e-02, eta: 17:02:32, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9900, loss_cls: 0.7147, loss: 0.7147 +2025-06-24 14:03:18,180 - pyskl - INFO - Epoch [24][1000/1281] lr: 2.348e-02, eta: 17:01:52, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9881, loss_cls: 0.7074, loss: 0.7074 +2025-06-24 14:03:56,158 - pyskl - INFO - Epoch [24][1100/1281] lr: 2.347e-02, eta: 17:01:15, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9919, loss_cls: 0.7174, loss: 0.7174 +2025-06-24 14:04:33,896 - pyskl - INFO - Epoch [24][1200/1281] lr: 2.346e-02, eta: 17:00:36, time: 0.377, data_time: 0.001, memory: 4082, top1_acc: 0.8619, top5_acc: 0.9906, loss_cls: 0.6889, loss: 0.6889 +2025-06-24 14:05:00,498 - pyskl - INFO - Saving checkpoint at 24 epochs +2025-06-24 14:06:08,514 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:06:08,570 - pyskl - INFO - +top1_acc 0.7967 +top5_acc 0.9837 +2025-06-24 14:06:08,570 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:06:08,579 - pyskl - INFO - +mean_acc 0.7086 +2025-06-24 14:06:08,582 - pyskl - INFO - Epoch(val) [24][533] top1_acc: 0.7967, top5_acc: 0.9837, mean_class_accuracy: 0.7086 +2025-06-24 14:07:05,753 - pyskl - INFO - Epoch [25][100/1281] lr: 2.344e-02, eta: 16:58:27, time: 0.572, data_time: 0.198, memory: 4082, top1_acc: 0.8656, top5_acc: 0.9900, loss_cls: 0.6716, loss: 0.6716 +2025-06-24 14:07:43,387 - pyskl - INFO - Epoch [25][200/1281] lr: 2.343e-02, eta: 16:57:48, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8494, top5_acc: 0.9919, loss_cls: 0.6901, loss: 0.6901 +2025-06-24 14:08:20,810 - pyskl - INFO - Epoch [25][300/1281] lr: 2.342e-02, eta: 16:57:08, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8619, top5_acc: 0.9944, loss_cls: 0.6582, loss: 0.6582 +2025-06-24 14:08:58,450 - pyskl - INFO - Epoch [25][400/1281] lr: 2.341e-02, eta: 16:56:29, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9919, loss_cls: 0.7256, loss: 0.7256 +2025-06-24 14:09:35,450 - pyskl - INFO - Epoch [25][500/1281] lr: 2.340e-02, eta: 16:55:46, time: 0.370, data_time: 0.000, memory: 4082, top1_acc: 0.8569, top5_acc: 0.9962, loss_cls: 0.6606, loss: 0.6606 +2025-06-24 14:10:13,492 - pyskl - INFO - Epoch [25][600/1281] lr: 2.339e-02, eta: 16:55:09, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9894, loss_cls: 0.6963, loss: 0.6963 +2025-06-24 14:10:50,885 - pyskl - INFO - Epoch [25][700/1281] lr: 2.338e-02, eta: 16:54:29, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8750, top5_acc: 0.9944, loss_cls: 0.6065, loss: 0.6065 +2025-06-24 14:11:28,706 - pyskl - INFO - Epoch [25][800/1281] lr: 2.337e-02, eta: 16:53:51, time: 0.378, data_time: 0.001, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9938, loss_cls: 0.7336, loss: 0.7336 +2025-06-24 14:12:05,899 - pyskl - INFO - Epoch [25][900/1281] lr: 2.336e-02, eta: 16:53:09, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8694, top5_acc: 0.9919, loss_cls: 0.6514, loss: 0.6514 +2025-06-24 14:12:43,243 - pyskl - INFO - Epoch [25][1000/1281] lr: 2.335e-02, eta: 16:52:29, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8456, top5_acc: 0.9856, loss_cls: 0.6997, loss: 0.6997 +2025-06-24 14:13:20,588 - pyskl - INFO - Epoch [25][1100/1281] lr: 2.334e-02, eta: 16:51:48, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8562, top5_acc: 0.9931, loss_cls: 0.6575, loss: 0.6575 +2025-06-24 14:13:57,610 - pyskl - INFO - Epoch [25][1200/1281] lr: 2.333e-02, eta: 16:51:06, time: 0.370, data_time: 0.000, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9906, loss_cls: 0.7020, loss: 0.7020 +2025-06-24 14:14:29,177 - pyskl - INFO - Saving checkpoint at 25 epochs +2025-06-24 14:15:28,752 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:15:28,807 - pyskl - INFO - +top1_acc 0.8163 +top5_acc 0.9877 +2025-06-24 14:15:28,807 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:15:28,813 - pyskl - INFO - +mean_acc 0.7488 +2025-06-24 14:15:28,815 - pyskl - INFO - Epoch(val) [25][533] top1_acc: 0.8163, top5_acc: 0.9877, mean_class_accuracy: 0.7488 +2025-06-24 14:16:25,802 - pyskl - INFO - Epoch [26][100/1281] lr: 2.332e-02, eta: 16:49:00, time: 0.570, data_time: 0.190, memory: 4082, top1_acc: 0.8494, top5_acc: 0.9938, loss_cls: 0.6498, loss: 0.6498 +2025-06-24 14:17:03,826 - pyskl - INFO - Epoch [26][200/1281] lr: 2.330e-02, eta: 16:48:23, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9931, loss_cls: 0.7146, loss: 0.7146 +2025-06-24 14:17:29,211 - pyskl - INFO - Epoch [26][300/1281] lr: 2.329e-02, eta: 16:46:44, time: 0.254, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9938, loss_cls: 0.6427, loss: 0.6427 +2025-06-24 14:18:14,643 - pyskl - INFO - Epoch [26][400/1281] lr: 2.328e-02, eta: 16:46:43, time: 0.454, data_time: 0.000, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9956, loss_cls: 0.6326, loss: 0.6326 +2025-06-24 14:18:38,520 - pyskl - INFO - Epoch [26][500/1281] lr: 2.327e-02, eta: 16:44:57, time: 0.239, data_time: 0.000, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9912, loss_cls: 0.6721, loss: 0.6721 +2025-06-24 14:19:09,207 - pyskl - INFO - Epoch [26][600/1281] lr: 2.326e-02, eta: 16:43:45, time: 0.307, data_time: 0.000, memory: 4082, top1_acc: 0.8756, top5_acc: 0.9944, loss_cls: 0.5960, loss: 0.5960 +2025-06-24 14:19:46,756 - pyskl - INFO - Epoch [26][700/1281] lr: 2.325e-02, eta: 16:43:06, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8512, top5_acc: 0.9944, loss_cls: 0.6740, loss: 0.6740 +2025-06-24 14:20:24,577 - pyskl - INFO - Epoch [26][800/1281] lr: 2.324e-02, eta: 16:42:29, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8562, top5_acc: 0.9906, loss_cls: 0.6603, loss: 0.6603 +2025-06-24 14:21:03,045 - pyskl - INFO - Epoch [26][900/1281] lr: 2.323e-02, eta: 16:41:54, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8612, top5_acc: 0.9950, loss_cls: 0.6616, loss: 0.6616 +2025-06-24 14:21:40,524 - pyskl - INFO - Epoch [26][1000/1281] lr: 2.322e-02, eta: 16:41:15, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8500, top5_acc: 0.9931, loss_cls: 0.6375, loss: 0.6375 +2025-06-24 14:22:17,863 - pyskl - INFO - Epoch [26][1100/1281] lr: 2.321e-02, eta: 16:40:35, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8581, top5_acc: 0.9919, loss_cls: 0.6594, loss: 0.6594 +2025-06-24 14:22:56,053 - pyskl - INFO - Epoch [26][1200/1281] lr: 2.320e-02, eta: 16:40:00, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8581, top5_acc: 0.9925, loss_cls: 0.6587, loss: 0.6587 +2025-06-24 14:23:27,325 - pyskl - INFO - Saving checkpoint at 26 epochs +2025-06-24 14:24:26,371 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:24:26,441 - pyskl - INFO - +top1_acc 0.8400 +top5_acc 0.9891 +2025-06-24 14:24:26,441 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:24:26,451 - pyskl - INFO - +mean_acc 0.7744 +2025-06-24 14:24:26,457 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_23.pth was removed +2025-06-24 14:24:26,770 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_26.pth. +2025-06-24 14:24:26,770 - pyskl - INFO - Best top1_acc is 0.8400 at 26 epoch. +2025-06-24 14:24:26,772 - pyskl - INFO - Epoch(val) [26][533] top1_acc: 0.8400, top5_acc: 0.9891, mean_class_accuracy: 0.7744 +2025-06-24 14:25:24,215 - pyskl - INFO - Epoch [27][100/1281] lr: 2.318e-02, eta: 16:38:00, time: 0.574, data_time: 0.195, memory: 4082, top1_acc: 0.8812, top5_acc: 0.9925, loss_cls: 0.6033, loss: 0.6033 +2025-06-24 14:26:02,028 - pyskl - INFO - Epoch [27][200/1281] lr: 2.317e-02, eta: 16:37:22, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9938, loss_cls: 0.7227, loss: 0.7227 +2025-06-24 14:26:39,479 - pyskl - INFO - Epoch [27][300/1281] lr: 2.316e-02, eta: 16:36:43, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8612, top5_acc: 0.9944, loss_cls: 0.6399, loss: 0.6399 +2025-06-24 14:27:16,807 - pyskl - INFO - Epoch [27][400/1281] lr: 2.315e-02, eta: 16:36:04, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8688, top5_acc: 0.9944, loss_cls: 0.6041, loss: 0.6041 +2025-06-24 14:27:53,566 - pyskl - INFO - Epoch [27][500/1281] lr: 2.314e-02, eta: 16:35:22, time: 0.368, data_time: 0.000, memory: 4082, top1_acc: 0.8556, top5_acc: 0.9950, loss_cls: 0.6457, loss: 0.6457 +2025-06-24 14:28:30,968 - pyskl - INFO - Epoch [27][600/1281] lr: 2.313e-02, eta: 16:34:42, time: 0.374, data_time: 0.001, memory: 4082, top1_acc: 0.8706, top5_acc: 0.9956, loss_cls: 0.6153, loss: 0.6153 +2025-06-24 14:29:08,461 - pyskl - INFO - Epoch [27][700/1281] lr: 2.312e-02, eta: 16:34:04, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9931, loss_cls: 0.6488, loss: 0.6488 +2025-06-24 14:29:44,488 - pyskl - INFO - Epoch [27][800/1281] lr: 2.311e-02, eta: 16:33:18, time: 0.360, data_time: 0.000, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9919, loss_cls: 0.6604, loss: 0.6604 +2025-06-24 14:30:12,704 - pyskl - INFO - Epoch [27][900/1281] lr: 2.310e-02, eta: 16:31:57, time: 0.282, data_time: 0.000, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9925, loss_cls: 0.6646, loss: 0.6646 +2025-06-24 14:30:55,835 - pyskl - INFO - Epoch [27][1000/1281] lr: 2.308e-02, eta: 16:31:44, time: 0.431, data_time: 0.000, memory: 4082, top1_acc: 0.8531, top5_acc: 0.9912, loss_cls: 0.6604, loss: 0.6604 +2025-06-24 14:31:18,937 - pyskl - INFO - Epoch [27][1100/1281] lr: 2.307e-02, eta: 16:29:59, time: 0.231, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9906, loss_cls: 0.7622, loss: 0.7622 +2025-06-24 14:31:53,798 - pyskl - INFO - Epoch [27][1200/1281] lr: 2.306e-02, eta: 16:29:09, time: 0.349, data_time: 0.000, memory: 4082, top1_acc: 0.8650, top5_acc: 0.9919, loss_cls: 0.6029, loss: 0.6029 +2025-06-24 14:32:25,213 - pyskl - INFO - Saving checkpoint at 27 epochs +2025-06-24 14:33:25,442 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:33:25,500 - pyskl - INFO - +top1_acc 0.8087 +top5_acc 0.9785 +2025-06-24 14:33:25,500 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:33:25,508 - pyskl - INFO - +mean_acc 0.7331 +2025-06-24 14:33:25,510 - pyskl - INFO - Epoch(val) [27][533] top1_acc: 0.8087, top5_acc: 0.9785, mean_class_accuracy: 0.7331 +2025-06-24 14:34:22,562 - pyskl - INFO - Epoch [28][100/1281] lr: 2.304e-02, eta: 16:27:10, time: 0.570, data_time: 0.197, memory: 4082, top1_acc: 0.8738, top5_acc: 0.9931, loss_cls: 0.6264, loss: 0.6264 +2025-06-24 14:35:00,036 - pyskl - INFO - Epoch [28][200/1281] lr: 2.303e-02, eta: 16:26:32, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9938, loss_cls: 0.6908, loss: 0.6908 +2025-06-24 14:35:38,074 - pyskl - INFO - Epoch [28][300/1281] lr: 2.302e-02, eta: 16:25:56, time: 0.380, data_time: 0.001, memory: 4082, top1_acc: 0.8544, top5_acc: 0.9912, loss_cls: 0.6930, loss: 0.6930 +2025-06-24 14:36:15,226 - pyskl - INFO - Epoch [28][400/1281] lr: 2.301e-02, eta: 16:25:17, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9938, loss_cls: 0.6897, loss: 0.6897 +2025-06-24 14:36:53,187 - pyskl - INFO - Epoch [28][500/1281] lr: 2.300e-02, eta: 16:24:41, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9950, loss_cls: 0.6391, loss: 0.6391 +2025-06-24 14:37:31,195 - pyskl - INFO - Epoch [28][600/1281] lr: 2.299e-02, eta: 16:24:05, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8544, top5_acc: 0.9950, loss_cls: 0.6652, loss: 0.6652 +2025-06-24 14:38:08,746 - pyskl - INFO - Epoch [28][700/1281] lr: 2.298e-02, eta: 16:23:27, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8525, top5_acc: 0.9906, loss_cls: 0.6908, loss: 0.6908 +2025-06-24 14:38:46,151 - pyskl - INFO - Epoch [28][800/1281] lr: 2.297e-02, eta: 16:22:48, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8650, top5_acc: 0.9919, loss_cls: 0.6623, loss: 0.6623 +2025-06-24 14:39:23,620 - pyskl - INFO - Epoch [28][900/1281] lr: 2.295e-02, eta: 16:22:10, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8569, top5_acc: 0.9931, loss_cls: 0.6469, loss: 0.6469 +2025-06-24 14:40:01,084 - pyskl - INFO - Epoch [28][1000/1281] lr: 2.294e-02, eta: 16:21:32, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8512, top5_acc: 0.9906, loss_cls: 0.6711, loss: 0.6711 +2025-06-24 14:40:38,873 - pyskl - INFO - Epoch [28][1100/1281] lr: 2.293e-02, eta: 16:20:55, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8556, top5_acc: 0.9950, loss_cls: 0.6637, loss: 0.6637 +2025-06-24 14:41:16,928 - pyskl - INFO - Epoch [28][1200/1281] lr: 2.292e-02, eta: 16:20:19, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8694, top5_acc: 0.9944, loss_cls: 0.6332, loss: 0.6332 +2025-06-24 14:41:48,773 - pyskl - INFO - Saving checkpoint at 28 epochs +2025-06-24 14:42:54,560 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:42:54,616 - pyskl - INFO - +top1_acc 0.8039 +top5_acc 0.9831 +2025-06-24 14:42:54,616 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:42:54,624 - pyskl - INFO - +mean_acc 0.7365 +2025-06-24 14:42:54,626 - pyskl - INFO - Epoch(val) [28][533] top1_acc: 0.8039, top5_acc: 0.9831, mean_class_accuracy: 0.7365 +2025-06-24 14:43:40,747 - pyskl - INFO - Epoch [29][100/1281] lr: 2.290e-02, eta: 16:17:36, time: 0.461, data_time: 0.203, memory: 4082, top1_acc: 0.8531, top5_acc: 0.9944, loss_cls: 0.6491, loss: 0.6491 +2025-06-24 14:44:18,601 - pyskl - INFO - Epoch [29][200/1281] lr: 2.289e-02, eta: 16:16:59, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8681, top5_acc: 0.9969, loss_cls: 0.5767, loss: 0.5767 +2025-06-24 14:44:56,716 - pyskl - INFO - Epoch [29][300/1281] lr: 2.288e-02, eta: 16:16:24, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8744, top5_acc: 0.9931, loss_cls: 0.5811, loss: 0.5811 +2025-06-24 14:45:33,906 - pyskl - INFO - Epoch [29][400/1281] lr: 2.287e-02, eta: 16:15:45, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9894, loss_cls: 0.6851, loss: 0.6851 +2025-06-24 14:46:11,956 - pyskl - INFO - Epoch [29][500/1281] lr: 2.285e-02, eta: 16:15:10, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8575, top5_acc: 0.9894, loss_cls: 0.7014, loss: 0.7014 +2025-06-24 14:46:49,895 - pyskl - INFO - Epoch [29][600/1281] lr: 2.284e-02, eta: 16:14:34, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9962, loss_cls: 0.6432, loss: 0.6432 +2025-06-24 14:47:27,321 - pyskl - INFO - Epoch [29][700/1281] lr: 2.283e-02, eta: 16:13:56, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8406, top5_acc: 0.9938, loss_cls: 0.6866, loss: 0.6866 +2025-06-24 14:48:05,251 - pyskl - INFO - Epoch [29][800/1281] lr: 2.282e-02, eta: 16:13:20, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8806, top5_acc: 0.9981, loss_cls: 0.5672, loss: 0.5672 +2025-06-24 14:48:42,976 - pyskl - INFO - Epoch [29][900/1281] lr: 2.281e-02, eta: 16:12:43, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8494, top5_acc: 0.9931, loss_cls: 0.6465, loss: 0.6465 +2025-06-24 14:49:19,819 - pyskl - INFO - Epoch [29][1000/1281] lr: 2.280e-02, eta: 16:12:02, time: 0.368, data_time: 0.000, memory: 4082, top1_acc: 0.8588, top5_acc: 0.9931, loss_cls: 0.6453, loss: 0.6453 +2025-06-24 14:49:57,139 - pyskl - INFO - Epoch [29][1100/1281] lr: 2.279e-02, eta: 16:11:24, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8450, top5_acc: 0.9912, loss_cls: 0.6635, loss: 0.6635 +2025-06-24 14:50:34,461 - pyskl - INFO - Epoch [29][1200/1281] lr: 2.277e-02, eta: 16:10:45, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8700, top5_acc: 0.9919, loss_cls: 0.6147, loss: 0.6147 +2025-06-24 14:51:06,015 - pyskl - INFO - Saving checkpoint at 29 epochs +2025-06-24 14:52:05,945 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:52:06,015 - pyskl - INFO - +top1_acc 0.8123 +top5_acc 0.9858 +2025-06-24 14:52:06,016 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:52:06,024 - pyskl - INFO - +mean_acc 0.7502 +2025-06-24 14:52:06,026 - pyskl - INFO - Epoch(val) [29][533] top1_acc: 0.8123, top5_acc: 0.9858, mean_class_accuracy: 0.7502 +2025-06-24 14:53:13,520 - pyskl - INFO - Epoch [30][100/1281] lr: 2.275e-02, eta: 16:09:35, time: 0.675, data_time: 0.193, memory: 4082, top1_acc: 0.8762, top5_acc: 0.9975, loss_cls: 0.5631, loss: 0.5631 +2025-06-24 14:54:01,630 - pyskl - INFO - Epoch [30][200/1281] lr: 2.274e-02, eta: 16:09:41, time: 0.481, data_time: 0.000, memory: 4082, top1_acc: 0.8844, top5_acc: 0.9969, loss_cls: 0.5544, loss: 0.5544 +2025-06-24 14:54:29,638 - pyskl - INFO - Epoch [30][300/1281] lr: 2.273e-02, eta: 16:08:24, time: 0.280, data_time: 0.000, memory: 4082, top1_acc: 0.8769, top5_acc: 0.9969, loss_cls: 0.5763, loss: 0.5763 +2025-06-24 14:55:20,859 - pyskl - INFO - Epoch [30][400/1281] lr: 2.272e-02, eta: 16:08:43, time: 0.512, data_time: 0.000, memory: 4082, top1_acc: 0.8538, top5_acc: 0.9919, loss_cls: 0.6900, loss: 0.6900 +2025-06-24 14:55:50,260 - pyskl - INFO - Epoch [30][500/1281] lr: 2.271e-02, eta: 16:07:31, time: 0.294, data_time: 0.000, memory: 4082, top1_acc: 0.8681, top5_acc: 0.9919, loss_cls: 0.6365, loss: 0.6365 +2025-06-24 14:56:38,247 - pyskl - INFO - Epoch [30][600/1281] lr: 2.269e-02, eta: 16:07:36, time: 0.480, data_time: 0.000, memory: 4082, top1_acc: 0.8725, top5_acc: 0.9906, loss_cls: 0.6059, loss: 0.6059 +2025-06-24 14:57:26,481 - pyskl - INFO - Epoch [30][700/1281] lr: 2.268e-02, eta: 16:07:42, time: 0.482, data_time: 0.000, memory: 4082, top1_acc: 0.8762, top5_acc: 0.9931, loss_cls: 0.5946, loss: 0.5946 +2025-06-24 14:58:14,775 - pyskl - INFO - Epoch [30][800/1281] lr: 2.267e-02, eta: 16:07:48, time: 0.483, data_time: 0.000, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9881, loss_cls: 0.6995, loss: 0.6995 +2025-06-24 14:59:03,140 - pyskl - INFO - Epoch [30][900/1281] lr: 2.266e-02, eta: 16:07:54, time: 0.484, data_time: 0.001, memory: 4082, top1_acc: 0.8669, top5_acc: 0.9919, loss_cls: 0.6247, loss: 0.6247 +2025-06-24 14:59:51,660 - pyskl - INFO - Epoch [30][1000/1281] lr: 2.265e-02, eta: 16:08:00, time: 0.485, data_time: 0.000, memory: 4082, top1_acc: 0.8619, top5_acc: 0.9938, loss_cls: 0.6500, loss: 0.6500 +2025-06-24 15:00:40,226 - pyskl - INFO - Epoch [30][1100/1281] lr: 2.263e-02, eta: 16:08:06, time: 0.486, data_time: 0.000, memory: 4082, top1_acc: 0.8650, top5_acc: 0.9944, loss_cls: 0.6363, loss: 0.6363 +2025-06-24 15:01:28,353 - pyskl - INFO - Epoch [30][1200/1281] lr: 2.262e-02, eta: 16:08:10, time: 0.481, data_time: 0.000, memory: 4082, top1_acc: 0.8675, top5_acc: 0.9919, loss_cls: 0.6290, loss: 0.6290 +2025-06-24 15:02:08,199 - pyskl - INFO - Saving checkpoint at 30 epochs +2025-06-24 15:03:07,653 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:03:07,711 - pyskl - INFO - +top1_acc 0.8236 +top5_acc 0.9826 +2025-06-24 15:03:07,711 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:03:07,718 - pyskl - INFO - +mean_acc 0.7519 +2025-06-24 15:03:07,721 - pyskl - INFO - Epoch(val) [30][533] top1_acc: 0.8236, top5_acc: 0.9826, mean_class_accuracy: 0.7519 +2025-06-24 15:04:32,514 - pyskl - INFO - Epoch [31][100/1281] lr: 2.260e-02, eta: 16:08:07, time: 0.848, data_time: 0.194, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9956, loss_cls: 0.7221, loss: 0.7221 +2025-06-24 15:05:21,653 - pyskl - INFO - Epoch [31][200/1281] lr: 2.259e-02, eta: 16:08:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9969, loss_cls: 0.6988, loss: 0.6988 +2025-06-24 15:05:51,451 - pyskl - INFO - Epoch [31][300/1281] lr: 2.258e-02, eta: 16:07:04, time: 0.298, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9962, loss_cls: 0.7133, loss: 0.7133 +2025-06-24 15:06:38,354 - pyskl - INFO - Epoch [31][400/1281] lr: 2.256e-02, eta: 16:07:02, time: 0.469, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9944, loss_cls: 0.6989, loss: 0.6989 +2025-06-24 15:07:11,078 - pyskl - INFO - Epoch [31][500/1281] lr: 2.255e-02, eta: 16:06:04, time: 0.327, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9950, loss_cls: 0.7676, loss: 0.7676 +2025-06-24 15:08:00,351 - pyskl - INFO - Epoch [31][600/1281] lr: 2.254e-02, eta: 16:06:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8562, top5_acc: 0.9931, loss_cls: 0.7914, loss: 0.7914 +2025-06-24 15:08:49,896 - pyskl - INFO - Epoch [31][700/1281] lr: 2.253e-02, eta: 16:06:19, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8525, top5_acc: 0.9925, loss_cls: 0.7969, loss: 0.7969 +2025-06-24 15:09:39,149 - pyskl - INFO - Epoch [31][800/1281] lr: 2.252e-02, eta: 16:06:25, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8544, top5_acc: 0.9925, loss_cls: 0.7746, loss: 0.7746 +2025-06-24 15:10:28,427 - pyskl - INFO - Epoch [31][900/1281] lr: 2.250e-02, eta: 16:06:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9956, loss_cls: 0.7335, loss: 0.7335 +2025-06-24 15:11:17,568 - pyskl - INFO - Epoch [31][1000/1281] lr: 2.249e-02, eta: 16:06:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9894, loss_cls: 0.7859, loss: 0.7859 +2025-06-24 15:12:07,151 - pyskl - INFO - Epoch [31][1100/1281] lr: 2.248e-02, eta: 16:06:44, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9906, loss_cls: 0.7851, loss: 0.7851 +2025-06-24 15:12:56,430 - pyskl - INFO - Epoch [31][1200/1281] lr: 2.247e-02, eta: 16:06:49, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9969, loss_cls: 0.6744, loss: 0.6744 +2025-06-24 15:13:36,956 - pyskl - INFO - Saving checkpoint at 31 epochs +2025-06-24 15:14:36,940 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:14:36,995 - pyskl - INFO - +top1_acc 0.7633 +top5_acc 0.9758 +2025-06-24 15:14:36,996 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:14:37,003 - pyskl - INFO - +mean_acc 0.6728 +2025-06-24 15:14:37,005 - pyskl - INFO - Epoch(val) [31][533] top1_acc: 0.7633, top5_acc: 0.9758, mean_class_accuracy: 0.6728 +2025-06-24 15:15:57,808 - pyskl - INFO - Epoch [32][100/1281] lr: 2.244e-02, eta: 16:06:26, time: 0.808, data_time: 0.193, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9944, loss_cls: 0.6608, loss: 0.6608 +2025-06-24 15:16:46,969 - pyskl - INFO - Epoch [32][200/1281] lr: 2.243e-02, eta: 16:06:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9944, loss_cls: 0.7053, loss: 0.7053 +2025-06-24 15:17:17,461 - pyskl - INFO - Epoch [32][300/1281] lr: 2.242e-02, eta: 16:05:23, time: 0.305, data_time: 0.001, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9969, loss_cls: 0.6396, loss: 0.6396 +2025-06-24 15:18:02,256 - pyskl - INFO - Epoch [32][400/1281] lr: 2.241e-02, eta: 16:05:11, time: 0.448, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9938, loss_cls: 0.7242, loss: 0.7242 +2025-06-24 15:18:36,146 - pyskl - INFO - Epoch [32][500/1281] lr: 2.239e-02, eta: 16:04:17, time: 0.339, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9944, loss_cls: 0.6790, loss: 0.6790 +2025-06-24 15:19:25,452 - pyskl - INFO - Epoch [32][600/1281] lr: 2.238e-02, eta: 16:04:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9925, loss_cls: 0.7517, loss: 0.7517 +2025-06-24 15:20:14,610 - pyskl - INFO - Epoch [32][700/1281] lr: 2.237e-02, eta: 16:04:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9975, loss_cls: 0.7001, loss: 0.7001 +2025-06-24 15:21:03,538 - pyskl - INFO - Epoch [32][800/1281] lr: 2.236e-02, eta: 16:04:27, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8500, top5_acc: 0.9919, loss_cls: 0.7625, loss: 0.7625 +2025-06-24 15:21:52,549 - pyskl - INFO - Epoch [32][900/1281] lr: 2.234e-02, eta: 16:04:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9931, loss_cls: 0.7439, loss: 0.7439 +2025-06-24 15:22:41,453 - pyskl - INFO - Epoch [32][1000/1281] lr: 2.233e-02, eta: 16:04:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9938, loss_cls: 0.7265, loss: 0.7265 +2025-06-24 15:23:30,600 - pyskl - INFO - Epoch [32][1100/1281] lr: 2.232e-02, eta: 16:04:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9938, loss_cls: 0.6637, loss: 0.6637 +2025-06-24 15:24:19,694 - pyskl - INFO - Epoch [32][1200/1281] lr: 2.231e-02, eta: 16:04:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8438, top5_acc: 0.9900, loss_cls: 0.7811, loss: 0.7811 +2025-06-24 15:25:00,296 - pyskl - INFO - Saving checkpoint at 32 epochs +2025-06-24 15:26:00,387 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:26:00,443 - pyskl - INFO - +top1_acc 0.7897 +top5_acc 0.9779 +2025-06-24 15:26:00,443 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:26:00,450 - pyskl - INFO - +mean_acc 0.7164 +2025-06-24 15:26:00,451 - pyskl - INFO - Epoch(val) [32][533] top1_acc: 0.7897, top5_acc: 0.9779, mean_class_accuracy: 0.7164 +2025-06-24 15:27:22,162 - pyskl - INFO - Epoch [33][100/1281] lr: 2.228e-02, eta: 16:04:11, time: 0.817, data_time: 0.197, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9931, loss_cls: 0.7224, loss: 0.7224 +2025-06-24 15:28:11,425 - pyskl - INFO - Epoch [33][200/1281] lr: 2.227e-02, eta: 16:04:13, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9931, loss_cls: 0.5763, loss: 0.5763 +2025-06-24 15:28:45,360 - pyskl - INFO - Epoch [33][300/1281] lr: 2.226e-02, eta: 16:03:19, time: 0.339, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9956, loss_cls: 0.6538, loss: 0.6538 +2025-06-24 15:29:25,905 - pyskl - INFO - Epoch [33][400/1281] lr: 2.225e-02, eta: 16:02:49, time: 0.405, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9944, loss_cls: 0.6373, loss: 0.6373 +2025-06-24 15:30:02,270 - pyskl - INFO - Epoch [33][500/1281] lr: 2.223e-02, eta: 16:02:03, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9931, loss_cls: 0.6806, loss: 0.6806 +2025-06-24 15:30:51,424 - pyskl - INFO - Epoch [33][600/1281] lr: 2.222e-02, eta: 16:02:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9919, loss_cls: 0.7108, loss: 0.7108 +2025-06-24 15:31:41,018 - pyskl - INFO - Epoch [33][700/1281] lr: 2.221e-02, eta: 16:02:06, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9938, loss_cls: 0.6768, loss: 0.6768 +2025-06-24 15:32:30,239 - pyskl - INFO - Epoch [33][800/1281] lr: 2.219e-02, eta: 16:02:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9950, loss_cls: 0.6496, loss: 0.6496 +2025-06-24 15:33:19,758 - pyskl - INFO - Epoch [33][900/1281] lr: 2.218e-02, eta: 16:02:09, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9931, loss_cls: 0.6778, loss: 0.6778 +2025-06-24 15:34:09,061 - pyskl - INFO - Epoch [33][1000/1281] lr: 2.217e-02, eta: 16:02:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9925, loss_cls: 0.6731, loss: 0.6731 +2025-06-24 15:34:58,297 - pyskl - INFO - Epoch [33][1100/1281] lr: 2.216e-02, eta: 16:02:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9925, loss_cls: 0.6854, loss: 0.6854 +2025-06-24 15:35:47,744 - pyskl - INFO - Epoch [33][1200/1281] lr: 2.214e-02, eta: 16:02:10, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9938, loss_cls: 0.6945, loss: 0.6945 +2025-06-24 15:36:28,547 - pyskl - INFO - Saving checkpoint at 33 epochs +2025-06-24 15:37:28,430 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:37:28,498 - pyskl - INFO - +top1_acc 0.8362 +top5_acc 0.9897 +2025-06-24 15:37:28,498 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:37:28,505 - pyskl - INFO - +mean_acc 0.7713 +2025-06-24 15:37:28,507 - pyskl - INFO - Epoch(val) [33][533] top1_acc: 0.8362, top5_acc: 0.9897, mean_class_accuracy: 0.7713 +2025-06-24 15:38:49,571 - pyskl - INFO - Epoch [34][100/1281] lr: 2.212e-02, eta: 16:01:40, time: 0.811, data_time: 0.196, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9925, loss_cls: 0.6606, loss: 0.6606 +2025-06-24 15:39:36,807 - pyskl - INFO - Epoch [34][200/1281] lr: 2.211e-02, eta: 16:01:32, time: 0.472, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9931, loss_cls: 0.5976, loss: 0.5976 +2025-06-24 15:40:11,984 - pyskl - INFO - Epoch [34][300/1281] lr: 2.209e-02, eta: 16:00:42, time: 0.352, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9925, loss_cls: 0.6248, loss: 0.6248 +2025-06-24 15:40:51,172 - pyskl - INFO - Epoch [34][400/1281] lr: 2.208e-02, eta: 16:00:06, time: 0.392, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9956, loss_cls: 0.6483, loss: 0.6483 +2025-06-24 15:41:28,729 - pyskl - INFO - Epoch [34][500/1281] lr: 2.207e-02, eta: 15:59:24, time: 0.376, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9925, loss_cls: 0.6516, loss: 0.6516 +2025-06-24 15:42:17,999 - pyskl - INFO - Epoch [34][600/1281] lr: 2.205e-02, eta: 15:59:23, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9944, loss_cls: 0.6768, loss: 0.6768 +2025-06-24 15:43:07,426 - pyskl - INFO - Epoch [34][700/1281] lr: 2.204e-02, eta: 15:59:22, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9956, loss_cls: 0.6411, loss: 0.6411 +2025-06-24 15:43:56,666 - pyskl - INFO - Epoch [34][800/1281] lr: 2.203e-02, eta: 15:59:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9944, loss_cls: 0.6176, loss: 0.6176 +2025-06-24 15:44:45,949 - pyskl - INFO - Epoch [34][900/1281] lr: 2.201e-02, eta: 15:59:18, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9931, loss_cls: 0.6520, loss: 0.6520 +2025-06-24 15:45:35,111 - pyskl - INFO - Epoch [34][1000/1281] lr: 2.200e-02, eta: 15:59:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8488, top5_acc: 0.9906, loss_cls: 0.7340, loss: 0.7340 +2025-06-24 15:46:24,206 - pyskl - INFO - Epoch [34][1100/1281] lr: 2.199e-02, eta: 15:59:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8438, top5_acc: 0.9900, loss_cls: 0.7412, loss: 0.7412 +2025-06-24 15:47:13,206 - pyskl - INFO - Epoch [34][1200/1281] lr: 2.197e-02, eta: 15:59:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9938, loss_cls: 0.6574, loss: 0.6574 +2025-06-24 15:47:54,094 - pyskl - INFO - Saving checkpoint at 34 epochs +2025-06-24 15:48:53,276 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:48:53,334 - pyskl - INFO - +top1_acc 0.8184 +top5_acc 0.9860 +2025-06-24 15:48:53,334 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:48:53,341 - pyskl - INFO - +mean_acc 0.7755 +2025-06-24 15:48:53,343 - pyskl - INFO - Epoch(val) [34][533] top1_acc: 0.8184, top5_acc: 0.9860, mean_class_accuracy: 0.7755 +2025-06-24 15:50:13,404 - pyskl - INFO - Epoch [35][100/1281] lr: 2.195e-02, eta: 15:58:34, time: 0.801, data_time: 0.195, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9925, loss_cls: 0.6361, loss: 0.6361 +2025-06-24 15:51:01,630 - pyskl - INFO - Epoch [35][200/1281] lr: 2.194e-02, eta: 15:58:27, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9925, loss_cls: 0.6229, loss: 0.6229 +2025-06-24 15:51:36,489 - pyskl - INFO - Epoch [35][300/1281] lr: 2.192e-02, eta: 15:57:35, time: 0.349, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9969, loss_cls: 0.5921, loss: 0.5921 +2025-06-24 15:52:15,846 - pyskl - INFO - Epoch [35][400/1281] lr: 2.191e-02, eta: 15:56:58, time: 0.394, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9950, loss_cls: 0.6563, loss: 0.6563 +2025-06-24 15:52:53,385 - pyskl - INFO - Epoch [35][500/1281] lr: 2.190e-02, eta: 15:56:16, time: 0.375, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9956, loss_cls: 0.6239, loss: 0.6239 +2025-06-24 15:53:42,508 - pyskl - INFO - Epoch [35][600/1281] lr: 2.188e-02, eta: 15:56:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9956, loss_cls: 0.6289, loss: 0.6289 +2025-06-24 15:54:31,775 - pyskl - INFO - Epoch [35][700/1281] lr: 2.187e-02, eta: 15:56:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9925, loss_cls: 0.6138, loss: 0.6138 +2025-06-24 15:55:21,114 - pyskl - INFO - Epoch [35][800/1281] lr: 2.185e-02, eta: 15:56:04, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9938, loss_cls: 0.6536, loss: 0.6536 +2025-06-24 15:56:10,373 - pyskl - INFO - Epoch [35][900/1281] lr: 2.184e-02, eta: 15:56:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9938, loss_cls: 0.6440, loss: 0.6440 +2025-06-24 15:56:59,962 - pyskl - INFO - Epoch [35][1000/1281] lr: 2.183e-02, eta: 15:55:57, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9950, loss_cls: 0.6912, loss: 0.6912 +2025-06-24 15:57:49,266 - pyskl - INFO - Epoch [35][1100/1281] lr: 2.181e-02, eta: 15:55:53, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9881, loss_cls: 0.6549, loss: 0.6549 +2025-06-24 15:58:38,592 - pyskl - INFO - Epoch [35][1200/1281] lr: 2.180e-02, eta: 15:55:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9950, loss_cls: 0.6216, loss: 0.6216 +2025-06-24 15:59:19,164 - pyskl - INFO - Saving checkpoint at 35 epochs +2025-06-24 16:00:18,796 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:00:18,866 - pyskl - INFO - +top1_acc 0.8269 +top5_acc 0.9833 +2025-06-24 16:00:18,866 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:00:18,874 - pyskl - INFO - +mean_acc 0.7441 +2025-06-24 16:00:18,876 - pyskl - INFO - Epoch(val) [35][533] top1_acc: 0.8269, top5_acc: 0.9833, mean_class_accuracy: 0.7441 +2025-06-24 16:01:40,240 - pyskl - INFO - Epoch [36][100/1281] lr: 2.178e-02, eta: 15:55:14, time: 0.814, data_time: 0.198, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9969, loss_cls: 0.5653, loss: 0.5653 +2025-06-24 16:02:26,223 - pyskl - INFO - Epoch [36][200/1281] lr: 2.176e-02, eta: 15:54:58, time: 0.460, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9981, loss_cls: 0.5574, loss: 0.5574 +2025-06-24 16:03:06,089 - pyskl - INFO - Epoch [36][300/1281] lr: 2.175e-02, eta: 15:54:22, time: 0.399, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9919, loss_cls: 0.6112, loss: 0.6112 +2025-06-24 16:03:40,565 - pyskl - INFO - Epoch [36][400/1281] lr: 2.173e-02, eta: 15:53:28, time: 0.345, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9931, loss_cls: 0.6183, loss: 0.6183 +2025-06-24 16:04:19,285 - pyskl - INFO - Epoch [36][500/1281] lr: 2.172e-02, eta: 15:52:49, time: 0.387, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9950, loss_cls: 0.6317, loss: 0.6317 +2025-06-24 16:05:08,339 - pyskl - INFO - Epoch [36][600/1281] lr: 2.171e-02, eta: 15:52:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9950, loss_cls: 0.5978, loss: 0.5978 +2025-06-24 16:05:57,285 - pyskl - INFO - Epoch [36][700/1281] lr: 2.169e-02, eta: 15:52:35, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9919, loss_cls: 0.6927, loss: 0.6927 +2025-06-24 16:06:46,609 - pyskl - INFO - Epoch [36][800/1281] lr: 2.168e-02, eta: 15:52:30, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9962, loss_cls: 0.7094, loss: 0.7094 +2025-06-24 16:07:35,876 - pyskl - INFO - Epoch [36][900/1281] lr: 2.167e-02, eta: 15:52:23, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9912, loss_cls: 0.6097, loss: 0.6097 +2025-06-24 16:08:24,983 - pyskl - INFO - Epoch [36][1000/1281] lr: 2.165e-02, eta: 15:52:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9925, loss_cls: 0.6557, loss: 0.6557 +2025-06-24 16:09:14,003 - pyskl - INFO - Epoch [36][1100/1281] lr: 2.164e-02, eta: 15:52:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9944, loss_cls: 0.6308, loss: 0.6308 +2025-06-24 16:10:03,063 - pyskl - INFO - Epoch [36][1200/1281] lr: 2.162e-02, eta: 15:52:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8619, top5_acc: 0.9938, loss_cls: 0.6640, loss: 0.6640 +2025-06-24 16:10:43,560 - pyskl - INFO - Saving checkpoint at 36 epochs +2025-06-24 16:11:43,257 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:11:43,314 - pyskl - INFO - +top1_acc 0.8512 +top5_acc 0.9903 +2025-06-24 16:11:43,314 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:11:43,321 - pyskl - INFO - +mean_acc 0.7857 +2025-06-24 16:11:43,325 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_26.pth was removed +2025-06-24 16:11:43,532 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_36.pth. +2025-06-24 16:11:43,533 - pyskl - INFO - Best top1_acc is 0.8512 at 36 epoch. +2025-06-24 16:11:43,536 - pyskl - INFO - Epoch(val) [36][533] top1_acc: 0.8512, top5_acc: 0.9903, mean_class_accuracy: 0.7857 +2025-06-24 16:13:04,097 - pyskl - INFO - Epoch [37][100/1281] lr: 2.160e-02, eta: 15:51:22, time: 0.806, data_time: 0.194, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9956, loss_cls: 0.5687, loss: 0.5687 +2025-06-24 16:13:49,574 - pyskl - INFO - Epoch [37][200/1281] lr: 2.158e-02, eta: 15:51:03, time: 0.455, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9969, loss_cls: 0.5723, loss: 0.5723 +2025-06-24 16:14:28,381 - pyskl - INFO - Epoch [37][300/1281] lr: 2.157e-02, eta: 15:50:23, time: 0.388, data_time: 0.001, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9969, loss_cls: 0.6248, loss: 0.6248 +2025-06-24 16:15:04,105 - pyskl - INFO - Epoch [37][400/1281] lr: 2.156e-02, eta: 15:49:33, time: 0.357, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9981, loss_cls: 0.6162, loss: 0.6162 +2025-06-24 16:15:44,922 - pyskl - INFO - Epoch [37][500/1281] lr: 2.154e-02, eta: 15:48:59, time: 0.408, data_time: 0.001, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9919, loss_cls: 0.6270, loss: 0.6270 +2025-06-24 16:16:34,060 - pyskl - INFO - Epoch [37][600/1281] lr: 2.153e-02, eta: 15:48:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9956, loss_cls: 0.6122, loss: 0.6122 +2025-06-24 16:17:23,063 - pyskl - INFO - Epoch [37][700/1281] lr: 2.151e-02, eta: 15:48:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9956, loss_cls: 0.5853, loss: 0.5853 +2025-06-24 16:18:12,537 - pyskl - INFO - Epoch [37][800/1281] lr: 2.150e-02, eta: 15:48:35, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9925, loss_cls: 0.6483, loss: 0.6483 +2025-06-24 16:19:01,831 - pyskl - INFO - Epoch [37][900/1281] lr: 2.149e-02, eta: 15:48:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9969, loss_cls: 0.6115, loss: 0.6115 +2025-06-24 16:19:51,104 - pyskl - INFO - Epoch [37][1000/1281] lr: 2.147e-02, eta: 15:48:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9912, loss_cls: 0.7027, loss: 0.7027 +2025-06-24 16:20:40,510 - pyskl - INFO - Epoch [37][1100/1281] lr: 2.146e-02, eta: 15:48:11, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9919, loss_cls: 0.6837, loss: 0.6837 +2025-06-24 16:21:29,682 - pyskl - INFO - Epoch [37][1200/1281] lr: 2.144e-02, eta: 15:48:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9906, loss_cls: 0.6292, loss: 0.6292 +2025-06-24 16:22:10,077 - pyskl - INFO - Saving checkpoint at 37 epochs +2025-06-24 16:23:08,998 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:23:09,054 - pyskl - INFO - +top1_acc 0.8376 +top5_acc 0.9892 +2025-06-24 16:23:09,054 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:23:09,064 - pyskl - INFO - +mean_acc 0.7567 +2025-06-24 16:23:09,066 - pyskl - INFO - Epoch(val) [37][533] top1_acc: 0.8376, top5_acc: 0.9892, mean_class_accuracy: 0.7567 +2025-06-24 16:24:29,245 - pyskl - INFO - Epoch [38][100/1281] lr: 2.142e-02, eta: 15:47:19, time: 0.802, data_time: 0.196, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9962, loss_cls: 0.5787, loss: 0.5787 +2025-06-24 16:25:13,699 - pyskl - INFO - Epoch [38][200/1281] lr: 2.140e-02, eta: 15:46:55, time: 0.445, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9969, loss_cls: 0.5568, loss: 0.5568 +2025-06-24 16:25:55,914 - pyskl - INFO - Epoch [38][300/1281] lr: 2.139e-02, eta: 15:46:25, time: 0.422, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9938, loss_cls: 0.5643, loss: 0.5643 +2025-06-24 16:26:27,877 - pyskl - INFO - Epoch [38][400/1281] lr: 2.137e-02, eta: 15:45:23, time: 0.320, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9938, loss_cls: 0.5783, loss: 0.5783 +2025-06-24 16:27:09,710 - pyskl - INFO - Epoch [38][500/1281] lr: 2.136e-02, eta: 15:44:52, time: 0.418, data_time: 0.001, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9931, loss_cls: 0.5947, loss: 0.5947 +2025-06-24 16:27:58,712 - pyskl - INFO - Epoch [38][600/1281] lr: 2.134e-02, eta: 15:44:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9962, loss_cls: 0.6115, loss: 0.6115 +2025-06-24 16:28:47,810 - pyskl - INFO - Epoch [38][700/1281] lr: 2.133e-02, eta: 15:44:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9950, loss_cls: 0.6565, loss: 0.6565 +2025-06-24 16:29:36,888 - pyskl - INFO - Epoch [38][800/1281] lr: 2.132e-02, eta: 15:44:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9931, loss_cls: 0.6279, loss: 0.6279 +2025-06-24 16:30:26,147 - pyskl - INFO - Epoch [38][900/1281] lr: 2.130e-02, eta: 15:44:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9925, loss_cls: 0.6479, loss: 0.6479 +2025-06-24 16:31:15,612 - pyskl - INFO - Epoch [38][1000/1281] lr: 2.129e-02, eta: 15:44:02, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9912, loss_cls: 0.6690, loss: 0.6690 +2025-06-24 16:32:04,795 - pyskl - INFO - Epoch [38][1100/1281] lr: 2.127e-02, eta: 15:43:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9950, loss_cls: 0.5923, loss: 0.5923 +2025-06-24 16:32:53,989 - pyskl - INFO - Epoch [38][1200/1281] lr: 2.126e-02, eta: 15:43:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9981, loss_cls: 0.5626, loss: 0.5626 +2025-06-24 16:33:34,715 - pyskl - INFO - Saving checkpoint at 38 epochs +2025-06-24 16:34:33,777 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:34:33,834 - pyskl - INFO - +top1_acc 0.8201 +top5_acc 0.9839 +2025-06-24 16:34:33,834 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:34:33,840 - pyskl - INFO - +mean_acc 0.7624 +2025-06-24 16:34:33,842 - pyskl - INFO - Epoch(val) [38][533] top1_acc: 0.8201, top5_acc: 0.9839, mean_class_accuracy: 0.7624 +2025-06-24 16:35:53,325 - pyskl - INFO - Epoch [39][100/1281] lr: 2.123e-02, eta: 15:42:53, time: 0.795, data_time: 0.200, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9912, loss_cls: 0.6144, loss: 0.6144 +2025-06-24 16:36:37,383 - pyskl - INFO - Epoch [39][200/1281] lr: 2.122e-02, eta: 15:42:27, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9931, loss_cls: 0.5408, loss: 0.5408 +2025-06-24 16:37:19,991 - pyskl - INFO - Epoch [39][300/1281] lr: 2.120e-02, eta: 15:41:57, time: 0.426, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9969, loss_cls: 0.5688, loss: 0.5688 +2025-06-24 16:37:51,683 - pyskl - INFO - Epoch [39][400/1281] lr: 2.119e-02, eta: 15:40:55, time: 0.317, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9975, loss_cls: 0.6189, loss: 0.6189 +2025-06-24 16:38:33,207 - pyskl - INFO - Epoch [39][500/1281] lr: 2.117e-02, eta: 15:40:21, time: 0.415, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9981, loss_cls: 0.5938, loss: 0.5938 +2025-06-24 16:39:22,215 - pyskl - INFO - Epoch [39][600/1281] lr: 2.116e-02, eta: 15:40:10, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9944, loss_cls: 0.5446, loss: 0.5446 +2025-06-24 16:40:11,576 - pyskl - INFO - Epoch [39][700/1281] lr: 2.114e-02, eta: 15:39:59, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9956, loss_cls: 0.5681, loss: 0.5681 +2025-06-24 16:41:00,750 - pyskl - INFO - Epoch [39][800/1281] lr: 2.113e-02, eta: 15:39:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9956, loss_cls: 0.6196, loss: 0.6196 +2025-06-24 16:41:49,773 - pyskl - INFO - Epoch [39][900/1281] lr: 2.111e-02, eta: 15:39:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9962, loss_cls: 0.6023, loss: 0.6023 +2025-06-24 16:42:38,884 - pyskl - INFO - Epoch [39][1000/1281] lr: 2.110e-02, eta: 15:39:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9962, loss_cls: 0.6819, loss: 0.6819 +2025-06-24 16:43:28,373 - pyskl - INFO - Epoch [39][1100/1281] lr: 2.108e-02, eta: 15:39:11, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9950, loss_cls: 0.6671, loss: 0.6671 +2025-06-24 16:44:17,588 - pyskl - INFO - Epoch [39][1200/1281] lr: 2.107e-02, eta: 15:38:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9925, loss_cls: 0.6302, loss: 0.6302 +2025-06-24 16:44:58,006 - pyskl - INFO - Saving checkpoint at 39 epochs +2025-06-24 16:45:57,802 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:45:57,860 - pyskl - INFO - +top1_acc 0.8614 +top5_acc 0.9900 +2025-06-24 16:45:57,861 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:45:57,868 - pyskl - INFO - +mean_acc 0.8048 +2025-06-24 16:45:57,872 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_36.pth was removed +2025-06-24 16:45:58,051 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_39.pth. +2025-06-24 16:45:58,051 - pyskl - INFO - Best top1_acc is 0.8614 at 39 epoch. +2025-06-24 16:45:58,055 - pyskl - INFO - Epoch(val) [39][533] top1_acc: 0.8614, top5_acc: 0.9900, mean_class_accuracy: 0.8048 +2025-06-24 16:47:19,510 - pyskl - INFO - Epoch [40][100/1281] lr: 2.104e-02, eta: 15:38:15, time: 0.814, data_time: 0.200, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9950, loss_cls: 0.5493, loss: 0.5493 +2025-06-24 16:48:00,569 - pyskl - INFO - Epoch [40][200/1281] lr: 2.103e-02, eta: 15:37:40, time: 0.411, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9956, loss_cls: 0.6215, loss: 0.6215 +2025-06-24 16:48:47,917 - pyskl - INFO - Epoch [40][300/1281] lr: 2.101e-02, eta: 15:37:22, time: 0.473, data_time: 0.001, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9938, loss_cls: 0.5332, loss: 0.5332 +2025-06-24 16:49:14,955 - pyskl - INFO - Epoch [40][400/1281] lr: 2.100e-02, eta: 15:36:07, time: 0.270, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9950, loss_cls: 0.5950, loss: 0.5950 +2025-06-24 16:49:57,436 - pyskl - INFO - Epoch [40][500/1281] lr: 2.098e-02, eta: 15:35:35, time: 0.425, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9950, loss_cls: 0.5798, loss: 0.5798 +2025-06-24 16:50:46,558 - pyskl - INFO - Epoch [40][600/1281] lr: 2.097e-02, eta: 15:35:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9975, loss_cls: 0.5995, loss: 0.5995 +2025-06-24 16:51:35,770 - pyskl - INFO - Epoch [40][700/1281] lr: 2.095e-02, eta: 15:35:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9925, loss_cls: 0.5920, loss: 0.5920 +2025-06-24 16:52:25,051 - pyskl - INFO - Epoch [40][800/1281] lr: 2.094e-02, eta: 15:34:57, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9919, loss_cls: 0.6282, loss: 0.6282 +2025-06-24 16:53:14,486 - pyskl - INFO - Epoch [40][900/1281] lr: 2.092e-02, eta: 15:34:44, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9925, loss_cls: 0.6018, loss: 0.6018 +2025-06-24 16:54:03,954 - pyskl - INFO - Epoch [40][1000/1281] lr: 2.091e-02, eta: 15:34:31, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9938, loss_cls: 0.6374, loss: 0.6374 +2025-06-24 16:54:53,489 - pyskl - INFO - Epoch [40][1100/1281] lr: 2.089e-02, eta: 15:34:19, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9956, loss_cls: 0.5717, loss: 0.5717 +2025-06-24 16:55:42,781 - pyskl - INFO - Epoch [40][1200/1281] lr: 2.088e-02, eta: 15:34:06, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9938, loss_cls: 0.6678, loss: 0.6678 +2025-06-24 16:56:23,406 - pyskl - INFO - Saving checkpoint at 40 epochs +2025-06-24 16:57:22,888 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:57:22,958 - pyskl - INFO - +top1_acc 0.8453 +top5_acc 0.9860 +2025-06-24 16:57:22,958 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:57:22,967 - pyskl - INFO - +mean_acc 0.7889 +2025-06-24 16:57:22,970 - pyskl - INFO - Epoch(val) [40][533] top1_acc: 0.8453, top5_acc: 0.9860, mean_class_accuracy: 0.7889 +2025-06-24 16:58:45,491 - pyskl - INFO - Epoch [41][100/1281] lr: 2.085e-02, eta: 15:33:23, time: 0.825, data_time: 0.201, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9969, loss_cls: 0.5439, loss: 0.5439 +2025-06-24 16:59:25,232 - pyskl - INFO - Epoch [41][200/1281] lr: 2.083e-02, eta: 15:32:43, time: 0.397, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9956, loss_cls: 0.5520, loss: 0.5520 +2025-06-24 17:00:16,658 - pyskl - INFO - Epoch [41][300/1281] lr: 2.082e-02, eta: 15:32:35, time: 0.514, data_time: 0.001, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9931, loss_cls: 0.5845, loss: 0.5845 +2025-06-24 17:00:40,318 - pyskl - INFO - Epoch [41][400/1281] lr: 2.080e-02, eta: 15:31:11, time: 0.237, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9938, loss_cls: 0.5983, loss: 0.5983 +2025-06-24 17:01:24,512 - pyskl - INFO - Epoch [41][500/1281] lr: 2.079e-02, eta: 15:30:43, time: 0.442, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9956, loss_cls: 0.6101, loss: 0.6101 +2025-06-24 17:02:13,324 - pyskl - INFO - Epoch [41][600/1281] lr: 2.077e-02, eta: 15:30:28, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9956, loss_cls: 0.6437, loss: 0.6437 +2025-06-24 17:03:02,673 - pyskl - INFO - Epoch [41][700/1281] lr: 2.076e-02, eta: 15:30:14, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9981, loss_cls: 0.5577, loss: 0.5577 +2025-06-24 17:03:51,727 - pyskl - INFO - Epoch [41][800/1281] lr: 2.074e-02, eta: 15:29:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9931, loss_cls: 0.5919, loss: 0.5919 +2025-06-24 17:04:40,960 - pyskl - INFO - Epoch [41][900/1281] lr: 2.073e-02, eta: 15:29:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9956, loss_cls: 0.5805, loss: 0.5805 +2025-06-24 17:05:30,086 - pyskl - INFO - Epoch [41][1000/1281] lr: 2.071e-02, eta: 15:29:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9950, loss_cls: 0.6152, loss: 0.6152 +2025-06-24 17:06:19,524 - pyskl - INFO - Epoch [41][1100/1281] lr: 2.070e-02, eta: 15:29:15, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9912, loss_cls: 0.6340, loss: 0.6340 +2025-06-24 17:07:09,273 - pyskl - INFO - Epoch [41][1200/1281] lr: 2.068e-02, eta: 15:29:02, time: 0.497, data_time: 0.001, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9950, loss_cls: 0.6125, loss: 0.6125 +2025-06-24 17:07:49,071 - pyskl - INFO - Saving checkpoint at 41 epochs +2025-06-24 17:08:49,454 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:08:49,526 - pyskl - INFO - +top1_acc 0.8551 +top5_acc 0.9877 +2025-06-24 17:08:49,526 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:08:49,536 - pyskl - INFO - +mean_acc 0.8120 +2025-06-24 17:08:49,539 - pyskl - INFO - Epoch(val) [41][533] top1_acc: 0.8551, top5_acc: 0.9877, mean_class_accuracy: 0.8120 +2025-06-24 17:10:10,255 - pyskl - INFO - Epoch [42][100/1281] lr: 2.065e-02, eta: 15:28:12, time: 0.807, data_time: 0.195, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9950, loss_cls: 0.6107, loss: 0.6107 +2025-06-24 17:10:49,009 - pyskl - INFO - Epoch [42][200/1281] lr: 2.064e-02, eta: 15:27:29, time: 0.388, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9975, loss_cls: 0.5475, loss: 0.5475 +2025-06-24 17:11:40,372 - pyskl - INFO - Epoch [42][300/1281] lr: 2.062e-02, eta: 15:27:19, time: 0.514, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9962, loss_cls: 0.5872, loss: 0.5872 +2025-06-24 17:12:04,543 - pyskl - INFO - Epoch [42][400/1281] lr: 2.061e-02, eta: 15:25:58, time: 0.242, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9962, loss_cls: 0.6265, loss: 0.6265 +2025-06-24 17:12:48,932 - pyskl - INFO - Epoch [42][500/1281] lr: 2.059e-02, eta: 15:25:30, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9944, loss_cls: 0.5946, loss: 0.5946 +2025-06-24 17:13:38,305 - pyskl - INFO - Epoch [42][600/1281] lr: 2.057e-02, eta: 15:25:15, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9950, loss_cls: 0.5665, loss: 0.5665 +2025-06-24 17:14:27,538 - pyskl - INFO - Epoch [42][700/1281] lr: 2.056e-02, eta: 15:24:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9975, loss_cls: 0.5920, loss: 0.5920 +2025-06-24 17:15:16,599 - pyskl - INFO - Epoch [42][800/1281] lr: 2.054e-02, eta: 15:24:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9950, loss_cls: 0.5920, loss: 0.5920 +2025-06-24 17:16:05,527 - pyskl - INFO - Epoch [42][900/1281] lr: 2.053e-02, eta: 15:24:26, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9956, loss_cls: 0.6067, loss: 0.6067 +2025-06-24 17:16:54,935 - pyskl - INFO - Epoch [42][1000/1281] lr: 2.051e-02, eta: 15:24:10, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9956, loss_cls: 0.5511, loss: 0.5511 +2025-06-24 17:17:44,369 - pyskl - INFO - Epoch [42][1100/1281] lr: 2.050e-02, eta: 15:23:55, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9944, loss_cls: 0.5902, loss: 0.5902 +2025-06-24 17:18:33,930 - pyskl - INFO - Epoch [42][1200/1281] lr: 2.048e-02, eta: 15:23:39, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9938, loss_cls: 0.6546, loss: 0.6546 +2025-06-24 17:19:14,626 - pyskl - INFO - Saving checkpoint at 42 epochs +2025-06-24 17:20:13,255 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:20:13,317 - pyskl - INFO - +top1_acc 0.8724 +top5_acc 0.9893 +2025-06-24 17:20:13,318 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:20:13,326 - pyskl - INFO - +mean_acc 0.8168 +2025-06-24 17:20:13,331 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_39.pth was removed +2025-06-24 17:20:13,530 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_42.pth. +2025-06-24 17:20:13,530 - pyskl - INFO - Best top1_acc is 0.8724 at 42 epoch. +2025-06-24 17:20:13,534 - pyskl - INFO - Epoch(val) [42][533] top1_acc: 0.8724, top5_acc: 0.9893, mean_class_accuracy: 0.8168 +2025-06-24 17:21:35,986 - pyskl - INFO - Epoch [43][100/1281] lr: 2.045e-02, eta: 15:22:52, time: 0.824, data_time: 0.200, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9975, loss_cls: 0.4848, loss: 0.4848 +2025-06-24 17:22:13,368 - pyskl - INFO - Epoch [43][200/1281] lr: 2.044e-02, eta: 15:22:06, time: 0.374, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9962, loss_cls: 0.5541, loss: 0.5541 +2025-06-24 17:23:04,823 - pyskl - INFO - Epoch [43][300/1281] lr: 2.042e-02, eta: 15:21:55, time: 0.515, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9950, loss_cls: 0.5286, loss: 0.5286 +2025-06-24 17:23:29,365 - pyskl - INFO - Epoch [43][400/1281] lr: 2.040e-02, eta: 15:20:35, time: 0.245, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9969, loss_cls: 0.5896, loss: 0.5896 +2025-06-24 17:24:15,466 - pyskl - INFO - Epoch [43][500/1281] lr: 2.039e-02, eta: 15:20:10, time: 0.461, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9944, loss_cls: 0.5585, loss: 0.5585 +2025-06-24 17:25:05,233 - pyskl - INFO - Epoch [43][600/1281] lr: 2.037e-02, eta: 15:19:55, time: 0.498, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9944, loss_cls: 0.6379, loss: 0.6379 +2025-06-24 17:25:54,696 - pyskl - INFO - Epoch [43][700/1281] lr: 2.036e-02, eta: 15:19:39, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9969, loss_cls: 0.5541, loss: 0.5541 +2025-06-24 17:26:43,724 - pyskl - INFO - Epoch [43][800/1281] lr: 2.034e-02, eta: 15:19:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9912, loss_cls: 0.5718, loss: 0.5718 +2025-06-24 17:27:32,839 - pyskl - INFO - Epoch [43][900/1281] lr: 2.033e-02, eta: 15:19:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9894, loss_cls: 0.6315, loss: 0.6315 +2025-06-24 17:28:21,779 - pyskl - INFO - Epoch [43][1000/1281] lr: 2.031e-02, eta: 15:18:46, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9956, loss_cls: 0.6055, loss: 0.6055 +2025-06-24 17:29:10,793 - pyskl - INFO - Epoch [43][1100/1281] lr: 2.029e-02, eta: 15:18:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9931, loss_cls: 0.5895, loss: 0.5895 +2025-06-24 17:29:59,987 - pyskl - INFO - Epoch [43][1200/1281] lr: 2.028e-02, eta: 15:18:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9981, loss_cls: 0.5682, loss: 0.5682 +2025-06-24 17:30:40,577 - pyskl - INFO - Saving checkpoint at 43 epochs +2025-06-24 17:31:39,933 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:31:40,014 - pyskl - INFO - +top1_acc 0.8310 +top5_acc 0.9862 +2025-06-24 17:31:40,014 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:31:40,024 - pyskl - INFO - +mean_acc 0.7874 +2025-06-24 17:31:40,027 - pyskl - INFO - Epoch(val) [43][533] top1_acc: 0.8310, top5_acc: 0.9862, mean_class_accuracy: 0.7874 +2025-06-24 17:32:59,526 - pyskl - INFO - Epoch [44][100/1281] lr: 2.025e-02, eta: 15:17:14, time: 0.795, data_time: 0.191, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9962, loss_cls: 0.5079, loss: 0.5079 +2025-06-24 17:33:37,304 - pyskl - INFO - Epoch [44][200/1281] lr: 2.023e-02, eta: 15:16:28, time: 0.378, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9975, loss_cls: 0.5132, loss: 0.5132 +2025-06-24 17:34:28,750 - pyskl - INFO - Epoch [44][300/1281] lr: 2.022e-02, eta: 15:16:16, time: 0.514, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9950, loss_cls: 0.5397, loss: 0.5397 +2025-06-24 17:34:52,918 - pyskl - INFO - Epoch [44][400/1281] lr: 2.020e-02, eta: 15:14:56, time: 0.242, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9969, loss_cls: 0.5316, loss: 0.5316 +2025-06-24 17:35:38,479 - pyskl - INFO - Epoch [44][500/1281] lr: 2.018e-02, eta: 15:14:29, time: 0.456, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9950, loss_cls: 0.5529, loss: 0.5529 +2025-06-24 17:36:27,685 - pyskl - INFO - Epoch [44][600/1281] lr: 2.017e-02, eta: 15:14:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9962, loss_cls: 0.5689, loss: 0.5689 +2025-06-24 17:37:16,786 - pyskl - INFO - Epoch [44][700/1281] lr: 2.015e-02, eta: 15:13:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9931, loss_cls: 0.5712, loss: 0.5712 +2025-06-24 17:38:06,131 - pyskl - INFO - Epoch [44][800/1281] lr: 2.014e-02, eta: 15:13:35, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9938, loss_cls: 0.6378, loss: 0.6378 +2025-06-24 17:38:55,326 - pyskl - INFO - Epoch [44][900/1281] lr: 2.012e-02, eta: 15:13:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9962, loss_cls: 0.6140, loss: 0.6140 +2025-06-24 17:39:44,547 - pyskl - INFO - Epoch [44][1000/1281] lr: 2.010e-02, eta: 15:12:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9944, loss_cls: 0.5381, loss: 0.5381 +2025-06-24 17:40:33,668 - pyskl - INFO - Epoch [44][1100/1281] lr: 2.009e-02, eta: 15:12:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9962, loss_cls: 0.5094, loss: 0.5094 +2025-06-24 17:41:23,015 - pyskl - INFO - Epoch [44][1200/1281] lr: 2.007e-02, eta: 15:12:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9975, loss_cls: 0.5809, loss: 0.5809 +2025-06-24 17:42:03,918 - pyskl - INFO - Saving checkpoint at 44 epochs +2025-06-24 17:43:03,368 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:43:03,450 - pyskl - INFO - +top1_acc 0.8363 +top5_acc 0.9900 +2025-06-24 17:43:03,450 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:43:03,458 - pyskl - INFO - +mean_acc 0.7720 +2025-06-24 17:43:03,461 - pyskl - INFO - Epoch(val) [44][533] top1_acc: 0.8363, top5_acc: 0.9900, mean_class_accuracy: 0.7720 +2025-06-24 17:44:24,163 - pyskl - INFO - Epoch [45][100/1281] lr: 2.004e-02, eta: 15:11:26, time: 0.807, data_time: 0.191, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9988, loss_cls: 0.6026, loss: 0.6026 +2025-06-24 17:45:01,772 - pyskl - INFO - Epoch [45][200/1281] lr: 2.003e-02, eta: 15:10:40, time: 0.376, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9988, loss_cls: 0.5446, loss: 0.5446 +2025-06-24 17:45:53,361 - pyskl - INFO - Epoch [45][300/1281] lr: 2.001e-02, eta: 15:10:26, time: 0.516, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9956, loss_cls: 0.5631, loss: 0.5631 +2025-06-24 17:46:17,786 - pyskl - INFO - Epoch [45][400/1281] lr: 1.999e-02, eta: 15:09:08, time: 0.244, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9962, loss_cls: 0.4732, loss: 0.4732 +2025-06-24 17:47:02,530 - pyskl - INFO - Epoch [45][500/1281] lr: 1.998e-02, eta: 15:08:38, time: 0.447, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9975, loss_cls: 0.5395, loss: 0.5395 +2025-06-24 17:47:51,941 - pyskl - INFO - Epoch [45][600/1281] lr: 1.996e-02, eta: 15:08:20, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9962, loss_cls: 0.5339, loss: 0.5339 +2025-06-24 17:48:41,239 - pyskl - INFO - Epoch [45][700/1281] lr: 1.994e-02, eta: 15:08:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9975, loss_cls: 0.5410, loss: 0.5410 +2025-06-24 17:49:30,311 - pyskl - INFO - Epoch [45][800/1281] lr: 1.993e-02, eta: 15:07:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9975, loss_cls: 0.5339, loss: 0.5339 +2025-06-24 17:50:19,701 - pyskl - INFO - Epoch [45][900/1281] lr: 1.991e-02, eta: 15:07:22, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9912, loss_cls: 0.5963, loss: 0.5963 +2025-06-24 17:51:08,699 - pyskl - INFO - Epoch [45][1000/1281] lr: 1.989e-02, eta: 15:07:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9950, loss_cls: 0.5747, loss: 0.5747 +2025-06-24 17:51:58,401 - pyskl - INFO - Epoch [45][1100/1281] lr: 1.988e-02, eta: 15:06:43, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9956, loss_cls: 0.5965, loss: 0.5965 +2025-06-24 17:52:47,793 - pyskl - INFO - Epoch [45][1200/1281] lr: 1.986e-02, eta: 15:06:24, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9975, loss_cls: 0.5250, loss: 0.5250 +2025-06-24 17:53:28,293 - pyskl - INFO - Saving checkpoint at 45 epochs +2025-06-24 17:54:27,956 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:54:28,027 - pyskl - INFO - +top1_acc 0.8471 +top5_acc 0.9905 +2025-06-24 17:54:28,027 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:54:28,036 - pyskl - INFO - +mean_acc 0.7844 +2025-06-24 17:54:28,038 - pyskl - INFO - Epoch(val) [45][533] top1_acc: 0.8471, top5_acc: 0.9905, mean_class_accuracy: 0.7844 +2025-06-24 17:55:49,059 - pyskl - INFO - Epoch [46][100/1281] lr: 1.983e-02, eta: 15:05:29, time: 0.810, data_time: 0.194, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9975, loss_cls: 0.5218, loss: 0.5218 +2025-06-24 17:56:26,303 - pyskl - INFO - Epoch [46][200/1281] lr: 1.981e-02, eta: 15:04:41, time: 0.372, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9981, loss_cls: 0.4750, loss: 0.4750 +2025-06-24 17:57:17,624 - pyskl - INFO - Epoch [46][300/1281] lr: 1.980e-02, eta: 15:04:26, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9981, loss_cls: 0.4677, loss: 0.4677 +2025-06-24 17:57:41,815 - pyskl - INFO - Epoch [46][400/1281] lr: 1.978e-02, eta: 15:03:08, time: 0.242, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9956, loss_cls: 0.5596, loss: 0.5596 +2025-06-24 17:58:28,735 - pyskl - INFO - Epoch [46][500/1281] lr: 1.976e-02, eta: 15:02:43, time: 0.469, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9944, loss_cls: 0.4999, loss: 0.4999 +2025-06-24 17:59:18,127 - pyskl - INFO - Epoch [46][600/1281] lr: 1.975e-02, eta: 15:02:23, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9969, loss_cls: 0.5740, loss: 0.5740 +2025-06-24 18:00:07,099 - pyskl - INFO - Epoch [46][700/1281] lr: 1.973e-02, eta: 15:02:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9944, loss_cls: 0.5618, loss: 0.5618 +2025-06-24 18:00:56,089 - pyskl - INFO - Epoch [46][800/1281] lr: 1.971e-02, eta: 15:01:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9962, loss_cls: 0.5669, loss: 0.5669 +2025-06-24 18:01:45,325 - pyskl - INFO - Epoch [46][900/1281] lr: 1.970e-02, eta: 15:01:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9962, loss_cls: 0.5844, loss: 0.5844 +2025-06-24 18:02:34,217 - pyskl - INFO - Epoch [46][1000/1281] lr: 1.968e-02, eta: 15:00:59, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9944, loss_cls: 0.5841, loss: 0.5841 +2025-06-24 18:03:23,478 - pyskl - INFO - Epoch [46][1100/1281] lr: 1.966e-02, eta: 15:00:39, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9944, loss_cls: 0.5275, loss: 0.5275 +2025-06-24 18:04:12,480 - pyskl - INFO - Epoch [46][1200/1281] lr: 1.965e-02, eta: 15:00:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9956, loss_cls: 0.5729, loss: 0.5729 +2025-06-24 18:04:53,154 - pyskl - INFO - Saving checkpoint at 46 epochs +2025-06-24 18:05:52,049 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:05:52,104 - pyskl - INFO - +top1_acc 0.8616 +top5_acc 0.9897 +2025-06-24 18:05:52,105 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:05:52,112 - pyskl - INFO - +mean_acc 0.8110 +2025-06-24 18:05:52,114 - pyskl - INFO - Epoch(val) [46][533] top1_acc: 0.8616, top5_acc: 0.9897, mean_class_accuracy: 0.8110 +2025-06-24 18:07:12,128 - pyskl - INFO - Epoch [47][100/1281] lr: 1.962e-02, eta: 14:59:19, time: 0.800, data_time: 0.195, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9969, loss_cls: 0.5545, loss: 0.5545 +2025-06-24 18:07:48,511 - pyskl - INFO - Epoch [47][200/1281] lr: 1.960e-02, eta: 14:58:29, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9962, loss_cls: 0.5404, loss: 0.5404 +2025-06-24 18:08:39,784 - pyskl - INFO - Epoch [47][300/1281] lr: 1.958e-02, eta: 14:58:13, time: 0.513, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9969, loss_cls: 0.5457, loss: 0.5457 +2025-06-24 18:09:04,132 - pyskl - INFO - Epoch [47][400/1281] lr: 1.957e-02, eta: 14:56:56, time: 0.244, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9962, loss_cls: 0.5276, loss: 0.5276 +2025-06-24 18:09:49,634 - pyskl - INFO - Epoch [47][500/1281] lr: 1.955e-02, eta: 14:56:27, time: 0.455, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9944, loss_cls: 0.5447, loss: 0.5447 +2025-06-24 18:10:38,808 - pyskl - INFO - Epoch [47][600/1281] lr: 1.953e-02, eta: 14:56:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9950, loss_cls: 0.4855, loss: 0.4855 +2025-06-24 18:11:28,050 - pyskl - INFO - Epoch [47][700/1281] lr: 1.952e-02, eta: 14:55:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9950, loss_cls: 0.5320, loss: 0.5320 +2025-06-24 18:12:17,025 - pyskl - INFO - Epoch [47][800/1281] lr: 1.950e-02, eta: 14:55:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9969, loss_cls: 0.5630, loss: 0.5630 +2025-06-24 18:13:06,338 - pyskl - INFO - Epoch [47][900/1281] lr: 1.948e-02, eta: 14:55:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9981, loss_cls: 0.5871, loss: 0.5871 +2025-06-24 18:13:55,347 - pyskl - INFO - Epoch [47][1000/1281] lr: 1.947e-02, eta: 14:54:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9962, loss_cls: 0.5310, loss: 0.5310 +2025-06-24 18:14:44,237 - pyskl - INFO - Epoch [47][1100/1281] lr: 1.945e-02, eta: 14:54:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9938, loss_cls: 0.6007, loss: 0.6007 +2025-06-24 18:15:33,338 - pyskl - INFO - Epoch [47][1200/1281] lr: 1.943e-02, eta: 14:53:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9919, loss_cls: 0.5316, loss: 0.5316 +2025-06-24 18:16:13,632 - pyskl - INFO - Saving checkpoint at 47 epochs +2025-06-24 18:17:12,981 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:17:13,049 - pyskl - INFO - +top1_acc 0.8522 +top5_acc 0.9889 +2025-06-24 18:17:13,049 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:17:13,059 - pyskl - INFO - +mean_acc 0.8050 +2025-06-24 18:17:13,061 - pyskl - INFO - Epoch(val) [47][533] top1_acc: 0.8522, top5_acc: 0.9889, mean_class_accuracy: 0.8050 +2025-06-24 18:18:32,562 - pyskl - INFO - Epoch [48][100/1281] lr: 1.940e-02, eta: 14:52:54, time: 0.795, data_time: 0.193, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9988, loss_cls: 0.4676, loss: 0.4676 +2025-06-24 18:19:10,860 - pyskl - INFO - Epoch [48][200/1281] lr: 1.938e-02, eta: 14:52:09, time: 0.383, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9962, loss_cls: 0.5387, loss: 0.5387 +2025-06-24 18:20:02,006 - pyskl - INFO - Epoch [48][300/1281] lr: 1.937e-02, eta: 14:51:51, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9962, loss_cls: 0.5426, loss: 0.5426 +2025-06-24 18:20:25,878 - pyskl - INFO - Epoch [48][400/1281] lr: 1.935e-02, eta: 14:50:34, time: 0.239, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9981, loss_cls: 0.5781, loss: 0.5781 +2025-06-24 18:21:10,364 - pyskl - INFO - Epoch [48][500/1281] lr: 1.933e-02, eta: 14:50:02, time: 0.445, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9969, loss_cls: 0.5690, loss: 0.5690 +2025-06-24 18:21:59,882 - pyskl - INFO - Epoch [48][600/1281] lr: 1.932e-02, eta: 14:49:40, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9956, loss_cls: 0.5176, loss: 0.5176 +2025-06-24 18:22:48,853 - pyskl - INFO - Epoch [48][700/1281] lr: 1.930e-02, eta: 14:49:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9950, loss_cls: 0.5479, loss: 0.5479 +2025-06-24 18:23:37,948 - pyskl - INFO - Epoch [48][800/1281] lr: 1.928e-02, eta: 14:48:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9931, loss_cls: 0.6655, loss: 0.6655 +2025-06-24 18:24:26,943 - pyskl - INFO - Epoch [48][900/1281] lr: 1.926e-02, eta: 14:48:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9938, loss_cls: 0.6134, loss: 0.6134 +2025-06-24 18:25:16,499 - pyskl - INFO - Epoch [48][1000/1281] lr: 1.925e-02, eta: 14:48:10, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9962, loss_cls: 0.5692, loss: 0.5692 +2025-06-24 18:26:05,598 - pyskl - INFO - Epoch [48][1100/1281] lr: 1.923e-02, eta: 14:47:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9962, loss_cls: 0.4978, loss: 0.4978 +2025-06-24 18:26:55,013 - pyskl - INFO - Epoch [48][1200/1281] lr: 1.921e-02, eta: 14:47:25, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9906, loss_cls: 0.5901, loss: 0.5901 +2025-06-24 18:27:35,214 - pyskl - INFO - Saving checkpoint at 48 epochs +2025-06-24 18:28:34,270 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:28:34,337 - pyskl - INFO - +top1_acc 0.8582 +top5_acc 0.9932 +2025-06-24 18:28:34,338 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:28:34,347 - pyskl - INFO - +mean_acc 0.7981 +2025-06-24 18:28:34,349 - pyskl - INFO - Epoch(val) [48][533] top1_acc: 0.8582, top5_acc: 0.9932, mean_class_accuracy: 0.7981 +2025-06-24 18:29:51,818 - pyskl - INFO - Epoch [49][100/1281] lr: 1.918e-02, eta: 14:46:20, time: 0.775, data_time: 0.188, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9975, loss_cls: 0.5357, loss: 0.5357 +2025-06-24 18:30:34,042 - pyskl - INFO - Epoch [49][200/1281] lr: 1.916e-02, eta: 14:45:42, time: 0.422, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9962, loss_cls: 0.4979, loss: 0.4979 +2025-06-24 18:31:18,823 - pyskl - INFO - Epoch [49][300/1281] lr: 1.915e-02, eta: 14:45:10, time: 0.448, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9969, loss_cls: 0.4977, loss: 0.4977 +2025-06-24 18:31:47,713 - pyskl - INFO - Epoch [49][400/1281] lr: 1.913e-02, eta: 14:44:04, time: 0.289, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9956, loss_cls: 0.5910, loss: 0.5910 +2025-06-24 18:32:28,323 - pyskl - INFO - Epoch [49][500/1281] lr: 1.911e-02, eta: 14:43:23, time: 0.406, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9988, loss_cls: 0.5077, loss: 0.5077 +2025-06-24 18:33:17,684 - pyskl - INFO - Epoch [49][600/1281] lr: 1.909e-02, eta: 14:43:01, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9956, loss_cls: 0.5832, loss: 0.5832 +2025-06-24 18:34:06,765 - pyskl - INFO - Epoch [49][700/1281] lr: 1.908e-02, eta: 14:42:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9962, loss_cls: 0.5465, loss: 0.5465 +2025-06-24 18:34:55,797 - pyskl - INFO - Epoch [49][800/1281] lr: 1.906e-02, eta: 14:42:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9912, loss_cls: 0.6083, loss: 0.6083 +2025-06-24 18:35:45,055 - pyskl - INFO - Epoch [49][900/1281] lr: 1.904e-02, eta: 14:41:51, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9956, loss_cls: 0.5231, loss: 0.5231 +2025-06-24 18:36:34,039 - pyskl - INFO - Epoch [49][1000/1281] lr: 1.902e-02, eta: 14:41:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9956, loss_cls: 0.5364, loss: 0.5364 +2025-06-24 18:37:23,438 - pyskl - INFO - Epoch [49][1100/1281] lr: 1.901e-02, eta: 14:41:04, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9950, loss_cls: 0.5729, loss: 0.5729 +2025-06-24 18:38:12,907 - pyskl - INFO - Epoch [49][1200/1281] lr: 1.899e-02, eta: 14:40:41, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9981, loss_cls: 0.5701, loss: 0.5701 +2025-06-24 18:38:53,345 - pyskl - INFO - Saving checkpoint at 49 epochs +2025-06-24 18:39:53,103 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:39:53,166 - pyskl - INFO - +top1_acc 0.8608 +top5_acc 0.9905 +2025-06-24 18:39:53,166 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:39:53,174 - pyskl - INFO - +mean_acc 0.7944 +2025-06-24 18:39:53,177 - pyskl - INFO - Epoch(val) [49][533] top1_acc: 0.8608, top5_acc: 0.9905, mean_class_accuracy: 0.7944 +2025-06-24 18:41:13,735 - pyskl - INFO - Epoch [50][100/1281] lr: 1.896e-02, eta: 14:39:41, time: 0.806, data_time: 0.195, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9988, loss_cls: 0.4753, loss: 0.4753 +2025-06-24 18:41:58,343 - pyskl - INFO - Epoch [50][200/1281] lr: 1.894e-02, eta: 14:39:08, time: 0.446, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9981, loss_cls: 0.4401, loss: 0.4401 +2025-06-24 18:42:38,526 - pyskl - INFO - Epoch [50][300/1281] lr: 1.892e-02, eta: 14:38:26, time: 0.402, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9969, loss_cls: 0.5053, loss: 0.5053 +2025-06-24 18:43:11,978 - pyskl - INFO - Epoch [50][400/1281] lr: 1.891e-02, eta: 14:37:30, time: 0.335, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9994, loss_cls: 0.4580, loss: 0.4580 +2025-06-24 18:43:51,946 - pyskl - INFO - Epoch [50][500/1281] lr: 1.889e-02, eta: 14:36:47, time: 0.400, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9975, loss_cls: 0.5205, loss: 0.5205 +2025-06-24 18:44:41,013 - pyskl - INFO - Epoch [50][600/1281] lr: 1.887e-02, eta: 14:36:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9962, loss_cls: 0.5242, loss: 0.5242 +2025-06-24 18:45:30,257 - pyskl - INFO - Epoch [50][700/1281] lr: 1.885e-02, eta: 14:35:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9962, loss_cls: 0.5484, loss: 0.5484 +2025-06-24 18:46:19,319 - pyskl - INFO - Epoch [50][800/1281] lr: 1.884e-02, eta: 14:35:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9962, loss_cls: 0.5283, loss: 0.5283 +2025-06-24 18:47:08,184 - pyskl - INFO - Epoch [50][900/1281] lr: 1.882e-02, eta: 14:35:11, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9931, loss_cls: 0.5738, loss: 0.5738 +2025-06-24 18:47:57,499 - pyskl - INFO - Epoch [50][1000/1281] lr: 1.880e-02, eta: 14:34:47, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9950, loss_cls: 0.5877, loss: 0.5877 +2025-06-24 18:48:46,558 - pyskl - INFO - Epoch [50][1100/1281] lr: 1.878e-02, eta: 14:34:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9950, loss_cls: 0.5193, loss: 0.5193 +2025-06-24 18:49:35,795 - pyskl - INFO - Epoch [50][1200/1281] lr: 1.876e-02, eta: 14:33:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9950, loss_cls: 0.5676, loss: 0.5676 +2025-06-24 18:50:16,311 - pyskl - INFO - Saving checkpoint at 50 epochs +2025-06-24 18:51:15,961 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:51:16,030 - pyskl - INFO - +top1_acc 0.8571 +top5_acc 0.9881 +2025-06-24 18:51:16,030 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:51:16,038 - pyskl - INFO - +mean_acc 0.7956 +2025-06-24 18:51:16,040 - pyskl - INFO - Epoch(val) [50][533] top1_acc: 0.8571, top5_acc: 0.9881, mean_class_accuracy: 0.7956 +2025-06-24 18:52:36,099 - pyskl - INFO - Epoch [51][100/1281] lr: 1.873e-02, eta: 14:32:56, time: 0.801, data_time: 0.195, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9962, loss_cls: 0.4835, loss: 0.4835 +2025-06-24 18:53:20,707 - pyskl - INFO - Epoch [51][200/1281] lr: 1.871e-02, eta: 14:32:22, time: 0.446, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9988, loss_cls: 0.4758, loss: 0.4758 +2025-06-24 18:54:01,539 - pyskl - INFO - Epoch [51][300/1281] lr: 1.870e-02, eta: 14:31:41, time: 0.408, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9981, loss_cls: 0.5143, loss: 0.5143 +2025-06-24 18:54:34,245 - pyskl - INFO - Epoch [51][400/1281] lr: 1.868e-02, eta: 14:30:44, time: 0.327, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9931, loss_cls: 0.5682, loss: 0.5682 +2025-06-24 18:55:12,959 - pyskl - INFO - Epoch [51][500/1281] lr: 1.866e-02, eta: 14:29:59, time: 0.387, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9950, loss_cls: 0.4849, loss: 0.4849 +2025-06-24 18:56:02,206 - pyskl - INFO - Epoch [51][600/1281] lr: 1.864e-02, eta: 14:29:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9975, loss_cls: 0.5442, loss: 0.5442 +2025-06-24 18:56:51,469 - pyskl - INFO - Epoch [51][700/1281] lr: 1.863e-02, eta: 14:29:10, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9969, loss_cls: 0.5189, loss: 0.5189 +2025-06-24 18:57:40,781 - pyskl - INFO - Epoch [51][800/1281] lr: 1.861e-02, eta: 14:28:45, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9956, loss_cls: 0.5725, loss: 0.5725 +2025-06-24 18:58:29,825 - pyskl - INFO - Epoch [51][900/1281] lr: 1.859e-02, eta: 14:28:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9956, loss_cls: 0.5385, loss: 0.5385 +2025-06-24 18:59:18,966 - pyskl - INFO - Epoch [51][1000/1281] lr: 1.857e-02, eta: 14:27:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9956, loss_cls: 0.5058, loss: 0.5058 +2025-06-24 19:00:08,160 - pyskl - INFO - Epoch [51][1100/1281] lr: 1.855e-02, eta: 14:27:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9944, loss_cls: 0.5666, loss: 0.5666 +2025-06-24 19:00:57,440 - pyskl - INFO - Epoch [51][1200/1281] lr: 1.854e-02, eta: 14:27:05, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9981, loss_cls: 0.5130, loss: 0.5130 +2025-06-24 19:01:37,952 - pyskl - INFO - Saving checkpoint at 51 epochs +2025-06-24 19:02:36,778 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:02:36,833 - pyskl - INFO - +top1_acc 0.8620 +top5_acc 0.9896 +2025-06-24 19:02:36,833 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:02:36,840 - pyskl - INFO - +mean_acc 0.8167 +2025-06-24 19:02:36,843 - pyskl - INFO - Epoch(val) [51][533] top1_acc: 0.8620, top5_acc: 0.9896, mean_class_accuracy: 0.8167 +2025-06-24 19:03:55,887 - pyskl - INFO - Epoch [52][100/1281] lr: 1.850e-02, eta: 14:26:01, time: 0.790, data_time: 0.186, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9962, loss_cls: 0.4764, loss: 0.4764 +2025-06-24 19:04:44,028 - pyskl - INFO - Epoch [52][200/1281] lr: 1.849e-02, eta: 14:25:33, time: 0.481, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9975, loss_cls: 0.4956, loss: 0.4956 +2025-06-24 19:05:18,841 - pyskl - INFO - Epoch [52][300/1281] lr: 1.847e-02, eta: 14:24:40, time: 0.348, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9969, loss_cls: 0.5253, loss: 0.5253 +2025-06-24 19:05:57,914 - pyskl - INFO - Epoch [52][400/1281] lr: 1.845e-02, eta: 14:23:56, time: 0.391, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9962, loss_cls: 0.5256, loss: 0.5256 +2025-06-24 19:06:31,975 - pyskl - INFO - Epoch [52][500/1281] lr: 1.843e-02, eta: 14:23:01, time: 0.341, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9969, loss_cls: 0.5386, loss: 0.5386 +2025-06-24 19:07:21,258 - pyskl - INFO - Epoch [52][600/1281] lr: 1.841e-02, eta: 14:22:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9944, loss_cls: 0.5419, loss: 0.5419 +2025-06-24 19:08:10,728 - pyskl - INFO - Epoch [52][700/1281] lr: 1.840e-02, eta: 14:22:11, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9962, loss_cls: 0.4934, loss: 0.4934 +2025-06-24 19:08:59,732 - pyskl - INFO - Epoch [52][800/1281] lr: 1.838e-02, eta: 14:21:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9925, loss_cls: 0.5740, loss: 0.5740 +2025-06-24 19:09:49,143 - pyskl - INFO - Epoch [52][900/1281] lr: 1.836e-02, eta: 14:21:20, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9944, loss_cls: 0.5884, loss: 0.5884 +2025-06-24 19:10:38,631 - pyskl - INFO - Epoch [52][1000/1281] lr: 1.834e-02, eta: 14:20:55, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9969, loss_cls: 0.4608, loss: 0.4608 +2025-06-24 19:11:28,117 - pyskl - INFO - Epoch [52][1100/1281] lr: 1.832e-02, eta: 14:20:30, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9950, loss_cls: 0.5301, loss: 0.5301 +2025-06-24 19:12:17,462 - pyskl - INFO - Epoch [52][1200/1281] lr: 1.831e-02, eta: 14:20:05, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9956, loss_cls: 0.4903, loss: 0.4903 +2025-06-24 19:12:58,019 - pyskl - INFO - Saving checkpoint at 52 epochs +2025-06-24 19:13:57,208 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:13:57,265 - pyskl - INFO - +top1_acc 0.8632 +top5_acc 0.9897 +2025-06-24 19:13:57,265 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:13:57,272 - pyskl - INFO - +mean_acc 0.8089 +2025-06-24 19:13:57,275 - pyskl - INFO - Epoch(val) [52][533] top1_acc: 0.8632, top5_acc: 0.9897, mean_class_accuracy: 0.8089 +2025-06-24 19:15:16,319 - pyskl - INFO - Epoch [53][100/1281] lr: 1.827e-02, eta: 14:18:59, time: 0.790, data_time: 0.192, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9975, loss_cls: 0.4925, loss: 0.4925 +2025-06-24 19:16:05,578 - pyskl - INFO - Epoch [53][200/1281] lr: 1.826e-02, eta: 14:18:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9969, loss_cls: 0.5011, loss: 0.5011 +2025-06-24 19:16:34,360 - pyskl - INFO - Epoch [53][300/1281] lr: 1.824e-02, eta: 14:17:29, time: 0.288, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9956, loss_cls: 0.5054, loss: 0.5054 +2025-06-24 19:17:22,015 - pyskl - INFO - Epoch [53][400/1281] lr: 1.822e-02, eta: 14:17:01, time: 0.477, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9975, loss_cls: 0.4883, loss: 0.4883 +2025-06-24 19:17:53,688 - pyskl - INFO - Epoch [53][500/1281] lr: 1.820e-02, eta: 14:16:02, time: 0.317, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9975, loss_cls: 0.4858, loss: 0.4858 +2025-06-24 19:18:43,243 - pyskl - INFO - Epoch [53][600/1281] lr: 1.818e-02, eta: 14:15:37, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9969, loss_cls: 0.4686, loss: 0.4686 +2025-06-24 19:19:32,339 - pyskl - INFO - Epoch [53][700/1281] lr: 1.816e-02, eta: 14:15:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9944, loss_cls: 0.5459, loss: 0.5459 +2025-06-24 19:20:21,782 - pyskl - INFO - Epoch [53][800/1281] lr: 1.815e-02, eta: 14:14:45, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9956, loss_cls: 0.5460, loss: 0.5460 +2025-06-24 19:21:11,014 - pyskl - INFO - Epoch [53][900/1281] lr: 1.813e-02, eta: 14:14:19, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9962, loss_cls: 0.5435, loss: 0.5435 +2025-06-24 19:21:59,976 - pyskl - INFO - Epoch [53][1000/1281] lr: 1.811e-02, eta: 14:13:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9975, loss_cls: 0.5126, loss: 0.5126 +2025-06-24 19:22:49,325 - pyskl - INFO - Epoch [53][1100/1281] lr: 1.809e-02, eta: 14:13:26, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9969, loss_cls: 0.4739, loss: 0.4739 +2025-06-24 19:23:38,789 - pyskl - INFO - Epoch [53][1200/1281] lr: 1.807e-02, eta: 14:13:00, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9975, loss_cls: 0.4695, loss: 0.4695 +2025-06-24 19:24:19,353 - pyskl - INFO - Saving checkpoint at 53 epochs +2025-06-24 19:25:17,569 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:25:17,631 - pyskl - INFO - +top1_acc 0.8520 +top5_acc 0.9908 +2025-06-24 19:25:17,631 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:25:17,638 - pyskl - INFO - +mean_acc 0.7933 +2025-06-24 19:25:17,640 - pyskl - INFO - Epoch(val) [53][533] top1_acc: 0.8520, top5_acc: 0.9908, mean_class_accuracy: 0.7933 +2025-06-24 19:26:36,304 - pyskl - INFO - Epoch [54][100/1281] lr: 1.804e-02, eta: 14:11:53, time: 0.787, data_time: 0.191, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9981, loss_cls: 0.5165, loss: 0.5165 +2025-06-24 19:27:25,569 - pyskl - INFO - Epoch [54][200/1281] lr: 1.802e-02, eta: 14:11:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9962, loss_cls: 0.4756, loss: 0.4756 +2025-06-24 19:27:53,967 - pyskl - INFO - Epoch [54][300/1281] lr: 1.800e-02, eta: 14:10:22, time: 0.284, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9988, loss_cls: 0.4334, loss: 0.4334 +2025-06-24 19:28:45,070 - pyskl - INFO - Epoch [54][400/1281] lr: 1.798e-02, eta: 14:09:59, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9962, loss_cls: 0.4484, loss: 0.4484 +2025-06-24 19:29:12,640 - pyskl - INFO - Epoch [54][500/1281] lr: 1.797e-02, eta: 14:08:53, time: 0.276, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9975, loss_cls: 0.4944, loss: 0.4944 +2025-06-24 19:30:02,149 - pyskl - INFO - Epoch [54][600/1281] lr: 1.795e-02, eta: 14:08:27, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9938, loss_cls: 0.5298, loss: 0.5298 +2025-06-24 19:30:51,321 - pyskl - INFO - Epoch [54][700/1281] lr: 1.793e-02, eta: 14:08:00, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9969, loss_cls: 0.4712, loss: 0.4712 +2025-06-24 19:31:40,265 - pyskl - INFO - Epoch [54][800/1281] lr: 1.791e-02, eta: 14:07:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9956, loss_cls: 0.5606, loss: 0.5606 +2025-06-24 19:32:29,305 - pyskl - INFO - Epoch [54][900/1281] lr: 1.789e-02, eta: 14:07:06, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9962, loss_cls: 0.4887, loss: 0.4887 +2025-06-24 19:33:18,553 - pyskl - INFO - Epoch [54][1000/1281] lr: 1.787e-02, eta: 14:06:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9994, loss_cls: 0.5431, loss: 0.5431 +2025-06-24 19:34:07,815 - pyskl - INFO - Epoch [54][1100/1281] lr: 1.786e-02, eta: 14:06:12, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.5096, loss: 0.5096 +2025-06-24 19:34:56,773 - pyskl - INFO - Epoch [54][1200/1281] lr: 1.784e-02, eta: 14:05:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9981, loss_cls: 0.4922, loss: 0.4922 +2025-06-24 19:35:37,171 - pyskl - INFO - Saving checkpoint at 54 epochs +2025-06-24 19:36:35,994 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:36:36,052 - pyskl - INFO - +top1_acc 0.8567 +top5_acc 0.9925 +2025-06-24 19:36:36,052 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:36:36,059 - pyskl - INFO - +mean_acc 0.7972 +2025-06-24 19:36:36,061 - pyskl - INFO - Epoch(val) [54][533] top1_acc: 0.8567, top5_acc: 0.9925, mean_class_accuracy: 0.7972 +2025-06-24 19:37:56,572 - pyskl - INFO - Epoch [55][100/1281] lr: 1.780e-02, eta: 14:04:41, time: 0.805, data_time: 0.191, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9962, loss_cls: 0.5028, loss: 0.5028 +2025-06-24 19:38:45,681 - pyskl - INFO - Epoch [55][200/1281] lr: 1.779e-02, eta: 14:04:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9981, loss_cls: 0.5088, loss: 0.5088 +2025-06-24 19:39:17,115 - pyskl - INFO - Epoch [55][300/1281] lr: 1.777e-02, eta: 14:03:15, time: 0.314, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9975, loss_cls: 0.5430, loss: 0.5430 +2025-06-24 19:40:08,229 - pyskl - INFO - Epoch [55][400/1281] lr: 1.775e-02, eta: 14:02:51, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4410, loss: 0.4410 +2025-06-24 19:40:33,900 - pyskl - INFO - Epoch [55][500/1281] lr: 1.773e-02, eta: 14:01:42, time: 0.257, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9988, loss_cls: 0.4993, loss: 0.4993 +2025-06-24 19:41:23,645 - pyskl - INFO - Epoch [55][600/1281] lr: 1.771e-02, eta: 14:01:16, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9969, loss_cls: 0.5034, loss: 0.5034 +2025-06-24 19:42:12,728 - pyskl - INFO - Epoch [55][700/1281] lr: 1.769e-02, eta: 14:00:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9944, loss_cls: 0.4782, loss: 0.4782 +2025-06-24 19:43:01,878 - pyskl - INFO - Epoch [55][800/1281] lr: 1.767e-02, eta: 14:00:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9956, loss_cls: 0.4915, loss: 0.4915 +2025-06-24 19:43:51,139 - pyskl - INFO - Epoch [55][900/1281] lr: 1.766e-02, eta: 13:59:54, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9969, loss_cls: 0.4953, loss: 0.4953 +2025-06-24 19:44:40,608 - pyskl - INFO - Epoch [55][1000/1281] lr: 1.764e-02, eta: 13:59:27, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9962, loss_cls: 0.5072, loss: 0.5072 +2025-06-24 19:45:29,942 - pyskl - INFO - Epoch [55][1100/1281] lr: 1.762e-02, eta: 13:58:59, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9956, loss_cls: 0.5149, loss: 0.5149 +2025-06-24 19:46:19,675 - pyskl - INFO - Epoch [55][1200/1281] lr: 1.760e-02, eta: 13:58:33, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9969, loss_cls: 0.5397, loss: 0.5397 +2025-06-24 19:47:00,104 - pyskl - INFO - Saving checkpoint at 55 epochs +2025-06-24 19:47:59,591 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:47:59,646 - pyskl - INFO - +top1_acc 0.8285 +top5_acc 0.9851 +2025-06-24 19:47:59,646 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:47:59,656 - pyskl - INFO - +mean_acc 0.7705 +2025-06-24 19:47:59,658 - pyskl - INFO - Epoch(val) [55][533] top1_acc: 0.8285, top5_acc: 0.9851, mean_class_accuracy: 0.7705 +2025-06-24 19:49:18,563 - pyskl - INFO - Epoch [56][100/1281] lr: 1.757e-02, eta: 13:57:25, time: 0.789, data_time: 0.195, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9956, loss_cls: 0.5188, loss: 0.5188 +2025-06-24 19:50:07,401 - pyskl - INFO - Epoch [56][200/1281] lr: 1.755e-02, eta: 13:56:56, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9975, loss_cls: 0.4644, loss: 0.4644 +2025-06-24 19:50:40,398 - pyskl - INFO - Epoch [56][300/1281] lr: 1.753e-02, eta: 13:56:01, time: 0.330, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9950, loss_cls: 0.4604, loss: 0.4604 +2025-06-24 19:51:31,417 - pyskl - INFO - Epoch [56][400/1281] lr: 1.751e-02, eta: 13:55:36, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9969, loss_cls: 0.5024, loss: 0.5024 +2025-06-24 19:51:56,843 - pyskl - INFO - Epoch [56][500/1281] lr: 1.749e-02, eta: 13:54:28, time: 0.254, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9981, loss_cls: 0.4738, loss: 0.4738 +2025-06-24 19:52:45,648 - pyskl - INFO - Epoch [56][600/1281] lr: 1.747e-02, eta: 13:53:59, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9988, loss_cls: 0.5142, loss: 0.5142 +2025-06-24 19:53:34,650 - pyskl - INFO - Epoch [56][700/1281] lr: 1.745e-02, eta: 13:53:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9969, loss_cls: 0.5770, loss: 0.5770 +2025-06-24 19:54:23,931 - pyskl - INFO - Epoch [56][800/1281] lr: 1.743e-02, eta: 13:53:03, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9969, loss_cls: 0.4405, loss: 0.4405 +2025-06-24 19:55:13,254 - pyskl - INFO - Epoch [56][900/1281] lr: 1.742e-02, eta: 13:52:35, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9969, loss_cls: 0.4947, loss: 0.4947 +2025-06-24 19:56:02,426 - pyskl - INFO - Epoch [56][1000/1281] lr: 1.740e-02, eta: 13:52:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9931, loss_cls: 0.4742, loss: 0.4742 +2025-06-24 19:56:51,994 - pyskl - INFO - Epoch [56][1100/1281] lr: 1.738e-02, eta: 13:51:39, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9956, loss_cls: 0.5185, loss: 0.5185 +2025-06-24 19:57:41,319 - pyskl - INFO - Epoch [56][1200/1281] lr: 1.736e-02, eta: 13:51:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9969, loss_cls: 0.5061, loss: 0.5061 +2025-06-24 19:58:21,315 - pyskl - INFO - Saving checkpoint at 56 epochs +2025-06-24 19:59:20,010 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:59:20,067 - pyskl - INFO - +top1_acc 0.8545 +top5_acc 0.9867 +2025-06-24 19:59:20,067 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:59:20,074 - pyskl - INFO - +mean_acc 0.7918 +2025-06-24 19:59:20,075 - pyskl - INFO - Epoch(val) [56][533] top1_acc: 0.8545, top5_acc: 0.9867, mean_class_accuracy: 0.7918 +2025-06-24 20:00:40,962 - pyskl - INFO - Epoch [57][100/1281] lr: 1.733e-02, eta: 13:50:06, time: 0.809, data_time: 0.193, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9981, loss_cls: 0.5197, loss: 0.5197 +2025-06-24 20:01:30,214 - pyskl - INFO - Epoch [57][200/1281] lr: 1.731e-02, eta: 13:49:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9994, loss_cls: 0.4325, loss: 0.4325 +2025-06-24 20:02:03,422 - pyskl - INFO - Epoch [57][300/1281] lr: 1.729e-02, eta: 13:48:43, time: 0.332, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9969, loss_cls: 0.4210, loss: 0.4210 +2025-06-24 20:02:54,485 - pyskl - INFO - Epoch [57][400/1281] lr: 1.727e-02, eta: 13:48:18, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9975, loss_cls: 0.4822, loss: 0.4822 +2025-06-24 20:03:19,567 - pyskl - INFO - Epoch [57][500/1281] lr: 1.725e-02, eta: 13:47:09, time: 0.251, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9988, loss_cls: 0.4143, loss: 0.4143 +2025-06-24 20:04:07,877 - pyskl - INFO - Epoch [57][600/1281] lr: 1.723e-02, eta: 13:46:39, time: 0.483, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9962, loss_cls: 0.4746, loss: 0.4746 +2025-06-24 20:04:56,842 - pyskl - INFO - Epoch [57][700/1281] lr: 1.721e-02, eta: 13:46:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9969, loss_cls: 0.5086, loss: 0.5086 +2025-06-24 20:05:45,753 - pyskl - INFO - Epoch [57][800/1281] lr: 1.719e-02, eta: 13:45:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9994, loss_cls: 0.4312, loss: 0.4312 +2025-06-24 20:06:35,273 - pyskl - INFO - Epoch [57][900/1281] lr: 1.717e-02, eta: 13:45:13, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9944, loss_cls: 0.4984, loss: 0.4984 +2025-06-24 20:07:24,421 - pyskl - INFO - Epoch [57][1000/1281] lr: 1.716e-02, eta: 13:44:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9975, loss_cls: 0.5652, loss: 0.5652 +2025-06-24 20:08:14,038 - pyskl - INFO - Epoch [57][1100/1281] lr: 1.714e-02, eta: 13:44:16, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9956, loss_cls: 0.5038, loss: 0.5038 +2025-06-24 20:09:03,485 - pyskl - INFO - Epoch [57][1200/1281] lr: 1.712e-02, eta: 13:43:48, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9925, loss_cls: 0.4981, loss: 0.4981 +2025-06-24 20:09:43,763 - pyskl - INFO - Saving checkpoint at 57 epochs +2025-06-24 20:10:42,630 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:10:42,687 - pyskl - INFO - +top1_acc 0.8535 +top5_acc 0.9923 +2025-06-24 20:10:42,687 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:10:42,694 - pyskl - INFO - +mean_acc 0.8040 +2025-06-24 20:10:42,696 - pyskl - INFO - Epoch(val) [57][533] top1_acc: 0.8535, top5_acc: 0.9923, mean_class_accuracy: 0.8040 +2025-06-24 20:12:00,802 - pyskl - INFO - Epoch [58][100/1281] lr: 1.708e-02, eta: 13:42:38, time: 0.781, data_time: 0.188, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9981, loss_cls: 0.4565, loss: 0.4565 +2025-06-24 20:12:50,057 - pyskl - INFO - Epoch [58][200/1281] lr: 1.706e-02, eta: 13:42:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9981, loss_cls: 0.4720, loss: 0.4720 +2025-06-24 20:13:26,521 - pyskl - INFO - Epoch [58][300/1281] lr: 1.704e-02, eta: 13:41:20, time: 0.365, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9981, loss_cls: 0.4699, loss: 0.4699 +2025-06-24 20:14:17,484 - pyskl - INFO - Epoch [58][400/1281] lr: 1.703e-02, eta: 13:40:53, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9950, loss_cls: 0.4522, loss: 0.4522 +2025-06-24 20:14:42,048 - pyskl - INFO - Epoch [58][500/1281] lr: 1.701e-02, eta: 13:39:45, time: 0.246, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9975, loss_cls: 0.4835, loss: 0.4835 +2025-06-24 20:15:28,834 - pyskl - INFO - Epoch [58][600/1281] lr: 1.699e-02, eta: 13:39:12, time: 0.468, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9962, loss_cls: 0.4973, loss: 0.4973 +2025-06-24 20:16:18,021 - pyskl - INFO - Epoch [58][700/1281] lr: 1.697e-02, eta: 13:38:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9988, loss_cls: 0.4689, loss: 0.4689 +2025-06-24 20:17:07,069 - pyskl - INFO - Epoch [58][800/1281] lr: 1.695e-02, eta: 13:38:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9931, loss_cls: 0.5499, loss: 0.5499 +2025-06-24 20:17:56,149 - pyskl - INFO - Epoch [58][900/1281] lr: 1.693e-02, eta: 13:37:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9962, loss_cls: 0.4750, loss: 0.4750 +2025-06-24 20:18:45,511 - pyskl - INFO - Epoch [58][1000/1281] lr: 1.691e-02, eta: 13:37:15, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9988, loss_cls: 0.4752, loss: 0.4752 +2025-06-24 20:19:34,727 - pyskl - INFO - Epoch [58][1100/1281] lr: 1.689e-02, eta: 13:36:46, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9969, loss_cls: 0.4766, loss: 0.4766 +2025-06-24 20:20:23,756 - pyskl - INFO - Epoch [58][1200/1281] lr: 1.687e-02, eta: 13:36:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9981, loss_cls: 0.4168, loss: 0.4168 +2025-06-24 20:21:04,338 - pyskl - INFO - Saving checkpoint at 58 epochs +2025-06-24 20:22:03,586 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:22:03,659 - pyskl - INFO - +top1_acc 0.8560 +top5_acc 0.9913 +2025-06-24 20:22:03,659 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:22:03,667 - pyskl - INFO - +mean_acc 0.7981 +2025-06-24 20:22:03,669 - pyskl - INFO - Epoch(val) [58][533] top1_acc: 0.8560, top5_acc: 0.9913, mean_class_accuracy: 0.7981 +2025-06-24 20:23:24,868 - pyskl - INFO - Epoch [59][100/1281] lr: 1.684e-02, eta: 13:35:11, time: 0.812, data_time: 0.198, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9950, loss_cls: 0.4957, loss: 0.4957 +2025-06-24 20:24:14,141 - pyskl - INFO - Epoch [59][200/1281] lr: 1.682e-02, eta: 13:34:41, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9988, loss_cls: 0.4478, loss: 0.4478 +2025-06-24 20:24:49,322 - pyskl - INFO - Epoch [59][300/1281] lr: 1.680e-02, eta: 13:33:50, time: 0.352, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9962, loss_cls: 0.4591, loss: 0.4591 +2025-06-24 20:25:40,358 - pyskl - INFO - Epoch [59][400/1281] lr: 1.678e-02, eta: 13:33:23, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9956, loss_cls: 0.4227, loss: 0.4227 +2025-06-24 20:26:04,759 - pyskl - INFO - Epoch [59][500/1281] lr: 1.676e-02, eta: 13:32:15, time: 0.244, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9981, loss_cls: 0.4461, loss: 0.4461 +2025-06-24 20:26:50,510 - pyskl - INFO - Epoch [59][600/1281] lr: 1.674e-02, eta: 13:31:40, time: 0.458, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9962, loss_cls: 0.4496, loss: 0.4496 +2025-06-24 20:27:39,737 - pyskl - INFO - Epoch [59][700/1281] lr: 1.672e-02, eta: 13:31:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9975, loss_cls: 0.4789, loss: 0.4789 +2025-06-24 20:28:29,036 - pyskl - INFO - Epoch [59][800/1281] lr: 1.670e-02, eta: 13:30:41, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9981, loss_cls: 0.4582, loss: 0.4582 +2025-06-24 20:29:18,324 - pyskl - INFO - Epoch [59][900/1281] lr: 1.668e-02, eta: 13:30:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9975, loss_cls: 0.4345, loss: 0.4345 +2025-06-24 20:30:07,613 - pyskl - INFO - Epoch [59][1000/1281] lr: 1.667e-02, eta: 13:29:42, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9975, loss_cls: 0.4839, loss: 0.4839 +2025-06-24 20:30:56,941 - pyskl - INFO - Epoch [59][1100/1281] lr: 1.665e-02, eta: 13:29:12, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9969, loss_cls: 0.5008, loss: 0.5008 +2025-06-24 20:31:46,264 - pyskl - INFO - Epoch [59][1200/1281] lr: 1.663e-02, eta: 13:28:42, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9950, loss_cls: 0.5444, loss: 0.5444 +2025-06-24 20:32:26,541 - pyskl - INFO - Saving checkpoint at 59 epochs +2025-06-24 20:33:26,127 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:33:26,182 - pyskl - INFO - +top1_acc 0.8618 +top5_acc 0.9906 +2025-06-24 20:33:26,182 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:33:26,189 - pyskl - INFO - +mean_acc 0.8111 +2025-06-24 20:33:26,190 - pyskl - INFO - Epoch(val) [59][533] top1_acc: 0.8618, top5_acc: 0.9906, mean_class_accuracy: 0.8111 +2025-06-24 20:34:45,977 - pyskl - INFO - Epoch [60][100/1281] lr: 1.659e-02, eta: 13:27:34, time: 0.798, data_time: 0.193, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9969, loss_cls: 0.4250, loss: 0.4250 +2025-06-24 20:35:35,212 - pyskl - INFO - Epoch [60][200/1281] lr: 1.657e-02, eta: 13:27:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9994, loss_cls: 0.4111, loss: 0.4111 +2025-06-24 20:36:12,312 - pyskl - INFO - Epoch [60][300/1281] lr: 1.655e-02, eta: 13:26:16, time: 0.371, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9950, loss_cls: 0.5100, loss: 0.5100 +2025-06-24 20:37:03,438 - pyskl - INFO - Epoch [60][400/1281] lr: 1.653e-02, eta: 13:25:49, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9975, loss_cls: 0.4522, loss: 0.4522 +2025-06-24 20:37:26,377 - pyskl - INFO - Epoch [60][500/1281] lr: 1.651e-02, eta: 13:24:39, time: 0.229, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9962, loss_cls: 0.5076, loss: 0.5076 +2025-06-24 20:38:10,526 - pyskl - INFO - Epoch [60][600/1281] lr: 1.650e-02, eta: 13:24:01, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9988, loss_cls: 0.4583, loss: 0.4583 +2025-06-24 20:38:59,950 - pyskl - INFO - Epoch [60][700/1281] lr: 1.648e-02, eta: 13:23:31, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9975, loss_cls: 0.4973, loss: 0.4973 +2025-06-24 20:39:49,463 - pyskl - INFO - Epoch [60][800/1281] lr: 1.646e-02, eta: 13:23:01, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9975, loss_cls: 0.4674, loss: 0.4674 +2025-06-24 20:40:38,975 - pyskl - INFO - Epoch [60][900/1281] lr: 1.644e-02, eta: 13:22:32, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9981, loss_cls: 0.4316, loss: 0.4316 +2025-06-24 20:41:28,082 - pyskl - INFO - Epoch [60][1000/1281] lr: 1.642e-02, eta: 13:22:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9944, loss_cls: 0.4973, loss: 0.4973 +2025-06-24 20:42:17,438 - pyskl - INFO - Epoch [60][1100/1281] lr: 1.640e-02, eta: 13:21:31, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9969, loss_cls: 0.4619, loss: 0.4619 +2025-06-24 20:43:06,890 - pyskl - INFO - Epoch [60][1200/1281] lr: 1.638e-02, eta: 13:21:01, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9981, loss_cls: 0.4583, loss: 0.4583 +2025-06-24 20:43:47,299 - pyskl - INFO - Saving checkpoint at 60 epochs +2025-06-24 20:44:46,474 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:44:46,529 - pyskl - INFO - +top1_acc 0.8772 +top5_acc 0.9918 +2025-06-24 20:44:46,529 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:44:46,536 - pyskl - INFO - +mean_acc 0.8209 +2025-06-24 20:44:46,540 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_42.pth was removed +2025-06-24 20:44:46,732 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_60.pth. +2025-06-24 20:44:46,733 - pyskl - INFO - Best top1_acc is 0.8772 at 60 epoch. +2025-06-24 20:44:46,735 - pyskl - INFO - Epoch(val) [60][533] top1_acc: 0.8772, top5_acc: 0.9918, mean_class_accuracy: 0.8209 +2025-06-24 20:46:05,877 - pyskl - INFO - Epoch [61][100/1281] lr: 1.634e-02, eta: 13:19:52, time: 0.791, data_time: 0.186, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9975, loss_cls: 0.4410, loss: 0.4410 +2025-06-24 20:46:54,816 - pyskl - INFO - Epoch [61][200/1281] lr: 1.632e-02, eta: 13:19:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9994, loss_cls: 0.4670, loss: 0.4670 +2025-06-24 20:47:34,982 - pyskl - INFO - Epoch [61][300/1281] lr: 1.630e-02, eta: 13:18:37, time: 0.402, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9988, loss_cls: 0.4055, loss: 0.4055 +2025-06-24 20:48:23,443 - pyskl - INFO - Epoch [61][400/1281] lr: 1.629e-02, eta: 13:18:05, time: 0.485, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9981, loss_cls: 0.3868, loss: 0.3868 +2025-06-24 20:48:48,401 - pyskl - INFO - Epoch [61][500/1281] lr: 1.627e-02, eta: 13:16:59, time: 0.250, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9950, loss_cls: 0.5143, loss: 0.5143 +2025-06-24 20:49:30,952 - pyskl - INFO - Epoch [61][600/1281] lr: 1.625e-02, eta: 13:16:19, time: 0.425, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9981, loss_cls: 0.4298, loss: 0.4298 +2025-06-24 20:50:20,166 - pyskl - INFO - Epoch [61][700/1281] lr: 1.623e-02, eta: 13:15:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9969, loss_cls: 0.5043, loss: 0.5043 +2025-06-24 20:51:09,412 - pyskl - INFO - Epoch [61][800/1281] lr: 1.621e-02, eta: 13:15:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9981, loss_cls: 0.4478, loss: 0.4478 +2025-06-24 20:51:58,497 - pyskl - INFO - Epoch [61][900/1281] lr: 1.619e-02, eta: 13:14:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9981, loss_cls: 0.4640, loss: 0.4640 +2025-06-24 20:52:47,562 - pyskl - INFO - Epoch [61][1000/1281] lr: 1.617e-02, eta: 13:14:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9962, loss_cls: 0.4541, loss: 0.4541 +2025-06-24 20:53:36,606 - pyskl - INFO - Epoch [61][1100/1281] lr: 1.615e-02, eta: 13:13:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9950, loss_cls: 0.4967, loss: 0.4967 +2025-06-24 20:54:25,757 - pyskl - INFO - Epoch [61][1200/1281] lr: 1.613e-02, eta: 13:13:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9962, loss_cls: 0.5087, loss: 0.5087 +2025-06-24 20:55:06,183 - pyskl - INFO - Saving checkpoint at 61 epochs +2025-06-24 20:56:05,400 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:56:05,465 - pyskl - INFO - +top1_acc 0.8488 +top5_acc 0.9898 +2025-06-24 20:56:05,465 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:56:05,473 - pyskl - INFO - +mean_acc 0.8209 +2025-06-24 20:56:05,475 - pyskl - INFO - Epoch(val) [61][533] top1_acc: 0.8488, top5_acc: 0.9898, mean_class_accuracy: 0.8209 +2025-06-24 20:57:24,116 - pyskl - INFO - Epoch [62][100/1281] lr: 1.609e-02, eta: 13:12:03, time: 0.786, data_time: 0.191, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9969, loss_cls: 0.4858, loss: 0.4858 +2025-06-24 20:58:13,228 - pyskl - INFO - Epoch [62][200/1281] lr: 1.607e-02, eta: 13:11:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9981, loss_cls: 0.3989, loss: 0.3989 +2025-06-24 20:58:56,960 - pyskl - INFO - Epoch [62][300/1281] lr: 1.605e-02, eta: 13:10:53, time: 0.437, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9981, loss_cls: 0.4569, loss: 0.4569 +2025-06-24 20:59:38,415 - pyskl - INFO - Epoch [62][400/1281] lr: 1.603e-02, eta: 13:10:11, time: 0.415, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9975, loss_cls: 0.4629, loss: 0.4629 +2025-06-24 21:00:10,303 - pyskl - INFO - Epoch [62][500/1281] lr: 1.602e-02, eta: 13:09:15, time: 0.319, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9962, loss_cls: 0.4815, loss: 0.4815 +2025-06-24 21:00:50,533 - pyskl - INFO - Epoch [62][600/1281] lr: 1.600e-02, eta: 13:08:31, time: 0.402, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9981, loss_cls: 0.4272, loss: 0.4272 +2025-06-24 21:01:39,927 - pyskl - INFO - Epoch [62][700/1281] lr: 1.598e-02, eta: 13:08:01, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9962, loss_cls: 0.4403, loss: 0.4403 +2025-06-24 21:02:28,965 - pyskl - INFO - Epoch [62][800/1281] lr: 1.596e-02, eta: 13:07:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9988, loss_cls: 0.4397, loss: 0.4397 +2025-06-24 21:03:18,455 - pyskl - INFO - Epoch [62][900/1281] lr: 1.594e-02, eta: 13:06:59, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9969, loss_cls: 0.4425, loss: 0.4425 +2025-06-24 21:04:07,575 - pyskl - INFO - Epoch [62][1000/1281] lr: 1.592e-02, eta: 13:06:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9994, loss_cls: 0.4472, loss: 0.4472 +2025-06-24 21:04:56,535 - pyskl - INFO - Epoch [62][1100/1281] lr: 1.590e-02, eta: 13:05:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9975, loss_cls: 0.4999, loss: 0.4999 +2025-06-24 21:05:45,956 - pyskl - INFO - Epoch [62][1200/1281] lr: 1.588e-02, eta: 13:05:25, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9956, loss_cls: 0.5013, loss: 0.5013 +2025-06-24 21:06:26,288 - pyskl - INFO - Saving checkpoint at 62 epochs +2025-06-24 21:07:25,285 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:07:25,354 - pyskl - INFO - +top1_acc 0.8809 +top5_acc 0.9923 +2025-06-24 21:07:25,355 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:07:25,362 - pyskl - INFO - +mean_acc 0.8427 +2025-06-24 21:07:25,366 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_60.pth was removed +2025-06-24 21:07:25,535 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_62.pth. +2025-06-24 21:07:25,535 - pyskl - INFO - Best top1_acc is 0.8809 at 62 epoch. +2025-06-24 21:07:25,538 - pyskl - INFO - Epoch(val) [62][533] top1_acc: 0.8809, top5_acc: 0.9923, mean_class_accuracy: 0.8427 +2025-06-24 21:08:43,676 - pyskl - INFO - Epoch [63][100/1281] lr: 1.584e-02, eta: 13:04:13, time: 0.781, data_time: 0.185, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9988, loss_cls: 0.3936, loss: 0.3936 +2025-06-24 21:09:33,190 - pyskl - INFO - Epoch [63][200/1281] lr: 1.582e-02, eta: 13:03:42, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 1.0000, loss_cls: 0.4039, loss: 0.4039 +2025-06-24 21:10:20,119 - pyskl - INFO - Epoch [63][300/1281] lr: 1.580e-02, eta: 13:03:08, time: 0.469, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9981, loss_cls: 0.4236, loss: 0.4236 +2025-06-24 21:10:57,117 - pyskl - INFO - Epoch [63][400/1281] lr: 1.578e-02, eta: 13:02:19, time: 0.370, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9981, loss_cls: 0.4227, loss: 0.4227 +2025-06-24 21:11:33,861 - pyskl - INFO - Epoch [63][500/1281] lr: 1.576e-02, eta: 13:01:30, time: 0.367, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9962, loss_cls: 0.4490, loss: 0.4490 +2025-06-24 21:12:12,794 - pyskl - INFO - Epoch [63][600/1281] lr: 1.574e-02, eta: 13:00:45, time: 0.389, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9981, loss_cls: 0.4749, loss: 0.4749 +2025-06-24 21:13:01,836 - pyskl - INFO - Epoch [63][700/1281] lr: 1.572e-02, eta: 13:00:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9956, loss_cls: 0.4778, loss: 0.4778 +2025-06-24 21:13:50,986 - pyskl - INFO - Epoch [63][800/1281] lr: 1.570e-02, eta: 12:59:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9962, loss_cls: 0.4255, loss: 0.4255 +2025-06-24 21:14:39,815 - pyskl - INFO - Epoch [63][900/1281] lr: 1.568e-02, eta: 12:59:09, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9981, loss_cls: 0.4350, loss: 0.4350 +2025-06-24 21:15:29,069 - pyskl - INFO - Epoch [63][1000/1281] lr: 1.566e-02, eta: 12:58:38, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9975, loss_cls: 0.4571, loss: 0.4571 +2025-06-24 21:16:18,801 - pyskl - INFO - Epoch [63][1100/1281] lr: 1.564e-02, eta: 12:58:07, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9988, loss_cls: 0.4289, loss: 0.4289 +2025-06-24 21:17:07,909 - pyskl - INFO - Epoch [63][1200/1281] lr: 1.562e-02, eta: 12:57:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9962, loss_cls: 0.4703, loss: 0.4703 +2025-06-24 21:17:48,140 - pyskl - INFO - Saving checkpoint at 63 epochs +2025-06-24 21:18:47,743 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:18:47,815 - pyskl - INFO - +top1_acc 0.8630 +top5_acc 0.9930 +2025-06-24 21:18:47,815 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:18:47,823 - pyskl - INFO - +mean_acc 0.7980 +2025-06-24 21:18:47,825 - pyskl - INFO - Epoch(val) [63][533] top1_acc: 0.8630, top5_acc: 0.9930, mean_class_accuracy: 0.7980 +2025-06-24 21:20:06,626 - pyskl - INFO - Epoch [64][100/1281] lr: 1.559e-02, eta: 12:56:24, time: 0.788, data_time: 0.191, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9975, loss_cls: 0.3967, loss: 0.3967 +2025-06-24 21:20:55,811 - pyskl - INFO - Epoch [64][200/1281] lr: 1.557e-02, eta: 12:55:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9988, loss_cls: 0.4168, loss: 0.4168 +2025-06-24 21:21:42,872 - pyskl - INFO - Epoch [64][300/1281] lr: 1.555e-02, eta: 12:55:18, time: 0.471, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9988, loss_cls: 0.4106, loss: 0.4106 +2025-06-24 21:22:18,104 - pyskl - INFO - Epoch [64][400/1281] lr: 1.553e-02, eta: 12:54:27, time: 0.352, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9950, loss_cls: 0.3690, loss: 0.3690 +2025-06-24 21:22:56,493 - pyskl - INFO - Epoch [64][500/1281] lr: 1.551e-02, eta: 12:53:40, time: 0.384, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9981, loss_cls: 0.4199, loss: 0.4199 +2025-06-24 21:23:32,978 - pyskl - INFO - Epoch [64][600/1281] lr: 1.549e-02, eta: 12:52:51, time: 0.365, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9969, loss_cls: 0.4903, loss: 0.4903 +2025-06-24 21:24:22,297 - pyskl - INFO - Epoch [64][700/1281] lr: 1.547e-02, eta: 12:52:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9950, loss_cls: 0.5002, loss: 0.5002 +2025-06-24 21:25:11,279 - pyskl - INFO - Epoch [64][800/1281] lr: 1.545e-02, eta: 12:51:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9988, loss_cls: 0.4062, loss: 0.4062 +2025-06-24 21:26:00,442 - pyskl - INFO - Epoch [64][900/1281] lr: 1.543e-02, eta: 12:51:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9981, loss_cls: 0.4566, loss: 0.4566 +2025-06-24 21:26:49,616 - pyskl - INFO - Epoch [64][1000/1281] lr: 1.541e-02, eta: 12:50:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9988, loss_cls: 0.4759, loss: 0.4759 +2025-06-24 21:27:38,890 - pyskl - INFO - Epoch [64][1100/1281] lr: 1.539e-02, eta: 12:50:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9975, loss_cls: 0.4370, loss: 0.4370 +2025-06-24 21:28:28,408 - pyskl - INFO - Epoch [64][1200/1281] lr: 1.537e-02, eta: 12:49:40, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9956, loss_cls: 0.4752, loss: 0.4752 +2025-06-24 21:29:08,985 - pyskl - INFO - Saving checkpoint at 64 epochs +2025-06-24 21:30:07,641 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:30:07,698 - pyskl - INFO - +top1_acc 0.8749 +top5_acc 0.9917 +2025-06-24 21:30:07,698 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:30:07,710 - pyskl - INFO - +mean_acc 0.8276 +2025-06-24 21:30:07,712 - pyskl - INFO - Epoch(val) [64][533] top1_acc: 0.8749, top5_acc: 0.9917, mean_class_accuracy: 0.8276 +2025-06-24 21:31:27,835 - pyskl - INFO - Epoch [65][100/1281] lr: 1.533e-02, eta: 12:48:30, time: 0.801, data_time: 0.201, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9969, loss_cls: 0.4210, loss: 0.4210 +2025-06-24 21:32:17,010 - pyskl - INFO - Epoch [65][200/1281] lr: 1.531e-02, eta: 12:47:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.4132, loss: 0.4132 +2025-06-24 21:33:05,259 - pyskl - INFO - Epoch [65][300/1281] lr: 1.529e-02, eta: 12:47:24, time: 0.482, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9988, loss_cls: 0.3877, loss: 0.3877 +2025-06-24 21:33:37,276 - pyskl - INFO - Epoch [65][400/1281] lr: 1.527e-02, eta: 12:46:29, time: 0.320, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9981, loss_cls: 0.3992, loss: 0.3992 +2025-06-24 21:34:18,829 - pyskl - INFO - Epoch [65][500/1281] lr: 1.526e-02, eta: 12:45:47, time: 0.416, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9994, loss_cls: 0.4309, loss: 0.4309 +2025-06-24 21:34:53,173 - pyskl - INFO - Epoch [65][600/1281] lr: 1.524e-02, eta: 12:44:55, time: 0.343, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9981, loss_cls: 0.3793, loss: 0.3793 +2025-06-24 21:35:42,623 - pyskl - INFO - Epoch [65][700/1281] lr: 1.522e-02, eta: 12:44:23, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9975, loss_cls: 0.4628, loss: 0.4628 +2025-06-24 21:36:31,751 - pyskl - INFO - Epoch [65][800/1281] lr: 1.520e-02, eta: 12:43:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9962, loss_cls: 0.4699, loss: 0.4699 +2025-06-24 21:37:20,426 - pyskl - INFO - Epoch [65][900/1281] lr: 1.518e-02, eta: 12:43:18, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9944, loss_cls: 0.5286, loss: 0.5286 +2025-06-24 21:38:09,468 - pyskl - INFO - Epoch [65][1000/1281] lr: 1.516e-02, eta: 12:42:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9938, loss_cls: 0.4984, loss: 0.4984 +2025-06-24 21:38:58,762 - pyskl - INFO - Epoch [65][1100/1281] lr: 1.514e-02, eta: 12:42:13, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9969, loss_cls: 0.4372, loss: 0.4372 +2025-06-24 21:39:47,603 - pyskl - INFO - Epoch [65][1200/1281] lr: 1.512e-02, eta: 12:41:40, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9962, loss_cls: 0.4728, loss: 0.4728 +2025-06-24 21:40:28,126 - pyskl - INFO - Saving checkpoint at 65 epochs +2025-06-24 21:41:27,295 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:41:27,350 - pyskl - INFO - +top1_acc 0.8858 +top5_acc 0.9939 +2025-06-24 21:41:27,351 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:41:27,357 - pyskl - INFO - +mean_acc 0.8445 +2025-06-24 21:41:27,361 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_62.pth was removed +2025-06-24 21:41:27,528 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_65.pth. +2025-06-24 21:41:27,528 - pyskl - INFO - Best top1_acc is 0.8858 at 65 epoch. +2025-06-24 21:41:27,531 - pyskl - INFO - Epoch(val) [65][533] top1_acc: 0.8858, top5_acc: 0.9939, mean_class_accuracy: 0.8445 +2025-06-24 21:42:46,913 - pyskl - INFO - Epoch [66][100/1281] lr: 1.508e-02, eta: 12:40:28, time: 0.794, data_time: 0.190, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9981, loss_cls: 0.4114, loss: 0.4114 +2025-06-24 21:43:36,257 - pyskl - INFO - Epoch [66][200/1281] lr: 1.506e-02, eta: 12:39:56, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9975, loss_cls: 0.4078, loss: 0.4078 +2025-06-24 21:44:25,465 - pyskl - INFO - Epoch [66][300/1281] lr: 1.504e-02, eta: 12:39:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9981, loss_cls: 0.4255, loss: 0.4255 +2025-06-24 21:44:53,335 - pyskl - INFO - Epoch [66][400/1281] lr: 1.502e-02, eta: 12:38:23, time: 0.279, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9981, loss_cls: 0.3669, loss: 0.3669 +2025-06-24 21:45:40,372 - pyskl - INFO - Epoch [66][500/1281] lr: 1.500e-02, eta: 12:37:48, time: 0.470, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9969, loss_cls: 0.4241, loss: 0.4241 +2025-06-24 21:46:12,278 - pyskl - INFO - Epoch [66][600/1281] lr: 1.498e-02, eta: 12:36:53, time: 0.319, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9962, loss_cls: 0.5017, loss: 0.5017 +2025-06-24 21:47:01,527 - pyskl - INFO - Epoch [66][700/1281] lr: 1.496e-02, eta: 12:36:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 1.0000, loss_cls: 0.3868, loss: 0.3868 +2025-06-24 21:47:50,973 - pyskl - INFO - Epoch [66][800/1281] lr: 1.494e-02, eta: 12:35:48, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9962, loss_cls: 0.4578, loss: 0.4578 +2025-06-24 21:48:39,983 - pyskl - INFO - Epoch [66][900/1281] lr: 1.492e-02, eta: 12:35:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9956, loss_cls: 0.4139, loss: 0.4139 +2025-06-24 21:49:29,456 - pyskl - INFO - Epoch [66][1000/1281] lr: 1.490e-02, eta: 12:34:43, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9988, loss_cls: 0.4028, loss: 0.4028 +2025-06-24 21:50:18,650 - pyskl - INFO - Epoch [66][1100/1281] lr: 1.488e-02, eta: 12:34:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9950, loss_cls: 0.4361, loss: 0.4361 +2025-06-24 21:51:07,661 - pyskl - INFO - Epoch [66][1200/1281] lr: 1.486e-02, eta: 12:33:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9981, loss_cls: 0.4322, loss: 0.4322 +2025-06-24 21:51:48,148 - pyskl - INFO - Saving checkpoint at 66 epochs +2025-06-24 21:52:46,637 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:52:46,693 - pyskl - INFO - +top1_acc 0.8711 +top5_acc 0.9907 +2025-06-24 21:52:46,693 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:52:46,701 - pyskl - INFO - +mean_acc 0.8289 +2025-06-24 21:52:46,703 - pyskl - INFO - Epoch(val) [66][533] top1_acc: 0.8711, top5_acc: 0.9907, mean_class_accuracy: 0.8289 +2025-06-24 21:54:06,320 - pyskl - INFO - Epoch [67][100/1281] lr: 1.482e-02, eta: 12:32:26, time: 0.796, data_time: 0.188, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9969, loss_cls: 0.3607, loss: 0.3607 +2025-06-24 21:54:55,512 - pyskl - INFO - Epoch [67][200/1281] lr: 1.480e-02, eta: 12:31:53, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9994, loss_cls: 0.3697, loss: 0.3697 +2025-06-24 21:55:44,285 - pyskl - INFO - Epoch [67][300/1281] lr: 1.478e-02, eta: 12:31:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9981, loss_cls: 0.4326, loss: 0.4326 +2025-06-24 21:56:11,394 - pyskl - INFO - Epoch [67][400/1281] lr: 1.476e-02, eta: 12:30:19, time: 0.271, data_time: 0.001, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9956, loss_cls: 0.4282, loss: 0.4282 +2025-06-24 21:57:02,415 - pyskl - INFO - Epoch [67][500/1281] lr: 1.474e-02, eta: 12:29:48, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9981, loss_cls: 0.3471, loss: 0.3471 +2025-06-24 21:57:30,914 - pyskl - INFO - Epoch [67][600/1281] lr: 1.472e-02, eta: 12:28:49, time: 0.285, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9969, loss_cls: 0.4537, loss: 0.4537 +2025-06-24 21:58:20,291 - pyskl - INFO - Epoch [67][700/1281] lr: 1.470e-02, eta: 12:28:16, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9988, loss_cls: 0.4060, loss: 0.4060 +2025-06-24 21:59:09,702 - pyskl - INFO - Epoch [67][800/1281] lr: 1.468e-02, eta: 12:27:44, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9975, loss_cls: 0.4349, loss: 0.4349 +2025-06-24 21:59:59,118 - pyskl - INFO - Epoch [67][900/1281] lr: 1.466e-02, eta: 12:27:11, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 1.0000, loss_cls: 0.3845, loss: 0.3845 +2025-06-24 22:00:48,168 - pyskl - INFO - Epoch [67][1000/1281] lr: 1.464e-02, eta: 12:26:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9981, loss_cls: 0.4306, loss: 0.4306 +2025-06-24 22:01:37,312 - pyskl - INFO - Epoch [67][1100/1281] lr: 1.462e-02, eta: 12:26:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4246, loss: 0.4246 +2025-06-24 22:02:26,874 - pyskl - INFO - Epoch [67][1200/1281] lr: 1.460e-02, eta: 12:25:32, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9988, loss_cls: 0.4262, loss: 0.4262 +2025-06-24 22:03:07,205 - pyskl - INFO - Saving checkpoint at 67 epochs +2025-06-24 22:04:05,476 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:04:05,542 - pyskl - INFO - +top1_acc 0.8865 +top5_acc 0.9933 +2025-06-24 22:04:05,542 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:04:05,550 - pyskl - INFO - +mean_acc 0.8429 +2025-06-24 22:04:05,556 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_65.pth was removed +2025-06-24 22:04:05,751 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_67.pth. +2025-06-24 22:04:05,752 - pyskl - INFO - Best top1_acc is 0.8865 at 67 epoch. +2025-06-24 22:04:05,754 - pyskl - INFO - Epoch(val) [67][533] top1_acc: 0.8865, top5_acc: 0.9933, mean_class_accuracy: 0.8429 +2025-06-24 22:05:26,285 - pyskl - INFO - Epoch [68][100/1281] lr: 1.456e-02, eta: 12:24:21, time: 0.805, data_time: 0.191, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9981, loss_cls: 0.4291, loss: 0.4291 +2025-06-24 22:06:15,163 - pyskl - INFO - Epoch [68][200/1281] lr: 1.454e-02, eta: 12:23:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9975, loss_cls: 0.3742, loss: 0.3742 +2025-06-24 22:07:04,559 - pyskl - INFO - Epoch [68][300/1281] lr: 1.452e-02, eta: 12:23:15, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9981, loss_cls: 0.4192, loss: 0.4192 +2025-06-24 22:07:35,547 - pyskl - INFO - Epoch [68][400/1281] lr: 1.450e-02, eta: 12:22:19, time: 0.310, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9962, loss_cls: 0.4032, loss: 0.4032 +2025-06-24 22:08:26,505 - pyskl - INFO - Epoch [68][500/1281] lr: 1.448e-02, eta: 12:21:48, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 1.0000, loss_cls: 0.3981, loss: 0.3981 +2025-06-24 22:08:52,589 - pyskl - INFO - Epoch [68][600/1281] lr: 1.446e-02, eta: 12:20:46, time: 0.261, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3770, loss: 0.3770 +2025-06-24 22:09:42,017 - pyskl - INFO - Epoch [68][700/1281] lr: 1.444e-02, eta: 12:20:13, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9981, loss_cls: 0.3984, loss: 0.3984 +2025-06-24 22:10:31,069 - pyskl - INFO - Epoch [68][800/1281] lr: 1.442e-02, eta: 12:19:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9969, loss_cls: 0.4539, loss: 0.4539 +2025-06-24 22:11:20,257 - pyskl - INFO - Epoch [68][900/1281] lr: 1.440e-02, eta: 12:19:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9962, loss_cls: 0.4348, loss: 0.4348 +2025-06-24 22:12:09,248 - pyskl - INFO - Epoch [68][1000/1281] lr: 1.438e-02, eta: 12:18:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9988, loss_cls: 0.3743, loss: 0.3743 +2025-06-24 22:12:58,449 - pyskl - INFO - Epoch [68][1100/1281] lr: 1.436e-02, eta: 12:17:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.3870, loss: 0.3870 +2025-06-24 22:13:47,670 - pyskl - INFO - Epoch [68][1200/1281] lr: 1.434e-02, eta: 12:17:26, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9956, loss_cls: 0.4379, loss: 0.4379 +2025-06-24 22:14:28,228 - pyskl - INFO - Saving checkpoint at 68 epochs +2025-06-24 22:15:26,364 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:15:26,421 - pyskl - INFO - +top1_acc 0.8660 +top5_acc 0.9906 +2025-06-24 22:15:26,421 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:15:26,429 - pyskl - INFO - +mean_acc 0.8267 +2025-06-24 22:15:26,430 - pyskl - INFO - Epoch(val) [68][533] top1_acc: 0.8660, top5_acc: 0.9906, mean_class_accuracy: 0.8267 +2025-06-24 22:16:46,183 - pyskl - INFO - Epoch [69][100/1281] lr: 1.431e-02, eta: 12:16:14, time: 0.797, data_time: 0.192, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9956, loss_cls: 0.4054, loss: 0.4054 +2025-06-24 22:17:35,441 - pyskl - INFO - Epoch [69][200/1281] lr: 1.429e-02, eta: 12:15:40, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9981, loss_cls: 0.3912, loss: 0.3912 +2025-06-24 22:18:24,500 - pyskl - INFO - Epoch [69][300/1281] lr: 1.427e-02, eta: 12:15:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 1.0000, loss_cls: 0.4219, loss: 0.4219 +2025-06-24 22:18:58,278 - pyskl - INFO - Epoch [69][400/1281] lr: 1.425e-02, eta: 12:14:14, time: 0.338, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 1.0000, loss_cls: 0.3860, loss: 0.3860 +2025-06-24 22:19:49,398 - pyskl - INFO - Epoch [69][500/1281] lr: 1.423e-02, eta: 12:13:43, time: 0.511, data_time: 0.001, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.3999, loss: 0.3999 +2025-06-24 22:20:14,430 - pyskl - INFO - Epoch [69][600/1281] lr: 1.420e-02, eta: 12:12:41, time: 0.250, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9981, loss_cls: 0.3779, loss: 0.3779 +2025-06-24 22:21:01,906 - pyskl - INFO - Epoch [69][700/1281] lr: 1.418e-02, eta: 12:12:05, time: 0.475, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9994, loss_cls: 0.3835, loss: 0.3835 +2025-06-24 22:21:51,360 - pyskl - INFO - Epoch [69][800/1281] lr: 1.416e-02, eta: 12:11:32, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9981, loss_cls: 0.3845, loss: 0.3845 +2025-06-24 22:22:40,591 - pyskl - INFO - Epoch [69][900/1281] lr: 1.414e-02, eta: 12:10:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4266, loss: 0.4266 +2025-06-24 22:23:29,798 - pyskl - INFO - Epoch [69][1000/1281] lr: 1.412e-02, eta: 12:10:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9994, loss_cls: 0.4011, loss: 0.4011 +2025-06-24 22:24:19,105 - pyskl - INFO - Epoch [69][1100/1281] lr: 1.410e-02, eta: 12:09:50, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9975, loss_cls: 0.4300, loss: 0.4300 +2025-06-24 22:25:08,503 - pyskl - INFO - Epoch [69][1200/1281] lr: 1.408e-02, eta: 12:09:17, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9950, loss_cls: 0.4603, loss: 0.4603 +2025-06-24 22:25:48,997 - pyskl - INFO - Saving checkpoint at 69 epochs +2025-06-24 22:26:48,194 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:26:48,261 - pyskl - INFO - +top1_acc 0.8862 +top5_acc 0.9919 +2025-06-24 22:26:48,261 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:26:48,268 - pyskl - INFO - +mean_acc 0.8383 +2025-06-24 22:26:48,270 - pyskl - INFO - Epoch(val) [69][533] top1_acc: 0.8862, top5_acc: 0.9919, mean_class_accuracy: 0.8383 +2025-06-24 22:28:08,688 - pyskl - INFO - Epoch [70][100/1281] lr: 1.405e-02, eta: 12:08:05, time: 0.804, data_time: 0.196, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9988, loss_cls: 0.3361, loss: 0.3361 +2025-06-24 22:28:57,960 - pyskl - INFO - Epoch [70][200/1281] lr: 1.403e-02, eta: 12:07:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9975, loss_cls: 0.3664, loss: 0.3664 +2025-06-24 22:29:47,180 - pyskl - INFO - Epoch [70][300/1281] lr: 1.401e-02, eta: 12:06:58, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9988, loss_cls: 0.3292, loss: 0.3292 +2025-06-24 22:30:21,470 - pyskl - INFO - Epoch [70][400/1281] lr: 1.399e-02, eta: 12:06:06, time: 0.343, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9962, loss_cls: 0.3771, loss: 0.3771 +2025-06-24 22:31:12,479 - pyskl - INFO - Epoch [70][500/1281] lr: 1.397e-02, eta: 12:05:35, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9969, loss_cls: 0.3977, loss: 0.3977 +2025-06-24 22:31:37,039 - pyskl - INFO - Epoch [70][600/1281] lr: 1.395e-02, eta: 12:04:32, time: 0.246, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9981, loss_cls: 0.3457, loss: 0.3457 +2025-06-24 22:32:23,942 - pyskl - INFO - Epoch [70][700/1281] lr: 1.392e-02, eta: 12:03:55, time: 0.469, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9969, loss_cls: 0.3688, loss: 0.3688 +2025-06-24 22:33:13,032 - pyskl - INFO - Epoch [70][800/1281] lr: 1.390e-02, eta: 12:03:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9988, loss_cls: 0.3890, loss: 0.3890 +2025-06-24 22:34:01,978 - pyskl - INFO - Epoch [70][900/1281] lr: 1.388e-02, eta: 12:02:47, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4422, loss: 0.4422 +2025-06-24 22:34:51,194 - pyskl - INFO - Epoch [70][1000/1281] lr: 1.386e-02, eta: 12:02:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9962, loss_cls: 0.5132, loss: 0.5132 +2025-06-24 22:35:40,635 - pyskl - INFO - Epoch [70][1100/1281] lr: 1.384e-02, eta: 12:01:39, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9988, loss_cls: 0.4442, loss: 0.4442 +2025-06-24 22:36:30,000 - pyskl - INFO - Epoch [70][1200/1281] lr: 1.382e-02, eta: 12:01:05, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.4309, loss: 0.4309 +2025-06-24 22:37:10,726 - pyskl - INFO - Saving checkpoint at 70 epochs +2025-06-24 22:38:09,657 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:38:09,749 - pyskl - INFO - +top1_acc 0.8718 +top5_acc 0.9918 +2025-06-24 22:38:09,749 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:38:09,756 - pyskl - INFO - +mean_acc 0.8277 +2025-06-24 22:38:09,758 - pyskl - INFO - Epoch(val) [70][533] top1_acc: 0.8718, top5_acc: 0.9918, mean_class_accuracy: 0.8277 +2025-06-24 22:39:29,808 - pyskl - INFO - Epoch [71][100/1281] lr: 1.379e-02, eta: 11:59:53, time: 0.800, data_time: 0.194, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9969, loss_cls: 0.3880, loss: 0.3880 +2025-06-24 22:40:19,207 - pyskl - INFO - Epoch [71][200/1281] lr: 1.377e-02, eta: 11:59:19, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 0.9988, loss_cls: 0.3482, loss: 0.3482 +2025-06-24 22:41:08,673 - pyskl - INFO - Epoch [71][300/1281] lr: 1.375e-02, eta: 11:58:45, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3363, loss: 0.3363 +2025-06-24 22:41:44,212 - pyskl - INFO - Epoch [71][400/1281] lr: 1.373e-02, eta: 11:57:55, time: 0.355, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9994, loss_cls: 0.4055, loss: 0.4055 +2025-06-24 22:42:35,279 - pyskl - INFO - Epoch [71][500/1281] lr: 1.371e-02, eta: 11:57:23, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9988, loss_cls: 0.4375, loss: 0.4375 +2025-06-24 22:42:59,425 - pyskl - INFO - Epoch [71][600/1281] lr: 1.368e-02, eta: 11:56:20, time: 0.241, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9938, loss_cls: 0.4617, loss: 0.4617 +2025-06-24 22:43:45,014 - pyskl - INFO - Epoch [71][700/1281] lr: 1.366e-02, eta: 11:55:42, time: 0.456, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9994, loss_cls: 0.4042, loss: 0.4042 +2025-06-24 22:44:34,151 - pyskl - INFO - Epoch [71][800/1281] lr: 1.364e-02, eta: 11:55:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9969, loss_cls: 0.4209, loss: 0.4209 +2025-06-24 22:45:23,308 - pyskl - INFO - Epoch [71][900/1281] lr: 1.362e-02, eta: 11:54:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9969, loss_cls: 0.3959, loss: 0.3959 +2025-06-24 22:46:12,827 - pyskl - INFO - Epoch [71][1000/1281] lr: 1.360e-02, eta: 11:53:59, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4175, loss: 0.4175 +2025-06-24 22:47:02,221 - pyskl - INFO - Epoch [71][1100/1281] lr: 1.358e-02, eta: 11:53:25, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9988, loss_cls: 0.4207, loss: 0.4207 +2025-06-24 22:47:51,313 - pyskl - INFO - Epoch [71][1200/1281] lr: 1.356e-02, eta: 11:52:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9988, loss_cls: 0.3414, loss: 0.3414 +2025-06-24 22:48:31,903 - pyskl - INFO - Saving checkpoint at 71 epochs +2025-06-24 22:49:30,495 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:49:30,559 - pyskl - INFO - +top1_acc 0.8879 +top5_acc 0.9938 +2025-06-24 22:49:30,559 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:49:30,569 - pyskl - INFO - +mean_acc 0.8481 +2025-06-24 22:49:30,575 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_67.pth was removed +2025-06-24 22:49:30,763 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_71.pth. +2025-06-24 22:49:30,764 - pyskl - INFO - Best top1_acc is 0.8879 at 71 epoch. +2025-06-24 22:49:30,767 - pyskl - INFO - Epoch(val) [71][533] top1_acc: 0.8879, top5_acc: 0.9938, mean_class_accuracy: 0.8481 +2025-06-24 22:50:50,104 - pyskl - INFO - Epoch [72][100/1281] lr: 1.353e-02, eta: 11:51:37, time: 0.793, data_time: 0.193, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9994, loss_cls: 0.3509, loss: 0.3509 +2025-06-24 22:51:39,252 - pyskl - INFO - Epoch [72][200/1281] lr: 1.351e-02, eta: 11:51:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9988, loss_cls: 0.3432, loss: 0.3432 +2025-06-24 22:52:28,553 - pyskl - INFO - Epoch [72][300/1281] lr: 1.349e-02, eta: 11:50:28, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.4187, loss: 0.4187 +2025-06-24 22:53:07,088 - pyskl - INFO - Epoch [72][400/1281] lr: 1.346e-02, eta: 11:49:42, time: 0.385, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9981, loss_cls: 0.3474, loss: 0.3474 +2025-06-24 22:53:58,277 - pyskl - INFO - Epoch [72][500/1281] lr: 1.344e-02, eta: 11:49:10, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9988, loss_cls: 0.3957, loss: 0.3957 +2025-06-24 22:54:21,745 - pyskl - INFO - Epoch [72][600/1281] lr: 1.342e-02, eta: 11:48:07, time: 0.235, data_time: 0.001, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9981, loss_cls: 0.3604, loss: 0.3604 +2025-06-24 22:55:05,484 - pyskl - INFO - Epoch [72][700/1281] lr: 1.340e-02, eta: 11:47:26, time: 0.437, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9988, loss_cls: 0.3691, loss: 0.3691 +2025-06-24 22:55:54,612 - pyskl - INFO - Epoch [72][800/1281] lr: 1.338e-02, eta: 11:46:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9988, loss_cls: 0.4339, loss: 0.4339 +2025-06-24 22:56:43,886 - pyskl - INFO - Epoch [72][900/1281] lr: 1.336e-02, eta: 11:46:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9969, loss_cls: 0.4471, loss: 0.4471 +2025-06-24 22:57:33,238 - pyskl - INFO - Epoch [72][1000/1281] lr: 1.334e-02, eta: 11:45:42, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9975, loss_cls: 0.3770, loss: 0.3770 +2025-06-24 22:58:22,468 - pyskl - INFO - Epoch [72][1100/1281] lr: 1.332e-02, eta: 11:45:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9956, loss_cls: 0.4671, loss: 0.4671 +2025-06-24 22:59:11,352 - pyskl - INFO - Epoch [72][1200/1281] lr: 1.330e-02, eta: 11:44:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9950, loss_cls: 0.4084, loss: 0.4084 +2025-06-24 22:59:51,523 - pyskl - INFO - Saving checkpoint at 72 epochs +2025-06-24 23:00:50,026 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:00:50,101 - pyskl - INFO - +top1_acc 0.8415 +top5_acc 0.9877 +2025-06-24 23:00:50,101 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:00:50,109 - pyskl - INFO - +mean_acc 0.7944 +2025-06-24 23:00:50,111 - pyskl - INFO - Epoch(val) [72][533] top1_acc: 0.8415, top5_acc: 0.9877, mean_class_accuracy: 0.7944 +2025-06-24 23:02:09,124 - pyskl - INFO - Epoch [73][100/1281] lr: 1.326e-02, eta: 11:43:19, time: 0.790, data_time: 0.190, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9981, loss_cls: 0.4297, loss: 0.4297 +2025-06-24 23:02:58,376 - pyskl - INFO - Epoch [73][200/1281] lr: 1.324e-02, eta: 11:42:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.3839, loss: 0.3839 +2025-06-24 23:03:47,646 - pyskl - INFO - Epoch [73][300/1281] lr: 1.322e-02, eta: 11:42:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9975, loss_cls: 0.3700, loss: 0.3700 +2025-06-24 23:04:30,627 - pyskl - INFO - Epoch [73][400/1281] lr: 1.320e-02, eta: 11:41:28, time: 0.430, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9981, loss_cls: 0.3818, loss: 0.3818 +2025-06-24 23:05:14,325 - pyskl - INFO - Epoch [73][500/1281] lr: 1.318e-02, eta: 11:40:47, time: 0.437, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 1.0000, loss_cls: 0.3629, loss: 0.3629 +2025-06-24 23:05:44,077 - pyskl - INFO - Epoch [73][600/1281] lr: 1.316e-02, eta: 11:39:51, time: 0.298, data_time: 0.001, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9981, loss_cls: 0.3704, loss: 0.3704 +2025-06-24 23:06:25,278 - pyskl - INFO - Epoch [73][700/1281] lr: 1.314e-02, eta: 11:39:08, time: 0.412, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9994, loss_cls: 0.3860, loss: 0.3860 +2025-06-24 23:07:14,273 - pyskl - INFO - Epoch [73][800/1281] lr: 1.312e-02, eta: 11:38:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9988, loss_cls: 0.3531, loss: 0.3531 +2025-06-24 23:08:03,462 - pyskl - INFO - Epoch [73][900/1281] lr: 1.310e-02, eta: 11:37:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9988, loss_cls: 0.3835, loss: 0.3835 +2025-06-24 23:08:52,661 - pyskl - INFO - Epoch [73][1000/1281] lr: 1.308e-02, eta: 11:37:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9988, loss_cls: 0.3024, loss: 0.3024 +2025-06-24 23:09:41,851 - pyskl - INFO - Epoch [73][1100/1281] lr: 1.306e-02, eta: 11:36:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9956, loss_cls: 0.3917, loss: 0.3917 +2025-06-24 23:10:31,388 - pyskl - INFO - Epoch [73][1200/1281] lr: 1.304e-02, eta: 11:36:13, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4251, loss: 0.4251 +2025-06-24 23:11:11,805 - pyskl - INFO - Saving checkpoint at 73 epochs +2025-06-24 23:12:09,809 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:12:09,871 - pyskl - INFO - +top1_acc 0.8674 +top5_acc 0.9905 +2025-06-24 23:12:09,871 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:12:09,878 - pyskl - INFO - +mean_acc 0.8350 +2025-06-24 23:12:09,879 - pyskl - INFO - Epoch(val) [73][533] top1_acc: 0.8674, top5_acc: 0.9905, mean_class_accuracy: 0.8350 +2025-06-24 23:13:29,351 - pyskl - INFO - Epoch [74][100/1281] lr: 1.300e-02, eta: 11:34:59, time: 0.795, data_time: 0.191, memory: 4083, top1_acc: 0.9431, top5_acc: 1.0000, loss_cls: 0.3444, loss: 0.3444 +2025-06-24 23:14:18,493 - pyskl - INFO - Epoch [74][200/1281] lr: 1.298e-02, eta: 11:34:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9381, top5_acc: 1.0000, loss_cls: 0.3111, loss: 0.3111 +2025-06-24 23:15:07,897 - pyskl - INFO - Epoch [74][300/1281] lr: 1.296e-02, eta: 11:33:49, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.2973, loss: 0.2973 +2025-06-24 23:15:53,265 - pyskl - INFO - Epoch [74][400/1281] lr: 1.294e-02, eta: 11:33:10, time: 0.454, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9994, loss_cls: 0.3604, loss: 0.3604 +2025-06-24 23:16:32,227 - pyskl - INFO - Epoch [74][500/1281] lr: 1.292e-02, eta: 11:32:24, time: 0.390, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9975, loss_cls: 0.3453, loss: 0.3453 +2025-06-24 23:17:07,175 - pyskl - INFO - Epoch [74][600/1281] lr: 1.290e-02, eta: 11:31:34, time: 0.349, data_time: 0.001, memory: 4083, top1_acc: 0.9206, top5_acc: 1.0000, loss_cls: 0.3738, loss: 0.3738 +2025-06-24 23:17:45,131 - pyskl - INFO - Epoch [74][700/1281] lr: 1.288e-02, eta: 11:30:47, time: 0.380, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9975, loss_cls: 0.4164, loss: 0.4164 +2025-06-24 23:18:34,273 - pyskl - INFO - Epoch [74][800/1281] lr: 1.286e-02, eta: 11:30:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9975, loss_cls: 0.3993, loss: 0.3993 +2025-06-24 23:19:23,582 - pyskl - INFO - Epoch [74][900/1281] lr: 1.284e-02, eta: 11:29:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4021, loss: 0.4021 +2025-06-24 23:20:12,760 - pyskl - INFO - Epoch [74][1000/1281] lr: 1.282e-02, eta: 11:29:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9975, loss_cls: 0.3702, loss: 0.3702 +2025-06-24 23:21:02,013 - pyskl - INFO - Epoch [74][1100/1281] lr: 1.280e-02, eta: 11:28:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9988, loss_cls: 0.3484, loss: 0.3484 +2025-06-24 23:21:51,101 - pyskl - INFO - Epoch [74][1200/1281] lr: 1.278e-02, eta: 11:27:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9981, loss_cls: 0.3623, loss: 0.3623 +2025-06-24 23:22:31,395 - pyskl - INFO - Saving checkpoint at 74 epochs +2025-06-24 23:23:30,348 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:23:30,415 - pyskl - INFO - +top1_acc 0.9019 +top5_acc 0.9941 +2025-06-24 23:23:30,415 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:23:30,422 - pyskl - INFO - +mean_acc 0.8648 +2025-06-24 23:23:30,426 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_71.pth was removed +2025-06-24 23:23:30,602 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_74.pth. +2025-06-24 23:23:30,603 - pyskl - INFO - Best top1_acc is 0.9019 at 74 epoch. +2025-06-24 23:23:30,605 - pyskl - INFO - Epoch(val) [74][533] top1_acc: 0.9019, top5_acc: 0.9941, mean_class_accuracy: 0.8648 +2025-06-24 23:24:50,506 - pyskl - INFO - Epoch [75][100/1281] lr: 1.274e-02, eta: 11:26:38, time: 0.799, data_time: 0.186, memory: 4083, top1_acc: 0.9481, top5_acc: 1.0000, loss_cls: 0.3263, loss: 0.3263 +2025-06-24 23:25:39,887 - pyskl - INFO - Epoch [75][200/1281] lr: 1.272e-02, eta: 11:26:03, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 1.0000, loss_cls: 0.3328, loss: 0.3328 +2025-06-24 23:26:29,063 - pyskl - INFO - Epoch [75][300/1281] lr: 1.270e-02, eta: 11:25:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3438, loss: 0.3438 +2025-06-24 23:27:16,731 - pyskl - INFO - Epoch [75][400/1281] lr: 1.268e-02, eta: 11:24:50, time: 0.477, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3613, loss: 0.3613 +2025-06-24 23:27:51,586 - pyskl - INFO - Epoch [75][500/1281] lr: 1.266e-02, eta: 11:24:00, time: 0.349, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9988, loss_cls: 0.4112, loss: 0.4112 +2025-06-24 23:28:30,242 - pyskl - INFO - Epoch [75][600/1281] lr: 1.264e-02, eta: 11:23:14, time: 0.387, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9988, loss_cls: 0.3911, loss: 0.3911 +2025-06-24 23:29:07,404 - pyskl - INFO - Epoch [75][700/1281] lr: 1.262e-02, eta: 11:22:26, time: 0.372, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9994, loss_cls: 0.3984, loss: 0.3984 +2025-06-24 23:29:56,611 - pyskl - INFO - Epoch [75][800/1281] lr: 1.260e-02, eta: 11:21:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9994, loss_cls: 0.3910, loss: 0.3910 +2025-06-24 23:30:45,842 - pyskl - INFO - Epoch [75][900/1281] lr: 1.258e-02, eta: 11:21:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9975, loss_cls: 0.3989, loss: 0.3989 +2025-06-24 23:31:34,978 - pyskl - INFO - Epoch [75][1000/1281] lr: 1.256e-02, eta: 11:20:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9969, loss_cls: 0.3677, loss: 0.3677 +2025-06-24 23:32:24,552 - pyskl - INFO - Epoch [75][1100/1281] lr: 1.254e-02, eta: 11:20:05, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9975, loss_cls: 0.3649, loss: 0.3649 +2025-06-24 23:33:13,875 - pyskl - INFO - Epoch [75][1200/1281] lr: 1.252e-02, eta: 11:19:29, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9994, loss_cls: 0.3598, loss: 0.3598 +2025-06-24 23:33:54,380 - pyskl - INFO - Saving checkpoint at 75 epochs +2025-06-24 23:34:52,543 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:34:52,606 - pyskl - INFO - +top1_acc 0.8782 +top5_acc 0.9920 +2025-06-24 23:34:52,606 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:34:52,613 - pyskl - INFO - +mean_acc 0.8410 +2025-06-24 23:34:52,614 - pyskl - INFO - Epoch(val) [75][533] top1_acc: 0.8782, top5_acc: 0.9920, mean_class_accuracy: 0.8410 +2025-06-24 23:36:11,055 - pyskl - INFO - Epoch [76][100/1281] lr: 1.248e-02, eta: 11:18:14, time: 0.784, data_time: 0.185, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9994, loss_cls: 0.3677, loss: 0.3677 +2025-06-24 23:36:59,947 - pyskl - INFO - Epoch [76][200/1281] lr: 1.246e-02, eta: 11:17:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9994, loss_cls: 0.3751, loss: 0.3751 +2025-06-24 23:37:49,109 - pyskl - INFO - Epoch [76][300/1281] lr: 1.244e-02, eta: 11:17:03, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9981, loss_cls: 0.3273, loss: 0.3273 +2025-06-24 23:38:38,671 - pyskl - INFO - Epoch [76][400/1281] lr: 1.242e-02, eta: 11:16:27, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9981, loss_cls: 0.2837, loss: 0.2837 +2025-06-24 23:39:10,840 - pyskl - INFO - Epoch [76][500/1281] lr: 1.240e-02, eta: 11:15:35, time: 0.322, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9981, loss_cls: 0.3683, loss: 0.3683 +2025-06-24 23:39:53,089 - pyskl - INFO - Epoch [76][600/1281] lr: 1.238e-02, eta: 11:14:52, time: 0.422, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 1.0000, loss_cls: 0.3526, loss: 0.3526 +2025-06-24 23:40:29,507 - pyskl - INFO - Epoch [76][700/1281] lr: 1.236e-02, eta: 11:14:04, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9994, loss_cls: 0.3939, loss: 0.3939 +2025-06-24 23:41:18,547 - pyskl - INFO - Epoch [76][800/1281] lr: 1.234e-02, eta: 11:13:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9981, loss_cls: 0.3897, loss: 0.3897 +2025-06-24 23:42:07,711 - pyskl - INFO - Epoch [76][900/1281] lr: 1.232e-02, eta: 11:12:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 1.0000, loss_cls: 0.3621, loss: 0.3621 +2025-06-24 23:42:56,820 - pyskl - INFO - Epoch [76][1000/1281] lr: 1.230e-02, eta: 11:12:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9969, loss_cls: 0.4027, loss: 0.4027 +2025-06-24 23:43:45,758 - pyskl - INFO - Epoch [76][1100/1281] lr: 1.228e-02, eta: 11:11:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9962, loss_cls: 0.4251, loss: 0.4251 +2025-06-24 23:44:35,126 - pyskl - INFO - Epoch [76][1200/1281] lr: 1.225e-02, eta: 11:11:05, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9975, loss_cls: 0.4294, loss: 0.4294 +2025-06-24 23:45:15,726 - pyskl - INFO - Saving checkpoint at 76 epochs +2025-06-24 23:46:14,713 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:46:14,768 - pyskl - INFO - +top1_acc 0.8796 +top5_acc 0.9924 +2025-06-24 23:46:14,768 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:46:14,775 - pyskl - INFO - +mean_acc 0.8574 +2025-06-24 23:46:14,776 - pyskl - INFO - Epoch(val) [76][533] top1_acc: 0.8796, top5_acc: 0.9924, mean_class_accuracy: 0.8574 +2025-06-24 23:47:33,574 - pyskl - INFO - Epoch [77][100/1281] lr: 1.222e-02, eta: 11:09:50, time: 0.788, data_time: 0.187, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9988, loss_cls: 0.3305, loss: 0.3305 +2025-06-24 23:48:22,773 - pyskl - INFO - Epoch [77][200/1281] lr: 1.220e-02, eta: 11:09:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.2902, loss: 0.2902 +2025-06-24 23:49:11,886 - pyskl - INFO - Epoch [77][300/1281] lr: 1.218e-02, eta: 11:08:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.3064, loss: 0.3064 +2025-06-24 23:50:00,931 - pyskl - INFO - Epoch [77][400/1281] lr: 1.216e-02, eta: 11:08:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9981, loss_cls: 0.3592, loss: 0.3592 +2025-06-24 23:50:32,183 - pyskl - INFO - Epoch [77][500/1281] lr: 1.214e-02, eta: 11:07:08, time: 0.313, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9988, loss_cls: 0.3296, loss: 0.3296 +2025-06-24 23:51:15,213 - pyskl - INFO - Epoch [77][600/1281] lr: 1.212e-02, eta: 11:06:26, time: 0.430, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9981, loss_cls: 0.3684, loss: 0.3684 +2025-06-24 23:51:51,193 - pyskl - INFO - Epoch [77][700/1281] lr: 1.210e-02, eta: 11:05:38, time: 0.360, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9981, loss_cls: 0.3624, loss: 0.3624 +2025-06-24 23:52:40,416 - pyskl - INFO - Epoch [77][800/1281] lr: 1.207e-02, eta: 11:05:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.3489, loss: 0.3489 +2025-06-24 23:53:29,444 - pyskl - INFO - Epoch [77][900/1281] lr: 1.205e-02, eta: 11:04:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9969, loss_cls: 0.3837, loss: 0.3837 +2025-06-24 23:54:18,848 - pyskl - INFO - Epoch [77][1000/1281] lr: 1.203e-02, eta: 11:03:50, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9988, loss_cls: 0.3794, loss: 0.3794 +2025-06-24 23:55:08,622 - pyskl - INFO - Epoch [77][1100/1281] lr: 1.201e-02, eta: 11:03:14, time: 0.498, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9981, loss_cls: 0.3459, loss: 0.3459 +2025-06-24 23:55:57,693 - pyskl - INFO - Epoch [77][1200/1281] lr: 1.199e-02, eta: 11:02:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9988, loss_cls: 0.3588, loss: 0.3588 +2025-06-24 23:56:37,517 - pyskl - INFO - Saving checkpoint at 77 epochs +2025-06-24 23:57:35,770 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:57:35,835 - pyskl - INFO - +top1_acc 0.8947 +top5_acc 0.9930 +2025-06-24 23:57:35,835 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:57:35,844 - pyskl - INFO - +mean_acc 0.8510 +2025-06-24 23:57:35,847 - pyskl - INFO - Epoch(val) [77][533] top1_acc: 0.8947, top5_acc: 0.9930, mean_class_accuracy: 0.8510 +2025-06-24 23:58:56,816 - pyskl - INFO - Epoch [78][100/1281] lr: 1.196e-02, eta: 11:01:25, time: 0.810, data_time: 0.186, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.3080, loss: 0.3080 +2025-06-24 23:59:46,065 - pyskl - INFO - Epoch [78][200/1281] lr: 1.194e-02, eta: 11:00:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.3142, loss: 0.3142 +2025-06-25 00:00:35,310 - pyskl - INFO - Epoch [78][300/1281] lr: 1.192e-02, eta: 11:00:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9988, loss_cls: 0.2575, loss: 0.2575 +2025-06-25 00:01:24,799 - pyskl - INFO - Epoch [78][400/1281] lr: 1.190e-02, eta: 10:59:37, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.3178, loss: 0.3178 +2025-06-25 00:01:54,442 - pyskl - INFO - Epoch [78][500/1281] lr: 1.187e-02, eta: 10:58:42, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 1.0000, loss_cls: 0.3605, loss: 0.3605 +2025-06-25 00:02:38,910 - pyskl - INFO - Epoch [78][600/1281] lr: 1.185e-02, eta: 10:58:02, time: 0.445, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.3901, loss: 0.3901 +2025-06-25 00:03:12,821 - pyskl - INFO - Epoch [78][700/1281] lr: 1.183e-02, eta: 10:57:11, time: 0.339, data_time: 0.001, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9975, loss_cls: 0.3737, loss: 0.3737 +2025-06-25 00:04:01,945 - pyskl - INFO - Epoch [78][800/1281] lr: 1.181e-02, eta: 10:56:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9981, loss_cls: 0.3265, loss: 0.3265 +2025-06-25 00:04:50,983 - pyskl - INFO - Epoch [78][900/1281] lr: 1.179e-02, eta: 10:55:59, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9988, loss_cls: 0.3299, loss: 0.3299 +2025-06-25 00:05:40,421 - pyskl - INFO - Epoch [78][1000/1281] lr: 1.177e-02, eta: 10:55:22, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 1.0000, loss_cls: 0.3738, loss: 0.3738 +2025-06-25 00:06:29,758 - pyskl - INFO - Epoch [78][1100/1281] lr: 1.175e-02, eta: 10:54:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9981, loss_cls: 0.3458, loss: 0.3458 +2025-06-25 00:07:18,863 - pyskl - INFO - Epoch [78][1200/1281] lr: 1.173e-02, eta: 10:54:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9988, loss_cls: 0.4057, loss: 0.4057 +2025-06-25 00:07:59,295 - pyskl - INFO - Saving checkpoint at 78 epochs +2025-06-25 00:08:57,888 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:08:57,943 - pyskl - INFO - +top1_acc 0.8973 +top5_acc 0.9946 +2025-06-25 00:08:57,943 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:08:57,950 - pyskl - INFO - +mean_acc 0.8564 +2025-06-25 00:08:57,951 - pyskl - INFO - Epoch(val) [78][533] top1_acc: 0.8973, top5_acc: 0.9946, mean_class_accuracy: 0.8564 +2025-06-25 00:10:16,880 - pyskl - INFO - Epoch [79][100/1281] lr: 1.169e-02, eta: 10:52:55, time: 0.789, data_time: 0.187, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9981, loss_cls: 0.3417, loss: 0.3417 +2025-06-25 00:11:06,239 - pyskl - INFO - Epoch [79][200/1281] lr: 1.167e-02, eta: 10:52:19, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.2987, loss: 0.2987 +2025-06-25 00:11:54,980 - pyskl - INFO - Epoch [79][300/1281] lr: 1.165e-02, eta: 10:51:42, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2872, loss: 0.2872 +2025-06-25 00:12:44,181 - pyskl - INFO - Epoch [79][400/1281] lr: 1.163e-02, eta: 10:51:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9988, loss_cls: 0.3630, loss: 0.3630 +2025-06-25 00:13:12,166 - pyskl - INFO - Epoch [79][500/1281] lr: 1.161e-02, eta: 10:50:09, time: 0.280, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9981, loss_cls: 0.3932, loss: 0.3932 +2025-06-25 00:14:00,920 - pyskl - INFO - Epoch [79][600/1281] lr: 1.159e-02, eta: 10:49:33, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9994, loss_cls: 0.4055, loss: 0.4055 +2025-06-25 00:14:33,191 - pyskl - INFO - Epoch [79][700/1281] lr: 1.157e-02, eta: 10:48:41, time: 0.323, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9975, loss_cls: 0.3465, loss: 0.3465 +2025-06-25 00:15:21,863 - pyskl - INFO - Epoch [79][800/1281] lr: 1.155e-02, eta: 10:48:04, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3065, loss: 0.3065 +2025-06-25 00:16:10,829 - pyskl - INFO - Epoch [79][900/1281] lr: 1.153e-02, eta: 10:47:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9994, loss_cls: 0.3041, loss: 0.3041 +2025-06-25 00:16:59,800 - pyskl - INFO - Epoch [79][1000/1281] lr: 1.151e-02, eta: 10:46:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3092, loss: 0.3092 +2025-06-25 00:17:49,103 - pyskl - INFO - Epoch [79][1100/1281] lr: 1.149e-02, eta: 10:46:14, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3198, loss: 0.3198 +2025-06-25 00:18:38,381 - pyskl - INFO - Epoch [79][1200/1281] lr: 1.147e-02, eta: 10:45:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9975, loss_cls: 0.3647, loss: 0.3647 +2025-06-25 00:19:18,835 - pyskl - INFO - Saving checkpoint at 79 epochs +2025-06-25 00:20:17,002 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:20:17,057 - pyskl - INFO - +top1_acc 0.8943 +top5_acc 0.9948 +2025-06-25 00:20:17,058 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:20:17,064 - pyskl - INFO - +mean_acc 0.8607 +2025-06-25 00:20:17,066 - pyskl - INFO - Epoch(val) [79][533] top1_acc: 0.8943, top5_acc: 0.9948, mean_class_accuracy: 0.8607 +2025-06-25 00:21:34,687 - pyskl - INFO - Epoch [80][100/1281] lr: 1.143e-02, eta: 10:44:21, time: 0.776, data_time: 0.183, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9988, loss_cls: 0.3513, loss: 0.3513 +2025-06-25 00:22:23,723 - pyskl - INFO - Epoch [80][200/1281] lr: 1.141e-02, eta: 10:43:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9988, loss_cls: 0.3242, loss: 0.3242 +2025-06-25 00:23:12,893 - pyskl - INFO - Epoch [80][300/1281] lr: 1.139e-02, eta: 10:43:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9988, loss_cls: 0.2646, loss: 0.2646 +2025-06-25 00:24:02,263 - pyskl - INFO - Epoch [80][400/1281] lr: 1.137e-02, eta: 10:42:31, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2916, loss: 0.2916 +2025-06-25 00:24:31,975 - pyskl - INFO - Epoch [80][500/1281] lr: 1.135e-02, eta: 10:41:37, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 1.0000, loss_cls: 0.3304, loss: 0.3304 +2025-06-25 00:25:23,009 - pyskl - INFO - Epoch [80][600/1281] lr: 1.133e-02, eta: 10:41:02, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9975, loss_cls: 0.2990, loss: 0.2990 +2025-06-25 00:25:53,273 - pyskl - INFO - Epoch [80][700/1281] lr: 1.131e-02, eta: 10:40:08, time: 0.303, data_time: 0.001, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9988, loss_cls: 0.3138, loss: 0.3138 +2025-06-25 00:26:42,416 - pyskl - INFO - Epoch [80][800/1281] lr: 1.129e-02, eta: 10:39:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9981, loss_cls: 0.3428, loss: 0.3428 +2025-06-25 00:27:31,541 - pyskl - INFO - Epoch [80][900/1281] lr: 1.127e-02, eta: 10:38:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9988, loss_cls: 0.3175, loss: 0.3175 +2025-06-25 00:28:20,313 - pyskl - INFO - Epoch [80][1000/1281] lr: 1.125e-02, eta: 10:38:18, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.3952, loss: 0.3952 +2025-06-25 00:29:09,481 - pyskl - INFO - Epoch [80][1100/1281] lr: 1.123e-02, eta: 10:37:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9988, loss_cls: 0.3164, loss: 0.3164 +2025-06-25 00:29:58,834 - pyskl - INFO - Epoch [80][1200/1281] lr: 1.121e-02, eta: 10:37:04, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3363, loss: 0.3363 +2025-06-25 00:30:39,309 - pyskl - INFO - Saving checkpoint at 80 epochs +2025-06-25 00:31:37,776 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:31:37,835 - pyskl - INFO - +top1_acc 0.8991 +top5_acc 0.9947 +2025-06-25 00:31:37,836 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:31:37,845 - pyskl - INFO - +mean_acc 0.8700 +2025-06-25 00:31:37,847 - pyskl - INFO - Epoch(val) [80][533] top1_acc: 0.8991, top5_acc: 0.9947, mean_class_accuracy: 0.8700 +2025-06-25 00:32:58,215 - pyskl - INFO - Epoch [81][100/1281] lr: 1.117e-02, eta: 10:35:50, time: 0.804, data_time: 0.193, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.2707, loss: 0.2707 +2025-06-25 00:33:47,288 - pyskl - INFO - Epoch [81][200/1281] lr: 1.115e-02, eta: 10:35:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3205, loss: 0.3205 +2025-06-25 00:34:36,401 - pyskl - INFO - Epoch [81][300/1281] lr: 1.113e-02, eta: 10:34:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9988, loss_cls: 0.2804, loss: 0.2804 +2025-06-25 00:35:25,504 - pyskl - INFO - Epoch [81][400/1281] lr: 1.111e-02, eta: 10:33:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9994, loss_cls: 0.3737, loss: 0.3737 +2025-06-25 00:35:53,236 - pyskl - INFO - Epoch [81][500/1281] lr: 1.109e-02, eta: 10:33:04, time: 0.277, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9981, loss_cls: 0.3641, loss: 0.3641 +2025-06-25 00:36:44,272 - pyskl - INFO - Epoch [81][600/1281] lr: 1.107e-02, eta: 10:32:29, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9988, loss_cls: 0.3243, loss: 0.3243 +2025-06-25 00:37:14,243 - pyskl - INFO - Epoch [81][700/1281] lr: 1.105e-02, eta: 10:31:35, time: 0.300, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 1.0000, loss_cls: 0.3222, loss: 0.3222 +2025-06-25 00:38:03,086 - pyskl - INFO - Epoch [81][800/1281] lr: 1.103e-02, eta: 10:30:58, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 1.0000, loss_cls: 0.3347, loss: 0.3347 +2025-06-25 00:38:52,098 - pyskl - INFO - Epoch [81][900/1281] lr: 1.101e-02, eta: 10:30:21, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9988, loss_cls: 0.3107, loss: 0.3107 +2025-06-25 00:39:41,362 - pyskl - INFO - Epoch [81][1000/1281] lr: 1.099e-02, eta: 10:29:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9975, loss_cls: 0.4247, loss: 0.4247 +2025-06-25 00:40:30,639 - pyskl - INFO - Epoch [81][1100/1281] lr: 1.097e-02, eta: 10:29:07, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9981, loss_cls: 0.3014, loss: 0.3014 +2025-06-25 00:41:19,707 - pyskl - INFO - Epoch [81][1200/1281] lr: 1.095e-02, eta: 10:28:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9988, loss_cls: 0.3276, loss: 0.3276 +2025-06-25 00:41:59,939 - pyskl - INFO - Saving checkpoint at 81 epochs +2025-06-25 00:42:58,560 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:42:58,615 - pyskl - INFO - +top1_acc 0.8933 +top5_acc 0.9924 +2025-06-25 00:42:58,615 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:42:58,621 - pyskl - INFO - +mean_acc 0.8577 +2025-06-25 00:42:58,623 - pyskl - INFO - Epoch(val) [81][533] top1_acc: 0.8933, top5_acc: 0.9924, mean_class_accuracy: 0.8577 +2025-06-25 00:44:17,563 - pyskl - INFO - Epoch [82][100/1281] lr: 1.091e-02, eta: 10:27:14, time: 0.789, data_time: 0.188, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3364, loss: 0.3364 +2025-06-25 00:45:06,639 - pyskl - INFO - Epoch [82][200/1281] lr: 1.089e-02, eta: 10:26:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9994, loss_cls: 0.2953, loss: 0.2953 +2025-06-25 00:45:55,789 - pyskl - INFO - Epoch [82][300/1281] lr: 1.087e-02, eta: 10:26:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2500, loss: 0.2500 +2025-06-25 00:46:45,148 - pyskl - INFO - Epoch [82][400/1281] lr: 1.085e-02, eta: 10:25:23, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.3167, loss: 0.3167 +2025-06-25 00:47:14,849 - pyskl - INFO - Epoch [82][500/1281] lr: 1.083e-02, eta: 10:24:29, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.3277, loss: 0.3277 +2025-06-25 00:48:05,963 - pyskl - INFO - Epoch [82][600/1281] lr: 1.081e-02, eta: 10:23:54, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9988, loss_cls: 0.3071, loss: 0.3071 +2025-06-25 00:48:34,773 - pyskl - INFO - Epoch [82][700/1281] lr: 1.079e-02, eta: 10:23:00, time: 0.288, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3588, loss: 0.3588 +2025-06-25 00:49:24,136 - pyskl - INFO - Epoch [82][800/1281] lr: 1.077e-02, eta: 10:22:23, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9988, loss_cls: 0.3279, loss: 0.3279 +2025-06-25 00:50:12,790 - pyskl - INFO - Epoch [82][900/1281] lr: 1.075e-02, eta: 10:21:45, time: 0.487, data_time: 0.001, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9988, loss_cls: 0.2839, loss: 0.2839 +2025-06-25 00:51:02,035 - pyskl - INFO - Epoch [82][1000/1281] lr: 1.073e-02, eta: 10:21:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9975, loss_cls: 0.3465, loss: 0.3465 +2025-06-25 00:51:51,456 - pyskl - INFO - Epoch [82][1100/1281] lr: 1.071e-02, eta: 10:20:31, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.3105, loss: 0.3105 +2025-06-25 00:52:40,531 - pyskl - INFO - Epoch [82][1200/1281] lr: 1.069e-02, eta: 10:19:54, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9981, loss_cls: 0.3418, loss: 0.3418 +2025-06-25 00:53:21,028 - pyskl - INFO - Saving checkpoint at 82 epochs +2025-06-25 00:54:19,835 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:54:19,890 - pyskl - INFO - +top1_acc 0.9069 +top5_acc 0.9950 +2025-06-25 00:54:19,890 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:54:19,897 - pyskl - INFO - +mean_acc 0.8699 +2025-06-25 00:54:19,901 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_74.pth was removed +2025-06-25 00:54:20,064 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_82.pth. +2025-06-25 00:54:20,064 - pyskl - INFO - Best top1_acc is 0.9069 at 82 epoch. +2025-06-25 00:54:20,066 - pyskl - INFO - Epoch(val) [82][533] top1_acc: 0.9069, top5_acc: 0.9950, mean_class_accuracy: 0.8699 +2025-06-25 00:55:41,079 - pyskl - INFO - Epoch [83][100/1281] lr: 1.065e-02, eta: 10:18:39, time: 0.810, data_time: 0.189, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9975, loss_cls: 0.2761, loss: 0.2761 +2025-06-25 00:56:30,282 - pyskl - INFO - Epoch [83][200/1281] lr: 1.063e-02, eta: 10:18:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2514, loss: 0.2514 +2025-06-25 00:57:19,401 - pyskl - INFO - Epoch [83][300/1281] lr: 1.061e-02, eta: 10:17:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9988, loss_cls: 0.2663, loss: 0.2663 +2025-06-25 00:58:08,968 - pyskl - INFO - Epoch [83][400/1281] lr: 1.059e-02, eta: 10:16:48, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3142, loss: 0.3142 +2025-06-25 00:58:36,925 - pyskl - INFO - Epoch [83][500/1281] lr: 1.057e-02, eta: 10:15:53, time: 0.280, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9981, loss_cls: 0.3148, loss: 0.3148 +2025-06-25 00:59:27,760 - pyskl - INFO - Epoch [83][600/1281] lr: 1.055e-02, eta: 10:15:17, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 1.0000, loss_cls: 0.2849, loss: 0.2849 +2025-06-25 00:59:56,695 - pyskl - INFO - Epoch [83][700/1281] lr: 1.053e-02, eta: 10:14:23, time: 0.289, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9975, loss_cls: 0.3145, loss: 0.3145 +2025-06-25 01:00:45,565 - pyskl - INFO - Epoch [83][800/1281] lr: 1.051e-02, eta: 10:13:46, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 1.0000, loss_cls: 0.2829, loss: 0.2829 +2025-06-25 01:01:34,800 - pyskl - INFO - Epoch [83][900/1281] lr: 1.049e-02, eta: 10:13:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9981, loss_cls: 0.3089, loss: 0.3089 +2025-06-25 01:02:23,729 - pyskl - INFO - Epoch [83][1000/1281] lr: 1.047e-02, eta: 10:12:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9981, loss_cls: 0.3101, loss: 0.3101 +2025-06-25 01:03:13,018 - pyskl - INFO - Epoch [83][1100/1281] lr: 1.045e-02, eta: 10:11:53, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.3123, loss: 0.3123 +2025-06-25 01:04:02,079 - pyskl - INFO - Epoch [83][1200/1281] lr: 1.043e-02, eta: 10:11:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 1.0000, loss_cls: 0.3263, loss: 0.3263 +2025-06-25 01:04:42,402 - pyskl - INFO - Saving checkpoint at 83 epochs +2025-06-25 01:05:40,394 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:05:40,450 - pyskl - INFO - +top1_acc 0.9017 +top5_acc 0.9933 +2025-06-25 01:05:40,450 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:05:40,457 - pyskl - INFO - +mean_acc 0.8640 +2025-06-25 01:05:40,459 - pyskl - INFO - Epoch(val) [83][533] top1_acc: 0.9017, top5_acc: 0.9933, mean_class_accuracy: 0.8640 +2025-06-25 01:07:00,927 - pyskl - INFO - Epoch [84][100/1281] lr: 1.040e-02, eta: 10:10:01, time: 0.805, data_time: 0.187, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2457, loss: 0.2457 +2025-06-25 01:07:50,114 - pyskl - INFO - Epoch [84][200/1281] lr: 1.038e-02, eta: 10:09:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2775, loss: 0.2775 +2025-06-25 01:08:39,191 - pyskl - INFO - Epoch [84][300/1281] lr: 1.036e-02, eta: 10:08:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9994, loss_cls: 0.2993, loss: 0.2993 +2025-06-25 01:09:28,451 - pyskl - INFO - Epoch [84][400/1281] lr: 1.034e-02, eta: 10:08:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.2965, loss: 0.2965 +2025-06-25 01:09:57,726 - pyskl - INFO - Epoch [84][500/1281] lr: 1.031e-02, eta: 10:07:15, time: 0.293, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2784, loss: 0.2784 +2025-06-25 01:10:48,741 - pyskl - INFO - Epoch [84][600/1281] lr: 1.029e-02, eta: 10:06:39, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.3140, loss: 0.3140 +2025-06-25 01:11:19,421 - pyskl - INFO - Epoch [84][700/1281] lr: 1.027e-02, eta: 10:05:47, time: 0.307, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9988, loss_cls: 0.3263, loss: 0.3263 +2025-06-25 01:12:08,471 - pyskl - INFO - Epoch [84][800/1281] lr: 1.025e-02, eta: 10:05:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9994, loss_cls: 0.3261, loss: 0.3261 +2025-06-25 01:12:57,461 - pyskl - INFO - Epoch [84][900/1281] lr: 1.023e-02, eta: 10:04:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9981, loss_cls: 0.2970, loss: 0.2970 +2025-06-25 01:13:46,222 - pyskl - INFO - Epoch [84][1000/1281] lr: 1.021e-02, eta: 10:03:53, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.2748, loss: 0.2748 +2025-06-25 01:14:35,328 - pyskl - INFO - Epoch [84][1100/1281] lr: 1.019e-02, eta: 10:03:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9988, loss_cls: 0.3623, loss: 0.3623 +2025-06-25 01:15:24,495 - pyskl - INFO - Epoch [84][1200/1281] lr: 1.017e-02, eta: 10:02:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9988, loss_cls: 0.3622, loss: 0.3622 +2025-06-25 01:16:04,769 - pyskl - INFO - Saving checkpoint at 84 epochs +2025-06-25 01:17:03,763 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:17:03,836 - pyskl - INFO - +top1_acc 0.8891 +top5_acc 0.9935 +2025-06-25 01:17:03,836 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:17:03,843 - pyskl - INFO - +mean_acc 0.8589 +2025-06-25 01:17:03,845 - pyskl - INFO - Epoch(val) [84][533] top1_acc: 0.8891, top5_acc: 0.9935, mean_class_accuracy: 0.8589 +2025-06-25 01:18:21,797 - pyskl - INFO - Epoch [85][100/1281] lr: 1.014e-02, eta: 10:01:21, time: 0.779, data_time: 0.179, memory: 4083, top1_acc: 0.9463, top5_acc: 1.0000, loss_cls: 0.2852, loss: 0.2852 +2025-06-25 01:19:11,043 - pyskl - INFO - Epoch [85][200/1281] lr: 1.012e-02, eta: 10:00:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9988, loss_cls: 0.2390, loss: 0.2390 +2025-06-25 01:20:00,350 - pyskl - INFO - Epoch [85][300/1281] lr: 1.010e-02, eta: 10:00:06, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2608, loss: 0.2608 +2025-06-25 01:20:49,660 - pyskl - INFO - Epoch [85][400/1281] lr: 1.008e-02, eta: 9:59:28, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2547, loss: 0.2547 +2025-06-25 01:21:18,395 - pyskl - INFO - Epoch [85][500/1281] lr: 1.006e-02, eta: 9:58:35, time: 0.287, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3290, loss: 0.3290 +2025-06-25 01:22:09,411 - pyskl - INFO - Epoch [85][600/1281] lr: 1.004e-02, eta: 9:57:58, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2265, loss: 0.2265 +2025-06-25 01:22:39,628 - pyskl - INFO - Epoch [85][700/1281] lr: 1.002e-02, eta: 9:57:06, time: 0.302, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3209, loss: 0.3209 +2025-06-25 01:23:28,633 - pyskl - INFO - Epoch [85][800/1281] lr: 9.998e-03, eta: 9:56:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 1.0000, loss_cls: 0.3326, loss: 0.3326 +2025-06-25 01:24:17,775 - pyskl - INFO - Epoch [85][900/1281] lr: 9.978e-03, eta: 9:55:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.3100, loss: 0.3100 +2025-06-25 01:25:06,835 - pyskl - INFO - Epoch [85][1000/1281] lr: 9.958e-03, eta: 9:55:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 1.0000, loss_cls: 0.3044, loss: 0.3044 +2025-06-25 01:25:55,811 - pyskl - INFO - Epoch [85][1100/1281] lr: 9.937e-03, eta: 9:54:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9988, loss_cls: 0.2863, loss: 0.2863 +2025-06-25 01:26:44,855 - pyskl - INFO - Epoch [85][1200/1281] lr: 9.917e-03, eta: 9:53:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.3606, loss: 0.3606 +2025-06-25 01:27:24,960 - pyskl - INFO - Saving checkpoint at 85 epochs +2025-06-25 01:28:23,117 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:28:23,175 - pyskl - INFO - +top1_acc 0.8772 +top5_acc 0.9885 +2025-06-25 01:28:23,175 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:28:23,181 - pyskl - INFO - +mean_acc 0.8463 +2025-06-25 01:28:23,183 - pyskl - INFO - Epoch(val) [85][533] top1_acc: 0.8772, top5_acc: 0.9885, mean_class_accuracy: 0.8463 +2025-06-25 01:29:42,995 - pyskl - INFO - Epoch [86][100/1281] lr: 9.881e-03, eta: 9:52:41, time: 0.798, data_time: 0.185, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9994, loss_cls: 0.3680, loss: 0.3680 +2025-06-25 01:30:32,097 - pyskl - INFO - Epoch [86][200/1281] lr: 9.861e-03, eta: 9:52:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2700, loss: 0.2700 +2025-06-25 01:31:21,135 - pyskl - INFO - Epoch [86][300/1281] lr: 9.841e-03, eta: 9:51:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3017, loss: 0.3017 +2025-06-25 01:32:10,194 - pyskl - INFO - Epoch [86][400/1281] lr: 9.821e-03, eta: 9:50:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2976, loss: 0.2976 +2025-06-25 01:32:38,845 - pyskl - INFO - Epoch [86][500/1281] lr: 9.801e-03, eta: 9:49:54, time: 0.287, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9988, loss_cls: 0.2720, loss: 0.2720 +2025-06-25 01:33:29,541 - pyskl - INFO - Epoch [86][600/1281] lr: 9.781e-03, eta: 9:49:17, time: 0.507, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.3058, loss: 0.3058 +2025-06-25 01:33:59,789 - pyskl - INFO - Epoch [86][700/1281] lr: 9.762e-03, eta: 9:48:25, time: 0.302, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9988, loss_cls: 0.2823, loss: 0.2823 +2025-06-25 01:34:48,519 - pyskl - INFO - Epoch [86][800/1281] lr: 9.742e-03, eta: 9:47:46, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9975, loss_cls: 0.3035, loss: 0.3035 +2025-06-25 01:35:37,583 - pyskl - INFO - Epoch [86][900/1281] lr: 9.722e-03, eta: 9:47:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 1.0000, loss_cls: 0.2907, loss: 0.2907 +2025-06-25 01:36:26,766 - pyskl - INFO - Epoch [86][1000/1281] lr: 9.702e-03, eta: 9:46:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9962, loss_cls: 0.3304, loss: 0.3304 +2025-06-25 01:37:16,051 - pyskl - INFO - Epoch [86][1100/1281] lr: 9.682e-03, eta: 9:45:52, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9988, loss_cls: 0.2806, loss: 0.2806 +2025-06-25 01:38:04,692 - pyskl - INFO - Epoch [86][1200/1281] lr: 9.662e-03, eta: 9:45:14, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3134, loss: 0.3134 +2025-06-25 01:38:45,111 - pyskl - INFO - Saving checkpoint at 86 epochs +2025-06-25 01:39:42,721 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:39:42,777 - pyskl - INFO - +top1_acc 0.8991 +top5_acc 0.9952 +2025-06-25 01:39:42,777 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:39:42,784 - pyskl - INFO - +mean_acc 0.8640 +2025-06-25 01:39:42,786 - pyskl - INFO - Epoch(val) [86][533] top1_acc: 0.8991, top5_acc: 0.9952, mean_class_accuracy: 0.8640 +2025-06-25 01:41:02,158 - pyskl - INFO - Epoch [87][100/1281] lr: 9.626e-03, eta: 9:43:58, time: 0.794, data_time: 0.189, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2324, loss: 0.2324 +2025-06-25 01:41:51,108 - pyskl - INFO - Epoch [87][200/1281] lr: 9.606e-03, eta: 9:43:20, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9975, loss_cls: 0.2382, loss: 0.2382 +2025-06-25 01:42:40,331 - pyskl - INFO - Epoch [87][300/1281] lr: 9.586e-03, eta: 9:42:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2177, loss: 0.2177 +2025-06-25 01:43:29,693 - pyskl - INFO - Epoch [87][400/1281] lr: 9.566e-03, eta: 9:42:04, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2262, loss: 0.2262 +2025-06-25 01:43:59,501 - pyskl - INFO - Epoch [87][500/1281] lr: 9.546e-03, eta: 9:41:11, time: 0.298, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.2796, loss: 0.2796 +2025-06-25 01:44:50,475 - pyskl - INFO - Epoch [87][600/1281] lr: 9.527e-03, eta: 9:40:35, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.3379, loss: 0.3379 +2025-06-25 01:45:19,057 - pyskl - INFO - Epoch [87][700/1281] lr: 9.507e-03, eta: 9:39:41, time: 0.286, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9988, loss_cls: 0.2688, loss: 0.2688 +2025-06-25 01:46:07,906 - pyskl - INFO - Epoch [87][800/1281] lr: 9.487e-03, eta: 9:39:03, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9994, loss_cls: 0.2644, loss: 0.2644 +2025-06-25 01:46:57,112 - pyskl - INFO - Epoch [87][900/1281] lr: 9.467e-03, eta: 9:38:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 1.0000, loss_cls: 0.3225, loss: 0.3225 +2025-06-25 01:47:46,189 - pyskl - INFO - Epoch [87][1000/1281] lr: 9.447e-03, eta: 9:37:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9981, loss_cls: 0.3245, loss: 0.3245 +2025-06-25 01:48:35,098 - pyskl - INFO - Epoch [87][1100/1281] lr: 9.427e-03, eta: 9:37:08, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 1.0000, loss_cls: 0.3304, loss: 0.3304 +2025-06-25 01:49:24,156 - pyskl - INFO - Epoch [87][1200/1281] lr: 9.408e-03, eta: 9:36:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9988, loss_cls: 0.3098, loss: 0.3098 +2025-06-25 01:50:04,437 - pyskl - INFO - Saving checkpoint at 87 epochs +2025-06-25 01:51:02,587 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:51:02,642 - pyskl - INFO - +top1_acc 0.8855 +top5_acc 0.9930 +2025-06-25 01:51:02,642 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:51:02,649 - pyskl - INFO - +mean_acc 0.8462 +2025-06-25 01:51:02,650 - pyskl - INFO - Epoch(val) [87][533] top1_acc: 0.8855, top5_acc: 0.9930, mean_class_accuracy: 0.8462 +2025-06-25 01:52:22,777 - pyskl - INFO - Epoch [88][100/1281] lr: 9.372e-03, eta: 9:35:14, time: 0.801, data_time: 0.185, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2655, loss: 0.2655 +2025-06-25 01:53:11,866 - pyskl - INFO - Epoch [88][200/1281] lr: 9.352e-03, eta: 9:34:36, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2733, loss: 0.2733 +2025-06-25 01:54:00,855 - pyskl - INFO - Epoch [88][300/1281] lr: 9.332e-03, eta: 9:33:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2518, loss: 0.2518 +2025-06-25 01:54:50,129 - pyskl - INFO - Epoch [88][400/1281] lr: 9.312e-03, eta: 9:33:20, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9994, loss_cls: 0.2590, loss: 0.2590 +2025-06-25 01:55:19,756 - pyskl - INFO - Epoch [88][500/1281] lr: 9.293e-03, eta: 9:32:27, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9988, loss_cls: 0.2841, loss: 0.2841 +2025-06-25 01:56:10,667 - pyskl - INFO - Epoch [88][600/1281] lr: 9.273e-03, eta: 9:31:50, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9988, loss_cls: 0.2690, loss: 0.2690 +2025-06-25 01:56:39,683 - pyskl - INFO - Epoch [88][700/1281] lr: 9.253e-03, eta: 9:30:57, time: 0.290, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2678, loss: 0.2678 +2025-06-25 01:57:28,785 - pyskl - INFO - Epoch [88][800/1281] lr: 9.233e-03, eta: 9:30:19, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 1.0000, loss_cls: 0.2850, loss: 0.2850 +2025-06-25 01:58:17,681 - pyskl - INFO - Epoch [88][900/1281] lr: 9.214e-03, eta: 9:29:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2837, loss: 0.2837 +2025-06-25 01:59:06,694 - pyskl - INFO - Epoch [88][1000/1281] lr: 9.194e-03, eta: 9:29:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2512, loss: 0.2512 +2025-06-25 01:59:55,669 - pyskl - INFO - Epoch [88][1100/1281] lr: 9.174e-03, eta: 9:28:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9994, loss_cls: 0.2610, loss: 0.2610 +2025-06-25 02:00:44,618 - pyskl - INFO - Epoch [88][1200/1281] lr: 9.155e-03, eta: 9:27:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3134, loss: 0.3134 +2025-06-25 02:01:25,050 - pyskl - INFO - Saving checkpoint at 88 epochs +2025-06-25 02:02:23,932 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:02:24,002 - pyskl - INFO - +top1_acc 0.8973 +top5_acc 0.9931 +2025-06-25 02:02:24,002 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:02:24,012 - pyskl - INFO - +mean_acc 0.8666 +2025-06-25 02:02:24,014 - pyskl - INFO - Epoch(val) [88][533] top1_acc: 0.8973, top5_acc: 0.9931, mean_class_accuracy: 0.8666 +2025-06-25 02:03:43,805 - pyskl - INFO - Epoch [89][100/1281] lr: 9.119e-03, eta: 9:26:29, time: 0.798, data_time: 0.189, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2289, loss: 0.2289 +2025-06-25 02:04:33,009 - pyskl - INFO - Epoch [89][200/1281] lr: 9.099e-03, eta: 9:25:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2359, loss: 0.2359 +2025-06-25 02:05:22,086 - pyskl - INFO - Epoch [89][300/1281] lr: 9.080e-03, eta: 9:25:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2497, loss: 0.2497 +2025-06-25 02:06:11,463 - pyskl - INFO - Epoch [89][400/1281] lr: 9.060e-03, eta: 9:24:34, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2260, loss: 0.2260 +2025-06-25 02:06:40,064 - pyskl - INFO - Epoch [89][500/1281] lr: 9.040e-03, eta: 9:23:41, time: 0.286, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2866, loss: 0.2866 +2025-06-25 02:07:31,008 - pyskl - INFO - Epoch [89][600/1281] lr: 9.021e-03, eta: 9:23:04, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2803, loss: 0.2803 +2025-06-25 02:08:01,227 - pyskl - INFO - Epoch [89][700/1281] lr: 9.001e-03, eta: 9:22:12, time: 0.302, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9994, loss_cls: 0.2553, loss: 0.2553 +2025-06-25 02:08:50,100 - pyskl - INFO - Epoch [89][800/1281] lr: 8.982e-03, eta: 9:21:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9981, loss_cls: 0.2562, loss: 0.2562 +2025-06-25 02:09:39,132 - pyskl - INFO - Epoch [89][900/1281] lr: 8.962e-03, eta: 9:20:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 1.0000, loss_cls: 0.3118, loss: 0.3118 +2025-06-25 02:10:28,117 - pyskl - INFO - Epoch [89][1000/1281] lr: 8.942e-03, eta: 9:20:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9988, loss_cls: 0.2859, loss: 0.2859 +2025-06-25 02:11:17,623 - pyskl - INFO - Epoch [89][1100/1281] lr: 8.923e-03, eta: 9:19:38, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 1.0000, loss_cls: 0.2993, loss: 0.2993 +2025-06-25 02:12:06,849 - pyskl - INFO - Epoch [89][1200/1281] lr: 8.903e-03, eta: 9:19:00, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.2832, loss: 0.2832 +2025-06-25 02:12:47,208 - pyskl - INFO - Saving checkpoint at 89 epochs +2025-06-25 02:13:45,365 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:13:45,420 - pyskl - INFO - +top1_acc 0.9059 +top5_acc 0.9934 +2025-06-25 02:13:45,420 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:13:45,427 - pyskl - INFO - +mean_acc 0.8848 +2025-06-25 02:13:45,428 - pyskl - INFO - Epoch(val) [89][533] top1_acc: 0.9059, top5_acc: 0.9934, mean_class_accuracy: 0.8848 +2025-06-25 02:15:05,061 - pyskl - INFO - Epoch [90][100/1281] lr: 8.868e-03, eta: 9:17:43, time: 0.796, data_time: 0.182, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2867, loss: 0.2867 +2025-06-25 02:15:53,954 - pyskl - INFO - Epoch [90][200/1281] lr: 8.848e-03, eta: 9:17:05, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2620, loss: 0.2620 +2025-06-25 02:16:43,053 - pyskl - INFO - Epoch [90][300/1281] lr: 8.829e-03, eta: 9:16:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9988, loss_cls: 0.2778, loss: 0.2778 +2025-06-25 02:17:32,339 - pyskl - INFO - Epoch [90][400/1281] lr: 8.809e-03, eta: 9:15:47, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.2963, loss: 0.2963 +2025-06-25 02:18:02,033 - pyskl - INFO - Epoch [90][500/1281] lr: 8.790e-03, eta: 9:14:55, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9981, loss_cls: 0.3085, loss: 0.3085 +2025-06-25 02:18:52,824 - pyskl - INFO - Epoch [90][600/1281] lr: 8.770e-03, eta: 9:14:18, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2770, loss: 0.2770 +2025-06-25 02:19:22,772 - pyskl - INFO - Epoch [90][700/1281] lr: 8.751e-03, eta: 9:13:26, time: 0.299, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.2628, loss: 0.2628 +2025-06-25 02:20:12,085 - pyskl - INFO - Epoch [90][800/1281] lr: 8.731e-03, eta: 9:12:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 0.9988, loss_cls: 0.2721, loss: 0.2721 +2025-06-25 02:21:01,402 - pyskl - INFO - Epoch [90][900/1281] lr: 8.712e-03, eta: 9:12:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9988, loss_cls: 0.2499, loss: 0.2499 +2025-06-25 02:21:50,582 - pyskl - INFO - Epoch [90][1000/1281] lr: 8.692e-03, eta: 9:11:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.2927, loss: 0.2927 +2025-06-25 02:22:39,640 - pyskl - INFO - Epoch [90][1100/1281] lr: 8.673e-03, eta: 9:10:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 1.0000, loss_cls: 0.2859, loss: 0.2859 +2025-06-25 02:23:28,767 - pyskl - INFO - Epoch [90][1200/1281] lr: 8.653e-03, eta: 9:10:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9981, loss_cls: 0.2738, loss: 0.2738 +2025-06-25 02:24:08,833 - pyskl - INFO - Saving checkpoint at 90 epochs +2025-06-25 02:25:07,698 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:25:07,765 - pyskl - INFO - +top1_acc 0.8934 +top5_acc 0.9933 +2025-06-25 02:25:07,765 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:25:07,773 - pyskl - INFO - +mean_acc 0.8495 +2025-06-25 02:25:07,775 - pyskl - INFO - Epoch(val) [90][533] top1_acc: 0.8934, top5_acc: 0.9933, mean_class_accuracy: 0.8495 +2025-06-25 02:26:26,110 - pyskl - INFO - Epoch [91][100/1281] lr: 8.618e-03, eta: 9:08:56, time: 0.783, data_time: 0.185, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2647, loss: 0.2647 +2025-06-25 02:27:15,077 - pyskl - INFO - Epoch [91][200/1281] lr: 8.599e-03, eta: 9:08:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9975, loss_cls: 0.2640, loss: 0.2640 +2025-06-25 02:28:04,009 - pyskl - INFO - Epoch [91][300/1281] lr: 8.579e-03, eta: 9:07:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2446, loss: 0.2446 +2025-06-25 02:28:52,869 - pyskl - INFO - Epoch [91][400/1281] lr: 8.560e-03, eta: 9:06:59, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9988, loss_cls: 0.2821, loss: 0.2821 +2025-06-25 02:29:21,418 - pyskl - INFO - Epoch [91][500/1281] lr: 8.540e-03, eta: 9:06:07, time: 0.285, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2423, loss: 0.2423 +2025-06-25 02:30:12,405 - pyskl - INFO - Epoch [91][600/1281] lr: 8.521e-03, eta: 9:05:29, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9988, loss_cls: 0.2611, loss: 0.2611 +2025-06-25 02:30:41,046 - pyskl - INFO - Epoch [91][700/1281] lr: 8.502e-03, eta: 9:04:37, time: 0.286, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2500, loss: 0.2500 +2025-06-25 02:31:30,031 - pyskl - INFO - Epoch [91][800/1281] lr: 8.482e-03, eta: 9:03:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9994, loss_cls: 0.2565, loss: 0.2565 +2025-06-25 02:32:19,145 - pyskl - INFO - Epoch [91][900/1281] lr: 8.463e-03, eta: 9:03:19, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2534, loss: 0.2534 +2025-06-25 02:33:08,402 - pyskl - INFO - Epoch [91][1000/1281] lr: 8.444e-03, eta: 9:02:40, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9981, loss_cls: 0.2734, loss: 0.2734 +2025-06-25 02:33:57,679 - pyskl - INFO - Epoch [91][1100/1281] lr: 8.424e-03, eta: 9:02:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2489, loss: 0.2489 +2025-06-25 02:34:46,809 - pyskl - INFO - Epoch [91][1200/1281] lr: 8.405e-03, eta: 9:01:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9994, loss_cls: 0.2254, loss: 0.2254 +2025-06-25 02:35:27,039 - pyskl - INFO - Saving checkpoint at 91 epochs +2025-06-25 02:36:25,223 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:36:25,285 - pyskl - INFO - +top1_acc 0.8898 +top5_acc 0.9934 +2025-06-25 02:36:25,285 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:36:25,295 - pyskl - INFO - +mean_acc 0.8658 +2025-06-25 02:36:25,298 - pyskl - INFO - Epoch(val) [91][533] top1_acc: 0.8898, top5_acc: 0.9934, mean_class_accuracy: 0.8658 +2025-06-25 02:37:44,988 - pyskl - INFO - Epoch [92][100/1281] lr: 8.370e-03, eta: 9:00:06, time: 0.797, data_time: 0.186, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.1995, loss: 0.1995 +2025-06-25 02:38:34,037 - pyskl - INFO - Epoch [92][200/1281] lr: 8.351e-03, eta: 8:59:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2155, loss: 0.2155 +2025-06-25 02:39:23,080 - pyskl - INFO - Epoch [92][300/1281] lr: 8.332e-03, eta: 8:58:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 0.9994, loss_cls: 0.2166, loss: 0.2166 +2025-06-25 02:40:12,215 - pyskl - INFO - Epoch [92][400/1281] lr: 8.312e-03, eta: 8:58:09, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9994, loss_cls: 0.2346, loss: 0.2346 +2025-06-25 02:40:41,545 - pyskl - INFO - Epoch [92][500/1281] lr: 8.293e-03, eta: 8:57:17, time: 0.293, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2095, loss: 0.2095 +2025-06-25 02:41:32,436 - pyskl - INFO - Epoch [92][600/1281] lr: 8.274e-03, eta: 8:56:40, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9988, loss_cls: 0.2254, loss: 0.2254 +2025-06-25 02:42:01,950 - pyskl - INFO - Epoch [92][700/1281] lr: 8.255e-03, eta: 8:55:48, time: 0.295, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2428, loss: 0.2428 +2025-06-25 02:42:51,298 - pyskl - INFO - Epoch [92][800/1281] lr: 8.235e-03, eta: 8:55:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9988, loss_cls: 0.2416, loss: 0.2416 +2025-06-25 02:43:40,287 - pyskl - INFO - Epoch [92][900/1281] lr: 8.216e-03, eta: 8:54:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2288, loss: 0.2288 +2025-06-25 02:44:29,410 - pyskl - INFO - Epoch [92][1000/1281] lr: 8.197e-03, eta: 8:53:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9994, loss_cls: 0.2141, loss: 0.2141 +2025-06-25 02:45:18,567 - pyskl - INFO - Epoch [92][1100/1281] lr: 8.178e-03, eta: 8:53:12, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 1.0000, loss_cls: 0.2861, loss: 0.2861 +2025-06-25 02:46:07,659 - pyskl - INFO - Epoch [92][1200/1281] lr: 8.159e-03, eta: 8:52:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2605, loss: 0.2605 +2025-06-25 02:46:47,849 - pyskl - INFO - Saving checkpoint at 92 epochs +2025-06-25 02:47:46,312 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:47:46,368 - pyskl - INFO - +top1_acc 0.9067 +top5_acc 0.9942 +2025-06-25 02:47:46,368 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:47:46,375 - pyskl - INFO - +mean_acc 0.8744 +2025-06-25 02:47:46,377 - pyskl - INFO - Epoch(val) [92][533] top1_acc: 0.9067, top5_acc: 0.9942, mean_class_accuracy: 0.8744 +2025-06-25 02:49:05,883 - pyskl - INFO - Epoch [93][100/1281] lr: 8.124e-03, eta: 8:51:16, time: 0.795, data_time: 0.186, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9988, loss_cls: 0.2507, loss: 0.2507 +2025-06-25 02:49:54,717 - pyskl - INFO - Epoch [93][200/1281] lr: 8.105e-03, eta: 8:50:37, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1784, loss: 0.1784 +2025-06-25 02:50:43,901 - pyskl - INFO - Epoch [93][300/1281] lr: 8.086e-03, eta: 8:49:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9988, loss_cls: 0.2132, loss: 0.2132 +2025-06-25 02:51:33,372 - pyskl - INFO - Epoch [93][400/1281] lr: 8.067e-03, eta: 8:49:19, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.2001, loss: 0.2001 +2025-06-25 02:52:02,607 - pyskl - INFO - Epoch [93][500/1281] lr: 8.047e-03, eta: 8:48:27, time: 0.292, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1955, loss: 0.1955 +2025-06-25 02:52:53,459 - pyskl - INFO - Epoch [93][600/1281] lr: 8.028e-03, eta: 8:47:49, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2200, loss: 0.2200 +2025-06-25 02:53:23,278 - pyskl - INFO - Epoch [93][700/1281] lr: 8.009e-03, eta: 8:46:58, time: 0.298, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2272, loss: 0.2272 +2025-06-25 02:54:12,365 - pyskl - INFO - Epoch [93][800/1281] lr: 7.990e-03, eta: 8:46:19, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2164, loss: 0.2164 +2025-06-25 02:55:01,301 - pyskl - INFO - Epoch [93][900/1281] lr: 7.971e-03, eta: 8:45:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2200, loss: 0.2200 +2025-06-25 02:55:50,527 - pyskl - INFO - Epoch [93][1000/1281] lr: 7.952e-03, eta: 8:45:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2118, loss: 0.2118 +2025-06-25 02:56:39,391 - pyskl - INFO - Epoch [93][1100/1281] lr: 7.933e-03, eta: 8:44:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 0.9988, loss_cls: 0.2522, loss: 0.2522 +2025-06-25 02:57:28,483 - pyskl - INFO - Epoch [93][1200/1281] lr: 7.914e-03, eta: 8:43:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2325, loss: 0.2325 +2025-06-25 02:58:08,629 - pyskl - INFO - Saving checkpoint at 93 epochs +2025-06-25 02:59:07,499 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:59:07,553 - pyskl - INFO - +top1_acc 0.9166 +top5_acc 0.9942 +2025-06-25 02:59:07,553 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:59:07,560 - pyskl - INFO - +mean_acc 0.8888 +2025-06-25 02:59:07,564 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_82.pth was removed +2025-06-25 02:59:07,732 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_93.pth. +2025-06-25 02:59:07,733 - pyskl - INFO - Best top1_acc is 0.9166 at 93 epoch. +2025-06-25 02:59:07,736 - pyskl - INFO - Epoch(val) [93][533] top1_acc: 0.9166, top5_acc: 0.9942, mean_class_accuracy: 0.8888 +2025-06-25 03:00:27,267 - pyskl - INFO - Epoch [94][100/1281] lr: 7.880e-03, eta: 8:42:25, time: 0.795, data_time: 0.186, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2029, loss: 0.2029 +2025-06-25 03:01:16,480 - pyskl - INFO - Epoch [94][200/1281] lr: 7.861e-03, eta: 8:41:46, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1865, loss: 0.1865 +2025-06-25 03:02:05,806 - pyskl - INFO - Epoch [94][300/1281] lr: 7.842e-03, eta: 8:41:07, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1927, loss: 0.1927 +2025-06-25 03:02:55,447 - pyskl - INFO - Epoch [94][400/1281] lr: 7.823e-03, eta: 8:40:28, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2362, loss: 0.2362 +2025-06-25 03:03:23,970 - pyskl - INFO - Epoch [94][500/1281] lr: 7.804e-03, eta: 8:39:36, time: 0.285, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9988, loss_cls: 0.2275, loss: 0.2275 +2025-06-25 03:04:14,877 - pyskl - INFO - Epoch [94][600/1281] lr: 7.785e-03, eta: 8:38:58, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2201, loss: 0.2201 +2025-06-25 03:04:45,670 - pyskl - INFO - Epoch [94][700/1281] lr: 7.766e-03, eta: 8:38:08, time: 0.308, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2302, loss: 0.2302 +2025-06-25 03:05:34,886 - pyskl - INFO - Epoch [94][800/1281] lr: 7.747e-03, eta: 8:37:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.2240, loss: 0.2240 +2025-06-25 03:06:24,129 - pyskl - INFO - Epoch [94][900/1281] lr: 7.728e-03, eta: 8:36:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 1.0000, loss_cls: 0.2647, loss: 0.2647 +2025-06-25 03:07:13,407 - pyskl - INFO - Epoch [94][1000/1281] lr: 7.709e-03, eta: 8:36:10, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9988, loss_cls: 0.2476, loss: 0.2476 +2025-06-25 03:08:02,763 - pyskl - INFO - Epoch [94][1100/1281] lr: 7.690e-03, eta: 8:35:31, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2272, loss: 0.2272 +2025-06-25 03:08:51,889 - pyskl - INFO - Epoch [94][1200/1281] lr: 7.672e-03, eta: 8:34:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2156, loss: 0.2156 +2025-06-25 03:09:32,166 - pyskl - INFO - Saving checkpoint at 94 epochs +2025-06-25 03:10:30,554 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:10:30,611 - pyskl - INFO - +top1_acc 0.8965 +top5_acc 0.9935 +2025-06-25 03:10:30,611 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:10:30,618 - pyskl - INFO - +mean_acc 0.8621 +2025-06-25 03:10:30,620 - pyskl - INFO - Epoch(val) [94][533] top1_acc: 0.8965, top5_acc: 0.9935, mean_class_accuracy: 0.8621 +2025-06-25 03:11:49,900 - pyskl - INFO - Epoch [95][100/1281] lr: 7.637e-03, eta: 8:33:34, time: 0.793, data_time: 0.188, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9988, loss_cls: 0.2706, loss: 0.2706 +2025-06-25 03:12:38,857 - pyskl - INFO - Epoch [95][200/1281] lr: 7.619e-03, eta: 8:32:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9981, loss_cls: 0.2787, loss: 0.2787 +2025-06-25 03:13:27,990 - pyskl - INFO - Epoch [95][300/1281] lr: 7.600e-03, eta: 8:32:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2254, loss: 0.2254 +2025-06-25 03:14:17,205 - pyskl - INFO - Epoch [95][400/1281] lr: 7.581e-03, eta: 8:31:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.2203, loss: 0.2203 +2025-06-25 03:14:45,334 - pyskl - INFO - Epoch [95][500/1281] lr: 7.562e-03, eta: 8:30:44, time: 0.281, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1909, loss: 0.1909 +2025-06-25 03:15:36,409 - pyskl - INFO - Epoch [95][600/1281] lr: 7.543e-03, eta: 8:30:06, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9981, loss_cls: 0.2186, loss: 0.2186 +2025-06-25 03:16:08,381 - pyskl - INFO - Epoch [95][700/1281] lr: 7.525e-03, eta: 8:29:17, time: 0.320, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.2132, loss: 0.2132 +2025-06-25 03:16:57,590 - pyskl - INFO - Epoch [95][800/1281] lr: 7.506e-03, eta: 8:28:37, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.1953, loss: 0.1953 +2025-06-25 03:17:46,938 - pyskl - INFO - Epoch [95][900/1281] lr: 7.487e-03, eta: 8:27:58, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2249, loss: 0.2249 +2025-06-25 03:18:36,431 - pyskl - INFO - Epoch [95][1000/1281] lr: 7.468e-03, eta: 8:27:19, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9988, loss_cls: 0.2224, loss: 0.2224 +2025-06-25 03:19:25,774 - pyskl - INFO - Epoch [95][1100/1281] lr: 7.450e-03, eta: 8:26:39, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.2469, loss: 0.2469 +2025-06-25 03:20:14,887 - pyskl - INFO - Epoch [95][1200/1281] lr: 7.431e-03, eta: 8:26:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2551, loss: 0.2551 +2025-06-25 03:20:55,302 - pyskl - INFO - Saving checkpoint at 95 epochs +2025-06-25 03:21:53,555 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:21:53,617 - pyskl - INFO - +top1_acc 0.9051 +top5_acc 0.9935 +2025-06-25 03:21:53,618 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:21:53,624 - pyskl - INFO - +mean_acc 0.8778 +2025-06-25 03:21:53,626 - pyskl - INFO - Epoch(val) [95][533] top1_acc: 0.9051, top5_acc: 0.9935, mean_class_accuracy: 0.8778 +2025-06-25 03:23:12,141 - pyskl - INFO - Epoch [96][100/1281] lr: 7.397e-03, eta: 8:24:42, time: 0.785, data_time: 0.184, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9994, loss_cls: 0.2156, loss: 0.2156 +2025-06-25 03:24:01,520 - pyskl - INFO - Epoch [96][200/1281] lr: 7.379e-03, eta: 8:24:03, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2286, loss: 0.2286 +2025-06-25 03:24:50,926 - pyskl - INFO - Epoch [96][300/1281] lr: 7.360e-03, eta: 8:23:23, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9969, loss_cls: 0.2358, loss: 0.2358 +2025-06-25 03:25:40,319 - pyskl - INFO - Epoch [96][400/1281] lr: 7.341e-03, eta: 8:22:44, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1833, loss: 0.1833 +2025-06-25 03:26:08,169 - pyskl - INFO - Epoch [96][500/1281] lr: 7.323e-03, eta: 8:21:52, time: 0.279, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1456, loss: 0.1456 +2025-06-25 03:26:58,993 - pyskl - INFO - Epoch [96][600/1281] lr: 7.304e-03, eta: 8:21:14, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2177, loss: 0.2177 +2025-06-25 03:27:29,481 - pyskl - INFO - Epoch [96][700/1281] lr: 7.286e-03, eta: 8:20:23, time: 0.305, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.2075, loss: 0.2075 +2025-06-25 03:28:18,171 - pyskl - INFO - Epoch [96][800/1281] lr: 7.267e-03, eta: 8:19:43, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2399, loss: 0.2399 +2025-06-25 03:29:07,223 - pyskl - INFO - Epoch [96][900/1281] lr: 7.249e-03, eta: 8:19:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9988, loss_cls: 0.2193, loss: 0.2193 +2025-06-25 03:29:56,404 - pyskl - INFO - Epoch [96][1000/1281] lr: 7.230e-03, eta: 8:18:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2108, loss: 0.2108 +2025-06-25 03:30:45,394 - pyskl - INFO - Epoch [96][1100/1281] lr: 7.211e-03, eta: 8:17:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2129, loss: 0.2129 +2025-06-25 03:31:34,637 - pyskl - INFO - Epoch [96][1200/1281] lr: 7.193e-03, eta: 8:17:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2273, loss: 0.2273 +2025-06-25 03:32:15,009 - pyskl - INFO - Saving checkpoint at 96 epochs +2025-06-25 03:33:13,291 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:33:13,347 - pyskl - INFO - +top1_acc 0.9100 +top5_acc 0.9951 +2025-06-25 03:33:13,348 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:33:13,354 - pyskl - INFO - +mean_acc 0.8739 +2025-06-25 03:33:13,356 - pyskl - INFO - Epoch(val) [96][533] top1_acc: 0.9100, top5_acc: 0.9951, mean_class_accuracy: 0.8739 +2025-06-25 03:34:34,291 - pyskl - INFO - Epoch [97][100/1281] lr: 7.159e-03, eta: 8:15:49, time: 0.809, data_time: 0.192, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2021, loss: 0.2021 +2025-06-25 03:35:23,488 - pyskl - INFO - Epoch [97][200/1281] lr: 7.141e-03, eta: 8:15:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 0.9994, loss_cls: 0.1649, loss: 0.1649 +2025-06-25 03:36:13,033 - pyskl - INFO - Epoch [97][300/1281] lr: 7.123e-03, eta: 8:14:30, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1721, loss: 0.1721 +2025-06-25 03:37:02,268 - pyskl - INFO - Epoch [97][400/1281] lr: 7.104e-03, eta: 8:13:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2174, loss: 0.2174 +2025-06-25 03:37:29,325 - pyskl - INFO - Epoch [97][500/1281] lr: 7.086e-03, eta: 8:12:58, time: 0.271, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.1905, loss: 0.1905 +2025-06-25 03:38:20,350 - pyskl - INFO - Epoch [97][600/1281] lr: 7.067e-03, eta: 8:12:19, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2214, loss: 0.2214 +2025-06-25 03:38:52,029 - pyskl - INFO - Epoch [97][700/1281] lr: 7.049e-03, eta: 8:11:30, time: 0.317, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2486, loss: 0.2486 +2025-06-25 03:39:41,211 - pyskl - INFO - Epoch [97][800/1281] lr: 7.030e-03, eta: 8:10:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2244, loss: 0.2244 +2025-06-25 03:40:30,519 - pyskl - INFO - Epoch [97][900/1281] lr: 7.012e-03, eta: 8:10:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.1978, loss: 0.1978 +2025-06-25 03:41:19,521 - pyskl - INFO - Epoch [97][1000/1281] lr: 6.994e-03, eta: 8:09:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.2081, loss: 0.2081 +2025-06-25 03:42:08,332 - pyskl - INFO - Epoch [97][1100/1281] lr: 6.975e-03, eta: 8:08:51, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2045, loss: 0.2045 +2025-06-25 03:42:57,304 - pyskl - INFO - Epoch [97][1200/1281] lr: 6.957e-03, eta: 8:08:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2361, loss: 0.2361 +2025-06-25 03:43:37,343 - pyskl - INFO - Saving checkpoint at 97 epochs +2025-06-25 03:44:35,876 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:44:35,938 - pyskl - INFO - +top1_acc 0.9071 +top5_acc 0.9948 +2025-06-25 03:44:35,938 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:44:35,945 - pyskl - INFO - +mean_acc 0.8865 +2025-06-25 03:44:35,947 - pyskl - INFO - Epoch(val) [97][533] top1_acc: 0.9071, top5_acc: 0.9948, mean_class_accuracy: 0.8865 +2025-06-25 03:45:54,107 - pyskl - INFO - Epoch [98][100/1281] lr: 6.924e-03, eta: 8:06:53, time: 0.782, data_time: 0.186, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1758, loss: 0.1758 +2025-06-25 03:46:43,346 - pyskl - INFO - Epoch [98][200/1281] lr: 6.906e-03, eta: 8:06:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1937, loss: 0.1937 +2025-06-25 03:47:32,639 - pyskl - INFO - Epoch [98][300/1281] lr: 6.887e-03, eta: 8:05:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1505, loss: 0.1505 +2025-06-25 03:48:21,940 - pyskl - INFO - Epoch [98][400/1281] lr: 6.869e-03, eta: 8:04:54, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1863, loss: 0.1863 +2025-06-25 03:48:50,458 - pyskl - INFO - Epoch [98][500/1281] lr: 6.851e-03, eta: 8:04:03, time: 0.285, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 0.9994, loss_cls: 0.1823, loss: 0.1823 +2025-06-25 03:49:41,322 - pyskl - INFO - Epoch [98][600/1281] lr: 6.833e-03, eta: 8:03:24, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 0.9988, loss_cls: 0.1964, loss: 0.1964 +2025-06-25 03:50:13,008 - pyskl - INFO - Epoch [98][700/1281] lr: 6.814e-03, eta: 8:02:35, time: 0.317, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9994, loss_cls: 0.2232, loss: 0.2232 +2025-06-25 03:51:01,598 - pyskl - INFO - Epoch [98][800/1281] lr: 6.796e-03, eta: 8:01:55, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9994, loss_cls: 0.1865, loss: 0.1865 +2025-06-25 03:51:50,713 - pyskl - INFO - Epoch [98][900/1281] lr: 6.778e-03, eta: 8:01:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1955, loss: 0.1955 +2025-06-25 03:52:39,829 - pyskl - INFO - Epoch [98][1000/1281] lr: 6.760e-03, eta: 8:00:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9994, loss_cls: 0.2114, loss: 0.2114 +2025-06-25 03:53:28,922 - pyskl - INFO - Epoch [98][1100/1281] lr: 6.742e-03, eta: 7:59:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 0.9994, loss_cls: 0.2129, loss: 0.2129 +2025-06-25 03:54:18,008 - pyskl - INFO - Epoch [98][1200/1281] lr: 6.724e-03, eta: 7:59:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.1972, loss: 0.1972 +2025-06-25 03:54:58,604 - pyskl - INFO - Saving checkpoint at 98 epochs +2025-06-25 03:55:56,686 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:55:56,741 - pyskl - INFO - +top1_acc 0.9125 +top5_acc 0.9958 +2025-06-25 03:55:56,742 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:55:56,748 - pyskl - INFO - +mean_acc 0.8886 +2025-06-25 03:55:56,750 - pyskl - INFO - Epoch(val) [98][533] top1_acc: 0.9125, top5_acc: 0.9958, mean_class_accuracy: 0.8886 +2025-06-25 03:57:15,356 - pyskl - INFO - Epoch [99][100/1281] lr: 6.691e-03, eta: 7:57:57, time: 0.786, data_time: 0.189, memory: 4083, top1_acc: 0.9806, top5_acc: 0.9994, loss_cls: 0.1779, loss: 0.1779 +2025-06-25 03:58:04,671 - pyskl - INFO - Epoch [99][200/1281] lr: 6.673e-03, eta: 7:57:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9988, loss_cls: 0.1845, loss: 0.1845 +2025-06-25 03:58:53,891 - pyskl - INFO - Epoch [99][300/1281] lr: 6.655e-03, eta: 7:56:37, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1767, loss: 0.1767 +2025-06-25 03:59:43,560 - pyskl - INFO - Epoch [99][400/1281] lr: 6.637e-03, eta: 7:55:58, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1552, loss: 0.1552 +2025-06-25 04:00:13,192 - pyskl - INFO - Epoch [99][500/1281] lr: 6.619e-03, eta: 7:55:08, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1740, loss: 0.1740 +2025-06-25 04:01:04,367 - pyskl - INFO - Epoch [99][600/1281] lr: 6.601e-03, eta: 7:54:29, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1372, loss: 0.1372 +2025-06-25 04:01:34,439 - pyskl - INFO - Epoch [99][700/1281] lr: 6.583e-03, eta: 7:53:39, time: 0.301, data_time: 0.001, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.1825, loss: 0.1825 +2025-06-25 04:02:23,734 - pyskl - INFO - Epoch [99][800/1281] lr: 6.565e-03, eta: 7:52:59, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2120, loss: 0.2120 +2025-06-25 04:03:12,583 - pyskl - INFO - Epoch [99][900/1281] lr: 6.547e-03, eta: 7:52:19, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1959, loss: 0.1959 +2025-06-25 04:04:01,604 - pyskl - INFO - Epoch [99][1000/1281] lr: 6.529e-03, eta: 7:51:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.1899, loss: 0.1899 +2025-06-25 04:04:50,951 - pyskl - INFO - Epoch [99][1100/1281] lr: 6.511e-03, eta: 7:50:59, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9994, loss_cls: 0.2179, loss: 0.2179 +2025-06-25 04:05:40,070 - pyskl - INFO - Epoch [99][1200/1281] lr: 6.493e-03, eta: 7:50:19, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2451, loss: 0.2451 +2025-06-25 04:06:20,561 - pyskl - INFO - Saving checkpoint at 99 epochs +2025-06-25 04:07:18,586 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:07:18,641 - pyskl - INFO - +top1_acc 0.9167 +top5_acc 0.9955 +2025-06-25 04:07:18,641 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:07:18,648 - pyskl - INFO - +mean_acc 0.8844 +2025-06-25 04:07:18,652 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_93.pth was removed +2025-06-25 04:07:18,816 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_99.pth. +2025-06-25 04:07:18,816 - pyskl - INFO - Best top1_acc is 0.9167 at 99 epoch. +2025-06-25 04:07:18,819 - pyskl - INFO - Epoch(val) [99][533] top1_acc: 0.9167, top5_acc: 0.9955, mean_class_accuracy: 0.8844 +2025-06-25 04:08:38,177 - pyskl - INFO - Epoch [100][100/1281] lr: 6.460e-03, eta: 7:49:01, time: 0.794, data_time: 0.192, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2016, loss: 0.2016 +2025-06-25 04:09:27,314 - pyskl - INFO - Epoch [100][200/1281] lr: 6.442e-03, eta: 7:48:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1628, loss: 0.1628 +2025-06-25 04:10:16,477 - pyskl - INFO - Epoch [100][300/1281] lr: 6.425e-03, eta: 7:47:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.1770, loss: 0.1770 +2025-06-25 04:11:05,641 - pyskl - INFO - Epoch [100][400/1281] lr: 6.407e-03, eta: 7:47:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1426, loss: 0.1426 +2025-06-25 04:11:34,333 - pyskl - INFO - Epoch [100][500/1281] lr: 6.389e-03, eta: 7:46:10, time: 0.287, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.1769, loss: 0.1769 +2025-06-25 04:12:25,389 - pyskl - INFO - Epoch [100][600/1281] lr: 6.371e-03, eta: 7:45:31, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1884, loss: 0.1884 +2025-06-25 04:12:53,754 - pyskl - INFO - Epoch [100][700/1281] lr: 6.353e-03, eta: 7:44:41, time: 0.284, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1561, loss: 0.1561 +2025-06-25 04:13:43,172 - pyskl - INFO - Epoch [100][800/1281] lr: 6.336e-03, eta: 7:44:01, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1550, loss: 0.1550 +2025-06-25 04:14:32,301 - pyskl - INFO - Epoch [100][900/1281] lr: 6.318e-03, eta: 7:43:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2556, loss: 0.2556 +2025-06-25 04:15:21,374 - pyskl - INFO - Epoch [100][1000/1281] lr: 6.300e-03, eta: 7:42:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2164, loss: 0.2164 +2025-06-25 04:16:10,155 - pyskl - INFO - Epoch [100][1100/1281] lr: 6.282e-03, eta: 7:42:00, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2175, loss: 0.2175 +2025-06-25 04:16:59,319 - pyskl - INFO - Epoch [100][1200/1281] lr: 6.265e-03, eta: 7:41:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 1.0000, loss_cls: 0.2601, loss: 0.2601 +2025-06-25 04:17:39,803 - pyskl - INFO - Saving checkpoint at 100 epochs +2025-06-25 04:18:38,349 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:18:38,404 - pyskl - INFO - +top1_acc 0.9008 +top5_acc 0.9964 +2025-06-25 04:18:38,404 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:18:38,411 - pyskl - INFO - +mean_acc 0.8748 +2025-06-25 04:18:38,413 - pyskl - INFO - Epoch(val) [100][533] top1_acc: 0.9008, top5_acc: 0.9964, mean_class_accuracy: 0.8748 +2025-06-25 04:19:58,293 - pyskl - INFO - Epoch [101][100/1281] lr: 6.232e-03, eta: 7:40:03, time: 0.799, data_time: 0.190, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2277, loss: 0.2277 +2025-06-25 04:20:47,677 - pyskl - INFO - Epoch [101][200/1281] lr: 6.215e-03, eta: 7:39:23, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2244, loss: 0.2244 +2025-06-25 04:21:36,574 - pyskl - INFO - Epoch [101][300/1281] lr: 6.197e-03, eta: 7:38:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1544, loss: 0.1544 +2025-06-25 04:22:25,850 - pyskl - INFO - Epoch [101][400/1281] lr: 6.180e-03, eta: 7:38:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1620, loss: 0.1620 +2025-06-25 04:22:56,604 - pyskl - INFO - Epoch [101][500/1281] lr: 6.162e-03, eta: 7:37:13, time: 0.308, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9994, loss_cls: 0.1710, loss: 0.1710 +2025-06-25 04:23:47,638 - pyskl - INFO - Epoch [101][600/1281] lr: 6.144e-03, eta: 7:36:34, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1901, loss: 0.1901 +2025-06-25 04:24:15,005 - pyskl - INFO - Epoch [101][700/1281] lr: 6.127e-03, eta: 7:35:43, time: 0.274, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1460, loss: 0.1460 +2025-06-25 04:25:03,970 - pyskl - INFO - Epoch [101][800/1281] lr: 6.109e-03, eta: 7:35:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 0.9994, loss_cls: 0.1483, loss: 0.1483 +2025-06-25 04:25:52,968 - pyskl - INFO - Epoch [101][900/1281] lr: 6.092e-03, eta: 7:34:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1883, loss: 0.1883 +2025-06-25 04:26:42,020 - pyskl - INFO - Epoch [101][1000/1281] lr: 6.074e-03, eta: 7:33:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1819, loss: 0.1819 +2025-06-25 04:27:31,523 - pyskl - INFO - Epoch [101][1100/1281] lr: 6.057e-03, eta: 7:33:02, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1686, loss: 0.1686 +2025-06-25 04:28:20,426 - pyskl - INFO - Epoch [101][1200/1281] lr: 6.039e-03, eta: 7:32:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1836, loss: 0.1836 +2025-06-25 04:29:00,719 - pyskl - INFO - Saving checkpoint at 101 epochs +2025-06-25 04:29:58,590 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:29:58,646 - pyskl - INFO - +top1_acc 0.9100 +top5_acc 0.9946 +2025-06-25 04:29:58,646 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:29:58,655 - pyskl - INFO - +mean_acc 0.8807 +2025-06-25 04:29:58,658 - pyskl - INFO - Epoch(val) [101][533] top1_acc: 0.9100, top5_acc: 0.9946, mean_class_accuracy: 0.8807 +2025-06-25 04:31:19,517 - pyskl - INFO - Epoch [102][100/1281] lr: 6.007e-03, eta: 7:31:04, time: 0.809, data_time: 0.191, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1486, loss: 0.1486 +2025-06-25 04:32:08,678 - pyskl - INFO - Epoch [102][200/1281] lr: 5.990e-03, eta: 7:30:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1437, loss: 0.1437 +2025-06-25 04:32:57,747 - pyskl - INFO - Epoch [102][300/1281] lr: 5.972e-03, eta: 7:29:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1556, loss: 0.1556 +2025-06-25 04:33:47,056 - pyskl - INFO - Epoch [102][400/1281] lr: 5.955e-03, eta: 7:29:03, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1890, loss: 0.1890 +2025-06-25 04:34:18,237 - pyskl - INFO - Epoch [102][500/1281] lr: 5.938e-03, eta: 7:28:14, time: 0.312, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1564, loss: 0.1564 +2025-06-25 04:35:09,261 - pyskl - INFO - Epoch [102][600/1281] lr: 5.920e-03, eta: 7:27:35, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1387, loss: 0.1387 +2025-06-25 04:35:37,179 - pyskl - INFO - Epoch [102][700/1281] lr: 5.903e-03, eta: 7:26:44, time: 0.279, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1704, loss: 0.1704 +2025-06-25 04:36:25,918 - pyskl - INFO - Epoch [102][800/1281] lr: 5.886e-03, eta: 7:26:04, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1455, loss: 0.1455 +2025-06-25 04:37:14,739 - pyskl - INFO - Epoch [102][900/1281] lr: 5.868e-03, eta: 7:25:23, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1822, loss: 0.1822 +2025-06-25 04:38:03,444 - pyskl - INFO - Epoch [102][1000/1281] lr: 5.851e-03, eta: 7:24:43, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1889, loss: 0.1889 +2025-06-25 04:38:52,809 - pyskl - INFO - Epoch [102][1100/1281] lr: 5.834e-03, eta: 7:24:03, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1996, loss: 0.1996 +2025-06-25 04:39:42,174 - pyskl - INFO - Epoch [102][1200/1281] lr: 5.816e-03, eta: 7:23:22, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1756, loss: 0.1756 +2025-06-25 04:40:22,268 - pyskl - INFO - Saving checkpoint at 102 epochs +2025-06-25 04:41:20,759 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:41:20,827 - pyskl - INFO - +top1_acc 0.9107 +top5_acc 0.9947 +2025-06-25 04:41:20,827 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:41:20,837 - pyskl - INFO - +mean_acc 0.8845 +2025-06-25 04:41:20,839 - pyskl - INFO - Epoch(val) [102][533] top1_acc: 0.9107, top5_acc: 0.9947, mean_class_accuracy: 0.8845 +2025-06-25 04:42:41,678 - pyskl - INFO - Epoch [103][100/1281] lr: 5.785e-03, eta: 7:22:05, time: 0.808, data_time: 0.196, memory: 4083, top1_acc: 0.9719, top5_acc: 0.9988, loss_cls: 0.1622, loss: 0.1622 +2025-06-25 04:43:30,593 - pyskl - INFO - Epoch [103][200/1281] lr: 5.768e-03, eta: 7:21:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1290, loss: 0.1290 +2025-06-25 04:44:19,476 - pyskl - INFO - Epoch [103][300/1281] lr: 5.751e-03, eta: 7:20:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 0.9988, loss_cls: 0.1628, loss: 0.1628 +2025-06-25 04:45:08,774 - pyskl - INFO - Epoch [103][400/1281] lr: 5.733e-03, eta: 7:20:04, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1435, loss: 0.1435 +2025-06-25 04:45:38,686 - pyskl - INFO - Epoch [103][500/1281] lr: 5.716e-03, eta: 7:19:14, time: 0.299, data_time: 0.001, memory: 4083, top1_acc: 0.9756, top5_acc: 1.0000, loss_cls: 0.1551, loss: 0.1551 +2025-06-25 04:46:29,705 - pyskl - INFO - Epoch [103][600/1281] lr: 5.699e-03, eta: 7:18:35, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 0.9994, loss_cls: 0.1884, loss: 0.1884 +2025-06-25 04:46:57,215 - pyskl - INFO - Epoch [103][700/1281] lr: 5.682e-03, eta: 7:17:44, time: 0.275, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1585, loss: 0.1585 +2025-06-25 04:47:46,433 - pyskl - INFO - Epoch [103][800/1281] lr: 5.665e-03, eta: 7:17:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1392, loss: 0.1392 +2025-06-25 04:48:35,479 - pyskl - INFO - Epoch [103][900/1281] lr: 5.648e-03, eta: 7:16:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1088, loss: 0.1088 +2025-06-25 04:49:24,668 - pyskl - INFO - Epoch [103][1000/1281] lr: 5.631e-03, eta: 7:15:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2081, loss: 0.2081 +2025-06-25 04:50:13,706 - pyskl - INFO - Epoch [103][1100/1281] lr: 5.614e-03, eta: 7:15:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1711, loss: 0.1711 +2025-06-25 04:51:02,715 - pyskl - INFO - Epoch [103][1200/1281] lr: 5.597e-03, eta: 7:14:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2174, loss: 0.2174 +2025-06-25 04:51:43,153 - pyskl - INFO - Saving checkpoint at 103 epochs +2025-06-25 04:52:41,152 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:52:41,242 - pyskl - INFO - +top1_acc 0.9176 +top5_acc 0.9948 +2025-06-25 04:52:41,243 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:52:41,253 - pyskl - INFO - +mean_acc 0.8851 +2025-06-25 04:52:41,257 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_99.pth was removed +2025-06-25 04:52:41,432 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_103.pth. +2025-06-25 04:52:41,433 - pyskl - INFO - Best top1_acc is 0.9176 at 103 epoch. +2025-06-25 04:52:41,437 - pyskl - INFO - Epoch(val) [103][533] top1_acc: 0.9176, top5_acc: 0.9948, mean_class_accuracy: 0.8851 +2025-06-25 04:54:02,078 - pyskl - INFO - Epoch [104][100/1281] lr: 5.566e-03, eta: 7:13:05, time: 0.806, data_time: 0.187, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1418, loss: 0.1418 +2025-06-25 04:54:51,074 - pyskl - INFO - Epoch [104][200/1281] lr: 5.549e-03, eta: 7:12:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1228, loss: 0.1228 +2025-06-25 04:55:40,244 - pyskl - INFO - Epoch [104][300/1281] lr: 5.532e-03, eta: 7:11:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1710, loss: 0.1710 +2025-06-25 04:56:29,391 - pyskl - INFO - Epoch [104][400/1281] lr: 5.515e-03, eta: 7:11:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1101, loss: 0.1101 +2025-06-25 04:56:59,393 - pyskl - INFO - Epoch [104][500/1281] lr: 5.498e-03, eta: 7:10:14, time: 0.300, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1372, loss: 0.1372 +2025-06-25 04:57:50,335 - pyskl - INFO - Epoch [104][600/1281] lr: 5.481e-03, eta: 7:09:34, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1451, loss: 0.1451 +2025-06-25 04:58:20,222 - pyskl - INFO - Epoch [104][700/1281] lr: 5.464e-03, eta: 7:08:45, time: 0.299, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1574, loss: 0.1574 +2025-06-25 04:59:09,288 - pyskl - INFO - Epoch [104][800/1281] lr: 5.447e-03, eta: 7:08:04, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1682, loss: 0.1682 +2025-06-25 04:59:58,390 - pyskl - INFO - Epoch [104][900/1281] lr: 5.430e-03, eta: 7:07:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 0.9994, loss_cls: 0.1702, loss: 0.1702 +2025-06-25 05:00:47,370 - pyskl - INFO - Epoch [104][1000/1281] lr: 5.413e-03, eta: 7:06:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1394, loss: 0.1394 +2025-06-25 05:01:36,584 - pyskl - INFO - Epoch [104][1100/1281] lr: 5.397e-03, eta: 7:06:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 0.9988, loss_cls: 0.1618, loss: 0.1618 +2025-06-25 05:02:25,685 - pyskl - INFO - Epoch [104][1200/1281] lr: 5.380e-03, eta: 7:05:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 0.9994, loss_cls: 0.1875, loss: 0.1875 +2025-06-25 05:03:05,886 - pyskl - INFO - Saving checkpoint at 104 epochs +2025-06-25 05:04:04,574 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:04:04,631 - pyskl - INFO - +top1_acc 0.9159 +top5_acc 0.9947 +2025-06-25 05:04:04,631 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:04:04,638 - pyskl - INFO - +mean_acc 0.8935 +2025-06-25 05:04:04,640 - pyskl - INFO - Epoch(val) [104][533] top1_acc: 0.9159, top5_acc: 0.9947, mean_class_accuracy: 0.8935 +2025-06-25 05:05:25,238 - pyskl - INFO - Epoch [105][100/1281] lr: 5.349e-03, eta: 7:04:04, time: 0.806, data_time: 0.189, memory: 4083, top1_acc: 0.9819, top5_acc: 0.9994, loss_cls: 0.1259, loss: 0.1259 +2025-06-25 05:06:14,363 - pyskl - INFO - Epoch [105][200/1281] lr: 5.333e-03, eta: 7:03:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1253, loss: 0.1253 +2025-06-25 05:07:03,625 - pyskl - INFO - Epoch [105][300/1281] lr: 5.316e-03, eta: 7:02:43, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0982, loss: 0.0982 +2025-06-25 05:07:52,788 - pyskl - INFO - Epoch [105][400/1281] lr: 5.299e-03, eta: 7:02:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1315, loss: 0.1315 +2025-06-25 05:08:20,344 - pyskl - INFO - Epoch [105][500/1281] lr: 5.283e-03, eta: 7:01:12, time: 0.276, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.1789, loss: 0.1789 +2025-06-25 05:09:11,419 - pyskl - INFO - Epoch [105][600/1281] lr: 5.266e-03, eta: 7:00:32, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.1783, loss: 0.1783 +2025-06-25 05:09:42,112 - pyskl - INFO - Epoch [105][700/1281] lr: 5.249e-03, eta: 6:59:44, time: 0.307, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1865, loss: 0.1865 +2025-06-25 05:10:31,342 - pyskl - INFO - Epoch [105][800/1281] lr: 5.233e-03, eta: 6:59:03, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1578, loss: 0.1578 +2025-06-25 05:11:20,513 - pyskl - INFO - Epoch [105][900/1281] lr: 5.216e-03, eta: 6:58:22, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1299, loss: 0.1299 +2025-06-25 05:12:09,611 - pyskl - INFO - Epoch [105][1000/1281] lr: 5.199e-03, eta: 6:57:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1227, loss: 0.1227 +2025-06-25 05:12:58,836 - pyskl - INFO - Epoch [105][1100/1281] lr: 5.183e-03, eta: 6:57:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1189, loss: 0.1189 +2025-06-25 05:13:48,350 - pyskl - INFO - Epoch [105][1200/1281] lr: 5.166e-03, eta: 6:56:20, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 0.9994, loss_cls: 0.1354, loss: 0.1354 +2025-06-25 05:14:28,234 - pyskl - INFO - Saving checkpoint at 105 epochs +2025-06-25 05:15:26,355 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:15:26,423 - pyskl - INFO - +top1_acc 0.9121 +top5_acc 0.9952 +2025-06-25 05:15:26,423 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:15:26,431 - pyskl - INFO - +mean_acc 0.8738 +2025-06-25 05:15:26,433 - pyskl - INFO - Epoch(val) [105][533] top1_acc: 0.9121, top5_acc: 0.9952, mean_class_accuracy: 0.8738 +2025-06-25 05:16:46,807 - pyskl - INFO - Epoch [106][100/1281] lr: 5.136e-03, eta: 6:55:03, time: 0.804, data_time: 0.187, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1427, loss: 0.1427 +2025-06-25 05:17:35,827 - pyskl - INFO - Epoch [106][200/1281] lr: 5.120e-03, eta: 6:54:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1318, loss: 0.1318 +2025-06-25 05:18:24,701 - pyskl - INFO - Epoch [106][300/1281] lr: 5.103e-03, eta: 6:53:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 0.9994, loss_cls: 0.1311, loss: 0.1311 +2025-06-25 05:19:13,976 - pyskl - INFO - Epoch [106][400/1281] lr: 5.087e-03, eta: 6:53:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1525, loss: 0.1525 +2025-06-25 05:19:41,848 - pyskl - INFO - Epoch [106][500/1281] lr: 5.070e-03, eta: 6:52:10, time: 0.279, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1532, loss: 0.1532 +2025-06-25 05:20:32,842 - pyskl - INFO - Epoch [106][600/1281] lr: 5.054e-03, eta: 6:51:30, time: 0.510, data_time: 0.001, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9994, loss_cls: 0.1925, loss: 0.1925 +2025-06-25 05:21:04,076 - pyskl - INFO - Epoch [106][700/1281] lr: 5.038e-03, eta: 6:50:42, time: 0.312, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 0.9994, loss_cls: 0.1261, loss: 0.1261 +2025-06-25 05:21:53,016 - pyskl - INFO - Epoch [106][800/1281] lr: 5.021e-03, eta: 6:50:01, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 0.9988, loss_cls: 0.1590, loss: 0.1590 +2025-06-25 05:22:41,834 - pyskl - INFO - Epoch [106][900/1281] lr: 5.005e-03, eta: 6:49:20, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1513, loss: 0.1513 +2025-06-25 05:23:30,841 - pyskl - INFO - Epoch [106][1000/1281] lr: 4.988e-03, eta: 6:48:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1569, loss: 0.1569 +2025-06-25 05:24:19,869 - pyskl - INFO - Epoch [106][1100/1281] lr: 4.972e-03, eta: 6:47:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1550, loss: 0.1550 +2025-06-25 05:25:09,197 - pyskl - INFO - Epoch [106][1200/1281] lr: 4.956e-03, eta: 6:47:18, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2143, loss: 0.2143 +2025-06-25 05:25:49,378 - pyskl - INFO - Saving checkpoint at 106 epochs +2025-06-25 05:26:47,444 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:26:47,514 - pyskl - INFO - +top1_acc 0.9147 +top5_acc 0.9941 +2025-06-25 05:26:47,514 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:26:47,522 - pyskl - INFO - +mean_acc 0.8822 +2025-06-25 05:26:47,524 - pyskl - INFO - Epoch(val) [106][533] top1_acc: 0.9147, top5_acc: 0.9941, mean_class_accuracy: 0.8822 +2025-06-25 05:28:07,184 - pyskl - INFO - Epoch [107][100/1281] lr: 4.926e-03, eta: 6:46:00, time: 0.797, data_time: 0.185, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1391, loss: 0.1391 +2025-06-25 05:28:56,066 - pyskl - INFO - Epoch [107][200/1281] lr: 4.910e-03, eta: 6:45:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1046, loss: 0.1046 +2025-06-25 05:29:45,223 - pyskl - INFO - Epoch [107][300/1281] lr: 4.894e-03, eta: 6:44:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1358, loss: 0.1358 +2025-06-25 05:30:34,198 - pyskl - INFO - Epoch [107][400/1281] lr: 4.878e-03, eta: 6:43:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1293, loss: 0.1293 +2025-06-25 05:31:01,878 - pyskl - INFO - Epoch [107][500/1281] lr: 4.862e-03, eta: 6:43:07, time: 0.277, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1477, loss: 0.1477 +2025-06-25 05:31:52,770 - pyskl - INFO - Epoch [107][600/1281] lr: 4.845e-03, eta: 6:42:27, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1009, loss: 0.1009 +2025-06-25 05:32:23,070 - pyskl - INFO - Epoch [107][700/1281] lr: 4.829e-03, eta: 6:41:38, time: 0.303, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1281, loss: 0.1281 +2025-06-25 05:33:12,151 - pyskl - INFO - Epoch [107][800/1281] lr: 4.813e-03, eta: 6:40:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1264, loss: 0.1264 +2025-06-25 05:34:01,479 - pyskl - INFO - Epoch [107][900/1281] lr: 4.797e-03, eta: 6:40:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1509, loss: 0.1509 +2025-06-25 05:34:50,753 - pyskl - INFO - Epoch [107][1000/1281] lr: 4.781e-03, eta: 6:39:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 0.9988, loss_cls: 0.1370, loss: 0.1370 +2025-06-25 05:35:39,985 - pyskl - INFO - Epoch [107][1100/1281] lr: 4.765e-03, eta: 6:38:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1340, loss: 0.1340 +2025-06-25 05:36:29,156 - pyskl - INFO - Epoch [107][1200/1281] lr: 4.749e-03, eta: 6:38:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1309, loss: 0.1309 +2025-06-25 05:37:09,512 - pyskl - INFO - Saving checkpoint at 107 epochs +2025-06-25 05:38:07,593 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:38:07,663 - pyskl - INFO - +top1_acc 0.9208 +top5_acc 0.9952 +2025-06-25 05:38:07,663 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:38:07,671 - pyskl - INFO - +mean_acc 0.8970 +2025-06-25 05:38:07,676 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_103.pth was removed +2025-06-25 05:38:07,855 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_107.pth. +2025-06-25 05:38:07,855 - pyskl - INFO - Best top1_acc is 0.9208 at 107 epoch. +2025-06-25 05:38:07,858 - pyskl - INFO - Epoch(val) [107][533] top1_acc: 0.9208, top5_acc: 0.9952, mean_class_accuracy: 0.8970 +2025-06-25 05:39:27,211 - pyskl - INFO - Epoch [108][100/1281] lr: 4.720e-03, eta: 6:36:56, time: 0.793, data_time: 0.184, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1199, loss: 0.1199 +2025-06-25 05:40:16,288 - pyskl - INFO - Epoch [108][200/1281] lr: 4.704e-03, eta: 6:36:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0887, loss: 0.0887 +2025-06-25 05:41:05,457 - pyskl - INFO - Epoch [108][300/1281] lr: 4.688e-03, eta: 6:35:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0946, loss: 0.0946 +2025-06-25 05:41:54,486 - pyskl - INFO - Epoch [108][400/1281] lr: 4.672e-03, eta: 6:34:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1007, loss: 0.1007 +2025-06-25 05:42:23,692 - pyskl - INFO - Epoch [108][500/1281] lr: 4.656e-03, eta: 6:34:04, time: 0.292, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1242, loss: 0.1242 +2025-06-25 05:43:14,675 - pyskl - INFO - Epoch [108][600/1281] lr: 4.640e-03, eta: 6:33:24, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 0.9994, loss_cls: 0.1406, loss: 0.1406 +2025-06-25 05:43:44,744 - pyskl - INFO - Epoch [108][700/1281] lr: 4.624e-03, eta: 6:32:35, time: 0.301, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1336, loss: 0.1336 +2025-06-25 05:44:33,926 - pyskl - INFO - Epoch [108][800/1281] lr: 4.608e-03, eta: 6:31:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1297, loss: 0.1297 +2025-06-25 05:45:23,253 - pyskl - INFO - Epoch [108][900/1281] lr: 4.593e-03, eta: 6:31:13, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1221, loss: 0.1221 +2025-06-25 05:46:12,070 - pyskl - INFO - Epoch [108][1000/1281] lr: 4.577e-03, eta: 6:30:32, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1346, loss: 0.1346 +2025-06-25 05:47:01,087 - pyskl - INFO - Epoch [108][1100/1281] lr: 4.561e-03, eta: 6:29:51, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1488, loss: 0.1488 +2025-06-25 05:47:50,123 - pyskl - INFO - Epoch [108][1200/1281] lr: 4.545e-03, eta: 6:29:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1363, loss: 0.1363 +2025-06-25 05:48:30,436 - pyskl - INFO - Saving checkpoint at 108 epochs +2025-06-25 05:49:28,451 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:49:28,509 - pyskl - INFO - +top1_acc 0.9237 +top5_acc 0.9945 +2025-06-25 05:49:28,509 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:49:28,516 - pyskl - INFO - +mean_acc 0.8962 +2025-06-25 05:49:28,520 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_107.pth was removed +2025-06-25 05:49:28,689 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_108.pth. +2025-06-25 05:49:28,689 - pyskl - INFO - Best top1_acc is 0.9237 at 108 epoch. +2025-06-25 05:49:28,692 - pyskl - INFO - Epoch(val) [108][533] top1_acc: 0.9237, top5_acc: 0.9945, mean_class_accuracy: 0.8962 +2025-06-25 05:50:48,423 - pyskl - INFO - Epoch [109][100/1281] lr: 4.517e-03, eta: 6:27:52, time: 0.797, data_time: 0.184, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0978, loss: 0.0978 +2025-06-25 05:51:37,424 - pyskl - INFO - Epoch [109][200/1281] lr: 4.501e-03, eta: 6:27:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 0.9994, loss_cls: 0.1358, loss: 0.1358 +2025-06-25 05:52:26,537 - pyskl - INFO - Epoch [109][300/1281] lr: 4.485e-03, eta: 6:26:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 0.9994, loss_cls: 0.1279, loss: 0.1279 +2025-06-25 05:53:15,714 - pyskl - INFO - Epoch [109][400/1281] lr: 4.470e-03, eta: 6:25:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.1000, loss: 0.1000 +2025-06-25 05:53:44,197 - pyskl - INFO - Epoch [109][500/1281] lr: 4.454e-03, eta: 6:24:59, time: 0.285, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0967, loss: 0.0967 +2025-06-25 05:54:35,343 - pyskl - INFO - Epoch [109][600/1281] lr: 4.438e-03, eta: 6:24:19, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1057, loss: 0.1057 +2025-06-25 05:55:06,596 - pyskl - INFO - Epoch [109][700/1281] lr: 4.423e-03, eta: 6:23:31, time: 0.313, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1361, loss: 0.1361 +2025-06-25 05:55:55,683 - pyskl - INFO - Epoch [109][800/1281] lr: 4.407e-03, eta: 6:22:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1413, loss: 0.1413 +2025-06-25 05:56:44,603 - pyskl - INFO - Epoch [109][900/1281] lr: 4.391e-03, eta: 6:22:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1246, loss: 0.1246 +2025-06-25 05:57:33,538 - pyskl - INFO - Epoch [109][1000/1281] lr: 4.376e-03, eta: 6:21:27, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1149, loss: 0.1149 +2025-06-25 05:58:22,783 - pyskl - INFO - Epoch [109][1100/1281] lr: 4.360e-03, eta: 6:20:46, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0899, loss: 0.0899 +2025-06-25 05:59:11,600 - pyskl - INFO - Epoch [109][1200/1281] lr: 4.345e-03, eta: 6:20:05, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1085, loss: 0.1085 +2025-06-25 05:59:51,673 - pyskl - INFO - Saving checkpoint at 109 epochs +2025-06-25 06:00:49,452 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:00:49,507 - pyskl - INFO - +top1_acc 0.9140 +top5_acc 0.9935 +2025-06-25 06:00:49,507 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:00:49,514 - pyskl - INFO - +mean_acc 0.8807 +2025-06-25 06:00:49,515 - pyskl - INFO - Epoch(val) [109][533] top1_acc: 0.9140, top5_acc: 0.9935, mean_class_accuracy: 0.8807 +2025-06-25 06:02:10,215 - pyskl - INFO - Epoch [110][100/1281] lr: 4.317e-03, eta: 6:18:47, time: 0.807, data_time: 0.183, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1132, loss: 0.1132 +2025-06-25 06:02:59,550 - pyskl - INFO - Epoch [110][200/1281] lr: 4.301e-03, eta: 6:18:06, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1012, loss: 0.1012 +2025-06-25 06:03:48,947 - pyskl - INFO - Epoch [110][300/1281] lr: 4.286e-03, eta: 6:17:25, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0798, loss: 0.0798 +2025-06-25 06:04:38,084 - pyskl - INFO - Epoch [110][400/1281] lr: 4.271e-03, eta: 6:16:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0808, loss: 0.0808 +2025-06-25 06:05:06,043 - pyskl - INFO - Epoch [110][500/1281] lr: 4.255e-03, eta: 6:15:55, time: 0.280, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0831, loss: 0.0831 +2025-06-25 06:05:57,023 - pyskl - INFO - Epoch [110][600/1281] lr: 4.240e-03, eta: 6:15:14, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1034, loss: 0.1034 +2025-06-25 06:06:28,769 - pyskl - INFO - Epoch [110][700/1281] lr: 4.225e-03, eta: 6:14:27, time: 0.317, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0949, loss: 0.0949 +2025-06-25 06:07:17,356 - pyskl - INFO - Epoch [110][800/1281] lr: 4.209e-03, eta: 6:13:45, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 0.9994, loss_cls: 0.1171, loss: 0.1171 +2025-06-25 06:08:06,556 - pyskl - INFO - Epoch [110][900/1281] lr: 4.194e-03, eta: 6:13:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1402, loss: 0.1402 +2025-06-25 06:08:55,514 - pyskl - INFO - Epoch [110][1000/1281] lr: 4.179e-03, eta: 6:12:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1318, loss: 0.1318 +2025-06-25 06:09:44,924 - pyskl - INFO - Epoch [110][1100/1281] lr: 4.164e-03, eta: 6:11:42, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1324, loss: 0.1324 +2025-06-25 06:10:34,159 - pyskl - INFO - Epoch [110][1200/1281] lr: 4.148e-03, eta: 6:11:00, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1089, loss: 0.1089 +2025-06-25 06:11:14,466 - pyskl - INFO - Saving checkpoint at 110 epochs +2025-06-25 06:12:12,670 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:12:12,725 - pyskl - INFO - +top1_acc 0.9244 +top5_acc 0.9952 +2025-06-25 06:12:12,725 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:12:12,731 - pyskl - INFO - +mean_acc 0.9036 +2025-06-25 06:12:12,735 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_108.pth was removed +2025-06-25 06:12:12,898 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_110.pth. +2025-06-25 06:12:12,898 - pyskl - INFO - Best top1_acc is 0.9244 at 110 epoch. +2025-06-25 06:12:12,901 - pyskl - INFO - Epoch(val) [110][533] top1_acc: 0.9244, top5_acc: 0.9952, mean_class_accuracy: 0.9036 +2025-06-25 06:13:33,186 - pyskl - INFO - Epoch [111][100/1281] lr: 4.121e-03, eta: 6:09:42, time: 0.803, data_time: 0.186, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1085, loss: 0.1085 +2025-06-25 06:14:22,381 - pyskl - INFO - Epoch [111][200/1281] lr: 4.106e-03, eta: 6:09:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0984, loss: 0.0984 +2025-06-25 06:15:12,065 - pyskl - INFO - Epoch [111][300/1281] lr: 4.091e-03, eta: 6:08:20, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0756, loss: 0.0756 +2025-06-25 06:16:01,192 - pyskl - INFO - Epoch [111][400/1281] lr: 4.075e-03, eta: 6:07:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0872, loss: 0.0872 +2025-06-25 06:16:28,747 - pyskl - INFO - Epoch [111][500/1281] lr: 4.060e-03, eta: 6:06:50, time: 0.276, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 0.9988, loss_cls: 0.0847, loss: 0.0847 +2025-06-25 06:17:18,725 - pyskl - INFO - Epoch [111][600/1281] lr: 4.045e-03, eta: 6:06:09, time: 0.500, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0844, loss: 0.0844 +2025-06-25 06:17:51,663 - pyskl - INFO - Epoch [111][700/1281] lr: 4.030e-03, eta: 6:05:22, time: 0.329, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0894, loss: 0.0894 +2025-06-25 06:18:40,626 - pyskl - INFO - Epoch [111][800/1281] lr: 4.015e-03, eta: 6:04:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1134, loss: 0.1134 +2025-06-25 06:19:29,953 - pyskl - INFO - Epoch [111][900/1281] lr: 4.000e-03, eta: 6:03:59, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0997, loss: 0.0997 +2025-06-25 06:20:19,031 - pyskl - INFO - Epoch [111][1000/1281] lr: 3.985e-03, eta: 6:03:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 0.9994, loss_cls: 0.0913, loss: 0.0913 +2025-06-25 06:21:08,211 - pyskl - INFO - Epoch [111][1100/1281] lr: 3.970e-03, eta: 6:02:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0716, loss: 0.0716 +2025-06-25 06:21:57,336 - pyskl - INFO - Epoch [111][1200/1281] lr: 3.955e-03, eta: 6:01:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0873, loss: 0.0873 +2025-06-25 06:22:37,483 - pyskl - INFO - Saving checkpoint at 111 epochs +2025-06-25 06:23:35,630 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:23:35,705 - pyskl - INFO - +top1_acc 0.9251 +top5_acc 0.9959 +2025-06-25 06:23:35,705 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:23:35,712 - pyskl - INFO - +mean_acc 0.9053 +2025-06-25 06:23:35,715 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_110.pth was removed +2025-06-25 06:23:35,875 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_111.pth. +2025-06-25 06:23:35,875 - pyskl - INFO - Best top1_acc is 0.9251 at 111 epoch. +2025-06-25 06:23:35,878 - pyskl - INFO - Epoch(val) [111][533] top1_acc: 0.9251, top5_acc: 0.9959, mean_class_accuracy: 0.9053 +2025-06-25 06:24:56,065 - pyskl - INFO - Epoch [112][100/1281] lr: 3.928e-03, eta: 6:00:37, time: 0.802, data_time: 0.186, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0991, loss: 0.0991 +2025-06-25 06:25:45,320 - pyskl - INFO - Epoch [112][200/1281] lr: 3.914e-03, eta: 5:59:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0810, loss: 0.0810 +2025-06-25 06:26:34,174 - pyskl - INFO - Epoch [112][300/1281] lr: 3.899e-03, eta: 5:59:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1046, loss: 0.1046 +2025-06-25 06:27:23,328 - pyskl - INFO - Epoch [112][400/1281] lr: 3.884e-03, eta: 5:58:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1020, loss: 0.1020 +2025-06-25 06:27:52,231 - pyskl - INFO - Epoch [112][500/1281] lr: 3.869e-03, eta: 5:57:44, time: 0.289, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 0.9994, loss_cls: 0.1038, loss: 0.1038 +2025-06-25 06:28:39,550 - pyskl - INFO - Epoch [112][600/1281] lr: 3.854e-03, eta: 5:57:02, time: 0.473, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.0996, loss: 0.0996 +2025-06-25 06:29:13,203 - pyskl - INFO - Epoch [112][700/1281] lr: 3.840e-03, eta: 5:56:15, time: 0.337, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1011, loss: 0.1011 +2025-06-25 06:30:02,266 - pyskl - INFO - Epoch [112][800/1281] lr: 3.825e-03, eta: 5:55:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1155, loss: 0.1155 +2025-06-25 06:30:51,410 - pyskl - INFO - Epoch [112][900/1281] lr: 3.810e-03, eta: 5:54:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1011, loss: 0.1011 +2025-06-25 06:31:40,437 - pyskl - INFO - Epoch [112][1000/1281] lr: 3.795e-03, eta: 5:54:11, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.0976, loss: 0.0976 +2025-06-25 06:32:29,564 - pyskl - INFO - Epoch [112][1100/1281] lr: 3.781e-03, eta: 5:53:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1159, loss: 0.1159 +2025-06-25 06:33:18,997 - pyskl - INFO - Epoch [112][1200/1281] lr: 3.766e-03, eta: 5:52:48, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1006, loss: 0.1006 +2025-06-25 06:33:59,662 - pyskl - INFO - Saving checkpoint at 112 epochs +2025-06-25 06:34:57,882 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:34:57,954 - pyskl - INFO - +top1_acc 0.9274 +top5_acc 0.9961 +2025-06-25 06:34:57,955 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:34:57,964 - pyskl - INFO - +mean_acc 0.9019 +2025-06-25 06:34:57,968 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_111.pth was removed +2025-06-25 06:34:58,149 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_112.pth. +2025-06-25 06:34:58,149 - pyskl - INFO - Best top1_acc is 0.9274 at 112 epoch. +2025-06-25 06:34:58,152 - pyskl - INFO - Epoch(val) [112][533] top1_acc: 0.9274, top5_acc: 0.9961, mean_class_accuracy: 0.9019 +2025-06-25 06:36:16,734 - pyskl - INFO - Epoch [113][100/1281] lr: 3.740e-03, eta: 5:51:30, time: 0.786, data_time: 0.187, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1114, loss: 0.1114 +2025-06-25 06:37:05,986 - pyskl - INFO - Epoch [113][200/1281] lr: 3.725e-03, eta: 5:50:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1115, loss: 0.1115 +2025-06-25 06:37:55,614 - pyskl - INFO - Epoch [113][300/1281] lr: 3.711e-03, eta: 5:50:07, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1228, loss: 0.1228 +2025-06-25 06:38:44,967 - pyskl - INFO - Epoch [113][400/1281] lr: 3.696e-03, eta: 5:49:25, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0937, loss: 0.0937 +2025-06-25 06:39:14,671 - pyskl - INFO - Epoch [113][500/1281] lr: 3.682e-03, eta: 5:48:37, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1063, loss: 0.1063 +2025-06-25 06:40:01,294 - pyskl - INFO - Epoch [113][600/1281] lr: 3.667e-03, eta: 5:47:55, time: 0.466, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0953, loss: 0.0953 +2025-06-25 06:40:35,588 - pyskl - INFO - Epoch [113][700/1281] lr: 3.653e-03, eta: 5:47:09, time: 0.343, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0901, loss: 0.0901 +2025-06-25 06:41:24,748 - pyskl - INFO - Epoch [113][800/1281] lr: 3.638e-03, eta: 5:46:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0884, loss: 0.0884 +2025-06-25 06:42:14,074 - pyskl - INFO - Epoch [113][900/1281] lr: 3.624e-03, eta: 5:45:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0984, loss: 0.0984 +2025-06-25 06:43:03,327 - pyskl - INFO - Epoch [113][1000/1281] lr: 3.610e-03, eta: 5:45:04, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0870, loss: 0.0870 +2025-06-25 06:43:52,456 - pyskl - INFO - Epoch [113][1100/1281] lr: 3.595e-03, eta: 5:44:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0912, loss: 0.0912 +2025-06-25 06:44:41,815 - pyskl - INFO - Epoch [113][1200/1281] lr: 3.581e-03, eta: 5:43:41, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1023, loss: 0.1023 +2025-06-25 06:45:22,143 - pyskl - INFO - Saving checkpoint at 113 epochs +2025-06-25 06:46:20,519 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:46:20,585 - pyskl - INFO - +top1_acc 0.9228 +top5_acc 0.9952 +2025-06-25 06:46:20,585 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:46:20,591 - pyskl - INFO - +mean_acc 0.9034 +2025-06-25 06:46:20,592 - pyskl - INFO - Epoch(val) [113][533] top1_acc: 0.9228, top5_acc: 0.9952, mean_class_accuracy: 0.9034 +2025-06-25 06:47:38,978 - pyskl - INFO - Epoch [114][100/1281] lr: 3.555e-03, eta: 5:42:22, time: 0.784, data_time: 0.180, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0770, loss: 0.0770 +2025-06-25 06:48:28,311 - pyskl - INFO - Epoch [114][200/1281] lr: 3.541e-03, eta: 5:41:41, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0683, loss: 0.0683 +2025-06-25 06:49:17,543 - pyskl - INFO - Epoch [114][300/1281] lr: 3.526e-03, eta: 5:40:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0856, loss: 0.0856 +2025-06-25 06:50:06,651 - pyskl - INFO - Epoch [114][400/1281] lr: 3.512e-03, eta: 5:40:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0842, loss: 0.0842 +2025-06-25 06:50:36,765 - pyskl - INFO - Epoch [114][500/1281] lr: 3.498e-03, eta: 5:39:30, time: 0.301, data_time: 0.001, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.0960, loss: 0.0960 +2025-06-25 06:51:23,511 - pyskl - INFO - Epoch [114][600/1281] lr: 3.484e-03, eta: 5:38:48, time: 0.467, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1039, loss: 0.1039 +2025-06-25 06:51:57,020 - pyskl - INFO - Epoch [114][700/1281] lr: 3.470e-03, eta: 5:38:01, time: 0.335, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0847, loss: 0.0847 +2025-06-25 06:52:46,059 - pyskl - INFO - Epoch [114][800/1281] lr: 3.456e-03, eta: 5:37:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1063, loss: 0.1063 +2025-06-25 06:53:35,155 - pyskl - INFO - Epoch [114][900/1281] lr: 3.442e-03, eta: 5:36:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0881, loss: 0.0881 +2025-06-25 06:54:24,321 - pyskl - INFO - Epoch [114][1000/1281] lr: 3.427e-03, eta: 5:35:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0928, loss: 0.0928 +2025-06-25 06:55:13,404 - pyskl - INFO - Epoch [114][1100/1281] lr: 3.413e-03, eta: 5:35:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 0.9994, loss_cls: 0.0985, loss: 0.0985 +2025-06-25 06:56:02,429 - pyskl - INFO - Epoch [114][1200/1281] lr: 3.399e-03, eta: 5:34:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0951, loss: 0.0951 +2025-06-25 06:56:42,791 - pyskl - INFO - Saving checkpoint at 114 epochs +2025-06-25 06:57:40,857 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:57:40,912 - pyskl - INFO - +top1_acc 0.9229 +top5_acc 0.9953 +2025-06-25 06:57:40,912 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:57:40,918 - pyskl - INFO - +mean_acc 0.8948 +2025-06-25 06:57:40,919 - pyskl - INFO - Epoch(val) [114][533] top1_acc: 0.9229, top5_acc: 0.9953, mean_class_accuracy: 0.8948 +2025-06-25 06:59:00,405 - pyskl - INFO - Epoch [115][100/1281] lr: 3.374e-03, eta: 5:33:14, time: 0.795, data_time: 0.182, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1046, loss: 0.1046 +2025-06-25 06:59:49,341 - pyskl - INFO - Epoch [115][200/1281] lr: 3.360e-03, eta: 5:32:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0741, loss: 0.0741 +2025-06-25 07:00:38,463 - pyskl - INFO - Epoch [115][300/1281] lr: 3.346e-03, eta: 5:31:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0687, loss: 0.0687 +2025-06-25 07:01:27,439 - pyskl - INFO - Epoch [115][400/1281] lr: 3.332e-03, eta: 5:31:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0744, loss: 0.0744 +2025-06-25 07:01:55,642 - pyskl - INFO - Epoch [115][500/1281] lr: 3.318e-03, eta: 5:30:21, time: 0.282, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0742, loss: 0.0742 +2025-06-25 07:02:44,462 - pyskl - INFO - Epoch [115][600/1281] lr: 3.305e-03, eta: 5:29:39, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0740, loss: 0.0740 +2025-06-25 07:03:15,950 - pyskl - INFO - Epoch [115][700/1281] lr: 3.291e-03, eta: 5:28:52, time: 0.315, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0888, loss: 0.0888 +2025-06-25 07:04:04,971 - pyskl - INFO - Epoch [115][800/1281] lr: 3.277e-03, eta: 5:28:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0671, loss: 0.0671 +2025-06-25 07:04:53,981 - pyskl - INFO - Epoch [115][900/1281] lr: 3.263e-03, eta: 5:27:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0848, loss: 0.0848 +2025-06-25 07:05:43,401 - pyskl - INFO - Epoch [115][1000/1281] lr: 3.249e-03, eta: 5:26:47, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0991, loss: 0.0991 +2025-06-25 07:06:32,511 - pyskl - INFO - Epoch [115][1100/1281] lr: 3.236e-03, eta: 5:26:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0723, loss: 0.0723 +2025-06-25 07:07:21,582 - pyskl - INFO - Epoch [115][1200/1281] lr: 3.222e-03, eta: 5:25:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1017, loss: 0.1017 +2025-06-25 07:08:01,944 - pyskl - INFO - Saving checkpoint at 115 epochs +2025-06-25 07:08:59,948 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:09:00,007 - pyskl - INFO - +top1_acc 0.9196 +top5_acc 0.9942 +2025-06-25 07:09:00,007 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:09:00,014 - pyskl - INFO - +mean_acc 0.8993 +2025-06-25 07:09:00,016 - pyskl - INFO - Epoch(val) [115][533] top1_acc: 0.9196, top5_acc: 0.9942, mean_class_accuracy: 0.8993 +2025-06-25 07:10:20,032 - pyskl - INFO - Epoch [116][100/1281] lr: 3.197e-03, eta: 5:24:05, time: 0.800, data_time: 0.188, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.1027, loss: 0.1027 +2025-06-25 07:11:09,326 - pyskl - INFO - Epoch [116][200/1281] lr: 3.184e-03, eta: 5:23:23, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.0983, loss: 0.0983 +2025-06-25 07:11:58,299 - pyskl - INFO - Epoch [116][300/1281] lr: 3.170e-03, eta: 5:22:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0941, loss: 0.0941 +2025-06-25 07:12:47,358 - pyskl - INFO - Epoch [116][400/1281] lr: 3.156e-03, eta: 5:22:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0797, loss: 0.0797 +2025-06-25 07:13:14,882 - pyskl - INFO - Epoch [116][500/1281] lr: 3.143e-03, eta: 5:21:12, time: 0.275, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0701, loss: 0.0701 +2025-06-25 07:14:05,949 - pyskl - INFO - Epoch [116][600/1281] lr: 3.129e-03, eta: 5:20:30, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0746, loss: 0.0746 +2025-06-25 07:14:35,582 - pyskl - INFO - Epoch [116][700/1281] lr: 3.116e-03, eta: 5:19:43, time: 0.296, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0617, loss: 0.0617 +2025-06-25 07:15:24,559 - pyskl - INFO - Epoch [116][800/1281] lr: 3.102e-03, eta: 5:19:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0622, loss: 0.0622 +2025-06-25 07:16:13,448 - pyskl - INFO - Epoch [116][900/1281] lr: 3.089e-03, eta: 5:18:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0713, loss: 0.0713 +2025-06-25 07:17:02,555 - pyskl - INFO - Epoch [116][1000/1281] lr: 3.075e-03, eta: 5:17:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0850, loss: 0.0850 +2025-06-25 07:17:51,416 - pyskl - INFO - Epoch [116][1100/1281] lr: 3.062e-03, eta: 5:16:55, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0690, loss: 0.0690 +2025-06-25 07:18:40,480 - pyskl - INFO - Epoch [116][1200/1281] lr: 3.049e-03, eta: 5:16:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0841, loss: 0.0841 +2025-06-25 07:19:20,741 - pyskl - INFO - Saving checkpoint at 116 epochs +2025-06-25 07:20:18,699 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:20:18,755 - pyskl - INFO - +top1_acc 0.9309 +top5_acc 0.9952 +2025-06-25 07:20:18,755 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:20:18,762 - pyskl - INFO - +mean_acc 0.9117 +2025-06-25 07:20:18,766 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_112.pth was removed +2025-06-25 07:20:18,932 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_116.pth. +2025-06-25 07:20:18,932 - pyskl - INFO - Best top1_acc is 0.9309 at 116 epoch. +2025-06-25 07:20:18,935 - pyskl - INFO - Epoch(val) [116][533] top1_acc: 0.9309, top5_acc: 0.9952, mean_class_accuracy: 0.9117 +2025-06-25 07:21:37,409 - pyskl - INFO - Epoch [117][100/1281] lr: 3.024e-03, eta: 5:14:55, time: 0.785, data_time: 0.187, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0468, loss: 0.0468 +2025-06-25 07:22:26,293 - pyskl - INFO - Epoch [117][200/1281] lr: 3.011e-03, eta: 5:14:13, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0409, loss: 0.0409 +2025-06-25 07:23:15,506 - pyskl - INFO - Epoch [117][300/1281] lr: 2.998e-03, eta: 5:13:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0621, loss: 0.0621 +2025-06-25 07:24:04,573 - pyskl - INFO - Epoch [117][400/1281] lr: 2.984e-03, eta: 5:12:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0648, loss: 0.0648 +2025-06-25 07:24:35,568 - pyskl - INFO - Epoch [117][500/1281] lr: 2.971e-03, eta: 5:12:02, time: 0.310, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0586, loss: 0.0586 +2025-06-25 07:25:26,538 - pyskl - INFO - Epoch [117][600/1281] lr: 2.958e-03, eta: 5:11:21, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0648, loss: 0.0648 +2025-06-25 07:25:55,030 - pyskl - INFO - Epoch [117][700/1281] lr: 2.945e-03, eta: 5:10:33, time: 0.285, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.0803, loss: 0.0803 +2025-06-25 07:26:44,002 - pyskl - INFO - Epoch [117][800/1281] lr: 2.932e-03, eta: 5:09:51, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0794, loss: 0.0794 +2025-06-25 07:27:32,627 - pyskl - INFO - Epoch [117][900/1281] lr: 2.919e-03, eta: 5:09:09, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0486, loss: 0.0486 +2025-06-25 07:28:21,521 - pyskl - INFO - Epoch [117][1000/1281] lr: 2.905e-03, eta: 5:08:27, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0699, loss: 0.0699 +2025-06-25 07:29:10,478 - pyskl - INFO - Epoch [117][1100/1281] lr: 2.892e-03, eta: 5:07:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0730, loss: 0.0730 +2025-06-25 07:29:59,555 - pyskl - INFO - Epoch [117][1200/1281] lr: 2.879e-03, eta: 5:07:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0947, loss: 0.0947 +2025-06-25 07:30:39,975 - pyskl - INFO - Saving checkpoint at 117 epochs +2025-06-25 07:31:38,485 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:31:38,554 - pyskl - INFO - +top1_acc 0.9283 +top5_acc 0.9951 +2025-06-25 07:31:38,554 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:31:38,562 - pyskl - INFO - +mean_acc 0.9032 +2025-06-25 07:31:38,564 - pyskl - INFO - Epoch(val) [117][533] top1_acc: 0.9283, top5_acc: 0.9951, mean_class_accuracy: 0.9032 +2025-06-25 07:32:57,021 - pyskl - INFO - Epoch [118][100/1281] lr: 2.856e-03, eta: 5:05:44, time: 0.785, data_time: 0.192, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0735, loss: 0.0735 +2025-06-25 07:33:46,450 - pyskl - INFO - Epoch [118][200/1281] lr: 2.843e-03, eta: 5:05:03, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0478, loss: 0.0478 +2025-06-25 07:34:35,807 - pyskl - INFO - Epoch [118][300/1281] lr: 2.830e-03, eta: 5:04:21, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0561, loss: 0.0561 +2025-06-25 07:35:24,960 - pyskl - INFO - Epoch [118][400/1281] lr: 2.817e-03, eta: 5:03:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0589, loss: 0.0589 +2025-06-25 07:35:56,059 - pyskl - INFO - Epoch [118][500/1281] lr: 2.804e-03, eta: 5:02:52, time: 0.311, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0582, loss: 0.0582 +2025-06-25 07:36:47,229 - pyskl - INFO - Epoch [118][600/1281] lr: 2.791e-03, eta: 5:02:10, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0571, loss: 0.0571 +2025-06-25 07:37:12,849 - pyskl - INFO - Epoch [118][700/1281] lr: 2.778e-03, eta: 5:01:22, time: 0.256, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0598, loss: 0.0598 +2025-06-25 07:38:01,961 - pyskl - INFO - Epoch [118][800/1281] lr: 2.765e-03, eta: 5:00:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0576, loss: 0.0576 +2025-06-25 07:38:50,994 - pyskl - INFO - Epoch [118][900/1281] lr: 2.753e-03, eta: 4:59:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0406, loss: 0.0406 +2025-06-25 07:39:40,144 - pyskl - INFO - Epoch [118][1000/1281] lr: 2.740e-03, eta: 4:59:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0465, loss: 0.0465 +2025-06-25 07:40:29,394 - pyskl - INFO - Epoch [118][1100/1281] lr: 2.727e-03, eta: 4:58:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0918, loss: 0.0918 +2025-06-25 07:41:18,330 - pyskl - INFO - Epoch [118][1200/1281] lr: 2.714e-03, eta: 4:57:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0585, loss: 0.0585 +2025-06-25 07:41:58,574 - pyskl - INFO - Saving checkpoint at 118 epochs +2025-06-25 07:42:57,148 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:42:57,208 - pyskl - INFO - +top1_acc 0.9322 +top5_acc 0.9954 +2025-06-25 07:42:57,208 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:42:57,215 - pyskl - INFO - +mean_acc 0.9053 +2025-06-25 07:42:57,219 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_116.pth was removed +2025-06-25 07:42:57,385 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2025-06-25 07:42:57,386 - pyskl - INFO - Best top1_acc is 0.9322 at 118 epoch. +2025-06-25 07:42:57,388 - pyskl - INFO - Epoch(val) [118][533] top1_acc: 0.9322, top5_acc: 0.9954, mean_class_accuracy: 0.9053 +2025-06-25 07:44:16,669 - pyskl - INFO - Epoch [119][100/1281] lr: 2.691e-03, eta: 4:56:33, time: 0.793, data_time: 0.187, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0481, loss: 0.0481 +2025-06-25 07:45:05,806 - pyskl - INFO - Epoch [119][200/1281] lr: 2.679e-03, eta: 4:55:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0758, loss: 0.0758 +2025-06-25 07:45:54,800 - pyskl - INFO - Epoch [119][300/1281] lr: 2.666e-03, eta: 4:55:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 0.9994, loss_cls: 0.0721, loss: 0.0721 +2025-06-25 07:46:43,939 - pyskl - INFO - Epoch [119][400/1281] lr: 2.653e-03, eta: 4:54:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0693, loss: 0.0693 +2025-06-25 07:47:17,052 - pyskl - INFO - Epoch [119][500/1281] lr: 2.641e-03, eta: 4:53:41, time: 0.331, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0699, loss: 0.0699 +2025-06-25 07:48:07,984 - pyskl - INFO - Epoch [119][600/1281] lr: 2.628e-03, eta: 4:53:00, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0610, loss: 0.0610 +2025-06-25 07:48:34,590 - pyskl - INFO - Epoch [119][700/1281] lr: 2.616e-03, eta: 4:52:12, time: 0.266, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0570, loss: 0.0570 +2025-06-25 07:49:23,681 - pyskl - INFO - Epoch [119][800/1281] lr: 2.603e-03, eta: 4:51:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0478, loss: 0.0478 +2025-06-25 07:50:12,630 - pyskl - INFO - Epoch [119][900/1281] lr: 2.591e-03, eta: 4:50:47, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0531, loss: 0.0531 +2025-06-25 07:51:01,937 - pyskl - INFO - Epoch [119][1000/1281] lr: 2.578e-03, eta: 4:50:05, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0660, loss: 0.0660 +2025-06-25 07:51:51,054 - pyskl - INFO - Epoch [119][1100/1281] lr: 2.566e-03, eta: 4:49:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0651, loss: 0.0651 +2025-06-25 07:52:40,102 - pyskl - INFO - Epoch [119][1200/1281] lr: 2.554e-03, eta: 4:48:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0712, loss: 0.0712 +2025-06-25 07:53:20,586 - pyskl - INFO - Saving checkpoint at 119 epochs +2025-06-25 07:54:18,661 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:54:18,716 - pyskl - INFO - +top1_acc 0.9337 +top5_acc 0.9959 +2025-06-25 07:54:18,716 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:54:18,723 - pyskl - INFO - +mean_acc 0.9082 +2025-06-25 07:54:18,727 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_118.pth was removed +2025-06-25 07:54:18,897 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_119.pth. +2025-06-25 07:54:18,897 - pyskl - INFO - Best top1_acc is 0.9337 at 119 epoch. +2025-06-25 07:54:18,900 - pyskl - INFO - Epoch(val) [119][533] top1_acc: 0.9337, top5_acc: 0.9959, mean_class_accuracy: 0.9082 +2025-06-25 07:55:37,561 - pyskl - INFO - Epoch [120][100/1281] lr: 2.531e-03, eta: 4:47:22, time: 0.787, data_time: 0.188, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0533, loss: 0.0533 +2025-06-25 07:56:26,809 - pyskl - INFO - Epoch [120][200/1281] lr: 2.519e-03, eta: 4:46:40, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0396, loss: 0.0396 +2025-06-25 07:57:15,644 - pyskl - INFO - Epoch [120][300/1281] lr: 2.507e-03, eta: 4:45:58, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0413, loss: 0.0413 +2025-06-25 07:58:04,627 - pyskl - INFO - Epoch [120][400/1281] lr: 2.494e-03, eta: 4:45:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0499, loss: 0.0499 +2025-06-25 07:58:38,580 - pyskl - INFO - Epoch [120][500/1281] lr: 2.482e-03, eta: 4:44:30, time: 0.340, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0429, loss: 0.0429 +2025-06-25 07:59:29,769 - pyskl - INFO - Epoch [120][600/1281] lr: 2.470e-03, eta: 4:43:48, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0413, loss: 0.0413 +2025-06-25 07:59:55,210 - pyskl - INFO - Epoch [120][700/1281] lr: 2.458e-03, eta: 4:43:00, time: 0.254, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0452, loss: 0.0452 +2025-06-25 08:00:44,437 - pyskl - INFO - Epoch [120][800/1281] lr: 2.446e-03, eta: 4:42:18, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0429, loss: 0.0429 +2025-06-25 08:01:33,220 - pyskl - INFO - Epoch [120][900/1281] lr: 2.433e-03, eta: 4:41:36, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0412, loss: 0.0412 +2025-06-25 08:02:22,569 - pyskl - INFO - Epoch [120][1000/1281] lr: 2.421e-03, eta: 4:40:54, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0478, loss: 0.0478 +2025-06-25 08:03:11,865 - pyskl - INFO - Epoch [120][1100/1281] lr: 2.409e-03, eta: 4:40:12, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0679, loss: 0.0679 +2025-06-25 08:04:01,120 - pyskl - INFO - Epoch [120][1200/1281] lr: 2.397e-03, eta: 4:39:30, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0579, loss: 0.0579 +2025-06-25 08:04:41,725 - pyskl - INFO - Saving checkpoint at 120 epochs +2025-06-25 08:05:40,327 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:05:40,394 - pyskl - INFO - +top1_acc 0.9323 +top5_acc 0.9966 +2025-06-25 08:05:40,395 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:05:40,403 - pyskl - INFO - +mean_acc 0.9092 +2025-06-25 08:05:40,405 - pyskl - INFO - Epoch(val) [120][533] top1_acc: 0.9323, top5_acc: 0.9966, mean_class_accuracy: 0.9092 +2025-06-25 08:07:00,407 - pyskl - INFO - Epoch [121][100/1281] lr: 2.375e-03, eta: 4:38:11, time: 0.800, data_time: 0.188, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0472, loss: 0.0472 +2025-06-25 08:07:49,455 - pyskl - INFO - Epoch [121][200/1281] lr: 2.363e-03, eta: 4:37:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0579, loss: 0.0579 +2025-06-25 08:08:38,441 - pyskl - INFO - Epoch [121][300/1281] lr: 2.351e-03, eta: 4:36:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0519, loss: 0.0519 +2025-06-25 08:09:27,315 - pyskl - INFO - Epoch [121][400/1281] lr: 2.340e-03, eta: 4:36:05, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0423, loss: 0.0423 +2025-06-25 08:09:59,590 - pyskl - INFO - Epoch [121][500/1281] lr: 2.328e-03, eta: 4:35:18, time: 0.323, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0454, loss: 0.0454 +2025-06-25 08:10:50,479 - pyskl - INFO - Epoch [121][600/1281] lr: 2.316e-03, eta: 4:34:37, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0421, loss: 0.0421 +2025-06-25 08:11:16,622 - pyskl - INFO - Epoch [121][700/1281] lr: 2.304e-03, eta: 4:33:49, time: 0.261, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0400, loss: 0.0400 +2025-06-25 08:12:05,498 - pyskl - INFO - Epoch [121][800/1281] lr: 2.292e-03, eta: 4:33:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0536, loss: 0.0536 +2025-06-25 08:12:54,600 - pyskl - INFO - Epoch [121][900/1281] lr: 2.280e-03, eta: 4:32:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0447, loss: 0.0447 +2025-06-25 08:13:43,505 - pyskl - INFO - Epoch [121][1000/1281] lr: 2.269e-03, eta: 4:31:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0587, loss: 0.0587 +2025-06-25 08:14:32,601 - pyskl - INFO - Epoch [121][1100/1281] lr: 2.257e-03, eta: 4:31:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0601, loss: 0.0601 +2025-06-25 08:15:21,982 - pyskl - INFO - Epoch [121][1200/1281] lr: 2.245e-03, eta: 4:30:18, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0503, loss: 0.0503 +2025-06-25 08:16:02,432 - pyskl - INFO - Saving checkpoint at 121 epochs +2025-06-25 08:17:00,972 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:17:01,034 - pyskl - INFO - +top1_acc 0.9275 +top5_acc 0.9948 +2025-06-25 08:17:01,034 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:17:01,044 - pyskl - INFO - +mean_acc 0.9017 +2025-06-25 08:17:01,046 - pyskl - INFO - Epoch(val) [121][533] top1_acc: 0.9275, top5_acc: 0.9948, mean_class_accuracy: 0.9017 +2025-06-25 08:18:20,854 - pyskl - INFO - Epoch [122][100/1281] lr: 2.224e-03, eta: 4:28:59, time: 0.798, data_time: 0.187, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0501, loss: 0.0501 +2025-06-25 08:19:09,910 - pyskl - INFO - Epoch [122][200/1281] lr: 2.212e-03, eta: 4:28:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0460, loss: 0.0460 +2025-06-25 08:19:58,776 - pyskl - INFO - Epoch [122][300/1281] lr: 2.201e-03, eta: 4:27:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0558, loss: 0.0558 +2025-06-25 08:20:47,634 - pyskl - INFO - Epoch [122][400/1281] lr: 2.189e-03, eta: 4:26:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0416, loss: 0.0416 +2025-06-25 08:21:19,826 - pyskl - INFO - Epoch [122][500/1281] lr: 2.178e-03, eta: 4:26:06, time: 0.322, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0401, loss: 0.0401 +2025-06-25 08:22:10,703 - pyskl - INFO - Epoch [122][600/1281] lr: 2.166e-03, eta: 4:25:24, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0399, loss: 0.0399 +2025-06-25 08:22:37,742 - pyskl - INFO - Epoch [122][700/1281] lr: 2.155e-03, eta: 4:24:37, time: 0.270, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0483, loss: 0.0483 +2025-06-25 08:23:26,773 - pyskl - INFO - Epoch [122][800/1281] lr: 2.143e-03, eta: 4:23:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0470, loss: 0.0470 +2025-06-25 08:24:15,696 - pyskl - INFO - Epoch [122][900/1281] lr: 2.132e-03, eta: 4:23:12, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0354, loss: 0.0354 +2025-06-25 08:25:04,991 - pyskl - INFO - Epoch [122][1000/1281] lr: 2.120e-03, eta: 4:22:30, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0492, loss: 0.0492 +2025-06-25 08:25:53,956 - pyskl - INFO - Epoch [122][1100/1281] lr: 2.109e-03, eta: 4:21:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0324, loss: 0.0324 +2025-06-25 08:26:42,887 - pyskl - INFO - Epoch [122][1200/1281] lr: 2.098e-03, eta: 4:21:05, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0453, loss: 0.0453 +2025-06-25 08:27:23,004 - pyskl - INFO - Saving checkpoint at 122 epochs +2025-06-25 08:28:21,704 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:28:21,761 - pyskl - INFO - +top1_acc 0.9277 +top5_acc 0.9960 +2025-06-25 08:28:21,761 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:28:21,768 - pyskl - INFO - +mean_acc 0.9043 +2025-06-25 08:28:21,771 - pyskl - INFO - Epoch(val) [122][533] top1_acc: 0.9277, top5_acc: 0.9960, mean_class_accuracy: 0.9043 +2025-06-25 08:29:40,351 - pyskl - INFO - Epoch [123][100/1281] lr: 2.077e-03, eta: 4:19:46, time: 0.786, data_time: 0.187, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0636, loss: 0.0636 +2025-06-25 08:30:29,326 - pyskl - INFO - Epoch [123][200/1281] lr: 2.066e-03, eta: 4:19:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0604, loss: 0.0604 +2025-06-25 08:31:18,752 - pyskl - INFO - Epoch [123][300/1281] lr: 2.055e-03, eta: 4:18:22, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0507, loss: 0.0507 +2025-06-25 08:32:07,788 - pyskl - INFO - Epoch [123][400/1281] lr: 2.044e-03, eta: 4:17:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0686, loss: 0.0686 +2025-06-25 08:32:40,543 - pyskl - INFO - Epoch [123][500/1281] lr: 2.032e-03, eta: 4:16:53, time: 0.328, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0568, loss: 0.0568 +2025-06-25 08:33:31,685 - pyskl - INFO - Epoch [123][600/1281] lr: 2.021e-03, eta: 4:16:11, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0405, loss: 0.0405 +2025-06-25 08:33:57,862 - pyskl - INFO - Epoch [123][700/1281] lr: 2.010e-03, eta: 4:15:24, time: 0.262, data_time: 0.001, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0332, loss: 0.0332 +2025-06-25 08:34:46,803 - pyskl - INFO - Epoch [123][800/1281] lr: 1.999e-03, eta: 4:14:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0294, loss: 0.0294 +2025-06-25 08:35:35,817 - pyskl - INFO - Epoch [123][900/1281] lr: 1.988e-03, eta: 4:13:59, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0371, loss: 0.0371 +2025-06-25 08:36:24,595 - pyskl - INFO - Epoch [123][1000/1281] lr: 1.977e-03, eta: 4:13:17, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0467, loss: 0.0467 +2025-06-25 08:37:13,648 - pyskl - INFO - Epoch [123][1100/1281] lr: 1.966e-03, eta: 4:12:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0306, loss: 0.0306 +2025-06-25 08:38:02,862 - pyskl - INFO - Epoch [123][1200/1281] lr: 1.955e-03, eta: 4:11:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0411, loss: 0.0411 +2025-06-25 08:38:43,299 - pyskl - INFO - Saving checkpoint at 123 epochs +2025-06-25 08:39:41,702 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:39:41,759 - pyskl - INFO - +top1_acc 0.9336 +top5_acc 0.9964 +2025-06-25 08:39:41,759 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:39:41,766 - pyskl - INFO - +mean_acc 0.9065 +2025-06-25 08:39:41,768 - pyskl - INFO - Epoch(val) [123][533] top1_acc: 0.9336, top5_acc: 0.9964, mean_class_accuracy: 0.9065 +2025-06-25 08:41:02,397 - pyskl - INFO - Epoch [124][100/1281] lr: 1.935e-03, eta: 4:10:33, time: 0.806, data_time: 0.192, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0369, loss: 0.0369 +2025-06-25 08:41:51,367 - pyskl - INFO - Epoch [124][200/1281] lr: 1.924e-03, eta: 4:09:51, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0355, loss: 0.0355 +2025-06-25 08:42:40,427 - pyskl - INFO - Epoch [124][300/1281] lr: 1.913e-03, eta: 4:09:09, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0411, loss: 0.0411 +2025-06-25 08:43:29,772 - pyskl - INFO - Epoch [124][400/1281] lr: 1.902e-03, eta: 4:08:26, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0281, loss: 0.0281 +2025-06-25 08:44:01,746 - pyskl - INFO - Epoch [124][500/1281] lr: 1.892e-03, eta: 4:07:40, time: 0.320, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0371, loss: 0.0371 +2025-06-25 08:44:52,784 - pyskl - INFO - Epoch [124][600/1281] lr: 1.881e-03, eta: 4:06:58, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0298, loss: 0.0298 +2025-06-25 08:45:20,899 - pyskl - INFO - Epoch [124][700/1281] lr: 1.870e-03, eta: 4:06:11, time: 0.281, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0303, loss: 0.0303 +2025-06-25 08:46:10,070 - pyskl - INFO - Epoch [124][800/1281] lr: 1.859e-03, eta: 4:05:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0259, loss: 0.0259 +2025-06-25 08:46:59,433 - pyskl - INFO - Epoch [124][900/1281] lr: 1.849e-03, eta: 4:04:46, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0252, loss: 0.0252 +2025-06-25 08:47:48,600 - pyskl - INFO - Epoch [124][1000/1281] lr: 1.838e-03, eta: 4:04:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0411, loss: 0.0411 +2025-06-25 08:48:37,699 - pyskl - INFO - Epoch [124][1100/1281] lr: 1.827e-03, eta: 4:03:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0309, loss: 0.0309 +2025-06-25 08:49:26,885 - pyskl - INFO - Epoch [124][1200/1281] lr: 1.817e-03, eta: 4:02:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0428, loss: 0.0428 +2025-06-25 08:50:07,444 - pyskl - INFO - Saving checkpoint at 124 epochs +2025-06-25 08:51:05,512 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:51:05,567 - pyskl - INFO - +top1_acc 0.9351 +top5_acc 0.9958 +2025-06-25 08:51:05,567 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:51:05,573 - pyskl - INFO - +mean_acc 0.9134 +2025-06-25 08:51:05,577 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_119.pth was removed +2025-06-25 08:51:05,744 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_124.pth. +2025-06-25 08:51:05,744 - pyskl - INFO - Best top1_acc is 0.9351 at 124 epoch. +2025-06-25 08:51:05,747 - pyskl - INFO - Epoch(val) [124][533] top1_acc: 0.9351, top5_acc: 0.9958, mean_class_accuracy: 0.9134 +2025-06-25 08:52:25,889 - pyskl - INFO - Epoch [125][100/1281] lr: 1.797e-03, eta: 4:01:20, time: 0.801, data_time: 0.188, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0395, loss: 0.0395 +2025-06-25 08:53:15,323 - pyskl - INFO - Epoch [125][200/1281] lr: 1.787e-03, eta: 4:00:38, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0281, loss: 0.0281 +2025-06-25 08:54:04,507 - pyskl - INFO - Epoch [125][300/1281] lr: 1.776e-03, eta: 3:59:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0273, loss: 0.0273 +2025-06-25 08:54:53,942 - pyskl - INFO - Epoch [125][400/1281] lr: 1.766e-03, eta: 3:59:13, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0310, loss: 0.0310 +2025-06-25 08:55:24,537 - pyskl - INFO - Epoch [125][500/1281] lr: 1.755e-03, eta: 3:58:27, time: 0.306, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0276, loss: 0.0276 +2025-06-25 08:56:15,633 - pyskl - INFO - Epoch [125][600/1281] lr: 1.745e-03, eta: 3:57:45, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0335, loss: 0.0335 +2025-06-25 08:56:42,474 - pyskl - INFO - Epoch [125][700/1281] lr: 1.735e-03, eta: 3:56:58, time: 0.268, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0362, loss: 0.0362 +2025-06-25 08:57:31,483 - pyskl - INFO - Epoch [125][800/1281] lr: 1.724e-03, eta: 3:56:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0283, loss: 0.0283 +2025-06-25 08:58:20,503 - pyskl - INFO - Epoch [125][900/1281] lr: 1.714e-03, eta: 3:55:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0308, loss: 0.0308 +2025-06-25 08:59:09,666 - pyskl - INFO - Epoch [125][1000/1281] lr: 1.704e-03, eta: 3:54:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0332, loss: 0.0332 +2025-06-25 08:59:58,915 - pyskl - INFO - Epoch [125][1100/1281] lr: 1.693e-03, eta: 3:54:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0270, loss: 0.0270 +2025-06-25 09:00:48,096 - pyskl - INFO - Epoch [125][1200/1281] lr: 1.683e-03, eta: 3:53:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0318, loss: 0.0318 +2025-06-25 09:01:28,424 - pyskl - INFO - Saving checkpoint at 125 epochs +2025-06-25 09:02:26,583 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:02:26,640 - pyskl - INFO - +top1_acc 0.9359 +top5_acc 0.9962 +2025-06-25 09:02:26,640 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:02:26,650 - pyskl - INFO - +mean_acc 0.9112 +2025-06-25 09:02:26,656 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_124.pth was removed +2025-06-25 09:02:26,880 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_125.pth. +2025-06-25 09:02:26,881 - pyskl - INFO - Best top1_acc is 0.9359 at 125 epoch. +2025-06-25 09:02:26,883 - pyskl - INFO - Epoch(val) [125][533] top1_acc: 0.9359, top5_acc: 0.9962, mean_class_accuracy: 0.9112 +2025-06-25 09:03:46,659 - pyskl - INFO - Epoch [126][100/1281] lr: 1.665e-03, eta: 3:52:06, time: 0.798, data_time: 0.188, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 09:04:35,873 - pyskl - INFO - Epoch [126][200/1281] lr: 1.654e-03, eta: 3:51:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0256, loss: 0.0256 +2025-06-25 09:05:24,775 - pyskl - INFO - Epoch [126][300/1281] lr: 1.644e-03, eta: 3:50:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0324, loss: 0.0324 +2025-06-25 09:06:14,019 - pyskl - INFO - Epoch [126][400/1281] lr: 1.634e-03, eta: 3:49:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0250, loss: 0.0250 +2025-06-25 09:06:45,412 - pyskl - INFO - Epoch [126][500/1281] lr: 1.624e-03, eta: 3:49:13, time: 0.314, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0255, loss: 0.0255 +2025-06-25 09:07:36,542 - pyskl - INFO - Epoch [126][600/1281] lr: 1.614e-03, eta: 3:48:31, time: 0.511, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-25 09:08:02,908 - pyskl - INFO - Epoch [126][700/1281] lr: 1.604e-03, eta: 3:47:44, time: 0.264, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 09:08:52,065 - pyskl - INFO - Epoch [126][800/1281] lr: 1.594e-03, eta: 3:47:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0321, loss: 0.0321 +2025-06-25 09:09:41,333 - pyskl - INFO - Epoch [126][900/1281] lr: 1.584e-03, eta: 3:46:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0414, loss: 0.0414 +2025-06-25 09:10:30,297 - pyskl - INFO - Epoch [126][1000/1281] lr: 1.574e-03, eta: 3:45:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0277, loss: 0.0277 +2025-06-25 09:11:19,604 - pyskl - INFO - Epoch [126][1100/1281] lr: 1.564e-03, eta: 3:44:53, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0335, loss: 0.0335 +2025-06-25 09:12:08,868 - pyskl - INFO - Epoch [126][1200/1281] lr: 1.554e-03, eta: 3:44:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0367, loss: 0.0367 +2025-06-25 09:12:48,842 - pyskl - INFO - Saving checkpoint at 126 epochs +2025-06-25 09:13:46,929 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:13:46,998 - pyskl - INFO - +top1_acc 0.9386 +top5_acc 0.9961 +2025-06-25 09:13:46,998 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:13:47,006 - pyskl - INFO - +mean_acc 0.9137 +2025-06-25 09:13:47,011 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_125.pth was removed +2025-06-25 09:13:47,189 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_126.pth. +2025-06-25 09:13:47,190 - pyskl - INFO - Best top1_acc is 0.9386 at 126 epoch. +2025-06-25 09:13:47,192 - pyskl - INFO - Epoch(val) [126][533] top1_acc: 0.9386, top5_acc: 0.9961, mean_class_accuracy: 0.9137 +2025-06-25 09:15:06,129 - pyskl - INFO - Epoch [127][100/1281] lr: 1.536e-03, eta: 3:42:52, time: 0.789, data_time: 0.186, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 09:15:55,420 - pyskl - INFO - Epoch [127][200/1281] lr: 1.527e-03, eta: 3:42:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 09:16:44,568 - pyskl - INFO - Epoch [127][300/1281] lr: 1.517e-03, eta: 3:41:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0311, loss: 0.0311 +2025-06-25 09:17:33,538 - pyskl - INFO - Epoch [127][400/1281] lr: 1.507e-03, eta: 3:40:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0315, loss: 0.0315 +2025-06-25 09:18:06,542 - pyskl - INFO - Epoch [127][500/1281] lr: 1.497e-03, eta: 3:39:58, time: 0.330, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0282, loss: 0.0282 +2025-06-25 09:18:57,558 - pyskl - INFO - Epoch [127][600/1281] lr: 1.488e-03, eta: 3:39:16, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0346, loss: 0.0346 +2025-06-25 09:19:22,612 - pyskl - INFO - Epoch [127][700/1281] lr: 1.478e-03, eta: 3:38:29, time: 0.251, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0258, loss: 0.0258 +2025-06-25 09:20:09,777 - pyskl - INFO - Epoch [127][800/1281] lr: 1.468e-03, eta: 3:37:46, time: 0.472, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0270, loss: 0.0270 +2025-06-25 09:20:58,989 - pyskl - INFO - Epoch [127][900/1281] lr: 1.459e-03, eta: 3:37:03, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0237, loss: 0.0237 +2025-06-25 09:21:48,071 - pyskl - INFO - Epoch [127][1000/1281] lr: 1.449e-03, eta: 3:36:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 09:22:37,096 - pyskl - INFO - Epoch [127][1100/1281] lr: 1.440e-03, eta: 3:35:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0270, loss: 0.0270 +2025-06-25 09:23:26,201 - pyskl - INFO - Epoch [127][1200/1281] lr: 1.430e-03, eta: 3:34:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0354, loss: 0.0354 +2025-06-25 09:24:06,860 - pyskl - INFO - Saving checkpoint at 127 epochs +2025-06-25 09:25:05,328 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:25:05,384 - pyskl - INFO - +top1_acc 0.9397 +top5_acc 0.9969 +2025-06-25 09:25:05,384 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:25:05,391 - pyskl - INFO - +mean_acc 0.9123 +2025-06-25 09:25:05,395 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_126.pth was removed +2025-06-25 09:25:05,563 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_127.pth. +2025-06-25 09:25:05,563 - pyskl - INFO - Best top1_acc is 0.9397 at 127 epoch. +2025-06-25 09:25:05,566 - pyskl - INFO - Epoch(val) [127][533] top1_acc: 0.9397, top5_acc: 0.9969, mean_class_accuracy: 0.9123 +2025-06-25 09:26:25,827 - pyskl - INFO - Epoch [128][100/1281] lr: 1.413e-03, eta: 3:33:37, time: 0.803, data_time: 0.190, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 09:27:15,535 - pyskl - INFO - Epoch [128][200/1281] lr: 1.404e-03, eta: 3:32:54, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 09:28:05,053 - pyskl - INFO - Epoch [128][300/1281] lr: 1.394e-03, eta: 3:32:12, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-25 09:28:54,315 - pyskl - INFO - Epoch [128][400/1281] lr: 1.385e-03, eta: 3:31:29, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0260, loss: 0.0260 +2025-06-25 09:29:28,668 - pyskl - INFO - Epoch [128][500/1281] lr: 1.376e-03, eta: 3:30:44, time: 0.344, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0260, loss: 0.0260 +2025-06-25 09:30:19,709 - pyskl - INFO - Epoch [128][600/1281] lr: 1.366e-03, eta: 3:30:01, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 09:30:44,417 - pyskl - INFO - Epoch [128][700/1281] lr: 1.357e-03, eta: 3:29:14, time: 0.247, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0249, loss: 0.0249 +2025-06-25 09:31:31,459 - pyskl - INFO - Epoch [128][800/1281] lr: 1.348e-03, eta: 3:28:31, time: 0.470, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0276, loss: 0.0276 +2025-06-25 09:32:20,444 - pyskl - INFO - Epoch [128][900/1281] lr: 1.339e-03, eta: 3:27:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0255, loss: 0.0255 +2025-06-25 09:33:09,666 - pyskl - INFO - Epoch [128][1000/1281] lr: 1.329e-03, eta: 3:27:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 09:33:58,594 - pyskl - INFO - Epoch [128][1100/1281] lr: 1.320e-03, eta: 3:26:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0262, loss: 0.0262 +2025-06-25 09:34:47,606 - pyskl - INFO - Epoch [128][1200/1281] lr: 1.311e-03, eta: 3:25:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0257, loss: 0.0257 +2025-06-25 09:35:27,848 - pyskl - INFO - Saving checkpoint at 128 epochs +2025-06-25 09:36:25,928 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:36:25,985 - pyskl - INFO - +top1_acc 0.9386 +top5_acc 0.9959 +2025-06-25 09:36:25,985 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:36:25,993 - pyskl - INFO - +mean_acc 0.9190 +2025-06-25 09:36:25,995 - pyskl - INFO - Epoch(val) [128][533] top1_acc: 0.9386, top5_acc: 0.9959, mean_class_accuracy: 0.9190 +2025-06-25 09:37:45,127 - pyskl - INFO - Epoch [129][100/1281] lr: 1.295e-03, eta: 3:24:21, time: 0.791, data_time: 0.180, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 09:38:34,379 - pyskl - INFO - Epoch [129][200/1281] lr: 1.286e-03, eta: 3:23:39, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0358, loss: 0.0358 +2025-06-25 09:39:23,426 - pyskl - INFO - Epoch [129][300/1281] lr: 1.277e-03, eta: 3:22:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0265, loss: 0.0265 +2025-06-25 09:40:12,606 - pyskl - INFO - Epoch [129][400/1281] lr: 1.268e-03, eta: 3:22:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0266, loss: 0.0266 +2025-06-25 09:40:48,979 - pyskl - INFO - Epoch [129][500/1281] lr: 1.259e-03, eta: 3:21:28, time: 0.364, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-25 09:41:40,041 - pyskl - INFO - Epoch [129][600/1281] lr: 1.250e-03, eta: 3:20:46, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 09:42:04,797 - pyskl - INFO - Epoch [129][700/1281] lr: 1.241e-03, eta: 3:19:59, time: 0.248, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0205, loss: 0.0205 +2025-06-25 09:42:52,228 - pyskl - INFO - Epoch [129][800/1281] lr: 1.232e-03, eta: 3:19:16, time: 0.474, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0254, loss: 0.0254 +2025-06-25 09:43:41,138 - pyskl - INFO - Epoch [129][900/1281] lr: 1.223e-03, eta: 3:18:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 09:44:30,132 - pyskl - INFO - Epoch [129][1000/1281] lr: 1.214e-03, eta: 3:17:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 09:45:19,374 - pyskl - INFO - Epoch [129][1100/1281] lr: 1.206e-03, eta: 3:17:07, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 09:46:08,604 - pyskl - INFO - Epoch [129][1200/1281] lr: 1.197e-03, eta: 3:16:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 09:46:48,729 - pyskl - INFO - Saving checkpoint at 129 epochs +2025-06-25 09:47:47,295 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:47:47,366 - pyskl - INFO - +top1_acc 0.9392 +top5_acc 0.9966 +2025-06-25 09:47:47,366 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:47:47,373 - pyskl - INFO - +mean_acc 0.9195 +2025-06-25 09:47:47,375 - pyskl - INFO - Epoch(val) [129][533] top1_acc: 0.9392, top5_acc: 0.9966, mean_class_accuracy: 0.9195 +2025-06-25 09:49:08,398 - pyskl - INFO - Epoch [130][100/1281] lr: 1.181e-03, eta: 3:15:06, time: 0.810, data_time: 0.189, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0223, loss: 0.0223 +2025-06-25 09:49:57,543 - pyskl - INFO - Epoch [130][200/1281] lr: 1.172e-03, eta: 3:14:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0233, loss: 0.0233 +2025-06-25 09:50:46,991 - pyskl - INFO - Epoch [130][300/1281] lr: 1.164e-03, eta: 3:13:40, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0263, loss: 0.0263 +2025-06-25 09:51:36,301 - pyskl - INFO - Epoch [130][400/1281] lr: 1.155e-03, eta: 3:12:58, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 09:52:10,090 - pyskl - INFO - Epoch [130][500/1281] lr: 1.147e-03, eta: 3:12:12, time: 0.338, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 09:53:01,243 - pyskl - INFO - Epoch [130][600/1281] lr: 1.138e-03, eta: 3:11:30, time: 0.512, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0254, loss: 0.0254 +2025-06-25 09:53:26,334 - pyskl - INFO - Epoch [130][700/1281] lr: 1.130e-03, eta: 3:10:43, time: 0.251, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 09:54:14,041 - pyskl - INFO - Epoch [130][800/1281] lr: 1.121e-03, eta: 3:10:00, time: 0.477, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 09:55:03,366 - pyskl - INFO - Epoch [130][900/1281] lr: 1.113e-03, eta: 3:09:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 09:55:52,577 - pyskl - INFO - Epoch [130][1000/1281] lr: 1.104e-03, eta: 3:08:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 09:56:41,798 - pyskl - INFO - Epoch [130][1100/1281] lr: 1.096e-03, eta: 3:07:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0202, loss: 0.0202 +2025-06-25 09:57:31,019 - pyskl - INFO - Epoch [130][1200/1281] lr: 1.088e-03, eta: 3:07:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0221, loss: 0.0221 +2025-06-25 09:58:11,155 - pyskl - INFO - Saving checkpoint at 130 epochs +2025-06-25 09:59:09,843 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:59:09,903 - pyskl - INFO - +top1_acc 0.9377 +top5_acc 0.9961 +2025-06-25 09:59:09,903 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:59:09,910 - pyskl - INFO - +mean_acc 0.9117 +2025-06-25 09:59:09,912 - pyskl - INFO - Epoch(val) [130][533] top1_acc: 0.9377, top5_acc: 0.9961, mean_class_accuracy: 0.9117 +2025-06-25 10:00:29,645 - pyskl - INFO - Epoch [131][100/1281] lr: 1.072e-03, eta: 3:05:50, time: 0.797, data_time: 0.184, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 10:01:18,718 - pyskl - INFO - Epoch [131][200/1281] lr: 1.064e-03, eta: 3:05:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 10:02:07,924 - pyskl - INFO - Epoch [131][300/1281] lr: 1.056e-03, eta: 3:04:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 10:02:56,765 - pyskl - INFO - Epoch [131][400/1281] lr: 1.048e-03, eta: 3:03:41, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 10:03:30,568 - pyskl - INFO - Epoch [131][500/1281] lr: 1.040e-03, eta: 3:02:56, time: 0.338, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 10:04:21,636 - pyskl - INFO - Epoch [131][600/1281] lr: 1.031e-03, eta: 3:02:14, time: 0.511, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0237, loss: 0.0237 +2025-06-25 10:04:46,776 - pyskl - INFO - Epoch [131][700/1281] lr: 1.023e-03, eta: 3:01:27, time: 0.251, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 10:05:35,194 - pyskl - INFO - Epoch [131][800/1281] lr: 1.015e-03, eta: 3:00:44, time: 0.484, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 10:06:24,180 - pyskl - INFO - Epoch [131][900/1281] lr: 1.007e-03, eta: 3:00:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 10:07:13,454 - pyskl - INFO - Epoch [131][1000/1281] lr: 9.992e-04, eta: 2:59:18, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 10:08:02,594 - pyskl - INFO - Epoch [131][1100/1281] lr: 9.912e-04, eta: 2:58:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0276, loss: 0.0276 +2025-06-25 10:08:51,711 - pyskl - INFO - Epoch [131][1200/1281] lr: 9.832e-04, eta: 2:57:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 10:09:31,969 - pyskl - INFO - Saving checkpoint at 131 epochs +2025-06-25 10:10:30,256 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:10:30,320 - pyskl - INFO - +top1_acc 0.9391 +top5_acc 0.9961 +2025-06-25 10:10:30,320 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:10:30,327 - pyskl - INFO - +mean_acc 0.9167 +2025-06-25 10:10:30,328 - pyskl - INFO - Epoch(val) [131][533] top1_acc: 0.9391, top5_acc: 0.9961, mean_class_accuracy: 0.9167 +2025-06-25 10:11:50,318 - pyskl - INFO - Epoch [132][100/1281] lr: 9.689e-04, eta: 2:56:33, time: 0.800, data_time: 0.187, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 10:12:39,600 - pyskl - INFO - Epoch [132][200/1281] lr: 9.610e-04, eta: 2:55:51, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 10:13:28,967 - pyskl - INFO - Epoch [132][300/1281] lr: 9.532e-04, eta: 2:55:08, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 10:14:18,100 - pyskl - INFO - Epoch [132][400/1281] lr: 9.454e-04, eta: 2:54:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 10:14:52,542 - pyskl - INFO - Epoch [132][500/1281] lr: 9.376e-04, eta: 2:53:40, time: 0.344, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 10:15:43,393 - pyskl - INFO - Epoch [132][600/1281] lr: 9.298e-04, eta: 2:52:57, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0210, loss: 0.0210 +2025-06-25 10:16:08,369 - pyskl - INFO - Epoch [132][700/1281] lr: 9.221e-04, eta: 2:52:11, time: 0.250, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 10:16:56,071 - pyskl - INFO - Epoch [132][800/1281] lr: 9.144e-04, eta: 2:51:27, time: 0.477, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 10:17:45,224 - pyskl - INFO - Epoch [132][900/1281] lr: 9.068e-04, eta: 2:50:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 10:18:34,352 - pyskl - INFO - Epoch [132][1000/1281] lr: 8.991e-04, eta: 2:50:02, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 10:19:23,520 - pyskl - INFO - Epoch [132][1100/1281] lr: 8.915e-04, eta: 2:49:19, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 10:20:12,618 - pyskl - INFO - Epoch [132][1200/1281] lr: 8.840e-04, eta: 2:48:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 10:20:52,941 - pyskl - INFO - Saving checkpoint at 132 epochs +2025-06-25 10:21:51,189 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:21:51,244 - pyskl - INFO - +top1_acc 0.9404 +top5_acc 0.9961 +2025-06-25 10:21:51,245 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:21:51,251 - pyskl - INFO - +mean_acc 0.9171 +2025-06-25 10:21:51,255 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_127.pth was removed +2025-06-25 10:21:51,426 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_132.pth. +2025-06-25 10:21:51,426 - pyskl - INFO - Best top1_acc is 0.9404 at 132 epoch. +2025-06-25 10:21:51,429 - pyskl - INFO - Epoch(val) [132][533] top1_acc: 0.9404, top5_acc: 0.9961, mean_class_accuracy: 0.9171 +2025-06-25 10:23:11,940 - pyskl - INFO - Epoch [133][100/1281] lr: 8.704e-04, eta: 2:47:17, time: 0.805, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 10:24:01,216 - pyskl - INFO - Epoch [133][200/1281] lr: 8.629e-04, eta: 2:46:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 10:24:50,356 - pyskl - INFO - Epoch [133][300/1281] lr: 8.554e-04, eta: 2:45:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 10:25:39,599 - pyskl - INFO - Epoch [133][400/1281] lr: 8.480e-04, eta: 2:45:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 10:26:13,968 - pyskl - INFO - Epoch [133][500/1281] lr: 8.406e-04, eta: 2:44:23, time: 0.344, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 10:27:04,742 - pyskl - INFO - Epoch [133][600/1281] lr: 8.333e-04, eta: 2:43:40, time: 0.508, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 10:27:30,300 - pyskl - INFO - Epoch [133][700/1281] lr: 8.260e-04, eta: 2:42:54, time: 0.256, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 10:28:19,010 - pyskl - INFO - Epoch [133][800/1281] lr: 8.187e-04, eta: 2:42:11, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 10:29:08,112 - pyskl - INFO - Epoch [133][900/1281] lr: 8.114e-04, eta: 2:41:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 10:29:57,416 - pyskl - INFO - Epoch [133][1000/1281] lr: 8.042e-04, eta: 2:40:45, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 10:30:46,326 - pyskl - INFO - Epoch [133][1100/1281] lr: 7.970e-04, eta: 2:40:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 10:31:35,533 - pyskl - INFO - Epoch [133][1200/1281] lr: 7.898e-04, eta: 2:39:19, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 10:32:15,863 - pyskl - INFO - Saving checkpoint at 133 epochs +2025-06-25 10:33:14,267 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:33:14,322 - pyskl - INFO - +top1_acc 0.9393 +top5_acc 0.9966 +2025-06-25 10:33:14,322 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:33:14,328 - pyskl - INFO - +mean_acc 0.9179 +2025-06-25 10:33:14,330 - pyskl - INFO - Epoch(val) [133][533] top1_acc: 0.9393, top5_acc: 0.9966, mean_class_accuracy: 0.9179 +2025-06-25 10:34:33,826 - pyskl - INFO - Epoch [134][100/1281] lr: 7.769e-04, eta: 2:38:00, time: 0.795, data_time: 0.184, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 10:35:22,642 - pyskl - INFO - Epoch [134][200/1281] lr: 7.699e-04, eta: 2:37:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 10:36:11,960 - pyskl - INFO - Epoch [134][300/1281] lr: 7.628e-04, eta: 2:36:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 10:37:01,172 - pyskl - INFO - Epoch [134][400/1281] lr: 7.558e-04, eta: 2:35:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 10:37:34,513 - pyskl - INFO - Epoch [134][500/1281] lr: 7.488e-04, eta: 2:35:05, time: 0.333, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 10:38:25,425 - pyskl - INFO - Epoch [134][600/1281] lr: 7.419e-04, eta: 2:34:23, time: 0.509, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 10:38:51,598 - pyskl - INFO - Epoch [134][700/1281] lr: 7.349e-04, eta: 2:33:37, time: 0.262, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 10:39:40,548 - pyskl - INFO - Epoch [134][800/1281] lr: 7.281e-04, eta: 2:32:54, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 10:40:29,910 - pyskl - INFO - Epoch [134][900/1281] lr: 7.212e-04, eta: 2:32:10, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 10:41:19,031 - pyskl - INFO - Epoch [134][1000/1281] lr: 7.144e-04, eta: 2:31:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0232, loss: 0.0232 +2025-06-25 10:42:08,031 - pyskl - INFO - Epoch [134][1100/1281] lr: 7.076e-04, eta: 2:30:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 10:42:57,251 - pyskl - INFO - Epoch [134][1200/1281] lr: 7.008e-04, eta: 2:30:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 10:43:37,542 - pyskl - INFO - Saving checkpoint at 134 epochs +2025-06-25 10:44:35,769 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:44:35,824 - pyskl - INFO - +top1_acc 0.9398 +top5_acc 0.9960 +2025-06-25 10:44:35,824 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:44:35,830 - pyskl - INFO - +mean_acc 0.9176 +2025-06-25 10:44:35,832 - pyskl - INFO - Epoch(val) [134][533] top1_acc: 0.9398, top5_acc: 0.9960, mean_class_accuracy: 0.9176 +2025-06-25 10:45:55,525 - pyskl - INFO - Epoch [135][100/1281] lr: 6.887e-04, eta: 2:28:42, time: 0.797, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 10:46:44,763 - pyskl - INFO - Epoch [135][200/1281] lr: 6.820e-04, eta: 2:27:59, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 10:47:34,094 - pyskl - INFO - Epoch [135][300/1281] lr: 6.753e-04, eta: 2:27:16, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 10:48:23,369 - pyskl - INFO - Epoch [135][400/1281] lr: 6.687e-04, eta: 2:26:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 10:48:56,617 - pyskl - INFO - Epoch [135][500/1281] lr: 6.622e-04, eta: 2:25:48, time: 0.332, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 10:49:47,636 - pyskl - INFO - Epoch [135][600/1281] lr: 6.556e-04, eta: 2:25:05, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 10:50:13,363 - pyskl - INFO - Epoch [135][700/1281] lr: 6.491e-04, eta: 2:24:19, time: 0.257, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 10:51:02,188 - pyskl - INFO - Epoch [135][800/1281] lr: 6.426e-04, eta: 2:23:36, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 10:51:51,090 - pyskl - INFO - Epoch [135][900/1281] lr: 6.362e-04, eta: 2:22:53, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 10:52:39,923 - pyskl - INFO - Epoch [135][1000/1281] lr: 6.297e-04, eta: 2:22:10, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 10:53:29,185 - pyskl - INFO - Epoch [135][1100/1281] lr: 6.233e-04, eta: 2:21:26, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 10:54:18,407 - pyskl - INFO - Epoch [135][1200/1281] lr: 6.170e-04, eta: 2:20:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 10:54:58,963 - pyskl - INFO - Saving checkpoint at 135 epochs +2025-06-25 10:55:57,325 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:55:57,394 - pyskl - INFO - +top1_acc 0.9399 +top5_acc 0.9961 +2025-06-25 10:55:57,394 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:55:57,402 - pyskl - INFO - +mean_acc 0.9183 +2025-06-25 10:55:57,403 - pyskl - INFO - Epoch(val) [135][533] top1_acc: 0.9399, top5_acc: 0.9961, mean_class_accuracy: 0.9183 +2025-06-25 10:57:15,604 - pyskl - INFO - Epoch [136][100/1281] lr: 6.056e-04, eta: 2:19:24, time: 0.782, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 10:58:04,813 - pyskl - INFO - Epoch [136][200/1281] lr: 5.993e-04, eta: 2:18:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 10:58:54,130 - pyskl - INFO - Epoch [136][300/1281] lr: 5.931e-04, eta: 2:17:58, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 10:59:43,328 - pyskl - INFO - Epoch [136][400/1281] lr: 5.868e-04, eta: 2:17:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 11:00:16,457 - pyskl - INFO - Epoch [136][500/1281] lr: 5.807e-04, eta: 2:16:30, time: 0.331, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 11:01:07,465 - pyskl - INFO - Epoch [136][600/1281] lr: 5.745e-04, eta: 2:15:47, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 11:01:33,230 - pyskl - INFO - Epoch [136][700/1281] lr: 5.684e-04, eta: 2:15:01, time: 0.258, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 11:02:22,112 - pyskl - INFO - Epoch [136][800/1281] lr: 5.623e-04, eta: 2:14:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 11:03:11,010 - pyskl - INFO - Epoch [136][900/1281] lr: 5.563e-04, eta: 2:13:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 11:04:00,553 - pyskl - INFO - Epoch [136][1000/1281] lr: 5.503e-04, eta: 2:12:51, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 11:04:50,280 - pyskl - INFO - Epoch [136][1100/1281] lr: 5.443e-04, eta: 2:12:08, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 11:05:39,248 - pyskl - INFO - Epoch [136][1200/1281] lr: 5.384e-04, eta: 2:11:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 11:06:19,621 - pyskl - INFO - Saving checkpoint at 136 epochs +2025-06-25 11:07:17,746 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:07:17,800 - pyskl - INFO - +top1_acc 0.9400 +top5_acc 0.9964 +2025-06-25 11:07:17,801 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:07:17,807 - pyskl - INFO - +mean_acc 0.9171 +2025-06-25 11:07:17,809 - pyskl - INFO - Epoch(val) [136][533] top1_acc: 0.9400, top5_acc: 0.9964, mean_class_accuracy: 0.9171 +2025-06-25 11:08:38,285 - pyskl - INFO - Epoch [137][100/1281] lr: 5.277e-04, eta: 2:10:06, time: 0.805, data_time: 0.187, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 11:09:27,455 - pyskl - INFO - Epoch [137][200/1281] lr: 5.218e-04, eta: 2:09:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 11:10:17,271 - pyskl - INFO - Epoch [137][300/1281] lr: 5.160e-04, eta: 2:08:39, time: 0.498, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 11:11:06,401 - pyskl - INFO - Epoch [137][400/1281] lr: 5.102e-04, eta: 2:07:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 11:11:38,862 - pyskl - INFO - Epoch [137][500/1281] lr: 5.044e-04, eta: 2:07:11, time: 0.325, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 11:12:29,820 - pyskl - INFO - Epoch [137][600/1281] lr: 4.987e-04, eta: 2:06:28, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 11:12:56,512 - pyskl - INFO - Epoch [137][700/1281] lr: 4.930e-04, eta: 2:05:43, time: 0.267, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0204, loss: 0.0204 +2025-06-25 11:13:45,725 - pyskl - INFO - Epoch [137][800/1281] lr: 4.873e-04, eta: 2:05:00, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 11:14:34,973 - pyskl - INFO - Epoch [137][900/1281] lr: 4.817e-04, eta: 2:04:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 11:15:24,535 - pyskl - INFO - Epoch [137][1000/1281] lr: 4.761e-04, eta: 2:03:33, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 11:16:13,687 - pyskl - INFO - Epoch [137][1100/1281] lr: 4.705e-04, eta: 2:02:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 11:17:03,168 - pyskl - INFO - Epoch [137][1200/1281] lr: 4.650e-04, eta: 2:02:07, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 11:17:43,478 - pyskl - INFO - Saving checkpoint at 137 epochs +2025-06-25 11:18:41,956 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:18:42,026 - pyskl - INFO - +top1_acc 0.9407 +top5_acc 0.9961 +2025-06-25 11:18:42,026 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:18:42,034 - pyskl - INFO - +mean_acc 0.9190 +2025-06-25 11:18:42,039 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_132.pth was removed +2025-06-25 11:18:42,212 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_137.pth. +2025-06-25 11:18:42,212 - pyskl - INFO - Best top1_acc is 0.9407 at 137 epoch. +2025-06-25 11:18:42,215 - pyskl - INFO - Epoch(val) [137][533] top1_acc: 0.9407, top5_acc: 0.9961, mean_class_accuracy: 0.9190 +2025-06-25 11:20:01,956 - pyskl - INFO - Epoch [138][100/1281] lr: 4.550e-04, eta: 2:00:47, time: 0.797, data_time: 0.193, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 11:20:51,024 - pyskl - INFO - Epoch [138][200/1281] lr: 4.496e-04, eta: 2:00:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 11:21:40,499 - pyskl - INFO - Epoch [138][300/1281] lr: 4.442e-04, eta: 1:59:21, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 11:22:29,765 - pyskl - INFO - Epoch [138][400/1281] lr: 4.388e-04, eta: 1:58:38, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 11:23:01,232 - pyskl - INFO - Epoch [138][500/1281] lr: 4.334e-04, eta: 1:57:53, time: 0.315, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0144, loss: 0.0144 +2025-06-25 11:23:52,227 - pyskl - INFO - Epoch [138][600/1281] lr: 4.281e-04, eta: 1:57:09, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 11:24:21,076 - pyskl - INFO - Epoch [138][700/1281] lr: 4.228e-04, eta: 1:56:24, time: 0.288, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 11:25:10,075 - pyskl - INFO - Epoch [138][800/1281] lr: 4.176e-04, eta: 1:55:41, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 11:25:59,234 - pyskl - INFO - Epoch [138][900/1281] lr: 4.124e-04, eta: 1:54:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 11:26:48,354 - pyskl - INFO - Epoch [138][1000/1281] lr: 4.072e-04, eta: 1:54:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0199, loss: 0.0199 +2025-06-25 11:27:37,524 - pyskl - INFO - Epoch [138][1100/1281] lr: 4.020e-04, eta: 1:53:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 11:28:26,645 - pyskl - INFO - Epoch [138][1200/1281] lr: 3.969e-04, eta: 1:52:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 11:29:07,172 - pyskl - INFO - Saving checkpoint at 138 epochs +2025-06-25 11:30:05,858 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:30:05,914 - pyskl - INFO - +top1_acc 0.9417 +top5_acc 0.9965 +2025-06-25 11:30:05,915 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:30:05,921 - pyskl - INFO - +mean_acc 0.9193 +2025-06-25 11:30:05,925 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_137.pth was removed +2025-06-25 11:30:06,089 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_138.pth. +2025-06-25 11:30:06,089 - pyskl - INFO - Best top1_acc is 0.9417 at 138 epoch. +2025-06-25 11:30:06,092 - pyskl - INFO - Epoch(val) [138][533] top1_acc: 0.9417, top5_acc: 0.9965, mean_class_accuracy: 0.9193 +2025-06-25 11:31:25,977 - pyskl - INFO - Epoch [139][100/1281] lr: 3.877e-04, eta: 1:51:28, time: 0.799, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 11:32:15,073 - pyskl - INFO - Epoch [139][200/1281] lr: 3.827e-04, eta: 1:50:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 11:33:04,077 - pyskl - INFO - Epoch [139][300/1281] lr: 3.777e-04, eta: 1:50:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 11:33:53,261 - pyskl - INFO - Epoch [139][400/1281] lr: 3.727e-04, eta: 1:49:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 11:34:22,940 - pyskl - INFO - Epoch [139][500/1281] lr: 3.678e-04, eta: 1:48:33, time: 0.297, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 11:35:13,919 - pyskl - INFO - Epoch [139][600/1281] lr: 3.628e-04, eta: 1:47:50, time: 0.510, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 11:35:55,153 - pyskl - INFO - Epoch [139][700/1281] lr: 3.580e-04, eta: 1:47:06, time: 0.412, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 11:37:06,408 - pyskl - INFO - Epoch [139][800/1281] lr: 3.531e-04, eta: 1:46:25, time: 0.713, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 11:38:16,926 - pyskl - INFO - Epoch [139][900/1281] lr: 3.483e-04, eta: 1:45:43, time: 0.705, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 11:39:25,588 - pyskl - INFO - Epoch [139][1000/1281] lr: 3.436e-04, eta: 1:45:01, time: 0.687, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 11:40:35,152 - pyskl - INFO - Epoch [139][1100/1281] lr: 3.388e-04, eta: 1:44:19, time: 0.696, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 11:41:45,375 - pyskl - INFO - Epoch [139][1200/1281] lr: 3.341e-04, eta: 1:43:38, time: 0.702, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 11:42:40,824 - pyskl - INFO - Saving checkpoint at 139 epochs +2025-06-25 11:43:41,667 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:43:41,735 - pyskl - INFO - +top1_acc 0.9423 +top5_acc 0.9968 +2025-06-25 11:43:41,736 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:43:41,743 - pyskl - INFO - +mean_acc 0.9210 +2025-06-25 11:43:41,748 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_138.pth was removed +2025-06-25 11:43:41,911 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_139.pth. +2025-06-25 11:43:41,911 - pyskl - INFO - Best top1_acc is 0.9423 at 139 epoch. +2025-06-25 11:43:41,914 - pyskl - INFO - Epoch(val) [139][533] top1_acc: 0.9423, top5_acc: 0.9968, mean_class_accuracy: 0.9210 +2025-06-25 11:45:38,352 - pyskl - INFO - Epoch [140][100/1281] lr: 3.257e-04, eta: 1:42:21, time: 1.164, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 11:46:47,641 - pyskl - INFO - Epoch [140][200/1281] lr: 3.210e-04, eta: 1:41:39, time: 0.693, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 11:47:55,685 - pyskl - INFO - Epoch [140][300/1281] lr: 3.165e-04, eta: 1:40:57, time: 0.680, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 11:49:05,538 - pyskl - INFO - Epoch [140][400/1281] lr: 3.119e-04, eta: 1:40:15, time: 0.699, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 11:50:13,737 - pyskl - INFO - Epoch [140][500/1281] lr: 3.074e-04, eta: 1:39:33, time: 0.682, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 11:50:43,779 - pyskl - INFO - Epoch [140][600/1281] lr: 3.029e-04, eta: 1:38:48, time: 0.300, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 11:51:05,891 - pyskl - INFO - Epoch [140][700/1281] lr: 2.984e-04, eta: 1:38:03, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0139, loss: 0.0139 +2025-06-25 11:51:27,609 - pyskl - INFO - Epoch [140][800/1281] lr: 2.940e-04, eta: 1:37:17, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0141, loss: 0.0141 +2025-06-25 11:51:49,494 - pyskl - INFO - Epoch [140][900/1281] lr: 2.896e-04, eta: 1:36:31, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 11:52:11,306 - pyskl - INFO - Epoch [140][1000/1281] lr: 2.853e-04, eta: 1:35:46, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0144, loss: 0.0144 +2025-06-25 11:52:33,086 - pyskl - INFO - Epoch [140][1100/1281] lr: 2.809e-04, eta: 1:35:00, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0137, loss: 0.0137 +2025-06-25 11:52:55,206 - pyskl - INFO - Epoch [140][1200/1281] lr: 2.767e-04, eta: 1:34:15, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 11:53:13,474 - pyskl - INFO - Saving checkpoint at 140 epochs +2025-06-25 11:53:56,339 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:53:56,396 - pyskl - INFO - +top1_acc 0.9410 +top5_acc 0.9961 +2025-06-25 11:53:56,396 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:53:56,404 - pyskl - INFO - +mean_acc 0.9187 +2025-06-25 11:53:56,407 - pyskl - INFO - Epoch(val) [140][533] top1_acc: 0.9410, top5_acc: 0.9961, mean_class_accuracy: 0.9187 +2025-06-25 11:54:37,291 - pyskl - INFO - Epoch [141][100/1281] lr: 2.690e-04, eta: 1:32:53, time: 0.409, data_time: 0.178, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 11:54:59,373 - pyskl - INFO - Epoch [141][200/1281] lr: 2.648e-04, eta: 1:32:07, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 11:55:21,311 - pyskl - INFO - Epoch [141][300/1281] lr: 2.606e-04, eta: 1:31:22, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 11:55:43,135 - pyskl - INFO - Epoch [141][400/1281] lr: 2.565e-04, eta: 1:30:37, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 11:56:05,295 - pyskl - INFO - Epoch [141][500/1281] lr: 2.524e-04, eta: 1:29:51, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0140, loss: 0.0140 +2025-06-25 11:56:27,323 - pyskl - INFO - Epoch [141][600/1281] lr: 2.483e-04, eta: 1:29:06, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 11:56:49,267 - pyskl - INFO - Epoch [141][700/1281] lr: 2.443e-04, eta: 1:28:21, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 11:57:11,330 - pyskl - INFO - Epoch [141][800/1281] lr: 2.402e-04, eta: 1:27:36, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 11:57:33,547 - pyskl - INFO - Epoch [141][900/1281] lr: 2.363e-04, eta: 1:26:51, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 11:57:55,755 - pyskl - INFO - Epoch [141][1000/1281] lr: 2.323e-04, eta: 1:26:05, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 11:58:17,835 - pyskl - INFO - Epoch [141][1100/1281] lr: 2.284e-04, eta: 1:25:20, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 11:58:39,859 - pyskl - INFO - Epoch [141][1200/1281] lr: 2.246e-04, eta: 1:24:35, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0133, loss: 0.0133 +2025-06-25 11:58:58,189 - pyskl - INFO - Saving checkpoint at 141 epochs +2025-06-25 11:59:40,702 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:59:40,757 - pyskl - INFO - +top1_acc 0.9412 +top5_acc 0.9969 +2025-06-25 11:59:40,757 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:59:40,764 - pyskl - INFO - +mean_acc 0.9186 +2025-06-25 11:59:40,765 - pyskl - INFO - Epoch(val) [141][533] top1_acc: 0.9412, top5_acc: 0.9969, mean_class_accuracy: 0.9186 +2025-06-25 12:00:21,689 - pyskl - INFO - Epoch [142][100/1281] lr: 2.176e-04, eta: 1:23:14, time: 0.409, data_time: 0.178, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 12:00:43,791 - pyskl - INFO - Epoch [142][200/1281] lr: 2.139e-04, eta: 1:22:28, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 12:01:05,499 - pyskl - INFO - Epoch [142][300/1281] lr: 2.101e-04, eta: 1:21:43, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0129, loss: 0.0129 +2025-06-25 12:01:27,516 - pyskl - INFO - Epoch [142][400/1281] lr: 2.064e-04, eta: 1:20:58, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0137, loss: 0.0137 +2025-06-25 12:01:49,500 - pyskl - INFO - Epoch [142][500/1281] lr: 2.027e-04, eta: 1:20:13, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 12:02:11,446 - pyskl - INFO - Epoch [142][600/1281] lr: 1.991e-04, eta: 1:19:28, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:02:33,331 - pyskl - INFO - Epoch [142][700/1281] lr: 1.954e-04, eta: 1:18:44, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0136, loss: 0.0136 +2025-06-25 12:02:55,238 - pyskl - INFO - Epoch [142][800/1281] lr: 1.919e-04, eta: 1:17:59, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 12:03:17,055 - pyskl - INFO - Epoch [142][900/1281] lr: 1.883e-04, eta: 1:17:14, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 12:03:39,148 - pyskl - INFO - Epoch [142][1000/1281] lr: 1.848e-04, eta: 1:16:29, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 12:04:01,061 - pyskl - INFO - Epoch [142][1100/1281] lr: 1.813e-04, eta: 1:15:44, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0144, loss: 0.0144 +2025-06-25 12:04:23,007 - pyskl - INFO - Epoch [142][1200/1281] lr: 1.779e-04, eta: 1:14:59, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0137, loss: 0.0137 +2025-06-25 12:04:41,909 - pyskl - INFO - Saving checkpoint at 142 epochs +2025-06-25 12:05:24,630 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:05:24,686 - pyskl - INFO - +top1_acc 0.9404 +top5_acc 0.9962 +2025-06-25 12:05:24,687 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:05:24,694 - pyskl - INFO - +mean_acc 0.9192 +2025-06-25 12:05:24,696 - pyskl - INFO - Epoch(val) [142][533] top1_acc: 0.9404, top5_acc: 0.9962, mean_class_accuracy: 0.9192 +2025-06-25 12:06:05,791 - pyskl - INFO - Epoch [143][100/1281] lr: 1.717e-04, eta: 1:13:38, time: 0.411, data_time: 0.181, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 12:06:28,023 - pyskl - INFO - Epoch [143][200/1281] lr: 1.683e-04, eta: 1:12:54, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 12:06:49,646 - pyskl - INFO - Epoch [143][300/1281] lr: 1.650e-04, eta: 1:12:09, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0140, loss: 0.0140 +2025-06-25 12:07:11,729 - pyskl - INFO - Epoch [143][400/1281] lr: 1.617e-04, eta: 1:11:24, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 12:07:33,452 - pyskl - INFO - Epoch [143][500/1281] lr: 1.585e-04, eta: 1:10:40, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0146, loss: 0.0146 +2025-06-25 12:07:55,509 - pyskl - INFO - Epoch [143][600/1281] lr: 1.552e-04, eta: 1:09:55, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 12:08:17,412 - pyskl - INFO - Epoch [143][700/1281] lr: 1.520e-04, eta: 1:09:10, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 12:08:39,330 - pyskl - INFO - Epoch [143][800/1281] lr: 1.489e-04, eta: 1:08:26, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 12:09:01,273 - pyskl - INFO - Epoch [143][900/1281] lr: 1.457e-04, eta: 1:07:41, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0144, loss: 0.0144 +2025-06-25 12:09:23,440 - pyskl - INFO - Epoch [143][1000/1281] lr: 1.426e-04, eta: 1:06:57, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 12:09:45,206 - pyskl - INFO - Epoch [143][1100/1281] lr: 1.396e-04, eta: 1:06:12, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0120, loss: 0.0120 +2025-06-25 12:10:07,297 - pyskl - INFO - Epoch [143][1200/1281] lr: 1.366e-04, eta: 1:05:28, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0141, loss: 0.0141 +2025-06-25 12:10:25,661 - pyskl - INFO - Saving checkpoint at 143 epochs +2025-06-25 12:11:08,638 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:11:08,693 - pyskl - INFO - +top1_acc 0.9414 +top5_acc 0.9961 +2025-06-25 12:11:08,693 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:11:08,700 - pyskl - INFO - +mean_acc 0.9194 +2025-06-25 12:11:08,701 - pyskl - INFO - Epoch(val) [143][533] top1_acc: 0.9414, top5_acc: 0.9961, mean_class_accuracy: 0.9194 +2025-06-25 12:11:49,979 - pyskl - INFO - Epoch [144][100/1281] lr: 1.312e-04, eta: 1:04:07, time: 0.413, data_time: 0.182, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 12:12:11,985 - pyskl - INFO - Epoch [144][200/1281] lr: 1.282e-04, eta: 1:03:23, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 12:12:34,108 - pyskl - INFO - Epoch [144][300/1281] lr: 1.253e-04, eta: 1:02:38, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 12:12:56,085 - pyskl - INFO - Epoch [144][400/1281] lr: 1.224e-04, eta: 1:01:54, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0134, loss: 0.0134 +2025-06-25 12:13:17,916 - pyskl - INFO - Epoch [144][500/1281] lr: 1.196e-04, eta: 1:01:10, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 12:13:40,018 - pyskl - INFO - Epoch [144][600/1281] lr: 1.168e-04, eta: 1:00:25, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 12:14:02,536 - pyskl - INFO - Epoch [144][700/1281] lr: 1.140e-04, eta: 0:59:41, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 12:14:24,715 - pyskl - INFO - Epoch [144][800/1281] lr: 1.113e-04, eta: 0:58:57, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 12:14:46,869 - pyskl - INFO - Epoch [144][900/1281] lr: 1.086e-04, eta: 0:58:13, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 12:15:08,862 - pyskl - INFO - Epoch [144][1000/1281] lr: 1.059e-04, eta: 0:57:28, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0145, loss: 0.0145 +2025-06-25 12:15:30,656 - pyskl - INFO - Epoch [144][1100/1281] lr: 1.033e-04, eta: 0:56:44, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0145, loss: 0.0145 +2025-06-25 12:15:52,562 - pyskl - INFO - Epoch [144][1200/1281] lr: 1.007e-04, eta: 0:56:00, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 12:16:11,272 - pyskl - INFO - Saving checkpoint at 144 epochs +2025-06-25 12:16:53,802 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:16:53,869 - pyskl - INFO - +top1_acc 0.9416 +top5_acc 0.9967 +2025-06-25 12:16:53,870 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:16:53,878 - pyskl - INFO - +mean_acc 0.9190 +2025-06-25 12:16:53,880 - pyskl - INFO - Epoch(val) [144][533] top1_acc: 0.9416, top5_acc: 0.9967, mean_class_accuracy: 0.9190 +2025-06-25 12:17:35,247 - pyskl - INFO - Epoch [145][100/1281] lr: 9.605e-05, eta: 0:54:40, time: 0.414, data_time: 0.183, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 12:17:57,465 - pyskl - INFO - Epoch [145][200/1281] lr: 9.353e-05, eta: 0:53:56, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 12:18:19,246 - pyskl - INFO - Epoch [145][300/1281] lr: 9.106e-05, eta: 0:53:12, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 12:18:40,882 - pyskl - INFO - Epoch [145][400/1281] lr: 8.861e-05, eta: 0:52:28, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 12:19:02,551 - pyskl - INFO - Epoch [145][500/1281] lr: 8.620e-05, eta: 0:51:44, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0135, loss: 0.0135 +2025-06-25 12:19:24,464 - pyskl - INFO - Epoch [145][600/1281] lr: 8.382e-05, eta: 0:51:00, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0145, loss: 0.0145 +2025-06-25 12:19:46,430 - pyskl - INFO - Epoch [145][700/1281] lr: 8.147e-05, eta: 0:50:16, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 12:20:08,408 - pyskl - INFO - Epoch [145][800/1281] lr: 7.916e-05, eta: 0:49:32, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0131, loss: 0.0131 +2025-06-25 12:20:30,306 - pyskl - INFO - Epoch [145][900/1281] lr: 7.688e-05, eta: 0:48:48, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0142, loss: 0.0142 +2025-06-25 12:20:52,337 - pyskl - INFO - Epoch [145][1000/1281] lr: 7.463e-05, eta: 0:48:04, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 12:21:14,099 - pyskl - INFO - Epoch [145][1100/1281] lr: 7.242e-05, eta: 0:47:20, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:21:36,048 - pyskl - INFO - Epoch [145][1200/1281] lr: 7.024e-05, eta: 0:46:36, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0145, loss: 0.0145 +2025-06-25 12:21:54,513 - pyskl - INFO - Saving checkpoint at 145 epochs +2025-06-25 12:22:37,205 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:22:37,260 - pyskl - INFO - +top1_acc 0.9424 +top5_acc 0.9962 +2025-06-25 12:22:37,260 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:22:37,266 - pyskl - INFO - +mean_acc 0.9195 +2025-06-25 12:22:37,270 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_139.pth was removed +2025-06-25 12:22:37,430 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_145.pth. +2025-06-25 12:22:37,430 - pyskl - INFO - Best top1_acc is 0.9424 at 145 epoch. +2025-06-25 12:22:37,433 - pyskl - INFO - Epoch(val) [145][533] top1_acc: 0.9424, top5_acc: 0.9962, mean_class_accuracy: 0.9195 +2025-06-25 12:23:18,892 - pyskl - INFO - Epoch [146][100/1281] lr: 6.638e-05, eta: 0:45:17, time: 0.415, data_time: 0.184, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:23:40,826 - pyskl - INFO - Epoch [146][200/1281] lr: 6.429e-05, eta: 0:44:33, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 12:24:02,857 - pyskl - INFO - Epoch [146][300/1281] lr: 6.224e-05, eta: 0:43:49, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0137, loss: 0.0137 +2025-06-25 12:24:24,972 - pyskl - INFO - Epoch [146][400/1281] lr: 6.022e-05, eta: 0:43:06, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 12:24:47,093 - pyskl - INFO - Epoch [146][500/1281] lr: 5.823e-05, eta: 0:42:22, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0140, loss: 0.0140 +2025-06-25 12:25:08,951 - pyskl - INFO - Epoch [146][600/1281] lr: 5.628e-05, eta: 0:41:38, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 12:25:31,246 - pyskl - INFO - Epoch [146][700/1281] lr: 5.436e-05, eta: 0:40:54, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 12:25:53,456 - pyskl - INFO - Epoch [146][800/1281] lr: 5.247e-05, eta: 0:40:11, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 12:26:15,512 - pyskl - INFO - Epoch [146][900/1281] lr: 5.061e-05, eta: 0:39:27, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 12:26:37,240 - pyskl - INFO - Epoch [146][1000/1281] lr: 4.879e-05, eta: 0:38:44, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 12:26:59,045 - pyskl - INFO - Epoch [146][1100/1281] lr: 4.701e-05, eta: 0:38:00, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 12:27:20,711 - pyskl - INFO - Epoch [146][1200/1281] lr: 4.525e-05, eta: 0:37:16, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 12:27:39,160 - pyskl - INFO - Saving checkpoint at 146 epochs +2025-06-25 12:28:22,333 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:28:22,389 - pyskl - INFO - +top1_acc 0.9412 +top5_acc 0.9968 +2025-06-25 12:28:22,389 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:28:22,396 - pyskl - INFO - +mean_acc 0.9174 +2025-06-25 12:28:22,398 - pyskl - INFO - Epoch(val) [146][533] top1_acc: 0.9412, top5_acc: 0.9968, mean_class_accuracy: 0.9174 +2025-06-25 12:29:03,950 - pyskl - INFO - Epoch [147][100/1281] lr: 4.216e-05, eta: 0:35:58, time: 0.415, data_time: 0.185, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0144, loss: 0.0144 +2025-06-25 12:29:26,083 - pyskl - INFO - Epoch [147][200/1281] lr: 4.050e-05, eta: 0:35:14, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0139, loss: 0.0139 +2025-06-25 12:29:48,255 - pyskl - INFO - Epoch [147][300/1281] lr: 3.887e-05, eta: 0:34:31, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 12:30:09,984 - pyskl - INFO - Epoch [147][400/1281] lr: 3.728e-05, eta: 0:33:47, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:30:32,040 - pyskl - INFO - Epoch [147][500/1281] lr: 3.572e-05, eta: 0:33:04, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 12:30:54,048 - pyskl - INFO - Epoch [147][600/1281] lr: 3.419e-05, eta: 0:32:20, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0139, loss: 0.0139 +2025-06-25 12:31:15,957 - pyskl - INFO - Epoch [147][700/1281] lr: 3.270e-05, eta: 0:31:37, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 12:31:37,725 - pyskl - INFO - Epoch [147][800/1281] lr: 3.124e-05, eta: 0:30:54, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:31:59,685 - pyskl - INFO - Epoch [147][900/1281] lr: 2.981e-05, eta: 0:30:10, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0145, loss: 0.0145 +2025-06-25 12:32:21,682 - pyskl - INFO - Epoch [147][1000/1281] lr: 2.842e-05, eta: 0:29:27, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 12:32:43,166 - pyskl - INFO - Epoch [147][1100/1281] lr: 2.706e-05, eta: 0:28:44, time: 0.215, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 12:33:05,124 - pyskl - INFO - Epoch [147][1200/1281] lr: 2.573e-05, eta: 0:28:00, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0132, loss: 0.0132 +2025-06-25 12:33:23,377 - pyskl - INFO - Saving checkpoint at 147 epochs +2025-06-25 12:34:06,533 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:34:06,590 - pyskl - INFO - +top1_acc 0.9425 +top5_acc 0.9966 +2025-06-25 12:34:06,590 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:34:06,596 - pyskl - INFO - +mean_acc 0.9209 +2025-06-25 12:34:06,600 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_145.pth was removed +2025-06-25 12:34:06,757 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_147.pth. +2025-06-25 12:34:06,757 - pyskl - INFO - Best top1_acc is 0.9425 at 147 epoch. +2025-06-25 12:34:06,760 - pyskl - INFO - Epoch(val) [147][533] top1_acc: 0.9425, top5_acc: 0.9966, mean_class_accuracy: 0.9209 +2025-06-25 12:34:47,607 - pyskl - INFO - Epoch [148][100/1281] lr: 2.341e-05, eta: 0:26:42, time: 0.408, data_time: 0.178, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 12:35:09,697 - pyskl - INFO - Epoch [148][200/1281] lr: 2.218e-05, eta: 0:25:59, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 12:35:31,508 - pyskl - INFO - Epoch [148][300/1281] lr: 2.098e-05, eta: 0:25:16, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 12:35:53,311 - pyskl - INFO - Epoch [148][400/1281] lr: 1.981e-05, eta: 0:24:32, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 12:36:15,091 - pyskl - INFO - Epoch [148][500/1281] lr: 1.868e-05, eta: 0:23:49, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 12:36:36,744 - pyskl - INFO - Epoch [148][600/1281] lr: 1.758e-05, eta: 0:23:06, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 12:36:58,719 - pyskl - INFO - Epoch [148][700/1281] lr: 1.651e-05, eta: 0:22:23, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0146, loss: 0.0146 +2025-06-25 12:37:20,454 - pyskl - INFO - Epoch [148][800/1281] lr: 1.548e-05, eta: 0:21:40, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 12:37:42,465 - pyskl - INFO - Epoch [148][900/1281] lr: 1.448e-05, eta: 0:20:57, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0146, loss: 0.0146 +2025-06-25 12:38:04,390 - pyskl - INFO - Epoch [148][1000/1281] lr: 1.351e-05, eta: 0:20:14, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 12:38:26,265 - pyskl - INFO - Epoch [148][1100/1281] lr: 1.258e-05, eta: 0:19:31, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 12:38:48,183 - pyskl - INFO - Epoch [148][1200/1281] lr: 1.168e-05, eta: 0:18:48, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0135, loss: 0.0135 +2025-06-25 12:39:06,854 - pyskl - INFO - Saving checkpoint at 148 epochs +2025-06-25 12:39:50,189 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:39:50,244 - pyskl - INFO - +top1_acc 0.9425 +top5_acc 0.9962 +2025-06-25 12:39:50,244 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:39:50,251 - pyskl - INFO - +mean_acc 0.9213 +2025-06-25 12:39:50,253 - pyskl - INFO - Epoch(val) [148][533] top1_acc: 0.9425, top5_acc: 0.9962, mean_class_accuracy: 0.9213 +2025-06-25 12:40:31,710 - pyskl - INFO - Epoch [149][100/1281] lr: 1.013e-05, eta: 0:17:30, time: 0.415, data_time: 0.182, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 12:40:53,639 - pyskl - INFO - Epoch [149][200/1281] lr: 9.328e-06, eta: 0:16:47, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 12:41:15,908 - pyskl - INFO - Epoch [149][300/1281] lr: 8.555e-06, eta: 0:16:04, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0136, loss: 0.0136 +2025-06-25 12:41:38,157 - pyskl - INFO - Epoch [149][400/1281] lr: 7.816e-06, eta: 0:15:21, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 12:42:00,325 - pyskl - INFO - Epoch [149][500/1281] lr: 7.110e-06, eta: 0:14:39, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 12:42:22,563 - pyskl - INFO - Epoch [149][600/1281] lr: 6.437e-06, eta: 0:13:56, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 12:42:44,880 - pyskl - INFO - Epoch [149][700/1281] lr: 5.798e-06, eta: 0:13:13, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:43:06,951 - pyskl - INFO - Epoch [149][800/1281] lr: 5.192e-06, eta: 0:12:30, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0134, loss: 0.0134 +2025-06-25 12:43:29,375 - pyskl - INFO - Epoch [149][900/1281] lr: 4.620e-06, eta: 0:11:47, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 12:43:51,298 - pyskl - INFO - Epoch [149][1000/1281] lr: 4.081e-06, eta: 0:11:05, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 12:44:13,138 - pyskl - INFO - Epoch [149][1100/1281] lr: 3.576e-06, eta: 0:10:22, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0139, loss: 0.0139 +2025-06-25 12:44:35,033 - pyskl - INFO - Epoch [149][1200/1281] lr: 3.104e-06, eta: 0:09:39, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 12:44:53,567 - pyskl - INFO - Saving checkpoint at 149 epochs +2025-06-25 12:45:36,632 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:45:36,689 - pyskl - INFO - +top1_acc 0.9412 +top5_acc 0.9967 +2025-06-25 12:45:36,689 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:45:36,696 - pyskl - INFO - +mean_acc 0.9202 +2025-06-25 12:45:36,697 - pyskl - INFO - Epoch(val) [149][533] top1_acc: 0.9412, top5_acc: 0.9967, mean_class_accuracy: 0.9202 +2025-06-25 12:46:17,530 - pyskl - INFO - Epoch [150][100/1281] lr: 2.334e-06, eta: 0:08:22, time: 0.408, data_time: 0.181, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 12:46:39,147 - pyskl - INFO - Epoch [150][200/1281] lr: 1.956e-06, eta: 0:07:39, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 12:47:00,675 - pyskl - INFO - Epoch [150][300/1281] lr: 1.611e-06, eta: 0:06:57, time: 0.215, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 12:47:22,435 - pyskl - INFO - Epoch [150][400/1281] lr: 1.300e-06, eta: 0:06:14, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0138, loss: 0.0138 +2025-06-25 12:47:44,074 - pyskl - INFO - Epoch [150][500/1281] lr: 1.022e-06, eta: 0:05:31, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 12:48:05,786 - pyskl - INFO - Epoch [150][600/1281] lr: 7.771e-07, eta: 0:04:49, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:48:27,514 - pyskl - INFO - Epoch [150][700/1281] lr: 5.659e-07, eta: 0:04:06, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 12:48:49,386 - pyskl - INFO - Epoch [150][800/1281] lr: 3.881e-07, eta: 0:03:24, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 12:49:10,880 - pyskl - INFO - Epoch [150][900/1281] lr: 2.438e-07, eta: 0:02:41, time: 0.215, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:49:32,696 - pyskl - INFO - Epoch [150][1000/1281] lr: 1.329e-07, eta: 0:01:59, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0133, loss: 0.0133 +2025-06-25 12:49:54,387 - pyskl - INFO - Epoch [150][1100/1281] lr: 5.534e-08, eta: 0:01:16, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 12:50:16,329 - pyskl - INFO - Epoch [150][1200/1281] lr: 1.123e-08, eta: 0:00:34, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0139, loss: 0.0139 +2025-06-25 12:50:34,416 - pyskl - INFO - Saving checkpoint at 150 epochs +2025-06-25 12:51:17,222 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:51:17,277 - pyskl - INFO - +top1_acc 0.9418 +top5_acc 0.9965 +2025-06-25 12:51:17,277 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:51:17,283 - pyskl - INFO - +mean_acc 0.9210 +2025-06-25 12:51:17,285 - pyskl - INFO - Epoch(val) [150][533] top1_acc: 0.9418, top5_acc: 0.9965, mean_class_accuracy: 0.9210 +2025-06-25 12:51:21,589 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-25 12:56:28,152 - pyskl - INFO - Testing results of the last checkpoint +2025-06-25 12:56:28,152 - pyskl - INFO - top1_acc: 0.9428 +2025-06-25 12:56:28,152 - pyskl - INFO - top5_acc: 0.9968 +2025-06-25 12:56:28,152 - pyskl - INFO - mean_class_accuracy: 0.9213 +2025-06-25 12:56:28,153 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/finegym/k_3/best_top1_acc_epoch_147.pth +2025-06-25 13:01:31,490 - pyskl - INFO - Testing results of the best checkpoint +2025-06-25 13:01:31,490 - pyskl - INFO - top1_acc: 0.9430 +2025-06-25 13:01:31,490 - pyskl - INFO - top5_acc: 0.9966 +2025-06-25 13:01:31,490 - pyskl - INFO - mean_class_accuracy: 0.9215 diff --git a/finegym/k_3/20250624_101238.log.json b/finegym/k_3/20250624_101238.log.json new file mode 100644 index 0000000000000000000000000000000000000000..af61bf567b0b48ec01f46ebe4308c3b94704ebe0 --- /dev/null +++ b/finegym/k_3/20250624_101238.log.json @@ -0,0 +1,1951 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 1966239539, "config_name": "k_3.py", "work_dir": "k_3", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.19045, "top1_acc": 0.05125, "top5_acc": 0.18938, "loss_cls": 4.6418, "loss": 4.6418, "time": 0.62796} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.09188, "top5_acc": 0.33312, "loss_cls": 4.61287, "loss": 4.61287, "time": 0.27851} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.105, "top5_acc": 0.38875, "loss_cls": 4.32221, "loss": 4.32221, "time": 0.40191} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.11438, "top5_acc": 0.41188, "loss_cls": 4.13841, "loss": 4.13841, "time": 0.41731} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.025, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.14562, "top5_acc": 0.47125, "loss_cls": 3.91313, "loss": 3.91313, "time": 0.41447} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.025, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.1825, "top5_acc": 0.4975, "loss_cls": 3.7642, "loss": 3.7642, "time": 0.4144} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.025, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.20125, "top5_acc": 0.5575, "loss_cls": 3.56146, "loss": 3.56146, "time": 0.4146} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.025, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.21312, "top5_acc": 0.58312, "loss_cls": 3.40719, "loss": 3.40719, "time": 0.41324} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.025, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.26812, "top5_acc": 0.63125, "loss_cls": 3.17894, "loss": 3.17894, "time": 0.41581} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.025, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.30375, "top5_acc": 0.685, "loss_cls": 3.08854, "loss": 3.08854, "time": 0.41546} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.025, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.28562, "top5_acc": 0.69375, "loss_cls": 3.02044, "loss": 3.02044, "time": 0.41613} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.025, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.31438, "top5_acc": 0.71688, "loss_cls": 2.88676, "loss": 2.88676, "time": 0.41481} +{"mode": "val", "epoch": 1, "iter": 533, "lr": 0.025, "top1_acc": 0.30994, "top5_acc": 0.72867, "mean_class_accuracy": 0.16173} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.19879, "top1_acc": 0.35625, "top5_acc": 0.76188, "loss_cls": 2.69393, "loss": 2.69393, "time": 0.64683} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.37312, "top5_acc": 0.785, "loss_cls": 2.56177, "loss": 2.56177, "time": 0.26605} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.39312, "top5_acc": 0.79188, "loss_cls": 2.5156, "loss": 2.5156, "time": 0.39316} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.41938, "top5_acc": 0.83, "loss_cls": 2.35736, "loss": 2.35736, "time": 0.41536} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.02499, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.40312, "top5_acc": 0.82188, "loss_cls": 2.4284, "loss": 2.4284, "time": 0.41613} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.02499, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.43875, "top5_acc": 0.845, "loss_cls": 2.25421, "loss": 2.25421, "time": 0.41623} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.02499, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.43562, "top5_acc": 0.84312, "loss_cls": 2.24269, "loss": 2.24269, "time": 0.41536} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.02499, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.46438, "top5_acc": 0.84688, "loss_cls": 2.24226, "loss": 2.24226, "time": 0.41482} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.02499, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.46375, "top5_acc": 0.8625, "loss_cls": 2.15372, "loss": 2.15372, "time": 0.41414} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.02499, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.46875, "top5_acc": 0.855, "loss_cls": 2.17287, "loss": 2.17287, "time": 0.41627} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.02499, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.49688, "top5_acc": 0.87188, "loss_cls": 2.06548, "loss": 2.06548, "time": 0.41453} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.02499, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.4825, "top5_acc": 0.88062, "loss_cls": 2.0421, "loss": 2.0421, "time": 0.41565} +{"mode": "val", "epoch": 2, "iter": 533, "lr": 0.02499, "top1_acc": 0.50276, "top5_acc": 0.88476, "mean_class_accuracy": 0.2922} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.02499, "memory": 4082, "data_time": 0.19953, "top1_acc": 0.54188, "top5_acc": 0.91062, "loss_cls": 1.90972, "loss": 1.90972, "time": 0.64648} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.02499, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.52125, "top5_acc": 0.90938, "loss_cls": 1.88722, "loss": 1.88722, "time": 0.26426} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.02499, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.54125, "top5_acc": 0.91375, "loss_cls": 1.84494, "loss": 1.84494, "time": 0.39061} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.02499, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.53875, "top5_acc": 0.91125, "loss_cls": 1.8669, "loss": 1.8669, "time": 0.41475} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.02498, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.56563, "top5_acc": 0.92938, "loss_cls": 1.7411, "loss": 1.7411, "time": 0.41619} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.02498, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.53812, "top5_acc": 0.9175, "loss_cls": 1.83884, "loss": 1.83884, "time": 0.41954} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.02498, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.58125, "top5_acc": 0.93688, "loss_cls": 1.69217, "loss": 1.69217, "time": 0.41499} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.02498, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.56125, "top5_acc": 0.92375, "loss_cls": 1.73707, "loss": 1.73707, "time": 0.41806} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.02498, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.57063, "top5_acc": 0.93812, "loss_cls": 1.71662, "loss": 1.71662, "time": 0.43652} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.02498, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.595, "top5_acc": 0.93938, "loss_cls": 1.63778, "loss": 1.63778, "time": 0.41649} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.02498, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.5975, "top5_acc": 0.94625, "loss_cls": 1.60056, "loss": 1.60056, "time": 0.41516} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.02498, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.60312, "top5_acc": 0.94188, "loss_cls": 1.61413, "loss": 1.61413, "time": 0.41382} +{"mode": "val", "epoch": 3, "iter": 533, "lr": 0.02498, "top1_acc": 0.61061, "top5_acc": 0.93405, "mean_class_accuracy": 0.44854} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.02497, "memory": 4082, "data_time": 0.19492, "top1_acc": 0.62125, "top5_acc": 0.94438, "loss_cls": 1.55148, "loss": 1.55148, "time": 0.63832} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.02497, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.61125, "top5_acc": 0.95, "loss_cls": 1.5375, "loss": 1.5375, "time": 0.27082} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.02497, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.62062, "top5_acc": 0.94312, "loss_cls": 1.54547, "loss": 1.54547, "time": 0.38568} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.02497, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.6125, "top5_acc": 0.94812, "loss_cls": 1.55787, "loss": 1.55787, "time": 0.4155} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.02497, "memory": 4082, "data_time": 0.00054, "top1_acc": 0.60688, "top5_acc": 0.95438, "loss_cls": 1.52583, "loss": 1.52583, "time": 0.41494} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.02497, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.63062, "top5_acc": 0.95, "loss_cls": 1.49983, "loss": 1.49983, "time": 0.4161} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.02497, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.64938, "top5_acc": 0.95, "loss_cls": 1.46038, "loss": 1.46038, "time": 0.41613} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.02496, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.63938, "top5_acc": 0.95688, "loss_cls": 1.44052, "loss": 1.44052, "time": 0.41536} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.02496, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.6225, "top5_acc": 0.95812, "loss_cls": 1.51783, "loss": 1.51783, "time": 0.41529} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.02496, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.64812, "top5_acc": 0.95562, "loss_cls": 1.4297, "loss": 1.4297, "time": 0.41632} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.02496, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.65438, "top5_acc": 0.96312, "loss_cls": 1.3951, "loss": 1.3951, "time": 0.41617} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.02496, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.6675, "top5_acc": 0.9675, "loss_cls": 1.38181, "loss": 1.38181, "time": 0.4169} +{"mode": "val", "epoch": 4, "iter": 533, "lr": 0.02496, "top1_acc": 0.6376, "top5_acc": 0.94578, "mean_class_accuracy": 0.50585} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.02495, "memory": 4082, "data_time": 0.1971, "top1_acc": 0.6625, "top5_acc": 0.95562, "loss_cls": 1.36936, "loss": 1.36936, "time": 0.64431} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.02495, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.6675, "top5_acc": 0.96062, "loss_cls": 1.37117, "loss": 1.37117, "time": 0.26752} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.02495, "memory": 4082, "data_time": 0.00049, "top1_acc": 0.69812, "top5_acc": 0.95688, "loss_cls": 1.32856, "loss": 1.32856, "time": 0.40143} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.02495, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.67688, "top5_acc": 0.96625, "loss_cls": 1.34894, "loss": 1.34894, "time": 0.4231} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.02495, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.6925, "top5_acc": 0.96062, "loss_cls": 1.32486, "loss": 1.32486, "time": 0.41491} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.02495, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.67938, "top5_acc": 0.96375, "loss_cls": 1.35133, "loss": 1.35133, "time": 0.41565} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.02494, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.66125, "top5_acc": 0.9675, "loss_cls": 1.35805, "loss": 1.35805, "time": 0.41576} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.02494, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.685, "top5_acc": 0.95812, "loss_cls": 1.33621, "loss": 1.33621, "time": 0.4155} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.02494, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.69062, "top5_acc": 0.96375, "loss_cls": 1.28207, "loss": 1.28207, "time": 0.41521} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.02494, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.66688, "top5_acc": 0.97438, "loss_cls": 1.31447, "loss": 1.31447, "time": 0.41528} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.02494, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.6825, "top5_acc": 0.9625, "loss_cls": 1.30876, "loss": 1.30876, "time": 0.41443} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.02493, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.675, "top5_acc": 0.96938, "loss_cls": 1.28758, "loss": 1.28758, "time": 0.41511} +{"mode": "val", "epoch": 5, "iter": 533, "lr": 0.02493, "top1_acc": 0.67832, "top5_acc": 0.96338, "mean_class_accuracy": 0.54647} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.02493, "memory": 4082, "data_time": 0.1965, "top1_acc": 0.68125, "top5_acc": 0.96625, "loss_cls": 1.29799, "loss": 1.29799, "time": 0.65306} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.02493, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.71312, "top5_acc": 0.97812, "loss_cls": 1.222, "loss": 1.222, "time": 0.25774} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.02492, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.68875, "top5_acc": 0.96688, "loss_cls": 1.2833, "loss": 1.2833, "time": 0.40126} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.02492, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.69938, "top5_acc": 0.975, "loss_cls": 1.24755, "loss": 1.24755, "time": 0.41516} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.02492, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.71625, "top5_acc": 0.97625, "loss_cls": 1.20154, "loss": 1.20154, "time": 0.41568} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.02492, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.69125, "top5_acc": 0.9675, "loss_cls": 1.26016, "loss": 1.26016, "time": 0.41375} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.02492, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.70938, "top5_acc": 0.97688, "loss_cls": 1.21109, "loss": 1.21109, "time": 0.41371} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.02491, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.72312, "top5_acc": 0.96812, "loss_cls": 1.20028, "loss": 1.20028, "time": 0.41341} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.02491, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.7, "top5_acc": 0.97312, "loss_cls": 1.22564, "loss": 1.22564, "time": 0.41604} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.02491, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.70875, "top5_acc": 0.97188, "loss_cls": 1.24104, "loss": 1.24104, "time": 0.41521} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.02491, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.70938, "top5_acc": 0.96812, "loss_cls": 1.24087, "loss": 1.24087, "time": 0.41419} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.0249, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.72812, "top5_acc": 0.98062, "loss_cls": 1.14224, "loss": 1.14224, "time": 0.41494} +{"mode": "val", "epoch": 6, "iter": 533, "lr": 0.0249, "top1_acc": 0.67081, "top5_acc": 0.95928, "mean_class_accuracy": 0.5413} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0249, "memory": 4082, "data_time": 0.19754, "top1_acc": 0.70938, "top5_acc": 0.97438, "loss_cls": 1.18046, "loss": 1.18046, "time": 0.6539} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0249, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.73188, "top5_acc": 0.97875, "loss_cls": 1.1081, "loss": 1.1081, "time": 0.25278} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.02489, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.72625, "top5_acc": 0.97125, "loss_cls": 1.16325, "loss": 1.16325, "time": 0.40261} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.02489, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.70562, "top5_acc": 0.97, "loss_cls": 1.19434, "loss": 1.19434, "time": 0.41677} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.02489, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.74625, "top5_acc": 0.98, "loss_cls": 1.12259, "loss": 1.12259, "time": 0.41602} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.02489, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.74438, "top5_acc": 0.97688, "loss_cls": 1.08436, "loss": 1.08436, "time": 0.41569} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.02488, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.73, "top5_acc": 0.97438, "loss_cls": 1.10063, "loss": 1.10063, "time": 0.41633} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.02488, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7125, "top5_acc": 0.975, "loss_cls": 1.1873, "loss": 1.1873, "time": 0.41628} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.02488, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.74688, "top5_acc": 0.97938, "loss_cls": 1.11319, "loss": 1.11319, "time": 0.41555} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.02487, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.72812, "top5_acc": 0.97375, "loss_cls": 1.1437, "loss": 1.1437, "time": 0.417} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.02487, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.7325, "top5_acc": 0.97938, "loss_cls": 1.08961, "loss": 1.08961, "time": 0.41496} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.02487, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.71125, "top5_acc": 0.96688, "loss_cls": 1.21726, "loss": 1.21726, "time": 0.4167} +{"mode": "val", "epoch": 7, "iter": 533, "lr": 0.02487, "top1_acc": 0.71318, "top5_acc": 0.97336, "mean_class_accuracy": 0.60242} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.02486, "memory": 4082, "data_time": 0.19316, "top1_acc": 0.75625, "top5_acc": 0.985, "loss_cls": 1.03428, "loss": 1.03428, "time": 0.64834} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.02486, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.72812, "top5_acc": 0.97, "loss_cls": 1.18375, "loss": 1.18375, "time": 0.25689} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.02486, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.74812, "top5_acc": 0.98062, "loss_cls": 1.06926, "loss": 1.06926, "time": 0.40901} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.02485, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.745, "top5_acc": 0.98, "loss_cls": 1.06971, "loss": 1.06971, "time": 0.41558} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.02485, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.75625, "top5_acc": 0.98062, "loss_cls": 1.05262, "loss": 1.05262, "time": 0.41626} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.02485, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.75062, "top5_acc": 0.98375, "loss_cls": 1.05356, "loss": 1.05356, "time": 0.41639} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.02484, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.74188, "top5_acc": 0.98375, "loss_cls": 1.06294, "loss": 1.06294, "time": 0.41528} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.02484, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.75688, "top5_acc": 0.97875, "loss_cls": 1.04289, "loss": 1.04289, "time": 0.41667} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.02484, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.73625, "top5_acc": 0.9775, "loss_cls": 1.05465, "loss": 1.05465, "time": 0.4175} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.02483, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.74875, "top5_acc": 0.98188, "loss_cls": 1.08306, "loss": 1.08306, "time": 0.41571} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.02483, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.73438, "top5_acc": 0.97562, "loss_cls": 1.12457, "loss": 1.12457, "time": 0.41628} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.02483, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.73812, "top5_acc": 0.97125, "loss_cls": 1.10261, "loss": 1.10261, "time": 0.417} +{"mode": "val", "epoch": 8, "iter": 533, "lr": 0.02482, "top1_acc": 0.70614, "top5_acc": 0.96151, "mean_class_accuracy": 0.62394} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.02482, "memory": 4082, "data_time": 0.18743, "top1_acc": 0.76125, "top5_acc": 0.98688, "loss_cls": 1.03835, "loss": 1.03835, "time": 0.64167} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.02482, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.74625, "top5_acc": 0.9825, "loss_cls": 1.0454, "loss": 1.0454, "time": 0.26088} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.02481, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.7475, "top5_acc": 0.98375, "loss_cls": 1.05151, "loss": 1.05151, "time": 0.404} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.02481, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.77375, "top5_acc": 0.97812, "loss_cls": 1.03573, "loss": 1.03573, "time": 0.41366} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.02481, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.745, "top5_acc": 0.97875, "loss_cls": 1.06449, "loss": 1.06449, "time": 0.41664} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.0248, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.75312, "top5_acc": 0.98125, "loss_cls": 1.03081, "loss": 1.03081, "time": 0.41604} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.0248, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.7625, "top5_acc": 0.985, "loss_cls": 1.00339, "loss": 1.00339, "time": 0.41425} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.0248, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.75438, "top5_acc": 0.97875, "loss_cls": 1.03046, "loss": 1.03046, "time": 0.4152} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.02479, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7375, "top5_acc": 0.98125, "loss_cls": 1.06255, "loss": 1.06255, "time": 0.41449} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.02479, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.74688, "top5_acc": 0.97312, "loss_cls": 1.07865, "loss": 1.07865, "time": 0.41515} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.02479, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.75062, "top5_acc": 0.97875, "loss_cls": 1.04647, "loss": 1.04647, "time": 0.41441} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.02478, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.75938, "top5_acc": 0.9875, "loss_cls": 1.00502, "loss": 1.00502, "time": 0.41539} +{"mode": "val", "epoch": 9, "iter": 533, "lr": 0.02478, "top1_acc": 0.71271, "top5_acc": 0.96902, "mean_class_accuracy": 0.6024} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.02477, "memory": 4082, "data_time": 0.19194, "top1_acc": 0.75375, "top5_acc": 0.98375, "loss_cls": 1.02112, "loss": 1.02112, "time": 0.65115} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.02477, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.775, "top5_acc": 0.98438, "loss_cls": 0.98918, "loss": 0.98918, "time": 0.24373} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.02477, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.76688, "top5_acc": 0.985, "loss_cls": 0.99544, "loss": 0.99544, "time": 0.43231} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.02476, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.77312, "top5_acc": 0.98562, "loss_cls": 0.97089, "loss": 0.97089, "time": 0.41601} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.02476, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.755, "top5_acc": 0.985, "loss_cls": 0.99699, "loss": 0.99699, "time": 0.4171} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.02476, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.76938, "top5_acc": 0.98875, "loss_cls": 0.95982, "loss": 0.95982, "time": 0.41603} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.02475, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.75938, "top5_acc": 0.98, "loss_cls": 1.00943, "loss": 1.00943, "time": 0.415} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.02475, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.76188, "top5_acc": 0.98812, "loss_cls": 1.02197, "loss": 1.02197, "time": 0.41544} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.02474, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.77125, "top5_acc": 0.97812, "loss_cls": 1.00481, "loss": 1.00481, "time": 0.41667} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.02474, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.76125, "top5_acc": 0.98188, "loss_cls": 1.01092, "loss": 1.01092, "time": 0.41475} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.02473, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.75562, "top5_acc": 0.97625, "loss_cls": 1.03681, "loss": 1.03681, "time": 0.41465} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.02473, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.74812, "top5_acc": 0.97812, "loss_cls": 1.04409, "loss": 1.04409, "time": 0.41554} +{"mode": "val", "epoch": 10, "iter": 533, "lr": 0.02473, "top1_acc": 0.73524, "top5_acc": 0.97547, "mean_class_accuracy": 0.62711} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.02472, "memory": 4082, "data_time": 0.19548, "top1_acc": 0.75688, "top5_acc": 0.98375, "loss_cls": 0.98708, "loss": 0.98708, "time": 0.65353} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.02472, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.76875, "top5_acc": 0.98062, "loss_cls": 0.99489, "loss": 0.99489, "time": 0.2406} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.02471, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.76125, "top5_acc": 0.98062, "loss_cls": 0.9992, "loss": 0.9992, "time": 0.41456} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.02471, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.76312, "top5_acc": 0.9825, "loss_cls": 0.98539, "loss": 0.98539, "time": 0.41527} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.02471, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.775, "top5_acc": 0.98875, "loss_cls": 0.94924, "loss": 0.94924, "time": 0.415} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.0247, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.75375, "top5_acc": 0.97938, "loss_cls": 1.0367, "loss": 1.0367, "time": 0.41603} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.0247, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.77875, "top5_acc": 0.98625, "loss_cls": 0.9528, "loss": 0.9528, "time": 0.41536} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.02469, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.76875, "top5_acc": 0.985, "loss_cls": 0.96303, "loss": 0.96303, "time": 0.41508} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.02469, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.75188, "top5_acc": 0.98625, "loss_cls": 0.98911, "loss": 0.98911, "time": 0.42919} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.02468, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.7925, "top5_acc": 0.985, "loss_cls": 0.91322, "loss": 0.91322, "time": 0.44072} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.02468, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.76688, "top5_acc": 0.98562, "loss_cls": 0.97043, "loss": 0.97043, "time": 0.42778} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.02467, "memory": 4082, "data_time": 0.00056, "top1_acc": 0.77, "top5_acc": 0.9875, "loss_cls": 0.93463, "loss": 0.93463, "time": 0.41522} +{"mode": "val", "epoch": 11, "iter": 533, "lr": 0.02467, "top1_acc": 0.73994, "top5_acc": 0.97371, "mean_class_accuracy": 0.66143} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.02467, "memory": 4082, "data_time": 0.19531, "top1_acc": 0.78188, "top5_acc": 0.98562, "loss_cls": 0.92558, "loss": 0.92558, "time": 0.65299} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.02466, "memory": 4082, "data_time": 0.00056, "top1_acc": 0.78812, "top5_acc": 0.98875, "loss_cls": 0.88733, "loss": 0.88733, "time": 0.23283} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.02466, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.77625, "top5_acc": 0.98438, "loss_cls": 0.93851, "loss": 0.93851, "time": 0.41852} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.02465, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.7775, "top5_acc": 0.97875, "loss_cls": 0.97804, "loss": 0.97804, "time": 0.41507} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.02465, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.78, "top5_acc": 0.9875, "loss_cls": 0.94996, "loss": 0.94996, "time": 0.41666} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.02464, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.78812, "top5_acc": 0.98688, "loss_cls": 0.90332, "loss": 0.90332, "time": 0.41567} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.02464, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.76562, "top5_acc": 0.99, "loss_cls": 0.95539, "loss": 0.95539, "time": 0.41586} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.02463, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79125, "top5_acc": 0.98688, "loss_cls": 0.88775, "loss": 0.88775, "time": 0.41409} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.02463, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.78812, "top5_acc": 0.98438, "loss_cls": 0.89177, "loss": 0.89177, "time": 0.41501} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.02462, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.77562, "top5_acc": 0.98562, "loss_cls": 0.96661, "loss": 0.96661, "time": 0.41577} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.02462, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77812, "top5_acc": 0.985, "loss_cls": 0.952, "loss": 0.952, "time": 0.41579} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.02461, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.78062, "top5_acc": 0.97938, "loss_cls": 0.97589, "loss": 0.97589, "time": 0.41658} +{"mode": "val", "epoch": 12, "iter": 533, "lr": 0.02461, "top1_acc": 0.74522, "top5_acc": 0.97629, "mean_class_accuracy": 0.64634} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.0246, "memory": 4082, "data_time": 0.19048, "top1_acc": 0.8025, "top5_acc": 0.9875, "loss_cls": 0.86082, "loss": 0.86082, "time": 0.64858} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.0246, "memory": 4082, "data_time": 0.0006, "top1_acc": 0.79125, "top5_acc": 0.9875, "loss_cls": 0.9208, "loss": 0.9208, "time": 0.23395} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.02459, "memory": 4082, "data_time": 0.00051, "top1_acc": 0.78438, "top5_acc": 0.98875, "loss_cls": 0.89974, "loss": 0.89974, "time": 0.41599} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.02459, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78125, "top5_acc": 0.98625, "loss_cls": 0.94111, "loss": 0.94111, "time": 0.41782} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.02458, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.78875, "top5_acc": 0.98562, "loss_cls": 0.9166, "loss": 0.9166, "time": 0.4403} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.02458, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.77875, "top5_acc": 0.98438, "loss_cls": 0.92061, "loss": 0.92061, "time": 0.43765} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.02457, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.77125, "top5_acc": 0.98062, "loss_cls": 0.93887, "loss": 0.93887, "time": 0.43499} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.02457, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.79375, "top5_acc": 0.98938, "loss_cls": 0.88905, "loss": 0.88905, "time": 0.42499} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.02456, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.78438, "top5_acc": 0.97688, "loss_cls": 0.94228, "loss": 0.94228, "time": 0.41423} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.02455, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.7825, "top5_acc": 0.98812, "loss_cls": 0.92471, "loss": 0.92471, "time": 0.41502} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.02455, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.79938, "top5_acc": 0.98875, "loss_cls": 0.87463, "loss": 0.87463, "time": 0.41491} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.02454, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80625, "top5_acc": 0.98688, "loss_cls": 0.83343, "loss": 0.83343, "time": 0.41514} +{"mode": "val", "epoch": 13, "iter": 533, "lr": 0.02454, "top1_acc": 0.73853, "top5_acc": 0.97442, "mean_class_accuracy": 0.64229} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.02453, "memory": 4082, "data_time": 0.19981, "top1_acc": 0.79188, "top5_acc": 0.98875, "loss_cls": 0.87435, "loss": 0.87435, "time": 0.65954} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.02453, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.78625, "top5_acc": 0.9925, "loss_cls": 0.89711, "loss": 0.89711, "time": 0.24032} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.02452, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.78688, "top5_acc": 0.98438, "loss_cls": 0.92373, "loss": 0.92373, "time": 0.38805} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.02452, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.78688, "top5_acc": 0.98562, "loss_cls": 0.89058, "loss": 0.89058, "time": 0.39399} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.02451, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.78688, "top5_acc": 0.98688, "loss_cls": 0.90638, "loss": 0.90638, "time": 0.39398} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.02451, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.78938, "top5_acc": 0.98625, "loss_cls": 0.90226, "loss": 0.90226, "time": 0.38922} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.0245, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8075, "top5_acc": 0.99062, "loss_cls": 0.83707, "loss": 0.83707, "time": 0.38623} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.02449, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.79938, "top5_acc": 0.98625, "loss_cls": 0.85813, "loss": 0.85813, "time": 0.38639} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.02449, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.7925, "top5_acc": 0.98375, "loss_cls": 0.88725, "loss": 0.88725, "time": 0.38262} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.02448, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.79562, "top5_acc": 0.98562, "loss_cls": 0.87421, "loss": 0.87421, "time": 0.38521} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.02448, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.78438, "top5_acc": 0.9825, "loss_cls": 0.89929, "loss": 0.89929, "time": 0.38899} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.02447, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79938, "top5_acc": 0.9825, "loss_cls": 0.90912, "loss": 0.90912, "time": 0.38965} +{"mode": "val", "epoch": 14, "iter": 533, "lr": 0.02447, "top1_acc": 0.71999, "top5_acc": 0.9716, "mean_class_accuracy": 0.63109} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.02446, "memory": 4082, "data_time": 0.19515, "top1_acc": 0.81188, "top5_acc": 0.98812, "loss_cls": 0.82368, "loss": 0.82368, "time": 0.59325} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.02445, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.80188, "top5_acc": 0.9925, "loss_cls": 0.82235, "loss": 0.82235, "time": 0.38928} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.02445, "memory": 4082, "data_time": 0.00061, "top1_acc": 0.80312, "top5_acc": 0.99062, "loss_cls": 0.84028, "loss": 0.84028, "time": 0.37527} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.02444, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.8, "top5_acc": 0.98938, "loss_cls": 0.87066, "loss": 0.87066, "time": 0.27568} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.02444, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80062, "top5_acc": 0.99188, "loss_cls": 0.85315, "loss": 0.85315, "time": 0.44009} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.02443, "memory": 4082, "data_time": 0.00059, "top1_acc": 0.79688, "top5_acc": 0.98938, "loss_cls": 0.87546, "loss": 0.87546, "time": 0.22975} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.02442, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79375, "top5_acc": 0.98875, "loss_cls": 0.84925, "loss": 0.84925, "time": 0.28144} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.02442, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.81125, "top5_acc": 0.98938, "loss_cls": 0.83458, "loss": 0.83458, "time": 0.38164} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.02441, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8, "top5_acc": 0.98812, "loss_cls": 0.85528, "loss": 0.85528, "time": 0.38219} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.02441, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.80938, "top5_acc": 0.99, "loss_cls": 0.82005, "loss": 0.82005, "time": 0.37352} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.0244, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7975, "top5_acc": 0.985, "loss_cls": 0.86988, "loss": 0.86988, "time": 0.37986} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.02439, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.79875, "top5_acc": 0.98312, "loss_cls": 0.89101, "loss": 0.89101, "time": 0.38248} +{"mode": "val", "epoch": 15, "iter": 533, "lr": 0.02439, "top1_acc": 0.76752, "top5_acc": 0.97805, "mean_class_accuracy": 0.68478} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.02438, "memory": 4082, "data_time": 0.19372, "top1_acc": 0.8275, "top5_acc": 0.99062, "loss_cls": 0.73909, "loss": 0.73909, "time": 0.56906} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.02438, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.82438, "top5_acc": 0.9925, "loss_cls": 0.77316, "loss": 0.77316, "time": 0.37815} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.02437, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.79312, "top5_acc": 0.98688, "loss_cls": 0.88095, "loss": 0.88095, "time": 0.38561} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.02436, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8175, "top5_acc": 0.98812, "loss_cls": 0.82455, "loss": 0.82455, "time": 0.37556} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.02436, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.80438, "top5_acc": 0.99, "loss_cls": 0.82979, "loss": 0.82979, "time": 0.37978} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.02435, "memory": 4082, "data_time": 0.00055, "top1_acc": 0.78062, "top5_acc": 0.98562, "loss_cls": 0.90962, "loss": 0.90962, "time": 0.38139} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.02434, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81875, "top5_acc": 0.98875, "loss_cls": 0.80267, "loss": 0.80267, "time": 0.37145} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.02434, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82125, "top5_acc": 0.99062, "loss_cls": 0.7847, "loss": 0.7847, "time": 0.37807} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.02433, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.81312, "top5_acc": 0.9875, "loss_cls": 0.80666, "loss": 0.80666, "time": 0.35089} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.02432, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.83188, "top5_acc": 0.99125, "loss_cls": 0.7543, "loss": 0.7543, "time": 0.28403} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.02432, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.79938, "top5_acc": 0.9875, "loss_cls": 0.84501, "loss": 0.84501, "time": 0.42879} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.02431, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.79938, "top5_acc": 0.98812, "loss_cls": 0.85749, "loss": 0.85749, "time": 0.22537} +{"mode": "val", "epoch": 16, "iter": 533, "lr": 0.0243, "top1_acc": 0.75977, "top5_acc": 0.98216, "mean_class_accuracy": 0.67046} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.0243, "memory": 4082, "data_time": 0.19295, "top1_acc": 0.81688, "top5_acc": 0.99125, "loss_cls": 0.83762, "loss": 0.83762, "time": 0.57507} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.02429, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.81375, "top5_acc": 0.99312, "loss_cls": 0.81123, "loss": 0.81123, "time": 0.38236} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.02428, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.82, "top5_acc": 0.98812, "loss_cls": 0.80544, "loss": 0.80544, "time": 0.37683} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.02428, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.79938, "top5_acc": 0.99125, "loss_cls": 0.87652, "loss": 0.87652, "time": 0.37823} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.02427, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.8125, "top5_acc": 0.98812, "loss_cls": 0.8251, "loss": 0.8251, "time": 0.37755} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.02426, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.80188, "top5_acc": 0.98625, "loss_cls": 0.84428, "loss": 0.84428, "time": 0.37447} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.02426, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.81125, "top5_acc": 0.99188, "loss_cls": 0.81494, "loss": 0.81494, "time": 0.37569} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.02425, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.8325, "top5_acc": 0.9925, "loss_cls": 0.76485, "loss": 0.76485, "time": 0.37316} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.02424, "memory": 4082, "data_time": 0.00065, "top1_acc": 0.82, "top5_acc": 0.98875, "loss_cls": 0.7806, "loss": 0.7806, "time": 0.37877} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.02424, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.81188, "top5_acc": 0.99312, "loss_cls": 0.80068, "loss": 0.80068, "time": 0.37402} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.02423, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.815, "top5_acc": 0.98688, "loss_cls": 0.80464, "loss": 0.80464, "time": 0.37899} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.02422, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81562, "top5_acc": 0.9875, "loss_cls": 0.8157, "loss": 0.8157, "time": 0.37176} +{"mode": "val", "epoch": 17, "iter": 533, "lr": 0.02422, "top1_acc": 0.78911, "top5_acc": 0.98463, "mean_class_accuracy": 0.69593} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.02421, "memory": 4082, "data_time": 0.19599, "top1_acc": 0.84438, "top5_acc": 0.99125, "loss_cls": 0.70635, "loss": 0.70635, "time": 0.65074} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.0242, "memory": 4082, "data_time": 0.00054, "top1_acc": 0.83188, "top5_acc": 0.99062, "loss_cls": 0.75746, "loss": 0.75746, "time": 0.2573} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.02419, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81562, "top5_acc": 0.985, "loss_cls": 0.80584, "loss": 0.80584, "time": 0.28702} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.02419, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8125, "top5_acc": 0.99, "loss_cls": 0.82093, "loss": 0.82093, "time": 0.37464} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.02418, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.80625, "top5_acc": 0.98812, "loss_cls": 0.82891, "loss": 0.82891, "time": 0.37607} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.02417, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.81438, "top5_acc": 0.99188, "loss_cls": 0.78462, "loss": 0.78462, "time": 0.37687} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.02417, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8275, "top5_acc": 0.98562, "loss_cls": 0.79048, "loss": 0.79048, "time": 0.37446} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.02416, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80188, "top5_acc": 0.9875, "loss_cls": 0.80951, "loss": 0.80951, "time": 0.3777} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.02415, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8275, "top5_acc": 0.99062, "loss_cls": 0.74795, "loss": 0.74795, "time": 0.37357} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.02414, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.82188, "top5_acc": 0.98875, "loss_cls": 0.78539, "loss": 0.78539, "time": 0.3712} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.02414, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.79875, "top5_acc": 0.98562, "loss_cls": 0.85314, "loss": 0.85314, "time": 0.37342} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.02413, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.815, "top5_acc": 0.99125, "loss_cls": 0.78799, "loss": 0.78799, "time": 0.37283} +{"mode": "val", "epoch": 18, "iter": 533, "lr": 0.02412, "top1_acc": 0.80296, "top5_acc": 0.98439, "mean_class_accuracy": 0.71931} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.02411, "memory": 4082, "data_time": 0.19187, "top1_acc": 0.82312, "top5_acc": 0.99062, "loss_cls": 0.76174, "loss": 0.76174, "time": 0.56865} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.02411, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82125, "top5_acc": 0.99062, "loss_cls": 0.79791, "loss": 0.79791, "time": 0.3736} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.0241, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.82188, "top5_acc": 0.99125, "loss_cls": 0.7485, "loss": 0.7485, "time": 0.38417} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.02409, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82438, "top5_acc": 0.98938, "loss_cls": 0.76995, "loss": 0.76995, "time": 0.3745} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.02408, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.85, "top5_acc": 0.98812, "loss_cls": 0.6792, "loss": 0.6792, "time": 0.3763} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.02408, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.83688, "top5_acc": 0.995, "loss_cls": 0.73054, "loss": 0.73054, "time": 0.24338} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.02407, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8225, "top5_acc": 0.99, "loss_cls": 0.78306, "loss": 0.78306, "time": 0.45175} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.02406, "memory": 4082, "data_time": 0.00065, "top1_acc": 0.8425, "top5_acc": 0.99438, "loss_cls": 0.71073, "loss": 0.71073, "time": 0.26013} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.02405, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.83312, "top5_acc": 0.98812, "loss_cls": 0.7764, "loss": 0.7764, "time": 0.29819} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.02405, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82375, "top5_acc": 0.98812, "loss_cls": 0.81228, "loss": 0.81228, "time": 0.37291} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.02404, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.82812, "top5_acc": 0.99125, "loss_cls": 0.75982, "loss": 0.75982, "time": 0.38009} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.02403, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.8225, "top5_acc": 0.98812, "loss_cls": 0.80284, "loss": 0.80284, "time": 0.37263} +{"mode": "val", "epoch": 19, "iter": 533, "lr": 0.02402, "top1_acc": 0.75707, "top5_acc": 0.97688, "mean_class_accuracy": 0.70058} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.02402, "memory": 4082, "data_time": 0.20145, "top1_acc": 0.83812, "top5_acc": 0.99375, "loss_cls": 0.71911, "loss": 0.71911, "time": 0.58237} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.02401, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.84312, "top5_acc": 0.99062, "loss_cls": 0.7106, "loss": 0.7106, "time": 0.38505} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.024, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.84375, "top5_acc": 0.9925, "loss_cls": 0.69003, "loss": 0.69003, "time": 0.38158} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.02399, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.8575, "top5_acc": 0.9925, "loss_cls": 0.69122, "loss": 0.69122, "time": 0.37513} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.02398, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.8525, "top5_acc": 0.99312, "loss_cls": 0.69059, "loss": 0.69059, "time": 0.37359} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.02398, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.82812, "top5_acc": 0.98938, "loss_cls": 0.74433, "loss": 0.74433, "time": 0.37806} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.02397, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.825, "top5_acc": 0.98688, "loss_cls": 0.81074, "loss": 0.81074, "time": 0.37718} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.02396, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.83562, "top5_acc": 0.98875, "loss_cls": 0.76488, "loss": 0.76488, "time": 0.37468} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.02395, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83562, "top5_acc": 0.99062, "loss_cls": 0.73915, "loss": 0.73915, "time": 0.37677} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.02394, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.82688, "top5_acc": 0.98688, "loss_cls": 0.78639, "loss": 0.78639, "time": 0.37286} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.02393, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.8225, "top5_acc": 0.9875, "loss_cls": 0.79122, "loss": 0.79122, "time": 0.33659} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.02393, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.82375, "top5_acc": 0.99, "loss_cls": 0.77493, "loss": 0.77493, "time": 0.31452} +{"mode": "val", "epoch": 20, "iter": 533, "lr": 0.02392, "top1_acc": 0.80284, "top5_acc": 0.98275, "mean_class_accuracy": 0.73586} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.02391, "memory": 4082, "data_time": 0.20128, "top1_acc": 0.82438, "top5_acc": 0.99125, "loss_cls": 0.76421, "loss": 0.76421, "time": 0.57617} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.0239, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.84375, "top5_acc": 0.99125, "loss_cls": 0.72803, "loss": 0.72803, "time": 0.38512} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.02389, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.82562, "top5_acc": 0.9925, "loss_cls": 0.77719, "loss": 0.77719, "time": 0.37395} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.02389, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84688, "top5_acc": 0.98875, "loss_cls": 0.70848, "loss": 0.70848, "time": 0.37493} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.02388, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.835, "top5_acc": 0.99188, "loss_cls": 0.76795, "loss": 0.76795, "time": 0.37214} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.02387, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85438, "top5_acc": 0.99438, "loss_cls": 0.67976, "loss": 0.67976, "time": 0.37829} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.02386, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.83125, "top5_acc": 0.99062, "loss_cls": 0.76359, "loss": 0.76359, "time": 0.37596} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.02385, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85438, "top5_acc": 0.99438, "loss_cls": 0.68503, "loss": 0.68503, "time": 0.37141} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.02384, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83125, "top5_acc": 0.99312, "loss_cls": 0.72215, "loss": 0.72215, "time": 0.37478} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.02383, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.84188, "top5_acc": 0.99375, "loss_cls": 0.70996, "loss": 0.70996, "time": 0.37521} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.02383, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.83312, "top5_acc": 0.98938, "loss_cls": 0.76727, "loss": 0.76727, "time": 0.37496} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.02382, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83188, "top5_acc": 0.99, "loss_cls": 0.76917, "loss": 0.76917, "time": 0.37805} +{"mode": "val", "epoch": 21, "iter": 533, "lr": 0.02381, "top1_acc": 0.81422, "top5_acc": 0.98791, "mean_class_accuracy": 0.72321} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.0238, "memory": 4082, "data_time": 0.20216, "top1_acc": 0.84188, "top5_acc": 0.995, "loss_cls": 0.67377, "loss": 0.67377, "time": 0.57752} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.02379, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.84812, "top5_acc": 0.99312, "loss_cls": 0.67279, "loss": 0.67279, "time": 0.26292} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.02378, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.86188, "top5_acc": 0.995, "loss_cls": 0.67029, "loss": 0.67029, "time": 0.45236} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.02378, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.84188, "top5_acc": 0.98938, "loss_cls": 0.72071, "loss": 0.72071, "time": 0.22472} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.02377, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.84438, "top5_acc": 0.99125, "loss_cls": 0.70859, "loss": 0.70859, "time": 0.3171} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.02376, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.845, "top5_acc": 0.99125, "loss_cls": 0.69902, "loss": 0.69902, "time": 0.38253} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.02375, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.8675, "top5_acc": 0.99125, "loss_cls": 0.64132, "loss": 0.64132, "time": 0.37142} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.02374, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.855, "top5_acc": 0.98938, "loss_cls": 0.70623, "loss": 0.70623, "time": 0.3812} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.02373, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.82688, "top5_acc": 0.99, "loss_cls": 0.76805, "loss": 0.76805, "time": 0.3706} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.02372, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83062, "top5_acc": 0.985, "loss_cls": 0.79001, "loss": 0.79001, "time": 0.37069} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.02371, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.8475, "top5_acc": 0.9875, "loss_cls": 0.73875, "loss": 0.73875, "time": 0.3729} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0237, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.84062, "top5_acc": 0.99438, "loss_cls": 0.7311, "loss": 0.7311, "time": 0.37743} +{"mode": "val", "epoch": 22, "iter": 533, "lr": 0.0237, "top1_acc": 0.77773, "top5_acc": 0.98111, "mean_class_accuracy": 0.71748} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.02369, "memory": 4082, "data_time": 0.19814, "top1_acc": 0.8525, "top5_acc": 0.99125, "loss_cls": 0.66037, "loss": 0.66037, "time": 0.58105} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.02368, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.83375, "top5_acc": 0.9925, "loss_cls": 0.71404, "loss": 0.71404, "time": 0.37373} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.02367, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.86562, "top5_acc": 0.99125, "loss_cls": 0.63845, "loss": 0.63845, "time": 0.37579} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.02366, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85438, "top5_acc": 0.99438, "loss_cls": 0.66125, "loss": 0.66125, "time": 0.37721} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.02365, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.82812, "top5_acc": 0.99, "loss_cls": 0.76047, "loss": 0.76047, "time": 0.37416} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.02364, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83125, "top5_acc": 0.99125, "loss_cls": 0.75261, "loss": 0.75261, "time": 0.37144} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.02363, "memory": 4082, "data_time": 0.0006, "top1_acc": 0.86062, "top5_acc": 0.995, "loss_cls": 0.6904, "loss": 0.6904, "time": 0.34232} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.02362, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.84562, "top5_acc": 0.99688, "loss_cls": 0.68289, "loss": 0.68289, "time": 0.30506} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.02361, "memory": 4082, "data_time": 0.00051, "top1_acc": 0.83625, "top5_acc": 0.995, "loss_cls": 0.71215, "loss": 0.71215, "time": 0.40715} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.0236, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84375, "top5_acc": 0.9925, "loss_cls": 0.68959, "loss": 0.68959, "time": 0.23005} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.02359, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.835, "top5_acc": 0.99125, "loss_cls": 0.76243, "loss": 0.76243, "time": 0.33394} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.02359, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.8425, "top5_acc": 0.99, "loss_cls": 0.69943, "loss": 0.69943, "time": 0.37228} +{"mode": "val", "epoch": 23, "iter": 533, "lr": 0.02358, "top1_acc": 0.81727, "top5_acc": 0.98533, "mean_class_accuracy": 0.74423} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.02357, "memory": 4082, "data_time": 0.1981, "top1_acc": 0.83312, "top5_acc": 0.99312, "loss_cls": 0.72058, "loss": 0.72058, "time": 0.5815} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.02356, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.85125, "top5_acc": 0.99125, "loss_cls": 0.71822, "loss": 0.71822, "time": 0.37773} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.02355, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.8575, "top5_acc": 0.99188, "loss_cls": 0.65391, "loss": 0.65391, "time": 0.38122} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.02354, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.85875, "top5_acc": 0.98938, "loss_cls": 0.6878, "loss": 0.6878, "time": 0.37737} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.02353, "memory": 4082, "data_time": 0.00058, "top1_acc": 0.84438, "top5_acc": 0.99438, "loss_cls": 0.69224, "loss": 0.69224, "time": 0.38429} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.02352, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.855, "top5_acc": 0.99125, "loss_cls": 0.66057, "loss": 0.66057, "time": 0.37898} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.02351, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.84812, "top5_acc": 0.99375, "loss_cls": 0.67298, "loss": 0.67298, "time": 0.37695} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.0235, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84125, "top5_acc": 0.995, "loss_cls": 0.68716, "loss": 0.68716, "time": 0.37175} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.02349, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.84625, "top5_acc": 0.99, "loss_cls": 0.71468, "loss": 0.71468, "time": 0.37353} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.02348, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84875, "top5_acc": 0.98812, "loss_cls": 0.70741, "loss": 0.70741, "time": 0.37531} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.02347, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.83562, "top5_acc": 0.99188, "loss_cls": 0.71738, "loss": 0.71738, "time": 0.37978} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.02346, "memory": 4082, "data_time": 0.00067, "top1_acc": 0.86188, "top5_acc": 0.99062, "loss_cls": 0.68894, "loss": 0.68894, "time": 0.37737} +{"mode": "val", "epoch": 24, "iter": 533, "lr": 0.02345, "top1_acc": 0.79674, "top5_acc": 0.98369, "mean_class_accuracy": 0.70859} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.02344, "memory": 4082, "data_time": 0.19765, "top1_acc": 0.86562, "top5_acc": 0.99, "loss_cls": 0.6716, "loss": 0.6716, "time": 0.57163} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.02343, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.84938, "top5_acc": 0.99188, "loss_cls": 0.69013, "loss": 0.69013, "time": 0.37634} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.02342, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86188, "top5_acc": 0.99438, "loss_cls": 0.65818, "loss": 0.65818, "time": 0.37422} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.02341, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83062, "top5_acc": 0.99188, "loss_cls": 0.7256, "loss": 0.7256, "time": 0.3764} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.0234, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.85688, "top5_acc": 0.99625, "loss_cls": 0.66062, "loss": 0.66062, "time": 0.36999} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.02339, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.845, "top5_acc": 0.98938, "loss_cls": 0.69635, "loss": 0.69635, "time": 0.38039} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.02338, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.875, "top5_acc": 0.99438, "loss_cls": 0.60651, "loss": 0.60651, "time": 0.37391} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.02337, "memory": 4082, "data_time": 0.00057, "top1_acc": 0.83875, "top5_acc": 0.99375, "loss_cls": 0.7336, "loss": 0.7336, "time": 0.37822} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.02336, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86938, "top5_acc": 0.99188, "loss_cls": 0.6514, "loss": 0.6514, "time": 0.37192} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.02335, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84562, "top5_acc": 0.98562, "loss_cls": 0.69967, "loss": 0.69967, "time": 0.37345} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.02334, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.85625, "top5_acc": 0.99312, "loss_cls": 0.65754, "loss": 0.65754, "time": 0.37342} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.02333, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.84312, "top5_acc": 0.99062, "loss_cls": 0.70201, "loss": 0.70201, "time": 0.37022} +{"mode": "val", "epoch": 25, "iter": 533, "lr": 0.02333, "top1_acc": 0.81634, "top5_acc": 0.98768, "mean_class_accuracy": 0.74882} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.02332, "memory": 4082, "data_time": 0.1899, "top1_acc": 0.84938, "top5_acc": 0.99375, "loss_cls": 0.6498, "loss": 0.6498, "time": 0.56979} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.0233, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.835, "top5_acc": 0.99312, "loss_cls": 0.71459, "loss": 0.71459, "time": 0.38026} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.02329, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86, "top5_acc": 0.99375, "loss_cls": 0.64267, "loss": 0.64267, "time": 0.25384} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.02328, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.86375, "top5_acc": 0.99562, "loss_cls": 0.63256, "loss": 0.63256, "time": 0.45431} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.02327, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.855, "top5_acc": 0.99125, "loss_cls": 0.67209, "loss": 0.67209, "time": 0.23876} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.02326, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.87562, "top5_acc": 0.99438, "loss_cls": 0.59598, "loss": 0.59598, "time": 0.30685} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.02325, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.85125, "top5_acc": 0.99438, "loss_cls": 0.67396, "loss": 0.67396, "time": 0.37549} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.02324, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.85625, "top5_acc": 0.99062, "loss_cls": 0.66027, "loss": 0.66027, "time": 0.37819} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.02323, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.86125, "top5_acc": 0.995, "loss_cls": 0.66157, "loss": 0.66157, "time": 0.38467} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.02322, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.85, "top5_acc": 0.99312, "loss_cls": 0.63747, "loss": 0.63747, "time": 0.37476} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.02321, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.85812, "top5_acc": 0.99188, "loss_cls": 0.65937, "loss": 0.65937, "time": 0.3734} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.0232, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85812, "top5_acc": 0.9925, "loss_cls": 0.65866, "loss": 0.65866, "time": 0.38187} +{"mode": "val", "epoch": 26, "iter": 533, "lr": 0.02319, "top1_acc": 0.84004, "top5_acc": 0.98909, "mean_class_accuracy": 0.77439} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.02318, "memory": 4082, "data_time": 0.1954, "top1_acc": 0.88125, "top5_acc": 0.9925, "loss_cls": 0.60333, "loss": 0.60333, "time": 0.57438} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.02317, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.83625, "top5_acc": 0.99375, "loss_cls": 0.72271, "loss": 0.72271, "time": 0.3781} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.02316, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86125, "top5_acc": 0.99438, "loss_cls": 0.63995, "loss": 0.63995, "time": 0.37451} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.02315, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.86875, "top5_acc": 0.99438, "loss_cls": 0.60408, "loss": 0.60408, "time": 0.37327} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.02314, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85562, "top5_acc": 0.995, "loss_cls": 0.64569, "loss": 0.64569, "time": 0.36758} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.02313, "memory": 4082, "data_time": 0.00051, "top1_acc": 0.87062, "top5_acc": 0.99562, "loss_cls": 0.61526, "loss": 0.61526, "time": 0.37401} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.02312, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.855, "top5_acc": 0.99312, "loss_cls": 0.64875, "loss": 0.64875, "time": 0.37492} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.02311, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.84875, "top5_acc": 0.99188, "loss_cls": 0.6604, "loss": 0.6604, "time": 0.36027} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.0231, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.855, "top5_acc": 0.9925, "loss_cls": 0.66462, "loss": 0.66462, "time": 0.28214} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.02308, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.85312, "top5_acc": 0.99125, "loss_cls": 0.66042, "loss": 0.66042, "time": 0.43131} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.02307, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83375, "top5_acc": 0.99062, "loss_cls": 0.76218, "loss": 0.76218, "time": 0.23099} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.02306, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.865, "top5_acc": 0.99188, "loss_cls": 0.60286, "loss": 0.60286, "time": 0.34861} +{"mode": "val", "epoch": 27, "iter": 533, "lr": 0.02305, "top1_acc": 0.80871, "top5_acc": 0.97852, "mean_class_accuracy": 0.73307} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.02304, "memory": 4082, "data_time": 0.19732, "top1_acc": 0.87375, "top5_acc": 0.99312, "loss_cls": 0.62637, "loss": 0.62637, "time": 0.57046} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.02303, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8325, "top5_acc": 0.99375, "loss_cls": 0.69078, "loss": 0.69078, "time": 0.37472} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.02302, "memory": 4082, "data_time": 0.00068, "top1_acc": 0.85438, "top5_acc": 0.99125, "loss_cls": 0.69299, "loss": 0.69299, "time": 0.38037} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.02301, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.845, "top5_acc": 0.99375, "loss_cls": 0.68969, "loss": 0.68969, "time": 0.3715} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.023, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.86, "top5_acc": 0.995, "loss_cls": 0.63905, "loss": 0.63905, "time": 0.3796} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.02299, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85438, "top5_acc": 0.995, "loss_cls": 0.66524, "loss": 0.66524, "time": 0.38008} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.02298, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.8525, "top5_acc": 0.99062, "loss_cls": 0.69078, "loss": 0.69078, "time": 0.37548} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.02297, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.865, "top5_acc": 0.99188, "loss_cls": 0.66227, "loss": 0.66227, "time": 0.37406} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.02295, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.85688, "top5_acc": 0.99312, "loss_cls": 0.64686, "loss": 0.64686, "time": 0.37467} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.02294, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85125, "top5_acc": 0.99062, "loss_cls": 0.67115, "loss": 0.67115, "time": 0.37463} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.02293, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.85562, "top5_acc": 0.995, "loss_cls": 0.66371, "loss": 0.66371, "time": 0.3779} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.02292, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.86938, "top5_acc": 0.99438, "loss_cls": 0.63321, "loss": 0.63321, "time": 0.38054} +{"mode": "val", "epoch": 28, "iter": 533, "lr": 0.02291, "top1_acc": 0.8039, "top5_acc": 0.9831, "mean_class_accuracy": 0.73655} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.0229, "memory": 4082, "data_time": 0.20281, "top1_acc": 0.85312, "top5_acc": 0.99438, "loss_cls": 0.64908, "loss": 0.64908, "time": 0.46116} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.02289, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86812, "top5_acc": 0.99688, "loss_cls": 0.57672, "loss": 0.57672, "time": 0.37853} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.02288, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.87438, "top5_acc": 0.99312, "loss_cls": 0.58108, "loss": 0.58108, "time": 0.38114} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.02287, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84688, "top5_acc": 0.98938, "loss_cls": 0.6851, "loss": 0.6851, "time": 0.37189} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.02285, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.8575, "top5_acc": 0.98938, "loss_cls": 0.70142, "loss": 0.70142, "time": 0.38048} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.02284, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.855, "top5_acc": 0.99625, "loss_cls": 0.64318, "loss": 0.64318, "time": 0.37938} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.02283, "memory": 4082, "data_time": 0.00049, "top1_acc": 0.84062, "top5_acc": 0.99375, "loss_cls": 0.68664, "loss": 0.68664, "time": 0.37426} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.02282, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.88062, "top5_acc": 0.99812, "loss_cls": 0.56718, "loss": 0.56718, "time": 0.37929} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.02281, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.84938, "top5_acc": 0.99312, "loss_cls": 0.64652, "loss": 0.64652, "time": 0.37723} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.0228, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.85875, "top5_acc": 0.99312, "loss_cls": 0.64534, "loss": 0.64534, "time": 0.36844} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.02279, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.845, "top5_acc": 0.99125, "loss_cls": 0.66346, "loss": 0.66346, "time": 0.37317} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.02277, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.87, "top5_acc": 0.99188, "loss_cls": 0.61469, "loss": 0.61469, "time": 0.37323} +{"mode": "val", "epoch": 29, "iter": 533, "lr": 0.02276, "top1_acc": 0.81235, "top5_acc": 0.9858, "mean_class_accuracy": 0.75017} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.02275, "memory": 4082, "data_time": 0.19288, "top1_acc": 0.87625, "top5_acc": 0.9975, "loss_cls": 0.56311, "loss": 0.56311, "time": 0.67488} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.02274, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.88438, "top5_acc": 0.99688, "loss_cls": 0.55435, "loss": 0.55435, "time": 0.48109} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.02273, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.87688, "top5_acc": 0.99688, "loss_cls": 0.57626, "loss": 0.57626, "time": 0.28007} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.02272, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.85375, "top5_acc": 0.99188, "loss_cls": 0.68998, "loss": 0.68998, "time": 0.51219} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.02271, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.86812, "top5_acc": 0.99188, "loss_cls": 0.63649, "loss": 0.63649, "time": 0.294} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.02269, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.8725, "top5_acc": 0.99062, "loss_cls": 0.60586, "loss": 0.60586, "time": 0.47986} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.02268, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.87625, "top5_acc": 0.99312, "loss_cls": 0.59464, "loss": 0.59464, "time": 0.48234} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.02267, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84625, "top5_acc": 0.98812, "loss_cls": 0.69952, "loss": 0.69952, "time": 0.48291} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.02266, "memory": 4082, "data_time": 0.00059, "top1_acc": 0.86688, "top5_acc": 0.99188, "loss_cls": 0.62474, "loss": 0.62474, "time": 0.48365} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.02265, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.86188, "top5_acc": 0.99375, "loss_cls": 0.64997, "loss": 0.64997, "time": 0.48518} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.02263, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.865, "top5_acc": 0.99438, "loss_cls": 0.63625, "loss": 0.63625, "time": 0.48565} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.02262, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.8675, "top5_acc": 0.99188, "loss_cls": 0.62902, "loss": 0.62902, "time": 0.48129} +{"mode": "val", "epoch": 30, "iter": 533, "lr": 0.02261, "top1_acc": 0.82361, "top5_acc": 0.98263, "mean_class_accuracy": 0.75194} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.0226, "memory": 4083, "data_time": 0.19398, "top1_acc": 0.87938, "top5_acc": 0.99562, "loss_cls": 0.7221, "loss": 0.7221, "time": 0.84788} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.02259, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87562, "top5_acc": 0.99688, "loss_cls": 0.69884, "loss": 0.69884, "time": 0.49138} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.02258, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.87, "top5_acc": 0.99625, "loss_cls": 0.71326, "loss": 0.71326, "time": 0.29795} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.02256, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88, "top5_acc": 0.99438, "loss_cls": 0.69886, "loss": 0.69886, "time": 0.46904} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.02255, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8575, "top5_acc": 0.995, "loss_cls": 0.76762, "loss": 0.76762, "time": 0.32723} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.02254, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85625, "top5_acc": 0.99312, "loss_cls": 0.7914, "loss": 0.7914, "time": 0.49272} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.02253, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.8525, "top5_acc": 0.9925, "loss_cls": 0.79691, "loss": 0.79691, "time": 0.49542} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.02252, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.85438, "top5_acc": 0.9925, "loss_cls": 0.77463, "loss": 0.77463, "time": 0.49253} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0225, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86688, "top5_acc": 0.99562, "loss_cls": 0.73355, "loss": 0.73355, "time": 0.49275} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.02249, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.8625, "top5_acc": 0.98938, "loss_cls": 0.78594, "loss": 0.78594, "time": 0.49143} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.02248, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.85938, "top5_acc": 0.99062, "loss_cls": 0.7851, "loss": 0.7851, "time": 0.49581} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.02247, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88438, "top5_acc": 0.99688, "loss_cls": 0.67436, "loss": 0.67436, "time": 0.49279} +{"mode": "val", "epoch": 31, "iter": 533, "lr": 0.02246, "top1_acc": 0.76329, "top5_acc": 0.97582, "mean_class_accuracy": 0.67279} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.02244, "memory": 4083, "data_time": 0.19274, "top1_acc": 0.8825, "top5_acc": 0.99438, "loss_cls": 0.66083, "loss": 0.66083, "time": 0.80796} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.02243, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86938, "top5_acc": 0.99438, "loss_cls": 0.70532, "loss": 0.70532, "time": 0.49161} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.02242, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.88125, "top5_acc": 0.99688, "loss_cls": 0.63958, "loss": 0.63958, "time": 0.30489} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.02241, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86125, "top5_acc": 0.99375, "loss_cls": 0.72417, "loss": 0.72417, "time": 0.44797} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.02239, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.87062, "top5_acc": 0.99438, "loss_cls": 0.67902, "loss": 0.67902, "time": 0.33889} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.02238, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8575, "top5_acc": 0.9925, "loss_cls": 0.75169, "loss": 0.75169, "time": 0.49304} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.02237, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86312, "top5_acc": 0.9975, "loss_cls": 0.70012, "loss": 0.70012, "time": 0.49157} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.02236, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85, "top5_acc": 0.99188, "loss_cls": 0.76248, "loss": 0.76248, "time": 0.48928} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.02234, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.85688, "top5_acc": 0.99312, "loss_cls": 0.74389, "loss": 0.74389, "time": 0.49009} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.02233, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86375, "top5_acc": 0.99375, "loss_cls": 0.72645, "loss": 0.72645, "time": 0.48904} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.02232, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.86062, "top5_acc": 0.99375, "loss_cls": 0.66365, "loss": 0.66365, "time": 0.49146} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.02231, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.84375, "top5_acc": 0.99, "loss_cls": 0.7811, "loss": 0.7811, "time": 0.49092} +{"mode": "val", "epoch": 32, "iter": 533, "lr": 0.0223, "top1_acc": 0.7897, "top5_acc": 0.97794, "mean_class_accuracy": 0.71637} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.02228, "memory": 4083, "data_time": 0.19689, "top1_acc": 0.855, "top5_acc": 0.99312, "loss_cls": 0.72237, "loss": 0.72237, "time": 0.81707} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.02227, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89062, "top5_acc": 0.99312, "loss_cls": 0.57626, "loss": 0.57626, "time": 0.49261} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.02226, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.8725, "top5_acc": 0.99562, "loss_cls": 0.65375, "loss": 0.65375, "time": 0.33932} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.02225, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.87438, "top5_acc": 0.99438, "loss_cls": 0.63725, "loss": 0.63725, "time": 0.40547} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.02223, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8675, "top5_acc": 0.99312, "loss_cls": 0.68055, "loss": 0.68055, "time": 0.36364} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.02222, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85938, "top5_acc": 0.99188, "loss_cls": 0.71082, "loss": 0.71082, "time": 0.49153} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.02221, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.86, "top5_acc": 0.99375, "loss_cls": 0.67679, "loss": 0.67679, "time": 0.49593} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.02219, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.8725, "top5_acc": 0.995, "loss_cls": 0.64956, "loss": 0.64956, "time": 0.4922} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.02218, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.86375, "top5_acc": 0.99312, "loss_cls": 0.67775, "loss": 0.67775, "time": 0.49518} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.02217, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.85938, "top5_acc": 0.9925, "loss_cls": 0.6731, "loss": 0.6731, "time": 0.49302} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.02216, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86812, "top5_acc": 0.9925, "loss_cls": 0.68537, "loss": 0.68537, "time": 0.49236} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.02214, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86062, "top5_acc": 0.99375, "loss_cls": 0.69452, "loss": 0.69452, "time": 0.49444} +{"mode": "val", "epoch": 33, "iter": 533, "lr": 0.02213, "top1_acc": 0.83617, "top5_acc": 0.98967, "mean_class_accuracy": 0.77131} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.02212, "memory": 4083, "data_time": 0.19571, "top1_acc": 0.86812, "top5_acc": 0.9925, "loss_cls": 0.66056, "loss": 0.66056, "time": 0.81059} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.02211, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88625, "top5_acc": 0.99312, "loss_cls": 0.59764, "loss": 0.59764, "time": 0.47235} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.02209, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88438, "top5_acc": 0.9925, "loss_cls": 0.62484, "loss": 0.62484, "time": 0.35175} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.02208, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.85812, "top5_acc": 0.99562, "loss_cls": 0.64835, "loss": 0.64835, "time": 0.39188} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.02207, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.87625, "top5_acc": 0.9925, "loss_cls": 0.65163, "loss": 0.65163, "time": 0.37554} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.02205, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86688, "top5_acc": 0.99438, "loss_cls": 0.67681, "loss": 0.67681, "time": 0.4927} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.02204, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87375, "top5_acc": 0.99562, "loss_cls": 0.64108, "loss": 0.64108, "time": 0.49426} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.02203, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.87312, "top5_acc": 0.99438, "loss_cls": 0.61763, "loss": 0.61763, "time": 0.49239} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.02201, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.865, "top5_acc": 0.99312, "loss_cls": 0.65204, "loss": 0.65204, "time": 0.4928} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.022, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.84875, "top5_acc": 0.99062, "loss_cls": 0.734, "loss": 0.734, "time": 0.49162} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.02199, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.84375, "top5_acc": 0.99, "loss_cls": 0.74124, "loss": 0.74124, "time": 0.49093} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.02197, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8725, "top5_acc": 0.99375, "loss_cls": 0.65737, "loss": 0.65737, "time": 0.48999} +{"mode": "val", "epoch": 34, "iter": 533, "lr": 0.02196, "top1_acc": 0.81845, "top5_acc": 0.98603, "mean_class_accuracy": 0.77548} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.02195, "memory": 4083, "data_time": 0.19489, "top1_acc": 0.8775, "top5_acc": 0.9925, "loss_cls": 0.6361, "loss": 0.6361, "time": 0.80054} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.02194, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87562, "top5_acc": 0.9925, "loss_cls": 0.62286, "loss": 0.62286, "time": 0.48228} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.02192, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87688, "top5_acc": 0.99688, "loss_cls": 0.59207, "loss": 0.59207, "time": 0.34857} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.02191, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.87125, "top5_acc": 0.995, "loss_cls": 0.65632, "loss": 0.65632, "time": 0.39356} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.0219, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.87562, "top5_acc": 0.99562, "loss_cls": 0.62388, "loss": 0.62388, "time": 0.37538} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.02188, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87375, "top5_acc": 0.99562, "loss_cls": 0.6289, "loss": 0.6289, "time": 0.49122} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.02187, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88125, "top5_acc": 0.9925, "loss_cls": 0.61377, "loss": 0.61377, "time": 0.49266} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.02185, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87375, "top5_acc": 0.99375, "loss_cls": 0.65359, "loss": 0.65359, "time": 0.49337} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.02184, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87062, "top5_acc": 0.99375, "loss_cls": 0.64403, "loss": 0.64403, "time": 0.49257} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.02183, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86188, "top5_acc": 0.995, "loss_cls": 0.69122, "loss": 0.69122, "time": 0.49587} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.02181, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.865, "top5_acc": 0.98812, "loss_cls": 0.65487, "loss": 0.65487, "time": 0.49303} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.0218, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8725, "top5_acc": 0.995, "loss_cls": 0.62164, "loss": 0.62164, "time": 0.49328} +{"mode": "val", "epoch": 35, "iter": 533, "lr": 0.02179, "top1_acc": 0.8269, "top5_acc": 0.98334, "mean_class_accuracy": 0.74408} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.02178, "memory": 4083, "data_time": 0.1979, "top1_acc": 0.8875, "top5_acc": 0.99688, "loss_cls": 0.56527, "loss": 0.56527, "time": 0.81358} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.02176, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89375, "top5_acc": 0.99812, "loss_cls": 0.55737, "loss": 0.55737, "time": 0.45983} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.02175, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87062, "top5_acc": 0.99188, "loss_cls": 0.61122, "loss": 0.61122, "time": 0.39863} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.02173, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.88875, "top5_acc": 0.99312, "loss_cls": 0.61832, "loss": 0.61832, "time": 0.34476} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.02172, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8625, "top5_acc": 0.995, "loss_cls": 0.63174, "loss": 0.63174, "time": 0.3872} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.02171, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8875, "top5_acc": 0.995, "loss_cls": 0.5978, "loss": 0.5978, "time": 0.49053} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.02169, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.85938, "top5_acc": 0.99188, "loss_cls": 0.69269, "loss": 0.69269, "time": 0.48945} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.02168, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.865, "top5_acc": 0.99625, "loss_cls": 0.70937, "loss": 0.70937, "time": 0.49323} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.02167, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.88812, "top5_acc": 0.99125, "loss_cls": 0.60972, "loss": 0.60972, "time": 0.49265} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.02165, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87438, "top5_acc": 0.9925, "loss_cls": 0.65572, "loss": 0.65572, "time": 0.49107} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.02164, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87312, "top5_acc": 0.99438, "loss_cls": 0.63084, "loss": 0.63084, "time": 0.49019} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.02162, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86188, "top5_acc": 0.99375, "loss_cls": 0.66398, "loss": 0.66398, "time": 0.4906} +{"mode": "val", "epoch": 36, "iter": 533, "lr": 0.02161, "top1_acc": 0.85119, "top5_acc": 0.99026, "mean_class_accuracy": 0.78565} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.0216, "memory": 4083, "data_time": 0.19444, "top1_acc": 0.88625, "top5_acc": 0.99562, "loss_cls": 0.56873, "loss": 0.56873, "time": 0.80555} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.02158, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8825, "top5_acc": 0.99688, "loss_cls": 0.57227, "loss": 0.57227, "time": 0.45476} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.02157, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.86688, "top5_acc": 0.99688, "loss_cls": 0.62478, "loss": 0.62478, "time": 0.38806} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.02156, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.87562, "top5_acc": 0.99812, "loss_cls": 0.6162, "loss": 0.6162, "time": 0.35722} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.02154, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.88938, "top5_acc": 0.99188, "loss_cls": 0.62695, "loss": 0.62695, "time": 0.40816} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.02153, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86875, "top5_acc": 0.99562, "loss_cls": 0.61217, "loss": 0.61217, "time": 0.49136} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.02151, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88562, "top5_acc": 0.99562, "loss_cls": 0.58531, "loss": 0.58531, "time": 0.49004} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0215, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.86438, "top5_acc": 0.9925, "loss_cls": 0.64832, "loss": 0.64832, "time": 0.49473} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.02149, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88688, "top5_acc": 0.99688, "loss_cls": 0.61154, "loss": 0.61154, "time": 0.49294} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.02147, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8575, "top5_acc": 0.99125, "loss_cls": 0.70273, "loss": 0.70273, "time": 0.49272} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.02146, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86312, "top5_acc": 0.99188, "loss_cls": 0.68368, "loss": 0.68368, "time": 0.49405} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.02144, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.88125, "top5_acc": 0.99062, "loss_cls": 0.62916, "loss": 0.62916, "time": 0.49171} +{"mode": "val", "epoch": 37, "iter": 533, "lr": 0.02143, "top1_acc": 0.83758, "top5_acc": 0.9892, "mean_class_accuracy": 0.75672} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.02142, "memory": 4083, "data_time": 0.19622, "top1_acc": 0.88625, "top5_acc": 0.99625, "loss_cls": 0.5787, "loss": 0.5787, "time": 0.80172} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.0214, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89125, "top5_acc": 0.99688, "loss_cls": 0.55681, "loss": 0.55681, "time": 0.44453} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.02139, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89875, "top5_acc": 0.99375, "loss_cls": 0.56429, "loss": 0.56429, "time": 0.42214} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.02137, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.895, "top5_acc": 0.99375, "loss_cls": 0.5783, "loss": 0.5783, "time": 0.31962} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.02136, "memory": 4083, "data_time": 0.00094, "top1_acc": 0.88625, "top5_acc": 0.99312, "loss_cls": 0.5947, "loss": 0.5947, "time": 0.41832} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.02134, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87875, "top5_acc": 0.99625, "loss_cls": 0.61154, "loss": 0.61154, "time": 0.49001} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.02133, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.86438, "top5_acc": 0.995, "loss_cls": 0.65652, "loss": 0.65652, "time": 0.49096} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.02132, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87688, "top5_acc": 0.99312, "loss_cls": 0.62786, "loss": 0.62786, "time": 0.49079} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.0213, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87188, "top5_acc": 0.9925, "loss_cls": 0.64785, "loss": 0.64785, "time": 0.49257} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.02129, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86688, "top5_acc": 0.99125, "loss_cls": 0.66903, "loss": 0.66903, "time": 0.49464} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.02127, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89188, "top5_acc": 0.995, "loss_cls": 0.59226, "loss": 0.59226, "time": 0.49182} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.02126, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8875, "top5_acc": 0.99812, "loss_cls": 0.56264, "loss": 0.56264, "time": 0.49192} +{"mode": "val", "epoch": 38, "iter": 533, "lr": 0.02125, "top1_acc": 0.82009, "top5_acc": 0.98392, "mean_class_accuracy": 0.76242} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.02123, "memory": 4083, "data_time": 0.20028, "top1_acc": 0.87562, "top5_acc": 0.99125, "loss_cls": 0.6144, "loss": 0.6144, "time": 0.79479} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.02122, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90188, "top5_acc": 0.99312, "loss_cls": 0.54081, "loss": 0.54081, "time": 0.44057} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.0212, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88875, "top5_acc": 0.99688, "loss_cls": 0.56879, "loss": 0.56879, "time": 0.42607} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.02119, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8825, "top5_acc": 0.9975, "loss_cls": 0.61889, "loss": 0.61889, "time": 0.31691} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.02117, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88125, "top5_acc": 0.99812, "loss_cls": 0.59383, "loss": 0.59383, "time": 0.4152} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.02116, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.89062, "top5_acc": 0.99438, "loss_cls": 0.54462, "loss": 0.54462, "time": 0.49009} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.02114, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.89438, "top5_acc": 0.99562, "loss_cls": 0.5681, "loss": 0.5681, "time": 0.49358} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.02113, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.87562, "top5_acc": 0.99562, "loss_cls": 0.61958, "loss": 0.61958, "time": 0.49175} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.02111, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88875, "top5_acc": 0.99625, "loss_cls": 0.6023, "loss": 0.6023, "time": 0.49022} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.0211, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.865, "top5_acc": 0.99625, "loss_cls": 0.68195, "loss": 0.68195, "time": 0.49109} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.02108, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.86438, "top5_acc": 0.995, "loss_cls": 0.66714, "loss": 0.66714, "time": 0.49489} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.02107, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.885, "top5_acc": 0.9925, "loss_cls": 0.63017, "loss": 0.63017, "time": 0.49213} +{"mode": "val", "epoch": 39, "iter": 533, "lr": 0.02106, "top1_acc": 0.8614, "top5_acc": 0.99002, "mean_class_accuracy": 0.80483} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.02104, "memory": 4083, "data_time": 0.19987, "top1_acc": 0.89375, "top5_acc": 0.995, "loss_cls": 0.54929, "loss": 0.54929, "time": 0.81449} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.02103, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86438, "top5_acc": 0.99562, "loss_cls": 0.62153, "loss": 0.62153, "time": 0.41058} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.02101, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.89438, "top5_acc": 0.99375, "loss_cls": 0.53315, "loss": 0.53315, "time": 0.47346} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.021, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87875, "top5_acc": 0.995, "loss_cls": 0.59496, "loss": 0.59496, "time": 0.27038} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.02098, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.885, "top5_acc": 0.995, "loss_cls": 0.57984, "loss": 0.57984, "time": 0.4248} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.02097, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88, "top5_acc": 0.9975, "loss_cls": 0.59948, "loss": 0.59948, "time": 0.4912} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.02095, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.88688, "top5_acc": 0.9925, "loss_cls": 0.59196, "loss": 0.59196, "time": 0.49212} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.02094, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87688, "top5_acc": 0.99188, "loss_cls": 0.62822, "loss": 0.62822, "time": 0.4928} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.02092, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88312, "top5_acc": 0.9925, "loss_cls": 0.60178, "loss": 0.60178, "time": 0.49433} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.02091, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87125, "top5_acc": 0.99375, "loss_cls": 0.63735, "loss": 0.63735, "time": 0.49468} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.02089, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89562, "top5_acc": 0.99562, "loss_cls": 0.57169, "loss": 0.57169, "time": 0.49533} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.02088, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.865, "top5_acc": 0.99375, "loss_cls": 0.66782, "loss": 0.66782, "time": 0.49291} +{"mode": "val", "epoch": 40, "iter": 533, "lr": 0.02086, "top1_acc": 0.84532, "top5_acc": 0.98603, "mean_class_accuracy": 0.78895} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.02085, "memory": 4083, "data_time": 0.2013, "top1_acc": 0.89125, "top5_acc": 0.99688, "loss_cls": 0.54386, "loss": 0.54386, "time": 0.82514} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.02083, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88875, "top5_acc": 0.99562, "loss_cls": 0.55203, "loss": 0.55203, "time": 0.3974} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.02082, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.88938, "top5_acc": 0.99312, "loss_cls": 0.58447, "loss": 0.58447, "time": 0.51426} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.0208, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88625, "top5_acc": 0.99375, "loss_cls": 0.59826, "loss": 0.59826, "time": 0.23659} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.02079, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87625, "top5_acc": 0.99562, "loss_cls": 0.61014, "loss": 0.61014, "time": 0.44191} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.02077, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87938, "top5_acc": 0.99562, "loss_cls": 0.64367, "loss": 0.64367, "time": 0.48811} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.02076, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.88938, "top5_acc": 0.99812, "loss_cls": 0.5577, "loss": 0.5577, "time": 0.49347} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.02074, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88562, "top5_acc": 0.99312, "loss_cls": 0.59188, "loss": 0.59188, "time": 0.49056} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.02073, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88125, "top5_acc": 0.99562, "loss_cls": 0.58046, "loss": 0.58046, "time": 0.49232} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.02071, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.88562, "top5_acc": 0.995, "loss_cls": 0.6152, "loss": 0.6152, "time": 0.49125} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.0207, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8775, "top5_acc": 0.99125, "loss_cls": 0.63398, "loss": 0.63398, "time": 0.49437} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.02068, "memory": 4083, "data_time": 0.00063, "top1_acc": 0.88562, "top5_acc": 0.995, "loss_cls": 0.6125, "loss": 0.6125, "time": 0.49748} +{"mode": "val", "epoch": 41, "iter": 533, "lr": 0.02067, "top1_acc": 0.85506, "top5_acc": 0.98768, "mean_class_accuracy": 0.81201} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.02065, "memory": 4083, "data_time": 0.19534, "top1_acc": 0.87812, "top5_acc": 0.995, "loss_cls": 0.61065, "loss": 0.61065, "time": 0.80711} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.02064, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89375, "top5_acc": 0.9975, "loss_cls": 0.54751, "loss": 0.54751, "time": 0.38753} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.02062, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88875, "top5_acc": 0.99625, "loss_cls": 0.58717, "loss": 0.58717, "time": 0.51362} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.02061, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88188, "top5_acc": 0.99625, "loss_cls": 0.62652, "loss": 0.62652, "time": 0.2417} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.02059, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.88375, "top5_acc": 0.99438, "loss_cls": 0.59464, "loss": 0.59464, "time": 0.44388} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.02057, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.88812, "top5_acc": 0.995, "loss_cls": 0.56648, "loss": 0.56648, "time": 0.49371} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.02056, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.88688, "top5_acc": 0.9975, "loss_cls": 0.59201, "loss": 0.59201, "time": 0.49232} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.02054, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.88938, "top5_acc": 0.995, "loss_cls": 0.59204, "loss": 0.59204, "time": 0.4906} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.02053, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88438, "top5_acc": 0.99562, "loss_cls": 0.60675, "loss": 0.60675, "time": 0.48928} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.02051, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.8875, "top5_acc": 0.99562, "loss_cls": 0.55107, "loss": 0.55107, "time": 0.49407} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.0205, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.8775, "top5_acc": 0.99438, "loss_cls": 0.59022, "loss": 0.59022, "time": 0.49433} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.02048, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.86375, "top5_acc": 0.99375, "loss_cls": 0.65465, "loss": 0.65465, "time": 0.4956} +{"mode": "val", "epoch": 42, "iter": 533, "lr": 0.02047, "top1_acc": 0.87243, "top5_acc": 0.98932, "mean_class_accuracy": 0.81683} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.02045, "memory": 4083, "data_time": 0.19985, "top1_acc": 0.90938, "top5_acc": 0.9975, "loss_cls": 0.48483, "loss": 0.48483, "time": 0.82446} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.02044, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88875, "top5_acc": 0.99625, "loss_cls": 0.55406, "loss": 0.55406, "time": 0.37382} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.02042, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.9025, "top5_acc": 0.995, "loss_cls": 0.52855, "loss": 0.52855, "time": 0.51454} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.0204, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87438, "top5_acc": 0.99688, "loss_cls": 0.58956, "loss": 0.58956, "time": 0.24541} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.02039, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88812, "top5_acc": 0.99438, "loss_cls": 0.55847, "loss": 0.55847, "time": 0.46098} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.02037, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86938, "top5_acc": 0.99438, "loss_cls": 0.63787, "loss": 0.63787, "time": 0.49768} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.02036, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88938, "top5_acc": 0.99688, "loss_cls": 0.55407, "loss": 0.55407, "time": 0.49461} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.02034, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88375, "top5_acc": 0.99125, "loss_cls": 0.57183, "loss": 0.57183, "time": 0.49029} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.02033, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8775, "top5_acc": 0.98938, "loss_cls": 0.63152, "loss": 0.63152, "time": 0.49113} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.02031, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.885, "top5_acc": 0.99562, "loss_cls": 0.60553, "loss": 0.60553, "time": 0.4894} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.02029, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8875, "top5_acc": 0.99312, "loss_cls": 0.58953, "loss": 0.58953, "time": 0.49012} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.02028, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8875, "top5_acc": 0.99812, "loss_cls": 0.56816, "loss": 0.56816, "time": 0.49193} +{"mode": "val", "epoch": 43, "iter": 533, "lr": 0.02026, "top1_acc": 0.83101, "top5_acc": 0.98615, "mean_class_accuracy": 0.78738} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.02025, "memory": 4083, "data_time": 0.19085, "top1_acc": 0.89938, "top5_acc": 0.99625, "loss_cls": 0.50795, "loss": 0.50795, "time": 0.79494} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.02023, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9, "top5_acc": 0.9975, "loss_cls": 0.5132, "loss": 0.5132, "time": 0.37777} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.02022, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.895, "top5_acc": 0.995, "loss_cls": 0.53966, "loss": 0.53966, "time": 0.51445} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.0202, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89938, "top5_acc": 0.99688, "loss_cls": 0.53165, "loss": 0.53165, "time": 0.24167} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.02018, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89625, "top5_acc": 0.995, "loss_cls": 0.55293, "loss": 0.55293, "time": 0.45558} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.02017, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88438, "top5_acc": 0.99625, "loss_cls": 0.56894, "loss": 0.56894, "time": 0.49206} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.02015, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8925, "top5_acc": 0.99312, "loss_cls": 0.57121, "loss": 0.57121, "time": 0.49101} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.02014, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.88062, "top5_acc": 0.99375, "loss_cls": 0.63775, "loss": 0.63775, "time": 0.49344} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.02012, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8825, "top5_acc": 0.99625, "loss_cls": 0.61399, "loss": 0.61399, "time": 0.49194} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.0201, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.89, "top5_acc": 0.99438, "loss_cls": 0.53806, "loss": 0.53806, "time": 0.49221} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.02009, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.905, "top5_acc": 0.99625, "loss_cls": 0.50941, "loss": 0.50941, "time": 0.49119} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.02007, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88062, "top5_acc": 0.9975, "loss_cls": 0.58088, "loss": 0.58088, "time": 0.49345} +{"mode": "val", "epoch": 44, "iter": 533, "lr": 0.02006, "top1_acc": 0.83629, "top5_acc": 0.99002, "mean_class_accuracy": 0.77202} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.02004, "memory": 4083, "data_time": 0.19108, "top1_acc": 0.87938, "top5_acc": 0.99875, "loss_cls": 0.60259, "loss": 0.60259, "time": 0.80697} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.02003, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89125, "top5_acc": 0.99875, "loss_cls": 0.54455, "loss": 0.54455, "time": 0.37608} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.02001, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.895, "top5_acc": 0.99562, "loss_cls": 0.56314, "loss": 0.56314, "time": 0.51588} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.01999, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91312, "top5_acc": 0.99625, "loss_cls": 0.47318, "loss": 0.47318, "time": 0.24423} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.01998, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.895, "top5_acc": 0.9975, "loss_cls": 0.53948, "loss": 0.53948, "time": 0.44743} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.01996, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89938, "top5_acc": 0.99625, "loss_cls": 0.5339, "loss": 0.5339, "time": 0.49411} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.01994, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.89875, "top5_acc": 0.9975, "loss_cls": 0.54096, "loss": 0.54096, "time": 0.49297} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.01993, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.89625, "top5_acc": 0.9975, "loss_cls": 0.53385, "loss": 0.53385, "time": 0.49069} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.01991, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.88062, "top5_acc": 0.99125, "loss_cls": 0.59632, "loss": 0.59632, "time": 0.49375} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.01989, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88688, "top5_acc": 0.995, "loss_cls": 0.57472, "loss": 0.57472, "time": 0.48999} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.01988, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88125, "top5_acc": 0.99562, "loss_cls": 0.59647, "loss": 0.59647, "time": 0.49701} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.01986, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.89562, "top5_acc": 0.9975, "loss_cls": 0.52504, "loss": 0.52504, "time": 0.49391} +{"mode": "val", "epoch": 45, "iter": 533, "lr": 0.01985, "top1_acc": 0.84708, "top5_acc": 0.99049, "mean_class_accuracy": 0.78435} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.01983, "memory": 4083, "data_time": 0.19391, "top1_acc": 0.905, "top5_acc": 0.9975, "loss_cls": 0.52175, "loss": 0.52175, "time": 0.81016} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.01981, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91062, "top5_acc": 0.99812, "loss_cls": 0.47496, "loss": 0.47496, "time": 0.37243} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.0198, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91062, "top5_acc": 0.99812, "loss_cls": 0.46766, "loss": 0.46766, "time": 0.51319} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.01978, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.88812, "top5_acc": 0.99562, "loss_cls": 0.55962, "loss": 0.55962, "time": 0.2419} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.01976, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90375, "top5_acc": 0.99438, "loss_cls": 0.49995, "loss": 0.49995, "time": 0.46918} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.01975, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8875, "top5_acc": 0.99688, "loss_cls": 0.57395, "loss": 0.57395, "time": 0.49392} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.01973, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89125, "top5_acc": 0.99438, "loss_cls": 0.56179, "loss": 0.56179, "time": 0.48969} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.01971, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8925, "top5_acc": 0.99625, "loss_cls": 0.56691, "loss": 0.56691, "time": 0.48991} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.0197, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87938, "top5_acc": 0.99625, "loss_cls": 0.58437, "loss": 0.58437, "time": 0.49236} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.01968, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.88812, "top5_acc": 0.99438, "loss_cls": 0.5841, "loss": 0.5841, "time": 0.4889} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.01966, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90688, "top5_acc": 0.99438, "loss_cls": 0.52751, "loss": 0.52751, "time": 0.4926} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.01965, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88812, "top5_acc": 0.99562, "loss_cls": 0.57287, "loss": 0.57287, "time": 0.49001} +{"mode": "val", "epoch": 46, "iter": 533, "lr": 0.01963, "top1_acc": 0.86164, "top5_acc": 0.98967, "mean_class_accuracy": 0.811} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.01962, "memory": 4083, "data_time": 0.1949, "top1_acc": 0.87938, "top5_acc": 0.99688, "loss_cls": 0.55449, "loss": 0.55449, "time": 0.80009} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.0196, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89188, "top5_acc": 0.99625, "loss_cls": 0.54041, "loss": 0.54041, "time": 0.36383} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.01958, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88812, "top5_acc": 0.99688, "loss_cls": 0.54572, "loss": 0.54572, "time": 0.51263} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.01957, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.90125, "top5_acc": 0.99625, "loss_cls": 0.5276, "loss": 0.5276, "time": 0.24356} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.01955, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89688, "top5_acc": 0.99438, "loss_cls": 0.54468, "loss": 0.54468, "time": 0.45499} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.01953, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90938, "top5_acc": 0.995, "loss_cls": 0.48545, "loss": 0.48545, "time": 0.49174} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.01952, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.89688, "top5_acc": 0.995, "loss_cls": 0.53197, "loss": 0.53197, "time": 0.49242} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.0195, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.87812, "top5_acc": 0.99688, "loss_cls": 0.56299, "loss": 0.56299, "time": 0.48974} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.01948, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.88188, "top5_acc": 0.99812, "loss_cls": 0.58713, "loss": 0.58713, "time": 0.49311} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.01947, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89312, "top5_acc": 0.99625, "loss_cls": 0.53102, "loss": 0.53102, "time": 0.49008} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.01945, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89062, "top5_acc": 0.99375, "loss_cls": 0.60073, "loss": 0.60073, "time": 0.4889} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.01943, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8925, "top5_acc": 0.99188, "loss_cls": 0.53164, "loss": 0.53164, "time": 0.491} +{"mode": "val", "epoch": 47, "iter": 533, "lr": 0.01942, "top1_acc": 0.85225, "top5_acc": 0.98885, "mean_class_accuracy": 0.80495} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.0194, "memory": 4083, "data_time": 0.19251, "top1_acc": 0.91312, "top5_acc": 0.99875, "loss_cls": 0.46764, "loss": 0.46764, "time": 0.79493} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.01938, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89312, "top5_acc": 0.99625, "loss_cls": 0.53866, "loss": 0.53866, "time": 0.383} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.01937, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88875, "top5_acc": 0.99625, "loss_cls": 0.54256, "loss": 0.54256, "time": 0.51145} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.01935, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88125, "top5_acc": 0.99812, "loss_cls": 0.57808, "loss": 0.57808, "time": 0.23871} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.01933, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.895, "top5_acc": 0.99688, "loss_cls": 0.569, "loss": 0.569, "time": 0.44484} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.01932, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89438, "top5_acc": 0.99562, "loss_cls": 0.51756, "loss": 0.51756, "time": 0.49518} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.0193, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89438, "top5_acc": 0.995, "loss_cls": 0.54789, "loss": 0.54789, "time": 0.4897} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.01928, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86, "top5_acc": 0.99312, "loss_cls": 0.66554, "loss": 0.66554, "time": 0.49094} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.01926, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.88125, "top5_acc": 0.99375, "loss_cls": 0.61342, "loss": 0.61342, "time": 0.48993} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.01925, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.88812, "top5_acc": 0.99625, "loss_cls": 0.56923, "loss": 0.56923, "time": 0.49554} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.01923, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90312, "top5_acc": 0.99625, "loss_cls": 0.49777, "loss": 0.49777, "time": 0.49101} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.01921, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.88125, "top5_acc": 0.99062, "loss_cls": 0.59012, "loss": 0.59012, "time": 0.49414} +{"mode": "val", "epoch": 48, "iter": 533, "lr": 0.0192, "top1_acc": 0.85823, "top5_acc": 0.99319, "mean_class_accuracy": 0.7981} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.01918, "memory": 4083, "data_time": 0.18765, "top1_acc": 0.89875, "top5_acc": 0.9975, "loss_cls": 0.53568, "loss": 0.53568, "time": 0.77465} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.01916, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.90625, "top5_acc": 0.99625, "loss_cls": 0.49793, "loss": 0.49793, "time": 0.42223} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.01915, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.90375, "top5_acc": 0.99688, "loss_cls": 0.49773, "loss": 0.49773, "time": 0.44778} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.01913, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86688, "top5_acc": 0.99562, "loss_cls": 0.59097, "loss": 0.59097, "time": 0.28891} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.01911, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90062, "top5_acc": 0.99875, "loss_cls": 0.50766, "loss": 0.50766, "time": 0.40609} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.01909, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88062, "top5_acc": 0.99562, "loss_cls": 0.58322, "loss": 0.58322, "time": 0.49359} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.01908, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89812, "top5_acc": 0.99625, "loss_cls": 0.54646, "loss": 0.54646, "time": 0.49078} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.01906, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.88188, "top5_acc": 0.99125, "loss_cls": 0.60833, "loss": 0.60833, "time": 0.49033} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.01904, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9025, "top5_acc": 0.99562, "loss_cls": 0.52307, "loss": 0.52307, "time": 0.49257} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.01902, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89812, "top5_acc": 0.99562, "loss_cls": 0.53645, "loss": 0.53645, "time": 0.48982} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.01901, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88562, "top5_acc": 0.995, "loss_cls": 0.5729, "loss": 0.5729, "time": 0.49399} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.01899, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89625, "top5_acc": 0.99812, "loss_cls": 0.57014, "loss": 0.57014, "time": 0.49468} +{"mode": "val", "epoch": 49, "iter": 533, "lr": 0.01898, "top1_acc": 0.86081, "top5_acc": 0.99049, "mean_class_accuracy": 0.79444} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.01896, "memory": 4083, "data_time": 0.19534, "top1_acc": 0.91562, "top5_acc": 0.99875, "loss_cls": 0.47535, "loss": 0.47535, "time": 0.80552} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.01894, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.91812, "top5_acc": 0.99812, "loss_cls": 0.44008, "loss": 0.44008, "time": 0.44607} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.01892, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.90062, "top5_acc": 0.99688, "loss_cls": 0.50531, "loss": 0.50531, "time": 0.40181} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.01891, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9125, "top5_acc": 0.99938, "loss_cls": 0.458, "loss": 0.458, "time": 0.33452} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.01889, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89375, "top5_acc": 0.9975, "loss_cls": 0.52054, "loss": 0.52054, "time": 0.39966} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.01887, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89875, "top5_acc": 0.99625, "loss_cls": 0.52423, "loss": 0.52423, "time": 0.49066} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.01885, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88438, "top5_acc": 0.99625, "loss_cls": 0.54843, "loss": 0.54843, "time": 0.49243} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.01884, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89375, "top5_acc": 0.99625, "loss_cls": 0.52828, "loss": 0.52828, "time": 0.49062} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.01882, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89062, "top5_acc": 0.99312, "loss_cls": 0.57383, "loss": 0.57383, "time": 0.48864} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.0188, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87812, "top5_acc": 0.995, "loss_cls": 0.58772, "loss": 0.58772, "time": 0.49313} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.01878, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90062, "top5_acc": 0.995, "loss_cls": 0.51933, "loss": 0.51933, "time": 0.49058} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.01876, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8875, "top5_acc": 0.995, "loss_cls": 0.56764, "loss": 0.56764, "time": 0.49236} +{"mode": "val", "epoch": 50, "iter": 533, "lr": 0.01875, "top1_acc": 0.85706, "top5_acc": 0.98815, "mean_class_accuracy": 0.79561} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.01873, "memory": 4083, "data_time": 0.19543, "top1_acc": 0.91062, "top5_acc": 0.99625, "loss_cls": 0.48345, "loss": 0.48345, "time": 0.80054} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.01871, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.90625, "top5_acc": 0.99875, "loss_cls": 0.47581, "loss": 0.47581, "time": 0.44607} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.0187, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9025, "top5_acc": 0.99812, "loss_cls": 0.51426, "loss": 0.51426, "time": 0.4083} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.01868, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8875, "top5_acc": 0.99312, "loss_cls": 0.56818, "loss": 0.56818, "time": 0.32705} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.01866, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.90938, "top5_acc": 0.995, "loss_cls": 0.48487, "loss": 0.48487, "time": 0.38713} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.01864, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.90375, "top5_acc": 0.9975, "loss_cls": 0.5442, "loss": 0.5442, "time": 0.49244} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.01863, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.895, "top5_acc": 0.99688, "loss_cls": 0.51887, "loss": 0.51887, "time": 0.49258} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.01861, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88812, "top5_acc": 0.99562, "loss_cls": 0.57251, "loss": 0.57251, "time": 0.49317} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.01859, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89312, "top5_acc": 0.99562, "loss_cls": 0.53847, "loss": 0.53847, "time": 0.49042} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.01857, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89625, "top5_acc": 0.99562, "loss_cls": 0.50584, "loss": 0.50584, "time": 0.49139} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.01855, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.88625, "top5_acc": 0.99438, "loss_cls": 0.56661, "loss": 0.56661, "time": 0.49194} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.01854, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.89812, "top5_acc": 0.99812, "loss_cls": 0.51297, "loss": 0.51297, "time": 0.49279} +{"mode": "val", "epoch": 51, "iter": 533, "lr": 0.01852, "top1_acc": 0.86199, "top5_acc": 0.98956, "mean_class_accuracy": 0.81673} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.0185, "memory": 4083, "data_time": 0.18604, "top1_acc": 0.91188, "top5_acc": 0.99625, "loss_cls": 0.47638, "loss": 0.47638, "time": 0.79039} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.01849, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.89438, "top5_acc": 0.9975, "loss_cls": 0.49559, "loss": 0.49559, "time": 0.48141} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.01847, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.89625, "top5_acc": 0.99688, "loss_cls": 0.52533, "loss": 0.52533, "time": 0.3481} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.01845, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.895, "top5_acc": 0.99625, "loss_cls": 0.52562, "loss": 0.52562, "time": 0.39074} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.01843, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88875, "top5_acc": 0.99688, "loss_cls": 0.5386, "loss": 0.5386, "time": 0.34059} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.01841, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89438, "top5_acc": 0.99438, "loss_cls": 0.54185, "loss": 0.54185, "time": 0.49282} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.0184, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90062, "top5_acc": 0.99625, "loss_cls": 0.4934, "loss": 0.4934, "time": 0.49467} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.01838, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88312, "top5_acc": 0.9925, "loss_cls": 0.57401, "loss": 0.57401, "time": 0.49005} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.01836, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88125, "top5_acc": 0.99438, "loss_cls": 0.58839, "loss": 0.58839, "time": 0.49409} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.01834, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91062, "top5_acc": 0.99688, "loss_cls": 0.46082, "loss": 0.46082, "time": 0.49489} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.01832, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89188, "top5_acc": 0.995, "loss_cls": 0.53012, "loss": 0.53012, "time": 0.49483} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.01831, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.895, "top5_acc": 0.99562, "loss_cls": 0.49026, "loss": 0.49026, "time": 0.49346} +{"mode": "val", "epoch": 52, "iter": 533, "lr": 0.01829, "top1_acc": 0.86316, "top5_acc": 0.98967, "mean_class_accuracy": 0.80886} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.01827, "memory": 4083, "data_time": 0.19186, "top1_acc": 0.90562, "top5_acc": 0.9975, "loss_cls": 0.49252, "loss": 0.49252, "time": 0.7904} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.01826, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90688, "top5_acc": 0.99688, "loss_cls": 0.50113, "loss": 0.50113, "time": 0.49257} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.01824, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8975, "top5_acc": 0.99562, "loss_cls": 0.50537, "loss": 0.50537, "time": 0.2878} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.01822, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.90812, "top5_acc": 0.9975, "loss_cls": 0.4883, "loss": 0.4883, "time": 0.47656} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.0182, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.90812, "top5_acc": 0.9975, "loss_cls": 0.48583, "loss": 0.48583, "time": 0.31671} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.01818, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91625, "top5_acc": 0.99688, "loss_cls": 0.46858, "loss": 0.46858, "time": 0.49554} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.01816, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89438, "top5_acc": 0.99438, "loss_cls": 0.54593, "loss": 0.54593, "time": 0.49095} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.01815, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89312, "top5_acc": 0.99562, "loss_cls": 0.54598, "loss": 0.54598, "time": 0.49442} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.01813, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.8975, "top5_acc": 0.99625, "loss_cls": 0.54351, "loss": 0.54351, "time": 0.49229} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.01811, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.905, "top5_acc": 0.9975, "loss_cls": 0.51257, "loss": 0.51257, "time": 0.48962} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.01809, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.91375, "top5_acc": 0.99688, "loss_cls": 0.47386, "loss": 0.47386, "time": 0.49351} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.01807, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9125, "top5_acc": 0.9975, "loss_cls": 0.4695, "loss": 0.4695, "time": 0.49463} +{"mode": "val", "epoch": 53, "iter": 533, "lr": 0.01806, "top1_acc": 0.85201, "top5_acc": 0.99085, "mean_class_accuracy": 0.7933} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.01804, "memory": 4083, "data_time": 0.1906, "top1_acc": 0.90562, "top5_acc": 0.99812, "loss_cls": 0.51653, "loss": 0.51653, "time": 0.78658} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.01802, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91625, "top5_acc": 0.99625, "loss_cls": 0.47565, "loss": 0.47565, "time": 0.49264} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.018, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91438, "top5_acc": 0.99875, "loss_cls": 0.43342, "loss": 0.43342, "time": 0.28399} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.01798, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91125, "top5_acc": 0.99625, "loss_cls": 0.4484, "loss": 0.4484, "time": 0.51101} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.01797, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.90625, "top5_acc": 0.9975, "loss_cls": 0.49438, "loss": 0.49438, "time": 0.27569} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.01795, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88875, "top5_acc": 0.99375, "loss_cls": 0.52981, "loss": 0.52981, "time": 0.49506} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.01793, "memory": 4083, "data_time": 0.00055, "top1_acc": 0.91375, "top5_acc": 0.99688, "loss_cls": 0.4712, "loss": 0.4712, "time": 0.49172} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.01791, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90062, "top5_acc": 0.99562, "loss_cls": 0.56064, "loss": 0.56064, "time": 0.48944} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.01789, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91125, "top5_acc": 0.99625, "loss_cls": 0.48875, "loss": 0.48875, "time": 0.49038} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.01787, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.89562, "top5_acc": 0.99938, "loss_cls": 0.54309, "loss": 0.54309, "time": 0.49247} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.01786, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.50956, "loss": 0.50956, "time": 0.49263} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.01784, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.90188, "top5_acc": 0.99812, "loss_cls": 0.49224, "loss": 0.49224, "time": 0.48956} +{"mode": "val", "epoch": 54, "iter": 533, "lr": 0.01782, "top1_acc": 0.85671, "top5_acc": 0.99249, "mean_class_accuracy": 0.79717} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.0178, "memory": 4083, "data_time": 0.19129, "top1_acc": 0.90125, "top5_acc": 0.99625, "loss_cls": 0.50275, "loss": 0.50275, "time": 0.80506} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.01779, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9025, "top5_acc": 0.99812, "loss_cls": 0.50877, "loss": 0.50877, "time": 0.49106} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.01777, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.89375, "top5_acc": 0.9975, "loss_cls": 0.54295, "loss": 0.54295, "time": 0.31436} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.01775, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.44105, "loss": 0.44105, "time": 0.51113} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.01773, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.90812, "top5_acc": 0.99875, "loss_cls": 0.49926, "loss": 0.49926, "time": 0.2567} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.01771, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90062, "top5_acc": 0.99688, "loss_cls": 0.50344, "loss": 0.50344, "time": 0.49745} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.01769, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.915, "top5_acc": 0.99438, "loss_cls": 0.47819, "loss": 0.47819, "time": 0.49081} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.01767, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9075, "top5_acc": 0.99562, "loss_cls": 0.49153, "loss": 0.49153, "time": 0.4915} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.01766, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89938, "top5_acc": 0.99688, "loss_cls": 0.4953, "loss": 0.4953, "time": 0.49258} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.01764, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89812, "top5_acc": 0.99625, "loss_cls": 0.50717, "loss": 0.50717, "time": 0.49468} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.01762, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90062, "top5_acc": 0.99562, "loss_cls": 0.5149, "loss": 0.5149, "time": 0.49334} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.0176, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89625, "top5_acc": 0.99688, "loss_cls": 0.53973, "loss": 0.53973, "time": 0.49733} +{"mode": "val", "epoch": 55, "iter": 533, "lr": 0.01758, "top1_acc": 0.82854, "top5_acc": 0.9851, "mean_class_accuracy": 0.77054} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.01757, "memory": 4083, "data_time": 0.19466, "top1_acc": 0.90438, "top5_acc": 0.99562, "loss_cls": 0.51882, "loss": 0.51882, "time": 0.789} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.01755, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90438, "top5_acc": 0.9975, "loss_cls": 0.46443, "loss": 0.46443, "time": 0.48837} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.01753, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.91, "top5_acc": 0.995, "loss_cls": 0.46043, "loss": 0.46043, "time": 0.32996} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.01751, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90312, "top5_acc": 0.99688, "loss_cls": 0.50241, "loss": 0.50241, "time": 0.51018} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.01749, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90688, "top5_acc": 0.99812, "loss_cls": 0.47381, "loss": 0.47381, "time": 0.25425} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.01747, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90188, "top5_acc": 0.99875, "loss_cls": 0.51419, "loss": 0.51419, "time": 0.48804} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.01745, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88875, "top5_acc": 0.99688, "loss_cls": 0.577, "loss": 0.577, "time": 0.49001} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.01743, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.91438, "top5_acc": 0.99688, "loss_cls": 0.44046, "loss": 0.44046, "time": 0.4928} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.01742, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89375, "top5_acc": 0.99688, "loss_cls": 0.49472, "loss": 0.49472, "time": 0.49322} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.0174, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91938, "top5_acc": 0.99312, "loss_cls": 0.47419, "loss": 0.47419, "time": 0.4917} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.01738, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.90125, "top5_acc": 0.99562, "loss_cls": 0.51849, "loss": 0.51849, "time": 0.49568} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.01736, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89875, "top5_acc": 0.99688, "loss_cls": 0.50612, "loss": 0.50612, "time": 0.49322} +{"mode": "val", "epoch": 56, "iter": 533, "lr": 0.01734, "top1_acc": 0.85448, "top5_acc": 0.98674, "mean_class_accuracy": 0.79178} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.01733, "memory": 4083, "data_time": 0.19316, "top1_acc": 0.89875, "top5_acc": 0.99812, "loss_cls": 0.51973, "loss": 0.51973, "time": 0.80881} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.01731, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91125, "top5_acc": 0.99938, "loss_cls": 0.4325, "loss": 0.4325, "time": 0.4925} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.01729, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92438, "top5_acc": 0.99688, "loss_cls": 0.42096, "loss": 0.42096, "time": 0.33208} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.01727, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.9125, "top5_acc": 0.9975, "loss_cls": 0.48221, "loss": 0.48221, "time": 0.51062} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.01725, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.92062, "top5_acc": 0.99875, "loss_cls": 0.41433, "loss": 0.41433, "time": 0.25081} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.01723, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9125, "top5_acc": 0.99625, "loss_cls": 0.47461, "loss": 0.47461, "time": 0.48309} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.01721, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.90875, "top5_acc": 0.99688, "loss_cls": 0.50858, "loss": 0.50858, "time": 0.48962} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.01719, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91312, "top5_acc": 0.99938, "loss_cls": 0.43121, "loss": 0.43121, "time": 0.48912} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.01717, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89938, "top5_acc": 0.99438, "loss_cls": 0.49842, "loss": 0.49842, "time": 0.49519} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.01716, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.88812, "top5_acc": 0.9975, "loss_cls": 0.5652, "loss": 0.5652, "time": 0.49146} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.01714, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9125, "top5_acc": 0.99562, "loss_cls": 0.50384, "loss": 0.50384, "time": 0.49616} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.01712, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.91125, "top5_acc": 0.9925, "loss_cls": 0.49812, "loss": 0.49812, "time": 0.49446} +{"mode": "val", "epoch": 57, "iter": 533, "lr": 0.0171, "top1_acc": 0.85354, "top5_acc": 0.99225, "mean_class_accuracy": 0.80399} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.01708, "memory": 4083, "data_time": 0.18764, "top1_acc": 0.90812, "top5_acc": 0.99812, "loss_cls": 0.45651, "loss": 0.45651, "time": 0.781} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.01706, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9125, "top5_acc": 0.99812, "loss_cls": 0.47204, "loss": 0.47204, "time": 0.49256} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.01704, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.90812, "top5_acc": 0.99812, "loss_cls": 0.46994, "loss": 0.46994, "time": 0.36463} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.01703, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91875, "top5_acc": 0.995, "loss_cls": 0.45217, "loss": 0.45217, "time": 0.50962} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.01701, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90875, "top5_acc": 0.9975, "loss_cls": 0.48347, "loss": 0.48347, "time": 0.24564} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.01699, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91125, "top5_acc": 0.99625, "loss_cls": 0.49732, "loss": 0.49732, "time": 0.46783} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.01697, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91062, "top5_acc": 0.99875, "loss_cls": 0.46893, "loss": 0.46893, "time": 0.49187} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.01695, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89875, "top5_acc": 0.99312, "loss_cls": 0.5499, "loss": 0.5499, "time": 0.49048} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.01693, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91438, "top5_acc": 0.99625, "loss_cls": 0.47499, "loss": 0.47499, "time": 0.49078} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.01691, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9025, "top5_acc": 0.99875, "loss_cls": 0.47523, "loss": 0.47523, "time": 0.4936} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.01689, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90938, "top5_acc": 0.99688, "loss_cls": 0.47664, "loss": 0.47664, "time": 0.49217} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.01687, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92625, "top5_acc": 0.99812, "loss_cls": 0.41684, "loss": 0.41684, "time": 0.49027} +{"mode": "val", "epoch": 58, "iter": 533, "lr": 0.01686, "top1_acc": 0.856, "top5_acc": 0.99132, "mean_class_accuracy": 0.79805} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.01684, "memory": 4083, "data_time": 0.19754, "top1_acc": 0.90688, "top5_acc": 0.995, "loss_cls": 0.49574, "loss": 0.49574, "time": 0.81192} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.01682, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91688, "top5_acc": 0.99875, "loss_cls": 0.44785, "loss": 0.44785, "time": 0.49272} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.0168, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91438, "top5_acc": 0.99625, "loss_cls": 0.45909, "loss": 0.45909, "time": 0.35182} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.01678, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9175, "top5_acc": 0.99562, "loss_cls": 0.42272, "loss": 0.42272, "time": 0.51035} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.01676, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.905, "top5_acc": 0.99812, "loss_cls": 0.4461, "loss": 0.4461, "time": 0.244} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.01674, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9125, "top5_acc": 0.99625, "loss_cls": 0.44963, "loss": 0.44963, "time": 0.4575} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.01672, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91, "top5_acc": 0.9975, "loss_cls": 0.4789, "loss": 0.4789, "time": 0.49225} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.0167, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91438, "top5_acc": 0.99812, "loss_cls": 0.4582, "loss": 0.4582, "time": 0.49297} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.01668, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9175, "top5_acc": 0.9975, "loss_cls": 0.43454, "loss": 0.43454, "time": 0.49288} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.01667, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90625, "top5_acc": 0.9975, "loss_cls": 0.48388, "loss": 0.48388, "time": 0.49289} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.01665, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9075, "top5_acc": 0.99688, "loss_cls": 0.50076, "loss": 0.50076, "time": 0.49326} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.01663, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9, "top5_acc": 0.995, "loss_cls": 0.54438, "loss": 0.54438, "time": 0.49323} +{"mode": "val", "epoch": 59, "iter": 533, "lr": 0.01661, "top1_acc": 0.86175, "top5_acc": 0.99061, "mean_class_accuracy": 0.81107} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.01659, "memory": 4083, "data_time": 0.19313, "top1_acc": 0.92562, "top5_acc": 0.99688, "loss_cls": 0.42499, "loss": 0.42499, "time": 0.7978} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.01657, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.92562, "top5_acc": 0.99938, "loss_cls": 0.41113, "loss": 0.41113, "time": 0.49235} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.01655, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.905, "top5_acc": 0.995, "loss_cls": 0.51, "loss": 0.51, "time": 0.37098} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.01653, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.91562, "top5_acc": 0.9975, "loss_cls": 0.45219, "loss": 0.45219, "time": 0.51125} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.01651, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89688, "top5_acc": 0.99625, "loss_cls": 0.50757, "loss": 0.50757, "time": 0.22938} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.0165, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91438, "top5_acc": 0.99875, "loss_cls": 0.45827, "loss": 0.45827, "time": 0.44146} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.01648, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.895, "top5_acc": 0.9975, "loss_cls": 0.49733, "loss": 0.49733, "time": 0.49424} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.01646, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90562, "top5_acc": 0.9975, "loss_cls": 0.46743, "loss": 0.46743, "time": 0.49513} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.01644, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91062, "top5_acc": 0.99812, "loss_cls": 0.43161, "loss": 0.43161, "time": 0.4951} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.01642, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90875, "top5_acc": 0.99438, "loss_cls": 0.4973, "loss": 0.4973, "time": 0.49106} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.0164, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91062, "top5_acc": 0.99688, "loss_cls": 0.46185, "loss": 0.46185, "time": 0.49354} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.01638, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.91188, "top5_acc": 0.99812, "loss_cls": 0.45826, "loss": 0.45826, "time": 0.49453} +{"mode": "val", "epoch": 60, "iter": 533, "lr": 0.01636, "top1_acc": 0.87724, "top5_acc": 0.99179, "mean_class_accuracy": 0.8209} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.01634, "memory": 4083, "data_time": 0.18642, "top1_acc": 0.91625, "top5_acc": 0.9975, "loss_cls": 0.44099, "loss": 0.44099, "time": 0.79137} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.01632, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90188, "top5_acc": 0.99938, "loss_cls": 0.46698, "loss": 0.46698, "time": 0.48938} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.0163, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91375, "top5_acc": 0.99875, "loss_cls": 0.40554, "loss": 0.40554, "time": 0.40165} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.01629, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9275, "top5_acc": 0.99812, "loss_cls": 0.38676, "loss": 0.38676, "time": 0.48458} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.01627, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.89812, "top5_acc": 0.995, "loss_cls": 0.51429, "loss": 0.51429, "time": 0.24958} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.01625, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92375, "top5_acc": 0.99812, "loss_cls": 0.42978, "loss": 0.42978, "time": 0.42549} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.01623, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89938, "top5_acc": 0.99688, "loss_cls": 0.50428, "loss": 0.50428, "time": 0.49213} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.01621, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91125, "top5_acc": 0.99812, "loss_cls": 0.44777, "loss": 0.44777, "time": 0.49245} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.01619, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90812, "top5_acc": 0.99812, "loss_cls": 0.46399, "loss": 0.46399, "time": 0.49085} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.01617, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91, "top5_acc": 0.99625, "loss_cls": 0.45409, "loss": 0.45409, "time": 0.49065} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.01615, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89938, "top5_acc": 0.995, "loss_cls": 0.49665, "loss": 0.49665, "time": 0.49043} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.01613, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90625, "top5_acc": 0.99625, "loss_cls": 0.50872, "loss": 0.50872, "time": 0.4915} +{"mode": "val", "epoch": 61, "iter": 533, "lr": 0.01611, "top1_acc": 0.84884, "top5_acc": 0.98979, "mean_class_accuracy": 0.8209} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.01609, "memory": 4083, "data_time": 0.1909, "top1_acc": 0.90438, "top5_acc": 0.99688, "loss_cls": 0.48578, "loss": 0.48578, "time": 0.78635} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.01607, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.925, "top5_acc": 0.99812, "loss_cls": 0.39891, "loss": 0.39891, "time": 0.49112} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.01605, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90875, "top5_acc": 0.99812, "loss_cls": 0.45691, "loss": 0.45691, "time": 0.43731} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.01603, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91375, "top5_acc": 0.9975, "loss_cls": 0.46289, "loss": 0.46289, "time": 0.41452} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.01602, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.90875, "top5_acc": 0.99625, "loss_cls": 0.48146, "loss": 0.48146, "time": 0.31889} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.016, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92625, "top5_acc": 0.99812, "loss_cls": 0.42724, "loss": 0.42724, "time": 0.40229} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.01598, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91688, "top5_acc": 0.99625, "loss_cls": 0.44026, "loss": 0.44026, "time": 0.49393} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.01596, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91688, "top5_acc": 0.99875, "loss_cls": 0.43973, "loss": 0.43973, "time": 0.49037} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.01594, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9175, "top5_acc": 0.99688, "loss_cls": 0.44253, "loss": 0.44253, "time": 0.49489} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.01592, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9125, "top5_acc": 0.99938, "loss_cls": 0.44723, "loss": 0.44723, "time": 0.49118} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.0159, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90625, "top5_acc": 0.9975, "loss_cls": 0.49985, "loss": 0.49985, "time": 0.4896} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.01588, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90625, "top5_acc": 0.99562, "loss_cls": 0.50125, "loss": 0.50125, "time": 0.49421} +{"mode": "val", "epoch": 62, "iter": 533, "lr": 0.01586, "top1_acc": 0.88088, "top5_acc": 0.99225, "mean_class_accuracy": 0.8427} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.01584, "memory": 4083, "data_time": 0.18493, "top1_acc": 0.9275, "top5_acc": 0.99875, "loss_cls": 0.3936, "loss": 0.3936, "time": 0.78132} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.01582, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92, "top5_acc": 1.0, "loss_cls": 0.40391, "loss": 0.40391, "time": 0.49515} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.0158, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92625, "top5_acc": 0.99812, "loss_cls": 0.42364, "loss": 0.42364, "time": 0.46927} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.01578, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91938, "top5_acc": 0.99812, "loss_cls": 0.42269, "loss": 0.42269, "time": 0.36996} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.01576, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.91312, "top5_acc": 0.99625, "loss_cls": 0.44903, "loss": 0.44903, "time": 0.36745} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.01574, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91375, "top5_acc": 0.99812, "loss_cls": 0.47486, "loss": 0.47486, "time": 0.38932} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.01572, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.91375, "top5_acc": 0.99562, "loss_cls": 0.47777, "loss": 0.47777, "time": 0.4904} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.0157, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92375, "top5_acc": 0.99625, "loss_cls": 0.42553, "loss": 0.42553, "time": 0.4915} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.01568, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92062, "top5_acc": 0.99812, "loss_cls": 0.43504, "loss": 0.43504, "time": 0.48827} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.01566, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91562, "top5_acc": 0.9975, "loss_cls": 0.45709, "loss": 0.45709, "time": 0.49253} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.01564, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92188, "top5_acc": 0.99875, "loss_cls": 0.42893, "loss": 0.42893, "time": 0.49732} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.01562, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91062, "top5_acc": 0.99625, "loss_cls": 0.47029, "loss": 0.47029, "time": 0.49107} +{"mode": "val", "epoch": 63, "iter": 533, "lr": 0.01561, "top1_acc": 0.86304, "top5_acc": 0.99296, "mean_class_accuracy": 0.79797} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.01559, "memory": 4083, "data_time": 0.19085, "top1_acc": 0.93062, "top5_acc": 0.9975, "loss_cls": 0.39674, "loss": 0.39674, "time": 0.78796} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.01557, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91938, "top5_acc": 0.99875, "loss_cls": 0.41683, "loss": 0.41683, "time": 0.49185} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.01555, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9275, "top5_acc": 0.99875, "loss_cls": 0.41061, "loss": 0.41061, "time": 0.47051} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.01553, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.94, "top5_acc": 0.995, "loss_cls": 0.36898, "loss": 0.36898, "time": 0.35238} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.01551, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92562, "top5_acc": 0.99812, "loss_cls": 0.41993, "loss": 0.41993, "time": 0.38389} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.01549, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90562, "top5_acc": 0.99688, "loss_cls": 0.4903, "loss": 0.4903, "time": 0.36484} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.01547, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.90125, "top5_acc": 0.995, "loss_cls": 0.5002, "loss": 0.5002, "time": 0.49317} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.01545, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91688, "top5_acc": 0.99875, "loss_cls": 0.40621, "loss": 0.40621, "time": 0.4898} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.01543, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90938, "top5_acc": 0.99812, "loss_cls": 0.45657, "loss": 0.45657, "time": 0.49162} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.01541, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9125, "top5_acc": 0.99875, "loss_cls": 0.4759, "loss": 0.4759, "time": 0.49174} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.01539, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91562, "top5_acc": 0.9975, "loss_cls": 0.43701, "loss": 0.43701, "time": 0.49272} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.01537, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91062, "top5_acc": 0.99562, "loss_cls": 0.47524, "loss": 0.47524, "time": 0.49518} +{"mode": "val", "epoch": 64, "iter": 533, "lr": 0.01535, "top1_acc": 0.8749, "top5_acc": 0.99167, "mean_class_accuracy": 0.82762} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.01533, "memory": 4083, "data_time": 0.20075, "top1_acc": 0.92062, "top5_acc": 0.99688, "loss_cls": 0.42096, "loss": 0.42096, "time": 0.80117} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.01531, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.41321, "loss": 0.41321, "time": 0.49174} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.01529, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92875, "top5_acc": 0.99875, "loss_cls": 0.38771, "loss": 0.38771, "time": 0.48249} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.01527, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.91938, "top5_acc": 0.99812, "loss_cls": 0.39919, "loss": 0.39919, "time": 0.32015} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.01526, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91625, "top5_acc": 0.99938, "loss_cls": 0.43093, "loss": 0.43093, "time": 0.41553} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.01524, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93438, "top5_acc": 0.99812, "loss_cls": 0.37931, "loss": 0.37931, "time": 0.34342} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.01522, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91062, "top5_acc": 0.9975, "loss_cls": 0.46278, "loss": 0.46278, "time": 0.4945} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0152, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.90688, "top5_acc": 0.99625, "loss_cls": 0.46991, "loss": 0.46991, "time": 0.49124} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.01518, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.8875, "top5_acc": 0.99438, "loss_cls": 0.52861, "loss": 0.52861, "time": 0.48676} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.01516, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.90688, "top5_acc": 0.99375, "loss_cls": 0.49845, "loss": 0.49845, "time": 0.49041} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.01514, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92375, "top5_acc": 0.99688, "loss_cls": 0.43724, "loss": 0.43724, "time": 0.49294} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.01512, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90438, "top5_acc": 0.99625, "loss_cls": 0.47282, "loss": 0.47282, "time": 0.48839} +{"mode": "val", "epoch": 65, "iter": 533, "lr": 0.0151, "top1_acc": 0.88581, "top5_acc": 0.9939, "mean_class_accuracy": 0.84447} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.01508, "memory": 4083, "data_time": 0.1903, "top1_acc": 0.91812, "top5_acc": 0.99812, "loss_cls": 0.41141, "loss": 0.41141, "time": 0.79378} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.01506, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9275, "top5_acc": 0.9975, "loss_cls": 0.40778, "loss": 0.40778, "time": 0.49341} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.01504, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91625, "top5_acc": 0.99812, "loss_cls": 0.42548, "loss": 0.42548, "time": 0.49206} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.01502, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.93375, "top5_acc": 0.99812, "loss_cls": 0.36692, "loss": 0.36692, "time": 0.27867} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.015, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91125, "top5_acc": 0.99688, "loss_cls": 0.42409, "loss": 0.42409, "time": 0.47041} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.01498, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90062, "top5_acc": 0.99625, "loss_cls": 0.50167, "loss": 0.50167, "time": 0.31904} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.01496, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92562, "top5_acc": 1.0, "loss_cls": 0.38676, "loss": 0.38676, "time": 0.49248} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.01494, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91812, "top5_acc": 0.99625, "loss_cls": 0.45778, "loss": 0.45778, "time": 0.49446} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.01492, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92562, "top5_acc": 0.99562, "loss_cls": 0.41387, "loss": 0.41387, "time": 0.49008} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.0149, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92, "top5_acc": 0.99875, "loss_cls": 0.40281, "loss": 0.40281, "time": 0.49473} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.01488, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92062, "top5_acc": 0.995, "loss_cls": 0.43606, "loss": 0.43606, "time": 0.49192} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.01486, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92625, "top5_acc": 0.99812, "loss_cls": 0.43223, "loss": 0.43223, "time": 0.4901} +{"mode": "val", "epoch": 66, "iter": 533, "lr": 0.01484, "top1_acc": 0.87114, "top5_acc": 0.99073, "mean_class_accuracy": 0.82887} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.01482, "memory": 4083, "data_time": 0.18845, "top1_acc": 0.93375, "top5_acc": 0.99688, "loss_cls": 0.36073, "loss": 0.36073, "time": 0.79613} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.0148, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92938, "top5_acc": 0.99938, "loss_cls": 0.36972, "loss": 0.36972, "time": 0.49191} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.01478, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91625, "top5_acc": 0.99812, "loss_cls": 0.43264, "loss": 0.43264, "time": 0.48771} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.01476, "memory": 4083, "data_time": 0.00073, "top1_acc": 0.92, "top5_acc": 0.99562, "loss_cls": 0.42824, "loss": 0.42824, "time": 0.27109} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.01474, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94375, "top5_acc": 0.99812, "loss_cls": 0.34711, "loss": 0.34711, "time": 0.51019} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.01472, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91125, "top5_acc": 0.99688, "loss_cls": 0.45373, "loss": 0.45373, "time": 0.28497} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.0147, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93312, "top5_acc": 0.99875, "loss_cls": 0.40595, "loss": 0.40595, "time": 0.49378} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.01468, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.91312, "top5_acc": 0.9975, "loss_cls": 0.43488, "loss": 0.43488, "time": 0.49409} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.01466, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93812, "top5_acc": 1.0, "loss_cls": 0.38446, "loss": 0.38446, "time": 0.49415} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.01464, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91938, "top5_acc": 0.99812, "loss_cls": 0.43064, "loss": 0.43064, "time": 0.4905} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.01462, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.42461, "loss": 0.42461, "time": 0.49142} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.0146, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91562, "top5_acc": 0.99875, "loss_cls": 0.42618, "loss": 0.42618, "time": 0.49561} +{"mode": "val", "epoch": 67, "iter": 533, "lr": 0.01458, "top1_acc": 0.88652, "top5_acc": 0.99331, "mean_class_accuracy": 0.84286} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.01456, "memory": 4083, "data_time": 0.19087, "top1_acc": 0.92312, "top5_acc": 0.99812, "loss_cls": 0.42906, "loss": 0.42906, "time": 0.80525} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.01454, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93875, "top5_acc": 0.9975, "loss_cls": 0.37425, "loss": 0.37425, "time": 0.48876} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.01452, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92062, "top5_acc": 0.99812, "loss_cls": 0.41915, "loss": 0.41915, "time": 0.49396} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.0145, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.9275, "top5_acc": 0.99625, "loss_cls": 0.40323, "loss": 0.40323, "time": 0.30988} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.01448, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9275, "top5_acc": 1.0, "loss_cls": 0.39812, "loss": 0.39812, "time": 0.50956} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.01446, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.37696, "loss": 0.37696, "time": 0.26083} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.01444, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9325, "top5_acc": 0.99812, "loss_cls": 0.39839, "loss": 0.39839, "time": 0.49427} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.01442, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91938, "top5_acc": 0.99688, "loss_cls": 0.45392, "loss": 0.45392, "time": 0.49052} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.0144, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91875, "top5_acc": 0.99625, "loss_cls": 0.43481, "loss": 0.43481, "time": 0.49186} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.01438, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9325, "top5_acc": 0.99875, "loss_cls": 0.37433, "loss": 0.37433, "time": 0.4899} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.01436, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.38696, "loss": 0.38696, "time": 0.49201} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.01434, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91312, "top5_acc": 0.99562, "loss_cls": 0.43792, "loss": 0.43792, "time": 0.49218} +{"mode": "val", "epoch": 68, "iter": 533, "lr": 0.01433, "top1_acc": 0.86598, "top5_acc": 0.99061, "mean_class_accuracy": 0.82674} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.01431, "memory": 4083, "data_time": 0.19153, "top1_acc": 0.92625, "top5_acc": 0.99562, "loss_cls": 0.40539, "loss": 0.40539, "time": 0.79743} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.01429, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.92812, "top5_acc": 0.99812, "loss_cls": 0.39125, "loss": 0.39125, "time": 0.49262} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.01427, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.915, "top5_acc": 1.0, "loss_cls": 0.42188, "loss": 0.42188, "time": 0.49056} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.01425, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92625, "top5_acc": 1.0, "loss_cls": 0.38596, "loss": 0.38596, "time": 0.33778} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.01423, "memory": 4083, "data_time": 0.00067, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.39985, "loss": 0.39985, "time": 0.51119} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.0142, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93188, "top5_acc": 0.99812, "loss_cls": 0.37787, "loss": 0.37787, "time": 0.25031} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.01418, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92812, "top5_acc": 0.99938, "loss_cls": 0.38346, "loss": 0.38346, "time": 0.47475} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.01416, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.92938, "top5_acc": 0.99812, "loss_cls": 0.38447, "loss": 0.38447, "time": 0.49454} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.01414, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.42662, "loss": 0.42662, "time": 0.4923} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.01412, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92688, "top5_acc": 0.99938, "loss_cls": 0.40107, "loss": 0.40107, "time": 0.49204} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.0141, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.9125, "top5_acc": 0.9975, "loss_cls": 0.43004, "loss": 0.43004, "time": 0.49293} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.01408, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91, "top5_acc": 0.995, "loss_cls": 0.46029, "loss": 0.46029, "time": 0.49398} +{"mode": "val", "epoch": 69, "iter": 533, "lr": 0.01407, "top1_acc": 0.88616, "top5_acc": 0.9919, "mean_class_accuracy": 0.83834} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.01405, "memory": 4083, "data_time": 0.19568, "top1_acc": 0.93625, "top5_acc": 0.99875, "loss_cls": 0.33608, "loss": 0.33608, "time": 0.80414} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.01403, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93625, "top5_acc": 0.9975, "loss_cls": 0.36643, "loss": 0.36643, "time": 0.49269} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.01401, "memory": 4083, "data_time": 0.00063, "top1_acc": 0.935, "top5_acc": 0.99875, "loss_cls": 0.3292, "loss": 0.3292, "time": 0.49219} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.01399, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.93188, "top5_acc": 0.99625, "loss_cls": 0.37707, "loss": 0.37707, "time": 0.34291} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.01397, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.92562, "top5_acc": 0.99688, "loss_cls": 0.39774, "loss": 0.39774, "time": 0.51008} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.01395, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9375, "top5_acc": 0.99812, "loss_cls": 0.34566, "loss": 0.34566, "time": 0.24558} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.01392, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93188, "top5_acc": 0.99688, "loss_cls": 0.36879, "loss": 0.36879, "time": 0.469} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.0139, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.92688, "top5_acc": 0.99875, "loss_cls": 0.389, "loss": 0.389, "time": 0.49089} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.01388, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.44219, "loss": 0.44219, "time": 0.48947} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.01386, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89438, "top5_acc": 0.99625, "loss_cls": 0.51324, "loss": 0.51324, "time": 0.49214} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.01384, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91375, "top5_acc": 0.99875, "loss_cls": 0.44417, "loss": 0.44417, "time": 0.49442} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.01382, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.43094, "loss": 0.43094, "time": 0.49362} +{"mode": "val", "epoch": 70, "iter": 533, "lr": 0.01381, "top1_acc": 0.87185, "top5_acc": 0.99179, "mean_class_accuracy": 0.82769} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.01379, "memory": 4083, "data_time": 0.19444, "top1_acc": 0.93188, "top5_acc": 0.99688, "loss_cls": 0.38797, "loss": 0.38797, "time": 0.80044} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.01377, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93812, "top5_acc": 0.99875, "loss_cls": 0.34819, "loss": 0.34819, "time": 0.494} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.01375, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.33633, "loss": 0.33633, "time": 0.49464} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.01373, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.92125, "top5_acc": 0.99938, "loss_cls": 0.40548, "loss": 0.40548, "time": 0.35538} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.01371, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.915, "top5_acc": 0.99875, "loss_cls": 0.43751, "loss": 0.43751, "time": 0.51066} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.01368, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91312, "top5_acc": 0.99375, "loss_cls": 0.46173, "loss": 0.46173, "time": 0.24145} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.01366, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9225, "top5_acc": 0.99938, "loss_cls": 0.4042, "loss": 0.4042, "time": 0.45588} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.01364, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91938, "top5_acc": 0.99688, "loss_cls": 0.42088, "loss": 0.42088, "time": 0.49137} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.01362, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93375, "top5_acc": 0.99688, "loss_cls": 0.39585, "loss": 0.39585, "time": 0.49154} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.0136, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.41747, "loss": 0.41747, "time": 0.4952} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.01358, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92125, "top5_acc": 0.99875, "loss_cls": 0.42071, "loss": 0.42071, "time": 0.49392} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.01356, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94188, "top5_acc": 0.99875, "loss_cls": 0.34142, "loss": 0.34142, "time": 0.4909} +{"mode": "val", "epoch": 71, "iter": 533, "lr": 0.01355, "top1_acc": 0.88792, "top5_acc": 0.99378, "mean_class_accuracy": 0.8481} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.01353, "memory": 4083, "data_time": 0.19289, "top1_acc": 0.93188, "top5_acc": 0.99938, "loss_cls": 0.3509, "loss": 0.3509, "time": 0.79329} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.01351, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.94125, "top5_acc": 0.99875, "loss_cls": 0.34321, "loss": 0.34321, "time": 0.49147} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.01349, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.41869, "loss": 0.41869, "time": 0.493} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.01346, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93562, "top5_acc": 0.99812, "loss_cls": 0.34739, "loss": 0.34739, "time": 0.38536} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.01344, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93062, "top5_acc": 0.99875, "loss_cls": 0.39568, "loss": 0.39568, "time": 0.51188} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.01342, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.93562, "top5_acc": 0.99812, "loss_cls": 0.36045, "loss": 0.36045, "time": 0.23467} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.0134, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92562, "top5_acc": 0.99875, "loss_cls": 0.36911, "loss": 0.36911, "time": 0.43738} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.01338, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9125, "top5_acc": 0.99875, "loss_cls": 0.43387, "loss": 0.43387, "time": 0.49128} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.01336, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9075, "top5_acc": 0.99688, "loss_cls": 0.44707, "loss": 0.44707, "time": 0.49273} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.01334, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93, "top5_acc": 0.9975, "loss_cls": 0.37699, "loss": 0.37699, "time": 0.4935} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.01332, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91438, "top5_acc": 0.99562, "loss_cls": 0.46713, "loss": 0.46713, "time": 0.49228} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.0133, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92188, "top5_acc": 0.995, "loss_cls": 0.4084, "loss": 0.4084, "time": 0.48884} +{"mode": "val", "epoch": 72, "iter": 533, "lr": 0.01329, "top1_acc": 0.84145, "top5_acc": 0.98768, "mean_class_accuracy": 0.79436} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.01326, "memory": 4083, "data_time": 0.19034, "top1_acc": 0.92062, "top5_acc": 0.99812, "loss_cls": 0.42974, "loss": 0.42974, "time": 0.79008} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.01324, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.38386, "loss": 0.38386, "time": 0.4925} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.01322, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93562, "top5_acc": 0.9975, "loss_cls": 0.37005, "loss": 0.37005, "time": 0.49269} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.0132, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92375, "top5_acc": 0.99812, "loss_cls": 0.38183, "loss": 0.38183, "time": 0.42981} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.01318, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.92938, "top5_acc": 1.0, "loss_cls": 0.36287, "loss": 0.36287, "time": 0.43696} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.01316, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.93062, "top5_acc": 0.99812, "loss_cls": 0.37038, "loss": 0.37038, "time": 0.29752} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.01314, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.92438, "top5_acc": 0.99938, "loss_cls": 0.38604, "loss": 0.38604, "time": 0.412} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.01312, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9325, "top5_acc": 0.99875, "loss_cls": 0.35309, "loss": 0.35309, "time": 0.48995} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.0131, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92812, "top5_acc": 0.99875, "loss_cls": 0.38348, "loss": 0.38348, "time": 0.49187} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.01308, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94688, "top5_acc": 0.99875, "loss_cls": 0.30236, "loss": 0.30236, "time": 0.49198} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.01306, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92375, "top5_acc": 0.99562, "loss_cls": 0.39165, "loss": 0.39165, "time": 0.4919} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.01304, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.42508, "loss": 0.42508, "time": 0.49535} +{"mode": "val", "epoch": 73, "iter": 533, "lr": 0.01302, "top1_acc": 0.86739, "top5_acc": 0.99049, "mean_class_accuracy": 0.83503} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.013, "memory": 4083, "data_time": 0.19055, "top1_acc": 0.94312, "top5_acc": 1.0, "loss_cls": 0.34443, "loss": 0.34443, "time": 0.79467} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.01298, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93812, "top5_acc": 1.0, "loss_cls": 0.31114, "loss": 0.31114, "time": 0.49141} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.01296, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.29733, "loss": 0.29733, "time": 0.49402} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.01294, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.93312, "top5_acc": 0.99938, "loss_cls": 0.36045, "loss": 0.36045, "time": 0.45367} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.01292, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.93875, "top5_acc": 0.9975, "loss_cls": 0.34533, "loss": 0.34533, "time": 0.3896} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.0129, "memory": 4083, "data_time": 0.0007, "top1_acc": 0.92062, "top5_acc": 1.0, "loss_cls": 0.37383, "loss": 0.37383, "time": 0.34949} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.01288, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91812, "top5_acc": 0.9975, "loss_cls": 0.41643, "loss": 0.41643, "time": 0.37953} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.01286, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.9225, "top5_acc": 0.9975, "loss_cls": 0.39926, "loss": 0.39926, "time": 0.49143} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.01284, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.40205, "loss": 0.40205, "time": 0.49308} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.01282, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93312, "top5_acc": 0.9975, "loss_cls": 0.37022, "loss": 0.37022, "time": 0.49177} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.0128, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93062, "top5_acc": 0.99875, "loss_cls": 0.34843, "loss": 0.34843, "time": 0.49252} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.01278, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93875, "top5_acc": 0.99812, "loss_cls": 0.36226, "loss": 0.36226, "time": 0.49088} +{"mode": "val", "epoch": 74, "iter": 533, "lr": 0.01276, "top1_acc": 0.90189, "top5_acc": 0.99413, "mean_class_accuracy": 0.86481} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.01274, "memory": 4083, "data_time": 0.18648, "top1_acc": 0.94812, "top5_acc": 1.0, "loss_cls": 0.32626, "loss": 0.32626, "time": 0.79896} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.01272, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94, "top5_acc": 1.0, "loss_cls": 0.33278, "loss": 0.33278, "time": 0.49378} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.0127, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.34376, "loss": 0.34376, "time": 0.49176} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.01268, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.36126, "loss": 0.36126, "time": 0.47668} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.01266, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92375, "top5_acc": 0.99875, "loss_cls": 0.41123, "loss": 0.41123, "time": 0.34852} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.01264, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.92312, "top5_acc": 0.99875, "loss_cls": 0.39114, "loss": 0.39114, "time": 0.38657} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.01262, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.92625, "top5_acc": 0.99938, "loss_cls": 0.39842, "loss": 0.39842, "time": 0.37161} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.0126, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92438, "top5_acc": 0.99938, "loss_cls": 0.39104, "loss": 0.39104, "time": 0.49204} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.01258, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92375, "top5_acc": 0.9975, "loss_cls": 0.39886, "loss": 0.39886, "time": 0.49232} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.01256, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93375, "top5_acc": 0.99688, "loss_cls": 0.36775, "loss": 0.36775, "time": 0.49136} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.01254, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93562, "top5_acc": 0.9975, "loss_cls": 0.36488, "loss": 0.36488, "time": 0.49574} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.01252, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.93312, "top5_acc": 0.99938, "loss_cls": 0.35985, "loss": 0.35985, "time": 0.49321} +{"mode": "val", "epoch": 75, "iter": 533, "lr": 0.0125, "top1_acc": 0.87818, "top5_acc": 0.99202, "mean_class_accuracy": 0.84098} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.01248, "memory": 4083, "data_time": 0.18465, "top1_acc": 0.92562, "top5_acc": 0.99938, "loss_cls": 0.36767, "loss": 0.36767, "time": 0.78435} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.01246, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.935, "top5_acc": 0.99938, "loss_cls": 0.37512, "loss": 0.37512, "time": 0.48892} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.01244, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93875, "top5_acc": 0.99812, "loss_cls": 0.3273, "loss": 0.3273, "time": 0.49161} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.01242, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95062, "top5_acc": 0.99812, "loss_cls": 0.28371, "loss": 0.28371, "time": 0.49561} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.0124, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.93312, "top5_acc": 0.99812, "loss_cls": 0.36832, "loss": 0.36832, "time": 0.32168} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.01238, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92625, "top5_acc": 1.0, "loss_cls": 0.35258, "loss": 0.35258, "time": 0.42249} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.01236, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9225, "top5_acc": 0.99938, "loss_cls": 0.39393, "loss": 0.39393, "time": 0.36417} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.01234, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.925, "top5_acc": 0.99812, "loss_cls": 0.38966, "loss": 0.38966, "time": 0.49038} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.01232, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.935, "top5_acc": 1.0, "loss_cls": 0.36205, "loss": 0.36205, "time": 0.49164} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.0123, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92625, "top5_acc": 0.99688, "loss_cls": 0.40266, "loss": 0.40266, "time": 0.49108} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.01228, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92188, "top5_acc": 0.99625, "loss_cls": 0.4251, "loss": 0.4251, "time": 0.48938} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.01225, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91625, "top5_acc": 0.9975, "loss_cls": 0.42937, "loss": 0.42937, "time": 0.49367} +{"mode": "val", "epoch": 76, "iter": 533, "lr": 0.01224, "top1_acc": 0.87959, "top5_acc": 0.99237, "mean_class_accuracy": 0.85743} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.01222, "memory": 4083, "data_time": 0.18688, "top1_acc": 0.9375, "top5_acc": 0.99875, "loss_cls": 0.3305, "loss": 0.3305, "time": 0.7879} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0122, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.29018, "loss": 0.29018, "time": 0.49201} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.01218, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.30644, "loss": 0.30644, "time": 0.4911} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.01216, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93375, "top5_acc": 0.99812, "loss_cls": 0.35921, "loss": 0.35921, "time": 0.49045} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.01214, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.94188, "top5_acc": 0.99875, "loss_cls": 0.32958, "loss": 0.32958, "time": 0.3125} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.01212, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.9275, "top5_acc": 0.99812, "loss_cls": 0.36845, "loss": 0.36845, "time": 0.4303} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.0121, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.93562, "top5_acc": 0.99812, "loss_cls": 0.36236, "loss": 0.36236, "time": 0.35979} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.01207, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.34886, "loss": 0.34886, "time": 0.49222} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.01205, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92062, "top5_acc": 0.99688, "loss_cls": 0.38374, "loss": 0.38374, "time": 0.49028} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.01203, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93625, "top5_acc": 0.99875, "loss_cls": 0.37941, "loss": 0.37941, "time": 0.49402} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.01201, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93562, "top5_acc": 0.99812, "loss_cls": 0.34592, "loss": 0.34592, "time": 0.49774} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.01199, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93188, "top5_acc": 0.99875, "loss_cls": 0.35884, "loss": 0.35884, "time": 0.4907} +{"mode": "val", "epoch": 77, "iter": 533, "lr": 0.01198, "top1_acc": 0.89473, "top5_acc": 0.99296, "mean_class_accuracy": 0.85105} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.01196, "memory": 4083, "data_time": 0.1857, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.308, "loss": 0.308, "time": 0.80963} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.01194, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.31424, "loss": 0.31424, "time": 0.49248} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.01192, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9575, "top5_acc": 0.99875, "loss_cls": 0.25747, "loss": 0.25747, "time": 0.49244} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.0119, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.3178, "loss": 0.3178, "time": 0.49487} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.01187, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93688, "top5_acc": 1.0, "loss_cls": 0.36052, "loss": 0.36052, "time": 0.29641} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.01185, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.39013, "loss": 0.39013, "time": 0.44469} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.01183, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.93312, "top5_acc": 0.9975, "loss_cls": 0.37368, "loss": 0.37368, "time": 0.3391} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.01181, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.94438, "top5_acc": 0.99812, "loss_cls": 0.32653, "loss": 0.32653, "time": 0.49123} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.01179, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94625, "top5_acc": 0.99875, "loss_cls": 0.32987, "loss": 0.32987, "time": 0.49038} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.01177, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.925, "top5_acc": 1.0, "loss_cls": 0.37383, "loss": 0.37383, "time": 0.49437} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.01175, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92875, "top5_acc": 0.99812, "loss_cls": 0.34584, "loss": 0.34584, "time": 0.49336} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.01173, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.92438, "top5_acc": 0.99875, "loss_cls": 0.40567, "loss": 0.40567, "time": 0.49104} +{"mode": "val", "epoch": 78, "iter": 533, "lr": 0.01172, "top1_acc": 0.89731, "top5_acc": 0.9946, "mean_class_accuracy": 0.8564} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.01169, "memory": 4083, "data_time": 0.18693, "top1_acc": 0.94438, "top5_acc": 0.99812, "loss_cls": 0.34172, "loss": 0.34172, "time": 0.78924} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.01167, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.29874, "loss": 0.29874, "time": 0.49358} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.01165, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.28716, "loss": 0.28716, "time": 0.4874} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.01163, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93562, "top5_acc": 0.99875, "loss_cls": 0.36305, "loss": 0.36305, "time": 0.492} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.01161, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92125, "top5_acc": 0.99812, "loss_cls": 0.39318, "loss": 0.39318, "time": 0.27983} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.01159, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92438, "top5_acc": 0.99938, "loss_cls": 0.40549, "loss": 0.40549, "time": 0.48754} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.01157, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.93375, "top5_acc": 0.9975, "loss_cls": 0.34653, "loss": 0.34653, "time": 0.32269} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.01155, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.30653, "loss": 0.30653, "time": 0.48673} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.01153, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.945, "top5_acc": 0.99938, "loss_cls": 0.30407, "loss": 0.30407, "time": 0.48965} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.01151, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.30918, "loss": 0.30918, "time": 0.4897} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.01149, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.31981, "loss": 0.31981, "time": 0.49303} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.01147, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.93375, "top5_acc": 0.9975, "loss_cls": 0.36472, "loss": 0.36472, "time": 0.49277} +{"mode": "val", "epoch": 79, "iter": 533, "lr": 0.01145, "top1_acc": 0.89426, "top5_acc": 0.99484, "mean_class_accuracy": 0.86067} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.01143, "memory": 4083, "data_time": 0.18265, "top1_acc": 0.94562, "top5_acc": 0.99875, "loss_cls": 0.35134, "loss": 0.35134, "time": 0.77617} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.01141, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94188, "top5_acc": 0.99875, "loss_cls": 0.3242, "loss": 0.3242, "time": 0.49035} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.01139, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95812, "top5_acc": 0.99875, "loss_cls": 0.26459, "loss": 0.26459, "time": 0.49168} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.01137, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.29165, "loss": 0.29165, "time": 0.49369} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.01135, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93375, "top5_acc": 1.0, "loss_cls": 0.33037, "loss": 0.33037, "time": 0.29712} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.01133, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9425, "top5_acc": 0.9975, "loss_cls": 0.29902, "loss": 0.29902, "time": 0.51032} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.01131, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.94562, "top5_acc": 0.99875, "loss_cls": 0.31378, "loss": 0.31378, "time": 0.30263} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.01129, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.94125, "top5_acc": 0.99812, "loss_cls": 0.34277, "loss": 0.34277, "time": 0.49141} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.01127, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9475, "top5_acc": 0.99875, "loss_cls": 0.31753, "loss": 0.31753, "time": 0.49123} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.01125, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.3952, "loss": 0.3952, "time": 0.4877} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.01123, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95125, "top5_acc": 0.99875, "loss_cls": 0.31638, "loss": 0.31638, "time": 0.49168} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.01121, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.33629, "loss": 0.33629, "time": 0.49354} +{"mode": "val", "epoch": 80, "iter": 533, "lr": 0.01119, "top1_acc": 0.89907, "top5_acc": 0.99472, "mean_class_accuracy": 0.86997} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.01117, "memory": 4083, "data_time": 0.1932, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.27073, "loss": 0.27073, "time": 0.80362} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.01115, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.32051, "loss": 0.32051, "time": 0.49073} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.01113, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95625, "top5_acc": 0.99875, "loss_cls": 0.28042, "loss": 0.28042, "time": 0.49113} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.01111, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92375, "top5_acc": 0.99938, "loss_cls": 0.37367, "loss": 0.37367, "time": 0.49101} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.01109, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.93375, "top5_acc": 0.99812, "loss_cls": 0.36415, "loss": 0.36415, "time": 0.27732} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.01107, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93625, "top5_acc": 0.99875, "loss_cls": 0.32434, "loss": 0.32434, "time": 0.51035} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.01105, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.93688, "top5_acc": 1.0, "loss_cls": 0.3222, "loss": 0.3222, "time": 0.2997} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.01103, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94375, "top5_acc": 1.0, "loss_cls": 0.33467, "loss": 0.33467, "time": 0.48842} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.01101, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.94562, "top5_acc": 0.99875, "loss_cls": 0.31069, "loss": 0.31069, "time": 0.49011} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.01099, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91938, "top5_acc": 0.9975, "loss_cls": 0.42469, "loss": 0.42469, "time": 0.49263} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.01097, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.94625, "top5_acc": 0.99812, "loss_cls": 0.30137, "loss": 0.30137, "time": 0.49276} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.01095, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93062, "top5_acc": 0.99875, "loss_cls": 0.3276, "loss": 0.3276, "time": 0.49066} +{"mode": "val", "epoch": 81, "iter": 533, "lr": 0.01093, "top1_acc": 0.89332, "top5_acc": 0.99237, "mean_class_accuracy": 0.85773} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.01091, "memory": 4083, "data_time": 0.18833, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.33642, "loss": 0.33642, "time": 0.78935} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.01089, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94188, "top5_acc": 0.99938, "loss_cls": 0.29533, "loss": 0.29533, "time": 0.49075} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.01087, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.25004, "loss": 0.25004, "time": 0.49148} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.01085, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.31673, "loss": 0.31673, "time": 0.49358} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.01083, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.32768, "loss": 0.32768, "time": 0.29701} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.01081, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93688, "top5_acc": 0.99875, "loss_cls": 0.30714, "loss": 0.30714, "time": 0.51113} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.01079, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.35879, "loss": 0.35879, "time": 0.2881} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.01077, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.94375, "top5_acc": 0.99875, "loss_cls": 0.32787, "loss": 0.32787, "time": 0.49361} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.01075, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.9475, "top5_acc": 0.99875, "loss_cls": 0.28391, "loss": 0.28391, "time": 0.48653} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.01073, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93938, "top5_acc": 0.9975, "loss_cls": 0.34647, "loss": 0.34647, "time": 0.49243} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.01071, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.31054, "loss": 0.31054, "time": 0.49421} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.01069, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93688, "top5_acc": 0.99812, "loss_cls": 0.34176, "loss": 0.34176, "time": 0.49075} +{"mode": "val", "epoch": 82, "iter": 533, "lr": 0.01067, "top1_acc": 0.90694, "top5_acc": 0.99495, "mean_class_accuracy": 0.86988} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.01065, "memory": 4083, "data_time": 0.18917, "top1_acc": 0.95438, "top5_acc": 0.9975, "loss_cls": 0.27606, "loss": 0.27606, "time": 0.81007} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.01063, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.25135, "loss": 0.25135, "time": 0.49202} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.01061, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94875, "top5_acc": 0.99875, "loss_cls": 0.26628, "loss": 0.26628, "time": 0.49117} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.01059, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.31419, "loss": 0.31419, "time": 0.49566} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.01057, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.94562, "top5_acc": 0.99812, "loss_cls": 0.3148, "loss": 0.3148, "time": 0.27958} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.01055, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9475, "top5_acc": 1.0, "loss_cls": 0.28489, "loss": 0.28489, "time": 0.50834} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.01053, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.94625, "top5_acc": 0.9975, "loss_cls": 0.31452, "loss": 0.31452, "time": 0.28935} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.01051, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.94562, "top5_acc": 1.0, "loss_cls": 0.28293, "loss": 0.28293, "time": 0.48868} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.01049, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.94562, "top5_acc": 0.99812, "loss_cls": 0.30886, "loss": 0.30886, "time": 0.49235} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.01047, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94875, "top5_acc": 0.99812, "loss_cls": 0.31009, "loss": 0.31009, "time": 0.48925} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.01045, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.31235, "loss": 0.31235, "time": 0.4929} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.01043, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.93562, "top5_acc": 1.0, "loss_cls": 0.32634, "loss": 0.32634, "time": 0.49061} +{"mode": "val", "epoch": 83, "iter": 533, "lr": 0.01042, "top1_acc": 0.90165, "top5_acc": 0.99331, "mean_class_accuracy": 0.86396} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.0104, "memory": 4083, "data_time": 0.1866, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.24575, "loss": 0.24575, "time": 0.80463} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.01038, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.27753, "loss": 0.27753, "time": 0.49185} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.01036, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93938, "top5_acc": 0.99938, "loss_cls": 0.29932, "loss": 0.29932, "time": 0.49077} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.01034, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.29648, "loss": 0.29648, "time": 0.49258} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.01031, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.27836, "loss": 0.27836, "time": 0.29275} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.01029, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.31401, "loss": 0.31401, "time": 0.51014} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.01027, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.9475, "top5_acc": 0.99875, "loss_cls": 0.32633, "loss": 0.32633, "time": 0.30679} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.01025, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94375, "top5_acc": 0.99938, "loss_cls": 0.32612, "loss": 0.32612, "time": 0.49049} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.01023, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.95188, "top5_acc": 0.99812, "loss_cls": 0.29701, "loss": 0.29701, "time": 0.4899} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.01021, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.2748, "loss": 0.2748, "time": 0.48759} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.01019, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92938, "top5_acc": 0.99875, "loss_cls": 0.36233, "loss": 0.36233, "time": 0.49105} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.01017, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9375, "top5_acc": 0.99875, "loss_cls": 0.36224, "loss": 0.36224, "time": 0.49168} +{"mode": "val", "epoch": 84, "iter": 533, "lr": 0.01016, "top1_acc": 0.8891, "top5_acc": 0.99355, "mean_class_accuracy": 0.85889} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.01014, "memory": 4083, "data_time": 0.17902, "top1_acc": 0.94625, "top5_acc": 1.0, "loss_cls": 0.28519, "loss": 0.28519, "time": 0.77947} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.01012, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96125, "top5_acc": 0.99875, "loss_cls": 0.23905, "loss": 0.23905, "time": 0.49245} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.0101, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.26078, "loss": 0.26078, "time": 0.49305} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.01008, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.2547, "loss": 0.2547, "time": 0.4931} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.01006, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.32902, "loss": 0.32902, "time": 0.28735} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.01004, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.22646, "loss": 0.22646, "time": 0.51015} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.01002, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.3209, "loss": 0.3209, "time": 0.30216} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.01, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93562, "top5_acc": 1.0, "loss_cls": 0.33264, "loss": 0.33264, "time": 0.49004} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.00998, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.30997, "loss": 0.30997, "time": 0.4914} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.00996, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9425, "top5_acc": 1.0, "loss_cls": 0.30443, "loss": 0.30443, "time": 0.4906} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.00994, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.95, "top5_acc": 0.99875, "loss_cls": 0.28625, "loss": 0.28625, "time": 0.48975} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.00992, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.3606, "loss": 0.3606, "time": 0.49043} +{"mode": "val", "epoch": 85, "iter": 533, "lr": 0.0099, "top1_acc": 0.87724, "top5_acc": 0.9885, "mean_class_accuracy": 0.8463} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.00988, "memory": 4083, "data_time": 0.18501, "top1_acc": 0.93125, "top5_acc": 0.99938, "loss_cls": 0.36796, "loss": 0.36796, "time": 0.79807} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.00986, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.27, "loss": 0.27, "time": 0.49101} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.00984, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.30173, "loss": 0.30173, "time": 0.49036} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.00982, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.29763, "loss": 0.29763, "time": 0.49058} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.0098, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95562, "top5_acc": 0.99875, "loss_cls": 0.272, "loss": 0.272, "time": 0.28651} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.00978, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.3058, "loss": 0.3058, "time": 0.50695} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.00976, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94812, "top5_acc": 0.99875, "loss_cls": 0.28235, "loss": 0.28235, "time": 0.30246} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.00974, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94312, "top5_acc": 0.9975, "loss_cls": 0.30352, "loss": 0.30352, "time": 0.48727} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.00972, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95125, "top5_acc": 1.0, "loss_cls": 0.2907, "loss": 0.2907, "time": 0.49065} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.0097, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94188, "top5_acc": 0.99625, "loss_cls": 0.33044, "loss": 0.33044, "time": 0.49182} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.00968, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94875, "top5_acc": 0.99875, "loss_cls": 0.2806, "loss": 0.2806, "time": 0.49283} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.00966, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.31341, "loss": 0.31341, "time": 0.48641} +{"mode": "val", "epoch": 86, "iter": 533, "lr": 0.00965, "top1_acc": 0.89907, "top5_acc": 0.99519, "mean_class_accuracy": 0.86398} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.00963, "memory": 4083, "data_time": 0.18877, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.23245, "loss": 0.23245, "time": 0.79367} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.00961, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95875, "top5_acc": 0.9975, "loss_cls": 0.23819, "loss": 0.23819, "time": 0.48948} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.00959, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.21769, "loss": 0.21769, "time": 0.49223} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.00957, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.22625, "loss": 0.22625, "time": 0.49361} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.00955, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.27956, "loss": 0.27956, "time": 0.29807} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.00953, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.3379, "loss": 0.3379, "time": 0.50972} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.00951, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95, "top5_acc": 0.99875, "loss_cls": 0.26879, "loss": 0.26879, "time": 0.2858} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.00949, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.95, "top5_acc": 0.99938, "loss_cls": 0.26439, "loss": 0.26439, "time": 0.48849} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.00947, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.93875, "top5_acc": 1.0, "loss_cls": 0.32251, "loss": 0.32251, "time": 0.49204} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.00945, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94, "top5_acc": 0.99812, "loss_cls": 0.32451, "loss": 0.32451, "time": 0.49077} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.00943, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94438, "top5_acc": 1.0, "loss_cls": 0.33045, "loss": 0.33045, "time": 0.48909} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.00941, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94562, "top5_acc": 0.99875, "loss_cls": 0.30981, "loss": 0.30981, "time": 0.49056} +{"mode": "val", "epoch": 87, "iter": 533, "lr": 0.00939, "top1_acc": 0.88546, "top5_acc": 0.99296, "mean_class_accuracy": 0.84617} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.00937, "memory": 4083, "data_time": 0.18456, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.26552, "loss": 0.26552, "time": 0.80119} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.00935, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.27332, "loss": 0.27332, "time": 0.49091} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.00933, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.25182, "loss": 0.25182, "time": 0.48987} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.00931, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9575, "top5_acc": 0.99938, "loss_cls": 0.25898, "loss": 0.25898, "time": 0.49274} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.00929, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94875, "top5_acc": 0.99875, "loss_cls": 0.28412, "loss": 0.28412, "time": 0.29627} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.00927, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95438, "top5_acc": 0.99875, "loss_cls": 0.26904, "loss": 0.26904, "time": 0.5091} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.00925, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.26775, "loss": 0.26775, "time": 0.29015} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.00923, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95, "top5_acc": 1.0, "loss_cls": 0.28503, "loss": 0.28503, "time": 0.49101} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.00921, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.2837, "loss": 0.2837, "time": 0.48895} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.00919, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.25119, "loss": 0.25119, "time": 0.4901} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.00917, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95812, "top5_acc": 0.99938, "loss_cls": 0.26101, "loss": 0.26101, "time": 0.48975} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.00915, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.31336, "loss": 0.31336, "time": 0.4895} +{"mode": "val", "epoch": 88, "iter": 533, "lr": 0.00914, "top1_acc": 0.89731, "top5_acc": 0.99308, "mean_class_accuracy": 0.86664} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.00912, "memory": 4083, "data_time": 0.18919, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.2289, "loss": 0.2289, "time": 0.79786} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0091, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.23587, "loss": 0.23587, "time": 0.49203} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.00908, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.2497, "loss": 0.2497, "time": 0.49076} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.00906, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.22603, "loss": 0.22603, "time": 0.49375} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.00904, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.28656, "loss": 0.28656, "time": 0.28601} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.00902, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.28029, "loss": 0.28029, "time": 0.50943} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.009, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95188, "top5_acc": 0.99938, "loss_cls": 0.25529, "loss": 0.25529, "time": 0.30217} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.00898, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9525, "top5_acc": 0.99812, "loss_cls": 0.2562, "loss": 0.2562, "time": 0.48874} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.00896, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.945, "top5_acc": 1.0, "loss_cls": 0.31183, "loss": 0.31183, "time": 0.49031} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.00894, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94438, "top5_acc": 0.99875, "loss_cls": 0.28587, "loss": 0.28587, "time": 0.48979} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.00892, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94062, "top5_acc": 1.0, "loss_cls": 0.29933, "loss": 0.29933, "time": 0.49504} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.0089, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.2832, "loss": 0.2832, "time": 0.49225} +{"mode": "val", "epoch": 89, "iter": 533, "lr": 0.00889, "top1_acc": 0.90588, "top5_acc": 0.99343, "mean_class_accuracy": 0.88484} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.00887, "memory": 4083, "data_time": 0.18242, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.28673, "loss": 0.28673, "time": 0.79627} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.00885, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.26197, "loss": 0.26197, "time": 0.48892} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.00883, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9525, "top5_acc": 0.99875, "loss_cls": 0.2778, "loss": 0.2778, "time": 0.49099} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.00881, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.2963, "loss": 0.2963, "time": 0.49285} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.00879, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94625, "top5_acc": 0.99812, "loss_cls": 0.30854, "loss": 0.30854, "time": 0.29694} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.00877, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.27699, "loss": 0.27699, "time": 0.5079} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.00875, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.26277, "loss": 0.26277, "time": 0.29947} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.00873, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95, "top5_acc": 0.99875, "loss_cls": 0.27213, "loss": 0.27213, "time": 0.49312} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.00871, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95625, "top5_acc": 0.99875, "loss_cls": 0.24989, "loss": 0.24989, "time": 0.49316} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.00869, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.29272, "loss": 0.29272, "time": 0.4918} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.00867, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9525, "top5_acc": 1.0, "loss_cls": 0.28588, "loss": 0.28588, "time": 0.49057} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.00865, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95062, "top5_acc": 0.99812, "loss_cls": 0.27379, "loss": 0.27379, "time": 0.49126} +{"mode": "val", "epoch": 90, "iter": 533, "lr": 0.00864, "top1_acc": 0.89344, "top5_acc": 0.99331, "mean_class_accuracy": 0.84955} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.00862, "memory": 4083, "data_time": 0.1845, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.2647, "loss": 0.2647, "time": 0.78329} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0086, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.95812, "top5_acc": 0.9975, "loss_cls": 0.26402, "loss": 0.26402, "time": 0.48967} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.00858, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.24459, "loss": 0.24459, "time": 0.48932} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.00856, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94812, "top5_acc": 0.99875, "loss_cls": 0.28209, "loss": 0.28209, "time": 0.4886} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.00854, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.24225, "loss": 0.24225, "time": 0.28548} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.00852, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95375, "top5_acc": 0.99875, "loss_cls": 0.2611, "loss": 0.2611, "time": 0.50986} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.0085, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.25002, "loss": 0.25002, "time": 0.2864} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.00848, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94875, "top5_acc": 0.99938, "loss_cls": 0.25647, "loss": 0.25647, "time": 0.48984} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.00846, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.25337, "loss": 0.25337, "time": 0.49112} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.00844, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95375, "top5_acc": 0.99812, "loss_cls": 0.27343, "loss": 0.27343, "time": 0.49257} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.00842, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.24888, "loss": 0.24888, "time": 0.49277} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.0084, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96125, "top5_acc": 0.99938, "loss_cls": 0.22541, "loss": 0.22541, "time": 0.49129} +{"mode": "val", "epoch": 91, "iter": 533, "lr": 0.00839, "top1_acc": 0.8898, "top5_acc": 0.99343, "mean_class_accuracy": 0.86577} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.00837, "memory": 4083, "data_time": 0.18615, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.19951, "loss": 0.19951, "time": 0.79684} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.00835, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.21549, "loss": 0.21549, "time": 0.49047} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.00833, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96938, "top5_acc": 0.99938, "loss_cls": 0.21661, "loss": 0.21661, "time": 0.49041} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.00831, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95375, "top5_acc": 0.99938, "loss_cls": 0.23463, "loss": 0.23463, "time": 0.49136} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.00829, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.20951, "loss": 0.20951, "time": 0.29329} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.00827, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.95875, "top5_acc": 0.99875, "loss_cls": 0.22545, "loss": 0.22545, "time": 0.5089} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.00825, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.24277, "loss": 0.24277, "time": 0.29512} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.00824, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.95562, "top5_acc": 0.99875, "loss_cls": 0.24163, "loss": 0.24163, "time": 0.49349} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.00822, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.22877, "loss": 0.22877, "time": 0.48988} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.0082, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96375, "top5_acc": 0.99938, "loss_cls": 0.2141, "loss": 0.2141, "time": 0.49122} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.00818, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94812, "top5_acc": 1.0, "loss_cls": 0.28606, "loss": 0.28606, "time": 0.49157} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.00816, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.26054, "loss": 0.26054, "time": 0.49088} +{"mode": "val", "epoch": 92, "iter": 533, "lr": 0.00814, "top1_acc": 0.9067, "top5_acc": 0.99425, "mean_class_accuracy": 0.8744} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.00812, "memory": 4083, "data_time": 0.18557, "top1_acc": 0.95688, "top5_acc": 0.99875, "loss_cls": 0.25075, "loss": 0.25075, "time": 0.79502} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.0081, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.17844, "loss": 0.17844, "time": 0.48833} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.00809, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96625, "top5_acc": 0.99875, "loss_cls": 0.21322, "loss": 0.21322, "time": 0.49182} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.00807, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.20013, "loss": 0.20013, "time": 0.4947} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.00805, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.19551, "loss": 0.19551, "time": 0.29234} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.00803, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.21995, "loss": 0.21995, "time": 0.50851} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.00801, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.22719, "loss": 0.22719, "time": 0.29818} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.00799, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.2164, "loss": 0.2164, "time": 0.49085} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.00797, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.21998, "loss": 0.21998, "time": 0.48936} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.00795, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.21181, "loss": 0.21181, "time": 0.49225} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.00793, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95375, "top5_acc": 0.99875, "loss_cls": 0.25222, "loss": 0.25222, "time": 0.48863} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.00791, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.23252, "loss": 0.23252, "time": 0.49091} +{"mode": "val", "epoch": 93, "iter": 533, "lr": 0.0079, "top1_acc": 0.91656, "top5_acc": 0.99425, "mean_class_accuracy": 0.88876} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.00788, "memory": 4083, "data_time": 0.18634, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.20287, "loss": 0.20287, "time": 0.79524} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.00786, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.18652, "loss": 0.18652, "time": 0.49214} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.00784, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.19268, "loss": 0.19268, "time": 0.49325} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.00782, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.23616, "loss": 0.23616, "time": 0.49639} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.0078, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.95812, "top5_acc": 0.99875, "loss_cls": 0.22752, "loss": 0.22752, "time": 0.28523} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.00778, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.22011, "loss": 0.22011, "time": 0.50907} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.00777, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.23023, "loss": 0.23023, "time": 0.30792} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.00775, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.22404, "loss": 0.22404, "time": 0.49216} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.00773, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95, "top5_acc": 1.0, "loss_cls": 0.2647, "loss": 0.2647, "time": 0.49241} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.00771, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9575, "top5_acc": 0.99875, "loss_cls": 0.24761, "loss": 0.24761, "time": 0.49276} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.00769, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.22716, "loss": 0.22716, "time": 0.49356} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.00767, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.2156, "loss": 0.2156, "time": 0.49125} +{"mode": "val", "epoch": 94, "iter": 533, "lr": 0.00766, "top1_acc": 0.89649, "top5_acc": 0.99355, "mean_class_accuracy": 0.86206} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.00764, "memory": 4083, "data_time": 0.18767, "top1_acc": 0.95312, "top5_acc": 0.99875, "loss_cls": 0.27062, "loss": 0.27062, "time": 0.79275} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.00762, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95062, "top5_acc": 0.99812, "loss_cls": 0.27872, "loss": 0.27872, "time": 0.48955} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.0076, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.22544, "loss": 0.22544, "time": 0.49133} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.00758, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.22029, "loss": 0.22029, "time": 0.49214} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.00756, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.19086, "loss": 0.19086, "time": 0.28128} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.00754, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96562, "top5_acc": 0.99812, "loss_cls": 0.21864, "loss": 0.21864, "time": 0.51074} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.00752, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.21322, "loss": 0.21322, "time": 0.31971} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.00751, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.1953, "loss": 0.1953, "time": 0.49208} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.00749, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.22487, "loss": 0.22487, "time": 0.49348} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.00747, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96625, "top5_acc": 0.99875, "loss_cls": 0.2224, "loss": 0.2224, "time": 0.49492} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.00745, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.24689, "loss": 0.24689, "time": 0.49342} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.00743, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.2551, "loss": 0.2551, "time": 0.49111} +{"mode": "val", "epoch": 95, "iter": 533, "lr": 0.00742, "top1_acc": 0.90506, "top5_acc": 0.99355, "mean_class_accuracy": 0.87782} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.0074, "memory": 4083, "data_time": 0.18448, "top1_acc": 0.96688, "top5_acc": 0.99938, "loss_cls": 0.21559, "loss": 0.21559, "time": 0.7851} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.00738, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.22862, "loss": 0.22862, "time": 0.49377} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.00736, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96312, "top5_acc": 0.99688, "loss_cls": 0.23577, "loss": 0.23577, "time": 0.49406} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.00734, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.18329, "loss": 0.18329, "time": 0.49391} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.00732, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.14556, "loss": 0.14556, "time": 0.2785} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.0073, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.2177, "loss": 0.2177, "time": 0.50823} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.00729, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.20752, "loss": 0.20752, "time": 0.30487} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.00727, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.2399, "loss": 0.2399, "time": 0.48688} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.00725, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96688, "top5_acc": 0.99875, "loss_cls": 0.21929, "loss": 0.21929, "time": 0.49052} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.00723, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.21079, "loss": 0.21079, "time": 0.49181} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.00721, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.2129, "loss": 0.2129, "time": 0.48989} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.00719, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.22731, "loss": 0.22731, "time": 0.49243} +{"mode": "val", "epoch": 96, "iter": 533, "lr": 0.00718, "top1_acc": 0.90999, "top5_acc": 0.99507, "mean_class_accuracy": 0.8739} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.00716, "memory": 4083, "data_time": 0.19203, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.20207, "loss": 0.20207, "time": 0.8093} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.00714, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97562, "top5_acc": 0.99938, "loss_cls": 0.16494, "loss": 0.16494, "time": 0.49195} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.00712, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.17208, "loss": 0.17208, "time": 0.49546} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.0071, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.2174, "loss": 0.2174, "time": 0.49234} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.00709, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.19047, "loss": 0.19047, "time": 0.27056} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.00707, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.22144, "loss": 0.22144, "time": 0.51024} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.00705, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.2486, "loss": 0.2486, "time": 0.31678} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.00703, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.22435, "loss": 0.22435, "time": 0.49182} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.00701, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.19777, "loss": 0.19777, "time": 0.49307} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.00699, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.2081, "loss": 0.2081, "time": 0.49001} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.00698, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.20448, "loss": 0.20448, "time": 0.48809} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.00696, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.23612, "loss": 0.23612, "time": 0.48971} +{"mode": "val", "epoch": 97, "iter": 533, "lr": 0.00694, "top1_acc": 0.90705, "top5_acc": 0.99484, "mean_class_accuracy": 0.88648} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.00692, "memory": 4083, "data_time": 0.18606, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.17582, "loss": 0.17582, "time": 0.78154} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.00691, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.19373, "loss": 0.19373, "time": 0.49238} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.00689, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.15055, "loss": 0.15055, "time": 0.49293} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.00687, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.18627, "loss": 0.18627, "time": 0.49299} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.00685, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96938, "top5_acc": 0.99938, "loss_cls": 0.18228, "loss": 0.18228, "time": 0.28519} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.00683, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97312, "top5_acc": 0.99875, "loss_cls": 0.19645, "loss": 0.19645, "time": 0.50863} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.00681, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9625, "top5_acc": 0.99938, "loss_cls": 0.22322, "loss": 0.22322, "time": 0.31683} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.0068, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.96875, "top5_acc": 0.99938, "loss_cls": 0.18648, "loss": 0.18648, "time": 0.4859} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.00678, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.19551, "loss": 0.19551, "time": 0.49114} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.00676, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96625, "top5_acc": 0.99938, "loss_cls": 0.21143, "loss": 0.21143, "time": 0.49116} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.00674, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96562, "top5_acc": 0.99938, "loss_cls": 0.2129, "loss": 0.2129, "time": 0.49091} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.00672, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.19724, "loss": 0.19724, "time": 0.49086} +{"mode": "val", "epoch": 98, "iter": 533, "lr": 0.00671, "top1_acc": 0.91245, "top5_acc": 0.99578, "mean_class_accuracy": 0.88863} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.00669, "memory": 4083, "data_time": 0.18932, "top1_acc": 0.98062, "top5_acc": 0.99938, "loss_cls": 0.17792, "loss": 0.17792, "time": 0.78602} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.00667, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97062, "top5_acc": 0.99875, "loss_cls": 0.18448, "loss": 0.18448, "time": 0.49313} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.00665, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.17669, "loss": 0.17669, "time": 0.4922} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.00664, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.15518, "loss": 0.15518, "time": 0.49668} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.00662, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.17405, "loss": 0.17405, "time": 0.2963} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.0066, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.13724, "loss": 0.13724, "time": 0.51174} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.00658, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.18253, "loss": 0.18253, "time": 0.30071} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.00656, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.21205, "loss": 0.21205, "time": 0.49293} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.00655, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.19588, "loss": 0.19588, "time": 0.48847} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.00653, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.18995, "loss": 0.18995, "time": 0.49019} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.00651, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.96125, "top5_acc": 0.99938, "loss_cls": 0.21786, "loss": 0.21786, "time": 0.49349} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.00649, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.2451, "loss": 0.2451, "time": 0.49119} +{"mode": "val", "epoch": 99, "iter": 533, "lr": 0.00648, "top1_acc": 0.91668, "top5_acc": 0.99554, "mean_class_accuracy": 0.88435} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.00646, "memory": 4083, "data_time": 0.19206, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.20157, "loss": 0.20157, "time": 0.79354} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.00644, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.16278, "loss": 0.16278, "time": 0.49136} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.00642, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.17695, "loss": 0.17695, "time": 0.49162} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.00641, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.14258, "loss": 0.14258, "time": 0.49162} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.00639, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.17688, "loss": 0.17688, "time": 0.28693} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.00637, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.18845, "loss": 0.18845, "time": 0.51055} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.00635, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.15612, "loss": 0.15612, "time": 0.28364} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.00634, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.15503, "loss": 0.15503, "time": 0.49415} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.00632, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.25562, "loss": 0.25562, "time": 0.49129} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.0063, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.2164, "loss": 0.2164, "time": 0.49073} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.00628, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.21748, "loss": 0.21748, "time": 0.4878} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.00626, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.95188, "top5_acc": 1.0, "loss_cls": 0.26009, "loss": 0.26009, "time": 0.49164} +{"mode": "val", "epoch": 100, "iter": 533, "lr": 0.00625, "top1_acc": 0.90083, "top5_acc": 0.99636, "mean_class_accuracy": 0.87477} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.00623, "memory": 4083, "data_time": 0.18973, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.22768, "loss": 0.22768, "time": 0.79876} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.00621, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.22439, "loss": 0.22439, "time": 0.49382} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.0062, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.15436, "loss": 0.15436, "time": 0.48895} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.00618, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.16204, "loss": 0.16204, "time": 0.49276} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.00616, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96875, "top5_acc": 0.99938, "loss_cls": 0.17096, "loss": 0.17096, "time": 0.30753} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.00614, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.19009, "loss": 0.19009, "time": 0.51034} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.00613, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.14599, "loss": 0.14599, "time": 0.27364} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.00611, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97875, "top5_acc": 0.99938, "loss_cls": 0.14831, "loss": 0.14831, "time": 0.48966} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.00609, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.18829, "loss": 0.18829, "time": 0.48995} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.00607, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.18192, "loss": 0.18192, "time": 0.49052} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.00606, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.16856, "loss": 0.16856, "time": 0.49502} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.00604, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.18357, "loss": 0.18357, "time": 0.48903} +{"mode": "val", "epoch": 101, "iter": 533, "lr": 0.00602, "top1_acc": 0.90999, "top5_acc": 0.9946, "mean_class_accuracy": 0.8807} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.00601, "memory": 4083, "data_time": 0.19089, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14859, "loss": 0.14859, "time": 0.80852} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.00599, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.1437, "loss": 0.1437, "time": 0.4916} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.00597, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.15557, "loss": 0.15557, "time": 0.49068} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.00596, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.18903, "loss": 0.18903, "time": 0.49306} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.00594, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.15638, "loss": 0.15638, "time": 0.31182} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.00592, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.13874, "loss": 0.13874, "time": 0.51023} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.0059, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.17037, "loss": 0.17037, "time": 0.27916} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.00589, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.14554, "loss": 0.14554, "time": 0.48738} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.00587, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.18221, "loss": 0.18221, "time": 0.48821} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.00585, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.18888, "loss": 0.18888, "time": 0.48703} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.00583, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.1996, "loss": 0.1996, "time": 0.49365} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.00582, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.1756, "loss": 0.1756, "time": 0.49363} +{"mode": "val", "epoch": 102, "iter": 533, "lr": 0.0058, "top1_acc": 0.91069, "top5_acc": 0.99472, "mean_class_accuracy": 0.8845} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.00579, "memory": 4083, "data_time": 0.19604, "top1_acc": 0.97188, "top5_acc": 0.99875, "loss_cls": 0.16218, "loss": 0.16218, "time": 0.80834} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.00577, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.12901, "loss": 0.12901, "time": 0.48913} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.00575, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97438, "top5_acc": 0.99875, "loss_cls": 0.16283, "loss": 0.16283, "time": 0.48883} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.00573, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14354, "loss": 0.14354, "time": 0.49297} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.00572, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.97562, "top5_acc": 1.0, "loss_cls": 0.15506, "loss": 0.15506, "time": 0.29912} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.0057, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96062, "top5_acc": 0.99938, "loss_cls": 0.18844, "loss": 0.18844, "time": 0.51017} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.00568, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.15848, "loss": 0.15848, "time": 0.27509} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.00566, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.13916, "loss": 0.13916, "time": 0.49216} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.00565, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.10876, "loss": 0.10876, "time": 0.49047} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.00563, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.20811, "loss": 0.20811, "time": 0.49188} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.00561, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.17109, "loss": 0.17109, "time": 0.49037} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.0056, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.21737, "loss": 0.21737, "time": 0.49007} +{"mode": "val", "epoch": 103, "iter": 533, "lr": 0.00558, "top1_acc": 0.91762, "top5_acc": 0.99484, "mean_class_accuracy": 0.88506} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.00557, "memory": 4083, "data_time": 0.18719, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.14176, "loss": 0.14176, "time": 0.80636} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.00555, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.12275, "loss": 0.12275, "time": 0.48995} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.00553, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.17101, "loss": 0.17101, "time": 0.49169} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.00551, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.11014, "loss": 0.11014, "time": 0.49145} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.0055, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.1372, "loss": 0.1372, "time": 0.30001} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.00548, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.14512, "loss": 0.14512, "time": 0.50941} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.00546, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.15744, "loss": 0.15744, "time": 0.29885} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.00545, "memory": 4083, "data_time": 0.00053, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.16824, "loss": 0.16824, "time": 0.49066} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.00543, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97438, "top5_acc": 0.99938, "loss_cls": 0.17021, "loss": 0.17021, "time": 0.49101} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.00541, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.1394, "loss": 0.1394, "time": 0.4898} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.0054, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.97438, "top5_acc": 0.99875, "loss_cls": 0.16183, "loss": 0.16183, "time": 0.49212} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.00538, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9725, "top5_acc": 0.99938, "loss_cls": 0.18754, "loss": 0.18754, "time": 0.49099} +{"mode": "val", "epoch": 104, "iter": 533, "lr": 0.00537, "top1_acc": 0.91585, "top5_acc": 0.99472, "mean_class_accuracy": 0.89352} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.00535, "memory": 4083, "data_time": 0.1891, "top1_acc": 0.98188, "top5_acc": 0.99938, "loss_cls": 0.12588, "loss": 0.12588, "time": 0.80593} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.00533, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.12533, "loss": 0.12533, "time": 0.49124} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.00532, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.09818, "loss": 0.09818, "time": 0.4926} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.0053, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.13151, "loss": 0.13151, "time": 0.49164} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.00528, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.17889, "loss": 0.17889, "time": 0.27555} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.00527, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.17826, "loss": 0.17826, "time": 0.51074} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.00525, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.18653, "loss": 0.18653, "time": 0.30691} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.00523, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.15782, "loss": 0.15782, "time": 0.49229} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.00522, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.12993, "loss": 0.12993, "time": 0.4917} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.0052, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.1227, "loss": 0.1227, "time": 0.49097} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.00518, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11891, "loss": 0.11891, "time": 0.49224} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.00517, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97625, "top5_acc": 0.99938, "loss_cls": 0.13541, "loss": 0.13541, "time": 0.49512} +{"mode": "val", "epoch": 105, "iter": 533, "lr": 0.00515, "top1_acc": 0.9121, "top5_acc": 0.99519, "mean_class_accuracy": 0.87376} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.00514, "memory": 4083, "data_time": 0.18664, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.14268, "loss": 0.14268, "time": 0.80368} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.00512, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13177, "loss": 0.13177, "time": 0.4902} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.0051, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.98062, "top5_acc": 0.99938, "loss_cls": 0.13105, "loss": 0.13105, "time": 0.48873} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.00509, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.15247, "loss": 0.15247, "time": 0.49273} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.00507, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.15317, "loss": 0.15317, "time": 0.27872} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.00505, "memory": 4083, "data_time": 0.00064, "top1_acc": 0.96438, "top5_acc": 0.99938, "loss_cls": 0.1925, "loss": 0.1925, "time": 0.50994} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.00504, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98125, "top5_acc": 0.99938, "loss_cls": 0.12613, "loss": 0.12613, "time": 0.31233} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.00502, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97688, "top5_acc": 0.99875, "loss_cls": 0.15904, "loss": 0.15904, "time": 0.48937} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.005, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.15127, "loss": 0.15127, "time": 0.48819} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.00499, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.15692, "loss": 0.15692, "time": 0.49005} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.00497, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.15498, "loss": 0.15498, "time": 0.49029} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.00496, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.21431, "loss": 0.21431, "time": 0.49326} +{"mode": "val", "epoch": 106, "iter": 533, "lr": 0.00494, "top1_acc": 0.91468, "top5_acc": 0.99413, "mean_class_accuracy": 0.88221} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.00493, "memory": 4083, "data_time": 0.18484, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.13907, "loss": 0.13907, "time": 0.79653} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.00491, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10463, "loss": 0.10463, "time": 0.48883} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.00489, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.13576, "loss": 0.13576, "time": 0.49156} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.00488, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.12931, "loss": 0.12931, "time": 0.48973} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.00486, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.14773, "loss": 0.14773, "time": 0.2768} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.00485, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10092, "loss": 0.10092, "time": 0.50891} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.00483, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.12806, "loss": 0.12806, "time": 0.30297} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.00481, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.12635, "loss": 0.12635, "time": 0.49081} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.0048, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.15091, "loss": 0.15091, "time": 0.49329} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.00478, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98125, "top5_acc": 0.99875, "loss_cls": 0.13698, "loss": 0.13698, "time": 0.49272} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.00476, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13404, "loss": 0.13404, "time": 0.49232} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.00475, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.13091, "loss": 0.13091, "time": 0.4917} +{"mode": "val", "epoch": 107, "iter": 533, "lr": 0.00474, "top1_acc": 0.92078, "top5_acc": 0.99519, "mean_class_accuracy": 0.89697} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.00472, "memory": 4083, "data_time": 0.18405, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.11987, "loss": 0.11987, "time": 0.79343} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0047, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08873, "loss": 0.08873, "time": 0.49081} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.00469, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.09461, "loss": 0.09461, "time": 0.49169} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.00467, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.1007, "loss": 0.1007, "time": 0.49027} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.00466, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.12422, "loss": 0.12422, "time": 0.29205} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.00464, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97812, "top5_acc": 0.99938, "loss_cls": 0.14056, "loss": 0.14056, "time": 0.50982} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.00462, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.13362, "loss": 0.13362, "time": 0.30068} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.00461, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12974, "loss": 0.12974, "time": 0.49181} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.00459, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.12215, "loss": 0.12215, "time": 0.49327} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.00458, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.13465, "loss": 0.13465, "time": 0.48815} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.00456, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.14881, "loss": 0.14881, "time": 0.49017} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.00455, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.13635, "loss": 0.13635, "time": 0.49035} +{"mode": "val", "epoch": 108, "iter": 533, "lr": 0.00453, "top1_acc": 0.92372, "top5_acc": 0.99448, "mean_class_accuracy": 0.89615} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.00452, "memory": 4083, "data_time": 0.18385, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09782, "loss": 0.09782, "time": 0.79725} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.0045, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.98062, "top5_acc": 0.99938, "loss_cls": 0.13583, "loss": 0.13583, "time": 0.49} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.00449, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.98188, "top5_acc": 0.99938, "loss_cls": 0.12789, "loss": 0.12789, "time": 0.49114} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.00447, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.1, "loss": 0.1, "time": 0.49176} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.00445, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.09669, "loss": 0.09669, "time": 0.28482} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.00444, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.10568, "loss": 0.10568, "time": 0.51145} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.00442, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.13609, "loss": 0.13609, "time": 0.31251} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.00441, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.1413, "loss": 0.1413, "time": 0.49085} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.00439, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.12464, "loss": 0.12464, "time": 0.48921} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.00438, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.11489, "loss": 0.11489, "time": 0.48934} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.00436, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08988, "loss": 0.08988, "time": 0.49243} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.00434, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.10854, "loss": 0.10854, "time": 0.48818} +{"mode": "val", "epoch": 109, "iter": 533, "lr": 0.00433, "top1_acc": 0.91398, "top5_acc": 0.99355, "mean_class_accuracy": 0.88075} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.00432, "memory": 4083, "data_time": 0.18303, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11316, "loss": 0.11316, "time": 0.80695} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.0043, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.10115, "loss": 0.10115, "time": 0.49334} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.00429, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07976, "loss": 0.07976, "time": 0.49396} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.00427, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08077, "loss": 0.08077, "time": 0.49136} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.00426, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.08308, "loss": 0.08308, "time": 0.27958} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.00424, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.1034, "loss": 0.1034, "time": 0.50979} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.00422, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.09487, "loss": 0.09487, "time": 0.31745} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.00421, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.98438, "top5_acc": 0.99938, "loss_cls": 0.1171, "loss": 0.1171, "time": 0.48586} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.00419, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.14021, "loss": 0.14021, "time": 0.49199} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.00418, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.13179, "loss": 0.13179, "time": 0.48956} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.00416, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.13236, "loss": 0.13236, "time": 0.4941} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.00415, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.1089, "loss": 0.1089, "time": 0.49235} +{"mode": "val", "epoch": 110, "iter": 533, "lr": 0.00414, "top1_acc": 0.92442, "top5_acc": 0.99519, "mean_class_accuracy": 0.90364} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.00412, "memory": 4083, "data_time": 0.18567, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10845, "loss": 0.10845, "time": 0.8028} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.00411, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.09843, "loss": 0.09843, "time": 0.49193} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.00409, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07557, "loss": 0.07557, "time": 0.49684} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.00408, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08724, "loss": 0.08724, "time": 0.49123} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.00406, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99, "top5_acc": 0.99875, "loss_cls": 0.08474, "loss": 0.08474, "time": 0.27556} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.00405, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08444, "loss": 0.08444, "time": 0.49977} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.00403, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08936, "loss": 0.08936, "time": 0.32937} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.00402, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.11338, "loss": 0.11338, "time": 0.48963} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.004, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.0997, "loss": 0.0997, "time": 0.49326} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.00399, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98938, "top5_acc": 0.99938, "loss_cls": 0.09126, "loss": 0.09126, "time": 0.49077} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.00397, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.07163, "loss": 0.07163, "time": 0.49179} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.00396, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08733, "loss": 0.08733, "time": 0.49124} +{"mode": "val", "epoch": 111, "iter": 533, "lr": 0.00394, "top1_acc": 0.92513, "top5_acc": 0.99589, "mean_class_accuracy": 0.90527} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.00393, "memory": 4083, "data_time": 0.18588, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.09907, "loss": 0.09907, "time": 0.80182} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.00391, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.081, "loss": 0.081, "time": 0.49254} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.0039, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10461, "loss": 0.10461, "time": 0.48854} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.00388, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.10205, "loss": 0.10205, "time": 0.49151} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.00387, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9875, "top5_acc": 0.99938, "loss_cls": 0.10378, "loss": 0.10378, "time": 0.28903} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.00385, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.09962, "loss": 0.09962, "time": 0.4732} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.00384, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.10113, "loss": 0.10113, "time": 0.33653} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.00382, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.11547, "loss": 0.11547, "time": 0.49061} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.00381, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.10115, "loss": 0.10115, "time": 0.49144} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.0038, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.09765, "loss": 0.09765, "time": 0.49025} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.00378, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11589, "loss": 0.11589, "time": 0.49127} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.00377, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.10063, "loss": 0.10063, "time": 0.49432} +{"mode": "val", "epoch": 112, "iter": 533, "lr": 0.00375, "top1_acc": 0.92736, "top5_acc": 0.99613, "mean_class_accuracy": 0.90192} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.00374, "memory": 4083, "data_time": 0.18678, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.1114, "loss": 0.1114, "time": 0.78577} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.00373, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11149, "loss": 0.11149, "time": 0.49251} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.00371, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.12284, "loss": 0.12284, "time": 0.49627} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.0037, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.09372, "loss": 0.09372, "time": 0.49351} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.00368, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.10627, "loss": 0.10627, "time": 0.29704} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.00367, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.0953, "loss": 0.0953, "time": 0.46622} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.00365, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.09014, "loss": 0.09014, "time": 0.34292} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.00364, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.08845, "loss": 0.08845, "time": 0.4916} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.00362, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.09835, "loss": 0.09835, "time": 0.49324} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.00361, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.087, "loss": 0.087, "time": 0.4925} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0036, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.09121, "loss": 0.09121, "time": 0.49131} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.00358, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.10233, "loss": 0.10233, "time": 0.49358} +{"mode": "val", "epoch": 113, "iter": 533, "lr": 0.00357, "top1_acc": 0.92278, "top5_acc": 0.99519, "mean_class_accuracy": 0.9034} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.00355, "memory": 4083, "data_time": 0.18028, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07703, "loss": 0.07703, "time": 0.78381} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.00354, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06831, "loss": 0.06831, "time": 0.49331} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.00353, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.08556, "loss": 0.08556, "time": 0.49231} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.00351, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.08421, "loss": 0.08421, "time": 0.49107} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.0035, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.09597, "loss": 0.09597, "time": 0.30112} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.00348, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.10386, "loss": 0.10386, "time": 0.46746} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.00347, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.0847, "loss": 0.0847, "time": 0.33506} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.00346, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10627, "loss": 0.10627, "time": 0.4904} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.00344, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08813, "loss": 0.08813, "time": 0.49096} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.00343, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09277, "loss": 0.09277, "time": 0.49165} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.00341, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98688, "top5_acc": 0.99938, "loss_cls": 0.09848, "loss": 0.09848, "time": 0.49081} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.0034, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.09513, "loss": 0.09513, "time": 0.49025} +{"mode": "val", "epoch": 114, "iter": 533, "lr": 0.00339, "top1_acc": 0.9229, "top5_acc": 0.99531, "mean_class_accuracy": 0.89479} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.00337, "memory": 4083, "data_time": 0.18167, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10458, "loss": 0.10458, "time": 0.79482} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.00336, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07411, "loss": 0.07411, "time": 0.48935} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.00335, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06868, "loss": 0.06868, "time": 0.49119} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.00333, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.07443, "loss": 0.07443, "time": 0.48976} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.00332, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.0742, "loss": 0.0742, "time": 0.28201} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.0033, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.07398, "loss": 0.07398, "time": 0.4882} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.00329, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.0888, "loss": 0.0888, "time": 0.31487} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.00328, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06714, "loss": 0.06714, "time": 0.49019} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.00326, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.08483, "loss": 0.08483, "time": 0.4901} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.00325, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09911, "loss": 0.09911, "time": 0.49419} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.00324, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.07229, "loss": 0.07229, "time": 0.4911} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.00322, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10165, "loss": 0.10165, "time": 0.4907} +{"mode": "val", "epoch": 115, "iter": 533, "lr": 0.00321, "top1_acc": 0.91961, "top5_acc": 0.99425, "mean_class_accuracy": 0.89926} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.0032, "memory": 4083, "data_time": 0.18843, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.10269, "loss": 0.10269, "time": 0.8001} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.00318, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.09826, "loss": 0.09826, "time": 0.49293} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.00317, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.09407, "loss": 0.09407, "time": 0.48973} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.00316, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07966, "loss": 0.07966, "time": 0.49058} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.00314, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.07014, "loss": 0.07014, "time": 0.27524} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.00313, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07457, "loss": 0.07457, "time": 0.51066} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.00312, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.06166, "loss": 0.06166, "time": 0.29631} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.0031, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.06217, "loss": 0.06217, "time": 0.48977} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.00309, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07131, "loss": 0.07131, "time": 0.48888} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.00308, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08499, "loss": 0.08499, "time": 0.49107} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.00306, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.06903, "loss": 0.06903, "time": 0.4886} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.00305, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08413, "loss": 0.08413, "time": 0.49061} +{"mode": "val", "epoch": 116, "iter": 533, "lr": 0.00304, "top1_acc": 0.93088, "top5_acc": 0.99519, "mean_class_accuracy": 0.91168} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.00302, "memory": 4083, "data_time": 0.18715, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04681, "loss": 0.04681, "time": 0.78468} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.00301, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.04095, "loss": 0.04095, "time": 0.48882} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.003, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06206, "loss": 0.06206, "time": 0.49215} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.00298, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06483, "loss": 0.06483, "time": 0.49065} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.00297, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.05859, "loss": 0.05859, "time": 0.30995} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.00296, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.06477, "loss": 0.06477, "time": 0.50969} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.00294, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.08026, "loss": 0.08026, "time": 0.28492} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.00293, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.07941, "loss": 0.07941, "time": 0.48971} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.00292, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.0486, "loss": 0.0486, "time": 0.48621} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.00291, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06992, "loss": 0.06992, "time": 0.48896} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.00289, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07301, "loss": 0.07301, "time": 0.48955} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.00288, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.09466, "loss": 0.09466, "time": 0.49077} +{"mode": "val", "epoch": 117, "iter": 533, "lr": 0.00287, "top1_acc": 0.92829, "top5_acc": 0.99507, "mean_class_accuracy": 0.90321} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.00286, "memory": 4083, "data_time": 0.19205, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.07351, "loss": 0.07351, "time": 0.78452} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.00284, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.0478, "loss": 0.0478, "time": 0.49428} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.00283, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05608, "loss": 0.05608, "time": 0.49356} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.00282, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.05887, "loss": 0.05887, "time": 0.49152} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.0028, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05821, "loss": 0.05821, "time": 0.31098} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.00279, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05711, "loss": 0.05711, "time": 0.51169} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.00278, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05976, "loss": 0.05976, "time": 0.25619} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.00277, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.05762, "loss": 0.05762, "time": 0.49111} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.00275, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04057, "loss": 0.04057, "time": 0.49032} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.00274, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.04653, "loss": 0.04653, "time": 0.49149} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.00273, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.09178, "loss": 0.09178, "time": 0.4925} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.00271, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.05846, "loss": 0.05846, "time": 0.48936} +{"mode": "val", "epoch": 118, "iter": 533, "lr": 0.0027, "top1_acc": 0.93217, "top5_acc": 0.99542, "mean_class_accuracy": 0.90531} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.00269, "memory": 4083, "data_time": 0.18709, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.0481, "loss": 0.0481, "time": 0.79276} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.00268, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.07582, "loss": 0.07582, "time": 0.49137} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.00267, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99125, "top5_acc": 0.99938, "loss_cls": 0.07211, "loss": 0.07211, "time": 0.48993} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.00265, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06929, "loss": 0.06929, "time": 0.49139} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.00264, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06992, "loss": 0.06992, "time": 0.33112} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.00263, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.06098, "loss": 0.06098, "time": 0.50931} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.00262, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.05697, "loss": 0.05697, "time": 0.26606} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.0026, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04776, "loss": 0.04776, "time": 0.49088} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.00259, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05313, "loss": 0.05313, "time": 0.48949} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.00258, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06596, "loss": 0.06596, "time": 0.49307} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.00257, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06511, "loss": 0.06511, "time": 0.49115} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.00255, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07118, "loss": 0.07118, "time": 0.49047} +{"mode": "val", "epoch": 119, "iter": 533, "lr": 0.00254, "top1_acc": 0.93369, "top5_acc": 0.99589, "mean_class_accuracy": 0.90819} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.00253, "memory": 4083, "data_time": 0.18803, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05328, "loss": 0.05328, "time": 0.78655} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.00252, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03959, "loss": 0.03959, "time": 0.49248} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.00251, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04126, "loss": 0.04126, "time": 0.48834} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.00249, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.0499, "loss": 0.0499, "time": 0.48981} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.00248, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.04291, "loss": 0.04291, "time": 0.33952} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.00247, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04127, "loss": 0.04127, "time": 0.51188} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.00246, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.04523, "loss": 0.04523, "time": 0.2544} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.00245, "memory": 4083, "data_time": 0.00062, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04293, "loss": 0.04293, "time": 0.49226} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.00243, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0412, "loss": 0.0412, "time": 0.4878} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.00242, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.04775, "loss": 0.04775, "time": 0.4935} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00241, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06787, "loss": 0.06787, "time": 0.49294} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.0024, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05788, "loss": 0.05788, "time": 0.49254} +{"mode": "val", "epoch": 120, "iter": 533, "lr": 0.00239, "top1_acc": 0.93228, "top5_acc": 0.9966, "mean_class_accuracy": 0.9092} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00238, "memory": 4083, "data_time": 0.18829, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04724, "loss": 0.04724, "time": 0.79996} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00236, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05792, "loss": 0.05792, "time": 0.49047} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.00235, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05194, "loss": 0.05194, "time": 0.48984} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00234, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04228, "loss": 0.04228, "time": 0.48874} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00233, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04537, "loss": 0.04537, "time": 0.32275} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00232, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04208, "loss": 0.04208, "time": 0.50888} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.0023, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04005, "loss": 0.04005, "time": 0.26141} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00229, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05362, "loss": 0.05362, "time": 0.48875} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.00228, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.0447, "loss": 0.0447, "time": 0.49101} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00227, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.05866, "loss": 0.05866, "time": 0.48903} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00226, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06011, "loss": 0.06011, "time": 0.49096} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00225, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05025, "loss": 0.05025, "time": 0.49381} +{"mode": "val", "epoch": 121, "iter": 533, "lr": 0.00224, "top1_acc": 0.92747, "top5_acc": 0.99484, "mean_class_accuracy": 0.9017} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00222, "memory": 4083, "data_time": 0.18716, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05013, "loss": 0.05013, "time": 0.79803} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00221, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04598, "loss": 0.04598, "time": 0.49053} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.0022, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.0558, "loss": 0.0558, "time": 0.48867} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00219, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04161, "loss": 0.04161, "time": 0.48857} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00218, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04011, "loss": 0.04011, "time": 0.32191} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00217, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03994, "loss": 0.03994, "time": 0.50876} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00215, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04832, "loss": 0.04832, "time": 0.27035} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00214, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04705, "loss": 0.04705, "time": 0.49033} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.00213, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03537, "loss": 0.03537, "time": 0.4892} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00212, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.04917, "loss": 0.04917, "time": 0.49295} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00211, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03244, "loss": 0.03244, "time": 0.48965} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.0021, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04531, "loss": 0.04531, "time": 0.4893} +{"mode": "val", "epoch": 122, "iter": 533, "lr": 0.00209, "top1_acc": 0.92771, "top5_acc": 0.99601, "mean_class_accuracy": 0.90427} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00208, "memory": 4083, "data_time": 0.18682, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06364, "loss": 0.06364, "time": 0.78576} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00207, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06036, "loss": 0.06036, "time": 0.48972} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00205, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05069, "loss": 0.05069, "time": 0.49426} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00204, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.06864, "loss": 0.06864, "time": 0.49035} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00203, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05676, "loss": 0.05676, "time": 0.32754} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00202, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04054, "loss": 0.04054, "time": 0.51141} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00201, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03317, "loss": 0.03317, "time": 0.26175} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.002, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02942, "loss": 0.02942, "time": 0.48941} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00199, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03714, "loss": 0.03714, "time": 0.49013} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.00198, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04668, "loss": 0.04668, "time": 0.48777} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00197, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03058, "loss": 0.03058, "time": 0.49052} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00195, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04115, "loss": 0.04115, "time": 0.49214} +{"mode": "val", "epoch": 123, "iter": 533, "lr": 0.00195, "top1_acc": 0.93358, "top5_acc": 0.99636, "mean_class_accuracy": 0.90646} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00194, "memory": 4083, "data_time": 0.19232, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03687, "loss": 0.03687, "time": 0.80625} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00192, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0355, "loss": 0.0355, "time": 0.48968} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00191, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.04109, "loss": 0.04109, "time": 0.4906} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.0019, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02808, "loss": 0.02808, "time": 0.49344} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00189, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.03712, "loss": 0.03712, "time": 0.31973} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00188, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02983, "loss": 0.02983, "time": 0.51037} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00187, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03029, "loss": 0.03029, "time": 0.28114} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00186, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02595, "loss": 0.02595, "time": 0.49169} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00185, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02515, "loss": 0.02515, "time": 0.49362} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00184, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04111, "loss": 0.04111, "time": 0.49167} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00183, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03094, "loss": 0.03094, "time": 0.49097} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.00182, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.04278, "loss": 0.04278, "time": 0.49184} +{"mode": "val", "epoch": 124, "iter": 533, "lr": 0.00181, "top1_acc": 0.9351, "top5_acc": 0.99578, "mean_class_accuracy": 0.9134} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.0018, "memory": 4083, "data_time": 0.18836, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.03949, "loss": 0.03949, "time": 0.80138} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.00179, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02806, "loss": 0.02806, "time": 0.49432} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00178, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02728, "loss": 0.02728, "time": 0.49184} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00177, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03102, "loss": 0.03102, "time": 0.49433} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00176, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02763, "loss": 0.02763, "time": 0.30596} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00175, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03353, "loss": 0.03353, "time": 0.51095} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00173, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.0362, "loss": 0.0362, "time": 0.26839} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00172, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02826, "loss": 0.02826, "time": 0.4901} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.00171, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0308, "loss": 0.0308, "time": 0.49018} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.0017, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03322, "loss": 0.03322, "time": 0.4916} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00169, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02699, "loss": 0.02699, "time": 0.49249} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00168, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03182, "loss": 0.03182, "time": 0.49182} +{"mode": "val", "epoch": 125, "iter": 533, "lr": 0.00167, "top1_acc": 0.93592, "top5_acc": 0.99624, "mean_class_accuracy": 0.91123} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00166, "memory": 4083, "data_time": 0.18807, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02442, "loss": 0.02442, "time": 0.79771} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00165, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02557, "loss": 0.02557, "time": 0.49211} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00164, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03237, "loss": 0.03237, "time": 0.48903} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00163, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02503, "loss": 0.02503, "time": 0.49244} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00162, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02553, "loss": 0.02553, "time": 0.31391} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00161, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02145, "loss": 0.02145, "time": 0.51129} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0016, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.0244, "loss": 0.0244, "time": 0.26365} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00159, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03209, "loss": 0.03209, "time": 0.49154} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00158, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04142, "loss": 0.04142, "time": 0.49268} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00157, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.02769, "loss": 0.02769, "time": 0.48964} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00156, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03352, "loss": 0.03352, "time": 0.49306} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00155, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03672, "loss": 0.03672, "time": 0.49263} +{"mode": "val", "epoch": 126, "iter": 533, "lr": 0.00155, "top1_acc": 0.93862, "top5_acc": 0.99613, "mean_class_accuracy": 0.91367} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00154, "memory": 4083, "data_time": 0.18605, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02427, "loss": 0.02427, "time": 0.7893} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00153, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01874, "loss": 0.01874, "time": 0.49292} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00152, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03105, "loss": 0.03105, "time": 0.49147} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00151, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03154, "loss": 0.03154, "time": 0.48969} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.0015, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02817, "loss": 0.02817, "time": 0.33003} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.00149, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0346, "loss": 0.0346, "time": 0.51015} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00148, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02584, "loss": 0.02584, "time": 0.25054} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00147, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02698, "loss": 0.02698, "time": 0.47163} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00146, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02366, "loss": 0.02366, "time": 0.49212} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00145, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02399, "loss": 0.02399, "time": 0.49079} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00144, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02705, "loss": 0.02705, "time": 0.49026} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00143, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03535, "loss": 0.03535, "time": 0.49105} +{"mode": "val", "epoch": 127, "iter": 533, "lr": 0.00142, "top1_acc": 0.93968, "top5_acc": 0.99695, "mean_class_accuracy": 0.91229} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00141, "memory": 4083, "data_time": 0.18956, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02096, "loss": 0.02096, "time": 0.80256} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.0014, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02246, "loss": 0.02246, "time": 0.49707} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00139, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02148, "loss": 0.02148, "time": 0.49516} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00138, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.026, "loss": 0.026, "time": 0.49262} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00138, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02602, "loss": 0.02602, "time": 0.34352} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00137, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02401, "loss": 0.02401, "time": 0.5104} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.00136, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.0249, "loss": 0.0249, "time": 0.24707} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00135, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02762, "loss": 0.02762, "time": 0.47042} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00134, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02552, "loss": 0.02552, "time": 0.48984} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00133, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01841, "loss": 0.01841, "time": 0.4922} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00132, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.02623, "loss": 0.02623, "time": 0.48928} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00131, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0257, "loss": 0.0257, "time": 0.49011} +{"mode": "val", "epoch": 128, "iter": 533, "lr": 0.0013, "top1_acc": 0.93862, "top5_acc": 0.99589, "mean_class_accuracy": 0.91898} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.00129, "memory": 4083, "data_time": 0.17978, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02005, "loss": 0.02005, "time": 0.79125} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00129, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.03577, "loss": 0.03577, "time": 0.49252} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00128, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02651, "loss": 0.02651, "time": 0.49047} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00127, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02659, "loss": 0.02659, "time": 0.49178} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00126, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02423, "loss": 0.02423, "time": 0.36373} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00125, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02139, "loss": 0.02139, "time": 0.5106} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00124, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02048, "loss": 0.02048, "time": 0.24756} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00123, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02536, "loss": 0.02536, "time": 0.4743} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.00122, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01784, "loss": 0.01784, "time": 0.48907} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00121, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0217, "loss": 0.0217, "time": 0.48995} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00121, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01981, "loss": 0.01981, "time": 0.49239} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.0012, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01847, "loss": 0.01847, "time": 0.49231} +{"mode": "val", "epoch": 129, "iter": 533, "lr": 0.00119, "top1_acc": 0.93921, "top5_acc": 0.9966, "mean_class_accuracy": 0.91945} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00118, "memory": 4083, "data_time": 0.18901, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0223, "loss": 0.0223, "time": 0.81018} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00117, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02328, "loss": 0.02328, "time": 0.49143} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00116, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02628, "loss": 0.02628, "time": 0.49448} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00116, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02338, "loss": 0.02338, "time": 0.49309} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.00115, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0176, "loss": 0.0176, "time": 0.33788} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00114, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02542, "loss": 0.02542, "time": 0.51152} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00113, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02095, "loss": 0.02095, "time": 0.2509} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00112, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01992, "loss": 0.01992, "time": 0.47705} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00111, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01803, "loss": 0.01803, "time": 0.49323} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.0011, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02028, "loss": 0.02028, "time": 0.4921} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.0011, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0202, "loss": 0.0202, "time": 0.49222} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00109, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02208, "loss": 0.02208, "time": 0.49218} +{"mode": "val", "epoch": 130, "iter": 533, "lr": 0.00108, "top1_acc": 0.93768, "top5_acc": 0.99613, "mean_class_accuracy": 0.91165} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00107, "memory": 4083, "data_time": 0.18365, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01833, "loss": 0.01833, "time": 0.79727} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.00106, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01799, "loss": 0.01799, "time": 0.49073} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00106, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01601, "loss": 0.01601, "time": 0.49205} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00105, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01984, "loss": 0.01984, "time": 0.48839} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00104, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01944, "loss": 0.01944, "time": 0.33803} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00103, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02373, "loss": 0.02373, "time": 0.51066} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00102, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01846, "loss": 0.01846, "time": 0.25139} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00102, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01964, "loss": 0.01964, "time": 0.48416} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00101, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01828, "loss": 0.01828, "time": 0.48986} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.001, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0176, "loss": 0.0176, "time": 0.49273} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.00099, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02758, "loss": 0.02758, "time": 0.49139} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00098, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01701, "loss": 0.01701, "time": 0.49117} +{"mode": "val", "epoch": 131, "iter": 533, "lr": 0.00098, "top1_acc": 0.93909, "top5_acc": 0.99613, "mean_class_accuracy": 0.91669} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.00097, "memory": 4083, "data_time": 0.18669, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01846, "loss": 0.01846, "time": 0.79985} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00096, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01966, "loss": 0.01966, "time": 0.49281} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00095, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01953, "loss": 0.01953, "time": 0.49365} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00095, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02032, "loss": 0.02032, "time": 0.49132} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00094, "memory": 4083, "data_time": 0.00041, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01647, "loss": 0.01647, "time": 0.34442} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00093, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02102, "loss": 0.02102, "time": 0.50851} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00092, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01721, "loss": 0.01721, "time": 0.24975} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00091, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01633, "loss": 0.01633, "time": 0.477} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00091, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0172, "loss": 0.0172, "time": 0.49152} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0009, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01865, "loss": 0.01865, "time": 0.49126} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00089, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01922, "loss": 0.01922, "time": 0.49169} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00088, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02144, "loss": 0.02144, "time": 0.49097} +{"mode": "val", "epoch": 132, "iter": 533, "lr": 0.00088, "top1_acc": 0.94038, "top5_acc": 0.99613, "mean_class_accuracy": 0.9171} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.00087, "memory": 4083, "data_time": 0.18597, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01602, "loss": 0.01602, "time": 0.80507} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00086, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01738, "loss": 0.01738, "time": 0.49272} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00086, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01535, "loss": 0.01535, "time": 0.49141} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00085, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01658, "loss": 0.01658, "time": 0.49241} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00084, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01617, "loss": 0.01617, "time": 0.3437} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00083, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01623, "loss": 0.01623, "time": 0.50772} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00083, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01833, "loss": 0.01833, "time": 0.25558} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00082, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01987, "loss": 0.01987, "time": 0.4871} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00081, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01766, "loss": 0.01766, "time": 0.49099} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.0008, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01613, "loss": 0.01613, "time": 0.49305} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0008, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01626, "loss": 0.01626, "time": 0.48908} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00079, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01651, "loss": 0.01651, "time": 0.49206} +{"mode": "val", "epoch": 133, "iter": 533, "lr": 0.00078, "top1_acc": 0.93933, "top5_acc": 0.9966, "mean_class_accuracy": 0.9179} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00078, "memory": 4083, "data_time": 0.18433, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01674, "loss": 0.01674, "time": 0.79491} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00077, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01545, "loss": 0.01545, "time": 0.48814} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00076, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01554, "loss": 0.01554, "time": 0.49318} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.00076, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01607, "loss": 0.01607, "time": 0.4921} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00075, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0161, "loss": 0.0161, "time": 0.33341} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00074, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01466, "loss": 0.01466, "time": 0.50911} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00073, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01952, "loss": 0.01952, "time": 0.2617} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00073, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0201, "loss": 0.0201, "time": 0.4895} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00072, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01561, "loss": 0.01561, "time": 0.49361} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00071, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02317, "loss": 0.02317, "time": 0.4912} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00071, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02169, "loss": 0.02169, "time": 0.49} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.0007, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01849, "loss": 0.01849, "time": 0.49219} +{"mode": "val", "epoch": 134, "iter": 533, "lr": 0.0007, "top1_acc": 0.9398, "top5_acc": 0.99601, "mean_class_accuracy": 0.91756} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00069, "memory": 4083, "data_time": 0.1849, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01696, "loss": 0.01696, "time": 0.79687} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00068, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01869, "loss": 0.01869, "time": 0.49237} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00068, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02314, "loss": 0.02314, "time": 0.4933} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00067, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01564, "loss": 0.01564, "time": 0.49273} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00066, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01905, "loss": 0.01905, "time": 0.33249} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00066, "memory": 4083, "data_time": 0.0004, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01679, "loss": 0.01679, "time": 0.51018} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00065, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01829, "loss": 0.01829, "time": 0.25726} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00064, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01637, "loss": 0.01637, "time": 0.48824} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.00064, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01542, "loss": 0.01542, "time": 0.48902} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00063, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01722, "loss": 0.01722, "time": 0.4883} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00062, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01686, "loss": 0.01686, "time": 0.49261} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00062, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01487, "loss": 0.01487, "time": 0.49222} +{"mode": "val", "epoch": 135, "iter": 533, "lr": 0.00061, "top1_acc": 0.93991, "top5_acc": 0.99613, "mean_class_accuracy": 0.91833} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00061, "memory": 4083, "data_time": 0.18631, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01624, "loss": 0.01624, "time": 0.78195} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.0006, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01575, "loss": 0.01575, "time": 0.49209} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00059, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01465, "loss": 0.01465, "time": 0.49315} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00059, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01564, "loss": 0.01564, "time": 0.49198} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.00058, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02138, "loss": 0.02138, "time": 0.33128} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.00057, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01862, "loss": 0.01862, "time": 0.51008} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00057, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01712, "loss": 0.01712, "time": 0.25764} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00056, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01851, "loss": 0.01851, "time": 0.48882} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00056, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01533, "loss": 0.01533, "time": 0.48896} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00055, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01476, "loss": 0.01476, "time": 0.49541} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00054, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0179, "loss": 0.0179, "time": 0.49727} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00054, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0158, "loss": 0.0158, "time": 0.48968} +{"mode": "val", "epoch": 136, "iter": 533, "lr": 0.00053, "top1_acc": 0.94003, "top5_acc": 0.99636, "mean_class_accuracy": 0.91707} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00053, "memory": 4083, "data_time": 0.18663, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01669, "loss": 0.01669, "time": 0.8047} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00052, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01517, "loss": 0.01517, "time": 0.49169} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00052, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01871, "loss": 0.01871, "time": 0.49815} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.00051, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01763, "loss": 0.01763, "time": 0.49128} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.0005, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01728, "loss": 0.01728, "time": 0.32463} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.0005, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01494, "loss": 0.01494, "time": 0.50957} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00049, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02044, "loss": 0.02044, "time": 0.26689} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00049, "memory": 4083, "data_time": 0.0004, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01623, "loss": 0.01623, "time": 0.49213} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00048, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01505, "loss": 0.01505, "time": 0.49248} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00048, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01562, "loss": 0.01562, "time": 0.49561} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00047, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01924, "loss": 0.01924, "time": 0.49151} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00046, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01799, "loss": 0.01799, "time": 0.49481} +{"mode": "val", "epoch": 137, "iter": 533, "lr": 0.00046, "top1_acc": 0.94073, "top5_acc": 0.99613, "mean_class_accuracy": 0.91902} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00046, "memory": 4083, "data_time": 0.19341, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01604, "loss": 0.01604, "time": 0.79736} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00045, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0156, "loss": 0.0156, "time": 0.49067} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00044, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01596, "loss": 0.01596, "time": 0.49474} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00044, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01574, "loss": 0.01574, "time": 0.49266} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.00043, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01435, "loss": 0.01435, "time": 0.31467} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.00043, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01759, "loss": 0.01759, "time": 0.50994} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00042, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01633, "loss": 0.01633, "time": 0.28847} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00042, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01498, "loss": 0.01498, "time": 0.48997} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00041, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01561, "loss": 0.01561, "time": 0.4916} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00041, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01988, "loss": 0.01988, "time": 0.49119} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.0004, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01482, "loss": 0.01482, "time": 0.49169} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.0004, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01544, "loss": 0.01544, "time": 0.49118} +{"mode": "val", "epoch": 138, "iter": 533, "lr": 0.00039, "top1_acc": 0.94167, "top5_acc": 0.99648, "mean_class_accuracy": 0.91926} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00039, "memory": 4083, "data_time": 0.18525, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01593, "loss": 0.01593, "time": 0.79879} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00038, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01521, "loss": 0.01521, "time": 0.49095} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00038, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01499, "loss": 0.01499, "time": 0.49003} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00037, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01429, "loss": 0.01429, "time": 0.49182} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00037, "memory": 4083, "data_time": 0.00038, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01537, "loss": 0.01537, "time": 0.2968} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00036, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01481, "loss": 0.01481, "time": 0.50979} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00036, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01544, "loss": 0.01544, "time": 0.41231} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00035, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01575, "loss": 0.01575, "time": 0.71253} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00035, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01832, "loss": 0.01832, "time": 0.7052} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.00034, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01579, "loss": 0.01579, "time": 0.6866} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.00034, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01837, "loss": 0.01837, "time": 0.69564} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00033, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01526, "loss": 0.01526, "time": 0.70222} +{"mode": "val", "epoch": 139, "iter": 533, "lr": 0.00033, "top1_acc": 0.94226, "top5_acc": 0.99683, "mean_class_accuracy": 0.92099} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00033, "memory": 4083, "data_time": 0.18501, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01575, "loss": 0.01575, "time": 1.16434} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00032, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.017, "loss": 0.017, "time": 0.69286} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.00032, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01609, "loss": 0.01609, "time": 0.68043} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.00031, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01496, "loss": 0.01496, "time": 0.69851} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00031, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01585, "loss": 0.01585, "time": 0.682} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.0003, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01529, "loss": 0.01529, "time": 0.30042} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.0003, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01388, "loss": 0.01388, "time": 0.22112} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00029, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01407, "loss": 0.01407, "time": 0.21716} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00029, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01772, "loss": 0.01772, "time": 0.21884} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00029, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01439, "loss": 0.01439, "time": 0.21812} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00028, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01367, "loss": 0.01367, "time": 0.21778} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00028, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01507, "loss": 0.01507, "time": 0.22118} +{"mode": "val", "epoch": 140, "iter": 533, "lr": 0.00027, "top1_acc": 0.94097, "top5_acc": 0.99613, "mean_class_accuracy": 0.91867} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00027, "memory": 4083, "data_time": 0.17829, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01522, "loss": 0.01522, "time": 0.4088} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00026, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01514, "loss": 0.01514, "time": 0.22081} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00026, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01637, "loss": 0.01637, "time": 0.21937} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00026, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01521, "loss": 0.01521, "time": 0.21824} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00025, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.014, "loss": 0.014, "time": 0.22158} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00025, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01471, "loss": 0.01471, "time": 0.22027} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00024, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0178, "loss": 0.0178, "time": 0.21943} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00024, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01577, "loss": 0.01577, "time": 0.22064} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00024, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01597, "loss": 0.01597, "time": 0.22215} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00023, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01495, "loss": 0.01495, "time": 0.22207} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00023, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0143, "loss": 0.0143, "time": 0.2208} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00022, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01335, "loss": 0.01335, "time": 0.22023} +{"mode": "val", "epoch": 141, "iter": 533, "lr": 0.00022, "top1_acc": 0.9412, "top5_acc": 0.99695, "mean_class_accuracy": 0.91862} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00022, "memory": 4083, "data_time": 0.17848, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01488, "loss": 0.01488, "time": 0.40919} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00021, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01675, "loss": 0.01675, "time": 0.22101} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00021, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01293, "loss": 0.01293, "time": 0.21707} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00021, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01366, "loss": 0.01366, "time": 0.22016} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.0002, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01836, "loss": 0.01836, "time": 0.21983} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.0002, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01533, "loss": 0.01533, "time": 0.21945} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.0002, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01358, "loss": 0.01358, "time": 0.21885} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00019, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01432, "loss": 0.01432, "time": 0.21906} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00019, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01655, "loss": 0.01655, "time": 0.21816} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00018, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01516, "loss": 0.01516, "time": 0.22092} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00018, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01445, "loss": 0.01445, "time": 0.21913} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00018, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01373, "loss": 0.01373, "time": 0.21945} +{"mode": "val", "epoch": 142, "iter": 533, "lr": 0.00018, "top1_acc": 0.94038, "top5_acc": 0.99624, "mean_class_accuracy": 0.91917} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.00017, "memory": 4083, "data_time": 0.18146, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01637, "loss": 0.01637, "time": 0.4109} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00017, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01754, "loss": 0.01754, "time": 0.22232} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00017, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01403, "loss": 0.01403, "time": 0.21622} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00016, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01689, "loss": 0.01689, "time": 0.22082} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00016, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01457, "loss": 0.01457, "time": 0.21722} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00016, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01722, "loss": 0.01722, "time": 0.22056} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00015, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01668, "loss": 0.01668, "time": 0.21902} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00015, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01828, "loss": 0.01828, "time": 0.21917} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00015, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01444, "loss": 0.01444, "time": 0.21942} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00014, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01489, "loss": 0.01489, "time": 0.22166} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00014, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01201, "loss": 0.01201, "time": 0.21766} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00014, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0141, "loss": 0.0141, "time": 0.2209} +{"mode": "val", "epoch": 143, "iter": 533, "lr": 0.00013, "top1_acc": 0.94144, "top5_acc": 0.99613, "mean_class_accuracy": 0.91937} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00013, "memory": 4083, "data_time": 0.18194, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01546, "loss": 0.01546, "time": 0.41273} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00013, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01435, "loss": 0.01435, "time": 0.22005} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00013, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01645, "loss": 0.01645, "time": 0.22122} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00012, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01343, "loss": 0.01343, "time": 0.21976} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00012, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01484, "loss": 0.01484, "time": 0.2183} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00012, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01596, "loss": 0.01596, "time": 0.22101} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00011, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01721, "loss": 0.01721, "time": 0.22517} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.00011, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0147, "loss": 0.0147, "time": 0.22177} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.00011, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01579, "loss": 0.01579, "time": 0.22153} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.00011, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01453, "loss": 0.01453, "time": 0.21991} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.0001, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01446, "loss": 0.01446, "time": 0.21794} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.0001, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01654, "loss": 0.01654, "time": 0.21905} +{"mode": "val", "epoch": 144, "iter": 533, "lr": 0.0001, "top1_acc": 0.94156, "top5_acc": 0.99671, "mean_class_accuracy": 0.91901} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.0001, "memory": 4083, "data_time": 0.18271, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01563, "loss": 0.01563, "time": 0.41361} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 9e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02071, "loss": 0.02071, "time": 0.22217} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 9e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01803, "loss": 0.01803, "time": 0.2178} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 9e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01465, "loss": 0.01465, "time": 0.21635} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 9e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01346, "loss": 0.01346, "time": 0.21669} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 8e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01449, "loss": 0.01449, "time": 0.21911} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 8e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01553, "loss": 0.01553, "time": 0.21965} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 8e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0131, "loss": 0.0131, "time": 0.21978} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 8e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01419, "loss": 0.01419, "time": 0.21896} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 7e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01513, "loss": 0.01513, "time": 0.2203} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 7e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0162, "loss": 0.0162, "time": 0.21762} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 7e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01446, "loss": 0.01446, "time": 0.21948} +{"mode": "val", "epoch": 145, "iter": 533, "lr": 7e-05, "top1_acc": 0.94238, "top5_acc": 0.99624, "mean_class_accuracy": 0.91946} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 7e-05, "memory": 4083, "data_time": 0.184, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01539, "loss": 0.01539, "time": 0.41454} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 6e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01558, "loss": 0.01558, "time": 0.21934} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 6e-05, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0137, "loss": 0.0137, "time": 0.22029} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 6e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02066, "loss": 0.02066, "time": 0.22114} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 6e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01402, "loss": 0.01402, "time": 0.2212} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 6e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01425, "loss": 0.01425, "time": 0.21857} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 5e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01783, "loss": 0.01783, "time": 0.22295} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 5e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01468, "loss": 0.01468, "time": 0.22209} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 5e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01518, "loss": 0.01518, "time": 0.22055} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 5e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01798, "loss": 0.01798, "time": 0.21727} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 5e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0148, "loss": 0.0148, "time": 0.21805} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 5e-05, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01493, "loss": 0.01493, "time": 0.21665} +{"mode": "val", "epoch": 146, "iter": 533, "lr": 4e-05, "top1_acc": 0.9412, "top5_acc": 0.99683, "mean_class_accuracy": 0.91738} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 4e-05, "memory": 4083, "data_time": 0.1848, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01439, "loss": 0.01439, "time": 0.41548} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 4e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01386, "loss": 0.01386, "time": 0.22132} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 4e-05, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01632, "loss": 0.01632, "time": 0.2217} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 4e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0154, "loss": 0.0154, "time": 0.21728} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 4e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01507, "loss": 0.01507, "time": 0.22055} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 3e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01394, "loss": 0.01394, "time": 0.22007} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 3e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01839, "loss": 0.01839, "time": 0.21908} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 3e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0154, "loss": 0.0154, "time": 0.21767} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 3e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01453, "loss": 0.01453, "time": 0.21959} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 3e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01625, "loss": 0.01625, "time": 0.21997} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 3e-05, "memory": 4083, "data_time": 0.00019, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01575, "loss": 0.01575, "time": 0.21483} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 3e-05, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01318, "loss": 0.01318, "time": 0.21957} +{"mode": "val", "epoch": 147, "iter": 533, "lr": 2e-05, "top1_acc": 0.9425, "top5_acc": 0.9966, "mean_class_accuracy": 0.9209} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 2e-05, "memory": 4083, "data_time": 0.17817, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01736, "loss": 0.01736, "time": 0.40842} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 2e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0147, "loss": 0.0147, "time": 0.22089} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 2e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01434, "loss": 0.01434, "time": 0.2181} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 2e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01491, "loss": 0.01491, "time": 0.21802} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 2e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0166, "loss": 0.0166, "time": 0.21779} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 2e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01473, "loss": 0.01473, "time": 0.21652} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 2e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01458, "loss": 0.01458, "time": 0.21975} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 2e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0174, "loss": 0.0174, "time": 0.21734} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 1e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01456, "loss": 0.01456, "time": 0.22011} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 1e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01687, "loss": 0.01687, "time": 0.21924} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 1e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01516, "loss": 0.01516, "time": 0.21874} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 1e-05, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01346, "loss": 0.01346, "time": 0.21917} +{"mode": "val", "epoch": 148, "iter": 533, "lr": 1e-05, "top1_acc": 0.9425, "top5_acc": 0.99624, "mean_class_accuracy": 0.92134} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 1e-05, "memory": 4083, "data_time": 0.18205, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01755, "loss": 0.01755, "time": 0.41453} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 1e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01552, "loss": 0.01552, "time": 0.21928} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 1e-05, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01362, "loss": 0.01362, "time": 0.22268} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 1e-05, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01484, "loss": 0.01484, "time": 0.22247} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 1e-05, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01489, "loss": 0.01489, "time": 0.22167} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 1e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01701, "loss": 0.01701, "time": 0.22237} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 1e-05, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0153, "loss": 0.0153, "time": 0.22316} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 1e-05, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01336, "loss": 0.01336, "time": 0.22071} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01511, "loss": 0.01511, "time": 0.22422} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01491, "loss": 0.01491, "time": 0.21923} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01392, "loss": 0.01392, "time": 0.21839} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0147, "loss": 0.0147, "time": 0.21895} +{"mode": "val", "epoch": 149, "iter": 533, "lr": 0.0, "top1_acc": 0.9412, "top5_acc": 0.99671, "mean_class_accuracy": 0.92021} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 0.0, "memory": 4083, "data_time": 0.18129, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01513, "loss": 0.01513, "time": 0.40829} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01601, "loss": 0.01601, "time": 0.21616} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01427, "loss": 0.01427, "time": 0.21526} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01381, "loss": 0.01381, "time": 0.21759} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 0.0, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01712, "loss": 0.01712, "time": 0.21638} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0153, "loss": 0.0153, "time": 0.21711} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 0.0, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01425, "loss": 0.01425, "time": 0.21727} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 0.0, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01556, "loss": 0.01556, "time": 0.21871} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0153, "loss": 0.0153, "time": 0.21493} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01328, "loss": 0.01328, "time": 0.21814} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01434, "loss": 0.01434, "time": 0.21691} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01393, "loss": 0.01393, "time": 0.2194} +{"mode": "val", "epoch": 150, "iter": 533, "lr": 0.0, "top1_acc": 0.94179, "top5_acc": 0.99648, "mean_class_accuracy": 0.92104} diff --git a/finegym/k_3/best_pred.pkl b/finegym/k_3/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..f8343b7242b996705d37a7bad304b803604ecb15 --- /dev/null +++ b/finegym/k_3/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:29ec47e946dec928a97a5985656bc8890cb62f4df904ec8e440aaaacf0ea1156 +size 5254451 diff --git a/finegym/k_3/best_top1_acc_epoch_147.pth b/finegym/k_3/best_top1_acc_epoch_147.pth new file mode 100644 index 0000000000000000000000000000000000000000..ac7a22c369cbf0d71660fff604f437c280c1d759 --- /dev/null +++ b/finegym/k_3/best_top1_acc_epoch_147.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8d649349a05325a267d3704fda3ba334247d5c6c653c71d82c822d2dc7a00180 +size 31999601 diff --git a/finegym/k_3/k_3.py b/finegym/k_3/k_3.py new file mode 100644 index 0000000000000000000000000000000000000000..c897bf38ce3ead76ce0a7c30ae48da373ba4cc4e --- /dev/null +++ b/finegym/k_3/k_3.py @@ -0,0 +1,113 @@ +modality = 'k' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/k_3' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/finegym/km/20250624_101502.log b/finegym/km/20250624_101502.log new file mode 100644 index 0000000000000000000000000000000000000000..ab7030dbb5f8966a1429e2022142d0bb1b42934c --- /dev/null +++ b/finegym/km/20250624_101502.log @@ -0,0 +1,3471 @@ +2025-06-24 10:15:02,722 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2025-06-24 10:15:02,955 - pyskl - INFO - Config: modality = 'km' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/km' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2025-06-24 10:15:02,956 - pyskl - INFO - Set random seed to 221931424, deterministic: False +2025-06-24 10:15:04,573 - pyskl - INFO - 20484 videos remain after valid thresholding +2025-06-24 10:15:08,898 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-24 10:15:08,899 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km +2025-06-24 10:15:08,899 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CustomCheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2025-06-24 10:15:08,899 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2025-06-24 10:15:08,899 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km by HardDiskBackend. +2025-06-24 10:16:11,638 - pyskl - INFO - Epoch [1][100/1281] lr: 2.500e-02, eta: 1 day, 9:28:01, time: 0.627, data_time: 0.196, memory: 4082, top1_acc: 0.0506, top5_acc: 0.2125, loss_cls: 4.6163, loss: 4.6163 +2025-06-24 10:16:53,278 - pyskl - INFO - Epoch [1][200/1281] lr: 2.500e-02, eta: 1 day, 3:49:32, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.0925, top5_acc: 0.2944, loss_cls: 4.6548, loss: 4.6548 +2025-06-24 10:17:35,116 - pyskl - INFO - Epoch [1][300/1281] lr: 2.500e-02, eta: 1 day, 1:58:20, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.0869, top5_acc: 0.3156, loss_cls: 4.5094, loss: 4.5094 +2025-06-24 10:18:16,827 - pyskl - INFO - Epoch [1][400/1281] lr: 2.500e-02, eta: 1 day, 1:01:23, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.0969, top5_acc: 0.3494, loss_cls: 4.3248, loss: 4.3248 +2025-06-24 10:18:58,887 - pyskl - INFO - Epoch [1][500/1281] lr: 2.500e-02, eta: 1 day, 0:29:10, time: 0.421, data_time: 0.000, memory: 4082, top1_acc: 0.0956, top5_acc: 0.4056, loss_cls: 4.1567, loss: 4.1567 +2025-06-24 10:19:40,481 - pyskl - INFO - Epoch [1][600/1281] lr: 2.500e-02, eta: 1 day, 0:04:58, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.1300, top5_acc: 0.4444, loss_cls: 3.9782, loss: 3.9782 +2025-06-24 10:20:22,164 - pyskl - INFO - Epoch [1][700/1281] lr: 2.500e-02, eta: 23:47:55, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.1338, top5_acc: 0.4344, loss_cls: 3.9153, loss: 3.9153 +2025-06-24 10:21:04,251 - pyskl - INFO - Epoch [1][800/1281] lr: 2.500e-02, eta: 23:36:32, time: 0.421, data_time: 0.000, memory: 4082, top1_acc: 0.1844, top5_acc: 0.5125, loss_cls: 3.6924, loss: 3.6924 +2025-06-24 10:21:45,960 - pyskl - INFO - Epoch [1][900/1281] lr: 2.500e-02, eta: 23:26:12, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.2156, top5_acc: 0.5394, loss_cls: 3.5527, loss: 3.5527 +2025-06-24 10:22:27,772 - pyskl - INFO - Epoch [1][1000/1281] lr: 2.500e-02, eta: 23:18:08, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.2313, top5_acc: 0.5600, loss_cls: 3.4496, loss: 3.4496 +2025-06-24 10:23:09,582 - pyskl - INFO - Epoch [1][1100/1281] lr: 2.500e-02, eta: 23:11:23, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.2444, top5_acc: 0.6006, loss_cls: 3.3527, loss: 3.3527 +2025-06-24 10:23:52,773 - pyskl - INFO - Epoch [1][1200/1281] lr: 2.500e-02, eta: 23:09:18, time: 0.432, data_time: 0.000, memory: 4082, top1_acc: 0.2644, top5_acc: 0.6331, loss_cls: 3.2224, loss: 3.2224 +2025-06-24 10:24:22,636 - pyskl - INFO - Saving checkpoint at 1 epochs +2025-06-24 10:25:31,979 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:25:32,050 - pyskl - INFO - +top1_acc 0.3109 +top5_acc 0.6821 +2025-06-24 10:25:32,050 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:25:32,058 - pyskl - INFO - +mean_acc 0.1465 +2025-06-24 10:25:32,248 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2025-06-24 10:25:32,248 - pyskl - INFO - Best top1_acc is 0.3109 at 1 epoch. +2025-06-24 10:25:32,252 - pyskl - INFO - Epoch(val) [1][533] top1_acc: 0.3109, top5_acc: 0.6821, mean_class_accuracy: 0.1465 +2025-06-24 10:26:36,525 - pyskl - INFO - Epoch [2][100/1281] lr: 2.500e-02, eta: 22:34:02, time: 0.643, data_time: 0.205, memory: 4082, top1_acc: 0.3438, top5_acc: 0.6987, loss_cls: 2.8869, loss: 2.8869 +2025-06-24 10:27:18,506 - pyskl - INFO - Epoch [2][200/1281] lr: 2.500e-02, eta: 22:32:01, time: 0.420, data_time: 0.000, memory: 4082, top1_acc: 0.3356, top5_acc: 0.7281, loss_cls: 2.8757, loss: 2.8757 +2025-06-24 10:28:00,185 - pyskl - INFO - Epoch [2][300/1281] lr: 2.500e-02, eta: 22:29:34, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.3513, top5_acc: 0.7312, loss_cls: 2.7645, loss: 2.7645 +2025-06-24 10:28:41,971 - pyskl - INFO - Epoch [2][400/1281] lr: 2.500e-02, eta: 22:27:32, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.3481, top5_acc: 0.7575, loss_cls: 2.6782, loss: 2.6782 +2025-06-24 10:29:23,573 - pyskl - INFO - Epoch [2][500/1281] lr: 2.499e-02, eta: 22:25:19, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.3912, top5_acc: 0.7975, loss_cls: 2.5395, loss: 2.5395 +2025-06-24 10:30:05,256 - pyskl - INFO - Epoch [2][600/1281] lr: 2.499e-02, eta: 22:23:23, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.3887, top5_acc: 0.7931, loss_cls: 2.5480, loss: 2.5480 +2025-06-24 10:30:46,758 - pyskl - INFO - Epoch [2][700/1281] lr: 2.499e-02, eta: 22:21:18, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.4044, top5_acc: 0.7925, loss_cls: 2.5329, loss: 2.5329 +2025-06-24 10:31:28,429 - pyskl - INFO - Epoch [2][800/1281] lr: 2.499e-02, eta: 22:19:37, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.4125, top5_acc: 0.8344, loss_cls: 2.3862, loss: 2.3862 +2025-06-24 10:32:10,244 - pyskl - INFO - Epoch [2][900/1281] lr: 2.499e-02, eta: 22:18:13, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.4156, top5_acc: 0.8269, loss_cls: 2.4069, loss: 2.4069 +2025-06-24 10:32:51,961 - pyskl - INFO - Epoch [2][1000/1281] lr: 2.499e-02, eta: 22:16:45, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.4238, top5_acc: 0.8512, loss_cls: 2.3501, loss: 2.3501 +2025-06-24 10:33:33,808 - pyskl - INFO - Epoch [2][1100/1281] lr: 2.499e-02, eta: 22:15:31, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.4344, top5_acc: 0.8319, loss_cls: 2.3077, loss: 2.3077 +2025-06-24 10:34:15,564 - pyskl - INFO - Epoch [2][1200/1281] lr: 2.499e-02, eta: 22:14:13, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.4681, top5_acc: 0.8431, loss_cls: 2.2211, loss: 2.2211 +2025-06-24 10:34:43,797 - pyskl - INFO - Saving checkpoint at 2 epochs +2025-06-24 10:35:50,664 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:35:50,738 - pyskl - INFO - +top1_acc 0.4516 +top5_acc 0.8359 +2025-06-24 10:35:50,738 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:35:50,750 - pyskl - INFO - +mean_acc 0.2673 +2025-06-24 10:35:50,755 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_1.pth was removed +2025-06-24 10:35:50,964 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2025-06-24 10:35:50,964 - pyskl - INFO - Best top1_acc is 0.4516 at 2 epoch. +2025-06-24 10:35:50,967 - pyskl - INFO - Epoch(val) [2][533] top1_acc: 0.4516, top5_acc: 0.8359, mean_class_accuracy: 0.2673 +2025-06-24 10:36:54,346 - pyskl - INFO - Epoch [3][100/1281] lr: 2.499e-02, eta: 21:57:30, time: 0.634, data_time: 0.197, memory: 4082, top1_acc: 0.4788, top5_acc: 0.8688, loss_cls: 2.1325, loss: 2.1325 +2025-06-24 10:37:37,276 - pyskl - INFO - Epoch [3][200/1281] lr: 2.499e-02, eta: 21:58:11, time: 0.429, data_time: 0.000, memory: 4082, top1_acc: 0.4925, top5_acc: 0.8944, loss_cls: 2.0369, loss: 2.0369 +2025-06-24 10:38:19,135 - pyskl - INFO - Epoch [3][300/1281] lr: 2.499e-02, eta: 21:57:36, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.4963, top5_acc: 0.8994, loss_cls: 2.0152, loss: 2.0152 +2025-06-24 10:39:00,865 - pyskl - INFO - Epoch [3][400/1281] lr: 2.499e-02, eta: 21:56:52, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.5050, top5_acc: 0.8919, loss_cls: 2.0283, loss: 2.0283 +2025-06-24 10:39:42,563 - pyskl - INFO - Epoch [3][500/1281] lr: 2.498e-02, eta: 21:56:06, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.5256, top5_acc: 0.8906, loss_cls: 1.9821, loss: 1.9821 +2025-06-24 10:40:24,326 - pyskl - INFO - Epoch [3][600/1281] lr: 2.498e-02, eta: 21:55:24, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.5119, top5_acc: 0.9044, loss_cls: 1.9255, loss: 1.9255 +2025-06-24 10:41:06,104 - pyskl - INFO - Epoch [3][700/1281] lr: 2.498e-02, eta: 21:54:43, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.5225, top5_acc: 0.8938, loss_cls: 1.9646, loss: 1.9646 +2025-06-24 10:41:47,940 - pyskl - INFO - Epoch [3][800/1281] lr: 2.498e-02, eta: 21:54:05, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.5181, top5_acc: 0.9062, loss_cls: 1.9398, loss: 1.9398 +2025-06-24 10:42:29,775 - pyskl - INFO - Epoch [3][900/1281] lr: 2.498e-02, eta: 21:53:27, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.5350, top5_acc: 0.9038, loss_cls: 1.9138, loss: 1.9138 +2025-06-24 10:43:11,642 - pyskl - INFO - Epoch [3][1000/1281] lr: 2.498e-02, eta: 21:52:51, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.5262, top5_acc: 0.9156, loss_cls: 1.8921, loss: 1.8921 +2025-06-24 10:43:53,441 - pyskl - INFO - Epoch [3][1100/1281] lr: 2.498e-02, eta: 21:52:11, time: 0.418, data_time: 0.001, memory: 4082, top1_acc: 0.5450, top5_acc: 0.9031, loss_cls: 1.8763, loss: 1.8763 +2025-06-24 10:44:35,233 - pyskl - INFO - Epoch [3][1200/1281] lr: 2.498e-02, eta: 21:51:30, time: 0.418, data_time: 0.001, memory: 4082, top1_acc: 0.5381, top5_acc: 0.9169, loss_cls: 1.8748, loss: 1.8748 +2025-06-24 10:45:02,394 - pyskl - INFO - Saving checkpoint at 3 epochs +2025-06-24 10:46:08,677 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:46:08,740 - pyskl - INFO - +top1_acc 0.5153 +top5_acc 0.9142 +2025-06-24 10:46:08,740 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:46:08,749 - pyskl - INFO - +mean_acc 0.3718 +2025-06-24 10:46:08,754 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_2.pth was removed +2025-06-24 10:46:08,943 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2025-06-24 10:46:08,943 - pyskl - INFO - Best top1_acc is 0.5153 at 3 epoch. +2025-06-24 10:46:08,946 - pyskl - INFO - Epoch(val) [3][533] top1_acc: 0.5153, top5_acc: 0.9142, mean_class_accuracy: 0.3718 +2025-06-24 10:47:12,185 - pyskl - INFO - Epoch [4][100/1281] lr: 2.497e-02, eta: 21:40:24, time: 0.632, data_time: 0.201, memory: 4082, top1_acc: 0.5563, top5_acc: 0.9163, loss_cls: 1.7965, loss: 1.7965 +2025-06-24 10:47:53,800 - pyskl - INFO - Epoch [4][200/1281] lr: 2.497e-02, eta: 21:39:50, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.5850, top5_acc: 0.9350, loss_cls: 1.7107, loss: 1.7107 +2025-06-24 10:48:35,839 - pyskl - INFO - Epoch [4][300/1281] lr: 2.497e-02, eta: 21:39:35, time: 0.420, data_time: 0.000, memory: 4082, top1_acc: 0.5944, top5_acc: 0.9400, loss_cls: 1.6285, loss: 1.6285 +2025-06-24 10:49:17,722 - pyskl - INFO - Epoch [4][400/1281] lr: 2.497e-02, eta: 21:39:11, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.5863, top5_acc: 0.9337, loss_cls: 1.6610, loss: 1.6610 +2025-06-24 10:49:59,540 - pyskl - INFO - Epoch [4][500/1281] lr: 2.497e-02, eta: 21:38:44, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.5756, top5_acc: 0.9387, loss_cls: 1.7163, loss: 1.7163 +2025-06-24 10:50:41,413 - pyskl - INFO - Epoch [4][600/1281] lr: 2.497e-02, eta: 21:38:19, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.5831, top5_acc: 0.9281, loss_cls: 1.6961, loss: 1.6961 +2025-06-24 10:51:23,311 - pyskl - INFO - Epoch [4][700/1281] lr: 2.497e-02, eta: 21:37:53, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.5887, top5_acc: 0.9369, loss_cls: 1.6627, loss: 1.6627 +2025-06-24 10:52:05,207 - pyskl - INFO - Epoch [4][800/1281] lr: 2.496e-02, eta: 21:37:28, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.5969, top5_acc: 0.9425, loss_cls: 1.6231, loss: 1.6231 +2025-06-24 10:52:47,171 - pyskl - INFO - Epoch [4][900/1281] lr: 2.496e-02, eta: 21:37:04, time: 0.420, data_time: 0.000, memory: 4082, top1_acc: 0.6000, top5_acc: 0.9350, loss_cls: 1.6053, loss: 1.6053 +2025-06-24 10:53:28,968 - pyskl - INFO - Epoch [4][1000/1281] lr: 2.496e-02, eta: 21:36:32, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.5919, top5_acc: 0.9344, loss_cls: 1.6468, loss: 1.6468 +2025-06-24 10:54:10,632 - pyskl - INFO - Epoch [4][1100/1281] lr: 2.496e-02, eta: 21:35:56, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.5869, top5_acc: 0.9419, loss_cls: 1.6503, loss: 1.6503 +2025-06-24 10:54:52,604 - pyskl - INFO - Epoch [4][1200/1281] lr: 2.496e-02, eta: 21:35:31, time: 0.420, data_time: 0.000, memory: 4082, top1_acc: 0.6212, top5_acc: 0.9450, loss_cls: 1.5430, loss: 1.5430 +2025-06-24 10:55:19,833 - pyskl - INFO - Saving checkpoint at 4 epochs +2025-06-24 10:56:25,539 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 10:56:25,618 - pyskl - INFO - +top1_acc 0.5672 +top5_acc 0.9283 +2025-06-24 10:56:25,618 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 10:56:25,627 - pyskl - INFO - +mean_acc 0.4261 +2025-06-24 10:56:25,632 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_3.pth was removed +2025-06-24 10:56:25,865 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2025-06-24 10:56:25,865 - pyskl - INFO - Best top1_acc is 0.5672 at 4 epoch. +2025-06-24 10:56:25,867 - pyskl - INFO - Epoch(val) [4][533] top1_acc: 0.5672, top5_acc: 0.9283, mean_class_accuracy: 0.4261 +2025-06-24 10:57:27,654 - pyskl - INFO - Epoch [5][100/1281] lr: 2.495e-02, eta: 21:26:15, time: 0.618, data_time: 0.200, memory: 4082, top1_acc: 0.6231, top5_acc: 0.9406, loss_cls: 1.5321, loss: 1.5321 +2025-06-24 10:58:09,300 - pyskl - INFO - Epoch [5][200/1281] lr: 2.495e-02, eta: 21:25:47, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6250, top5_acc: 0.9500, loss_cls: 1.5084, loss: 1.5084 +2025-06-24 10:58:51,055 - pyskl - INFO - Epoch [5][300/1281] lr: 2.495e-02, eta: 21:25:21, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.6419, top5_acc: 0.9544, loss_cls: 1.5038, loss: 1.5038 +2025-06-24 10:59:33,145 - pyskl - INFO - Epoch [5][400/1281] lr: 2.495e-02, eta: 21:25:07, time: 0.421, data_time: 0.000, memory: 4082, top1_acc: 0.6300, top5_acc: 0.9569, loss_cls: 1.5033, loss: 1.5033 +2025-06-24 11:00:14,912 - pyskl - INFO - Epoch [5][500/1281] lr: 2.495e-02, eta: 21:24:40, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.6375, top5_acc: 0.9594, loss_cls: 1.4796, loss: 1.4796 +2025-06-24 11:00:56,615 - pyskl - INFO - Epoch [5][600/1281] lr: 2.495e-02, eta: 21:24:11, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.6262, top5_acc: 0.9425, loss_cls: 1.5275, loss: 1.5275 +2025-06-24 11:01:38,394 - pyskl - INFO - Epoch [5][700/1281] lr: 2.494e-02, eta: 21:23:44, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.6344, top5_acc: 0.9544, loss_cls: 1.4957, loss: 1.4957 +2025-06-24 11:02:20,292 - pyskl - INFO - Epoch [5][800/1281] lr: 2.494e-02, eta: 21:23:20, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.6412, top5_acc: 0.9550, loss_cls: 1.4619, loss: 1.4619 +2025-06-24 11:03:02,090 - pyskl - INFO - Epoch [5][900/1281] lr: 2.494e-02, eta: 21:22:53, time: 0.418, data_time: 0.001, memory: 4082, top1_acc: 0.6719, top5_acc: 0.9556, loss_cls: 1.4156, loss: 1.4156 +2025-06-24 11:03:45,048 - pyskl - INFO - Epoch [5][1000/1281] lr: 2.494e-02, eta: 21:23:00, time: 0.430, data_time: 0.000, memory: 4082, top1_acc: 0.6581, top5_acc: 0.9525, loss_cls: 1.4879, loss: 1.4879 +2025-06-24 11:04:28,992 - pyskl - INFO - Epoch [5][1100/1281] lr: 2.494e-02, eta: 21:23:35, time: 0.439, data_time: 0.001, memory: 4082, top1_acc: 0.6569, top5_acc: 0.9544, loss_cls: 1.4246, loss: 1.4246 +2025-06-24 11:05:12,801 - pyskl - INFO - Epoch [5][1200/1281] lr: 2.493e-02, eta: 21:24:04, time: 0.438, data_time: 0.000, memory: 4082, top1_acc: 0.6294, top5_acc: 0.9637, loss_cls: 1.4462, loss: 1.4462 +2025-06-24 11:05:39,193 - pyskl - INFO - Saving checkpoint at 5 epochs +2025-06-24 11:06:44,204 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:06:44,261 - pyskl - INFO - +top1_acc 0.5522 +top5_acc 0.9194 +2025-06-24 11:06:44,261 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:06:44,268 - pyskl - INFO - +mean_acc 0.4432 +2025-06-24 11:06:44,270 - pyskl - INFO - Epoch(val) [5][533] top1_acc: 0.5522, top5_acc: 0.9194, mean_class_accuracy: 0.4432 +2025-06-24 11:07:46,659 - pyskl - INFO - Epoch [6][100/1281] lr: 2.493e-02, eta: 21:16:48, time: 0.624, data_time: 0.193, memory: 4082, top1_acc: 0.6575, top5_acc: 0.9625, loss_cls: 1.3693, loss: 1.3693 +2025-06-24 11:08:28,528 - pyskl - INFO - Epoch [6][200/1281] lr: 2.493e-02, eta: 21:16:23, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.6625, top5_acc: 0.9544, loss_cls: 1.4165, loss: 1.4165 +2025-06-24 11:09:10,251 - pyskl - INFO - Epoch [6][300/1281] lr: 2.492e-02, eta: 21:15:54, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.6462, top5_acc: 0.9631, loss_cls: 1.4100, loss: 1.4100 +2025-06-24 11:09:51,986 - pyskl - INFO - Epoch [6][400/1281] lr: 2.492e-02, eta: 21:15:25, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.6694, top5_acc: 0.9644, loss_cls: 1.3946, loss: 1.3946 +2025-06-24 11:10:33,840 - pyskl - INFO - Epoch [6][500/1281] lr: 2.492e-02, eta: 21:14:59, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.6663, top5_acc: 0.9656, loss_cls: 1.3302, loss: 1.3302 +2025-06-24 11:11:15,530 - pyskl - INFO - Epoch [6][600/1281] lr: 2.492e-02, eta: 21:14:28, time: 0.417, data_time: 0.001, memory: 4082, top1_acc: 0.6756, top5_acc: 0.9625, loss_cls: 1.3680, loss: 1.3680 +2025-06-24 11:11:57,463 - pyskl - INFO - Epoch [6][700/1281] lr: 2.492e-02, eta: 21:14:03, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.6631, top5_acc: 0.9600, loss_cls: 1.3656, loss: 1.3656 +2025-06-24 11:12:39,277 - pyskl - INFO - Epoch [6][800/1281] lr: 2.491e-02, eta: 21:13:35, time: 0.418, data_time: 0.001, memory: 4082, top1_acc: 0.6769, top5_acc: 0.9625, loss_cls: 1.3549, loss: 1.3549 +2025-06-24 11:13:21,148 - pyskl - INFO - Epoch [6][900/1281] lr: 2.491e-02, eta: 21:13:08, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.6756, top5_acc: 0.9581, loss_cls: 1.3321, loss: 1.3321 +2025-06-24 11:14:02,988 - pyskl - INFO - Epoch [6][1000/1281] lr: 2.491e-02, eta: 21:12:39, time: 0.418, data_time: 0.001, memory: 4082, top1_acc: 0.6444, top5_acc: 0.9569, loss_cls: 1.4402, loss: 1.4402 +2025-06-24 11:14:46,963 - pyskl - INFO - Epoch [6][1100/1281] lr: 2.491e-02, eta: 21:13:03, time: 0.440, data_time: 0.000, memory: 4082, top1_acc: 0.6800, top5_acc: 0.9712, loss_cls: 1.3271, loss: 1.3271 +2025-06-24 11:15:29,846 - pyskl - INFO - Epoch [6][1200/1281] lr: 2.490e-02, eta: 21:12:58, time: 0.429, data_time: 0.000, memory: 4082, top1_acc: 0.6913, top5_acc: 0.9681, loss_cls: 1.2830, loss: 1.2830 +2025-06-24 11:15:56,229 - pyskl - INFO - Saving checkpoint at 6 epochs +2025-06-24 11:17:00,704 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:17:00,778 - pyskl - INFO - +top1_acc 0.6527 +top5_acc 0.9519 +2025-06-24 11:17:00,778 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:17:00,789 - pyskl - INFO - +mean_acc 0.5610 +2025-06-24 11:17:00,793 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_4.pth was removed +2025-06-24 11:17:01,022 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_6.pth. +2025-06-24 11:17:01,022 - pyskl - INFO - Best top1_acc is 0.6527 at 6 epoch. +2025-06-24 11:17:01,026 - pyskl - INFO - Epoch(val) [6][533] top1_acc: 0.6527, top5_acc: 0.9519, mean_class_accuracy: 0.5610 +2025-06-24 11:18:03,496 - pyskl - INFO - Epoch [7][100/1281] lr: 2.490e-02, eta: 21:06:48, time: 0.625, data_time: 0.202, memory: 4082, top1_acc: 0.6806, top5_acc: 0.9675, loss_cls: 1.2954, loss: 1.2954 +2025-06-24 11:18:46,257 - pyskl - INFO - Epoch [7][200/1281] lr: 2.490e-02, eta: 21:06:43, time: 0.428, data_time: 0.001, memory: 4082, top1_acc: 0.7031, top5_acc: 0.9631, loss_cls: 1.2867, loss: 1.2867 +2025-06-24 11:19:28,022 - pyskl - INFO - Epoch [7][300/1281] lr: 2.489e-02, eta: 21:06:14, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7019, top5_acc: 0.9712, loss_cls: 1.2522, loss: 1.2522 +2025-06-24 11:20:09,795 - pyskl - INFO - Epoch [7][400/1281] lr: 2.489e-02, eta: 21:05:44, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.6650, top5_acc: 0.9625, loss_cls: 1.3709, loss: 1.3709 +2025-06-24 11:20:51,485 - pyskl - INFO - Epoch [7][500/1281] lr: 2.489e-02, eta: 21:05:13, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.6956, top5_acc: 0.9750, loss_cls: 1.2541, loss: 1.2541 +2025-06-24 11:21:33,079 - pyskl - INFO - Epoch [7][600/1281] lr: 2.489e-02, eta: 21:04:39, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6894, top5_acc: 0.9669, loss_cls: 1.3006, loss: 1.3006 +2025-06-24 11:22:14,774 - pyskl - INFO - Epoch [7][700/1281] lr: 2.488e-02, eta: 21:04:07, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7356, top5_acc: 0.9750, loss_cls: 1.1979, loss: 1.1979 +2025-06-24 11:22:56,590 - pyskl - INFO - Epoch [7][800/1281] lr: 2.488e-02, eta: 21:03:37, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7087, top5_acc: 0.9712, loss_cls: 1.2312, loss: 1.2312 +2025-06-24 11:23:38,598 - pyskl - INFO - Epoch [7][900/1281] lr: 2.488e-02, eta: 21:03:11, time: 0.420, data_time: 0.000, memory: 4082, top1_acc: 0.7050, top5_acc: 0.9694, loss_cls: 1.2536, loss: 1.2536 +2025-06-24 11:24:20,481 - pyskl - INFO - Epoch [7][1000/1281] lr: 2.487e-02, eta: 21:02:43, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7056, top5_acc: 0.9738, loss_cls: 1.2586, loss: 1.2586 +2025-06-24 11:25:02,243 - pyskl - INFO - Epoch [7][1100/1281] lr: 2.487e-02, eta: 21:02:11, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7219, top5_acc: 0.9650, loss_cls: 1.2436, loss: 1.2436 +2025-06-24 11:25:44,166 - pyskl - INFO - Epoch [7][1200/1281] lr: 2.487e-02, eta: 21:01:42, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.6925, top5_acc: 0.9637, loss_cls: 1.2993, loss: 1.2993 +2025-06-24 11:26:09,172 - pyskl - INFO - Saving checkpoint at 7 epochs +2025-06-24 11:27:12,278 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:27:12,348 - pyskl - INFO - +top1_acc 0.7021 +top5_acc 0.9712 +2025-06-24 11:27:12,348 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:27:12,358 - pyskl - INFO - +mean_acc 0.5710 +2025-06-24 11:27:12,363 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_6.pth was removed +2025-06-24 11:27:12,578 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2025-06-24 11:27:12,579 - pyskl - INFO - Best top1_acc is 0.7021 at 7 epoch. +2025-06-24 11:27:12,582 - pyskl - INFO - Epoch(val) [7][533] top1_acc: 0.7021, top5_acc: 0.9712, mean_class_accuracy: 0.5710 +2025-06-24 11:28:15,874 - pyskl - INFO - Epoch [8][100/1281] lr: 2.486e-02, eta: 20:56:36, time: 0.633, data_time: 0.203, memory: 4082, top1_acc: 0.7163, top5_acc: 0.9762, loss_cls: 1.1528, loss: 1.1528 +2025-06-24 11:28:57,625 - pyskl - INFO - Epoch [8][200/1281] lr: 2.486e-02, eta: 20:56:06, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7194, top5_acc: 0.9750, loss_cls: 1.2098, loss: 1.2098 +2025-06-24 11:29:39,351 - pyskl - INFO - Epoch [8][300/1281] lr: 2.486e-02, eta: 20:55:35, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7206, top5_acc: 0.9756, loss_cls: 1.1578, loss: 1.1578 +2025-06-24 11:30:21,024 - pyskl - INFO - Epoch [8][400/1281] lr: 2.485e-02, eta: 20:55:04, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7081, top5_acc: 0.9700, loss_cls: 1.2370, loss: 1.2370 +2025-06-24 11:31:02,939 - pyskl - INFO - Epoch [8][500/1281] lr: 2.485e-02, eta: 20:54:36, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7125, top5_acc: 0.9756, loss_cls: 1.2036, loss: 1.2036 +2025-06-24 11:31:44,636 - pyskl - INFO - Epoch [8][600/1281] lr: 2.485e-02, eta: 20:54:04, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7150, top5_acc: 0.9794, loss_cls: 1.1740, loss: 1.1740 +2025-06-24 11:32:26,278 - pyskl - INFO - Epoch [8][700/1281] lr: 2.484e-02, eta: 20:53:31, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.6650, top5_acc: 0.9775, loss_cls: 1.2674, loss: 1.2674 +2025-06-24 11:33:07,955 - pyskl - INFO - Epoch [8][800/1281] lr: 2.484e-02, eta: 20:52:59, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7181, top5_acc: 0.9738, loss_cls: 1.1884, loss: 1.1884 +2025-06-24 11:33:49,779 - pyskl - INFO - Epoch [8][900/1281] lr: 2.484e-02, eta: 20:52:29, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7150, top5_acc: 0.9719, loss_cls: 1.1683, loss: 1.1683 +2025-06-24 11:34:31,714 - pyskl - INFO - Epoch [8][1000/1281] lr: 2.483e-02, eta: 20:52:00, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7444, top5_acc: 0.9800, loss_cls: 1.0940, loss: 1.0940 +2025-06-24 11:35:13,420 - pyskl - INFO - Epoch [8][1100/1281] lr: 2.483e-02, eta: 20:51:27, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7163, top5_acc: 0.9694, loss_cls: 1.1786, loss: 1.1786 +2025-06-24 11:35:55,265 - pyskl - INFO - Epoch [8][1200/1281] lr: 2.483e-02, eta: 20:50:57, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7275, top5_acc: 0.9794, loss_cls: 1.1391, loss: 1.1391 +2025-06-24 11:36:19,761 - pyskl - INFO - Saving checkpoint at 8 epochs +2025-06-24 11:37:22,459 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:37:22,529 - pyskl - INFO - +top1_acc 0.6617 +top5_acc 0.9532 +2025-06-24 11:37:22,530 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:37:22,538 - pyskl - INFO - +mean_acc 0.5727 +2025-06-24 11:37:22,540 - pyskl - INFO - Epoch(val) [8][533] top1_acc: 0.6617, top5_acc: 0.9532, mean_class_accuracy: 0.5727 +2025-06-24 11:38:24,239 - pyskl - INFO - Epoch [9][100/1281] lr: 2.482e-02, eta: 20:45:54, time: 0.617, data_time: 0.200, memory: 4082, top1_acc: 0.7412, top5_acc: 0.9762, loss_cls: 1.1255, loss: 1.1255 +2025-06-24 11:39:06,032 - pyskl - INFO - Epoch [9][200/1281] lr: 2.482e-02, eta: 20:45:25, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7219, top5_acc: 0.9756, loss_cls: 1.1649, loss: 1.1649 +2025-06-24 11:39:47,719 - pyskl - INFO - Epoch [9][300/1281] lr: 2.481e-02, eta: 20:44:53, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7131, top5_acc: 0.9750, loss_cls: 1.1785, loss: 1.1785 +2025-06-24 11:40:29,355 - pyskl - INFO - Epoch [9][400/1281] lr: 2.481e-02, eta: 20:44:21, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7238, top5_acc: 0.9794, loss_cls: 1.1552, loss: 1.1552 +2025-06-24 11:41:10,987 - pyskl - INFO - Epoch [9][500/1281] lr: 2.481e-02, eta: 20:43:48, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7481, top5_acc: 0.9788, loss_cls: 1.0810, loss: 1.0810 +2025-06-24 11:41:52,558 - pyskl - INFO - Epoch [9][600/1281] lr: 2.480e-02, eta: 20:43:14, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7544, top5_acc: 0.9781, loss_cls: 1.0803, loss: 1.0803 +2025-06-24 11:42:34,309 - pyskl - INFO - Epoch [9][700/1281] lr: 2.480e-02, eta: 20:42:43, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7169, top5_acc: 0.9812, loss_cls: 1.1675, loss: 1.1675 +2025-06-24 11:43:16,110 - pyskl - INFO - Epoch [9][800/1281] lr: 2.480e-02, eta: 20:42:13, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7288, top5_acc: 0.9744, loss_cls: 1.1443, loss: 1.1443 +2025-06-24 11:43:57,786 - pyskl - INFO - Epoch [9][900/1281] lr: 2.479e-02, eta: 20:41:40, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7356, top5_acc: 0.9769, loss_cls: 1.1201, loss: 1.1201 +2025-06-24 11:44:39,507 - pyskl - INFO - Epoch [9][1000/1281] lr: 2.479e-02, eta: 20:41:08, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7700, top5_acc: 0.9794, loss_cls: 1.0411, loss: 1.0411 +2025-06-24 11:45:21,247 - pyskl - INFO - Epoch [9][1100/1281] lr: 2.479e-02, eta: 20:40:36, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7544, top5_acc: 0.9806, loss_cls: 1.0615, loss: 1.0615 +2025-06-24 11:46:03,079 - pyskl - INFO - Epoch [9][1200/1281] lr: 2.478e-02, eta: 20:40:05, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7412, top5_acc: 0.9775, loss_cls: 1.0718, loss: 1.0718 +2025-06-24 11:46:27,931 - pyskl - INFO - Saving checkpoint at 9 epochs +2025-06-24 11:47:30,034 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:47:30,093 - pyskl - INFO - +top1_acc 0.7127 +top5_acc 0.9720 +2025-06-24 11:47:30,093 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:47:30,101 - pyskl - INFO - +mean_acc 0.6024 +2025-06-24 11:47:30,106 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_7.pth was removed +2025-06-24 11:47:30,289 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_9.pth. +2025-06-24 11:47:30,289 - pyskl - INFO - Best top1_acc is 0.7127 at 9 epoch. +2025-06-24 11:47:30,292 - pyskl - INFO - Epoch(val) [9][533] top1_acc: 0.7127, top5_acc: 0.9720, mean_class_accuracy: 0.6024 +2025-06-24 11:48:34,074 - pyskl - INFO - Epoch [10][100/1281] lr: 2.477e-02, eta: 20:36:04, time: 0.638, data_time: 0.200, memory: 4082, top1_acc: 0.7656, top5_acc: 0.9806, loss_cls: 1.0208, loss: 1.0208 +2025-06-24 11:49:15,774 - pyskl - INFO - Epoch [10][200/1281] lr: 2.477e-02, eta: 20:35:32, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7656, top5_acc: 0.9794, loss_cls: 1.0646, loss: 1.0646 +2025-06-24 11:49:57,390 - pyskl - INFO - Epoch [10][300/1281] lr: 2.477e-02, eta: 20:34:59, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7512, top5_acc: 0.9825, loss_cls: 1.0633, loss: 1.0633 +2025-06-24 11:50:39,081 - pyskl - INFO - Epoch [10][400/1281] lr: 2.476e-02, eta: 20:34:27, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7412, top5_acc: 0.9719, loss_cls: 1.1163, loss: 1.1163 +2025-06-24 11:51:20,807 - pyskl - INFO - Epoch [10][500/1281] lr: 2.476e-02, eta: 20:33:55, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7544, top5_acc: 0.9812, loss_cls: 1.0418, loss: 1.0418 +2025-06-24 11:52:02,730 - pyskl - INFO - Epoch [10][600/1281] lr: 2.476e-02, eta: 20:33:26, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7450, top5_acc: 0.9788, loss_cls: 1.0902, loss: 1.0902 +2025-06-24 11:52:44,220 - pyskl - INFO - Epoch [10][700/1281] lr: 2.475e-02, eta: 20:32:51, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7556, top5_acc: 0.9806, loss_cls: 1.0535, loss: 1.0535 +2025-06-24 11:53:26,056 - pyskl - INFO - Epoch [10][800/1281] lr: 2.475e-02, eta: 20:32:20, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7394, top5_acc: 0.9769, loss_cls: 1.0680, loss: 1.0680 +2025-06-24 11:54:07,731 - pyskl - INFO - Epoch [10][900/1281] lr: 2.474e-02, eta: 20:31:47, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7525, top5_acc: 0.9819, loss_cls: 1.0663, loss: 1.0663 +2025-06-24 11:54:49,645 - pyskl - INFO - Epoch [10][1000/1281] lr: 2.474e-02, eta: 20:31:17, time: 0.419, data_time: 0.001, memory: 4082, top1_acc: 0.7212, top5_acc: 0.9794, loss_cls: 1.1382, loss: 1.1382 +2025-06-24 11:55:31,406 - pyskl - INFO - Epoch [10][1100/1281] lr: 2.473e-02, eta: 20:30:45, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7438, top5_acc: 0.9806, loss_cls: 1.1002, loss: 1.1002 +2025-06-24 11:56:12,979 - pyskl - INFO - Epoch [10][1200/1281] lr: 2.473e-02, eta: 20:30:10, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7338, top5_acc: 0.9812, loss_cls: 1.0726, loss: 1.0726 +2025-06-24 11:56:36,915 - pyskl - INFO - Saving checkpoint at 10 epochs +2025-06-24 11:57:39,127 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 11:57:39,187 - pyskl - INFO - +top1_acc 0.7243 +top5_acc 0.9620 +2025-06-24 11:57:39,188 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 11:57:39,196 - pyskl - INFO - +mean_acc 0.6263 +2025-06-24 11:57:39,200 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_9.pth was removed +2025-06-24 11:57:39,380 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_10.pth. +2025-06-24 11:57:39,381 - pyskl - INFO - Best top1_acc is 0.7243 at 10 epoch. +2025-06-24 11:57:39,383 - pyskl - INFO - Epoch(val) [10][533] top1_acc: 0.7243, top5_acc: 0.9620, mean_class_accuracy: 0.6263 +2025-06-24 11:58:42,950 - pyskl - INFO - Epoch [11][100/1281] lr: 2.472e-02, eta: 20:26:24, time: 0.636, data_time: 0.200, memory: 4082, top1_acc: 0.7750, top5_acc: 0.9825, loss_cls: 1.0020, loss: 1.0020 +2025-06-24 11:59:24,835 - pyskl - INFO - Epoch [11][200/1281] lr: 2.472e-02, eta: 20:25:55, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7662, top5_acc: 0.9781, loss_cls: 1.0247, loss: 1.0247 +2025-06-24 12:00:06,441 - pyskl - INFO - Epoch [11][300/1281] lr: 2.471e-02, eta: 20:25:21, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7744, top5_acc: 0.9838, loss_cls: 0.9912, loss: 0.9912 +2025-06-24 12:00:48,071 - pyskl - INFO - Epoch [11][400/1281] lr: 2.471e-02, eta: 20:24:48, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7706, top5_acc: 0.9838, loss_cls: 1.0360, loss: 1.0360 +2025-06-24 12:01:29,937 - pyskl - INFO - Epoch [11][500/1281] lr: 2.471e-02, eta: 20:24:17, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7588, top5_acc: 0.9838, loss_cls: 1.0230, loss: 1.0230 +2025-06-24 12:02:11,575 - pyskl - INFO - Epoch [11][600/1281] lr: 2.470e-02, eta: 20:23:44, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7756, top5_acc: 0.9819, loss_cls: 1.0099, loss: 1.0099 +2025-06-24 12:02:53,361 - pyskl - INFO - Epoch [11][700/1281] lr: 2.470e-02, eta: 20:23:12, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7562, top5_acc: 0.9744, loss_cls: 1.0949, loss: 1.0949 +2025-06-24 12:03:35,072 - pyskl - INFO - Epoch [11][800/1281] lr: 2.469e-02, eta: 20:22:39, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7362, top5_acc: 0.9756, loss_cls: 1.0912, loss: 1.0912 +2025-06-24 12:04:16,930 - pyskl - INFO - Epoch [11][900/1281] lr: 2.469e-02, eta: 20:22:08, time: 0.419, data_time: 0.000, memory: 4082, top1_acc: 0.7669, top5_acc: 0.9819, loss_cls: 1.0531, loss: 1.0531 +2025-06-24 12:04:58,739 - pyskl - INFO - Epoch [11][1000/1281] lr: 2.468e-02, eta: 20:21:36, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7300, top5_acc: 0.9738, loss_cls: 1.1236, loss: 1.1236 +2025-06-24 12:05:40,781 - pyskl - INFO - Epoch [11][1100/1281] lr: 2.468e-02, eta: 20:21:07, time: 0.421, data_time: 0.001, memory: 4082, top1_acc: 0.7650, top5_acc: 0.9819, loss_cls: 1.0315, loss: 1.0315 +2025-06-24 12:06:22,614 - pyskl - INFO - Epoch [11][1200/1281] lr: 2.467e-02, eta: 20:20:35, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7494, top5_acc: 0.9738, loss_cls: 1.0652, loss: 1.0652 +2025-06-24 12:06:46,573 - pyskl - INFO - Saving checkpoint at 11 epochs +2025-06-24 12:07:47,282 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:07:47,339 - pyskl - INFO - +top1_acc 0.7261 +top5_acc 0.9688 +2025-06-24 12:07:47,339 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:07:47,346 - pyskl - INFO - +mean_acc 0.6273 +2025-06-24 12:07:47,350 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_10.pth was removed +2025-06-24 12:07:47,584 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_11.pth. +2025-06-24 12:07:47,584 - pyskl - INFO - Best top1_acc is 0.7261 at 11 epoch. +2025-06-24 12:07:47,587 - pyskl - INFO - Epoch(val) [11][533] top1_acc: 0.7261, top5_acc: 0.9688, mean_class_accuracy: 0.6273 +2025-06-24 12:08:50,939 - pyskl - INFO - Epoch [12][100/1281] lr: 2.467e-02, eta: 20:17:02, time: 0.633, data_time: 0.195, memory: 4082, top1_acc: 0.7475, top5_acc: 0.9800, loss_cls: 1.0329, loss: 1.0329 +2025-06-24 12:09:34,114 - pyskl - INFO - Epoch [12][200/1281] lr: 2.466e-02, eta: 20:16:48, time: 0.432, data_time: 0.000, memory: 4082, top1_acc: 0.7844, top5_acc: 0.9894, loss_cls: 0.9136, loss: 0.9136 +2025-06-24 12:10:15,635 - pyskl - INFO - Epoch [12][300/1281] lr: 2.466e-02, eta: 20:16:12, time: 0.415, data_time: 0.000, memory: 4082, top1_acc: 0.7738, top5_acc: 0.9869, loss_cls: 0.9716, loss: 0.9716 +2025-06-24 12:10:57,252 - pyskl - INFO - Epoch [12][400/1281] lr: 2.465e-02, eta: 20:15:38, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7606, top5_acc: 0.9838, loss_cls: 1.0238, loss: 1.0238 +2025-06-24 12:11:38,976 - pyskl - INFO - Epoch [12][500/1281] lr: 2.465e-02, eta: 20:15:05, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7788, top5_acc: 0.9800, loss_cls: 1.0245, loss: 1.0245 +2025-06-24 12:12:21,482 - pyskl - INFO - Epoch [12][600/1281] lr: 2.464e-02, eta: 20:14:42, time: 0.425, data_time: 0.000, memory: 4082, top1_acc: 0.7650, top5_acc: 0.9819, loss_cls: 1.0126, loss: 1.0126 +2025-06-24 12:13:05,240 - pyskl - INFO - Epoch [12][700/1281] lr: 2.464e-02, eta: 20:14:33, time: 0.438, data_time: 0.000, memory: 4082, top1_acc: 0.7588, top5_acc: 0.9875, loss_cls: 1.0077, loss: 1.0077 +2025-06-24 12:13:46,977 - pyskl - INFO - Epoch [12][800/1281] lr: 2.463e-02, eta: 20:13:59, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7600, top5_acc: 0.9844, loss_cls: 1.0344, loss: 1.0344 +2025-06-24 12:14:28,761 - pyskl - INFO - Epoch [12][900/1281] lr: 2.463e-02, eta: 20:13:27, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7662, top5_acc: 0.9844, loss_cls: 1.0408, loss: 1.0408 +2025-06-24 12:15:10,529 - pyskl - INFO - Epoch [12][1000/1281] lr: 2.462e-02, eta: 20:12:53, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7762, top5_acc: 0.9812, loss_cls: 1.0241, loss: 1.0241 +2025-06-24 12:15:52,314 - pyskl - INFO - Epoch [12][1100/1281] lr: 2.462e-02, eta: 20:12:20, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7662, top5_acc: 0.9781, loss_cls: 1.0117, loss: 1.0117 +2025-06-24 12:16:33,987 - pyskl - INFO - Epoch [12][1200/1281] lr: 2.461e-02, eta: 20:11:46, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7750, top5_acc: 0.9844, loss_cls: 0.9916, loss: 0.9916 +2025-06-24 12:16:56,952 - pyskl - INFO - Saving checkpoint at 12 epochs +2025-06-24 12:17:56,984 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:17:57,041 - pyskl - INFO - +top1_acc 0.7178 +top5_acc 0.9712 +2025-06-24 12:17:57,042 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:17:57,049 - pyskl - INFO - +mean_acc 0.6310 +2025-06-24 12:17:57,050 - pyskl - INFO - Epoch(val) [12][533] top1_acc: 0.7178, top5_acc: 0.9712, mean_class_accuracy: 0.6310 +2025-06-24 12:19:01,282 - pyskl - INFO - Epoch [13][100/1281] lr: 2.460e-02, eta: 20:08:35, time: 0.642, data_time: 0.204, memory: 4082, top1_acc: 0.7956, top5_acc: 0.9894, loss_cls: 0.9195, loss: 0.9195 +2025-06-24 12:19:45,020 - pyskl - INFO - Epoch [13][200/1281] lr: 2.460e-02, eta: 20:08:24, time: 0.437, data_time: 0.000, memory: 4082, top1_acc: 0.7688, top5_acc: 0.9888, loss_cls: 0.9642, loss: 0.9642 +2025-06-24 12:20:28,790 - pyskl - INFO - Epoch [13][300/1281] lr: 2.459e-02, eta: 20:08:14, time: 0.438, data_time: 0.000, memory: 4082, top1_acc: 0.7638, top5_acc: 0.9894, loss_cls: 0.9814, loss: 0.9814 +2025-06-24 12:21:12,070 - pyskl - INFO - Epoch [13][400/1281] lr: 2.459e-02, eta: 20:07:57, time: 0.433, data_time: 0.000, memory: 4082, top1_acc: 0.7875, top5_acc: 0.9850, loss_cls: 0.9327, loss: 0.9327 +2025-06-24 12:21:55,714 - pyskl - INFO - Epoch [13][500/1281] lr: 2.458e-02, eta: 20:07:45, time: 0.436, data_time: 0.000, memory: 4082, top1_acc: 0.7919, top5_acc: 0.9888, loss_cls: 0.9421, loss: 0.9421 +2025-06-24 12:22:39,475 - pyskl - INFO - Epoch [13][600/1281] lr: 2.458e-02, eta: 20:07:33, time: 0.438, data_time: 0.000, memory: 4082, top1_acc: 0.7638, top5_acc: 0.9869, loss_cls: 0.9710, loss: 0.9710 +2025-06-24 12:23:23,056 - pyskl - INFO - Epoch [13][700/1281] lr: 2.457e-02, eta: 20:07:19, time: 0.436, data_time: 0.000, memory: 4082, top1_acc: 0.7662, top5_acc: 0.9812, loss_cls: 1.0094, loss: 1.0094 +2025-06-24 12:24:05,484 - pyskl - INFO - Epoch [13][800/1281] lr: 2.457e-02, eta: 20:06:51, time: 0.424, data_time: 0.000, memory: 4082, top1_acc: 0.7675, top5_acc: 0.9788, loss_cls: 1.0083, loss: 1.0083 +2025-06-24 12:24:47,052 - pyskl - INFO - Epoch [13][900/1281] lr: 2.456e-02, eta: 20:06:15, time: 0.416, data_time: 0.000, memory: 4082, top1_acc: 0.7806, top5_acc: 0.9869, loss_cls: 0.9423, loss: 0.9423 +2025-06-24 12:25:28,864 - pyskl - INFO - Epoch [13][1000/1281] lr: 2.455e-02, eta: 20:05:41, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.7650, top5_acc: 0.9819, loss_cls: 0.9899, loss: 0.9899 +2025-06-24 12:26:10,590 - pyskl - INFO - Epoch [13][1100/1281] lr: 2.455e-02, eta: 20:05:06, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7762, top5_acc: 0.9831, loss_cls: 0.9654, loss: 0.9654 +2025-06-24 12:26:52,319 - pyskl - INFO - Epoch [13][1200/1281] lr: 2.454e-02, eta: 20:04:30, time: 0.417, data_time: 0.000, memory: 4082, top1_acc: 0.7612, top5_acc: 0.9819, loss_cls: 1.0175, loss: 1.0175 +2025-06-24 12:27:14,965 - pyskl - INFO - Saving checkpoint at 13 epochs +2025-06-24 12:28:13,716 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:28:13,771 - pyskl - INFO - +top1_acc 0.7147 +top5_acc 0.9700 +2025-06-24 12:28:13,772 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:28:13,782 - pyskl - INFO - +mean_acc 0.6417 +2025-06-24 12:28:13,785 - pyskl - INFO - Epoch(val) [13][533] top1_acc: 0.7147, top5_acc: 0.9700, mean_class_accuracy: 0.6417 +2025-06-24 12:29:12,447 - pyskl - INFO - Epoch [14][100/1281] lr: 2.453e-02, eta: 20:00:30, time: 0.587, data_time: 0.195, memory: 4082, top1_acc: 0.8069, top5_acc: 0.9881, loss_cls: 0.8664, loss: 0.8664 +2025-06-24 12:29:50,495 - pyskl - INFO - Epoch [14][200/1281] lr: 2.453e-02, eta: 19:59:18, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.7937, top5_acc: 0.9906, loss_cls: 0.8854, loss: 0.8854 +2025-06-24 12:30:28,937 - pyskl - INFO - Epoch [14][300/1281] lr: 2.452e-02, eta: 19:58:10, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.7775, top5_acc: 0.9819, loss_cls: 0.9913, loss: 0.9913 +2025-06-24 12:31:07,046 - pyskl - INFO - Epoch [14][400/1281] lr: 2.452e-02, eta: 19:56:59, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.7987, top5_acc: 0.9869, loss_cls: 0.9010, loss: 0.9010 +2025-06-24 12:31:46,012 - pyskl - INFO - Epoch [14][500/1281] lr: 2.451e-02, eta: 19:55:57, time: 0.390, data_time: 0.000, memory: 4082, top1_acc: 0.7700, top5_acc: 0.9856, loss_cls: 0.9772, loss: 0.9772 +2025-06-24 12:32:23,721 - pyskl - INFO - Epoch [14][600/1281] lr: 2.451e-02, eta: 19:54:42, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.7913, top5_acc: 0.9844, loss_cls: 0.9269, loss: 0.9269 +2025-06-24 12:33:02,410 - pyskl - INFO - Epoch [14][700/1281] lr: 2.450e-02, eta: 19:53:38, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.7775, top5_acc: 0.9875, loss_cls: 0.9407, loss: 0.9407 +2025-06-24 12:33:42,121 - pyskl - INFO - Epoch [14][800/1281] lr: 2.449e-02, eta: 19:52:44, time: 0.397, data_time: 0.000, memory: 4082, top1_acc: 0.7800, top5_acc: 0.9869, loss_cls: 0.9879, loss: 0.9879 +2025-06-24 12:34:20,773 - pyskl - INFO - Epoch [14][900/1281] lr: 2.449e-02, eta: 19:51:40, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.7800, top5_acc: 0.9825, loss_cls: 0.9614, loss: 0.9614 +2025-06-24 12:34:59,027 - pyskl - INFO - Epoch [14][1000/1281] lr: 2.448e-02, eta: 19:50:33, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.7769, top5_acc: 0.9800, loss_cls: 0.9688, loss: 0.9688 +2025-06-24 12:35:38,130 - pyskl - INFO - Epoch [14][1100/1281] lr: 2.448e-02, eta: 19:49:34, time: 0.391, data_time: 0.001, memory: 4082, top1_acc: 0.7575, top5_acc: 0.9844, loss_cls: 1.0343, loss: 1.0343 +2025-06-24 12:36:17,021 - pyskl - INFO - Epoch [14][1200/1281] lr: 2.447e-02, eta: 19:48:33, time: 0.389, data_time: 0.000, memory: 4082, top1_acc: 0.7662, top5_acc: 0.9812, loss_cls: 0.9928, loss: 0.9928 +2025-06-24 12:36:49,090 - pyskl - INFO - Saving checkpoint at 14 epochs +2025-06-24 12:37:49,622 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:37:49,678 - pyskl - INFO - +top1_acc 0.7473 +top5_acc 0.9791 +2025-06-24 12:37:49,678 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:37:49,685 - pyskl - INFO - +mean_acc 0.6627 +2025-06-24 12:37:49,689 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_11.pth was removed +2025-06-24 12:37:49,871 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_14.pth. +2025-06-24 12:37:49,871 - pyskl - INFO - Best top1_acc is 0.7473 at 14 epoch. +2025-06-24 12:37:49,874 - pyskl - INFO - Epoch(val) [14][533] top1_acc: 0.7473, top5_acc: 0.9791, mean_class_accuracy: 0.6627 +2025-06-24 12:38:36,842 - pyskl - INFO - Epoch [15][100/1281] lr: 2.446e-02, eta: 19:42:57, time: 0.470, data_time: 0.190, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9888, loss_cls: 0.8347, loss: 0.8347 +2025-06-24 12:39:17,962 - pyskl - INFO - Epoch [15][200/1281] lr: 2.445e-02, eta: 19:42:20, time: 0.411, data_time: 0.000, memory: 4082, top1_acc: 0.7887, top5_acc: 0.9838, loss_cls: 0.9363, loss: 0.9363 +2025-06-24 12:39:48,150 - pyskl - INFO - Epoch [15][300/1281] lr: 2.445e-02, eta: 19:39:58, time: 0.302, data_time: 0.000, memory: 4082, top1_acc: 0.7963, top5_acc: 0.9869, loss_cls: 0.9038, loss: 0.9038 +2025-06-24 12:40:12,659 - pyskl - INFO - Epoch [15][400/1281] lr: 2.444e-02, eta: 19:36:44, time: 0.245, data_time: 0.000, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9812, loss_cls: 0.9152, loss: 0.9152 +2025-06-24 12:40:50,052 - pyskl - INFO - Epoch [15][500/1281] lr: 2.444e-02, eta: 19:35:33, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.7769, top5_acc: 0.9850, loss_cls: 0.9184, loss: 0.9184 +2025-06-24 12:41:27,750 - pyskl - INFO - Epoch [15][600/1281] lr: 2.443e-02, eta: 19:34:25, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.7650, top5_acc: 0.9788, loss_cls: 1.0136, loss: 1.0136 +2025-06-24 12:42:05,868 - pyskl - INFO - Epoch [15][700/1281] lr: 2.442e-02, eta: 19:33:22, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.7963, top5_acc: 0.9894, loss_cls: 0.8941, loss: 0.8941 +2025-06-24 12:42:43,777 - pyskl - INFO - Epoch [15][800/1281] lr: 2.442e-02, eta: 19:32:16, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8006, top5_acc: 0.9875, loss_cls: 0.9170, loss: 0.9170 +2025-06-24 12:43:23,030 - pyskl - INFO - Epoch [15][900/1281] lr: 2.441e-02, eta: 19:31:24, time: 0.393, data_time: 0.000, memory: 4082, top1_acc: 0.7800, top5_acc: 0.9825, loss_cls: 0.9578, loss: 0.9578 +2025-06-24 12:44:00,413 - pyskl - INFO - Epoch [15][1000/1281] lr: 2.441e-02, eta: 19:30:14, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.7925, top5_acc: 0.9831, loss_cls: 0.9201, loss: 0.9201 +2025-06-24 12:44:38,487 - pyskl - INFO - Epoch [15][1100/1281] lr: 2.440e-02, eta: 19:29:11, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.7744, top5_acc: 0.9869, loss_cls: 0.9235, loss: 0.9235 +2025-06-24 12:45:16,251 - pyskl - INFO - Epoch [15][1200/1281] lr: 2.439e-02, eta: 19:28:06, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.7806, top5_acc: 0.9888, loss_cls: 0.9097, loss: 0.9097 +2025-06-24 12:45:47,157 - pyskl - INFO - Saving checkpoint at 15 epochs +2025-06-24 12:46:46,974 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:46:47,031 - pyskl - INFO - +top1_acc 0.7248 +top5_acc 0.9708 +2025-06-24 12:46:47,032 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:46:47,039 - pyskl - INFO - +mean_acc 0.6377 +2025-06-24 12:46:47,040 - pyskl - INFO - Epoch(val) [15][533] top1_acc: 0.7248, top5_acc: 0.9708, mean_class_accuracy: 0.6377 +2025-06-24 12:47:45,170 - pyskl - INFO - Epoch [16][100/1281] lr: 2.438e-02, eta: 19:24:36, time: 0.581, data_time: 0.198, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9869, loss_cls: 0.7961, loss: 0.7961 +2025-06-24 12:48:22,611 - pyskl - INFO - Epoch [16][200/1281] lr: 2.438e-02, eta: 19:23:29, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.7700, top5_acc: 0.9825, loss_cls: 0.9907, loss: 0.9907 +2025-06-24 12:49:00,411 - pyskl - INFO - Epoch [16][300/1281] lr: 2.437e-02, eta: 19:22:26, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8031, top5_acc: 0.9825, loss_cls: 0.8962, loss: 0.8962 +2025-06-24 12:49:38,128 - pyskl - INFO - Epoch [16][400/1281] lr: 2.436e-02, eta: 19:21:22, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9894, loss_cls: 0.8928, loss: 0.8928 +2025-06-24 12:50:16,250 - pyskl - INFO - Epoch [16][500/1281] lr: 2.436e-02, eta: 19:20:22, time: 0.381, data_time: 0.001, memory: 4082, top1_acc: 0.7937, top5_acc: 0.9844, loss_cls: 0.9120, loss: 0.9120 +2025-06-24 12:50:53,516 - pyskl - INFO - Epoch [16][600/1281] lr: 2.435e-02, eta: 19:19:14, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.7837, top5_acc: 0.9812, loss_cls: 0.9467, loss: 0.9467 +2025-06-24 12:51:18,667 - pyskl - INFO - Epoch [16][700/1281] lr: 2.434e-02, eta: 19:16:22, time: 0.251, data_time: 0.000, memory: 4082, top1_acc: 0.7950, top5_acc: 0.9850, loss_cls: 0.8854, loss: 0.8854 +2025-06-24 12:52:04,287 - pyskl - INFO - Epoch [16][800/1281] lr: 2.434e-02, eta: 19:16:28, time: 0.456, data_time: 0.000, memory: 4082, top1_acc: 0.7731, top5_acc: 0.9825, loss_cls: 0.9407, loss: 0.9407 +2025-06-24 12:52:28,171 - pyskl - INFO - Epoch [16][900/1281] lr: 2.433e-02, eta: 19:13:27, time: 0.239, data_time: 0.000, memory: 4082, top1_acc: 0.8163, top5_acc: 0.9869, loss_cls: 0.8716, loss: 0.8716 +2025-06-24 12:52:57,863 - pyskl - INFO - Epoch [16][1000/1281] lr: 2.432e-02, eta: 19:11:17, time: 0.297, data_time: 0.000, memory: 4082, top1_acc: 0.7913, top5_acc: 0.9919, loss_cls: 0.8723, loss: 0.8723 +2025-06-24 12:53:35,914 - pyskl - INFO - Epoch [16][1100/1281] lr: 2.432e-02, eta: 19:10:19, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.7900, top5_acc: 0.9862, loss_cls: 0.9090, loss: 0.9090 +2025-06-24 12:54:13,606 - pyskl - INFO - Epoch [16][1200/1281] lr: 2.431e-02, eta: 19:09:18, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.7881, top5_acc: 0.9844, loss_cls: 0.9326, loss: 0.9326 +2025-06-24 12:54:44,959 - pyskl - INFO - Saving checkpoint at 16 epochs +2025-06-24 12:55:43,936 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 12:55:43,996 - pyskl - INFO - +top1_acc 0.7608 +top5_acc 0.9770 +2025-06-24 12:55:43,996 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 12:55:44,006 - pyskl - INFO - +mean_acc 0.6721 +2025-06-24 12:55:44,010 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_14.pth was removed +2025-06-24 12:55:44,187 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_16.pth. +2025-06-24 12:55:44,187 - pyskl - INFO - Best top1_acc is 0.7608 at 16 epoch. +2025-06-24 12:55:44,191 - pyskl - INFO - Epoch(val) [16][533] top1_acc: 0.7608, top5_acc: 0.9770, mean_class_accuracy: 0.6721 +2025-06-24 12:56:41,309 - pyskl - INFO - Epoch [17][100/1281] lr: 2.430e-02, eta: 19:05:56, time: 0.571, data_time: 0.194, memory: 4082, top1_acc: 0.7969, top5_acc: 0.9862, loss_cls: 0.9061, loss: 0.9061 +2025-06-24 12:57:19,369 - pyskl - INFO - Epoch [17][200/1281] lr: 2.429e-02, eta: 19:04:59, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8063, top5_acc: 0.9881, loss_cls: 0.8555, loss: 0.8555 +2025-06-24 12:57:56,811 - pyskl - INFO - Epoch [17][300/1281] lr: 2.428e-02, eta: 19:03:57, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9906, loss_cls: 0.7985, loss: 0.7985 +2025-06-24 12:58:35,294 - pyskl - INFO - Epoch [17][400/1281] lr: 2.428e-02, eta: 19:03:04, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.7950, top5_acc: 0.9919, loss_cls: 0.8504, loss: 0.8504 +2025-06-24 12:59:13,906 - pyskl - INFO - Epoch [17][500/1281] lr: 2.427e-02, eta: 19:02:12, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.8081, top5_acc: 0.9850, loss_cls: 0.8649, loss: 0.8649 +2025-06-24 12:59:52,489 - pyskl - INFO - Epoch [17][600/1281] lr: 2.426e-02, eta: 19:01:21, time: 0.386, data_time: 0.000, memory: 4082, top1_acc: 0.7975, top5_acc: 0.9825, loss_cls: 0.9316, loss: 0.9316 +2025-06-24 13:00:31,593 - pyskl - INFO - Epoch [17][700/1281] lr: 2.426e-02, eta: 19:00:33, time: 0.391, data_time: 0.000, memory: 4082, top1_acc: 0.7900, top5_acc: 0.9856, loss_cls: 0.9057, loss: 0.9057 +2025-06-24 13:01:09,157 - pyskl - INFO - Epoch [17][800/1281] lr: 2.425e-02, eta: 18:59:33, time: 0.376, data_time: 0.001, memory: 4082, top1_acc: 0.8131, top5_acc: 0.9919, loss_cls: 0.8448, loss: 0.8448 +2025-06-24 13:01:46,952 - pyskl - INFO - Epoch [17][900/1281] lr: 2.424e-02, eta: 18:58:35, time: 0.378, data_time: 0.001, memory: 4082, top1_acc: 0.7875, top5_acc: 0.9794, loss_cls: 0.9459, loss: 0.9459 +2025-06-24 13:02:24,376 - pyskl - INFO - Epoch [17][1000/1281] lr: 2.424e-02, eta: 18:57:35, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8019, top5_acc: 0.9906, loss_cls: 0.8686, loss: 0.8686 +2025-06-24 13:03:02,166 - pyskl - INFO - Epoch [17][1100/1281] lr: 2.423e-02, eta: 18:56:37, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8094, top5_acc: 0.9894, loss_cls: 0.8452, loss: 0.8452 +2025-06-24 13:03:34,630 - pyskl - INFO - Epoch [17][1200/1281] lr: 2.422e-02, eta: 18:54:58, time: 0.325, data_time: 0.000, memory: 4082, top1_acc: 0.8056, top5_acc: 0.9838, loss_cls: 0.8906, loss: 0.8906 +2025-06-24 13:04:00,260 - pyskl - INFO - Saving checkpoint at 17 epochs +2025-06-24 13:04:46,081 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:04:46,138 - pyskl - INFO - +top1_acc 0.7405 +top5_acc 0.9715 +2025-06-24 13:04:46,138 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:04:46,147 - pyskl - INFO - +mean_acc 0.6765 +2025-06-24 13:04:46,149 - pyskl - INFO - Epoch(val) [17][533] top1_acc: 0.7405, top5_acc: 0.9715, mean_class_accuracy: 0.6765 +2025-06-24 13:05:43,609 - pyskl - INFO - Epoch [18][100/1281] lr: 2.421e-02, eta: 18:51:50, time: 0.575, data_time: 0.196, memory: 4082, top1_acc: 0.7950, top5_acc: 0.9875, loss_cls: 0.8780, loss: 0.8780 +2025-06-24 13:06:22,363 - pyskl - INFO - Epoch [18][200/1281] lr: 2.420e-02, eta: 18:51:02, time: 0.387, data_time: 0.000, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9869, loss_cls: 0.8194, loss: 0.8194 +2025-06-24 13:06:59,259 - pyskl - INFO - Epoch [18][300/1281] lr: 2.419e-02, eta: 18:49:59, time: 0.369, data_time: 0.000, memory: 4082, top1_acc: 0.7956, top5_acc: 0.9875, loss_cls: 0.8834, loss: 0.8834 +2025-06-24 13:07:36,451 - pyskl - INFO - Epoch [18][400/1281] lr: 2.419e-02, eta: 18:48:59, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.7956, top5_acc: 0.9856, loss_cls: 0.8761, loss: 0.8761 +2025-06-24 13:08:13,854 - pyskl - INFO - Epoch [18][500/1281] lr: 2.418e-02, eta: 18:48:00, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8013, top5_acc: 0.9888, loss_cls: 0.8906, loss: 0.8906 +2025-06-24 13:08:51,389 - pyskl - INFO - Epoch [18][600/1281] lr: 2.417e-02, eta: 18:47:03, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8025, top5_acc: 0.9862, loss_cls: 0.8454, loss: 0.8454 +2025-06-24 13:09:29,163 - pyskl - INFO - Epoch [18][700/1281] lr: 2.417e-02, eta: 18:46:07, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.7963, top5_acc: 0.9875, loss_cls: 0.8969, loss: 0.8969 +2025-06-24 13:10:07,038 - pyskl - INFO - Epoch [18][800/1281] lr: 2.416e-02, eta: 18:45:13, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8031, top5_acc: 0.9881, loss_cls: 0.8320, loss: 0.8320 +2025-06-24 13:10:45,025 - pyskl - INFO - Epoch [18][900/1281] lr: 2.415e-02, eta: 18:44:19, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8050, top5_acc: 0.9869, loss_cls: 0.8688, loss: 0.8688 +2025-06-24 13:11:22,562 - pyskl - INFO - Epoch [18][1000/1281] lr: 2.414e-02, eta: 18:43:23, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8050, top5_acc: 0.9856, loss_cls: 0.8355, loss: 0.8355 +2025-06-24 13:12:00,386 - pyskl - INFO - Epoch [18][1100/1281] lr: 2.414e-02, eta: 18:42:28, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.7944, top5_acc: 0.9869, loss_cls: 0.8978, loss: 0.8978 +2025-06-24 13:12:38,028 - pyskl - INFO - Epoch [18][1200/1281] lr: 2.413e-02, eta: 18:41:33, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.7994, top5_acc: 0.9869, loss_cls: 0.8436, loss: 0.8436 +2025-06-24 13:13:09,078 - pyskl - INFO - Saving checkpoint at 18 epochs +2025-06-24 13:14:08,590 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:14:08,646 - pyskl - INFO - +top1_acc 0.7499 +top5_acc 0.9750 +2025-06-24 13:14:08,646 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:14:08,652 - pyskl - INFO - +mean_acc 0.6643 +2025-06-24 13:14:08,654 - pyskl - INFO - Epoch(val) [18][533] top1_acc: 0.7499, top5_acc: 0.9750, mean_class_accuracy: 0.6643 +2025-06-24 13:15:05,464 - pyskl - INFO - Epoch [19][100/1281] lr: 2.411e-02, eta: 18:38:30, time: 0.568, data_time: 0.195, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9869, loss_cls: 0.7939, loss: 0.7939 +2025-06-24 13:15:32,082 - pyskl - INFO - Epoch [19][200/1281] lr: 2.411e-02, eta: 18:36:15, time: 0.266, data_time: 0.000, memory: 4082, top1_acc: 0.8013, top5_acc: 0.9875, loss_cls: 0.8706, loss: 0.8706 +2025-06-24 13:16:16,809 - pyskl - INFO - Epoch [19][300/1281] lr: 2.410e-02, eta: 18:36:12, time: 0.447, data_time: 0.000, memory: 4082, top1_acc: 0.8387, top5_acc: 0.9906, loss_cls: 0.7586, loss: 0.7586 +2025-06-24 13:16:39,208 - pyskl - INFO - Epoch [19][400/1281] lr: 2.409e-02, eta: 18:33:28, time: 0.224, data_time: 0.000, memory: 4082, top1_acc: 0.8113, top5_acc: 0.9919, loss_cls: 0.8240, loss: 0.8240 +2025-06-24 13:17:11,162 - pyskl - INFO - Epoch [19][500/1281] lr: 2.408e-02, eta: 18:31:54, time: 0.320, data_time: 0.000, memory: 4082, top1_acc: 0.8319, top5_acc: 0.9894, loss_cls: 0.8114, loss: 0.8114 +2025-06-24 13:17:48,880 - pyskl - INFO - Epoch [19][600/1281] lr: 2.408e-02, eta: 18:31:01, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.7887, top5_acc: 0.9912, loss_cls: 0.8637, loss: 0.8637 +2025-06-24 13:18:26,401 - pyskl - INFO - Epoch [19][700/1281] lr: 2.407e-02, eta: 18:30:07, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9900, loss_cls: 0.8318, loss: 0.8318 +2025-06-24 13:19:03,967 - pyskl - INFO - Epoch [19][800/1281] lr: 2.406e-02, eta: 18:29:13, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8075, top5_acc: 0.9900, loss_cls: 0.8485, loss: 0.8485 +2025-06-24 13:19:41,910 - pyskl - INFO - Epoch [19][900/1281] lr: 2.405e-02, eta: 18:28:23, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8100, top5_acc: 0.9894, loss_cls: 0.8098, loss: 0.8098 +2025-06-24 13:20:19,630 - pyskl - INFO - Epoch [19][1000/1281] lr: 2.405e-02, eta: 18:27:30, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8044, top5_acc: 0.9894, loss_cls: 0.8272, loss: 0.8272 +2025-06-24 13:20:57,554 - pyskl - INFO - Epoch [19][1100/1281] lr: 2.404e-02, eta: 18:26:40, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9919, loss_cls: 0.8283, loss: 0.8283 +2025-06-24 13:21:35,885 - pyskl - INFO - Epoch [19][1200/1281] lr: 2.403e-02, eta: 18:25:52, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.7931, top5_acc: 0.9831, loss_cls: 0.9075, loss: 0.9075 +2025-06-24 13:22:07,015 - pyskl - INFO - Saving checkpoint at 19 epochs +2025-06-24 13:23:06,786 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:23:06,855 - pyskl - INFO - +top1_acc 0.7599 +top5_acc 0.9722 +2025-06-24 13:23:06,855 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:23:06,863 - pyskl - INFO - +mean_acc 0.6461 +2025-06-24 13:23:06,866 - pyskl - INFO - Epoch(val) [19][533] top1_acc: 0.7599, top5_acc: 0.9722, mean_class_accuracy: 0.6461 +2025-06-24 13:24:05,248 - pyskl - INFO - Epoch [20][100/1281] lr: 2.402e-02, eta: 18:23:10, time: 0.584, data_time: 0.197, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9938, loss_cls: 0.7607, loss: 0.7607 +2025-06-24 13:24:43,180 - pyskl - INFO - Epoch [20][200/1281] lr: 2.401e-02, eta: 18:22:20, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9919, loss_cls: 0.7493, loss: 0.7493 +2025-06-24 13:25:20,799 - pyskl - INFO - Epoch [20][300/1281] lr: 2.400e-02, eta: 18:21:28, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9906, loss_cls: 0.8333, loss: 0.8333 +2025-06-24 13:25:58,454 - pyskl - INFO - Epoch [20][400/1281] lr: 2.399e-02, eta: 18:20:36, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9912, loss_cls: 0.7740, loss: 0.7740 +2025-06-24 13:26:36,644 - pyskl - INFO - Epoch [20][500/1281] lr: 2.398e-02, eta: 18:19:48, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9875, loss_cls: 0.7987, loss: 0.7987 +2025-06-24 13:27:14,041 - pyskl - INFO - Epoch [20][600/1281] lr: 2.398e-02, eta: 18:18:55, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8044, top5_acc: 0.9888, loss_cls: 0.8519, loss: 0.8519 +2025-06-24 13:27:44,784 - pyskl - INFO - Epoch [20][700/1281] lr: 2.397e-02, eta: 18:17:18, time: 0.307, data_time: 0.001, memory: 4082, top1_acc: 0.8231, top5_acc: 0.9875, loss_cls: 0.7969, loss: 0.7969 +2025-06-24 13:28:21,804 - pyskl - INFO - Epoch [20][800/1281] lr: 2.396e-02, eta: 18:16:23, time: 0.370, data_time: 0.000, memory: 4082, top1_acc: 0.8237, top5_acc: 0.9894, loss_cls: 0.7943, loss: 0.7943 +2025-06-24 13:28:56,141 - pyskl - INFO - Epoch [20][900/1281] lr: 2.395e-02, eta: 18:15:10, time: 0.343, data_time: 0.000, memory: 4082, top1_acc: 0.8113, top5_acc: 0.9912, loss_cls: 0.7987, loss: 0.7987 +2025-06-24 13:29:19,801 - pyskl - INFO - Epoch [20][1000/1281] lr: 2.394e-02, eta: 18:12:47, time: 0.237, data_time: 0.000, memory: 4082, top1_acc: 0.8050, top5_acc: 0.9931, loss_cls: 0.7971, loss: 0.7971 +2025-06-24 13:29:57,112 - pyskl - INFO - Epoch [20][1100/1281] lr: 2.393e-02, eta: 18:11:55, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.7956, top5_acc: 0.9906, loss_cls: 0.8441, loss: 0.8441 +2025-06-24 13:30:35,045 - pyskl - INFO - Epoch [20][1200/1281] lr: 2.393e-02, eta: 18:11:06, time: 0.379, data_time: 0.001, memory: 4082, top1_acc: 0.8050, top5_acc: 0.9869, loss_cls: 0.8887, loss: 0.8887 +2025-06-24 13:31:06,050 - pyskl - INFO - Saving checkpoint at 20 epochs +2025-06-24 13:32:05,538 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:32:05,609 - pyskl - INFO - +top1_acc 0.7775 +top5_acc 0.9796 +2025-06-24 13:32:05,609 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:32:05,619 - pyskl - INFO - +mean_acc 0.6983 +2025-06-24 13:32:05,624 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_16.pth was removed +2025-06-24 13:32:05,807 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_20.pth. +2025-06-24 13:32:05,807 - pyskl - INFO - Best top1_acc is 0.7775 at 20 epoch. +2025-06-24 13:32:05,810 - pyskl - INFO - Epoch(val) [20][533] top1_acc: 0.7775, top5_acc: 0.9796, mean_class_accuracy: 0.6983 +2025-06-24 13:33:04,180 - pyskl - INFO - Epoch [21][100/1281] lr: 2.391e-02, eta: 18:08:33, time: 0.584, data_time: 0.197, memory: 4082, top1_acc: 0.8000, top5_acc: 0.9938, loss_cls: 0.8252, loss: 0.8252 +2025-06-24 13:33:41,977 - pyskl - INFO - Epoch [21][200/1281] lr: 2.390e-02, eta: 18:07:44, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8187, top5_acc: 0.9919, loss_cls: 0.8071, loss: 0.8071 +2025-06-24 13:34:19,680 - pyskl - INFO - Epoch [21][300/1281] lr: 2.389e-02, eta: 18:06:55, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8300, top5_acc: 0.9906, loss_cls: 0.7741, loss: 0.7741 +2025-06-24 13:34:57,440 - pyskl - INFO - Epoch [21][400/1281] lr: 2.389e-02, eta: 18:06:06, time: 0.378, data_time: 0.001, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9888, loss_cls: 0.7743, loss: 0.7743 +2025-06-24 13:35:34,993 - pyskl - INFO - Epoch [21][500/1281] lr: 2.388e-02, eta: 18:05:17, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9912, loss_cls: 0.7641, loss: 0.7641 +2025-06-24 13:36:12,606 - pyskl - INFO - Epoch [21][600/1281] lr: 2.387e-02, eta: 18:04:27, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8106, top5_acc: 0.9881, loss_cls: 0.8279, loss: 0.8279 +2025-06-24 13:36:50,324 - pyskl - INFO - Epoch [21][700/1281] lr: 2.386e-02, eta: 18:03:39, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8163, top5_acc: 0.9869, loss_cls: 0.8685, loss: 0.8685 +2025-06-24 13:37:28,024 - pyskl - INFO - Epoch [21][800/1281] lr: 2.385e-02, eta: 18:02:50, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8287, top5_acc: 0.9900, loss_cls: 0.7849, loss: 0.7849 +2025-06-24 13:38:06,425 - pyskl - INFO - Epoch [21][900/1281] lr: 2.384e-02, eta: 18:02:06, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9931, loss_cls: 0.7533, loss: 0.7533 +2025-06-24 13:38:44,017 - pyskl - INFO - Epoch [21][1000/1281] lr: 2.383e-02, eta: 18:01:16, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8056, top5_acc: 0.9881, loss_cls: 0.8113, loss: 0.8113 +2025-06-24 13:39:22,013 - pyskl - INFO - Epoch [21][1100/1281] lr: 2.383e-02, eta: 18:00:30, time: 0.380, data_time: 0.001, memory: 4082, top1_acc: 0.8175, top5_acc: 0.9844, loss_cls: 0.8277, loss: 0.8277 +2025-06-24 13:39:58,868 - pyskl - INFO - Epoch [21][1200/1281] lr: 2.382e-02, eta: 17:59:36, time: 0.369, data_time: 0.000, memory: 4082, top1_acc: 0.8150, top5_acc: 0.9862, loss_cls: 0.8464, loss: 0.8464 +2025-06-24 13:40:19,915 - pyskl - INFO - Saving checkpoint at 21 epochs +2025-06-24 13:41:18,691 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:41:18,747 - pyskl - INFO - +top1_acc 0.8041 +top5_acc 0.9843 +2025-06-24 13:41:18,747 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:41:18,754 - pyskl - INFO - +mean_acc 0.7373 +2025-06-24 13:41:18,758 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_20.pth was removed +2025-06-24 13:41:18,936 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_21.pth. +2025-06-24 13:41:18,936 - pyskl - INFO - Best top1_acc is 0.8041 at 21 epoch. +2025-06-24 13:41:18,939 - pyskl - INFO - Epoch(val) [21][533] top1_acc: 0.8041, top5_acc: 0.9843, mean_class_accuracy: 0.7373 +2025-06-24 13:42:16,375 - pyskl - INFO - Epoch [22][100/1281] lr: 2.380e-02, eta: 17:57:03, time: 0.574, data_time: 0.195, memory: 4082, top1_acc: 0.8406, top5_acc: 0.9950, loss_cls: 0.7382, loss: 0.7382 +2025-06-24 13:42:54,133 - pyskl - INFO - Epoch [22][200/1281] lr: 2.379e-02, eta: 17:56:15, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9894, loss_cls: 0.7688, loss: 0.7688 +2025-06-24 13:43:31,853 - pyskl - INFO - Epoch [22][300/1281] lr: 2.378e-02, eta: 17:55:28, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9919, loss_cls: 0.7971, loss: 0.7971 +2025-06-24 13:44:10,025 - pyskl - INFO - Epoch [22][400/1281] lr: 2.378e-02, eta: 17:54:43, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8106, top5_acc: 0.9925, loss_cls: 0.7955, loss: 0.7955 +2025-06-24 13:44:47,458 - pyskl - INFO - Epoch [22][500/1281] lr: 2.377e-02, eta: 17:53:54, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8044, top5_acc: 0.9862, loss_cls: 0.8426, loss: 0.8426 +2025-06-24 13:45:25,079 - pyskl - INFO - Epoch [22][600/1281] lr: 2.376e-02, eta: 17:53:06, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8087, top5_acc: 0.9869, loss_cls: 0.8504, loss: 0.8504 +2025-06-24 13:46:02,108 - pyskl - INFO - Epoch [22][700/1281] lr: 2.375e-02, eta: 17:52:14, time: 0.370, data_time: 0.000, memory: 4082, top1_acc: 0.7931, top5_acc: 0.9875, loss_cls: 0.8948, loss: 0.8948 +2025-06-24 13:46:40,199 - pyskl - INFO - Epoch [22][800/1281] lr: 2.374e-02, eta: 17:51:29, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8331, top5_acc: 0.9919, loss_cls: 0.7644, loss: 0.7644 +2025-06-24 13:47:18,311 - pyskl - INFO - Epoch [22][900/1281] lr: 2.373e-02, eta: 17:50:44, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9906, loss_cls: 0.7541, loss: 0.7541 +2025-06-24 13:47:56,228 - pyskl - INFO - Epoch [22][1000/1281] lr: 2.372e-02, eta: 17:49:58, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9875, loss_cls: 0.8006, loss: 0.8006 +2025-06-24 13:48:33,670 - pyskl - INFO - Epoch [22][1100/1281] lr: 2.371e-02, eta: 17:49:09, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8219, top5_acc: 0.9875, loss_cls: 0.8049, loss: 0.8049 +2025-06-24 13:49:11,374 - pyskl - INFO - Epoch [22][1200/1281] lr: 2.370e-02, eta: 17:48:22, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8144, top5_acc: 0.9862, loss_cls: 0.8179, loss: 0.8179 +2025-06-24 13:49:42,406 - pyskl - INFO - Saving checkpoint at 22 epochs +2025-06-24 13:50:41,557 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:50:41,612 - pyskl - INFO - +top1_acc 0.8038 +top5_acc 0.9830 +2025-06-24 13:50:41,612 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:50:41,619 - pyskl - INFO - +mean_acc 0.7413 +2025-06-24 13:50:41,621 - pyskl - INFO - Epoch(val) [22][533] top1_acc: 0.8038, top5_acc: 0.9830, mean_class_accuracy: 0.7413 +2025-06-24 13:51:39,288 - pyskl - INFO - Epoch [23][100/1281] lr: 2.369e-02, eta: 17:45:56, time: 0.577, data_time: 0.196, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9925, loss_cls: 0.7110, loss: 0.7110 +2025-06-24 13:52:07,878 - pyskl - INFO - Epoch [23][200/1281] lr: 2.368e-02, eta: 17:44:17, time: 0.286, data_time: 0.000, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9919, loss_cls: 0.7112, loss: 0.7112 +2025-06-24 13:52:46,907 - pyskl - INFO - Epoch [23][300/1281] lr: 2.367e-02, eta: 17:43:38, time: 0.390, data_time: 0.000, memory: 4082, top1_acc: 0.8344, top5_acc: 0.9912, loss_cls: 0.7464, loss: 0.7464 +2025-06-24 13:53:19,096 - pyskl - INFO - Epoch [23][400/1281] lr: 2.366e-02, eta: 17:42:20, time: 0.322, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9888, loss_cls: 0.7656, loss: 0.7656 +2025-06-24 13:53:44,455 - pyskl - INFO - Epoch [23][500/1281] lr: 2.365e-02, eta: 17:40:23, time: 0.254, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9900, loss_cls: 0.7843, loss: 0.7843 +2025-06-24 13:54:21,913 - pyskl - INFO - Epoch [23][600/1281] lr: 2.364e-02, eta: 17:39:36, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9875, loss_cls: 0.7648, loss: 0.7648 +2025-06-24 13:54:58,812 - pyskl - INFO - Epoch [23][700/1281] lr: 2.363e-02, eta: 17:38:46, time: 0.369, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9881, loss_cls: 0.7796, loss: 0.7796 +2025-06-24 13:55:36,394 - pyskl - INFO - Epoch [23][800/1281] lr: 2.362e-02, eta: 17:37:59, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8494, top5_acc: 0.9919, loss_cls: 0.6976, loss: 0.6976 +2025-06-24 13:56:13,387 - pyskl - INFO - Epoch [23][900/1281] lr: 2.361e-02, eta: 17:37:10, time: 0.370, data_time: 0.000, memory: 4082, top1_acc: 0.8200, top5_acc: 0.9900, loss_cls: 0.8090, loss: 0.8090 +2025-06-24 13:56:50,446 - pyskl - INFO - Epoch [23][1000/1281] lr: 2.360e-02, eta: 17:36:21, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8213, top5_acc: 0.9912, loss_cls: 0.7764, loss: 0.7764 +2025-06-24 13:57:27,867 - pyskl - INFO - Epoch [23][1100/1281] lr: 2.359e-02, eta: 17:35:34, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8256, top5_acc: 0.9912, loss_cls: 0.7968, loss: 0.7968 +2025-06-24 13:58:05,011 - pyskl - INFO - Epoch [23][1200/1281] lr: 2.359e-02, eta: 17:34:45, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8156, top5_acc: 0.9862, loss_cls: 0.8231, loss: 0.8231 +2025-06-24 13:58:36,168 - pyskl - INFO - Saving checkpoint at 23 epochs +2025-06-24 13:59:35,472 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 13:59:35,527 - pyskl - INFO - +top1_acc 0.8037 +top5_acc 0.9835 +2025-06-24 13:59:35,527 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 13:59:35,534 - pyskl - INFO - +mean_acc 0.7322 +2025-06-24 13:59:35,535 - pyskl - INFO - Epoch(val) [23][533] top1_acc: 0.8037, top5_acc: 0.9835, mean_class_accuracy: 0.7322 +2025-06-24 14:00:33,516 - pyskl - INFO - Epoch [24][100/1281] lr: 2.357e-02, eta: 17:32:26, time: 0.580, data_time: 0.198, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9894, loss_cls: 0.7221, loss: 0.7221 +2025-06-24 14:01:11,524 - pyskl - INFO - Epoch [24][200/1281] lr: 2.356e-02, eta: 17:31:43, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8281, top5_acc: 0.9919, loss_cls: 0.7328, loss: 0.7328 +2025-06-24 14:01:49,650 - pyskl - INFO - Epoch [24][300/1281] lr: 2.355e-02, eta: 17:31:00, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8169, top5_acc: 0.9906, loss_cls: 0.7968, loss: 0.7968 +2025-06-24 14:02:26,980 - pyskl - INFO - Epoch [24][400/1281] lr: 2.354e-02, eta: 17:30:13, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9919, loss_cls: 0.7216, loss: 0.7216 +2025-06-24 14:03:05,109 - pyskl - INFO - Epoch [24][500/1281] lr: 2.353e-02, eta: 17:29:31, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8313, top5_acc: 0.9900, loss_cls: 0.7646, loss: 0.7646 +2025-06-24 14:03:43,282 - pyskl - INFO - Epoch [24][600/1281] lr: 2.352e-02, eta: 17:28:48, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8206, top5_acc: 0.9894, loss_cls: 0.7713, loss: 0.7713 +2025-06-24 14:04:21,652 - pyskl - INFO - Epoch [24][700/1281] lr: 2.351e-02, eta: 17:28:07, time: 0.384, data_time: 0.001, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9900, loss_cls: 0.7247, loss: 0.7247 +2025-06-24 14:04:46,628 - pyskl - INFO - Epoch [24][800/1281] lr: 2.350e-02, eta: 17:26:14, time: 0.250, data_time: 0.000, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9888, loss_cls: 0.7594, loss: 0.7594 +2025-06-24 14:05:30,806 - pyskl - INFO - Epoch [24][900/1281] lr: 2.349e-02, eta: 17:26:04, time: 0.442, data_time: 0.000, memory: 4082, top1_acc: 0.8175, top5_acc: 0.9881, loss_cls: 0.8065, loss: 0.8065 +2025-06-24 14:05:57,804 - pyskl - INFO - Epoch [24][1000/1281] lr: 2.348e-02, eta: 17:24:23, time: 0.270, data_time: 0.000, memory: 4082, top1_acc: 0.8131, top5_acc: 0.9869, loss_cls: 0.8273, loss: 0.8273 +2025-06-24 14:06:28,391 - pyskl - INFO - Epoch [24][1100/1281] lr: 2.347e-02, eta: 17:23:01, time: 0.306, data_time: 0.000, memory: 4082, top1_acc: 0.8244, top5_acc: 0.9875, loss_cls: 0.7985, loss: 0.7985 +2025-06-24 14:07:05,702 - pyskl - INFO - Epoch [24][1200/1281] lr: 2.346e-02, eta: 17:22:15, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8181, top5_acc: 0.9925, loss_cls: 0.7746, loss: 0.7746 +2025-06-24 14:07:36,556 - pyskl - INFO - Saving checkpoint at 24 epochs +2025-06-24 14:08:35,927 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:08:35,982 - pyskl - INFO - +top1_acc 0.7916 +top5_acc 0.9844 +2025-06-24 14:08:35,982 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:08:35,989 - pyskl - INFO - +mean_acc 0.7307 +2025-06-24 14:08:35,991 - pyskl - INFO - Epoch(val) [24][533] top1_acc: 0.7916, top5_acc: 0.9844, mean_class_accuracy: 0.7307 +2025-06-24 14:09:33,936 - pyskl - INFO - Epoch [25][100/1281] lr: 2.344e-02, eta: 17:20:01, time: 0.579, data_time: 0.202, memory: 4082, top1_acc: 0.8456, top5_acc: 0.9944, loss_cls: 0.7195, loss: 0.7195 +2025-06-24 14:10:11,424 - pyskl - INFO - Epoch [25][200/1281] lr: 2.343e-02, eta: 17:19:16, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8381, top5_acc: 0.9950, loss_cls: 0.6989, loss: 0.6989 +2025-06-24 14:10:49,638 - pyskl - INFO - Epoch [25][300/1281] lr: 2.342e-02, eta: 17:18:35, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8575, top5_acc: 0.9944, loss_cls: 0.6961, loss: 0.6961 +2025-06-24 14:11:27,462 - pyskl - INFO - Epoch [25][400/1281] lr: 2.341e-02, eta: 17:17:52, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9856, loss_cls: 0.7210, loss: 0.7210 +2025-06-24 14:12:05,282 - pyskl - INFO - Epoch [25][500/1281] lr: 2.340e-02, eta: 17:17:09, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8350, top5_acc: 0.9894, loss_cls: 0.7435, loss: 0.7435 +2025-06-24 14:12:42,781 - pyskl - INFO - Epoch [25][600/1281] lr: 2.339e-02, eta: 17:16:24, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8275, top5_acc: 0.9906, loss_cls: 0.7753, loss: 0.7753 +2025-06-24 14:13:20,585 - pyskl - INFO - Epoch [25][700/1281] lr: 2.338e-02, eta: 17:15:41, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8381, top5_acc: 0.9912, loss_cls: 0.7520, loss: 0.7520 +2025-06-24 14:13:57,987 - pyskl - INFO - Epoch [25][800/1281] lr: 2.337e-02, eta: 17:14:56, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8306, top5_acc: 0.9881, loss_cls: 0.7585, loss: 0.7585 +2025-06-24 14:14:36,340 - pyskl - INFO - Epoch [25][900/1281] lr: 2.336e-02, eta: 17:14:16, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8356, top5_acc: 0.9919, loss_cls: 0.7164, loss: 0.7164 +2025-06-24 14:15:13,590 - pyskl - INFO - Epoch [25][1000/1281] lr: 2.335e-02, eta: 17:13:30, time: 0.373, data_time: 0.000, memory: 4082, top1_acc: 0.8344, top5_acc: 0.9894, loss_cls: 0.7670, loss: 0.7670 +2025-06-24 14:15:51,269 - pyskl - INFO - Epoch [25][1100/1281] lr: 2.334e-02, eta: 17:12:46, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8119, top5_acc: 0.9850, loss_cls: 0.8225, loss: 0.8225 +2025-06-24 14:16:29,702 - pyskl - INFO - Epoch [25][1200/1281] lr: 2.333e-02, eta: 17:12:06, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9850, loss_cls: 0.7795, loss: 0.7795 +2025-06-24 14:16:59,793 - pyskl - INFO - Saving checkpoint at 25 epochs +2025-06-24 14:18:13,284 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:18:13,339 - pyskl - INFO - +top1_acc 0.7814 +top5_acc 0.9804 +2025-06-24 14:18:13,339 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:18:13,346 - pyskl - INFO - +mean_acc 0.7278 +2025-06-24 14:18:13,348 - pyskl - INFO - Epoch(val) [25][533] top1_acc: 0.7814, top5_acc: 0.9804, mean_class_accuracy: 0.7278 +2025-06-24 14:19:10,767 - pyskl - INFO - Epoch [26][100/1281] lr: 2.332e-02, eta: 17:09:54, time: 0.574, data_time: 0.199, memory: 4082, top1_acc: 0.8556, top5_acc: 0.9962, loss_cls: 0.6970, loss: 0.6970 +2025-06-24 14:19:48,314 - pyskl - INFO - Epoch [26][200/1281] lr: 2.330e-02, eta: 17:09:10, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8525, top5_acc: 0.9912, loss_cls: 0.6786, loss: 0.6786 +2025-06-24 14:20:25,792 - pyskl - INFO - Epoch [26][300/1281] lr: 2.329e-02, eta: 17:08:26, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8512, top5_acc: 0.9944, loss_cls: 0.7029, loss: 0.7029 +2025-06-24 14:21:03,743 - pyskl - INFO - Epoch [26][400/1281] lr: 2.328e-02, eta: 17:07:44, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8425, top5_acc: 0.9862, loss_cls: 0.7294, loss: 0.7294 +2025-06-24 14:21:40,952 - pyskl - INFO - Epoch [26][500/1281] lr: 2.327e-02, eta: 17:06:58, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8325, top5_acc: 0.9906, loss_cls: 0.7485, loss: 0.7485 +2025-06-24 14:22:18,982 - pyskl - INFO - Epoch [26][600/1281] lr: 2.326e-02, eta: 17:06:17, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8431, top5_acc: 0.9925, loss_cls: 0.7268, loss: 0.7268 +2025-06-24 14:22:56,042 - pyskl - INFO - Epoch [26][700/1281] lr: 2.325e-02, eta: 17:05:31, time: 0.371, data_time: 0.000, memory: 4082, top1_acc: 0.8237, top5_acc: 0.9912, loss_cls: 0.7664, loss: 0.7664 +2025-06-24 14:23:33,987 - pyskl - INFO - Epoch [26][800/1281] lr: 2.324e-02, eta: 17:04:49, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8206, top5_acc: 0.9912, loss_cls: 0.7729, loss: 0.7729 +2025-06-24 14:24:11,749 - pyskl - INFO - Epoch [26][900/1281] lr: 2.323e-02, eta: 17:04:07, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8275, top5_acc: 0.9850, loss_cls: 0.7981, loss: 0.7981 +2025-06-24 14:24:49,460 - pyskl - INFO - Epoch [26][1000/1281] lr: 2.322e-02, eta: 17:03:24, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8562, top5_acc: 0.9925, loss_cls: 0.6930, loss: 0.6930 +2025-06-24 14:25:26,859 - pyskl - INFO - Epoch [26][1100/1281] lr: 2.321e-02, eta: 17:02:39, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8438, top5_acc: 0.9906, loss_cls: 0.7629, loss: 0.7629 +2025-06-24 14:26:05,372 - pyskl - INFO - Epoch [26][1200/1281] lr: 2.320e-02, eta: 17:02:00, time: 0.385, data_time: 0.000, memory: 4082, top1_acc: 0.8519, top5_acc: 0.9912, loss_cls: 0.7047, loss: 0.7047 +2025-06-24 14:26:36,144 - pyskl - INFO - Saving checkpoint at 26 epochs +2025-06-24 14:27:35,504 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:27:35,559 - pyskl - INFO - +top1_acc 0.7704 +top5_acc 0.9776 +2025-06-24 14:27:35,559 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:27:35,565 - pyskl - INFO - +mean_acc 0.6658 +2025-06-24 14:27:35,567 - pyskl - INFO - Epoch(val) [26][533] top1_acc: 0.7704, top5_acc: 0.9776, mean_class_accuracy: 0.6658 +2025-06-24 14:28:32,957 - pyskl - INFO - Epoch [27][100/1281] lr: 2.318e-02, eta: 16:59:51, time: 0.574, data_time: 0.194, memory: 4082, top1_acc: 0.8313, top5_acc: 0.9912, loss_cls: 0.7387, loss: 0.7387 +2025-06-24 14:29:10,425 - pyskl - INFO - Epoch [27][200/1281] lr: 2.317e-02, eta: 16:59:08, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8562, top5_acc: 0.9900, loss_cls: 0.6670, loss: 0.6670 +2025-06-24 14:29:36,289 - pyskl - INFO - Epoch [27][300/1281] lr: 2.316e-02, eta: 16:57:29, time: 0.259, data_time: 0.000, memory: 4082, top1_acc: 0.8656, top5_acc: 0.9906, loss_cls: 0.6704, loss: 0.6704 +2025-06-24 14:30:19,810 - pyskl - INFO - Epoch [27][400/1281] lr: 2.315e-02, eta: 16:57:14, time: 0.435, data_time: 0.000, memory: 4082, top1_acc: 0.8475, top5_acc: 0.9919, loss_cls: 0.6895, loss: 0.6895 +2025-06-24 14:30:47,711 - pyskl - INFO - Epoch [27][500/1281] lr: 2.314e-02, eta: 16:55:46, time: 0.279, data_time: 0.000, memory: 4082, top1_acc: 0.8462, top5_acc: 0.9944, loss_cls: 0.7177, loss: 0.7177 +2025-06-24 14:31:16,307 - pyskl - INFO - Epoch [27][600/1281] lr: 2.313e-02, eta: 16:54:22, time: 0.286, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9894, loss_cls: 0.7145, loss: 0.7145 +2025-06-24 14:31:53,923 - pyskl - INFO - Epoch [27][700/1281] lr: 2.312e-02, eta: 16:53:39, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8381, top5_acc: 0.9875, loss_cls: 0.7373, loss: 0.7373 +2025-06-24 14:32:31,566 - pyskl - INFO - Epoch [27][800/1281] lr: 2.311e-02, eta: 16:52:57, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8544, top5_acc: 0.9888, loss_cls: 0.6789, loss: 0.6789 +2025-06-24 14:33:09,372 - pyskl - INFO - Epoch [27][900/1281] lr: 2.310e-02, eta: 16:52:15, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9944, loss_cls: 0.7153, loss: 0.7153 +2025-06-24 14:33:47,345 - pyskl - INFO - Epoch [27][1000/1281] lr: 2.308e-02, eta: 16:51:35, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8337, top5_acc: 0.9888, loss_cls: 0.7290, loss: 0.7290 +2025-06-24 14:34:25,167 - pyskl - INFO - Epoch [27][1100/1281] lr: 2.307e-02, eta: 16:50:53, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8137, top5_acc: 0.9931, loss_cls: 0.8077, loss: 0.8077 +2025-06-24 14:35:02,650 - pyskl - INFO - Epoch [27][1200/1281] lr: 2.306e-02, eta: 16:50:11, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8294, top5_acc: 0.9900, loss_cls: 0.7690, loss: 0.7690 +2025-06-24 14:35:33,911 - pyskl - INFO - Saving checkpoint at 27 epochs +2025-06-24 14:36:33,561 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:36:33,632 - pyskl - INFO - +top1_acc 0.8138 +top5_acc 0.9849 +2025-06-24 14:36:33,632 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:36:33,640 - pyskl - INFO - +mean_acc 0.7569 +2025-06-24 14:36:33,644 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_21.pth was removed +2025-06-24 14:36:33,846 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_27.pth. +2025-06-24 14:36:33,846 - pyskl - INFO - Best top1_acc is 0.8138 at 27 epoch. +2025-06-24 14:36:33,849 - pyskl - INFO - Epoch(val) [27][533] top1_acc: 0.8138, top5_acc: 0.9849, mean_class_accuracy: 0.7569 +2025-06-24 14:37:31,684 - pyskl - INFO - Epoch [28][100/1281] lr: 2.304e-02, eta: 16:48:08, time: 0.578, data_time: 0.196, memory: 4082, top1_acc: 0.8406, top5_acc: 0.9938, loss_cls: 0.7047, loss: 0.7047 +2025-06-24 14:38:09,148 - pyskl - INFO - Epoch [28][200/1281] lr: 2.303e-02, eta: 16:47:25, time: 0.375, data_time: 0.000, memory: 4082, top1_acc: 0.8531, top5_acc: 0.9931, loss_cls: 0.6750, loss: 0.6750 +2025-06-24 14:38:47,174 - pyskl - INFO - Epoch [28][300/1281] lr: 2.302e-02, eta: 16:46:45, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8394, top5_acc: 0.9912, loss_cls: 0.7449, loss: 0.7449 +2025-06-24 14:39:24,534 - pyskl - INFO - Epoch [28][400/1281] lr: 2.301e-02, eta: 16:46:02, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9925, loss_cls: 0.6680, loss: 0.6680 +2025-06-24 14:40:02,413 - pyskl - INFO - Epoch [28][500/1281] lr: 2.300e-02, eta: 16:45:21, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8569, top5_acc: 0.9925, loss_cls: 0.6637, loss: 0.6637 +2025-06-24 14:40:40,424 - pyskl - INFO - Epoch [28][600/1281] lr: 2.299e-02, eta: 16:44:41, time: 0.380, data_time: 0.000, memory: 4082, top1_acc: 0.8619, top5_acc: 0.9938, loss_cls: 0.6674, loss: 0.6674 +2025-06-24 14:41:18,347 - pyskl - INFO - Epoch [28][700/1281] lr: 2.298e-02, eta: 16:44:00, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8363, top5_acc: 0.9931, loss_cls: 0.7316, loss: 0.7316 +2025-06-24 14:41:53,234 - pyskl - INFO - Epoch [28][800/1281] lr: 2.297e-02, eta: 16:43:06, time: 0.349, data_time: 0.000, memory: 4082, top1_acc: 0.8631, top5_acc: 0.9912, loss_cls: 0.7241, loss: 0.7241 +2025-06-24 14:42:22,932 - pyskl - INFO - Epoch [28][900/1281] lr: 2.295e-02, eta: 16:41:49, time: 0.297, data_time: 0.000, memory: 4082, top1_acc: 0.8569, top5_acc: 0.9900, loss_cls: 0.6761, loss: 0.6761 +2025-06-24 14:43:04,704 - pyskl - INFO - Epoch [28][1000/1281] lr: 2.294e-02, eta: 16:41:26, time: 0.418, data_time: 0.000, memory: 4082, top1_acc: 0.8344, top5_acc: 0.9912, loss_cls: 0.7816, loss: 0.7816 +2025-06-24 14:43:27,736 - pyskl - INFO - Epoch [28][1100/1281] lr: 2.293e-02, eta: 16:39:40, time: 0.230, data_time: 0.000, memory: 4082, top1_acc: 0.8413, top5_acc: 0.9938, loss_cls: 0.7163, loss: 0.7163 +2025-06-24 14:44:01,761 - pyskl - INFO - Epoch [28][1200/1281] lr: 2.292e-02, eta: 16:38:43, time: 0.340, data_time: 0.000, memory: 4082, top1_acc: 0.8488, top5_acc: 0.9912, loss_cls: 0.7218, loss: 0.7218 +2025-06-24 14:44:32,957 - pyskl - INFO - Saving checkpoint at 28 epochs +2025-06-24 14:45:32,210 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:45:32,265 - pyskl - INFO - +top1_acc 0.7965 +top5_acc 0.9857 +2025-06-24 14:45:32,265 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:45:32,272 - pyskl - INFO - +mean_acc 0.7313 +2025-06-24 14:45:32,273 - pyskl - INFO - Epoch(val) [28][533] top1_acc: 0.7965, top5_acc: 0.9857, mean_class_accuracy: 0.7313 +2025-06-24 14:46:30,341 - pyskl - INFO - Epoch [29][100/1281] lr: 2.290e-02, eta: 16:36:45, time: 0.581, data_time: 0.200, memory: 4082, top1_acc: 0.8550, top5_acc: 0.9931, loss_cls: 0.6728, loss: 0.6728 +2025-06-24 14:47:08,630 - pyskl - INFO - Epoch [29][200/1281] lr: 2.289e-02, eta: 16:36:06, time: 0.383, data_time: 0.000, memory: 4082, top1_acc: 0.8638, top5_acc: 0.9944, loss_cls: 0.6384, loss: 0.6384 +2025-06-24 14:47:46,211 - pyskl - INFO - Epoch [29][300/1281] lr: 2.288e-02, eta: 16:35:25, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8512, top5_acc: 0.9938, loss_cls: 0.6681, loss: 0.6681 +2025-06-24 14:48:24,125 - pyskl - INFO - Epoch [29][400/1281] lr: 2.287e-02, eta: 16:34:45, time: 0.379, data_time: 0.000, memory: 4082, top1_acc: 0.8644, top5_acc: 0.9938, loss_cls: 0.6830, loss: 0.6830 +2025-06-24 14:49:01,813 - pyskl - INFO - Epoch [29][500/1281] lr: 2.285e-02, eta: 16:34:04, time: 0.377, data_time: 0.000, memory: 4082, top1_acc: 0.8650, top5_acc: 0.9900, loss_cls: 0.6937, loss: 0.6937 +2025-06-24 14:49:39,436 - pyskl - INFO - Epoch [29][600/1281] lr: 2.284e-02, eta: 16:33:23, time: 0.376, data_time: 0.000, memory: 4082, top1_acc: 0.8562, top5_acc: 0.9912, loss_cls: 0.6672, loss: 0.6672 +2025-06-24 14:50:16,869 - pyskl - INFO - Epoch [29][700/1281] lr: 2.283e-02, eta: 16:32:41, time: 0.374, data_time: 0.000, memory: 4082, top1_acc: 0.8606, top5_acc: 0.9950, loss_cls: 0.6591, loss: 0.6591 +2025-06-24 14:50:54,081 - pyskl - INFO - Epoch [29][800/1281] lr: 2.282e-02, eta: 16:31:58, time: 0.372, data_time: 0.000, memory: 4082, top1_acc: 0.8413, top5_acc: 0.9912, loss_cls: 0.6885, loss: 0.6885 +2025-06-24 14:51:31,837 - pyskl - INFO - Epoch [29][900/1281] lr: 2.281e-02, eta: 16:31:18, time: 0.378, data_time: 0.000, memory: 4082, top1_acc: 0.8269, top5_acc: 0.9900, loss_cls: 0.7721, loss: 0.7721 +2025-06-24 14:52:10,243 - pyskl - INFO - Epoch [29][1000/1281] lr: 2.280e-02, eta: 16:30:40, time: 0.384, data_time: 0.000, memory: 4082, top1_acc: 0.8569, top5_acc: 0.9944, loss_cls: 0.6821, loss: 0.6821 +2025-06-24 14:52:48,374 - pyskl - INFO - Epoch [29][1100/1281] lr: 2.279e-02, eta: 16:30:01, time: 0.381, data_time: 0.000, memory: 4082, top1_acc: 0.8413, top5_acc: 0.9950, loss_cls: 0.7198, loss: 0.7198 +2025-06-24 14:53:26,604 - pyskl - INFO - Epoch [29][1200/1281] lr: 2.277e-02, eta: 16:29:23, time: 0.382, data_time: 0.000, memory: 4082, top1_acc: 0.8419, top5_acc: 0.9894, loss_cls: 0.7307, loss: 0.7307 +2025-06-24 14:53:58,300 - pyskl - INFO - Saving checkpoint at 29 epochs +2025-06-24 14:55:04,697 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 14:55:04,765 - pyskl - INFO - +top1_acc 0.7881 +top5_acc 0.9781 +2025-06-24 14:55:04,765 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 14:55:04,773 - pyskl - INFO - +mean_acc 0.7214 +2025-06-24 14:55:04,775 - pyskl - INFO - Epoch(val) [29][533] top1_acc: 0.7881, top5_acc: 0.9781, mean_class_accuracy: 0.7214 +2025-06-24 14:55:57,047 - pyskl - INFO - Epoch [30][100/1281] lr: 2.275e-02, eta: 16:27:03, time: 0.523, data_time: 0.192, memory: 4082, top1_acc: 0.8375, top5_acc: 0.9912, loss_cls: 0.7284, loss: 0.7284 +2025-06-24 14:56:45,023 - pyskl - INFO - Epoch [30][200/1281] lr: 2.274e-02, eta: 16:27:05, time: 0.480, data_time: 0.000, memory: 4082, top1_acc: 0.8812, top5_acc: 0.9950, loss_cls: 0.5995, loss: 0.5995 +2025-06-24 14:57:33,112 - pyskl - INFO - Epoch [30][300/1281] lr: 2.273e-02, eta: 16:27:07, time: 0.481, data_time: 0.000, memory: 4082, top1_acc: 0.8531, top5_acc: 0.9962, loss_cls: 0.6557, loss: 0.6557 +2025-06-24 14:58:20,750 - pyskl - INFO - Epoch [30][400/1281] lr: 2.272e-02, eta: 16:27:07, time: 0.476, data_time: 0.000, memory: 4082, top1_acc: 0.8600, top5_acc: 0.9881, loss_cls: 0.6815, loss: 0.6815 +2025-06-24 14:59:08,759 - pyskl - INFO - Epoch [30][500/1281] lr: 2.271e-02, eta: 16:27:09, time: 0.480, data_time: 0.000, memory: 4082, top1_acc: 0.8581, top5_acc: 0.9969, loss_cls: 0.6644, loss: 0.6644 +2025-06-24 14:59:56,700 - pyskl - INFO - Epoch [30][600/1281] lr: 2.269e-02, eta: 16:27:10, time: 0.479, data_time: 0.000, memory: 4082, top1_acc: 0.8569, top5_acc: 0.9925, loss_cls: 0.6710, loss: 0.6710 +2025-06-24 15:00:45,024 - pyskl - INFO - Epoch [30][700/1281] lr: 2.268e-02, eta: 16:27:12, time: 0.483, data_time: 0.000, memory: 4082, top1_acc: 0.8531, top5_acc: 0.9919, loss_cls: 0.6963, loss: 0.6963 +2025-06-24 15:01:33,305 - pyskl - INFO - Epoch [30][800/1281] lr: 2.267e-02, eta: 16:27:14, time: 0.483, data_time: 0.000, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9938, loss_cls: 0.6999, loss: 0.6999 +2025-06-24 15:02:21,628 - pyskl - INFO - Epoch [30][900/1281] lr: 2.266e-02, eta: 16:27:16, time: 0.483, data_time: 0.000, memory: 4082, top1_acc: 0.8581, top5_acc: 0.9900, loss_cls: 0.6813, loss: 0.6813 +2025-06-24 15:03:09,795 - pyskl - INFO - Epoch [30][1000/1281] lr: 2.265e-02, eta: 16:27:16, time: 0.482, data_time: 0.000, memory: 4082, top1_acc: 0.8481, top5_acc: 0.9919, loss_cls: 0.7226, loss: 0.7226 +2025-06-24 15:03:58,104 - pyskl - INFO - Epoch [30][1100/1281] lr: 2.263e-02, eta: 16:27:17, time: 0.483, data_time: 0.001, memory: 4082, top1_acc: 0.8469, top5_acc: 0.9894, loss_cls: 0.7161, loss: 0.7161 +2025-06-24 15:04:46,079 - pyskl - INFO - Epoch [30][1200/1281] lr: 2.262e-02, eta: 16:27:17, time: 0.480, data_time: 0.000, memory: 4082, top1_acc: 0.8375, top5_acc: 0.9906, loss_cls: 0.7269, loss: 0.7269 +2025-06-24 15:05:25,750 - pyskl - INFO - Saving checkpoint at 30 epochs +2025-06-24 15:06:23,266 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:06:23,321 - pyskl - INFO - +top1_acc 0.7565 +top5_acc 0.9683 +2025-06-24 15:06:23,321 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:06:23,328 - pyskl - INFO - +mean_acc 0.6603 +2025-06-24 15:06:23,330 - pyskl - INFO - Epoch(val) [30][533] top1_acc: 0.7565, top5_acc: 0.9683, mean_class_accuracy: 0.6603 +2025-06-24 15:07:09,092 - pyskl - INFO - Epoch [31][100/1281] lr: 2.260e-02, eta: 16:24:32, time: 0.458, data_time: 0.187, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9969, loss_cls: 0.7793, loss: 0.7793 +2025-06-24 15:07:57,932 - pyskl - INFO - Epoch [31][200/1281] lr: 2.259e-02, eta: 16:24:35, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8550, top5_acc: 0.9912, loss_cls: 0.8110, loss: 0.8110 +2025-06-24 15:08:47,201 - pyskl - INFO - Epoch [31][300/1281] lr: 2.258e-02, eta: 16:24:39, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8525, top5_acc: 0.9950, loss_cls: 0.7956, loss: 0.7956 +2025-06-24 15:09:36,216 - pyskl - INFO - Epoch [31][400/1281] lr: 2.256e-02, eta: 16:24:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8444, top5_acc: 0.9944, loss_cls: 0.8134, loss: 0.8134 +2025-06-24 15:10:25,050 - pyskl - INFO - Epoch [31][500/1281] lr: 2.255e-02, eta: 16:24:44, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9919, loss_cls: 0.7859, loss: 0.7859 +2025-06-24 15:11:14,223 - pyskl - INFO - Epoch [31][600/1281] lr: 2.254e-02, eta: 16:24:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8250, top5_acc: 0.9894, loss_cls: 0.9229, loss: 0.9229 +2025-06-24 15:12:03,447 - pyskl - INFO - Epoch [31][700/1281] lr: 2.253e-02, eta: 16:24:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8506, top5_acc: 0.9888, loss_cls: 0.8289, loss: 0.8289 +2025-06-24 15:12:52,527 - pyskl - INFO - Epoch [31][800/1281] lr: 2.252e-02, eta: 16:24:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9912, loss_cls: 0.8068, loss: 0.8068 +2025-06-24 15:13:41,709 - pyskl - INFO - Epoch [31][900/1281] lr: 2.250e-02, eta: 16:24:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8400, top5_acc: 0.9912, loss_cls: 0.8776, loss: 0.8776 +2025-06-24 15:14:30,959 - pyskl - INFO - Epoch [31][1000/1281] lr: 2.249e-02, eta: 16:24:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8450, top5_acc: 0.9944, loss_cls: 0.8750, loss: 0.8750 +2025-06-24 15:15:20,104 - pyskl - INFO - Epoch [31][1100/1281] lr: 2.248e-02, eta: 16:24:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8500, top5_acc: 0.9894, loss_cls: 0.8774, loss: 0.8774 +2025-06-24 15:16:09,210 - pyskl - INFO - Epoch [31][1200/1281] lr: 2.247e-02, eta: 16:25:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8394, top5_acc: 0.9906, loss_cls: 0.8668, loss: 0.8668 +2025-06-24 15:16:49,332 - pyskl - INFO - Saving checkpoint at 31 epochs +2025-06-24 15:17:39,233 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:17:39,308 - pyskl - INFO - +top1_acc 0.7663 +top5_acc 0.9725 +2025-06-24 15:17:39,308 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:17:39,315 - pyskl - INFO - +mean_acc 0.6921 +2025-06-24 15:17:39,317 - pyskl - INFO - Epoch(val) [31][533] top1_acc: 0.7663, top5_acc: 0.9725, mean_class_accuracy: 0.6921 +2025-06-24 15:18:30,339 - pyskl - INFO - Epoch [32][100/1281] lr: 2.244e-02, eta: 16:22:36, time: 0.510, data_time: 0.189, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9912, loss_cls: 0.7192, loss: 0.7192 +2025-06-24 15:19:14,202 - pyskl - INFO - Epoch [32][200/1281] lr: 2.243e-02, eta: 16:22:17, time: 0.439, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9975, loss_cls: 0.6675, loss: 0.6675 +2025-06-24 15:20:03,308 - pyskl - INFO - Epoch [32][300/1281] lr: 2.242e-02, eta: 16:22:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8500, top5_acc: 0.9925, loss_cls: 0.7596, loss: 0.7596 +2025-06-24 15:20:52,528 - pyskl - INFO - Epoch [32][400/1281] lr: 2.241e-02, eta: 16:22:19, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9944, loss_cls: 0.6600, loss: 0.6600 +2025-06-24 15:21:41,550 - pyskl - INFO - Epoch [32][500/1281] lr: 2.239e-02, eta: 16:22:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8450, top5_acc: 0.9919, loss_cls: 0.7713, loss: 0.7713 +2025-06-24 15:22:30,504 - pyskl - INFO - Epoch [32][600/1281] lr: 2.238e-02, eta: 16:22:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8350, top5_acc: 0.9919, loss_cls: 0.7910, loss: 0.7910 +2025-06-24 15:23:19,788 - pyskl - INFO - Epoch [32][700/1281] lr: 2.237e-02, eta: 16:22:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8450, top5_acc: 0.9956, loss_cls: 0.7724, loss: 0.7724 +2025-06-24 15:24:08,928 - pyskl - INFO - Epoch [32][800/1281] lr: 2.236e-02, eta: 16:22:19, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8419, top5_acc: 0.9881, loss_cls: 0.7758, loss: 0.7758 +2025-06-24 15:24:58,219 - pyskl - INFO - Epoch [32][900/1281] lr: 2.234e-02, eta: 16:22:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8525, top5_acc: 0.9919, loss_cls: 0.7688, loss: 0.7688 +2025-06-24 15:25:47,364 - pyskl - INFO - Epoch [32][1000/1281] lr: 2.233e-02, eta: 16:22:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9931, loss_cls: 0.7331, loss: 0.7331 +2025-06-24 15:26:36,332 - pyskl - INFO - Epoch [32][1100/1281] lr: 2.232e-02, eta: 16:22:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8544, top5_acc: 0.9969, loss_cls: 0.7402, loss: 0.7402 +2025-06-24 15:27:25,629 - pyskl - INFO - Epoch [32][1200/1281] lr: 2.231e-02, eta: 16:22:16, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8444, top5_acc: 0.9919, loss_cls: 0.7805, loss: 0.7805 +2025-06-24 15:28:06,413 - pyskl - INFO - Saving checkpoint at 32 epochs +2025-06-24 15:29:03,892 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:29:03,947 - pyskl - INFO - +top1_acc 0.8055 +top5_acc 0.9830 +2025-06-24 15:29:03,947 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:29:03,954 - pyskl - INFO - +mean_acc 0.7408 +2025-06-24 15:29:03,956 - pyskl - INFO - Epoch(val) [32][533] top1_acc: 0.8055, top5_acc: 0.9830, mean_class_accuracy: 0.7408 +2025-06-24 15:30:13,092 - pyskl - INFO - Epoch [33][100/1281] lr: 2.228e-02, eta: 16:21:00, time: 0.691, data_time: 0.193, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9950, loss_cls: 0.6898, loss: 0.6898 +2025-06-24 15:30:57,846 - pyskl - INFO - Epoch [33][200/1281] lr: 2.227e-02, eta: 16:20:42, time: 0.448, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9956, loss_cls: 0.6669, loss: 0.6669 +2025-06-24 15:31:47,222 - pyskl - INFO - Epoch [33][300/1281] lr: 2.226e-02, eta: 16:20:41, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9944, loss_cls: 0.6634, loss: 0.6634 +2025-06-24 15:32:36,272 - pyskl - INFO - Epoch [33][400/1281] lr: 2.225e-02, eta: 16:20:39, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9912, loss_cls: 0.7437, loss: 0.7437 +2025-06-24 15:33:25,241 - pyskl - INFO - Epoch [33][500/1281] lr: 2.223e-02, eta: 16:20:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9931, loss_cls: 0.7023, loss: 0.7023 +2025-06-24 15:34:14,370 - pyskl - INFO - Epoch [33][600/1281] lr: 2.222e-02, eta: 16:20:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9925, loss_cls: 0.7455, loss: 0.7455 +2025-06-24 15:35:03,855 - pyskl - INFO - Epoch [33][700/1281] lr: 2.221e-02, eta: 16:20:32, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9938, loss_cls: 0.7420, loss: 0.7420 +2025-06-24 15:35:53,282 - pyskl - INFO - Epoch [33][800/1281] lr: 2.219e-02, eta: 16:20:30, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9944, loss_cls: 0.6951, loss: 0.6951 +2025-06-24 15:36:42,857 - pyskl - INFO - Epoch [33][900/1281] lr: 2.218e-02, eta: 16:20:28, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9975, loss_cls: 0.7153, loss: 0.7153 +2025-06-24 15:37:32,045 - pyskl - INFO - Epoch [33][1000/1281] lr: 2.217e-02, eta: 16:20:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8425, top5_acc: 0.9925, loss_cls: 0.7609, loss: 0.7609 +2025-06-24 15:38:21,174 - pyskl - INFO - Epoch [33][1100/1281] lr: 2.216e-02, eta: 16:20:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8456, top5_acc: 0.9900, loss_cls: 0.7932, loss: 0.7932 +2025-06-24 15:39:09,976 - pyskl - INFO - Epoch [33][1200/1281] lr: 2.214e-02, eta: 16:20:16, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8369, top5_acc: 0.9925, loss_cls: 0.8028, loss: 0.8028 +2025-06-24 15:39:50,116 - pyskl - INFO - Saving checkpoint at 33 epochs +2025-06-24 15:40:46,999 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:40:47,056 - pyskl - INFO - +top1_acc 0.8315 +top5_acc 0.9837 +2025-06-24 15:40:47,056 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:40:47,063 - pyskl - INFO - +mean_acc 0.7950 +2025-06-24 15:40:47,067 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_27.pth was removed +2025-06-24 15:40:47,269 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_33.pth. +2025-06-24 15:40:47,270 - pyskl - INFO - Best top1_acc is 0.8315 at 33 epoch. +2025-06-24 15:40:47,273 - pyskl - INFO - Epoch(val) [33][533] top1_acc: 0.8315, top5_acc: 0.9837, mean_class_accuracy: 0.7950 +2025-06-24 15:41:54,859 - pyskl - INFO - Epoch [34][100/1281] lr: 2.212e-02, eta: 16:18:53, time: 0.676, data_time: 0.188, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9938, loss_cls: 0.6657, loss: 0.6657 +2025-06-24 15:42:39,990 - pyskl - INFO - Epoch [34][200/1281] lr: 2.211e-02, eta: 16:18:35, time: 0.451, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9956, loss_cls: 0.6490, loss: 0.6490 +2025-06-24 15:43:29,422 - pyskl - INFO - Epoch [34][300/1281] lr: 2.209e-02, eta: 16:18:31, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9919, loss_cls: 0.6540, loss: 0.6540 +2025-06-24 15:44:18,711 - pyskl - INFO - Epoch [34][400/1281] lr: 2.208e-02, eta: 16:18:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9931, loss_cls: 0.6909, loss: 0.6909 +2025-06-24 15:45:07,819 - pyskl - INFO - Epoch [34][500/1281] lr: 2.207e-02, eta: 16:18:22, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9925, loss_cls: 0.6889, loss: 0.6889 +2025-06-24 15:45:57,036 - pyskl - INFO - Epoch [34][600/1281] lr: 2.205e-02, eta: 16:18:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8500, top5_acc: 0.9919, loss_cls: 0.7322, loss: 0.7322 +2025-06-24 15:46:46,207 - pyskl - INFO - Epoch [34][700/1281] lr: 2.204e-02, eta: 16:18:12, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9962, loss_cls: 0.7123, loss: 0.7123 +2025-06-24 15:47:35,617 - pyskl - INFO - Epoch [34][800/1281] lr: 2.203e-02, eta: 16:18:08, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9931, loss_cls: 0.7013, loss: 0.7013 +2025-06-24 15:48:25,101 - pyskl - INFO - Epoch [34][900/1281] lr: 2.201e-02, eta: 16:18:03, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8644, top5_acc: 0.9944, loss_cls: 0.6899, loss: 0.6899 +2025-06-24 15:49:14,061 - pyskl - INFO - Epoch [34][1000/1281] lr: 2.200e-02, eta: 16:17:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8488, top5_acc: 0.9944, loss_cls: 0.7111, loss: 0.7111 +2025-06-24 15:50:03,320 - pyskl - INFO - Epoch [34][1100/1281] lr: 2.199e-02, eta: 16:17:51, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9944, loss_cls: 0.6613, loss: 0.6613 +2025-06-24 15:50:52,557 - pyskl - INFO - Epoch [34][1200/1281] lr: 2.197e-02, eta: 16:17:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9931, loss_cls: 0.6997, loss: 0.6997 +2025-06-24 15:51:32,911 - pyskl - INFO - Saving checkpoint at 34 epochs +2025-06-24 15:52:28,734 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 15:52:28,806 - pyskl - INFO - +top1_acc 0.8311 +top5_acc 0.9885 +2025-06-24 15:52:28,807 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 15:52:28,819 - pyskl - INFO - +mean_acc 0.7782 +2025-06-24 15:52:28,822 - pyskl - INFO - Epoch(val) [34][533] top1_acc: 0.8311, top5_acc: 0.9885, mean_class_accuracy: 0.7782 +2025-06-24 15:53:33,412 - pyskl - INFO - Epoch [35][100/1281] lr: 2.195e-02, eta: 16:16:11, time: 0.646, data_time: 0.200, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9950, loss_cls: 0.6155, loss: 0.6155 +2025-06-24 15:54:17,302 - pyskl - INFO - Epoch [35][200/1281] lr: 2.194e-02, eta: 16:15:46, time: 0.439, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9975, loss_cls: 0.6784, loss: 0.6784 +2025-06-24 15:55:06,842 - pyskl - INFO - Epoch [35][300/1281] lr: 2.192e-02, eta: 16:15:41, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9938, loss_cls: 0.6475, loss: 0.6475 +2025-06-24 15:55:56,068 - pyskl - INFO - Epoch [35][400/1281] lr: 2.191e-02, eta: 16:15:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9944, loss_cls: 0.7127, loss: 0.7127 +2025-06-24 15:56:45,706 - pyskl - INFO - Epoch [35][500/1281] lr: 2.190e-02, eta: 16:15:29, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8525, top5_acc: 0.9919, loss_cls: 0.7426, loss: 0.7426 +2025-06-24 15:57:35,003 - pyskl - INFO - Epoch [35][600/1281] lr: 2.188e-02, eta: 16:15:22, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8638, top5_acc: 0.9962, loss_cls: 0.6716, loss: 0.6716 +2025-06-24 15:58:24,345 - pyskl - INFO - Epoch [35][700/1281] lr: 2.187e-02, eta: 16:15:15, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8494, top5_acc: 0.9944, loss_cls: 0.7056, loss: 0.7056 +2025-06-24 15:59:13,661 - pyskl - INFO - Epoch [35][800/1281] lr: 2.185e-02, eta: 16:15:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9931, loss_cls: 0.6584, loss: 0.6584 +2025-06-24 16:00:02,728 - pyskl - INFO - Epoch [35][900/1281] lr: 2.184e-02, eta: 16:15:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8569, top5_acc: 0.9912, loss_cls: 0.7040, loss: 0.7040 +2025-06-24 16:00:52,012 - pyskl - INFO - Epoch [35][1000/1281] lr: 2.183e-02, eta: 16:14:53, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8588, top5_acc: 0.9938, loss_cls: 0.7050, loss: 0.7050 +2025-06-24 16:01:41,410 - pyskl - INFO - Epoch [35][1100/1281] lr: 2.181e-02, eta: 16:14:45, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8594, top5_acc: 0.9956, loss_cls: 0.7266, loss: 0.7266 +2025-06-24 16:02:30,751 - pyskl - INFO - Epoch [35][1200/1281] lr: 2.180e-02, eta: 16:14:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8600, top5_acc: 0.9938, loss_cls: 0.7252, loss: 0.7252 +2025-06-24 16:03:11,209 - pyskl - INFO - Saving checkpoint at 35 epochs +2025-06-24 16:04:07,877 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:04:07,932 - pyskl - INFO - +top1_acc 0.8142 +top5_acc 0.9784 +2025-06-24 16:04:07,932 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:04:07,939 - pyskl - INFO - +mean_acc 0.7564 +2025-06-24 16:04:07,941 - pyskl - INFO - Epoch(val) [35][533] top1_acc: 0.8142, top5_acc: 0.9784, mean_class_accuracy: 0.7564 +2025-06-24 16:05:14,580 - pyskl - INFO - Epoch [36][100/1281] lr: 2.178e-02, eta: 16:13:09, time: 0.666, data_time: 0.192, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9956, loss_cls: 0.6431, loss: 0.6431 +2025-06-24 16:05:58,858 - pyskl - INFO - Epoch [36][200/1281] lr: 2.176e-02, eta: 16:12:44, time: 0.443, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9925, loss_cls: 0.6348, loss: 0.6348 +2025-06-24 16:06:47,756 - pyskl - INFO - Epoch [36][300/1281] lr: 2.175e-02, eta: 16:12:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8675, top5_acc: 0.9969, loss_cls: 0.6509, loss: 0.6509 +2025-06-24 16:07:36,533 - pyskl - INFO - Epoch [36][400/1281] lr: 2.173e-02, eta: 16:12:24, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9975, loss_cls: 0.6123, loss: 0.6123 +2025-06-24 16:08:25,736 - pyskl - INFO - Epoch [36][500/1281] lr: 2.172e-02, eta: 16:12:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9938, loss_cls: 0.6559, loss: 0.6559 +2025-06-24 16:09:14,854 - pyskl - INFO - Epoch [36][600/1281] lr: 2.171e-02, eta: 16:12:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9962, loss_cls: 0.6428, loss: 0.6428 +2025-06-24 16:10:03,797 - pyskl - INFO - Epoch [36][700/1281] lr: 2.169e-02, eta: 16:11:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9956, loss_cls: 0.6541, loss: 0.6541 +2025-06-24 16:10:53,145 - pyskl - INFO - Epoch [36][800/1281] lr: 2.168e-02, eta: 16:11:47, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9925, loss_cls: 0.6963, loss: 0.6963 +2025-06-24 16:11:41,823 - pyskl - INFO - Epoch [36][900/1281] lr: 2.167e-02, eta: 16:11:35, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8369, top5_acc: 0.9931, loss_cls: 0.7597, loss: 0.7597 +2025-06-24 16:12:30,872 - pyskl - INFO - Epoch [36][1000/1281] lr: 2.165e-02, eta: 16:11:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9925, loss_cls: 0.7137, loss: 0.7137 +2025-06-24 16:13:20,231 - pyskl - INFO - Epoch [36][1100/1281] lr: 2.164e-02, eta: 16:11:15, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8669, top5_acc: 0.9925, loss_cls: 0.7077, loss: 0.7077 +2025-06-24 16:14:09,364 - pyskl - INFO - Epoch [36][1200/1281] lr: 2.162e-02, eta: 16:11:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9900, loss_cls: 0.6963, loss: 0.6963 +2025-06-24 16:14:49,707 - pyskl - INFO - Saving checkpoint at 36 epochs +2025-06-24 16:15:47,147 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:15:47,203 - pyskl - INFO - +top1_acc 0.8053 +top5_acc 0.9811 +2025-06-24 16:15:47,203 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:15:47,210 - pyskl - INFO - +mean_acc 0.7677 +2025-06-24 16:15:47,212 - pyskl - INFO - Epoch(val) [36][533] top1_acc: 0.8053, top5_acc: 0.9811, mean_class_accuracy: 0.7677 +2025-06-24 16:16:54,690 - pyskl - INFO - Epoch [37][100/1281] lr: 2.160e-02, eta: 16:09:38, time: 0.675, data_time: 0.198, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9938, loss_cls: 0.6546, loss: 0.6546 +2025-06-24 16:17:38,551 - pyskl - INFO - Epoch [37][200/1281] lr: 2.158e-02, eta: 16:09:11, time: 0.439, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9969, loss_cls: 0.6102, loss: 0.6102 +2025-06-24 16:18:27,744 - pyskl - INFO - Epoch [37][300/1281] lr: 2.157e-02, eta: 16:09:00, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9944, loss_cls: 0.6258, loss: 0.6258 +2025-06-24 16:19:16,897 - pyskl - INFO - Epoch [37][400/1281] lr: 2.156e-02, eta: 16:08:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8500, top5_acc: 0.9938, loss_cls: 0.7212, loss: 0.7212 +2025-06-24 16:20:06,088 - pyskl - INFO - Epoch [37][500/1281] lr: 2.154e-02, eta: 16:08:38, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9950, loss_cls: 0.6173, loss: 0.6173 +2025-06-24 16:20:55,301 - pyskl - INFO - Epoch [37][600/1281] lr: 2.153e-02, eta: 16:08:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9956, loss_cls: 0.6561, loss: 0.6561 +2025-06-24 16:21:44,252 - pyskl - INFO - Epoch [37][700/1281] lr: 2.151e-02, eta: 16:08:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9938, loss_cls: 0.6422, loss: 0.6422 +2025-06-24 16:22:33,714 - pyskl - INFO - Epoch [37][800/1281] lr: 2.150e-02, eta: 16:08:04, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9931, loss_cls: 0.6621, loss: 0.6621 +2025-06-24 16:23:23,045 - pyskl - INFO - Epoch [37][900/1281] lr: 2.149e-02, eta: 16:07:53, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9956, loss_cls: 0.6389, loss: 0.6389 +2025-06-24 16:24:12,271 - pyskl - INFO - Epoch [37][1000/1281] lr: 2.147e-02, eta: 16:07:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8488, top5_acc: 0.9906, loss_cls: 0.7385, loss: 0.7385 +2025-06-24 16:25:01,417 - pyskl - INFO - Epoch [37][1100/1281] lr: 2.146e-02, eta: 16:07:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9925, loss_cls: 0.7176, loss: 0.7176 +2025-06-24 16:25:50,689 - pyskl - INFO - Epoch [37][1200/1281] lr: 2.144e-02, eta: 16:07:18, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8656, top5_acc: 0.9938, loss_cls: 0.6698, loss: 0.6698 +2025-06-24 16:26:31,090 - pyskl - INFO - Saving checkpoint at 37 epochs +2025-06-24 16:27:28,142 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:27:28,200 - pyskl - INFO - +top1_acc 0.8400 +top5_acc 0.9879 +2025-06-24 16:27:28,200 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:27:28,208 - pyskl - INFO - +mean_acc 0.7884 +2025-06-24 16:27:28,213 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_33.pth was removed +2025-06-24 16:27:28,425 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_37.pth. +2025-06-24 16:27:28,425 - pyskl - INFO - Best top1_acc is 0.8400 at 37 epoch. +2025-06-24 16:27:28,428 - pyskl - INFO - Epoch(val) [37][533] top1_acc: 0.8400, top5_acc: 0.9879, mean_class_accuracy: 0.7884 +2025-06-24 16:28:35,181 - pyskl - INFO - Epoch [38][100/1281] lr: 2.142e-02, eta: 16:05:48, time: 0.667, data_time: 0.193, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9969, loss_cls: 0.6219, loss: 0.6219 +2025-06-24 16:29:18,599 - pyskl - INFO - Epoch [38][200/1281] lr: 2.140e-02, eta: 16:05:18, time: 0.434, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9938, loss_cls: 0.6590, loss: 0.6590 +2025-06-24 16:30:07,469 - pyskl - INFO - Epoch [38][300/1281] lr: 2.139e-02, eta: 16:05:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8650, top5_acc: 0.9950, loss_cls: 0.7085, loss: 0.7085 +2025-06-24 16:30:56,706 - pyskl - INFO - Epoch [38][400/1281] lr: 2.137e-02, eta: 16:04:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8625, top5_acc: 0.9938, loss_cls: 0.6823, loss: 0.6823 +2025-06-24 16:31:46,241 - pyskl - INFO - Epoch [38][500/1281] lr: 2.136e-02, eta: 16:04:40, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9931, loss_cls: 0.6212, loss: 0.6212 +2025-06-24 16:32:35,771 - pyskl - INFO - Epoch [38][600/1281] lr: 2.134e-02, eta: 16:04:28, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9956, loss_cls: 0.6445, loss: 0.6445 +2025-06-24 16:33:25,065 - pyskl - INFO - Epoch [38][700/1281] lr: 2.133e-02, eta: 16:04:16, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9931, loss_cls: 0.6051, loss: 0.6051 +2025-06-24 16:34:14,273 - pyskl - INFO - Epoch [38][800/1281] lr: 2.132e-02, eta: 16:04:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8469, top5_acc: 0.9925, loss_cls: 0.7149, loss: 0.7149 +2025-06-24 16:35:03,613 - pyskl - INFO - Epoch [38][900/1281] lr: 2.130e-02, eta: 16:03:50, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.8581, top5_acc: 0.9931, loss_cls: 0.6991, loss: 0.6991 +2025-06-24 16:35:52,557 - pyskl - INFO - Epoch [38][1000/1281] lr: 2.129e-02, eta: 16:03:35, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9931, loss_cls: 0.6488, loss: 0.6488 +2025-06-24 16:36:41,940 - pyskl - INFO - Epoch [38][1100/1281] lr: 2.127e-02, eta: 16:03:22, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9931, loss_cls: 0.6780, loss: 0.6780 +2025-06-24 16:37:31,113 - pyskl - INFO - Epoch [38][1200/1281] lr: 2.126e-02, eta: 16:03:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8612, top5_acc: 0.9906, loss_cls: 0.7154, loss: 0.7154 +2025-06-24 16:38:11,699 - pyskl - INFO - Saving checkpoint at 38 epochs +2025-06-24 16:39:08,738 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:39:08,793 - pyskl - INFO - +top1_acc 0.8305 +top5_acc 0.9881 +2025-06-24 16:39:08,793 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:39:08,799 - pyskl - INFO - +mean_acc 0.7872 +2025-06-24 16:39:08,801 - pyskl - INFO - Epoch(val) [38][533] top1_acc: 0.8305, top5_acc: 0.9881, mean_class_accuracy: 0.7872 +2025-06-24 16:40:15,735 - pyskl - INFO - Epoch [39][100/1281] lr: 2.123e-02, eta: 16:01:38, time: 0.669, data_time: 0.195, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9944, loss_cls: 0.5679, loss: 0.5679 +2025-06-24 16:41:01,022 - pyskl - INFO - Epoch [39][200/1281] lr: 2.122e-02, eta: 16:01:13, time: 0.453, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9988, loss_cls: 0.5729, loss: 0.5729 +2025-06-24 16:41:50,163 - pyskl - INFO - Epoch [39][300/1281] lr: 2.120e-02, eta: 16:00:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9956, loss_cls: 0.6091, loss: 0.6091 +2025-06-24 16:42:39,398 - pyskl - INFO - Epoch [39][400/1281] lr: 2.119e-02, eta: 16:00:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9925, loss_cls: 0.6792, loss: 0.6792 +2025-06-24 16:43:28,850 - pyskl - INFO - Epoch [39][500/1281] lr: 2.117e-02, eta: 16:00:30, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8606, top5_acc: 0.9938, loss_cls: 0.6943, loss: 0.6943 +2025-06-24 16:44:17,786 - pyskl - INFO - Epoch [39][600/1281] lr: 2.116e-02, eta: 16:00:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8531, top5_acc: 0.9938, loss_cls: 0.7265, loss: 0.7265 +2025-06-24 16:45:06,884 - pyskl - INFO - Epoch [39][700/1281] lr: 2.114e-02, eta: 16:00:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9962, loss_cls: 0.6714, loss: 0.6714 +2025-06-24 16:45:55,960 - pyskl - INFO - Epoch [39][800/1281] lr: 2.113e-02, eta: 15:59:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9962, loss_cls: 0.6885, loss: 0.6885 +2025-06-24 16:46:45,012 - pyskl - INFO - Epoch [39][900/1281] lr: 2.111e-02, eta: 15:59:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9894, loss_cls: 0.6625, loss: 0.6625 +2025-06-24 16:47:33,929 - pyskl - INFO - Epoch [39][1000/1281] lr: 2.110e-02, eta: 15:59:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9944, loss_cls: 0.6125, loss: 0.6125 +2025-06-24 16:48:22,979 - pyskl - INFO - Epoch [39][1100/1281] lr: 2.108e-02, eta: 15:58:58, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9938, loss_cls: 0.6592, loss: 0.6592 +2025-06-24 16:49:12,075 - pyskl - INFO - Epoch [39][1200/1281] lr: 2.107e-02, eta: 15:58:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9925, loss_cls: 0.6563, loss: 0.6563 +2025-06-24 16:49:52,320 - pyskl - INFO - Saving checkpoint at 39 epochs +2025-06-24 16:50:47,753 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 16:50:47,810 - pyskl - INFO - +top1_acc 0.7997 +top5_acc 0.9853 +2025-06-24 16:50:47,810 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 16:50:47,817 - pyskl - INFO - +mean_acc 0.7443 +2025-06-24 16:50:47,820 - pyskl - INFO - Epoch(val) [39][533] top1_acc: 0.7997, top5_acc: 0.9853, mean_class_accuracy: 0.7443 +2025-06-24 16:51:51,644 - pyskl - INFO - Epoch [40][100/1281] lr: 2.104e-02, eta: 15:57:03, time: 0.638, data_time: 0.193, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9956, loss_cls: 0.6820, loss: 0.6820 +2025-06-24 16:52:34,890 - pyskl - INFO - Epoch [40][200/1281] lr: 2.103e-02, eta: 15:56:30, time: 0.432, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9931, loss_cls: 0.6767, loss: 0.6767 +2025-06-24 16:53:24,348 - pyskl - INFO - Epoch [40][300/1281] lr: 2.101e-02, eta: 15:56:15, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9938, loss_cls: 0.6276, loss: 0.6276 +2025-06-24 16:54:13,911 - pyskl - INFO - Epoch [40][400/1281] lr: 2.100e-02, eta: 15:56:01, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9950, loss_cls: 0.6646, loss: 0.6646 +2025-06-24 16:55:03,206 - pyskl - INFO - Epoch [40][500/1281] lr: 2.098e-02, eta: 15:55:45, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9944, loss_cls: 0.5975, loss: 0.5975 +2025-06-24 16:55:52,379 - pyskl - INFO - Epoch [40][600/1281] lr: 2.097e-02, eta: 15:55:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9931, loss_cls: 0.6674, loss: 0.6674 +2025-06-24 16:56:41,460 - pyskl - INFO - Epoch [40][700/1281] lr: 2.095e-02, eta: 15:55:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9962, loss_cls: 0.6140, loss: 0.6140 +2025-06-24 16:57:30,517 - pyskl - INFO - Epoch [40][800/1281] lr: 2.094e-02, eta: 15:54:56, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8700, top5_acc: 0.9969, loss_cls: 0.6444, loss: 0.6444 +2025-06-24 16:58:19,706 - pyskl - INFO - Epoch [40][900/1281] lr: 2.092e-02, eta: 15:54:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8575, top5_acc: 0.9950, loss_cls: 0.7030, loss: 0.7030 +2025-06-24 16:59:09,248 - pyskl - INFO - Epoch [40][1000/1281] lr: 2.091e-02, eta: 15:54:24, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9975, loss_cls: 0.6567, loss: 0.6567 +2025-06-24 16:59:58,295 - pyskl - INFO - Epoch [40][1100/1281] lr: 2.089e-02, eta: 15:54:07, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9944, loss_cls: 0.5972, loss: 0.5972 +2025-06-24 17:00:47,382 - pyskl - INFO - Epoch [40][1200/1281] lr: 2.088e-02, eta: 15:53:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9931, loss_cls: 0.6619, loss: 0.6619 +2025-06-24 17:01:27,896 - pyskl - INFO - Saving checkpoint at 40 epochs +2025-06-24 17:02:26,307 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:02:26,364 - pyskl - INFO - +top1_acc 0.8146 +top5_acc 0.9839 +2025-06-24 17:02:26,364 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:02:26,372 - pyskl - INFO - +mean_acc 0.7505 +2025-06-24 17:02:26,374 - pyskl - INFO - Epoch(val) [40][533] top1_acc: 0.8146, top5_acc: 0.9839, mean_class_accuracy: 0.7505 +2025-06-24 17:03:36,151 - pyskl - INFO - Epoch [41][100/1281] lr: 2.085e-02, eta: 15:52:26, time: 0.698, data_time: 0.195, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9938, loss_cls: 0.6097, loss: 0.6097 +2025-06-24 17:04:19,802 - pyskl - INFO - Epoch [41][200/1281] lr: 2.083e-02, eta: 15:51:54, time: 0.436, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9944, loss_cls: 0.6034, loss: 0.6034 +2025-06-24 17:05:08,928 - pyskl - INFO - Epoch [41][300/1281] lr: 2.082e-02, eta: 15:51:36, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9950, loss_cls: 0.6232, loss: 0.6232 +2025-06-24 17:05:57,899 - pyskl - INFO - Epoch [41][400/1281] lr: 2.080e-02, eta: 15:51:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9912, loss_cls: 0.6994, loss: 0.6994 +2025-06-24 17:06:47,259 - pyskl - INFO - Epoch [41][500/1281] lr: 2.079e-02, eta: 15:51:02, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9944, loss_cls: 0.6150, loss: 0.6150 +2025-06-24 17:07:36,763 - pyskl - INFO - Epoch [41][600/1281] lr: 2.077e-02, eta: 15:50:45, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9981, loss_cls: 0.6031, loss: 0.6031 +2025-06-24 17:08:25,677 - pyskl - INFO - Epoch [41][700/1281] lr: 2.076e-02, eta: 15:50:27, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9962, loss_cls: 0.6186, loss: 0.6186 +2025-06-24 17:09:14,745 - pyskl - INFO - Epoch [41][800/1281] lr: 2.074e-02, eta: 15:50:09, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8738, top5_acc: 0.9938, loss_cls: 0.6364, loss: 0.6364 +2025-06-24 17:10:04,371 - pyskl - INFO - Epoch [41][900/1281] lr: 2.073e-02, eta: 15:49:52, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9931, loss_cls: 0.6164, loss: 0.6164 +2025-06-24 17:10:53,740 - pyskl - INFO - Epoch [41][1000/1281] lr: 2.071e-02, eta: 15:49:34, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9931, loss_cls: 0.6032, loss: 0.6032 +2025-06-24 17:11:42,922 - pyskl - INFO - Epoch [41][1100/1281] lr: 2.070e-02, eta: 15:49:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9919, loss_cls: 0.6347, loss: 0.6347 +2025-06-24 17:12:32,026 - pyskl - INFO - Epoch [41][1200/1281] lr: 2.068e-02, eta: 15:48:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8725, top5_acc: 0.9931, loss_cls: 0.6524, loss: 0.6524 +2025-06-24 17:13:12,157 - pyskl - INFO - Saving checkpoint at 41 epochs +2025-06-24 17:14:10,105 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:14:10,160 - pyskl - INFO - +top1_acc 0.8317 +top5_acc 0.9847 +2025-06-24 17:14:10,160 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:14:10,168 - pyskl - INFO - +mean_acc 0.7612 +2025-06-24 17:14:10,170 - pyskl - INFO - Epoch(val) [41][533] top1_acc: 0.8317, top5_acc: 0.9847, mean_class_accuracy: 0.7612 +2025-06-24 17:15:20,234 - pyskl - INFO - Epoch [42][100/1281] lr: 2.065e-02, eta: 15:47:34, time: 0.701, data_time: 0.203, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9950, loss_cls: 0.6099, loss: 0.6099 +2025-06-24 17:16:03,360 - pyskl - INFO - Epoch [42][200/1281] lr: 2.064e-02, eta: 15:47:00, time: 0.431, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9956, loss_cls: 0.6040, loss: 0.6040 +2025-06-24 17:16:52,428 - pyskl - INFO - Epoch [42][300/1281] lr: 2.062e-02, eta: 15:46:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9969, loss_cls: 0.5905, loss: 0.5905 +2025-06-24 17:17:41,871 - pyskl - INFO - Epoch [42][400/1281] lr: 2.061e-02, eta: 15:46:23, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9938, loss_cls: 0.6141, loss: 0.6141 +2025-06-24 17:18:31,465 - pyskl - INFO - Epoch [42][500/1281] lr: 2.059e-02, eta: 15:46:05, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9975, loss_cls: 0.5957, loss: 0.5957 +2025-06-24 17:19:20,817 - pyskl - INFO - Epoch [42][600/1281] lr: 2.057e-02, eta: 15:45:47, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9938, loss_cls: 0.6381, loss: 0.6381 +2025-06-24 17:20:10,143 - pyskl - INFO - Epoch [42][700/1281] lr: 2.056e-02, eta: 15:45:28, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9931, loss_cls: 0.5906, loss: 0.5906 +2025-06-24 17:20:59,374 - pyskl - INFO - Epoch [42][800/1281] lr: 2.054e-02, eta: 15:45:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9956, loss_cls: 0.5986, loss: 0.5986 +2025-06-24 17:21:48,817 - pyskl - INFO - Epoch [42][900/1281] lr: 2.053e-02, eta: 15:44:51, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8731, top5_acc: 0.9950, loss_cls: 0.6466, loss: 0.6466 +2025-06-24 17:22:37,776 - pyskl - INFO - Epoch [42][1000/1281] lr: 2.051e-02, eta: 15:44:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8631, top5_acc: 0.9938, loss_cls: 0.6757, loss: 0.6757 +2025-06-24 17:23:27,313 - pyskl - INFO - Epoch [42][1100/1281] lr: 2.050e-02, eta: 15:44:12, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9931, loss_cls: 0.6373, loss: 0.6373 +2025-06-24 17:24:16,987 - pyskl - INFO - Epoch [42][1200/1281] lr: 2.048e-02, eta: 15:43:54, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.8556, top5_acc: 0.9950, loss_cls: 0.6763, loss: 0.6763 +2025-06-24 17:24:57,978 - pyskl - INFO - Saving checkpoint at 42 epochs +2025-06-24 17:25:56,073 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:25:56,130 - pyskl - INFO - +top1_acc 0.8433 +top5_acc 0.9898 +2025-06-24 17:25:56,130 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:25:56,139 - pyskl - INFO - +mean_acc 0.8024 +2025-06-24 17:25:56,143 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_37.pth was removed +2025-06-24 17:25:56,320 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_42.pth. +2025-06-24 17:25:56,321 - pyskl - INFO - Best top1_acc is 0.8433 at 42 epoch. +2025-06-24 17:25:56,324 - pyskl - INFO - Epoch(val) [42][533] top1_acc: 0.8433, top5_acc: 0.9898, mean_class_accuracy: 0.8024 +2025-06-24 17:27:04,260 - pyskl - INFO - Epoch [43][100/1281] lr: 2.045e-02, eta: 15:42:24, time: 0.679, data_time: 0.191, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9950, loss_cls: 0.6102, loss: 0.6102 +2025-06-24 17:27:47,652 - pyskl - INFO - Epoch [43][200/1281] lr: 2.044e-02, eta: 15:41:50, time: 0.434, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9975, loss_cls: 0.6231, loss: 0.6231 +2025-06-24 17:28:36,645 - pyskl - INFO - Epoch [43][300/1281] lr: 2.042e-02, eta: 15:41:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8856, top5_acc: 0.9938, loss_cls: 0.6235, loss: 0.6235 +2025-06-24 17:29:25,773 - pyskl - INFO - Epoch [43][400/1281] lr: 2.040e-02, eta: 15:41:09, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8750, top5_acc: 0.9944, loss_cls: 0.6149, loss: 0.6149 +2025-06-24 17:30:14,838 - pyskl - INFO - Epoch [43][500/1281] lr: 2.039e-02, eta: 15:40:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9950, loss_cls: 0.6177, loss: 0.6177 +2025-06-24 17:31:03,976 - pyskl - INFO - Epoch [43][600/1281] lr: 2.037e-02, eta: 15:40:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9925, loss_cls: 0.6201, loss: 0.6201 +2025-06-24 17:31:53,102 - pyskl - INFO - Epoch [43][700/1281] lr: 2.036e-02, eta: 15:40:09, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9956, loss_cls: 0.6225, loss: 0.6225 +2025-06-24 17:32:42,303 - pyskl - INFO - Epoch [43][800/1281] lr: 2.034e-02, eta: 15:39:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9950, loss_cls: 0.6382, loss: 0.6382 +2025-06-24 17:33:31,338 - pyskl - INFO - Epoch [43][900/1281] lr: 2.033e-02, eta: 15:39:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9975, loss_cls: 0.6179, loss: 0.6179 +2025-06-24 17:34:20,359 - pyskl - INFO - Epoch [43][1000/1281] lr: 2.031e-02, eta: 15:39:07, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9938, loss_cls: 0.6443, loss: 0.6443 +2025-06-24 17:35:09,762 - pyskl - INFO - Epoch [43][1100/1281] lr: 2.029e-02, eta: 15:38:46, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8706, top5_acc: 0.9906, loss_cls: 0.6850, loss: 0.6850 +2025-06-24 17:35:59,175 - pyskl - INFO - Epoch [43][1200/1281] lr: 2.028e-02, eta: 15:38:26, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8719, top5_acc: 0.9931, loss_cls: 0.6653, loss: 0.6653 +2025-06-24 17:36:39,535 - pyskl - INFO - Saving checkpoint at 43 epochs +2025-06-24 17:37:37,075 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:37:37,142 - pyskl - INFO - +top1_acc 0.8334 +top5_acc 0.9881 +2025-06-24 17:37:37,142 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:37:37,150 - pyskl - INFO - +mean_acc 0.7884 +2025-06-24 17:37:37,152 - pyskl - INFO - Epoch(val) [43][533] top1_acc: 0.8334, top5_acc: 0.9881, mean_class_accuracy: 0.7884 +2025-06-24 17:38:44,911 - pyskl - INFO - Epoch [44][100/1281] lr: 2.025e-02, eta: 15:36:56, time: 0.678, data_time: 0.197, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9969, loss_cls: 0.5684, loss: 0.5684 +2025-06-24 17:39:29,093 - pyskl - INFO - Epoch [44][200/1281] lr: 2.023e-02, eta: 15:36:22, time: 0.442, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9994, loss_cls: 0.5502, loss: 0.5502 +2025-06-24 17:40:18,261 - pyskl - INFO - Epoch [44][300/1281] lr: 2.022e-02, eta: 15:36:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9944, loss_cls: 0.5616, loss: 0.5616 +2025-06-24 17:41:07,712 - pyskl - INFO - Epoch [44][400/1281] lr: 2.020e-02, eta: 15:35:41, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8762, top5_acc: 0.9919, loss_cls: 0.6402, loss: 0.6402 +2025-06-24 17:41:57,371 - pyskl - INFO - Epoch [44][500/1281] lr: 2.018e-02, eta: 15:35:21, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.8775, top5_acc: 0.9938, loss_cls: 0.6373, loss: 0.6373 +2025-06-24 17:42:46,692 - pyskl - INFO - Epoch [44][600/1281] lr: 2.017e-02, eta: 15:35:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9938, loss_cls: 0.6325, loss: 0.6325 +2025-06-24 17:43:35,664 - pyskl - INFO - Epoch [44][700/1281] lr: 2.015e-02, eta: 15:34:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9950, loss_cls: 0.6335, loss: 0.6335 +2025-06-24 17:44:24,843 - pyskl - INFO - Epoch [44][800/1281] lr: 2.014e-02, eta: 15:34:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8694, top5_acc: 0.9919, loss_cls: 0.6925, loss: 0.6925 +2025-06-24 17:45:13,840 - pyskl - INFO - Epoch [44][900/1281] lr: 2.012e-02, eta: 15:33:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9962, loss_cls: 0.6284, loss: 0.6284 +2025-06-24 17:46:02,780 - pyskl - INFO - Epoch [44][1000/1281] lr: 2.010e-02, eta: 15:33:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9969, loss_cls: 0.5399, loss: 0.5399 +2025-06-24 17:46:51,755 - pyskl - INFO - Epoch [44][1100/1281] lr: 2.009e-02, eta: 15:33:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9975, loss_cls: 0.6410, loss: 0.6410 +2025-06-24 17:47:41,068 - pyskl - INFO - Epoch [44][1200/1281] lr: 2.007e-02, eta: 15:32:49, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9969, loss_cls: 0.5873, loss: 0.5873 +2025-06-24 17:48:21,637 - pyskl - INFO - Saving checkpoint at 44 epochs +2025-06-24 17:49:18,810 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 17:49:18,865 - pyskl - INFO - +top1_acc 0.8404 +top5_acc 0.9866 +2025-06-24 17:49:18,866 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 17:49:18,873 - pyskl - INFO - +mean_acc 0.7935 +2025-06-24 17:49:18,875 - pyskl - INFO - Epoch(val) [44][533] top1_acc: 0.8404, top5_acc: 0.9866, mean_class_accuracy: 0.7935 +2025-06-24 17:50:25,529 - pyskl - INFO - Epoch [45][100/1281] lr: 2.004e-02, eta: 15:31:15, time: 0.666, data_time: 0.201, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9969, loss_cls: 0.5489, loss: 0.5489 +2025-06-24 17:51:08,991 - pyskl - INFO - Epoch [45][200/1281] lr: 2.003e-02, eta: 15:30:39, time: 0.435, data_time: 0.000, memory: 4083, top1_acc: 0.8881, top5_acc: 0.9981, loss_cls: 0.5865, loss: 0.5865 +2025-06-24 17:51:58,363 - pyskl - INFO - Epoch [45][300/1281] lr: 2.001e-02, eta: 15:30:18, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9950, loss_cls: 0.5652, loss: 0.5652 +2025-06-24 17:52:47,811 - pyskl - INFO - Epoch [45][400/1281] lr: 1.999e-02, eta: 15:29:56, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9956, loss_cls: 0.5436, loss: 0.5436 +2025-06-24 17:53:37,231 - pyskl - INFO - Epoch [45][500/1281] lr: 1.998e-02, eta: 15:29:34, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9944, loss_cls: 0.6029, loss: 0.6029 +2025-06-24 17:54:26,484 - pyskl - INFO - Epoch [45][600/1281] lr: 1.996e-02, eta: 15:29:12, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9944, loss_cls: 0.6181, loss: 0.6181 +2025-06-24 17:55:15,371 - pyskl - INFO - Epoch [45][700/1281] lr: 1.994e-02, eta: 15:28:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9969, loss_cls: 0.5823, loss: 0.5823 +2025-06-24 17:56:04,784 - pyskl - INFO - Epoch [45][800/1281] lr: 1.993e-02, eta: 15:28:27, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9919, loss_cls: 0.6054, loss: 0.6054 +2025-06-24 17:56:54,044 - pyskl - INFO - Epoch [45][900/1281] lr: 1.991e-02, eta: 15:28:05, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9950, loss_cls: 0.6172, loss: 0.6172 +2025-06-24 17:57:42,723 - pyskl - INFO - Epoch [45][1000/1281] lr: 1.989e-02, eta: 15:27:41, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9944, loss_cls: 0.6371, loss: 0.6371 +2025-06-24 17:58:32,121 - pyskl - INFO - Epoch [45][1100/1281] lr: 1.988e-02, eta: 15:27:18, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9938, loss_cls: 0.6296, loss: 0.6296 +2025-06-24 17:59:21,325 - pyskl - INFO - Epoch [45][1200/1281] lr: 1.986e-02, eta: 15:26:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9950, loss_cls: 0.5836, loss: 0.5836 +2025-06-24 18:00:01,753 - pyskl - INFO - Saving checkpoint at 45 epochs +2025-06-24 18:00:59,302 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:00:59,360 - pyskl - INFO - +top1_acc 0.8206 +top5_acc 0.9890 +2025-06-24 18:00:59,360 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:00:59,367 - pyskl - INFO - +mean_acc 0.7687 +2025-06-24 18:00:59,369 - pyskl - INFO - Epoch(val) [45][533] top1_acc: 0.8206, top5_acc: 0.9890, mean_class_accuracy: 0.7687 +2025-06-24 18:02:06,872 - pyskl - INFO - Epoch [46][100/1281] lr: 1.983e-02, eta: 15:25:24, time: 0.675, data_time: 0.192, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9944, loss_cls: 0.5929, loss: 0.5929 +2025-06-24 18:02:49,541 - pyskl - INFO - Epoch [46][200/1281] lr: 1.981e-02, eta: 15:24:46, time: 0.427, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9962, loss_cls: 0.5855, loss: 0.5855 +2025-06-24 18:03:38,731 - pyskl - INFO - Epoch [46][300/1281] lr: 1.980e-02, eta: 15:24:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9944, loss_cls: 0.6142, loss: 0.6142 +2025-06-24 18:04:27,836 - pyskl - INFO - Epoch [46][400/1281] lr: 1.978e-02, eta: 15:23:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9938, loss_cls: 0.5885, loss: 0.5885 +2025-06-24 18:05:17,384 - pyskl - INFO - Epoch [46][500/1281] lr: 1.976e-02, eta: 15:23:37, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9975, loss_cls: 0.5222, loss: 0.5222 +2025-06-24 18:06:06,527 - pyskl - INFO - Epoch [46][600/1281] lr: 1.975e-02, eta: 15:23:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9944, loss_cls: 0.5540, loss: 0.5540 +2025-06-24 18:06:55,869 - pyskl - INFO - Epoch [46][700/1281] lr: 1.973e-02, eta: 15:22:50, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8869, top5_acc: 0.9956, loss_cls: 0.5843, loss: 0.5843 +2025-06-24 18:07:45,045 - pyskl - INFO - Epoch [46][800/1281] lr: 1.971e-02, eta: 15:22:27, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9938, loss_cls: 0.6064, loss: 0.6064 +2025-06-24 18:08:34,280 - pyskl - INFO - Epoch [46][900/1281] lr: 1.970e-02, eta: 15:22:03, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9944, loss_cls: 0.6077, loss: 0.6077 +2025-06-24 18:09:23,385 - pyskl - INFO - Epoch [46][1000/1281] lr: 1.968e-02, eta: 15:21:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9975, loss_cls: 0.5943, loss: 0.5943 +2025-06-24 18:10:12,569 - pyskl - INFO - Epoch [46][1100/1281] lr: 1.966e-02, eta: 15:21:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8712, top5_acc: 0.9956, loss_cls: 0.6323, loss: 0.6323 +2025-06-24 18:11:01,765 - pyskl - INFO - Epoch [46][1200/1281] lr: 1.965e-02, eta: 15:20:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9906, loss_cls: 0.6217, loss: 0.6217 +2025-06-24 18:11:42,023 - pyskl - INFO - Saving checkpoint at 46 epochs +2025-06-24 18:12:41,153 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:12:41,209 - pyskl - INFO - +top1_acc 0.8456 +top5_acc 0.9883 +2025-06-24 18:12:41,209 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:12:41,216 - pyskl - INFO - +mean_acc 0.7974 +2025-06-24 18:12:41,220 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_42.pth was removed +2025-06-24 18:12:41,397 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_46.pth. +2025-06-24 18:12:41,397 - pyskl - INFO - Best top1_acc is 0.8456 at 46 epoch. +2025-06-24 18:12:41,400 - pyskl - INFO - Epoch(val) [46][533] top1_acc: 0.8456, top5_acc: 0.9883, mean_class_accuracy: 0.7974 +2025-06-24 18:13:52,709 - pyskl - INFO - Epoch [47][100/1281] lr: 1.962e-02, eta: 15:19:28, time: 0.713, data_time: 0.198, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9956, loss_cls: 0.5158, loss: 0.5158 +2025-06-24 18:14:35,691 - pyskl - INFO - Epoch [47][200/1281] lr: 1.960e-02, eta: 15:18:50, time: 0.430, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9969, loss_cls: 0.5262, loss: 0.5262 +2025-06-24 18:15:24,659 - pyskl - INFO - Epoch [47][300/1281] lr: 1.958e-02, eta: 15:18:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9956, loss_cls: 0.5373, loss: 0.5373 +2025-06-24 18:16:13,591 - pyskl - INFO - Epoch [47][400/1281] lr: 1.957e-02, eta: 15:18:01, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9950, loss_cls: 0.6227, loss: 0.6227 +2025-06-24 18:17:02,912 - pyskl - INFO - Epoch [47][500/1281] lr: 1.955e-02, eta: 15:17:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9988, loss_cls: 0.5116, loss: 0.5116 +2025-06-24 18:17:51,938 - pyskl - INFO - Epoch [47][600/1281] lr: 1.953e-02, eta: 15:17:12, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9975, loss_cls: 0.5633, loss: 0.5633 +2025-06-24 18:18:40,899 - pyskl - INFO - Epoch [47][700/1281] lr: 1.952e-02, eta: 15:16:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9931, loss_cls: 0.5844, loss: 0.5844 +2025-06-24 18:19:30,049 - pyskl - INFO - Epoch [47][800/1281] lr: 1.950e-02, eta: 15:16:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9931, loss_cls: 0.6240, loss: 0.6240 +2025-06-24 18:20:19,175 - pyskl - INFO - Epoch [47][900/1281] lr: 1.948e-02, eta: 15:15:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9962, loss_cls: 0.5818, loss: 0.5818 +2025-06-24 18:21:08,402 - pyskl - INFO - Epoch [47][1000/1281] lr: 1.947e-02, eta: 15:15:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9969, loss_cls: 0.6132, loss: 0.6132 +2025-06-24 18:21:57,892 - pyskl - INFO - Epoch [47][1100/1281] lr: 1.945e-02, eta: 15:15:10, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8662, top5_acc: 0.9919, loss_cls: 0.6745, loss: 0.6745 +2025-06-24 18:22:46,893 - pyskl - INFO - Epoch [47][1200/1281] lr: 1.943e-02, eta: 15:14:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9969, loss_cls: 0.6022, loss: 0.6022 +2025-06-24 18:23:27,096 - pyskl - INFO - Saving checkpoint at 47 epochs +2025-06-24 18:24:25,022 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:24:25,089 - pyskl - INFO - +top1_acc 0.8395 +top5_acc 0.9812 +2025-06-24 18:24:25,089 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:24:25,097 - pyskl - INFO - +mean_acc 0.7951 +2025-06-24 18:24:25,099 - pyskl - INFO - Epoch(val) [47][533] top1_acc: 0.8395, top5_acc: 0.9812, mean_class_accuracy: 0.7951 +2025-06-24 18:25:33,000 - pyskl - INFO - Epoch [48][100/1281] lr: 1.940e-02, eta: 15:13:13, time: 0.679, data_time: 0.193, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9962, loss_cls: 0.5291, loss: 0.5291 +2025-06-24 18:26:16,674 - pyskl - INFO - Epoch [48][200/1281] lr: 1.938e-02, eta: 15:12:36, time: 0.437, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9969, loss_cls: 0.5049, loss: 0.5049 +2025-06-24 18:27:05,761 - pyskl - INFO - Epoch [48][300/1281] lr: 1.937e-02, eta: 15:12:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9975, loss_cls: 0.5318, loss: 0.5318 +2025-06-24 18:27:55,039 - pyskl - INFO - Epoch [48][400/1281] lr: 1.935e-02, eta: 15:11:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9950, loss_cls: 0.5809, loss: 0.5809 +2025-06-24 18:28:44,292 - pyskl - INFO - Epoch [48][500/1281] lr: 1.933e-02, eta: 15:11:21, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9975, loss_cls: 0.5308, loss: 0.5308 +2025-06-24 18:29:33,684 - pyskl - INFO - Epoch [48][600/1281] lr: 1.932e-02, eta: 15:10:56, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9950, loss_cls: 0.5585, loss: 0.5585 +2025-06-24 18:30:22,527 - pyskl - INFO - Epoch [48][700/1281] lr: 1.930e-02, eta: 15:10:30, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9950, loss_cls: 0.6128, loss: 0.6128 +2025-06-24 18:31:11,594 - pyskl - INFO - Epoch [48][800/1281] lr: 1.928e-02, eta: 15:10:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8806, top5_acc: 0.9931, loss_cls: 0.6152, loss: 0.6152 +2025-06-24 18:32:01,012 - pyskl - INFO - Epoch [48][900/1281] lr: 1.926e-02, eta: 15:09:40, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8831, top5_acc: 0.9919, loss_cls: 0.6401, loss: 0.6401 +2025-06-24 18:32:50,248 - pyskl - INFO - Epoch [48][1000/1281] lr: 1.925e-02, eta: 15:09:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8819, top5_acc: 0.9969, loss_cls: 0.5863, loss: 0.5863 +2025-06-24 18:33:39,487 - pyskl - INFO - Epoch [48][1100/1281] lr: 1.923e-02, eta: 15:08:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9938, loss_cls: 0.6163, loss: 0.6163 +2025-06-24 18:34:28,577 - pyskl - INFO - Epoch [48][1200/1281] lr: 1.921e-02, eta: 15:08:23, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.8812, top5_acc: 0.9969, loss_cls: 0.5882, loss: 0.5882 +2025-06-24 18:35:08,997 - pyskl - INFO - Saving checkpoint at 48 epochs +2025-06-24 18:36:06,193 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:36:06,260 - pyskl - INFO - +top1_acc 0.8411 +top5_acc 0.9899 +2025-06-24 18:36:06,261 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:36:06,269 - pyskl - INFO - +mean_acc 0.7894 +2025-06-24 18:36:06,271 - pyskl - INFO - Epoch(val) [48][533] top1_acc: 0.8411, top5_acc: 0.9899, mean_class_accuracy: 0.7894 +2025-06-24 18:37:13,166 - pyskl - INFO - Epoch [49][100/1281] lr: 1.918e-02, eta: 15:06:49, time: 0.669, data_time: 0.194, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9975, loss_cls: 0.5160, loss: 0.5160 +2025-06-24 18:37:56,887 - pyskl - INFO - Epoch [49][200/1281] lr: 1.916e-02, eta: 15:06:12, time: 0.437, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9950, loss_cls: 0.5315, loss: 0.5315 +2025-06-24 18:38:46,468 - pyskl - INFO - Epoch [49][300/1281] lr: 1.915e-02, eta: 15:05:47, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9956, loss_cls: 0.5849, loss: 0.5849 +2025-06-24 18:39:35,513 - pyskl - INFO - Epoch [49][400/1281] lr: 1.913e-02, eta: 15:05:21, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9975, loss_cls: 0.5274, loss: 0.5274 +2025-06-24 18:40:24,626 - pyskl - INFO - Epoch [49][500/1281] lr: 1.911e-02, eta: 15:04:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9975, loss_cls: 0.5159, loss: 0.5159 +2025-06-24 18:41:13,740 - pyskl - INFO - Epoch [49][600/1281] lr: 1.909e-02, eta: 15:04:28, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8788, top5_acc: 0.9956, loss_cls: 0.6101, loss: 0.6101 +2025-06-24 18:42:02,768 - pyskl - INFO - Epoch [49][700/1281] lr: 1.908e-02, eta: 15:04:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9938, loss_cls: 0.5846, loss: 0.5846 +2025-06-24 18:42:51,743 - pyskl - INFO - Epoch [49][800/1281] lr: 1.906e-02, eta: 15:03:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9950, loss_cls: 0.5520, loss: 0.5520 +2025-06-24 18:43:41,171 - pyskl - INFO - Epoch [49][900/1281] lr: 1.904e-02, eta: 15:03:09, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9950, loss_cls: 0.5856, loss: 0.5856 +2025-06-24 18:44:30,517 - pyskl - INFO - Epoch [49][1000/1281] lr: 1.902e-02, eta: 15:02:43, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9925, loss_cls: 0.6006, loss: 0.6006 +2025-06-24 18:45:19,635 - pyskl - INFO - Epoch [49][1100/1281] lr: 1.901e-02, eta: 15:02:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8844, top5_acc: 0.9944, loss_cls: 0.6138, loss: 0.6138 +2025-06-24 18:46:08,747 - pyskl - INFO - Epoch [49][1200/1281] lr: 1.899e-02, eta: 15:01:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8756, top5_acc: 0.9938, loss_cls: 0.6504, loss: 0.6504 +2025-06-24 18:46:49,178 - pyskl - INFO - Saving checkpoint at 49 epochs +2025-06-24 18:47:47,657 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:47:47,734 - pyskl - INFO - +top1_acc 0.8398 +top5_acc 0.9889 +2025-06-24 18:47:47,734 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:47:47,746 - pyskl - INFO - +mean_acc 0.7683 +2025-06-24 18:47:47,750 - pyskl - INFO - Epoch(val) [49][533] top1_acc: 0.8398, top5_acc: 0.9889, mean_class_accuracy: 0.7683 +2025-06-24 18:48:56,586 - pyskl - INFO - Epoch [50][100/1281] lr: 1.896e-02, eta: 15:00:21, time: 0.688, data_time: 0.198, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9956, loss_cls: 0.5421, loss: 0.5421 +2025-06-24 18:49:40,425 - pyskl - INFO - Epoch [50][200/1281] lr: 1.894e-02, eta: 14:59:43, time: 0.438, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9969, loss_cls: 0.5203, loss: 0.5203 +2025-06-24 18:50:29,483 - pyskl - INFO - Epoch [50][300/1281] lr: 1.892e-02, eta: 14:59:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9938, loss_cls: 0.5585, loss: 0.5585 +2025-06-24 18:51:18,553 - pyskl - INFO - Epoch [50][400/1281] lr: 1.891e-02, eta: 14:58:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9988, loss_cls: 0.5523, loss: 0.5523 +2025-06-24 18:52:07,174 - pyskl - INFO - Epoch [50][500/1281] lr: 1.889e-02, eta: 14:58:21, time: 0.486, data_time: 0.001, memory: 4083, top1_acc: 0.8681, top5_acc: 0.9950, loss_cls: 0.6408, loss: 0.6408 +2025-06-24 18:52:55,857 - pyskl - INFO - Epoch [50][600/1281] lr: 1.887e-02, eta: 14:57:53, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9981, loss_cls: 0.5642, loss: 0.5642 +2025-06-24 18:53:45,068 - pyskl - INFO - Epoch [50][700/1281] lr: 1.885e-02, eta: 14:57:26, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9981, loss_cls: 0.5319, loss: 0.5319 +2025-06-24 18:54:34,206 - pyskl - INFO - Epoch [50][800/1281] lr: 1.884e-02, eta: 14:56:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9962, loss_cls: 0.5672, loss: 0.5672 +2025-06-24 18:55:23,559 - pyskl - INFO - Epoch [50][900/1281] lr: 1.882e-02, eta: 14:56:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8688, top5_acc: 0.9988, loss_cls: 0.6227, loss: 0.6227 +2025-06-24 18:56:12,707 - pyskl - INFO - Epoch [50][1000/1281] lr: 1.880e-02, eta: 14:56:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9950, loss_cls: 0.5443, loss: 0.5443 +2025-06-24 18:57:01,829 - pyskl - INFO - Epoch [50][1100/1281] lr: 1.878e-02, eta: 14:55:38, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9925, loss_cls: 0.6048, loss: 0.6048 +2025-06-24 18:57:51,234 - pyskl - INFO - Epoch [50][1200/1281] lr: 1.876e-02, eta: 14:55:11, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8850, top5_acc: 0.9962, loss_cls: 0.5847, loss: 0.5847 +2025-06-24 18:58:31,599 - pyskl - INFO - Saving checkpoint at 50 epochs +2025-06-24 18:59:29,464 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 18:59:29,523 - pyskl - INFO - +top1_acc 0.8390 +top5_acc 0.9879 +2025-06-24 18:59:29,523 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 18:59:29,530 - pyskl - INFO - +mean_acc 0.7750 +2025-06-24 18:59:29,532 - pyskl - INFO - Epoch(val) [50][533] top1_acc: 0.8390, top5_acc: 0.9879, mean_class_accuracy: 0.7750 +2025-06-24 19:00:39,150 - pyskl - INFO - Epoch [51][100/1281] lr: 1.873e-02, eta: 14:53:43, time: 0.696, data_time: 0.186, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9975, loss_cls: 0.4652, loss: 0.4652 +2025-06-24 19:01:23,558 - pyskl - INFO - Epoch [51][200/1281] lr: 1.871e-02, eta: 14:53:06, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9975, loss_cls: 0.4595, loss: 0.4595 +2025-06-24 19:02:12,753 - pyskl - INFO - Epoch [51][300/1281] lr: 1.870e-02, eta: 14:52:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9988, loss_cls: 0.5104, loss: 0.5104 +2025-06-24 19:03:02,023 - pyskl - INFO - Epoch [51][400/1281] lr: 1.868e-02, eta: 14:52:11, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9956, loss_cls: 0.5891, loss: 0.5891 +2025-06-24 19:03:51,461 - pyskl - INFO - Epoch [51][500/1281] lr: 1.866e-02, eta: 14:51:44, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9912, loss_cls: 0.6681, loss: 0.6681 +2025-06-24 19:04:40,852 - pyskl - INFO - Epoch [51][600/1281] lr: 1.864e-02, eta: 14:51:17, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9944, loss_cls: 0.5283, loss: 0.5283 +2025-06-24 19:05:29,904 - pyskl - INFO - Epoch [51][700/1281] lr: 1.863e-02, eta: 14:50:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9962, loss_cls: 0.5349, loss: 0.5349 +2025-06-24 19:06:19,082 - pyskl - INFO - Epoch [51][800/1281] lr: 1.861e-02, eta: 14:50:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8825, top5_acc: 0.9931, loss_cls: 0.5844, loss: 0.5844 +2025-06-24 19:07:08,281 - pyskl - INFO - Epoch [51][900/1281] lr: 1.859e-02, eta: 14:49:53, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9962, loss_cls: 0.5481, loss: 0.5481 +2025-06-24 19:07:57,448 - pyskl - INFO - Epoch [51][1000/1281] lr: 1.857e-02, eta: 14:49:26, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9962, loss_cls: 0.5556, loss: 0.5556 +2025-06-24 19:08:46,551 - pyskl - INFO - Epoch [51][1100/1281] lr: 1.855e-02, eta: 14:48:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9962, loss_cls: 0.5577, loss: 0.5577 +2025-06-24 19:09:35,802 - pyskl - INFO - Epoch [51][1200/1281] lr: 1.854e-02, eta: 14:48:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8769, top5_acc: 0.9975, loss_cls: 0.5663, loss: 0.5663 +2025-06-24 19:10:16,400 - pyskl - INFO - Saving checkpoint at 51 epochs +2025-06-24 19:11:13,283 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:11:13,338 - pyskl - INFO - +top1_acc 0.8580 +top5_acc 0.9893 +2025-06-24 19:11:13,338 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:11:13,346 - pyskl - INFO - +mean_acc 0.8086 +2025-06-24 19:11:13,352 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_46.pth was removed +2025-06-24 19:11:13,543 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_51.pth. +2025-06-24 19:11:13,543 - pyskl - INFO - Best top1_acc is 0.8580 at 51 epoch. +2025-06-24 19:11:13,546 - pyskl - INFO - Epoch(val) [51][533] top1_acc: 0.8580, top5_acc: 0.9893, mean_class_accuracy: 0.8086 +2025-06-24 19:12:19,939 - pyskl - INFO - Epoch [52][100/1281] lr: 1.850e-02, eta: 14:46:55, time: 0.664, data_time: 0.192, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9969, loss_cls: 0.4787, loss: 0.4787 +2025-06-24 19:13:03,392 - pyskl - INFO - Epoch [52][200/1281] lr: 1.849e-02, eta: 14:46:16, time: 0.435, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4625, loss: 0.4625 +2025-06-24 19:13:52,373 - pyskl - INFO - Epoch [52][300/1281] lr: 1.847e-02, eta: 14:45:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9956, loss_cls: 0.4824, loss: 0.4824 +2025-06-24 19:14:41,656 - pyskl - INFO - Epoch [52][400/1281] lr: 1.845e-02, eta: 14:45:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9956, loss_cls: 0.4752, loss: 0.4752 +2025-06-24 19:15:31,229 - pyskl - INFO - Epoch [52][500/1281] lr: 1.843e-02, eta: 14:44:52, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9931, loss_cls: 0.5473, loss: 0.5473 +2025-06-24 19:16:20,469 - pyskl - INFO - Epoch [52][600/1281] lr: 1.841e-02, eta: 14:44:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9950, loss_cls: 0.5604, loss: 0.5604 +2025-06-24 19:17:09,791 - pyskl - INFO - Epoch [52][700/1281] lr: 1.840e-02, eta: 14:43:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9975, loss_cls: 0.4994, loss: 0.4994 +2025-06-24 19:17:58,846 - pyskl - INFO - Epoch [52][800/1281] lr: 1.838e-02, eta: 14:43:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9944, loss_cls: 0.5213, loss: 0.5213 +2025-06-24 19:18:47,895 - pyskl - INFO - Epoch [52][900/1281] lr: 1.836e-02, eta: 14:42:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9975, loss_cls: 0.5776, loss: 0.5776 +2025-06-24 19:19:37,430 - pyskl - INFO - Epoch [52][1000/1281] lr: 1.834e-02, eta: 14:42:30, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8838, top5_acc: 0.9956, loss_cls: 0.5590, loss: 0.5590 +2025-06-24 19:20:26,724 - pyskl - INFO - Epoch [52][1100/1281] lr: 1.832e-02, eta: 14:42:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8800, top5_acc: 0.9919, loss_cls: 0.6134, loss: 0.6134 +2025-06-24 19:21:15,906 - pyskl - INFO - Epoch [52][1200/1281] lr: 1.831e-02, eta: 14:41:33, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9956, loss_cls: 0.5599, loss: 0.5599 +2025-06-24 19:21:56,096 - pyskl - INFO - Saving checkpoint at 52 epochs +2025-06-24 19:22:54,081 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:22:54,148 - pyskl - INFO - +top1_acc 0.8433 +top5_acc 0.9846 +2025-06-24 19:22:54,148 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:22:54,156 - pyskl - INFO - +mean_acc 0.7922 +2025-06-24 19:22:54,158 - pyskl - INFO - Epoch(val) [52][533] top1_acc: 0.8433, top5_acc: 0.9846, mean_class_accuracy: 0.7922 +2025-06-24 19:24:03,680 - pyskl - INFO - Epoch [53][100/1281] lr: 1.827e-02, eta: 14:40:04, time: 0.695, data_time: 0.195, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9969, loss_cls: 0.4627, loss: 0.4627 +2025-06-24 19:24:47,215 - pyskl - INFO - Epoch [53][200/1281] lr: 1.826e-02, eta: 14:39:24, time: 0.435, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9962, loss_cls: 0.4575, loss: 0.4575 +2025-06-24 19:25:36,416 - pyskl - INFO - Epoch [53][300/1281] lr: 1.824e-02, eta: 14:38:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9981, loss_cls: 0.5017, loss: 0.5017 +2025-06-24 19:26:26,014 - pyskl - INFO - Epoch [53][400/1281] lr: 1.822e-02, eta: 14:38:27, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9981, loss_cls: 0.5595, loss: 0.5595 +2025-06-24 19:27:15,156 - pyskl - INFO - Epoch [53][500/1281] lr: 1.820e-02, eta: 14:37:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9944, loss_cls: 0.5457, loss: 0.5457 +2025-06-24 19:28:04,479 - pyskl - INFO - Epoch [53][600/1281] lr: 1.818e-02, eta: 14:37:30, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9931, loss_cls: 0.6046, loss: 0.6046 +2025-06-24 19:28:53,808 - pyskl - INFO - Epoch [53][700/1281] lr: 1.816e-02, eta: 14:37:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8975, top5_acc: 0.9969, loss_cls: 0.5471, loss: 0.5471 +2025-06-24 19:29:43,353 - pyskl - INFO - Epoch [53][800/1281] lr: 1.815e-02, eta: 14:36:32, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9956, loss_cls: 0.5603, loss: 0.5603 +2025-06-24 19:30:32,744 - pyskl - INFO - Epoch [53][900/1281] lr: 1.813e-02, eta: 14:36:04, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9950, loss_cls: 0.5450, loss: 0.5450 +2025-06-24 19:31:21,890 - pyskl - INFO - Epoch [53][1000/1281] lr: 1.811e-02, eta: 14:35:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8794, top5_acc: 0.9931, loss_cls: 0.5926, loss: 0.5926 +2025-06-24 19:32:11,098 - pyskl - INFO - Epoch [53][1100/1281] lr: 1.809e-02, eta: 14:35:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9938, loss_cls: 0.5609, loss: 0.5609 +2025-06-24 19:33:00,360 - pyskl - INFO - Epoch [53][1200/1281] lr: 1.807e-02, eta: 14:34:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8744, top5_acc: 0.9956, loss_cls: 0.6303, loss: 0.6303 +2025-06-24 19:33:40,801 - pyskl - INFO - Saving checkpoint at 53 epochs +2025-06-24 19:34:38,619 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:34:38,679 - pyskl - INFO - +top1_acc 0.8499 +top5_acc 0.9870 +2025-06-24 19:34:38,679 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:34:38,687 - pyskl - INFO - +mean_acc 0.8036 +2025-06-24 19:34:38,690 - pyskl - INFO - Epoch(val) [53][533] top1_acc: 0.8499, top5_acc: 0.9870, mean_class_accuracy: 0.8036 +2025-06-24 19:35:47,885 - pyskl - INFO - Epoch [54][100/1281] lr: 1.804e-02, eta: 14:33:06, time: 0.692, data_time: 0.196, memory: 4083, top1_acc: 0.8944, top5_acc: 0.9962, loss_cls: 0.5196, loss: 0.5196 +2025-06-24 19:36:32,984 - pyskl - INFO - Epoch [54][200/1281] lr: 1.802e-02, eta: 14:32:29, time: 0.451, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9962, loss_cls: 0.5309, loss: 0.5309 +2025-06-24 19:37:22,362 - pyskl - INFO - Epoch [54][300/1281] lr: 1.800e-02, eta: 14:32:00, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9950, loss_cls: 0.5210, loss: 0.5210 +2025-06-24 19:38:11,511 - pyskl - INFO - Epoch [54][400/1281] lr: 1.798e-02, eta: 14:31:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9981, loss_cls: 0.4760, loss: 0.4760 +2025-06-24 19:39:00,331 - pyskl - INFO - Epoch [54][500/1281] lr: 1.797e-02, eta: 14:31:00, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9962, loss_cls: 0.4679, loss: 0.4679 +2025-06-24 19:39:49,460 - pyskl - INFO - Epoch [54][600/1281] lr: 1.795e-02, eta: 14:30:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9950, loss_cls: 0.5126, loss: 0.5126 +2025-06-24 19:40:38,547 - pyskl - INFO - Epoch [54][700/1281] lr: 1.793e-02, eta: 14:30:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9950, loss_cls: 0.5526, loss: 0.5526 +2025-06-24 19:41:28,033 - pyskl - INFO - Epoch [54][800/1281] lr: 1.791e-02, eta: 14:29:31, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9975, loss_cls: 0.5541, loss: 0.5541 +2025-06-24 19:42:17,289 - pyskl - INFO - Epoch [54][900/1281] lr: 1.789e-02, eta: 14:29:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8925, top5_acc: 0.9975, loss_cls: 0.5454, loss: 0.5454 +2025-06-24 19:43:06,414 - pyskl - INFO - Epoch [54][1000/1281] lr: 1.787e-02, eta: 14:28:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8781, top5_acc: 0.9925, loss_cls: 0.6138, loss: 0.6138 +2025-06-24 19:43:55,648 - pyskl - INFO - Epoch [54][1100/1281] lr: 1.786e-02, eta: 14:28:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9956, loss_cls: 0.5341, loss: 0.5341 +2025-06-24 19:44:44,833 - pyskl - INFO - Epoch [54][1200/1281] lr: 1.784e-02, eta: 14:27:32, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8862, top5_acc: 0.9981, loss_cls: 0.5821, loss: 0.5821 +2025-06-24 19:45:24,914 - pyskl - INFO - Saving checkpoint at 54 epochs +2025-06-24 19:46:19,950 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:46:20,005 - pyskl - INFO - +top1_acc 0.8460 +top5_acc 0.9883 +2025-06-24 19:46:20,005 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:46:20,012 - pyskl - INFO - +mean_acc 0.7867 +2025-06-24 19:46:20,014 - pyskl - INFO - Epoch(val) [54][533] top1_acc: 0.8460, top5_acc: 0.9883, mean_class_accuracy: 0.7867 +2025-06-24 19:47:22,810 - pyskl - INFO - Epoch [55][100/1281] lr: 1.780e-02, eta: 14:25:51, time: 0.628, data_time: 0.196, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9981, loss_cls: 0.5083, loss: 0.5083 +2025-06-24 19:48:06,922 - pyskl - INFO - Epoch [55][200/1281] lr: 1.779e-02, eta: 14:25:12, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9962, loss_cls: 0.4636, loss: 0.4636 +2025-06-24 19:48:56,008 - pyskl - INFO - Epoch [55][300/1281] lr: 1.777e-02, eta: 14:24:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.5465, loss: 0.5465 +2025-06-24 19:49:45,060 - pyskl - INFO - Epoch [55][400/1281] lr: 1.775e-02, eta: 14:24:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9981, loss_cls: 0.4801, loss: 0.4801 +2025-06-24 19:50:34,192 - pyskl - INFO - Epoch [55][500/1281] lr: 1.773e-02, eta: 14:23:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9962, loss_cls: 0.4856, loss: 0.4856 +2025-06-24 19:51:23,281 - pyskl - INFO - Epoch [55][600/1281] lr: 1.771e-02, eta: 14:23:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9994, loss_cls: 0.4852, loss: 0.4852 +2025-06-24 19:52:12,352 - pyskl - INFO - Epoch [55][700/1281] lr: 1.769e-02, eta: 14:22:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8956, top5_acc: 0.9981, loss_cls: 0.5193, loss: 0.5193 +2025-06-24 19:53:01,530 - pyskl - INFO - Epoch [55][800/1281] lr: 1.767e-02, eta: 14:22:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9944, loss_cls: 0.5256, loss: 0.5256 +2025-06-24 19:53:50,605 - pyskl - INFO - Epoch [55][900/1281] lr: 1.766e-02, eta: 14:21:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9938, loss_cls: 0.5545, loss: 0.5545 +2025-06-24 19:54:39,699 - pyskl - INFO - Epoch [55][1000/1281] lr: 1.764e-02, eta: 14:21:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9962, loss_cls: 0.5055, loss: 0.5055 +2025-06-24 19:55:28,931 - pyskl - INFO - Epoch [55][1100/1281] lr: 1.762e-02, eta: 14:20:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8994, top5_acc: 0.9944, loss_cls: 0.5599, loss: 0.5599 +2025-06-24 19:56:18,217 - pyskl - INFO - Epoch [55][1200/1281] lr: 1.760e-02, eta: 14:20:07, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9969, loss_cls: 0.5151, loss: 0.5151 +2025-06-24 19:56:58,943 - pyskl - INFO - Saving checkpoint at 55 epochs +2025-06-24 19:57:55,471 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 19:57:55,533 - pyskl - INFO - +top1_acc 0.8187 +top5_acc 0.9842 +2025-06-24 19:57:55,533 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 19:57:55,541 - pyskl - INFO - +mean_acc 0.7834 +2025-06-24 19:57:55,543 - pyskl - INFO - Epoch(val) [55][533] top1_acc: 0.8187, top5_acc: 0.9842, mean_class_accuracy: 0.7834 +2025-06-24 19:59:01,821 - pyskl - INFO - Epoch [56][100/1281] lr: 1.757e-02, eta: 14:18:33, time: 0.663, data_time: 0.195, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9969, loss_cls: 0.5530, loss: 0.5530 +2025-06-24 19:59:46,046 - pyskl - INFO - Epoch [56][200/1281] lr: 1.755e-02, eta: 14:17:53, time: 0.442, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9962, loss_cls: 0.5219, loss: 0.5219 +2025-06-24 20:00:35,462 - pyskl - INFO - Epoch [56][300/1281] lr: 1.753e-02, eta: 14:17:23, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9975, loss_cls: 0.4493, loss: 0.4493 +2025-06-24 20:01:24,560 - pyskl - INFO - Epoch [56][400/1281] lr: 1.751e-02, eta: 14:16:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9962, loss_cls: 0.4758, loss: 0.4758 +2025-06-24 20:02:13,709 - pyskl - INFO - Epoch [56][500/1281] lr: 1.749e-02, eta: 14:16:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9962, loss_cls: 0.5059, loss: 0.5059 +2025-06-24 20:03:02,413 - pyskl - INFO - Epoch [56][600/1281] lr: 1.747e-02, eta: 14:15:50, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9962, loss_cls: 0.5389, loss: 0.5389 +2025-06-24 20:03:51,696 - pyskl - INFO - Epoch [56][700/1281] lr: 1.745e-02, eta: 14:15:19, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9969, loss_cls: 0.4697, loss: 0.4697 +2025-06-24 20:04:40,460 - pyskl - INFO - Epoch [56][800/1281] lr: 1.743e-02, eta: 14:14:47, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9962, loss_cls: 0.4967, loss: 0.4967 +2025-06-24 20:05:29,763 - pyskl - INFO - Epoch [56][900/1281] lr: 1.742e-02, eta: 14:14:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9938, loss_cls: 0.5352, loss: 0.5352 +2025-06-24 20:06:18,874 - pyskl - INFO - Epoch [56][1000/1281] lr: 1.740e-02, eta: 14:13:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9969, loss_cls: 0.5498, loss: 0.5498 +2025-06-24 20:07:07,864 - pyskl - INFO - Epoch [56][1100/1281] lr: 1.738e-02, eta: 14:13:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9956, loss_cls: 0.5544, loss: 0.5544 +2025-06-24 20:07:57,099 - pyskl - INFO - Epoch [56][1200/1281] lr: 1.736e-02, eta: 14:12:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8888, top5_acc: 0.9950, loss_cls: 0.5854, loss: 0.5854 +2025-06-24 20:08:37,666 - pyskl - INFO - Saving checkpoint at 56 epochs +2025-06-24 20:09:34,068 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:09:34,124 - pyskl - INFO - +top1_acc 0.8598 +top5_acc 0.9892 +2025-06-24 20:09:34,124 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:09:34,131 - pyskl - INFO - +mean_acc 0.8186 +2025-06-24 20:09:34,135 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_51.pth was removed +2025-06-24 20:09:34,304 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_56.pth. +2025-06-24 20:09:34,304 - pyskl - INFO - Best top1_acc is 0.8598 at 56 epoch. +2025-06-24 20:09:34,307 - pyskl - INFO - Epoch(val) [56][533] top1_acc: 0.8598, top5_acc: 0.9892, mean_class_accuracy: 0.8186 +2025-06-24 20:10:39,621 - pyskl - INFO - Epoch [57][100/1281] lr: 1.733e-02, eta: 14:11:07, time: 0.653, data_time: 0.193, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9962, loss_cls: 0.4714, loss: 0.4714 +2025-06-24 20:11:24,751 - pyskl - INFO - Epoch [57][200/1281] lr: 1.731e-02, eta: 14:10:29, time: 0.451, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9950, loss_cls: 0.4367, loss: 0.4367 +2025-06-24 20:12:14,020 - pyskl - INFO - Epoch [57][300/1281] lr: 1.729e-02, eta: 14:09:58, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9975, loss_cls: 0.4349, loss: 0.4349 +2025-06-24 20:13:03,388 - pyskl - INFO - Epoch [57][400/1281] lr: 1.727e-02, eta: 14:09:27, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9950, loss_cls: 0.5121, loss: 0.5121 +2025-06-24 20:13:52,699 - pyskl - INFO - Epoch [57][500/1281] lr: 1.725e-02, eta: 14:08:56, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9019, top5_acc: 0.9988, loss_cls: 0.5250, loss: 0.5250 +2025-06-24 20:14:41,873 - pyskl - INFO - Epoch [57][600/1281] lr: 1.723e-02, eta: 14:08:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9944, loss_cls: 0.5542, loss: 0.5542 +2025-06-24 20:15:30,853 - pyskl - INFO - Epoch [57][700/1281] lr: 1.721e-02, eta: 14:07:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9950, loss_cls: 0.5918, loss: 0.5918 +2025-06-24 20:16:20,030 - pyskl - INFO - Epoch [57][800/1281] lr: 1.719e-02, eta: 14:07:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8950, top5_acc: 0.9956, loss_cls: 0.5757, loss: 0.5757 +2025-06-24 20:17:09,145 - pyskl - INFO - Epoch [57][900/1281] lr: 1.717e-02, eta: 14:06:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9981, loss_cls: 0.4813, loss: 0.4813 +2025-06-24 20:17:58,411 - pyskl - INFO - Epoch [57][1000/1281] lr: 1.716e-02, eta: 14:06:18, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9981, loss_cls: 0.5257, loss: 0.5257 +2025-06-24 20:18:47,458 - pyskl - INFO - Epoch [57][1100/1281] lr: 1.714e-02, eta: 14:05:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9962, loss_cls: 0.5339, loss: 0.5339 +2025-06-24 20:19:36,473 - pyskl - INFO - Epoch [57][1200/1281] lr: 1.712e-02, eta: 14:05:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9969, loss_cls: 0.4916, loss: 0.4916 +2025-06-24 20:20:16,809 - pyskl - INFO - Saving checkpoint at 57 epochs +2025-06-24 20:21:11,580 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:21:11,642 - pyskl - INFO - +top1_acc 0.8386 +top5_acc 0.9864 +2025-06-24 20:21:11,642 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:21:11,649 - pyskl - INFO - +mean_acc 0.7791 +2025-06-24 20:21:11,651 - pyskl - INFO - Epoch(val) [57][533] top1_acc: 0.8386, top5_acc: 0.9864, mean_class_accuracy: 0.7791 +2025-06-24 20:22:13,939 - pyskl - INFO - Epoch [58][100/1281] lr: 1.708e-02, eta: 14:03:33, time: 0.623, data_time: 0.188, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9988, loss_cls: 0.4748, loss: 0.4748 +2025-06-24 20:22:58,353 - pyskl - INFO - Epoch [58][200/1281] lr: 1.706e-02, eta: 14:02:54, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9994, loss_cls: 0.3964, loss: 0.3964 +2025-06-24 20:23:47,529 - pyskl - INFO - Epoch [58][300/1281] lr: 1.704e-02, eta: 14:02:22, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9981, loss_cls: 0.4477, loss: 0.4477 +2025-06-24 20:24:36,543 - pyskl - INFO - Epoch [58][400/1281] lr: 1.703e-02, eta: 14:01:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9981, loss_cls: 0.4398, loss: 0.4398 +2025-06-24 20:25:25,740 - pyskl - INFO - Epoch [58][500/1281] lr: 1.701e-02, eta: 14:01:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9938, loss_cls: 0.5289, loss: 0.5289 +2025-06-24 20:26:14,631 - pyskl - INFO - Epoch [58][600/1281] lr: 1.699e-02, eta: 14:00:46, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9012, top5_acc: 0.9950, loss_cls: 0.5645, loss: 0.5645 +2025-06-24 20:27:03,745 - pyskl - INFO - Epoch [58][700/1281] lr: 1.697e-02, eta: 14:00:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9975, loss_cls: 0.5429, loss: 0.5429 +2025-06-24 20:27:53,165 - pyskl - INFO - Epoch [58][800/1281] lr: 1.695e-02, eta: 13:59:42, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9938, loss_cls: 0.5411, loss: 0.5411 +2025-06-24 20:28:42,329 - pyskl - INFO - Epoch [58][900/1281] lr: 1.693e-02, eta: 13:59:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9969, loss_cls: 0.5550, loss: 0.5550 +2025-06-24 20:29:31,718 - pyskl - INFO - Epoch [58][1000/1281] lr: 1.691e-02, eta: 13:58:39, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8931, top5_acc: 0.9962, loss_cls: 0.5585, loss: 0.5585 +2025-06-24 20:30:20,832 - pyskl - INFO - Epoch [58][1100/1281] lr: 1.689e-02, eta: 13:58:06, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9950, loss_cls: 0.5354, loss: 0.5354 +2025-06-24 20:31:09,949 - pyskl - INFO - Epoch [58][1200/1281] lr: 1.687e-02, eta: 13:57:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8894, top5_acc: 0.9956, loss_cls: 0.5604, loss: 0.5604 +2025-06-24 20:31:50,540 - pyskl - INFO - Saving checkpoint at 58 epochs +2025-06-24 20:32:46,995 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:32:47,054 - pyskl - INFO - +top1_acc 0.8574 +top5_acc 0.9885 +2025-06-24 20:32:47,054 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:32:47,065 - pyskl - INFO - +mean_acc 0.8223 +2025-06-24 20:32:47,067 - pyskl - INFO - Epoch(val) [58][533] top1_acc: 0.8574, top5_acc: 0.9885, mean_class_accuracy: 0.8223 +2025-06-24 20:33:52,083 - pyskl - INFO - Epoch [59][100/1281] lr: 1.684e-02, eta: 13:55:58, time: 0.650, data_time: 0.193, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9956, loss_cls: 0.4655, loss: 0.4655 +2025-06-24 20:34:35,156 - pyskl - INFO - Epoch [59][200/1281] lr: 1.682e-02, eta: 13:55:16, time: 0.431, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9975, loss_cls: 0.4223, loss: 0.4223 +2025-06-24 20:35:24,354 - pyskl - INFO - Epoch [59][300/1281] lr: 1.680e-02, eta: 13:54:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9969, loss_cls: 0.4820, loss: 0.4820 +2025-06-24 20:36:13,412 - pyskl - INFO - Epoch [59][400/1281] lr: 1.678e-02, eta: 13:54:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9988, loss_cls: 0.5269, loss: 0.5269 +2025-06-24 20:37:02,641 - pyskl - INFO - Epoch [59][500/1281] lr: 1.676e-02, eta: 13:53:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9962, loss_cls: 0.4832, loss: 0.4832 +2025-06-24 20:37:51,772 - pyskl - INFO - Epoch [59][600/1281] lr: 1.674e-02, eta: 13:53:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9962, loss_cls: 0.5301, loss: 0.5301 +2025-06-24 20:38:40,650 - pyskl - INFO - Epoch [59][700/1281] lr: 1.672e-02, eta: 13:52:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9969, loss_cls: 0.5051, loss: 0.5051 +2025-06-24 20:39:30,103 - pyskl - INFO - Epoch [59][800/1281] lr: 1.670e-02, eta: 13:52:02, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9969, loss_cls: 0.5212, loss: 0.5212 +2025-06-24 20:40:19,269 - pyskl - INFO - Epoch [59][900/1281] lr: 1.668e-02, eta: 13:51:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9006, top5_acc: 0.9981, loss_cls: 0.4811, loss: 0.4811 +2025-06-24 20:41:08,540 - pyskl - INFO - Epoch [59][1000/1281] lr: 1.667e-02, eta: 13:50:57, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9975, loss_cls: 0.5162, loss: 0.5162 +2025-06-24 20:41:57,850 - pyskl - INFO - Epoch [59][1100/1281] lr: 1.665e-02, eta: 13:50:24, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9975, loss_cls: 0.5223, loss: 0.5223 +2025-06-24 20:42:47,135 - pyskl - INFO - Epoch [59][1200/1281] lr: 1.663e-02, eta: 13:49:52, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8900, top5_acc: 0.9962, loss_cls: 0.5580, loss: 0.5580 +2025-06-24 20:43:27,650 - pyskl - INFO - Saving checkpoint at 59 epochs +2025-06-24 20:44:25,949 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:44:26,011 - pyskl - INFO - +top1_acc 0.8533 +top5_acc 0.9876 +2025-06-24 20:44:26,011 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:44:26,022 - pyskl - INFO - +mean_acc 0.8050 +2025-06-24 20:44:26,029 - pyskl - INFO - Epoch(val) [59][533] top1_acc: 0.8533, top5_acc: 0.9876, mean_class_accuracy: 0.8050 +2025-06-24 20:45:34,140 - pyskl - INFO - Epoch [60][100/1281] lr: 1.659e-02, eta: 13:48:21, time: 0.681, data_time: 0.189, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9962, loss_cls: 0.4540, loss: 0.4540 +2025-06-24 20:46:18,633 - pyskl - INFO - Epoch [60][200/1281] lr: 1.657e-02, eta: 13:47:41, time: 0.445, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9975, loss_cls: 0.4291, loss: 0.4291 +2025-06-24 20:47:07,863 - pyskl - INFO - Epoch [60][300/1281] lr: 1.655e-02, eta: 13:47:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9956, loss_cls: 0.4572, loss: 0.4572 +2025-06-24 20:47:56,750 - pyskl - INFO - Epoch [60][400/1281] lr: 1.653e-02, eta: 13:46:35, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9044, top5_acc: 0.9969, loss_cls: 0.5121, loss: 0.5121 +2025-06-24 20:48:45,757 - pyskl - INFO - Epoch [60][500/1281] lr: 1.651e-02, eta: 13:46:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9962, loss_cls: 0.4844, loss: 0.4844 +2025-06-24 20:49:34,604 - pyskl - INFO - Epoch [60][600/1281] lr: 1.650e-02, eta: 13:45:29, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9962, loss_cls: 0.4946, loss: 0.4946 +2025-06-24 20:50:23,350 - pyskl - INFO - Epoch [60][700/1281] lr: 1.648e-02, eta: 13:44:55, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9975, loss_cls: 0.5132, loss: 0.5132 +2025-06-24 20:51:12,255 - pyskl - INFO - Epoch [60][800/1281] lr: 1.646e-02, eta: 13:44:22, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9969, loss_cls: 0.4765, loss: 0.4765 +2025-06-24 20:52:01,089 - pyskl - INFO - Epoch [60][900/1281] lr: 1.644e-02, eta: 13:43:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.8988, top5_acc: 0.9981, loss_cls: 0.5089, loss: 0.5089 +2025-06-24 20:52:50,085 - pyskl - INFO - Epoch [60][1000/1281] lr: 1.642e-02, eta: 13:43:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9000, top5_acc: 0.9962, loss_cls: 0.5043, loss: 0.5043 +2025-06-24 20:53:39,093 - pyskl - INFO - Epoch [60][1100/1281] lr: 1.640e-02, eta: 13:42:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8906, top5_acc: 0.9962, loss_cls: 0.5945, loss: 0.5945 +2025-06-24 20:54:28,419 - pyskl - INFO - Epoch [60][1200/1281] lr: 1.638e-02, eta: 13:42:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9975, loss_cls: 0.4831, loss: 0.4831 +2025-06-24 20:55:08,722 - pyskl - INFO - Saving checkpoint at 60 epochs +2025-06-24 20:56:05,589 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 20:56:05,644 - pyskl - INFO - +top1_acc 0.8473 +top5_acc 0.9881 +2025-06-24 20:56:05,644 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 20:56:05,650 - pyskl - INFO - +mean_acc 0.8050 +2025-06-24 20:56:05,652 - pyskl - INFO - Epoch(val) [60][533] top1_acc: 0.8473, top5_acc: 0.9881, mean_class_accuracy: 0.8050 +2025-06-24 20:57:11,706 - pyskl - INFO - Epoch [61][100/1281] lr: 1.634e-02, eta: 13:40:34, time: 0.661, data_time: 0.193, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9956, loss_cls: 0.4660, loss: 0.4660 +2025-06-24 20:57:55,581 - pyskl - INFO - Epoch [61][200/1281] lr: 1.632e-02, eta: 13:39:53, time: 0.439, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9975, loss_cls: 0.4071, loss: 0.4071 +2025-06-24 20:58:44,779 - pyskl - INFO - Epoch [61][300/1281] lr: 1.630e-02, eta: 13:39:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9994, loss_cls: 0.4219, loss: 0.4219 +2025-06-24 20:59:34,074 - pyskl - INFO - Epoch [61][400/1281] lr: 1.629e-02, eta: 13:38:47, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9962, loss_cls: 0.4377, loss: 0.4377 +2025-06-24 21:00:23,302 - pyskl - INFO - Epoch [61][500/1281] lr: 1.627e-02, eta: 13:38:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9975, loss_cls: 0.4524, loss: 0.4524 +2025-06-24 21:01:12,640 - pyskl - INFO - Epoch [61][600/1281] lr: 1.625e-02, eta: 13:37:41, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9969, loss_cls: 0.5143, loss: 0.5143 +2025-06-24 21:02:02,042 - pyskl - INFO - Epoch [61][700/1281] lr: 1.623e-02, eta: 13:37:08, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9962, loss_cls: 0.5091, loss: 0.5091 +2025-06-24 21:02:51,239 - pyskl - INFO - Epoch [61][800/1281] lr: 1.621e-02, eta: 13:36:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9994, loss_cls: 0.4534, loss: 0.4534 +2025-06-24 21:03:40,586 - pyskl - INFO - Epoch [61][900/1281] lr: 1.619e-02, eta: 13:36:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8938, top5_acc: 0.9950, loss_cls: 0.5429, loss: 0.5429 +2025-06-24 21:04:29,867 - pyskl - INFO - Epoch [61][1000/1281] lr: 1.617e-02, eta: 13:35:28, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9069, top5_acc: 0.9969, loss_cls: 0.4876, loss: 0.4876 +2025-06-24 21:05:19,173 - pyskl - INFO - Epoch [61][1100/1281] lr: 1.615e-02, eta: 13:34:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9975, loss_cls: 0.4870, loss: 0.4870 +2025-06-24 21:06:08,684 - pyskl - INFO - Epoch [61][1200/1281] lr: 1.613e-02, eta: 13:34:22, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9988, loss_cls: 0.4684, loss: 0.4684 +2025-06-24 21:06:49,137 - pyskl - INFO - Saving checkpoint at 61 epochs +2025-06-24 21:07:46,078 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:07:46,132 - pyskl - INFO - +top1_acc 0.8670 +top5_acc 0.9885 +2025-06-24 21:07:46,132 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:07:46,140 - pyskl - INFO - +mean_acc 0.8269 +2025-06-24 21:07:46,144 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_56.pth was removed +2025-06-24 21:07:46,326 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_61.pth. +2025-06-24 21:07:46,327 - pyskl - INFO - Best top1_acc is 0.8670 at 61 epoch. +2025-06-24 21:07:46,329 - pyskl - INFO - Epoch(val) [61][533] top1_acc: 0.8670, top5_acc: 0.9885, mean_class_accuracy: 0.8269 +2025-06-24 21:08:51,605 - pyskl - INFO - Epoch [62][100/1281] lr: 1.609e-02, eta: 13:32:46, time: 0.653, data_time: 0.192, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9994, loss_cls: 0.4527, loss: 0.4527 +2025-06-24 21:09:34,875 - pyskl - INFO - Epoch [62][200/1281] lr: 1.607e-02, eta: 13:32:04, time: 0.433, data_time: 0.000, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9969, loss_cls: 0.4557, loss: 0.4557 +2025-06-24 21:10:24,209 - pyskl - INFO - Epoch [62][300/1281] lr: 1.605e-02, eta: 13:31:31, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 1.0000, loss_cls: 0.3972, loss: 0.3972 +2025-06-24 21:11:13,388 - pyskl - INFO - Epoch [62][400/1281] lr: 1.603e-02, eta: 13:30:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9975, loss_cls: 0.3903, loss: 0.3903 +2025-06-24 21:12:02,565 - pyskl - INFO - Epoch [62][500/1281] lr: 1.602e-02, eta: 13:30:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9981, loss_cls: 0.4503, loss: 0.4503 +2025-06-24 21:12:51,442 - pyskl - INFO - Epoch [62][600/1281] lr: 1.600e-02, eta: 13:29:50, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9981, loss_cls: 0.4773, loss: 0.4773 +2025-06-24 21:13:40,555 - pyskl - INFO - Epoch [62][700/1281] lr: 1.598e-02, eta: 13:29:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9975, loss_cls: 0.4954, loss: 0.4954 +2025-06-24 21:14:30,013 - pyskl - INFO - Epoch [62][800/1281] lr: 1.596e-02, eta: 13:28:42, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.8962, top5_acc: 0.9969, loss_cls: 0.5222, loss: 0.5222 +2025-06-24 21:15:19,277 - pyskl - INFO - Epoch [62][900/1281] lr: 1.594e-02, eta: 13:28:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9950, loss_cls: 0.4787, loss: 0.4787 +2025-06-24 21:16:08,832 - pyskl - INFO - Epoch [62][1000/1281] lr: 1.592e-02, eta: 13:27:35, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.8912, top5_acc: 0.9981, loss_cls: 0.5388, loss: 0.5388 +2025-06-24 21:16:58,054 - pyskl - INFO - Epoch [62][1100/1281] lr: 1.590e-02, eta: 13:27:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.8919, top5_acc: 0.9931, loss_cls: 0.5519, loss: 0.5519 +2025-06-24 21:17:47,392 - pyskl - INFO - Epoch [62][1200/1281] lr: 1.588e-02, eta: 13:26:28, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.8969, top5_acc: 0.9962, loss_cls: 0.5273, loss: 0.5273 +2025-06-24 21:18:27,781 - pyskl - INFO - Saving checkpoint at 62 epochs +2025-06-24 21:19:25,300 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:19:25,373 - pyskl - INFO - +top1_acc 0.8636 +top5_acc 0.9914 +2025-06-24 21:19:25,373 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:19:25,381 - pyskl - INFO - +mean_acc 0.8250 +2025-06-24 21:19:25,382 - pyskl - INFO - Epoch(val) [62][533] top1_acc: 0.8636, top5_acc: 0.9914, mean_class_accuracy: 0.8250 +2025-06-24 21:20:32,485 - pyskl - INFO - Epoch [63][100/1281] lr: 1.584e-02, eta: 13:24:55, time: 0.671, data_time: 0.194, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9975, loss_cls: 0.4469, loss: 0.4469 +2025-06-24 21:21:16,494 - pyskl - INFO - Epoch [63][200/1281] lr: 1.582e-02, eta: 13:24:14, time: 0.440, data_time: 0.001, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9988, loss_cls: 0.4116, loss: 0.4116 +2025-06-24 21:22:05,914 - pyskl - INFO - Epoch [63][300/1281] lr: 1.580e-02, eta: 13:23:40, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9981, loss_cls: 0.4088, loss: 0.4088 +2025-06-24 21:22:55,348 - pyskl - INFO - Epoch [63][400/1281] lr: 1.578e-02, eta: 13:23:07, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9969, loss_cls: 0.4807, loss: 0.4807 +2025-06-24 21:23:44,458 - pyskl - INFO - Epoch [63][500/1281] lr: 1.576e-02, eta: 13:22:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9969, loss_cls: 0.5017, loss: 0.5017 +2025-06-24 21:24:33,822 - pyskl - INFO - Epoch [63][600/1281] lr: 1.574e-02, eta: 13:21:58, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9962, loss_cls: 0.4363, loss: 0.4363 +2025-06-24 21:25:23,142 - pyskl - INFO - Epoch [63][700/1281] lr: 1.572e-02, eta: 13:21:25, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9969, loss_cls: 0.4468, loss: 0.4468 +2025-06-24 21:26:11,937 - pyskl - INFO - Epoch [63][800/1281] lr: 1.570e-02, eta: 13:20:50, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9144, top5_acc: 0.9981, loss_cls: 0.4875, loss: 0.4875 +2025-06-24 21:27:00,822 - pyskl - INFO - Epoch [63][900/1281] lr: 1.568e-02, eta: 13:20:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9962, loss_cls: 0.4725, loss: 0.4725 +2025-06-24 21:27:50,268 - pyskl - INFO - Epoch [63][1000/1281] lr: 1.566e-02, eta: 13:19:41, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.8875, top5_acc: 0.9925, loss_cls: 0.5617, loss: 0.5617 +2025-06-24 21:28:39,774 - pyskl - INFO - Epoch [63][1100/1281] lr: 1.564e-02, eta: 13:19:07, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9988, loss_cls: 0.4582, loss: 0.4582 +2025-06-24 21:29:28,840 - pyskl - INFO - Epoch [63][1200/1281] lr: 1.562e-02, eta: 13:18:33, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9981, loss_cls: 0.4978, loss: 0.4978 +2025-06-24 21:30:09,162 - pyskl - INFO - Saving checkpoint at 63 epochs +2025-06-24 21:31:05,708 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:31:05,765 - pyskl - INFO - +top1_acc 0.8558 +top5_acc 0.9893 +2025-06-24 21:31:05,765 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:31:05,772 - pyskl - INFO - +mean_acc 0.8028 +2025-06-24 21:31:05,774 - pyskl - INFO - Epoch(val) [63][533] top1_acc: 0.8558, top5_acc: 0.9893, mean_class_accuracy: 0.8028 +2025-06-24 21:32:10,831 - pyskl - INFO - Epoch [64][100/1281] lr: 1.559e-02, eta: 13:16:58, time: 0.651, data_time: 0.196, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9962, loss_cls: 0.4588, loss: 0.4588 +2025-06-24 21:32:54,417 - pyskl - INFO - Epoch [64][200/1281] lr: 1.557e-02, eta: 13:16:16, time: 0.436, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9969, loss_cls: 0.4206, loss: 0.4206 +2025-06-24 21:33:43,637 - pyskl - INFO - Epoch [64][300/1281] lr: 1.555e-02, eta: 13:15:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9938, loss_cls: 0.4463, loss: 0.4463 +2025-06-24 21:34:32,479 - pyskl - INFO - Epoch [64][400/1281] lr: 1.553e-02, eta: 13:15:06, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9988, loss_cls: 0.4350, loss: 0.4350 +2025-06-24 21:35:21,506 - pyskl - INFO - Epoch [64][500/1281] lr: 1.551e-02, eta: 13:14:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9994, loss_cls: 0.4217, loss: 0.4217 +2025-06-24 21:36:10,458 - pyskl - INFO - Epoch [64][600/1281] lr: 1.549e-02, eta: 13:13:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.4078, loss: 0.4078 +2025-06-24 21:36:59,457 - pyskl - INFO - Epoch [64][700/1281] lr: 1.547e-02, eta: 13:13:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9962, loss_cls: 0.4284, loss: 0.4284 +2025-06-24 21:37:48,338 - pyskl - INFO - Epoch [64][800/1281] lr: 1.545e-02, eta: 13:12:47, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9038, top5_acc: 0.9962, loss_cls: 0.5103, loss: 0.5103 +2025-06-24 21:38:37,637 - pyskl - INFO - Epoch [64][900/1281] lr: 1.543e-02, eta: 13:12:13, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9113, top5_acc: 0.9938, loss_cls: 0.4853, loss: 0.4853 +2025-06-24 21:39:26,524 - pyskl - INFO - Epoch [64][1000/1281] lr: 1.541e-02, eta: 13:11:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9962, loss_cls: 0.5482, loss: 0.5482 +2025-06-24 21:40:15,688 - pyskl - INFO - Epoch [64][1100/1281] lr: 1.539e-02, eta: 13:11:03, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9975, loss_cls: 0.4862, loss: 0.4862 +2025-06-24 21:41:04,856 - pyskl - INFO - Epoch [64][1200/1281] lr: 1.537e-02, eta: 13:10:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9950, loss_cls: 0.4600, loss: 0.4600 +2025-06-24 21:41:45,160 - pyskl - INFO - Saving checkpoint at 64 epochs +2025-06-24 21:42:42,404 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:42:42,460 - pyskl - INFO - +top1_acc 0.8540 +top5_acc 0.9900 +2025-06-24 21:42:42,460 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:42:42,468 - pyskl - INFO - +mean_acc 0.7847 +2025-06-24 21:42:42,470 - pyskl - INFO - Epoch(val) [64][533] top1_acc: 0.8540, top5_acc: 0.9900, mean_class_accuracy: 0.7847 +2025-06-24 21:43:49,188 - pyskl - INFO - Epoch [65][100/1281] lr: 1.533e-02, eta: 13:08:55, time: 0.667, data_time: 0.192, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9962, loss_cls: 0.5061, loss: 0.5061 +2025-06-24 21:44:33,308 - pyskl - INFO - Epoch [65][200/1281] lr: 1.531e-02, eta: 13:08:14, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9237, top5_acc: 0.9969, loss_cls: 0.4502, loss: 0.4502 +2025-06-24 21:45:22,528 - pyskl - INFO - Epoch [65][300/1281] lr: 1.529e-02, eta: 13:07:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4329, loss: 0.4329 +2025-06-24 21:46:11,543 - pyskl - INFO - Epoch [65][400/1281] lr: 1.527e-02, eta: 13:07:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 1.0000, loss_cls: 0.3905, loss: 0.3905 +2025-06-24 21:47:00,754 - pyskl - INFO - Epoch [65][500/1281] lr: 1.526e-02, eta: 13:06:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9975, loss_cls: 0.4344, loss: 0.4344 +2025-06-24 21:47:50,003 - pyskl - INFO - Epoch [65][600/1281] lr: 1.524e-02, eta: 13:05:55, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9981, loss_cls: 0.4192, loss: 0.4192 +2025-06-24 21:48:39,001 - pyskl - INFO - Epoch [65][700/1281] lr: 1.522e-02, eta: 13:05:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9969, loss_cls: 0.4147, loss: 0.4147 +2025-06-24 21:49:28,471 - pyskl - INFO - Epoch [65][800/1281] lr: 1.520e-02, eta: 13:04:45, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9988, loss_cls: 0.3850, loss: 0.3850 +2025-06-24 21:50:17,518 - pyskl - INFO - Epoch [65][900/1281] lr: 1.518e-02, eta: 13:04:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9219, top5_acc: 0.9975, loss_cls: 0.4422, loss: 0.4422 +2025-06-24 21:51:06,699 - pyskl - INFO - Epoch [65][1000/1281] lr: 1.516e-02, eta: 13:03:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9950, loss_cls: 0.4671, loss: 0.4671 +2025-06-24 21:51:55,997 - pyskl - INFO - Epoch [65][1100/1281] lr: 1.514e-02, eta: 13:03:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9962, loss_cls: 0.4839, loss: 0.4839 +2025-06-24 21:52:45,168 - pyskl - INFO - Epoch [65][1200/1281] lr: 1.512e-02, eta: 13:02:25, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9100, top5_acc: 0.9956, loss_cls: 0.5100, loss: 0.5100 +2025-06-24 21:53:25,409 - pyskl - INFO - Saving checkpoint at 65 epochs +2025-06-24 21:54:22,170 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 21:54:22,235 - pyskl - INFO - +top1_acc 0.8662 +top5_acc 0.9890 +2025-06-24 21:54:22,236 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 21:54:22,247 - pyskl - INFO - +mean_acc 0.8179 +2025-06-24 21:54:22,249 - pyskl - INFO - Epoch(val) [65][533] top1_acc: 0.8662, top5_acc: 0.9890, mean_class_accuracy: 0.8179 +2025-06-24 21:55:27,774 - pyskl - INFO - Epoch [66][100/1281] lr: 1.508e-02, eta: 13:00:50, time: 0.655, data_time: 0.190, memory: 4083, top1_acc: 0.9156, top5_acc: 1.0000, loss_cls: 0.4663, loss: 0.4663 +2025-06-24 21:56:11,854 - pyskl - INFO - Epoch [66][200/1281] lr: 1.506e-02, eta: 13:00:09, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.3666, loss: 0.3666 +2025-06-24 21:57:01,079 - pyskl - INFO - Epoch [66][300/1281] lr: 1.504e-02, eta: 12:59:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9975, loss_cls: 0.4117, loss: 0.4117 +2025-06-24 21:57:50,187 - pyskl - INFO - Epoch [66][400/1281] lr: 1.502e-02, eta: 12:58:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9988, loss_cls: 0.4163, loss: 0.4163 +2025-06-24 21:58:39,241 - pyskl - INFO - Epoch [66][500/1281] lr: 1.500e-02, eta: 12:58:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9988, loss_cls: 0.4433, loss: 0.4433 +2025-06-24 21:59:28,542 - pyskl - INFO - Epoch [66][600/1281] lr: 1.498e-02, eta: 12:57:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9969, loss_cls: 0.4593, loss: 0.4593 +2025-06-24 22:00:17,828 - pyskl - INFO - Epoch [66][700/1281] lr: 1.496e-02, eta: 12:57:13, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9969, loss_cls: 0.4537, loss: 0.4537 +2025-06-24 22:01:06,817 - pyskl - INFO - Epoch [66][800/1281] lr: 1.494e-02, eta: 12:56:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9075, top5_acc: 0.9988, loss_cls: 0.4738, loss: 0.4738 +2025-06-24 22:01:56,168 - pyskl - INFO - Epoch [66][900/1281] lr: 1.492e-02, eta: 12:56:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9994, loss_cls: 0.4288, loss: 0.4288 +2025-06-24 22:02:45,562 - pyskl - INFO - Epoch [66][1000/1281] lr: 1.490e-02, eta: 12:55:27, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9981, loss_cls: 0.4672, loss: 0.4672 +2025-06-24 22:03:34,736 - pyskl - INFO - Epoch [66][1100/1281] lr: 1.488e-02, eta: 12:54:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9962, loss_cls: 0.4892, loss: 0.4892 +2025-06-24 22:04:23,778 - pyskl - INFO - Epoch [66][1200/1281] lr: 1.486e-02, eta: 12:54:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.8981, top5_acc: 0.9988, loss_cls: 0.5221, loss: 0.5221 +2025-06-24 22:05:03,795 - pyskl - INFO - Saving checkpoint at 66 epochs +2025-06-24 22:06:01,142 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:06:01,199 - pyskl - INFO - +top1_acc 0.8595 +top5_acc 0.9859 +2025-06-24 22:06:01,199 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:06:01,205 - pyskl - INFO - +mean_acc 0.8241 +2025-06-24 22:06:01,207 - pyskl - INFO - Epoch(val) [66][533] top1_acc: 0.8595, top5_acc: 0.9859, mean_class_accuracy: 0.8241 +2025-06-24 22:07:08,529 - pyskl - INFO - Epoch [67][100/1281] lr: 1.482e-02, eta: 12:52:44, time: 0.673, data_time: 0.188, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9975, loss_cls: 0.3657, loss: 0.3657 +2025-06-24 22:07:52,044 - pyskl - INFO - Epoch [67][200/1281] lr: 1.480e-02, eta: 12:52:02, time: 0.435, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3697, loss: 0.3697 +2025-06-24 22:08:41,073 - pyskl - INFO - Epoch [67][300/1281] lr: 1.478e-02, eta: 12:51:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9981, loss_cls: 0.3870, loss: 0.3870 +2025-06-24 22:09:30,242 - pyskl - INFO - Epoch [67][400/1281] lr: 1.476e-02, eta: 12:50:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9975, loss_cls: 0.4413, loss: 0.4413 +2025-06-24 22:10:19,337 - pyskl - INFO - Epoch [67][500/1281] lr: 1.474e-02, eta: 12:50:15, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9163, top5_acc: 0.9975, loss_cls: 0.4551, loss: 0.4551 +2025-06-24 22:11:08,414 - pyskl - INFO - Epoch [67][600/1281] lr: 1.472e-02, eta: 12:49:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 1.0000, loss_cls: 0.3944, loss: 0.3944 +2025-06-24 22:11:57,821 - pyskl - INFO - Epoch [67][700/1281] lr: 1.470e-02, eta: 12:49:04, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9137, top5_acc: 0.9969, loss_cls: 0.4548, loss: 0.4548 +2025-06-24 22:12:47,258 - pyskl - INFO - Epoch [67][800/1281] lr: 1.468e-02, eta: 12:48:28, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9975, loss_cls: 0.4563, loss: 0.4563 +2025-06-24 22:13:36,858 - pyskl - INFO - Epoch [67][900/1281] lr: 1.466e-02, eta: 12:47:53, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9050, top5_acc: 0.9988, loss_cls: 0.5065, loss: 0.5065 +2025-06-24 22:14:26,207 - pyskl - INFO - Epoch [67][1000/1281] lr: 1.464e-02, eta: 12:47:18, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9056, top5_acc: 0.9994, loss_cls: 0.4787, loss: 0.4787 +2025-06-24 22:15:15,312 - pyskl - INFO - Epoch [67][1100/1281] lr: 1.462e-02, eta: 12:46:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9950, loss_cls: 0.5275, loss: 0.5275 +2025-06-24 22:16:04,677 - pyskl - INFO - Epoch [67][1200/1281] lr: 1.460e-02, eta: 12:46:06, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9981, loss_cls: 0.4221, loss: 0.4221 +2025-06-24 22:16:44,853 - pyskl - INFO - Saving checkpoint at 67 epochs +2025-06-24 22:17:42,773 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:17:42,832 - pyskl - INFO - +top1_acc 0.8542 +top5_acc 0.9898 +2025-06-24 22:17:42,832 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:17:42,840 - pyskl - INFO - +mean_acc 0.8060 +2025-06-24 22:17:42,843 - pyskl - INFO - Epoch(val) [67][533] top1_acc: 0.8542, top5_acc: 0.9898, mean_class_accuracy: 0.8060 +2025-06-24 22:18:51,063 - pyskl - INFO - Epoch [68][100/1281] lr: 1.456e-02, eta: 12:44:36, time: 0.682, data_time: 0.189, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9994, loss_cls: 0.4007, loss: 0.4007 +2025-06-24 22:19:34,830 - pyskl - INFO - Epoch [68][200/1281] lr: 1.454e-02, eta: 12:43:53, time: 0.438, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.3819, loss: 0.3819 +2025-06-24 22:20:24,013 - pyskl - INFO - Epoch [68][300/1281] lr: 1.452e-02, eta: 12:43:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9988, loss_cls: 0.4045, loss: 0.4045 +2025-06-24 22:21:13,178 - pyskl - INFO - Epoch [68][400/1281] lr: 1.450e-02, eta: 12:42:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3841, loss: 0.3841 +2025-06-24 22:22:02,524 - pyskl - INFO - Epoch [68][500/1281] lr: 1.448e-02, eta: 12:42:06, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9956, loss_cls: 0.4438, loss: 0.4438 +2025-06-24 22:22:51,502 - pyskl - INFO - Epoch [68][600/1281] lr: 1.446e-02, eta: 12:41:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9081, top5_acc: 0.9981, loss_cls: 0.4794, loss: 0.4794 +2025-06-24 22:23:40,711 - pyskl - INFO - Epoch [68][700/1281] lr: 1.444e-02, eta: 12:40:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9988, loss_cls: 0.4031, loss: 0.4031 +2025-06-24 22:24:29,690 - pyskl - INFO - Epoch [68][800/1281] lr: 1.442e-02, eta: 12:40:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.4293, loss: 0.4293 +2025-06-24 22:25:18,886 - pyskl - INFO - Epoch [68][900/1281] lr: 1.440e-02, eta: 12:39:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9981, loss_cls: 0.4252, loss: 0.4252 +2025-06-24 22:26:08,049 - pyskl - INFO - Epoch [68][1000/1281] lr: 1.438e-02, eta: 12:39:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9975, loss_cls: 0.4095, loss: 0.4095 +2025-06-24 22:26:57,126 - pyskl - INFO - Epoch [68][1100/1281] lr: 1.436e-02, eta: 12:38:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9031, top5_acc: 0.9950, loss_cls: 0.4858, loss: 0.4858 +2025-06-24 22:27:46,089 - pyskl - INFO - Epoch [68][1200/1281] lr: 1.434e-02, eta: 12:37:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9156, top5_acc: 0.9969, loss_cls: 0.4894, loss: 0.4894 +2025-06-24 22:28:26,486 - pyskl - INFO - Saving checkpoint at 68 epochs +2025-06-24 22:29:23,538 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:29:23,606 - pyskl - INFO - +top1_acc 0.8648 +top5_acc 0.9890 +2025-06-24 22:29:23,606 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:29:23,615 - pyskl - INFO - +mean_acc 0.8189 +2025-06-24 22:29:23,617 - pyskl - INFO - Epoch(val) [68][533] top1_acc: 0.8648, top5_acc: 0.9890, mean_class_accuracy: 0.8189 +2025-06-24 22:30:30,514 - pyskl - INFO - Epoch [69][100/1281] lr: 1.431e-02, eta: 12:36:21, time: 0.669, data_time: 0.192, memory: 4083, top1_acc: 0.9300, top5_acc: 1.0000, loss_cls: 0.3723, loss: 0.3723 +2025-06-24 22:31:14,507 - pyskl - INFO - Epoch [69][200/1281] lr: 1.429e-02, eta: 12:35:39, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3595, loss: 0.3595 +2025-06-24 22:32:03,556 - pyskl - INFO - Epoch [69][300/1281] lr: 1.427e-02, eta: 12:35:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9975, loss_cls: 0.4377, loss: 0.4377 +2025-06-24 22:32:52,868 - pyskl - INFO - Epoch [69][400/1281] lr: 1.425e-02, eta: 12:34:27, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9975, loss_cls: 0.4495, loss: 0.4495 +2025-06-24 22:33:41,940 - pyskl - INFO - Epoch [69][500/1281] lr: 1.423e-02, eta: 12:33:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 0.9988, loss_cls: 0.4043, loss: 0.4043 +2025-06-24 22:34:31,034 - pyskl - INFO - Epoch [69][600/1281] lr: 1.420e-02, eta: 12:33:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9969, loss_cls: 0.4007, loss: 0.4007 +2025-06-24 22:35:20,472 - pyskl - INFO - Epoch [69][700/1281] lr: 1.418e-02, eta: 12:32:38, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9344, top5_acc: 0.9969, loss_cls: 0.3968, loss: 0.3968 +2025-06-24 22:36:09,912 - pyskl - INFO - Epoch [69][800/1281] lr: 1.416e-02, eta: 12:32:02, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9962, loss_cls: 0.4178, loss: 0.4178 +2025-06-24 22:36:59,404 - pyskl - INFO - Epoch [69][900/1281] lr: 1.414e-02, eta: 12:31:26, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9994, loss_cls: 0.4363, loss: 0.4363 +2025-06-24 22:37:48,329 - pyskl - INFO - Epoch [69][1000/1281] lr: 1.412e-02, eta: 12:30:49, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9994, loss_cls: 0.4451, loss: 0.4451 +2025-06-24 22:38:37,597 - pyskl - INFO - Epoch [69][1100/1281] lr: 1.410e-02, eta: 12:30:13, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9106, top5_acc: 0.9969, loss_cls: 0.4699, loss: 0.4699 +2025-06-24 22:39:26,868 - pyskl - INFO - Epoch [69][1200/1281] lr: 1.408e-02, eta: 12:29:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9062, top5_acc: 0.9988, loss_cls: 0.4836, loss: 0.4836 +2025-06-24 22:40:07,445 - pyskl - INFO - Saving checkpoint at 69 epochs +2025-06-24 22:41:03,847 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:41:03,909 - pyskl - INFO - +top1_acc 0.8620 +top5_acc 0.9894 +2025-06-24 22:41:03,909 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:41:03,917 - pyskl - INFO - +mean_acc 0.8201 +2025-06-24 22:41:03,919 - pyskl - INFO - Epoch(val) [69][533] top1_acc: 0.8620, top5_acc: 0.9894, mean_class_accuracy: 0.8201 +2025-06-24 22:42:09,600 - pyskl - INFO - Epoch [70][100/1281] lr: 1.405e-02, eta: 12:28:04, time: 0.657, data_time: 0.190, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9988, loss_cls: 0.3631, loss: 0.3631 +2025-06-24 22:42:54,177 - pyskl - INFO - Epoch [70][200/1281] lr: 1.403e-02, eta: 12:27:22, time: 0.446, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9981, loss_cls: 0.3475, loss: 0.3475 +2025-06-24 22:43:43,087 - pyskl - INFO - Epoch [70][300/1281] lr: 1.401e-02, eta: 12:26:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9988, loss_cls: 0.3802, loss: 0.3802 +2025-06-24 22:44:32,161 - pyskl - INFO - Epoch [70][400/1281] lr: 1.399e-02, eta: 12:26:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9981, loss_cls: 0.4142, loss: 0.4142 +2025-06-24 22:45:21,431 - pyskl - INFO - Epoch [70][500/1281] lr: 1.397e-02, eta: 12:25:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9087, top5_acc: 0.9988, loss_cls: 0.4544, loss: 0.4544 +2025-06-24 22:46:10,826 - pyskl - INFO - Epoch [70][600/1281] lr: 1.395e-02, eta: 12:24:56, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9119, top5_acc: 0.9981, loss_cls: 0.4276, loss: 0.4276 +2025-06-24 22:47:00,232 - pyskl - INFO - Epoch [70][700/1281] lr: 1.392e-02, eta: 12:24:19, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9094, top5_acc: 0.9975, loss_cls: 0.4634, loss: 0.4634 +2025-06-24 22:47:49,412 - pyskl - INFO - Epoch [70][800/1281] lr: 1.390e-02, eta: 12:23:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9231, top5_acc: 0.9981, loss_cls: 0.4170, loss: 0.4170 +2025-06-24 22:48:38,748 - pyskl - INFO - Epoch [70][900/1281] lr: 1.388e-02, eta: 12:23:06, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9981, loss_cls: 0.4166, loss: 0.4166 +2025-06-24 22:49:27,911 - pyskl - INFO - Epoch [70][1000/1281] lr: 1.386e-02, eta: 12:22:30, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9994, loss_cls: 0.4083, loss: 0.4083 +2025-06-24 22:50:17,043 - pyskl - INFO - Epoch [70][1100/1281] lr: 1.384e-02, eta: 12:21:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9287, top5_acc: 0.9988, loss_cls: 0.3918, loss: 0.3918 +2025-06-24 22:51:06,280 - pyskl - INFO - Epoch [70][1200/1281] lr: 1.382e-02, eta: 12:21:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9969, loss_cls: 0.4332, loss: 0.4332 +2025-06-24 22:51:46,479 - pyskl - INFO - Saving checkpoint at 70 epochs +2025-06-24 22:52:42,835 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 22:52:42,896 - pyskl - INFO - +top1_acc 0.8622 +top5_acc 0.9885 +2025-06-24 22:52:42,896 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 22:52:42,903 - pyskl - INFO - +mean_acc 0.8231 +2025-06-24 22:52:42,905 - pyskl - INFO - Epoch(val) [70][533] top1_acc: 0.8622, top5_acc: 0.9885, mean_class_accuracy: 0.8231 +2025-06-24 22:53:49,225 - pyskl - INFO - Epoch [71][100/1281] lr: 1.379e-02, eta: 12:19:44, time: 0.663, data_time: 0.191, memory: 4083, top1_acc: 0.9437, top5_acc: 1.0000, loss_cls: 0.3434, loss: 0.3434 +2025-06-24 22:54:32,910 - pyskl - INFO - Epoch [71][200/1281] lr: 1.377e-02, eta: 12:19:01, time: 0.437, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9988, loss_cls: 0.3927, loss: 0.3927 +2025-06-24 22:55:22,223 - pyskl - INFO - Epoch [71][300/1281] lr: 1.375e-02, eta: 12:18:24, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3410, loss: 0.3410 +2025-06-24 22:56:11,375 - pyskl - INFO - Epoch [71][400/1281] lr: 1.373e-02, eta: 12:17:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9988, loss_cls: 0.3574, loss: 0.3574 +2025-06-24 22:57:00,131 - pyskl - INFO - Epoch [71][500/1281] lr: 1.371e-02, eta: 12:17:10, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9975, loss_cls: 0.3810, loss: 0.3810 +2025-06-24 22:57:49,588 - pyskl - INFO - Epoch [71][600/1281] lr: 1.368e-02, eta: 12:16:34, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9994, loss_cls: 0.3974, loss: 0.3974 +2025-06-24 22:58:38,786 - pyskl - INFO - Epoch [71][700/1281] lr: 1.366e-02, eta: 12:15:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9981, loss_cls: 0.4074, loss: 0.4074 +2025-06-24 22:59:27,894 - pyskl - INFO - Epoch [71][800/1281] lr: 1.364e-02, eta: 12:15:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3566, loss: 0.3566 +2025-06-24 23:00:16,997 - pyskl - INFO - Epoch [71][900/1281] lr: 1.362e-02, eta: 12:14:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.3826, loss: 0.3826 +2025-06-24 23:01:06,293 - pyskl - INFO - Epoch [71][1000/1281] lr: 1.360e-02, eta: 12:14:06, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9281, top5_acc: 0.9975, loss_cls: 0.4202, loss: 0.4202 +2025-06-24 23:01:55,519 - pyskl - INFO - Epoch [71][1100/1281] lr: 1.358e-02, eta: 12:13:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9975, loss_cls: 0.4612, loss: 0.4612 +2025-06-24 23:02:44,589 - pyskl - INFO - Epoch [71][1200/1281] lr: 1.356e-02, eta: 12:12:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9981, loss_cls: 0.4426, loss: 0.4426 +2025-06-24 23:03:24,994 - pyskl - INFO - Saving checkpoint at 71 epochs +2025-06-24 23:04:22,412 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:04:22,507 - pyskl - INFO - +top1_acc 0.8534 +top5_acc 0.9858 +2025-06-24 23:04:22,508 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:04:22,518 - pyskl - INFO - +mean_acc 0.8190 +2025-06-24 23:04:22,520 - pyskl - INFO - Epoch(val) [71][533] top1_acc: 0.8534, top5_acc: 0.9858, mean_class_accuracy: 0.8190 +2025-06-24 23:05:30,687 - pyskl - INFO - Epoch [72][100/1281] lr: 1.353e-02, eta: 12:11:22, time: 0.682, data_time: 0.195, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.3819, loss: 0.3819 +2025-06-24 23:06:15,479 - pyskl - INFO - Epoch [72][200/1281] lr: 1.351e-02, eta: 12:10:40, time: 0.448, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9988, loss_cls: 0.3760, loss: 0.3760 +2025-06-24 23:07:04,497 - pyskl - INFO - Epoch [72][300/1281] lr: 1.349e-02, eta: 12:10:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9988, loss_cls: 0.3468, loss: 0.3468 +2025-06-24 23:07:53,555 - pyskl - INFO - Epoch [72][400/1281] lr: 1.346e-02, eta: 12:09:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9988, loss_cls: 0.3683, loss: 0.3683 +2025-06-24 23:08:42,710 - pyskl - INFO - Epoch [72][500/1281] lr: 1.344e-02, eta: 12:08:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9975, loss_cls: 0.3776, loss: 0.3776 +2025-06-24 23:09:32,145 - pyskl - INFO - Epoch [72][600/1281] lr: 1.342e-02, eta: 12:08:12, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9969, loss_cls: 0.4303, loss: 0.4303 +2025-06-24 23:10:21,579 - pyskl - INFO - Epoch [72][700/1281] lr: 1.340e-02, eta: 12:07:35, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9969, loss_cls: 0.4326, loss: 0.4326 +2025-06-24 23:11:10,800 - pyskl - INFO - Epoch [72][800/1281] lr: 1.338e-02, eta: 12:06:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 1.0000, loss_cls: 0.4016, loss: 0.4016 +2025-06-24 23:11:59,993 - pyskl - INFO - Epoch [72][900/1281] lr: 1.336e-02, eta: 12:06:21, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9206, top5_acc: 0.9975, loss_cls: 0.4460, loss: 0.4460 +2025-06-24 23:12:49,369 - pyskl - INFO - Epoch [72][1000/1281] lr: 1.334e-02, eta: 12:05:44, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9975, loss_cls: 0.3705, loss: 0.3705 +2025-06-24 23:13:38,791 - pyskl - INFO - Epoch [72][1100/1281] lr: 1.332e-02, eta: 12:05:07, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9975, loss_cls: 0.3588, loss: 0.3588 +2025-06-24 23:14:27,916 - pyskl - INFO - Epoch [72][1200/1281] lr: 1.330e-02, eta: 12:04:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9181, top5_acc: 0.9962, loss_cls: 0.4512, loss: 0.4512 +2025-06-24 23:15:08,270 - pyskl - INFO - Saving checkpoint at 72 epochs +2025-06-24 23:16:04,522 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:16:04,577 - pyskl - INFO - +top1_acc 0.8817 +top5_acc 0.9925 +2025-06-24 23:16:04,577 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:16:04,584 - pyskl - INFO - +mean_acc 0.8437 +2025-06-24 23:16:04,589 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_61.pth was removed +2025-06-24 23:16:04,773 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_72.pth. +2025-06-24 23:16:04,773 - pyskl - INFO - Best top1_acc is 0.8817 at 72 epoch. +2025-06-24 23:16:04,776 - pyskl - INFO - Epoch(val) [72][533] top1_acc: 0.8817, top5_acc: 0.9925, mean_class_accuracy: 0.8437 +2025-06-24 23:17:10,039 - pyskl - INFO - Epoch [73][100/1281] lr: 1.326e-02, eta: 12:02:56, time: 0.653, data_time: 0.194, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3484, loss: 0.3484 +2025-06-24 23:17:53,459 - pyskl - INFO - Epoch [73][200/1281] lr: 1.324e-02, eta: 12:02:13, time: 0.434, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3467, loss: 0.3467 +2025-06-24 23:18:42,379 - pyskl - INFO - Epoch [73][300/1281] lr: 1.322e-02, eta: 12:01:35, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9981, loss_cls: 0.3381, loss: 0.3381 +2025-06-24 23:19:31,429 - pyskl - INFO - Epoch [73][400/1281] lr: 1.320e-02, eta: 12:00:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9975, loss_cls: 0.3329, loss: 0.3329 +2025-06-24 23:20:20,426 - pyskl - INFO - Epoch [73][500/1281] lr: 1.318e-02, eta: 12:00:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9975, loss_cls: 0.3399, loss: 0.3399 +2025-06-24 23:21:09,455 - pyskl - INFO - Epoch [73][600/1281] lr: 1.316e-02, eta: 11:59:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9988, loss_cls: 0.4563, loss: 0.4563 +2025-06-24 23:21:58,462 - pyskl - INFO - Epoch [73][700/1281] lr: 1.314e-02, eta: 11:59:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9994, loss_cls: 0.3933, loss: 0.3933 +2025-06-24 23:22:47,722 - pyskl - INFO - Epoch [73][800/1281] lr: 1.312e-02, eta: 11:58:28, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9969, loss_cls: 0.4451, loss: 0.4451 +2025-06-24 23:23:36,573 - pyskl - INFO - Epoch [73][900/1281] lr: 1.310e-02, eta: 11:57:50, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9994, loss_cls: 0.3627, loss: 0.3627 +2025-06-24 23:24:26,159 - pyskl - INFO - Epoch [73][1000/1281] lr: 1.308e-02, eta: 11:57:13, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9125, top5_acc: 0.9944, loss_cls: 0.4642, loss: 0.4642 +2025-06-24 23:25:15,808 - pyskl - INFO - Epoch [73][1100/1281] lr: 1.306e-02, eta: 11:56:36, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9988, loss_cls: 0.3987, loss: 0.3987 +2025-06-24 23:26:05,078 - pyskl - INFO - Epoch [73][1200/1281] lr: 1.304e-02, eta: 11:55:59, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9244, top5_acc: 0.9969, loss_cls: 0.4140, loss: 0.4140 +2025-06-24 23:26:45,181 - pyskl - INFO - Saving checkpoint at 73 epochs +2025-06-24 23:27:43,517 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:27:43,584 - pyskl - INFO - +top1_acc 0.8513 +top5_acc 0.9863 +2025-06-24 23:27:43,584 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:27:43,592 - pyskl - INFO - +mean_acc 0.8230 +2025-06-24 23:27:43,594 - pyskl - INFO - Epoch(val) [73][533] top1_acc: 0.8513, top5_acc: 0.9863, mean_class_accuracy: 0.8230 +2025-06-24 23:28:53,348 - pyskl - INFO - Epoch [74][100/1281] lr: 1.300e-02, eta: 11:54:30, time: 0.697, data_time: 0.190, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9969, loss_cls: 0.3560, loss: 0.3560 +2025-06-24 23:29:37,077 - pyskl - INFO - Epoch [74][200/1281] lr: 1.298e-02, eta: 11:53:47, time: 0.437, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9975, loss_cls: 0.3299, loss: 0.3299 +2025-06-24 23:30:26,080 - pyskl - INFO - Epoch [74][300/1281] lr: 1.296e-02, eta: 11:53:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.3888, loss: 0.3888 +2025-06-24 23:31:15,498 - pyskl - INFO - Epoch [74][400/1281] lr: 1.294e-02, eta: 11:52:32, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9988, loss_cls: 0.3972, loss: 0.3972 +2025-06-24 23:32:04,742 - pyskl - INFO - Epoch [74][500/1281] lr: 1.292e-02, eta: 11:51:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9200, top5_acc: 0.9994, loss_cls: 0.4328, loss: 0.4328 +2025-06-24 23:32:54,180 - pyskl - INFO - Epoch [74][600/1281] lr: 1.290e-02, eta: 11:51:17, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9187, top5_acc: 0.9981, loss_cls: 0.4318, loss: 0.4318 +2025-06-24 23:33:43,522 - pyskl - INFO - Epoch [74][700/1281] lr: 1.288e-02, eta: 11:50:40, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9981, loss_cls: 0.3803, loss: 0.3803 +2025-06-24 23:34:32,684 - pyskl - INFO - Epoch [74][800/1281] lr: 1.286e-02, eta: 11:50:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9994, loss_cls: 0.3613, loss: 0.3613 +2025-06-24 23:35:21,668 - pyskl - INFO - Epoch [74][900/1281] lr: 1.284e-02, eta: 11:49:24, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9988, loss_cls: 0.3668, loss: 0.3668 +2025-06-24 23:36:10,624 - pyskl - INFO - Epoch [74][1000/1281] lr: 1.282e-02, eta: 11:48:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9981, loss_cls: 0.3894, loss: 0.3894 +2025-06-24 23:36:59,459 - pyskl - INFO - Epoch [74][1100/1281] lr: 1.280e-02, eta: 11:48:08, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9213, top5_acc: 0.9975, loss_cls: 0.4189, loss: 0.4189 +2025-06-24 23:37:48,660 - pyskl - INFO - Epoch [74][1200/1281] lr: 1.278e-02, eta: 11:47:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9175, top5_acc: 0.9975, loss_cls: 0.4454, loss: 0.4454 +2025-06-24 23:38:28,852 - pyskl - INFO - Saving checkpoint at 74 epochs +2025-06-24 23:39:26,636 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:39:26,691 - pyskl - INFO - +top1_acc 0.8650 +top5_acc 0.9903 +2025-06-24 23:39:26,692 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:39:26,699 - pyskl - INFO - +mean_acc 0.8149 +2025-06-24 23:39:26,701 - pyskl - INFO - Epoch(val) [74][533] top1_acc: 0.8650, top5_acc: 0.9903, mean_class_accuracy: 0.8149 +2025-06-24 23:40:34,445 - pyskl - INFO - Epoch [75][100/1281] lr: 1.274e-02, eta: 11:46:00, time: 0.677, data_time: 0.190, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9988, loss_cls: 0.4017, loss: 0.4017 +2025-06-24 23:41:18,570 - pyskl - INFO - Epoch [75][200/1281] lr: 1.272e-02, eta: 11:45:17, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9994, loss_cls: 0.3263, loss: 0.3263 +2025-06-24 23:42:07,726 - pyskl - INFO - Epoch [75][300/1281] lr: 1.270e-02, eta: 11:44:39, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9994, loss_cls: 0.3036, loss: 0.3036 +2025-06-24 23:42:56,684 - pyskl - INFO - Epoch [75][400/1281] lr: 1.268e-02, eta: 11:44:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 1.0000, loss_cls: 0.3054, loss: 0.3054 +2025-06-24 23:43:45,737 - pyskl - INFO - Epoch [75][500/1281] lr: 1.266e-02, eta: 11:43:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9994, loss_cls: 0.3836, loss: 0.3836 +2025-06-24 23:44:35,138 - pyskl - INFO - Epoch [75][600/1281] lr: 1.264e-02, eta: 11:42:45, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9950, loss_cls: 0.4198, loss: 0.4198 +2025-06-24 23:45:24,358 - pyskl - INFO - Epoch [75][700/1281] lr: 1.262e-02, eta: 11:42:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9988, loss_cls: 0.3624, loss: 0.3624 +2025-06-24 23:46:13,371 - pyskl - INFO - Epoch [75][800/1281] lr: 1.260e-02, eta: 11:41:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9150, top5_acc: 0.9975, loss_cls: 0.4503, loss: 0.4503 +2025-06-24 23:47:02,651 - pyskl - INFO - Epoch [75][900/1281] lr: 1.258e-02, eta: 11:40:51, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9975, loss_cls: 0.3506, loss: 0.3506 +2025-06-24 23:47:51,864 - pyskl - INFO - Epoch [75][1000/1281] lr: 1.256e-02, eta: 11:40:14, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9225, top5_acc: 0.9988, loss_cls: 0.3945, loss: 0.3945 +2025-06-24 23:48:41,075 - pyskl - INFO - Epoch [75][1100/1281] lr: 1.254e-02, eta: 11:39:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9981, loss_cls: 0.3617, loss: 0.3617 +2025-06-24 23:49:29,982 - pyskl - INFO - Epoch [75][1200/1281] lr: 1.252e-02, eta: 11:38:57, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9194, top5_acc: 0.9994, loss_cls: 0.4172, loss: 0.4172 +2025-06-24 23:50:10,295 - pyskl - INFO - Saving checkpoint at 75 epochs +2025-06-24 23:51:07,214 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-24 23:51:07,270 - pyskl - INFO - +top1_acc 0.8499 +top5_acc 0.9853 +2025-06-24 23:51:07,270 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-24 23:51:07,276 - pyskl - INFO - +mean_acc 0.8055 +2025-06-24 23:51:07,278 - pyskl - INFO - Epoch(val) [75][533] top1_acc: 0.8499, top5_acc: 0.9853, mean_class_accuracy: 0.8055 +2025-06-24 23:52:15,282 - pyskl - INFO - Epoch [76][100/1281] lr: 1.248e-02, eta: 11:37:27, time: 0.680, data_time: 0.189, memory: 4083, top1_acc: 0.9294, top5_acc: 0.9981, loss_cls: 0.3977, loss: 0.3977 +2025-06-24 23:52:59,483 - pyskl - INFO - Epoch [76][200/1281] lr: 1.246e-02, eta: 11:36:44, time: 0.442, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9994, loss_cls: 0.3516, loss: 0.3516 +2025-06-24 23:53:48,308 - pyskl - INFO - Epoch [76][300/1281] lr: 1.244e-02, eta: 11:36:06, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9988, loss_cls: 0.2817, loss: 0.2817 +2025-06-24 23:54:37,712 - pyskl - INFO - Epoch [76][400/1281] lr: 1.242e-02, eta: 11:35:28, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9994, loss_cls: 0.3493, loss: 0.3493 +2025-06-24 23:55:27,667 - pyskl - INFO - Epoch [76][500/1281] lr: 1.240e-02, eta: 11:34:51, time: 0.500, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 1.0000, loss_cls: 0.3064, loss: 0.3064 +2025-06-24 23:56:16,883 - pyskl - INFO - Epoch [76][600/1281] lr: 1.238e-02, eta: 11:34:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9981, loss_cls: 0.3307, loss: 0.3307 +2025-06-24 23:57:06,160 - pyskl - INFO - Epoch [76][700/1281] lr: 1.236e-02, eta: 11:33:35, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.9425, top5_acc: 1.0000, loss_cls: 0.3279, loss: 0.3279 +2025-06-24 23:57:55,022 - pyskl - INFO - Epoch [76][800/1281] lr: 1.234e-02, eta: 11:32:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4413, loss: 0.4413 +2025-06-24 23:58:44,581 - pyskl - INFO - Epoch [76][900/1281] lr: 1.232e-02, eta: 11:32:18, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.3720, loss: 0.3720 +2025-06-24 23:59:33,768 - pyskl - INFO - Epoch [76][1000/1281] lr: 1.230e-02, eta: 11:31:40, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9981, loss_cls: 0.3824, loss: 0.3824 +2025-06-25 00:00:22,969 - pyskl - INFO - Epoch [76][1100/1281] lr: 1.228e-02, eta: 11:31:02, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.4177, loss: 0.4177 +2025-06-25 00:01:12,222 - pyskl - INFO - Epoch [76][1200/1281] lr: 1.225e-02, eta: 11:30:24, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9981, loss_cls: 0.3850, loss: 0.3850 +2025-06-25 00:01:52,501 - pyskl - INFO - Saving checkpoint at 76 epochs +2025-06-25 00:02:48,597 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:02:48,662 - pyskl - INFO - +top1_acc 0.8710 +top5_acc 0.9884 +2025-06-25 00:02:48,662 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:02:48,672 - pyskl - INFO - +mean_acc 0.8392 +2025-06-25 00:02:48,674 - pyskl - INFO - Epoch(val) [76][533] top1_acc: 0.8710, top5_acc: 0.9884, mean_class_accuracy: 0.8392 +2025-06-25 00:03:54,889 - pyskl - INFO - Epoch [77][100/1281] lr: 1.222e-02, eta: 11:28:52, time: 0.662, data_time: 0.190, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9988, loss_cls: 0.3052, loss: 0.3052 +2025-06-25 00:04:39,365 - pyskl - INFO - Epoch [77][200/1281] lr: 1.220e-02, eta: 11:28:09, time: 0.445, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2897, loss: 0.2897 +2025-06-25 00:05:28,580 - pyskl - INFO - Epoch [77][300/1281] lr: 1.218e-02, eta: 11:27:31, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9988, loss_cls: 0.2986, loss: 0.2986 +2025-06-25 00:06:18,033 - pyskl - INFO - Epoch [77][400/1281] lr: 1.216e-02, eta: 11:26:53, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9994, loss_cls: 0.3466, loss: 0.3466 +2025-06-25 00:07:07,077 - pyskl - INFO - Epoch [77][500/1281] lr: 1.214e-02, eta: 11:26:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9975, loss_cls: 0.3577, loss: 0.3577 +2025-06-25 00:07:56,373 - pyskl - INFO - Epoch [77][600/1281] lr: 1.212e-02, eta: 11:25:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9250, top5_acc: 0.9988, loss_cls: 0.3599, loss: 0.3599 +2025-06-25 00:08:45,375 - pyskl - INFO - Epoch [77][700/1281] lr: 1.210e-02, eta: 11:24:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9988, loss_cls: 0.3619, loss: 0.3619 +2025-06-25 00:09:34,344 - pyskl - INFO - Epoch [77][800/1281] lr: 1.207e-02, eta: 11:24:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9263, top5_acc: 0.9956, loss_cls: 0.4312, loss: 0.4312 +2025-06-25 00:10:23,241 - pyskl - INFO - Epoch [77][900/1281] lr: 1.205e-02, eta: 11:23:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9969, loss_cls: 0.3637, loss: 0.3637 +2025-06-25 00:11:12,600 - pyskl - INFO - Epoch [77][1000/1281] lr: 1.203e-02, eta: 11:23:02, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9994, loss_cls: 0.3559, loss: 0.3559 +2025-06-25 00:12:01,574 - pyskl - INFO - Epoch [77][1100/1281] lr: 1.201e-02, eta: 11:22:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9275, top5_acc: 1.0000, loss_cls: 0.3822, loss: 0.3822 +2025-06-25 00:12:50,635 - pyskl - INFO - Epoch [77][1200/1281] lr: 1.199e-02, eta: 11:21:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9025, top5_acc: 0.9994, loss_cls: 0.4814, loss: 0.4814 +2025-06-25 00:13:31,021 - pyskl - INFO - Saving checkpoint at 77 epochs +2025-06-25 00:14:26,623 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:14:26,680 - pyskl - INFO - +top1_acc 0.8831 +top5_acc 0.9920 +2025-06-25 00:14:26,680 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:14:26,687 - pyskl - INFO - +mean_acc 0.8430 +2025-06-25 00:14:26,691 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_72.pth was removed +2025-06-25 00:14:26,866 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_77.pth. +2025-06-25 00:14:26,866 - pyskl - INFO - Best top1_acc is 0.8831 at 77 epoch. +2025-06-25 00:14:26,870 - pyskl - INFO - Epoch(val) [77][533] top1_acc: 0.8831, top5_acc: 0.9920, mean_class_accuracy: 0.8430 +2025-06-25 00:15:29,391 - pyskl - INFO - Epoch [78][100/1281] lr: 1.196e-02, eta: 11:20:10, time: 0.625, data_time: 0.191, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2965, loss: 0.2965 +2025-06-25 00:16:13,375 - pyskl - INFO - Epoch [78][200/1281] lr: 1.194e-02, eta: 11:19:27, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 1.0000, loss_cls: 0.2889, loss: 0.2889 +2025-06-25 00:17:02,306 - pyskl - INFO - Epoch [78][300/1281] lr: 1.192e-02, eta: 11:18:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 1.0000, loss_cls: 0.3837, loss: 0.3837 +2025-06-25 00:17:51,176 - pyskl - INFO - Epoch [78][400/1281] lr: 1.190e-02, eta: 11:18:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9981, loss_cls: 0.3544, loss: 0.3544 +2025-06-25 00:18:40,183 - pyskl - INFO - Epoch [78][500/1281] lr: 1.187e-02, eta: 11:17:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9988, loss_cls: 0.3409, loss: 0.3409 +2025-06-25 00:19:29,209 - pyskl - INFO - Epoch [78][600/1281] lr: 1.185e-02, eta: 11:16:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9988, loss_cls: 0.3330, loss: 0.3330 +2025-06-25 00:20:18,193 - pyskl - INFO - Epoch [78][700/1281] lr: 1.183e-02, eta: 11:16:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 1.0000, loss_cls: 0.3373, loss: 0.3373 +2025-06-25 00:21:07,342 - pyskl - INFO - Epoch [78][800/1281] lr: 1.181e-02, eta: 11:15:34, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9319, top5_acc: 0.9981, loss_cls: 0.3777, loss: 0.3777 +2025-06-25 00:21:56,469 - pyskl - INFO - Epoch [78][900/1281] lr: 1.179e-02, eta: 11:14:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9269, top5_acc: 0.9988, loss_cls: 0.4102, loss: 0.4102 +2025-06-25 00:22:45,613 - pyskl - INFO - Epoch [78][1000/1281] lr: 1.177e-02, eta: 11:14:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9369, top5_acc: 0.9994, loss_cls: 0.3680, loss: 0.3680 +2025-06-25 00:23:34,922 - pyskl - INFO - Epoch [78][1100/1281] lr: 1.175e-02, eta: 11:13:38, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9306, top5_acc: 0.9981, loss_cls: 0.3610, loss: 0.3610 +2025-06-25 00:24:24,264 - pyskl - INFO - Epoch [78][1200/1281] lr: 1.173e-02, eta: 11:13:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9169, top5_acc: 0.9975, loss_cls: 0.4848, loss: 0.4848 +2025-06-25 00:25:04,579 - pyskl - INFO - Saving checkpoint at 78 epochs +2025-06-25 00:26:01,367 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:26:01,425 - pyskl - INFO - +top1_acc 0.8608 +top5_acc 0.9904 +2025-06-25 00:26:01,425 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:26:01,435 - pyskl - INFO - +mean_acc 0.8092 +2025-06-25 00:26:01,437 - pyskl - INFO - Epoch(val) [78][533] top1_acc: 0.8608, top5_acc: 0.9904, mean_class_accuracy: 0.8092 +2025-06-25 00:27:07,582 - pyskl - INFO - Epoch [79][100/1281] lr: 1.169e-02, eta: 11:11:28, time: 0.661, data_time: 0.188, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9981, loss_cls: 0.3899, loss: 0.3899 +2025-06-25 00:27:51,804 - pyskl - INFO - Epoch [79][200/1281] lr: 1.167e-02, eta: 11:10:45, time: 0.442, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9994, loss_cls: 0.3234, loss: 0.3234 +2025-06-25 00:28:40,963 - pyskl - INFO - Epoch [79][300/1281] lr: 1.165e-02, eta: 11:10:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9994, loss_cls: 0.3309, loss: 0.3309 +2025-06-25 00:29:30,334 - pyskl - INFO - Epoch [79][400/1281] lr: 1.163e-02, eta: 11:09:28, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9994, loss_cls: 0.3303, loss: 0.3303 +2025-06-25 00:30:19,512 - pyskl - INFO - Epoch [79][500/1281] lr: 1.161e-02, eta: 11:08:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9981, loss_cls: 0.3620, loss: 0.3620 +2025-06-25 00:31:08,743 - pyskl - INFO - Epoch [79][600/1281] lr: 1.159e-02, eta: 11:08:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9988, loss_cls: 0.3101, loss: 0.3101 +2025-06-25 00:31:57,667 - pyskl - INFO - Epoch [79][700/1281] lr: 1.157e-02, eta: 11:07:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.3089, loss: 0.3089 +2025-06-25 00:32:46,959 - pyskl - INFO - Epoch [79][800/1281] lr: 1.155e-02, eta: 11:06:53, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9981, loss_cls: 0.3282, loss: 0.3282 +2025-06-25 00:33:36,039 - pyskl - INFO - Epoch [79][900/1281] lr: 1.153e-02, eta: 11:06:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 1.0000, loss_cls: 0.3614, loss: 0.3614 +2025-06-25 00:34:24,960 - pyskl - INFO - Epoch [79][1000/1281] lr: 1.151e-02, eta: 11:05:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9994, loss_cls: 0.3779, loss: 0.3779 +2025-06-25 00:35:13,918 - pyskl - INFO - Epoch [79][1100/1281] lr: 1.149e-02, eta: 11:04:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9131, top5_acc: 0.9981, loss_cls: 0.4043, loss: 0.4043 +2025-06-25 00:36:02,788 - pyskl - INFO - Epoch [79][1200/1281] lr: 1.147e-02, eta: 11:04:16, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9981, loss_cls: 0.4051, loss: 0.4051 +2025-06-25 00:36:42,994 - pyskl - INFO - Saving checkpoint at 79 epochs +2025-06-25 00:37:39,403 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:37:39,464 - pyskl - INFO - +top1_acc 0.8634 +top5_acc 0.9873 +2025-06-25 00:37:39,464 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:37:39,471 - pyskl - INFO - +mean_acc 0.8220 +2025-06-25 00:37:39,473 - pyskl - INFO - Epoch(val) [79][533] top1_acc: 0.8634, top5_acc: 0.9873, mean_class_accuracy: 0.8220 +2025-06-25 00:38:43,572 - pyskl - INFO - Epoch [80][100/1281] lr: 1.143e-02, eta: 11:02:43, time: 0.641, data_time: 0.190, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9975, loss_cls: 0.3078, loss: 0.3078 +2025-06-25 00:39:27,589 - pyskl - INFO - Epoch [80][200/1281] lr: 1.141e-02, eta: 11:02:00, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 0.9994, loss_cls: 0.2861, loss: 0.2861 +2025-06-25 00:40:16,601 - pyskl - INFO - Epoch [80][300/1281] lr: 1.139e-02, eta: 11:01:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3015, loss: 0.3015 +2025-06-25 00:41:05,729 - pyskl - INFO - Epoch [80][400/1281] lr: 1.137e-02, eta: 11:00:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3397, loss: 0.3397 +2025-06-25 00:41:54,755 - pyskl - INFO - Epoch [80][500/1281] lr: 1.135e-02, eta: 11:00:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9981, loss_cls: 0.3721, loss: 0.3721 +2025-06-25 00:42:44,080 - pyskl - INFO - Epoch [80][600/1281] lr: 1.133e-02, eta: 10:59:24, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9350, top5_acc: 0.9981, loss_cls: 0.3710, loss: 0.3710 +2025-06-25 00:43:33,094 - pyskl - INFO - Epoch [80][700/1281] lr: 1.131e-02, eta: 10:58:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9975, loss_cls: 0.3209, loss: 0.3209 +2025-06-25 00:44:22,179 - pyskl - INFO - Epoch [80][800/1281] lr: 1.129e-02, eta: 10:58:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 0.9981, loss_cls: 0.3311, loss: 0.3311 +2025-06-25 00:45:11,277 - pyskl - INFO - Epoch [80][900/1281] lr: 1.127e-02, eta: 10:57:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.3018, loss: 0.3018 +2025-06-25 00:46:00,352 - pyskl - INFO - Epoch [80][1000/1281] lr: 1.125e-02, eta: 10:56:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 1.0000, loss_cls: 0.3260, loss: 0.3260 +2025-06-25 00:46:49,558 - pyskl - INFO - Epoch [80][1100/1281] lr: 1.123e-02, eta: 10:56:08, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9387, top5_acc: 0.9994, loss_cls: 0.3271, loss: 0.3271 +2025-06-25 00:47:38,779 - pyskl - INFO - Epoch [80][1200/1281] lr: 1.121e-02, eta: 10:55:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9313, top5_acc: 0.9988, loss_cls: 0.3806, loss: 0.3806 +2025-06-25 00:48:19,056 - pyskl - INFO - Saving checkpoint at 80 epochs +2025-06-25 00:49:16,054 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 00:49:16,110 - pyskl - INFO - +top1_acc 0.8831 +top5_acc 0.9911 +2025-06-25 00:49:16,110 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 00:49:16,118 - pyskl - INFO - +mean_acc 0.8604 +2025-06-25 00:49:16,120 - pyskl - INFO - Epoch(val) [80][533] top1_acc: 0.8831, top5_acc: 0.9911, mean_class_accuracy: 0.8604 +2025-06-25 00:50:23,564 - pyskl - INFO - Epoch [81][100/1281] lr: 1.117e-02, eta: 10:53:59, time: 0.674, data_time: 0.191, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3497, loss: 0.3497 +2025-06-25 00:51:06,931 - pyskl - INFO - Epoch [81][200/1281] lr: 1.115e-02, eta: 10:53:15, time: 0.434, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9994, loss_cls: 0.3244, loss: 0.3244 +2025-06-25 00:51:56,228 - pyskl - INFO - Epoch [81][300/1281] lr: 1.113e-02, eta: 10:52:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2602, loss: 0.2602 +2025-06-25 00:52:45,036 - pyskl - INFO - Epoch [81][400/1281] lr: 1.111e-02, eta: 10:51:57, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9994, loss_cls: 0.2962, loss: 0.2962 +2025-06-25 00:53:34,059 - pyskl - INFO - Epoch [81][500/1281] lr: 1.109e-02, eta: 10:51:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 1.0000, loss_cls: 0.3163, loss: 0.3163 +2025-06-25 00:54:22,964 - pyskl - INFO - Epoch [81][600/1281] lr: 1.107e-02, eta: 10:50:38, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9988, loss_cls: 0.3262, loss: 0.3262 +2025-06-25 00:55:12,503 - pyskl - INFO - Epoch [81][700/1281] lr: 1.105e-02, eta: 10:49:59, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9363, top5_acc: 0.9969, loss_cls: 0.3614, loss: 0.3614 +2025-06-25 00:56:01,597 - pyskl - INFO - Epoch [81][800/1281] lr: 1.103e-02, eta: 10:49:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9962, loss_cls: 0.3475, loss: 0.3475 +2025-06-25 00:56:50,872 - pyskl - INFO - Epoch [81][900/1281] lr: 1.101e-02, eta: 10:48:40, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3288, loss: 0.3288 +2025-06-25 00:57:40,300 - pyskl - INFO - Epoch [81][1000/1281] lr: 1.099e-02, eta: 10:48:01, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9994, loss_cls: 0.3494, loss: 0.3494 +2025-06-25 00:58:29,552 - pyskl - INFO - Epoch [81][1100/1281] lr: 1.097e-02, eta: 10:47:22, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9356, top5_acc: 0.9988, loss_cls: 0.3718, loss: 0.3718 +2025-06-25 00:59:18,630 - pyskl - INFO - Epoch [81][1200/1281] lr: 1.095e-02, eta: 10:46:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9994, loss_cls: 0.3509, loss: 0.3509 +2025-06-25 00:59:59,195 - pyskl - INFO - Saving checkpoint at 81 epochs +2025-06-25 01:00:56,875 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:00:56,943 - pyskl - INFO - +top1_acc 0.8495 +top5_acc 0.9852 +2025-06-25 01:00:56,943 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:00:56,950 - pyskl - INFO - +mean_acc 0.8293 +2025-06-25 01:00:56,952 - pyskl - INFO - Epoch(val) [81][533] top1_acc: 0.8495, top5_acc: 0.9852, mean_class_accuracy: 0.8293 +2025-06-25 01:02:06,589 - pyskl - INFO - Epoch [82][100/1281] lr: 1.091e-02, eta: 10:45:15, time: 0.696, data_time: 0.184, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9994, loss_cls: 0.3344, loss: 0.3344 +2025-06-25 01:02:49,440 - pyskl - INFO - Epoch [82][200/1281] lr: 1.089e-02, eta: 10:44:30, time: 0.429, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 1.0000, loss_cls: 0.2856, loss: 0.2856 +2025-06-25 01:03:38,537 - pyskl - INFO - Epoch [82][300/1281] lr: 1.087e-02, eta: 10:43:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9419, top5_acc: 0.9981, loss_cls: 0.3304, loss: 0.3304 +2025-06-25 01:04:27,544 - pyskl - INFO - Epoch [82][400/1281] lr: 1.085e-02, eta: 10:43:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9300, top5_acc: 0.9988, loss_cls: 0.3498, loss: 0.3498 +2025-06-25 01:05:16,792 - pyskl - INFO - Epoch [82][500/1281] lr: 1.083e-02, eta: 10:42:32, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9481, top5_acc: 1.0000, loss_cls: 0.3087, loss: 0.3087 +2025-06-25 01:06:05,695 - pyskl - INFO - Epoch [82][600/1281] lr: 1.081e-02, eta: 10:41:53, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 0.9988, loss_cls: 0.3212, loss: 0.3212 +2025-06-25 01:06:54,683 - pyskl - INFO - Epoch [82][700/1281] lr: 1.079e-02, eta: 10:41:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9988, loss_cls: 0.3415, loss: 0.3415 +2025-06-25 01:07:43,440 - pyskl - INFO - Epoch [82][800/1281] lr: 1.077e-02, eta: 10:40:33, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9981, loss_cls: 0.3264, loss: 0.3264 +2025-06-25 01:08:32,314 - pyskl - INFO - Epoch [82][900/1281] lr: 1.075e-02, eta: 10:39:53, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9994, loss_cls: 0.3376, loss: 0.3376 +2025-06-25 01:09:21,444 - pyskl - INFO - Epoch [82][1000/1281] lr: 1.073e-02, eta: 10:39:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9325, top5_acc: 0.9975, loss_cls: 0.3879, loss: 0.3879 +2025-06-25 01:10:10,237 - pyskl - INFO - Epoch [82][1100/1281] lr: 1.071e-02, eta: 10:38:34, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 0.9988, loss_cls: 0.3592, loss: 0.3592 +2025-06-25 01:10:59,144 - pyskl - INFO - Epoch [82][1200/1281] lr: 1.069e-02, eta: 10:37:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9988, loss_cls: 0.3312, loss: 0.3312 +2025-06-25 01:11:39,581 - pyskl - INFO - Saving checkpoint at 82 epochs +2025-06-25 01:12:37,747 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:12:37,804 - pyskl - INFO - +top1_acc 0.8907 +top5_acc 0.9906 +2025-06-25 01:12:37,804 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:12:37,810 - pyskl - INFO - +mean_acc 0.8537 +2025-06-25 01:12:37,814 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_77.pth was removed +2025-06-25 01:12:37,978 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_82.pth. +2025-06-25 01:12:37,979 - pyskl - INFO - Best top1_acc is 0.8907 at 82 epoch. +2025-06-25 01:12:37,981 - pyskl - INFO - Epoch(val) [82][533] top1_acc: 0.8907, top5_acc: 0.9906, mean_class_accuracy: 0.8537 +2025-06-25 01:13:46,460 - pyskl - INFO - Epoch [83][100/1281] lr: 1.065e-02, eta: 10:36:26, time: 0.685, data_time: 0.189, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9981, loss_cls: 0.2647, loss: 0.2647 +2025-06-25 01:14:30,412 - pyskl - INFO - Epoch [83][200/1281] lr: 1.063e-02, eta: 10:35:42, time: 0.439, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.2996, loss: 0.2996 +2025-06-25 01:15:19,353 - pyskl - INFO - Epoch [83][300/1281] lr: 1.061e-02, eta: 10:35:02, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 1.0000, loss_cls: 0.2803, loss: 0.2803 +2025-06-25 01:16:08,212 - pyskl - INFO - Epoch [83][400/1281] lr: 1.059e-02, eta: 10:34:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2723, loss: 0.2723 +2025-06-25 01:16:57,208 - pyskl - INFO - Epoch [83][500/1281] lr: 1.057e-02, eta: 10:33:43, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9994, loss_cls: 0.2960, loss: 0.2960 +2025-06-25 01:17:46,291 - pyskl - INFO - Epoch [83][600/1281] lr: 1.055e-02, eta: 10:33:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 1.0000, loss_cls: 0.3266, loss: 0.3266 +2025-06-25 01:18:35,250 - pyskl - INFO - Epoch [83][700/1281] lr: 1.053e-02, eta: 10:32:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9406, top5_acc: 1.0000, loss_cls: 0.3107, loss: 0.3107 +2025-06-25 01:19:24,432 - pyskl - INFO - Epoch [83][800/1281] lr: 1.051e-02, eta: 10:31:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9994, loss_cls: 0.3207, loss: 0.3207 +2025-06-25 01:20:13,714 - pyskl - INFO - Epoch [83][900/1281] lr: 1.049e-02, eta: 10:31:04, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9431, top5_acc: 1.0000, loss_cls: 0.3273, loss: 0.3273 +2025-06-25 01:21:02,816 - pyskl - INFO - Epoch [83][1000/1281] lr: 1.047e-02, eta: 10:30:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9337, top5_acc: 0.9994, loss_cls: 0.3446, loss: 0.3446 +2025-06-25 01:21:51,782 - pyskl - INFO - Epoch [83][1100/1281] lr: 1.045e-02, eta: 10:29:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9981, loss_cls: 0.3369, loss: 0.3369 +2025-06-25 01:22:40,814 - pyskl - INFO - Epoch [83][1200/1281] lr: 1.043e-02, eta: 10:29:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9513, top5_acc: 0.9969, loss_cls: 0.2934, loss: 0.2934 +2025-06-25 01:23:21,031 - pyskl - INFO - Saving checkpoint at 83 epochs +2025-06-25 01:24:18,118 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:24:18,175 - pyskl - INFO - +top1_acc 0.8728 +top5_acc 0.9905 +2025-06-25 01:24:18,175 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:24:18,183 - pyskl - INFO - +mean_acc 0.8311 +2025-06-25 01:24:18,185 - pyskl - INFO - Epoch(val) [83][533] top1_acc: 0.8728, top5_acc: 0.9905, mean_class_accuracy: 0.8311 +2025-06-25 01:25:25,980 - pyskl - INFO - Epoch [84][100/1281] lr: 1.040e-02, eta: 10:27:36, time: 0.678, data_time: 0.188, memory: 4083, top1_acc: 0.9256, top5_acc: 0.9988, loss_cls: 0.3870, loss: 0.3870 +2025-06-25 01:26:09,635 - pyskl - INFO - Epoch [84][200/1281] lr: 1.038e-02, eta: 10:26:52, time: 0.437, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.3076, loss: 0.3076 +2025-06-25 01:26:58,739 - pyskl - INFO - Epoch [84][300/1281] lr: 1.036e-02, eta: 10:26:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2803, loss: 0.2803 +2025-06-25 01:27:47,607 - pyskl - INFO - Epoch [84][400/1281] lr: 1.034e-02, eta: 10:25:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.3042, loss: 0.3042 +2025-06-25 01:28:36,921 - pyskl - INFO - Epoch [84][500/1281] lr: 1.031e-02, eta: 10:24:52, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9994, loss_cls: 0.3167, loss: 0.3167 +2025-06-25 01:29:25,574 - pyskl - INFO - Epoch [84][600/1281] lr: 1.029e-02, eta: 10:24:12, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9400, top5_acc: 0.9994, loss_cls: 0.3355, loss: 0.3355 +2025-06-25 01:30:14,459 - pyskl - INFO - Epoch [84][700/1281] lr: 1.027e-02, eta: 10:23:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9988, loss_cls: 0.3379, loss: 0.3379 +2025-06-25 01:31:03,521 - pyskl - INFO - Epoch [84][800/1281] lr: 1.025e-02, eta: 10:22:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 1.0000, loss_cls: 0.3004, loss: 0.3004 +2025-06-25 01:31:52,603 - pyskl - INFO - Epoch [84][900/1281] lr: 1.023e-02, eta: 10:22:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9988, loss_cls: 0.3300, loss: 0.3300 +2025-06-25 01:32:41,627 - pyskl - INFO - Epoch [84][1000/1281] lr: 1.021e-02, eta: 10:21:32, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.3221, loss: 0.3221 +2025-06-25 01:33:30,583 - pyskl - INFO - Epoch [84][1100/1281] lr: 1.019e-02, eta: 10:20:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2668, loss: 0.2668 +2025-06-25 01:34:19,377 - pyskl - INFO - Epoch [84][1200/1281] lr: 1.017e-02, eta: 10:20:12, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9463, top5_acc: 0.9988, loss_cls: 0.3166, loss: 0.3166 +2025-06-25 01:34:59,435 - pyskl - INFO - Saving checkpoint at 84 epochs +2025-06-25 01:35:56,439 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:35:56,562 - pyskl - INFO - +top1_acc 0.8720 +top5_acc 0.9901 +2025-06-25 01:35:56,562 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:35:56,569 - pyskl - INFO - +mean_acc 0.8363 +2025-06-25 01:35:56,571 - pyskl - INFO - Epoch(val) [84][533] top1_acc: 0.8720, top5_acc: 0.9901, mean_class_accuracy: 0.8363 +2025-06-25 01:37:04,318 - pyskl - INFO - Epoch [85][100/1281] lr: 1.014e-02, eta: 10:18:43, time: 0.677, data_time: 0.189, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2670, loss: 0.2670 +2025-06-25 01:37:48,799 - pyskl - INFO - Epoch [85][200/1281] lr: 1.012e-02, eta: 10:18:00, time: 0.445, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2678, loss: 0.2678 +2025-06-25 01:38:37,745 - pyskl - INFO - Epoch [85][300/1281] lr: 1.010e-02, eta: 10:17:20, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9981, loss_cls: 0.2613, loss: 0.2613 +2025-06-25 01:39:26,837 - pyskl - INFO - Epoch [85][400/1281] lr: 1.008e-02, eta: 10:16:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2545, loss: 0.2545 +2025-06-25 01:40:15,868 - pyskl - INFO - Epoch [85][500/1281] lr: 1.006e-02, eta: 10:16:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2375, loss: 0.2375 +2025-06-25 01:41:05,099 - pyskl - INFO - Epoch [85][600/1281] lr: 1.004e-02, eta: 10:15:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 0.9994, loss_cls: 0.2543, loss: 0.2543 +2025-06-25 01:41:53,768 - pyskl - INFO - Epoch [85][700/1281] lr: 1.002e-02, eta: 10:14:39, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2781, loss: 0.2781 +2025-06-25 01:42:42,891 - pyskl - INFO - Epoch [85][800/1281] lr: 9.998e-03, eta: 10:13:59, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9437, top5_acc: 0.9975, loss_cls: 0.3011, loss: 0.3011 +2025-06-25 01:43:32,120 - pyskl - INFO - Epoch [85][900/1281] lr: 9.978e-03, eta: 10:13:19, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9500, top5_acc: 1.0000, loss_cls: 0.3016, loss: 0.3016 +2025-06-25 01:44:21,066 - pyskl - INFO - Epoch [85][1000/1281] lr: 9.958e-03, eta: 10:12:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.2214, loss: 0.2214 +2025-06-25 01:45:09,936 - pyskl - INFO - Epoch [85][1100/1281] lr: 9.937e-03, eta: 10:11:59, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2752, loss: 0.2752 +2025-06-25 01:45:59,068 - pyskl - INFO - Epoch [85][1200/1281] lr: 9.917e-03, eta: 10:11:19, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9981, loss_cls: 0.3666, loss: 0.3666 +2025-06-25 01:46:39,497 - pyskl - INFO - Saving checkpoint at 85 epochs +2025-06-25 01:47:35,396 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:47:35,452 - pyskl - INFO - +top1_acc 0.8777 +top5_acc 0.9914 +2025-06-25 01:47:35,452 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:47:35,459 - pyskl - INFO - +mean_acc 0.8470 +2025-06-25 01:47:35,461 - pyskl - INFO - Epoch(val) [85][533] top1_acc: 0.8777, top5_acc: 0.9914, mean_class_accuracy: 0.8470 +2025-06-25 01:48:40,639 - pyskl - INFO - Epoch [86][100/1281] lr: 9.881e-03, eta: 10:09:48, time: 0.652, data_time: 0.189, memory: 4083, top1_acc: 0.9506, top5_acc: 1.0000, loss_cls: 0.2889, loss: 0.2889 +2025-06-25 01:49:24,913 - pyskl - INFO - Epoch [86][200/1281] lr: 9.861e-03, eta: 10:09:04, time: 0.443, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9988, loss_cls: 0.2370, loss: 0.2370 +2025-06-25 01:50:13,987 - pyskl - INFO - Epoch [86][300/1281] lr: 9.841e-03, eta: 10:08:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2439, loss: 0.2439 +2025-06-25 01:51:02,790 - pyskl - INFO - Epoch [86][400/1281] lr: 9.821e-03, eta: 10:07:44, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9981, loss_cls: 0.2815, loss: 0.2815 +2025-06-25 01:51:51,503 - pyskl - INFO - Epoch [86][500/1281] lr: 9.801e-03, eta: 10:07:03, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 1.0000, loss_cls: 0.2831, loss: 0.2831 +2025-06-25 01:52:40,510 - pyskl - INFO - Epoch [86][600/1281] lr: 9.781e-03, eta: 10:06:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9994, loss_cls: 0.2774, loss: 0.2774 +2025-06-25 01:53:29,772 - pyskl - INFO - Epoch [86][700/1281] lr: 9.762e-03, eta: 10:05:43, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 1.0000, loss_cls: 0.2874, loss: 0.2874 +2025-06-25 01:54:18,955 - pyskl - INFO - Epoch [86][800/1281] lr: 9.742e-03, eta: 10:05:03, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2896, loss: 0.2896 +2025-06-25 01:55:08,247 - pyskl - INFO - Epoch [86][900/1281] lr: 9.722e-03, eta: 10:04:23, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9456, top5_acc: 0.9988, loss_cls: 0.2958, loss: 0.2958 +2025-06-25 01:55:57,031 - pyskl - INFO - Epoch [86][1000/1281] lr: 9.702e-03, eta: 10:03:43, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9475, top5_acc: 1.0000, loss_cls: 0.3192, loss: 0.3192 +2025-06-25 01:56:45,867 - pyskl - INFO - Epoch [86][1100/1281] lr: 9.682e-03, eta: 10:03:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9994, loss_cls: 0.2656, loss: 0.2656 +2025-06-25 01:57:34,670 - pyskl - INFO - Epoch [86][1200/1281] lr: 9.662e-03, eta: 10:02:22, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9425, top5_acc: 0.9988, loss_cls: 0.3095, loss: 0.3095 +2025-06-25 01:58:15,031 - pyskl - INFO - Saving checkpoint at 86 epochs +2025-06-25 01:59:11,074 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 01:59:11,129 - pyskl - INFO - +top1_acc 0.8797 +top5_acc 0.9887 +2025-06-25 01:59:11,129 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 01:59:11,136 - pyskl - INFO - +mean_acc 0.8519 +2025-06-25 01:59:11,138 - pyskl - INFO - Epoch(val) [86][533] top1_acc: 0.8797, top5_acc: 0.9887, mean_class_accuracy: 0.8519 +2025-06-25 02:00:16,514 - pyskl - INFO - Epoch [87][100/1281] lr: 9.626e-03, eta: 10:00:51, time: 0.654, data_time: 0.188, memory: 4083, top1_acc: 0.9475, top5_acc: 0.9988, loss_cls: 0.3042, loss: 0.3042 +2025-06-25 02:01:00,587 - pyskl - INFO - Epoch [87][200/1281] lr: 9.606e-03, eta: 10:00:07, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.3111, loss: 0.3111 +2025-06-25 02:01:49,381 - pyskl - INFO - Epoch [87][300/1281] lr: 9.586e-03, eta: 9:59:27, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2462, loss: 0.2462 +2025-06-25 02:02:38,414 - pyskl - INFO - Epoch [87][400/1281] lr: 9.566e-03, eta: 9:58:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 0.9994, loss_cls: 0.2505, loss: 0.2505 +2025-06-25 02:03:27,342 - pyskl - INFO - Epoch [87][500/1281] lr: 9.546e-03, eta: 9:58:06, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2611, loss: 0.2611 +2025-06-25 02:04:16,307 - pyskl - INFO - Epoch [87][600/1281] lr: 9.527e-03, eta: 9:57:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 1.0000, loss_cls: 0.2496, loss: 0.2496 +2025-06-25 02:05:05,495 - pyskl - INFO - Epoch [87][700/1281] lr: 9.507e-03, eta: 9:56:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9469, top5_acc: 0.9988, loss_cls: 0.2811, loss: 0.2811 +2025-06-25 02:05:54,513 - pyskl - INFO - Epoch [87][800/1281] lr: 9.487e-03, eta: 9:56:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9988, loss_cls: 0.2707, loss: 0.2707 +2025-06-25 02:06:43,617 - pyskl - INFO - Epoch [87][900/1281] lr: 9.467e-03, eta: 9:55:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.3305, loss: 0.3305 +2025-06-25 02:07:32,903 - pyskl - INFO - Epoch [87][1000/1281] lr: 9.447e-03, eta: 9:54:44, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2518, loss: 0.2518 +2025-06-25 02:08:21,740 - pyskl - INFO - Epoch [87][1100/1281] lr: 9.427e-03, eta: 9:54:04, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9981, loss_cls: 0.3055, loss: 0.3055 +2025-06-25 02:09:10,682 - pyskl - INFO - Epoch [87][1200/1281] lr: 9.408e-03, eta: 9:53:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9331, top5_acc: 0.9981, loss_cls: 0.3532, loss: 0.3532 +2025-06-25 02:09:50,860 - pyskl - INFO - Saving checkpoint at 87 epochs +2025-06-25 02:10:47,886 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:10:47,942 - pyskl - INFO - +top1_acc 0.8770 +top5_acc 0.9898 +2025-06-25 02:10:47,942 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:10:47,949 - pyskl - INFO - +mean_acc 0.8416 +2025-06-25 02:10:47,951 - pyskl - INFO - Epoch(val) [87][533] top1_acc: 0.8770, top5_acc: 0.9898, mean_class_accuracy: 0.8416 +2025-06-25 02:11:56,934 - pyskl - INFO - Epoch [88][100/1281] lr: 9.372e-03, eta: 9:51:56, time: 0.690, data_time: 0.188, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2732, loss: 0.2732 +2025-06-25 02:12:41,794 - pyskl - INFO - Epoch [88][200/1281] lr: 9.352e-03, eta: 9:51:12, time: 0.449, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9994, loss_cls: 0.2829, loss: 0.2829 +2025-06-25 02:13:30,671 - pyskl - INFO - Epoch [88][300/1281] lr: 9.332e-03, eta: 9:50:31, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2511, loss: 0.2511 +2025-06-25 02:14:19,861 - pyskl - INFO - Epoch [88][400/1281] lr: 9.312e-03, eta: 9:49:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 0.9994, loss_cls: 0.2733, loss: 0.2733 +2025-06-25 02:15:08,972 - pyskl - INFO - Epoch [88][500/1281] lr: 9.293e-03, eta: 9:49:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9988, loss_cls: 0.2627, loss: 0.2627 +2025-06-25 02:15:57,934 - pyskl - INFO - Epoch [88][600/1281] lr: 9.273e-03, eta: 9:48:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2405, loss: 0.2405 +2025-06-25 02:16:47,040 - pyskl - INFO - Epoch [88][700/1281] lr: 9.253e-03, eta: 9:47:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2358, loss: 0.2358 +2025-06-25 02:17:36,279 - pyskl - INFO - Epoch [88][800/1281] lr: 9.233e-03, eta: 9:47:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2796, loss: 0.2796 +2025-06-25 02:18:25,534 - pyskl - INFO - Epoch [88][900/1281] lr: 9.214e-03, eta: 9:46:29, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9988, loss_cls: 0.2696, loss: 0.2696 +2025-06-25 02:19:14,840 - pyskl - INFO - Epoch [88][1000/1281] lr: 9.194e-03, eta: 9:45:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9994, loss_cls: 0.2811, loss: 0.2811 +2025-06-25 02:20:04,226 - pyskl - INFO - Epoch [88][1100/1281] lr: 9.174e-03, eta: 9:45:08, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9494, top5_acc: 1.0000, loss_cls: 0.2827, loss: 0.2827 +2025-06-25 02:20:53,309 - pyskl - INFO - Epoch [88][1200/1281] lr: 9.155e-03, eta: 9:44:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 1.0000, loss_cls: 0.2943, loss: 0.2943 +2025-06-25 02:21:33,555 - pyskl - INFO - Saving checkpoint at 88 epochs +2025-06-25 02:22:29,210 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:22:29,266 - pyskl - INFO - +top1_acc 0.8782 +top5_acc 0.9901 +2025-06-25 02:22:29,267 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:22:29,274 - pyskl - INFO - +mean_acc 0.8338 +2025-06-25 02:22:29,277 - pyskl - INFO - Epoch(val) [88][533] top1_acc: 0.8782, top5_acc: 0.9901, mean_class_accuracy: 0.8338 +2025-06-25 02:23:34,097 - pyskl - INFO - Epoch [89][100/1281] lr: 9.119e-03, eta: 9:42:57, time: 0.648, data_time: 0.187, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2230, loss: 0.2230 +2025-06-25 02:24:18,203 - pyskl - INFO - Epoch [89][200/1281] lr: 9.099e-03, eta: 9:42:13, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9550, top5_acc: 0.9994, loss_cls: 0.2568, loss: 0.2568 +2025-06-25 02:25:07,462 - pyskl - INFO - Epoch [89][300/1281] lr: 9.080e-03, eta: 9:41:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2494, loss: 0.2494 +2025-06-25 02:25:56,735 - pyskl - INFO - Epoch [89][400/1281] lr: 9.060e-03, eta: 9:40:52, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9988, loss_cls: 0.2481, loss: 0.2481 +2025-06-25 02:26:45,658 - pyskl - INFO - Epoch [89][500/1281] lr: 9.040e-03, eta: 9:40:11, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2300, loss: 0.2300 +2025-06-25 02:27:34,911 - pyskl - INFO - Epoch [89][600/1281] lr: 9.021e-03, eta: 9:39:31, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9519, top5_acc: 0.9981, loss_cls: 0.2953, loss: 0.2953 +2025-06-25 02:28:23,894 - pyskl - INFO - Epoch [89][700/1281] lr: 9.001e-03, eta: 9:38:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9988, loss_cls: 0.2956, loss: 0.2956 +2025-06-25 02:29:13,053 - pyskl - INFO - Epoch [89][800/1281] lr: 8.982e-03, eta: 9:38:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9544, top5_acc: 0.9975, loss_cls: 0.2734, loss: 0.2734 +2025-06-25 02:30:02,190 - pyskl - INFO - Epoch [89][900/1281] lr: 8.962e-03, eta: 9:37:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2390, loss: 0.2390 +2025-06-25 02:30:51,244 - pyskl - INFO - Epoch [89][1000/1281] lr: 8.942e-03, eta: 9:36:48, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2747, loss: 0.2747 +2025-06-25 02:31:40,328 - pyskl - INFO - Epoch [89][1100/1281] lr: 8.923e-03, eta: 9:36:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9444, top5_acc: 0.9988, loss_cls: 0.3061, loss: 0.3061 +2025-06-25 02:32:29,508 - pyskl - INFO - Epoch [89][1200/1281] lr: 8.903e-03, eta: 9:35:26, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9988, loss_cls: 0.2723, loss: 0.2723 +2025-06-25 02:33:09,689 - pyskl - INFO - Saving checkpoint at 89 epochs +2025-06-25 02:34:05,880 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:34:05,945 - pyskl - INFO - +top1_acc 0.8857 +top5_acc 0.9908 +2025-06-25 02:34:05,945 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:34:05,952 - pyskl - INFO - +mean_acc 0.8501 +2025-06-25 02:34:05,954 - pyskl - INFO - Epoch(val) [89][533] top1_acc: 0.8857, top5_acc: 0.9908, mean_class_accuracy: 0.8501 +2025-06-25 02:35:12,032 - pyskl - INFO - Epoch [90][100/1281] lr: 8.868e-03, eta: 9:33:57, time: 0.661, data_time: 0.188, memory: 4083, top1_acc: 0.9650, top5_acc: 0.9994, loss_cls: 0.2182, loss: 0.2182 +2025-06-25 02:35:56,486 - pyskl - INFO - Epoch [90][200/1281] lr: 8.848e-03, eta: 9:33:13, time: 0.445, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2391, loss: 0.2391 +2025-06-25 02:36:45,953 - pyskl - INFO - Epoch [90][300/1281] lr: 8.829e-03, eta: 9:32:33, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2726, loss: 0.2726 +2025-06-25 02:37:34,988 - pyskl - INFO - Epoch [90][400/1281] lr: 8.809e-03, eta: 9:31:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2309, loss: 0.2309 +2025-06-25 02:38:24,210 - pyskl - INFO - Epoch [90][500/1281] lr: 8.790e-03, eta: 9:31:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 0.9994, loss_cls: 0.2425, loss: 0.2425 +2025-06-25 02:39:13,300 - pyskl - INFO - Epoch [90][600/1281] lr: 8.770e-03, eta: 9:30:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2056, loss: 0.2056 +2025-06-25 02:40:02,312 - pyskl - INFO - Epoch [90][700/1281] lr: 8.751e-03, eta: 9:29:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.2228, loss: 0.2228 +2025-06-25 02:40:51,487 - pyskl - INFO - Epoch [90][800/1281] lr: 8.731e-03, eta: 9:29:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 0.9988, loss_cls: 0.2254, loss: 0.2254 +2025-06-25 02:41:40,659 - pyskl - INFO - Epoch [90][900/1281] lr: 8.712e-03, eta: 9:28:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2918, loss: 0.2918 +2025-06-25 02:42:29,528 - pyskl - INFO - Epoch [90][1000/1281] lr: 8.692e-03, eta: 9:27:47, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9487, top5_acc: 1.0000, loss_cls: 0.2915, loss: 0.2915 +2025-06-25 02:43:18,775 - pyskl - INFO - Epoch [90][1100/1281] lr: 8.673e-03, eta: 9:27:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9394, top5_acc: 0.9956, loss_cls: 0.3654, loss: 0.3654 +2025-06-25 02:44:07,988 - pyskl - INFO - Epoch [90][1200/1281] lr: 8.653e-03, eta: 9:26:25, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.3016, loss: 0.3016 +2025-06-25 02:44:48,725 - pyskl - INFO - Saving checkpoint at 90 epochs +2025-06-25 02:45:44,907 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:45:44,964 - pyskl - INFO - +top1_acc 0.8608 +top5_acc 0.9899 +2025-06-25 02:45:44,964 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:45:44,972 - pyskl - INFO - +mean_acc 0.8368 +2025-06-25 02:45:44,974 - pyskl - INFO - Epoch(val) [90][533] top1_acc: 0.8608, top5_acc: 0.9899, mean_class_accuracy: 0.8368 +2025-06-25 02:46:51,925 - pyskl - INFO - Epoch [91][100/1281] lr: 8.618e-03, eta: 9:24:57, time: 0.669, data_time: 0.191, memory: 4083, top1_acc: 0.9450, top5_acc: 0.9988, loss_cls: 0.3338, loss: 0.3338 +2025-06-25 02:47:36,384 - pyskl - INFO - Epoch [91][200/1281] lr: 8.599e-03, eta: 9:24:13, time: 0.445, data_time: 0.000, memory: 4083, top1_acc: 0.9594, top5_acc: 0.9994, loss_cls: 0.2462, loss: 0.2462 +2025-06-25 02:48:25,508 - pyskl - INFO - Epoch [91][300/1281] lr: 8.579e-03, eta: 9:23:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9994, loss_cls: 0.2358, loss: 0.2358 +2025-06-25 02:49:14,684 - pyskl - INFO - Epoch [91][400/1281] lr: 8.560e-03, eta: 9:22:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 0.9988, loss_cls: 0.2295, loss: 0.2295 +2025-06-25 02:50:03,568 - pyskl - INFO - Epoch [91][500/1281] lr: 8.540e-03, eta: 9:22:10, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 1.0000, loss_cls: 0.2596, loss: 0.2596 +2025-06-25 02:50:52,814 - pyskl - INFO - Epoch [91][600/1281] lr: 8.521e-03, eta: 9:21:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9988, loss_cls: 0.2299, loss: 0.2299 +2025-06-25 02:51:42,010 - pyskl - INFO - Epoch [91][700/1281] lr: 8.502e-03, eta: 9:20:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2257, loss: 0.2257 +2025-06-25 02:52:31,040 - pyskl - INFO - Epoch [91][800/1281] lr: 8.482e-03, eta: 9:20:07, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9413, top5_acc: 0.9994, loss_cls: 0.2995, loss: 0.2995 +2025-06-25 02:53:20,060 - pyskl - INFO - Epoch [91][900/1281] lr: 8.463e-03, eta: 9:19:26, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9994, loss_cls: 0.2515, loss: 0.2515 +2025-06-25 02:54:09,193 - pyskl - INFO - Epoch [91][1000/1281] lr: 8.444e-03, eta: 9:18:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 0.9988, loss_cls: 0.2808, loss: 0.2808 +2025-06-25 02:54:58,314 - pyskl - INFO - Epoch [91][1100/1281] lr: 8.424e-03, eta: 9:18:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9581, top5_acc: 1.0000, loss_cls: 0.2570, loss: 0.2570 +2025-06-25 02:55:47,510 - pyskl - INFO - Epoch [91][1200/1281] lr: 8.405e-03, eta: 9:17:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 1.0000, loss_cls: 0.2656, loss: 0.2656 +2025-06-25 02:56:27,981 - pyskl - INFO - Saving checkpoint at 91 epochs +2025-06-25 02:57:24,190 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 02:57:24,248 - pyskl - INFO - +top1_acc 0.8728 +top5_acc 0.9878 +2025-06-25 02:57:24,248 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 02:57:24,256 - pyskl - INFO - +mean_acc 0.8328 +2025-06-25 02:57:24,258 - pyskl - INFO - Epoch(val) [91][533] top1_acc: 0.8728, top5_acc: 0.9878, mean_class_accuracy: 0.8328 +2025-06-25 02:58:30,652 - pyskl - INFO - Epoch [92][100/1281] lr: 8.370e-03, eta: 9:15:55, time: 0.664, data_time: 0.184, memory: 4083, top1_acc: 0.9594, top5_acc: 1.0000, loss_cls: 0.2439, loss: 0.2439 +2025-06-25 02:59:14,098 - pyskl - INFO - Epoch [92][200/1281] lr: 8.351e-03, eta: 9:15:10, time: 0.434, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2391, loss: 0.2391 +2025-06-25 03:00:03,485 - pyskl - INFO - Epoch [92][300/1281] lr: 8.332e-03, eta: 9:14:29, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9981, loss_cls: 0.2518, loss: 0.2518 +2025-06-25 03:00:52,763 - pyskl - INFO - Epoch [92][400/1281] lr: 8.312e-03, eta: 9:13:48, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.2169, loss: 0.2169 +2025-06-25 03:01:42,152 - pyskl - INFO - Epoch [92][500/1281] lr: 8.293e-03, eta: 9:13:07, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 0.9981, loss_cls: 0.2260, loss: 0.2260 +2025-06-25 03:02:31,651 - pyskl - INFO - Epoch [92][600/1281] lr: 8.274e-03, eta: 9:12:26, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9375, top5_acc: 0.9994, loss_cls: 0.3074, loss: 0.3074 +2025-06-25 03:03:20,946 - pyskl - INFO - Epoch [92][700/1281] lr: 8.255e-03, eta: 9:11:45, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9537, top5_acc: 1.0000, loss_cls: 0.2674, loss: 0.2674 +2025-06-25 03:04:10,043 - pyskl - INFO - Epoch [92][800/1281] lr: 8.235e-03, eta: 9:11:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2446, loss: 0.2446 +2025-06-25 03:04:59,036 - pyskl - INFO - Epoch [92][900/1281] lr: 8.216e-03, eta: 9:10:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 1.0000, loss_cls: 0.2159, loss: 0.2159 +2025-06-25 03:05:47,951 - pyskl - INFO - Epoch [92][1000/1281] lr: 8.197e-03, eta: 9:09:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 1.0000, loss_cls: 0.2702, loss: 0.2702 +2025-06-25 03:06:37,155 - pyskl - INFO - Epoch [92][1100/1281] lr: 8.178e-03, eta: 9:09:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9525, top5_acc: 0.9988, loss_cls: 0.2754, loss: 0.2754 +2025-06-25 03:07:26,219 - pyskl - INFO - Epoch [92][1200/1281] lr: 8.159e-03, eta: 9:08:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9531, top5_acc: 0.9994, loss_cls: 0.2700, loss: 0.2700 +2025-06-25 03:08:06,631 - pyskl - INFO - Saving checkpoint at 92 epochs +2025-06-25 03:09:04,497 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:09:04,552 - pyskl - INFO - +top1_acc 0.8930 +top5_acc 0.9911 +2025-06-25 03:09:04,552 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:09:04,559 - pyskl - INFO - +mean_acc 0.8547 +2025-06-25 03:09:04,563 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_82.pth was removed +2025-06-25 03:09:04,774 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_92.pth. +2025-06-25 03:09:04,774 - pyskl - INFO - Best top1_acc is 0.8930 at 92 epoch. +2025-06-25 03:09:04,777 - pyskl - INFO - Epoch(val) [92][533] top1_acc: 0.8930, top5_acc: 0.9911, mean_class_accuracy: 0.8547 +2025-06-25 03:10:14,509 - pyskl - INFO - Epoch [93][100/1281] lr: 8.124e-03, eta: 9:06:53, time: 0.697, data_time: 0.187, memory: 4083, top1_acc: 0.9719, top5_acc: 0.9988, loss_cls: 0.2000, loss: 0.2000 +2025-06-25 03:10:57,932 - pyskl - INFO - Epoch [93][200/1281] lr: 8.105e-03, eta: 9:06:08, time: 0.434, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.1922, loss: 0.1922 +2025-06-25 03:11:46,998 - pyskl - INFO - Epoch [93][300/1281] lr: 8.086e-03, eta: 9:05:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2207, loss: 0.2207 +2025-06-25 03:12:35,906 - pyskl - INFO - Epoch [93][400/1281] lr: 8.067e-03, eta: 9:04:46, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 0.9988, loss_cls: 0.2668, loss: 0.2668 +2025-06-25 03:13:24,886 - pyskl - INFO - Epoch [93][500/1281] lr: 8.047e-03, eta: 9:04:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2385, loss: 0.2385 +2025-06-25 03:14:14,018 - pyskl - INFO - Epoch [93][600/1281] lr: 8.028e-03, eta: 9:03:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 0.9994, loss_cls: 0.2041, loss: 0.2041 +2025-06-25 03:15:03,218 - pyskl - INFO - Epoch [93][700/1281] lr: 8.009e-03, eta: 9:02:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2032, loss: 0.2032 +2025-06-25 03:15:52,189 - pyskl - INFO - Epoch [93][800/1281] lr: 7.990e-03, eta: 9:02:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.2142, loss: 0.2142 +2025-06-25 03:16:41,405 - pyskl - INFO - Epoch [93][900/1281] lr: 7.971e-03, eta: 9:01:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2274, loss: 0.2274 +2025-06-25 03:17:30,503 - pyskl - INFO - Epoch [93][1000/1281] lr: 7.952e-03, eta: 9:00:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2412, loss: 0.2412 +2025-06-25 03:18:20,093 - pyskl - INFO - Epoch [93][1100/1281] lr: 7.933e-03, eta: 8:59:58, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2293, loss: 0.2293 +2025-06-25 03:19:09,554 - pyskl - INFO - Epoch [93][1200/1281] lr: 7.914e-03, eta: 8:59:17, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 0.9994, loss_cls: 0.2660, loss: 0.2660 +2025-06-25 03:19:49,901 - pyskl - INFO - Saving checkpoint at 93 epochs +2025-06-25 03:20:48,016 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:20:48,071 - pyskl - INFO - +top1_acc 0.8835 +top5_acc 0.9913 +2025-06-25 03:20:48,071 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:20:48,078 - pyskl - INFO - +mean_acc 0.8601 +2025-06-25 03:20:48,079 - pyskl - INFO - Epoch(val) [93][533] top1_acc: 0.8835, top5_acc: 0.9913, mean_class_accuracy: 0.8601 +2025-06-25 03:21:56,846 - pyskl - INFO - Epoch [94][100/1281] lr: 7.880e-03, eta: 8:57:49, time: 0.688, data_time: 0.191, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1894, loss: 0.1894 +2025-06-25 03:22:41,273 - pyskl - INFO - Epoch [94][200/1281] lr: 7.861e-03, eta: 8:57:05, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1984, loss: 0.1984 +2025-06-25 03:23:30,372 - pyskl - INFO - Epoch [94][300/1281] lr: 7.842e-03, eta: 8:56:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1860, loss: 0.1860 +2025-06-25 03:24:19,870 - pyskl - INFO - Epoch [94][400/1281] lr: 7.823e-03, eta: 8:55:43, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 1.0000, loss_cls: 0.2155, loss: 0.2155 +2025-06-25 03:25:09,255 - pyskl - INFO - Epoch [94][500/1281] lr: 7.804e-03, eta: 8:55:02, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2362, loss: 0.2362 +2025-06-25 03:25:58,026 - pyskl - INFO - Epoch [94][600/1281] lr: 7.785e-03, eta: 8:54:20, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.2170, loss: 0.2170 +2025-06-25 03:26:47,140 - pyskl - INFO - Epoch [94][700/1281] lr: 7.766e-03, eta: 8:53:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1996, loss: 0.1996 +2025-06-25 03:27:36,399 - pyskl - INFO - Epoch [94][800/1281] lr: 7.747e-03, eta: 8:52:58, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9606, top5_acc: 1.0000, loss_cls: 0.2379, loss: 0.2379 +2025-06-25 03:28:25,602 - pyskl - INFO - Epoch [94][900/1281] lr: 7.728e-03, eta: 8:52:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1906, loss: 0.1906 +2025-06-25 03:29:14,792 - pyskl - INFO - Epoch [94][1000/1281] lr: 7.709e-03, eta: 8:51:35, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.2083, loss: 0.2083 +2025-06-25 03:30:04,008 - pyskl - INFO - Epoch [94][1100/1281] lr: 7.690e-03, eta: 8:50:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9625, top5_acc: 1.0000, loss_cls: 0.2199, loss: 0.2199 +2025-06-25 03:30:53,124 - pyskl - INFO - Epoch [94][1200/1281] lr: 7.672e-03, eta: 8:50:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 1.0000, loss_cls: 0.2416, loss: 0.2416 +2025-06-25 03:31:33,386 - pyskl - INFO - Saving checkpoint at 94 epochs +2025-06-25 03:32:30,143 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:32:30,199 - pyskl - INFO - +top1_acc 0.8803 +top5_acc 0.9896 +2025-06-25 03:32:30,199 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:32:30,206 - pyskl - INFO - +mean_acc 0.8580 +2025-06-25 03:32:30,207 - pyskl - INFO - Epoch(val) [94][533] top1_acc: 0.8803, top5_acc: 0.9896, mean_class_accuracy: 0.8580 +2025-06-25 03:33:36,791 - pyskl - INFO - Epoch [95][100/1281] lr: 7.637e-03, eta: 8:48:44, time: 0.666, data_time: 0.184, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1948, loss: 0.1948 +2025-06-25 03:34:20,533 - pyskl - INFO - Epoch [95][200/1281] lr: 7.619e-03, eta: 8:47:59, time: 0.437, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.1858, loss: 0.1858 +2025-06-25 03:35:09,477 - pyskl - INFO - Epoch [95][300/1281] lr: 7.600e-03, eta: 8:47:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9994, loss_cls: 0.2198, loss: 0.2198 +2025-06-25 03:35:58,634 - pyskl - INFO - Epoch [95][400/1281] lr: 7.581e-03, eta: 8:46:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 0.9994, loss_cls: 0.2115, loss: 0.2115 +2025-06-25 03:36:47,887 - pyskl - INFO - Epoch [95][500/1281] lr: 7.562e-03, eta: 8:45:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2255, loss: 0.2255 +2025-06-25 03:37:37,291 - pyskl - INFO - Epoch [95][600/1281] lr: 7.543e-03, eta: 8:45:14, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2088, loss: 0.2088 +2025-06-25 03:38:26,449 - pyskl - INFO - Epoch [95][700/1281] lr: 7.525e-03, eta: 8:44:32, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9575, top5_acc: 0.9988, loss_cls: 0.2490, loss: 0.2490 +2025-06-25 03:39:15,743 - pyskl - INFO - Epoch [95][800/1281] lr: 7.506e-03, eta: 8:43:51, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9569, top5_acc: 0.9988, loss_cls: 0.2478, loss: 0.2478 +2025-06-25 03:40:04,960 - pyskl - INFO - Epoch [95][900/1281] lr: 7.487e-03, eta: 8:43:09, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 1.0000, loss_cls: 0.2117, loss: 0.2117 +2025-06-25 03:40:53,959 - pyskl - INFO - Epoch [95][1000/1281] lr: 7.468e-03, eta: 8:42:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9506, top5_acc: 1.0000, loss_cls: 0.2698, loss: 0.2698 +2025-06-25 03:41:42,707 - pyskl - INFO - Epoch [95][1100/1281] lr: 7.450e-03, eta: 8:41:46, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.2151, loss: 0.2151 +2025-06-25 03:42:31,912 - pyskl - INFO - Epoch [95][1200/1281] lr: 7.431e-03, eta: 8:41:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9556, top5_acc: 1.0000, loss_cls: 0.2390, loss: 0.2390 +2025-06-25 03:43:12,390 - pyskl - INFO - Saving checkpoint at 95 epochs +2025-06-25 03:44:10,155 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:44:10,223 - pyskl - INFO - +top1_acc 0.8985 +top5_acc 0.9927 +2025-06-25 03:44:10,223 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:44:10,231 - pyskl - INFO - +mean_acc 0.8661 +2025-06-25 03:44:10,235 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_92.pth was removed +2025-06-25 03:44:10,424 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_95.pth. +2025-06-25 03:44:10,424 - pyskl - INFO - Best top1_acc is 0.8985 at 95 epoch. +2025-06-25 03:44:10,426 - pyskl - INFO - Epoch(val) [95][533] top1_acc: 0.8985, top5_acc: 0.9927, mean_class_accuracy: 0.8661 +2025-06-25 03:45:18,872 - pyskl - INFO - Epoch [96][100/1281] lr: 7.397e-03, eta: 8:39:38, time: 0.684, data_time: 0.182, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1627, loss: 0.1627 +2025-06-25 03:46:04,011 - pyskl - INFO - Epoch [96][200/1281] lr: 7.379e-03, eta: 8:38:54, time: 0.451, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1743, loss: 0.1743 +2025-06-25 03:46:53,434 - pyskl - INFO - Epoch [96][300/1281] lr: 7.360e-03, eta: 8:38:12, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1352, loss: 0.1352 +2025-06-25 03:47:42,582 - pyskl - INFO - Epoch [96][400/1281] lr: 7.341e-03, eta: 8:37:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1640, loss: 0.1640 +2025-06-25 03:48:31,548 - pyskl - INFO - Epoch [96][500/1281] lr: 7.323e-03, eta: 8:36:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9613, top5_acc: 1.0000, loss_cls: 0.2361, loss: 0.2361 +2025-06-25 03:49:20,867 - pyskl - INFO - Epoch [96][600/1281] lr: 7.304e-03, eta: 8:36:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2247, loss: 0.2247 +2025-06-25 03:50:09,917 - pyskl - INFO - Epoch [96][700/1281] lr: 7.286e-03, eta: 8:35:26, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2270, loss: 0.2270 +2025-06-25 03:50:59,026 - pyskl - INFO - Epoch [96][800/1281] lr: 7.267e-03, eta: 8:34:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1710, loss: 0.1710 +2025-06-25 03:51:48,169 - pyskl - INFO - Epoch [96][900/1281] lr: 7.249e-03, eta: 8:34:03, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1791, loss: 0.1791 +2025-06-25 03:52:37,622 - pyskl - INFO - Epoch [96][1000/1281] lr: 7.230e-03, eta: 8:33:21, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9988, loss_cls: 0.1936, loss: 0.1936 +2025-06-25 03:53:26,683 - pyskl - INFO - Epoch [96][1100/1281] lr: 7.211e-03, eta: 8:32:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2394, loss: 0.2394 +2025-06-25 03:54:15,542 - pyskl - INFO - Epoch [96][1200/1281] lr: 7.193e-03, eta: 8:31:58, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9587, top5_acc: 0.9988, loss_cls: 0.2432, loss: 0.2432 +2025-06-25 03:54:56,319 - pyskl - INFO - Saving checkpoint at 96 epochs +2025-06-25 03:55:52,331 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 03:55:52,388 - pyskl - INFO - +top1_acc 0.8738 +top5_acc 0.9908 +2025-06-25 03:55:52,388 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 03:55:52,395 - pyskl - INFO - +mean_acc 0.8338 +2025-06-25 03:55:52,397 - pyskl - INFO - Epoch(val) [96][533] top1_acc: 0.8738, top5_acc: 0.9908, mean_class_accuracy: 0.8338 +2025-06-25 03:56:58,144 - pyskl - INFO - Epoch [97][100/1281] lr: 7.159e-03, eta: 8:30:29, time: 0.657, data_time: 0.192, memory: 4083, top1_acc: 0.9563, top5_acc: 1.0000, loss_cls: 0.2586, loss: 0.2586 +2025-06-25 03:57:42,879 - pyskl - INFO - Epoch [97][200/1281] lr: 7.141e-03, eta: 8:29:45, time: 0.447, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2204, loss: 0.2204 +2025-06-25 03:58:32,079 - pyskl - INFO - Epoch [97][300/1281] lr: 7.123e-03, eta: 8:29:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 1.0000, loss_cls: 0.1823, loss: 0.1823 +2025-06-25 03:59:21,789 - pyskl - INFO - Epoch [97][400/1281] lr: 7.104e-03, eta: 8:28:22, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9631, top5_acc: 0.9994, loss_cls: 0.2133, loss: 0.2133 +2025-06-25 04:00:11,211 - pyskl - INFO - Epoch [97][500/1281] lr: 7.086e-03, eta: 8:27:41, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 0.9994, loss_cls: 0.1996, loss: 0.1996 +2025-06-25 04:01:00,473 - pyskl - INFO - Epoch [97][600/1281] lr: 7.067e-03, eta: 8:26:59, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2084, loss: 0.2084 +2025-06-25 04:01:49,415 - pyskl - INFO - Epoch [97][700/1281] lr: 7.049e-03, eta: 8:26:17, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1987, loss: 0.1987 +2025-06-25 04:02:38,443 - pyskl - INFO - Epoch [97][800/1281] lr: 7.030e-03, eta: 8:25:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 0.9994, loss_cls: 0.1812, loss: 0.1812 +2025-06-25 04:03:27,331 - pyskl - INFO - Epoch [97][900/1281] lr: 7.012e-03, eta: 8:24:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9994, loss_cls: 0.1871, loss: 0.1871 +2025-06-25 04:04:16,382 - pyskl - INFO - Epoch [97][1000/1281] lr: 6.994e-03, eta: 8:24:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9675, top5_acc: 0.9994, loss_cls: 0.2015, loss: 0.2015 +2025-06-25 04:05:05,773 - pyskl - INFO - Epoch [97][1100/1281] lr: 6.975e-03, eta: 8:23:30, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9669, top5_acc: 1.0000, loss_cls: 0.2175, loss: 0.2175 +2025-06-25 04:05:55,129 - pyskl - INFO - Epoch [97][1200/1281] lr: 6.957e-03, eta: 8:22:48, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1928, loss: 0.1928 +2025-06-25 04:06:35,216 - pyskl - INFO - Saving checkpoint at 97 epochs +2025-06-25 04:07:30,495 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:07:30,549 - pyskl - INFO - +top1_acc 0.9027 +top5_acc 0.9918 +2025-06-25 04:07:30,549 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:07:30,556 - pyskl - INFO - +mean_acc 0.8646 +2025-06-25 04:07:30,560 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_95.pth was removed +2025-06-25 04:07:30,768 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_97.pth. +2025-06-25 04:07:30,768 - pyskl - INFO - Best top1_acc is 0.9027 at 97 epoch. +2025-06-25 04:07:30,771 - pyskl - INFO - Epoch(val) [97][533] top1_acc: 0.9027, top5_acc: 0.9918, mean_class_accuracy: 0.8646 +2025-06-25 04:08:35,304 - pyskl - INFO - Epoch [98][100/1281] lr: 6.924e-03, eta: 8:21:20, time: 0.645, data_time: 0.192, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1830, loss: 0.1830 +2025-06-25 04:09:19,453 - pyskl - INFO - Epoch [98][200/1281] lr: 6.906e-03, eta: 8:20:35, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1687, loss: 0.1687 +2025-06-25 04:10:08,600 - pyskl - INFO - Epoch [98][300/1281] lr: 6.887e-03, eta: 8:19:53, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1874, loss: 0.1874 +2025-06-25 04:10:57,693 - pyskl - INFO - Epoch [98][400/1281] lr: 6.869e-03, eta: 8:19:11, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.2111, loss: 0.2111 +2025-06-25 04:11:47,188 - pyskl - INFO - Epoch [98][500/1281] lr: 6.851e-03, eta: 8:18:30, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9656, top5_acc: 1.0000, loss_cls: 0.1987, loss: 0.1987 +2025-06-25 04:12:36,096 - pyskl - INFO - Epoch [98][600/1281] lr: 6.833e-03, eta: 8:17:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9725, top5_acc: 1.0000, loss_cls: 0.1820, loss: 0.1820 +2025-06-25 04:13:25,447 - pyskl - INFO - Epoch [98][700/1281] lr: 6.814e-03, eta: 8:17:06, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1757, loss: 0.1757 +2025-06-25 04:14:14,657 - pyskl - INFO - Epoch [98][800/1281] lr: 6.796e-03, eta: 8:16:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1980, loss: 0.1980 +2025-06-25 04:15:03,649 - pyskl - INFO - Epoch [98][900/1281] lr: 6.778e-03, eta: 8:15:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9681, top5_acc: 1.0000, loss_cls: 0.2021, loss: 0.2021 +2025-06-25 04:15:52,822 - pyskl - INFO - Epoch [98][1000/1281] lr: 6.760e-03, eta: 8:15:01, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.2077, loss: 0.2077 +2025-06-25 04:16:41,999 - pyskl - INFO - Epoch [98][1100/1281] lr: 6.742e-03, eta: 8:14:19, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9650, top5_acc: 1.0000, loss_cls: 0.1999, loss: 0.1999 +2025-06-25 04:17:31,320 - pyskl - INFO - Epoch [98][1200/1281] lr: 6.724e-03, eta: 8:13:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 0.9994, loss_cls: 0.2168, loss: 0.2168 +2025-06-25 04:18:11,567 - pyskl - INFO - Saving checkpoint at 98 epochs +2025-06-25 04:19:07,761 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:19:07,816 - pyskl - INFO - +top1_acc 0.8848 +top5_acc 0.9911 +2025-06-25 04:19:07,816 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:19:07,823 - pyskl - INFO - +mean_acc 0.8521 +2025-06-25 04:19:07,824 - pyskl - INFO - Epoch(val) [98][533] top1_acc: 0.8848, top5_acc: 0.9911, mean_class_accuracy: 0.8521 +2025-06-25 04:20:14,643 - pyskl - INFO - Epoch [99][100/1281] lr: 6.691e-03, eta: 8:12:09, time: 0.668, data_time: 0.191, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1874, loss: 0.1874 +2025-06-25 04:20:59,509 - pyskl - INFO - Epoch [99][200/1281] lr: 6.673e-03, eta: 8:11:25, time: 0.449, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.2161, loss: 0.2161 +2025-06-25 04:21:48,576 - pyskl - INFO - Epoch [99][300/1281] lr: 6.655e-03, eta: 8:10:43, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1953, loss: 0.1953 +2025-06-25 04:22:37,875 - pyskl - INFO - Epoch [99][400/1281] lr: 6.637e-03, eta: 8:10:01, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9731, top5_acc: 1.0000, loss_cls: 0.1820, loss: 0.1820 +2025-06-25 04:23:26,777 - pyskl - INFO - Epoch [99][500/1281] lr: 6.619e-03, eta: 8:09:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 0.9994, loss_cls: 0.1518, loss: 0.1518 +2025-06-25 04:24:15,826 - pyskl - INFO - Epoch [99][600/1281] lr: 6.601e-03, eta: 8:08:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.1729, loss: 0.1729 +2025-06-25 04:25:04,744 - pyskl - INFO - Epoch [99][700/1281] lr: 6.583e-03, eta: 8:07:55, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1783, loss: 0.1783 +2025-06-25 04:25:53,946 - pyskl - INFO - Epoch [99][800/1281] lr: 6.565e-03, eta: 8:07:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9663, top5_acc: 1.0000, loss_cls: 0.1879, loss: 0.1879 +2025-06-25 04:26:43,021 - pyskl - INFO - Epoch [99][900/1281] lr: 6.547e-03, eta: 8:06:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9637, top5_acc: 1.0000, loss_cls: 0.2121, loss: 0.2121 +2025-06-25 04:27:32,389 - pyskl - INFO - Epoch [99][1000/1281] lr: 6.529e-03, eta: 8:05:50, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2225, loss: 0.2225 +2025-06-25 04:28:21,465 - pyskl - INFO - Epoch [99][1100/1281] lr: 6.511e-03, eta: 8:05:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9619, top5_acc: 0.9994, loss_cls: 0.2252, loss: 0.2252 +2025-06-25 04:29:10,554 - pyskl - INFO - Epoch [99][1200/1281] lr: 6.493e-03, eta: 8:04:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1629, loss: 0.1629 +2025-06-25 04:29:50,989 - pyskl - INFO - Saving checkpoint at 99 epochs +2025-06-25 04:30:46,528 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:30:46,583 - pyskl - INFO - +top1_acc 0.8864 +top5_acc 0.9914 +2025-06-25 04:30:46,583 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:30:46,590 - pyskl - INFO - +mean_acc 0.8535 +2025-06-25 04:30:46,591 - pyskl - INFO - Epoch(val) [99][533] top1_acc: 0.8864, top5_acc: 0.9914, mean_class_accuracy: 0.8535 +2025-06-25 04:31:51,308 - pyskl - INFO - Epoch [100][100/1281] lr: 6.460e-03, eta: 8:02:57, time: 0.647, data_time: 0.185, memory: 4083, top1_acc: 0.9762, top5_acc: 1.0000, loss_cls: 0.1620, loss: 0.1620 +2025-06-25 04:32:34,474 - pyskl - INFO - Epoch [100][200/1281] lr: 6.442e-03, eta: 8:02:12, time: 0.432, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 0.9994, loss_cls: 0.1509, loss: 0.1509 +2025-06-25 04:33:23,478 - pyskl - INFO - Epoch [100][300/1281] lr: 6.425e-03, eta: 8:01:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1512, loss: 0.1512 +2025-06-25 04:34:12,831 - pyskl - INFO - Epoch [100][400/1281] lr: 6.407e-03, eta: 8:00:48, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1477, loss: 0.1477 +2025-06-25 04:35:02,013 - pyskl - INFO - Epoch [100][500/1281] lr: 6.389e-03, eta: 8:00:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1523, loss: 0.1523 +2025-06-25 04:35:51,081 - pyskl - INFO - Epoch [100][600/1281] lr: 6.371e-03, eta: 7:59:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1668, loss: 0.1668 +2025-06-25 04:36:40,012 - pyskl - INFO - Epoch [100][700/1281] lr: 6.353e-03, eta: 7:58:42, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1594, loss: 0.1594 +2025-06-25 04:37:28,844 - pyskl - INFO - Epoch [100][800/1281] lr: 6.336e-03, eta: 7:57:59, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1365, loss: 0.1365 +2025-06-25 04:38:18,071 - pyskl - INFO - Epoch [100][900/1281] lr: 6.318e-03, eta: 7:57:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9688, top5_acc: 1.0000, loss_cls: 0.1896, loss: 0.1896 +2025-06-25 04:39:07,645 - pyskl - INFO - Epoch [100][1000/1281] lr: 6.300e-03, eta: 7:56:36, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1643, loss: 0.1643 +2025-06-25 04:39:56,855 - pyskl - INFO - Epoch [100][1100/1281] lr: 6.282e-03, eta: 7:55:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9644, top5_acc: 0.9988, loss_cls: 0.2255, loss: 0.2255 +2025-06-25 04:40:46,011 - pyskl - INFO - Epoch [100][1200/1281] lr: 6.265e-03, eta: 7:55:11, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1708, loss: 0.1708 +2025-06-25 04:41:26,501 - pyskl - INFO - Saving checkpoint at 100 epochs +2025-06-25 04:42:24,649 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:42:24,705 - pyskl - INFO - +top1_acc 0.8948 +top5_acc 0.9928 +2025-06-25 04:42:24,705 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:42:24,711 - pyskl - INFO - +mean_acc 0.8638 +2025-06-25 04:42:24,714 - pyskl - INFO - Epoch(val) [100][533] top1_acc: 0.8948, top5_acc: 0.9928, mean_class_accuracy: 0.8638 +2025-06-25 04:43:34,423 - pyskl - INFO - Epoch [101][100/1281] lr: 6.232e-03, eta: 7:53:46, time: 0.697, data_time: 0.188, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1563, loss: 0.1563 +2025-06-25 04:44:17,897 - pyskl - INFO - Epoch [101][200/1281] lr: 6.215e-03, eta: 7:53:01, time: 0.435, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1409, loss: 0.1409 +2025-06-25 04:45:07,265 - pyskl - INFO - Epoch [101][300/1281] lr: 6.197e-03, eta: 7:52:19, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1331, loss: 0.1331 +2025-06-25 04:45:56,677 - pyskl - INFO - Epoch [101][400/1281] lr: 6.180e-03, eta: 7:51:37, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1518, loss: 0.1518 +2025-06-25 04:46:45,813 - pyskl - INFO - Epoch [101][500/1281] lr: 6.162e-03, eta: 7:50:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1224, loss: 0.1224 +2025-06-25 04:47:34,877 - pyskl - INFO - Epoch [101][600/1281] lr: 6.144e-03, eta: 7:50:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1853, loss: 0.1853 +2025-06-25 04:48:24,071 - pyskl - INFO - Epoch [101][700/1281] lr: 6.127e-03, eta: 7:49:30, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 0.9994, loss_cls: 0.1394, loss: 0.1394 +2025-06-25 04:49:13,614 - pyskl - INFO - Epoch [101][800/1281] lr: 6.109e-03, eta: 7:48:48, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9706, top5_acc: 1.0000, loss_cls: 0.1579, loss: 0.1579 +2025-06-25 04:50:02,645 - pyskl - INFO - Epoch [101][900/1281] lr: 6.092e-03, eta: 7:48:06, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.1836, loss: 0.1836 +2025-06-25 04:50:51,929 - pyskl - INFO - Epoch [101][1000/1281] lr: 6.074e-03, eta: 7:47:24, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1603, loss: 0.1603 +2025-06-25 04:51:41,120 - pyskl - INFO - Epoch [101][1100/1281] lr: 6.057e-03, eta: 7:46:42, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9694, top5_acc: 1.0000, loss_cls: 0.1835, loss: 0.1835 +2025-06-25 04:52:30,416 - pyskl - INFO - Epoch [101][1200/1281] lr: 6.039e-03, eta: 7:46:00, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9600, top5_acc: 1.0000, loss_cls: 0.2325, loss: 0.2325 +2025-06-25 04:53:10,494 - pyskl - INFO - Saving checkpoint at 101 epochs +2025-06-25 04:54:07,136 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 04:54:07,191 - pyskl - INFO - +top1_acc 0.8876 +top5_acc 0.9917 +2025-06-25 04:54:07,191 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 04:54:07,198 - pyskl - INFO - +mean_acc 0.8496 +2025-06-25 04:54:07,199 - pyskl - INFO - Epoch(val) [101][533] top1_acc: 0.8876, top5_acc: 0.9917, mean_class_accuracy: 0.8496 +2025-06-25 04:55:15,585 - pyskl - INFO - Epoch [102][100/1281] lr: 6.007e-03, eta: 7:44:33, time: 0.684, data_time: 0.187, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1637, loss: 0.1637 +2025-06-25 04:55:59,535 - pyskl - INFO - Epoch [102][200/1281] lr: 5.990e-03, eta: 7:43:48, time: 0.439, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1443, loss: 0.1443 +2025-06-25 04:56:49,090 - pyskl - INFO - Epoch [102][300/1281] lr: 5.972e-03, eta: 7:43:06, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9719, top5_acc: 1.0000, loss_cls: 0.1764, loss: 0.1764 +2025-06-25 04:57:38,247 - pyskl - INFO - Epoch [102][400/1281] lr: 5.955e-03, eta: 7:42:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1853, loss: 0.1853 +2025-06-25 04:58:27,393 - pyskl - INFO - Epoch [102][500/1281] lr: 5.938e-03, eta: 7:41:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9988, loss_cls: 0.1905, loss: 0.1905 +2025-06-25 04:59:16,426 - pyskl - INFO - Epoch [102][600/1281] lr: 5.920e-03, eta: 7:41:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1621, loss: 0.1621 +2025-06-25 05:00:05,522 - pyskl - INFO - Epoch [102][700/1281] lr: 5.903e-03, eta: 7:40:17, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1345, loss: 0.1345 +2025-06-25 05:00:54,486 - pyskl - INFO - Epoch [102][800/1281] lr: 5.886e-03, eta: 7:39:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9700, top5_acc: 1.0000, loss_cls: 0.1800, loss: 0.1800 +2025-06-25 05:01:43,414 - pyskl - INFO - Epoch [102][900/1281] lr: 5.868e-03, eta: 7:38:52, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1602, loss: 0.1602 +2025-06-25 05:02:32,485 - pyskl - INFO - Epoch [102][1000/1281] lr: 5.851e-03, eta: 7:38:10, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1521, loss: 0.1521 +2025-06-25 05:03:21,466 - pyskl - INFO - Epoch [102][1100/1281] lr: 5.834e-03, eta: 7:37:28, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1604, loss: 0.1604 +2025-06-25 05:04:10,658 - pyskl - INFO - Epoch [102][1200/1281] lr: 5.816e-03, eta: 7:36:45, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 1.0000, loss_cls: 0.1757, loss: 0.1757 +2025-06-25 05:04:50,856 - pyskl - INFO - Saving checkpoint at 102 epochs +2025-06-25 05:05:47,791 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:05:47,856 - pyskl - INFO - +top1_acc 0.9014 +top5_acc 0.9910 +2025-06-25 05:05:47,856 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:05:47,863 - pyskl - INFO - +mean_acc 0.8693 +2025-06-25 05:05:47,865 - pyskl - INFO - Epoch(val) [102][533] top1_acc: 0.9014, top5_acc: 0.9910, mean_class_accuracy: 0.8693 +2025-06-25 05:06:54,803 - pyskl - INFO - Epoch [103][100/1281] lr: 5.785e-03, eta: 7:35:18, time: 0.669, data_time: 0.188, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1224, loss: 0.1224 +2025-06-25 05:07:37,821 - pyskl - INFO - Epoch [103][200/1281] lr: 5.768e-03, eta: 7:34:33, time: 0.430, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1372, loss: 0.1372 +2025-06-25 05:08:27,296 - pyskl - INFO - Epoch [103][300/1281] lr: 5.751e-03, eta: 7:33:51, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1022, loss: 0.1022 +2025-06-25 05:09:16,559 - pyskl - INFO - Epoch [103][400/1281] lr: 5.733e-03, eta: 7:33:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1197, loss: 0.1197 +2025-06-25 05:10:05,692 - pyskl - INFO - Epoch [103][500/1281] lr: 5.716e-03, eta: 7:32:26, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9769, top5_acc: 1.0000, loss_cls: 0.1524, loss: 0.1524 +2025-06-25 05:10:54,631 - pyskl - INFO - Epoch [103][600/1281] lr: 5.699e-03, eta: 7:31:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1401, loss: 0.1401 +2025-06-25 05:11:43,962 - pyskl - INFO - Epoch [103][700/1281] lr: 5.682e-03, eta: 7:31:02, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1216, loss: 0.1216 +2025-06-25 05:12:33,026 - pyskl - INFO - Epoch [103][800/1281] lr: 5.665e-03, eta: 7:30:19, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1269, loss: 0.1269 +2025-06-25 05:13:22,024 - pyskl - INFO - Epoch [103][900/1281] lr: 5.648e-03, eta: 7:29:37, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1421, loss: 0.1421 +2025-06-25 05:14:11,388 - pyskl - INFO - Epoch [103][1000/1281] lr: 5.631e-03, eta: 7:28:55, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1356, loss: 0.1356 +2025-06-25 05:15:00,601 - pyskl - INFO - Epoch [103][1100/1281] lr: 5.614e-03, eta: 7:28:12, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1658, loss: 0.1658 +2025-06-25 05:15:49,244 - pyskl - INFO - Epoch [103][1200/1281] lr: 5.597e-03, eta: 7:27:29, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1154, loss: 0.1154 +2025-06-25 05:16:29,587 - pyskl - INFO - Saving checkpoint at 103 epochs +2025-06-25 05:17:28,076 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:17:28,131 - pyskl - INFO - +top1_acc 0.8998 +top5_acc 0.9908 +2025-06-25 05:17:28,131 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:17:28,137 - pyskl - INFO - +mean_acc 0.8712 +2025-06-25 05:17:28,139 - pyskl - INFO - Epoch(val) [103][533] top1_acc: 0.8998, top5_acc: 0.9908, mean_class_accuracy: 0.8712 +2025-06-25 05:18:39,490 - pyskl - INFO - Epoch [104][100/1281] lr: 5.566e-03, eta: 7:26:05, time: 0.713, data_time: 0.187, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1196, loss: 0.1196 +2025-06-25 05:19:23,412 - pyskl - INFO - Epoch [104][200/1281] lr: 5.549e-03, eta: 7:25:20, time: 0.439, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1280, loss: 0.1280 +2025-06-25 05:20:12,489 - pyskl - INFO - Epoch [104][300/1281] lr: 5.532e-03, eta: 7:24:37, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.1012, loss: 0.1012 +2025-06-25 05:21:01,447 - pyskl - INFO - Epoch [104][400/1281] lr: 5.515e-03, eta: 7:23:55, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 1.0000, loss_cls: 0.1351, loss: 0.1351 +2025-06-25 05:21:50,050 - pyskl - INFO - Epoch [104][500/1281] lr: 5.498e-03, eta: 7:23:12, time: 0.486, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1223, loss: 0.1223 +2025-06-25 05:22:38,887 - pyskl - INFO - Epoch [104][600/1281] lr: 5.481e-03, eta: 7:22:30, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 0.9994, loss_cls: 0.1332, loss: 0.1332 +2025-06-25 05:23:28,014 - pyskl - INFO - Epoch [104][700/1281] lr: 5.464e-03, eta: 7:21:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9756, top5_acc: 0.9994, loss_cls: 0.1625, loss: 0.1625 +2025-06-25 05:24:17,044 - pyskl - INFO - Epoch [104][800/1281] lr: 5.447e-03, eta: 7:21:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 0.9994, loss_cls: 0.1373, loss: 0.1373 +2025-06-25 05:25:06,710 - pyskl - INFO - Epoch [104][900/1281] lr: 5.430e-03, eta: 7:20:22, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1276, loss: 0.1276 +2025-06-25 05:25:55,683 - pyskl - INFO - Epoch [104][1000/1281] lr: 5.413e-03, eta: 7:19:40, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 0.9994, loss_cls: 0.1737, loss: 0.1737 +2025-06-25 05:26:44,931 - pyskl - INFO - Epoch [104][1100/1281] lr: 5.397e-03, eta: 7:18:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1267, loss: 0.1267 +2025-06-25 05:27:33,860 - pyskl - INFO - Epoch [104][1200/1281] lr: 5.380e-03, eta: 7:18:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 0.9994, loss_cls: 0.1823, loss: 0.1823 +2025-06-25 05:28:14,109 - pyskl - INFO - Saving checkpoint at 104 epochs +2025-06-25 05:29:11,098 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:29:11,166 - pyskl - INFO - +top1_acc 0.9006 +top5_acc 0.9930 +2025-06-25 05:29:11,166 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:29:11,175 - pyskl - INFO - +mean_acc 0.8719 +2025-06-25 05:29:11,177 - pyskl - INFO - Epoch(val) [104][533] top1_acc: 0.9006, top5_acc: 0.9930, mean_class_accuracy: 0.8719 +2025-06-25 05:30:18,735 - pyskl - INFO - Epoch [105][100/1281] lr: 5.349e-03, eta: 7:16:48, time: 0.676, data_time: 0.185, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.1119, loss: 0.1119 +2025-06-25 05:31:02,833 - pyskl - INFO - Epoch [105][200/1281] lr: 5.333e-03, eta: 7:16:03, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9800, top5_acc: 0.9988, loss_cls: 0.1454, loss: 0.1454 +2025-06-25 05:31:51,934 - pyskl - INFO - Epoch [105][300/1281] lr: 5.316e-03, eta: 7:15:21, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 0.9994, loss_cls: 0.1420, loss: 0.1420 +2025-06-25 05:32:40,719 - pyskl - INFO - Epoch [105][400/1281] lr: 5.299e-03, eta: 7:14:38, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1328, loss: 0.1328 +2025-06-25 05:33:30,067 - pyskl - INFO - Epoch [105][500/1281] lr: 5.283e-03, eta: 7:13:56, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1276, loss: 0.1276 +2025-06-25 05:34:19,292 - pyskl - INFO - Epoch [105][600/1281] lr: 5.266e-03, eta: 7:13:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1196, loss: 0.1196 +2025-06-25 05:35:08,616 - pyskl - INFO - Epoch [105][700/1281] lr: 5.249e-03, eta: 7:12:31, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 0.9994, loss_cls: 0.1271, loss: 0.1271 +2025-06-25 05:35:57,609 - pyskl - INFO - Epoch [105][800/1281] lr: 5.233e-03, eta: 7:11:48, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 0.9994, loss_cls: 0.1453, loss: 0.1453 +2025-06-25 05:36:47,127 - pyskl - INFO - Epoch [105][900/1281] lr: 5.216e-03, eta: 7:11:06, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1253, loss: 0.1253 +2025-06-25 05:37:36,037 - pyskl - INFO - Epoch [105][1000/1281] lr: 5.199e-03, eta: 7:10:23, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1125, loss: 0.1125 +2025-06-25 05:38:25,122 - pyskl - INFO - Epoch [105][1100/1281] lr: 5.183e-03, eta: 7:09:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1026, loss: 0.1026 +2025-06-25 05:39:14,261 - pyskl - INFO - Epoch [105][1200/1281] lr: 5.166e-03, eta: 7:08:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9744, top5_acc: 1.0000, loss_cls: 0.1677, loss: 0.1677 +2025-06-25 05:39:54,761 - pyskl - INFO - Saving checkpoint at 105 epochs +2025-06-25 05:40:51,427 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:40:51,483 - pyskl - INFO - +top1_acc 0.9004 +top5_acc 0.9917 +2025-06-25 05:40:51,483 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:40:51,489 - pyskl - INFO - +mean_acc 0.8718 +2025-06-25 05:40:51,492 - pyskl - INFO - Epoch(val) [105][533] top1_acc: 0.9004, top5_acc: 0.9917, mean_class_accuracy: 0.8718 +2025-06-25 05:41:58,754 - pyskl - INFO - Epoch [106][100/1281] lr: 5.136e-03, eta: 7:07:31, time: 0.673, data_time: 0.192, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1344, loss: 0.1344 +2025-06-25 05:42:42,889 - pyskl - INFO - Epoch [106][200/1281] lr: 5.120e-03, eta: 7:06:46, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1115, loss: 0.1115 +2025-06-25 05:43:31,660 - pyskl - INFO - Epoch [106][300/1281] lr: 5.103e-03, eta: 7:06:04, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1108, loss: 0.1108 +2025-06-25 05:44:20,667 - pyskl - INFO - Epoch [106][400/1281] lr: 5.087e-03, eta: 7:05:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9775, top5_acc: 1.0000, loss_cls: 0.1357, loss: 0.1357 +2025-06-25 05:45:09,832 - pyskl - INFO - Epoch [106][500/1281] lr: 5.070e-03, eta: 7:04:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1508, loss: 0.1508 +2025-06-25 05:45:58,988 - pyskl - INFO - Epoch [106][600/1281] lr: 5.054e-03, eta: 7:03:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9819, top5_acc: 1.0000, loss_cls: 0.1305, loss: 0.1305 +2025-06-25 05:46:47,968 - pyskl - INFO - Epoch [106][700/1281] lr: 5.038e-03, eta: 7:03:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1159, loss: 0.1159 +2025-06-25 05:47:36,923 - pyskl - INFO - Epoch [106][800/1281] lr: 5.021e-03, eta: 7:02:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1104, loss: 0.1104 +2025-06-25 05:48:26,078 - pyskl - INFO - Epoch [106][900/1281] lr: 5.005e-03, eta: 7:01:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1125, loss: 0.1125 +2025-06-25 05:49:15,479 - pyskl - INFO - Epoch [106][1000/1281] lr: 4.988e-03, eta: 7:01:05, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1104, loss: 0.1104 +2025-06-25 05:50:04,504 - pyskl - INFO - Epoch [106][1100/1281] lr: 4.972e-03, eta: 7:00:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1269, loss: 0.1269 +2025-06-25 05:50:53,941 - pyskl - INFO - Epoch [106][1200/1281] lr: 4.956e-03, eta: 6:59:39, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9750, top5_acc: 0.9994, loss_cls: 0.1534, loss: 0.1534 +2025-06-25 05:51:34,268 - pyskl - INFO - Saving checkpoint at 106 epochs +2025-06-25 05:52:31,673 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 05:52:31,730 - pyskl - INFO - +top1_acc 0.8992 +top5_acc 0.9904 +2025-06-25 05:52:31,730 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 05:52:31,736 - pyskl - INFO - +mean_acc 0.8720 +2025-06-25 05:52:31,738 - pyskl - INFO - Epoch(val) [106][533] top1_acc: 0.8992, top5_acc: 0.9904, mean_class_accuracy: 0.8720 +2025-06-25 05:53:40,260 - pyskl - INFO - Epoch [107][100/1281] lr: 4.926e-03, eta: 6:58:14, time: 0.685, data_time: 0.190, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0902, loss: 0.0902 +2025-06-25 05:54:24,765 - pyskl - INFO - Epoch [107][200/1281] lr: 4.910e-03, eta: 6:57:29, time: 0.445, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0993, loss: 0.0993 +2025-06-25 05:55:14,097 - pyskl - INFO - Epoch [107][300/1281] lr: 4.894e-03, eta: 6:56:46, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0986, loss: 0.0986 +2025-06-25 05:56:02,840 - pyskl - INFO - Epoch [107][400/1281] lr: 4.878e-03, eta: 6:56:03, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9812, top5_acc: 1.0000, loss_cls: 0.1178, loss: 0.1178 +2025-06-25 05:56:51,783 - pyskl - INFO - Epoch [107][500/1281] lr: 4.862e-03, eta: 6:55:21, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1024, loss: 0.1024 +2025-06-25 05:57:40,971 - pyskl - INFO - Epoch [107][600/1281] lr: 4.845e-03, eta: 6:54:38, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1490, loss: 0.1490 +2025-06-25 05:58:30,239 - pyskl - INFO - Epoch [107][700/1281] lr: 4.829e-03, eta: 6:53:55, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 0.9994, loss_cls: 0.1240, loss: 0.1240 +2025-06-25 05:59:19,213 - pyskl - INFO - Epoch [107][800/1281] lr: 4.813e-03, eta: 6:53:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1358, loss: 0.1358 +2025-06-25 06:00:08,258 - pyskl - INFO - Epoch [107][900/1281] lr: 4.797e-03, eta: 6:52:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1276, loss: 0.1276 +2025-06-25 06:00:57,451 - pyskl - INFO - Epoch [107][1000/1281] lr: 4.781e-03, eta: 6:51:47, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 0.9994, loss_cls: 0.1304, loss: 0.1304 +2025-06-25 06:01:46,599 - pyskl - INFO - Epoch [107][1100/1281] lr: 4.765e-03, eta: 6:51:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1411, loss: 0.1411 +2025-06-25 06:02:35,958 - pyskl - INFO - Epoch [107][1200/1281] lr: 4.749e-03, eta: 6:50:21, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9781, top5_acc: 1.0000, loss_cls: 0.1735, loss: 0.1735 +2025-06-25 06:03:16,508 - pyskl - INFO - Saving checkpoint at 107 epochs +2025-06-25 06:04:12,762 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:04:12,829 - pyskl - INFO - +top1_acc 0.8934 +top5_acc 0.9914 +2025-06-25 06:04:12,830 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:04:12,836 - pyskl - INFO - +mean_acc 0.8592 +2025-06-25 06:04:12,838 - pyskl - INFO - Epoch(val) [107][533] top1_acc: 0.8934, top5_acc: 0.9914, mean_class_accuracy: 0.8592 +2025-06-25 06:05:19,085 - pyskl - INFO - Epoch [108][100/1281] lr: 4.720e-03, eta: 6:48:55, time: 0.662, data_time: 0.186, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1198, loss: 0.1198 +2025-06-25 06:06:03,123 - pyskl - INFO - Epoch [108][200/1281] lr: 4.704e-03, eta: 6:48:10, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0899, loss: 0.0899 +2025-06-25 06:06:52,110 - pyskl - INFO - Epoch [108][300/1281] lr: 4.688e-03, eta: 6:47:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0940, loss: 0.0940 +2025-06-25 06:07:40,972 - pyskl - INFO - Epoch [108][400/1281] lr: 4.672e-03, eta: 6:46:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0837, loss: 0.0837 +2025-06-25 06:08:30,069 - pyskl - INFO - Epoch [108][500/1281] lr: 4.656e-03, eta: 6:46:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1123, loss: 0.1123 +2025-06-25 06:09:19,184 - pyskl - INFO - Epoch [108][600/1281] lr: 4.640e-03, eta: 6:45:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9712, top5_acc: 1.0000, loss_cls: 0.1505, loss: 0.1505 +2025-06-25 06:10:08,199 - pyskl - INFO - Epoch [108][700/1281] lr: 4.624e-03, eta: 6:44:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1216, loss: 0.1216 +2025-06-25 06:10:57,357 - pyskl - INFO - Epoch [108][800/1281] lr: 4.608e-03, eta: 6:43:52, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 1.0000, loss_cls: 0.1583, loss: 0.1583 +2025-06-25 06:11:46,585 - pyskl - INFO - Epoch [108][900/1281] lr: 4.593e-03, eta: 6:43:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9738, top5_acc: 0.9994, loss_cls: 0.1571, loss: 0.1571 +2025-06-25 06:12:35,423 - pyskl - INFO - Epoch [108][1000/1281] lr: 4.577e-03, eta: 6:42:27, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1290, loss: 0.1290 +2025-06-25 06:13:24,367 - pyskl - INFO - Epoch [108][1100/1281] lr: 4.561e-03, eta: 6:41:44, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1455, loss: 0.1455 +2025-06-25 06:14:13,341 - pyskl - INFO - Epoch [108][1200/1281] lr: 4.545e-03, eta: 6:41:01, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1038, loss: 0.1038 +2025-06-25 06:14:54,088 - pyskl - INFO - Saving checkpoint at 108 epochs +2025-06-25 06:15:52,130 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:15:52,201 - pyskl - INFO - +top1_acc 0.8953 +top5_acc 0.9914 +2025-06-25 06:15:52,201 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:15:52,208 - pyskl - INFO - +mean_acc 0.8700 +2025-06-25 06:15:52,210 - pyskl - INFO - Epoch(val) [108][533] top1_acc: 0.8953, top5_acc: 0.9914, mean_class_accuracy: 0.8700 +2025-06-25 06:17:02,214 - pyskl - INFO - Epoch [109][100/1281] lr: 4.517e-03, eta: 6:39:36, time: 0.700, data_time: 0.184, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.1115, loss: 0.1115 +2025-06-25 06:17:46,235 - pyskl - INFO - Epoch [109][200/1281] lr: 4.501e-03, eta: 6:38:51, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 0.9844, top5_acc: 1.0000, loss_cls: 0.1074, loss: 0.1074 +2025-06-25 06:18:35,342 - pyskl - INFO - Epoch [109][300/1281] lr: 4.485e-03, eta: 6:38:08, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0941, loss: 0.0941 +2025-06-25 06:19:24,430 - pyskl - INFO - Epoch [109][400/1281] lr: 4.470e-03, eta: 6:37:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1082, loss: 0.1082 +2025-06-25 06:20:13,572 - pyskl - INFO - Epoch [109][500/1281] lr: 4.454e-03, eta: 6:36:42, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0920, loss: 0.0920 +2025-06-25 06:21:02,585 - pyskl - INFO - Epoch [109][600/1281] lr: 4.438e-03, eta: 6:35:59, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.1139, loss: 0.1139 +2025-06-25 06:21:51,992 - pyskl - INFO - Epoch [109][700/1281] lr: 4.423e-03, eta: 6:35:16, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0929, loss: 0.0929 +2025-06-25 06:22:41,126 - pyskl - INFO - Epoch [109][800/1281] lr: 4.407e-03, eta: 6:34:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9788, top5_acc: 1.0000, loss_cls: 0.1359, loss: 0.1359 +2025-06-25 06:23:29,971 - pyskl - INFO - Epoch [109][900/1281] lr: 4.391e-03, eta: 6:33:50, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1105, loss: 0.1105 +2025-06-25 06:24:19,145 - pyskl - INFO - Epoch [109][1000/1281] lr: 4.376e-03, eta: 6:33:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0961, loss: 0.0961 +2025-06-25 06:25:08,476 - pyskl - INFO - Epoch [109][1100/1281] lr: 4.360e-03, eta: 6:32:24, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0866, loss: 0.0866 +2025-06-25 06:25:57,424 - pyskl - INFO - Epoch [109][1200/1281] lr: 4.345e-03, eta: 6:31:41, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.1043, loss: 0.1043 +2025-06-25 06:26:37,883 - pyskl - INFO - Saving checkpoint at 109 epochs +2025-06-25 06:27:34,870 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:27:34,925 - pyskl - INFO - +top1_acc 0.9011 +top5_acc 0.9932 +2025-06-25 06:27:34,925 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:27:34,931 - pyskl - INFO - +mean_acc 0.8684 +2025-06-25 06:27:34,933 - pyskl - INFO - Epoch(val) [109][533] top1_acc: 0.9011, top5_acc: 0.9932, mean_class_accuracy: 0.8684 +2025-06-25 06:28:42,337 - pyskl - INFO - Epoch [110][100/1281] lr: 4.317e-03, eta: 6:30:15, time: 0.674, data_time: 0.189, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.1089, loss: 0.1089 +2025-06-25 06:29:27,730 - pyskl - INFO - Epoch [110][200/1281] lr: 4.301e-03, eta: 6:29:31, time: 0.454, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1033, loss: 0.1033 +2025-06-25 06:30:16,825 - pyskl - INFO - Epoch [110][300/1281] lr: 4.286e-03, eta: 6:28:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0865, loss: 0.0865 +2025-06-25 06:31:05,844 - pyskl - INFO - Epoch [110][400/1281] lr: 4.271e-03, eta: 6:28:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1110, loss: 0.1110 +2025-06-25 06:31:55,062 - pyskl - INFO - Epoch [110][500/1281] lr: 4.255e-03, eta: 6:27:22, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 1.0000, loss_cls: 0.1160, loss: 0.1160 +2025-06-25 06:32:44,490 - pyskl - INFO - Epoch [110][600/1281] lr: 4.240e-03, eta: 6:26:39, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0959, loss: 0.0959 +2025-06-25 06:33:33,796 - pyskl - INFO - Epoch [110][700/1281] lr: 4.225e-03, eta: 6:25:56, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0785, loss: 0.0785 +2025-06-25 06:34:22,926 - pyskl - INFO - Epoch [110][800/1281] lr: 4.209e-03, eta: 6:25:13, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9850, top5_acc: 0.9994, loss_cls: 0.0983, loss: 0.0983 +2025-06-25 06:35:12,016 - pyskl - INFO - Epoch [110][900/1281] lr: 4.194e-03, eta: 6:24:30, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1162, loss: 0.1162 +2025-06-25 06:36:01,040 - pyskl - INFO - Epoch [110][1000/1281] lr: 4.179e-03, eta: 6:23:47, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 0.9994, loss_cls: 0.1207, loss: 0.1207 +2025-06-25 06:36:50,169 - pyskl - INFO - Epoch [110][1100/1281] lr: 4.164e-03, eta: 6:23:04, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 1.0000, loss_cls: 0.1330, loss: 0.1330 +2025-06-25 06:37:39,661 - pyskl - INFO - Epoch [110][1200/1281] lr: 4.148e-03, eta: 6:22:21, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9825, top5_acc: 1.0000, loss_cls: 0.1324, loss: 0.1324 +2025-06-25 06:38:20,044 - pyskl - INFO - Saving checkpoint at 110 epochs +2025-06-25 06:39:15,193 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:39:15,250 - pyskl - INFO - +top1_acc 0.9048 +top5_acc 0.9921 +2025-06-25 06:39:15,250 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:39:15,257 - pyskl - INFO - +mean_acc 0.8757 +2025-06-25 06:39:15,261 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_97.pth was removed +2025-06-25 06:39:15,428 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_110.pth. +2025-06-25 06:39:15,428 - pyskl - INFO - Best top1_acc is 0.9048 at 110 epoch. +2025-06-25 06:39:15,431 - pyskl - INFO - Epoch(val) [110][533] top1_acc: 0.9048, top5_acc: 0.9921, mean_class_accuracy: 0.8757 +2025-06-25 06:40:19,535 - pyskl - INFO - Epoch [111][100/1281] lr: 4.121e-03, eta: 6:20:54, time: 0.641, data_time: 0.186, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0741, loss: 0.0741 +2025-06-25 06:41:04,100 - pyskl - INFO - Epoch [111][200/1281] lr: 4.106e-03, eta: 6:20:09, time: 0.446, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0745, loss: 0.0745 +2025-06-25 06:41:53,445 - pyskl - INFO - Epoch [111][300/1281] lr: 4.091e-03, eta: 6:19:26, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 0.9994, loss_cls: 0.1017, loss: 0.1017 +2025-06-25 06:42:42,657 - pyskl - INFO - Epoch [111][400/1281] lr: 4.075e-03, eta: 6:18:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0815, loss: 0.0815 +2025-06-25 06:43:31,710 - pyskl - INFO - Epoch [111][500/1281] lr: 4.060e-03, eta: 6:18:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0753, loss: 0.0753 +2025-06-25 06:44:20,993 - pyskl - INFO - Epoch [111][600/1281] lr: 4.045e-03, eta: 6:17:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0785, loss: 0.0785 +2025-06-25 06:45:10,332 - pyskl - INFO - Epoch [111][700/1281] lr: 4.030e-03, eta: 6:16:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 1.0000, loss_cls: 0.0957, loss: 0.0957 +2025-06-25 06:45:59,386 - pyskl - INFO - Epoch [111][800/1281] lr: 4.015e-03, eta: 6:15:50, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0964, loss: 0.0964 +2025-06-25 06:46:48,312 - pyskl - INFO - Epoch [111][900/1281] lr: 4.000e-03, eta: 6:15:07, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0868, loss: 0.0868 +2025-06-25 06:47:37,317 - pyskl - INFO - Epoch [111][1000/1281] lr: 3.985e-03, eta: 6:14:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0973, loss: 0.0973 +2025-06-25 06:48:26,589 - pyskl - INFO - Epoch [111][1100/1281] lr: 3.970e-03, eta: 6:13:41, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 1.0000, loss_cls: 0.0873, loss: 0.0873 +2025-06-25 06:49:15,804 - pyskl - INFO - Epoch [111][1200/1281] lr: 3.955e-03, eta: 6:12:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9806, top5_acc: 1.0000, loss_cls: 0.1175, loss: 0.1175 +2025-06-25 06:49:56,108 - pyskl - INFO - Saving checkpoint at 111 epochs +2025-06-25 06:50:52,351 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 06:50:52,417 - pyskl - INFO - +top1_acc 0.9036 +top5_acc 0.9927 +2025-06-25 06:50:52,417 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 06:50:52,423 - pyskl - INFO - +mean_acc 0.8771 +2025-06-25 06:50:52,424 - pyskl - INFO - Epoch(val) [111][533] top1_acc: 0.9036, top5_acc: 0.9927, mean_class_accuracy: 0.8771 +2025-06-25 06:51:56,612 - pyskl - INFO - Epoch [112][100/1281] lr: 3.928e-03, eta: 6:11:31, time: 0.642, data_time: 0.185, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.1043, loss: 0.1043 +2025-06-25 06:52:41,232 - pyskl - INFO - Epoch [112][200/1281] lr: 3.914e-03, eta: 6:10:46, time: 0.446, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0591, loss: 0.0591 +2025-06-25 06:53:30,059 - pyskl - INFO - Epoch [112][300/1281] lr: 3.899e-03, eta: 6:10:03, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0772, loss: 0.0772 +2025-06-25 06:54:19,207 - pyskl - INFO - Epoch [112][400/1281] lr: 3.884e-03, eta: 6:09:20, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0774, loss: 0.0774 +2025-06-25 06:55:08,558 - pyskl - INFO - Epoch [112][500/1281] lr: 3.869e-03, eta: 6:08:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0634, loss: 0.0634 +2025-06-25 06:55:57,538 - pyskl - INFO - Epoch [112][600/1281] lr: 3.854e-03, eta: 6:07:53, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1007, loss: 0.1007 +2025-06-25 06:56:46,715 - pyskl - INFO - Epoch [112][700/1281] lr: 3.840e-03, eta: 6:07:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9838, top5_acc: 1.0000, loss_cls: 0.1055, loss: 0.1055 +2025-06-25 06:57:35,797 - pyskl - INFO - Epoch [112][800/1281] lr: 3.825e-03, eta: 6:06:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9856, top5_acc: 1.0000, loss_cls: 0.1147, loss: 0.1147 +2025-06-25 06:58:24,926 - pyskl - INFO - Epoch [112][900/1281] lr: 3.810e-03, eta: 6:05:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0710, loss: 0.0710 +2025-06-25 06:59:13,844 - pyskl - INFO - Epoch [112][1000/1281] lr: 3.795e-03, eta: 6:05:00, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0804, loss: 0.0804 +2025-06-25 07:00:03,083 - pyskl - INFO - Epoch [112][1100/1281] lr: 3.781e-03, eta: 6:04:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0806, loss: 0.0806 +2025-06-25 07:00:52,364 - pyskl - INFO - Epoch [112][1200/1281] lr: 3.766e-03, eta: 6:03:34, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9794, top5_acc: 0.9994, loss_cls: 0.1416, loss: 0.1416 +2025-06-25 07:01:32,438 - pyskl - INFO - Saving checkpoint at 112 epochs +2025-06-25 07:02:27,977 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:02:28,033 - pyskl - INFO - +top1_acc 0.9027 +top5_acc 0.9924 +2025-06-25 07:02:28,033 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:02:28,040 - pyskl - INFO - +mean_acc 0.8768 +2025-06-25 07:02:28,042 - pyskl - INFO - Epoch(val) [112][533] top1_acc: 0.9027, top5_acc: 0.9924, mean_class_accuracy: 0.8768 +2025-06-25 07:03:31,320 - pyskl - INFO - Epoch [113][100/1281] lr: 3.740e-03, eta: 6:02:07, time: 0.633, data_time: 0.191, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0589, loss: 0.0589 +2025-06-25 07:04:15,949 - pyskl - INFO - Epoch [113][200/1281] lr: 3.725e-03, eta: 6:01:22, time: 0.446, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0589, loss: 0.0589 +2025-06-25 07:05:04,807 - pyskl - INFO - Epoch [113][300/1281] lr: 3.711e-03, eta: 6:00:39, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9869, top5_acc: 0.9994, loss_cls: 0.0954, loss: 0.0954 +2025-06-25 07:05:54,238 - pyskl - INFO - Epoch [113][400/1281] lr: 3.696e-03, eta: 5:59:56, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0899, loss: 0.0899 +2025-06-25 07:06:43,067 - pyskl - INFO - Epoch [113][500/1281] lr: 3.682e-03, eta: 5:59:12, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0600, loss: 0.0600 +2025-06-25 07:07:32,084 - pyskl - INFO - Epoch [113][600/1281] lr: 3.667e-03, eta: 5:58:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0518, loss: 0.0518 +2025-06-25 07:08:21,028 - pyskl - INFO - Epoch [113][700/1281] lr: 3.653e-03, eta: 5:57:46, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0530, loss: 0.0530 +2025-06-25 07:09:10,019 - pyskl - INFO - Epoch [113][800/1281] lr: 3.638e-03, eta: 5:57:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0973, loss: 0.0973 +2025-06-25 07:09:59,047 - pyskl - INFO - Epoch [113][900/1281] lr: 3.624e-03, eta: 5:56:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0809, loss: 0.0809 +2025-06-25 07:10:48,245 - pyskl - INFO - Epoch [113][1000/1281] lr: 3.610e-03, eta: 5:55:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0763, loss: 0.0763 +2025-06-25 07:11:37,422 - pyskl - INFO - Epoch [113][1100/1281] lr: 3.595e-03, eta: 5:54:53, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0734, loss: 0.0734 +2025-06-25 07:12:26,353 - pyskl - INFO - Epoch [113][1200/1281] lr: 3.581e-03, eta: 5:54:09, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0759, loss: 0.0759 +2025-06-25 07:13:06,578 - pyskl - INFO - Saving checkpoint at 113 epochs +2025-06-25 07:14:03,013 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:14:03,086 - pyskl - INFO - +top1_acc 0.9049 +top5_acc 0.9927 +2025-06-25 07:14:03,086 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:14:03,095 - pyskl - INFO - +mean_acc 0.8747 +2025-06-25 07:14:03,100 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_110.pth was removed +2025-06-25 07:14:03,274 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_113.pth. +2025-06-25 07:14:03,275 - pyskl - INFO - Best top1_acc is 0.9049 at 113 epoch. +2025-06-25 07:14:03,278 - pyskl - INFO - Epoch(val) [113][533] top1_acc: 0.9049, top5_acc: 0.9927, mean_class_accuracy: 0.8747 +2025-06-25 07:15:09,795 - pyskl - INFO - Epoch [114][100/1281] lr: 3.555e-03, eta: 5:52:43, time: 0.665, data_time: 0.190, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0657, loss: 0.0657 +2025-06-25 07:15:54,184 - pyskl - INFO - Epoch [114][200/1281] lr: 3.541e-03, eta: 5:51:59, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0659, loss: 0.0659 +2025-06-25 07:16:43,197 - pyskl - INFO - Epoch [114][300/1281] lr: 3.526e-03, eta: 5:51:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0783, loss: 0.0783 +2025-06-25 07:17:32,311 - pyskl - INFO - Epoch [114][400/1281] lr: 3.512e-03, eta: 5:50:32, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0641, loss: 0.0641 +2025-06-25 07:18:21,386 - pyskl - INFO - Epoch [114][500/1281] lr: 3.498e-03, eta: 5:49:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.1114, loss: 0.1114 +2025-06-25 07:19:10,387 - pyskl - INFO - Epoch [114][600/1281] lr: 3.484e-03, eta: 5:49:05, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 0.9994, loss_cls: 0.1101, loss: 0.1101 +2025-06-25 07:19:59,392 - pyskl - INFO - Epoch [114][700/1281] lr: 3.470e-03, eta: 5:48:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9831, top5_acc: 1.0000, loss_cls: 0.0954, loss: 0.0954 +2025-06-25 07:20:48,353 - pyskl - INFO - Epoch [114][800/1281] lr: 3.456e-03, eta: 5:47:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9862, top5_acc: 1.0000, loss_cls: 0.0945, loss: 0.0945 +2025-06-25 07:21:37,428 - pyskl - INFO - Epoch [114][900/1281] lr: 3.442e-03, eta: 5:46:55, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 1.0000, loss_cls: 0.0890, loss: 0.0890 +2025-06-25 07:22:26,531 - pyskl - INFO - Epoch [114][1000/1281] lr: 3.427e-03, eta: 5:46:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0904, loss: 0.0904 +2025-06-25 07:23:15,728 - pyskl - INFO - Epoch [114][1100/1281] lr: 3.413e-03, eta: 5:45:28, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0759, loss: 0.0759 +2025-06-25 07:24:04,645 - pyskl - INFO - Epoch [114][1200/1281] lr: 3.399e-03, eta: 5:44:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0669, loss: 0.0669 +2025-06-25 07:24:45,427 - pyskl - INFO - Saving checkpoint at 114 epochs +2025-06-25 07:25:41,444 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:25:41,501 - pyskl - INFO - +top1_acc 0.9105 +top5_acc 0.9917 +2025-06-25 07:25:41,501 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:25:41,508 - pyskl - INFO - +mean_acc 0.8854 +2025-06-25 07:25:41,512 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_113.pth was removed +2025-06-25 07:25:41,863 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_114.pth. +2025-06-25 07:25:41,863 - pyskl - INFO - Best top1_acc is 0.9105 at 114 epoch. +2025-06-25 07:25:41,867 - pyskl - INFO - Epoch(val) [114][533] top1_acc: 0.9105, top5_acc: 0.9917, mean_class_accuracy: 0.8854 +2025-06-25 07:26:46,899 - pyskl - INFO - Epoch [115][100/1281] lr: 3.374e-03, eta: 5:43:19, time: 0.650, data_time: 0.186, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0546, loss: 0.0546 +2025-06-25 07:27:30,864 - pyskl - INFO - Epoch [115][200/1281] lr: 3.360e-03, eta: 5:42:34, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 0.9894, top5_acc: 1.0000, loss_cls: 0.0771, loss: 0.0771 +2025-06-25 07:28:20,110 - pyskl - INFO - Epoch [115][300/1281] lr: 3.346e-03, eta: 5:41:50, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0754, loss: 0.0754 +2025-06-25 07:29:08,888 - pyskl - INFO - Epoch [115][400/1281] lr: 3.332e-03, eta: 5:41:07, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0805, loss: 0.0805 +2025-06-25 07:29:58,152 - pyskl - INFO - Epoch [115][500/1281] lr: 3.318e-03, eta: 5:40:23, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0625, loss: 0.0625 +2025-06-25 07:30:47,292 - pyskl - INFO - Epoch [115][600/1281] lr: 3.305e-03, eta: 5:39:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0828, loss: 0.0828 +2025-06-25 07:31:36,338 - pyskl - INFO - Epoch [115][700/1281] lr: 3.291e-03, eta: 5:38:56, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0847, loss: 0.0847 +2025-06-25 07:32:25,505 - pyskl - INFO - Epoch [115][800/1281] lr: 3.277e-03, eta: 5:38:13, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0898, loss: 0.0898 +2025-06-25 07:33:14,461 - pyskl - INFO - Epoch [115][900/1281] lr: 3.263e-03, eta: 5:37:30, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0787, loss: 0.0787 +2025-06-25 07:34:03,659 - pyskl - INFO - Epoch [115][1000/1281] lr: 3.249e-03, eta: 5:36:46, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0758, loss: 0.0758 +2025-06-25 07:34:52,963 - pyskl - INFO - Epoch [115][1100/1281] lr: 3.236e-03, eta: 5:36:03, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0525, loss: 0.0525 +2025-06-25 07:35:41,874 - pyskl - INFO - Epoch [115][1200/1281] lr: 3.222e-03, eta: 5:35:19, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9888, top5_acc: 0.9994, loss_cls: 0.0806, loss: 0.0806 +2025-06-25 07:36:21,974 - pyskl - INFO - Saving checkpoint at 115 epochs +2025-06-25 07:37:19,483 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:37:19,551 - pyskl - INFO - +top1_acc 0.9092 +top5_acc 0.9924 +2025-06-25 07:37:19,551 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:37:19,559 - pyskl - INFO - +mean_acc 0.8788 +2025-06-25 07:37:19,561 - pyskl - INFO - Epoch(val) [115][533] top1_acc: 0.9092, top5_acc: 0.9924, mean_class_accuracy: 0.8788 +2025-06-25 07:38:29,687 - pyskl - INFO - Epoch [116][100/1281] lr: 3.197e-03, eta: 5:33:55, time: 0.701, data_time: 0.185, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0695, loss: 0.0695 +2025-06-25 07:39:14,338 - pyskl - INFO - Epoch [116][200/1281] lr: 3.184e-03, eta: 5:33:10, time: 0.446, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0622, loss: 0.0622 +2025-06-25 07:40:03,703 - pyskl - INFO - Epoch [116][300/1281] lr: 3.170e-03, eta: 5:32:27, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 1.0000, loss_cls: 0.0781, loss: 0.0781 +2025-06-25 07:40:53,014 - pyskl - INFO - Epoch [116][400/1281] lr: 3.156e-03, eta: 5:31:43, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0589, loss: 0.0589 +2025-06-25 07:41:41,846 - pyskl - INFO - Epoch [116][500/1281] lr: 3.143e-03, eta: 5:31:00, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0577, loss: 0.0577 +2025-06-25 07:42:31,035 - pyskl - INFO - Epoch [116][600/1281] lr: 3.129e-03, eta: 5:30:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0517, loss: 0.0517 +2025-06-25 07:43:20,096 - pyskl - INFO - Epoch [116][700/1281] lr: 3.116e-03, eta: 5:29:33, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9912, top5_acc: 1.0000, loss_cls: 0.0707, loss: 0.0707 +2025-06-25 07:44:09,318 - pyskl - INFO - Epoch [116][800/1281] lr: 3.102e-03, eta: 5:28:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0651, loss: 0.0651 +2025-06-25 07:44:58,511 - pyskl - INFO - Epoch [116][900/1281] lr: 3.089e-03, eta: 5:28:06, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0620, loss: 0.0620 +2025-06-25 07:45:47,178 - pyskl - INFO - Epoch [116][1000/1281] lr: 3.075e-03, eta: 5:27:22, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0503, loss: 0.0503 +2025-06-25 07:46:36,443 - pyskl - INFO - Epoch [116][1100/1281] lr: 3.062e-03, eta: 5:26:38, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0583, loss: 0.0583 +2025-06-25 07:47:25,957 - pyskl - INFO - Epoch [116][1200/1281] lr: 3.049e-03, eta: 5:25:55, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9881, top5_acc: 0.9994, loss_cls: 0.0811, loss: 0.0811 +2025-06-25 07:48:06,440 - pyskl - INFO - Saving checkpoint at 116 epochs +2025-06-25 07:49:02,467 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 07:49:02,535 - pyskl - INFO - +top1_acc 0.9114 +top5_acc 0.9939 +2025-06-25 07:49:02,536 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 07:49:02,544 - pyskl - INFO - +mean_acc 0.8865 +2025-06-25 07:49:02,549 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_114.pth was removed +2025-06-25 07:49:02,707 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_116.pth. +2025-06-25 07:49:02,707 - pyskl - INFO - Best top1_acc is 0.9114 at 116 epoch. +2025-06-25 07:49:02,710 - pyskl - INFO - Epoch(val) [116][533] top1_acc: 0.9114, top5_acc: 0.9939, mean_class_accuracy: 0.8865 +2025-06-25 07:50:09,480 - pyskl - INFO - Epoch [117][100/1281] lr: 3.024e-03, eta: 5:24:30, time: 0.668, data_time: 0.182, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0692, loss: 0.0692 +2025-06-25 07:50:53,637 - pyskl - INFO - Epoch [117][200/1281] lr: 3.011e-03, eta: 5:23:45, time: 0.442, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0606, loss: 0.0606 +2025-06-25 07:51:42,758 - pyskl - INFO - Epoch [117][300/1281] lr: 2.998e-03, eta: 5:23:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0677, loss: 0.0677 +2025-06-25 07:52:31,962 - pyskl - INFO - Epoch [117][400/1281] lr: 2.984e-03, eta: 5:22:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0772, loss: 0.0772 +2025-06-25 07:53:21,130 - pyskl - INFO - Epoch [117][500/1281] lr: 2.971e-03, eta: 5:21:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0560, loss: 0.0560 +2025-06-25 07:54:10,130 - pyskl - INFO - Epoch [117][600/1281] lr: 2.958e-03, eta: 5:20:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9919, top5_acc: 1.0000, loss_cls: 0.0614, loss: 0.0614 +2025-06-25 07:54:58,991 - pyskl - INFO - Epoch [117][700/1281] lr: 2.945e-03, eta: 5:20:07, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0537, loss: 0.0537 +2025-06-25 07:55:48,018 - pyskl - INFO - Epoch [117][800/1281] lr: 2.932e-03, eta: 5:19:23, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0556, loss: 0.0556 +2025-06-25 07:56:37,137 - pyskl - INFO - Epoch [117][900/1281] lr: 2.919e-03, eta: 5:18:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9906, top5_acc: 1.0000, loss_cls: 0.0624, loss: 0.0624 +2025-06-25 07:57:25,946 - pyskl - INFO - Epoch [117][1000/1281] lr: 2.905e-03, eta: 5:17:56, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9900, top5_acc: 0.9994, loss_cls: 0.0749, loss: 0.0749 +2025-06-25 07:58:14,998 - pyskl - INFO - Epoch [117][1100/1281] lr: 2.892e-03, eta: 5:17:12, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0618, loss: 0.0618 +2025-06-25 07:59:04,311 - pyskl - INFO - Epoch [117][1200/1281] lr: 2.879e-03, eta: 5:16:29, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9931, top5_acc: 1.0000, loss_cls: 0.0575, loss: 0.0575 +2025-06-25 07:59:44,557 - pyskl - INFO - Saving checkpoint at 117 epochs +2025-06-25 08:00:41,057 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:00:41,112 - pyskl - INFO - +top1_acc 0.9117 +top5_acc 0.9931 +2025-06-25 08:00:41,112 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:00:41,118 - pyskl - INFO - +mean_acc 0.8881 +2025-06-25 08:00:41,122 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_116.pth was removed +2025-06-25 08:00:41,301 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_117.pth. +2025-06-25 08:00:41,302 - pyskl - INFO - Best top1_acc is 0.9117 at 117 epoch. +2025-06-25 08:00:41,305 - pyskl - INFO - Epoch(val) [117][533] top1_acc: 0.9117, top5_acc: 0.9931, mean_class_accuracy: 0.8881 +2025-06-25 08:01:47,778 - pyskl - INFO - Epoch [118][100/1281] lr: 2.856e-03, eta: 5:15:03, time: 0.665, data_time: 0.182, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0484, loss: 0.0484 +2025-06-25 08:02:32,850 - pyskl - INFO - Epoch [118][200/1281] lr: 2.843e-03, eta: 5:14:19, time: 0.451, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0520, loss: 0.0520 +2025-06-25 08:03:22,264 - pyskl - INFO - Epoch [118][300/1281] lr: 2.830e-03, eta: 5:13:35, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0490, loss: 0.0490 +2025-06-25 08:04:11,582 - pyskl - INFO - Epoch [118][400/1281] lr: 2.817e-03, eta: 5:12:51, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0530, loss: 0.0530 +2025-06-25 08:05:00,923 - pyskl - INFO - Epoch [118][500/1281] lr: 2.804e-03, eta: 5:12:08, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0344, loss: 0.0344 +2025-06-25 08:05:49,894 - pyskl - INFO - Epoch [118][600/1281] lr: 2.791e-03, eta: 5:11:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0477, loss: 0.0477 +2025-06-25 08:06:39,130 - pyskl - INFO - Epoch [118][700/1281] lr: 2.778e-03, eta: 5:10:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0621, loss: 0.0621 +2025-06-25 08:07:28,171 - pyskl - INFO - Epoch [118][800/1281] lr: 2.765e-03, eta: 5:09:57, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0478, loss: 0.0478 +2025-06-25 08:08:17,130 - pyskl - INFO - Epoch [118][900/1281] lr: 2.753e-03, eta: 5:09:13, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0366, loss: 0.0366 +2025-06-25 08:09:06,178 - pyskl - INFO - Epoch [118][1000/1281] lr: 2.740e-03, eta: 5:08:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0432, loss: 0.0432 +2025-06-25 08:09:55,010 - pyskl - INFO - Epoch [118][1100/1281] lr: 2.727e-03, eta: 5:07:46, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0386, loss: 0.0386 +2025-06-25 08:10:43,989 - pyskl - INFO - Epoch [118][1200/1281] lr: 2.714e-03, eta: 5:07:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0447, loss: 0.0447 +2025-06-25 08:11:24,143 - pyskl - INFO - Saving checkpoint at 118 epochs +2025-06-25 08:12:19,968 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:12:20,023 - pyskl - INFO - +top1_acc 0.9224 +top5_acc 0.9940 +2025-06-25 08:12:20,023 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:12:20,031 - pyskl - INFO - +mean_acc 0.8965 +2025-06-25 08:12:20,035 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_117.pth was removed +2025-06-25 08:12:20,209 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2025-06-25 08:12:20,209 - pyskl - INFO - Best top1_acc is 0.9224 at 118 epoch. +2025-06-25 08:12:20,213 - pyskl - INFO - Epoch(val) [118][533] top1_acc: 0.9224, top5_acc: 0.9940, mean_class_accuracy: 0.8965 +2025-06-25 08:13:25,262 - pyskl - INFO - Epoch [119][100/1281] lr: 2.691e-03, eta: 5:05:36, time: 0.650, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0279, loss: 0.0279 +2025-06-25 08:14:09,630 - pyskl - INFO - Epoch [119][200/1281] lr: 2.679e-03, eta: 5:04:51, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0338, loss: 0.0338 +2025-06-25 08:14:59,102 - pyskl - INFO - Epoch [119][300/1281] lr: 2.666e-03, eta: 5:04:08, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0385, loss: 0.0385 +2025-06-25 08:15:48,281 - pyskl - INFO - Epoch [119][400/1281] lr: 2.653e-03, eta: 5:03:24, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0393, loss: 0.0393 +2025-06-25 08:16:37,344 - pyskl - INFO - Epoch [119][500/1281] lr: 2.641e-03, eta: 5:02:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0318, loss: 0.0318 +2025-06-25 08:17:26,462 - pyskl - INFO - Epoch [119][600/1281] lr: 2.628e-03, eta: 5:01:57, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0408, loss: 0.0408 +2025-06-25 08:18:15,302 - pyskl - INFO - Epoch [119][700/1281] lr: 2.616e-03, eta: 5:01:13, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0661, loss: 0.0661 +2025-06-25 08:19:04,301 - pyskl - INFO - Epoch [119][800/1281] lr: 2.603e-03, eta: 5:00:29, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0446, loss: 0.0446 +2025-06-25 08:19:53,201 - pyskl - INFO - Epoch [119][900/1281] lr: 2.591e-03, eta: 4:59:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0459, loss: 0.0459 +2025-06-25 08:20:42,191 - pyskl - INFO - Epoch [119][1000/1281] lr: 2.578e-03, eta: 4:59:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0377, loss: 0.0377 +2025-06-25 08:21:31,249 - pyskl - INFO - Epoch [119][1100/1281] lr: 2.566e-03, eta: 4:58:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0346, loss: 0.0346 +2025-06-25 08:22:20,436 - pyskl - INFO - Epoch [119][1200/1281] lr: 2.554e-03, eta: 4:57:34, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0362, loss: 0.0362 +2025-06-25 08:23:00,645 - pyskl - INFO - Saving checkpoint at 119 epochs +2025-06-25 08:23:57,241 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:23:57,305 - pyskl - INFO - +top1_acc 0.9161 +top5_acc 0.9939 +2025-06-25 08:23:57,306 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:23:57,313 - pyskl - INFO - +mean_acc 0.8901 +2025-06-25 08:23:57,315 - pyskl - INFO - Epoch(val) [119][533] top1_acc: 0.9161, top5_acc: 0.9939, mean_class_accuracy: 0.8901 +2025-06-25 08:25:03,515 - pyskl - INFO - Epoch [120][100/1281] lr: 2.531e-03, eta: 4:56:09, time: 0.662, data_time: 0.185, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0370, loss: 0.0370 +2025-06-25 08:25:47,929 - pyskl - INFO - Epoch [120][200/1281] lr: 2.519e-03, eta: 4:55:24, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0354, loss: 0.0354 +2025-06-25 08:26:36,991 - pyskl - INFO - Epoch [120][300/1281] lr: 2.507e-03, eta: 4:54:40, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0341, loss: 0.0341 +2025-06-25 08:27:25,842 - pyskl - INFO - Epoch [120][400/1281] lr: 2.494e-03, eta: 4:53:56, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0391, loss: 0.0391 +2025-06-25 08:28:14,762 - pyskl - INFO - Epoch [120][500/1281] lr: 2.482e-03, eta: 4:53:13, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0349, loss: 0.0349 +2025-06-25 08:29:03,934 - pyskl - INFO - Epoch [120][600/1281] lr: 2.470e-03, eta: 4:52:29, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0610, loss: 0.0610 +2025-06-25 08:29:52,668 - pyskl - INFO - Epoch [120][700/1281] lr: 2.458e-03, eta: 4:51:45, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0405, loss: 0.0405 +2025-06-25 08:30:41,733 - pyskl - INFO - Epoch [120][800/1281] lr: 2.446e-03, eta: 4:51:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0434, loss: 0.0434 +2025-06-25 08:31:30,990 - pyskl - INFO - Epoch [120][900/1281] lr: 2.433e-03, eta: 4:50:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0454, loss: 0.0454 +2025-06-25 08:32:20,088 - pyskl - INFO - Epoch [120][1000/1281] lr: 2.421e-03, eta: 4:49:34, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0420, loss: 0.0420 +2025-06-25 08:33:09,083 - pyskl - INFO - Epoch [120][1100/1281] lr: 2.409e-03, eta: 4:48:50, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9938, top5_acc: 1.0000, loss_cls: 0.0561, loss: 0.0561 +2025-06-25 08:33:57,765 - pyskl - INFO - Epoch [120][1200/1281] lr: 2.397e-03, eta: 4:48:06, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0579, loss: 0.0579 +2025-06-25 08:34:38,011 - pyskl - INFO - Saving checkpoint at 120 epochs +2025-06-25 08:35:34,447 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:35:34,503 - pyskl - INFO - +top1_acc 0.9141 +top5_acc 0.9937 +2025-06-25 08:35:34,503 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:35:34,510 - pyskl - INFO - +mean_acc 0.8866 +2025-06-25 08:35:34,512 - pyskl - INFO - Epoch(val) [120][533] top1_acc: 0.9141, top5_acc: 0.9937, mean_class_accuracy: 0.8866 +2025-06-25 08:36:43,203 - pyskl - INFO - Epoch [121][100/1281] lr: 2.375e-03, eta: 4:46:41, time: 0.687, data_time: 0.191, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0465, loss: 0.0465 +2025-06-25 08:37:27,616 - pyskl - INFO - Epoch [121][200/1281] lr: 2.363e-03, eta: 4:45:56, time: 0.444, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0638, loss: 0.0638 +2025-06-25 08:38:16,407 - pyskl - INFO - Epoch [121][300/1281] lr: 2.351e-03, eta: 4:45:13, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0431, loss: 0.0431 +2025-06-25 08:39:05,309 - pyskl - INFO - Epoch [121][400/1281] lr: 2.340e-03, eta: 4:44:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0434, loss: 0.0434 +2025-06-25 08:39:54,287 - pyskl - INFO - Epoch [121][500/1281] lr: 2.328e-03, eta: 4:43:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0487, loss: 0.0487 +2025-06-25 08:40:43,689 - pyskl - INFO - Epoch [121][600/1281] lr: 2.316e-03, eta: 4:43:01, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9944, top5_acc: 1.0000, loss_cls: 0.0558, loss: 0.0558 +2025-06-25 08:41:32,871 - pyskl - INFO - Epoch [121][700/1281] lr: 2.304e-03, eta: 4:42:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9875, top5_acc: 1.0000, loss_cls: 0.0654, loss: 0.0654 +2025-06-25 08:42:21,876 - pyskl - INFO - Epoch [121][800/1281] lr: 2.292e-03, eta: 4:41:33, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0429, loss: 0.0429 +2025-06-25 08:43:11,057 - pyskl - INFO - Epoch [121][900/1281] lr: 2.280e-03, eta: 4:40:49, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0378, loss: 0.0378 +2025-06-25 08:44:00,402 - pyskl - INFO - Epoch [121][1000/1281] lr: 2.269e-03, eta: 4:40:06, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9925, top5_acc: 1.0000, loss_cls: 0.0566, loss: 0.0566 +2025-06-25 08:44:49,446 - pyskl - INFO - Epoch [121][1100/1281] lr: 2.257e-03, eta: 4:39:22, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0495, loss: 0.0495 +2025-06-25 08:45:38,751 - pyskl - INFO - Epoch [121][1200/1281] lr: 2.245e-03, eta: 4:38:38, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9950, top5_acc: 1.0000, loss_cls: 0.0457, loss: 0.0457 +2025-06-25 08:46:18,897 - pyskl - INFO - Saving checkpoint at 121 epochs +2025-06-25 08:47:14,906 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:47:14,966 - pyskl - INFO - +top1_acc 0.9128 +top5_acc 0.9923 +2025-06-25 08:47:14,966 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:47:14,973 - pyskl - INFO - +mean_acc 0.8884 +2025-06-25 08:47:14,975 - pyskl - INFO - Epoch(val) [121][533] top1_acc: 0.9128, top5_acc: 0.9923, mean_class_accuracy: 0.8884 +2025-06-25 08:48:21,185 - pyskl - INFO - Epoch [122][100/1281] lr: 2.224e-03, eta: 4:37:13, time: 0.662, data_time: 0.188, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0325, loss: 0.0325 +2025-06-25 08:49:04,476 - pyskl - INFO - Epoch [122][200/1281] lr: 2.212e-03, eta: 4:36:28, time: 0.433, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0373, loss: 0.0373 +2025-06-25 08:49:53,622 - pyskl - INFO - Epoch [122][300/1281] lr: 2.201e-03, eta: 4:35:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0341, loss: 0.0341 +2025-06-25 08:50:42,610 - pyskl - INFO - Epoch [122][400/1281] lr: 2.189e-03, eta: 4:35:00, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0404, loss: 0.0404 +2025-06-25 08:51:31,863 - pyskl - INFO - Epoch [122][500/1281] lr: 2.178e-03, eta: 4:34:16, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0338, loss: 0.0338 +2025-06-25 08:52:21,212 - pyskl - INFO - Epoch [122][600/1281] lr: 2.166e-03, eta: 4:33:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0344, loss: 0.0344 +2025-06-25 08:53:10,322 - pyskl - INFO - Epoch [122][700/1281] lr: 2.155e-03, eta: 4:32:48, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0311, loss: 0.0311 +2025-06-25 08:53:59,498 - pyskl - INFO - Epoch [122][800/1281] lr: 2.143e-03, eta: 4:32:04, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0299, loss: 0.0299 +2025-06-25 08:54:48,961 - pyskl - INFO - Epoch [122][900/1281] lr: 2.132e-03, eta: 4:31:20, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0339, loss: 0.0339 +2025-06-25 08:55:37,968 - pyskl - INFO - Epoch [122][1000/1281] lr: 2.120e-03, eta: 4:30:36, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0397, loss: 0.0397 +2025-06-25 08:56:27,154 - pyskl - INFO - Epoch [122][1100/1281] lr: 2.109e-03, eta: 4:29:53, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0295, loss: 0.0295 +2025-06-25 08:57:16,201 - pyskl - INFO - Epoch [122][1200/1281] lr: 2.098e-03, eta: 4:29:09, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0404, loss: 0.0404 +2025-06-25 08:57:56,517 - pyskl - INFO - Saving checkpoint at 122 epochs +2025-06-25 08:58:54,333 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 08:58:54,401 - pyskl - INFO - +top1_acc 0.9156 +top5_acc 0.9940 +2025-06-25 08:58:54,401 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 08:58:54,409 - pyskl - INFO - +mean_acc 0.8944 +2025-06-25 08:58:54,411 - pyskl - INFO - Epoch(val) [122][533] top1_acc: 0.9156, top5_acc: 0.9940, mean_class_accuracy: 0.8944 +2025-06-25 09:00:06,061 - pyskl - INFO - Epoch [123][100/1281] lr: 2.077e-03, eta: 4:27:45, time: 0.716, data_time: 0.187, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0330, loss: 0.0330 +2025-06-25 09:00:51,382 - pyskl - INFO - Epoch [123][200/1281] lr: 2.066e-03, eta: 4:27:00, time: 0.453, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 09:01:40,510 - pyskl - INFO - Epoch [123][300/1281] lr: 2.055e-03, eta: 4:26:16, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0302, loss: 0.0302 +2025-06-25 09:02:29,363 - pyskl - INFO - Epoch [123][400/1281] lr: 2.044e-03, eta: 4:25:32, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0285, loss: 0.0285 +2025-06-25 09:03:18,284 - pyskl - INFO - Epoch [123][500/1281] lr: 2.032e-03, eta: 4:24:48, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 09:04:07,234 - pyskl - INFO - Epoch [123][600/1281] lr: 2.021e-03, eta: 4:24:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0293, loss: 0.0293 +2025-06-25 09:04:56,326 - pyskl - INFO - Epoch [123][700/1281] lr: 2.010e-03, eta: 4:23:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 09:05:45,557 - pyskl - INFO - Epoch [123][800/1281] lr: 1.999e-03, eta: 4:22:36, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0277, loss: 0.0277 +2025-06-25 09:06:34,641 - pyskl - INFO - Epoch [123][900/1281] lr: 1.988e-03, eta: 4:21:52, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 09:07:23,643 - pyskl - INFO - Epoch [123][1000/1281] lr: 1.977e-03, eta: 4:21:08, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0312, loss: 0.0312 +2025-06-25 09:08:12,696 - pyskl - INFO - Epoch [123][1100/1281] lr: 1.966e-03, eta: 4:20:24, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0365, loss: 0.0365 +2025-06-25 09:09:01,857 - pyskl - INFO - Epoch [123][1200/1281] lr: 1.955e-03, eta: 4:19:40, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0275, loss: 0.0275 +2025-06-25 09:09:42,241 - pyskl - INFO - Saving checkpoint at 123 epochs +2025-06-25 09:10:37,168 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:10:37,224 - pyskl - INFO - +top1_acc 0.9224 +top5_acc 0.9940 +2025-06-25 09:10:37,224 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:10:37,231 - pyskl - INFO - +mean_acc 0.8959 +2025-06-25 09:10:37,233 - pyskl - INFO - Epoch(val) [123][533] top1_acc: 0.9224, top5_acc: 0.9940, mean_class_accuracy: 0.8959 +2025-06-25 09:11:40,234 - pyskl - INFO - Epoch [124][100/1281] lr: 1.935e-03, eta: 4:18:15, time: 0.630, data_time: 0.188, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0260, loss: 0.0260 +2025-06-25 09:12:24,547 - pyskl - INFO - Epoch [124][200/1281] lr: 1.924e-03, eta: 4:17:30, time: 0.443, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0315, loss: 0.0315 +2025-06-25 09:13:13,398 - pyskl - INFO - Epoch [124][300/1281] lr: 1.913e-03, eta: 4:16:46, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0349, loss: 0.0349 +2025-06-25 09:14:02,236 - pyskl - INFO - Epoch [124][400/1281] lr: 1.902e-03, eta: 4:16:02, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0261, loss: 0.0261 +2025-06-25 09:14:51,319 - pyskl - INFO - Epoch [124][500/1281] lr: 1.892e-03, eta: 4:15:18, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0293, loss: 0.0293 +2025-06-25 09:15:40,573 - pyskl - INFO - Epoch [124][600/1281] lr: 1.881e-03, eta: 4:14:33, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9956, top5_acc: 1.0000, loss_cls: 0.0363, loss: 0.0363 +2025-06-25 09:16:29,700 - pyskl - INFO - Epoch [124][700/1281] lr: 1.870e-03, eta: 4:13:49, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0293, loss: 0.0293 +2025-06-25 09:17:18,829 - pyskl - INFO - Epoch [124][800/1281] lr: 1.859e-03, eta: 4:13:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 09:18:07,853 - pyskl - INFO - Epoch [124][900/1281] lr: 1.849e-03, eta: 4:12:21, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0294, loss: 0.0294 +2025-06-25 09:18:57,111 - pyskl - INFO - Epoch [124][1000/1281] lr: 1.838e-03, eta: 4:11:37, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0279, loss: 0.0279 +2025-06-25 09:19:46,555 - pyskl - INFO - Epoch [124][1100/1281] lr: 1.827e-03, eta: 4:10:53, time: 0.494, data_time: 0.001, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-25 09:20:35,988 - pyskl - INFO - Epoch [124][1200/1281] lr: 1.817e-03, eta: 4:10:09, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-25 09:21:16,236 - pyskl - INFO - Saving checkpoint at 124 epochs +2025-06-25 09:22:12,570 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:22:12,624 - pyskl - INFO - +top1_acc 0.9243 +top5_acc 0.9950 +2025-06-25 09:22:12,624 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:22:12,631 - pyskl - INFO - +mean_acc 0.9005 +2025-06-25 09:22:12,638 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_118.pth was removed +2025-06-25 09:22:12,801 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_124.pth. +2025-06-25 09:22:12,802 - pyskl - INFO - Best top1_acc is 0.9243 at 124 epoch. +2025-06-25 09:22:12,805 - pyskl - INFO - Epoch(val) [124][533] top1_acc: 0.9243, top5_acc: 0.9950, mean_class_accuracy: 0.9005 +2025-06-25 09:23:17,810 - pyskl - INFO - Epoch [125][100/1281] lr: 1.797e-03, eta: 4:08:44, time: 0.650, data_time: 0.185, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0341, loss: 0.0341 +2025-06-25 09:24:02,820 - pyskl - INFO - Epoch [125][200/1281] lr: 1.787e-03, eta: 4:08:00, time: 0.450, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0308, loss: 0.0308 +2025-06-25 09:24:51,815 - pyskl - INFO - Epoch [125][300/1281] lr: 1.776e-03, eta: 4:07:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0247, loss: 0.0247 +2025-06-25 09:25:40,915 - pyskl - INFO - Epoch [125][400/1281] lr: 1.766e-03, eta: 4:06:31, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0285, loss: 0.0285 +2025-06-25 09:26:30,013 - pyskl - INFO - Epoch [125][500/1281] lr: 1.755e-03, eta: 4:05:47, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0234, loss: 0.0234 +2025-06-25 09:27:19,475 - pyskl - INFO - Epoch [125][600/1281] lr: 1.745e-03, eta: 4:05:03, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0278, loss: 0.0278 +2025-06-25 09:28:09,010 - pyskl - INFO - Epoch [125][700/1281] lr: 1.735e-03, eta: 4:04:19, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-25 09:28:58,107 - pyskl - INFO - Epoch [125][800/1281] lr: 1.724e-03, eta: 4:03:35, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0253, loss: 0.0253 +2025-06-25 09:29:47,252 - pyskl - INFO - Epoch [125][900/1281] lr: 1.714e-03, eta: 4:02:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0299, loss: 0.0299 +2025-06-25 09:30:36,449 - pyskl - INFO - Epoch [125][1000/1281] lr: 1.704e-03, eta: 4:02:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0272, loss: 0.0272 +2025-06-25 09:31:25,551 - pyskl - INFO - Epoch [125][1100/1281] lr: 1.693e-03, eta: 4:01:23, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0279, loss: 0.0279 +2025-06-25 09:32:14,685 - pyskl - INFO - Epoch [125][1200/1281] lr: 1.683e-03, eta: 4:00:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0286, loss: 0.0286 +2025-06-25 09:32:54,945 - pyskl - INFO - Saving checkpoint at 125 epochs +2025-06-25 09:33:51,101 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:33:51,169 - pyskl - INFO - +top1_acc 0.9163 +top5_acc 0.9947 +2025-06-25 09:33:51,169 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:33:51,177 - pyskl - INFO - +mean_acc 0.8914 +2025-06-25 09:33:51,179 - pyskl - INFO - Epoch(val) [125][533] top1_acc: 0.9163, top5_acc: 0.9947, mean_class_accuracy: 0.8914 +2025-06-25 09:34:57,602 - pyskl - INFO - Epoch [126][100/1281] lr: 1.665e-03, eta: 3:59:14, time: 0.664, data_time: 0.188, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0238, loss: 0.0238 +2025-06-25 09:35:42,283 - pyskl - INFO - Epoch [126][200/1281] lr: 1.654e-03, eta: 3:58:29, time: 0.447, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 09:36:31,191 - pyskl - INFO - Epoch [126][300/1281] lr: 1.644e-03, eta: 3:57:45, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-25 09:37:20,066 - pyskl - INFO - Epoch [126][400/1281] lr: 1.634e-03, eta: 3:57:01, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0229, loss: 0.0229 +2025-06-25 09:38:09,105 - pyskl - INFO - Epoch [126][500/1281] lr: 1.624e-03, eta: 3:56:17, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 09:38:58,399 - pyskl - INFO - Epoch [126][600/1281] lr: 1.614e-03, eta: 3:55:32, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 09:39:47,612 - pyskl - INFO - Epoch [126][700/1281] lr: 1.604e-03, eta: 3:54:48, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 09:40:36,650 - pyskl - INFO - Epoch [126][800/1281] lr: 1.594e-03, eta: 3:54:04, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0183, loss: 0.0183 +2025-06-25 09:41:26,034 - pyskl - INFO - Epoch [126][900/1281] lr: 1.584e-03, eta: 3:53:20, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 09:42:15,071 - pyskl - INFO - Epoch [126][1000/1281] lr: 1.574e-03, eta: 3:52:36, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0291, loss: 0.0291 +2025-06-25 09:43:04,049 - pyskl - INFO - Epoch [126][1100/1281] lr: 1.564e-03, eta: 3:51:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 09:43:53,210 - pyskl - INFO - Epoch [126][1200/1281] lr: 1.554e-03, eta: 3:51:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0240, loss: 0.0240 +2025-06-25 09:44:33,273 - pyskl - INFO - Saving checkpoint at 126 epochs +2025-06-25 09:45:28,856 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:45:28,911 - pyskl - INFO - +top1_acc 0.9228 +top5_acc 0.9938 +2025-06-25 09:45:28,912 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:45:28,919 - pyskl - INFO - +mean_acc 0.8995 +2025-06-25 09:45:28,921 - pyskl - INFO - Epoch(val) [126][533] top1_acc: 0.9228, top5_acc: 0.9938, mean_class_accuracy: 0.8995 +2025-06-25 09:46:34,364 - pyskl - INFO - Epoch [127][100/1281] lr: 1.536e-03, eta: 3:49:43, time: 0.654, data_time: 0.188, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0200, loss: 0.0200 +2025-06-25 09:47:18,875 - pyskl - INFO - Epoch [127][200/1281] lr: 1.527e-03, eta: 3:48:58, time: 0.445, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 09:48:07,844 - pyskl - INFO - Epoch [127][300/1281] lr: 1.517e-03, eta: 3:48:14, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 09:48:56,761 - pyskl - INFO - Epoch [127][400/1281] lr: 1.507e-03, eta: 3:47:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-25 09:49:45,848 - pyskl - INFO - Epoch [127][500/1281] lr: 1.497e-03, eta: 3:46:45, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0275, loss: 0.0275 +2025-06-25 09:50:35,227 - pyskl - INFO - Epoch [127][600/1281] lr: 1.488e-03, eta: 3:46:01, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0213, loss: 0.0213 +2025-06-25 09:51:24,395 - pyskl - INFO - Epoch [127][700/1281] lr: 1.478e-03, eta: 3:45:17, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0201, loss: 0.0201 +2025-06-25 09:52:13,842 - pyskl - INFO - Epoch [127][800/1281] lr: 1.468e-03, eta: 3:44:33, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0217, loss: 0.0217 +2025-06-25 09:53:02,584 - pyskl - INFO - Epoch [127][900/1281] lr: 1.459e-03, eta: 3:43:48, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 09:53:51,471 - pyskl - INFO - Epoch [127][1000/1281] lr: 1.449e-03, eta: 3:43:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 09:54:40,688 - pyskl - INFO - Epoch [127][1100/1281] lr: 1.440e-03, eta: 3:42:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 09:55:30,028 - pyskl - INFO - Epoch [127][1200/1281] lr: 1.430e-03, eta: 3:41:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 09:56:10,523 - pyskl - INFO - Saving checkpoint at 127 epochs +2025-06-25 09:57:06,449 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 09:57:06,503 - pyskl - INFO - +top1_acc 0.9259 +top5_acc 0.9945 +2025-06-25 09:57:06,503 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 09:57:06,510 - pyskl - INFO - +mean_acc 0.8996 +2025-06-25 09:57:06,514 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_124.pth was removed +2025-06-25 09:57:06,675 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_127.pth. +2025-06-25 09:57:06,676 - pyskl - INFO - Best top1_acc is 0.9259 at 127 epoch. +2025-06-25 09:57:06,678 - pyskl - INFO - Epoch(val) [127][533] top1_acc: 0.9259, top5_acc: 0.9945, mean_class_accuracy: 0.8996 +2025-06-25 09:58:12,920 - pyskl - INFO - Epoch [128][100/1281] lr: 1.413e-03, eta: 3:40:11, time: 0.662, data_time: 0.187, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 09:58:57,961 - pyskl - INFO - Epoch [128][200/1281] lr: 1.404e-03, eta: 3:39:26, time: 0.450, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 09:59:46,976 - pyskl - INFO - Epoch [128][300/1281] lr: 1.394e-03, eta: 3:38:42, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 10:00:36,015 - pyskl - INFO - Epoch [128][400/1281] lr: 1.385e-03, eta: 3:37:58, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 10:01:25,159 - pyskl - INFO - Epoch [128][500/1281] lr: 1.376e-03, eta: 3:37:14, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0207, loss: 0.0207 +2025-06-25 10:02:14,227 - pyskl - INFO - Epoch [128][600/1281] lr: 1.366e-03, eta: 3:36:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0285, loss: 0.0285 +2025-06-25 10:03:03,234 - pyskl - INFO - Epoch [128][700/1281] lr: 1.357e-03, eta: 3:35:45, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0250, loss: 0.0250 +2025-06-25 10:03:52,294 - pyskl - INFO - Epoch [128][800/1281] lr: 1.348e-03, eta: 3:35:01, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 10:04:41,505 - pyskl - INFO - Epoch [128][900/1281] lr: 1.339e-03, eta: 3:34:16, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0262, loss: 0.0262 +2025-06-25 10:05:30,681 - pyskl - INFO - Epoch [128][1000/1281] lr: 1.329e-03, eta: 3:33:32, time: 0.492, data_time: 0.001, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0297, loss: 0.0297 +2025-06-25 10:06:20,210 - pyskl - INFO - Epoch [128][1100/1281] lr: 1.320e-03, eta: 3:32:48, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0277, loss: 0.0277 +2025-06-25 10:07:09,471 - pyskl - INFO - Epoch [128][1200/1281] lr: 1.311e-03, eta: 3:32:04, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9969, top5_acc: 1.0000, loss_cls: 0.0317, loss: 0.0317 +2025-06-25 10:07:49,883 - pyskl - INFO - Saving checkpoint at 128 epochs +2025-06-25 10:08:45,010 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:08:45,065 - pyskl - INFO - +top1_acc 0.9201 +top5_acc 0.9933 +2025-06-25 10:08:45,066 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:08:45,072 - pyskl - INFO - +mean_acc 0.8931 +2025-06-25 10:08:45,073 - pyskl - INFO - Epoch(val) [128][533] top1_acc: 0.9201, top5_acc: 0.9933, mean_class_accuracy: 0.8931 +2025-06-25 10:09:49,727 - pyskl - INFO - Epoch [129][100/1281] lr: 1.295e-03, eta: 3:30:39, time: 0.646, data_time: 0.184, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0263, loss: 0.0263 +2025-06-25 10:10:34,569 - pyskl - INFO - Epoch [129][200/1281] lr: 1.286e-03, eta: 3:29:54, time: 0.448, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 10:11:23,361 - pyskl - INFO - Epoch [129][300/1281] lr: 1.277e-03, eta: 3:29:10, time: 0.488, data_time: 0.001, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 10:12:12,852 - pyskl - INFO - Epoch [129][400/1281] lr: 1.268e-03, eta: 3:28:26, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0243, loss: 0.0243 +2025-06-25 10:13:02,099 - pyskl - INFO - Epoch [129][500/1281] lr: 1.259e-03, eta: 3:27:41, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0209, loss: 0.0209 +2025-06-25 10:13:51,292 - pyskl - INFO - Epoch [129][600/1281] lr: 1.250e-03, eta: 3:26:57, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0239, loss: 0.0239 +2025-06-25 10:14:40,631 - pyskl - INFO - Epoch [129][700/1281] lr: 1.241e-03, eta: 3:26:13, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0300, loss: 0.0300 +2025-06-25 10:15:29,906 - pyskl - INFO - Epoch [129][800/1281] lr: 1.232e-03, eta: 3:25:28, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 10:16:19,116 - pyskl - INFO - Epoch [129][900/1281] lr: 1.223e-03, eta: 3:24:44, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9975, top5_acc: 1.0000, loss_cls: 0.0271, loss: 0.0271 +2025-06-25 10:17:08,173 - pyskl - INFO - Epoch [129][1000/1281] lr: 1.214e-03, eta: 3:24:00, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9962, top5_acc: 1.0000, loss_cls: 0.0310, loss: 0.0310 +2025-06-25 10:17:57,199 - pyskl - INFO - Epoch [129][1100/1281] lr: 1.206e-03, eta: 3:23:15, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0218, loss: 0.0218 +2025-06-25 10:18:46,229 - pyskl - INFO - Epoch [129][1200/1281] lr: 1.197e-03, eta: 3:22:31, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0237, loss: 0.0237 +2025-06-25 10:19:26,477 - pyskl - INFO - Saving checkpoint at 129 epochs +2025-06-25 10:20:23,137 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:20:23,206 - pyskl - INFO - +top1_acc 0.9227 +top5_acc 0.9941 +2025-06-25 10:20:23,206 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:20:23,214 - pyskl - INFO - +mean_acc 0.8990 +2025-06-25 10:20:23,217 - pyskl - INFO - Epoch(val) [129][533] top1_acc: 0.9227, top5_acc: 0.9941, mean_class_accuracy: 0.8990 +2025-06-25 10:21:32,031 - pyskl - INFO - Epoch [130][100/1281] lr: 1.181e-03, eta: 3:21:07, time: 0.688, data_time: 0.195, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0214, loss: 0.0214 +2025-06-25 10:22:16,931 - pyskl - INFO - Epoch [130][200/1281] lr: 1.172e-03, eta: 3:20:22, time: 0.449, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 10:23:06,527 - pyskl - INFO - Epoch [130][300/1281] lr: 1.164e-03, eta: 3:19:38, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0208, loss: 0.0208 +2025-06-25 10:23:55,746 - pyskl - INFO - Epoch [130][400/1281] lr: 1.155e-03, eta: 3:18:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 10:24:45,001 - pyskl - INFO - Epoch [130][500/1281] lr: 1.147e-03, eta: 3:18:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 10:25:34,099 - pyskl - INFO - Epoch [130][600/1281] lr: 1.138e-03, eta: 3:17:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0222, loss: 0.0222 +2025-06-25 10:26:23,513 - pyskl - INFO - Epoch [130][700/1281] lr: 1.130e-03, eta: 3:16:41, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0212, loss: 0.0212 +2025-06-25 10:27:12,690 - pyskl - INFO - Epoch [130][800/1281] lr: 1.121e-03, eta: 3:15:56, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 10:28:01,649 - pyskl - INFO - Epoch [130][900/1281] lr: 1.113e-03, eta: 3:15:12, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0262, loss: 0.0262 +2025-06-25 10:28:50,749 - pyskl - INFO - Epoch [130][1000/1281] lr: 1.104e-03, eta: 3:14:27, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0253, loss: 0.0253 +2025-06-25 10:29:39,966 - pyskl - INFO - Epoch [130][1100/1281] lr: 1.096e-03, eta: 3:13:43, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0226, loss: 0.0226 +2025-06-25 10:30:29,030 - pyskl - INFO - Epoch [130][1200/1281] lr: 1.088e-03, eta: 3:12:58, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 10:31:09,263 - pyskl - INFO - Saving checkpoint at 130 epochs +2025-06-25 10:32:04,297 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:32:04,366 - pyskl - INFO - +top1_acc 0.9231 +top5_acc 0.9945 +2025-06-25 10:32:04,367 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:32:04,377 - pyskl - INFO - +mean_acc 0.9005 +2025-06-25 10:32:04,380 - pyskl - INFO - Epoch(val) [130][533] top1_acc: 0.9231, top5_acc: 0.9945, mean_class_accuracy: 0.9005 +2025-06-25 10:33:08,563 - pyskl - INFO - Epoch [131][100/1281] lr: 1.072e-03, eta: 3:11:34, time: 0.642, data_time: 0.188, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0241, loss: 0.0241 +2025-06-25 10:33:52,590 - pyskl - INFO - Epoch [131][200/1281] lr: 1.064e-03, eta: 3:10:49, time: 0.440, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0206, loss: 0.0206 +2025-06-25 10:34:42,223 - pyskl - INFO - Epoch [131][300/1281] lr: 1.056e-03, eta: 3:10:05, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 10:35:31,411 - pyskl - INFO - Epoch [131][400/1281] lr: 1.048e-03, eta: 3:09:20, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0230, loss: 0.0230 +2025-06-25 10:36:20,812 - pyskl - INFO - Epoch [131][500/1281] lr: 1.040e-03, eta: 3:08:36, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 10:37:09,882 - pyskl - INFO - Epoch [131][600/1281] lr: 1.031e-03, eta: 3:07:51, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 10:37:58,998 - pyskl - INFO - Epoch [131][700/1281] lr: 1.023e-03, eta: 3:07:07, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0198, loss: 0.0198 +2025-06-25 10:38:48,153 - pyskl - INFO - Epoch [131][800/1281] lr: 1.015e-03, eta: 3:06:23, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 10:39:37,203 - pyskl - INFO - Epoch [131][900/1281] lr: 1.007e-03, eta: 3:05:38, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0226, loss: 0.0226 +2025-06-25 10:40:26,373 - pyskl - INFO - Epoch [131][1000/1281] lr: 9.992e-04, eta: 3:04:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-25 10:41:15,669 - pyskl - INFO - Epoch [131][1100/1281] lr: 9.912e-04, eta: 3:04:09, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0273, loss: 0.0273 +2025-06-25 10:42:04,741 - pyskl - INFO - Epoch [131][1200/1281] lr: 9.832e-04, eta: 3:03:25, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0236, loss: 0.0236 +2025-06-25 10:42:45,024 - pyskl - INFO - Saving checkpoint at 131 epochs +2025-06-25 10:43:41,552 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:43:41,618 - pyskl - INFO - +top1_acc 0.9240 +top5_acc 0.9945 +2025-06-25 10:43:41,618 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:43:41,627 - pyskl - INFO - +mean_acc 0.8998 +2025-06-25 10:43:41,629 - pyskl - INFO - Epoch(val) [131][533] top1_acc: 0.9240, top5_acc: 0.9945, mean_class_accuracy: 0.8998 +2025-06-25 10:44:49,121 - pyskl - INFO - Epoch [132][100/1281] lr: 9.689e-04, eta: 3:02:01, time: 0.675, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 10:45:32,937 - pyskl - INFO - Epoch [132][200/1281] lr: 9.610e-04, eta: 3:01:16, time: 0.438, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0244, loss: 0.0244 +2025-06-25 10:46:22,398 - pyskl - INFO - Epoch [132][300/1281] lr: 9.532e-04, eta: 3:00:31, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0184, loss: 0.0184 +2025-06-25 10:47:11,745 - pyskl - INFO - Epoch [132][400/1281] lr: 9.454e-04, eta: 2:59:47, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 10:48:00,775 - pyskl - INFO - Epoch [132][500/1281] lr: 9.376e-04, eta: 2:59:03, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 10:48:50,002 - pyskl - INFO - Epoch [132][600/1281] lr: 9.298e-04, eta: 2:58:18, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 10:49:38,911 - pyskl - INFO - Epoch [132][700/1281] lr: 9.221e-04, eta: 2:57:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 10:50:28,245 - pyskl - INFO - Epoch [132][800/1281] lr: 9.144e-04, eta: 2:56:49, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0225, loss: 0.0225 +2025-06-25 10:51:17,378 - pyskl - INFO - Epoch [132][900/1281] lr: 9.068e-04, eta: 2:56:05, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 10:52:06,430 - pyskl - INFO - Epoch [132][1000/1281] lr: 8.991e-04, eta: 2:55:20, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 10:52:55,762 - pyskl - INFO - Epoch [132][1100/1281] lr: 8.915e-04, eta: 2:54:36, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0231, loss: 0.0231 +2025-06-25 10:53:44,593 - pyskl - INFO - Epoch [132][1200/1281] lr: 8.840e-04, eta: 2:53:51, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 10:54:24,716 - pyskl - INFO - Saving checkpoint at 132 epochs +2025-06-25 10:55:22,226 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 10:55:22,309 - pyskl - INFO - +top1_acc 0.9259 +top5_acc 0.9952 +2025-06-25 10:55:22,310 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 10:55:22,320 - pyskl - INFO - +mean_acc 0.9030 +2025-06-25 10:55:22,323 - pyskl - INFO - Epoch(val) [132][533] top1_acc: 0.9259, top5_acc: 0.9952, mean_class_accuracy: 0.9030 +2025-06-25 10:56:30,501 - pyskl - INFO - Epoch [133][100/1281] lr: 8.704e-04, eta: 2:52:28, time: 0.682, data_time: 0.186, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 10:57:14,618 - pyskl - INFO - Epoch [133][200/1281] lr: 8.629e-04, eta: 2:51:42, time: 0.441, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0195, loss: 0.0195 +2025-06-25 10:58:03,867 - pyskl - INFO - Epoch [133][300/1281] lr: 8.554e-04, eta: 2:50:58, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 10:58:53,169 - pyskl - INFO - Epoch [133][400/1281] lr: 8.480e-04, eta: 2:50:13, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 10:59:42,088 - pyskl - INFO - Epoch [133][500/1281] lr: 8.406e-04, eta: 2:49:29, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 11:00:31,197 - pyskl - INFO - Epoch [133][600/1281] lr: 8.333e-04, eta: 2:48:44, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0242, loss: 0.0242 +2025-06-25 11:01:20,525 - pyskl - INFO - Epoch [133][700/1281] lr: 8.260e-04, eta: 2:48:00, time: 0.493, data_time: 0.001, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 11:02:09,723 - pyskl - INFO - Epoch [133][800/1281] lr: 8.187e-04, eta: 2:47:15, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 11:02:59,072 - pyskl - INFO - Epoch [133][900/1281] lr: 8.114e-04, eta: 2:46:31, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 11:03:48,095 - pyskl - INFO - Epoch [133][1000/1281] lr: 8.042e-04, eta: 2:45:46, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 11:04:37,758 - pyskl - INFO - Epoch [133][1100/1281] lr: 7.970e-04, eta: 2:45:01, time: 0.497, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 11:05:27,068 - pyskl - INFO - Epoch [133][1200/1281] lr: 7.898e-04, eta: 2:44:17, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0176, loss: 0.0176 +2025-06-25 11:06:07,539 - pyskl - INFO - Saving checkpoint at 133 epochs +2025-06-25 11:07:03,582 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:07:03,648 - pyskl - INFO - +top1_acc 0.9249 +top5_acc 0.9955 +2025-06-25 11:07:03,648 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:07:03,655 - pyskl - INFO - +mean_acc 0.9036 +2025-06-25 11:07:03,657 - pyskl - INFO - Epoch(val) [133][533] top1_acc: 0.9249, top5_acc: 0.9955, mean_class_accuracy: 0.9036 +2025-06-25 11:08:09,761 - pyskl - INFO - Epoch [134][100/1281] lr: 7.769e-04, eta: 2:42:53, time: 0.661, data_time: 0.189, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 11:08:54,512 - pyskl - INFO - Epoch [134][200/1281] lr: 7.699e-04, eta: 2:42:08, time: 0.447, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 11:09:43,989 - pyskl - INFO - Epoch [134][300/1281] lr: 7.628e-04, eta: 2:41:24, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 11:10:33,076 - pyskl - INFO - Epoch [134][400/1281] lr: 7.558e-04, eta: 2:40:39, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 11:11:22,275 - pyskl - INFO - Epoch [134][500/1281] lr: 7.488e-04, eta: 2:39:54, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 11:12:11,467 - pyskl - INFO - Epoch [134][600/1281] lr: 7.419e-04, eta: 2:39:10, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0203, loss: 0.0203 +2025-06-25 11:13:00,477 - pyskl - INFO - Epoch [134][700/1281] lr: 7.349e-04, eta: 2:38:25, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 11:13:49,571 - pyskl - INFO - Epoch [134][800/1281] lr: 7.281e-04, eta: 2:37:41, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 11:14:38,943 - pyskl - INFO - Epoch [134][900/1281] lr: 7.212e-04, eta: 2:36:56, time: 0.494, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 11:15:28,525 - pyskl - INFO - Epoch [134][1000/1281] lr: 7.144e-04, eta: 2:36:12, time: 0.496, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 11:16:17,574 - pyskl - INFO - Epoch [134][1100/1281] lr: 7.076e-04, eta: 2:35:27, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 11:17:06,882 - pyskl - INFO - Epoch [134][1200/1281] lr: 7.008e-04, eta: 2:34:42, time: 0.493, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0196, loss: 0.0196 +2025-06-25 11:17:47,372 - pyskl - INFO - Saving checkpoint at 134 epochs +2025-06-25 11:18:43,249 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:18:43,318 - pyskl - INFO - +top1_acc 0.9257 +top5_acc 0.9946 +2025-06-25 11:18:43,319 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:18:43,327 - pyskl - INFO - +mean_acc 0.9052 +2025-06-25 11:18:43,329 - pyskl - INFO - Epoch(val) [134][533] top1_acc: 0.9257, top5_acc: 0.9946, mean_class_accuracy: 0.9052 +2025-06-25 11:19:49,759 - pyskl - INFO - Epoch [135][100/1281] lr: 6.887e-04, eta: 2:33:19, time: 0.664, data_time: 0.185, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 11:20:34,738 - pyskl - INFO - Epoch [135][200/1281] lr: 6.820e-04, eta: 2:32:34, time: 0.450, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 11:21:24,275 - pyskl - INFO - Epoch [135][300/1281] lr: 6.753e-04, eta: 2:31:49, time: 0.495, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 11:22:13,523 - pyskl - INFO - Epoch [135][400/1281] lr: 6.687e-04, eta: 2:31:05, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 11:23:02,538 - pyskl - INFO - Epoch [135][500/1281] lr: 6.622e-04, eta: 2:30:20, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 11:23:51,507 - pyskl - INFO - Epoch [135][600/1281] lr: 6.556e-04, eta: 2:29:35, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 11:24:40,737 - pyskl - INFO - Epoch [135][700/1281] lr: 6.491e-04, eta: 2:28:51, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 11:25:29,778 - pyskl - INFO - Epoch [135][800/1281] lr: 6.426e-04, eta: 2:28:06, time: 0.490, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0177, loss: 0.0177 +2025-06-25 11:26:18,568 - pyskl - INFO - Epoch [135][900/1281] lr: 6.362e-04, eta: 2:27:21, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0178, loss: 0.0178 +2025-06-25 11:27:07,734 - pyskl - INFO - Epoch [135][1000/1281] lr: 6.297e-04, eta: 2:26:37, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 11:27:56,776 - pyskl - INFO - Epoch [135][1100/1281] lr: 6.233e-04, eta: 2:25:52, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 11:28:45,952 - pyskl - INFO - Epoch [135][1200/1281] lr: 6.170e-04, eta: 2:25:07, time: 0.492, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 11:29:26,281 - pyskl - INFO - Saving checkpoint at 135 epochs +2025-06-25 11:30:22,146 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:30:22,201 - pyskl - INFO - +top1_acc 0.9271 +top5_acc 0.9951 +2025-06-25 11:30:22,201 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:30:22,208 - pyskl - INFO - +mean_acc 0.9040 +2025-06-25 11:30:22,212 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_127.pth was removed +2025-06-25 11:30:22,382 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_135.pth. +2025-06-25 11:30:22,382 - pyskl - INFO - Best top1_acc is 0.9271 at 135 epoch. +2025-06-25 11:30:22,385 - pyskl - INFO - Epoch(val) [135][533] top1_acc: 0.9271, top5_acc: 0.9951, mean_class_accuracy: 0.9040 +2025-06-25 11:31:29,216 - pyskl - INFO - Epoch [136][100/1281] lr: 6.056e-04, eta: 2:23:44, time: 0.668, data_time: 0.181, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0190, loss: 0.0190 +2025-06-25 11:32:12,349 - pyskl - INFO - Epoch [136][200/1281] lr: 5.993e-04, eta: 2:22:59, time: 0.431, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 11:33:01,215 - pyskl - INFO - Epoch [136][300/1281] lr: 5.931e-04, eta: 2:22:14, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 11:33:50,284 - pyskl - INFO - Epoch [136][400/1281] lr: 5.868e-04, eta: 2:21:29, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 11:34:39,313 - pyskl - INFO - Epoch [136][500/1281] lr: 5.807e-04, eta: 2:20:44, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 11:35:28,062 - pyskl - INFO - Epoch [136][600/1281] lr: 5.745e-04, eta: 2:20:00, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 11:36:16,958 - pyskl - INFO - Epoch [136][700/1281] lr: 5.684e-04, eta: 2:19:15, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 11:37:05,787 - pyskl - INFO - Epoch [136][800/1281] lr: 5.623e-04, eta: 2:18:30, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 11:37:54,867 - pyskl - INFO - Epoch [136][900/1281] lr: 5.563e-04, eta: 2:17:46, time: 0.491, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 11:38:43,594 - pyskl - INFO - Epoch [136][1000/1281] lr: 5.503e-04, eta: 2:17:01, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0187, loss: 0.0187 +2025-06-25 11:39:32,637 - pyskl - INFO - Epoch [136][1100/1281] lr: 5.443e-04, eta: 2:16:16, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 11:40:21,738 - pyskl - INFO - Epoch [136][1200/1281] lr: 5.384e-04, eta: 2:15:31, time: 0.491, data_time: 0.001, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 11:41:01,903 - pyskl - INFO - Saving checkpoint at 136 epochs +2025-06-25 11:42:00,041 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:42:00,098 - pyskl - INFO - +top1_acc 0.9245 +top5_acc 0.9951 +2025-06-25 11:42:00,098 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:42:00,105 - pyskl - INFO - +mean_acc 0.8998 +2025-06-25 11:42:00,107 - pyskl - INFO - Epoch(val) [136][533] top1_acc: 0.9245, top5_acc: 0.9951, mean_class_accuracy: 0.8998 +2025-06-25 11:43:11,148 - pyskl - INFO - Epoch [137][100/1281] lr: 5.277e-04, eta: 2:14:08, time: 0.710, data_time: 0.188, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 11:43:55,446 - pyskl - INFO - Epoch [137][200/1281] lr: 5.218e-04, eta: 2:13:23, time: 0.443, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 11:44:44,181 - pyskl - INFO - Epoch [137][300/1281] lr: 5.160e-04, eta: 2:12:39, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 11:45:33,033 - pyskl - INFO - Epoch [137][400/1281] lr: 5.102e-04, eta: 2:11:54, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0215, loss: 0.0215 +2025-06-25 11:46:21,784 - pyskl - INFO - Epoch [137][500/1281] lr: 5.044e-04, eta: 2:11:09, time: 0.487, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 11:47:10,756 - pyskl - INFO - Epoch [137][600/1281] lr: 4.987e-04, eta: 2:10:24, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 11:47:59,525 - pyskl - INFO - Epoch [137][700/1281] lr: 4.930e-04, eta: 2:09:39, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 11:48:48,289 - pyskl - INFO - Epoch [137][800/1281] lr: 4.873e-04, eta: 2:08:55, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 11:49:37,242 - pyskl - INFO - Epoch [137][900/1281] lr: 4.817e-04, eta: 2:08:10, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0182, loss: 0.0182 +2025-06-25 11:50:26,163 - pyskl - INFO - Epoch [137][1000/1281] lr: 4.761e-04, eta: 2:07:25, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 11:51:15,081 - pyskl - INFO - Epoch [137][1100/1281] lr: 4.705e-04, eta: 2:06:40, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 11:52:03,874 - pyskl - INFO - Epoch [137][1200/1281] lr: 4.650e-04, eta: 2:05:55, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 11:52:43,943 - pyskl - INFO - Saving checkpoint at 137 epochs +2025-06-25 11:53:40,962 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 11:53:41,018 - pyskl - INFO - +top1_acc 0.9268 +top5_acc 0.9948 +2025-06-25 11:53:41,018 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 11:53:41,025 - pyskl - INFO - +mean_acc 0.9037 +2025-06-25 11:53:41,027 - pyskl - INFO - Epoch(val) [137][533] top1_acc: 0.9268, top5_acc: 0.9948, mean_class_accuracy: 0.9037 +2025-06-25 11:54:49,080 - pyskl - INFO - Epoch [138][100/1281] lr: 4.550e-04, eta: 2:04:32, time: 0.680, data_time: 0.180, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 11:55:33,291 - pyskl - INFO - Epoch [138][200/1281] lr: 4.496e-04, eta: 2:03:47, time: 0.442, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 11:56:22,298 - pyskl - INFO - Epoch [138][300/1281] lr: 4.442e-04, eta: 2:03:02, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 11:57:11,252 - pyskl - INFO - Epoch [138][400/1281] lr: 4.388e-04, eta: 2:02:18, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 11:58:00,170 - pyskl - INFO - Epoch [138][500/1281] lr: 4.334e-04, eta: 2:01:33, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 11:58:48,966 - pyskl - INFO - Epoch [138][600/1281] lr: 4.281e-04, eta: 2:00:48, time: 0.488, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 11:59:37,884 - pyskl - INFO - Epoch [138][700/1281] lr: 4.228e-04, eta: 2:00:03, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 12:00:26,815 - pyskl - INFO - Epoch [138][800/1281] lr: 4.176e-04, eta: 1:59:18, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:01:15,722 - pyskl - INFO - Epoch [138][900/1281] lr: 4.124e-04, eta: 1:58:34, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 12:02:04,721 - pyskl - INFO - Epoch [138][1000/1281] lr: 4.072e-04, eta: 1:57:49, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 12:02:53,645 - pyskl - INFO - Epoch [138][1100/1281] lr: 4.020e-04, eta: 1:57:04, time: 0.489, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 12:03:42,636 - pyskl - INFO - Epoch [138][1200/1281] lr: 3.969e-04, eta: 1:56:19, time: 0.490, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 12:04:22,842 - pyskl - INFO - Saving checkpoint at 138 epochs +2025-06-25 12:05:19,388 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:05:19,444 - pyskl - INFO - +top1_acc 0.9231 +top5_acc 0.9955 +2025-06-25 12:05:19,445 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:05:19,457 - pyskl - INFO - +mean_acc 0.8978 +2025-06-25 12:05:19,459 - pyskl - INFO - Epoch(val) [138][533] top1_acc: 0.9231, top5_acc: 0.9955, mean_class_accuracy: 0.8978 +2025-06-25 12:06:26,770 - pyskl - INFO - Epoch [139][100/1281] lr: 3.877e-04, eta: 1:54:56, time: 0.673, data_time: 0.187, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 12:07:40,155 - pyskl - INFO - Epoch [139][200/1281] lr: 3.827e-04, eta: 1:54:13, time: 0.734, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 12:08:53,141 - pyskl - INFO - Epoch [139][300/1281] lr: 3.777e-04, eta: 1:53:31, time: 0.730, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 12:10:04,809 - pyskl - INFO - Epoch [139][400/1281] lr: 3.727e-04, eta: 1:52:48, time: 0.717, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 12:11:17,168 - pyskl - INFO - Epoch [139][500/1281] lr: 3.678e-04, eta: 1:52:05, time: 0.724, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 12:12:29,857 - pyskl - INFO - Epoch [139][600/1281] lr: 3.628e-04, eta: 1:51:22, time: 0.727, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 12:13:43,719 - pyskl - INFO - Epoch [139][700/1281] lr: 3.580e-04, eta: 1:50:39, time: 0.739, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 12:14:14,613 - pyskl - INFO - Epoch [139][800/1281] lr: 3.531e-04, eta: 1:49:52, time: 0.309, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 12:15:27,235 - pyskl - INFO - Epoch [139][900/1281] lr: 3.483e-04, eta: 1:49:09, time: 0.726, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 12:16:39,069 - pyskl - INFO - Epoch [139][1000/1281] lr: 3.436e-04, eta: 1:48:26, time: 0.718, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 12:17:49,088 - pyskl - INFO - Epoch [139][1100/1281] lr: 3.388e-04, eta: 1:47:43, time: 0.700, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 12:19:02,531 - pyskl - INFO - Epoch [139][1200/1281] lr: 3.341e-04, eta: 1:47:00, time: 0.734, data_time: 0.000, memory: 4083, top1_acc: 0.9988, top5_acc: 1.0000, loss_cls: 0.0197, loss: 0.0197 +2025-06-25 12:20:00,889 - pyskl - INFO - Saving checkpoint at 139 epochs +2025-06-25 12:21:15,333 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:21:15,389 - pyskl - INFO - +top1_acc 0.9286 +top5_acc 0.9957 +2025-06-25 12:21:15,389 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:21:15,396 - pyskl - INFO - +mean_acc 0.9062 +2025-06-25 12:21:15,400 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_135.pth was removed +2025-06-25 12:21:15,561 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_139.pth. +2025-06-25 12:21:15,561 - pyskl - INFO - Best top1_acc is 0.9286 at 139 epoch. +2025-06-25 12:21:15,563 - pyskl - INFO - Epoch(val) [139][533] top1_acc: 0.9286, top5_acc: 0.9957, mean_class_accuracy: 0.9062 +2025-06-25 12:21:57,183 - pyskl - INFO - Epoch [140][100/1281] lr: 3.257e-04, eta: 1:45:35, time: 0.416, data_time: 0.181, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0185, loss: 0.0185 +2025-06-25 12:22:19,559 - pyskl - INFO - Epoch [140][200/1281] lr: 3.210e-04, eta: 1:44:48, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 12:22:41,411 - pyskl - INFO - Epoch [140][300/1281] lr: 3.165e-04, eta: 1:44:01, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 12:23:03,540 - pyskl - INFO - Epoch [140][400/1281] lr: 3.119e-04, eta: 1:43:14, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0173, loss: 0.0173 +2025-06-25 12:23:25,835 - pyskl - INFO - Epoch [140][500/1281] lr: 3.074e-04, eta: 1:42:27, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 12:23:48,161 - pyskl - INFO - Epoch [140][600/1281] lr: 3.029e-04, eta: 1:41:40, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0186, loss: 0.0186 +2025-06-25 12:24:10,359 - pyskl - INFO - Epoch [140][700/1281] lr: 2.984e-04, eta: 1:40:53, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 12:24:32,545 - pyskl - INFO - Epoch [140][800/1281] lr: 2.940e-04, eta: 1:40:06, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 12:24:54,727 - pyskl - INFO - Epoch [140][900/1281] lr: 2.896e-04, eta: 1:39:19, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 12:25:16,818 - pyskl - INFO - Epoch [140][1000/1281] lr: 2.853e-04, eta: 1:38:32, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 12:25:39,165 - pyskl - INFO - Epoch [140][1100/1281] lr: 2.809e-04, eta: 1:37:45, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 12:26:01,066 - pyskl - INFO - Epoch [140][1200/1281] lr: 2.767e-04, eta: 1:36:58, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0145, loss: 0.0145 +2025-06-25 12:26:19,939 - pyskl - INFO - Saving checkpoint at 140 epochs +2025-06-25 12:27:02,738 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:27:02,807 - pyskl - INFO - +top1_acc 0.9268 +top5_acc 0.9955 +2025-06-25 12:27:02,807 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:27:02,814 - pyskl - INFO - +mean_acc 0.9044 +2025-06-25 12:27:02,816 - pyskl - INFO - Epoch(val) [140][533] top1_acc: 0.9268, top5_acc: 0.9955, mean_class_accuracy: 0.9044 +2025-06-25 12:27:44,692 - pyskl - INFO - Epoch [141][100/1281] lr: 2.690e-04, eta: 1:35:34, time: 0.419, data_time: 0.184, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 12:28:06,908 - pyskl - INFO - Epoch [141][200/1281] lr: 2.648e-04, eta: 1:34:47, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 12:28:29,152 - pyskl - INFO - Epoch [141][300/1281] lr: 2.606e-04, eta: 1:34:00, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:28:51,365 - pyskl - INFO - Epoch [141][400/1281] lr: 2.565e-04, eta: 1:33:14, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 12:29:13,646 - pyskl - INFO - Epoch [141][500/1281] lr: 2.524e-04, eta: 1:32:27, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:29:35,484 - pyskl - INFO - Epoch [141][600/1281] lr: 2.483e-04, eta: 1:31:41, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:29:57,529 - pyskl - INFO - Epoch [141][700/1281] lr: 2.443e-04, eta: 1:30:54, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 12:30:19,386 - pyskl - INFO - Epoch [141][800/1281] lr: 2.402e-04, eta: 1:30:07, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 12:30:41,569 - pyskl - INFO - Epoch [141][900/1281] lr: 2.363e-04, eta: 1:29:21, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 12:31:03,727 - pyskl - INFO - Epoch [141][1000/1281] lr: 2.323e-04, eta: 1:28:34, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 12:31:25,940 - pyskl - INFO - Epoch [141][1100/1281] lr: 2.284e-04, eta: 1:27:48, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 12:31:47,797 - pyskl - INFO - Epoch [141][1200/1281] lr: 2.246e-04, eta: 1:27:01, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0143, loss: 0.0143 +2025-06-25 12:32:06,584 - pyskl - INFO - Saving checkpoint at 141 epochs +2025-06-25 12:32:49,496 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:32:49,549 - pyskl - INFO - +top1_acc 0.9283 +top5_acc 0.9954 +2025-06-25 12:32:49,549 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:32:49,556 - pyskl - INFO - +mean_acc 0.9074 +2025-06-25 12:32:49,557 - pyskl - INFO - Epoch(val) [141][533] top1_acc: 0.9283, top5_acc: 0.9954, mean_class_accuracy: 0.9074 +2025-06-25 12:33:31,331 - pyskl - INFO - Epoch [142][100/1281] lr: 2.176e-04, eta: 1:25:37, time: 0.418, data_time: 0.184, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:33:53,493 - pyskl - INFO - Epoch [142][200/1281] lr: 2.139e-04, eta: 1:24:51, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 12:34:15,709 - pyskl - INFO - Epoch [142][300/1281] lr: 2.101e-04, eta: 1:24:05, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 12:34:38,031 - pyskl - INFO - Epoch [142][400/1281] lr: 2.064e-04, eta: 1:23:18, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 12:35:00,422 - pyskl - INFO - Epoch [142][500/1281] lr: 2.027e-04, eta: 1:22:32, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 12:35:23,060 - pyskl - INFO - Epoch [142][600/1281] lr: 1.991e-04, eta: 1:21:46, time: 0.226, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:35:45,265 - pyskl - INFO - Epoch [142][700/1281] lr: 1.954e-04, eta: 1:21:00, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 12:36:07,690 - pyskl - INFO - Epoch [142][800/1281] lr: 1.919e-04, eta: 1:20:13, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 12:36:29,875 - pyskl - INFO - Epoch [142][900/1281] lr: 1.883e-04, eta: 1:19:27, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 12:36:52,191 - pyskl - INFO - Epoch [142][1000/1281] lr: 1.848e-04, eta: 1:18:41, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 12:37:14,584 - pyskl - INFO - Epoch [142][1100/1281] lr: 1.813e-04, eta: 1:17:55, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 12:37:36,982 - pyskl - INFO - Epoch [142][1200/1281] lr: 1.779e-04, eta: 1:17:09, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 12:37:55,930 - pyskl - INFO - Saving checkpoint at 142 epochs +2025-06-25 12:38:39,281 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:38:39,334 - pyskl - INFO - +top1_acc 0.9264 +top5_acc 0.9947 +2025-06-25 12:38:39,334 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:38:39,339 - pyskl - INFO - +mean_acc 0.9041 +2025-06-25 12:38:39,341 - pyskl - INFO - Epoch(val) [142][533] top1_acc: 0.9264, top5_acc: 0.9947, mean_class_accuracy: 0.9041 +2025-06-25 12:39:21,580 - pyskl - INFO - Epoch [143][100/1281] lr: 1.717e-04, eta: 1:15:45, time: 0.422, data_time: 0.187, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 12:39:43,627 - pyskl - INFO - Epoch [143][200/1281] lr: 1.683e-04, eta: 1:14:59, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 12:40:05,658 - pyskl - INFO - Epoch [143][300/1281] lr: 1.650e-04, eta: 1:14:13, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 12:40:27,665 - pyskl - INFO - Epoch [143][400/1281] lr: 1.617e-04, eta: 1:13:27, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 12:40:49,857 - pyskl - INFO - Epoch [143][500/1281] lr: 1.585e-04, eta: 1:12:41, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 12:41:11,942 - pyskl - INFO - Epoch [143][600/1281] lr: 1.552e-04, eta: 1:11:56, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0174, loss: 0.0174 +2025-06-25 12:41:34,365 - pyskl - INFO - Epoch [143][700/1281] lr: 1.520e-04, eta: 1:11:10, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 12:41:56,733 - pyskl - INFO - Epoch [143][800/1281] lr: 1.489e-04, eta: 1:10:24, time: 0.224, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 12:42:18,966 - pyskl - INFO - Epoch [143][900/1281] lr: 1.457e-04, eta: 1:09:38, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 12:42:41,096 - pyskl - INFO - Epoch [143][1000/1281] lr: 1.426e-04, eta: 1:08:52, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 12:43:03,051 - pyskl - INFO - Epoch [143][1100/1281] lr: 1.396e-04, eta: 1:08:06, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:43:25,248 - pyskl - INFO - Epoch [143][1200/1281] lr: 1.366e-04, eta: 1:07:20, time: 0.222, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 12:43:43,814 - pyskl - INFO - Saving checkpoint at 143 epochs +2025-06-25 12:44:27,404 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:44:27,471 - pyskl - INFO - +top1_acc 0.9269 +top5_acc 0.9953 +2025-06-25 12:44:27,472 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:44:27,479 - pyskl - INFO - +mean_acc 0.9005 +2025-06-25 12:44:27,480 - pyskl - INFO - Epoch(val) [143][533] top1_acc: 0.9269, top5_acc: 0.9953, mean_class_accuracy: 0.9005 +2025-06-25 12:45:08,885 - pyskl - INFO - Epoch [144][100/1281] lr: 1.312e-04, eta: 1:05:58, time: 0.414, data_time: 0.183, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0194, loss: 0.0194 +2025-06-25 12:45:30,766 - pyskl - INFO - Epoch [144][200/1281] lr: 1.282e-04, eta: 1:05:12, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 12:45:53,028 - pyskl - INFO - Epoch [144][300/1281] lr: 1.253e-04, eta: 1:04:26, time: 0.223, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0146, loss: 0.0146 +2025-06-25 12:46:15,075 - pyskl - INFO - Epoch [144][400/1281] lr: 1.224e-04, eta: 1:03:41, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:46:36,908 - pyskl - INFO - Epoch [144][500/1281] lr: 1.196e-04, eta: 1:02:55, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0219, loss: 0.0219 +2025-06-25 12:46:58,655 - pyskl - INFO - Epoch [144][600/1281] lr: 1.168e-04, eta: 1:02:09, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 12:47:20,545 - pyskl - INFO - Epoch [144][700/1281] lr: 1.140e-04, eta: 1:01:24, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:47:42,456 - pyskl - INFO - Epoch [144][800/1281] lr: 1.113e-04, eta: 1:00:38, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0171, loss: 0.0171 +2025-06-25 12:48:04,243 - pyskl - INFO - Epoch [144][900/1281] lr: 1.086e-04, eta: 0:59:53, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 12:48:26,942 - pyskl - INFO - Epoch [144][1000/1281] lr: 1.059e-04, eta: 0:59:07, time: 0.227, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0138, loss: 0.0138 +2025-06-25 12:48:49,485 - pyskl - INFO - Epoch [144][1100/1281] lr: 1.033e-04, eta: 0:58:22, time: 0.225, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0157, loss: 0.0157 +2025-06-25 12:49:11,144 - pyskl - INFO - Epoch [144][1200/1281] lr: 1.007e-04, eta: 0:57:36, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:49:29,779 - pyskl - INFO - Saving checkpoint at 144 epochs +2025-06-25 12:50:12,356 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:50:12,411 - pyskl - INFO - +top1_acc 0.9276 +top5_acc 0.9951 +2025-06-25 12:50:12,411 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:50:12,418 - pyskl - INFO - +mean_acc 0.9035 +2025-06-25 12:50:12,420 - pyskl - INFO - Epoch(val) [144][533] top1_acc: 0.9276, top5_acc: 0.9951, mean_class_accuracy: 0.9035 +2025-06-25 12:50:53,679 - pyskl - INFO - Epoch [145][100/1281] lr: 9.605e-05, eta: 0:56:14, time: 0.413, data_time: 0.183, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:51:15,744 - pyskl - INFO - Epoch [145][200/1281] lr: 9.353e-05, eta: 0:55:29, time: 0.221, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0172, loss: 0.0172 +2025-06-25 12:51:37,583 - pyskl - INFO - Epoch [145][300/1281] lr: 9.106e-05, eta: 0:54:43, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 12:51:59,515 - pyskl - INFO - Epoch [145][400/1281] lr: 8.861e-05, eta: 0:53:58, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 12:52:21,086 - pyskl - INFO - Epoch [145][500/1281] lr: 8.620e-05, eta: 0:53:13, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 12:52:42,592 - pyskl - INFO - Epoch [145][600/1281] lr: 8.382e-05, eta: 0:52:27, time: 0.215, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:53:04,473 - pyskl - INFO - Epoch [145][700/1281] lr: 8.147e-05, eta: 0:51:42, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 12:53:26,260 - pyskl - INFO - Epoch [145][800/1281] lr: 7.916e-05, eta: 0:50:57, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 12:53:48,215 - pyskl - INFO - Epoch [145][900/1281] lr: 7.688e-05, eta: 0:50:11, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0154, loss: 0.0154 +2025-06-25 12:54:09,894 - pyskl - INFO - Epoch [145][1000/1281] lr: 7.463e-05, eta: 0:49:26, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0150, loss: 0.0150 +2025-06-25 12:54:31,558 - pyskl - INFO - Epoch [145][1100/1281] lr: 7.242e-05, eta: 0:48:41, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0166, loss: 0.0166 +2025-06-25 12:54:53,278 - pyskl - INFO - Epoch [145][1200/1281] lr: 7.024e-05, eta: 0:47:56, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 12:55:11,621 - pyskl - INFO - Saving checkpoint at 145 epochs +2025-06-25 12:55:53,406 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 12:55:53,459 - pyskl - INFO - +top1_acc 0.9277 +top5_acc 0.9953 +2025-06-25 12:55:53,459 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 12:55:53,466 - pyskl - INFO - +mean_acc 0.9050 +2025-06-25 12:55:53,467 - pyskl - INFO - Epoch(val) [145][533] top1_acc: 0.9277, top5_acc: 0.9953, mean_class_accuracy: 0.9050 +2025-06-25 12:56:32,566 - pyskl - INFO - Epoch [146][100/1281] lr: 6.638e-05, eta: 0:46:34, time: 0.391, data_time: 0.170, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 12:56:53,866 - pyskl - INFO - Epoch [146][200/1281] lr: 6.429e-05, eta: 0:45:49, time: 0.213, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:57:15,083 - pyskl - INFO - Epoch [146][300/1281] lr: 6.224e-05, eta: 0:45:04, time: 0.212, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0158, loss: 0.0158 +2025-06-25 12:57:36,079 - pyskl - INFO - Epoch [146][400/1281] lr: 6.022e-05, eta: 0:44:19, time: 0.210, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 12:57:56,951 - pyskl - INFO - Epoch [146][500/1281] lr: 5.823e-05, eta: 0:43:34, time: 0.209, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 12:58:17,854 - pyskl - INFO - Epoch [146][600/1281] lr: 5.628e-05, eta: 0:42:49, time: 0.209, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 12:58:38,754 - pyskl - INFO - Epoch [146][700/1281] lr: 5.436e-05, eta: 0:42:04, time: 0.209, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0169, loss: 0.0169 +2025-06-25 12:59:00,263 - pyskl - INFO - Epoch [146][800/1281] lr: 5.247e-05, eta: 0:41:19, time: 0.215, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 12:59:22,139 - pyskl - INFO - Epoch [146][900/1281] lr: 5.061e-05, eta: 0:40:34, time: 0.219, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0168, loss: 0.0168 +2025-06-25 12:59:43,685 - pyskl - INFO - Epoch [146][1000/1281] lr: 4.879e-05, eta: 0:39:49, time: 0.215, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 13:00:05,272 - pyskl - INFO - Epoch [146][1100/1281] lr: 4.701e-05, eta: 0:39:04, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 13:00:26,211 - pyskl - INFO - Epoch [146][1200/1281] lr: 4.525e-05, eta: 0:38:20, time: 0.209, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0139, loss: 0.0139 +2025-06-25 13:00:43,696 - pyskl - INFO - Saving checkpoint at 146 epochs +2025-06-25 13:01:24,367 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 13:01:24,419 - pyskl - INFO - +top1_acc 0.9267 +top5_acc 0.9954 +2025-06-25 13:01:24,419 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 13:01:24,425 - pyskl - INFO - +mean_acc 0.9015 +2025-06-25 13:01:24,426 - pyskl - INFO - Epoch(val) [146][533] top1_acc: 0.9267, top5_acc: 0.9954, mean_class_accuracy: 0.9015 +2025-06-25 13:02:02,513 - pyskl - INFO - Epoch [147][100/1281] lr: 4.216e-05, eta: 0:36:59, time: 0.381, data_time: 0.164, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 13:02:24,244 - pyskl - INFO - Epoch [147][200/1281] lr: 4.050e-05, eta: 0:36:14, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 13:02:45,875 - pyskl - INFO - Epoch [147][300/1281] lr: 3.887e-05, eta: 0:35:29, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0165, loss: 0.0165 +2025-06-25 13:03:07,605 - pyskl - INFO - Epoch [147][400/1281] lr: 3.728e-05, eta: 0:34:44, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 13:03:29,249 - pyskl - INFO - Epoch [147][500/1281] lr: 3.572e-05, eta: 0:34:00, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 13:03:50,879 - pyskl - INFO - Epoch [147][600/1281] lr: 3.419e-05, eta: 0:33:15, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 13:04:12,865 - pyskl - INFO - Epoch [147][700/1281] lr: 3.270e-05, eta: 0:32:30, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0175, loss: 0.0175 +2025-06-25 13:04:34,697 - pyskl - INFO - Epoch [147][800/1281] lr: 3.124e-05, eta: 0:31:46, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 13:04:55,814 - pyskl - INFO - Epoch [147][900/1281] lr: 2.981e-05, eta: 0:31:01, time: 0.211, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0191, loss: 0.0191 +2025-06-25 13:05:16,766 - pyskl - INFO - Epoch [147][1000/1281] lr: 2.842e-05, eta: 0:30:17, time: 0.210, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 13:05:38,220 - pyskl - INFO - Epoch [147][1100/1281] lr: 2.706e-05, eta: 0:29:32, time: 0.215, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 13:05:59,385 - pyskl - INFO - Epoch [147][1200/1281] lr: 2.573e-05, eta: 0:28:47, time: 0.212, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 13:06:17,029 - pyskl - INFO - Saving checkpoint at 147 epochs +2025-06-25 13:06:58,041 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 13:06:58,092 - pyskl - INFO - +top1_acc 0.9279 +top5_acc 0.9952 +2025-06-25 13:06:58,092 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 13:06:58,097 - pyskl - INFO - +mean_acc 0.9051 +2025-06-25 13:06:58,098 - pyskl - INFO - Epoch(val) [147][533] top1_acc: 0.9279, top5_acc: 0.9952, mean_class_accuracy: 0.9051 +2025-06-25 13:07:36,125 - pyskl - INFO - Epoch [148][100/1281] lr: 2.341e-05, eta: 0:27:27, time: 0.380, data_time: 0.163, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 13:07:56,727 - pyskl - INFO - Epoch [148][200/1281] lr: 2.218e-05, eta: 0:26:42, time: 0.206, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0179, loss: 0.0179 +2025-06-25 13:08:17,484 - pyskl - INFO - Epoch [148][300/1281] lr: 2.098e-05, eta: 0:25:58, time: 0.208, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 13:08:39,165 - pyskl - INFO - Epoch [148][400/1281] lr: 1.981e-05, eta: 0:25:14, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0160, loss: 0.0160 +2025-06-25 13:09:00,773 - pyskl - INFO - Epoch [148][500/1281] lr: 1.868e-05, eta: 0:24:29, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0149, loss: 0.0149 +2025-06-25 13:09:21,787 - pyskl - INFO - Epoch [148][600/1281] lr: 1.758e-05, eta: 0:23:45, time: 0.210, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0156, loss: 0.0156 +2025-06-25 13:09:42,943 - pyskl - INFO - Epoch [148][700/1281] lr: 1.651e-05, eta: 0:23:01, time: 0.212, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0147, loss: 0.0147 +2025-06-25 13:10:03,811 - pyskl - INFO - Epoch [148][800/1281] lr: 1.548e-05, eta: 0:22:16, time: 0.209, data_time: 0.000, memory: 4083, top1_acc: 0.9981, top5_acc: 1.0000, loss_cls: 0.0192, loss: 0.0192 +2025-06-25 13:10:24,774 - pyskl - INFO - Epoch [148][900/1281] lr: 1.448e-05, eta: 0:21:32, time: 0.210, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 13:10:45,751 - pyskl - INFO - Epoch [148][1000/1281] lr: 1.351e-05, eta: 0:20:48, time: 0.210, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 13:11:07,151 - pyskl - INFO - Epoch [148][1100/1281] lr: 1.258e-05, eta: 0:20:03, time: 0.214, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 13:11:28,760 - pyskl - INFO - Epoch [148][1200/1281] lr: 1.168e-05, eta: 0:19:19, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 13:11:46,912 - pyskl - INFO - Saving checkpoint at 148 epochs +2025-06-25 13:12:27,853 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 13:12:27,903 - pyskl - INFO - +top1_acc 0.9257 +top5_acc 0.9957 +2025-06-25 13:12:27,903 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 13:12:27,909 - pyskl - INFO - +mean_acc 0.9028 +2025-06-25 13:12:27,910 - pyskl - INFO - Epoch(val) [148][533] top1_acc: 0.9257, top5_acc: 0.9957, mean_class_accuracy: 0.9028 +2025-06-25 13:13:06,908 - pyskl - INFO - Epoch [149][100/1281] lr: 1.013e-05, eta: 0:17:59, time: 0.390, data_time: 0.165, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0181, loss: 0.0181 +2025-06-25 13:13:28,672 - pyskl - INFO - Epoch [149][200/1281] lr: 9.328e-06, eta: 0:17:15, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 13:13:50,201 - pyskl - INFO - Epoch [149][300/1281] lr: 8.555e-06, eta: 0:16:31, time: 0.215, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0155, loss: 0.0155 +2025-06-25 13:14:11,820 - pyskl - INFO - Epoch [149][400/1281] lr: 7.816e-06, eta: 0:15:47, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 13:14:32,889 - pyskl - INFO - Epoch [149][500/1281] lr: 7.110e-06, eta: 0:15:03, time: 0.211, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 13:14:54,524 - pyskl - INFO - Epoch [149][600/1281] lr: 6.437e-06, eta: 0:14:19, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0148, loss: 0.0148 +2025-06-25 13:15:16,564 - pyskl - INFO - Epoch [149][700/1281] lr: 5.798e-06, eta: 0:13:35, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0146, loss: 0.0146 +2025-06-25 13:15:38,234 - pyskl - INFO - Epoch [149][800/1281] lr: 5.192e-06, eta: 0:12:51, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0136, loss: 0.0136 +2025-06-25 13:15:59,944 - pyskl - INFO - Epoch [149][900/1281] lr: 4.620e-06, eta: 0:12:07, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0180, loss: 0.0180 +2025-06-25 13:16:21,624 - pyskl - INFO - Epoch [149][1000/1281] lr: 4.081e-06, eta: 0:11:23, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0163, loss: 0.0163 +2025-06-25 13:16:42,567 - pyskl - INFO - Epoch [149][1100/1281] lr: 3.576e-06, eta: 0:10:39, time: 0.209, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0167, loss: 0.0167 +2025-06-25 13:17:03,401 - pyskl - INFO - Epoch [149][1200/1281] lr: 3.104e-06, eta: 0:09:55, time: 0.208, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0162, loss: 0.0162 +2025-06-25 13:17:21,398 - pyskl - INFO - Saving checkpoint at 149 epochs +2025-06-25 13:18:02,199 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 13:18:02,250 - pyskl - INFO - +top1_acc 0.9277 +top5_acc 0.9953 +2025-06-25 13:18:02,250 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 13:18:02,255 - pyskl - INFO - +mean_acc 0.9039 +2025-06-25 13:18:02,256 - pyskl - INFO - Epoch(val) [149][533] top1_acc: 0.9277, top5_acc: 0.9953, mean_class_accuracy: 0.9039 +2025-06-25 13:18:41,640 - pyskl - INFO - Epoch [150][100/1281] lr: 2.334e-06, eta: 0:08:36, time: 0.394, data_time: 0.163, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0146, loss: 0.0146 +2025-06-25 13:19:03,684 - pyskl - INFO - Epoch [150][200/1281] lr: 1.956e-06, eta: 0:07:52, time: 0.220, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0170, loss: 0.0170 +2025-06-25 13:19:25,436 - pyskl - INFO - Epoch [150][300/1281] lr: 1.611e-06, eta: 0:07:08, time: 0.218, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0151, loss: 0.0151 +2025-06-25 13:19:47,068 - pyskl - INFO - Epoch [150][400/1281] lr: 1.300e-06, eta: 0:06:24, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 13:20:08,675 - pyskl - INFO - Epoch [150][500/1281] lr: 1.022e-06, eta: 0:05:41, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 13:20:30,309 - pyskl - INFO - Epoch [150][600/1281] lr: 7.771e-07, eta: 0:04:57, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 13:20:51,956 - pyskl - INFO - Epoch [150][700/1281] lr: 5.659e-07, eta: 0:04:13, time: 0.216, data_time: 0.000, memory: 4083, top1_acc: 0.9994, top5_acc: 1.0000, loss_cls: 0.0189, loss: 0.0189 +2025-06-25 13:21:13,613 - pyskl - INFO - Epoch [150][800/1281] lr: 3.881e-07, eta: 0:03:29, time: 0.217, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0152, loss: 0.0152 +2025-06-25 13:21:34,900 - pyskl - INFO - Epoch [150][900/1281] lr: 2.438e-07, eta: 0:02:46, time: 0.213, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0153, loss: 0.0153 +2025-06-25 13:21:55,530 - pyskl - INFO - Epoch [150][1000/1281] lr: 1.329e-07, eta: 0:02:02, time: 0.206, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0164, loss: 0.0164 +2025-06-25 13:22:16,913 - pyskl - INFO - Epoch [150][1100/1281] lr: 5.534e-08, eta: 0:01:18, time: 0.214, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0161, loss: 0.0161 +2025-06-25 13:22:38,361 - pyskl - INFO - Epoch [150][1200/1281] lr: 1.123e-08, eta: 0:00:35, time: 0.214, data_time: 0.000, memory: 4083, top1_acc: 1.0000, top5_acc: 1.0000, loss_cls: 0.0159, loss: 0.0159 +2025-06-25 13:22:56,134 - pyskl - INFO - Saving checkpoint at 150 epochs +2025-06-25 13:23:36,780 - pyskl - INFO - Evaluating top_k_accuracy ... +2025-06-25 13:23:36,830 - pyskl - INFO - +top1_acc 0.9275 +top5_acc 0.9957 +2025-06-25 13:23:36,830 - pyskl - INFO - Evaluating mean_class_accuracy ... +2025-06-25 13:23:36,835 - pyskl - INFO - +mean_acc 0.9039 +2025-06-25 13:23:36,837 - pyskl - INFO - Epoch(val) [150][533] top1_acc: 0.9275, top5_acc: 0.9957, mean_class_accuracy: 0.9039 +2025-06-25 13:23:41,061 - pyskl - INFO - 8521 videos remain after valid thresholding +2025-06-25 13:28:43,742 - pyskl - INFO - Testing results of the last checkpoint +2025-06-25 13:28:43,742 - pyskl - INFO - top1_acc: 0.9308 +2025-06-25 13:28:43,742 - pyskl - INFO - top5_acc: 0.9954 +2025-06-25 13:28:43,743 - pyskl - INFO - mean_class_accuracy: 0.9088 +2025-06-25 13:28:43,743 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/finegym/km/best_top1_acc_epoch_139.pth +2025-06-25 13:33:49,887 - pyskl - INFO - Testing results of the best checkpoint +2025-06-25 13:33:49,887 - pyskl - INFO - top1_acc: 0.9301 +2025-06-25 13:33:49,887 - pyskl - INFO - top5_acc: 0.9959 +2025-06-25 13:33:49,887 - pyskl - INFO - mean_class_accuracy: 0.9090 diff --git a/finegym/km/20250624_101502.log.json b/finegym/km/20250624_101502.log.json new file mode 100644 index 0000000000000000000000000000000000000000..02463caf01a26b782a3869bd8cd0c16ed5dc4d05 --- /dev/null +++ b/finegym/km/20250624_101502.log.json @@ -0,0 +1,1951 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 221931424, "config_name": "km.py", "work_dir": "km", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.19614, "top1_acc": 0.05062, "top5_acc": 0.2125, "loss_cls": 4.61628, "loss": 4.61628, "time": 0.62735} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.0925, "top5_acc": 0.29438, "loss_cls": 4.65479, "loss": 4.65479, "time": 0.41639} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.08688, "top5_acc": 0.31562, "loss_cls": 4.50937, "loss": 4.50937, "time": 0.41836} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.09688, "top5_acc": 0.34938, "loss_cls": 4.32478, "loss": 4.32478, "time": 0.4171} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.025, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.09562, "top5_acc": 0.40562, "loss_cls": 4.15673, "loss": 4.15673, "time": 0.4206} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.025, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.13, "top5_acc": 0.44438, "loss_cls": 3.97823, "loss": 3.97823, "time": 0.41592} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.025, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.13375, "top5_acc": 0.43438, "loss_cls": 3.91529, "loss": 3.91529, "time": 0.41684} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.025, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.18438, "top5_acc": 0.5125, "loss_cls": 3.69236, "loss": 3.69236, "time": 0.42086} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.025, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.21562, "top5_acc": 0.53938, "loss_cls": 3.55268, "loss": 3.55268, "time": 0.41707} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.23125, "top5_acc": 0.56, "loss_cls": 3.44962, "loss": 3.44962, "time": 0.41813} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.025, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.24438, "top5_acc": 0.60062, "loss_cls": 3.35269, "loss": 3.35269, "time": 0.41807} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.025, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.26438, "top5_acc": 0.63313, "loss_cls": 3.22244, "loss": 3.22244, "time": 0.43191} +{"mode": "val", "epoch": 1, "iter": 533, "lr": 0.025, "top1_acc": 0.31088, "top5_acc": 0.68208, "mean_class_accuracy": 0.14652} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.025, "memory": 4082, "data_time": 0.20537, "top1_acc": 0.34375, "top5_acc": 0.69875, "loss_cls": 2.88686, "loss": 2.88686, "time": 0.64267} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.025, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.33562, "top5_acc": 0.72812, "loss_cls": 2.87574, "loss": 2.87574, "time": 0.41978} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.025, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.35125, "top5_acc": 0.73125, "loss_cls": 2.76453, "loss": 2.76453, "time": 0.41679} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.025, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.34812, "top5_acc": 0.7575, "loss_cls": 2.67819, "loss": 2.67819, "time": 0.41785} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.02499, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.39125, "top5_acc": 0.7975, "loss_cls": 2.5395, "loss": 2.5395, "time": 0.41601} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.02499, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.38875, "top5_acc": 0.79312, "loss_cls": 2.54798, "loss": 2.54798, "time": 0.41683} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.02499, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.40438, "top5_acc": 0.7925, "loss_cls": 2.53294, "loss": 2.53294, "time": 0.41499} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.02499, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.4125, "top5_acc": 0.83438, "loss_cls": 2.38624, "loss": 2.38624, "time": 0.41672} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.02499, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.41562, "top5_acc": 0.82688, "loss_cls": 2.40688, "loss": 2.40688, "time": 0.41814} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.02499, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.42375, "top5_acc": 0.85125, "loss_cls": 2.35006, "loss": 2.35006, "time": 0.41717} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.02499, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.43438, "top5_acc": 0.83188, "loss_cls": 2.30775, "loss": 2.30775, "time": 0.41844} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.02499, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.46812, "top5_acc": 0.84312, "loss_cls": 2.2211, "loss": 2.2211, "time": 0.41757} +{"mode": "val", "epoch": 2, "iter": 533, "lr": 0.02499, "top1_acc": 0.45159, "top5_acc": 0.83593, "mean_class_accuracy": 0.26726} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.02499, "memory": 4082, "data_time": 0.19678, "top1_acc": 0.47875, "top5_acc": 0.86875, "loss_cls": 2.13254, "loss": 2.13254, "time": 0.63375} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.02499, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.4925, "top5_acc": 0.89438, "loss_cls": 2.03693, "loss": 2.03693, "time": 0.42929} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.02499, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.49625, "top5_acc": 0.89938, "loss_cls": 2.0152, "loss": 2.0152, "time": 0.41856} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.02499, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.505, "top5_acc": 0.89188, "loss_cls": 2.02826, "loss": 2.02826, "time": 0.41729} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.02498, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.52562, "top5_acc": 0.89062, "loss_cls": 1.98211, "loss": 1.98211, "time": 0.41697} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.02498, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.51188, "top5_acc": 0.90438, "loss_cls": 1.92554, "loss": 1.92554, "time": 0.41762} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.02498, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.5225, "top5_acc": 0.89375, "loss_cls": 1.96463, "loss": 1.96463, "time": 0.41778} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.02498, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.51812, "top5_acc": 0.90625, "loss_cls": 1.93978, "loss": 1.93978, "time": 0.41833} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.02498, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.535, "top5_acc": 0.90375, "loss_cls": 1.91375, "loss": 1.91375, "time": 0.41835} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.02498, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.52625, "top5_acc": 0.91562, "loss_cls": 1.89212, "loss": 1.89212, "time": 0.41868} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.02498, "memory": 4082, "data_time": 0.00057, "top1_acc": 0.545, "top5_acc": 0.90312, "loss_cls": 1.8763, "loss": 1.8763, "time": 0.41796} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.02498, "memory": 4082, "data_time": 0.00062, "top1_acc": 0.53812, "top5_acc": 0.91688, "loss_cls": 1.87478, "loss": 1.87478, "time": 0.41792} +{"mode": "val", "epoch": 3, "iter": 533, "lr": 0.02498, "top1_acc": 0.51532, "top5_acc": 0.91421, "mean_class_accuracy": 0.37176} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.02497, "memory": 4082, "data_time": 0.20106, "top1_acc": 0.55625, "top5_acc": 0.91625, "loss_cls": 1.79655, "loss": 1.79655, "time": 0.63233} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.02497, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.585, "top5_acc": 0.935, "loss_cls": 1.71066, "loss": 1.71066, "time": 0.41614} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.02497, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.59438, "top5_acc": 0.94, "loss_cls": 1.62851, "loss": 1.62851, "time": 0.42038} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.02497, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.58625, "top5_acc": 0.93375, "loss_cls": 1.66103, "loss": 1.66103, "time": 0.41881} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.02497, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.57563, "top5_acc": 0.93875, "loss_cls": 1.71634, "loss": 1.71634, "time": 0.41818} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.02497, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.58312, "top5_acc": 0.92812, "loss_cls": 1.69608, "loss": 1.69608, "time": 0.41873} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.02497, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.58875, "top5_acc": 0.93688, "loss_cls": 1.66267, "loss": 1.66267, "time": 0.41897} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.02496, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.59688, "top5_acc": 0.9425, "loss_cls": 1.62312, "loss": 1.62312, "time": 0.41894} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.02496, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.6, "top5_acc": 0.935, "loss_cls": 1.60535, "loss": 1.60535, "time": 0.41963} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.02496, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.59188, "top5_acc": 0.93438, "loss_cls": 1.64682, "loss": 1.64682, "time": 0.41796} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.02496, "memory": 4082, "data_time": 0.00046, "top1_acc": 0.58688, "top5_acc": 0.94188, "loss_cls": 1.65033, "loss": 1.65033, "time": 0.41665} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.02496, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.62125, "top5_acc": 0.945, "loss_cls": 1.54297, "loss": 1.54297, "time": 0.41971} +{"mode": "val", "epoch": 4, "iter": 533, "lr": 0.02496, "top1_acc": 0.56719, "top5_acc": 0.92829, "mean_class_accuracy": 0.42606} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.02495, "memory": 4082, "data_time": 0.19995, "top1_acc": 0.62313, "top5_acc": 0.94062, "loss_cls": 1.53215, "loss": 1.53215, "time": 0.6178} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.02495, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.625, "top5_acc": 0.95, "loss_cls": 1.50839, "loss": 1.50839, "time": 0.41647} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.02495, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.64188, "top5_acc": 0.95438, "loss_cls": 1.50383, "loss": 1.50383, "time": 0.41753} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.02495, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.63, "top5_acc": 0.95688, "loss_cls": 1.50333, "loss": 1.50333, "time": 0.42089} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.02495, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.6375, "top5_acc": 0.95938, "loss_cls": 1.47964, "loss": 1.47964, "time": 0.41766} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.02495, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.62625, "top5_acc": 0.9425, "loss_cls": 1.52747, "loss": 1.52747, "time": 0.41701} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.02494, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.63438, "top5_acc": 0.95438, "loss_cls": 1.49572, "loss": 1.49572, "time": 0.41778} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.02494, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.64125, "top5_acc": 0.955, "loss_cls": 1.4619, "loss": 1.4619, "time": 0.41897} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.02494, "memory": 4082, "data_time": 0.00059, "top1_acc": 0.67188, "top5_acc": 0.95562, "loss_cls": 1.41564, "loss": 1.41564, "time": 0.41798} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.02494, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.65812, "top5_acc": 0.9525, "loss_cls": 1.48786, "loss": 1.48786, "time": 0.42955} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.02494, "memory": 4082, "data_time": 0.00052, "top1_acc": 0.65688, "top5_acc": 0.95438, "loss_cls": 1.42459, "loss": 1.42459, "time": 0.43945} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.02493, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.62938, "top5_acc": 0.96375, "loss_cls": 1.44615, "loss": 1.44615, "time": 0.43807} +{"mode": "val", "epoch": 5, "iter": 533, "lr": 0.02493, "top1_acc": 0.55217, "top5_acc": 0.91938, "mean_class_accuracy": 0.44324} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.02493, "memory": 4082, "data_time": 0.19341, "top1_acc": 0.6575, "top5_acc": 0.9625, "loss_cls": 1.36934, "loss": 1.36934, "time": 0.62382} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.02493, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.6625, "top5_acc": 0.95438, "loss_cls": 1.41648, "loss": 1.41648, "time": 0.41867} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.02492, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.64625, "top5_acc": 0.96312, "loss_cls": 1.40999, "loss": 1.40999, "time": 0.41724} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.02492, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.66938, "top5_acc": 0.96438, "loss_cls": 1.39459, "loss": 1.39459, "time": 0.41734} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.02492, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.66625, "top5_acc": 0.96562, "loss_cls": 1.3302, "loss": 1.3302, "time": 0.41852} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.02492, "memory": 4082, "data_time": 0.00053, "top1_acc": 0.67562, "top5_acc": 0.9625, "loss_cls": 1.36799, "loss": 1.36799, "time": 0.4169} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.02492, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.66312, "top5_acc": 0.96, "loss_cls": 1.36564, "loss": 1.36564, "time": 0.41932} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.02491, "memory": 4082, "data_time": 0.00076, "top1_acc": 0.67688, "top5_acc": 0.9625, "loss_cls": 1.35492, "loss": 1.35492, "time": 0.41813} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.02491, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.67562, "top5_acc": 0.95812, "loss_cls": 1.33209, "loss": 1.33209, "time": 0.41868} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.02491, "memory": 4082, "data_time": 0.00059, "top1_acc": 0.64438, "top5_acc": 0.95688, "loss_cls": 1.44017, "loss": 1.44017, "time": 0.41841} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.02491, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.68, "top5_acc": 0.97125, "loss_cls": 1.32715, "loss": 1.32715, "time": 0.43974} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.0249, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.69125, "top5_acc": 0.96812, "loss_cls": 1.28298, "loss": 1.28298, "time": 0.42881} +{"mode": "val", "epoch": 6, "iter": 533, "lr": 0.0249, "top1_acc": 0.65274, "top5_acc": 0.95188, "mean_class_accuracy": 0.56096} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0249, "memory": 4082, "data_time": 0.20225, "top1_acc": 0.68062, "top5_acc": 0.9675, "loss_cls": 1.29536, "loss": 1.29536, "time": 0.62462} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0249, "memory": 4082, "data_time": 0.00061, "top1_acc": 0.70312, "top5_acc": 0.96312, "loss_cls": 1.28665, "loss": 1.28665, "time": 0.42761} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.02489, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.70188, "top5_acc": 0.97125, "loss_cls": 1.2522, "loss": 1.2522, "time": 0.41765} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.02489, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.665, "top5_acc": 0.9625, "loss_cls": 1.37092, "loss": 1.37092, "time": 0.41771} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.02489, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.69562, "top5_acc": 0.975, "loss_cls": 1.25413, "loss": 1.25413, "time": 0.41687} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.02489, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.68938, "top5_acc": 0.96688, "loss_cls": 1.30064, "loss": 1.30064, "time": 0.41594} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.02488, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.73562, "top5_acc": 0.975, "loss_cls": 1.19792, "loss": 1.19792, "time": 0.41694} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.02488, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.70875, "top5_acc": 0.97125, "loss_cls": 1.23117, "loss": 1.23117, "time": 0.41815} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.02488, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.705, "top5_acc": 0.96938, "loss_cls": 1.25357, "loss": 1.25357, "time": 0.42006} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.02487, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.70562, "top5_acc": 0.97375, "loss_cls": 1.2586, "loss": 1.2586, "time": 0.41883} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.02487, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.72188, "top5_acc": 0.965, "loss_cls": 1.24364, "loss": 1.24364, "time": 0.41761} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.02487, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.6925, "top5_acc": 0.96375, "loss_cls": 1.29931, "loss": 1.29931, "time": 0.41921} +{"mode": "val", "epoch": 7, "iter": 533, "lr": 0.02487, "top1_acc": 0.70215, "top5_acc": 0.97125, "mean_class_accuracy": 0.57098} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.02486, "memory": 4082, "data_time": 0.20325, "top1_acc": 0.71625, "top5_acc": 0.97625, "loss_cls": 1.15282, "loss": 1.15282, "time": 0.63287} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.02486, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.71938, "top5_acc": 0.975, "loss_cls": 1.20982, "loss": 1.20982, "time": 0.4175} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.02486, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.72062, "top5_acc": 0.97562, "loss_cls": 1.15775, "loss": 1.15775, "time": 0.41725} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.02485, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.70812, "top5_acc": 0.97, "loss_cls": 1.23705, "loss": 1.23705, "time": 0.4167} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.02485, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.7125, "top5_acc": 0.97562, "loss_cls": 1.20365, "loss": 1.20365, "time": 0.41916} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.02485, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.715, "top5_acc": 0.97938, "loss_cls": 1.17398, "loss": 1.17398, "time": 0.41694} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.02484, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.665, "top5_acc": 0.9775, "loss_cls": 1.26743, "loss": 1.26743, "time": 0.41644} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.02484, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.71812, "top5_acc": 0.97375, "loss_cls": 1.18839, "loss": 1.18839, "time": 0.41675} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.02484, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.715, "top5_acc": 0.97188, "loss_cls": 1.16826, "loss": 1.16826, "time": 0.41824} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.02483, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.74438, "top5_acc": 0.98, "loss_cls": 1.09404, "loss": 1.09404, "time": 0.41907} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.02483, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.71625, "top5_acc": 0.96938, "loss_cls": 1.17856, "loss": 1.17856, "time": 0.41706} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.02483, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.7275, "top5_acc": 0.97938, "loss_cls": 1.13909, "loss": 1.13909, "time": 0.41843} +{"mode": "val", "epoch": 8, "iter": 533, "lr": 0.02482, "top1_acc": 0.66166, "top5_acc": 0.95317, "mean_class_accuracy": 0.57274} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.02482, "memory": 4082, "data_time": 0.2, "top1_acc": 0.74125, "top5_acc": 0.97625, "loss_cls": 1.12551, "loss": 1.12551, "time": 0.61693} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.02482, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.72188, "top5_acc": 0.97562, "loss_cls": 1.16487, "loss": 1.16487, "time": 0.41792} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.02481, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.71312, "top5_acc": 0.975, "loss_cls": 1.17854, "loss": 1.17854, "time": 0.41686} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.02481, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.72375, "top5_acc": 0.97938, "loss_cls": 1.15516, "loss": 1.15516, "time": 0.41636} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.02481, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.74812, "top5_acc": 0.97875, "loss_cls": 1.08103, "loss": 1.08103, "time": 0.41631} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.0248, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.75438, "top5_acc": 0.97812, "loss_cls": 1.08034, "loss": 1.08034, "time": 0.4157} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.0248, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.71688, "top5_acc": 0.98125, "loss_cls": 1.16751, "loss": 1.16751, "time": 0.41749} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.0248, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.72875, "top5_acc": 0.97438, "loss_cls": 1.14427, "loss": 1.14427, "time": 0.418} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.02479, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.73562, "top5_acc": 0.97688, "loss_cls": 1.12007, "loss": 1.12007, "time": 0.41675} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.02479, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.77, "top5_acc": 0.97938, "loss_cls": 1.04113, "loss": 1.04113, "time": 0.4172} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.02479, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.75438, "top5_acc": 0.98062, "loss_cls": 1.06152, "loss": 1.06152, "time": 0.41739} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.02478, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.74125, "top5_acc": 0.9775, "loss_cls": 1.07176, "loss": 1.07176, "time": 0.41832} +{"mode": "val", "epoch": 9, "iter": 533, "lr": 0.02478, "top1_acc": 0.71271, "top5_acc": 0.97195, "mean_class_accuracy": 0.60236} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.02477, "memory": 4082, "data_time": 0.20017, "top1_acc": 0.76562, "top5_acc": 0.98062, "loss_cls": 1.02076, "loss": 1.02076, "time": 0.63777} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.02477, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.76562, "top5_acc": 0.97938, "loss_cls": 1.06456, "loss": 1.06456, "time": 0.41699} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.02477, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.75125, "top5_acc": 0.9825, "loss_cls": 1.06334, "loss": 1.06334, "time": 0.41615} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.02476, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.74125, "top5_acc": 0.97188, "loss_cls": 1.11634, "loss": 1.11634, "time": 0.41688} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.02476, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.75438, "top5_acc": 0.98125, "loss_cls": 1.04181, "loss": 1.04181, "time": 0.41729} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.02476, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.745, "top5_acc": 0.97875, "loss_cls": 1.09019, "loss": 1.09019, "time": 0.4192} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.02475, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.75562, "top5_acc": 0.98062, "loss_cls": 1.05349, "loss": 1.05349, "time": 0.41489} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.02475, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.73938, "top5_acc": 0.97688, "loss_cls": 1.06798, "loss": 1.06798, "time": 0.41836} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.02474, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.7525, "top5_acc": 0.98188, "loss_cls": 1.06628, "loss": 1.06628, "time": 0.41674} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.02474, "memory": 4082, "data_time": 0.00057, "top1_acc": 0.72125, "top5_acc": 0.97938, "loss_cls": 1.1382, "loss": 1.1382, "time": 0.41912} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.02473, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.74375, "top5_acc": 0.98062, "loss_cls": 1.10016, "loss": 1.10016, "time": 0.41761} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.02473, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.73375, "top5_acc": 0.98125, "loss_cls": 1.07261, "loss": 1.07261, "time": 0.41571} +{"mode": "val", "epoch": 10, "iter": 533, "lr": 0.02473, "top1_acc": 0.72433, "top5_acc": 0.96198, "mean_class_accuracy": 0.62632} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.02472, "memory": 4082, "data_time": 0.19974, "top1_acc": 0.775, "top5_acc": 0.9825, "loss_cls": 1.00198, "loss": 1.00198, "time": 0.6356} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.02472, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.76625, "top5_acc": 0.97812, "loss_cls": 1.02465, "loss": 1.02465, "time": 0.41884} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.02471, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.77438, "top5_acc": 0.98375, "loss_cls": 0.99121, "loss": 0.99121, "time": 0.41606} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.02471, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.77062, "top5_acc": 0.98375, "loss_cls": 1.036, "loss": 1.036, "time": 0.41628} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.02471, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.75875, "top5_acc": 0.98375, "loss_cls": 1.02303, "loss": 1.02303, "time": 0.41865} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.0247, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77562, "top5_acc": 0.98188, "loss_cls": 1.00985, "loss": 1.00985, "time": 0.41636} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.0247, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.75625, "top5_acc": 0.97438, "loss_cls": 1.09489, "loss": 1.09489, "time": 0.41787} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.02469, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.73625, "top5_acc": 0.97562, "loss_cls": 1.09117, "loss": 1.09117, "time": 0.4171} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.02469, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.76688, "top5_acc": 0.98188, "loss_cls": 1.05314, "loss": 1.05314, "time": 0.41856} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.02468, "memory": 4082, "data_time": 0.00046, "top1_acc": 0.73, "top5_acc": 0.97375, "loss_cls": 1.12359, "loss": 1.12359, "time": 0.41794} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.02468, "memory": 4082, "data_time": 0.0007, "top1_acc": 0.765, "top5_acc": 0.98188, "loss_cls": 1.03154, "loss": 1.03154, "time": 0.42054} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.02467, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.74938, "top5_acc": 0.97375, "loss_cls": 1.0652, "loss": 1.0652, "time": 0.41832} +{"mode": "val", "epoch": 11, "iter": 533, "lr": 0.02467, "top1_acc": 0.72609, "top5_acc": 0.96878, "mean_class_accuracy": 0.62728} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.02467, "memory": 4082, "data_time": 0.19508, "top1_acc": 0.7475, "top5_acc": 0.98, "loss_cls": 1.0329, "loss": 1.0329, "time": 0.63347} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.02466, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.78438, "top5_acc": 0.98938, "loss_cls": 0.91357, "loss": 0.91357, "time": 0.43173} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.02466, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.77375, "top5_acc": 0.98688, "loss_cls": 0.97163, "loss": 0.97163, "time": 0.4152} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.02465, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.76062, "top5_acc": 0.98375, "loss_cls": 1.02377, "loss": 1.02377, "time": 0.41616} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.02465, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77875, "top5_acc": 0.98, "loss_cls": 1.02452, "loss": 1.02452, "time": 0.41725} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.02464, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.765, "top5_acc": 0.98188, "loss_cls": 1.01258, "loss": 1.01258, "time": 0.42505} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.02464, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.75875, "top5_acc": 0.9875, "loss_cls": 1.00771, "loss": 1.00771, "time": 0.43758} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.02463, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.76, "top5_acc": 0.98438, "loss_cls": 1.03437, "loss": 1.03437, "time": 0.41734} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.02463, "memory": 4082, "data_time": 0.00049, "top1_acc": 0.76625, "top5_acc": 0.98438, "loss_cls": 1.0408, "loss": 1.0408, "time": 0.41784} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.02462, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.77625, "top5_acc": 0.98125, "loss_cls": 1.02411, "loss": 1.02411, "time": 0.41766} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.02462, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.76625, "top5_acc": 0.97812, "loss_cls": 1.01167, "loss": 1.01167, "time": 0.41783} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.02461, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.775, "top5_acc": 0.98438, "loss_cls": 0.99163, "loss": 0.99163, "time": 0.41675} +{"mode": "val", "epoch": 12, "iter": 533, "lr": 0.02461, "top1_acc": 0.71776, "top5_acc": 0.97125, "mean_class_accuracy": 0.63103} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.0246, "memory": 4082, "data_time": 0.20356, "top1_acc": 0.79562, "top5_acc": 0.98938, "loss_cls": 0.91946, "loss": 0.91946, "time": 0.64227} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.0246, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.76875, "top5_acc": 0.98875, "loss_cls": 0.96425, "loss": 0.96425, "time": 0.43736} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.02459, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.76375, "top5_acc": 0.98938, "loss_cls": 0.98141, "loss": 0.98141, "time": 0.4377} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.02459, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.7875, "top5_acc": 0.985, "loss_cls": 0.93268, "loss": 0.93268, "time": 0.43279} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.02458, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.79188, "top5_acc": 0.98875, "loss_cls": 0.94212, "loss": 0.94212, "time": 0.43641} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.02458, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.76375, "top5_acc": 0.98688, "loss_cls": 0.971, "loss": 0.971, "time": 0.43761} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.02457, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.76625, "top5_acc": 0.98125, "loss_cls": 1.00936, "loss": 1.00936, "time": 0.4358} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.02457, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.7675, "top5_acc": 0.97875, "loss_cls": 1.00831, "loss": 1.00831, "time": 0.42427} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.02456, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.78062, "top5_acc": 0.98688, "loss_cls": 0.94234, "loss": 0.94234, "time": 0.41567} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.02455, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.765, "top5_acc": 0.98188, "loss_cls": 0.98987, "loss": 0.98987, "time": 0.41812} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.02455, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.77625, "top5_acc": 0.98312, "loss_cls": 0.96541, "loss": 0.96541, "time": 0.41724} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.02454, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.76125, "top5_acc": 0.98188, "loss_cls": 1.01752, "loss": 1.01752, "time": 0.41728} +{"mode": "val", "epoch": 13, "iter": 533, "lr": 0.02454, "top1_acc": 0.7147, "top5_acc": 0.96996, "mean_class_accuracy": 0.64174} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.02453, "memory": 4082, "data_time": 0.19494, "top1_acc": 0.80688, "top5_acc": 0.98812, "loss_cls": 0.86642, "loss": 0.86642, "time": 0.58656} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.02453, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.79375, "top5_acc": 0.99062, "loss_cls": 0.88541, "loss": 0.88541, "time": 0.38047} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.02452, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.7775, "top5_acc": 0.98188, "loss_cls": 0.99133, "loss": 0.99133, "time": 0.3844} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.02452, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.79875, "top5_acc": 0.98688, "loss_cls": 0.90097, "loss": 0.90097, "time": 0.38109} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.02451, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77, "top5_acc": 0.98562, "loss_cls": 0.97723, "loss": 0.97723, "time": 0.38965} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.02451, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79125, "top5_acc": 0.98438, "loss_cls": 0.92692, "loss": 0.92692, "time": 0.37709} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.0245, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.7775, "top5_acc": 0.9875, "loss_cls": 0.94074, "loss": 0.94074, "time": 0.38688} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.02449, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78, "top5_acc": 0.98688, "loss_cls": 0.98791, "loss": 0.98791, "time": 0.3971} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.02449, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.78, "top5_acc": 0.9825, "loss_cls": 0.96139, "loss": 0.96139, "time": 0.38651} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.02448, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.77688, "top5_acc": 0.98, "loss_cls": 0.96878, "loss": 0.96878, "time": 0.38253} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.02448, "memory": 4082, "data_time": 0.00058, "top1_acc": 0.7575, "top5_acc": 0.98438, "loss_cls": 1.0343, "loss": 1.0343, "time": 0.39102} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.02447, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.76625, "top5_acc": 0.98125, "loss_cls": 0.99277, "loss": 0.99277, "time": 0.38879} +{"mode": "val", "epoch": 14, "iter": 533, "lr": 0.02447, "top1_acc": 0.74733, "top5_acc": 0.97911, "mean_class_accuracy": 0.66267} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.02446, "memory": 4082, "data_time": 0.1903, "top1_acc": 0.81562, "top5_acc": 0.98875, "loss_cls": 0.83465, "loss": 0.83465, "time": 0.46964} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.02445, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.78875, "top5_acc": 0.98375, "loss_cls": 0.93629, "loss": 0.93629, "time": 0.41117} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.02445, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.79625, "top5_acc": 0.98688, "loss_cls": 0.90381, "loss": 0.90381, "time": 0.30188} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.02444, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.78812, "top5_acc": 0.98125, "loss_cls": 0.91525, "loss": 0.91525, "time": 0.24508} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.02444, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.77688, "top5_acc": 0.985, "loss_cls": 0.91838, "loss": 0.91838, "time": 0.37392} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.02443, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.765, "top5_acc": 0.97875, "loss_cls": 1.01359, "loss": 1.01359, "time": 0.37697} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.02442, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.79625, "top5_acc": 0.98938, "loss_cls": 0.89411, "loss": 0.89411, "time": 0.38117} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.02442, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.80062, "top5_acc": 0.9875, "loss_cls": 0.91704, "loss": 0.91704, "time": 0.37908} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.02441, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.78, "top5_acc": 0.9825, "loss_cls": 0.95782, "loss": 0.95782, "time": 0.39253} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.02441, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.7925, "top5_acc": 0.98312, "loss_cls": 0.92013, "loss": 0.92013, "time": 0.37382} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.0244, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.77438, "top5_acc": 0.98688, "loss_cls": 0.92347, "loss": 0.92347, "time": 0.38074} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.02439, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78062, "top5_acc": 0.98875, "loss_cls": 0.90966, "loss": 0.90966, "time": 0.37761} +{"mode": "val", "epoch": 15, "iter": 533, "lr": 0.02439, "top1_acc": 0.7248, "top5_acc": 0.97078, "mean_class_accuracy": 0.63773} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.02438, "memory": 4082, "data_time": 0.19756, "top1_acc": 0.82938, "top5_acc": 0.98688, "loss_cls": 0.79606, "loss": 0.79606, "time": 0.58125} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.02438, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.77, "top5_acc": 0.9825, "loss_cls": 0.99065, "loss": 0.99065, "time": 0.37439} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.02437, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80312, "top5_acc": 0.9825, "loss_cls": 0.89616, "loss": 0.89616, "time": 0.37798} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.02436, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.80188, "top5_acc": 0.98938, "loss_cls": 0.89281, "loss": 0.89281, "time": 0.37716} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.02436, "memory": 4082, "data_time": 0.00069, "top1_acc": 0.79375, "top5_acc": 0.98438, "loss_cls": 0.91205, "loss": 0.91205, "time": 0.38122} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.02435, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.78375, "top5_acc": 0.98125, "loss_cls": 0.9467, "loss": 0.9467, "time": 0.37266} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.02434, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.795, "top5_acc": 0.985, "loss_cls": 0.88537, "loss": 0.88537, "time": 0.25148} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.02434, "memory": 4082, "data_time": 0.00049, "top1_acc": 0.77312, "top5_acc": 0.9825, "loss_cls": 0.94069, "loss": 0.94069, "time": 0.45619} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.02433, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.81625, "top5_acc": 0.98688, "loss_cls": 0.8716, "loss": 0.8716, "time": 0.23885} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.02432, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.79125, "top5_acc": 0.99188, "loss_cls": 0.87229, "loss": 0.87229, "time": 0.29691} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.02432, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.79, "top5_acc": 0.98625, "loss_cls": 0.90901, "loss": 0.90901, "time": 0.3805} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.02431, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.78812, "top5_acc": 0.98438, "loss_cls": 0.93257, "loss": 0.93257, "time": 0.37691} +{"mode": "val", "epoch": 16, "iter": 533, "lr": 0.0243, "top1_acc": 0.76083, "top5_acc": 0.977, "mean_class_accuracy": 0.67214} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.0243, "memory": 4082, "data_time": 0.1937, "top1_acc": 0.79688, "top5_acc": 0.98625, "loss_cls": 0.90612, "loss": 0.90612, "time": 0.57114} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.02429, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80625, "top5_acc": 0.98812, "loss_cls": 0.85553, "loss": 0.85553, "time": 0.38059} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.02428, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.81562, "top5_acc": 0.99062, "loss_cls": 0.79852, "loss": 0.79852, "time": 0.37439} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.02428, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.795, "top5_acc": 0.99188, "loss_cls": 0.85039, "loss": 0.85039, "time": 0.38484} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.02427, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.80812, "top5_acc": 0.985, "loss_cls": 0.86488, "loss": 0.86488, "time": 0.38611} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.02426, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.7975, "top5_acc": 0.9825, "loss_cls": 0.9316, "loss": 0.9316, "time": 0.38582} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.02426, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.79, "top5_acc": 0.98562, "loss_cls": 0.90571, "loss": 0.90571, "time": 0.39103} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.02425, "memory": 4082, "data_time": 0.00055, "top1_acc": 0.81312, "top5_acc": 0.99188, "loss_cls": 0.84479, "loss": 0.84479, "time": 0.37564} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.02424, "memory": 4082, "data_time": 0.00066, "top1_acc": 0.7875, "top5_acc": 0.97938, "loss_cls": 0.94594, "loss": 0.94594, "time": 0.37793} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.02424, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.80188, "top5_acc": 0.99062, "loss_cls": 0.86857, "loss": 0.86857, "time": 0.37421} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.02423, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.80938, "top5_acc": 0.98938, "loss_cls": 0.84518, "loss": 0.84518, "time": 0.37791} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.02422, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.80562, "top5_acc": 0.98375, "loss_cls": 0.89061, "loss": 0.89061, "time": 0.32464} +{"mode": "val", "epoch": 17, "iter": 533, "lr": 0.02422, "top1_acc": 0.74052, "top5_acc": 0.97148, "mean_class_accuracy": 0.6765} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.02421, "memory": 4082, "data_time": 0.19583, "top1_acc": 0.795, "top5_acc": 0.9875, "loss_cls": 0.87797, "loss": 0.87797, "time": 0.57456} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.0242, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81688, "top5_acc": 0.98688, "loss_cls": 0.81941, "loss": 0.81941, "time": 0.38749} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.02419, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.79562, "top5_acc": 0.9875, "loss_cls": 0.88341, "loss": 0.88341, "time": 0.36899} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.02419, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.79562, "top5_acc": 0.98562, "loss_cls": 0.8761, "loss": 0.8761, "time": 0.37191} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.02418, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.80125, "top5_acc": 0.98875, "loss_cls": 0.89058, "loss": 0.89058, "time": 0.37402} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.02417, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.8025, "top5_acc": 0.98625, "loss_cls": 0.84539, "loss": 0.84539, "time": 0.37534} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.02417, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.79625, "top5_acc": 0.9875, "loss_cls": 0.89688, "loss": 0.89688, "time": 0.37772} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.02416, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.80312, "top5_acc": 0.98812, "loss_cls": 0.83198, "loss": 0.83198, "time": 0.37875} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.02415, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.805, "top5_acc": 0.98688, "loss_cls": 0.86878, "loss": 0.86878, "time": 0.37986} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.02414, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.805, "top5_acc": 0.98562, "loss_cls": 0.83546, "loss": 0.83546, "time": 0.37537} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.02414, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.79438, "top5_acc": 0.98688, "loss_cls": 0.8978, "loss": 0.8978, "time": 0.37823} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.02413, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.79938, "top5_acc": 0.98688, "loss_cls": 0.84356, "loss": 0.84356, "time": 0.37639} +{"mode": "val", "epoch": 18, "iter": 533, "lr": 0.02412, "top1_acc": 0.74991, "top5_acc": 0.975, "mean_class_accuracy": 0.66431} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.02411, "memory": 4082, "data_time": 0.19461, "top1_acc": 0.82875, "top5_acc": 0.98688, "loss_cls": 0.79395, "loss": 0.79395, "time": 0.56806} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.02411, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.80125, "top5_acc": 0.9875, "loss_cls": 0.87063, "loss": 0.87063, "time": 0.26616} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.0241, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83875, "top5_acc": 0.99062, "loss_cls": 0.75859, "loss": 0.75859, "time": 0.44726} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.02409, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81125, "top5_acc": 0.99188, "loss_cls": 0.82399, "loss": 0.82399, "time": 0.22398} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.02408, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83188, "top5_acc": 0.98938, "loss_cls": 0.81136, "loss": 0.81136, "time": 0.31953} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.02408, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.78875, "top5_acc": 0.99125, "loss_cls": 0.86375, "loss": 0.86375, "time": 0.37717} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.02407, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.815, "top5_acc": 0.99, "loss_cls": 0.83181, "loss": 0.83181, "time": 0.37519} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.02406, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.8075, "top5_acc": 0.99, "loss_cls": 0.84851, "loss": 0.84851, "time": 0.37567} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.02405, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.81, "top5_acc": 0.98938, "loss_cls": 0.80983, "loss": 0.80983, "time": 0.37943} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.02405, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.80438, "top5_acc": 0.98938, "loss_cls": 0.82721, "loss": 0.82721, "time": 0.37718} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.02404, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.8, "top5_acc": 0.99188, "loss_cls": 0.82828, "loss": 0.82828, "time": 0.37924} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.02403, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.79312, "top5_acc": 0.98312, "loss_cls": 0.90751, "loss": 0.90751, "time": 0.38328} +{"mode": "val", "epoch": 19, "iter": 533, "lr": 0.02402, "top1_acc": 0.75989, "top5_acc": 0.97219, "mean_class_accuracy": 0.6461} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.02402, "memory": 4082, "data_time": 0.19679, "top1_acc": 0.82688, "top5_acc": 0.99375, "loss_cls": 0.76069, "loss": 0.76069, "time": 0.58377} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.02401, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83062, "top5_acc": 0.99188, "loss_cls": 0.74933, "loss": 0.74933, "time": 0.37931} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.024, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.81875, "top5_acc": 0.99062, "loss_cls": 0.83335, "loss": 0.83335, "time": 0.37616} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.02399, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.835, "top5_acc": 0.99125, "loss_cls": 0.77403, "loss": 0.77403, "time": 0.37656} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.02398, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.82938, "top5_acc": 0.9875, "loss_cls": 0.79866, "loss": 0.79866, "time": 0.38189} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.02398, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.80438, "top5_acc": 0.98875, "loss_cls": 0.85195, "loss": 0.85195, "time": 0.37396} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.02397, "memory": 4082, "data_time": 0.0005, "top1_acc": 0.82312, "top5_acc": 0.9875, "loss_cls": 0.7969, "loss": 0.7969, "time": 0.30742} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.02396, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.82375, "top5_acc": 0.98938, "loss_cls": 0.7943, "loss": 0.7943, "time": 0.37017} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.02395, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81125, "top5_acc": 0.99125, "loss_cls": 0.79868, "loss": 0.79868, "time": 0.34338} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.02394, "memory": 4082, "data_time": 0.00022, "top1_acc": 0.805, "top5_acc": 0.99312, "loss_cls": 0.79715, "loss": 0.79715, "time": 0.23659} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.02393, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.79562, "top5_acc": 0.99062, "loss_cls": 0.84408, "loss": 0.84408, "time": 0.37309} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.02393, "memory": 4082, "data_time": 0.00051, "top1_acc": 0.805, "top5_acc": 0.98688, "loss_cls": 0.8887, "loss": 0.8887, "time": 0.37934} +{"mode": "val", "epoch": 20, "iter": 533, "lr": 0.02392, "top1_acc": 0.77749, "top5_acc": 0.97958, "mean_class_accuracy": 0.69831} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.02391, "memory": 4082, "data_time": 0.1967, "top1_acc": 0.8, "top5_acc": 0.99375, "loss_cls": 0.82516, "loss": 0.82516, "time": 0.58366} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.0239, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81875, "top5_acc": 0.99188, "loss_cls": 0.80707, "loss": 0.80707, "time": 0.37795} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.02389, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83, "top5_acc": 0.99062, "loss_cls": 0.77412, "loss": 0.77412, "time": 0.37702} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.02389, "memory": 4082, "data_time": 0.00055, "top1_acc": 0.83062, "top5_acc": 0.98875, "loss_cls": 0.77432, "loss": 0.77432, "time": 0.37759} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.02388, "memory": 4082, "data_time": 0.00035, "top1_acc": 0.83062, "top5_acc": 0.99125, "loss_cls": 0.76412, "loss": 0.76412, "time": 0.37552} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.02387, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.81062, "top5_acc": 0.98812, "loss_cls": 0.82788, "loss": 0.82788, "time": 0.37611} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.02386, "memory": 4082, "data_time": 0.00042, "top1_acc": 0.81625, "top5_acc": 0.98688, "loss_cls": 0.86852, "loss": 0.86852, "time": 0.37717} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.02385, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82875, "top5_acc": 0.99, "loss_cls": 0.78489, "loss": 0.78489, "time": 0.37701} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.02384, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83312, "top5_acc": 0.99312, "loss_cls": 0.75331, "loss": 0.75331, "time": 0.384} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.02383, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.80562, "top5_acc": 0.98812, "loss_cls": 0.81128, "loss": 0.81128, "time": 0.37591} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.02383, "memory": 4082, "data_time": 0.00054, "top1_acc": 0.8175, "top5_acc": 0.98438, "loss_cls": 0.82771, "loss": 0.82771, "time": 0.37993} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.02382, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.815, "top5_acc": 0.98625, "loss_cls": 0.84645, "loss": 0.84645, "time": 0.36856} +{"mode": "val", "epoch": 21, "iter": 533, "lr": 0.02381, "top1_acc": 0.80413, "top5_acc": 0.98427, "mean_class_accuracy": 0.73734} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.0238, "memory": 4082, "data_time": 0.19462, "top1_acc": 0.84062, "top5_acc": 0.995, "loss_cls": 0.73816, "loss": 0.73816, "time": 0.5743} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.02379, "memory": 4082, "data_time": 0.00036, "top1_acc": 0.83312, "top5_acc": 0.98938, "loss_cls": 0.76877, "loss": 0.76877, "time": 0.37759} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.02378, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.835, "top5_acc": 0.99188, "loss_cls": 0.79709, "loss": 0.79709, "time": 0.37718} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.02378, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.81062, "top5_acc": 0.9925, "loss_cls": 0.79549, "loss": 0.79549, "time": 0.38172} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.02377, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.80438, "top5_acc": 0.98625, "loss_cls": 0.8426, "loss": 0.8426, "time": 0.37432} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.02376, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.80875, "top5_acc": 0.98688, "loss_cls": 0.85041, "loss": 0.85041, "time": 0.37621} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.02375, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.79312, "top5_acc": 0.9875, "loss_cls": 0.89476, "loss": 0.89476, "time": 0.37028} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.02374, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83312, "top5_acc": 0.99188, "loss_cls": 0.76441, "loss": 0.76441, "time": 0.3809} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.02373, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.835, "top5_acc": 0.99062, "loss_cls": 0.75413, "loss": 0.75413, "time": 0.38111} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.02372, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.82688, "top5_acc": 0.9875, "loss_cls": 0.80055, "loss": 0.80055, "time": 0.37916} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.02371, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82188, "top5_acc": 0.9875, "loss_cls": 0.80491, "loss": 0.80491, "time": 0.37441} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0237, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.81438, "top5_acc": 0.98625, "loss_cls": 0.81795, "loss": 0.81795, "time": 0.37705} +{"mode": "val", "epoch": 22, "iter": 533, "lr": 0.0237, "top1_acc": 0.80378, "top5_acc": 0.98298, "mean_class_accuracy": 0.7413} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.02369, "memory": 4082, "data_time": 0.19645, "top1_acc": 0.84312, "top5_acc": 0.9925, "loss_cls": 0.71102, "loss": 0.71102, "time": 0.57661} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.02368, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.8475, "top5_acc": 0.99188, "loss_cls": 0.71121, "loss": 0.71121, "time": 0.2859} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.02367, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.83438, "top5_acc": 0.99125, "loss_cls": 0.74639, "loss": 0.74639, "time": 0.39027} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.02366, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82938, "top5_acc": 0.98875, "loss_cls": 0.76561, "loss": 0.76561, "time": 0.32189} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.02365, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.82812, "top5_acc": 0.99, "loss_cls": 0.78428, "loss": 0.78428, "time": 0.25358} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.02364, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.835, "top5_acc": 0.9875, "loss_cls": 0.76483, "loss": 0.76483, "time": 0.37457} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.02363, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.82688, "top5_acc": 0.98812, "loss_cls": 0.77963, "loss": 0.77963, "time": 0.36898} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.02362, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.84938, "top5_acc": 0.99188, "loss_cls": 0.69762, "loss": 0.69762, "time": 0.37582} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.02361, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82, "top5_acc": 0.99, "loss_cls": 0.80905, "loss": 0.80905, "time": 0.36991} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.0236, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.82125, "top5_acc": 0.99125, "loss_cls": 0.77638, "loss": 0.77638, "time": 0.37059} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.02359, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.82562, "top5_acc": 0.99125, "loss_cls": 0.79683, "loss": 0.79683, "time": 0.37421} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.02359, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.81562, "top5_acc": 0.98625, "loss_cls": 0.82312, "loss": 0.82312, "time": 0.37143} +{"mode": "val", "epoch": 23, "iter": 533, "lr": 0.02358, "top1_acc": 0.80366, "top5_acc": 0.98345, "mean_class_accuracy": 0.7322} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.02357, "memory": 4082, "data_time": 0.19835, "top1_acc": 0.84625, "top5_acc": 0.98938, "loss_cls": 0.72214, "loss": 0.72214, "time": 0.57976} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.02356, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.82812, "top5_acc": 0.99188, "loss_cls": 0.73283, "loss": 0.73283, "time": 0.38006} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.02355, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.81688, "top5_acc": 0.99062, "loss_cls": 0.79685, "loss": 0.79685, "time": 0.38127} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.02354, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.84312, "top5_acc": 0.99188, "loss_cls": 0.72159, "loss": 0.72159, "time": 0.37329} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.02353, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.83125, "top5_acc": 0.99, "loss_cls": 0.76462, "loss": 0.76462, "time": 0.38128} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.02352, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.82062, "top5_acc": 0.98938, "loss_cls": 0.77134, "loss": 0.77134, "time": 0.38173} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.02351, "memory": 4082, "data_time": 0.00092, "top1_acc": 0.83375, "top5_acc": 0.99, "loss_cls": 0.72472, "loss": 0.72472, "time": 0.38367} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.0235, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.83625, "top5_acc": 0.98875, "loss_cls": 0.75936, "loss": 0.75936, "time": 0.24978} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.02349, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8175, "top5_acc": 0.98812, "loss_cls": 0.80646, "loss": 0.80646, "time": 0.44175} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.02348, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.81312, "top5_acc": 0.98688, "loss_cls": 0.82734, "loss": 0.82734, "time": 0.26999} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.02347, "memory": 4082, "data_time": 0.00044, "top1_acc": 0.82438, "top5_acc": 0.9875, "loss_cls": 0.7985, "loss": 0.7985, "time": 0.30586} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.02346, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.81812, "top5_acc": 0.9925, "loss_cls": 0.7746, "loss": 0.7746, "time": 0.3731} +{"mode": "val", "epoch": 24, "iter": 533, "lr": 0.02345, "top1_acc": 0.79157, "top5_acc": 0.98439, "mean_class_accuracy": 0.73069} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.02344, "memory": 4082, "data_time": 0.20167, "top1_acc": 0.84562, "top5_acc": 0.99438, "loss_cls": 0.71945, "loss": 0.71945, "time": 0.57941} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.02343, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.83812, "top5_acc": 0.995, "loss_cls": 0.69889, "loss": 0.69889, "time": 0.37487} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.02342, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.8575, "top5_acc": 0.99438, "loss_cls": 0.69614, "loss": 0.69614, "time": 0.38213} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.02341, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.855, "top5_acc": 0.98562, "loss_cls": 0.72101, "loss": 0.72101, "time": 0.37823} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.0234, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.835, "top5_acc": 0.98938, "loss_cls": 0.74346, "loss": 0.74346, "time": 0.37819} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.02339, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.8275, "top5_acc": 0.99062, "loss_cls": 0.77528, "loss": 0.77528, "time": 0.37496} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.02338, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83812, "top5_acc": 0.99125, "loss_cls": 0.75199, "loss": 0.75199, "time": 0.37804} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.02337, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83062, "top5_acc": 0.98812, "loss_cls": 0.75852, "loss": 0.75852, "time": 0.37401} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.02336, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83562, "top5_acc": 0.99188, "loss_cls": 0.71636, "loss": 0.71636, "time": 0.3835} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.02335, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83438, "top5_acc": 0.98938, "loss_cls": 0.76704, "loss": 0.76704, "time": 0.37251} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.02334, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.81188, "top5_acc": 0.985, "loss_cls": 0.82246, "loss": 0.82246, "time": 0.37677} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.02333, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.83375, "top5_acc": 0.985, "loss_cls": 0.77951, "loss": 0.77951, "time": 0.38433} +{"mode": "val", "epoch": 25, "iter": 533, "lr": 0.02333, "top1_acc": 0.78136, "top5_acc": 0.9804, "mean_class_accuracy": 0.72778} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.02332, "memory": 4082, "data_time": 0.19904, "top1_acc": 0.85562, "top5_acc": 0.99625, "loss_cls": 0.697, "loss": 0.697, "time": 0.57412} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.0233, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.8525, "top5_acc": 0.99125, "loss_cls": 0.67862, "loss": 0.67862, "time": 0.37548} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.02329, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85125, "top5_acc": 0.99438, "loss_cls": 0.70289, "loss": 0.70289, "time": 0.37477} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.02328, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.8425, "top5_acc": 0.98625, "loss_cls": 0.72942, "loss": 0.72942, "time": 0.37949} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.02327, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.8325, "top5_acc": 0.99062, "loss_cls": 0.74846, "loss": 0.74846, "time": 0.37208} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.02326, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.84312, "top5_acc": 0.9925, "loss_cls": 0.72685, "loss": 0.72685, "time": 0.38028} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.02325, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.82375, "top5_acc": 0.99125, "loss_cls": 0.76644, "loss": 0.76644, "time": 0.37059} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.02324, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.82062, "top5_acc": 0.99125, "loss_cls": 0.77293, "loss": 0.77293, "time": 0.37946} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.02323, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.8275, "top5_acc": 0.985, "loss_cls": 0.79813, "loss": 0.79813, "time": 0.37761} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.02322, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.85625, "top5_acc": 0.9925, "loss_cls": 0.69299, "loss": 0.69299, "time": 0.3771} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.02321, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84375, "top5_acc": 0.99062, "loss_cls": 0.7629, "loss": 0.7629, "time": 0.37398} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.0232, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85188, "top5_acc": 0.99125, "loss_cls": 0.70473, "loss": 0.70473, "time": 0.38512} +{"mode": "val", "epoch": 26, "iter": 533, "lr": 0.02319, "top1_acc": 0.77045, "top5_acc": 0.97758, "mean_class_accuracy": 0.66581} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.02318, "memory": 4082, "data_time": 0.19449, "top1_acc": 0.83125, "top5_acc": 0.99125, "loss_cls": 0.73867, "loss": 0.73867, "time": 0.57385} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.02317, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85625, "top5_acc": 0.99, "loss_cls": 0.66698, "loss": 0.66698, "time": 0.37468} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.02316, "memory": 4082, "data_time": 0.00033, "top1_acc": 0.86562, "top5_acc": 0.99062, "loss_cls": 0.67037, "loss": 0.67037, "time": 0.25864} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.02315, "memory": 4082, "data_time": 0.00038, "top1_acc": 0.8475, "top5_acc": 0.99188, "loss_cls": 0.68948, "loss": 0.68948, "time": 0.43518} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.02314, "memory": 4082, "data_time": 0.0003, "top1_acc": 0.84625, "top5_acc": 0.99438, "loss_cls": 0.71772, "loss": 0.71772, "time": 0.27901} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.02313, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84688, "top5_acc": 0.98938, "loss_cls": 0.71451, "loss": 0.71451, "time": 0.28595} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.02312, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.83812, "top5_acc": 0.9875, "loss_cls": 0.73734, "loss": 0.73734, "time": 0.37616} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.02311, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.85438, "top5_acc": 0.98875, "loss_cls": 0.67887, "loss": 0.67887, "time": 0.37642} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.0231, "memory": 4082, "data_time": 0.00041, "top1_acc": 0.83938, "top5_acc": 0.99438, "loss_cls": 0.71535, "loss": 0.71535, "time": 0.37806} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.02308, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.83375, "top5_acc": 0.98875, "loss_cls": 0.72902, "loss": 0.72902, "time": 0.37972} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.02307, "memory": 4082, "data_time": 0.0004, "top1_acc": 0.81375, "top5_acc": 0.99312, "loss_cls": 0.80772, "loss": 0.80772, "time": 0.37819} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.02306, "memory": 4082, "data_time": 0.00045, "top1_acc": 0.82938, "top5_acc": 0.99, "loss_cls": 0.769, "loss": 0.769, "time": 0.37485} +{"mode": "val", "epoch": 27, "iter": 533, "lr": 0.02305, "top1_acc": 0.81375, "top5_acc": 0.98486, "mean_class_accuracy": 0.75686} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.02304, "memory": 4082, "data_time": 0.19619, "top1_acc": 0.84062, "top5_acc": 0.99375, "loss_cls": 0.7047, "loss": 0.7047, "time": 0.57829} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.02303, "memory": 4082, "data_time": 0.00029, "top1_acc": 0.85312, "top5_acc": 0.99312, "loss_cls": 0.675, "loss": 0.675, "time": 0.37464} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.02302, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.83938, "top5_acc": 0.99125, "loss_cls": 0.74486, "loss": 0.74486, "time": 0.38025} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.02301, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.86062, "top5_acc": 0.9925, "loss_cls": 0.668, "loss": 0.668, "time": 0.37357} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.023, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.85688, "top5_acc": 0.9925, "loss_cls": 0.66369, "loss": 0.66369, "time": 0.3788} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.02299, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.86188, "top5_acc": 0.99375, "loss_cls": 0.66741, "loss": 0.66741, "time": 0.3801} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.02298, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.83625, "top5_acc": 0.99312, "loss_cls": 0.73162, "loss": 0.73162, "time": 0.37921} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.02297, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.86312, "top5_acc": 0.99125, "loss_cls": 0.72414, "loss": 0.72414, "time": 0.34888} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.02295, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.85688, "top5_acc": 0.99, "loss_cls": 0.67614, "loss": 0.67614, "time": 0.29697} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.02294, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.83438, "top5_acc": 0.99125, "loss_cls": 0.78163, "loss": 0.78163, "time": 0.41771} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.02293, "memory": 4082, "data_time": 0.00043, "top1_acc": 0.84125, "top5_acc": 0.99375, "loss_cls": 0.71632, "loss": 0.71632, "time": 0.23032} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.02292, "memory": 4082, "data_time": 0.00027, "top1_acc": 0.84875, "top5_acc": 0.99125, "loss_cls": 0.72185, "loss": 0.72185, "time": 0.34023} +{"mode": "val", "epoch": 28, "iter": 533, "lr": 0.02291, "top1_acc": 0.7965, "top5_acc": 0.98568, "mean_class_accuracy": 0.7313} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.0229, "memory": 4082, "data_time": 0.20032, "top1_acc": 0.855, "top5_acc": 0.99312, "loss_cls": 0.67277, "loss": 0.67277, "time": 0.58063} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.02289, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86375, "top5_acc": 0.99438, "loss_cls": 0.6384, "loss": 0.6384, "time": 0.38286} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.02288, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.85125, "top5_acc": 0.99375, "loss_cls": 0.66807, "loss": 0.66807, "time": 0.37581} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.02287, "memory": 4082, "data_time": 0.00025, "top1_acc": 0.86438, "top5_acc": 0.99375, "loss_cls": 0.68297, "loss": 0.68297, "time": 0.37911} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.02285, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.865, "top5_acc": 0.99, "loss_cls": 0.69374, "loss": 0.69374, "time": 0.37689} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.02284, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85625, "top5_acc": 0.99125, "loss_cls": 0.66722, "loss": 0.66722, "time": 0.37621} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.02283, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.86062, "top5_acc": 0.995, "loss_cls": 0.65909, "loss": 0.65909, "time": 0.37433} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.02282, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.84125, "top5_acc": 0.99125, "loss_cls": 0.68846, "loss": 0.68846, "time": 0.37211} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.02281, "memory": 4082, "data_time": 0.00037, "top1_acc": 0.82688, "top5_acc": 0.99, "loss_cls": 0.77206, "loss": 0.77206, "time": 0.37755} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.0228, "memory": 4082, "data_time": 0.00048, "top1_acc": 0.85688, "top5_acc": 0.99438, "loss_cls": 0.68213, "loss": 0.68213, "time": 0.38405} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.02279, "memory": 4082, "data_time": 0.00039, "top1_acc": 0.84125, "top5_acc": 0.995, "loss_cls": 0.71976, "loss": 0.71976, "time": 0.38128} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.02277, "memory": 4082, "data_time": 0.00031, "top1_acc": 0.84188, "top5_acc": 0.98938, "loss_cls": 0.73068, "loss": 0.73068, "time": 0.38231} +{"mode": "val", "epoch": 29, "iter": 533, "lr": 0.02276, "top1_acc": 0.78805, "top5_acc": 0.97805, "mean_class_accuracy": 0.72138} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.02275, "memory": 4082, "data_time": 0.19223, "top1_acc": 0.8375, "top5_acc": 0.99125, "loss_cls": 0.72836, "loss": 0.72836, "time": 0.52266} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.02274, "memory": 4082, "data_time": 0.00028, "top1_acc": 0.88125, "top5_acc": 0.995, "loss_cls": 0.59947, "loss": 0.59947, "time": 0.47973} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.02273, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85312, "top5_acc": 0.99625, "loss_cls": 0.6557, "loss": 0.6557, "time": 0.4809} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.02272, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.86, "top5_acc": 0.98812, "loss_cls": 0.68148, "loss": 0.68148, "time": 0.47637} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.02271, "memory": 4082, "data_time": 0.00032, "top1_acc": 0.85812, "top5_acc": 0.99688, "loss_cls": 0.66437, "loss": 0.66437, "time": 0.48008} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.02269, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.85688, "top5_acc": 0.9925, "loss_cls": 0.67103, "loss": 0.67103, "time": 0.47939} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.02268, "memory": 4082, "data_time": 0.00024, "top1_acc": 0.85312, "top5_acc": 0.99188, "loss_cls": 0.69628, "loss": 0.69628, "time": 0.48323} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.02267, "memory": 4082, "data_time": 0.00026, "top1_acc": 0.84688, "top5_acc": 0.99375, "loss_cls": 0.69988, "loss": 0.69988, "time": 0.48281} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.02266, "memory": 4082, "data_time": 0.00034, "top1_acc": 0.85812, "top5_acc": 0.99, "loss_cls": 0.68131, "loss": 0.68131, "time": 0.48322} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.02265, "memory": 4082, "data_time": 0.00047, "top1_acc": 0.84812, "top5_acc": 0.99188, "loss_cls": 0.72258, "loss": 0.72258, "time": 0.48165} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.02263, "memory": 4082, "data_time": 0.0007, "top1_acc": 0.84688, "top5_acc": 0.98938, "loss_cls": 0.7161, "loss": 0.7161, "time": 0.48309} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.02262, "memory": 4082, "data_time": 0.00023, "top1_acc": 0.8375, "top5_acc": 0.99062, "loss_cls": 0.72687, "loss": 0.72687, "time": 0.47975} +{"mode": "val", "epoch": 30, "iter": 533, "lr": 0.02261, "top1_acc": 0.75648, "top5_acc": 0.96831, "mean_class_accuracy": 0.66025} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.0226, "memory": 4083, "data_time": 0.18731, "top1_acc": 0.86625, "top5_acc": 0.99688, "loss_cls": 0.77928, "loss": 0.77928, "time": 0.45758} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.02259, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.855, "top5_acc": 0.99125, "loss_cls": 0.81101, "loss": 0.81101, "time": 0.48836} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.02258, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8525, "top5_acc": 0.995, "loss_cls": 0.79558, "loss": 0.79558, "time": 0.4927} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.02256, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.84438, "top5_acc": 0.99438, "loss_cls": 0.81344, "loss": 0.81344, "time": 0.49014} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.02255, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.865, "top5_acc": 0.99188, "loss_cls": 0.78589, "loss": 0.78589, "time": 0.48833} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.02254, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.825, "top5_acc": 0.98938, "loss_cls": 0.92287, "loss": 0.92287, "time": 0.49171} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.02253, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.85062, "top5_acc": 0.98875, "loss_cls": 0.82889, "loss": 0.82889, "time": 0.49223} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.02252, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.85312, "top5_acc": 0.99125, "loss_cls": 0.80681, "loss": 0.80681, "time": 0.49079} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0225, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84, "top5_acc": 0.99125, "loss_cls": 0.87761, "loss": 0.87761, "time": 0.49182} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.02249, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.845, "top5_acc": 0.99438, "loss_cls": 0.87502, "loss": 0.87502, "time": 0.49248} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.02248, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.85, "top5_acc": 0.98938, "loss_cls": 0.87738, "loss": 0.87738, "time": 0.49144} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.02247, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.83938, "top5_acc": 0.99062, "loss_cls": 0.8668, "loss": 0.8668, "time": 0.49105} +{"mode": "val", "epoch": 31, "iter": 533, "lr": 0.02246, "top1_acc": 0.76634, "top5_acc": 0.97254, "mean_class_accuracy": 0.69208} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.02244, "memory": 4083, "data_time": 0.18873, "top1_acc": 0.87062, "top5_acc": 0.99125, "loss_cls": 0.71917, "loss": 0.71917, "time": 0.51018} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.02243, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87625, "top5_acc": 0.9975, "loss_cls": 0.66749, "loss": 0.66749, "time": 0.43861} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.02242, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85, "top5_acc": 0.9925, "loss_cls": 0.75964, "loss": 0.75964, "time": 0.49104} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.02241, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.8775, "top5_acc": 0.99438, "loss_cls": 0.66003, "loss": 0.66003, "time": 0.49218} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.02239, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.845, "top5_acc": 0.99188, "loss_cls": 0.77133, "loss": 0.77133, "time": 0.49022} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.02238, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.835, "top5_acc": 0.99188, "loss_cls": 0.79099, "loss": 0.79099, "time": 0.48952} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.02237, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.845, "top5_acc": 0.99562, "loss_cls": 0.77243, "loss": 0.77243, "time": 0.49284} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.02236, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84188, "top5_acc": 0.98812, "loss_cls": 0.77583, "loss": 0.77583, "time": 0.49139} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.02234, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8525, "top5_acc": 0.99188, "loss_cls": 0.76879, "loss": 0.76879, "time": 0.49289} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.02233, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85875, "top5_acc": 0.99312, "loss_cls": 0.73312, "loss": 0.73312, "time": 0.49143} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.02232, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.85438, "top5_acc": 0.99688, "loss_cls": 0.74024, "loss": 0.74024, "time": 0.48969} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.02231, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84438, "top5_acc": 0.99188, "loss_cls": 0.78053, "loss": 0.78053, "time": 0.49295} +{"mode": "val", "epoch": 32, "iter": 533, "lr": 0.0223, "top1_acc": 0.80554, "top5_acc": 0.98298, "mean_class_accuracy": 0.74078} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.02228, "memory": 4083, "data_time": 0.19289, "top1_acc": 0.87188, "top5_acc": 0.995, "loss_cls": 0.68976, "loss": 0.68976, "time": 0.69131} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.02227, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86812, "top5_acc": 0.99562, "loss_cls": 0.66687, "loss": 0.66687, "time": 0.44753} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.02226, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87, "top5_acc": 0.99438, "loss_cls": 0.66343, "loss": 0.66343, "time": 0.49374} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.02225, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.84562, "top5_acc": 0.99125, "loss_cls": 0.7437, "loss": 0.7437, "time": 0.49049} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.02223, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85812, "top5_acc": 0.99312, "loss_cls": 0.70234, "loss": 0.70234, "time": 0.48969} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.02222, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85938, "top5_acc": 0.9925, "loss_cls": 0.74548, "loss": 0.74548, "time": 0.49127} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.02221, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.85938, "top5_acc": 0.99375, "loss_cls": 0.74198, "loss": 0.74198, "time": 0.49485} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.02219, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86375, "top5_acc": 0.99438, "loss_cls": 0.6951, "loss": 0.6951, "time": 0.49425} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.02218, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.84938, "top5_acc": 0.9975, "loss_cls": 0.71528, "loss": 0.71528, "time": 0.49575} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.02217, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8425, "top5_acc": 0.9925, "loss_cls": 0.76094, "loss": 0.76094, "time": 0.49187} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.02216, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.84562, "top5_acc": 0.99, "loss_cls": 0.79323, "loss": 0.79323, "time": 0.49126} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.02214, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.83688, "top5_acc": 0.9925, "loss_cls": 0.80276, "loss": 0.80276, "time": 0.48803} +{"mode": "val", "epoch": 33, "iter": 533, "lr": 0.02213, "top1_acc": 0.83148, "top5_acc": 0.98369, "mean_class_accuracy": 0.79502} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.02212, "memory": 4083, "data_time": 0.18829, "top1_acc": 0.8725, "top5_acc": 0.99375, "loss_cls": 0.66573, "loss": 0.66573, "time": 0.67582} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.02211, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.875, "top5_acc": 0.99562, "loss_cls": 0.64905, "loss": 0.64905, "time": 0.45129} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.02209, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87812, "top5_acc": 0.99188, "loss_cls": 0.65403, "loss": 0.65403, "time": 0.49432} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.02208, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86625, "top5_acc": 0.99312, "loss_cls": 0.69087, "loss": 0.69087, "time": 0.49285} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.02207, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.86625, "top5_acc": 0.9925, "loss_cls": 0.68893, "loss": 0.68893, "time": 0.49109} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.02205, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.85, "top5_acc": 0.99188, "loss_cls": 0.73223, "loss": 0.73223, "time": 0.49215} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.02204, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.85812, "top5_acc": 0.99625, "loss_cls": 0.71226, "loss": 0.71226, "time": 0.4917} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.02203, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8625, "top5_acc": 0.99312, "loss_cls": 0.70134, "loss": 0.70134, "time": 0.49409} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.02201, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.86438, "top5_acc": 0.99438, "loss_cls": 0.6899, "loss": 0.6899, "time": 0.49484} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.022, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.84875, "top5_acc": 0.99438, "loss_cls": 0.71114, "loss": 0.71114, "time": 0.48958} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.02199, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88, "top5_acc": 0.99438, "loss_cls": 0.66132, "loss": 0.66132, "time": 0.49259} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.02197, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86062, "top5_acc": 0.99312, "loss_cls": 0.69973, "loss": 0.69973, "time": 0.49236} +{"mode": "val", "epoch": 34, "iter": 533, "lr": 0.02196, "top1_acc": 0.83112, "top5_acc": 0.9885, "mean_class_accuracy": 0.77819} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.02195, "memory": 4083, "data_time": 0.19978, "top1_acc": 0.8825, "top5_acc": 0.995, "loss_cls": 0.6155, "loss": 0.6155, "time": 0.64584} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.02194, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86875, "top5_acc": 0.9975, "loss_cls": 0.67843, "loss": 0.67843, "time": 0.43887} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.02192, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87312, "top5_acc": 0.99375, "loss_cls": 0.64746, "loss": 0.64746, "time": 0.4954} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.02191, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8575, "top5_acc": 0.99438, "loss_cls": 0.71269, "loss": 0.71269, "time": 0.49224} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.0219, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8525, "top5_acc": 0.99188, "loss_cls": 0.74265, "loss": 0.74265, "time": 0.4964} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.02188, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.86375, "top5_acc": 0.99625, "loss_cls": 0.67165, "loss": 0.67165, "time": 0.49296} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.02187, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.84938, "top5_acc": 0.99438, "loss_cls": 0.7056, "loss": 0.7056, "time": 0.4934} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.02185, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87375, "top5_acc": 0.99312, "loss_cls": 0.65844, "loss": 0.65844, "time": 0.49316} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.02184, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85688, "top5_acc": 0.99125, "loss_cls": 0.704, "loss": 0.704, "time": 0.49067} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.02183, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.85875, "top5_acc": 0.99375, "loss_cls": 0.70495, "loss": 0.70495, "time": 0.49283} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.02181, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.85938, "top5_acc": 0.99562, "loss_cls": 0.7266, "loss": 0.7266, "time": 0.49397} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.0218, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.86, "top5_acc": 0.99375, "loss_cls": 0.72523, "loss": 0.72523, "time": 0.49338} +{"mode": "val", "epoch": 35, "iter": 533, "lr": 0.02179, "top1_acc": 0.81422, "top5_acc": 0.97841, "mean_class_accuracy": 0.75636} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.02178, "memory": 4083, "data_time": 0.19154, "top1_acc": 0.87125, "top5_acc": 0.99562, "loss_cls": 0.64309, "loss": 0.64309, "time": 0.66635} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.02176, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87812, "top5_acc": 0.9925, "loss_cls": 0.63481, "loss": 0.63481, "time": 0.44276} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.02175, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8675, "top5_acc": 0.99688, "loss_cls": 0.65087, "loss": 0.65087, "time": 0.48898} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.02173, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87688, "top5_acc": 0.9975, "loss_cls": 0.61232, "loss": 0.61232, "time": 0.48775} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.02172, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88375, "top5_acc": 0.99375, "loss_cls": 0.65588, "loss": 0.65588, "time": 0.49203} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.02171, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87688, "top5_acc": 0.99625, "loss_cls": 0.64281, "loss": 0.64281, "time": 0.49118} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.02169, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87188, "top5_acc": 0.99562, "loss_cls": 0.65407, "loss": 0.65407, "time": 0.48942} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.02168, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.86875, "top5_acc": 0.9925, "loss_cls": 0.6963, "loss": 0.6963, "time": 0.49346} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.02167, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.83688, "top5_acc": 0.99312, "loss_cls": 0.75969, "loss": 0.75969, "time": 0.48676} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.02165, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.86125, "top5_acc": 0.9925, "loss_cls": 0.71373, "loss": 0.71373, "time": 0.4905} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.02164, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86688, "top5_acc": 0.9925, "loss_cls": 0.70767, "loss": 0.70767, "time": 0.49358} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.02162, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.86562, "top5_acc": 0.99, "loss_cls": 0.69634, "loss": 0.69634, "time": 0.49131} +{"mode": "val", "epoch": 36, "iter": 533, "lr": 0.02161, "top1_acc": 0.8053, "top5_acc": 0.98111, "mean_class_accuracy": 0.76769} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.0216, "memory": 4083, "data_time": 0.19801, "top1_acc": 0.875, "top5_acc": 0.99375, "loss_cls": 0.6546, "loss": 0.6546, "time": 0.67473} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.02158, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88688, "top5_acc": 0.99688, "loss_cls": 0.61023, "loss": 0.61023, "time": 0.4386} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.02157, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87562, "top5_acc": 0.99438, "loss_cls": 0.6258, "loss": 0.6258, "time": 0.49192} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.02156, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.85, "top5_acc": 0.99375, "loss_cls": 0.7212, "loss": 0.7212, "time": 0.4915} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.02154, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.89188, "top5_acc": 0.995, "loss_cls": 0.61726, "loss": 0.61726, "time": 0.49191} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.02153, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.88062, "top5_acc": 0.99562, "loss_cls": 0.65609, "loss": 0.65609, "time": 0.49211} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.02151, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87188, "top5_acc": 0.99375, "loss_cls": 0.64221, "loss": 0.64221, "time": 0.48951} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0215, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.86562, "top5_acc": 0.99312, "loss_cls": 0.66212, "loss": 0.66212, "time": 0.4946} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.02149, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.87188, "top5_acc": 0.99562, "loss_cls": 0.6389, "loss": 0.6389, "time": 0.49332} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.02147, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.84875, "top5_acc": 0.99062, "loss_cls": 0.73853, "loss": 0.73853, "time": 0.49223} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.02146, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.85812, "top5_acc": 0.9925, "loss_cls": 0.71764, "loss": 0.71764, "time": 0.49147} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.02144, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.86562, "top5_acc": 0.99375, "loss_cls": 0.66978, "loss": 0.66978, "time": 0.4927} +{"mode": "val", "epoch": 37, "iter": 533, "lr": 0.02143, "top1_acc": 0.84004, "top5_acc": 0.98791, "mean_class_accuracy": 0.78842} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.02142, "memory": 4083, "data_time": 0.19281, "top1_acc": 0.8825, "top5_acc": 0.99688, "loss_cls": 0.62186, "loss": 0.62186, "time": 0.66749} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.0214, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.87562, "top5_acc": 0.99375, "loss_cls": 0.65901, "loss": 0.65901, "time": 0.43416} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.02139, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.865, "top5_acc": 0.995, "loss_cls": 0.7085, "loss": 0.7085, "time": 0.48869} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.02137, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.8625, "top5_acc": 0.99375, "loss_cls": 0.68227, "loss": 0.68227, "time": 0.49237} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.02136, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87938, "top5_acc": 0.99312, "loss_cls": 0.62122, "loss": 0.62122, "time": 0.49533} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.02134, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87062, "top5_acc": 0.99562, "loss_cls": 0.6445, "loss": 0.6445, "time": 0.49528} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.02133, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.885, "top5_acc": 0.99312, "loss_cls": 0.60513, "loss": 0.60513, "time": 0.49293} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.02132, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.84688, "top5_acc": 0.9925, "loss_cls": 0.71492, "loss": 0.71492, "time": 0.49208} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.0213, "memory": 4083, "data_time": 0.0006, "top1_acc": 0.85812, "top5_acc": 0.99312, "loss_cls": 0.69907, "loss": 0.69907, "time": 0.49338} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.02129, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.88562, "top5_acc": 0.99312, "loss_cls": 0.64878, "loss": 0.64878, "time": 0.48946} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.02127, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87688, "top5_acc": 0.99312, "loss_cls": 0.67796, "loss": 0.67796, "time": 0.49381} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.02126, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86125, "top5_acc": 0.99062, "loss_cls": 0.7154, "loss": 0.7154, "time": 0.49171} +{"mode": "val", "epoch": 38, "iter": 533, "lr": 0.02125, "top1_acc": 0.83054, "top5_acc": 0.98815, "mean_class_accuracy": 0.78722} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.02123, "memory": 4083, "data_time": 0.19519, "top1_acc": 0.8975, "top5_acc": 0.99438, "loss_cls": 0.5679, "loss": 0.5679, "time": 0.66929} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.02122, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.8775, "top5_acc": 0.99875, "loss_cls": 0.57287, "loss": 0.57287, "time": 0.45284} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.0212, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.8875, "top5_acc": 0.99562, "loss_cls": 0.60911, "loss": 0.60911, "time": 0.4914} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.02119, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87125, "top5_acc": 0.9925, "loss_cls": 0.67917, "loss": 0.67917, "time": 0.49235} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.02117, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.86062, "top5_acc": 0.99375, "loss_cls": 0.69431, "loss": 0.69431, "time": 0.49452} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.02116, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.85312, "top5_acc": 0.99375, "loss_cls": 0.72655, "loss": 0.72655, "time": 0.48934} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.02114, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86938, "top5_acc": 0.99625, "loss_cls": 0.67138, "loss": 0.67138, "time": 0.49097} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.02113, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.86875, "top5_acc": 0.99625, "loss_cls": 0.6885, "loss": 0.6885, "time": 0.49076} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.02111, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87625, "top5_acc": 0.98938, "loss_cls": 0.66251, "loss": 0.66251, "time": 0.4905} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.0211, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8875, "top5_acc": 0.99438, "loss_cls": 0.61245, "loss": 0.61245, "time": 0.48917} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.02108, "memory": 4083, "data_time": 0.00056, "top1_acc": 0.87062, "top5_acc": 0.99375, "loss_cls": 0.6592, "loss": 0.6592, "time": 0.49048} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.02107, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87562, "top5_acc": 0.9925, "loss_cls": 0.65628, "loss": 0.65628, "time": 0.49096} +{"mode": "val", "epoch": 39, "iter": 533, "lr": 0.02106, "top1_acc": 0.79967, "top5_acc": 0.98533, "mean_class_accuracy": 0.74432} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.02104, "memory": 4083, "data_time": 0.19282, "top1_acc": 0.86938, "top5_acc": 0.99562, "loss_cls": 0.68196, "loss": 0.68196, "time": 0.63819} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.02103, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86812, "top5_acc": 0.99312, "loss_cls": 0.67673, "loss": 0.67673, "time": 0.43244} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.02101, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.86812, "top5_acc": 0.99375, "loss_cls": 0.62765, "loss": 0.62765, "time": 0.49459} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.021, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.86625, "top5_acc": 0.995, "loss_cls": 0.66465, "loss": 0.66465, "time": 0.4956} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.02098, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.885, "top5_acc": 0.99438, "loss_cls": 0.59755, "loss": 0.59755, "time": 0.49295} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.02097, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8725, "top5_acc": 0.99312, "loss_cls": 0.66743, "loss": 0.66743, "time": 0.49173} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.02095, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88188, "top5_acc": 0.99625, "loss_cls": 0.61398, "loss": 0.61398, "time": 0.49081} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.02094, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87, "top5_acc": 0.99688, "loss_cls": 0.64442, "loss": 0.64442, "time": 0.49056} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.02092, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8575, "top5_acc": 0.995, "loss_cls": 0.70302, "loss": 0.70302, "time": 0.49186} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.02091, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88, "top5_acc": 0.9975, "loss_cls": 0.65673, "loss": 0.65673, "time": 0.49543} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.02089, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.885, "top5_acc": 0.99438, "loss_cls": 0.59716, "loss": 0.59716, "time": 0.49046} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.02088, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.87938, "top5_acc": 0.99312, "loss_cls": 0.66188, "loss": 0.66188, "time": 0.49087} +{"mode": "val", "epoch": 40, "iter": 533, "lr": 0.02086, "top1_acc": 0.81458, "top5_acc": 0.98392, "mean_class_accuracy": 0.75046} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.02085, "memory": 4083, "data_time": 0.19536, "top1_acc": 0.8825, "top5_acc": 0.99375, "loss_cls": 0.60969, "loss": 0.60969, "time": 0.69772} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.02083, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88125, "top5_acc": 0.99438, "loss_cls": 0.60342, "loss": 0.60342, "time": 0.43649} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.02082, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87688, "top5_acc": 0.995, "loss_cls": 0.62319, "loss": 0.62319, "time": 0.49123} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.0208, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86938, "top5_acc": 0.99125, "loss_cls": 0.69939, "loss": 0.69939, "time": 0.4897} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.02079, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88625, "top5_acc": 0.99438, "loss_cls": 0.61501, "loss": 0.61501, "time": 0.49357} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.02077, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88188, "top5_acc": 0.99812, "loss_cls": 0.60312, "loss": 0.60312, "time": 0.49505} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.02076, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88625, "top5_acc": 0.99625, "loss_cls": 0.61863, "loss": 0.61863, "time": 0.48913} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.02074, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87375, "top5_acc": 0.99375, "loss_cls": 0.63636, "loss": 0.63636, "time": 0.49068} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.02073, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87562, "top5_acc": 0.99312, "loss_cls": 0.61642, "loss": 0.61642, "time": 0.49622} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.02071, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.8875, "top5_acc": 0.99312, "loss_cls": 0.60319, "loss": 0.60319, "time": 0.4937} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.0207, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.8775, "top5_acc": 0.99188, "loss_cls": 0.63473, "loss": 0.63473, "time": 0.49182} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.02068, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8725, "top5_acc": 0.99312, "loss_cls": 0.65245, "loss": 0.65245, "time": 0.49102} +{"mode": "val", "epoch": 41, "iter": 533, "lr": 0.02067, "top1_acc": 0.83171, "top5_acc": 0.98474, "mean_class_accuracy": 0.76115} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.02065, "memory": 4083, "data_time": 0.20308, "top1_acc": 0.89, "top5_acc": 0.995, "loss_cls": 0.60991, "loss": 0.60991, "time": 0.70059} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.02064, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87875, "top5_acc": 0.99562, "loss_cls": 0.60402, "loss": 0.60402, "time": 0.43124} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.02062, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87938, "top5_acc": 0.99688, "loss_cls": 0.59048, "loss": 0.59048, "time": 0.49066} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.02061, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88625, "top5_acc": 0.99375, "loss_cls": 0.61405, "loss": 0.61405, "time": 0.49444} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.02059, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.88188, "top5_acc": 0.9975, "loss_cls": 0.59575, "loss": 0.59575, "time": 0.49593} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.02057, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88, "top5_acc": 0.99375, "loss_cls": 0.63807, "loss": 0.63807, "time": 0.49349} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.02056, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88875, "top5_acc": 0.99312, "loss_cls": 0.59057, "loss": 0.59057, "time": 0.49328} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.02054, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87938, "top5_acc": 0.99562, "loss_cls": 0.59861, "loss": 0.59861, "time": 0.4923} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.02053, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87312, "top5_acc": 0.995, "loss_cls": 0.64658, "loss": 0.64658, "time": 0.49442} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.02051, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.86312, "top5_acc": 0.99375, "loss_cls": 0.67571, "loss": 0.67571, "time": 0.48957} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.0205, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87062, "top5_acc": 0.99312, "loss_cls": 0.63733, "loss": 0.63733, "time": 0.49538} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.02048, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.85562, "top5_acc": 0.995, "loss_cls": 0.67629, "loss": 0.67629, "time": 0.49672} +{"mode": "val", "epoch": 42, "iter": 533, "lr": 0.02047, "top1_acc": 0.84333, "top5_acc": 0.98979, "mean_class_accuracy": 0.80236} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.02045, "memory": 4083, "data_time": 0.19058, "top1_acc": 0.88625, "top5_acc": 0.995, "loss_cls": 0.61021, "loss": 0.61021, "time": 0.67931} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.02044, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.88312, "top5_acc": 0.9975, "loss_cls": 0.62312, "loss": 0.62312, "time": 0.43391} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.02042, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88562, "top5_acc": 0.99375, "loss_cls": 0.62347, "loss": 0.62347, "time": 0.48991} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.0204, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.875, "top5_acc": 0.99438, "loss_cls": 0.61494, "loss": 0.61494, "time": 0.49126} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.02039, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88125, "top5_acc": 0.995, "loss_cls": 0.61772, "loss": 0.61772, "time": 0.49066} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.02037, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.8775, "top5_acc": 0.9925, "loss_cls": 0.62011, "loss": 0.62011, "time": 0.49136} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.02036, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88062, "top5_acc": 0.99562, "loss_cls": 0.62254, "loss": 0.62254, "time": 0.49126} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.02034, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87938, "top5_acc": 0.995, "loss_cls": 0.63822, "loss": 0.63822, "time": 0.49199} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.02033, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88, "top5_acc": 0.9975, "loss_cls": 0.61789, "loss": 0.61789, "time": 0.49035} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.02031, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87625, "top5_acc": 0.99375, "loss_cls": 0.64432, "loss": 0.64432, "time": 0.49019} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.02029, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87062, "top5_acc": 0.99062, "loss_cls": 0.68501, "loss": 0.68501, "time": 0.49404} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.02028, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.87188, "top5_acc": 0.99312, "loss_cls": 0.66532, "loss": 0.66532, "time": 0.49412} +{"mode": "val", "epoch": 43, "iter": 533, "lr": 0.02026, "top1_acc": 0.83335, "top5_acc": 0.98815, "mean_class_accuracy": 0.78842} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.02025, "memory": 4083, "data_time": 0.19656, "top1_acc": 0.89062, "top5_acc": 0.99688, "loss_cls": 0.56843, "loss": 0.56843, "time": 0.67753} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.02023, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89625, "top5_acc": 0.99938, "loss_cls": 0.55016, "loss": 0.55016, "time": 0.4418} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.02022, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89062, "top5_acc": 0.99438, "loss_cls": 0.56159, "loss": 0.56159, "time": 0.49167} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.0202, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.87625, "top5_acc": 0.99188, "loss_cls": 0.64016, "loss": 0.64016, "time": 0.49449} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.02018, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.8775, "top5_acc": 0.99375, "loss_cls": 0.63732, "loss": 0.63732, "time": 0.49659} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.02017, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.87562, "top5_acc": 0.99375, "loss_cls": 0.63247, "loss": 0.63247, "time": 0.49319} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.02015, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.87875, "top5_acc": 0.995, "loss_cls": 0.6335, "loss": 0.6335, "time": 0.48971} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.02014, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.86938, "top5_acc": 0.99188, "loss_cls": 0.69248, "loss": 0.69248, "time": 0.49178} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.02012, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.88062, "top5_acc": 0.99625, "loss_cls": 0.62842, "loss": 0.62842, "time": 0.48996} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.0201, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89812, "top5_acc": 0.99688, "loss_cls": 0.53986, "loss": 0.53986, "time": 0.4894} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.02009, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.87562, "top5_acc": 0.9975, "loss_cls": 0.64104, "loss": 0.64104, "time": 0.48975} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.02007, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88938, "top5_acc": 0.99688, "loss_cls": 0.58725, "loss": 0.58725, "time": 0.49311} +{"mode": "val", "epoch": 44, "iter": 533, "lr": 0.02006, "top1_acc": 0.84039, "top5_acc": 0.98662, "mean_class_accuracy": 0.79347} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.02004, "memory": 4083, "data_time": 0.20108, "top1_acc": 0.89562, "top5_acc": 0.99688, "loss_cls": 0.54886, "loss": 0.54886, "time": 0.66649} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.02003, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88812, "top5_acc": 0.99812, "loss_cls": 0.5865, "loss": 0.5865, "time": 0.4346} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.02001, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90625, "top5_acc": 0.995, "loss_cls": 0.56521, "loss": 0.56521, "time": 0.49372} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.01999, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.89875, "top5_acc": 0.99562, "loss_cls": 0.5436, "loss": 0.5436, "time": 0.49447} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.01998, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.8825, "top5_acc": 0.99438, "loss_cls": 0.60287, "loss": 0.60287, "time": 0.49418} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.01996, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.88125, "top5_acc": 0.99438, "loss_cls": 0.61809, "loss": 0.61809, "time": 0.49252} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.01994, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89188, "top5_acc": 0.99688, "loss_cls": 0.58232, "loss": 0.58232, "time": 0.48888} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.01993, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88875, "top5_acc": 0.99188, "loss_cls": 0.60537, "loss": 0.60537, "time": 0.49411} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.01991, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87875, "top5_acc": 0.995, "loss_cls": 0.61716, "loss": 0.61716, "time": 0.49257} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.01989, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87562, "top5_acc": 0.99438, "loss_cls": 0.6371, "loss": 0.6371, "time": 0.48678} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.01988, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88125, "top5_acc": 0.99375, "loss_cls": 0.62963, "loss": 0.62963, "time": 0.49398} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.01986, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88062, "top5_acc": 0.995, "loss_cls": 0.5836, "loss": 0.5836, "time": 0.49202} +{"mode": "val", "epoch": 45, "iter": 533, "lr": 0.01985, "top1_acc": 0.82056, "top5_acc": 0.98897, "mean_class_accuracy": 0.76866} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.01983, "memory": 4083, "data_time": 0.19207, "top1_acc": 0.89438, "top5_acc": 0.99438, "loss_cls": 0.59295, "loss": 0.59295, "time": 0.67498} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.01981, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88125, "top5_acc": 0.99625, "loss_cls": 0.58547, "loss": 0.58547, "time": 0.42668} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.0198, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88438, "top5_acc": 0.99438, "loss_cls": 0.61422, "loss": 0.61422, "time": 0.49189} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.01978, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88062, "top5_acc": 0.99375, "loss_cls": 0.5885, "loss": 0.5885, "time": 0.49105} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.01976, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.9, "top5_acc": 0.9975, "loss_cls": 0.52219, "loss": 0.52219, "time": 0.49546} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.01975, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89875, "top5_acc": 0.99438, "loss_cls": 0.554, "loss": 0.554, "time": 0.49142} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.01973, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88688, "top5_acc": 0.99562, "loss_cls": 0.58433, "loss": 0.58433, "time": 0.49341} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.01971, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.8825, "top5_acc": 0.99375, "loss_cls": 0.60637, "loss": 0.60637, "time": 0.49175} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.0197, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.88, "top5_acc": 0.99438, "loss_cls": 0.60771, "loss": 0.60771, "time": 0.49233} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.01968, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.88, "top5_acc": 0.9975, "loss_cls": 0.59428, "loss": 0.59428, "time": 0.49102} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.01966, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87125, "top5_acc": 0.99562, "loss_cls": 0.63232, "loss": 0.63232, "time": 0.49186} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.01965, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88125, "top5_acc": 0.99062, "loss_cls": 0.62167, "loss": 0.62167, "time": 0.49194} +{"mode": "val", "epoch": 46, "iter": 533, "lr": 0.01963, "top1_acc": 0.84556, "top5_acc": 0.98826, "mean_class_accuracy": 0.79741} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.01962, "memory": 4083, "data_time": 0.19839, "top1_acc": 0.90938, "top5_acc": 0.99562, "loss_cls": 0.51583, "loss": 0.51583, "time": 0.71305} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.0196, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89688, "top5_acc": 0.99688, "loss_cls": 0.52625, "loss": 0.52625, "time": 0.42979} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.01958, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.8925, "top5_acc": 0.99562, "loss_cls": 0.53727, "loss": 0.53727, "time": 0.48968} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.01957, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.88188, "top5_acc": 0.995, "loss_cls": 0.62272, "loss": 0.62272, "time": 0.48931} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.01955, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.90625, "top5_acc": 0.99875, "loss_cls": 0.51157, "loss": 0.51157, "time": 0.4932} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.01953, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.89375, "top5_acc": 0.9975, "loss_cls": 0.56328, "loss": 0.56328, "time": 0.49025} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.01952, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89375, "top5_acc": 0.99312, "loss_cls": 0.58437, "loss": 0.58437, "time": 0.48959} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.0195, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88062, "top5_acc": 0.99312, "loss_cls": 0.62399, "loss": 0.62399, "time": 0.49149} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.01948, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88938, "top5_acc": 0.99625, "loss_cls": 0.58177, "loss": 0.58177, "time": 0.49125} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.01947, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89125, "top5_acc": 0.99688, "loss_cls": 0.61322, "loss": 0.61322, "time": 0.49224} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.01945, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.86625, "top5_acc": 0.99188, "loss_cls": 0.67455, "loss": 0.67455, "time": 0.49492} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.01943, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88188, "top5_acc": 0.99688, "loss_cls": 0.60216, "loss": 0.60216, "time": 0.49} +{"mode": "val", "epoch": 47, "iter": 533, "lr": 0.01942, "top1_acc": 0.83946, "top5_acc": 0.98122, "mean_class_accuracy": 0.79515} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.0194, "memory": 4083, "data_time": 0.19339, "top1_acc": 0.90875, "top5_acc": 0.99625, "loss_cls": 0.52913, "loss": 0.52913, "time": 0.67895} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.01938, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90812, "top5_acc": 0.99688, "loss_cls": 0.50487, "loss": 0.50487, "time": 0.43672} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.01937, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.8975, "top5_acc": 0.9975, "loss_cls": 0.53181, "loss": 0.53181, "time": 0.49087} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.01935, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88438, "top5_acc": 0.995, "loss_cls": 0.5809, "loss": 0.5809, "time": 0.49278} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.01933, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90312, "top5_acc": 0.9975, "loss_cls": 0.53083, "loss": 0.53083, "time": 0.49251} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.01932, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.89875, "top5_acc": 0.995, "loss_cls": 0.55855, "loss": 0.55855, "time": 0.4939} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.0193, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89188, "top5_acc": 0.995, "loss_cls": 0.61278, "loss": 0.61278, "time": 0.48843} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.01928, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88062, "top5_acc": 0.99312, "loss_cls": 0.61516, "loss": 0.61516, "time": 0.49066} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.01926, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88312, "top5_acc": 0.99188, "loss_cls": 0.64011, "loss": 0.64011, "time": 0.49415} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.01925, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.88188, "top5_acc": 0.99688, "loss_cls": 0.58634, "loss": 0.58634, "time": 0.49237} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.01923, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.88375, "top5_acc": 0.99375, "loss_cls": 0.61627, "loss": 0.61627, "time": 0.49238} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.01921, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.88125, "top5_acc": 0.99688, "loss_cls": 0.58816, "loss": 0.58816, "time": 0.49087} +{"mode": "val", "epoch": 48, "iter": 533, "lr": 0.0192, "top1_acc": 0.8411, "top5_acc": 0.98991, "mean_class_accuracy": 0.78937} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.01918, "memory": 4083, "data_time": 0.19417, "top1_acc": 0.90438, "top5_acc": 0.9975, "loss_cls": 0.51602, "loss": 0.51602, "time": 0.6689} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.01916, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90312, "top5_acc": 0.995, "loss_cls": 0.53147, "loss": 0.53147, "time": 0.4372} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.01915, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89062, "top5_acc": 0.99562, "loss_cls": 0.58494, "loss": 0.58494, "time": 0.4958} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.01913, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.90188, "top5_acc": 0.9975, "loss_cls": 0.52744, "loss": 0.52744, "time": 0.49044} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.01911, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90562, "top5_acc": 0.9975, "loss_cls": 0.51591, "loss": 0.51591, "time": 0.49112} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.01909, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87875, "top5_acc": 0.99562, "loss_cls": 0.61012, "loss": 0.61012, "time": 0.49113} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.01908, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88625, "top5_acc": 0.99375, "loss_cls": 0.58459, "loss": 0.58459, "time": 0.49027} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.01906, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89812, "top5_acc": 0.995, "loss_cls": 0.55205, "loss": 0.55205, "time": 0.48973} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.01904, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88625, "top5_acc": 0.995, "loss_cls": 0.58559, "loss": 0.58559, "time": 0.49428} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.01902, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.88625, "top5_acc": 0.9925, "loss_cls": 0.60057, "loss": 0.60057, "time": 0.49344} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.01901, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.88438, "top5_acc": 0.99438, "loss_cls": 0.61384, "loss": 0.61384, "time": 0.49119} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.01899, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.87562, "top5_acc": 0.99375, "loss_cls": 0.6504, "loss": 0.6504, "time": 0.49111} +{"mode": "val", "epoch": 49, "iter": 533, "lr": 0.01898, "top1_acc": 0.83981, "top5_acc": 0.98885, "mean_class_accuracy": 0.76827} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.01896, "memory": 4083, "data_time": 0.19773, "top1_acc": 0.89812, "top5_acc": 0.99562, "loss_cls": 0.54215, "loss": 0.54215, "time": 0.68831} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.01894, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90625, "top5_acc": 0.99688, "loss_cls": 0.52025, "loss": 0.52025, "time": 0.43837} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.01892, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.89188, "top5_acc": 0.99375, "loss_cls": 0.55851, "loss": 0.55851, "time": 0.49057} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.01891, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89562, "top5_acc": 0.99875, "loss_cls": 0.55227, "loss": 0.55227, "time": 0.49069} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.01889, "memory": 4083, "data_time": 0.00061, "top1_acc": 0.86812, "top5_acc": 0.995, "loss_cls": 0.64081, "loss": 0.64081, "time": 0.4862} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.01887, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.89125, "top5_acc": 0.99812, "loss_cls": 0.5642, "loss": 0.5642, "time": 0.48681} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.01885, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89438, "top5_acc": 0.99812, "loss_cls": 0.53188, "loss": 0.53188, "time": 0.49209} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.01884, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.895, "top5_acc": 0.99625, "loss_cls": 0.56724, "loss": 0.56724, "time": 0.49139} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.01882, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.86875, "top5_acc": 0.99875, "loss_cls": 0.62272, "loss": 0.62272, "time": 0.4935} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.0188, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89562, "top5_acc": 0.995, "loss_cls": 0.54429, "loss": 0.54429, "time": 0.49149} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.01878, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.87438, "top5_acc": 0.9925, "loss_cls": 0.60476, "loss": 0.60476, "time": 0.49121} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.01876, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.885, "top5_acc": 0.99625, "loss_cls": 0.58466, "loss": 0.58466, "time": 0.49404} +{"mode": "val", "epoch": 50, "iter": 533, "lr": 0.01875, "top1_acc": 0.83899, "top5_acc": 0.98791, "mean_class_accuracy": 0.775} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.01873, "memory": 4083, "data_time": 0.18616, "top1_acc": 0.91, "top5_acc": 0.9975, "loss_cls": 0.46519, "loss": 0.46519, "time": 0.69613} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.01871, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91812, "top5_acc": 0.9975, "loss_cls": 0.45947, "loss": 0.45947, "time": 0.44407} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.0187, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89875, "top5_acc": 0.99875, "loss_cls": 0.51037, "loss": 0.51037, "time": 0.49194} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.01868, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.88625, "top5_acc": 0.99562, "loss_cls": 0.58913, "loss": 0.58913, "time": 0.49268} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.01866, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.87438, "top5_acc": 0.99125, "loss_cls": 0.66807, "loss": 0.66807, "time": 0.49438} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.01864, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90875, "top5_acc": 0.99438, "loss_cls": 0.52833, "loss": 0.52833, "time": 0.49391} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.01863, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90312, "top5_acc": 0.99625, "loss_cls": 0.53488, "loss": 0.53488, "time": 0.4905} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.01861, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.8825, "top5_acc": 0.99312, "loss_cls": 0.58444, "loss": 0.58444, "time": 0.49178} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.01859, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.89688, "top5_acc": 0.99625, "loss_cls": 0.54807, "loss": 0.54807, "time": 0.49198} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.01857, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.89062, "top5_acc": 0.99625, "loss_cls": 0.55561, "loss": 0.55561, "time": 0.49166} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.01855, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89625, "top5_acc": 0.99625, "loss_cls": 0.55768, "loss": 0.55768, "time": 0.49103} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.01854, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.87688, "top5_acc": 0.9975, "loss_cls": 0.56632, "loss": 0.56632, "time": 0.4925} +{"mode": "val", "epoch": 51, "iter": 533, "lr": 0.01852, "top1_acc": 0.858, "top5_acc": 0.98932, "mean_class_accuracy": 0.80865} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.0185, "memory": 4083, "data_time": 0.19248, "top1_acc": 0.91875, "top5_acc": 0.99688, "loss_cls": 0.47867, "loss": 0.47867, "time": 0.66389} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.01849, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.46253, "loss": 0.46253, "time": 0.43451} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.01847, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.91125, "top5_acc": 0.99562, "loss_cls": 0.48236, "loss": 0.48236, "time": 0.48981} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.01845, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91938, "top5_acc": 0.99562, "loss_cls": 0.47516, "loss": 0.47516, "time": 0.4928} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.01843, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.89312, "top5_acc": 0.99312, "loss_cls": 0.54731, "loss": 0.54731, "time": 0.49574} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.01841, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.895, "top5_acc": 0.995, "loss_cls": 0.56043, "loss": 0.56043, "time": 0.49237} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.0184, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91062, "top5_acc": 0.9975, "loss_cls": 0.49943, "loss": 0.49943, "time": 0.49322} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.01838, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90188, "top5_acc": 0.99438, "loss_cls": 0.52126, "loss": 0.52126, "time": 0.49055} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.01836, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89625, "top5_acc": 0.9975, "loss_cls": 0.57762, "loss": 0.57762, "time": 0.49048} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.01834, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88375, "top5_acc": 0.99562, "loss_cls": 0.55905, "loss": 0.55905, "time": 0.49532} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.01832, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.88, "top5_acc": 0.99188, "loss_cls": 0.61335, "loss": 0.61335, "time": 0.49292} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.01831, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89938, "top5_acc": 0.99562, "loss_cls": 0.55986, "loss": 0.55986, "time": 0.49183} +{"mode": "val", "epoch": 52, "iter": 533, "lr": 0.01829, "top1_acc": 0.84333, "top5_acc": 0.98463, "mean_class_accuracy": 0.79224} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.01827, "memory": 4083, "data_time": 0.19519, "top1_acc": 0.92, "top5_acc": 0.99688, "loss_cls": 0.46268, "loss": 0.46268, "time": 0.69517} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.01826, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92062, "top5_acc": 0.99625, "loss_cls": 0.45753, "loss": 0.45753, "time": 0.43534} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.01824, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90562, "top5_acc": 0.99812, "loss_cls": 0.50167, "loss": 0.50167, "time": 0.492} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.01822, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89438, "top5_acc": 0.99812, "loss_cls": 0.55952, "loss": 0.55952, "time": 0.49598} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.0182, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89938, "top5_acc": 0.99438, "loss_cls": 0.54567, "loss": 0.54567, "time": 0.49141} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.01818, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.88625, "top5_acc": 0.99312, "loss_cls": 0.60457, "loss": 0.60457, "time": 0.49322} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.01816, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.8975, "top5_acc": 0.99688, "loss_cls": 0.54709, "loss": 0.54709, "time": 0.49328} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.01815, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90125, "top5_acc": 0.99562, "loss_cls": 0.56034, "loss": 0.56034, "time": 0.49544} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.01813, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.89625, "top5_acc": 0.995, "loss_cls": 0.54502, "loss": 0.54502, "time": 0.49391} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.01811, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87938, "top5_acc": 0.99312, "loss_cls": 0.59258, "loss": 0.59258, "time": 0.49144} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.01809, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.89375, "top5_acc": 0.99375, "loss_cls": 0.56095, "loss": 0.56095, "time": 0.49206} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.01807, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.87438, "top5_acc": 0.99562, "loss_cls": 0.63032, "loss": 0.63032, "time": 0.49263} +{"mode": "val", "epoch": 53, "iter": 533, "lr": 0.01806, "top1_acc": 0.8499, "top5_acc": 0.98697, "mean_class_accuracy": 0.8036} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.01804, "memory": 4083, "data_time": 0.19553, "top1_acc": 0.89438, "top5_acc": 0.99625, "loss_cls": 0.51955, "loss": 0.51955, "time": 0.69189} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.01802, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89375, "top5_acc": 0.99625, "loss_cls": 0.53095, "loss": 0.53095, "time": 0.45097} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.018, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.905, "top5_acc": 0.995, "loss_cls": 0.52098, "loss": 0.52098, "time": 0.49376} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.01798, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91375, "top5_acc": 0.99812, "loss_cls": 0.47601, "loss": 0.47601, "time": 0.4915} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.01797, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91625, "top5_acc": 0.99625, "loss_cls": 0.46786, "loss": 0.46786, "time": 0.48819} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.01795, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90188, "top5_acc": 0.995, "loss_cls": 0.51262, "loss": 0.51262, "time": 0.49127} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.01793, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89062, "top5_acc": 0.995, "loss_cls": 0.55258, "loss": 0.55258, "time": 0.49086} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.01791, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89312, "top5_acc": 0.9975, "loss_cls": 0.55406, "loss": 0.55406, "time": 0.49485} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.01789, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.8925, "top5_acc": 0.9975, "loss_cls": 0.54544, "loss": 0.54544, "time": 0.49255} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.01787, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.87812, "top5_acc": 0.9925, "loss_cls": 0.61377, "loss": 0.61377, "time": 0.49124} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.01786, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89812, "top5_acc": 0.99562, "loss_cls": 0.5341, "loss": 0.5341, "time": 0.49232} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.01784, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88625, "top5_acc": 0.99812, "loss_cls": 0.58212, "loss": 0.58212, "time": 0.49183} +{"mode": "val", "epoch": 54, "iter": 533, "lr": 0.01782, "top1_acc": 0.84603, "top5_acc": 0.98826, "mean_class_accuracy": 0.78674} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.0178, "memory": 4083, "data_time": 0.19577, "top1_acc": 0.90062, "top5_acc": 0.99812, "loss_cls": 0.50828, "loss": 0.50828, "time": 0.62791} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.01779, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.9125, "top5_acc": 0.99625, "loss_cls": 0.46363, "loss": 0.46363, "time": 0.44111} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.01777, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.54651, "loss": 0.54651, "time": 0.49085} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.01775, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.915, "top5_acc": 0.99812, "loss_cls": 0.48011, "loss": 0.48011, "time": 0.49051} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.01773, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.90875, "top5_acc": 0.99625, "loss_cls": 0.48556, "loss": 0.48556, "time": 0.49132} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.01771, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9125, "top5_acc": 0.99938, "loss_cls": 0.48519, "loss": 0.48519, "time": 0.49088} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.01769, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.89562, "top5_acc": 0.99812, "loss_cls": 0.51929, "loss": 0.51929, "time": 0.4907} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.01767, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.9, "top5_acc": 0.99438, "loss_cls": 0.52556, "loss": 0.52556, "time": 0.49176} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.01766, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.89812, "top5_acc": 0.99375, "loss_cls": 0.5545, "loss": 0.5545, "time": 0.49075} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.01764, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9075, "top5_acc": 0.99625, "loss_cls": 0.50547, "loss": 0.50547, "time": 0.49092} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.01762, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89938, "top5_acc": 0.99438, "loss_cls": 0.5599, "loss": 0.5599, "time": 0.49233} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.0176, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89875, "top5_acc": 0.99688, "loss_cls": 0.51511, "loss": 0.51511, "time": 0.49283} +{"mode": "val", "epoch": 55, "iter": 533, "lr": 0.01758, "top1_acc": 0.81868, "top5_acc": 0.98416, "mean_class_accuracy": 0.78345} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.01757, "memory": 4083, "data_time": 0.19497, "top1_acc": 0.90125, "top5_acc": 0.99688, "loss_cls": 0.55297, "loss": 0.55297, "time": 0.66273} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.01755, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90188, "top5_acc": 0.99625, "loss_cls": 0.52192, "loss": 0.52192, "time": 0.44223} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.01753, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.91562, "top5_acc": 0.9975, "loss_cls": 0.4493, "loss": 0.4493, "time": 0.49416} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.01751, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91688, "top5_acc": 0.99625, "loss_cls": 0.47578, "loss": 0.47578, "time": 0.49095} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.01749, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9075, "top5_acc": 0.99625, "loss_cls": 0.50591, "loss": 0.50591, "time": 0.49149} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.01747, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89188, "top5_acc": 0.99625, "loss_cls": 0.53891, "loss": 0.53891, "time": 0.48701} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.01745, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.91562, "top5_acc": 0.99688, "loss_cls": 0.46971, "loss": 0.46971, "time": 0.49283} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.01743, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90125, "top5_acc": 0.99625, "loss_cls": 0.49675, "loss": 0.49675, "time": 0.48764} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.01742, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90188, "top5_acc": 0.99375, "loss_cls": 0.53518, "loss": 0.53518, "time": 0.49303} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.0174, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90062, "top5_acc": 0.99688, "loss_cls": 0.54985, "loss": 0.54985, "time": 0.49111} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.01738, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.89062, "top5_acc": 0.99562, "loss_cls": 0.55437, "loss": 0.55437, "time": 0.48988} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.01736, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.88875, "top5_acc": 0.995, "loss_cls": 0.58543, "loss": 0.58543, "time": 0.49234} +{"mode": "val", "epoch": 56, "iter": 533, "lr": 0.01734, "top1_acc": 0.85976, "top5_acc": 0.9892, "mean_class_accuracy": 0.81859} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.01733, "memory": 4083, "data_time": 0.19318, "top1_acc": 0.91438, "top5_acc": 0.99625, "loss_cls": 0.47142, "loss": 0.47142, "time": 0.65309} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.01731, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92562, "top5_acc": 0.995, "loss_cls": 0.43673, "loss": 0.43673, "time": 0.45128} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.01729, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91625, "top5_acc": 0.9975, "loss_cls": 0.43486, "loss": 0.43486, "time": 0.49267} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.01727, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9025, "top5_acc": 0.995, "loss_cls": 0.51209, "loss": 0.51209, "time": 0.49368} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.01725, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90188, "top5_acc": 0.99875, "loss_cls": 0.52497, "loss": 0.52497, "time": 0.49311} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.01723, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89812, "top5_acc": 0.99438, "loss_cls": 0.55424, "loss": 0.55424, "time": 0.49172} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.01721, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.8875, "top5_acc": 0.995, "loss_cls": 0.59184, "loss": 0.59184, "time": 0.4898} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.01719, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.895, "top5_acc": 0.99562, "loss_cls": 0.57572, "loss": 0.57572, "time": 0.49176} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.01717, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91062, "top5_acc": 0.99812, "loss_cls": 0.48135, "loss": 0.48135, "time": 0.49114} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.01716, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90438, "top5_acc": 0.99812, "loss_cls": 0.5257, "loss": 0.5257, "time": 0.49263} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.01714, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89375, "top5_acc": 0.99625, "loss_cls": 0.53392, "loss": 0.53392, "time": 0.49048} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.01712, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90375, "top5_acc": 0.99688, "loss_cls": 0.49161, "loss": 0.49161, "time": 0.49012} +{"mode": "val", "epoch": 57, "iter": 533, "lr": 0.0171, "top1_acc": 0.83863, "top5_acc": 0.98639, "mean_class_accuracy": 0.77908} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.01708, "memory": 4083, "data_time": 0.18829, "top1_acc": 0.905, "top5_acc": 0.99875, "loss_cls": 0.47483, "loss": 0.47483, "time": 0.62283} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.01706, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92375, "top5_acc": 0.99938, "loss_cls": 0.3964, "loss": 0.3964, "time": 0.44413} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.01704, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.91625, "top5_acc": 0.99812, "loss_cls": 0.44774, "loss": 0.44774, "time": 0.49175} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.01703, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.915, "top5_acc": 0.99812, "loss_cls": 0.43984, "loss": 0.43984, "time": 0.49011} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.01701, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9, "top5_acc": 0.99375, "loss_cls": 0.52889, "loss": 0.52889, "time": 0.49199} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.01699, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90125, "top5_acc": 0.995, "loss_cls": 0.56453, "loss": 0.56453, "time": 0.4889} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.01697, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.89625, "top5_acc": 0.9975, "loss_cls": 0.5429, "loss": 0.5429, "time": 0.49112} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.01695, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89188, "top5_acc": 0.99375, "loss_cls": 0.54113, "loss": 0.54113, "time": 0.49419} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.01693, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.88938, "top5_acc": 0.99688, "loss_cls": 0.55495, "loss": 0.55495, "time": 0.49162} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.01691, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.89312, "top5_acc": 0.99625, "loss_cls": 0.55848, "loss": 0.55848, "time": 0.49391} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.01689, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9025, "top5_acc": 0.995, "loss_cls": 0.53535, "loss": 0.53535, "time": 0.49112} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.01687, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.88938, "top5_acc": 0.99562, "loss_cls": 0.56038, "loss": 0.56038, "time": 0.49117} +{"mode": "val", "epoch": 58, "iter": 533, "lr": 0.01686, "top1_acc": 0.85741, "top5_acc": 0.9885, "mean_class_accuracy": 0.82231} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.01684, "memory": 4083, "data_time": 0.19303, "top1_acc": 0.91438, "top5_acc": 0.99562, "loss_cls": 0.46555, "loss": 0.46555, "time": 0.65011} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.01682, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92, "top5_acc": 0.9975, "loss_cls": 0.42228, "loss": 0.42228, "time": 0.43072} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.0168, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91, "top5_acc": 0.99688, "loss_cls": 0.48199, "loss": 0.48199, "time": 0.49196} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.01678, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.89625, "top5_acc": 0.99875, "loss_cls": 0.52689, "loss": 0.52689, "time": 0.49057} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.01676, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.90938, "top5_acc": 0.99625, "loss_cls": 0.48323, "loss": 0.48323, "time": 0.49228} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.01674, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9, "top5_acc": 0.99625, "loss_cls": 0.53012, "loss": 0.53012, "time": 0.49129} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.01672, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9075, "top5_acc": 0.99688, "loss_cls": 0.50513, "loss": 0.50513, "time": 0.48877} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.0167, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91312, "top5_acc": 0.99688, "loss_cls": 0.52116, "loss": 0.52116, "time": 0.49454} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.01668, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.90062, "top5_acc": 0.99812, "loss_cls": 0.48113, "loss": 0.48113, "time": 0.49165} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.01667, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89875, "top5_acc": 0.9975, "loss_cls": 0.51622, "loss": 0.51622, "time": 0.49269} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.01665, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.89812, "top5_acc": 0.9975, "loss_cls": 0.52232, "loss": 0.52232, "time": 0.4931} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.01663, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89, "top5_acc": 0.99625, "loss_cls": 0.55803, "loss": 0.55803, "time": 0.49283} +{"mode": "val", "epoch": 59, "iter": 533, "lr": 0.01661, "top1_acc": 0.8533, "top5_acc": 0.98756, "mean_class_accuracy": 0.80502} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.01659, "memory": 4083, "data_time": 0.18896, "top1_acc": 0.91062, "top5_acc": 0.99625, "loss_cls": 0.45398, "loss": 0.45398, "time": 0.68104} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.01657, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9225, "top5_acc": 0.9975, "loss_cls": 0.42907, "loss": 0.42907, "time": 0.44491} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.01655, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92125, "top5_acc": 0.99562, "loss_cls": 0.45722, "loss": 0.45722, "time": 0.49229} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.01653, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.90438, "top5_acc": 0.99688, "loss_cls": 0.51208, "loss": 0.51208, "time": 0.48887} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.01651, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9125, "top5_acc": 0.99625, "loss_cls": 0.48442, "loss": 0.48442, "time": 0.49007} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.0165, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.90375, "top5_acc": 0.99625, "loss_cls": 0.49459, "loss": 0.49459, "time": 0.48844} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.01648, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.89875, "top5_acc": 0.9975, "loss_cls": 0.51319, "loss": 0.51319, "time": 0.48746} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.01646, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.90562, "top5_acc": 0.99688, "loss_cls": 0.4765, "loss": 0.4765, "time": 0.48905} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.01644, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89875, "top5_acc": 0.99812, "loss_cls": 0.5089, "loss": 0.5089, "time": 0.48833} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.01642, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9, "top5_acc": 0.99625, "loss_cls": 0.50433, "loss": 0.50433, "time": 0.48995} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.0164, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.89062, "top5_acc": 0.99625, "loss_cls": 0.59452, "loss": 0.59452, "time": 0.49006} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.01638, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.90875, "top5_acc": 0.9975, "loss_cls": 0.48314, "loss": 0.48314, "time": 0.49326} +{"mode": "val", "epoch": 60, "iter": 533, "lr": 0.01636, "top1_acc": 0.84732, "top5_acc": 0.98815, "mean_class_accuracy": 0.80503} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.01634, "memory": 4083, "data_time": 0.19293, "top1_acc": 0.9175, "top5_acc": 0.99562, "loss_cls": 0.46597, "loss": 0.46597, "time": 0.6605} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.01632, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93688, "top5_acc": 0.9975, "loss_cls": 0.40712, "loss": 0.40712, "time": 0.43872} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.0163, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.91688, "top5_acc": 0.99938, "loss_cls": 0.42186, "loss": 0.42186, "time": 0.49199} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.01629, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92812, "top5_acc": 0.99625, "loss_cls": 0.43773, "loss": 0.43773, "time": 0.49292} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.01627, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90688, "top5_acc": 0.9975, "loss_cls": 0.45242, "loss": 0.45242, "time": 0.49229} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.01625, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90562, "top5_acc": 0.99688, "loss_cls": 0.51427, "loss": 0.51427, "time": 0.49337} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.01623, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90938, "top5_acc": 0.99625, "loss_cls": 0.5091, "loss": 0.5091, "time": 0.49401} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.01621, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91375, "top5_acc": 0.99938, "loss_cls": 0.45344, "loss": 0.45344, "time": 0.49196} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.01619, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89375, "top5_acc": 0.995, "loss_cls": 0.54295, "loss": 0.54295, "time": 0.49346} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.01617, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.90688, "top5_acc": 0.99688, "loss_cls": 0.48763, "loss": 0.48763, "time": 0.49278} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.01615, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9125, "top5_acc": 0.9975, "loss_cls": 0.48696, "loss": 0.48696, "time": 0.49306} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.01613, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9125, "top5_acc": 0.99875, "loss_cls": 0.46843, "loss": 0.46843, "time": 0.49511} +{"mode": "val", "epoch": 61, "iter": 533, "lr": 0.01611, "top1_acc": 0.86703, "top5_acc": 0.9885, "mean_class_accuracy": 0.82685} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.01609, "memory": 4083, "data_time": 0.19159, "top1_acc": 0.9175, "top5_acc": 0.99938, "loss_cls": 0.45269, "loss": 0.45269, "time": 0.65271} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.01607, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91, "top5_acc": 0.99688, "loss_cls": 0.4557, "loss": 0.4557, "time": 0.43269} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.01605, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93562, "top5_acc": 1.0, "loss_cls": 0.39722, "loss": 0.39722, "time": 0.49333} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.01603, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93688, "top5_acc": 0.9975, "loss_cls": 0.39029, "loss": 0.39029, "time": 0.49176} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.01602, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91562, "top5_acc": 0.99812, "loss_cls": 0.4503, "loss": 0.4503, "time": 0.49178} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.016, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91062, "top5_acc": 0.99812, "loss_cls": 0.4773, "loss": 0.4773, "time": 0.48874} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.01598, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.91375, "top5_acc": 0.9975, "loss_cls": 0.49538, "loss": 0.49538, "time": 0.49114} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.01596, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.89625, "top5_acc": 0.99688, "loss_cls": 0.52217, "loss": 0.52217, "time": 0.49458} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.01594, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.91875, "top5_acc": 0.995, "loss_cls": 0.47866, "loss": 0.47866, "time": 0.49263} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.01592, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.89125, "top5_acc": 0.99812, "loss_cls": 0.53878, "loss": 0.53878, "time": 0.49554} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.0159, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89188, "top5_acc": 0.99312, "loss_cls": 0.55188, "loss": 0.55188, "time": 0.49221} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.01588, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.89688, "top5_acc": 0.99625, "loss_cls": 0.52735, "loss": 0.52735, "time": 0.49338} +{"mode": "val", "epoch": 62, "iter": 533, "lr": 0.01586, "top1_acc": 0.86363, "top5_acc": 0.99143, "mean_class_accuracy": 0.82499} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.01584, "memory": 4083, "data_time": 0.19448, "top1_acc": 0.9225, "top5_acc": 0.9975, "loss_cls": 0.44693, "loss": 0.44693, "time": 0.67098} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.01582, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.9325, "top5_acc": 0.99875, "loss_cls": 0.4116, "loss": 0.4116, "time": 0.44008} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.0158, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9275, "top5_acc": 0.99812, "loss_cls": 0.40878, "loss": 0.40878, "time": 0.49419} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.01578, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91562, "top5_acc": 0.99688, "loss_cls": 0.48067, "loss": 0.48067, "time": 0.49433} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.01576, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90938, "top5_acc": 0.99688, "loss_cls": 0.50172, "loss": 0.50172, "time": 0.49109} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.01574, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.9225, "top5_acc": 0.99625, "loss_cls": 0.43628, "loss": 0.43628, "time": 0.49362} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.01572, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9175, "top5_acc": 0.99688, "loss_cls": 0.44683, "loss": 0.44683, "time": 0.49318} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.0157, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.91438, "top5_acc": 0.99812, "loss_cls": 0.48754, "loss": 0.48754, "time": 0.48795} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.01568, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9075, "top5_acc": 0.99625, "loss_cls": 0.47248, "loss": 0.47248, "time": 0.48885} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.01566, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.8875, "top5_acc": 0.9925, "loss_cls": 0.5617, "loss": 0.5617, "time": 0.49442} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.01564, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.91188, "top5_acc": 0.99875, "loss_cls": 0.45823, "loss": 0.45823, "time": 0.49508} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.01562, "memory": 4083, "data_time": 0.00063, "top1_acc": 0.90562, "top5_acc": 0.99812, "loss_cls": 0.49783, "loss": 0.49783, "time": 0.49066} +{"mode": "val", "epoch": 63, "iter": 533, "lr": 0.01561, "top1_acc": 0.85577, "top5_acc": 0.98932, "mean_class_accuracy": 0.80277} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.01559, "memory": 4083, "data_time": 0.19552, "top1_acc": 0.925, "top5_acc": 0.99625, "loss_cls": 0.45883, "loss": 0.45883, "time": 0.65052} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.01557, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9275, "top5_acc": 0.99688, "loss_cls": 0.42059, "loss": 0.42059, "time": 0.43584} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.01555, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.91875, "top5_acc": 0.99375, "loss_cls": 0.44627, "loss": 0.44627, "time": 0.4922} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.01553, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91188, "top5_acc": 0.99875, "loss_cls": 0.43504, "loss": 0.43504, "time": 0.4884} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.01551, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.9225, "top5_acc": 0.99938, "loss_cls": 0.42167, "loss": 0.42167, "time": 0.49027} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.01549, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.40781, "loss": 0.40781, "time": 0.48951} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.01547, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.91875, "top5_acc": 0.99625, "loss_cls": 0.4284, "loss": 0.4284, "time": 0.48997} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.01545, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90375, "top5_acc": 0.99625, "loss_cls": 0.51034, "loss": 0.51034, "time": 0.48881} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.01543, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.91125, "top5_acc": 0.99375, "loss_cls": 0.48533, "loss": 0.48533, "time": 0.49296} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.01541, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.89812, "top5_acc": 0.99625, "loss_cls": 0.54816, "loss": 0.54816, "time": 0.48886} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.01539, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9075, "top5_acc": 0.9975, "loss_cls": 0.48618, "loss": 0.48618, "time": 0.49165} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.01537, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.91625, "top5_acc": 0.995, "loss_cls": 0.45999, "loss": 0.45999, "time": 0.49167} +{"mode": "val", "epoch": 64, "iter": 533, "lr": 0.01535, "top1_acc": 0.85401, "top5_acc": 0.99002, "mean_class_accuracy": 0.7847} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.01533, "memory": 4083, "data_time": 0.19204, "top1_acc": 0.90875, "top5_acc": 0.99625, "loss_cls": 0.5061, "loss": 0.5061, "time": 0.66713} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.01531, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92375, "top5_acc": 0.99688, "loss_cls": 0.45016, "loss": 0.45016, "time": 0.44118} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.01529, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.43291, "loss": 0.43291, "time": 0.49219} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.01527, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.925, "top5_acc": 1.0, "loss_cls": 0.39045, "loss": 0.39045, "time": 0.49014} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.01526, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91562, "top5_acc": 0.9975, "loss_cls": 0.43441, "loss": 0.43441, "time": 0.49209} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.01524, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.92562, "top5_acc": 0.99812, "loss_cls": 0.41917, "loss": 0.41917, "time": 0.49249} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.01522, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.92562, "top5_acc": 0.99688, "loss_cls": 0.41471, "loss": 0.41471, "time": 0.48997} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0152, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9325, "top5_acc": 0.99875, "loss_cls": 0.38503, "loss": 0.38503, "time": 0.49469} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.01518, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92188, "top5_acc": 0.9975, "loss_cls": 0.44216, "loss": 0.44216, "time": 0.49046} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.01516, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.91188, "top5_acc": 0.995, "loss_cls": 0.46707, "loss": 0.46707, "time": 0.4918} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.01514, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.90625, "top5_acc": 0.99625, "loss_cls": 0.48394, "loss": 0.48394, "time": 0.49296} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.01512, "memory": 4083, "data_time": 0.00052, "top1_acc": 0.91, "top5_acc": 0.99562, "loss_cls": 0.50995, "loss": 0.50995, "time": 0.49171} +{"mode": "val", "epoch": 65, "iter": 533, "lr": 0.0151, "top1_acc": 0.86621, "top5_acc": 0.98897, "mean_class_accuracy": 0.81794} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.01508, "memory": 4083, "data_time": 0.18973, "top1_acc": 0.91562, "top5_acc": 1.0, "loss_cls": 0.46635, "loss": 0.46635, "time": 0.65521} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.01506, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.3666, "loss": 0.3666, "time": 0.44079} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.01504, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92125, "top5_acc": 0.9975, "loss_cls": 0.41166, "loss": 0.41166, "time": 0.49224} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.01502, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92688, "top5_acc": 0.99875, "loss_cls": 0.41632, "loss": 0.41632, "time": 0.49105} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.015, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91375, "top5_acc": 0.99875, "loss_cls": 0.4433, "loss": 0.4433, "time": 0.49054} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.01498, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.91688, "top5_acc": 0.99688, "loss_cls": 0.45929, "loss": 0.45929, "time": 0.49298} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.01496, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91938, "top5_acc": 0.99688, "loss_cls": 0.45365, "loss": 0.45365, "time": 0.49284} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.01494, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9075, "top5_acc": 0.99875, "loss_cls": 0.47377, "loss": 0.47377, "time": 0.4899} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.01492, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92, "top5_acc": 0.99938, "loss_cls": 0.42877, "loss": 0.42877, "time": 0.4935} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.0149, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9175, "top5_acc": 0.99812, "loss_cls": 0.46723, "loss": 0.46723, "time": 0.49394} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.01488, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91375, "top5_acc": 0.99625, "loss_cls": 0.48923, "loss": 0.48923, "time": 0.49173} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.01486, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.89812, "top5_acc": 0.99875, "loss_cls": 0.52207, "loss": 0.52207, "time": 0.49041} +{"mode": "val", "epoch": 66, "iter": 533, "lr": 0.01484, "top1_acc": 0.85952, "top5_acc": 0.98592, "mean_class_accuracy": 0.82406} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.01482, "memory": 4083, "data_time": 0.18806, "top1_acc": 0.9375, "top5_acc": 0.9975, "loss_cls": 0.36566, "loss": 0.36566, "time": 0.67317} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.0148, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.36974, "loss": 0.36974, "time": 0.43512} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.01478, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.92438, "top5_acc": 0.99812, "loss_cls": 0.387, "loss": 0.387, "time": 0.4903} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.01476, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.92125, "top5_acc": 0.9975, "loss_cls": 0.44131, "loss": 0.44131, "time": 0.49166} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.01474, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.91625, "top5_acc": 0.9975, "loss_cls": 0.45507, "loss": 0.45507, "time": 0.49096} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.01472, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93062, "top5_acc": 1.0, "loss_cls": 0.39439, "loss": 0.39439, "time": 0.49077} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.0147, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91375, "top5_acc": 0.99688, "loss_cls": 0.4548, "loss": 0.4548, "time": 0.49406} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.01468, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92062, "top5_acc": 0.9975, "loss_cls": 0.45625, "loss": 0.45625, "time": 0.49435} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.01466, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.905, "top5_acc": 0.99875, "loss_cls": 0.50655, "loss": 0.50655, "time": 0.49599} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.01464, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90562, "top5_acc": 0.99938, "loss_cls": 0.47873, "loss": 0.47873, "time": 0.49349} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.01462, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.90812, "top5_acc": 0.995, "loss_cls": 0.52748, "loss": 0.52748, "time": 0.49104} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.0146, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.92875, "top5_acc": 0.99812, "loss_cls": 0.42206, "loss": 0.42206, "time": 0.49364} +{"mode": "val", "epoch": 67, "iter": 533, "lr": 0.01458, "top1_acc": 0.85424, "top5_acc": 0.98979, "mean_class_accuracy": 0.80601} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.01456, "memory": 4083, "data_time": 0.18903, "top1_acc": 0.9275, "top5_acc": 0.99938, "loss_cls": 0.40074, "loss": 0.40074, "time": 0.68216} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.01454, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.38191, "loss": 0.38191, "time": 0.43764} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.01452, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92812, "top5_acc": 0.99875, "loss_cls": 0.4045, "loss": 0.4045, "time": 0.49184} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.0145, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.38409, "loss": 0.38409, "time": 0.49161} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.01448, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.91312, "top5_acc": 0.99562, "loss_cls": 0.44377, "loss": 0.44377, "time": 0.49347} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.01446, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.90812, "top5_acc": 0.99812, "loss_cls": 0.47944, "loss": 0.47944, "time": 0.48977} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.01444, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.92875, "top5_acc": 0.99875, "loss_cls": 0.40311, "loss": 0.40311, "time": 0.49209} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.01442, "memory": 4083, "data_time": 0.00048, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.42926, "loss": 0.42926, "time": 0.48978} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.0144, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91938, "top5_acc": 0.99812, "loss_cls": 0.42523, "loss": 0.42523, "time": 0.49193} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.01438, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9275, "top5_acc": 0.9975, "loss_cls": 0.40955, "loss": 0.40955, "time": 0.49164} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.01436, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.90312, "top5_acc": 0.995, "loss_cls": 0.48576, "loss": 0.48576, "time": 0.49077} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.01434, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91562, "top5_acc": 0.99688, "loss_cls": 0.48945, "loss": 0.48945, "time": 0.48961} +{"mode": "val", "epoch": 68, "iter": 533, "lr": 0.01433, "top1_acc": 0.8648, "top5_acc": 0.98897, "mean_class_accuracy": 0.81889} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.01431, "memory": 4083, "data_time": 0.19203, "top1_acc": 0.93, "top5_acc": 1.0, "loss_cls": 0.37226, "loss": 0.37226, "time": 0.66892} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.01429, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.35949, "loss": 0.35949, "time": 0.43991} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.01427, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91938, "top5_acc": 0.9975, "loss_cls": 0.43765, "loss": 0.43765, "time": 0.49048} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.01425, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9125, "top5_acc": 0.9975, "loss_cls": 0.44948, "loss": 0.44948, "time": 0.49311} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.01423, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9275, "top5_acc": 0.99875, "loss_cls": 0.40429, "loss": 0.40429, "time": 0.4907} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.0142, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93, "top5_acc": 0.99688, "loss_cls": 0.40073, "loss": 0.40073, "time": 0.49094} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.01418, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93438, "top5_acc": 0.99688, "loss_cls": 0.39676, "loss": 0.39676, "time": 0.49436} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.01416, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92562, "top5_acc": 0.99625, "loss_cls": 0.41776, "loss": 0.41776, "time": 0.49439} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.01414, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.915, "top5_acc": 0.99938, "loss_cls": 0.43629, "loss": 0.43629, "time": 0.49491} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.01412, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92, "top5_acc": 0.99938, "loss_cls": 0.44515, "loss": 0.44515, "time": 0.48924} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.0141, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.91062, "top5_acc": 0.99688, "loss_cls": 0.46986, "loss": 0.46986, "time": 0.49269} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.01408, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90625, "top5_acc": 0.99875, "loss_cls": 0.4836, "loss": 0.4836, "time": 0.4927} +{"mode": "val", "epoch": 69, "iter": 533, "lr": 0.01407, "top1_acc": 0.86199, "top5_acc": 0.98944, "mean_class_accuracy": 0.82015} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.01405, "memory": 4083, "data_time": 0.18961, "top1_acc": 0.93688, "top5_acc": 0.99875, "loss_cls": 0.3631, "loss": 0.3631, "time": 0.65676} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.01403, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.93938, "top5_acc": 0.99812, "loss_cls": 0.34747, "loss": 0.34747, "time": 0.44574} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.01401, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92688, "top5_acc": 0.99875, "loss_cls": 0.38023, "loss": 0.38023, "time": 0.48909} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.01399, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92562, "top5_acc": 0.99812, "loss_cls": 0.41416, "loss": 0.41416, "time": 0.49075} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.01397, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.90875, "top5_acc": 0.99875, "loss_cls": 0.45439, "loss": 0.45439, "time": 0.49269} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.01395, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91188, "top5_acc": 0.99812, "loss_cls": 0.42762, "loss": 0.42762, "time": 0.49392} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.01392, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.90938, "top5_acc": 0.9975, "loss_cls": 0.46345, "loss": 0.46345, "time": 0.49405} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.0139, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.92312, "top5_acc": 0.99812, "loss_cls": 0.41697, "loss": 0.41697, "time": 0.4918} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.01388, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.92125, "top5_acc": 0.99812, "loss_cls": 0.41657, "loss": 0.41657, "time": 0.49337} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.01386, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.92938, "top5_acc": 0.99938, "loss_cls": 0.40829, "loss": 0.40829, "time": 0.49161} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.01384, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.92875, "top5_acc": 0.99875, "loss_cls": 0.39181, "loss": 0.39181, "time": 0.49132} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.01382, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9175, "top5_acc": 0.99688, "loss_cls": 0.43323, "loss": 0.43323, "time": 0.49234} +{"mode": "val", "epoch": 70, "iter": 533, "lr": 0.01381, "top1_acc": 0.86222, "top5_acc": 0.9885, "mean_class_accuracy": 0.82308} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.01379, "memory": 4083, "data_time": 0.19135, "top1_acc": 0.94375, "top5_acc": 1.0, "loss_cls": 0.34342, "loss": 0.34342, "time": 0.66313} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.01377, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92812, "top5_acc": 0.99875, "loss_cls": 0.3927, "loss": 0.3927, "time": 0.43684} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.01375, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.34097, "loss": 0.34097, "time": 0.49311} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.01373, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.93375, "top5_acc": 0.99875, "loss_cls": 0.35736, "loss": 0.35736, "time": 0.49152} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.01371, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93, "top5_acc": 0.9975, "loss_cls": 0.38104, "loss": 0.38104, "time": 0.48756} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.01368, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93188, "top5_acc": 0.99938, "loss_cls": 0.39738, "loss": 0.39738, "time": 0.49455} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.01366, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92625, "top5_acc": 0.99812, "loss_cls": 0.4074, "loss": 0.4074, "time": 0.49197} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.01364, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.35658, "loss": 0.35658, "time": 0.49106} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.01362, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.38258, "loss": 0.38258, "time": 0.49104} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.0136, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92812, "top5_acc": 0.9975, "loss_cls": 0.42022, "loss": 0.42022, "time": 0.49294} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.01358, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91812, "top5_acc": 0.9975, "loss_cls": 0.46125, "loss": 0.46125, "time": 0.49226} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.01356, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9175, "top5_acc": 0.99812, "loss_cls": 0.44262, "loss": 0.44262, "time": 0.49068} +{"mode": "val", "epoch": 71, "iter": 533, "lr": 0.01355, "top1_acc": 0.85342, "top5_acc": 0.9858, "mean_class_accuracy": 0.81899} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.01353, "memory": 4083, "data_time": 0.19519, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.38194, "loss": 0.38194, "time": 0.68161} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.01351, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.93062, "top5_acc": 0.99875, "loss_cls": 0.37599, "loss": 0.37599, "time": 0.44789} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.01349, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93875, "top5_acc": 0.99875, "loss_cls": 0.34677, "loss": 0.34677, "time": 0.49017} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.01346, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.93688, "top5_acc": 0.99875, "loss_cls": 0.36831, "loss": 0.36831, "time": 0.49059} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.01344, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93062, "top5_acc": 0.9975, "loss_cls": 0.37755, "loss": 0.37755, "time": 0.49154} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.01342, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91938, "top5_acc": 0.99688, "loss_cls": 0.43032, "loss": 0.43032, "time": 0.49434} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.0134, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92062, "top5_acc": 0.99688, "loss_cls": 0.43262, "loss": 0.43262, "time": 0.49432} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.01338, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93, "top5_acc": 1.0, "loss_cls": 0.40157, "loss": 0.40157, "time": 0.4922} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.01336, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.92062, "top5_acc": 0.9975, "loss_cls": 0.446, "loss": 0.446, "time": 0.49193} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.01334, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.93875, "top5_acc": 0.9975, "loss_cls": 0.37045, "loss": 0.37045, "time": 0.49375} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.01332, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93188, "top5_acc": 0.9975, "loss_cls": 0.35882, "loss": 0.35882, "time": 0.49421} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.0133, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91812, "top5_acc": 0.99625, "loss_cls": 0.45121, "loss": 0.45121, "time": 0.49125} +{"mode": "val", "epoch": 72, "iter": 533, "lr": 0.01329, "top1_acc": 0.8817, "top5_acc": 0.99249, "mean_class_accuracy": 0.84366} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.01326, "memory": 4083, "data_time": 0.19399, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.34843, "loss": 0.34843, "time": 0.65258} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.01324, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.34669, "loss": 0.34669, "time": 0.4342} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.01322, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9425, "top5_acc": 0.99812, "loss_cls": 0.33813, "loss": 0.33813, "time": 0.48917} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.0132, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9425, "top5_acc": 0.9975, "loss_cls": 0.33287, "loss": 0.33287, "time": 0.49051} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.01318, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94375, "top5_acc": 0.9975, "loss_cls": 0.33986, "loss": 0.33986, "time": 0.48996} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.01316, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91312, "top5_acc": 0.99875, "loss_cls": 0.45632, "loss": 0.45632, "time": 0.49028} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.01314, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.92938, "top5_acc": 0.99938, "loss_cls": 0.39325, "loss": 0.39325, "time": 0.49006} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.01312, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.9225, "top5_acc": 0.99688, "loss_cls": 0.44513, "loss": 0.44513, "time": 0.49258} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.0131, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.935, "top5_acc": 0.99938, "loss_cls": 0.36275, "loss": 0.36275, "time": 0.48851} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.01308, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9125, "top5_acc": 0.99438, "loss_cls": 0.46417, "loss": 0.46417, "time": 0.49584} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.01306, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93125, "top5_acc": 0.99875, "loss_cls": 0.39875, "loss": 0.39875, "time": 0.49648} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.01304, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92438, "top5_acc": 0.99688, "loss_cls": 0.41401, "loss": 0.41401, "time": 0.49268} +{"mode": "val", "epoch": 73, "iter": 533, "lr": 0.01302, "top1_acc": 0.85131, "top5_acc": 0.98627, "mean_class_accuracy": 0.823} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.013, "memory": 4083, "data_time": 0.18995, "top1_acc": 0.93625, "top5_acc": 0.99688, "loss_cls": 0.356, "loss": 0.356, "time": 0.69749} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.01298, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.94, "top5_acc": 0.9975, "loss_cls": 0.32987, "loss": 0.32987, "time": 0.43727} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.01296, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.3888, "loss": 0.3888, "time": 0.49003} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.01294, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.92625, "top5_acc": 0.99875, "loss_cls": 0.39721, "loss": 0.39721, "time": 0.49417} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.01292, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.92, "top5_acc": 0.99938, "loss_cls": 0.43281, "loss": 0.43281, "time": 0.49241} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.0129, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.91875, "top5_acc": 0.99812, "loss_cls": 0.43181, "loss": 0.43181, "time": 0.49438} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.01288, "memory": 4083, "data_time": 0.00063, "top1_acc": 0.93188, "top5_acc": 0.99812, "loss_cls": 0.38027, "loss": 0.38027, "time": 0.4934} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.01286, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.93562, "top5_acc": 0.99938, "loss_cls": 0.36131, "loss": 0.36131, "time": 0.49163} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.01284, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.93375, "top5_acc": 0.99875, "loss_cls": 0.36678, "loss": 0.36678, "time": 0.48982} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.01282, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93062, "top5_acc": 0.99812, "loss_cls": 0.38938, "loss": 0.38938, "time": 0.48955} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.0128, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92125, "top5_acc": 0.9975, "loss_cls": 0.41888, "loss": 0.41888, "time": 0.48834} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.01278, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9175, "top5_acc": 0.9975, "loss_cls": 0.44537, "loss": 0.44537, "time": 0.49202} +{"mode": "val", "epoch": 74, "iter": 533, "lr": 0.01276, "top1_acc": 0.86504, "top5_acc": 0.99026, "mean_class_accuracy": 0.81488} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.01274, "memory": 4083, "data_time": 0.18998, "top1_acc": 0.9225, "top5_acc": 0.99875, "loss_cls": 0.40171, "loss": 0.40171, "time": 0.67739} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.01272, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.945, "top5_acc": 0.99938, "loss_cls": 0.32629, "loss": 0.32629, "time": 0.44122} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.0127, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9525, "top5_acc": 0.99938, "loss_cls": 0.30362, "loss": 0.30362, "time": 0.49155} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.01268, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94125, "top5_acc": 1.0, "loss_cls": 0.30539, "loss": 0.30539, "time": 0.48956} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.01266, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.925, "top5_acc": 0.99938, "loss_cls": 0.3836, "loss": 0.3836, "time": 0.49054} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.01264, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.92688, "top5_acc": 0.995, "loss_cls": 0.41976, "loss": 0.41976, "time": 0.49398} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.01262, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.93562, "top5_acc": 0.99875, "loss_cls": 0.36243, "loss": 0.36243, "time": 0.49221} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.0126, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.915, "top5_acc": 0.9975, "loss_cls": 0.45034, "loss": 0.45034, "time": 0.49011} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.01258, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.94375, "top5_acc": 0.9975, "loss_cls": 0.35064, "loss": 0.35064, "time": 0.49281} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.01256, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9225, "top5_acc": 0.99875, "loss_cls": 0.39451, "loss": 0.39451, "time": 0.49209} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.01254, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9325, "top5_acc": 0.99812, "loss_cls": 0.36165, "loss": 0.36165, "time": 0.49213} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.01252, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.91938, "top5_acc": 0.99938, "loss_cls": 0.41722, "loss": 0.41722, "time": 0.48906} +{"mode": "val", "epoch": 75, "iter": 533, "lr": 0.0125, "top1_acc": 0.8499, "top5_acc": 0.98533, "mean_class_accuracy": 0.80554} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.01248, "memory": 4083, "data_time": 0.18857, "top1_acc": 0.92938, "top5_acc": 0.99812, "loss_cls": 0.3977, "loss": 0.3977, "time": 0.67999} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.01246, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.93062, "top5_acc": 0.99938, "loss_cls": 0.35158, "loss": 0.35158, "time": 0.442} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.01244, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95188, "top5_acc": 0.99875, "loss_cls": 0.2817, "loss": 0.2817, "time": 0.48825} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.01242, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93688, "top5_acc": 0.99938, "loss_cls": 0.34935, "loss": 0.34935, "time": 0.49403} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.0124, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.94625, "top5_acc": 1.0, "loss_cls": 0.3064, "loss": 0.3064, "time": 0.49952} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.01238, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94625, "top5_acc": 0.99812, "loss_cls": 0.33067, "loss": 0.33067, "time": 0.49216} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.01236, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.9425, "top5_acc": 1.0, "loss_cls": 0.32787, "loss": 0.32787, "time": 0.49277} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.01234, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.44131, "loss": 0.44131, "time": 0.48862} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.01232, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.37201, "loss": 0.37201, "time": 0.49557} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.0123, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93375, "top5_acc": 0.99812, "loss_cls": 0.38239, "loss": 0.38239, "time": 0.49187} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.01228, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.4177, "loss": 0.4177, "time": 0.492} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.01225, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.92625, "top5_acc": 0.99812, "loss_cls": 0.38496, "loss": 0.38496, "time": 0.49253} +{"mode": "val", "epoch": 76, "iter": 533, "lr": 0.01224, "top1_acc": 0.87102, "top5_acc": 0.98838, "mean_class_accuracy": 0.83923} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.01222, "memory": 4083, "data_time": 0.18979, "top1_acc": 0.94875, "top5_acc": 0.99875, "loss_cls": 0.30524, "loss": 0.30524, "time": 0.66209} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0122, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.28969, "loss": 0.28969, "time": 0.44475} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.01218, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94938, "top5_acc": 0.99875, "loss_cls": 0.2986, "loss": 0.2986, "time": 0.49214} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.01216, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93375, "top5_acc": 0.99938, "loss_cls": 0.34658, "loss": 0.34658, "time": 0.49453} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.01214, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93312, "top5_acc": 0.9975, "loss_cls": 0.35769, "loss": 0.35769, "time": 0.4904} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.01212, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.925, "top5_acc": 0.99875, "loss_cls": 0.35992, "loss": 0.35992, "time": 0.49298} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.0121, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.9375, "top5_acc": 0.99875, "loss_cls": 0.36191, "loss": 0.36191, "time": 0.48999} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.01207, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.92625, "top5_acc": 0.99562, "loss_cls": 0.43119, "loss": 0.43119, "time": 0.4897} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.01205, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94062, "top5_acc": 0.99688, "loss_cls": 0.36366, "loss": 0.36366, "time": 0.48896} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.01203, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93938, "top5_acc": 0.99938, "loss_cls": 0.3559, "loss": 0.3559, "time": 0.49359} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.01201, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9275, "top5_acc": 1.0, "loss_cls": 0.38225, "loss": 0.38225, "time": 0.48973} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.01199, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9025, "top5_acc": 0.99938, "loss_cls": 0.48141, "loss": 0.48141, "time": 0.49061} +{"mode": "val", "epoch": 77, "iter": 533, "lr": 0.01198, "top1_acc": 0.88311, "top5_acc": 0.99202, "mean_class_accuracy": 0.84303} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.01196, "memory": 4083, "data_time": 0.19055, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.29649, "loss": 0.29649, "time": 0.62517} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.01194, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94438, "top5_acc": 1.0, "loss_cls": 0.28892, "loss": 0.28892, "time": 0.43982} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.01192, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.93312, "top5_acc": 1.0, "loss_cls": 0.38368, "loss": 0.38368, "time": 0.4893} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.0119, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9375, "top5_acc": 0.99812, "loss_cls": 0.35442, "loss": 0.35442, "time": 0.48869} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.01187, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.93562, "top5_acc": 0.99875, "loss_cls": 0.34095, "loss": 0.34095, "time": 0.49004} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.01185, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9475, "top5_acc": 0.99875, "loss_cls": 0.33296, "loss": 0.33296, "time": 0.49027} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.01183, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94562, "top5_acc": 1.0, "loss_cls": 0.33732, "loss": 0.33732, "time": 0.48984} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.01181, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.93188, "top5_acc": 0.99812, "loss_cls": 0.37771, "loss": 0.37771, "time": 0.49145} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.01179, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.92688, "top5_acc": 0.99875, "loss_cls": 0.41024, "loss": 0.41024, "time": 0.49128} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.01177, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93688, "top5_acc": 0.99938, "loss_cls": 0.36801, "loss": 0.36801, "time": 0.49143} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.01175, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.93062, "top5_acc": 0.99812, "loss_cls": 0.36095, "loss": 0.36095, "time": 0.49308} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.01173, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.91688, "top5_acc": 0.9975, "loss_cls": 0.48477, "loss": 0.48477, "time": 0.49342} +{"mode": "val", "epoch": 78, "iter": 533, "lr": 0.01172, "top1_acc": 0.86081, "top5_acc": 0.99038, "mean_class_accuracy": 0.80922} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.01169, "memory": 4083, "data_time": 0.18843, "top1_acc": 0.9375, "top5_acc": 0.99812, "loss_cls": 0.38994, "loss": 0.38994, "time": 0.6614} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.01167, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94, "top5_acc": 0.99938, "loss_cls": 0.3234, "loss": 0.3234, "time": 0.44221} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.01165, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94625, "top5_acc": 0.99938, "loss_cls": 0.33092, "loss": 0.33092, "time": 0.49159} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.01163, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.945, "top5_acc": 0.99938, "loss_cls": 0.33028, "loss": 0.33028, "time": 0.49368} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.01161, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93375, "top5_acc": 0.99812, "loss_cls": 0.36195, "loss": 0.36195, "time": 0.49179} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.01159, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9525, "top5_acc": 0.99875, "loss_cls": 0.31014, "loss": 0.31014, "time": 0.49229} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.01157, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.30893, "loss": 0.30893, "time": 0.48924} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.01155, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.945, "top5_acc": 0.99812, "loss_cls": 0.32819, "loss": 0.32819, "time": 0.49291} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.01153, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93125, "top5_acc": 1.0, "loss_cls": 0.36141, "loss": 0.36141, "time": 0.49079} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.01151, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93125, "top5_acc": 0.99938, "loss_cls": 0.37786, "loss": 0.37786, "time": 0.4892} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.01149, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.91312, "top5_acc": 0.99812, "loss_cls": 0.40433, "loss": 0.40433, "time": 0.48957} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.01147, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93125, "top5_acc": 0.99812, "loss_cls": 0.40511, "loss": 0.40511, "time": 0.4887} +{"mode": "val", "epoch": 79, "iter": 533, "lr": 0.01145, "top1_acc": 0.8634, "top5_acc": 0.98733, "mean_class_accuracy": 0.82201} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.01143, "memory": 4083, "data_time": 0.18968, "top1_acc": 0.95125, "top5_acc": 0.9975, "loss_cls": 0.3078, "loss": 0.3078, "time": 0.64094} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.01141, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94812, "top5_acc": 0.99938, "loss_cls": 0.2861, "loss": 0.2861, "time": 0.44013} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.01139, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.30148, "loss": 0.30148, "time": 0.49013} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.01137, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.33969, "loss": 0.33969, "time": 0.49125} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.01135, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.93312, "top5_acc": 0.99812, "loss_cls": 0.37215, "loss": 0.37215, "time": 0.49028} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.01133, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.935, "top5_acc": 0.99812, "loss_cls": 0.37096, "loss": 0.37096, "time": 0.49323} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.01131, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9425, "top5_acc": 0.9975, "loss_cls": 0.32089, "loss": 0.32089, "time": 0.49014} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.01129, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94875, "top5_acc": 0.99812, "loss_cls": 0.33108, "loss": 0.33108, "time": 0.49083} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.01127, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.30182, "loss": 0.30182, "time": 0.49097} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.01125, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94188, "top5_acc": 1.0, "loss_cls": 0.32603, "loss": 0.32603, "time": 0.49074} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.01123, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93875, "top5_acc": 0.99938, "loss_cls": 0.32711, "loss": 0.32711, "time": 0.49205} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.01121, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.93125, "top5_acc": 0.99875, "loss_cls": 0.38061, "loss": 0.38061, "time": 0.4922} +{"mode": "val", "epoch": 80, "iter": 533, "lr": 0.01119, "top1_acc": 0.88311, "top5_acc": 0.99108, "mean_class_accuracy": 0.86035} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.01117, "memory": 4083, "data_time": 0.19107, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.34972, "loss": 0.34972, "time": 0.6744} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.01115, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.945, "top5_acc": 0.99938, "loss_cls": 0.3244, "loss": 0.3244, "time": 0.43366} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.01113, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.26021, "loss": 0.26021, "time": 0.49296} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.01111, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.94438, "top5_acc": 0.99938, "loss_cls": 0.29621, "loss": 0.29621, "time": 0.48806} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.01109, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9475, "top5_acc": 1.0, "loss_cls": 0.31626, "loss": 0.31626, "time": 0.49023} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.01107, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94562, "top5_acc": 0.99875, "loss_cls": 0.32619, "loss": 0.32619, "time": 0.48905} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.01105, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.93625, "top5_acc": 0.99688, "loss_cls": 0.36138, "loss": 0.36138, "time": 0.49538} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.01103, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94562, "top5_acc": 0.99625, "loss_cls": 0.34751, "loss": 0.34751, "time": 0.49093} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.01101, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.32881, "loss": 0.32881, "time": 0.49274} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.01099, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93375, "top5_acc": 0.99938, "loss_cls": 0.34936, "loss": 0.34936, "time": 0.49427} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.01097, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.93562, "top5_acc": 0.99875, "loss_cls": 0.37181, "loss": 0.37181, "time": 0.49252} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.01095, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94062, "top5_acc": 0.99938, "loss_cls": 0.35087, "loss": 0.35087, "time": 0.49077} +{"mode": "val", "epoch": 81, "iter": 533, "lr": 0.01093, "top1_acc": 0.84955, "top5_acc": 0.98521, "mean_class_accuracy": 0.8293} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.01091, "memory": 4083, "data_time": 0.18397, "top1_acc": 0.94312, "top5_acc": 0.99938, "loss_cls": 0.33444, "loss": 0.33444, "time": 0.69632} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.01089, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95062, "top5_acc": 1.0, "loss_cls": 0.28562, "loss": 0.28562, "time": 0.4285} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.01087, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94188, "top5_acc": 0.99812, "loss_cls": 0.33038, "loss": 0.33038, "time": 0.49096} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.01085, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.93, "top5_acc": 0.99875, "loss_cls": 0.34981, "loss": 0.34981, "time": 0.49005} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.01083, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94812, "top5_acc": 1.0, "loss_cls": 0.30875, "loss": 0.30875, "time": 0.49248} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.01081, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94312, "top5_acc": 0.99875, "loss_cls": 0.32123, "loss": 0.32123, "time": 0.48902} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.01079, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94, "top5_acc": 0.99875, "loss_cls": 0.34148, "loss": 0.34148, "time": 0.48986} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.01077, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94125, "top5_acc": 0.99812, "loss_cls": 0.32637, "loss": 0.32637, "time": 0.48757} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.01075, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.94, "top5_acc": 0.99938, "loss_cls": 0.33763, "loss": 0.33763, "time": 0.48873} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.01073, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9325, "top5_acc": 0.9975, "loss_cls": 0.38791, "loss": 0.38791, "time": 0.49129} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.01071, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94062, "top5_acc": 0.99875, "loss_cls": 0.35918, "loss": 0.35918, "time": 0.48792} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.01069, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.93938, "top5_acc": 0.99875, "loss_cls": 0.33118, "loss": 0.33118, "time": 0.48906} +{"mode": "val", "epoch": 82, "iter": 533, "lr": 0.01067, "top1_acc": 0.89074, "top5_acc": 0.99061, "mean_class_accuracy": 0.85373} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.01065, "memory": 4083, "data_time": 0.18937, "top1_acc": 0.95688, "top5_acc": 0.99812, "loss_cls": 0.26469, "loss": 0.26469, "time": 0.68473} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.01063, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.29965, "loss": 0.29965, "time": 0.4395} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.01061, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.95438, "top5_acc": 1.0, "loss_cls": 0.28031, "loss": 0.28031, "time": 0.48942} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.01059, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.27227, "loss": 0.27227, "time": 0.48858} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.01057, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9425, "top5_acc": 0.99938, "loss_cls": 0.29602, "loss": 0.29602, "time": 0.48994} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.01055, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.94562, "top5_acc": 1.0, "loss_cls": 0.32657, "loss": 0.32657, "time": 0.49081} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.01053, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.94062, "top5_acc": 1.0, "loss_cls": 0.31067, "loss": 0.31067, "time": 0.48958} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.01051, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.94562, "top5_acc": 0.99938, "loss_cls": 0.32068, "loss": 0.32068, "time": 0.49181} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.01049, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.94312, "top5_acc": 1.0, "loss_cls": 0.3273, "loss": 0.3273, "time": 0.49281} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.01047, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.93375, "top5_acc": 0.99938, "loss_cls": 0.34459, "loss": 0.34459, "time": 0.49101} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.01045, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94125, "top5_acc": 0.99812, "loss_cls": 0.33693, "loss": 0.33693, "time": 0.48967} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.01043, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.95125, "top5_acc": 0.99688, "loss_cls": 0.2934, "loss": 0.2934, "time": 0.4903} +{"mode": "val", "epoch": 83, "iter": 533, "lr": 0.01042, "top1_acc": 0.87278, "top5_acc": 0.99049, "mean_class_accuracy": 0.83105} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.0104, "memory": 4083, "data_time": 0.18767, "top1_acc": 0.92562, "top5_acc": 0.99875, "loss_cls": 0.38697, "loss": 0.38697, "time": 0.6779} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.01038, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.30764, "loss": 0.30764, "time": 0.43654} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.01036, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.28025, "loss": 0.28025, "time": 0.49102} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.01034, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.30424, "loss": 0.30424, "time": 0.48866} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.01031, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9475, "top5_acc": 0.99938, "loss_cls": 0.31672, "loss": 0.31672, "time": 0.49311} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.01029, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.94, "top5_acc": 0.99938, "loss_cls": 0.33554, "loss": 0.33554, "time": 0.48654} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.01027, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94125, "top5_acc": 0.99875, "loss_cls": 0.33791, "loss": 0.33791, "time": 0.48885} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.01025, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94688, "top5_acc": 1.0, "loss_cls": 0.30038, "loss": 0.30038, "time": 0.49062} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.01023, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9425, "top5_acc": 0.99875, "loss_cls": 0.32998, "loss": 0.32998, "time": 0.49082} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.01021, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.32211, "loss": 0.32211, "time": 0.49022} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.01019, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.2668, "loss": 0.2668, "time": 0.48956} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.01017, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94625, "top5_acc": 0.99875, "loss_cls": 0.31661, "loss": 0.31661, "time": 0.48792} +{"mode": "val", "epoch": 84, "iter": 533, "lr": 0.01016, "top1_acc": 0.87196, "top5_acc": 0.99014, "mean_class_accuracy": 0.8363} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.01014, "memory": 4083, "data_time": 0.18943, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.26699, "loss": 0.26699, "time": 0.67742} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.01012, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.26781, "loss": 0.26781, "time": 0.44479} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.0101, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.9625, "top5_acc": 0.99812, "loss_cls": 0.26129, "loss": 0.26129, "time": 0.48945} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.01008, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.25445, "loss": 0.25445, "time": 0.4909} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.01006, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.23748, "loss": 0.23748, "time": 0.4903} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.01004, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96, "top5_acc": 0.99938, "loss_cls": 0.2543, "loss": 0.2543, "time": 0.49231} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.01002, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.27806, "loss": 0.27806, "time": 0.48668} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.01, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.94375, "top5_acc": 0.9975, "loss_cls": 0.30113, "loss": 0.30113, "time": 0.49121} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.00998, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.95, "top5_acc": 1.0, "loss_cls": 0.30162, "loss": 0.30162, "time": 0.4923} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.00996, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.22139, "loss": 0.22139, "time": 0.48944} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.00994, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.27524, "loss": 0.27524, "time": 0.4887} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.00992, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9375, "top5_acc": 0.99812, "loss_cls": 0.3666, "loss": 0.3666, "time": 0.49132} +{"mode": "val", "epoch": 85, "iter": 533, "lr": 0.0099, "top1_acc": 0.87771, "top5_acc": 0.99143, "mean_class_accuracy": 0.84703} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.00988, "memory": 4083, "data_time": 0.1885, "top1_acc": 0.95062, "top5_acc": 1.0, "loss_cls": 0.28894, "loss": 0.28894, "time": 0.65174} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.00986, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96312, "top5_acc": 0.99875, "loss_cls": 0.23699, "loss": 0.23699, "time": 0.44272} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.00984, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.24386, "loss": 0.24386, "time": 0.49072} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.00982, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95625, "top5_acc": 0.99812, "loss_cls": 0.28152, "loss": 0.28152, "time": 0.48801} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.0098, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.94938, "top5_acc": 1.0, "loss_cls": 0.28313, "loss": 0.28313, "time": 0.48714} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.00978, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.94688, "top5_acc": 0.99938, "loss_cls": 0.27735, "loss": 0.27735, "time": 0.49007} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.00976, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.955, "top5_acc": 1.0, "loss_cls": 0.28744, "loss": 0.28744, "time": 0.4926} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.00974, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.28955, "loss": 0.28955, "time": 0.49183} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.00972, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94562, "top5_acc": 0.99875, "loss_cls": 0.29584, "loss": 0.29584, "time": 0.49291} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.0097, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.9475, "top5_acc": 1.0, "loss_cls": 0.31918, "loss": 0.31918, "time": 0.48783} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.00968, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95875, "top5_acc": 0.99938, "loss_cls": 0.26559, "loss": 0.26559, "time": 0.48835} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.00966, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9425, "top5_acc": 0.99875, "loss_cls": 0.30952, "loss": 0.30952, "time": 0.48802} +{"mode": "val", "epoch": 86, "iter": 533, "lr": 0.00965, "top1_acc": 0.87971, "top5_acc": 0.98873, "mean_class_accuracy": 0.8519} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.00963, "memory": 4083, "data_time": 0.18799, "top1_acc": 0.9475, "top5_acc": 0.99875, "loss_cls": 0.30424, "loss": 0.30424, "time": 0.6537} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.00961, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.3111, "loss": 0.3111, "time": 0.44072} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.00959, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.24622, "loss": 0.24622, "time": 0.48793} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.00957, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95812, "top5_acc": 0.99938, "loss_cls": 0.25051, "loss": 0.25051, "time": 0.49032} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.00955, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.26113, "loss": 0.26113, "time": 0.48927} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.00953, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95688, "top5_acc": 1.0, "loss_cls": 0.2496, "loss": 0.2496, "time": 0.48964} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.00951, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94688, "top5_acc": 0.99875, "loss_cls": 0.28111, "loss": 0.28111, "time": 0.49186} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.00949, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95562, "top5_acc": 0.99875, "loss_cls": 0.27066, "loss": 0.27066, "time": 0.49019} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.00947, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.33052, "loss": 0.33052, "time": 0.49101} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.00945, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.25177, "loss": 0.25177, "time": 0.49287} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.00943, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.945, "top5_acc": 0.99812, "loss_cls": 0.30549, "loss": 0.30549, "time": 0.48837} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.00941, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.93312, "top5_acc": 0.99812, "loss_cls": 0.35323, "loss": 0.35323, "time": 0.48941} +{"mode": "val", "epoch": 87, "iter": 533, "lr": 0.00939, "top1_acc": 0.87701, "top5_acc": 0.98979, "mean_class_accuracy": 0.84157} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.00937, "memory": 4083, "data_time": 0.18811, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.27321, "loss": 0.27321, "time": 0.68977} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.00935, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.95625, "top5_acc": 0.99938, "loss_cls": 0.28293, "loss": 0.28293, "time": 0.44859} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.00933, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.25109, "loss": 0.25109, "time": 0.48876} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.00931, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94938, "top5_acc": 0.99938, "loss_cls": 0.27334, "loss": 0.27334, "time": 0.49189} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.00929, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95688, "top5_acc": 0.99875, "loss_cls": 0.26267, "loss": 0.26267, "time": 0.4911} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.00927, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.24045, "loss": 0.24045, "time": 0.48959} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.00925, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.23585, "loss": 0.23585, "time": 0.49107} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.00923, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.27961, "loss": 0.27961, "time": 0.49238} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.00921, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95625, "top5_acc": 0.99875, "loss_cls": 0.26956, "loss": 0.26956, "time": 0.49253} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.00919, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9575, "top5_acc": 0.99938, "loss_cls": 0.28109, "loss": 0.28109, "time": 0.49306} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.00917, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.94938, "top5_acc": 1.0, "loss_cls": 0.28275, "loss": 0.28275, "time": 0.49384} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.00915, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95188, "top5_acc": 1.0, "loss_cls": 0.29431, "loss": 0.29431, "time": 0.49081} +{"mode": "val", "epoch": 88, "iter": 533, "lr": 0.00914, "top1_acc": 0.87818, "top5_acc": 0.99014, "mean_class_accuracy": 0.83379} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.00912, "memory": 4083, "data_time": 0.18673, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.223, "loss": 0.223, "time": 0.64816} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0091, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.955, "top5_acc": 0.99938, "loss_cls": 0.25685, "loss": 0.25685, "time": 0.44103} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.00908, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.24941, "loss": 0.24941, "time": 0.49257} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.00906, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95688, "top5_acc": 0.99875, "loss_cls": 0.24808, "loss": 0.24808, "time": 0.49273} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.00904, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.23002, "loss": 0.23002, "time": 0.48923} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.00902, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95188, "top5_acc": 0.99812, "loss_cls": 0.29533, "loss": 0.29533, "time": 0.49253} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.009, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95312, "top5_acc": 0.99875, "loss_cls": 0.2956, "loss": 0.2956, "time": 0.48982} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.00898, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95438, "top5_acc": 0.9975, "loss_cls": 0.2734, "loss": 0.2734, "time": 0.49157} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.00896, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.23898, "loss": 0.23898, "time": 0.49138} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.00894, "memory": 4083, "data_time": 0.0005, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.2747, "loss": 0.2747, "time": 0.49052} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.00892, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94438, "top5_acc": 0.99875, "loss_cls": 0.30615, "loss": 0.30615, "time": 0.49067} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.0089, "memory": 4083, "data_time": 0.00068, "top1_acc": 0.95062, "top5_acc": 0.99875, "loss_cls": 0.2723, "loss": 0.2723, "time": 0.49197} +{"mode": "val", "epoch": 89, "iter": 533, "lr": 0.00889, "top1_acc": 0.88569, "top5_acc": 0.99085, "mean_class_accuracy": 0.85014} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.00887, "memory": 4083, "data_time": 0.18813, "top1_acc": 0.965, "top5_acc": 0.99938, "loss_cls": 0.21819, "loss": 0.21819, "time": 0.66074} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.00885, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.23913, "loss": 0.23913, "time": 0.4445} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.00883, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.27261, "loss": 0.27261, "time": 0.49469} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.00881, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.23087, "loss": 0.23087, "time": 0.49032} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.00879, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96375, "top5_acc": 0.99938, "loss_cls": 0.24248, "loss": 0.24248, "time": 0.49223} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.00877, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.20563, "loss": 0.20563, "time": 0.49089} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.00875, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.22285, "loss": 0.22285, "time": 0.4901} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.00873, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96688, "top5_acc": 0.99875, "loss_cls": 0.22539, "loss": 0.22539, "time": 0.49175} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.00871, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.29175, "loss": 0.29175, "time": 0.49172} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.00869, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94875, "top5_acc": 1.0, "loss_cls": 0.29155, "loss": 0.29155, "time": 0.48866} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.00867, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.93938, "top5_acc": 0.99562, "loss_cls": 0.36541, "loss": 0.36541, "time": 0.49249} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.00865, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.30157, "loss": 0.30157, "time": 0.49211} +{"mode": "val", "epoch": 90, "iter": 533, "lr": 0.00864, "top1_acc": 0.86081, "top5_acc": 0.98991, "mean_class_accuracy": 0.83679} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.00862, "memory": 4083, "data_time": 0.19131, "top1_acc": 0.945, "top5_acc": 0.99875, "loss_cls": 0.33382, "loss": 0.33382, "time": 0.66946} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0086, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95938, "top5_acc": 0.99938, "loss_cls": 0.24619, "loss": 0.24619, "time": 0.44457} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.00858, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96125, "top5_acc": 0.99938, "loss_cls": 0.23576, "loss": 0.23576, "time": 0.49123} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.00856, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9625, "top5_acc": 0.99875, "loss_cls": 0.22953, "loss": 0.22953, "time": 0.49173} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.00854, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95312, "top5_acc": 1.0, "loss_cls": 0.25962, "loss": 0.25962, "time": 0.48884} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.00852, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96312, "top5_acc": 0.99875, "loss_cls": 0.22994, "loss": 0.22994, "time": 0.49246} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.0085, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.22567, "loss": 0.22567, "time": 0.49195} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.00848, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.94125, "top5_acc": 0.99938, "loss_cls": 0.29946, "loss": 0.29946, "time": 0.4903} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.00846, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.96125, "top5_acc": 0.99938, "loss_cls": 0.25148, "loss": 0.25148, "time": 0.49019} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.00844, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95062, "top5_acc": 0.99875, "loss_cls": 0.28081, "loss": 0.28081, "time": 0.49133} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.00842, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95812, "top5_acc": 1.0, "loss_cls": 0.25705, "loss": 0.25705, "time": 0.49119} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.0084, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9575, "top5_acc": 1.0, "loss_cls": 0.26556, "loss": 0.26556, "time": 0.49194} +{"mode": "val", "epoch": 91, "iter": 533, "lr": 0.00839, "top1_acc": 0.87278, "top5_acc": 0.98779, "mean_class_accuracy": 0.83276} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.00837, "memory": 4083, "data_time": 0.18438, "top1_acc": 0.95938, "top5_acc": 1.0, "loss_cls": 0.24386, "loss": 0.24386, "time": 0.66389} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.00835, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.23912, "loss": 0.23912, "time": 0.43444} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.00833, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96312, "top5_acc": 0.99812, "loss_cls": 0.25184, "loss": 0.25184, "time": 0.49388} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.00831, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.21689, "loss": 0.21689, "time": 0.49275} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.00829, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96125, "top5_acc": 0.99812, "loss_cls": 0.22605, "loss": 0.22605, "time": 0.49388} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.00827, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9375, "top5_acc": 0.99938, "loss_cls": 0.30743, "loss": 0.30743, "time": 0.49499} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.00825, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95375, "top5_acc": 1.0, "loss_cls": 0.26737, "loss": 0.26737, "time": 0.49295} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.00824, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.24459, "loss": 0.24459, "time": 0.49096} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.00822, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.95875, "top5_acc": 1.0, "loss_cls": 0.21586, "loss": 0.21586, "time": 0.48992} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.0082, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.95062, "top5_acc": 1.0, "loss_cls": 0.2702, "loss": 0.2702, "time": 0.48913} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.00818, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9525, "top5_acc": 0.99875, "loss_cls": 0.27543, "loss": 0.27543, "time": 0.49204} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.00816, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95312, "top5_acc": 0.99938, "loss_cls": 0.27, "loss": 0.27, "time": 0.49062} +{"mode": "val", "epoch": 92, "iter": 533, "lr": 0.00814, "top1_acc": 0.89297, "top5_acc": 0.99108, "mean_class_accuracy": 0.85468} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.00812, "memory": 4083, "data_time": 0.18719, "top1_acc": 0.97188, "top5_acc": 0.99875, "loss_cls": 0.19995, "loss": 0.19995, "time": 0.69728} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.0081, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.19219, "loss": 0.19219, "time": 0.43422} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.00809, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.22073, "loss": 0.22073, "time": 0.49065} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.00807, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.95625, "top5_acc": 0.99875, "loss_cls": 0.2668, "loss": 0.2668, "time": 0.48907} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.00805, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.23854, "loss": 0.23854, "time": 0.4898} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.00803, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97, "top5_acc": 0.99938, "loss_cls": 0.20413, "loss": 0.20413, "time": 0.49131} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.00801, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.20322, "loss": 0.20322, "time": 0.49199} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.00799, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.21419, "loss": 0.21419, "time": 0.4897} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.00797, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.22738, "loss": 0.22738, "time": 0.49215} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.00795, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.2412, "loss": 0.2412, "time": 0.49098} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.00793, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.2293, "loss": 0.2293, "time": 0.49589} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.00791, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.95562, "top5_acc": 0.99938, "loss_cls": 0.26601, "loss": 0.26601, "time": 0.4946} +{"mode": "val", "epoch": 93, "iter": 533, "lr": 0.0079, "top1_acc": 0.88346, "top5_acc": 0.99132, "mean_class_accuracy": 0.86005} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.00788, "memory": 4083, "data_time": 0.19064, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.18935, "loss": 0.18935, "time": 0.68762} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.00786, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.19843, "loss": 0.19843, "time": 0.44424} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.00784, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.18603, "loss": 0.18603, "time": 0.491} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.00782, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96312, "top5_acc": 1.0, "loss_cls": 0.21554, "loss": 0.21554, "time": 0.49497} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.0078, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.23619, "loss": 0.23619, "time": 0.49384} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.00778, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.217, "loss": 0.217, "time": 0.4877} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.00777, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.19956, "loss": 0.19956, "time": 0.49114} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.00775, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96062, "top5_acc": 1.0, "loss_cls": 0.23793, "loss": 0.23793, "time": 0.49257} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.00773, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.19064, "loss": 0.19064, "time": 0.49202} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.00771, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.20827, "loss": 0.20827, "time": 0.49188} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.00769, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.9625, "top5_acc": 1.0, "loss_cls": 0.21988, "loss": 0.21988, "time": 0.49215} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.00767, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96188, "top5_acc": 1.0, "loss_cls": 0.24163, "loss": 0.24163, "time": 0.49117} +{"mode": "val", "epoch": 94, "iter": 533, "lr": 0.00766, "top1_acc": 0.8803, "top5_acc": 0.98956, "mean_class_accuracy": 0.85804} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.00764, "memory": 4083, "data_time": 0.18449, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.19479, "loss": 0.19479, "time": 0.6658} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.00762, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.1858, "loss": 0.1858, "time": 0.4374} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.0076, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96625, "top5_acc": 0.99938, "loss_cls": 0.21982, "loss": 0.21982, "time": 0.48944} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.00758, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96625, "top5_acc": 0.99938, "loss_cls": 0.21147, "loss": 0.21147, "time": 0.49156} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.00756, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.22553, "loss": 0.22553, "time": 0.4925} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.00754, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.20884, "loss": 0.20884, "time": 0.49405} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.00752, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.9575, "top5_acc": 0.99875, "loss_cls": 0.24904, "loss": 0.24904, "time": 0.49155} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.00751, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.95688, "top5_acc": 0.99875, "loss_cls": 0.24779, "loss": 0.24779, "time": 0.49295} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.00749, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.96438, "top5_acc": 1.0, "loss_cls": 0.21169, "loss": 0.21169, "time": 0.49216} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.00747, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95062, "top5_acc": 1.0, "loss_cls": 0.2698, "loss": 0.2698, "time": 0.48999} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.00745, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.21508, "loss": 0.21508, "time": 0.48747} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.00743, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.95562, "top5_acc": 1.0, "loss_cls": 0.23895, "loss": 0.23895, "time": 0.49204} +{"mode": "val", "epoch": 95, "iter": 533, "lr": 0.00742, "top1_acc": 0.89849, "top5_acc": 0.99272, "mean_class_accuracy": 0.86614} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.0074, "memory": 4083, "data_time": 0.182, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.16269, "loss": 0.16269, "time": 0.68442} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.00738, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.17426, "loss": 0.17426, "time": 0.45136} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.00736, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.13517, "loss": 0.13517, "time": 0.49423} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.00734, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.16402, "loss": 0.16402, "time": 0.49147} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.00732, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96125, "top5_acc": 1.0, "loss_cls": 0.23614, "loss": 0.23614, "time": 0.48966} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.0073, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.22469, "loss": 0.22469, "time": 0.49318} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.00729, "memory": 4083, "data_time": 0.00059, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.22696, "loss": 0.22696, "time": 0.49049} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.00727, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.17095, "loss": 0.17095, "time": 0.49107} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.00725, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.17913, "loss": 0.17913, "time": 0.49144} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.00723, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97375, "top5_acc": 0.99875, "loss_cls": 0.19361, "loss": 0.19361, "time": 0.4945} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.00721, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.2394, "loss": 0.2394, "time": 0.49062} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.00719, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.95875, "top5_acc": 0.99875, "loss_cls": 0.24325, "loss": 0.24325, "time": 0.48857} +{"mode": "val", "epoch": 96, "iter": 533, "lr": 0.00718, "top1_acc": 0.87384, "top5_acc": 0.99085, "mean_class_accuracy": 0.83375} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.00716, "memory": 4083, "data_time": 0.19249, "top1_acc": 0.95625, "top5_acc": 1.0, "loss_cls": 0.2586, "loss": 0.2586, "time": 0.65743} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.00714, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.22037, "loss": 0.22037, "time": 0.44733} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.00712, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9675, "top5_acc": 1.0, "loss_cls": 0.18233, "loss": 0.18233, "time": 0.49198} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.0071, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96312, "top5_acc": 0.99938, "loss_cls": 0.21332, "loss": 0.21332, "time": 0.49711} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.00709, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97062, "top5_acc": 0.99938, "loss_cls": 0.19964, "loss": 0.19964, "time": 0.49421} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.00707, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.20841, "loss": 0.20841, "time": 0.49261} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.00705, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.19874, "loss": 0.19874, "time": 0.48941} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.00703, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9725, "top5_acc": 0.99938, "loss_cls": 0.18121, "loss": 0.18121, "time": 0.49027} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.00701, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96875, "top5_acc": 0.99938, "loss_cls": 0.18706, "loss": 0.18706, "time": 0.48887} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.00699, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9675, "top5_acc": 0.99938, "loss_cls": 0.20154, "loss": 0.20154, "time": 0.49051} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.00698, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96688, "top5_acc": 1.0, "loss_cls": 0.21745, "loss": 0.21745, "time": 0.49389} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.00696, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.1928, "loss": 0.1928, "time": 0.49356} +{"mode": "val", "epoch": 97, "iter": 533, "lr": 0.00694, "top1_acc": 0.90271, "top5_acc": 0.99179, "mean_class_accuracy": 0.86462} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.00692, "memory": 4083, "data_time": 0.19194, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.183, "loss": 0.183, "time": 0.64528} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.00691, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.16868, "loss": 0.16868, "time": 0.44148} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.00689, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.18743, "loss": 0.18743, "time": 0.49145} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.00687, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.21107, "loss": 0.21107, "time": 0.49091} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.00685, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96562, "top5_acc": 1.0, "loss_cls": 0.19874, "loss": 0.19874, "time": 0.49493} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.00683, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9725, "top5_acc": 1.0, "loss_cls": 0.18198, "loss": 0.18198, "time": 0.48909} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.00681, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.17567, "loss": 0.17567, "time": 0.49351} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.0068, "memory": 4083, "data_time": 0.00041, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.19801, "loss": 0.19801, "time": 0.4921} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.00678, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96812, "top5_acc": 1.0, "loss_cls": 0.20211, "loss": 0.20211, "time": 0.48989} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.00676, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.20773, "loss": 0.20773, "time": 0.49173} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.00674, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.965, "top5_acc": 1.0, "loss_cls": 0.19988, "loss": 0.19988, "time": 0.49177} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.00672, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96875, "top5_acc": 0.99938, "loss_cls": 0.21679, "loss": 0.21679, "time": 0.49319} +{"mode": "val", "epoch": 98, "iter": 533, "lr": 0.00671, "top1_acc": 0.88476, "top5_acc": 0.99108, "mean_class_accuracy": 0.85215} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.00669, "memory": 4083, "data_time": 0.19146, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.18739, "loss": 0.18739, "time": 0.66814} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.00667, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.21611, "loss": 0.21611, "time": 0.44863} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.00665, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.19527, "loss": 0.19527, "time": 0.49066} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.00664, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97312, "top5_acc": 1.0, "loss_cls": 0.18195, "loss": 0.18195, "time": 0.493} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.00662, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97875, "top5_acc": 0.99938, "loss_cls": 0.15181, "loss": 0.15181, "time": 0.48901} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.0066, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.17287, "loss": 0.17287, "time": 0.49048} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.00658, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.1783, "loss": 0.1783, "time": 0.48917} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.00656, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96625, "top5_acc": 1.0, "loss_cls": 0.18791, "loss": 0.18791, "time": 0.492} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.00655, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.96375, "top5_acc": 1.0, "loss_cls": 0.2121, "loss": 0.2121, "time": 0.49073} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.00653, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.2225, "loss": 0.2225, "time": 0.49369} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.00651, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.96188, "top5_acc": 0.99938, "loss_cls": 0.22523, "loss": 0.22523, "time": 0.49073} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.00649, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.16293, "loss": 0.16293, "time": 0.49089} +{"mode": "val", "epoch": 99, "iter": 533, "lr": 0.00648, "top1_acc": 0.8864, "top5_acc": 0.99143, "mean_class_accuracy": 0.85352} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.00646, "memory": 4083, "data_time": 0.1846, "top1_acc": 0.97625, "top5_acc": 1.0, "loss_cls": 0.16197, "loss": 0.16197, "time": 0.64712} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.00644, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98062, "top5_acc": 0.99938, "loss_cls": 0.15089, "loss": 0.15089, "time": 0.43164} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.00642, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.15125, "loss": 0.15125, "time": 0.49004} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.00641, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.1477, "loss": 0.1477, "time": 0.49352} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.00639, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.15227, "loss": 0.15227, "time": 0.49181} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.00637, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.16682, "loss": 0.16682, "time": 0.49068} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.00635, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.15937, "loss": 0.15937, "time": 0.4893} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.00634, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.13647, "loss": 0.13647, "time": 0.48831} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.00632, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.96875, "top5_acc": 1.0, "loss_cls": 0.18963, "loss": 0.18963, "time": 0.49226} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.0063, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.16431, "loss": 0.16431, "time": 0.49574} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.00628, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.96438, "top5_acc": 0.99875, "loss_cls": 0.22553, "loss": 0.22553, "time": 0.49209} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.00626, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.17082, "loss": 0.17082, "time": 0.49156} +{"mode": "val", "epoch": 100, "iter": 533, "lr": 0.00625, "top1_acc": 0.89485, "top5_acc": 0.99284, "mean_class_accuracy": 0.86378} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.00623, "memory": 4083, "data_time": 0.18828, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.15629, "loss": 0.15629, "time": 0.69705} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.00621, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.14087, "loss": 0.14087, "time": 0.43473} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.0062, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.13311, "loss": 0.13311, "time": 0.49367} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.00618, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.15184, "loss": 0.15184, "time": 0.49411} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.00616, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.12236, "loss": 0.12236, "time": 0.49135} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.00614, "memory": 4083, "data_time": 0.00036, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.18529, "loss": 0.18529, "time": 0.49062} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.00613, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98438, "top5_acc": 0.99938, "loss_cls": 0.13944, "loss": 0.13944, "time": 0.49192} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.00611, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97062, "top5_acc": 1.0, "loss_cls": 0.15791, "loss": 0.15791, "time": 0.49543} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.00609, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.18363, "loss": 0.18363, "time": 0.49031} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.00607, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.16025, "loss": 0.16025, "time": 0.49283} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.00606, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.96938, "top5_acc": 1.0, "loss_cls": 0.18351, "loss": 0.18351, "time": 0.4919} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.00604, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.96, "top5_acc": 1.0, "loss_cls": 0.23255, "loss": 0.23255, "time": 0.49296} +{"mode": "val", "epoch": 101, "iter": 533, "lr": 0.00602, "top1_acc": 0.88757, "top5_acc": 0.99167, "mean_class_accuracy": 0.8496} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.00601, "memory": 4083, "data_time": 0.18704, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.16374, "loss": 0.16374, "time": 0.68381} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.00599, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.14433, "loss": 0.14433, "time": 0.43948} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.00597, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97188, "top5_acc": 1.0, "loss_cls": 0.1764, "loss": 0.1764, "time": 0.49555} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.00596, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.18525, "loss": 0.18525, "time": 0.49154} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.00594, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97125, "top5_acc": 0.99875, "loss_cls": 0.19054, "loss": 0.19054, "time": 0.49147} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.00592, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.16212, "loss": 0.16212, "time": 0.49032} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.0059, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.1345, "loss": 0.1345, "time": 0.49094} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.00589, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.97, "top5_acc": 1.0, "loss_cls": 0.17998, "loss": 0.17998, "time": 0.48959} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.00587, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.16024, "loss": 0.16024, "time": 0.48928} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.00585, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.15206, "loss": 0.15206, "time": 0.4907} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.00583, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.16041, "loss": 0.16041, "time": 0.4898} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.00582, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.975, "top5_acc": 1.0, "loss_cls": 0.1757, "loss": 0.1757, "time": 0.49191} +{"mode": "val", "epoch": 102, "iter": 533, "lr": 0.0058, "top1_acc": 0.90142, "top5_acc": 0.99096, "mean_class_accuracy": 0.86933} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.00579, "memory": 4083, "data_time": 0.18768, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12243, "loss": 0.12243, "time": 0.66934} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.00577, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.13721, "loss": 0.13721, "time": 0.43015} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.00575, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.10217, "loss": 0.10217, "time": 0.49475} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.00573, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11973, "loss": 0.11973, "time": 0.49262} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.00572, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97688, "top5_acc": 1.0, "loss_cls": 0.15238, "loss": 0.15238, "time": 0.49132} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.0057, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.14006, "loss": 0.14006, "time": 0.48938} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.00568, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.12156, "loss": 0.12156, "time": 0.4933} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.00566, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.12686, "loss": 0.12686, "time": 0.49064} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.00565, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.14211, "loss": 0.14211, "time": 0.48997} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.00563, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.1356, "loss": 0.1356, "time": 0.49363} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.00561, "memory": 4083, "data_time": 0.00051, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.16576, "loss": 0.16576, "time": 0.49211} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.0056, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11544, "loss": 0.11544, "time": 0.48644} +{"mode": "val", "epoch": 103, "iter": 533, "lr": 0.00558, "top1_acc": 0.89978, "top5_acc": 0.99085, "mean_class_accuracy": 0.87119} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.00557, "memory": 4083, "data_time": 0.18663, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.11964, "loss": 0.11964, "time": 0.71346} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.00555, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12802, "loss": 0.12802, "time": 0.4392} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.00553, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.10124, "loss": 0.10124, "time": 0.49074} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.00551, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98, "top5_acc": 1.0, "loss_cls": 0.13507, "loss": 0.13507, "time": 0.48958} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.0055, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12225, "loss": 0.12225, "time": 0.48601} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.00548, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98, "top5_acc": 0.99938, "loss_cls": 0.1332, "loss": 0.1332, "time": 0.48837} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.00546, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97562, "top5_acc": 0.99938, "loss_cls": 0.16246, "loss": 0.16246, "time": 0.49127} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.00545, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97938, "top5_acc": 0.99938, "loss_cls": 0.13732, "loss": 0.13732, "time": 0.49029} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.00543, "memory": 4083, "data_time": 0.00043, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.12762, "loss": 0.12762, "time": 0.49664} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.00541, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97438, "top5_acc": 0.99938, "loss_cls": 0.17375, "loss": 0.17375, "time": 0.48974} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.0054, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.12673, "loss": 0.12673, "time": 0.49247} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.00538, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.97125, "top5_acc": 0.99938, "loss_cls": 0.1823, "loss": 0.1823, "time": 0.48928} +{"mode": "val", "epoch": 104, "iter": 533, "lr": 0.00537, "top1_acc": 0.9006, "top5_acc": 0.99296, "mean_class_accuracy": 0.87194} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.00535, "memory": 4083, "data_time": 0.18504, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.1119, "loss": 0.1119, "time": 0.67553} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.00533, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98, "top5_acc": 0.99875, "loss_cls": 0.14543, "loss": 0.14543, "time": 0.44096} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.00532, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9775, "top5_acc": 0.99938, "loss_cls": 0.14203, "loss": 0.14203, "time": 0.491} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.0053, "memory": 4083, "data_time": 0.00067, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.13282, "loss": 0.13282, "time": 0.48784} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.00528, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.12764, "loss": 0.12764, "time": 0.49347} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.00527, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.11963, "loss": 0.11963, "time": 0.49223} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.00525, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.98188, "top5_acc": 0.99938, "loss_cls": 0.1271, "loss": 0.1271, "time": 0.49324} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.00523, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98125, "top5_acc": 0.99938, "loss_cls": 0.14531, "loss": 0.14531, "time": 0.48993} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.00522, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.12534, "loss": 0.12534, "time": 0.49517} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.0052, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.11248, "loss": 0.11248, "time": 0.48908} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.00518, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.10262, "loss": 0.10262, "time": 0.49086} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.00517, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.97438, "top5_acc": 1.0, "loss_cls": 0.16767, "loss": 0.16767, "time": 0.49136} +{"mode": "val", "epoch": 105, "iter": 533, "lr": 0.00515, "top1_acc": 0.90036, "top5_acc": 0.99167, "mean_class_accuracy": 0.87183} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.00514, "memory": 4083, "data_time": 0.1916, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.13439, "loss": 0.13439, "time": 0.67257} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.00512, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.11145, "loss": 0.11145, "time": 0.44134} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.0051, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.11078, "loss": 0.11078, "time": 0.4877} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.00509, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9775, "top5_acc": 1.0, "loss_cls": 0.13567, "loss": 0.13567, "time": 0.49004} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.00507, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.15084, "loss": 0.15084, "time": 0.49165} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.00505, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98188, "top5_acc": 1.0, "loss_cls": 0.13045, "loss": 0.13045, "time": 0.49155} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.00504, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.1159, "loss": 0.1159, "time": 0.4898} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.00502, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11042, "loss": 0.11042, "time": 0.48953} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.005, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.11253, "loss": 0.11253, "time": 0.49155} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.00499, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.11035, "loss": 0.11035, "time": 0.494} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.00497, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.12694, "loss": 0.12694, "time": 0.49024} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.00496, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.975, "top5_acc": 0.99938, "loss_cls": 0.15336, "loss": 0.15336, "time": 0.49437} +{"mode": "val", "epoch": 106, "iter": 533, "lr": 0.00494, "top1_acc": 0.89919, "top5_acc": 0.99038, "mean_class_accuracy": 0.87196} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.00493, "memory": 4083, "data_time": 0.18988, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.09024, "loss": 0.09024, "time": 0.68518} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.00491, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09935, "loss": 0.09935, "time": 0.44504} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.00489, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.09858, "loss": 0.09858, "time": 0.49331} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.00488, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98125, "top5_acc": 1.0, "loss_cls": 0.11775, "loss": 0.11775, "time": 0.48743} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.00486, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10244, "loss": 0.10244, "time": 0.48942} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.00485, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.14905, "loss": 0.14905, "time": 0.49186} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.00483, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98312, "top5_acc": 0.99938, "loss_cls": 0.12404, "loss": 0.12404, "time": 0.49268} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.00481, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.13584, "loss": 0.13584, "time": 0.48973} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.0048, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.1276, "loss": 0.1276, "time": 0.49044} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.00478, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9825, "top5_acc": 0.99938, "loss_cls": 0.13043, "loss": 0.13043, "time": 0.49193} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.00476, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.14107, "loss": 0.14107, "time": 0.49146} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.00475, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97812, "top5_acc": 1.0, "loss_cls": 0.17351, "loss": 0.17351, "time": 0.4936} +{"mode": "val", "epoch": 107, "iter": 533, "lr": 0.00474, "top1_acc": 0.89344, "top5_acc": 0.99143, "mean_class_accuracy": 0.85921} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.00472, "memory": 4083, "data_time": 0.18562, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.11983, "loss": 0.11983, "time": 0.66242} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0047, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.08987, "loss": 0.08987, "time": 0.44037} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.00469, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.09402, "loss": 0.09402, "time": 0.48984} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.00467, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.08373, "loss": 0.08373, "time": 0.48863} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.00466, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.11231, "loss": 0.11231, "time": 0.49096} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.00464, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97125, "top5_acc": 1.0, "loss_cls": 0.15054, "loss": 0.15054, "time": 0.49113} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.00462, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.12161, "loss": 0.12161, "time": 0.49017} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.00461, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.97375, "top5_acc": 1.0, "loss_cls": 0.15835, "loss": 0.15835, "time": 0.49156} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.00459, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.97375, "top5_acc": 0.99938, "loss_cls": 0.15707, "loss": 0.15707, "time": 0.49228} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.00458, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.12902, "loss": 0.12902, "time": 0.48836} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.00456, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.1455, "loss": 0.1455, "time": 0.48945} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.00455, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.1038, "loss": 0.1038, "time": 0.48974} +{"mode": "val", "epoch": 108, "iter": 533, "lr": 0.00453, "top1_acc": 0.89532, "top5_acc": 0.99143, "mean_class_accuracy": 0.86996} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.00452, "memory": 4083, "data_time": 0.18434, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.11154, "loss": 0.11154, "time": 0.7} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.0045, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98438, "top5_acc": 1.0, "loss_cls": 0.10743, "loss": 0.10743, "time": 0.44021} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.00449, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.09409, "loss": 0.09409, "time": 0.49104} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.00447, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.10816, "loss": 0.10816, "time": 0.49089} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.00445, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.09202, "loss": 0.09202, "time": 0.4914} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.00444, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.11395, "loss": 0.11395, "time": 0.49013} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.00442, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.09289, "loss": 0.09289, "time": 0.49407} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.00441, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.97875, "top5_acc": 1.0, "loss_cls": 0.13591, "loss": 0.13591, "time": 0.49132} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.00439, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.1105, "loss": 0.1105, "time": 0.48845} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.00438, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.09613, "loss": 0.09613, "time": 0.49174} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.00436, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08657, "loss": 0.08657, "time": 0.49329} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.00434, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.10427, "loss": 0.10427, "time": 0.48949} +{"mode": "val", "epoch": 109, "iter": 533, "lr": 0.00433, "top1_acc": 0.90107, "top5_acc": 0.99319, "mean_class_accuracy": 0.86837} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.00432, "memory": 4083, "data_time": 0.18911, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.10889, "loss": 0.10889, "time": 0.67399} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.0043, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.10327, "loss": 0.10327, "time": 0.45392} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.00429, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.08647, "loss": 0.08647, "time": 0.49095} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.00427, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.11099, "loss": 0.11099, "time": 0.49018} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.00426, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.985, "top5_acc": 1.0, "loss_cls": 0.11604, "loss": 0.11604, "time": 0.49217} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.00424, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.09586, "loss": 0.09586, "time": 0.49426} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.00422, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.07853, "loss": 0.07853, "time": 0.49306} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.00421, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.985, "top5_acc": 0.99938, "loss_cls": 0.09833, "loss": 0.09833, "time": 0.49128} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.00419, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11624, "loss": 0.11624, "time": 0.4909} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.00418, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.98562, "top5_acc": 0.99938, "loss_cls": 0.12074, "loss": 0.12074, "time": 0.49022} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.00416, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.97938, "top5_acc": 1.0, "loss_cls": 0.13299, "loss": 0.13299, "time": 0.49128} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.00415, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9825, "top5_acc": 1.0, "loss_cls": 0.13241, "loss": 0.13241, "time": 0.49492} +{"mode": "val", "epoch": 110, "iter": 533, "lr": 0.00414, "top1_acc": 0.90482, "top5_acc": 0.99214, "mean_class_accuracy": 0.87572} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.00412, "memory": 4083, "data_time": 0.1862, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.07407, "loss": 0.07407, "time": 0.64099} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.00411, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.07452, "loss": 0.07452, "time": 0.44565} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.00409, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98625, "top5_acc": 0.99938, "loss_cls": 0.10173, "loss": 0.10173, "time": 0.49344} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.00408, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08152, "loss": 0.08152, "time": 0.4921} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.00406, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07533, "loss": 0.07533, "time": 0.49053} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.00405, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.0785, "loss": 0.0785, "time": 0.49281} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.00403, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98688, "top5_acc": 1.0, "loss_cls": 0.09565, "loss": 0.09565, "time": 0.49338} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.00402, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.09637, "loss": 0.09637, "time": 0.49054} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.004, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.08683, "loss": 0.08683, "time": 0.48924} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.00399, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.0973, "loss": 0.0973, "time": 0.49002} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.00397, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.98875, "top5_acc": 1.0, "loss_cls": 0.08729, "loss": 0.08729, "time": 0.49274} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.00396, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98062, "top5_acc": 1.0, "loss_cls": 0.11748, "loss": 0.11748, "time": 0.49211} +{"mode": "val", "epoch": 111, "iter": 533, "lr": 0.00394, "top1_acc": 0.90365, "top5_acc": 0.99272, "mean_class_accuracy": 0.87707} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.00393, "memory": 4083, "data_time": 0.18457, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.10427, "loss": 0.10427, "time": 0.64183} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.00391, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.05907, "loss": 0.05907, "time": 0.44619} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.0039, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.07716, "loss": 0.07716, "time": 0.48825} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.00388, "memory": 4083, "data_time": 0.00057, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.07745, "loss": 0.07745, "time": 0.49147} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.00387, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.06343, "loss": 0.06343, "time": 0.4935} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.00385, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.10073, "loss": 0.10073, "time": 0.48979} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.00384, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98375, "top5_acc": 1.0, "loss_cls": 0.10551, "loss": 0.10551, "time": 0.49178} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.00382, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98562, "top5_acc": 1.0, "loss_cls": 0.11472, "loss": 0.11472, "time": 0.49078} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.00381, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.07102, "loss": 0.07102, "time": 0.49132} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.0038, "memory": 4083, "data_time": 0.00021, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.08038, "loss": 0.08038, "time": 0.48916} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.00378, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.08063, "loss": 0.08063, "time": 0.49238} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.00377, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.97938, "top5_acc": 0.99938, "loss_cls": 0.14157, "loss": 0.14157, "time": 0.49279} +{"mode": "val", "epoch": 112, "iter": 533, "lr": 0.00375, "top1_acc": 0.90271, "top5_acc": 0.99237, "mean_class_accuracy": 0.87681} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.00374, "memory": 4083, "data_time": 0.19146, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05889, "loss": 0.05889, "time": 0.63273} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.00373, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.0589, "loss": 0.0589, "time": 0.44626} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.00371, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.98688, "top5_acc": 0.99938, "loss_cls": 0.09543, "loss": 0.09543, "time": 0.48859} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.0037, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08986, "loss": 0.08986, "time": 0.4943} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.00368, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.06004, "loss": 0.06004, "time": 0.48828} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.00367, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05177, "loss": 0.05177, "time": 0.49014} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.00365, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05304, "loss": 0.05304, "time": 0.48945} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.00364, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.09727, "loss": 0.09727, "time": 0.4899} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.00362, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.08086, "loss": 0.08086, "time": 0.49026} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.00361, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.07627, "loss": 0.07627, "time": 0.49197} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0036, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.07341, "loss": 0.07341, "time": 0.49178} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.00358, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.07594, "loss": 0.07594, "time": 0.48927} +{"mode": "val", "epoch": 113, "iter": 533, "lr": 0.00357, "top1_acc": 0.90494, "top5_acc": 0.99272, "mean_class_accuracy": 0.87467} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.00355, "memory": 4083, "data_time": 0.18965, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06572, "loss": 0.06572, "time": 0.66512} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.00354, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06591, "loss": 0.06591, "time": 0.44388} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.00353, "memory": 4083, "data_time": 0.00049, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.0783, "loss": 0.0783, "time": 0.49009} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.00351, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.06412, "loss": 0.06412, "time": 0.49116} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.0035, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.11138, "loss": 0.11138, "time": 0.49073} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.00348, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98812, "top5_acc": 0.99938, "loss_cls": 0.11008, "loss": 0.11008, "time": 0.49001} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.00347, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98312, "top5_acc": 1.0, "loss_cls": 0.09541, "loss": 0.09541, "time": 0.49005} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.00346, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.98625, "top5_acc": 1.0, "loss_cls": 0.09454, "loss": 0.09454, "time": 0.48959} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.00344, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98812, "top5_acc": 1.0, "loss_cls": 0.08905, "loss": 0.08905, "time": 0.49073} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.00343, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.09035, "loss": 0.09035, "time": 0.49103} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.00341, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07593, "loss": 0.07593, "time": 0.49196} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.0034, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06691, "loss": 0.06691, "time": 0.48916} +{"mode": "val", "epoch": 114, "iter": 533, "lr": 0.00339, "top1_acc": 0.91046, "top5_acc": 0.99167, "mean_class_accuracy": 0.88536} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.00337, "memory": 4083, "data_time": 0.18614, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.05464, "loss": 0.05464, "time": 0.65028} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.00336, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.98938, "top5_acc": 1.0, "loss_cls": 0.07714, "loss": 0.07714, "time": 0.43964} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.00335, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07536, "loss": 0.07536, "time": 0.49242} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.00333, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.08046, "loss": 0.08046, "time": 0.4878} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.00332, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.06247, "loss": 0.06247, "time": 0.49263} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.0033, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.08278, "loss": 0.08278, "time": 0.49138} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.00329, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.08472, "loss": 0.08472, "time": 0.49046} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.00328, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.08976, "loss": 0.08976, "time": 0.49167} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.00326, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.07871, "loss": 0.07871, "time": 0.48955} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.00325, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.07582, "loss": 0.07582, "time": 0.49196} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.00324, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05255, "loss": 0.05255, "time": 0.49306} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.00322, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.98875, "top5_acc": 0.99938, "loss_cls": 0.08059, "loss": 0.08059, "time": 0.48908} +{"mode": "val", "epoch": 115, "iter": 533, "lr": 0.00321, "top1_acc": 0.90917, "top5_acc": 0.99237, "mean_class_accuracy": 0.87884} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.0032, "memory": 4083, "data_time": 0.18483, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06949, "loss": 0.06949, "time": 0.70122} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.00318, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.0622, "loss": 0.0622, "time": 0.4465} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.00317, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99, "top5_acc": 1.0, "loss_cls": 0.07809, "loss": 0.07809, "time": 0.49364} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.00316, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05891, "loss": 0.05891, "time": 0.49308} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.00314, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05769, "loss": 0.05769, "time": 0.48832} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.00313, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.05167, "loss": 0.05167, "time": 0.49189} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.00312, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99125, "top5_acc": 1.0, "loss_cls": 0.07073, "loss": 0.07073, "time": 0.49058} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.0031, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06507, "loss": 0.06507, "time": 0.4922} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.00309, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.062, "loss": 0.062, "time": 0.49193} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.00308, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.05025, "loss": 0.05025, "time": 0.48665} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.00306, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.0583, "loss": 0.0583, "time": 0.49265} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.00305, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.98812, "top5_acc": 0.99938, "loss_cls": 0.08105, "loss": 0.08105, "time": 0.49514} +{"mode": "val", "epoch": 116, "iter": 533, "lr": 0.00304, "top1_acc": 0.9114, "top5_acc": 0.9939, "mean_class_accuracy": 0.88651} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.00302, "memory": 4083, "data_time": 0.18175, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.06921, "loss": 0.06921, "time": 0.66766} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.00301, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.06056, "loss": 0.06056, "time": 0.44156} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.003, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.06767, "loss": 0.06767, "time": 0.4912} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.00298, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.0772, "loss": 0.0772, "time": 0.49203} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.00297, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.05597, "loss": 0.05597, "time": 0.49167} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.00296, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99188, "top5_acc": 1.0, "loss_cls": 0.0614, "loss": 0.0614, "time": 0.48999} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.00294, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.0537, "loss": 0.0537, "time": 0.4886} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.00293, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05558, "loss": 0.05558, "time": 0.49026} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.00292, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99062, "top5_acc": 1.0, "loss_cls": 0.06242, "loss": 0.06242, "time": 0.49119} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.00291, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99, "top5_acc": 0.99938, "loss_cls": 0.07486, "loss": 0.07486, "time": 0.48806} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.00289, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.06176, "loss": 0.06176, "time": 0.49052} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.00288, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99312, "top5_acc": 1.0, "loss_cls": 0.05753, "loss": 0.05753, "time": 0.49312} +{"mode": "val", "epoch": 117, "iter": 533, "lr": 0.00287, "top1_acc": 0.91175, "top5_acc": 0.99308, "mean_class_accuracy": 0.88812} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.00286, "memory": 4083, "data_time": 0.18238, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04839, "loss": 0.04839, "time": 0.66467} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.00284, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.052, "loss": 0.052, "time": 0.45071} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.00283, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04905, "loss": 0.04905, "time": 0.49413} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.00282, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.053, "loss": 0.053, "time": 0.49317} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.0028, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.03441, "loss": 0.03441, "time": 0.4934} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.00279, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04769, "loss": 0.04769, "time": 0.4897} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.00278, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06205, "loss": 0.06205, "time": 0.49236} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.00277, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.04781, "loss": 0.04781, "time": 0.49037} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.00275, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03658, "loss": 0.03658, "time": 0.4896} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.00274, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04323, "loss": 0.04323, "time": 0.49048} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.00273, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03855, "loss": 0.03855, "time": 0.48831} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.00271, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04473, "loss": 0.04473, "time": 0.48978} +{"mode": "val", "epoch": 118, "iter": 533, "lr": 0.0027, "top1_acc": 0.92243, "top5_acc": 0.99401, "mean_class_accuracy": 0.89646} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.00269, "memory": 4083, "data_time": 0.18596, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02794, "loss": 0.02794, "time": 0.65043} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.00268, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03379, "loss": 0.03379, "time": 0.44366} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.00267, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.0385, "loss": 0.0385, "time": 0.4947} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.00265, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03934, "loss": 0.03934, "time": 0.49178} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.00264, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03182, "loss": 0.03182, "time": 0.49063} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.00263, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04079, "loss": 0.04079, "time": 0.49117} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.00262, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06606, "loss": 0.06606, "time": 0.48839} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.0026, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.0446, "loss": 0.0446, "time": 0.48998} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.00259, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04594, "loss": 0.04594, "time": 0.489} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.00258, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03773, "loss": 0.03773, "time": 0.48989} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.00257, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03464, "loss": 0.03464, "time": 0.49057} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.00255, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03621, "loss": 0.03621, "time": 0.49186} +{"mode": "val", "epoch": 119, "iter": 533, "lr": 0.00254, "top1_acc": 0.91609, "top5_acc": 0.9939, "mean_class_accuracy": 0.89009} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.00253, "memory": 4083, "data_time": 0.18546, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03703, "loss": 0.03703, "time": 0.66195} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.00252, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03537, "loss": 0.03537, "time": 0.44414} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.00251, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03406, "loss": 0.03406, "time": 0.49061} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.00249, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03913, "loss": 0.03913, "time": 0.4885} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.00248, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03487, "loss": 0.03487, "time": 0.48919} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.00247, "memory": 4083, "data_time": 0.00039, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.06095, "loss": 0.06095, "time": 0.49171} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.00246, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.04049, "loss": 0.04049, "time": 0.48733} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.00245, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04344, "loss": 0.04344, "time": 0.49064} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.00243, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04538, "loss": 0.04538, "time": 0.49256} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.00242, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.04204, "loss": 0.04204, "time": 0.49096} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00241, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99375, "top5_acc": 1.0, "loss_cls": 0.05614, "loss": 0.05614, "time": 0.48997} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.0024, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.0579, "loss": 0.0579, "time": 0.48681} +{"mode": "val", "epoch": 120, "iter": 533, "lr": 0.00239, "top1_acc": 0.91409, "top5_acc": 0.99366, "mean_class_accuracy": 0.88664} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00238, "memory": 4083, "data_time": 0.19067, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04646, "loss": 0.04646, "time": 0.68687} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00236, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.06377, "loss": 0.06377, "time": 0.44412} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.00235, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.04311, "loss": 0.04311, "time": 0.4879} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00234, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04338, "loss": 0.04338, "time": 0.48901} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00233, "memory": 4083, "data_time": 0.00042, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04873, "loss": 0.04873, "time": 0.48976} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00232, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99438, "top5_acc": 1.0, "loss_cls": 0.05585, "loss": 0.05585, "time": 0.49402} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.0023, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9875, "top5_acc": 1.0, "loss_cls": 0.06536, "loss": 0.06536, "time": 0.49181} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00229, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.04289, "loss": 0.04289, "time": 0.49003} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.00228, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03778, "loss": 0.03778, "time": 0.49182} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00227, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.9925, "top5_acc": 1.0, "loss_cls": 0.05664, "loss": 0.05664, "time": 0.49344} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00226, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04951, "loss": 0.04951, "time": 0.49042} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00225, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.995, "top5_acc": 1.0, "loss_cls": 0.04565, "loss": 0.04565, "time": 0.49305} +{"mode": "val", "epoch": 121, "iter": 533, "lr": 0.00224, "top1_acc": 0.9128, "top5_acc": 0.99225, "mean_class_accuracy": 0.88845} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00222, "memory": 4083, "data_time": 0.18778, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0325, "loss": 0.0325, "time": 0.66206} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00221, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03733, "loss": 0.03733, "time": 0.4329} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.0022, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03408, "loss": 0.03408, "time": 0.49145} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00219, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.04035, "loss": 0.04035, "time": 0.48988} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00218, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.0338, "loss": 0.0338, "time": 0.49253} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00217, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03443, "loss": 0.03443, "time": 0.49348} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00215, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03106, "loss": 0.03106, "time": 0.49109} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00214, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02991, "loss": 0.02991, "time": 0.49174} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.00213, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03385, "loss": 0.03385, "time": 0.49461} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00212, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.03969, "loss": 0.03969, "time": 0.49007} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00211, "memory": 4083, "data_time": 0.00034, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02948, "loss": 0.02948, "time": 0.49182} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.0021, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.04042, "loss": 0.04042, "time": 0.49046} +{"mode": "val", "epoch": 122, "iter": 533, "lr": 0.00209, "top1_acc": 0.91562, "top5_acc": 0.99401, "mean_class_accuracy": 0.89438} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00208, "memory": 4083, "data_time": 0.18654, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.033, "loss": 0.033, "time": 0.71645} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00207, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0247, "loss": 0.0247, "time": 0.4532} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00205, "memory": 4083, "data_time": 0.00046, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.03019, "loss": 0.03019, "time": 0.49127} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00204, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0285, "loss": 0.0285, "time": 0.48852} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00203, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01953, "loss": 0.01953, "time": 0.4892} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00202, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02929, "loss": 0.02929, "time": 0.48949} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00201, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02249, "loss": 0.02249, "time": 0.49089} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.002, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02767, "loss": 0.02767, "time": 0.49231} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00199, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02431, "loss": 0.02431, "time": 0.49085} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.00198, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03119, "loss": 0.03119, "time": 0.49} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00197, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.03649, "loss": 0.03649, "time": 0.49052} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00195, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02749, "loss": 0.02749, "time": 0.49159} +{"mode": "val", "epoch": 123, "iter": 533, "lr": 0.00195, "top1_acc": 0.92243, "top5_acc": 0.99401, "mean_class_accuracy": 0.89588} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00194, "memory": 4083, "data_time": 0.18785, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02597, "loss": 0.02597, "time": 0.62996} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00192, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03149, "loss": 0.03149, "time": 0.44311} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00191, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.03492, "loss": 0.03492, "time": 0.4885} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.0019, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02608, "loss": 0.02608, "time": 0.48838} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00189, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02935, "loss": 0.02935, "time": 0.49082} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00188, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99562, "top5_acc": 1.0, "loss_cls": 0.03629, "loss": 0.03629, "time": 0.49253} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00187, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0293, "loss": 0.0293, "time": 0.49127} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00186, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02471, "loss": 0.02471, "time": 0.49127} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00185, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02935, "loss": 0.02935, "time": 0.49024} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00184, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02791, "loss": 0.02791, "time": 0.49257} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00183, "memory": 4083, "data_time": 0.0007, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02364, "loss": 0.02364, "time": 0.49443} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.00182, "memory": 4083, "data_time": 0.00045, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02409, "loss": 0.02409, "time": 0.49431} +{"mode": "val", "epoch": 124, "iter": 533, "lr": 0.00181, "top1_acc": 0.9243, "top5_acc": 0.99495, "mean_class_accuracy": 0.90054} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.0018, "memory": 4083, "data_time": 0.18486, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.0341, "loss": 0.0341, "time": 0.65001} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.00179, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.0308, "loss": 0.0308, "time": 0.45008} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00178, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02466, "loss": 0.02466, "time": 0.48995} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00177, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02848, "loss": 0.02848, "time": 0.49099} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00176, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02343, "loss": 0.02343, "time": 0.49095} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00175, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02776, "loss": 0.02776, "time": 0.49462} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00173, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02086, "loss": 0.02086, "time": 0.49536} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00172, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0253, "loss": 0.0253, "time": 0.49096} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.00171, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02987, "loss": 0.02987, "time": 0.49144} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.0017, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.0272, "loss": 0.0272, "time": 0.49196} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00169, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.0279, "loss": 0.0279, "time": 0.49102} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00168, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02859, "loss": 0.02859, "time": 0.49133} +{"mode": "val", "epoch": 125, "iter": 533, "lr": 0.00167, "top1_acc": 0.91632, "top5_acc": 0.99472, "mean_class_accuracy": 0.89135} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00166, "memory": 4083, "data_time": 0.18798, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02379, "loss": 0.02379, "time": 0.66416} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00165, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01952, "loss": 0.01952, "time": 0.44679} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00164, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02086, "loss": 0.02086, "time": 0.48907} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00163, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02285, "loss": 0.02285, "time": 0.48874} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00162, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01868, "loss": 0.01868, "time": 0.4904} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00161, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01789, "loss": 0.01789, "time": 0.49292} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0016, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01788, "loss": 0.01788, "time": 0.49211} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00159, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01827, "loss": 0.01827, "time": 0.49038} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00158, "memory": 4083, "data_time": 0.00041, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01781, "loss": 0.01781, "time": 0.49385} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00157, "memory": 4083, "data_time": 0.00089, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02906, "loss": 0.02906, "time": 0.49036} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00156, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02032, "loss": 0.02032, "time": 0.48978} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00155, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02403, "loss": 0.02403, "time": 0.4916} +{"mode": "val", "epoch": 126, "iter": 533, "lr": 0.00155, "top1_acc": 0.92278, "top5_acc": 0.99378, "mean_class_accuracy": 0.89954} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00154, "memory": 4083, "data_time": 0.18751, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02003, "loss": 0.02003, "time": 0.65439} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00153, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0207, "loss": 0.0207, "time": 0.44508} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00152, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01942, "loss": 0.01942, "time": 0.48969} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00151, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02406, "loss": 0.02406, "time": 0.48917} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.0015, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02747, "loss": 0.02747, "time": 0.49086} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.00149, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.0213, "loss": 0.0213, "time": 0.49378} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00148, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02006, "loss": 0.02006, "time": 0.49167} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00147, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02169, "loss": 0.02169, "time": 0.49445} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00146, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02075, "loss": 0.02075, "time": 0.48741} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00145, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02027, "loss": 0.02027, "time": 0.48887} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00144, "memory": 4083, "data_time": 0.00033, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01845, "loss": 0.01845, "time": 0.49214} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00143, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01961, "loss": 0.01961, "time": 0.4934} +{"mode": "val", "epoch": 127, "iter": 533, "lr": 0.00142, "top1_acc": 0.92595, "top5_acc": 0.99448, "mean_class_accuracy": 0.89963} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00141, "memory": 4083, "data_time": 0.187, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02116, "loss": 0.02116, "time": 0.66238} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.0014, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01762, "loss": 0.01762, "time": 0.45037} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00139, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01875, "loss": 0.01875, "time": 0.49017} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00138, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01813, "loss": 0.01813, "time": 0.49036} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00138, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02074, "loss": 0.02074, "time": 0.49143} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00137, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02849, "loss": 0.02849, "time": 0.49067} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.00136, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02497, "loss": 0.02497, "time": 0.49007} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00135, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01945, "loss": 0.01945, "time": 0.4906} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00134, "memory": 4083, "data_time": 0.0004, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02617, "loss": 0.02617, "time": 0.49208} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00133, "memory": 4083, "data_time": 0.00054, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02968, "loss": 0.02968, "time": 0.49177} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00132, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02769, "loss": 0.02769, "time": 0.49526} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00131, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99688, "top5_acc": 1.0, "loss_cls": 0.03166, "loss": 0.03166, "time": 0.49262} +{"mode": "val", "epoch": 128, "iter": 533, "lr": 0.0013, "top1_acc": 0.92008, "top5_acc": 0.99331, "mean_class_accuracy": 0.89313} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.00129, "memory": 4083, "data_time": 0.18375, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02625, "loss": 0.02625, "time": 0.64649} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00129, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02297, "loss": 0.02297, "time": 0.4484} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00128, "memory": 4083, "data_time": 0.00058, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02303, "loss": 0.02303, "time": 0.4879} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00127, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02433, "loss": 0.02433, "time": 0.4949} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00126, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02091, "loss": 0.02091, "time": 0.49248} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00125, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02394, "loss": 0.02394, "time": 0.49192} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00124, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02998, "loss": 0.02998, "time": 0.49338} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00123, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02057, "loss": 0.02057, "time": 0.49273} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.00122, "memory": 4083, "data_time": 0.00044, "top1_acc": 0.9975, "top5_acc": 1.0, "loss_cls": 0.02706, "loss": 0.02706, "time": 0.49209} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00121, "memory": 4083, "data_time": 0.00047, "top1_acc": 0.99625, "top5_acc": 1.0, "loss_cls": 0.03099, "loss": 0.03099, "time": 0.49055} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00121, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02183, "loss": 0.02183, "time": 0.49028} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.0012, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02367, "loss": 0.02367, "time": 0.49028} +{"mode": "val", "epoch": 129, "iter": 533, "lr": 0.00119, "top1_acc": 0.92266, "top5_acc": 0.99413, "mean_class_accuracy": 0.89901} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00118, "memory": 4083, "data_time": 0.19456, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.02141, "loss": 0.02141, "time": 0.68808} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00117, "memory": 4083, "data_time": 0.00032, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02057, "loss": 0.02057, "time": 0.44899} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00116, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0208, "loss": 0.0208, "time": 0.49595} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00116, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0198, "loss": 0.0198, "time": 0.49218} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.00115, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01648, "loss": 0.01648, "time": 0.49254} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00114, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02225, "loss": 0.02225, "time": 0.49096} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00113, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02122, "loss": 0.02122, "time": 0.49415} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00112, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0186, "loss": 0.0186, "time": 0.49174} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00111, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02623, "loss": 0.02623, "time": 0.48959} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.0011, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02534, "loss": 0.02534, "time": 0.49099} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.0011, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02262, "loss": 0.02262, "time": 0.49214} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00109, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02254, "loss": 0.02254, "time": 0.49067} +{"mode": "val", "epoch": 130, "iter": 533, "lr": 0.00108, "top1_acc": 0.92313, "top5_acc": 0.99448, "mean_class_accuracy": 0.90053} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00107, "memory": 4083, "data_time": 0.18779, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02413, "loss": 0.02413, "time": 0.64178} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.00106, "memory": 4083, "data_time": 0.00037, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02061, "loss": 0.02061, "time": 0.44025} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00106, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01782, "loss": 0.01782, "time": 0.49633} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00105, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02298, "loss": 0.02298, "time": 0.49188} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00104, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01897, "loss": 0.01897, "time": 0.49399} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00103, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01809, "loss": 0.01809, "time": 0.4907} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00102, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01977, "loss": 0.01977, "time": 0.49115} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00102, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01739, "loss": 0.01739, "time": 0.49154} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00101, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02262, "loss": 0.02262, "time": 0.49048} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.001, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02421, "loss": 0.02421, "time": 0.49168} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.00099, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02733, "loss": 0.02733, "time": 0.49294} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00098, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02357, "loss": 0.02357, "time": 0.49072} +{"mode": "val", "epoch": 131, "iter": 533, "lr": 0.00098, "top1_acc": 0.92395, "top5_acc": 0.99448, "mean_class_accuracy": 0.89981} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.00097, "memory": 4083, "data_time": 0.18508, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01902, "loss": 0.01902, "time": 0.67487} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00096, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02442, "loss": 0.02442, "time": 0.43814} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00095, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01841, "loss": 0.01841, "time": 0.49461} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00095, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01952, "loss": 0.01952, "time": 0.49346} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00094, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01749, "loss": 0.01749, "time": 0.49028} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00093, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01962, "loss": 0.01962, "time": 0.49228} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00092, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01774, "loss": 0.01774, "time": 0.48908} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00091, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02246, "loss": 0.02246, "time": 0.49332} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00091, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01913, "loss": 0.01913, "time": 0.49133} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0009, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01938, "loss": 0.01938, "time": 0.49051} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00089, "memory": 4083, "data_time": 0.00028, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.02313, "loss": 0.02313, "time": 0.49331} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00088, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01969, "loss": 0.01969, "time": 0.48828} +{"mode": "val", "epoch": 132, "iter": 533, "lr": 0.00088, "top1_acc": 0.92595, "top5_acc": 0.99519, "mean_class_accuracy": 0.90295} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.00087, "memory": 4083, "data_time": 0.18586, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01809, "loss": 0.01809, "time": 0.68173} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00086, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01947, "loss": 0.01947, "time": 0.44116} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00086, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01823, "loss": 0.01823, "time": 0.49248} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00085, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01873, "loss": 0.01873, "time": 0.49299} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00084, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01775, "loss": 0.01775, "time": 0.48919} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00083, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02425, "loss": 0.02425, "time": 0.49106} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00083, "memory": 4083, "data_time": 0.00079, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01957, "loss": 0.01957, "time": 0.49329} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00082, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01649, "loss": 0.01649, "time": 0.49197} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00081, "memory": 4083, "data_time": 0.00038, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01961, "loss": 0.01961, "time": 0.49349} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.0008, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01761, "loss": 0.01761, "time": 0.49021} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0008, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01638, "loss": 0.01638, "time": 0.49662} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00079, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01761, "loss": 0.01761, "time": 0.49308} +{"mode": "val", "epoch": 133, "iter": 533, "lr": 0.00078, "top1_acc": 0.92489, "top5_acc": 0.99554, "mean_class_accuracy": 0.90355} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00078, "memory": 4083, "data_time": 0.18897, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01721, "loss": 0.01721, "time": 0.66099} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00077, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01961, "loss": 0.01961, "time": 0.4475} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00076, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01914, "loss": 0.01914, "time": 0.49476} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.00076, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01712, "loss": 0.01712, "time": 0.49087} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00075, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01859, "loss": 0.01859, "time": 0.49198} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00074, "memory": 4083, "data_time": 0.00035, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02026, "loss": 0.02026, "time": 0.49189} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00073, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01886, "loss": 0.01886, "time": 0.49012} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00073, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01778, "loss": 0.01778, "time": 0.49093} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00072, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01782, "loss": 0.01782, "time": 0.49371} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00071, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01669, "loss": 0.01669, "time": 0.49581} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00071, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01824, "loss": 0.01824, "time": 0.49048} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.0007, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01955, "loss": 0.01955, "time": 0.49306} +{"mode": "val", "epoch": 134, "iter": 533, "lr": 0.0007, "top1_acc": 0.92571, "top5_acc": 0.9946, "mean_class_accuracy": 0.90518} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00069, "memory": 4083, "data_time": 0.18459, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01609, "loss": 0.01609, "time": 0.66426} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00068, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01846, "loss": 0.01846, "time": 0.44976} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00068, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0164, "loss": 0.0164, "time": 0.49536} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00067, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01575, "loss": 0.01575, "time": 0.49249} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00066, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0167, "loss": 0.0167, "time": 0.49013} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00066, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01614, "loss": 0.01614, "time": 0.48969} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00065, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01725, "loss": 0.01725, "time": 0.49227} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00064, "memory": 4083, "data_time": 0.00053, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01768, "loss": 0.01768, "time": 0.49042} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.00064, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01778, "loss": 0.01778, "time": 0.48789} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00063, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01611, "loss": 0.01611, "time": 0.49164} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00062, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01792, "loss": 0.01792, "time": 0.49042} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00062, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01804, "loss": 0.01804, "time": 0.49175} +{"mode": "val", "epoch": 135, "iter": 533, "lr": 0.00061, "top1_acc": 0.92712, "top5_acc": 0.99507, "mean_class_accuracy": 0.90404} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00061, "memory": 4083, "data_time": 0.18138, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01901, "loss": 0.01901, "time": 0.66826} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.0006, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01865, "loss": 0.01865, "time": 0.43131} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00059, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01944, "loss": 0.01944, "time": 0.48866} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00059, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01945, "loss": 0.01945, "time": 0.49067} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.00058, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0166, "loss": 0.0166, "time": 0.49029} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.00057, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01605, "loss": 0.01605, "time": 0.48746} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00057, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01753, "loss": 0.01753, "time": 0.48897} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00056, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01723, "loss": 0.01723, "time": 0.48829} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00056, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01655, "loss": 0.01655, "time": 0.49079} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00055, "memory": 4083, "data_time": 0.00031, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01874, "loss": 0.01874, "time": 0.48726} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00054, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01656, "loss": 0.01656, "time": 0.49041} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00054, "memory": 4083, "data_time": 0.00065, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01678, "loss": 0.01678, "time": 0.49101} +{"mode": "val", "epoch": 136, "iter": 533, "lr": 0.00053, "top1_acc": 0.92454, "top5_acc": 0.99507, "mean_class_accuracy": 0.89982} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00053, "memory": 4083, "data_time": 0.18807, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01745, "loss": 0.01745, "time": 0.71035} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00052, "memory": 4083, "data_time": 0.00029, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01694, "loss": 0.01694, "time": 0.44297} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00052, "memory": 4083, "data_time": 0.00027, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01743, "loss": 0.01743, "time": 0.48734} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.00051, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.02151, "loss": 0.02151, "time": 0.48852} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.0005, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01709, "loss": 0.01709, "time": 0.48749} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.0005, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01618, "loss": 0.01618, "time": 0.4897} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00049, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01484, "loss": 0.01484, "time": 0.48771} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00049, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01618, "loss": 0.01618, "time": 0.4876} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00048, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01819, "loss": 0.01819, "time": 0.48954} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00048, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01719, "loss": 0.01719, "time": 0.4892} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00047, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01655, "loss": 0.01655, "time": 0.48917} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00046, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01489, "loss": 0.01489, "time": 0.48793} +{"mode": "val", "epoch": 137, "iter": 533, "lr": 0.00046, "top1_acc": 0.92677, "top5_acc": 0.99484, "mean_class_accuracy": 0.90368} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00046, "memory": 4083, "data_time": 0.18035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01714, "loss": 0.01714, "time": 0.68049} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00045, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01607, "loss": 0.01607, "time": 0.4421} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00044, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01616, "loss": 0.01616, "time": 0.49004} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00044, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01602, "loss": 0.01602, "time": 0.48955} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.00043, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01468, "loss": 0.01468, "time": 0.48915} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.00043, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01704, "loss": 0.01704, "time": 0.48797} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00042, "memory": 4083, "data_time": 0.00037, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01707, "loss": 0.01707, "time": 0.48917} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00042, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01535, "loss": 0.01535, "time": 0.4893} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00041, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01815, "loss": 0.01815, "time": 0.48907} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00041, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01562, "loss": 0.01562, "time": 0.48996} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.0004, "memory": 4083, "data_time": 0.0003, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0171, "loss": 0.0171, "time": 0.48925} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.0004, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01731, "loss": 0.01731, "time": 0.48991} +{"mode": "val", "epoch": 138, "iter": 533, "lr": 0.00039, "top1_acc": 0.92313, "top5_acc": 0.99554, "mean_class_accuracy": 0.89784} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00039, "memory": 4083, "data_time": 0.18668, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01677, "loss": 0.01677, "time": 0.67305} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00038, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01638, "loss": 0.01638, "time": 0.73384} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00038, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01563, "loss": 0.01563, "time": 0.72984} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00037, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0168, "loss": 0.0168, "time": 0.7167} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00037, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01721, "loss": 0.01721, "time": 0.72357} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00036, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01706, "loss": 0.01706, "time": 0.72688} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00036, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01509, "loss": 0.01509, "time": 0.73862} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00035, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01681, "loss": 0.01681, "time": 0.30892} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00035, "memory": 4083, "data_time": 0.00035, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01613, "loss": 0.01613, "time": 0.72622} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.00034, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01656, "loss": 0.01656, "time": 0.71832} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.00034, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0163, "loss": 0.0163, "time": 0.70018} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00033, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99875, "top5_acc": 1.0, "loss_cls": 0.01965, "loss": 0.01965, "time": 0.73443} +{"mode": "val", "epoch": 139, "iter": 533, "lr": 0.00033, "top1_acc": 0.92865, "top5_acc": 0.99566, "mean_class_accuracy": 0.90618} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00033, "memory": 4083, "data_time": 0.18071, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01851, "loss": 0.01851, "time": 0.41615} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00032, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01675, "loss": 0.01675, "time": 0.22375} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.00032, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01602, "loss": 0.01602, "time": 0.2185} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.00031, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01726, "loss": 0.01726, "time": 0.22129} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00031, "memory": 4083, "data_time": 0.00022, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01746, "loss": 0.01746, "time": 0.22294} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.0003, "memory": 4083, "data_time": 0.00026, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01862, "loss": 0.01862, "time": 0.22325} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.0003, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01637, "loss": 0.01637, "time": 0.22198} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00029, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0152, "loss": 0.0152, "time": 0.22186} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00029, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01591, "loss": 0.01591, "time": 0.22181} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00029, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01587, "loss": 0.01587, "time": 0.2209} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00028, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0151, "loss": 0.0151, "time": 0.22346} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00028, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01449, "loss": 0.01449, "time": 0.21901} +{"mode": "val", "epoch": 140, "iter": 533, "lr": 0.00027, "top1_acc": 0.92677, "top5_acc": 0.99554, "mean_class_accuracy": 0.90442} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00027, "memory": 4083, "data_time": 0.18393, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01643, "loss": 0.01643, "time": 0.41871} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00026, "memory": 4083, "data_time": 0.00034, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01585, "loss": 0.01585, "time": 0.22216} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00026, "memory": 4083, "data_time": 0.00036, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01543, "loss": 0.01543, "time": 0.22243} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00026, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01429, "loss": 0.01429, "time": 0.22213} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00025, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01621, "loss": 0.01621, "time": 0.2228} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00025, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01616, "loss": 0.01616, "time": 0.21837} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00024, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01702, "loss": 0.01702, "time": 0.22044} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00024, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01599, "loss": 0.01599, "time": 0.21857} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00024, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01666, "loss": 0.01666, "time": 0.22182} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00023, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01626, "loss": 0.01626, "time": 0.22158} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00023, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01563, "loss": 0.01563, "time": 0.22212} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00022, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0143, "loss": 0.0143, "time": 0.21857} +{"mode": "val", "epoch": 141, "iter": 533, "lr": 0.00022, "top1_acc": 0.92829, "top5_acc": 0.99542, "mean_class_accuracy": 0.9074} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00022, "memory": 4083, "data_time": 0.18401, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01616, "loss": 0.01616, "time": 0.4177} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00021, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01709, "loss": 0.01709, "time": 0.22157} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00021, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01471, "loss": 0.01471, "time": 0.22219} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00021, "memory": 4083, "data_time": 0.00033, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01641, "loss": 0.01641, "time": 0.22321} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.0002, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0161, "loss": 0.0161, "time": 0.2239} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.0002, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01624, "loss": 0.01624, "time": 0.22636} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.0002, "memory": 4083, "data_time": 0.00023, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01721, "loss": 0.01721, "time": 0.22205} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00019, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01634, "loss": 0.01634, "time": 0.22424} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00019, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01665, "loss": 0.01665, "time": 0.22185} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00018, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01561, "loss": 0.01561, "time": 0.22314} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00018, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01578, "loss": 0.01578, "time": 0.22393} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00018, "memory": 4083, "data_time": 0.00029, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01701, "loss": 0.01701, "time": 0.22396} +{"mode": "val", "epoch": 142, "iter": 533, "lr": 0.00018, "top1_acc": 0.92642, "top5_acc": 0.99472, "mean_class_accuracy": 0.90407} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.00017, "memory": 4083, "data_time": 0.18735, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01627, "loss": 0.01627, "time": 0.42235} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00017, "memory": 4083, "data_time": 0.00031, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01483, "loss": 0.01483, "time": 0.22045} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00017, "memory": 4083, "data_time": 0.0003, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01571, "loss": 0.01571, "time": 0.22031} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00016, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01561, "loss": 0.01561, "time": 0.22005} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00016, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01549, "loss": 0.01549, "time": 0.22191} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00016, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01745, "loss": 0.01745, "time": 0.22084} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00015, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01642, "loss": 0.01642, "time": 0.22421} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00015, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01668, "loss": 0.01668, "time": 0.22368} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00015, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0172, "loss": 0.0172, "time": 0.22232} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00014, "memory": 4083, "data_time": 0.00027, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01515, "loss": 0.01515, "time": 0.2213} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00014, "memory": 4083, "data_time": 0.00028, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01542, "loss": 0.01542, "time": 0.21953} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00014, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01785, "loss": 0.01785, "time": 0.22197} +{"mode": "val", "epoch": 143, "iter": 533, "lr": 0.00013, "top1_acc": 0.92689, "top5_acc": 0.99531, "mean_class_accuracy": 0.90047} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00013, "memory": 4083, "data_time": 0.18347, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0194, "loss": 0.0194, "time": 0.41399} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00013, "memory": 4083, "data_time": 0.00041, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01521, "loss": 0.01521, "time": 0.21881} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00013, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01457, "loss": 0.01457, "time": 0.22261} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00012, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01623, "loss": 0.01623, "time": 0.22046} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00012, "memory": 4083, "data_time": 0.00025, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.02188, "loss": 0.02188, "time": 0.21831} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00012, "memory": 4083, "data_time": 0.00024, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01636, "loss": 0.01636, "time": 0.21747} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00011, "memory": 4083, "data_time": 0.00023, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01619, "loss": 0.01619, "time": 0.21889} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.00011, "memory": 4083, "data_time": 0.00025, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01709, "loss": 0.01709, "time": 0.2191} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.00011, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01567, "loss": 0.01567, "time": 0.21787} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.00011, "memory": 4083, "data_time": 0.00026, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01378, "loss": 0.01378, "time": 0.22698} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.0001, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01566, "loss": 0.01566, "time": 0.22543} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.0001, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01532, "loss": 0.01532, "time": 0.21658} +{"mode": "val", "epoch": 144, "iter": 533, "lr": 0.0001, "top1_acc": 0.92759, "top5_acc": 0.99507, "mean_class_accuracy": 0.90354} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.0001, "memory": 4083, "data_time": 0.18299, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01621, "loss": 0.01621, "time": 0.41256} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 9e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01718, "loss": 0.01718, "time": 0.22063} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 9e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01693, "loss": 0.01693, "time": 0.21839} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 9e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01591, "loss": 0.01591, "time": 0.21931} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 9e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01615, "loss": 0.01615, "time": 0.2157} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 8e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01539, "loss": 0.01539, "time": 0.21506} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 8e-05, "memory": 4083, "data_time": 0.00024, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01497, "loss": 0.01497, "time": 0.2188} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 8e-05, "memory": 4083, "data_time": 0.00021, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01554, "loss": 0.01554, "time": 0.21786} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 8e-05, "memory": 4083, "data_time": 0.00022, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01544, "loss": 0.01544, "time": 0.21955} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 7e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01504, "loss": 0.01504, "time": 0.21678} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 7e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01659, "loss": 0.01659, "time": 0.21664} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 7e-05, "memory": 4083, "data_time": 0.0002, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01555, "loss": 0.01555, "time": 0.21719} +{"mode": "val", "epoch": 145, "iter": 533, "lr": 7e-05, "top1_acc": 0.92771, "top5_acc": 0.99531, "mean_class_accuracy": 0.90502} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 7e-05, "memory": 4083, "data_time": 0.1697, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01609, "loss": 0.01609, "time": 0.39094} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 6e-05, "memory": 4083, "data_time": 0.00018, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01531, "loss": 0.01531, "time": 0.21299} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 6e-05, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01584, "loss": 0.01584, "time": 0.21217} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 6e-05, "memory": 4083, "data_time": 0.00014, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01596, "loss": 0.01596, "time": 0.20996} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 6e-05, "memory": 4083, "data_time": 0.00014, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01482, "loss": 0.01482, "time": 0.20871} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 6e-05, "memory": 4083, "data_time": 0.00014, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01745, "loss": 0.01745, "time": 0.20903} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 5e-05, "memory": 4083, "data_time": 0.00014, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01693, "loss": 0.01693, "time": 0.209} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 5e-05, "memory": 4083, "data_time": 0.00014, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01534, "loss": 0.01534, "time": 0.21508} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 5e-05, "memory": 4083, "data_time": 0.00015, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01682, "loss": 0.01682, "time": 0.21876} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 5e-05, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01597, "loss": 0.01597, "time": 0.21545} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 5e-05, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0159, "loss": 0.0159, "time": 0.21587} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 5e-05, "memory": 4083, "data_time": 0.00015, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01388, "loss": 0.01388, "time": 0.20939} +{"mode": "val", "epoch": 146, "iter": 533, "lr": 4e-05, "top1_acc": 0.92665, "top5_acc": 0.99542, "mean_class_accuracy": 0.90154} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 4e-05, "memory": 4083, "data_time": 0.16351, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01609, "loss": 0.01609, "time": 0.38083} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 4e-05, "memory": 4083, "data_time": 0.00017, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01553, "loss": 0.01553, "time": 0.2173} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 4e-05, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01654, "loss": 0.01654, "time": 0.2163} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 4e-05, "memory": 4083, "data_time": 0.00017, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01629, "loss": 0.01629, "time": 0.2173} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 4e-05, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01616, "loss": 0.01616, "time": 0.21643} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 3e-05, "memory": 4083, "data_time": 0.00015, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01552, "loss": 0.01552, "time": 0.21629} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 3e-05, "memory": 4083, "data_time": 0.00015, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01753, "loss": 0.01753, "time": 0.21986} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 3e-05, "memory": 4083, "data_time": 0.00015, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01624, "loss": 0.01624, "time": 0.21831} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 3e-05, "memory": 4083, "data_time": 0.00014, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01913, "loss": 0.01913, "time": 0.21117} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 3e-05, "memory": 4083, "data_time": 0.00014, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0164, "loss": 0.0164, "time": 0.20952} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 3e-05, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01607, "loss": 0.01607, "time": 0.21454} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 3e-05, "memory": 4083, "data_time": 0.00015, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01557, "loss": 0.01557, "time": 0.21165} +{"mode": "val", "epoch": 147, "iter": 533, "lr": 2e-05, "top1_acc": 0.92794, "top5_acc": 0.99519, "mean_class_accuracy": 0.90513} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 2e-05, "memory": 4083, "data_time": 0.16336, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01703, "loss": 0.01703, "time": 0.38023} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 2e-05, "memory": 4083, "data_time": 0.00014, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.0179, "loss": 0.0179, "time": 0.20602} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 2e-05, "memory": 4083, "data_time": 0.00014, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01534, "loss": 0.01534, "time": 0.20756} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 2e-05, "memory": 4083, "data_time": 0.00017, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01598, "loss": 0.01598, "time": 0.21681} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 2e-05, "memory": 4083, "data_time": 0.00017, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01486, "loss": 0.01486, "time": 0.21607} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 2e-05, "memory": 4083, "data_time": 0.00014, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01558, "loss": 0.01558, "time": 0.21014} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 2e-05, "memory": 4083, "data_time": 0.00015, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01475, "loss": 0.01475, "time": 0.21156} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 2e-05, "memory": 4083, "data_time": 0.00012, "top1_acc": 0.99812, "top5_acc": 1.0, "loss_cls": 0.01916, "loss": 0.01916, "time": 0.20868} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 1e-05, "memory": 4083, "data_time": 0.00014, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0151, "loss": 0.0151, "time": 0.20962} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 1e-05, "memory": 4083, "data_time": 0.00015, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01608, "loss": 0.01608, "time": 0.20976} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 1e-05, "memory": 4083, "data_time": 0.00014, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01672, "loss": 0.01672, "time": 0.21399} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 1e-05, "memory": 4083, "data_time": 0.00016, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01637, "loss": 0.01637, "time": 0.21609} +{"mode": "val", "epoch": 148, "iter": 533, "lr": 1e-05, "top1_acc": 0.92571, "top5_acc": 0.99566, "mean_class_accuracy": 0.90285} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 1e-05, "memory": 4083, "data_time": 0.16452, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01808, "loss": 0.01808, "time": 0.38994} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 1e-05, "memory": 4083, "data_time": 0.00018, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01666, "loss": 0.01666, "time": 0.21763} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 1e-05, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01549, "loss": 0.01549, "time": 0.21528} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 1e-05, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01622, "loss": 0.01622, "time": 0.21619} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 1e-05, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01595, "loss": 0.01595, "time": 0.21068} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 1e-05, "memory": 4083, "data_time": 0.00015, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01484, "loss": 0.01484, "time": 0.21634} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 1e-05, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0146, "loss": 0.0146, "time": 0.2204} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 1e-05, "memory": 4083, "data_time": 0.00017, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01361, "loss": 0.01361, "time": 0.21669} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00017, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01803, "loss": 0.01803, "time": 0.2171} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01632, "loss": 0.01632, "time": 0.2168} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00015, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01672, "loss": 0.01672, "time": 0.20942} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00015, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01624, "loss": 0.01624, "time": 0.20834} +{"mode": "val", "epoch": 149, "iter": 533, "lr": 0.0, "top1_acc": 0.92771, "top5_acc": 0.99531, "mean_class_accuracy": 0.90394} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 0.0, "memory": 4083, "data_time": 0.1633, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.0146, "loss": 0.0146, "time": 0.3938} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 0.0, "memory": 4083, "data_time": 0.00014, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01697, "loss": 0.01697, "time": 0.22043} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 0.0, "memory": 4083, "data_time": 0.00015, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01515, "loss": 0.01515, "time": 0.21751} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 0.0, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01532, "loss": 0.01532, "time": 0.21632} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 0.0, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01641, "loss": 0.01641, "time": 0.21606} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 0.0, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01605, "loss": 0.01605, "time": 0.21634} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 0.0, "memory": 4083, "data_time": 0.00016, "top1_acc": 0.99938, "top5_acc": 1.0, "loss_cls": 0.01888, "loss": 0.01888, "time": 0.21646} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 0.0, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01522, "loss": 0.01522, "time": 0.21657} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 0.0, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01529, "loss": 0.01529, "time": 0.21287} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 0.0, "memory": 4083, "data_time": 0.00014, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01643, "loss": 0.01643, "time": 0.2063} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 0.0, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01609, "loss": 0.01609, "time": 0.21382} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 0.0, "memory": 4083, "data_time": 0.00016, "top1_acc": 1.0, "top5_acc": 1.0, "loss_cls": 0.01588, "loss": 0.01588, "time": 0.21448} +{"mode": "val", "epoch": 150, "iter": 533, "lr": 0.0, "top1_acc": 0.92747, "top5_acc": 0.99566, "mean_class_accuracy": 0.90387} diff --git a/finegym/km/best_pred.pkl b/finegym/km/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..3a9892df64e76bb89f262435e172564df8954322 --- /dev/null +++ b/finegym/km/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bee39a9be3ad8a21e1ae352c79900735f8f669cc73454223d368d1ab3c36af5c +size 5255495 diff --git a/finegym/km/best_top1_acc_epoch_150.pth b/finegym/km/best_top1_acc_epoch_150.pth new file mode 100644 index 0000000000000000000000000000000000000000..8a4ce64261f48365b3f185bb382d1d9e1e0dd7ed --- /dev/null +++ b/finegym/km/best_top1_acc_epoch_150.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b034840cd3db9526e4ba02a31d889d53cc9e9828731d3c52f1552f67439936cf +size 16118201 diff --git a/finegym/km/km.py b/finegym/km/km.py new file mode 100644 index 0000000000000000000000000000000000000000..2401232483a71a9b476a15807735bf15b3d495a0 --- /dev/null +++ b/finegym/km/km.py @@ -0,0 +1,113 @@ +modality = 'km' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/finegym/km' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='finegym', num_classes=99, in_channels=384)) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='train'), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val'), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/gym/gym_hrnet.pkl', + pipeline=[ + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + split='val')) +optimizer = dict( + type='SGD', lr=0.025, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/k400/b_1/20240722_022356.log b/k400/b_1/20240722_022356.log new file mode 100644 index 0000000000000000000000000000000000000000..9250e879bb06bceb056ee4380c8429ee8c9a66de --- /dev/null +++ b/k400/b_1/20240722_022356.log @@ -0,0 +1,7313 @@ +2024-07-22 02:23:56,087 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2024-07-22 02:23:56,399 - pyskl - INFO - Config: modality = 'b' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/b_1' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2024-07-22 02:23:56,399 - pyskl - INFO - Set random seed to 601143061, deterministic: False +2024-07-22 02:24:06,184 - pyskl - INFO - 239737 videos remain after valid thresholding +2024-07-22 02:24:19,510 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-07-22 02:24:19,512 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1 +2024-07-22 02:24:19,520 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2024-07-22 02:24:19,537 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2024-07-22 02:24:19,540 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1 by HardDiskBackend. +2024-07-22 02:27:22,186 - pyskl - INFO - Epoch [1][100/3746] lr: 1.000e-01, eta: 11 days, 21:01:10, time: 1.826, data_time: 1.136, memory: 15990, top1_acc: 0.0070, top5_acc: 0.0317, loss_cls: 6.4193, loss: 6.4193 +2024-07-22 02:28:31,238 - pyskl - INFO - Epoch [1][200/3746] lr: 1.000e-01, eta: 8 days, 4:21:13, time: 0.691, data_time: 0.000, memory: 15990, top1_acc: 0.0131, top5_acc: 0.0537, loss_cls: 6.2960, loss: 6.2960 +2024-07-22 02:29:41,449 - pyskl - INFO - Epoch [1][300/3746] lr: 1.000e-01, eta: 6 days, 23:23:18, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0181, top5_acc: 0.0809, loss_cls: 6.0987, loss: 6.0987 +2024-07-22 02:30:52,020 - pyskl - INFO - Epoch [1][400/3746] lr: 1.000e-01, eta: 6 days, 9:02:11, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.0233, top5_acc: 0.0964, loss_cls: 5.9446, loss: 5.9446 +2024-07-22 02:32:02,428 - pyskl - INFO - Epoch [1][500/3746] lr: 1.000e-01, eta: 6 days, 0:21:59, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0277, top5_acc: 0.1080, loss_cls: 5.8819, loss: 5.8819 +2024-07-22 02:33:12,661 - pyskl - INFO - Epoch [1][600/3746] lr: 1.000e-01, eta: 5 days, 18:32:04, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0308, top5_acc: 0.1194, loss_cls: 5.8175, loss: 5.8175 +2024-07-22 02:34:23,038 - pyskl - INFO - Epoch [1][700/3746] lr: 1.000e-01, eta: 5 days, 14:23:43, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0344, top5_acc: 0.1228, loss_cls: 5.7814, loss: 5.7814 +2024-07-22 02:35:33,287 - pyskl - INFO - Epoch [1][800/3746] lr: 1.000e-01, eta: 5 days, 11:15:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0347, top5_acc: 0.1344, loss_cls: 5.7140, loss: 5.7140 +2024-07-22 02:36:43,287 - pyskl - INFO - Epoch [1][900/3746] lr: 1.000e-01, eta: 5 days, 8:46:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0378, top5_acc: 0.1403, loss_cls: 5.7194, loss: 5.7194 +2024-07-22 02:37:53,284 - pyskl - INFO - Epoch [1][1000/3746] lr: 1.000e-01, eta: 5 days, 6:47:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0437, top5_acc: 0.1480, loss_cls: 5.6409, loss: 5.6409 +2024-07-22 02:39:03,233 - pyskl - INFO - Epoch [1][1100/3746] lr: 1.000e-01, eta: 5 days, 5:08:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0453, top5_acc: 0.1573, loss_cls: 5.6396, loss: 5.6396 +2024-07-22 02:40:13,370 - pyskl - INFO - Epoch [1][1200/3746] lr: 1.000e-01, eta: 5 days, 3:47:49, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0450, top5_acc: 0.1661, loss_cls: 5.5811, loss: 5.5811 +2024-07-22 02:41:23,210 - pyskl - INFO - Epoch [1][1300/3746] lr: 1.000e-01, eta: 5 days, 2:37:10, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.0483, top5_acc: 0.1694, loss_cls: 5.5756, loss: 5.5756 +2024-07-22 02:42:33,124 - pyskl - INFO - Epoch [1][1400/3746] lr: 1.000e-01, eta: 5 days, 1:36:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0592, top5_acc: 0.1853, loss_cls: 5.4915, loss: 5.4915 +2024-07-22 02:43:42,999 - pyskl - INFO - Epoch [1][1500/3746] lr: 1.000e-01, eta: 5 days, 0:44:21, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0600, top5_acc: 0.1853, loss_cls: 5.5031, loss: 5.5031 +2024-07-22 02:44:52,791 - pyskl - INFO - Epoch [1][1600/3746] lr: 1.000e-01, eta: 4 days, 23:57:42, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.0536, top5_acc: 0.1822, loss_cls: 5.4964, loss: 5.4964 +2024-07-22 02:46:02,783 - pyskl - INFO - Epoch [1][1700/3746] lr: 1.000e-01, eta: 4 days, 23:17:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0606, top5_acc: 0.2022, loss_cls: 5.4535, loss: 5.4535 +2024-07-22 02:47:12,779 - pyskl - INFO - Epoch [1][1800/3746] lr: 1.000e-01, eta: 4 days, 22:41:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0733, top5_acc: 0.2105, loss_cls: 5.4038, loss: 5.4038 +2024-07-22 02:48:23,189 - pyskl - INFO - Epoch [1][1900/3746] lr: 1.000e-01, eta: 4 days, 22:11:30, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0744, top5_acc: 0.2197, loss_cls: 5.3907, loss: 5.3907 +2024-07-22 02:49:33,060 - pyskl - INFO - Epoch [1][2000/3746] lr: 1.000e-01, eta: 4 days, 21:41:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0764, top5_acc: 0.2219, loss_cls: 5.3568, loss: 5.3568 +2024-07-22 02:50:43,366 - pyskl - INFO - Epoch [1][2100/3746] lr: 1.000e-01, eta: 4 days, 21:16:36, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0748, top5_acc: 0.2256, loss_cls: 5.3655, loss: 5.3655 +2024-07-22 02:51:53,460 - pyskl - INFO - Epoch [1][2200/3746] lr: 1.000e-01, eta: 4 days, 20:52:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0748, top5_acc: 0.2336, loss_cls: 5.3119, loss: 5.3119 +2024-07-22 02:53:03,950 - pyskl - INFO - Epoch [1][2300/3746] lr: 1.000e-01, eta: 4 days, 20:32:30, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0811, top5_acc: 0.2292, loss_cls: 5.3492, loss: 5.3492 +2024-07-22 02:54:14,872 - pyskl - INFO - Epoch [1][2400/3746] lr: 1.000e-01, eta: 4 days, 20:15:31, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.0866, top5_acc: 0.2441, loss_cls: 5.2815, loss: 5.2815 +2024-07-22 02:55:27,079 - pyskl - INFO - Epoch [1][2500/3746] lr: 1.000e-01, eta: 4 days, 20:04:34, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.0845, top5_acc: 0.2397, loss_cls: 5.2824, loss: 5.2824 +2024-07-22 02:56:38,259 - pyskl - INFO - Epoch [1][2600/3746] lr: 9.999e-02, eta: 4 days, 19:50:42, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.0958, top5_acc: 0.2461, loss_cls: 5.2279, loss: 5.2279 +2024-07-22 02:57:48,579 - pyskl - INFO - Epoch [1][2700/3746] lr: 9.999e-02, eta: 4 days, 19:34:48, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0945, top5_acc: 0.2542, loss_cls: 5.2381, loss: 5.2381 +2024-07-22 02:58:58,545 - pyskl - INFO - Epoch [1][2800/3746] lr: 9.999e-02, eta: 4 days, 19:18:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0953, top5_acc: 0.2537, loss_cls: 5.2042, loss: 5.2042 +2024-07-22 03:00:08,649 - pyskl - INFO - Epoch [1][2900/3746] lr: 9.999e-02, eta: 4 days, 19:04:13, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0908, top5_acc: 0.2650, loss_cls: 5.1758, loss: 5.1758 +2024-07-22 03:01:18,553 - pyskl - INFO - Epoch [1][3000/3746] lr: 9.999e-02, eta: 4 days, 18:49:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0998, top5_acc: 0.2798, loss_cls: 5.1307, loss: 5.1307 +2024-07-22 03:02:28,705 - pyskl - INFO - Epoch [1][3100/3746] lr: 9.999e-02, eta: 4 days, 18:37:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1047, top5_acc: 0.2770, loss_cls: 5.1191, loss: 5.1191 +2024-07-22 03:03:38,844 - pyskl - INFO - Epoch [1][3200/3746] lr: 9.999e-02, eta: 4 days, 18:25:14, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0989, top5_acc: 0.2730, loss_cls: 5.1298, loss: 5.1298 +2024-07-22 03:04:49,109 - pyskl - INFO - Epoch [1][3300/3746] lr: 9.999e-02, eta: 4 days, 18:14:14, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1069, top5_acc: 0.2772, loss_cls: 5.1051, loss: 5.1051 +2024-07-22 03:05:58,833 - pyskl - INFO - Epoch [1][3400/3746] lr: 9.999e-02, eta: 4 days, 18:02:20, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1061, top5_acc: 0.2869, loss_cls: 5.0674, loss: 5.0674 +2024-07-22 03:07:08,949 - pyskl - INFO - Epoch [1][3500/3746] lr: 9.999e-02, eta: 4 days, 17:52:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1142, top5_acc: 0.2906, loss_cls: 5.0425, loss: 5.0425 +2024-07-22 03:08:19,083 - pyskl - INFO - Epoch [1][3600/3746] lr: 9.999e-02, eta: 4 days, 17:42:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1217, top5_acc: 0.3152, loss_cls: 4.9633, loss: 4.9633 +2024-07-22 03:09:28,845 - pyskl - INFO - Epoch [1][3700/3746] lr: 9.999e-02, eta: 4 days, 17:32:13, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1152, top5_acc: 0.3016, loss_cls: 5.0133, loss: 5.0133 +2024-07-22 03:10:03,195 - pyskl - INFO - Saving checkpoint at 1 epochs +2024-07-22 03:11:54,830 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 03:11:55,488 - pyskl - INFO - +top1_acc 0.0821 +top5_acc 0.2338 +2024-07-22 03:11:55,488 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 03:11:55,527 - pyskl - INFO - +mean_acc 0.0820 +2024-07-22 03:11:55,759 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2024-07-22 03:11:55,759 - pyskl - INFO - Best top1_acc is 0.0821 at 1 epoch. +2024-07-22 03:11:55,769 - pyskl - INFO - Epoch(val) [1][309] top1_acc: 0.0821, top5_acc: 0.2338, mean_class_accuracy: 0.0820 +2024-07-22 03:15:12,663 - pyskl - INFO - Epoch [2][100/3746] lr: 9.999e-02, eta: 4 days, 21:08:02, time: 1.969, data_time: 1.265, memory: 15990, top1_acc: 0.1187, top5_acc: 0.3098, loss_cls: 4.9737, loss: 4.9737 +2024-07-22 03:16:23,995 - pyskl - INFO - Epoch [2][200/3746] lr: 9.999e-02, eta: 4 days, 20:56:48, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1209, top5_acc: 0.3175, loss_cls: 4.9312, loss: 4.9312 +2024-07-22 03:17:35,363 - pyskl - INFO - Epoch [2][300/3746] lr: 9.999e-02, eta: 4 days, 20:46:09, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1261, top5_acc: 0.3267, loss_cls: 4.9492, loss: 4.9492 +2024-07-22 03:18:46,459 - pyskl - INFO - Epoch [2][400/3746] lr: 9.999e-02, eta: 4 days, 20:35:21, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1203, top5_acc: 0.3297, loss_cls: 4.9333, loss: 4.9333 +2024-07-22 03:19:57,079 - pyskl - INFO - Epoch [2][500/3746] lr: 9.999e-02, eta: 4 days, 20:23:57, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1231, top5_acc: 0.3234, loss_cls: 4.9018, loss: 4.9018 +2024-07-22 03:21:07,122 - pyskl - INFO - Epoch [2][600/3746] lr: 9.999e-02, eta: 4 days, 20:11:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1380, top5_acc: 0.3386, loss_cls: 4.8628, loss: 4.8628 +2024-07-22 03:22:17,397 - pyskl - INFO - Epoch [2][700/3746] lr: 9.998e-02, eta: 4 days, 20:00:37, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1403, top5_acc: 0.3383, loss_cls: 4.8628, loss: 4.8628 +2024-07-22 03:23:27,411 - pyskl - INFO - Epoch [2][800/3746] lr: 9.998e-02, eta: 4 days, 19:49:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1364, top5_acc: 0.3317, loss_cls: 4.8833, loss: 4.8833 +2024-07-22 03:24:37,452 - pyskl - INFO - Epoch [2][900/3746] lr: 9.998e-02, eta: 4 days, 19:38:33, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1305, top5_acc: 0.3302, loss_cls: 4.8952, loss: 4.8952 +2024-07-22 03:25:47,550 - pyskl - INFO - Epoch [2][1000/3746] lr: 9.998e-02, eta: 4 days, 19:28:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1447, top5_acc: 0.3416, loss_cls: 4.8312, loss: 4.8312 +2024-07-22 03:26:57,507 - pyskl - INFO - Epoch [2][1100/3746] lr: 9.998e-02, eta: 4 days, 19:18:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1425, top5_acc: 0.3422, loss_cls: 4.8311, loss: 4.8311 +2024-07-22 03:28:07,878 - pyskl - INFO - Epoch [2][1200/3746] lr: 9.998e-02, eta: 4 days, 19:09:07, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1384, top5_acc: 0.3447, loss_cls: 4.8466, loss: 4.8466 +2024-07-22 03:29:18,033 - pyskl - INFO - Epoch [2][1300/3746] lr: 9.998e-02, eta: 4 days, 19:00:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1494, top5_acc: 0.3513, loss_cls: 4.8050, loss: 4.8050 +2024-07-22 03:30:28,012 - pyskl - INFO - Epoch [2][1400/3746] lr: 9.998e-02, eta: 4 days, 18:50:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1380, top5_acc: 0.3438, loss_cls: 4.8515, loss: 4.8515 +2024-07-22 03:31:37,989 - pyskl - INFO - Epoch [2][1500/3746] lr: 9.998e-02, eta: 4 days, 18:42:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1472, top5_acc: 0.3466, loss_cls: 4.8201, loss: 4.8201 +2024-07-22 03:32:47,823 - pyskl - INFO - Epoch [2][1600/3746] lr: 9.998e-02, eta: 4 days, 18:33:17, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1491, top5_acc: 0.3542, loss_cls: 4.8014, loss: 4.8014 +2024-07-22 03:33:57,759 - pyskl - INFO - Epoch [2][1700/3746] lr: 9.998e-02, eta: 4 days, 18:24:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1480, top5_acc: 0.3514, loss_cls: 4.8099, loss: 4.8099 +2024-07-22 03:35:07,694 - pyskl - INFO - Epoch [2][1800/3746] lr: 9.998e-02, eta: 4 days, 18:16:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1519, top5_acc: 0.3613, loss_cls: 4.7511, loss: 4.7511 +2024-07-22 03:36:17,656 - pyskl - INFO - Epoch [2][1900/3746] lr: 9.998e-02, eta: 4 days, 18:09:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1541, top5_acc: 0.3638, loss_cls: 4.7361, loss: 4.7361 +2024-07-22 03:37:27,607 - pyskl - INFO - Epoch [2][2000/3746] lr: 9.997e-02, eta: 4 days, 18:01:33, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1539, top5_acc: 0.3653, loss_cls: 4.7299, loss: 4.7299 +2024-07-22 03:38:37,697 - pyskl - INFO - Epoch [2][2100/3746] lr: 9.997e-02, eta: 4 days, 17:54:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1553, top5_acc: 0.3673, loss_cls: 4.7424, loss: 4.7424 +2024-07-22 03:39:47,671 - pyskl - INFO - Epoch [2][2200/3746] lr: 9.997e-02, eta: 4 days, 17:47:19, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1458, top5_acc: 0.3684, loss_cls: 4.7562, loss: 4.7562 +2024-07-22 03:40:57,922 - pyskl - INFO - Epoch [2][2300/3746] lr: 9.997e-02, eta: 4 days, 17:40:50, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1600, top5_acc: 0.3789, loss_cls: 4.6885, loss: 4.6885 +2024-07-22 03:42:07,740 - pyskl - INFO - Epoch [2][2400/3746] lr: 9.997e-02, eta: 4 days, 17:33:52, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1541, top5_acc: 0.3784, loss_cls: 4.7079, loss: 4.7079 +2024-07-22 03:43:17,480 - pyskl - INFO - Epoch [2][2500/3746] lr: 9.997e-02, eta: 4 days, 17:26:58, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1644, top5_acc: 0.3797, loss_cls: 4.6715, loss: 4.6715 +2024-07-22 03:44:27,554 - pyskl - INFO - Epoch [2][2600/3746] lr: 9.997e-02, eta: 4 days, 17:20:44, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1613, top5_acc: 0.3833, loss_cls: 4.6899, loss: 4.6899 +2024-07-22 03:45:37,229 - pyskl - INFO - Epoch [2][2700/3746] lr: 9.997e-02, eta: 4 days, 17:14:06, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1623, top5_acc: 0.3723, loss_cls: 4.7133, loss: 4.7133 +2024-07-22 03:46:47,135 - pyskl - INFO - Epoch [2][2800/3746] lr: 9.997e-02, eta: 4 days, 17:07:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1647, top5_acc: 0.3819, loss_cls: 4.6736, loss: 4.6736 +2024-07-22 03:47:56,902 - pyskl - INFO - Epoch [2][2900/3746] lr: 9.997e-02, eta: 4 days, 17:01:45, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1655, top5_acc: 0.3892, loss_cls: 4.6470, loss: 4.6470 +2024-07-22 03:49:06,671 - pyskl - INFO - Epoch [2][3000/3746] lr: 9.996e-02, eta: 4 days, 16:55:43, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1620, top5_acc: 0.3820, loss_cls: 4.6638, loss: 4.6638 +2024-07-22 03:50:16,426 - pyskl - INFO - Epoch [2][3100/3746] lr: 9.996e-02, eta: 4 days, 16:49:48, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1711, top5_acc: 0.3941, loss_cls: 4.6247, loss: 4.6247 +2024-07-22 03:51:26,311 - pyskl - INFO - Epoch [2][3200/3746] lr: 9.996e-02, eta: 4 days, 16:44:11, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1597, top5_acc: 0.3830, loss_cls: 4.6642, loss: 4.6642 +2024-07-22 03:52:36,223 - pyskl - INFO - Epoch [2][3300/3746] lr: 9.996e-02, eta: 4 days, 16:38:44, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1758, top5_acc: 0.3887, loss_cls: 4.6051, loss: 4.6051 +2024-07-22 03:53:46,533 - pyskl - INFO - Epoch [2][3400/3746] lr: 9.996e-02, eta: 4 days, 16:33:56, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1659, top5_acc: 0.3889, loss_cls: 4.6063, loss: 4.6063 +2024-07-22 03:54:56,288 - pyskl - INFO - Epoch [2][3500/3746] lr: 9.996e-02, eta: 4 days, 16:28:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1713, top5_acc: 0.3955, loss_cls: 4.6295, loss: 4.6295 +2024-07-22 03:56:06,279 - pyskl - INFO - Epoch [2][3600/3746] lr: 9.996e-02, eta: 4 days, 16:23:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1698, top5_acc: 0.3948, loss_cls: 4.6259, loss: 4.6259 +2024-07-22 03:57:16,104 - pyskl - INFO - Epoch [2][3700/3746] lr: 9.996e-02, eta: 4 days, 16:18:23, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1652, top5_acc: 0.3844, loss_cls: 4.6605, loss: 4.6605 +2024-07-22 03:57:50,610 - pyskl - INFO - Saving checkpoint at 2 epochs +2024-07-22 03:59:41,165 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 03:59:41,830 - pyskl - INFO - +top1_acc 0.1230 +top5_acc 0.3152 +2024-07-22 03:59:41,831 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 03:59:41,871 - pyskl - INFO - +mean_acc 0.1231 +2024-07-22 03:59:41,875 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_1.pth was removed +2024-07-22 03:59:42,115 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2024-07-22 03:59:42,116 - pyskl - INFO - Best top1_acc is 0.1230 at 2 epoch. +2024-07-22 03:59:42,126 - pyskl - INFO - Epoch(val) [2][309] top1_acc: 0.1230, top5_acc: 0.3152, mean_class_accuracy: 0.1231 +2024-07-22 04:02:58,991 - pyskl - INFO - Epoch [3][100/3746] lr: 9.995e-02, eta: 4 days, 18:06:37, time: 1.969, data_time: 1.264, memory: 15990, top1_acc: 0.1737, top5_acc: 0.4031, loss_cls: 4.5846, loss: 4.5846 +2024-07-22 04:04:10,593 - pyskl - INFO - Epoch [3][200/3746] lr: 9.995e-02, eta: 4 days, 18:02:22, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1817, top5_acc: 0.4070, loss_cls: 4.5536, loss: 4.5536 +2024-07-22 04:05:21,464 - pyskl - INFO - Epoch [3][300/3746] lr: 9.995e-02, eta: 4 days, 17:57:20, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1794, top5_acc: 0.4045, loss_cls: 4.5651, loss: 4.5651 +2024-07-22 04:06:32,410 - pyskl - INFO - Epoch [3][400/3746] lr: 9.995e-02, eta: 4 days, 17:52:29, time: 0.709, data_time: 0.001, memory: 15990, top1_acc: 0.1894, top5_acc: 0.4134, loss_cls: 4.5334, loss: 4.5334 +2024-07-22 04:07:43,180 - pyskl - INFO - Epoch [3][500/3746] lr: 9.995e-02, eta: 4 days, 17:47:31, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1784, top5_acc: 0.4113, loss_cls: 4.5395, loss: 4.5395 +2024-07-22 04:08:53,294 - pyskl - INFO - Epoch [3][600/3746] lr: 9.995e-02, eta: 4 days, 17:41:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1834, top5_acc: 0.4244, loss_cls: 4.5077, loss: 4.5077 +2024-07-22 04:10:03,529 - pyskl - INFO - Epoch [3][700/3746] lr: 9.995e-02, eta: 4 days, 17:36:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1794, top5_acc: 0.4033, loss_cls: 4.5796, loss: 4.5796 +2024-07-22 04:11:13,374 - pyskl - INFO - Epoch [3][800/3746] lr: 9.995e-02, eta: 4 days, 17:30:49, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1905, top5_acc: 0.4150, loss_cls: 4.5458, loss: 4.5458 +2024-07-22 04:12:23,614 - pyskl - INFO - Epoch [3][900/3746] lr: 9.994e-02, eta: 4 days, 17:25:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1845, top5_acc: 0.4080, loss_cls: 4.5452, loss: 4.5452 +2024-07-22 04:13:33,675 - pyskl - INFO - Epoch [3][1000/3746] lr: 9.994e-02, eta: 4 days, 17:20:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1827, top5_acc: 0.4053, loss_cls: 4.5511, loss: 4.5511 +2024-07-22 04:14:43,788 - pyskl - INFO - Epoch [3][1100/3746] lr: 9.994e-02, eta: 4 days, 17:15:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1828, top5_acc: 0.4105, loss_cls: 4.5506, loss: 4.5506 +2024-07-22 04:15:53,783 - pyskl - INFO - Epoch [3][1200/3746] lr: 9.994e-02, eta: 4 days, 17:10:09, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1814, top5_acc: 0.4148, loss_cls: 4.5355, loss: 4.5355 +2024-07-22 04:17:03,638 - pyskl - INFO - Epoch [3][1300/3746] lr: 9.994e-02, eta: 4 days, 17:04:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1866, top5_acc: 0.4142, loss_cls: 4.5182, loss: 4.5182 +2024-07-22 04:18:13,729 - pyskl - INFO - Epoch [3][1400/3746] lr: 9.994e-02, eta: 4 days, 17:00:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1825, top5_acc: 0.4081, loss_cls: 4.5472, loss: 4.5472 +2024-07-22 04:19:23,784 - pyskl - INFO - Epoch [3][1500/3746] lr: 9.994e-02, eta: 4 days, 16:55:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1917, top5_acc: 0.4225, loss_cls: 4.4892, loss: 4.4892 +2024-07-22 04:20:33,707 - pyskl - INFO - Epoch [3][1600/3746] lr: 9.994e-02, eta: 4 days, 16:50:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1953, top5_acc: 0.4334, loss_cls: 4.4821, loss: 4.4821 +2024-07-22 04:21:43,595 - pyskl - INFO - Epoch [3][1700/3746] lr: 9.993e-02, eta: 4 days, 16:45:33, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1837, top5_acc: 0.4089, loss_cls: 4.5469, loss: 4.5469 +2024-07-22 04:22:53,597 - pyskl - INFO - Epoch [3][1800/3746] lr: 9.993e-02, eta: 4 days, 16:40:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1911, top5_acc: 0.4278, loss_cls: 4.4863, loss: 4.4863 +2024-07-22 04:24:03,712 - pyskl - INFO - Epoch [3][1900/3746] lr: 9.993e-02, eta: 4 days, 16:36:28, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1861, top5_acc: 0.4175, loss_cls: 4.5007, loss: 4.5007 +2024-07-22 04:25:13,757 - pyskl - INFO - Epoch [3][2000/3746] lr: 9.993e-02, eta: 4 days, 16:32:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1875, top5_acc: 0.4184, loss_cls: 4.4825, loss: 4.4825 +2024-07-22 04:26:23,486 - pyskl - INFO - Epoch [3][2100/3746] lr: 9.993e-02, eta: 4 days, 16:27:19, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1927, top5_acc: 0.4231, loss_cls: 4.5038, loss: 4.5038 +2024-07-22 04:27:33,469 - pyskl - INFO - Epoch [3][2200/3746] lr: 9.993e-02, eta: 4 days, 16:22:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1975, top5_acc: 0.4355, loss_cls: 4.4610, loss: 4.4610 +2024-07-22 04:28:43,244 - pyskl - INFO - Epoch [3][2300/3746] lr: 9.993e-02, eta: 4 days, 16:18:27, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1931, top5_acc: 0.4239, loss_cls: 4.4648, loss: 4.4648 +2024-07-22 04:29:53,291 - pyskl - INFO - Epoch [3][2400/3746] lr: 9.992e-02, eta: 4 days, 16:14:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1959, top5_acc: 0.4300, loss_cls: 4.4627, loss: 4.4627 +2024-07-22 04:31:03,312 - pyskl - INFO - Epoch [3][2500/3746] lr: 9.992e-02, eta: 4 days, 16:10:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1908, top5_acc: 0.4239, loss_cls: 4.4771, loss: 4.4771 +2024-07-22 04:32:13,103 - pyskl - INFO - Epoch [3][2600/3746] lr: 9.992e-02, eta: 4 days, 16:05:50, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1914, top5_acc: 0.4233, loss_cls: 4.4893, loss: 4.4893 +2024-07-22 04:33:23,109 - pyskl - INFO - Epoch [3][2700/3746] lr: 9.992e-02, eta: 4 days, 16:01:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1945, top5_acc: 0.4383, loss_cls: 4.4411, loss: 4.4411 +2024-07-22 04:34:32,893 - pyskl - INFO - Epoch [3][2800/3746] lr: 9.992e-02, eta: 4 days, 15:57:36, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4323, loss_cls: 4.4501, loss: 4.4501 +2024-07-22 04:35:42,775 - pyskl - INFO - Epoch [3][2900/3746] lr: 9.992e-02, eta: 4 days, 15:53:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1952, top5_acc: 0.4313, loss_cls: 4.4591, loss: 4.4591 +2024-07-22 04:36:52,673 - pyskl - INFO - Epoch [3][3000/3746] lr: 9.991e-02, eta: 4 days, 15:49:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1927, top5_acc: 0.4245, loss_cls: 4.4865, loss: 4.4865 +2024-07-22 04:38:02,940 - pyskl - INFO - Epoch [3][3100/3746] lr: 9.991e-02, eta: 4 days, 15:46:00, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1923, top5_acc: 0.4314, loss_cls: 4.4623, loss: 4.4623 +2024-07-22 04:39:12,856 - pyskl - INFO - Epoch [3][3200/3746] lr: 9.991e-02, eta: 4 days, 15:42:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4327, loss_cls: 4.4294, loss: 4.4294 +2024-07-22 04:40:22,772 - pyskl - INFO - Epoch [3][3300/3746] lr: 9.991e-02, eta: 4 days, 15:38:21, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4339, loss_cls: 4.4221, loss: 4.4221 +2024-07-22 04:41:32,517 - pyskl - INFO - Epoch [3][3400/3746] lr: 9.991e-02, eta: 4 days, 15:34:27, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1983, top5_acc: 0.4345, loss_cls: 4.4400, loss: 4.4400 +2024-07-22 04:42:42,430 - pyskl - INFO - Epoch [3][3500/3746] lr: 9.991e-02, eta: 4 days, 15:30:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4381, loss_cls: 4.4091, loss: 4.4091 +2024-07-22 04:43:52,460 - pyskl - INFO - Epoch [3][3600/3746] lr: 9.990e-02, eta: 4 days, 15:27:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4334, loss_cls: 4.4145, loss: 4.4145 +2024-07-22 04:45:02,190 - pyskl - INFO - Epoch [3][3700/3746] lr: 9.990e-02, eta: 4 days, 15:23:25, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4370, loss_cls: 4.4067, loss: 4.4067 +2024-07-22 04:45:36,717 - pyskl - INFO - Saving checkpoint at 3 epochs +2024-07-22 04:47:28,253 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 04:47:28,917 - pyskl - INFO - +top1_acc 0.1181 +top5_acc 0.3101 +2024-07-22 04:47:28,917 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 04:47:28,957 - pyskl - INFO - +mean_acc 0.1182 +2024-07-22 04:47:28,967 - pyskl - INFO - Epoch(val) [3][309] top1_acc: 0.1181, top5_acc: 0.3101, mean_class_accuracy: 0.1182 +2024-07-22 04:50:49,010 - pyskl - INFO - Epoch [4][100/3746] lr: 9.990e-02, eta: 4 days, 16:37:29, time: 2.000, data_time: 1.294, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4381, loss_cls: 4.3971, loss: 4.3971 +2024-07-22 04:52:00,046 - pyskl - INFO - Epoch [4][200/3746] lr: 9.990e-02, eta: 4 days, 16:34:10, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2027, top5_acc: 0.4327, loss_cls: 4.4158, loss: 4.4158 +2024-07-22 04:53:10,889 - pyskl - INFO - Epoch [4][300/3746] lr: 9.990e-02, eta: 4 days, 16:30:44, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2036, top5_acc: 0.4358, loss_cls: 4.4103, loss: 4.4103 +2024-07-22 04:54:22,242 - pyskl - INFO - Epoch [4][400/3746] lr: 9.989e-02, eta: 4 days, 16:27:44, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2028, top5_acc: 0.4400, loss_cls: 4.3938, loss: 4.3938 +2024-07-22 04:55:32,930 - pyskl - INFO - Epoch [4][500/3746] lr: 9.989e-02, eta: 4 days, 16:24:15, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4436, loss_cls: 4.3501, loss: 4.3501 +2024-07-22 04:56:43,440 - pyskl - INFO - Epoch [4][600/3746] lr: 9.989e-02, eta: 4 days, 16:20:40, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4392, loss_cls: 4.4053, loss: 4.4053 +2024-07-22 04:57:53,787 - pyskl - INFO - Epoch [4][700/3746] lr: 9.989e-02, eta: 4 days, 16:17:00, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4403, loss_cls: 4.3833, loss: 4.3833 +2024-07-22 04:59:03,804 - pyskl - INFO - Epoch [4][800/3746] lr: 9.989e-02, eta: 4 days, 16:13:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4372, loss_cls: 4.4070, loss: 4.4070 +2024-07-22 05:00:14,030 - pyskl - INFO - Epoch [4][900/3746] lr: 9.988e-02, eta: 4 days, 16:09:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4242, loss_cls: 4.4383, loss: 4.4383 +2024-07-22 05:01:24,577 - pyskl - INFO - Epoch [4][1000/3746] lr: 9.988e-02, eta: 4 days, 16:06:04, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4516, loss_cls: 4.3591, loss: 4.3591 +2024-07-22 05:02:34,908 - pyskl - INFO - Epoch [4][1100/3746] lr: 9.988e-02, eta: 4 days, 16:02:33, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4355, loss_cls: 4.4025, loss: 4.4025 +2024-07-22 05:03:45,683 - pyskl - INFO - Epoch [4][1200/3746] lr: 9.988e-02, eta: 4 days, 15:59:23, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4378, loss_cls: 4.3785, loss: 4.3785 +2024-07-22 05:04:56,180 - pyskl - INFO - Epoch [4][1300/3746] lr: 9.988e-02, eta: 4 days, 15:56:04, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4481, loss_cls: 4.3658, loss: 4.3658 +2024-07-22 05:06:06,692 - pyskl - INFO - Epoch [4][1400/3746] lr: 9.988e-02, eta: 4 days, 15:52:47, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2056, top5_acc: 0.4405, loss_cls: 4.3910, loss: 4.3910 +2024-07-22 05:07:16,803 - pyskl - INFO - Epoch [4][1500/3746] lr: 9.987e-02, eta: 4 days, 15:49:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4466, loss_cls: 4.3714, loss: 4.3714 +2024-07-22 05:08:27,087 - pyskl - INFO - Epoch [4][1600/3746] lr: 9.987e-02, eta: 4 days, 15:45:53, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1995, top5_acc: 0.4400, loss_cls: 4.4204, loss: 4.4204 +2024-07-22 05:09:37,510 - pyskl - INFO - Epoch [4][1700/3746] lr: 9.987e-02, eta: 4 days, 15:42:38, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4523, loss_cls: 4.3608, loss: 4.3608 +2024-07-22 05:10:48,254 - pyskl - INFO - Epoch [4][1800/3746] lr: 9.987e-02, eta: 4 days, 15:39:39, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4450, loss_cls: 4.3921, loss: 4.3921 +2024-07-22 05:11:58,670 - pyskl - INFO - Epoch [4][1900/3746] lr: 9.987e-02, eta: 4 days, 15:36:28, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4502, loss_cls: 4.3819, loss: 4.3819 +2024-07-22 05:13:08,880 - pyskl - INFO - Epoch [4][2000/3746] lr: 9.986e-02, eta: 4 days, 15:33:10, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2059, top5_acc: 0.4528, loss_cls: 4.3640, loss: 4.3640 +2024-07-22 05:14:19,083 - pyskl - INFO - Epoch [4][2100/3746] lr: 9.986e-02, eta: 4 days, 15:29:54, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4519, loss_cls: 4.3551, loss: 4.3551 +2024-07-22 05:15:29,109 - pyskl - INFO - Epoch [4][2200/3746] lr: 9.986e-02, eta: 4 days, 15:26:32, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4405, loss_cls: 4.3794, loss: 4.3794 +2024-07-22 05:16:39,222 - pyskl - INFO - Epoch [4][2300/3746] lr: 9.986e-02, eta: 4 days, 15:23:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4394, loss_cls: 4.3950, loss: 4.3950 +2024-07-22 05:17:49,374 - pyskl - INFO - Epoch [4][2400/3746] lr: 9.985e-02, eta: 4 days, 15:20:03, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4412, loss_cls: 4.3982, loss: 4.3982 +2024-07-22 05:18:59,685 - pyskl - INFO - Epoch [4][2500/3746] lr: 9.985e-02, eta: 4 days, 15:16:59, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4489, loss_cls: 4.3529, loss: 4.3529 +2024-07-22 05:20:10,066 - pyskl - INFO - Epoch [4][2600/3746] lr: 9.985e-02, eta: 4 days, 15:13:58, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4547, loss_cls: 4.3447, loss: 4.3447 +2024-07-22 05:21:20,406 - pyskl - INFO - Epoch [4][2700/3746] lr: 9.985e-02, eta: 4 days, 15:10:58, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4430, loss_cls: 4.3809, loss: 4.3809 +2024-07-22 05:22:30,513 - pyskl - INFO - Epoch [4][2800/3746] lr: 9.985e-02, eta: 4 days, 15:07:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2092, top5_acc: 0.4511, loss_cls: 4.3723, loss: 4.3723 +2024-07-22 05:23:40,825 - pyskl - INFO - Epoch [4][2900/3746] lr: 9.984e-02, eta: 4 days, 15:04:52, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4444, loss_cls: 4.3900, loss: 4.3900 +2024-07-22 05:24:50,826 - pyskl - INFO - Epoch [4][3000/3746] lr: 9.984e-02, eta: 4 days, 15:01:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4406, loss_cls: 4.3739, loss: 4.3739 +2024-07-22 05:26:00,691 - pyskl - INFO - Epoch [4][3100/3746] lr: 9.984e-02, eta: 4 days, 14:58:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4378, loss_cls: 4.3706, loss: 4.3706 +2024-07-22 05:27:10,490 - pyskl - INFO - Epoch [4][3200/3746] lr: 9.984e-02, eta: 4 days, 14:55:19, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4527, loss_cls: 4.3729, loss: 4.3729 +2024-07-22 05:28:20,457 - pyskl - INFO - Epoch [4][3300/3746] lr: 9.983e-02, eta: 4 days, 14:52:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4472, loss_cls: 4.3667, loss: 4.3667 +2024-07-22 05:29:30,278 - pyskl - INFO - Epoch [4][3400/3746] lr: 9.983e-02, eta: 4 days, 14:49:05, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4477, loss_cls: 4.3548, loss: 4.3548 +2024-07-22 05:30:40,242 - pyskl - INFO - Epoch [4][3500/3746] lr: 9.983e-02, eta: 4 days, 14:46:03, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4495, loss_cls: 4.3663, loss: 4.3663 +2024-07-22 05:31:50,245 - pyskl - INFO - Epoch [4][3600/3746] lr: 9.983e-02, eta: 4 days, 14:43:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4494, loss_cls: 4.3762, loss: 4.3762 +2024-07-22 05:33:00,699 - pyskl - INFO - Epoch [4][3700/3746] lr: 9.983e-02, eta: 4 days, 14:40:23, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4527, loss_cls: 4.3640, loss: 4.3640 +2024-07-22 05:33:35,177 - pyskl - INFO - Saving checkpoint at 4 epochs +2024-07-22 05:35:26,626 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 05:35:27,283 - pyskl - INFO - +top1_acc 0.1481 +top5_acc 0.3592 +2024-07-22 05:35:27,283 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 05:35:27,321 - pyskl - INFO - +mean_acc 0.1482 +2024-07-22 05:35:27,326 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_2.pth was removed +2024-07-22 05:35:27,569 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2024-07-22 05:35:27,569 - pyskl - INFO - Best top1_acc is 0.1481 at 4 epoch. +2024-07-22 05:35:27,580 - pyskl - INFO - Epoch(val) [4][309] top1_acc: 0.1481, top5_acc: 0.3592, mean_class_accuracy: 0.1482 +2024-07-22 05:38:45,563 - pyskl - INFO - Epoch [5][100/3746] lr: 9.982e-02, eta: 4 days, 15:33:58, time: 1.980, data_time: 1.273, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4623, loss_cls: 4.2941, loss: 4.2941 +2024-07-22 05:39:57,411 - pyskl - INFO - Epoch [5][200/3746] lr: 9.982e-02, eta: 4 days, 15:31:47, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4519, loss_cls: 4.3575, loss: 4.3575 +2024-07-22 05:41:08,871 - pyskl - INFO - Epoch [5][300/3746] lr: 9.982e-02, eta: 4 days, 15:29:23, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4617, loss_cls: 4.3250, loss: 4.3250 +2024-07-22 05:42:19,637 - pyskl - INFO - Epoch [5][400/3746] lr: 9.982e-02, eta: 4 days, 15:26:35, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4547, loss_cls: 4.3306, loss: 4.3306 +2024-07-22 05:43:29,850 - pyskl - INFO - Epoch [5][500/3746] lr: 9.981e-02, eta: 4 days, 15:23:29, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4522, loss_cls: 4.3396, loss: 4.3396 +2024-07-22 05:44:40,127 - pyskl - INFO - Epoch [5][600/3746] lr: 9.981e-02, eta: 4 days, 15:20:26, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4564, loss_cls: 4.3191, loss: 4.3191 +2024-07-22 05:45:50,247 - pyskl - INFO - Epoch [5][700/3746] lr: 9.981e-02, eta: 4 days, 15:17:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4534, loss_cls: 4.3528, loss: 4.3528 +2024-07-22 05:47:00,462 - pyskl - INFO - Epoch [5][800/3746] lr: 9.981e-02, eta: 4 days, 15:14:18, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4553, loss_cls: 4.3384, loss: 4.3384 +2024-07-22 05:48:10,656 - pyskl - INFO - Epoch [5][900/3746] lr: 9.980e-02, eta: 4 days, 15:11:17, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4533, loss_cls: 4.3377, loss: 4.3377 +2024-07-22 05:49:20,679 - pyskl - INFO - Epoch [5][1000/3746] lr: 9.980e-02, eta: 4 days, 15:08:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4612, loss_cls: 4.3143, loss: 4.3143 +2024-07-22 05:50:30,846 - pyskl - INFO - Epoch [5][1100/3746] lr: 9.980e-02, eta: 4 days, 15:05:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4531, loss_cls: 4.3260, loss: 4.3260 +2024-07-22 05:51:41,035 - pyskl - INFO - Epoch [5][1200/3746] lr: 9.980e-02, eta: 4 days, 15:02:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4659, loss_cls: 4.2598, loss: 4.2598 +2024-07-22 05:52:51,025 - pyskl - INFO - Epoch [5][1300/3746] lr: 9.979e-02, eta: 4 days, 14:59:12, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4561, loss_cls: 4.2967, loss: 4.2967 +2024-07-22 05:54:01,194 - pyskl - INFO - Epoch [5][1400/3746] lr: 9.979e-02, eta: 4 days, 14:56:17, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4530, loss_cls: 4.3338, loss: 4.3338 +2024-07-22 05:55:11,500 - pyskl - INFO - Epoch [5][1500/3746] lr: 9.979e-02, eta: 4 days, 14:53:28, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4598, loss_cls: 4.3082, loss: 4.3082 +2024-07-22 05:56:21,490 - pyskl - INFO - Epoch [5][1600/3746] lr: 9.979e-02, eta: 4 days, 14:50:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4567, loss_cls: 4.3196, loss: 4.3196 +2024-07-22 05:57:31,899 - pyskl - INFO - Epoch [5][1700/3746] lr: 9.978e-02, eta: 4 days, 14:47:46, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4558, loss_cls: 4.3265, loss: 4.3265 +2024-07-22 05:58:42,028 - pyskl - INFO - Epoch [5][1800/3746] lr: 9.978e-02, eta: 4 days, 14:44:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4572, loss_cls: 4.3298, loss: 4.3298 +2024-07-22 05:59:52,160 - pyskl - INFO - Epoch [5][1900/3746] lr: 9.978e-02, eta: 4 days, 14:42:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4530, loss_cls: 4.3185, loss: 4.3185 +2024-07-22 06:01:02,261 - pyskl - INFO - Epoch [5][2000/3746] lr: 9.977e-02, eta: 4 days, 14:39:14, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4666, loss_cls: 4.3129, loss: 4.3129 +2024-07-22 06:02:12,215 - pyskl - INFO - Epoch [5][2100/3746] lr: 9.977e-02, eta: 4 days, 14:36:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4662, loss_cls: 4.2824, loss: 4.2824 +2024-07-22 06:03:22,257 - pyskl - INFO - Epoch [5][2200/3746] lr: 9.977e-02, eta: 4 days, 14:33:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4631, loss_cls: 4.3000, loss: 4.3000 +2024-07-22 06:04:32,571 - pyskl - INFO - Epoch [5][2300/3746] lr: 9.977e-02, eta: 4 days, 14:30:51, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4573, loss_cls: 4.3215, loss: 4.3215 +2024-07-22 06:05:42,497 - pyskl - INFO - Epoch [5][2400/3746] lr: 9.976e-02, eta: 4 days, 14:28:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4628, loss_cls: 4.2788, loss: 4.2788 +2024-07-22 06:06:52,584 - pyskl - INFO - Epoch [5][2500/3746] lr: 9.976e-02, eta: 4 days, 14:25:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4736, loss_cls: 4.2378, loss: 4.2378 +2024-07-22 06:08:02,522 - pyskl - INFO - Epoch [5][2600/3746] lr: 9.976e-02, eta: 4 days, 14:22:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4631, loss_cls: 4.2703, loss: 4.2703 +2024-07-22 06:09:12,491 - pyskl - INFO - Epoch [5][2700/3746] lr: 9.976e-02, eta: 4 days, 14:19:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4536, loss_cls: 4.3124, loss: 4.3124 +2024-07-22 06:10:22,632 - pyskl - INFO - Epoch [5][2800/3746] lr: 9.975e-02, eta: 4 days, 14:17:00, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4648, loss_cls: 4.2828, loss: 4.2828 +2024-07-22 06:11:32,833 - pyskl - INFO - Epoch [5][2900/3746] lr: 9.975e-02, eta: 4 days, 14:14:23, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4631, loss_cls: 4.2897, loss: 4.2897 +2024-07-22 06:12:42,816 - pyskl - INFO - Epoch [5][3000/3746] lr: 9.975e-02, eta: 4 days, 14:11:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4670, loss_cls: 4.3027, loss: 4.3027 +2024-07-22 06:13:52,692 - pyskl - INFO - Epoch [5][3100/3746] lr: 9.974e-02, eta: 4 days, 14:08:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4466, loss_cls: 4.3466, loss: 4.3466 +2024-07-22 06:15:02,806 - pyskl - INFO - Epoch [5][3200/3746] lr: 9.974e-02, eta: 4 days, 14:06:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4619, loss_cls: 4.2768, loss: 4.2768 +2024-07-22 06:16:12,606 - pyskl - INFO - Epoch [5][3300/3746] lr: 9.974e-02, eta: 4 days, 14:03:34, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4617, loss_cls: 4.2998, loss: 4.2998 +2024-07-22 06:17:23,218 - pyskl - INFO - Epoch [5][3400/3746] lr: 9.974e-02, eta: 4 days, 14:01:14, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4664, loss_cls: 4.2810, loss: 4.2810 +2024-07-22 06:18:33,272 - pyskl - INFO - Epoch [5][3500/3746] lr: 9.973e-02, eta: 4 days, 13:58:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4653, loss_cls: 4.2910, loss: 4.2910 +2024-07-22 06:19:43,767 - pyskl - INFO - Epoch [5][3600/3746] lr: 9.973e-02, eta: 4 days, 13:56:16, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4734, loss_cls: 4.2535, loss: 4.2535 +2024-07-22 06:20:53,672 - pyskl - INFO - Epoch [5][3700/3746] lr: 9.973e-02, eta: 4 days, 13:53:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4619, loss_cls: 4.3030, loss: 4.3030 +2024-07-22 06:21:27,873 - pyskl - INFO - Saving checkpoint at 5 epochs +2024-07-22 06:23:19,430 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 06:23:20,090 - pyskl - INFO - +top1_acc 0.1420 +top5_acc 0.3389 +2024-07-22 06:23:20,090 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 06:23:20,132 - pyskl - INFO - +mean_acc 0.1419 +2024-07-22 06:23:20,144 - pyskl - INFO - Epoch(val) [5][309] top1_acc: 0.1420, top5_acc: 0.3389, mean_class_accuracy: 0.1419 +2024-07-22 06:26:38,013 - pyskl - INFO - Epoch [6][100/3746] lr: 9.972e-02, eta: 4 days, 14:35:51, time: 1.979, data_time: 1.272, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4636, loss_cls: 4.2544, loss: 4.2544 +2024-07-22 06:27:49,345 - pyskl - INFO - Epoch [6][200/3746] lr: 9.972e-02, eta: 4 days, 14:33:41, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4680, loss_cls: 4.2665, loss: 4.2665 +2024-07-22 06:29:00,770 - pyskl - INFO - Epoch [6][300/3746] lr: 9.972e-02, eta: 4 days, 14:31:34, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4670, loss_cls: 4.2485, loss: 4.2485 +2024-07-22 06:30:12,433 - pyskl - INFO - Epoch [6][400/3746] lr: 9.971e-02, eta: 4 days, 14:29:34, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4683, loss_cls: 4.2679, loss: 4.2679 +2024-07-22 06:31:23,127 - pyskl - INFO - Epoch [6][500/3746] lr: 9.971e-02, eta: 4 days, 14:27:08, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4688, loss_cls: 4.2905, loss: 4.2905 +2024-07-22 06:32:33,707 - pyskl - INFO - Epoch [6][600/3746] lr: 9.971e-02, eta: 4 days, 14:24:39, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4642, loss_cls: 4.2562, loss: 4.2562 +2024-07-22 06:33:43,633 - pyskl - INFO - Epoch [6][700/3746] lr: 9.971e-02, eta: 4 days, 14:21:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4720, loss_cls: 4.2624, loss: 4.2624 +2024-07-22 06:34:53,450 - pyskl - INFO - Epoch [6][800/3746] lr: 9.970e-02, eta: 4 days, 14:19:04, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4611, loss_cls: 4.3044, loss: 4.3044 +2024-07-22 06:36:03,388 - pyskl - INFO - Epoch [6][900/3746] lr: 9.970e-02, eta: 4 days, 14:16:20, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4652, loss_cls: 4.2659, loss: 4.2659 +2024-07-22 06:37:13,619 - pyskl - INFO - Epoch [6][1000/3746] lr: 9.970e-02, eta: 4 days, 14:13:45, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4734, loss_cls: 4.2525, loss: 4.2525 +2024-07-22 06:38:23,711 - pyskl - INFO - Epoch [6][1100/3746] lr: 9.969e-02, eta: 4 days, 14:11:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4584, loss_cls: 4.3103, loss: 4.3103 +2024-07-22 06:39:33,992 - pyskl - INFO - Epoch [6][1200/3746] lr: 9.969e-02, eta: 4 days, 14:08:35, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4600, loss_cls: 4.3058, loss: 4.3058 +2024-07-22 06:40:44,713 - pyskl - INFO - Epoch [6][1300/3746] lr: 9.969e-02, eta: 4 days, 14:06:16, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4619, loss_cls: 4.2802, loss: 4.2802 +2024-07-22 06:41:54,947 - pyskl - INFO - Epoch [6][1400/3746] lr: 9.968e-02, eta: 4 days, 14:03:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4695, loss_cls: 4.2634, loss: 4.2634 +2024-07-22 06:43:04,861 - pyskl - INFO - Epoch [6][1500/3746] lr: 9.968e-02, eta: 4 days, 14:01:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4706, loss_cls: 4.2472, loss: 4.2472 +2024-07-22 06:44:14,943 - pyskl - INFO - Epoch [6][1600/3746] lr: 9.968e-02, eta: 4 days, 13:58:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4705, loss_cls: 4.2460, loss: 4.2460 +2024-07-22 06:45:25,014 - pyskl - INFO - Epoch [6][1700/3746] lr: 9.967e-02, eta: 4 days, 13:55:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4597, loss_cls: 4.2904, loss: 4.2904 +2024-07-22 06:46:35,176 - pyskl - INFO - Epoch [6][1800/3746] lr: 9.967e-02, eta: 4 days, 13:53:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4677, loss_cls: 4.2674, loss: 4.2674 +2024-07-22 06:47:45,066 - pyskl - INFO - Epoch [6][1900/3746] lr: 9.967e-02, eta: 4 days, 13:50:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4639, loss_cls: 4.2626, loss: 4.2626 +2024-07-22 06:48:55,178 - pyskl - INFO - Epoch [6][2000/3746] lr: 9.966e-02, eta: 4 days, 13:48:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4589, loss_cls: 4.2711, loss: 4.2711 +2024-07-22 06:50:05,642 - pyskl - INFO - Epoch [6][2100/3746] lr: 9.966e-02, eta: 4 days, 13:46:00, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4627, loss_cls: 4.2845, loss: 4.2845 +2024-07-22 06:51:15,737 - pyskl - INFO - Epoch [6][2200/3746] lr: 9.966e-02, eta: 4 days, 13:43:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4652, loss_cls: 4.2925, loss: 4.2925 +2024-07-22 06:52:25,761 - pyskl - INFO - Epoch [6][2300/3746] lr: 9.965e-02, eta: 4 days, 13:41:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4673, loss_cls: 4.2953, loss: 4.2953 +2024-07-22 06:53:35,693 - pyskl - INFO - Epoch [6][2400/3746] lr: 9.965e-02, eta: 4 days, 13:38:30, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4653, loss_cls: 4.2712, loss: 4.2712 +2024-07-22 06:54:45,876 - pyskl - INFO - Epoch [6][2500/3746] lr: 9.965e-02, eta: 4 days, 13:36:05, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4780, loss_cls: 4.2093, loss: 4.2093 +2024-07-22 06:55:56,020 - pyskl - INFO - Epoch [6][2600/3746] lr: 9.964e-02, eta: 4 days, 13:33:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4631, loss_cls: 4.2740, loss: 4.2740 +2024-07-22 06:57:06,168 - pyskl - INFO - Epoch [6][2700/3746] lr: 9.964e-02, eta: 4 days, 13:31:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4664, loss_cls: 4.2945, loss: 4.2945 +2024-07-22 06:58:16,583 - pyskl - INFO - Epoch [6][2800/3746] lr: 9.964e-02, eta: 4 days, 13:29:00, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4717, loss_cls: 4.2559, loss: 4.2559 +2024-07-22 06:59:26,474 - pyskl - INFO - Epoch [6][2900/3746] lr: 9.963e-02, eta: 4 days, 13:26:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4594, loss_cls: 4.2811, loss: 4.2811 +2024-07-22 07:00:36,821 - pyskl - INFO - Epoch [6][3000/3746] lr: 9.963e-02, eta: 4 days, 13:24:14, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4773, loss_cls: 4.2347, loss: 4.2347 +2024-07-22 07:01:46,721 - pyskl - INFO - Epoch [6][3100/3746] lr: 9.963e-02, eta: 4 days, 13:21:46, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4622, loss_cls: 4.2569, loss: 4.2569 +2024-07-22 07:02:56,796 - pyskl - INFO - Epoch [6][3200/3746] lr: 9.962e-02, eta: 4 days, 13:19:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4661, loss_cls: 4.2730, loss: 4.2730 +2024-07-22 07:04:06,559 - pyskl - INFO - Epoch [6][3300/3746] lr: 9.962e-02, eta: 4 days, 13:16:54, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4786, loss_cls: 4.2316, loss: 4.2316 +2024-07-22 07:05:16,535 - pyskl - INFO - Epoch [6][3400/3746] lr: 9.962e-02, eta: 4 days, 13:14:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4789, loss_cls: 4.2359, loss: 4.2359 +2024-07-22 07:06:26,829 - pyskl - INFO - Epoch [6][3500/3746] lr: 9.961e-02, eta: 4 days, 13:12:16, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4653, loss_cls: 4.2874, loss: 4.2874 +2024-07-22 07:07:36,760 - pyskl - INFO - Epoch [6][3600/3746] lr: 9.961e-02, eta: 4 days, 13:09:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4703, loss_cls: 4.2892, loss: 4.2892 +2024-07-22 07:08:46,969 - pyskl - INFO - Epoch [6][3700/3746] lr: 9.961e-02, eta: 4 days, 13:07:36, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4670, loss_cls: 4.2672, loss: 4.2672 +2024-07-22 07:09:21,548 - pyskl - INFO - Saving checkpoint at 6 epochs +2024-07-22 07:11:12,030 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 07:11:12,699 - pyskl - INFO - +top1_acc 0.1417 +top5_acc 0.3402 +2024-07-22 07:11:12,699 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 07:11:12,738 - pyskl - INFO - +mean_acc 0.1417 +2024-07-22 07:11:12,750 - pyskl - INFO - Epoch(val) [6][309] top1_acc: 0.1417, top5_acc: 0.3402, mean_class_accuracy: 0.1417 +2024-07-22 07:14:29,153 - pyskl - INFO - Epoch [7][100/3746] lr: 9.960e-02, eta: 4 days, 13:41:42, time: 1.964, data_time: 1.257, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4611, loss_cls: 4.2605, loss: 4.2605 +2024-07-22 07:15:40,370 - pyskl - INFO - Epoch [7][200/3746] lr: 9.960e-02, eta: 4 days, 13:39:41, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4742, loss_cls: 4.2604, loss: 4.2604 +2024-07-22 07:16:51,134 - pyskl - INFO - Epoch [7][300/3746] lr: 9.960e-02, eta: 4 days, 13:37:30, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4744, loss_cls: 4.2396, loss: 4.2396 +2024-07-22 07:18:02,323 - pyskl - INFO - Epoch [7][400/3746] lr: 9.959e-02, eta: 4 days, 13:35:29, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4717, loss_cls: 4.2470, loss: 4.2470 +2024-07-22 07:19:12,840 - pyskl - INFO - Epoch [7][500/3746] lr: 9.959e-02, eta: 4 days, 13:33:13, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4675, loss_cls: 4.3073, loss: 4.3073 +2024-07-22 07:20:23,433 - pyskl - INFO - Epoch [7][600/3746] lr: 9.958e-02, eta: 4 days, 13:30:59, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4720, loss_cls: 4.2095, loss: 4.2095 +2024-07-22 07:21:33,715 - pyskl - INFO - Epoch [7][700/3746] lr: 9.958e-02, eta: 4 days, 13:28:39, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4770, loss_cls: 4.2289, loss: 4.2289 +2024-07-22 07:22:43,660 - pyskl - INFO - Epoch [7][800/3746] lr: 9.958e-02, eta: 4 days, 13:26:11, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4733, loss_cls: 4.2474, loss: 4.2474 +2024-07-22 07:23:53,857 - pyskl - INFO - Epoch [7][900/3746] lr: 9.957e-02, eta: 4 days, 13:23:50, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4667, loss_cls: 4.2488, loss: 4.2488 +2024-07-22 07:25:03,807 - pyskl - INFO - Epoch [7][1000/3746] lr: 9.957e-02, eta: 4 days, 13:21:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4625, loss_cls: 4.2685, loss: 4.2685 +2024-07-22 07:26:14,044 - pyskl - INFO - Epoch [7][1100/3746] lr: 9.957e-02, eta: 4 days, 13:19:05, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4814, loss_cls: 4.1892, loss: 4.1892 +2024-07-22 07:27:24,320 - pyskl - INFO - Epoch [7][1200/3746] lr: 9.956e-02, eta: 4 days, 13:16:48, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4720, loss_cls: 4.2436, loss: 4.2436 +2024-07-22 07:28:34,734 - pyskl - INFO - Epoch [7][1300/3746] lr: 9.956e-02, eta: 4 days, 13:14:34, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4803, loss_cls: 4.2225, loss: 4.2225 +2024-07-22 07:29:44,856 - pyskl - INFO - Epoch [7][1400/3746] lr: 9.956e-02, eta: 4 days, 13:12:14, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4803, loss_cls: 4.2069, loss: 4.2069 +2024-07-22 07:30:54,903 - pyskl - INFO - Epoch [7][1500/3746] lr: 9.955e-02, eta: 4 days, 13:09:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4794, loss_cls: 4.2174, loss: 4.2174 +2024-07-22 07:32:05,185 - pyskl - INFO - Epoch [7][1600/3746] lr: 9.955e-02, eta: 4 days, 13:07:38, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4719, loss_cls: 4.2544, loss: 4.2544 +2024-07-22 07:33:15,261 - pyskl - INFO - Epoch [7][1700/3746] lr: 9.954e-02, eta: 4 days, 13:05:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4900, loss_cls: 4.1894, loss: 4.1894 +2024-07-22 07:34:25,356 - pyskl - INFO - Epoch [7][1800/3746] lr: 9.954e-02, eta: 4 days, 13:03:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4814, loss_cls: 4.1899, loss: 4.1899 +2024-07-22 07:35:35,609 - pyskl - INFO - Epoch [7][1900/3746] lr: 9.954e-02, eta: 4 days, 13:00:47, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4758, loss_cls: 4.2176, loss: 4.2176 +2024-07-22 07:36:45,868 - pyskl - INFO - Epoch [7][2000/3746] lr: 9.953e-02, eta: 4 days, 12:58:33, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4802, loss_cls: 4.2574, loss: 4.2574 +2024-07-22 07:37:56,188 - pyskl - INFO - Epoch [7][2100/3746] lr: 9.953e-02, eta: 4 days, 12:56:22, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4748, loss_cls: 4.2085, loss: 4.2085 +2024-07-22 07:39:06,244 - pyskl - INFO - Epoch [7][2200/3746] lr: 9.952e-02, eta: 4 days, 12:54:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4700, loss_cls: 4.2556, loss: 4.2556 +2024-07-22 07:40:15,991 - pyskl - INFO - Epoch [7][2300/3746] lr: 9.952e-02, eta: 4 days, 12:51:42, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4766, loss_cls: 4.2212, loss: 4.2212 +2024-07-22 07:41:25,886 - pyskl - INFO - Epoch [7][2400/3746] lr: 9.952e-02, eta: 4 days, 12:49:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4636, loss_cls: 4.2500, loss: 4.2500 +2024-07-22 07:42:36,127 - pyskl - INFO - Epoch [7][2500/3746] lr: 9.951e-02, eta: 4 days, 12:47:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4630, loss_cls: 4.2599, loss: 4.2599 +2024-07-22 07:43:46,247 - pyskl - INFO - Epoch [7][2600/3746] lr: 9.951e-02, eta: 4 days, 12:44:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4694, loss_cls: 4.2448, loss: 4.2448 +2024-07-22 07:44:56,298 - pyskl - INFO - Epoch [7][2700/3746] lr: 9.951e-02, eta: 4 days, 12:42:44, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4914, loss_cls: 4.1507, loss: 4.1507 +2024-07-22 07:46:06,694 - pyskl - INFO - Epoch [7][2800/3746] lr: 9.950e-02, eta: 4 days, 12:40:38, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4581, loss_cls: 4.2643, loss: 4.2643 +2024-07-22 07:47:16,839 - pyskl - INFO - Epoch [7][2900/3746] lr: 9.950e-02, eta: 4 days, 12:38:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4730, loss_cls: 4.2624, loss: 4.2624 +2024-07-22 07:48:27,193 - pyskl - INFO - Epoch [7][3000/3746] lr: 9.949e-02, eta: 4 days, 12:36:20, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4767, loss_cls: 4.2239, loss: 4.2239 +2024-07-22 07:49:37,206 - pyskl - INFO - Epoch [7][3100/3746] lr: 9.949e-02, eta: 4 days, 12:34:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4780, loss_cls: 4.2087, loss: 4.2087 +2024-07-22 07:50:47,182 - pyskl - INFO - Epoch [7][3200/3746] lr: 9.949e-02, eta: 4 days, 12:31:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4767, loss_cls: 4.2315, loss: 4.2315 +2024-07-22 07:51:57,285 - pyskl - INFO - Epoch [7][3300/3746] lr: 9.948e-02, eta: 4 days, 12:29:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4819, loss_cls: 4.2131, loss: 4.2131 +2024-07-22 07:53:06,926 - pyskl - INFO - Epoch [7][3400/3746] lr: 9.948e-02, eta: 4 days, 12:27:23, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4709, loss_cls: 4.2674, loss: 4.2674 +2024-07-22 07:54:16,859 - pyskl - INFO - Epoch [7][3500/3746] lr: 9.947e-02, eta: 4 days, 12:25:11, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4755, loss_cls: 4.2415, loss: 4.2415 +2024-07-22 07:55:26,764 - pyskl - INFO - Epoch [7][3600/3746] lr: 9.947e-02, eta: 4 days, 12:22:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4931, loss_cls: 4.1712, loss: 4.1712 +2024-07-22 07:56:37,108 - pyskl - INFO - Epoch [7][3700/3746] lr: 9.947e-02, eta: 4 days, 12:20:54, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4764, loss_cls: 4.2187, loss: 4.2187 +2024-07-22 07:57:11,760 - pyskl - INFO - Saving checkpoint at 7 epochs +2024-07-22 07:59:03,609 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 07:59:04,267 - pyskl - INFO - +top1_acc 0.1687 +top5_acc 0.3865 +2024-07-22 07:59:04,267 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 07:59:04,307 - pyskl - INFO - +mean_acc 0.1686 +2024-07-22 07:59:04,311 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_4.pth was removed +2024-07-22 07:59:04,535 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2024-07-22 07:59:04,536 - pyskl - INFO - Best top1_acc is 0.1687 at 7 epoch. +2024-07-22 07:59:04,546 - pyskl - INFO - Epoch(val) [7][309] top1_acc: 0.1687, top5_acc: 0.3865, mean_class_accuracy: 0.1686 +2024-07-22 08:02:21,149 - pyskl - INFO - Epoch [8][100/3746] lr: 9.946e-02, eta: 4 days, 12:49:45, time: 1.966, data_time: 1.259, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4695, loss_cls: 4.2309, loss: 4.2309 +2024-07-22 08:03:32,404 - pyskl - INFO - Epoch [8][200/3746] lr: 9.946e-02, eta: 4 days, 12:47:53, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4722, loss_cls: 4.2245, loss: 4.2245 +2024-07-22 08:04:43,273 - pyskl - INFO - Epoch [8][300/3746] lr: 9.945e-02, eta: 4 days, 12:45:54, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4855, loss_cls: 4.1918, loss: 4.1918 +2024-07-22 08:05:54,264 - pyskl - INFO - Epoch [8][400/3746] lr: 9.945e-02, eta: 4 days, 12:43:58, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4817, loss_cls: 4.2187, loss: 4.2187 +2024-07-22 08:07:05,159 - pyskl - INFO - Epoch [8][500/3746] lr: 9.944e-02, eta: 4 days, 12:42:00, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4886, loss_cls: 4.2053, loss: 4.2053 +2024-07-22 08:08:15,665 - pyskl - INFO - Epoch [8][600/3746] lr: 9.944e-02, eta: 4 days, 12:39:55, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4748, loss_cls: 4.2117, loss: 4.2117 +2024-07-22 08:09:26,688 - pyskl - INFO - Epoch [8][700/3746] lr: 9.943e-02, eta: 4 days, 12:38:00, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4694, loss_cls: 4.2435, loss: 4.2435 +2024-07-22 08:10:36,816 - pyskl - INFO - Epoch [8][800/3746] lr: 9.943e-02, eta: 4 days, 12:35:48, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4806, loss_cls: 4.1708, loss: 4.1708 +2024-07-22 08:11:47,048 - pyskl - INFO - Epoch [8][900/3746] lr: 9.943e-02, eta: 4 days, 12:33:39, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4788, loss_cls: 4.1937, loss: 4.1937 +2024-07-22 08:12:57,302 - pyskl - INFO - Epoch [8][1000/3746] lr: 9.942e-02, eta: 4 days, 12:31:30, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4836, loss_cls: 4.1844, loss: 4.1844 +2024-07-22 08:14:07,548 - pyskl - INFO - Epoch [8][1100/3746] lr: 9.942e-02, eta: 4 days, 12:29:22, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4698, loss_cls: 4.2585, loss: 4.2585 +2024-07-22 08:15:17,967 - pyskl - INFO - Epoch [8][1200/3746] lr: 9.941e-02, eta: 4 days, 12:27:17, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4794, loss_cls: 4.1969, loss: 4.1969 +2024-07-22 08:16:28,221 - pyskl - INFO - Epoch [8][1300/3746] lr: 9.941e-02, eta: 4 days, 12:25:10, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4830, loss_cls: 4.2174, loss: 4.2174 +2024-07-22 08:17:38,652 - pyskl - INFO - Epoch [8][1400/3746] lr: 9.940e-02, eta: 4 days, 12:23:06, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4778, loss_cls: 4.2221, loss: 4.2221 +2024-07-22 08:18:48,893 - pyskl - INFO - Epoch [8][1500/3746] lr: 9.940e-02, eta: 4 days, 12:21:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4802, loss_cls: 4.2003, loss: 4.2003 +2024-07-22 08:19:59,187 - pyskl - INFO - Epoch [8][1600/3746] lr: 9.940e-02, eta: 4 days, 12:18:54, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4886, loss_cls: 4.1624, loss: 4.1624 +2024-07-22 08:21:09,394 - pyskl - INFO - Epoch [8][1700/3746] lr: 9.939e-02, eta: 4 days, 12:16:48, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4817, loss_cls: 4.2035, loss: 4.2035 +2024-07-22 08:22:19,547 - pyskl - INFO - Epoch [8][1800/3746] lr: 9.939e-02, eta: 4 days, 12:14:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4697, loss_cls: 4.2794, loss: 4.2794 +2024-07-22 08:23:29,962 - pyskl - INFO - Epoch [8][1900/3746] lr: 9.938e-02, eta: 4 days, 12:12:39, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4689, loss_cls: 4.2379, loss: 4.2379 +2024-07-22 08:24:39,992 - pyskl - INFO - Epoch [8][2000/3746] lr: 9.938e-02, eta: 4 days, 12:10:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4781, loss_cls: 4.2109, loss: 4.2109 +2024-07-22 08:25:50,050 - pyskl - INFO - Epoch [8][2100/3746] lr: 9.937e-02, eta: 4 days, 12:08:22, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4830, loss_cls: 4.2069, loss: 4.2069 +2024-07-22 08:27:00,736 - pyskl - INFO - Epoch [8][2200/3746] lr: 9.937e-02, eta: 4 days, 12:06:26, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4756, loss_cls: 4.2160, loss: 4.2160 +2024-07-22 08:28:11,001 - pyskl - INFO - Epoch [8][2300/3746] lr: 9.937e-02, eta: 4 days, 12:04:23, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4967, loss_cls: 4.1580, loss: 4.1580 +2024-07-22 08:29:21,242 - pyskl - INFO - Epoch [8][2400/3746] lr: 9.936e-02, eta: 4 days, 12:02:20, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4828, loss_cls: 4.2168, loss: 4.2168 +2024-07-22 08:30:31,378 - pyskl - INFO - Epoch [8][2500/3746] lr: 9.936e-02, eta: 4 days, 12:00:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4784, loss_cls: 4.1892, loss: 4.1892 +2024-07-22 08:31:41,460 - pyskl - INFO - Epoch [8][2600/3746] lr: 9.935e-02, eta: 4 days, 11:58:09, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4789, loss_cls: 4.1982, loss: 4.1982 +2024-07-22 08:32:51,513 - pyskl - INFO - Epoch [8][2700/3746] lr: 9.935e-02, eta: 4 days, 11:56:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4867, loss_cls: 4.1950, loss: 4.1950 +2024-07-22 08:34:01,605 - pyskl - INFO - Epoch [8][2800/3746] lr: 9.934e-02, eta: 4 days, 11:53:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4814, loss_cls: 4.2003, loss: 4.2003 +2024-07-22 08:35:11,478 - pyskl - INFO - Epoch [8][2900/3746] lr: 9.934e-02, eta: 4 days, 11:51:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4920, loss_cls: 4.1361, loss: 4.1361 +2024-07-22 08:36:21,478 - pyskl - INFO - Epoch [8][3000/3746] lr: 9.933e-02, eta: 4 days, 11:49:45, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4823, loss_cls: 4.1728, loss: 4.1728 +2024-07-22 08:37:31,788 - pyskl - INFO - Epoch [8][3100/3746] lr: 9.933e-02, eta: 4 days, 11:47:46, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4720, loss_cls: 4.2375, loss: 4.2375 +2024-07-22 08:38:41,973 - pyskl - INFO - Epoch [8][3200/3746] lr: 9.933e-02, eta: 4 days, 11:45:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4755, loss_cls: 4.2292, loss: 4.2292 +2024-07-22 08:39:52,105 - pyskl - INFO - Epoch [8][3300/3746] lr: 9.932e-02, eta: 4 days, 11:43:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4919, loss_cls: 4.1543, loss: 4.1543 +2024-07-22 08:41:02,730 - pyskl - INFO - Epoch [8][3400/3746] lr: 9.932e-02, eta: 4 days, 11:41:50, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4808, loss_cls: 4.2142, loss: 4.2142 +2024-07-22 08:42:13,058 - pyskl - INFO - Epoch [8][3500/3746] lr: 9.931e-02, eta: 4 days, 11:39:52, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4844, loss_cls: 4.2179, loss: 4.2179 +2024-07-22 08:43:23,340 - pyskl - INFO - Epoch [8][3600/3746] lr: 9.931e-02, eta: 4 days, 11:37:54, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4788, loss_cls: 4.2258, loss: 4.2258 +2024-07-22 08:44:33,403 - pyskl - INFO - Epoch [8][3700/3746] lr: 9.930e-02, eta: 4 days, 11:35:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4811, loss_cls: 4.2147, loss: 4.2147 +2024-07-22 08:45:07,935 - pyskl - INFO - Saving checkpoint at 8 epochs +2024-07-22 08:46:59,729 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 08:47:00,400 - pyskl - INFO - +top1_acc 0.1550 +top5_acc 0.3585 +2024-07-22 08:47:00,400 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 08:47:00,443 - pyskl - INFO - +mean_acc 0.1548 +2024-07-22 08:47:00,455 - pyskl - INFO - Epoch(val) [8][309] top1_acc: 0.1550, top5_acc: 0.3585, mean_class_accuracy: 0.1548 +2024-07-22 08:50:17,565 - pyskl - INFO - Epoch [9][100/3746] lr: 9.930e-02, eta: 4 days, 12:00:51, time: 1.971, data_time: 1.264, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4848, loss_cls: 4.1764, loss: 4.1764 +2024-07-22 08:51:28,760 - pyskl - INFO - Epoch [9][200/3746] lr: 9.929e-02, eta: 4 days, 11:59:04, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4969, loss_cls: 4.1368, loss: 4.1368 +2024-07-22 08:52:40,039 - pyskl - INFO - Epoch [9][300/3746] lr: 9.929e-02, eta: 4 days, 11:57:19, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4803, loss_cls: 4.1935, loss: 4.1935 +2024-07-22 08:53:51,625 - pyskl - INFO - Epoch [9][400/3746] lr: 9.928e-02, eta: 4 days, 11:55:39, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4850, loss_cls: 4.1992, loss: 4.1992 +2024-07-22 08:55:02,708 - pyskl - INFO - Epoch [9][500/3746] lr: 9.928e-02, eta: 4 days, 11:53:51, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4772, loss_cls: 4.2394, loss: 4.2394 +2024-07-22 08:56:13,112 - pyskl - INFO - Epoch [9][600/3746] lr: 9.927e-02, eta: 4 days, 11:51:51, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4916, loss_cls: 4.1301, loss: 4.1301 +2024-07-22 08:57:23,765 - pyskl - INFO - Epoch [9][700/3746] lr: 9.927e-02, eta: 4 days, 11:49:56, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4828, loss_cls: 4.1662, loss: 4.1662 +2024-07-22 08:58:33,921 - pyskl - INFO - Epoch [9][800/3746] lr: 9.926e-02, eta: 4 days, 11:47:53, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4881, loss_cls: 4.1650, loss: 4.1650 +2024-07-22 08:59:44,429 - pyskl - INFO - Epoch [9][900/3746] lr: 9.926e-02, eta: 4 days, 11:45:56, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4897, loss_cls: 4.1567, loss: 4.1567 +2024-07-22 09:00:54,633 - pyskl - INFO - Epoch [9][1000/3746] lr: 9.925e-02, eta: 4 days, 11:43:54, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4809, loss_cls: 4.1862, loss: 4.1862 +2024-07-22 09:02:05,013 - pyskl - INFO - Epoch [9][1100/3746] lr: 9.925e-02, eta: 4 days, 11:41:55, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4791, loss_cls: 4.1680, loss: 4.1680 +2024-07-22 09:03:15,403 - pyskl - INFO - Epoch [9][1200/3746] lr: 9.924e-02, eta: 4 days, 11:39:57, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4747, loss_cls: 4.2009, loss: 4.2009 +2024-07-22 09:04:25,701 - pyskl - INFO - Epoch [9][1300/3746] lr: 9.924e-02, eta: 4 days, 11:37:57, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4850, loss_cls: 4.1521, loss: 4.1521 +2024-07-22 09:05:35,622 - pyskl - INFO - Epoch [9][1400/3746] lr: 9.923e-02, eta: 4 days, 11:35:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4839, loss_cls: 4.1795, loss: 4.1795 +2024-07-22 09:06:45,715 - pyskl - INFO - Epoch [9][1500/3746] lr: 9.923e-02, eta: 4 days, 11:33:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4731, loss_cls: 4.2332, loss: 4.2332 +2024-07-22 09:07:55,838 - pyskl - INFO - Epoch [9][1600/3746] lr: 9.922e-02, eta: 4 days, 11:31:48, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4711, loss_cls: 4.2422, loss: 4.2422 +2024-07-22 09:09:05,892 - pyskl - INFO - Epoch [9][1700/3746] lr: 9.922e-02, eta: 4 days, 11:29:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4753, loss_cls: 4.2298, loss: 4.2298 +2024-07-22 09:10:15,885 - pyskl - INFO - Epoch [9][1800/3746] lr: 9.921e-02, eta: 4 days, 11:27:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4814, loss_cls: 4.2045, loss: 4.2045 +2024-07-22 09:11:25,674 - pyskl - INFO - Epoch [9][1900/3746] lr: 9.921e-02, eta: 4 days, 11:25:37, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4844, loss_cls: 4.1872, loss: 4.1872 +2024-07-22 09:12:35,840 - pyskl - INFO - Epoch [9][2000/3746] lr: 9.920e-02, eta: 4 days, 11:23:38, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4825, loss_cls: 4.1635, loss: 4.1635 +2024-07-22 09:13:45,918 - pyskl - INFO - Epoch [9][2100/3746] lr: 9.920e-02, eta: 4 days, 11:21:37, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4927, loss_cls: 4.1309, loss: 4.1309 +2024-07-22 09:14:56,272 - pyskl - INFO - Epoch [9][2200/3746] lr: 9.919e-02, eta: 4 days, 11:19:42, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4805, loss_cls: 4.1878, loss: 4.1878 +2024-07-22 09:16:06,941 - pyskl - INFO - Epoch [9][2300/3746] lr: 9.919e-02, eta: 4 days, 11:17:51, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4939, loss_cls: 4.1587, loss: 4.1587 +2024-07-22 09:17:17,017 - pyskl - INFO - Epoch [9][2400/3746] lr: 9.918e-02, eta: 4 days, 11:15:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4681, loss_cls: 4.2271, loss: 4.2271 +2024-07-22 09:18:27,040 - pyskl - INFO - Epoch [9][2500/3746] lr: 9.918e-02, eta: 4 days, 11:13:51, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4866, loss_cls: 4.1646, loss: 4.1646 +2024-07-22 09:19:37,181 - pyskl - INFO - Epoch [9][2600/3746] lr: 9.917e-02, eta: 4 days, 11:11:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4859, loss_cls: 4.1688, loss: 4.1688 +2024-07-22 09:20:47,478 - pyskl - INFO - Epoch [9][2700/3746] lr: 9.917e-02, eta: 4 days, 11:09:58, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4667, loss_cls: 4.2871, loss: 4.2871 +2024-07-22 09:21:57,411 - pyskl - INFO - Epoch [9][2800/3746] lr: 9.916e-02, eta: 4 days, 11:07:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4875, loss_cls: 4.1783, loss: 4.1783 +2024-07-22 09:23:07,442 - pyskl - INFO - Epoch [9][2900/3746] lr: 9.916e-02, eta: 4 days, 11:05:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4683, loss_cls: 4.2210, loss: 4.2210 +2024-07-22 09:24:17,418 - pyskl - INFO - Epoch [9][3000/3746] lr: 9.915e-02, eta: 4 days, 11:03:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4834, loss_cls: 4.2147, loss: 4.2147 +2024-07-22 09:25:27,467 - pyskl - INFO - Epoch [9][3100/3746] lr: 9.915e-02, eta: 4 days, 11:02:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4927, loss_cls: 4.1542, loss: 4.1542 +2024-07-22 09:26:37,532 - pyskl - INFO - Epoch [9][3200/3746] lr: 9.914e-02, eta: 4 days, 11:00:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4917, loss_cls: 4.1571, loss: 4.1571 +2024-07-22 09:27:47,538 - pyskl - INFO - Epoch [9][3300/3746] lr: 9.914e-02, eta: 4 days, 10:58:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4764, loss_cls: 4.2092, loss: 4.2092 +2024-07-22 09:28:57,748 - pyskl - INFO - Epoch [9][3400/3746] lr: 9.913e-02, eta: 4 days, 10:56:11, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4789, loss_cls: 4.1751, loss: 4.1751 +2024-07-22 09:30:07,955 - pyskl - INFO - Epoch [9][3500/3746] lr: 9.913e-02, eta: 4 days, 10:54:16, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4784, loss_cls: 4.2184, loss: 4.2184 +2024-07-22 09:31:18,182 - pyskl - INFO - Epoch [9][3600/3746] lr: 9.912e-02, eta: 4 days, 10:52:23, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4892, loss_cls: 4.1601, loss: 4.1601 +2024-07-22 09:32:28,293 - pyskl - INFO - Epoch [9][3700/3746] lr: 9.912e-02, eta: 4 days, 10:50:27, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4766, loss_cls: 4.2310, loss: 4.2310 +2024-07-22 09:33:02,806 - pyskl - INFO - Saving checkpoint at 9 epochs +2024-07-22 09:34:54,493 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 09:34:55,155 - pyskl - INFO - +top1_acc 0.1747 +top5_acc 0.3924 +2024-07-22 09:34:55,155 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 09:34:55,193 - pyskl - INFO - +mean_acc 0.1745 +2024-07-22 09:34:55,198 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_7.pth was removed +2024-07-22 09:34:55,437 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_9.pth. +2024-07-22 09:34:55,437 - pyskl - INFO - Best top1_acc is 0.1747 at 9 epoch. +2024-07-22 09:34:55,449 - pyskl - INFO - Epoch(val) [9][309] top1_acc: 0.1747, top5_acc: 0.3924, mean_class_accuracy: 0.1745 +2024-07-22 09:38:15,010 - pyskl - INFO - Epoch [10][100/3746] lr: 9.911e-02, eta: 4 days, 11:12:57, time: 1.996, data_time: 1.286, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5002, loss_cls: 4.1198, loss: 4.1198 +2024-07-22 09:39:26,044 - pyskl - INFO - Epoch [10][200/3746] lr: 9.910e-02, eta: 4 days, 11:11:12, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4803, loss_cls: 4.1904, loss: 4.1904 +2024-07-22 09:40:36,949 - pyskl - INFO - Epoch [10][300/3746] lr: 9.910e-02, eta: 4 days, 11:09:25, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4828, loss_cls: 4.1761, loss: 4.1761 +2024-07-22 09:41:47,669 - pyskl - INFO - Epoch [10][400/3746] lr: 9.909e-02, eta: 4 days, 11:07:36, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4817, loss_cls: 4.2204, loss: 4.2204 +2024-07-22 09:42:58,205 - pyskl - INFO - Epoch [10][500/3746] lr: 9.909e-02, eta: 4 days, 11:05:43, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4842, loss_cls: 4.2043, loss: 4.2043 +2024-07-22 09:44:08,755 - pyskl - INFO - Epoch [10][600/3746] lr: 9.908e-02, eta: 4 days, 11:03:52, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4908, loss_cls: 4.1667, loss: 4.1667 +2024-07-22 09:45:19,174 - pyskl - INFO - Epoch [10][700/3746] lr: 9.908e-02, eta: 4 days, 11:01:58, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4927, loss_cls: 4.1644, loss: 4.1644 +2024-07-22 09:46:29,765 - pyskl - INFO - Epoch [10][800/3746] lr: 9.907e-02, eta: 4 days, 11:00:08, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4864, loss_cls: 4.1853, loss: 4.1853 +2024-07-22 09:47:39,898 - pyskl - INFO - Epoch [10][900/3746] lr: 9.907e-02, eta: 4 days, 10:58:10, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4955, loss_cls: 4.1455, loss: 4.1455 +2024-07-22 09:48:50,203 - pyskl - INFO - Epoch [10][1000/3746] lr: 9.906e-02, eta: 4 days, 10:56:16, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4867, loss_cls: 4.2002, loss: 4.2002 +2024-07-22 09:50:00,292 - pyskl - INFO - Epoch [10][1100/3746] lr: 9.906e-02, eta: 4 days, 10:54:18, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4861, loss_cls: 4.1945, loss: 4.1945 +2024-07-22 09:51:10,689 - pyskl - INFO - Epoch [10][1200/3746] lr: 9.905e-02, eta: 4 days, 10:52:26, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4753, loss_cls: 4.2078, loss: 4.2078 +2024-07-22 09:52:21,152 - pyskl - INFO - Epoch [10][1300/3746] lr: 9.905e-02, eta: 4 days, 10:50:34, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4931, loss_cls: 4.1630, loss: 4.1630 +2024-07-22 09:53:31,141 - pyskl - INFO - Epoch [10][1400/3746] lr: 9.904e-02, eta: 4 days, 10:48:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4842, loss_cls: 4.1838, loss: 4.1838 +2024-07-22 09:54:41,252 - pyskl - INFO - Epoch [10][1500/3746] lr: 9.903e-02, eta: 4 days, 10:46:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4869, loss_cls: 4.1686, loss: 4.1686 +2024-07-22 09:55:51,595 - pyskl - INFO - Epoch [10][1600/3746] lr: 9.903e-02, eta: 4 days, 10:44:48, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4805, loss_cls: 4.1847, loss: 4.1847 +2024-07-22 09:57:01,862 - pyskl - INFO - Epoch [10][1700/3746] lr: 9.902e-02, eta: 4 days, 10:42:54, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4897, loss_cls: 4.1518, loss: 4.1518 +2024-07-22 09:58:12,077 - pyskl - INFO - Epoch [10][1800/3746] lr: 9.902e-02, eta: 4 days, 10:41:01, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4744, loss_cls: 4.2131, loss: 4.2131 +2024-07-22 09:59:22,079 - pyskl - INFO - Epoch [10][1900/3746] lr: 9.901e-02, eta: 4 days, 10:39:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4697, loss_cls: 4.2207, loss: 4.2207 +2024-07-22 10:00:32,044 - pyskl - INFO - Epoch [10][2000/3746] lr: 9.901e-02, eta: 4 days, 10:37:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4977, loss_cls: 4.1176, loss: 4.1176 +2024-07-22 10:01:41,912 - pyskl - INFO - Epoch [10][2100/3746] lr: 9.900e-02, eta: 4 days, 10:35:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4822, loss_cls: 4.1733, loss: 4.1733 +2024-07-22 10:02:52,078 - pyskl - INFO - Epoch [10][2200/3746] lr: 9.900e-02, eta: 4 days, 10:33:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4819, loss_cls: 4.1857, loss: 4.1857 +2024-07-22 10:04:02,350 - pyskl - INFO - Epoch [10][2300/3746] lr: 9.899e-02, eta: 4 days, 10:31:23, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4769, loss_cls: 4.1812, loss: 4.1812 +2024-07-22 10:05:12,443 - pyskl - INFO - Epoch [10][2400/3746] lr: 9.898e-02, eta: 4 days, 10:29:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4850, loss_cls: 4.1908, loss: 4.1908 +2024-07-22 10:06:22,530 - pyskl - INFO - Epoch [10][2500/3746] lr: 9.898e-02, eta: 4 days, 10:27:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4775, loss_cls: 4.1910, loss: 4.1910 +2024-07-22 10:07:32,598 - pyskl - INFO - Epoch [10][2600/3746] lr: 9.897e-02, eta: 4 days, 10:25:41, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4836, loss_cls: 4.1815, loss: 4.1815 +2024-07-22 10:08:42,768 - pyskl - INFO - Epoch [10][2700/3746] lr: 9.897e-02, eta: 4 days, 10:23:49, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4845, loss_cls: 4.1661, loss: 4.1661 +2024-07-22 10:09:53,173 - pyskl - INFO - Epoch [10][2800/3746] lr: 9.896e-02, eta: 4 days, 10:22:00, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4936, loss_cls: 4.1798, loss: 4.1798 +2024-07-22 10:11:03,093 - pyskl - INFO - Epoch [10][2900/3746] lr: 9.896e-02, eta: 4 days, 10:20:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4903, loss_cls: 4.1738, loss: 4.1738 +2024-07-22 10:12:13,189 - pyskl - INFO - Epoch [10][3000/3746] lr: 9.895e-02, eta: 4 days, 10:18:12, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4828, loss_cls: 4.2207, loss: 4.2207 +2024-07-22 10:13:23,159 - pyskl - INFO - Epoch [10][3100/3746] lr: 9.894e-02, eta: 4 days, 10:16:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4828, loss_cls: 4.1932, loss: 4.1932 +2024-07-22 10:14:32,938 - pyskl - INFO - Epoch [10][3200/3746] lr: 9.894e-02, eta: 4 days, 10:14:22, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4855, loss_cls: 4.1577, loss: 4.1577 +2024-07-22 10:15:42,963 - pyskl - INFO - Epoch [10][3300/3746] lr: 9.893e-02, eta: 4 days, 10:12:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4883, loss_cls: 4.1520, loss: 4.1520 +2024-07-22 10:16:52,884 - pyskl - INFO - Epoch [10][3400/3746] lr: 9.893e-02, eta: 4 days, 10:10:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4844, loss_cls: 4.1822, loss: 4.1822 +2024-07-22 10:18:02,993 - pyskl - INFO - Epoch [10][3500/3746] lr: 9.892e-02, eta: 4 days, 10:08:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5047, loss_cls: 4.0987, loss: 4.0987 +2024-07-22 10:19:13,296 - pyskl - INFO - Epoch [10][3600/3746] lr: 9.892e-02, eta: 4 days, 10:06:55, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4897, loss_cls: 4.1791, loss: 4.1791 +2024-07-22 10:20:23,380 - pyskl - INFO - Epoch [10][3700/3746] lr: 9.891e-02, eta: 4 days, 10:05:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4838, loss_cls: 4.1929, loss: 4.1929 +2024-07-22 10:20:57,970 - pyskl - INFO - Saving checkpoint at 10 epochs +2024-07-22 10:22:50,102 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 10:22:50,765 - pyskl - INFO - +top1_acc 0.1732 +top5_acc 0.3833 +2024-07-22 10:22:50,765 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 10:22:50,807 - pyskl - INFO - +mean_acc 0.1731 +2024-07-22 10:22:50,818 - pyskl - INFO - Epoch(val) [10][309] top1_acc: 0.1732, top5_acc: 0.3833, mean_class_accuracy: 0.1731 +2024-07-22 10:26:09,526 - pyskl - INFO - Epoch [11][100/3746] lr: 9.890e-02, eta: 4 days, 10:24:47, time: 1.987, data_time: 1.276, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4967, loss_cls: 4.1123, loss: 4.1123 +2024-07-22 10:27:20,143 - pyskl - INFO - Epoch [11][200/3746] lr: 9.890e-02, eta: 4 days, 10:23:00, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4919, loss_cls: 4.1507, loss: 4.1507 +2024-07-22 10:28:30,884 - pyskl - INFO - Epoch [11][300/3746] lr: 9.889e-02, eta: 4 days, 10:21:15, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4927, loss_cls: 4.1457, loss: 4.1457 +2024-07-22 10:29:42,048 - pyskl - INFO - Epoch [11][400/3746] lr: 9.888e-02, eta: 4 days, 10:19:36, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4714, loss_cls: 4.2280, loss: 4.2280 +2024-07-22 10:30:53,112 - pyskl - INFO - Epoch [11][500/3746] lr: 9.888e-02, eta: 4 days, 10:17:55, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4930, loss_cls: 4.1191, loss: 4.1191 +2024-07-22 10:32:03,982 - pyskl - INFO - Epoch [11][600/3746] lr: 9.887e-02, eta: 4 days, 10:16:12, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4942, loss_cls: 4.1614, loss: 4.1614 +2024-07-22 10:33:14,436 - pyskl - INFO - Epoch [11][700/3746] lr: 9.887e-02, eta: 4 days, 10:14:24, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4844, loss_cls: 4.2017, loss: 4.2017 +2024-07-22 10:34:24,757 - pyskl - INFO - Epoch [11][800/3746] lr: 9.886e-02, eta: 4 days, 10:12:34, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4884, loss_cls: 4.1747, loss: 4.1747 +2024-07-22 10:35:34,910 - pyskl - INFO - Epoch [11][900/3746] lr: 9.885e-02, eta: 4 days, 10:10:42, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4922, loss_cls: 4.1668, loss: 4.1668 +2024-07-22 10:36:45,018 - pyskl - INFO - Epoch [11][1000/3746] lr: 9.885e-02, eta: 4 days, 10:08:49, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4938, loss_cls: 4.1434, loss: 4.1434 +2024-07-22 10:37:55,180 - pyskl - INFO - Epoch [11][1100/3746] lr: 9.884e-02, eta: 4 days, 10:06:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4889, loss_cls: 4.1714, loss: 4.1714 +2024-07-22 10:39:05,161 - pyskl - INFO - Epoch [11][1200/3746] lr: 9.884e-02, eta: 4 days, 10:05:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4933, loss_cls: 4.1408, loss: 4.1408 +2024-07-22 10:40:15,657 - pyskl - INFO - Epoch [11][1300/3746] lr: 9.883e-02, eta: 4 days, 10:03:17, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4688, loss_cls: 4.2143, loss: 4.2143 +2024-07-22 10:41:25,623 - pyskl - INFO - Epoch [11][1400/3746] lr: 9.882e-02, eta: 4 days, 10:01:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4948, loss_cls: 4.1068, loss: 4.1068 +2024-07-22 10:42:35,515 - pyskl - INFO - Epoch [11][1500/3746] lr: 9.882e-02, eta: 4 days, 9:59:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4931, loss_cls: 4.1400, loss: 4.1400 +2024-07-22 10:43:45,341 - pyskl - INFO - Epoch [11][1600/3746] lr: 9.881e-02, eta: 4 days, 9:57:34, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4884, loss_cls: 4.1422, loss: 4.1422 +2024-07-22 10:44:55,259 - pyskl - INFO - Epoch [11][1700/3746] lr: 9.881e-02, eta: 4 days, 9:55:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4889, loss_cls: 4.1525, loss: 4.1525 +2024-07-22 10:46:05,227 - pyskl - INFO - Epoch [11][1800/3746] lr: 9.880e-02, eta: 4 days, 9:53:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4839, loss_cls: 4.1670, loss: 4.1670 +2024-07-22 10:47:15,217 - pyskl - INFO - Epoch [11][1900/3746] lr: 9.879e-02, eta: 4 days, 9:51:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4969, loss_cls: 4.1292, loss: 4.1292 +2024-07-22 10:48:25,063 - pyskl - INFO - Epoch [11][2000/3746] lr: 9.879e-02, eta: 4 days, 9:50:02, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4863, loss_cls: 4.1991, loss: 4.1991 +2024-07-22 10:49:34,950 - pyskl - INFO - Epoch [11][2100/3746] lr: 9.878e-02, eta: 4 days, 9:48:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4777, loss_cls: 4.1936, loss: 4.1936 +2024-07-22 10:50:44,673 - pyskl - INFO - Epoch [11][2200/3746] lr: 9.878e-02, eta: 4 days, 9:46:14, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4811, loss_cls: 4.1822, loss: 4.1822 +2024-07-22 10:51:54,969 - pyskl - INFO - Epoch [11][2300/3746] lr: 9.877e-02, eta: 4 days, 9:44:27, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4820, loss_cls: 4.1894, loss: 4.1894 +2024-07-22 10:53:05,004 - pyskl - INFO - Epoch [11][2400/3746] lr: 9.876e-02, eta: 4 days, 9:42:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4855, loss_cls: 4.1625, loss: 4.1625 +2024-07-22 10:54:14,993 - pyskl - INFO - Epoch [11][2500/3746] lr: 9.876e-02, eta: 4 days, 9:40:45, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.4908, loss_cls: 4.1414, loss: 4.1414 +2024-07-22 10:55:24,744 - pyskl - INFO - Epoch [11][2600/3746] lr: 9.875e-02, eta: 4 days, 9:38:51, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4856, loss_cls: 4.1413, loss: 4.1413 +2024-07-22 10:56:34,743 - pyskl - INFO - Epoch [11][2700/3746] lr: 9.874e-02, eta: 4 days, 9:37:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5002, loss_cls: 4.1250, loss: 4.1250 +2024-07-22 10:57:44,953 - pyskl - INFO - Epoch [11][2800/3746] lr: 9.874e-02, eta: 4 days, 9:35:14, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4925, loss_cls: 4.1598, loss: 4.1598 +2024-07-22 10:58:54,925 - pyskl - INFO - Epoch [11][2900/3746] lr: 9.873e-02, eta: 4 days, 9:33:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4884, loss_cls: 4.1413, loss: 4.1413 +2024-07-22 11:00:05,146 - pyskl - INFO - Epoch [11][3000/3746] lr: 9.873e-02, eta: 4 days, 9:31:36, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4711, loss_cls: 4.2073, loss: 4.2073 +2024-07-22 11:01:15,106 - pyskl - INFO - Epoch [11][3100/3746] lr: 9.872e-02, eta: 4 days, 9:29:46, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5002, loss_cls: 4.1149, loss: 4.1149 +2024-07-22 11:02:25,100 - pyskl - INFO - Epoch [11][3200/3746] lr: 9.871e-02, eta: 4 days, 9:27:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4936, loss_cls: 4.1740, loss: 4.1740 +2024-07-22 11:03:35,002 - pyskl - INFO - Epoch [11][3300/3746] lr: 9.871e-02, eta: 4 days, 9:26:06, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5023, loss_cls: 4.0855, loss: 4.0855 +2024-07-22 11:04:45,300 - pyskl - INFO - Epoch [11][3400/3746] lr: 9.870e-02, eta: 4 days, 9:24:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4902, loss_cls: 4.1505, loss: 4.1505 +2024-07-22 11:05:55,385 - pyskl - INFO - Epoch [11][3500/3746] lr: 9.869e-02, eta: 4 days, 9:22:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4914, loss_cls: 4.1621, loss: 4.1621 +2024-07-22 11:07:05,576 - pyskl - INFO - Epoch [11][3600/3746] lr: 9.869e-02, eta: 4 days, 9:20:47, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4822, loss_cls: 4.1847, loss: 4.1847 +2024-07-22 11:08:15,563 - pyskl - INFO - Epoch [11][3700/3746] lr: 9.868e-02, eta: 4 days, 9:18:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4913, loss_cls: 4.1529, loss: 4.1529 +2024-07-22 11:08:49,920 - pyskl - INFO - Saving checkpoint at 11 epochs +2024-07-22 11:10:41,343 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 11:10:42,005 - pyskl - INFO - +top1_acc 0.1710 +top5_acc 0.3885 +2024-07-22 11:10:42,005 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 11:10:42,044 - pyskl - INFO - +mean_acc 0.1710 +2024-07-22 11:10:42,054 - pyskl - INFO - Epoch(val) [11][309] top1_acc: 0.1710, top5_acc: 0.3885, mean_class_accuracy: 0.1710 +2024-07-22 11:13:57,840 - pyskl - INFO - Epoch [12][100/3746] lr: 9.867e-02, eta: 4 days, 9:36:00, time: 1.958, data_time: 1.252, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4911, loss_cls: 4.1355, loss: 4.1355 +2024-07-22 11:15:08,830 - pyskl - INFO - Epoch [12][200/3746] lr: 9.867e-02, eta: 4 days, 9:34:22, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4930, loss_cls: 4.1243, loss: 4.1243 +2024-07-22 11:16:19,758 - pyskl - INFO - Epoch [12][300/3746] lr: 9.866e-02, eta: 4 days, 9:32:42, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4931, loss_cls: 4.1408, loss: 4.1408 +2024-07-22 11:17:30,580 - pyskl - INFO - Epoch [12][400/3746] lr: 9.865e-02, eta: 4 days, 9:31:02, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4922, loss_cls: 4.1480, loss: 4.1480 +2024-07-22 11:18:40,804 - pyskl - INFO - Epoch [12][500/3746] lr: 9.865e-02, eta: 4 days, 9:29:14, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.5012, loss_cls: 4.0855, loss: 4.0855 +2024-07-22 11:19:51,797 - pyskl - INFO - Epoch [12][600/3746] lr: 9.864e-02, eta: 4 days, 9:27:36, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4941, loss_cls: 4.1234, loss: 4.1234 +2024-07-22 11:21:02,418 - pyskl - INFO - Epoch [12][700/3746] lr: 9.863e-02, eta: 4 days, 9:25:54, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4930, loss_cls: 4.1585, loss: 4.1585 +2024-07-22 11:22:12,681 - pyskl - INFO - Epoch [12][800/3746] lr: 9.863e-02, eta: 4 days, 9:24:07, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4900, loss_cls: 4.1343, loss: 4.1343 +2024-07-22 11:23:22,988 - pyskl - INFO - Epoch [12][900/3746] lr: 9.862e-02, eta: 4 days, 9:22:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4878, loss_cls: 4.1481, loss: 4.1481 +2024-07-22 11:24:32,930 - pyskl - INFO - Epoch [12][1000/3746] lr: 9.861e-02, eta: 4 days, 9:20:30, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4819, loss_cls: 4.2002, loss: 4.2002 +2024-07-22 11:25:42,872 - pyskl - INFO - Epoch [12][1100/3746] lr: 9.861e-02, eta: 4 days, 9:18:40, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4927, loss_cls: 4.1594, loss: 4.1594 +2024-07-22 11:26:53,032 - pyskl - INFO - Epoch [12][1200/3746] lr: 9.860e-02, eta: 4 days, 9:16:53, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4919, loss_cls: 4.1137, loss: 4.1137 +2024-07-22 11:28:03,109 - pyskl - INFO - Epoch [12][1300/3746] lr: 9.859e-02, eta: 4 days, 9:15:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4914, loss_cls: 4.1499, loss: 4.1499 +2024-07-22 11:29:13,247 - pyskl - INFO - Epoch [12][1400/3746] lr: 9.859e-02, eta: 4 days, 9:13:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4948, loss_cls: 4.1267, loss: 4.1267 +2024-07-22 11:30:23,301 - pyskl - INFO - Epoch [12][1500/3746] lr: 9.858e-02, eta: 4 days, 9:11:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4850, loss_cls: 4.1595, loss: 4.1595 +2024-07-22 11:31:33,659 - pyskl - INFO - Epoch [12][1600/3746] lr: 9.857e-02, eta: 4 days, 9:09:45, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4909, loss_cls: 4.1458, loss: 4.1458 +2024-07-22 11:32:43,558 - pyskl - INFO - Epoch [12][1700/3746] lr: 9.857e-02, eta: 4 days, 9:07:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.4969, loss_cls: 4.1365, loss: 4.1365 +2024-07-22 11:33:53,979 - pyskl - INFO - Epoch [12][1800/3746] lr: 9.856e-02, eta: 4 days, 9:06:12, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4906, loss_cls: 4.1381, loss: 4.1381 +2024-07-22 11:35:04,157 - pyskl - INFO - Epoch [12][1900/3746] lr: 9.855e-02, eta: 4 days, 9:04:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4788, loss_cls: 4.2145, loss: 4.2145 +2024-07-22 11:36:14,159 - pyskl - INFO - Epoch [12][2000/3746] lr: 9.855e-02, eta: 4 days, 9:02:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4845, loss_cls: 4.1828, loss: 4.1828 +2024-07-22 11:37:24,022 - pyskl - INFO - Epoch [12][2100/3746] lr: 9.854e-02, eta: 4 days, 9:00:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4953, loss_cls: 4.1123, loss: 4.1123 +2024-07-22 11:38:34,345 - pyskl - INFO - Epoch [12][2200/3746] lr: 9.853e-02, eta: 4 days, 8:59:05, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4970, loss_cls: 4.1106, loss: 4.1106 +2024-07-22 11:39:44,184 - pyskl - INFO - Epoch [12][2300/3746] lr: 9.853e-02, eta: 4 days, 8:57:16, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4909, loss_cls: 4.1270, loss: 4.1270 +2024-07-22 11:40:54,344 - pyskl - INFO - Epoch [12][2400/3746] lr: 9.852e-02, eta: 4 days, 8:55:31, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4813, loss_cls: 4.1892, loss: 4.1892 +2024-07-22 11:42:04,321 - pyskl - INFO - Epoch [12][2500/3746] lr: 9.851e-02, eta: 4 days, 8:53:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4881, loss_cls: 4.1895, loss: 4.1895 +2024-07-22 11:43:14,680 - pyskl - INFO - Epoch [12][2600/3746] lr: 9.851e-02, eta: 4 days, 8:52:01, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4855, loss_cls: 4.1605, loss: 4.1605 +2024-07-22 11:44:24,752 - pyskl - INFO - Epoch [12][2700/3746] lr: 9.850e-02, eta: 4 days, 8:50:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4883, loss_cls: 4.1495, loss: 4.1495 +2024-07-22 11:45:35,053 - pyskl - INFO - Epoch [12][2800/3746] lr: 9.849e-02, eta: 4 days, 8:48:32, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4853, loss_cls: 4.1767, loss: 4.1767 +2024-07-22 11:46:45,151 - pyskl - INFO - Epoch [12][2900/3746] lr: 9.849e-02, eta: 4 days, 8:46:47, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4894, loss_cls: 4.1611, loss: 4.1611 +2024-07-22 11:47:55,065 - pyskl - INFO - Epoch [12][3000/3746] lr: 9.848e-02, eta: 4 days, 8:45:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4881, loss_cls: 4.1484, loss: 4.1484 +2024-07-22 11:49:04,990 - pyskl - INFO - Epoch [12][3100/3746] lr: 9.847e-02, eta: 4 days, 8:43:13, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4873, loss_cls: 4.1439, loss: 4.1439 +2024-07-22 11:50:15,068 - pyskl - INFO - Epoch [12][3200/3746] lr: 9.847e-02, eta: 4 days, 8:41:28, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4961, loss_cls: 4.1245, loss: 4.1245 +2024-07-22 11:51:25,344 - pyskl - INFO - Epoch [12][3300/3746] lr: 9.846e-02, eta: 4 days, 8:39:45, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4855, loss_cls: 4.1387, loss: 4.1387 +2024-07-22 11:52:35,439 - pyskl - INFO - Epoch [12][3400/3746] lr: 9.845e-02, eta: 4 days, 8:38:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4870, loss_cls: 4.1642, loss: 4.1642 +2024-07-22 11:53:45,598 - pyskl - INFO - Epoch [12][3500/3746] lr: 9.845e-02, eta: 4 days, 8:36:17, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4894, loss_cls: 4.1160, loss: 4.1160 +2024-07-22 11:54:55,687 - pyskl - INFO - Epoch [12][3600/3746] lr: 9.844e-02, eta: 4 days, 8:34:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4952, loss_cls: 4.1447, loss: 4.1447 +2024-07-22 11:56:05,503 - pyskl - INFO - Epoch [12][3700/3746] lr: 9.843e-02, eta: 4 days, 8:32:46, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4997, loss_cls: 4.1238, loss: 4.1238 +2024-07-22 11:56:40,434 - pyskl - INFO - Saving checkpoint at 12 epochs +2024-07-22 11:58:31,883 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 11:58:32,539 - pyskl - INFO - +top1_acc 0.1769 +top5_acc 0.4013 +2024-07-22 11:58:32,539 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 11:58:32,578 - pyskl - INFO - +mean_acc 0.1768 +2024-07-22 11:58:32,582 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_9.pth was removed +2024-07-22 11:58:32,811 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_12.pth. +2024-07-22 11:58:32,811 - pyskl - INFO - Best top1_acc is 0.1769 at 12 epoch. +2024-07-22 11:58:32,821 - pyskl - INFO - Epoch(val) [12][309] top1_acc: 0.1769, top5_acc: 0.4013, mean_class_accuracy: 0.1768 +2024-07-22 12:01:50,074 - pyskl - INFO - Epoch [13][100/3746] lr: 9.842e-02, eta: 4 days, 8:48:23, time: 1.972, data_time: 1.265, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5053, loss_cls: 4.0628, loss: 4.0628 +2024-07-22 12:03:01,145 - pyskl - INFO - Epoch [13][200/3746] lr: 9.842e-02, eta: 4 days, 8:46:48, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5041, loss_cls: 4.0750, loss: 4.0750 +2024-07-22 12:04:11,740 - pyskl - INFO - Epoch [13][300/3746] lr: 9.841e-02, eta: 4 days, 8:45:07, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5053, loss_cls: 4.0965, loss: 4.0965 +2024-07-22 12:05:22,763 - pyskl - INFO - Epoch [13][400/3746] lr: 9.840e-02, eta: 4 days, 8:43:32, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4956, loss_cls: 4.1131, loss: 4.1131 +2024-07-22 12:06:33,819 - pyskl - INFO - Epoch [13][500/3746] lr: 9.839e-02, eta: 4 days, 8:41:57, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4942, loss_cls: 4.1276, loss: 4.1276 +2024-07-22 12:07:44,617 - pyskl - INFO - Epoch [13][600/3746] lr: 9.839e-02, eta: 4 days, 8:40:19, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.4959, loss_cls: 4.1034, loss: 4.1034 +2024-07-22 12:08:54,972 - pyskl - INFO - Epoch [13][700/3746] lr: 9.838e-02, eta: 4 days, 8:38:37, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5022, loss_cls: 4.1128, loss: 4.1128 +2024-07-22 12:10:05,400 - pyskl - INFO - Epoch [13][800/3746] lr: 9.837e-02, eta: 4 days, 8:36:55, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4981, loss_cls: 4.1195, loss: 4.1195 +2024-07-22 12:11:15,478 - pyskl - INFO - Epoch [13][900/3746] lr: 9.837e-02, eta: 4 days, 8:35:09, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4856, loss_cls: 4.1678, loss: 4.1678 +2024-07-22 12:12:26,071 - pyskl - INFO - Epoch [13][1000/3746] lr: 9.836e-02, eta: 4 days, 8:33:30, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4834, loss_cls: 4.1861, loss: 4.1861 +2024-07-22 12:13:36,142 - pyskl - INFO - Epoch [13][1100/3746] lr: 9.835e-02, eta: 4 days, 8:31:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.4977, loss_cls: 4.1219, loss: 4.1219 +2024-07-22 12:14:45,990 - pyskl - INFO - Epoch [13][1200/3746] lr: 9.834e-02, eta: 4 days, 8:29:57, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5003, loss_cls: 4.1066, loss: 4.1066 +2024-07-22 12:15:56,318 - pyskl - INFO - Epoch [13][1300/3746] lr: 9.834e-02, eta: 4 days, 8:28:15, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.5059, loss_cls: 4.1145, loss: 4.1145 +2024-07-22 12:17:06,809 - pyskl - INFO - Epoch [13][1400/3746] lr: 9.833e-02, eta: 4 days, 8:26:35, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4895, loss_cls: 4.1474, loss: 4.1474 +2024-07-22 12:18:16,917 - pyskl - INFO - Epoch [13][1500/3746] lr: 9.832e-02, eta: 4 days, 8:24:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.5017, loss_cls: 4.1276, loss: 4.1276 +2024-07-22 12:19:26,852 - pyskl - INFO - Epoch [13][1600/3746] lr: 9.832e-02, eta: 4 days, 8:23:04, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4852, loss_cls: 4.1544, loss: 4.1544 +2024-07-22 12:20:36,903 - pyskl - INFO - Epoch [13][1700/3746] lr: 9.831e-02, eta: 4 days, 8:21:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4992, loss_cls: 4.1279, loss: 4.1279 +2024-07-22 12:21:47,100 - pyskl - INFO - Epoch [13][1800/3746] lr: 9.830e-02, eta: 4 days, 8:19:37, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4875, loss_cls: 4.1789, loss: 4.1789 +2024-07-22 12:22:57,379 - pyskl - INFO - Epoch [13][1900/3746] lr: 9.829e-02, eta: 4 days, 8:17:55, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5056, loss_cls: 4.0876, loss: 4.0876 +2024-07-22 12:24:07,399 - pyskl - INFO - Epoch [13][2000/3746] lr: 9.829e-02, eta: 4 days, 8:16:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4989, loss_cls: 4.0843, loss: 4.0843 +2024-07-22 12:25:17,565 - pyskl - INFO - Epoch [13][2100/3746] lr: 9.828e-02, eta: 4 days, 8:14:28, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4813, loss_cls: 4.1534, loss: 4.1534 +2024-07-22 12:26:27,561 - pyskl - INFO - Epoch [13][2200/3746] lr: 9.827e-02, eta: 4 days, 8:12:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4941, loss_cls: 4.1378, loss: 4.1378 +2024-07-22 12:27:37,814 - pyskl - INFO - Epoch [13][2300/3746] lr: 9.827e-02, eta: 4 days, 8:11:02, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4891, loss_cls: 4.1824, loss: 4.1824 +2024-07-22 12:28:48,041 - pyskl - INFO - Epoch [13][2400/3746] lr: 9.826e-02, eta: 4 days, 8:09:20, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4947, loss_cls: 4.1533, loss: 4.1533 +2024-07-22 12:29:58,369 - pyskl - INFO - Epoch [13][2500/3746] lr: 9.825e-02, eta: 4 days, 8:07:40, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4997, loss_cls: 4.1344, loss: 4.1344 +2024-07-22 12:31:08,522 - pyskl - INFO - Epoch [13][2600/3746] lr: 9.824e-02, eta: 4 days, 8:05:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4992, loss_cls: 4.0920, loss: 4.0920 +2024-07-22 12:32:18,697 - pyskl - INFO - Epoch [13][2700/3746] lr: 9.824e-02, eta: 4 days, 8:04:16, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4916, loss_cls: 4.1749, loss: 4.1749 +2024-07-22 12:33:28,614 - pyskl - INFO - Epoch [13][2800/3746] lr: 9.823e-02, eta: 4 days, 8:02:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4817, loss_cls: 4.1918, loss: 4.1918 +2024-07-22 12:34:38,778 - pyskl - INFO - Epoch [13][2900/3746] lr: 9.822e-02, eta: 4 days, 8:00:49, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4867, loss_cls: 4.1451, loss: 4.1451 +2024-07-22 12:35:49,206 - pyskl - INFO - Epoch [13][3000/3746] lr: 9.821e-02, eta: 4 days, 7:59:11, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4906, loss_cls: 4.1443, loss: 4.1443 +2024-07-22 12:36:59,509 - pyskl - INFO - Epoch [13][3100/3746] lr: 9.821e-02, eta: 4 days, 7:57:31, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4972, loss_cls: 4.1434, loss: 4.1434 +2024-07-22 12:38:09,787 - pyskl - INFO - Epoch [13][3200/3746] lr: 9.820e-02, eta: 4 days, 7:55:51, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5109, loss_cls: 4.0701, loss: 4.0701 +2024-07-22 12:39:20,097 - pyskl - INFO - Epoch [13][3300/3746] lr: 9.819e-02, eta: 4 days, 7:54:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4870, loss_cls: 4.1714, loss: 4.1714 +2024-07-22 12:40:30,453 - pyskl - INFO - Epoch [13][3400/3746] lr: 9.818e-02, eta: 4 days, 7:52:32, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4920, loss_cls: 4.1469, loss: 4.1469 +2024-07-22 12:41:40,641 - pyskl - INFO - Epoch [13][3500/3746] lr: 9.818e-02, eta: 4 days, 7:50:51, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4795, loss_cls: 4.2099, loss: 4.2099 +2024-07-22 12:42:50,457 - pyskl - INFO - Epoch [13][3600/3746] lr: 9.817e-02, eta: 4 days, 7:49:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4888, loss_cls: 4.1785, loss: 4.1785 +2024-07-22 12:44:00,886 - pyskl - INFO - Epoch [13][3700/3746] lr: 9.816e-02, eta: 4 days, 7:47:29, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4988, loss_cls: 4.1211, loss: 4.1211 +2024-07-22 12:44:35,559 - pyskl - INFO - Saving checkpoint at 13 epochs +2024-07-22 12:46:27,090 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 12:46:27,751 - pyskl - INFO - +top1_acc 0.1911 +top5_acc 0.4116 +2024-07-22 12:46:27,751 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 12:46:27,791 - pyskl - INFO - +mean_acc 0.1910 +2024-07-22 12:46:27,795 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_12.pth was removed +2024-07-22 12:46:28,027 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_13.pth. +2024-07-22 12:46:28,027 - pyskl - INFO - Best top1_acc is 0.1911 at 13 epoch. +2024-07-22 12:46:28,037 - pyskl - INFO - Epoch(val) [13][309] top1_acc: 0.1911, top5_acc: 0.4116, mean_class_accuracy: 0.1910 +2024-07-22 12:49:43,690 - pyskl - INFO - Epoch [14][100/3746] lr: 9.815e-02, eta: 4 days, 8:01:22, time: 1.956, data_time: 1.249, memory: 15990, top1_acc: 0.2483, top5_acc: 0.5003, loss_cls: 4.1089, loss: 4.1089 +2024-07-22 12:50:54,822 - pyskl - INFO - Epoch [14][200/3746] lr: 9.814e-02, eta: 4 days, 7:59:50, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5033, loss_cls: 4.0802, loss: 4.0802 +2024-07-22 12:52:05,904 - pyskl - INFO - Epoch [14][300/3746] lr: 9.814e-02, eta: 4 days, 7:58:17, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5078, loss_cls: 4.0816, loss: 4.0816 +2024-07-22 12:53:17,314 - pyskl - INFO - Epoch [14][400/3746] lr: 9.813e-02, eta: 4 days, 7:56:48, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4958, loss_cls: 4.1082, loss: 4.1082 +2024-07-22 12:54:27,754 - pyskl - INFO - Epoch [14][500/3746] lr: 9.812e-02, eta: 4 days, 7:55:08, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.5009, loss_cls: 4.1065, loss: 4.1065 +2024-07-22 12:55:38,386 - pyskl - INFO - Epoch [14][600/3746] lr: 9.811e-02, eta: 4 days, 7:53:31, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4961, loss_cls: 4.1147, loss: 4.1147 +2024-07-22 12:56:48,789 - pyskl - INFO - Epoch [14][700/3746] lr: 9.811e-02, eta: 4 days, 7:51:51, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5109, loss_cls: 4.0750, loss: 4.0750 +2024-07-22 12:57:59,253 - pyskl - INFO - Epoch [14][800/3746] lr: 9.810e-02, eta: 4 days, 7:50:13, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4994, loss_cls: 4.1282, loss: 4.1282 +2024-07-22 12:59:09,419 - pyskl - INFO - Epoch [14][900/3746] lr: 9.809e-02, eta: 4 days, 7:48:31, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4992, loss_cls: 4.1019, loss: 4.1019 +2024-07-22 13:00:19,311 - pyskl - INFO - Epoch [14][1000/3746] lr: 9.808e-02, eta: 4 days, 7:46:46, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4939, loss_cls: 4.0996, loss: 4.0996 +2024-07-22 13:01:29,605 - pyskl - INFO - Epoch [14][1100/3746] lr: 9.807e-02, eta: 4 days, 7:45:06, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.4992, loss_cls: 4.0973, loss: 4.0973 +2024-07-22 13:02:39,580 - pyskl - INFO - Epoch [14][1200/3746] lr: 9.807e-02, eta: 4 days, 7:43:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4816, loss_cls: 4.1710, loss: 4.1710 +2024-07-22 13:03:49,856 - pyskl - INFO - Epoch [14][1300/3746] lr: 9.806e-02, eta: 4 days, 7:41:43, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4966, loss_cls: 4.1203, loss: 4.1203 +2024-07-22 13:05:00,098 - pyskl - INFO - Epoch [14][1400/3746] lr: 9.805e-02, eta: 4 days, 7:40:02, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5039, loss_cls: 4.0691, loss: 4.0691 +2024-07-22 13:06:10,238 - pyskl - INFO - Epoch [14][1500/3746] lr: 9.804e-02, eta: 4 days, 7:38:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4889, loss_cls: 4.1444, loss: 4.1444 +2024-07-22 13:07:20,308 - pyskl - INFO - Epoch [14][1600/3746] lr: 9.804e-02, eta: 4 days, 7:36:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5042, loss_cls: 4.0712, loss: 4.0712 +2024-07-22 13:08:30,471 - pyskl - INFO - Epoch [14][1700/3746] lr: 9.803e-02, eta: 4 days, 7:34:59, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5022, loss_cls: 4.1265, loss: 4.1265 +2024-07-22 13:09:40,646 - pyskl - INFO - Epoch [14][1800/3746] lr: 9.802e-02, eta: 4 days, 7:33:18, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4981, loss_cls: 4.1371, loss: 4.1371 +2024-07-22 13:10:50,578 - pyskl - INFO - Epoch [14][1900/3746] lr: 9.801e-02, eta: 4 days, 7:31:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5020, loss_cls: 4.1029, loss: 4.1029 +2024-07-22 13:12:00,889 - pyskl - INFO - Epoch [14][2000/3746] lr: 9.800e-02, eta: 4 days, 7:29:56, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5006, loss_cls: 4.1263, loss: 4.1263 +2024-07-22 13:13:11,030 - pyskl - INFO - Epoch [14][2100/3746] lr: 9.800e-02, eta: 4 days, 7:28:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4814, loss_cls: 4.1727, loss: 4.1727 +2024-07-22 13:14:21,044 - pyskl - INFO - Epoch [14][2200/3746] lr: 9.799e-02, eta: 4 days, 7:26:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4888, loss_cls: 4.1414, loss: 4.1414 +2024-07-22 13:15:31,695 - pyskl - INFO - Epoch [14][2300/3746] lr: 9.798e-02, eta: 4 days, 7:24:59, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4897, loss_cls: 4.1701, loss: 4.1701 +2024-07-22 13:16:41,731 - pyskl - INFO - Epoch [14][2400/3746] lr: 9.797e-02, eta: 4 days, 7:23:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4895, loss_cls: 4.1278, loss: 4.1278 +2024-07-22 13:17:52,157 - pyskl - INFO - Epoch [14][2500/3746] lr: 9.797e-02, eta: 4 days, 7:21:40, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5041, loss_cls: 4.0874, loss: 4.0874 +2024-07-22 13:19:02,120 - pyskl - INFO - Epoch [14][2600/3746] lr: 9.796e-02, eta: 4 days, 7:19:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4900, loss_cls: 4.1402, loss: 4.1402 +2024-07-22 13:20:12,417 - pyskl - INFO - Epoch [14][2700/3746] lr: 9.795e-02, eta: 4 days, 7:18:20, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4875, loss_cls: 4.1458, loss: 4.1458 +2024-07-22 13:21:22,922 - pyskl - INFO - Epoch [14][2800/3746] lr: 9.794e-02, eta: 4 days, 7:16:44, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5083, loss_cls: 4.0886, loss: 4.0886 +2024-07-22 13:22:33,013 - pyskl - INFO - Epoch [14][2900/3746] lr: 9.793e-02, eta: 4 days, 7:15:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4920, loss_cls: 4.1522, loss: 4.1522 +2024-07-22 13:23:43,074 - pyskl - INFO - Epoch [14][3000/3746] lr: 9.793e-02, eta: 4 days, 7:13:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4941, loss_cls: 4.1523, loss: 4.1523 +2024-07-22 13:24:53,442 - pyskl - INFO - Epoch [14][3100/3746] lr: 9.792e-02, eta: 4 days, 7:11:47, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4942, loss_cls: 4.1487, loss: 4.1487 +2024-07-22 13:26:03,479 - pyskl - INFO - Epoch [14][3200/3746] lr: 9.791e-02, eta: 4 days, 7:10:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5056, loss_cls: 4.1054, loss: 4.1054 +2024-07-22 13:27:13,351 - pyskl - INFO - Epoch [14][3300/3746] lr: 9.790e-02, eta: 4 days, 7:08:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5041, loss_cls: 4.0770, loss: 4.0770 +2024-07-22 13:28:23,621 - pyskl - INFO - Epoch [14][3400/3746] lr: 9.789e-02, eta: 4 days, 7:06:47, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4956, loss_cls: 4.1448, loss: 4.1448 +2024-07-22 13:29:33,575 - pyskl - INFO - Epoch [14][3500/3746] lr: 9.789e-02, eta: 4 days, 7:05:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4923, loss_cls: 4.1508, loss: 4.1508 +2024-07-22 13:30:43,594 - pyskl - INFO - Epoch [14][3600/3746] lr: 9.788e-02, eta: 4 days, 7:03:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4897, loss_cls: 4.1650, loss: 4.1650 +2024-07-22 13:31:53,809 - pyskl - INFO - Epoch [14][3700/3746] lr: 9.787e-02, eta: 4 days, 7:01:48, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4830, loss_cls: 4.1453, loss: 4.1453 +2024-07-22 13:32:28,205 - pyskl - INFO - Saving checkpoint at 14 epochs +2024-07-22 13:34:20,418 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 13:34:21,075 - pyskl - INFO - +top1_acc 0.1755 +top5_acc 0.3991 +2024-07-22 13:34:21,075 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 13:34:21,113 - pyskl - INFO - +mean_acc 0.1753 +2024-07-22 13:34:21,123 - pyskl - INFO - Epoch(val) [14][309] top1_acc: 0.1755, top5_acc: 0.3991, mean_class_accuracy: 0.1753 +2024-07-22 13:37:37,302 - pyskl - INFO - Epoch [15][100/3746] lr: 9.786e-02, eta: 4 days, 7:14:33, time: 1.962, data_time: 1.253, memory: 15990, top1_acc: 0.2564, top5_acc: 0.4983, loss_cls: 4.0984, loss: 4.0984 +2024-07-22 13:38:48,002 - pyskl - INFO - Epoch [15][200/3746] lr: 9.785e-02, eta: 4 days, 7:12:58, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5012, loss_cls: 4.0854, loss: 4.0854 +2024-07-22 13:39:58,318 - pyskl - INFO - Epoch [15][300/3746] lr: 9.784e-02, eta: 4 days, 7:11:20, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4908, loss_cls: 4.1459, loss: 4.1459 +2024-07-22 13:41:09,571 - pyskl - INFO - Epoch [15][400/3746] lr: 9.783e-02, eta: 4 days, 7:09:50, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4908, loss_cls: 4.1470, loss: 4.1470 +2024-07-22 13:42:19,776 - pyskl - INFO - Epoch [15][500/3746] lr: 9.783e-02, eta: 4 days, 7:08:11, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5086, loss_cls: 4.0627, loss: 4.0627 +2024-07-22 13:43:30,410 - pyskl - INFO - Epoch [15][600/3746] lr: 9.782e-02, eta: 4 days, 7:06:36, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5030, loss_cls: 4.0772, loss: 4.0772 +2024-07-22 13:44:41,367 - pyskl - INFO - Epoch [15][700/3746] lr: 9.781e-02, eta: 4 days, 7:05:04, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5045, loss_cls: 4.0741, loss: 4.0741 +2024-07-22 13:45:51,752 - pyskl - INFO - Epoch [15][800/3746] lr: 9.780e-02, eta: 4 days, 7:03:26, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.5023, loss_cls: 4.1263, loss: 4.1263 +2024-07-22 13:47:02,096 - pyskl - INFO - Epoch [15][900/3746] lr: 9.779e-02, eta: 4 days, 7:01:49, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5034, loss_cls: 4.0815, loss: 4.0815 +2024-07-22 13:48:12,028 - pyskl - INFO - Epoch [15][1000/3746] lr: 9.778e-02, eta: 4 days, 7:00:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.5009, loss_cls: 4.1037, loss: 4.1037 +2024-07-22 13:49:22,000 - pyskl - INFO - Epoch [15][1100/3746] lr: 9.778e-02, eta: 4 days, 6:58:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.4938, loss_cls: 4.1201, loss: 4.1201 +2024-07-22 13:50:31,839 - pyskl - INFO - Epoch [15][1200/3746] lr: 9.777e-02, eta: 4 days, 6:56:44, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5034, loss_cls: 4.0942, loss: 4.0942 +2024-07-22 13:51:41,999 - pyskl - INFO - Epoch [15][1300/3746] lr: 9.776e-02, eta: 4 days, 6:55:05, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5052, loss_cls: 4.1212, loss: 4.1212 +2024-07-22 13:52:51,976 - pyskl - INFO - Epoch [15][1400/3746] lr: 9.775e-02, eta: 4 days, 6:53:25, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4975, loss_cls: 4.1295, loss: 4.1295 +2024-07-22 13:54:01,832 - pyskl - INFO - Epoch [15][1500/3746] lr: 9.774e-02, eta: 4 days, 6:51:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5023, loss_cls: 4.1254, loss: 4.1254 +2024-07-22 13:55:11,793 - pyskl - INFO - Epoch [15][1600/3746] lr: 9.773e-02, eta: 4 days, 6:50:02, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.4981, loss_cls: 4.0758, loss: 4.0758 +2024-07-22 13:56:21,536 - pyskl - INFO - Epoch [15][1700/3746] lr: 9.773e-02, eta: 4 days, 6:48:20, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4934, loss_cls: 4.1563, loss: 4.1563 +2024-07-22 13:57:31,545 - pyskl - INFO - Epoch [15][1800/3746] lr: 9.772e-02, eta: 4 days, 6:46:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.4995, loss_cls: 4.1026, loss: 4.1026 +2024-07-22 13:58:41,384 - pyskl - INFO - Epoch [15][1900/3746] lr: 9.771e-02, eta: 4 days, 6:44:59, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5009, loss_cls: 4.0659, loss: 4.0659 +2024-07-22 13:59:51,487 - pyskl - INFO - Epoch [15][2000/3746] lr: 9.770e-02, eta: 4 days, 6:43:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5012, loss_cls: 4.1231, loss: 4.1231 +2024-07-22 14:01:01,556 - pyskl - INFO - Epoch [15][2100/3746] lr: 9.769e-02, eta: 4 days, 6:41:41, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4984, loss_cls: 4.0932, loss: 4.0932 +2024-07-22 14:02:11,336 - pyskl - INFO - Epoch [15][2200/3746] lr: 9.768e-02, eta: 4 days, 6:39:59, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4975, loss_cls: 4.1439, loss: 4.1439 +2024-07-22 14:03:21,374 - pyskl - INFO - Epoch [15][2300/3746] lr: 9.768e-02, eta: 4 days, 6:38:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5091, loss_cls: 4.0716, loss: 4.0716 +2024-07-22 14:04:31,467 - pyskl - INFO - Epoch [15][2400/3746] lr: 9.767e-02, eta: 4 days, 6:36:42, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4884, loss_cls: 4.1500, loss: 4.1500 +2024-07-22 14:05:41,365 - pyskl - INFO - Epoch [15][2500/3746] lr: 9.766e-02, eta: 4 days, 6:35:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4867, loss_cls: 4.1631, loss: 4.1631 +2024-07-22 14:06:51,188 - pyskl - INFO - Epoch [15][2600/3746] lr: 9.765e-02, eta: 4 days, 6:33:21, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4884, loss_cls: 4.1352, loss: 4.1352 +2024-07-22 14:08:00,888 - pyskl - INFO - Epoch [15][2700/3746] lr: 9.764e-02, eta: 4 days, 6:31:39, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5078, loss_cls: 4.0732, loss: 4.0732 +2024-07-22 14:09:10,729 - pyskl - INFO - Epoch [15][2800/3746] lr: 9.763e-02, eta: 4 days, 6:29:59, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4952, loss_cls: 4.1475, loss: 4.1475 +2024-07-22 14:10:20,381 - pyskl - INFO - Epoch [15][2900/3746] lr: 9.763e-02, eta: 4 days, 6:28:17, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4852, loss_cls: 4.1700, loss: 4.1700 +2024-07-22 14:11:30,259 - pyskl - INFO - Epoch [15][3000/3746] lr: 9.762e-02, eta: 4 days, 6:26:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4959, loss_cls: 4.1385, loss: 4.1385 +2024-07-22 14:12:40,211 - pyskl - INFO - Epoch [15][3100/3746] lr: 9.761e-02, eta: 4 days, 6:24:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4992, loss_cls: 4.1457, loss: 4.1457 +2024-07-22 14:13:50,324 - pyskl - INFO - Epoch [15][3200/3746] lr: 9.760e-02, eta: 4 days, 6:23:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.5016, loss_cls: 4.1165, loss: 4.1165 +2024-07-22 14:15:00,314 - pyskl - INFO - Epoch [15][3300/3746] lr: 9.759e-02, eta: 4 days, 6:21:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5056, loss_cls: 4.0686, loss: 4.0686 +2024-07-22 14:16:10,164 - pyskl - INFO - Epoch [15][3400/3746] lr: 9.758e-02, eta: 4 days, 6:20:03, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4956, loss_cls: 4.1112, loss: 4.1112 +2024-07-22 14:17:19,927 - pyskl - INFO - Epoch [15][3500/3746] lr: 9.757e-02, eta: 4 days, 6:18:23, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4836, loss_cls: 4.1635, loss: 4.1635 +2024-07-22 14:18:30,127 - pyskl - INFO - Epoch [15][3600/3746] lr: 9.757e-02, eta: 4 days, 6:16:47, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4950, loss_cls: 4.1315, loss: 4.1315 +2024-07-22 14:19:39,855 - pyskl - INFO - Epoch [15][3700/3746] lr: 9.756e-02, eta: 4 days, 6:15:06, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4955, loss_cls: 4.1248, loss: 4.1248 +2024-07-22 14:20:14,357 - pyskl - INFO - Saving checkpoint at 15 epochs +2024-07-22 14:22:05,745 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 14:22:06,404 - pyskl - INFO - +top1_acc 0.1774 +top5_acc 0.3995 +2024-07-22 14:22:06,404 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 14:22:06,441 - pyskl - INFO - +mean_acc 0.1773 +2024-07-22 14:22:06,451 - pyskl - INFO - Epoch(val) [15][309] top1_acc: 0.1774, top5_acc: 0.3995, mean_class_accuracy: 0.1773 +2024-07-22 14:25:27,904 - pyskl - INFO - Epoch [16][100/3746] lr: 9.754e-02, eta: 4 days, 6:27:35, time: 2.014, data_time: 1.307, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5153, loss_cls: 4.0338, loss: 4.0338 +2024-07-22 14:26:38,214 - pyskl - INFO - Epoch [16][200/3746] lr: 9.754e-02, eta: 4 days, 6:25:58, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.5027, loss_cls: 4.0859, loss: 4.0859 +2024-07-22 14:27:48,467 - pyskl - INFO - Epoch [16][300/3746] lr: 9.753e-02, eta: 4 days, 6:24:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4995, loss_cls: 4.1103, loss: 4.1103 +2024-07-22 14:28:59,194 - pyskl - INFO - Epoch [16][400/3746] lr: 9.752e-02, eta: 4 days, 6:22:48, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5012, loss_cls: 4.0758, loss: 4.0758 +2024-07-22 14:30:09,448 - pyskl - INFO - Epoch [16][500/3746] lr: 9.751e-02, eta: 4 days, 6:21:12, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5039, loss_cls: 4.0789, loss: 4.0789 +2024-07-22 14:31:20,158 - pyskl - INFO - Epoch [16][600/3746] lr: 9.750e-02, eta: 4 days, 6:19:39, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.4938, loss_cls: 4.1207, loss: 4.1207 +2024-07-22 14:32:30,983 - pyskl - INFO - Epoch [16][700/3746] lr: 9.749e-02, eta: 4 days, 6:18:07, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4978, loss_cls: 4.0881, loss: 4.0881 +2024-07-22 14:33:41,658 - pyskl - INFO - Epoch [16][800/3746] lr: 9.748e-02, eta: 4 days, 6:16:34, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5008, loss_cls: 4.0971, loss: 4.0971 +2024-07-22 14:34:53,011 - pyskl - INFO - Epoch [16][900/3746] lr: 9.747e-02, eta: 4 days, 6:15:08, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4980, loss_cls: 4.1144, loss: 4.1144 +2024-07-22 14:36:03,160 - pyskl - INFO - Epoch [16][1000/3746] lr: 9.747e-02, eta: 4 days, 6:13:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4933, loss_cls: 4.1482, loss: 4.1482 +2024-07-22 14:37:13,288 - pyskl - INFO - Epoch [16][1100/3746] lr: 9.746e-02, eta: 4 days, 6:11:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.5012, loss_cls: 4.1069, loss: 4.1069 +2024-07-22 14:38:23,077 - pyskl - INFO - Epoch [16][1200/3746] lr: 9.745e-02, eta: 4 days, 6:10:12, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.4975, loss_cls: 4.0932, loss: 4.0932 +2024-07-22 14:39:33,205 - pyskl - INFO - Epoch [16][1300/3746] lr: 9.744e-02, eta: 4 days, 6:08:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4942, loss_cls: 4.1283, loss: 4.1283 +2024-07-22 14:40:43,345 - pyskl - INFO - Epoch [16][1400/3746] lr: 9.743e-02, eta: 4 days, 6:06:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.4908, loss_cls: 4.0979, loss: 4.0979 +2024-07-22 14:41:53,499 - pyskl - INFO - Epoch [16][1500/3746] lr: 9.742e-02, eta: 4 days, 6:05:21, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.4983, loss_cls: 4.1107, loss: 4.1107 +2024-07-22 14:43:03,311 - pyskl - INFO - Epoch [16][1600/3746] lr: 9.741e-02, eta: 4 days, 6:03:41, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4969, loss_cls: 4.1177, loss: 4.1177 +2024-07-22 14:44:13,252 - pyskl - INFO - Epoch [16][1700/3746] lr: 9.740e-02, eta: 4 days, 6:02:03, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4938, loss_cls: 4.1340, loss: 4.1340 +2024-07-22 14:45:22,959 - pyskl - INFO - Epoch [16][1800/3746] lr: 9.740e-02, eta: 4 days, 6:00:22, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4927, loss_cls: 4.1419, loss: 4.1419 +2024-07-22 14:46:32,767 - pyskl - INFO - Epoch [16][1900/3746] lr: 9.739e-02, eta: 4 days, 5:58:43, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5048, loss_cls: 4.0549, loss: 4.0549 +2024-07-22 14:47:42,754 - pyskl - INFO - Epoch [16][2000/3746] lr: 9.738e-02, eta: 4 days, 5:57:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4958, loss_cls: 4.1157, loss: 4.1157 +2024-07-22 14:48:52,661 - pyskl - INFO - Epoch [16][2100/3746] lr: 9.737e-02, eta: 4 days, 5:55:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4914, loss_cls: 4.1332, loss: 4.1332 +2024-07-22 14:50:02,581 - pyskl - INFO - Epoch [16][2200/3746] lr: 9.736e-02, eta: 4 days, 5:53:48, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5033, loss_cls: 4.0881, loss: 4.0881 +2024-07-22 14:51:12,551 - pyskl - INFO - Epoch [16][2300/3746] lr: 9.735e-02, eta: 4 days, 5:52:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5048, loss_cls: 4.0787, loss: 4.0787 +2024-07-22 14:52:22,602 - pyskl - INFO - Epoch [16][2400/3746] lr: 9.734e-02, eta: 4 days, 5:50:34, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.5003, loss_cls: 4.1149, loss: 4.1149 +2024-07-22 14:53:32,492 - pyskl - INFO - Epoch [16][2500/3746] lr: 9.733e-02, eta: 4 days, 5:48:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.4986, loss_cls: 4.0908, loss: 4.0908 +2024-07-22 14:54:42,319 - pyskl - INFO - Epoch [16][2600/3746] lr: 9.732e-02, eta: 4 days, 5:47:17, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.4952, loss_cls: 4.0896, loss: 4.0896 +2024-07-22 14:55:52,199 - pyskl - INFO - Epoch [16][2700/3746] lr: 9.731e-02, eta: 4 days, 5:45:39, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5044, loss_cls: 4.0816, loss: 4.0816 +2024-07-22 14:57:01,986 - pyskl - INFO - Epoch [16][2800/3746] lr: 9.731e-02, eta: 4 days, 5:44:00, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5033, loss_cls: 4.0897, loss: 4.0897 +2024-07-22 14:58:11,766 - pyskl - INFO - Epoch [16][2900/3746] lr: 9.730e-02, eta: 4 days, 5:42:21, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4959, loss_cls: 4.1067, loss: 4.1067 +2024-07-22 14:59:21,955 - pyskl - INFO - Epoch [16][3000/3746] lr: 9.729e-02, eta: 4 days, 5:40:46, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4913, loss_cls: 4.1550, loss: 4.1550 +2024-07-22 15:00:31,862 - pyskl - INFO - Epoch [16][3100/3746] lr: 9.728e-02, eta: 4 days, 5:39:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4956, loss_cls: 4.1049, loss: 4.1049 +2024-07-22 15:01:41,638 - pyskl - INFO - Epoch [16][3200/3746] lr: 9.727e-02, eta: 4 days, 5:37:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5027, loss_cls: 4.0665, loss: 4.0665 +2024-07-22 15:02:51,331 - pyskl - INFO - Epoch [16][3300/3746] lr: 9.726e-02, eta: 4 days, 5:35:51, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4948, loss_cls: 4.1200, loss: 4.1200 +2024-07-22 15:04:01,262 - pyskl - INFO - Epoch [16][3400/3746] lr: 9.725e-02, eta: 4 days, 5:34:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5031, loss_cls: 4.0939, loss: 4.0939 +2024-07-22 15:05:11,147 - pyskl - INFO - Epoch [16][3500/3746] lr: 9.724e-02, eta: 4 days, 5:32:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5009, loss_cls: 4.1029, loss: 4.1029 +2024-07-22 15:06:20,949 - pyskl - INFO - Epoch [16][3600/3746] lr: 9.723e-02, eta: 4 days, 5:30:59, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.4989, loss_cls: 4.1145, loss: 4.1145 +2024-07-22 15:07:31,004 - pyskl - INFO - Epoch [16][3700/3746] lr: 9.722e-02, eta: 4 days, 5:29:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5002, loss_cls: 4.1015, loss: 4.1015 +2024-07-22 15:08:05,237 - pyskl - INFO - Saving checkpoint at 16 epochs +2024-07-22 15:09:56,392 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 15:09:57,052 - pyskl - INFO - +top1_acc 0.1894 +top5_acc 0.4147 +2024-07-22 15:09:57,053 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 15:09:57,091 - pyskl - INFO - +mean_acc 0.1892 +2024-07-22 15:09:57,101 - pyskl - INFO - Epoch(val) [16][309] top1_acc: 0.1894, top5_acc: 0.4147, mean_class_accuracy: 0.1892 +2024-07-22 15:13:15,672 - pyskl - INFO - Epoch [17][100/3746] lr: 9.721e-02, eta: 4 days, 5:40:29, time: 1.986, data_time: 1.278, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5047, loss_cls: 4.0817, loss: 4.0817 +2024-07-22 15:14:26,063 - pyskl - INFO - Epoch [17][200/3746] lr: 9.720e-02, eta: 4 days, 5:38:55, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5062, loss_cls: 4.0954, loss: 4.0954 +2024-07-22 15:15:36,278 - pyskl - INFO - Epoch [17][300/3746] lr: 9.719e-02, eta: 4 days, 5:37:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4966, loss_cls: 4.1188, loss: 4.1188 +2024-07-22 15:16:47,201 - pyskl - INFO - Epoch [17][400/3746] lr: 9.718e-02, eta: 4 days, 5:35:50, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5005, loss_cls: 4.1088, loss: 4.1088 +2024-07-22 15:17:57,610 - pyskl - INFO - Epoch [17][500/3746] lr: 9.717e-02, eta: 4 days, 5:34:16, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4856, loss_cls: 4.1610, loss: 4.1610 +2024-07-22 15:19:08,389 - pyskl - INFO - Epoch [17][600/3746] lr: 9.716e-02, eta: 4 days, 5:32:45, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5091, loss_cls: 4.0987, loss: 4.0987 +2024-07-22 15:20:19,242 - pyskl - INFO - Epoch [17][700/3746] lr: 9.715e-02, eta: 4 days, 5:31:15, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4997, loss_cls: 4.1020, loss: 4.1020 +2024-07-22 15:21:29,875 - pyskl - INFO - Epoch [17][800/3746] lr: 9.714e-02, eta: 4 days, 5:29:43, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5009, loss_cls: 4.0796, loss: 4.0796 +2024-07-22 15:22:40,194 - pyskl - INFO - Epoch [17][900/3746] lr: 9.714e-02, eta: 4 days, 5:28:09, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5130, loss_cls: 4.0398, loss: 4.0398 +2024-07-22 15:23:50,613 - pyskl - INFO - Epoch [17][1000/3746] lr: 9.713e-02, eta: 4 days, 5:26:36, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4970, loss_cls: 4.1329, loss: 4.1329 +2024-07-22 15:25:00,635 - pyskl - INFO - Epoch [17][1100/3746] lr: 9.712e-02, eta: 4 days, 5:24:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4992, loss_cls: 4.1071, loss: 4.1071 +2024-07-22 15:26:10,594 - pyskl - INFO - Epoch [17][1200/3746] lr: 9.711e-02, eta: 4 days, 5:23:22, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5019, loss_cls: 4.1039, loss: 4.1039 +2024-07-22 15:27:20,475 - pyskl - INFO - Epoch [17][1300/3746] lr: 9.710e-02, eta: 4 days, 5:21:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5078, loss_cls: 4.0770, loss: 4.0770 +2024-07-22 15:28:30,310 - pyskl - INFO - Epoch [17][1400/3746] lr: 9.709e-02, eta: 4 days, 5:20:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5027, loss_cls: 4.0729, loss: 4.0729 +2024-07-22 15:29:40,109 - pyskl - INFO - Epoch [17][1500/3746] lr: 9.708e-02, eta: 4 days, 5:18:29, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5041, loss_cls: 4.0914, loss: 4.0914 +2024-07-22 15:30:49,823 - pyskl - INFO - Epoch [17][1600/3746] lr: 9.707e-02, eta: 4 days, 5:16:51, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5064, loss_cls: 4.0748, loss: 4.0748 +2024-07-22 15:31:59,659 - pyskl - INFO - Epoch [17][1700/3746] lr: 9.706e-02, eta: 4 days, 5:15:13, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5038, loss_cls: 4.1010, loss: 4.1010 +2024-07-22 15:33:09,503 - pyskl - INFO - Epoch [17][1800/3746] lr: 9.705e-02, eta: 4 days, 5:13:36, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5009, loss_cls: 4.1070, loss: 4.1070 +2024-07-22 15:34:19,618 - pyskl - INFO - Epoch [17][1900/3746] lr: 9.704e-02, eta: 4 days, 5:12:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4981, loss_cls: 4.0964, loss: 4.0964 +2024-07-22 15:35:29,347 - pyskl - INFO - Epoch [17][2000/3746] lr: 9.703e-02, eta: 4 days, 5:10:23, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5117, loss_cls: 4.0749, loss: 4.0749 +2024-07-22 15:36:39,033 - pyskl - INFO - Epoch [17][2100/3746] lr: 9.702e-02, eta: 4 days, 5:08:44, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5028, loss_cls: 4.0535, loss: 4.0535 +2024-07-22 15:37:48,817 - pyskl - INFO - Epoch [17][2200/3746] lr: 9.701e-02, eta: 4 days, 5:07:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5023, loss_cls: 4.0648, loss: 4.0648 +2024-07-22 15:38:58,436 - pyskl - INFO - Epoch [17][2300/3746] lr: 9.700e-02, eta: 4 days, 5:05:28, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4908, loss_cls: 4.1430, loss: 4.1430 +2024-07-22 15:40:08,197 - pyskl - INFO - Epoch [17][2400/3746] lr: 9.699e-02, eta: 4 days, 5:03:51, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5073, loss_cls: 4.0754, loss: 4.0754 +2024-07-22 15:41:18,142 - pyskl - INFO - Epoch [17][2500/3746] lr: 9.698e-02, eta: 4 days, 5:02:15, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4958, loss_cls: 4.1105, loss: 4.1105 +2024-07-22 15:42:28,104 - pyskl - INFO - Epoch [17][2600/3746] lr: 9.697e-02, eta: 4 days, 5:00:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5005, loss_cls: 4.0909, loss: 4.0909 +2024-07-22 15:43:37,984 - pyskl - INFO - Epoch [17][2700/3746] lr: 9.697e-02, eta: 4 days, 4:59:03, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5042, loss_cls: 4.0845, loss: 4.0845 +2024-07-22 15:44:47,748 - pyskl - INFO - Epoch [17][2800/3746] lr: 9.696e-02, eta: 4 days, 4:57:26, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5002, loss_cls: 4.0820, loss: 4.0820 +2024-07-22 15:45:57,741 - pyskl - INFO - Epoch [17][2900/3746] lr: 9.695e-02, eta: 4 days, 4:55:50, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5016, loss_cls: 4.1160, loss: 4.1160 +2024-07-22 15:47:07,674 - pyskl - INFO - Epoch [17][3000/3746] lr: 9.694e-02, eta: 4 days, 4:54:15, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4959, loss_cls: 4.1338, loss: 4.1338 +2024-07-22 15:48:17,673 - pyskl - INFO - Epoch [17][3100/3746] lr: 9.693e-02, eta: 4 days, 4:52:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5025, loss_cls: 4.0864, loss: 4.0864 +2024-07-22 15:49:27,542 - pyskl - INFO - Epoch [17][3200/3746] lr: 9.692e-02, eta: 4 days, 4:51:04, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.4913, loss_cls: 4.1005, loss: 4.1005 +2024-07-22 15:50:37,465 - pyskl - INFO - Epoch [17][3300/3746] lr: 9.691e-02, eta: 4 days, 4:49:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5100, loss_cls: 4.0480, loss: 4.0480 +2024-07-22 15:51:47,231 - pyskl - INFO - Epoch [17][3400/3746] lr: 9.690e-02, eta: 4 days, 4:47:52, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5138, loss_cls: 4.0402, loss: 4.0402 +2024-07-22 15:52:57,187 - pyskl - INFO - Epoch [17][3500/3746] lr: 9.689e-02, eta: 4 days, 4:46:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4934, loss_cls: 4.1382, loss: 4.1382 +2024-07-22 15:54:06,952 - pyskl - INFO - Epoch [17][3600/3746] lr: 9.688e-02, eta: 4 days, 4:44:40, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5114, loss_cls: 4.0701, loss: 4.0701 +2024-07-22 15:55:17,161 - pyskl - INFO - Epoch [17][3700/3746] lr: 9.687e-02, eta: 4 days, 4:43:08, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4994, loss_cls: 4.1055, loss: 4.1055 +2024-07-22 15:55:51,236 - pyskl - INFO - Saving checkpoint at 17 epochs +2024-07-22 15:57:41,442 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 15:57:42,105 - pyskl - INFO - +top1_acc 0.1776 +top5_acc 0.4002 +2024-07-22 15:57:42,106 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 15:57:42,144 - pyskl - INFO - +mean_acc 0.1774 +2024-07-22 15:57:42,155 - pyskl - INFO - Epoch(val) [17][309] top1_acc: 0.1776, top5_acc: 0.4002, mean_class_accuracy: 0.1774 +2024-07-22 16:01:00,304 - pyskl - INFO - Epoch [18][100/3746] lr: 9.685e-02, eta: 4 days, 4:53:19, time: 1.981, data_time: 1.278, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5158, loss_cls: 4.0355, loss: 4.0355 +2024-07-22 16:02:10,812 - pyskl - INFO - Epoch [18][200/3746] lr: 9.684e-02, eta: 4 days, 4:51:47, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5123, loss_cls: 4.0523, loss: 4.0523 +2024-07-22 16:03:20,938 - pyskl - INFO - Epoch [18][300/3746] lr: 9.683e-02, eta: 4 days, 4:50:13, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5111, loss_cls: 4.0730, loss: 4.0730 +2024-07-22 16:04:31,824 - pyskl - INFO - Epoch [18][400/3746] lr: 9.683e-02, eta: 4 days, 4:48:44, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5198, loss_cls: 3.9969, loss: 3.9969 +2024-07-22 16:05:42,270 - pyskl - INFO - Epoch [18][500/3746] lr: 9.682e-02, eta: 4 days, 4:47:12, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5102, loss_cls: 4.0542, loss: 4.0542 +2024-07-22 16:06:52,690 - pyskl - INFO - Epoch [18][600/3746] lr: 9.681e-02, eta: 4 days, 4:45:40, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5044, loss_cls: 4.0750, loss: 4.0750 +2024-07-22 16:08:03,817 - pyskl - INFO - Epoch [18][700/3746] lr: 9.680e-02, eta: 4 days, 4:44:14, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4894, loss_cls: 4.1213, loss: 4.1213 +2024-07-22 16:09:15,081 - pyskl - INFO - Epoch [18][800/3746] lr: 9.679e-02, eta: 4 days, 4:42:48, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5002, loss_cls: 4.0895, loss: 4.0895 +2024-07-22 16:10:25,553 - pyskl - INFO - Epoch [18][900/3746] lr: 9.678e-02, eta: 4 days, 4:41:17, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5081, loss_cls: 4.0735, loss: 4.0735 +2024-07-22 16:11:35,456 - pyskl - INFO - Epoch [18][1000/3746] lr: 9.677e-02, eta: 4 days, 4:39:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5000, loss_cls: 4.0661, loss: 4.0661 +2024-07-22 16:12:45,535 - pyskl - INFO - Epoch [18][1100/3746] lr: 9.676e-02, eta: 4 days, 4:38:06, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5038, loss_cls: 4.0706, loss: 4.0706 +2024-07-22 16:13:55,578 - pyskl - INFO - Epoch [18][1200/3746] lr: 9.675e-02, eta: 4 days, 4:36:32, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5097, loss_cls: 4.0722, loss: 4.0722 +2024-07-22 16:15:05,382 - pyskl - INFO - Epoch [18][1300/3746] lr: 9.674e-02, eta: 4 days, 4:34:55, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5000, loss_cls: 4.1086, loss: 4.1086 +2024-07-22 16:16:15,237 - pyskl - INFO - Epoch [18][1400/3746] lr: 9.673e-02, eta: 4 days, 4:33:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5034, loss_cls: 4.0714, loss: 4.0714 +2024-07-22 16:17:25,353 - pyskl - INFO - Epoch [18][1500/3746] lr: 9.672e-02, eta: 4 days, 4:31:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5033, loss_cls: 4.0871, loss: 4.0871 +2024-07-22 16:18:35,297 - pyskl - INFO - Epoch [18][1600/3746] lr: 9.671e-02, eta: 4 days, 4:30:11, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5034, loss_cls: 4.0649, loss: 4.0649 +2024-07-22 16:19:45,250 - pyskl - INFO - Epoch [18][1700/3746] lr: 9.670e-02, eta: 4 days, 4:28:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5023, loss_cls: 4.0899, loss: 4.0899 +2024-07-22 16:20:55,239 - pyskl - INFO - Epoch [18][1800/3746] lr: 9.669e-02, eta: 4 days, 4:27:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4963, loss_cls: 4.1082, loss: 4.1082 +2024-07-22 16:22:05,017 - pyskl - INFO - Epoch [18][1900/3746] lr: 9.668e-02, eta: 4 days, 4:25:25, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4875, loss_cls: 4.1251, loss: 4.1251 +2024-07-22 16:23:14,803 - pyskl - INFO - Epoch [18][2000/3746] lr: 9.667e-02, eta: 4 days, 4:23:49, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4992, loss_cls: 4.0989, loss: 4.0989 +2024-07-22 16:24:24,699 - pyskl - INFO - Epoch [18][2100/3746] lr: 9.666e-02, eta: 4 days, 4:22:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5025, loss_cls: 4.0947, loss: 4.0947 +2024-07-22 16:25:34,691 - pyskl - INFO - Epoch [18][2200/3746] lr: 9.665e-02, eta: 4 days, 4:20:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4919, loss_cls: 4.1323, loss: 4.1323 +2024-07-22 16:26:44,551 - pyskl - INFO - Epoch [18][2300/3746] lr: 9.664e-02, eta: 4 days, 4:19:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4980, loss_cls: 4.1182, loss: 4.1182 +2024-07-22 16:27:54,363 - pyskl - INFO - Epoch [18][2400/3746] lr: 9.663e-02, eta: 4 days, 4:17:29, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5008, loss_cls: 4.0850, loss: 4.0850 +2024-07-22 16:29:04,372 - pyskl - INFO - Epoch [18][2500/3746] lr: 9.662e-02, eta: 4 days, 4:15:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5012, loss_cls: 4.0977, loss: 4.0977 +2024-07-22 16:30:14,074 - pyskl - INFO - Epoch [18][2600/3746] lr: 9.661e-02, eta: 4 days, 4:14:19, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5144, loss_cls: 4.0500, loss: 4.0500 +2024-07-22 16:31:24,364 - pyskl - INFO - Epoch [18][2700/3746] lr: 9.660e-02, eta: 4 days, 4:12:48, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5039, loss_cls: 4.1006, loss: 4.1006 +2024-07-22 16:32:34,003 - pyskl - INFO - Epoch [18][2800/3746] lr: 9.659e-02, eta: 4 days, 4:11:11, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5166, loss_cls: 4.0370, loss: 4.0370 +2024-07-22 16:33:43,901 - pyskl - INFO - Epoch [18][2900/3746] lr: 9.658e-02, eta: 4 days, 4:09:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5045, loss_cls: 4.0880, loss: 4.0880 +2024-07-22 16:34:53,781 - pyskl - INFO - Epoch [18][3000/3746] lr: 9.657e-02, eta: 4 days, 4:08:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4850, loss_cls: 4.1578, loss: 4.1578 +2024-07-22 16:36:03,585 - pyskl - INFO - Epoch [18][3100/3746] lr: 9.656e-02, eta: 4 days, 4:06:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5128, loss_cls: 4.0586, loss: 4.0586 +2024-07-22 16:37:13,261 - pyskl - INFO - Epoch [18][3200/3746] lr: 9.654e-02, eta: 4 days, 4:04:52, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4939, loss_cls: 4.0990, loss: 4.0990 +2024-07-22 16:38:23,201 - pyskl - INFO - Epoch [18][3300/3746] lr: 9.653e-02, eta: 4 days, 4:03:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4989, loss_cls: 4.1195, loss: 4.1195 +2024-07-22 16:39:33,094 - pyskl - INFO - Epoch [18][3400/3746] lr: 9.652e-02, eta: 4 days, 4:01:44, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5052, loss_cls: 4.0980, loss: 4.0980 +2024-07-22 16:40:43,051 - pyskl - INFO - Epoch [18][3500/3746] lr: 9.651e-02, eta: 4 days, 4:00:10, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5036, loss_cls: 4.0806, loss: 4.0806 +2024-07-22 16:41:53,041 - pyskl - INFO - Epoch [18][3600/3746] lr: 9.650e-02, eta: 4 days, 3:58:37, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4964, loss_cls: 4.1282, loss: 4.1282 +2024-07-22 16:43:02,923 - pyskl - INFO - Epoch [18][3700/3746] lr: 9.649e-02, eta: 4 days, 3:57:03, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5066, loss_cls: 4.0882, loss: 4.0882 +2024-07-22 16:43:37,216 - pyskl - INFO - Saving checkpoint at 18 epochs +2024-07-22 16:45:27,769 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 16:45:28,428 - pyskl - INFO - +top1_acc 0.1939 +top5_acc 0.4244 +2024-07-22 16:45:28,428 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 16:45:28,467 - pyskl - INFO - +mean_acc 0.1937 +2024-07-22 16:45:28,472 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_13.pth was removed +2024-07-22 16:45:28,707 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_18.pth. +2024-07-22 16:45:28,708 - pyskl - INFO - Best top1_acc is 0.1939 at 18 epoch. +2024-07-22 16:45:28,718 - pyskl - INFO - Epoch(val) [18][309] top1_acc: 0.1939, top5_acc: 0.4244, mean_class_accuracy: 0.1937 +2024-07-22 16:48:47,321 - pyskl - INFO - Epoch [19][100/3746] lr: 9.648e-02, eta: 4 days, 4:06:33, time: 1.986, data_time: 1.281, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5158, loss_cls: 3.9967, loss: 3.9967 +2024-07-22 16:49:58,571 - pyskl - INFO - Epoch [19][200/3746] lr: 9.647e-02, eta: 4 days, 4:05:08, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5038, loss_cls: 4.0386, loss: 4.0386 +2024-07-22 16:51:08,957 - pyskl - INFO - Epoch [19][300/3746] lr: 9.646e-02, eta: 4 days, 4:03:37, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5130, loss_cls: 4.0279, loss: 4.0279 +2024-07-22 16:52:19,555 - pyskl - INFO - Epoch [19][400/3746] lr: 9.645e-02, eta: 4 days, 4:02:07, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4986, loss_cls: 4.0995, loss: 4.0995 +2024-07-22 16:53:29,549 - pyskl - INFO - Epoch [19][500/3746] lr: 9.644e-02, eta: 4 days, 4:00:33, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5058, loss_cls: 4.0609, loss: 4.0609 +2024-07-22 16:54:40,031 - pyskl - INFO - Epoch [19][600/3746] lr: 9.643e-02, eta: 4 days, 3:59:03, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.4983, loss_cls: 4.1140, loss: 4.1140 +2024-07-22 16:55:50,969 - pyskl - INFO - Epoch [19][700/3746] lr: 9.642e-02, eta: 4 days, 3:57:36, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.4986, loss_cls: 4.0737, loss: 4.0737 +2024-07-22 16:57:01,136 - pyskl - INFO - Epoch [19][800/3746] lr: 9.641e-02, eta: 4 days, 3:56:03, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5039, loss_cls: 4.0642, loss: 4.0642 +2024-07-22 16:58:11,857 - pyskl - INFO - Epoch [19][900/3746] lr: 9.640e-02, eta: 4 days, 3:54:35, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5098, loss_cls: 4.0536, loss: 4.0536 +2024-07-22 16:59:22,253 - pyskl - INFO - Epoch [19][1000/3746] lr: 9.639e-02, eta: 4 days, 3:53:04, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5008, loss_cls: 4.0842, loss: 4.0842 +2024-07-22 17:00:32,372 - pyskl - INFO - Epoch [19][1100/3746] lr: 9.637e-02, eta: 4 days, 3:51:32, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5031, loss_cls: 4.1012, loss: 4.1012 +2024-07-22 17:01:42,289 - pyskl - INFO - Epoch [19][1200/3746] lr: 9.636e-02, eta: 4 days, 3:49:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5061, loss_cls: 4.0639, loss: 4.0639 +2024-07-22 17:02:52,358 - pyskl - INFO - Epoch [19][1300/3746] lr: 9.635e-02, eta: 4 days, 3:48:25, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5089, loss_cls: 4.0928, loss: 4.0928 +2024-07-22 17:04:02,344 - pyskl - INFO - Epoch [19][1400/3746] lr: 9.634e-02, eta: 4 days, 3:46:51, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5073, loss_cls: 4.0779, loss: 4.0779 +2024-07-22 17:05:12,387 - pyskl - INFO - Epoch [19][1500/3746] lr: 9.633e-02, eta: 4 days, 3:45:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5075, loss_cls: 4.0719, loss: 4.0719 +2024-07-22 17:06:22,063 - pyskl - INFO - Epoch [19][1600/3746] lr: 9.632e-02, eta: 4 days, 3:43:43, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5134, loss_cls: 4.0509, loss: 4.0509 +2024-07-22 17:07:31,991 - pyskl - INFO - Epoch [19][1700/3746] lr: 9.631e-02, eta: 4 days, 3:42:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4925, loss_cls: 4.1134, loss: 4.1134 +2024-07-22 17:08:42,356 - pyskl - INFO - Epoch [19][1800/3746] lr: 9.630e-02, eta: 4 days, 3:40:39, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5000, loss_cls: 4.0833, loss: 4.0833 +2024-07-22 17:09:52,204 - pyskl - INFO - Epoch [19][1900/3746] lr: 9.629e-02, eta: 4 days, 3:39:05, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5052, loss_cls: 4.0754, loss: 4.0754 +2024-07-22 17:11:01,897 - pyskl - INFO - Epoch [19][2000/3746] lr: 9.628e-02, eta: 4 days, 3:37:30, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5019, loss_cls: 4.0785, loss: 4.0785 +2024-07-22 17:12:11,811 - pyskl - INFO - Epoch [19][2100/3746] lr: 9.627e-02, eta: 4 days, 3:35:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5131, loss_cls: 4.0437, loss: 4.0437 +2024-07-22 17:13:21,739 - pyskl - INFO - Epoch [19][2200/3746] lr: 9.626e-02, eta: 4 days, 3:34:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.4989, loss_cls: 4.0813, loss: 4.0813 +2024-07-22 17:14:31,611 - pyskl - INFO - Epoch [19][2300/3746] lr: 9.625e-02, eta: 4 days, 3:32:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5089, loss_cls: 4.0634, loss: 4.0634 +2024-07-22 17:15:41,369 - pyskl - INFO - Epoch [19][2400/3746] lr: 9.624e-02, eta: 4 days, 3:31:15, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4980, loss_cls: 4.1159, loss: 4.1159 +2024-07-22 17:16:51,737 - pyskl - INFO - Epoch [19][2500/3746] lr: 9.623e-02, eta: 4 days, 3:29:45, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5042, loss_cls: 4.1043, loss: 4.1043 +2024-07-22 17:18:01,643 - pyskl - INFO - Epoch [19][2600/3746] lr: 9.622e-02, eta: 4 days, 3:28:12, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.4981, loss_cls: 4.1165, loss: 4.1165 +2024-07-22 17:19:11,632 - pyskl - INFO - Epoch [19][2700/3746] lr: 9.621e-02, eta: 4 days, 3:26:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.4969, loss_cls: 4.1133, loss: 4.1133 +2024-07-22 17:20:21,403 - pyskl - INFO - Epoch [19][2800/3746] lr: 9.620e-02, eta: 4 days, 3:25:05, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5127, loss_cls: 4.0161, loss: 4.0161 +2024-07-22 17:21:31,206 - pyskl - INFO - Epoch [19][2900/3746] lr: 9.618e-02, eta: 4 days, 3:23:31, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5152, loss_cls: 4.0602, loss: 4.0602 +2024-07-22 17:22:40,919 - pyskl - INFO - Epoch [19][3000/3746] lr: 9.617e-02, eta: 4 days, 3:21:57, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5084, loss_cls: 4.0355, loss: 4.0355 +2024-07-22 17:23:50,809 - pyskl - INFO - Epoch [19][3100/3746] lr: 9.616e-02, eta: 4 days, 3:20:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4961, loss_cls: 4.1242, loss: 4.1242 +2024-07-22 17:25:00,575 - pyskl - INFO - Epoch [19][3200/3746] lr: 9.615e-02, eta: 4 days, 3:18:51, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5073, loss_cls: 4.0754, loss: 4.0754 +2024-07-22 17:26:10,414 - pyskl - INFO - Epoch [19][3300/3746] lr: 9.614e-02, eta: 4 days, 3:17:17, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5058, loss_cls: 4.0997, loss: 4.0997 +2024-07-22 17:27:19,978 - pyskl - INFO - Epoch [19][3400/3746] lr: 9.613e-02, eta: 4 days, 3:15:42, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4950, loss_cls: 4.1310, loss: 4.1310 +2024-07-22 17:28:29,985 - pyskl - INFO - Epoch [19][3500/3746] lr: 9.612e-02, eta: 4 days, 3:14:10, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5066, loss_cls: 4.0392, loss: 4.0392 +2024-07-22 17:29:40,140 - pyskl - INFO - Epoch [19][3600/3746] lr: 9.611e-02, eta: 4 days, 3:12:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5014, loss_cls: 4.1102, loss: 4.1102 +2024-07-22 17:30:49,934 - pyskl - INFO - Epoch [19][3700/3746] lr: 9.610e-02, eta: 4 days, 3:11:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5061, loss_cls: 4.0578, loss: 4.0578 +2024-07-22 17:31:24,321 - pyskl - INFO - Saving checkpoint at 19 epochs +2024-07-22 17:33:14,841 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 17:33:15,508 - pyskl - INFO - +top1_acc 0.1932 +top5_acc 0.4212 +2024-07-22 17:33:15,509 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 17:33:15,548 - pyskl - INFO - +mean_acc 0.1929 +2024-07-22 17:33:15,559 - pyskl - INFO - Epoch(val) [19][309] top1_acc: 0.1932, top5_acc: 0.4212, mean_class_accuracy: 0.1929 +2024-07-22 17:36:31,345 - pyskl - INFO - Epoch [20][100/3746] lr: 9.608e-02, eta: 4 days, 3:19:37, time: 1.958, data_time: 1.254, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5197, loss_cls: 3.9927, loss: 3.9927 +2024-07-22 17:37:41,800 - pyskl - INFO - Epoch [20][200/3746] lr: 9.607e-02, eta: 4 days, 3:18:07, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.4936, loss_cls: 4.1039, loss: 4.1039 +2024-07-22 17:38:52,041 - pyskl - INFO - Epoch [20][300/3746] lr: 9.606e-02, eta: 4 days, 3:16:36, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5052, loss_cls: 4.0677, loss: 4.0677 +2024-07-22 17:40:02,840 - pyskl - INFO - Epoch [20][400/3746] lr: 9.605e-02, eta: 4 days, 3:15:09, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5092, loss_cls: 4.0443, loss: 4.0443 +2024-07-22 17:41:13,460 - pyskl - INFO - Epoch [20][500/3746] lr: 9.604e-02, eta: 4 days, 3:13:41, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5008, loss_cls: 4.0911, loss: 4.0911 +2024-07-22 17:42:23,739 - pyskl - INFO - Epoch [20][600/3746] lr: 9.603e-02, eta: 4 days, 3:12:10, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5128, loss_cls: 4.0236, loss: 4.0236 +2024-07-22 17:43:34,597 - pyskl - INFO - Epoch [20][700/3746] lr: 9.602e-02, eta: 4 days, 3:10:44, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5011, loss_cls: 4.0812, loss: 4.0812 +2024-07-22 17:44:45,579 - pyskl - INFO - Epoch [20][800/3746] lr: 9.601e-02, eta: 4 days, 3:09:18, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5161, loss_cls: 4.0411, loss: 4.0411 +2024-07-22 17:45:55,997 - pyskl - INFO - Epoch [20][900/3746] lr: 9.600e-02, eta: 4 days, 3:07:48, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5086, loss_cls: 4.0451, loss: 4.0451 +2024-07-22 17:47:06,056 - pyskl - INFO - Epoch [20][1000/3746] lr: 9.598e-02, eta: 4 days, 3:06:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5050, loss_cls: 4.0514, loss: 4.0514 +2024-07-22 17:48:16,281 - pyskl - INFO - Epoch [20][1100/3746] lr: 9.597e-02, eta: 4 days, 3:04:46, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4986, loss_cls: 4.0979, loss: 4.0979 +2024-07-22 17:49:26,371 - pyskl - INFO - Epoch [20][1200/3746] lr: 9.596e-02, eta: 4 days, 3:03:14, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4897, loss_cls: 4.1252, loss: 4.1252 +2024-07-22 17:50:36,235 - pyskl - INFO - Epoch [20][1300/3746] lr: 9.595e-02, eta: 4 days, 3:01:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5219, loss_cls: 4.0092, loss: 4.0092 +2024-07-22 17:51:46,040 - pyskl - INFO - Epoch [20][1400/3746] lr: 9.594e-02, eta: 4 days, 3:00:08, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5050, loss_cls: 4.0502, loss: 4.0502 +2024-07-22 17:52:55,967 - pyskl - INFO - Epoch [20][1500/3746] lr: 9.593e-02, eta: 4 days, 2:58:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.4955, loss_cls: 4.0880, loss: 4.0880 +2024-07-22 17:54:05,802 - pyskl - INFO - Epoch [20][1600/3746] lr: 9.592e-02, eta: 4 days, 2:57:03, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5128, loss_cls: 4.0298, loss: 4.0298 +2024-07-22 17:55:15,626 - pyskl - INFO - Epoch [20][1700/3746] lr: 9.591e-02, eta: 4 days, 2:55:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4942, loss_cls: 4.1502, loss: 4.1502 +2024-07-22 17:56:25,987 - pyskl - INFO - Epoch [20][1800/3746] lr: 9.590e-02, eta: 4 days, 2:54:00, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5077, loss_cls: 4.0764, loss: 4.0764 +2024-07-22 17:57:35,851 - pyskl - INFO - Epoch [20][1900/3746] lr: 9.588e-02, eta: 4 days, 2:52:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5162, loss_cls: 4.0351, loss: 4.0351 +2024-07-22 17:58:45,820 - pyskl - INFO - Epoch [20][2000/3746] lr: 9.587e-02, eta: 4 days, 2:50:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5144, loss_cls: 4.0351, loss: 4.0351 +2024-07-22 17:59:56,071 - pyskl - INFO - Epoch [20][2100/3746] lr: 9.586e-02, eta: 4 days, 2:49:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5109, loss_cls: 4.0533, loss: 4.0533 +2024-07-22 18:01:05,903 - pyskl - INFO - Epoch [20][2200/3746] lr: 9.585e-02, eta: 4 days, 2:47:53, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5173, loss_cls: 4.0236, loss: 4.0236 +2024-07-22 18:02:15,811 - pyskl - INFO - Epoch [20][2300/3746] lr: 9.584e-02, eta: 4 days, 2:46:21, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5069, loss_cls: 4.0740, loss: 4.0740 +2024-07-22 18:03:25,819 - pyskl - INFO - Epoch [20][2400/3746] lr: 9.583e-02, eta: 4 days, 2:44:50, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5041, loss_cls: 4.0716, loss: 4.0716 +2024-07-22 18:04:35,696 - pyskl - INFO - Epoch [20][2500/3746] lr: 9.582e-02, eta: 4 days, 2:43:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.5006, loss_cls: 4.1221, loss: 4.1221 +2024-07-22 18:05:45,668 - pyskl - INFO - Epoch [20][2600/3746] lr: 9.581e-02, eta: 4 days, 2:41:46, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5117, loss_cls: 4.0463, loss: 4.0463 +2024-07-22 18:06:55,523 - pyskl - INFO - Epoch [20][2700/3746] lr: 9.580e-02, eta: 4 days, 2:40:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5061, loss_cls: 4.0764, loss: 4.0764 +2024-07-22 18:08:05,407 - pyskl - INFO - Epoch [20][2800/3746] lr: 9.578e-02, eta: 4 days, 2:38:42, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5067, loss_cls: 4.0720, loss: 4.0720 +2024-07-22 18:09:15,167 - pyskl - INFO - Epoch [20][2900/3746] lr: 9.577e-02, eta: 4 days, 2:37:10, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5022, loss_cls: 4.0927, loss: 4.0927 +2024-07-22 18:10:25,125 - pyskl - INFO - Epoch [20][3000/3746] lr: 9.576e-02, eta: 4 days, 2:35:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5003, loss_cls: 4.0945, loss: 4.0945 +2024-07-22 18:11:34,856 - pyskl - INFO - Epoch [20][3100/3746] lr: 9.575e-02, eta: 4 days, 2:34:05, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5112, loss_cls: 4.0430, loss: 4.0430 +2024-07-22 18:12:45,034 - pyskl - INFO - Epoch [20][3200/3746] lr: 9.574e-02, eta: 4 days, 2:32:36, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5047, loss_cls: 4.0762, loss: 4.0762 +2024-07-22 18:13:54,791 - pyskl - INFO - Epoch [20][3300/3746] lr: 9.573e-02, eta: 4 days, 2:31:03, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5000, loss_cls: 4.0904, loss: 4.0904 +2024-07-22 18:15:04,850 - pyskl - INFO - Epoch [20][3400/3746] lr: 9.572e-02, eta: 4 days, 2:29:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.4992, loss_cls: 4.0885, loss: 4.0885 +2024-07-22 18:16:14,551 - pyskl - INFO - Epoch [20][3500/3746] lr: 9.571e-02, eta: 4 days, 2:28:00, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5016, loss_cls: 4.0661, loss: 4.0661 +2024-07-22 18:17:24,286 - pyskl - INFO - Epoch [20][3600/3746] lr: 9.569e-02, eta: 4 days, 2:26:28, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4995, loss_cls: 4.0874, loss: 4.0874 +2024-07-22 18:18:34,083 - pyskl - INFO - Epoch [20][3700/3746] lr: 9.568e-02, eta: 4 days, 2:24:56, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4977, loss_cls: 4.0908, loss: 4.0908 +2024-07-22 18:19:08,103 - pyskl - INFO - Saving checkpoint at 20 epochs +2024-07-22 18:20:59,325 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 18:20:59,990 - pyskl - INFO - +top1_acc 0.1863 +top5_acc 0.4151 +2024-07-22 18:20:59,990 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 18:21:00,030 - pyskl - INFO - +mean_acc 0.1863 +2024-07-22 18:21:00,040 - pyskl - INFO - Epoch(val) [20][309] top1_acc: 0.1863, top5_acc: 0.4151, mean_class_accuracy: 0.1863 +2024-07-22 18:24:16,576 - pyskl - INFO - Epoch [21][100/3746] lr: 9.567e-02, eta: 4 days, 2:32:56, time: 1.965, data_time: 1.260, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5073, loss_cls: 4.0476, loss: 4.0476 +2024-07-22 18:25:26,958 - pyskl - INFO - Epoch [21][200/3746] lr: 9.565e-02, eta: 4 days, 2:31:27, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5030, loss_cls: 4.0425, loss: 4.0425 +2024-07-22 18:26:37,134 - pyskl - INFO - Epoch [21][300/3746] lr: 9.564e-02, eta: 4 days, 2:29:56, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5130, loss_cls: 4.0188, loss: 4.0188 +2024-07-22 18:27:48,018 - pyskl - INFO - Epoch [21][400/3746] lr: 9.563e-02, eta: 4 days, 2:28:31, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5041, loss_cls: 4.0935, loss: 4.0935 +2024-07-22 18:28:58,377 - pyskl - INFO - Epoch [21][500/3746] lr: 9.562e-02, eta: 4 days, 2:27:02, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5005, loss_cls: 4.0833, loss: 4.0833 +2024-07-22 18:30:08,361 - pyskl - INFO - Epoch [21][600/3746] lr: 9.561e-02, eta: 4 days, 2:25:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5064, loss_cls: 4.0591, loss: 4.0591 +2024-07-22 18:31:19,496 - pyskl - INFO - Epoch [21][700/3746] lr: 9.560e-02, eta: 4 days, 2:24:07, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5059, loss_cls: 4.0732, loss: 4.0732 +2024-07-22 18:32:29,602 - pyskl - INFO - Epoch [21][800/3746] lr: 9.559e-02, eta: 4 days, 2:22:36, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5098, loss_cls: 4.0593, loss: 4.0593 +2024-07-22 18:33:39,825 - pyskl - INFO - Epoch [21][900/3746] lr: 9.557e-02, eta: 4 days, 2:21:06, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5077, loss_cls: 4.0276, loss: 4.0276 +2024-07-22 18:34:50,015 - pyskl - INFO - Epoch [21][1000/3746] lr: 9.556e-02, eta: 4 days, 2:19:37, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5059, loss_cls: 4.0393, loss: 4.0393 +2024-07-22 18:35:59,885 - pyskl - INFO - Epoch [21][1100/3746] lr: 9.555e-02, eta: 4 days, 2:18:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5114, loss_cls: 4.0437, loss: 4.0437 +2024-07-22 18:37:09,962 - pyskl - INFO - Epoch [21][1200/3746] lr: 9.554e-02, eta: 4 days, 2:16:34, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5083, loss_cls: 4.0724, loss: 4.0724 +2024-07-22 18:38:19,691 - pyskl - INFO - Epoch [21][1300/3746] lr: 9.553e-02, eta: 4 days, 2:15:02, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5170, loss_cls: 4.0465, loss: 4.0465 +2024-07-22 18:39:29,364 - pyskl - INFO - Epoch [21][1400/3746] lr: 9.552e-02, eta: 4 days, 2:13:29, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.4991, loss_cls: 4.0688, loss: 4.0688 +2024-07-22 18:40:39,308 - pyskl - INFO - Epoch [21][1500/3746] lr: 9.551e-02, eta: 4 days, 2:11:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4917, loss_cls: 4.1311, loss: 4.1311 +2024-07-22 18:41:49,482 - pyskl - INFO - Epoch [21][1600/3746] lr: 9.549e-02, eta: 4 days, 2:10:28, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5206, loss_cls: 4.0155, loss: 4.0155 +2024-07-22 18:42:59,226 - pyskl - INFO - Epoch [21][1700/3746] lr: 9.548e-02, eta: 4 days, 2:08:56, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.4997, loss_cls: 4.0837, loss: 4.0837 +2024-07-22 18:44:09,014 - pyskl - INFO - Epoch [21][1800/3746] lr: 9.547e-02, eta: 4 days, 2:07:24, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5156, loss_cls: 4.0502, loss: 4.0502 +2024-07-22 18:45:19,031 - pyskl - INFO - Epoch [21][1900/3746] lr: 9.546e-02, eta: 4 days, 2:05:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5116, loss_cls: 4.0533, loss: 4.0533 +2024-07-22 18:46:28,681 - pyskl - INFO - Epoch [21][2000/3746] lr: 9.545e-02, eta: 4 days, 2:04:21, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5069, loss_cls: 4.0380, loss: 4.0380 +2024-07-22 18:47:38,279 - pyskl - INFO - Epoch [21][2100/3746] lr: 9.544e-02, eta: 4 days, 2:02:48, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5144, loss_cls: 4.0677, loss: 4.0677 +2024-07-22 18:48:48,571 - pyskl - INFO - Epoch [21][2200/3746] lr: 9.542e-02, eta: 4 days, 2:01:19, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.5020, loss_cls: 4.0964, loss: 4.0964 +2024-07-22 18:49:58,423 - pyskl - INFO - Epoch [21][2300/3746] lr: 9.541e-02, eta: 4 days, 1:59:48, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5133, loss_cls: 4.0431, loss: 4.0431 +2024-07-22 18:51:08,420 - pyskl - INFO - Epoch [21][2400/3746] lr: 9.540e-02, eta: 4 days, 1:58:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5119, loss_cls: 4.0380, loss: 4.0380 +2024-07-22 18:52:18,504 - pyskl - INFO - Epoch [21][2500/3746] lr: 9.539e-02, eta: 4 days, 1:56:48, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5027, loss_cls: 4.0814, loss: 4.0814 +2024-07-22 18:53:28,313 - pyskl - INFO - Epoch [21][2600/3746] lr: 9.538e-02, eta: 4 days, 1:55:17, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5128, loss_cls: 4.0388, loss: 4.0388 +2024-07-22 18:54:37,936 - pyskl - INFO - Epoch [21][2700/3746] lr: 9.537e-02, eta: 4 days, 1:53:44, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5105, loss_cls: 4.0440, loss: 4.0440 +2024-07-22 18:55:47,761 - pyskl - INFO - Epoch [21][2800/3746] lr: 9.535e-02, eta: 4 days, 1:52:13, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5095, loss_cls: 4.0753, loss: 4.0753 +2024-07-22 18:56:57,779 - pyskl - INFO - Epoch [21][2900/3746] lr: 9.534e-02, eta: 4 days, 1:50:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5022, loss_cls: 4.0749, loss: 4.0749 +2024-07-22 18:58:07,656 - pyskl - INFO - Epoch [21][3000/3746] lr: 9.533e-02, eta: 4 days, 1:49:13, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5011, loss_cls: 4.0736, loss: 4.0736 +2024-07-22 18:59:17,440 - pyskl - INFO - Epoch [21][3100/3746] lr: 9.532e-02, eta: 4 days, 1:47:41, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5180, loss_cls: 4.0288, loss: 4.0288 +2024-07-22 19:00:27,396 - pyskl - INFO - Epoch [21][3200/3746] lr: 9.531e-02, eta: 4 days, 1:46:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5109, loss_cls: 4.0782, loss: 4.0782 +2024-07-22 19:01:37,332 - pyskl - INFO - Epoch [21][3300/3746] lr: 9.529e-02, eta: 4 days, 1:44:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5148, loss_cls: 4.0461, loss: 4.0461 +2024-07-22 19:02:47,204 - pyskl - INFO - Epoch [21][3400/3746] lr: 9.528e-02, eta: 4 days, 1:43:10, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.4983, loss_cls: 4.0916, loss: 4.0916 +2024-07-22 19:03:57,158 - pyskl - INFO - Epoch [21][3500/3746] lr: 9.527e-02, eta: 4 days, 1:41:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5116, loss_cls: 4.0627, loss: 4.0627 +2024-07-22 19:05:06,873 - pyskl - INFO - Epoch [21][3600/3746] lr: 9.526e-02, eta: 4 days, 1:40:09, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5064, loss_cls: 4.0581, loss: 4.0581 +2024-07-22 19:06:16,681 - pyskl - INFO - Epoch [21][3700/3746] lr: 9.525e-02, eta: 4 days, 1:38:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5131, loss_cls: 4.0552, loss: 4.0552 +2024-07-22 19:06:50,756 - pyskl - INFO - Saving checkpoint at 21 epochs +2024-07-22 19:08:42,358 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 19:08:43,015 - pyskl - INFO - +top1_acc 0.1680 +top5_acc 0.3730 +2024-07-22 19:08:43,016 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 19:08:43,056 - pyskl - INFO - +mean_acc 0.1677 +2024-07-22 19:08:43,066 - pyskl - INFO - Epoch(val) [21][309] top1_acc: 0.1680, top5_acc: 0.3730, mean_class_accuracy: 0.1677 +2024-07-22 19:12:00,108 - pyskl - INFO - Epoch [22][100/3746] lr: 9.523e-02, eta: 4 days, 1:46:09, time: 1.970, data_time: 1.264, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5122, loss_cls: 4.0283, loss: 4.0283 +2024-07-22 19:13:10,737 - pyskl - INFO - Epoch [22][200/3746] lr: 9.522e-02, eta: 4 days, 1:44:43, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5089, loss_cls: 4.0668, loss: 4.0668 +2024-07-22 19:14:20,867 - pyskl - INFO - Epoch [22][300/3746] lr: 9.521e-02, eta: 4 days, 1:43:13, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5250, loss_cls: 3.9966, loss: 3.9966 +2024-07-22 19:15:31,394 - pyskl - INFO - Epoch [22][400/3746] lr: 9.519e-02, eta: 4 days, 1:41:46, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5000, loss_cls: 4.0918, loss: 4.0918 +2024-07-22 19:16:41,340 - pyskl - INFO - Epoch [22][500/3746] lr: 9.518e-02, eta: 4 days, 1:40:16, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5042, loss_cls: 4.0692, loss: 4.0692 +2024-07-22 19:17:51,436 - pyskl - INFO - Epoch [22][600/3746] lr: 9.517e-02, eta: 4 days, 1:38:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5009, loss_cls: 4.0572, loss: 4.0572 +2024-07-22 19:19:02,821 - pyskl - INFO - Epoch [22][700/3746] lr: 9.516e-02, eta: 4 days, 1:37:25, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5077, loss_cls: 4.0462, loss: 4.0462 +2024-07-22 19:20:13,433 - pyskl - INFO - Epoch [22][800/3746] lr: 9.515e-02, eta: 4 days, 1:35:58, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5162, loss_cls: 4.0375, loss: 4.0375 +2024-07-22 19:21:24,089 - pyskl - INFO - Epoch [22][900/3746] lr: 9.513e-02, eta: 4 days, 1:34:32, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5036, loss_cls: 4.0699, loss: 4.0699 +2024-07-22 19:22:34,683 - pyskl - INFO - Epoch [22][1000/3746] lr: 9.512e-02, eta: 4 days, 1:33:06, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5138, loss_cls: 4.0527, loss: 4.0527 +2024-07-22 19:23:44,977 - pyskl - INFO - Epoch [22][1100/3746] lr: 9.511e-02, eta: 4 days, 1:31:38, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5031, loss_cls: 4.1056, loss: 4.1056 +2024-07-22 19:24:54,985 - pyskl - INFO - Epoch [22][1200/3746] lr: 9.510e-02, eta: 4 days, 1:30:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5134, loss_cls: 4.0602, loss: 4.0602 +2024-07-22 19:26:04,848 - pyskl - INFO - Epoch [22][1300/3746] lr: 9.509e-02, eta: 4 days, 1:28:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5048, loss_cls: 4.0694, loss: 4.0694 +2024-07-22 19:27:14,764 - pyskl - INFO - Epoch [22][1400/3746] lr: 9.507e-02, eta: 4 days, 1:27:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5080, loss_cls: 4.0439, loss: 4.0439 +2024-07-22 19:28:24,549 - pyskl - INFO - Epoch [22][1500/3746] lr: 9.506e-02, eta: 4 days, 1:25:36, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5100, loss_cls: 4.0309, loss: 4.0309 +2024-07-22 19:29:34,657 - pyskl - INFO - Epoch [22][1600/3746] lr: 9.505e-02, eta: 4 days, 1:24:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5083, loss_cls: 4.0561, loss: 4.0561 +2024-07-22 19:30:44,381 - pyskl - INFO - Epoch [22][1700/3746] lr: 9.504e-02, eta: 4 days, 1:22:36, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5123, loss_cls: 4.0272, loss: 4.0272 +2024-07-22 19:31:54,416 - pyskl - INFO - Epoch [22][1800/3746] lr: 9.502e-02, eta: 4 days, 1:21:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5055, loss_cls: 4.0558, loss: 4.0558 +2024-07-22 19:33:04,117 - pyskl - INFO - Epoch [22][1900/3746] lr: 9.501e-02, eta: 4 days, 1:19:35, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5102, loss_cls: 4.0587, loss: 4.0587 +2024-07-22 19:34:14,094 - pyskl - INFO - Epoch [22][2000/3746] lr: 9.500e-02, eta: 4 days, 1:18:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5158, loss_cls: 4.0286, loss: 4.0286 +2024-07-22 19:35:24,075 - pyskl - INFO - Epoch [22][2100/3746] lr: 9.499e-02, eta: 4 days, 1:16:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5092, loss_cls: 4.0269, loss: 4.0269 +2024-07-22 19:36:34,150 - pyskl - INFO - Epoch [22][2200/3746] lr: 9.498e-02, eta: 4 days, 1:15:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5080, loss_cls: 4.0682, loss: 4.0682 +2024-07-22 19:37:43,838 - pyskl - INFO - Epoch [22][2300/3746] lr: 9.496e-02, eta: 4 days, 1:13:36, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5070, loss_cls: 4.0790, loss: 4.0790 +2024-07-22 19:38:53,856 - pyskl - INFO - Epoch [22][2400/3746] lr: 9.495e-02, eta: 4 days, 1:12:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.4981, loss_cls: 4.0716, loss: 4.0716 +2024-07-22 19:40:03,706 - pyskl - INFO - Epoch [22][2500/3746] lr: 9.494e-02, eta: 4 days, 1:10:37, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5041, loss_cls: 4.0838, loss: 4.0838 +2024-07-22 19:41:14,057 - pyskl - INFO - Epoch [22][2600/3746] lr: 9.493e-02, eta: 4 days, 1:09:10, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5117, loss_cls: 4.0385, loss: 4.0385 +2024-07-22 19:42:24,256 - pyskl - INFO - Epoch [22][2700/3746] lr: 9.491e-02, eta: 4 days, 1:07:42, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.4975, loss_cls: 4.1216, loss: 4.1216 +2024-07-22 19:43:34,263 - pyskl - INFO - Epoch [22][2800/3746] lr: 9.490e-02, eta: 4 days, 1:06:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5138, loss_cls: 4.0531, loss: 4.0531 +2024-07-22 19:44:44,264 - pyskl - INFO - Epoch [22][2900/3746] lr: 9.489e-02, eta: 4 days, 1:04:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5133, loss_cls: 4.0108, loss: 4.0108 +2024-07-22 19:45:54,098 - pyskl - INFO - Epoch [22][3000/3746] lr: 9.488e-02, eta: 4 days, 1:03:14, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5125, loss_cls: 4.0459, loss: 4.0459 +2024-07-22 19:47:03,960 - pyskl - INFO - Epoch [22][3100/3746] lr: 9.487e-02, eta: 4 days, 1:01:44, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5125, loss_cls: 4.0470, loss: 4.0470 +2024-07-22 19:48:14,138 - pyskl - INFO - Epoch [22][3200/3746] lr: 9.485e-02, eta: 4 days, 1:00:16, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5091, loss_cls: 4.0702, loss: 4.0702 +2024-07-22 19:49:23,944 - pyskl - INFO - Epoch [22][3300/3746] lr: 9.484e-02, eta: 4 days, 0:58:46, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5109, loss_cls: 4.0552, loss: 4.0552 +2024-07-22 19:50:33,781 - pyskl - INFO - Epoch [22][3400/3746] lr: 9.483e-02, eta: 4 days, 0:57:16, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5200, loss_cls: 4.0192, loss: 4.0192 +2024-07-22 19:51:43,626 - pyskl - INFO - Epoch [22][3500/3746] lr: 9.482e-02, eta: 4 days, 0:55:47, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5114, loss_cls: 4.0399, loss: 4.0399 +2024-07-22 19:52:53,299 - pyskl - INFO - Epoch [22][3600/3746] lr: 9.480e-02, eta: 4 days, 0:54:16, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5148, loss_cls: 4.0253, loss: 4.0253 +2024-07-22 19:54:02,994 - pyskl - INFO - Epoch [22][3700/3746] lr: 9.479e-02, eta: 4 days, 0:52:46, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5091, loss_cls: 4.0300, loss: 4.0300 +2024-07-22 19:54:37,210 - pyskl - INFO - Saving checkpoint at 22 epochs +2024-07-22 19:56:28,309 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 19:56:28,973 - pyskl - INFO - +top1_acc 0.1784 +top5_acc 0.4105 +2024-07-22 19:56:28,973 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 19:56:29,012 - pyskl - INFO - +mean_acc 0.1782 +2024-07-22 19:56:29,022 - pyskl - INFO - Epoch(val) [22][309] top1_acc: 0.1784, top5_acc: 0.4105, mean_class_accuracy: 0.1782 +2024-07-22 19:59:47,834 - pyskl - INFO - Epoch [23][100/3746] lr: 9.477e-02, eta: 4 days, 0:59:58, time: 1.988, data_time: 1.279, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5203, loss_cls: 3.9858, loss: 3.9858 +2024-07-22 20:00:58,548 - pyskl - INFO - Epoch [23][200/3746] lr: 9.476e-02, eta: 4 days, 0:58:32, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5095, loss_cls: 4.0188, loss: 4.0188 +2024-07-22 20:02:08,697 - pyskl - INFO - Epoch [23][300/3746] lr: 9.475e-02, eta: 4 days, 0:57:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5053, loss_cls: 4.0267, loss: 4.0267 +2024-07-22 20:03:19,104 - pyskl - INFO - Epoch [23][400/3746] lr: 9.474e-02, eta: 4 days, 0:55:37, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.4947, loss_cls: 4.0716, loss: 4.0716 +2024-07-22 20:04:29,540 - pyskl - INFO - Epoch [23][500/3746] lr: 9.472e-02, eta: 4 days, 0:54:10, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5139, loss_cls: 4.0278, loss: 4.0278 +2024-07-22 20:05:39,898 - pyskl - INFO - Epoch [23][600/3746] lr: 9.471e-02, eta: 4 days, 0:52:43, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5148, loss_cls: 4.0376, loss: 4.0376 +2024-07-22 20:06:51,335 - pyskl - INFO - Epoch [23][700/3746] lr: 9.470e-02, eta: 4 days, 0:51:22, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5092, loss_cls: 4.0411, loss: 4.0411 +2024-07-22 20:08:01,625 - pyskl - INFO - Epoch [23][800/3746] lr: 9.469e-02, eta: 4 days, 0:49:55, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5112, loss_cls: 4.0431, loss: 4.0431 +2024-07-22 20:09:12,013 - pyskl - INFO - Epoch [23][900/3746] lr: 9.467e-02, eta: 4 days, 0:48:28, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5128, loss_cls: 4.0051, loss: 4.0051 +2024-07-22 20:10:22,285 - pyskl - INFO - Epoch [23][1000/3746] lr: 9.466e-02, eta: 4 days, 0:47:01, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5016, loss_cls: 4.0824, loss: 4.0824 +2024-07-22 20:11:32,798 - pyskl - INFO - Epoch [23][1100/3746] lr: 9.465e-02, eta: 4 days, 0:45:35, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5114, loss_cls: 4.0345, loss: 4.0345 +2024-07-22 20:12:42,967 - pyskl - INFO - Epoch [23][1200/3746] lr: 9.464e-02, eta: 4 days, 0:44:07, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5075, loss_cls: 4.0639, loss: 4.0639 +2024-07-22 20:13:53,091 - pyskl - INFO - Epoch [23][1300/3746] lr: 9.462e-02, eta: 4 days, 0:42:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5170, loss_cls: 4.0048, loss: 4.0048 +2024-07-22 20:15:02,895 - pyskl - INFO - Epoch [23][1400/3746] lr: 9.461e-02, eta: 4 days, 0:41:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5083, loss_cls: 4.0803, loss: 4.0803 +2024-07-22 20:16:12,977 - pyskl - INFO - Epoch [23][1500/3746] lr: 9.460e-02, eta: 4 days, 0:39:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5092, loss_cls: 4.0733, loss: 4.0733 +2024-07-22 20:17:23,061 - pyskl - INFO - Epoch [23][1600/3746] lr: 9.459e-02, eta: 4 days, 0:38:12, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5042, loss_cls: 4.0056, loss: 4.0056 +2024-07-22 20:18:33,025 - pyskl - INFO - Epoch [23][1700/3746] lr: 9.457e-02, eta: 4 days, 0:36:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5122, loss_cls: 4.0457, loss: 4.0457 +2024-07-22 20:19:42,793 - pyskl - INFO - Epoch [23][1800/3746] lr: 9.456e-02, eta: 4 days, 0:35:13, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5044, loss_cls: 4.0612, loss: 4.0612 +2024-07-22 20:20:52,609 - pyskl - INFO - Epoch [23][1900/3746] lr: 9.455e-02, eta: 4 days, 0:33:44, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5102, loss_cls: 4.0147, loss: 4.0147 +2024-07-22 20:22:02,535 - pyskl - INFO - Epoch [23][2000/3746] lr: 9.453e-02, eta: 4 days, 0:32:15, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5069, loss_cls: 4.0795, loss: 4.0795 +2024-07-22 20:23:12,421 - pyskl - INFO - Epoch [23][2100/3746] lr: 9.452e-02, eta: 4 days, 0:30:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5083, loss_cls: 4.0769, loss: 4.0769 +2024-07-22 20:24:22,532 - pyskl - INFO - Epoch [23][2200/3746] lr: 9.451e-02, eta: 4 days, 0:29:18, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5105, loss_cls: 4.0663, loss: 4.0663 +2024-07-22 20:25:32,343 - pyskl - INFO - Epoch [23][2300/3746] lr: 9.450e-02, eta: 4 days, 0:27:48, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5048, loss_cls: 4.0855, loss: 4.0855 +2024-07-22 20:26:42,201 - pyskl - INFO - Epoch [23][2400/3746] lr: 9.448e-02, eta: 4 days, 0:26:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5036, loss_cls: 4.0814, loss: 4.0814 +2024-07-22 20:27:51,969 - pyskl - INFO - Epoch [23][2500/3746] lr: 9.447e-02, eta: 4 days, 0:24:49, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5130, loss_cls: 4.0481, loss: 4.0481 +2024-07-22 20:29:01,751 - pyskl - INFO - Epoch [23][2600/3746] lr: 9.446e-02, eta: 4 days, 0:23:20, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5012, loss_cls: 4.0748, loss: 4.0748 +2024-07-22 20:30:11,680 - pyskl - INFO - Epoch [23][2700/3746] lr: 9.445e-02, eta: 4 days, 0:21:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5022, loss_cls: 4.0498, loss: 4.0498 +2024-07-22 20:31:21,427 - pyskl - INFO - Epoch [23][2800/3746] lr: 9.443e-02, eta: 4 days, 0:20:22, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5159, loss_cls: 4.0314, loss: 4.0314 +2024-07-22 20:32:31,291 - pyskl - INFO - Epoch [23][2900/3746] lr: 9.442e-02, eta: 4 days, 0:18:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5150, loss_cls: 4.0367, loss: 4.0367 +2024-07-22 20:33:41,223 - pyskl - INFO - Epoch [23][3000/3746] lr: 9.441e-02, eta: 4 days, 0:17:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5200, loss_cls: 4.0027, loss: 4.0027 +2024-07-22 20:34:50,895 - pyskl - INFO - Epoch [23][3100/3746] lr: 9.439e-02, eta: 4 days, 0:15:54, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5130, loss_cls: 4.0671, loss: 4.0671 +2024-07-22 20:36:00,897 - pyskl - INFO - Epoch [23][3200/3746] lr: 9.438e-02, eta: 4 days, 0:14:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5142, loss_cls: 4.0416, loss: 4.0416 +2024-07-22 20:37:10,622 - pyskl - INFO - Epoch [23][3300/3746] lr: 9.437e-02, eta: 4 days, 0:12:57, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4931, loss_cls: 4.1239, loss: 4.1239 +2024-07-22 20:38:20,414 - pyskl - INFO - Epoch [23][3400/3746] lr: 9.436e-02, eta: 4 days, 0:11:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5223, loss_cls: 4.0218, loss: 4.0218 +2024-07-22 20:39:30,375 - pyskl - INFO - Epoch [23][3500/3746] lr: 9.434e-02, eta: 4 days, 0:09:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.4994, loss_cls: 4.0749, loss: 4.0749 +2024-07-22 20:40:40,056 - pyskl - INFO - Epoch [23][3600/3746] lr: 9.433e-02, eta: 4 days, 0:08:30, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5005, loss_cls: 4.0700, loss: 4.0700 +2024-07-22 20:41:50,188 - pyskl - INFO - Epoch [23][3700/3746] lr: 9.432e-02, eta: 4 days, 0:07:03, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5116, loss_cls: 4.0346, loss: 4.0346 +2024-07-22 20:42:24,537 - pyskl - INFO - Saving checkpoint at 23 epochs +2024-07-22 20:44:15,044 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 20:44:15,704 - pyskl - INFO - +top1_acc 0.1961 +top5_acc 0.4284 +2024-07-22 20:44:15,704 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 20:44:15,742 - pyskl - INFO - +mean_acc 0.1959 +2024-07-22 20:44:15,747 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_18.pth was removed +2024-07-22 20:44:15,986 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_23.pth. +2024-07-22 20:44:15,987 - pyskl - INFO - Best top1_acc is 0.1961 at 23 epoch. +2024-07-22 20:44:15,997 - pyskl - INFO - Epoch(val) [23][309] top1_acc: 0.1961, top5_acc: 0.4284, mean_class_accuracy: 0.1959 +2024-07-22 20:47:34,388 - pyskl - INFO - Epoch [24][100/3746] lr: 9.430e-02, eta: 4 days, 0:13:45, time: 1.984, data_time: 1.277, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5144, loss_cls: 4.0078, loss: 4.0078 +2024-07-22 20:48:45,378 - pyskl - INFO - Epoch [24][200/3746] lr: 9.428e-02, eta: 4 days, 0:12:22, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5252, loss_cls: 3.9771, loss: 3.9771 +2024-07-22 20:49:55,513 - pyskl - INFO - Epoch [24][300/3746] lr: 9.427e-02, eta: 4 days, 0:10:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5148, loss_cls: 4.0208, loss: 4.0208 +2024-07-22 20:51:06,413 - pyskl - INFO - Epoch [24][400/3746] lr: 9.426e-02, eta: 4 days, 0:09:31, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5264, loss_cls: 4.0088, loss: 4.0088 +2024-07-22 20:52:16,530 - pyskl - INFO - Epoch [24][500/3746] lr: 9.425e-02, eta: 4 days, 0:08:03, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5075, loss_cls: 4.0551, loss: 4.0551 +2024-07-22 20:53:26,579 - pyskl - INFO - Epoch [24][600/3746] lr: 9.423e-02, eta: 4 days, 0:06:35, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5186, loss_cls: 4.0195, loss: 4.0195 +2024-07-22 20:54:37,120 - pyskl - INFO - Epoch [24][700/3746] lr: 9.422e-02, eta: 4 days, 0:05:10, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5180, loss_cls: 4.0053, loss: 4.0053 +2024-07-22 20:55:47,703 - pyskl - INFO - Epoch [24][800/3746] lr: 9.421e-02, eta: 4 days, 0:03:45, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5088, loss_cls: 4.0652, loss: 4.0652 +2024-07-22 20:56:57,894 - pyskl - INFO - Epoch [24][900/3746] lr: 9.419e-02, eta: 4 days, 0:02:18, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5062, loss_cls: 4.0788, loss: 4.0788 +2024-07-22 20:58:08,394 - pyskl - INFO - Epoch [24][1000/3746] lr: 9.418e-02, eta: 4 days, 0:00:52, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5112, loss_cls: 4.0658, loss: 4.0658 +2024-07-22 20:59:18,550 - pyskl - INFO - Epoch [24][1100/3746] lr: 9.417e-02, eta: 3 days, 23:59:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5084, loss_cls: 4.0631, loss: 4.0631 +2024-07-22 21:00:28,306 - pyskl - INFO - Epoch [24][1200/3746] lr: 9.415e-02, eta: 3 days, 23:57:56, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5038, loss_cls: 4.0676, loss: 4.0676 +2024-07-22 21:01:38,386 - pyskl - INFO - Epoch [24][1300/3746] lr: 9.414e-02, eta: 3 days, 23:56:28, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5050, loss_cls: 4.0243, loss: 4.0243 +2024-07-22 21:02:48,688 - pyskl - INFO - Epoch [24][1400/3746] lr: 9.413e-02, eta: 3 days, 23:55:02, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5138, loss_cls: 4.0115, loss: 4.0115 +2024-07-22 21:03:58,738 - pyskl - INFO - Epoch [24][1500/3746] lr: 9.411e-02, eta: 3 days, 23:53:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5077, loss_cls: 4.0489, loss: 4.0489 +2024-07-22 21:05:08,799 - pyskl - INFO - Epoch [24][1600/3746] lr: 9.410e-02, eta: 3 days, 23:52:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5034, loss_cls: 4.0400, loss: 4.0400 +2024-07-22 21:06:18,817 - pyskl - INFO - Epoch [24][1700/3746] lr: 9.409e-02, eta: 3 days, 23:50:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5038, loss_cls: 4.0420, loss: 4.0420 +2024-07-22 21:07:28,677 - pyskl - INFO - Epoch [24][1800/3746] lr: 9.407e-02, eta: 3 days, 23:49:10, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4939, loss_cls: 4.1280, loss: 4.1280 +2024-07-22 21:08:38,254 - pyskl - INFO - Epoch [24][1900/3746] lr: 9.406e-02, eta: 3 days, 23:47:40, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5092, loss_cls: 4.0326, loss: 4.0326 +2024-07-22 21:09:48,258 - pyskl - INFO - Epoch [24][2000/3746] lr: 9.405e-02, eta: 3 days, 23:46:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5047, loss_cls: 4.0878, loss: 4.0878 +2024-07-22 21:10:57,963 - pyskl - INFO - Epoch [24][2100/3746] lr: 9.404e-02, eta: 3 days, 23:44:43, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5119, loss_cls: 4.0647, loss: 4.0647 +2024-07-22 21:12:08,024 - pyskl - INFO - Epoch [24][2200/3746] lr: 9.402e-02, eta: 3 days, 23:43:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5169, loss_cls: 3.9991, loss: 3.9991 +2024-07-22 21:13:17,671 - pyskl - INFO - Epoch [24][2300/3746] lr: 9.401e-02, eta: 3 days, 23:41:47, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5159, loss_cls: 4.0368, loss: 4.0368 +2024-07-22 21:14:27,398 - pyskl - INFO - Epoch [24][2400/3746] lr: 9.400e-02, eta: 3 days, 23:40:17, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5048, loss_cls: 4.0733, loss: 4.0733 +2024-07-22 21:15:37,262 - pyskl - INFO - Epoch [24][2500/3746] lr: 9.398e-02, eta: 3 days, 23:38:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5114, loss_cls: 4.0342, loss: 4.0342 +2024-07-22 21:16:46,899 - pyskl - INFO - Epoch [24][2600/3746] lr: 9.397e-02, eta: 3 days, 23:37:20, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.4958, loss_cls: 4.0979, loss: 4.0979 +2024-07-22 21:17:56,776 - pyskl - INFO - Epoch [24][2700/3746] lr: 9.396e-02, eta: 3 days, 23:35:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5070, loss_cls: 4.0410, loss: 4.0410 +2024-07-22 21:19:06,444 - pyskl - INFO - Epoch [24][2800/3746] lr: 9.394e-02, eta: 3 days, 23:34:23, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4983, loss_cls: 4.0890, loss: 4.0890 +2024-07-22 21:20:16,449 - pyskl - INFO - Epoch [24][2900/3746] lr: 9.393e-02, eta: 3 days, 23:32:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5116, loss_cls: 4.0663, loss: 4.0663 +2024-07-22 21:21:26,226 - pyskl - INFO - Epoch [24][3000/3746] lr: 9.392e-02, eta: 3 days, 23:31:27, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5030, loss_cls: 4.0772, loss: 4.0772 +2024-07-22 21:22:36,068 - pyskl - INFO - Epoch [24][3100/3746] lr: 9.390e-02, eta: 3 days, 23:29:59, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5323, loss_cls: 3.9878, loss: 3.9878 +2024-07-22 21:23:46,072 - pyskl - INFO - Epoch [24][3200/3746] lr: 9.389e-02, eta: 3 days, 23:28:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5073, loss_cls: 4.0624, loss: 4.0624 +2024-07-22 21:24:55,914 - pyskl - INFO - Epoch [24][3300/3746] lr: 9.388e-02, eta: 3 days, 23:27:03, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5122, loss_cls: 4.0511, loss: 4.0511 +2024-07-22 21:26:05,651 - pyskl - INFO - Epoch [24][3400/3746] lr: 9.386e-02, eta: 3 days, 23:25:35, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5030, loss_cls: 4.0654, loss: 4.0654 +2024-07-22 21:27:15,525 - pyskl - INFO - Epoch [24][3500/3746] lr: 9.385e-02, eta: 3 days, 23:24:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5094, loss_cls: 4.0305, loss: 4.0305 +2024-07-22 21:28:25,151 - pyskl - INFO - Epoch [24][3600/3746] lr: 9.384e-02, eta: 3 days, 23:22:38, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5105, loss_cls: 4.0614, loss: 4.0614 +2024-07-22 21:29:35,069 - pyskl - INFO - Epoch [24][3700/3746] lr: 9.382e-02, eta: 3 days, 23:21:11, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5112, loss_cls: 4.0350, loss: 4.0350 +2024-07-22 21:30:09,077 - pyskl - INFO - Saving checkpoint at 24 epochs +2024-07-22 21:32:00,310 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 21:32:00,970 - pyskl - INFO - +top1_acc 0.1971 +top5_acc 0.4257 +2024-07-22 21:32:00,970 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 21:32:01,008 - pyskl - INFO - +mean_acc 0.1971 +2024-07-22 21:32:01,013 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_23.pth was removed +2024-07-22 21:32:01,242 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_24.pth. +2024-07-22 21:32:01,243 - pyskl - INFO - Best top1_acc is 0.1971 at 24 epoch. +2024-07-22 21:32:01,255 - pyskl - INFO - Epoch(val) [24][309] top1_acc: 0.1971, top5_acc: 0.4257, mean_class_accuracy: 0.1971 +2024-07-22 21:35:19,327 - pyskl - INFO - Epoch [25][100/3746] lr: 9.380e-02, eta: 3 days, 23:27:26, time: 1.981, data_time: 1.279, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5195, loss_cls: 3.9988, loss: 3.9988 +2024-07-22 21:36:30,356 - pyskl - INFO - Epoch [25][200/3746] lr: 9.379e-02, eta: 3 days, 23:26:04, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5242, loss_cls: 3.9767, loss: 3.9767 +2024-07-22 21:37:40,833 - pyskl - INFO - Epoch [25][300/3746] lr: 9.378e-02, eta: 3 days, 23:24:39, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5136, loss_cls: 4.0297, loss: 4.0297 +2024-07-22 21:38:51,702 - pyskl - INFO - Epoch [25][400/3746] lr: 9.376e-02, eta: 3 days, 23:23:16, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5077, loss_cls: 4.0214, loss: 4.0214 +2024-07-22 21:40:01,986 - pyskl - INFO - Epoch [25][500/3746] lr: 9.375e-02, eta: 3 days, 23:21:50, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5148, loss_cls: 4.0482, loss: 4.0482 +2024-07-22 21:41:12,025 - pyskl - INFO - Epoch [25][600/3746] lr: 9.373e-02, eta: 3 days, 23:20:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5181, loss_cls: 4.0277, loss: 4.0277 +2024-07-22 21:42:23,095 - pyskl - INFO - Epoch [25][700/3746] lr: 9.372e-02, eta: 3 days, 23:19:01, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5128, loss_cls: 4.0123, loss: 4.0123 +2024-07-22 21:43:33,673 - pyskl - INFO - Epoch [25][800/3746] lr: 9.371e-02, eta: 3 days, 23:17:36, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5162, loss_cls: 4.0478, loss: 4.0478 +2024-07-22 21:44:44,245 - pyskl - INFO - Epoch [25][900/3746] lr: 9.369e-02, eta: 3 days, 23:16:12, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5117, loss_cls: 4.0382, loss: 4.0382 +2024-07-22 21:45:54,929 - pyskl - INFO - Epoch [25][1000/3746] lr: 9.368e-02, eta: 3 days, 23:14:48, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5091, loss_cls: 4.0276, loss: 4.0276 +2024-07-22 21:47:04,943 - pyskl - INFO - Epoch [25][1100/3746] lr: 9.367e-02, eta: 3 days, 23:13:21, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5217, loss_cls: 3.9808, loss: 3.9808 +2024-07-22 21:48:14,938 - pyskl - INFO - Epoch [25][1200/3746] lr: 9.365e-02, eta: 3 days, 23:11:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5155, loss_cls: 4.0453, loss: 4.0453 +2024-07-22 21:49:24,972 - pyskl - INFO - Epoch [25][1300/3746] lr: 9.364e-02, eta: 3 days, 23:10:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5112, loss_cls: 4.0648, loss: 4.0648 +2024-07-22 21:50:34,871 - pyskl - INFO - Epoch [25][1400/3746] lr: 9.363e-02, eta: 3 days, 23:08:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5105, loss_cls: 4.0422, loss: 4.0422 +2024-07-22 21:51:44,813 - pyskl - INFO - Epoch [25][1500/3746] lr: 9.361e-02, eta: 3 days, 23:07:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5095, loss_cls: 4.0515, loss: 4.0515 +2024-07-22 21:52:54,758 - pyskl - INFO - Epoch [25][1600/3746] lr: 9.360e-02, eta: 3 days, 23:06:04, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5145, loss_cls: 4.0433, loss: 4.0433 +2024-07-22 21:54:04,547 - pyskl - INFO - Epoch [25][1700/3746] lr: 9.358e-02, eta: 3 days, 23:04:36, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5102, loss_cls: 4.0582, loss: 4.0582 +2024-07-22 21:55:14,239 - pyskl - INFO - Epoch [25][1800/3746] lr: 9.357e-02, eta: 3 days, 23:03:07, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5030, loss_cls: 4.0764, loss: 4.0764 +2024-07-22 21:56:24,244 - pyskl - INFO - Epoch [25][1900/3746] lr: 9.356e-02, eta: 3 days, 23:01:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5184, loss_cls: 4.0195, loss: 4.0195 +2024-07-22 21:57:34,283 - pyskl - INFO - Epoch [25][2000/3746] lr: 9.354e-02, eta: 3 days, 23:00:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5200, loss_cls: 4.0039, loss: 4.0039 +2024-07-22 21:58:44,132 - pyskl - INFO - Epoch [25][2100/3746] lr: 9.353e-02, eta: 3 days, 22:58:46, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5080, loss_cls: 4.0317, loss: 4.0317 +2024-07-22 21:59:54,010 - pyskl - INFO - Epoch [25][2200/3746] lr: 9.352e-02, eta: 3 days, 22:57:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5114, loss_cls: 4.0649, loss: 4.0649 +2024-07-22 22:01:04,104 - pyskl - INFO - Epoch [25][2300/3746] lr: 9.350e-02, eta: 3 days, 22:55:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5017, loss_cls: 4.0805, loss: 4.0805 +2024-07-22 22:02:13,856 - pyskl - INFO - Epoch [25][2400/3746] lr: 9.349e-02, eta: 3 days, 22:54:24, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5050, loss_cls: 4.0760, loss: 4.0760 +2024-07-22 22:03:23,770 - pyskl - INFO - Epoch [25][2500/3746] lr: 9.347e-02, eta: 3 days, 22:52:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5127, loss_cls: 4.0258, loss: 4.0258 +2024-07-22 22:04:33,793 - pyskl - INFO - Epoch [25][2600/3746] lr: 9.346e-02, eta: 3 days, 22:51:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5095, loss_cls: 4.0376, loss: 4.0376 +2024-07-22 22:05:43,689 - pyskl - INFO - Epoch [25][2700/3746] lr: 9.345e-02, eta: 3 days, 22:50:03, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5127, loss_cls: 4.0016, loss: 4.0016 +2024-07-22 22:06:53,559 - pyskl - INFO - Epoch [25][2800/3746] lr: 9.343e-02, eta: 3 days, 22:48:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5044, loss_cls: 4.0536, loss: 4.0536 +2024-07-22 22:08:03,433 - pyskl - INFO - Epoch [25][2900/3746] lr: 9.342e-02, eta: 3 days, 22:47:08, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5188, loss_cls: 4.0163, loss: 4.0163 +2024-07-22 22:09:13,119 - pyskl - INFO - Epoch [25][3000/3746] lr: 9.341e-02, eta: 3 days, 22:45:40, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5098, loss_cls: 4.0277, loss: 4.0277 +2024-07-22 22:10:22,983 - pyskl - INFO - Epoch [25][3100/3746] lr: 9.339e-02, eta: 3 days, 22:44:13, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5150, loss_cls: 4.0154, loss: 4.0154 +2024-07-22 22:11:32,781 - pyskl - INFO - Epoch [25][3200/3746] lr: 9.338e-02, eta: 3 days, 22:42:45, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5114, loss_cls: 4.0563, loss: 4.0563 +2024-07-22 22:12:42,666 - pyskl - INFO - Epoch [25][3300/3746] lr: 9.336e-02, eta: 3 days, 22:41:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5156, loss_cls: 4.0464, loss: 4.0464 +2024-07-22 22:13:52,679 - pyskl - INFO - Epoch [25][3400/3746] lr: 9.335e-02, eta: 3 days, 22:39:52, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5056, loss_cls: 4.0749, loss: 4.0749 +2024-07-22 22:15:02,439 - pyskl - INFO - Epoch [25][3500/3746] lr: 9.334e-02, eta: 3 days, 22:38:24, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5158, loss_cls: 4.0273, loss: 4.0273 +2024-07-22 22:16:12,265 - pyskl - INFO - Epoch [25][3600/3746] lr: 9.332e-02, eta: 3 days, 22:36:57, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5092, loss_cls: 4.0510, loss: 4.0510 +2024-07-22 22:17:22,051 - pyskl - INFO - Epoch [25][3700/3746] lr: 9.331e-02, eta: 3 days, 22:35:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5047, loss_cls: 4.0250, loss: 4.0250 +2024-07-22 22:17:56,279 - pyskl - INFO - Saving checkpoint at 25 epochs +2024-07-22 22:19:46,127 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 22:19:46,790 - pyskl - INFO - +top1_acc 0.1932 +top5_acc 0.4115 +2024-07-22 22:19:46,790 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 22:19:46,831 - pyskl - INFO - +mean_acc 0.1930 +2024-07-22 22:19:46,841 - pyskl - INFO - Epoch(val) [25][309] top1_acc: 0.1932, top5_acc: 0.4115, mean_class_accuracy: 0.1930 +2024-07-22 22:23:05,808 - pyskl - INFO - Epoch [26][100/3746] lr: 9.329e-02, eta: 3 days, 22:41:27, time: 1.990, data_time: 1.284, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5217, loss_cls: 4.0067, loss: 4.0067 +2024-07-22 22:24:16,460 - pyskl - INFO - Epoch [26][200/3746] lr: 9.327e-02, eta: 3 days, 22:40:03, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5212, loss_cls: 4.0019, loss: 4.0019 +2024-07-22 22:25:26,883 - pyskl - INFO - Epoch [26][300/3746] lr: 9.326e-02, eta: 3 days, 22:38:39, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5252, loss_cls: 3.9855, loss: 3.9855 +2024-07-22 22:26:37,492 - pyskl - INFO - Epoch [26][400/3746] lr: 9.325e-02, eta: 3 days, 22:37:15, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5116, loss_cls: 4.0434, loss: 4.0434 +2024-07-22 22:27:48,160 - pyskl - INFO - Epoch [26][500/3746] lr: 9.323e-02, eta: 3 days, 22:35:51, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5111, loss_cls: 4.0186, loss: 4.0186 +2024-07-22 22:28:58,388 - pyskl - INFO - Epoch [26][600/3746] lr: 9.322e-02, eta: 3 days, 22:34:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5102, loss_cls: 4.0401, loss: 4.0401 +2024-07-22 22:30:08,985 - pyskl - INFO - Epoch [26][700/3746] lr: 9.320e-02, eta: 3 days, 22:33:02, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5019, loss_cls: 4.0824, loss: 4.0824 +2024-07-22 22:31:19,518 - pyskl - INFO - Epoch [26][800/3746] lr: 9.319e-02, eta: 3 days, 22:31:38, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5173, loss_cls: 4.0057, loss: 4.0057 +2024-07-22 22:32:29,810 - pyskl - INFO - Epoch [26][900/3746] lr: 9.318e-02, eta: 3 days, 22:30:13, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5145, loss_cls: 4.0271, loss: 4.0271 +2024-07-22 22:33:40,300 - pyskl - INFO - Epoch [26][1000/3746] lr: 9.316e-02, eta: 3 days, 22:28:49, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5111, loss_cls: 4.0473, loss: 4.0473 +2024-07-22 22:34:50,439 - pyskl - INFO - Epoch [26][1100/3746] lr: 9.315e-02, eta: 3 days, 22:27:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5122, loss_cls: 4.0476, loss: 4.0476 +2024-07-22 22:36:00,514 - pyskl - INFO - Epoch [26][1200/3746] lr: 9.313e-02, eta: 3 days, 22:25:57, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5145, loss_cls: 4.0131, loss: 4.0131 +2024-07-22 22:37:10,752 - pyskl - INFO - Epoch [26][1300/3746] lr: 9.312e-02, eta: 3 days, 22:24:31, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5067, loss_cls: 4.0542, loss: 4.0542 +2024-07-22 22:38:20,794 - pyskl - INFO - Epoch [26][1400/3746] lr: 9.310e-02, eta: 3 days, 22:23:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5192, loss_cls: 3.9961, loss: 3.9961 +2024-07-22 22:39:30,760 - pyskl - INFO - Epoch [26][1500/3746] lr: 9.309e-02, eta: 3 days, 22:21:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5092, loss_cls: 4.0188, loss: 4.0188 +2024-07-22 22:40:40,713 - pyskl - INFO - Epoch [26][1600/3746] lr: 9.308e-02, eta: 3 days, 22:20:12, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5094, loss_cls: 4.0314, loss: 4.0314 +2024-07-22 22:41:50,569 - pyskl - INFO - Epoch [26][1700/3746] lr: 9.306e-02, eta: 3 days, 22:18:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5195, loss_cls: 4.0172, loss: 4.0172 +2024-07-22 22:43:00,495 - pyskl - INFO - Epoch [26][1800/3746] lr: 9.305e-02, eta: 3 days, 22:17:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5073, loss_cls: 4.0631, loss: 4.0631 +2024-07-22 22:44:10,546 - pyskl - INFO - Epoch [26][1900/3746] lr: 9.303e-02, eta: 3 days, 22:15:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5053, loss_cls: 4.0600, loss: 4.0600 +2024-07-22 22:45:20,453 - pyskl - INFO - Epoch [26][2000/3746] lr: 9.302e-02, eta: 3 days, 22:14:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.4989, loss_cls: 4.0680, loss: 4.0680 +2024-07-22 22:46:30,206 - pyskl - INFO - Epoch [26][2100/3746] lr: 9.300e-02, eta: 3 days, 22:12:58, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5194, loss_cls: 4.0056, loss: 4.0056 +2024-07-22 22:47:40,119 - pyskl - INFO - Epoch [26][2200/3746] lr: 9.299e-02, eta: 3 days, 22:11:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5167, loss_cls: 4.0262, loss: 4.0262 +2024-07-22 22:48:50,191 - pyskl - INFO - Epoch [26][2300/3746] lr: 9.298e-02, eta: 3 days, 22:10:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5066, loss_cls: 4.0437, loss: 4.0437 +2024-07-22 22:50:00,124 - pyskl - INFO - Epoch [26][2400/3746] lr: 9.296e-02, eta: 3 days, 22:08:39, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5159, loss_cls: 4.0273, loss: 4.0273 +2024-07-22 22:51:10,080 - pyskl - INFO - Epoch [26][2500/3746] lr: 9.295e-02, eta: 3 days, 22:07:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5083, loss_cls: 4.0461, loss: 4.0461 +2024-07-22 22:52:19,892 - pyskl - INFO - Epoch [26][2600/3746] lr: 9.293e-02, eta: 3 days, 22:05:46, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5078, loss_cls: 4.0723, loss: 4.0723 +2024-07-22 22:53:29,610 - pyskl - INFO - Epoch [26][2700/3746] lr: 9.292e-02, eta: 3 days, 22:04:18, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5262, loss_cls: 4.0208, loss: 4.0208 +2024-07-22 22:54:39,757 - pyskl - INFO - Epoch [26][2800/3746] lr: 9.290e-02, eta: 3 days, 22:02:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5006, loss_cls: 4.0716, loss: 4.0716 +2024-07-22 22:55:49,379 - pyskl - INFO - Epoch [26][2900/3746] lr: 9.289e-02, eta: 3 days, 22:01:25, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5080, loss_cls: 4.0734, loss: 4.0734 +2024-07-22 22:56:59,569 - pyskl - INFO - Epoch [26][3000/3746] lr: 9.288e-02, eta: 3 days, 22:00:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5136, loss_cls: 4.0182, loss: 4.0182 +2024-07-22 22:58:09,405 - pyskl - INFO - Epoch [26][3100/3746] lr: 9.286e-02, eta: 3 days, 21:58:34, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5231, loss_cls: 3.9948, loss: 3.9948 +2024-07-22 22:59:19,219 - pyskl - INFO - Epoch [26][3200/3746] lr: 9.285e-02, eta: 3 days, 21:57:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5098, loss_cls: 4.0318, loss: 4.0318 +2024-07-22 23:00:29,643 - pyskl - INFO - Epoch [26][3300/3746] lr: 9.283e-02, eta: 3 days, 21:55:43, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5177, loss_cls: 4.0060, loss: 4.0060 +2024-07-22 23:01:39,610 - pyskl - INFO - Epoch [26][3400/3746] lr: 9.282e-02, eta: 3 days, 21:54:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5123, loss_cls: 4.0108, loss: 4.0108 +2024-07-22 23:02:49,435 - pyskl - INFO - Epoch [26][3500/3746] lr: 9.280e-02, eta: 3 days, 21:52:50, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5139, loss_cls: 4.0309, loss: 4.0309 +2024-07-22 23:03:59,263 - pyskl - INFO - Epoch [26][3600/3746] lr: 9.279e-02, eta: 3 days, 21:51:24, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5181, loss_cls: 4.0081, loss: 4.0081 +2024-07-22 23:05:09,357 - pyskl - INFO - Epoch [26][3700/3746] lr: 9.278e-02, eta: 3 days, 21:49:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5248, loss_cls: 3.9732, loss: 3.9732 +2024-07-22 23:05:43,327 - pyskl - INFO - Saving checkpoint at 26 epochs +2024-07-22 23:07:33,058 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 23:07:33,720 - pyskl - INFO - +top1_acc 0.1953 +top5_acc 0.4271 +2024-07-22 23:07:33,720 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 23:07:33,758 - pyskl - INFO - +mean_acc 0.1951 +2024-07-22 23:07:33,768 - pyskl - INFO - Epoch(val) [26][309] top1_acc: 0.1953, top5_acc: 0.4271, mean_class_accuracy: 0.1951 +2024-07-22 23:10:52,209 - pyskl - INFO - Epoch [27][100/3746] lr: 9.275e-02, eta: 3 days, 21:55:32, time: 1.984, data_time: 1.281, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5244, loss_cls: 3.9823, loss: 3.9823 +2024-07-22 23:12:03,349 - pyskl - INFO - Epoch [27][200/3746] lr: 9.274e-02, eta: 3 days, 21:54:11, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5277, loss_cls: 4.0020, loss: 4.0020 +2024-07-22 23:13:13,725 - pyskl - INFO - Epoch [27][300/3746] lr: 9.272e-02, eta: 3 days, 21:52:47, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5073, loss_cls: 4.0331, loss: 4.0331 +2024-07-22 23:14:24,194 - pyskl - INFO - Epoch [27][400/3746] lr: 9.271e-02, eta: 3 days, 21:51:23, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5234, loss_cls: 3.9826, loss: 3.9826 +2024-07-22 23:15:34,491 - pyskl - INFO - Epoch [27][500/3746] lr: 9.270e-02, eta: 3 days, 21:49:58, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5234, loss_cls: 3.9862, loss: 3.9862 +2024-07-22 23:16:44,571 - pyskl - INFO - Epoch [27][600/3746] lr: 9.268e-02, eta: 3 days, 21:48:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5056, loss_cls: 4.0664, loss: 4.0664 +2024-07-22 23:17:54,602 - pyskl - INFO - Epoch [27][700/3746] lr: 9.267e-02, eta: 3 days, 21:47:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5116, loss_cls: 4.0408, loss: 4.0408 +2024-07-22 23:19:05,387 - pyskl - INFO - Epoch [27][800/3746] lr: 9.265e-02, eta: 3 days, 21:45:44, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5145, loss_cls: 4.0032, loss: 4.0032 +2024-07-22 23:20:15,801 - pyskl - INFO - Epoch [27][900/3746] lr: 9.264e-02, eta: 3 days, 21:44:20, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5064, loss_cls: 4.0625, loss: 4.0625 +2024-07-22 23:21:26,275 - pyskl - INFO - Epoch [27][1000/3746] lr: 9.262e-02, eta: 3 days, 21:42:57, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5130, loss_cls: 4.0237, loss: 4.0237 +2024-07-22 23:22:36,252 - pyskl - INFO - Epoch [27][1100/3746] lr: 9.261e-02, eta: 3 days, 21:41:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5117, loss_cls: 4.0407, loss: 4.0407 +2024-07-22 23:23:46,456 - pyskl - INFO - Epoch [27][1200/3746] lr: 9.259e-02, eta: 3 days, 21:40:06, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5159, loss_cls: 4.0189, loss: 4.0189 +2024-07-22 23:24:56,531 - pyskl - INFO - Epoch [27][1300/3746] lr: 9.258e-02, eta: 3 days, 21:38:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5153, loss_cls: 3.9958, loss: 3.9958 +2024-07-22 23:26:06,595 - pyskl - INFO - Epoch [27][1400/3746] lr: 9.256e-02, eta: 3 days, 21:37:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5152, loss_cls: 4.0116, loss: 4.0116 +2024-07-22 23:27:16,554 - pyskl - INFO - Epoch [27][1500/3746] lr: 9.255e-02, eta: 3 days, 21:35:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5209, loss_cls: 3.9851, loss: 3.9851 +2024-07-22 23:28:26,409 - pyskl - INFO - Epoch [27][1600/3746] lr: 9.253e-02, eta: 3 days, 21:34:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5170, loss_cls: 4.0407, loss: 4.0407 +2024-07-22 23:29:36,228 - pyskl - INFO - Epoch [27][1700/3746] lr: 9.252e-02, eta: 3 days, 21:32:56, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5089, loss_cls: 4.0368, loss: 4.0368 +2024-07-22 23:30:46,078 - pyskl - INFO - Epoch [27][1800/3746] lr: 9.251e-02, eta: 3 days, 21:31:29, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5159, loss_cls: 4.0123, loss: 4.0123 +2024-07-22 23:31:55,871 - pyskl - INFO - Epoch [27][1900/3746] lr: 9.249e-02, eta: 3 days, 21:30:03, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5103, loss_cls: 4.0308, loss: 4.0308 +2024-07-22 23:33:05,615 - pyskl - INFO - Epoch [27][2000/3746] lr: 9.248e-02, eta: 3 days, 21:28:36, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5095, loss_cls: 4.0294, loss: 4.0294 +2024-07-22 23:34:15,629 - pyskl - INFO - Epoch [27][2100/3746] lr: 9.246e-02, eta: 3 days, 21:27:10, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5091, loss_cls: 4.0525, loss: 4.0525 +2024-07-22 23:35:25,525 - pyskl - INFO - Epoch [27][2200/3746] lr: 9.245e-02, eta: 3 days, 21:25:44, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5105, loss_cls: 4.0486, loss: 4.0486 +2024-07-22 23:36:35,149 - pyskl - INFO - Epoch [27][2300/3746] lr: 9.243e-02, eta: 3 days, 21:24:17, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5078, loss_cls: 4.0671, loss: 4.0671 +2024-07-22 23:37:44,886 - pyskl - INFO - Epoch [27][2400/3746] lr: 9.242e-02, eta: 3 days, 21:22:50, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5144, loss_cls: 4.0251, loss: 4.0251 +2024-07-22 23:38:54,796 - pyskl - INFO - Epoch [27][2500/3746] lr: 9.240e-02, eta: 3 days, 21:21:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5159, loss_cls: 4.0374, loss: 4.0374 +2024-07-22 23:40:04,652 - pyskl - INFO - Epoch [27][2600/3746] lr: 9.239e-02, eta: 3 days, 21:19:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5219, loss_cls: 4.0054, loss: 4.0054 +2024-07-22 23:41:14,550 - pyskl - INFO - Epoch [27][2700/3746] lr: 9.237e-02, eta: 3 days, 21:18:33, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5083, loss_cls: 4.0602, loss: 4.0602 +2024-07-22 23:42:24,336 - pyskl - INFO - Epoch [27][2800/3746] lr: 9.236e-02, eta: 3 days, 21:17:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5116, loss_cls: 4.0592, loss: 4.0592 +2024-07-22 23:43:34,074 - pyskl - INFO - Epoch [27][2900/3746] lr: 9.234e-02, eta: 3 days, 21:15:40, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5191, loss_cls: 4.0087, loss: 4.0087 +2024-07-22 23:44:43,851 - pyskl - INFO - Epoch [27][3000/3746] lr: 9.233e-02, eta: 3 days, 21:14:13, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5244, loss_cls: 4.0082, loss: 4.0082 +2024-07-22 23:45:53,720 - pyskl - INFO - Epoch [27][3100/3746] lr: 9.231e-02, eta: 3 days, 21:12:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5250, loss_cls: 4.0049, loss: 4.0049 +2024-07-22 23:47:03,624 - pyskl - INFO - Epoch [27][3200/3746] lr: 9.230e-02, eta: 3 days, 21:11:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5170, loss_cls: 3.9943, loss: 3.9943 +2024-07-22 23:48:13,312 - pyskl - INFO - Epoch [27][3300/3746] lr: 9.228e-02, eta: 3 days, 21:09:55, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5208, loss_cls: 4.0346, loss: 4.0346 +2024-07-22 23:49:23,335 - pyskl - INFO - Epoch [27][3400/3746] lr: 9.227e-02, eta: 3 days, 21:08:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5144, loss_cls: 4.0332, loss: 4.0332 +2024-07-22 23:50:33,157 - pyskl - INFO - Epoch [27][3500/3746] lr: 9.225e-02, eta: 3 days, 21:07:04, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5138, loss_cls: 4.0341, loss: 4.0341 +2024-07-22 23:51:42,971 - pyskl - INFO - Epoch [27][3600/3746] lr: 9.224e-02, eta: 3 days, 21:05:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5011, loss_cls: 4.0823, loss: 4.0823 +2024-07-22 23:52:52,773 - pyskl - INFO - Epoch [27][3700/3746] lr: 9.222e-02, eta: 3 days, 21:04:12, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5130, loss_cls: 4.0464, loss: 4.0464 +2024-07-22 23:53:26,994 - pyskl - INFO - Saving checkpoint at 27 epochs +2024-07-22 23:55:16,731 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 23:55:17,393 - pyskl - INFO - +top1_acc 0.2003 +top5_acc 0.4295 +2024-07-22 23:55:17,393 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 23:55:17,431 - pyskl - INFO - +mean_acc 0.2002 +2024-07-22 23:55:17,435 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_24.pth was removed +2024-07-22 23:55:17,668 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_27.pth. +2024-07-22 23:55:17,668 - pyskl - INFO - Best top1_acc is 0.2003 at 27 epoch. +2024-07-22 23:55:17,679 - pyskl - INFO - Epoch(val) [27][309] top1_acc: 0.2003, top5_acc: 0.4295, mean_class_accuracy: 0.2002 +2024-07-22 23:58:35,878 - pyskl - INFO - Epoch [28][100/3746] lr: 9.220e-02, eta: 3 days, 21:09:25, time: 1.982, data_time: 1.277, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5255, loss_cls: 3.9805, loss: 3.9805 +2024-07-22 23:59:46,546 - pyskl - INFO - Epoch [28][200/3746] lr: 9.219e-02, eta: 3 days, 21:08:02, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5236, loss_cls: 3.9893, loss: 3.9893 +2024-07-23 00:00:57,235 - pyskl - INFO - Epoch [28][300/3746] lr: 9.217e-02, eta: 3 days, 21:06:40, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5217, loss_cls: 4.0156, loss: 4.0156 +2024-07-23 00:02:08,499 - pyskl - INFO - Epoch [28][400/3746] lr: 9.216e-02, eta: 3 days, 21:05:20, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5098, loss_cls: 4.0080, loss: 4.0080 +2024-07-23 00:03:19,047 - pyskl - INFO - Epoch [28][500/3746] lr: 9.214e-02, eta: 3 days, 21:03:57, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5173, loss_cls: 4.0026, loss: 4.0026 +2024-07-23 00:04:29,277 - pyskl - INFO - Epoch [28][600/3746] lr: 9.213e-02, eta: 3 days, 21:02:33, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5123, loss_cls: 4.0042, loss: 4.0042 +2024-07-23 00:05:39,228 - pyskl - INFO - Epoch [28][700/3746] lr: 9.211e-02, eta: 3 days, 21:01:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5180, loss_cls: 4.0333, loss: 4.0333 +2024-07-23 00:06:50,063 - pyskl - INFO - Epoch [28][800/3746] lr: 9.210e-02, eta: 3 days, 20:59:46, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5103, loss_cls: 4.0457, loss: 4.0457 +2024-07-23 00:08:00,621 - pyskl - INFO - Epoch [28][900/3746] lr: 9.208e-02, eta: 3 days, 20:58:23, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5109, loss_cls: 4.0137, loss: 4.0137 +2024-07-23 00:09:10,863 - pyskl - INFO - Epoch [28][1000/3746] lr: 9.207e-02, eta: 3 days, 20:56:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5178, loss_cls: 3.9947, loss: 3.9947 +2024-07-23 00:10:21,408 - pyskl - INFO - Epoch [28][1100/3746] lr: 9.205e-02, eta: 3 days, 20:55:36, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5194, loss_cls: 4.0193, loss: 4.0193 +2024-07-23 00:11:31,437 - pyskl - INFO - Epoch [28][1200/3746] lr: 9.204e-02, eta: 3 days, 20:54:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5136, loss_cls: 4.0281, loss: 4.0281 +2024-07-23 00:12:41,289 - pyskl - INFO - Epoch [28][1300/3746] lr: 9.202e-02, eta: 3 days, 20:52:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5222, loss_cls: 3.9812, loss: 3.9812 +2024-07-23 00:13:51,232 - pyskl - INFO - Epoch [28][1400/3746] lr: 9.201e-02, eta: 3 days, 20:51:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5173, loss_cls: 4.0209, loss: 4.0209 +2024-07-23 00:15:00,825 - pyskl - INFO - Epoch [28][1500/3746] lr: 9.199e-02, eta: 3 days, 20:49:52, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5150, loss_cls: 4.0417, loss: 4.0417 +2024-07-23 00:16:10,719 - pyskl - INFO - Epoch [28][1600/3746] lr: 9.198e-02, eta: 3 days, 20:48:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5169, loss_cls: 4.0092, loss: 4.0092 +2024-07-23 00:17:20,482 - pyskl - INFO - Epoch [28][1700/3746] lr: 9.196e-02, eta: 3 days, 20:47:00, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5133, loss_cls: 4.0332, loss: 4.0332 +2024-07-23 00:18:30,455 - pyskl - INFO - Epoch [28][1800/3746] lr: 9.194e-02, eta: 3 days, 20:45:35, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5181, loss_cls: 3.9987, loss: 3.9987 +2024-07-23 00:19:40,536 - pyskl - INFO - Epoch [28][1900/3746] lr: 9.193e-02, eta: 3 days, 20:44:10, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5081, loss_cls: 4.0804, loss: 4.0804 +2024-07-23 00:20:50,323 - pyskl - INFO - Epoch [28][2000/3746] lr: 9.191e-02, eta: 3 days, 20:42:45, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5084, loss_cls: 4.0354, loss: 4.0354 +2024-07-23 00:22:00,368 - pyskl - INFO - Epoch [28][2100/3746] lr: 9.190e-02, eta: 3 days, 20:41:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5050, loss_cls: 4.0398, loss: 4.0398 +2024-07-23 00:23:10,328 - pyskl - INFO - Epoch [28][2200/3746] lr: 9.188e-02, eta: 3 days, 20:39:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5223, loss_cls: 3.9880, loss: 3.9880 +2024-07-23 00:24:20,345 - pyskl - INFO - Epoch [28][2300/3746] lr: 9.187e-02, eta: 3 days, 20:38:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5195, loss_cls: 4.0137, loss: 4.0137 +2024-07-23 00:25:30,139 - pyskl - INFO - Epoch [28][2400/3746] lr: 9.185e-02, eta: 3 days, 20:37:04, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5188, loss_cls: 4.0286, loss: 4.0286 +2024-07-23 00:26:40,110 - pyskl - INFO - Epoch [28][2500/3746] lr: 9.184e-02, eta: 3 days, 20:35:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5220, loss_cls: 4.0123, loss: 4.0123 +2024-07-23 00:27:50,081 - pyskl - INFO - Epoch [28][2600/3746] lr: 9.182e-02, eta: 3 days, 20:34:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5259, loss_cls: 4.0052, loss: 4.0052 +2024-07-23 00:29:00,068 - pyskl - INFO - Epoch [28][2700/3746] lr: 9.181e-02, eta: 3 days, 20:32:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5097, loss_cls: 4.0509, loss: 4.0509 +2024-07-23 00:30:10,108 - pyskl - INFO - Epoch [28][2800/3746] lr: 9.179e-02, eta: 3 days, 20:31:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5119, loss_cls: 4.0259, loss: 4.0259 +2024-07-23 00:31:19,927 - pyskl - INFO - Epoch [28][2900/3746] lr: 9.178e-02, eta: 3 days, 20:29:59, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5150, loss_cls: 4.0157, loss: 4.0157 +2024-07-23 00:32:29,935 - pyskl - INFO - Epoch [28][3000/3746] lr: 9.176e-02, eta: 3 days, 20:28:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5114, loss_cls: 3.9912, loss: 3.9912 +2024-07-23 00:33:39,543 - pyskl - INFO - Epoch [28][3100/3746] lr: 9.175e-02, eta: 3 days, 20:27:08, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5061, loss_cls: 4.0366, loss: 4.0366 +2024-07-23 00:34:49,576 - pyskl - INFO - Epoch [28][3200/3746] lr: 9.173e-02, eta: 3 days, 20:25:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5203, loss_cls: 3.9969, loss: 3.9969 +2024-07-23 00:35:59,406 - pyskl - INFO - Epoch [28][3300/3746] lr: 9.172e-02, eta: 3 days, 20:24:18, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5181, loss_cls: 4.0182, loss: 4.0182 +2024-07-23 00:37:09,639 - pyskl - INFO - Epoch [28][3400/3746] lr: 9.170e-02, eta: 3 days, 20:22:54, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5166, loss_cls: 4.0342, loss: 4.0342 +2024-07-23 00:38:19,516 - pyskl - INFO - Epoch [28][3500/3746] lr: 9.168e-02, eta: 3 days, 20:21:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5119, loss_cls: 4.0372, loss: 4.0372 +2024-07-23 00:39:29,353 - pyskl - INFO - Epoch [28][3600/3746] lr: 9.167e-02, eta: 3 days, 20:20:04, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5038, loss_cls: 4.0522, loss: 4.0522 +2024-07-23 00:40:39,184 - pyskl - INFO - Epoch [28][3700/3746] lr: 9.165e-02, eta: 3 days, 20:18:39, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5108, loss_cls: 4.0134, loss: 4.0134 +2024-07-23 00:41:13,308 - pyskl - INFO - Saving checkpoint at 28 epochs +2024-07-23 00:43:04,821 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 00:43:05,483 - pyskl - INFO - +top1_acc 0.1985 +top5_acc 0.4306 +2024-07-23 00:43:05,483 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 00:43:05,522 - pyskl - INFO - +mean_acc 0.1985 +2024-07-23 00:43:05,533 - pyskl - INFO - Epoch(val) [28][309] top1_acc: 0.1985, top5_acc: 0.4306, mean_class_accuracy: 0.1985 +2024-07-23 00:46:23,049 - pyskl - INFO - Epoch [29][100/3746] lr: 9.163e-02, eta: 3 days, 20:23:30, time: 1.975, data_time: 1.273, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5161, loss_cls: 3.9854, loss: 3.9854 +2024-07-23 00:47:33,589 - pyskl - INFO - Epoch [29][200/3746] lr: 9.162e-02, eta: 3 days, 20:22:07, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5225, loss_cls: 4.0158, loss: 4.0158 +2024-07-23 00:48:44,240 - pyskl - INFO - Epoch [29][300/3746] lr: 9.160e-02, eta: 3 days, 20:20:45, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5141, loss_cls: 3.9994, loss: 3.9994 +2024-07-23 00:49:54,683 - pyskl - INFO - Epoch [29][400/3746] lr: 9.158e-02, eta: 3 days, 20:19:22, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5088, loss_cls: 4.0224, loss: 4.0224 +2024-07-23 00:51:05,239 - pyskl - INFO - Epoch [29][500/3746] lr: 9.157e-02, eta: 3 days, 20:18:00, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5133, loss_cls: 3.9965, loss: 3.9965 +2024-07-23 00:52:15,637 - pyskl - INFO - Epoch [29][600/3746] lr: 9.155e-02, eta: 3 days, 20:16:37, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5186, loss_cls: 3.9994, loss: 3.9994 +2024-07-23 00:53:25,565 - pyskl - INFO - Epoch [29][700/3746] lr: 9.154e-02, eta: 3 days, 20:15:12, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5242, loss_cls: 3.9788, loss: 3.9788 +2024-07-23 00:54:36,640 - pyskl - INFO - Epoch [29][800/3746] lr: 9.152e-02, eta: 3 days, 20:13:51, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5266, loss_cls: 3.9914, loss: 3.9914 +2024-07-23 00:55:47,306 - pyskl - INFO - Epoch [29][900/3746] lr: 9.151e-02, eta: 3 days, 20:12:30, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5245, loss_cls: 3.9794, loss: 3.9794 +2024-07-23 00:56:57,291 - pyskl - INFO - Epoch [29][1000/3746] lr: 9.149e-02, eta: 3 days, 20:11:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5183, loss_cls: 3.9997, loss: 3.9997 +2024-07-23 00:58:07,766 - pyskl - INFO - Epoch [29][1100/3746] lr: 9.148e-02, eta: 3 days, 20:09:42, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5178, loss_cls: 4.0017, loss: 4.0017 +2024-07-23 00:59:18,074 - pyskl - INFO - Epoch [29][1200/3746] lr: 9.146e-02, eta: 3 days, 20:08:19, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5156, loss_cls: 4.0079, loss: 4.0079 +2024-07-23 01:00:28,173 - pyskl - INFO - Epoch [29][1300/3746] lr: 9.144e-02, eta: 3 days, 20:06:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5131, loss_cls: 4.0134, loss: 4.0134 +2024-07-23 01:01:38,143 - pyskl - INFO - Epoch [29][1400/3746] lr: 9.143e-02, eta: 3 days, 20:05:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5067, loss_cls: 4.0285, loss: 4.0285 +2024-07-23 01:02:47,912 - pyskl - INFO - Epoch [29][1500/3746] lr: 9.141e-02, eta: 3 days, 20:04:04, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5262, loss_cls: 3.9795, loss: 3.9795 +2024-07-23 01:03:57,880 - pyskl - INFO - Epoch [29][1600/3746] lr: 9.140e-02, eta: 3 days, 20:02:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5130, loss_cls: 4.0387, loss: 4.0387 +2024-07-23 01:05:07,948 - pyskl - INFO - Epoch [29][1700/3746] lr: 9.138e-02, eta: 3 days, 20:01:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5098, loss_cls: 4.0598, loss: 4.0598 +2024-07-23 01:06:17,795 - pyskl - INFO - Epoch [29][1800/3746] lr: 9.137e-02, eta: 3 days, 19:59:50, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5281, loss_cls: 3.9900, loss: 3.9900 +2024-07-23 01:07:27,424 - pyskl - INFO - Epoch [29][1900/3746] lr: 9.135e-02, eta: 3 days, 19:58:24, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5184, loss_cls: 4.0598, loss: 4.0598 +2024-07-23 01:08:37,531 - pyskl - INFO - Epoch [29][2000/3746] lr: 9.133e-02, eta: 3 days, 19:57:00, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5100, loss_cls: 4.0365, loss: 4.0365 +2024-07-23 01:09:47,744 - pyskl - INFO - Epoch [29][2100/3746] lr: 9.132e-02, eta: 3 days, 19:55:36, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5162, loss_cls: 4.0207, loss: 4.0207 +2024-07-23 01:10:57,460 - pyskl - INFO - Epoch [29][2200/3746] lr: 9.130e-02, eta: 3 days, 19:54:11, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5206, loss_cls: 3.9938, loss: 3.9938 +2024-07-23 01:12:07,253 - pyskl - INFO - Epoch [29][2300/3746] lr: 9.129e-02, eta: 3 days, 19:52:45, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5162, loss_cls: 4.0423, loss: 4.0423 +2024-07-23 01:13:17,192 - pyskl - INFO - Epoch [29][2400/3746] lr: 9.127e-02, eta: 3 days, 19:51:21, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5325, loss_cls: 3.9350, loss: 3.9350 +2024-07-23 01:14:27,215 - pyskl - INFO - Epoch [29][2500/3746] lr: 9.126e-02, eta: 3 days, 19:49:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5211, loss_cls: 4.0427, loss: 4.0427 +2024-07-23 01:15:37,079 - pyskl - INFO - Epoch [29][2600/3746] lr: 9.124e-02, eta: 3 days, 19:48:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5052, loss_cls: 4.0548, loss: 4.0548 +2024-07-23 01:16:46,872 - pyskl - INFO - Epoch [29][2700/3746] lr: 9.122e-02, eta: 3 days, 19:47:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5216, loss_cls: 4.0129, loss: 4.0129 +2024-07-23 01:17:56,955 - pyskl - INFO - Epoch [29][2800/3746] lr: 9.121e-02, eta: 3 days, 19:45:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5192, loss_cls: 4.0072, loss: 4.0072 +2024-07-23 01:19:06,865 - pyskl - INFO - Epoch [29][2900/3746] lr: 9.119e-02, eta: 3 days, 19:44:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5136, loss_cls: 4.0297, loss: 4.0297 +2024-07-23 01:20:16,688 - pyskl - INFO - Epoch [29][3000/3746] lr: 9.118e-02, eta: 3 days, 19:42:53, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5053, loss_cls: 4.0657, loss: 4.0657 +2024-07-23 01:21:26,517 - pyskl - INFO - Epoch [29][3100/3746] lr: 9.116e-02, eta: 3 days, 19:41:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5142, loss_cls: 4.0251, loss: 4.0251 +2024-07-23 01:22:36,398 - pyskl - INFO - Epoch [29][3200/3746] lr: 9.114e-02, eta: 3 days, 19:40:03, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5091, loss_cls: 4.0309, loss: 4.0309 +2024-07-23 01:23:46,348 - pyskl - INFO - Epoch [29][3300/3746] lr: 9.113e-02, eta: 3 days, 19:38:39, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5194, loss_cls: 3.9901, loss: 3.9901 +2024-07-23 01:24:56,093 - pyskl - INFO - Epoch [29][3400/3746] lr: 9.111e-02, eta: 3 days, 19:37:14, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5122, loss_cls: 4.0485, loss: 4.0485 +2024-07-23 01:26:06,091 - pyskl - INFO - Epoch [29][3500/3746] lr: 9.110e-02, eta: 3 days, 19:35:50, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5173, loss_cls: 3.9857, loss: 3.9857 +2024-07-23 01:27:15,979 - pyskl - INFO - Epoch [29][3600/3746] lr: 9.108e-02, eta: 3 days, 19:34:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5088, loss_cls: 4.0754, loss: 4.0754 +2024-07-23 01:28:25,931 - pyskl - INFO - Epoch [29][3700/3746] lr: 9.106e-02, eta: 3 days, 19:33:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5081, loss_cls: 4.0102, loss: 4.0102 +2024-07-23 01:29:00,240 - pyskl - INFO - Saving checkpoint at 29 epochs +2024-07-23 01:30:50,109 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 01:30:50,789 - pyskl - INFO - +top1_acc 0.1608 +top5_acc 0.3595 +2024-07-23 01:30:50,789 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 01:30:50,829 - pyskl - INFO - +mean_acc 0.1606 +2024-07-23 01:30:50,840 - pyskl - INFO - Epoch(val) [29][309] top1_acc: 0.1608, top5_acc: 0.3595, mean_class_accuracy: 0.1606 +2024-07-23 01:34:17,402 - pyskl - INFO - Epoch [30][100/3746] lr: 9.104e-02, eta: 3 days, 19:38:13, time: 2.066, data_time: 1.263, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5336, loss_cls: 3.9536, loss: 3.9536 +2024-07-23 01:35:38,158 - pyskl - INFO - Epoch [30][200/3746] lr: 9.103e-02, eta: 3 days, 19:37:34, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5333, loss_cls: 3.9271, loss: 3.9271 +2024-07-23 01:36:59,088 - pyskl - INFO - Epoch [30][300/3746] lr: 9.101e-02, eta: 3 days, 19:36:55, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5197, loss_cls: 3.9788, loss: 3.9788 +2024-07-23 01:38:19,290 - pyskl - INFO - Epoch [30][400/3746] lr: 9.099e-02, eta: 3 days, 19:36:13, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5209, loss_cls: 3.9796, loss: 3.9796 +2024-07-23 01:39:39,830 - pyskl - INFO - Epoch [30][500/3746] lr: 9.098e-02, eta: 3 days, 19:35:32, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5195, loss_cls: 4.0257, loss: 4.0257 +2024-07-23 01:40:59,853 - pyskl - INFO - Epoch [30][600/3746] lr: 9.096e-02, eta: 3 days, 19:34:49, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5144, loss_cls: 4.0472, loss: 4.0472 +2024-07-23 01:42:20,744 - pyskl - INFO - Epoch [30][700/3746] lr: 9.095e-02, eta: 3 days, 19:34:09, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5208, loss_cls: 3.9952, loss: 3.9952 +2024-07-23 01:43:41,701 - pyskl - INFO - Epoch [30][800/3746] lr: 9.093e-02, eta: 3 days, 19:33:30, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5158, loss_cls: 4.0071, loss: 4.0071 +2024-07-23 01:45:02,495 - pyskl - INFO - Epoch [30][900/3746] lr: 9.091e-02, eta: 3 days, 19:32:50, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5239, loss_cls: 3.9676, loss: 3.9676 +2024-07-23 01:46:22,688 - pyskl - INFO - Epoch [30][1000/3746] lr: 9.090e-02, eta: 3 days, 19:32:07, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5134, loss_cls: 4.0197, loss: 4.0197 +2024-07-23 01:47:42,718 - pyskl - INFO - Epoch [30][1100/3746] lr: 9.088e-02, eta: 3 days, 19:31:24, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5211, loss_cls: 3.9914, loss: 3.9914 +2024-07-23 01:49:02,974 - pyskl - INFO - Epoch [30][1200/3746] lr: 9.087e-02, eta: 3 days, 19:30:42, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5152, loss_cls: 4.0221, loss: 4.0221 +2024-07-23 01:50:22,900 - pyskl - INFO - Epoch [30][1300/3746] lr: 9.085e-02, eta: 3 days, 19:29:58, time: 0.799, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5234, loss_cls: 3.9995, loss: 3.9995 +2024-07-23 01:51:43,375 - pyskl - INFO - Epoch [30][1400/3746] lr: 9.083e-02, eta: 3 days, 19:29:16, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5208, loss_cls: 3.9991, loss: 3.9991 +2024-07-23 01:53:03,037 - pyskl - INFO - Epoch [30][1500/3746] lr: 9.082e-02, eta: 3 days, 19:28:31, time: 0.797, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5123, loss_cls: 4.0198, loss: 4.0198 +2024-07-23 01:54:23,106 - pyskl - INFO - Epoch [30][1600/3746] lr: 9.080e-02, eta: 3 days, 19:27:47, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5161, loss_cls: 4.0297, loss: 4.0297 +2024-07-23 01:55:43,050 - pyskl - INFO - Epoch [30][1700/3746] lr: 9.078e-02, eta: 3 days, 19:27:03, time: 0.799, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5198, loss_cls: 4.0097, loss: 4.0097 +2024-07-23 01:57:02,670 - pyskl - INFO - Epoch [30][1800/3746] lr: 9.077e-02, eta: 3 days, 19:26:18, time: 0.796, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5175, loss_cls: 4.0015, loss: 4.0015 +2024-07-23 01:58:22,295 - pyskl - INFO - Epoch [30][1900/3746] lr: 9.075e-02, eta: 3 days, 19:25:32, time: 0.796, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5155, loss_cls: 4.0148, loss: 4.0148 +2024-07-23 01:59:42,598 - pyskl - INFO - Epoch [30][2000/3746] lr: 9.074e-02, eta: 3 days, 19:24:50, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5048, loss_cls: 4.0493, loss: 4.0493 +2024-07-23 02:01:02,616 - pyskl - INFO - Epoch [30][2100/3746] lr: 9.072e-02, eta: 3 days, 19:24:06, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5070, loss_cls: 4.0279, loss: 4.0279 +2024-07-23 02:02:22,678 - pyskl - INFO - Epoch [30][2200/3746] lr: 9.070e-02, eta: 3 days, 19:23:22, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5062, loss_cls: 4.0632, loss: 4.0632 +2024-07-23 02:03:42,803 - pyskl - INFO - Epoch [30][2300/3746] lr: 9.069e-02, eta: 3 days, 19:22:38, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5053, loss_cls: 4.0575, loss: 4.0575 +2024-07-23 02:05:02,800 - pyskl - INFO - Epoch [30][2400/3746] lr: 9.067e-02, eta: 3 days, 19:21:54, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5162, loss_cls: 4.0263, loss: 4.0263 +2024-07-23 02:06:22,721 - pyskl - INFO - Epoch [30][2500/3746] lr: 9.065e-02, eta: 3 days, 19:21:09, time: 0.799, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5141, loss_cls: 4.0290, loss: 4.0290 +2024-07-23 02:07:42,788 - pyskl - INFO - Epoch [30][2600/3746] lr: 9.064e-02, eta: 3 days, 19:20:25, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5200, loss_cls: 3.9835, loss: 3.9835 +2024-07-23 02:09:02,507 - pyskl - INFO - Epoch [30][2700/3746] lr: 9.062e-02, eta: 3 days, 19:19:39, time: 0.797, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5250, loss_cls: 3.9928, loss: 3.9928 +2024-07-23 02:10:22,690 - pyskl - INFO - Epoch [30][2800/3746] lr: 9.061e-02, eta: 3 days, 19:18:55, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5206, loss_cls: 4.0185, loss: 4.0185 +2024-07-23 02:11:42,459 - pyskl - INFO - Epoch [30][2900/3746] lr: 9.059e-02, eta: 3 days, 19:18:10, time: 0.798, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5173, loss_cls: 3.9977, loss: 3.9977 +2024-07-23 02:13:02,494 - pyskl - INFO - Epoch [30][3000/3746] lr: 9.057e-02, eta: 3 days, 19:17:25, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5181, loss_cls: 4.0232, loss: 4.0232 +2024-07-23 02:14:22,376 - pyskl - INFO - Epoch [30][3100/3746] lr: 9.056e-02, eta: 3 days, 19:16:40, time: 0.799, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5250, loss_cls: 3.9697, loss: 3.9697 +2024-07-23 02:15:42,268 - pyskl - INFO - Epoch [30][3200/3746] lr: 9.054e-02, eta: 3 days, 19:15:55, time: 0.799, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5166, loss_cls: 4.0183, loss: 4.0183 +2024-07-23 02:17:02,109 - pyskl - INFO - Epoch [30][3300/3746] lr: 9.052e-02, eta: 3 days, 19:15:09, time: 0.798, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5158, loss_cls: 4.0137, loss: 4.0137 +2024-07-23 02:18:22,320 - pyskl - INFO - Epoch [30][3400/3746] lr: 9.051e-02, eta: 3 days, 19:14:25, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5112, loss_cls: 4.0165, loss: 4.0165 +2024-07-23 02:19:42,021 - pyskl - INFO - Epoch [30][3500/3746] lr: 9.049e-02, eta: 3 days, 19:13:39, time: 0.797, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5167, loss_cls: 4.0178, loss: 4.0178 +2024-07-23 02:21:02,229 - pyskl - INFO - Epoch [30][3600/3746] lr: 9.047e-02, eta: 3 days, 19:12:55, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5230, loss_cls: 3.9569, loss: 3.9569 +2024-07-23 02:22:22,342 - pyskl - INFO - Epoch [30][3700/3746] lr: 9.046e-02, eta: 3 days, 19:12:10, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5197, loss_cls: 4.0221, loss: 4.0221 +2024-07-23 02:23:01,486 - pyskl - INFO - Saving checkpoint at 30 epochs +2024-07-23 02:24:51,892 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 02:24:52,551 - pyskl - INFO - +top1_acc 0.2033 +top5_acc 0.4404 +2024-07-23 02:24:52,551 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 02:24:52,590 - pyskl - INFO - +mean_acc 0.2030 +2024-07-23 02:24:52,594 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_27.pth was removed +2024-07-23 02:24:52,825 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_30.pth. +2024-07-23 02:24:52,826 - pyskl - INFO - Best top1_acc is 0.2033 at 30 epoch. +2024-07-23 02:24:52,836 - pyskl - INFO - Epoch(val) [30][309] top1_acc: 0.2033, top5_acc: 0.4404, mean_class_accuracy: 0.2030 +2024-07-23 02:28:41,995 - pyskl - INFO - Epoch [31][100/3746] lr: 9.043e-02, eta: 3 days, 19:18:33, time: 2.292, data_time: 1.318, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5272, loss_cls: 4.1661, loss: 4.1661 +2024-07-23 02:30:04,572 - pyskl - INFO - Epoch [31][200/3746] lr: 9.042e-02, eta: 3 days, 19:17:58, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5141, loss_cls: 4.2517, loss: 4.2517 +2024-07-23 02:31:27,133 - pyskl - INFO - Epoch [31][300/3746] lr: 9.040e-02, eta: 3 days, 19:17:22, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5220, loss_cls: 4.2240, loss: 4.2240 +2024-07-23 02:32:49,274 - pyskl - INFO - Epoch [31][400/3746] lr: 9.039e-02, eta: 3 days, 19:16:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5208, loss_cls: 4.2120, loss: 4.2120 +2024-07-23 02:34:10,988 - pyskl - INFO - Epoch [31][500/3746] lr: 9.037e-02, eta: 3 days, 19:16:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5152, loss_cls: 4.2335, loss: 4.2335 +2024-07-23 02:35:33,097 - pyskl - INFO - Epoch [31][600/3746] lr: 9.035e-02, eta: 3 days, 19:15:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5181, loss_cls: 4.2473, loss: 4.2473 +2024-07-23 02:36:56,341 - pyskl - INFO - Epoch [31][700/3746] lr: 9.034e-02, eta: 3 days, 19:14:54, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5189, loss_cls: 4.2436, loss: 4.2436 +2024-07-23 02:38:18,993 - pyskl - INFO - Epoch [31][800/3746] lr: 9.032e-02, eta: 3 days, 19:14:19, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5178, loss_cls: 4.2340, loss: 4.2340 +2024-07-23 02:39:40,912 - pyskl - INFO - Epoch [31][900/3746] lr: 9.030e-02, eta: 3 days, 19:13:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5248, loss_cls: 4.1840, loss: 4.1840 +2024-07-23 02:41:02,564 - pyskl - INFO - Epoch [31][1000/3746] lr: 9.029e-02, eta: 3 days, 19:13:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5128, loss_cls: 4.2256, loss: 4.2256 +2024-07-23 02:42:24,001 - pyskl - INFO - Epoch [31][1100/3746] lr: 9.027e-02, eta: 3 days, 19:12:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5238, loss_cls: 4.1903, loss: 4.1903 +2024-07-23 02:43:45,325 - pyskl - INFO - Epoch [31][1200/3746] lr: 9.025e-02, eta: 3 days, 19:11:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5253, loss_cls: 4.2115, loss: 4.2115 +2024-07-23 02:45:07,014 - pyskl - INFO - Epoch [31][1300/3746] lr: 9.024e-02, eta: 3 days, 19:10:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5205, loss_cls: 4.2101, loss: 4.2101 +2024-07-23 02:46:29,096 - pyskl - INFO - Epoch [31][1400/3746] lr: 9.022e-02, eta: 3 days, 19:10:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5156, loss_cls: 4.2441, loss: 4.2441 +2024-07-23 02:47:50,079 - pyskl - INFO - Epoch [31][1500/3746] lr: 9.020e-02, eta: 3 days, 19:09:37, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5255, loss_cls: 4.1974, loss: 4.1974 +2024-07-23 02:49:11,769 - pyskl - INFO - Epoch [31][1600/3746] lr: 9.019e-02, eta: 3 days, 19:08:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5131, loss_cls: 4.2302, loss: 4.2302 +2024-07-23 02:50:33,167 - pyskl - INFO - Epoch [31][1700/3746] lr: 9.017e-02, eta: 3 days, 19:08:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5138, loss_cls: 4.2503, loss: 4.2503 +2024-07-23 02:51:54,748 - pyskl - INFO - Epoch [31][1800/3746] lr: 9.015e-02, eta: 3 days, 19:07:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5241, loss_cls: 4.2298, loss: 4.2298 +2024-07-23 02:53:16,254 - pyskl - INFO - Epoch [31][1900/3746] lr: 9.014e-02, eta: 3 days, 19:06:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5122, loss_cls: 4.2357, loss: 4.2357 +2024-07-23 02:54:37,619 - pyskl - INFO - Epoch [31][2000/3746] lr: 9.012e-02, eta: 3 days, 19:06:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5062, loss_cls: 4.2834, loss: 4.2834 +2024-07-23 02:55:59,383 - pyskl - INFO - Epoch [31][2100/3746] lr: 9.010e-02, eta: 3 days, 19:05:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5212, loss_cls: 4.2206, loss: 4.2206 +2024-07-23 02:57:21,001 - pyskl - INFO - Epoch [31][2200/3746] lr: 9.009e-02, eta: 3 days, 19:04:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5178, loss_cls: 4.2086, loss: 4.2086 +2024-07-23 02:58:42,333 - pyskl - INFO - Epoch [31][2300/3746] lr: 9.007e-02, eta: 3 days, 19:04:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5159, loss_cls: 4.2167, loss: 4.2167 +2024-07-23 03:00:03,989 - pyskl - INFO - Epoch [31][2400/3746] lr: 9.005e-02, eta: 3 days, 19:03:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5116, loss_cls: 4.2649, loss: 4.2649 +2024-07-23 03:01:25,176 - pyskl - INFO - Epoch [31][2500/3746] lr: 9.004e-02, eta: 3 days, 19:02:46, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5281, loss_cls: 4.1748, loss: 4.1748 +2024-07-23 03:02:46,903 - pyskl - INFO - Epoch [31][2600/3746] lr: 9.002e-02, eta: 3 days, 19:02:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5080, loss_cls: 4.2628, loss: 4.2628 +2024-07-23 03:04:08,522 - pyskl - INFO - Epoch [31][2700/3746] lr: 9.000e-02, eta: 3 days, 19:01:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5266, loss_cls: 4.2212, loss: 4.2212 +2024-07-23 03:05:30,006 - pyskl - INFO - Epoch [31][2800/3746] lr: 8.999e-02, eta: 3 days, 19:00:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5152, loss_cls: 4.1934, loss: 4.1934 +2024-07-23 03:06:51,489 - pyskl - INFO - Epoch [31][2900/3746] lr: 8.997e-02, eta: 3 days, 19:00:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5155, loss_cls: 4.2213, loss: 4.2213 +2024-07-23 03:08:12,935 - pyskl - INFO - Epoch [31][3000/3746] lr: 8.995e-02, eta: 3 days, 18:59:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5166, loss_cls: 4.2396, loss: 4.2396 +2024-07-23 03:09:34,796 - pyskl - INFO - Epoch [31][3100/3746] lr: 8.994e-02, eta: 3 days, 18:58:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5223, loss_cls: 4.2001, loss: 4.2001 +2024-07-23 03:10:56,342 - pyskl - INFO - Epoch [31][3200/3746] lr: 8.992e-02, eta: 3 days, 18:57:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5153, loss_cls: 4.2408, loss: 4.2408 +2024-07-23 03:12:18,140 - pyskl - INFO - Epoch [31][3300/3746] lr: 8.990e-02, eta: 3 days, 18:57:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5258, loss_cls: 4.1707, loss: 4.1707 +2024-07-23 03:13:40,212 - pyskl - INFO - Epoch [31][3400/3746] lr: 8.989e-02, eta: 3 days, 18:56:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5217, loss_cls: 4.2053, loss: 4.2053 +2024-07-23 03:15:02,273 - pyskl - INFO - Epoch [31][3500/3746] lr: 8.987e-02, eta: 3 days, 18:55:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5202, loss_cls: 4.2562, loss: 4.2562 +2024-07-23 03:16:24,087 - pyskl - INFO - Epoch [31][3600/3746] lr: 8.985e-02, eta: 3 days, 18:55:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5170, loss_cls: 4.2119, loss: 4.2119 +2024-07-23 03:17:45,799 - pyskl - INFO - Epoch [31][3700/3746] lr: 8.983e-02, eta: 3 days, 18:54:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5211, loss_cls: 4.2479, loss: 4.2479 +2024-07-23 03:18:25,849 - pyskl - INFO - Saving checkpoint at 31 epochs +2024-07-23 03:20:16,666 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 03:20:17,331 - pyskl - INFO - +top1_acc 0.2027 +top5_acc 0.4381 +2024-07-23 03:20:17,331 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 03:20:17,369 - pyskl - INFO - +mean_acc 0.2023 +2024-07-23 03:20:17,378 - pyskl - INFO - Epoch(val) [31][309] top1_acc: 0.2027, top5_acc: 0.4381, mean_class_accuracy: 0.2023 +2024-07-23 03:24:05,532 - pyskl - INFO - Epoch [32][100/3746] lr: 8.981e-02, eta: 3 days, 19:00:30, time: 2.281, data_time: 1.306, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5312, loss_cls: 4.1665, loss: 4.1665 +2024-07-23 03:25:27,461 - pyskl - INFO - Epoch [32][200/3746] lr: 8.979e-02, eta: 3 days, 18:59:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5319, loss_cls: 4.1492, loss: 4.1492 +2024-07-23 03:26:49,811 - pyskl - INFO - Epoch [32][300/3746] lr: 8.978e-02, eta: 3 days, 18:59:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5292, loss_cls: 4.1786, loss: 4.1786 +2024-07-23 03:28:11,989 - pyskl - INFO - Epoch [32][400/3746] lr: 8.976e-02, eta: 3 days, 18:58:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5262, loss_cls: 4.1769, loss: 4.1769 +2024-07-23 03:29:33,875 - pyskl - INFO - Epoch [32][500/3746] lr: 8.974e-02, eta: 3 days, 18:57:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5158, loss_cls: 4.2348, loss: 4.2348 +2024-07-23 03:30:55,882 - pyskl - INFO - Epoch [32][600/3746] lr: 8.973e-02, eta: 3 days, 18:57:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5288, loss_cls: 4.2194, loss: 4.2194 +2024-07-23 03:32:18,217 - pyskl - INFO - Epoch [32][700/3746] lr: 8.971e-02, eta: 3 days, 18:56:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5256, loss_cls: 4.1966, loss: 4.1966 +2024-07-23 03:33:40,501 - pyskl - INFO - Epoch [32][800/3746] lr: 8.969e-02, eta: 3 days, 18:55:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5238, loss_cls: 4.1974, loss: 4.1974 +2024-07-23 03:35:02,635 - pyskl - INFO - Epoch [32][900/3746] lr: 8.967e-02, eta: 3 days, 18:55:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5130, loss_cls: 4.2490, loss: 4.2490 +2024-07-23 03:36:25,496 - pyskl - INFO - Epoch [32][1000/3746] lr: 8.966e-02, eta: 3 days, 18:54:26, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5222, loss_cls: 4.1925, loss: 4.1925 +2024-07-23 03:37:47,359 - pyskl - INFO - Epoch [32][1100/3746] lr: 8.964e-02, eta: 3 days, 18:53:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5222, loss_cls: 4.2225, loss: 4.2225 +2024-07-23 03:39:09,141 - pyskl - INFO - Epoch [32][1200/3746] lr: 8.962e-02, eta: 3 days, 18:53:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5209, loss_cls: 4.2452, loss: 4.2452 +2024-07-23 03:40:30,812 - pyskl - INFO - Epoch [32][1300/3746] lr: 8.961e-02, eta: 3 days, 18:52:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5277, loss_cls: 4.1576, loss: 4.1576 +2024-07-23 03:41:52,073 - pyskl - INFO - Epoch [32][1400/3746] lr: 8.959e-02, eta: 3 days, 18:51:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5236, loss_cls: 4.1835, loss: 4.1835 +2024-07-23 03:43:13,399 - pyskl - INFO - Epoch [32][1500/3746] lr: 8.957e-02, eta: 3 days, 18:50:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5109, loss_cls: 4.2266, loss: 4.2266 +2024-07-23 03:44:34,881 - pyskl - INFO - Epoch [32][1600/3746] lr: 8.955e-02, eta: 3 days, 18:50:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5156, loss_cls: 4.2095, loss: 4.2095 +2024-07-23 03:45:56,840 - pyskl - INFO - Epoch [32][1700/3746] lr: 8.954e-02, eta: 3 days, 18:49:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5125, loss_cls: 4.2672, loss: 4.2672 +2024-07-23 03:47:18,250 - pyskl - INFO - Epoch [32][1800/3746] lr: 8.952e-02, eta: 3 days, 18:48:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5152, loss_cls: 4.2124, loss: 4.2124 +2024-07-23 03:48:40,101 - pyskl - INFO - Epoch [32][1900/3746] lr: 8.950e-02, eta: 3 days, 18:47:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5050, loss_cls: 4.2870, loss: 4.2870 +2024-07-23 03:50:01,478 - pyskl - INFO - Epoch [32][2000/3746] lr: 8.949e-02, eta: 3 days, 18:47:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5272, loss_cls: 4.2003, loss: 4.2003 +2024-07-23 03:51:22,905 - pyskl - INFO - Epoch [32][2100/3746] lr: 8.947e-02, eta: 3 days, 18:46:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5103, loss_cls: 4.2571, loss: 4.2571 +2024-07-23 03:52:44,646 - pyskl - INFO - Epoch [32][2200/3746] lr: 8.945e-02, eta: 3 days, 18:45:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5214, loss_cls: 4.2034, loss: 4.2034 +2024-07-23 03:54:06,121 - pyskl - INFO - Epoch [32][2300/3746] lr: 8.943e-02, eta: 3 days, 18:44:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5252, loss_cls: 4.1779, loss: 4.1779 +2024-07-23 03:55:28,266 - pyskl - INFO - Epoch [32][2400/3746] lr: 8.942e-02, eta: 3 days, 18:44:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5244, loss_cls: 4.2007, loss: 4.2007 +2024-07-23 03:56:50,139 - pyskl - INFO - Epoch [32][2500/3746] lr: 8.940e-02, eta: 3 days, 18:43:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5075, loss_cls: 4.2342, loss: 4.2342 +2024-07-23 03:58:12,120 - pyskl - INFO - Epoch [32][2600/3746] lr: 8.938e-02, eta: 3 days, 18:42:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5116, loss_cls: 4.2355, loss: 4.2355 +2024-07-23 03:59:33,709 - pyskl - INFO - Epoch [32][2700/3746] lr: 8.937e-02, eta: 3 days, 18:42:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5255, loss_cls: 4.1775, loss: 4.1775 +2024-07-23 04:00:55,322 - pyskl - INFO - Epoch [32][2800/3746] lr: 8.935e-02, eta: 3 days, 18:41:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5173, loss_cls: 4.2203, loss: 4.2203 +2024-07-23 04:02:17,362 - pyskl - INFO - Epoch [32][2900/3746] lr: 8.933e-02, eta: 3 days, 18:40:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5192, loss_cls: 4.2353, loss: 4.2353 +2024-07-23 04:03:38,688 - pyskl - INFO - Epoch [32][3000/3746] lr: 8.931e-02, eta: 3 days, 18:39:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5134, loss_cls: 4.2673, loss: 4.2673 +2024-07-23 04:04:59,994 - pyskl - INFO - Epoch [32][3100/3746] lr: 8.930e-02, eta: 3 days, 18:39:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5238, loss_cls: 4.1783, loss: 4.1783 +2024-07-23 04:06:21,127 - pyskl - INFO - Epoch [32][3200/3746] lr: 8.928e-02, eta: 3 days, 18:38:23, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5202, loss_cls: 4.2426, loss: 4.2426 +2024-07-23 04:07:42,158 - pyskl - INFO - Epoch [32][3300/3746] lr: 8.926e-02, eta: 3 days, 18:37:36, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5256, loss_cls: 4.2165, loss: 4.2165 +2024-07-23 04:09:04,023 - pyskl - INFO - Epoch [32][3400/3746] lr: 8.924e-02, eta: 3 days, 18:36:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5253, loss_cls: 4.1915, loss: 4.1915 +2024-07-23 04:10:25,889 - pyskl - INFO - Epoch [32][3500/3746] lr: 8.923e-02, eta: 3 days, 18:36:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5138, loss_cls: 4.2606, loss: 4.2606 +2024-07-23 04:11:47,420 - pyskl - INFO - Epoch [32][3600/3746] lr: 8.921e-02, eta: 3 days, 18:35:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5155, loss_cls: 4.2244, loss: 4.2244 +2024-07-23 04:13:08,697 - pyskl - INFO - Epoch [32][3700/3746] lr: 8.919e-02, eta: 3 days, 18:34:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5209, loss_cls: 4.2482, loss: 4.2482 +2024-07-23 04:13:48,267 - pyskl - INFO - Saving checkpoint at 32 epochs +2024-07-23 04:15:38,624 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 04:15:39,286 - pyskl - INFO - +top1_acc 0.2101 +top5_acc 0.4404 +2024-07-23 04:15:39,286 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 04:15:39,325 - pyskl - INFO - +mean_acc 0.2098 +2024-07-23 04:15:39,329 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_30.pth was removed +2024-07-23 04:15:39,556 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_32.pth. +2024-07-23 04:15:39,557 - pyskl - INFO - Best top1_acc is 0.2101 at 32 epoch. +2024-07-23 04:15:39,568 - pyskl - INFO - Epoch(val) [32][309] top1_acc: 0.2101, top5_acc: 0.4404, mean_class_accuracy: 0.2098 +2024-07-23 04:19:25,853 - pyskl - INFO - Epoch [33][100/3746] lr: 8.917e-02, eta: 3 days, 18:40:07, time: 2.263, data_time: 1.290, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5262, loss_cls: 4.1611, loss: 4.1611 +2024-07-23 04:20:47,641 - pyskl - INFO - Epoch [33][200/3746] lr: 8.915e-02, eta: 3 days, 18:39:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5208, loss_cls: 4.2080, loss: 4.2080 +2024-07-23 04:22:10,034 - pyskl - INFO - Epoch [33][300/3746] lr: 8.913e-02, eta: 3 days, 18:38:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5308, loss_cls: 4.1614, loss: 4.1614 +2024-07-23 04:23:32,177 - pyskl - INFO - Epoch [33][400/3746] lr: 8.912e-02, eta: 3 days, 18:37:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5228, loss_cls: 4.2195, loss: 4.2195 +2024-07-23 04:24:54,469 - pyskl - INFO - Epoch [33][500/3746] lr: 8.910e-02, eta: 3 days, 18:37:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5244, loss_cls: 4.1792, loss: 4.1792 +2024-07-23 04:26:16,397 - pyskl - INFO - Epoch [33][600/3746] lr: 8.908e-02, eta: 3 days, 18:36:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5336, loss_cls: 4.1676, loss: 4.1676 +2024-07-23 04:27:39,371 - pyskl - INFO - Epoch [33][700/3746] lr: 8.906e-02, eta: 3 days, 18:35:48, time: 0.830, data_time: 0.001, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5269, loss_cls: 4.1586, loss: 4.1586 +2024-07-23 04:29:01,511 - pyskl - INFO - Epoch [33][800/3746] lr: 8.905e-02, eta: 3 days, 18:35:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5227, loss_cls: 4.1930, loss: 4.1930 +2024-07-23 04:30:23,548 - pyskl - INFO - Epoch [33][900/3746] lr: 8.903e-02, eta: 3 days, 18:34:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5170, loss_cls: 4.2369, loss: 4.2369 +2024-07-23 04:31:45,064 - pyskl - INFO - Epoch [33][1000/3746] lr: 8.901e-02, eta: 3 days, 18:33:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5219, loss_cls: 4.1958, loss: 4.1958 +2024-07-23 04:33:06,913 - pyskl - INFO - Epoch [33][1100/3746] lr: 8.899e-02, eta: 3 days, 18:32:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5200, loss_cls: 4.2544, loss: 4.2544 +2024-07-23 04:34:29,071 - pyskl - INFO - Epoch [33][1200/3746] lr: 8.898e-02, eta: 3 days, 18:32:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5214, loss_cls: 4.2182, loss: 4.2182 +2024-07-23 04:35:50,438 - pyskl - INFO - Epoch [33][1300/3746] lr: 8.896e-02, eta: 3 days, 18:31:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5183, loss_cls: 4.2228, loss: 4.2228 +2024-07-23 04:37:12,078 - pyskl - INFO - Epoch [33][1400/3746] lr: 8.894e-02, eta: 3 days, 18:30:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5212, loss_cls: 4.1897, loss: 4.1897 +2024-07-23 04:38:33,422 - pyskl - INFO - Epoch [33][1500/3746] lr: 8.892e-02, eta: 3 days, 18:29:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5161, loss_cls: 4.2395, loss: 4.2395 +2024-07-23 04:39:54,898 - pyskl - INFO - Epoch [33][1600/3746] lr: 8.891e-02, eta: 3 days, 18:28:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5227, loss_cls: 4.2109, loss: 4.2109 +2024-07-23 04:41:16,492 - pyskl - INFO - Epoch [33][1700/3746] lr: 8.889e-02, eta: 3 days, 18:28:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5114, loss_cls: 4.2367, loss: 4.2367 +2024-07-23 04:42:37,896 - pyskl - INFO - Epoch [33][1800/3746] lr: 8.887e-02, eta: 3 days, 18:27:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5262, loss_cls: 4.2282, loss: 4.2282 +2024-07-23 04:43:59,822 - pyskl - INFO - Epoch [33][1900/3746] lr: 8.885e-02, eta: 3 days, 18:26:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5214, loss_cls: 4.2354, loss: 4.2354 +2024-07-23 04:45:21,575 - pyskl - INFO - Epoch [33][2000/3746] lr: 8.884e-02, eta: 3 days, 18:25:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5256, loss_cls: 4.2275, loss: 4.2275 +2024-07-23 04:46:43,185 - pyskl - INFO - Epoch [33][2100/3746] lr: 8.882e-02, eta: 3 days, 18:25:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5259, loss_cls: 4.1981, loss: 4.1981 +2024-07-23 04:48:05,083 - pyskl - INFO - Epoch [33][2200/3746] lr: 8.880e-02, eta: 3 days, 18:24:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5189, loss_cls: 4.1890, loss: 4.1890 +2024-07-23 04:49:26,803 - pyskl - INFO - Epoch [33][2300/3746] lr: 8.878e-02, eta: 3 days, 18:23:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5192, loss_cls: 4.2064, loss: 4.2064 +2024-07-23 04:50:48,855 - pyskl - INFO - Epoch [33][2400/3746] lr: 8.876e-02, eta: 3 days, 18:22:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5134, loss_cls: 4.2255, loss: 4.2255 +2024-07-23 04:52:10,640 - pyskl - INFO - Epoch [33][2500/3746] lr: 8.875e-02, eta: 3 days, 18:22:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5264, loss_cls: 4.1923, loss: 4.1923 +2024-07-23 04:53:32,774 - pyskl - INFO - Epoch [33][2600/3746] lr: 8.873e-02, eta: 3 days, 18:21:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5216, loss_cls: 4.1810, loss: 4.1810 +2024-07-23 04:54:54,750 - pyskl - INFO - Epoch [33][2700/3746] lr: 8.871e-02, eta: 3 days, 18:20:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5138, loss_cls: 4.2601, loss: 4.2601 +2024-07-23 04:56:16,611 - pyskl - INFO - Epoch [33][2800/3746] lr: 8.869e-02, eta: 3 days, 18:19:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5208, loss_cls: 4.2111, loss: 4.2111 +2024-07-23 04:57:39,006 - pyskl - INFO - Epoch [33][2900/3746] lr: 8.868e-02, eta: 3 days, 18:19:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5211, loss_cls: 4.2084, loss: 4.2084 +2024-07-23 04:59:00,614 - pyskl - INFO - Epoch [33][3000/3746] lr: 8.866e-02, eta: 3 days, 18:18:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5233, loss_cls: 4.2074, loss: 4.2074 +2024-07-23 05:00:22,082 - pyskl - INFO - Epoch [33][3100/3746] lr: 8.864e-02, eta: 3 days, 18:17:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5281, loss_cls: 4.1883, loss: 4.1883 +2024-07-23 05:01:43,721 - pyskl - INFO - Epoch [33][3200/3746] lr: 8.862e-02, eta: 3 days, 18:16:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5206, loss_cls: 4.2335, loss: 4.2335 +2024-07-23 05:03:04,893 - pyskl - INFO - Epoch [33][3300/3746] lr: 8.861e-02, eta: 3 days, 18:15:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5253, loss_cls: 4.1718, loss: 4.1718 +2024-07-23 05:04:26,477 - pyskl - INFO - Epoch [33][3400/3746] lr: 8.859e-02, eta: 3 days, 18:15:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5177, loss_cls: 4.2154, loss: 4.2154 +2024-07-23 05:05:48,110 - pyskl - INFO - Epoch [33][3500/3746] lr: 8.857e-02, eta: 3 days, 18:14:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5169, loss_cls: 4.2120, loss: 4.2120 +2024-07-23 05:07:09,852 - pyskl - INFO - Epoch [33][3600/3746] lr: 8.855e-02, eta: 3 days, 18:13:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5081, loss_cls: 4.2617, loss: 4.2617 +2024-07-23 05:08:31,840 - pyskl - INFO - Epoch [33][3700/3746] lr: 8.853e-02, eta: 3 days, 18:12:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5214, loss_cls: 4.2224, loss: 4.2224 +2024-07-23 05:09:11,425 - pyskl - INFO - Saving checkpoint at 33 epochs +2024-07-23 05:11:01,943 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 05:11:02,603 - pyskl - INFO - +top1_acc 0.2112 +top5_acc 0.4455 +2024-07-23 05:11:02,603 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 05:11:02,647 - pyskl - INFO - +mean_acc 0.2110 +2024-07-23 05:11:02,653 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_32.pth was removed +2024-07-23 05:11:02,915 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_33.pth. +2024-07-23 05:11:02,916 - pyskl - INFO - Best top1_acc is 0.2112 at 33 epoch. +2024-07-23 05:11:02,926 - pyskl - INFO - Epoch(val) [33][309] top1_acc: 0.2112, top5_acc: 0.4455, mean_class_accuracy: 0.2110 +2024-07-23 05:14:54,273 - pyskl - INFO - Epoch [34][100/3746] lr: 8.851e-02, eta: 3 days, 18:18:14, time: 2.313, data_time: 1.337, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5325, loss_cls: 4.1591, loss: 4.1591 +2024-07-23 05:16:15,832 - pyskl - INFO - Epoch [34][200/3746] lr: 8.849e-02, eta: 3 days, 18:17:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5250, loss_cls: 4.1382, loss: 4.1382 +2024-07-23 05:17:38,367 - pyskl - INFO - Epoch [34][300/3746] lr: 8.847e-02, eta: 3 days, 18:16:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5284, loss_cls: 4.1464, loss: 4.1464 +2024-07-23 05:19:00,251 - pyskl - INFO - Epoch [34][400/3746] lr: 8.845e-02, eta: 3 days, 18:15:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5242, loss_cls: 4.1995, loss: 4.1995 +2024-07-23 05:20:22,271 - pyskl - INFO - Epoch [34][500/3746] lr: 8.844e-02, eta: 3 days, 18:15:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5152, loss_cls: 4.2235, loss: 4.2235 +2024-07-23 05:21:44,853 - pyskl - INFO - Epoch [34][600/3746] lr: 8.842e-02, eta: 3 days, 18:14:23, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5280, loss_cls: 4.1927, loss: 4.1927 +2024-07-23 05:23:07,976 - pyskl - INFO - Epoch [34][700/3746] lr: 8.840e-02, eta: 3 days, 18:13:40, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5212, loss_cls: 4.1921, loss: 4.1921 +2024-07-23 05:24:30,222 - pyskl - INFO - Epoch [34][800/3746] lr: 8.838e-02, eta: 3 days, 18:12:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5270, loss_cls: 4.1883, loss: 4.1883 +2024-07-23 05:25:52,745 - pyskl - INFO - Epoch [34][900/3746] lr: 8.836e-02, eta: 3 days, 18:12:09, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5181, loss_cls: 4.2344, loss: 4.2344 +2024-07-23 05:27:14,543 - pyskl - INFO - Epoch [34][1000/3746] lr: 8.835e-02, eta: 3 days, 18:11:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5206, loss_cls: 4.2001, loss: 4.2001 +2024-07-23 05:28:36,181 - pyskl - INFO - Epoch [34][1100/3746] lr: 8.833e-02, eta: 3 days, 18:10:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5183, loss_cls: 4.2230, loss: 4.2230 +2024-07-23 05:29:58,051 - pyskl - INFO - Epoch [34][1200/3746] lr: 8.831e-02, eta: 3 days, 18:09:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5241, loss_cls: 4.1887, loss: 4.1887 +2024-07-23 05:31:19,750 - pyskl - INFO - Epoch [34][1300/3746] lr: 8.829e-02, eta: 3 days, 18:08:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5119, loss_cls: 4.2337, loss: 4.2337 +2024-07-23 05:32:41,125 - pyskl - INFO - Epoch [34][1400/3746] lr: 8.828e-02, eta: 3 days, 18:08:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5195, loss_cls: 4.1951, loss: 4.1951 +2024-07-23 05:34:02,590 - pyskl - INFO - Epoch [34][1500/3746] lr: 8.826e-02, eta: 3 days, 18:07:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5242, loss_cls: 4.1772, loss: 4.1772 +2024-07-23 05:35:23,899 - pyskl - INFO - Epoch [34][1600/3746] lr: 8.824e-02, eta: 3 days, 18:06:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5211, loss_cls: 4.2051, loss: 4.2051 +2024-07-23 05:36:45,184 - pyskl - INFO - Epoch [34][1700/3746] lr: 8.822e-02, eta: 3 days, 18:05:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5200, loss_cls: 4.2196, loss: 4.2196 +2024-07-23 05:38:06,803 - pyskl - INFO - Epoch [34][1800/3746] lr: 8.820e-02, eta: 3 days, 18:04:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5311, loss_cls: 4.1633, loss: 4.1633 +2024-07-23 05:39:28,540 - pyskl - INFO - Epoch [34][1900/3746] lr: 8.819e-02, eta: 3 days, 18:04:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5245, loss_cls: 4.1905, loss: 4.1905 +2024-07-23 05:40:50,436 - pyskl - INFO - Epoch [34][2000/3746] lr: 8.817e-02, eta: 3 days, 18:03:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5250, loss_cls: 4.1741, loss: 4.1741 +2024-07-23 05:42:11,858 - pyskl - INFO - Epoch [34][2100/3746] lr: 8.815e-02, eta: 3 days, 18:02:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5103, loss_cls: 4.2630, loss: 4.2630 +2024-07-23 05:43:33,357 - pyskl - INFO - Epoch [34][2200/3746] lr: 8.813e-02, eta: 3 days, 18:01:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5203, loss_cls: 4.2081, loss: 4.2081 +2024-07-23 05:44:55,253 - pyskl - INFO - Epoch [34][2300/3746] lr: 8.811e-02, eta: 3 days, 18:00:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5241, loss_cls: 4.1909, loss: 4.1909 +2024-07-23 05:46:16,918 - pyskl - INFO - Epoch [34][2400/3746] lr: 8.809e-02, eta: 3 days, 17:59:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5178, loss_cls: 4.2323, loss: 4.2323 +2024-07-23 05:47:38,414 - pyskl - INFO - Epoch [34][2500/3746] lr: 8.808e-02, eta: 3 days, 17:59:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5275, loss_cls: 4.1710, loss: 4.1710 +2024-07-23 05:48:59,933 - pyskl - INFO - Epoch [34][2600/3746] lr: 8.806e-02, eta: 3 days, 17:58:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5141, loss_cls: 4.2462, loss: 4.2462 +2024-07-23 05:50:21,592 - pyskl - INFO - Epoch [34][2700/3746] lr: 8.804e-02, eta: 3 days, 17:57:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5147, loss_cls: 4.2256, loss: 4.2256 +2024-07-23 05:51:42,940 - pyskl - INFO - Epoch [34][2800/3746] lr: 8.802e-02, eta: 3 days, 17:56:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5208, loss_cls: 4.2056, loss: 4.2056 +2024-07-23 05:53:04,561 - pyskl - INFO - Epoch [34][2900/3746] lr: 8.800e-02, eta: 3 days, 17:55:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5192, loss_cls: 4.2013, loss: 4.2013 +2024-07-23 05:54:26,077 - pyskl - INFO - Epoch [34][3000/3746] lr: 8.799e-02, eta: 3 days, 17:54:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5255, loss_cls: 4.2108, loss: 4.2108 +2024-07-23 05:55:47,738 - pyskl - INFO - Epoch [34][3100/3746] lr: 8.797e-02, eta: 3 days, 17:54:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5267, loss_cls: 4.2019, loss: 4.2019 +2024-07-23 05:57:09,093 - pyskl - INFO - Epoch [34][3200/3746] lr: 8.795e-02, eta: 3 days, 17:53:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5195, loss_cls: 4.2107, loss: 4.2107 +2024-07-23 05:58:30,481 - pyskl - INFO - Epoch [34][3300/3746] lr: 8.793e-02, eta: 3 days, 17:52:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5220, loss_cls: 4.2103, loss: 4.2103 +2024-07-23 05:59:52,107 - pyskl - INFO - Epoch [34][3400/3746] lr: 8.791e-02, eta: 3 days, 17:51:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5211, loss_cls: 4.1836, loss: 4.1836 +2024-07-23 06:01:13,550 - pyskl - INFO - Epoch [34][3500/3746] lr: 8.789e-02, eta: 3 days, 17:50:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5122, loss_cls: 4.2157, loss: 4.2157 +2024-07-23 06:02:35,185 - pyskl - INFO - Epoch [34][3600/3746] lr: 8.788e-02, eta: 3 days, 17:50:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5128, loss_cls: 4.2560, loss: 4.2560 +2024-07-23 06:03:56,372 - pyskl - INFO - Epoch [34][3700/3746] lr: 8.786e-02, eta: 3 days, 17:49:09, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5252, loss_cls: 4.1883, loss: 4.1883 +2024-07-23 06:04:36,258 - pyskl - INFO - Saving checkpoint at 34 epochs +2024-07-23 06:06:27,365 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 06:06:28,033 - pyskl - INFO - +top1_acc 0.1922 +top5_acc 0.4221 +2024-07-23 06:06:28,033 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 06:06:28,072 - pyskl - INFO - +mean_acc 0.1919 +2024-07-23 06:06:28,084 - pyskl - INFO - Epoch(val) [34][309] top1_acc: 0.1922, top5_acc: 0.4221, mean_class_accuracy: 0.1919 +2024-07-23 06:10:15,159 - pyskl - INFO - Epoch [35][100/3746] lr: 8.783e-02, eta: 3 days, 17:54:04, time: 2.271, data_time: 1.296, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5208, loss_cls: 4.1655, loss: 4.1655 +2024-07-23 06:11:37,065 - pyskl - INFO - Epoch [35][200/3746] lr: 8.781e-02, eta: 3 days, 17:53:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5264, loss_cls: 4.1604, loss: 4.1604 +2024-07-23 06:12:58,662 - pyskl - INFO - Epoch [35][300/3746] lr: 8.780e-02, eta: 3 days, 17:52:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5280, loss_cls: 4.1764, loss: 4.1764 +2024-07-23 06:14:21,733 - pyskl - INFO - Epoch [35][400/3746] lr: 8.778e-02, eta: 3 days, 17:51:39, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5162, loss_cls: 4.2211, loss: 4.2211 +2024-07-23 06:15:44,040 - pyskl - INFO - Epoch [35][500/3746] lr: 8.776e-02, eta: 3 days, 17:50:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5281, loss_cls: 4.1392, loss: 4.1392 +2024-07-23 06:17:06,738 - pyskl - INFO - Epoch [35][600/3746] lr: 8.774e-02, eta: 3 days, 17:50:04, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5169, loss_cls: 4.2200, loss: 4.2200 +2024-07-23 06:18:29,736 - pyskl - INFO - Epoch [35][700/3746] lr: 8.772e-02, eta: 3 days, 17:49:18, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5233, loss_cls: 4.1802, loss: 4.1802 +2024-07-23 06:19:52,066 - pyskl - INFO - Epoch [35][800/3746] lr: 8.770e-02, eta: 3 days, 17:48:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5255, loss_cls: 4.2046, loss: 4.2046 +2024-07-23 06:21:14,770 - pyskl - INFO - Epoch [35][900/3746] lr: 8.769e-02, eta: 3 days, 17:47:43, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5184, loss_cls: 4.2074, loss: 4.2074 +2024-07-23 06:22:36,152 - pyskl - INFO - Epoch [35][1000/3746] lr: 8.767e-02, eta: 3 days, 17:46:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5236, loss_cls: 4.2153, loss: 4.2153 +2024-07-23 06:23:57,961 - pyskl - INFO - Epoch [35][1100/3746] lr: 8.765e-02, eta: 3 days, 17:46:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5286, loss_cls: 4.1690, loss: 4.1690 +2024-07-23 06:25:19,447 - pyskl - INFO - Epoch [35][1200/3746] lr: 8.763e-02, eta: 3 days, 17:45:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5288, loss_cls: 4.1708, loss: 4.1708 +2024-07-23 06:26:41,303 - pyskl - INFO - Epoch [35][1300/3746] lr: 8.761e-02, eta: 3 days, 17:44:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5206, loss_cls: 4.2345, loss: 4.2345 +2024-07-23 06:28:02,787 - pyskl - INFO - Epoch [35][1400/3746] lr: 8.759e-02, eta: 3 days, 17:43:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5203, loss_cls: 4.2251, loss: 4.2251 +2024-07-23 06:29:24,738 - pyskl - INFO - Epoch [35][1500/3746] lr: 8.757e-02, eta: 3 days, 17:42:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5208, loss_cls: 4.2194, loss: 4.2194 +2024-07-23 06:30:46,343 - pyskl - INFO - Epoch [35][1600/3746] lr: 8.756e-02, eta: 3 days, 17:41:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5194, loss_cls: 4.2129, loss: 4.2129 +2024-07-23 06:32:08,690 - pyskl - INFO - Epoch [35][1700/3746] lr: 8.754e-02, eta: 3 days, 17:40:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5150, loss_cls: 4.2055, loss: 4.2055 +2024-07-23 06:33:30,096 - pyskl - INFO - Epoch [35][1800/3746] lr: 8.752e-02, eta: 3 days, 17:40:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5175, loss_cls: 4.2048, loss: 4.2048 +2024-07-23 06:34:51,701 - pyskl - INFO - Epoch [35][1900/3746] lr: 8.750e-02, eta: 3 days, 17:39:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5273, loss_cls: 4.1524, loss: 4.1524 +2024-07-23 06:36:13,456 - pyskl - INFO - Epoch [35][2000/3746] lr: 8.748e-02, eta: 3 days, 17:38:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5244, loss_cls: 4.1747, loss: 4.1747 +2024-07-23 06:37:35,327 - pyskl - INFO - Epoch [35][2100/3746] lr: 8.746e-02, eta: 3 days, 17:37:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5230, loss_cls: 4.1947, loss: 4.1947 +2024-07-23 06:38:56,685 - pyskl - INFO - Epoch [35][2200/3746] lr: 8.745e-02, eta: 3 days, 17:36:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5233, loss_cls: 4.2186, loss: 4.2186 +2024-07-23 06:40:18,385 - pyskl - INFO - Epoch [35][2300/3746] lr: 8.743e-02, eta: 3 days, 17:35:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5356, loss_cls: 4.1610, loss: 4.1610 +2024-07-23 06:41:40,569 - pyskl - INFO - Epoch [35][2400/3746] lr: 8.741e-02, eta: 3 days, 17:35:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5323, loss_cls: 4.1879, loss: 4.1879 +2024-07-23 06:43:02,148 - pyskl - INFO - Epoch [35][2500/3746] lr: 8.739e-02, eta: 3 days, 17:34:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5172, loss_cls: 4.2080, loss: 4.2080 +2024-07-23 06:44:24,128 - pyskl - INFO - Epoch [35][2600/3746] lr: 8.737e-02, eta: 3 days, 17:33:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5230, loss_cls: 4.1983, loss: 4.1983 +2024-07-23 06:45:45,691 - pyskl - INFO - Epoch [35][2700/3746] lr: 8.735e-02, eta: 3 days, 17:32:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5275, loss_cls: 4.1729, loss: 4.1729 +2024-07-23 06:47:07,530 - pyskl - INFO - Epoch [35][2800/3746] lr: 8.733e-02, eta: 3 days, 17:31:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5145, loss_cls: 4.2500, loss: 4.2500 +2024-07-23 06:48:28,658 - pyskl - INFO - Epoch [35][2900/3746] lr: 8.732e-02, eta: 3 days, 17:30:45, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5214, loss_cls: 4.1943, loss: 4.1943 +2024-07-23 06:49:50,115 - pyskl - INFO - Epoch [35][3000/3746] lr: 8.730e-02, eta: 3 days, 17:29:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5248, loss_cls: 4.1938, loss: 4.1938 +2024-07-23 06:51:12,786 - pyskl - INFO - Epoch [35][3100/3746] lr: 8.728e-02, eta: 3 days, 17:29:05, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5114, loss_cls: 4.2520, loss: 4.2520 +2024-07-23 06:52:35,154 - pyskl - INFO - Epoch [35][3200/3746] lr: 8.726e-02, eta: 3 days, 17:28:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5228, loss_cls: 4.2236, loss: 4.2236 +2024-07-23 06:53:57,024 - pyskl - INFO - Epoch [35][3300/3746] lr: 8.724e-02, eta: 3 days, 17:27:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5230, loss_cls: 4.2124, loss: 4.2124 +2024-07-23 06:55:19,485 - pyskl - INFO - Epoch [35][3400/3746] lr: 8.722e-02, eta: 3 days, 17:26:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5250, loss_cls: 4.2220, loss: 4.2220 +2024-07-23 06:56:41,550 - pyskl - INFO - Epoch [35][3500/3746] lr: 8.720e-02, eta: 3 days, 17:25:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5141, loss_cls: 4.2190, loss: 4.2190 +2024-07-23 06:58:03,233 - pyskl - INFO - Epoch [35][3600/3746] lr: 8.718e-02, eta: 3 days, 17:24:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5317, loss_cls: 4.1703, loss: 4.1703 +2024-07-23 06:59:25,276 - pyskl - INFO - Epoch [35][3700/3746] lr: 8.717e-02, eta: 3 days, 17:24:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5220, loss_cls: 4.1917, loss: 4.1917 +2024-07-23 07:00:04,986 - pyskl - INFO - Saving checkpoint at 35 epochs +2024-07-23 07:01:57,180 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 07:01:57,851 - pyskl - INFO - +top1_acc 0.1681 +top5_acc 0.3775 +2024-07-23 07:01:57,851 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 07:01:57,890 - pyskl - INFO - +mean_acc 0.1678 +2024-07-23 07:01:57,900 - pyskl - INFO - Epoch(val) [35][309] top1_acc: 0.1681, top5_acc: 0.3775, mean_class_accuracy: 0.1678 +2024-07-23 07:05:44,112 - pyskl - INFO - Epoch [36][100/3746] lr: 8.714e-02, eta: 3 days, 17:28:39, time: 2.262, data_time: 1.290, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5419, loss_cls: 4.1181, loss: 4.1181 +2024-07-23 07:07:05,888 - pyskl - INFO - Epoch [36][200/3746] lr: 8.712e-02, eta: 3 days, 17:27:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5170, loss_cls: 4.2067, loss: 4.2067 +2024-07-23 07:08:27,439 - pyskl - INFO - Epoch [36][300/3746] lr: 8.710e-02, eta: 3 days, 17:26:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5303, loss_cls: 4.1689, loss: 4.1689 +2024-07-23 07:09:49,622 - pyskl - INFO - Epoch [36][400/3746] lr: 8.708e-02, eta: 3 days, 17:26:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5305, loss_cls: 4.2068, loss: 4.2068 +2024-07-23 07:11:11,612 - pyskl - INFO - Epoch [36][500/3746] lr: 8.706e-02, eta: 3 days, 17:25:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5367, loss_cls: 4.1396, loss: 4.1396 +2024-07-23 07:12:34,220 - pyskl - INFO - Epoch [36][600/3746] lr: 8.704e-02, eta: 3 days, 17:24:23, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5266, loss_cls: 4.1656, loss: 4.1656 +2024-07-23 07:13:56,665 - pyskl - INFO - Epoch [36][700/3746] lr: 8.703e-02, eta: 3 days, 17:23:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5183, loss_cls: 4.2117, loss: 4.2117 +2024-07-23 07:15:19,052 - pyskl - INFO - Epoch [36][800/3746] lr: 8.701e-02, eta: 3 days, 17:22:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5220, loss_cls: 4.2067, loss: 4.2067 +2024-07-23 07:16:41,338 - pyskl - INFO - Epoch [36][900/3746] lr: 8.699e-02, eta: 3 days, 17:21:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5352, loss_cls: 4.1603, loss: 4.1603 +2024-07-23 07:18:03,276 - pyskl - INFO - Epoch [36][1000/3746] lr: 8.697e-02, eta: 3 days, 17:21:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5231, loss_cls: 4.1637, loss: 4.1637 +2024-07-23 07:19:24,959 - pyskl - INFO - Epoch [36][1100/3746] lr: 8.695e-02, eta: 3 days, 17:20:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5252, loss_cls: 4.1853, loss: 4.1853 +2024-07-23 07:20:46,836 - pyskl - INFO - Epoch [36][1200/3746] lr: 8.693e-02, eta: 3 days, 17:19:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5300, loss_cls: 4.1375, loss: 4.1375 +2024-07-23 07:22:08,173 - pyskl - INFO - Epoch [36][1300/3746] lr: 8.691e-02, eta: 3 days, 17:18:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5269, loss_cls: 4.1554, loss: 4.1554 +2024-07-23 07:23:30,040 - pyskl - INFO - Epoch [36][1400/3746] lr: 8.689e-02, eta: 3 days, 17:17:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5141, loss_cls: 4.2411, loss: 4.2411 +2024-07-23 07:24:51,911 - pyskl - INFO - Epoch [36][1500/3746] lr: 8.688e-02, eta: 3 days, 17:16:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5200, loss_cls: 4.1741, loss: 4.1741 +2024-07-23 07:26:13,284 - pyskl - INFO - Epoch [36][1600/3746] lr: 8.686e-02, eta: 3 days, 17:15:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5194, loss_cls: 4.2170, loss: 4.2170 +2024-07-23 07:27:35,166 - pyskl - INFO - Epoch [36][1700/3746] lr: 8.684e-02, eta: 3 days, 17:14:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5198, loss_cls: 4.2043, loss: 4.2043 +2024-07-23 07:28:57,174 - pyskl - INFO - Epoch [36][1800/3746] lr: 8.682e-02, eta: 3 days, 17:14:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5372, loss_cls: 4.1302, loss: 4.1302 +2024-07-23 07:30:19,223 - pyskl - INFO - Epoch [36][1900/3746] lr: 8.680e-02, eta: 3 days, 17:13:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5298, loss_cls: 4.1598, loss: 4.1598 +2024-07-23 07:31:40,716 - pyskl - INFO - Epoch [36][2000/3746] lr: 8.678e-02, eta: 3 days, 17:12:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5266, loss_cls: 4.1819, loss: 4.1819 +2024-07-23 07:33:02,590 - pyskl - INFO - Epoch [36][2100/3746] lr: 8.676e-02, eta: 3 days, 17:11:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5103, loss_cls: 4.2557, loss: 4.2557 +2024-07-23 07:34:23,861 - pyskl - INFO - Epoch [36][2200/3746] lr: 8.674e-02, eta: 3 days, 17:10:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5212, loss_cls: 4.2295, loss: 4.2295 +2024-07-23 07:35:45,568 - pyskl - INFO - Epoch [36][2300/3746] lr: 8.672e-02, eta: 3 days, 17:09:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5228, loss_cls: 4.1700, loss: 4.1700 +2024-07-23 07:37:07,186 - pyskl - INFO - Epoch [36][2400/3746] lr: 8.671e-02, eta: 3 days, 17:08:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5256, loss_cls: 4.1893, loss: 4.1893 +2024-07-23 07:38:29,600 - pyskl - INFO - Epoch [36][2500/3746] lr: 8.669e-02, eta: 3 days, 17:07:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5245, loss_cls: 4.1902, loss: 4.1902 +2024-07-23 07:39:51,291 - pyskl - INFO - Epoch [36][2600/3746] lr: 8.667e-02, eta: 3 days, 17:06:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5166, loss_cls: 4.1981, loss: 4.1981 +2024-07-23 07:41:13,019 - pyskl - INFO - Epoch [36][2700/3746] lr: 8.665e-02, eta: 3 days, 17:06:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5231, loss_cls: 4.1829, loss: 4.1829 +2024-07-23 07:42:34,760 - pyskl - INFO - Epoch [36][2800/3746] lr: 8.663e-02, eta: 3 days, 17:05:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5088, loss_cls: 4.2707, loss: 4.2707 +2024-07-23 07:43:56,788 - pyskl - INFO - Epoch [36][2900/3746] lr: 8.661e-02, eta: 3 days, 17:04:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5128, loss_cls: 4.2373, loss: 4.2373 +2024-07-23 07:45:18,491 - pyskl - INFO - Epoch [36][3000/3746] lr: 8.659e-02, eta: 3 days, 17:03:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5283, loss_cls: 4.1714, loss: 4.1714 +2024-07-23 07:46:40,132 - pyskl - INFO - Epoch [36][3100/3746] lr: 8.657e-02, eta: 3 days, 17:02:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5181, loss_cls: 4.2150, loss: 4.2150 +2024-07-23 07:48:01,767 - pyskl - INFO - Epoch [36][3200/3746] lr: 8.655e-02, eta: 3 days, 17:01:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5298, loss_cls: 4.1799, loss: 4.1799 +2024-07-23 07:49:23,641 - pyskl - INFO - Epoch [36][3300/3746] lr: 8.653e-02, eta: 3 days, 17:00:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5275, loss_cls: 4.1866, loss: 4.1866 +2024-07-23 07:50:45,550 - pyskl - INFO - Epoch [36][3400/3746] lr: 8.651e-02, eta: 3 days, 16:59:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5233, loss_cls: 4.1982, loss: 4.1982 +2024-07-23 07:52:07,791 - pyskl - INFO - Epoch [36][3500/3746] lr: 8.650e-02, eta: 3 days, 16:59:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5222, loss_cls: 4.2114, loss: 4.2114 +2024-07-23 07:53:29,137 - pyskl - INFO - Epoch [36][3600/3746] lr: 8.648e-02, eta: 3 days, 16:58:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5208, loss_cls: 4.2135, loss: 4.2135 +2024-07-23 07:54:50,717 - pyskl - INFO - Epoch [36][3700/3746] lr: 8.646e-02, eta: 3 days, 16:57:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5333, loss_cls: 4.1533, loss: 4.1533 +2024-07-23 07:55:30,697 - pyskl - INFO - Saving checkpoint at 36 epochs +2024-07-23 07:57:23,256 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 07:57:23,919 - pyskl - INFO - +top1_acc 0.1965 +top5_acc 0.4287 +2024-07-23 07:57:23,919 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 07:57:23,960 - pyskl - INFO - +mean_acc 0.1965 +2024-07-23 07:57:23,970 - pyskl - INFO - Epoch(val) [36][309] top1_acc: 0.1965, top5_acc: 0.4287, mean_class_accuracy: 0.1965 +2024-07-23 08:01:09,927 - pyskl - INFO - Epoch [37][100/3746] lr: 8.643e-02, eta: 3 days, 17:01:30, time: 2.259, data_time: 1.283, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5225, loss_cls: 4.1838, loss: 4.1838 +2024-07-23 08:02:31,050 - pyskl - INFO - Epoch [37][200/3746] lr: 8.641e-02, eta: 3 days, 17:00:34, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5384, loss_cls: 4.1525, loss: 4.1525 +2024-07-23 08:03:53,065 - pyskl - INFO - Epoch [37][300/3746] lr: 8.639e-02, eta: 3 days, 16:59:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5289, loss_cls: 4.1803, loss: 4.1803 +2024-07-23 08:05:14,492 - pyskl - INFO - Epoch [37][400/3746] lr: 8.637e-02, eta: 3 days, 16:58:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5317, loss_cls: 4.1649, loss: 4.1649 +2024-07-23 08:06:36,522 - pyskl - INFO - Epoch [37][500/3746] lr: 8.635e-02, eta: 3 days, 16:57:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5311, loss_cls: 4.1700, loss: 4.1700 +2024-07-23 08:07:57,898 - pyskl - INFO - Epoch [37][600/3746] lr: 8.633e-02, eta: 3 days, 16:56:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5316, loss_cls: 4.1728, loss: 4.1728 +2024-07-23 08:09:21,013 - pyskl - INFO - Epoch [37][700/3746] lr: 8.631e-02, eta: 3 days, 16:56:08, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5283, loss_cls: 4.1926, loss: 4.1926 +2024-07-23 08:10:43,083 - pyskl - INFO - Epoch [37][800/3746] lr: 8.630e-02, eta: 3 days, 16:55:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5245, loss_cls: 4.2056, loss: 4.2056 +2024-07-23 08:12:05,700 - pyskl - INFO - Epoch [37][900/3746] lr: 8.628e-02, eta: 3 days, 16:54:23, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5247, loss_cls: 4.1602, loss: 4.1602 +2024-07-23 08:13:27,676 - pyskl - INFO - Epoch [37][1000/3746] lr: 8.626e-02, eta: 3 days, 16:53:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5308, loss_cls: 4.1596, loss: 4.1596 +2024-07-23 08:14:49,676 - pyskl - INFO - Epoch [37][1100/3746] lr: 8.624e-02, eta: 3 days, 16:52:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5341, loss_cls: 4.1545, loss: 4.1545 +2024-07-23 08:16:11,520 - pyskl - INFO - Epoch [37][1200/3746] lr: 8.622e-02, eta: 3 days, 16:51:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5097, loss_cls: 4.2428, loss: 4.2428 +2024-07-23 08:17:33,108 - pyskl - INFO - Epoch [37][1300/3746] lr: 8.620e-02, eta: 3 days, 16:50:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5233, loss_cls: 4.2035, loss: 4.2035 +2024-07-23 08:18:55,222 - pyskl - INFO - Epoch [37][1400/3746] lr: 8.618e-02, eta: 3 days, 16:49:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5225, loss_cls: 4.1882, loss: 4.1882 +2024-07-23 08:20:16,908 - pyskl - INFO - Epoch [37][1500/3746] lr: 8.616e-02, eta: 3 days, 16:48:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5198, loss_cls: 4.2016, loss: 4.2016 +2024-07-23 08:21:38,687 - pyskl - INFO - Epoch [37][1600/3746] lr: 8.614e-02, eta: 3 days, 16:48:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5328, loss_cls: 4.1825, loss: 4.1825 +2024-07-23 08:22:59,849 - pyskl - INFO - Epoch [37][1700/3746] lr: 8.612e-02, eta: 3 days, 16:47:08, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5323, loss_cls: 4.1485, loss: 4.1485 +2024-07-23 08:24:21,435 - pyskl - INFO - Epoch [37][1800/3746] lr: 8.610e-02, eta: 3 days, 16:46:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5255, loss_cls: 4.1696, loss: 4.1696 +2024-07-23 08:25:43,440 - pyskl - INFO - Epoch [37][1900/3746] lr: 8.608e-02, eta: 3 days, 16:45:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5273, loss_cls: 4.1666, loss: 4.1666 +2024-07-23 08:27:05,176 - pyskl - INFO - Epoch [37][2000/3746] lr: 8.606e-02, eta: 3 days, 16:44:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5136, loss_cls: 4.2339, loss: 4.2339 +2024-07-23 08:28:27,064 - pyskl - INFO - Epoch [37][2100/3746] lr: 8.604e-02, eta: 3 days, 16:43:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5291, loss_cls: 4.1792, loss: 4.1792 +2024-07-23 08:29:48,985 - pyskl - INFO - Epoch [37][2200/3746] lr: 8.602e-02, eta: 3 days, 16:42:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5250, loss_cls: 4.1822, loss: 4.1822 +2024-07-23 08:31:10,593 - pyskl - INFO - Epoch [37][2300/3746] lr: 8.601e-02, eta: 3 days, 16:41:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5248, loss_cls: 4.2149, loss: 4.2149 +2024-07-23 08:32:32,284 - pyskl - INFO - Epoch [37][2400/3746] lr: 8.599e-02, eta: 3 days, 16:40:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5127, loss_cls: 4.2421, loss: 4.2421 +2024-07-23 08:33:54,281 - pyskl - INFO - Epoch [37][2500/3746] lr: 8.597e-02, eta: 3 days, 16:39:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5295, loss_cls: 4.1496, loss: 4.1496 +2024-07-23 08:35:16,326 - pyskl - INFO - Epoch [37][2600/3746] lr: 8.595e-02, eta: 3 days, 16:38:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5220, loss_cls: 4.1853, loss: 4.1853 +2024-07-23 08:36:37,820 - pyskl - INFO - Epoch [37][2700/3746] lr: 8.593e-02, eta: 3 days, 16:38:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5188, loss_cls: 4.2089, loss: 4.2089 +2024-07-23 08:37:59,192 - pyskl - INFO - Epoch [37][2800/3746] lr: 8.591e-02, eta: 3 days, 16:37:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5169, loss_cls: 4.2385, loss: 4.2385 +2024-07-23 08:39:21,184 - pyskl - INFO - Epoch [37][2900/3746] lr: 8.589e-02, eta: 3 days, 16:36:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5173, loss_cls: 4.2104, loss: 4.2104 +2024-07-23 08:40:42,322 - pyskl - INFO - Epoch [37][3000/3746] lr: 8.587e-02, eta: 3 days, 16:35:15, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5192, loss_cls: 4.1830, loss: 4.1830 +2024-07-23 08:42:04,606 - pyskl - INFO - Epoch [37][3100/3746] lr: 8.585e-02, eta: 3 days, 16:34:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5248, loss_cls: 4.1942, loss: 4.1942 +2024-07-23 08:43:26,158 - pyskl - INFO - Epoch [37][3200/3746] lr: 8.583e-02, eta: 3 days, 16:33:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5344, loss_cls: 4.1365, loss: 4.1365 +2024-07-23 08:44:48,198 - pyskl - INFO - Epoch [37][3300/3746] lr: 8.581e-02, eta: 3 days, 16:32:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5342, loss_cls: 4.1280, loss: 4.1280 +2024-07-23 08:46:09,886 - pyskl - INFO - Epoch [37][3400/3746] lr: 8.579e-02, eta: 3 days, 16:31:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5166, loss_cls: 4.1905, loss: 4.1905 +2024-07-23 08:47:31,418 - pyskl - INFO - Epoch [37][3500/3746] lr: 8.577e-02, eta: 3 days, 16:30:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5300, loss_cls: 4.1649, loss: 4.1649 +2024-07-23 08:48:53,229 - pyskl - INFO - Epoch [37][3600/3746] lr: 8.575e-02, eta: 3 days, 16:29:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5300, loss_cls: 4.1654, loss: 4.1654 +2024-07-23 08:50:15,146 - pyskl - INFO - Epoch [37][3700/3746] lr: 8.573e-02, eta: 3 days, 16:28:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5211, loss_cls: 4.2361, loss: 4.2361 +2024-07-23 08:50:54,690 - pyskl - INFO - Saving checkpoint at 37 epochs +2024-07-23 08:52:46,970 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 08:52:47,623 - pyskl - INFO - +top1_acc 0.1959 +top5_acc 0.4199 +2024-07-23 08:52:47,623 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 08:52:47,662 - pyskl - INFO - +mean_acc 0.1956 +2024-07-23 08:52:47,673 - pyskl - INFO - Epoch(val) [37][309] top1_acc: 0.1959, top5_acc: 0.4199, mean_class_accuracy: 0.1956 +2024-07-23 08:56:31,814 - pyskl - INFO - Epoch [38][100/3746] lr: 8.570e-02, eta: 3 days, 16:32:49, time: 2.241, data_time: 1.266, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5270, loss_cls: 4.1759, loss: 4.1759 +2024-07-23 08:57:53,841 - pyskl - INFO - Epoch [38][200/3746] lr: 8.568e-02, eta: 3 days, 16:31:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5378, loss_cls: 4.1034, loss: 4.1034 +2024-07-23 08:59:15,405 - pyskl - INFO - Epoch [38][300/3746] lr: 8.567e-02, eta: 3 days, 16:30:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5345, loss_cls: 4.1550, loss: 4.1550 +2024-07-23 09:00:37,009 - pyskl - INFO - Epoch [38][400/3746] lr: 8.565e-02, eta: 3 days, 16:30:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5339, loss_cls: 4.1537, loss: 4.1537 +2024-07-23 09:01:58,856 - pyskl - INFO - Epoch [38][500/3746] lr: 8.563e-02, eta: 3 days, 16:29:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5345, loss_cls: 4.1365, loss: 4.1365 +2024-07-23 09:03:22,273 - pyskl - INFO - Epoch [38][600/3746] lr: 8.561e-02, eta: 3 days, 16:28:15, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5262, loss_cls: 4.2054, loss: 4.2054 +2024-07-23 09:04:45,493 - pyskl - INFO - Epoch [38][700/3746] lr: 8.559e-02, eta: 3 days, 16:27:23, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5295, loss_cls: 4.1730, loss: 4.1730 +2024-07-23 09:06:08,677 - pyskl - INFO - Epoch [38][800/3746] lr: 8.557e-02, eta: 3 days, 16:26:32, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5250, loss_cls: 4.1873, loss: 4.1873 +2024-07-23 09:07:31,153 - pyskl - INFO - Epoch [38][900/3746] lr: 8.555e-02, eta: 3 days, 16:25:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5228, loss_cls: 4.2246, loss: 4.2246 +2024-07-23 09:08:53,552 - pyskl - INFO - Epoch [38][1000/3746] lr: 8.553e-02, eta: 3 days, 16:24:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5197, loss_cls: 4.1826, loss: 4.1826 +2024-07-23 09:10:15,463 - pyskl - INFO - Epoch [38][1100/3746] lr: 8.551e-02, eta: 3 days, 16:23:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5364, loss_cls: 4.1494, loss: 4.1494 +2024-07-23 09:11:37,315 - pyskl - INFO - Epoch [38][1200/3746] lr: 8.549e-02, eta: 3 days, 16:22:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5281, loss_cls: 4.1736, loss: 4.1736 +2024-07-23 09:12:59,139 - pyskl - INFO - Epoch [38][1300/3746] lr: 8.547e-02, eta: 3 days, 16:21:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5300, loss_cls: 4.1561, loss: 4.1561 +2024-07-23 09:14:20,753 - pyskl - INFO - Epoch [38][1400/3746] lr: 8.545e-02, eta: 3 days, 16:21:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5295, loss_cls: 4.2023, loss: 4.2023 +2024-07-23 09:15:43,422 - pyskl - INFO - Epoch [38][1500/3746] lr: 8.543e-02, eta: 3 days, 16:20:06, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5189, loss_cls: 4.2118, loss: 4.2118 +2024-07-23 09:17:06,372 - pyskl - INFO - Epoch [38][1600/3746] lr: 8.541e-02, eta: 3 days, 16:19:14, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5345, loss_cls: 4.1395, loss: 4.1395 +2024-07-23 09:18:28,072 - pyskl - INFO - Epoch [38][1700/3746] lr: 8.539e-02, eta: 3 days, 16:18:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5197, loss_cls: 4.1809, loss: 4.1809 +2024-07-23 09:19:50,103 - pyskl - INFO - Epoch [38][1800/3746] lr: 8.537e-02, eta: 3 days, 16:17:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5106, loss_cls: 4.2178, loss: 4.2178 +2024-07-23 09:21:11,694 - pyskl - INFO - Epoch [38][1900/3746] lr: 8.535e-02, eta: 3 days, 16:16:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5247, loss_cls: 4.1849, loss: 4.1849 +2024-07-23 09:22:33,205 - pyskl - INFO - Epoch [38][2000/3746] lr: 8.533e-02, eta: 3 days, 16:15:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5214, loss_cls: 4.1877, loss: 4.1877 +2024-07-23 09:23:55,491 - pyskl - INFO - Epoch [38][2100/3746] lr: 8.531e-02, eta: 3 days, 16:14:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5300, loss_cls: 4.1655, loss: 4.1655 +2024-07-23 09:25:17,045 - pyskl - INFO - Epoch [38][2200/3746] lr: 8.529e-02, eta: 3 days, 16:13:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5220, loss_cls: 4.1996, loss: 4.1996 +2024-07-23 09:26:38,655 - pyskl - INFO - Epoch [38][2300/3746] lr: 8.527e-02, eta: 3 days, 16:12:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5286, loss_cls: 4.1417, loss: 4.1417 +2024-07-23 09:28:00,581 - pyskl - INFO - Epoch [38][2400/3746] lr: 8.525e-02, eta: 3 days, 16:11:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5256, loss_cls: 4.1565, loss: 4.1565 +2024-07-23 09:29:22,413 - pyskl - INFO - Epoch [38][2500/3746] lr: 8.523e-02, eta: 3 days, 16:10:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5275, loss_cls: 4.1835, loss: 4.1835 +2024-07-23 09:30:43,844 - pyskl - INFO - Epoch [38][2600/3746] lr: 8.521e-02, eta: 3 days, 16:09:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5291, loss_cls: 4.1520, loss: 4.1520 +2024-07-23 09:32:05,302 - pyskl - INFO - Epoch [38][2700/3746] lr: 8.519e-02, eta: 3 days, 16:08:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5223, loss_cls: 4.2005, loss: 4.2005 +2024-07-23 09:33:26,787 - pyskl - INFO - Epoch [38][2800/3746] lr: 8.517e-02, eta: 3 days, 16:07:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5292, loss_cls: 4.1975, loss: 4.1975 +2024-07-23 09:34:48,317 - pyskl - INFO - Epoch [38][2900/3746] lr: 8.515e-02, eta: 3 days, 16:06:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5128, loss_cls: 4.2102, loss: 4.2102 +2024-07-23 09:36:09,606 - pyskl - INFO - Epoch [38][3000/3746] lr: 8.513e-02, eta: 3 days, 16:05:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5305, loss_cls: 4.1615, loss: 4.1615 +2024-07-23 09:37:31,606 - pyskl - INFO - Epoch [38][3100/3746] lr: 8.511e-02, eta: 3 days, 16:05:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5206, loss_cls: 4.1935, loss: 4.1935 +2024-07-23 09:38:53,466 - pyskl - INFO - Epoch [38][3200/3746] lr: 8.509e-02, eta: 3 days, 16:04:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5230, loss_cls: 4.2050, loss: 4.2050 +2024-07-23 09:40:15,644 - pyskl - INFO - Epoch [38][3300/3746] lr: 8.507e-02, eta: 3 days, 16:03:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5311, loss_cls: 4.1365, loss: 4.1365 +2024-07-23 09:41:36,940 - pyskl - INFO - Epoch [38][3400/3746] lr: 8.505e-02, eta: 3 days, 16:02:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5238, loss_cls: 4.1894, loss: 4.1894 +2024-07-23 09:42:58,348 - pyskl - INFO - Epoch [38][3500/3746] lr: 8.503e-02, eta: 3 days, 16:01:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5200, loss_cls: 4.1595, loss: 4.1595 +2024-07-23 09:44:20,190 - pyskl - INFO - Epoch [38][3600/3746] lr: 8.501e-02, eta: 3 days, 16:00:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5233, loss_cls: 4.2198, loss: 4.2198 +2024-07-23 09:45:42,771 - pyskl - INFO - Epoch [38][3700/3746] lr: 8.499e-02, eta: 3 days, 15:59:22, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5172, loss_cls: 4.2109, loss: 4.2109 +2024-07-23 09:46:22,499 - pyskl - INFO - Saving checkpoint at 38 epochs +2024-07-23 09:48:14,618 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 09:48:15,276 - pyskl - INFO - +top1_acc 0.2194 +top5_acc 0.4569 +2024-07-23 09:48:15,276 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 09:48:15,314 - pyskl - INFO - +mean_acc 0.2191 +2024-07-23 09:48:15,319 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_33.pth was removed +2024-07-23 09:48:15,558 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_38.pth. +2024-07-23 09:48:15,559 - pyskl - INFO - Best top1_acc is 0.2194 at 38 epoch. +2024-07-23 09:48:15,570 - pyskl - INFO - Epoch(val) [38][309] top1_acc: 0.2194, top5_acc: 0.4569, mean_class_accuracy: 0.2191 +2024-07-23 09:52:04,561 - pyskl - INFO - Epoch [39][100/3746] lr: 8.496e-02, eta: 3 days, 16:03:22, time: 2.290, data_time: 1.269, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5298, loss_cls: 4.1535, loss: 4.1535 +2024-07-23 09:53:27,572 - pyskl - INFO - Epoch [39][200/3746] lr: 8.494e-02, eta: 3 days, 16:02:28, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5250, loss_cls: 4.1710, loss: 4.1710 +2024-07-23 09:54:49,518 - pyskl - INFO - Epoch [39][300/3746] lr: 8.492e-02, eta: 3 days, 16:01:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5222, loss_cls: 4.1836, loss: 4.1836 +2024-07-23 09:56:11,695 - pyskl - INFO - Epoch [39][400/3746] lr: 8.490e-02, eta: 3 days, 16:00:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5284, loss_cls: 4.1649, loss: 4.1649 +2024-07-23 09:57:33,939 - pyskl - INFO - Epoch [39][500/3746] lr: 8.488e-02, eta: 3 days, 15:59:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5403, loss_cls: 4.1422, loss: 4.1422 +2024-07-23 09:58:57,012 - pyskl - INFO - Epoch [39][600/3746] lr: 8.486e-02, eta: 3 days, 15:58:45, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5316, loss_cls: 4.1458, loss: 4.1458 +2024-07-23 10:00:20,594 - pyskl - INFO - Epoch [39][700/3746] lr: 8.484e-02, eta: 3 days, 15:57:53, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5363, loss_cls: 4.1435, loss: 4.1435 +2024-07-23 10:01:43,367 - pyskl - INFO - Epoch [39][800/3746] lr: 8.482e-02, eta: 3 days, 15:56:59, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5283, loss_cls: 4.1707, loss: 4.1707 +2024-07-23 10:03:05,600 - pyskl - INFO - Epoch [39][900/3746] lr: 8.480e-02, eta: 3 days, 15:56:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5256, loss_cls: 4.1370, loss: 4.1370 +2024-07-23 10:04:27,428 - pyskl - INFO - Epoch [39][1000/3746] lr: 8.478e-02, eta: 3 days, 15:55:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5225, loss_cls: 4.2010, loss: 4.2010 +2024-07-23 10:05:48,940 - pyskl - INFO - Epoch [39][1100/3746] lr: 8.476e-02, eta: 3 days, 15:54:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5178, loss_cls: 4.1999, loss: 4.1999 +2024-07-23 10:07:10,503 - pyskl - INFO - Epoch [39][1200/3746] lr: 8.474e-02, eta: 3 days, 15:53:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5294, loss_cls: 4.1713, loss: 4.1713 +2024-07-23 10:08:32,170 - pyskl - INFO - Epoch [39][1300/3746] lr: 8.472e-02, eta: 3 days, 15:52:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5453, loss_cls: 4.1236, loss: 4.1236 +2024-07-23 10:09:54,365 - pyskl - INFO - Epoch [39][1400/3746] lr: 8.470e-02, eta: 3 days, 15:51:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5252, loss_cls: 4.1524, loss: 4.1524 +2024-07-23 10:11:16,211 - pyskl - INFO - Epoch [39][1500/3746] lr: 8.468e-02, eta: 3 days, 15:50:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5266, loss_cls: 4.1958, loss: 4.1958 +2024-07-23 10:12:38,182 - pyskl - INFO - Epoch [39][1600/3746] lr: 8.466e-02, eta: 3 days, 15:49:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5255, loss_cls: 4.1994, loss: 4.1994 +2024-07-23 10:14:00,251 - pyskl - INFO - Epoch [39][1700/3746] lr: 8.464e-02, eta: 3 days, 15:48:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5255, loss_cls: 4.1883, loss: 4.1883 +2024-07-23 10:15:22,149 - pyskl - INFO - Epoch [39][1800/3746] lr: 8.462e-02, eta: 3 days, 15:47:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5302, loss_cls: 4.1770, loss: 4.1770 +2024-07-23 10:16:43,711 - pyskl - INFO - Epoch [39][1900/3746] lr: 8.460e-02, eta: 3 days, 15:46:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5217, loss_cls: 4.1902, loss: 4.1902 +2024-07-23 10:18:06,518 - pyskl - INFO - Epoch [39][2000/3746] lr: 8.458e-02, eta: 3 days, 15:45:31, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5245, loss_cls: 4.1704, loss: 4.1704 +2024-07-23 10:19:28,248 - pyskl - INFO - Epoch [39][2100/3746] lr: 8.456e-02, eta: 3 days, 15:44:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5280, loss_cls: 4.1619, loss: 4.1619 +2024-07-23 10:20:50,156 - pyskl - INFO - Epoch [39][2200/3746] lr: 8.454e-02, eta: 3 days, 15:43:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5264, loss_cls: 4.1926, loss: 4.1926 +2024-07-23 10:22:12,018 - pyskl - INFO - Epoch [39][2300/3746] lr: 8.452e-02, eta: 3 days, 15:42:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5386, loss_cls: 4.1090, loss: 4.1090 +2024-07-23 10:23:34,210 - pyskl - INFO - Epoch [39][2400/3746] lr: 8.450e-02, eta: 3 days, 15:41:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5300, loss_cls: 4.1630, loss: 4.1630 +2024-07-23 10:24:55,874 - pyskl - INFO - Epoch [39][2500/3746] lr: 8.448e-02, eta: 3 days, 15:40:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5216, loss_cls: 4.2203, loss: 4.2203 +2024-07-23 10:26:17,431 - pyskl - INFO - Epoch [39][2600/3746] lr: 8.446e-02, eta: 3 days, 15:39:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5216, loss_cls: 4.1693, loss: 4.1693 +2024-07-23 10:27:39,521 - pyskl - INFO - Epoch [39][2700/3746] lr: 8.444e-02, eta: 3 days, 15:38:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5292, loss_cls: 4.1786, loss: 4.1786 +2024-07-23 10:29:00,880 - pyskl - INFO - Epoch [39][2800/3746] lr: 8.442e-02, eta: 3 days, 15:37:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5222, loss_cls: 4.1801, loss: 4.1801 +2024-07-23 10:30:22,464 - pyskl - INFO - Epoch [39][2900/3746] lr: 8.440e-02, eta: 3 days, 15:36:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5325, loss_cls: 4.1527, loss: 4.1527 +2024-07-23 10:31:44,659 - pyskl - INFO - Epoch [39][3000/3746] lr: 8.438e-02, eta: 3 days, 15:35:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5212, loss_cls: 4.2338, loss: 4.2338 +2024-07-23 10:33:06,761 - pyskl - INFO - Epoch [39][3100/3746] lr: 8.436e-02, eta: 3 days, 15:34:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5252, loss_cls: 4.2037, loss: 4.2037 +2024-07-23 10:34:29,652 - pyskl - INFO - Epoch [39][3200/3746] lr: 8.434e-02, eta: 3 days, 15:33:58, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5202, loss_cls: 4.1763, loss: 4.1763 +2024-07-23 10:35:51,990 - pyskl - INFO - Epoch [39][3300/3746] lr: 8.432e-02, eta: 3 days, 15:33:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5144, loss_cls: 4.2325, loss: 4.2325 +2024-07-23 10:37:13,702 - pyskl - INFO - Epoch [39][3400/3746] lr: 8.430e-02, eta: 3 days, 15:32:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5247, loss_cls: 4.1720, loss: 4.1720 +2024-07-23 10:38:35,950 - pyskl - INFO - Epoch [39][3500/3746] lr: 8.428e-02, eta: 3 days, 15:31:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5322, loss_cls: 4.1475, loss: 4.1475 +2024-07-23 10:39:57,773 - pyskl - INFO - Epoch [39][3600/3746] lr: 8.426e-02, eta: 3 days, 15:30:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5300, loss_cls: 4.1639, loss: 4.1639 +2024-07-23 10:41:20,092 - pyskl - INFO - Epoch [39][3700/3746] lr: 8.424e-02, eta: 3 days, 15:29:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5202, loss_cls: 4.1848, loss: 4.1848 +2024-07-23 10:42:00,065 - pyskl - INFO - Saving checkpoint at 39 epochs +2024-07-23 10:43:52,925 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 10:43:53,590 - pyskl - INFO - +top1_acc 0.2039 +top5_acc 0.4349 +2024-07-23 10:43:53,590 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 10:43:53,631 - pyskl - INFO - +mean_acc 0.2037 +2024-07-23 10:43:53,642 - pyskl - INFO - Epoch(val) [39][309] top1_acc: 0.2039, top5_acc: 0.4349, mean_class_accuracy: 0.2037 +2024-07-23 10:47:44,164 - pyskl - INFO - Epoch [40][100/3746] lr: 8.421e-02, eta: 3 days, 15:33:00, time: 2.305, data_time: 1.285, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5378, loss_cls: 4.1347, loss: 4.1347 +2024-07-23 10:49:05,794 - pyskl - INFO - Epoch [40][200/3746] lr: 8.419e-02, eta: 3 days, 15:32:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5245, loss_cls: 4.1622, loss: 4.1622 +2024-07-23 10:50:27,513 - pyskl - INFO - Epoch [40][300/3746] lr: 8.417e-02, eta: 3 days, 15:31:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5275, loss_cls: 4.1485, loss: 4.1485 +2024-07-23 10:51:49,862 - pyskl - INFO - Epoch [40][400/3746] lr: 8.415e-02, eta: 3 days, 15:30:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5298, loss_cls: 4.1691, loss: 4.1691 +2024-07-23 10:53:11,715 - pyskl - INFO - Epoch [40][500/3746] lr: 8.413e-02, eta: 3 days, 15:29:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5402, loss_cls: 4.1061, loss: 4.1061 +2024-07-23 10:54:33,864 - pyskl - INFO - Epoch [40][600/3746] lr: 8.411e-02, eta: 3 days, 15:28:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5280, loss_cls: 4.1788, loss: 4.1788 +2024-07-23 10:55:57,648 - pyskl - INFO - Epoch [40][700/3746] lr: 8.408e-02, eta: 3 days, 15:27:14, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5261, loss_cls: 4.1772, loss: 4.1772 +2024-07-23 10:57:20,505 - pyskl - INFO - Epoch [40][800/3746] lr: 8.406e-02, eta: 3 days, 15:26:18, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5222, loss_cls: 4.2228, loss: 4.2228 +2024-07-23 10:58:43,349 - pyskl - INFO - Epoch [40][900/3746] lr: 8.404e-02, eta: 3 days, 15:25:22, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5292, loss_cls: 4.1413, loss: 4.1413 +2024-07-23 11:00:05,381 - pyskl - INFO - Epoch [40][1000/3746] lr: 8.402e-02, eta: 3 days, 15:24:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5336, loss_cls: 4.1104, loss: 4.1104 +2024-07-23 11:01:27,281 - pyskl - INFO - Epoch [40][1100/3746] lr: 8.400e-02, eta: 3 days, 15:23:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5320, loss_cls: 4.1391, loss: 4.1391 +2024-07-23 11:02:49,408 - pyskl - INFO - Epoch [40][1200/3746] lr: 8.398e-02, eta: 3 days, 15:22:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5483, loss_cls: 4.0569, loss: 4.0569 +2024-07-23 11:04:11,745 - pyskl - INFO - Epoch [40][1300/3746] lr: 8.396e-02, eta: 3 days, 15:21:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5200, loss_cls: 4.1840, loss: 4.1840 +2024-07-23 11:05:33,551 - pyskl - INFO - Epoch [40][1400/3746] lr: 8.394e-02, eta: 3 days, 15:20:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5317, loss_cls: 4.1433, loss: 4.1433 +2024-07-23 11:06:55,468 - pyskl - INFO - Epoch [40][1500/3746] lr: 8.392e-02, eta: 3 days, 15:19:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5273, loss_cls: 4.1634, loss: 4.1634 +2024-07-23 11:08:17,613 - pyskl - INFO - Epoch [40][1600/3746] lr: 8.390e-02, eta: 3 days, 15:18:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5291, loss_cls: 4.1596, loss: 4.1596 +2024-07-23 11:09:39,195 - pyskl - INFO - Epoch [40][1700/3746] lr: 8.388e-02, eta: 3 days, 15:17:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5317, loss_cls: 4.1386, loss: 4.1386 +2024-07-23 11:11:00,982 - pyskl - INFO - Epoch [40][1800/3746] lr: 8.386e-02, eta: 3 days, 15:16:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5277, loss_cls: 4.1560, loss: 4.1560 +2024-07-23 11:12:23,461 - pyskl - INFO - Epoch [40][1900/3746] lr: 8.384e-02, eta: 3 days, 15:15:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5339, loss_cls: 4.1675, loss: 4.1675 +2024-07-23 11:13:45,626 - pyskl - INFO - Epoch [40][2000/3746] lr: 8.382e-02, eta: 3 days, 15:14:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5231, loss_cls: 4.1883, loss: 4.1883 +2024-07-23 11:15:07,085 - pyskl - INFO - Epoch [40][2100/3746] lr: 8.380e-02, eta: 3 days, 15:13:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5297, loss_cls: 4.1802, loss: 4.1802 +2024-07-23 11:16:29,024 - pyskl - INFO - Epoch [40][2200/3746] lr: 8.378e-02, eta: 3 days, 15:12:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5120, loss_cls: 4.1995, loss: 4.1995 +2024-07-23 11:17:51,036 - pyskl - INFO - Epoch [40][2300/3746] lr: 8.376e-02, eta: 3 days, 15:11:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5192, loss_cls: 4.2044, loss: 4.2044 +2024-07-23 11:19:13,806 - pyskl - INFO - Epoch [40][2400/3746] lr: 8.374e-02, eta: 3 days, 15:10:42, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5314, loss_cls: 4.1724, loss: 4.1724 +2024-07-23 11:20:35,696 - pyskl - INFO - Epoch [40][2500/3746] lr: 8.371e-02, eta: 3 days, 15:09:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5400, loss_cls: 4.1149, loss: 4.1149 +2024-07-23 11:21:57,616 - pyskl - INFO - Epoch [40][2600/3746] lr: 8.369e-02, eta: 3 days, 15:08:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5289, loss_cls: 4.1672, loss: 4.1672 +2024-07-23 11:23:19,057 - pyskl - INFO - Epoch [40][2700/3746] lr: 8.367e-02, eta: 3 days, 15:07:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5352, loss_cls: 4.1359, loss: 4.1359 +2024-07-23 11:24:41,167 - pyskl - INFO - Epoch [40][2800/3746] lr: 8.365e-02, eta: 3 days, 15:06:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5266, loss_cls: 4.1745, loss: 4.1745 +2024-07-23 11:26:03,058 - pyskl - INFO - Epoch [40][2900/3746] lr: 8.363e-02, eta: 3 days, 15:05:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5305, loss_cls: 4.1732, loss: 4.1732 +2024-07-23 11:27:25,377 - pyskl - INFO - Epoch [40][3000/3746] lr: 8.361e-02, eta: 3 days, 15:04:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5231, loss_cls: 4.1836, loss: 4.1836 +2024-07-23 11:28:48,463 - pyskl - INFO - Epoch [40][3100/3746] lr: 8.359e-02, eta: 3 days, 15:03:51, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5261, loss_cls: 4.1311, loss: 4.1311 +2024-07-23 11:30:10,920 - pyskl - INFO - Epoch [40][3200/3746] lr: 8.357e-02, eta: 3 days, 15:02:53, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5344, loss_cls: 4.1417, loss: 4.1417 +2024-07-23 11:31:33,351 - pyskl - INFO - Epoch [40][3300/3746] lr: 8.355e-02, eta: 3 days, 15:01:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5195, loss_cls: 4.1981, loss: 4.1981 +2024-07-23 11:32:55,322 - pyskl - INFO - Epoch [40][3400/3746] lr: 8.353e-02, eta: 3 days, 15:00:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5242, loss_cls: 4.1989, loss: 4.1989 +2024-07-23 11:34:17,088 - pyskl - INFO - Epoch [40][3500/3746] lr: 8.351e-02, eta: 3 days, 14:59:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5250, loss_cls: 4.1846, loss: 4.1846 +2024-07-23 11:35:38,583 - pyskl - INFO - Epoch [40][3600/3746] lr: 8.349e-02, eta: 3 days, 14:58:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5212, loss_cls: 4.1678, loss: 4.1678 +2024-07-23 11:37:00,514 - pyskl - INFO - Epoch [40][3700/3746] lr: 8.347e-02, eta: 3 days, 14:57:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5286, loss_cls: 4.1685, loss: 4.1685 +2024-07-23 11:37:40,318 - pyskl - INFO - Saving checkpoint at 40 epochs +2024-07-23 11:39:32,159 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 11:39:32,817 - pyskl - INFO - +top1_acc 0.2040 +top5_acc 0.4365 +2024-07-23 11:39:32,818 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 11:39:32,857 - pyskl - INFO - +mean_acc 0.2038 +2024-07-23 11:39:32,868 - pyskl - INFO - Epoch(val) [40][309] top1_acc: 0.2040, top5_acc: 0.4365, mean_class_accuracy: 0.2038 +2024-07-23 11:43:17,859 - pyskl - INFO - Epoch [41][100/3746] lr: 8.344e-02, eta: 3 days, 15:01:17, time: 2.250, data_time: 1.266, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5452, loss_cls: 4.0719, loss: 4.0719 +2024-07-23 11:44:39,581 - pyskl - INFO - Epoch [41][200/3746] lr: 8.342e-02, eta: 3 days, 15:00:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5414, loss_cls: 4.1107, loss: 4.1107 +2024-07-23 11:46:01,892 - pyskl - INFO - Epoch [41][300/3746] lr: 8.339e-02, eta: 3 days, 14:59:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5297, loss_cls: 4.1642, loss: 4.1642 +2024-07-23 11:47:23,995 - pyskl - INFO - Epoch [41][400/3746] lr: 8.337e-02, eta: 3 days, 14:58:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5330, loss_cls: 4.1602, loss: 4.1602 +2024-07-23 11:48:45,989 - pyskl - INFO - Epoch [41][500/3746] lr: 8.335e-02, eta: 3 days, 14:57:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5302, loss_cls: 4.1376, loss: 4.1376 +2024-07-23 11:50:08,195 - pyskl - INFO - Epoch [41][600/3746] lr: 8.333e-02, eta: 3 days, 14:56:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5269, loss_cls: 4.1516, loss: 4.1516 +2024-07-23 11:51:30,628 - pyskl - INFO - Epoch [41][700/3746] lr: 8.331e-02, eta: 3 days, 14:55:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5298, loss_cls: 4.1469, loss: 4.1469 +2024-07-23 11:52:54,242 - pyskl - INFO - Epoch [41][800/3746] lr: 8.329e-02, eta: 3 days, 14:54:25, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5297, loss_cls: 4.1285, loss: 4.1285 +2024-07-23 11:54:16,410 - pyskl - INFO - Epoch [41][900/3746] lr: 8.327e-02, eta: 3 days, 14:53:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5383, loss_cls: 4.1038, loss: 4.1038 +2024-07-23 11:55:38,583 - pyskl - INFO - Epoch [41][1000/3746] lr: 8.325e-02, eta: 3 days, 14:52:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5238, loss_cls: 4.1725, loss: 4.1725 +2024-07-23 11:57:00,477 - pyskl - INFO - Epoch [41][1100/3746] lr: 8.323e-02, eta: 3 days, 14:51:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5247, loss_cls: 4.1882, loss: 4.1882 +2024-07-23 11:58:22,123 - pyskl - INFO - Epoch [41][1200/3746] lr: 8.321e-02, eta: 3 days, 14:50:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5353, loss_cls: 4.1500, loss: 4.1500 +2024-07-23 11:59:44,348 - pyskl - INFO - Epoch [41][1300/3746] lr: 8.319e-02, eta: 3 days, 14:49:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5209, loss_cls: 4.2210, loss: 4.2210 +2024-07-23 12:01:06,191 - pyskl - INFO - Epoch [41][1400/3746] lr: 8.316e-02, eta: 3 days, 14:48:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5203, loss_cls: 4.1807, loss: 4.1807 +2024-07-23 12:02:28,063 - pyskl - INFO - Epoch [41][1500/3746] lr: 8.314e-02, eta: 3 days, 14:47:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5306, loss_cls: 4.1366, loss: 4.1366 +2024-07-23 12:03:49,435 - pyskl - INFO - Epoch [41][1600/3746] lr: 8.312e-02, eta: 3 days, 14:46:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5256, loss_cls: 4.1509, loss: 4.1509 +2024-07-23 12:05:11,183 - pyskl - INFO - Epoch [41][1700/3746] lr: 8.310e-02, eta: 3 days, 14:45:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5352, loss_cls: 4.1365, loss: 4.1365 +2024-07-23 12:06:33,535 - pyskl - INFO - Epoch [41][1800/3746] lr: 8.308e-02, eta: 3 days, 14:44:23, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5280, loss_cls: 4.1869, loss: 4.1869 +2024-07-23 12:07:55,003 - pyskl - INFO - Epoch [41][1900/3746] lr: 8.306e-02, eta: 3 days, 14:43:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5256, loss_cls: 4.1627, loss: 4.1627 +2024-07-23 12:09:17,071 - pyskl - INFO - Epoch [41][2000/3746] lr: 8.304e-02, eta: 3 days, 14:42:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5247, loss_cls: 4.1684, loss: 4.1684 +2024-07-23 12:10:39,902 - pyskl - INFO - Epoch [41][2100/3746] lr: 8.302e-02, eta: 3 days, 14:41:23, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5342, loss_cls: 4.1497, loss: 4.1497 +2024-07-23 12:12:01,560 - pyskl - INFO - Epoch [41][2200/3746] lr: 8.300e-02, eta: 3 days, 14:40:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5333, loss_cls: 4.1156, loss: 4.1156 +2024-07-23 12:13:23,188 - pyskl - INFO - Epoch [41][2300/3746] lr: 8.298e-02, eta: 3 days, 14:39:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5281, loss_cls: 4.1714, loss: 4.1714 +2024-07-23 12:14:45,448 - pyskl - INFO - Epoch [41][2400/3746] lr: 8.296e-02, eta: 3 days, 14:38:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5381, loss_cls: 4.1433, loss: 4.1433 +2024-07-23 12:16:07,062 - pyskl - INFO - Epoch [41][2500/3746] lr: 8.293e-02, eta: 3 days, 14:37:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5359, loss_cls: 4.1541, loss: 4.1541 +2024-07-23 12:17:28,715 - pyskl - INFO - Epoch [41][2600/3746] lr: 8.291e-02, eta: 3 days, 14:36:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5330, loss_cls: 4.1566, loss: 4.1566 +2024-07-23 12:18:50,105 - pyskl - INFO - Epoch [41][2700/3746] lr: 8.289e-02, eta: 3 days, 14:35:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5206, loss_cls: 4.2063, loss: 4.2063 +2024-07-23 12:20:11,972 - pyskl - INFO - Epoch [41][2800/3746] lr: 8.287e-02, eta: 3 days, 14:34:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5359, loss_cls: 4.1270, loss: 4.1270 +2024-07-23 12:21:34,117 - pyskl - INFO - Epoch [41][2900/3746] lr: 8.285e-02, eta: 3 days, 14:33:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5234, loss_cls: 4.1844, loss: 4.1844 +2024-07-23 12:22:55,968 - pyskl - INFO - Epoch [41][3000/3746] lr: 8.283e-02, eta: 3 days, 14:32:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5234, loss_cls: 4.1916, loss: 4.1916 +2024-07-23 12:24:17,353 - pyskl - INFO - Epoch [41][3100/3746] lr: 8.281e-02, eta: 3 days, 14:31:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5288, loss_cls: 4.1848, loss: 4.1848 +2024-07-23 12:25:39,277 - pyskl - INFO - Epoch [41][3200/3746] lr: 8.279e-02, eta: 3 days, 14:30:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5406, loss_cls: 4.1317, loss: 4.1317 +2024-07-23 12:27:01,045 - pyskl - INFO - Epoch [41][3300/3746] lr: 8.277e-02, eta: 3 days, 14:29:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5269, loss_cls: 4.1967, loss: 4.1967 +2024-07-23 12:28:22,184 - pyskl - INFO - Epoch [41][3400/3746] lr: 8.274e-02, eta: 3 days, 14:28:07, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5367, loss_cls: 4.1519, loss: 4.1519 +2024-07-23 12:29:43,972 - pyskl - INFO - Epoch [41][3500/3746] lr: 8.272e-02, eta: 3 days, 14:27:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5366, loss_cls: 4.1275, loss: 4.1275 +2024-07-23 12:31:05,822 - pyskl - INFO - Epoch [41][3600/3746] lr: 8.270e-02, eta: 3 days, 14:26:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5259, loss_cls: 4.1443, loss: 4.1443 +2024-07-23 12:32:27,367 - pyskl - INFO - Epoch [41][3700/3746] lr: 8.268e-02, eta: 3 days, 14:25:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5219, loss_cls: 4.1980, loss: 4.1980 +2024-07-23 12:33:06,999 - pyskl - INFO - Saving checkpoint at 41 epochs +2024-07-23 12:34:58,936 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 12:34:59,593 - pyskl - INFO - +top1_acc 0.1871 +top5_acc 0.4191 +2024-07-23 12:34:59,594 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 12:34:59,632 - pyskl - INFO - +mean_acc 0.1869 +2024-07-23 12:34:59,643 - pyskl - INFO - Epoch(val) [41][309] top1_acc: 0.1871, top5_acc: 0.4191, mean_class_accuracy: 0.1869 +2024-07-23 12:38:44,863 - pyskl - INFO - Epoch [42][100/3746] lr: 8.265e-02, eta: 3 days, 14:28:15, time: 2.252, data_time: 1.275, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5294, loss_cls: 4.1220, loss: 4.1220 +2024-07-23 12:40:07,163 - pyskl - INFO - Epoch [42][200/3746] lr: 8.263e-02, eta: 3 days, 14:27:14, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5502, loss_cls: 4.0707, loss: 4.0707 +2024-07-23 12:41:29,067 - pyskl - INFO - Epoch [42][300/3746] lr: 8.261e-02, eta: 3 days, 14:26:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5389, loss_cls: 4.1244, loss: 4.1244 +2024-07-23 12:42:50,300 - pyskl - INFO - Epoch [42][400/3746] lr: 8.259e-02, eta: 3 days, 14:25:10, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5350, loss_cls: 4.1356, loss: 4.1356 +2024-07-23 12:44:11,294 - pyskl - INFO - Epoch [42][500/3746] lr: 8.257e-02, eta: 3 days, 14:24:06, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5328, loss_cls: 4.1361, loss: 4.1361 +2024-07-23 12:45:33,348 - pyskl - INFO - Epoch [42][600/3746] lr: 8.254e-02, eta: 3 days, 14:23:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5389, loss_cls: 4.1432, loss: 4.1432 +2024-07-23 12:46:55,180 - pyskl - INFO - Epoch [42][700/3746] lr: 8.252e-02, eta: 3 days, 14:22:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5348, loss_cls: 4.1029, loss: 4.1029 +2024-07-23 12:48:17,581 - pyskl - INFO - Epoch [42][800/3746] lr: 8.250e-02, eta: 3 days, 14:21:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5359, loss_cls: 4.0968, loss: 4.0968 +2024-07-23 12:49:39,758 - pyskl - INFO - Epoch [42][900/3746] lr: 8.248e-02, eta: 3 days, 14:20:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5303, loss_cls: 4.1669, loss: 4.1669 +2024-07-23 12:51:01,713 - pyskl - INFO - Epoch [42][1000/3746] lr: 8.246e-02, eta: 3 days, 14:19:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5383, loss_cls: 4.0820, loss: 4.0820 +2024-07-23 12:52:24,036 - pyskl - INFO - Epoch [42][1100/3746] lr: 8.244e-02, eta: 3 days, 14:18:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5284, loss_cls: 4.1570, loss: 4.1570 +2024-07-23 12:53:46,045 - pyskl - INFO - Epoch [42][1200/3746] lr: 8.242e-02, eta: 3 days, 14:17:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5344, loss_cls: 4.1193, loss: 4.1193 +2024-07-23 12:55:07,869 - pyskl - INFO - Epoch [42][1300/3746] lr: 8.240e-02, eta: 3 days, 14:15:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5408, loss_cls: 4.1095, loss: 4.1095 +2024-07-23 12:56:29,521 - pyskl - INFO - Epoch [42][1400/3746] lr: 8.237e-02, eta: 3 days, 14:14:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5403, loss_cls: 4.1288, loss: 4.1288 +2024-07-23 12:57:50,960 - pyskl - INFO - Epoch [42][1500/3746] lr: 8.235e-02, eta: 3 days, 14:13:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5277, loss_cls: 4.1347, loss: 4.1347 +2024-07-23 12:59:12,296 - pyskl - INFO - Epoch [42][1600/3746] lr: 8.233e-02, eta: 3 days, 14:12:50, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5297, loss_cls: 4.1511, loss: 4.1511 +2024-07-23 13:00:33,826 - pyskl - INFO - Epoch [42][1700/3746] lr: 8.231e-02, eta: 3 days, 14:11:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5219, loss_cls: 4.1755, loss: 4.1755 +2024-07-23 13:01:55,689 - pyskl - INFO - Epoch [42][1800/3746] lr: 8.229e-02, eta: 3 days, 14:10:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5288, loss_cls: 4.1590, loss: 4.1590 +2024-07-23 13:03:17,583 - pyskl - INFO - Epoch [42][1900/3746] lr: 8.227e-02, eta: 3 days, 14:09:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5370, loss_cls: 4.1231, loss: 4.1231 +2024-07-23 13:04:39,455 - pyskl - INFO - Epoch [42][2000/3746] lr: 8.225e-02, eta: 3 days, 14:08:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5286, loss_cls: 4.1845, loss: 4.1845 +2024-07-23 13:06:00,956 - pyskl - INFO - Epoch [42][2100/3746] lr: 8.222e-02, eta: 3 days, 14:07:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5273, loss_cls: 4.1686, loss: 4.1686 +2024-07-23 13:07:22,417 - pyskl - INFO - Epoch [42][2200/3746] lr: 8.220e-02, eta: 3 days, 14:06:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5327, loss_cls: 4.1658, loss: 4.1658 +2024-07-23 13:08:43,991 - pyskl - INFO - Epoch [42][2300/3746] lr: 8.218e-02, eta: 3 days, 14:05:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5239, loss_cls: 4.2067, loss: 4.2067 +2024-07-23 13:10:06,103 - pyskl - INFO - Epoch [42][2400/3746] lr: 8.216e-02, eta: 3 days, 14:04:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5281, loss_cls: 4.1640, loss: 4.1640 +2024-07-23 13:11:27,739 - pyskl - INFO - Epoch [42][2500/3746] lr: 8.214e-02, eta: 3 days, 14:03:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5286, loss_cls: 4.1412, loss: 4.1412 +2024-07-23 13:12:49,622 - pyskl - INFO - Epoch [42][2600/3746] lr: 8.212e-02, eta: 3 days, 14:02:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5345, loss_cls: 4.1127, loss: 4.1127 +2024-07-23 13:14:11,184 - pyskl - INFO - Epoch [42][2700/3746] lr: 8.210e-02, eta: 3 days, 14:01:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5341, loss_cls: 4.1332, loss: 4.1332 +2024-07-23 13:15:33,116 - pyskl - INFO - Epoch [42][2800/3746] lr: 8.207e-02, eta: 3 days, 14:00:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5328, loss_cls: 4.1467, loss: 4.1467 +2024-07-23 13:16:54,529 - pyskl - INFO - Epoch [42][2900/3746] lr: 8.205e-02, eta: 3 days, 13:59:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5258, loss_cls: 4.1767, loss: 4.1767 +2024-07-23 13:18:16,359 - pyskl - INFO - Epoch [42][3000/3746] lr: 8.203e-02, eta: 3 days, 13:58:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5238, loss_cls: 4.2272, loss: 4.2272 +2024-07-23 13:19:38,657 - pyskl - INFO - Epoch [42][3100/3746] lr: 8.201e-02, eta: 3 days, 13:57:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5245, loss_cls: 4.1973, loss: 4.1973 +2024-07-23 13:21:00,599 - pyskl - INFO - Epoch [42][3200/3746] lr: 8.199e-02, eta: 3 days, 13:56:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5164, loss_cls: 4.2091, loss: 4.2091 +2024-07-23 13:22:22,067 - pyskl - INFO - Epoch [42][3300/3746] lr: 8.197e-02, eta: 3 days, 13:55:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5209, loss_cls: 4.1852, loss: 4.1852 +2024-07-23 13:23:43,598 - pyskl - INFO - Epoch [42][3400/3746] lr: 8.195e-02, eta: 3 days, 13:54:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5370, loss_cls: 4.1450, loss: 4.1450 +2024-07-23 13:25:05,364 - pyskl - INFO - Epoch [42][3500/3746] lr: 8.192e-02, eta: 3 days, 13:53:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5225, loss_cls: 4.2001, loss: 4.2001 +2024-07-23 13:26:27,156 - pyskl - INFO - Epoch [42][3600/3746] lr: 8.190e-02, eta: 3 days, 13:52:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5258, loss_cls: 4.2002, loss: 4.2002 +2024-07-23 13:27:48,745 - pyskl - INFO - Epoch [42][3700/3746] lr: 8.188e-02, eta: 3 days, 13:51:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5417, loss_cls: 4.1125, loss: 4.1125 +2024-07-23 13:28:28,347 - pyskl - INFO - Saving checkpoint at 42 epochs +2024-07-23 13:30:20,803 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 13:30:21,461 - pyskl - INFO - +top1_acc 0.2025 +top5_acc 0.4312 +2024-07-23 13:30:21,461 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 13:30:21,501 - pyskl - INFO - +mean_acc 0.2023 +2024-07-23 13:30:21,511 - pyskl - INFO - Epoch(val) [42][309] top1_acc: 0.2025, top5_acc: 0.4312, mean_class_accuracy: 0.2023 +2024-07-23 13:34:03,944 - pyskl - INFO - Epoch [43][100/3746] lr: 8.185e-02, eta: 3 days, 13:53:54, time: 2.224, data_time: 1.251, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5406, loss_cls: 4.0969, loss: 4.0969 +2024-07-23 13:35:25,764 - pyskl - INFO - Epoch [43][200/3746] lr: 8.183e-02, eta: 3 days, 13:52:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5453, loss_cls: 4.0980, loss: 4.0980 +2024-07-23 13:36:48,393 - pyskl - INFO - Epoch [43][300/3746] lr: 8.181e-02, eta: 3 days, 13:51:50, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5280, loss_cls: 4.1387, loss: 4.1387 +2024-07-23 13:38:10,473 - pyskl - INFO - Epoch [43][400/3746] lr: 8.179e-02, eta: 3 days, 13:50:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5356, loss_cls: 4.1151, loss: 4.1151 +2024-07-23 13:39:32,022 - pyskl - INFO - Epoch [43][500/3746] lr: 8.176e-02, eta: 3 days, 13:49:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5286, loss_cls: 4.1532, loss: 4.1532 +2024-07-23 13:40:53,666 - pyskl - INFO - Epoch [43][600/3746] lr: 8.174e-02, eta: 3 days, 13:48:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5350, loss_cls: 4.1554, loss: 4.1554 +2024-07-23 13:42:15,708 - pyskl - INFO - Epoch [43][700/3746] lr: 8.172e-02, eta: 3 days, 13:47:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5325, loss_cls: 4.1173, loss: 4.1173 +2024-07-23 13:43:37,870 - pyskl - INFO - Epoch [43][800/3746] lr: 8.170e-02, eta: 3 days, 13:46:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5359, loss_cls: 4.1293, loss: 4.1293 +2024-07-23 13:45:00,881 - pyskl - INFO - Epoch [43][900/3746] lr: 8.168e-02, eta: 3 days, 13:45:38, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5336, loss_cls: 4.1420, loss: 4.1420 +2024-07-23 13:46:22,388 - pyskl - INFO - Epoch [43][1000/3746] lr: 8.166e-02, eta: 3 days, 13:44:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5327, loss_cls: 4.1576, loss: 4.1576 +2024-07-23 13:47:44,807 - pyskl - INFO - Epoch [43][1100/3746] lr: 8.163e-02, eta: 3 days, 13:43:32, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5358, loss_cls: 4.1136, loss: 4.1136 +2024-07-23 13:49:06,502 - pyskl - INFO - Epoch [43][1200/3746] lr: 8.161e-02, eta: 3 days, 13:42:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5245, loss_cls: 4.1652, loss: 4.1652 +2024-07-23 13:50:27,930 - pyskl - INFO - Epoch [43][1300/3746] lr: 8.159e-02, eta: 3 days, 13:41:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5239, loss_cls: 4.1899, loss: 4.1899 +2024-07-23 13:51:49,663 - pyskl - INFO - Epoch [43][1400/3746] lr: 8.157e-02, eta: 3 days, 13:40:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5309, loss_cls: 4.1352, loss: 4.1352 +2024-07-23 13:53:10,974 - pyskl - INFO - Epoch [43][1500/3746] lr: 8.155e-02, eta: 3 days, 13:39:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5416, loss_cls: 4.1227, loss: 4.1227 +2024-07-23 13:54:32,844 - pyskl - INFO - Epoch [43][1600/3746] lr: 8.153e-02, eta: 3 days, 13:38:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5395, loss_cls: 4.1110, loss: 4.1110 +2024-07-23 13:55:54,480 - pyskl - INFO - Epoch [43][1700/3746] lr: 8.150e-02, eta: 3 days, 13:37:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5275, loss_cls: 4.1594, loss: 4.1594 +2024-07-23 13:57:16,555 - pyskl - INFO - Epoch [43][1800/3746] lr: 8.148e-02, eta: 3 days, 13:36:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5317, loss_cls: 4.1503, loss: 4.1503 +2024-07-23 13:58:38,359 - pyskl - INFO - Epoch [43][1900/3746] lr: 8.146e-02, eta: 3 days, 13:35:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5464, loss_cls: 4.0626, loss: 4.0626 +2024-07-23 14:00:00,372 - pyskl - INFO - Epoch [43][2000/3746] lr: 8.144e-02, eta: 3 days, 13:34:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5273, loss_cls: 4.1709, loss: 4.1709 +2024-07-23 14:01:21,868 - pyskl - INFO - Epoch [43][2100/3746] lr: 8.142e-02, eta: 3 days, 13:32:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5342, loss_cls: 4.1630, loss: 4.1630 +2024-07-23 14:02:43,535 - pyskl - INFO - Epoch [43][2200/3746] lr: 8.140e-02, eta: 3 days, 13:31:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5377, loss_cls: 4.1267, loss: 4.1267 +2024-07-23 14:04:05,325 - pyskl - INFO - Epoch [43][2300/3746] lr: 8.137e-02, eta: 3 days, 13:30:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5214, loss_cls: 4.2155, loss: 4.2155 +2024-07-23 14:05:27,073 - pyskl - INFO - Epoch [43][2400/3746] lr: 8.135e-02, eta: 3 days, 13:29:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5256, loss_cls: 4.1719, loss: 4.1719 +2024-07-23 14:06:48,776 - pyskl - INFO - Epoch [43][2500/3746] lr: 8.133e-02, eta: 3 days, 13:28:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5291, loss_cls: 4.1554, loss: 4.1554 +2024-07-23 14:08:10,222 - pyskl - INFO - Epoch [43][2600/3746] lr: 8.131e-02, eta: 3 days, 13:27:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5258, loss_cls: 4.1695, loss: 4.1695 +2024-07-23 14:09:31,783 - pyskl - INFO - Epoch [43][2700/3746] lr: 8.129e-02, eta: 3 days, 13:26:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5337, loss_cls: 4.1526, loss: 4.1526 +2024-07-23 14:10:53,247 - pyskl - INFO - Epoch [43][2800/3746] lr: 8.126e-02, eta: 3 days, 13:25:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5378, loss_cls: 4.1443, loss: 4.1443 +2024-07-23 14:12:14,862 - pyskl - INFO - Epoch [43][2900/3746] lr: 8.124e-02, eta: 3 days, 13:24:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5219, loss_cls: 4.1822, loss: 4.1822 +2024-07-23 14:13:36,901 - pyskl - INFO - Epoch [43][3000/3746] lr: 8.122e-02, eta: 3 days, 13:23:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5427, loss_cls: 4.0919, loss: 4.0919 +2024-07-23 14:14:58,818 - pyskl - INFO - Epoch [43][3100/3746] lr: 8.120e-02, eta: 3 days, 13:22:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5361, loss_cls: 4.1523, loss: 4.1523 +2024-07-23 14:16:20,090 - pyskl - INFO - Epoch [43][3200/3746] lr: 8.118e-02, eta: 3 days, 13:21:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5348, loss_cls: 4.1371, loss: 4.1371 +2024-07-23 14:17:41,769 - pyskl - INFO - Epoch [43][3300/3746] lr: 8.116e-02, eta: 3 days, 13:20:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5314, loss_cls: 4.1434, loss: 4.1434 +2024-07-23 14:19:03,340 - pyskl - INFO - Epoch [43][3400/3746] lr: 8.113e-02, eta: 3 days, 13:19:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5316, loss_cls: 4.1346, loss: 4.1346 +2024-07-23 14:20:25,667 - pyskl - INFO - Epoch [43][3500/3746] lr: 8.111e-02, eta: 3 days, 13:18:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5312, loss_cls: 4.1447, loss: 4.1447 +2024-07-23 14:21:47,614 - pyskl - INFO - Epoch [43][3600/3746] lr: 8.109e-02, eta: 3 days, 13:17:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5352, loss_cls: 4.1053, loss: 4.1053 +2024-07-23 14:23:09,619 - pyskl - INFO - Epoch [43][3700/3746] lr: 8.107e-02, eta: 3 days, 13:16:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5395, loss_cls: 4.1229, loss: 4.1229 +2024-07-23 14:23:49,168 - pyskl - INFO - Saving checkpoint at 43 epochs +2024-07-23 14:25:40,624 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 14:25:41,285 - pyskl - INFO - +top1_acc 0.2098 +top5_acc 0.4505 +2024-07-23 14:25:41,285 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 14:25:41,326 - pyskl - INFO - +mean_acc 0.2097 +2024-07-23 14:25:41,336 - pyskl - INFO - Epoch(val) [43][309] top1_acc: 0.2098, top5_acc: 0.4505, mean_class_accuracy: 0.2097 +2024-07-23 14:29:24,901 - pyskl - INFO - Epoch [44][100/3746] lr: 8.104e-02, eta: 3 days, 13:18:48, time: 2.236, data_time: 1.264, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5405, loss_cls: 4.1361, loss: 4.1361 +2024-07-23 14:30:46,386 - pyskl - INFO - Epoch [44][200/3746] lr: 8.101e-02, eta: 3 days, 13:17:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5334, loss_cls: 4.1380, loss: 4.1380 +2024-07-23 14:32:08,729 - pyskl - INFO - Epoch [44][300/3746] lr: 8.099e-02, eta: 3 days, 13:16:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5323, loss_cls: 4.1284, loss: 4.1284 +2024-07-23 14:33:30,498 - pyskl - INFO - Epoch [44][400/3746] lr: 8.097e-02, eta: 3 days, 13:15:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5350, loss_cls: 4.1408, loss: 4.1408 +2024-07-23 14:34:52,242 - pyskl - INFO - Epoch [44][500/3746] lr: 8.095e-02, eta: 3 days, 13:14:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5331, loss_cls: 4.1359, loss: 4.1359 +2024-07-23 14:36:14,559 - pyskl - INFO - Epoch [44][600/3746] lr: 8.093e-02, eta: 3 days, 13:13:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5434, loss_cls: 4.1072, loss: 4.1072 +2024-07-23 14:37:36,683 - pyskl - INFO - Epoch [44][700/3746] lr: 8.090e-02, eta: 3 days, 13:12:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5375, loss_cls: 4.0981, loss: 4.0981 +2024-07-23 14:38:58,913 - pyskl - INFO - Epoch [44][800/3746] lr: 8.088e-02, eta: 3 days, 13:11:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5355, loss_cls: 4.0874, loss: 4.0874 +2024-07-23 14:40:20,811 - pyskl - INFO - Epoch [44][900/3746] lr: 8.086e-02, eta: 3 days, 13:10:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5445, loss_cls: 4.1062, loss: 4.1062 +2024-07-23 14:41:43,999 - pyskl - INFO - Epoch [44][1000/3746] lr: 8.084e-02, eta: 3 days, 13:09:20, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5364, loss_cls: 4.1249, loss: 4.1249 +2024-07-23 14:43:05,734 - pyskl - INFO - Epoch [44][1100/3746] lr: 8.082e-02, eta: 3 days, 13:08:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5392, loss_cls: 4.1256, loss: 4.1256 +2024-07-23 14:44:27,623 - pyskl - INFO - Epoch [44][1200/3746] lr: 8.079e-02, eta: 3 days, 13:07:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5294, loss_cls: 4.1463, loss: 4.1463 +2024-07-23 14:45:49,430 - pyskl - INFO - Epoch [44][1300/3746] lr: 8.077e-02, eta: 3 days, 13:06:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5298, loss_cls: 4.1497, loss: 4.1497 +2024-07-23 14:47:10,919 - pyskl - INFO - Epoch [44][1400/3746] lr: 8.075e-02, eta: 3 days, 13:05:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5298, loss_cls: 4.1121, loss: 4.1121 +2024-07-23 14:48:32,341 - pyskl - INFO - Epoch [44][1500/3746] lr: 8.073e-02, eta: 3 days, 13:03:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5312, loss_cls: 4.1567, loss: 4.1567 +2024-07-23 14:49:53,528 - pyskl - INFO - Epoch [44][1600/3746] lr: 8.071e-02, eta: 3 days, 13:02:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5386, loss_cls: 4.1234, loss: 4.1234 +2024-07-23 14:51:15,127 - pyskl - INFO - Epoch [44][1700/3746] lr: 8.068e-02, eta: 3 days, 13:01:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5450, loss_cls: 4.1058, loss: 4.1058 +2024-07-23 14:52:36,381 - pyskl - INFO - Epoch [44][1800/3746] lr: 8.066e-02, eta: 3 days, 13:00:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5331, loss_cls: 4.1397, loss: 4.1397 +2024-07-23 14:53:58,142 - pyskl - INFO - Epoch [44][1900/3746] lr: 8.064e-02, eta: 3 days, 12:59:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5366, loss_cls: 4.1421, loss: 4.1421 +2024-07-23 14:55:19,757 - pyskl - INFO - Epoch [44][2000/3746] lr: 8.062e-02, eta: 3 days, 12:58:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5411, loss_cls: 4.1105, loss: 4.1105 +2024-07-23 14:56:41,541 - pyskl - INFO - Epoch [44][2100/3746] lr: 8.060e-02, eta: 3 days, 12:57:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5266, loss_cls: 4.1620, loss: 4.1620 +2024-07-23 14:58:02,932 - pyskl - INFO - Epoch [44][2200/3746] lr: 8.057e-02, eta: 3 days, 12:56:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5223, loss_cls: 4.1842, loss: 4.1842 +2024-07-23 14:59:24,800 - pyskl - INFO - Epoch [44][2300/3746] lr: 8.055e-02, eta: 3 days, 12:55:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5363, loss_cls: 4.1386, loss: 4.1386 +2024-07-23 15:00:45,829 - pyskl - INFO - Epoch [44][2400/3746] lr: 8.053e-02, eta: 3 days, 12:54:13, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5197, loss_cls: 4.1922, loss: 4.1922 +2024-07-23 15:02:07,621 - pyskl - INFO - Epoch [44][2500/3746] lr: 8.051e-02, eta: 3 days, 12:53:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5375, loss_cls: 4.1333, loss: 4.1333 +2024-07-23 15:03:29,344 - pyskl - INFO - Epoch [44][2600/3746] lr: 8.048e-02, eta: 3 days, 12:52:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5428, loss_cls: 4.0873, loss: 4.0873 +2024-07-23 15:04:51,886 - pyskl - INFO - Epoch [44][2700/3746] lr: 8.046e-02, eta: 3 days, 12:51:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5367, loss_cls: 4.1244, loss: 4.1244 +2024-07-23 15:06:13,797 - pyskl - INFO - Epoch [44][2800/3746] lr: 8.044e-02, eta: 3 days, 12:49:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5197, loss_cls: 4.1707, loss: 4.1707 +2024-07-23 15:07:36,133 - pyskl - INFO - Epoch [44][2900/3746] lr: 8.042e-02, eta: 3 days, 12:48:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5370, loss_cls: 4.1278, loss: 4.1278 +2024-07-23 15:08:57,790 - pyskl - INFO - Epoch [44][3000/3746] lr: 8.040e-02, eta: 3 days, 12:47:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5408, loss_cls: 4.0594, loss: 4.0594 +2024-07-23 15:10:19,168 - pyskl - INFO - Epoch [44][3100/3746] lr: 8.037e-02, eta: 3 days, 12:46:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5297, loss_cls: 4.1886, loss: 4.1886 +2024-07-23 15:11:40,709 - pyskl - INFO - Epoch [44][3200/3746] lr: 8.035e-02, eta: 3 days, 12:45:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5291, loss_cls: 4.1678, loss: 4.1678 +2024-07-23 15:13:02,330 - pyskl - INFO - Epoch [44][3300/3746] lr: 8.033e-02, eta: 3 days, 12:44:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5252, loss_cls: 4.1886, loss: 4.1886 +2024-07-23 15:14:24,075 - pyskl - INFO - Epoch [44][3400/3746] lr: 8.031e-02, eta: 3 days, 12:43:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5413, loss_cls: 4.1157, loss: 4.1157 +2024-07-23 15:15:45,544 - pyskl - INFO - Epoch [44][3500/3746] lr: 8.028e-02, eta: 3 days, 12:42:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5345, loss_cls: 4.1349, loss: 4.1349 +2024-07-23 15:17:07,866 - pyskl - INFO - Epoch [44][3600/3746] lr: 8.026e-02, eta: 3 days, 12:41:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5256, loss_cls: 4.1596, loss: 4.1596 +2024-07-23 15:18:29,482 - pyskl - INFO - Epoch [44][3700/3746] lr: 8.024e-02, eta: 3 days, 12:40:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5261, loss_cls: 4.1499, loss: 4.1499 +2024-07-23 15:19:09,233 - pyskl - INFO - Saving checkpoint at 44 epochs +2024-07-23 15:21:00,235 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 15:21:00,886 - pyskl - INFO - +top1_acc 0.2065 +top5_acc 0.4346 +2024-07-23 15:21:00,886 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 15:21:00,926 - pyskl - INFO - +mean_acc 0.2063 +2024-07-23 15:21:00,937 - pyskl - INFO - Epoch(val) [44][309] top1_acc: 0.2065, top5_acc: 0.4346, mean_class_accuracy: 0.2063 +2024-07-23 15:24:48,835 - pyskl - INFO - Epoch [45][100/3746] lr: 8.021e-02, eta: 3 days, 12:43:03, time: 2.279, data_time: 1.268, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5489, loss_cls: 4.0495, loss: 4.0495 +2024-07-23 15:26:10,530 - pyskl - INFO - Epoch [45][200/3746] lr: 8.019e-02, eta: 3 days, 12:41:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5397, loss_cls: 4.1029, loss: 4.1029 +2024-07-23 15:27:32,748 - pyskl - INFO - Epoch [45][300/3746] lr: 8.016e-02, eta: 3 days, 12:40:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5455, loss_cls: 4.0769, loss: 4.0769 +2024-07-23 15:28:54,705 - pyskl - INFO - Epoch [45][400/3746] lr: 8.014e-02, eta: 3 days, 12:39:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5423, loss_cls: 4.0907, loss: 4.0907 +2024-07-23 15:30:16,650 - pyskl - INFO - Epoch [45][500/3746] lr: 8.012e-02, eta: 3 days, 12:38:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5377, loss_cls: 4.1059, loss: 4.1059 +2024-07-23 15:31:38,724 - pyskl - INFO - Epoch [45][600/3746] lr: 8.010e-02, eta: 3 days, 12:37:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5339, loss_cls: 4.1577, loss: 4.1577 +2024-07-23 15:33:01,352 - pyskl - INFO - Epoch [45][700/3746] lr: 8.007e-02, eta: 3 days, 12:36:37, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5381, loss_cls: 4.1157, loss: 4.1157 +2024-07-23 15:34:22,864 - pyskl - INFO - Epoch [45][800/3746] lr: 8.005e-02, eta: 3 days, 12:35:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5322, loss_cls: 4.1269, loss: 4.1269 +2024-07-23 15:35:44,675 - pyskl - INFO - Epoch [45][900/3746] lr: 8.003e-02, eta: 3 days, 12:34:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5456, loss_cls: 4.0990, loss: 4.0990 +2024-07-23 15:37:07,182 - pyskl - INFO - Epoch [45][1000/3746] lr: 8.001e-02, eta: 3 days, 12:33:23, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5384, loss_cls: 4.1046, loss: 4.1046 +2024-07-23 15:38:29,649 - pyskl - INFO - Epoch [45][1100/3746] lr: 7.998e-02, eta: 3 days, 12:32:20, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5323, loss_cls: 4.1135, loss: 4.1135 +2024-07-23 15:39:52,709 - pyskl - INFO - Epoch [45][1200/3746] lr: 7.996e-02, eta: 3 days, 12:31:18, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5325, loss_cls: 4.1416, loss: 4.1416 +2024-07-23 15:41:14,749 - pyskl - INFO - Epoch [45][1300/3746] lr: 7.994e-02, eta: 3 days, 12:30:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5311, loss_cls: 4.1193, loss: 4.1193 +2024-07-23 15:42:36,584 - pyskl - INFO - Epoch [45][1400/3746] lr: 7.992e-02, eta: 3 days, 12:29:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5308, loss_cls: 4.1290, loss: 4.1290 +2024-07-23 15:43:58,227 - pyskl - INFO - Epoch [45][1500/3746] lr: 7.990e-02, eta: 3 days, 12:28:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5141, loss_cls: 4.1964, loss: 4.1964 +2024-07-23 15:45:19,693 - pyskl - INFO - Epoch [45][1600/3746] lr: 7.987e-02, eta: 3 days, 12:26:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5345, loss_cls: 4.1414, loss: 4.1414 +2024-07-23 15:46:41,059 - pyskl - INFO - Epoch [45][1700/3746] lr: 7.985e-02, eta: 3 days, 12:25:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5370, loss_cls: 4.1160, loss: 4.1160 +2024-07-23 15:48:03,193 - pyskl - INFO - Epoch [45][1800/3746] lr: 7.983e-02, eta: 3 days, 12:24:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5414, loss_cls: 4.1066, loss: 4.1066 +2024-07-23 15:49:25,218 - pyskl - INFO - Epoch [45][1900/3746] lr: 7.981e-02, eta: 3 days, 12:23:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5283, loss_cls: 4.1563, loss: 4.1563 +2024-07-23 15:50:47,423 - pyskl - INFO - Epoch [45][2000/3746] lr: 7.978e-02, eta: 3 days, 12:22:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5300, loss_cls: 4.1534, loss: 4.1534 +2024-07-23 15:52:08,809 - pyskl - INFO - Epoch [45][2100/3746] lr: 7.976e-02, eta: 3 days, 12:21:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5308, loss_cls: 4.1706, loss: 4.1706 +2024-07-23 15:53:30,979 - pyskl - INFO - Epoch [45][2200/3746] lr: 7.974e-02, eta: 3 days, 12:20:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5317, loss_cls: 4.1311, loss: 4.1311 +2024-07-23 15:54:52,418 - pyskl - INFO - Epoch [45][2300/3746] lr: 7.972e-02, eta: 3 days, 12:19:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5345, loss_cls: 4.1483, loss: 4.1483 +2024-07-23 15:56:13,845 - pyskl - INFO - Epoch [45][2400/3746] lr: 7.969e-02, eta: 3 days, 12:18:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5283, loss_cls: 4.1407, loss: 4.1407 +2024-07-23 15:57:35,698 - pyskl - INFO - Epoch [45][2500/3746] lr: 7.967e-02, eta: 3 days, 12:17:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5236, loss_cls: 4.1649, loss: 4.1649 +2024-07-23 15:58:56,774 - pyskl - INFO - Epoch [45][2600/3746] lr: 7.965e-02, eta: 3 days, 12:16:02, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5342, loss_cls: 4.1026, loss: 4.1026 +2024-07-23 16:00:18,444 - pyskl - INFO - Epoch [45][2700/3746] lr: 7.963e-02, eta: 3 days, 12:14:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5392, loss_cls: 4.0950, loss: 4.0950 +2024-07-23 16:01:39,957 - pyskl - INFO - Epoch [45][2800/3746] lr: 7.960e-02, eta: 3 days, 12:13:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5291, loss_cls: 4.1692, loss: 4.1692 +2024-07-23 16:03:01,967 - pyskl - INFO - Epoch [45][2900/3746] lr: 7.958e-02, eta: 3 days, 12:12:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5253, loss_cls: 4.1583, loss: 4.1583 +2024-07-23 16:04:23,472 - pyskl - INFO - Epoch [45][3000/3746] lr: 7.956e-02, eta: 3 days, 12:11:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5361, loss_cls: 4.1521, loss: 4.1521 +2024-07-23 16:05:45,180 - pyskl - INFO - Epoch [45][3100/3746] lr: 7.954e-02, eta: 3 days, 12:10:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5217, loss_cls: 4.1914, loss: 4.1914 +2024-07-23 16:07:06,762 - pyskl - INFO - Epoch [45][3200/3746] lr: 7.951e-02, eta: 3 days, 12:09:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5255, loss_cls: 4.1476, loss: 4.1476 +2024-07-23 16:08:28,237 - pyskl - INFO - Epoch [45][3300/3746] lr: 7.949e-02, eta: 3 days, 12:08:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5347, loss_cls: 4.1164, loss: 4.1164 +2024-07-23 16:09:49,719 - pyskl - INFO - Epoch [45][3400/3746] lr: 7.947e-02, eta: 3 days, 12:07:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5392, loss_cls: 4.0934, loss: 4.0934 +2024-07-23 16:11:11,170 - pyskl - INFO - Epoch [45][3500/3746] lr: 7.945e-02, eta: 3 days, 12:06:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5387, loss_cls: 4.0932, loss: 4.0932 +2024-07-23 16:12:33,117 - pyskl - INFO - Epoch [45][3600/3746] lr: 7.942e-02, eta: 3 days, 12:05:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5322, loss_cls: 4.1427, loss: 4.1427 +2024-07-23 16:13:54,706 - pyskl - INFO - Epoch [45][3700/3746] lr: 7.940e-02, eta: 3 days, 12:03:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5350, loss_cls: 4.1466, loss: 4.1466 +2024-07-23 16:14:34,614 - pyskl - INFO - Saving checkpoint at 45 epochs +2024-07-23 16:16:25,998 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 16:16:26,655 - pyskl - INFO - +top1_acc 0.2083 +top5_acc 0.4353 +2024-07-23 16:16:26,655 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 16:16:26,695 - pyskl - INFO - +mean_acc 0.2083 +2024-07-23 16:16:26,705 - pyskl - INFO - Epoch(val) [45][309] top1_acc: 0.2083, top5_acc: 0.4353, mean_class_accuracy: 0.2083 +2024-07-23 16:20:10,785 - pyskl - INFO - Epoch [46][100/3746] lr: 7.937e-02, eta: 3 days, 12:06:26, time: 2.241, data_time: 1.265, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5347, loss_cls: 4.1139, loss: 4.1139 +2024-07-23 16:21:33,247 - pyskl - INFO - Epoch [46][200/3746] lr: 7.934e-02, eta: 3 days, 12:05:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5480, loss_cls: 4.0748, loss: 4.0748 +2024-07-23 16:22:55,011 - pyskl - INFO - Epoch [46][300/3746] lr: 7.932e-02, eta: 3 days, 12:04:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5387, loss_cls: 4.1111, loss: 4.1111 +2024-07-23 16:24:17,248 - pyskl - INFO - Epoch [46][400/3746] lr: 7.930e-02, eta: 3 days, 12:03:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5455, loss_cls: 4.1144, loss: 4.1144 +2024-07-23 16:25:39,211 - pyskl - INFO - Epoch [46][500/3746] lr: 7.928e-02, eta: 3 days, 12:02:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5406, loss_cls: 4.1124, loss: 4.1124 +2024-07-23 16:27:01,151 - pyskl - INFO - Epoch [46][600/3746] lr: 7.925e-02, eta: 3 days, 12:00:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5489, loss_cls: 4.0543, loss: 4.0543 +2024-07-23 16:28:22,668 - pyskl - INFO - Epoch [46][700/3746] lr: 7.923e-02, eta: 3 days, 11:59:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5391, loss_cls: 4.0909, loss: 4.0909 +2024-07-23 16:29:45,057 - pyskl - INFO - Epoch [46][800/3746] lr: 7.921e-02, eta: 3 days, 11:58:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5331, loss_cls: 4.1465, loss: 4.1465 +2024-07-23 16:31:07,008 - pyskl - INFO - Epoch [46][900/3746] lr: 7.919e-02, eta: 3 days, 11:57:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5459, loss_cls: 4.0669, loss: 4.0669 +2024-07-23 16:32:29,239 - pyskl - INFO - Epoch [46][1000/3746] lr: 7.916e-02, eta: 3 days, 11:56:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5278, loss_cls: 4.1389, loss: 4.1389 +2024-07-23 16:33:51,842 - pyskl - INFO - Epoch [46][1100/3746] lr: 7.914e-02, eta: 3 days, 11:55:34, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5283, loss_cls: 4.1518, loss: 4.1518 +2024-07-23 16:35:14,384 - pyskl - INFO - Epoch [46][1200/3746] lr: 7.912e-02, eta: 3 days, 11:54:29, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5219, loss_cls: 4.1921, loss: 4.1921 +2024-07-23 16:36:36,246 - pyskl - INFO - Epoch [46][1300/3746] lr: 7.909e-02, eta: 3 days, 11:53:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5322, loss_cls: 4.1393, loss: 4.1393 +2024-07-23 16:37:57,963 - pyskl - INFO - Epoch [46][1400/3746] lr: 7.907e-02, eta: 3 days, 11:52:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5428, loss_cls: 4.1083, loss: 4.1083 +2024-07-23 16:39:19,654 - pyskl - INFO - Epoch [46][1500/3746] lr: 7.905e-02, eta: 3 days, 11:51:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5331, loss_cls: 4.1433, loss: 4.1433 +2024-07-23 16:40:41,372 - pyskl - INFO - Epoch [46][1600/3746] lr: 7.903e-02, eta: 3 days, 11:50:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5319, loss_cls: 4.1228, loss: 4.1228 +2024-07-23 16:42:03,236 - pyskl - INFO - Epoch [46][1700/3746] lr: 7.900e-02, eta: 3 days, 11:48:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5333, loss_cls: 4.1484, loss: 4.1484 +2024-07-23 16:43:25,168 - pyskl - INFO - Epoch [46][1800/3746] lr: 7.898e-02, eta: 3 days, 11:47:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5344, loss_cls: 4.1507, loss: 4.1507 +2024-07-23 16:44:46,793 - pyskl - INFO - Epoch [46][1900/3746] lr: 7.896e-02, eta: 3 days, 11:46:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5397, loss_cls: 4.1162, loss: 4.1162 +2024-07-23 16:46:08,780 - pyskl - INFO - Epoch [46][2000/3746] lr: 7.894e-02, eta: 3 days, 11:45:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5428, loss_cls: 4.0947, loss: 4.0947 +2024-07-23 16:47:30,043 - pyskl - INFO - Epoch [46][2100/3746] lr: 7.891e-02, eta: 3 days, 11:44:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5225, loss_cls: 4.1757, loss: 4.1757 +2024-07-23 16:48:51,887 - pyskl - INFO - Epoch [46][2200/3746] lr: 7.889e-02, eta: 3 days, 11:43:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5486, loss_cls: 4.0948, loss: 4.0948 +2024-07-23 16:50:13,281 - pyskl - INFO - Epoch [46][2300/3746] lr: 7.887e-02, eta: 3 days, 11:42:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5330, loss_cls: 4.1630, loss: 4.1630 +2024-07-23 16:51:35,214 - pyskl - INFO - Epoch [46][2400/3746] lr: 7.884e-02, eta: 3 days, 11:41:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5344, loss_cls: 4.1490, loss: 4.1490 +2024-07-23 16:52:56,931 - pyskl - INFO - Epoch [46][2500/3746] lr: 7.882e-02, eta: 3 days, 11:40:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5417, loss_cls: 4.0898, loss: 4.0898 +2024-07-23 16:54:18,604 - pyskl - INFO - Epoch [46][2600/3746] lr: 7.880e-02, eta: 3 days, 11:39:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5377, loss_cls: 4.1260, loss: 4.1260 +2024-07-23 16:55:40,397 - pyskl - INFO - Epoch [46][2700/3746] lr: 7.878e-02, eta: 3 days, 11:37:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5334, loss_cls: 4.1470, loss: 4.1470 +2024-07-23 16:57:01,793 - pyskl - INFO - Epoch [46][2800/3746] lr: 7.875e-02, eta: 3 days, 11:36:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5459, loss_cls: 4.0783, loss: 4.0783 +2024-07-23 16:58:23,671 - pyskl - INFO - Epoch [46][2900/3746] lr: 7.873e-02, eta: 3 days, 11:35:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5277, loss_cls: 4.1440, loss: 4.1440 +2024-07-23 16:59:45,337 - pyskl - INFO - Epoch [46][3000/3746] lr: 7.871e-02, eta: 3 days, 11:34:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5320, loss_cls: 4.1388, loss: 4.1388 +2024-07-23 17:01:06,532 - pyskl - INFO - Epoch [46][3100/3746] lr: 7.868e-02, eta: 3 days, 11:33:27, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5308, loss_cls: 4.1331, loss: 4.1331 +2024-07-23 17:02:28,337 - pyskl - INFO - Epoch [46][3200/3746] lr: 7.866e-02, eta: 3 days, 11:32:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5284, loss_cls: 4.1596, loss: 4.1596 +2024-07-23 17:03:50,032 - pyskl - INFO - Epoch [46][3300/3746] lr: 7.864e-02, eta: 3 days, 11:31:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5423, loss_cls: 4.0874, loss: 4.0874 +2024-07-23 17:05:11,336 - pyskl - INFO - Epoch [46][3400/3746] lr: 7.862e-02, eta: 3 days, 11:30:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5297, loss_cls: 4.1861, loss: 4.1861 +2024-07-23 17:06:32,805 - pyskl - INFO - Epoch [46][3500/3746] lr: 7.859e-02, eta: 3 days, 11:28:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5403, loss_cls: 4.1307, loss: 4.1307 +2024-07-23 17:07:54,623 - pyskl - INFO - Epoch [46][3600/3746] lr: 7.857e-02, eta: 3 days, 11:27:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5278, loss_cls: 4.1758, loss: 4.1758 +2024-07-23 17:09:16,607 - pyskl - INFO - Epoch [46][3700/3746] lr: 7.855e-02, eta: 3 days, 11:26:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5419, loss_cls: 4.1102, loss: 4.1102 +2024-07-23 17:09:56,397 - pyskl - INFO - Saving checkpoint at 46 epochs +2024-07-23 17:11:48,885 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 17:11:49,546 - pyskl - INFO - +top1_acc 0.2169 +top5_acc 0.4575 +2024-07-23 17:11:49,547 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 17:11:49,586 - pyskl - INFO - +mean_acc 0.2167 +2024-07-23 17:11:49,602 - pyskl - INFO - Epoch(val) [46][309] top1_acc: 0.2169, top5_acc: 0.4575, mean_class_accuracy: 0.2167 +2024-07-23 17:15:35,163 - pyskl - INFO - Epoch [47][100/3746] lr: 7.851e-02, eta: 3 days, 11:29:09, time: 2.256, data_time: 1.280, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5417, loss_cls: 4.0928, loss: 4.0928 +2024-07-23 17:16:56,552 - pyskl - INFO - Epoch [47][200/3746] lr: 7.849e-02, eta: 3 days, 11:28:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5391, loss_cls: 4.1131, loss: 4.1131 +2024-07-23 17:18:18,481 - pyskl - INFO - Epoch [47][300/3746] lr: 7.847e-02, eta: 3 days, 11:26:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5523, loss_cls: 4.0412, loss: 4.0412 +2024-07-23 17:19:40,290 - pyskl - INFO - Epoch [47][400/3746] lr: 7.844e-02, eta: 3 days, 11:25:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5342, loss_cls: 4.1175, loss: 4.1175 +2024-07-23 17:21:01,793 - pyskl - INFO - Epoch [47][500/3746] lr: 7.842e-02, eta: 3 days, 11:24:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5372, loss_cls: 4.1207, loss: 4.1207 +2024-07-23 17:22:24,531 - pyskl - INFO - Epoch [47][600/3746] lr: 7.840e-02, eta: 3 days, 11:23:36, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5400, loss_cls: 4.1034, loss: 4.1034 +2024-07-23 17:23:46,195 - pyskl - INFO - Epoch [47][700/3746] lr: 7.838e-02, eta: 3 days, 11:22:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5408, loss_cls: 4.1288, loss: 4.1288 +2024-07-23 17:25:08,467 - pyskl - INFO - Epoch [47][800/3746] lr: 7.835e-02, eta: 3 days, 11:21:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5475, loss_cls: 4.0650, loss: 4.0650 +2024-07-23 17:26:30,863 - pyskl - INFO - Epoch [47][900/3746] lr: 7.833e-02, eta: 3 days, 11:20:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5302, loss_cls: 4.0972, loss: 4.0972 +2024-07-23 17:27:52,747 - pyskl - INFO - Epoch [47][1000/3746] lr: 7.831e-02, eta: 3 days, 11:19:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5392, loss_cls: 4.0731, loss: 4.0731 +2024-07-23 17:29:14,795 - pyskl - INFO - Epoch [47][1100/3746] lr: 7.828e-02, eta: 3 days, 11:18:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5302, loss_cls: 4.1330, loss: 4.1330 +2024-07-23 17:30:37,429 - pyskl - INFO - Epoch [47][1200/3746] lr: 7.826e-02, eta: 3 days, 11:17:00, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5344, loss_cls: 4.0929, loss: 4.0929 +2024-07-23 17:31:59,661 - pyskl - INFO - Epoch [47][1300/3746] lr: 7.824e-02, eta: 3 days, 11:15:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5334, loss_cls: 4.1456, loss: 4.1456 +2024-07-23 17:33:21,264 - pyskl - INFO - Epoch [47][1400/3746] lr: 7.821e-02, eta: 3 days, 11:14:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5392, loss_cls: 4.1132, loss: 4.1132 +2024-07-23 17:34:42,645 - pyskl - INFO - Epoch [47][1500/3746] lr: 7.819e-02, eta: 3 days, 11:13:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5281, loss_cls: 4.1281, loss: 4.1281 +2024-07-23 17:36:03,924 - pyskl - INFO - Epoch [47][1600/3746] lr: 7.817e-02, eta: 3 days, 11:12:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5317, loss_cls: 4.1323, loss: 4.1323 +2024-07-23 17:37:25,515 - pyskl - INFO - Epoch [47][1700/3746] lr: 7.814e-02, eta: 3 days, 11:11:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5406, loss_cls: 4.1209, loss: 4.1209 +2024-07-23 17:38:47,152 - pyskl - INFO - Epoch [47][1800/3746] lr: 7.812e-02, eta: 3 days, 11:10:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5347, loss_cls: 4.1268, loss: 4.1268 +2024-07-23 17:40:09,124 - pyskl - INFO - Epoch [47][1900/3746] lr: 7.810e-02, eta: 3 days, 11:09:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5386, loss_cls: 4.1216, loss: 4.1216 +2024-07-23 17:41:31,125 - pyskl - INFO - Epoch [47][2000/3746] lr: 7.808e-02, eta: 3 days, 11:08:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5461, loss_cls: 4.1295, loss: 4.1295 +2024-07-23 17:42:52,403 - pyskl - INFO - Epoch [47][2100/3746] lr: 7.805e-02, eta: 3 days, 11:06:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5406, loss_cls: 4.0955, loss: 4.0955 +2024-07-23 17:44:14,456 - pyskl - INFO - Epoch [47][2200/3746] lr: 7.803e-02, eta: 3 days, 11:05:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5414, loss_cls: 4.1187, loss: 4.1187 +2024-07-23 17:45:36,958 - pyskl - INFO - Epoch [47][2300/3746] lr: 7.801e-02, eta: 3 days, 11:04:43, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5366, loss_cls: 4.1358, loss: 4.1358 +2024-07-23 17:46:58,880 - pyskl - INFO - Epoch [47][2400/3746] lr: 7.798e-02, eta: 3 days, 11:03:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5311, loss_cls: 4.1607, loss: 4.1607 +2024-07-23 17:48:20,872 - pyskl - INFO - Epoch [47][2500/3746] lr: 7.796e-02, eta: 3 days, 11:02:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5331, loss_cls: 4.1110, loss: 4.1110 +2024-07-23 17:49:42,394 - pyskl - INFO - Epoch [47][2600/3746] lr: 7.794e-02, eta: 3 days, 11:01:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5417, loss_cls: 4.1208, loss: 4.1208 +2024-07-23 17:51:04,179 - pyskl - INFO - Epoch [47][2700/3746] lr: 7.791e-02, eta: 3 days, 11:00:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5334, loss_cls: 4.1182, loss: 4.1182 +2024-07-23 17:52:25,793 - pyskl - INFO - Epoch [47][2800/3746] lr: 7.789e-02, eta: 3 days, 10:59:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5288, loss_cls: 4.1470, loss: 4.1470 +2024-07-23 17:53:47,224 - pyskl - INFO - Epoch [47][2900/3746] lr: 7.787e-02, eta: 3 days, 10:57:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5334, loss_cls: 4.1253, loss: 4.1253 +2024-07-23 17:55:09,048 - pyskl - INFO - Epoch [47][3000/3746] lr: 7.784e-02, eta: 3 days, 10:56:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5387, loss_cls: 4.1077, loss: 4.1077 +2024-07-23 17:56:30,630 - pyskl - INFO - Epoch [47][3100/3746] lr: 7.782e-02, eta: 3 days, 10:55:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5302, loss_cls: 4.1146, loss: 4.1146 +2024-07-23 17:57:52,353 - pyskl - INFO - Epoch [47][3200/3746] lr: 7.780e-02, eta: 3 days, 10:54:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5378, loss_cls: 4.1194, loss: 4.1194 +2024-07-23 17:59:13,516 - pyskl - INFO - Epoch [47][3300/3746] lr: 7.777e-02, eta: 3 days, 10:53:28, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5331, loss_cls: 4.1267, loss: 4.1267 +2024-07-23 18:00:35,179 - pyskl - INFO - Epoch [47][3400/3746] lr: 7.775e-02, eta: 3 days, 10:52:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5300, loss_cls: 4.1517, loss: 4.1517 +2024-07-23 18:01:56,699 - pyskl - INFO - Epoch [47][3500/3746] lr: 7.773e-02, eta: 3 days, 10:51:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5298, loss_cls: 4.1274, loss: 4.1274 +2024-07-23 18:03:18,013 - pyskl - INFO - Epoch [47][3600/3746] lr: 7.770e-02, eta: 3 days, 10:50:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5392, loss_cls: 4.1009, loss: 4.1009 +2024-07-23 18:04:39,407 - pyskl - INFO - Epoch [47][3700/3746] lr: 7.768e-02, eta: 3 days, 10:48:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5238, loss_cls: 4.1856, loss: 4.1856 +2024-07-23 18:05:19,187 - pyskl - INFO - Saving checkpoint at 47 epochs +2024-07-23 18:07:10,004 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 18:07:10,665 - pyskl - INFO - +top1_acc 0.2153 +top5_acc 0.4547 +2024-07-23 18:07:10,665 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 18:07:10,705 - pyskl - INFO - +mean_acc 0.2153 +2024-07-23 18:07:10,715 - pyskl - INFO - Epoch(val) [47][309] top1_acc: 0.2153, top5_acc: 0.4547, mean_class_accuracy: 0.2153 +2024-07-23 18:10:59,684 - pyskl - INFO - Epoch [48][100/3746] lr: 7.765e-02, eta: 3 days, 10:51:17, time: 2.290, data_time: 1.312, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5469, loss_cls: 4.0765, loss: 4.0765 +2024-07-23 18:12:21,687 - pyskl - INFO - Epoch [48][200/3746] lr: 7.762e-02, eta: 3 days, 10:50:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5475, loss_cls: 4.0527, loss: 4.0527 +2024-07-23 18:13:43,564 - pyskl - INFO - Epoch [48][300/3746] lr: 7.760e-02, eta: 3 days, 10:49:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5450, loss_cls: 4.0563, loss: 4.0563 +2024-07-23 18:15:05,176 - pyskl - INFO - Epoch [48][400/3746] lr: 7.758e-02, eta: 3 days, 10:47:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5386, loss_cls: 4.1078, loss: 4.1078 +2024-07-23 18:16:26,707 - pyskl - INFO - Epoch [48][500/3746] lr: 7.755e-02, eta: 3 days, 10:46:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5466, loss_cls: 4.0854, loss: 4.0854 +2024-07-23 18:17:48,631 - pyskl - INFO - Epoch [48][600/3746] lr: 7.753e-02, eta: 3 days, 10:45:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5448, loss_cls: 4.0665, loss: 4.0665 +2024-07-23 18:19:10,407 - pyskl - INFO - Epoch [48][700/3746] lr: 7.751e-02, eta: 3 days, 10:44:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5336, loss_cls: 4.1044, loss: 4.1044 +2024-07-23 18:20:32,703 - pyskl - INFO - Epoch [48][800/3746] lr: 7.748e-02, eta: 3 days, 10:43:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5525, loss_cls: 4.0573, loss: 4.0573 +2024-07-23 18:21:54,283 - pyskl - INFO - Epoch [48][900/3746] lr: 7.746e-02, eta: 3 days, 10:42:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5353, loss_cls: 4.1011, loss: 4.1011 +2024-07-23 18:23:16,651 - pyskl - INFO - Epoch [48][1000/3746] lr: 7.744e-02, eta: 3 days, 10:41:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5389, loss_cls: 4.1254, loss: 4.1254 +2024-07-23 18:24:39,278 - pyskl - INFO - Epoch [48][1100/3746] lr: 7.741e-02, eta: 3 days, 10:40:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5391, loss_cls: 4.1056, loss: 4.1056 +2024-07-23 18:26:01,868 - pyskl - INFO - Epoch [48][1200/3746] lr: 7.739e-02, eta: 3 days, 10:38:59, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5242, loss_cls: 4.1549, loss: 4.1549 +2024-07-23 18:27:23,936 - pyskl - INFO - Epoch [48][1300/3746] lr: 7.737e-02, eta: 3 days, 10:37:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5370, loss_cls: 4.1126, loss: 4.1126 +2024-07-23 18:28:45,739 - pyskl - INFO - Epoch [48][1400/3746] lr: 7.734e-02, eta: 3 days, 10:36:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5381, loss_cls: 4.1143, loss: 4.1143 +2024-07-23 18:30:07,798 - pyskl - INFO - Epoch [48][1500/3746] lr: 7.732e-02, eta: 3 days, 10:35:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5397, loss_cls: 4.1369, loss: 4.1369 +2024-07-23 18:31:29,246 - pyskl - INFO - Epoch [48][1600/3746] lr: 7.730e-02, eta: 3 days, 10:34:28, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5330, loss_cls: 4.1414, loss: 4.1414 +2024-07-23 18:32:51,556 - pyskl - INFO - Epoch [48][1700/3746] lr: 7.727e-02, eta: 3 days, 10:33:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5375, loss_cls: 4.1078, loss: 4.1078 +2024-07-23 18:34:13,346 - pyskl - INFO - Epoch [48][1800/3746] lr: 7.725e-02, eta: 3 days, 10:32:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5373, loss_cls: 4.0935, loss: 4.0935 +2024-07-23 18:35:35,592 - pyskl - INFO - Epoch [48][1900/3746] lr: 7.723e-02, eta: 3 days, 10:31:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5406, loss_cls: 4.1162, loss: 4.1162 +2024-07-23 18:36:57,420 - pyskl - INFO - Epoch [48][2000/3746] lr: 7.720e-02, eta: 3 days, 10:29:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5430, loss_cls: 4.0676, loss: 4.0676 +2024-07-23 18:38:19,510 - pyskl - INFO - Epoch [48][2100/3746] lr: 7.718e-02, eta: 3 days, 10:28:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5358, loss_cls: 4.1002, loss: 4.1002 +2024-07-23 18:39:40,775 - pyskl - INFO - Epoch [48][2200/3746] lr: 7.716e-02, eta: 3 days, 10:27:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5383, loss_cls: 4.1118, loss: 4.1118 +2024-07-23 18:41:02,533 - pyskl - INFO - Epoch [48][2300/3746] lr: 7.713e-02, eta: 3 days, 10:26:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5309, loss_cls: 4.1285, loss: 4.1285 +2024-07-23 18:42:24,222 - pyskl - INFO - Epoch [48][2400/3746] lr: 7.711e-02, eta: 3 days, 10:25:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5347, loss_cls: 4.1163, loss: 4.1163 +2024-07-23 18:43:45,534 - pyskl - INFO - Epoch [48][2500/3746] lr: 7.709e-02, eta: 3 days, 10:24:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5422, loss_cls: 4.0890, loss: 4.0890 +2024-07-23 18:45:07,403 - pyskl - INFO - Epoch [48][2600/3746] lr: 7.706e-02, eta: 3 days, 10:23:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5344, loss_cls: 4.1510, loss: 4.1510 +2024-07-23 18:46:28,904 - pyskl - INFO - Epoch [48][2700/3746] lr: 7.704e-02, eta: 3 days, 10:22:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5322, loss_cls: 4.1282, loss: 4.1282 +2024-07-23 18:47:50,320 - pyskl - INFO - Epoch [48][2800/3746] lr: 7.701e-02, eta: 3 days, 10:20:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5359, loss_cls: 4.1266, loss: 4.1266 +2024-07-23 18:49:11,894 - pyskl - INFO - Epoch [48][2900/3746] lr: 7.699e-02, eta: 3 days, 10:19:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5467, loss_cls: 4.0766, loss: 4.0766 +2024-07-23 18:50:33,590 - pyskl - INFO - Epoch [48][3000/3746] lr: 7.697e-02, eta: 3 days, 10:18:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5395, loss_cls: 4.1309, loss: 4.1309 +2024-07-23 18:51:55,209 - pyskl - INFO - Epoch [48][3100/3746] lr: 7.694e-02, eta: 3 days, 10:17:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5337, loss_cls: 4.1142, loss: 4.1142 +2024-07-23 18:53:16,970 - pyskl - INFO - Epoch [48][3200/3746] lr: 7.692e-02, eta: 3 days, 10:16:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5333, loss_cls: 4.1160, loss: 4.1160 +2024-07-23 18:54:38,762 - pyskl - INFO - Epoch [48][3300/3746] lr: 7.690e-02, eta: 3 days, 10:15:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5369, loss_cls: 4.1164, loss: 4.1164 +2024-07-23 18:56:00,297 - pyskl - INFO - Epoch [48][3400/3746] lr: 7.687e-02, eta: 3 days, 10:14:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5300, loss_cls: 4.1362, loss: 4.1362 +2024-07-23 18:57:22,528 - pyskl - INFO - Epoch [48][3500/3746] lr: 7.685e-02, eta: 3 days, 10:12:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5383, loss_cls: 4.1153, loss: 4.1153 +2024-07-23 18:58:44,051 - pyskl - INFO - Epoch [48][3600/3746] lr: 7.683e-02, eta: 3 days, 10:11:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5339, loss_cls: 4.1053, loss: 4.1053 +2024-07-23 19:00:05,595 - pyskl - INFO - Epoch [48][3700/3746] lr: 7.680e-02, eta: 3 days, 10:10:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5400, loss_cls: 4.1119, loss: 4.1119 +2024-07-23 19:00:45,533 - pyskl - INFO - Saving checkpoint at 48 epochs +2024-07-23 19:02:37,294 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 19:02:37,960 - pyskl - INFO - +top1_acc 0.2181 +top5_acc 0.4576 +2024-07-23 19:02:37,960 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 19:02:37,999 - pyskl - INFO - +mean_acc 0.2179 +2024-07-23 19:02:38,011 - pyskl - INFO - Epoch(val) [48][309] top1_acc: 0.2181, top5_acc: 0.4576, mean_class_accuracy: 0.2179 +2024-07-23 19:06:30,217 - pyskl - INFO - Epoch [49][100/3746] lr: 7.677e-02, eta: 3 days, 10:12:58, time: 2.322, data_time: 1.344, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5519, loss_cls: 4.0235, loss: 4.0235 +2024-07-23 19:07:51,674 - pyskl - INFO - Epoch [49][200/3746] lr: 7.674e-02, eta: 3 days, 10:11:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5448, loss_cls: 4.0969, loss: 4.0969 +2024-07-23 19:09:13,349 - pyskl - INFO - Epoch [49][300/3746] lr: 7.672e-02, eta: 3 days, 10:10:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5530, loss_cls: 4.0346, loss: 4.0346 +2024-07-23 19:10:35,702 - pyskl - INFO - Epoch [49][400/3746] lr: 7.670e-02, eta: 3 days, 10:09:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5356, loss_cls: 4.1054, loss: 4.1054 +2024-07-23 19:11:57,677 - pyskl - INFO - Epoch [49][500/3746] lr: 7.667e-02, eta: 3 days, 10:08:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5327, loss_cls: 4.1211, loss: 4.1211 +2024-07-23 19:13:19,730 - pyskl - INFO - Epoch [49][600/3746] lr: 7.665e-02, eta: 3 days, 10:07:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5361, loss_cls: 4.1099, loss: 4.1099 +2024-07-23 19:14:41,689 - pyskl - INFO - Epoch [49][700/3746] lr: 7.663e-02, eta: 3 days, 10:06:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5411, loss_cls: 4.1209, loss: 4.1209 +2024-07-23 19:16:03,843 - pyskl - INFO - Epoch [49][800/3746] lr: 7.660e-02, eta: 3 days, 10:05:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5334, loss_cls: 4.1347, loss: 4.1347 +2024-07-23 19:17:25,812 - pyskl - INFO - Epoch [49][900/3746] lr: 7.658e-02, eta: 3 days, 10:03:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5447, loss_cls: 4.0762, loss: 4.0762 +2024-07-23 19:18:48,506 - pyskl - INFO - Epoch [49][1000/3746] lr: 7.656e-02, eta: 3 days, 10:02:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5377, loss_cls: 4.1196, loss: 4.1196 +2024-07-23 19:20:10,751 - pyskl - INFO - Epoch [49][1100/3746] lr: 7.653e-02, eta: 3 days, 10:01:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5561, loss_cls: 4.0529, loss: 4.0529 +2024-07-23 19:21:34,284 - pyskl - INFO - Epoch [49][1200/3746] lr: 7.651e-02, eta: 3 days, 10:00:35, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5453, loss_cls: 4.0646, loss: 4.0646 +2024-07-23 19:22:56,334 - pyskl - INFO - Epoch [49][1300/3746] lr: 7.648e-02, eta: 3 days, 9:59:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5347, loss_cls: 4.1399, loss: 4.1399 +2024-07-23 19:24:17,946 - pyskl - INFO - Epoch [49][1400/3746] lr: 7.646e-02, eta: 3 days, 9:58:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5367, loss_cls: 4.1325, loss: 4.1325 +2024-07-23 19:25:39,738 - pyskl - INFO - Epoch [49][1500/3746] lr: 7.644e-02, eta: 3 days, 9:57:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5387, loss_cls: 4.1137, loss: 4.1137 +2024-07-23 19:27:01,349 - pyskl - INFO - Epoch [49][1600/3746] lr: 7.641e-02, eta: 3 days, 9:56:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5370, loss_cls: 4.1225, loss: 4.1225 +2024-07-23 19:28:23,171 - pyskl - INFO - Epoch [49][1700/3746] lr: 7.639e-02, eta: 3 days, 9:54:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5414, loss_cls: 4.0926, loss: 4.0926 +2024-07-23 19:29:44,842 - pyskl - INFO - Epoch [49][1800/3746] lr: 7.637e-02, eta: 3 days, 9:53:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5425, loss_cls: 4.1140, loss: 4.1140 +2024-07-23 19:31:06,924 - pyskl - INFO - Epoch [49][1900/3746] lr: 7.634e-02, eta: 3 days, 9:52:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5441, loss_cls: 4.0730, loss: 4.0730 +2024-07-23 19:32:28,671 - pyskl - INFO - Epoch [49][2000/3746] lr: 7.632e-02, eta: 3 days, 9:51:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5527, loss_cls: 4.0617, loss: 4.0617 +2024-07-23 19:33:50,752 - pyskl - INFO - Epoch [49][2100/3746] lr: 7.629e-02, eta: 3 days, 9:50:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5370, loss_cls: 4.1322, loss: 4.1322 +2024-07-23 19:35:12,017 - pyskl - INFO - Epoch [49][2200/3746] lr: 7.627e-02, eta: 3 days, 9:49:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5339, loss_cls: 4.1137, loss: 4.1137 +2024-07-23 19:36:34,180 - pyskl - INFO - Epoch [49][2300/3746] lr: 7.625e-02, eta: 3 days, 9:48:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5275, loss_cls: 4.1221, loss: 4.1221 +2024-07-23 19:37:55,784 - pyskl - INFO - Epoch [49][2400/3746] lr: 7.622e-02, eta: 3 days, 9:46:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5300, loss_cls: 4.1191, loss: 4.1191 +2024-07-23 19:39:17,928 - pyskl - INFO - Epoch [49][2500/3746] lr: 7.620e-02, eta: 3 days, 9:45:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5366, loss_cls: 4.1246, loss: 4.1246 +2024-07-23 19:40:39,489 - pyskl - INFO - Epoch [49][2600/3746] lr: 7.618e-02, eta: 3 days, 9:44:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5387, loss_cls: 4.1370, loss: 4.1370 +2024-07-23 19:42:01,041 - pyskl - INFO - Epoch [49][2700/3746] lr: 7.615e-02, eta: 3 days, 9:43:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5352, loss_cls: 4.1070, loss: 4.1070 +2024-07-23 19:43:22,654 - pyskl - INFO - Epoch [49][2800/3746] lr: 7.613e-02, eta: 3 days, 9:42:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5413, loss_cls: 4.0676, loss: 4.0676 +2024-07-23 19:44:44,804 - pyskl - INFO - Epoch [49][2900/3746] lr: 7.610e-02, eta: 3 days, 9:41:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5411, loss_cls: 4.0881, loss: 4.0881 +2024-07-23 19:46:06,159 - pyskl - INFO - Epoch [49][3000/3746] lr: 7.608e-02, eta: 3 days, 9:39:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5398, loss_cls: 4.0885, loss: 4.0885 +2024-07-23 19:47:27,885 - pyskl - INFO - Epoch [49][3100/3746] lr: 7.606e-02, eta: 3 days, 9:38:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5308, loss_cls: 4.1519, loss: 4.1519 +2024-07-23 19:48:49,356 - pyskl - INFO - Epoch [49][3200/3746] lr: 7.603e-02, eta: 3 days, 9:37:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5428, loss_cls: 4.0850, loss: 4.0850 +2024-07-23 19:50:10,978 - pyskl - INFO - Epoch [49][3300/3746] lr: 7.601e-02, eta: 3 days, 9:36:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5411, loss_cls: 4.0788, loss: 4.0788 +2024-07-23 19:51:32,525 - pyskl - INFO - Epoch [49][3400/3746] lr: 7.598e-02, eta: 3 days, 9:35:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5327, loss_cls: 4.1428, loss: 4.1428 +2024-07-23 19:52:53,924 - pyskl - INFO - Epoch [49][3500/3746] lr: 7.596e-02, eta: 3 days, 9:34:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5427, loss_cls: 4.1017, loss: 4.1017 +2024-07-23 19:54:15,481 - pyskl - INFO - Epoch [49][3600/3746] lr: 7.594e-02, eta: 3 days, 9:33:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5266, loss_cls: 4.1609, loss: 4.1609 +2024-07-23 19:55:37,065 - pyskl - INFO - Epoch [49][3700/3746] lr: 7.591e-02, eta: 3 days, 9:31:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5381, loss_cls: 4.0961, loss: 4.0961 +2024-07-23 19:56:16,474 - pyskl - INFO - Saving checkpoint at 49 epochs +2024-07-23 19:58:07,964 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 19:58:08,634 - pyskl - INFO - +top1_acc 0.2250 +top5_acc 0.4529 +2024-07-23 19:58:08,635 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 19:58:08,676 - pyskl - INFO - +mean_acc 0.2248 +2024-07-23 19:58:08,681 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_38.pth was removed +2024-07-23 19:58:08,910 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_49.pth. +2024-07-23 19:58:08,911 - pyskl - INFO - Best top1_acc is 0.2250 at 49 epoch. +2024-07-23 19:58:08,922 - pyskl - INFO - Epoch(val) [49][309] top1_acc: 0.2250, top5_acc: 0.4529, mean_class_accuracy: 0.2248 +2024-07-23 20:01:57,389 - pyskl - INFO - Epoch [50][100/3746] lr: 7.588e-02, eta: 3 days, 9:33:57, time: 2.285, data_time: 1.307, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5477, loss_cls: 4.0535, loss: 4.0535 +2024-07-23 20:03:19,733 - pyskl - INFO - Epoch [50][200/3746] lr: 7.585e-02, eta: 3 days, 9:32:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5419, loss_cls: 4.0566, loss: 4.0566 +2024-07-23 20:04:41,203 - pyskl - INFO - Epoch [50][300/3746] lr: 7.583e-02, eta: 3 days, 9:31:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5491, loss_cls: 4.0415, loss: 4.0415 +2024-07-23 20:06:02,507 - pyskl - INFO - Epoch [50][400/3746] lr: 7.581e-02, eta: 3 days, 9:30:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5500, loss_cls: 4.0224, loss: 4.0224 +2024-07-23 20:07:24,097 - pyskl - INFO - Epoch [50][500/3746] lr: 7.578e-02, eta: 3 days, 9:29:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5425, loss_cls: 4.1023, loss: 4.1023 +2024-07-23 20:08:45,896 - pyskl - INFO - Epoch [50][600/3746] lr: 7.576e-02, eta: 3 days, 9:28:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5483, loss_cls: 4.0406, loss: 4.0406 +2024-07-23 20:10:07,944 - pyskl - INFO - Epoch [50][700/3746] lr: 7.573e-02, eta: 3 days, 9:27:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5416, loss_cls: 4.1228, loss: 4.1228 +2024-07-23 20:11:29,755 - pyskl - INFO - Epoch [50][800/3746] lr: 7.571e-02, eta: 3 days, 9:25:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5461, loss_cls: 4.0858, loss: 4.0858 +2024-07-23 20:12:51,813 - pyskl - INFO - Epoch [50][900/3746] lr: 7.569e-02, eta: 3 days, 9:24:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5495, loss_cls: 4.0447, loss: 4.0447 +2024-07-23 20:14:13,882 - pyskl - INFO - Epoch [50][1000/3746] lr: 7.566e-02, eta: 3 days, 9:23:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5398, loss_cls: 4.1257, loss: 4.1257 +2024-07-23 20:15:35,257 - pyskl - INFO - Epoch [50][1100/3746] lr: 7.564e-02, eta: 3 days, 9:22:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5423, loss_cls: 4.0739, loss: 4.0739 +2024-07-23 20:16:58,070 - pyskl - INFO - Epoch [50][1200/3746] lr: 7.561e-02, eta: 3 days, 9:21:19, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5389, loss_cls: 4.1132, loss: 4.1132 +2024-07-23 20:18:20,322 - pyskl - INFO - Epoch [50][1300/3746] lr: 7.559e-02, eta: 3 days, 9:20:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5420, loss_cls: 4.1029, loss: 4.1029 +2024-07-23 20:19:42,641 - pyskl - INFO - Epoch [50][1400/3746] lr: 7.557e-02, eta: 3 days, 9:19:02, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5461, loss_cls: 4.0623, loss: 4.0623 +2024-07-23 20:21:04,139 - pyskl - INFO - Epoch [50][1500/3746] lr: 7.554e-02, eta: 3 days, 9:17:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5314, loss_cls: 4.1502, loss: 4.1502 +2024-07-23 20:22:26,235 - pyskl - INFO - Epoch [50][1600/3746] lr: 7.552e-02, eta: 3 days, 9:16:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5470, loss_cls: 4.0728, loss: 4.0728 +2024-07-23 20:23:48,046 - pyskl - INFO - Epoch [50][1700/3746] lr: 7.549e-02, eta: 3 days, 9:15:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5369, loss_cls: 4.1245, loss: 4.1245 +2024-07-23 20:25:09,608 - pyskl - INFO - Epoch [50][1800/3746] lr: 7.547e-02, eta: 3 days, 9:14:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5437, loss_cls: 4.1343, loss: 4.1343 +2024-07-23 20:26:31,348 - pyskl - INFO - Epoch [50][1900/3746] lr: 7.545e-02, eta: 3 days, 9:13:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5275, loss_cls: 4.1161, loss: 4.1161 +2024-07-23 20:27:52,922 - pyskl - INFO - Epoch [50][2000/3746] lr: 7.542e-02, eta: 3 days, 9:12:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5348, loss_cls: 4.1055, loss: 4.1055 +2024-07-23 20:29:14,494 - pyskl - INFO - Epoch [50][2100/3746] lr: 7.540e-02, eta: 3 days, 9:10:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5369, loss_cls: 4.1212, loss: 4.1212 +2024-07-23 20:30:35,980 - pyskl - INFO - Epoch [50][2200/3746] lr: 7.537e-02, eta: 3 days, 9:09:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5363, loss_cls: 4.1168, loss: 4.1168 +2024-07-23 20:31:58,346 - pyskl - INFO - Epoch [50][2300/3746] lr: 7.535e-02, eta: 3 days, 9:08:38, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5381, loss_cls: 4.1225, loss: 4.1225 +2024-07-23 20:33:19,636 - pyskl - INFO - Epoch [50][2400/3746] lr: 7.533e-02, eta: 3 days, 9:07:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5541, loss_cls: 4.0433, loss: 4.0433 +2024-07-23 20:34:41,763 - pyskl - INFO - Epoch [50][2500/3746] lr: 7.530e-02, eta: 3 days, 9:06:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5341, loss_cls: 4.1133, loss: 4.1133 +2024-07-23 20:36:03,274 - pyskl - INFO - Epoch [50][2600/3746] lr: 7.528e-02, eta: 3 days, 9:05:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5447, loss_cls: 4.0721, loss: 4.0721 +2024-07-23 20:37:25,027 - pyskl - INFO - Epoch [50][2700/3746] lr: 7.525e-02, eta: 3 days, 9:03:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5477, loss_cls: 4.0679, loss: 4.0679 +2024-07-23 20:38:46,905 - pyskl - INFO - Epoch [50][2800/3746] lr: 7.523e-02, eta: 3 days, 9:02:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5289, loss_cls: 4.1190, loss: 4.1190 +2024-07-23 20:40:08,887 - pyskl - INFO - Epoch [50][2900/3746] lr: 7.520e-02, eta: 3 days, 9:01:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5369, loss_cls: 4.1120, loss: 4.1120 +2024-07-23 20:41:30,453 - pyskl - INFO - Epoch [50][3000/3746] lr: 7.518e-02, eta: 3 days, 9:00:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5514, loss_cls: 4.0696, loss: 4.0696 +2024-07-23 20:42:52,170 - pyskl - INFO - Epoch [50][3100/3746] lr: 7.516e-02, eta: 3 days, 8:59:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5373, loss_cls: 4.1223, loss: 4.1223 +2024-07-23 20:44:14,071 - pyskl - INFO - Epoch [50][3200/3746] lr: 7.513e-02, eta: 3 days, 8:58:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5380, loss_cls: 4.0996, loss: 4.0996 +2024-07-23 20:45:35,315 - pyskl - INFO - Epoch [50][3300/3746] lr: 7.511e-02, eta: 3 days, 8:57:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5339, loss_cls: 4.1100, loss: 4.1100 +2024-07-23 20:46:57,176 - pyskl - INFO - Epoch [50][3400/3746] lr: 7.508e-02, eta: 3 days, 8:55:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5334, loss_cls: 4.1077, loss: 4.1077 +2024-07-23 20:48:18,633 - pyskl - INFO - Epoch [50][3500/3746] lr: 7.506e-02, eta: 3 days, 8:54:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5398, loss_cls: 4.1149, loss: 4.1149 +2024-07-23 20:49:40,360 - pyskl - INFO - Epoch [50][3600/3746] lr: 7.504e-02, eta: 3 days, 8:53:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5402, loss_cls: 4.0915, loss: 4.0915 +2024-07-23 20:51:02,034 - pyskl - INFO - Epoch [50][3700/3746] lr: 7.501e-02, eta: 3 days, 8:52:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5353, loss_cls: 4.1347, loss: 4.1347 +2024-07-23 20:51:41,494 - pyskl - INFO - Saving checkpoint at 50 epochs +2024-07-23 20:53:32,705 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 20:53:33,364 - pyskl - INFO - +top1_acc 0.2301 +top5_acc 0.4715 +2024-07-23 20:53:33,365 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 20:53:33,403 - pyskl - INFO - +mean_acc 0.2299 +2024-07-23 20:53:33,407 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_49.pth was removed +2024-07-23 20:53:33,635 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_50.pth. +2024-07-23 20:53:33,636 - pyskl - INFO - Best top1_acc is 0.2301 at 50 epoch. +2024-07-23 20:53:33,646 - pyskl - INFO - Epoch(val) [50][309] top1_acc: 0.2301, top5_acc: 0.4715, mean_class_accuracy: 0.2299 +2024-07-23 20:57:24,456 - pyskl - INFO - Epoch [51][100/3746] lr: 7.498e-02, eta: 3 days, 8:54:24, time: 2.308, data_time: 1.331, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5523, loss_cls: 4.0319, loss: 4.0319 +2024-07-23 20:58:46,219 - pyskl - INFO - Epoch [51][200/3746] lr: 7.495e-02, eta: 3 days, 8:53:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5494, loss_cls: 4.0237, loss: 4.0237 +2024-07-23 21:00:08,317 - pyskl - INFO - Epoch [51][300/3746] lr: 7.493e-02, eta: 3 days, 8:52:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5459, loss_cls: 4.0640, loss: 4.0640 +2024-07-23 21:01:29,918 - pyskl - INFO - Epoch [51][400/3746] lr: 7.490e-02, eta: 3 days, 8:50:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5450, loss_cls: 4.0794, loss: 4.0794 +2024-07-23 21:02:51,748 - pyskl - INFO - Epoch [51][500/3746] lr: 7.488e-02, eta: 3 days, 8:49:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5423, loss_cls: 4.0478, loss: 4.0478 +2024-07-23 21:04:13,661 - pyskl - INFO - Epoch [51][600/3746] lr: 7.485e-02, eta: 3 days, 8:48:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5473, loss_cls: 4.0655, loss: 4.0655 +2024-07-23 21:05:35,475 - pyskl - INFO - Epoch [51][700/3746] lr: 7.483e-02, eta: 3 days, 8:47:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5373, loss_cls: 4.1137, loss: 4.1137 +2024-07-23 21:06:57,565 - pyskl - INFO - Epoch [51][800/3746] lr: 7.481e-02, eta: 3 days, 8:46:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5394, loss_cls: 4.1096, loss: 4.1096 +2024-07-23 21:08:19,597 - pyskl - INFO - Epoch [51][900/3746] lr: 7.478e-02, eta: 3 days, 8:45:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5380, loss_cls: 4.1052, loss: 4.1052 +2024-07-23 21:09:42,114 - pyskl - INFO - Epoch [51][1000/3746] lr: 7.476e-02, eta: 3 days, 8:43:58, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5419, loss_cls: 4.1043, loss: 4.1043 +2024-07-23 21:11:03,882 - pyskl - INFO - Epoch [51][1100/3746] lr: 7.473e-02, eta: 3 days, 8:42:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5448, loss_cls: 4.0950, loss: 4.0950 +2024-07-23 21:12:26,719 - pyskl - INFO - Epoch [51][1200/3746] lr: 7.471e-02, eta: 3 days, 8:41:41, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5453, loss_cls: 4.0684, loss: 4.0684 +2024-07-23 21:13:48,732 - pyskl - INFO - Epoch [51][1300/3746] lr: 7.468e-02, eta: 3 days, 8:40:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5402, loss_cls: 4.1179, loss: 4.1179 +2024-07-23 21:15:10,765 - pyskl - INFO - Epoch [51][1400/3746] lr: 7.466e-02, eta: 3 days, 8:39:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5384, loss_cls: 4.0709, loss: 4.0709 +2024-07-23 21:16:32,815 - pyskl - INFO - Epoch [51][1500/3746] lr: 7.464e-02, eta: 3 days, 8:38:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5347, loss_cls: 4.1169, loss: 4.1169 +2024-07-23 21:17:54,314 - pyskl - INFO - Epoch [51][1600/3746] lr: 7.461e-02, eta: 3 days, 8:37:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5414, loss_cls: 4.0756, loss: 4.0756 +2024-07-23 21:19:15,707 - pyskl - INFO - Epoch [51][1700/3746] lr: 7.459e-02, eta: 3 days, 8:35:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5289, loss_cls: 4.1359, loss: 4.1359 +2024-07-23 21:20:37,316 - pyskl - INFO - Epoch [51][1800/3746] lr: 7.456e-02, eta: 3 days, 8:34:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5394, loss_cls: 4.1100, loss: 4.1100 +2024-07-23 21:21:58,787 - pyskl - INFO - Epoch [51][1900/3746] lr: 7.454e-02, eta: 3 days, 8:33:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5508, loss_cls: 4.0549, loss: 4.0549 +2024-07-23 21:23:20,352 - pyskl - INFO - Epoch [51][2000/3746] lr: 7.451e-02, eta: 3 days, 8:32:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5394, loss_cls: 4.0895, loss: 4.0895 +2024-07-23 21:24:42,643 - pyskl - INFO - Epoch [51][2100/3746] lr: 7.449e-02, eta: 3 days, 8:31:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5363, loss_cls: 4.0986, loss: 4.0986 +2024-07-23 21:26:04,368 - pyskl - INFO - Epoch [51][2200/3746] lr: 7.447e-02, eta: 3 days, 8:30:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5478, loss_cls: 4.0639, loss: 4.0639 +2024-07-23 21:27:26,365 - pyskl - INFO - Epoch [51][2300/3746] lr: 7.444e-02, eta: 3 days, 8:28:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5381, loss_cls: 4.1127, loss: 4.1127 +2024-07-23 21:28:47,907 - pyskl - INFO - Epoch [51][2400/3746] lr: 7.442e-02, eta: 3 days, 8:27:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5337, loss_cls: 4.1087, loss: 4.1087 +2024-07-23 21:30:09,387 - pyskl - INFO - Epoch [51][2500/3746] lr: 7.439e-02, eta: 3 days, 8:26:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5397, loss_cls: 4.0855, loss: 4.0855 +2024-07-23 21:31:30,876 - pyskl - INFO - Epoch [51][2600/3746] lr: 7.437e-02, eta: 3 days, 8:25:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5478, loss_cls: 4.0679, loss: 4.0679 +2024-07-23 21:32:52,340 - pyskl - INFO - Epoch [51][2700/3746] lr: 7.434e-02, eta: 3 days, 8:24:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5425, loss_cls: 4.0546, loss: 4.0546 +2024-07-23 21:34:14,288 - pyskl - INFO - Epoch [51][2800/3746] lr: 7.432e-02, eta: 3 days, 8:22:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5400, loss_cls: 4.0971, loss: 4.0971 +2024-07-23 21:35:35,875 - pyskl - INFO - Epoch [51][2900/3746] lr: 7.429e-02, eta: 3 days, 8:21:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5584, loss_cls: 4.0255, loss: 4.0255 +2024-07-23 21:36:57,215 - pyskl - INFO - Epoch [51][3000/3746] lr: 7.427e-02, eta: 3 days, 8:20:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5492, loss_cls: 4.0523, loss: 4.0523 +2024-07-23 21:38:18,496 - pyskl - INFO - Epoch [51][3100/3746] lr: 7.425e-02, eta: 3 days, 8:19:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5325, loss_cls: 4.1034, loss: 4.1034 +2024-07-23 21:39:40,221 - pyskl - INFO - Epoch [51][3200/3746] lr: 7.422e-02, eta: 3 days, 8:18:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5361, loss_cls: 4.1362, loss: 4.1362 +2024-07-23 21:41:01,715 - pyskl - INFO - Epoch [51][3300/3746] lr: 7.420e-02, eta: 3 days, 8:17:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5470, loss_cls: 4.0644, loss: 4.0644 +2024-07-23 21:42:23,031 - pyskl - INFO - Epoch [51][3400/3746] lr: 7.417e-02, eta: 3 days, 8:15:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5373, loss_cls: 4.0967, loss: 4.0967 +2024-07-23 21:43:44,870 - pyskl - INFO - Epoch [51][3500/3746] lr: 7.415e-02, eta: 3 days, 8:14:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5400, loss_cls: 4.1003, loss: 4.1003 +2024-07-23 21:45:06,389 - pyskl - INFO - Epoch [51][3600/3746] lr: 7.412e-02, eta: 3 days, 8:13:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5397, loss_cls: 4.0906, loss: 4.0906 +2024-07-23 21:46:27,978 - pyskl - INFO - Epoch [51][3700/3746] lr: 7.410e-02, eta: 3 days, 8:12:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5358, loss_cls: 4.1171, loss: 4.1171 +2024-07-23 21:47:07,546 - pyskl - INFO - Saving checkpoint at 51 epochs +2024-07-23 21:48:59,054 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 21:48:59,714 - pyskl - INFO - +top1_acc 0.2149 +top5_acc 0.4542 +2024-07-23 21:48:59,714 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 21:48:59,756 - pyskl - INFO - +mean_acc 0.2147 +2024-07-23 21:48:59,766 - pyskl - INFO - Epoch(val) [51][309] top1_acc: 0.2149, top5_acc: 0.4542, mean_class_accuracy: 0.2147 +2024-07-23 21:52:50,713 - pyskl - INFO - Epoch [52][100/3746] lr: 7.406e-02, eta: 3 days, 8:14:17, time: 2.309, data_time: 1.326, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5663, loss_cls: 4.0119, loss: 4.0119 +2024-07-23 21:54:12,683 - pyskl - INFO - Epoch [52][200/3746] lr: 7.404e-02, eta: 3 days, 8:13:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5413, loss_cls: 4.1006, loss: 4.1006 +2024-07-23 21:55:34,354 - pyskl - INFO - Epoch [52][300/3746] lr: 7.401e-02, eta: 3 days, 8:11:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5533, loss_cls: 4.0350, loss: 4.0350 +2024-07-23 21:56:55,947 - pyskl - INFO - Epoch [52][400/3746] lr: 7.399e-02, eta: 3 days, 8:10:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5531, loss_cls: 4.0497, loss: 4.0497 +2024-07-23 21:58:17,715 - pyskl - INFO - Epoch [52][500/3746] lr: 7.397e-02, eta: 3 days, 8:09:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5453, loss_cls: 4.0868, loss: 4.0868 +2024-07-23 21:59:39,499 - pyskl - INFO - Epoch [52][600/3746] lr: 7.394e-02, eta: 3 days, 8:08:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5475, loss_cls: 4.0582, loss: 4.0582 +2024-07-23 22:01:01,068 - pyskl - INFO - Epoch [52][700/3746] lr: 7.392e-02, eta: 3 days, 8:07:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5475, loss_cls: 4.0924, loss: 4.0924 +2024-07-23 22:02:22,589 - pyskl - INFO - Epoch [52][800/3746] lr: 7.389e-02, eta: 3 days, 8:06:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5308, loss_cls: 4.1163, loss: 4.1163 +2024-07-23 22:03:44,755 - pyskl - INFO - Epoch [52][900/3746] lr: 7.387e-02, eta: 3 days, 8:04:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5405, loss_cls: 4.1085, loss: 4.1085 +2024-07-23 22:05:06,878 - pyskl - INFO - Epoch [52][1000/3746] lr: 7.384e-02, eta: 3 days, 8:03:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5423, loss_cls: 4.0776, loss: 4.0776 +2024-07-23 22:06:28,799 - pyskl - INFO - Epoch [52][1100/3746] lr: 7.382e-02, eta: 3 days, 8:02:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5456, loss_cls: 4.0595, loss: 4.0595 +2024-07-23 22:07:51,812 - pyskl - INFO - Epoch [52][1200/3746] lr: 7.379e-02, eta: 3 days, 8:01:24, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5456, loss_cls: 4.0928, loss: 4.0928 +2024-07-23 22:09:13,923 - pyskl - INFO - Epoch [52][1300/3746] lr: 7.377e-02, eta: 3 days, 8:00:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5497, loss_cls: 4.0538, loss: 4.0538 +2024-07-23 22:10:36,123 - pyskl - INFO - Epoch [52][1400/3746] lr: 7.374e-02, eta: 3 days, 7:59:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5406, loss_cls: 4.0780, loss: 4.0780 +2024-07-23 22:11:57,804 - pyskl - INFO - Epoch [52][1500/3746] lr: 7.372e-02, eta: 3 days, 7:57:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5377, loss_cls: 4.0828, loss: 4.0828 +2024-07-23 22:13:19,532 - pyskl - INFO - Epoch [52][1600/3746] lr: 7.370e-02, eta: 3 days, 7:56:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5413, loss_cls: 4.1056, loss: 4.1056 +2024-07-23 22:14:41,147 - pyskl - INFO - Epoch [52][1700/3746] lr: 7.367e-02, eta: 3 days, 7:55:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5352, loss_cls: 4.0751, loss: 4.0751 +2024-07-23 22:16:02,650 - pyskl - INFO - Epoch [52][1800/3746] lr: 7.365e-02, eta: 3 days, 7:54:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5520, loss_cls: 4.0470, loss: 4.0470 +2024-07-23 22:17:24,427 - pyskl - INFO - Epoch [52][1900/3746] lr: 7.362e-02, eta: 3 days, 7:53:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5311, loss_cls: 4.1443, loss: 4.1443 +2024-07-23 22:18:46,178 - pyskl - INFO - Epoch [52][2000/3746] lr: 7.360e-02, eta: 3 days, 7:52:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5408, loss_cls: 4.0653, loss: 4.0653 +2024-07-23 22:20:08,179 - pyskl - INFO - Epoch [52][2100/3746] lr: 7.357e-02, eta: 3 days, 7:50:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5392, loss_cls: 4.0952, loss: 4.0952 +2024-07-23 22:21:29,761 - pyskl - INFO - Epoch [52][2200/3746] lr: 7.355e-02, eta: 3 days, 7:49:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5569, loss_cls: 4.0413, loss: 4.0413 +2024-07-23 22:22:51,547 - pyskl - INFO - Epoch [52][2300/3746] lr: 7.352e-02, eta: 3 days, 7:48:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5336, loss_cls: 4.1446, loss: 4.1446 +2024-07-23 22:24:12,734 - pyskl - INFO - Epoch [52][2400/3746] lr: 7.350e-02, eta: 3 days, 7:47:16, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5345, loss_cls: 4.1311, loss: 4.1311 +2024-07-23 22:25:34,383 - pyskl - INFO - Epoch [52][2500/3746] lr: 7.347e-02, eta: 3 days, 7:46:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5395, loss_cls: 4.0731, loss: 4.0731 +2024-07-23 22:26:56,073 - pyskl - INFO - Epoch [52][2600/3746] lr: 7.345e-02, eta: 3 days, 7:44:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5387, loss_cls: 4.1078, loss: 4.1078 +2024-07-23 22:28:17,723 - pyskl - INFO - Epoch [52][2700/3746] lr: 7.342e-02, eta: 3 days, 7:43:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5375, loss_cls: 4.0769, loss: 4.0769 +2024-07-23 22:29:40,465 - pyskl - INFO - Epoch [52][2800/3746] lr: 7.340e-02, eta: 3 days, 7:42:34, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5437, loss_cls: 4.0661, loss: 4.0661 +2024-07-23 22:31:02,637 - pyskl - INFO - Epoch [52][2900/3746] lr: 7.337e-02, eta: 3 days, 7:41:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5403, loss_cls: 4.0973, loss: 4.0973 +2024-07-23 22:32:23,851 - pyskl - INFO - Epoch [52][3000/3746] lr: 7.335e-02, eta: 3 days, 7:40:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5395, loss_cls: 4.1121, loss: 4.1121 +2024-07-23 22:33:45,345 - pyskl - INFO - Epoch [52][3100/3746] lr: 7.332e-02, eta: 3 days, 7:39:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5539, loss_cls: 4.0518, loss: 4.0518 +2024-07-23 22:35:06,705 - pyskl - INFO - Epoch [52][3200/3746] lr: 7.330e-02, eta: 3 days, 7:37:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5472, loss_cls: 4.0581, loss: 4.0581 +2024-07-23 22:36:28,291 - pyskl - INFO - Epoch [52][3300/3746] lr: 7.328e-02, eta: 3 days, 7:36:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5352, loss_cls: 4.1208, loss: 4.1208 +2024-07-23 22:37:50,015 - pyskl - INFO - Epoch [52][3400/3746] lr: 7.325e-02, eta: 3 days, 7:35:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5355, loss_cls: 4.0820, loss: 4.0820 +2024-07-23 22:39:12,014 - pyskl - INFO - Epoch [52][3500/3746] lr: 7.323e-02, eta: 3 days, 7:34:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5364, loss_cls: 4.0909, loss: 4.0909 +2024-07-23 22:40:33,592 - pyskl - INFO - Epoch [52][3600/3746] lr: 7.320e-02, eta: 3 days, 7:33:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5437, loss_cls: 4.0815, loss: 4.0815 +2024-07-23 22:41:54,948 - pyskl - INFO - Epoch [52][3700/3746] lr: 7.318e-02, eta: 3 days, 7:31:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5477, loss_cls: 4.0736, loss: 4.0736 +2024-07-23 22:42:34,296 - pyskl - INFO - Saving checkpoint at 52 epochs +2024-07-23 22:44:24,730 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 22:44:25,388 - pyskl - INFO - +top1_acc 0.2033 +top5_acc 0.4380 +2024-07-23 22:44:25,388 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 22:44:25,426 - pyskl - INFO - +mean_acc 0.2030 +2024-07-23 22:44:25,437 - pyskl - INFO - Epoch(val) [52][309] top1_acc: 0.2033, top5_acc: 0.4380, mean_class_accuracy: 0.2030 +2024-07-23 22:48:14,930 - pyskl - INFO - Epoch [53][100/3746] lr: 7.314e-02, eta: 3 days, 7:33:38, time: 2.295, data_time: 1.317, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5464, loss_cls: 4.0415, loss: 4.0415 +2024-07-23 22:49:36,810 - pyskl - INFO - Epoch [53][200/3746] lr: 7.312e-02, eta: 3 days, 7:32:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5581, loss_cls: 4.0272, loss: 4.0272 +2024-07-23 22:50:58,709 - pyskl - INFO - Epoch [53][300/3746] lr: 7.309e-02, eta: 3 days, 7:31:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5422, loss_cls: 4.0602, loss: 4.0602 +2024-07-23 22:52:20,297 - pyskl - INFO - Epoch [53][400/3746] lr: 7.307e-02, eta: 3 days, 7:30:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5569, loss_cls: 4.0226, loss: 4.0226 +2024-07-23 22:53:41,701 - pyskl - INFO - Epoch [53][500/3746] lr: 7.304e-02, eta: 3 days, 7:28:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5519, loss_cls: 4.0178, loss: 4.0178 +2024-07-23 22:55:03,913 - pyskl - INFO - Epoch [53][600/3746] lr: 7.302e-02, eta: 3 days, 7:27:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5416, loss_cls: 4.0962, loss: 4.0962 +2024-07-23 22:56:26,001 - pyskl - INFO - Epoch [53][700/3746] lr: 7.299e-02, eta: 3 days, 7:26:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5416, loss_cls: 4.0904, loss: 4.0904 +2024-07-23 22:57:47,674 - pyskl - INFO - Epoch [53][800/3746] lr: 7.297e-02, eta: 3 days, 7:25:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5458, loss_cls: 4.0935, loss: 4.0935 +2024-07-23 22:59:09,345 - pyskl - INFO - Epoch [53][900/3746] lr: 7.294e-02, eta: 3 days, 7:24:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5553, loss_cls: 4.0518, loss: 4.0518 +2024-07-23 23:00:32,586 - pyskl - INFO - Epoch [53][1000/3746] lr: 7.292e-02, eta: 3 days, 7:23:02, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5522, loss_cls: 4.0635, loss: 4.0635 +2024-07-23 23:01:54,632 - pyskl - INFO - Epoch [53][1100/3746] lr: 7.289e-02, eta: 3 days, 7:21:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5531, loss_cls: 4.0642, loss: 4.0642 +2024-07-23 23:03:17,350 - pyskl - INFO - Epoch [53][1200/3746] lr: 7.287e-02, eta: 3 days, 7:20:42, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5405, loss_cls: 4.0844, loss: 4.0844 +2024-07-23 23:04:39,685 - pyskl - INFO - Epoch [53][1300/3746] lr: 7.284e-02, eta: 3 days, 7:19:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5406, loss_cls: 4.1000, loss: 4.1000 +2024-07-23 23:06:02,255 - pyskl - INFO - Epoch [53][1400/3746] lr: 7.282e-02, eta: 3 days, 7:18:22, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5481, loss_cls: 4.0620, loss: 4.0620 +2024-07-23 23:07:24,237 - pyskl - INFO - Epoch [53][1500/3746] lr: 7.279e-02, eta: 3 days, 7:17:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5403, loss_cls: 4.0830, loss: 4.0830 +2024-07-23 23:08:45,191 - pyskl - INFO - Epoch [53][1600/3746] lr: 7.277e-02, eta: 3 days, 7:15:59, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5473, loss_cls: 4.0487, loss: 4.0487 +2024-07-23 23:10:06,685 - pyskl - INFO - Epoch [53][1700/3746] lr: 7.274e-02, eta: 3 days, 7:14:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5380, loss_cls: 4.1009, loss: 4.1009 +2024-07-23 23:11:28,927 - pyskl - INFO - Epoch [53][1800/3746] lr: 7.272e-02, eta: 3 days, 7:13:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5541, loss_cls: 4.0717, loss: 4.0717 +2024-07-23 23:12:50,722 - pyskl - INFO - Epoch [53][1900/3746] lr: 7.269e-02, eta: 3 days, 7:12:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5447, loss_cls: 4.0703, loss: 4.0703 +2024-07-23 23:14:12,686 - pyskl - INFO - Epoch [53][2000/3746] lr: 7.267e-02, eta: 3 days, 7:11:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5517, loss_cls: 4.0524, loss: 4.0524 +2024-07-23 23:15:34,476 - pyskl - INFO - Epoch [53][2100/3746] lr: 7.264e-02, eta: 3 days, 7:10:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5459, loss_cls: 4.0699, loss: 4.0699 +2024-07-23 23:16:56,300 - pyskl - INFO - Epoch [53][2200/3746] lr: 7.262e-02, eta: 3 days, 7:08:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5455, loss_cls: 4.0453, loss: 4.0453 +2024-07-23 23:18:17,471 - pyskl - INFO - Epoch [53][2300/3746] lr: 7.259e-02, eta: 3 days, 7:07:40, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5517, loss_cls: 4.0371, loss: 4.0371 +2024-07-23 23:19:38,922 - pyskl - INFO - Epoch [53][2400/3746] lr: 7.257e-02, eta: 3 days, 7:06:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5481, loss_cls: 4.0402, loss: 4.0402 +2024-07-23 23:21:00,783 - pyskl - INFO - Epoch [53][2500/3746] lr: 7.254e-02, eta: 3 days, 7:05:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5456, loss_cls: 4.0539, loss: 4.0539 +2024-07-23 23:22:22,233 - pyskl - INFO - Epoch [53][2600/3746] lr: 7.252e-02, eta: 3 days, 7:04:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5275, loss_cls: 4.1334, loss: 4.1334 +2024-07-23 23:23:43,628 - pyskl - INFO - Epoch [53][2700/3746] lr: 7.249e-02, eta: 3 days, 7:02:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5419, loss_cls: 4.1084, loss: 4.1084 +2024-07-23 23:25:05,357 - pyskl - INFO - Epoch [53][2800/3746] lr: 7.247e-02, eta: 3 days, 7:01:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5428, loss_cls: 4.0745, loss: 4.0745 +2024-07-23 23:26:27,219 - pyskl - INFO - Epoch [53][2900/3746] lr: 7.244e-02, eta: 3 days, 7:00:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5305, loss_cls: 4.1186, loss: 4.1186 +2024-07-23 23:27:49,262 - pyskl - INFO - Epoch [53][3000/3746] lr: 7.242e-02, eta: 3 days, 6:59:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5395, loss_cls: 4.0690, loss: 4.0690 +2024-07-23 23:29:10,893 - pyskl - INFO - Epoch [53][3100/3746] lr: 7.239e-02, eta: 3 days, 6:58:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5427, loss_cls: 4.1108, loss: 4.1108 +2024-07-23 23:30:32,991 - pyskl - INFO - Epoch [53][3200/3746] lr: 7.237e-02, eta: 3 days, 6:56:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5450, loss_cls: 4.0824, loss: 4.0824 +2024-07-23 23:31:54,836 - pyskl - INFO - Epoch [53][3300/3746] lr: 7.234e-02, eta: 3 days, 6:55:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5509, loss_cls: 4.0635, loss: 4.0635 +2024-07-23 23:33:16,690 - pyskl - INFO - Epoch [53][3400/3746] lr: 7.232e-02, eta: 3 days, 6:54:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5483, loss_cls: 4.0901, loss: 4.0901 +2024-07-23 23:34:38,190 - pyskl - INFO - Epoch [53][3500/3746] lr: 7.229e-02, eta: 3 days, 6:53:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5506, loss_cls: 4.0612, loss: 4.0612 +2024-07-23 23:35:59,645 - pyskl - INFO - Epoch [53][3600/3746] lr: 7.227e-02, eta: 3 days, 6:52:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5434, loss_cls: 4.0946, loss: 4.0946 +2024-07-23 23:37:21,496 - pyskl - INFO - Epoch [53][3700/3746] lr: 7.224e-02, eta: 3 days, 6:51:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5416, loss_cls: 4.0702, loss: 4.0702 +2024-07-23 23:38:01,080 - pyskl - INFO - Saving checkpoint at 53 epochs +2024-07-23 23:39:53,075 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 23:39:53,744 - pyskl - INFO - +top1_acc 0.2263 +top5_acc 0.4676 +2024-07-23 23:39:53,744 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 23:39:53,787 - pyskl - INFO - +mean_acc 0.2261 +2024-07-23 23:39:53,801 - pyskl - INFO - Epoch(val) [53][309] top1_acc: 0.2263, top5_acc: 0.4676, mean_class_accuracy: 0.2261 +2024-07-23 23:43:44,080 - pyskl - INFO - Epoch [54][100/3746] lr: 7.221e-02, eta: 3 days, 6:52:38, time: 2.303, data_time: 1.323, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5531, loss_cls: 4.0043, loss: 4.0043 +2024-07-23 23:45:05,978 - pyskl - INFO - Epoch [54][200/3746] lr: 7.218e-02, eta: 3 days, 6:51:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5550, loss_cls: 4.0733, loss: 4.0733 +2024-07-23 23:46:27,429 - pyskl - INFO - Epoch [54][300/3746] lr: 7.216e-02, eta: 3 days, 6:50:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5408, loss_cls: 4.0652, loss: 4.0652 +2024-07-23 23:47:48,986 - pyskl - INFO - Epoch [54][400/3746] lr: 7.213e-02, eta: 3 days, 6:49:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5528, loss_cls: 4.0317, loss: 4.0317 +2024-07-23 23:49:11,104 - pyskl - INFO - Epoch [54][500/3746] lr: 7.211e-02, eta: 3 days, 6:47:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5450, loss_cls: 4.0668, loss: 4.0668 +2024-07-23 23:50:33,420 - pyskl - INFO - Epoch [54][600/3746] lr: 7.208e-02, eta: 3 days, 6:46:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5487, loss_cls: 4.0755, loss: 4.0755 +2024-07-23 23:51:55,629 - pyskl - INFO - Epoch [54][700/3746] lr: 7.206e-02, eta: 3 days, 6:45:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5483, loss_cls: 4.0348, loss: 4.0348 +2024-07-23 23:53:17,384 - pyskl - INFO - Epoch [54][800/3746] lr: 7.203e-02, eta: 3 days, 6:44:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5477, loss_cls: 4.0368, loss: 4.0368 +2024-07-23 23:54:39,133 - pyskl - INFO - Epoch [54][900/3746] lr: 7.201e-02, eta: 3 days, 6:43:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5463, loss_cls: 4.0704, loss: 4.0704 +2024-07-23 23:56:01,348 - pyskl - INFO - Epoch [54][1000/3746] lr: 7.198e-02, eta: 3 days, 6:41:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5391, loss_cls: 4.0563, loss: 4.0563 +2024-07-23 23:57:23,211 - pyskl - INFO - Epoch [54][1100/3746] lr: 7.196e-02, eta: 3 days, 6:40:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5466, loss_cls: 4.0823, loss: 4.0823 +2024-07-23 23:58:45,343 - pyskl - INFO - Epoch [54][1200/3746] lr: 7.193e-02, eta: 3 days, 6:39:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5572, loss_cls: 4.0007, loss: 4.0007 +2024-07-24 00:00:07,793 - pyskl - INFO - Epoch [54][1300/3746] lr: 7.191e-02, eta: 3 days, 6:38:23, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5442, loss_cls: 4.0765, loss: 4.0765 +2024-07-24 00:01:29,847 - pyskl - INFO - Epoch [54][1400/3746] lr: 7.188e-02, eta: 3 days, 6:37:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5364, loss_cls: 4.0819, loss: 4.0819 +2024-07-24 00:02:52,238 - pyskl - INFO - Epoch [54][1500/3746] lr: 7.186e-02, eta: 3 days, 6:36:01, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5395, loss_cls: 4.0928, loss: 4.0928 +2024-07-24 00:04:14,161 - pyskl - INFO - Epoch [54][1600/3746] lr: 7.183e-02, eta: 3 days, 6:34:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5433, loss_cls: 4.0436, loss: 4.0436 +2024-07-24 00:05:36,490 - pyskl - INFO - Epoch [54][1700/3746] lr: 7.181e-02, eta: 3 days, 6:33:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5502, loss_cls: 4.0547, loss: 4.0547 +2024-07-24 00:06:58,024 - pyskl - INFO - Epoch [54][1800/3746] lr: 7.178e-02, eta: 3 days, 6:32:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5444, loss_cls: 4.0420, loss: 4.0420 +2024-07-24 00:08:19,533 - pyskl - INFO - Epoch [54][1900/3746] lr: 7.176e-02, eta: 3 days, 6:31:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5389, loss_cls: 4.0707, loss: 4.0707 +2024-07-24 00:09:41,004 - pyskl - INFO - Epoch [54][2000/3746] lr: 7.173e-02, eta: 3 days, 6:30:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5475, loss_cls: 4.0926, loss: 4.0926 +2024-07-24 00:11:02,919 - pyskl - INFO - Epoch [54][2100/3746] lr: 7.170e-02, eta: 3 days, 6:28:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5344, loss_cls: 4.1192, loss: 4.1192 +2024-07-24 00:12:24,527 - pyskl - INFO - Epoch [54][2200/3746] lr: 7.168e-02, eta: 3 days, 6:27:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5437, loss_cls: 4.0769, loss: 4.0769 +2024-07-24 00:13:46,785 - pyskl - INFO - Epoch [54][2300/3746] lr: 7.165e-02, eta: 3 days, 6:26:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5600, loss_cls: 4.0081, loss: 4.0081 +2024-07-24 00:15:08,144 - pyskl - INFO - Epoch [54][2400/3746] lr: 7.163e-02, eta: 3 days, 6:25:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5467, loss_cls: 4.0907, loss: 4.0907 +2024-07-24 00:16:29,241 - pyskl - INFO - Epoch [54][2500/3746] lr: 7.160e-02, eta: 3 days, 6:24:02, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5572, loss_cls: 4.0576, loss: 4.0576 +2024-07-24 00:17:50,495 - pyskl - INFO - Epoch [54][2600/3746] lr: 7.158e-02, eta: 3 days, 6:22:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5409, loss_cls: 4.0615, loss: 4.0615 +2024-07-24 00:19:12,172 - pyskl - INFO - Epoch [54][2700/3746] lr: 7.155e-02, eta: 3 days, 6:21:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5414, loss_cls: 4.0653, loss: 4.0653 +2024-07-24 00:20:33,496 - pyskl - INFO - Epoch [54][2800/3746] lr: 7.153e-02, eta: 3 days, 6:20:25, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5475, loss_cls: 4.0665, loss: 4.0665 +2024-07-24 00:21:55,152 - pyskl - INFO - Epoch [54][2900/3746] lr: 7.150e-02, eta: 3 days, 6:19:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5372, loss_cls: 4.1207, loss: 4.1207 +2024-07-24 00:23:16,900 - pyskl - INFO - Epoch [54][3000/3746] lr: 7.148e-02, eta: 3 days, 6:18:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5472, loss_cls: 4.0467, loss: 4.0467 +2024-07-24 00:24:38,582 - pyskl - INFO - Epoch [54][3100/3746] lr: 7.145e-02, eta: 3 days, 6:16:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5452, loss_cls: 4.0442, loss: 4.0442 +2024-07-24 00:26:00,358 - pyskl - INFO - Epoch [54][3200/3746] lr: 7.143e-02, eta: 3 days, 6:15:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5477, loss_cls: 4.0568, loss: 4.0568 +2024-07-24 00:27:21,924 - pyskl - INFO - Epoch [54][3300/3746] lr: 7.140e-02, eta: 3 days, 6:14:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5333, loss_cls: 4.1449, loss: 4.1449 +2024-07-24 00:28:43,377 - pyskl - INFO - Epoch [54][3400/3746] lr: 7.138e-02, eta: 3 days, 6:13:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5472, loss_cls: 4.0621, loss: 4.0621 +2024-07-24 00:30:04,622 - pyskl - INFO - Epoch [54][3500/3746] lr: 7.135e-02, eta: 3 days, 6:11:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5414, loss_cls: 4.0694, loss: 4.0694 +2024-07-24 00:31:26,059 - pyskl - INFO - Epoch [54][3600/3746] lr: 7.133e-02, eta: 3 days, 6:10:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5483, loss_cls: 4.0215, loss: 4.0215 +2024-07-24 00:32:48,309 - pyskl - INFO - Epoch [54][3700/3746] lr: 7.130e-02, eta: 3 days, 6:09:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5450, loss_cls: 4.0905, loss: 4.0905 +2024-07-24 00:33:27,667 - pyskl - INFO - Saving checkpoint at 54 epochs +2024-07-24 00:35:18,842 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 00:35:19,512 - pyskl - INFO - +top1_acc 0.2054 +top5_acc 0.4344 +2024-07-24 00:35:19,512 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 00:35:19,551 - pyskl - INFO - +mean_acc 0.2052 +2024-07-24 00:35:19,563 - pyskl - INFO - Epoch(val) [54][309] top1_acc: 0.2054, top5_acc: 0.4344, mean_class_accuracy: 0.2052 +2024-07-24 00:39:10,097 - pyskl - INFO - Epoch [55][100/3746] lr: 7.126e-02, eta: 3 days, 6:11:08, time: 2.305, data_time: 1.333, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5495, loss_cls: 4.0116, loss: 4.0116 +2024-07-24 00:40:32,129 - pyskl - INFO - Epoch [55][200/3746] lr: 7.124e-02, eta: 3 days, 6:09:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5495, loss_cls: 4.0265, loss: 4.0265 +2024-07-24 00:41:54,164 - pyskl - INFO - Epoch [55][300/3746] lr: 7.121e-02, eta: 3 days, 6:08:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5475, loss_cls: 4.0540, loss: 4.0540 +2024-07-24 00:43:16,464 - pyskl - INFO - Epoch [55][400/3746] lr: 7.119e-02, eta: 3 days, 6:07:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5539, loss_cls: 4.0222, loss: 4.0222 +2024-07-24 00:44:38,341 - pyskl - INFO - Epoch [55][500/3746] lr: 7.116e-02, eta: 3 days, 6:06:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5423, loss_cls: 4.0664, loss: 4.0664 +2024-07-24 00:45:59,655 - pyskl - INFO - Epoch [55][600/3746] lr: 7.114e-02, eta: 3 days, 6:05:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5464, loss_cls: 4.0577, loss: 4.0577 +2024-07-24 00:47:21,496 - pyskl - INFO - Epoch [55][700/3746] lr: 7.111e-02, eta: 3 days, 6:03:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5586, loss_cls: 4.0078, loss: 4.0078 +2024-07-24 00:48:43,638 - pyskl - INFO - Epoch [55][800/3746] lr: 7.109e-02, eta: 3 days, 6:02:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5487, loss_cls: 4.0469, loss: 4.0469 +2024-07-24 00:50:05,624 - pyskl - INFO - Epoch [55][900/3746] lr: 7.106e-02, eta: 3 days, 6:01:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5555, loss_cls: 4.0091, loss: 4.0091 +2024-07-24 00:51:27,943 - pyskl - INFO - Epoch [55][1000/3746] lr: 7.104e-02, eta: 3 days, 6:00:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5537, loss_cls: 4.0409, loss: 4.0409 +2024-07-24 00:52:49,703 - pyskl - INFO - Epoch [55][1100/3746] lr: 7.101e-02, eta: 3 days, 5:59:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5453, loss_cls: 4.0607, loss: 4.0607 +2024-07-24 00:54:12,100 - pyskl - INFO - Epoch [55][1200/3746] lr: 7.099e-02, eta: 3 days, 5:57:59, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5469, loss_cls: 4.0319, loss: 4.0319 +2024-07-24 00:55:34,522 - pyskl - INFO - Epoch [55][1300/3746] lr: 7.096e-02, eta: 3 days, 5:56:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5519, loss_cls: 4.0383, loss: 4.0383 +2024-07-24 00:56:57,074 - pyskl - INFO - Epoch [55][1400/3746] lr: 7.093e-02, eta: 3 days, 5:55:37, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5380, loss_cls: 4.0996, loss: 4.0996 +2024-07-24 00:58:18,888 - pyskl - INFO - Epoch [55][1500/3746] lr: 7.091e-02, eta: 3 days, 5:54:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5500, loss_cls: 4.0391, loss: 4.0391 +2024-07-24 00:59:40,475 - pyskl - INFO - Epoch [55][1600/3746] lr: 7.088e-02, eta: 3 days, 5:53:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5580, loss_cls: 3.9905, loss: 3.9905 +2024-07-24 01:01:02,074 - pyskl - INFO - Epoch [55][1700/3746] lr: 7.086e-02, eta: 3 days, 5:51:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5464, loss_cls: 4.0804, loss: 4.0804 +2024-07-24 01:02:23,221 - pyskl - INFO - Epoch [55][1800/3746] lr: 7.083e-02, eta: 3 days, 5:50:46, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5364, loss_cls: 4.0773, loss: 4.0773 +2024-07-24 01:03:44,574 - pyskl - INFO - Epoch [55][1900/3746] lr: 7.081e-02, eta: 3 days, 5:49:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5433, loss_cls: 4.0518, loss: 4.0518 +2024-07-24 01:05:06,249 - pyskl - INFO - Epoch [55][2000/3746] lr: 7.078e-02, eta: 3 days, 5:48:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5406, loss_cls: 4.1115, loss: 4.1115 +2024-07-24 01:06:28,066 - pyskl - INFO - Epoch [55][2100/3746] lr: 7.076e-02, eta: 3 days, 5:47:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5559, loss_cls: 4.0626, loss: 4.0626 +2024-07-24 01:07:49,924 - pyskl - INFO - Epoch [55][2200/3746] lr: 7.073e-02, eta: 3 days, 5:45:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5436, loss_cls: 4.0784, loss: 4.0784 +2024-07-24 01:09:11,467 - pyskl - INFO - Epoch [55][2300/3746] lr: 7.071e-02, eta: 3 days, 5:44:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5506, loss_cls: 4.0449, loss: 4.0449 +2024-07-24 01:10:33,218 - pyskl - INFO - Epoch [55][2400/3746] lr: 7.068e-02, eta: 3 days, 5:43:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5387, loss_cls: 4.0739, loss: 4.0739 +2024-07-24 01:11:54,659 - pyskl - INFO - Epoch [55][2500/3746] lr: 7.065e-02, eta: 3 days, 5:42:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5437, loss_cls: 4.0853, loss: 4.0853 +2024-07-24 01:13:16,189 - pyskl - INFO - Epoch [55][2600/3746] lr: 7.063e-02, eta: 3 days, 5:41:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5494, loss_cls: 4.0376, loss: 4.0376 +2024-07-24 01:14:37,771 - pyskl - INFO - Epoch [55][2700/3746] lr: 7.060e-02, eta: 3 days, 5:39:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5522, loss_cls: 4.0336, loss: 4.0336 +2024-07-24 01:15:59,193 - pyskl - INFO - Epoch [55][2800/3746] lr: 7.058e-02, eta: 3 days, 5:38:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5595, loss_cls: 4.0257, loss: 4.0257 +2024-07-24 01:17:20,626 - pyskl - INFO - Epoch [55][2900/3746] lr: 7.055e-02, eta: 3 days, 5:37:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5419, loss_cls: 4.0928, loss: 4.0928 +2024-07-24 01:18:42,334 - pyskl - INFO - Epoch [55][3000/3746] lr: 7.053e-02, eta: 3 days, 5:36:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5555, loss_cls: 4.0394, loss: 4.0394 +2024-07-24 01:20:03,871 - pyskl - INFO - Epoch [55][3100/3746] lr: 7.050e-02, eta: 3 days, 5:35:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5544, loss_cls: 4.0481, loss: 4.0481 +2024-07-24 01:21:25,177 - pyskl - INFO - Epoch [55][3200/3746] lr: 7.048e-02, eta: 3 days, 5:33:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5427, loss_cls: 4.0717, loss: 4.0717 +2024-07-24 01:22:46,651 - pyskl - INFO - Epoch [55][3300/3746] lr: 7.045e-02, eta: 3 days, 5:32:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5353, loss_cls: 4.0991, loss: 4.0991 +2024-07-24 01:24:08,501 - pyskl - INFO - Epoch [55][3400/3746] lr: 7.043e-02, eta: 3 days, 5:31:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5420, loss_cls: 4.0974, loss: 4.0974 +2024-07-24 01:25:30,107 - pyskl - INFO - Epoch [55][3500/3746] lr: 7.040e-02, eta: 3 days, 5:30:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5445, loss_cls: 4.0750, loss: 4.0750 +2024-07-24 01:26:51,458 - pyskl - INFO - Epoch [55][3600/3746] lr: 7.037e-02, eta: 3 days, 5:28:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5428, loss_cls: 4.0726, loss: 4.0726 +2024-07-24 01:28:13,422 - pyskl - INFO - Epoch [55][3700/3746] lr: 7.035e-02, eta: 3 days, 5:27:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5431, loss_cls: 4.0511, loss: 4.0511 +2024-07-24 01:28:53,145 - pyskl - INFO - Saving checkpoint at 55 epochs +2024-07-24 01:30:44,389 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 01:30:45,055 - pyskl - INFO - +top1_acc 0.2267 +top5_acc 0.4639 +2024-07-24 01:30:45,055 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 01:30:45,096 - pyskl - INFO - +mean_acc 0.2263 +2024-07-24 01:30:45,107 - pyskl - INFO - Epoch(val) [55][309] top1_acc: 0.2267, top5_acc: 0.4639, mean_class_accuracy: 0.2263 +2024-07-24 01:34:35,817 - pyskl - INFO - Epoch [56][100/3746] lr: 7.031e-02, eta: 3 days, 5:29:12, time: 2.307, data_time: 1.321, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5634, loss_cls: 3.9700, loss: 3.9700 +2024-07-24 01:35:57,625 - pyskl - INFO - Epoch [56][200/3746] lr: 7.029e-02, eta: 3 days, 5:27:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5533, loss_cls: 4.0270, loss: 4.0270 +2024-07-24 01:37:19,088 - pyskl - INFO - Epoch [56][300/3746] lr: 7.026e-02, eta: 3 days, 5:26:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5461, loss_cls: 4.0767, loss: 4.0767 +2024-07-24 01:38:40,847 - pyskl - INFO - Epoch [56][400/3746] lr: 7.023e-02, eta: 3 days, 5:25:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5533, loss_cls: 4.0272, loss: 4.0272 +2024-07-24 01:40:02,783 - pyskl - INFO - Epoch [56][500/3746] lr: 7.021e-02, eta: 3 days, 5:24:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5537, loss_cls: 4.0263, loss: 4.0263 +2024-07-24 01:41:24,325 - pyskl - INFO - Epoch [56][600/3746] lr: 7.018e-02, eta: 3 days, 5:23:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5513, loss_cls: 4.0295, loss: 4.0295 +2024-07-24 01:42:46,655 - pyskl - INFO - Epoch [56][700/3746] lr: 7.016e-02, eta: 3 days, 5:21:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5619, loss_cls: 3.9905, loss: 3.9905 +2024-07-24 01:44:08,944 - pyskl - INFO - Epoch [56][800/3746] lr: 7.013e-02, eta: 3 days, 5:20:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5578, loss_cls: 3.9952, loss: 3.9952 +2024-07-24 01:45:30,739 - pyskl - INFO - Epoch [56][900/3746] lr: 7.011e-02, eta: 3 days, 5:19:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5495, loss_cls: 4.0401, loss: 4.0401 +2024-07-24 01:46:52,598 - pyskl - INFO - Epoch [56][1000/3746] lr: 7.008e-02, eta: 3 days, 5:18:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5528, loss_cls: 4.0115, loss: 4.0115 +2024-07-24 01:48:14,313 - pyskl - INFO - Epoch [56][1100/3746] lr: 7.006e-02, eta: 3 days, 5:17:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5470, loss_cls: 4.0474, loss: 4.0474 +2024-07-24 01:49:36,550 - pyskl - INFO - Epoch [56][1200/3746] lr: 7.003e-02, eta: 3 days, 5:15:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5395, loss_cls: 4.0639, loss: 4.0639 +2024-07-24 01:50:58,892 - pyskl - INFO - Epoch [56][1300/3746] lr: 7.000e-02, eta: 3 days, 5:14:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5505, loss_cls: 4.0158, loss: 4.0158 +2024-07-24 01:52:20,977 - pyskl - INFO - Epoch [56][1400/3746] lr: 6.998e-02, eta: 3 days, 5:13:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5550, loss_cls: 4.0436, loss: 4.0436 +2024-07-24 01:53:43,490 - pyskl - INFO - Epoch [56][1500/3746] lr: 6.995e-02, eta: 3 days, 5:12:19, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5550, loss_cls: 4.0242, loss: 4.0242 +2024-07-24 01:55:05,399 - pyskl - INFO - Epoch [56][1600/3746] lr: 6.993e-02, eta: 3 days, 5:11:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5397, loss_cls: 4.0888, loss: 4.0888 +2024-07-24 01:56:27,727 - pyskl - INFO - Epoch [56][1700/3746] lr: 6.990e-02, eta: 3 days, 5:09:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5522, loss_cls: 4.0135, loss: 4.0135 +2024-07-24 01:57:49,489 - pyskl - INFO - Epoch [56][1800/3746] lr: 6.988e-02, eta: 3 days, 5:08:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5508, loss_cls: 4.0541, loss: 4.0541 +2024-07-24 01:59:11,197 - pyskl - INFO - Epoch [56][1900/3746] lr: 6.985e-02, eta: 3 days, 5:07:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5600, loss_cls: 4.0198, loss: 4.0198 +2024-07-24 02:00:32,706 - pyskl - INFO - Epoch [56][2000/3746] lr: 6.983e-02, eta: 3 days, 5:06:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5364, loss_cls: 4.0875, loss: 4.0875 +2024-07-24 02:01:54,166 - pyskl - INFO - Epoch [56][2100/3746] lr: 6.980e-02, eta: 3 days, 5:05:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5531, loss_cls: 4.0420, loss: 4.0420 +2024-07-24 02:03:16,205 - pyskl - INFO - Epoch [56][2200/3746] lr: 6.977e-02, eta: 3 days, 5:03:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5556, loss_cls: 4.0111, loss: 4.0111 +2024-07-24 02:04:38,048 - pyskl - INFO - Epoch [56][2300/3746] lr: 6.975e-02, eta: 3 days, 5:02:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5455, loss_cls: 4.0847, loss: 4.0847 +2024-07-24 02:05:59,440 - pyskl - INFO - Epoch [56][2400/3746] lr: 6.972e-02, eta: 3 days, 5:01:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5506, loss_cls: 4.0170, loss: 4.0170 +2024-07-24 02:07:21,557 - pyskl - INFO - Epoch [56][2500/3746] lr: 6.970e-02, eta: 3 days, 5:00:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5494, loss_cls: 4.0360, loss: 4.0360 +2024-07-24 02:08:43,464 - pyskl - INFO - Epoch [56][2600/3746] lr: 6.967e-02, eta: 3 days, 4:58:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5444, loss_cls: 4.0504, loss: 4.0504 +2024-07-24 02:10:05,158 - pyskl - INFO - Epoch [56][2700/3746] lr: 6.965e-02, eta: 3 days, 4:57:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5459, loss_cls: 4.0613, loss: 4.0613 +2024-07-24 02:11:27,268 - pyskl - INFO - Epoch [56][2800/3746] lr: 6.962e-02, eta: 3 days, 4:56:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5405, loss_cls: 4.0527, loss: 4.0527 +2024-07-24 02:12:48,675 - pyskl - INFO - Epoch [56][2900/3746] lr: 6.959e-02, eta: 3 days, 4:55:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5431, loss_cls: 4.0490, loss: 4.0490 +2024-07-24 02:14:09,768 - pyskl - INFO - Epoch [56][3000/3746] lr: 6.957e-02, eta: 3 days, 4:54:07, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5444, loss_cls: 4.0768, loss: 4.0768 +2024-07-24 02:15:31,438 - pyskl - INFO - Epoch [56][3100/3746] lr: 6.954e-02, eta: 3 days, 4:52:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5309, loss_cls: 4.1261, loss: 4.1261 +2024-07-24 02:16:52,936 - pyskl - INFO - Epoch [56][3200/3746] lr: 6.952e-02, eta: 3 days, 4:51:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5447, loss_cls: 4.0629, loss: 4.0629 +2024-07-24 02:18:14,316 - pyskl - INFO - Epoch [56][3300/3746] lr: 6.949e-02, eta: 3 days, 4:50:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5481, loss_cls: 4.0157, loss: 4.0157 +2024-07-24 02:19:35,990 - pyskl - INFO - Epoch [56][3400/3746] lr: 6.947e-02, eta: 3 days, 4:49:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5391, loss_cls: 4.0783, loss: 4.0783 +2024-07-24 02:20:57,700 - pyskl - INFO - Epoch [56][3500/3746] lr: 6.944e-02, eta: 3 days, 4:48:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5444, loss_cls: 4.0675, loss: 4.0675 +2024-07-24 02:22:19,083 - pyskl - INFO - Epoch [56][3600/3746] lr: 6.941e-02, eta: 3 days, 4:46:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5514, loss_cls: 4.0506, loss: 4.0506 +2024-07-24 02:23:40,732 - pyskl - INFO - Epoch [56][3700/3746] lr: 6.939e-02, eta: 3 days, 4:45:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5466, loss_cls: 4.0408, loss: 4.0408 +2024-07-24 02:24:20,776 - pyskl - INFO - Saving checkpoint at 56 epochs +2024-07-24 02:26:12,374 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 02:26:13,036 - pyskl - INFO - +top1_acc 0.2284 +top5_acc 0.4696 +2024-07-24 02:26:13,036 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 02:26:13,076 - pyskl - INFO - +mean_acc 0.2282 +2024-07-24 02:26:13,088 - pyskl - INFO - Epoch(val) [56][309] top1_acc: 0.2284, top5_acc: 0.4696, mean_class_accuracy: 0.2282 +2024-07-24 02:30:01,836 - pyskl - INFO - Epoch [57][100/3746] lr: 6.935e-02, eta: 3 days, 4:46:51, time: 2.287, data_time: 1.312, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5445, loss_cls: 4.0381, loss: 4.0381 +2024-07-24 02:31:23,764 - pyskl - INFO - Epoch [57][200/3746] lr: 6.932e-02, eta: 3 days, 4:45:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5589, loss_cls: 3.9561, loss: 3.9561 +2024-07-24 02:32:45,460 - pyskl - INFO - Epoch [57][300/3746] lr: 6.930e-02, eta: 3 days, 4:44:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5583, loss_cls: 3.9992, loss: 3.9992 +2024-07-24 02:34:07,698 - pyskl - INFO - Epoch [57][400/3746] lr: 6.927e-02, eta: 3 days, 4:43:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5563, loss_cls: 4.0133, loss: 4.0133 +2024-07-24 02:35:29,796 - pyskl - INFO - Epoch [57][500/3746] lr: 6.925e-02, eta: 3 days, 4:42:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5583, loss_cls: 3.9995, loss: 3.9995 +2024-07-24 02:36:51,851 - pyskl - INFO - Epoch [57][600/3746] lr: 6.922e-02, eta: 3 days, 4:40:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5628, loss_cls: 3.9547, loss: 3.9547 +2024-07-24 02:38:13,871 - pyskl - INFO - Epoch [57][700/3746] lr: 6.920e-02, eta: 3 days, 4:39:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5575, loss_cls: 3.9897, loss: 3.9897 +2024-07-24 02:39:36,145 - pyskl - INFO - Epoch [57][800/3746] lr: 6.917e-02, eta: 3 days, 4:38:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5513, loss_cls: 4.0224, loss: 4.0224 +2024-07-24 02:40:58,092 - pyskl - INFO - Epoch [57][900/3746] lr: 6.914e-02, eta: 3 days, 4:37:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5559, loss_cls: 4.0092, loss: 4.0092 +2024-07-24 02:42:19,979 - pyskl - INFO - Epoch [57][1000/3746] lr: 6.912e-02, eta: 3 days, 4:35:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5437, loss_cls: 4.0795, loss: 4.0795 +2024-07-24 02:43:41,809 - pyskl - INFO - Epoch [57][1100/3746] lr: 6.909e-02, eta: 3 days, 4:34:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5533, loss_cls: 4.0639, loss: 4.0639 +2024-07-24 02:45:04,447 - pyskl - INFO - Epoch [57][1200/3746] lr: 6.907e-02, eta: 3 days, 4:33:32, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5517, loss_cls: 4.0584, loss: 4.0584 +2024-07-24 02:46:27,747 - pyskl - INFO - Epoch [57][1300/3746] lr: 6.904e-02, eta: 3 days, 4:32:21, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5513, loss_cls: 4.0380, loss: 4.0380 +2024-07-24 02:47:49,680 - pyskl - INFO - Epoch [57][1400/3746] lr: 6.901e-02, eta: 3 days, 4:31:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5411, loss_cls: 4.0776, loss: 4.0776 +2024-07-24 02:49:11,643 - pyskl - INFO - Epoch [57][1500/3746] lr: 6.899e-02, eta: 3 days, 4:29:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5463, loss_cls: 4.0513, loss: 4.0513 +2024-07-24 02:50:33,165 - pyskl - INFO - Epoch [57][1600/3746] lr: 6.896e-02, eta: 3 days, 4:28:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5566, loss_cls: 3.9731, loss: 3.9731 +2024-07-24 02:51:55,022 - pyskl - INFO - Epoch [57][1700/3746] lr: 6.894e-02, eta: 3 days, 4:27:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5430, loss_cls: 4.0619, loss: 4.0619 +2024-07-24 02:53:16,884 - pyskl - INFO - Epoch [57][1800/3746] lr: 6.891e-02, eta: 3 days, 4:26:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5570, loss_cls: 4.0181, loss: 4.0181 +2024-07-24 02:54:38,560 - pyskl - INFO - Epoch [57][1900/3746] lr: 6.889e-02, eta: 3 days, 4:25:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5448, loss_cls: 4.0878, loss: 4.0878 +2024-07-24 02:56:00,187 - pyskl - INFO - Epoch [57][2000/3746] lr: 6.886e-02, eta: 3 days, 4:23:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5495, loss_cls: 4.0382, loss: 4.0382 +2024-07-24 02:57:22,100 - pyskl - INFO - Epoch [57][2100/3746] lr: 6.883e-02, eta: 3 days, 4:22:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5539, loss_cls: 4.0404, loss: 4.0404 +2024-07-24 02:58:43,422 - pyskl - INFO - Epoch [57][2200/3746] lr: 6.881e-02, eta: 3 days, 4:21:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5500, loss_cls: 4.0862, loss: 4.0862 +2024-07-24 03:00:04,761 - pyskl - INFO - Epoch [57][2300/3746] lr: 6.878e-02, eta: 3 days, 4:20:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5503, loss_cls: 4.0288, loss: 4.0288 +2024-07-24 03:01:26,091 - pyskl - INFO - Epoch [57][2400/3746] lr: 6.876e-02, eta: 3 days, 4:18:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5380, loss_cls: 4.0789, loss: 4.0789 +2024-07-24 03:02:47,927 - pyskl - INFO - Epoch [57][2500/3746] lr: 6.873e-02, eta: 3 days, 4:17:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5470, loss_cls: 4.0763, loss: 4.0763 +2024-07-24 03:04:09,598 - pyskl - INFO - Epoch [57][2600/3746] lr: 6.870e-02, eta: 3 days, 4:16:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5447, loss_cls: 4.0807, loss: 4.0807 +2024-07-24 03:05:31,532 - pyskl - INFO - Epoch [57][2700/3746] lr: 6.868e-02, eta: 3 days, 4:15:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5517, loss_cls: 4.0773, loss: 4.0773 +2024-07-24 03:06:53,076 - pyskl - INFO - Epoch [57][2800/3746] lr: 6.865e-02, eta: 3 days, 4:14:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5502, loss_cls: 4.0660, loss: 4.0660 +2024-07-24 03:08:14,969 - pyskl - INFO - Epoch [57][2900/3746] lr: 6.863e-02, eta: 3 days, 4:12:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5547, loss_cls: 3.9965, loss: 3.9965 +2024-07-24 03:09:36,350 - pyskl - INFO - Epoch [57][3000/3746] lr: 6.860e-02, eta: 3 days, 4:11:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5437, loss_cls: 4.0668, loss: 4.0668 +2024-07-24 03:10:57,598 - pyskl - INFO - Epoch [57][3100/3746] lr: 6.857e-02, eta: 3 days, 4:10:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5502, loss_cls: 4.0572, loss: 4.0572 +2024-07-24 03:12:19,093 - pyskl - INFO - Epoch [57][3200/3746] lr: 6.855e-02, eta: 3 days, 4:09:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5425, loss_cls: 4.0671, loss: 4.0671 +2024-07-24 03:13:40,761 - pyskl - INFO - Epoch [57][3300/3746] lr: 6.852e-02, eta: 3 days, 4:07:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5508, loss_cls: 4.0102, loss: 4.0102 +2024-07-24 03:15:02,977 - pyskl - INFO - Epoch [57][3400/3746] lr: 6.850e-02, eta: 3 days, 4:06:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5484, loss_cls: 4.0604, loss: 4.0604 +2024-07-24 03:16:24,932 - pyskl - INFO - Epoch [57][3500/3746] lr: 6.847e-02, eta: 3 days, 4:05:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5395, loss_cls: 4.0603, loss: 4.0603 +2024-07-24 03:17:46,889 - pyskl - INFO - Epoch [57][3600/3746] lr: 6.844e-02, eta: 3 days, 4:04:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5402, loss_cls: 4.0723, loss: 4.0723 +2024-07-24 03:19:08,550 - pyskl - INFO - Epoch [57][3700/3746] lr: 6.842e-02, eta: 3 days, 4:03:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5463, loss_cls: 4.0385, loss: 4.0385 +2024-07-24 03:19:48,015 - pyskl - INFO - Saving checkpoint at 57 epochs +2024-07-24 03:21:38,525 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 03:21:39,189 - pyskl - INFO - +top1_acc 0.2216 +top5_acc 0.4658 +2024-07-24 03:21:39,189 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 03:21:39,230 - pyskl - INFO - +mean_acc 0.2212 +2024-07-24 03:21:39,244 - pyskl - INFO - Epoch(val) [57][309] top1_acc: 0.2216, top5_acc: 0.4658, mean_class_accuracy: 0.2212 +2024-07-24 03:25:29,728 - pyskl - INFO - Epoch [58][100/3746] lr: 6.838e-02, eta: 3 days, 4:04:14, time: 2.305, data_time: 1.321, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5589, loss_cls: 4.0246, loss: 4.0246 +2024-07-24 03:26:51,622 - pyskl - INFO - Epoch [58][200/3746] lr: 6.835e-02, eta: 3 days, 4:03:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5594, loss_cls: 4.0306, loss: 4.0306 +2024-07-24 03:28:13,713 - pyskl - INFO - Epoch [58][300/3746] lr: 6.833e-02, eta: 3 days, 4:01:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5484, loss_cls: 4.0431, loss: 4.0431 +2024-07-24 03:29:35,238 - pyskl - INFO - Epoch [58][400/3746] lr: 6.830e-02, eta: 3 days, 4:00:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5523, loss_cls: 4.0420, loss: 4.0420 +2024-07-24 03:30:57,435 - pyskl - INFO - Epoch [58][500/3746] lr: 6.828e-02, eta: 3 days, 3:59:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5570, loss_cls: 3.9987, loss: 3.9987 +2024-07-24 03:32:19,450 - pyskl - INFO - Epoch [58][600/3746] lr: 6.825e-02, eta: 3 days, 3:58:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5509, loss_cls: 4.0082, loss: 4.0082 +2024-07-24 03:33:41,204 - pyskl - INFO - Epoch [58][700/3746] lr: 6.822e-02, eta: 3 days, 3:56:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5489, loss_cls: 4.0233, loss: 4.0233 +2024-07-24 03:35:03,771 - pyskl - INFO - Epoch [58][800/3746] lr: 6.820e-02, eta: 3 days, 3:55:42, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5552, loss_cls: 4.0303, loss: 4.0303 +2024-07-24 03:36:25,905 - pyskl - INFO - Epoch [58][900/3746] lr: 6.817e-02, eta: 3 days, 3:54:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5563, loss_cls: 3.9776, loss: 3.9776 +2024-07-24 03:37:48,044 - pyskl - INFO - Epoch [58][1000/3746] lr: 6.815e-02, eta: 3 days, 3:53:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5470, loss_cls: 4.0619, loss: 4.0619 +2024-07-24 03:39:10,092 - pyskl - INFO - Epoch [58][1100/3746] lr: 6.812e-02, eta: 3 days, 3:52:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5461, loss_cls: 4.0289, loss: 4.0289 +2024-07-24 03:40:32,245 - pyskl - INFO - Epoch [58][1200/3746] lr: 6.809e-02, eta: 3 days, 3:50:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5494, loss_cls: 4.0130, loss: 4.0130 +2024-07-24 03:41:55,178 - pyskl - INFO - Epoch [58][1300/3746] lr: 6.807e-02, eta: 3 days, 3:49:38, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5580, loss_cls: 4.0023, loss: 4.0023 +2024-07-24 03:43:16,907 - pyskl - INFO - Epoch [58][1400/3746] lr: 6.804e-02, eta: 3 days, 3:48:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5519, loss_cls: 4.0527, loss: 4.0527 +2024-07-24 03:44:39,199 - pyskl - INFO - Epoch [58][1500/3746] lr: 6.802e-02, eta: 3 days, 3:47:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5677, loss_cls: 4.0060, loss: 4.0060 +2024-07-24 03:46:01,544 - pyskl - INFO - Epoch [58][1600/3746] lr: 6.799e-02, eta: 3 days, 3:45:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5414, loss_cls: 4.0759, loss: 4.0759 +2024-07-24 03:47:23,232 - pyskl - INFO - Epoch [58][1700/3746] lr: 6.796e-02, eta: 3 days, 3:44:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5578, loss_cls: 4.0132, loss: 4.0132 +2024-07-24 03:48:45,039 - pyskl - INFO - Epoch [58][1800/3746] lr: 6.794e-02, eta: 3 days, 3:43:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5559, loss_cls: 4.0304, loss: 4.0304 +2024-07-24 03:50:06,929 - pyskl - INFO - Epoch [58][1900/3746] lr: 6.791e-02, eta: 3 days, 3:42:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5536, loss_cls: 4.0250, loss: 4.0250 +2024-07-24 03:51:28,360 - pyskl - INFO - Epoch [58][2000/3746] lr: 6.789e-02, eta: 3 days, 3:41:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5289, loss_cls: 4.1274, loss: 4.1274 +2024-07-24 03:52:49,906 - pyskl - INFO - Epoch [58][2100/3746] lr: 6.786e-02, eta: 3 days, 3:39:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5537, loss_cls: 4.0568, loss: 4.0568 +2024-07-24 03:54:11,924 - pyskl - INFO - Epoch [58][2200/3746] lr: 6.783e-02, eta: 3 days, 3:38:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5480, loss_cls: 4.0578, loss: 4.0578 +2024-07-24 03:55:33,573 - pyskl - INFO - Epoch [58][2300/3746] lr: 6.781e-02, eta: 3 days, 3:37:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5473, loss_cls: 4.0448, loss: 4.0448 +2024-07-24 03:56:55,523 - pyskl - INFO - Epoch [58][2400/3746] lr: 6.778e-02, eta: 3 days, 3:36:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5567, loss_cls: 4.0248, loss: 4.0248 +2024-07-24 03:58:17,881 - pyskl - INFO - Epoch [58][2500/3746] lr: 6.775e-02, eta: 3 days, 3:34:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5573, loss_cls: 3.9999, loss: 3.9999 +2024-07-24 03:59:40,046 - pyskl - INFO - Epoch [58][2600/3746] lr: 6.773e-02, eta: 3 days, 3:33:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5498, loss_cls: 4.0002, loss: 4.0002 +2024-07-24 04:01:01,907 - pyskl - INFO - Epoch [58][2700/3746] lr: 6.770e-02, eta: 3 days, 3:32:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5439, loss_cls: 4.0504, loss: 4.0504 +2024-07-24 04:02:23,906 - pyskl - INFO - Epoch [58][2800/3746] lr: 6.768e-02, eta: 3 days, 3:31:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5505, loss_cls: 4.0558, loss: 4.0558 +2024-07-24 04:03:45,120 - pyskl - INFO - Epoch [58][2900/3746] lr: 6.765e-02, eta: 3 days, 3:30:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5597, loss_cls: 3.9951, loss: 3.9951 +2024-07-24 04:05:06,911 - pyskl - INFO - Epoch [58][3000/3746] lr: 6.762e-02, eta: 3 days, 3:28:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5437, loss_cls: 4.0661, loss: 4.0661 +2024-07-24 04:06:28,852 - pyskl - INFO - Epoch [58][3100/3746] lr: 6.760e-02, eta: 3 days, 3:27:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5513, loss_cls: 4.0555, loss: 4.0555 +2024-07-24 04:07:50,815 - pyskl - INFO - Epoch [58][3200/3746] lr: 6.757e-02, eta: 3 days, 3:26:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5517, loss_cls: 4.0396, loss: 4.0396 +2024-07-24 04:09:12,345 - pyskl - INFO - Epoch [58][3300/3746] lr: 6.755e-02, eta: 3 days, 3:25:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5566, loss_cls: 4.0079, loss: 4.0079 +2024-07-24 04:10:33,927 - pyskl - INFO - Epoch [58][3400/3746] lr: 6.752e-02, eta: 3 days, 3:23:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5439, loss_cls: 4.0526, loss: 4.0526 +2024-07-24 04:11:55,019 - pyskl - INFO - Epoch [58][3500/3746] lr: 6.749e-02, eta: 3 days, 3:22:38, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5591, loss_cls: 4.0185, loss: 4.0185 +2024-07-24 04:13:16,675 - pyskl - INFO - Epoch [58][3600/3746] lr: 6.747e-02, eta: 3 days, 3:21:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5448, loss_cls: 4.0573, loss: 4.0573 +2024-07-24 04:14:38,402 - pyskl - INFO - Epoch [58][3700/3746] lr: 6.744e-02, eta: 3 days, 3:20:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5459, loss_cls: 4.0626, loss: 4.0626 +2024-07-24 04:15:17,885 - pyskl - INFO - Saving checkpoint at 58 epochs +2024-07-24 04:17:09,882 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 04:17:10,544 - pyskl - INFO - +top1_acc 0.2156 +top5_acc 0.4530 +2024-07-24 04:17:10,544 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 04:17:10,584 - pyskl - INFO - +mean_acc 0.2155 +2024-07-24 04:17:10,595 - pyskl - INFO - Epoch(val) [58][309] top1_acc: 0.2156, top5_acc: 0.4530, mean_class_accuracy: 0.2155 +2024-07-24 04:20:59,330 - pyskl - INFO - Epoch [59][100/3746] lr: 6.740e-02, eta: 3 days, 3:21:16, time: 2.287, data_time: 1.310, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5594, loss_cls: 3.9734, loss: 3.9734 +2024-07-24 04:22:21,069 - pyskl - INFO - Epoch [59][200/3746] lr: 6.738e-02, eta: 3 days, 3:20:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5491, loss_cls: 4.0355, loss: 4.0355 +2024-07-24 04:23:42,874 - pyskl - INFO - Epoch [59][300/3746] lr: 6.735e-02, eta: 3 days, 3:18:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5573, loss_cls: 3.9908, loss: 3.9908 +2024-07-24 04:25:04,761 - pyskl - INFO - Epoch [59][400/3746] lr: 6.732e-02, eta: 3 days, 3:17:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5713, loss_cls: 3.9745, loss: 3.9745 +2024-07-24 04:26:25,943 - pyskl - INFO - Epoch [59][500/3746] lr: 6.730e-02, eta: 3 days, 3:16:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5473, loss_cls: 4.0663, loss: 4.0663 +2024-07-24 04:27:47,487 - pyskl - INFO - Epoch [59][600/3746] lr: 6.727e-02, eta: 3 days, 3:15:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5547, loss_cls: 3.9966, loss: 3.9966 +2024-07-24 04:29:09,649 - pyskl - INFO - Epoch [59][700/3746] lr: 6.725e-02, eta: 3 days, 3:13:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5586, loss_cls: 3.9994, loss: 3.9994 +2024-07-24 04:30:31,069 - pyskl - INFO - Epoch [59][800/3746] lr: 6.722e-02, eta: 3 days, 3:12:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5556, loss_cls: 3.9871, loss: 3.9871 +2024-07-24 04:31:53,431 - pyskl - INFO - Epoch [59][900/3746] lr: 6.719e-02, eta: 3 days, 3:11:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5513, loss_cls: 4.0085, loss: 4.0085 +2024-07-24 04:33:14,915 - pyskl - INFO - Epoch [59][1000/3746] lr: 6.717e-02, eta: 3 days, 3:10:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5481, loss_cls: 4.0452, loss: 4.0452 +2024-07-24 04:34:37,343 - pyskl - INFO - Epoch [59][1100/3746] lr: 6.714e-02, eta: 3 days, 3:08:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5513, loss_cls: 4.0311, loss: 4.0311 +2024-07-24 04:35:59,367 - pyskl - INFO - Epoch [59][1200/3746] lr: 6.711e-02, eta: 3 days, 3:07:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5470, loss_cls: 4.0553, loss: 4.0553 +2024-07-24 04:37:22,037 - pyskl - INFO - Epoch [59][1300/3746] lr: 6.709e-02, eta: 3 days, 3:06:31, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5473, loss_cls: 4.0658, loss: 4.0658 +2024-07-24 04:38:44,460 - pyskl - INFO - Epoch [59][1400/3746] lr: 6.706e-02, eta: 3 days, 3:05:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5492, loss_cls: 4.0428, loss: 4.0428 +2024-07-24 04:40:07,213 - pyskl - INFO - Epoch [59][1500/3746] lr: 6.704e-02, eta: 3 days, 3:04:05, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5609, loss_cls: 4.0079, loss: 4.0079 +2024-07-24 04:41:29,284 - pyskl - INFO - Epoch [59][1600/3746] lr: 6.701e-02, eta: 3 days, 3:02:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5352, loss_cls: 4.0754, loss: 4.0754 +2024-07-24 04:42:50,929 - pyskl - INFO - Epoch [59][1700/3746] lr: 6.698e-02, eta: 3 days, 3:01:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5384, loss_cls: 4.0596, loss: 4.0596 +2024-07-24 04:44:12,831 - pyskl - INFO - Epoch [59][1800/3746] lr: 6.696e-02, eta: 3 days, 3:00:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5534, loss_cls: 4.0426, loss: 4.0426 +2024-07-24 04:45:34,219 - pyskl - INFO - Epoch [59][1900/3746] lr: 6.693e-02, eta: 3 days, 2:59:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5484, loss_cls: 4.0617, loss: 4.0617 +2024-07-24 04:46:56,053 - pyskl - INFO - Epoch [59][2000/3746] lr: 6.690e-02, eta: 3 days, 2:57:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5508, loss_cls: 4.0458, loss: 4.0458 +2024-07-24 04:48:18,518 - pyskl - INFO - Epoch [59][2100/3746] lr: 6.688e-02, eta: 3 days, 2:56:42, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5530, loss_cls: 3.9729, loss: 3.9729 +2024-07-24 04:49:40,175 - pyskl - INFO - Epoch [59][2200/3746] lr: 6.685e-02, eta: 3 days, 2:55:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5516, loss_cls: 4.0323, loss: 4.0323 +2024-07-24 04:51:01,540 - pyskl - INFO - Epoch [59][2300/3746] lr: 6.682e-02, eta: 3 days, 2:54:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5553, loss_cls: 3.9874, loss: 3.9874 +2024-07-24 04:52:23,759 - pyskl - INFO - Epoch [59][2400/3746] lr: 6.680e-02, eta: 3 days, 2:53:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5608, loss_cls: 4.0195, loss: 4.0195 +2024-07-24 04:53:45,488 - pyskl - INFO - Epoch [59][2500/3746] lr: 6.677e-02, eta: 3 days, 2:51:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5603, loss_cls: 3.9493, loss: 3.9493 +2024-07-24 04:55:06,771 - pyskl - INFO - Epoch [59][2600/3746] lr: 6.675e-02, eta: 3 days, 2:50:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5578, loss_cls: 4.0058, loss: 4.0058 +2024-07-24 04:56:28,510 - pyskl - INFO - Epoch [59][2700/3746] lr: 6.672e-02, eta: 3 days, 2:49:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5583, loss_cls: 4.0078, loss: 4.0078 +2024-07-24 04:57:49,749 - pyskl - INFO - Epoch [59][2800/3746] lr: 6.669e-02, eta: 3 days, 2:48:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5633, loss_cls: 4.0130, loss: 4.0130 +2024-07-24 04:59:11,324 - pyskl - INFO - Epoch [59][2900/3746] lr: 6.667e-02, eta: 3 days, 2:46:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5545, loss_cls: 4.0452, loss: 4.0452 +2024-07-24 05:00:32,544 - pyskl - INFO - Epoch [59][3000/3746] lr: 6.664e-02, eta: 3 days, 2:45:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5528, loss_cls: 4.0212, loss: 4.0212 +2024-07-24 05:01:54,084 - pyskl - INFO - Epoch [59][3100/3746] lr: 6.661e-02, eta: 3 days, 2:44:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5595, loss_cls: 3.9958, loss: 3.9958 +2024-07-24 05:03:15,910 - pyskl - INFO - Epoch [59][3200/3746] lr: 6.659e-02, eta: 3 days, 2:43:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5600, loss_cls: 4.0303, loss: 4.0303 +2024-07-24 05:04:37,538 - pyskl - INFO - Epoch [59][3300/3746] lr: 6.656e-02, eta: 3 days, 2:41:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5552, loss_cls: 4.0308, loss: 4.0308 +2024-07-24 05:05:59,221 - pyskl - INFO - Epoch [59][3400/3746] lr: 6.653e-02, eta: 3 days, 2:40:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5511, loss_cls: 4.0293, loss: 4.0293 +2024-07-24 05:07:20,913 - pyskl - INFO - Epoch [59][3500/3746] lr: 6.651e-02, eta: 3 days, 2:39:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5619, loss_cls: 3.9791, loss: 3.9791 +2024-07-24 05:08:42,685 - pyskl - INFO - Epoch [59][3600/3746] lr: 6.648e-02, eta: 3 days, 2:38:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5498, loss_cls: 4.0442, loss: 4.0442 +2024-07-24 05:10:04,620 - pyskl - INFO - Epoch [59][3700/3746] lr: 6.646e-02, eta: 3 days, 2:36:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5456, loss_cls: 4.0443, loss: 4.0443 +2024-07-24 05:10:44,732 - pyskl - INFO - Saving checkpoint at 59 epochs +2024-07-24 05:12:35,493 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 05:12:36,155 - pyskl - INFO - +top1_acc 0.2330 +top5_acc 0.4704 +2024-07-24 05:12:36,155 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 05:12:36,194 - pyskl - INFO - +mean_acc 0.2329 +2024-07-24 05:12:36,199 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_50.pth was removed +2024-07-24 05:12:36,447 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_59.pth. +2024-07-24 05:12:36,447 - pyskl - INFO - Best top1_acc is 0.2330 at 59 epoch. +2024-07-24 05:12:36,457 - pyskl - INFO - Epoch(val) [59][309] top1_acc: 0.2330, top5_acc: 0.4704, mean_class_accuracy: 0.2329 +2024-07-24 05:16:25,803 - pyskl - INFO - Epoch [60][100/3746] lr: 6.642e-02, eta: 3 days, 2:37:53, time: 2.293, data_time: 1.292, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5614, loss_cls: 3.9811, loss: 3.9811 +2024-07-24 05:17:48,098 - pyskl - INFO - Epoch [60][200/3746] lr: 6.639e-02, eta: 3 days, 2:36:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5608, loss_cls: 3.9682, loss: 3.9682 +2024-07-24 05:19:09,775 - pyskl - INFO - Epoch [60][300/3746] lr: 6.636e-02, eta: 3 days, 2:35:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5689, loss_cls: 3.9117, loss: 3.9117 +2024-07-24 05:20:31,744 - pyskl - INFO - Epoch [60][400/3746] lr: 6.634e-02, eta: 3 days, 2:34:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5586, loss_cls: 3.9834, loss: 3.9834 +2024-07-24 05:21:53,453 - pyskl - INFO - Epoch [60][500/3746] lr: 6.631e-02, eta: 3 days, 2:32:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5545, loss_cls: 4.0076, loss: 4.0076 +2024-07-24 05:23:15,136 - pyskl - INFO - Epoch [60][600/3746] lr: 6.629e-02, eta: 3 days, 2:31:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5600, loss_cls: 4.0128, loss: 4.0128 +2024-07-24 05:24:37,664 - pyskl - INFO - Epoch [60][700/3746] lr: 6.626e-02, eta: 3 days, 2:30:29, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5767, loss_cls: 3.9426, loss: 3.9426 +2024-07-24 05:25:59,722 - pyskl - INFO - Epoch [60][800/3746] lr: 6.623e-02, eta: 3 days, 2:29:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5652, loss_cls: 3.9537, loss: 3.9537 +2024-07-24 05:27:21,752 - pyskl - INFO - Epoch [60][900/3746] lr: 6.621e-02, eta: 3 days, 2:28:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5634, loss_cls: 3.9666, loss: 3.9666 +2024-07-24 05:28:43,303 - pyskl - INFO - Epoch [60][1000/3746] lr: 6.618e-02, eta: 3 days, 2:26:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5589, loss_cls: 4.0064, loss: 4.0064 +2024-07-24 05:30:05,537 - pyskl - INFO - Epoch [60][1100/3746] lr: 6.615e-02, eta: 3 days, 2:25:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5417, loss_cls: 4.0451, loss: 4.0451 +2024-07-24 05:31:28,250 - pyskl - INFO - Epoch [60][1200/3746] lr: 6.613e-02, eta: 3 days, 2:24:20, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5550, loss_cls: 4.0145, loss: 4.0145 +2024-07-24 05:32:50,197 - pyskl - INFO - Epoch [60][1300/3746] lr: 6.610e-02, eta: 3 days, 2:23:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5428, loss_cls: 4.0819, loss: 4.0819 +2024-07-24 05:34:12,641 - pyskl - INFO - Epoch [60][1400/3746] lr: 6.607e-02, eta: 3 days, 2:21:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5570, loss_cls: 3.9919, loss: 3.9919 +2024-07-24 05:35:35,111 - pyskl - INFO - Epoch [60][1500/3746] lr: 6.605e-02, eta: 3 days, 2:20:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5542, loss_cls: 4.0474, loss: 4.0474 +2024-07-24 05:36:57,276 - pyskl - INFO - Epoch [60][1600/3746] lr: 6.602e-02, eta: 3 days, 2:19:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5563, loss_cls: 3.9701, loss: 3.9701 +2024-07-24 05:38:18,960 - pyskl - INFO - Epoch [60][1700/3746] lr: 6.599e-02, eta: 3 days, 2:18:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5483, loss_cls: 4.0242, loss: 4.0242 +2024-07-24 05:39:40,559 - pyskl - INFO - Epoch [60][1800/3746] lr: 6.597e-02, eta: 3 days, 2:16:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5630, loss_cls: 3.9807, loss: 3.9807 +2024-07-24 05:41:02,516 - pyskl - INFO - Epoch [60][1900/3746] lr: 6.594e-02, eta: 3 days, 2:15:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5511, loss_cls: 4.0658, loss: 4.0658 +2024-07-24 05:42:24,171 - pyskl - INFO - Epoch [60][2000/3746] lr: 6.591e-02, eta: 3 days, 2:14:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5419, loss_cls: 4.0635, loss: 4.0635 +2024-07-24 05:43:45,904 - pyskl - INFO - Epoch [60][2100/3746] lr: 6.589e-02, eta: 3 days, 2:13:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5492, loss_cls: 4.0433, loss: 4.0433 +2024-07-24 05:45:07,574 - pyskl - INFO - Epoch [60][2200/3746] lr: 6.586e-02, eta: 3 days, 2:11:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5453, loss_cls: 4.0576, loss: 4.0576 +2024-07-24 05:46:29,101 - pyskl - INFO - Epoch [60][2300/3746] lr: 6.584e-02, eta: 3 days, 2:10:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5413, loss_cls: 4.0780, loss: 4.0780 +2024-07-24 05:47:50,572 - pyskl - INFO - Epoch [60][2400/3746] lr: 6.581e-02, eta: 3 days, 2:09:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5539, loss_cls: 4.0046, loss: 4.0046 +2024-07-24 05:49:12,202 - pyskl - INFO - Epoch [60][2500/3746] lr: 6.578e-02, eta: 3 days, 2:08:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5548, loss_cls: 4.0079, loss: 4.0079 +2024-07-24 05:50:33,762 - pyskl - INFO - Epoch [60][2600/3746] lr: 6.576e-02, eta: 3 days, 2:06:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5591, loss_cls: 3.9877, loss: 3.9877 +2024-07-24 05:51:55,183 - pyskl - INFO - Epoch [60][2700/3746] lr: 6.573e-02, eta: 3 days, 2:05:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5450, loss_cls: 4.0661, loss: 4.0661 +2024-07-24 05:53:17,587 - pyskl - INFO - Epoch [60][2800/3746] lr: 6.570e-02, eta: 3 days, 2:04:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5513, loss_cls: 4.0264, loss: 4.0264 +2024-07-24 05:54:39,085 - pyskl - INFO - Epoch [60][2900/3746] lr: 6.568e-02, eta: 3 days, 2:03:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5589, loss_cls: 3.9695, loss: 3.9695 +2024-07-24 05:56:01,579 - pyskl - INFO - Epoch [60][3000/3746] lr: 6.565e-02, eta: 3 days, 2:02:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5552, loss_cls: 4.0293, loss: 4.0293 +2024-07-24 05:57:23,818 - pyskl - INFO - Epoch [60][3100/3746] lr: 6.562e-02, eta: 3 days, 2:00:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5420, loss_cls: 4.0722, loss: 4.0722 +2024-07-24 05:58:45,822 - pyskl - INFO - Epoch [60][3200/3746] lr: 6.560e-02, eta: 3 days, 1:59:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5602, loss_cls: 3.9868, loss: 3.9868 +2024-07-24 06:00:07,705 - pyskl - INFO - Epoch [60][3300/3746] lr: 6.557e-02, eta: 3 days, 1:58:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5589, loss_cls: 4.0046, loss: 4.0046 +2024-07-24 06:01:29,181 - pyskl - INFO - Epoch [60][3400/3746] lr: 6.554e-02, eta: 3 days, 1:57:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5483, loss_cls: 4.0386, loss: 4.0386 +2024-07-24 06:02:51,226 - pyskl - INFO - Epoch [60][3500/3746] lr: 6.552e-02, eta: 3 days, 1:55:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5648, loss_cls: 3.9437, loss: 3.9437 +2024-07-24 06:04:13,583 - pyskl - INFO - Epoch [60][3600/3746] lr: 6.549e-02, eta: 3 days, 1:54:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5480, loss_cls: 4.0578, loss: 4.0578 +2024-07-24 06:05:35,310 - pyskl - INFO - Epoch [60][3700/3746] lr: 6.546e-02, eta: 3 days, 1:53:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5503, loss_cls: 4.0371, loss: 4.0371 +2024-07-24 06:06:15,003 - pyskl - INFO - Saving checkpoint at 60 epochs +2024-07-24 06:08:06,981 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 06:08:07,644 - pyskl - INFO - +top1_acc 0.2413 +top5_acc 0.4840 +2024-07-24 06:08:07,644 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 06:08:07,683 - pyskl - INFO - +mean_acc 0.2410 +2024-07-24 06:08:07,687 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_59.pth was removed +2024-07-24 06:08:07,934 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_60.pth. +2024-07-24 06:08:07,935 - pyskl - INFO - Best top1_acc is 0.2413 at 60 epoch. +2024-07-24 06:08:07,945 - pyskl - INFO - Epoch(val) [60][309] top1_acc: 0.2413, top5_acc: 0.4840, mean_class_accuracy: 0.2410 +2024-07-24 06:11:56,057 - pyskl - INFO - Epoch [61][100/3746] lr: 6.542e-02, eta: 3 days, 1:54:16, time: 2.281, data_time: 1.303, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5720, loss_cls: 3.9281, loss: 3.9281 +2024-07-24 06:13:18,562 - pyskl - INFO - Epoch [61][200/3746] lr: 6.540e-02, eta: 3 days, 1:53:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5669, loss_cls: 3.9440, loss: 3.9440 +2024-07-24 06:14:40,825 - pyskl - INFO - Epoch [61][300/3746] lr: 6.537e-02, eta: 3 days, 1:51:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5584, loss_cls: 3.9705, loss: 3.9705 +2024-07-24 06:16:02,389 - pyskl - INFO - Epoch [61][400/3746] lr: 6.534e-02, eta: 3 days, 1:50:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5564, loss_cls: 4.0001, loss: 4.0001 +2024-07-24 06:17:23,986 - pyskl - INFO - Epoch [61][500/3746] lr: 6.532e-02, eta: 3 days, 1:49:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5619, loss_cls: 3.9973, loss: 3.9973 +2024-07-24 06:18:45,440 - pyskl - INFO - Epoch [61][600/3746] lr: 6.529e-02, eta: 3 days, 1:48:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5552, loss_cls: 3.9757, loss: 3.9757 +2024-07-24 06:20:07,146 - pyskl - INFO - Epoch [61][700/3746] lr: 6.526e-02, eta: 3 days, 1:46:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5566, loss_cls: 3.9839, loss: 3.9839 +2024-07-24 06:21:29,364 - pyskl - INFO - Epoch [61][800/3746] lr: 6.524e-02, eta: 3 days, 1:45:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5692, loss_cls: 3.9492, loss: 3.9492 +2024-07-24 06:22:51,291 - pyskl - INFO - Epoch [61][900/3746] lr: 6.521e-02, eta: 3 days, 1:44:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5577, loss_cls: 3.9858, loss: 3.9858 +2024-07-24 06:24:12,344 - pyskl - INFO - Epoch [61][1000/3746] lr: 6.519e-02, eta: 3 days, 1:43:04, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5580, loss_cls: 3.9992, loss: 3.9992 +2024-07-24 06:25:33,879 - pyskl - INFO - Epoch [61][1100/3746] lr: 6.516e-02, eta: 3 days, 1:41:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5527, loss_cls: 4.0200, loss: 4.0200 +2024-07-24 06:26:56,024 - pyskl - INFO - Epoch [61][1200/3746] lr: 6.513e-02, eta: 3 days, 1:40:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5514, loss_cls: 4.0194, loss: 4.0194 +2024-07-24 06:28:18,197 - pyskl - INFO - Epoch [61][1300/3746] lr: 6.511e-02, eta: 3 days, 1:39:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5578, loss_cls: 4.0087, loss: 4.0087 +2024-07-24 06:29:41,415 - pyskl - INFO - Epoch [61][1400/3746] lr: 6.508e-02, eta: 3 days, 1:38:08, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5591, loss_cls: 3.9770, loss: 3.9770 +2024-07-24 06:31:03,653 - pyskl - INFO - Epoch [61][1500/3746] lr: 6.505e-02, eta: 3 days, 1:36:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5616, loss_cls: 4.0044, loss: 4.0044 +2024-07-24 06:32:25,627 - pyskl - INFO - Epoch [61][1600/3746] lr: 6.503e-02, eta: 3 days, 1:35:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5505, loss_cls: 4.0289, loss: 4.0289 +2024-07-24 06:33:47,737 - pyskl - INFO - Epoch [61][1700/3746] lr: 6.500e-02, eta: 3 days, 1:34:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5570, loss_cls: 3.9993, loss: 3.9993 +2024-07-24 06:35:09,348 - pyskl - INFO - Epoch [61][1800/3746] lr: 6.497e-02, eta: 3 days, 1:33:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5506, loss_cls: 4.0316, loss: 4.0316 +2024-07-24 06:36:31,317 - pyskl - INFO - Epoch [61][1900/3746] lr: 6.495e-02, eta: 3 days, 1:31:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5550, loss_cls: 4.0115, loss: 4.0115 +2024-07-24 06:37:52,874 - pyskl - INFO - Epoch [61][2000/3746] lr: 6.492e-02, eta: 3 days, 1:30:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5661, loss_cls: 3.9781, loss: 3.9781 +2024-07-24 06:39:14,897 - pyskl - INFO - Epoch [61][2100/3746] lr: 6.489e-02, eta: 3 days, 1:29:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5542, loss_cls: 4.0084, loss: 4.0084 +2024-07-24 06:40:37,391 - pyskl - INFO - Epoch [61][2200/3746] lr: 6.487e-02, eta: 3 days, 1:28:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5533, loss_cls: 4.0106, loss: 4.0106 +2024-07-24 06:41:59,250 - pyskl - INFO - Epoch [61][2300/3746] lr: 6.484e-02, eta: 3 days, 1:26:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5569, loss_cls: 4.0031, loss: 4.0031 +2024-07-24 06:43:20,991 - pyskl - INFO - Epoch [61][2400/3746] lr: 6.481e-02, eta: 3 days, 1:25:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5536, loss_cls: 4.0055, loss: 4.0055 +2024-07-24 06:44:42,785 - pyskl - INFO - Epoch [61][2500/3746] lr: 6.478e-02, eta: 3 days, 1:24:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5547, loss_cls: 3.9848, loss: 3.9848 +2024-07-24 06:46:04,732 - pyskl - INFO - Epoch [61][2600/3746] lr: 6.476e-02, eta: 3 days, 1:23:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5498, loss_cls: 4.0268, loss: 4.0268 +2024-07-24 06:47:26,160 - pyskl - INFO - Epoch [61][2700/3746] lr: 6.473e-02, eta: 3 days, 1:21:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5648, loss_cls: 4.0092, loss: 4.0092 +2024-07-24 06:48:47,514 - pyskl - INFO - Epoch [61][2800/3746] lr: 6.470e-02, eta: 3 days, 1:20:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5528, loss_cls: 4.0180, loss: 4.0180 +2024-07-24 06:50:09,133 - pyskl - INFO - Epoch [61][2900/3746] lr: 6.468e-02, eta: 3 days, 1:19:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5592, loss_cls: 4.0026, loss: 4.0026 +2024-07-24 06:51:30,681 - pyskl - INFO - Epoch [61][3000/3746] lr: 6.465e-02, eta: 3 days, 1:18:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5613, loss_cls: 4.0083, loss: 4.0083 +2024-07-24 06:52:51,812 - pyskl - INFO - Epoch [61][3100/3746] lr: 6.462e-02, eta: 3 days, 1:16:56, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5403, loss_cls: 4.0954, loss: 4.0954 +2024-07-24 06:54:13,405 - pyskl - INFO - Epoch [61][3200/3746] lr: 6.460e-02, eta: 3 days, 1:15:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5566, loss_cls: 4.0068, loss: 4.0068 +2024-07-24 06:55:34,920 - pyskl - INFO - Epoch [61][3300/3746] lr: 6.457e-02, eta: 3 days, 1:14:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5561, loss_cls: 3.9849, loss: 3.9849 +2024-07-24 06:56:56,844 - pyskl - INFO - Epoch [61][3400/3746] lr: 6.454e-02, eta: 3 days, 1:13:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5580, loss_cls: 4.0227, loss: 4.0227 +2024-07-24 06:58:18,223 - pyskl - INFO - Epoch [61][3500/3746] lr: 6.452e-02, eta: 3 days, 1:11:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5595, loss_cls: 3.9964, loss: 3.9964 +2024-07-24 06:59:40,005 - pyskl - INFO - Epoch [61][3600/3746] lr: 6.449e-02, eta: 3 days, 1:10:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5533, loss_cls: 4.0175, loss: 4.0175 +2024-07-24 07:01:01,463 - pyskl - INFO - Epoch [61][3700/3746] lr: 6.446e-02, eta: 3 days, 1:09:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5450, loss_cls: 4.0642, loss: 4.0642 +2024-07-24 07:01:41,570 - pyskl - INFO - Saving checkpoint at 61 epochs +2024-07-24 07:03:33,058 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 07:03:33,718 - pyskl - INFO - +top1_acc 0.2379 +top5_acc 0.4800 +2024-07-24 07:03:33,718 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 07:03:33,758 - pyskl - INFO - +mean_acc 0.2375 +2024-07-24 07:03:33,769 - pyskl - INFO - Epoch(val) [61][309] top1_acc: 0.2379, top5_acc: 0.4800, mean_class_accuracy: 0.2375 +2024-07-24 07:07:22,305 - pyskl - INFO - Epoch [62][100/3746] lr: 6.443e-02, eta: 3 days, 1:10:15, time: 2.285, data_time: 1.299, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5594, loss_cls: 3.9935, loss: 3.9935 +2024-07-24 07:08:44,102 - pyskl - INFO - Epoch [62][200/3746] lr: 6.440e-02, eta: 3 days, 1:09:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5664, loss_cls: 3.9034, loss: 3.9034 +2024-07-24 07:10:06,292 - pyskl - INFO - Epoch [62][300/3746] lr: 6.437e-02, eta: 3 days, 1:07:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5606, loss_cls: 3.9737, loss: 3.9737 +2024-07-24 07:11:28,092 - pyskl - INFO - Epoch [62][400/3746] lr: 6.434e-02, eta: 3 days, 1:06:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5614, loss_cls: 3.9797, loss: 3.9797 +2024-07-24 07:12:49,649 - pyskl - INFO - Epoch [62][500/3746] lr: 6.432e-02, eta: 3 days, 1:05:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5700, loss_cls: 3.9476, loss: 3.9476 +2024-07-24 07:14:11,370 - pyskl - INFO - Epoch [62][600/3746] lr: 6.429e-02, eta: 3 days, 1:04:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5673, loss_cls: 3.9767, loss: 3.9767 +2024-07-24 07:15:33,697 - pyskl - INFO - Epoch [62][700/3746] lr: 6.426e-02, eta: 3 days, 1:02:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5591, loss_cls: 3.9749, loss: 3.9749 +2024-07-24 07:16:55,665 - pyskl - INFO - Epoch [62][800/3746] lr: 6.424e-02, eta: 3 days, 1:01:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5630, loss_cls: 3.9724, loss: 3.9724 +2024-07-24 07:18:17,246 - pyskl - INFO - Epoch [62][900/3746] lr: 6.421e-02, eta: 3 days, 1:00:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5598, loss_cls: 3.9899, loss: 3.9899 +2024-07-24 07:19:39,204 - pyskl - INFO - Epoch [62][1000/3746] lr: 6.418e-02, eta: 3 days, 0:59:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5569, loss_cls: 4.0153, loss: 4.0153 +2024-07-24 07:21:00,427 - pyskl - INFO - Epoch [62][1100/3746] lr: 6.416e-02, eta: 3 days, 0:57:45, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5427, loss_cls: 4.0283, loss: 4.0283 +2024-07-24 07:22:22,126 - pyskl - INFO - Epoch [62][1200/3746] lr: 6.413e-02, eta: 3 days, 0:56:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5502, loss_cls: 4.0168, loss: 4.0168 +2024-07-24 07:23:44,355 - pyskl - INFO - Epoch [62][1300/3746] lr: 6.410e-02, eta: 3 days, 0:55:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5550, loss_cls: 4.0038, loss: 4.0038 +2024-07-24 07:25:06,858 - pyskl - INFO - Epoch [62][1400/3746] lr: 6.408e-02, eta: 3 days, 0:54:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5528, loss_cls: 4.0121, loss: 4.0121 +2024-07-24 07:26:29,772 - pyskl - INFO - Epoch [62][1500/3746] lr: 6.405e-02, eta: 3 days, 0:52:48, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5600, loss_cls: 4.0116, loss: 4.0116 +2024-07-24 07:27:52,039 - pyskl - INFO - Epoch [62][1600/3746] lr: 6.402e-02, eta: 3 days, 0:51:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5622, loss_cls: 3.9552, loss: 3.9552 +2024-07-24 07:29:13,664 - pyskl - INFO - Epoch [62][1700/3746] lr: 6.400e-02, eta: 3 days, 0:50:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5589, loss_cls: 3.9998, loss: 3.9998 +2024-07-24 07:30:35,535 - pyskl - INFO - Epoch [62][1800/3746] lr: 6.397e-02, eta: 3 days, 0:49:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5609, loss_cls: 3.9369, loss: 3.9369 +2024-07-24 07:31:57,096 - pyskl - INFO - Epoch [62][1900/3746] lr: 6.394e-02, eta: 3 days, 0:47:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5573, loss_cls: 3.9861, loss: 3.9861 +2024-07-24 07:33:18,753 - pyskl - INFO - Epoch [62][2000/3746] lr: 6.392e-02, eta: 3 days, 0:46:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5644, loss_cls: 3.9628, loss: 3.9628 +2024-07-24 07:34:40,208 - pyskl - INFO - Epoch [62][2100/3746] lr: 6.389e-02, eta: 3 days, 0:45:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5523, loss_cls: 4.0507, loss: 4.0507 +2024-07-24 07:36:01,588 - pyskl - INFO - Epoch [62][2200/3746] lr: 6.386e-02, eta: 3 days, 0:44:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5500, loss_cls: 4.0266, loss: 4.0266 +2024-07-24 07:37:22,673 - pyskl - INFO - Epoch [62][2300/3746] lr: 6.384e-02, eta: 3 days, 0:42:45, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5592, loss_cls: 3.9971, loss: 3.9971 +2024-07-24 07:38:44,622 - pyskl - INFO - Epoch [62][2400/3746] lr: 6.381e-02, eta: 3 days, 0:41:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5594, loss_cls: 3.9818, loss: 3.9818 +2024-07-24 07:40:06,460 - pyskl - INFO - Epoch [62][2500/3746] lr: 6.378e-02, eta: 3 days, 0:40:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5606, loss_cls: 4.0078, loss: 4.0078 +2024-07-24 07:41:27,609 - pyskl - INFO - Epoch [62][2600/3746] lr: 6.375e-02, eta: 3 days, 0:38:59, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5603, loss_cls: 3.9849, loss: 3.9849 +2024-07-24 07:42:49,040 - pyskl - INFO - Epoch [62][2700/3746] lr: 6.373e-02, eta: 3 days, 0:37:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5481, loss_cls: 4.0229, loss: 4.0229 +2024-07-24 07:44:10,395 - pyskl - INFO - Epoch [62][2800/3746] lr: 6.370e-02, eta: 3 days, 0:36:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5548, loss_cls: 4.0066, loss: 4.0066 +2024-07-24 07:45:32,485 - pyskl - INFO - Epoch [62][2900/3746] lr: 6.367e-02, eta: 3 days, 0:35:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5561, loss_cls: 4.0160, loss: 4.0160 +2024-07-24 07:46:54,319 - pyskl - INFO - Epoch [62][3000/3746] lr: 6.365e-02, eta: 3 days, 0:33:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5605, loss_cls: 4.0073, loss: 4.0073 +2024-07-24 07:48:16,165 - pyskl - INFO - Epoch [62][3100/3746] lr: 6.362e-02, eta: 3 days, 0:32:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5509, loss_cls: 4.0321, loss: 4.0321 +2024-07-24 07:49:37,815 - pyskl - INFO - Epoch [62][3200/3746] lr: 6.359e-02, eta: 3 days, 0:31:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5575, loss_cls: 3.9947, loss: 3.9947 +2024-07-24 07:50:59,585 - pyskl - INFO - Epoch [62][3300/3746] lr: 6.357e-02, eta: 3 days, 0:30:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5430, loss_cls: 4.0662, loss: 4.0662 +2024-07-24 07:52:20,879 - pyskl - INFO - Epoch [62][3400/3746] lr: 6.354e-02, eta: 3 days, 0:28:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5670, loss_cls: 3.9583, loss: 3.9583 +2024-07-24 07:53:42,410 - pyskl - INFO - Epoch [62][3500/3746] lr: 6.351e-02, eta: 3 days, 0:27:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5609, loss_cls: 3.9975, loss: 3.9975 +2024-07-24 07:55:04,106 - pyskl - INFO - Epoch [62][3600/3746] lr: 6.349e-02, eta: 3 days, 0:26:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5511, loss_cls: 4.0156, loss: 4.0156 +2024-07-24 07:56:25,363 - pyskl - INFO - Epoch [62][3700/3746] lr: 6.346e-02, eta: 3 days, 0:25:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5539, loss_cls: 4.0117, loss: 4.0117 +2024-07-24 07:57:04,865 - pyskl - INFO - Saving checkpoint at 62 epochs +2024-07-24 07:58:56,917 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 07:58:57,593 - pyskl - INFO - +top1_acc 0.2432 +top5_acc 0.4855 +2024-07-24 07:58:57,593 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 07:58:57,637 - pyskl - INFO - +mean_acc 0.2430 +2024-07-24 07:58:57,642 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_60.pth was removed +2024-07-24 07:58:57,882 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_62.pth. +2024-07-24 07:58:57,883 - pyskl - INFO - Best top1_acc is 0.2432 at 62 epoch. +2024-07-24 07:58:57,896 - pyskl - INFO - Epoch(val) [62][309] top1_acc: 0.2432, top5_acc: 0.4855, mean_class_accuracy: 0.2430 +2024-07-24 08:02:47,728 - pyskl - INFO - Epoch [63][100/3746] lr: 6.342e-02, eta: 3 days, 0:25:56, time: 2.298, data_time: 1.328, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5670, loss_cls: 3.9602, loss: 3.9602 +2024-07-24 08:04:09,737 - pyskl - INFO - Epoch [63][200/3746] lr: 6.339e-02, eta: 3 days, 0:24:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5652, loss_cls: 3.9400, loss: 3.9400 +2024-07-24 08:05:32,000 - pyskl - INFO - Epoch [63][300/3746] lr: 6.337e-02, eta: 3 days, 0:23:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5577, loss_cls: 3.9950, loss: 3.9950 +2024-07-24 08:06:53,930 - pyskl - INFO - Epoch [63][400/3746] lr: 6.334e-02, eta: 3 days, 0:22:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5642, loss_cls: 3.9676, loss: 3.9676 +2024-07-24 08:08:15,320 - pyskl - INFO - Epoch [63][500/3746] lr: 6.331e-02, eta: 3 days, 0:20:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5603, loss_cls: 3.9748, loss: 3.9748 +2024-07-24 08:09:37,190 - pyskl - INFO - Epoch [63][600/3746] lr: 6.328e-02, eta: 3 days, 0:19:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5600, loss_cls: 3.9839, loss: 3.9839 +2024-07-24 08:10:59,048 - pyskl - INFO - Epoch [63][700/3746] lr: 6.326e-02, eta: 3 days, 0:18:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5686, loss_cls: 3.9532, loss: 3.9532 +2024-07-24 08:12:20,826 - pyskl - INFO - Epoch [63][800/3746] lr: 6.323e-02, eta: 3 days, 0:17:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5480, loss_cls: 4.0133, loss: 4.0133 +2024-07-24 08:13:43,051 - pyskl - INFO - Epoch [63][900/3746] lr: 6.320e-02, eta: 3 days, 0:15:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5653, loss_cls: 3.9385, loss: 3.9385 +2024-07-24 08:15:05,422 - pyskl - INFO - Epoch [63][1000/3746] lr: 6.318e-02, eta: 3 days, 0:14:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5509, loss_cls: 3.9969, loss: 3.9969 +2024-07-24 08:16:26,725 - pyskl - INFO - Epoch [63][1100/3746] lr: 6.315e-02, eta: 3 days, 0:13:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5589, loss_cls: 3.9737, loss: 3.9737 +2024-07-24 08:17:48,406 - pyskl - INFO - Epoch [63][1200/3746] lr: 6.312e-02, eta: 3 days, 0:12:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5541, loss_cls: 4.0037, loss: 4.0037 +2024-07-24 08:19:10,874 - pyskl - INFO - Epoch [63][1300/3746] lr: 6.310e-02, eta: 3 days, 0:10:54, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5645, loss_cls: 3.9776, loss: 3.9776 +2024-07-24 08:20:33,509 - pyskl - INFO - Epoch [63][1400/3746] lr: 6.307e-02, eta: 3 days, 0:09:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5591, loss_cls: 3.9779, loss: 3.9779 +2024-07-24 08:21:55,739 - pyskl - INFO - Epoch [63][1500/3746] lr: 6.304e-02, eta: 3 days, 0:08:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5655, loss_cls: 3.9745, loss: 3.9745 +2024-07-24 08:23:18,385 - pyskl - INFO - Epoch [63][1600/3746] lr: 6.301e-02, eta: 3 days, 0:07:10, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5620, loss_cls: 3.9825, loss: 3.9825 +2024-07-24 08:24:40,012 - pyskl - INFO - Epoch [63][1700/3746] lr: 6.299e-02, eta: 3 days, 0:05:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5572, loss_cls: 4.0161, loss: 4.0161 +2024-07-24 08:26:01,575 - pyskl - INFO - Epoch [63][1800/3746] lr: 6.296e-02, eta: 3 days, 0:04:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5677, loss_cls: 3.9330, loss: 3.9330 +2024-07-24 08:27:22,739 - pyskl - INFO - Epoch [63][1900/3746] lr: 6.293e-02, eta: 3 days, 0:03:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5530, loss_cls: 4.0396, loss: 4.0396 +2024-07-24 08:28:45,080 - pyskl - INFO - Epoch [63][2000/3746] lr: 6.291e-02, eta: 3 days, 0:02:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5670, loss_cls: 3.9655, loss: 3.9655 +2024-07-24 08:30:07,053 - pyskl - INFO - Epoch [63][2100/3746] lr: 6.288e-02, eta: 3 days, 0:00:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5452, loss_cls: 4.0522, loss: 4.0522 +2024-07-24 08:31:29,164 - pyskl - INFO - Epoch [63][2200/3746] lr: 6.285e-02, eta: 2 days, 23:59:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5734, loss_cls: 3.9016, loss: 3.9016 +2024-07-24 08:32:51,111 - pyskl - INFO - Epoch [63][2300/3746] lr: 6.283e-02, eta: 2 days, 23:58:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5695, loss_cls: 3.9905, loss: 3.9905 +2024-07-24 08:34:13,085 - pyskl - INFO - Epoch [63][2400/3746] lr: 6.280e-02, eta: 2 days, 23:57:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5633, loss_cls: 3.9837, loss: 3.9837 +2024-07-24 08:35:34,734 - pyskl - INFO - Epoch [63][2500/3746] lr: 6.277e-02, eta: 2 days, 23:55:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5589, loss_cls: 3.9927, loss: 3.9927 +2024-07-24 08:36:56,561 - pyskl - INFO - Epoch [63][2600/3746] lr: 6.274e-02, eta: 2 days, 23:54:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5620, loss_cls: 3.9694, loss: 3.9694 +2024-07-24 08:38:18,215 - pyskl - INFO - Epoch [63][2700/3746] lr: 6.272e-02, eta: 2 days, 23:53:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5489, loss_cls: 4.0595, loss: 4.0595 +2024-07-24 08:39:39,411 - pyskl - INFO - Epoch [63][2800/3746] lr: 6.269e-02, eta: 2 days, 23:52:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5506, loss_cls: 4.0237, loss: 4.0237 +2024-07-24 08:41:01,739 - pyskl - INFO - Epoch [63][2900/3746] lr: 6.266e-02, eta: 2 days, 23:50:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5567, loss_cls: 4.0035, loss: 4.0035 +2024-07-24 08:42:23,340 - pyskl - INFO - Epoch [63][3000/3746] lr: 6.264e-02, eta: 2 days, 23:49:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5569, loss_cls: 3.9995, loss: 3.9995 +2024-07-24 08:43:45,330 - pyskl - INFO - Epoch [63][3100/3746] lr: 6.261e-02, eta: 2 days, 23:48:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5575, loss_cls: 4.0091, loss: 4.0091 +2024-07-24 08:45:06,736 - pyskl - INFO - Epoch [63][3200/3746] lr: 6.258e-02, eta: 2 days, 23:47:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5602, loss_cls: 3.9615, loss: 3.9615 +2024-07-24 08:46:28,778 - pyskl - INFO - Epoch [63][3300/3746] lr: 6.256e-02, eta: 2 days, 23:45:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5531, loss_cls: 4.0228, loss: 4.0228 +2024-07-24 08:47:50,634 - pyskl - INFO - Epoch [63][3400/3746] lr: 6.253e-02, eta: 2 days, 23:44:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5548, loss_cls: 3.9666, loss: 3.9666 +2024-07-24 08:49:12,083 - pyskl - INFO - Epoch [63][3500/3746] lr: 6.250e-02, eta: 2 days, 23:43:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5498, loss_cls: 4.0047, loss: 4.0047 +2024-07-24 08:50:33,377 - pyskl - INFO - Epoch [63][3600/3746] lr: 6.247e-02, eta: 2 days, 23:41:59, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5492, loss_cls: 4.0446, loss: 4.0446 +2024-07-24 08:51:55,055 - pyskl - INFO - Epoch [63][3700/3746] lr: 6.245e-02, eta: 2 days, 23:40:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5553, loss_cls: 4.0277, loss: 4.0277 +2024-07-24 08:52:34,969 - pyskl - INFO - Saving checkpoint at 63 epochs +2024-07-24 08:54:26,764 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 08:54:27,425 - pyskl - INFO - +top1_acc 0.2393 +top5_acc 0.4876 +2024-07-24 08:54:27,425 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 08:54:27,465 - pyskl - INFO - +mean_acc 0.2389 +2024-07-24 08:54:27,476 - pyskl - INFO - Epoch(val) [63][309] top1_acc: 0.2393, top5_acc: 0.4876, mean_class_accuracy: 0.2389 +2024-07-24 08:58:16,748 - pyskl - INFO - Epoch [64][100/3746] lr: 6.241e-02, eta: 2 days, 23:41:25, time: 2.293, data_time: 1.306, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5658, loss_cls: 3.9369, loss: 3.9369 +2024-07-24 08:59:38,737 - pyskl - INFO - Epoch [64][200/3746] lr: 6.238e-02, eta: 2 days, 23:40:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5677, loss_cls: 3.9185, loss: 3.9185 +2024-07-24 09:01:00,220 - pyskl - INFO - Epoch [64][300/3746] lr: 6.235e-02, eta: 2 days, 23:38:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5725, loss_cls: 3.9205, loss: 3.9205 +2024-07-24 09:02:21,927 - pyskl - INFO - Epoch [64][400/3746] lr: 6.233e-02, eta: 2 days, 23:37:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5592, loss_cls: 3.9882, loss: 3.9882 +2024-07-24 09:03:43,041 - pyskl - INFO - Epoch [64][500/3746] lr: 6.230e-02, eta: 2 days, 23:36:21, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5628, loss_cls: 3.9652, loss: 3.9652 +2024-07-24 09:05:04,330 - pyskl - INFO - Epoch [64][600/3746] lr: 6.227e-02, eta: 2 days, 23:35:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5680, loss_cls: 3.9666, loss: 3.9666 +2024-07-24 09:06:25,956 - pyskl - INFO - Epoch [64][700/3746] lr: 6.225e-02, eta: 2 days, 23:33:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5555, loss_cls: 3.9769, loss: 3.9769 +2024-07-24 09:07:48,069 - pyskl - INFO - Epoch [64][800/3746] lr: 6.222e-02, eta: 2 days, 23:32:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5545, loss_cls: 3.9843, loss: 3.9843 +2024-07-24 09:09:10,716 - pyskl - INFO - Epoch [64][900/3746] lr: 6.219e-02, eta: 2 days, 23:31:19, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5681, loss_cls: 3.9646, loss: 3.9646 +2024-07-24 09:10:32,961 - pyskl - INFO - Epoch [64][1000/3746] lr: 6.216e-02, eta: 2 days, 23:30:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5609, loss_cls: 3.9553, loss: 3.9553 +2024-07-24 09:11:54,631 - pyskl - INFO - Epoch [64][1100/3746] lr: 6.214e-02, eta: 2 days, 23:28:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5623, loss_cls: 3.9760, loss: 3.9760 +2024-07-24 09:13:16,465 - pyskl - INFO - Epoch [64][1200/3746] lr: 6.211e-02, eta: 2 days, 23:27:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5716, loss_cls: 3.9490, loss: 3.9490 +2024-07-24 09:14:38,040 - pyskl - INFO - Epoch [64][1300/3746] lr: 6.208e-02, eta: 2 days, 23:26:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5623, loss_cls: 4.0073, loss: 4.0073 +2024-07-24 09:16:00,750 - pyskl - INFO - Epoch [64][1400/3746] lr: 6.206e-02, eta: 2 days, 23:25:02, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5639, loss_cls: 3.9590, loss: 3.9590 +2024-07-24 09:17:23,987 - pyskl - INFO - Epoch [64][1500/3746] lr: 6.203e-02, eta: 2 days, 23:23:48, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5583, loss_cls: 3.9916, loss: 3.9916 +2024-07-24 09:18:46,434 - pyskl - INFO - Epoch [64][1600/3746] lr: 6.200e-02, eta: 2 days, 23:22:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5625, loss_cls: 3.9725, loss: 3.9725 +2024-07-24 09:20:08,653 - pyskl - INFO - Epoch [64][1700/3746] lr: 6.197e-02, eta: 2 days, 23:21:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5450, loss_cls: 4.0391, loss: 4.0391 +2024-07-24 09:21:30,346 - pyskl - INFO - Epoch [64][1800/3746] lr: 6.195e-02, eta: 2 days, 23:20:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5563, loss_cls: 3.9836, loss: 3.9836 +2024-07-24 09:22:51,957 - pyskl - INFO - Epoch [64][1900/3746] lr: 6.192e-02, eta: 2 days, 23:18:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5667, loss_cls: 3.9708, loss: 3.9708 +2024-07-24 09:24:13,263 - pyskl - INFO - Epoch [64][2000/3746] lr: 6.189e-02, eta: 2 days, 23:17:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5717, loss_cls: 3.9510, loss: 3.9510 +2024-07-24 09:25:34,801 - pyskl - INFO - Epoch [64][2100/3746] lr: 6.187e-02, eta: 2 days, 23:16:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5655, loss_cls: 3.9654, loss: 3.9654 +2024-07-24 09:26:56,475 - pyskl - INFO - Epoch [64][2200/3746] lr: 6.184e-02, eta: 2 days, 23:14:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5655, loss_cls: 3.9664, loss: 3.9664 +2024-07-24 09:28:18,332 - pyskl - INFO - Epoch [64][2300/3746] lr: 6.181e-02, eta: 2 days, 23:13:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5709, loss_cls: 3.9604, loss: 3.9604 +2024-07-24 09:29:40,072 - pyskl - INFO - Epoch [64][2400/3746] lr: 6.178e-02, eta: 2 days, 23:12:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5541, loss_cls: 4.0129, loss: 4.0129 +2024-07-24 09:31:01,797 - pyskl - INFO - Epoch [64][2500/3746] lr: 6.176e-02, eta: 2 days, 23:11:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5659, loss_cls: 3.9815, loss: 3.9815 +2024-07-24 09:32:24,258 - pyskl - INFO - Epoch [64][2600/3746] lr: 6.173e-02, eta: 2 days, 23:09:56, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5536, loss_cls: 3.9999, loss: 3.9999 +2024-07-24 09:33:45,708 - pyskl - INFO - Epoch [64][2700/3746] lr: 6.170e-02, eta: 2 days, 23:08:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5633, loss_cls: 3.9690, loss: 3.9690 +2024-07-24 09:35:06,768 - pyskl - INFO - Epoch [64][2800/3746] lr: 6.168e-02, eta: 2 days, 23:07:22, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5573, loss_cls: 4.0001, loss: 4.0001 +2024-07-24 09:36:28,567 - pyskl - INFO - Epoch [64][2900/3746] lr: 6.165e-02, eta: 2 days, 23:06:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5561, loss_cls: 3.9801, loss: 3.9801 +2024-07-24 09:37:49,907 - pyskl - INFO - Epoch [64][3000/3746] lr: 6.162e-02, eta: 2 days, 23:04:50, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5558, loss_cls: 4.0232, loss: 4.0232 +2024-07-24 09:39:11,600 - pyskl - INFO - Epoch [64][3100/3746] lr: 6.159e-02, eta: 2 days, 23:03:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5497, loss_cls: 4.0067, loss: 4.0067 +2024-07-24 09:40:33,399 - pyskl - INFO - Epoch [64][3200/3746] lr: 6.157e-02, eta: 2 days, 23:02:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5664, loss_cls: 3.9589, loss: 3.9589 +2024-07-24 09:41:54,812 - pyskl - INFO - Epoch [64][3300/3746] lr: 6.154e-02, eta: 2 days, 23:01:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5563, loss_cls: 3.9640, loss: 3.9640 +2024-07-24 09:43:16,283 - pyskl - INFO - Epoch [64][3400/3746] lr: 6.151e-02, eta: 2 days, 22:59:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5545, loss_cls: 3.9914, loss: 3.9914 +2024-07-24 09:44:38,145 - pyskl - INFO - Epoch [64][3500/3746] lr: 6.148e-02, eta: 2 days, 22:58:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5547, loss_cls: 3.9936, loss: 3.9936 +2024-07-24 09:45:59,291 - pyskl - INFO - Epoch [64][3600/3746] lr: 6.146e-02, eta: 2 days, 22:57:13, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5597, loss_cls: 4.0037, loss: 4.0037 +2024-07-24 09:47:21,403 - pyskl - INFO - Epoch [64][3700/3746] lr: 6.143e-02, eta: 2 days, 22:55:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5631, loss_cls: 3.9798, loss: 3.9798 +2024-07-24 09:48:01,005 - pyskl - INFO - Saving checkpoint at 64 epochs +2024-07-24 09:49:53,432 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 09:49:54,088 - pyskl - INFO - +top1_acc 0.2421 +top5_acc 0.4870 +2024-07-24 09:49:54,089 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 09:49:54,129 - pyskl - INFO - +mean_acc 0.2420 +2024-07-24 09:49:54,141 - pyskl - INFO - Epoch(val) [64][309] top1_acc: 0.2421, top5_acc: 0.4870, mean_class_accuracy: 0.2420 +2024-07-24 09:53:41,477 - pyskl - INFO - Epoch [65][100/3746] lr: 6.139e-02, eta: 2 days, 22:56:32, time: 2.273, data_time: 1.298, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5769, loss_cls: 3.8805, loss: 3.8805 +2024-07-24 09:55:03,371 - pyskl - INFO - Epoch [65][200/3746] lr: 6.136e-02, eta: 2 days, 22:55:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5713, loss_cls: 3.9137, loss: 3.9137 +2024-07-24 09:56:25,077 - pyskl - INFO - Epoch [65][300/3746] lr: 6.134e-02, eta: 2 days, 22:54:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5637, loss_cls: 3.9314, loss: 3.9314 +2024-07-24 09:57:47,894 - pyskl - INFO - Epoch [65][400/3746] lr: 6.131e-02, eta: 2 days, 22:52:46, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5702, loss_cls: 3.9601, loss: 3.9601 +2024-07-24 09:59:09,745 - pyskl - INFO - Epoch [65][500/3746] lr: 6.128e-02, eta: 2 days, 22:51:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5716, loss_cls: 3.9337, loss: 3.9337 +2024-07-24 10:00:31,514 - pyskl - INFO - Epoch [65][600/3746] lr: 6.125e-02, eta: 2 days, 22:50:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5603, loss_cls: 3.9787, loss: 3.9787 +2024-07-24 10:01:53,878 - pyskl - INFO - Epoch [65][700/3746] lr: 6.123e-02, eta: 2 days, 22:48:59, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5583, loss_cls: 3.9581, loss: 3.9581 +2024-07-24 10:03:15,678 - pyskl - INFO - Epoch [65][800/3746] lr: 6.120e-02, eta: 2 days, 22:47:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5591, loss_cls: 3.9701, loss: 3.9701 +2024-07-24 10:04:38,224 - pyskl - INFO - Epoch [65][900/3746] lr: 6.117e-02, eta: 2 days, 22:46:28, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5587, loss_cls: 3.9713, loss: 3.9713 +2024-07-24 10:06:00,474 - pyskl - INFO - Epoch [65][1000/3746] lr: 6.115e-02, eta: 2 days, 22:45:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5570, loss_cls: 3.9811, loss: 3.9811 +2024-07-24 10:07:22,262 - pyskl - INFO - Epoch [65][1100/3746] lr: 6.112e-02, eta: 2 days, 22:43:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5678, loss_cls: 3.9135, loss: 3.9135 +2024-07-24 10:08:43,657 - pyskl - INFO - Epoch [65][1200/3746] lr: 6.109e-02, eta: 2 days, 22:42:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5513, loss_cls: 4.0122, loss: 4.0122 +2024-07-24 10:10:05,045 - pyskl - INFO - Epoch [65][1300/3746] lr: 6.106e-02, eta: 2 days, 22:41:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5484, loss_cls: 4.0375, loss: 4.0375 +2024-07-24 10:11:26,857 - pyskl - INFO - Epoch [65][1400/3746] lr: 6.104e-02, eta: 2 days, 22:40:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5630, loss_cls: 3.9911, loss: 3.9911 +2024-07-24 10:12:49,213 - pyskl - INFO - Epoch [65][1500/3746] lr: 6.101e-02, eta: 2 days, 22:38:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5645, loss_cls: 3.9661, loss: 3.9661 +2024-07-24 10:14:12,253 - pyskl - INFO - Epoch [65][1600/3746] lr: 6.098e-02, eta: 2 days, 22:37:37, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5634, loss_cls: 3.9585, loss: 3.9585 +2024-07-24 10:15:33,998 - pyskl - INFO - Epoch [65][1700/3746] lr: 6.095e-02, eta: 2 days, 22:36:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5628, loss_cls: 3.9946, loss: 3.9946 +2024-07-24 10:16:55,963 - pyskl - INFO - Epoch [65][1800/3746] lr: 6.093e-02, eta: 2 days, 22:35:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5742, loss_cls: 3.9383, loss: 3.9383 +2024-07-24 10:18:17,543 - pyskl - INFO - Epoch [65][1900/3746] lr: 6.090e-02, eta: 2 days, 22:33:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5603, loss_cls: 3.9672, loss: 3.9672 +2024-07-24 10:19:39,269 - pyskl - INFO - Epoch [65][2000/3746] lr: 6.087e-02, eta: 2 days, 22:32:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5658, loss_cls: 3.9519, loss: 3.9519 +2024-07-24 10:21:00,648 - pyskl - INFO - Epoch [65][2100/3746] lr: 6.085e-02, eta: 2 days, 22:31:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5669, loss_cls: 3.9697, loss: 3.9697 +2024-07-24 10:22:23,203 - pyskl - INFO - Epoch [65][2200/3746] lr: 6.082e-02, eta: 2 days, 22:30:01, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5625, loss_cls: 3.9351, loss: 3.9351 +2024-07-24 10:23:45,509 - pyskl - INFO - Epoch [65][2300/3746] lr: 6.079e-02, eta: 2 days, 22:28:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5641, loss_cls: 3.9674, loss: 3.9674 +2024-07-24 10:25:07,825 - pyskl - INFO - Epoch [65][2400/3746] lr: 6.076e-02, eta: 2 days, 22:27:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5714, loss_cls: 3.9438, loss: 3.9438 +2024-07-24 10:26:29,344 - pyskl - INFO - Epoch [65][2500/3746] lr: 6.074e-02, eta: 2 days, 22:26:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5645, loss_cls: 3.9440, loss: 3.9440 +2024-07-24 10:27:51,212 - pyskl - INFO - Epoch [65][2600/3746] lr: 6.071e-02, eta: 2 days, 22:24:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5637, loss_cls: 3.9636, loss: 3.9636 +2024-07-24 10:29:12,706 - pyskl - INFO - Epoch [65][2700/3746] lr: 6.068e-02, eta: 2 days, 22:23:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5613, loss_cls: 3.9860, loss: 3.9860 +2024-07-24 10:30:34,473 - pyskl - INFO - Epoch [65][2800/3746] lr: 6.065e-02, eta: 2 days, 22:22:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5603, loss_cls: 4.0097, loss: 4.0097 +2024-07-24 10:31:56,779 - pyskl - INFO - Epoch [65][2900/3746] lr: 6.063e-02, eta: 2 days, 22:21:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5589, loss_cls: 3.9943, loss: 3.9943 +2024-07-24 10:33:18,153 - pyskl - INFO - Epoch [65][3000/3746] lr: 6.060e-02, eta: 2 days, 22:19:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5584, loss_cls: 3.9930, loss: 3.9930 +2024-07-24 10:34:39,518 - pyskl - INFO - Epoch [65][3100/3746] lr: 6.057e-02, eta: 2 days, 22:18:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5705, loss_cls: 3.9493, loss: 3.9493 +2024-07-24 10:36:00,982 - pyskl - INFO - Epoch [65][3200/3746] lr: 6.055e-02, eta: 2 days, 22:17:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5617, loss_cls: 3.9928, loss: 3.9928 +2024-07-24 10:37:22,305 - pyskl - INFO - Epoch [65][3300/3746] lr: 6.052e-02, eta: 2 days, 22:16:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5608, loss_cls: 4.0011, loss: 4.0011 +2024-07-24 10:38:43,485 - pyskl - INFO - Epoch [65][3400/3746] lr: 6.049e-02, eta: 2 days, 22:14:47, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5614, loss_cls: 3.9785, loss: 3.9785 +2024-07-24 10:40:04,924 - pyskl - INFO - Epoch [65][3500/3746] lr: 6.046e-02, eta: 2 days, 22:13:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5619, loss_cls: 3.9863, loss: 3.9863 +2024-07-24 10:41:26,566 - pyskl - INFO - Epoch [65][3600/3746] lr: 6.044e-02, eta: 2 days, 22:12:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5631, loss_cls: 3.9718, loss: 3.9718 +2024-07-24 10:42:48,161 - pyskl - INFO - Epoch [65][3700/3746] lr: 6.041e-02, eta: 2 days, 22:10:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5655, loss_cls: 3.9418, loss: 3.9418 +2024-07-24 10:43:27,722 - pyskl - INFO - Saving checkpoint at 65 epochs +2024-07-24 10:45:19,820 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 10:45:20,493 - pyskl - INFO - +top1_acc 0.2301 +top5_acc 0.4753 +2024-07-24 10:45:20,493 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 10:45:20,538 - pyskl - INFO - +mean_acc 0.2299 +2024-07-24 10:45:20,550 - pyskl - INFO - Epoch(val) [65][309] top1_acc: 0.2301, top5_acc: 0.4753, mean_class_accuracy: 0.2299 +2024-07-24 10:49:11,088 - pyskl - INFO - Epoch [66][100/3746] lr: 6.037e-02, eta: 2 days, 22:11:32, time: 2.305, data_time: 1.311, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5708, loss_cls: 3.9142, loss: 3.9142 +2024-07-24 10:50:32,907 - pyskl - INFO - Epoch [66][200/3746] lr: 6.034e-02, eta: 2 days, 22:10:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5720, loss_cls: 3.9398, loss: 3.9398 +2024-07-24 10:51:54,611 - pyskl - INFO - Epoch [66][300/3746] lr: 6.031e-02, eta: 2 days, 22:08:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5692, loss_cls: 3.9527, loss: 3.9527 +2024-07-24 10:53:16,221 - pyskl - INFO - Epoch [66][400/3746] lr: 6.029e-02, eta: 2 days, 22:07:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5587, loss_cls: 4.0017, loss: 4.0017 +2024-07-24 10:54:38,106 - pyskl - INFO - Epoch [66][500/3746] lr: 6.026e-02, eta: 2 days, 22:06:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5758, loss_cls: 3.9002, loss: 3.9002 +2024-07-24 10:56:00,085 - pyskl - INFO - Epoch [66][600/3746] lr: 6.023e-02, eta: 2 days, 22:05:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5667, loss_cls: 3.9165, loss: 3.9165 +2024-07-24 10:57:21,748 - pyskl - INFO - Epoch [66][700/3746] lr: 6.020e-02, eta: 2 days, 22:03:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5619, loss_cls: 3.9557, loss: 3.9557 +2024-07-24 10:58:44,004 - pyskl - INFO - Epoch [66][800/3746] lr: 6.018e-02, eta: 2 days, 22:02:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5684, loss_cls: 3.9418, loss: 3.9418 +2024-07-24 11:00:06,338 - pyskl - INFO - Epoch [66][900/3746] lr: 6.015e-02, eta: 2 days, 22:01:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5680, loss_cls: 3.9655, loss: 3.9655 +2024-07-24 11:01:27,790 - pyskl - INFO - Epoch [66][1000/3746] lr: 6.012e-02, eta: 2 days, 22:00:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5703, loss_cls: 3.9257, loss: 3.9257 +2024-07-24 11:02:49,787 - pyskl - INFO - Epoch [66][1100/3746] lr: 6.009e-02, eta: 2 days, 21:58:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5653, loss_cls: 3.9578, loss: 3.9578 +2024-07-24 11:04:11,156 - pyskl - INFO - Epoch [66][1200/3746] lr: 6.007e-02, eta: 2 days, 21:57:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5736, loss_cls: 3.9296, loss: 3.9296 +2024-07-24 11:05:33,149 - pyskl - INFO - Epoch [66][1300/3746] lr: 6.004e-02, eta: 2 days, 21:56:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5587, loss_cls: 3.9690, loss: 3.9690 +2024-07-24 11:06:55,620 - pyskl - INFO - Epoch [66][1400/3746] lr: 6.001e-02, eta: 2 days, 21:55:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5573, loss_cls: 3.9588, loss: 3.9588 +2024-07-24 11:08:17,932 - pyskl - INFO - Epoch [66][1500/3746] lr: 5.999e-02, eta: 2 days, 21:53:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5563, loss_cls: 3.9928, loss: 3.9928 +2024-07-24 11:09:40,635 - pyskl - INFO - Epoch [66][1600/3746] lr: 5.996e-02, eta: 2 days, 21:52:31, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5616, loss_cls: 3.9720, loss: 3.9720 +2024-07-24 11:11:02,677 - pyskl - INFO - Epoch [66][1700/3746] lr: 5.993e-02, eta: 2 days, 21:51:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5636, loss_cls: 3.9788, loss: 3.9788 +2024-07-24 11:12:24,443 - pyskl - INFO - Epoch [66][1800/3746] lr: 5.990e-02, eta: 2 days, 21:49:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5775, loss_cls: 3.9155, loss: 3.9155 +2024-07-24 11:13:46,550 - pyskl - INFO - Epoch [66][1900/3746] lr: 5.988e-02, eta: 2 days, 21:48:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5567, loss_cls: 4.0042, loss: 4.0042 +2024-07-24 11:15:08,109 - pyskl - INFO - Epoch [66][2000/3746] lr: 5.985e-02, eta: 2 days, 21:47:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5630, loss_cls: 3.9780, loss: 3.9780 +2024-07-24 11:16:29,653 - pyskl - INFO - Epoch [66][2100/3746] lr: 5.982e-02, eta: 2 days, 21:46:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5594, loss_cls: 3.9717, loss: 3.9717 +2024-07-24 11:17:51,722 - pyskl - INFO - Epoch [66][2200/3746] lr: 5.979e-02, eta: 2 days, 21:44:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5622, loss_cls: 3.9509, loss: 3.9509 +2024-07-24 11:19:13,472 - pyskl - INFO - Epoch [66][2300/3746] lr: 5.977e-02, eta: 2 days, 21:43:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5606, loss_cls: 3.9622, loss: 3.9622 +2024-07-24 11:20:35,811 - pyskl - INFO - Epoch [66][2400/3746] lr: 5.974e-02, eta: 2 days, 21:42:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5722, loss_cls: 3.8974, loss: 3.8974 +2024-07-24 11:21:57,396 - pyskl - INFO - Epoch [66][2500/3746] lr: 5.971e-02, eta: 2 days, 21:41:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5700, loss_cls: 3.9471, loss: 3.9471 +2024-07-24 11:23:19,024 - pyskl - INFO - Epoch [66][2600/3746] lr: 5.968e-02, eta: 2 days, 21:39:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5556, loss_cls: 3.9794, loss: 3.9794 +2024-07-24 11:24:40,977 - pyskl - INFO - Epoch [66][2700/3746] lr: 5.966e-02, eta: 2 days, 21:38:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5663, loss_cls: 3.9883, loss: 3.9883 +2024-07-24 11:26:02,587 - pyskl - INFO - Epoch [66][2800/3746] lr: 5.963e-02, eta: 2 days, 21:37:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5466, loss_cls: 4.0448, loss: 4.0448 +2024-07-24 11:27:24,689 - pyskl - INFO - Epoch [66][2900/3746] lr: 5.960e-02, eta: 2 days, 21:35:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5597, loss_cls: 3.9624, loss: 3.9624 +2024-07-24 11:28:46,711 - pyskl - INFO - Epoch [66][3000/3746] lr: 5.957e-02, eta: 2 days, 21:34:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5777, loss_cls: 3.9144, loss: 3.9144 +2024-07-24 11:30:08,224 - pyskl - INFO - Epoch [66][3100/3746] lr: 5.955e-02, eta: 2 days, 21:33:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5577, loss_cls: 3.9979, loss: 3.9979 +2024-07-24 11:31:29,852 - pyskl - INFO - Epoch [66][3200/3746] lr: 5.952e-02, eta: 2 days, 21:32:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5656, loss_cls: 3.9572, loss: 3.9572 +2024-07-24 11:32:50,985 - pyskl - INFO - Epoch [66][3300/3746] lr: 5.949e-02, eta: 2 days, 21:30:53, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5536, loss_cls: 4.0285, loss: 4.0285 +2024-07-24 11:34:12,370 - pyskl - INFO - Epoch [66][3400/3746] lr: 5.946e-02, eta: 2 days, 21:29:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5634, loss_cls: 3.9860, loss: 3.9860 +2024-07-24 11:35:34,497 - pyskl - INFO - Epoch [66][3500/3746] lr: 5.944e-02, eta: 2 days, 21:28:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5728, loss_cls: 3.8980, loss: 3.8980 +2024-07-24 11:36:55,870 - pyskl - INFO - Epoch [66][3600/3746] lr: 5.941e-02, eta: 2 days, 21:27:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5570, loss_cls: 3.9813, loss: 3.9813 +2024-07-24 11:38:17,512 - pyskl - INFO - Epoch [66][3700/3746] lr: 5.938e-02, eta: 2 days, 21:25:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5661, loss_cls: 3.9240, loss: 3.9240 +2024-07-24 11:38:57,188 - pyskl - INFO - Saving checkpoint at 66 epochs +2024-07-24 11:40:47,557 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 11:40:48,220 - pyskl - INFO - +top1_acc 0.2496 +top5_acc 0.4997 +2024-07-24 11:40:48,220 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 11:40:48,261 - pyskl - INFO - +mean_acc 0.2493 +2024-07-24 11:40:48,266 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_62.pth was removed +2024-07-24 11:40:48,492 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_66.pth. +2024-07-24 11:40:48,493 - pyskl - INFO - Best top1_acc is 0.2496 at 66 epoch. +2024-07-24 11:40:48,504 - pyskl - INFO - Epoch(val) [66][309] top1_acc: 0.2496, top5_acc: 0.4997, mean_class_accuracy: 0.2493 +2024-07-24 11:44:41,710 - pyskl - INFO - Epoch [67][100/3746] lr: 5.934e-02, eta: 2 days, 21:26:20, time: 2.332, data_time: 1.344, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5769, loss_cls: 3.9028, loss: 3.9028 +2024-07-24 11:46:04,108 - pyskl - INFO - Epoch [67][200/3746] lr: 5.931e-02, eta: 2 days, 21:25:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5686, loss_cls: 3.9327, loss: 3.9327 +2024-07-24 11:47:26,155 - pyskl - INFO - Epoch [67][300/3746] lr: 5.929e-02, eta: 2 days, 21:23:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5636, loss_cls: 3.9629, loss: 3.9629 +2024-07-24 11:48:47,969 - pyskl - INFO - Epoch [67][400/3746] lr: 5.926e-02, eta: 2 days, 21:22:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5789, loss_cls: 3.9588, loss: 3.9588 +2024-07-24 11:50:09,981 - pyskl - INFO - Epoch [67][500/3746] lr: 5.923e-02, eta: 2 days, 21:21:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5636, loss_cls: 3.9572, loss: 3.9572 +2024-07-24 11:51:32,344 - pyskl - INFO - Epoch [67][600/3746] lr: 5.920e-02, eta: 2 days, 21:19:59, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5753, loss_cls: 3.9081, loss: 3.9081 +2024-07-24 11:52:54,443 - pyskl - INFO - Epoch [67][700/3746] lr: 5.918e-02, eta: 2 days, 21:18:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5787, loss_cls: 3.8685, loss: 3.8685 +2024-07-24 11:54:16,433 - pyskl - INFO - Epoch [67][800/3746] lr: 5.915e-02, eta: 2 days, 21:17:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5700, loss_cls: 3.8913, loss: 3.8913 +2024-07-24 11:55:38,704 - pyskl - INFO - Epoch [67][900/3746] lr: 5.912e-02, eta: 2 days, 21:16:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5697, loss_cls: 3.9258, loss: 3.9258 +2024-07-24 11:57:00,152 - pyskl - INFO - Epoch [67][1000/3746] lr: 5.909e-02, eta: 2 days, 21:14:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5645, loss_cls: 3.9433, loss: 3.9433 +2024-07-24 11:58:21,901 - pyskl - INFO - Epoch [67][1100/3746] lr: 5.907e-02, eta: 2 days, 21:13:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5698, loss_cls: 3.9459, loss: 3.9459 +2024-07-24 11:59:43,502 - pyskl - INFO - Epoch [67][1200/3746] lr: 5.904e-02, eta: 2 days, 21:12:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5720, loss_cls: 3.8991, loss: 3.8991 +2024-07-24 12:01:05,068 - pyskl - INFO - Epoch [67][1300/3746] lr: 5.901e-02, eta: 2 days, 21:11:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5683, loss_cls: 3.9200, loss: 3.9200 +2024-07-24 12:02:27,333 - pyskl - INFO - Epoch [67][1400/3746] lr: 5.898e-02, eta: 2 days, 21:09:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5770, loss_cls: 3.9174, loss: 3.9174 +2024-07-24 12:03:49,583 - pyskl - INFO - Epoch [67][1500/3746] lr: 5.896e-02, eta: 2 days, 21:08:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5717, loss_cls: 3.9369, loss: 3.9369 +2024-07-24 12:05:12,279 - pyskl - INFO - Epoch [67][1600/3746] lr: 5.893e-02, eta: 2 days, 21:07:16, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5614, loss_cls: 3.9963, loss: 3.9963 +2024-07-24 12:06:33,992 - pyskl - INFO - Epoch [67][1700/3746] lr: 5.890e-02, eta: 2 days, 21:05:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5675, loss_cls: 3.9213, loss: 3.9213 +2024-07-24 12:07:56,239 - pyskl - INFO - Epoch [67][1800/3746] lr: 5.887e-02, eta: 2 days, 21:04:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5678, loss_cls: 3.9557, loss: 3.9557 +2024-07-24 12:09:18,784 - pyskl - INFO - Epoch [67][1900/3746] lr: 5.885e-02, eta: 2 days, 21:03:28, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5606, loss_cls: 3.9881, loss: 3.9881 +2024-07-24 12:10:40,657 - pyskl - INFO - Epoch [67][2000/3746] lr: 5.882e-02, eta: 2 days, 21:02:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5673, loss_cls: 3.9477, loss: 3.9477 +2024-07-24 12:12:02,156 - pyskl - INFO - Epoch [67][2100/3746] lr: 5.879e-02, eta: 2 days, 21:00:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5592, loss_cls: 3.9824, loss: 3.9824 +2024-07-24 12:13:23,721 - pyskl - INFO - Epoch [67][2200/3746] lr: 5.876e-02, eta: 2 days, 20:59:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5533, loss_cls: 3.9978, loss: 3.9978 +2024-07-24 12:14:45,426 - pyskl - INFO - Epoch [67][2300/3746] lr: 5.874e-02, eta: 2 days, 20:58:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5641, loss_cls: 3.9623, loss: 3.9623 +2024-07-24 12:16:07,202 - pyskl - INFO - Epoch [67][2400/3746] lr: 5.871e-02, eta: 2 days, 20:57:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5583, loss_cls: 3.9946, loss: 3.9946 +2024-07-24 12:17:28,743 - pyskl - INFO - Epoch [67][2500/3746] lr: 5.868e-02, eta: 2 days, 20:55:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5494, loss_cls: 4.0427, loss: 4.0427 +2024-07-24 12:18:50,104 - pyskl - INFO - Epoch [67][2600/3746] lr: 5.865e-02, eta: 2 days, 20:54:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5613, loss_cls: 3.9440, loss: 3.9440 +2024-07-24 12:20:11,785 - pyskl - INFO - Epoch [67][2700/3746] lr: 5.863e-02, eta: 2 days, 20:53:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5667, loss_cls: 3.9322, loss: 3.9322 +2024-07-24 12:21:33,466 - pyskl - INFO - Epoch [67][2800/3746] lr: 5.860e-02, eta: 2 days, 20:51:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5556, loss_cls: 3.9992, loss: 3.9992 +2024-07-24 12:22:55,104 - pyskl - INFO - Epoch [67][2900/3746] lr: 5.857e-02, eta: 2 days, 20:50:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5637, loss_cls: 3.9447, loss: 3.9447 +2024-07-24 12:24:16,744 - pyskl - INFO - Epoch [67][3000/3746] lr: 5.854e-02, eta: 2 days, 20:49:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5619, loss_cls: 3.9726, loss: 3.9726 +2024-07-24 12:25:38,474 - pyskl - INFO - Epoch [67][3100/3746] lr: 5.852e-02, eta: 2 days, 20:48:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5597, loss_cls: 3.9973, loss: 3.9973 +2024-07-24 12:27:00,202 - pyskl - INFO - Epoch [67][3200/3746] lr: 5.849e-02, eta: 2 days, 20:46:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5505, loss_cls: 4.0149, loss: 4.0149 +2024-07-24 12:28:21,678 - pyskl - INFO - Epoch [67][3300/3746] lr: 5.846e-02, eta: 2 days, 20:45:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5616, loss_cls: 3.9910, loss: 3.9910 +2024-07-24 12:29:43,219 - pyskl - INFO - Epoch [67][3400/3746] lr: 5.843e-02, eta: 2 days, 20:44:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5628, loss_cls: 3.9767, loss: 3.9767 +2024-07-24 12:31:04,740 - pyskl - INFO - Epoch [67][3500/3746] lr: 5.841e-02, eta: 2 days, 20:42:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5753, loss_cls: 3.9001, loss: 3.9001 +2024-07-24 12:32:26,532 - pyskl - INFO - Epoch [67][3600/3746] lr: 5.838e-02, eta: 2 days, 20:41:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5652, loss_cls: 3.9139, loss: 3.9139 +2024-07-24 12:33:47,731 - pyskl - INFO - Epoch [67][3700/3746] lr: 5.835e-02, eta: 2 days, 20:40:24, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5720, loss_cls: 3.9032, loss: 3.9032 +2024-07-24 12:34:26,981 - pyskl - INFO - Saving checkpoint at 67 epochs +2024-07-24 12:36:18,723 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 12:36:19,395 - pyskl - INFO - +top1_acc 0.2491 +top5_acc 0.4916 +2024-07-24 12:36:19,395 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 12:36:19,437 - pyskl - INFO - +mean_acc 0.2487 +2024-07-24 12:36:19,449 - pyskl - INFO - Epoch(val) [67][309] top1_acc: 0.2491, top5_acc: 0.4916, mean_class_accuracy: 0.2487 +2024-07-24 12:40:13,405 - pyskl - INFO - Epoch [68][100/3746] lr: 5.831e-02, eta: 2 days, 20:40:54, time: 2.339, data_time: 1.362, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5794, loss_cls: 3.9060, loss: 3.9060 +2024-07-24 12:41:35,724 - pyskl - INFO - Epoch [68][200/3746] lr: 5.828e-02, eta: 2 days, 20:39:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5698, loss_cls: 3.9118, loss: 3.9118 +2024-07-24 12:42:57,507 - pyskl - INFO - Epoch [68][300/3746] lr: 5.826e-02, eta: 2 days, 20:38:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5680, loss_cls: 3.9514, loss: 3.9514 +2024-07-24 12:44:19,440 - pyskl - INFO - Epoch [68][400/3746] lr: 5.823e-02, eta: 2 days, 20:37:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5800, loss_cls: 3.8837, loss: 3.8837 +2024-07-24 12:45:41,397 - pyskl - INFO - Epoch [68][500/3746] lr: 5.820e-02, eta: 2 days, 20:35:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5769, loss_cls: 3.8820, loss: 3.8820 +2024-07-24 12:47:03,494 - pyskl - INFO - Epoch [68][600/3746] lr: 5.817e-02, eta: 2 days, 20:34:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5734, loss_cls: 3.9133, loss: 3.9133 +2024-07-24 12:48:25,490 - pyskl - INFO - Epoch [68][700/3746] lr: 5.815e-02, eta: 2 days, 20:33:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5713, loss_cls: 3.9240, loss: 3.9240 +2024-07-24 12:49:47,464 - pyskl - INFO - Epoch [68][800/3746] lr: 5.812e-02, eta: 2 days, 20:31:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5711, loss_cls: 3.9325, loss: 3.9325 +2024-07-24 12:51:10,082 - pyskl - INFO - Epoch [68][900/3746] lr: 5.809e-02, eta: 2 days, 20:30:43, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5802, loss_cls: 3.8540, loss: 3.8540 +2024-07-24 12:52:31,433 - pyskl - INFO - Epoch [68][1000/3746] lr: 5.806e-02, eta: 2 days, 20:29:25, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5700, loss_cls: 3.9427, loss: 3.9427 +2024-07-24 12:53:52,940 - pyskl - INFO - Epoch [68][1100/3746] lr: 5.804e-02, eta: 2 days, 20:28:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5598, loss_cls: 3.9665, loss: 3.9665 +2024-07-24 12:55:14,965 - pyskl - INFO - Epoch [68][1200/3746] lr: 5.801e-02, eta: 2 days, 20:26:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5669, loss_cls: 3.9303, loss: 3.9303 +2024-07-24 12:56:36,430 - pyskl - INFO - Epoch [68][1300/3746] lr: 5.798e-02, eta: 2 days, 20:25:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5622, loss_cls: 3.9504, loss: 3.9504 +2024-07-24 12:57:58,573 - pyskl - INFO - Epoch [68][1400/3746] lr: 5.795e-02, eta: 2 days, 20:24:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5608, loss_cls: 3.9712, loss: 3.9712 +2024-07-24 12:59:20,457 - pyskl - INFO - Epoch [68][1500/3746] lr: 5.792e-02, eta: 2 days, 20:23:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5708, loss_cls: 3.9077, loss: 3.9077 +2024-07-24 13:00:43,629 - pyskl - INFO - Epoch [68][1600/3746] lr: 5.790e-02, eta: 2 days, 20:21:46, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5677, loss_cls: 3.9445, loss: 3.9445 +2024-07-24 13:02:05,171 - pyskl - INFO - Epoch [68][1700/3746] lr: 5.787e-02, eta: 2 days, 20:20:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5745, loss_cls: 3.9033, loss: 3.9033 +2024-07-24 13:03:27,220 - pyskl - INFO - Epoch [68][1800/3746] lr: 5.784e-02, eta: 2 days, 20:19:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5666, loss_cls: 3.9470, loss: 3.9470 +2024-07-24 13:04:49,355 - pyskl - INFO - Epoch [68][1900/3746] lr: 5.781e-02, eta: 2 days, 20:17:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5613, loss_cls: 3.9618, loss: 3.9618 +2024-07-24 13:06:11,122 - pyskl - INFO - Epoch [68][2000/3746] lr: 5.779e-02, eta: 2 days, 20:16:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5769, loss_cls: 3.8985, loss: 3.8985 +2024-07-24 13:07:33,043 - pyskl - INFO - Epoch [68][2100/3746] lr: 5.776e-02, eta: 2 days, 20:15:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5613, loss_cls: 3.9758, loss: 3.9758 +2024-07-24 13:08:54,739 - pyskl - INFO - Epoch [68][2200/3746] lr: 5.773e-02, eta: 2 days, 20:14:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5633, loss_cls: 3.9346, loss: 3.9346 +2024-07-24 13:10:16,427 - pyskl - INFO - Epoch [68][2300/3746] lr: 5.770e-02, eta: 2 days, 20:12:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5614, loss_cls: 3.9471, loss: 3.9471 +2024-07-24 13:11:38,667 - pyskl - INFO - Epoch [68][2400/3746] lr: 5.768e-02, eta: 2 days, 20:11:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5722, loss_cls: 3.9284, loss: 3.9284 +2024-07-24 13:13:00,587 - pyskl - INFO - Epoch [68][2500/3746] lr: 5.765e-02, eta: 2 days, 20:10:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5517, loss_cls: 3.9790, loss: 3.9790 +2024-07-24 13:14:21,903 - pyskl - INFO - Epoch [68][2600/3746] lr: 5.762e-02, eta: 2 days, 20:08:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5733, loss_cls: 3.9269, loss: 3.9269 +2024-07-24 13:15:43,122 - pyskl - INFO - Epoch [68][2700/3746] lr: 5.759e-02, eta: 2 days, 20:07:40, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5697, loss_cls: 3.9246, loss: 3.9246 +2024-07-24 13:17:04,586 - pyskl - INFO - Epoch [68][2800/3746] lr: 5.757e-02, eta: 2 days, 20:06:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5713, loss_cls: 3.9631, loss: 3.9631 +2024-07-24 13:18:26,513 - pyskl - INFO - Epoch [68][2900/3746] lr: 5.754e-02, eta: 2 days, 20:05:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5719, loss_cls: 3.9035, loss: 3.9035 +2024-07-24 13:19:47,850 - pyskl - INFO - Epoch [68][3000/3746] lr: 5.751e-02, eta: 2 days, 20:03:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5556, loss_cls: 3.9774, loss: 3.9774 +2024-07-24 13:21:09,281 - pyskl - INFO - Epoch [68][3100/3746] lr: 5.748e-02, eta: 2 days, 20:02:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5675, loss_cls: 3.9371, loss: 3.9371 +2024-07-24 13:22:30,600 - pyskl - INFO - Epoch [68][3200/3746] lr: 5.746e-02, eta: 2 days, 20:01:14, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5577, loss_cls: 3.9741, loss: 3.9741 +2024-07-24 13:23:52,868 - pyskl - INFO - Epoch [68][3300/3746] lr: 5.743e-02, eta: 2 days, 19:59:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5627, loss_cls: 3.9419, loss: 3.9419 +2024-07-24 13:25:14,638 - pyskl - INFO - Epoch [68][3400/3746] lr: 5.740e-02, eta: 2 days, 19:58:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5627, loss_cls: 3.9779, loss: 3.9779 +2024-07-24 13:26:36,197 - pyskl - INFO - Epoch [68][3500/3746] lr: 5.737e-02, eta: 2 days, 19:57:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5709, loss_cls: 3.9472, loss: 3.9472 +2024-07-24 13:27:57,817 - pyskl - INFO - Epoch [68][3600/3746] lr: 5.734e-02, eta: 2 days, 19:56:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5659, loss_cls: 3.9464, loss: 3.9464 +2024-07-24 13:29:19,526 - pyskl - INFO - Epoch [68][3700/3746] lr: 5.732e-02, eta: 2 days, 19:54:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5648, loss_cls: 3.9376, loss: 3.9376 +2024-07-24 13:29:58,869 - pyskl - INFO - Saving checkpoint at 68 epochs +2024-07-24 13:31:49,276 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 13:31:49,936 - pyskl - INFO - +top1_acc 0.2435 +top5_acc 0.4865 +2024-07-24 13:31:49,936 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 13:31:49,976 - pyskl - INFO - +mean_acc 0.2435 +2024-07-24 13:31:49,988 - pyskl - INFO - Epoch(val) [68][309] top1_acc: 0.2435, top5_acc: 0.4865, mean_class_accuracy: 0.2435 +2024-07-24 13:35:40,890 - pyskl - INFO - Epoch [69][100/3746] lr: 5.728e-02, eta: 2 days, 19:55:12, time: 2.309, data_time: 1.309, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5903, loss_cls: 3.8104, loss: 3.8104 +2024-07-24 13:37:03,354 - pyskl - INFO - Epoch [69][200/3746] lr: 5.725e-02, eta: 2 days, 19:53:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5841, loss_cls: 3.8442, loss: 3.8442 +2024-07-24 13:38:24,651 - pyskl - INFO - Epoch [69][300/3746] lr: 5.722e-02, eta: 2 days, 19:52:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5795, loss_cls: 3.9147, loss: 3.9147 +2024-07-24 13:39:46,758 - pyskl - INFO - Epoch [69][400/3746] lr: 5.719e-02, eta: 2 days, 19:51:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5745, loss_cls: 3.9048, loss: 3.9048 +2024-07-24 13:41:08,778 - pyskl - INFO - Epoch [69][500/3746] lr: 5.717e-02, eta: 2 days, 19:50:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5714, loss_cls: 3.9008, loss: 3.9008 +2024-07-24 13:42:30,077 - pyskl - INFO - Epoch [69][600/3746] lr: 5.714e-02, eta: 2 days, 19:48:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5664, loss_cls: 3.9338, loss: 3.9338 +2024-07-24 13:43:52,219 - pyskl - INFO - Epoch [69][700/3746] lr: 5.711e-02, eta: 2 days, 19:47:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5755, loss_cls: 3.9266, loss: 3.9266 +2024-07-24 13:45:13,905 - pyskl - INFO - Epoch [69][800/3746] lr: 5.708e-02, eta: 2 days, 19:46:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5678, loss_cls: 3.9270, loss: 3.9270 +2024-07-24 13:46:35,712 - pyskl - INFO - Epoch [69][900/3746] lr: 5.706e-02, eta: 2 days, 19:44:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5781, loss_cls: 3.8697, loss: 3.8697 +2024-07-24 13:47:57,765 - pyskl - INFO - Epoch [69][1000/3746] lr: 5.703e-02, eta: 2 days, 19:43:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5600, loss_cls: 3.9453, loss: 3.9453 +2024-07-24 13:49:19,342 - pyskl - INFO - Epoch [69][1100/3746] lr: 5.700e-02, eta: 2 days, 19:42:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5683, loss_cls: 3.9179, loss: 3.9179 +2024-07-24 13:50:40,906 - pyskl - INFO - Epoch [69][1200/3746] lr: 5.697e-02, eta: 2 days, 19:41:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5681, loss_cls: 3.9312, loss: 3.9312 +2024-07-24 13:52:02,109 - pyskl - INFO - Epoch [69][1300/3746] lr: 5.694e-02, eta: 2 days, 19:39:47, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5830, loss_cls: 3.8948, loss: 3.8948 +2024-07-24 13:53:24,500 - pyskl - INFO - Epoch [69][1400/3746] lr: 5.692e-02, eta: 2 days, 19:38:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5713, loss_cls: 3.9521, loss: 3.9521 +2024-07-24 13:54:46,540 - pyskl - INFO - Epoch [69][1500/3746] lr: 5.689e-02, eta: 2 days, 19:37:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5630, loss_cls: 3.9418, loss: 3.9418 +2024-07-24 13:56:10,092 - pyskl - INFO - Epoch [69][1600/3746] lr: 5.686e-02, eta: 2 days, 19:35:59, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5773, loss_cls: 3.9072, loss: 3.9072 +2024-07-24 13:57:32,009 - pyskl - INFO - Epoch [69][1700/3746] lr: 5.683e-02, eta: 2 days, 19:34:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5644, loss_cls: 3.9708, loss: 3.9708 +2024-07-24 13:58:54,446 - pyskl - INFO - Epoch [69][1800/3746] lr: 5.681e-02, eta: 2 days, 19:33:25, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5645, loss_cls: 3.9438, loss: 3.9438 +2024-07-24 14:00:15,932 - pyskl - INFO - Epoch [69][1900/3746] lr: 5.678e-02, eta: 2 days, 19:32:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5672, loss_cls: 3.9383, loss: 3.9383 +2024-07-24 14:01:37,846 - pyskl - INFO - Epoch [69][2000/3746] lr: 5.675e-02, eta: 2 days, 19:30:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5733, loss_cls: 3.9104, loss: 3.9104 +2024-07-24 14:02:59,649 - pyskl - INFO - Epoch [69][2100/3746] lr: 5.672e-02, eta: 2 days, 19:29:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5719, loss_cls: 3.9232, loss: 3.9232 +2024-07-24 14:04:20,917 - pyskl - INFO - Epoch [69][2200/3746] lr: 5.670e-02, eta: 2 days, 19:28:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5639, loss_cls: 3.9626, loss: 3.9626 +2024-07-24 14:05:42,269 - pyskl - INFO - Epoch [69][2300/3746] lr: 5.667e-02, eta: 2 days, 19:26:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5533, loss_cls: 4.0060, loss: 4.0060 +2024-07-24 14:07:03,792 - pyskl - INFO - Epoch [69][2400/3746] lr: 5.664e-02, eta: 2 days, 19:25:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5552, loss_cls: 3.9885, loss: 3.9885 +2024-07-24 14:08:25,503 - pyskl - INFO - Epoch [69][2500/3746] lr: 5.661e-02, eta: 2 days, 19:24:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5700, loss_cls: 3.9571, loss: 3.9571 +2024-07-24 14:09:47,027 - pyskl - INFO - Epoch [69][2600/3746] lr: 5.658e-02, eta: 2 days, 19:23:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5684, loss_cls: 3.9393, loss: 3.9393 +2024-07-24 14:11:08,422 - pyskl - INFO - Epoch [69][2700/3746] lr: 5.656e-02, eta: 2 days, 19:21:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5736, loss_cls: 3.8971, loss: 3.8971 +2024-07-24 14:12:29,821 - pyskl - INFO - Epoch [69][2800/3746] lr: 5.653e-02, eta: 2 days, 19:20:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5714, loss_cls: 3.9274, loss: 3.9274 +2024-07-24 14:13:51,084 - pyskl - INFO - Epoch [69][2900/3746] lr: 5.650e-02, eta: 2 days, 19:19:14, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5697, loss_cls: 3.9358, loss: 3.9358 +2024-07-24 14:15:12,986 - pyskl - INFO - Epoch [69][3000/3746] lr: 5.647e-02, eta: 2 days, 19:17:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5781, loss_cls: 3.8888, loss: 3.8888 +2024-07-24 14:16:34,239 - pyskl - INFO - Epoch [69][3100/3746] lr: 5.645e-02, eta: 2 days, 19:16:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5683, loss_cls: 3.9485, loss: 3.9485 +2024-07-24 14:17:55,889 - pyskl - INFO - Epoch [69][3200/3746] lr: 5.642e-02, eta: 2 days, 19:15:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5727, loss_cls: 3.9417, loss: 3.9417 +2024-07-24 14:19:17,488 - pyskl - INFO - Epoch [69][3300/3746] lr: 5.639e-02, eta: 2 days, 19:14:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5753, loss_cls: 3.8944, loss: 3.8944 +2024-07-24 14:20:39,254 - pyskl - INFO - Epoch [69][3400/3746] lr: 5.636e-02, eta: 2 days, 19:12:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5645, loss_cls: 3.9875, loss: 3.9875 +2024-07-24 14:22:00,802 - pyskl - INFO - Epoch [69][3500/3746] lr: 5.634e-02, eta: 2 days, 19:11:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5727, loss_cls: 3.9256, loss: 3.9256 +2024-07-24 14:23:22,956 - pyskl - INFO - Epoch [69][3600/3746] lr: 5.631e-02, eta: 2 days, 19:10:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5681, loss_cls: 3.9640, loss: 3.9640 +2024-07-24 14:24:44,405 - pyskl - INFO - Epoch [69][3700/3746] lr: 5.628e-02, eta: 2 days, 19:08:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5608, loss_cls: 3.9570, loss: 3.9570 +2024-07-24 14:25:23,992 - pyskl - INFO - Saving checkpoint at 69 epochs +2024-07-24 14:27:15,821 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 14:27:16,487 - pyskl - INFO - +top1_acc 0.2353 +top5_acc 0.4754 +2024-07-24 14:27:16,487 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 14:27:16,528 - pyskl - INFO - +mean_acc 0.2350 +2024-07-24 14:27:16,540 - pyskl - INFO - Epoch(val) [69][309] top1_acc: 0.2353, top5_acc: 0.4754, mean_class_accuracy: 0.2350 +2024-07-24 14:31:10,941 - pyskl - INFO - Epoch [70][100/3746] lr: 5.624e-02, eta: 2 days, 19:09:17, time: 2.344, data_time: 1.360, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5791, loss_cls: 3.8840, loss: 3.8840 +2024-07-24 14:32:32,971 - pyskl - INFO - Epoch [70][200/3746] lr: 5.621e-02, eta: 2 days, 19:08:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5733, loss_cls: 3.8892, loss: 3.8892 +2024-07-24 14:33:54,970 - pyskl - INFO - Epoch [70][300/3746] lr: 5.618e-02, eta: 2 days, 19:06:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5761, loss_cls: 3.8940, loss: 3.8940 +2024-07-24 14:35:16,656 - pyskl - INFO - Epoch [70][400/3746] lr: 5.616e-02, eta: 2 days, 19:05:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5686, loss_cls: 3.9432, loss: 3.9432 +2024-07-24 14:36:38,001 - pyskl - INFO - Epoch [70][500/3746] lr: 5.613e-02, eta: 2 days, 19:04:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5733, loss_cls: 3.9053, loss: 3.9053 +2024-07-24 14:37:59,738 - pyskl - INFO - Epoch [70][600/3746] lr: 5.610e-02, eta: 2 days, 19:02:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5756, loss_cls: 3.8811, loss: 3.8811 +2024-07-24 14:39:21,364 - pyskl - INFO - Epoch [70][700/3746] lr: 5.607e-02, eta: 2 days, 19:01:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5733, loss_cls: 3.9201, loss: 3.9201 +2024-07-24 14:40:44,212 - pyskl - INFO - Epoch [70][800/3746] lr: 5.605e-02, eta: 2 days, 19:00:17, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5805, loss_cls: 3.8689, loss: 3.8689 +2024-07-24 14:42:05,420 - pyskl - INFO - Epoch [70][900/3746] lr: 5.602e-02, eta: 2 days, 18:59:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5797, loss_cls: 3.8750, loss: 3.8750 +2024-07-24 14:43:27,172 - pyskl - INFO - Epoch [70][1000/3746] lr: 5.599e-02, eta: 2 days, 18:57:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5731, loss_cls: 3.9072, loss: 3.9072 +2024-07-24 14:44:48,794 - pyskl - INFO - Epoch [70][1100/3746] lr: 5.596e-02, eta: 2 days, 18:56:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5633, loss_cls: 3.9662, loss: 3.9662 +2024-07-24 14:46:10,864 - pyskl - INFO - Epoch [70][1200/3746] lr: 5.593e-02, eta: 2 days, 18:55:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5720, loss_cls: 3.8710, loss: 3.8710 +2024-07-24 14:47:33,496 - pyskl - INFO - Epoch [70][1300/3746] lr: 5.591e-02, eta: 2 days, 18:53:51, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5702, loss_cls: 3.9254, loss: 3.9254 +2024-07-24 14:48:55,926 - pyskl - INFO - Epoch [70][1400/3746] lr: 5.588e-02, eta: 2 days, 18:52:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5764, loss_cls: 3.8991, loss: 3.8991 +2024-07-24 14:50:17,861 - pyskl - INFO - Epoch [70][1500/3746] lr: 5.585e-02, eta: 2 days, 18:51:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5622, loss_cls: 3.9605, loss: 3.9605 +2024-07-24 14:51:41,159 - pyskl - INFO - Epoch [70][1600/3746] lr: 5.582e-02, eta: 2 days, 18:50:02, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5702, loss_cls: 3.9176, loss: 3.9176 +2024-07-24 14:53:03,140 - pyskl - INFO - Epoch [70][1700/3746] lr: 5.580e-02, eta: 2 days, 18:48:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5709, loss_cls: 3.9137, loss: 3.9137 +2024-07-24 14:54:25,287 - pyskl - INFO - Epoch [70][1800/3746] lr: 5.577e-02, eta: 2 days, 18:47:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5783, loss_cls: 3.9045, loss: 3.9045 +2024-07-24 14:55:47,049 - pyskl - INFO - Epoch [70][1900/3746] lr: 5.574e-02, eta: 2 days, 18:46:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5653, loss_cls: 3.9024, loss: 3.9024 +2024-07-24 14:57:08,716 - pyskl - INFO - Epoch [70][2000/3746] lr: 5.571e-02, eta: 2 days, 18:44:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5734, loss_cls: 3.9165, loss: 3.9165 +2024-07-24 14:58:30,797 - pyskl - INFO - Epoch [70][2100/3746] lr: 5.568e-02, eta: 2 days, 18:43:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5683, loss_cls: 3.9440, loss: 3.9440 +2024-07-24 14:59:52,682 - pyskl - INFO - Epoch [70][2200/3746] lr: 5.566e-02, eta: 2 days, 18:42:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5686, loss_cls: 3.9224, loss: 3.9224 +2024-07-24 15:01:14,369 - pyskl - INFO - Epoch [70][2300/3746] lr: 5.563e-02, eta: 2 days, 18:41:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5711, loss_cls: 3.9265, loss: 3.9265 +2024-07-24 15:02:36,230 - pyskl - INFO - Epoch [70][2400/3746] lr: 5.560e-02, eta: 2 days, 18:39:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5806, loss_cls: 3.8594, loss: 3.8594 +2024-07-24 15:03:58,096 - pyskl - INFO - Epoch [70][2500/3746] lr: 5.557e-02, eta: 2 days, 18:38:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5727, loss_cls: 3.8778, loss: 3.8778 +2024-07-24 15:05:19,426 - pyskl - INFO - Epoch [70][2600/3746] lr: 5.555e-02, eta: 2 days, 18:37:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5722, loss_cls: 3.8858, loss: 3.8858 +2024-07-24 15:06:41,049 - pyskl - INFO - Epoch [70][2700/3746] lr: 5.552e-02, eta: 2 days, 18:35:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5652, loss_cls: 3.9584, loss: 3.9584 +2024-07-24 15:08:02,942 - pyskl - INFO - Epoch [70][2800/3746] lr: 5.549e-02, eta: 2 days, 18:34:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5575, loss_cls: 3.9725, loss: 3.9725 +2024-07-24 15:09:24,861 - pyskl - INFO - Epoch [70][2900/3746] lr: 5.546e-02, eta: 2 days, 18:33:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5653, loss_cls: 3.9305, loss: 3.9305 +2024-07-24 15:10:46,428 - pyskl - INFO - Epoch [70][3000/3746] lr: 5.543e-02, eta: 2 days, 18:32:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5711, loss_cls: 3.9259, loss: 3.9259 +2024-07-24 15:12:08,135 - pyskl - INFO - Epoch [70][3100/3746] lr: 5.541e-02, eta: 2 days, 18:30:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5695, loss_cls: 3.9428, loss: 3.9428 +2024-07-24 15:13:29,994 - pyskl - INFO - Epoch [70][3200/3746] lr: 5.538e-02, eta: 2 days, 18:29:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5684, loss_cls: 3.9267, loss: 3.9267 +2024-07-24 15:14:51,615 - pyskl - INFO - Epoch [70][3300/3746] lr: 5.535e-02, eta: 2 days, 18:28:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5611, loss_cls: 3.9259, loss: 3.9259 +2024-07-24 15:16:13,159 - pyskl - INFO - Epoch [70][3400/3746] lr: 5.532e-02, eta: 2 days, 18:26:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5620, loss_cls: 3.9620, loss: 3.9620 +2024-07-24 15:17:34,726 - pyskl - INFO - Epoch [70][3500/3746] lr: 5.530e-02, eta: 2 days, 18:25:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5631, loss_cls: 3.9671, loss: 3.9671 +2024-07-24 15:18:56,226 - pyskl - INFO - Epoch [70][3600/3746] lr: 5.527e-02, eta: 2 days, 18:24:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5661, loss_cls: 3.9383, loss: 3.9383 +2024-07-24 15:20:17,521 - pyskl - INFO - Epoch [70][3700/3746] lr: 5.524e-02, eta: 2 days, 18:22:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5755, loss_cls: 3.9193, loss: 3.9193 +2024-07-24 15:20:57,077 - pyskl - INFO - Saving checkpoint at 70 epochs +2024-07-24 15:22:50,279 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 15:22:50,951 - pyskl - INFO - +top1_acc 0.2566 +top5_acc 0.5032 +2024-07-24 15:22:50,952 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 15:22:50,994 - pyskl - INFO - +mean_acc 0.2562 +2024-07-24 15:22:50,999 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_66.pth was removed +2024-07-24 15:22:51,233 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_70.pth. +2024-07-24 15:22:51,234 - pyskl - INFO - Best top1_acc is 0.2566 at 70 epoch. +2024-07-24 15:22:51,250 - pyskl - INFO - Epoch(val) [70][309] top1_acc: 0.2566, top5_acc: 0.5032, mean_class_accuracy: 0.2562 +2024-07-24 15:26:45,597 - pyskl - INFO - Epoch [71][100/3746] lr: 5.520e-02, eta: 2 days, 18:23:15, time: 2.343, data_time: 1.360, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5625, loss_cls: 3.9464, loss: 3.9464 +2024-07-24 15:28:08,548 - pyskl - INFO - Epoch [71][200/3746] lr: 5.517e-02, eta: 2 days, 18:21:59, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5800, loss_cls: 3.8745, loss: 3.8745 +2024-07-24 15:29:30,865 - pyskl - INFO - Epoch [71][300/3746] lr: 5.514e-02, eta: 2 days, 18:20:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5766, loss_cls: 3.8760, loss: 3.8760 +2024-07-24 15:30:52,345 - pyskl - INFO - Epoch [71][400/3746] lr: 5.512e-02, eta: 2 days, 18:19:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5717, loss_cls: 3.8844, loss: 3.8844 +2024-07-24 15:32:14,056 - pyskl - INFO - Epoch [71][500/3746] lr: 5.509e-02, eta: 2 days, 18:18:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5752, loss_cls: 3.8994, loss: 3.8994 +2024-07-24 15:33:35,701 - pyskl - INFO - Epoch [71][600/3746] lr: 5.506e-02, eta: 2 days, 18:16:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5667, loss_cls: 3.9314, loss: 3.9314 +2024-07-24 15:34:57,734 - pyskl - INFO - Epoch [71][700/3746] lr: 5.503e-02, eta: 2 days, 18:15:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5728, loss_cls: 3.9038, loss: 3.9038 +2024-07-24 15:36:19,783 - pyskl - INFO - Epoch [71][800/3746] lr: 5.500e-02, eta: 2 days, 18:14:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5814, loss_cls: 3.8823, loss: 3.8823 +2024-07-24 15:37:41,730 - pyskl - INFO - Epoch [71][900/3746] lr: 5.498e-02, eta: 2 days, 18:12:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5748, loss_cls: 3.9231, loss: 3.9231 +2024-07-24 15:39:04,308 - pyskl - INFO - Epoch [71][1000/3746] lr: 5.495e-02, eta: 2 days, 18:11:40, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5816, loss_cls: 3.8894, loss: 3.8894 +2024-07-24 15:40:26,648 - pyskl - INFO - Epoch [71][1100/3746] lr: 5.492e-02, eta: 2 days, 18:10:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5717, loss_cls: 3.9160, loss: 3.9160 +2024-07-24 15:41:48,951 - pyskl - INFO - Epoch [71][1200/3746] lr: 5.489e-02, eta: 2 days, 18:09:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5789, loss_cls: 3.8811, loss: 3.8811 +2024-07-24 15:43:10,793 - pyskl - INFO - Epoch [71][1300/3746] lr: 5.487e-02, eta: 2 days, 18:07:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5753, loss_cls: 3.8747, loss: 3.8747 +2024-07-24 15:44:32,911 - pyskl - INFO - Epoch [71][1400/3746] lr: 5.484e-02, eta: 2 days, 18:06:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5734, loss_cls: 3.9190, loss: 3.9190 +2024-07-24 15:45:54,645 - pyskl - INFO - Epoch [71][1500/3746] lr: 5.481e-02, eta: 2 days, 18:05:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5691, loss_cls: 3.9080, loss: 3.9080 +2024-07-24 15:47:17,622 - pyskl - INFO - Epoch [71][1600/3746] lr: 5.478e-02, eta: 2 days, 18:03:58, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5703, loss_cls: 3.9029, loss: 3.9029 +2024-07-24 15:48:40,308 - pyskl - INFO - Epoch [71][1700/3746] lr: 5.475e-02, eta: 2 days, 18:02:42, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5708, loss_cls: 3.9602, loss: 3.9602 +2024-07-24 15:50:02,649 - pyskl - INFO - Epoch [71][1800/3746] lr: 5.473e-02, eta: 2 days, 18:01:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5748, loss_cls: 3.9073, loss: 3.9073 +2024-07-24 15:51:25,022 - pyskl - INFO - Epoch [71][1900/3746] lr: 5.470e-02, eta: 2 days, 18:00:08, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5825, loss_cls: 3.8743, loss: 3.8743 +2024-07-24 15:52:46,592 - pyskl - INFO - Epoch [71][2000/3746] lr: 5.467e-02, eta: 2 days, 17:58:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5698, loss_cls: 3.9259, loss: 3.9259 +2024-07-24 15:54:08,193 - pyskl - INFO - Epoch [71][2100/3746] lr: 5.464e-02, eta: 2 days, 17:57:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5713, loss_cls: 3.9114, loss: 3.9114 +2024-07-24 15:55:29,634 - pyskl - INFO - Epoch [71][2200/3746] lr: 5.461e-02, eta: 2 days, 17:56:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5613, loss_cls: 3.9604, loss: 3.9604 +2024-07-24 15:56:51,375 - pyskl - INFO - Epoch [71][2300/3746] lr: 5.459e-02, eta: 2 days, 17:54:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5730, loss_cls: 3.9423, loss: 3.9423 +2024-07-24 15:58:13,724 - pyskl - INFO - Epoch [71][2400/3746] lr: 5.456e-02, eta: 2 days, 17:53:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5723, loss_cls: 3.8914, loss: 3.8914 +2024-07-24 15:59:35,371 - pyskl - INFO - Epoch [71][2500/3746] lr: 5.453e-02, eta: 2 days, 17:52:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5656, loss_cls: 3.9368, loss: 3.9368 +2024-07-24 16:00:57,388 - pyskl - INFO - Epoch [71][2600/3746] lr: 5.450e-02, eta: 2 days, 17:51:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5778, loss_cls: 3.9129, loss: 3.9129 +2024-07-24 16:02:18,397 - pyskl - INFO - Epoch [71][2700/3746] lr: 5.448e-02, eta: 2 days, 17:49:46, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5678, loss_cls: 3.8899, loss: 3.8899 +2024-07-24 16:03:40,445 - pyskl - INFO - Epoch [71][2800/3746] lr: 5.445e-02, eta: 2 days, 17:48:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5639, loss_cls: 3.9598, loss: 3.9598 +2024-07-24 16:05:02,263 - pyskl - INFO - Epoch [71][2900/3746] lr: 5.442e-02, eta: 2 days, 17:47:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5733, loss_cls: 3.9046, loss: 3.9046 +2024-07-24 16:06:24,065 - pyskl - INFO - Epoch [71][3000/3746] lr: 5.439e-02, eta: 2 days, 17:45:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5833, loss_cls: 3.8812, loss: 3.8812 +2024-07-24 16:07:45,318 - pyskl - INFO - Epoch [71][3100/3746] lr: 5.436e-02, eta: 2 days, 17:44:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5653, loss_cls: 3.9453, loss: 3.9453 +2024-07-24 16:09:06,775 - pyskl - INFO - Epoch [71][3200/3746] lr: 5.434e-02, eta: 2 days, 17:43:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5677, loss_cls: 3.9228, loss: 3.9228 +2024-07-24 16:10:28,178 - pyskl - INFO - Epoch [71][3300/3746] lr: 5.431e-02, eta: 2 days, 17:42:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5719, loss_cls: 3.9099, loss: 3.9099 +2024-07-24 16:11:49,794 - pyskl - INFO - Epoch [71][3400/3746] lr: 5.428e-02, eta: 2 days, 17:40:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5648, loss_cls: 3.9525, loss: 3.9525 +2024-07-24 16:13:11,074 - pyskl - INFO - Epoch [71][3500/3746] lr: 5.425e-02, eta: 2 days, 17:39:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5680, loss_cls: 3.9226, loss: 3.9226 +2024-07-24 16:14:32,417 - pyskl - INFO - Epoch [71][3600/3746] lr: 5.422e-02, eta: 2 days, 17:38:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5744, loss_cls: 3.9025, loss: 3.9025 +2024-07-24 16:15:53,833 - pyskl - INFO - Epoch [71][3700/3746] lr: 5.420e-02, eta: 2 days, 17:36:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5678, loss_cls: 3.9388, loss: 3.9388 +2024-07-24 16:16:33,556 - pyskl - INFO - Saving checkpoint at 71 epochs +2024-07-24 16:18:25,707 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 16:18:26,385 - pyskl - INFO - +top1_acc 0.2411 +top5_acc 0.4862 +2024-07-24 16:18:26,385 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 16:18:26,438 - pyskl - INFO - +mean_acc 0.2411 +2024-07-24 16:18:26,453 - pyskl - INFO - Epoch(val) [71][309] top1_acc: 0.2411, top5_acc: 0.4862, mean_class_accuracy: 0.2411 +2024-07-24 16:22:19,656 - pyskl - INFO - Epoch [72][100/3746] lr: 5.416e-02, eta: 2 days, 17:37:01, time: 2.332, data_time: 1.355, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5889, loss_cls: 3.8575, loss: 3.8575 +2024-07-24 16:23:41,384 - pyskl - INFO - Epoch [72][200/3746] lr: 5.413e-02, eta: 2 days, 17:35:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5767, loss_cls: 3.8942, loss: 3.8942 +2024-07-24 16:25:03,037 - pyskl - INFO - Epoch [72][300/3746] lr: 5.410e-02, eta: 2 days, 17:34:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5830, loss_cls: 3.8519, loss: 3.8519 +2024-07-24 16:26:24,578 - pyskl - INFO - Epoch [72][400/3746] lr: 5.407e-02, eta: 2 days, 17:33:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5886, loss_cls: 3.8518, loss: 3.8518 +2024-07-24 16:27:45,982 - pyskl - INFO - Epoch [72][500/3746] lr: 5.404e-02, eta: 2 days, 17:31:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5705, loss_cls: 3.8996, loss: 3.8996 +2024-07-24 16:29:07,495 - pyskl - INFO - Epoch [72][600/3746] lr: 5.402e-02, eta: 2 days, 17:30:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5791, loss_cls: 3.8893, loss: 3.8893 +2024-07-24 16:30:29,621 - pyskl - INFO - Epoch [72][700/3746] lr: 5.399e-02, eta: 2 days, 17:29:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5687, loss_cls: 3.9190, loss: 3.9190 +2024-07-24 16:31:51,708 - pyskl - INFO - Epoch [72][800/3746] lr: 5.396e-02, eta: 2 days, 17:27:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5755, loss_cls: 3.8865, loss: 3.8865 +2024-07-24 16:33:13,693 - pyskl - INFO - Epoch [72][900/3746] lr: 5.393e-02, eta: 2 days, 17:26:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5822, loss_cls: 3.8295, loss: 3.8295 +2024-07-24 16:34:35,678 - pyskl - INFO - Epoch [72][1000/3746] lr: 5.391e-02, eta: 2 days, 17:25:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5766, loss_cls: 3.9031, loss: 3.9031 +2024-07-24 16:35:57,875 - pyskl - INFO - Epoch [72][1100/3746] lr: 5.388e-02, eta: 2 days, 17:24:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5694, loss_cls: 3.9016, loss: 3.9016 +2024-07-24 16:37:19,469 - pyskl - INFO - Epoch [72][1200/3746] lr: 5.385e-02, eta: 2 days, 17:22:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5695, loss_cls: 3.8950, loss: 3.8950 +2024-07-24 16:38:41,984 - pyskl - INFO - Epoch [72][1300/3746] lr: 5.382e-02, eta: 2 days, 17:21:29, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5817, loss_cls: 3.8886, loss: 3.8886 +2024-07-24 16:40:04,361 - pyskl - INFO - Epoch [72][1400/3746] lr: 5.379e-02, eta: 2 days, 17:20:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5680, loss_cls: 3.9397, loss: 3.9397 +2024-07-24 16:41:26,218 - pyskl - INFO - Epoch [72][1500/3746] lr: 5.377e-02, eta: 2 days, 17:18:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5786, loss_cls: 3.8788, loss: 3.8788 +2024-07-24 16:42:49,094 - pyskl - INFO - Epoch [72][1600/3746] lr: 5.374e-02, eta: 2 days, 17:17:38, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5716, loss_cls: 3.8916, loss: 3.8916 +2024-07-24 16:44:11,180 - pyskl - INFO - Epoch [72][1700/3746] lr: 5.371e-02, eta: 2 days, 17:16:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5792, loss_cls: 3.9009, loss: 3.9009 +2024-07-24 16:45:33,625 - pyskl - INFO - Epoch [72][1800/3746] lr: 5.368e-02, eta: 2 days, 17:15:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5587, loss_cls: 3.9722, loss: 3.9722 +2024-07-24 16:46:55,172 - pyskl - INFO - Epoch [72][1900/3746] lr: 5.365e-02, eta: 2 days, 17:13:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5736, loss_cls: 3.9034, loss: 3.9034 +2024-07-24 16:48:16,859 - pyskl - INFO - Epoch [72][2000/3746] lr: 5.363e-02, eta: 2 days, 17:12:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5825, loss_cls: 3.8908, loss: 3.8908 +2024-07-24 16:49:38,724 - pyskl - INFO - Epoch [72][2100/3746] lr: 5.360e-02, eta: 2 days, 17:11:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5837, loss_cls: 3.8401, loss: 3.8401 +2024-07-24 16:51:00,390 - pyskl - INFO - Epoch [72][2200/3746] lr: 5.357e-02, eta: 2 days, 17:09:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5716, loss_cls: 3.9317, loss: 3.9317 +2024-07-24 16:52:21,921 - pyskl - INFO - Epoch [72][2300/3746] lr: 5.354e-02, eta: 2 days, 17:08:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5734, loss_cls: 3.9163, loss: 3.9163 +2024-07-24 16:53:44,088 - pyskl - INFO - Epoch [72][2400/3746] lr: 5.352e-02, eta: 2 days, 17:07:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5964, loss_cls: 3.8071, loss: 3.8071 +2024-07-24 16:55:05,587 - pyskl - INFO - Epoch [72][2500/3746] lr: 5.349e-02, eta: 2 days, 17:05:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5731, loss_cls: 3.9039, loss: 3.9039 +2024-07-24 16:56:27,453 - pyskl - INFO - Epoch [72][2600/3746] lr: 5.346e-02, eta: 2 days, 17:04:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5759, loss_cls: 3.9017, loss: 3.9017 +2024-07-24 16:57:49,075 - pyskl - INFO - Epoch [72][2700/3746] lr: 5.343e-02, eta: 2 days, 17:03:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5748, loss_cls: 3.9098, loss: 3.9098 +2024-07-24 16:59:10,560 - pyskl - INFO - Epoch [72][2800/3746] lr: 5.340e-02, eta: 2 days, 17:02:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5752, loss_cls: 3.9239, loss: 3.9239 +2024-07-24 17:00:32,270 - pyskl - INFO - Epoch [72][2900/3746] lr: 5.338e-02, eta: 2 days, 17:00:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5784, loss_cls: 3.9111, loss: 3.9111 +2024-07-24 17:01:53,834 - pyskl - INFO - Epoch [72][3000/3746] lr: 5.335e-02, eta: 2 days, 16:59:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5787, loss_cls: 3.8892, loss: 3.8892 +2024-07-24 17:03:15,745 - pyskl - INFO - Epoch [72][3100/3746] lr: 5.332e-02, eta: 2 days, 16:58:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5709, loss_cls: 3.9107, loss: 3.9107 +2024-07-24 17:04:37,245 - pyskl - INFO - Epoch [72][3200/3746] lr: 5.329e-02, eta: 2 days, 16:56:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5717, loss_cls: 3.9131, loss: 3.9131 +2024-07-24 17:05:59,024 - pyskl - INFO - Epoch [72][3300/3746] lr: 5.326e-02, eta: 2 days, 16:55:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5770, loss_cls: 3.9071, loss: 3.9071 +2024-07-24 17:07:20,582 - pyskl - INFO - Epoch [72][3400/3746] lr: 5.324e-02, eta: 2 days, 16:54:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5792, loss_cls: 3.8918, loss: 3.8918 +2024-07-24 17:08:42,396 - pyskl - INFO - Epoch [72][3500/3746] lr: 5.321e-02, eta: 2 days, 16:53:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5570, loss_cls: 3.9953, loss: 3.9953 +2024-07-24 17:10:03,629 - pyskl - INFO - Epoch [72][3600/3746] lr: 5.318e-02, eta: 2 days, 16:51:41, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5764, loss_cls: 3.8936, loss: 3.8936 +2024-07-24 17:11:25,031 - pyskl - INFO - Epoch [72][3700/3746] lr: 5.315e-02, eta: 2 days, 16:50:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5672, loss_cls: 3.9184, loss: 3.9184 +2024-07-24 17:12:04,779 - pyskl - INFO - Saving checkpoint at 72 epochs +2024-07-24 17:13:55,604 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 17:13:56,276 - pyskl - INFO - +top1_acc 0.2274 +top5_acc 0.4632 +2024-07-24 17:13:56,276 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 17:13:56,317 - pyskl - INFO - +mean_acc 0.2272 +2024-07-24 17:13:56,329 - pyskl - INFO - Epoch(val) [72][309] top1_acc: 0.2274, top5_acc: 0.4632, mean_class_accuracy: 0.2272 +2024-07-24 17:17:48,728 - pyskl - INFO - Epoch [73][100/3746] lr: 5.311e-02, eta: 2 days, 16:50:32, time: 2.324, data_time: 1.346, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5825, loss_cls: 3.8506, loss: 3.8506 +2024-07-24 17:19:10,693 - pyskl - INFO - Epoch [73][200/3746] lr: 5.308e-02, eta: 2 days, 16:49:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5844, loss_cls: 3.8457, loss: 3.8457 +2024-07-24 17:20:32,224 - pyskl - INFO - Epoch [73][300/3746] lr: 5.306e-02, eta: 2 days, 16:47:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5750, loss_cls: 3.8688, loss: 3.8688 +2024-07-24 17:21:53,852 - pyskl - INFO - Epoch [73][400/3746] lr: 5.303e-02, eta: 2 days, 16:46:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5836, loss_cls: 3.8415, loss: 3.8415 +2024-07-24 17:23:15,267 - pyskl - INFO - Epoch [73][500/3746] lr: 5.300e-02, eta: 2 days, 16:45:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5697, loss_cls: 3.9024, loss: 3.9024 +2024-07-24 17:24:37,553 - pyskl - INFO - Epoch [73][600/3746] lr: 5.297e-02, eta: 2 days, 16:44:02, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5803, loss_cls: 3.8652, loss: 3.8652 +2024-07-24 17:25:59,475 - pyskl - INFO - Epoch [73][700/3746] lr: 5.294e-02, eta: 2 days, 16:42:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5736, loss_cls: 3.8704, loss: 3.8704 +2024-07-24 17:27:21,637 - pyskl - INFO - Epoch [73][800/3746] lr: 5.292e-02, eta: 2 days, 16:41:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5769, loss_cls: 3.8904, loss: 3.8904 +2024-07-24 17:28:43,495 - pyskl - INFO - Epoch [73][900/3746] lr: 5.289e-02, eta: 2 days, 16:40:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5759, loss_cls: 3.8935, loss: 3.8935 +2024-07-24 17:30:05,847 - pyskl - INFO - Epoch [73][1000/3746] lr: 5.286e-02, eta: 2 days, 16:38:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5780, loss_cls: 3.9088, loss: 3.9088 +2024-07-24 17:31:27,949 - pyskl - INFO - Epoch [73][1100/3746] lr: 5.283e-02, eta: 2 days, 16:37:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5913, loss_cls: 3.8342, loss: 3.8342 +2024-07-24 17:32:49,691 - pyskl - INFO - Epoch [73][1200/3746] lr: 5.280e-02, eta: 2 days, 16:36:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5781, loss_cls: 3.8632, loss: 3.8632 +2024-07-24 17:34:11,237 - pyskl - INFO - Epoch [73][1300/3746] lr: 5.278e-02, eta: 2 days, 16:34:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5817, loss_cls: 3.8836, loss: 3.8836 +2024-07-24 17:35:33,014 - pyskl - INFO - Epoch [73][1400/3746] lr: 5.275e-02, eta: 2 days, 16:33:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5733, loss_cls: 3.8852, loss: 3.8852 +2024-07-24 17:36:55,140 - pyskl - INFO - Epoch [73][1500/3746] lr: 5.272e-02, eta: 2 days, 16:32:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5677, loss_cls: 3.9404, loss: 3.9404 +2024-07-24 17:38:17,549 - pyskl - INFO - Epoch [73][1600/3746] lr: 5.269e-02, eta: 2 days, 16:31:05, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5711, loss_cls: 3.8772, loss: 3.8772 +2024-07-24 17:39:40,409 - pyskl - INFO - Epoch [73][1700/3746] lr: 5.267e-02, eta: 2 days, 16:29:48, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5697, loss_cls: 3.8900, loss: 3.8900 +2024-07-24 17:41:02,753 - pyskl - INFO - Epoch [73][1800/3746] lr: 5.264e-02, eta: 2 days, 16:28:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5713, loss_cls: 3.9287, loss: 3.9287 +2024-07-24 17:42:24,495 - pyskl - INFO - Epoch [73][1900/3746] lr: 5.261e-02, eta: 2 days, 16:27:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5711, loss_cls: 3.8951, loss: 3.8951 +2024-07-24 17:43:45,949 - pyskl - INFO - Epoch [73][2000/3746] lr: 5.258e-02, eta: 2 days, 16:25:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5714, loss_cls: 3.9215, loss: 3.9215 +2024-07-24 17:45:07,384 - pyskl - INFO - Epoch [73][2100/3746] lr: 5.255e-02, eta: 2 days, 16:24:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5730, loss_cls: 3.9307, loss: 3.9307 +2024-07-24 17:46:29,215 - pyskl - INFO - Epoch [73][2200/3746] lr: 5.253e-02, eta: 2 days, 16:23:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5816, loss_cls: 3.8842, loss: 3.8842 +2024-07-24 17:47:51,032 - pyskl - INFO - Epoch [73][2300/3746] lr: 5.250e-02, eta: 2 days, 16:22:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5689, loss_cls: 3.8919, loss: 3.8919 +2024-07-24 17:49:12,607 - pyskl - INFO - Epoch [73][2400/3746] lr: 5.247e-02, eta: 2 days, 16:20:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5753, loss_cls: 3.9118, loss: 3.9118 +2024-07-24 17:50:34,464 - pyskl - INFO - Epoch [73][2500/3746] lr: 5.244e-02, eta: 2 days, 16:19:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5869, loss_cls: 3.8494, loss: 3.8494 +2024-07-24 17:51:55,703 - pyskl - INFO - Epoch [73][2600/3746] lr: 5.241e-02, eta: 2 days, 16:18:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5684, loss_cls: 3.9434, loss: 3.9434 +2024-07-24 17:53:17,558 - pyskl - INFO - Epoch [73][2700/3746] lr: 5.239e-02, eta: 2 days, 16:16:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5795, loss_cls: 3.8636, loss: 3.8636 +2024-07-24 17:54:38,644 - pyskl - INFO - Epoch [73][2800/3746] lr: 5.236e-02, eta: 2 days, 16:15:29, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5813, loss_cls: 3.8697, loss: 3.8697 +2024-07-24 17:56:00,097 - pyskl - INFO - Epoch [73][2900/3746] lr: 5.233e-02, eta: 2 days, 16:14:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5787, loss_cls: 3.8770, loss: 3.8770 +2024-07-24 17:57:21,897 - pyskl - INFO - Epoch [73][3000/3746] lr: 5.230e-02, eta: 2 days, 16:12:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5722, loss_cls: 3.9163, loss: 3.9163 +2024-07-24 17:58:43,531 - pyskl - INFO - Epoch [73][3100/3746] lr: 5.227e-02, eta: 2 days, 16:11:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5780, loss_cls: 3.8898, loss: 3.8898 +2024-07-24 18:00:05,476 - pyskl - INFO - Epoch [73][3200/3746] lr: 5.225e-02, eta: 2 days, 16:10:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5781, loss_cls: 3.9219, loss: 3.9219 +2024-07-24 18:01:27,506 - pyskl - INFO - Epoch [73][3300/3746] lr: 5.222e-02, eta: 2 days, 16:08:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5795, loss_cls: 3.9096, loss: 3.9096 +2024-07-24 18:02:49,139 - pyskl - INFO - Epoch [73][3400/3746] lr: 5.219e-02, eta: 2 days, 16:07:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5916, loss_cls: 3.8260, loss: 3.8260 +2024-07-24 18:04:10,809 - pyskl - INFO - Epoch [73][3500/3746] lr: 5.216e-02, eta: 2 days, 16:06:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5698, loss_cls: 3.8947, loss: 3.8947 +2024-07-24 18:05:32,293 - pyskl - INFO - Epoch [73][3600/3746] lr: 5.213e-02, eta: 2 days, 16:05:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5697, loss_cls: 3.9223, loss: 3.9223 +2024-07-24 18:06:54,191 - pyskl - INFO - Epoch [73][3700/3746] lr: 5.211e-02, eta: 2 days, 16:03:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5802, loss_cls: 3.8464, loss: 3.8464 +2024-07-24 18:07:33,768 - pyskl - INFO - Saving checkpoint at 73 epochs +2024-07-24 18:09:25,871 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 18:09:26,539 - pyskl - INFO - +top1_acc 0.2323 +top5_acc 0.4766 +2024-07-24 18:09:26,540 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 18:09:26,582 - pyskl - INFO - +mean_acc 0.2321 +2024-07-24 18:09:26,593 - pyskl - INFO - Epoch(val) [73][309] top1_acc: 0.2323, top5_acc: 0.4766, mean_class_accuracy: 0.2321 +2024-07-24 18:13:21,055 - pyskl - INFO - Epoch [74][100/3746] lr: 5.207e-02, eta: 2 days, 16:03:54, time: 2.345, data_time: 1.355, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5927, loss_cls: 3.8075, loss: 3.8075 +2024-07-24 18:14:43,449 - pyskl - INFO - Epoch [74][200/3746] lr: 5.204e-02, eta: 2 days, 16:02:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5833, loss_cls: 3.8462, loss: 3.8462 +2024-07-24 18:16:05,427 - pyskl - INFO - Epoch [74][300/3746] lr: 5.201e-02, eta: 2 days, 16:01:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5791, loss_cls: 3.8403, loss: 3.8403 +2024-07-24 18:17:27,217 - pyskl - INFO - Epoch [74][400/3746] lr: 5.198e-02, eta: 2 days, 16:00:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5844, loss_cls: 3.8562, loss: 3.8562 +2024-07-24 18:18:49,124 - pyskl - INFO - Epoch [74][500/3746] lr: 5.195e-02, eta: 2 days, 15:58:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5764, loss_cls: 3.8853, loss: 3.8853 +2024-07-24 18:20:10,976 - pyskl - INFO - Epoch [74][600/3746] lr: 5.193e-02, eta: 2 days, 15:57:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5869, loss_cls: 3.8407, loss: 3.8407 +2024-07-24 18:21:33,375 - pyskl - INFO - Epoch [74][700/3746] lr: 5.190e-02, eta: 2 days, 15:56:07, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5848, loss_cls: 3.8013, loss: 3.8013 +2024-07-24 18:22:55,661 - pyskl - INFO - Epoch [74][800/3746] lr: 5.187e-02, eta: 2 days, 15:54:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5800, loss_cls: 3.8510, loss: 3.8510 +2024-07-24 18:24:17,111 - pyskl - INFO - Epoch [74][900/3746] lr: 5.184e-02, eta: 2 days, 15:53:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5859, loss_cls: 3.8377, loss: 3.8377 +2024-07-24 18:25:39,063 - pyskl - INFO - Epoch [74][1000/3746] lr: 5.181e-02, eta: 2 days, 15:52:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5847, loss_cls: 3.8709, loss: 3.8709 +2024-07-24 18:27:00,809 - pyskl - INFO - Epoch [74][1100/3746] lr: 5.179e-02, eta: 2 days, 15:50:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5814, loss_cls: 3.8906, loss: 3.8906 +2024-07-24 18:28:22,276 - pyskl - INFO - Epoch [74][1200/3746] lr: 5.176e-02, eta: 2 days, 15:49:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5770, loss_cls: 3.8866, loss: 3.8866 +2024-07-24 18:29:43,825 - pyskl - INFO - Epoch [74][1300/3746] lr: 5.173e-02, eta: 2 days, 15:48:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5905, loss_cls: 3.8449, loss: 3.8449 +2024-07-24 18:31:05,803 - pyskl - INFO - Epoch [74][1400/3746] lr: 5.170e-02, eta: 2 days, 15:47:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5752, loss_cls: 3.8558, loss: 3.8558 +2024-07-24 18:32:27,827 - pyskl - INFO - Epoch [74][1500/3746] lr: 5.168e-02, eta: 2 days, 15:45:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5764, loss_cls: 3.8913, loss: 3.8913 +2024-07-24 18:33:50,171 - pyskl - INFO - Epoch [74][1600/3746] lr: 5.165e-02, eta: 2 days, 15:44:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5753, loss_cls: 3.9178, loss: 3.9178 +2024-07-24 18:35:13,060 - pyskl - INFO - Epoch [74][1700/3746] lr: 5.162e-02, eta: 2 days, 15:43:08, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5780, loss_cls: 3.8654, loss: 3.8654 +2024-07-24 18:36:34,813 - pyskl - INFO - Epoch [74][1800/3746] lr: 5.159e-02, eta: 2 days, 15:41:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5820, loss_cls: 3.8708, loss: 3.8708 +2024-07-24 18:37:56,525 - pyskl - INFO - Epoch [74][1900/3746] lr: 5.156e-02, eta: 2 days, 15:40:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5859, loss_cls: 3.8550, loss: 3.8550 +2024-07-24 18:39:18,404 - pyskl - INFO - Epoch [74][2000/3746] lr: 5.154e-02, eta: 2 days, 15:39:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5775, loss_cls: 3.8903, loss: 3.8903 +2024-07-24 18:40:40,114 - pyskl - INFO - Epoch [74][2100/3746] lr: 5.151e-02, eta: 2 days, 15:37:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5839, loss_cls: 3.8576, loss: 3.8576 +2024-07-24 18:42:01,395 - pyskl - INFO - Epoch [74][2200/3746] lr: 5.148e-02, eta: 2 days, 15:36:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5769, loss_cls: 3.9035, loss: 3.9035 +2024-07-24 18:43:23,049 - pyskl - INFO - Epoch [74][2300/3746] lr: 5.145e-02, eta: 2 days, 15:35:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5767, loss_cls: 3.8888, loss: 3.8888 +2024-07-24 18:44:44,955 - pyskl - INFO - Epoch [74][2400/3746] lr: 5.142e-02, eta: 2 days, 15:34:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5684, loss_cls: 3.9263, loss: 3.9263 +2024-07-24 18:46:06,511 - pyskl - INFO - Epoch [74][2500/3746] lr: 5.140e-02, eta: 2 days, 15:32:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5672, loss_cls: 3.9481, loss: 3.9481 +2024-07-24 18:47:27,898 - pyskl - INFO - Epoch [74][2600/3746] lr: 5.137e-02, eta: 2 days, 15:31:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5714, loss_cls: 3.8891, loss: 3.8891 +2024-07-24 18:48:49,616 - pyskl - INFO - Epoch [74][2700/3746] lr: 5.134e-02, eta: 2 days, 15:30:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5730, loss_cls: 3.9245, loss: 3.9245 +2024-07-24 18:50:11,309 - pyskl - INFO - Epoch [74][2800/3746] lr: 5.131e-02, eta: 2 days, 15:28:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5742, loss_cls: 3.8985, loss: 3.8985 +2024-07-24 18:51:32,920 - pyskl - INFO - Epoch [74][2900/3746] lr: 5.128e-02, eta: 2 days, 15:27:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5856, loss_cls: 3.8281, loss: 3.8281 +2024-07-24 18:52:54,166 - pyskl - INFO - Epoch [74][3000/3746] lr: 5.126e-02, eta: 2 days, 15:26:10, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5805, loss_cls: 3.8723, loss: 3.8723 +2024-07-24 18:54:15,859 - pyskl - INFO - Epoch [74][3100/3746] lr: 5.123e-02, eta: 2 days, 15:24:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5736, loss_cls: 3.9176, loss: 3.9176 +2024-07-24 18:55:37,615 - pyskl - INFO - Epoch [74][3200/3746] lr: 5.120e-02, eta: 2 days, 15:23:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5817, loss_cls: 3.8494, loss: 3.8494 +2024-07-24 18:56:59,077 - pyskl - INFO - Epoch [74][3300/3746] lr: 5.117e-02, eta: 2 days, 15:22:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5698, loss_cls: 3.9257, loss: 3.9257 +2024-07-24 18:58:20,175 - pyskl - INFO - Epoch [74][3400/3746] lr: 5.114e-02, eta: 2 days, 15:20:56, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5748, loss_cls: 3.8972, loss: 3.8972 +2024-07-24 18:59:41,637 - pyskl - INFO - Epoch [74][3500/3746] lr: 5.112e-02, eta: 2 days, 15:19:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5681, loss_cls: 3.8788, loss: 3.8788 +2024-07-24 19:01:02,979 - pyskl - INFO - Epoch [74][3600/3746] lr: 5.109e-02, eta: 2 days, 15:18:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5761, loss_cls: 3.8861, loss: 3.8861 +2024-07-24 19:02:24,562 - pyskl - INFO - Epoch [74][3700/3746] lr: 5.106e-02, eta: 2 days, 15:17:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5761, loss_cls: 3.8692, loss: 3.8692 +2024-07-24 19:03:04,260 - pyskl - INFO - Saving checkpoint at 74 epochs +2024-07-24 19:04:57,798 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 19:04:58,461 - pyskl - INFO - +top1_acc 0.2509 +top5_acc 0.4841 +2024-07-24 19:04:58,461 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 19:04:58,500 - pyskl - INFO - +mean_acc 0.2507 +2024-07-24 19:04:58,512 - pyskl - INFO - Epoch(val) [74][309] top1_acc: 0.2509, top5_acc: 0.4841, mean_class_accuracy: 0.2507 +2024-07-24 19:08:52,797 - pyskl - INFO - Epoch [75][100/3746] lr: 5.102e-02, eta: 2 days, 15:17:04, time: 2.343, data_time: 1.362, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5895, loss_cls: 3.8099, loss: 3.8099 +2024-07-24 19:10:14,789 - pyskl - INFO - Epoch [75][200/3746] lr: 5.099e-02, eta: 2 days, 15:15:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5988, loss_cls: 3.7732, loss: 3.7732 +2024-07-24 19:11:37,016 - pyskl - INFO - Epoch [75][300/3746] lr: 5.096e-02, eta: 2 days, 15:14:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5873, loss_cls: 3.8296, loss: 3.8296 +2024-07-24 19:12:58,528 - pyskl - INFO - Epoch [75][400/3746] lr: 5.094e-02, eta: 2 days, 15:13:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5886, loss_cls: 3.8264, loss: 3.8264 +2024-07-24 19:14:19,981 - pyskl - INFO - Epoch [75][500/3746] lr: 5.091e-02, eta: 2 days, 15:11:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5863, loss_cls: 3.8398, loss: 3.8398 +2024-07-24 19:15:41,656 - pyskl - INFO - Epoch [75][600/3746] lr: 5.088e-02, eta: 2 days, 15:10:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5700, loss_cls: 3.9023, loss: 3.9023 +2024-07-24 19:17:02,847 - pyskl - INFO - Epoch [75][700/3746] lr: 5.085e-02, eta: 2 days, 15:09:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5780, loss_cls: 3.8665, loss: 3.8665 +2024-07-24 19:18:24,782 - pyskl - INFO - Epoch [75][800/3746] lr: 5.082e-02, eta: 2 days, 15:07:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5827, loss_cls: 3.8458, loss: 3.8458 +2024-07-24 19:19:46,524 - pyskl - INFO - Epoch [75][900/3746] lr: 5.080e-02, eta: 2 days, 15:06:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5787, loss_cls: 3.8646, loss: 3.8646 +2024-07-24 19:21:08,621 - pyskl - INFO - Epoch [75][1000/3746] lr: 5.077e-02, eta: 2 days, 15:05:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5798, loss_cls: 3.8820, loss: 3.8820 +2024-07-24 19:22:30,632 - pyskl - INFO - Epoch [75][1100/3746] lr: 5.074e-02, eta: 2 days, 15:04:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5706, loss_cls: 3.8848, loss: 3.8848 +2024-07-24 19:23:52,056 - pyskl - INFO - Epoch [75][1200/3746] lr: 5.071e-02, eta: 2 days, 15:02:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5755, loss_cls: 3.8895, loss: 3.8895 +2024-07-24 19:25:13,848 - pyskl - INFO - Epoch [75][1300/3746] lr: 5.068e-02, eta: 2 days, 15:01:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5805, loss_cls: 3.8574, loss: 3.8574 +2024-07-24 19:26:35,849 - pyskl - INFO - Epoch [75][1400/3746] lr: 5.066e-02, eta: 2 days, 15:00:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5819, loss_cls: 3.8523, loss: 3.8523 +2024-07-24 19:27:58,026 - pyskl - INFO - Epoch [75][1500/3746] lr: 5.063e-02, eta: 2 days, 14:58:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5853, loss_cls: 3.8379, loss: 3.8379 +2024-07-24 19:29:20,520 - pyskl - INFO - Epoch [75][1600/3746] lr: 5.060e-02, eta: 2 days, 14:57:31, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5820, loss_cls: 3.8586, loss: 3.8586 +2024-07-24 19:30:43,493 - pyskl - INFO - Epoch [75][1700/3746] lr: 5.057e-02, eta: 2 days, 14:56:14, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5836, loss_cls: 3.8579, loss: 3.8579 +2024-07-24 19:32:06,107 - pyskl - INFO - Epoch [75][1800/3746] lr: 5.054e-02, eta: 2 days, 14:54:56, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5836, loss_cls: 3.8597, loss: 3.8597 +2024-07-24 19:33:28,113 - pyskl - INFO - Epoch [75][1900/3746] lr: 5.052e-02, eta: 2 days, 14:53:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5823, loss_cls: 3.8673, loss: 3.8673 +2024-07-24 19:34:49,971 - pyskl - INFO - Epoch [75][2000/3746] lr: 5.049e-02, eta: 2 days, 14:52:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5697, loss_cls: 3.9187, loss: 3.9187 +2024-07-24 19:36:11,773 - pyskl - INFO - Epoch [75][2100/3746] lr: 5.046e-02, eta: 2 days, 14:51:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5761, loss_cls: 3.8763, loss: 3.8763 +2024-07-24 19:37:33,756 - pyskl - INFO - Epoch [75][2200/3746] lr: 5.043e-02, eta: 2 days, 14:49:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5658, loss_cls: 3.9067, loss: 3.9067 +2024-07-24 19:38:55,218 - pyskl - INFO - Epoch [75][2300/3746] lr: 5.040e-02, eta: 2 days, 14:48:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5842, loss_cls: 3.8538, loss: 3.8538 +2024-07-24 19:40:16,857 - pyskl - INFO - Epoch [75][2400/3746] lr: 5.038e-02, eta: 2 days, 14:47:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5625, loss_cls: 3.9295, loss: 3.9295 +2024-07-24 19:41:38,715 - pyskl - INFO - Epoch [75][2500/3746] lr: 5.035e-02, eta: 2 days, 14:45:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5825, loss_cls: 3.8542, loss: 3.8542 +2024-07-24 19:43:01,078 - pyskl - INFO - Epoch [75][2600/3746] lr: 5.032e-02, eta: 2 days, 14:44:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5633, loss_cls: 3.9155, loss: 3.9155 +2024-07-24 19:44:22,731 - pyskl - INFO - Epoch [75][2700/3746] lr: 5.029e-02, eta: 2 days, 14:43:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5877, loss_cls: 3.8606, loss: 3.8606 +2024-07-24 19:45:44,375 - pyskl - INFO - Epoch [75][2800/3746] lr: 5.026e-02, eta: 2 days, 14:41:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5775, loss_cls: 3.8972, loss: 3.8972 +2024-07-24 19:47:05,932 - pyskl - INFO - Epoch [75][2900/3746] lr: 5.024e-02, eta: 2 days, 14:40:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5681, loss_cls: 3.9266, loss: 3.9266 +2024-07-24 19:48:27,736 - pyskl - INFO - Epoch [75][3000/3746] lr: 5.021e-02, eta: 2 days, 14:39:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5748, loss_cls: 3.9025, loss: 3.9025 +2024-07-24 19:49:49,589 - pyskl - INFO - Epoch [75][3100/3746] lr: 5.018e-02, eta: 2 days, 14:37:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5845, loss_cls: 3.8653, loss: 3.8653 +2024-07-24 19:51:11,543 - pyskl - INFO - Epoch [75][3200/3746] lr: 5.015e-02, eta: 2 days, 14:36:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5748, loss_cls: 3.8810, loss: 3.8810 +2024-07-24 19:52:33,450 - pyskl - INFO - Epoch [75][3300/3746] lr: 5.012e-02, eta: 2 days, 14:35:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5747, loss_cls: 3.9172, loss: 3.9172 +2024-07-24 19:53:54,734 - pyskl - INFO - Epoch [75][3400/3746] lr: 5.010e-02, eta: 2 days, 14:34:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5881, loss_cls: 3.8308, loss: 3.8308 +2024-07-24 19:55:16,610 - pyskl - INFO - Epoch [75][3500/3746] lr: 5.007e-02, eta: 2 days, 14:32:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5745, loss_cls: 3.9007, loss: 3.9007 +2024-07-24 19:56:38,297 - pyskl - INFO - Epoch [75][3600/3746] lr: 5.004e-02, eta: 2 days, 14:31:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5752, loss_cls: 3.8902, loss: 3.8902 +2024-07-24 19:57:59,549 - pyskl - INFO - Epoch [75][3700/3746] lr: 5.001e-02, eta: 2 days, 14:30:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5933, loss_cls: 3.8031, loss: 3.8031 +2024-07-24 19:58:38,819 - pyskl - INFO - Saving checkpoint at 75 epochs +2024-07-24 20:00:30,172 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 20:00:30,893 - pyskl - INFO - +top1_acc 0.2506 +top5_acc 0.4947 +2024-07-24 20:00:30,893 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 20:00:30,939 - pyskl - INFO - +mean_acc 0.2504 +2024-07-24 20:00:30,952 - pyskl - INFO - Epoch(val) [75][309] top1_acc: 0.2506, top5_acc: 0.4947, mean_class_accuracy: 0.2504 +2024-07-24 20:04:24,038 - pyskl - INFO - Epoch [76][100/3746] lr: 4.997e-02, eta: 2 days, 14:30:06, time: 2.331, data_time: 1.349, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5913, loss_cls: 3.8209, loss: 3.8209 +2024-07-24 20:05:46,010 - pyskl - INFO - Epoch [76][200/3746] lr: 4.994e-02, eta: 2 days, 14:28:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5917, loss_cls: 3.8049, loss: 3.8049 +2024-07-24 20:07:08,504 - pyskl - INFO - Epoch [76][300/3746] lr: 4.992e-02, eta: 2 days, 14:27:30, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5820, loss_cls: 3.8489, loss: 3.8489 +2024-07-24 20:08:29,999 - pyskl - INFO - Epoch [76][400/3746] lr: 4.989e-02, eta: 2 days, 14:26:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5869, loss_cls: 3.8245, loss: 3.8245 +2024-07-24 20:09:51,979 - pyskl - INFO - Epoch [76][500/3746] lr: 4.986e-02, eta: 2 days, 14:24:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5859, loss_cls: 3.8012, loss: 3.8012 +2024-07-24 20:11:13,879 - pyskl - INFO - Epoch [76][600/3746] lr: 4.983e-02, eta: 2 days, 14:23:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5761, loss_cls: 3.8645, loss: 3.8645 +2024-07-24 20:12:35,130 - pyskl - INFO - Epoch [76][700/3746] lr: 4.980e-02, eta: 2 days, 14:22:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5814, loss_cls: 3.8635, loss: 3.8635 +2024-07-24 20:13:57,159 - pyskl - INFO - Epoch [76][800/3746] lr: 4.978e-02, eta: 2 days, 14:20:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5797, loss_cls: 3.8636, loss: 3.8636 +2024-07-24 20:15:19,005 - pyskl - INFO - Epoch [76][900/3746] lr: 4.975e-02, eta: 2 days, 14:19:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5884, loss_cls: 3.8334, loss: 3.8334 +2024-07-24 20:16:40,937 - pyskl - INFO - Epoch [76][1000/3746] lr: 4.972e-02, eta: 2 days, 14:18:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5794, loss_cls: 3.8670, loss: 3.8670 +2024-07-24 20:18:02,141 - pyskl - INFO - Epoch [76][1100/3746] lr: 4.969e-02, eta: 2 days, 14:17:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5842, loss_cls: 3.8552, loss: 3.8552 +2024-07-24 20:19:23,947 - pyskl - INFO - Epoch [76][1200/3746] lr: 4.966e-02, eta: 2 days, 14:15:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5786, loss_cls: 3.8427, loss: 3.8427 +2024-07-24 20:20:45,783 - pyskl - INFO - Epoch [76][1300/3746] lr: 4.964e-02, eta: 2 days, 14:14:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5858, loss_cls: 3.8270, loss: 3.8270 +2024-07-24 20:22:07,184 - pyskl - INFO - Epoch [76][1400/3746] lr: 4.961e-02, eta: 2 days, 14:13:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5961, loss_cls: 3.8030, loss: 3.8030 +2024-07-24 20:23:29,132 - pyskl - INFO - Epoch [76][1500/3746] lr: 4.958e-02, eta: 2 days, 14:11:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5741, loss_cls: 3.9018, loss: 3.9018 +2024-07-24 20:24:51,896 - pyskl - INFO - Epoch [76][1600/3746] lr: 4.955e-02, eta: 2 days, 14:10:30, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5878, loss_cls: 3.8357, loss: 3.8357 +2024-07-24 20:26:14,508 - pyskl - INFO - Epoch [76][1700/3746] lr: 4.953e-02, eta: 2 days, 14:09:13, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5803, loss_cls: 3.8480, loss: 3.8480 +2024-07-24 20:27:37,007 - pyskl - INFO - Epoch [76][1800/3746] lr: 4.950e-02, eta: 2 days, 14:07:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5917, loss_cls: 3.7951, loss: 3.7951 +2024-07-24 20:28:59,243 - pyskl - INFO - Epoch [76][1900/3746] lr: 4.947e-02, eta: 2 days, 14:06:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5678, loss_cls: 3.9137, loss: 3.9137 +2024-07-24 20:30:20,792 - pyskl - INFO - Epoch [76][2000/3746] lr: 4.944e-02, eta: 2 days, 14:05:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5803, loss_cls: 3.8664, loss: 3.8664 +2024-07-24 20:31:42,180 - pyskl - INFO - Epoch [76][2100/3746] lr: 4.941e-02, eta: 2 days, 14:03:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5875, loss_cls: 3.8181, loss: 3.8181 +2024-07-24 20:33:04,315 - pyskl - INFO - Epoch [76][2200/3746] lr: 4.939e-02, eta: 2 days, 14:02:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5708, loss_cls: 3.9209, loss: 3.9209 +2024-07-24 20:34:26,099 - pyskl - INFO - Epoch [76][2300/3746] lr: 4.936e-02, eta: 2 days, 14:01:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5920, loss_cls: 3.8254, loss: 3.8254 +2024-07-24 20:35:47,641 - pyskl - INFO - Epoch [76][2400/3746] lr: 4.933e-02, eta: 2 days, 14:00:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5839, loss_cls: 3.8134, loss: 3.8134 +2024-07-24 20:37:09,111 - pyskl - INFO - Epoch [76][2500/3746] lr: 4.930e-02, eta: 2 days, 13:58:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5880, loss_cls: 3.8433, loss: 3.8433 +2024-07-24 20:38:30,285 - pyskl - INFO - Epoch [76][2600/3746] lr: 4.927e-02, eta: 2 days, 13:57:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5680, loss_cls: 3.9072, loss: 3.9072 +2024-07-24 20:39:51,724 - pyskl - INFO - Epoch [76][2700/3746] lr: 4.925e-02, eta: 2 days, 13:56:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5783, loss_cls: 3.8793, loss: 3.8793 +2024-07-24 20:41:13,164 - pyskl - INFO - Epoch [76][2800/3746] lr: 4.922e-02, eta: 2 days, 13:54:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5837, loss_cls: 3.8506, loss: 3.8506 +2024-07-24 20:42:34,518 - pyskl - INFO - Epoch [76][2900/3746] lr: 4.919e-02, eta: 2 days, 13:53:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5803, loss_cls: 3.8819, loss: 3.8819 +2024-07-24 20:43:56,189 - pyskl - INFO - Epoch [76][3000/3746] lr: 4.916e-02, eta: 2 days, 13:52:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5847, loss_cls: 3.8107, loss: 3.8107 +2024-07-24 20:45:17,760 - pyskl - INFO - Epoch [76][3100/3746] lr: 4.913e-02, eta: 2 days, 13:50:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5802, loss_cls: 3.8572, loss: 3.8572 +2024-07-24 20:46:39,340 - pyskl - INFO - Epoch [76][3200/3746] lr: 4.911e-02, eta: 2 days, 13:49:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5727, loss_cls: 3.8570, loss: 3.8570 +2024-07-24 20:48:01,256 - pyskl - INFO - Epoch [76][3300/3746] lr: 4.908e-02, eta: 2 days, 13:48:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5895, loss_cls: 3.8074, loss: 3.8074 +2024-07-24 20:49:22,955 - pyskl - INFO - Epoch [76][3400/3746] lr: 4.905e-02, eta: 2 days, 13:46:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5716, loss_cls: 3.8884, loss: 3.8884 +2024-07-24 20:50:44,371 - pyskl - INFO - Epoch [76][3500/3746] lr: 4.902e-02, eta: 2 days, 13:45:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5752, loss_cls: 3.8801, loss: 3.8801 +2024-07-24 20:52:05,764 - pyskl - INFO - Epoch [76][3600/3746] lr: 4.899e-02, eta: 2 days, 13:44:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5742, loss_cls: 3.8835, loss: 3.8835 +2024-07-24 20:53:27,537 - pyskl - INFO - Epoch [76][3700/3746] lr: 4.897e-02, eta: 2 days, 13:43:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5902, loss_cls: 3.8355, loss: 3.8355 +2024-07-24 20:54:07,035 - pyskl - INFO - Saving checkpoint at 76 epochs +2024-07-24 20:55:58,680 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 20:55:59,342 - pyskl - INFO - +top1_acc 0.2485 +top5_acc 0.4952 +2024-07-24 20:55:59,343 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 20:55:59,385 - pyskl - INFO - +mean_acc 0.2483 +2024-07-24 20:55:59,397 - pyskl - INFO - Epoch(val) [76][309] top1_acc: 0.2485, top5_acc: 0.4952, mean_class_accuracy: 0.2483 +2024-07-24 20:59:49,630 - pyskl - INFO - Epoch [77][100/3746] lr: 4.893e-02, eta: 2 days, 13:42:53, time: 2.302, data_time: 1.316, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5945, loss_cls: 3.7762, loss: 3.7762 +2024-07-24 21:01:11,673 - pyskl - INFO - Epoch [77][200/3746] lr: 4.890e-02, eta: 2 days, 13:41:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5948, loss_cls: 3.8035, loss: 3.8035 +2024-07-24 21:02:33,560 - pyskl - INFO - Epoch [77][300/3746] lr: 4.887e-02, eta: 2 days, 13:40:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5916, loss_cls: 3.8246, loss: 3.8246 +2024-07-24 21:03:55,923 - pyskl - INFO - Epoch [77][400/3746] lr: 4.884e-02, eta: 2 days, 13:38:58, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5852, loss_cls: 3.8246, loss: 3.8246 +2024-07-24 21:05:17,987 - pyskl - INFO - Epoch [77][500/3746] lr: 4.881e-02, eta: 2 days, 13:37:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5941, loss_cls: 3.8296, loss: 3.8296 +2024-07-24 21:06:39,286 - pyskl - INFO - Epoch [77][600/3746] lr: 4.879e-02, eta: 2 days, 13:36:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5897, loss_cls: 3.7971, loss: 3.7971 +2024-07-24 21:08:01,490 - pyskl - INFO - Epoch [77][700/3746] lr: 4.876e-02, eta: 2 days, 13:35:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.5894, loss_cls: 3.7723, loss: 3.7723 +2024-07-24 21:09:23,426 - pyskl - INFO - Epoch [77][800/3746] lr: 4.873e-02, eta: 2 days, 13:33:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5805, loss_cls: 3.8510, loss: 3.8510 +2024-07-24 21:10:45,378 - pyskl - INFO - Epoch [77][900/3746] lr: 4.870e-02, eta: 2 days, 13:32:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5891, loss_cls: 3.8339, loss: 3.8339 +2024-07-24 21:12:07,597 - pyskl - INFO - Epoch [77][1000/3746] lr: 4.867e-02, eta: 2 days, 13:31:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5916, loss_cls: 3.8057, loss: 3.8057 +2024-07-24 21:13:29,636 - pyskl - INFO - Epoch [77][1100/3746] lr: 4.865e-02, eta: 2 days, 13:29:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5855, loss_cls: 3.8336, loss: 3.8336 +2024-07-24 21:14:51,119 - pyskl - INFO - Epoch [77][1200/3746] lr: 4.862e-02, eta: 2 days, 13:28:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5800, loss_cls: 3.8973, loss: 3.8973 +2024-07-24 21:16:12,409 - pyskl - INFO - Epoch [77][1300/3746] lr: 4.859e-02, eta: 2 days, 13:27:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5808, loss_cls: 3.8380, loss: 3.8380 +2024-07-24 21:17:34,078 - pyskl - INFO - Epoch [77][1400/3746] lr: 4.856e-02, eta: 2 days, 13:25:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5836, loss_cls: 3.8706, loss: 3.8706 +2024-07-24 21:18:55,836 - pyskl - INFO - Epoch [77][1500/3746] lr: 4.853e-02, eta: 2 days, 13:24:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5781, loss_cls: 3.8569, loss: 3.8569 +2024-07-24 21:20:18,019 - pyskl - INFO - Epoch [77][1600/3746] lr: 4.851e-02, eta: 2 days, 13:23:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5902, loss_cls: 3.7926, loss: 3.7926 +2024-07-24 21:21:40,637 - pyskl - INFO - Epoch [77][1700/3746] lr: 4.848e-02, eta: 2 days, 13:21:57, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5886, loss_cls: 3.8107, loss: 3.8107 +2024-07-24 21:23:03,100 - pyskl - INFO - Epoch [77][1800/3746] lr: 4.845e-02, eta: 2 days, 13:20:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5813, loss_cls: 3.8495, loss: 3.8495 +2024-07-24 21:24:25,532 - pyskl - INFO - Epoch [77][1900/3746] lr: 4.842e-02, eta: 2 days, 13:19:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5845, loss_cls: 3.8191, loss: 3.8191 +2024-07-24 21:25:47,215 - pyskl - INFO - Epoch [77][2000/3746] lr: 4.839e-02, eta: 2 days, 13:18:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5847, loss_cls: 3.8354, loss: 3.8354 +2024-07-24 21:27:09,362 - pyskl - INFO - Epoch [77][2100/3746] lr: 4.837e-02, eta: 2 days, 13:16:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5853, loss_cls: 3.8229, loss: 3.8229 +2024-07-24 21:28:30,757 - pyskl - INFO - Epoch [77][2200/3746] lr: 4.834e-02, eta: 2 days, 13:15:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5852, loss_cls: 3.8493, loss: 3.8493 +2024-07-24 21:29:52,595 - pyskl - INFO - Epoch [77][2300/3746] lr: 4.831e-02, eta: 2 days, 13:14:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5944, loss_cls: 3.7940, loss: 3.7940 +2024-07-24 21:31:14,206 - pyskl - INFO - Epoch [77][2400/3746] lr: 4.828e-02, eta: 2 days, 13:12:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5841, loss_cls: 3.8343, loss: 3.8343 +2024-07-24 21:32:36,014 - pyskl - INFO - Epoch [77][2500/3746] lr: 4.825e-02, eta: 2 days, 13:11:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5881, loss_cls: 3.8325, loss: 3.8325 +2024-07-24 21:33:57,934 - pyskl - INFO - Epoch [77][2600/3746] lr: 4.823e-02, eta: 2 days, 13:10:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5819, loss_cls: 3.8473, loss: 3.8473 +2024-07-24 21:35:20,352 - pyskl - INFO - Epoch [77][2700/3746] lr: 4.820e-02, eta: 2 days, 13:08:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5900, loss_cls: 3.8253, loss: 3.8253 +2024-07-24 21:36:42,256 - pyskl - INFO - Epoch [77][2800/3746] lr: 4.817e-02, eta: 2 days, 13:07:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5817, loss_cls: 3.8649, loss: 3.8649 +2024-07-24 21:38:03,855 - pyskl - INFO - Epoch [77][2900/3746] lr: 4.814e-02, eta: 2 days, 13:06:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5716, loss_cls: 3.9061, loss: 3.9061 +2024-07-24 21:39:25,972 - pyskl - INFO - Epoch [77][3000/3746] lr: 4.811e-02, eta: 2 days, 13:04:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5752, loss_cls: 3.8860, loss: 3.8860 +2024-07-24 21:40:48,479 - pyskl - INFO - Epoch [77][3100/3746] lr: 4.809e-02, eta: 2 days, 13:03:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5806, loss_cls: 3.8377, loss: 3.8377 +2024-07-24 21:42:09,971 - pyskl - INFO - Epoch [77][3200/3746] lr: 4.806e-02, eta: 2 days, 13:02:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5791, loss_cls: 3.8497, loss: 3.8497 +2024-07-24 21:43:31,710 - pyskl - INFO - Epoch [77][3300/3746] lr: 4.803e-02, eta: 2 days, 13:01:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5725, loss_cls: 3.8965, loss: 3.8965 +2024-07-24 21:44:53,755 - pyskl - INFO - Epoch [77][3400/3746] lr: 4.800e-02, eta: 2 days, 12:59:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5791, loss_cls: 3.8749, loss: 3.8749 +2024-07-24 21:46:15,468 - pyskl - INFO - Epoch [77][3500/3746] lr: 4.798e-02, eta: 2 days, 12:58:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5722, loss_cls: 3.9139, loss: 3.9139 +2024-07-24 21:47:37,025 - pyskl - INFO - Epoch [77][3600/3746] lr: 4.795e-02, eta: 2 days, 12:57:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5792, loss_cls: 3.8558, loss: 3.8558 +2024-07-24 21:48:58,966 - pyskl - INFO - Epoch [77][3700/3746] lr: 4.792e-02, eta: 2 days, 12:55:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5869, loss_cls: 3.8566, loss: 3.8566 +2024-07-24 21:49:38,679 - pyskl - INFO - Saving checkpoint at 77 epochs +2024-07-24 21:51:30,873 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 21:51:31,547 - pyskl - INFO - +top1_acc 0.2524 +top5_acc 0.4869 +2024-07-24 21:51:31,547 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 21:51:31,591 - pyskl - INFO - +mean_acc 0.2521 +2024-07-24 21:51:31,603 - pyskl - INFO - Epoch(val) [77][309] top1_acc: 0.2524, top5_acc: 0.4869, mean_class_accuracy: 0.2521 +2024-07-24 21:55:18,445 - pyskl - INFO - Epoch [78][100/3746] lr: 4.788e-02, eta: 2 days, 12:55:33, time: 2.268, data_time: 1.294, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5997, loss_cls: 3.7662, loss: 3.7662 +2024-07-24 21:56:40,409 - pyskl - INFO - Epoch [78][200/3746] lr: 4.785e-02, eta: 2 days, 12:54:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6009, loss_cls: 3.7444, loss: 3.7444 +2024-07-24 21:58:02,205 - pyskl - INFO - Epoch [78][300/3746] lr: 4.782e-02, eta: 2 days, 12:52:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5922, loss_cls: 3.7841, loss: 3.7841 +2024-07-24 21:59:23,622 - pyskl - INFO - Epoch [78][400/3746] lr: 4.779e-02, eta: 2 days, 12:51:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5913, loss_cls: 3.8177, loss: 3.8177 +2024-07-24 22:00:44,685 - pyskl - INFO - Epoch [78][500/3746] lr: 4.777e-02, eta: 2 days, 12:50:17, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5806, loss_cls: 3.8276, loss: 3.8276 +2024-07-24 22:02:06,182 - pyskl - INFO - Epoch [78][600/3746] lr: 4.774e-02, eta: 2 days, 12:48:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5800, loss_cls: 3.8376, loss: 3.8376 +2024-07-24 22:03:27,588 - pyskl - INFO - Epoch [78][700/3746] lr: 4.771e-02, eta: 2 days, 12:47:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5867, loss_cls: 3.8151, loss: 3.8151 +2024-07-24 22:04:49,477 - pyskl - INFO - Epoch [78][800/3746] lr: 4.768e-02, eta: 2 days, 12:46:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.6034, loss_cls: 3.7514, loss: 3.7514 +2024-07-24 22:06:11,376 - pyskl - INFO - Epoch [78][900/3746] lr: 4.766e-02, eta: 2 days, 12:45:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5880, loss_cls: 3.8004, loss: 3.8004 +2024-07-24 22:07:33,661 - pyskl - INFO - Epoch [78][1000/3746] lr: 4.763e-02, eta: 2 days, 12:43:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.5917, loss_cls: 3.7980, loss: 3.7980 +2024-07-24 22:08:55,281 - pyskl - INFO - Epoch [78][1100/3746] lr: 4.760e-02, eta: 2 days, 12:42:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5867, loss_cls: 3.8354, loss: 3.8354 +2024-07-24 22:10:17,301 - pyskl - INFO - Epoch [78][1200/3746] lr: 4.757e-02, eta: 2 days, 12:41:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5769, loss_cls: 3.8521, loss: 3.8521 +2024-07-24 22:11:38,936 - pyskl - INFO - Epoch [78][1300/3746] lr: 4.754e-02, eta: 2 days, 12:39:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5866, loss_cls: 3.8307, loss: 3.8307 +2024-07-24 22:13:00,742 - pyskl - INFO - Epoch [78][1400/3746] lr: 4.752e-02, eta: 2 days, 12:38:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5866, loss_cls: 3.8390, loss: 3.8390 +2024-07-24 22:14:22,738 - pyskl - INFO - Epoch [78][1500/3746] lr: 4.749e-02, eta: 2 days, 12:37:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5803, loss_cls: 3.8874, loss: 3.8874 +2024-07-24 22:15:44,787 - pyskl - INFO - Epoch [78][1600/3746] lr: 4.746e-02, eta: 2 days, 12:35:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5845, loss_cls: 3.8565, loss: 3.8565 +2024-07-24 22:17:07,501 - pyskl - INFO - Epoch [78][1700/3746] lr: 4.743e-02, eta: 2 days, 12:34:33, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5755, loss_cls: 3.8408, loss: 3.8408 +2024-07-24 22:18:29,852 - pyskl - INFO - Epoch [78][1800/3746] lr: 4.740e-02, eta: 2 days, 12:33:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5791, loss_cls: 3.8690, loss: 3.8690 +2024-07-24 22:19:51,886 - pyskl - INFO - Epoch [78][1900/3746] lr: 4.738e-02, eta: 2 days, 12:31:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5808, loss_cls: 3.8540, loss: 3.8540 +2024-07-24 22:21:13,681 - pyskl - INFO - Epoch [78][2000/3746] lr: 4.735e-02, eta: 2 days, 12:30:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5725, loss_cls: 3.8837, loss: 3.8837 +2024-07-24 22:22:35,709 - pyskl - INFO - Epoch [78][2100/3746] lr: 4.732e-02, eta: 2 days, 12:29:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5895, loss_cls: 3.8148, loss: 3.8148 +2024-07-24 22:23:57,488 - pyskl - INFO - Epoch [78][2200/3746] lr: 4.729e-02, eta: 2 days, 12:28:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5897, loss_cls: 3.8116, loss: 3.8116 +2024-07-24 22:25:18,826 - pyskl - INFO - Epoch [78][2300/3746] lr: 4.726e-02, eta: 2 days, 12:26:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5719, loss_cls: 3.9183, loss: 3.9183 +2024-07-24 22:26:40,470 - pyskl - INFO - Epoch [78][2400/3746] lr: 4.724e-02, eta: 2 days, 12:25:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5758, loss_cls: 3.8659, loss: 3.8659 +2024-07-24 22:28:02,555 - pyskl - INFO - Epoch [78][2500/3746] lr: 4.721e-02, eta: 2 days, 12:24:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5867, loss_cls: 3.8205, loss: 3.8205 +2024-07-24 22:29:24,693 - pyskl - INFO - Epoch [78][2600/3746] lr: 4.718e-02, eta: 2 days, 12:22:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.6019, loss_cls: 3.7763, loss: 3.7763 +2024-07-24 22:30:46,349 - pyskl - INFO - Epoch [78][2700/3746] lr: 4.715e-02, eta: 2 days, 12:21:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5870, loss_cls: 3.8641, loss: 3.8641 +2024-07-24 22:32:08,337 - pyskl - INFO - Epoch [78][2800/3746] lr: 4.712e-02, eta: 2 days, 12:20:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5931, loss_cls: 3.8055, loss: 3.8055 +2024-07-24 22:33:30,411 - pyskl - INFO - Epoch [78][2900/3746] lr: 4.710e-02, eta: 2 days, 12:18:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5880, loss_cls: 3.8433, loss: 3.8433 +2024-07-24 22:34:52,557 - pyskl - INFO - Epoch [78][3000/3746] lr: 4.707e-02, eta: 2 days, 12:17:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5861, loss_cls: 3.8241, loss: 3.8241 +2024-07-24 22:36:14,213 - pyskl - INFO - Epoch [78][3100/3746] lr: 4.704e-02, eta: 2 days, 12:16:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5830, loss_cls: 3.8339, loss: 3.8339 +2024-07-24 22:37:35,909 - pyskl - INFO - Epoch [78][3200/3746] lr: 4.701e-02, eta: 2 days, 12:14:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5875, loss_cls: 3.8440, loss: 3.8440 +2024-07-24 22:38:57,352 - pyskl - INFO - Epoch [78][3300/3746] lr: 4.699e-02, eta: 2 days, 12:13:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5787, loss_cls: 3.8558, loss: 3.8558 +2024-07-24 22:40:18,905 - pyskl - INFO - Epoch [78][3400/3746] lr: 4.696e-02, eta: 2 days, 12:12:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.5887, loss_cls: 3.7991, loss: 3.7991 +2024-07-24 22:41:40,910 - pyskl - INFO - Epoch [78][3500/3746] lr: 4.693e-02, eta: 2 days, 12:10:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5797, loss_cls: 3.8462, loss: 3.8462 +2024-07-24 22:43:02,187 - pyskl - INFO - Epoch [78][3600/3746] lr: 4.690e-02, eta: 2 days, 12:09:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5970, loss_cls: 3.7896, loss: 3.7896 +2024-07-24 22:44:23,673 - pyskl - INFO - Epoch [78][3700/3746] lr: 4.687e-02, eta: 2 days, 12:08:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.5975, loss_cls: 3.7868, loss: 3.7868 +2024-07-24 22:45:03,173 - pyskl - INFO - Saving checkpoint at 78 epochs +2024-07-24 22:46:55,570 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 22:46:56,234 - pyskl - INFO - +top1_acc 0.2536 +top5_acc 0.5017 +2024-07-24 22:46:56,235 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 22:46:56,276 - pyskl - INFO - +mean_acc 0.2533 +2024-07-24 22:46:56,288 - pyskl - INFO - Epoch(val) [78][309] top1_acc: 0.2536, top5_acc: 0.5017, mean_class_accuracy: 0.2533 +2024-07-24 22:50:48,103 - pyskl - INFO - Epoch [79][100/3746] lr: 4.683e-02, eta: 2 days, 12:08:06, time: 2.318, data_time: 1.336, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5925, loss_cls: 3.7734, loss: 3.7734 +2024-07-24 22:52:10,231 - pyskl - INFO - Epoch [79][200/3746] lr: 4.680e-02, eta: 2 days, 12:06:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5872, loss_cls: 3.8172, loss: 3.8172 +2024-07-24 22:53:32,552 - pyskl - INFO - Epoch [79][300/3746] lr: 4.678e-02, eta: 2 days, 12:05:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5983, loss_cls: 3.7933, loss: 3.7933 +2024-07-24 22:54:54,450 - pyskl - INFO - Epoch [79][400/3746] lr: 4.675e-02, eta: 2 days, 12:04:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.5955, loss_cls: 3.7758, loss: 3.7758 +2024-07-24 22:56:16,076 - pyskl - INFO - Epoch [79][500/3746] lr: 4.672e-02, eta: 2 days, 12:02:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5770, loss_cls: 3.8659, loss: 3.8659 +2024-07-24 22:57:37,407 - pyskl - INFO - Epoch [79][600/3746] lr: 4.669e-02, eta: 2 days, 12:01:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5837, loss_cls: 3.8368, loss: 3.8368 +2024-07-24 22:58:59,139 - pyskl - INFO - Epoch [79][700/3746] lr: 4.667e-02, eta: 2 days, 12:00:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5927, loss_cls: 3.7730, loss: 3.7730 +2024-07-24 23:00:21,234 - pyskl - INFO - Epoch [79][800/3746] lr: 4.664e-02, eta: 2 days, 11:58:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5995, loss_cls: 3.7843, loss: 3.7843 +2024-07-24 23:01:43,188 - pyskl - INFO - Epoch [79][900/3746] lr: 4.661e-02, eta: 2 days, 11:57:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5889, loss_cls: 3.8017, loss: 3.8017 +2024-07-24 23:03:05,070 - pyskl - INFO - Epoch [79][1000/3746] lr: 4.658e-02, eta: 2 days, 11:56:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.6019, loss_cls: 3.7683, loss: 3.7683 +2024-07-24 23:04:26,701 - pyskl - INFO - Epoch [79][1100/3746] lr: 4.655e-02, eta: 2 days, 11:54:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5941, loss_cls: 3.7868, loss: 3.7868 +2024-07-24 23:05:48,412 - pyskl - INFO - Epoch [79][1200/3746] lr: 4.653e-02, eta: 2 days, 11:53:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5853, loss_cls: 3.8492, loss: 3.8492 +2024-07-24 23:07:10,652 - pyskl - INFO - Epoch [79][1300/3746] lr: 4.650e-02, eta: 2 days, 11:52:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5872, loss_cls: 3.8300, loss: 3.8300 +2024-07-24 23:08:32,426 - pyskl - INFO - Epoch [79][1400/3746] lr: 4.647e-02, eta: 2 days, 11:51:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.5917, loss_cls: 3.8203, loss: 3.8203 +2024-07-24 23:09:54,395 - pyskl - INFO - Epoch [79][1500/3746] lr: 4.644e-02, eta: 2 days, 11:49:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5927, loss_cls: 3.8056, loss: 3.8056 +2024-07-24 23:11:16,150 - pyskl - INFO - Epoch [79][1600/3746] lr: 4.641e-02, eta: 2 days, 11:48:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5858, loss_cls: 3.8298, loss: 3.8298 +2024-07-24 23:12:38,852 - pyskl - INFO - Epoch [79][1700/3746] lr: 4.639e-02, eta: 2 days, 11:47:05, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5967, loss_cls: 3.7651, loss: 3.7651 +2024-07-24 23:14:01,584 - pyskl - INFO - Epoch [79][1800/3746] lr: 4.636e-02, eta: 2 days, 11:45:46, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5897, loss_cls: 3.8103, loss: 3.8103 +2024-07-24 23:15:24,244 - pyskl - INFO - Epoch [79][1900/3746] lr: 4.633e-02, eta: 2 days, 11:44:28, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.5908, loss_cls: 3.7745, loss: 3.7745 +2024-07-24 23:16:45,746 - pyskl - INFO - Epoch [79][2000/3746] lr: 4.630e-02, eta: 2 days, 11:43:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5919, loss_cls: 3.8117, loss: 3.8117 +2024-07-24 23:18:07,666 - pyskl - INFO - Epoch [79][2100/3746] lr: 4.628e-02, eta: 2 days, 11:41:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5902, loss_cls: 3.8066, loss: 3.8066 +2024-07-24 23:19:29,073 - pyskl - INFO - Epoch [79][2200/3746] lr: 4.625e-02, eta: 2 days, 11:40:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5850, loss_cls: 3.8521, loss: 3.8521 +2024-07-24 23:20:50,513 - pyskl - INFO - Epoch [79][2300/3746] lr: 4.622e-02, eta: 2 days, 11:39:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5920, loss_cls: 3.8298, loss: 3.8298 +2024-07-24 23:22:12,471 - pyskl - INFO - Epoch [79][2400/3746] lr: 4.619e-02, eta: 2 days, 11:37:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5867, loss_cls: 3.8343, loss: 3.8343 +2024-07-24 23:23:34,376 - pyskl - INFO - Epoch [79][2500/3746] lr: 4.616e-02, eta: 2 days, 11:36:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5856, loss_cls: 3.8265, loss: 3.8265 +2024-07-24 23:24:56,203 - pyskl - INFO - Epoch [79][2600/3746] lr: 4.614e-02, eta: 2 days, 11:35:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5873, loss_cls: 3.8065, loss: 3.8065 +2024-07-24 23:26:17,695 - pyskl - INFO - Epoch [79][2700/3746] lr: 4.611e-02, eta: 2 days, 11:33:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5784, loss_cls: 3.8471, loss: 3.8471 +2024-07-24 23:27:39,328 - pyskl - INFO - Epoch [79][2800/3746] lr: 4.608e-02, eta: 2 days, 11:32:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5909, loss_cls: 3.8049, loss: 3.8049 +2024-07-24 23:29:01,085 - pyskl - INFO - Epoch [79][2900/3746] lr: 4.605e-02, eta: 2 days, 11:31:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5769, loss_cls: 3.8656, loss: 3.8656 +2024-07-24 23:30:22,531 - pyskl - INFO - Epoch [79][3000/3746] lr: 4.602e-02, eta: 2 days, 11:29:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5891, loss_cls: 3.8234, loss: 3.8234 +2024-07-24 23:31:44,395 - pyskl - INFO - Epoch [79][3100/3746] lr: 4.600e-02, eta: 2 days, 11:28:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5828, loss_cls: 3.8108, loss: 3.8108 +2024-07-24 23:33:06,848 - pyskl - INFO - Epoch [79][3200/3746] lr: 4.597e-02, eta: 2 days, 11:27:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5805, loss_cls: 3.8748, loss: 3.8748 +2024-07-24 23:34:28,350 - pyskl - INFO - Epoch [79][3300/3746] lr: 4.594e-02, eta: 2 days, 11:26:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5794, loss_cls: 3.8441, loss: 3.8441 +2024-07-24 23:35:50,085 - pyskl - INFO - Epoch [79][3400/3746] lr: 4.591e-02, eta: 2 days, 11:24:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5900, loss_cls: 3.8555, loss: 3.8555 +2024-07-24 23:37:11,717 - pyskl - INFO - Epoch [79][3500/3746] lr: 4.588e-02, eta: 2 days, 11:23:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5791, loss_cls: 3.8655, loss: 3.8655 +2024-07-24 23:38:33,134 - pyskl - INFO - Epoch [79][3600/3746] lr: 4.586e-02, eta: 2 days, 11:22:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5808, loss_cls: 3.8464, loss: 3.8464 +2024-07-24 23:39:54,664 - pyskl - INFO - Epoch [79][3700/3746] lr: 4.583e-02, eta: 2 days, 11:20:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5831, loss_cls: 3.8341, loss: 3.8341 +2024-07-24 23:40:34,589 - pyskl - INFO - Saving checkpoint at 79 epochs +2024-07-24 23:42:26,841 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 23:42:27,517 - pyskl - INFO - +top1_acc 0.2468 +top5_acc 0.4993 +2024-07-24 23:42:27,517 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 23:42:27,562 - pyskl - INFO - +mean_acc 0.2466 +2024-07-24 23:42:27,575 - pyskl - INFO - Epoch(val) [79][309] top1_acc: 0.2468, top5_acc: 0.4993, mean_class_accuracy: 0.2466 +2024-07-24 23:46:23,352 - pyskl - INFO - Epoch [80][100/3746] lr: 4.579e-02, eta: 2 days, 11:20:34, time: 2.358, data_time: 1.349, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5877, loss_cls: 3.7984, loss: 3.7984 +2024-07-24 23:47:46,032 - pyskl - INFO - Epoch [80][200/3746] lr: 4.576e-02, eta: 2 days, 11:19:16, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.6108, loss_cls: 3.7084, loss: 3.7084 +2024-07-24 23:49:07,912 - pyskl - INFO - Epoch [80][300/3746] lr: 4.573e-02, eta: 2 days, 11:17:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5881, loss_cls: 3.7859, loss: 3.7859 +2024-07-24 23:50:29,513 - pyskl - INFO - Epoch [80][400/3746] lr: 4.570e-02, eta: 2 days, 11:16:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5848, loss_cls: 3.8495, loss: 3.8495 +2024-07-24 23:51:50,922 - pyskl - INFO - Epoch [80][500/3746] lr: 4.568e-02, eta: 2 days, 11:15:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5952, loss_cls: 3.7960, loss: 3.7960 +2024-07-24 23:53:12,510 - pyskl - INFO - Epoch [80][600/3746] lr: 4.565e-02, eta: 2 days, 11:13:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5923, loss_cls: 3.7805, loss: 3.7805 +2024-07-24 23:54:34,189 - pyskl - INFO - Epoch [80][700/3746] lr: 4.562e-02, eta: 2 days, 11:12:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5950, loss_cls: 3.7574, loss: 3.7574 +2024-07-24 23:55:56,669 - pyskl - INFO - Epoch [80][800/3746] lr: 4.559e-02, eta: 2 days, 11:11:22, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5880, loss_cls: 3.8650, loss: 3.8650 +2024-07-24 23:57:19,226 - pyskl - INFO - Epoch [80][900/3746] lr: 4.557e-02, eta: 2 days, 11:10:03, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5998, loss_cls: 3.7942, loss: 3.7942 +2024-07-24 23:58:41,242 - pyskl - INFO - Epoch [80][1000/3746] lr: 4.554e-02, eta: 2 days, 11:08:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5887, loss_cls: 3.7943, loss: 3.7943 +2024-07-25 00:00:03,412 - pyskl - INFO - Epoch [80][1100/3746] lr: 4.551e-02, eta: 2 days, 11:07:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5925, loss_cls: 3.8186, loss: 3.8186 +2024-07-25 00:01:24,734 - pyskl - INFO - Epoch [80][1200/3746] lr: 4.548e-02, eta: 2 days, 11:06:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5913, loss_cls: 3.7983, loss: 3.7983 +2024-07-25 00:02:47,360 - pyskl - INFO - Epoch [80][1300/3746] lr: 4.545e-02, eta: 2 days, 11:04:48, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5978, loss_cls: 3.7791, loss: 3.7791 +2024-07-25 00:04:08,851 - pyskl - INFO - Epoch [80][1400/3746] lr: 4.543e-02, eta: 2 days, 11:03:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5933, loss_cls: 3.8060, loss: 3.8060 +2024-07-25 00:05:30,450 - pyskl - INFO - Epoch [80][1500/3746] lr: 4.540e-02, eta: 2 days, 11:02:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5967, loss_cls: 3.7738, loss: 3.7738 +2024-07-25 00:06:52,523 - pyskl - INFO - Epoch [80][1600/3746] lr: 4.537e-02, eta: 2 days, 11:00:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5997, loss_cls: 3.7604, loss: 3.7604 +2024-07-25 00:08:14,755 - pyskl - INFO - Epoch [80][1700/3746] lr: 4.534e-02, eta: 2 days, 10:59:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5792, loss_cls: 3.8505, loss: 3.8505 +2024-07-25 00:09:37,118 - pyskl - INFO - Epoch [80][1800/3746] lr: 4.532e-02, eta: 2 days, 10:58:13, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5895, loss_cls: 3.7953, loss: 3.7953 +2024-07-25 00:10:58,526 - pyskl - INFO - Epoch [80][1900/3746] lr: 4.529e-02, eta: 2 days, 10:56:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5970, loss_cls: 3.7706, loss: 3.7706 +2024-07-25 00:12:20,343 - pyskl - INFO - Epoch [80][2000/3746] lr: 4.526e-02, eta: 2 days, 10:55:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5917, loss_cls: 3.8123, loss: 3.8123 +2024-07-25 00:13:42,415 - pyskl - INFO - Epoch [80][2100/3746] lr: 4.523e-02, eta: 2 days, 10:54:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.5930, loss_cls: 3.7653, loss: 3.7653 +2024-07-25 00:15:04,314 - pyskl - INFO - Epoch [80][2200/3746] lr: 4.520e-02, eta: 2 days, 10:52:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5939, loss_cls: 3.8010, loss: 3.8010 +2024-07-25 00:16:25,977 - pyskl - INFO - Epoch [80][2300/3746] lr: 4.518e-02, eta: 2 days, 10:51:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5964, loss_cls: 3.8077, loss: 3.8077 +2024-07-25 00:17:47,248 - pyskl - INFO - Epoch [80][2400/3746] lr: 4.515e-02, eta: 2 days, 10:50:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5822, loss_cls: 3.8426, loss: 3.8426 +2024-07-25 00:19:08,449 - pyskl - INFO - Epoch [80][2500/3746] lr: 4.512e-02, eta: 2 days, 10:48:58, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.6006, loss_cls: 3.7852, loss: 3.7852 +2024-07-25 00:20:29,885 - pyskl - INFO - Epoch [80][2600/3746] lr: 4.509e-02, eta: 2 days, 10:47:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5806, loss_cls: 3.8431, loss: 3.8431 +2024-07-25 00:21:51,581 - pyskl - INFO - Epoch [80][2700/3746] lr: 4.506e-02, eta: 2 days, 10:46:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5908, loss_cls: 3.8215, loss: 3.8215 +2024-07-25 00:23:13,718 - pyskl - INFO - Epoch [80][2800/3746] lr: 4.504e-02, eta: 2 days, 10:45:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5863, loss_cls: 3.8397, loss: 3.8397 +2024-07-25 00:24:35,283 - pyskl - INFO - Epoch [80][2900/3746] lr: 4.501e-02, eta: 2 days, 10:43:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5861, loss_cls: 3.8196, loss: 3.8196 +2024-07-25 00:25:56,596 - pyskl - INFO - Epoch [80][3000/3746] lr: 4.498e-02, eta: 2 days, 10:42:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5964, loss_cls: 3.7593, loss: 3.7593 +2024-07-25 00:27:18,358 - pyskl - INFO - Epoch [80][3100/3746] lr: 4.495e-02, eta: 2 days, 10:41:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.5953, loss_cls: 3.7695, loss: 3.7695 +2024-07-25 00:28:40,429 - pyskl - INFO - Epoch [80][3200/3746] lr: 4.493e-02, eta: 2 days, 10:39:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5800, loss_cls: 3.8536, loss: 3.8536 +2024-07-25 00:30:01,917 - pyskl - INFO - Epoch [80][3300/3746] lr: 4.490e-02, eta: 2 days, 10:38:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5809, loss_cls: 3.8249, loss: 3.8249 +2024-07-25 00:31:23,763 - pyskl - INFO - Epoch [80][3400/3746] lr: 4.487e-02, eta: 2 days, 10:37:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5936, loss_cls: 3.8204, loss: 3.8204 +2024-07-25 00:32:46,164 - pyskl - INFO - Epoch [80][3500/3746] lr: 4.484e-02, eta: 2 days, 10:35:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5803, loss_cls: 3.8676, loss: 3.8676 +2024-07-25 00:34:07,922 - pyskl - INFO - Epoch [80][3600/3746] lr: 4.481e-02, eta: 2 days, 10:34:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5894, loss_cls: 3.8026, loss: 3.8026 +2024-07-25 00:35:29,801 - pyskl - INFO - Epoch [80][3700/3746] lr: 4.479e-02, eta: 2 days, 10:33:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5839, loss_cls: 3.8265, loss: 3.8265 +2024-07-25 00:36:09,495 - pyskl - INFO - Saving checkpoint at 80 epochs +2024-07-25 00:38:01,767 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 00:38:02,433 - pyskl - INFO - +top1_acc 0.2721 +top5_acc 0.5145 +2024-07-25 00:38:02,434 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 00:38:02,475 - pyskl - INFO - +mean_acc 0.2717 +2024-07-25 00:38:02,480 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_70.pth was removed +2024-07-25 00:38:02,717 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_80.pth. +2024-07-25 00:38:02,718 - pyskl - INFO - Best top1_acc is 0.2721 at 80 epoch. +2024-07-25 00:38:02,730 - pyskl - INFO - Epoch(val) [80][309] top1_acc: 0.2721, top5_acc: 0.5145, mean_class_accuracy: 0.2717 +2024-07-25 00:41:57,038 - pyskl - INFO - Epoch [81][100/3746] lr: 4.475e-02, eta: 2 days, 10:32:54, time: 2.343, data_time: 1.367, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5997, loss_cls: 3.7539, loss: 3.7539 +2024-07-25 00:43:19,824 - pyskl - INFO - Epoch [81][200/3746] lr: 4.472e-02, eta: 2 days, 10:31:35, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5892, loss_cls: 3.7843, loss: 3.7843 +2024-07-25 00:44:42,600 - pyskl - INFO - Epoch [81][300/3746] lr: 4.469e-02, eta: 2 days, 10:30:17, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5977, loss_cls: 3.7825, loss: 3.7825 +2024-07-25 00:46:03,983 - pyskl - INFO - Epoch [81][400/3746] lr: 4.466e-02, eta: 2 days, 10:28:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.6023, loss_cls: 3.7538, loss: 3.7538 +2024-07-25 00:47:25,832 - pyskl - INFO - Epoch [81][500/3746] lr: 4.463e-02, eta: 2 days, 10:27:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5938, loss_cls: 3.7928, loss: 3.7928 +2024-07-25 00:48:47,662 - pyskl - INFO - Epoch [81][600/3746] lr: 4.461e-02, eta: 2 days, 10:26:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5886, loss_cls: 3.7942, loss: 3.7942 +2024-07-25 00:50:09,124 - pyskl - INFO - Epoch [81][700/3746] lr: 4.458e-02, eta: 2 days, 10:24:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5850, loss_cls: 3.7992, loss: 3.7992 +2024-07-25 00:51:31,384 - pyskl - INFO - Epoch [81][800/3746] lr: 4.455e-02, eta: 2 days, 10:23:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5973, loss_cls: 3.7657, loss: 3.7657 +2024-07-25 00:52:53,159 - pyskl - INFO - Epoch [81][900/3746] lr: 4.452e-02, eta: 2 days, 10:22:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6048, loss_cls: 3.7579, loss: 3.7579 +2024-07-25 00:54:15,299 - pyskl - INFO - Epoch [81][1000/3746] lr: 4.450e-02, eta: 2 days, 10:21:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5922, loss_cls: 3.7878, loss: 3.7878 +2024-07-25 00:55:36,752 - pyskl - INFO - Epoch [81][1100/3746] lr: 4.447e-02, eta: 2 days, 10:19:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.5938, loss_cls: 3.7816, loss: 3.7816 +2024-07-25 00:56:58,595 - pyskl - INFO - Epoch [81][1200/3746] lr: 4.444e-02, eta: 2 days, 10:18:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6012, loss_cls: 3.7620, loss: 3.7620 +2024-07-25 00:58:20,139 - pyskl - INFO - Epoch [81][1300/3746] lr: 4.441e-02, eta: 2 days, 10:17:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5880, loss_cls: 3.8161, loss: 3.8161 +2024-07-25 00:59:41,601 - pyskl - INFO - Epoch [81][1400/3746] lr: 4.438e-02, eta: 2 days, 10:15:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5830, loss_cls: 3.8568, loss: 3.8568 +2024-07-25 01:01:03,493 - pyskl - INFO - Epoch [81][1500/3746] lr: 4.436e-02, eta: 2 days, 10:14:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5948, loss_cls: 3.7977, loss: 3.7977 +2024-07-25 01:02:25,682 - pyskl - INFO - Epoch [81][1600/3746] lr: 4.433e-02, eta: 2 days, 10:13:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5856, loss_cls: 3.8108, loss: 3.8108 +2024-07-25 01:03:47,506 - pyskl - INFO - Epoch [81][1700/3746] lr: 4.430e-02, eta: 2 days, 10:11:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5895, loss_cls: 3.8110, loss: 3.8110 +2024-07-25 01:05:10,437 - pyskl - INFO - Epoch [81][1800/3746] lr: 4.427e-02, eta: 2 days, 10:10:29, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5783, loss_cls: 3.8571, loss: 3.8571 +2024-07-25 01:06:32,343 - pyskl - INFO - Epoch [81][1900/3746] lr: 4.425e-02, eta: 2 days, 10:09:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5875, loss_cls: 3.8126, loss: 3.8126 +2024-07-25 01:07:54,138 - pyskl - INFO - Epoch [81][2000/3746] lr: 4.422e-02, eta: 2 days, 10:07:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5936, loss_cls: 3.7934, loss: 3.7934 +2024-07-25 01:09:15,747 - pyskl - INFO - Epoch [81][2100/3746] lr: 4.419e-02, eta: 2 days, 10:06:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5945, loss_cls: 3.7984, loss: 3.7984 +2024-07-25 01:10:37,254 - pyskl - INFO - Epoch [81][2200/3746] lr: 4.416e-02, eta: 2 days, 10:05:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5891, loss_cls: 3.8388, loss: 3.8388 +2024-07-25 01:11:58,999 - pyskl - INFO - Epoch [81][2300/3746] lr: 4.413e-02, eta: 2 days, 10:03:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.6000, loss_cls: 3.7372, loss: 3.7372 +2024-07-25 01:13:20,679 - pyskl - INFO - Epoch [81][2400/3746] lr: 4.411e-02, eta: 2 days, 10:02:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5955, loss_cls: 3.7719, loss: 3.7719 +2024-07-25 01:14:42,820 - pyskl - INFO - Epoch [81][2500/3746] lr: 4.408e-02, eta: 2 days, 10:01:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5905, loss_cls: 3.7824, loss: 3.7824 +2024-07-25 01:16:04,476 - pyskl - INFO - Epoch [81][2600/3746] lr: 4.405e-02, eta: 2 days, 9:59:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.6008, loss_cls: 3.7607, loss: 3.7607 +2024-07-25 01:17:26,384 - pyskl - INFO - Epoch [81][2700/3746] lr: 4.402e-02, eta: 2 days, 9:58:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5887, loss_cls: 3.7927, loss: 3.7927 +2024-07-25 01:18:47,870 - pyskl - INFO - Epoch [81][2800/3746] lr: 4.400e-02, eta: 2 days, 9:57:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5923, loss_cls: 3.7885, loss: 3.7885 +2024-07-25 01:20:09,772 - pyskl - INFO - Epoch [81][2900/3746] lr: 4.397e-02, eta: 2 days, 9:55:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5961, loss_cls: 3.7953, loss: 3.7953 +2024-07-25 01:21:31,719 - pyskl - INFO - Epoch [81][3000/3746] lr: 4.394e-02, eta: 2 days, 9:54:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5827, loss_cls: 3.8015, loss: 3.8015 +2024-07-25 01:22:53,075 - pyskl - INFO - Epoch [81][3100/3746] lr: 4.391e-02, eta: 2 days, 9:53:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5975, loss_cls: 3.7841, loss: 3.7841 +2024-07-25 01:24:15,144 - pyskl - INFO - Epoch [81][3200/3746] lr: 4.389e-02, eta: 2 days, 9:52:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5892, loss_cls: 3.7850, loss: 3.7850 +2024-07-25 01:25:37,024 - pyskl - INFO - Epoch [81][3300/3746] lr: 4.386e-02, eta: 2 days, 9:50:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.5953, loss_cls: 3.7863, loss: 3.7863 +2024-07-25 01:26:58,625 - pyskl - INFO - Epoch [81][3400/3746] lr: 4.383e-02, eta: 2 days, 9:49:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5811, loss_cls: 3.8209, loss: 3.8209 +2024-07-25 01:28:20,373 - pyskl - INFO - Epoch [81][3500/3746] lr: 4.380e-02, eta: 2 days, 9:48:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.6003, loss_cls: 3.7827, loss: 3.7827 +2024-07-25 01:29:42,225 - pyskl - INFO - Epoch [81][3600/3746] lr: 4.377e-02, eta: 2 days, 9:46:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5752, loss_cls: 3.8550, loss: 3.8550 +2024-07-25 01:31:03,524 - pyskl - INFO - Epoch [81][3700/3746] lr: 4.375e-02, eta: 2 days, 9:45:23, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5948, loss_cls: 3.7848, loss: 3.7848 +2024-07-25 01:31:43,121 - pyskl - INFO - Saving checkpoint at 81 epochs +2024-07-25 01:33:34,757 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 01:33:35,420 - pyskl - INFO - +top1_acc 0.2685 +top5_acc 0.5203 +2024-07-25 01:33:35,421 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 01:33:35,461 - pyskl - INFO - +mean_acc 0.2683 +2024-07-25 01:33:35,473 - pyskl - INFO - Epoch(val) [81][309] top1_acc: 0.2685, top5_acc: 0.5203, mean_class_accuracy: 0.2683 +2024-07-25 01:37:28,274 - pyskl - INFO - Epoch [82][100/3746] lr: 4.371e-02, eta: 2 days, 9:45:04, time: 2.328, data_time: 1.346, memory: 15990, top1_acc: 0.3386, top5_acc: 0.6045, loss_cls: 3.7327, loss: 3.7327 +2024-07-25 01:38:50,431 - pyskl - INFO - Epoch [82][200/3746] lr: 4.368e-02, eta: 2 days, 9:43:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5997, loss_cls: 3.7704, loss: 3.7704 +2024-07-25 01:40:13,037 - pyskl - INFO - Epoch [82][300/3746] lr: 4.365e-02, eta: 2 days, 9:42:26, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6025, loss_cls: 3.7366, loss: 3.7366 +2024-07-25 01:41:35,299 - pyskl - INFO - Epoch [82][400/3746] lr: 4.362e-02, eta: 2 days, 9:41:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6041, loss_cls: 3.7375, loss: 3.7375 +2024-07-25 01:42:56,630 - pyskl - INFO - Epoch [82][500/3746] lr: 4.359e-02, eta: 2 days, 9:39:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5872, loss_cls: 3.8086, loss: 3.8086 +2024-07-25 01:44:18,145 - pyskl - INFO - Epoch [82][600/3746] lr: 4.357e-02, eta: 2 days, 9:38:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5869, loss_cls: 3.7911, loss: 3.7911 +2024-07-25 01:45:40,111 - pyskl - INFO - Epoch [82][700/3746] lr: 4.354e-02, eta: 2 days, 9:37:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6025, loss_cls: 3.7380, loss: 3.7380 +2024-07-25 01:47:02,959 - pyskl - INFO - Epoch [82][800/3746] lr: 4.351e-02, eta: 2 days, 9:35:50, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5995, loss_cls: 3.7389, loss: 3.7389 +2024-07-25 01:48:25,122 - pyskl - INFO - Epoch [82][900/3746] lr: 4.348e-02, eta: 2 days, 9:34:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.5952, loss_cls: 3.7821, loss: 3.7821 +2024-07-25 01:49:46,855 - pyskl - INFO - Epoch [82][1000/3746] lr: 4.346e-02, eta: 2 days, 9:33:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5864, loss_cls: 3.7832, loss: 3.7832 +2024-07-25 01:51:08,488 - pyskl - INFO - Epoch [82][1100/3746] lr: 4.343e-02, eta: 2 days, 9:31:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5934, loss_cls: 3.7784, loss: 3.7784 +2024-07-25 01:52:29,801 - pyskl - INFO - Epoch [82][1200/3746] lr: 4.340e-02, eta: 2 days, 9:30:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5988, loss_cls: 3.7859, loss: 3.7859 +2024-07-25 01:53:51,827 - pyskl - INFO - Epoch [82][1300/3746] lr: 4.337e-02, eta: 2 days, 9:29:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.6030, loss_cls: 3.7530, loss: 3.7530 +2024-07-25 01:55:13,955 - pyskl - INFO - Epoch [82][1400/3746] lr: 4.335e-02, eta: 2 days, 9:27:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5917, loss_cls: 3.7812, loss: 3.7812 +2024-07-25 01:56:35,781 - pyskl - INFO - Epoch [82][1500/3746] lr: 4.332e-02, eta: 2 days, 9:26:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5877, loss_cls: 3.8078, loss: 3.8078 +2024-07-25 01:57:58,271 - pyskl - INFO - Epoch [82][1600/3746] lr: 4.329e-02, eta: 2 days, 9:25:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5794, loss_cls: 3.8604, loss: 3.8604 +2024-07-25 01:59:20,542 - pyskl - INFO - Epoch [82][1700/3746] lr: 4.326e-02, eta: 2 days, 9:23:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5877, loss_cls: 3.8389, loss: 3.8389 +2024-07-25 02:00:42,952 - pyskl - INFO - Epoch [82][1800/3746] lr: 4.323e-02, eta: 2 days, 9:22:38, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3452, top5_acc: 0.5931, loss_cls: 3.7572, loss: 3.7572 +2024-07-25 02:02:05,492 - pyskl - INFO - Epoch [82][1900/3746] lr: 4.321e-02, eta: 2 days, 9:21:19, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5980, loss_cls: 3.7761, loss: 3.7761 +2024-07-25 02:03:27,668 - pyskl - INFO - Epoch [82][2000/3746] lr: 4.318e-02, eta: 2 days, 9:20:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6053, loss_cls: 3.7612, loss: 3.7612 +2024-07-25 02:04:49,707 - pyskl - INFO - Epoch [82][2100/3746] lr: 4.315e-02, eta: 2 days, 9:18:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.6020, loss_cls: 3.7305, loss: 3.7305 +2024-07-25 02:06:11,112 - pyskl - INFO - Epoch [82][2200/3746] lr: 4.312e-02, eta: 2 days, 9:17:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5881, loss_cls: 3.7930, loss: 3.7930 +2024-07-25 02:07:33,038 - pyskl - INFO - Epoch [82][2300/3746] lr: 4.310e-02, eta: 2 days, 9:16:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.5988, loss_cls: 3.7931, loss: 3.7931 +2024-07-25 02:08:54,888 - pyskl - INFO - Epoch [82][2400/3746] lr: 4.307e-02, eta: 2 days, 9:14:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5984, loss_cls: 3.7618, loss: 3.7618 +2024-07-25 02:10:16,486 - pyskl - INFO - Epoch [82][2500/3746] lr: 4.304e-02, eta: 2 days, 9:13:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5925, loss_cls: 3.7873, loss: 3.7873 +2024-07-25 02:11:38,345 - pyskl - INFO - Epoch [82][2600/3746] lr: 4.301e-02, eta: 2 days, 9:12:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6034, loss_cls: 3.7387, loss: 3.7387 +2024-07-25 02:13:00,127 - pyskl - INFO - Epoch [82][2700/3746] lr: 4.299e-02, eta: 2 days, 9:10:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.6062, loss_cls: 3.7795, loss: 3.7795 +2024-07-25 02:14:21,506 - pyskl - INFO - Epoch [82][2800/3746] lr: 4.296e-02, eta: 2 days, 9:09:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5962, loss_cls: 3.7839, loss: 3.7839 +2024-07-25 02:15:42,635 - pyskl - INFO - Epoch [82][2900/3746] lr: 4.293e-02, eta: 2 days, 9:08:05, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5969, loss_cls: 3.7863, loss: 3.7863 +2024-07-25 02:17:04,009 - pyskl - INFO - Epoch [82][3000/3746] lr: 4.290e-02, eta: 2 days, 9:06:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5814, loss_cls: 3.8640, loss: 3.8640 +2024-07-25 02:18:25,376 - pyskl - INFO - Epoch [82][3100/3746] lr: 4.287e-02, eta: 2 days, 9:05:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5942, loss_cls: 3.7906, loss: 3.7906 +2024-07-25 02:19:47,387 - pyskl - INFO - Epoch [82][3200/3746] lr: 4.285e-02, eta: 2 days, 9:04:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.6000, loss_cls: 3.7777, loss: 3.7777 +2024-07-25 02:21:08,677 - pyskl - INFO - Epoch [82][3300/3746] lr: 4.282e-02, eta: 2 days, 9:02:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5875, loss_cls: 3.8282, loss: 3.8282 +2024-07-25 02:22:30,421 - pyskl - INFO - Epoch [82][3400/3746] lr: 4.279e-02, eta: 2 days, 9:01:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5892, loss_cls: 3.7954, loss: 3.7954 +2024-07-25 02:23:51,737 - pyskl - INFO - Epoch [82][3500/3746] lr: 4.276e-02, eta: 2 days, 9:00:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5902, loss_cls: 3.8131, loss: 3.8131 +2024-07-25 02:25:13,268 - pyskl - INFO - Epoch [82][3600/3746] lr: 4.274e-02, eta: 2 days, 8:58:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5908, loss_cls: 3.7961, loss: 3.7961 +2024-07-25 02:26:34,981 - pyskl - INFO - Epoch [82][3700/3746] lr: 4.271e-02, eta: 2 days, 8:57:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5998, loss_cls: 3.7519, loss: 3.7519 +2024-07-25 02:27:14,521 - pyskl - INFO - Saving checkpoint at 82 epochs +2024-07-25 02:29:05,403 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 02:29:06,388 - pyskl - INFO - +top1_acc 0.2673 +top5_acc 0.5186 +2024-07-25 02:29:06,388 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 02:29:06,439 - pyskl - INFO - +mean_acc 0.2672 +2024-07-25 02:29:06,450 - pyskl - INFO - Epoch(val) [82][309] top1_acc: 0.2673, top5_acc: 0.5186, mean_class_accuracy: 0.2672 +2024-07-25 02:33:00,634 - pyskl - INFO - Epoch [83][100/3746] lr: 4.267e-02, eta: 2 days, 8:57:07, time: 2.342, data_time: 1.363, memory: 15990, top1_acc: 0.3425, top5_acc: 0.5986, loss_cls: 3.7498, loss: 3.7498 +2024-07-25 02:34:23,810 - pyskl - INFO - Epoch [83][200/3746] lr: 4.264e-02, eta: 2 days, 8:55:49, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.6008, loss_cls: 3.7349, loss: 3.7349 +2024-07-25 02:35:47,984 - pyskl - INFO - Epoch [83][300/3746] lr: 4.261e-02, eta: 2 days, 8:54:31, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5998, loss_cls: 3.7687, loss: 3.7687 +2024-07-25 02:37:11,681 - pyskl - INFO - Epoch [83][400/3746] lr: 4.259e-02, eta: 2 days, 8:53:14, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.5984, loss_cls: 3.7278, loss: 3.7278 +2024-07-25 02:38:35,243 - pyskl - INFO - Epoch [83][500/3746] lr: 4.256e-02, eta: 2 days, 8:51:56, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6044, loss_cls: 3.7358, loss: 3.7358 +2024-07-25 02:39:59,310 - pyskl - INFO - Epoch [83][600/3746] lr: 4.253e-02, eta: 2 days, 8:50:38, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.5992, loss_cls: 3.7592, loss: 3.7592 +2024-07-25 02:41:23,088 - pyskl - INFO - Epoch [83][700/3746] lr: 4.250e-02, eta: 2 days, 8:49:20, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5905, loss_cls: 3.7763, loss: 3.7763 +2024-07-25 02:42:46,437 - pyskl - INFO - Epoch [83][800/3746] lr: 4.247e-02, eta: 2 days, 8:48:02, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6048, loss_cls: 3.7384, loss: 3.7384 +2024-07-25 02:44:08,654 - pyskl - INFO - Epoch [83][900/3746] lr: 4.245e-02, eta: 2 days, 8:46:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6073, loss_cls: 3.7066, loss: 3.7066 +2024-07-25 02:45:31,425 - pyskl - INFO - Epoch [83][1000/3746] lr: 4.242e-02, eta: 2 days, 8:45:24, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6056, loss_cls: 3.7099, loss: 3.7099 +2024-07-25 02:46:54,540 - pyskl - INFO - Epoch [83][1100/3746] lr: 4.239e-02, eta: 2 days, 8:44:06, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.6008, loss_cls: 3.7709, loss: 3.7709 +2024-07-25 02:48:17,914 - pyskl - INFO - Epoch [83][1200/3746] lr: 4.236e-02, eta: 2 days, 8:42:47, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5958, loss_cls: 3.7824, loss: 3.7824 +2024-07-25 02:49:41,387 - pyskl - INFO - Epoch [83][1300/3746] lr: 4.234e-02, eta: 2 days, 8:41:29, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5933, loss_cls: 3.7539, loss: 3.7539 +2024-07-25 02:51:04,754 - pyskl - INFO - Epoch [83][1400/3746] lr: 4.231e-02, eta: 2 days, 8:40:11, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5998, loss_cls: 3.7665, loss: 3.7665 +2024-07-25 02:52:28,181 - pyskl - INFO - Epoch [83][1500/3746] lr: 4.228e-02, eta: 2 days, 8:38:53, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5872, loss_cls: 3.7787, loss: 3.7787 +2024-07-25 02:53:51,665 - pyskl - INFO - Epoch [83][1600/3746] lr: 4.225e-02, eta: 2 days, 8:37:35, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.5991, loss_cls: 3.7461, loss: 3.7461 +2024-07-25 02:55:14,355 - pyskl - INFO - Epoch [83][1700/3746] lr: 4.223e-02, eta: 2 days, 8:36:16, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5997, loss_cls: 3.7653, loss: 3.7653 +2024-07-25 02:56:37,159 - pyskl - INFO - Epoch [83][1800/3746] lr: 4.220e-02, eta: 2 days, 8:34:57, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.5991, loss_cls: 3.7418, loss: 3.7418 +2024-07-25 02:58:00,970 - pyskl - INFO - Epoch [83][1900/3746] lr: 4.217e-02, eta: 2 days, 8:33:40, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.5900, loss_cls: 3.7946, loss: 3.7946 +2024-07-25 02:59:24,055 - pyskl - INFO - Epoch [83][2000/3746] lr: 4.214e-02, eta: 2 days, 8:32:21, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5923, loss_cls: 3.7777, loss: 3.7777 +2024-07-25 03:00:47,205 - pyskl - INFO - Epoch [83][2100/3746] lr: 4.212e-02, eta: 2 days, 8:31:03, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5881, loss_cls: 3.7998, loss: 3.7998 +2024-07-25 03:02:09,890 - pyskl - INFO - Epoch [83][2200/3746] lr: 4.209e-02, eta: 2 days, 8:29:44, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.6008, loss_cls: 3.7492, loss: 3.7492 +2024-07-25 03:03:33,312 - pyskl - INFO - Epoch [83][2300/3746] lr: 4.206e-02, eta: 2 days, 8:28:26, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.5969, loss_cls: 3.7400, loss: 3.7400 +2024-07-25 03:04:55,944 - pyskl - INFO - Epoch [83][2400/3746] lr: 4.203e-02, eta: 2 days, 8:27:07, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5975, loss_cls: 3.7634, loss: 3.7634 +2024-07-25 03:06:18,792 - pyskl - INFO - Epoch [83][2500/3746] lr: 4.201e-02, eta: 2 days, 8:25:48, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5955, loss_cls: 3.7624, loss: 3.7624 +2024-07-25 03:07:42,259 - pyskl - INFO - Epoch [83][2600/3746] lr: 4.198e-02, eta: 2 days, 8:24:30, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5986, loss_cls: 3.7880, loss: 3.7880 +2024-07-25 03:09:05,569 - pyskl - INFO - Epoch [83][2700/3746] lr: 4.195e-02, eta: 2 days, 8:23:12, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5994, loss_cls: 3.7648, loss: 3.7648 +2024-07-25 03:10:28,513 - pyskl - INFO - Epoch [83][2800/3746] lr: 4.192e-02, eta: 2 days, 8:21:53, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5855, loss_cls: 3.8114, loss: 3.8114 +2024-07-25 03:11:51,733 - pyskl - INFO - Epoch [83][2900/3746] lr: 4.190e-02, eta: 2 days, 8:20:35, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6055, loss_cls: 3.7232, loss: 3.7232 +2024-07-25 03:13:14,336 - pyskl - INFO - Epoch [83][3000/3746] lr: 4.187e-02, eta: 2 days, 8:19:16, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.5852, loss_cls: 3.8058, loss: 3.8058 +2024-07-25 03:14:36,431 - pyskl - INFO - Epoch [83][3100/3746] lr: 4.184e-02, eta: 2 days, 8:17:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5941, loss_cls: 3.7807, loss: 3.7807 +2024-07-25 03:15:58,240 - pyskl - INFO - Epoch [83][3200/3746] lr: 4.181e-02, eta: 2 days, 8:16:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6050, loss_cls: 3.7297, loss: 3.7297 +2024-07-25 03:17:19,486 - pyskl - INFO - Epoch [83][3300/3746] lr: 4.178e-02, eta: 2 days, 8:15:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5941, loss_cls: 3.7788, loss: 3.7788 +2024-07-25 03:18:40,892 - pyskl - INFO - Epoch [83][3400/3746] lr: 4.176e-02, eta: 2 days, 8:13:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5834, loss_cls: 3.8423, loss: 3.8423 +2024-07-25 03:20:02,703 - pyskl - INFO - Epoch [83][3500/3746] lr: 4.173e-02, eta: 2 days, 8:12:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5938, loss_cls: 3.7806, loss: 3.7806 +2024-07-25 03:21:24,477 - pyskl - INFO - Epoch [83][3600/3746] lr: 4.170e-02, eta: 2 days, 8:11:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.5952, loss_cls: 3.8117, loss: 3.8117 +2024-07-25 03:22:46,202 - pyskl - INFO - Epoch [83][3700/3746] lr: 4.167e-02, eta: 2 days, 8:09:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5889, loss_cls: 3.8083, loss: 3.8083 +2024-07-25 03:23:25,768 - pyskl - INFO - Saving checkpoint at 83 epochs +2024-07-25 03:25:17,422 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 03:25:18,091 - pyskl - INFO - +top1_acc 0.2887 +top5_acc 0.5377 +2024-07-25 03:25:18,092 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 03:25:18,131 - pyskl - INFO - +mean_acc 0.2884 +2024-07-25 03:25:18,136 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_80.pth was removed +2024-07-25 03:25:18,370 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_83.pth. +2024-07-25 03:25:18,371 - pyskl - INFO - Best top1_acc is 0.2887 at 83 epoch. +2024-07-25 03:25:18,383 - pyskl - INFO - Epoch(val) [83][309] top1_acc: 0.2887, top5_acc: 0.5377, mean_class_accuracy: 0.2884 +2024-07-25 03:29:10,164 - pyskl - INFO - Epoch [84][100/3746] lr: 4.163e-02, eta: 2 days, 8:09:33, time: 2.318, data_time: 1.337, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6039, loss_cls: 3.7176, loss: 3.7176 +2024-07-25 03:30:32,794 - pyskl - INFO - Epoch [84][200/3746] lr: 4.161e-02, eta: 2 days, 8:08:14, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6025, loss_cls: 3.7073, loss: 3.7073 +2024-07-25 03:31:54,824 - pyskl - INFO - Epoch [84][300/3746] lr: 4.158e-02, eta: 2 days, 8:06:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6052, loss_cls: 3.7194, loss: 3.7194 +2024-07-25 03:33:16,553 - pyskl - INFO - Epoch [84][400/3746] lr: 4.155e-02, eta: 2 days, 8:05:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.5959, loss_cls: 3.7429, loss: 3.7429 +2024-07-25 03:34:38,163 - pyskl - INFO - Epoch [84][500/3746] lr: 4.152e-02, eta: 2 days, 8:04:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5920, loss_cls: 3.7867, loss: 3.7867 +2024-07-25 03:35:59,785 - pyskl - INFO - Epoch [84][600/3746] lr: 4.150e-02, eta: 2 days, 8:02:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.5938, loss_cls: 3.7749, loss: 3.7749 +2024-07-25 03:37:22,265 - pyskl - INFO - Epoch [84][700/3746] lr: 4.147e-02, eta: 2 days, 8:01:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6039, loss_cls: 3.7451, loss: 3.7451 +2024-07-25 03:38:44,167 - pyskl - INFO - Epoch [84][800/3746] lr: 4.144e-02, eta: 2 days, 8:00:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5980, loss_cls: 3.7676, loss: 3.7676 +2024-07-25 03:40:06,172 - pyskl - INFO - Epoch [84][900/3746] lr: 4.141e-02, eta: 2 days, 7:58:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6025, loss_cls: 3.7554, loss: 3.7554 +2024-07-25 03:41:28,206 - pyskl - INFO - Epoch [84][1000/3746] lr: 4.139e-02, eta: 2 days, 7:57:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5955, loss_cls: 3.7639, loss: 3.7639 +2024-07-25 03:42:49,914 - pyskl - INFO - Epoch [84][1100/3746] lr: 4.136e-02, eta: 2 days, 7:56:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6098, loss_cls: 3.6972, loss: 3.6972 +2024-07-25 03:44:11,679 - pyskl - INFO - Epoch [84][1200/3746] lr: 4.133e-02, eta: 2 days, 7:54:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5967, loss_cls: 3.7700, loss: 3.7700 +2024-07-25 03:45:33,325 - pyskl - INFO - Epoch [84][1300/3746] lr: 4.130e-02, eta: 2 days, 7:53:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5978, loss_cls: 3.7648, loss: 3.7648 +2024-07-25 03:46:55,092 - pyskl - INFO - Epoch [84][1400/3746] lr: 4.128e-02, eta: 2 days, 7:52:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5938, loss_cls: 3.7557, loss: 3.7557 +2024-07-25 03:48:17,397 - pyskl - INFO - Epoch [84][1500/3746] lr: 4.125e-02, eta: 2 days, 7:51:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5938, loss_cls: 3.7715, loss: 3.7715 +2024-07-25 03:49:39,378 - pyskl - INFO - Epoch [84][1600/3746] lr: 4.122e-02, eta: 2 days, 7:49:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6030, loss_cls: 3.7120, loss: 3.7120 +2024-07-25 03:51:01,874 - pyskl - INFO - Epoch [84][1700/3746] lr: 4.119e-02, eta: 2 days, 7:48:22, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.5972, loss_cls: 3.7729, loss: 3.7729 +2024-07-25 03:52:24,896 - pyskl - INFO - Epoch [84][1800/3746] lr: 4.117e-02, eta: 2 days, 7:47:03, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.6008, loss_cls: 3.7587, loss: 3.7587 +2024-07-25 03:53:47,097 - pyskl - INFO - Epoch [84][1900/3746] lr: 4.114e-02, eta: 2 days, 7:45:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5994, loss_cls: 3.7734, loss: 3.7734 +2024-07-25 03:55:08,950 - pyskl - INFO - Epoch [84][2000/3746] lr: 4.111e-02, eta: 2 days, 7:44:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5959, loss_cls: 3.7827, loss: 3.7827 +2024-07-25 03:56:31,052 - pyskl - INFO - Epoch [84][2100/3746] lr: 4.108e-02, eta: 2 days, 7:43:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5900, loss_cls: 3.8144, loss: 3.8144 +2024-07-25 03:57:52,596 - pyskl - INFO - Epoch [84][2200/3746] lr: 4.106e-02, eta: 2 days, 7:41:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.5998, loss_cls: 3.7597, loss: 3.7597 +2024-07-25 03:59:14,125 - pyskl - INFO - Epoch [84][2300/3746] lr: 4.103e-02, eta: 2 days, 7:40:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5900, loss_cls: 3.8178, loss: 3.8178 +2024-07-25 04:00:36,209 - pyskl - INFO - Epoch [84][2400/3746] lr: 4.100e-02, eta: 2 days, 7:39:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6052, loss_cls: 3.7142, loss: 3.7142 +2024-07-25 04:01:57,859 - pyskl - INFO - Epoch [84][2500/3746] lr: 4.097e-02, eta: 2 days, 7:37:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.5944, loss_cls: 3.7635, loss: 3.7635 +2024-07-25 04:03:19,392 - pyskl - INFO - Epoch [84][2600/3746] lr: 4.095e-02, eta: 2 days, 7:36:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6022, loss_cls: 3.7302, loss: 3.7302 +2024-07-25 04:04:41,361 - pyskl - INFO - Epoch [84][2700/3746] lr: 4.092e-02, eta: 2 days, 7:35:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.6059, loss_cls: 3.7283, loss: 3.7283 +2024-07-25 04:06:03,186 - pyskl - INFO - Epoch [84][2800/3746] lr: 4.089e-02, eta: 2 days, 7:33:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5927, loss_cls: 3.7704, loss: 3.7704 +2024-07-25 04:07:25,017 - pyskl - INFO - Epoch [84][2900/3746] lr: 4.086e-02, eta: 2 days, 7:32:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5920, loss_cls: 3.8019, loss: 3.8019 +2024-07-25 04:08:46,868 - pyskl - INFO - Epoch [84][3000/3746] lr: 4.084e-02, eta: 2 days, 7:31:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.5972, loss_cls: 3.7478, loss: 3.7478 +2024-07-25 04:10:08,476 - pyskl - INFO - Epoch [84][3100/3746] lr: 4.081e-02, eta: 2 days, 7:29:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.5934, loss_cls: 3.7656, loss: 3.7656 +2024-07-25 04:11:30,342 - pyskl - INFO - Epoch [84][3200/3746] lr: 4.078e-02, eta: 2 days, 7:28:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5941, loss_cls: 3.7880, loss: 3.7880 +2024-07-25 04:12:51,886 - pyskl - INFO - Epoch [84][3300/3746] lr: 4.075e-02, eta: 2 days, 7:27:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6166, loss_cls: 3.6860, loss: 3.6860 +2024-07-25 04:14:13,469 - pyskl - INFO - Epoch [84][3400/3746] lr: 4.073e-02, eta: 2 days, 7:25:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5891, loss_cls: 3.8055, loss: 3.8055 +2024-07-25 04:15:35,375 - pyskl - INFO - Epoch [84][3500/3746] lr: 4.070e-02, eta: 2 days, 7:24:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5991, loss_cls: 3.7735, loss: 3.7735 +2024-07-25 04:16:56,891 - pyskl - INFO - Epoch [84][3600/3746] lr: 4.067e-02, eta: 2 days, 7:23:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6039, loss_cls: 3.7242, loss: 3.7242 +2024-07-25 04:18:18,978 - pyskl - INFO - Epoch [84][3700/3746] lr: 4.064e-02, eta: 2 days, 7:21:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.6012, loss_cls: 3.7487, loss: 3.7487 +2024-07-25 04:18:58,839 - pyskl - INFO - Saving checkpoint at 84 epochs +2024-07-25 04:20:51,375 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 04:20:52,035 - pyskl - INFO - +top1_acc 0.2651 +top5_acc 0.5072 +2024-07-25 04:20:52,036 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 04:20:52,077 - pyskl - INFO - +mean_acc 0.2649 +2024-07-25 04:20:52,088 - pyskl - INFO - Epoch(val) [84][309] top1_acc: 0.2651, top5_acc: 0.5072, mean_class_accuracy: 0.2649 +2024-07-25 04:24:41,126 - pyskl - INFO - Epoch [85][100/3746] lr: 4.060e-02, eta: 2 days, 7:21:19, time: 2.290, data_time: 1.310, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6139, loss_cls: 3.6613, loss: 3.6613 +2024-07-25 04:26:03,267 - pyskl - INFO - Epoch [85][200/3746] lr: 4.058e-02, eta: 2 days, 7:20:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6122, loss_cls: 3.6564, loss: 3.6564 +2024-07-25 04:27:25,376 - pyskl - INFO - Epoch [85][300/3746] lr: 4.055e-02, eta: 2 days, 7:18:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6052, loss_cls: 3.6942, loss: 3.6942 +2024-07-25 04:28:46,848 - pyskl - INFO - Epoch [85][400/3746] lr: 4.052e-02, eta: 2 days, 7:17:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6034, loss_cls: 3.7057, loss: 3.7057 +2024-07-25 04:30:08,978 - pyskl - INFO - Epoch [85][500/3746] lr: 4.049e-02, eta: 2 days, 7:16:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6095, loss_cls: 3.6944, loss: 3.6944 +2024-07-25 04:31:31,299 - pyskl - INFO - Epoch [85][600/3746] lr: 4.047e-02, eta: 2 days, 7:14:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6034, loss_cls: 3.7221, loss: 3.7221 +2024-07-25 04:32:53,654 - pyskl - INFO - Epoch [85][700/3746] lr: 4.044e-02, eta: 2 days, 7:13:22, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6048, loss_cls: 3.6717, loss: 3.6717 +2024-07-25 04:34:15,602 - pyskl - INFO - Epoch [85][800/3746] lr: 4.041e-02, eta: 2 days, 7:12:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.5933, loss_cls: 3.7380, loss: 3.7380 +2024-07-25 04:35:37,219 - pyskl - INFO - Epoch [85][900/3746] lr: 4.038e-02, eta: 2 days, 7:10:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.5939, loss_cls: 3.7672, loss: 3.7672 +2024-07-25 04:36:59,240 - pyskl - INFO - Epoch [85][1000/3746] lr: 4.036e-02, eta: 2 days, 7:09:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.6073, loss_cls: 3.7412, loss: 3.7412 +2024-07-25 04:38:20,676 - pyskl - INFO - Epoch [85][1100/3746] lr: 4.033e-02, eta: 2 days, 7:08:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.5969, loss_cls: 3.7267, loss: 3.7267 +2024-07-25 04:39:42,642 - pyskl - INFO - Epoch [85][1200/3746] lr: 4.030e-02, eta: 2 days, 7:06:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6019, loss_cls: 3.7396, loss: 3.7396 +2024-07-25 04:41:04,412 - pyskl - INFO - Epoch [85][1300/3746] lr: 4.027e-02, eta: 2 days, 7:05:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5909, loss_cls: 3.7788, loss: 3.7788 +2024-07-25 04:42:26,186 - pyskl - INFO - Epoch [85][1400/3746] lr: 4.025e-02, eta: 2 days, 7:04:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6034, loss_cls: 3.7592, loss: 3.7592 +2024-07-25 04:43:49,238 - pyskl - INFO - Epoch [85][1500/3746] lr: 4.022e-02, eta: 2 days, 7:02:45, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6045, loss_cls: 3.6944, loss: 3.6944 +2024-07-25 04:45:10,947 - pyskl - INFO - Epoch [85][1600/3746] lr: 4.019e-02, eta: 2 days, 7:01:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6017, loss_cls: 3.7237, loss: 3.7237 +2024-07-25 04:46:33,232 - pyskl - INFO - Epoch [85][1700/3746] lr: 4.016e-02, eta: 2 days, 7:00:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5962, loss_cls: 3.7496, loss: 3.7496 +2024-07-25 04:47:55,851 - pyskl - INFO - Epoch [85][1800/3746] lr: 4.014e-02, eta: 2 days, 6:58:47, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.5998, loss_cls: 3.7633, loss: 3.7633 +2024-07-25 04:49:18,696 - pyskl - INFO - Epoch [85][1900/3746] lr: 4.011e-02, eta: 2 days, 6:57:28, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5978, loss_cls: 3.7746, loss: 3.7746 +2024-07-25 04:50:40,468 - pyskl - INFO - Epoch [85][2000/3746] lr: 4.008e-02, eta: 2 days, 6:56:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5997, loss_cls: 3.7309, loss: 3.7309 +2024-07-25 04:52:01,969 - pyskl - INFO - Epoch [85][2100/3746] lr: 4.006e-02, eta: 2 days, 6:54:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5933, loss_cls: 3.7737, loss: 3.7737 +2024-07-25 04:53:24,137 - pyskl - INFO - Epoch [85][2200/3746] lr: 4.003e-02, eta: 2 days, 6:53:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.6047, loss_cls: 3.7544, loss: 3.7544 +2024-07-25 04:54:45,204 - pyskl - INFO - Epoch [85][2300/3746] lr: 4.000e-02, eta: 2 days, 6:52:09, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6039, loss_cls: 3.7292, loss: 3.7292 +2024-07-25 04:56:06,578 - pyskl - INFO - Epoch [85][2400/3746] lr: 3.997e-02, eta: 2 days, 6:50:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.6009, loss_cls: 3.7636, loss: 3.7636 +2024-07-25 04:57:28,420 - pyskl - INFO - Epoch [85][2500/3746] lr: 3.995e-02, eta: 2 days, 6:49:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5828, loss_cls: 3.8220, loss: 3.8220 +2024-07-25 04:58:49,545 - pyskl - INFO - Epoch [85][2600/3746] lr: 3.992e-02, eta: 2 days, 6:48:08, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5934, loss_cls: 3.7688, loss: 3.7688 +2024-07-25 05:00:10,876 - pyskl - INFO - Epoch [85][2700/3746] lr: 3.989e-02, eta: 2 days, 6:46:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6011, loss_cls: 3.7491, loss: 3.7491 +2024-07-25 05:01:32,169 - pyskl - INFO - Epoch [85][2800/3746] lr: 3.986e-02, eta: 2 days, 6:45:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6055, loss_cls: 3.7325, loss: 3.7325 +2024-07-25 05:02:53,717 - pyskl - INFO - Epoch [85][2900/3746] lr: 3.984e-02, eta: 2 days, 6:44:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.5936, loss_cls: 3.7665, loss: 3.7665 +2024-07-25 05:04:15,955 - pyskl - INFO - Epoch [85][3000/3746] lr: 3.981e-02, eta: 2 days, 6:42:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5939, loss_cls: 3.8091, loss: 3.8091 +2024-07-25 05:05:37,913 - pyskl - INFO - Epoch [85][3100/3746] lr: 3.978e-02, eta: 2 days, 6:41:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6061, loss_cls: 3.7196, loss: 3.7196 +2024-07-25 05:06:59,498 - pyskl - INFO - Epoch [85][3200/3746] lr: 3.975e-02, eta: 2 days, 6:40:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5939, loss_cls: 3.7667, loss: 3.7667 +2024-07-25 05:08:21,501 - pyskl - INFO - Epoch [85][3300/3746] lr: 3.973e-02, eta: 2 days, 6:38:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.5962, loss_cls: 3.7369, loss: 3.7369 +2024-07-25 05:09:43,105 - pyskl - INFO - Epoch [85][3400/3746] lr: 3.970e-02, eta: 2 days, 6:37:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.5961, loss_cls: 3.7793, loss: 3.7793 +2024-07-25 05:11:04,901 - pyskl - INFO - Epoch [85][3500/3746] lr: 3.967e-02, eta: 2 days, 6:36:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6025, loss_cls: 3.7487, loss: 3.7487 +2024-07-25 05:12:26,511 - pyskl - INFO - Epoch [85][3600/3746] lr: 3.964e-02, eta: 2 days, 6:34:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.6059, loss_cls: 3.7486, loss: 3.7486 +2024-07-25 05:13:48,041 - pyskl - INFO - Epoch [85][3700/3746] lr: 3.962e-02, eta: 2 days, 6:33:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6069, loss_cls: 3.7060, loss: 3.7060 +2024-07-25 05:14:27,877 - pyskl - INFO - Saving checkpoint at 85 epochs +2024-07-25 05:16:20,276 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 05:16:20,945 - pyskl - INFO - +top1_acc 0.2860 +top5_acc 0.5419 +2024-07-25 05:16:20,945 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 05:16:20,988 - pyskl - INFO - +mean_acc 0.2857 +2024-07-25 05:16:21,000 - pyskl - INFO - Epoch(val) [85][309] top1_acc: 0.2860, top5_acc: 0.5419, mean_class_accuracy: 0.2857 +2024-07-25 05:20:17,488 - pyskl - INFO - Epoch [86][100/3746] lr: 3.958e-02, eta: 2 days, 6:33:03, time: 2.365, data_time: 1.367, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6222, loss_cls: 3.6336, loss: 3.6336 +2024-07-25 05:21:40,367 - pyskl - INFO - Epoch [86][200/3746] lr: 3.955e-02, eta: 2 days, 6:31:44, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6094, loss_cls: 3.6634, loss: 3.6634 +2024-07-25 05:23:02,991 - pyskl - INFO - Epoch [86][300/3746] lr: 3.952e-02, eta: 2 days, 6:30:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6122, loss_cls: 3.7450, loss: 3.7450 +2024-07-25 05:24:25,022 - pyskl - INFO - Epoch [86][400/3746] lr: 3.950e-02, eta: 2 days, 6:29:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6012, loss_cls: 3.7362, loss: 3.7362 +2024-07-25 05:25:47,083 - pyskl - INFO - Epoch [86][500/3746] lr: 3.947e-02, eta: 2 days, 6:27:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.5998, loss_cls: 3.7355, loss: 3.7355 +2024-07-25 05:27:08,595 - pyskl - INFO - Epoch [86][600/3746] lr: 3.944e-02, eta: 2 days, 6:26:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6123, loss_cls: 3.6755, loss: 3.6755 +2024-07-25 05:28:30,524 - pyskl - INFO - Epoch [86][700/3746] lr: 3.941e-02, eta: 2 days, 6:25:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6070, loss_cls: 3.7217, loss: 3.7217 +2024-07-25 05:29:52,518 - pyskl - INFO - Epoch [86][800/3746] lr: 3.939e-02, eta: 2 days, 6:23:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6117, loss_cls: 3.7125, loss: 3.7125 +2024-07-25 05:31:14,863 - pyskl - INFO - Epoch [86][900/3746] lr: 3.936e-02, eta: 2 days, 6:22:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6077, loss_cls: 3.6991, loss: 3.6991 +2024-07-25 05:32:36,470 - pyskl - INFO - Epoch [86][1000/3746] lr: 3.933e-02, eta: 2 days, 6:21:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6080, loss_cls: 3.7234, loss: 3.7234 +2024-07-25 05:33:57,803 - pyskl - INFO - Epoch [86][1100/3746] lr: 3.930e-02, eta: 2 days, 6:19:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.6006, loss_cls: 3.7513, loss: 3.7513 +2024-07-25 05:35:19,897 - pyskl - INFO - Epoch [86][1200/3746] lr: 3.928e-02, eta: 2 days, 6:18:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6047, loss_cls: 3.6970, loss: 3.6970 +2024-07-25 05:36:41,758 - pyskl - INFO - Epoch [86][1300/3746] lr: 3.925e-02, eta: 2 days, 6:17:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6095, loss_cls: 3.6989, loss: 3.6989 +2024-07-25 05:38:03,463 - pyskl - INFO - Epoch [86][1400/3746] lr: 3.922e-02, eta: 2 days, 6:15:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6211, loss_cls: 3.6412, loss: 3.6412 +2024-07-25 05:39:25,266 - pyskl - INFO - Epoch [86][1500/3746] lr: 3.919e-02, eta: 2 days, 6:14:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5953, loss_cls: 3.7698, loss: 3.7698 +2024-07-25 05:40:46,890 - pyskl - INFO - Epoch [86][1600/3746] lr: 3.917e-02, eta: 2 days, 6:13:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5958, loss_cls: 3.7601, loss: 3.7601 +2024-07-25 05:42:08,745 - pyskl - INFO - Epoch [86][1700/3746] lr: 3.914e-02, eta: 2 days, 6:11:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6020, loss_cls: 3.7309, loss: 3.7309 +2024-07-25 05:43:31,661 - pyskl - INFO - Epoch [86][1800/3746] lr: 3.911e-02, eta: 2 days, 6:10:28, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5923, loss_cls: 3.7433, loss: 3.7433 +2024-07-25 05:44:53,349 - pyskl - INFO - Epoch [86][1900/3746] lr: 3.909e-02, eta: 2 days, 6:09:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6041, loss_cls: 3.7366, loss: 3.7366 +2024-07-25 05:46:15,412 - pyskl - INFO - Epoch [86][2000/3746] lr: 3.906e-02, eta: 2 days, 6:07:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.6014, loss_cls: 3.7547, loss: 3.7547 +2024-07-25 05:47:37,458 - pyskl - INFO - Epoch [86][2100/3746] lr: 3.903e-02, eta: 2 days, 6:06:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6083, loss_cls: 3.7046, loss: 3.7046 +2024-07-25 05:48:58,999 - pyskl - INFO - Epoch [86][2200/3746] lr: 3.900e-02, eta: 2 days, 6:05:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6014, loss_cls: 3.7484, loss: 3.7484 +2024-07-25 05:50:21,071 - pyskl - INFO - Epoch [86][2300/3746] lr: 3.898e-02, eta: 2 days, 6:03:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.5997, loss_cls: 3.7303, loss: 3.7303 +2024-07-25 05:51:42,655 - pyskl - INFO - Epoch [86][2400/3746] lr: 3.895e-02, eta: 2 days, 6:02:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5947, loss_cls: 3.7616, loss: 3.7616 +2024-07-25 05:53:04,475 - pyskl - INFO - Epoch [86][2500/3746] lr: 3.892e-02, eta: 2 days, 6:01:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6048, loss_cls: 3.7126, loss: 3.7126 +2024-07-25 05:54:26,628 - pyskl - INFO - Epoch [86][2600/3746] lr: 3.889e-02, eta: 2 days, 5:59:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6038, loss_cls: 3.7133, loss: 3.7133 +2024-07-25 05:55:48,289 - pyskl - INFO - Epoch [86][2700/3746] lr: 3.887e-02, eta: 2 days, 5:58:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6034, loss_cls: 3.7487, loss: 3.7487 +2024-07-25 05:57:09,947 - pyskl - INFO - Epoch [86][2800/3746] lr: 3.884e-02, eta: 2 days, 5:57:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.6027, loss_cls: 3.7282, loss: 3.7282 +2024-07-25 05:58:31,574 - pyskl - INFO - Epoch [86][2900/3746] lr: 3.881e-02, eta: 2 days, 5:55:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6069, loss_cls: 3.7065, loss: 3.7065 +2024-07-25 05:59:53,106 - pyskl - INFO - Epoch [86][3000/3746] lr: 3.879e-02, eta: 2 days, 5:54:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5916, loss_cls: 3.7658, loss: 3.7658 +2024-07-25 06:01:14,714 - pyskl - INFO - Epoch [86][3100/3746] lr: 3.876e-02, eta: 2 days, 5:53:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6039, loss_cls: 3.7239, loss: 3.7239 +2024-07-25 06:02:35,936 - pyskl - INFO - Epoch [86][3200/3746] lr: 3.873e-02, eta: 2 days, 5:51:50, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.6000, loss_cls: 3.7543, loss: 3.7543 +2024-07-25 06:03:57,389 - pyskl - INFO - Epoch [86][3300/3746] lr: 3.870e-02, eta: 2 days, 5:50:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.6009, loss_cls: 3.7667, loss: 3.7667 +2024-07-25 06:05:18,986 - pyskl - INFO - Epoch [86][3400/3746] lr: 3.868e-02, eta: 2 days, 5:49:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6038, loss_cls: 3.7267, loss: 3.7267 +2024-07-25 06:06:41,140 - pyskl - INFO - Epoch [86][3500/3746] lr: 3.865e-02, eta: 2 days, 5:47:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5994, loss_cls: 3.7425, loss: 3.7425 +2024-07-25 06:08:02,790 - pyskl - INFO - Epoch [86][3600/3746] lr: 3.862e-02, eta: 2 days, 5:46:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.5978, loss_cls: 3.7392, loss: 3.7392 +2024-07-25 06:09:24,484 - pyskl - INFO - Epoch [86][3700/3746] lr: 3.860e-02, eta: 2 days, 5:45:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5978, loss_cls: 3.7640, loss: 3.7640 +2024-07-25 06:10:03,794 - pyskl - INFO - Saving checkpoint at 86 epochs +2024-07-25 06:11:53,931 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 06:11:54,591 - pyskl - INFO - +top1_acc 0.2789 +top5_acc 0.5334 +2024-07-25 06:11:54,591 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 06:11:54,631 - pyskl - INFO - +mean_acc 0.2788 +2024-07-25 06:11:54,642 - pyskl - INFO - Epoch(val) [86][309] top1_acc: 0.2789, top5_acc: 0.5334, mean_class_accuracy: 0.2788 +2024-07-25 06:15:42,649 - pyskl - INFO - Epoch [87][100/3746] lr: 3.856e-02, eta: 2 days, 5:44:34, time: 2.280, data_time: 1.301, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6192, loss_cls: 3.6454, loss: 3.6454 +2024-07-25 06:17:04,821 - pyskl - INFO - Epoch [87][200/3746] lr: 3.853e-02, eta: 2 days, 5:43:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6144, loss_cls: 3.6682, loss: 3.6682 +2024-07-25 06:18:26,249 - pyskl - INFO - Epoch [87][300/3746] lr: 3.850e-02, eta: 2 days, 5:41:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6028, loss_cls: 3.7290, loss: 3.7290 +2024-07-25 06:19:48,219 - pyskl - INFO - Epoch [87][400/3746] lr: 3.847e-02, eta: 2 days, 5:40:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6217, loss_cls: 3.6297, loss: 3.6297 +2024-07-25 06:21:10,029 - pyskl - INFO - Epoch [87][500/3746] lr: 3.845e-02, eta: 2 days, 5:39:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6173, loss_cls: 3.6771, loss: 3.6771 +2024-07-25 06:22:32,557 - pyskl - INFO - Epoch [87][600/3746] lr: 3.842e-02, eta: 2 days, 5:37:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.6023, loss_cls: 3.7403, loss: 3.7403 +2024-07-25 06:23:55,673 - pyskl - INFO - Epoch [87][700/3746] lr: 3.839e-02, eta: 2 days, 5:36:36, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6105, loss_cls: 3.7045, loss: 3.7045 +2024-07-25 06:25:17,621 - pyskl - INFO - Epoch [87][800/3746] lr: 3.837e-02, eta: 2 days, 5:35:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6072, loss_cls: 3.7059, loss: 3.7059 +2024-07-25 06:26:40,081 - pyskl - INFO - Epoch [87][900/3746] lr: 3.834e-02, eta: 2 days, 5:33:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5992, loss_cls: 3.7501, loss: 3.7501 +2024-07-25 06:28:01,595 - pyskl - INFO - Epoch [87][1000/3746] lr: 3.831e-02, eta: 2 days, 5:32:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6105, loss_cls: 3.7109, loss: 3.7109 +2024-07-25 06:29:23,405 - pyskl - INFO - Epoch [87][1100/3746] lr: 3.828e-02, eta: 2 days, 5:31:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6059, loss_cls: 3.7120, loss: 3.7120 +2024-07-25 06:30:45,127 - pyskl - INFO - Epoch [87][1200/3746] lr: 3.826e-02, eta: 2 days, 5:29:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6041, loss_cls: 3.7385, loss: 3.7385 +2024-07-25 06:32:06,444 - pyskl - INFO - Epoch [87][1300/3746] lr: 3.823e-02, eta: 2 days, 5:28:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.5889, loss_cls: 3.7712, loss: 3.7712 +2024-07-25 06:33:27,975 - pyskl - INFO - Epoch [87][1400/3746] lr: 3.820e-02, eta: 2 days, 5:27:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6066, loss_cls: 3.7164, loss: 3.7164 +2024-07-25 06:34:49,627 - pyskl - INFO - Epoch [87][1500/3746] lr: 3.817e-02, eta: 2 days, 5:25:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.6042, loss_cls: 3.7303, loss: 3.7303 +2024-07-25 06:36:11,803 - pyskl - INFO - Epoch [87][1600/3746] lr: 3.815e-02, eta: 2 days, 5:24:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6095, loss_cls: 3.7007, loss: 3.7007 +2024-07-25 06:37:33,882 - pyskl - INFO - Epoch [87][1700/3746] lr: 3.812e-02, eta: 2 days, 5:23:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6061, loss_cls: 3.7163, loss: 3.7163 +2024-07-25 06:38:56,056 - pyskl - INFO - Epoch [87][1800/3746] lr: 3.809e-02, eta: 2 days, 5:21:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6091, loss_cls: 3.6960, loss: 3.6960 +2024-07-25 06:40:18,945 - pyskl - INFO - Epoch [87][1900/3746] lr: 3.807e-02, eta: 2 days, 5:20:38, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6041, loss_cls: 3.7320, loss: 3.7320 +2024-07-25 06:41:41,540 - pyskl - INFO - Epoch [87][2000/3746] lr: 3.804e-02, eta: 2 days, 5:19:18, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5947, loss_cls: 3.7575, loss: 3.7575 +2024-07-25 06:43:02,946 - pyskl - INFO - Epoch [87][2100/3746] lr: 3.801e-02, eta: 2 days, 5:17:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6031, loss_cls: 3.7135, loss: 3.7135 +2024-07-25 06:44:25,149 - pyskl - INFO - Epoch [87][2200/3746] lr: 3.798e-02, eta: 2 days, 5:16:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6102, loss_cls: 3.7091, loss: 3.7091 +2024-07-25 06:45:47,255 - pyskl - INFO - Epoch [87][2300/3746] lr: 3.796e-02, eta: 2 days, 5:15:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6084, loss_cls: 3.7123, loss: 3.7123 +2024-07-25 06:47:08,991 - pyskl - INFO - Epoch [87][2400/3746] lr: 3.793e-02, eta: 2 days, 5:13:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6053, loss_cls: 3.7118, loss: 3.7118 +2024-07-25 06:48:30,516 - pyskl - INFO - Epoch [87][2500/3746] lr: 3.790e-02, eta: 2 days, 5:12:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5953, loss_cls: 3.7588, loss: 3.7588 +2024-07-25 06:49:52,531 - pyskl - INFO - Epoch [87][2600/3746] lr: 3.788e-02, eta: 2 days, 5:11:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.5989, loss_cls: 3.7548, loss: 3.7548 +2024-07-25 06:51:14,728 - pyskl - INFO - Epoch [87][2700/3746] lr: 3.785e-02, eta: 2 days, 5:09:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6030, loss_cls: 3.7518, loss: 3.7518 +2024-07-25 06:52:36,204 - pyskl - INFO - Epoch [87][2800/3746] lr: 3.782e-02, eta: 2 days, 5:08:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.5964, loss_cls: 3.7288, loss: 3.7288 +2024-07-25 06:53:57,816 - pyskl - INFO - Epoch [87][2900/3746] lr: 3.779e-02, eta: 2 days, 5:07:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6084, loss_cls: 3.6937, loss: 3.6937 +2024-07-25 06:55:19,545 - pyskl - INFO - Epoch [87][3000/3746] lr: 3.777e-02, eta: 2 days, 5:05:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6036, loss_cls: 3.7305, loss: 3.7305 +2024-07-25 06:56:41,272 - pyskl - INFO - Epoch [87][3100/3746] lr: 3.774e-02, eta: 2 days, 5:04:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.6062, loss_cls: 3.7516, loss: 3.7516 +2024-07-25 06:58:03,103 - pyskl - INFO - Epoch [87][3200/3746] lr: 3.771e-02, eta: 2 days, 5:03:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6062, loss_cls: 3.6958, loss: 3.6958 +2024-07-25 06:59:24,791 - pyskl - INFO - Epoch [87][3300/3746] lr: 3.769e-02, eta: 2 days, 5:01:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3452, top5_acc: 0.6028, loss_cls: 3.7273, loss: 3.7273 +2024-07-25 07:00:46,709 - pyskl - INFO - Epoch [87][3400/3746] lr: 3.766e-02, eta: 2 days, 5:00:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.5997, loss_cls: 3.7324, loss: 3.7324 +2024-07-25 07:02:08,259 - pyskl - INFO - Epoch [87][3500/3746] lr: 3.763e-02, eta: 2 days, 4:59:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.6056, loss_cls: 3.7193, loss: 3.7193 +2024-07-25 07:03:30,058 - pyskl - INFO - Epoch [87][3600/3746] lr: 3.761e-02, eta: 2 days, 4:57:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6102, loss_cls: 3.6989, loss: 3.6989 +2024-07-25 07:04:51,755 - pyskl - INFO - Epoch [87][3700/3746] lr: 3.758e-02, eta: 2 days, 4:56:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.6042, loss_cls: 3.7119, loss: 3.7119 +2024-07-25 07:05:31,831 - pyskl - INFO - Saving checkpoint at 87 epochs +2024-07-25 07:07:24,893 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 07:07:25,562 - pyskl - INFO - +top1_acc 0.2882 +top5_acc 0.5409 +2024-07-25 07:07:25,562 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 07:07:25,607 - pyskl - INFO - +mean_acc 0.2880 +2024-07-25 07:07:25,618 - pyskl - INFO - Epoch(val) [87][309] top1_acc: 0.2882, top5_acc: 0.5409, mean_class_accuracy: 0.2880 +2024-07-25 07:11:12,429 - pyskl - INFO - Epoch [88][100/3746] lr: 3.754e-02, eta: 2 days, 4:55:59, time: 2.268, data_time: 1.290, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6095, loss_cls: 3.6818, loss: 3.6818 +2024-07-25 07:12:35,468 - pyskl - INFO - Epoch [88][200/3746] lr: 3.751e-02, eta: 2 days, 4:54:40, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6166, loss_cls: 3.6020, loss: 3.6020 +2024-07-25 07:13:58,119 - pyskl - INFO - Epoch [88][300/3746] lr: 3.748e-02, eta: 2 days, 4:53:21, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6092, loss_cls: 3.6841, loss: 3.6841 +2024-07-25 07:15:20,399 - pyskl - INFO - Epoch [88][400/3746] lr: 3.746e-02, eta: 2 days, 4:52:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6145, loss_cls: 3.6611, loss: 3.6611 +2024-07-25 07:16:41,945 - pyskl - INFO - Epoch [88][500/3746] lr: 3.743e-02, eta: 2 days, 4:50:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6125, loss_cls: 3.6822, loss: 3.6822 +2024-07-25 07:18:03,681 - pyskl - INFO - Epoch [88][600/3746] lr: 3.740e-02, eta: 2 days, 4:49:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6145, loss_cls: 3.6885, loss: 3.6885 +2024-07-25 07:19:26,262 - pyskl - INFO - Epoch [88][700/3746] lr: 3.738e-02, eta: 2 days, 4:48:01, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6152, loss_cls: 3.6715, loss: 3.6715 +2024-07-25 07:20:48,250 - pyskl - INFO - Epoch [88][800/3746] lr: 3.735e-02, eta: 2 days, 4:46:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6034, loss_cls: 3.7121, loss: 3.7121 +2024-07-25 07:22:10,764 - pyskl - INFO - Epoch [88][900/3746] lr: 3.732e-02, eta: 2 days, 4:45:22, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6117, loss_cls: 3.6828, loss: 3.6828 +2024-07-25 07:23:32,555 - pyskl - INFO - Epoch [88][1000/3746] lr: 3.730e-02, eta: 2 days, 4:44:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6119, loss_cls: 3.7000, loss: 3.7000 +2024-07-25 07:24:54,232 - pyskl - INFO - Epoch [88][1100/3746] lr: 3.727e-02, eta: 2 days, 4:42:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.6061, loss_cls: 3.7235, loss: 3.7235 +2024-07-25 07:26:15,596 - pyskl - INFO - Epoch [88][1200/3746] lr: 3.724e-02, eta: 2 days, 4:41:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6059, loss_cls: 3.7219, loss: 3.7219 +2024-07-25 07:27:37,322 - pyskl - INFO - Epoch [88][1300/3746] lr: 3.721e-02, eta: 2 days, 4:40:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6123, loss_cls: 3.6660, loss: 3.6660 +2024-07-25 07:28:59,093 - pyskl - INFO - Epoch [88][1400/3746] lr: 3.719e-02, eta: 2 days, 4:38:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6136, loss_cls: 3.6751, loss: 3.6751 +2024-07-25 07:30:21,001 - pyskl - INFO - Epoch [88][1500/3746] lr: 3.716e-02, eta: 2 days, 4:37:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6098, loss_cls: 3.7128, loss: 3.7128 +2024-07-25 07:31:43,178 - pyskl - INFO - Epoch [88][1600/3746] lr: 3.713e-02, eta: 2 days, 4:36:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6044, loss_cls: 3.7300, loss: 3.7300 +2024-07-25 07:33:05,189 - pyskl - INFO - Epoch [88][1700/3746] lr: 3.711e-02, eta: 2 days, 4:34:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.6017, loss_cls: 3.7312, loss: 3.7312 +2024-07-25 07:34:27,810 - pyskl - INFO - Epoch [88][1800/3746] lr: 3.708e-02, eta: 2 days, 4:33:22, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6056, loss_cls: 3.6944, loss: 3.6944 +2024-07-25 07:35:50,370 - pyskl - INFO - Epoch [88][1900/3746] lr: 3.705e-02, eta: 2 days, 4:32:02, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6022, loss_cls: 3.7181, loss: 3.7181 +2024-07-25 07:37:12,613 - pyskl - INFO - Epoch [88][2000/3746] lr: 3.703e-02, eta: 2 days, 4:30:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6133, loss_cls: 3.6575, loss: 3.6575 +2024-07-25 07:38:34,441 - pyskl - INFO - Epoch [88][2100/3746] lr: 3.700e-02, eta: 2 days, 4:29:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6130, loss_cls: 3.6966, loss: 3.6966 +2024-07-25 07:39:56,680 - pyskl - INFO - Epoch [88][2200/3746] lr: 3.697e-02, eta: 2 days, 4:28:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6081, loss_cls: 3.7049, loss: 3.7049 +2024-07-25 07:41:18,342 - pyskl - INFO - Epoch [88][2300/3746] lr: 3.694e-02, eta: 2 days, 4:26:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6070, loss_cls: 3.7255, loss: 3.7255 +2024-07-25 07:42:39,719 - pyskl - INFO - Epoch [88][2400/3746] lr: 3.692e-02, eta: 2 days, 4:25:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6144, loss_cls: 3.7071, loss: 3.7071 +2024-07-25 07:44:01,949 - pyskl - INFO - Epoch [88][2500/3746] lr: 3.689e-02, eta: 2 days, 4:24:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6095, loss_cls: 3.7116, loss: 3.7116 +2024-07-25 07:45:23,304 - pyskl - INFO - Epoch [88][2600/3746] lr: 3.686e-02, eta: 2 days, 4:22:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6092, loss_cls: 3.6970, loss: 3.6970 +2024-07-25 07:46:45,068 - pyskl - INFO - Epoch [88][2700/3746] lr: 3.684e-02, eta: 2 days, 4:21:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6092, loss_cls: 3.7136, loss: 3.7136 +2024-07-25 07:48:06,752 - pyskl - INFO - Epoch [88][2800/3746] lr: 3.681e-02, eta: 2 days, 4:20:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6109, loss_cls: 3.7093, loss: 3.7093 +2024-07-25 07:49:28,617 - pyskl - INFO - Epoch [88][2900/3746] lr: 3.678e-02, eta: 2 days, 4:18:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6052, loss_cls: 3.7381, loss: 3.7381 +2024-07-25 07:50:50,412 - pyskl - INFO - Epoch [88][3000/3746] lr: 3.676e-02, eta: 2 days, 4:17:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6147, loss_cls: 3.6735, loss: 3.6735 +2024-07-25 07:52:12,129 - pyskl - INFO - Epoch [88][3100/3746] lr: 3.673e-02, eta: 2 days, 4:16:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6056, loss_cls: 3.7104, loss: 3.7104 +2024-07-25 07:53:33,825 - pyskl - INFO - Epoch [88][3200/3746] lr: 3.670e-02, eta: 2 days, 4:14:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.5973, loss_cls: 3.7436, loss: 3.7436 +2024-07-25 07:54:55,455 - pyskl - INFO - Epoch [88][3300/3746] lr: 3.667e-02, eta: 2 days, 4:13:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6002, loss_cls: 3.7460, loss: 3.7460 +2024-07-25 07:56:17,395 - pyskl - INFO - Epoch [88][3400/3746] lr: 3.665e-02, eta: 2 days, 4:12:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.6034, loss_cls: 3.7115, loss: 3.7115 +2024-07-25 07:57:38,951 - pyskl - INFO - Epoch [88][3500/3746] lr: 3.662e-02, eta: 2 days, 4:10:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6066, loss_cls: 3.7131, loss: 3.7131 +2024-07-25 07:59:00,323 - pyskl - INFO - Epoch [88][3600/3746] lr: 3.659e-02, eta: 2 days, 4:09:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6050, loss_cls: 3.7240, loss: 3.7240 +2024-07-25 08:00:22,008 - pyskl - INFO - Epoch [88][3700/3746] lr: 3.657e-02, eta: 2 days, 4:08:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6034, loss_cls: 3.7331, loss: 3.7331 +2024-07-25 08:01:01,780 - pyskl - INFO - Saving checkpoint at 88 epochs +2024-07-25 08:02:54,687 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 08:02:55,343 - pyskl - INFO - +top1_acc 0.2959 +top5_acc 0.5504 +2024-07-25 08:02:55,343 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 08:02:55,384 - pyskl - INFO - +mean_acc 0.2957 +2024-07-25 08:02:55,389 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_83.pth was removed +2024-07-25 08:02:55,620 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_88.pth. +2024-07-25 08:02:55,621 - pyskl - INFO - Best top1_acc is 0.2959 at 88 epoch. +2024-07-25 08:02:55,632 - pyskl - INFO - Epoch(val) [88][309] top1_acc: 0.2959, top5_acc: 0.5504, mean_class_accuracy: 0.2957 +2024-07-25 08:06:40,029 - pyskl - INFO - Epoch [89][100/3746] lr: 3.653e-02, eta: 2 days, 4:07:17, time: 2.244, data_time: 1.274, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6197, loss_cls: 3.6049, loss: 3.6049 +2024-07-25 08:08:01,603 - pyskl - INFO - Epoch [89][200/3746] lr: 3.650e-02, eta: 2 days, 4:05:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6052, loss_cls: 3.6897, loss: 3.6897 +2024-07-25 08:09:23,313 - pyskl - INFO - Epoch [89][300/3746] lr: 3.647e-02, eta: 2 days, 4:04:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6170, loss_cls: 3.6478, loss: 3.6478 +2024-07-25 08:10:45,085 - pyskl - INFO - Epoch [89][400/3746] lr: 3.645e-02, eta: 2 days, 4:03:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6158, loss_cls: 3.6617, loss: 3.6617 +2024-07-25 08:12:06,431 - pyskl - INFO - Epoch [89][500/3746] lr: 3.642e-02, eta: 2 days, 4:01:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6161, loss_cls: 3.6513, loss: 3.6513 +2024-07-25 08:13:28,119 - pyskl - INFO - Epoch [89][600/3746] lr: 3.639e-02, eta: 2 days, 4:00:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6211, loss_cls: 3.6367, loss: 3.6367 +2024-07-25 08:14:50,609 - pyskl - INFO - Epoch [89][700/3746] lr: 3.637e-02, eta: 2 days, 3:59:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6059, loss_cls: 3.6902, loss: 3.6902 +2024-07-25 08:16:12,642 - pyskl - INFO - Epoch [89][800/3746] lr: 3.634e-02, eta: 2 days, 3:57:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6142, loss_cls: 3.6750, loss: 3.6750 +2024-07-25 08:17:34,650 - pyskl - INFO - Epoch [89][900/3746] lr: 3.631e-02, eta: 2 days, 3:56:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6066, loss_cls: 3.6770, loss: 3.6770 +2024-07-25 08:18:56,231 - pyskl - INFO - Epoch [89][1000/3746] lr: 3.629e-02, eta: 2 days, 3:55:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6102, loss_cls: 3.6812, loss: 3.6812 +2024-07-25 08:20:17,742 - pyskl - INFO - Epoch [89][1100/3746] lr: 3.626e-02, eta: 2 days, 3:53:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6091, loss_cls: 3.6920, loss: 3.6920 +2024-07-25 08:21:39,265 - pyskl - INFO - Epoch [89][1200/3746] lr: 3.623e-02, eta: 2 days, 3:52:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.5980, loss_cls: 3.7209, loss: 3.7209 +2024-07-25 08:23:00,447 - pyskl - INFO - Epoch [89][1300/3746] lr: 3.620e-02, eta: 2 days, 3:51:15, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6112, loss_cls: 3.6926, loss: 3.6926 +2024-07-25 08:24:22,009 - pyskl - INFO - Epoch [89][1400/3746] lr: 3.618e-02, eta: 2 days, 3:49:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6070, loss_cls: 3.7066, loss: 3.7066 +2024-07-25 08:25:43,882 - pyskl - INFO - Epoch [89][1500/3746] lr: 3.615e-02, eta: 2 days, 3:48:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6159, loss_cls: 3.6579, loss: 3.6579 +2024-07-25 08:27:05,682 - pyskl - INFO - Epoch [89][1600/3746] lr: 3.612e-02, eta: 2 days, 3:47:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6178, loss_cls: 3.6413, loss: 3.6413 +2024-07-25 08:28:27,528 - pyskl - INFO - Epoch [89][1700/3746] lr: 3.610e-02, eta: 2 days, 3:45:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6133, loss_cls: 3.6904, loss: 3.6904 +2024-07-25 08:29:49,598 - pyskl - INFO - Epoch [89][1800/3746] lr: 3.607e-02, eta: 2 days, 3:44:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.6147, loss_cls: 3.7097, loss: 3.7097 +2024-07-25 08:31:11,923 - pyskl - INFO - Epoch [89][1900/3746] lr: 3.604e-02, eta: 2 days, 3:43:14, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.6064, loss_cls: 3.7063, loss: 3.7063 +2024-07-25 08:32:34,516 - pyskl - INFO - Epoch [89][2000/3746] lr: 3.602e-02, eta: 2 days, 3:41:55, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6078, loss_cls: 3.7173, loss: 3.7173 +2024-07-25 08:33:56,778 - pyskl - INFO - Epoch [89][2100/3746] lr: 3.599e-02, eta: 2 days, 3:40:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6108, loss_cls: 3.6944, loss: 3.6944 +2024-07-25 08:35:18,819 - pyskl - INFO - Epoch [89][2200/3746] lr: 3.596e-02, eta: 2 days, 3:39:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6027, loss_cls: 3.7200, loss: 3.7200 +2024-07-25 08:36:40,459 - pyskl - INFO - Epoch [89][2300/3746] lr: 3.594e-02, eta: 2 days, 3:37:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6169, loss_cls: 3.6814, loss: 3.6814 +2024-07-25 08:38:01,980 - pyskl - INFO - Epoch [89][2400/3746] lr: 3.591e-02, eta: 2 days, 3:36:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.6006, loss_cls: 3.7363, loss: 3.7363 +2024-07-25 08:39:23,252 - pyskl - INFO - Epoch [89][2500/3746] lr: 3.588e-02, eta: 2 days, 3:35:14, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6123, loss_cls: 3.6501, loss: 3.6501 +2024-07-25 08:40:44,943 - pyskl - INFO - Epoch [89][2600/3746] lr: 3.586e-02, eta: 2 days, 3:33:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6072, loss_cls: 3.6936, loss: 3.6936 +2024-07-25 08:42:06,299 - pyskl - INFO - Epoch [89][2700/3746] lr: 3.583e-02, eta: 2 days, 3:32:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6019, loss_cls: 3.7142, loss: 3.7142 +2024-07-25 08:43:27,870 - pyskl - INFO - Epoch [89][2800/3746] lr: 3.580e-02, eta: 2 days, 3:31:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6022, loss_cls: 3.7267, loss: 3.7267 +2024-07-25 08:44:49,793 - pyskl - INFO - Epoch [89][2900/3746] lr: 3.578e-02, eta: 2 days, 3:29:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6139, loss_cls: 3.6841, loss: 3.6841 +2024-07-25 08:46:12,014 - pyskl - INFO - Epoch [89][3000/3746] lr: 3.575e-02, eta: 2 days, 3:28:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6155, loss_cls: 3.6670, loss: 3.6670 +2024-07-25 08:47:33,517 - pyskl - INFO - Epoch [89][3100/3746] lr: 3.572e-02, eta: 2 days, 3:27:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6088, loss_cls: 3.7001, loss: 3.7001 +2024-07-25 08:48:55,620 - pyskl - INFO - Epoch [89][3200/3746] lr: 3.569e-02, eta: 2 days, 3:25:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6119, loss_cls: 3.6764, loss: 3.6764 +2024-07-25 08:50:17,589 - pyskl - INFO - Epoch [89][3300/3746] lr: 3.567e-02, eta: 2 days, 3:24:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6081, loss_cls: 3.7039, loss: 3.7039 +2024-07-25 08:51:39,367 - pyskl - INFO - Epoch [89][3400/3746] lr: 3.564e-02, eta: 2 days, 3:23:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6152, loss_cls: 3.6703, loss: 3.6703 +2024-07-25 08:53:01,189 - pyskl - INFO - Epoch [89][3500/3746] lr: 3.561e-02, eta: 2 days, 3:21:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6139, loss_cls: 3.6570, loss: 3.6570 +2024-07-25 08:54:23,024 - pyskl - INFO - Epoch [89][3600/3746] lr: 3.559e-02, eta: 2 days, 3:20:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6075, loss_cls: 3.6833, loss: 3.6833 +2024-07-25 08:55:44,292 - pyskl - INFO - Epoch [89][3700/3746] lr: 3.556e-02, eta: 2 days, 3:19:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6089, loss_cls: 3.6882, loss: 3.6882 +2024-07-25 08:56:24,251 - pyskl - INFO - Saving checkpoint at 89 epochs +2024-07-25 08:58:15,261 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 08:58:15,924 - pyskl - INFO - +top1_acc 0.2796 +top5_acc 0.5318 +2024-07-25 08:58:15,924 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 08:58:15,964 - pyskl - INFO - +mean_acc 0.2794 +2024-07-25 08:58:15,975 - pyskl - INFO - Epoch(val) [89][309] top1_acc: 0.2796, top5_acc: 0.5318, mean_class_accuracy: 0.2794 +2024-07-25 09:02:06,964 - pyskl - INFO - Epoch [90][100/3746] lr: 3.552e-02, eta: 2 days, 3:18:31, time: 2.310, data_time: 1.331, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6230, loss_cls: 3.6123, loss: 3.6123 +2024-07-25 09:03:28,563 - pyskl - INFO - Epoch [90][200/3746] lr: 3.550e-02, eta: 2 days, 3:17:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6142, loss_cls: 3.6523, loss: 3.6523 +2024-07-25 09:04:50,490 - pyskl - INFO - Epoch [90][300/3746] lr: 3.547e-02, eta: 2 days, 3:15:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6183, loss_cls: 3.6593, loss: 3.6593 +2024-07-25 09:06:11,897 - pyskl - INFO - Epoch [90][400/3746] lr: 3.544e-02, eta: 2 days, 3:14:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6202, loss_cls: 3.5954, loss: 3.5954 +2024-07-25 09:07:33,522 - pyskl - INFO - Epoch [90][500/3746] lr: 3.541e-02, eta: 2 days, 3:13:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6109, loss_cls: 3.6867, loss: 3.6867 +2024-07-25 09:08:54,958 - pyskl - INFO - Epoch [90][600/3746] lr: 3.539e-02, eta: 2 days, 3:11:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6123, loss_cls: 3.6678, loss: 3.6678 +2024-07-25 09:10:17,042 - pyskl - INFO - Epoch [90][700/3746] lr: 3.536e-02, eta: 2 days, 3:10:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6133, loss_cls: 3.6695, loss: 3.6695 +2024-07-25 09:11:38,856 - pyskl - INFO - Epoch [90][800/3746] lr: 3.533e-02, eta: 2 days, 3:09:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6150, loss_cls: 3.6813, loss: 3.6813 +2024-07-25 09:13:00,744 - pyskl - INFO - Epoch [90][900/3746] lr: 3.531e-02, eta: 2 days, 3:07:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6169, loss_cls: 3.6763, loss: 3.6763 +2024-07-25 09:14:21,767 - pyskl - INFO - Epoch [90][1000/3746] lr: 3.528e-02, eta: 2 days, 3:06:28, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6136, loss_cls: 3.6713, loss: 3.6713 +2024-07-25 09:15:43,250 - pyskl - INFO - Epoch [90][1100/3746] lr: 3.525e-02, eta: 2 days, 3:05:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6150, loss_cls: 3.6598, loss: 3.6598 +2024-07-25 09:17:04,963 - pyskl - INFO - Epoch [90][1200/3746] lr: 3.523e-02, eta: 2 days, 3:03:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6119, loss_cls: 3.7088, loss: 3.7088 +2024-07-25 09:18:26,441 - pyskl - INFO - Epoch [90][1300/3746] lr: 3.520e-02, eta: 2 days, 3:02:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6122, loss_cls: 3.6570, loss: 3.6570 +2024-07-25 09:19:47,900 - pyskl - INFO - Epoch [90][1400/3746] lr: 3.517e-02, eta: 2 days, 3:01:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6080, loss_cls: 3.6687, loss: 3.6687 +2024-07-25 09:21:09,190 - pyskl - INFO - Epoch [90][1500/3746] lr: 3.515e-02, eta: 2 days, 2:59:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6123, loss_cls: 3.6621, loss: 3.6621 +2024-07-25 09:22:30,706 - pyskl - INFO - Epoch [90][1600/3746] lr: 3.512e-02, eta: 2 days, 2:58:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6103, loss_cls: 3.6523, loss: 3.6523 +2024-07-25 09:23:52,977 - pyskl - INFO - Epoch [90][1700/3746] lr: 3.509e-02, eta: 2 days, 2:57:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6080, loss_cls: 3.6860, loss: 3.6860 +2024-07-25 09:25:15,209 - pyskl - INFO - Epoch [90][1800/3746] lr: 3.507e-02, eta: 2 days, 2:55:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6159, loss_cls: 3.6672, loss: 3.6672 +2024-07-25 09:26:37,145 - pyskl - INFO - Epoch [90][1900/3746] lr: 3.504e-02, eta: 2 days, 2:54:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6103, loss_cls: 3.6834, loss: 3.6834 +2024-07-25 09:28:00,665 - pyskl - INFO - Epoch [90][2000/3746] lr: 3.501e-02, eta: 2 days, 2:53:06, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6183, loss_cls: 3.6628, loss: 3.6628 +2024-07-25 09:29:23,489 - pyskl - INFO - Epoch [90][2100/3746] lr: 3.499e-02, eta: 2 days, 2:51:46, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6120, loss_cls: 3.6900, loss: 3.6900 +2024-07-25 09:30:45,208 - pyskl - INFO - Epoch [90][2200/3746] lr: 3.496e-02, eta: 2 days, 2:50:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6092, loss_cls: 3.6900, loss: 3.6900 +2024-07-25 09:32:06,602 - pyskl - INFO - Epoch [90][2300/3746] lr: 3.493e-02, eta: 2 days, 2:49:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6112, loss_cls: 3.6695, loss: 3.6695 +2024-07-25 09:33:28,007 - pyskl - INFO - Epoch [90][2400/3746] lr: 3.491e-02, eta: 2 days, 2:47:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.5998, loss_cls: 3.7024, loss: 3.7024 +2024-07-25 09:34:49,956 - pyskl - INFO - Epoch [90][2500/3746] lr: 3.488e-02, eta: 2 days, 2:46:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6147, loss_cls: 3.6799, loss: 3.6799 +2024-07-25 09:36:11,863 - pyskl - INFO - Epoch [90][2600/3746] lr: 3.485e-02, eta: 2 days, 2:45:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.5969, loss_cls: 3.7317, loss: 3.7317 +2024-07-25 09:37:33,075 - pyskl - INFO - Epoch [90][2700/3746] lr: 3.483e-02, eta: 2 days, 2:43:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6162, loss_cls: 3.6812, loss: 3.6812 +2024-07-25 09:38:55,341 - pyskl - INFO - Epoch [90][2800/3746] lr: 3.480e-02, eta: 2 days, 2:42:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6166, loss_cls: 3.6750, loss: 3.6750 +2024-07-25 09:40:16,761 - pyskl - INFO - Epoch [90][2900/3746] lr: 3.477e-02, eta: 2 days, 2:41:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6078, loss_cls: 3.7279, loss: 3.7279 +2024-07-25 09:41:38,522 - pyskl - INFO - Epoch [90][3000/3746] lr: 3.475e-02, eta: 2 days, 2:39:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6080, loss_cls: 3.7039, loss: 3.7039 +2024-07-25 09:42:59,890 - pyskl - INFO - Epoch [90][3100/3746] lr: 3.472e-02, eta: 2 days, 2:38:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6112, loss_cls: 3.6615, loss: 3.6615 +2024-07-25 09:44:21,586 - pyskl - INFO - Epoch [90][3200/3746] lr: 3.469e-02, eta: 2 days, 2:37:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6205, loss_cls: 3.6445, loss: 3.6445 +2024-07-25 09:45:43,114 - pyskl - INFO - Epoch [90][3300/3746] lr: 3.467e-02, eta: 2 days, 2:35:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6153, loss_cls: 3.6651, loss: 3.6651 +2024-07-25 09:47:04,641 - pyskl - INFO - Epoch [90][3400/3746] lr: 3.464e-02, eta: 2 days, 2:34:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6091, loss_cls: 3.6991, loss: 3.6991 +2024-07-25 09:48:26,159 - pyskl - INFO - Epoch [90][3500/3746] lr: 3.461e-02, eta: 2 days, 2:33:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6055, loss_cls: 3.7074, loss: 3.7074 +2024-07-25 09:49:47,792 - pyskl - INFO - Epoch [90][3600/3746] lr: 3.459e-02, eta: 2 days, 2:31:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6161, loss_cls: 3.6595, loss: 3.6595 +2024-07-25 09:51:09,290 - pyskl - INFO - Epoch [90][3700/3746] lr: 3.456e-02, eta: 2 days, 2:30:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6155, loss_cls: 3.6555, loss: 3.6555 +2024-07-25 09:51:48,922 - pyskl - INFO - Saving checkpoint at 90 epochs +2024-07-25 09:53:39,889 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 09:53:40,549 - pyskl - INFO - +top1_acc 0.2939 +top5_acc 0.5491 +2024-07-25 09:53:40,550 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 09:53:40,590 - pyskl - INFO - +mean_acc 0.2936 +2024-07-25 09:53:40,600 - pyskl - INFO - Epoch(val) [90][309] top1_acc: 0.2939, top5_acc: 0.5491, mean_class_accuracy: 0.2936 +2024-07-25 09:57:27,785 - pyskl - INFO - Epoch [91][100/3746] lr: 3.452e-02, eta: 2 days, 2:29:35, time: 2.272, data_time: 1.290, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6256, loss_cls: 3.5819, loss: 3.5819 +2024-07-25 09:58:49,181 - pyskl - INFO - Epoch [91][200/3746] lr: 3.450e-02, eta: 2 days, 2:28:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6191, loss_cls: 3.5928, loss: 3.5928 +2024-07-25 10:00:11,507 - pyskl - INFO - Epoch [91][300/3746] lr: 3.447e-02, eta: 2 days, 2:26:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6142, loss_cls: 3.6747, loss: 3.6747 +2024-07-25 10:01:33,568 - pyskl - INFO - Epoch [91][400/3746] lr: 3.444e-02, eta: 2 days, 2:25:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6239, loss_cls: 3.6242, loss: 3.6242 +2024-07-25 10:02:55,673 - pyskl - INFO - Epoch [91][500/3746] lr: 3.442e-02, eta: 2 days, 2:24:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6145, loss_cls: 3.6646, loss: 3.6646 +2024-07-25 10:04:18,569 - pyskl - INFO - Epoch [91][600/3746] lr: 3.439e-02, eta: 2 days, 2:22:54, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6152, loss_cls: 3.6775, loss: 3.6775 +2024-07-25 10:05:40,823 - pyskl - INFO - Epoch [91][700/3746] lr: 3.436e-02, eta: 2 days, 2:21:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6183, loss_cls: 3.6421, loss: 3.6421 +2024-07-25 10:07:03,206 - pyskl - INFO - Epoch [91][800/3746] lr: 3.434e-02, eta: 2 days, 2:20:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6133, loss_cls: 3.6679, loss: 3.6679 +2024-07-25 10:08:25,516 - pyskl - INFO - Epoch [91][900/3746] lr: 3.431e-02, eta: 2 days, 2:18:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6127, loss_cls: 3.6892, loss: 3.6892 +2024-07-25 10:09:47,340 - pyskl - INFO - Epoch [91][1000/3746] lr: 3.428e-02, eta: 2 days, 2:17:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6147, loss_cls: 3.6680, loss: 3.6680 +2024-07-25 10:11:09,120 - pyskl - INFO - Epoch [91][1100/3746] lr: 3.426e-02, eta: 2 days, 2:16:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6211, loss_cls: 3.6112, loss: 3.6112 +2024-07-25 10:12:30,664 - pyskl - INFO - Epoch [91][1200/3746] lr: 3.423e-02, eta: 2 days, 2:14:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3594, top5_acc: 0.6166, loss_cls: 3.6543, loss: 3.6543 +2024-07-25 10:13:52,524 - pyskl - INFO - Epoch [91][1300/3746] lr: 3.420e-02, eta: 2 days, 2:13:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6098, loss_cls: 3.6926, loss: 3.6926 +2024-07-25 10:15:14,356 - pyskl - INFO - Epoch [91][1400/3746] lr: 3.418e-02, eta: 2 days, 2:12:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6105, loss_cls: 3.6860, loss: 3.6860 +2024-07-25 10:16:35,968 - pyskl - INFO - Epoch [91][1500/3746] lr: 3.415e-02, eta: 2 days, 2:10:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6170, loss_cls: 3.6547, loss: 3.6547 +2024-07-25 10:17:57,372 - pyskl - INFO - Epoch [91][1600/3746] lr: 3.412e-02, eta: 2 days, 2:09:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6130, loss_cls: 3.6667, loss: 3.6667 +2024-07-25 10:19:19,827 - pyskl - INFO - Epoch [91][1700/3746] lr: 3.410e-02, eta: 2 days, 2:08:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6069, loss_cls: 3.6887, loss: 3.6887 +2024-07-25 10:20:42,127 - pyskl - INFO - Epoch [91][1800/3746] lr: 3.407e-02, eta: 2 days, 2:06:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6256, loss_cls: 3.6112, loss: 3.6112 +2024-07-25 10:22:04,037 - pyskl - INFO - Epoch [91][1900/3746] lr: 3.405e-02, eta: 2 days, 2:05:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6288, loss_cls: 3.5749, loss: 3.5749 +2024-07-25 10:23:25,912 - pyskl - INFO - Epoch [91][2000/3746] lr: 3.402e-02, eta: 2 days, 2:04:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6211, loss_cls: 3.6566, loss: 3.6566 +2024-07-25 10:24:48,584 - pyskl - INFO - Epoch [91][2100/3746] lr: 3.399e-02, eta: 2 days, 2:02:51, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6064, loss_cls: 3.7181, loss: 3.7181 +2024-07-25 10:26:10,447 - pyskl - INFO - Epoch [91][2200/3746] lr: 3.397e-02, eta: 2 days, 2:01:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6095, loss_cls: 3.6867, loss: 3.6867 +2024-07-25 10:27:31,888 - pyskl - INFO - Epoch [91][2300/3746] lr: 3.394e-02, eta: 2 days, 2:00:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6131, loss_cls: 3.6637, loss: 3.6637 +2024-07-25 10:28:53,097 - pyskl - INFO - Epoch [91][2400/3746] lr: 3.391e-02, eta: 2 days, 1:58:50, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6045, loss_cls: 3.6878, loss: 3.6878 +2024-07-25 10:30:14,644 - pyskl - INFO - Epoch [91][2500/3746] lr: 3.389e-02, eta: 2 days, 1:57:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6142, loss_cls: 3.6586, loss: 3.6586 +2024-07-25 10:31:36,727 - pyskl - INFO - Epoch [91][2600/3746] lr: 3.386e-02, eta: 2 days, 1:56:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.6062, loss_cls: 3.7040, loss: 3.7040 +2024-07-25 10:32:57,971 - pyskl - INFO - Epoch [91][2700/3746] lr: 3.383e-02, eta: 2 days, 1:54:48, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6133, loss_cls: 3.6477, loss: 3.6477 +2024-07-25 10:34:20,098 - pyskl - INFO - Epoch [91][2800/3746] lr: 3.381e-02, eta: 2 days, 1:53:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.5984, loss_cls: 3.7202, loss: 3.7202 +2024-07-25 10:35:41,766 - pyskl - INFO - Epoch [91][2900/3746] lr: 3.378e-02, eta: 2 days, 1:52:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6183, loss_cls: 3.6577, loss: 3.6577 +2024-07-25 10:37:03,839 - pyskl - INFO - Epoch [91][3000/3746] lr: 3.375e-02, eta: 2 days, 1:50:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6145, loss_cls: 3.6549, loss: 3.6549 +2024-07-25 10:38:25,251 - pyskl - INFO - Epoch [91][3100/3746] lr: 3.373e-02, eta: 2 days, 1:49:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6011, loss_cls: 3.6984, loss: 3.6984 +2024-07-25 10:39:47,120 - pyskl - INFO - Epoch [91][3200/3746] lr: 3.370e-02, eta: 2 days, 1:48:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6145, loss_cls: 3.6586, loss: 3.6586 +2024-07-25 10:41:08,639 - pyskl - INFO - Epoch [91][3300/3746] lr: 3.367e-02, eta: 2 days, 1:46:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6211, loss_cls: 3.6280, loss: 3.6280 +2024-07-25 10:42:30,501 - pyskl - INFO - Epoch [91][3400/3746] lr: 3.365e-02, eta: 2 days, 1:45:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6175, loss_cls: 3.6746, loss: 3.6746 +2024-07-25 10:43:52,059 - pyskl - INFO - Epoch [91][3500/3746] lr: 3.362e-02, eta: 2 days, 1:44:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6178, loss_cls: 3.6572, loss: 3.6572 +2024-07-25 10:45:13,084 - pyskl - INFO - Epoch [91][3600/3746] lr: 3.360e-02, eta: 2 days, 1:42:44, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6189, loss_cls: 3.6335, loss: 3.6335 +2024-07-25 10:46:34,344 - pyskl - INFO - Epoch [91][3700/3746] lr: 3.357e-02, eta: 2 days, 1:41:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6131, loss_cls: 3.6964, loss: 3.6964 +2024-07-25 10:47:13,850 - pyskl - INFO - Saving checkpoint at 91 epochs +2024-07-25 10:49:05,828 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 10:49:06,633 - pyskl - INFO - +top1_acc 0.2843 +top5_acc 0.5332 +2024-07-25 10:49:06,634 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 10:49:06,681 - pyskl - INFO - +mean_acc 0.2840 +2024-07-25 10:49:06,692 - pyskl - INFO - Epoch(val) [91][309] top1_acc: 0.2843, top5_acc: 0.5332, mean_class_accuracy: 0.2840 +2024-07-25 10:52:56,078 - pyskl - INFO - Epoch [92][100/3746] lr: 3.353e-02, eta: 2 days, 1:40:38, time: 2.294, data_time: 1.316, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6231, loss_cls: 3.6106, loss: 3.6106 +2024-07-25 10:54:17,314 - pyskl - INFO - Epoch [92][200/3746] lr: 3.350e-02, eta: 2 days, 1:39:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6231, loss_cls: 3.6073, loss: 3.6073 +2024-07-25 10:55:39,604 - pyskl - INFO - Epoch [92][300/3746] lr: 3.348e-02, eta: 2 days, 1:37:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6222, loss_cls: 3.6231, loss: 3.6231 +2024-07-25 10:57:01,708 - pyskl - INFO - Epoch [92][400/3746] lr: 3.345e-02, eta: 2 days, 1:36:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6281, loss_cls: 3.6140, loss: 3.6140 +2024-07-25 10:58:24,183 - pyskl - INFO - Epoch [92][500/3746] lr: 3.342e-02, eta: 2 days, 1:35:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6234, loss_cls: 3.6155, loss: 3.6155 +2024-07-25 10:59:45,762 - pyskl - INFO - Epoch [92][600/3746] lr: 3.340e-02, eta: 2 days, 1:33:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6205, loss_cls: 3.6290, loss: 3.6290 +2024-07-25 11:01:08,223 - pyskl - INFO - Epoch [92][700/3746] lr: 3.337e-02, eta: 2 days, 1:32:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6197, loss_cls: 3.5959, loss: 3.5959 +2024-07-25 11:02:29,971 - pyskl - INFO - Epoch [92][800/3746] lr: 3.335e-02, eta: 2 days, 1:31:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6194, loss_cls: 3.6360, loss: 3.6360 +2024-07-25 11:03:51,404 - pyskl - INFO - Epoch [92][900/3746] lr: 3.332e-02, eta: 2 days, 1:29:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6306, loss_cls: 3.5961, loss: 3.5961 +2024-07-25 11:05:12,877 - pyskl - INFO - Epoch [92][1000/3746] lr: 3.329e-02, eta: 2 days, 1:28:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6180, loss_cls: 3.6226, loss: 3.6226 +2024-07-25 11:06:34,408 - pyskl - INFO - Epoch [92][1100/3746] lr: 3.327e-02, eta: 2 days, 1:27:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6127, loss_cls: 3.6516, loss: 3.6516 +2024-07-25 11:07:56,395 - pyskl - INFO - Epoch [92][1200/3746] lr: 3.324e-02, eta: 2 days, 1:25:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6208, loss_cls: 3.6141, loss: 3.6141 +2024-07-25 11:09:17,763 - pyskl - INFO - Epoch [92][1300/3746] lr: 3.321e-02, eta: 2 days, 1:24:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6155, loss_cls: 3.6507, loss: 3.6507 +2024-07-25 11:10:39,759 - pyskl - INFO - Epoch [92][1400/3746] lr: 3.319e-02, eta: 2 days, 1:23:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6198, loss_cls: 3.6338, loss: 3.6338 +2024-07-25 11:12:02,276 - pyskl - INFO - Epoch [92][1500/3746] lr: 3.316e-02, eta: 2 days, 1:21:52, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6170, loss_cls: 3.6688, loss: 3.6688 +2024-07-25 11:13:24,097 - pyskl - INFO - Epoch [92][1600/3746] lr: 3.314e-02, eta: 2 days, 1:20:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6216, loss_cls: 3.6531, loss: 3.6531 +2024-07-25 11:14:45,973 - pyskl - INFO - Epoch [92][1700/3746] lr: 3.311e-02, eta: 2 days, 1:19:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6230, loss_cls: 3.6141, loss: 3.6141 +2024-07-25 11:16:08,641 - pyskl - INFO - Epoch [92][1800/3746] lr: 3.308e-02, eta: 2 days, 1:17:52, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6139, loss_cls: 3.6748, loss: 3.6748 +2024-07-25 11:17:30,854 - pyskl - INFO - Epoch [92][1900/3746] lr: 3.306e-02, eta: 2 days, 1:16:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6094, loss_cls: 3.6624, loss: 3.6624 +2024-07-25 11:18:53,329 - pyskl - INFO - Epoch [92][2000/3746] lr: 3.303e-02, eta: 2 days, 1:15:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6095, loss_cls: 3.6780, loss: 3.6780 +2024-07-25 11:20:15,489 - pyskl - INFO - Epoch [92][2100/3746] lr: 3.300e-02, eta: 2 days, 1:13:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6128, loss_cls: 3.6555, loss: 3.6555 +2024-07-25 11:21:38,004 - pyskl - INFO - Epoch [92][2200/3746] lr: 3.298e-02, eta: 2 days, 1:12:32, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6209, loss_cls: 3.6391, loss: 3.6391 +2024-07-25 11:23:00,207 - pyskl - INFO - Epoch [92][2300/3746] lr: 3.295e-02, eta: 2 days, 1:11:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6111, loss_cls: 3.6803, loss: 3.6803 +2024-07-25 11:24:22,495 - pyskl - INFO - Epoch [92][2400/3746] lr: 3.292e-02, eta: 2 days, 1:09:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6147, loss_cls: 3.6532, loss: 3.6532 +2024-07-25 11:25:43,981 - pyskl - INFO - Epoch [92][2500/3746] lr: 3.290e-02, eta: 2 days, 1:08:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6158, loss_cls: 3.6316, loss: 3.6316 +2024-07-25 11:27:05,494 - pyskl - INFO - Epoch [92][2600/3746] lr: 3.287e-02, eta: 2 days, 1:07:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6117, loss_cls: 3.6839, loss: 3.6839 +2024-07-25 11:28:27,544 - pyskl - INFO - Epoch [92][2700/3746] lr: 3.285e-02, eta: 2 days, 1:05:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6269, loss_cls: 3.6136, loss: 3.6136 +2024-07-25 11:29:48,955 - pyskl - INFO - Epoch [92][2800/3746] lr: 3.282e-02, eta: 2 days, 1:04:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6069, loss_cls: 3.6321, loss: 3.6321 +2024-07-25 11:31:10,494 - pyskl - INFO - Epoch [92][2900/3746] lr: 3.279e-02, eta: 2 days, 1:03:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6105, loss_cls: 3.6710, loss: 3.6710 +2024-07-25 11:32:32,188 - pyskl - INFO - Epoch [92][3000/3746] lr: 3.277e-02, eta: 2 days, 1:01:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6203, loss_cls: 3.6278, loss: 3.6278 +2024-07-25 11:33:53,729 - pyskl - INFO - Epoch [92][3100/3746] lr: 3.274e-02, eta: 2 days, 1:00:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6138, loss_cls: 3.6595, loss: 3.6595 +2024-07-25 11:35:15,093 - pyskl - INFO - Epoch [92][3200/3746] lr: 3.271e-02, eta: 2 days, 0:59:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6081, loss_cls: 3.6699, loss: 3.6699 +2024-07-25 11:36:36,595 - pyskl - INFO - Epoch [92][3300/3746] lr: 3.269e-02, eta: 2 days, 0:57:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6102, loss_cls: 3.6713, loss: 3.6713 +2024-07-25 11:37:58,403 - pyskl - INFO - Epoch [92][3400/3746] lr: 3.266e-02, eta: 2 days, 0:56:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6180, loss_cls: 3.6367, loss: 3.6367 +2024-07-25 11:39:20,140 - pyskl - INFO - Epoch [92][3500/3746] lr: 3.264e-02, eta: 2 days, 0:55:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6188, loss_cls: 3.6417, loss: 3.6417 +2024-07-25 11:40:41,369 - pyskl - INFO - Epoch [92][3600/3746] lr: 3.261e-02, eta: 2 days, 0:53:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6047, loss_cls: 3.6917, loss: 3.6917 +2024-07-25 11:42:02,872 - pyskl - INFO - Epoch [92][3700/3746] lr: 3.258e-02, eta: 2 days, 0:52:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6269, loss_cls: 3.6224, loss: 3.6224 +2024-07-25 11:42:42,217 - pyskl - INFO - Saving checkpoint at 92 epochs +2024-07-25 11:44:35,076 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 11:44:35,750 - pyskl - INFO - +top1_acc 0.3107 +top5_acc 0.5596 +2024-07-25 11:44:35,750 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 11:44:35,793 - pyskl - INFO - +mean_acc 0.3106 +2024-07-25 11:44:35,798 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_88.pth was removed +2024-07-25 11:44:36,035 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_92.pth. +2024-07-25 11:44:36,036 - pyskl - INFO - Best top1_acc is 0.3107 at 92 epoch. +2024-07-25 11:44:36,047 - pyskl - INFO - Epoch(val) [92][309] top1_acc: 0.3107, top5_acc: 0.5596, mean_class_accuracy: 0.3106 +2024-07-25 11:48:31,452 - pyskl - INFO - Epoch [93][100/3746] lr: 3.255e-02, eta: 2 days, 0:51:39, time: 2.354, data_time: 1.375, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6308, loss_cls: 3.5680, loss: 3.5680 +2024-07-25 11:49:53,497 - pyskl - INFO - Epoch [93][200/3746] lr: 3.252e-02, eta: 2 days, 0:50:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6252, loss_cls: 3.6143, loss: 3.6143 +2024-07-25 11:51:15,656 - pyskl - INFO - Epoch [93][300/3746] lr: 3.249e-02, eta: 2 days, 0:48:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6183, loss_cls: 3.6184, loss: 3.6184 +2024-07-25 11:52:37,461 - pyskl - INFO - Epoch [93][400/3746] lr: 3.247e-02, eta: 2 days, 0:47:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6303, loss_cls: 3.5740, loss: 3.5740 +2024-07-25 11:53:59,602 - pyskl - INFO - Epoch [93][500/3746] lr: 3.244e-02, eta: 2 days, 0:46:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6258, loss_cls: 3.5949, loss: 3.5949 +2024-07-25 11:55:21,849 - pyskl - INFO - Epoch [93][600/3746] lr: 3.241e-02, eta: 2 days, 0:44:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6273, loss_cls: 3.6032, loss: 3.6032 +2024-07-25 11:56:44,102 - pyskl - INFO - Epoch [93][700/3746] lr: 3.239e-02, eta: 2 days, 0:43:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6241, loss_cls: 3.5910, loss: 3.5910 +2024-07-25 11:58:05,844 - pyskl - INFO - Epoch [93][800/3746] lr: 3.236e-02, eta: 2 days, 0:42:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6177, loss_cls: 3.6380, loss: 3.6380 +2024-07-25 11:59:28,376 - pyskl - INFO - Epoch [93][900/3746] lr: 3.234e-02, eta: 2 days, 0:40:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6169, loss_cls: 3.6762, loss: 3.6762 +2024-07-25 12:00:49,939 - pyskl - INFO - Epoch [93][1000/3746] lr: 3.231e-02, eta: 2 days, 0:39:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6253, loss_cls: 3.5905, loss: 3.5905 +2024-07-25 12:02:11,592 - pyskl - INFO - Epoch [93][1100/3746] lr: 3.228e-02, eta: 2 days, 0:38:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6273, loss_cls: 3.6090, loss: 3.6090 +2024-07-25 12:03:33,589 - pyskl - INFO - Epoch [93][1200/3746] lr: 3.226e-02, eta: 2 days, 0:36:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6208, loss_cls: 3.6264, loss: 3.6264 +2024-07-25 12:04:55,221 - pyskl - INFO - Epoch [93][1300/3746] lr: 3.223e-02, eta: 2 days, 0:35:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6122, loss_cls: 3.6454, loss: 3.6454 +2024-07-25 12:06:16,732 - pyskl - INFO - Epoch [93][1400/3746] lr: 3.221e-02, eta: 2 days, 0:34:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6227, loss_cls: 3.5873, loss: 3.5873 +2024-07-25 12:07:38,590 - pyskl - INFO - Epoch [93][1500/3746] lr: 3.218e-02, eta: 2 days, 0:32:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6144, loss_cls: 3.6700, loss: 3.6700 +2024-07-25 12:09:00,181 - pyskl - INFO - Epoch [93][1600/3746] lr: 3.215e-02, eta: 2 days, 0:31:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6267, loss_cls: 3.5967, loss: 3.5967 +2024-07-25 12:10:22,095 - pyskl - INFO - Epoch [93][1700/3746] lr: 3.213e-02, eta: 2 days, 0:30:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6291, loss_cls: 3.5943, loss: 3.5943 +2024-07-25 12:11:44,637 - pyskl - INFO - Epoch [93][1800/3746] lr: 3.210e-02, eta: 2 days, 0:28:52, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6248, loss_cls: 3.5884, loss: 3.5884 +2024-07-25 12:13:06,938 - pyskl - INFO - Epoch [93][1900/3746] lr: 3.207e-02, eta: 2 days, 0:27:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6112, loss_cls: 3.6914, loss: 3.6914 +2024-07-25 12:14:29,479 - pyskl - INFO - Epoch [93][2000/3746] lr: 3.205e-02, eta: 2 days, 0:26:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6081, loss_cls: 3.6725, loss: 3.6725 +2024-07-25 12:15:51,826 - pyskl - INFO - Epoch [93][2100/3746] lr: 3.202e-02, eta: 2 days, 0:24:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6297, loss_cls: 3.6132, loss: 3.6132 +2024-07-25 12:17:13,971 - pyskl - INFO - Epoch [93][2200/3746] lr: 3.200e-02, eta: 2 days, 0:23:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6206, loss_cls: 3.6268, loss: 3.6268 +2024-07-25 12:18:35,800 - pyskl - INFO - Epoch [93][2300/3746] lr: 3.197e-02, eta: 2 days, 0:22:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6227, loss_cls: 3.6428, loss: 3.6428 +2024-07-25 12:19:57,788 - pyskl - INFO - Epoch [93][2400/3746] lr: 3.194e-02, eta: 2 days, 0:20:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6197, loss_cls: 3.6484, loss: 3.6484 +2024-07-25 12:21:19,405 - pyskl - INFO - Epoch [93][2500/3746] lr: 3.192e-02, eta: 2 days, 0:19:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6133, loss_cls: 3.6399, loss: 3.6399 +2024-07-25 12:22:41,590 - pyskl - INFO - Epoch [93][2600/3746] lr: 3.189e-02, eta: 2 days, 0:18:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6169, loss_cls: 3.6800, loss: 3.6800 +2024-07-25 12:24:03,200 - pyskl - INFO - Epoch [93][2700/3746] lr: 3.187e-02, eta: 2 days, 0:16:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6195, loss_cls: 3.6102, loss: 3.6102 +2024-07-25 12:25:24,982 - pyskl - INFO - Epoch [93][2800/3746] lr: 3.184e-02, eta: 2 days, 0:15:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6181, loss_cls: 3.6386, loss: 3.6386 +2024-07-25 12:26:46,364 - pyskl - INFO - Epoch [93][2900/3746] lr: 3.181e-02, eta: 2 days, 0:14:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6108, loss_cls: 3.6342, loss: 3.6342 +2024-07-25 12:28:07,858 - pyskl - INFO - Epoch [93][3000/3746] lr: 3.179e-02, eta: 2 days, 0:12:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6181, loss_cls: 3.6071, loss: 3.6071 +2024-07-25 12:29:29,451 - pyskl - INFO - Epoch [93][3100/3746] lr: 3.176e-02, eta: 2 days, 0:11:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6216, loss_cls: 3.6178, loss: 3.6178 +2024-07-25 12:30:50,948 - pyskl - INFO - Epoch [93][3200/3746] lr: 3.174e-02, eta: 2 days, 0:10:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6192, loss_cls: 3.6525, loss: 3.6525 +2024-07-25 12:32:12,721 - pyskl - INFO - Epoch [93][3300/3746] lr: 3.171e-02, eta: 2 days, 0:08:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6194, loss_cls: 3.6391, loss: 3.6391 +2024-07-25 12:33:34,502 - pyskl - INFO - Epoch [93][3400/3746] lr: 3.168e-02, eta: 2 days, 0:07:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6041, loss_cls: 3.6680, loss: 3.6680 +2024-07-25 12:34:56,113 - pyskl - INFO - Epoch [93][3500/3746] lr: 3.166e-02, eta: 2 days, 0:06:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6238, loss_cls: 3.6343, loss: 3.6343 +2024-07-25 12:36:17,348 - pyskl - INFO - Epoch [93][3600/3746] lr: 3.163e-02, eta: 2 days, 0:04:43, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6094, loss_cls: 3.6653, loss: 3.6653 +2024-07-25 12:37:38,672 - pyskl - INFO - Epoch [93][3700/3746] lr: 3.161e-02, eta: 2 days, 0:03:23, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3588, top5_acc: 0.6133, loss_cls: 3.6393, loss: 3.6393 +2024-07-25 12:38:18,435 - pyskl - INFO - Saving checkpoint at 93 epochs +2024-07-25 12:40:10,180 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 12:40:10,848 - pyskl - INFO - +top1_acc 0.2921 +top5_acc 0.5460 +2024-07-25 12:40:10,848 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 12:40:10,889 - pyskl - INFO - +mean_acc 0.2918 +2024-07-25 12:40:10,900 - pyskl - INFO - Epoch(val) [93][309] top1_acc: 0.2921, top5_acc: 0.5460, mean_class_accuracy: 0.2918 +2024-07-25 12:44:03,000 - pyskl - INFO - Epoch [94][100/3746] lr: 3.157e-02, eta: 2 days, 0:02:34, time: 2.321, data_time: 1.345, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6317, loss_cls: 3.5613, loss: 3.5613 +2024-07-25 12:45:24,695 - pyskl - INFO - Epoch [94][200/3746] lr: 3.154e-02, eta: 2 days, 0:01:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6303, loss_cls: 3.5997, loss: 3.5997 +2024-07-25 12:46:46,651 - pyskl - INFO - Epoch [94][300/3746] lr: 3.152e-02, eta: 1 day, 23:59:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6317, loss_cls: 3.5687, loss: 3.5687 +2024-07-25 12:48:08,371 - pyskl - INFO - Epoch [94][400/3746] lr: 3.149e-02, eta: 1 day, 23:58:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6266, loss_cls: 3.5978, loss: 3.5978 +2024-07-25 12:49:29,775 - pyskl - INFO - Epoch [94][500/3746] lr: 3.146e-02, eta: 1 day, 23:57:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6270, loss_cls: 3.5993, loss: 3.5993 +2024-07-25 12:50:51,505 - pyskl - INFO - Epoch [94][600/3746] lr: 3.144e-02, eta: 1 day, 23:55:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6130, loss_cls: 3.6355, loss: 3.6355 +2024-07-25 12:52:13,829 - pyskl - INFO - Epoch [94][700/3746] lr: 3.141e-02, eta: 1 day, 23:54:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6195, loss_cls: 3.6146, loss: 3.6146 +2024-07-25 12:53:35,817 - pyskl - INFO - Epoch [94][800/3746] lr: 3.139e-02, eta: 1 day, 23:53:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6258, loss_cls: 3.5871, loss: 3.5871 +2024-07-25 12:54:57,861 - pyskl - INFO - Epoch [94][900/3746] lr: 3.136e-02, eta: 1 day, 23:51:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6278, loss_cls: 3.5622, loss: 3.5622 +2024-07-25 12:56:20,031 - pyskl - INFO - Epoch [94][1000/3746] lr: 3.133e-02, eta: 1 day, 23:50:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6273, loss_cls: 3.6076, loss: 3.6076 +2024-07-25 12:57:41,604 - pyskl - INFO - Epoch [94][1100/3746] lr: 3.131e-02, eta: 1 day, 23:49:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6225, loss_cls: 3.6040, loss: 3.6040 +2024-07-25 12:59:03,478 - pyskl - INFO - Epoch [94][1200/3746] lr: 3.128e-02, eta: 1 day, 23:47:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6166, loss_cls: 3.6463, loss: 3.6463 +2024-07-25 13:00:25,678 - pyskl - INFO - Epoch [94][1300/3746] lr: 3.126e-02, eta: 1 day, 23:46:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6258, loss_cls: 3.6034, loss: 3.6034 +2024-07-25 13:01:47,332 - pyskl - INFO - Epoch [94][1400/3746] lr: 3.123e-02, eta: 1 day, 23:45:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6253, loss_cls: 3.5798, loss: 3.5798 +2024-07-25 13:03:09,264 - pyskl - INFO - Epoch [94][1500/3746] lr: 3.120e-02, eta: 1 day, 23:43:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6289, loss_cls: 3.5590, loss: 3.5590 +2024-07-25 13:04:31,251 - pyskl - INFO - Epoch [94][1600/3746] lr: 3.118e-02, eta: 1 day, 23:42:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6206, loss_cls: 3.6288, loss: 3.6288 +2024-07-25 13:05:53,689 - pyskl - INFO - Epoch [94][1700/3746] lr: 3.115e-02, eta: 1 day, 23:41:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6045, loss_cls: 3.6959, loss: 3.6959 +2024-07-25 13:07:16,526 - pyskl - INFO - Epoch [94][1800/3746] lr: 3.113e-02, eta: 1 day, 23:39:46, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3594, top5_acc: 0.6205, loss_cls: 3.6307, loss: 3.6307 +2024-07-25 13:08:38,692 - pyskl - INFO - Epoch [94][1900/3746] lr: 3.110e-02, eta: 1 day, 23:38:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6264, loss_cls: 3.6073, loss: 3.6073 +2024-07-25 13:10:01,380 - pyskl - INFO - Epoch [94][2000/3746] lr: 3.108e-02, eta: 1 day, 23:37:06, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6239, loss_cls: 3.5877, loss: 3.5877 +2024-07-25 13:11:23,821 - pyskl - INFO - Epoch [94][2100/3746] lr: 3.105e-02, eta: 1 day, 23:35:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6259, loss_cls: 3.6124, loss: 3.6124 +2024-07-25 13:12:45,465 - pyskl - INFO - Epoch [94][2200/3746] lr: 3.102e-02, eta: 1 day, 23:34:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6086, loss_cls: 3.6835, loss: 3.6835 +2024-07-25 13:14:07,229 - pyskl - INFO - Epoch [94][2300/3746] lr: 3.100e-02, eta: 1 day, 23:33:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6319, loss_cls: 3.5848, loss: 3.5848 +2024-07-25 13:15:29,048 - pyskl - INFO - Epoch [94][2400/3746] lr: 3.097e-02, eta: 1 day, 23:31:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6361, loss_cls: 3.5946, loss: 3.5946 +2024-07-25 13:16:50,513 - pyskl - INFO - Epoch [94][2500/3746] lr: 3.095e-02, eta: 1 day, 23:30:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6181, loss_cls: 3.6858, loss: 3.6858 +2024-07-25 13:18:12,698 - pyskl - INFO - Epoch [94][2600/3746] lr: 3.092e-02, eta: 1 day, 23:29:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6289, loss_cls: 3.5793, loss: 3.5793 +2024-07-25 13:19:34,425 - pyskl - INFO - Epoch [94][2700/3746] lr: 3.089e-02, eta: 1 day, 23:27:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6227, loss_cls: 3.6438, loss: 3.6438 +2024-07-25 13:20:56,316 - pyskl - INFO - Epoch [94][2800/3746] lr: 3.087e-02, eta: 1 day, 23:26:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6214, loss_cls: 3.5966, loss: 3.5966 +2024-07-25 13:22:18,088 - pyskl - INFO - Epoch [94][2900/3746] lr: 3.084e-02, eta: 1 day, 23:25:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6250, loss_cls: 3.5888, loss: 3.5888 +2024-07-25 13:23:39,597 - pyskl - INFO - Epoch [94][3000/3746] lr: 3.082e-02, eta: 1 day, 23:23:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6177, loss_cls: 3.6204, loss: 3.6204 +2024-07-25 13:25:02,338 - pyskl - INFO - Epoch [94][3100/3746] lr: 3.079e-02, eta: 1 day, 23:22:20, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6209, loss_cls: 3.6204, loss: 3.6204 +2024-07-25 13:26:23,765 - pyskl - INFO - Epoch [94][3200/3746] lr: 3.077e-02, eta: 1 day, 23:20:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6252, loss_cls: 3.6136, loss: 3.6136 +2024-07-25 13:27:45,160 - pyskl - INFO - Epoch [94][3300/3746] lr: 3.074e-02, eta: 1 day, 23:19:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6202, loss_cls: 3.6214, loss: 3.6214 +2024-07-25 13:29:06,840 - pyskl - INFO - Epoch [94][3400/3746] lr: 3.071e-02, eta: 1 day, 23:18:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6141, loss_cls: 3.6666, loss: 3.6666 +2024-07-25 13:30:28,461 - pyskl - INFO - Epoch [94][3500/3746] lr: 3.069e-02, eta: 1 day, 23:16:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6222, loss_cls: 3.6018, loss: 3.6018 +2024-07-25 13:31:50,041 - pyskl - INFO - Epoch [94][3600/3746] lr: 3.066e-02, eta: 1 day, 23:15:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6178, loss_cls: 3.6740, loss: 3.6740 +2024-07-25 13:33:11,713 - pyskl - INFO - Epoch [94][3700/3746] lr: 3.064e-02, eta: 1 day, 23:14:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6272, loss_cls: 3.6124, loss: 3.6124 +2024-07-25 13:33:51,329 - pyskl - INFO - Saving checkpoint at 94 epochs +2024-07-25 13:35:43,675 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 13:35:44,336 - pyskl - INFO - +top1_acc 0.2986 +top5_acc 0.5593 +2024-07-25 13:35:44,336 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 13:35:44,380 - pyskl - INFO - +mean_acc 0.2985 +2024-07-25 13:35:44,392 - pyskl - INFO - Epoch(val) [94][309] top1_acc: 0.2986, top5_acc: 0.5593, mean_class_accuracy: 0.2985 +2024-07-25 13:39:36,726 - pyskl - INFO - Epoch [95][100/3746] lr: 3.060e-02, eta: 1 day, 23:13:25, time: 2.323, data_time: 1.343, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6312, loss_cls: 3.5393, loss: 3.5393 +2024-07-25 13:40:57,942 - pyskl - INFO - Epoch [95][200/3746] lr: 3.057e-02, eta: 1 day, 23:12:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6328, loss_cls: 3.5372, loss: 3.5372 +2024-07-25 13:42:19,313 - pyskl - INFO - Epoch [95][300/3746] lr: 3.055e-02, eta: 1 day, 23:10:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6342, loss_cls: 3.5303, loss: 3.5303 +2024-07-25 13:43:41,115 - pyskl - INFO - Epoch [95][400/3746] lr: 3.052e-02, eta: 1 day, 23:09:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3837, top5_acc: 0.6378, loss_cls: 3.4925, loss: 3.4925 +2024-07-25 13:45:03,181 - pyskl - INFO - Epoch [95][500/3746] lr: 3.050e-02, eta: 1 day, 23:08:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6266, loss_cls: 3.5664, loss: 3.5664 +2024-07-25 13:46:24,677 - pyskl - INFO - Epoch [95][600/3746] lr: 3.047e-02, eta: 1 day, 23:06:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6239, loss_cls: 3.6079, loss: 3.6079 +2024-07-25 13:47:47,132 - pyskl - INFO - Epoch [95][700/3746] lr: 3.044e-02, eta: 1 day, 23:05:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6372, loss_cls: 3.5583, loss: 3.5583 +2024-07-25 13:49:08,548 - pyskl - INFO - Epoch [95][800/3746] lr: 3.042e-02, eta: 1 day, 23:04:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6372, loss_cls: 3.5727, loss: 3.5727 +2024-07-25 13:50:30,732 - pyskl - INFO - Epoch [95][900/3746] lr: 3.039e-02, eta: 1 day, 23:02:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6272, loss_cls: 3.5638, loss: 3.5638 +2024-07-25 13:51:52,667 - pyskl - INFO - Epoch [95][1000/3746] lr: 3.037e-02, eta: 1 day, 23:01:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6164, loss_cls: 3.6567, loss: 3.6567 +2024-07-25 13:53:14,331 - pyskl - INFO - Epoch [95][1100/3746] lr: 3.034e-02, eta: 1 day, 22:59:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6247, loss_cls: 3.6033, loss: 3.6033 +2024-07-25 13:54:36,029 - pyskl - INFO - Epoch [95][1200/3746] lr: 3.032e-02, eta: 1 day, 22:58:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6178, loss_cls: 3.6111, loss: 3.6111 +2024-07-25 13:55:58,138 - pyskl - INFO - Epoch [95][1300/3746] lr: 3.029e-02, eta: 1 day, 22:57:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6164, loss_cls: 3.6105, loss: 3.6105 +2024-07-25 13:57:19,262 - pyskl - INFO - Epoch [95][1400/3746] lr: 3.026e-02, eta: 1 day, 22:55:56, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6202, loss_cls: 3.5953, loss: 3.5953 +2024-07-25 13:58:40,968 - pyskl - INFO - Epoch [95][1500/3746] lr: 3.024e-02, eta: 1 day, 22:54:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6192, loss_cls: 3.6166, loss: 3.6166 +2024-07-25 14:00:02,716 - pyskl - INFO - Epoch [95][1600/3746] lr: 3.021e-02, eta: 1 day, 22:53:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6197, loss_cls: 3.6089, loss: 3.6089 +2024-07-25 14:01:24,370 - pyskl - INFO - Epoch [95][1700/3746] lr: 3.019e-02, eta: 1 day, 22:51:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6230, loss_cls: 3.6155, loss: 3.6155 +2024-07-25 14:02:46,639 - pyskl - INFO - Epoch [95][1800/3746] lr: 3.016e-02, eta: 1 day, 22:50:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6230, loss_cls: 3.6037, loss: 3.6037 +2024-07-25 14:04:08,871 - pyskl - INFO - Epoch [95][1900/3746] lr: 3.014e-02, eta: 1 day, 22:49:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6162, loss_cls: 3.6518, loss: 3.6518 +2024-07-25 14:05:31,829 - pyskl - INFO - Epoch [95][2000/3746] lr: 3.011e-02, eta: 1 day, 22:47:53, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6186, loss_cls: 3.6721, loss: 3.6721 +2024-07-25 14:06:53,613 - pyskl - INFO - Epoch [95][2100/3746] lr: 3.008e-02, eta: 1 day, 22:46:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3691, top5_acc: 0.6281, loss_cls: 3.5724, loss: 3.5724 +2024-07-25 14:08:15,598 - pyskl - INFO - Epoch [95][2200/3746] lr: 3.006e-02, eta: 1 day, 22:45:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6297, loss_cls: 3.5895, loss: 3.5895 +2024-07-25 14:09:37,792 - pyskl - INFO - Epoch [95][2300/3746] lr: 3.003e-02, eta: 1 day, 22:43:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3812, top5_acc: 0.6361, loss_cls: 3.5120, loss: 3.5120 +2024-07-25 14:10:59,557 - pyskl - INFO - Epoch [95][2400/3746] lr: 3.001e-02, eta: 1 day, 22:42:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6264, loss_cls: 3.6051, loss: 3.6051 +2024-07-25 14:12:21,041 - pyskl - INFO - Epoch [95][2500/3746] lr: 2.998e-02, eta: 1 day, 22:41:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6188, loss_cls: 3.6009, loss: 3.6009 +2024-07-25 14:13:42,931 - pyskl - INFO - Epoch [95][2600/3746] lr: 2.996e-02, eta: 1 day, 22:39:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6266, loss_cls: 3.5969, loss: 3.5969 +2024-07-25 14:15:04,488 - pyskl - INFO - Epoch [95][2700/3746] lr: 2.993e-02, eta: 1 day, 22:38:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6194, loss_cls: 3.6192, loss: 3.6192 +2024-07-25 14:16:25,882 - pyskl - INFO - Epoch [95][2800/3746] lr: 2.991e-02, eta: 1 day, 22:37:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6250, loss_cls: 3.6310, loss: 3.6310 +2024-07-25 14:17:47,555 - pyskl - INFO - Epoch [95][2900/3746] lr: 2.988e-02, eta: 1 day, 22:35:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6156, loss_cls: 3.6614, loss: 3.6614 +2024-07-25 14:19:09,181 - pyskl - INFO - Epoch [95][3000/3746] lr: 2.985e-02, eta: 1 day, 22:34:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6289, loss_cls: 3.5882, loss: 3.5882 +2024-07-25 14:20:30,881 - pyskl - INFO - Epoch [95][3100/3746] lr: 2.983e-02, eta: 1 day, 22:33:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6133, loss_cls: 3.6448, loss: 3.6448 +2024-07-25 14:21:52,685 - pyskl - INFO - Epoch [95][3200/3746] lr: 2.980e-02, eta: 1 day, 22:31:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6212, loss_cls: 3.6273, loss: 3.6273 +2024-07-25 14:23:14,754 - pyskl - INFO - Epoch [95][3300/3746] lr: 2.978e-02, eta: 1 day, 22:30:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6192, loss_cls: 3.6091, loss: 3.6091 +2024-07-25 14:24:36,878 - pyskl - INFO - Epoch [95][3400/3746] lr: 2.975e-02, eta: 1 day, 22:29:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6253, loss_cls: 3.6300, loss: 3.6300 +2024-07-25 14:25:58,986 - pyskl - INFO - Epoch [95][3500/3746] lr: 2.973e-02, eta: 1 day, 22:27:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6230, loss_cls: 3.6097, loss: 3.6097 +2024-07-25 14:27:20,685 - pyskl - INFO - Epoch [95][3600/3746] lr: 2.970e-02, eta: 1 day, 22:26:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6214, loss_cls: 3.5862, loss: 3.5862 +2024-07-25 14:28:42,006 - pyskl - INFO - Epoch [95][3700/3746] lr: 2.968e-02, eta: 1 day, 22:25:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6239, loss_cls: 3.6230, loss: 3.6230 +2024-07-25 14:29:21,499 - pyskl - INFO - Saving checkpoint at 95 epochs +2024-07-25 14:31:14,127 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 14:31:14,794 - pyskl - INFO - +top1_acc 0.2945 +top5_acc 0.5487 +2024-07-25 14:31:14,795 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 14:31:14,835 - pyskl - INFO - +mean_acc 0.2943 +2024-07-25 14:31:14,846 - pyskl - INFO - Epoch(val) [95][309] top1_acc: 0.2945, top5_acc: 0.5487, mean_class_accuracy: 0.2943 +2024-07-25 14:35:06,234 - pyskl - INFO - Epoch [96][100/3746] lr: 2.964e-02, eta: 1 day, 22:24:08, time: 2.314, data_time: 1.333, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6375, loss_cls: 3.4958, loss: 3.4958 +2024-07-25 14:36:28,122 - pyskl - INFO - Epoch [96][200/3746] lr: 2.961e-02, eta: 1 day, 22:22:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6348, loss_cls: 3.5384, loss: 3.5384 +2024-07-25 14:37:49,646 - pyskl - INFO - Epoch [96][300/3746] lr: 2.959e-02, eta: 1 day, 22:21:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6361, loss_cls: 3.5416, loss: 3.5416 +2024-07-25 14:39:11,762 - pyskl - INFO - Epoch [96][400/3746] lr: 2.956e-02, eta: 1 day, 22:20:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6339, loss_cls: 3.5415, loss: 3.5415 +2024-07-25 14:40:33,842 - pyskl - INFO - Epoch [96][500/3746] lr: 2.954e-02, eta: 1 day, 22:18:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6398, loss_cls: 3.5181, loss: 3.5181 +2024-07-25 14:41:56,093 - pyskl - INFO - Epoch [96][600/3746] lr: 2.951e-02, eta: 1 day, 22:17:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6377, loss_cls: 3.5272, loss: 3.5272 +2024-07-25 14:43:18,400 - pyskl - INFO - Epoch [96][700/3746] lr: 2.948e-02, eta: 1 day, 22:16:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6303, loss_cls: 3.5551, loss: 3.5551 +2024-07-25 14:44:40,097 - pyskl - INFO - Epoch [96][800/3746] lr: 2.946e-02, eta: 1 day, 22:14:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6298, loss_cls: 3.5565, loss: 3.5565 +2024-07-25 14:46:02,201 - pyskl - INFO - Epoch [96][900/3746] lr: 2.943e-02, eta: 1 day, 22:13:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6336, loss_cls: 3.5680, loss: 3.5680 +2024-07-25 14:47:23,920 - pyskl - INFO - Epoch [96][1000/3746] lr: 2.941e-02, eta: 1 day, 22:12:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6297, loss_cls: 3.6146, loss: 3.6146 +2024-07-25 14:48:45,952 - pyskl - INFO - Epoch [96][1100/3746] lr: 2.938e-02, eta: 1 day, 22:10:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6283, loss_cls: 3.6189, loss: 3.6189 +2024-07-25 14:50:07,582 - pyskl - INFO - Epoch [96][1200/3746] lr: 2.936e-02, eta: 1 day, 22:09:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6300, loss_cls: 3.5821, loss: 3.5821 +2024-07-25 14:51:28,715 - pyskl - INFO - Epoch [96][1300/3746] lr: 2.933e-02, eta: 1 day, 22:08:00, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6227, loss_cls: 3.6152, loss: 3.6152 +2024-07-25 14:52:50,407 - pyskl - INFO - Epoch [96][1400/3746] lr: 2.931e-02, eta: 1 day, 22:06:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6147, loss_cls: 3.6406, loss: 3.6406 +2024-07-25 14:54:12,514 - pyskl - INFO - Epoch [96][1500/3746] lr: 2.928e-02, eta: 1 day, 22:05:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6291, loss_cls: 3.5623, loss: 3.5623 +2024-07-25 14:55:35,014 - pyskl - INFO - Epoch [96][1600/3746] lr: 2.926e-02, eta: 1 day, 22:03:58, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6378, loss_cls: 3.5433, loss: 3.5433 +2024-07-25 14:56:57,293 - pyskl - INFO - Epoch [96][1700/3746] lr: 2.923e-02, eta: 1 day, 22:02:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6300, loss_cls: 3.5717, loss: 3.5717 +2024-07-25 14:58:19,841 - pyskl - INFO - Epoch [96][1800/3746] lr: 2.920e-02, eta: 1 day, 22:01:18, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6231, loss_cls: 3.6336, loss: 3.6336 +2024-07-25 14:59:41,520 - pyskl - INFO - Epoch [96][1900/3746] lr: 2.918e-02, eta: 1 day, 21:59:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6205, loss_cls: 3.6108, loss: 3.6108 +2024-07-25 15:01:04,955 - pyskl - INFO - Epoch [96][2000/3746] lr: 2.915e-02, eta: 1 day, 21:58:37, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6336, loss_cls: 3.5576, loss: 3.5576 +2024-07-25 15:02:26,272 - pyskl - INFO - Epoch [96][2100/3746] lr: 2.913e-02, eta: 1 day, 21:57:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6228, loss_cls: 3.5956, loss: 3.5956 +2024-07-25 15:03:48,198 - pyskl - INFO - Epoch [96][2200/3746] lr: 2.910e-02, eta: 1 day, 21:55:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6238, loss_cls: 3.5889, loss: 3.5889 +2024-07-25 15:05:09,398 - pyskl - INFO - Epoch [96][2300/3746] lr: 2.908e-02, eta: 1 day, 21:54:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3686, top5_acc: 0.6294, loss_cls: 3.5800, loss: 3.5800 +2024-07-25 15:06:31,342 - pyskl - INFO - Epoch [96][2400/3746] lr: 2.905e-02, eta: 1 day, 21:53:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6222, loss_cls: 3.5975, loss: 3.5975 +2024-07-25 15:07:53,238 - pyskl - INFO - Epoch [96][2500/3746] lr: 2.903e-02, eta: 1 day, 21:51:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6309, loss_cls: 3.5640, loss: 3.5640 +2024-07-25 15:09:15,191 - pyskl - INFO - Epoch [96][2600/3746] lr: 2.900e-02, eta: 1 day, 21:50:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6286, loss_cls: 3.5833, loss: 3.5833 +2024-07-25 15:10:36,522 - pyskl - INFO - Epoch [96][2700/3746] lr: 2.898e-02, eta: 1 day, 21:49:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6294, loss_cls: 3.5737, loss: 3.5737 +2024-07-25 15:11:58,042 - pyskl - INFO - Epoch [96][2800/3746] lr: 2.895e-02, eta: 1 day, 21:47:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6281, loss_cls: 3.5916, loss: 3.5916 +2024-07-25 15:13:19,906 - pyskl - INFO - Epoch [96][2900/3746] lr: 2.893e-02, eta: 1 day, 21:46:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6273, loss_cls: 3.5913, loss: 3.5913 +2024-07-25 15:14:41,435 - pyskl - INFO - Epoch [96][3000/3746] lr: 2.890e-02, eta: 1 day, 21:45:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6316, loss_cls: 3.5667, loss: 3.5667 +2024-07-25 15:16:02,991 - pyskl - INFO - Epoch [96][3100/3746] lr: 2.887e-02, eta: 1 day, 21:43:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6233, loss_cls: 3.5745, loss: 3.5745 +2024-07-25 15:17:24,733 - pyskl - INFO - Epoch [96][3200/3746] lr: 2.885e-02, eta: 1 day, 21:42:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6317, loss_cls: 3.5864, loss: 3.5864 +2024-07-25 15:18:46,231 - pyskl - INFO - Epoch [96][3300/3746] lr: 2.882e-02, eta: 1 day, 21:41:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6277, loss_cls: 3.6313, loss: 3.6313 +2024-07-25 15:20:07,919 - pyskl - INFO - Epoch [96][3400/3746] lr: 2.880e-02, eta: 1 day, 21:39:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6145, loss_cls: 3.6193, loss: 3.6193 +2024-07-25 15:21:29,220 - pyskl - INFO - Epoch [96][3500/3746] lr: 2.877e-02, eta: 1 day, 21:38:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6183, loss_cls: 3.6372, loss: 3.6372 +2024-07-25 15:22:50,637 - pyskl - INFO - Epoch [96][3600/3746] lr: 2.875e-02, eta: 1 day, 21:37:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6103, loss_cls: 3.6648, loss: 3.6648 +2024-07-25 15:24:12,414 - pyskl - INFO - Epoch [96][3700/3746] lr: 2.872e-02, eta: 1 day, 21:35:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6269, loss_cls: 3.5814, loss: 3.5814 +2024-07-25 15:24:52,225 - pyskl - INFO - Saving checkpoint at 96 epochs +2024-07-25 15:26:45,760 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 15:26:46,422 - pyskl - INFO - +top1_acc 0.3030 +top5_acc 0.5554 +2024-07-25 15:26:46,423 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 15:26:46,463 - pyskl - INFO - +mean_acc 0.3026 +2024-07-25 15:26:46,475 - pyskl - INFO - Epoch(val) [96][309] top1_acc: 0.3030, top5_acc: 0.5554, mean_class_accuracy: 0.3026 +2024-07-25 15:30:33,462 - pyskl - INFO - Epoch [97][100/3746] lr: 2.869e-02, eta: 1 day, 21:34:45, time: 2.270, data_time: 1.293, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6309, loss_cls: 3.5452, loss: 3.5452 +2024-07-25 15:31:55,900 - pyskl - INFO - Epoch [97][200/3746] lr: 2.866e-02, eta: 1 day, 21:33:25, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6389, loss_cls: 3.5353, loss: 3.5353 +2024-07-25 15:33:17,783 - pyskl - INFO - Epoch [97][300/3746] lr: 2.864e-02, eta: 1 day, 21:32:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6306, loss_cls: 3.5555, loss: 3.5555 +2024-07-25 15:34:39,472 - pyskl - INFO - Epoch [97][400/3746] lr: 2.861e-02, eta: 1 day, 21:30:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6497, loss_cls: 3.4977, loss: 3.4977 +2024-07-25 15:36:01,413 - pyskl - INFO - Epoch [97][500/3746] lr: 2.858e-02, eta: 1 day, 21:29:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6397, loss_cls: 3.5270, loss: 3.5270 +2024-07-25 15:37:22,869 - pyskl - INFO - Epoch [97][600/3746] lr: 2.856e-02, eta: 1 day, 21:28:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3739, top5_acc: 0.6339, loss_cls: 3.5728, loss: 3.5728 +2024-07-25 15:38:45,370 - pyskl - INFO - Epoch [97][700/3746] lr: 2.853e-02, eta: 1 day, 21:26:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6338, loss_cls: 3.5588, loss: 3.5588 +2024-07-25 15:40:07,610 - pyskl - INFO - Epoch [97][800/3746] lr: 2.851e-02, eta: 1 day, 21:25:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6414, loss_cls: 3.5282, loss: 3.5282 +2024-07-25 15:41:29,603 - pyskl - INFO - Epoch [97][900/3746] lr: 2.848e-02, eta: 1 day, 21:24:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6208, loss_cls: 3.6092, loss: 3.6092 +2024-07-25 15:42:51,627 - pyskl - INFO - Epoch [97][1000/3746] lr: 2.846e-02, eta: 1 day, 21:22:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6288, loss_cls: 3.5457, loss: 3.5457 +2024-07-25 15:44:13,192 - pyskl - INFO - Epoch [97][1100/3746] lr: 2.843e-02, eta: 1 day, 21:21:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6339, loss_cls: 3.5429, loss: 3.5429 +2024-07-25 15:45:35,112 - pyskl - INFO - Epoch [97][1200/3746] lr: 2.841e-02, eta: 1 day, 21:19:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6427, loss_cls: 3.5296, loss: 3.5296 +2024-07-25 15:46:57,408 - pyskl - INFO - Epoch [97][1300/3746] lr: 2.838e-02, eta: 1 day, 21:18:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6241, loss_cls: 3.6032, loss: 3.6032 +2024-07-25 15:48:19,114 - pyskl - INFO - Epoch [97][1400/3746] lr: 2.836e-02, eta: 1 day, 21:17:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6347, loss_cls: 3.5304, loss: 3.5304 +2024-07-25 15:49:40,583 - pyskl - INFO - Epoch [97][1500/3746] lr: 2.833e-02, eta: 1 day, 21:15:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6131, loss_cls: 3.6128, loss: 3.6128 +2024-07-25 15:51:01,889 - pyskl - INFO - Epoch [97][1600/3746] lr: 2.831e-02, eta: 1 day, 21:14:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6308, loss_cls: 3.5581, loss: 3.5581 +2024-07-25 15:52:23,578 - pyskl - INFO - Epoch [97][1700/3746] lr: 2.828e-02, eta: 1 day, 21:13:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6144, loss_cls: 3.6549, loss: 3.6549 +2024-07-25 15:53:45,402 - pyskl - INFO - Epoch [97][1800/3746] lr: 2.826e-02, eta: 1 day, 21:11:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6372, loss_cls: 3.5704, loss: 3.5704 +2024-07-25 15:55:08,510 - pyskl - INFO - Epoch [97][1900/3746] lr: 2.823e-02, eta: 1 day, 21:10:32, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6303, loss_cls: 3.5472, loss: 3.5472 +2024-07-25 15:56:30,446 - pyskl - INFO - Epoch [97][2000/3746] lr: 2.821e-02, eta: 1 day, 21:09:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3741, top5_acc: 0.6267, loss_cls: 3.5684, loss: 3.5684 +2024-07-25 15:57:52,461 - pyskl - INFO - Epoch [97][2100/3746] lr: 2.818e-02, eta: 1 day, 21:07:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6286, loss_cls: 3.5617, loss: 3.5617 +2024-07-25 15:59:14,596 - pyskl - INFO - Epoch [97][2200/3746] lr: 2.816e-02, eta: 1 day, 21:06:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6208, loss_cls: 3.5637, loss: 3.5637 +2024-07-25 16:00:36,731 - pyskl - INFO - Epoch [97][2300/3746] lr: 2.813e-02, eta: 1 day, 21:05:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6211, loss_cls: 3.5917, loss: 3.5917 +2024-07-25 16:01:58,808 - pyskl - INFO - Epoch [97][2400/3746] lr: 2.811e-02, eta: 1 day, 21:03:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6278, loss_cls: 3.5833, loss: 3.5833 +2024-07-25 16:03:20,819 - pyskl - INFO - Epoch [97][2500/3746] lr: 2.808e-02, eta: 1 day, 21:02:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6217, loss_cls: 3.6086, loss: 3.6086 +2024-07-25 16:04:42,481 - pyskl - INFO - Epoch [97][2600/3746] lr: 2.806e-02, eta: 1 day, 21:01:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6325, loss_cls: 3.5319, loss: 3.5319 +2024-07-25 16:06:04,419 - pyskl - INFO - Epoch [97][2700/3746] lr: 2.803e-02, eta: 1 day, 20:59:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6388, loss_cls: 3.5317, loss: 3.5317 +2024-07-25 16:07:26,023 - pyskl - INFO - Epoch [97][2800/3746] lr: 2.801e-02, eta: 1 day, 20:58:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6372, loss_cls: 3.5069, loss: 3.5069 +2024-07-25 16:08:48,236 - pyskl - INFO - Epoch [97][2900/3746] lr: 2.798e-02, eta: 1 day, 20:57:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6203, loss_cls: 3.6093, loss: 3.6093 +2024-07-25 16:10:09,923 - pyskl - INFO - Epoch [97][3000/3746] lr: 2.796e-02, eta: 1 day, 20:55:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6269, loss_cls: 3.5521, loss: 3.5521 +2024-07-25 16:11:31,772 - pyskl - INFO - Epoch [97][3100/3746] lr: 2.793e-02, eta: 1 day, 20:54:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6256, loss_cls: 3.6010, loss: 3.6010 +2024-07-25 16:12:53,582 - pyskl - INFO - Epoch [97][3200/3746] lr: 2.791e-02, eta: 1 day, 20:53:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6347, loss_cls: 3.5430, loss: 3.5430 +2024-07-25 16:14:14,776 - pyskl - INFO - Epoch [97][3300/3746] lr: 2.788e-02, eta: 1 day, 20:51:41, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6292, loss_cls: 3.5778, loss: 3.5778 +2024-07-25 16:15:36,186 - pyskl - INFO - Epoch [97][3400/3746] lr: 2.786e-02, eta: 1 day, 20:50:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6280, loss_cls: 3.5729, loss: 3.5729 +2024-07-25 16:16:57,592 - pyskl - INFO - Epoch [97][3500/3746] lr: 2.783e-02, eta: 1 day, 20:48:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6305, loss_cls: 3.5590, loss: 3.5590 +2024-07-25 16:18:19,576 - pyskl - INFO - Epoch [97][3600/3746] lr: 2.781e-02, eta: 1 day, 20:47:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6212, loss_cls: 3.6336, loss: 3.6336 +2024-07-25 16:19:41,773 - pyskl - INFO - Epoch [97][3700/3746] lr: 2.778e-02, eta: 1 day, 20:46:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6312, loss_cls: 3.5388, loss: 3.5388 +2024-07-25 16:20:21,283 - pyskl - INFO - Saving checkpoint at 97 epochs +2024-07-25 16:22:13,117 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 16:22:13,985 - pyskl - INFO - +top1_acc 0.3170 +top5_acc 0.5708 +2024-07-25 16:22:13,985 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 16:22:14,035 - pyskl - INFO - +mean_acc 0.3167 +2024-07-25 16:22:14,040 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_92.pth was removed +2024-07-25 16:22:14,288 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_97.pth. +2024-07-25 16:22:14,289 - pyskl - INFO - Best top1_acc is 0.3170 at 97 epoch. +2024-07-25 16:22:14,301 - pyskl - INFO - Epoch(val) [97][309] top1_acc: 0.3170, top5_acc: 0.5708, mean_class_accuracy: 0.3167 +2024-07-25 16:26:04,774 - pyskl - INFO - Epoch [98][100/3746] lr: 2.774e-02, eta: 1 day, 20:45:21, time: 2.305, data_time: 1.329, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6383, loss_cls: 3.5054, loss: 3.5054 +2024-07-25 16:27:25,941 - pyskl - INFO - Epoch [98][200/3746] lr: 2.772e-02, eta: 1 day, 20:43:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6344, loss_cls: 3.4903, loss: 3.4903 +2024-07-25 16:28:47,339 - pyskl - INFO - Epoch [98][300/3746] lr: 2.769e-02, eta: 1 day, 20:42:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6438, loss_cls: 3.4953, loss: 3.4953 +2024-07-25 16:30:09,186 - pyskl - INFO - Epoch [98][400/3746] lr: 2.767e-02, eta: 1 day, 20:41:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6383, loss_cls: 3.5084, loss: 3.5084 +2024-07-25 16:31:31,258 - pyskl - INFO - Epoch [98][500/3746] lr: 2.764e-02, eta: 1 day, 20:39:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6359, loss_cls: 3.5131, loss: 3.5131 +2024-07-25 16:32:53,175 - pyskl - INFO - Epoch [98][600/3746] lr: 2.762e-02, eta: 1 day, 20:38:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3812, top5_acc: 0.6320, loss_cls: 3.5113, loss: 3.5113 +2024-07-25 16:34:15,723 - pyskl - INFO - Epoch [98][700/3746] lr: 2.759e-02, eta: 1 day, 20:37:15, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3848, top5_acc: 0.6416, loss_cls: 3.5084, loss: 3.5084 +2024-07-25 16:35:37,086 - pyskl - INFO - Epoch [98][800/3746] lr: 2.757e-02, eta: 1 day, 20:35:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6259, loss_cls: 3.5961, loss: 3.5961 +2024-07-25 16:36:59,243 - pyskl - INFO - Epoch [98][900/3746] lr: 2.754e-02, eta: 1 day, 20:34:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6352, loss_cls: 3.5404, loss: 3.5404 +2024-07-25 16:38:21,325 - pyskl - INFO - Epoch [98][1000/3746] lr: 2.752e-02, eta: 1 day, 20:33:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6350, loss_cls: 3.5418, loss: 3.5418 +2024-07-25 16:39:43,543 - pyskl - INFO - Epoch [98][1100/3746] lr: 2.749e-02, eta: 1 day, 20:31:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3748, top5_acc: 0.6323, loss_cls: 3.5184, loss: 3.5184 +2024-07-25 16:41:05,518 - pyskl - INFO - Epoch [98][1200/3746] lr: 2.747e-02, eta: 1 day, 20:30:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6244, loss_cls: 3.5688, loss: 3.5688 +2024-07-25 16:42:27,509 - pyskl - INFO - Epoch [98][1300/3746] lr: 2.744e-02, eta: 1 day, 20:29:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6362, loss_cls: 3.5462, loss: 3.5462 +2024-07-25 16:43:48,871 - pyskl - INFO - Epoch [98][1400/3746] lr: 2.742e-02, eta: 1 day, 20:27:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6394, loss_cls: 3.5511, loss: 3.5511 +2024-07-25 16:45:10,357 - pyskl - INFO - Epoch [98][1500/3746] lr: 2.739e-02, eta: 1 day, 20:26:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6252, loss_cls: 3.5760, loss: 3.5760 +2024-07-25 16:46:32,036 - pyskl - INFO - Epoch [98][1600/3746] lr: 2.737e-02, eta: 1 day, 20:25:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3691, top5_acc: 0.6322, loss_cls: 3.5503, loss: 3.5503 +2024-07-25 16:47:53,727 - pyskl - INFO - Epoch [98][1700/3746] lr: 2.734e-02, eta: 1 day, 20:23:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6308, loss_cls: 3.5522, loss: 3.5522 +2024-07-25 16:49:15,865 - pyskl - INFO - Epoch [98][1800/3746] lr: 2.732e-02, eta: 1 day, 20:22:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6312, loss_cls: 3.5944, loss: 3.5944 +2024-07-25 16:50:38,597 - pyskl - INFO - Epoch [98][1900/3746] lr: 2.729e-02, eta: 1 day, 20:21:06, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6341, loss_cls: 3.5481, loss: 3.5481 +2024-07-25 16:52:00,914 - pyskl - INFO - Epoch [98][2000/3746] lr: 2.727e-02, eta: 1 day, 20:19:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6336, loss_cls: 3.5307, loss: 3.5307 +2024-07-25 16:53:23,176 - pyskl - INFO - Epoch [98][2100/3746] lr: 2.724e-02, eta: 1 day, 20:18:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6258, loss_cls: 3.5770, loss: 3.5770 +2024-07-25 16:54:45,270 - pyskl - INFO - Epoch [98][2200/3746] lr: 2.722e-02, eta: 1 day, 20:17:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6334, loss_cls: 3.5187, loss: 3.5187 +2024-07-25 16:56:06,863 - pyskl - INFO - Epoch [98][2300/3746] lr: 2.719e-02, eta: 1 day, 20:15:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6312, loss_cls: 3.5367, loss: 3.5367 +2024-07-25 16:57:28,871 - pyskl - INFO - Epoch [98][2400/3746] lr: 2.717e-02, eta: 1 day, 20:14:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6305, loss_cls: 3.5649, loss: 3.5649 +2024-07-25 16:58:50,247 - pyskl - INFO - Epoch [98][2500/3746] lr: 2.714e-02, eta: 1 day, 20:13:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3656, top5_acc: 0.6367, loss_cls: 3.5552, loss: 3.5552 +2024-07-25 17:00:11,710 - pyskl - INFO - Epoch [98][2600/3746] lr: 2.712e-02, eta: 1 day, 20:11:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3731, top5_acc: 0.6330, loss_cls: 3.5465, loss: 3.5465 +2024-07-25 17:01:33,237 - pyskl - INFO - Epoch [98][2700/3746] lr: 2.709e-02, eta: 1 day, 20:10:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6306, loss_cls: 3.5581, loss: 3.5581 +2024-07-25 17:02:54,623 - pyskl - INFO - Epoch [98][2800/3746] lr: 2.707e-02, eta: 1 day, 20:08:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6270, loss_cls: 3.5812, loss: 3.5812 +2024-07-25 17:04:16,296 - pyskl - INFO - Epoch [98][2900/3746] lr: 2.705e-02, eta: 1 day, 20:07:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6294, loss_cls: 3.5585, loss: 3.5585 +2024-07-25 17:05:38,074 - pyskl - INFO - Epoch [98][3000/3746] lr: 2.702e-02, eta: 1 day, 20:06:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6275, loss_cls: 3.5778, loss: 3.5778 +2024-07-25 17:06:59,257 - pyskl - INFO - Epoch [98][3100/3746] lr: 2.700e-02, eta: 1 day, 20:04:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6241, loss_cls: 3.5760, loss: 3.5760 +2024-07-25 17:08:20,785 - pyskl - INFO - Epoch [98][3200/3746] lr: 2.697e-02, eta: 1 day, 20:03:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6361, loss_cls: 3.5580, loss: 3.5580 +2024-07-25 17:09:42,390 - pyskl - INFO - Epoch [98][3300/3746] lr: 2.695e-02, eta: 1 day, 20:02:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3748, top5_acc: 0.6316, loss_cls: 3.5421, loss: 3.5421 +2024-07-25 17:11:04,224 - pyskl - INFO - Epoch [98][3400/3746] lr: 2.692e-02, eta: 1 day, 20:00:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6303, loss_cls: 3.5998, loss: 3.5998 +2024-07-25 17:12:25,648 - pyskl - INFO - Epoch [98][3500/3746] lr: 2.690e-02, eta: 1 day, 19:59:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6339, loss_cls: 3.5516, loss: 3.5516 +2024-07-25 17:13:47,058 - pyskl - INFO - Epoch [98][3600/3746] lr: 2.687e-02, eta: 1 day, 19:58:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6338, loss_cls: 3.5424, loss: 3.5424 +2024-07-25 17:15:09,060 - pyskl - INFO - Epoch [98][3700/3746] lr: 2.685e-02, eta: 1 day, 19:56:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6345, loss_cls: 3.5543, loss: 3.5543 +2024-07-25 17:15:48,654 - pyskl - INFO - Saving checkpoint at 98 epochs +2024-07-25 17:17:40,667 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 17:17:41,324 - pyskl - INFO - +top1_acc 0.3166 +top5_acc 0.5676 +2024-07-25 17:17:41,324 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 17:17:41,362 - pyskl - INFO - +mean_acc 0.3163 +2024-07-25 17:17:41,373 - pyskl - INFO - Epoch(val) [98][309] top1_acc: 0.3166, top5_acc: 0.5676, mean_class_accuracy: 0.3163 +2024-07-25 17:21:29,714 - pyskl - INFO - Epoch [99][100/3746] lr: 2.681e-02, eta: 1 day, 19:55:49, time: 2.283, data_time: 1.314, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6434, loss_cls: 3.4715, loss: 3.4715 +2024-07-25 17:22:50,945 - pyskl - INFO - Epoch [99][200/3746] lr: 2.679e-02, eta: 1 day, 19:54:27, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6467, loss_cls: 3.4787, loss: 3.4787 +2024-07-25 17:24:12,120 - pyskl - INFO - Epoch [99][300/3746] lr: 2.676e-02, eta: 1 day, 19:53:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6358, loss_cls: 3.5095, loss: 3.5095 +2024-07-25 17:25:33,821 - pyskl - INFO - Epoch [99][400/3746] lr: 2.674e-02, eta: 1 day, 19:51:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6383, loss_cls: 3.4937, loss: 3.4937 +2024-07-25 17:26:55,074 - pyskl - INFO - Epoch [99][500/3746] lr: 2.671e-02, eta: 1 day, 19:50:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6439, loss_cls: 3.4850, loss: 3.4850 +2024-07-25 17:28:16,912 - pyskl - INFO - Epoch [99][600/3746] lr: 2.669e-02, eta: 1 day, 19:49:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6414, loss_cls: 3.5141, loss: 3.5141 +2024-07-25 17:29:39,036 - pyskl - INFO - Epoch [99][700/3746] lr: 2.666e-02, eta: 1 day, 19:47:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6386, loss_cls: 3.5071, loss: 3.5071 +2024-07-25 17:31:01,049 - pyskl - INFO - Epoch [99][800/3746] lr: 2.664e-02, eta: 1 day, 19:46:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6436, loss_cls: 3.4798, loss: 3.4798 +2024-07-25 17:32:23,159 - pyskl - INFO - Epoch [99][900/3746] lr: 2.661e-02, eta: 1 day, 19:45:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6392, loss_cls: 3.5153, loss: 3.5153 +2024-07-25 17:33:45,108 - pyskl - INFO - Epoch [99][1000/3746] lr: 2.659e-02, eta: 1 day, 19:43:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6336, loss_cls: 3.5513, loss: 3.5513 +2024-07-25 17:35:06,706 - pyskl - INFO - Epoch [99][1100/3746] lr: 2.656e-02, eta: 1 day, 19:42:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6378, loss_cls: 3.5353, loss: 3.5353 +2024-07-25 17:36:28,015 - pyskl - INFO - Epoch [99][1200/3746] lr: 2.654e-02, eta: 1 day, 19:40:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6372, loss_cls: 3.5383, loss: 3.5383 +2024-07-25 17:37:49,832 - pyskl - INFO - Epoch [99][1300/3746] lr: 2.651e-02, eta: 1 day, 19:39:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6295, loss_cls: 3.5625, loss: 3.5625 +2024-07-25 17:39:11,392 - pyskl - INFO - Epoch [99][1400/3746] lr: 2.649e-02, eta: 1 day, 19:38:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6241, loss_cls: 3.5688, loss: 3.5688 +2024-07-25 17:40:32,932 - pyskl - INFO - Epoch [99][1500/3746] lr: 2.646e-02, eta: 1 day, 19:36:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6267, loss_cls: 3.5952, loss: 3.5952 +2024-07-25 17:41:54,976 - pyskl - INFO - Epoch [99][1600/3746] lr: 2.644e-02, eta: 1 day, 19:35:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3812, top5_acc: 0.6362, loss_cls: 3.5302, loss: 3.5302 +2024-07-25 17:43:16,449 - pyskl - INFO - Epoch [99][1700/3746] lr: 2.642e-02, eta: 1 day, 19:34:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6314, loss_cls: 3.5593, loss: 3.5593 +2024-07-25 17:44:38,162 - pyskl - INFO - Epoch [99][1800/3746] lr: 2.639e-02, eta: 1 day, 19:32:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6350, loss_cls: 3.5175, loss: 3.5175 +2024-07-25 17:46:01,128 - pyskl - INFO - Epoch [99][1900/3746] lr: 2.637e-02, eta: 1 day, 19:31:31, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6347, loss_cls: 3.5509, loss: 3.5509 +2024-07-25 17:47:23,217 - pyskl - INFO - Epoch [99][2000/3746] lr: 2.634e-02, eta: 1 day, 19:30:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6323, loss_cls: 3.5515, loss: 3.5515 +2024-07-25 17:48:45,996 - pyskl - INFO - Epoch [99][2100/3746] lr: 2.632e-02, eta: 1 day, 19:28:50, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6325, loss_cls: 3.5247, loss: 3.5247 +2024-07-25 17:50:07,749 - pyskl - INFO - Epoch [99][2200/3746] lr: 2.629e-02, eta: 1 day, 19:27:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6366, loss_cls: 3.5285, loss: 3.5285 +2024-07-25 17:51:30,264 - pyskl - INFO - Epoch [99][2300/3746] lr: 2.627e-02, eta: 1 day, 19:26:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6455, loss_cls: 3.5047, loss: 3.5047 +2024-07-25 17:52:52,516 - pyskl - INFO - Epoch [99][2400/3746] lr: 2.624e-02, eta: 1 day, 19:24:48, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6275, loss_cls: 3.6015, loss: 3.6015 +2024-07-25 17:54:14,689 - pyskl - INFO - Epoch [99][2500/3746] lr: 2.622e-02, eta: 1 day, 19:23:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6423, loss_cls: 3.5379, loss: 3.5379 +2024-07-25 17:55:36,562 - pyskl - INFO - Epoch [99][2600/3746] lr: 2.619e-02, eta: 1 day, 19:22:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6362, loss_cls: 3.5566, loss: 3.5566 +2024-07-25 17:56:58,063 - pyskl - INFO - Epoch [99][2700/3746] lr: 2.617e-02, eta: 1 day, 19:20:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6400, loss_cls: 3.4794, loss: 3.4794 +2024-07-25 17:58:19,859 - pyskl - INFO - Epoch [99][2800/3746] lr: 2.614e-02, eta: 1 day, 19:19:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6258, loss_cls: 3.5860, loss: 3.5860 +2024-07-25 17:59:41,532 - pyskl - INFO - Epoch [99][2900/3746] lr: 2.612e-02, eta: 1 day, 19:18:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6373, loss_cls: 3.5461, loss: 3.5461 +2024-07-25 18:01:03,352 - pyskl - INFO - Epoch [99][3000/3746] lr: 2.610e-02, eta: 1 day, 19:16:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3741, top5_acc: 0.6378, loss_cls: 3.5029, loss: 3.5029 +2024-07-25 18:02:24,963 - pyskl - INFO - Epoch [99][3100/3746] lr: 2.607e-02, eta: 1 day, 19:15:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6234, loss_cls: 3.5846, loss: 3.5846 +2024-07-25 18:03:46,840 - pyskl - INFO - Epoch [99][3200/3746] lr: 2.605e-02, eta: 1 day, 19:14:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6317, loss_cls: 3.5328, loss: 3.5328 +2024-07-25 18:05:08,260 - pyskl - INFO - Epoch [99][3300/3746] lr: 2.602e-02, eta: 1 day, 19:12:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6355, loss_cls: 3.5195, loss: 3.5195 +2024-07-25 18:06:29,709 - pyskl - INFO - Epoch [99][3400/3746] lr: 2.600e-02, eta: 1 day, 19:11:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6312, loss_cls: 3.5730, loss: 3.5730 +2024-07-25 18:07:50,998 - pyskl - INFO - Epoch [99][3500/3746] lr: 2.597e-02, eta: 1 day, 19:09:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6373, loss_cls: 3.5355, loss: 3.5355 +2024-07-25 18:09:12,972 - pyskl - INFO - Epoch [99][3600/3746] lr: 2.595e-02, eta: 1 day, 19:08:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6372, loss_cls: 3.5432, loss: 3.5432 +2024-07-25 18:10:34,627 - pyskl - INFO - Epoch [99][3700/3746] lr: 2.592e-02, eta: 1 day, 19:07:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6359, loss_cls: 3.5248, loss: 3.5248 +2024-07-25 18:11:14,077 - pyskl - INFO - Saving checkpoint at 99 epochs +2024-07-25 18:13:05,799 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 18:13:06,462 - pyskl - INFO - +top1_acc 0.3184 +top5_acc 0.5767 +2024-07-25 18:13:06,463 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 18:13:06,503 - pyskl - INFO - +mean_acc 0.3182 +2024-07-25 18:13:06,508 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_97.pth was removed +2024-07-25 18:13:06,745 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_99.pth. +2024-07-25 18:13:06,746 - pyskl - INFO - Best top1_acc is 0.3184 at 99 epoch. +2024-07-25 18:13:06,757 - pyskl - INFO - Epoch(val) [99][309] top1_acc: 0.3184, top5_acc: 0.5767, mean_class_accuracy: 0.3182 +2024-07-25 18:16:51,779 - pyskl - INFO - Epoch [100][100/3746] lr: 2.589e-02, eta: 1 day, 19:06:11, time: 2.250, data_time: 1.277, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6525, loss_cls: 3.4303, loss: 3.4303 +2024-07-25 18:18:14,093 - pyskl - INFO - Epoch [100][200/3746] lr: 2.586e-02, eta: 1 day, 19:04:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6481, loss_cls: 3.4634, loss: 3.4634 +2024-07-25 18:19:35,579 - pyskl - INFO - Epoch [100][300/3746] lr: 2.584e-02, eta: 1 day, 19:03:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6405, loss_cls: 3.4774, loss: 3.4774 +2024-07-25 18:20:57,690 - pyskl - INFO - Epoch [100][400/3746] lr: 2.581e-02, eta: 1 day, 19:02:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6373, loss_cls: 3.4923, loss: 3.4923 +2024-07-25 18:22:19,215 - pyskl - INFO - Epoch [100][500/3746] lr: 2.579e-02, eta: 1 day, 19:00:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6427, loss_cls: 3.5266, loss: 3.5266 +2024-07-25 18:23:40,985 - pyskl - INFO - Epoch [100][600/3746] lr: 2.577e-02, eta: 1 day, 18:59:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6331, loss_cls: 3.5263, loss: 3.5263 +2024-07-25 18:25:03,790 - pyskl - INFO - Epoch [100][700/3746] lr: 2.574e-02, eta: 1 day, 18:58:06, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6436, loss_cls: 3.5070, loss: 3.5070 +2024-07-25 18:26:25,680 - pyskl - INFO - Epoch [100][800/3746] lr: 2.572e-02, eta: 1 day, 18:56:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6227, loss_cls: 3.5749, loss: 3.5749 +2024-07-25 18:27:47,663 - pyskl - INFO - Epoch [100][900/3746] lr: 2.569e-02, eta: 1 day, 18:55:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3822, top5_acc: 0.6425, loss_cls: 3.4702, loss: 3.4702 +2024-07-25 18:29:08,951 - pyskl - INFO - Epoch [100][1000/3746] lr: 2.567e-02, eta: 1 day, 18:54:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6445, loss_cls: 3.4874, loss: 3.4874 +2024-07-25 18:30:30,684 - pyskl - INFO - Epoch [100][1100/3746] lr: 2.564e-02, eta: 1 day, 18:52:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6398, loss_cls: 3.5221, loss: 3.5221 +2024-07-25 18:31:51,872 - pyskl - INFO - Epoch [100][1200/3746] lr: 2.562e-02, eta: 1 day, 18:51:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6494, loss_cls: 3.4595, loss: 3.4595 +2024-07-25 18:33:13,693 - pyskl - INFO - Epoch [100][1300/3746] lr: 2.559e-02, eta: 1 day, 18:49:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6375, loss_cls: 3.5301, loss: 3.5301 +2024-07-25 18:34:35,539 - pyskl - INFO - Epoch [100][1400/3746] lr: 2.557e-02, eta: 1 day, 18:48:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6414, loss_cls: 3.4574, loss: 3.4574 +2024-07-25 18:35:56,534 - pyskl - INFO - Epoch [100][1500/3746] lr: 2.555e-02, eta: 1 day, 18:47:17, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6277, loss_cls: 3.5592, loss: 3.5592 +2024-07-25 18:37:18,350 - pyskl - INFO - Epoch [100][1600/3746] lr: 2.552e-02, eta: 1 day, 18:45:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3741, top5_acc: 0.6352, loss_cls: 3.5279, loss: 3.5279 +2024-07-25 18:38:39,744 - pyskl - INFO - Epoch [100][1700/3746] lr: 2.550e-02, eta: 1 day, 18:44:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6322, loss_cls: 3.5490, loss: 3.5490 +2024-07-25 18:40:01,420 - pyskl - INFO - Epoch [100][1800/3746] lr: 2.547e-02, eta: 1 day, 18:43:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6380, loss_cls: 3.5128, loss: 3.5128 +2024-07-25 18:41:23,736 - pyskl - INFO - Epoch [100][1900/3746] lr: 2.545e-02, eta: 1 day, 18:41:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6394, loss_cls: 3.4918, loss: 3.4918 +2024-07-25 18:42:46,296 - pyskl - INFO - Epoch [100][2000/3746] lr: 2.542e-02, eta: 1 day, 18:40:32, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6380, loss_cls: 3.5060, loss: 3.5060 +2024-07-25 18:44:09,265 - pyskl - INFO - Epoch [100][2100/3746] lr: 2.540e-02, eta: 1 day, 18:39:12, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6416, loss_cls: 3.5187, loss: 3.5187 +2024-07-25 18:45:31,343 - pyskl - INFO - Epoch [100][2200/3746] lr: 2.538e-02, eta: 1 day, 18:37:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6355, loss_cls: 3.5044, loss: 3.5044 +2024-07-25 18:46:53,346 - pyskl - INFO - Epoch [100][2300/3746] lr: 2.535e-02, eta: 1 day, 18:36:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3819, top5_acc: 0.6369, loss_cls: 3.5184, loss: 3.5184 +2024-07-25 18:48:15,545 - pyskl - INFO - Epoch [100][2400/3746] lr: 2.533e-02, eta: 1 day, 18:35:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6325, loss_cls: 3.5456, loss: 3.5456 +2024-07-25 18:49:37,544 - pyskl - INFO - Epoch [100][2500/3746] lr: 2.530e-02, eta: 1 day, 18:33:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6359, loss_cls: 3.5295, loss: 3.5295 +2024-07-25 18:50:59,217 - pyskl - INFO - Epoch [100][2600/3746] lr: 2.528e-02, eta: 1 day, 18:32:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6456, loss_cls: 3.5136, loss: 3.5136 +2024-07-25 18:52:20,976 - pyskl - INFO - Epoch [100][2700/3746] lr: 2.525e-02, eta: 1 day, 18:31:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6328, loss_cls: 3.5324, loss: 3.5324 +2024-07-25 18:53:42,591 - pyskl - INFO - Epoch [100][2800/3746] lr: 2.523e-02, eta: 1 day, 18:29:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6289, loss_cls: 3.5450, loss: 3.5450 +2024-07-25 18:55:04,349 - pyskl - INFO - Epoch [100][2900/3746] lr: 2.521e-02, eta: 1 day, 18:28:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3708, top5_acc: 0.6277, loss_cls: 3.5522, loss: 3.5522 +2024-07-25 18:56:25,970 - pyskl - INFO - Epoch [100][3000/3746] lr: 2.518e-02, eta: 1 day, 18:27:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6370, loss_cls: 3.5128, loss: 3.5128 +2024-07-25 18:57:47,761 - pyskl - INFO - Epoch [100][3100/3746] lr: 2.516e-02, eta: 1 day, 18:25:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6442, loss_cls: 3.4816, loss: 3.4816 +2024-07-25 18:59:09,096 - pyskl - INFO - Epoch [100][3200/3746] lr: 2.513e-02, eta: 1 day, 18:24:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6294, loss_cls: 3.5634, loss: 3.5634 +2024-07-25 19:00:30,700 - pyskl - INFO - Epoch [100][3300/3746] lr: 2.511e-02, eta: 1 day, 18:23:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6345, loss_cls: 3.5317, loss: 3.5317 +2024-07-25 19:01:52,975 - pyskl - INFO - Epoch [100][3400/3746] lr: 2.508e-02, eta: 1 day, 18:21:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6459, loss_cls: 3.4950, loss: 3.4950 +2024-07-25 19:03:14,308 - pyskl - INFO - Epoch [100][3500/3746] lr: 2.506e-02, eta: 1 day, 18:20:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6338, loss_cls: 3.5333, loss: 3.5333 +2024-07-25 19:04:36,425 - pyskl - INFO - Epoch [100][3600/3746] lr: 2.504e-02, eta: 1 day, 18:18:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6417, loss_cls: 3.5191, loss: 3.5191 +2024-07-25 19:05:58,122 - pyskl - INFO - Epoch [100][3700/3746] lr: 2.501e-02, eta: 1 day, 18:17:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3622, top5_acc: 0.6289, loss_cls: 3.5791, loss: 3.5791 +2024-07-25 19:06:37,819 - pyskl - INFO - Saving checkpoint at 100 epochs +2024-07-25 19:08:30,207 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 19:08:30,878 - pyskl - INFO - +top1_acc 0.3072 +top5_acc 0.5671 +2024-07-25 19:08:30,879 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 19:08:30,921 - pyskl - INFO - +mean_acc 0.3068 +2024-07-25 19:08:30,933 - pyskl - INFO - Epoch(val) [100][309] top1_acc: 0.3072, top5_acc: 0.5671, mean_class_accuracy: 0.3068 +2024-07-25 19:12:24,459 - pyskl - INFO - Epoch [101][100/3746] lr: 2.498e-02, eta: 1 day, 18:16:35, time: 2.335, data_time: 1.363, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6530, loss_cls: 3.4022, loss: 3.4022 +2024-07-25 19:13:45,963 - pyskl - INFO - Epoch [101][200/3746] lr: 2.495e-02, eta: 1 day, 18:15:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3909, top5_acc: 0.6489, loss_cls: 3.4552, loss: 3.4552 +2024-07-25 19:15:07,615 - pyskl - INFO - Epoch [101][300/3746] lr: 2.493e-02, eta: 1 day, 18:13:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6538, loss_cls: 3.4579, loss: 3.4579 +2024-07-25 19:16:29,170 - pyskl - INFO - Epoch [101][400/3746] lr: 2.490e-02, eta: 1 day, 18:12:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3927, top5_acc: 0.6555, loss_cls: 3.4287, loss: 3.4287 +2024-07-25 19:17:50,867 - pyskl - INFO - Epoch [101][500/3746] lr: 2.488e-02, eta: 1 day, 18:11:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6489, loss_cls: 3.4567, loss: 3.4567 +2024-07-25 19:19:12,505 - pyskl - INFO - Epoch [101][600/3746] lr: 2.486e-02, eta: 1 day, 18:09:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3989, top5_acc: 0.6470, loss_cls: 3.4213, loss: 3.4213 +2024-07-25 19:20:35,177 - pyskl - INFO - Epoch [101][700/3746] lr: 2.483e-02, eta: 1 day, 18:08:28, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6483, loss_cls: 3.4586, loss: 3.4586 +2024-07-25 19:21:57,246 - pyskl - INFO - Epoch [101][800/3746] lr: 2.481e-02, eta: 1 day, 18:07:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6325, loss_cls: 3.5536, loss: 3.5536 +2024-07-25 19:23:18,993 - pyskl - INFO - Epoch [101][900/3746] lr: 2.478e-02, eta: 1 day, 18:05:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6508, loss_cls: 3.4299, loss: 3.4299 +2024-07-25 19:24:40,480 - pyskl - INFO - Epoch [101][1000/3746] lr: 2.476e-02, eta: 1 day, 18:04:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3819, top5_acc: 0.6433, loss_cls: 3.4821, loss: 3.4821 +2024-07-25 19:26:01,988 - pyskl - INFO - Epoch [101][1100/3746] lr: 2.473e-02, eta: 1 day, 18:03:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6447, loss_cls: 3.4953, loss: 3.4953 +2024-07-25 19:27:23,648 - pyskl - INFO - Epoch [101][1200/3746] lr: 2.471e-02, eta: 1 day, 18:01:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6386, loss_cls: 3.5059, loss: 3.5059 +2024-07-25 19:28:45,024 - pyskl - INFO - Epoch [101][1300/3746] lr: 2.469e-02, eta: 1 day, 18:00:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6286, loss_cls: 3.5619, loss: 3.5619 +2024-07-25 19:30:07,110 - pyskl - INFO - Epoch [101][1400/3746] lr: 2.466e-02, eta: 1 day, 17:59:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6378, loss_cls: 3.4987, loss: 3.4987 +2024-07-25 19:31:28,565 - pyskl - INFO - Epoch [101][1500/3746] lr: 2.464e-02, eta: 1 day, 17:57:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6489, loss_cls: 3.4468, loss: 3.4468 +2024-07-25 19:32:50,142 - pyskl - INFO - Epoch [101][1600/3746] lr: 2.461e-02, eta: 1 day, 17:56:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6502, loss_cls: 3.4615, loss: 3.4615 +2024-07-25 19:34:11,765 - pyskl - INFO - Epoch [101][1700/3746] lr: 2.459e-02, eta: 1 day, 17:54:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6378, loss_cls: 3.5561, loss: 3.5561 +2024-07-25 19:35:33,964 - pyskl - INFO - Epoch [101][1800/3746] lr: 2.457e-02, eta: 1 day, 17:53:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6366, loss_cls: 3.5447, loss: 3.5447 +2024-07-25 19:36:55,700 - pyskl - INFO - Epoch [101][1900/3746] lr: 2.454e-02, eta: 1 day, 17:52:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6302, loss_cls: 3.5156, loss: 3.5156 +2024-07-25 19:38:18,268 - pyskl - INFO - Epoch [101][2000/3746] lr: 2.452e-02, eta: 1 day, 17:50:54, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3844, top5_acc: 0.6417, loss_cls: 3.4918, loss: 3.4918 +2024-07-25 19:39:40,382 - pyskl - INFO - Epoch [101][2100/3746] lr: 2.449e-02, eta: 1 day, 17:49:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3908, top5_acc: 0.6447, loss_cls: 3.4649, loss: 3.4649 +2024-07-25 19:41:02,351 - pyskl - INFO - Epoch [101][2200/3746] lr: 2.447e-02, eta: 1 day, 17:48:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6422, loss_cls: 3.4959, loss: 3.4959 +2024-07-25 19:42:24,165 - pyskl - INFO - Epoch [101][2300/3746] lr: 2.445e-02, eta: 1 day, 17:46:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6359, loss_cls: 3.5270, loss: 3.5270 +2024-07-25 19:43:46,019 - pyskl - INFO - Epoch [101][2400/3746] lr: 2.442e-02, eta: 1 day, 17:45:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6481, loss_cls: 3.4598, loss: 3.4598 +2024-07-25 19:45:07,530 - pyskl - INFO - Epoch [101][2500/3746] lr: 2.440e-02, eta: 1 day, 17:44:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6423, loss_cls: 3.4917, loss: 3.4917 +2024-07-25 19:46:29,196 - pyskl - INFO - Epoch [101][2600/3746] lr: 2.437e-02, eta: 1 day, 17:42:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6531, loss_cls: 3.4710, loss: 3.4710 +2024-07-25 19:47:50,541 - pyskl - INFO - Epoch [101][2700/3746] lr: 2.435e-02, eta: 1 day, 17:41:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6441, loss_cls: 3.4666, loss: 3.4666 +2024-07-25 19:49:12,126 - pyskl - INFO - Epoch [101][2800/3746] lr: 2.433e-02, eta: 1 day, 17:40:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6352, loss_cls: 3.5333, loss: 3.5333 +2024-07-25 19:50:33,346 - pyskl - INFO - Epoch [101][2900/3746] lr: 2.430e-02, eta: 1 day, 17:38:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6283, loss_cls: 3.5543, loss: 3.5543 +2024-07-25 19:51:54,717 - pyskl - INFO - Epoch [101][3000/3746] lr: 2.428e-02, eta: 1 day, 17:37:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6380, loss_cls: 3.5043, loss: 3.5043 +2024-07-25 19:53:16,754 - pyskl - INFO - Epoch [101][3100/3746] lr: 2.425e-02, eta: 1 day, 17:36:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6281, loss_cls: 3.5423, loss: 3.5423 +2024-07-25 19:54:38,594 - pyskl - INFO - Epoch [101][3200/3746] lr: 2.423e-02, eta: 1 day, 17:34:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6409, loss_cls: 3.5138, loss: 3.5138 +2024-07-25 19:56:00,180 - pyskl - INFO - Epoch [101][3300/3746] lr: 2.421e-02, eta: 1 day, 17:33:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6264, loss_cls: 3.5497, loss: 3.5497 +2024-07-25 19:57:21,853 - pyskl - INFO - Epoch [101][3400/3746] lr: 2.418e-02, eta: 1 day, 17:31:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6323, loss_cls: 3.5751, loss: 3.5751 +2024-07-25 19:58:43,248 - pyskl - INFO - Epoch [101][3500/3746] lr: 2.416e-02, eta: 1 day, 17:30:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6389, loss_cls: 3.5309, loss: 3.5309 +2024-07-25 20:00:04,593 - pyskl - INFO - Epoch [101][3600/3746] lr: 2.413e-02, eta: 1 day, 17:29:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6445, loss_cls: 3.4809, loss: 3.4809 +2024-07-25 20:01:26,605 - pyskl - INFO - Epoch [101][3700/3746] lr: 2.411e-02, eta: 1 day, 17:27:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6327, loss_cls: 3.5280, loss: 3.5280 +2024-07-25 20:02:06,283 - pyskl - INFO - Saving checkpoint at 101 epochs +2024-07-25 20:03:59,141 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 20:03:59,802 - pyskl - INFO - +top1_acc 0.3196 +top5_acc 0.5744 +2024-07-25 20:03:59,803 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 20:03:59,843 - pyskl - INFO - +mean_acc 0.3193 +2024-07-25 20:03:59,848 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_99.pth was removed +2024-07-25 20:04:00,077 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_101.pth. +2024-07-25 20:04:00,078 - pyskl - INFO - Best top1_acc is 0.3196 at 101 epoch. +2024-07-25 20:04:00,089 - pyskl - INFO - Epoch(val) [101][309] top1_acc: 0.3196, top5_acc: 0.5744, mean_class_accuracy: 0.3193 +2024-07-25 20:07:50,865 - pyskl - INFO - Epoch [102][100/3746] lr: 2.407e-02, eta: 1 day, 17:26:51, time: 2.308, data_time: 1.293, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6558, loss_cls: 3.4183, loss: 3.4183 +2024-07-25 20:09:12,623 - pyskl - INFO - Epoch [102][200/3746] lr: 2.405e-02, eta: 1 day, 17:25:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6611, loss_cls: 3.4435, loss: 3.4435 +2024-07-25 20:10:33,900 - pyskl - INFO - Epoch [102][300/3746] lr: 2.403e-02, eta: 1 day, 17:24:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6484, loss_cls: 3.4299, loss: 3.4299 +2024-07-25 20:11:55,229 - pyskl - INFO - Epoch [102][400/3746] lr: 2.400e-02, eta: 1 day, 17:22:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3941, top5_acc: 0.6542, loss_cls: 3.4139, loss: 3.4139 +2024-07-25 20:13:16,824 - pyskl - INFO - Epoch [102][500/3746] lr: 2.398e-02, eta: 1 day, 17:21:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6525, loss_cls: 3.4093, loss: 3.4093 +2024-07-25 20:14:38,442 - pyskl - INFO - Epoch [102][600/3746] lr: 2.396e-02, eta: 1 day, 17:20:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6416, loss_cls: 3.5134, loss: 3.5134 +2024-07-25 20:16:00,398 - pyskl - INFO - Epoch [102][700/3746] lr: 2.393e-02, eta: 1 day, 17:18:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6483, loss_cls: 3.4777, loss: 3.4777 +2024-07-25 20:17:22,310 - pyskl - INFO - Epoch [102][800/3746] lr: 2.391e-02, eta: 1 day, 17:17:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6486, loss_cls: 3.4557, loss: 3.4557 +2024-07-25 20:18:44,327 - pyskl - INFO - Epoch [102][900/3746] lr: 2.388e-02, eta: 1 day, 17:16:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3720, top5_acc: 0.6422, loss_cls: 3.5245, loss: 3.5245 +2024-07-25 20:20:06,104 - pyskl - INFO - Epoch [102][1000/3746] lr: 2.386e-02, eta: 1 day, 17:14:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6472, loss_cls: 3.4370, loss: 3.4370 +2024-07-25 20:21:28,014 - pyskl - INFO - Epoch [102][1100/3746] lr: 2.384e-02, eta: 1 day, 17:13:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6442, loss_cls: 3.4656, loss: 3.4656 +2024-07-25 20:22:49,895 - pyskl - INFO - Epoch [102][1200/3746] lr: 2.381e-02, eta: 1 day, 17:11:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6542, loss_cls: 3.4130, loss: 3.4130 +2024-07-25 20:24:11,656 - pyskl - INFO - Epoch [102][1300/3746] lr: 2.379e-02, eta: 1 day, 17:10:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6347, loss_cls: 3.5079, loss: 3.5079 +2024-07-25 20:25:33,386 - pyskl - INFO - Epoch [102][1400/3746] lr: 2.376e-02, eta: 1 day, 17:09:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6580, loss_cls: 3.3872, loss: 3.3872 +2024-07-25 20:26:55,213 - pyskl - INFO - Epoch [102][1500/3746] lr: 2.374e-02, eta: 1 day, 17:07:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3909, top5_acc: 0.6498, loss_cls: 3.4579, loss: 3.4579 +2024-07-25 20:28:16,400 - pyskl - INFO - Epoch [102][1600/3746] lr: 2.372e-02, eta: 1 day, 17:06:33, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6473, loss_cls: 3.4718, loss: 3.4718 +2024-07-25 20:29:37,527 - pyskl - INFO - Epoch [102][1700/3746] lr: 2.369e-02, eta: 1 day, 17:05:12, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6409, loss_cls: 3.5101, loss: 3.5101 +2024-07-25 20:30:58,763 - pyskl - INFO - Epoch [102][1800/3746] lr: 2.367e-02, eta: 1 day, 17:03:51, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6467, loss_cls: 3.4842, loss: 3.4842 +2024-07-25 20:32:20,131 - pyskl - INFO - Epoch [102][1900/3746] lr: 2.365e-02, eta: 1 day, 17:02:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6303, loss_cls: 3.5210, loss: 3.5210 +2024-07-25 20:33:42,009 - pyskl - INFO - Epoch [102][2000/3746] lr: 2.362e-02, eta: 1 day, 17:01:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6314, loss_cls: 3.5130, loss: 3.5130 +2024-07-25 20:35:04,132 - pyskl - INFO - Epoch [102][2100/3746] lr: 2.360e-02, eta: 1 day, 16:59:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6428, loss_cls: 3.4741, loss: 3.4741 +2024-07-25 20:36:25,991 - pyskl - INFO - Epoch [102][2200/3746] lr: 2.357e-02, eta: 1 day, 16:58:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6438, loss_cls: 3.5034, loss: 3.5034 +2024-07-25 20:37:48,594 - pyskl - INFO - Epoch [102][2300/3746] lr: 2.355e-02, eta: 1 day, 16:57:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6477, loss_cls: 3.4443, loss: 3.4443 +2024-07-25 20:39:10,167 - pyskl - INFO - Epoch [102][2400/3746] lr: 2.353e-02, eta: 1 day, 16:55:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6433, loss_cls: 3.4915, loss: 3.4915 +2024-07-25 20:40:31,850 - pyskl - INFO - Epoch [102][2500/3746] lr: 2.350e-02, eta: 1 day, 16:54:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6391, loss_cls: 3.5045, loss: 3.5045 +2024-07-25 20:41:53,314 - pyskl - INFO - Epoch [102][2600/3746] lr: 2.348e-02, eta: 1 day, 16:53:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6505, loss_cls: 3.4683, loss: 3.4683 +2024-07-25 20:43:14,872 - pyskl - INFO - Epoch [102][2700/3746] lr: 2.346e-02, eta: 1 day, 16:51:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6472, loss_cls: 3.4623, loss: 3.4623 +2024-07-25 20:44:36,428 - pyskl - INFO - Epoch [102][2800/3746] lr: 2.343e-02, eta: 1 day, 16:50:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6492, loss_cls: 3.4901, loss: 3.4901 +2024-07-25 20:45:57,437 - pyskl - INFO - Epoch [102][2900/3746] lr: 2.341e-02, eta: 1 day, 16:48:58, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3708, top5_acc: 0.6283, loss_cls: 3.5767, loss: 3.5767 +2024-07-25 20:47:18,809 - pyskl - INFO - Epoch [102][3000/3746] lr: 2.339e-02, eta: 1 day, 16:47:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6392, loss_cls: 3.4782, loss: 3.4782 +2024-07-25 20:48:40,400 - pyskl - INFO - Epoch [102][3100/3746] lr: 2.336e-02, eta: 1 day, 16:46:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6389, loss_cls: 3.5033, loss: 3.5033 +2024-07-25 20:50:01,763 - pyskl - INFO - Epoch [102][3200/3746] lr: 2.334e-02, eta: 1 day, 16:44:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6375, loss_cls: 3.4849, loss: 3.4849 +2024-07-25 20:51:23,381 - pyskl - INFO - Epoch [102][3300/3746] lr: 2.331e-02, eta: 1 day, 16:43:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6455, loss_cls: 3.4826, loss: 3.4826 +2024-07-25 20:52:44,972 - pyskl - INFO - Epoch [102][3400/3746] lr: 2.329e-02, eta: 1 day, 16:42:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6345, loss_cls: 3.5207, loss: 3.5207 +2024-07-25 20:54:06,919 - pyskl - INFO - Epoch [102][3500/3746] lr: 2.327e-02, eta: 1 day, 16:40:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6355, loss_cls: 3.5299, loss: 3.5299 +2024-07-25 20:55:28,572 - pyskl - INFO - Epoch [102][3600/3746] lr: 2.324e-02, eta: 1 day, 16:39:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6352, loss_cls: 3.5236, loss: 3.5236 +2024-07-25 20:56:49,823 - pyskl - INFO - Epoch [102][3700/3746] lr: 2.322e-02, eta: 1 day, 16:38:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3812, top5_acc: 0.6452, loss_cls: 3.4542, loss: 3.4542 +2024-07-25 20:57:29,208 - pyskl - INFO - Saving checkpoint at 102 epochs +2024-07-25 20:59:19,920 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 20:59:20,581 - pyskl - INFO - +top1_acc 0.3190 +top5_acc 0.5743 +2024-07-25 20:59:20,581 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 20:59:20,623 - pyskl - INFO - +mean_acc 0.3186 +2024-07-25 20:59:20,634 - pyskl - INFO - Epoch(val) [102][309] top1_acc: 0.3190, top5_acc: 0.5743, mean_class_accuracy: 0.3186 +2024-07-25 21:03:10,719 - pyskl - INFO - Epoch [103][100/3746] lr: 2.319e-02, eta: 1 day, 16:37:02, time: 2.301, data_time: 1.312, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6616, loss_cls: 3.4091, loss: 3.4091 +2024-07-25 21:04:32,357 - pyskl - INFO - Epoch [103][200/3746] lr: 2.316e-02, eta: 1 day, 16:35:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6548, loss_cls: 3.4298, loss: 3.4298 +2024-07-25 21:05:54,234 - pyskl - INFO - Epoch [103][300/3746] lr: 2.314e-02, eta: 1 day, 16:34:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6573, loss_cls: 3.4222, loss: 3.4222 +2024-07-25 21:07:15,896 - pyskl - INFO - Epoch [103][400/3746] lr: 2.311e-02, eta: 1 day, 16:32:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6525, loss_cls: 3.4465, loss: 3.4465 +2024-07-25 21:08:37,259 - pyskl - INFO - Epoch [103][500/3746] lr: 2.309e-02, eta: 1 day, 16:31:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4070, top5_acc: 0.6603, loss_cls: 3.3755, loss: 3.3755 +2024-07-25 21:09:59,182 - pyskl - INFO - Epoch [103][600/3746] lr: 2.307e-02, eta: 1 day, 16:30:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6525, loss_cls: 3.4285, loss: 3.4285 +2024-07-25 21:11:21,958 - pyskl - INFO - Epoch [103][700/3746] lr: 2.304e-02, eta: 1 day, 16:28:55, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6445, loss_cls: 3.4387, loss: 3.4387 +2024-07-25 21:12:43,864 - pyskl - INFO - Epoch [103][800/3746] lr: 2.302e-02, eta: 1 day, 16:27:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6511, loss_cls: 3.4104, loss: 3.4104 +2024-07-25 21:14:05,583 - pyskl - INFO - Epoch [103][900/3746] lr: 2.300e-02, eta: 1 day, 16:26:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6486, loss_cls: 3.4685, loss: 3.4685 +2024-07-25 21:15:27,318 - pyskl - INFO - Epoch [103][1000/3746] lr: 2.297e-02, eta: 1 day, 16:24:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6516, loss_cls: 3.4230, loss: 3.4230 +2024-07-25 21:16:49,314 - pyskl - INFO - Epoch [103][1100/3746] lr: 2.295e-02, eta: 1 day, 16:23:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6541, loss_cls: 3.3965, loss: 3.3965 +2024-07-25 21:18:11,046 - pyskl - INFO - Epoch [103][1200/3746] lr: 2.293e-02, eta: 1 day, 16:22:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6470, loss_cls: 3.4321, loss: 3.4321 +2024-07-25 21:19:32,527 - pyskl - INFO - Epoch [103][1300/3746] lr: 2.290e-02, eta: 1 day, 16:20:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6470, loss_cls: 3.4696, loss: 3.4696 +2024-07-25 21:20:53,921 - pyskl - INFO - Epoch [103][1400/3746] lr: 2.288e-02, eta: 1 day, 16:19:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6373, loss_cls: 3.4999, loss: 3.4999 +2024-07-25 21:22:15,369 - pyskl - INFO - Epoch [103][1500/3746] lr: 2.286e-02, eta: 1 day, 16:18:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6516, loss_cls: 3.4269, loss: 3.4269 +2024-07-25 21:23:37,583 - pyskl - INFO - Epoch [103][1600/3746] lr: 2.283e-02, eta: 1 day, 16:16:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6489, loss_cls: 3.5075, loss: 3.5075 +2024-07-25 21:24:58,949 - pyskl - INFO - Epoch [103][1700/3746] lr: 2.281e-02, eta: 1 day, 16:15:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6386, loss_cls: 3.5023, loss: 3.5023 +2024-07-25 21:26:20,089 - pyskl - INFO - Epoch [103][1800/3746] lr: 2.279e-02, eta: 1 day, 16:14:01, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6411, loss_cls: 3.4810, loss: 3.4810 +2024-07-25 21:27:41,714 - pyskl - INFO - Epoch [103][1900/3746] lr: 2.276e-02, eta: 1 day, 16:12:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6417, loss_cls: 3.4722, loss: 3.4722 +2024-07-25 21:29:02,996 - pyskl - INFO - Epoch [103][2000/3746] lr: 2.274e-02, eta: 1 day, 16:11:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6383, loss_cls: 3.4739, loss: 3.4739 +2024-07-25 21:30:26,487 - pyskl - INFO - Epoch [103][2100/3746] lr: 2.272e-02, eta: 1 day, 16:09:58, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6420, loss_cls: 3.5018, loss: 3.5018 +2024-07-25 21:31:48,635 - pyskl - INFO - Epoch [103][2200/3746] lr: 2.269e-02, eta: 1 day, 16:08:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6448, loss_cls: 3.4593, loss: 3.4593 +2024-07-25 21:33:11,333 - pyskl - INFO - Epoch [103][2300/3746] lr: 2.267e-02, eta: 1 day, 16:07:16, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3923, top5_acc: 0.6473, loss_cls: 3.4430, loss: 3.4430 +2024-07-25 21:34:33,351 - pyskl - INFO - Epoch [103][2400/3746] lr: 2.264e-02, eta: 1 day, 16:05:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6538, loss_cls: 3.4429, loss: 3.4429 +2024-07-25 21:35:55,597 - pyskl - INFO - Epoch [103][2500/3746] lr: 2.262e-02, eta: 1 day, 16:04:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6398, loss_cls: 3.4952, loss: 3.4952 +2024-07-25 21:37:17,888 - pyskl - INFO - Epoch [103][2600/3746] lr: 2.260e-02, eta: 1 day, 16:03:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6481, loss_cls: 3.4299, loss: 3.4299 +2024-07-25 21:38:38,969 - pyskl - INFO - Epoch [103][2700/3746] lr: 2.257e-02, eta: 1 day, 16:01:52, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6367, loss_cls: 3.5431, loss: 3.5431 +2024-07-25 21:40:00,384 - pyskl - INFO - Epoch [103][2800/3746] lr: 2.255e-02, eta: 1 day, 16:00:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6427, loss_cls: 3.4820, loss: 3.4820 +2024-07-25 21:41:21,389 - pyskl - INFO - Epoch [103][2900/3746] lr: 2.253e-02, eta: 1 day, 15:59:09, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6286, loss_cls: 3.5505, loss: 3.5505 +2024-07-25 21:42:42,804 - pyskl - INFO - Epoch [103][3000/3746] lr: 2.250e-02, eta: 1 day, 15:57:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6505, loss_cls: 3.4797, loss: 3.4797 +2024-07-25 21:44:04,304 - pyskl - INFO - Epoch [103][3100/3746] lr: 2.248e-02, eta: 1 day, 15:56:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6438, loss_cls: 3.4971, loss: 3.4971 +2024-07-25 21:45:25,942 - pyskl - INFO - Epoch [103][3200/3746] lr: 2.246e-02, eta: 1 day, 15:55:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6517, loss_cls: 3.4242, loss: 3.4242 +2024-07-25 21:46:46,979 - pyskl - INFO - Epoch [103][3300/3746] lr: 2.243e-02, eta: 1 day, 15:53:44, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6353, loss_cls: 3.5025, loss: 3.5025 +2024-07-25 21:48:08,193 - pyskl - INFO - Epoch [103][3400/3746] lr: 2.241e-02, eta: 1 day, 15:52:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6488, loss_cls: 3.4305, loss: 3.4305 +2024-07-25 21:49:29,306 - pyskl - INFO - Epoch [103][3500/3746] lr: 2.239e-02, eta: 1 day, 15:51:01, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6488, loss_cls: 3.4472, loss: 3.4472 +2024-07-25 21:50:50,837 - pyskl - INFO - Epoch [103][3600/3746] lr: 2.236e-02, eta: 1 day, 15:49:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3898, top5_acc: 0.6439, loss_cls: 3.4626, loss: 3.4626 +2024-07-25 21:52:11,961 - pyskl - INFO - Epoch [103][3700/3746] lr: 2.234e-02, eta: 1 day, 15:48:18, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6494, loss_cls: 3.4541, loss: 3.4541 +2024-07-25 21:52:51,212 - pyskl - INFO - Saving checkpoint at 103 epochs +2024-07-25 21:54:41,267 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 21:54:41,927 - pyskl - INFO - +top1_acc 0.3278 +top5_acc 0.5858 +2024-07-25 21:54:41,927 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 21:54:41,972 - pyskl - INFO - +mean_acc 0.3275 +2024-07-25 21:54:41,977 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_101.pth was removed +2024-07-25 21:54:42,208 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_103.pth. +2024-07-25 21:54:42,209 - pyskl - INFO - Best top1_acc is 0.3278 at 103 epoch. +2024-07-25 21:54:42,221 - pyskl - INFO - Epoch(val) [103][309] top1_acc: 0.3278, top5_acc: 0.5858, mean_class_accuracy: 0.3275 +2024-07-25 21:58:33,045 - pyskl - INFO - Epoch [104][100/3746] lr: 2.231e-02, eta: 1 day, 15:47:10, time: 2.308, data_time: 1.309, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6686, loss_cls: 3.3380, loss: 3.3380 +2024-07-25 21:59:54,822 - pyskl - INFO - Epoch [104][200/3746] lr: 2.228e-02, eta: 1 day, 15:45:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6533, loss_cls: 3.4271, loss: 3.4271 +2024-07-25 22:01:16,425 - pyskl - INFO - Epoch [104][300/3746] lr: 2.226e-02, eta: 1 day, 15:44:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6569, loss_cls: 3.4025, loss: 3.4025 +2024-07-25 22:02:38,132 - pyskl - INFO - Epoch [104][400/3746] lr: 2.224e-02, eta: 1 day, 15:43:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6503, loss_cls: 3.4319, loss: 3.4319 +2024-07-25 22:03:59,304 - pyskl - INFO - Epoch [104][500/3746] lr: 2.221e-02, eta: 1 day, 15:41:45, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6577, loss_cls: 3.3904, loss: 3.3904 +2024-07-25 22:05:20,404 - pyskl - INFO - Epoch [104][600/3746] lr: 2.219e-02, eta: 1 day, 15:40:23, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6462, loss_cls: 3.4304, loss: 3.4304 +2024-07-25 22:06:42,844 - pyskl - INFO - Epoch [104][700/3746] lr: 2.217e-02, eta: 1 day, 15:39:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6542, loss_cls: 3.4282, loss: 3.4282 +2024-07-25 22:08:04,630 - pyskl - INFO - Epoch [104][800/3746] lr: 2.214e-02, eta: 1 day, 15:37:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6464, loss_cls: 3.4311, loss: 3.4311 +2024-07-25 22:09:25,950 - pyskl - INFO - Epoch [104][900/3746] lr: 2.212e-02, eta: 1 day, 15:36:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6544, loss_cls: 3.4207, loss: 3.4207 +2024-07-25 22:10:48,285 - pyskl - INFO - Epoch [104][1000/3746] lr: 2.210e-02, eta: 1 day, 15:34:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6467, loss_cls: 3.4421, loss: 3.4421 +2024-07-25 22:12:09,944 - pyskl - INFO - Epoch [104][1100/3746] lr: 2.208e-02, eta: 1 day, 15:33:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6530, loss_cls: 3.3907, loss: 3.3907 +2024-07-25 22:13:31,168 - pyskl - INFO - Epoch [104][1200/3746] lr: 2.205e-02, eta: 1 day, 15:32:16, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6531, loss_cls: 3.3977, loss: 3.3977 +2024-07-25 22:14:52,847 - pyskl - INFO - Epoch [104][1300/3746] lr: 2.203e-02, eta: 1 day, 15:30:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4008, top5_acc: 0.6575, loss_cls: 3.3691, loss: 3.3691 +2024-07-25 22:16:14,304 - pyskl - INFO - Epoch [104][1400/3746] lr: 2.201e-02, eta: 1 day, 15:29:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6447, loss_cls: 3.4590, loss: 3.4590 +2024-07-25 22:17:36,291 - pyskl - INFO - Epoch [104][1500/3746] lr: 2.198e-02, eta: 1 day, 15:28:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3844, top5_acc: 0.6431, loss_cls: 3.4958, loss: 3.4958 +2024-07-25 22:18:57,545 - pyskl - INFO - Epoch [104][1600/3746] lr: 2.196e-02, eta: 1 day, 15:26:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6484, loss_cls: 3.4595, loss: 3.4595 +2024-07-25 22:20:19,264 - pyskl - INFO - Epoch [104][1700/3746] lr: 2.194e-02, eta: 1 day, 15:25:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6442, loss_cls: 3.4688, loss: 3.4688 +2024-07-25 22:21:40,448 - pyskl - INFO - Epoch [104][1800/3746] lr: 2.191e-02, eta: 1 day, 15:24:08, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6514, loss_cls: 3.4477, loss: 3.4477 +2024-07-25 22:23:01,703 - pyskl - INFO - Epoch [104][1900/3746] lr: 2.189e-02, eta: 1 day, 15:22:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6573, loss_cls: 3.4148, loss: 3.4148 +2024-07-25 22:24:23,108 - pyskl - INFO - Epoch [104][2000/3746] lr: 2.187e-02, eta: 1 day, 15:21:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6519, loss_cls: 3.4230, loss: 3.4230 +2024-07-25 22:25:46,075 - pyskl - INFO - Epoch [104][2100/3746] lr: 2.184e-02, eta: 1 day, 15:20:04, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6422, loss_cls: 3.4586, loss: 3.4586 +2024-07-25 22:27:08,417 - pyskl - INFO - Epoch [104][2200/3746] lr: 2.182e-02, eta: 1 day, 15:18:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6481, loss_cls: 3.4445, loss: 3.4445 +2024-07-25 22:28:30,689 - pyskl - INFO - Epoch [104][2300/3746] lr: 2.180e-02, eta: 1 day, 15:17:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6525, loss_cls: 3.4318, loss: 3.4318 +2024-07-25 22:29:52,948 - pyskl - INFO - Epoch [104][2400/3746] lr: 2.177e-02, eta: 1 day, 15:16:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6525, loss_cls: 3.4556, loss: 3.4556 +2024-07-25 22:31:14,921 - pyskl - INFO - Epoch [104][2500/3746] lr: 2.175e-02, eta: 1 day, 15:14:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6466, loss_cls: 3.4834, loss: 3.4834 +2024-07-25 22:32:36,820 - pyskl - INFO - Epoch [104][2600/3746] lr: 2.173e-02, eta: 1 day, 15:13:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6481, loss_cls: 3.4140, loss: 3.4140 +2024-07-25 22:33:57,987 - pyskl - INFO - Epoch [104][2700/3746] lr: 2.171e-02, eta: 1 day, 15:11:58, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3903, top5_acc: 0.6486, loss_cls: 3.4285, loss: 3.4285 +2024-07-25 22:35:19,453 - pyskl - INFO - Epoch [104][2800/3746] lr: 2.168e-02, eta: 1 day, 15:10:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6492, loss_cls: 3.4678, loss: 3.4678 +2024-07-25 22:36:41,300 - pyskl - INFO - Epoch [104][2900/3746] lr: 2.166e-02, eta: 1 day, 15:09:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6431, loss_cls: 3.4871, loss: 3.4871 +2024-07-25 22:38:02,453 - pyskl - INFO - Epoch [104][3000/3746] lr: 2.164e-02, eta: 1 day, 15:07:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6531, loss_cls: 3.4315, loss: 3.4315 +2024-07-25 22:39:24,060 - pyskl - INFO - Epoch [104][3100/3746] lr: 2.161e-02, eta: 1 day, 15:06:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3947, top5_acc: 0.6558, loss_cls: 3.4204, loss: 3.4204 +2024-07-25 22:40:45,582 - pyskl - INFO - Epoch [104][3200/3746] lr: 2.159e-02, eta: 1 day, 15:05:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6491, loss_cls: 3.4592, loss: 3.4592 +2024-07-25 22:42:07,798 - pyskl - INFO - Epoch [104][3300/3746] lr: 2.157e-02, eta: 1 day, 15:03:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6472, loss_cls: 3.4806, loss: 3.4806 +2024-07-25 22:43:29,709 - pyskl - INFO - Epoch [104][3400/3746] lr: 2.154e-02, eta: 1 day, 15:02:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6541, loss_cls: 3.4252, loss: 3.4252 +2024-07-25 22:44:51,230 - pyskl - INFO - Epoch [104][3500/3746] lr: 2.152e-02, eta: 1 day, 15:01:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6331, loss_cls: 3.4756, loss: 3.4756 +2024-07-25 22:46:12,608 - pyskl - INFO - Epoch [104][3600/3746] lr: 2.150e-02, eta: 1 day, 14:59:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6542, loss_cls: 3.4596, loss: 3.4596 +2024-07-25 22:47:33,886 - pyskl - INFO - Epoch [104][3700/3746] lr: 2.148e-02, eta: 1 day, 14:58:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6397, loss_cls: 3.4861, loss: 3.4861 +2024-07-25 22:48:13,361 - pyskl - INFO - Saving checkpoint at 104 epochs +2024-07-25 22:50:04,027 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 22:50:04,692 - pyskl - INFO - +top1_acc 0.3006 +top5_acc 0.5502 +2024-07-25 22:50:04,693 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 22:50:04,737 - pyskl - INFO - +mean_acc 0.3004 +2024-07-25 22:50:04,748 - pyskl - INFO - Epoch(val) [104][309] top1_acc: 0.3006, top5_acc: 0.5502, mean_class_accuracy: 0.3004 +2024-07-25 22:53:51,033 - pyskl - INFO - Epoch [105][100/3746] lr: 2.144e-02, eta: 1 day, 14:57:13, time: 2.263, data_time: 1.289, memory: 15990, top1_acc: 0.4113, top5_acc: 0.6677, loss_cls: 3.3195, loss: 3.3195 +2024-07-25 22:55:12,974 - pyskl - INFO - Epoch [105][200/3746] lr: 2.142e-02, eta: 1 day, 14:55:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6659, loss_cls: 3.3319, loss: 3.3319 +2024-07-25 22:56:34,461 - pyskl - INFO - Epoch [105][300/3746] lr: 2.140e-02, eta: 1 day, 14:54:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6609, loss_cls: 3.3738, loss: 3.3738 +2024-07-25 22:57:55,736 - pyskl - INFO - Epoch [105][400/3746] lr: 2.137e-02, eta: 1 day, 14:53:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3914, top5_acc: 0.6481, loss_cls: 3.4150, loss: 3.4150 +2024-07-25 22:59:16,921 - pyskl - INFO - Epoch [105][500/3746] lr: 2.135e-02, eta: 1 day, 14:51:47, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6506, loss_cls: 3.4162, loss: 3.4162 +2024-07-25 23:00:38,490 - pyskl - INFO - Epoch [105][600/3746] lr: 2.133e-02, eta: 1 day, 14:50:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6492, loss_cls: 3.3969, loss: 3.3969 +2024-07-25 23:02:00,490 - pyskl - INFO - Epoch [105][700/3746] lr: 2.130e-02, eta: 1 day, 14:49:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6542, loss_cls: 3.4298, loss: 3.4298 +2024-07-25 23:03:21,971 - pyskl - INFO - Epoch [105][800/3746] lr: 2.128e-02, eta: 1 day, 14:47:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6545, loss_cls: 3.3993, loss: 3.3993 +2024-07-25 23:04:43,600 - pyskl - INFO - Epoch [105][900/3746] lr: 2.126e-02, eta: 1 day, 14:46:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6509, loss_cls: 3.4092, loss: 3.4092 +2024-07-25 23:06:05,612 - pyskl - INFO - Epoch [105][1000/3746] lr: 2.124e-02, eta: 1 day, 14:45:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4009, top5_acc: 0.6531, loss_cls: 3.3840, loss: 3.3840 +2024-07-25 23:07:27,442 - pyskl - INFO - Epoch [105][1100/3746] lr: 2.121e-02, eta: 1 day, 14:43:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6452, loss_cls: 3.4628, loss: 3.4628 +2024-07-25 23:08:49,317 - pyskl - INFO - Epoch [105][1200/3746] lr: 2.119e-02, eta: 1 day, 14:42:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3966, top5_acc: 0.6627, loss_cls: 3.3893, loss: 3.3893 +2024-07-25 23:10:11,203 - pyskl - INFO - Epoch [105][1300/3746] lr: 2.117e-02, eta: 1 day, 14:40:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6558, loss_cls: 3.4158, loss: 3.4158 +2024-07-25 23:11:33,112 - pyskl - INFO - Epoch [105][1400/3746] lr: 2.114e-02, eta: 1 day, 14:39:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3966, top5_acc: 0.6527, loss_cls: 3.3842, loss: 3.3842 +2024-07-25 23:12:54,743 - pyskl - INFO - Epoch [105][1500/3746] lr: 2.112e-02, eta: 1 day, 14:38:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6566, loss_cls: 3.4121, loss: 3.4121 +2024-07-25 23:14:16,654 - pyskl - INFO - Epoch [105][1600/3746] lr: 2.110e-02, eta: 1 day, 14:36:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6509, loss_cls: 3.4177, loss: 3.4177 +2024-07-25 23:15:38,393 - pyskl - INFO - Epoch [105][1700/3746] lr: 2.108e-02, eta: 1 day, 14:35:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3848, top5_acc: 0.6475, loss_cls: 3.4425, loss: 3.4425 +2024-07-25 23:17:00,082 - pyskl - INFO - Epoch [105][1800/3746] lr: 2.105e-02, eta: 1 day, 14:34:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6583, loss_cls: 3.3717, loss: 3.3717 +2024-07-25 23:18:21,741 - pyskl - INFO - Epoch [105][1900/3746] lr: 2.103e-02, eta: 1 day, 14:32:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6564, loss_cls: 3.3785, loss: 3.3785 +2024-07-25 23:19:43,273 - pyskl - INFO - Epoch [105][2000/3746] lr: 2.101e-02, eta: 1 day, 14:31:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6494, loss_cls: 3.4350, loss: 3.4350 +2024-07-25 23:21:05,346 - pyskl - INFO - Epoch [105][2100/3746] lr: 2.098e-02, eta: 1 day, 14:30:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3927, top5_acc: 0.6592, loss_cls: 3.3808, loss: 3.3808 +2024-07-25 23:22:27,957 - pyskl - INFO - Epoch [105][2200/3746] lr: 2.096e-02, eta: 1 day, 14:28:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6408, loss_cls: 3.4710, loss: 3.4710 +2024-07-25 23:23:50,069 - pyskl - INFO - Epoch [105][2300/3746] lr: 2.094e-02, eta: 1 day, 14:27:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6550, loss_cls: 3.4281, loss: 3.4281 +2024-07-25 23:25:12,796 - pyskl - INFO - Epoch [105][2400/3746] lr: 2.092e-02, eta: 1 day, 14:26:04, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6448, loss_cls: 3.4400, loss: 3.4400 +2024-07-25 23:26:34,952 - pyskl - INFO - Epoch [105][2500/3746] lr: 2.089e-02, eta: 1 day, 14:24:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6480, loss_cls: 3.4203, loss: 3.4203 +2024-07-25 23:27:56,321 - pyskl - INFO - Epoch [105][2600/3746] lr: 2.087e-02, eta: 1 day, 14:23:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3933, top5_acc: 0.6481, loss_cls: 3.4150, loss: 3.4150 +2024-07-25 23:29:17,625 - pyskl - INFO - Epoch [105][2700/3746] lr: 2.085e-02, eta: 1 day, 14:22:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6573, loss_cls: 3.4236, loss: 3.4236 +2024-07-25 23:30:39,103 - pyskl - INFO - Epoch [105][2800/3746] lr: 2.083e-02, eta: 1 day, 14:20:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6431, loss_cls: 3.4670, loss: 3.4670 +2024-07-25 23:32:01,087 - pyskl - INFO - Epoch [105][2900/3746] lr: 2.080e-02, eta: 1 day, 14:19:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6564, loss_cls: 3.4353, loss: 3.4353 +2024-07-25 23:33:22,408 - pyskl - INFO - Epoch [105][3000/3746] lr: 2.078e-02, eta: 1 day, 14:17:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6400, loss_cls: 3.4904, loss: 3.4904 +2024-07-25 23:34:44,042 - pyskl - INFO - Epoch [105][3100/3746] lr: 2.076e-02, eta: 1 day, 14:16:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6509, loss_cls: 3.4389, loss: 3.4389 +2024-07-25 23:36:05,875 - pyskl - INFO - Epoch [105][3200/3746] lr: 2.073e-02, eta: 1 day, 14:15:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3837, top5_acc: 0.6388, loss_cls: 3.4839, loss: 3.4839 +2024-07-25 23:37:26,930 - pyskl - INFO - Epoch [105][3300/3746] lr: 2.071e-02, eta: 1 day, 14:13:52, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6516, loss_cls: 3.4550, loss: 3.4550 +2024-07-25 23:38:48,315 - pyskl - INFO - Epoch [105][3400/3746] lr: 2.069e-02, eta: 1 day, 14:12:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6475, loss_cls: 3.4570, loss: 3.4570 +2024-07-25 23:40:09,847 - pyskl - INFO - Epoch [105][3500/3746] lr: 2.067e-02, eta: 1 day, 14:11:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6464, loss_cls: 3.4578, loss: 3.4578 +2024-07-25 23:41:31,727 - pyskl - INFO - Epoch [105][3600/3746] lr: 2.064e-02, eta: 1 day, 14:09:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6495, loss_cls: 3.4593, loss: 3.4593 +2024-07-25 23:42:53,098 - pyskl - INFO - Epoch [105][3700/3746] lr: 2.062e-02, eta: 1 day, 14:08:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6477, loss_cls: 3.4170, loss: 3.4170 +2024-07-25 23:43:32,996 - pyskl - INFO - Saving checkpoint at 105 epochs +2024-07-25 23:45:23,784 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 23:45:24,445 - pyskl - INFO - +top1_acc 0.3313 +top5_acc 0.5885 +2024-07-25 23:45:24,446 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 23:45:24,484 - pyskl - INFO - +mean_acc 0.3309 +2024-07-25 23:45:24,488 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_103.pth was removed +2024-07-25 23:45:24,731 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_105.pth. +2024-07-25 23:45:24,732 - pyskl - INFO - Best top1_acc is 0.3313 at 105 epoch. +2024-07-25 23:45:24,741 - pyskl - INFO - Epoch(val) [105][309] top1_acc: 0.3313, top5_acc: 0.5885, mean_class_accuracy: 0.3309 +2024-07-25 23:49:11,225 - pyskl - INFO - Epoch [106][100/3746] lr: 2.059e-02, eta: 1 day, 14:07:13, time: 2.265, data_time: 1.295, memory: 15990, top1_acc: 0.4116, top5_acc: 0.6713, loss_cls: 3.3334, loss: 3.3334 +2024-07-25 23:50:32,851 - pyskl - INFO - Epoch [106][200/3746] lr: 2.057e-02, eta: 1 day, 14:05:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6673, loss_cls: 3.3245, loss: 3.3245 +2024-07-25 23:51:54,522 - pyskl - INFO - Epoch [106][300/3746] lr: 2.054e-02, eta: 1 day, 14:04:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6663, loss_cls: 3.3387, loss: 3.3387 +2024-07-25 23:53:16,263 - pyskl - INFO - Epoch [106][400/3746] lr: 2.052e-02, eta: 1 day, 14:03:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6581, loss_cls: 3.3842, loss: 3.3842 +2024-07-25 23:54:37,524 - pyskl - INFO - Epoch [106][500/3746] lr: 2.050e-02, eta: 1 day, 14:01:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6625, loss_cls: 3.3520, loss: 3.3520 +2024-07-25 23:55:59,404 - pyskl - INFO - Epoch [106][600/3746] lr: 2.048e-02, eta: 1 day, 14:00:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6559, loss_cls: 3.3803, loss: 3.3803 +2024-07-25 23:57:21,436 - pyskl - INFO - Epoch [106][700/3746] lr: 2.045e-02, eta: 1 day, 13:59:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6628, loss_cls: 3.3742, loss: 3.3742 +2024-07-25 23:58:43,201 - pyskl - INFO - Epoch [106][800/3746] lr: 2.043e-02, eta: 1 day, 13:57:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6586, loss_cls: 3.3993, loss: 3.3993 +2024-07-26 00:00:04,844 - pyskl - INFO - Epoch [106][900/3746] lr: 2.041e-02, eta: 1 day, 13:56:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6519, loss_cls: 3.4136, loss: 3.4136 +2024-07-26 00:01:26,818 - pyskl - INFO - Epoch [106][1000/3746] lr: 2.039e-02, eta: 1 day, 13:55:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6516, loss_cls: 3.3979, loss: 3.3979 +2024-07-26 00:02:48,383 - pyskl - INFO - Epoch [106][1100/3746] lr: 2.036e-02, eta: 1 day, 13:53:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6577, loss_cls: 3.3751, loss: 3.3751 +2024-07-26 00:04:10,005 - pyskl - INFO - Epoch [106][1200/3746] lr: 2.034e-02, eta: 1 day, 13:52:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6550, loss_cls: 3.4403, loss: 3.4403 +2024-07-26 00:05:31,618 - pyskl - INFO - Epoch [106][1300/3746] lr: 2.032e-02, eta: 1 day, 13:50:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6583, loss_cls: 3.3952, loss: 3.3952 +2024-07-26 00:06:53,403 - pyskl - INFO - Epoch [106][1400/3746] lr: 2.030e-02, eta: 1 day, 13:49:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3914, top5_acc: 0.6544, loss_cls: 3.4096, loss: 3.4096 +2024-07-26 00:08:15,301 - pyskl - INFO - Epoch [106][1500/3746] lr: 2.027e-02, eta: 1 day, 13:48:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6577, loss_cls: 3.4152, loss: 3.4152 +2024-07-26 00:09:37,568 - pyskl - INFO - Epoch [106][1600/3746] lr: 2.025e-02, eta: 1 day, 13:46:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6462, loss_cls: 3.4338, loss: 3.4338 +2024-07-26 00:10:59,652 - pyskl - INFO - Epoch [106][1700/3746] lr: 2.023e-02, eta: 1 day, 13:45:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6634, loss_cls: 3.3845, loss: 3.3845 +2024-07-26 00:12:21,467 - pyskl - INFO - Epoch [106][1800/3746] lr: 2.021e-02, eta: 1 day, 13:44:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6559, loss_cls: 3.4240, loss: 3.4240 +2024-07-26 00:13:43,301 - pyskl - INFO - Epoch [106][1900/3746] lr: 2.018e-02, eta: 1 day, 13:42:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6594, loss_cls: 3.3783, loss: 3.3783 +2024-07-26 00:15:04,514 - pyskl - INFO - Epoch [106][2000/3746] lr: 2.016e-02, eta: 1 day, 13:41:28, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3989, top5_acc: 0.6598, loss_cls: 3.3723, loss: 3.3723 +2024-07-26 00:16:26,800 - pyskl - INFO - Epoch [106][2100/3746] lr: 2.014e-02, eta: 1 day, 13:40:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6627, loss_cls: 3.3833, loss: 3.3833 +2024-07-26 00:17:50,042 - pyskl - INFO - Epoch [106][2200/3746] lr: 2.012e-02, eta: 1 day, 13:38:46, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6655, loss_cls: 3.3611, loss: 3.3611 +2024-07-26 00:19:12,365 - pyskl - INFO - Epoch [106][2300/3746] lr: 2.009e-02, eta: 1 day, 13:37:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6555, loss_cls: 3.4024, loss: 3.4024 +2024-07-26 00:20:34,995 - pyskl - INFO - Epoch [106][2400/3746] lr: 2.007e-02, eta: 1 day, 13:36:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4017, top5_acc: 0.6531, loss_cls: 3.4128, loss: 3.4128 +2024-07-26 00:21:56,900 - pyskl - INFO - Epoch [106][2500/3746] lr: 2.005e-02, eta: 1 day, 13:34:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6558, loss_cls: 3.3845, loss: 3.3845 +2024-07-26 00:23:18,243 - pyskl - INFO - Epoch [106][2600/3746] lr: 2.003e-02, eta: 1 day, 13:33:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6542, loss_cls: 3.4279, loss: 3.4279 +2024-07-26 00:24:40,029 - pyskl - INFO - Epoch [106][2700/3746] lr: 2.000e-02, eta: 1 day, 13:32:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6434, loss_cls: 3.4778, loss: 3.4778 +2024-07-26 00:26:01,340 - pyskl - INFO - Epoch [106][2800/3746] lr: 1.998e-02, eta: 1 day, 13:30:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6514, loss_cls: 3.4394, loss: 3.4394 +2024-07-26 00:27:22,617 - pyskl - INFO - Epoch [106][2900/3746] lr: 1.996e-02, eta: 1 day, 13:29:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3986, top5_acc: 0.6569, loss_cls: 3.3916, loss: 3.3916 +2024-07-26 00:28:44,012 - pyskl - INFO - Epoch [106][3000/3746] lr: 1.994e-02, eta: 1 day, 13:27:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3923, top5_acc: 0.6587, loss_cls: 3.3992, loss: 3.3992 +2024-07-26 00:30:05,715 - pyskl - INFO - Epoch [106][3100/3746] lr: 1.991e-02, eta: 1 day, 13:26:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6603, loss_cls: 3.3701, loss: 3.3701 +2024-07-26 00:31:27,441 - pyskl - INFO - Epoch [106][3200/3746] lr: 1.989e-02, eta: 1 day, 13:25:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6570, loss_cls: 3.3984, loss: 3.3984 +2024-07-26 00:32:49,057 - pyskl - INFO - Epoch [106][3300/3746] lr: 1.987e-02, eta: 1 day, 13:23:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6403, loss_cls: 3.5214, loss: 3.5214 +2024-07-26 00:34:11,476 - pyskl - INFO - Epoch [106][3400/3746] lr: 1.985e-02, eta: 1 day, 13:22:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6528, loss_cls: 3.4139, loss: 3.4139 +2024-07-26 00:35:32,601 - pyskl - INFO - Epoch [106][3500/3746] lr: 1.983e-02, eta: 1 day, 13:21:09, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6483, loss_cls: 3.4496, loss: 3.4496 +2024-07-26 00:36:54,014 - pyskl - INFO - Epoch [106][3600/3746] lr: 1.980e-02, eta: 1 day, 13:19:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6544, loss_cls: 3.3690, loss: 3.3690 +2024-07-26 00:38:15,584 - pyskl - INFO - Epoch [106][3700/3746] lr: 1.978e-02, eta: 1 day, 13:18:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6544, loss_cls: 3.3981, loss: 3.3981 +2024-07-26 00:38:55,251 - pyskl - INFO - Saving checkpoint at 106 epochs +2024-07-26 00:40:47,664 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 00:40:48,411 - pyskl - INFO - +top1_acc 0.3311 +top5_acc 0.5941 +2024-07-26 00:40:48,411 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 00:40:48,450 - pyskl - INFO - +mean_acc 0.3310 +2024-07-26 00:40:48,460 - pyskl - INFO - Epoch(val) [106][309] top1_acc: 0.3311, top5_acc: 0.5941, mean_class_accuracy: 0.3310 +2024-07-26 00:44:37,594 - pyskl - INFO - Epoch [107][100/3746] lr: 1.975e-02, eta: 1 day, 13:17:12, time: 2.291, data_time: 1.316, memory: 15990, top1_acc: 0.4073, top5_acc: 0.6683, loss_cls: 3.3318, loss: 3.3318 +2024-07-26 00:45:58,996 - pyskl - INFO - Epoch [107][200/3746] lr: 1.973e-02, eta: 1 day, 13:15:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6628, loss_cls: 3.3675, loss: 3.3675 +2024-07-26 00:47:20,528 - pyskl - INFO - Epoch [107][300/3746] lr: 1.970e-02, eta: 1 day, 13:14:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4078, top5_acc: 0.6741, loss_cls: 3.2955, loss: 3.2955 +2024-07-26 00:48:42,722 - pyskl - INFO - Epoch [107][400/3746] lr: 1.968e-02, eta: 1 day, 13:13:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6664, loss_cls: 3.3323, loss: 3.3323 +2024-07-26 00:50:04,838 - pyskl - INFO - Epoch [107][500/3746] lr: 1.966e-02, eta: 1 day, 13:11:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6675, loss_cls: 3.3333, loss: 3.3333 +2024-07-26 00:51:26,965 - pyskl - INFO - Epoch [107][600/3746] lr: 1.964e-02, eta: 1 day, 13:10:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6630, loss_cls: 3.3619, loss: 3.3619 +2024-07-26 00:52:48,933 - pyskl - INFO - Epoch [107][700/3746] lr: 1.961e-02, eta: 1 day, 13:09:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6605, loss_cls: 3.3692, loss: 3.3692 +2024-07-26 00:54:10,579 - pyskl - INFO - Epoch [107][800/3746] lr: 1.959e-02, eta: 1 day, 13:07:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6564, loss_cls: 3.3856, loss: 3.3856 +2024-07-26 00:55:33,094 - pyskl - INFO - Epoch [107][900/3746] lr: 1.957e-02, eta: 1 day, 13:06:22, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6680, loss_cls: 3.3243, loss: 3.3243 +2024-07-26 00:56:54,947 - pyskl - INFO - Epoch [107][1000/3746] lr: 1.955e-02, eta: 1 day, 13:05:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6530, loss_cls: 3.4046, loss: 3.4046 +2024-07-26 00:58:16,895 - pyskl - INFO - Epoch [107][1100/3746] lr: 1.953e-02, eta: 1 day, 13:03:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6566, loss_cls: 3.4083, loss: 3.4083 +2024-07-26 00:59:38,546 - pyskl - INFO - Epoch [107][1200/3746] lr: 1.950e-02, eta: 1 day, 13:02:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6569, loss_cls: 3.4075, loss: 3.4075 +2024-07-26 01:01:00,732 - pyskl - INFO - Epoch [107][1300/3746] lr: 1.948e-02, eta: 1 day, 13:00:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6584, loss_cls: 3.3701, loss: 3.3701 +2024-07-26 01:02:22,490 - pyskl - INFO - Epoch [107][1400/3746] lr: 1.946e-02, eta: 1 day, 12:59:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3941, top5_acc: 0.6711, loss_cls: 3.3762, loss: 3.3762 +2024-07-26 01:03:44,423 - pyskl - INFO - Epoch [107][1500/3746] lr: 1.944e-02, eta: 1 day, 12:58:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6619, loss_cls: 3.3740, loss: 3.3740 +2024-07-26 01:05:05,780 - pyskl - INFO - Epoch [107][1600/3746] lr: 1.942e-02, eta: 1 day, 12:56:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6641, loss_cls: 3.3587, loss: 3.3587 +2024-07-26 01:06:27,454 - pyskl - INFO - Epoch [107][1700/3746] lr: 1.939e-02, eta: 1 day, 12:55:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6622, loss_cls: 3.3844, loss: 3.3844 +2024-07-26 01:07:49,266 - pyskl - INFO - Epoch [107][1800/3746] lr: 1.937e-02, eta: 1 day, 12:54:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3989, top5_acc: 0.6577, loss_cls: 3.4086, loss: 3.4086 +2024-07-26 01:09:11,404 - pyskl - INFO - Epoch [107][1900/3746] lr: 1.935e-02, eta: 1 day, 12:52:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6614, loss_cls: 3.3654, loss: 3.3654 +2024-07-26 01:10:33,048 - pyskl - INFO - Epoch [107][2000/3746] lr: 1.933e-02, eta: 1 day, 12:51:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6544, loss_cls: 3.4002, loss: 3.4002 +2024-07-26 01:11:54,997 - pyskl - INFO - Epoch [107][2100/3746] lr: 1.930e-02, eta: 1 day, 12:50:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6561, loss_cls: 3.3716, loss: 3.3716 +2024-07-26 01:13:18,090 - pyskl - INFO - Epoch [107][2200/3746] lr: 1.928e-02, eta: 1 day, 12:48:45, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6578, loss_cls: 3.3817, loss: 3.3817 +2024-07-26 01:14:40,100 - pyskl - INFO - Epoch [107][2300/3746] lr: 1.926e-02, eta: 1 day, 12:47:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6630, loss_cls: 3.3575, loss: 3.3575 +2024-07-26 01:16:02,487 - pyskl - INFO - Epoch [107][2400/3746] lr: 1.924e-02, eta: 1 day, 12:46:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6550, loss_cls: 3.4099, loss: 3.4099 +2024-07-26 01:17:24,404 - pyskl - INFO - Epoch [107][2500/3746] lr: 1.922e-02, eta: 1 day, 12:44:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6555, loss_cls: 3.3840, loss: 3.3840 +2024-07-26 01:18:46,421 - pyskl - INFO - Epoch [107][2600/3746] lr: 1.919e-02, eta: 1 day, 12:43:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6592, loss_cls: 3.3863, loss: 3.3863 +2024-07-26 01:20:08,072 - pyskl - INFO - Epoch [107][2700/3746] lr: 1.917e-02, eta: 1 day, 12:41:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6603, loss_cls: 3.4033, loss: 3.4033 +2024-07-26 01:21:29,487 - pyskl - INFO - Epoch [107][2800/3746] lr: 1.915e-02, eta: 1 day, 12:40:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3972, top5_acc: 0.6711, loss_cls: 3.3657, loss: 3.3657 +2024-07-26 01:22:50,881 - pyskl - INFO - Epoch [107][2900/3746] lr: 1.913e-02, eta: 1 day, 12:39:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6577, loss_cls: 3.3740, loss: 3.3740 +2024-07-26 01:24:12,192 - pyskl - INFO - Epoch [107][3000/3746] lr: 1.911e-02, eta: 1 day, 12:37:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6472, loss_cls: 3.4459, loss: 3.4459 +2024-07-26 01:25:34,039 - pyskl - INFO - Epoch [107][3100/3746] lr: 1.908e-02, eta: 1 day, 12:36:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3872, top5_acc: 0.6519, loss_cls: 3.4317, loss: 3.4317 +2024-07-26 01:26:55,319 - pyskl - INFO - Epoch [107][3200/3746] lr: 1.906e-02, eta: 1 day, 12:35:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6467, loss_cls: 3.4411, loss: 3.4411 +2024-07-26 01:28:17,065 - pyskl - INFO - Epoch [107][3300/3746] lr: 1.904e-02, eta: 1 day, 12:33:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6591, loss_cls: 3.3628, loss: 3.3628 +2024-07-26 01:29:38,793 - pyskl - INFO - Epoch [107][3400/3746] lr: 1.902e-02, eta: 1 day, 12:32:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6552, loss_cls: 3.3897, loss: 3.3897 +2024-07-26 01:31:00,188 - pyskl - INFO - Epoch [107][3500/3746] lr: 1.900e-02, eta: 1 day, 12:31:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4008, top5_acc: 0.6584, loss_cls: 3.3843, loss: 3.3843 +2024-07-26 01:32:21,661 - pyskl - INFO - Epoch [107][3600/3746] lr: 1.897e-02, eta: 1 day, 12:29:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6522, loss_cls: 3.3827, loss: 3.3827 +2024-07-26 01:33:43,161 - pyskl - INFO - Epoch [107][3700/3746] lr: 1.895e-02, eta: 1 day, 12:28:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6652, loss_cls: 3.3862, loss: 3.3862 +2024-07-26 01:34:22,926 - pyskl - INFO - Saving checkpoint at 107 epochs +2024-07-26 01:36:13,938 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 01:36:14,691 - pyskl - INFO - +top1_acc 0.3442 +top5_acc 0.5950 +2024-07-26 01:36:14,691 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 01:36:14,730 - pyskl - INFO - +mean_acc 0.3439 +2024-07-26 01:36:14,735 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_105.pth was removed +2024-07-26 01:36:14,987 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_107.pth. +2024-07-26 01:36:14,988 - pyskl - INFO - Best top1_acc is 0.3442 at 107 epoch. +2024-07-26 01:36:15,001 - pyskl - INFO - Epoch(val) [107][309] top1_acc: 0.3442, top5_acc: 0.5950, mean_class_accuracy: 0.3439 +2024-07-26 01:40:10,474 - pyskl - INFO - Epoch [108][100/3746] lr: 1.892e-02, eta: 1 day, 12:27:11, time: 2.355, data_time: 1.382, memory: 15990, top1_acc: 0.3970, top5_acc: 0.6614, loss_cls: 3.3840, loss: 3.3840 +2024-07-26 01:41:32,070 - pyskl - INFO - Epoch [108][200/3746] lr: 1.890e-02, eta: 1 day, 12:25:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4163, top5_acc: 0.6791, loss_cls: 3.2586, loss: 3.2586 +2024-07-26 01:42:53,420 - pyskl - INFO - Epoch [108][300/3746] lr: 1.888e-02, eta: 1 day, 12:24:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6736, loss_cls: 3.3038, loss: 3.3038 +2024-07-26 01:44:15,266 - pyskl - INFO - Epoch [108][400/3746] lr: 1.886e-02, eta: 1 day, 12:23:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6722, loss_cls: 3.3226, loss: 3.3226 +2024-07-26 01:45:36,832 - pyskl - INFO - Epoch [108][500/3746] lr: 1.883e-02, eta: 1 day, 12:21:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6652, loss_cls: 3.3700, loss: 3.3700 +2024-07-26 01:46:58,643 - pyskl - INFO - Epoch [108][600/3746] lr: 1.881e-02, eta: 1 day, 12:20:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6617, loss_cls: 3.3624, loss: 3.3624 +2024-07-26 01:48:20,866 - pyskl - INFO - Epoch [108][700/3746] lr: 1.879e-02, eta: 1 day, 12:19:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6652, loss_cls: 3.3481, loss: 3.3481 +2024-07-26 01:49:42,742 - pyskl - INFO - Epoch [108][800/3746] lr: 1.877e-02, eta: 1 day, 12:17:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4109, top5_acc: 0.6633, loss_cls: 3.3196, loss: 3.3196 +2024-07-26 01:51:04,944 - pyskl - INFO - Epoch [108][900/3746] lr: 1.875e-02, eta: 1 day, 12:16:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6598, loss_cls: 3.3420, loss: 3.3420 +2024-07-26 01:52:26,702 - pyskl - INFO - Epoch [108][1000/3746] lr: 1.872e-02, eta: 1 day, 12:14:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6684, loss_cls: 3.3413, loss: 3.3413 +2024-07-26 01:53:48,104 - pyskl - INFO - Epoch [108][1100/3746] lr: 1.870e-02, eta: 1 day, 12:13:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6627, loss_cls: 3.3612, loss: 3.3612 +2024-07-26 01:55:10,279 - pyskl - INFO - Epoch [108][1200/3746] lr: 1.868e-02, eta: 1 day, 12:12:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6587, loss_cls: 3.3839, loss: 3.3839 +2024-07-26 01:56:32,086 - pyskl - INFO - Epoch [108][1300/3746] lr: 1.866e-02, eta: 1 day, 12:10:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6703, loss_cls: 3.3345, loss: 3.3345 +2024-07-26 01:57:53,582 - pyskl - INFO - Epoch [108][1400/3746] lr: 1.864e-02, eta: 1 day, 12:09:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6670, loss_cls: 3.3774, loss: 3.3774 +2024-07-26 01:59:15,490 - pyskl - INFO - Epoch [108][1500/3746] lr: 1.862e-02, eta: 1 day, 12:08:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4227, top5_acc: 0.6741, loss_cls: 3.2732, loss: 3.2732 +2024-07-26 02:00:37,334 - pyskl - INFO - Epoch [108][1600/3746] lr: 1.859e-02, eta: 1 day, 12:06:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6575, loss_cls: 3.3858, loss: 3.3858 +2024-07-26 02:01:58,839 - pyskl - INFO - Epoch [108][1700/3746] lr: 1.857e-02, eta: 1 day, 12:05:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6670, loss_cls: 3.3802, loss: 3.3802 +2024-07-26 02:03:20,222 - pyskl - INFO - Epoch [108][1800/3746] lr: 1.855e-02, eta: 1 day, 12:04:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6591, loss_cls: 3.3815, loss: 3.3815 +2024-07-26 02:04:41,421 - pyskl - INFO - Epoch [108][1900/3746] lr: 1.853e-02, eta: 1 day, 12:02:45, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6619, loss_cls: 3.3743, loss: 3.3743 +2024-07-26 02:06:02,551 - pyskl - INFO - Epoch [108][2000/3746] lr: 1.851e-02, eta: 1 day, 12:01:24, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6589, loss_cls: 3.3748, loss: 3.3748 +2024-07-26 02:07:24,351 - pyskl - INFO - Epoch [108][2100/3746] lr: 1.848e-02, eta: 1 day, 12:00:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6508, loss_cls: 3.3891, loss: 3.3891 +2024-07-26 02:08:47,076 - pyskl - INFO - Epoch [108][2200/3746] lr: 1.846e-02, eta: 1 day, 11:58:41, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6606, loss_cls: 3.3827, loss: 3.3827 +2024-07-26 02:10:09,853 - pyskl - INFO - Epoch [108][2300/3746] lr: 1.844e-02, eta: 1 day, 11:57:20, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6527, loss_cls: 3.4035, loss: 3.4035 +2024-07-26 02:11:32,760 - pyskl - INFO - Epoch [108][2400/3746] lr: 1.842e-02, eta: 1 day, 11:55:59, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4066, top5_acc: 0.6653, loss_cls: 3.3484, loss: 3.3484 +2024-07-26 02:12:54,741 - pyskl - INFO - Epoch [108][2500/3746] lr: 1.840e-02, eta: 1 day, 11:54:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6561, loss_cls: 3.3776, loss: 3.3776 +2024-07-26 02:14:15,894 - pyskl - INFO - Epoch [108][2600/3746] lr: 1.838e-02, eta: 1 day, 11:53:16, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6631, loss_cls: 3.3787, loss: 3.3787 +2024-07-26 02:15:37,463 - pyskl - INFO - Epoch [108][2700/3746] lr: 1.835e-02, eta: 1 day, 11:51:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6602, loss_cls: 3.3805, loss: 3.3805 +2024-07-26 02:16:59,010 - pyskl - INFO - Epoch [108][2800/3746] lr: 1.833e-02, eta: 1 day, 11:50:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6677, loss_cls: 3.3712, loss: 3.3712 +2024-07-26 02:18:20,599 - pyskl - INFO - Epoch [108][2900/3746] lr: 1.831e-02, eta: 1 day, 11:49:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4008, top5_acc: 0.6556, loss_cls: 3.4000, loss: 3.4000 +2024-07-26 02:19:42,118 - pyskl - INFO - Epoch [108][3000/3746] lr: 1.829e-02, eta: 1 day, 11:47:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6639, loss_cls: 3.3436, loss: 3.3436 +2024-07-26 02:21:03,917 - pyskl - INFO - Epoch [108][3100/3746] lr: 1.827e-02, eta: 1 day, 11:46:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6620, loss_cls: 3.3781, loss: 3.3781 +2024-07-26 02:22:25,418 - pyskl - INFO - Epoch [108][3200/3746] lr: 1.825e-02, eta: 1 day, 11:45:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6569, loss_cls: 3.3499, loss: 3.3499 +2024-07-26 02:23:46,850 - pyskl - INFO - Epoch [108][3300/3746] lr: 1.823e-02, eta: 1 day, 11:43:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6742, loss_cls: 3.3071, loss: 3.3071 +2024-07-26 02:25:08,330 - pyskl - INFO - Epoch [108][3400/3746] lr: 1.820e-02, eta: 1 day, 11:42:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4072, top5_acc: 0.6611, loss_cls: 3.3420, loss: 3.3420 +2024-07-26 02:26:30,499 - pyskl - INFO - Epoch [108][3500/3746] lr: 1.818e-02, eta: 1 day, 11:41:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4005, top5_acc: 0.6714, loss_cls: 3.3612, loss: 3.3612 +2024-07-26 02:27:52,192 - pyskl - INFO - Epoch [108][3600/3746] lr: 1.816e-02, eta: 1 day, 11:39:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6580, loss_cls: 3.3797, loss: 3.3797 +2024-07-26 02:29:13,235 - pyskl - INFO - Epoch [108][3700/3746] lr: 1.814e-02, eta: 1 day, 11:38:20, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6541, loss_cls: 3.3973, loss: 3.3973 +2024-07-26 02:29:53,064 - pyskl - INFO - Saving checkpoint at 108 epochs +2024-07-26 02:31:44,094 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 02:31:44,758 - pyskl - INFO - +top1_acc 0.3424 +top5_acc 0.6027 +2024-07-26 02:31:44,758 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 02:31:44,799 - pyskl - INFO - +mean_acc 0.3423 +2024-07-26 02:31:44,811 - pyskl - INFO - Epoch(val) [108][309] top1_acc: 0.3424, top5_acc: 0.6027, mean_class_accuracy: 0.3423 +2024-07-26 02:35:38,540 - pyskl - INFO - Epoch [109][100/3746] lr: 1.811e-02, eta: 1 day, 11:37:05, time: 2.337, data_time: 1.354, memory: 15990, top1_acc: 0.4270, top5_acc: 0.6777, loss_cls: 3.2636, loss: 3.2636 +2024-07-26 02:37:00,876 - pyskl - INFO - Epoch [109][200/3746] lr: 1.809e-02, eta: 1 day, 11:35:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6744, loss_cls: 3.2827, loss: 3.2827 +2024-07-26 02:38:23,233 - pyskl - INFO - Epoch [109][300/3746] lr: 1.806e-02, eta: 1 day, 11:34:23, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6720, loss_cls: 3.2903, loss: 3.2903 +2024-07-26 02:39:45,154 - pyskl - INFO - Epoch [109][400/3746] lr: 1.804e-02, eta: 1 day, 11:33:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4139, top5_acc: 0.6686, loss_cls: 3.2738, loss: 3.2738 +2024-07-26 02:41:06,964 - pyskl - INFO - Epoch [109][500/3746] lr: 1.802e-02, eta: 1 day, 11:31:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4145, top5_acc: 0.6827, loss_cls: 3.2411, loss: 3.2411 +2024-07-26 02:42:29,073 - pyskl - INFO - Epoch [109][600/3746] lr: 1.800e-02, eta: 1 day, 11:30:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6841, loss_cls: 3.2407, loss: 3.2407 +2024-07-26 02:43:51,041 - pyskl - INFO - Epoch [109][700/3746] lr: 1.798e-02, eta: 1 day, 11:28:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6808, loss_cls: 3.2726, loss: 3.2726 +2024-07-26 02:45:12,795 - pyskl - INFO - Epoch [109][800/3746] lr: 1.796e-02, eta: 1 day, 11:27:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4152, top5_acc: 0.6666, loss_cls: 3.3264, loss: 3.3264 +2024-07-26 02:46:35,371 - pyskl - INFO - Epoch [109][900/3746] lr: 1.794e-02, eta: 1 day, 11:26:15, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6645, loss_cls: 3.3489, loss: 3.3489 +2024-07-26 02:47:56,992 - pyskl - INFO - Epoch [109][1000/3746] lr: 1.791e-02, eta: 1 day, 11:24:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6670, loss_cls: 3.3271, loss: 3.3271 +2024-07-26 02:49:18,067 - pyskl - INFO - Epoch [109][1100/3746] lr: 1.789e-02, eta: 1 day, 11:23:31, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6752, loss_cls: 3.3171, loss: 3.3171 +2024-07-26 02:50:39,743 - pyskl - INFO - Epoch [109][1200/3746] lr: 1.787e-02, eta: 1 day, 11:22:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3977, top5_acc: 0.6558, loss_cls: 3.3962, loss: 3.3962 +2024-07-26 02:52:01,371 - pyskl - INFO - Epoch [109][1300/3746] lr: 1.785e-02, eta: 1 day, 11:20:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4033, top5_acc: 0.6708, loss_cls: 3.3109, loss: 3.3109 +2024-07-26 02:53:23,249 - pyskl - INFO - Epoch [109][1400/3746] lr: 1.783e-02, eta: 1 day, 11:19:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6628, loss_cls: 3.3584, loss: 3.3584 +2024-07-26 02:54:44,768 - pyskl - INFO - Epoch [109][1500/3746] lr: 1.781e-02, eta: 1 day, 11:18:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6611, loss_cls: 3.3669, loss: 3.3669 +2024-07-26 02:56:05,912 - pyskl - INFO - Epoch [109][1600/3746] lr: 1.779e-02, eta: 1 day, 11:16:44, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6544, loss_cls: 3.4122, loss: 3.4122 +2024-07-26 02:57:27,376 - pyskl - INFO - Epoch [109][1700/3746] lr: 1.776e-02, eta: 1 day, 11:15:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6720, loss_cls: 3.2889, loss: 3.2889 +2024-07-26 02:58:49,096 - pyskl - INFO - Epoch [109][1800/3746] lr: 1.774e-02, eta: 1 day, 11:14:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4072, top5_acc: 0.6716, loss_cls: 3.3203, loss: 3.3203 +2024-07-26 03:00:10,832 - pyskl - INFO - Epoch [109][1900/3746] lr: 1.772e-02, eta: 1 day, 11:12:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6595, loss_cls: 3.3565, loss: 3.3565 +2024-07-26 03:01:32,745 - pyskl - INFO - Epoch [109][2000/3746] lr: 1.770e-02, eta: 1 day, 11:11:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6561, loss_cls: 3.3857, loss: 3.3857 +2024-07-26 03:02:54,077 - pyskl - INFO - Epoch [109][2100/3746] lr: 1.768e-02, eta: 1 day, 11:09:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6634, loss_cls: 3.3505, loss: 3.3505 +2024-07-26 03:04:17,299 - pyskl - INFO - Epoch [109][2200/3746] lr: 1.766e-02, eta: 1 day, 11:08:35, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6639, loss_cls: 3.3370, loss: 3.3370 +2024-07-26 03:05:39,847 - pyskl - INFO - Epoch [109][2300/3746] lr: 1.764e-02, eta: 1 day, 11:07:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6625, loss_cls: 3.3481, loss: 3.3481 +2024-07-26 03:07:03,009 - pyskl - INFO - Epoch [109][2400/3746] lr: 1.761e-02, eta: 1 day, 11:05:53, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4070, top5_acc: 0.6673, loss_cls: 3.3287, loss: 3.3287 +2024-07-26 03:08:24,768 - pyskl - INFO - Epoch [109][2500/3746] lr: 1.759e-02, eta: 1 day, 11:04:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6655, loss_cls: 3.3316, loss: 3.3316 +2024-07-26 03:09:46,401 - pyskl - INFO - Epoch [109][2600/3746] lr: 1.757e-02, eta: 1 day, 11:03:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6491, loss_cls: 3.4162, loss: 3.4162 +2024-07-26 03:11:07,760 - pyskl - INFO - Epoch [109][2700/3746] lr: 1.755e-02, eta: 1 day, 11:01:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6581, loss_cls: 3.3832, loss: 3.3832 +2024-07-26 03:12:29,405 - pyskl - INFO - Epoch [109][2800/3746] lr: 1.753e-02, eta: 1 day, 11:00:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4073, top5_acc: 0.6630, loss_cls: 3.3429, loss: 3.3429 +2024-07-26 03:13:50,804 - pyskl - INFO - Epoch [109][2900/3746] lr: 1.751e-02, eta: 1 day, 10:59:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6675, loss_cls: 3.3491, loss: 3.3491 +2024-07-26 03:15:12,052 - pyskl - INFO - Epoch [109][3000/3746] lr: 1.749e-02, eta: 1 day, 10:57:43, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4095, top5_acc: 0.6678, loss_cls: 3.3340, loss: 3.3340 +2024-07-26 03:16:33,320 - pyskl - INFO - Epoch [109][3100/3746] lr: 1.747e-02, eta: 1 day, 10:56:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6655, loss_cls: 3.3618, loss: 3.3618 +2024-07-26 03:17:54,837 - pyskl - INFO - Epoch [109][3200/3746] lr: 1.744e-02, eta: 1 day, 10:55:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6477, loss_cls: 3.4163, loss: 3.4163 +2024-07-26 03:19:16,249 - pyskl - INFO - Epoch [109][3300/3746] lr: 1.742e-02, eta: 1 day, 10:53:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6655, loss_cls: 3.3587, loss: 3.3587 +2024-07-26 03:20:38,051 - pyskl - INFO - Epoch [109][3400/3746] lr: 1.740e-02, eta: 1 day, 10:52:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6634, loss_cls: 3.3638, loss: 3.3638 +2024-07-26 03:21:59,265 - pyskl - INFO - Epoch [109][3500/3746] lr: 1.738e-02, eta: 1 day, 10:50:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6567, loss_cls: 3.3897, loss: 3.3897 +2024-07-26 03:23:21,550 - pyskl - INFO - Epoch [109][3600/3746] lr: 1.736e-02, eta: 1 day, 10:49:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6622, loss_cls: 3.3587, loss: 3.3587 +2024-07-26 03:24:43,584 - pyskl - INFO - Epoch [109][3700/3746] lr: 1.734e-02, eta: 1 day, 10:48:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6578, loss_cls: 3.4060, loss: 3.4060 +2024-07-26 03:25:23,141 - pyskl - INFO - Saving checkpoint at 109 epochs +2024-07-26 03:27:15,463 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 03:27:16,126 - pyskl - INFO - +top1_acc 0.3298 +top5_acc 0.5905 +2024-07-26 03:27:16,126 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 03:27:16,164 - pyskl - INFO - +mean_acc 0.3296 +2024-07-26 03:27:16,176 - pyskl - INFO - Epoch(val) [109][309] top1_acc: 0.3298, top5_acc: 0.5905, mean_class_accuracy: 0.3296 +2024-07-26 03:31:09,688 - pyskl - INFO - Epoch [110][100/3746] lr: 1.731e-02, eta: 1 day, 10:46:57, time: 2.335, data_time: 1.353, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6827, loss_cls: 3.2278, loss: 3.2278 +2024-07-26 03:32:31,214 - pyskl - INFO - Epoch [110][200/3746] lr: 1.729e-02, eta: 1 day, 10:45:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6755, loss_cls: 3.3052, loss: 3.3052 +2024-07-26 03:33:53,809 - pyskl - INFO - Epoch [110][300/3746] lr: 1.727e-02, eta: 1 day, 10:44:14, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6767, loss_cls: 3.2626, loss: 3.2626 +2024-07-26 03:35:15,191 - pyskl - INFO - Epoch [110][400/3746] lr: 1.724e-02, eta: 1 day, 10:42:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4241, top5_acc: 0.6819, loss_cls: 3.2509, loss: 3.2509 +2024-07-26 03:36:36,679 - pyskl - INFO - Epoch [110][500/3746] lr: 1.722e-02, eta: 1 day, 10:41:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4102, top5_acc: 0.6692, loss_cls: 3.3106, loss: 3.3106 +2024-07-26 03:37:58,484 - pyskl - INFO - Epoch [110][600/3746] lr: 1.720e-02, eta: 1 day, 10:40:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6858, loss_cls: 3.2381, loss: 3.2381 +2024-07-26 03:39:20,508 - pyskl - INFO - Epoch [110][700/3746] lr: 1.718e-02, eta: 1 day, 10:38:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4147, top5_acc: 0.6719, loss_cls: 3.2950, loss: 3.2950 +2024-07-26 03:40:42,337 - pyskl - INFO - Epoch [110][800/3746] lr: 1.716e-02, eta: 1 day, 10:37:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4138, top5_acc: 0.6700, loss_cls: 3.3018, loss: 3.3018 +2024-07-26 03:42:04,325 - pyskl - INFO - Epoch [110][900/3746] lr: 1.714e-02, eta: 1 day, 10:36:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4155, top5_acc: 0.6719, loss_cls: 3.2985, loss: 3.2985 +2024-07-26 03:43:26,306 - pyskl - INFO - Epoch [110][1000/3746] lr: 1.712e-02, eta: 1 day, 10:34:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6694, loss_cls: 3.3010, loss: 3.3010 +2024-07-26 03:44:48,099 - pyskl - INFO - Epoch [110][1100/3746] lr: 1.710e-02, eta: 1 day, 10:33:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6580, loss_cls: 3.3915, loss: 3.3915 +2024-07-26 03:46:09,848 - pyskl - INFO - Epoch [110][1200/3746] lr: 1.708e-02, eta: 1 day, 10:32:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6713, loss_cls: 3.3135, loss: 3.3135 +2024-07-26 03:47:31,058 - pyskl - INFO - Epoch [110][1300/3746] lr: 1.705e-02, eta: 1 day, 10:30:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6669, loss_cls: 3.3131, loss: 3.3131 +2024-07-26 03:48:52,268 - pyskl - INFO - Epoch [110][1400/3746] lr: 1.703e-02, eta: 1 day, 10:29:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6705, loss_cls: 3.3378, loss: 3.3378 +2024-07-26 03:50:14,377 - pyskl - INFO - Epoch [110][1500/3746] lr: 1.701e-02, eta: 1 day, 10:27:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6761, loss_cls: 3.2649, loss: 3.2649 +2024-07-26 03:51:36,516 - pyskl - INFO - Epoch [110][1600/3746] lr: 1.699e-02, eta: 1 day, 10:26:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3992, top5_acc: 0.6614, loss_cls: 3.3727, loss: 3.3727 +2024-07-26 03:52:58,273 - pyskl - INFO - Epoch [110][1700/3746] lr: 1.697e-02, eta: 1 day, 10:25:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4202, top5_acc: 0.6737, loss_cls: 3.2906, loss: 3.2906 +2024-07-26 03:54:19,868 - pyskl - INFO - Epoch [110][1800/3746] lr: 1.695e-02, eta: 1 day, 10:23:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4095, top5_acc: 0.6634, loss_cls: 3.3219, loss: 3.3219 +2024-07-26 03:55:41,443 - pyskl - INFO - Epoch [110][1900/3746] lr: 1.693e-02, eta: 1 day, 10:22:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6756, loss_cls: 3.2931, loss: 3.2931 +2024-07-26 03:57:03,290 - pyskl - INFO - Epoch [110][2000/3746] lr: 1.691e-02, eta: 1 day, 10:21:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3997, top5_acc: 0.6611, loss_cls: 3.3700, loss: 3.3700 +2024-07-26 03:58:25,321 - pyskl - INFO - Epoch [110][2100/3746] lr: 1.689e-02, eta: 1 day, 10:19:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4106, top5_acc: 0.6595, loss_cls: 3.3300, loss: 3.3300 +2024-07-26 03:59:47,884 - pyskl - INFO - Epoch [110][2200/3746] lr: 1.687e-02, eta: 1 day, 10:18:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6677, loss_cls: 3.3453, loss: 3.3453 +2024-07-26 04:01:10,123 - pyskl - INFO - Epoch [110][2300/3746] lr: 1.685e-02, eta: 1 day, 10:17:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6681, loss_cls: 3.3444, loss: 3.3444 +2024-07-26 04:02:32,957 - pyskl - INFO - Epoch [110][2400/3746] lr: 1.682e-02, eta: 1 day, 10:15:43, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4195, top5_acc: 0.6773, loss_cls: 3.2759, loss: 3.2759 +2024-07-26 04:03:54,778 - pyskl - INFO - Epoch [110][2500/3746] lr: 1.680e-02, eta: 1 day, 10:14:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6652, loss_cls: 3.3185, loss: 3.3185 +2024-07-26 04:05:16,960 - pyskl - INFO - Epoch [110][2600/3746] lr: 1.678e-02, eta: 1 day, 10:13:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4072, top5_acc: 0.6587, loss_cls: 3.3599, loss: 3.3599 +2024-07-26 04:06:39,204 - pyskl - INFO - Epoch [110][2700/3746] lr: 1.676e-02, eta: 1 day, 10:11:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6602, loss_cls: 3.3697, loss: 3.3697 +2024-07-26 04:08:00,473 - pyskl - INFO - Epoch [110][2800/3746] lr: 1.674e-02, eta: 1 day, 10:10:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6525, loss_cls: 3.3664, loss: 3.3664 +2024-07-26 04:09:21,902 - pyskl - INFO - Epoch [110][2900/3746] lr: 1.672e-02, eta: 1 day, 10:08:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6545, loss_cls: 3.3489, loss: 3.3489 +2024-07-26 04:10:43,567 - pyskl - INFO - Epoch [110][3000/3746] lr: 1.670e-02, eta: 1 day, 10:07:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4033, top5_acc: 0.6608, loss_cls: 3.3381, loss: 3.3381 +2024-07-26 04:12:05,567 - pyskl - INFO - Epoch [110][3100/3746] lr: 1.668e-02, eta: 1 day, 10:06:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4116, top5_acc: 0.6681, loss_cls: 3.3059, loss: 3.3059 +2024-07-26 04:13:26,833 - pyskl - INFO - Epoch [110][3200/3746] lr: 1.666e-02, eta: 1 day, 10:04:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4125, top5_acc: 0.6708, loss_cls: 3.3238, loss: 3.3238 +2024-07-26 04:14:48,640 - pyskl - INFO - Epoch [110][3300/3746] lr: 1.664e-02, eta: 1 day, 10:03:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4134, top5_acc: 0.6713, loss_cls: 3.2988, loss: 3.2988 +2024-07-26 04:16:10,092 - pyskl - INFO - Epoch [110][3400/3746] lr: 1.662e-02, eta: 1 day, 10:02:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6775, loss_cls: 3.2906, loss: 3.2906 +2024-07-26 04:17:31,502 - pyskl - INFO - Epoch [110][3500/3746] lr: 1.659e-02, eta: 1 day, 10:00:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6658, loss_cls: 3.3047, loss: 3.3047 +2024-07-26 04:18:52,907 - pyskl - INFO - Epoch [110][3600/3746] lr: 1.657e-02, eta: 1 day, 9:59:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6631, loss_cls: 3.3791, loss: 3.3791 +2024-07-26 04:20:14,740 - pyskl - INFO - Epoch [110][3700/3746] lr: 1.655e-02, eta: 1 day, 9:58:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6717, loss_cls: 3.3114, loss: 3.3114 +2024-07-26 04:20:54,075 - pyskl - INFO - Saving checkpoint at 110 epochs +2024-07-26 04:22:46,045 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 04:22:46,739 - pyskl - INFO - +top1_acc 0.3497 +top5_acc 0.6018 +2024-07-26 04:22:46,739 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 04:22:46,800 - pyskl - INFO - +mean_acc 0.3496 +2024-07-26 04:22:46,806 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_107.pth was removed +2024-07-26 04:22:47,097 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_110.pth. +2024-07-26 04:22:47,098 - pyskl - INFO - Best top1_acc is 0.3497 at 110 epoch. +2024-07-26 04:22:47,121 - pyskl - INFO - Epoch(val) [110][309] top1_acc: 0.3497, top5_acc: 0.6018, mean_class_accuracy: 0.3496 +2024-07-26 04:26:40,837 - pyskl - INFO - Epoch [111][100/3746] lr: 1.652e-02, eta: 1 day, 9:56:45, time: 2.337, data_time: 1.363, memory: 15990, top1_acc: 0.4195, top5_acc: 0.6802, loss_cls: 3.2201, loss: 3.2201 +2024-07-26 04:28:03,165 - pyskl - INFO - Epoch [111][200/3746] lr: 1.650e-02, eta: 1 day, 9:55:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6805, loss_cls: 3.2495, loss: 3.2495 +2024-07-26 04:29:25,598 - pyskl - INFO - Epoch [111][300/3746] lr: 1.648e-02, eta: 1 day, 9:54:03, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4280, top5_acc: 0.6852, loss_cls: 3.2078, loss: 3.2078 +2024-07-26 04:30:47,497 - pyskl - INFO - Epoch [111][400/3746] lr: 1.646e-02, eta: 1 day, 9:52:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4330, top5_acc: 0.6842, loss_cls: 3.1834, loss: 3.1834 +2024-07-26 04:32:09,612 - pyskl - INFO - Epoch [111][500/3746] lr: 1.644e-02, eta: 1 day, 9:51:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4220, top5_acc: 0.6833, loss_cls: 3.2453, loss: 3.2453 +2024-07-26 04:33:31,460 - pyskl - INFO - Epoch [111][600/3746] lr: 1.642e-02, eta: 1 day, 9:49:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4211, top5_acc: 0.6797, loss_cls: 3.2785, loss: 3.2785 +2024-07-26 04:34:53,992 - pyskl - INFO - Epoch [111][700/3746] lr: 1.640e-02, eta: 1 day, 9:48:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6775, loss_cls: 3.2469, loss: 3.2469 +2024-07-26 04:36:15,708 - pyskl - INFO - Epoch [111][800/3746] lr: 1.638e-02, eta: 1 day, 9:47:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4142, top5_acc: 0.6711, loss_cls: 3.2763, loss: 3.2763 +2024-07-26 04:37:38,355 - pyskl - INFO - Epoch [111][900/3746] lr: 1.636e-02, eta: 1 day, 9:45:54, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4173, top5_acc: 0.6836, loss_cls: 3.2286, loss: 3.2286 +2024-07-26 04:38:59,886 - pyskl - INFO - Epoch [111][1000/3746] lr: 1.634e-02, eta: 1 day, 9:44:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4145, top5_acc: 0.6800, loss_cls: 3.2795, loss: 3.2795 +2024-07-26 04:40:21,950 - pyskl - INFO - Epoch [111][1100/3746] lr: 1.632e-02, eta: 1 day, 9:43:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6767, loss_cls: 3.2653, loss: 3.2653 +2024-07-26 04:41:43,454 - pyskl - INFO - Epoch [111][1200/3746] lr: 1.630e-02, eta: 1 day, 9:41:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6727, loss_cls: 3.2931, loss: 3.2931 +2024-07-26 04:43:05,241 - pyskl - INFO - Epoch [111][1300/3746] lr: 1.627e-02, eta: 1 day, 9:40:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6741, loss_cls: 3.3216, loss: 3.3216 +2024-07-26 04:44:26,428 - pyskl - INFO - Epoch [111][1400/3746] lr: 1.625e-02, eta: 1 day, 9:39:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4252, top5_acc: 0.6778, loss_cls: 3.2684, loss: 3.2684 +2024-07-26 04:45:48,156 - pyskl - INFO - Epoch [111][1500/3746] lr: 1.623e-02, eta: 1 day, 9:37:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4009, top5_acc: 0.6708, loss_cls: 3.3368, loss: 3.3368 +2024-07-26 04:47:10,109 - pyskl - INFO - Epoch [111][1600/3746] lr: 1.621e-02, eta: 1 day, 9:36:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4098, top5_acc: 0.6653, loss_cls: 3.3232, loss: 3.3232 +2024-07-26 04:48:32,222 - pyskl - INFO - Epoch [111][1700/3746] lr: 1.619e-02, eta: 1 day, 9:35:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6706, loss_cls: 3.3083, loss: 3.3083 +2024-07-26 04:49:53,810 - pyskl - INFO - Epoch [111][1800/3746] lr: 1.617e-02, eta: 1 day, 9:33:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6698, loss_cls: 3.3073, loss: 3.3073 +2024-07-26 04:51:15,134 - pyskl - INFO - Epoch [111][1900/3746] lr: 1.615e-02, eta: 1 day, 9:32:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6686, loss_cls: 3.3018, loss: 3.3018 +2024-07-26 04:52:36,716 - pyskl - INFO - Epoch [111][2000/3746] lr: 1.613e-02, eta: 1 day, 9:30:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6706, loss_cls: 3.3177, loss: 3.3177 +2024-07-26 04:53:59,061 - pyskl - INFO - Epoch [111][2100/3746] lr: 1.611e-02, eta: 1 day, 9:29:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4169, top5_acc: 0.6687, loss_cls: 3.3158, loss: 3.3158 +2024-07-26 04:55:21,396 - pyskl - INFO - Epoch [111][2200/3746] lr: 1.609e-02, eta: 1 day, 9:28:14, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4109, top5_acc: 0.6636, loss_cls: 3.3404, loss: 3.3404 +2024-07-26 04:56:43,793 - pyskl - INFO - Epoch [111][2300/3746] lr: 1.607e-02, eta: 1 day, 9:26:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6698, loss_cls: 3.3137, loss: 3.3137 +2024-07-26 04:58:06,103 - pyskl - INFO - Epoch [111][2400/3746] lr: 1.605e-02, eta: 1 day, 9:25:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6641, loss_cls: 3.3395, loss: 3.3395 +2024-07-26 04:59:28,472 - pyskl - INFO - Epoch [111][2500/3746] lr: 1.603e-02, eta: 1 day, 9:24:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6767, loss_cls: 3.2754, loss: 3.2754 +2024-07-26 05:00:50,394 - pyskl - INFO - Epoch [111][2600/3746] lr: 1.601e-02, eta: 1 day, 9:22:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6720, loss_cls: 3.2906, loss: 3.2906 +2024-07-26 05:02:12,687 - pyskl - INFO - Epoch [111][2700/3746] lr: 1.599e-02, eta: 1 day, 9:21:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6694, loss_cls: 3.3369, loss: 3.3369 +2024-07-26 05:03:34,191 - pyskl - INFO - Epoch [111][2800/3746] lr: 1.597e-02, eta: 1 day, 9:20:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4169, top5_acc: 0.6739, loss_cls: 3.3265, loss: 3.3265 +2024-07-26 05:04:55,771 - pyskl - INFO - Epoch [111][2900/3746] lr: 1.595e-02, eta: 1 day, 9:18:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6714, loss_cls: 3.2842, loss: 3.2842 +2024-07-26 05:06:17,751 - pyskl - INFO - Epoch [111][3000/3746] lr: 1.593e-02, eta: 1 day, 9:17:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6730, loss_cls: 3.2986, loss: 3.2986 +2024-07-26 05:07:39,542 - pyskl - INFO - Epoch [111][3100/3746] lr: 1.590e-02, eta: 1 day, 9:16:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4102, top5_acc: 0.6689, loss_cls: 3.3193, loss: 3.3193 +2024-07-26 05:09:01,212 - pyskl - INFO - Epoch [111][3200/3746] lr: 1.588e-02, eta: 1 day, 9:14:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6750, loss_cls: 3.2911, loss: 3.2911 +2024-07-26 05:10:22,806 - pyskl - INFO - Epoch [111][3300/3746] lr: 1.586e-02, eta: 1 day, 9:13:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6581, loss_cls: 3.3662, loss: 3.3662 +2024-07-26 05:11:44,041 - pyskl - INFO - Epoch [111][3400/3746] lr: 1.584e-02, eta: 1 day, 9:11:56, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6613, loss_cls: 3.3524, loss: 3.3524 +2024-07-26 05:13:05,619 - pyskl - INFO - Epoch [111][3500/3746] lr: 1.582e-02, eta: 1 day, 9:10:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6620, loss_cls: 3.3275, loss: 3.3275 +2024-07-26 05:14:27,057 - pyskl - INFO - Epoch [111][3600/3746] lr: 1.580e-02, eta: 1 day, 9:09:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4114, top5_acc: 0.6755, loss_cls: 3.3145, loss: 3.3145 +2024-07-26 05:15:48,589 - pyskl - INFO - Epoch [111][3700/3746] lr: 1.578e-02, eta: 1 day, 9:07:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6633, loss_cls: 3.3730, loss: 3.3730 +2024-07-26 05:16:27,993 - pyskl - INFO - Saving checkpoint at 111 epochs +2024-07-26 05:18:20,498 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 05:18:21,168 - pyskl - INFO - +top1_acc 0.3512 +top5_acc 0.6087 +2024-07-26 05:18:21,168 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 05:18:21,215 - pyskl - INFO - +mean_acc 0.3509 +2024-07-26 05:18:21,221 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_110.pth was removed +2024-07-26 05:18:21,546 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_111.pth. +2024-07-26 05:18:21,547 - pyskl - INFO - Best top1_acc is 0.3512 at 111 epoch. +2024-07-26 05:18:21,564 - pyskl - INFO - Epoch(val) [111][309] top1_acc: 0.3512, top5_acc: 0.6087, mean_class_accuracy: 0.3509 +2024-07-26 05:22:19,266 - pyskl - INFO - Epoch [112][100/3746] lr: 1.575e-02, eta: 1 day, 9:06:33, time: 2.377, data_time: 1.396, memory: 15990, top1_acc: 0.4323, top5_acc: 0.6847, loss_cls: 3.2114, loss: 3.2114 +2024-07-26 05:23:41,223 - pyskl - INFO - Epoch [112][200/3746] lr: 1.573e-02, eta: 1 day, 9:05:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4238, top5_acc: 0.6825, loss_cls: 3.2369, loss: 3.2369 +2024-07-26 05:25:03,745 - pyskl - INFO - Epoch [112][300/3746] lr: 1.571e-02, eta: 1 day, 9:03:50, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6803, loss_cls: 3.2461, loss: 3.2461 +2024-07-26 05:26:25,473 - pyskl - INFO - Epoch [112][400/3746] lr: 1.569e-02, eta: 1 day, 9:02:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4263, top5_acc: 0.6859, loss_cls: 3.2189, loss: 3.2189 +2024-07-26 05:27:47,411 - pyskl - INFO - Epoch [112][500/3746] lr: 1.567e-02, eta: 1 day, 9:01:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4286, top5_acc: 0.6778, loss_cls: 3.2388, loss: 3.2388 +2024-07-26 05:29:09,330 - pyskl - INFO - Epoch [112][600/3746] lr: 1.565e-02, eta: 1 day, 8:59:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4059, top5_acc: 0.6669, loss_cls: 3.3010, loss: 3.3010 +2024-07-26 05:30:32,334 - pyskl - INFO - Epoch [112][700/3746] lr: 1.563e-02, eta: 1 day, 8:58:24, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6772, loss_cls: 3.2686, loss: 3.2686 +2024-07-26 05:31:54,135 - pyskl - INFO - Epoch [112][800/3746] lr: 1.561e-02, eta: 1 day, 8:57:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6784, loss_cls: 3.2501, loss: 3.2501 +2024-07-26 05:33:16,344 - pyskl - INFO - Epoch [112][900/3746] lr: 1.559e-02, eta: 1 day, 8:55:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6866, loss_cls: 3.2365, loss: 3.2365 +2024-07-26 05:34:38,429 - pyskl - INFO - Epoch [112][1000/3746] lr: 1.557e-02, eta: 1 day, 8:54:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6745, loss_cls: 3.2613, loss: 3.2613 +2024-07-26 05:35:59,932 - pyskl - INFO - Epoch [112][1100/3746] lr: 1.555e-02, eta: 1 day, 8:52:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6806, loss_cls: 3.2657, loss: 3.2657 +2024-07-26 05:37:21,658 - pyskl - INFO - Epoch [112][1200/3746] lr: 1.553e-02, eta: 1 day, 8:51:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6772, loss_cls: 3.2557, loss: 3.2557 +2024-07-26 05:38:43,010 - pyskl - INFO - Epoch [112][1300/3746] lr: 1.551e-02, eta: 1 day, 8:50:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4188, top5_acc: 0.6725, loss_cls: 3.2627, loss: 3.2627 +2024-07-26 05:40:04,641 - pyskl - INFO - Epoch [112][1400/3746] lr: 1.549e-02, eta: 1 day, 8:48:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4317, top5_acc: 0.6781, loss_cls: 3.2117, loss: 3.2117 +2024-07-26 05:41:26,410 - pyskl - INFO - Epoch [112][1500/3746] lr: 1.547e-02, eta: 1 day, 8:47:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6794, loss_cls: 3.2598, loss: 3.2598 +2024-07-26 05:42:47,745 - pyskl - INFO - Epoch [112][1600/3746] lr: 1.545e-02, eta: 1 day, 8:46:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6733, loss_cls: 3.3052, loss: 3.3052 +2024-07-26 05:44:08,840 - pyskl - INFO - Epoch [112][1700/3746] lr: 1.543e-02, eta: 1 day, 8:44:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4166, top5_acc: 0.6789, loss_cls: 3.2721, loss: 3.2721 +2024-07-26 05:45:30,424 - pyskl - INFO - Epoch [112][1800/3746] lr: 1.541e-02, eta: 1 day, 8:43:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6703, loss_cls: 3.2853, loss: 3.2853 +2024-07-26 05:46:51,945 - pyskl - INFO - Epoch [112][1900/3746] lr: 1.539e-02, eta: 1 day, 8:42:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4164, top5_acc: 0.6745, loss_cls: 3.2915, loss: 3.2915 +2024-07-26 05:48:13,903 - pyskl - INFO - Epoch [112][2000/3746] lr: 1.537e-02, eta: 1 day, 8:40:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4286, top5_acc: 0.6767, loss_cls: 3.2441, loss: 3.2441 +2024-07-26 05:49:36,394 - pyskl - INFO - Epoch [112][2100/3746] lr: 1.535e-02, eta: 1 day, 8:39:22, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6659, loss_cls: 3.3357, loss: 3.3357 +2024-07-26 05:50:59,460 - pyskl - INFO - Epoch [112][2200/3746] lr: 1.533e-02, eta: 1 day, 8:38:01, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6686, loss_cls: 3.3111, loss: 3.3111 +2024-07-26 05:52:21,338 - pyskl - INFO - Epoch [112][2300/3746] lr: 1.531e-02, eta: 1 day, 8:36:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6709, loss_cls: 3.3041, loss: 3.3041 +2024-07-26 05:53:43,229 - pyskl - INFO - Epoch [112][2400/3746] lr: 1.529e-02, eta: 1 day, 8:35:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4152, top5_acc: 0.6798, loss_cls: 3.2841, loss: 3.2841 +2024-07-26 05:55:04,889 - pyskl - INFO - Epoch [112][2500/3746] lr: 1.527e-02, eta: 1 day, 8:33:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4141, top5_acc: 0.6764, loss_cls: 3.2908, loss: 3.2908 +2024-07-26 05:56:26,816 - pyskl - INFO - Epoch [112][2600/3746] lr: 1.525e-02, eta: 1 day, 8:32:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4163, top5_acc: 0.6703, loss_cls: 3.3072, loss: 3.3072 +2024-07-26 05:57:47,891 - pyskl - INFO - Epoch [112][2700/3746] lr: 1.523e-02, eta: 1 day, 8:31:12, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4117, top5_acc: 0.6681, loss_cls: 3.3053, loss: 3.3053 +2024-07-26 05:59:08,825 - pyskl - INFO - Epoch [112][2800/3746] lr: 1.521e-02, eta: 1 day, 8:29:50, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6733, loss_cls: 3.2770, loss: 3.2770 +2024-07-26 06:00:30,330 - pyskl - INFO - Epoch [112][2900/3746] lr: 1.519e-02, eta: 1 day, 8:28:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6613, loss_cls: 3.3344, loss: 3.3344 +2024-07-26 06:01:51,472 - pyskl - INFO - Epoch [112][3000/3746] lr: 1.517e-02, eta: 1 day, 8:27:07, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6817, loss_cls: 3.2352, loss: 3.2352 +2024-07-26 06:03:12,630 - pyskl - INFO - Epoch [112][3100/3746] lr: 1.515e-02, eta: 1 day, 8:25:45, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4256, top5_acc: 0.6791, loss_cls: 3.2487, loss: 3.2487 +2024-07-26 06:04:34,484 - pyskl - INFO - Epoch [112][3200/3746] lr: 1.513e-02, eta: 1 day, 8:24:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6764, loss_cls: 3.2786, loss: 3.2786 +2024-07-26 06:05:56,097 - pyskl - INFO - Epoch [112][3300/3746] lr: 1.511e-02, eta: 1 day, 8:23:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4130, top5_acc: 0.6713, loss_cls: 3.2813, loss: 3.2813 +2024-07-26 06:07:18,014 - pyskl - INFO - Epoch [112][3400/3746] lr: 1.509e-02, eta: 1 day, 8:21:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6639, loss_cls: 3.3473, loss: 3.3473 +2024-07-26 06:08:39,168 - pyskl - INFO - Epoch [112][3500/3746] lr: 1.507e-02, eta: 1 day, 8:20:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6820, loss_cls: 3.2855, loss: 3.2855 +2024-07-26 06:10:01,068 - pyskl - INFO - Epoch [112][3600/3746] lr: 1.505e-02, eta: 1 day, 8:18:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4195, top5_acc: 0.6828, loss_cls: 3.2292, loss: 3.2292 +2024-07-26 06:11:22,493 - pyskl - INFO - Epoch [112][3700/3746] lr: 1.503e-02, eta: 1 day, 8:17:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4106, top5_acc: 0.6786, loss_cls: 3.2869, loss: 3.2869 +2024-07-26 06:12:01,838 - pyskl - INFO - Saving checkpoint at 112 epochs +2024-07-26 06:13:52,490 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 06:13:53,156 - pyskl - INFO - +top1_acc 0.3585 +top5_acc 0.6193 +2024-07-26 06:13:53,156 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 06:13:53,199 - pyskl - INFO - +mean_acc 0.3582 +2024-07-26 06:13:53,205 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_111.pth was removed +2024-07-26 06:13:53,442 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_112.pth. +2024-07-26 06:13:53,443 - pyskl - INFO - Best top1_acc is 0.3585 at 112 epoch. +2024-07-26 06:13:53,455 - pyskl - INFO - Epoch(val) [112][309] top1_acc: 0.3585, top5_acc: 0.6193, mean_class_accuracy: 0.3582 +2024-07-26 06:17:41,511 - pyskl - INFO - Epoch [113][100/3746] lr: 1.500e-02, eta: 1 day, 8:16:13, time: 2.280, data_time: 1.307, memory: 15990, top1_acc: 0.4330, top5_acc: 0.6883, loss_cls: 3.1677, loss: 3.1677 +2024-07-26 06:19:03,222 - pyskl - INFO - Epoch [113][200/3746] lr: 1.498e-02, eta: 1 day, 8:14:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4356, top5_acc: 0.6909, loss_cls: 3.1748, loss: 3.1748 +2024-07-26 06:20:25,181 - pyskl - INFO - Epoch [113][300/3746] lr: 1.496e-02, eta: 1 day, 8:13:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4219, top5_acc: 0.6836, loss_cls: 3.2089, loss: 3.2089 +2024-07-26 06:21:46,913 - pyskl - INFO - Epoch [113][400/3746] lr: 1.494e-02, eta: 1 day, 8:12:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4313, top5_acc: 0.6909, loss_cls: 3.1842, loss: 3.1842 +2024-07-26 06:23:08,513 - pyskl - INFO - Epoch [113][500/3746] lr: 1.492e-02, eta: 1 day, 8:10:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4158, top5_acc: 0.6817, loss_cls: 3.2616, loss: 3.2616 +2024-07-26 06:24:29,915 - pyskl - INFO - Epoch [113][600/3746] lr: 1.490e-02, eta: 1 day, 8:09:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4316, top5_acc: 0.6906, loss_cls: 3.2003, loss: 3.2003 +2024-07-26 06:25:53,042 - pyskl - INFO - Epoch [113][700/3746] lr: 1.488e-02, eta: 1 day, 8:08:03, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4270, top5_acc: 0.6783, loss_cls: 3.2027, loss: 3.2027 +2024-07-26 06:27:15,033 - pyskl - INFO - Epoch [113][800/3746] lr: 1.486e-02, eta: 1 day, 8:06:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6798, loss_cls: 3.2292, loss: 3.2292 +2024-07-26 06:28:36,934 - pyskl - INFO - Epoch [113][900/3746] lr: 1.484e-02, eta: 1 day, 8:05:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4273, top5_acc: 0.6873, loss_cls: 3.1981, loss: 3.1981 +2024-07-26 06:29:58,851 - pyskl - INFO - Epoch [113][1000/3746] lr: 1.482e-02, eta: 1 day, 8:03:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4273, top5_acc: 0.6875, loss_cls: 3.2098, loss: 3.2098 +2024-07-26 06:31:20,608 - pyskl - INFO - Epoch [113][1100/3746] lr: 1.480e-02, eta: 1 day, 8:02:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6811, loss_cls: 3.2680, loss: 3.2680 +2024-07-26 06:32:42,088 - pyskl - INFO - Epoch [113][1200/3746] lr: 1.478e-02, eta: 1 day, 8:01:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4219, top5_acc: 0.6811, loss_cls: 3.2503, loss: 3.2503 +2024-07-26 06:34:04,048 - pyskl - INFO - Epoch [113][1300/3746] lr: 1.476e-02, eta: 1 day, 7:59:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6952, loss_cls: 3.1837, loss: 3.1837 +2024-07-26 06:35:25,833 - pyskl - INFO - Epoch [113][1400/3746] lr: 1.474e-02, eta: 1 day, 7:58:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4202, top5_acc: 0.6877, loss_cls: 3.2214, loss: 3.2214 +2024-07-26 06:36:47,126 - pyskl - INFO - Epoch [113][1500/3746] lr: 1.472e-02, eta: 1 day, 7:57:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6677, loss_cls: 3.2962, loss: 3.2962 +2024-07-26 06:38:09,110 - pyskl - INFO - Epoch [113][1600/3746] lr: 1.470e-02, eta: 1 day, 7:55:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4245, top5_acc: 0.6889, loss_cls: 3.2192, loss: 3.2192 +2024-07-26 06:39:30,865 - pyskl - INFO - Epoch [113][1700/3746] lr: 1.468e-02, eta: 1 day, 7:54:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4273, top5_acc: 0.6845, loss_cls: 3.2265, loss: 3.2265 +2024-07-26 06:40:52,542 - pyskl - INFO - Epoch [113][1800/3746] lr: 1.466e-02, eta: 1 day, 7:53:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4183, top5_acc: 0.6737, loss_cls: 3.2485, loss: 3.2485 +2024-07-26 06:42:14,281 - pyskl - INFO - Epoch [113][1900/3746] lr: 1.464e-02, eta: 1 day, 7:51:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6763, loss_cls: 3.2735, loss: 3.2735 +2024-07-26 06:43:35,808 - pyskl - INFO - Epoch [113][2000/3746] lr: 1.462e-02, eta: 1 day, 7:50:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6813, loss_cls: 3.2561, loss: 3.2561 +2024-07-26 06:44:57,669 - pyskl - INFO - Epoch [113][2100/3746] lr: 1.460e-02, eta: 1 day, 7:49:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4195, top5_acc: 0.6787, loss_cls: 3.2472, loss: 3.2472 +2024-07-26 06:46:20,096 - pyskl - INFO - Epoch [113][2200/3746] lr: 1.458e-02, eta: 1 day, 7:47:39, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6781, loss_cls: 3.2586, loss: 3.2586 +2024-07-26 06:47:43,163 - pyskl - INFO - Epoch [113][2300/3746] lr: 1.456e-02, eta: 1 day, 7:46:18, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6777, loss_cls: 3.3164, loss: 3.3164 +2024-07-26 06:49:05,143 - pyskl - INFO - Epoch [113][2400/3746] lr: 1.454e-02, eta: 1 day, 7:44:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6684, loss_cls: 3.2852, loss: 3.2852 +2024-07-26 06:50:28,736 - pyskl - INFO - Epoch [113][2500/3746] lr: 1.452e-02, eta: 1 day, 7:43:35, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6737, loss_cls: 3.2963, loss: 3.2963 +2024-07-26 06:51:50,149 - pyskl - INFO - Epoch [113][2600/3746] lr: 1.450e-02, eta: 1 day, 7:42:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4269, top5_acc: 0.6816, loss_cls: 3.2278, loss: 3.2278 +2024-07-26 06:53:11,773 - pyskl - INFO - Epoch [113][2700/3746] lr: 1.448e-02, eta: 1 day, 7:40:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6806, loss_cls: 3.2480, loss: 3.2480 +2024-07-26 06:54:33,410 - pyskl - INFO - Epoch [113][2800/3746] lr: 1.446e-02, eta: 1 day, 7:39:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6725, loss_cls: 3.2741, loss: 3.2741 +2024-07-26 06:55:54,772 - pyskl - INFO - Epoch [113][2900/3746] lr: 1.444e-02, eta: 1 day, 7:38:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6678, loss_cls: 3.3109, loss: 3.3109 +2024-07-26 06:57:16,651 - pyskl - INFO - Epoch [113][3000/3746] lr: 1.442e-02, eta: 1 day, 7:36:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6731, loss_cls: 3.2717, loss: 3.2717 +2024-07-26 06:58:38,197 - pyskl - INFO - Epoch [113][3100/3746] lr: 1.440e-02, eta: 1 day, 7:35:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6759, loss_cls: 3.2760, loss: 3.2760 +2024-07-26 06:59:59,942 - pyskl - INFO - Epoch [113][3200/3746] lr: 1.438e-02, eta: 1 day, 7:34:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4281, top5_acc: 0.6781, loss_cls: 3.2616, loss: 3.2616 +2024-07-26 07:01:21,175 - pyskl - INFO - Epoch [113][3300/3746] lr: 1.436e-02, eta: 1 day, 7:32:42, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4116, top5_acc: 0.6608, loss_cls: 3.3200, loss: 3.3200 +2024-07-26 07:02:42,810 - pyskl - INFO - Epoch [113][3400/3746] lr: 1.434e-02, eta: 1 day, 7:31:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4188, top5_acc: 0.6706, loss_cls: 3.2643, loss: 3.2643 +2024-07-26 07:04:04,026 - pyskl - INFO - Epoch [113][3500/3746] lr: 1.432e-02, eta: 1 day, 7:29:58, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6863, loss_cls: 3.2308, loss: 3.2308 +2024-07-26 07:05:25,318 - pyskl - INFO - Epoch [113][3600/3746] lr: 1.431e-02, eta: 1 day, 7:28:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6713, loss_cls: 3.2783, loss: 3.2783 +2024-07-26 07:06:47,141 - pyskl - INFO - Epoch [113][3700/3746] lr: 1.429e-02, eta: 1 day, 7:27:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4106, top5_acc: 0.6769, loss_cls: 3.2696, loss: 3.2696 +2024-07-26 07:07:26,511 - pyskl - INFO - Saving checkpoint at 113 epochs +2024-07-26 07:09:17,136 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 07:09:17,803 - pyskl - INFO - +top1_acc 0.3523 +top5_acc 0.6141 +2024-07-26 07:09:17,803 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 07:09:17,844 - pyskl - INFO - +mean_acc 0.3522 +2024-07-26 07:09:17,855 - pyskl - INFO - Epoch(val) [113][309] top1_acc: 0.3523, top5_acc: 0.6141, mean_class_accuracy: 0.3522 +2024-07-26 07:13:05,347 - pyskl - INFO - Epoch [114][100/3746] lr: 1.426e-02, eta: 1 day, 7:25:51, time: 2.275, data_time: 1.301, memory: 15990, top1_acc: 0.4280, top5_acc: 0.6845, loss_cls: 3.2102, loss: 3.2102 +2024-07-26 07:14:26,854 - pyskl - INFO - Epoch [114][200/3746] lr: 1.424e-02, eta: 1 day, 7:24:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6928, loss_cls: 3.1737, loss: 3.1737 +2024-07-26 07:15:49,032 - pyskl - INFO - Epoch [114][300/3746] lr: 1.422e-02, eta: 1 day, 7:23:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4380, top5_acc: 0.6941, loss_cls: 3.1602, loss: 3.1602 +2024-07-26 07:17:10,365 - pyskl - INFO - Epoch [114][400/3746] lr: 1.420e-02, eta: 1 day, 7:21:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4345, top5_acc: 0.6959, loss_cls: 3.1668, loss: 3.1668 +2024-07-26 07:18:31,853 - pyskl - INFO - Epoch [114][500/3746] lr: 1.418e-02, eta: 1 day, 7:20:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4275, top5_acc: 0.6939, loss_cls: 3.1996, loss: 3.1996 +2024-07-26 07:19:53,732 - pyskl - INFO - Epoch [114][600/3746] lr: 1.416e-02, eta: 1 day, 7:19:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4228, top5_acc: 0.6892, loss_cls: 3.2064, loss: 3.2064 +2024-07-26 07:21:16,252 - pyskl - INFO - Epoch [114][700/3746] lr: 1.414e-02, eta: 1 day, 7:17:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4141, top5_acc: 0.6775, loss_cls: 3.2509, loss: 3.2509 +2024-07-26 07:22:37,965 - pyskl - INFO - Epoch [114][800/3746] lr: 1.412e-02, eta: 1 day, 7:16:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4389, top5_acc: 0.6880, loss_cls: 3.1672, loss: 3.1672 +2024-07-26 07:23:59,424 - pyskl - INFO - Epoch [114][900/3746] lr: 1.410e-02, eta: 1 day, 7:14:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4197, top5_acc: 0.6802, loss_cls: 3.2309, loss: 3.2309 +2024-07-26 07:25:20,818 - pyskl - INFO - Epoch [114][1000/3746] lr: 1.408e-02, eta: 1 day, 7:13:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4248, top5_acc: 0.6864, loss_cls: 3.2101, loss: 3.2101 +2024-07-26 07:26:42,495 - pyskl - INFO - Epoch [114][1100/3746] lr: 1.406e-02, eta: 1 day, 7:12:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6900, loss_cls: 3.1911, loss: 3.1911 +2024-07-26 07:28:03,984 - pyskl - INFO - Epoch [114][1200/3746] lr: 1.404e-02, eta: 1 day, 7:10:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4252, top5_acc: 0.6853, loss_cls: 3.2167, loss: 3.2167 +2024-07-26 07:29:25,520 - pyskl - INFO - Epoch [114][1300/3746] lr: 1.402e-02, eta: 1 day, 7:09:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4347, top5_acc: 0.6931, loss_cls: 3.2014, loss: 3.2014 +2024-07-26 07:30:46,761 - pyskl - INFO - Epoch [114][1400/3746] lr: 1.400e-02, eta: 1 day, 7:08:09, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6772, loss_cls: 3.2615, loss: 3.2615 +2024-07-26 07:32:08,776 - pyskl - INFO - Epoch [114][1500/3746] lr: 1.398e-02, eta: 1 day, 7:06:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4264, top5_acc: 0.6847, loss_cls: 3.2141, loss: 3.2141 +2024-07-26 07:33:30,197 - pyskl - INFO - Epoch [114][1600/3746] lr: 1.397e-02, eta: 1 day, 7:05:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4344, top5_acc: 0.6816, loss_cls: 3.2238, loss: 3.2238 +2024-07-26 07:34:51,880 - pyskl - INFO - Epoch [114][1700/3746] lr: 1.395e-02, eta: 1 day, 7:04:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4283, top5_acc: 0.6797, loss_cls: 3.1983, loss: 3.1983 +2024-07-26 07:36:13,656 - pyskl - INFO - Epoch [114][1800/3746] lr: 1.393e-02, eta: 1 day, 7:02:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4275, top5_acc: 0.6989, loss_cls: 3.1676, loss: 3.1676 +2024-07-26 07:37:35,159 - pyskl - INFO - Epoch [114][1900/3746] lr: 1.391e-02, eta: 1 day, 7:01:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4281, top5_acc: 0.6878, loss_cls: 3.2116, loss: 3.2116 +2024-07-26 07:38:56,652 - pyskl - INFO - Epoch [114][2000/3746] lr: 1.389e-02, eta: 1 day, 6:59:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4188, top5_acc: 0.6819, loss_cls: 3.2207, loss: 3.2207 +2024-07-26 07:40:18,146 - pyskl - INFO - Epoch [114][2100/3746] lr: 1.387e-02, eta: 1 day, 6:58:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4266, top5_acc: 0.6819, loss_cls: 3.2252, loss: 3.2252 +2024-07-26 07:41:40,228 - pyskl - INFO - Epoch [114][2200/3746] lr: 1.385e-02, eta: 1 day, 6:57:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4269, top5_acc: 0.6837, loss_cls: 3.2102, loss: 3.2102 +2024-07-26 07:43:02,997 - pyskl - INFO - Epoch [114][2300/3746] lr: 1.383e-02, eta: 1 day, 6:55:54, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6850, loss_cls: 3.1948, loss: 3.1948 +2024-07-26 07:44:25,295 - pyskl - INFO - Epoch [114][2400/3746] lr: 1.381e-02, eta: 1 day, 6:54:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6837, loss_cls: 3.2073, loss: 3.2073 +2024-07-26 07:45:47,084 - pyskl - INFO - Epoch [114][2500/3746] lr: 1.379e-02, eta: 1 day, 6:53:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4106, top5_acc: 0.6725, loss_cls: 3.2772, loss: 3.2772 +2024-07-26 07:47:09,464 - pyskl - INFO - Epoch [114][2600/3746] lr: 1.377e-02, eta: 1 day, 6:51:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6783, loss_cls: 3.2399, loss: 3.2399 +2024-07-26 07:48:31,124 - pyskl - INFO - Epoch [114][2700/3746] lr: 1.375e-02, eta: 1 day, 6:50:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6683, loss_cls: 3.3108, loss: 3.3108 +2024-07-26 07:49:52,313 - pyskl - INFO - Epoch [114][2800/3746] lr: 1.373e-02, eta: 1 day, 6:49:05, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6791, loss_cls: 3.2619, loss: 3.2619 +2024-07-26 07:51:14,136 - pyskl - INFO - Epoch [114][2900/3746] lr: 1.371e-02, eta: 1 day, 6:47:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4169, top5_acc: 0.6697, loss_cls: 3.2862, loss: 3.2862 +2024-07-26 07:52:35,899 - pyskl - INFO - Epoch [114][3000/3746] lr: 1.369e-02, eta: 1 day, 6:46:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6870, loss_cls: 3.2138, loss: 3.2138 +2024-07-26 07:53:57,584 - pyskl - INFO - Epoch [114][3100/3746] lr: 1.368e-02, eta: 1 day, 6:45:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6739, loss_cls: 3.2586, loss: 3.2586 +2024-07-26 07:55:19,372 - pyskl - INFO - Epoch [114][3200/3746] lr: 1.366e-02, eta: 1 day, 6:43:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6869, loss_cls: 3.2112, loss: 3.2112 +2024-07-26 07:56:41,105 - pyskl - INFO - Epoch [114][3300/3746] lr: 1.364e-02, eta: 1 day, 6:42:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4275, top5_acc: 0.6839, loss_cls: 3.2368, loss: 3.2368 +2024-07-26 07:58:02,871 - pyskl - INFO - Epoch [114][3400/3746] lr: 1.362e-02, eta: 1 day, 6:40:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6764, loss_cls: 3.2619, loss: 3.2619 +2024-07-26 07:59:24,179 - pyskl - INFO - Epoch [114][3500/3746] lr: 1.360e-02, eta: 1 day, 6:39:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4178, top5_acc: 0.6780, loss_cls: 3.2495, loss: 3.2495 +2024-07-26 08:00:45,707 - pyskl - INFO - Epoch [114][3600/3746] lr: 1.358e-02, eta: 1 day, 6:38:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6725, loss_cls: 3.2892, loss: 3.2892 +2024-07-26 08:02:07,123 - pyskl - INFO - Epoch [114][3700/3746] lr: 1.356e-02, eta: 1 day, 6:36:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6795, loss_cls: 3.2733, loss: 3.2733 +2024-07-26 08:02:46,413 - pyskl - INFO - Saving checkpoint at 114 epochs +2024-07-26 08:04:36,810 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 08:04:37,479 - pyskl - INFO - +top1_acc 0.3655 +top5_acc 0.6206 +2024-07-26 08:04:37,479 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 08:04:37,520 - pyskl - INFO - +mean_acc 0.3652 +2024-07-26 08:04:37,525 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_112.pth was removed +2024-07-26 08:04:37,759 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_114.pth. +2024-07-26 08:04:37,759 - pyskl - INFO - Best top1_acc is 0.3655 at 114 epoch. +2024-07-26 08:04:37,771 - pyskl - INFO - Epoch(val) [114][309] top1_acc: 0.3655, top5_acc: 0.6206, mean_class_accuracy: 0.3652 +2024-07-26 08:08:24,817 - pyskl - INFO - Epoch [115][100/3746] lr: 1.353e-02, eta: 1 day, 6:35:25, time: 2.270, data_time: 1.298, memory: 15990, top1_acc: 0.4447, top5_acc: 0.6992, loss_cls: 3.0999, loss: 3.0999 +2024-07-26 08:09:46,577 - pyskl - INFO - Epoch [115][200/3746] lr: 1.351e-02, eta: 1 day, 6:34:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6922, loss_cls: 3.1497, loss: 3.1497 +2024-07-26 08:11:08,273 - pyskl - INFO - Epoch [115][300/3746] lr: 1.349e-02, eta: 1 day, 6:32:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4270, top5_acc: 0.6923, loss_cls: 3.1922, loss: 3.1922 +2024-07-26 08:12:30,277 - pyskl - INFO - Epoch [115][400/3746] lr: 1.348e-02, eta: 1 day, 6:31:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6883, loss_cls: 3.2127, loss: 3.2127 +2024-07-26 08:13:51,789 - pyskl - INFO - Epoch [115][500/3746] lr: 1.346e-02, eta: 1 day, 6:29:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4477, top5_acc: 0.7016, loss_cls: 3.0953, loss: 3.0953 +2024-07-26 08:15:13,325 - pyskl - INFO - Epoch [115][600/3746] lr: 1.344e-02, eta: 1 day, 6:28:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4406, top5_acc: 0.6952, loss_cls: 3.1727, loss: 3.1727 +2024-07-26 08:16:35,369 - pyskl - INFO - Epoch [115][700/3746] lr: 1.342e-02, eta: 1 day, 6:27:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4380, top5_acc: 0.6959, loss_cls: 3.1725, loss: 3.1725 +2024-07-26 08:17:57,144 - pyskl - INFO - Epoch [115][800/3746] lr: 1.340e-02, eta: 1 day, 6:25:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6945, loss_cls: 3.1751, loss: 3.1751 +2024-07-26 08:19:19,156 - pyskl - INFO - Epoch [115][900/3746] lr: 1.338e-02, eta: 1 day, 6:24:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.6994, loss_cls: 3.1307, loss: 3.1307 +2024-07-26 08:20:41,035 - pyskl - INFO - Epoch [115][1000/3746] lr: 1.336e-02, eta: 1 day, 6:23:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4289, top5_acc: 0.6853, loss_cls: 3.2016, loss: 3.2016 +2024-07-26 08:22:02,010 - pyskl - INFO - Epoch [115][1100/3746] lr: 1.334e-02, eta: 1 day, 6:21:47, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.6834, loss_cls: 3.2009, loss: 3.2009 +2024-07-26 08:23:23,357 - pyskl - INFO - Epoch [115][1200/3746] lr: 1.332e-02, eta: 1 day, 6:20:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4387, top5_acc: 0.6878, loss_cls: 3.1691, loss: 3.1691 +2024-07-26 08:24:44,684 - pyskl - INFO - Epoch [115][1300/3746] lr: 1.330e-02, eta: 1 day, 6:19:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4183, top5_acc: 0.6839, loss_cls: 3.2219, loss: 3.2219 +2024-07-26 08:26:06,121 - pyskl - INFO - Epoch [115][1400/3746] lr: 1.328e-02, eta: 1 day, 6:17:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6834, loss_cls: 3.1955, loss: 3.1955 +2024-07-26 08:27:27,429 - pyskl - INFO - Epoch [115][1500/3746] lr: 1.327e-02, eta: 1 day, 6:16:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6756, loss_cls: 3.2442, loss: 3.2442 +2024-07-26 08:28:48,788 - pyskl - INFO - Epoch [115][1600/3746] lr: 1.325e-02, eta: 1 day, 6:14:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6911, loss_cls: 3.2013, loss: 3.2013 +2024-07-26 08:30:10,373 - pyskl - INFO - Epoch [115][1700/3746] lr: 1.323e-02, eta: 1 day, 6:13:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4338, top5_acc: 0.6930, loss_cls: 3.1652, loss: 3.1652 +2024-07-26 08:31:31,753 - pyskl - INFO - Epoch [115][1800/3746] lr: 1.321e-02, eta: 1 day, 6:12:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4238, top5_acc: 0.6833, loss_cls: 3.2296, loss: 3.2296 +2024-07-26 08:32:53,811 - pyskl - INFO - Epoch [115][1900/3746] lr: 1.319e-02, eta: 1 day, 6:10:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6861, loss_cls: 3.2080, loss: 3.2080 +2024-07-26 08:34:15,134 - pyskl - INFO - Epoch [115][2000/3746] lr: 1.317e-02, eta: 1 day, 6:09:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4339, top5_acc: 0.6825, loss_cls: 3.2085, loss: 3.2085 +2024-07-26 08:35:37,124 - pyskl - INFO - Epoch [115][2100/3746] lr: 1.315e-02, eta: 1 day, 6:08:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6823, loss_cls: 3.2699, loss: 3.2699 +2024-07-26 08:36:58,736 - pyskl - INFO - Epoch [115][2200/3746] lr: 1.313e-02, eta: 1 day, 6:06:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4292, top5_acc: 0.6856, loss_cls: 3.1796, loss: 3.1796 +2024-07-26 08:38:20,600 - pyskl - INFO - Epoch [115][2300/3746] lr: 1.311e-02, eta: 1 day, 6:05:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4283, top5_acc: 0.6809, loss_cls: 3.2011, loss: 3.2011 +2024-07-26 08:39:43,828 - pyskl - INFO - Epoch [115][2400/3746] lr: 1.310e-02, eta: 1 day, 6:04:05, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4205, top5_acc: 0.6855, loss_cls: 3.2251, loss: 3.2251 +2024-07-26 08:41:05,700 - pyskl - INFO - Epoch [115][2500/3746] lr: 1.308e-02, eta: 1 day, 6:02:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6789, loss_cls: 3.2507, loss: 3.2507 +2024-07-26 08:42:27,621 - pyskl - INFO - Epoch [115][2600/3746] lr: 1.306e-02, eta: 1 day, 6:01:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6802, loss_cls: 3.2206, loss: 3.2206 +2024-07-26 08:43:49,032 - pyskl - INFO - Epoch [115][2700/3746] lr: 1.304e-02, eta: 1 day, 6:00:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.6889, loss_cls: 3.1895, loss: 3.1895 +2024-07-26 08:45:10,402 - pyskl - INFO - Epoch [115][2800/3746] lr: 1.302e-02, eta: 1 day, 5:58:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4258, top5_acc: 0.6737, loss_cls: 3.2351, loss: 3.2351 +2024-07-26 08:46:31,799 - pyskl - INFO - Epoch [115][2900/3746] lr: 1.300e-02, eta: 1 day, 5:57:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6847, loss_cls: 3.1939, loss: 3.1939 +2024-07-26 08:47:53,728 - pyskl - INFO - Epoch [115][3000/3746] lr: 1.298e-02, eta: 1 day, 5:55:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6822, loss_cls: 3.2263, loss: 3.2263 +2024-07-26 08:49:15,390 - pyskl - INFO - Epoch [115][3100/3746] lr: 1.296e-02, eta: 1 day, 5:54:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6844, loss_cls: 3.2149, loss: 3.2149 +2024-07-26 08:50:36,912 - pyskl - INFO - Epoch [115][3200/3746] lr: 1.295e-02, eta: 1 day, 5:53:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4211, top5_acc: 0.6833, loss_cls: 3.2167, loss: 3.2167 +2024-07-26 08:51:58,200 - pyskl - INFO - Epoch [115][3300/3746] lr: 1.293e-02, eta: 1 day, 5:51:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4283, top5_acc: 0.6823, loss_cls: 3.2386, loss: 3.2386 +2024-07-26 08:53:19,661 - pyskl - INFO - Epoch [115][3400/3746] lr: 1.291e-02, eta: 1 day, 5:50:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6883, loss_cls: 3.2103, loss: 3.2103 +2024-07-26 08:54:41,328 - pyskl - INFO - Epoch [115][3500/3746] lr: 1.289e-02, eta: 1 day, 5:49:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4247, top5_acc: 0.6772, loss_cls: 3.2460, loss: 3.2460 +2024-07-26 08:56:03,005 - pyskl - INFO - Epoch [115][3600/3746] lr: 1.287e-02, eta: 1 day, 5:47:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4281, top5_acc: 0.6803, loss_cls: 3.2346, loss: 3.2346 +2024-07-26 08:57:24,971 - pyskl - INFO - Epoch [115][3700/3746] lr: 1.285e-02, eta: 1 day, 5:46:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4328, top5_acc: 0.6778, loss_cls: 3.2147, loss: 3.2147 +2024-07-26 08:58:04,365 - pyskl - INFO - Saving checkpoint at 115 epochs +2024-07-26 08:59:56,833 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 08:59:57,494 - pyskl - INFO - +top1_acc 0.3648 +top5_acc 0.6209 +2024-07-26 08:59:57,495 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 08:59:57,537 - pyskl - INFO - +mean_acc 0.3646 +2024-07-26 08:59:57,549 - pyskl - INFO - Epoch(val) [115][309] top1_acc: 0.3648, top5_acc: 0.6209, mean_class_accuracy: 0.3646 +2024-07-26 09:03:51,510 - pyskl - INFO - Epoch [116][100/3746] lr: 1.282e-02, eta: 1 day, 5:44:58, time: 2.340, data_time: 1.360, memory: 15990, top1_acc: 0.4525, top5_acc: 0.7081, loss_cls: 3.0719, loss: 3.0719 +2024-07-26 09:05:13,604 - pyskl - INFO - Epoch [116][200/3746] lr: 1.281e-02, eta: 1 day, 5:43:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4378, top5_acc: 0.6922, loss_cls: 3.1635, loss: 3.1635 +2024-07-26 09:06:35,063 - pyskl - INFO - Epoch [116][300/3746] lr: 1.279e-02, eta: 1 day, 5:42:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4381, top5_acc: 0.6952, loss_cls: 3.1590, loss: 3.1590 +2024-07-26 09:07:56,941 - pyskl - INFO - Epoch [116][400/3746] lr: 1.277e-02, eta: 1 day, 5:40:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4317, top5_acc: 0.6902, loss_cls: 3.1647, loss: 3.1647 +2024-07-26 09:09:18,311 - pyskl - INFO - Epoch [116][500/3746] lr: 1.275e-02, eta: 1 day, 5:39:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4350, top5_acc: 0.6894, loss_cls: 3.1648, loss: 3.1648 +2024-07-26 09:10:39,885 - pyskl - INFO - Epoch [116][600/3746] lr: 1.273e-02, eta: 1 day, 5:38:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.6984, loss_cls: 3.1258, loss: 3.1258 +2024-07-26 09:12:02,226 - pyskl - INFO - Epoch [116][700/3746] lr: 1.271e-02, eta: 1 day, 5:36:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4359, top5_acc: 0.6942, loss_cls: 3.1594, loss: 3.1594 +2024-07-26 09:13:24,012 - pyskl - INFO - Epoch [116][800/3746] lr: 1.269e-02, eta: 1 day, 5:35:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4306, top5_acc: 0.6981, loss_cls: 3.1616, loss: 3.1616 +2024-07-26 09:14:46,093 - pyskl - INFO - Epoch [116][900/3746] lr: 1.268e-02, eta: 1 day, 5:34:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4372, top5_acc: 0.6953, loss_cls: 3.1454, loss: 3.1454 +2024-07-26 09:16:07,681 - pyskl - INFO - Epoch [116][1000/3746] lr: 1.266e-02, eta: 1 day, 5:32:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4359, top5_acc: 0.6939, loss_cls: 3.1755, loss: 3.1755 +2024-07-26 09:17:29,335 - pyskl - INFO - Epoch [116][1100/3746] lr: 1.264e-02, eta: 1 day, 5:31:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4428, top5_acc: 0.7077, loss_cls: 3.1119, loss: 3.1119 +2024-07-26 09:18:50,678 - pyskl - INFO - Epoch [116][1200/3746] lr: 1.262e-02, eta: 1 day, 5:29:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4302, top5_acc: 0.6881, loss_cls: 3.1905, loss: 3.1905 +2024-07-26 09:20:12,316 - pyskl - INFO - Epoch [116][1300/3746] lr: 1.260e-02, eta: 1 day, 5:28:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4320, top5_acc: 0.6986, loss_cls: 3.1471, loss: 3.1471 +2024-07-26 09:21:33,804 - pyskl - INFO - Epoch [116][1400/3746] lr: 1.258e-02, eta: 1 day, 5:27:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4327, top5_acc: 0.6834, loss_cls: 3.1903, loss: 3.1903 +2024-07-26 09:22:55,729 - pyskl - INFO - Epoch [116][1500/3746] lr: 1.256e-02, eta: 1 day, 5:25:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4378, top5_acc: 0.6939, loss_cls: 3.1452, loss: 3.1452 +2024-07-26 09:24:17,396 - pyskl - INFO - Epoch [116][1600/3746] lr: 1.255e-02, eta: 1 day, 5:24:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6823, loss_cls: 3.2036, loss: 3.2036 +2024-07-26 09:25:39,528 - pyskl - INFO - Epoch [116][1700/3746] lr: 1.253e-02, eta: 1 day, 5:23:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4345, top5_acc: 0.6948, loss_cls: 3.1523, loss: 3.1523 +2024-07-26 09:27:01,172 - pyskl - INFO - Epoch [116][1800/3746] lr: 1.251e-02, eta: 1 day, 5:21:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6903, loss_cls: 3.1814, loss: 3.1814 +2024-07-26 09:28:22,620 - pyskl - INFO - Epoch [116][1900/3746] lr: 1.249e-02, eta: 1 day, 5:20:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4302, top5_acc: 0.6930, loss_cls: 3.2013, loss: 3.2013 +2024-07-26 09:29:44,260 - pyskl - INFO - Epoch [116][2000/3746] lr: 1.247e-02, eta: 1 day, 5:19:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4383, top5_acc: 0.6934, loss_cls: 3.1523, loss: 3.1523 +2024-07-26 09:31:06,376 - pyskl - INFO - Epoch [116][2100/3746] lr: 1.245e-02, eta: 1 day, 5:17:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.6873, loss_cls: 3.1650, loss: 3.1650 +2024-07-26 09:32:28,685 - pyskl - INFO - Epoch [116][2200/3746] lr: 1.243e-02, eta: 1 day, 5:16:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6852, loss_cls: 3.2250, loss: 3.2250 +2024-07-26 09:33:51,293 - pyskl - INFO - Epoch [116][2300/3746] lr: 1.242e-02, eta: 1 day, 5:15:00, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4314, top5_acc: 0.6898, loss_cls: 3.2016, loss: 3.2016 +2024-07-26 09:35:14,523 - pyskl - INFO - Epoch [116][2400/3746] lr: 1.240e-02, eta: 1 day, 5:13:38, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6813, loss_cls: 3.2059, loss: 3.2059 +2024-07-26 09:36:36,596 - pyskl - INFO - Epoch [116][2500/3746] lr: 1.238e-02, eta: 1 day, 5:12:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4309, top5_acc: 0.6987, loss_cls: 3.1693, loss: 3.1693 +2024-07-26 09:37:58,741 - pyskl - INFO - Epoch [116][2600/3746] lr: 1.236e-02, eta: 1 day, 5:10:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4416, top5_acc: 0.6858, loss_cls: 3.1532, loss: 3.1532 +2024-07-26 09:39:21,007 - pyskl - INFO - Epoch [116][2700/3746] lr: 1.234e-02, eta: 1 day, 5:09:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6930, loss_cls: 3.1628, loss: 3.1628 +2024-07-26 09:40:42,826 - pyskl - INFO - Epoch [116][2800/3746] lr: 1.232e-02, eta: 1 day, 5:08:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4338, top5_acc: 0.6886, loss_cls: 3.2005, loss: 3.2005 +2024-07-26 09:42:04,278 - pyskl - INFO - Epoch [116][2900/3746] lr: 1.231e-02, eta: 1 day, 5:06:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6897, loss_cls: 3.1939, loss: 3.1939 +2024-07-26 09:43:25,966 - pyskl - INFO - Epoch [116][3000/3746] lr: 1.229e-02, eta: 1 day, 5:05:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4405, top5_acc: 0.6950, loss_cls: 3.1464, loss: 3.1464 +2024-07-26 09:44:47,492 - pyskl - INFO - Epoch [116][3100/3746] lr: 1.227e-02, eta: 1 day, 5:04:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6886, loss_cls: 3.1995, loss: 3.1995 +2024-07-26 09:46:09,087 - pyskl - INFO - Epoch [116][3200/3746] lr: 1.225e-02, eta: 1 day, 5:02:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4269, top5_acc: 0.6844, loss_cls: 3.2074, loss: 3.2074 +2024-07-26 09:47:30,444 - pyskl - INFO - Epoch [116][3300/3746] lr: 1.223e-02, eta: 1 day, 5:01:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4275, top5_acc: 0.6811, loss_cls: 3.2365, loss: 3.2365 +2024-07-26 09:48:52,704 - pyskl - INFO - Epoch [116][3400/3746] lr: 1.221e-02, eta: 1 day, 5:00:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4298, top5_acc: 0.6778, loss_cls: 3.2239, loss: 3.2239 +2024-07-26 09:50:14,321 - pyskl - INFO - Epoch [116][3500/3746] lr: 1.220e-02, eta: 1 day, 4:58:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6872, loss_cls: 3.2145, loss: 3.2145 +2024-07-26 09:51:35,867 - pyskl - INFO - Epoch [116][3600/3746] lr: 1.218e-02, eta: 1 day, 4:57:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4252, top5_acc: 0.6806, loss_cls: 3.2243, loss: 3.2243 +2024-07-26 09:52:57,923 - pyskl - INFO - Epoch [116][3700/3746] lr: 1.216e-02, eta: 1 day, 4:55:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4297, top5_acc: 0.6811, loss_cls: 3.2015, loss: 3.2015 +2024-07-26 09:53:37,415 - pyskl - INFO - Saving checkpoint at 116 epochs +2024-07-26 09:55:31,160 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 09:55:31,836 - pyskl - INFO - +top1_acc 0.3684 +top5_acc 0.6212 +2024-07-26 09:55:31,837 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 09:55:31,879 - pyskl - INFO - +mean_acc 0.3682 +2024-07-26 09:55:31,883 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_114.pth was removed +2024-07-26 09:55:32,123 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_116.pth. +2024-07-26 09:55:32,123 - pyskl - INFO - Best top1_acc is 0.3684 at 116 epoch. +2024-07-26 09:55:32,138 - pyskl - INFO - Epoch(val) [116][309] top1_acc: 0.3684, top5_acc: 0.6212, mean_class_accuracy: 0.3682 +2024-07-26 09:59:27,099 - pyskl - INFO - Epoch [117][100/3746] lr: 1.213e-02, eta: 1 day, 4:54:30, time: 2.350, data_time: 1.373, memory: 15990, top1_acc: 0.4491, top5_acc: 0.7133, loss_cls: 3.0451, loss: 3.0451 +2024-07-26 10:00:48,935 - pyskl - INFO - Epoch [117][200/3746] lr: 1.211e-02, eta: 1 day, 4:53:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4419, top5_acc: 0.6948, loss_cls: 3.1365, loss: 3.1365 +2024-07-26 10:02:10,204 - pyskl - INFO - Epoch [117][300/3746] lr: 1.210e-02, eta: 1 day, 4:51:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4511, top5_acc: 0.7067, loss_cls: 3.0560, loss: 3.0560 +2024-07-26 10:03:32,200 - pyskl - INFO - Epoch [117][400/3746] lr: 1.208e-02, eta: 1 day, 4:50:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4320, top5_acc: 0.6991, loss_cls: 3.1169, loss: 3.1169 +2024-07-26 10:04:53,772 - pyskl - INFO - Epoch [117][500/3746] lr: 1.206e-02, eta: 1 day, 4:49:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4458, top5_acc: 0.6950, loss_cls: 3.1125, loss: 3.1125 +2024-07-26 10:06:15,698 - pyskl - INFO - Epoch [117][600/3746] lr: 1.204e-02, eta: 1 day, 4:47:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4469, top5_acc: 0.7052, loss_cls: 3.1014, loss: 3.1014 +2024-07-26 10:07:37,852 - pyskl - INFO - Epoch [117][700/3746] lr: 1.202e-02, eta: 1 day, 4:46:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4437, top5_acc: 0.6959, loss_cls: 3.1359, loss: 3.1359 +2024-07-26 10:08:59,642 - pyskl - INFO - Epoch [117][800/3746] lr: 1.200e-02, eta: 1 day, 4:44:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4314, top5_acc: 0.6920, loss_cls: 3.1501, loss: 3.1501 +2024-07-26 10:10:22,256 - pyskl - INFO - Epoch [117][900/3746] lr: 1.199e-02, eta: 1 day, 4:43:36, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4398, top5_acc: 0.6945, loss_cls: 3.1350, loss: 3.1350 +2024-07-26 10:11:43,784 - pyskl - INFO - Epoch [117][1000/3746] lr: 1.197e-02, eta: 1 day, 4:42:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4303, top5_acc: 0.6920, loss_cls: 3.1617, loss: 3.1617 +2024-07-26 10:13:05,704 - pyskl - INFO - Epoch [117][1100/3746] lr: 1.195e-02, eta: 1 day, 4:40:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4416, top5_acc: 0.6973, loss_cls: 3.1374, loss: 3.1374 +2024-07-26 10:14:27,811 - pyskl - INFO - Epoch [117][1200/3746] lr: 1.193e-02, eta: 1 day, 4:39:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4506, top5_acc: 0.7072, loss_cls: 3.0719, loss: 3.0719 +2024-07-26 10:15:49,549 - pyskl - INFO - Epoch [117][1300/3746] lr: 1.191e-02, eta: 1 day, 4:38:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4398, top5_acc: 0.6939, loss_cls: 3.1256, loss: 3.1256 +2024-07-26 10:17:12,189 - pyskl - INFO - Epoch [117][1400/3746] lr: 1.190e-02, eta: 1 day, 4:36:48, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4436, top5_acc: 0.6969, loss_cls: 3.1356, loss: 3.1356 +2024-07-26 10:18:33,690 - pyskl - INFO - Epoch [117][1500/3746] lr: 1.188e-02, eta: 1 day, 4:35:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4406, top5_acc: 0.6961, loss_cls: 3.1519, loss: 3.1519 +2024-07-26 10:19:55,049 - pyskl - INFO - Epoch [117][1600/3746] lr: 1.186e-02, eta: 1 day, 4:34:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6948, loss_cls: 3.1781, loss: 3.1781 +2024-07-26 10:21:16,562 - pyskl - INFO - Epoch [117][1700/3746] lr: 1.184e-02, eta: 1 day, 4:32:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.6975, loss_cls: 3.1208, loss: 3.1208 +2024-07-26 10:22:38,188 - pyskl - INFO - Epoch [117][1800/3746] lr: 1.182e-02, eta: 1 day, 4:31:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4459, top5_acc: 0.6922, loss_cls: 3.1441, loss: 3.1441 +2024-07-26 10:24:00,106 - pyskl - INFO - Epoch [117][1900/3746] lr: 1.181e-02, eta: 1 day, 4:29:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4447, top5_acc: 0.7044, loss_cls: 3.1171, loss: 3.1171 +2024-07-26 10:25:21,781 - pyskl - INFO - Epoch [117][2000/3746] lr: 1.179e-02, eta: 1 day, 4:28:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4297, top5_acc: 0.7006, loss_cls: 3.1469, loss: 3.1469 +2024-07-26 10:26:43,144 - pyskl - INFO - Epoch [117][2100/3746] lr: 1.177e-02, eta: 1 day, 4:27:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6842, loss_cls: 3.2193, loss: 3.2193 +2024-07-26 10:28:05,036 - pyskl - INFO - Epoch [117][2200/3746] lr: 1.175e-02, eta: 1 day, 4:25:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.7005, loss_cls: 3.1096, loss: 3.1096 +2024-07-26 10:29:27,130 - pyskl - INFO - Epoch [117][2300/3746] lr: 1.173e-02, eta: 1 day, 4:24:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6920, loss_cls: 3.1909, loss: 3.1909 +2024-07-26 10:30:49,630 - pyskl - INFO - Epoch [117][2400/3746] lr: 1.172e-02, eta: 1 day, 4:23:10, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4313, top5_acc: 0.6786, loss_cls: 3.1948, loss: 3.1948 +2024-07-26 10:32:11,476 - pyskl - INFO - Epoch [117][2500/3746] lr: 1.170e-02, eta: 1 day, 4:21:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4348, top5_acc: 0.6944, loss_cls: 3.1764, loss: 3.1764 +2024-07-26 10:33:33,532 - pyskl - INFO - Epoch [117][2600/3746] lr: 1.168e-02, eta: 1 day, 4:20:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4291, top5_acc: 0.6892, loss_cls: 3.1800, loss: 3.1800 +2024-07-26 10:34:55,284 - pyskl - INFO - Epoch [117][2700/3746] lr: 1.166e-02, eta: 1 day, 4:19:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4330, top5_acc: 0.6845, loss_cls: 3.1834, loss: 3.1834 +2024-07-26 10:36:16,933 - pyskl - INFO - Epoch [117][2800/3746] lr: 1.164e-02, eta: 1 day, 4:17:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4341, top5_acc: 0.6894, loss_cls: 3.1710, loss: 3.1710 +2024-07-26 10:37:38,418 - pyskl - INFO - Epoch [117][2900/3746] lr: 1.163e-02, eta: 1 day, 4:16:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4316, top5_acc: 0.6927, loss_cls: 3.1521, loss: 3.1521 +2024-07-26 10:39:00,150 - pyskl - INFO - Epoch [117][3000/3746] lr: 1.161e-02, eta: 1 day, 4:14:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6920, loss_cls: 3.1779, loss: 3.1779 +2024-07-26 10:40:21,955 - pyskl - INFO - Epoch [117][3100/3746] lr: 1.159e-02, eta: 1 day, 4:13:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4392, top5_acc: 0.6956, loss_cls: 3.1315, loss: 3.1315 +2024-07-26 10:41:43,631 - pyskl - INFO - Epoch [117][3200/3746] lr: 1.157e-02, eta: 1 day, 4:12:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6852, loss_cls: 3.2064, loss: 3.2064 +2024-07-26 10:43:04,855 - pyskl - INFO - Epoch [117][3300/3746] lr: 1.155e-02, eta: 1 day, 4:10:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.7025, loss_cls: 3.1119, loss: 3.1119 +2024-07-26 10:44:26,437 - pyskl - INFO - Epoch [117][3400/3746] lr: 1.154e-02, eta: 1 day, 4:09:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4345, top5_acc: 0.6936, loss_cls: 3.1578, loss: 3.1578 +2024-07-26 10:45:48,461 - pyskl - INFO - Epoch [117][3500/3746] lr: 1.152e-02, eta: 1 day, 4:08:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6798, loss_cls: 3.2515, loss: 3.2515 +2024-07-26 10:47:10,298 - pyskl - INFO - Epoch [117][3600/3746] lr: 1.150e-02, eta: 1 day, 4:06:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6866, loss_cls: 3.2077, loss: 3.2077 +2024-07-26 10:48:32,184 - pyskl - INFO - Epoch [117][3700/3746] lr: 1.148e-02, eta: 1 day, 4:05:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4280, top5_acc: 0.6889, loss_cls: 3.1749, loss: 3.1749 +2024-07-26 10:49:11,758 - pyskl - INFO - Saving checkpoint at 117 epochs +2024-07-26 10:51:03,288 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 10:51:03,966 - pyskl - INFO - +top1_acc 0.3707 +top5_acc 0.6274 +2024-07-26 10:51:03,966 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 10:51:04,012 - pyskl - INFO - +mean_acc 0.3704 +2024-07-26 10:51:04,016 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_116.pth was removed +2024-07-26 10:51:04,255 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_117.pth. +2024-07-26 10:51:04,256 - pyskl - INFO - Best top1_acc is 0.3707 at 117 epoch. +2024-07-26 10:51:04,272 - pyskl - INFO - Epoch(val) [117][309] top1_acc: 0.3707, top5_acc: 0.6274, mean_class_accuracy: 0.3704 +2024-07-26 10:55:00,212 - pyskl - INFO - Epoch [118][100/3746] lr: 1.146e-02, eta: 1 day, 4:04:00, time: 2.359, data_time: 1.377, memory: 15990, top1_acc: 0.4561, top5_acc: 0.7144, loss_cls: 3.0325, loss: 3.0325 +2024-07-26 10:56:22,441 - pyskl - INFO - Epoch [118][200/3746] lr: 1.144e-02, eta: 1 day, 4:02:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4592, top5_acc: 0.7137, loss_cls: 3.0370, loss: 3.0370 +2024-07-26 10:57:45,247 - pyskl - INFO - Epoch [118][300/3746] lr: 1.142e-02, eta: 1 day, 4:01:17, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7144, loss_cls: 3.0518, loss: 3.0518 +2024-07-26 10:59:07,791 - pyskl - INFO - Epoch [118][400/3746] lr: 1.140e-02, eta: 1 day, 3:59:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4461, top5_acc: 0.7056, loss_cls: 3.0823, loss: 3.0823 +2024-07-26 11:00:30,467 - pyskl - INFO - Epoch [118][500/3746] lr: 1.139e-02, eta: 1 day, 3:58:34, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4500, top5_acc: 0.7037, loss_cls: 3.1093, loss: 3.1093 +2024-07-26 11:01:52,604 - pyskl - INFO - Epoch [118][600/3746] lr: 1.137e-02, eta: 1 day, 3:57:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4406, top5_acc: 0.7002, loss_cls: 3.1325, loss: 3.1325 +2024-07-26 11:03:15,823 - pyskl - INFO - Epoch [118][700/3746] lr: 1.135e-02, eta: 1 day, 3:55:51, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4414, top5_acc: 0.7095, loss_cls: 3.0790, loss: 3.0790 +2024-07-26 11:04:38,777 - pyskl - INFO - Epoch [118][800/3746] lr: 1.133e-02, eta: 1 day, 3:54:29, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4587, top5_acc: 0.7080, loss_cls: 3.0528, loss: 3.0528 +2024-07-26 11:06:01,267 - pyskl - INFO - Epoch [118][900/3746] lr: 1.131e-02, eta: 1 day, 3:53:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4330, top5_acc: 0.7041, loss_cls: 3.1206, loss: 3.1206 +2024-07-26 11:07:24,252 - pyskl - INFO - Epoch [118][1000/3746] lr: 1.130e-02, eta: 1 day, 3:51:46, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4511, top5_acc: 0.7064, loss_cls: 3.0432, loss: 3.0432 +2024-07-26 11:08:46,946 - pyskl - INFO - Epoch [118][1100/3746] lr: 1.128e-02, eta: 1 day, 3:50:25, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4527, top5_acc: 0.7055, loss_cls: 3.0826, loss: 3.0826 +2024-07-26 11:10:09,867 - pyskl - INFO - Epoch [118][1200/3746] lr: 1.126e-02, eta: 1 day, 3:49:03, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4439, top5_acc: 0.7036, loss_cls: 3.1034, loss: 3.1034 +2024-07-26 11:11:32,503 - pyskl - INFO - Epoch [118][1300/3746] lr: 1.124e-02, eta: 1 day, 3:47:41, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4259, top5_acc: 0.6841, loss_cls: 3.2043, loss: 3.2043 +2024-07-26 11:12:54,482 - pyskl - INFO - Epoch [118][1400/3746] lr: 1.123e-02, eta: 1 day, 3:46:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7037, loss_cls: 3.1028, loss: 3.1028 +2024-07-26 11:14:16,544 - pyskl - INFO - Epoch [118][1500/3746] lr: 1.121e-02, eta: 1 day, 3:44:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4389, top5_acc: 0.6959, loss_cls: 3.1324, loss: 3.1324 +2024-07-26 11:15:38,686 - pyskl - INFO - Epoch [118][1600/3746] lr: 1.119e-02, eta: 1 day, 3:43:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4269, top5_acc: 0.6806, loss_cls: 3.2012, loss: 3.2012 +2024-07-26 11:17:00,969 - pyskl - INFO - Epoch [118][1700/3746] lr: 1.117e-02, eta: 1 day, 3:42:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4473, top5_acc: 0.6998, loss_cls: 3.0959, loss: 3.0959 +2024-07-26 11:18:23,807 - pyskl - INFO - Epoch [118][1800/3746] lr: 1.116e-02, eta: 1 day, 3:40:53, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4450, top5_acc: 0.6964, loss_cls: 3.1306, loss: 3.1306 +2024-07-26 11:19:45,761 - pyskl - INFO - Epoch [118][1900/3746] lr: 1.114e-02, eta: 1 day, 3:39:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4313, top5_acc: 0.6898, loss_cls: 3.1540, loss: 3.1540 +2024-07-26 11:21:07,380 - pyskl - INFO - Epoch [118][2000/3746] lr: 1.112e-02, eta: 1 day, 3:38:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4264, top5_acc: 0.6873, loss_cls: 3.2020, loss: 3.2020 +2024-07-26 11:22:29,656 - pyskl - INFO - Epoch [118][2100/3746] lr: 1.110e-02, eta: 1 day, 3:36:48, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4380, top5_acc: 0.6883, loss_cls: 3.1682, loss: 3.1682 +2024-07-26 11:23:51,940 - pyskl - INFO - Epoch [118][2200/3746] lr: 1.109e-02, eta: 1 day, 3:35:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7063, loss_cls: 3.0942, loss: 3.0942 +2024-07-26 11:25:15,285 - pyskl - INFO - Epoch [118][2300/3746] lr: 1.107e-02, eta: 1 day, 3:34:05, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4405, top5_acc: 0.6883, loss_cls: 3.1561, loss: 3.1561 +2024-07-26 11:26:39,156 - pyskl - INFO - Epoch [118][2400/3746] lr: 1.105e-02, eta: 1 day, 3:32:43, time: 0.839, data_time: 0.001, memory: 15990, top1_acc: 0.4342, top5_acc: 0.6916, loss_cls: 3.1710, loss: 3.1710 +2024-07-26 11:28:01,777 - pyskl - INFO - Epoch [118][2500/3746] lr: 1.103e-02, eta: 1 day, 3:31:22, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4458, top5_acc: 0.6980, loss_cls: 3.1293, loss: 3.1293 +2024-07-26 11:29:24,318 - pyskl - INFO - Epoch [118][2600/3746] lr: 1.102e-02, eta: 1 day, 3:30:00, time: 0.825, data_time: 0.001, memory: 15990, top1_acc: 0.4434, top5_acc: 0.6987, loss_cls: 3.1456, loss: 3.1456 +2024-07-26 11:30:47,498 - pyskl - INFO - Epoch [118][2700/3746] lr: 1.100e-02, eta: 1 day, 3:28:39, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4309, top5_acc: 0.7000, loss_cls: 3.1604, loss: 3.1604 +2024-07-26 11:32:10,176 - pyskl - INFO - Epoch [118][2800/3746] lr: 1.098e-02, eta: 1 day, 3:27:17, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4330, top5_acc: 0.6975, loss_cls: 3.1498, loss: 3.1498 +2024-07-26 11:33:33,339 - pyskl - INFO - Epoch [118][2900/3746] lr: 1.096e-02, eta: 1 day, 3:25:56, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4389, top5_acc: 0.6917, loss_cls: 3.1532, loss: 3.1532 +2024-07-26 11:34:55,715 - pyskl - INFO - Epoch [118][3000/3746] lr: 1.095e-02, eta: 1 day, 3:24:34, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4419, top5_acc: 0.7039, loss_cls: 3.1338, loss: 3.1338 +2024-07-26 11:36:18,294 - pyskl - INFO - Epoch [118][3100/3746] lr: 1.093e-02, eta: 1 day, 3:23:13, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4405, top5_acc: 0.6986, loss_cls: 3.1133, loss: 3.1133 +2024-07-26 11:37:40,770 - pyskl - INFO - Epoch [118][3200/3746] lr: 1.091e-02, eta: 1 day, 3:21:51, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4341, top5_acc: 0.6942, loss_cls: 3.1622, loss: 3.1622 +2024-07-26 11:39:03,521 - pyskl - INFO - Epoch [118][3300/3746] lr: 1.089e-02, eta: 1 day, 3:20:29, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.6959, loss_cls: 3.1244, loss: 3.1244 +2024-07-26 11:40:26,104 - pyskl - INFO - Epoch [118][3400/3746] lr: 1.088e-02, eta: 1 day, 3:19:08, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4330, top5_acc: 0.6958, loss_cls: 3.1388, loss: 3.1388 +2024-07-26 11:41:48,614 - pyskl - INFO - Epoch [118][3500/3746] lr: 1.086e-02, eta: 1 day, 3:17:46, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4427, top5_acc: 0.6969, loss_cls: 3.1184, loss: 3.1184 +2024-07-26 11:43:10,917 - pyskl - INFO - Epoch [118][3600/3746] lr: 1.084e-02, eta: 1 day, 3:16:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4341, top5_acc: 0.6986, loss_cls: 3.1456, loss: 3.1456 +2024-07-26 11:44:32,697 - pyskl - INFO - Epoch [118][3700/3746] lr: 1.082e-02, eta: 1 day, 3:15:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.6891, loss_cls: 3.1367, loss: 3.1367 +2024-07-26 11:45:12,154 - pyskl - INFO - Saving checkpoint at 118 epochs +2024-07-26 11:47:04,916 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 11:47:05,590 - pyskl - INFO - +top1_acc 0.3724 +top5_acc 0.6266 +2024-07-26 11:47:05,590 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 11:47:05,633 - pyskl - INFO - +mean_acc 0.3721 +2024-07-26 11:47:05,639 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_117.pth was removed +2024-07-26 11:47:05,885 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2024-07-26 11:47:05,885 - pyskl - INFO - Best top1_acc is 0.3724 at 118 epoch. +2024-07-26 11:47:05,898 - pyskl - INFO - Epoch(val) [118][309] top1_acc: 0.3724, top5_acc: 0.6266, mean_class_accuracy: 0.3721 +2024-07-26 11:51:00,126 - pyskl - INFO - Epoch [119][100/3746] lr: 1.080e-02, eta: 1 day, 3:13:34, time: 2.342, data_time: 1.363, memory: 15990, top1_acc: 0.4584, top5_acc: 0.7159, loss_cls: 3.0320, loss: 3.0320 +2024-07-26 11:52:22,737 - pyskl - INFO - Epoch [119][200/3746] lr: 1.078e-02, eta: 1 day, 3:12:13, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4536, top5_acc: 0.7103, loss_cls: 3.0431, loss: 3.0431 +2024-07-26 11:53:45,095 - pyskl - INFO - Epoch [119][300/3746] lr: 1.076e-02, eta: 1 day, 3:10:51, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4500, top5_acc: 0.7066, loss_cls: 3.0656, loss: 3.0656 +2024-07-26 11:55:07,300 - pyskl - INFO - Epoch [119][400/3746] lr: 1.075e-02, eta: 1 day, 3:09:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7031, loss_cls: 3.0876, loss: 3.0876 +2024-07-26 11:56:28,837 - pyskl - INFO - Epoch [119][500/3746] lr: 1.073e-02, eta: 1 day, 3:08:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4502, top5_acc: 0.7050, loss_cls: 3.0578, loss: 3.0578 +2024-07-26 11:57:50,812 - pyskl - INFO - Epoch [119][600/3746] lr: 1.071e-02, eta: 1 day, 3:06:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4519, top5_acc: 0.7039, loss_cls: 3.0509, loss: 3.0509 +2024-07-26 11:59:12,537 - pyskl - INFO - Epoch [119][700/3746] lr: 1.069e-02, eta: 1 day, 3:05:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4492, top5_acc: 0.7084, loss_cls: 3.0548, loss: 3.0548 +2024-07-26 12:00:34,807 - pyskl - INFO - Epoch [119][800/3746] lr: 1.068e-02, eta: 1 day, 3:04:02, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4453, top5_acc: 0.6978, loss_cls: 3.1062, loss: 3.1062 +2024-07-26 12:01:56,219 - pyskl - INFO - Epoch [119][900/3746] lr: 1.066e-02, eta: 1 day, 3:02:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4502, top5_acc: 0.6936, loss_cls: 3.1034, loss: 3.1034 +2024-07-26 12:03:17,967 - pyskl - INFO - Epoch [119][1000/3746] lr: 1.064e-02, eta: 1 day, 3:01:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4478, top5_acc: 0.7048, loss_cls: 3.1012, loss: 3.1012 +2024-07-26 12:04:39,850 - pyskl - INFO - Epoch [119][1100/3746] lr: 1.063e-02, eta: 1 day, 2:59:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4503, top5_acc: 0.7073, loss_cls: 3.0674, loss: 3.0674 +2024-07-26 12:06:01,345 - pyskl - INFO - Epoch [119][1200/3746] lr: 1.061e-02, eta: 1 day, 2:58:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4506, top5_acc: 0.7063, loss_cls: 3.0814, loss: 3.0814 +2024-07-26 12:07:23,333 - pyskl - INFO - Epoch [119][1300/3746] lr: 1.059e-02, eta: 1 day, 2:57:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4406, top5_acc: 0.6964, loss_cls: 3.1259, loss: 3.1259 +2024-07-26 12:08:45,609 - pyskl - INFO - Epoch [119][1400/3746] lr: 1.057e-02, eta: 1 day, 2:55:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4500, top5_acc: 0.7089, loss_cls: 3.0731, loss: 3.0731 +2024-07-26 12:10:06,795 - pyskl - INFO - Epoch [119][1500/3746] lr: 1.056e-02, eta: 1 day, 2:54:29, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4414, top5_acc: 0.7025, loss_cls: 3.1288, loss: 3.1288 +2024-07-26 12:11:28,524 - pyskl - INFO - Epoch [119][1600/3746] lr: 1.054e-02, eta: 1 day, 2:53:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4486, top5_acc: 0.7056, loss_cls: 3.0719, loss: 3.0719 +2024-07-26 12:12:50,113 - pyskl - INFO - Epoch [119][1700/3746] lr: 1.052e-02, eta: 1 day, 2:51:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4416, top5_acc: 0.7031, loss_cls: 3.0965, loss: 3.0965 +2024-07-26 12:14:11,731 - pyskl - INFO - Epoch [119][1800/3746] lr: 1.050e-02, eta: 1 day, 2:50:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4514, top5_acc: 0.7023, loss_cls: 3.0879, loss: 3.0879 +2024-07-26 12:15:33,581 - pyskl - INFO - Epoch [119][1900/3746] lr: 1.049e-02, eta: 1 day, 2:49:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4387, top5_acc: 0.6989, loss_cls: 3.1485, loss: 3.1485 +2024-07-26 12:16:55,149 - pyskl - INFO - Epoch [119][2000/3746] lr: 1.047e-02, eta: 1 day, 2:47:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4466, top5_acc: 0.7008, loss_cls: 3.1086, loss: 3.1086 +2024-07-26 12:18:17,006 - pyskl - INFO - Epoch [119][2100/3746] lr: 1.045e-02, eta: 1 day, 2:46:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4477, top5_acc: 0.7042, loss_cls: 3.1093, loss: 3.1093 +2024-07-26 12:19:39,923 - pyskl - INFO - Epoch [119][2200/3746] lr: 1.044e-02, eta: 1 day, 2:44:56, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4514, top5_acc: 0.7064, loss_cls: 3.0863, loss: 3.0863 +2024-07-26 12:21:01,860 - pyskl - INFO - Epoch [119][2300/3746] lr: 1.042e-02, eta: 1 day, 2:43:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4491, top5_acc: 0.7042, loss_cls: 3.1065, loss: 3.1065 +2024-07-26 12:22:24,845 - pyskl - INFO - Epoch [119][2400/3746] lr: 1.040e-02, eta: 1 day, 2:42:13, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.7061, loss_cls: 3.0954, loss: 3.0954 +2024-07-26 12:23:46,643 - pyskl - INFO - Epoch [119][2500/3746] lr: 1.039e-02, eta: 1 day, 2:40:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4345, top5_acc: 0.6972, loss_cls: 3.1251, loss: 3.1251 +2024-07-26 12:25:08,392 - pyskl - INFO - Epoch [119][2600/3746] lr: 1.037e-02, eta: 1 day, 2:39:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4427, top5_acc: 0.7000, loss_cls: 3.1163, loss: 3.1163 +2024-07-26 12:26:30,013 - pyskl - INFO - Epoch [119][2700/3746] lr: 1.035e-02, eta: 1 day, 2:38:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7031, loss_cls: 3.0634, loss: 3.0634 +2024-07-26 12:27:51,974 - pyskl - INFO - Epoch [119][2800/3746] lr: 1.033e-02, eta: 1 day, 2:36:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4422, top5_acc: 0.6998, loss_cls: 3.1097, loss: 3.1097 +2024-07-26 12:29:13,422 - pyskl - INFO - Epoch [119][2900/3746] lr: 1.032e-02, eta: 1 day, 2:35:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4508, top5_acc: 0.7027, loss_cls: 3.0767, loss: 3.0767 +2024-07-26 12:30:35,045 - pyskl - INFO - Epoch [119][3000/3746] lr: 1.030e-02, eta: 1 day, 2:34:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4369, top5_acc: 0.6913, loss_cls: 3.1602, loss: 3.1602 +2024-07-26 12:31:56,760 - pyskl - INFO - Epoch [119][3100/3746] lr: 1.028e-02, eta: 1 day, 2:32:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4467, top5_acc: 0.7042, loss_cls: 3.1139, loss: 3.1139 +2024-07-26 12:33:17,965 - pyskl - INFO - Epoch [119][3200/3746] lr: 1.027e-02, eta: 1 day, 2:31:18, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4452, top5_acc: 0.7045, loss_cls: 3.0597, loss: 3.0597 +2024-07-26 12:34:39,232 - pyskl - INFO - Epoch [119][3300/3746] lr: 1.025e-02, eta: 1 day, 2:29:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.6995, loss_cls: 3.1260, loss: 3.1260 +2024-07-26 12:36:00,557 - pyskl - INFO - Epoch [119][3400/3746] lr: 1.023e-02, eta: 1 day, 2:28:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.6998, loss_cls: 3.1208, loss: 3.1208 +2024-07-26 12:37:22,244 - pyskl - INFO - Epoch [119][3500/3746] lr: 1.022e-02, eta: 1 day, 2:27:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.6981, loss_cls: 3.1270, loss: 3.1270 +2024-07-26 12:38:43,667 - pyskl - INFO - Epoch [119][3600/3746] lr: 1.020e-02, eta: 1 day, 2:25:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4461, top5_acc: 0.6989, loss_cls: 3.1138, loss: 3.1138 +2024-07-26 12:40:04,960 - pyskl - INFO - Epoch [119][3700/3746] lr: 1.018e-02, eta: 1 day, 2:24:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4400, top5_acc: 0.6887, loss_cls: 3.1642, loss: 3.1642 +2024-07-26 12:40:43,927 - pyskl - INFO - Saving checkpoint at 119 epochs +2024-07-26 12:42:35,459 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 12:42:36,122 - pyskl - INFO - +top1_acc 0.3867 +top5_acc 0.6404 +2024-07-26 12:42:36,123 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 12:42:36,164 - pyskl - INFO - +mean_acc 0.3866 +2024-07-26 12:42:36,169 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_118.pth was removed +2024-07-26 12:42:36,403 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_119.pth. +2024-07-26 12:42:36,404 - pyskl - INFO - Best top1_acc is 0.3867 at 119 epoch. +2024-07-26 12:42:36,415 - pyskl - INFO - Epoch(val) [119][309] top1_acc: 0.3867, top5_acc: 0.6404, mean_class_accuracy: 0.3866 +2024-07-26 12:46:25,496 - pyskl - INFO - Epoch [120][100/3746] lr: 1.016e-02, eta: 1 day, 2:22:57, time: 2.291, data_time: 1.309, memory: 15990, top1_acc: 0.4689, top5_acc: 0.7214, loss_cls: 2.9686, loss: 2.9686 +2024-07-26 12:47:47,579 - pyskl - INFO - Epoch [120][200/3746] lr: 1.014e-02, eta: 1 day, 2:21:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7248, loss_cls: 2.9655, loss: 2.9655 +2024-07-26 12:49:08,928 - pyskl - INFO - Epoch [120][300/3746] lr: 1.012e-02, eta: 1 day, 2:20:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7114, loss_cls: 3.0239, loss: 3.0239 +2024-07-26 12:50:30,223 - pyskl - INFO - Epoch [120][400/3746] lr: 1.011e-02, eta: 1 day, 2:18:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7080, loss_cls: 3.0159, loss: 3.0159 +2024-07-26 12:51:51,865 - pyskl - INFO - Epoch [120][500/3746] lr: 1.009e-02, eta: 1 day, 2:17:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4461, top5_acc: 0.7166, loss_cls: 3.0527, loss: 3.0527 +2024-07-26 12:53:13,725 - pyskl - INFO - Epoch [120][600/3746] lr: 1.007e-02, eta: 1 day, 2:16:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7100, loss_cls: 3.0537, loss: 3.0537 +2024-07-26 12:54:35,878 - pyskl - INFO - Epoch [120][700/3746] lr: 1.006e-02, eta: 1 day, 2:14:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7134, loss_cls: 3.0470, loss: 3.0470 +2024-07-26 12:55:57,813 - pyskl - INFO - Epoch [120][800/3746] lr: 1.004e-02, eta: 1 day, 2:13:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4492, top5_acc: 0.7142, loss_cls: 3.0506, loss: 3.0506 +2024-07-26 12:57:19,733 - pyskl - INFO - Epoch [120][900/3746] lr: 1.002e-02, eta: 1 day, 2:12:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4558, top5_acc: 0.7147, loss_cls: 3.0235, loss: 3.0235 +2024-07-26 12:58:41,267 - pyskl - INFO - Epoch [120][1000/3746] lr: 1.001e-02, eta: 1 day, 2:10:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7242, loss_cls: 2.9998, loss: 2.9998 +2024-07-26 13:00:02,958 - pyskl - INFO - Epoch [120][1100/3746] lr: 9.989e-03, eta: 1 day, 2:09:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4587, top5_acc: 0.7053, loss_cls: 3.0788, loss: 3.0788 +2024-07-26 13:01:24,448 - pyskl - INFO - Epoch [120][1200/3746] lr: 9.972e-03, eta: 1 day, 2:07:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4433, top5_acc: 0.6959, loss_cls: 3.1057, loss: 3.1057 +2024-07-26 13:02:45,995 - pyskl - INFO - Epoch [120][1300/3746] lr: 9.955e-03, eta: 1 day, 2:06:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.7094, loss_cls: 3.0798, loss: 3.0798 +2024-07-26 13:04:07,333 - pyskl - INFO - Epoch [120][1400/3746] lr: 9.938e-03, eta: 1 day, 2:05:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4478, top5_acc: 0.6995, loss_cls: 3.1135, loss: 3.1135 +2024-07-26 13:05:28,936 - pyskl - INFO - Epoch [120][1500/3746] lr: 9.922e-03, eta: 1 day, 2:03:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4442, top5_acc: 0.7008, loss_cls: 3.1026, loss: 3.1026 +2024-07-26 13:06:50,740 - pyskl - INFO - Epoch [120][1600/3746] lr: 9.905e-03, eta: 1 day, 2:02:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4508, top5_acc: 0.7031, loss_cls: 3.0952, loss: 3.0952 +2024-07-26 13:08:12,356 - pyskl - INFO - Epoch [120][1700/3746] lr: 9.888e-03, eta: 1 day, 2:01:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4544, top5_acc: 0.7087, loss_cls: 3.0461, loss: 3.0461 +2024-07-26 13:09:34,019 - pyskl - INFO - Epoch [120][1800/3746] lr: 9.871e-03, eta: 1 day, 1:59:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4448, top5_acc: 0.7114, loss_cls: 3.0712, loss: 3.0712 +2024-07-26 13:10:55,562 - pyskl - INFO - Epoch [120][1900/3746] lr: 9.855e-03, eta: 1 day, 1:58:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4439, top5_acc: 0.6978, loss_cls: 3.1064, loss: 3.1064 +2024-07-26 13:12:16,704 - pyskl - INFO - Epoch [120][2000/3746] lr: 9.838e-03, eta: 1 day, 1:57:01, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4506, top5_acc: 0.7048, loss_cls: 3.0934, loss: 3.0934 +2024-07-26 13:13:38,200 - pyskl - INFO - Epoch [120][2100/3746] lr: 9.821e-03, eta: 1 day, 1:55:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4564, top5_acc: 0.7125, loss_cls: 3.0365, loss: 3.0365 +2024-07-26 13:15:00,386 - pyskl - INFO - Epoch [120][2200/3746] lr: 9.805e-03, eta: 1 day, 1:54:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4450, top5_acc: 0.6989, loss_cls: 3.1173, loss: 3.1173 +2024-07-26 13:16:22,338 - pyskl - INFO - Epoch [120][2300/3746] lr: 9.788e-03, eta: 1 day, 1:52:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4470, top5_acc: 0.7122, loss_cls: 3.0498, loss: 3.0498 +2024-07-26 13:17:45,239 - pyskl - INFO - Epoch [120][2400/3746] lr: 9.772e-03, eta: 1 day, 1:51:34, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4539, top5_acc: 0.7036, loss_cls: 3.0903, loss: 3.0903 +2024-07-26 13:19:07,224 - pyskl - INFO - Epoch [120][2500/3746] lr: 9.755e-03, eta: 1 day, 1:50:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4436, top5_acc: 0.7008, loss_cls: 3.0856, loss: 3.0856 +2024-07-26 13:20:29,194 - pyskl - INFO - Epoch [120][2600/3746] lr: 9.738e-03, eta: 1 day, 1:48:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.6978, loss_cls: 3.1170, loss: 3.1170 +2024-07-26 13:21:50,566 - pyskl - INFO - Epoch [120][2700/3746] lr: 9.722e-03, eta: 1 day, 1:47:28, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.7044, loss_cls: 3.1003, loss: 3.1003 +2024-07-26 13:23:11,918 - pyskl - INFO - Epoch [120][2800/3746] lr: 9.705e-03, eta: 1 day, 1:46:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4503, top5_acc: 0.7117, loss_cls: 3.0493, loss: 3.0493 +2024-07-26 13:24:33,282 - pyskl - INFO - Epoch [120][2900/3746] lr: 9.689e-03, eta: 1 day, 1:44:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4534, top5_acc: 0.7153, loss_cls: 3.0384, loss: 3.0384 +2024-07-26 13:25:54,597 - pyskl - INFO - Epoch [120][3000/3746] lr: 9.672e-03, eta: 1 day, 1:43:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4447, top5_acc: 0.6975, loss_cls: 3.1001, loss: 3.1001 +2024-07-26 13:27:16,016 - pyskl - INFO - Epoch [120][3100/3746] lr: 9.656e-03, eta: 1 day, 1:42:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4466, top5_acc: 0.7053, loss_cls: 3.0565, loss: 3.0565 +2024-07-26 13:28:37,430 - pyskl - INFO - Epoch [120][3200/3746] lr: 9.639e-03, eta: 1 day, 1:40:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4461, top5_acc: 0.6928, loss_cls: 3.1273, loss: 3.1273 +2024-07-26 13:29:59,280 - pyskl - INFO - Epoch [120][3300/3746] lr: 9.623e-03, eta: 1 day, 1:39:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4512, top5_acc: 0.7067, loss_cls: 3.0475, loss: 3.0475 +2024-07-26 13:31:20,932 - pyskl - INFO - Epoch [120][3400/3746] lr: 9.606e-03, eta: 1 day, 1:37:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4453, top5_acc: 0.6914, loss_cls: 3.1250, loss: 3.1250 +2024-07-26 13:32:42,350 - pyskl - INFO - Epoch [120][3500/3746] lr: 9.590e-03, eta: 1 day, 1:36:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4550, top5_acc: 0.7130, loss_cls: 3.0486, loss: 3.0486 +2024-07-26 13:34:03,648 - pyskl - INFO - Epoch [120][3600/3746] lr: 9.573e-03, eta: 1 day, 1:35:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.7016, loss_cls: 3.0987, loss: 3.0987 +2024-07-26 13:35:25,637 - pyskl - INFO - Epoch [120][3700/3746] lr: 9.557e-03, eta: 1 day, 1:33:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4455, top5_acc: 0.6947, loss_cls: 3.1228, loss: 3.1228 +2024-07-26 13:36:04,961 - pyskl - INFO - Saving checkpoint at 120 epochs +2024-07-26 13:37:55,175 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 13:37:55,837 - pyskl - INFO - +top1_acc 0.3791 +top5_acc 0.6315 +2024-07-26 13:37:55,837 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 13:37:55,878 - pyskl - INFO - +mean_acc 0.3789 +2024-07-26 13:37:55,888 - pyskl - INFO - Epoch(val) [120][309] top1_acc: 0.3791, top5_acc: 0.6315, mean_class_accuracy: 0.3789 +2024-07-26 13:41:42,786 - pyskl - INFO - Epoch [121][100/3746] lr: 9.533e-03, eta: 1 day, 1:32:16, time: 2.269, data_time: 1.297, memory: 15990, top1_acc: 0.4630, top5_acc: 0.7230, loss_cls: 2.9839, loss: 2.9839 +2024-07-26 13:43:04,395 - pyskl - INFO - Epoch [121][200/3746] lr: 9.516e-03, eta: 1 day, 1:30:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4659, top5_acc: 0.7131, loss_cls: 3.0038, loss: 3.0038 +2024-07-26 13:44:25,940 - pyskl - INFO - Epoch [121][300/3746] lr: 9.500e-03, eta: 1 day, 1:29:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4669, top5_acc: 0.7294, loss_cls: 2.9599, loss: 2.9599 +2024-07-26 13:45:47,543 - pyskl - INFO - Epoch [121][400/3746] lr: 9.484e-03, eta: 1 day, 1:28:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4634, top5_acc: 0.7150, loss_cls: 3.0105, loss: 3.0105 +2024-07-26 13:47:08,787 - pyskl - INFO - Epoch [121][500/3746] lr: 9.467e-03, eta: 1 day, 1:26:48, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7186, loss_cls: 2.9869, loss: 2.9869 +2024-07-26 13:48:30,720 - pyskl - INFO - Epoch [121][600/3746] lr: 9.451e-03, eta: 1 day, 1:25:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4614, top5_acc: 0.7073, loss_cls: 3.0355, loss: 3.0355 +2024-07-26 13:49:52,417 - pyskl - INFO - Epoch [121][700/3746] lr: 9.435e-03, eta: 1 day, 1:24:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4570, top5_acc: 0.7095, loss_cls: 3.0362, loss: 3.0362 +2024-07-26 13:51:14,022 - pyskl - INFO - Epoch [121][800/3746] lr: 9.418e-03, eta: 1 day, 1:22:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4592, top5_acc: 0.7144, loss_cls: 3.0332, loss: 3.0332 +2024-07-26 13:52:35,828 - pyskl - INFO - Epoch [121][900/3746] lr: 9.402e-03, eta: 1 day, 1:21:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4709, top5_acc: 0.7180, loss_cls: 2.9850, loss: 2.9850 +2024-07-26 13:53:57,239 - pyskl - INFO - Epoch [121][1000/3746] lr: 9.386e-03, eta: 1 day, 1:19:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4544, top5_acc: 0.7133, loss_cls: 3.0382, loss: 3.0382 +2024-07-26 13:55:18,683 - pyskl - INFO - Epoch [121][1100/3746] lr: 9.369e-03, eta: 1 day, 1:18:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4583, top5_acc: 0.7205, loss_cls: 2.9843, loss: 2.9843 +2024-07-26 13:56:40,311 - pyskl - INFO - Epoch [121][1200/3746] lr: 9.353e-03, eta: 1 day, 1:17:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4427, top5_acc: 0.7063, loss_cls: 3.0823, loss: 3.0823 +2024-07-26 13:58:02,198 - pyskl - INFO - Epoch [121][1300/3746] lr: 9.337e-03, eta: 1 day, 1:15:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4597, top5_acc: 0.7170, loss_cls: 3.0217, loss: 3.0217 +2024-07-26 13:59:23,319 - pyskl - INFO - Epoch [121][1400/3746] lr: 9.321e-03, eta: 1 day, 1:14:30, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4525, top5_acc: 0.7033, loss_cls: 3.0675, loss: 3.0675 +2024-07-26 14:00:44,802 - pyskl - INFO - Epoch [121][1500/3746] lr: 9.304e-03, eta: 1 day, 1:13:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4573, top5_acc: 0.7084, loss_cls: 3.0715, loss: 3.0715 +2024-07-26 14:02:06,351 - pyskl - INFO - Epoch [121][1600/3746] lr: 9.288e-03, eta: 1 day, 1:11:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4634, top5_acc: 0.7108, loss_cls: 3.0220, loss: 3.0220 +2024-07-26 14:03:28,098 - pyskl - INFO - Epoch [121][1700/3746] lr: 9.272e-03, eta: 1 day, 1:10:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4433, top5_acc: 0.7086, loss_cls: 3.0735, loss: 3.0735 +2024-07-26 14:04:49,942 - pyskl - INFO - Epoch [121][1800/3746] lr: 9.256e-03, eta: 1 day, 1:09:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4575, top5_acc: 0.7116, loss_cls: 3.0436, loss: 3.0436 +2024-07-26 14:06:11,365 - pyskl - INFO - Epoch [121][1900/3746] lr: 9.239e-03, eta: 1 day, 1:07:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4520, top5_acc: 0.7105, loss_cls: 3.0362, loss: 3.0362 +2024-07-26 14:07:33,028 - pyskl - INFO - Epoch [121][2000/3746] lr: 9.223e-03, eta: 1 day, 1:06:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4586, top5_acc: 0.7130, loss_cls: 3.0260, loss: 3.0260 +2024-07-26 14:08:54,706 - pyskl - INFO - Epoch [121][2100/3746] lr: 9.207e-03, eta: 1 day, 1:04:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4514, top5_acc: 0.7067, loss_cls: 3.0641, loss: 3.0641 +2024-07-26 14:10:16,169 - pyskl - INFO - Epoch [121][2200/3746] lr: 9.191e-03, eta: 1 day, 1:03:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7077, loss_cls: 3.0218, loss: 3.0218 +2024-07-26 14:11:37,903 - pyskl - INFO - Epoch [121][2300/3746] lr: 9.175e-03, eta: 1 day, 1:02:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4467, top5_acc: 0.7081, loss_cls: 3.0666, loss: 3.0666 +2024-07-26 14:13:00,493 - pyskl - INFO - Epoch [121][2400/3746] lr: 9.159e-03, eta: 1 day, 1:00:51, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4503, top5_acc: 0.7028, loss_cls: 3.0641, loss: 3.0641 +2024-07-26 14:14:22,114 - pyskl - INFO - Epoch [121][2500/3746] lr: 9.142e-03, eta: 1 day, 0:59:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4534, top5_acc: 0.7147, loss_cls: 3.0330, loss: 3.0330 +2024-07-26 14:15:43,908 - pyskl - INFO - Epoch [121][2600/3746] lr: 9.126e-03, eta: 1 day, 0:58:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4448, top5_acc: 0.7008, loss_cls: 3.1031, loss: 3.1031 +2024-07-26 14:17:05,513 - pyskl - INFO - Epoch [121][2700/3746] lr: 9.110e-03, eta: 1 day, 0:56:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4489, top5_acc: 0.7033, loss_cls: 3.0978, loss: 3.0978 +2024-07-26 14:18:27,012 - pyskl - INFO - Epoch [121][2800/3746] lr: 9.094e-03, eta: 1 day, 0:55:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.7108, loss_cls: 3.0471, loss: 3.0471 +2024-07-26 14:19:49,162 - pyskl - INFO - Epoch [121][2900/3746] lr: 9.078e-03, eta: 1 day, 0:54:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4569, top5_acc: 0.7081, loss_cls: 3.0418, loss: 3.0418 +2024-07-26 14:21:10,849 - pyskl - INFO - Epoch [121][3000/3746] lr: 9.062e-03, eta: 1 day, 0:52:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4527, top5_acc: 0.7037, loss_cls: 3.0737, loss: 3.0737 +2024-07-26 14:22:32,276 - pyskl - INFO - Epoch [121][3100/3746] lr: 9.046e-03, eta: 1 day, 0:51:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4561, top5_acc: 0.7030, loss_cls: 3.0709, loss: 3.0709 +2024-07-26 14:23:53,233 - pyskl - INFO - Epoch [121][3200/3746] lr: 9.030e-03, eta: 1 day, 0:49:55, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4533, top5_acc: 0.7067, loss_cls: 3.0655, loss: 3.0655 +2024-07-26 14:25:14,915 - pyskl - INFO - Epoch [121][3300/3746] lr: 9.014e-03, eta: 1 day, 0:48:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4537, top5_acc: 0.7136, loss_cls: 3.0502, loss: 3.0502 +2024-07-26 14:26:36,237 - pyskl - INFO - Epoch [121][3400/3746] lr: 8.998e-03, eta: 1 day, 0:47:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4491, top5_acc: 0.7048, loss_cls: 3.0896, loss: 3.0896 +2024-07-26 14:27:57,699 - pyskl - INFO - Epoch [121][3500/3746] lr: 8.982e-03, eta: 1 day, 0:45:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4553, top5_acc: 0.7081, loss_cls: 3.0503, loss: 3.0503 +2024-07-26 14:29:19,286 - pyskl - INFO - Epoch [121][3600/3746] lr: 8.966e-03, eta: 1 day, 0:44:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4622, top5_acc: 0.7148, loss_cls: 3.0140, loss: 3.0140 +2024-07-26 14:30:40,740 - pyskl - INFO - Epoch [121][3700/3746] lr: 8.950e-03, eta: 1 day, 0:43:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7117, loss_cls: 3.0227, loss: 3.0227 +2024-07-26 14:31:20,184 - pyskl - INFO - Saving checkpoint at 121 epochs +2024-07-26 14:33:11,192 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 14:33:11,855 - pyskl - INFO - +top1_acc 0.3894 +top5_acc 0.6413 +2024-07-26 14:33:11,855 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 14:33:11,894 - pyskl - INFO - +mean_acc 0.3891 +2024-07-26 14:33:11,899 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_119.pth was removed +2024-07-26 14:33:12,131 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_121.pth. +2024-07-26 14:33:12,132 - pyskl - INFO - Best top1_acc is 0.3894 at 121 epoch. +2024-07-26 14:33:12,142 - pyskl - INFO - Epoch(val) [121][309] top1_acc: 0.3894, top5_acc: 0.6413, mean_class_accuracy: 0.3891 +2024-07-26 14:37:05,601 - pyskl - INFO - Epoch [122][100/3746] lr: 8.927e-03, eta: 1 day, 0:41:33, time: 2.334, data_time: 1.350, memory: 15990, top1_acc: 0.4616, top5_acc: 0.7128, loss_cls: 2.9876, loss: 2.9876 +2024-07-26 14:38:28,218 - pyskl - INFO - Epoch [122][200/3746] lr: 8.911e-03, eta: 1 day, 0:40:12, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4695, top5_acc: 0.7277, loss_cls: 2.9556, loss: 2.9556 +2024-07-26 14:39:51,188 - pyskl - INFO - Epoch [122][300/3746] lr: 8.895e-03, eta: 1 day, 0:38:50, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4677, top5_acc: 0.7248, loss_cls: 2.9850, loss: 2.9850 +2024-07-26 14:41:13,545 - pyskl - INFO - Epoch [122][400/3746] lr: 8.879e-03, eta: 1 day, 0:37:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4697, top5_acc: 0.7259, loss_cls: 2.9693, loss: 2.9693 +2024-07-26 14:42:35,641 - pyskl - INFO - Epoch [122][500/3746] lr: 8.863e-03, eta: 1 day, 0:36:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4698, top5_acc: 0.7203, loss_cls: 2.9587, loss: 2.9587 +2024-07-26 14:43:58,478 - pyskl - INFO - Epoch [122][600/3746] lr: 8.847e-03, eta: 1 day, 0:34:45, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7177, loss_cls: 3.0222, loss: 3.0222 +2024-07-26 14:45:21,403 - pyskl - INFO - Epoch [122][700/3746] lr: 8.831e-03, eta: 1 day, 0:33:23, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7102, loss_cls: 3.0194, loss: 3.0194 +2024-07-26 14:46:43,240 - pyskl - INFO - Epoch [122][800/3746] lr: 8.815e-03, eta: 1 day, 0:32:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4644, top5_acc: 0.7197, loss_cls: 2.9835, loss: 2.9835 +2024-07-26 14:48:05,680 - pyskl - INFO - Epoch [122][900/3746] lr: 8.800e-03, eta: 1 day, 0:30:39, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4559, top5_acc: 0.7180, loss_cls: 3.0323, loss: 3.0323 +2024-07-26 14:49:28,505 - pyskl - INFO - Epoch [122][1000/3746] lr: 8.784e-03, eta: 1 day, 0:29:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4678, top5_acc: 0.7225, loss_cls: 2.9701, loss: 2.9701 +2024-07-26 14:50:50,676 - pyskl - INFO - Epoch [122][1100/3746] lr: 8.768e-03, eta: 1 day, 0:27:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4627, top5_acc: 0.7208, loss_cls: 2.9762, loss: 2.9762 +2024-07-26 14:52:12,675 - pyskl - INFO - Epoch [122][1200/3746] lr: 8.752e-03, eta: 1 day, 0:26:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4652, top5_acc: 0.7167, loss_cls: 3.0060, loss: 3.0060 +2024-07-26 14:53:34,630 - pyskl - INFO - Epoch [122][1300/3746] lr: 8.736e-03, eta: 1 day, 0:25:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4589, top5_acc: 0.7175, loss_cls: 3.0127, loss: 3.0127 +2024-07-26 14:54:57,000 - pyskl - INFO - Epoch [122][1400/3746] lr: 8.721e-03, eta: 1 day, 0:23:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4633, top5_acc: 0.7100, loss_cls: 3.0201, loss: 3.0201 +2024-07-26 14:56:18,834 - pyskl - INFO - Epoch [122][1500/3746] lr: 8.705e-03, eta: 1 day, 0:22:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7091, loss_cls: 3.0229, loss: 3.0229 +2024-07-26 14:57:40,443 - pyskl - INFO - Epoch [122][1600/3746] lr: 8.689e-03, eta: 1 day, 0:21:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4628, top5_acc: 0.7206, loss_cls: 3.0105, loss: 3.0105 +2024-07-26 14:59:02,168 - pyskl - INFO - Epoch [122][1700/3746] lr: 8.673e-03, eta: 1 day, 0:19:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4605, top5_acc: 0.7172, loss_cls: 3.0140, loss: 3.0140 +2024-07-26 15:00:23,844 - pyskl - INFO - Epoch [122][1800/3746] lr: 8.658e-03, eta: 1 day, 0:18:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4537, top5_acc: 0.7144, loss_cls: 3.0617, loss: 3.0617 +2024-07-26 15:01:45,561 - pyskl - INFO - Epoch [122][1900/3746] lr: 8.642e-03, eta: 1 day, 0:17:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4445, top5_acc: 0.7056, loss_cls: 3.0772, loss: 3.0772 +2024-07-26 15:03:07,611 - pyskl - INFO - Epoch [122][2000/3746] lr: 8.626e-03, eta: 1 day, 0:15:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4536, top5_acc: 0.7189, loss_cls: 3.0359, loss: 3.0359 +2024-07-26 15:04:29,315 - pyskl - INFO - Epoch [122][2100/3746] lr: 8.610e-03, eta: 1 day, 0:14:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4387, top5_acc: 0.7084, loss_cls: 3.0798, loss: 3.0798 +2024-07-26 15:05:51,203 - pyskl - INFO - Epoch [122][2200/3746] lr: 8.595e-03, eta: 1 day, 0:12:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7072, loss_cls: 3.0601, loss: 3.0601 +2024-07-26 15:07:13,783 - pyskl - INFO - Epoch [122][2300/3746] lr: 8.579e-03, eta: 1 day, 0:11:33, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4736, top5_acc: 0.7172, loss_cls: 2.9971, loss: 2.9971 +2024-07-26 15:08:36,592 - pyskl - INFO - Epoch [122][2400/3746] lr: 8.563e-03, eta: 1 day, 0:10:11, time: 0.828, data_time: 0.001, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7175, loss_cls: 3.0046, loss: 3.0046 +2024-07-26 15:10:00,524 - pyskl - INFO - Epoch [122][2500/3746] lr: 8.548e-03, eta: 1 day, 0:08:50, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4636, top5_acc: 0.7142, loss_cls: 3.0093, loss: 3.0093 +2024-07-26 15:11:24,725 - pyskl - INFO - Epoch [122][2600/3746] lr: 8.532e-03, eta: 1 day, 0:07:28, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4628, top5_acc: 0.7222, loss_cls: 2.9718, loss: 2.9718 +2024-07-26 15:12:48,649 - pyskl - INFO - Epoch [122][2700/3746] lr: 8.517e-03, eta: 1 day, 0:06:07, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4639, top5_acc: 0.7061, loss_cls: 3.0017, loss: 3.0017 +2024-07-26 15:14:11,814 - pyskl - INFO - Epoch [122][2800/3746] lr: 8.501e-03, eta: 1 day, 0:04:45, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4716, top5_acc: 0.7156, loss_cls: 3.0030, loss: 3.0030 +2024-07-26 15:15:34,468 - pyskl - INFO - Epoch [122][2900/3746] lr: 8.485e-03, eta: 1 day, 0:03:24, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4623, top5_acc: 0.7137, loss_cls: 3.0170, loss: 3.0170 +2024-07-26 15:16:57,826 - pyskl - INFO - Epoch [122][3000/3746] lr: 8.470e-03, eta: 1 day, 0:02:02, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4534, top5_acc: 0.7177, loss_cls: 3.0356, loss: 3.0356 +2024-07-26 15:18:20,063 - pyskl - INFO - Epoch [122][3100/3746] lr: 8.454e-03, eta: 1 day, 0:00:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4481, top5_acc: 0.7061, loss_cls: 3.0552, loss: 3.0552 +2024-07-26 15:19:42,398 - pyskl - INFO - Epoch [122][3200/3746] lr: 8.439e-03, eta: 23:59:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4578, top5_acc: 0.7134, loss_cls: 3.0167, loss: 3.0167 +2024-07-26 15:21:04,515 - pyskl - INFO - Epoch [122][3300/3746] lr: 8.423e-03, eta: 23:57:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4495, top5_acc: 0.7039, loss_cls: 3.0753, loss: 3.0753 +2024-07-26 15:22:26,366 - pyskl - INFO - Epoch [122][3400/3746] lr: 8.408e-03, eta: 23:56:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4575, top5_acc: 0.7111, loss_cls: 2.9963, loss: 2.9963 +2024-07-26 15:23:48,204 - pyskl - INFO - Epoch [122][3500/3746] lr: 8.392e-03, eta: 23:55:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4547, top5_acc: 0.7078, loss_cls: 3.0481, loss: 3.0481 +2024-07-26 15:25:10,209 - pyskl - INFO - Epoch [122][3600/3746] lr: 8.377e-03, eta: 23:53:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4573, top5_acc: 0.7102, loss_cls: 3.0254, loss: 3.0254 +2024-07-26 15:26:32,598 - pyskl - INFO - Epoch [122][3700/3746] lr: 8.361e-03, eta: 23:52:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4602, top5_acc: 0.7098, loss_cls: 3.0120, loss: 3.0120 +2024-07-26 15:27:12,082 - pyskl - INFO - Saving checkpoint at 122 epochs +2024-07-26 15:29:05,500 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 15:29:06,421 - pyskl - INFO - +top1_acc 0.3926 +top5_acc 0.6433 +2024-07-26 15:29:06,421 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 15:29:06,469 - pyskl - INFO - +mean_acc 0.3924 +2024-07-26 15:29:06,474 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_121.pth was removed +2024-07-26 15:29:06,723 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_122.pth. +2024-07-26 15:29:06,723 - pyskl - INFO - Best top1_acc is 0.3926 at 122 epoch. +2024-07-26 15:29:06,735 - pyskl - INFO - Epoch(val) [122][309] top1_acc: 0.3926, top5_acc: 0.6433, mean_class_accuracy: 0.3924 +2024-07-26 15:33:02,784 - pyskl - INFO - Epoch [123][100/3746] lr: 8.339e-03, eta: 23:50:56, time: 2.360, data_time: 1.369, memory: 15990, top1_acc: 0.4727, top5_acc: 0.7262, loss_cls: 2.9396, loss: 2.9396 +2024-07-26 15:34:24,924 - pyskl - INFO - Epoch [123][200/3746] lr: 8.323e-03, eta: 23:49:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4834, top5_acc: 0.7334, loss_cls: 2.9054, loss: 2.9054 +2024-07-26 15:35:46,293 - pyskl - INFO - Epoch [123][300/3746] lr: 8.308e-03, eta: 23:48:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4734, top5_acc: 0.7291, loss_cls: 2.9165, loss: 2.9165 +2024-07-26 15:37:07,813 - pyskl - INFO - Epoch [123][400/3746] lr: 8.292e-03, eta: 23:46:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4795, top5_acc: 0.7312, loss_cls: 2.9396, loss: 2.9396 +2024-07-26 15:38:29,770 - pyskl - INFO - Epoch [123][500/3746] lr: 8.277e-03, eta: 23:45:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4625, top5_acc: 0.7178, loss_cls: 2.9881, loss: 2.9881 +2024-07-26 15:39:52,137 - pyskl - INFO - Epoch [123][600/3746] lr: 8.262e-03, eta: 23:44:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4697, top5_acc: 0.7241, loss_cls: 2.9538, loss: 2.9538 +2024-07-26 15:41:13,745 - pyskl - INFO - Epoch [123][700/3746] lr: 8.246e-03, eta: 23:42:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4684, top5_acc: 0.7222, loss_cls: 2.9466, loss: 2.9466 +2024-07-26 15:42:35,279 - pyskl - INFO - Epoch [123][800/3746] lr: 8.231e-03, eta: 23:41:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4587, top5_acc: 0.7223, loss_cls: 2.9699, loss: 2.9699 +2024-07-26 15:43:56,777 - pyskl - INFO - Epoch [123][900/3746] lr: 8.215e-03, eta: 23:40:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4597, top5_acc: 0.7141, loss_cls: 2.9997, loss: 2.9997 +2024-07-26 15:45:18,276 - pyskl - INFO - Epoch [123][1000/3746] lr: 8.200e-03, eta: 23:38:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4647, top5_acc: 0.7156, loss_cls: 2.9691, loss: 2.9691 +2024-07-26 15:46:39,581 - pyskl - INFO - Epoch [123][1100/3746] lr: 8.185e-03, eta: 23:37:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4662, top5_acc: 0.7175, loss_cls: 2.9698, loss: 2.9698 +2024-07-26 15:48:00,952 - pyskl - INFO - Epoch [123][1200/3746] lr: 8.169e-03, eta: 23:35:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4681, top5_acc: 0.7194, loss_cls: 2.9853, loss: 2.9853 +2024-07-26 15:49:22,709 - pyskl - INFO - Epoch [123][1300/3746] lr: 8.154e-03, eta: 23:34:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4623, top5_acc: 0.7233, loss_cls: 2.9729, loss: 2.9729 +2024-07-26 15:50:44,183 - pyskl - INFO - Epoch [123][1400/3746] lr: 8.139e-03, eta: 23:33:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7216, loss_cls: 2.9881, loss: 2.9881 +2024-07-26 15:52:06,023 - pyskl - INFO - Epoch [123][1500/3746] lr: 8.124e-03, eta: 23:31:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7070, loss_cls: 3.0509, loss: 3.0509 +2024-07-26 15:53:27,504 - pyskl - INFO - Epoch [123][1600/3746] lr: 8.108e-03, eta: 23:30:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4748, top5_acc: 0.7227, loss_cls: 2.9469, loss: 2.9469 +2024-07-26 15:54:48,753 - pyskl - INFO - Epoch [123][1700/3746] lr: 8.093e-03, eta: 23:29:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4641, top5_acc: 0.7145, loss_cls: 3.0011, loss: 3.0011 +2024-07-26 15:56:10,332 - pyskl - INFO - Epoch [123][1800/3746] lr: 8.078e-03, eta: 23:27:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4547, top5_acc: 0.7113, loss_cls: 3.0342, loss: 3.0342 +2024-07-26 15:57:31,792 - pyskl - INFO - Epoch [123][1900/3746] lr: 8.063e-03, eta: 23:26:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4756, top5_acc: 0.7233, loss_cls: 2.9429, loss: 2.9429 +2024-07-26 15:58:52,875 - pyskl - INFO - Epoch [123][2000/3746] lr: 8.047e-03, eta: 23:24:58, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7142, loss_cls: 3.0316, loss: 3.0316 +2024-07-26 16:00:15,075 - pyskl - INFO - Epoch [123][2100/3746] lr: 8.032e-03, eta: 23:23:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4752, top5_acc: 0.7192, loss_cls: 2.9420, loss: 2.9420 +2024-07-26 16:01:36,794 - pyskl - INFO - Epoch [123][2200/3746] lr: 8.017e-03, eta: 23:22:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4675, top5_acc: 0.7061, loss_cls: 3.0092, loss: 3.0092 +2024-07-26 16:02:58,398 - pyskl - INFO - Epoch [123][2300/3746] lr: 8.002e-03, eta: 23:20:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4641, top5_acc: 0.7169, loss_cls: 3.0029, loss: 3.0029 +2024-07-26 16:04:21,207 - pyskl - INFO - Epoch [123][2400/3746] lr: 7.987e-03, eta: 23:19:30, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7222, loss_cls: 2.9910, loss: 2.9910 +2024-07-26 16:05:43,059 - pyskl - INFO - Epoch [123][2500/3746] lr: 7.971e-03, eta: 23:18:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4634, top5_acc: 0.7166, loss_cls: 2.9804, loss: 2.9804 +2024-07-26 16:07:05,073 - pyskl - INFO - Epoch [123][2600/3746] lr: 7.956e-03, eta: 23:16:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4633, top5_acc: 0.7159, loss_cls: 2.9913, loss: 2.9913 +2024-07-26 16:08:26,645 - pyskl - INFO - Epoch [123][2700/3746] lr: 7.941e-03, eta: 23:15:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4603, top5_acc: 0.7205, loss_cls: 2.9978, loss: 2.9978 +2024-07-26 16:09:47,846 - pyskl - INFO - Epoch [123][2800/3746] lr: 7.926e-03, eta: 23:14:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4639, top5_acc: 0.7183, loss_cls: 2.9943, loss: 2.9943 +2024-07-26 16:11:09,220 - pyskl - INFO - Epoch [123][2900/3746] lr: 7.911e-03, eta: 23:12:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4733, top5_acc: 0.7219, loss_cls: 2.9672, loss: 2.9672 +2024-07-26 16:12:30,483 - pyskl - INFO - Epoch [123][3000/3746] lr: 7.896e-03, eta: 23:11:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4525, top5_acc: 0.7089, loss_cls: 3.0639, loss: 3.0639 +2024-07-26 16:13:52,537 - pyskl - INFO - Epoch [123][3100/3746] lr: 7.881e-03, eta: 23:09:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7150, loss_cls: 3.0324, loss: 3.0324 +2024-07-26 16:15:13,974 - pyskl - INFO - Epoch [123][3200/3746] lr: 7.866e-03, eta: 23:08:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4603, top5_acc: 0.7134, loss_cls: 2.9888, loss: 2.9888 +2024-07-26 16:16:35,497 - pyskl - INFO - Epoch [123][3300/3746] lr: 7.851e-03, eta: 23:07:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4753, top5_acc: 0.7291, loss_cls: 2.9510, loss: 2.9510 +2024-07-26 16:17:57,071 - pyskl - INFO - Epoch [123][3400/3746] lr: 7.836e-03, eta: 23:05:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4608, top5_acc: 0.7166, loss_cls: 3.0075, loss: 3.0075 +2024-07-26 16:19:18,477 - pyskl - INFO - Epoch [123][3500/3746] lr: 7.821e-03, eta: 23:04:28, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4684, top5_acc: 0.7188, loss_cls: 2.9892, loss: 2.9892 +2024-07-26 16:20:40,129 - pyskl - INFO - Epoch [123][3600/3746] lr: 7.806e-03, eta: 23:03:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4769, top5_acc: 0.7286, loss_cls: 2.9238, loss: 2.9238 +2024-07-26 16:22:01,317 - pyskl - INFO - Epoch [123][3700/3746] lr: 7.791e-03, eta: 23:01:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4658, top5_acc: 0.7106, loss_cls: 3.0190, loss: 3.0190 +2024-07-26 16:22:40,717 - pyskl - INFO - Saving checkpoint at 123 epochs +2024-07-26 16:24:33,066 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 16:24:33,746 - pyskl - INFO - +top1_acc 0.3923 +top5_acc 0.6487 +2024-07-26 16:24:33,746 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 16:24:33,786 - pyskl - INFO - +mean_acc 0.3921 +2024-07-26 16:24:33,798 - pyskl - INFO - Epoch(val) [123][309] top1_acc: 0.3923, top5_acc: 0.6487, mean_class_accuracy: 0.3921 +2024-07-26 16:28:27,720 - pyskl - INFO - Epoch [124][100/3746] lr: 7.769e-03, eta: 23:00:10, time: 2.339, data_time: 1.359, memory: 15990, top1_acc: 0.4813, top5_acc: 0.7408, loss_cls: 2.8799, loss: 2.8799 +2024-07-26 16:29:49,741 - pyskl - INFO - Epoch [124][200/3746] lr: 7.754e-03, eta: 22:58:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4920, top5_acc: 0.7377, loss_cls: 2.8346, loss: 2.8346 +2024-07-26 16:31:11,939 - pyskl - INFO - Epoch [124][300/3746] lr: 7.739e-03, eta: 22:57:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4839, top5_acc: 0.7283, loss_cls: 2.8917, loss: 2.8917 +2024-07-26 16:32:33,816 - pyskl - INFO - Epoch [124][400/3746] lr: 7.724e-03, eta: 22:56:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7309, loss_cls: 2.9023, loss: 2.9023 +2024-07-26 16:33:55,347 - pyskl - INFO - Epoch [124][500/3746] lr: 7.709e-03, eta: 22:54:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4661, top5_acc: 0.7317, loss_cls: 2.9352, loss: 2.9352 +2024-07-26 16:35:17,629 - pyskl - INFO - Epoch [124][600/3746] lr: 7.694e-03, eta: 22:53:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4756, top5_acc: 0.7291, loss_cls: 2.9415, loss: 2.9415 +2024-07-26 16:36:39,376 - pyskl - INFO - Epoch [124][700/3746] lr: 7.679e-03, eta: 22:51:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4803, top5_acc: 0.7288, loss_cls: 2.9130, loss: 2.9130 +2024-07-26 16:38:01,495 - pyskl - INFO - Epoch [124][800/3746] lr: 7.664e-03, eta: 22:50:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4623, top5_acc: 0.7191, loss_cls: 2.9892, loss: 2.9892 +2024-07-26 16:39:23,040 - pyskl - INFO - Epoch [124][900/3746] lr: 7.649e-03, eta: 22:49:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7231, loss_cls: 2.9516, loss: 2.9516 +2024-07-26 16:40:44,934 - pyskl - INFO - Epoch [124][1000/3746] lr: 7.635e-03, eta: 22:47:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4692, top5_acc: 0.7267, loss_cls: 2.9497, loss: 2.9497 +2024-07-26 16:42:06,483 - pyskl - INFO - Epoch [124][1100/3746] lr: 7.620e-03, eta: 22:46:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4770, top5_acc: 0.7262, loss_cls: 2.9355, loss: 2.9355 +2024-07-26 16:43:27,775 - pyskl - INFO - Epoch [124][1200/3746] lr: 7.605e-03, eta: 22:45:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4719, top5_acc: 0.7253, loss_cls: 2.9497, loss: 2.9497 +2024-07-26 16:44:49,183 - pyskl - INFO - Epoch [124][1300/3746] lr: 7.590e-03, eta: 22:43:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4691, top5_acc: 0.7188, loss_cls: 2.9577, loss: 2.9577 +2024-07-26 16:46:10,424 - pyskl - INFO - Epoch [124][1400/3746] lr: 7.575e-03, eta: 22:42:24, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4767, top5_acc: 0.7352, loss_cls: 2.9172, loss: 2.9172 +2024-07-26 16:47:32,005 - pyskl - INFO - Epoch [124][1500/3746] lr: 7.561e-03, eta: 22:41:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4688, top5_acc: 0.7217, loss_cls: 2.9664, loss: 2.9664 +2024-07-26 16:48:53,592 - pyskl - INFO - Epoch [124][1600/3746] lr: 7.546e-03, eta: 22:39:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4637, top5_acc: 0.7200, loss_cls: 2.9870, loss: 2.9870 +2024-07-26 16:50:15,227 - pyskl - INFO - Epoch [124][1700/3746] lr: 7.531e-03, eta: 22:38:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4755, top5_acc: 0.7308, loss_cls: 2.9297, loss: 2.9297 +2024-07-26 16:51:36,248 - pyskl - INFO - Epoch [124][1800/3746] lr: 7.516e-03, eta: 22:36:55, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4728, top5_acc: 0.7278, loss_cls: 2.9220, loss: 2.9220 +2024-07-26 16:52:57,672 - pyskl - INFO - Epoch [124][1900/3746] lr: 7.502e-03, eta: 22:35:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4550, top5_acc: 0.7113, loss_cls: 3.0362, loss: 3.0362 +2024-07-26 16:54:19,311 - pyskl - INFO - Epoch [124][2000/3746] lr: 7.487e-03, eta: 22:34:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4752, top5_acc: 0.7183, loss_cls: 2.9519, loss: 2.9519 +2024-07-26 16:55:41,395 - pyskl - INFO - Epoch [124][2100/3746] lr: 7.472e-03, eta: 22:32:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4739, top5_acc: 0.7216, loss_cls: 2.9545, loss: 2.9545 +2024-07-26 16:57:03,373 - pyskl - INFO - Epoch [124][2200/3746] lr: 7.457e-03, eta: 22:31:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4753, top5_acc: 0.7261, loss_cls: 2.9345, loss: 2.9345 +2024-07-26 16:58:24,948 - pyskl - INFO - Epoch [124][2300/3746] lr: 7.443e-03, eta: 22:30:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4662, top5_acc: 0.7203, loss_cls: 2.9615, loss: 2.9615 +2024-07-26 16:59:48,446 - pyskl - INFO - Epoch [124][2400/3746] lr: 7.428e-03, eta: 22:28:44, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4745, top5_acc: 0.7259, loss_cls: 2.9596, loss: 2.9596 +2024-07-26 17:01:10,746 - pyskl - INFO - Epoch [124][2500/3746] lr: 7.413e-03, eta: 22:27:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4580, top5_acc: 0.7206, loss_cls: 2.9907, loss: 2.9907 +2024-07-26 17:02:32,697 - pyskl - INFO - Epoch [124][2600/3746] lr: 7.399e-03, eta: 22:26:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4595, top5_acc: 0.7078, loss_cls: 3.0300, loss: 3.0300 +2024-07-26 17:03:54,106 - pyskl - INFO - Epoch [124][2700/3746] lr: 7.384e-03, eta: 22:24:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4781, top5_acc: 0.7280, loss_cls: 2.9283, loss: 2.9283 +2024-07-26 17:05:15,460 - pyskl - INFO - Epoch [124][2800/3746] lr: 7.370e-03, eta: 22:23:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4822, top5_acc: 0.7302, loss_cls: 2.8974, loss: 2.8974 +2024-07-26 17:06:36,836 - pyskl - INFO - Epoch [124][2900/3746] lr: 7.355e-03, eta: 22:21:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4616, top5_acc: 0.7122, loss_cls: 2.9923, loss: 2.9923 +2024-07-26 17:07:58,284 - pyskl - INFO - Epoch [124][3000/3746] lr: 7.340e-03, eta: 22:20:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4634, top5_acc: 0.7134, loss_cls: 3.0073, loss: 3.0073 +2024-07-26 17:09:19,632 - pyskl - INFO - Epoch [124][3100/3746] lr: 7.326e-03, eta: 22:19:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4681, top5_acc: 0.7164, loss_cls: 2.9879, loss: 2.9879 +2024-07-26 17:10:41,136 - pyskl - INFO - Epoch [124][3200/3746] lr: 7.311e-03, eta: 22:17:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4659, top5_acc: 0.7220, loss_cls: 2.9716, loss: 2.9716 +2024-07-26 17:12:02,693 - pyskl - INFO - Epoch [124][3300/3746] lr: 7.297e-03, eta: 22:16:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4634, top5_acc: 0.7183, loss_cls: 2.9880, loss: 2.9880 +2024-07-26 17:13:24,496 - pyskl - INFO - Epoch [124][3400/3746] lr: 7.282e-03, eta: 22:15:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4752, top5_acc: 0.7356, loss_cls: 2.9251, loss: 2.9251 +2024-07-26 17:14:46,064 - pyskl - INFO - Epoch [124][3500/3746] lr: 7.268e-03, eta: 22:13:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4695, top5_acc: 0.7175, loss_cls: 2.9320, loss: 2.9320 +2024-07-26 17:16:08,125 - pyskl - INFO - Epoch [124][3600/3746] lr: 7.253e-03, eta: 22:12:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4623, top5_acc: 0.7222, loss_cls: 2.9777, loss: 2.9777 +2024-07-26 17:17:29,416 - pyskl - INFO - Epoch [124][3700/3746] lr: 7.239e-03, eta: 22:10:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4648, top5_acc: 0.7212, loss_cls: 2.9850, loss: 2.9850 +2024-07-26 17:18:08,920 - pyskl - INFO - Saving checkpoint at 124 epochs +2024-07-26 17:19:59,715 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 17:20:00,378 - pyskl - INFO - +top1_acc 0.3989 +top5_acc 0.6527 +2024-07-26 17:20:00,379 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 17:20:00,419 - pyskl - INFO - +mean_acc 0.3986 +2024-07-26 17:20:00,424 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_122.pth was removed +2024-07-26 17:20:00,668 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_124.pth. +2024-07-26 17:20:00,669 - pyskl - INFO - Best top1_acc is 0.3989 at 124 epoch. +2024-07-26 17:20:00,680 - pyskl - INFO - Epoch(val) [124][309] top1_acc: 0.3989, top5_acc: 0.6527, mean_class_accuracy: 0.3986 +2024-07-26 17:23:50,548 - pyskl - INFO - Epoch [125][100/3746] lr: 7.217e-03, eta: 22:09:21, time: 2.299, data_time: 1.324, memory: 15990, top1_acc: 0.4953, top5_acc: 0.7477, loss_cls: 2.8218, loss: 2.8218 +2024-07-26 17:25:12,542 - pyskl - INFO - Epoch [125][200/3746] lr: 7.203e-03, eta: 22:07:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4745, top5_acc: 0.7289, loss_cls: 2.9257, loss: 2.9257 +2024-07-26 17:26:34,459 - pyskl - INFO - Epoch [125][300/3746] lr: 7.189e-03, eta: 22:06:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4833, top5_acc: 0.7428, loss_cls: 2.8535, loss: 2.8535 +2024-07-26 17:27:55,870 - pyskl - INFO - Epoch [125][400/3746] lr: 7.174e-03, eta: 22:05:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4723, top5_acc: 0.7289, loss_cls: 2.9353, loss: 2.9353 +2024-07-26 17:29:17,469 - pyskl - INFO - Epoch [125][500/3746] lr: 7.160e-03, eta: 22:03:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4916, top5_acc: 0.7378, loss_cls: 2.8495, loss: 2.8495 +2024-07-26 17:30:39,997 - pyskl - INFO - Epoch [125][600/3746] lr: 7.145e-03, eta: 22:02:31, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4813, top5_acc: 0.7384, loss_cls: 2.8753, loss: 2.8753 +2024-07-26 17:32:02,104 - pyskl - INFO - Epoch [125][700/3746] lr: 7.131e-03, eta: 22:01:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4917, top5_acc: 0.7427, loss_cls: 2.8538, loss: 2.8538 +2024-07-26 17:33:24,252 - pyskl - INFO - Epoch [125][800/3746] lr: 7.117e-03, eta: 21:59:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4872, top5_acc: 0.7397, loss_cls: 2.8588, loss: 2.8588 +2024-07-26 17:34:46,470 - pyskl - INFO - Epoch [125][900/3746] lr: 7.102e-03, eta: 21:58:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4805, top5_acc: 0.7283, loss_cls: 2.9093, loss: 2.9093 +2024-07-26 17:36:07,884 - pyskl - INFO - Epoch [125][1000/3746] lr: 7.088e-03, eta: 21:57:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4691, top5_acc: 0.7278, loss_cls: 2.9415, loss: 2.9415 +2024-07-26 17:37:29,476 - pyskl - INFO - Epoch [125][1100/3746] lr: 7.073e-03, eta: 21:55:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4775, top5_acc: 0.7241, loss_cls: 2.9047, loss: 2.9047 +2024-07-26 17:38:51,031 - pyskl - INFO - Epoch [125][1200/3746] lr: 7.059e-03, eta: 21:54:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7266, loss_cls: 2.9381, loss: 2.9381 +2024-07-26 17:40:12,478 - pyskl - INFO - Epoch [125][1300/3746] lr: 7.045e-03, eta: 21:52:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4825, top5_acc: 0.7320, loss_cls: 2.8791, loss: 2.8791 +2024-07-26 17:41:34,090 - pyskl - INFO - Epoch [125][1400/3746] lr: 7.031e-03, eta: 21:51:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4753, top5_acc: 0.7255, loss_cls: 2.9344, loss: 2.9344 +2024-07-26 17:42:55,538 - pyskl - INFO - Epoch [125][1500/3746] lr: 7.016e-03, eta: 21:50:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7230, loss_cls: 2.9371, loss: 2.9371 +2024-07-26 17:44:17,354 - pyskl - INFO - Epoch [125][1600/3746] lr: 7.002e-03, eta: 21:48:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4861, top5_acc: 0.7344, loss_cls: 2.8847, loss: 2.8847 +2024-07-26 17:45:38,675 - pyskl - INFO - Epoch [125][1700/3746] lr: 6.988e-03, eta: 21:47:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4753, top5_acc: 0.7320, loss_cls: 2.9182, loss: 2.9182 +2024-07-26 17:46:59,964 - pyskl - INFO - Epoch [125][1800/3746] lr: 6.973e-03, eta: 21:46:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4766, top5_acc: 0.7286, loss_cls: 2.9092, loss: 2.9092 +2024-07-26 17:48:21,742 - pyskl - INFO - Epoch [125][1900/3746] lr: 6.959e-03, eta: 21:44:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4658, top5_acc: 0.7247, loss_cls: 2.9429, loss: 2.9429 +2024-07-26 17:49:43,627 - pyskl - INFO - Epoch [125][2000/3746] lr: 6.945e-03, eta: 21:43:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4692, top5_acc: 0.7269, loss_cls: 2.9373, loss: 2.9373 +2024-07-26 17:51:05,186 - pyskl - INFO - Epoch [125][2100/3746] lr: 6.931e-03, eta: 21:42:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4680, top5_acc: 0.7256, loss_cls: 2.9449, loss: 2.9449 +2024-07-26 17:52:26,937 - pyskl - INFO - Epoch [125][2200/3746] lr: 6.917e-03, eta: 21:40:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4756, top5_acc: 0.7342, loss_cls: 2.8711, loss: 2.8711 +2024-07-26 17:53:48,880 - pyskl - INFO - Epoch [125][2300/3746] lr: 6.902e-03, eta: 21:39:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4747, top5_acc: 0.7261, loss_cls: 2.9450, loss: 2.9450 +2024-07-26 17:55:11,713 - pyskl - INFO - Epoch [125][2400/3746] lr: 6.888e-03, eta: 21:37:54, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7322, loss_cls: 2.9120, loss: 2.9120 +2024-07-26 17:56:34,387 - pyskl - INFO - Epoch [125][2500/3746] lr: 6.874e-03, eta: 21:36:33, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4706, top5_acc: 0.7242, loss_cls: 2.9507, loss: 2.9507 +2024-07-26 17:57:56,123 - pyskl - INFO - Epoch [125][2600/3746] lr: 6.860e-03, eta: 21:35:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4688, top5_acc: 0.7342, loss_cls: 2.9093, loss: 2.9093 +2024-07-26 17:59:18,363 - pyskl - INFO - Epoch [125][2700/3746] lr: 6.846e-03, eta: 21:33:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4763, top5_acc: 0.7278, loss_cls: 2.9312, loss: 2.9312 +2024-07-26 18:00:40,161 - pyskl - INFO - Epoch [125][2800/3746] lr: 6.832e-03, eta: 21:32:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4723, top5_acc: 0.7241, loss_cls: 2.9668, loss: 2.9668 +2024-07-26 18:02:01,751 - pyskl - INFO - Epoch [125][2900/3746] lr: 6.818e-03, eta: 21:31:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4683, top5_acc: 0.7300, loss_cls: 2.9250, loss: 2.9250 +2024-07-26 18:03:22,999 - pyskl - INFO - Epoch [125][3000/3746] lr: 6.804e-03, eta: 21:29:42, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4756, top5_acc: 0.7275, loss_cls: 2.9386, loss: 2.9386 +2024-07-26 18:04:44,713 - pyskl - INFO - Epoch [125][3100/3746] lr: 6.789e-03, eta: 21:28:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7275, loss_cls: 2.9594, loss: 2.9594 +2024-07-26 18:06:06,219 - pyskl - INFO - Epoch [125][3200/3746] lr: 6.775e-03, eta: 21:26:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4819, top5_acc: 0.7277, loss_cls: 2.9069, loss: 2.9069 +2024-07-26 18:07:27,573 - pyskl - INFO - Epoch [125][3300/3746] lr: 6.761e-03, eta: 21:25:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4658, top5_acc: 0.7173, loss_cls: 2.9883, loss: 2.9883 +2024-07-26 18:08:48,752 - pyskl - INFO - Epoch [125][3400/3746] lr: 6.747e-03, eta: 21:24:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4661, top5_acc: 0.7331, loss_cls: 2.9402, loss: 2.9402 +2024-07-26 18:10:10,301 - pyskl - INFO - Epoch [125][3500/3746] lr: 6.733e-03, eta: 21:22:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7256, loss_cls: 2.9589, loss: 2.9589 +2024-07-26 18:11:32,208 - pyskl - INFO - Epoch [125][3600/3746] lr: 6.719e-03, eta: 21:21:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4731, top5_acc: 0.7231, loss_cls: 2.9688, loss: 2.9688 +2024-07-26 18:12:53,807 - pyskl - INFO - Epoch [125][3700/3746] lr: 6.705e-03, eta: 21:20:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4673, top5_acc: 0.7275, loss_cls: 2.9231, loss: 2.9231 +2024-07-26 18:13:33,279 - pyskl - INFO - Saving checkpoint at 125 epochs +2024-07-26 18:15:25,065 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 18:15:25,726 - pyskl - INFO - +top1_acc 0.4075 +top5_acc 0.6611 +2024-07-26 18:15:25,726 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 18:15:25,767 - pyskl - INFO - +mean_acc 0.4073 +2024-07-26 18:15:25,772 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_124.pth was removed +2024-07-26 18:15:26,008 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_125.pth. +2024-07-26 18:15:26,009 - pyskl - INFO - Best top1_acc is 0.4075 at 125 epoch. +2024-07-26 18:15:26,021 - pyskl - INFO - Epoch(val) [125][309] top1_acc: 0.4075, top5_acc: 0.6611, mean_class_accuracy: 0.4073 +2024-07-26 18:19:21,559 - pyskl - INFO - Epoch [126][100/3746] lr: 6.685e-03, eta: 21:18:31, time: 2.355, data_time: 1.371, memory: 15990, top1_acc: 0.4900, top5_acc: 0.7516, loss_cls: 2.7839, loss: 2.7839 +2024-07-26 18:20:43,530 - pyskl - INFO - Epoch [126][200/3746] lr: 6.671e-03, eta: 21:17:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4948, top5_acc: 0.7386, loss_cls: 2.8306, loss: 2.8306 +2024-07-26 18:22:05,924 - pyskl - INFO - Epoch [126][300/3746] lr: 6.657e-03, eta: 21:15:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5039, top5_acc: 0.7422, loss_cls: 2.8130, loss: 2.8130 +2024-07-26 18:23:27,922 - pyskl - INFO - Epoch [126][400/3746] lr: 6.643e-03, eta: 21:14:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4866, top5_acc: 0.7448, loss_cls: 2.8352, loss: 2.8352 +2024-07-26 18:24:49,298 - pyskl - INFO - Epoch [126][500/3746] lr: 6.629e-03, eta: 21:13:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4877, top5_acc: 0.7433, loss_cls: 2.8222, loss: 2.8222 +2024-07-26 18:26:11,776 - pyskl - INFO - Epoch [126][600/3746] lr: 6.615e-03, eta: 21:11:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4733, top5_acc: 0.7247, loss_cls: 2.9088, loss: 2.9088 +2024-07-26 18:27:33,175 - pyskl - INFO - Epoch [126][700/3746] lr: 6.601e-03, eta: 21:10:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4889, top5_acc: 0.7362, loss_cls: 2.8319, loss: 2.8319 +2024-07-26 18:28:55,632 - pyskl - INFO - Epoch [126][800/3746] lr: 6.587e-03, eta: 21:08:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4828, top5_acc: 0.7348, loss_cls: 2.8778, loss: 2.8778 +2024-07-26 18:30:17,458 - pyskl - INFO - Epoch [126][900/3746] lr: 6.574e-03, eta: 21:07:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4850, top5_acc: 0.7286, loss_cls: 2.8721, loss: 2.8721 +2024-07-26 18:31:39,300 - pyskl - INFO - Epoch [126][1000/3746] lr: 6.560e-03, eta: 21:06:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7381, loss_cls: 2.8456, loss: 2.8456 +2024-07-26 18:33:01,141 - pyskl - INFO - Epoch [126][1100/3746] lr: 6.546e-03, eta: 21:04:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4738, top5_acc: 0.7322, loss_cls: 2.8928, loss: 2.8928 +2024-07-26 18:34:22,264 - pyskl - INFO - Epoch [126][1200/3746] lr: 6.532e-03, eta: 21:03:29, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4972, top5_acc: 0.7384, loss_cls: 2.8105, loss: 2.8105 +2024-07-26 18:35:43,837 - pyskl - INFO - Epoch [126][1300/3746] lr: 6.518e-03, eta: 21:02:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4795, top5_acc: 0.7317, loss_cls: 2.8959, loss: 2.8959 +2024-07-26 18:37:05,243 - pyskl - INFO - Epoch [126][1400/3746] lr: 6.505e-03, eta: 21:00:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4764, top5_acc: 0.7297, loss_cls: 2.9293, loss: 2.9293 +2024-07-26 18:38:26,679 - pyskl - INFO - Epoch [126][1500/3746] lr: 6.491e-03, eta: 20:59:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4764, top5_acc: 0.7316, loss_cls: 2.8999, loss: 2.8999 +2024-07-26 18:39:48,452 - pyskl - INFO - Epoch [126][1600/3746] lr: 6.477e-03, eta: 20:58:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4786, top5_acc: 0.7305, loss_cls: 2.8808, loss: 2.8808 +2024-07-26 18:41:09,732 - pyskl - INFO - Epoch [126][1700/3746] lr: 6.463e-03, eta: 20:56:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4833, top5_acc: 0.7389, loss_cls: 2.8307, loss: 2.8307 +2024-07-26 18:42:32,036 - pyskl - INFO - Epoch [126][1800/3746] lr: 6.449e-03, eta: 20:55:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4842, top5_acc: 0.7367, loss_cls: 2.8986, loss: 2.8986 +2024-07-26 18:43:54,009 - pyskl - INFO - Epoch [126][1900/3746] lr: 6.436e-03, eta: 20:53:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4831, top5_acc: 0.7416, loss_cls: 2.8798, loss: 2.8798 +2024-07-26 18:45:16,015 - pyskl - INFO - Epoch [126][2000/3746] lr: 6.422e-03, eta: 20:52:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4662, top5_acc: 0.7288, loss_cls: 2.9551, loss: 2.9551 +2024-07-26 18:46:37,538 - pyskl - INFO - Epoch [126][2100/3746] lr: 6.408e-03, eta: 20:51:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4778, top5_acc: 0.7273, loss_cls: 2.9116, loss: 2.9116 +2024-07-26 18:47:59,307 - pyskl - INFO - Epoch [126][2200/3746] lr: 6.395e-03, eta: 20:49:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4953, top5_acc: 0.7408, loss_cls: 2.8438, loss: 2.8438 +2024-07-26 18:49:21,676 - pyskl - INFO - Epoch [126][2300/3746] lr: 6.381e-03, eta: 20:48:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4769, top5_acc: 0.7330, loss_cls: 2.9020, loss: 2.9020 +2024-07-26 18:50:44,178 - pyskl - INFO - Epoch [126][2400/3746] lr: 6.367e-03, eta: 20:47:05, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4761, top5_acc: 0.7381, loss_cls: 2.9149, loss: 2.9149 +2024-07-26 18:52:07,101 - pyskl - INFO - Epoch [126][2500/3746] lr: 6.354e-03, eta: 20:45:43, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4770, top5_acc: 0.7252, loss_cls: 2.9407, loss: 2.9407 +2024-07-26 18:53:29,539 - pyskl - INFO - Epoch [126][2600/3746] lr: 6.340e-03, eta: 20:44:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4744, top5_acc: 0.7255, loss_cls: 2.9253, loss: 2.9253 +2024-07-26 18:54:51,879 - pyskl - INFO - Epoch [126][2700/3746] lr: 6.326e-03, eta: 20:42:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4736, top5_acc: 0.7297, loss_cls: 2.8956, loss: 2.8956 +2024-07-26 18:56:13,478 - pyskl - INFO - Epoch [126][2800/3746] lr: 6.313e-03, eta: 20:41:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4881, top5_acc: 0.7308, loss_cls: 2.9025, loss: 2.9025 +2024-07-26 18:57:34,942 - pyskl - INFO - Epoch [126][2900/3746] lr: 6.299e-03, eta: 20:40:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4811, top5_acc: 0.7280, loss_cls: 2.8894, loss: 2.8894 +2024-07-26 18:58:56,353 - pyskl - INFO - Epoch [126][3000/3746] lr: 6.286e-03, eta: 20:38:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4886, top5_acc: 0.7284, loss_cls: 2.8875, loss: 2.8875 +2024-07-26 19:00:18,172 - pyskl - INFO - Epoch [126][3100/3746] lr: 6.272e-03, eta: 20:37:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4830, top5_acc: 0.7281, loss_cls: 2.8952, loss: 2.8952 +2024-07-26 19:01:39,959 - pyskl - INFO - Epoch [126][3200/3746] lr: 6.259e-03, eta: 20:36:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4688, top5_acc: 0.7197, loss_cls: 2.9629, loss: 2.9629 +2024-07-26 19:03:01,984 - pyskl - INFO - Epoch [126][3300/3746] lr: 6.245e-03, eta: 20:34:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4783, top5_acc: 0.7311, loss_cls: 2.9031, loss: 2.9031 +2024-07-26 19:04:23,488 - pyskl - INFO - Epoch [126][3400/3746] lr: 6.231e-03, eta: 20:33:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4858, top5_acc: 0.7388, loss_cls: 2.8530, loss: 2.8530 +2024-07-26 19:05:45,123 - pyskl - INFO - Epoch [126][3500/3746] lr: 6.218e-03, eta: 20:32:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4775, top5_acc: 0.7347, loss_cls: 2.9252, loss: 2.9252 +2024-07-26 19:07:06,205 - pyskl - INFO - Epoch [126][3600/3746] lr: 6.204e-03, eta: 20:30:40, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4720, top5_acc: 0.7247, loss_cls: 2.9434, loss: 2.9434 +2024-07-26 19:08:27,977 - pyskl - INFO - Epoch [126][3700/3746] lr: 6.191e-03, eta: 20:29:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4775, top5_acc: 0.7264, loss_cls: 2.9383, loss: 2.9383 +2024-07-26 19:09:07,567 - pyskl - INFO - Saving checkpoint at 126 epochs +2024-07-26 19:10:59,227 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 19:10:59,892 - pyskl - INFO - +top1_acc 0.4098 +top5_acc 0.6651 +2024-07-26 19:10:59,892 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 19:10:59,933 - pyskl - INFO - +mean_acc 0.4095 +2024-07-26 19:10:59,937 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_125.pth was removed +2024-07-26 19:11:00,173 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_126.pth. +2024-07-26 19:11:00,174 - pyskl - INFO - Best top1_acc is 0.4098 at 126 epoch. +2024-07-26 19:11:00,186 - pyskl - INFO - Epoch(val) [126][309] top1_acc: 0.4098, top5_acc: 0.6651, mean_class_accuracy: 0.4095 +2024-07-26 19:14:52,337 - pyskl - INFO - Epoch [127][100/3746] lr: 6.171e-03, eta: 20:27:40, time: 2.321, data_time: 1.342, memory: 15990, top1_acc: 0.5225, top5_acc: 0.7627, loss_cls: 2.6948, loss: 2.6948 +2024-07-26 19:16:14,513 - pyskl - INFO - Epoch [127][200/3746] lr: 6.158e-03, eta: 20:26:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4970, top5_acc: 0.7494, loss_cls: 2.7796, loss: 2.7796 +2024-07-26 19:17:36,745 - pyskl - INFO - Epoch [127][300/3746] lr: 6.144e-03, eta: 20:24:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5075, top5_acc: 0.7558, loss_cls: 2.7661, loss: 2.7661 +2024-07-26 19:18:58,829 - pyskl - INFO - Epoch [127][400/3746] lr: 6.131e-03, eta: 20:23:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4978, top5_acc: 0.7498, loss_cls: 2.7948, loss: 2.7948 +2024-07-26 19:20:20,733 - pyskl - INFO - Epoch [127][500/3746] lr: 6.118e-03, eta: 20:22:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4753, top5_acc: 0.7388, loss_cls: 2.8813, loss: 2.8813 +2024-07-26 19:21:43,390 - pyskl - INFO - Epoch [127][600/3746] lr: 6.104e-03, eta: 20:20:50, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4997, top5_acc: 0.7427, loss_cls: 2.8216, loss: 2.8216 +2024-07-26 19:23:05,208 - pyskl - INFO - Epoch [127][700/3746] lr: 6.091e-03, eta: 20:19:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4859, top5_acc: 0.7438, loss_cls: 2.8516, loss: 2.8516 +2024-07-26 19:24:27,084 - pyskl - INFO - Epoch [127][800/3746] lr: 6.077e-03, eta: 20:18:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4928, top5_acc: 0.7531, loss_cls: 2.7865, loss: 2.7865 +2024-07-26 19:25:48,801 - pyskl - INFO - Epoch [127][900/3746] lr: 6.064e-03, eta: 20:16:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4981, top5_acc: 0.7484, loss_cls: 2.7925, loss: 2.7925 +2024-07-26 19:27:10,516 - pyskl - INFO - Epoch [127][1000/3746] lr: 6.051e-03, eta: 20:15:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4898, top5_acc: 0.7359, loss_cls: 2.8602, loss: 2.8602 +2024-07-26 19:28:31,922 - pyskl - INFO - Epoch [127][1100/3746] lr: 6.037e-03, eta: 20:14:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4864, top5_acc: 0.7327, loss_cls: 2.8644, loss: 2.8644 +2024-07-26 19:29:53,080 - pyskl - INFO - Epoch [127][1200/3746] lr: 6.024e-03, eta: 20:12:37, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5009, top5_acc: 0.7514, loss_cls: 2.7949, loss: 2.7949 +2024-07-26 19:31:14,706 - pyskl - INFO - Epoch [127][1300/3746] lr: 6.011e-03, eta: 20:11:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4964, top5_acc: 0.7420, loss_cls: 2.8229, loss: 2.8229 +2024-07-26 19:32:36,487 - pyskl - INFO - Epoch [127][1400/3746] lr: 5.998e-03, eta: 20:09:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4933, top5_acc: 0.7461, loss_cls: 2.8098, loss: 2.8098 +2024-07-26 19:33:58,370 - pyskl - INFO - Epoch [127][1500/3746] lr: 5.984e-03, eta: 20:08:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4864, top5_acc: 0.7277, loss_cls: 2.8824, loss: 2.8824 +2024-07-26 19:35:19,477 - pyskl - INFO - Epoch [127][1600/3746] lr: 5.971e-03, eta: 20:07:09, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4800, top5_acc: 0.7392, loss_cls: 2.8663, loss: 2.8663 +2024-07-26 19:36:41,379 - pyskl - INFO - Epoch [127][1700/3746] lr: 5.958e-03, eta: 20:05:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4900, top5_acc: 0.7330, loss_cls: 2.8763, loss: 2.8763 +2024-07-26 19:38:02,459 - pyskl - INFO - Epoch [127][1800/3746] lr: 5.945e-03, eta: 20:04:25, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4828, top5_acc: 0.7386, loss_cls: 2.8652, loss: 2.8652 +2024-07-26 19:39:23,883 - pyskl - INFO - Epoch [127][1900/3746] lr: 5.931e-03, eta: 20:03:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4892, top5_acc: 0.7455, loss_cls: 2.8102, loss: 2.8102 +2024-07-26 19:40:45,724 - pyskl - INFO - Epoch [127][2000/3746] lr: 5.918e-03, eta: 20:01:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4788, top5_acc: 0.7364, loss_cls: 2.8353, loss: 2.8353 +2024-07-26 19:42:07,914 - pyskl - INFO - Epoch [127][2100/3746] lr: 5.905e-03, eta: 20:00:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4811, top5_acc: 0.7373, loss_cls: 2.8487, loss: 2.8487 +2024-07-26 19:43:29,425 - pyskl - INFO - Epoch [127][2200/3746] lr: 5.892e-03, eta: 19:58:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4827, top5_acc: 0.7303, loss_cls: 2.8969, loss: 2.8969 +2024-07-26 19:44:51,671 - pyskl - INFO - Epoch [127][2300/3746] lr: 5.879e-03, eta: 19:57:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4847, top5_acc: 0.7347, loss_cls: 2.8908, loss: 2.8908 +2024-07-26 19:46:13,340 - pyskl - INFO - Epoch [127][2400/3746] lr: 5.866e-03, eta: 19:56:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4806, top5_acc: 0.7245, loss_cls: 2.9016, loss: 2.9016 +2024-07-26 19:47:35,911 - pyskl - INFO - Epoch [127][2500/3746] lr: 5.852e-03, eta: 19:54:50, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4809, top5_acc: 0.7273, loss_cls: 2.8919, loss: 2.8919 +2024-07-26 19:48:58,089 - pyskl - INFO - Epoch [127][2600/3746] lr: 5.839e-03, eta: 19:53:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4784, top5_acc: 0.7347, loss_cls: 2.8953, loss: 2.8953 +2024-07-26 19:50:20,458 - pyskl - INFO - Epoch [127][2700/3746] lr: 5.826e-03, eta: 19:52:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4852, top5_acc: 0.7297, loss_cls: 2.8939, loss: 2.8939 +2024-07-26 19:51:42,228 - pyskl - INFO - Epoch [127][2800/3746] lr: 5.813e-03, eta: 19:50:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4772, top5_acc: 0.7328, loss_cls: 2.8662, loss: 2.8662 +2024-07-26 19:53:03,973 - pyskl - INFO - Epoch [127][2900/3746] lr: 5.800e-03, eta: 19:49:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4878, top5_acc: 0.7397, loss_cls: 2.8549, loss: 2.8549 +2024-07-26 19:54:26,070 - pyskl - INFO - Epoch [127][3000/3746] lr: 5.787e-03, eta: 19:48:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4836, top5_acc: 0.7312, loss_cls: 2.8918, loss: 2.8918 +2024-07-26 19:55:47,672 - pyskl - INFO - Epoch [127][3100/3746] lr: 5.774e-03, eta: 19:46:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4778, top5_acc: 0.7241, loss_cls: 2.9203, loss: 2.9203 +2024-07-26 19:57:09,188 - pyskl - INFO - Epoch [127][3200/3746] lr: 5.761e-03, eta: 19:45:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4900, top5_acc: 0.7416, loss_cls: 2.8480, loss: 2.8480 +2024-07-26 19:58:30,756 - pyskl - INFO - Epoch [127][3300/3746] lr: 5.748e-03, eta: 19:43:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4802, top5_acc: 0.7402, loss_cls: 2.8791, loss: 2.8791 +2024-07-26 19:59:52,437 - pyskl - INFO - Epoch [127][3400/3746] lr: 5.735e-03, eta: 19:42:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4778, top5_acc: 0.7256, loss_cls: 2.9124, loss: 2.9124 +2024-07-26 20:01:14,174 - pyskl - INFO - Epoch [127][3500/3746] lr: 5.722e-03, eta: 19:41:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4830, top5_acc: 0.7383, loss_cls: 2.8565, loss: 2.8565 +2024-07-26 20:02:35,643 - pyskl - INFO - Epoch [127][3600/3746] lr: 5.709e-03, eta: 19:39:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4794, top5_acc: 0.7403, loss_cls: 2.8574, loss: 2.8574 +2024-07-26 20:03:57,218 - pyskl - INFO - Epoch [127][3700/3746] lr: 5.696e-03, eta: 19:38:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4784, top5_acc: 0.7398, loss_cls: 2.8696, loss: 2.8696 +2024-07-26 20:04:36,516 - pyskl - INFO - Saving checkpoint at 127 epochs +2024-07-26 20:06:26,350 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 20:06:27,011 - pyskl - INFO - +top1_acc 0.4111 +top5_acc 0.6599 +2024-07-26 20:06:27,011 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 20:06:27,053 - pyskl - INFO - +mean_acc 0.4107 +2024-07-26 20:06:27,058 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_126.pth was removed +2024-07-26 20:06:27,291 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_127.pth. +2024-07-26 20:06:27,292 - pyskl - INFO - Best top1_acc is 0.4111 at 127 epoch. +2024-07-26 20:06:27,304 - pyskl - INFO - Epoch(val) [127][309] top1_acc: 0.4111, top5_acc: 0.6599, mean_class_accuracy: 0.4107 +2024-07-26 20:10:16,544 - pyskl - INFO - Epoch [128][100/3746] lr: 5.677e-03, eta: 19:36:46, time: 2.292, data_time: 1.320, memory: 15990, top1_acc: 0.5134, top5_acc: 0.7592, loss_cls: 2.7040, loss: 2.7040 +2024-07-26 20:11:37,503 - pyskl - INFO - Epoch [128][200/3746] lr: 5.664e-03, eta: 19:35:23, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5002, top5_acc: 0.7533, loss_cls: 2.7803, loss: 2.7803 +2024-07-26 20:12:59,065 - pyskl - INFO - Epoch [128][300/3746] lr: 5.651e-03, eta: 19:34:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5062, top5_acc: 0.7578, loss_cls: 2.7335, loss: 2.7335 +2024-07-26 20:14:20,945 - pyskl - INFO - Epoch [128][400/3746] lr: 5.638e-03, eta: 19:32:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4919, top5_acc: 0.7489, loss_cls: 2.7800, loss: 2.7800 +2024-07-26 20:15:41,956 - pyskl - INFO - Epoch [128][500/3746] lr: 5.625e-03, eta: 19:31:17, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7530, loss_cls: 2.7635, loss: 2.7635 +2024-07-26 20:17:04,335 - pyskl - INFO - Epoch [128][600/3746] lr: 5.612e-03, eta: 19:29:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5005, top5_acc: 0.7550, loss_cls: 2.7829, loss: 2.7829 +2024-07-26 20:18:25,706 - pyskl - INFO - Epoch [128][700/3746] lr: 5.600e-03, eta: 19:28:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4941, top5_acc: 0.7439, loss_cls: 2.8138, loss: 2.8138 +2024-07-26 20:19:47,725 - pyskl - INFO - Epoch [128][800/3746] lr: 5.587e-03, eta: 19:27:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7400, loss_cls: 2.8269, loss: 2.8269 +2024-07-26 20:21:09,275 - pyskl - INFO - Epoch [128][900/3746] lr: 5.574e-03, eta: 19:25:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4961, top5_acc: 0.7456, loss_cls: 2.8026, loss: 2.8026 +2024-07-26 20:22:30,868 - pyskl - INFO - Epoch [128][1000/3746] lr: 5.561e-03, eta: 19:24:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5084, top5_acc: 0.7594, loss_cls: 2.7306, loss: 2.7306 +2024-07-26 20:23:52,625 - pyskl - INFO - Epoch [128][1100/3746] lr: 5.548e-03, eta: 19:23:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4845, top5_acc: 0.7438, loss_cls: 2.8390, loss: 2.8390 +2024-07-26 20:25:14,271 - pyskl - INFO - Epoch [128][1200/3746] lr: 5.536e-03, eta: 19:21:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4956, top5_acc: 0.7445, loss_cls: 2.8156, loss: 2.8156 +2024-07-26 20:26:35,608 - pyskl - INFO - Epoch [128][1300/3746] lr: 5.523e-03, eta: 19:20:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5036, top5_acc: 0.7416, loss_cls: 2.7991, loss: 2.7991 +2024-07-26 20:27:57,048 - pyskl - INFO - Epoch [128][1400/3746] lr: 5.510e-03, eta: 19:18:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4952, top5_acc: 0.7453, loss_cls: 2.7983, loss: 2.7983 +2024-07-26 20:29:19,217 - pyskl - INFO - Epoch [128][1500/3746] lr: 5.497e-03, eta: 19:17:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4852, top5_acc: 0.7353, loss_cls: 2.8663, loss: 2.8663 +2024-07-26 20:30:40,939 - pyskl - INFO - Epoch [128][1600/3746] lr: 5.485e-03, eta: 19:16:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4966, top5_acc: 0.7475, loss_cls: 2.7961, loss: 2.7961 +2024-07-26 20:32:02,109 - pyskl - INFO - Epoch [128][1700/3746] lr: 5.472e-03, eta: 19:14:52, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7428, loss_cls: 2.8302, loss: 2.8302 +2024-07-26 20:33:23,407 - pyskl - INFO - Epoch [128][1800/3746] lr: 5.459e-03, eta: 19:13:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4920, top5_acc: 0.7486, loss_cls: 2.7932, loss: 2.7932 +2024-07-26 20:34:45,085 - pyskl - INFO - Epoch [128][1900/3746] lr: 5.446e-03, eta: 19:12:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4877, top5_acc: 0.7422, loss_cls: 2.8472, loss: 2.8472 +2024-07-26 20:36:06,664 - pyskl - INFO - Epoch [128][2000/3746] lr: 5.434e-03, eta: 19:10:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4919, top5_acc: 0.7405, loss_cls: 2.8343, loss: 2.8343 +2024-07-26 20:37:27,869 - pyskl - INFO - Epoch [128][2100/3746] lr: 5.421e-03, eta: 19:09:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4945, top5_acc: 0.7412, loss_cls: 2.8218, loss: 2.8218 +2024-07-26 20:38:49,567 - pyskl - INFO - Epoch [128][2200/3746] lr: 5.408e-03, eta: 19:08:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4869, top5_acc: 0.7439, loss_cls: 2.8363, loss: 2.8363 +2024-07-26 20:40:11,531 - pyskl - INFO - Epoch [128][2300/3746] lr: 5.396e-03, eta: 19:06:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4888, top5_acc: 0.7461, loss_cls: 2.8326, loss: 2.8326 +2024-07-26 20:41:34,750 - pyskl - INFO - Epoch [128][2400/3746] lr: 5.383e-03, eta: 19:05:17, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4959, top5_acc: 0.7453, loss_cls: 2.8231, loss: 2.8231 +2024-07-26 20:42:57,445 - pyskl - INFO - Epoch [128][2500/3746] lr: 5.370e-03, eta: 19:03:55, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4867, top5_acc: 0.7378, loss_cls: 2.8421, loss: 2.8421 +2024-07-26 20:44:19,587 - pyskl - INFO - Epoch [128][2600/3746] lr: 5.358e-03, eta: 19:02:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7414, loss_cls: 2.8443, loss: 2.8443 +2024-07-26 20:45:41,475 - pyskl - INFO - Epoch [128][2700/3746] lr: 5.345e-03, eta: 19:01:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7403, loss_cls: 2.8618, loss: 2.8618 +2024-07-26 20:47:02,848 - pyskl - INFO - Epoch [128][2800/3746] lr: 5.333e-03, eta: 18:59:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4930, top5_acc: 0.7492, loss_cls: 2.7901, loss: 2.7901 +2024-07-26 20:48:24,344 - pyskl - INFO - Epoch [128][2900/3746] lr: 5.320e-03, eta: 18:58:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7398, loss_cls: 2.8229, loss: 2.8229 +2024-07-26 20:49:46,032 - pyskl - INFO - Epoch [128][3000/3746] lr: 5.308e-03, eta: 18:57:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4873, top5_acc: 0.7422, loss_cls: 2.8633, loss: 2.8633 +2024-07-26 20:51:07,175 - pyskl - INFO - Epoch [128][3100/3746] lr: 5.295e-03, eta: 18:55:42, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4892, top5_acc: 0.7470, loss_cls: 2.8199, loss: 2.8199 +2024-07-26 20:52:28,878 - pyskl - INFO - Epoch [128][3200/3746] lr: 5.283e-03, eta: 18:54:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4966, top5_acc: 0.7367, loss_cls: 2.8570, loss: 2.8570 +2024-07-26 20:53:50,612 - pyskl - INFO - Epoch [128][3300/3746] lr: 5.270e-03, eta: 18:52:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4866, top5_acc: 0.7367, loss_cls: 2.8566, loss: 2.8566 +2024-07-26 20:55:11,896 - pyskl - INFO - Epoch [128][3400/3746] lr: 5.258e-03, eta: 18:51:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4931, top5_acc: 0.7381, loss_cls: 2.8495, loss: 2.8495 +2024-07-26 20:56:33,483 - pyskl - INFO - Epoch [128][3500/3746] lr: 5.245e-03, eta: 18:50:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4853, top5_acc: 0.7388, loss_cls: 2.8311, loss: 2.8311 +2024-07-26 20:57:54,891 - pyskl - INFO - Epoch [128][3600/3746] lr: 5.233e-03, eta: 18:48:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4841, top5_acc: 0.7331, loss_cls: 2.8611, loss: 2.8611 +2024-07-26 20:59:16,931 - pyskl - INFO - Epoch [128][3700/3746] lr: 5.220e-03, eta: 18:47:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4933, top5_acc: 0.7500, loss_cls: 2.8081, loss: 2.8081 +2024-07-26 20:59:56,463 - pyskl - INFO - Saving checkpoint at 128 epochs +2024-07-26 21:01:47,603 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 21:01:48,269 - pyskl - INFO - +top1_acc 0.4153 +top5_acc 0.6697 +2024-07-26 21:01:48,269 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 21:01:48,310 - pyskl - INFO - +mean_acc 0.4150 +2024-07-26 21:01:48,314 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_127.pth was removed +2024-07-26 21:01:48,552 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_128.pth. +2024-07-26 21:01:48,554 - pyskl - INFO - Best top1_acc is 0.4153 at 128 epoch. +2024-07-26 21:01:48,570 - pyskl - INFO - Epoch(val) [128][309] top1_acc: 0.4153, top5_acc: 0.6697, mean_class_accuracy: 0.4150 +2024-07-26 21:05:41,119 - pyskl - INFO - Epoch [129][100/3746] lr: 5.202e-03, eta: 18:45:49, time: 2.325, data_time: 1.349, memory: 15990, top1_acc: 0.5061, top5_acc: 0.7539, loss_cls: 2.7401, loss: 2.7401 +2024-07-26 21:07:02,985 - pyskl - INFO - Epoch [129][200/3746] lr: 5.190e-03, eta: 18:44:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5045, top5_acc: 0.7575, loss_cls: 2.7416, loss: 2.7416 +2024-07-26 21:08:24,686 - pyskl - INFO - Epoch [129][300/3746] lr: 5.177e-03, eta: 18:43:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5069, top5_acc: 0.7589, loss_cls: 2.7390, loss: 2.7390 +2024-07-26 21:09:46,408 - pyskl - INFO - Epoch [129][400/3746] lr: 5.165e-03, eta: 18:41:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5120, top5_acc: 0.7702, loss_cls: 2.6828, loss: 2.6828 +2024-07-26 21:11:08,171 - pyskl - INFO - Epoch [129][500/3746] lr: 5.153e-03, eta: 18:40:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5155, top5_acc: 0.7673, loss_cls: 2.6967, loss: 2.6967 +2024-07-26 21:12:30,564 - pyskl - INFO - Epoch [129][600/3746] lr: 5.140e-03, eta: 18:38:59, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5062, top5_acc: 0.7569, loss_cls: 2.7390, loss: 2.7390 +2024-07-26 21:13:52,716 - pyskl - INFO - Epoch [129][700/3746] lr: 5.128e-03, eta: 18:37:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5039, top5_acc: 0.7553, loss_cls: 2.7380, loss: 2.7380 +2024-07-26 21:15:15,303 - pyskl - INFO - Epoch [129][800/3746] lr: 5.116e-03, eta: 18:36:15, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4994, top5_acc: 0.7473, loss_cls: 2.7716, loss: 2.7716 +2024-07-26 21:16:37,356 - pyskl - INFO - Epoch [129][900/3746] lr: 5.103e-03, eta: 18:34:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5022, top5_acc: 0.7534, loss_cls: 2.7630, loss: 2.7630 +2024-07-26 21:17:58,900 - pyskl - INFO - Epoch [129][1000/3746] lr: 5.091e-03, eta: 18:33:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5016, top5_acc: 0.7473, loss_cls: 2.7779, loss: 2.7779 +2024-07-26 21:19:20,418 - pyskl - INFO - Epoch [129][1100/3746] lr: 5.079e-03, eta: 18:32:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4942, top5_acc: 0.7538, loss_cls: 2.7587, loss: 2.7587 +2024-07-26 21:20:41,827 - pyskl - INFO - Epoch [129][1200/3746] lr: 5.066e-03, eta: 18:30:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5017, top5_acc: 0.7478, loss_cls: 2.7630, loss: 2.7630 +2024-07-26 21:22:03,191 - pyskl - INFO - Epoch [129][1300/3746] lr: 5.054e-03, eta: 18:29:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4902, top5_acc: 0.7430, loss_cls: 2.7917, loss: 2.7917 +2024-07-26 21:23:24,948 - pyskl - INFO - Epoch [129][1400/3746] lr: 5.042e-03, eta: 18:28:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5088, top5_acc: 0.7558, loss_cls: 2.7425, loss: 2.7425 +2024-07-26 21:24:46,696 - pyskl - INFO - Epoch [129][1500/3746] lr: 5.030e-03, eta: 18:26:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4938, top5_acc: 0.7458, loss_cls: 2.8133, loss: 2.8133 +2024-07-26 21:26:08,239 - pyskl - INFO - Epoch [129][1600/3746] lr: 5.017e-03, eta: 18:25:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7531, loss_cls: 2.7721, loss: 2.7721 +2024-07-26 21:27:29,886 - pyskl - INFO - Epoch [129][1700/3746] lr: 5.005e-03, eta: 18:23:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5061, top5_acc: 0.7506, loss_cls: 2.7674, loss: 2.7674 +2024-07-26 21:28:51,277 - pyskl - INFO - Epoch [129][1800/3746] lr: 4.993e-03, eta: 18:22:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5075, top5_acc: 0.7533, loss_cls: 2.7596, loss: 2.7596 +2024-07-26 21:30:13,007 - pyskl - INFO - Epoch [129][1900/3746] lr: 4.981e-03, eta: 18:21:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4989, top5_acc: 0.7478, loss_cls: 2.7994, loss: 2.7994 +2024-07-26 21:31:34,225 - pyskl - INFO - Epoch [129][2000/3746] lr: 4.969e-03, eta: 18:19:49, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5067, top5_acc: 0.7528, loss_cls: 2.7427, loss: 2.7427 +2024-07-26 21:32:56,216 - pyskl - INFO - Epoch [129][2100/3746] lr: 4.957e-03, eta: 18:18:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4981, top5_acc: 0.7488, loss_cls: 2.7916, loss: 2.7916 +2024-07-26 21:34:17,952 - pyskl - INFO - Epoch [129][2200/3746] lr: 4.944e-03, eta: 18:17:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4952, top5_acc: 0.7500, loss_cls: 2.7997, loss: 2.7997 +2024-07-26 21:35:39,269 - pyskl - INFO - Epoch [129][2300/3746] lr: 4.932e-03, eta: 18:15:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4944, top5_acc: 0.7461, loss_cls: 2.7924, loss: 2.7924 +2024-07-26 21:37:01,539 - pyskl - INFO - Epoch [129][2400/3746] lr: 4.920e-03, eta: 18:14:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4959, top5_acc: 0.7400, loss_cls: 2.8225, loss: 2.8225 +2024-07-26 21:38:24,645 - pyskl - INFO - Epoch [129][2500/3746] lr: 4.908e-03, eta: 18:12:59, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5019, top5_acc: 0.7508, loss_cls: 2.7864, loss: 2.7864 +2024-07-26 21:39:46,397 - pyskl - INFO - Epoch [129][2600/3746] lr: 4.896e-03, eta: 18:11:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7495, loss_cls: 2.8053, loss: 2.8053 +2024-07-26 21:41:08,685 - pyskl - INFO - Epoch [129][2700/3746] lr: 4.884e-03, eta: 18:10:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4952, top5_acc: 0.7523, loss_cls: 2.7906, loss: 2.7906 +2024-07-26 21:42:30,381 - pyskl - INFO - Epoch [129][2800/3746] lr: 4.872e-03, eta: 18:08:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4928, top5_acc: 0.7436, loss_cls: 2.8105, loss: 2.8105 +2024-07-26 21:43:52,051 - pyskl - INFO - Epoch [129][2900/3746] lr: 4.860e-03, eta: 18:07:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5047, top5_acc: 0.7583, loss_cls: 2.7548, loss: 2.7548 +2024-07-26 21:45:13,109 - pyskl - INFO - Epoch [129][3000/3746] lr: 4.848e-03, eta: 18:06:08, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4953, top5_acc: 0.7422, loss_cls: 2.8391, loss: 2.8391 +2024-07-26 21:46:34,667 - pyskl - INFO - Epoch [129][3100/3746] lr: 4.836e-03, eta: 18:04:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7461, loss_cls: 2.8180, loss: 2.8180 +2024-07-26 21:47:56,011 - pyskl - INFO - Epoch [129][3200/3746] lr: 4.824e-03, eta: 18:03:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4903, top5_acc: 0.7403, loss_cls: 2.8236, loss: 2.8236 +2024-07-26 21:49:17,456 - pyskl - INFO - Epoch [129][3300/3746] lr: 4.812e-03, eta: 18:02:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4973, top5_acc: 0.7464, loss_cls: 2.8097, loss: 2.8097 +2024-07-26 21:50:39,280 - pyskl - INFO - Epoch [129][3400/3746] lr: 4.800e-03, eta: 18:00:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4952, top5_acc: 0.7469, loss_cls: 2.7966, loss: 2.7966 +2024-07-26 21:52:01,351 - pyskl - INFO - Epoch [129][3500/3746] lr: 4.788e-03, eta: 17:59:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4991, top5_acc: 0.7467, loss_cls: 2.7955, loss: 2.7955 +2024-07-26 21:53:23,517 - pyskl - INFO - Epoch [129][3600/3746] lr: 4.776e-03, eta: 17:57:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5016, top5_acc: 0.7600, loss_cls: 2.7356, loss: 2.7356 +2024-07-26 21:54:45,113 - pyskl - INFO - Epoch [129][3700/3746] lr: 4.764e-03, eta: 17:56:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5109, top5_acc: 0.7616, loss_cls: 2.7389, loss: 2.7389 +2024-07-26 21:55:24,951 - pyskl - INFO - Saving checkpoint at 129 epochs +2024-07-26 21:57:17,486 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 21:57:18,156 - pyskl - INFO - +top1_acc 0.4159 +top5_acc 0.6652 +2024-07-26 21:57:18,156 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 21:57:18,198 - pyskl - INFO - +mean_acc 0.4157 +2024-07-26 21:57:18,203 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_128.pth was removed +2024-07-26 21:57:18,464 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_129.pth. +2024-07-26 21:57:18,465 - pyskl - INFO - Best top1_acc is 0.4159 at 129 epoch. +2024-07-26 21:57:18,478 - pyskl - INFO - Epoch(val) [129][309] top1_acc: 0.4159, top5_acc: 0.6652, mean_class_accuracy: 0.4157 +2024-07-26 22:01:11,927 - pyskl - INFO - Epoch [130][100/3746] lr: 4.747e-03, eta: 17:54:52, time: 2.334, data_time: 1.340, memory: 15990, top1_acc: 0.5206, top5_acc: 0.7694, loss_cls: 2.6845, loss: 2.6845 +2024-07-26 22:02:34,132 - pyskl - INFO - Epoch [130][200/3746] lr: 4.735e-03, eta: 17:53:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5219, top5_acc: 0.7603, loss_cls: 2.7051, loss: 2.7051 +2024-07-26 22:03:55,950 - pyskl - INFO - Epoch [130][300/3746] lr: 4.723e-03, eta: 17:52:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5114, top5_acc: 0.7561, loss_cls: 2.7280, loss: 2.7280 +2024-07-26 22:05:17,499 - pyskl - INFO - Epoch [130][400/3746] lr: 4.711e-03, eta: 17:50:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5131, top5_acc: 0.7609, loss_cls: 2.7041, loss: 2.7041 +2024-07-26 22:06:39,222 - pyskl - INFO - Epoch [130][500/3746] lr: 4.699e-03, eta: 17:49:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5222, top5_acc: 0.7695, loss_cls: 2.6349, loss: 2.6349 +2024-07-26 22:08:01,331 - pyskl - INFO - Epoch [130][600/3746] lr: 4.688e-03, eta: 17:48:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5239, top5_acc: 0.7722, loss_cls: 2.6489, loss: 2.6489 +2024-07-26 22:09:23,084 - pyskl - INFO - Epoch [130][700/3746] lr: 4.676e-03, eta: 17:46:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5042, top5_acc: 0.7544, loss_cls: 2.7258, loss: 2.7258 +2024-07-26 22:10:45,383 - pyskl - INFO - Epoch [130][800/3746] lr: 4.664e-03, eta: 17:45:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5136, top5_acc: 0.7612, loss_cls: 2.7281, loss: 2.7281 +2024-07-26 22:12:06,754 - pyskl - INFO - Epoch [130][900/3746] lr: 4.652e-03, eta: 17:43:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5095, top5_acc: 0.7694, loss_cls: 2.7026, loss: 2.7026 +2024-07-26 22:13:28,600 - pyskl - INFO - Epoch [130][1000/3746] lr: 4.640e-03, eta: 17:42:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5167, top5_acc: 0.7522, loss_cls: 2.7410, loss: 2.7410 +2024-07-26 22:14:49,936 - pyskl - INFO - Epoch [130][1100/3746] lr: 4.629e-03, eta: 17:41:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5203, top5_acc: 0.7658, loss_cls: 2.6777, loss: 2.6777 +2024-07-26 22:16:11,597 - pyskl - INFO - Epoch [130][1200/3746] lr: 4.617e-03, eta: 17:39:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5183, top5_acc: 0.7638, loss_cls: 2.7062, loss: 2.7062 +2024-07-26 22:17:33,190 - pyskl - INFO - Epoch [130][1300/3746] lr: 4.605e-03, eta: 17:38:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5048, top5_acc: 0.7520, loss_cls: 2.7293, loss: 2.7293 +2024-07-26 22:18:54,407 - pyskl - INFO - Epoch [130][1400/3746] lr: 4.594e-03, eta: 17:37:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5161, top5_acc: 0.7617, loss_cls: 2.6994, loss: 2.6994 +2024-07-26 22:20:15,894 - pyskl - INFO - Epoch [130][1500/3746] lr: 4.582e-03, eta: 17:35:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5116, top5_acc: 0.7577, loss_cls: 2.7224, loss: 2.7224 +2024-07-26 22:21:38,163 - pyskl - INFO - Epoch [130][1600/3746] lr: 4.570e-03, eta: 17:34:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5122, top5_acc: 0.7531, loss_cls: 2.7496, loss: 2.7496 +2024-07-26 22:23:00,042 - pyskl - INFO - Epoch [130][1700/3746] lr: 4.558e-03, eta: 17:32:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5100, top5_acc: 0.7589, loss_cls: 2.7480, loss: 2.7480 +2024-07-26 22:24:21,555 - pyskl - INFO - Epoch [130][1800/3746] lr: 4.547e-03, eta: 17:31:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5058, top5_acc: 0.7678, loss_cls: 2.7095, loss: 2.7095 +2024-07-26 22:25:43,225 - pyskl - INFO - Epoch [130][1900/3746] lr: 4.535e-03, eta: 17:30:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5025, top5_acc: 0.7453, loss_cls: 2.7575, loss: 2.7575 +2024-07-26 22:27:04,765 - pyskl - INFO - Epoch [130][2000/3746] lr: 4.524e-03, eta: 17:28:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5088, top5_acc: 0.7625, loss_cls: 2.7068, loss: 2.7068 +2024-07-26 22:28:27,212 - pyskl - INFO - Epoch [130][2100/3746] lr: 4.512e-03, eta: 17:27:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4972, top5_acc: 0.7453, loss_cls: 2.8142, loss: 2.8142 +2024-07-26 22:29:49,148 - pyskl - INFO - Epoch [130][2200/3746] lr: 4.500e-03, eta: 17:26:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5084, top5_acc: 0.7577, loss_cls: 2.7363, loss: 2.7363 +2024-07-26 22:31:10,378 - pyskl - INFO - Epoch [130][2300/3746] lr: 4.489e-03, eta: 17:24:45, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5012, top5_acc: 0.7559, loss_cls: 2.7547, loss: 2.7547 +2024-07-26 22:32:32,961 - pyskl - INFO - Epoch [130][2400/3746] lr: 4.477e-03, eta: 17:23:23, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5148, top5_acc: 0.7577, loss_cls: 2.7074, loss: 2.7074 +2024-07-26 22:33:55,563 - pyskl - INFO - Epoch [130][2500/3746] lr: 4.466e-03, eta: 17:22:01, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4961, top5_acc: 0.7458, loss_cls: 2.7880, loss: 2.7880 +2024-07-26 22:35:17,941 - pyskl - INFO - Epoch [130][2600/3746] lr: 4.454e-03, eta: 17:20:39, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5027, top5_acc: 0.7484, loss_cls: 2.7870, loss: 2.7870 +2024-07-26 22:36:40,266 - pyskl - INFO - Epoch [130][2700/3746] lr: 4.443e-03, eta: 17:19:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5112, top5_acc: 0.7561, loss_cls: 2.7637, loss: 2.7637 +2024-07-26 22:38:02,376 - pyskl - INFO - Epoch [130][2800/3746] lr: 4.431e-03, eta: 17:17:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5041, top5_acc: 0.7530, loss_cls: 2.7639, loss: 2.7639 +2024-07-26 22:39:24,704 - pyskl - INFO - Epoch [130][2900/3746] lr: 4.420e-03, eta: 17:16:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7481, loss_cls: 2.8108, loss: 2.8108 +2024-07-26 22:40:46,305 - pyskl - INFO - Epoch [130][3000/3746] lr: 4.408e-03, eta: 17:15:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5059, top5_acc: 0.7527, loss_cls: 2.7447, loss: 2.7447 +2024-07-26 22:42:07,705 - pyskl - INFO - Epoch [130][3100/3746] lr: 4.397e-03, eta: 17:13:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5000, top5_acc: 0.7489, loss_cls: 2.7798, loss: 2.7798 +2024-07-26 22:43:29,320 - pyskl - INFO - Epoch [130][3200/3746] lr: 4.385e-03, eta: 17:12:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4989, top5_acc: 0.7483, loss_cls: 2.7800, loss: 2.7800 +2024-07-26 22:44:50,920 - pyskl - INFO - Epoch [130][3300/3746] lr: 4.374e-03, eta: 17:11:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5008, top5_acc: 0.7484, loss_cls: 2.7942, loss: 2.7942 +2024-07-26 22:46:13,350 - pyskl - INFO - Epoch [130][3400/3746] lr: 4.362e-03, eta: 17:09:42, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5016, top5_acc: 0.7475, loss_cls: 2.8222, loss: 2.8222 +2024-07-26 22:47:35,159 - pyskl - INFO - Epoch [130][3500/3746] lr: 4.351e-03, eta: 17:08:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5069, top5_acc: 0.7561, loss_cls: 2.7502, loss: 2.7502 +2024-07-26 22:48:56,386 - pyskl - INFO - Epoch [130][3600/3746] lr: 4.339e-03, eta: 17:06:58, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5122, top5_acc: 0.7486, loss_cls: 2.7534, loss: 2.7534 +2024-07-26 22:50:17,889 - pyskl - INFO - Epoch [130][3700/3746] lr: 4.328e-03, eta: 17:05:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5134, top5_acc: 0.7645, loss_cls: 2.7080, loss: 2.7080 +2024-07-26 22:50:57,057 - pyskl - INFO - Saving checkpoint at 130 epochs +2024-07-26 22:52:47,859 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 22:52:48,519 - pyskl - INFO - +top1_acc 0.4156 +top5_acc 0.6700 +2024-07-26 22:52:48,519 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 22:52:48,560 - pyskl - INFO - +mean_acc 0.4152 +2024-07-26 22:52:48,572 - pyskl - INFO - Epoch(val) [130][309] top1_acc: 0.4156, top5_acc: 0.6700, mean_class_accuracy: 0.4152 +2024-07-26 22:56:35,575 - pyskl - INFO - Epoch [131][100/3746] lr: 4.311e-03, eta: 17:03:52, time: 2.270, data_time: 1.296, memory: 15990, top1_acc: 0.5192, top5_acc: 0.7681, loss_cls: 2.6428, loss: 2.6428 +2024-07-26 22:57:57,612 - pyskl - INFO - Epoch [131][200/3746] lr: 4.300e-03, eta: 17:02:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7736, loss_cls: 2.6429, loss: 2.6429 +2024-07-26 22:59:19,949 - pyskl - INFO - Epoch [131][300/3746] lr: 4.289e-03, eta: 17:01:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5145, top5_acc: 0.7681, loss_cls: 2.6752, loss: 2.6752 +2024-07-26 23:00:41,885 - pyskl - INFO - Epoch [131][400/3746] lr: 4.277e-03, eta: 16:59:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5306, top5_acc: 0.7719, loss_cls: 2.6103, loss: 2.6103 +2024-07-26 23:02:03,457 - pyskl - INFO - Epoch [131][500/3746] lr: 4.266e-03, eta: 16:58:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5264, top5_acc: 0.7709, loss_cls: 2.6184, loss: 2.6184 +2024-07-26 23:03:25,911 - pyskl - INFO - Epoch [131][600/3746] lr: 4.255e-03, eta: 16:57:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7673, loss_cls: 2.6683, loss: 2.6683 +2024-07-26 23:04:47,612 - pyskl - INFO - Epoch [131][700/3746] lr: 4.244e-03, eta: 16:55:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5269, top5_acc: 0.7709, loss_cls: 2.6345, loss: 2.6345 +2024-07-26 23:06:09,455 - pyskl - INFO - Epoch [131][800/3746] lr: 4.232e-03, eta: 16:54:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5109, top5_acc: 0.7630, loss_cls: 2.7003, loss: 2.7003 +2024-07-26 23:07:31,191 - pyskl - INFO - Epoch [131][900/3746] lr: 4.221e-03, eta: 16:52:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5038, top5_acc: 0.7547, loss_cls: 2.7325, loss: 2.7325 +2024-07-26 23:08:52,682 - pyskl - INFO - Epoch [131][1000/3746] lr: 4.210e-03, eta: 16:51:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5309, top5_acc: 0.7709, loss_cls: 2.6418, loss: 2.6418 +2024-07-26 23:10:14,550 - pyskl - INFO - Epoch [131][1100/3746] lr: 4.199e-03, eta: 16:50:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5095, top5_acc: 0.7575, loss_cls: 2.7118, loss: 2.7118 +2024-07-26 23:11:35,766 - pyskl - INFO - Epoch [131][1200/3746] lr: 4.187e-03, eta: 16:48:48, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5089, top5_acc: 0.7547, loss_cls: 2.7261, loss: 2.7261 +2024-07-26 23:12:57,000 - pyskl - INFO - Epoch [131][1300/3746] lr: 4.176e-03, eta: 16:47:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5206, top5_acc: 0.7619, loss_cls: 2.6781, loss: 2.6781 +2024-07-26 23:14:18,272 - pyskl - INFO - Epoch [131][1400/3746] lr: 4.165e-03, eta: 16:46:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5270, top5_acc: 0.7603, loss_cls: 2.6576, loss: 2.6576 +2024-07-26 23:15:39,855 - pyskl - INFO - Epoch [131][1500/3746] lr: 4.154e-03, eta: 16:44:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5103, top5_acc: 0.7594, loss_cls: 2.6871, loss: 2.6871 +2024-07-26 23:17:01,733 - pyskl - INFO - Epoch [131][1600/3746] lr: 4.143e-03, eta: 16:43:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5170, top5_acc: 0.7723, loss_cls: 2.6814, loss: 2.6814 +2024-07-26 23:18:23,308 - pyskl - INFO - Epoch [131][1700/3746] lr: 4.132e-03, eta: 16:41:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5034, top5_acc: 0.7584, loss_cls: 2.7079, loss: 2.7079 +2024-07-26 23:19:45,047 - pyskl - INFO - Epoch [131][1800/3746] lr: 4.120e-03, eta: 16:40:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5161, top5_acc: 0.7658, loss_cls: 2.7041, loss: 2.7041 +2024-07-26 23:21:06,641 - pyskl - INFO - Epoch [131][1900/3746] lr: 4.109e-03, eta: 16:39:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5197, top5_acc: 0.7620, loss_cls: 2.6730, loss: 2.6730 +2024-07-26 23:22:28,431 - pyskl - INFO - Epoch [131][2000/3746] lr: 4.098e-03, eta: 16:37:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5162, top5_acc: 0.7611, loss_cls: 2.7064, loss: 2.7064 +2024-07-26 23:23:50,237 - pyskl - INFO - Epoch [131][2100/3746] lr: 4.087e-03, eta: 16:36:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5130, top5_acc: 0.7591, loss_cls: 2.6900, loss: 2.6900 +2024-07-26 23:25:11,789 - pyskl - INFO - Epoch [131][2200/3746] lr: 4.076e-03, eta: 16:35:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5172, top5_acc: 0.7678, loss_cls: 2.6989, loss: 2.6989 +2024-07-26 23:26:32,976 - pyskl - INFO - Epoch [131][2300/3746] lr: 4.065e-03, eta: 16:33:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5084, top5_acc: 0.7633, loss_cls: 2.7154, loss: 2.7154 +2024-07-26 23:27:55,187 - pyskl - INFO - Epoch [131][2400/3746] lr: 4.054e-03, eta: 16:32:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5138, top5_acc: 0.7572, loss_cls: 2.7021, loss: 2.7021 +2024-07-26 23:29:16,847 - pyskl - INFO - Epoch [131][2500/3746] lr: 4.043e-03, eta: 16:31:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5097, top5_acc: 0.7514, loss_cls: 2.7618, loss: 2.7618 +2024-07-26 23:30:39,453 - pyskl - INFO - Epoch [131][2600/3746] lr: 4.032e-03, eta: 16:29:38, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5059, top5_acc: 0.7512, loss_cls: 2.7519, loss: 2.7519 +2024-07-26 23:32:01,093 - pyskl - INFO - Epoch [131][2700/3746] lr: 4.021e-03, eta: 16:28:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5264, top5_acc: 0.7602, loss_cls: 2.6827, loss: 2.6827 +2024-07-26 23:33:23,021 - pyskl - INFO - Epoch [131][2800/3746] lr: 4.010e-03, eta: 16:26:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5106, top5_acc: 0.7564, loss_cls: 2.7437, loss: 2.7437 +2024-07-26 23:34:45,168 - pyskl - INFO - Epoch [131][2900/3746] lr: 3.999e-03, eta: 16:25:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5217, top5_acc: 0.7683, loss_cls: 2.6755, loss: 2.6755 +2024-07-26 23:36:06,645 - pyskl - INFO - Epoch [131][3000/3746] lr: 3.988e-03, eta: 16:24:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5086, top5_acc: 0.7559, loss_cls: 2.7232, loss: 2.7232 +2024-07-26 23:37:28,104 - pyskl - INFO - Epoch [131][3100/3746] lr: 3.977e-03, eta: 16:22:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5164, top5_acc: 0.7572, loss_cls: 2.7152, loss: 2.7152 +2024-07-26 23:38:50,016 - pyskl - INFO - Epoch [131][3200/3746] lr: 3.966e-03, eta: 16:21:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5147, top5_acc: 0.7603, loss_cls: 2.6909, loss: 2.6909 +2024-07-26 23:40:11,434 - pyskl - INFO - Epoch [131][3300/3746] lr: 3.955e-03, eta: 16:20:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5152, top5_acc: 0.7630, loss_cls: 2.6897, loss: 2.6897 +2024-07-26 23:41:32,704 - pyskl - INFO - Epoch [131][3400/3746] lr: 3.945e-03, eta: 16:18:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5072, top5_acc: 0.7592, loss_cls: 2.7290, loss: 2.7290 +2024-07-26 23:42:54,302 - pyskl - INFO - Epoch [131][3500/3746] lr: 3.934e-03, eta: 16:17:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5078, top5_acc: 0.7591, loss_cls: 2.7257, loss: 2.7257 +2024-07-26 23:44:16,105 - pyskl - INFO - Epoch [131][3600/3746] lr: 3.923e-03, eta: 16:15:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4938, top5_acc: 0.7488, loss_cls: 2.7952, loss: 2.7952 +2024-07-26 23:45:37,278 - pyskl - INFO - Epoch [131][3700/3746] lr: 3.912e-03, eta: 16:14:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5028, top5_acc: 0.7508, loss_cls: 2.7626, loss: 2.7626 +2024-07-26 23:46:16,893 - pyskl - INFO - Saving checkpoint at 131 epochs +2024-07-26 23:48:08,780 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 23:48:09,476 - pyskl - INFO - +top1_acc 0.4209 +top5_acc 0.6730 +2024-07-26 23:48:09,476 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 23:48:09,532 - pyskl - INFO - +mean_acc 0.4206 +2024-07-26 23:48:09,538 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_129.pth was removed +2024-07-26 23:48:09,772 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_131.pth. +2024-07-26 23:48:09,773 - pyskl - INFO - Best top1_acc is 0.4209 at 131 epoch. +2024-07-26 23:48:09,787 - pyskl - INFO - Epoch(val) [131][309] top1_acc: 0.4209, top5_acc: 0.6730, mean_class_accuracy: 0.4206 +2024-07-26 23:51:59,931 - pyskl - INFO - Epoch [132][100/3746] lr: 3.896e-03, eta: 16:12:50, time: 2.301, data_time: 1.323, memory: 15990, top1_acc: 0.5250, top5_acc: 0.7731, loss_cls: 2.6226, loss: 2.6226 +2024-07-26 23:53:22,064 - pyskl - INFO - Epoch [132][200/3746] lr: 3.885e-03, eta: 16:11:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5328, top5_acc: 0.7762, loss_cls: 2.6005, loss: 2.6005 +2024-07-26 23:54:43,721 - pyskl - INFO - Epoch [132][300/3746] lr: 3.875e-03, eta: 16:10:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5328, top5_acc: 0.7786, loss_cls: 2.5884, loss: 2.5884 +2024-07-26 23:56:05,527 - pyskl - INFO - Epoch [132][400/3746] lr: 3.864e-03, eta: 16:08:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5278, top5_acc: 0.7775, loss_cls: 2.6077, loss: 2.6077 +2024-07-26 23:57:26,946 - pyskl - INFO - Epoch [132][500/3746] lr: 3.853e-03, eta: 16:07:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5320, top5_acc: 0.7795, loss_cls: 2.5998, loss: 2.5998 +2024-07-26 23:58:49,204 - pyskl - INFO - Epoch [132][600/3746] lr: 3.842e-03, eta: 16:05:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5266, top5_acc: 0.7739, loss_cls: 2.6160, loss: 2.6160 +2024-07-27 00:00:10,499 - pyskl - INFO - Epoch [132][700/3746] lr: 3.831e-03, eta: 16:04:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5353, top5_acc: 0.7750, loss_cls: 2.6090, loss: 2.6090 +2024-07-27 00:01:32,031 - pyskl - INFO - Epoch [132][800/3746] lr: 3.821e-03, eta: 16:03:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5170, top5_acc: 0.7720, loss_cls: 2.6503, loss: 2.6503 +2024-07-27 00:02:53,367 - pyskl - INFO - Epoch [132][900/3746] lr: 3.810e-03, eta: 16:01:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5292, top5_acc: 0.7717, loss_cls: 2.6295, loss: 2.6295 +2024-07-27 00:04:15,299 - pyskl - INFO - Epoch [132][1000/3746] lr: 3.799e-03, eta: 16:00:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5192, top5_acc: 0.7694, loss_cls: 2.6726, loss: 2.6726 +2024-07-27 00:05:36,704 - pyskl - INFO - Epoch [132][1100/3746] lr: 3.789e-03, eta: 15:59:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5153, top5_acc: 0.7550, loss_cls: 2.7152, loss: 2.7152 +2024-07-27 00:06:58,651 - pyskl - INFO - Epoch [132][1200/3746] lr: 3.778e-03, eta: 15:57:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5188, top5_acc: 0.7727, loss_cls: 2.6759, loss: 2.6759 +2024-07-27 00:08:20,210 - pyskl - INFO - Epoch [132][1300/3746] lr: 3.767e-03, eta: 15:56:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5095, top5_acc: 0.7602, loss_cls: 2.7058, loss: 2.7058 +2024-07-27 00:09:41,585 - pyskl - INFO - Epoch [132][1400/3746] lr: 3.757e-03, eta: 15:55:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5097, top5_acc: 0.7627, loss_cls: 2.6782, loss: 2.6782 +2024-07-27 00:11:02,782 - pyskl - INFO - Epoch [132][1500/3746] lr: 3.746e-03, eta: 15:53:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5328, top5_acc: 0.7733, loss_cls: 2.5990, loss: 2.5990 +2024-07-27 00:12:24,084 - pyskl - INFO - Epoch [132][1600/3746] lr: 3.735e-03, eta: 15:52:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5191, top5_acc: 0.7661, loss_cls: 2.6374, loss: 2.6374 +2024-07-27 00:13:45,843 - pyskl - INFO - Epoch [132][1700/3746] lr: 3.725e-03, eta: 15:50:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5308, top5_acc: 0.7738, loss_cls: 2.6205, loss: 2.6205 +2024-07-27 00:15:07,556 - pyskl - INFO - Epoch [132][1800/3746] lr: 3.714e-03, eta: 15:49:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5267, top5_acc: 0.7700, loss_cls: 2.6421, loss: 2.6421 +2024-07-27 00:16:28,659 - pyskl - INFO - Epoch [132][1900/3746] lr: 3.704e-03, eta: 15:48:11, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5298, top5_acc: 0.7709, loss_cls: 2.6268, loss: 2.6268 +2024-07-27 00:17:50,219 - pyskl - INFO - Epoch [132][2000/3746] lr: 3.693e-03, eta: 15:46:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5288, top5_acc: 0.7750, loss_cls: 2.6470, loss: 2.6470 +2024-07-27 00:19:11,388 - pyskl - INFO - Epoch [132][2100/3746] lr: 3.683e-03, eta: 15:45:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5230, top5_acc: 0.7688, loss_cls: 2.6499, loss: 2.6499 +2024-07-27 00:20:32,829 - pyskl - INFO - Epoch [132][2200/3746] lr: 3.672e-03, eta: 15:44:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5198, top5_acc: 0.7644, loss_cls: 2.6674, loss: 2.6674 +2024-07-27 00:21:54,206 - pyskl - INFO - Epoch [132][2300/3746] lr: 3.662e-03, eta: 15:42:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5105, top5_acc: 0.7530, loss_cls: 2.7342, loss: 2.7342 +2024-07-27 00:23:15,882 - pyskl - INFO - Epoch [132][2400/3746] lr: 3.651e-03, eta: 15:41:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5220, top5_acc: 0.7683, loss_cls: 2.6543, loss: 2.6543 +2024-07-27 00:24:37,553 - pyskl - INFO - Epoch [132][2500/3746] lr: 3.641e-03, eta: 15:39:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5306, top5_acc: 0.7650, loss_cls: 2.6384, loss: 2.6384 +2024-07-27 00:26:00,119 - pyskl - INFO - Epoch [132][2600/3746] lr: 3.630e-03, eta: 15:38:35, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5153, top5_acc: 0.7597, loss_cls: 2.6813, loss: 2.6813 +2024-07-27 00:27:21,995 - pyskl - INFO - Epoch [132][2700/3746] lr: 3.620e-03, eta: 15:37:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5181, top5_acc: 0.7661, loss_cls: 2.6646, loss: 2.6646 +2024-07-27 00:28:43,728 - pyskl - INFO - Epoch [132][2800/3746] lr: 3.609e-03, eta: 15:35:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5159, top5_acc: 0.7673, loss_cls: 2.6648, loss: 2.6648 +2024-07-27 00:30:05,928 - pyskl - INFO - Epoch [132][2900/3746] lr: 3.599e-03, eta: 15:34:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5033, top5_acc: 0.7502, loss_cls: 2.7724, loss: 2.7724 +2024-07-27 00:31:27,580 - pyskl - INFO - Epoch [132][3000/3746] lr: 3.588e-03, eta: 15:33:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5194, top5_acc: 0.7645, loss_cls: 2.6895, loss: 2.6895 +2024-07-27 00:32:48,715 - pyskl - INFO - Epoch [132][3100/3746] lr: 3.578e-03, eta: 15:31:44, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5172, top5_acc: 0.7617, loss_cls: 2.6836, loss: 2.6836 +2024-07-27 00:34:10,354 - pyskl - INFO - Epoch [132][3200/3746] lr: 3.568e-03, eta: 15:30:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5194, top5_acc: 0.7659, loss_cls: 2.6615, loss: 2.6615 +2024-07-27 00:35:31,533 - pyskl - INFO - Epoch [132][3300/3746] lr: 3.557e-03, eta: 15:29:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5178, top5_acc: 0.7627, loss_cls: 2.6728, loss: 2.6728 +2024-07-27 00:36:52,826 - pyskl - INFO - Epoch [132][3400/3746] lr: 3.547e-03, eta: 15:27:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5209, top5_acc: 0.7638, loss_cls: 2.6661, loss: 2.6661 +2024-07-27 00:38:13,993 - pyskl - INFO - Epoch [132][3500/3746] lr: 3.537e-03, eta: 15:26:15, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5134, top5_acc: 0.7514, loss_cls: 2.7261, loss: 2.7261 +2024-07-27 00:39:35,265 - pyskl - INFO - Epoch [132][3600/3746] lr: 3.526e-03, eta: 15:24:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5116, top5_acc: 0.7633, loss_cls: 2.6935, loss: 2.6935 +2024-07-27 00:40:56,487 - pyskl - INFO - Epoch [132][3700/3746] lr: 3.516e-03, eta: 15:23:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5255, top5_acc: 0.7680, loss_cls: 2.6678, loss: 2.6678 +2024-07-27 00:41:35,943 - pyskl - INFO - Saving checkpoint at 132 epochs +2024-07-27 00:43:26,239 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 00:43:26,913 - pyskl - INFO - +top1_acc 0.4277 +top5_acc 0.6825 +2024-07-27 00:43:26,913 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 00:43:26,954 - pyskl - INFO - +mean_acc 0.4273 +2024-07-27 00:43:26,958 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_131.pth was removed +2024-07-27 00:43:27,200 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_132.pth. +2024-07-27 00:43:27,201 - pyskl - INFO - Best top1_acc is 0.4277 at 132 epoch. +2024-07-27 00:43:27,213 - pyskl - INFO - Epoch(val) [132][309] top1_acc: 0.4277, top5_acc: 0.6825, mean_class_accuracy: 0.4273 +2024-07-27 00:47:14,524 - pyskl - INFO - Epoch [133][100/3746] lr: 3.501e-03, eta: 15:21:46, time: 2.273, data_time: 1.288, memory: 15990, top1_acc: 0.5375, top5_acc: 0.7827, loss_cls: 2.5463, loss: 2.5463 +2024-07-27 00:48:37,016 - pyskl - INFO - Epoch [133][200/3746] lr: 3.491e-03, eta: 15:20:24, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5495, top5_acc: 0.7883, loss_cls: 2.5017, loss: 2.5017 +2024-07-27 00:49:58,672 - pyskl - INFO - Epoch [133][300/3746] lr: 3.480e-03, eta: 15:19:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5375, top5_acc: 0.7798, loss_cls: 2.5632, loss: 2.5632 +2024-07-27 00:51:20,273 - pyskl - INFO - Epoch [133][400/3746] lr: 3.470e-03, eta: 15:17:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5469, top5_acc: 0.7828, loss_cls: 2.5450, loss: 2.5450 +2024-07-27 00:52:41,588 - pyskl - INFO - Epoch [133][500/3746] lr: 3.460e-03, eta: 15:16:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5466, top5_acc: 0.7809, loss_cls: 2.5528, loss: 2.5528 +2024-07-27 00:54:02,848 - pyskl - INFO - Epoch [133][600/3746] lr: 3.450e-03, eta: 15:14:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5381, top5_acc: 0.7794, loss_cls: 2.5841, loss: 2.5841 +2024-07-27 00:55:24,255 - pyskl - INFO - Epoch [133][700/3746] lr: 3.440e-03, eta: 15:13:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5289, top5_acc: 0.7791, loss_cls: 2.5981, loss: 2.5981 +2024-07-27 00:56:46,384 - pyskl - INFO - Epoch [133][800/3746] lr: 3.429e-03, eta: 15:12:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5330, top5_acc: 0.7833, loss_cls: 2.5778, loss: 2.5778 +2024-07-27 00:58:08,059 - pyskl - INFO - Epoch [133][900/3746] lr: 3.419e-03, eta: 15:10:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5380, top5_acc: 0.7875, loss_cls: 2.5174, loss: 2.5174 +2024-07-27 00:59:30,047 - pyskl - INFO - Epoch [133][1000/3746] lr: 3.409e-03, eta: 15:09:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5319, top5_acc: 0.7742, loss_cls: 2.5867, loss: 2.5867 +2024-07-27 01:00:51,645 - pyskl - INFO - Epoch [133][1100/3746] lr: 3.399e-03, eta: 15:08:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5400, top5_acc: 0.7817, loss_cls: 2.5570, loss: 2.5570 +2024-07-27 01:02:12,822 - pyskl - INFO - Epoch [133][1200/3746] lr: 3.389e-03, eta: 15:06:41, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5337, top5_acc: 0.7802, loss_cls: 2.5874, loss: 2.5874 +2024-07-27 01:03:34,831 - pyskl - INFO - Epoch [133][1300/3746] lr: 3.379e-03, eta: 15:05:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5219, top5_acc: 0.7750, loss_cls: 2.6250, loss: 2.6250 +2024-07-27 01:04:56,225 - pyskl - INFO - Epoch [133][1400/3746] lr: 3.369e-03, eta: 15:03:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5366, top5_acc: 0.7733, loss_cls: 2.5873, loss: 2.5873 +2024-07-27 01:06:17,860 - pyskl - INFO - Epoch [133][1500/3746] lr: 3.359e-03, eta: 15:02:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5330, top5_acc: 0.7783, loss_cls: 2.5847, loss: 2.5847 +2024-07-27 01:07:39,912 - pyskl - INFO - Epoch [133][1600/3746] lr: 3.348e-03, eta: 15:01:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5197, top5_acc: 0.7688, loss_cls: 2.6626, loss: 2.6626 +2024-07-27 01:09:01,790 - pyskl - INFO - Epoch [133][1700/3746] lr: 3.338e-03, eta: 14:59:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5225, top5_acc: 0.7786, loss_cls: 2.6092, loss: 2.6092 +2024-07-27 01:10:23,445 - pyskl - INFO - Epoch [133][1800/3746] lr: 3.328e-03, eta: 14:58:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5280, top5_acc: 0.7688, loss_cls: 2.6510, loss: 2.6510 +2024-07-27 01:11:44,983 - pyskl - INFO - Epoch [133][1900/3746] lr: 3.318e-03, eta: 14:57:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5308, top5_acc: 0.7683, loss_cls: 2.6210, loss: 2.6210 +2024-07-27 01:13:06,162 - pyskl - INFO - Epoch [133][2000/3746] lr: 3.308e-03, eta: 14:55:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5133, top5_acc: 0.7684, loss_cls: 2.6599, loss: 2.6599 +2024-07-27 01:14:27,659 - pyskl - INFO - Epoch [133][2100/3746] lr: 3.298e-03, eta: 14:54:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5280, top5_acc: 0.7694, loss_cls: 2.6229, loss: 2.6229 +2024-07-27 01:15:49,194 - pyskl - INFO - Epoch [133][2200/3746] lr: 3.288e-03, eta: 14:52:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5342, top5_acc: 0.7673, loss_cls: 2.6217, loss: 2.6217 +2024-07-27 01:17:10,633 - pyskl - INFO - Epoch [133][2300/3746] lr: 3.278e-03, eta: 14:51:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7669, loss_cls: 2.6743, loss: 2.6743 +2024-07-27 01:18:32,884 - pyskl - INFO - Epoch [133][2400/3746] lr: 3.268e-03, eta: 14:50:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5212, top5_acc: 0.7650, loss_cls: 2.6565, loss: 2.6565 +2024-07-27 01:19:54,832 - pyskl - INFO - Epoch [133][2500/3746] lr: 3.259e-03, eta: 14:48:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5194, top5_acc: 0.7703, loss_cls: 2.6820, loss: 2.6820 +2024-07-27 01:21:17,250 - pyskl - INFO - Epoch [133][2600/3746] lr: 3.249e-03, eta: 14:47:31, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5272, top5_acc: 0.7731, loss_cls: 2.6170, loss: 2.6170 +2024-07-27 01:22:39,256 - pyskl - INFO - Epoch [133][2700/3746] lr: 3.239e-03, eta: 14:46:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5267, top5_acc: 0.7708, loss_cls: 2.6403, loss: 2.6403 +2024-07-27 01:24:01,272 - pyskl - INFO - Epoch [133][2800/3746] lr: 3.229e-03, eta: 14:44:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5159, top5_acc: 0.7691, loss_cls: 2.6629, loss: 2.6629 +2024-07-27 01:25:23,124 - pyskl - INFO - Epoch [133][2900/3746] lr: 3.219e-03, eta: 14:43:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5206, top5_acc: 0.7606, loss_cls: 2.6785, loss: 2.6785 +2024-07-27 01:26:44,935 - pyskl - INFO - Epoch [133][3000/3746] lr: 3.209e-03, eta: 14:42:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5225, top5_acc: 0.7692, loss_cls: 2.6213, loss: 2.6213 +2024-07-27 01:28:06,260 - pyskl - INFO - Epoch [133][3100/3746] lr: 3.199e-03, eta: 14:40:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5317, top5_acc: 0.7711, loss_cls: 2.6230, loss: 2.6230 +2024-07-27 01:29:28,706 - pyskl - INFO - Epoch [133][3200/3746] lr: 3.189e-03, eta: 14:39:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5247, top5_acc: 0.7695, loss_cls: 2.6177, loss: 2.6177 +2024-07-27 01:30:50,009 - pyskl - INFO - Epoch [133][3300/3746] lr: 3.180e-03, eta: 14:37:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7639, loss_cls: 2.6532, loss: 2.6532 +2024-07-27 01:32:11,804 - pyskl - INFO - Epoch [133][3400/3746] lr: 3.170e-03, eta: 14:36:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5208, top5_acc: 0.7697, loss_cls: 2.6610, loss: 2.6610 +2024-07-27 01:33:33,150 - pyskl - INFO - Epoch [133][3500/3746] lr: 3.160e-03, eta: 14:35:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5205, top5_acc: 0.7627, loss_cls: 2.6772, loss: 2.6772 +2024-07-27 01:34:54,181 - pyskl - INFO - Epoch [133][3600/3746] lr: 3.150e-03, eta: 14:33:49, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5323, top5_acc: 0.7747, loss_cls: 2.6049, loss: 2.6049 +2024-07-27 01:36:15,559 - pyskl - INFO - Epoch [133][3700/3746] lr: 3.140e-03, eta: 14:32:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5195, top5_acc: 0.7673, loss_cls: 2.6624, loss: 2.6624 +2024-07-27 01:36:55,171 - pyskl - INFO - Saving checkpoint at 133 epochs +2024-07-27 01:38:45,747 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 01:38:46,406 - pyskl - INFO - +top1_acc 0.4312 +top5_acc 0.6788 +2024-07-27 01:38:46,406 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 01:38:46,447 - pyskl - INFO - +mean_acc 0.4309 +2024-07-27 01:38:46,451 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_132.pth was removed +2024-07-27 01:38:46,682 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_133.pth. +2024-07-27 01:38:46,683 - pyskl - INFO - Best top1_acc is 0.4312 at 133 epoch. +2024-07-27 01:38:46,694 - pyskl - INFO - Epoch(val) [133][309] top1_acc: 0.4312, top5_acc: 0.6788, mean_class_accuracy: 0.4309 +2024-07-27 01:42:33,765 - pyskl - INFO - Epoch [134][100/3746] lr: 3.126e-03, eta: 14:30:40, time: 2.271, data_time: 1.290, memory: 15990, top1_acc: 0.5531, top5_acc: 0.7898, loss_cls: 2.5262, loss: 2.5262 +2024-07-27 01:43:55,755 - pyskl - INFO - Epoch [134][200/3746] lr: 3.117e-03, eta: 14:29:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5650, top5_acc: 0.7995, loss_cls: 2.4501, loss: 2.4501 +2024-07-27 01:45:17,485 - pyskl - INFO - Epoch [134][300/3746] lr: 3.107e-03, eta: 14:27:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5534, top5_acc: 0.7973, loss_cls: 2.4808, loss: 2.4808 +2024-07-27 01:46:39,786 - pyskl - INFO - Epoch [134][400/3746] lr: 3.097e-03, eta: 14:26:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5445, top5_acc: 0.7822, loss_cls: 2.5233, loss: 2.5233 +2024-07-27 01:48:01,278 - pyskl - INFO - Epoch [134][500/3746] lr: 3.087e-03, eta: 14:25:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5359, top5_acc: 0.7842, loss_cls: 2.5357, loss: 2.5357 +2024-07-27 01:49:23,310 - pyskl - INFO - Epoch [134][600/3746] lr: 3.078e-03, eta: 14:23:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5420, top5_acc: 0.7812, loss_cls: 2.5442, loss: 2.5442 +2024-07-27 01:50:45,332 - pyskl - INFO - Epoch [134][700/3746] lr: 3.068e-03, eta: 14:22:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5369, top5_acc: 0.7811, loss_cls: 2.5618, loss: 2.5618 +2024-07-27 01:52:07,556 - pyskl - INFO - Epoch [134][800/3746] lr: 3.059e-03, eta: 14:21:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5466, top5_acc: 0.7927, loss_cls: 2.5171, loss: 2.5171 +2024-07-27 01:53:29,207 - pyskl - INFO - Epoch [134][900/3746] lr: 3.049e-03, eta: 14:19:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5448, top5_acc: 0.7827, loss_cls: 2.5424, loss: 2.5424 +2024-07-27 01:54:51,680 - pyskl - INFO - Epoch [134][1000/3746] lr: 3.039e-03, eta: 14:18:20, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5311, top5_acc: 0.7837, loss_cls: 2.5661, loss: 2.5661 +2024-07-27 01:56:12,936 - pyskl - INFO - Epoch [134][1100/3746] lr: 3.030e-03, eta: 14:16:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5417, top5_acc: 0.7902, loss_cls: 2.5149, loss: 2.5149 +2024-07-27 01:57:34,695 - pyskl - INFO - Epoch [134][1200/3746] lr: 3.020e-03, eta: 14:15:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5347, top5_acc: 0.7730, loss_cls: 2.6117, loss: 2.6117 +2024-07-27 01:58:55,947 - pyskl - INFO - Epoch [134][1300/3746] lr: 3.011e-03, eta: 14:14:14, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5380, top5_acc: 0.7816, loss_cls: 2.6030, loss: 2.6030 +2024-07-27 02:00:17,412 - pyskl - INFO - Epoch [134][1400/3746] lr: 3.001e-03, eta: 14:12:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5227, top5_acc: 0.7752, loss_cls: 2.5992, loss: 2.5992 +2024-07-27 02:01:39,283 - pyskl - INFO - Epoch [134][1500/3746] lr: 2.991e-03, eta: 14:11:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5375, top5_acc: 0.7816, loss_cls: 2.5893, loss: 2.5893 +2024-07-27 02:03:01,439 - pyskl - INFO - Epoch [134][1600/3746] lr: 2.982e-03, eta: 14:10:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5298, top5_acc: 0.7780, loss_cls: 2.6065, loss: 2.6065 +2024-07-27 02:04:22,859 - pyskl - INFO - Epoch [134][1700/3746] lr: 2.972e-03, eta: 14:08:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5331, top5_acc: 0.7827, loss_cls: 2.5956, loss: 2.5956 +2024-07-27 02:05:44,561 - pyskl - INFO - Epoch [134][1800/3746] lr: 2.963e-03, eta: 14:07:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5480, top5_acc: 0.7828, loss_cls: 2.5288, loss: 2.5288 +2024-07-27 02:07:06,353 - pyskl - INFO - Epoch [134][1900/3746] lr: 2.953e-03, eta: 14:06:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5450, top5_acc: 0.7770, loss_cls: 2.5695, loss: 2.5695 +2024-07-27 02:08:27,985 - pyskl - INFO - Epoch [134][2000/3746] lr: 2.944e-03, eta: 14:04:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5328, top5_acc: 0.7762, loss_cls: 2.5855, loss: 2.5855 +2024-07-27 02:09:49,474 - pyskl - INFO - Epoch [134][2100/3746] lr: 2.935e-03, eta: 14:03:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5405, top5_acc: 0.7831, loss_cls: 2.5547, loss: 2.5547 +2024-07-27 02:11:10,946 - pyskl - INFO - Epoch [134][2200/3746] lr: 2.925e-03, eta: 14:01:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5350, top5_acc: 0.7772, loss_cls: 2.5741, loss: 2.5741 +2024-07-27 02:12:33,219 - pyskl - INFO - Epoch [134][2300/3746] lr: 2.916e-03, eta: 14:00:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5278, top5_acc: 0.7741, loss_cls: 2.6171, loss: 2.6171 +2024-07-27 02:13:55,866 - pyskl - INFO - Epoch [134][2400/3746] lr: 2.906e-03, eta: 13:59:09, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5306, top5_acc: 0.7778, loss_cls: 2.5885, loss: 2.5885 +2024-07-27 02:15:17,439 - pyskl - INFO - Epoch [134][2500/3746] lr: 2.897e-03, eta: 13:57:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5283, top5_acc: 0.7783, loss_cls: 2.6100, loss: 2.6100 +2024-07-27 02:16:39,367 - pyskl - INFO - Epoch [134][2600/3746] lr: 2.888e-03, eta: 13:56:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5356, top5_acc: 0.7805, loss_cls: 2.5677, loss: 2.5677 +2024-07-27 02:18:02,724 - pyskl - INFO - Epoch [134][2700/3746] lr: 2.878e-03, eta: 13:55:03, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5295, top5_acc: 0.7756, loss_cls: 2.5970, loss: 2.5970 +2024-07-27 02:19:24,109 - pyskl - INFO - Epoch [134][2800/3746] lr: 2.869e-03, eta: 13:53:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5319, top5_acc: 0.7722, loss_cls: 2.5958, loss: 2.5958 +2024-07-27 02:20:46,325 - pyskl - INFO - Epoch [134][2900/3746] lr: 2.860e-03, eta: 13:52:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5244, top5_acc: 0.7839, loss_cls: 2.5975, loss: 2.5975 +2024-07-27 02:22:07,624 - pyskl - INFO - Epoch [134][3000/3746] lr: 2.850e-03, eta: 13:50:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5400, top5_acc: 0.7767, loss_cls: 2.5756, loss: 2.5756 +2024-07-27 02:23:29,151 - pyskl - INFO - Epoch [134][3100/3746] lr: 2.841e-03, eta: 13:49:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5348, top5_acc: 0.7755, loss_cls: 2.5861, loss: 2.5861 +2024-07-27 02:24:50,624 - pyskl - INFO - Epoch [134][3200/3746] lr: 2.832e-03, eta: 13:48:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5381, top5_acc: 0.7795, loss_cls: 2.5571, loss: 2.5571 +2024-07-27 02:26:12,317 - pyskl - INFO - Epoch [134][3300/3746] lr: 2.822e-03, eta: 13:46:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5339, top5_acc: 0.7734, loss_cls: 2.6017, loss: 2.6017 +2024-07-27 02:27:34,191 - pyskl - INFO - Epoch [134][3400/3746] lr: 2.813e-03, eta: 13:45:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5345, top5_acc: 0.7803, loss_cls: 2.5594, loss: 2.5594 +2024-07-27 02:28:56,209 - pyskl - INFO - Epoch [134][3500/3746] lr: 2.804e-03, eta: 13:44:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5288, top5_acc: 0.7736, loss_cls: 2.6232, loss: 2.6232 +2024-07-27 02:30:17,437 - pyskl - INFO - Epoch [134][3600/3746] lr: 2.795e-03, eta: 13:42:43, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5275, top5_acc: 0.7745, loss_cls: 2.6140, loss: 2.6140 +2024-07-27 02:31:38,880 - pyskl - INFO - Epoch [134][3700/3746] lr: 2.786e-03, eta: 13:41:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5350, top5_acc: 0.7863, loss_cls: 2.5505, loss: 2.5505 +2024-07-27 02:32:18,317 - pyskl - INFO - Saving checkpoint at 134 epochs +2024-07-27 02:34:08,947 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 02:34:09,604 - pyskl - INFO - +top1_acc 0.4344 +top5_acc 0.6852 +2024-07-27 02:34:09,604 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 02:34:09,644 - pyskl - INFO - +mean_acc 0.4340 +2024-07-27 02:34:09,648 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_133.pth was removed +2024-07-27 02:34:09,877 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_134.pth. +2024-07-27 02:34:09,878 - pyskl - INFO - Best top1_acc is 0.4344 at 134 epoch. +2024-07-27 02:34:09,889 - pyskl - INFO - Epoch(val) [134][309] top1_acc: 0.4344, top5_acc: 0.6852, mean_class_accuracy: 0.4340 +2024-07-27 02:37:56,248 - pyskl - INFO - Epoch [135][100/3746] lr: 2.772e-03, eta: 13:39:33, time: 2.264, data_time: 1.288, memory: 15990, top1_acc: 0.5659, top5_acc: 0.8037, loss_cls: 2.4210, loss: 2.4210 +2024-07-27 02:39:18,580 - pyskl - INFO - Epoch [135][200/3746] lr: 2.763e-03, eta: 13:38:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5575, top5_acc: 0.7984, loss_cls: 2.4427, loss: 2.4427 +2024-07-27 02:40:39,995 - pyskl - INFO - Epoch [135][300/3746] lr: 2.754e-03, eta: 13:36:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5517, top5_acc: 0.7898, loss_cls: 2.4903, loss: 2.4903 +2024-07-27 02:42:01,629 - pyskl - INFO - Epoch [135][400/3746] lr: 2.745e-03, eta: 13:35:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5497, top5_acc: 0.7909, loss_cls: 2.5099, loss: 2.5099 +2024-07-27 02:43:23,525 - pyskl - INFO - Epoch [135][500/3746] lr: 2.735e-03, eta: 13:34:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5591, top5_acc: 0.7911, loss_cls: 2.4725, loss: 2.4725 +2024-07-27 02:44:45,318 - pyskl - INFO - Epoch [135][600/3746] lr: 2.726e-03, eta: 13:32:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5603, top5_acc: 0.7920, loss_cls: 2.4733, loss: 2.4733 +2024-07-27 02:46:06,550 - pyskl - INFO - Epoch [135][700/3746] lr: 2.717e-03, eta: 13:31:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5472, top5_acc: 0.7870, loss_cls: 2.5215, loss: 2.5215 +2024-07-27 02:47:29,002 - pyskl - INFO - Epoch [135][800/3746] lr: 2.708e-03, eta: 13:29:58, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5494, top5_acc: 0.7945, loss_cls: 2.4926, loss: 2.4926 +2024-07-27 02:48:51,625 - pyskl - INFO - Epoch [135][900/3746] lr: 2.699e-03, eta: 13:28:36, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5344, top5_acc: 0.7864, loss_cls: 2.5410, loss: 2.5410 +2024-07-27 02:50:12,684 - pyskl - INFO - Epoch [135][1000/3746] lr: 2.690e-03, eta: 13:27:13, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5455, top5_acc: 0.7856, loss_cls: 2.5221, loss: 2.5221 +2024-07-27 02:51:34,093 - pyskl - INFO - Epoch [135][1100/3746] lr: 2.681e-03, eta: 13:25:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5497, top5_acc: 0.7887, loss_cls: 2.4997, loss: 2.4997 +2024-07-27 02:52:56,046 - pyskl - INFO - Epoch [135][1200/3746] lr: 2.672e-03, eta: 13:24:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5587, top5_acc: 0.7947, loss_cls: 2.4619, loss: 2.4619 +2024-07-27 02:54:17,503 - pyskl - INFO - Epoch [135][1300/3746] lr: 2.663e-03, eta: 13:23:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5453, top5_acc: 0.7867, loss_cls: 2.5095, loss: 2.5095 +2024-07-27 02:55:39,014 - pyskl - INFO - Epoch [135][1400/3746] lr: 2.654e-03, eta: 13:21:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5541, top5_acc: 0.7889, loss_cls: 2.4956, loss: 2.4956 +2024-07-27 02:57:00,413 - pyskl - INFO - Epoch [135][1500/3746] lr: 2.645e-03, eta: 13:20:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5431, top5_acc: 0.7841, loss_cls: 2.5280, loss: 2.5280 +2024-07-27 02:58:22,025 - pyskl - INFO - Epoch [135][1600/3746] lr: 2.636e-03, eta: 13:19:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5475, top5_acc: 0.7955, loss_cls: 2.5084, loss: 2.5084 +2024-07-27 02:59:43,637 - pyskl - INFO - Epoch [135][1700/3746] lr: 2.627e-03, eta: 13:17:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5522, top5_acc: 0.7897, loss_cls: 2.4775, loss: 2.4775 +2024-07-27 03:01:05,312 - pyskl - INFO - Epoch [135][1800/3746] lr: 2.618e-03, eta: 13:16:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5452, top5_acc: 0.7839, loss_cls: 2.5242, loss: 2.5242 +2024-07-27 03:02:27,216 - pyskl - INFO - Epoch [135][1900/3746] lr: 2.609e-03, eta: 13:14:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5403, top5_acc: 0.7795, loss_cls: 2.5406, loss: 2.5406 +2024-07-27 03:03:48,769 - pyskl - INFO - Epoch [135][2000/3746] lr: 2.600e-03, eta: 13:13:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5417, top5_acc: 0.7814, loss_cls: 2.5251, loss: 2.5251 +2024-07-27 03:05:09,893 - pyskl - INFO - Epoch [135][2100/3746] lr: 2.591e-03, eta: 13:12:09, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5416, top5_acc: 0.7891, loss_cls: 2.5139, loss: 2.5139 +2024-07-27 03:06:31,492 - pyskl - INFO - Epoch [135][2200/3746] lr: 2.583e-03, eta: 13:10:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5511, top5_acc: 0.7847, loss_cls: 2.5070, loss: 2.5070 +2024-07-27 03:07:53,088 - pyskl - INFO - Epoch [135][2300/3746] lr: 2.574e-03, eta: 13:09:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5387, top5_acc: 0.7902, loss_cls: 2.5311, loss: 2.5311 +2024-07-27 03:09:14,588 - pyskl - INFO - Epoch [135][2400/3746] lr: 2.565e-03, eta: 13:08:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5533, top5_acc: 0.7894, loss_cls: 2.4919, loss: 2.4919 +2024-07-27 03:10:35,776 - pyskl - INFO - Epoch [135][2500/3746] lr: 2.556e-03, eta: 13:06:40, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5377, top5_acc: 0.7833, loss_cls: 2.5371, loss: 2.5371 +2024-07-27 03:11:57,216 - pyskl - INFO - Epoch [135][2600/3746] lr: 2.547e-03, eta: 13:05:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5422, top5_acc: 0.7756, loss_cls: 2.5581, loss: 2.5581 +2024-07-27 03:13:19,391 - pyskl - INFO - Epoch [135][2700/3746] lr: 2.538e-03, eta: 13:03:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5431, top5_acc: 0.7794, loss_cls: 2.5723, loss: 2.5723 +2024-07-27 03:14:40,760 - pyskl - INFO - Epoch [135][2800/3746] lr: 2.530e-03, eta: 13:02:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5491, top5_acc: 0.7869, loss_cls: 2.5093, loss: 2.5093 +2024-07-27 03:16:02,602 - pyskl - INFO - Epoch [135][2900/3746] lr: 2.521e-03, eta: 13:01:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5337, top5_acc: 0.7809, loss_cls: 2.5769, loss: 2.5769 +2024-07-27 03:17:24,180 - pyskl - INFO - Epoch [135][3000/3746] lr: 2.512e-03, eta: 12:59:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5323, top5_acc: 0.7808, loss_cls: 2.5898, loss: 2.5898 +2024-07-27 03:18:46,104 - pyskl - INFO - Epoch [135][3100/3746] lr: 2.503e-03, eta: 12:58:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5483, top5_acc: 0.7844, loss_cls: 2.5389, loss: 2.5389 +2024-07-27 03:20:07,940 - pyskl - INFO - Epoch [135][3200/3746] lr: 2.495e-03, eta: 12:57:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5392, top5_acc: 0.7850, loss_cls: 2.5461, loss: 2.5461 +2024-07-27 03:21:29,375 - pyskl - INFO - Epoch [135][3300/3746] lr: 2.486e-03, eta: 12:55:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5352, top5_acc: 0.7827, loss_cls: 2.5637, loss: 2.5637 +2024-07-27 03:22:50,945 - pyskl - INFO - Epoch [135][3400/3746] lr: 2.477e-03, eta: 12:54:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5322, top5_acc: 0.7831, loss_cls: 2.5771, loss: 2.5771 +2024-07-27 03:24:12,239 - pyskl - INFO - Epoch [135][3500/3746] lr: 2.469e-03, eta: 12:52:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5334, top5_acc: 0.7762, loss_cls: 2.6086, loss: 2.6086 +2024-07-27 03:25:33,658 - pyskl - INFO - Epoch [135][3600/3746] lr: 2.460e-03, eta: 12:51:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5416, top5_acc: 0.7864, loss_cls: 2.5226, loss: 2.5226 +2024-07-27 03:26:54,940 - pyskl - INFO - Epoch [135][3700/3746] lr: 2.451e-03, eta: 12:50:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5467, top5_acc: 0.7744, loss_cls: 2.5579, loss: 2.5579 +2024-07-27 03:27:34,365 - pyskl - INFO - Saving checkpoint at 135 epochs +2024-07-27 03:29:25,331 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 03:29:25,987 - pyskl - INFO - +top1_acc 0.4368 +top5_acc 0.6873 +2024-07-27 03:29:25,987 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 03:29:26,027 - pyskl - INFO - +mean_acc 0.4365 +2024-07-27 03:29:26,032 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_134.pth was removed +2024-07-27 03:29:26,265 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_135.pth. +2024-07-27 03:29:26,266 - pyskl - INFO - Best top1_acc is 0.4368 at 135 epoch. +2024-07-27 03:29:26,278 - pyskl - INFO - Epoch(val) [135][309] top1_acc: 0.4368, top5_acc: 0.6873, mean_class_accuracy: 0.4365 +2024-07-27 03:33:15,420 - pyskl - INFO - Epoch [136][100/3746] lr: 2.439e-03, eta: 12:48:25, time: 2.291, data_time: 1.311, memory: 15990, top1_acc: 0.5678, top5_acc: 0.8145, loss_cls: 2.3752, loss: 2.3752 +2024-07-27 03:34:37,196 - pyskl - INFO - Epoch [136][200/3746] lr: 2.430e-03, eta: 12:47:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5586, top5_acc: 0.7942, loss_cls: 2.4438, loss: 2.4438 +2024-07-27 03:35:58,964 - pyskl - INFO - Epoch [136][300/3746] lr: 2.421e-03, eta: 12:45:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5573, top5_acc: 0.7959, loss_cls: 2.4688, loss: 2.4688 +2024-07-27 03:37:20,427 - pyskl - INFO - Epoch [136][400/3746] lr: 2.413e-03, eta: 12:44:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5672, top5_acc: 0.8025, loss_cls: 2.4037, loss: 2.4037 +2024-07-27 03:38:42,632 - pyskl - INFO - Epoch [136][500/3746] lr: 2.404e-03, eta: 12:42:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5673, top5_acc: 0.8100, loss_cls: 2.3833, loss: 2.3833 +2024-07-27 03:40:04,298 - pyskl - INFO - Epoch [136][600/3746] lr: 2.396e-03, eta: 12:41:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5619, top5_acc: 0.8061, loss_cls: 2.4174, loss: 2.4174 +2024-07-27 03:41:26,466 - pyskl - INFO - Epoch [136][700/3746] lr: 2.387e-03, eta: 12:40:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5553, top5_acc: 0.7991, loss_cls: 2.4538, loss: 2.4538 +2024-07-27 03:42:48,240 - pyskl - INFO - Epoch [136][800/3746] lr: 2.379e-03, eta: 12:38:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5578, top5_acc: 0.8002, loss_cls: 2.4510, loss: 2.4510 +2024-07-27 03:44:10,370 - pyskl - INFO - Epoch [136][900/3746] lr: 2.370e-03, eta: 12:37:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5700, top5_acc: 0.8102, loss_cls: 2.3756, loss: 2.3756 +2024-07-27 03:45:31,993 - pyskl - INFO - Epoch [136][1000/3746] lr: 2.362e-03, eta: 12:36:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5497, top5_acc: 0.7925, loss_cls: 2.4874, loss: 2.4874 +2024-07-27 03:46:53,623 - pyskl - INFO - Epoch [136][1100/3746] lr: 2.353e-03, eta: 12:34:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5477, top5_acc: 0.7897, loss_cls: 2.4972, loss: 2.4972 +2024-07-27 03:48:14,939 - pyskl - INFO - Epoch [136][1200/3746] lr: 2.345e-03, eta: 12:33:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5484, top5_acc: 0.7961, loss_cls: 2.4795, loss: 2.4795 +2024-07-27 03:49:36,649 - pyskl - INFO - Epoch [136][1300/3746] lr: 2.336e-03, eta: 12:31:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5505, top5_acc: 0.7900, loss_cls: 2.4823, loss: 2.4823 +2024-07-27 03:50:58,031 - pyskl - INFO - Epoch [136][1400/3746] lr: 2.328e-03, eta: 12:30:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5581, top5_acc: 0.7887, loss_cls: 2.4660, loss: 2.4660 +2024-07-27 03:52:19,976 - pyskl - INFO - Epoch [136][1500/3746] lr: 2.319e-03, eta: 12:29:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5556, top5_acc: 0.7939, loss_cls: 2.4780, loss: 2.4780 +2024-07-27 03:53:41,117 - pyskl - INFO - Epoch [136][1600/3746] lr: 2.311e-03, eta: 12:27:51, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5506, top5_acc: 0.7931, loss_cls: 2.4738, loss: 2.4738 +2024-07-27 03:55:02,985 - pyskl - INFO - Epoch [136][1700/3746] lr: 2.303e-03, eta: 12:26:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5566, top5_acc: 0.7995, loss_cls: 2.4527, loss: 2.4527 +2024-07-27 03:56:24,454 - pyskl - INFO - Epoch [136][1800/3746] lr: 2.294e-03, eta: 12:25:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5503, top5_acc: 0.7925, loss_cls: 2.4865, loss: 2.4865 +2024-07-27 03:57:45,900 - pyskl - INFO - Epoch [136][1900/3746] lr: 2.286e-03, eta: 12:23:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5559, top5_acc: 0.7942, loss_cls: 2.4993, loss: 2.4993 +2024-07-27 03:59:08,181 - pyskl - INFO - Epoch [136][2000/3746] lr: 2.277e-03, eta: 12:22:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5502, top5_acc: 0.7903, loss_cls: 2.4793, loss: 2.4793 +2024-07-27 04:00:29,710 - pyskl - INFO - Epoch [136][2100/3746] lr: 2.269e-03, eta: 12:21:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5427, top5_acc: 0.7841, loss_cls: 2.5274, loss: 2.5274 +2024-07-27 04:01:50,899 - pyskl - INFO - Epoch [136][2200/3746] lr: 2.261e-03, eta: 12:19:38, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5611, top5_acc: 0.7967, loss_cls: 2.4470, loss: 2.4470 +2024-07-27 04:03:12,512 - pyskl - INFO - Epoch [136][2300/3746] lr: 2.253e-03, eta: 12:18:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5552, top5_acc: 0.7963, loss_cls: 2.4542, loss: 2.4542 +2024-07-27 04:04:34,396 - pyskl - INFO - Epoch [136][2400/3746] lr: 2.244e-03, eta: 12:16:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5508, top5_acc: 0.7900, loss_cls: 2.4619, loss: 2.4619 +2024-07-27 04:05:55,643 - pyskl - INFO - Epoch [136][2500/3746] lr: 2.236e-03, eta: 12:15:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5697, top5_acc: 0.8009, loss_cls: 2.4203, loss: 2.4203 +2024-07-27 04:07:17,872 - pyskl - INFO - Epoch [136][2600/3746] lr: 2.228e-03, eta: 12:14:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5484, top5_acc: 0.7937, loss_cls: 2.4906, loss: 2.4906 +2024-07-27 04:08:39,560 - pyskl - INFO - Epoch [136][2700/3746] lr: 2.219e-03, eta: 12:12:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5514, top5_acc: 0.7941, loss_cls: 2.4730, loss: 2.4730 +2024-07-27 04:10:02,017 - pyskl - INFO - Epoch [136][2800/3746] lr: 2.211e-03, eta: 12:11:24, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5445, top5_acc: 0.7878, loss_cls: 2.5371, loss: 2.5371 +2024-07-27 04:11:24,194 - pyskl - INFO - Epoch [136][2900/3746] lr: 2.203e-03, eta: 12:10:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5584, top5_acc: 0.7922, loss_cls: 2.4731, loss: 2.4731 +2024-07-27 04:12:46,834 - pyskl - INFO - Epoch [136][3000/3746] lr: 2.195e-03, eta: 12:08:40, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5519, top5_acc: 0.7873, loss_cls: 2.4866, loss: 2.4866 +2024-07-27 04:14:08,656 - pyskl - INFO - Epoch [136][3100/3746] lr: 2.187e-03, eta: 12:07:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5567, top5_acc: 0.7881, loss_cls: 2.4857, loss: 2.4857 +2024-07-27 04:15:30,288 - pyskl - INFO - Epoch [136][3200/3746] lr: 2.178e-03, eta: 12:05:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5497, top5_acc: 0.7844, loss_cls: 2.5041, loss: 2.5041 +2024-07-27 04:16:51,626 - pyskl - INFO - Epoch [136][3300/3746] lr: 2.170e-03, eta: 12:04:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5537, top5_acc: 0.7864, loss_cls: 2.4990, loss: 2.4990 +2024-07-27 04:18:12,857 - pyskl - INFO - Epoch [136][3400/3746] lr: 2.162e-03, eta: 12:03:11, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5587, top5_acc: 0.7931, loss_cls: 2.4633, loss: 2.4633 +2024-07-27 04:19:34,997 - pyskl - INFO - Epoch [136][3500/3746] lr: 2.154e-03, eta: 12:01:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5458, top5_acc: 0.7794, loss_cls: 2.5321, loss: 2.5321 +2024-07-27 04:20:56,896 - pyskl - INFO - Epoch [136][3600/3746] lr: 2.146e-03, eta: 12:00:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5569, top5_acc: 0.7948, loss_cls: 2.4812, loss: 2.4812 +2024-07-27 04:22:18,470 - pyskl - INFO - Epoch [136][3700/3746] lr: 2.138e-03, eta: 11:59:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5456, top5_acc: 0.7867, loss_cls: 2.5162, loss: 2.5162 +2024-07-27 04:22:58,247 - pyskl - INFO - Saving checkpoint at 136 epochs +2024-07-27 04:24:48,830 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 04:24:49,493 - pyskl - INFO - +top1_acc 0.4330 +top5_acc 0.6855 +2024-07-27 04:24:49,493 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 04:24:49,533 - pyskl - INFO - +mean_acc 0.4326 +2024-07-27 04:24:49,545 - pyskl - INFO - Epoch(val) [136][309] top1_acc: 0.4330, top5_acc: 0.6855, mean_class_accuracy: 0.4326 +2024-07-27 04:28:42,185 - pyskl - INFO - Epoch [137][100/3746] lr: 2.126e-03, eta: 11:57:16, time: 2.326, data_time: 1.345, memory: 15990, top1_acc: 0.5739, top5_acc: 0.8120, loss_cls: 2.3690, loss: 2.3690 +2024-07-27 04:30:03,674 - pyskl - INFO - Epoch [137][200/3746] lr: 2.118e-03, eta: 11:55:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5887, top5_acc: 0.8189, loss_cls: 2.3037, loss: 2.3037 +2024-07-27 04:31:25,474 - pyskl - INFO - Epoch [137][300/3746] lr: 2.110e-03, eta: 11:54:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5770, top5_acc: 0.8014, loss_cls: 2.3878, loss: 2.3878 +2024-07-27 04:32:47,037 - pyskl - INFO - Epoch [137][400/3746] lr: 2.102e-03, eta: 11:53:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5602, top5_acc: 0.8067, loss_cls: 2.3780, loss: 2.3780 +2024-07-27 04:34:08,395 - pyskl - INFO - Epoch [137][500/3746] lr: 2.094e-03, eta: 11:51:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5673, top5_acc: 0.8084, loss_cls: 2.4108, loss: 2.4108 +2024-07-27 04:35:30,067 - pyskl - INFO - Epoch [137][600/3746] lr: 2.086e-03, eta: 11:50:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5781, top5_acc: 0.8133, loss_cls: 2.3654, loss: 2.3654 +2024-07-27 04:36:51,646 - pyskl - INFO - Epoch [137][700/3746] lr: 2.078e-03, eta: 11:49:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5778, top5_acc: 0.8120, loss_cls: 2.3629, loss: 2.3629 +2024-07-27 04:38:13,188 - pyskl - INFO - Epoch [137][800/3746] lr: 2.070e-03, eta: 11:47:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5597, top5_acc: 0.8025, loss_cls: 2.4223, loss: 2.4223 +2024-07-27 04:39:34,697 - pyskl - INFO - Epoch [137][900/3746] lr: 2.062e-03, eta: 11:46:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5669, top5_acc: 0.8056, loss_cls: 2.3946, loss: 2.3946 +2024-07-27 04:40:56,418 - pyskl - INFO - Epoch [137][1000/3746] lr: 2.054e-03, eta: 11:44:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5766, top5_acc: 0.8122, loss_cls: 2.3441, loss: 2.3441 +2024-07-27 04:42:17,837 - pyskl - INFO - Epoch [137][1100/3746] lr: 2.046e-03, eta: 11:43:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5659, top5_acc: 0.7927, loss_cls: 2.4230, loss: 2.4230 +2024-07-27 04:43:39,032 - pyskl - INFO - Epoch [137][1200/3746] lr: 2.038e-03, eta: 11:42:10, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5652, top5_acc: 0.7933, loss_cls: 2.4373, loss: 2.4373 +2024-07-27 04:45:00,360 - pyskl - INFO - Epoch [137][1300/3746] lr: 2.030e-03, eta: 11:40:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5667, top5_acc: 0.8047, loss_cls: 2.4004, loss: 2.4004 +2024-07-27 04:46:21,813 - pyskl - INFO - Epoch [137][1400/3746] lr: 2.022e-03, eta: 11:39:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5500, top5_acc: 0.7955, loss_cls: 2.4772, loss: 2.4772 +2024-07-27 04:47:42,896 - pyskl - INFO - Epoch [137][1500/3746] lr: 2.015e-03, eta: 11:38:04, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5613, top5_acc: 0.8056, loss_cls: 2.4162, loss: 2.4162 +2024-07-27 04:49:05,108 - pyskl - INFO - Epoch [137][1600/3746] lr: 2.007e-03, eta: 11:36:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5713, top5_acc: 0.8078, loss_cls: 2.3952, loss: 2.3952 +2024-07-27 04:50:26,472 - pyskl - INFO - Epoch [137][1700/3746] lr: 1.999e-03, eta: 11:35:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5642, top5_acc: 0.8058, loss_cls: 2.3990, loss: 2.3990 +2024-07-27 04:51:47,868 - pyskl - INFO - Epoch [137][1800/3746] lr: 1.991e-03, eta: 11:33:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5648, top5_acc: 0.8069, loss_cls: 2.3891, loss: 2.3891 +2024-07-27 04:53:09,503 - pyskl - INFO - Epoch [137][1900/3746] lr: 1.983e-03, eta: 11:32:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5625, top5_acc: 0.8078, loss_cls: 2.4048, loss: 2.4048 +2024-07-27 04:54:30,847 - pyskl - INFO - Epoch [137][2000/3746] lr: 1.976e-03, eta: 11:31:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5448, top5_acc: 0.7956, loss_cls: 2.4940, loss: 2.4940 +2024-07-27 04:55:52,546 - pyskl - INFO - Epoch [137][2100/3746] lr: 1.968e-03, eta: 11:29:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5552, top5_acc: 0.7933, loss_cls: 2.4720, loss: 2.4720 +2024-07-27 04:57:13,992 - pyskl - INFO - Epoch [137][2200/3746] lr: 1.960e-03, eta: 11:28:28, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5653, top5_acc: 0.8059, loss_cls: 2.3926, loss: 2.3926 +2024-07-27 04:58:35,465 - pyskl - INFO - Epoch [137][2300/3746] lr: 1.952e-03, eta: 11:27:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5697, top5_acc: 0.8055, loss_cls: 2.3842, loss: 2.3842 +2024-07-27 04:59:57,562 - pyskl - INFO - Epoch [137][2400/3746] lr: 1.944e-03, eta: 11:25:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5773, top5_acc: 0.8014, loss_cls: 2.3728, loss: 2.3728 +2024-07-27 05:01:19,400 - pyskl - INFO - Epoch [137][2500/3746] lr: 1.937e-03, eta: 11:24:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5648, top5_acc: 0.8017, loss_cls: 2.4241, loss: 2.4241 +2024-07-27 05:02:41,494 - pyskl - INFO - Epoch [137][2600/3746] lr: 1.929e-03, eta: 11:22:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5572, top5_acc: 0.7950, loss_cls: 2.4751, loss: 2.4751 +2024-07-27 05:04:03,255 - pyskl - INFO - Epoch [137][2700/3746] lr: 1.921e-03, eta: 11:21:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5545, top5_acc: 0.7928, loss_cls: 2.4698, loss: 2.4698 +2024-07-27 05:05:25,900 - pyskl - INFO - Epoch [137][2800/3746] lr: 1.914e-03, eta: 11:20:14, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5544, top5_acc: 0.7986, loss_cls: 2.4696, loss: 2.4696 +2024-07-27 05:06:48,003 - pyskl - INFO - Epoch [137][2900/3746] lr: 1.906e-03, eta: 11:18:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5545, top5_acc: 0.7894, loss_cls: 2.4803, loss: 2.4803 +2024-07-27 05:08:10,121 - pyskl - INFO - Epoch [137][3000/3746] lr: 1.898e-03, eta: 11:17:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5572, top5_acc: 0.7919, loss_cls: 2.4442, loss: 2.4442 +2024-07-27 05:09:31,845 - pyskl - INFO - Epoch [137][3100/3746] lr: 1.891e-03, eta: 11:16:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5592, top5_acc: 0.7952, loss_cls: 2.4296, loss: 2.4296 +2024-07-27 05:10:53,465 - pyskl - INFO - Epoch [137][3200/3746] lr: 1.883e-03, eta: 11:14:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5555, top5_acc: 0.7975, loss_cls: 2.4640, loss: 2.4640 +2024-07-27 05:12:15,032 - pyskl - INFO - Epoch [137][3300/3746] lr: 1.876e-03, eta: 11:13:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5466, top5_acc: 0.7923, loss_cls: 2.4723, loss: 2.4723 +2024-07-27 05:13:36,854 - pyskl - INFO - Epoch [137][3400/3746] lr: 1.868e-03, eta: 11:12:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5536, top5_acc: 0.7873, loss_cls: 2.4852, loss: 2.4852 +2024-07-27 05:14:58,318 - pyskl - INFO - Epoch [137][3500/3746] lr: 1.860e-03, eta: 11:10:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5614, top5_acc: 0.7978, loss_cls: 2.4338, loss: 2.4338 +2024-07-27 05:16:19,765 - pyskl - INFO - Epoch [137][3600/3746] lr: 1.853e-03, eta: 11:09:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5566, top5_acc: 0.7984, loss_cls: 2.4594, loss: 2.4594 +2024-07-27 05:17:41,456 - pyskl - INFO - Epoch [137][3700/3746] lr: 1.845e-03, eta: 11:07:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5567, top5_acc: 0.8006, loss_cls: 2.4380, loss: 2.4380 +2024-07-27 05:18:21,162 - pyskl - INFO - Saving checkpoint at 137 epochs +2024-07-27 05:20:12,572 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 05:20:13,233 - pyskl - INFO - +top1_acc 0.4416 +top5_acc 0.6910 +2024-07-27 05:20:13,233 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 05:20:13,274 - pyskl - INFO - +mean_acc 0.4413 +2024-07-27 05:20:13,278 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_135.pth was removed +2024-07-27 05:20:13,511 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_137.pth. +2024-07-27 05:20:13,512 - pyskl - INFO - Best top1_acc is 0.4416 at 137 epoch. +2024-07-27 05:20:13,524 - pyskl - INFO - Epoch(val) [137][309] top1_acc: 0.4416, top5_acc: 0.6910, mean_class_accuracy: 0.4413 +2024-07-27 05:24:02,260 - pyskl - INFO - Epoch [138][100/3746] lr: 1.834e-03, eta: 11:06:04, time: 2.287, data_time: 1.299, memory: 15990, top1_acc: 0.5902, top5_acc: 0.8234, loss_cls: 2.2684, loss: 2.2684 +2024-07-27 05:25:24,027 - pyskl - INFO - Epoch [138][200/3746] lr: 1.827e-03, eta: 11:04:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5845, top5_acc: 0.8225, loss_cls: 2.2822, loss: 2.2822 +2024-07-27 05:26:45,907 - pyskl - INFO - Epoch [138][300/3746] lr: 1.819e-03, eta: 11:03:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5742, top5_acc: 0.8086, loss_cls: 2.3748, loss: 2.3748 +2024-07-27 05:28:08,410 - pyskl - INFO - Epoch [138][400/3746] lr: 1.812e-03, eta: 11:01:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5809, top5_acc: 0.8113, loss_cls: 2.3379, loss: 2.3379 +2024-07-27 05:29:30,470 - pyskl - INFO - Epoch [138][500/3746] lr: 1.805e-03, eta: 11:00:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5753, top5_acc: 0.8083, loss_cls: 2.3493, loss: 2.3493 +2024-07-27 05:30:52,368 - pyskl - INFO - Epoch [138][600/3746] lr: 1.797e-03, eta: 10:59:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5794, top5_acc: 0.8173, loss_cls: 2.3150, loss: 2.3150 +2024-07-27 05:32:14,367 - pyskl - INFO - Epoch [138][700/3746] lr: 1.790e-03, eta: 10:57:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5736, top5_acc: 0.8119, loss_cls: 2.3728, loss: 2.3728 +2024-07-27 05:33:35,911 - pyskl - INFO - Epoch [138][800/3746] lr: 1.782e-03, eta: 10:56:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5725, top5_acc: 0.8059, loss_cls: 2.3688, loss: 2.3688 +2024-07-27 05:34:57,828 - pyskl - INFO - Epoch [138][900/3746] lr: 1.775e-03, eta: 10:55:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5767, top5_acc: 0.8114, loss_cls: 2.3286, loss: 2.3286 +2024-07-27 05:36:19,132 - pyskl - INFO - Epoch [138][1000/3746] lr: 1.768e-03, eta: 10:53:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5720, top5_acc: 0.8097, loss_cls: 2.3525, loss: 2.3525 +2024-07-27 05:37:40,654 - pyskl - INFO - Epoch [138][1100/3746] lr: 1.760e-03, eta: 10:52:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5745, top5_acc: 0.8067, loss_cls: 2.3749, loss: 2.3749 +2024-07-27 05:39:01,990 - pyskl - INFO - Epoch [138][1200/3746] lr: 1.753e-03, eta: 10:50:59, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5698, top5_acc: 0.8006, loss_cls: 2.4103, loss: 2.4103 +2024-07-27 05:40:23,291 - pyskl - INFO - Epoch [138][1300/3746] lr: 1.745e-03, eta: 10:49:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5686, top5_acc: 0.8123, loss_cls: 2.3533, loss: 2.3533 +2024-07-27 05:41:45,068 - pyskl - INFO - Epoch [138][1400/3746] lr: 1.738e-03, eta: 10:48:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5789, top5_acc: 0.8094, loss_cls: 2.3349, loss: 2.3349 +2024-07-27 05:43:06,684 - pyskl - INFO - Epoch [138][1500/3746] lr: 1.731e-03, eta: 10:46:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5802, top5_acc: 0.8014, loss_cls: 2.3628, loss: 2.3628 +2024-07-27 05:44:28,177 - pyskl - INFO - Epoch [138][1600/3746] lr: 1.724e-03, eta: 10:45:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5800, top5_acc: 0.8127, loss_cls: 2.3373, loss: 2.3373 +2024-07-27 05:45:49,422 - pyskl - INFO - Epoch [138][1700/3746] lr: 1.716e-03, eta: 10:44:08, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5786, top5_acc: 0.8159, loss_cls: 2.3286, loss: 2.3286 +2024-07-27 05:47:10,655 - pyskl - INFO - Epoch [138][1800/3746] lr: 1.709e-03, eta: 10:42:45, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5827, top5_acc: 0.8102, loss_cls: 2.3501, loss: 2.3501 +2024-07-27 05:48:32,311 - pyskl - INFO - Epoch [138][1900/3746] lr: 1.702e-03, eta: 10:41:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5737, top5_acc: 0.8106, loss_cls: 2.3600, loss: 2.3600 +2024-07-27 05:49:53,654 - pyskl - INFO - Epoch [138][2000/3746] lr: 1.695e-03, eta: 10:40:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5822, top5_acc: 0.8092, loss_cls: 2.3387, loss: 2.3387 +2024-07-27 05:51:15,163 - pyskl - INFO - Epoch [138][2100/3746] lr: 1.687e-03, eta: 10:38:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5692, top5_acc: 0.8048, loss_cls: 2.3904, loss: 2.3904 +2024-07-27 05:52:36,146 - pyskl - INFO - Epoch [138][2200/3746] lr: 1.680e-03, eta: 10:37:16, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5733, top5_acc: 0.8097, loss_cls: 2.3754, loss: 2.3754 +2024-07-27 05:53:57,755 - pyskl - INFO - Epoch [138][2300/3746] lr: 1.673e-03, eta: 10:35:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5680, top5_acc: 0.8064, loss_cls: 2.3874, loss: 2.3874 +2024-07-27 05:55:19,568 - pyskl - INFO - Epoch [138][2400/3746] lr: 1.666e-03, eta: 10:34:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5600, top5_acc: 0.8025, loss_cls: 2.3878, loss: 2.3878 +2024-07-27 05:56:40,932 - pyskl - INFO - Epoch [138][2500/3746] lr: 1.659e-03, eta: 10:33:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5637, top5_acc: 0.8056, loss_cls: 2.4284, loss: 2.4284 +2024-07-27 05:58:02,952 - pyskl - INFO - Epoch [138][2600/3746] lr: 1.652e-03, eta: 10:31:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5672, top5_acc: 0.8053, loss_cls: 2.4027, loss: 2.4027 +2024-07-27 05:59:24,857 - pyskl - INFO - Epoch [138][2700/3746] lr: 1.644e-03, eta: 10:30:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5709, top5_acc: 0.8056, loss_cls: 2.3920, loss: 2.3920 +2024-07-27 06:00:47,242 - pyskl - INFO - Epoch [138][2800/3746] lr: 1.637e-03, eta: 10:29:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5809, top5_acc: 0.8063, loss_cls: 2.3477, loss: 2.3477 +2024-07-27 06:02:09,174 - pyskl - INFO - Epoch [138][2900/3746] lr: 1.630e-03, eta: 10:27:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5677, top5_acc: 0.8056, loss_cls: 2.3817, loss: 2.3817 +2024-07-27 06:03:31,510 - pyskl - INFO - Epoch [138][3000/3746] lr: 1.623e-03, eta: 10:26:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5684, top5_acc: 0.8061, loss_cls: 2.3851, loss: 2.3851 +2024-07-27 06:04:53,238 - pyskl - INFO - Epoch [138][3100/3746] lr: 1.616e-03, eta: 10:24:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5727, top5_acc: 0.8052, loss_cls: 2.3769, loss: 2.3769 +2024-07-27 06:06:14,702 - pyskl - INFO - Epoch [138][3200/3746] lr: 1.609e-03, eta: 10:23:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5792, top5_acc: 0.8047, loss_cls: 2.3469, loss: 2.3469 +2024-07-27 06:07:36,359 - pyskl - INFO - Epoch [138][3300/3746] lr: 1.602e-03, eta: 10:22:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5584, top5_acc: 0.7948, loss_cls: 2.4449, loss: 2.4449 +2024-07-27 06:08:58,038 - pyskl - INFO - Epoch [138][3400/3746] lr: 1.595e-03, eta: 10:20:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5666, top5_acc: 0.8011, loss_cls: 2.3896, loss: 2.3896 +2024-07-27 06:10:19,758 - pyskl - INFO - Epoch [138][3500/3746] lr: 1.588e-03, eta: 10:19:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5602, top5_acc: 0.7966, loss_cls: 2.4286, loss: 2.4286 +2024-07-27 06:11:41,509 - pyskl - INFO - Epoch [138][3600/3746] lr: 1.581e-03, eta: 10:18:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5745, top5_acc: 0.8070, loss_cls: 2.3717, loss: 2.3717 +2024-07-27 06:13:02,997 - pyskl - INFO - Epoch [138][3700/3746] lr: 1.574e-03, eta: 10:16:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5595, top5_acc: 0.8030, loss_cls: 2.4158, loss: 2.4158 +2024-07-27 06:13:42,294 - pyskl - INFO - Saving checkpoint at 138 epochs +2024-07-27 06:15:31,482 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 06:15:32,138 - pyskl - INFO - +top1_acc 0.4448 +top5_acc 0.6893 +2024-07-27 06:15:32,139 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 06:15:32,180 - pyskl - INFO - +mean_acc 0.4446 +2024-07-27 06:15:32,184 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_137.pth was removed +2024-07-27 06:15:32,416 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_138.pth. +2024-07-27 06:15:32,416 - pyskl - INFO - Best top1_acc is 0.4448 at 138 epoch. +2024-07-27 06:15:32,428 - pyskl - INFO - Epoch(val) [138][309] top1_acc: 0.4448, top5_acc: 0.6893, mean_class_accuracy: 0.4446 +2024-07-27 06:19:18,165 - pyskl - INFO - Epoch [139][100/3746] lr: 1.564e-03, eta: 10:14:51, time: 2.257, data_time: 1.278, memory: 15990, top1_acc: 0.5911, top5_acc: 0.8269, loss_cls: 2.2702, loss: 2.2702 +2024-07-27 06:20:39,842 - pyskl - INFO - Epoch [139][200/3746] lr: 1.557e-03, eta: 10:13:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6019, top5_acc: 0.8259, loss_cls: 2.2631, loss: 2.2631 +2024-07-27 06:22:01,224 - pyskl - INFO - Epoch [139][300/3746] lr: 1.550e-03, eta: 10:12:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6016, top5_acc: 0.8280, loss_cls: 2.2488, loss: 2.2488 +2024-07-27 06:23:22,456 - pyskl - INFO - Epoch [139][400/3746] lr: 1.543e-03, eta: 10:10:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5947, top5_acc: 0.8178, loss_cls: 2.2720, loss: 2.2720 +2024-07-27 06:24:44,103 - pyskl - INFO - Epoch [139][500/3746] lr: 1.536e-03, eta: 10:09:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5972, top5_acc: 0.8237, loss_cls: 2.2551, loss: 2.2551 +2024-07-27 06:26:05,833 - pyskl - INFO - Epoch [139][600/3746] lr: 1.529e-03, eta: 10:07:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5964, top5_acc: 0.8155, loss_cls: 2.2886, loss: 2.2886 +2024-07-27 06:27:27,570 - pyskl - INFO - Epoch [139][700/3746] lr: 1.523e-03, eta: 10:06:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5881, top5_acc: 0.8177, loss_cls: 2.3150, loss: 2.3150 +2024-07-27 06:28:49,262 - pyskl - INFO - Epoch [139][800/3746] lr: 1.516e-03, eta: 10:05:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5936, top5_acc: 0.8231, loss_cls: 2.2609, loss: 2.2609 +2024-07-27 06:30:11,828 - pyskl - INFO - Epoch [139][900/3746] lr: 1.509e-03, eta: 10:03:53, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5855, top5_acc: 0.8166, loss_cls: 2.3060, loss: 2.3060 +2024-07-27 06:31:33,649 - pyskl - INFO - Epoch [139][1000/3746] lr: 1.502e-03, eta: 10:02:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5908, top5_acc: 0.8167, loss_cls: 2.2933, loss: 2.2933 +2024-07-27 06:32:55,395 - pyskl - INFO - Epoch [139][1100/3746] lr: 1.495e-03, eta: 10:01:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5822, top5_acc: 0.8178, loss_cls: 2.2877, loss: 2.2877 +2024-07-27 06:34:17,051 - pyskl - INFO - Epoch [139][1200/3746] lr: 1.489e-03, eta: 9:59:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5822, top5_acc: 0.8202, loss_cls: 2.3238, loss: 2.3238 +2024-07-27 06:35:38,738 - pyskl - INFO - Epoch [139][1300/3746] lr: 1.482e-03, eta: 9:58:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5903, top5_acc: 0.8203, loss_cls: 2.2751, loss: 2.2751 +2024-07-27 06:37:00,459 - pyskl - INFO - Epoch [139][1400/3746] lr: 1.475e-03, eta: 9:57:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5842, top5_acc: 0.8217, loss_cls: 2.2982, loss: 2.2982 +2024-07-27 06:38:22,000 - pyskl - INFO - Epoch [139][1500/3746] lr: 1.468e-03, eta: 9:55:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5817, top5_acc: 0.8080, loss_cls: 2.3578, loss: 2.3578 +2024-07-27 06:39:43,772 - pyskl - INFO - Epoch [139][1600/3746] lr: 1.462e-03, eta: 9:54:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5814, top5_acc: 0.8150, loss_cls: 2.3143, loss: 2.3143 +2024-07-27 06:41:05,840 - pyskl - INFO - Epoch [139][1700/3746] lr: 1.455e-03, eta: 9:52:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5808, top5_acc: 0.8187, loss_cls: 2.3329, loss: 2.3329 +2024-07-27 06:42:27,122 - pyskl - INFO - Epoch [139][1800/3746] lr: 1.448e-03, eta: 9:51:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5848, top5_acc: 0.8197, loss_cls: 2.3023, loss: 2.3023 +2024-07-27 06:43:48,631 - pyskl - INFO - Epoch [139][1900/3746] lr: 1.442e-03, eta: 9:50:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5853, top5_acc: 0.8159, loss_cls: 2.2977, loss: 2.2977 +2024-07-27 06:45:10,005 - pyskl - INFO - Epoch [139][2000/3746] lr: 1.435e-03, eta: 9:48:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5869, top5_acc: 0.8136, loss_cls: 2.3372, loss: 2.3372 +2024-07-27 06:46:31,477 - pyskl - INFO - Epoch [139][2100/3746] lr: 1.428e-03, eta: 9:47:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5855, top5_acc: 0.8103, loss_cls: 2.3137, loss: 2.3137 +2024-07-27 06:47:53,284 - pyskl - INFO - Epoch [139][2200/3746] lr: 1.422e-03, eta: 9:46:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5883, top5_acc: 0.8217, loss_cls: 2.2970, loss: 2.2970 +2024-07-27 06:49:14,927 - pyskl - INFO - Epoch [139][2300/3746] lr: 1.415e-03, eta: 9:44:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5775, top5_acc: 0.8122, loss_cls: 2.3224, loss: 2.3224 +2024-07-27 06:50:36,818 - pyskl - INFO - Epoch [139][2400/3746] lr: 1.408e-03, eta: 9:43:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5823, top5_acc: 0.8137, loss_cls: 2.3233, loss: 2.3233 +2024-07-27 06:51:58,776 - pyskl - INFO - Epoch [139][2500/3746] lr: 1.402e-03, eta: 9:41:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5764, top5_acc: 0.8173, loss_cls: 2.3208, loss: 2.3208 +2024-07-27 06:53:20,175 - pyskl - INFO - Epoch [139][2600/3746] lr: 1.395e-03, eta: 9:40:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5766, top5_acc: 0.8142, loss_cls: 2.3305, loss: 2.3305 +2024-07-27 06:54:42,187 - pyskl - INFO - Epoch [139][2700/3746] lr: 1.389e-03, eta: 9:39:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5852, top5_acc: 0.8223, loss_cls: 2.2959, loss: 2.2959 +2024-07-27 06:56:03,938 - pyskl - INFO - Epoch [139][2800/3746] lr: 1.382e-03, eta: 9:37:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5873, top5_acc: 0.8150, loss_cls: 2.3103, loss: 2.3103 +2024-07-27 06:57:25,866 - pyskl - INFO - Epoch [139][2900/3746] lr: 1.376e-03, eta: 9:36:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5756, top5_acc: 0.8066, loss_cls: 2.3698, loss: 2.3698 +2024-07-27 06:58:47,733 - pyskl - INFO - Epoch [139][3000/3746] lr: 1.369e-03, eta: 9:35:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5837, top5_acc: 0.8153, loss_cls: 2.3164, loss: 2.3164 +2024-07-27 07:00:09,788 - pyskl - INFO - Epoch [139][3100/3746] lr: 1.363e-03, eta: 9:33:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5884, top5_acc: 0.8202, loss_cls: 2.2744, loss: 2.2744 +2024-07-27 07:01:31,460 - pyskl - INFO - Epoch [139][3200/3746] lr: 1.356e-03, eta: 9:32:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5834, top5_acc: 0.8198, loss_cls: 2.2873, loss: 2.2873 +2024-07-27 07:02:52,703 - pyskl - INFO - Epoch [139][3300/3746] lr: 1.350e-03, eta: 9:30:58, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5769, top5_acc: 0.8081, loss_cls: 2.3429, loss: 2.3429 +2024-07-27 07:04:14,772 - pyskl - INFO - Epoch [139][3400/3746] lr: 1.343e-03, eta: 9:29:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5773, top5_acc: 0.8116, loss_cls: 2.3345, loss: 2.3345 +2024-07-27 07:05:36,478 - pyskl - INFO - Epoch [139][3500/3746] lr: 1.337e-03, eta: 9:28:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5831, top5_acc: 0.8116, loss_cls: 2.3194, loss: 2.3194 +2024-07-27 07:06:58,010 - pyskl - INFO - Epoch [139][3600/3746] lr: 1.330e-03, eta: 9:26:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5759, top5_acc: 0.8095, loss_cls: 2.3465, loss: 2.3465 +2024-07-27 07:08:19,253 - pyskl - INFO - Epoch [139][3700/3746] lr: 1.324e-03, eta: 9:25:28, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5678, top5_acc: 0.8045, loss_cls: 2.3866, loss: 2.3866 +2024-07-27 07:08:59,065 - pyskl - INFO - Saving checkpoint at 139 epochs +2024-07-27 07:10:49,358 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 07:10:50,013 - pyskl - INFO - +top1_acc 0.4486 +top5_acc 0.6980 +2024-07-27 07:10:50,013 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 07:10:50,053 - pyskl - INFO - +mean_acc 0.4483 +2024-07-27 07:10:50,058 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_138.pth was removed +2024-07-27 07:10:50,288 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_139.pth. +2024-07-27 07:10:50,289 - pyskl - INFO - Best top1_acc is 0.4486 at 139 epoch. +2024-07-27 07:10:50,300 - pyskl - INFO - Epoch(val) [139][309] top1_acc: 0.4486, top5_acc: 0.6980, mean_class_accuracy: 0.4483 +2024-07-27 07:14:39,462 - pyskl - INFO - Epoch [140][100/3746] lr: 1.315e-03, eta: 9:23:37, time: 2.292, data_time: 1.304, memory: 15990, top1_acc: 0.5988, top5_acc: 0.8331, loss_cls: 2.2184, loss: 2.2184 +2024-07-27 07:16:00,916 - pyskl - INFO - Epoch [140][200/3746] lr: 1.308e-03, eta: 9:22:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6055, top5_acc: 0.8320, loss_cls: 2.2010, loss: 2.2010 +2024-07-27 07:17:23,011 - pyskl - INFO - Epoch [140][300/3746] lr: 1.302e-03, eta: 9:20:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6098, top5_acc: 0.8234, loss_cls: 2.2276, loss: 2.2276 +2024-07-27 07:18:44,718 - pyskl - INFO - Epoch [140][400/3746] lr: 1.296e-03, eta: 9:19:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5941, top5_acc: 0.8195, loss_cls: 2.2651, loss: 2.2651 +2024-07-27 07:20:06,313 - pyskl - INFO - Epoch [140][500/3746] lr: 1.289e-03, eta: 9:18:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5967, top5_acc: 0.8213, loss_cls: 2.2564, loss: 2.2564 +2024-07-27 07:21:28,076 - pyskl - INFO - Epoch [140][600/3746] lr: 1.283e-03, eta: 9:16:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5994, top5_acc: 0.8322, loss_cls: 2.2053, loss: 2.2053 +2024-07-27 07:22:50,244 - pyskl - INFO - Epoch [140][700/3746] lr: 1.277e-03, eta: 9:15:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5956, top5_acc: 0.8273, loss_cls: 2.2453, loss: 2.2453 +2024-07-27 07:24:12,144 - pyskl - INFO - Epoch [140][800/3746] lr: 1.271e-03, eta: 9:14:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6003, top5_acc: 0.8245, loss_cls: 2.2462, loss: 2.2462 +2024-07-27 07:25:33,229 - pyskl - INFO - Epoch [140][900/3746] lr: 1.264e-03, eta: 9:12:38, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6083, top5_acc: 0.8261, loss_cls: 2.2264, loss: 2.2264 +2024-07-27 07:26:54,770 - pyskl - INFO - Epoch [140][1000/3746] lr: 1.258e-03, eta: 9:11:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6002, top5_acc: 0.8283, loss_cls: 2.2293, loss: 2.2293 +2024-07-27 07:28:16,172 - pyskl - INFO - Epoch [140][1100/3746] lr: 1.252e-03, eta: 9:09:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5931, top5_acc: 0.8198, loss_cls: 2.2764, loss: 2.2764 +2024-07-27 07:29:37,552 - pyskl - INFO - Epoch [140][1200/3746] lr: 1.246e-03, eta: 9:08:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5981, top5_acc: 0.8289, loss_cls: 2.2183, loss: 2.2183 +2024-07-27 07:30:58,910 - pyskl - INFO - Epoch [140][1300/3746] lr: 1.239e-03, eta: 9:07:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6067, top5_acc: 0.8308, loss_cls: 2.2084, loss: 2.2084 +2024-07-27 07:32:20,176 - pyskl - INFO - Epoch [140][1400/3746] lr: 1.233e-03, eta: 9:05:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5959, top5_acc: 0.8275, loss_cls: 2.2397, loss: 2.2397 +2024-07-27 07:33:41,629 - pyskl - INFO - Epoch [140][1500/3746] lr: 1.227e-03, eta: 9:04:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5992, top5_acc: 0.8275, loss_cls: 2.2351, loss: 2.2351 +2024-07-27 07:35:03,320 - pyskl - INFO - Epoch [140][1600/3746] lr: 1.221e-03, eta: 9:03:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5895, top5_acc: 0.8228, loss_cls: 2.2807, loss: 2.2807 +2024-07-27 07:36:24,856 - pyskl - INFO - Epoch [140][1700/3746] lr: 1.215e-03, eta: 9:01:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5966, top5_acc: 0.8206, loss_cls: 2.2673, loss: 2.2673 +2024-07-27 07:37:46,397 - pyskl - INFO - Epoch [140][1800/3746] lr: 1.209e-03, eta: 9:00:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5966, top5_acc: 0.8297, loss_cls: 2.2275, loss: 2.2275 +2024-07-27 07:39:07,822 - pyskl - INFO - Epoch [140][1900/3746] lr: 1.203e-03, eta: 8:58:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5969, top5_acc: 0.8283, loss_cls: 2.2349, loss: 2.2349 +2024-07-27 07:40:29,226 - pyskl - INFO - Epoch [140][2000/3746] lr: 1.196e-03, eta: 8:57:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5895, top5_acc: 0.8163, loss_cls: 2.2836, loss: 2.2836 +2024-07-27 07:41:50,968 - pyskl - INFO - Epoch [140][2100/3746] lr: 1.190e-03, eta: 8:56:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6009, top5_acc: 0.8297, loss_cls: 2.2108, loss: 2.2108 +2024-07-27 07:43:12,108 - pyskl - INFO - Epoch [140][2200/3746] lr: 1.184e-03, eta: 8:54:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5973, top5_acc: 0.8255, loss_cls: 2.2364, loss: 2.2364 +2024-07-27 07:44:33,603 - pyskl - INFO - Epoch [140][2300/3746] lr: 1.178e-03, eta: 8:53:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5978, top5_acc: 0.8264, loss_cls: 2.2311, loss: 2.2311 +2024-07-27 07:45:55,384 - pyskl - INFO - Epoch [140][2400/3746] lr: 1.172e-03, eta: 8:52:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5942, top5_acc: 0.8223, loss_cls: 2.2595, loss: 2.2595 +2024-07-27 07:47:17,985 - pyskl - INFO - Epoch [140][2500/3746] lr: 1.166e-03, eta: 8:50:41, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5875, top5_acc: 0.8153, loss_cls: 2.2886, loss: 2.2886 +2024-07-27 07:48:39,765 - pyskl - INFO - Epoch [140][2600/3746] lr: 1.160e-03, eta: 8:49:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5859, top5_acc: 0.8161, loss_cls: 2.3059, loss: 2.3059 +2024-07-27 07:50:01,557 - pyskl - INFO - Epoch [140][2700/3746] lr: 1.154e-03, eta: 8:47:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5919, top5_acc: 0.8186, loss_cls: 2.2673, loss: 2.2673 +2024-07-27 07:51:23,170 - pyskl - INFO - Epoch [140][2800/3746] lr: 1.148e-03, eta: 8:46:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6017, top5_acc: 0.8256, loss_cls: 2.2460, loss: 2.2460 +2024-07-27 07:52:45,536 - pyskl - INFO - Epoch [140][2900/3746] lr: 1.142e-03, eta: 8:45:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5916, top5_acc: 0.8206, loss_cls: 2.2727, loss: 2.2727 +2024-07-27 07:54:07,362 - pyskl - INFO - Epoch [140][3000/3746] lr: 1.136e-03, eta: 8:43:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5884, top5_acc: 0.8173, loss_cls: 2.2891, loss: 2.2891 +2024-07-27 07:55:29,275 - pyskl - INFO - Epoch [140][3100/3746] lr: 1.131e-03, eta: 8:42:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5966, top5_acc: 0.8275, loss_cls: 2.2372, loss: 2.2372 +2024-07-27 07:56:50,880 - pyskl - INFO - Epoch [140][3200/3746] lr: 1.125e-03, eta: 8:41:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5991, top5_acc: 0.8214, loss_cls: 2.2672, loss: 2.2672 +2024-07-27 07:58:13,166 - pyskl - INFO - Epoch [140][3300/3746] lr: 1.119e-03, eta: 8:39:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5980, top5_acc: 0.8236, loss_cls: 2.2502, loss: 2.2502 +2024-07-27 07:59:34,669 - pyskl - INFO - Epoch [140][3400/3746] lr: 1.113e-03, eta: 8:38:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5775, top5_acc: 0.8139, loss_cls: 2.2939, loss: 2.2939 +2024-07-27 08:00:56,124 - pyskl - INFO - Epoch [140][3500/3746] lr: 1.107e-03, eta: 8:36:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5906, top5_acc: 0.8189, loss_cls: 2.2618, loss: 2.2618 +2024-07-27 08:02:17,874 - pyskl - INFO - Epoch [140][3600/3746] lr: 1.101e-03, eta: 8:35:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5956, top5_acc: 0.8183, loss_cls: 2.2623, loss: 2.2623 +2024-07-27 08:03:39,485 - pyskl - INFO - Epoch [140][3700/3746] lr: 1.095e-03, eta: 8:34:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5959, top5_acc: 0.8225, loss_cls: 2.2415, loss: 2.2415 +2024-07-27 08:04:18,846 - pyskl - INFO - Saving checkpoint at 140 epochs +2024-07-27 08:06:09,624 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 08:06:10,287 - pyskl - INFO - +top1_acc 0.4538 +top5_acc 0.7006 +2024-07-27 08:06:10,287 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 08:06:10,328 - pyskl - INFO - +mean_acc 0.4535 +2024-07-27 08:06:10,332 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_139.pth was removed +2024-07-27 08:06:10,569 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_140.pth. +2024-07-27 08:06:10,570 - pyskl - INFO - Best top1_acc is 0.4538 at 140 epoch. +2024-07-27 08:06:10,582 - pyskl - INFO - Epoch(val) [140][309] top1_acc: 0.4538, top5_acc: 0.7006, mean_class_accuracy: 0.4535 +2024-07-27 08:09:57,733 - pyskl - INFO - Epoch [141][100/3746] lr: 1.087e-03, eta: 8:32:21, time: 2.271, data_time: 1.293, memory: 15990, top1_acc: 0.6253, top5_acc: 0.8389, loss_cls: 2.1243, loss: 2.1243 +2024-07-27 08:11:19,363 - pyskl - INFO - Epoch [141][200/3746] lr: 1.081e-03, eta: 8:30:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6122, top5_acc: 0.8383, loss_cls: 2.1710, loss: 2.1710 +2024-07-27 08:12:41,149 - pyskl - INFO - Epoch [141][300/3746] lr: 1.075e-03, eta: 8:29:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6191, top5_acc: 0.8381, loss_cls: 2.1678, loss: 2.1678 +2024-07-27 08:14:02,972 - pyskl - INFO - Epoch [141][400/3746] lr: 1.070e-03, eta: 8:28:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6294, top5_acc: 0.8398, loss_cls: 2.1033, loss: 2.1033 +2024-07-27 08:15:24,416 - pyskl - INFO - Epoch [141][500/3746] lr: 1.064e-03, eta: 8:26:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6177, top5_acc: 0.8383, loss_cls: 2.1640, loss: 2.1640 +2024-07-27 08:16:46,899 - pyskl - INFO - Epoch [141][600/3746] lr: 1.058e-03, eta: 8:25:30, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6167, top5_acc: 0.8414, loss_cls: 2.1373, loss: 2.1373 +2024-07-27 08:18:08,928 - pyskl - INFO - Epoch [141][700/3746] lr: 1.052e-03, eta: 8:24:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6097, top5_acc: 0.8322, loss_cls: 2.1787, loss: 2.1787 +2024-07-27 08:19:31,006 - pyskl - INFO - Epoch [141][800/3746] lr: 1.047e-03, eta: 8:22:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6053, top5_acc: 0.8322, loss_cls: 2.1922, loss: 2.1922 +2024-07-27 08:20:52,513 - pyskl - INFO - Epoch [141][900/3746] lr: 1.041e-03, eta: 8:21:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6084, top5_acc: 0.8308, loss_cls: 2.2047, loss: 2.2047 +2024-07-27 08:22:14,485 - pyskl - INFO - Epoch [141][1000/3746] lr: 1.035e-03, eta: 8:20:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6148, top5_acc: 0.8341, loss_cls: 2.1599, loss: 2.1599 +2024-07-27 08:23:36,417 - pyskl - INFO - Epoch [141][1100/3746] lr: 1.030e-03, eta: 8:18:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6108, top5_acc: 0.8308, loss_cls: 2.1835, loss: 2.1835 +2024-07-27 08:24:58,506 - pyskl - INFO - Epoch [141][1200/3746] lr: 1.024e-03, eta: 8:17:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6178, top5_acc: 0.8408, loss_cls: 2.1402, loss: 2.1402 +2024-07-27 08:26:20,108 - pyskl - INFO - Epoch [141][1300/3746] lr: 1.018e-03, eta: 8:15:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6033, top5_acc: 0.8320, loss_cls: 2.1889, loss: 2.1889 +2024-07-27 08:27:42,331 - pyskl - INFO - Epoch [141][1400/3746] lr: 1.013e-03, eta: 8:14:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6162, top5_acc: 0.8397, loss_cls: 2.1631, loss: 2.1631 +2024-07-27 08:29:03,841 - pyskl - INFO - Epoch [141][1500/3746] lr: 1.007e-03, eta: 8:13:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6036, top5_acc: 0.8419, loss_cls: 2.1650, loss: 2.1650 +2024-07-27 08:30:25,479 - pyskl - INFO - Epoch [141][1600/3746] lr: 1.002e-03, eta: 8:11:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5906, top5_acc: 0.8220, loss_cls: 2.2691, loss: 2.2691 +2024-07-27 08:31:46,920 - pyskl - INFO - Epoch [141][1700/3746] lr: 9.961e-04, eta: 8:10:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6061, top5_acc: 0.8323, loss_cls: 2.2108, loss: 2.2108 +2024-07-27 08:33:08,260 - pyskl - INFO - Epoch [141][1800/3746] lr: 9.905e-04, eta: 8:09:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6088, top5_acc: 0.8381, loss_cls: 2.1790, loss: 2.1790 +2024-07-27 08:34:29,520 - pyskl - INFO - Epoch [141][1900/3746] lr: 9.850e-04, eta: 8:07:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6069, top5_acc: 0.8347, loss_cls: 2.1619, loss: 2.1619 +2024-07-27 08:35:51,217 - pyskl - INFO - Epoch [141][2000/3746] lr: 9.795e-04, eta: 8:06:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6095, top5_acc: 0.8298, loss_cls: 2.1907, loss: 2.1907 +2024-07-27 08:37:12,840 - pyskl - INFO - Epoch [141][2100/3746] lr: 9.740e-04, eta: 8:04:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6098, top5_acc: 0.8348, loss_cls: 2.1840, loss: 2.1840 +2024-07-27 08:38:34,631 - pyskl - INFO - Epoch [141][2200/3746] lr: 9.685e-04, eta: 8:03:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5967, top5_acc: 0.8272, loss_cls: 2.2391, loss: 2.2391 +2024-07-27 08:39:56,352 - pyskl - INFO - Epoch [141][2300/3746] lr: 9.630e-04, eta: 8:02:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6127, top5_acc: 0.8313, loss_cls: 2.1851, loss: 2.1851 +2024-07-27 08:41:18,214 - pyskl - INFO - Epoch [141][2400/3746] lr: 9.576e-04, eta: 8:00:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6109, top5_acc: 0.8284, loss_cls: 2.2107, loss: 2.2107 +2024-07-27 08:42:39,927 - pyskl - INFO - Epoch [141][2500/3746] lr: 9.522e-04, eta: 7:59:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6061, top5_acc: 0.8280, loss_cls: 2.2032, loss: 2.2032 +2024-07-27 08:44:02,276 - pyskl - INFO - Epoch [141][2600/3746] lr: 9.467e-04, eta: 7:58:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6108, top5_acc: 0.8378, loss_cls: 2.1805, loss: 2.1805 +2024-07-27 08:45:23,869 - pyskl - INFO - Epoch [141][2700/3746] lr: 9.413e-04, eta: 7:56:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6012, top5_acc: 0.8327, loss_cls: 2.1972, loss: 2.1972 +2024-07-27 08:46:45,524 - pyskl - INFO - Epoch [141][2800/3746] lr: 9.359e-04, eta: 7:55:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6081, top5_acc: 0.8261, loss_cls: 2.1963, loss: 2.1963 +2024-07-27 08:48:07,100 - pyskl - INFO - Epoch [141][2900/3746] lr: 9.306e-04, eta: 7:53:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6177, top5_acc: 0.8361, loss_cls: 2.1539, loss: 2.1539 +2024-07-27 08:49:29,828 - pyskl - INFO - Epoch [141][3000/3746] lr: 9.252e-04, eta: 7:52:34, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6034, top5_acc: 0.8300, loss_cls: 2.2064, loss: 2.2064 +2024-07-27 08:50:51,798 - pyskl - INFO - Epoch [141][3100/3746] lr: 9.199e-04, eta: 7:51:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6023, top5_acc: 0.8306, loss_cls: 2.2103, loss: 2.2103 +2024-07-27 08:52:13,596 - pyskl - INFO - Epoch [141][3200/3746] lr: 9.145e-04, eta: 7:49:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5919, top5_acc: 0.8125, loss_cls: 2.2843, loss: 2.2843 +2024-07-27 08:53:35,053 - pyskl - INFO - Epoch [141][3300/3746] lr: 9.092e-04, eta: 7:48:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6089, top5_acc: 0.8383, loss_cls: 2.1522, loss: 2.1522 +2024-07-27 08:54:56,130 - pyskl - INFO - Epoch [141][3400/3746] lr: 9.039e-04, eta: 7:47:05, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6094, top5_acc: 0.8319, loss_cls: 2.2064, loss: 2.2064 +2024-07-27 08:56:17,825 - pyskl - INFO - Epoch [141][3500/3746] lr: 8.986e-04, eta: 7:45:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6070, top5_acc: 0.8239, loss_cls: 2.2169, loss: 2.2169 +2024-07-27 08:57:39,320 - pyskl - INFO - Epoch [141][3600/3746] lr: 8.934e-04, eta: 7:44:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5964, top5_acc: 0.8214, loss_cls: 2.2583, loss: 2.2583 +2024-07-27 08:59:00,870 - pyskl - INFO - Epoch [141][3700/3746] lr: 8.881e-04, eta: 7:42:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6092, top5_acc: 0.8248, loss_cls: 2.2121, loss: 2.2121 +2024-07-27 08:59:40,123 - pyskl - INFO - Saving checkpoint at 141 epochs +2024-07-27 09:01:31,968 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 09:01:32,639 - pyskl - INFO - +top1_acc 0.4535 +top5_acc 0.7005 +2024-07-27 09:01:32,639 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 09:01:32,684 - pyskl - INFO - +mean_acc 0.4532 +2024-07-27 09:01:32,698 - pyskl - INFO - Epoch(val) [141][309] top1_acc: 0.4535, top5_acc: 0.7005, mean_class_accuracy: 0.4532 +2024-07-27 09:05:23,700 - pyskl - INFO - Epoch [142][100/3746] lr: 8.805e-04, eta: 7:41:05, time: 2.310, data_time: 1.325, memory: 15990, top1_acc: 0.6252, top5_acc: 0.8456, loss_cls: 2.1018, loss: 2.1018 +2024-07-27 09:06:46,313 - pyskl - INFO - Epoch [142][200/3746] lr: 8.752e-04, eta: 7:39:42, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6308, top5_acc: 0.8480, loss_cls: 2.0657, loss: 2.0657 +2024-07-27 09:08:08,230 - pyskl - INFO - Epoch [142][300/3746] lr: 8.700e-04, eta: 7:38:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6256, top5_acc: 0.8442, loss_cls: 2.0996, loss: 2.0996 +2024-07-27 09:09:29,571 - pyskl - INFO - Epoch [142][400/3746] lr: 8.649e-04, eta: 7:36:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6270, top5_acc: 0.8427, loss_cls: 2.0993, loss: 2.0993 +2024-07-27 09:10:51,472 - pyskl - INFO - Epoch [142][500/3746] lr: 8.597e-04, eta: 7:35:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6231, top5_acc: 0.8483, loss_cls: 2.0899, loss: 2.0899 +2024-07-27 09:12:13,690 - pyskl - INFO - Epoch [142][600/3746] lr: 8.545e-04, eta: 7:34:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6242, top5_acc: 0.8430, loss_cls: 2.1206, loss: 2.1206 +2024-07-27 09:13:35,574 - pyskl - INFO - Epoch [142][700/3746] lr: 8.494e-04, eta: 7:32:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6273, top5_acc: 0.8452, loss_cls: 2.0988, loss: 2.0988 +2024-07-27 09:14:57,700 - pyskl - INFO - Epoch [142][800/3746] lr: 8.443e-04, eta: 7:31:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6188, top5_acc: 0.8381, loss_cls: 2.1482, loss: 2.1482 +2024-07-27 09:16:20,034 - pyskl - INFO - Epoch [142][900/3746] lr: 8.392e-04, eta: 7:30:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6181, top5_acc: 0.8364, loss_cls: 2.1461, loss: 2.1461 +2024-07-27 09:17:41,821 - pyskl - INFO - Epoch [142][1000/3746] lr: 8.341e-04, eta: 7:28:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6145, top5_acc: 0.8444, loss_cls: 2.1468, loss: 2.1468 +2024-07-27 09:19:03,867 - pyskl - INFO - Epoch [142][1100/3746] lr: 8.290e-04, eta: 7:27:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6167, top5_acc: 0.8411, loss_cls: 2.1442, loss: 2.1442 +2024-07-27 09:20:24,907 - pyskl - INFO - Epoch [142][1200/3746] lr: 8.239e-04, eta: 7:25:59, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6127, top5_acc: 0.8414, loss_cls: 2.1196, loss: 2.1196 +2024-07-27 09:21:46,483 - pyskl - INFO - Epoch [142][1300/3746] lr: 8.189e-04, eta: 7:24:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6191, top5_acc: 0.8406, loss_cls: 2.1343, loss: 2.1343 +2024-07-27 09:23:08,072 - pyskl - INFO - Epoch [142][1400/3746] lr: 8.139e-04, eta: 7:23:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6227, top5_acc: 0.8333, loss_cls: 2.1272, loss: 2.1272 +2024-07-27 09:24:29,529 - pyskl - INFO - Epoch [142][1500/3746] lr: 8.088e-04, eta: 7:21:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6250, top5_acc: 0.8378, loss_cls: 2.1230, loss: 2.1230 +2024-07-27 09:25:50,704 - pyskl - INFO - Epoch [142][1600/3746] lr: 8.038e-04, eta: 7:20:30, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6203, top5_acc: 0.8341, loss_cls: 2.1458, loss: 2.1458 +2024-07-27 09:27:12,290 - pyskl - INFO - Epoch [142][1700/3746] lr: 7.989e-04, eta: 7:19:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6073, top5_acc: 0.8341, loss_cls: 2.1627, loss: 2.1627 +2024-07-27 09:28:34,323 - pyskl - INFO - Epoch [142][1800/3746] lr: 7.939e-04, eta: 7:17:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6169, top5_acc: 0.8403, loss_cls: 2.1327, loss: 2.1327 +2024-07-27 09:29:56,346 - pyskl - INFO - Epoch [142][1900/3746] lr: 7.889e-04, eta: 7:16:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6166, top5_acc: 0.8330, loss_cls: 2.1356, loss: 2.1356 +2024-07-27 09:31:17,672 - pyskl - INFO - Epoch [142][2000/3746] lr: 7.840e-04, eta: 7:15:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6142, top5_acc: 0.8361, loss_cls: 2.1484, loss: 2.1484 +2024-07-27 09:32:39,446 - pyskl - INFO - Epoch [142][2100/3746] lr: 7.791e-04, eta: 7:13:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6209, top5_acc: 0.8389, loss_cls: 2.1277, loss: 2.1277 +2024-07-27 09:34:00,947 - pyskl - INFO - Epoch [142][2200/3746] lr: 7.742e-04, eta: 7:12:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6227, top5_acc: 0.8452, loss_cls: 2.1128, loss: 2.1128 +2024-07-27 09:35:22,976 - pyskl - INFO - Epoch [142][2300/3746] lr: 7.693e-04, eta: 7:10:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6203, top5_acc: 0.8367, loss_cls: 2.1275, loss: 2.1275 +2024-07-27 09:36:45,438 - pyskl - INFO - Epoch [142][2400/3746] lr: 7.644e-04, eta: 7:09:31, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6094, top5_acc: 0.8327, loss_cls: 2.1944, loss: 2.1944 +2024-07-27 09:38:06,694 - pyskl - INFO - Epoch [142][2500/3746] lr: 7.595e-04, eta: 7:08:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6247, top5_acc: 0.8427, loss_cls: 2.1067, loss: 2.1067 +2024-07-27 09:39:29,174 - pyskl - INFO - Epoch [142][2600/3746] lr: 7.547e-04, eta: 7:06:46, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6147, top5_acc: 0.8392, loss_cls: 2.1435, loss: 2.1435 +2024-07-27 09:40:51,177 - pyskl - INFO - Epoch [142][2700/3746] lr: 7.499e-04, eta: 7:05:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6245, top5_acc: 0.8408, loss_cls: 2.1011, loss: 2.1011 +2024-07-27 09:42:13,072 - pyskl - INFO - Epoch [142][2800/3746] lr: 7.450e-04, eta: 7:04:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6186, top5_acc: 0.8391, loss_cls: 2.1389, loss: 2.1389 +2024-07-27 09:43:34,786 - pyskl - INFO - Epoch [142][2900/3746] lr: 7.402e-04, eta: 7:02:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6203, top5_acc: 0.8436, loss_cls: 2.1185, loss: 2.1185 +2024-07-27 09:44:57,722 - pyskl - INFO - Epoch [142][3000/3746] lr: 7.355e-04, eta: 7:01:17, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6145, top5_acc: 0.8414, loss_cls: 2.1243, loss: 2.1243 +2024-07-27 09:46:19,312 - pyskl - INFO - Epoch [142][3100/3746] lr: 7.307e-04, eta: 6:59:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6108, top5_acc: 0.8344, loss_cls: 2.1813, loss: 2.1813 +2024-07-27 09:47:41,177 - pyskl - INFO - Epoch [142][3200/3746] lr: 7.259e-04, eta: 6:58:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6208, top5_acc: 0.8402, loss_cls: 2.1380, loss: 2.1380 +2024-07-27 09:49:02,811 - pyskl - INFO - Epoch [142][3300/3746] lr: 7.212e-04, eta: 6:57:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6111, top5_acc: 0.8363, loss_cls: 2.1418, loss: 2.1418 +2024-07-27 09:50:24,333 - pyskl - INFO - Epoch [142][3400/3746] lr: 7.165e-04, eta: 6:55:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6183, top5_acc: 0.8433, loss_cls: 2.1104, loss: 2.1104 +2024-07-27 09:51:45,990 - pyskl - INFO - Epoch [142][3500/3746] lr: 7.118e-04, eta: 6:54:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6192, top5_acc: 0.8417, loss_cls: 2.1227, loss: 2.1227 +2024-07-27 09:53:06,894 - pyskl - INFO - Epoch [142][3600/3746] lr: 7.071e-04, eta: 6:53:03, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6225, top5_acc: 0.8417, loss_cls: 2.1079, loss: 2.1079 +2024-07-27 09:54:28,304 - pyskl - INFO - Epoch [142][3700/3746] lr: 7.024e-04, eta: 6:51:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6169, top5_acc: 0.8408, loss_cls: 2.1498, loss: 2.1498 +2024-07-27 09:55:07,595 - pyskl - INFO - Saving checkpoint at 142 epochs +2024-07-27 09:56:58,171 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 09:56:58,834 - pyskl - INFO - +top1_acc 0.4549 +top5_acc 0.7017 +2024-07-27 09:56:58,834 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 09:56:58,876 - pyskl - INFO - +mean_acc 0.4547 +2024-07-27 09:56:58,881 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_140.pth was removed +2024-07-27 09:56:59,118 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_142.pth. +2024-07-27 09:56:59,119 - pyskl - INFO - Best top1_acc is 0.4549 at 142 epoch. +2024-07-27 09:56:59,135 - pyskl - INFO - Epoch(val) [142][309] top1_acc: 0.4549, top5_acc: 0.7017, mean_class_accuracy: 0.4547 +2024-07-27 10:00:47,660 - pyskl - INFO - Epoch [143][100/3746] lr: 6.956e-04, eta: 6:49:47, time: 2.285, data_time: 1.304, memory: 15990, top1_acc: 0.6445, top5_acc: 0.8517, loss_cls: 2.0158, loss: 2.0158 +2024-07-27 10:02:09,335 - pyskl - INFO - Epoch [143][200/3746] lr: 6.910e-04, eta: 6:48:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6389, top5_acc: 0.8506, loss_cls: 2.0325, loss: 2.0325 +2024-07-27 10:03:30,953 - pyskl - INFO - Epoch [143][300/3746] lr: 6.863e-04, eta: 6:47:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6441, top5_acc: 0.8606, loss_cls: 1.9955, loss: 1.9955 +2024-07-27 10:04:52,525 - pyskl - INFO - Epoch [143][400/3746] lr: 6.817e-04, eta: 6:45:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6300, top5_acc: 0.8491, loss_cls: 2.0721, loss: 2.0721 +2024-07-27 10:06:14,022 - pyskl - INFO - Epoch [143][500/3746] lr: 6.771e-04, eta: 6:44:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6330, top5_acc: 0.8498, loss_cls: 2.0520, loss: 2.0520 +2024-07-27 10:07:36,176 - pyskl - INFO - Epoch [143][600/3746] lr: 6.725e-04, eta: 6:42:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6347, top5_acc: 0.8548, loss_cls: 2.0384, loss: 2.0384 +2024-07-27 10:08:58,265 - pyskl - INFO - Epoch [143][700/3746] lr: 6.680e-04, eta: 6:41:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6303, top5_acc: 0.8538, loss_cls: 2.0518, loss: 2.0518 +2024-07-27 10:10:20,405 - pyskl - INFO - Epoch [143][800/3746] lr: 6.634e-04, eta: 6:40:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6344, top5_acc: 0.8541, loss_cls: 2.0443, loss: 2.0443 +2024-07-27 10:11:41,881 - pyskl - INFO - Epoch [143][900/3746] lr: 6.589e-04, eta: 6:38:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6412, top5_acc: 0.8534, loss_cls: 2.0363, loss: 2.0363 +2024-07-27 10:13:03,360 - pyskl - INFO - Epoch [143][1000/3746] lr: 6.544e-04, eta: 6:37:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6288, top5_acc: 0.8467, loss_cls: 2.0756, loss: 2.0756 +2024-07-27 10:14:24,852 - pyskl - INFO - Epoch [143][1100/3746] lr: 6.499e-04, eta: 6:36:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6366, top5_acc: 0.8541, loss_cls: 2.0498, loss: 2.0498 +2024-07-27 10:15:46,591 - pyskl - INFO - Epoch [143][1200/3746] lr: 6.454e-04, eta: 6:34:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6334, top5_acc: 0.8514, loss_cls: 2.0623, loss: 2.0623 +2024-07-27 10:17:08,069 - pyskl - INFO - Epoch [143][1300/3746] lr: 6.409e-04, eta: 6:33:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6306, top5_acc: 0.8483, loss_cls: 2.0787, loss: 2.0787 +2024-07-27 10:18:29,657 - pyskl - INFO - Epoch [143][1400/3746] lr: 6.365e-04, eta: 6:31:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6209, top5_acc: 0.8466, loss_cls: 2.0910, loss: 2.0910 +2024-07-27 10:19:50,921 - pyskl - INFO - Epoch [143][1500/3746] lr: 6.320e-04, eta: 6:30:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6273, top5_acc: 0.8542, loss_cls: 2.0528, loss: 2.0528 +2024-07-27 10:21:12,515 - pyskl - INFO - Epoch [143][1600/3746] lr: 6.276e-04, eta: 6:29:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6316, top5_acc: 0.8495, loss_cls: 2.0704, loss: 2.0704 +2024-07-27 10:22:33,420 - pyskl - INFO - Epoch [143][1700/3746] lr: 6.232e-04, eta: 6:27:49, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6253, top5_acc: 0.8483, loss_cls: 2.0995, loss: 2.0995 +2024-07-27 10:23:55,426 - pyskl - INFO - Epoch [143][1800/3746] lr: 6.188e-04, eta: 6:26:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6262, top5_acc: 0.8423, loss_cls: 2.1073, loss: 2.1073 +2024-07-27 10:25:16,940 - pyskl - INFO - Epoch [143][1900/3746] lr: 6.144e-04, eta: 6:25:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6325, top5_acc: 0.8494, loss_cls: 2.0628, loss: 2.0628 +2024-07-27 10:26:38,527 - pyskl - INFO - Epoch [143][2000/3746] lr: 6.101e-04, eta: 6:23:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6214, top5_acc: 0.8458, loss_cls: 2.1011, loss: 2.1011 +2024-07-27 10:27:59,944 - pyskl - INFO - Epoch [143][2100/3746] lr: 6.057e-04, eta: 6:22:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6439, top5_acc: 0.8466, loss_cls: 2.0667, loss: 2.0667 +2024-07-27 10:29:21,644 - pyskl - INFO - Epoch [143][2200/3746] lr: 6.014e-04, eta: 6:20:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6288, top5_acc: 0.8453, loss_cls: 2.0739, loss: 2.0739 +2024-07-27 10:30:42,688 - pyskl - INFO - Epoch [143][2300/3746] lr: 5.971e-04, eta: 6:19:35, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6266, top5_acc: 0.8402, loss_cls: 2.0986, loss: 2.0986 +2024-07-27 10:32:04,406 - pyskl - INFO - Epoch [143][2400/3746] lr: 5.928e-04, eta: 6:18:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6273, top5_acc: 0.8477, loss_cls: 2.0819, loss: 2.0819 +2024-07-27 10:33:26,269 - pyskl - INFO - Epoch [143][2500/3746] lr: 5.885e-04, eta: 6:16:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6280, top5_acc: 0.8416, loss_cls: 2.1039, loss: 2.1039 +2024-07-27 10:34:48,024 - pyskl - INFO - Epoch [143][2600/3746] lr: 5.842e-04, eta: 6:15:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6311, top5_acc: 0.8450, loss_cls: 2.0896, loss: 2.0896 +2024-07-27 10:36:09,893 - pyskl - INFO - Epoch [143][2700/3746] lr: 5.800e-04, eta: 6:14:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6283, top5_acc: 0.8445, loss_cls: 2.0879, loss: 2.0879 +2024-07-27 10:37:31,679 - pyskl - INFO - Epoch [143][2800/3746] lr: 5.757e-04, eta: 6:12:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6328, top5_acc: 0.8514, loss_cls: 2.0486, loss: 2.0486 +2024-07-27 10:38:53,574 - pyskl - INFO - Epoch [143][2900/3746] lr: 5.715e-04, eta: 6:11:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6264, top5_acc: 0.8480, loss_cls: 2.0710, loss: 2.0710 +2024-07-27 10:40:15,546 - pyskl - INFO - Epoch [143][3000/3746] lr: 5.673e-04, eta: 6:09:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6327, top5_acc: 0.8459, loss_cls: 2.0622, loss: 2.0622 +2024-07-27 10:41:37,535 - pyskl - INFO - Epoch [143][3100/3746] lr: 5.631e-04, eta: 6:08:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6348, top5_acc: 0.8503, loss_cls: 2.0625, loss: 2.0625 +2024-07-27 10:42:59,870 - pyskl - INFO - Epoch [143][3200/3746] lr: 5.590e-04, eta: 6:07:14, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6286, top5_acc: 0.8541, loss_cls: 2.0656, loss: 2.0656 +2024-07-27 10:44:21,765 - pyskl - INFO - Epoch [143][3300/3746] lr: 5.548e-04, eta: 6:05:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6352, top5_acc: 0.8483, loss_cls: 2.0544, loss: 2.0544 +2024-07-27 10:45:43,265 - pyskl - INFO - Epoch [143][3400/3746] lr: 5.506e-04, eta: 6:04:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6258, top5_acc: 0.8478, loss_cls: 2.0849, loss: 2.0849 +2024-07-27 10:47:05,520 - pyskl - INFO - Epoch [143][3500/3746] lr: 5.465e-04, eta: 6:03:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6156, top5_acc: 0.8361, loss_cls: 2.1460, loss: 2.1460 +2024-07-27 10:48:26,845 - pyskl - INFO - Epoch [143][3600/3746] lr: 5.424e-04, eta: 6:01:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6378, top5_acc: 0.8525, loss_cls: 2.0537, loss: 2.0537 +2024-07-27 10:49:48,205 - pyskl - INFO - Epoch [143][3700/3746] lr: 5.383e-04, eta: 6:00:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6309, top5_acc: 0.8509, loss_cls: 2.0781, loss: 2.0781 +2024-07-27 10:50:27,177 - pyskl - INFO - Saving checkpoint at 143 epochs +2024-07-27 10:52:17,391 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 10:52:18,049 - pyskl - INFO - +top1_acc 0.4539 +top5_acc 0.7002 +2024-07-27 10:52:18,049 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 10:52:18,089 - pyskl - INFO - +mean_acc 0.4537 +2024-07-27 10:52:18,100 - pyskl - INFO - Epoch(val) [143][309] top1_acc: 0.4539, top5_acc: 0.7002, mean_class_accuracy: 0.4537 +2024-07-27 10:56:06,183 - pyskl - INFO - Epoch [144][100/3746] lr: 5.323e-04, eta: 5:58:27, time: 2.281, data_time: 1.290, memory: 15990, top1_acc: 0.6461, top5_acc: 0.8606, loss_cls: 1.9923, loss: 1.9923 +2024-07-27 10:57:27,893 - pyskl - INFO - Epoch [144][200/3746] lr: 5.283e-04, eta: 5:57:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6530, top5_acc: 0.8662, loss_cls: 1.9494, loss: 1.9494 +2024-07-27 10:58:49,531 - pyskl - INFO - Epoch [144][300/3746] lr: 5.242e-04, eta: 5:55:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6448, top5_acc: 0.8561, loss_cls: 1.9984, loss: 1.9984 +2024-07-27 11:00:11,024 - pyskl - INFO - Epoch [144][400/3746] lr: 5.202e-04, eta: 5:54:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6380, top5_acc: 0.8509, loss_cls: 2.0367, loss: 2.0367 +2024-07-27 11:01:32,326 - pyskl - INFO - Epoch [144][500/3746] lr: 5.162e-04, eta: 5:52:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6447, top5_acc: 0.8583, loss_cls: 1.9822, loss: 1.9822 +2024-07-27 11:02:54,482 - pyskl - INFO - Epoch [144][600/3746] lr: 5.122e-04, eta: 5:51:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6489, top5_acc: 0.8548, loss_cls: 1.9851, loss: 1.9851 +2024-07-27 11:04:16,225 - pyskl - INFO - Epoch [144][700/3746] lr: 5.082e-04, eta: 5:50:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6514, top5_acc: 0.8641, loss_cls: 1.9728, loss: 1.9728 +2024-07-27 11:05:38,626 - pyskl - INFO - Epoch [144][800/3746] lr: 5.042e-04, eta: 5:48:51, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6512, top5_acc: 0.8595, loss_cls: 1.9657, loss: 1.9657 +2024-07-27 11:07:00,483 - pyskl - INFO - Epoch [144][900/3746] lr: 5.003e-04, eta: 5:47:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6508, top5_acc: 0.8580, loss_cls: 1.9786, loss: 1.9786 +2024-07-27 11:08:21,791 - pyskl - INFO - Epoch [144][1000/3746] lr: 4.964e-04, eta: 5:46:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6433, top5_acc: 0.8520, loss_cls: 2.0186, loss: 2.0186 +2024-07-27 11:09:42,749 - pyskl - INFO - Epoch [144][1100/3746] lr: 4.924e-04, eta: 5:44:43, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6547, top5_acc: 0.8628, loss_cls: 1.9642, loss: 1.9642 +2024-07-27 11:11:04,361 - pyskl - INFO - Epoch [144][1200/3746] lr: 4.885e-04, eta: 5:43:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6352, top5_acc: 0.8505, loss_cls: 2.0303, loss: 2.0303 +2024-07-27 11:12:25,823 - pyskl - INFO - Epoch [144][1300/3746] lr: 4.846e-04, eta: 5:41:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6411, top5_acc: 0.8633, loss_cls: 2.0002, loss: 2.0002 +2024-07-27 11:13:46,943 - pyskl - INFO - Epoch [144][1400/3746] lr: 4.808e-04, eta: 5:40:36, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6444, top5_acc: 0.8556, loss_cls: 2.0154, loss: 2.0154 +2024-07-27 11:15:08,579 - pyskl - INFO - Epoch [144][1500/3746] lr: 4.769e-04, eta: 5:39:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6511, top5_acc: 0.8602, loss_cls: 1.9878, loss: 1.9878 +2024-07-27 11:16:29,700 - pyskl - INFO - Epoch [144][1600/3746] lr: 4.731e-04, eta: 5:37:52, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6477, top5_acc: 0.8620, loss_cls: 1.9875, loss: 1.9875 +2024-07-27 11:17:51,240 - pyskl - INFO - Epoch [144][1700/3746] lr: 4.692e-04, eta: 5:36:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6458, top5_acc: 0.8562, loss_cls: 2.0145, loss: 2.0145 +2024-07-27 11:19:12,783 - pyskl - INFO - Epoch [144][1800/3746] lr: 4.654e-04, eta: 5:35:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6384, top5_acc: 0.8533, loss_cls: 2.0332, loss: 2.0332 +2024-07-27 11:20:34,178 - pyskl - INFO - Epoch [144][1900/3746] lr: 4.616e-04, eta: 5:33:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6428, top5_acc: 0.8509, loss_cls: 2.0181, loss: 2.0181 +2024-07-27 11:21:55,691 - pyskl - INFO - Epoch [144][2000/3746] lr: 4.578e-04, eta: 5:32:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6444, top5_acc: 0.8534, loss_cls: 2.0074, loss: 2.0074 +2024-07-27 11:23:18,096 - pyskl - INFO - Epoch [144][2100/3746] lr: 4.541e-04, eta: 5:31:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6319, top5_acc: 0.8477, loss_cls: 2.0725, loss: 2.0725 +2024-07-27 11:24:39,534 - pyskl - INFO - Epoch [144][2200/3746] lr: 4.503e-04, eta: 5:29:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6516, top5_acc: 0.8555, loss_cls: 1.9719, loss: 1.9719 +2024-07-27 11:26:00,992 - pyskl - INFO - Epoch [144][2300/3746] lr: 4.466e-04, eta: 5:28:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6359, top5_acc: 0.8547, loss_cls: 2.0305, loss: 2.0305 +2024-07-27 11:27:23,271 - pyskl - INFO - Epoch [144][2400/3746] lr: 4.429e-04, eta: 5:26:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6492, top5_acc: 0.8578, loss_cls: 1.9715, loss: 1.9715 +2024-07-27 11:28:44,906 - pyskl - INFO - Epoch [144][2500/3746] lr: 4.392e-04, eta: 5:25:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6336, top5_acc: 0.8469, loss_cls: 2.0561, loss: 2.0561 +2024-07-27 11:30:07,487 - pyskl - INFO - Epoch [144][2600/3746] lr: 4.355e-04, eta: 5:24:08, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6369, top5_acc: 0.8545, loss_cls: 2.0359, loss: 2.0359 +2024-07-27 11:31:29,001 - pyskl - INFO - Epoch [144][2700/3746] lr: 4.318e-04, eta: 5:22:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6383, top5_acc: 0.8500, loss_cls: 2.0562, loss: 2.0562 +2024-07-27 11:32:50,363 - pyskl - INFO - Epoch [144][2800/3746] lr: 4.281e-04, eta: 5:21:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6459, top5_acc: 0.8586, loss_cls: 2.0130, loss: 2.0130 +2024-07-27 11:34:11,549 - pyskl - INFO - Epoch [144][2900/3746] lr: 4.245e-04, eta: 5:20:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6356, top5_acc: 0.8538, loss_cls: 2.0197, loss: 2.0197 +2024-07-27 11:35:33,046 - pyskl - INFO - Epoch [144][3000/3746] lr: 4.209e-04, eta: 5:18:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6480, top5_acc: 0.8594, loss_cls: 1.9751, loss: 1.9751 +2024-07-27 11:36:56,011 - pyskl - INFO - Epoch [144][3100/3746] lr: 4.173e-04, eta: 5:17:16, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.6334, top5_acc: 0.8536, loss_cls: 2.0375, loss: 2.0375 +2024-07-27 11:38:17,440 - pyskl - INFO - Epoch [144][3200/3746] lr: 4.137e-04, eta: 5:15:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6431, top5_acc: 0.8534, loss_cls: 2.0224, loss: 2.0224 +2024-07-27 11:39:39,454 - pyskl - INFO - Epoch [144][3300/3746] lr: 4.101e-04, eta: 5:14:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6422, top5_acc: 0.8519, loss_cls: 2.0195, loss: 2.0195 +2024-07-27 11:41:00,671 - pyskl - INFO - Epoch [144][3400/3746] lr: 4.065e-04, eta: 5:13:09, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6331, top5_acc: 0.8547, loss_cls: 2.0401, loss: 2.0401 +2024-07-27 11:42:22,444 - pyskl - INFO - Epoch [144][3500/3746] lr: 4.030e-04, eta: 5:11:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6377, top5_acc: 0.8489, loss_cls: 2.0355, loss: 2.0355 +2024-07-27 11:43:43,523 - pyskl - INFO - Epoch [144][3600/3746] lr: 3.994e-04, eta: 5:10:24, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6520, top5_acc: 0.8616, loss_cls: 1.9689, loss: 1.9689 +2024-07-27 11:45:04,632 - pyskl - INFO - Epoch [144][3700/3746] lr: 3.959e-04, eta: 5:09:02, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6344, top5_acc: 0.8495, loss_cls: 2.0751, loss: 2.0751 +2024-07-27 11:45:44,231 - pyskl - INFO - Saving checkpoint at 144 epochs +2024-07-27 11:47:34,836 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 11:47:35,497 - pyskl - INFO - +top1_acc 0.4571 +top5_acc 0.7044 +2024-07-27 11:47:35,497 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 11:47:35,537 - pyskl - INFO - +mean_acc 0.4568 +2024-07-27 11:47:35,542 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_142.pth was removed +2024-07-27 11:47:35,783 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_144.pth. +2024-07-27 11:47:35,783 - pyskl - INFO - Best top1_acc is 0.4571 at 144 epoch. +2024-07-27 11:47:35,795 - pyskl - INFO - Epoch(val) [144][309] top1_acc: 0.4571, top5_acc: 0.7044, mean_class_accuracy: 0.4568 +2024-07-27 11:51:24,338 - pyskl - INFO - Epoch [145][100/3746] lr: 3.908e-04, eta: 5:07:06, time: 2.285, data_time: 1.290, memory: 15990, top1_acc: 0.6631, top5_acc: 0.8648, loss_cls: 1.9401, loss: 1.9401 +2024-07-27 11:52:45,905 - pyskl - INFO - Epoch [145][200/3746] lr: 3.873e-04, eta: 5:05:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6536, top5_acc: 0.8642, loss_cls: 1.9267, loss: 1.9267 +2024-07-27 11:54:07,185 - pyskl - INFO - Epoch [145][300/3746] lr: 3.839e-04, eta: 5:04:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6641, top5_acc: 0.8669, loss_cls: 1.9084, loss: 1.9084 +2024-07-27 11:55:28,908 - pyskl - INFO - Epoch [145][400/3746] lr: 3.804e-04, eta: 5:02:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6467, top5_acc: 0.8653, loss_cls: 1.9726, loss: 1.9726 +2024-07-27 11:56:50,614 - pyskl - INFO - Epoch [145][500/3746] lr: 3.770e-04, eta: 5:01:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6634, top5_acc: 0.8638, loss_cls: 1.9387, loss: 1.9387 +2024-07-27 11:58:13,071 - pyskl - INFO - Epoch [145][600/3746] lr: 3.736e-04, eta: 5:00:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6453, top5_acc: 0.8636, loss_cls: 1.9523, loss: 1.9523 +2024-07-27 11:59:35,045 - pyskl - INFO - Epoch [145][700/3746] lr: 3.702e-04, eta: 4:58:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6578, top5_acc: 0.8620, loss_cls: 1.9534, loss: 1.9534 +2024-07-27 12:00:56,855 - pyskl - INFO - Epoch [145][800/3746] lr: 3.668e-04, eta: 4:57:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6539, top5_acc: 0.8566, loss_cls: 1.9738, loss: 1.9738 +2024-07-27 12:02:18,726 - pyskl - INFO - Epoch [145][900/3746] lr: 3.634e-04, eta: 4:56:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6587, top5_acc: 0.8605, loss_cls: 1.9415, loss: 1.9415 +2024-07-27 12:03:40,244 - pyskl - INFO - Epoch [145][1000/3746] lr: 3.600e-04, eta: 4:54:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6619, top5_acc: 0.8725, loss_cls: 1.9099, loss: 1.9099 +2024-07-27 12:05:01,894 - pyskl - INFO - Epoch [145][1100/3746] lr: 3.567e-04, eta: 4:53:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6431, top5_acc: 0.8569, loss_cls: 1.9951, loss: 1.9951 +2024-07-27 12:06:23,300 - pyskl - INFO - Epoch [145][1200/3746] lr: 3.534e-04, eta: 4:52:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6580, top5_acc: 0.8678, loss_cls: 1.9456, loss: 1.9456 +2024-07-27 12:07:44,780 - pyskl - INFO - Epoch [145][1300/3746] lr: 3.501e-04, eta: 4:50:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6483, top5_acc: 0.8541, loss_cls: 1.9767, loss: 1.9767 +2024-07-27 12:09:05,850 - pyskl - INFO - Epoch [145][1400/3746] lr: 3.468e-04, eta: 4:49:15, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6488, top5_acc: 0.8653, loss_cls: 1.9497, loss: 1.9497 +2024-07-27 12:10:27,449 - pyskl - INFO - Epoch [145][1500/3746] lr: 3.435e-04, eta: 4:47:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6547, top5_acc: 0.8648, loss_cls: 1.9405, loss: 1.9405 +2024-07-27 12:11:48,889 - pyskl - INFO - Epoch [145][1600/3746] lr: 3.402e-04, eta: 4:46:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6436, top5_acc: 0.8584, loss_cls: 1.9864, loss: 1.9864 +2024-07-27 12:13:10,532 - pyskl - INFO - Epoch [145][1700/3746] lr: 3.370e-04, eta: 4:45:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6545, top5_acc: 0.8622, loss_cls: 1.9579, loss: 1.9579 +2024-07-27 12:14:31,717 - pyskl - INFO - Epoch [145][1800/3746] lr: 3.337e-04, eta: 4:43:46, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6575, top5_acc: 0.8608, loss_cls: 1.9605, loss: 1.9605 +2024-07-27 12:15:53,346 - pyskl - INFO - Epoch [145][1900/3746] lr: 3.305e-04, eta: 4:42:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6464, top5_acc: 0.8608, loss_cls: 1.9995, loss: 1.9995 +2024-07-27 12:17:15,037 - pyskl - INFO - Epoch [145][2000/3746] lr: 3.273e-04, eta: 4:41:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6456, top5_acc: 0.8598, loss_cls: 1.9976, loss: 1.9976 +2024-07-27 12:18:36,452 - pyskl - INFO - Epoch [145][2100/3746] lr: 3.241e-04, eta: 4:39:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6473, top5_acc: 0.8558, loss_cls: 1.9961, loss: 1.9961 +2024-07-27 12:19:58,016 - pyskl - INFO - Epoch [145][2200/3746] lr: 3.210e-04, eta: 4:38:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6519, top5_acc: 0.8558, loss_cls: 1.9734, loss: 1.9734 +2024-07-27 12:21:19,924 - pyskl - INFO - Epoch [145][2300/3746] lr: 3.178e-04, eta: 4:36:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6597, top5_acc: 0.8653, loss_cls: 1.9530, loss: 1.9530 +2024-07-27 12:22:42,286 - pyskl - INFO - Epoch [145][2400/3746] lr: 3.147e-04, eta: 4:35:32, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6539, top5_acc: 0.8608, loss_cls: 1.9412, loss: 1.9412 +2024-07-27 12:24:04,271 - pyskl - INFO - Epoch [145][2500/3746] lr: 3.116e-04, eta: 4:34:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6436, top5_acc: 0.8555, loss_cls: 1.9911, loss: 1.9911 +2024-07-27 12:25:26,488 - pyskl - INFO - Epoch [145][2600/3746] lr: 3.084e-04, eta: 4:32:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6511, top5_acc: 0.8628, loss_cls: 1.9686, loss: 1.9686 +2024-07-27 12:26:48,804 - pyskl - INFO - Epoch [145][2700/3746] lr: 3.054e-04, eta: 4:31:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6567, top5_acc: 0.8675, loss_cls: 1.9426, loss: 1.9426 +2024-07-27 12:28:10,212 - pyskl - INFO - Epoch [145][2800/3746] lr: 3.023e-04, eta: 4:30:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6475, top5_acc: 0.8630, loss_cls: 1.9666, loss: 1.9666 +2024-07-27 12:29:31,688 - pyskl - INFO - Epoch [145][2900/3746] lr: 2.992e-04, eta: 4:28:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6559, top5_acc: 0.8628, loss_cls: 1.9573, loss: 1.9573 +2024-07-27 12:30:53,459 - pyskl - INFO - Epoch [145][3000/3746] lr: 2.962e-04, eta: 4:27:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6605, top5_acc: 0.8625, loss_cls: 1.9497, loss: 1.9497 +2024-07-27 12:32:15,537 - pyskl - INFO - Epoch [145][3100/3746] lr: 2.931e-04, eta: 4:25:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6428, top5_acc: 0.8658, loss_cls: 1.9754, loss: 1.9754 +2024-07-27 12:33:37,238 - pyskl - INFO - Epoch [145][3200/3746] lr: 2.901e-04, eta: 4:24:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6409, top5_acc: 0.8591, loss_cls: 1.9694, loss: 1.9694 +2024-07-27 12:34:58,879 - pyskl - INFO - Epoch [145][3300/3746] lr: 2.871e-04, eta: 4:23:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6505, top5_acc: 0.8594, loss_cls: 1.9730, loss: 1.9730 +2024-07-27 12:36:20,389 - pyskl - INFO - Epoch [145][3400/3746] lr: 2.841e-04, eta: 4:21:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6687, top5_acc: 0.8672, loss_cls: 1.8881, loss: 1.8881 +2024-07-27 12:37:42,518 - pyskl - INFO - Epoch [145][3500/3746] lr: 2.812e-04, eta: 4:20:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6561, top5_acc: 0.8623, loss_cls: 1.9377, loss: 1.9377 +2024-07-27 12:39:03,833 - pyskl - INFO - Epoch [145][3600/3746] lr: 2.782e-04, eta: 4:19:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6545, top5_acc: 0.8622, loss_cls: 1.9488, loss: 1.9488 +2024-07-27 12:40:25,256 - pyskl - INFO - Epoch [145][3700/3746] lr: 2.753e-04, eta: 4:17:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6461, top5_acc: 0.8608, loss_cls: 1.9845, loss: 1.9845 +2024-07-27 12:41:04,567 - pyskl - INFO - Saving checkpoint at 145 epochs +2024-07-27 12:42:54,501 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 12:42:55,180 - pyskl - INFO - +top1_acc 0.4583 +top5_acc 0.7032 +2024-07-27 12:42:55,180 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 12:42:55,223 - pyskl - INFO - +mean_acc 0.4581 +2024-07-27 12:42:55,227 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_144.pth was removed +2024-07-27 12:42:55,463 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_145.pth. +2024-07-27 12:42:55,464 - pyskl - INFO - Best top1_acc is 0.4583 at 145 epoch. +2024-07-27 12:42:55,480 - pyskl - INFO - Epoch(val) [145][309] top1_acc: 0.4583, top5_acc: 0.7032, mean_class_accuracy: 0.4581 +2024-07-27 12:46:40,784 - pyskl - INFO - Epoch [146][100/3746] lr: 2.710e-04, eta: 4:15:44, time: 2.253, data_time: 1.277, memory: 15990, top1_acc: 0.6628, top5_acc: 0.8656, loss_cls: 1.9162, loss: 1.9162 +2024-07-27 12:48:02,247 - pyskl - INFO - Epoch [146][200/3746] lr: 2.681e-04, eta: 4:14:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6614, top5_acc: 0.8697, loss_cls: 1.8851, loss: 1.8851 +2024-07-27 12:49:23,356 - pyskl - INFO - Epoch [146][300/3746] lr: 2.652e-04, eta: 4:12:59, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6625, top5_acc: 0.8648, loss_cls: 1.9168, loss: 1.9168 +2024-07-27 12:50:45,221 - pyskl - INFO - Epoch [146][400/3746] lr: 2.624e-04, eta: 4:11:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6508, top5_acc: 0.8589, loss_cls: 1.9504, loss: 1.9504 +2024-07-27 12:52:06,932 - pyskl - INFO - Epoch [146][500/3746] lr: 2.595e-04, eta: 4:10:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6594, top5_acc: 0.8652, loss_cls: 1.9259, loss: 1.9259 +2024-07-27 12:53:28,660 - pyskl - INFO - Epoch [146][600/3746] lr: 2.567e-04, eta: 4:08:52, time: 0.817, data_time: 0.001, memory: 15990, top1_acc: 0.6653, top5_acc: 0.8723, loss_cls: 1.9009, loss: 1.9009 +2024-07-27 12:54:50,373 - pyskl - INFO - Epoch [146][700/3746] lr: 2.539e-04, eta: 4:07:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6597, top5_acc: 0.8691, loss_cls: 1.9210, loss: 1.9210 +2024-07-27 12:56:12,535 - pyskl - INFO - Epoch [146][800/3746] lr: 2.511e-04, eta: 4:06:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6669, top5_acc: 0.8733, loss_cls: 1.8836, loss: 1.8836 +2024-07-27 12:57:34,702 - pyskl - INFO - Epoch [146][900/3746] lr: 2.483e-04, eta: 4:04:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6702, top5_acc: 0.8727, loss_cls: 1.8730, loss: 1.8730 +2024-07-27 12:58:56,158 - pyskl - INFO - Epoch [146][1000/3746] lr: 2.455e-04, eta: 4:03:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6672, top5_acc: 0.8681, loss_cls: 1.9114, loss: 1.9114 +2024-07-27 13:00:17,737 - pyskl - INFO - Epoch [146][1100/3746] lr: 2.427e-04, eta: 4:02:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6603, top5_acc: 0.8694, loss_cls: 1.9089, loss: 1.9089 +2024-07-27 13:01:39,059 - pyskl - INFO - Epoch [146][1200/3746] lr: 2.400e-04, eta: 4:00:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6625, top5_acc: 0.8659, loss_cls: 1.9154, loss: 1.9154 +2024-07-27 13:03:00,820 - pyskl - INFO - Epoch [146][1300/3746] lr: 2.373e-04, eta: 3:59:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6625, top5_acc: 0.8728, loss_cls: 1.9059, loss: 1.9059 +2024-07-27 13:04:22,725 - pyskl - INFO - Epoch [146][1400/3746] lr: 2.345e-04, eta: 3:57:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6605, top5_acc: 0.8686, loss_cls: 1.9144, loss: 1.9144 +2024-07-27 13:05:44,225 - pyskl - INFO - Epoch [146][1500/3746] lr: 2.318e-04, eta: 3:56:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6647, top5_acc: 0.8681, loss_cls: 1.9065, loss: 1.9065 +2024-07-27 13:07:06,416 - pyskl - INFO - Epoch [146][1600/3746] lr: 2.292e-04, eta: 3:55:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6613, top5_acc: 0.8730, loss_cls: 1.9146, loss: 1.9146 +2024-07-27 13:08:27,713 - pyskl - INFO - Epoch [146][1700/3746] lr: 2.265e-04, eta: 3:53:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6745, top5_acc: 0.8700, loss_cls: 1.8793, loss: 1.8793 +2024-07-27 13:09:49,405 - pyskl - INFO - Epoch [146][1800/3746] lr: 2.239e-04, eta: 3:52:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6680, top5_acc: 0.8694, loss_cls: 1.8870, loss: 1.8870 +2024-07-27 13:11:10,922 - pyskl - INFO - Epoch [146][1900/3746] lr: 2.212e-04, eta: 3:51:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6631, top5_acc: 0.8667, loss_cls: 1.9255, loss: 1.9255 +2024-07-27 13:12:32,405 - pyskl - INFO - Epoch [146][2000/3746] lr: 2.186e-04, eta: 3:49:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6642, top5_acc: 0.8692, loss_cls: 1.9167, loss: 1.9167 +2024-07-27 13:13:54,156 - pyskl - INFO - Epoch [146][2100/3746] lr: 2.160e-04, eta: 3:48:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6636, top5_acc: 0.8681, loss_cls: 1.9268, loss: 1.9268 +2024-07-27 13:15:15,451 - pyskl - INFO - Epoch [146][2200/3746] lr: 2.134e-04, eta: 3:46:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6509, top5_acc: 0.8614, loss_cls: 1.9639, loss: 1.9639 +2024-07-27 13:16:37,346 - pyskl - INFO - Epoch [146][2300/3746] lr: 2.108e-04, eta: 3:45:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6562, top5_acc: 0.8552, loss_cls: 1.9678, loss: 1.9678 +2024-07-27 13:17:59,463 - pyskl - INFO - Epoch [146][2400/3746] lr: 2.083e-04, eta: 3:44:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6622, top5_acc: 0.8662, loss_cls: 1.8996, loss: 1.8996 +2024-07-27 13:19:20,970 - pyskl - INFO - Epoch [146][2500/3746] lr: 2.057e-04, eta: 3:42:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6605, top5_acc: 0.8705, loss_cls: 1.9090, loss: 1.9090 +2024-07-27 13:20:42,587 - pyskl - INFO - Epoch [146][2600/3746] lr: 2.032e-04, eta: 3:41:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6664, top5_acc: 0.8758, loss_cls: 1.8712, loss: 1.8712 +2024-07-27 13:22:04,792 - pyskl - INFO - Epoch [146][2700/3746] lr: 2.007e-04, eta: 3:40:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6692, top5_acc: 0.8709, loss_cls: 1.8860, loss: 1.8860 +2024-07-27 13:23:26,605 - pyskl - INFO - Epoch [146][2800/3746] lr: 1.982e-04, eta: 3:38:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6616, top5_acc: 0.8761, loss_cls: 1.9073, loss: 1.9073 +2024-07-27 13:24:47,947 - pyskl - INFO - Epoch [146][2900/3746] lr: 1.957e-04, eta: 3:37:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6631, top5_acc: 0.8675, loss_cls: 1.9045, loss: 1.9045 +2024-07-27 13:26:09,373 - pyskl - INFO - Epoch [146][3000/3746] lr: 1.933e-04, eta: 3:35:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6562, top5_acc: 0.8620, loss_cls: 1.9427, loss: 1.9427 +2024-07-27 13:27:31,269 - pyskl - INFO - Epoch [146][3100/3746] lr: 1.908e-04, eta: 3:34:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6716, top5_acc: 0.8720, loss_cls: 1.8723, loss: 1.8723 +2024-07-27 13:28:54,277 - pyskl - INFO - Epoch [146][3200/3746] lr: 1.884e-04, eta: 3:33:10, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.6592, top5_acc: 0.8623, loss_cls: 1.9474, loss: 1.9474 +2024-07-27 13:30:16,526 - pyskl - INFO - Epoch [146][3300/3746] lr: 1.860e-04, eta: 3:31:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6609, top5_acc: 0.8717, loss_cls: 1.8975, loss: 1.8975 +2024-07-27 13:31:39,281 - pyskl - INFO - Epoch [146][3400/3746] lr: 1.836e-04, eta: 3:30:26, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6727, top5_acc: 0.8703, loss_cls: 1.8770, loss: 1.8770 +2024-07-27 13:33:00,789 - pyskl - INFO - Epoch [146][3500/3746] lr: 1.812e-04, eta: 3:29:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6695, top5_acc: 0.8742, loss_cls: 1.8830, loss: 1.8830 +2024-07-27 13:34:22,668 - pyskl - INFO - Epoch [146][3600/3746] lr: 1.788e-04, eta: 3:27:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6667, top5_acc: 0.8753, loss_cls: 1.8928, loss: 1.8928 +2024-07-27 13:35:44,592 - pyskl - INFO - Epoch [146][3700/3746] lr: 1.765e-04, eta: 3:26:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6650, top5_acc: 0.8694, loss_cls: 1.9043, loss: 1.9043 +2024-07-27 13:36:23,903 - pyskl - INFO - Saving checkpoint at 146 epochs +2024-07-27 13:38:15,463 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 13:38:16,132 - pyskl - INFO - +top1_acc 0.4583 +top5_acc 0.7042 +2024-07-27 13:38:16,132 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 13:38:16,172 - pyskl - INFO - +mean_acc 0.4581 +2024-07-27 13:38:16,183 - pyskl - INFO - Epoch(val) [146][309] top1_acc: 0.4583, top5_acc: 0.7042, mean_class_accuracy: 0.4581 +2024-07-27 13:42:05,843 - pyskl - INFO - Epoch [147][100/3746] lr: 1.730e-04, eta: 3:24:21, time: 2.297, data_time: 1.309, memory: 15990, top1_acc: 0.6677, top5_acc: 0.8744, loss_cls: 1.8961, loss: 1.8961 +2024-07-27 13:43:27,402 - pyskl - INFO - Epoch [147][200/3746] lr: 1.707e-04, eta: 3:22:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6736, top5_acc: 0.8716, loss_cls: 1.8755, loss: 1.8755 +2024-07-27 13:44:49,106 - pyskl - INFO - Epoch [147][300/3746] lr: 1.684e-04, eta: 3:21:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6655, top5_acc: 0.8692, loss_cls: 1.9114, loss: 1.9114 +2024-07-27 13:46:11,396 - pyskl - INFO - Epoch [147][400/3746] lr: 1.661e-04, eta: 3:20:14, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6677, top5_acc: 0.8695, loss_cls: 1.8815, loss: 1.8815 +2024-07-27 13:47:32,762 - pyskl - INFO - Epoch [147][500/3746] lr: 1.639e-04, eta: 3:18:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6730, top5_acc: 0.8673, loss_cls: 1.8852, loss: 1.8852 +2024-07-27 13:48:55,051 - pyskl - INFO - Epoch [147][600/3746] lr: 1.616e-04, eta: 3:17:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6813, top5_acc: 0.8767, loss_cls: 1.8421, loss: 1.8421 +2024-07-27 13:50:16,757 - pyskl - INFO - Epoch [147][700/3746] lr: 1.594e-04, eta: 3:16:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6839, top5_acc: 0.8777, loss_cls: 1.8122, loss: 1.8122 +2024-07-27 13:51:38,245 - pyskl - INFO - Epoch [147][800/3746] lr: 1.572e-04, eta: 3:14:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6692, top5_acc: 0.8742, loss_cls: 1.8769, loss: 1.8769 +2024-07-27 13:52:59,706 - pyskl - INFO - Epoch [147][900/3746] lr: 1.550e-04, eta: 3:13:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6764, top5_acc: 0.8705, loss_cls: 1.8765, loss: 1.8765 +2024-07-27 13:54:21,294 - pyskl - INFO - Epoch [147][1000/3746] lr: 1.528e-04, eta: 3:12:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6705, top5_acc: 0.8717, loss_cls: 1.8668, loss: 1.8668 +2024-07-27 13:55:43,114 - pyskl - INFO - Epoch [147][1100/3746] lr: 1.506e-04, eta: 3:10:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6770, top5_acc: 0.8805, loss_cls: 1.8533, loss: 1.8533 +2024-07-27 13:57:05,304 - pyskl - INFO - Epoch [147][1200/3746] lr: 1.484e-04, eta: 3:09:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6825, top5_acc: 0.8750, loss_cls: 1.8351, loss: 1.8351 +2024-07-27 13:58:27,160 - pyskl - INFO - Epoch [147][1300/3746] lr: 1.463e-04, eta: 3:07:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6764, top5_acc: 0.8753, loss_cls: 1.8659, loss: 1.8659 +2024-07-27 13:59:48,926 - pyskl - INFO - Epoch [147][1400/3746] lr: 1.442e-04, eta: 3:06:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6741, top5_acc: 0.8697, loss_cls: 1.8613, loss: 1.8613 +2024-07-27 14:01:10,308 - pyskl - INFO - Epoch [147][1500/3746] lr: 1.420e-04, eta: 3:05:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6755, top5_acc: 0.8767, loss_cls: 1.8607, loss: 1.8607 +2024-07-27 14:02:32,096 - pyskl - INFO - Epoch [147][1600/3746] lr: 1.399e-04, eta: 3:03:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6641, top5_acc: 0.8686, loss_cls: 1.9036, loss: 1.9036 +2024-07-27 14:03:53,479 - pyskl - INFO - Epoch [147][1700/3746] lr: 1.379e-04, eta: 3:02:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6752, top5_acc: 0.8734, loss_cls: 1.8648, loss: 1.8648 +2024-07-27 14:05:15,154 - pyskl - INFO - Epoch [147][1800/3746] lr: 1.358e-04, eta: 3:01:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6708, top5_acc: 0.8727, loss_cls: 1.8800, loss: 1.8800 +2024-07-27 14:06:36,933 - pyskl - INFO - Epoch [147][1900/3746] lr: 1.337e-04, eta: 2:59:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6770, top5_acc: 0.8764, loss_cls: 1.8427, loss: 1.8427 +2024-07-27 14:07:58,509 - pyskl - INFO - Epoch [147][2000/3746] lr: 1.317e-04, eta: 2:58:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6647, top5_acc: 0.8716, loss_cls: 1.8820, loss: 1.8820 +2024-07-27 14:09:20,353 - pyskl - INFO - Epoch [147][2100/3746] lr: 1.297e-04, eta: 2:56:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6856, top5_acc: 0.8772, loss_cls: 1.8419, loss: 1.8419 +2024-07-27 14:10:41,979 - pyskl - INFO - Epoch [147][2200/3746] lr: 1.277e-04, eta: 2:55:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6759, top5_acc: 0.8723, loss_cls: 1.8598, loss: 1.8598 +2024-07-27 14:12:03,362 - pyskl - INFO - Epoch [147][2300/3746] lr: 1.257e-04, eta: 2:54:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6822, top5_acc: 0.8805, loss_cls: 1.8254, loss: 1.8254 +2024-07-27 14:13:25,767 - pyskl - INFO - Epoch [147][2400/3746] lr: 1.237e-04, eta: 2:52:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6763, top5_acc: 0.8742, loss_cls: 1.8482, loss: 1.8482 +2024-07-27 14:14:47,852 - pyskl - INFO - Epoch [147][2500/3746] lr: 1.218e-04, eta: 2:51:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6717, top5_acc: 0.8688, loss_cls: 1.8665, loss: 1.8665 +2024-07-27 14:16:09,740 - pyskl - INFO - Epoch [147][2600/3746] lr: 1.198e-04, eta: 2:50:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6756, top5_acc: 0.8756, loss_cls: 1.8487, loss: 1.8487 +2024-07-27 14:17:31,556 - pyskl - INFO - Epoch [147][2700/3746] lr: 1.179e-04, eta: 2:48:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6731, top5_acc: 0.8700, loss_cls: 1.8874, loss: 1.8874 +2024-07-27 14:18:53,258 - pyskl - INFO - Epoch [147][2800/3746] lr: 1.160e-04, eta: 2:47:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6708, top5_acc: 0.8775, loss_cls: 1.8521, loss: 1.8521 +2024-07-27 14:20:14,400 - pyskl - INFO - Epoch [147][2900/3746] lr: 1.141e-04, eta: 2:45:54, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6795, top5_acc: 0.8811, loss_cls: 1.8444, loss: 1.8444 +2024-07-27 14:21:35,969 - pyskl - INFO - Epoch [147][3000/3746] lr: 1.122e-04, eta: 2:44:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6791, top5_acc: 0.8745, loss_cls: 1.8469, loss: 1.8469 +2024-07-27 14:22:57,442 - pyskl - INFO - Epoch [147][3100/3746] lr: 1.103e-04, eta: 2:43:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6605, top5_acc: 0.8688, loss_cls: 1.9161, loss: 1.9161 +2024-07-27 14:24:20,173 - pyskl - INFO - Epoch [147][3200/3746] lr: 1.085e-04, eta: 2:41:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6709, top5_acc: 0.8700, loss_cls: 1.8704, loss: 1.8704 +2024-07-27 14:25:41,835 - pyskl - INFO - Epoch [147][3300/3746] lr: 1.067e-04, eta: 2:40:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6766, top5_acc: 0.8731, loss_cls: 1.8634, loss: 1.8634 +2024-07-27 14:27:04,728 - pyskl - INFO - Epoch [147][3400/3746] lr: 1.048e-04, eta: 2:39:02, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6767, top5_acc: 0.8747, loss_cls: 1.8696, loss: 1.8696 +2024-07-27 14:28:26,298 - pyskl - INFO - Epoch [147][3500/3746] lr: 1.030e-04, eta: 2:37:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6691, top5_acc: 0.8703, loss_cls: 1.8858, loss: 1.8858 +2024-07-27 14:29:47,733 - pyskl - INFO - Epoch [147][3600/3746] lr: 1.013e-04, eta: 2:36:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6727, top5_acc: 0.8791, loss_cls: 1.8677, loss: 1.8677 +2024-07-27 14:31:09,444 - pyskl - INFO - Epoch [147][3700/3746] lr: 9.949e-05, eta: 2:34:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6752, top5_acc: 0.8752, loss_cls: 1.8615, loss: 1.8615 +2024-07-27 14:31:48,878 - pyskl - INFO - Saving checkpoint at 147 epochs +2024-07-27 14:33:39,569 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 14:33:40,226 - pyskl - INFO - +top1_acc 0.4606 +top5_acc 0.7046 +2024-07-27 14:33:40,226 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 14:33:40,266 - pyskl - INFO - +mean_acc 0.4604 +2024-07-27 14:33:40,271 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_145.pth was removed +2024-07-27 14:33:40,502 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_147.pth. +2024-07-27 14:33:40,502 - pyskl - INFO - Best top1_acc is 0.4606 at 147 epoch. +2024-07-27 14:33:40,514 - pyskl - INFO - Epoch(val) [147][309] top1_acc: 0.4606, top5_acc: 0.7046, mean_class_accuracy: 0.4604 +2024-07-27 14:37:28,183 - pyskl - INFO - Epoch [148][100/3746] lr: 9.693e-05, eta: 2:32:57, time: 2.277, data_time: 1.296, memory: 15990, top1_acc: 0.6761, top5_acc: 0.8773, loss_cls: 1.8479, loss: 1.8479 +2024-07-27 14:38:50,524 - pyskl - INFO - Epoch [148][200/3746] lr: 9.520e-05, eta: 2:31:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6806, top5_acc: 0.8797, loss_cls: 1.8320, loss: 1.8320 +2024-07-27 14:40:12,404 - pyskl - INFO - Epoch [148][300/3746] lr: 9.348e-05, eta: 2:30:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6713, top5_acc: 0.8761, loss_cls: 1.8761, loss: 1.8761 +2024-07-27 14:41:33,998 - pyskl - INFO - Epoch [148][400/3746] lr: 9.178e-05, eta: 2:28:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6897, top5_acc: 0.8794, loss_cls: 1.7953, loss: 1.7953 +2024-07-27 14:42:55,698 - pyskl - INFO - Epoch [148][500/3746] lr: 9.010e-05, eta: 2:27:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6867, top5_acc: 0.8797, loss_cls: 1.8053, loss: 1.8053 +2024-07-27 14:44:18,435 - pyskl - INFO - Epoch [148][600/3746] lr: 8.843e-05, eta: 2:26:05, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6803, top5_acc: 0.8759, loss_cls: 1.8531, loss: 1.8531 +2024-07-27 14:45:40,484 - pyskl - INFO - Epoch [148][700/3746] lr: 8.678e-05, eta: 2:24:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6880, top5_acc: 0.8739, loss_cls: 1.8175, loss: 1.8175 +2024-07-27 14:47:02,239 - pyskl - INFO - Epoch [148][800/3746] lr: 8.514e-05, eta: 2:23:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6923, top5_acc: 0.8828, loss_cls: 1.8174, loss: 1.8174 +2024-07-27 14:48:23,784 - pyskl - INFO - Epoch [148][900/3746] lr: 8.351e-05, eta: 2:21:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6806, top5_acc: 0.8827, loss_cls: 1.8421, loss: 1.8421 +2024-07-27 14:49:45,332 - pyskl - INFO - Epoch [148][1000/3746] lr: 8.191e-05, eta: 2:20:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6809, top5_acc: 0.8769, loss_cls: 1.8294, loss: 1.8294 +2024-07-27 14:51:06,908 - pyskl - INFO - Epoch [148][1100/3746] lr: 8.031e-05, eta: 2:19:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6770, top5_acc: 0.8814, loss_cls: 1.8301, loss: 1.8301 +2024-07-27 14:52:28,364 - pyskl - INFO - Epoch [148][1200/3746] lr: 7.874e-05, eta: 2:17:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6820, top5_acc: 0.8750, loss_cls: 1.8466, loss: 1.8466 +2024-07-27 14:53:49,994 - pyskl - INFO - Epoch [148][1300/3746] lr: 7.718e-05, eta: 2:16:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6795, top5_acc: 0.8755, loss_cls: 1.8397, loss: 1.8397 +2024-07-27 14:55:12,237 - pyskl - INFO - Epoch [148][1400/3746] lr: 7.563e-05, eta: 2:15:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6816, top5_acc: 0.8741, loss_cls: 1.8444, loss: 1.8444 +2024-07-27 14:56:33,588 - pyskl - INFO - Epoch [148][1500/3746] lr: 7.410e-05, eta: 2:13:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6825, top5_acc: 0.8786, loss_cls: 1.8121, loss: 1.8121 +2024-07-27 14:57:55,246 - pyskl - INFO - Epoch [148][1600/3746] lr: 7.259e-05, eta: 2:12:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6752, top5_acc: 0.8758, loss_cls: 1.8468, loss: 1.8468 +2024-07-27 14:59:16,778 - pyskl - INFO - Epoch [148][1700/3746] lr: 7.109e-05, eta: 2:10:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6737, top5_acc: 0.8712, loss_cls: 1.8711, loss: 1.8711 +2024-07-27 15:00:38,276 - pyskl - INFO - Epoch [148][1800/3746] lr: 6.961e-05, eta: 2:09:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6766, top5_acc: 0.8784, loss_cls: 1.8332, loss: 1.8332 +2024-07-27 15:01:59,764 - pyskl - INFO - Epoch [148][1900/3746] lr: 6.814e-05, eta: 2:08:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6928, top5_acc: 0.8784, loss_cls: 1.8011, loss: 1.8011 +2024-07-27 15:03:20,832 - pyskl - INFO - Epoch [148][2000/3746] lr: 6.669e-05, eta: 2:06:51, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6748, top5_acc: 0.8816, loss_cls: 1.8486, loss: 1.8486 +2024-07-27 15:04:42,113 - pyskl - INFO - Epoch [148][2100/3746] lr: 6.526e-05, eta: 2:05:29, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6836, top5_acc: 0.8762, loss_cls: 1.8464, loss: 1.8464 +2024-07-27 15:06:03,720 - pyskl - INFO - Epoch [148][2200/3746] lr: 6.384e-05, eta: 2:04:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6816, top5_acc: 0.8753, loss_cls: 1.8472, loss: 1.8472 +2024-07-27 15:07:25,406 - pyskl - INFO - Epoch [148][2300/3746] lr: 6.243e-05, eta: 2:02:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6780, top5_acc: 0.8748, loss_cls: 1.8351, loss: 1.8351 +2024-07-27 15:08:47,608 - pyskl - INFO - Epoch [148][2400/3746] lr: 6.104e-05, eta: 2:01:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6787, top5_acc: 0.8791, loss_cls: 1.8308, loss: 1.8308 +2024-07-27 15:10:09,288 - pyskl - INFO - Epoch [148][2500/3746] lr: 5.967e-05, eta: 1:59:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6684, top5_acc: 0.8767, loss_cls: 1.8753, loss: 1.8753 +2024-07-27 15:11:30,746 - pyskl - INFO - Epoch [148][2600/3746] lr: 5.831e-05, eta: 1:58:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6716, top5_acc: 0.8756, loss_cls: 1.8638, loss: 1.8638 +2024-07-27 15:12:52,786 - pyskl - INFO - Epoch [148][2700/3746] lr: 5.697e-05, eta: 1:57:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6730, top5_acc: 0.8802, loss_cls: 1.8486, loss: 1.8486 +2024-07-27 15:14:14,242 - pyskl - INFO - Epoch [148][2800/3746] lr: 5.564e-05, eta: 1:55:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6891, top5_acc: 0.8745, loss_cls: 1.8239, loss: 1.8239 +2024-07-27 15:15:35,966 - pyskl - INFO - Epoch [148][2900/3746] lr: 5.433e-05, eta: 1:54:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6800, top5_acc: 0.8838, loss_cls: 1.8225, loss: 1.8225 +2024-07-27 15:16:57,115 - pyskl - INFO - Epoch [148][3000/3746] lr: 5.304e-05, eta: 1:53:07, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6858, top5_acc: 0.8827, loss_cls: 1.8093, loss: 1.8093 +2024-07-27 15:18:18,648 - pyskl - INFO - Epoch [148][3100/3746] lr: 5.176e-05, eta: 1:51:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6791, top5_acc: 0.8789, loss_cls: 1.8202, loss: 1.8202 +2024-07-27 15:19:41,879 - pyskl - INFO - Epoch [148][3200/3746] lr: 5.050e-05, eta: 1:50:22, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.6830, top5_acc: 0.8789, loss_cls: 1.8185, loss: 1.8185 +2024-07-27 15:21:03,803 - pyskl - INFO - Epoch [148][3300/3746] lr: 4.925e-05, eta: 1:49:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6769, top5_acc: 0.8784, loss_cls: 1.8277, loss: 1.8277 +2024-07-27 15:22:25,933 - pyskl - INFO - Epoch [148][3400/3746] lr: 4.801e-05, eta: 1:47:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6802, top5_acc: 0.8742, loss_cls: 1.8679, loss: 1.8679 +2024-07-27 15:23:47,761 - pyskl - INFO - Epoch [148][3500/3746] lr: 4.680e-05, eta: 1:46:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6773, top5_acc: 0.8734, loss_cls: 1.8590, loss: 1.8590 +2024-07-27 15:25:09,730 - pyskl - INFO - Epoch [148][3600/3746] lr: 4.560e-05, eta: 1:44:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6886, top5_acc: 0.8819, loss_cls: 1.8206, loss: 1.8206 +2024-07-27 15:26:31,309 - pyskl - INFO - Epoch [148][3700/3746] lr: 4.441e-05, eta: 1:43:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6787, top5_acc: 0.8838, loss_cls: 1.8350, loss: 1.8350 +2024-07-27 15:27:10,526 - pyskl - INFO - Saving checkpoint at 148 epochs +2024-07-27 15:29:01,701 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 15:29:02,415 - pyskl - INFO - +top1_acc 0.4585 +top5_acc 0.7026 +2024-07-27 15:29:02,415 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 15:29:02,472 - pyskl - INFO - +mean_acc 0.4582 +2024-07-27 15:29:02,484 - pyskl - INFO - Epoch(val) [148][309] top1_acc: 0.4585, top5_acc: 0.7026, mean_class_accuracy: 0.4582 +2024-07-27 15:32:46,930 - pyskl - INFO - Epoch [149][100/3746] lr: 4.271e-05, eta: 1:41:31, time: 2.244, data_time: 1.269, memory: 15990, top1_acc: 0.6784, top5_acc: 0.8739, loss_cls: 1.8430, loss: 1.8430 +2024-07-27 15:34:08,483 - pyskl - INFO - Epoch [149][200/3746] lr: 4.156e-05, eta: 1:40:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6734, top5_acc: 0.8769, loss_cls: 1.8317, loss: 1.8317 +2024-07-27 15:35:30,204 - pyskl - INFO - Epoch [149][300/3746] lr: 4.043e-05, eta: 1:38:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6916, top5_acc: 0.8831, loss_cls: 1.8043, loss: 1.8043 +2024-07-27 15:36:51,611 - pyskl - INFO - Epoch [149][400/3746] lr: 3.931e-05, eta: 1:37:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6791, top5_acc: 0.8748, loss_cls: 1.8399, loss: 1.8399 +2024-07-27 15:38:13,633 - pyskl - INFO - Epoch [149][500/3746] lr: 3.821e-05, eta: 1:36:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6823, top5_acc: 0.8873, loss_cls: 1.8054, loss: 1.8054 +2024-07-27 15:39:35,974 - pyskl - INFO - Epoch [149][600/3746] lr: 3.713e-05, eta: 1:34:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6755, top5_acc: 0.8814, loss_cls: 1.8303, loss: 1.8303 +2024-07-27 15:40:58,271 - pyskl - INFO - Epoch [149][700/3746] lr: 3.606e-05, eta: 1:33:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6911, top5_acc: 0.8870, loss_cls: 1.7934, loss: 1.7934 +2024-07-27 15:42:20,291 - pyskl - INFO - Epoch [149][800/3746] lr: 3.500e-05, eta: 1:31:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6823, top5_acc: 0.8809, loss_cls: 1.8212, loss: 1.8212 +2024-07-27 15:43:41,786 - pyskl - INFO - Epoch [149][900/3746] lr: 3.397e-05, eta: 1:30:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6814, top5_acc: 0.8777, loss_cls: 1.8318, loss: 1.8318 +2024-07-27 15:45:04,223 - pyskl - INFO - Epoch [149][1000/3746] lr: 3.294e-05, eta: 1:29:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6873, top5_acc: 0.8781, loss_cls: 1.8099, loss: 1.8099 +2024-07-27 15:46:25,878 - pyskl - INFO - Epoch [149][1100/3746] lr: 3.194e-05, eta: 1:27:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6825, top5_acc: 0.8836, loss_cls: 1.7998, loss: 1.7998 +2024-07-27 15:47:47,290 - pyskl - INFO - Epoch [149][1200/3746] lr: 3.095e-05, eta: 1:26:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6858, top5_acc: 0.8767, loss_cls: 1.8328, loss: 1.8328 +2024-07-27 15:49:08,721 - pyskl - INFO - Epoch [149][1300/3746] lr: 2.997e-05, eta: 1:25:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6916, top5_acc: 0.8831, loss_cls: 1.8069, loss: 1.8069 +2024-07-27 15:50:30,151 - pyskl - INFO - Epoch [149][1400/3746] lr: 2.901e-05, eta: 1:23:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6894, top5_acc: 0.8831, loss_cls: 1.7901, loss: 1.7901 +2024-07-27 15:51:51,293 - pyskl - INFO - Epoch [149][1500/3746] lr: 2.807e-05, eta: 1:22:17, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6816, top5_acc: 0.8828, loss_cls: 1.8368, loss: 1.8368 +2024-07-27 15:53:12,578 - pyskl - INFO - Epoch [149][1600/3746] lr: 2.714e-05, eta: 1:20:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6969, top5_acc: 0.8855, loss_cls: 1.7537, loss: 1.7537 +2024-07-27 15:54:33,717 - pyskl - INFO - Epoch [149][1700/3746] lr: 2.622e-05, eta: 1:19:33, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6847, top5_acc: 0.8784, loss_cls: 1.8341, loss: 1.8341 +2024-07-27 15:55:55,070 - pyskl - INFO - Epoch [149][1800/3746] lr: 2.533e-05, eta: 1:18:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6870, top5_acc: 0.8770, loss_cls: 1.8188, loss: 1.8188 +2024-07-27 15:57:16,432 - pyskl - INFO - Epoch [149][1900/3746] lr: 2.444e-05, eta: 1:16:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6859, top5_acc: 0.8819, loss_cls: 1.8041, loss: 1.8041 +2024-07-27 15:58:37,967 - pyskl - INFO - Epoch [149][2000/3746] lr: 2.358e-05, eta: 1:15:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6884, top5_acc: 0.8836, loss_cls: 1.7883, loss: 1.7883 +2024-07-27 15:59:59,447 - pyskl - INFO - Epoch [149][2100/3746] lr: 2.273e-05, eta: 1:14:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6834, top5_acc: 0.8817, loss_cls: 1.7854, loss: 1.7854 +2024-07-27 16:01:21,336 - pyskl - INFO - Epoch [149][2200/3746] lr: 2.189e-05, eta: 1:12:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6816, top5_acc: 0.8867, loss_cls: 1.8082, loss: 1.8082 +2024-07-27 16:02:42,887 - pyskl - INFO - Epoch [149][2300/3746] lr: 2.107e-05, eta: 1:11:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6834, top5_acc: 0.8823, loss_cls: 1.8109, loss: 1.8109 +2024-07-27 16:04:04,688 - pyskl - INFO - Epoch [149][2400/3746] lr: 2.027e-05, eta: 1:09:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6859, top5_acc: 0.8756, loss_cls: 1.8149, loss: 1.8149 +2024-07-27 16:05:27,086 - pyskl - INFO - Epoch [149][2500/3746] lr: 1.948e-05, eta: 1:08:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6891, top5_acc: 0.8883, loss_cls: 1.7763, loss: 1.7763 +2024-07-27 16:06:48,984 - pyskl - INFO - Epoch [149][2600/3746] lr: 1.871e-05, eta: 1:07:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6833, top5_acc: 0.8748, loss_cls: 1.8505, loss: 1.8505 +2024-07-27 16:08:10,410 - pyskl - INFO - Epoch [149][2700/3746] lr: 1.795e-05, eta: 1:05:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6744, top5_acc: 0.8745, loss_cls: 1.8465, loss: 1.8465 +2024-07-27 16:09:32,552 - pyskl - INFO - Epoch [149][2800/3746] lr: 1.721e-05, eta: 1:04:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6905, top5_acc: 0.8830, loss_cls: 1.8020, loss: 1.8020 +2024-07-27 16:10:54,644 - pyskl - INFO - Epoch [149][2900/3746] lr: 1.649e-05, eta: 1:03:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6817, top5_acc: 0.8856, loss_cls: 1.7788, loss: 1.7788 +2024-07-27 16:12:15,585 - pyskl - INFO - Epoch [149][3000/3746] lr: 1.578e-05, eta: 1:01:41, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6841, top5_acc: 0.8766, loss_cls: 1.8326, loss: 1.8326 +2024-07-27 16:13:37,111 - pyskl - INFO - Epoch [149][3100/3746] lr: 1.508e-05, eta: 1:00:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6817, top5_acc: 0.8797, loss_cls: 1.8143, loss: 1.8143 +2024-07-27 16:14:58,998 - pyskl - INFO - Epoch [149][3200/3746] lr: 1.440e-05, eta: 0:58:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6817, top5_acc: 0.8802, loss_cls: 1.8191, loss: 1.8191 +2024-07-27 16:16:21,881 - pyskl - INFO - Epoch [149][3300/3746] lr: 1.374e-05, eta: 0:57:34, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6852, top5_acc: 0.8797, loss_cls: 1.8286, loss: 1.8286 +2024-07-27 16:17:43,340 - pyskl - INFO - Epoch [149][3400/3746] lr: 1.309e-05, eta: 0:56:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6784, top5_acc: 0.8786, loss_cls: 1.8433, loss: 1.8433 +2024-07-27 16:19:05,252 - pyskl - INFO - Epoch [149][3500/3746] lr: 1.246e-05, eta: 0:54:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6728, top5_acc: 0.8722, loss_cls: 1.8839, loss: 1.8839 +2024-07-27 16:20:26,856 - pyskl - INFO - Epoch [149][3600/3746] lr: 1.184e-05, eta: 0:53:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6913, top5_acc: 0.8878, loss_cls: 1.7775, loss: 1.7775 +2024-07-27 16:21:48,249 - pyskl - INFO - Epoch [149][3700/3746] lr: 1.124e-05, eta: 0:52:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6880, top5_acc: 0.8756, loss_cls: 1.8301, loss: 1.8301 +2024-07-27 16:22:28,114 - pyskl - INFO - Saving checkpoint at 149 epochs +2024-07-27 16:24:18,528 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 16:24:19,203 - pyskl - INFO - +top1_acc 0.4597 +top5_acc 0.7036 +2024-07-27 16:24:19,203 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 16:24:19,250 - pyskl - INFO - +mean_acc 0.4594 +2024-07-27 16:24:19,263 - pyskl - INFO - Epoch(val) [149][309] top1_acc: 0.4597, top5_acc: 0.7036, mean_class_accuracy: 0.4594 +2024-07-27 16:28:07,345 - pyskl - INFO - Epoch [150][100/3746] lr: 1.039e-05, eta: 0:50:05, time: 2.281, data_time: 1.305, memory: 15990, top1_acc: 0.6880, top5_acc: 0.8800, loss_cls: 1.8146, loss: 1.8146 +2024-07-27 16:29:29,339 - pyskl - INFO - Epoch [150][200/3746] lr: 9.832e-06, eta: 0:48:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6769, top5_acc: 0.8775, loss_cls: 1.8407, loss: 1.8407 +2024-07-27 16:30:50,970 - pyskl - INFO - Epoch [150][300/3746] lr: 9.285e-06, eta: 0:47:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6895, top5_acc: 0.8844, loss_cls: 1.7878, loss: 1.7878 +2024-07-27 16:32:12,519 - pyskl - INFO - Epoch [150][400/3746] lr: 8.754e-06, eta: 0:45:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6802, top5_acc: 0.8770, loss_cls: 1.8442, loss: 1.8442 +2024-07-27 16:33:33,951 - pyskl - INFO - Epoch [150][500/3746] lr: 8.239e-06, eta: 0:44:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6823, top5_acc: 0.8845, loss_cls: 1.8200, loss: 1.8200 +2024-07-27 16:34:56,291 - pyskl - INFO - Epoch [150][600/3746] lr: 7.739e-06, eta: 0:43:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6797, top5_acc: 0.8816, loss_cls: 1.8368, loss: 1.8368 +2024-07-27 16:36:18,272 - pyskl - INFO - Epoch [150][700/3746] lr: 7.255e-06, eta: 0:41:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6958, top5_acc: 0.8816, loss_cls: 1.7969, loss: 1.7969 +2024-07-27 16:37:40,052 - pyskl - INFO - Epoch [150][800/3746] lr: 6.787e-06, eta: 0:40:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6887, top5_acc: 0.8864, loss_cls: 1.8086, loss: 1.8086 +2024-07-27 16:39:01,663 - pyskl - INFO - Epoch [150][900/3746] lr: 6.334e-06, eta: 0:39:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6922, top5_acc: 0.8856, loss_cls: 1.7743, loss: 1.7743 +2024-07-27 16:40:23,715 - pyskl - INFO - Epoch [150][1000/3746] lr: 5.897e-06, eta: 0:37:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6937, top5_acc: 0.8902, loss_cls: 1.7789, loss: 1.7789 +2024-07-27 16:41:44,828 - pyskl - INFO - Epoch [150][1100/3746] lr: 5.475e-06, eta: 0:36:20, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6900, top5_acc: 0.8875, loss_cls: 1.7794, loss: 1.7794 +2024-07-27 16:43:06,411 - pyskl - INFO - Epoch [150][1200/3746] lr: 5.070e-06, eta: 0:34:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6822, top5_acc: 0.8797, loss_cls: 1.8258, loss: 1.8258 +2024-07-27 16:44:27,936 - pyskl - INFO - Epoch [150][1300/3746] lr: 4.679e-06, eta: 0:33:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6831, top5_acc: 0.8739, loss_cls: 1.8262, loss: 1.8262 +2024-07-27 16:45:50,405 - pyskl - INFO - Epoch [150][1400/3746] lr: 4.305e-06, eta: 0:32:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6855, top5_acc: 0.8781, loss_cls: 1.8173, loss: 1.8173 +2024-07-27 16:47:12,436 - pyskl - INFO - Epoch [150][1500/3746] lr: 3.946e-06, eta: 0:30:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6761, top5_acc: 0.8802, loss_cls: 1.8552, loss: 1.8552 +2024-07-27 16:48:34,101 - pyskl - INFO - Epoch [150][1600/3746] lr: 3.602e-06, eta: 0:29:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6958, top5_acc: 0.8864, loss_cls: 1.7661, loss: 1.7661 +2024-07-27 16:49:55,908 - pyskl - INFO - Epoch [150][1700/3746] lr: 3.275e-06, eta: 0:28:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6958, top5_acc: 0.8806, loss_cls: 1.7889, loss: 1.7889 +2024-07-27 16:51:17,218 - pyskl - INFO - Epoch [150][1800/3746] lr: 2.962e-06, eta: 0:26:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6837, top5_acc: 0.8881, loss_cls: 1.7891, loss: 1.7891 +2024-07-27 16:52:38,824 - pyskl - INFO - Epoch [150][1900/3746] lr: 2.666e-06, eta: 0:25:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6819, top5_acc: 0.8783, loss_cls: 1.8153, loss: 1.8153 +2024-07-27 16:54:00,305 - pyskl - INFO - Epoch [150][2000/3746] lr: 2.385e-06, eta: 0:23:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6889, top5_acc: 0.8794, loss_cls: 1.8250, loss: 1.8250 +2024-07-27 16:55:21,550 - pyskl - INFO - Epoch [150][2100/3746] lr: 2.120e-06, eta: 0:22:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6758, top5_acc: 0.8794, loss_cls: 1.8225, loss: 1.8225 +2024-07-27 16:56:42,742 - pyskl - INFO - Epoch [150][2200/3746] lr: 1.870e-06, eta: 0:21:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6887, top5_acc: 0.8844, loss_cls: 1.8065, loss: 1.8065 +2024-07-27 16:58:03,953 - pyskl - INFO - Epoch [150][2300/3746] lr: 1.636e-06, eta: 0:19:51, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6905, top5_acc: 0.8845, loss_cls: 1.8079, loss: 1.8079 +2024-07-27 16:59:25,690 - pyskl - INFO - Epoch [150][2400/3746] lr: 1.418e-06, eta: 0:18:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6855, top5_acc: 0.8772, loss_cls: 1.8392, loss: 1.8392 +2024-07-27 17:00:47,424 - pyskl - INFO - Epoch [150][2500/3746] lr: 1.215e-06, eta: 0:17:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6837, top5_acc: 0.8800, loss_cls: 1.8092, loss: 1.8092 +2024-07-27 17:02:09,323 - pyskl - INFO - Epoch [150][2600/3746] lr: 1.028e-06, eta: 0:15:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6820, top5_acc: 0.8758, loss_cls: 1.8294, loss: 1.8294 +2024-07-27 17:03:31,166 - pyskl - INFO - Epoch [150][2700/3746] lr: 8.567e-07, eta: 0:14:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6897, top5_acc: 0.8889, loss_cls: 1.7740, loss: 1.7740 +2024-07-27 17:04:52,556 - pyskl - INFO - Epoch [150][2800/3746] lr: 7.008e-07, eta: 0:12:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6877, top5_acc: 0.8883, loss_cls: 1.7970, loss: 1.7970 +2024-07-27 17:06:14,490 - pyskl - INFO - Epoch [150][2900/3746] lr: 5.606e-07, eta: 0:11:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6837, top5_acc: 0.8856, loss_cls: 1.8203, loss: 1.8203 +2024-07-27 17:07:36,012 - pyskl - INFO - Epoch [150][3000/3746] lr: 4.361e-07, eta: 0:10:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6895, top5_acc: 0.8839, loss_cls: 1.7697, loss: 1.7697 +2024-07-27 17:08:57,220 - pyskl - INFO - Epoch [150][3100/3746] lr: 3.271e-07, eta: 0:08:52, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6933, top5_acc: 0.8816, loss_cls: 1.7671, loss: 1.7671 +2024-07-27 17:10:18,555 - pyskl - INFO - Epoch [150][3200/3746] lr: 2.338e-07, eta: 0:07:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6889, top5_acc: 0.8811, loss_cls: 1.8084, loss: 1.8084 +2024-07-27 17:11:41,314 - pyskl - INFO - Epoch [150][3300/3746] lr: 1.561e-07, eta: 0:06:07, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6942, top5_acc: 0.8845, loss_cls: 1.7973, loss: 1.7973 +2024-07-27 17:13:02,705 - pyskl - INFO - Epoch [150][3400/3746] lr: 9.410e-08, eta: 0:04:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6894, top5_acc: 0.8845, loss_cls: 1.7936, loss: 1.7936 +2024-07-27 17:14:25,019 - pyskl - INFO - Epoch [150][3500/3746] lr: 4.768e-08, eta: 0:03:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6961, top5_acc: 0.8892, loss_cls: 1.7526, loss: 1.7526 +2024-07-27 17:15:46,565 - pyskl - INFO - Epoch [150][3600/3746] lr: 1.689e-08, eta: 0:02:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6891, top5_acc: 0.8859, loss_cls: 1.7984, loss: 1.7984 +2024-07-27 17:17:07,945 - pyskl - INFO - Epoch [150][3700/3746] lr: 1.726e-09, eta: 0:00:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6900, top5_acc: 0.8795, loss_cls: 1.7785, loss: 1.7785 +2024-07-27 17:17:47,360 - pyskl - INFO - Saving checkpoint at 150 epochs +2024-07-27 17:19:37,065 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 17:19:37,724 - pyskl - INFO - +top1_acc 0.4578 +top5_acc 0.7016 +2024-07-27 17:19:37,724 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 17:19:37,766 - pyskl - INFO - +mean_acc 0.4576 +2024-07-27 17:19:37,778 - pyskl - INFO - Epoch(val) [150][309] top1_acc: 0.4578, top5_acc: 0.7016, mean_class_accuracy: 0.4576 +2024-07-27 17:19:52,648 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-07-27 17:31:54,810 - pyskl - INFO - Testing results of the last checkpoint +2024-07-27 17:31:54,810 - pyskl - INFO - top1_acc: 0.4692 +2024-07-27 17:31:54,810 - pyskl - INFO - top5_acc: 0.7118 +2024-07-27 17:31:54,810 - pyskl - INFO - mean_class_accuracy: 0.4690 +2024-07-27 17:31:54,811 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/k400/b_1/best_top1_acc_epoch_147.pth +2024-07-27 17:43:54,799 - pyskl - INFO - Testing results of the best checkpoint +2024-07-27 17:43:54,799 - pyskl - INFO - top1_acc: 0.4722 +2024-07-27 17:43:54,799 - pyskl - INFO - top5_acc: 0.7153 +2024-07-27 17:43:54,799 - pyskl - INFO - mean_class_accuracy: 0.4720 diff --git a/k400/b_1/20240722_022356.log.json b/k400/b_1/20240722_022356.log.json new file mode 100644 index 0000000000000000000000000000000000000000..fab4e70f2f2b67c60456aa1f6699fc6749a92b27 --- /dev/null +++ b/k400/b_1/20240722_022356.log.json @@ -0,0 +1,5701 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 601143061, "config_name": "b_1.py", "work_dir": "b_1", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.1, "memory": 15990, "data_time": 1.13646, "top1_acc": 0.00703, "top5_acc": 0.03172, "loss_cls": 6.41929, "loss": 6.41929, "time": 1.8264} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.1, "memory": 15990, "data_time": 0.00019, "top1_acc": 0.01312, "top5_acc": 0.05375, "loss_cls": 6.29598, "loss": 6.29598, "time": 0.69051} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.1, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.01812, "top5_acc": 0.08094, "loss_cls": 6.09868, "loss": 6.09868, "time": 0.7021} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.1, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.02328, "top5_acc": 0.09641, "loss_cls": 5.94457, "loss": 5.94457, "time": 0.7057} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.02766, "top5_acc": 0.10797, "loss_cls": 5.88187, "loss": 5.88187, "time": 0.70407} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.03078, "top5_acc": 0.11938, "loss_cls": 5.81753, "loss": 5.81753, "time": 0.70232} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.03438, "top5_acc": 0.12281, "loss_cls": 5.78143, "loss": 5.78143, "time": 0.70376} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.03469, "top5_acc": 0.13438, "loss_cls": 5.71397, "loss": 5.71397, "time": 0.70247} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.03781, "top5_acc": 0.14031, "loss_cls": 5.71938, "loss": 5.71938, "time": 0.7} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.04375, "top5_acc": 0.14797, "loss_cls": 5.64092, "loss": 5.64092, "time": 0.69996} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.04531, "top5_acc": 0.15734, "loss_cls": 5.63961, "loss": 5.63961, "time": 0.69948} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.045, "top5_acc": 0.16609, "loss_cls": 5.58114, "loss": 5.58114, "time": 0.70136} +{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.04828, "top5_acc": 0.16938, "loss_cls": 5.57563, "loss": 5.57563, "time": 0.69838} +{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.05922, "top5_acc": 0.18531, "loss_cls": 5.49147, "loss": 5.49147, "time": 0.69914} +{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.06, "top5_acc": 0.18531, "loss_cls": 5.50311, "loss": 5.50311, "time": 0.69874} +{"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.05359, "top5_acc": 0.18219, "loss_cls": 5.49641, "loss": 5.49641, "time": 0.69791} +{"mode": "train", "epoch": 1, "iter": 1700, "lr": 0.1, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.06062, "top5_acc": 0.20219, "loss_cls": 5.45348, "loss": 5.45348, "time": 0.69991} +{"mode": "train", "epoch": 1, "iter": 1800, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.07328, "top5_acc": 0.21047, "loss_cls": 5.40379, "loss": 5.40379, "time": 0.69995} +{"mode": "train", "epoch": 1, "iter": 1900, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.07438, "top5_acc": 0.21969, "loss_cls": 5.39065, "loss": 5.39065, "time": 0.70409} +{"mode": "train", "epoch": 1, "iter": 2000, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.07641, "top5_acc": 0.22188, "loss_cls": 5.35676, "loss": 5.35676, "time": 0.6987} +{"mode": "train", "epoch": 1, "iter": 2100, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.07484, "top5_acc": 0.22562, "loss_cls": 5.36546, "loss": 5.36546, "time": 0.70305} +{"mode": "train", "epoch": 1, "iter": 2200, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.07484, "top5_acc": 0.23359, "loss_cls": 5.3119, "loss": 5.3119, "time": 0.70093} +{"mode": "train", "epoch": 1, "iter": 2300, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.08109, "top5_acc": 0.22922, "loss_cls": 5.34916, "loss": 5.34916, "time": 0.70489} +{"mode": "train", "epoch": 1, "iter": 2400, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.08656, "top5_acc": 0.24406, "loss_cls": 5.28146, "loss": 5.28146, "time": 0.70921} +{"mode": "train", "epoch": 1, "iter": 2500, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.08453, "top5_acc": 0.23969, "loss_cls": 5.28245, "loss": 5.28245, "time": 0.72206} +{"mode": "train", "epoch": 1, "iter": 2600, "lr": 0.09999, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.09578, "top5_acc": 0.24609, "loss_cls": 5.22788, "loss": 5.22788, "time": 0.7118} +{"mode": "train", "epoch": 1, "iter": 2700, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.09453, "top5_acc": 0.25422, "loss_cls": 5.23806, "loss": 5.23806, "time": 0.70319} +{"mode": "train", "epoch": 1, "iter": 2800, "lr": 0.09999, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.09531, "top5_acc": 0.25375, "loss_cls": 5.20418, "loss": 5.20418, "time": 0.69965} +{"mode": "train", "epoch": 1, "iter": 2900, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.09078, "top5_acc": 0.265, "loss_cls": 5.17585, "loss": 5.17585, "time": 0.70104} +{"mode": "train", "epoch": 1, "iter": 3000, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.09984, "top5_acc": 0.27984, "loss_cls": 5.13067, "loss": 5.13067, "time": 0.69903} +{"mode": "train", "epoch": 1, "iter": 3100, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.10469, "top5_acc": 0.27703, "loss_cls": 5.11912, "loss": 5.11912, "time": 0.70152} +{"mode": "train", "epoch": 1, "iter": 3200, "lr": 0.09999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.09891, "top5_acc": 0.27297, "loss_cls": 5.12979, "loss": 5.12979, "time": 0.70137} +{"mode": "train", "epoch": 1, "iter": 3300, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.10688, "top5_acc": 0.27719, "loss_cls": 5.10508, "loss": 5.10508, "time": 0.70265} +{"mode": "train", "epoch": 1, "iter": 3400, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.10609, "top5_acc": 0.28688, "loss_cls": 5.06743, "loss": 5.06743, "time": 0.69723} +{"mode": "train", "epoch": 1, "iter": 3500, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.11422, "top5_acc": 0.29063, "loss_cls": 5.04253, "loss": 5.04253, "time": 0.70116} +{"mode": "train", "epoch": 1, "iter": 3600, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.12172, "top5_acc": 0.31516, "loss_cls": 4.96329, "loss": 4.96329, "time": 0.70133} +{"mode": "train", "epoch": 1, "iter": 3700, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.11516, "top5_acc": 0.30156, "loss_cls": 5.01333, "loss": 5.01333, "time": 0.69761} +{"mode": "val", "epoch": 1, "iter": 309, "lr": 0.09999, "top1_acc": 0.08205, "top5_acc": 0.23375, "mean_class_accuracy": 0.08202} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.09999, "memory": 15990, "data_time": 1.26491, "top1_acc": 0.11875, "top5_acc": 0.30984, "loss_cls": 4.97368, "loss": 4.97368, "time": 1.96886} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.09999, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.12094, "top5_acc": 0.3175, "loss_cls": 4.93121, "loss": 4.93121, "time": 0.71331} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.09999, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.12609, "top5_acc": 0.32672, "loss_cls": 4.94917, "loss": 4.94917, "time": 0.71367} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.09999, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.12031, "top5_acc": 0.32969, "loss_cls": 4.93333, "loss": 4.93333, "time": 0.71095} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.09999, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.12312, "top5_acc": 0.32344, "loss_cls": 4.90181, "loss": 4.90181, "time": 0.70619} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.09999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.13797, "top5_acc": 0.33859, "loss_cls": 4.86278, "loss": 4.86278, "time": 0.70041} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.09998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.14031, "top5_acc": 0.33828, "loss_cls": 4.8628, "loss": 4.8628, "time": 0.70275} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.13641, "top5_acc": 0.33172, "loss_cls": 4.88329, "loss": 4.88329, "time": 0.70013} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.13047, "top5_acc": 0.33016, "loss_cls": 4.89524, "loss": 4.89524, "time": 0.7004} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.09998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.14469, "top5_acc": 0.34156, "loss_cls": 4.83116, "loss": 4.83116, "time": 0.70098} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.09998, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.1425, "top5_acc": 0.34219, "loss_cls": 4.8311, "loss": 4.8311, "time": 0.69956} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.13844, "top5_acc": 0.34469, "loss_cls": 4.84661, "loss": 4.84661, "time": 0.7037} +{"mode": "train", "epoch": 2, "iter": 1300, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.14938, "top5_acc": 0.35125, "loss_cls": 4.80499, "loss": 4.80499, "time": 0.70154} +{"mode": "train", "epoch": 2, "iter": 1400, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.13797, "top5_acc": 0.34375, "loss_cls": 4.85154, "loss": 4.85154, "time": 0.69978} +{"mode": "train", "epoch": 2, "iter": 1500, "lr": 0.09998, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.14719, "top5_acc": 0.34656, "loss_cls": 4.82011, "loss": 4.82011, "time": 0.69977} +{"mode": "train", "epoch": 2, "iter": 1600, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.14906, "top5_acc": 0.35422, "loss_cls": 4.80135, "loss": 4.80135, "time": 0.69833} +{"mode": "train", "epoch": 2, "iter": 1700, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.14797, "top5_acc": 0.35141, "loss_cls": 4.80994, "loss": 4.80994, "time": 0.69935} +{"mode": "train", "epoch": 2, "iter": 1800, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15188, "top5_acc": 0.36125, "loss_cls": 4.7511, "loss": 4.7511, "time": 0.69934} +{"mode": "train", "epoch": 2, "iter": 1900, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.15406, "top5_acc": 0.36375, "loss_cls": 4.7361, "loss": 4.7361, "time": 0.69961} +{"mode": "train", "epoch": 2, "iter": 2000, "lr": 0.09997, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.15391, "top5_acc": 0.36531, "loss_cls": 4.72993, "loss": 4.72993, "time": 0.6995} +{"mode": "train", "epoch": 2, "iter": 2100, "lr": 0.09997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.15531, "top5_acc": 0.36734, "loss_cls": 4.74239, "loss": 4.74239, "time": 0.7009} +{"mode": "train", "epoch": 2, "iter": 2200, "lr": 0.09997, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.14578, "top5_acc": 0.36844, "loss_cls": 4.75619, "loss": 4.75619, "time": 0.69973} +{"mode": "train", "epoch": 2, "iter": 2300, "lr": 0.09997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16, "top5_acc": 0.37891, "loss_cls": 4.68849, "loss": 4.68849, "time": 0.7025} +{"mode": "train", "epoch": 2, "iter": 2400, "lr": 0.09997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.15406, "top5_acc": 0.37844, "loss_cls": 4.70787, "loss": 4.70787, "time": 0.69818} +{"mode": "train", "epoch": 2, "iter": 2500, "lr": 0.09997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.16438, "top5_acc": 0.37969, "loss_cls": 4.67154, "loss": 4.67154, "time": 0.69739} +{"mode": "train", "epoch": 2, "iter": 2600, "lr": 0.09997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16125, "top5_acc": 0.38328, "loss_cls": 4.68989, "loss": 4.68989, "time": 0.70074} +{"mode": "train", "epoch": 2, "iter": 2700, "lr": 0.09997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.16234, "top5_acc": 0.37234, "loss_cls": 4.7133, "loss": 4.7133, "time": 0.69674} +{"mode": "train", "epoch": 2, "iter": 2800, "lr": 0.09997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.16469, "top5_acc": 0.38188, "loss_cls": 4.67362, "loss": 4.67362, "time": 0.69905} +{"mode": "train", "epoch": 2, "iter": 2900, "lr": 0.09997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.16547, "top5_acc": 0.38922, "loss_cls": 4.64704, "loss": 4.64704, "time": 0.69766} +{"mode": "train", "epoch": 2, "iter": 3000, "lr": 0.09996, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16203, "top5_acc": 0.38203, "loss_cls": 4.66375, "loss": 4.66375, "time": 0.69768} +{"mode": "train", "epoch": 2, "iter": 3100, "lr": 0.09996, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17109, "top5_acc": 0.39406, "loss_cls": 4.62474, "loss": 4.62474, "time": 0.69755} +{"mode": "train", "epoch": 2, "iter": 3200, "lr": 0.09996, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.15969, "top5_acc": 0.38297, "loss_cls": 4.66418, "loss": 4.66418, "time": 0.69884} +{"mode": "train", "epoch": 2, "iter": 3300, "lr": 0.09996, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17578, "top5_acc": 0.38875, "loss_cls": 4.6051, "loss": 4.6051, "time": 0.69911} +{"mode": "train", "epoch": 2, "iter": 3400, "lr": 0.09996, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16594, "top5_acc": 0.38891, "loss_cls": 4.6063, "loss": 4.6063, "time": 0.7031} +{"mode": "train", "epoch": 2, "iter": 3500, "lr": 0.09996, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17125, "top5_acc": 0.39547, "loss_cls": 4.62946, "loss": 4.62946, "time": 0.69754} +{"mode": "train", "epoch": 2, "iter": 3600, "lr": 0.09996, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16984, "top5_acc": 0.39484, "loss_cls": 4.62592, "loss": 4.62592, "time": 0.6999} +{"mode": "train", "epoch": 2, "iter": 3700, "lr": 0.09996, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16516, "top5_acc": 0.38438, "loss_cls": 4.66047, "loss": 4.66047, "time": 0.69824} +{"mode": "val", "epoch": 2, "iter": 309, "lr": 0.09996, "top1_acc": 0.12303, "top5_acc": 0.3152, "mean_class_accuracy": 0.12309} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.09995, "memory": 15990, "data_time": 1.2638, "top1_acc": 0.17375, "top5_acc": 0.40312, "loss_cls": 4.58461, "loss": 4.58461, "time": 1.96857} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.09995, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.18172, "top5_acc": 0.40703, "loss_cls": 4.55364, "loss": 4.55364, "time": 0.71601} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.09995, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17938, "top5_acc": 0.40453, "loss_cls": 4.56506, "loss": 4.56506, "time": 0.7087} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.09995, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.18938, "top5_acc": 0.41344, "loss_cls": 4.53336, "loss": 4.53336, "time": 0.70944} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.09995, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.17844, "top5_acc": 0.41125, "loss_cls": 4.53947, "loss": 4.53947, "time": 0.70769} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.09995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18344, "top5_acc": 0.42438, "loss_cls": 4.50766, "loss": 4.50766, "time": 0.70114} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.09995, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.17938, "top5_acc": 0.40328, "loss_cls": 4.57965, "loss": 4.57965, "time": 0.70233} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.09995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19047, "top5_acc": 0.415, "loss_cls": 4.54579, "loss": 4.54579, "time": 0.69845} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.09994, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18453, "top5_acc": 0.40797, "loss_cls": 4.54522, "loss": 4.54522, "time": 0.70239} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.09994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18266, "top5_acc": 0.40531, "loss_cls": 4.55109, "loss": 4.55109, "time": 0.7006} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.09994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18281, "top5_acc": 0.41047, "loss_cls": 4.55056, "loss": 4.55056, "time": 0.70113} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.09994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18141, "top5_acc": 0.41484, "loss_cls": 4.53554, "loss": 4.53554, "time": 0.69994} +{"mode": "train", "epoch": 3, "iter": 1300, "lr": 0.09994, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18656, "top5_acc": 0.41422, "loss_cls": 4.51816, "loss": 4.51816, "time": 0.69854} +{"mode": "train", "epoch": 3, "iter": 1400, "lr": 0.09994, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.1825, "top5_acc": 0.40812, "loss_cls": 4.5472, "loss": 4.5472, "time": 0.70091} +{"mode": "train", "epoch": 3, "iter": 1500, "lr": 0.09994, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19172, "top5_acc": 0.4225, "loss_cls": 4.48919, "loss": 4.48919, "time": 0.70053} +{"mode": "train", "epoch": 3, "iter": 1600, "lr": 0.09994, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19531, "top5_acc": 0.43344, "loss_cls": 4.48206, "loss": 4.48206, "time": 0.69922} +{"mode": "train", "epoch": 3, "iter": 1700, "lr": 0.09993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18375, "top5_acc": 0.40891, "loss_cls": 4.54686, "loss": 4.54686, "time": 0.69888} +{"mode": "train", "epoch": 3, "iter": 1800, "lr": 0.09993, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19109, "top5_acc": 0.42781, "loss_cls": 4.48635, "loss": 4.48635, "time": 0.70001} +{"mode": "train", "epoch": 3, "iter": 1900, "lr": 0.09993, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.18609, "top5_acc": 0.4175, "loss_cls": 4.50067, "loss": 4.50067, "time": 0.70114} +{"mode": "train", "epoch": 3, "iter": 2000, "lr": 0.09993, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.1875, "top5_acc": 0.41844, "loss_cls": 4.48246, "loss": 4.48246, "time": 0.70044} +{"mode": "train", "epoch": 3, "iter": 2100, "lr": 0.09993, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19266, "top5_acc": 0.42312, "loss_cls": 4.50381, "loss": 4.50381, "time": 0.69728} +{"mode": "train", "epoch": 3, "iter": 2200, "lr": 0.09993, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.1975, "top5_acc": 0.43547, "loss_cls": 4.46098, "loss": 4.46098, "time": 0.69983} +{"mode": "train", "epoch": 3, "iter": 2300, "lr": 0.09993, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19312, "top5_acc": 0.42391, "loss_cls": 4.46481, "loss": 4.46481, "time": 0.69775} +{"mode": "train", "epoch": 3, "iter": 2400, "lr": 0.09992, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19594, "top5_acc": 0.43, "loss_cls": 4.46267, "loss": 4.46267, "time": 0.70045} +{"mode": "train", "epoch": 3, "iter": 2500, "lr": 0.09992, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19078, "top5_acc": 0.42391, "loss_cls": 4.47714, "loss": 4.47714, "time": 0.70021} +{"mode": "train", "epoch": 3, "iter": 2600, "lr": 0.09992, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19141, "top5_acc": 0.42328, "loss_cls": 4.48925, "loss": 4.48925, "time": 0.6979} +{"mode": "train", "epoch": 3, "iter": 2700, "lr": 0.09992, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19453, "top5_acc": 0.43828, "loss_cls": 4.44111, "loss": 4.44111, "time": 0.70005} +{"mode": "train", "epoch": 3, "iter": 2800, "lr": 0.09992, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20156, "top5_acc": 0.43234, "loss_cls": 4.45015, "loss": 4.45015, "time": 0.69783} +{"mode": "train", "epoch": 3, "iter": 2900, "lr": 0.09992, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19516, "top5_acc": 0.43125, "loss_cls": 4.45913, "loss": 4.45913, "time": 0.69881} +{"mode": "train", "epoch": 3, "iter": 3000, "lr": 0.09991, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19266, "top5_acc": 0.42453, "loss_cls": 4.48652, "loss": 4.48652, "time": 0.69897} +{"mode": "train", "epoch": 3, "iter": 3100, "lr": 0.09991, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19234, "top5_acc": 0.43141, "loss_cls": 4.46234, "loss": 4.46234, "time": 0.70266} +{"mode": "train", "epoch": 3, "iter": 3200, "lr": 0.09991, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20031, "top5_acc": 0.43266, "loss_cls": 4.42939, "loss": 4.42939, "time": 0.69915} +{"mode": "train", "epoch": 3, "iter": 3300, "lr": 0.09991, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20625, "top5_acc": 0.43391, "loss_cls": 4.42205, "loss": 4.42205, "time": 0.69915} +{"mode": "train", "epoch": 3, "iter": 3400, "lr": 0.09991, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19828, "top5_acc": 0.43453, "loss_cls": 4.43997, "loss": 4.43997, "time": 0.69744} +{"mode": "train", "epoch": 3, "iter": 3500, "lr": 0.09991, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20578, "top5_acc": 0.43812, "loss_cls": 4.40908, "loss": 4.40908, "time": 0.69912} +{"mode": "train", "epoch": 3, "iter": 3600, "lr": 0.0999, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.20312, "top5_acc": 0.43344, "loss_cls": 4.41453, "loss": 4.41453, "time": 0.70029} +{"mode": "train", "epoch": 3, "iter": 3700, "lr": 0.0999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20719, "top5_acc": 0.43703, "loss_cls": 4.4067, "loss": 4.4067, "time": 0.69729} +{"mode": "val", "epoch": 3, "iter": 309, "lr": 0.0999, "top1_acc": 0.11807, "top5_acc": 0.31008, "mean_class_accuracy": 0.11819} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.0999, "memory": 15990, "data_time": 1.29414, "top1_acc": 0.20641, "top5_acc": 0.43812, "loss_cls": 4.3971, "loss": 4.3971, "time": 2.00035} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.0999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20266, "top5_acc": 0.43266, "loss_cls": 4.41584, "loss": 4.41584, "time": 0.71034} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.0999, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20359, "top5_acc": 0.43578, "loss_cls": 4.41028, "loss": 4.41028, "time": 0.70843} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.09989, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20281, "top5_acc": 0.44, "loss_cls": 4.39377, "loss": 4.39377, "time": 0.71352} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.09989, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20828, "top5_acc": 0.44359, "loss_cls": 4.35011, "loss": 4.35011, "time": 0.70688} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.09989, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20719, "top5_acc": 0.43922, "loss_cls": 4.40534, "loss": 4.40534, "time": 0.70509} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.09989, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21516, "top5_acc": 0.44031, "loss_cls": 4.38327, "loss": 4.38327, "time": 0.70346} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.09989, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20422, "top5_acc": 0.43719, "loss_cls": 4.40701, "loss": 4.40701, "time": 0.70016} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.09988, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19859, "top5_acc": 0.42422, "loss_cls": 4.43834, "loss": 4.43834, "time": 0.70226} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.09988, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21688, "top5_acc": 0.45156, "loss_cls": 4.35912, "loss": 4.35912, "time": 0.70546} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.09988, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20734, "top5_acc": 0.43547, "loss_cls": 4.40253, "loss": 4.40253, "time": 0.7033} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.09988, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21031, "top5_acc": 0.43781, "loss_cls": 4.37848, "loss": 4.37848, "time": 0.70774} +{"mode": "train", "epoch": 4, "iter": 1300, "lr": 0.09988, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20984, "top5_acc": 0.44812, "loss_cls": 4.36576, "loss": 4.36576, "time": 0.70496} +{"mode": "train", "epoch": 4, "iter": 1400, "lr": 0.09988, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20562, "top5_acc": 0.44047, "loss_cls": 4.39098, "loss": 4.39098, "time": 0.70512} +{"mode": "train", "epoch": 4, "iter": 1500, "lr": 0.09987, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21625, "top5_acc": 0.44656, "loss_cls": 4.37142, "loss": 4.37142, "time": 0.7011} +{"mode": "train", "epoch": 4, "iter": 1600, "lr": 0.09987, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19953, "top5_acc": 0.44, "loss_cls": 4.42042, "loss": 4.42042, "time": 0.70283} +{"mode": "train", "epoch": 4, "iter": 1700, "lr": 0.09987, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21844, "top5_acc": 0.45234, "loss_cls": 4.36078, "loss": 4.36078, "time": 0.70422} +{"mode": "train", "epoch": 4, "iter": 1800, "lr": 0.09987, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20906, "top5_acc": 0.445, "loss_cls": 4.39205, "loss": 4.39205, "time": 0.70743} +{"mode": "train", "epoch": 4, "iter": 1900, "lr": 0.09987, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20312, "top5_acc": 0.45016, "loss_cls": 4.38193, "loss": 4.38193, "time": 0.70415} +{"mode": "train", "epoch": 4, "iter": 2000, "lr": 0.09986, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.20594, "top5_acc": 0.45281, "loss_cls": 4.36402, "loss": 4.36402, "time": 0.7021} +{"mode": "train", "epoch": 4, "iter": 2100, "lr": 0.09986, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21422, "top5_acc": 0.45188, "loss_cls": 4.35511, "loss": 4.35511, "time": 0.70202} +{"mode": "train", "epoch": 4, "iter": 2200, "lr": 0.09986, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21234, "top5_acc": 0.44047, "loss_cls": 4.3794, "loss": 4.3794, "time": 0.70026} +{"mode": "train", "epoch": 4, "iter": 2300, "lr": 0.09986, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20766, "top5_acc": 0.43938, "loss_cls": 4.395, "loss": 4.395, "time": 0.70112} +{"mode": "train", "epoch": 4, "iter": 2400, "lr": 0.09985, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20656, "top5_acc": 0.44125, "loss_cls": 4.39821, "loss": 4.39821, "time": 0.70151} +{"mode": "train", "epoch": 4, "iter": 2500, "lr": 0.09985, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21344, "top5_acc": 0.44891, "loss_cls": 4.35285, "loss": 4.35285, "time": 0.70311} +{"mode": "train", "epoch": 4, "iter": 2600, "lr": 0.09985, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21688, "top5_acc": 0.45469, "loss_cls": 4.34467, "loss": 4.34467, "time": 0.7038} +{"mode": "train", "epoch": 4, "iter": 2700, "lr": 0.09985, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20391, "top5_acc": 0.44297, "loss_cls": 4.38088, "loss": 4.38088, "time": 0.7034} +{"mode": "train", "epoch": 4, "iter": 2800, "lr": 0.09985, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.20922, "top5_acc": 0.45109, "loss_cls": 4.37232, "loss": 4.37232, "time": 0.70106} +{"mode": "train", "epoch": 4, "iter": 2900, "lr": 0.09984, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20938, "top5_acc": 0.44438, "loss_cls": 4.39002, "loss": 4.39002, "time": 0.70312} +{"mode": "train", "epoch": 4, "iter": 3000, "lr": 0.09984, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21578, "top5_acc": 0.44062, "loss_cls": 4.37386, "loss": 4.37386, "time": 0.7} +{"mode": "train", "epoch": 4, "iter": 3100, "lr": 0.09984, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20766, "top5_acc": 0.43781, "loss_cls": 4.37059, "loss": 4.37059, "time": 0.69864} +{"mode": "train", "epoch": 4, "iter": 3200, "lr": 0.09984, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20812, "top5_acc": 0.45266, "loss_cls": 4.37293, "loss": 4.37293, "time": 0.69798} +{"mode": "train", "epoch": 4, "iter": 3300, "lr": 0.09983, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21281, "top5_acc": 0.44719, "loss_cls": 4.36673, "loss": 4.36673, "time": 0.69966} +{"mode": "train", "epoch": 4, "iter": 3400, "lr": 0.09983, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21844, "top5_acc": 0.44766, "loss_cls": 4.3548, "loss": 4.3548, "time": 0.6982} +{"mode": "train", "epoch": 4, "iter": 3500, "lr": 0.09983, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21219, "top5_acc": 0.44953, "loss_cls": 4.36626, "loss": 4.36626, "time": 0.69963} +{"mode": "train", "epoch": 4, "iter": 3600, "lr": 0.09983, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20938, "top5_acc": 0.44938, "loss_cls": 4.37616, "loss": 4.37616, "time": 0.70002} +{"mode": "train", "epoch": 4, "iter": 3700, "lr": 0.09983, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21062, "top5_acc": 0.45266, "loss_cls": 4.36396, "loss": 4.36396, "time": 0.70454} +{"mode": "val", "epoch": 4, "iter": 309, "lr": 0.09982, "top1_acc": 0.14805, "top5_acc": 0.35917, "mean_class_accuracy": 0.14815} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.09982, "memory": 15990, "data_time": 1.27317, "top1_acc": 0.21516, "top5_acc": 0.46234, "loss_cls": 4.29414, "loss": 4.29414, "time": 1.97975} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.09982, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20578, "top5_acc": 0.45188, "loss_cls": 4.35754, "loss": 4.35754, "time": 0.71847} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.09982, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.21109, "top5_acc": 0.46172, "loss_cls": 4.32499, "loss": 4.32499, "time": 0.71459} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.09982, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21969, "top5_acc": 0.45469, "loss_cls": 4.33061, "loss": 4.33061, "time": 0.70765} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.09981, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21719, "top5_acc": 0.45219, "loss_cls": 4.3396, "loss": 4.3396, "time": 0.70212} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.09981, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21609, "top5_acc": 0.45641, "loss_cls": 4.31914, "loss": 4.31914, "time": 0.70276} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.09981, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20641, "top5_acc": 0.45344, "loss_cls": 4.35277, "loss": 4.35277, "time": 0.7012} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.09981, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21531, "top5_acc": 0.45531, "loss_cls": 4.33843, "loss": 4.33843, "time": 0.70214} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.0998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21266, "top5_acc": 0.45328, "loss_cls": 4.33768, "loss": 4.33768, "time": 0.70193} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.0998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21703, "top5_acc": 0.46125, "loss_cls": 4.31432, "loss": 4.31432, "time": 0.70022} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.0998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21328, "top5_acc": 0.45312, "loss_cls": 4.32604, "loss": 4.32604, "time": 0.70166} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.0998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23578, "top5_acc": 0.46594, "loss_cls": 4.25979, "loss": 4.25979, "time": 0.70188} +{"mode": "train", "epoch": 5, "iter": 1300, "lr": 0.09979, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21531, "top5_acc": 0.45609, "loss_cls": 4.29668, "loss": 4.29668, "time": 0.6999} +{"mode": "train", "epoch": 5, "iter": 1400, "lr": 0.09979, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22109, "top5_acc": 0.45297, "loss_cls": 4.33376, "loss": 4.33376, "time": 0.70168} +{"mode": "train", "epoch": 5, "iter": 1500, "lr": 0.09979, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21953, "top5_acc": 0.45984, "loss_cls": 4.30817, "loss": 4.30817, "time": 0.70306} +{"mode": "train", "epoch": 5, "iter": 1600, "lr": 0.09979, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22266, "top5_acc": 0.45672, "loss_cls": 4.31959, "loss": 4.31959, "time": 0.69989} +{"mode": "train", "epoch": 5, "iter": 1700, "lr": 0.09978, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21688, "top5_acc": 0.45578, "loss_cls": 4.32649, "loss": 4.32649, "time": 0.70408} +{"mode": "train", "epoch": 5, "iter": 1800, "lr": 0.09978, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21609, "top5_acc": 0.45719, "loss_cls": 4.32982, "loss": 4.32982, "time": 0.70129} +{"mode": "train", "epoch": 5, "iter": 1900, "lr": 0.09978, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22125, "top5_acc": 0.45297, "loss_cls": 4.31849, "loss": 4.31849, "time": 0.70131} +{"mode": "train", "epoch": 5, "iter": 2000, "lr": 0.09977, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22516, "top5_acc": 0.46656, "loss_cls": 4.3129, "loss": 4.3129, "time": 0.701} +{"mode": "train", "epoch": 5, "iter": 2100, "lr": 0.09977, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22078, "top5_acc": 0.46625, "loss_cls": 4.28235, "loss": 4.28235, "time": 0.69953} +{"mode": "train", "epoch": 5, "iter": 2200, "lr": 0.09977, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22094, "top5_acc": 0.46312, "loss_cls": 4.29996, "loss": 4.29996, "time": 0.70042} +{"mode": "train", "epoch": 5, "iter": 2300, "lr": 0.09977, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21828, "top5_acc": 0.45734, "loss_cls": 4.32148, "loss": 4.32148, "time": 0.70313} +{"mode": "train", "epoch": 5, "iter": 2400, "lr": 0.09976, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22062, "top5_acc": 0.46281, "loss_cls": 4.27877, "loss": 4.27877, "time": 0.69925} +{"mode": "train", "epoch": 5, "iter": 2500, "lr": 0.09976, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.235, "top5_acc": 0.47359, "loss_cls": 4.2378, "loss": 4.2378, "time": 0.70086} +{"mode": "train", "epoch": 5, "iter": 2600, "lr": 0.09976, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22266, "top5_acc": 0.46312, "loss_cls": 4.27034, "loss": 4.27034, "time": 0.69937} +{"mode": "train", "epoch": 5, "iter": 2700, "lr": 0.09976, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21656, "top5_acc": 0.45359, "loss_cls": 4.31242, "loss": 4.31242, "time": 0.69969} +{"mode": "train", "epoch": 5, "iter": 2800, "lr": 0.09975, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22453, "top5_acc": 0.46484, "loss_cls": 4.28276, "loss": 4.28276, "time": 0.70139} +{"mode": "train", "epoch": 5, "iter": 2900, "lr": 0.09975, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22109, "top5_acc": 0.46312, "loss_cls": 4.28968, "loss": 4.28968, "time": 0.70201} +{"mode": "train", "epoch": 5, "iter": 3000, "lr": 0.09975, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22453, "top5_acc": 0.46703, "loss_cls": 4.30266, "loss": 4.30266, "time": 0.69982} +{"mode": "train", "epoch": 5, "iter": 3100, "lr": 0.09974, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21891, "top5_acc": 0.44656, "loss_cls": 4.3466, "loss": 4.3466, "time": 0.69875} +{"mode": "train", "epoch": 5, "iter": 3200, "lr": 0.09974, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2225, "top5_acc": 0.46188, "loss_cls": 4.27683, "loss": 4.27683, "time": 0.70113} +{"mode": "train", "epoch": 5, "iter": 3300, "lr": 0.09974, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22156, "top5_acc": 0.46172, "loss_cls": 4.29978, "loss": 4.29978, "time": 0.69799} +{"mode": "train", "epoch": 5, "iter": 3400, "lr": 0.09974, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22844, "top5_acc": 0.46641, "loss_cls": 4.28096, "loss": 4.28096, "time": 0.70612} +{"mode": "train", "epoch": 5, "iter": 3500, "lr": 0.09973, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22266, "top5_acc": 0.46531, "loss_cls": 4.29103, "loss": 4.29103, "time": 0.70053} +{"mode": "train", "epoch": 5, "iter": 3600, "lr": 0.09973, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23141, "top5_acc": 0.47344, "loss_cls": 4.25355, "loss": 4.25355, "time": 0.70494} +{"mode": "train", "epoch": 5, "iter": 3700, "lr": 0.09973, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22422, "top5_acc": 0.46188, "loss_cls": 4.30301, "loss": 4.30301, "time": 0.69904} +{"mode": "val", "epoch": 5, "iter": 309, "lr": 0.09973, "top1_acc": 0.14203, "top5_acc": 0.3389, "mean_class_accuracy": 0.14188} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.09972, "memory": 15990, "data_time": 1.27218, "top1_acc": 0.22656, "top5_acc": 0.46359, "loss_cls": 4.25442, "loss": 4.25442, "time": 1.9786} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.09972, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22234, "top5_acc": 0.46797, "loss_cls": 4.26654, "loss": 4.26654, "time": 0.71331} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.09972, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22203, "top5_acc": 0.46703, "loss_cls": 4.24853, "loss": 4.24853, "time": 0.71424} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.09971, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22172, "top5_acc": 0.46828, "loss_cls": 4.26791, "loss": 4.26791, "time": 0.71662} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.09971, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22062, "top5_acc": 0.46875, "loss_cls": 4.29049, "loss": 4.29049, "time": 0.70693} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.09971, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21625, "top5_acc": 0.46422, "loss_cls": 4.25618, "loss": 4.25618, "time": 0.70579} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.09971, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22984, "top5_acc": 0.47203, "loss_cls": 4.26243, "loss": 4.26243, "time": 0.69926} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.0997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21953, "top5_acc": 0.46109, "loss_cls": 4.3044, "loss": 4.3044, "time": 0.69816} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.0997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22109, "top5_acc": 0.46516, "loss_cls": 4.26588, "loss": 4.26588, "time": 0.69937} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.0997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22594, "top5_acc": 0.47344, "loss_cls": 4.25249, "loss": 4.25249, "time": 0.70231} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.09969, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22, "top5_acc": 0.45844, "loss_cls": 4.31034, "loss": 4.31034, "time": 0.7009} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.09969, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21797, "top5_acc": 0.46, "loss_cls": 4.30576, "loss": 4.30576, "time": 0.70281} +{"mode": "train", "epoch": 6, "iter": 1300, "lr": 0.09969, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22422, "top5_acc": 0.46188, "loss_cls": 4.28017, "loss": 4.28017, "time": 0.7072} +{"mode": "train", "epoch": 6, "iter": 1400, "lr": 0.09968, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22938, "top5_acc": 0.46953, "loss_cls": 4.26343, "loss": 4.26343, "time": 0.70234} +{"mode": "train", "epoch": 6, "iter": 1500, "lr": 0.09968, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22766, "top5_acc": 0.47062, "loss_cls": 4.24722, "loss": 4.24722, "time": 0.69913} +{"mode": "train", "epoch": 6, "iter": 1600, "lr": 0.09968, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22484, "top5_acc": 0.47047, "loss_cls": 4.24596, "loss": 4.24596, "time": 0.70082} +{"mode": "train", "epoch": 6, "iter": 1700, "lr": 0.09967, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22953, "top5_acc": 0.45969, "loss_cls": 4.29036, "loss": 4.29036, "time": 0.7007} +{"mode": "train", "epoch": 6, "iter": 1800, "lr": 0.09967, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22516, "top5_acc": 0.46766, "loss_cls": 4.26739, "loss": 4.26739, "time": 0.70161} +{"mode": "train", "epoch": 6, "iter": 1900, "lr": 0.09967, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22125, "top5_acc": 0.46391, "loss_cls": 4.26262, "loss": 4.26262, "time": 0.69889} +{"mode": "train", "epoch": 6, "iter": 2000, "lr": 0.09966, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22609, "top5_acc": 0.45891, "loss_cls": 4.27113, "loss": 4.27113, "time": 0.70111} +{"mode": "train", "epoch": 6, "iter": 2100, "lr": 0.09966, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22828, "top5_acc": 0.46266, "loss_cls": 4.28449, "loss": 4.28449, "time": 0.70463} +{"mode": "train", "epoch": 6, "iter": 2200, "lr": 0.09966, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.225, "top5_acc": 0.46516, "loss_cls": 4.29246, "loss": 4.29246, "time": 0.70095} +{"mode": "train", "epoch": 6, "iter": 2300, "lr": 0.09965, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22359, "top5_acc": 0.46734, "loss_cls": 4.29535, "loss": 4.29535, "time": 0.70023} +{"mode": "train", "epoch": 6, "iter": 2400, "lr": 0.09965, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22547, "top5_acc": 0.46531, "loss_cls": 4.27119, "loss": 4.27119, "time": 0.69931} +{"mode": "train", "epoch": 6, "iter": 2500, "lr": 0.09965, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23281, "top5_acc": 0.47797, "loss_cls": 4.20926, "loss": 4.20926, "time": 0.70183} +{"mode": "train", "epoch": 6, "iter": 2600, "lr": 0.09964, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22969, "top5_acc": 0.46312, "loss_cls": 4.274, "loss": 4.274, "time": 0.70143} +{"mode": "train", "epoch": 6, "iter": 2700, "lr": 0.09964, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2225, "top5_acc": 0.46641, "loss_cls": 4.29451, "loss": 4.29451, "time": 0.70147} +{"mode": "train", "epoch": 6, "iter": 2800, "lr": 0.09964, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22984, "top5_acc": 0.47172, "loss_cls": 4.25588, "loss": 4.25588, "time": 0.70415} +{"mode": "train", "epoch": 6, "iter": 2900, "lr": 0.09963, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22188, "top5_acc": 0.45938, "loss_cls": 4.28115, "loss": 4.28115, "time": 0.6989} +{"mode": "train", "epoch": 6, "iter": 3000, "lr": 0.09963, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23203, "top5_acc": 0.47734, "loss_cls": 4.23467, "loss": 4.23467, "time": 0.70346} +{"mode": "train", "epoch": 6, "iter": 3100, "lr": 0.09963, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22078, "top5_acc": 0.46219, "loss_cls": 4.25693, "loss": 4.25693, "time": 0.69899} +{"mode": "train", "epoch": 6, "iter": 3200, "lr": 0.09962, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22094, "top5_acc": 0.46609, "loss_cls": 4.27305, "loss": 4.27305, "time": 0.70074} +{"mode": "train", "epoch": 6, "iter": 3300, "lr": 0.09962, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23391, "top5_acc": 0.47859, "loss_cls": 4.23163, "loss": 4.23163, "time": 0.69763} +{"mode": "train", "epoch": 6, "iter": 3400, "lr": 0.09962, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22906, "top5_acc": 0.47891, "loss_cls": 4.23585, "loss": 4.23585, "time": 0.69975} +{"mode": "train", "epoch": 6, "iter": 3500, "lr": 0.09961, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22969, "top5_acc": 0.46531, "loss_cls": 4.28735, "loss": 4.28735, "time": 0.70293} +{"mode": "train", "epoch": 6, "iter": 3600, "lr": 0.09961, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.22297, "top5_acc": 0.47031, "loss_cls": 4.28918, "loss": 4.28918, "time": 0.69931} +{"mode": "train", "epoch": 6, "iter": 3700, "lr": 0.09961, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.235, "top5_acc": 0.46703, "loss_cls": 4.26722, "loss": 4.26722, "time": 0.70208} +{"mode": "val", "epoch": 6, "iter": 309, "lr": 0.09961, "top1_acc": 0.14167, "top5_acc": 0.34017, "mean_class_accuracy": 0.14165} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0996, "memory": 15990, "data_time": 1.25712, "top1_acc": 0.22422, "top5_acc": 0.46109, "loss_cls": 4.26049, "loss": 4.26049, "time": 1.96394} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22734, "top5_acc": 0.47422, "loss_cls": 4.2604, "loss": 4.2604, "time": 0.71215} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.0996, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23562, "top5_acc": 0.47438, "loss_cls": 4.23964, "loss": 4.23964, "time": 0.70763} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.09959, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23031, "top5_acc": 0.47172, "loss_cls": 4.24703, "loss": 4.24703, "time": 0.71188} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.09959, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21953, "top5_acc": 0.4675, "loss_cls": 4.30725, "loss": 4.30725, "time": 0.70516} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.09958, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22797, "top5_acc": 0.47203, "loss_cls": 4.20949, "loss": 4.20949, "time": 0.70592} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.09958, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23328, "top5_acc": 0.47703, "loss_cls": 4.22893, "loss": 4.22893, "time": 0.70281} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.09958, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22953, "top5_acc": 0.47328, "loss_cls": 4.24743, "loss": 4.24743, "time": 0.69944} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.09957, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22344, "top5_acc": 0.46672, "loss_cls": 4.24882, "loss": 4.24882, "time": 0.70196} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.09957, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22547, "top5_acc": 0.4625, "loss_cls": 4.26846, "loss": 4.26846, "time": 0.69949} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.09957, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24062, "top5_acc": 0.48141, "loss_cls": 4.18921, "loss": 4.18921, "time": 0.70236} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.09956, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22422, "top5_acc": 0.47203, "loss_cls": 4.24359, "loss": 4.24359, "time": 0.70275} +{"mode": "train", "epoch": 7, "iter": 1300, "lr": 0.09956, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22656, "top5_acc": 0.48031, "loss_cls": 4.22253, "loss": 4.22253, "time": 0.70413} +{"mode": "train", "epoch": 7, "iter": 1400, "lr": 0.09956, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23656, "top5_acc": 0.48031, "loss_cls": 4.2069, "loss": 4.2069, "time": 0.70121} +{"mode": "train", "epoch": 7, "iter": 1500, "lr": 0.09955, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23625, "top5_acc": 0.47938, "loss_cls": 4.21736, "loss": 4.21736, "time": 0.70046} +{"mode": "train", "epoch": 7, "iter": 1600, "lr": 0.09955, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23172, "top5_acc": 0.47188, "loss_cls": 4.25436, "loss": 4.25436, "time": 0.70282} +{"mode": "train", "epoch": 7, "iter": 1700, "lr": 0.09954, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23812, "top5_acc": 0.49, "loss_cls": 4.18937, "loss": 4.18937, "time": 0.70075} +{"mode": "train", "epoch": 7, "iter": 1800, "lr": 0.09954, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23078, "top5_acc": 0.48141, "loss_cls": 4.18989, "loss": 4.18989, "time": 0.70095} +{"mode": "train", "epoch": 7, "iter": 1900, "lr": 0.09954, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22969, "top5_acc": 0.47578, "loss_cls": 4.21764, "loss": 4.21764, "time": 0.70252} +{"mode": "train", "epoch": 7, "iter": 2000, "lr": 0.09953, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22797, "top5_acc": 0.48016, "loss_cls": 4.25744, "loss": 4.25744, "time": 0.70258} +{"mode": "train", "epoch": 7, "iter": 2100, "lr": 0.09953, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23297, "top5_acc": 0.47484, "loss_cls": 4.20854, "loss": 4.20854, "time": 0.70319} +{"mode": "train", "epoch": 7, "iter": 2200, "lr": 0.09952, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22766, "top5_acc": 0.47, "loss_cls": 4.25564, "loss": 4.25564, "time": 0.70055} +{"mode": "train", "epoch": 7, "iter": 2300, "lr": 0.09952, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23406, "top5_acc": 0.47656, "loss_cls": 4.22117, "loss": 4.22117, "time": 0.69746} +{"mode": "train", "epoch": 7, "iter": 2400, "lr": 0.09952, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22219, "top5_acc": 0.46359, "loss_cls": 4.25, "loss": 4.25, "time": 0.69895} +{"mode": "train", "epoch": 7, "iter": 2500, "lr": 0.09951, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22297, "top5_acc": 0.46297, "loss_cls": 4.25989, "loss": 4.25989, "time": 0.7024} +{"mode": "train", "epoch": 7, "iter": 2600, "lr": 0.09951, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22797, "top5_acc": 0.46938, "loss_cls": 4.24483, "loss": 4.24483, "time": 0.70119} +{"mode": "train", "epoch": 7, "iter": 2700, "lr": 0.09951, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23781, "top5_acc": 0.49141, "loss_cls": 4.15072, "loss": 4.15072, "time": 0.70051} +{"mode": "train", "epoch": 7, "iter": 2800, "lr": 0.0995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21875, "top5_acc": 0.45812, "loss_cls": 4.26425, "loss": 4.26425, "time": 0.70395} +{"mode": "train", "epoch": 7, "iter": 2900, "lr": 0.0995, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22984, "top5_acc": 0.47297, "loss_cls": 4.26237, "loss": 4.26237, "time": 0.70144} +{"mode": "train", "epoch": 7, "iter": 3000, "lr": 0.09949, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23641, "top5_acc": 0.47672, "loss_cls": 4.22389, "loss": 4.22389, "time": 0.70353} +{"mode": "train", "epoch": 7, "iter": 3100, "lr": 0.09949, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2375, "top5_acc": 0.47797, "loss_cls": 4.20873, "loss": 4.20873, "time": 0.70012} +{"mode": "train", "epoch": 7, "iter": 3200, "lr": 0.09949, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23438, "top5_acc": 0.47672, "loss_cls": 4.2315, "loss": 4.2315, "time": 0.69975} +{"mode": "train", "epoch": 7, "iter": 3300, "lr": 0.09948, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23422, "top5_acc": 0.48188, "loss_cls": 4.21311, "loss": 4.21311, "time": 0.70103} +{"mode": "train", "epoch": 7, "iter": 3400, "lr": 0.09948, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22391, "top5_acc": 0.47094, "loss_cls": 4.26738, "loss": 4.26738, "time": 0.6964} +{"mode": "train", "epoch": 7, "iter": 3500, "lr": 0.09947, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23, "top5_acc": 0.47547, "loss_cls": 4.24148, "loss": 4.24148, "time": 0.69932} +{"mode": "train", "epoch": 7, "iter": 3600, "lr": 0.09947, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24469, "top5_acc": 0.49312, "loss_cls": 4.1712, "loss": 4.1712, "time": 0.69905} +{"mode": "train", "epoch": 7, "iter": 3700, "lr": 0.09947, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23672, "top5_acc": 0.47641, "loss_cls": 4.2187, "loss": 4.2187, "time": 0.70343} +{"mode": "val", "epoch": 7, "iter": 309, "lr": 0.09946, "top1_acc": 0.16867, "top5_acc": 0.38652, "mean_class_accuracy": 0.16856} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.09946, "memory": 15990, "data_time": 1.25855, "top1_acc": 0.23125, "top5_acc": 0.46953, "loss_cls": 4.23093, "loss": 4.23093, "time": 1.96594} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.09946, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23531, "top5_acc": 0.47219, "loss_cls": 4.22451, "loss": 4.22451, "time": 0.71254} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.09945, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23984, "top5_acc": 0.48547, "loss_cls": 4.19182, "loss": 4.19182, "time": 0.70869} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.09945, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.23484, "top5_acc": 0.48172, "loss_cls": 4.21873, "loss": 4.21873, "time": 0.7099} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.09944, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23703, "top5_acc": 0.48859, "loss_cls": 4.2053, "loss": 4.2053, "time": 0.70893} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.09944, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22906, "top5_acc": 0.47484, "loss_cls": 4.21175, "loss": 4.21175, "time": 0.70505} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.09943, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23, "top5_acc": 0.46938, "loss_cls": 4.24352, "loss": 4.24352, "time": 0.71022} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.09943, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23578, "top5_acc": 0.48062, "loss_cls": 4.17077, "loss": 4.17077, "time": 0.70127} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.09943, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23766, "top5_acc": 0.47875, "loss_cls": 4.19374, "loss": 4.19374, "time": 0.70231} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.09942, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2375, "top5_acc": 0.48359, "loss_cls": 4.18436, "loss": 4.18436, "time": 0.70253} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.09942, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22797, "top5_acc": 0.46984, "loss_cls": 4.25853, "loss": 4.25853, "time": 0.70245} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.09941, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23031, "top5_acc": 0.47938, "loss_cls": 4.19688, "loss": 4.19688, "time": 0.70418} +{"mode": "train", "epoch": 8, "iter": 1300, "lr": 0.09941, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23406, "top5_acc": 0.48297, "loss_cls": 4.21742, "loss": 4.21742, "time": 0.70253} +{"mode": "train", "epoch": 8, "iter": 1400, "lr": 0.0994, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.235, "top5_acc": 0.47781, "loss_cls": 4.22212, "loss": 4.22212, "time": 0.7043} +{"mode": "train", "epoch": 8, "iter": 1500, "lr": 0.0994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23781, "top5_acc": 0.48016, "loss_cls": 4.20029, "loss": 4.20029, "time": 0.7024} +{"mode": "train", "epoch": 8, "iter": 1600, "lr": 0.0994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24547, "top5_acc": 0.48859, "loss_cls": 4.16241, "loss": 4.16241, "time": 0.70294} +{"mode": "train", "epoch": 8, "iter": 1700, "lr": 0.09939, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23422, "top5_acc": 0.48172, "loss_cls": 4.20347, "loss": 4.20347, "time": 0.70206} +{"mode": "train", "epoch": 8, "iter": 1800, "lr": 0.09939, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22453, "top5_acc": 0.46969, "loss_cls": 4.27944, "loss": 4.27944, "time": 0.70152} +{"mode": "train", "epoch": 8, "iter": 1900, "lr": 0.09938, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23391, "top5_acc": 0.46891, "loss_cls": 4.23789, "loss": 4.23789, "time": 0.70415} +{"mode": "train", "epoch": 8, "iter": 2000, "lr": 0.09938, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23375, "top5_acc": 0.47812, "loss_cls": 4.2109, "loss": 4.2109, "time": 0.70029} +{"mode": "train", "epoch": 8, "iter": 2100, "lr": 0.09937, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23453, "top5_acc": 0.48297, "loss_cls": 4.2069, "loss": 4.2069, "time": 0.70057} +{"mode": "train", "epoch": 8, "iter": 2200, "lr": 0.09937, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23188, "top5_acc": 0.47562, "loss_cls": 4.216, "loss": 4.216, "time": 0.70684} +{"mode": "train", "epoch": 8, "iter": 2300, "lr": 0.09937, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24266, "top5_acc": 0.49672, "loss_cls": 4.158, "loss": 4.158, "time": 0.70266} +{"mode": "train", "epoch": 8, "iter": 2400, "lr": 0.09936, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23562, "top5_acc": 0.48281, "loss_cls": 4.21676, "loss": 4.21676, "time": 0.7024} +{"mode": "train", "epoch": 8, "iter": 2500, "lr": 0.09936, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23609, "top5_acc": 0.47844, "loss_cls": 4.18919, "loss": 4.18919, "time": 0.70135} +{"mode": "train", "epoch": 8, "iter": 2600, "lr": 0.09935, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23891, "top5_acc": 0.47891, "loss_cls": 4.19816, "loss": 4.19816, "time": 0.70081} +{"mode": "train", "epoch": 8, "iter": 2700, "lr": 0.09935, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23578, "top5_acc": 0.48672, "loss_cls": 4.19498, "loss": 4.19498, "time": 0.70052} +{"mode": "train", "epoch": 8, "iter": 2800, "lr": 0.09934, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23875, "top5_acc": 0.48141, "loss_cls": 4.20031, "loss": 4.20031, "time": 0.70092} +{"mode": "train", "epoch": 8, "iter": 2900, "lr": 0.09934, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.2475, "top5_acc": 0.49203, "loss_cls": 4.13611, "loss": 4.13611, "time": 0.69873} +{"mode": "train", "epoch": 8, "iter": 3000, "lr": 0.09933, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23766, "top5_acc": 0.48234, "loss_cls": 4.17284, "loss": 4.17284, "time": 0.69999} +{"mode": "train", "epoch": 8, "iter": 3100, "lr": 0.09933, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23797, "top5_acc": 0.47203, "loss_cls": 4.23751, "loss": 4.23751, "time": 0.70309} +{"mode": "train", "epoch": 8, "iter": 3200, "lr": 0.09933, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23438, "top5_acc": 0.47547, "loss_cls": 4.22917, "loss": 4.22917, "time": 0.70184} +{"mode": "train", "epoch": 8, "iter": 3300, "lr": 0.09932, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24422, "top5_acc": 0.49188, "loss_cls": 4.15434, "loss": 4.15434, "time": 0.70132} +{"mode": "train", "epoch": 8, "iter": 3400, "lr": 0.09932, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23656, "top5_acc": 0.48078, "loss_cls": 4.21424, "loss": 4.21424, "time": 0.70624} +{"mode": "train", "epoch": 8, "iter": 3500, "lr": 0.09931, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24266, "top5_acc": 0.48438, "loss_cls": 4.21794, "loss": 4.21794, "time": 0.70327} +{"mode": "train", "epoch": 8, "iter": 3600, "lr": 0.09931, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24078, "top5_acc": 0.47875, "loss_cls": 4.22576, "loss": 4.22576, "time": 0.70282} +{"mode": "train", "epoch": 8, "iter": 3700, "lr": 0.0993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2375, "top5_acc": 0.48109, "loss_cls": 4.21469, "loss": 4.21469, "time": 0.70062} +{"mode": "val", "epoch": 8, "iter": 309, "lr": 0.0993, "top1_acc": 0.15499, "top5_acc": 0.35846, "mean_class_accuracy": 0.15475} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.0993, "memory": 15990, "data_time": 1.26438, "top1_acc": 0.24141, "top5_acc": 0.48484, "loss_cls": 4.17637, "loss": 4.17637, "time": 1.97102} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.09929, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24844, "top5_acc": 0.49688, "loss_cls": 4.13677, "loss": 4.13677, "time": 0.71194} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.09929, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23984, "top5_acc": 0.48031, "loss_cls": 4.19349, "loss": 4.19349, "time": 0.71278} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.09928, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23469, "top5_acc": 0.485, "loss_cls": 4.19923, "loss": 4.19923, "time": 0.71585} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.09928, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2325, "top5_acc": 0.47719, "loss_cls": 4.23943, "loss": 4.23943, "time": 0.71082} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.09927, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23844, "top5_acc": 0.49156, "loss_cls": 4.13006, "loss": 4.13006, "time": 0.70404} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.09927, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22906, "top5_acc": 0.48281, "loss_cls": 4.16621, "loss": 4.16621, "time": 0.70652} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.09926, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24484, "top5_acc": 0.48812, "loss_cls": 4.16503, "loss": 4.16503, "time": 0.70155} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.09926, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24578, "top5_acc": 0.48969, "loss_cls": 4.15671, "loss": 4.15671, "time": 0.70507} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.09925, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23922, "top5_acc": 0.48094, "loss_cls": 4.18624, "loss": 4.18624, "time": 0.70204} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.09925, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23922, "top5_acc": 0.47906, "loss_cls": 4.16798, "loss": 4.16798, "time": 0.70379} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.09924, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23812, "top5_acc": 0.47469, "loss_cls": 4.20093, "loss": 4.20093, "time": 0.70389} +{"mode": "train", "epoch": 9, "iter": 1300, "lr": 0.09924, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24609, "top5_acc": 0.485, "loss_cls": 4.15214, "loss": 4.15214, "time": 0.70297} +{"mode": "train", "epoch": 9, "iter": 1400, "lr": 0.09923, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23828, "top5_acc": 0.48391, "loss_cls": 4.17948, "loss": 4.17948, "time": 0.69921} +{"mode": "train", "epoch": 9, "iter": 1500, "lr": 0.09923, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23734, "top5_acc": 0.47312, "loss_cls": 4.23323, "loss": 4.23323, "time": 0.70092} +{"mode": "train", "epoch": 9, "iter": 1600, "lr": 0.09922, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23391, "top5_acc": 0.47109, "loss_cls": 4.24224, "loss": 4.24224, "time": 0.70122} +{"mode": "train", "epoch": 9, "iter": 1700, "lr": 0.09922, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23719, "top5_acc": 0.47531, "loss_cls": 4.22975, "loss": 4.22975, "time": 0.70054} +{"mode": "train", "epoch": 9, "iter": 1800, "lr": 0.09921, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23719, "top5_acc": 0.48141, "loss_cls": 4.20451, "loss": 4.20451, "time": 0.69992} +{"mode": "train", "epoch": 9, "iter": 1900, "lr": 0.09921, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23141, "top5_acc": 0.48438, "loss_cls": 4.1872, "loss": 4.1872, "time": 0.69788} +{"mode": "train", "epoch": 9, "iter": 2000, "lr": 0.0992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23734, "top5_acc": 0.4825, "loss_cls": 4.16347, "loss": 4.16347, "time": 0.70166} +{"mode": "train", "epoch": 9, "iter": 2100, "lr": 0.0992, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24062, "top5_acc": 0.49266, "loss_cls": 4.13095, "loss": 4.13095, "time": 0.70077} +{"mode": "train", "epoch": 9, "iter": 2200, "lr": 0.09919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23656, "top5_acc": 0.48047, "loss_cls": 4.18785, "loss": 4.18785, "time": 0.70353} +{"mode": "train", "epoch": 9, "iter": 2300, "lr": 0.09919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24547, "top5_acc": 0.49391, "loss_cls": 4.1587, "loss": 4.1587, "time": 0.70668} +{"mode": "train", "epoch": 9, "iter": 2400, "lr": 0.09918, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23312, "top5_acc": 0.46812, "loss_cls": 4.22711, "loss": 4.22711, "time": 0.70075} +{"mode": "train", "epoch": 9, "iter": 2500, "lr": 0.09918, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2325, "top5_acc": 0.48656, "loss_cls": 4.16463, "loss": 4.16463, "time": 0.70022} +{"mode": "train", "epoch": 9, "iter": 2600, "lr": 0.09917, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24375, "top5_acc": 0.48594, "loss_cls": 4.16875, "loss": 4.16875, "time": 0.70141} +{"mode": "train", "epoch": 9, "iter": 2700, "lr": 0.09917, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2275, "top5_acc": 0.46672, "loss_cls": 4.28708, "loss": 4.28708, "time": 0.70296} +{"mode": "train", "epoch": 9, "iter": 2800, "lr": 0.09916, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23453, "top5_acc": 0.4875, "loss_cls": 4.17827, "loss": 4.17827, "time": 0.69932} +{"mode": "train", "epoch": 9, "iter": 2900, "lr": 0.09916, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23359, "top5_acc": 0.46828, "loss_cls": 4.22098, "loss": 4.22098, "time": 0.7003} +{"mode": "train", "epoch": 9, "iter": 3000, "lr": 0.09915, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23906, "top5_acc": 0.48344, "loss_cls": 4.21472, "loss": 4.21472, "time": 0.69975} +{"mode": "train", "epoch": 9, "iter": 3100, "lr": 0.09915, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24781, "top5_acc": 0.49266, "loss_cls": 4.15421, "loss": 4.15421, "time": 0.70048} +{"mode": "train", "epoch": 9, "iter": 3200, "lr": 0.09914, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25016, "top5_acc": 0.49172, "loss_cls": 4.15712, "loss": 4.15712, "time": 0.70064} +{"mode": "train", "epoch": 9, "iter": 3300, "lr": 0.09914, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23141, "top5_acc": 0.47641, "loss_cls": 4.20919, "loss": 4.20919, "time": 0.70006} +{"mode": "train", "epoch": 9, "iter": 3400, "lr": 0.09913, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23562, "top5_acc": 0.47891, "loss_cls": 4.17511, "loss": 4.17511, "time": 0.70209} +{"mode": "train", "epoch": 9, "iter": 3500, "lr": 0.09913, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23312, "top5_acc": 0.47844, "loss_cls": 4.21835, "loss": 4.21835, "time": 0.70207} +{"mode": "train", "epoch": 9, "iter": 3600, "lr": 0.09912, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24203, "top5_acc": 0.48922, "loss_cls": 4.1601, "loss": 4.1601, "time": 0.70225} +{"mode": "train", "epoch": 9, "iter": 3700, "lr": 0.09912, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23672, "top5_acc": 0.47656, "loss_cls": 4.23095, "loss": 4.23095, "time": 0.70111} +{"mode": "val", "epoch": 9, "iter": 309, "lr": 0.09911, "top1_acc": 0.17469, "top5_acc": 0.39244, "mean_class_accuracy": 0.17446} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.09911, "memory": 15990, "data_time": 1.28555, "top1_acc": 0.25062, "top5_acc": 0.50016, "loss_cls": 4.11975, "loss": 4.11975, "time": 1.99553} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.0991, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23828, "top5_acc": 0.48031, "loss_cls": 4.19044, "loss": 4.19044, "time": 0.71033} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.0991, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24281, "top5_acc": 0.48281, "loss_cls": 4.17609, "loss": 4.17609, "time": 0.70903} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.09909, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23969, "top5_acc": 0.48172, "loss_cls": 4.22043, "loss": 4.22043, "time": 0.7072} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.09909, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23875, "top5_acc": 0.48422, "loss_cls": 4.20425, "loss": 4.20425, "time": 0.70535} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.09908, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23969, "top5_acc": 0.49078, "loss_cls": 4.1667, "loss": 4.1667, "time": 0.70549} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.09908, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23516, "top5_acc": 0.49266, "loss_cls": 4.16435, "loss": 4.16435, "time": 0.70418} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.09907, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23766, "top5_acc": 0.48641, "loss_cls": 4.18529, "loss": 4.18529, "time": 0.70591} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.09907, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24078, "top5_acc": 0.49547, "loss_cls": 4.14552, "loss": 4.14552, "time": 0.70132} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.09906, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23656, "top5_acc": 0.48672, "loss_cls": 4.20019, "loss": 4.20019, "time": 0.70303} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.09906, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23703, "top5_acc": 0.48609, "loss_cls": 4.19447, "loss": 4.19447, "time": 0.70089} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.09905, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24016, "top5_acc": 0.47531, "loss_cls": 4.20782, "loss": 4.20782, "time": 0.70397} +{"mode": "train", "epoch": 10, "iter": 1300, "lr": 0.09905, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23891, "top5_acc": 0.49312, "loss_cls": 4.16297, "loss": 4.16297, "time": 0.70461} +{"mode": "train", "epoch": 10, "iter": 1400, "lr": 0.09904, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23828, "top5_acc": 0.48422, "loss_cls": 4.18375, "loss": 4.18375, "time": 0.69988} +{"mode": "train", "epoch": 10, "iter": 1500, "lr": 0.09903, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24031, "top5_acc": 0.48688, "loss_cls": 4.16857, "loss": 4.16857, "time": 0.7011} +{"mode": "train", "epoch": 10, "iter": 1600, "lr": 0.09903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23812, "top5_acc": 0.48047, "loss_cls": 4.18468, "loss": 4.18468, "time": 0.70342} +{"mode": "train", "epoch": 10, "iter": 1700, "lr": 0.09902, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24469, "top5_acc": 0.48969, "loss_cls": 4.15178, "loss": 4.15178, "time": 0.70266} +{"mode": "train", "epoch": 10, "iter": 1800, "lr": 0.09902, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23531, "top5_acc": 0.47438, "loss_cls": 4.21311, "loss": 4.21311, "time": 0.70215} +{"mode": "train", "epoch": 10, "iter": 1900, "lr": 0.09901, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23234, "top5_acc": 0.46969, "loss_cls": 4.22074, "loss": 4.22074, "time": 0.70001} +{"mode": "train", "epoch": 10, "iter": 2000, "lr": 0.09901, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25047, "top5_acc": 0.49766, "loss_cls": 4.11755, "loss": 4.11755, "time": 0.69964} +{"mode": "train", "epoch": 10, "iter": 2100, "lr": 0.099, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23859, "top5_acc": 0.48219, "loss_cls": 4.17326, "loss": 4.17326, "time": 0.69867} +{"mode": "train", "epoch": 10, "iter": 2200, "lr": 0.099, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24094, "top5_acc": 0.48188, "loss_cls": 4.18567, "loss": 4.18567, "time": 0.70166} +{"mode": "train", "epoch": 10, "iter": 2300, "lr": 0.09899, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23906, "top5_acc": 0.47688, "loss_cls": 4.1812, "loss": 4.1812, "time": 0.7027} +{"mode": "train", "epoch": 10, "iter": 2400, "lr": 0.09898, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24078, "top5_acc": 0.485, "loss_cls": 4.19081, "loss": 4.19081, "time": 0.70093} +{"mode": "train", "epoch": 10, "iter": 2500, "lr": 0.09898, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23734, "top5_acc": 0.4775, "loss_cls": 4.19096, "loss": 4.19096, "time": 0.70086} +{"mode": "train", "epoch": 10, "iter": 2600, "lr": 0.09897, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24094, "top5_acc": 0.48359, "loss_cls": 4.18153, "loss": 4.18153, "time": 0.70067} +{"mode": "train", "epoch": 10, "iter": 2700, "lr": 0.09897, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23875, "top5_acc": 0.48453, "loss_cls": 4.16606, "loss": 4.16606, "time": 0.7017} +{"mode": "train", "epoch": 10, "iter": 2800, "lr": 0.09896, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24203, "top5_acc": 0.49359, "loss_cls": 4.17978, "loss": 4.17978, "time": 0.70404} +{"mode": "train", "epoch": 10, "iter": 2900, "lr": 0.09896, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24031, "top5_acc": 0.49031, "loss_cls": 4.17382, "loss": 4.17382, "time": 0.6992} +{"mode": "train", "epoch": 10, "iter": 3000, "lr": 0.09895, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24094, "top5_acc": 0.48281, "loss_cls": 4.2207, "loss": 4.2207, "time": 0.70095} +{"mode": "train", "epoch": 10, "iter": 3100, "lr": 0.09894, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24297, "top5_acc": 0.48281, "loss_cls": 4.19325, "loss": 4.19325, "time": 0.69969} +{"mode": "train", "epoch": 10, "iter": 3200, "lr": 0.09894, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24328, "top5_acc": 0.48547, "loss_cls": 4.15769, "loss": 4.15769, "time": 0.69778} +{"mode": "train", "epoch": 10, "iter": 3300, "lr": 0.09893, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24344, "top5_acc": 0.48828, "loss_cls": 4.15204, "loss": 4.15204, "time": 0.70025} +{"mode": "train", "epoch": 10, "iter": 3400, "lr": 0.09893, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24016, "top5_acc": 0.48438, "loss_cls": 4.18223, "loss": 4.18223, "time": 0.6992} +{"mode": "train", "epoch": 10, "iter": 3500, "lr": 0.09892, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25359, "top5_acc": 0.50469, "loss_cls": 4.09874, "loss": 4.09874, "time": 0.70107} +{"mode": "train", "epoch": 10, "iter": 3600, "lr": 0.09892, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23781, "top5_acc": 0.48969, "loss_cls": 4.17911, "loss": 4.17911, "time": 0.70303} +{"mode": "train", "epoch": 10, "iter": 3700, "lr": 0.09891, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23766, "top5_acc": 0.48375, "loss_cls": 4.19294, "loss": 4.19294, "time": 0.70083} +{"mode": "val", "epoch": 10, "iter": 309, "lr": 0.09891, "top1_acc": 0.17323, "top5_acc": 0.38333, "mean_class_accuracy": 0.17315} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.0989, "memory": 15990, "data_time": 1.27648, "top1_acc": 0.24578, "top5_acc": 0.49672, "loss_cls": 4.11231, "loss": 4.11231, "time": 1.987} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.0989, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24531, "top5_acc": 0.49188, "loss_cls": 4.15068, "loss": 4.15068, "time": 0.70616} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.09889, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24906, "top5_acc": 0.49266, "loss_cls": 4.14572, "loss": 4.14572, "time": 0.7074} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.09888, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22672, "top5_acc": 0.47141, "loss_cls": 4.22796, "loss": 4.22796, "time": 0.71163} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.09888, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24312, "top5_acc": 0.49297, "loss_cls": 4.1191, "loss": 4.1191, "time": 0.71063} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.09887, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24156, "top5_acc": 0.49422, "loss_cls": 4.16141, "loss": 4.16141, "time": 0.70869} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.09887, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23422, "top5_acc": 0.48438, "loss_cls": 4.20174, "loss": 4.20174, "time": 0.70453} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.09886, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23531, "top5_acc": 0.48844, "loss_cls": 4.1747, "loss": 4.1747, "time": 0.70321} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.09885, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24484, "top5_acc": 0.49219, "loss_cls": 4.16678, "loss": 4.16678, "time": 0.70152} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.09885, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24516, "top5_acc": 0.49375, "loss_cls": 4.14337, "loss": 4.14337, "time": 0.70108} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.09884, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24469, "top5_acc": 0.48891, "loss_cls": 4.17143, "loss": 4.17143, "time": 0.70161} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.09884, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24594, "top5_acc": 0.49328, "loss_cls": 4.1408, "loss": 4.1408, "time": 0.6998} +{"mode": "train", "epoch": 11, "iter": 1300, "lr": 0.09883, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2275, "top5_acc": 0.46875, "loss_cls": 4.21434, "loss": 4.21434, "time": 0.70495} +{"mode": "train", "epoch": 11, "iter": 1400, "lr": 0.09882, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25188, "top5_acc": 0.49484, "loss_cls": 4.10681, "loss": 4.10681, "time": 0.69966} +{"mode": "train", "epoch": 11, "iter": 1500, "lr": 0.09882, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25031, "top5_acc": 0.49312, "loss_cls": 4.13999, "loss": 4.13999, "time": 0.6989} +{"mode": "train", "epoch": 11, "iter": 1600, "lr": 0.09881, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24766, "top5_acc": 0.48844, "loss_cls": 4.14216, "loss": 4.14216, "time": 0.69825} +{"mode": "train", "epoch": 11, "iter": 1700, "lr": 0.09881, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24281, "top5_acc": 0.48891, "loss_cls": 4.15252, "loss": 4.15252, "time": 0.69917} +{"mode": "train", "epoch": 11, "iter": 1800, "lr": 0.0988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24125, "top5_acc": 0.48391, "loss_cls": 4.16704, "loss": 4.16704, "time": 0.69968} +{"mode": "train", "epoch": 11, "iter": 1900, "lr": 0.09879, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24484, "top5_acc": 0.49688, "loss_cls": 4.12917, "loss": 4.12917, "time": 0.6999} +{"mode": "train", "epoch": 11, "iter": 2000, "lr": 0.09879, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24547, "top5_acc": 0.48625, "loss_cls": 4.19906, "loss": 4.19906, "time": 0.69845} +{"mode": "train", "epoch": 11, "iter": 2100, "lr": 0.09878, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2375, "top5_acc": 0.47766, "loss_cls": 4.19363, "loss": 4.19363, "time": 0.69886} +{"mode": "train", "epoch": 11, "iter": 2200, "lr": 0.09878, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24172, "top5_acc": 0.48109, "loss_cls": 4.18217, "loss": 4.18217, "time": 0.69723} +{"mode": "train", "epoch": 11, "iter": 2300, "lr": 0.09877, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23703, "top5_acc": 0.48203, "loss_cls": 4.18937, "loss": 4.18937, "time": 0.70295} +{"mode": "train", "epoch": 11, "iter": 2400, "lr": 0.09876, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24531, "top5_acc": 0.48547, "loss_cls": 4.16251, "loss": 4.16251, "time": 0.70035} +{"mode": "train", "epoch": 11, "iter": 2500, "lr": 0.09876, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25312, "top5_acc": 0.49078, "loss_cls": 4.14138, "loss": 4.14138, "time": 0.69988} +{"mode": "train", "epoch": 11, "iter": 2600, "lr": 0.09875, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24859, "top5_acc": 0.48562, "loss_cls": 4.14133, "loss": 4.14133, "time": 0.6975} +{"mode": "train", "epoch": 11, "iter": 2700, "lr": 0.09874, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25047, "top5_acc": 0.50016, "loss_cls": 4.12503, "loss": 4.12503, "time": 0.69998} +{"mode": "train", "epoch": 11, "iter": 2800, "lr": 0.09874, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24359, "top5_acc": 0.4925, "loss_cls": 4.15981, "loss": 4.15981, "time": 0.7021} +{"mode": "train", "epoch": 11, "iter": 2900, "lr": 0.09873, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25031, "top5_acc": 0.48844, "loss_cls": 4.14127, "loss": 4.14127, "time": 0.69972} +{"mode": "train", "epoch": 11, "iter": 3000, "lr": 0.09873, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23688, "top5_acc": 0.47109, "loss_cls": 4.20727, "loss": 4.20727, "time": 0.7022} +{"mode": "train", "epoch": 11, "iter": 3100, "lr": 0.09872, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25094, "top5_acc": 0.50016, "loss_cls": 4.11488, "loss": 4.11488, "time": 0.6996} +{"mode": "train", "epoch": 11, "iter": 3200, "lr": 0.09871, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24125, "top5_acc": 0.49359, "loss_cls": 4.17403, "loss": 4.17403, "time": 0.69993} +{"mode": "train", "epoch": 11, "iter": 3300, "lr": 0.09871, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2575, "top5_acc": 0.50234, "loss_cls": 4.08554, "loss": 4.08554, "time": 0.69902} +{"mode": "train", "epoch": 11, "iter": 3400, "lr": 0.0987, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24297, "top5_acc": 0.49016, "loss_cls": 4.15052, "loss": 4.15052, "time": 0.70297} +{"mode": "train", "epoch": 11, "iter": 3500, "lr": 0.09869, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24938, "top5_acc": 0.49141, "loss_cls": 4.16215, "loss": 4.16215, "time": 0.70085} +{"mode": "train", "epoch": 11, "iter": 3600, "lr": 0.09869, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24297, "top5_acc": 0.48219, "loss_cls": 4.18473, "loss": 4.18473, "time": 0.70191} +{"mode": "train", "epoch": 11, "iter": 3700, "lr": 0.09868, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24625, "top5_acc": 0.49125, "loss_cls": 4.15291, "loss": 4.15291, "time": 0.69985} +{"mode": "val", "epoch": 11, "iter": 309, "lr": 0.09868, "top1_acc": 0.17105, "top5_acc": 0.38854, "mean_class_accuracy": 0.171} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.09867, "memory": 15990, "data_time": 1.25167, "top1_acc": 0.25156, "top5_acc": 0.49109, "loss_cls": 4.13547, "loss": 4.13547, "time": 1.95778} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.09867, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24312, "top5_acc": 0.49297, "loss_cls": 4.12433, "loss": 4.12433, "time": 0.70989} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.09866, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24812, "top5_acc": 0.49312, "loss_cls": 4.14077, "loss": 4.14077, "time": 0.70926} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.09865, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24781, "top5_acc": 0.49219, "loss_cls": 4.14795, "loss": 4.14795, "time": 0.70821} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.09865, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24797, "top5_acc": 0.50125, "loss_cls": 4.08548, "loss": 4.08548, "time": 0.70223} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.09864, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2475, "top5_acc": 0.49406, "loss_cls": 4.12345, "loss": 4.12345, "time": 0.70992} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.09863, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24453, "top5_acc": 0.49297, "loss_cls": 4.15846, "loss": 4.15846, "time": 0.7062} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.09863, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24891, "top5_acc": 0.49, "loss_cls": 4.13426, "loss": 4.13426, "time": 0.70262} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.09862, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24531, "top5_acc": 0.48781, "loss_cls": 4.14812, "loss": 4.14812, "time": 0.70307} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.09861, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24047, "top5_acc": 0.48188, "loss_cls": 4.2002, "loss": 4.2002, "time": 0.6994} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.09861, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25219, "top5_acc": 0.49266, "loss_cls": 4.15943, "loss": 4.15943, "time": 0.69942} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.0986, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25406, "top5_acc": 0.49188, "loss_cls": 4.11367, "loss": 4.11367, "time": 0.70159} +{"mode": "train", "epoch": 12, "iter": 1300, "lr": 0.09859, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2475, "top5_acc": 0.49141, "loss_cls": 4.14986, "loss": 4.14986, "time": 0.70076} +{"mode": "train", "epoch": 12, "iter": 1400, "lr": 0.09859, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24344, "top5_acc": 0.49484, "loss_cls": 4.12671, "loss": 4.12671, "time": 0.70138} +{"mode": "train", "epoch": 12, "iter": 1500, "lr": 0.09858, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24516, "top5_acc": 0.485, "loss_cls": 4.15948, "loss": 4.15948, "time": 0.70053} +{"mode": "train", "epoch": 12, "iter": 1600, "lr": 0.09857, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24828, "top5_acc": 0.49094, "loss_cls": 4.14583, "loss": 4.14583, "time": 0.70357} +{"mode": "train", "epoch": 12, "iter": 1700, "lr": 0.09857, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25734, "top5_acc": 0.49688, "loss_cls": 4.13647, "loss": 4.13647, "time": 0.69898} +{"mode": "train", "epoch": 12, "iter": 1800, "lr": 0.09856, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24844, "top5_acc": 0.49062, "loss_cls": 4.13815, "loss": 4.13815, "time": 0.70421} +{"mode": "train", "epoch": 12, "iter": 1900, "lr": 0.09855, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23516, "top5_acc": 0.47875, "loss_cls": 4.21446, "loss": 4.21446, "time": 0.70177} +{"mode": "train", "epoch": 12, "iter": 2000, "lr": 0.09855, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23641, "top5_acc": 0.48453, "loss_cls": 4.18276, "loss": 4.18276, "time": 0.70002} +{"mode": "train", "epoch": 12, "iter": 2100, "lr": 0.09854, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24328, "top5_acc": 0.49531, "loss_cls": 4.11225, "loss": 4.11225, "time": 0.69862} +{"mode": "train", "epoch": 12, "iter": 2200, "lr": 0.09853, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25141, "top5_acc": 0.49703, "loss_cls": 4.11062, "loss": 4.11062, "time": 0.70322} +{"mode": "train", "epoch": 12, "iter": 2300, "lr": 0.09853, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24297, "top5_acc": 0.49094, "loss_cls": 4.12705, "loss": 4.12705, "time": 0.69838} +{"mode": "train", "epoch": 12, "iter": 2400, "lr": 0.09852, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24344, "top5_acc": 0.48125, "loss_cls": 4.18917, "loss": 4.18917, "time": 0.70159} +{"mode": "train", "epoch": 12, "iter": 2500, "lr": 0.09851, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24188, "top5_acc": 0.48812, "loss_cls": 4.18946, "loss": 4.18946, "time": 0.69976} +{"mode": "train", "epoch": 12, "iter": 2600, "lr": 0.09851, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24172, "top5_acc": 0.48547, "loss_cls": 4.16052, "loss": 4.16052, "time": 0.70359} +{"mode": "train", "epoch": 12, "iter": 2700, "lr": 0.0985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25062, "top5_acc": 0.48828, "loss_cls": 4.14949, "loss": 4.14949, "time": 0.70071} +{"mode": "train", "epoch": 12, "iter": 2800, "lr": 0.09849, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24, "top5_acc": 0.48531, "loss_cls": 4.17665, "loss": 4.17665, "time": 0.703} +{"mode": "train", "epoch": 12, "iter": 2900, "lr": 0.09849, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24562, "top5_acc": 0.48938, "loss_cls": 4.16107, "loss": 4.16107, "time": 0.70098} +{"mode": "train", "epoch": 12, "iter": 3000, "lr": 0.09848, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24125, "top5_acc": 0.48812, "loss_cls": 4.14839, "loss": 4.14839, "time": 0.69913} +{"mode": "train", "epoch": 12, "iter": 3100, "lr": 0.09847, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24406, "top5_acc": 0.48734, "loss_cls": 4.14391, "loss": 4.14391, "time": 0.69924} +{"mode": "train", "epoch": 12, "iter": 3200, "lr": 0.09847, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24328, "top5_acc": 0.49609, "loss_cls": 4.1245, "loss": 4.1245, "time": 0.70078} +{"mode": "train", "epoch": 12, "iter": 3300, "lr": 0.09846, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24359, "top5_acc": 0.48547, "loss_cls": 4.13866, "loss": 4.13866, "time": 0.70275} +{"mode": "train", "epoch": 12, "iter": 3400, "lr": 0.09845, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24234, "top5_acc": 0.48703, "loss_cls": 4.16419, "loss": 4.16419, "time": 0.70094} +{"mode": "train", "epoch": 12, "iter": 3500, "lr": 0.09845, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24344, "top5_acc": 0.48938, "loss_cls": 4.11595, "loss": 4.11595, "time": 0.70158} +{"mode": "train", "epoch": 12, "iter": 3600, "lr": 0.09844, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24312, "top5_acc": 0.49516, "loss_cls": 4.14472, "loss": 4.14472, "time": 0.70088} +{"mode": "train", "epoch": 12, "iter": 3700, "lr": 0.09843, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24422, "top5_acc": 0.49969, "loss_cls": 4.12383, "loss": 4.12383, "time": 0.69815} +{"mode": "val", "epoch": 12, "iter": 309, "lr": 0.09843, "top1_acc": 0.17692, "top5_acc": 0.40126, "mean_class_accuracy": 0.17685} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.09842, "memory": 15990, "data_time": 1.26483, "top1_acc": 0.26297, "top5_acc": 0.50531, "loss_cls": 4.06278, "loss": 4.06278, "time": 1.97245} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.09842, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25516, "top5_acc": 0.50406, "loss_cls": 4.07495, "loss": 4.07495, "time": 0.7107} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.09841, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25047, "top5_acc": 0.50531, "loss_cls": 4.09648, "loss": 4.09648, "time": 0.70593} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.0984, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25578, "top5_acc": 0.49562, "loss_cls": 4.11306, "loss": 4.11306, "time": 0.71023} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.09839, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24703, "top5_acc": 0.49422, "loss_cls": 4.12755, "loss": 4.12755, "time": 0.71055} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.09839, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25797, "top5_acc": 0.49594, "loss_cls": 4.10338, "loss": 4.10338, "time": 0.70798} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.09838, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25125, "top5_acc": 0.50219, "loss_cls": 4.1128, "loss": 4.1128, "time": 0.70353} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.09837, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25203, "top5_acc": 0.49812, "loss_cls": 4.11954, "loss": 4.11954, "time": 0.70428} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.09837, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24359, "top5_acc": 0.48562, "loss_cls": 4.16776, "loss": 4.16776, "time": 0.70077} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.09836, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24031, "top5_acc": 0.48344, "loss_cls": 4.18608, "loss": 4.18608, "time": 0.70592} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.09835, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26031, "top5_acc": 0.49766, "loss_cls": 4.12192, "loss": 4.12192, "time": 0.70071} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.09834, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25406, "top5_acc": 0.50031, "loss_cls": 4.10663, "loss": 4.10663, "time": 0.69847} +{"mode": "train", "epoch": 13, "iter": 1300, "lr": 0.09834, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24688, "top5_acc": 0.50594, "loss_cls": 4.11449, "loss": 4.11449, "time": 0.70327} +{"mode": "train", "epoch": 13, "iter": 1400, "lr": 0.09833, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24, "top5_acc": 0.48953, "loss_cls": 4.1474, "loss": 4.1474, "time": 0.7049} +{"mode": "train", "epoch": 13, "iter": 1500, "lr": 0.09832, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24859, "top5_acc": 0.50172, "loss_cls": 4.1276, "loss": 4.1276, "time": 0.70107} +{"mode": "train", "epoch": 13, "iter": 1600, "lr": 0.09832, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24219, "top5_acc": 0.48516, "loss_cls": 4.15445, "loss": 4.15445, "time": 0.69935} +{"mode": "train", "epoch": 13, "iter": 1700, "lr": 0.09831, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24953, "top5_acc": 0.49922, "loss_cls": 4.12791, "loss": 4.12791, "time": 0.7005} +{"mode": "train", "epoch": 13, "iter": 1800, "lr": 0.0983, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23438, "top5_acc": 0.4875, "loss_cls": 4.17889, "loss": 4.17889, "time": 0.70196} +{"mode": "train", "epoch": 13, "iter": 1900, "lr": 0.09829, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25219, "top5_acc": 0.50562, "loss_cls": 4.08762, "loss": 4.08762, "time": 0.70278} +{"mode": "train", "epoch": 13, "iter": 2000, "lr": 0.09829, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25125, "top5_acc": 0.49891, "loss_cls": 4.08435, "loss": 4.08435, "time": 0.70019} +{"mode": "train", "epoch": 13, "iter": 2100, "lr": 0.09828, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24641, "top5_acc": 0.48125, "loss_cls": 4.15343, "loss": 4.15343, "time": 0.70165} +{"mode": "train", "epoch": 13, "iter": 2200, "lr": 0.09827, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2525, "top5_acc": 0.49406, "loss_cls": 4.13784, "loss": 4.13784, "time": 0.69995} +{"mode": "train", "epoch": 13, "iter": 2300, "lr": 0.09827, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24812, "top5_acc": 0.48906, "loss_cls": 4.18245, "loss": 4.18245, "time": 0.70253} +{"mode": "train", "epoch": 13, "iter": 2400, "lr": 0.09826, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24156, "top5_acc": 0.49469, "loss_cls": 4.15333, "loss": 4.15333, "time": 0.70226} +{"mode": "train", "epoch": 13, "iter": 2500, "lr": 0.09825, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24688, "top5_acc": 0.49969, "loss_cls": 4.13445, "loss": 4.13445, "time": 0.70327} +{"mode": "train", "epoch": 13, "iter": 2600, "lr": 0.09824, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24781, "top5_acc": 0.49922, "loss_cls": 4.09201, "loss": 4.09201, "time": 0.70153} +{"mode": "train", "epoch": 13, "iter": 2700, "lr": 0.09824, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24594, "top5_acc": 0.49156, "loss_cls": 4.17493, "loss": 4.17493, "time": 0.70174} +{"mode": "train", "epoch": 13, "iter": 2800, "lr": 0.09823, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23984, "top5_acc": 0.48172, "loss_cls": 4.19179, "loss": 4.19179, "time": 0.69916} +{"mode": "train", "epoch": 13, "iter": 2900, "lr": 0.09822, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23922, "top5_acc": 0.48672, "loss_cls": 4.14512, "loss": 4.14512, "time": 0.70163} +{"mode": "train", "epoch": 13, "iter": 3000, "lr": 0.09821, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25172, "top5_acc": 0.49062, "loss_cls": 4.14425, "loss": 4.14425, "time": 0.70427} +{"mode": "train", "epoch": 13, "iter": 3100, "lr": 0.09821, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24484, "top5_acc": 0.49719, "loss_cls": 4.14337, "loss": 4.14337, "time": 0.70302} +{"mode": "train", "epoch": 13, "iter": 3200, "lr": 0.0982, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25156, "top5_acc": 0.51094, "loss_cls": 4.07008, "loss": 4.07008, "time": 0.70277} +{"mode": "train", "epoch": 13, "iter": 3300, "lr": 0.09819, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23734, "top5_acc": 0.48703, "loss_cls": 4.17137, "loss": 4.17137, "time": 0.7031} +{"mode": "train", "epoch": 13, "iter": 3400, "lr": 0.09818, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24641, "top5_acc": 0.49203, "loss_cls": 4.14691, "loss": 4.14691, "time": 0.70354} +{"mode": "train", "epoch": 13, "iter": 3500, "lr": 0.09818, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23641, "top5_acc": 0.47953, "loss_cls": 4.20992, "loss": 4.20992, "time": 0.70188} +{"mode": "train", "epoch": 13, "iter": 3600, "lr": 0.09817, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24578, "top5_acc": 0.48875, "loss_cls": 4.17852, "loss": 4.17852, "time": 0.69815} +{"mode": "train", "epoch": 13, "iter": 3700, "lr": 0.09816, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25328, "top5_acc": 0.49875, "loss_cls": 4.12108, "loss": 4.12108, "time": 0.70427} +{"mode": "val", "epoch": 13, "iter": 309, "lr": 0.09816, "top1_acc": 0.19106, "top5_acc": 0.41164, "mean_class_accuracy": 0.19103} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.09815, "memory": 15990, "data_time": 1.2486, "top1_acc": 0.24828, "top5_acc": 0.50031, "loss_cls": 4.10887, "loss": 4.10887, "time": 1.95646} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.09814, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25641, "top5_acc": 0.50328, "loss_cls": 4.08019, "loss": 4.08019, "time": 0.71131} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.09814, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25547, "top5_acc": 0.50781, "loss_cls": 4.08155, "loss": 4.08155, "time": 0.7108} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.09813, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25172, "top5_acc": 0.49578, "loss_cls": 4.10815, "loss": 4.10815, "time": 0.71409} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.09812, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2475, "top5_acc": 0.50094, "loss_cls": 4.10655, "loss": 4.10655, "time": 0.7044} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.09811, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24391, "top5_acc": 0.49609, "loss_cls": 4.11472, "loss": 4.11472, "time": 0.70631} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.09811, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25656, "top5_acc": 0.51094, "loss_cls": 4.07496, "loss": 4.07496, "time": 0.70401} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.0981, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24766, "top5_acc": 0.49938, "loss_cls": 4.12822, "loss": 4.12822, "time": 0.70463} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.09809, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24672, "top5_acc": 0.49922, "loss_cls": 4.10192, "loss": 4.10192, "time": 0.70165} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.09808, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25344, "top5_acc": 0.49391, "loss_cls": 4.09959, "loss": 4.09959, "time": 0.69891} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.09807, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25703, "top5_acc": 0.49922, "loss_cls": 4.09726, "loss": 4.09726, "time": 0.70293} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.09807, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24219, "top5_acc": 0.48156, "loss_cls": 4.17103, "loss": 4.17103, "time": 0.69974} +{"mode": "train", "epoch": 14, "iter": 1300, "lr": 0.09806, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24969, "top5_acc": 0.49656, "loss_cls": 4.12034, "loss": 4.12034, "time": 0.70276} +{"mode": "train", "epoch": 14, "iter": 1400, "lr": 0.09805, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26062, "top5_acc": 0.50391, "loss_cls": 4.06908, "loss": 4.06908, "time": 0.7024} +{"mode": "train", "epoch": 14, "iter": 1500, "lr": 0.09804, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25031, "top5_acc": 0.48891, "loss_cls": 4.14442, "loss": 4.14442, "time": 0.7014} +{"mode": "train", "epoch": 14, "iter": 1600, "lr": 0.09804, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26578, "top5_acc": 0.50422, "loss_cls": 4.07119, "loss": 4.07119, "time": 0.70069} +{"mode": "train", "epoch": 14, "iter": 1700, "lr": 0.09803, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25391, "top5_acc": 0.50219, "loss_cls": 4.12647, "loss": 4.12647, "time": 0.70163} +{"mode": "train", "epoch": 14, "iter": 1800, "lr": 0.09802, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25406, "top5_acc": 0.49812, "loss_cls": 4.13705, "loss": 4.13705, "time": 0.70174} +{"mode": "train", "epoch": 14, "iter": 1900, "lr": 0.09801, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25172, "top5_acc": 0.50203, "loss_cls": 4.10288, "loss": 4.10288, "time": 0.69931} +{"mode": "train", "epoch": 14, "iter": 2000, "lr": 0.098, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24953, "top5_acc": 0.50062, "loss_cls": 4.12629, "loss": 4.12629, "time": 0.70311} +{"mode": "train", "epoch": 14, "iter": 2100, "lr": 0.098, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24656, "top5_acc": 0.48141, "loss_cls": 4.17272, "loss": 4.17272, "time": 0.70141} +{"mode": "train", "epoch": 14, "iter": 2200, "lr": 0.09799, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24688, "top5_acc": 0.48875, "loss_cls": 4.14139, "loss": 4.14139, "time": 0.70013} +{"mode": "train", "epoch": 14, "iter": 2300, "lr": 0.09798, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23812, "top5_acc": 0.48969, "loss_cls": 4.17009, "loss": 4.17009, "time": 0.7065} +{"mode": "train", "epoch": 14, "iter": 2400, "lr": 0.09797, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24594, "top5_acc": 0.48953, "loss_cls": 4.12777, "loss": 4.12777, "time": 0.70036} +{"mode": "train", "epoch": 14, "iter": 2500, "lr": 0.09797, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25344, "top5_acc": 0.50406, "loss_cls": 4.08738, "loss": 4.08738, "time": 0.70426} +{"mode": "train", "epoch": 14, "iter": 2600, "lr": 0.09796, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24359, "top5_acc": 0.49, "loss_cls": 4.14018, "loss": 4.14018, "time": 0.69962} +{"mode": "train", "epoch": 14, "iter": 2700, "lr": 0.09795, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24422, "top5_acc": 0.4875, "loss_cls": 4.14577, "loss": 4.14577, "time": 0.70296} +{"mode": "train", "epoch": 14, "iter": 2800, "lr": 0.09794, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25781, "top5_acc": 0.50828, "loss_cls": 4.08856, "loss": 4.08856, "time": 0.70504} +{"mode": "train", "epoch": 14, "iter": 2900, "lr": 0.09793, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24922, "top5_acc": 0.49203, "loss_cls": 4.15218, "loss": 4.15218, "time": 0.7009} +{"mode": "train", "epoch": 14, "iter": 3000, "lr": 0.09793, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24969, "top5_acc": 0.49406, "loss_cls": 4.15228, "loss": 4.15228, "time": 0.70061} +{"mode": "train", "epoch": 14, "iter": 3100, "lr": 0.09792, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23656, "top5_acc": 0.49422, "loss_cls": 4.14874, "loss": 4.14874, "time": 0.70367} +{"mode": "train", "epoch": 14, "iter": 3200, "lr": 0.09791, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25609, "top5_acc": 0.50562, "loss_cls": 4.10541, "loss": 4.10541, "time": 0.70036} +{"mode": "train", "epoch": 14, "iter": 3300, "lr": 0.0979, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25641, "top5_acc": 0.50406, "loss_cls": 4.07705, "loss": 4.07705, "time": 0.69872} +{"mode": "train", "epoch": 14, "iter": 3400, "lr": 0.09789, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25, "top5_acc": 0.49562, "loss_cls": 4.14479, "loss": 4.14479, "time": 0.70269} +{"mode": "train", "epoch": 14, "iter": 3500, "lr": 0.09789, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24094, "top5_acc": 0.49234, "loss_cls": 4.15081, "loss": 4.15081, "time": 0.69953} +{"mode": "train", "epoch": 14, "iter": 3600, "lr": 0.09788, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24828, "top5_acc": 0.48969, "loss_cls": 4.16497, "loss": 4.16497, "time": 0.70018} +{"mode": "train", "epoch": 14, "iter": 3700, "lr": 0.09787, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24906, "top5_acc": 0.48297, "loss_cls": 4.14531, "loss": 4.14531, "time": 0.70215} +{"mode": "val", "epoch": 14, "iter": 309, "lr": 0.09787, "top1_acc": 0.17551, "top5_acc": 0.39913, "mean_class_accuracy": 0.17531} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.09786, "memory": 15990, "data_time": 1.25298, "top1_acc": 0.25641, "top5_acc": 0.49828, "loss_cls": 4.09837, "loss": 4.09837, "time": 1.96171} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.09785, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25156, "top5_acc": 0.50125, "loss_cls": 4.08542, "loss": 4.08542, "time": 0.70699} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.09784, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24641, "top5_acc": 0.49078, "loss_cls": 4.14586, "loss": 4.14586, "time": 0.70315} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.09783, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25016, "top5_acc": 0.49078, "loss_cls": 4.147, "loss": 4.147, "time": 0.71252} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.09783, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25891, "top5_acc": 0.50859, "loss_cls": 4.06274, "loss": 4.06274, "time": 0.70204} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.09782, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25578, "top5_acc": 0.50297, "loss_cls": 4.07722, "loss": 4.07722, "time": 0.70633} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.09781, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25156, "top5_acc": 0.50453, "loss_cls": 4.07405, "loss": 4.07405, "time": 0.70956} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.0978, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24797, "top5_acc": 0.50234, "loss_cls": 4.12635, "loss": 4.12635, "time": 0.70384} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.09779, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25578, "top5_acc": 0.50344, "loss_cls": 4.08149, "loss": 4.08149, "time": 0.70343} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.09778, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24391, "top5_acc": 0.50094, "loss_cls": 4.10373, "loss": 4.10373, "time": 0.69932} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.09778, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25234, "top5_acc": 0.49375, "loss_cls": 4.12008, "loss": 4.12008, "time": 0.69971} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.09777, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25094, "top5_acc": 0.50344, "loss_cls": 4.09419, "loss": 4.09419, "time": 0.69838} +{"mode": "train", "epoch": 15, "iter": 1300, "lr": 0.09776, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25125, "top5_acc": 0.50516, "loss_cls": 4.12125, "loss": 4.12125, "time": 0.7016} +{"mode": "train", "epoch": 15, "iter": 1400, "lr": 0.09775, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.245, "top5_acc": 0.4975, "loss_cls": 4.12947, "loss": 4.12947, "time": 0.69975} +{"mode": "train", "epoch": 15, "iter": 1500, "lr": 0.09774, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25109, "top5_acc": 0.50234, "loss_cls": 4.12539, "loss": 4.12539, "time": 0.69856} +{"mode": "train", "epoch": 15, "iter": 1600, "lr": 0.09773, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25797, "top5_acc": 0.49812, "loss_cls": 4.07579, "loss": 4.07579, "time": 0.6996} +{"mode": "train", "epoch": 15, "iter": 1700, "lr": 0.09773, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24688, "top5_acc": 0.49344, "loss_cls": 4.15627, "loss": 4.15627, "time": 0.69742} +{"mode": "train", "epoch": 15, "iter": 1800, "lr": 0.09772, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25281, "top5_acc": 0.49953, "loss_cls": 4.10265, "loss": 4.10265, "time": 0.70007} +{"mode": "train", "epoch": 15, "iter": 1900, "lr": 0.09771, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25703, "top5_acc": 0.50094, "loss_cls": 4.06586, "loss": 4.06586, "time": 0.69839} +{"mode": "train", "epoch": 15, "iter": 2000, "lr": 0.0977, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25438, "top5_acc": 0.50125, "loss_cls": 4.12313, "loss": 4.12313, "time": 0.70102} +{"mode": "train", "epoch": 15, "iter": 2100, "lr": 0.09769, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24469, "top5_acc": 0.49844, "loss_cls": 4.09319, "loss": 4.09319, "time": 0.70068} +{"mode": "train", "epoch": 15, "iter": 2200, "lr": 0.09768, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24875, "top5_acc": 0.4975, "loss_cls": 4.14393, "loss": 4.14393, "time": 0.6978} +{"mode": "train", "epoch": 15, "iter": 2300, "lr": 0.09768, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26016, "top5_acc": 0.50906, "loss_cls": 4.07164, "loss": 4.07164, "time": 0.70037} +{"mode": "train", "epoch": 15, "iter": 2400, "lr": 0.09767, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24812, "top5_acc": 0.48844, "loss_cls": 4.15, "loss": 4.15, "time": 0.70092} +{"mode": "train", "epoch": 15, "iter": 2500, "lr": 0.09766, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24656, "top5_acc": 0.48672, "loss_cls": 4.16308, "loss": 4.16308, "time": 0.69897} +{"mode": "train", "epoch": 15, "iter": 2600, "lr": 0.09765, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24438, "top5_acc": 0.48844, "loss_cls": 4.13525, "loss": 4.13525, "time": 0.69823} +{"mode": "train", "epoch": 15, "iter": 2700, "lr": 0.09764, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25641, "top5_acc": 0.50781, "loss_cls": 4.07323, "loss": 4.07323, "time": 0.69699} +{"mode": "train", "epoch": 15, "iter": 2800, "lr": 0.09763, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25266, "top5_acc": 0.49516, "loss_cls": 4.14753, "loss": 4.14753, "time": 0.69841} +{"mode": "train", "epoch": 15, "iter": 2900, "lr": 0.09763, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24094, "top5_acc": 0.48516, "loss_cls": 4.16995, "loss": 4.16995, "time": 0.69652} +{"mode": "train", "epoch": 15, "iter": 3000, "lr": 0.09762, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25047, "top5_acc": 0.49594, "loss_cls": 4.13851, "loss": 4.13851, "time": 0.69876} +{"mode": "train", "epoch": 15, "iter": 3100, "lr": 0.09761, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24797, "top5_acc": 0.49922, "loss_cls": 4.14566, "loss": 4.14566, "time": 0.69951} +{"mode": "train", "epoch": 15, "iter": 3200, "lr": 0.0976, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24344, "top5_acc": 0.50156, "loss_cls": 4.11654, "loss": 4.11654, "time": 0.70112} +{"mode": "train", "epoch": 15, "iter": 3300, "lr": 0.09759, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26, "top5_acc": 0.50562, "loss_cls": 4.06859, "loss": 4.06859, "time": 0.6999} +{"mode": "train", "epoch": 15, "iter": 3400, "lr": 0.09758, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25219, "top5_acc": 0.49562, "loss_cls": 4.11117, "loss": 4.11117, "time": 0.69849} +{"mode": "train", "epoch": 15, "iter": 3500, "lr": 0.09757, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24547, "top5_acc": 0.48359, "loss_cls": 4.16347, "loss": 4.16347, "time": 0.69762} +{"mode": "train", "epoch": 15, "iter": 3600, "lr": 0.09757, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24984, "top5_acc": 0.495, "loss_cls": 4.13152, "loss": 4.13152, "time": 0.70199} +{"mode": "train", "epoch": 15, "iter": 3700, "lr": 0.09756, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24828, "top5_acc": 0.49547, "loss_cls": 4.12481, "loss": 4.12481, "time": 0.69728} +{"mode": "val", "epoch": 15, "iter": 309, "lr": 0.09755, "top1_acc": 0.17738, "top5_acc": 0.39948, "mean_class_accuracy": 0.17729} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.09754, "memory": 15990, "data_time": 1.30725, "top1_acc": 0.26469, "top5_acc": 0.51531, "loss_cls": 4.03377, "loss": 4.03377, "time": 2.01445} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.09754, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24656, "top5_acc": 0.50266, "loss_cls": 4.08586, "loss": 4.08586, "time": 0.70309} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.09753, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24625, "top5_acc": 0.49953, "loss_cls": 4.11033, "loss": 4.11033, "time": 0.70252} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.09752, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25578, "top5_acc": 0.50125, "loss_cls": 4.07575, "loss": 4.07575, "time": 0.70726} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.09751, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25641, "top5_acc": 0.50391, "loss_cls": 4.07892, "loss": 4.07892, "time": 0.70254} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.0975, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25875, "top5_acc": 0.49375, "loss_cls": 4.12072, "loss": 4.12072, "time": 0.70709} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.09749, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25578, "top5_acc": 0.49781, "loss_cls": 4.08814, "loss": 4.08814, "time": 0.70825} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.09748, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25859, "top5_acc": 0.50078, "loss_cls": 4.09714, "loss": 4.09714, "time": 0.70674} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.09747, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24781, "top5_acc": 0.49797, "loss_cls": 4.11443, "loss": 4.11443, "time": 0.71352} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.09747, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24547, "top5_acc": 0.49328, "loss_cls": 4.14825, "loss": 4.14825, "time": 0.70148} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.09746, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24906, "top5_acc": 0.50125, "loss_cls": 4.10685, "loss": 4.10685, "time": 0.70127} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.09745, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25875, "top5_acc": 0.4975, "loss_cls": 4.09324, "loss": 4.09324, "time": 0.69788} +{"mode": "train", "epoch": 16, "iter": 1300, "lr": 0.09744, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24766, "top5_acc": 0.49422, "loss_cls": 4.12833, "loss": 4.12833, "time": 0.70127} +{"mode": "train", "epoch": 16, "iter": 1400, "lr": 0.09743, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25641, "top5_acc": 0.49078, "loss_cls": 4.09794, "loss": 4.09794, "time": 0.70139} +{"mode": "train", "epoch": 16, "iter": 1500, "lr": 0.09742, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25641, "top5_acc": 0.49828, "loss_cls": 4.11067, "loss": 4.11067, "time": 0.70153} +{"mode": "train", "epoch": 16, "iter": 1600, "lr": 0.09741, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25297, "top5_acc": 0.49688, "loss_cls": 4.11768, "loss": 4.11768, "time": 0.69812} +{"mode": "train", "epoch": 16, "iter": 1700, "lr": 0.0974, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25328, "top5_acc": 0.49375, "loss_cls": 4.13402, "loss": 4.13402, "time": 0.69939} +{"mode": "train", "epoch": 16, "iter": 1800, "lr": 0.0974, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25047, "top5_acc": 0.49266, "loss_cls": 4.14188, "loss": 4.14188, "time": 0.69707} +{"mode": "train", "epoch": 16, "iter": 1900, "lr": 0.09739, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25719, "top5_acc": 0.50484, "loss_cls": 4.05491, "loss": 4.05491, "time": 0.69807} +{"mode": "train", "epoch": 16, "iter": 2000, "lr": 0.09738, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25078, "top5_acc": 0.49578, "loss_cls": 4.11574, "loss": 4.11574, "time": 0.69986} +{"mode": "train", "epoch": 16, "iter": 2100, "lr": 0.09737, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24953, "top5_acc": 0.49141, "loss_cls": 4.13315, "loss": 4.13315, "time": 0.69906} +{"mode": "train", "epoch": 16, "iter": 2200, "lr": 0.09736, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26109, "top5_acc": 0.50328, "loss_cls": 4.08807, "loss": 4.08807, "time": 0.69919} +{"mode": "train", "epoch": 16, "iter": 2300, "lr": 0.09735, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25062, "top5_acc": 0.50484, "loss_cls": 4.07869, "loss": 4.07869, "time": 0.69969} +{"mode": "train", "epoch": 16, "iter": 2400, "lr": 0.09734, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24719, "top5_acc": 0.50031, "loss_cls": 4.11489, "loss": 4.11489, "time": 0.7005} +{"mode": "train", "epoch": 16, "iter": 2500, "lr": 0.09733, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25438, "top5_acc": 0.49859, "loss_cls": 4.09084, "loss": 4.09084, "time": 0.69889} +{"mode": "train", "epoch": 16, "iter": 2600, "lr": 0.09732, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25906, "top5_acc": 0.49516, "loss_cls": 4.08961, "loss": 4.08961, "time": 0.69827} +{"mode": "train", "epoch": 16, "iter": 2700, "lr": 0.09731, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26203, "top5_acc": 0.50438, "loss_cls": 4.08164, "loss": 4.08164, "time": 0.69879} +{"mode": "train", "epoch": 16, "iter": 2800, "lr": 0.09731, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25547, "top5_acc": 0.50328, "loss_cls": 4.0897, "loss": 4.0897, "time": 0.69787} +{"mode": "train", "epoch": 16, "iter": 2900, "lr": 0.0973, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25188, "top5_acc": 0.49594, "loss_cls": 4.10668, "loss": 4.10668, "time": 0.69779} +{"mode": "train", "epoch": 16, "iter": 3000, "lr": 0.09729, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24516, "top5_acc": 0.49125, "loss_cls": 4.15499, "loss": 4.15499, "time": 0.70187} +{"mode": "train", "epoch": 16, "iter": 3100, "lr": 0.09728, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24828, "top5_acc": 0.49562, "loss_cls": 4.10489, "loss": 4.10489, "time": 0.69907} +{"mode": "train", "epoch": 16, "iter": 3200, "lr": 0.09727, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26281, "top5_acc": 0.50266, "loss_cls": 4.06651, "loss": 4.06651, "time": 0.69775} +{"mode": "train", "epoch": 16, "iter": 3300, "lr": 0.09726, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25078, "top5_acc": 0.49484, "loss_cls": 4.12005, "loss": 4.12005, "time": 0.69693} +{"mode": "train", "epoch": 16, "iter": 3400, "lr": 0.09725, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25734, "top5_acc": 0.50313, "loss_cls": 4.09394, "loss": 4.09394, "time": 0.6993} +{"mode": "train", "epoch": 16, "iter": 3500, "lr": 0.09724, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25266, "top5_acc": 0.50094, "loss_cls": 4.10293, "loss": 4.10293, "time": 0.69884} +{"mode": "train", "epoch": 16, "iter": 3600, "lr": 0.09723, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25719, "top5_acc": 0.49891, "loss_cls": 4.11448, "loss": 4.11448, "time": 0.69802} +{"mode": "train", "epoch": 16, "iter": 3700, "lr": 0.09722, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25219, "top5_acc": 0.50016, "loss_cls": 4.10153, "loss": 4.10153, "time": 0.70054} +{"mode": "val", "epoch": 16, "iter": 309, "lr": 0.09722, "top1_acc": 0.18938, "top5_acc": 0.41473, "mean_class_accuracy": 0.18923} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.09721, "memory": 15990, "data_time": 1.27809, "top1_acc": 0.25844, "top5_acc": 0.50469, "loss_cls": 4.08169, "loss": 4.08169, "time": 1.98563} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.0972, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.2575, "top5_acc": 0.50625, "loss_cls": 4.09537, "loss": 4.09537, "time": 0.7039} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.09719, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24531, "top5_acc": 0.49656, "loss_cls": 4.11881, "loss": 4.11881, "time": 0.70213} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.09718, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25484, "top5_acc": 0.50047, "loss_cls": 4.10881, "loss": 4.10881, "time": 0.70923} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.09717, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23453, "top5_acc": 0.48562, "loss_cls": 4.16098, "loss": 4.16098, "time": 0.70408} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.09716, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26188, "top5_acc": 0.50906, "loss_cls": 4.09874, "loss": 4.09874, "time": 0.70778} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.09715, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25422, "top5_acc": 0.49969, "loss_cls": 4.10204, "loss": 4.10204, "time": 0.70853} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.09714, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25266, "top5_acc": 0.50094, "loss_cls": 4.0796, "loss": 4.0796, "time": 0.70632} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.09714, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26391, "top5_acc": 0.51297, "loss_cls": 4.03982, "loss": 4.03982, "time": 0.70319} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.09713, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25609, "top5_acc": 0.49703, "loss_cls": 4.13294, "loss": 4.13294, "time": 0.70418} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.09712, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25156, "top5_acc": 0.49922, "loss_cls": 4.10708, "loss": 4.10708, "time": 0.7002} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.09711, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25438, "top5_acc": 0.50187, "loss_cls": 4.10392, "loss": 4.10392, "time": 0.69958} +{"mode": "train", "epoch": 17, "iter": 1300, "lr": 0.0971, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26609, "top5_acc": 0.50781, "loss_cls": 4.07697, "loss": 4.07697, "time": 0.6988} +{"mode": "train", "epoch": 17, "iter": 1400, "lr": 0.09709, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.255, "top5_acc": 0.50266, "loss_cls": 4.07291, "loss": 4.07291, "time": 0.69835} +{"mode": "train", "epoch": 17, "iter": 1500, "lr": 0.09708, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25016, "top5_acc": 0.50406, "loss_cls": 4.09144, "loss": 4.09144, "time": 0.69798} +{"mode": "train", "epoch": 17, "iter": 1600, "lr": 0.09707, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25344, "top5_acc": 0.50641, "loss_cls": 4.07475, "loss": 4.07475, "time": 0.69713} +{"mode": "train", "epoch": 17, "iter": 1700, "lr": 0.09706, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25328, "top5_acc": 0.50375, "loss_cls": 4.10104, "loss": 4.10104, "time": 0.69835} +{"mode": "train", "epoch": 17, "iter": 1800, "lr": 0.09705, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25484, "top5_acc": 0.50094, "loss_cls": 4.10704, "loss": 4.10704, "time": 0.69844} +{"mode": "train", "epoch": 17, "iter": 1900, "lr": 0.09704, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25297, "top5_acc": 0.49812, "loss_cls": 4.09638, "loss": 4.09638, "time": 0.70113} +{"mode": "train", "epoch": 17, "iter": 2000, "lr": 0.09703, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25375, "top5_acc": 0.51172, "loss_cls": 4.07493, "loss": 4.07493, "time": 0.69729} +{"mode": "train", "epoch": 17, "iter": 2100, "lr": 0.09702, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25859, "top5_acc": 0.50281, "loss_cls": 4.05352, "loss": 4.05352, "time": 0.69685} +{"mode": "train", "epoch": 17, "iter": 2200, "lr": 0.09701, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25891, "top5_acc": 0.50234, "loss_cls": 4.06479, "loss": 4.06479, "time": 0.69783} +{"mode": "train", "epoch": 17, "iter": 2300, "lr": 0.097, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.24562, "top5_acc": 0.49078, "loss_cls": 4.14299, "loss": 4.14299, "time": 0.69618} +{"mode": "train", "epoch": 17, "iter": 2400, "lr": 0.09699, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25844, "top5_acc": 0.50734, "loss_cls": 4.0754, "loss": 4.0754, "time": 0.6976} +{"mode": "train", "epoch": 17, "iter": 2500, "lr": 0.09698, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24781, "top5_acc": 0.49578, "loss_cls": 4.11046, "loss": 4.11046, "time": 0.69944} +{"mode": "train", "epoch": 17, "iter": 2600, "lr": 0.09697, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25109, "top5_acc": 0.50047, "loss_cls": 4.09091, "loss": 4.09091, "time": 0.69961} +{"mode": "train", "epoch": 17, "iter": 2700, "lr": 0.09697, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26062, "top5_acc": 0.50422, "loss_cls": 4.08447, "loss": 4.08447, "time": 0.6988} +{"mode": "train", "epoch": 17, "iter": 2800, "lr": 0.09696, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25391, "top5_acc": 0.50016, "loss_cls": 4.08204, "loss": 4.08204, "time": 0.69763} +{"mode": "train", "epoch": 17, "iter": 2900, "lr": 0.09695, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25766, "top5_acc": 0.50156, "loss_cls": 4.11597, "loss": 4.11597, "time": 0.69992} +{"mode": "train", "epoch": 17, "iter": 3000, "lr": 0.09694, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24578, "top5_acc": 0.49594, "loss_cls": 4.13377, "loss": 4.13377, "time": 0.69932} +{"mode": "train", "epoch": 17, "iter": 3100, "lr": 0.09693, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25172, "top5_acc": 0.5025, "loss_cls": 4.08636, "loss": 4.08636, "time": 0.69999} +{"mode": "train", "epoch": 17, "iter": 3200, "lr": 0.09692, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25547, "top5_acc": 0.49125, "loss_cls": 4.10055, "loss": 4.10055, "time": 0.69868} +{"mode": "train", "epoch": 17, "iter": 3300, "lr": 0.09691, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.265, "top5_acc": 0.51, "loss_cls": 4.04797, "loss": 4.04797, "time": 0.69923} +{"mode": "train", "epoch": 17, "iter": 3400, "lr": 0.0969, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26641, "top5_acc": 0.51375, "loss_cls": 4.04017, "loss": 4.04017, "time": 0.69765} +{"mode": "train", "epoch": 17, "iter": 3500, "lr": 0.09689, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24234, "top5_acc": 0.49344, "loss_cls": 4.13821, "loss": 4.13821, "time": 0.69955} +{"mode": "train", "epoch": 17, "iter": 3600, "lr": 0.09688, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25922, "top5_acc": 0.51141, "loss_cls": 4.07014, "loss": 4.07014, "time": 0.69765} +{"mode": "train", "epoch": 17, "iter": 3700, "lr": 0.09687, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25, "top5_acc": 0.49938, "loss_cls": 4.10548, "loss": 4.10548, "time": 0.70208} +{"mode": "val", "epoch": 17, "iter": 309, "lr": 0.09686, "top1_acc": 0.17763, "top5_acc": 0.40024, "mean_class_accuracy": 0.17744} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.09685, "memory": 15990, "data_time": 1.2779, "top1_acc": 0.25516, "top5_acc": 0.51578, "loss_cls": 4.03547, "loss": 4.03547, "time": 1.98142} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.09684, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26109, "top5_acc": 0.51234, "loss_cls": 4.05233, "loss": 4.05233, "time": 0.70507} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.09683, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25375, "top5_acc": 0.51109, "loss_cls": 4.073, "loss": 4.073, "time": 0.70125} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.09683, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26719, "top5_acc": 0.51984, "loss_cls": 3.99689, "loss": 3.99689, "time": 0.70885} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.09682, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26094, "top5_acc": 0.51016, "loss_cls": 4.05424, "loss": 4.05424, "time": 0.70445} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.09681, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25656, "top5_acc": 0.50438, "loss_cls": 4.075, "loss": 4.075, "time": 0.70419} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.0968, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25156, "top5_acc": 0.48938, "loss_cls": 4.12125, "loss": 4.12125, "time": 0.71127} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.09679, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26047, "top5_acc": 0.50016, "loss_cls": 4.08954, "loss": 4.08954, "time": 0.71263} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.09678, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26031, "top5_acc": 0.50813, "loss_cls": 4.07346, "loss": 4.07346, "time": 0.70471} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.09677, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2575, "top5_acc": 0.5, "loss_cls": 4.0661, "loss": 4.0661, "time": 0.69903} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.09676, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25125, "top5_acc": 0.50375, "loss_cls": 4.07063, "loss": 4.07063, "time": 0.70078} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.09675, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25469, "top5_acc": 0.50969, "loss_cls": 4.07222, "loss": 4.07222, "time": 0.70042} +{"mode": "train", "epoch": 18, "iter": 1300, "lr": 0.09674, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24969, "top5_acc": 0.5, "loss_cls": 4.10856, "loss": 4.10856, "time": 0.69803} +{"mode": "train", "epoch": 18, "iter": 1400, "lr": 0.09673, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25641, "top5_acc": 0.50344, "loss_cls": 4.0714, "loss": 4.0714, "time": 0.69854} +{"mode": "train", "epoch": 18, "iter": 1500, "lr": 0.09672, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25125, "top5_acc": 0.50328, "loss_cls": 4.08708, "loss": 4.08708, "time": 0.70116} +{"mode": "train", "epoch": 18, "iter": 1600, "lr": 0.09671, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26, "top5_acc": 0.50344, "loss_cls": 4.06488, "loss": 4.06488, "time": 0.69943} +{"mode": "train", "epoch": 18, "iter": 1700, "lr": 0.0967, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25609, "top5_acc": 0.50234, "loss_cls": 4.08985, "loss": 4.08985, "time": 0.69952} +{"mode": "train", "epoch": 18, "iter": 1800, "lr": 0.09669, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24984, "top5_acc": 0.49625, "loss_cls": 4.1082, "loss": 4.1082, "time": 0.69988} +{"mode": "train", "epoch": 18, "iter": 1900, "lr": 0.09668, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25031, "top5_acc": 0.4875, "loss_cls": 4.12506, "loss": 4.12506, "time": 0.69778} +{"mode": "train", "epoch": 18, "iter": 2000, "lr": 0.09667, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24938, "top5_acc": 0.49922, "loss_cls": 4.09894, "loss": 4.09894, "time": 0.69785} +{"mode": "train", "epoch": 18, "iter": 2100, "lr": 0.09666, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25828, "top5_acc": 0.5025, "loss_cls": 4.0947, "loss": 4.0947, "time": 0.69895} +{"mode": "train", "epoch": 18, "iter": 2200, "lr": 0.09665, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25406, "top5_acc": 0.49188, "loss_cls": 4.13229, "loss": 4.13229, "time": 0.69991} +{"mode": "train", "epoch": 18, "iter": 2300, "lr": 0.09664, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25141, "top5_acc": 0.49797, "loss_cls": 4.1182, "loss": 4.1182, "time": 0.6986} +{"mode": "train", "epoch": 18, "iter": 2400, "lr": 0.09663, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25312, "top5_acc": 0.50078, "loss_cls": 4.08495, "loss": 4.08495, "time": 0.69811} +{"mode": "train", "epoch": 18, "iter": 2500, "lr": 0.09662, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26016, "top5_acc": 0.50125, "loss_cls": 4.0977, "loss": 4.0977, "time": 0.70009} +{"mode": "train", "epoch": 18, "iter": 2600, "lr": 0.09661, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26203, "top5_acc": 0.51438, "loss_cls": 4.05001, "loss": 4.05001, "time": 0.69701} +{"mode": "train", "epoch": 18, "iter": 2700, "lr": 0.0966, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25422, "top5_acc": 0.50391, "loss_cls": 4.10062, "loss": 4.10062, "time": 0.70289} +{"mode": "train", "epoch": 18, "iter": 2800, "lr": 0.09659, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25328, "top5_acc": 0.51656, "loss_cls": 4.037, "loss": 4.037, "time": 0.69638} +{"mode": "train", "epoch": 18, "iter": 2900, "lr": 0.09658, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26031, "top5_acc": 0.50453, "loss_cls": 4.088, "loss": 4.088, "time": 0.69897} +{"mode": "train", "epoch": 18, "iter": 3000, "lr": 0.09657, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24188, "top5_acc": 0.485, "loss_cls": 4.1578, "loss": 4.1578, "time": 0.6988} +{"mode": "train", "epoch": 18, "iter": 3100, "lr": 0.09656, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26391, "top5_acc": 0.51281, "loss_cls": 4.05859, "loss": 4.05859, "time": 0.69803} +{"mode": "train", "epoch": 18, "iter": 3200, "lr": 0.09654, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24922, "top5_acc": 0.49391, "loss_cls": 4.09899, "loss": 4.09899, "time": 0.69675} +{"mode": "train", "epoch": 18, "iter": 3300, "lr": 0.09653, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24844, "top5_acc": 0.49891, "loss_cls": 4.1195, "loss": 4.1195, "time": 0.69939} +{"mode": "train", "epoch": 18, "iter": 3400, "lr": 0.09652, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25672, "top5_acc": 0.50516, "loss_cls": 4.09805, "loss": 4.09805, "time": 0.69893} +{"mode": "train", "epoch": 18, "iter": 3500, "lr": 0.09651, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26203, "top5_acc": 0.50359, "loss_cls": 4.08059, "loss": 4.08059, "time": 0.69955} +{"mode": "train", "epoch": 18, "iter": 3600, "lr": 0.0965, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24203, "top5_acc": 0.49641, "loss_cls": 4.12822, "loss": 4.12822, "time": 0.6999} +{"mode": "train", "epoch": 18, "iter": 3700, "lr": 0.09649, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25797, "top5_acc": 0.50656, "loss_cls": 4.08821, "loss": 4.08821, "time": 0.69881} +{"mode": "val", "epoch": 18, "iter": 309, "lr": 0.09649, "top1_acc": 0.19394, "top5_acc": 0.42435, "mean_class_accuracy": 0.19366} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.09648, "memory": 15990, "data_time": 1.28105, "top1_acc": 0.26234, "top5_acc": 0.51578, "loss_cls": 3.99672, "loss": 3.99672, "time": 1.98594} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.09647, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26016, "top5_acc": 0.50375, "loss_cls": 4.03858, "loss": 4.03858, "time": 0.71249} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.09646, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26828, "top5_acc": 0.51297, "loss_cls": 4.02787, "loss": 4.02787, "time": 0.70385} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.09645, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25156, "top5_acc": 0.49859, "loss_cls": 4.0995, "loss": 4.0995, "time": 0.70597} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.09644, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25328, "top5_acc": 0.50578, "loss_cls": 4.06089, "loss": 4.06089, "time": 0.69994} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.09643, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25438, "top5_acc": 0.49828, "loss_cls": 4.114, "loss": 4.114, "time": 0.70481} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.09642, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26203, "top5_acc": 0.49859, "loss_cls": 4.07373, "loss": 4.07373, "time": 0.70938} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.09641, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25234, "top5_acc": 0.50391, "loss_cls": 4.06418, "loss": 4.06418, "time": 0.70166} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.0964, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26875, "top5_acc": 0.50984, "loss_cls": 4.05355, "loss": 4.05355, "time": 0.7072} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.09639, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2575, "top5_acc": 0.50078, "loss_cls": 4.08423, "loss": 4.08423, "time": 0.70395} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.09637, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25281, "top5_acc": 0.50313, "loss_cls": 4.10122, "loss": 4.10122, "time": 0.70118} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.09636, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26094, "top5_acc": 0.50609, "loss_cls": 4.06393, "loss": 4.06393, "time": 0.69916} +{"mode": "train", "epoch": 19, "iter": 1300, "lr": 0.09635, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25969, "top5_acc": 0.50891, "loss_cls": 4.09285, "loss": 4.09285, "time": 0.70068} +{"mode": "train", "epoch": 19, "iter": 1400, "lr": 0.09634, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25453, "top5_acc": 0.50734, "loss_cls": 4.07792, "loss": 4.07792, "time": 0.69985} +{"mode": "train", "epoch": 19, "iter": 1500, "lr": 0.09633, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26109, "top5_acc": 0.5075, "loss_cls": 4.07187, "loss": 4.07187, "time": 0.70042} +{"mode": "train", "epoch": 19, "iter": 1600, "lr": 0.09632, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25812, "top5_acc": 0.51344, "loss_cls": 4.05086, "loss": 4.05086, "time": 0.69675} +{"mode": "train", "epoch": 19, "iter": 1700, "lr": 0.09631, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24516, "top5_acc": 0.4925, "loss_cls": 4.11339, "loss": 4.11339, "time": 0.69927} +{"mode": "train", "epoch": 19, "iter": 1800, "lr": 0.0963, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25516, "top5_acc": 0.5, "loss_cls": 4.08329, "loss": 4.08329, "time": 0.70364} +{"mode": "train", "epoch": 19, "iter": 1900, "lr": 0.09629, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26047, "top5_acc": 0.50516, "loss_cls": 4.07539, "loss": 4.07539, "time": 0.69847} +{"mode": "train", "epoch": 19, "iter": 2000, "lr": 0.09628, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26297, "top5_acc": 0.50187, "loss_cls": 4.07852, "loss": 4.07852, "time": 0.69693} +{"mode": "train", "epoch": 19, "iter": 2100, "lr": 0.09627, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25891, "top5_acc": 0.51313, "loss_cls": 4.04369, "loss": 4.04369, "time": 0.69913} +{"mode": "train", "epoch": 19, "iter": 2200, "lr": 0.09626, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25453, "top5_acc": 0.49891, "loss_cls": 4.08132, "loss": 4.08132, "time": 0.69928} +{"mode": "train", "epoch": 19, "iter": 2300, "lr": 0.09625, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26156, "top5_acc": 0.50891, "loss_cls": 4.06336, "loss": 4.06336, "time": 0.69871} +{"mode": "train", "epoch": 19, "iter": 2400, "lr": 0.09624, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25406, "top5_acc": 0.49797, "loss_cls": 4.11594, "loss": 4.11594, "time": 0.69757} +{"mode": "train", "epoch": 19, "iter": 2500, "lr": 0.09623, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26578, "top5_acc": 0.50422, "loss_cls": 4.10425, "loss": 4.10425, "time": 0.70367} +{"mode": "train", "epoch": 19, "iter": 2600, "lr": 0.09622, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25812, "top5_acc": 0.49812, "loss_cls": 4.11651, "loss": 4.11651, "time": 0.69905} +{"mode": "train", "epoch": 19, "iter": 2700, "lr": 0.09621, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25938, "top5_acc": 0.49688, "loss_cls": 4.11332, "loss": 4.11332, "time": 0.69989} +{"mode": "train", "epoch": 19, "iter": 2800, "lr": 0.0962, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26906, "top5_acc": 0.51266, "loss_cls": 4.01613, "loss": 4.01613, "time": 0.69771} +{"mode": "train", "epoch": 19, "iter": 2900, "lr": 0.09618, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25844, "top5_acc": 0.51516, "loss_cls": 4.06022, "loss": 4.06022, "time": 0.69802} +{"mode": "train", "epoch": 19, "iter": 3000, "lr": 0.09617, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26172, "top5_acc": 0.50844, "loss_cls": 4.03547, "loss": 4.03547, "time": 0.69712} +{"mode": "train", "epoch": 19, "iter": 3100, "lr": 0.09616, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24797, "top5_acc": 0.49609, "loss_cls": 4.12421, "loss": 4.12421, "time": 0.6989} +{"mode": "train", "epoch": 19, "iter": 3200, "lr": 0.09615, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.2575, "top5_acc": 0.50734, "loss_cls": 4.07541, "loss": 4.07541, "time": 0.69765} +{"mode": "train", "epoch": 19, "iter": 3300, "lr": 0.09614, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25203, "top5_acc": 0.50578, "loss_cls": 4.09974, "loss": 4.09974, "time": 0.69839} +{"mode": "train", "epoch": 19, "iter": 3400, "lr": 0.09613, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24516, "top5_acc": 0.495, "loss_cls": 4.13096, "loss": 4.13096, "time": 0.69563} +{"mode": "train", "epoch": 19, "iter": 3500, "lr": 0.09612, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26578, "top5_acc": 0.50656, "loss_cls": 4.03923, "loss": 4.03923, "time": 0.70006} +{"mode": "train", "epoch": 19, "iter": 3600, "lr": 0.09611, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25594, "top5_acc": 0.50141, "loss_cls": 4.11022, "loss": 4.11022, "time": 0.70154} +{"mode": "train", "epoch": 19, "iter": 3700, "lr": 0.0961, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26234, "top5_acc": 0.50609, "loss_cls": 4.0578, "loss": 4.0578, "time": 0.69793} +{"mode": "val", "epoch": 19, "iter": 309, "lr": 0.09609, "top1_acc": 0.19323, "top5_acc": 0.42121, "mean_class_accuracy": 0.19289} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.09608, "memory": 15990, "data_time": 1.25409, "top1_acc": 0.27297, "top5_acc": 0.51969, "loss_cls": 3.99269, "loss": 3.99269, "time": 1.95778} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.09607, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25797, "top5_acc": 0.49359, "loss_cls": 4.10393, "loss": 4.10393, "time": 0.70454} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.09606, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25266, "top5_acc": 0.50516, "loss_cls": 4.06774, "loss": 4.06774, "time": 0.70239} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.09605, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26203, "top5_acc": 0.50922, "loss_cls": 4.04426, "loss": 4.04426, "time": 0.70798} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.09604, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25172, "top5_acc": 0.50078, "loss_cls": 4.0911, "loss": 4.0911, "time": 0.70619} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.09603, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26609, "top5_acc": 0.51281, "loss_cls": 4.0236, "loss": 4.0236, "time": 0.70279} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.09602, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25734, "top5_acc": 0.50109, "loss_cls": 4.08124, "loss": 4.08124, "time": 0.70857} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.09601, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26047, "top5_acc": 0.51609, "loss_cls": 4.04115, "loss": 4.04115, "time": 0.70981} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.096, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26531, "top5_acc": 0.50859, "loss_cls": 4.04506, "loss": 4.04506, "time": 0.70416} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.09598, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25953, "top5_acc": 0.505, "loss_cls": 4.05142, "loss": 4.05142, "time": 0.70059} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.09597, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.245, "top5_acc": 0.49859, "loss_cls": 4.09794, "loss": 4.09794, "time": 0.70225} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.09596, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24938, "top5_acc": 0.48969, "loss_cls": 4.12518, "loss": 4.12518, "time": 0.70089} +{"mode": "train", "epoch": 20, "iter": 1300, "lr": 0.09595, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26625, "top5_acc": 0.52188, "loss_cls": 4.00923, "loss": 4.00923, "time": 0.69864} +{"mode": "train", "epoch": 20, "iter": 1400, "lr": 0.09594, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26516, "top5_acc": 0.505, "loss_cls": 4.0502, "loss": 4.0502, "time": 0.69804} +{"mode": "train", "epoch": 20, "iter": 1500, "lr": 0.09593, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25859, "top5_acc": 0.49547, "loss_cls": 4.08801, "loss": 4.08801, "time": 0.69927} +{"mode": "train", "epoch": 20, "iter": 1600, "lr": 0.09592, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26609, "top5_acc": 0.51281, "loss_cls": 4.02984, "loss": 4.02984, "time": 0.69834} +{"mode": "train", "epoch": 20, "iter": 1700, "lr": 0.09591, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24688, "top5_acc": 0.49422, "loss_cls": 4.15021, "loss": 4.15021, "time": 0.69824} +{"mode": "train", "epoch": 20, "iter": 1800, "lr": 0.0959, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25375, "top5_acc": 0.50766, "loss_cls": 4.07636, "loss": 4.07636, "time": 0.7036} +{"mode": "train", "epoch": 20, "iter": 1900, "lr": 0.09588, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25969, "top5_acc": 0.51625, "loss_cls": 4.03514, "loss": 4.03514, "time": 0.69863} +{"mode": "train", "epoch": 20, "iter": 2000, "lr": 0.09587, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26062, "top5_acc": 0.51438, "loss_cls": 4.03507, "loss": 4.03507, "time": 0.69968} +{"mode": "train", "epoch": 20, "iter": 2100, "lr": 0.09586, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26297, "top5_acc": 0.51094, "loss_cls": 4.05334, "loss": 4.05334, "time": 0.7025} +{"mode": "train", "epoch": 20, "iter": 2200, "lr": 0.09585, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26406, "top5_acc": 0.51734, "loss_cls": 4.02358, "loss": 4.02358, "time": 0.69831} +{"mode": "train", "epoch": 20, "iter": 2300, "lr": 0.09584, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26062, "top5_acc": 0.50688, "loss_cls": 4.07404, "loss": 4.07404, "time": 0.69907} +{"mode": "train", "epoch": 20, "iter": 2400, "lr": 0.09583, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25812, "top5_acc": 0.50406, "loss_cls": 4.07164, "loss": 4.07164, "time": 0.70008} +{"mode": "train", "epoch": 20, "iter": 2500, "lr": 0.09582, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24531, "top5_acc": 0.50062, "loss_cls": 4.1221, "loss": 4.1221, "time": 0.69876} +{"mode": "train", "epoch": 20, "iter": 2600, "lr": 0.09581, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25891, "top5_acc": 0.51172, "loss_cls": 4.04629, "loss": 4.04629, "time": 0.69971} +{"mode": "train", "epoch": 20, "iter": 2700, "lr": 0.0958, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25328, "top5_acc": 0.50609, "loss_cls": 4.07641, "loss": 4.07641, "time": 0.69853} +{"mode": "train", "epoch": 20, "iter": 2800, "lr": 0.09578, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2525, "top5_acc": 0.50672, "loss_cls": 4.07199, "loss": 4.07199, "time": 0.69884} +{"mode": "train", "epoch": 20, "iter": 2900, "lr": 0.09577, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26375, "top5_acc": 0.50219, "loss_cls": 4.09271, "loss": 4.09271, "time": 0.69758} +{"mode": "train", "epoch": 20, "iter": 3000, "lr": 0.09576, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25, "top5_acc": 0.50031, "loss_cls": 4.09446, "loss": 4.09446, "time": 0.69957} +{"mode": "train", "epoch": 20, "iter": 3100, "lr": 0.09575, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25797, "top5_acc": 0.51125, "loss_cls": 4.04303, "loss": 4.04303, "time": 0.6973} +{"mode": "train", "epoch": 20, "iter": 3200, "lr": 0.09574, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26047, "top5_acc": 0.50469, "loss_cls": 4.07621, "loss": 4.07621, "time": 0.70177} +{"mode": "train", "epoch": 20, "iter": 3300, "lr": 0.09573, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.255, "top5_acc": 0.5, "loss_cls": 4.09039, "loss": 4.09039, "time": 0.69757} +{"mode": "train", "epoch": 20, "iter": 3400, "lr": 0.09572, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25438, "top5_acc": 0.49922, "loss_cls": 4.08855, "loss": 4.08855, "time": 0.70057} +{"mode": "train", "epoch": 20, "iter": 3500, "lr": 0.09571, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25875, "top5_acc": 0.50156, "loss_cls": 4.06606, "loss": 4.06606, "time": 0.69701} +{"mode": "train", "epoch": 20, "iter": 3600, "lr": 0.09569, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25062, "top5_acc": 0.49953, "loss_cls": 4.0874, "loss": 4.0874, "time": 0.69734} +{"mode": "train", "epoch": 20, "iter": 3700, "lr": 0.09568, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25219, "top5_acc": 0.49766, "loss_cls": 4.09084, "loss": 4.09084, "time": 0.69796} +{"mode": "val", "epoch": 20, "iter": 309, "lr": 0.09568, "top1_acc": 0.18629, "top5_acc": 0.41513, "mean_class_accuracy": 0.18625} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.09567, "memory": 15990, "data_time": 1.25955, "top1_acc": 0.25906, "top5_acc": 0.50734, "loss_cls": 4.04764, "loss": 4.04764, "time": 1.96528} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.09565, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25516, "top5_acc": 0.50297, "loss_cls": 4.04248, "loss": 4.04248, "time": 0.70381} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.09564, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26219, "top5_acc": 0.51297, "loss_cls": 4.01876, "loss": 4.01876, "time": 0.70174} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.09563, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26125, "top5_acc": 0.50406, "loss_cls": 4.09354, "loss": 4.09354, "time": 0.70884} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.09562, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25422, "top5_acc": 0.50047, "loss_cls": 4.08332, "loss": 4.08332, "time": 0.70358} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.09561, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25406, "top5_acc": 0.50641, "loss_cls": 4.05915, "loss": 4.05915, "time": 0.69983} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.0956, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26656, "top5_acc": 0.50594, "loss_cls": 4.07317, "loss": 4.07317, "time": 0.71134} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.09559, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25781, "top5_acc": 0.50984, "loss_cls": 4.05927, "loss": 4.05927, "time": 0.70104} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.09557, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26266, "top5_acc": 0.50766, "loss_cls": 4.02761, "loss": 4.02761, "time": 0.70223} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.09556, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26078, "top5_acc": 0.50594, "loss_cls": 4.03931, "loss": 4.03931, "time": 0.70188} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.09555, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26422, "top5_acc": 0.51141, "loss_cls": 4.04374, "loss": 4.04374, "time": 0.6987} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.09554, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.2575, "top5_acc": 0.50828, "loss_cls": 4.07243, "loss": 4.07243, "time": 0.70076} +{"mode": "train", "epoch": 21, "iter": 1300, "lr": 0.09553, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27, "top5_acc": 0.51703, "loss_cls": 4.04649, "loss": 4.04649, "time": 0.69727} +{"mode": "train", "epoch": 21, "iter": 1400, "lr": 0.09552, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25312, "top5_acc": 0.49906, "loss_cls": 4.0688, "loss": 4.0688, "time": 0.69673} +{"mode": "train", "epoch": 21, "iter": 1500, "lr": 0.09551, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25266, "top5_acc": 0.49172, "loss_cls": 4.13114, "loss": 4.13114, "time": 0.69943} +{"mode": "train", "epoch": 21, "iter": 1600, "lr": 0.09549, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26906, "top5_acc": 0.52062, "loss_cls": 4.01555, "loss": 4.01555, "time": 0.70174} +{"mode": "train", "epoch": 21, "iter": 1700, "lr": 0.09548, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25469, "top5_acc": 0.49969, "loss_cls": 4.0837, "loss": 4.0837, "time": 0.69743} +{"mode": "train", "epoch": 21, "iter": 1800, "lr": 0.09547, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25422, "top5_acc": 0.51562, "loss_cls": 4.05019, "loss": 4.05019, "time": 0.69787} +{"mode": "train", "epoch": 21, "iter": 1900, "lr": 0.09546, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26328, "top5_acc": 0.51156, "loss_cls": 4.0533, "loss": 4.0533, "time": 0.70017} +{"mode": "train", "epoch": 21, "iter": 2000, "lr": 0.09545, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25953, "top5_acc": 0.50688, "loss_cls": 4.03805, "loss": 4.03805, "time": 0.6965} +{"mode": "train", "epoch": 21, "iter": 2100, "lr": 0.09544, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25, "top5_acc": 0.51438, "loss_cls": 4.06775, "loss": 4.06775, "time": 0.69597} +{"mode": "train", "epoch": 21, "iter": 2200, "lr": 0.09542, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24891, "top5_acc": 0.50203, "loss_cls": 4.09638, "loss": 4.09638, "time": 0.70291} +{"mode": "train", "epoch": 21, "iter": 2300, "lr": 0.09541, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26609, "top5_acc": 0.51328, "loss_cls": 4.04311, "loss": 4.04311, "time": 0.69851} +{"mode": "train", "epoch": 21, "iter": 2400, "lr": 0.0954, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26156, "top5_acc": 0.51188, "loss_cls": 4.03802, "loss": 4.03802, "time": 0.69996} +{"mode": "train", "epoch": 21, "iter": 2500, "lr": 0.09539, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25547, "top5_acc": 0.50266, "loss_cls": 4.08139, "loss": 4.08139, "time": 0.70083} +{"mode": "train", "epoch": 21, "iter": 2600, "lr": 0.09538, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26266, "top5_acc": 0.51281, "loss_cls": 4.03881, "loss": 4.03881, "time": 0.69809} +{"mode": "train", "epoch": 21, "iter": 2700, "lr": 0.09537, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26375, "top5_acc": 0.51047, "loss_cls": 4.04397, "loss": 4.04397, "time": 0.69622} +{"mode": "train", "epoch": 21, "iter": 2800, "lr": 0.09535, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25625, "top5_acc": 0.50953, "loss_cls": 4.07532, "loss": 4.07532, "time": 0.69823} +{"mode": "train", "epoch": 21, "iter": 2900, "lr": 0.09534, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26094, "top5_acc": 0.50219, "loss_cls": 4.07485, "loss": 4.07485, "time": 0.70018} +{"mode": "train", "epoch": 21, "iter": 3000, "lr": 0.09533, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25703, "top5_acc": 0.50109, "loss_cls": 4.0736, "loss": 4.0736, "time": 0.69876} +{"mode": "train", "epoch": 21, "iter": 3100, "lr": 0.09532, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26672, "top5_acc": 0.51797, "loss_cls": 4.02878, "loss": 4.02878, "time": 0.69784} +{"mode": "train", "epoch": 21, "iter": 3200, "lr": 0.09531, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25938, "top5_acc": 0.51094, "loss_cls": 4.07822, "loss": 4.07822, "time": 0.69951} +{"mode": "train", "epoch": 21, "iter": 3300, "lr": 0.09529, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25844, "top5_acc": 0.51484, "loss_cls": 4.04607, "loss": 4.04607, "time": 0.69935} +{"mode": "train", "epoch": 21, "iter": 3400, "lr": 0.09528, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25906, "top5_acc": 0.49828, "loss_cls": 4.09165, "loss": 4.09165, "time": 0.69871} +{"mode": "train", "epoch": 21, "iter": 3500, "lr": 0.09527, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25266, "top5_acc": 0.51156, "loss_cls": 4.06271, "loss": 4.06271, "time": 0.69953} +{"mode": "train", "epoch": 21, "iter": 3600, "lr": 0.09526, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26734, "top5_acc": 0.50641, "loss_cls": 4.05811, "loss": 4.05811, "time": 0.69714} +{"mode": "train", "epoch": 21, "iter": 3700, "lr": 0.09525, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25969, "top5_acc": 0.51313, "loss_cls": 4.0552, "loss": 4.0552, "time": 0.69807} +{"mode": "val", "epoch": 21, "iter": 309, "lr": 0.09524, "top1_acc": 0.16796, "top5_acc": 0.37304, "mean_class_accuracy": 0.16767} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.09523, "memory": 15990, "data_time": 1.26398, "top1_acc": 0.26594, "top5_acc": 0.51219, "loss_cls": 4.02829, "loss": 4.02829, "time": 1.97034} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.09522, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25953, "top5_acc": 0.50891, "loss_cls": 4.06677, "loss": 4.06677, "time": 0.70627} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.09521, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26641, "top5_acc": 0.525, "loss_cls": 3.99658, "loss": 3.99658, "time": 0.70129} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.09519, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24781, "top5_acc": 0.5, "loss_cls": 4.0918, "loss": 4.0918, "time": 0.70527} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.09518, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26047, "top5_acc": 0.50422, "loss_cls": 4.06922, "loss": 4.06922, "time": 0.69945} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.09517, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26219, "top5_acc": 0.50094, "loss_cls": 4.05719, "loss": 4.05719, "time": 0.70095} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.09516, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26172, "top5_acc": 0.50766, "loss_cls": 4.04622, "loss": 4.04622, "time": 0.71385} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.09515, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26797, "top5_acc": 0.51625, "loss_cls": 4.03749, "loss": 4.03749, "time": 0.70611} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.09513, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25625, "top5_acc": 0.50359, "loss_cls": 4.06987, "loss": 4.06987, "time": 0.70655} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.09512, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26344, "top5_acc": 0.51375, "loss_cls": 4.05265, "loss": 4.05265, "time": 0.70593} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.09511, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25406, "top5_acc": 0.50313, "loss_cls": 4.10564, "loss": 4.10564, "time": 0.70294} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0951, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26047, "top5_acc": 0.51344, "loss_cls": 4.06021, "loss": 4.06021, "time": 0.70007} +{"mode": "train", "epoch": 22, "iter": 1300, "lr": 0.09509, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25406, "top5_acc": 0.50484, "loss_cls": 4.06944, "loss": 4.06944, "time": 0.69862} +{"mode": "train", "epoch": 22, "iter": 1400, "lr": 0.09507, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26656, "top5_acc": 0.50797, "loss_cls": 4.04391, "loss": 4.04391, "time": 0.69916} +{"mode": "train", "epoch": 22, "iter": 1500, "lr": 0.09506, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26438, "top5_acc": 0.51, "loss_cls": 4.03087, "loss": 4.03087, "time": 0.69784} +{"mode": "train", "epoch": 22, "iter": 1600, "lr": 0.09505, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26328, "top5_acc": 0.50828, "loss_cls": 4.05613, "loss": 4.05613, "time": 0.70107} +{"mode": "train", "epoch": 22, "iter": 1700, "lr": 0.09504, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26422, "top5_acc": 0.51234, "loss_cls": 4.02725, "loss": 4.02725, "time": 0.69724} +{"mode": "train", "epoch": 22, "iter": 1800, "lr": 0.09502, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25672, "top5_acc": 0.50547, "loss_cls": 4.05584, "loss": 4.05584, "time": 0.70034} +{"mode": "train", "epoch": 22, "iter": 1900, "lr": 0.09501, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26344, "top5_acc": 0.51016, "loss_cls": 4.05865, "loss": 4.05865, "time": 0.697} +{"mode": "train", "epoch": 22, "iter": 2000, "lr": 0.095, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26672, "top5_acc": 0.51578, "loss_cls": 4.02856, "loss": 4.02856, "time": 0.69976} +{"mode": "train", "epoch": 22, "iter": 2100, "lr": 0.09499, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26062, "top5_acc": 0.50922, "loss_cls": 4.02689, "loss": 4.02689, "time": 0.69981} +{"mode": "train", "epoch": 22, "iter": 2200, "lr": 0.09498, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25453, "top5_acc": 0.50797, "loss_cls": 4.06815, "loss": 4.06815, "time": 0.70074} +{"mode": "train", "epoch": 22, "iter": 2300, "lr": 0.09496, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25641, "top5_acc": 0.50703, "loss_cls": 4.07899, "loss": 4.07899, "time": 0.69687} +{"mode": "train", "epoch": 22, "iter": 2400, "lr": 0.09495, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26391, "top5_acc": 0.49812, "loss_cls": 4.07162, "loss": 4.07162, "time": 0.70017} +{"mode": "train", "epoch": 22, "iter": 2500, "lr": 0.09494, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26359, "top5_acc": 0.50406, "loss_cls": 4.08375, "loss": 4.08375, "time": 0.69849} +{"mode": "train", "epoch": 22, "iter": 2600, "lr": 0.09493, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26203, "top5_acc": 0.51172, "loss_cls": 4.03854, "loss": 4.03854, "time": 0.7035} +{"mode": "train", "epoch": 22, "iter": 2700, "lr": 0.09491, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25984, "top5_acc": 0.4975, "loss_cls": 4.12156, "loss": 4.12156, "time": 0.70199} +{"mode": "train", "epoch": 22, "iter": 2800, "lr": 0.0949, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26172, "top5_acc": 0.51375, "loss_cls": 4.05308, "loss": 4.05308, "time": 0.70006} +{"mode": "train", "epoch": 22, "iter": 2900, "lr": 0.09489, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26578, "top5_acc": 0.51328, "loss_cls": 4.01079, "loss": 4.01079, "time": 0.7} +{"mode": "train", "epoch": 22, "iter": 3000, "lr": 0.09488, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25953, "top5_acc": 0.5125, "loss_cls": 4.04585, "loss": 4.04585, "time": 0.69834} +{"mode": "train", "epoch": 22, "iter": 3100, "lr": 0.09487, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25859, "top5_acc": 0.5125, "loss_cls": 4.04695, "loss": 4.04695, "time": 0.69861} +{"mode": "train", "epoch": 22, "iter": 3200, "lr": 0.09485, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25219, "top5_acc": 0.50906, "loss_cls": 4.07017, "loss": 4.07017, "time": 0.70178} +{"mode": "train", "epoch": 22, "iter": 3300, "lr": 0.09484, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26562, "top5_acc": 0.51094, "loss_cls": 4.05524, "loss": 4.05524, "time": 0.69805} +{"mode": "train", "epoch": 22, "iter": 3400, "lr": 0.09483, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.2725, "top5_acc": 0.52, "loss_cls": 4.01921, "loss": 4.01921, "time": 0.69836} +{"mode": "train", "epoch": 22, "iter": 3500, "lr": 0.09482, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26609, "top5_acc": 0.51141, "loss_cls": 4.03994, "loss": 4.03994, "time": 0.69845} +{"mode": "train", "epoch": 22, "iter": 3600, "lr": 0.0948, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26203, "top5_acc": 0.51484, "loss_cls": 4.02525, "loss": 4.02525, "time": 0.69673} +{"mode": "train", "epoch": 22, "iter": 3700, "lr": 0.09479, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26906, "top5_acc": 0.50906, "loss_cls": 4.03, "loss": 4.03, "time": 0.69695} +{"mode": "val", "epoch": 22, "iter": 309, "lr": 0.09479, "top1_acc": 0.17844, "top5_acc": 0.41053, "mean_class_accuracy": 0.17822} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.09477, "memory": 15990, "data_time": 1.2787, "top1_acc": 0.27141, "top5_acc": 0.52031, "loss_cls": 3.98583, "loss": 3.98583, "time": 1.98804} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.09476, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26406, "top5_acc": 0.50953, "loss_cls": 4.01877, "loss": 4.01877, "time": 0.70714} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.09475, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26578, "top5_acc": 0.50531, "loss_cls": 4.02674, "loss": 4.02674, "time": 0.70148} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.09474, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25906, "top5_acc": 0.49469, "loss_cls": 4.07156, "loss": 4.07156, "time": 0.70406} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.09472, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26203, "top5_acc": 0.51391, "loss_cls": 4.02776, "loss": 4.02776, "time": 0.70435} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.09471, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26531, "top5_acc": 0.51484, "loss_cls": 4.03757, "loss": 4.03757, "time": 0.70357} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.0947, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25891, "top5_acc": 0.50922, "loss_cls": 4.04106, "loss": 4.04106, "time": 0.71436} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.09469, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26156, "top5_acc": 0.51125, "loss_cls": 4.04311, "loss": 4.04311, "time": 0.70289} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.09467, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26578, "top5_acc": 0.51281, "loss_cls": 4.0051, "loss": 4.0051, "time": 0.70387} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.09466, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25766, "top5_acc": 0.50156, "loss_cls": 4.08239, "loss": 4.08239, "time": 0.70271} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.09465, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26344, "top5_acc": 0.51141, "loss_cls": 4.03455, "loss": 4.03455, "time": 0.70512} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.09464, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25844, "top5_acc": 0.5075, "loss_cls": 4.06385, "loss": 4.06385, "time": 0.70168} +{"mode": "train", "epoch": 23, "iter": 1300, "lr": 0.09462, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26297, "top5_acc": 0.51703, "loss_cls": 4.00478, "loss": 4.00478, "time": 0.70123} +{"mode": "train", "epoch": 23, "iter": 1400, "lr": 0.09461, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25938, "top5_acc": 0.50828, "loss_cls": 4.08034, "loss": 4.08034, "time": 0.69803} +{"mode": "train", "epoch": 23, "iter": 1500, "lr": 0.0946, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25828, "top5_acc": 0.50922, "loss_cls": 4.07329, "loss": 4.07329, "time": 0.70081} +{"mode": "train", "epoch": 23, "iter": 1600, "lr": 0.09459, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26875, "top5_acc": 0.50422, "loss_cls": 4.0056, "loss": 4.0056, "time": 0.70083} +{"mode": "train", "epoch": 23, "iter": 1700, "lr": 0.09457, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26328, "top5_acc": 0.51219, "loss_cls": 4.04571, "loss": 4.04571, "time": 0.69963} +{"mode": "train", "epoch": 23, "iter": 1800, "lr": 0.09456, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26359, "top5_acc": 0.50438, "loss_cls": 4.06121, "loss": 4.06121, "time": 0.69768} +{"mode": "train", "epoch": 23, "iter": 1900, "lr": 0.09455, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26078, "top5_acc": 0.51016, "loss_cls": 4.01466, "loss": 4.01466, "time": 0.69815} +{"mode": "train", "epoch": 23, "iter": 2000, "lr": 0.09453, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26281, "top5_acc": 0.50688, "loss_cls": 4.07947, "loss": 4.07947, "time": 0.69925} +{"mode": "train", "epoch": 23, "iter": 2100, "lr": 0.09452, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25547, "top5_acc": 0.50828, "loss_cls": 4.07689, "loss": 4.07689, "time": 0.69886} +{"mode": "train", "epoch": 23, "iter": 2200, "lr": 0.09451, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25719, "top5_acc": 0.51047, "loss_cls": 4.06631, "loss": 4.06631, "time": 0.7011} +{"mode": "train", "epoch": 23, "iter": 2300, "lr": 0.0945, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.2575, "top5_acc": 0.50484, "loss_cls": 4.08549, "loss": 4.08549, "time": 0.6981} +{"mode": "train", "epoch": 23, "iter": 2400, "lr": 0.09448, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25594, "top5_acc": 0.50359, "loss_cls": 4.08145, "loss": 4.08145, "time": 0.69857} +{"mode": "train", "epoch": 23, "iter": 2500, "lr": 0.09447, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26406, "top5_acc": 0.51297, "loss_cls": 4.04806, "loss": 4.04806, "time": 0.69768} +{"mode": "train", "epoch": 23, "iter": 2600, "lr": 0.09446, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2525, "top5_acc": 0.50125, "loss_cls": 4.07484, "loss": 4.07484, "time": 0.69781} +{"mode": "train", "epoch": 23, "iter": 2700, "lr": 0.09445, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25125, "top5_acc": 0.50219, "loss_cls": 4.04976, "loss": 4.04976, "time": 0.69928} +{"mode": "train", "epoch": 23, "iter": 2800, "lr": 0.09443, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26203, "top5_acc": 0.51594, "loss_cls": 4.03135, "loss": 4.03135, "time": 0.69746} +{"mode": "train", "epoch": 23, "iter": 2900, "lr": 0.09442, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26188, "top5_acc": 0.515, "loss_cls": 4.03674, "loss": 4.03674, "time": 0.69863} +{"mode": "train", "epoch": 23, "iter": 3000, "lr": 0.09441, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26937, "top5_acc": 0.52, "loss_cls": 4.00271, "loss": 4.00271, "time": 0.69931} +{"mode": "train", "epoch": 23, "iter": 3100, "lr": 0.09439, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26266, "top5_acc": 0.51297, "loss_cls": 4.06712, "loss": 4.06712, "time": 0.69671} +{"mode": "train", "epoch": 23, "iter": 3200, "lr": 0.09438, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26422, "top5_acc": 0.51422, "loss_cls": 4.04163, "loss": 4.04163, "time": 0.70002} +{"mode": "train", "epoch": 23, "iter": 3300, "lr": 0.09437, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24719, "top5_acc": 0.49312, "loss_cls": 4.12387, "loss": 4.12387, "time": 0.69725} +{"mode": "train", "epoch": 23, "iter": 3400, "lr": 0.09436, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26891, "top5_acc": 0.52234, "loss_cls": 4.02178, "loss": 4.02178, "time": 0.69791} +{"mode": "train", "epoch": 23, "iter": 3500, "lr": 0.09434, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25312, "top5_acc": 0.49938, "loss_cls": 4.07491, "loss": 4.07491, "time": 0.6996} +{"mode": "train", "epoch": 23, "iter": 3600, "lr": 0.09433, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25562, "top5_acc": 0.50047, "loss_cls": 4.06997, "loss": 4.06997, "time": 0.6968} +{"mode": "train", "epoch": 23, "iter": 3700, "lr": 0.09432, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25891, "top5_acc": 0.51156, "loss_cls": 4.03458, "loss": 4.03458, "time": 0.70132} +{"mode": "val", "epoch": 23, "iter": 309, "lr": 0.09431, "top1_acc": 0.19607, "top5_acc": 0.42841, "mean_class_accuracy": 0.19588} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.0943, "memory": 15990, "data_time": 1.27702, "top1_acc": 0.27172, "top5_acc": 0.51438, "loss_cls": 4.00782, "loss": 4.00782, "time": 1.98383} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.09428, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27016, "top5_acc": 0.52516, "loss_cls": 3.9771, "loss": 3.9771, "time": 0.70989} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.09427, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26234, "top5_acc": 0.51484, "loss_cls": 4.02079, "loss": 4.02079, "time": 0.70134} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.09426, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27062, "top5_acc": 0.52641, "loss_cls": 4.00884, "loss": 4.00884, "time": 0.70899} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.09425, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25641, "top5_acc": 0.5075, "loss_cls": 4.05514, "loss": 4.05514, "time": 0.70116} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.09423, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26359, "top5_acc": 0.51859, "loss_cls": 4.01955, "loss": 4.01955, "time": 0.70049} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.09422, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26578, "top5_acc": 0.51797, "loss_cls": 4.00528, "loss": 4.00528, "time": 0.7054} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.09421, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25578, "top5_acc": 0.50875, "loss_cls": 4.0652, "loss": 4.0652, "time": 0.70581} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.09419, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25781, "top5_acc": 0.50625, "loss_cls": 4.07875, "loss": 4.07875, "time": 0.70191} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.09418, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25047, "top5_acc": 0.51125, "loss_cls": 4.06576, "loss": 4.06576, "time": 0.70498} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.09417, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25469, "top5_acc": 0.50844, "loss_cls": 4.0631, "loss": 4.0631, "time": 0.70155} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.09415, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26188, "top5_acc": 0.50375, "loss_cls": 4.06759, "loss": 4.06759, "time": 0.69756} +{"mode": "train", "epoch": 24, "iter": 1300, "lr": 0.09414, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26156, "top5_acc": 0.505, "loss_cls": 4.02429, "loss": 4.02429, "time": 0.70078} +{"mode": "train", "epoch": 24, "iter": 1400, "lr": 0.09413, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26438, "top5_acc": 0.51375, "loss_cls": 4.01151, "loss": 4.01151, "time": 0.70302} +{"mode": "train", "epoch": 24, "iter": 1500, "lr": 0.09411, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25875, "top5_acc": 0.50766, "loss_cls": 4.04891, "loss": 4.04891, "time": 0.70049} +{"mode": "train", "epoch": 24, "iter": 1600, "lr": 0.0941, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26797, "top5_acc": 0.50344, "loss_cls": 4.03995, "loss": 4.03995, "time": 0.7006} +{"mode": "train", "epoch": 24, "iter": 1700, "lr": 0.09409, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26328, "top5_acc": 0.50375, "loss_cls": 4.04198, "loss": 4.04198, "time": 0.70017} +{"mode": "train", "epoch": 24, "iter": 1800, "lr": 0.09407, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24672, "top5_acc": 0.49391, "loss_cls": 4.128, "loss": 4.128, "time": 0.69859} +{"mode": "train", "epoch": 24, "iter": 1900, "lr": 0.09406, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25984, "top5_acc": 0.50922, "loss_cls": 4.03263, "loss": 4.03263, "time": 0.69576} +{"mode": "train", "epoch": 24, "iter": 2000, "lr": 0.09405, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26359, "top5_acc": 0.50469, "loss_cls": 4.08776, "loss": 4.08776, "time": 0.70004} +{"mode": "train", "epoch": 24, "iter": 2100, "lr": 0.09404, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2575, "top5_acc": 0.51188, "loss_cls": 4.06468, "loss": 4.06468, "time": 0.69704} +{"mode": "train", "epoch": 24, "iter": 2200, "lr": 0.09402, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26875, "top5_acc": 0.51688, "loss_cls": 3.99908, "loss": 3.99908, "time": 0.70061} +{"mode": "train", "epoch": 24, "iter": 2300, "lr": 0.09401, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2675, "top5_acc": 0.51594, "loss_cls": 4.03675, "loss": 4.03675, "time": 0.69646} +{"mode": "train", "epoch": 24, "iter": 2400, "lr": 0.094, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26031, "top5_acc": 0.50484, "loss_cls": 4.07328, "loss": 4.07328, "time": 0.69726} +{"mode": "train", "epoch": 24, "iter": 2500, "lr": 0.09398, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26219, "top5_acc": 0.51141, "loss_cls": 4.03417, "loss": 4.03417, "time": 0.69864} +{"mode": "train", "epoch": 24, "iter": 2600, "lr": 0.09397, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25594, "top5_acc": 0.49578, "loss_cls": 4.09791, "loss": 4.09791, "time": 0.69636} +{"mode": "train", "epoch": 24, "iter": 2700, "lr": 0.09396, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26312, "top5_acc": 0.50703, "loss_cls": 4.041, "loss": 4.041, "time": 0.69876} +{"mode": "train", "epoch": 24, "iter": 2800, "lr": 0.09394, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25391, "top5_acc": 0.49828, "loss_cls": 4.08904, "loss": 4.08904, "time": 0.69668} +{"mode": "train", "epoch": 24, "iter": 2900, "lr": 0.09393, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25484, "top5_acc": 0.51156, "loss_cls": 4.06627, "loss": 4.06627, "time": 0.70004} +{"mode": "train", "epoch": 24, "iter": 3000, "lr": 0.09392, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25156, "top5_acc": 0.50297, "loss_cls": 4.07722, "loss": 4.07722, "time": 0.69775} +{"mode": "train", "epoch": 24, "iter": 3100, "lr": 0.0939, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27016, "top5_acc": 0.53234, "loss_cls": 3.98776, "loss": 3.98776, "time": 0.69841} +{"mode": "train", "epoch": 24, "iter": 3200, "lr": 0.09389, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25828, "top5_acc": 0.50734, "loss_cls": 4.06235, "loss": 4.06235, "time": 0.70004} +{"mode": "train", "epoch": 24, "iter": 3300, "lr": 0.09388, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26156, "top5_acc": 0.51219, "loss_cls": 4.05115, "loss": 4.05115, "time": 0.69842} +{"mode": "train", "epoch": 24, "iter": 3400, "lr": 0.09386, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25891, "top5_acc": 0.50297, "loss_cls": 4.06544, "loss": 4.06544, "time": 0.69735} +{"mode": "train", "epoch": 24, "iter": 3500, "lr": 0.09385, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26422, "top5_acc": 0.50938, "loss_cls": 4.03054, "loss": 4.03054, "time": 0.69874} +{"mode": "train", "epoch": 24, "iter": 3600, "lr": 0.09384, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26734, "top5_acc": 0.51047, "loss_cls": 4.06136, "loss": 4.06136, "time": 0.69625} +{"mode": "train", "epoch": 24, "iter": 3700, "lr": 0.09382, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26297, "top5_acc": 0.51125, "loss_cls": 4.03505, "loss": 4.03505, "time": 0.69917} +{"mode": "val", "epoch": 24, "iter": 309, "lr": 0.09382, "top1_acc": 0.19713, "top5_acc": 0.42572, "mean_class_accuracy": 0.19715} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.0938, "memory": 15990, "data_time": 1.27921, "top1_acc": 0.27391, "top5_acc": 0.51953, "loss_cls": 3.99879, "loss": 3.99879, "time": 1.98064} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.09379, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26828, "top5_acc": 0.52422, "loss_cls": 3.97667, "loss": 3.97667, "time": 0.71028} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.09378, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26734, "top5_acc": 0.51359, "loss_cls": 4.02966, "loss": 4.02966, "time": 0.70476} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.09376, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27047, "top5_acc": 0.50766, "loss_cls": 4.02137, "loss": 4.02137, "time": 0.70868} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.09375, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25844, "top5_acc": 0.51484, "loss_cls": 4.04823, "loss": 4.04823, "time": 0.70283} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.09373, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25953, "top5_acc": 0.51812, "loss_cls": 4.0277, "loss": 4.0277, "time": 0.70039} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.09372, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26375, "top5_acc": 0.51281, "loss_cls": 4.01229, "loss": 4.01229, "time": 0.71069} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.09371, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26031, "top5_acc": 0.51625, "loss_cls": 4.0478, "loss": 4.0478, "time": 0.70577} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.09369, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26594, "top5_acc": 0.51172, "loss_cls": 4.03822, "loss": 4.03822, "time": 0.70571} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.09368, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26734, "top5_acc": 0.50906, "loss_cls": 4.02757, "loss": 4.02757, "time": 0.70683} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.09367, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27531, "top5_acc": 0.52172, "loss_cls": 3.98077, "loss": 3.98077, "time": 0.70014} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.09365, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25797, "top5_acc": 0.51547, "loss_cls": 4.04531, "loss": 4.04531, "time": 0.69994} +{"mode": "train", "epoch": 25, "iter": 1300, "lr": 0.09364, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26391, "top5_acc": 0.51125, "loss_cls": 4.06477, "loss": 4.06477, "time": 0.70033} +{"mode": "train", "epoch": 25, "iter": 1400, "lr": 0.09363, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25938, "top5_acc": 0.51047, "loss_cls": 4.04222, "loss": 4.04222, "time": 0.69899} +{"mode": "train", "epoch": 25, "iter": 1500, "lr": 0.09361, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25906, "top5_acc": 0.50953, "loss_cls": 4.0515, "loss": 4.0515, "time": 0.6994} +{"mode": "train", "epoch": 25, "iter": 1600, "lr": 0.0936, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25469, "top5_acc": 0.51453, "loss_cls": 4.04334, "loss": 4.04334, "time": 0.69945} +{"mode": "train", "epoch": 25, "iter": 1700, "lr": 0.09358, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25906, "top5_acc": 0.51016, "loss_cls": 4.05817, "loss": 4.05817, "time": 0.69788} +{"mode": "train", "epoch": 25, "iter": 1800, "lr": 0.09357, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26062, "top5_acc": 0.50297, "loss_cls": 4.07637, "loss": 4.07637, "time": 0.69692} +{"mode": "train", "epoch": 25, "iter": 1900, "lr": 0.09356, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26562, "top5_acc": 0.51844, "loss_cls": 4.01946, "loss": 4.01946, "time": 0.70004} +{"mode": "train", "epoch": 25, "iter": 2000, "lr": 0.09354, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26609, "top5_acc": 0.52, "loss_cls": 4.00385, "loss": 4.00385, "time": 0.70038} +{"mode": "train", "epoch": 25, "iter": 2100, "lr": 0.09353, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26047, "top5_acc": 0.50797, "loss_cls": 4.03168, "loss": 4.03168, "time": 0.69848} +{"mode": "train", "epoch": 25, "iter": 2200, "lr": 0.09352, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25391, "top5_acc": 0.51141, "loss_cls": 4.06488, "loss": 4.06488, "time": 0.69878} +{"mode": "train", "epoch": 25, "iter": 2300, "lr": 0.0935, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25781, "top5_acc": 0.50172, "loss_cls": 4.08054, "loss": 4.08054, "time": 0.70093} +{"mode": "train", "epoch": 25, "iter": 2400, "lr": 0.09349, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25938, "top5_acc": 0.505, "loss_cls": 4.07601, "loss": 4.07601, "time": 0.69751} +{"mode": "train", "epoch": 25, "iter": 2500, "lr": 0.09347, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26297, "top5_acc": 0.51266, "loss_cls": 4.02577, "loss": 4.02577, "time": 0.69914} +{"mode": "train", "epoch": 25, "iter": 2600, "lr": 0.09346, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26719, "top5_acc": 0.50953, "loss_cls": 4.03761, "loss": 4.03761, "time": 0.70023} +{"mode": "train", "epoch": 25, "iter": 2700, "lr": 0.09345, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26422, "top5_acc": 0.51266, "loss_cls": 4.00156, "loss": 4.00156, "time": 0.69895} +{"mode": "train", "epoch": 25, "iter": 2800, "lr": 0.09343, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25922, "top5_acc": 0.50438, "loss_cls": 4.05358, "loss": 4.05358, "time": 0.69869} +{"mode": "train", "epoch": 25, "iter": 2900, "lr": 0.09342, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26328, "top5_acc": 0.51875, "loss_cls": 4.01627, "loss": 4.01627, "time": 0.69873} +{"mode": "train", "epoch": 25, "iter": 3000, "lr": 0.09341, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26203, "top5_acc": 0.50984, "loss_cls": 4.02769, "loss": 4.02769, "time": 0.69685} +{"mode": "train", "epoch": 25, "iter": 3100, "lr": 0.09339, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27187, "top5_acc": 0.515, "loss_cls": 4.01544, "loss": 4.01544, "time": 0.69863} +{"mode": "train", "epoch": 25, "iter": 3200, "lr": 0.09338, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25641, "top5_acc": 0.51141, "loss_cls": 4.05635, "loss": 4.05635, "time": 0.69797} +{"mode": "train", "epoch": 25, "iter": 3300, "lr": 0.09336, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25906, "top5_acc": 0.51562, "loss_cls": 4.04642, "loss": 4.04642, "time": 0.69885} +{"mode": "train", "epoch": 25, "iter": 3400, "lr": 0.09335, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25062, "top5_acc": 0.50562, "loss_cls": 4.07486, "loss": 4.07486, "time": 0.70012} +{"mode": "train", "epoch": 25, "iter": 3500, "lr": 0.09334, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26578, "top5_acc": 0.51578, "loss_cls": 4.02732, "loss": 4.02732, "time": 0.69759} +{"mode": "train", "epoch": 25, "iter": 3600, "lr": 0.09332, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26484, "top5_acc": 0.50922, "loss_cls": 4.05104, "loss": 4.05104, "time": 0.69826} +{"mode": "train", "epoch": 25, "iter": 3700, "lr": 0.09331, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26516, "top5_acc": 0.50469, "loss_cls": 4.02497, "loss": 4.02497, "time": 0.69786} +{"mode": "val", "epoch": 25, "iter": 309, "lr": 0.0933, "top1_acc": 0.19318, "top5_acc": 0.41154, "mean_class_accuracy": 0.19296} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.09329, "memory": 15990, "data_time": 1.28372, "top1_acc": 0.27031, "top5_acc": 0.52172, "loss_cls": 4.00671, "loss": 4.00671, "time": 1.98959} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.09327, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26969, "top5_acc": 0.52125, "loss_cls": 4.00194, "loss": 4.00194, "time": 0.70651} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.09326, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27656, "top5_acc": 0.52516, "loss_cls": 3.98548, "loss": 3.98548, "time": 0.70422} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.09325, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26, "top5_acc": 0.51156, "loss_cls": 4.0434, "loss": 4.0434, "time": 0.70608} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.09323, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26797, "top5_acc": 0.51109, "loss_cls": 4.01858, "loss": 4.01858, "time": 0.70667} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.09322, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26281, "top5_acc": 0.51016, "loss_cls": 4.04009, "loss": 4.04009, "time": 0.70227} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.0932, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24984, "top5_acc": 0.50187, "loss_cls": 4.08236, "loss": 4.08236, "time": 0.70596} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.09319, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26266, "top5_acc": 0.51734, "loss_cls": 4.00565, "loss": 4.00565, "time": 0.70532} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.09318, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26562, "top5_acc": 0.51453, "loss_cls": 4.02713, "loss": 4.02713, "time": 0.70291} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.09316, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26219, "top5_acc": 0.51109, "loss_cls": 4.04726, "loss": 4.04726, "time": 0.70489} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.09315, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26109, "top5_acc": 0.51219, "loss_cls": 4.04763, "loss": 4.04763, "time": 0.70138} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.09313, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26891, "top5_acc": 0.51453, "loss_cls": 4.01307, "loss": 4.01307, "time": 0.70074} +{"mode": "train", "epoch": 26, "iter": 1300, "lr": 0.09312, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2625, "top5_acc": 0.50672, "loss_cls": 4.05416, "loss": 4.05416, "time": 0.70238} +{"mode": "train", "epoch": 26, "iter": 1400, "lr": 0.0931, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27922, "top5_acc": 0.51922, "loss_cls": 3.99611, "loss": 3.99611, "time": 0.70041} +{"mode": "train", "epoch": 26, "iter": 1500, "lr": 0.09309, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25797, "top5_acc": 0.50922, "loss_cls": 4.0188, "loss": 4.0188, "time": 0.69965} +{"mode": "train", "epoch": 26, "iter": 1600, "lr": 0.09308, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26062, "top5_acc": 0.50938, "loss_cls": 4.03139, "loss": 4.03139, "time": 0.69953} +{"mode": "train", "epoch": 26, "iter": 1700, "lr": 0.09306, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26219, "top5_acc": 0.51953, "loss_cls": 4.01725, "loss": 4.01725, "time": 0.69856} +{"mode": "train", "epoch": 26, "iter": 1800, "lr": 0.09305, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26141, "top5_acc": 0.50734, "loss_cls": 4.06307, "loss": 4.06307, "time": 0.69924} +{"mode": "train", "epoch": 26, "iter": 1900, "lr": 0.09303, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25703, "top5_acc": 0.50531, "loss_cls": 4.06003, "loss": 4.06003, "time": 0.70051} +{"mode": "train", "epoch": 26, "iter": 2000, "lr": 0.09302, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26062, "top5_acc": 0.49891, "loss_cls": 4.06801, "loss": 4.06801, "time": 0.69906} +{"mode": "train", "epoch": 26, "iter": 2100, "lr": 0.093, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25906, "top5_acc": 0.51938, "loss_cls": 4.00563, "loss": 4.00563, "time": 0.69753} +{"mode": "train", "epoch": 26, "iter": 2200, "lr": 0.09299, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26906, "top5_acc": 0.51672, "loss_cls": 4.02615, "loss": 4.02615, "time": 0.69912} +{"mode": "train", "epoch": 26, "iter": 2300, "lr": 0.09298, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25547, "top5_acc": 0.50656, "loss_cls": 4.04371, "loss": 4.04371, "time": 0.70072} +{"mode": "train", "epoch": 26, "iter": 2400, "lr": 0.09296, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26344, "top5_acc": 0.51594, "loss_cls": 4.02728, "loss": 4.02728, "time": 0.69932} +{"mode": "train", "epoch": 26, "iter": 2500, "lr": 0.09295, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26281, "top5_acc": 0.50828, "loss_cls": 4.04614, "loss": 4.04614, "time": 0.69955} +{"mode": "train", "epoch": 26, "iter": 2600, "lr": 0.09293, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26094, "top5_acc": 0.50781, "loss_cls": 4.0723, "loss": 4.0723, "time": 0.69811} +{"mode": "train", "epoch": 26, "iter": 2700, "lr": 0.09292, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25781, "top5_acc": 0.52625, "loss_cls": 4.02081, "loss": 4.02081, "time": 0.69717} +{"mode": "train", "epoch": 26, "iter": 2800, "lr": 0.0929, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25641, "top5_acc": 0.50062, "loss_cls": 4.07162, "loss": 4.07162, "time": 0.70146} +{"mode": "train", "epoch": 26, "iter": 2900, "lr": 0.09289, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25844, "top5_acc": 0.50797, "loss_cls": 4.07336, "loss": 4.07336, "time": 0.69622} +{"mode": "train", "epoch": 26, "iter": 3000, "lr": 0.09288, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26687, "top5_acc": 0.51359, "loss_cls": 4.01816, "loss": 4.01816, "time": 0.70189} +{"mode": "train", "epoch": 26, "iter": 3100, "lr": 0.09286, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27844, "top5_acc": 0.52312, "loss_cls": 3.99481, "loss": 3.99481, "time": 0.69835} +{"mode": "train", "epoch": 26, "iter": 3200, "lr": 0.09285, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27047, "top5_acc": 0.50984, "loss_cls": 4.03184, "loss": 4.03184, "time": 0.69814} +{"mode": "train", "epoch": 26, "iter": 3300, "lr": 0.09283, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26797, "top5_acc": 0.51766, "loss_cls": 4.00596, "loss": 4.00596, "time": 0.70423} +{"mode": "train", "epoch": 26, "iter": 3400, "lr": 0.09282, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26625, "top5_acc": 0.51234, "loss_cls": 4.01076, "loss": 4.01076, "time": 0.69966} +{"mode": "train", "epoch": 26, "iter": 3500, "lr": 0.0928, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26375, "top5_acc": 0.51391, "loss_cls": 4.0309, "loss": 4.0309, "time": 0.69824} +{"mode": "train", "epoch": 26, "iter": 3600, "lr": 0.09279, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26703, "top5_acc": 0.51812, "loss_cls": 4.00813, "loss": 4.00813, "time": 0.69828} +{"mode": "train", "epoch": 26, "iter": 3700, "lr": 0.09278, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27531, "top5_acc": 0.52484, "loss_cls": 3.97318, "loss": 3.97318, "time": 0.70093} +{"mode": "val", "epoch": 26, "iter": 309, "lr": 0.09277, "top1_acc": 0.19526, "top5_acc": 0.42714, "mean_class_accuracy": 0.19509} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.09275, "memory": 15990, "data_time": 1.28055, "top1_acc": 0.27062, "top5_acc": 0.52438, "loss_cls": 3.98231, "loss": 3.98231, "time": 1.98433} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.09274, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26406, "top5_acc": 0.52766, "loss_cls": 4.00204, "loss": 4.00204, "time": 0.71138} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.09272, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26766, "top5_acc": 0.50734, "loss_cls": 4.03314, "loss": 4.03314, "time": 0.70375} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.09271, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27156, "top5_acc": 0.52344, "loss_cls": 3.98261, "loss": 3.98261, "time": 0.70468} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.0927, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25891, "top5_acc": 0.52344, "loss_cls": 3.98621, "loss": 3.98621, "time": 0.70296} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.09268, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25812, "top5_acc": 0.50562, "loss_cls": 4.06643, "loss": 4.06643, "time": 0.70079} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.09267, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27047, "top5_acc": 0.51156, "loss_cls": 4.04081, "loss": 4.04081, "time": 0.7003} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.09265, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27109, "top5_acc": 0.51453, "loss_cls": 4.0032, "loss": 4.0032, "time": 0.70784} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.09264, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25828, "top5_acc": 0.50641, "loss_cls": 4.06249, "loss": 4.06249, "time": 0.70413} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.09262, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26078, "top5_acc": 0.51297, "loss_cls": 4.02368, "loss": 4.02368, "time": 0.70474} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.09261, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25938, "top5_acc": 0.51172, "loss_cls": 4.04075, "loss": 4.04075, "time": 0.69976} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.09259, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27031, "top5_acc": 0.51594, "loss_cls": 4.01894, "loss": 4.01894, "time": 0.70203} +{"mode": "train", "epoch": 27, "iter": 1300, "lr": 0.09258, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27688, "top5_acc": 0.51531, "loss_cls": 3.99578, "loss": 3.99578, "time": 0.70074} +{"mode": "train", "epoch": 27, "iter": 1400, "lr": 0.09256, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26266, "top5_acc": 0.51516, "loss_cls": 4.0116, "loss": 4.0116, "time": 0.70063} +{"mode": "train", "epoch": 27, "iter": 1500, "lr": 0.09255, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27547, "top5_acc": 0.52094, "loss_cls": 3.98511, "loss": 3.98511, "time": 0.69959} +{"mode": "train", "epoch": 27, "iter": 1600, "lr": 0.09253, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26297, "top5_acc": 0.51703, "loss_cls": 4.04073, "loss": 4.04073, "time": 0.69854} +{"mode": "train", "epoch": 27, "iter": 1700, "lr": 0.09252, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26234, "top5_acc": 0.50891, "loss_cls": 4.03678, "loss": 4.03678, "time": 0.69818} +{"mode": "train", "epoch": 27, "iter": 1800, "lr": 0.09251, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26766, "top5_acc": 0.51594, "loss_cls": 4.01234, "loss": 4.01234, "time": 0.69849} +{"mode": "train", "epoch": 27, "iter": 1900, "lr": 0.09249, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26328, "top5_acc": 0.51031, "loss_cls": 4.03082, "loss": 4.03082, "time": 0.69792} +{"mode": "train", "epoch": 27, "iter": 2000, "lr": 0.09248, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26484, "top5_acc": 0.50953, "loss_cls": 4.02937, "loss": 4.02937, "time": 0.69743} +{"mode": "train", "epoch": 27, "iter": 2100, "lr": 0.09246, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25531, "top5_acc": 0.50906, "loss_cls": 4.05248, "loss": 4.05248, "time": 0.70014} +{"mode": "train", "epoch": 27, "iter": 2200, "lr": 0.09245, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26078, "top5_acc": 0.51047, "loss_cls": 4.04856, "loss": 4.04856, "time": 0.69895} +{"mode": "train", "epoch": 27, "iter": 2300, "lr": 0.09243, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25844, "top5_acc": 0.50781, "loss_cls": 4.0671, "loss": 4.0671, "time": 0.69623} +{"mode": "train", "epoch": 27, "iter": 2400, "lr": 0.09242, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26266, "top5_acc": 0.51438, "loss_cls": 4.02511, "loss": 4.02511, "time": 0.69736} +{"mode": "train", "epoch": 27, "iter": 2500, "lr": 0.0924, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26109, "top5_acc": 0.51594, "loss_cls": 4.0374, "loss": 4.0374, "time": 0.6991} +{"mode": "train", "epoch": 27, "iter": 2600, "lr": 0.09239, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27219, "top5_acc": 0.52188, "loss_cls": 4.00539, "loss": 4.00539, "time": 0.69855} +{"mode": "train", "epoch": 27, "iter": 2700, "lr": 0.09237, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25844, "top5_acc": 0.50828, "loss_cls": 4.06024, "loss": 4.06024, "time": 0.69897} +{"mode": "train", "epoch": 27, "iter": 2800, "lr": 0.09236, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26469, "top5_acc": 0.51156, "loss_cls": 4.05923, "loss": 4.05923, "time": 0.69786} +{"mode": "train", "epoch": 27, "iter": 2900, "lr": 0.09234, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27156, "top5_acc": 0.51906, "loss_cls": 4.00871, "loss": 4.00871, "time": 0.69737} +{"mode": "train", "epoch": 27, "iter": 3000, "lr": 0.09233, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27016, "top5_acc": 0.52438, "loss_cls": 4.00817, "loss": 4.00817, "time": 0.69776} +{"mode": "train", "epoch": 27, "iter": 3100, "lr": 0.09231, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26562, "top5_acc": 0.525, "loss_cls": 4.00495, "loss": 4.00495, "time": 0.69868} +{"mode": "train", "epoch": 27, "iter": 3200, "lr": 0.0923, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.27375, "top5_acc": 0.51703, "loss_cls": 3.99429, "loss": 3.99429, "time": 0.69903} +{"mode": "train", "epoch": 27, "iter": 3300, "lr": 0.09228, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26078, "top5_acc": 0.52078, "loss_cls": 4.03455, "loss": 4.03455, "time": 0.69687} +{"mode": "train", "epoch": 27, "iter": 3400, "lr": 0.09227, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26594, "top5_acc": 0.51438, "loss_cls": 4.03323, "loss": 4.03323, "time": 0.70022} +{"mode": "train", "epoch": 27, "iter": 3500, "lr": 0.09225, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26062, "top5_acc": 0.51375, "loss_cls": 4.03406, "loss": 4.03406, "time": 0.69821} +{"mode": "train", "epoch": 27, "iter": 3600, "lr": 0.09224, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25906, "top5_acc": 0.50109, "loss_cls": 4.08227, "loss": 4.08227, "time": 0.69813} +{"mode": "train", "epoch": 27, "iter": 3700, "lr": 0.09222, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26891, "top5_acc": 0.51297, "loss_cls": 4.04636, "loss": 4.04636, "time": 0.69802} +{"mode": "val", "epoch": 27, "iter": 309, "lr": 0.09222, "top1_acc": 0.20027, "top5_acc": 0.42952, "mean_class_accuracy": 0.20016} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.0922, "memory": 15990, "data_time": 1.27665, "top1_acc": 0.27234, "top5_acc": 0.52547, "loss_cls": 3.98051, "loss": 3.98051, "time": 1.98191} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.09219, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.275, "top5_acc": 0.52359, "loss_cls": 3.98928, "loss": 3.98928, "time": 0.70667} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.09217, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26359, "top5_acc": 0.52172, "loss_cls": 4.01557, "loss": 4.01557, "time": 0.70688} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.09216, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26344, "top5_acc": 0.50984, "loss_cls": 4.00803, "loss": 4.00803, "time": 0.71263} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.09214, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26172, "top5_acc": 0.51734, "loss_cls": 4.00262, "loss": 4.00262, "time": 0.70547} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.09213, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27203, "top5_acc": 0.51234, "loss_cls": 4.00423, "loss": 4.00423, "time": 0.70229} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.09211, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26625, "top5_acc": 0.51797, "loss_cls": 4.0333, "loss": 4.0333, "time": 0.69951} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.0921, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26203, "top5_acc": 0.51031, "loss_cls": 4.04571, "loss": 4.04571, "time": 0.70833} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.09208, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26656, "top5_acc": 0.51094, "loss_cls": 4.01367, "loss": 4.01367, "time": 0.70557} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.09207, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26922, "top5_acc": 0.51781, "loss_cls": 3.99474, "loss": 3.99474, "time": 0.70241} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.09205, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26844, "top5_acc": 0.51938, "loss_cls": 4.01932, "loss": 4.01932, "time": 0.70544} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.09204, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25609, "top5_acc": 0.51359, "loss_cls": 4.02808, "loss": 4.02808, "time": 0.70029} +{"mode": "train", "epoch": 28, "iter": 1300, "lr": 0.09202, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27172, "top5_acc": 0.52219, "loss_cls": 3.98124, "loss": 3.98124, "time": 0.69851} +{"mode": "train", "epoch": 28, "iter": 1400, "lr": 0.09201, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26656, "top5_acc": 0.51734, "loss_cls": 4.02092, "loss": 4.02092, "time": 0.69942} +{"mode": "train", "epoch": 28, "iter": 1500, "lr": 0.09199, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26359, "top5_acc": 0.515, "loss_cls": 4.04165, "loss": 4.04165, "time": 0.69592} +{"mode": "train", "epoch": 28, "iter": 1600, "lr": 0.09198, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.265, "top5_acc": 0.51688, "loss_cls": 4.00915, "loss": 4.00915, "time": 0.69893} +{"mode": "train", "epoch": 28, "iter": 1700, "lr": 0.09196, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26812, "top5_acc": 0.51328, "loss_cls": 4.03315, "loss": 4.03315, "time": 0.69762} +{"mode": "train", "epoch": 28, "iter": 1800, "lr": 0.09194, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27453, "top5_acc": 0.51812, "loss_cls": 3.99874, "loss": 3.99874, "time": 0.69972} +{"mode": "train", "epoch": 28, "iter": 1900, "lr": 0.09193, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25312, "top5_acc": 0.50813, "loss_cls": 4.08038, "loss": 4.08038, "time": 0.7008} +{"mode": "train", "epoch": 28, "iter": 2000, "lr": 0.09191, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25781, "top5_acc": 0.50844, "loss_cls": 4.03538, "loss": 4.03538, "time": 0.69787} +{"mode": "train", "epoch": 28, "iter": 2100, "lr": 0.0919, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26328, "top5_acc": 0.505, "loss_cls": 4.03981, "loss": 4.03981, "time": 0.70044} +{"mode": "train", "epoch": 28, "iter": 2200, "lr": 0.09188, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27156, "top5_acc": 0.52234, "loss_cls": 3.98801, "loss": 3.98801, "time": 0.69959} +{"mode": "train", "epoch": 28, "iter": 2300, "lr": 0.09187, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26422, "top5_acc": 0.51953, "loss_cls": 4.01365, "loss": 4.01365, "time": 0.70016} +{"mode": "train", "epoch": 28, "iter": 2400, "lr": 0.09185, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26594, "top5_acc": 0.51875, "loss_cls": 4.02863, "loss": 4.02863, "time": 0.69793} +{"mode": "train", "epoch": 28, "iter": 2500, "lr": 0.09184, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.2625, "top5_acc": 0.52203, "loss_cls": 4.01226, "loss": 4.01226, "time": 0.69971} +{"mode": "train", "epoch": 28, "iter": 2600, "lr": 0.09182, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26578, "top5_acc": 0.52594, "loss_cls": 4.00523, "loss": 4.00523, "time": 0.6997} +{"mode": "train", "epoch": 28, "iter": 2700, "lr": 0.09181, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25938, "top5_acc": 0.50969, "loss_cls": 4.05095, "loss": 4.05095, "time": 0.69987} +{"mode": "train", "epoch": 28, "iter": 2800, "lr": 0.09179, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26734, "top5_acc": 0.51188, "loss_cls": 4.02589, "loss": 4.02589, "time": 0.70039} +{"mode": "train", "epoch": 28, "iter": 2900, "lr": 0.09178, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26875, "top5_acc": 0.515, "loss_cls": 4.01566, "loss": 4.01566, "time": 0.69819} +{"mode": "train", "epoch": 28, "iter": 3000, "lr": 0.09176, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26781, "top5_acc": 0.51141, "loss_cls": 3.99121, "loss": 3.99121, "time": 0.70007} +{"mode": "train", "epoch": 28, "iter": 3100, "lr": 0.09175, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25844, "top5_acc": 0.50609, "loss_cls": 4.03659, "loss": 4.03659, "time": 0.69607} +{"mode": "train", "epoch": 28, "iter": 3200, "lr": 0.09173, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.265, "top5_acc": 0.52031, "loss_cls": 3.99694, "loss": 3.99694, "time": 0.70032} +{"mode": "train", "epoch": 28, "iter": 3300, "lr": 0.09172, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26984, "top5_acc": 0.51812, "loss_cls": 4.01815, "loss": 4.01815, "time": 0.6983} +{"mode": "train", "epoch": 28, "iter": 3400, "lr": 0.0917, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26359, "top5_acc": 0.51656, "loss_cls": 4.0342, "loss": 4.0342, "time": 0.70232} +{"mode": "train", "epoch": 28, "iter": 3500, "lr": 0.09168, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26312, "top5_acc": 0.51188, "loss_cls": 4.0372, "loss": 4.0372, "time": 0.69877} +{"mode": "train", "epoch": 28, "iter": 3600, "lr": 0.09167, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26344, "top5_acc": 0.50375, "loss_cls": 4.05217, "loss": 4.05217, "time": 0.69836} +{"mode": "train", "epoch": 28, "iter": 3700, "lr": 0.09165, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26531, "top5_acc": 0.51078, "loss_cls": 4.01341, "loss": 4.01341, "time": 0.6983} +{"mode": "val", "epoch": 28, "iter": 309, "lr": 0.09165, "top1_acc": 0.1985, "top5_acc": 0.43063, "mean_class_accuracy": 0.1985} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.09163, "memory": 15990, "data_time": 1.2731, "top1_acc": 0.27, "top5_acc": 0.51609, "loss_cls": 3.98542, "loss": 3.98542, "time": 1.97508} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.09162, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26391, "top5_acc": 0.5225, "loss_cls": 4.01583, "loss": 4.01583, "time": 0.70539} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.0916, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26406, "top5_acc": 0.51406, "loss_cls": 3.9994, "loss": 3.9994, "time": 0.70649} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.09158, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.26516, "top5_acc": 0.50875, "loss_cls": 4.02239, "loss": 4.02239, "time": 0.70442} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.09157, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26391, "top5_acc": 0.51328, "loss_cls": 3.99655, "loss": 3.99655, "time": 0.70555} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.09155, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27156, "top5_acc": 0.51859, "loss_cls": 3.9994, "loss": 3.9994, "time": 0.70397} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.09154, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27391, "top5_acc": 0.52422, "loss_cls": 3.97877, "loss": 3.97877, "time": 0.69928} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.09152, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26625, "top5_acc": 0.52656, "loss_cls": 3.99139, "loss": 3.99139, "time": 0.71074} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.09151, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26687, "top5_acc": 0.52453, "loss_cls": 3.97939, "loss": 3.97939, "time": 0.70665} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.09149, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27234, "top5_acc": 0.51828, "loss_cls": 3.99966, "loss": 3.99966, "time": 0.69985} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.09148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26859, "top5_acc": 0.51781, "loss_cls": 4.00173, "loss": 4.00173, "time": 0.70474} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.09146, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26047, "top5_acc": 0.51562, "loss_cls": 4.00786, "loss": 4.00786, "time": 0.70307} +{"mode": "train", "epoch": 29, "iter": 1300, "lr": 0.09144, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2575, "top5_acc": 0.51313, "loss_cls": 4.0134, "loss": 4.0134, "time": 0.70098} +{"mode": "train", "epoch": 29, "iter": 1400, "lr": 0.09143, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25891, "top5_acc": 0.50672, "loss_cls": 4.02851, "loss": 4.02851, "time": 0.69969} +{"mode": "train", "epoch": 29, "iter": 1500, "lr": 0.09141, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27031, "top5_acc": 0.52625, "loss_cls": 3.97952, "loss": 3.97952, "time": 0.69769} +{"mode": "train", "epoch": 29, "iter": 1600, "lr": 0.0914, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25812, "top5_acc": 0.51297, "loss_cls": 4.0387, "loss": 4.0387, "time": 0.69967} +{"mode": "train", "epoch": 29, "iter": 1700, "lr": 0.09138, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27031, "top5_acc": 0.50984, "loss_cls": 4.05982, "loss": 4.05982, "time": 0.70067} +{"mode": "train", "epoch": 29, "iter": 1800, "lr": 0.09137, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26703, "top5_acc": 0.52812, "loss_cls": 3.99, "loss": 3.99, "time": 0.69846} +{"mode": "train", "epoch": 29, "iter": 1900, "lr": 0.09135, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26188, "top5_acc": 0.51844, "loss_cls": 4.05983, "loss": 4.05983, "time": 0.69628} +{"mode": "train", "epoch": 29, "iter": 2000, "lr": 0.09133, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26312, "top5_acc": 0.51, "loss_cls": 4.03651, "loss": 4.03651, "time": 0.70106} +{"mode": "train", "epoch": 29, "iter": 2100, "lr": 0.09132, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2625, "top5_acc": 0.51625, "loss_cls": 4.02066, "loss": 4.02066, "time": 0.70212} +{"mode": "train", "epoch": 29, "iter": 2200, "lr": 0.0913, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.27062, "top5_acc": 0.52062, "loss_cls": 3.99379, "loss": 3.99379, "time": 0.69716} +{"mode": "train", "epoch": 29, "iter": 2300, "lr": 0.09129, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25672, "top5_acc": 0.51625, "loss_cls": 4.04231, "loss": 4.04231, "time": 0.69792} +{"mode": "train", "epoch": 29, "iter": 2400, "lr": 0.09127, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27234, "top5_acc": 0.5325, "loss_cls": 3.93497, "loss": 3.93497, "time": 0.69938} +{"mode": "train", "epoch": 29, "iter": 2500, "lr": 0.09126, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25938, "top5_acc": 0.52109, "loss_cls": 4.04273, "loss": 4.04273, "time": 0.70023} +{"mode": "train", "epoch": 29, "iter": 2600, "lr": 0.09124, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25422, "top5_acc": 0.50516, "loss_cls": 4.05483, "loss": 4.05483, "time": 0.69864} +{"mode": "train", "epoch": 29, "iter": 2700, "lr": 0.09122, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26078, "top5_acc": 0.52156, "loss_cls": 4.01287, "loss": 4.01287, "time": 0.69791} +{"mode": "train", "epoch": 29, "iter": 2800, "lr": 0.09121, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26844, "top5_acc": 0.51922, "loss_cls": 4.00725, "loss": 4.00725, "time": 0.70082} +{"mode": "train", "epoch": 29, "iter": 2900, "lr": 0.09119, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26172, "top5_acc": 0.51359, "loss_cls": 4.02965, "loss": 4.02965, "time": 0.69909} +{"mode": "train", "epoch": 29, "iter": 3000, "lr": 0.09118, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25578, "top5_acc": 0.50531, "loss_cls": 4.06572, "loss": 4.06572, "time": 0.69822} +{"mode": "train", "epoch": 29, "iter": 3100, "lr": 0.09116, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26859, "top5_acc": 0.51422, "loss_cls": 4.02507, "loss": 4.02507, "time": 0.69829} +{"mode": "train", "epoch": 29, "iter": 3200, "lr": 0.09114, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26344, "top5_acc": 0.50906, "loss_cls": 4.03088, "loss": 4.03088, "time": 0.6988} +{"mode": "train", "epoch": 29, "iter": 3300, "lr": 0.09113, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26562, "top5_acc": 0.51938, "loss_cls": 3.99008, "loss": 3.99008, "time": 0.69949} +{"mode": "train", "epoch": 29, "iter": 3400, "lr": 0.09111, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25984, "top5_acc": 0.51219, "loss_cls": 4.04847, "loss": 4.04847, "time": 0.69744} +{"mode": "train", "epoch": 29, "iter": 3500, "lr": 0.0911, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.275, "top5_acc": 0.51734, "loss_cls": 3.98569, "loss": 3.98569, "time": 0.69997} +{"mode": "train", "epoch": 29, "iter": 3600, "lr": 0.09108, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2625, "top5_acc": 0.50875, "loss_cls": 4.07545, "loss": 4.07545, "time": 0.69887} +{"mode": "train", "epoch": 29, "iter": 3700, "lr": 0.09106, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26031, "top5_acc": 0.50813, "loss_cls": 4.01015, "loss": 4.01015, "time": 0.69951} +{"mode": "val", "epoch": 29, "iter": 309, "lr": 0.09106, "top1_acc": 0.16077, "top5_acc": 0.35952, "mean_class_accuracy": 0.16056} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.09104, "memory": 15990, "data_time": 1.26275, "top1_acc": 0.27656, "top5_acc": 0.53359, "loss_cls": 3.95364, "loss": 3.95364, "time": 2.06554} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.09103, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27516, "top5_acc": 0.53328, "loss_cls": 3.92712, "loss": 3.92712, "time": 0.80755} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.09101, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27047, "top5_acc": 0.51969, "loss_cls": 3.97875, "loss": 3.97875, "time": 0.80928} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.09099, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27562, "top5_acc": 0.52094, "loss_cls": 3.97958, "loss": 3.97958, "time": 0.80201} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.09098, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27016, "top5_acc": 0.51953, "loss_cls": 4.02575, "loss": 4.02575, "time": 0.8054} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.09096, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27047, "top5_acc": 0.51438, "loss_cls": 4.0472, "loss": 4.0472, "time": 0.80022} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.09095, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26625, "top5_acc": 0.52078, "loss_cls": 3.99518, "loss": 3.99518, "time": 0.8089} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.09093, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26797, "top5_acc": 0.51578, "loss_cls": 4.00714, "loss": 4.00714, "time": 0.80956} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.09091, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27672, "top5_acc": 0.52391, "loss_cls": 3.96765, "loss": 3.96765, "time": 0.80793} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.0909, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25969, "top5_acc": 0.51344, "loss_cls": 4.01968, "loss": 4.01968, "time": 0.80192} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.09088, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26469, "top5_acc": 0.52109, "loss_cls": 3.99136, "loss": 3.99136, "time": 0.8003} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.09087, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26219, "top5_acc": 0.51516, "loss_cls": 4.02214, "loss": 4.02214, "time": 0.80255} +{"mode": "train", "epoch": 30, "iter": 1300, "lr": 0.09085, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26734, "top5_acc": 0.52344, "loss_cls": 3.99954, "loss": 3.99954, "time": 0.79925} +{"mode": "train", "epoch": 30, "iter": 1400, "lr": 0.09083, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27203, "top5_acc": 0.52078, "loss_cls": 3.99908, "loss": 3.99908, "time": 0.80475} +{"mode": "train", "epoch": 30, "iter": 1500, "lr": 0.09082, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26906, "top5_acc": 0.51234, "loss_cls": 4.01978, "loss": 4.01978, "time": 0.79661} +{"mode": "train", "epoch": 30, "iter": 1600, "lr": 0.0908, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25734, "top5_acc": 0.51609, "loss_cls": 4.02972, "loss": 4.02972, "time": 0.80068} +{"mode": "train", "epoch": 30, "iter": 1700, "lr": 0.09078, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27234, "top5_acc": 0.51984, "loss_cls": 4.00972, "loss": 4.00972, "time": 0.79943} +{"mode": "train", "epoch": 30, "iter": 1800, "lr": 0.09077, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26422, "top5_acc": 0.5175, "loss_cls": 4.00148, "loss": 4.00148, "time": 0.7962} +{"mode": "train", "epoch": 30, "iter": 1900, "lr": 0.09075, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26375, "top5_acc": 0.51547, "loss_cls": 4.01479, "loss": 4.01479, "time": 0.79624} +{"mode": "train", "epoch": 30, "iter": 2000, "lr": 0.09074, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25312, "top5_acc": 0.50484, "loss_cls": 4.04929, "loss": 4.04929, "time": 0.80301} +{"mode": "train", "epoch": 30, "iter": 2100, "lr": 0.09072, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26391, "top5_acc": 0.50703, "loss_cls": 4.02792, "loss": 4.02792, "time": 0.80017} +{"mode": "train", "epoch": 30, "iter": 2200, "lr": 0.0907, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25047, "top5_acc": 0.50625, "loss_cls": 4.06315, "loss": 4.06315, "time": 0.80061} +{"mode": "train", "epoch": 30, "iter": 2300, "lr": 0.09069, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25828, "top5_acc": 0.50531, "loss_cls": 4.0575, "loss": 4.0575, "time": 0.80125} +{"mode": "train", "epoch": 30, "iter": 2400, "lr": 0.09067, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26531, "top5_acc": 0.51625, "loss_cls": 4.02628, "loss": 4.02628, "time": 0.79995} +{"mode": "train", "epoch": 30, "iter": 2500, "lr": 0.09065, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26062, "top5_acc": 0.51406, "loss_cls": 4.02896, "loss": 4.02896, "time": 0.79921} +{"mode": "train", "epoch": 30, "iter": 2600, "lr": 0.09064, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26562, "top5_acc": 0.52, "loss_cls": 3.98354, "loss": 3.98354, "time": 0.80066} +{"mode": "train", "epoch": 30, "iter": 2700, "lr": 0.09062, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27516, "top5_acc": 0.525, "loss_cls": 3.99281, "loss": 3.99281, "time": 0.79718} +{"mode": "train", "epoch": 30, "iter": 2800, "lr": 0.09061, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26781, "top5_acc": 0.52062, "loss_cls": 4.01855, "loss": 4.01855, "time": 0.80183} +{"mode": "train", "epoch": 30, "iter": 2900, "lr": 0.09059, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26859, "top5_acc": 0.51734, "loss_cls": 3.99769, "loss": 3.99769, "time": 0.79769} +{"mode": "train", "epoch": 30, "iter": 3000, "lr": 0.09057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26687, "top5_acc": 0.51812, "loss_cls": 4.02322, "loss": 4.02322, "time": 0.80034} +{"mode": "train", "epoch": 30, "iter": 3100, "lr": 0.09056, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27875, "top5_acc": 0.525, "loss_cls": 3.96965, "loss": 3.96965, "time": 0.79881} +{"mode": "train", "epoch": 30, "iter": 3200, "lr": 0.09054, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27281, "top5_acc": 0.51656, "loss_cls": 4.01826, "loss": 4.01826, "time": 0.79891} +{"mode": "train", "epoch": 30, "iter": 3300, "lr": 0.09052, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26188, "top5_acc": 0.51578, "loss_cls": 4.01366, "loss": 4.01366, "time": 0.7984} +{"mode": "train", "epoch": 30, "iter": 3400, "lr": 0.09051, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26047, "top5_acc": 0.51125, "loss_cls": 4.01647, "loss": 4.01647, "time": 0.80211} +{"mode": "train", "epoch": 30, "iter": 3500, "lr": 0.09049, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26984, "top5_acc": 0.51672, "loss_cls": 4.01784, "loss": 4.01784, "time": 0.797} +{"mode": "train", "epoch": 30, "iter": 3600, "lr": 0.09047, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28031, "top5_acc": 0.52297, "loss_cls": 3.95689, "loss": 3.95689, "time": 0.80208} +{"mode": "train", "epoch": 30, "iter": 3700, "lr": 0.09046, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26297, "top5_acc": 0.51969, "loss_cls": 4.02207, "loss": 4.02207, "time": 0.80112} +{"mode": "val", "epoch": 30, "iter": 309, "lr": 0.09045, "top1_acc": 0.20331, "top5_acc": 0.44036, "mean_class_accuracy": 0.203} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.09043, "memory": 15990, "data_time": 1.31782, "top1_acc": 0.27734, "top5_acc": 0.52719, "loss_cls": 4.16608, "loss": 4.16608, "time": 2.29152} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.09042, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27016, "top5_acc": 0.51406, "loss_cls": 4.25166, "loss": 4.25166, "time": 0.82576} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.0904, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26328, "top5_acc": 0.52203, "loss_cls": 4.22402, "loss": 4.22402, "time": 0.82559} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.09039, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26484, "top5_acc": 0.52078, "loss_cls": 4.21203, "loss": 4.21203, "time": 0.8214} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.09037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26797, "top5_acc": 0.51516, "loss_cls": 4.23353, "loss": 4.23353, "time": 0.81713} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.09035, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26141, "top5_acc": 0.51812, "loss_cls": 4.24734, "loss": 4.24734, "time": 0.82107} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.09034, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26438, "top5_acc": 0.51891, "loss_cls": 4.24361, "loss": 4.24361, "time": 0.83243} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.09032, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26203, "top5_acc": 0.51781, "loss_cls": 4.23399, "loss": 4.23399, "time": 0.82652} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0903, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27547, "top5_acc": 0.52484, "loss_cls": 4.18402, "loss": 4.18402, "time": 0.81918} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.09029, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26469, "top5_acc": 0.51281, "loss_cls": 4.22565, "loss": 4.22565, "time": 0.8165} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.09027, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27688, "top5_acc": 0.52375, "loss_cls": 4.19029, "loss": 4.19029, "time": 0.81437} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.09025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26906, "top5_acc": 0.52531, "loss_cls": 4.21153, "loss": 4.21153, "time": 0.81323} +{"mode": "train", "epoch": 31, "iter": 1300, "lr": 0.09024, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2775, "top5_acc": 0.52047, "loss_cls": 4.21012, "loss": 4.21012, "time": 0.81689} +{"mode": "train", "epoch": 31, "iter": 1400, "lr": 0.09022, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26609, "top5_acc": 0.51562, "loss_cls": 4.24408, "loss": 4.24408, "time": 0.82081} +{"mode": "train", "epoch": 31, "iter": 1500, "lr": 0.0902, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26828, "top5_acc": 0.52547, "loss_cls": 4.19741, "loss": 4.19741, "time": 0.80981} +{"mode": "train", "epoch": 31, "iter": 1600, "lr": 0.09019, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26766, "top5_acc": 0.51313, "loss_cls": 4.23016, "loss": 4.23016, "time": 0.8169} +{"mode": "train", "epoch": 31, "iter": 1700, "lr": 0.09017, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25656, "top5_acc": 0.51375, "loss_cls": 4.25034, "loss": 4.25034, "time": 0.81397} +{"mode": "train", "epoch": 31, "iter": 1800, "lr": 0.09015, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26516, "top5_acc": 0.52406, "loss_cls": 4.22979, "loss": 4.22979, "time": 0.8158} +{"mode": "train", "epoch": 31, "iter": 1900, "lr": 0.09014, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26422, "top5_acc": 0.51219, "loss_cls": 4.23573, "loss": 4.23573, "time": 0.81506} +{"mode": "train", "epoch": 31, "iter": 2000, "lr": 0.09012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2625, "top5_acc": 0.50625, "loss_cls": 4.28335, "loss": 4.28335, "time": 0.81364} +{"mode": "train", "epoch": 31, "iter": 2100, "lr": 0.0901, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26594, "top5_acc": 0.52125, "loss_cls": 4.22061, "loss": 4.22061, "time": 0.81763} +{"mode": "train", "epoch": 31, "iter": 2200, "lr": 0.09009, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27516, "top5_acc": 0.51781, "loss_cls": 4.20856, "loss": 4.20856, "time": 0.81617} +{"mode": "train", "epoch": 31, "iter": 2300, "lr": 0.09007, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26625, "top5_acc": 0.51594, "loss_cls": 4.21667, "loss": 4.21667, "time": 0.81332} +{"mode": "train", "epoch": 31, "iter": 2400, "lr": 0.09005, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25719, "top5_acc": 0.51156, "loss_cls": 4.26495, "loss": 4.26495, "time": 0.81655} +{"mode": "train", "epoch": 31, "iter": 2500, "lr": 0.09004, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27547, "top5_acc": 0.52812, "loss_cls": 4.17476, "loss": 4.17476, "time": 0.81186} +{"mode": "train", "epoch": 31, "iter": 2600, "lr": 0.09002, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25984, "top5_acc": 0.50797, "loss_cls": 4.26278, "loss": 4.26278, "time": 0.81726} +{"mode": "train", "epoch": 31, "iter": 2700, "lr": 0.09, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26719, "top5_acc": 0.52656, "loss_cls": 4.22124, "loss": 4.22124, "time": 0.81618} +{"mode": "train", "epoch": 31, "iter": 2800, "lr": 0.08999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27437, "top5_acc": 0.51516, "loss_cls": 4.19338, "loss": 4.19338, "time": 0.81484} +{"mode": "train", "epoch": 31, "iter": 2900, "lr": 0.08997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26828, "top5_acc": 0.51547, "loss_cls": 4.22126, "loss": 4.22126, "time": 0.81482} +{"mode": "train", "epoch": 31, "iter": 3000, "lr": 0.08995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27234, "top5_acc": 0.51656, "loss_cls": 4.23963, "loss": 4.23963, "time": 0.81445} +{"mode": "train", "epoch": 31, "iter": 3100, "lr": 0.08994, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27141, "top5_acc": 0.52234, "loss_cls": 4.20013, "loss": 4.20013, "time": 0.8186} +{"mode": "train", "epoch": 31, "iter": 3200, "lr": 0.08992, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27344, "top5_acc": 0.51531, "loss_cls": 4.24081, "loss": 4.24081, "time": 0.81546} +{"mode": "train", "epoch": 31, "iter": 3300, "lr": 0.0899, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27703, "top5_acc": 0.52578, "loss_cls": 4.17071, "loss": 4.17071, "time": 0.81798} +{"mode": "train", "epoch": 31, "iter": 3400, "lr": 0.08989, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27391, "top5_acc": 0.52172, "loss_cls": 4.20535, "loss": 4.20535, "time": 0.8207} +{"mode": "train", "epoch": 31, "iter": 3500, "lr": 0.08987, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25812, "top5_acc": 0.52016, "loss_cls": 4.2562, "loss": 4.2562, "time": 0.8206} +{"mode": "train", "epoch": 31, "iter": 3600, "lr": 0.08985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26359, "top5_acc": 0.51703, "loss_cls": 4.21191, "loss": 4.21191, "time": 0.81813} +{"mode": "train", "epoch": 31, "iter": 3700, "lr": 0.08983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26531, "top5_acc": 0.52109, "loss_cls": 4.2479, "loss": 4.2479, "time": 0.81711} +{"mode": "val", "epoch": 31, "iter": 309, "lr": 0.08983, "top1_acc": 0.2027, "top5_acc": 0.43808, "mean_class_accuracy": 0.20229} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.08981, "memory": 15990, "data_time": 1.30583, "top1_acc": 0.27156, "top5_acc": 0.53125, "loss_cls": 4.16646, "loss": 4.16646, "time": 2.28146} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.08979, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28188, "top5_acc": 0.53188, "loss_cls": 4.14923, "loss": 4.14923, "time": 0.81928} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.08978, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27969, "top5_acc": 0.52922, "loss_cls": 4.17861, "loss": 4.17861, "time": 0.82348} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.08976, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27641, "top5_acc": 0.52625, "loss_cls": 4.17687, "loss": 4.17687, "time": 0.82178} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.08974, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26562, "top5_acc": 0.51578, "loss_cls": 4.23484, "loss": 4.23484, "time": 0.81885} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.08973, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27484, "top5_acc": 0.52875, "loss_cls": 4.21944, "loss": 4.21944, "time": 0.82006} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.08971, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26, "top5_acc": 0.52562, "loss_cls": 4.19659, "loss": 4.19659, "time": 0.82334} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.08969, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26953, "top5_acc": 0.52375, "loss_cls": 4.19741, "loss": 4.19741, "time": 0.82284} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.08967, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26766, "top5_acc": 0.51297, "loss_cls": 4.24904, "loss": 4.24904, "time": 0.82133} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.08966, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27391, "top5_acc": 0.52219, "loss_cls": 4.19247, "loss": 4.19247, "time": 0.8286} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.08964, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26875, "top5_acc": 0.52219, "loss_cls": 4.22251, "loss": 4.22251, "time": 0.81863} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.08962, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26438, "top5_acc": 0.52094, "loss_cls": 4.24517, "loss": 4.24517, "time": 0.81781} +{"mode": "train", "epoch": 32, "iter": 1300, "lr": 0.08961, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28031, "top5_acc": 0.52766, "loss_cls": 4.15763, "loss": 4.15763, "time": 0.8167} +{"mode": "train", "epoch": 32, "iter": 1400, "lr": 0.08959, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27797, "top5_acc": 0.52359, "loss_cls": 4.18352, "loss": 4.18352, "time": 0.81261} +{"mode": "train", "epoch": 32, "iter": 1500, "lr": 0.08957, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26969, "top5_acc": 0.51094, "loss_cls": 4.22664, "loss": 4.22664, "time": 0.81325} +{"mode": "train", "epoch": 32, "iter": 1600, "lr": 0.08955, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26375, "top5_acc": 0.51562, "loss_cls": 4.20946, "loss": 4.20946, "time": 0.81482} +{"mode": "train", "epoch": 32, "iter": 1700, "lr": 0.08954, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26594, "top5_acc": 0.5125, "loss_cls": 4.26716, "loss": 4.26716, "time": 0.81957} +{"mode": "train", "epoch": 32, "iter": 1800, "lr": 0.08952, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27688, "top5_acc": 0.51516, "loss_cls": 4.21239, "loss": 4.21239, "time": 0.8141} +{"mode": "train", "epoch": 32, "iter": 1900, "lr": 0.0895, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26156, "top5_acc": 0.505, "loss_cls": 4.28698, "loss": 4.28698, "time": 0.8185} +{"mode": "train", "epoch": 32, "iter": 2000, "lr": 0.08949, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26906, "top5_acc": 0.52719, "loss_cls": 4.20031, "loss": 4.20031, "time": 0.81377} +{"mode": "train", "epoch": 32, "iter": 2100, "lr": 0.08947, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26438, "top5_acc": 0.51031, "loss_cls": 4.25706, "loss": 4.25706, "time": 0.81425} +{"mode": "train", "epoch": 32, "iter": 2200, "lr": 0.08945, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27031, "top5_acc": 0.52141, "loss_cls": 4.20336, "loss": 4.20336, "time": 0.8174} +{"mode": "train", "epoch": 32, "iter": 2300, "lr": 0.08943, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27187, "top5_acc": 0.52516, "loss_cls": 4.1779, "loss": 4.1779, "time": 0.81474} +{"mode": "train", "epoch": 32, "iter": 2400, "lr": 0.08942, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26703, "top5_acc": 0.52438, "loss_cls": 4.2007, "loss": 4.2007, "time": 0.82145} +{"mode": "train", "epoch": 32, "iter": 2500, "lr": 0.0894, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26312, "top5_acc": 0.5075, "loss_cls": 4.23421, "loss": 4.23421, "time": 0.81872} +{"mode": "train", "epoch": 32, "iter": 2600, "lr": 0.08938, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26594, "top5_acc": 0.51156, "loss_cls": 4.23554, "loss": 4.23554, "time": 0.81981} +{"mode": "train", "epoch": 32, "iter": 2700, "lr": 0.08937, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27047, "top5_acc": 0.52547, "loss_cls": 4.17749, "loss": 4.17749, "time": 0.81588} +{"mode": "train", "epoch": 32, "iter": 2800, "lr": 0.08935, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26609, "top5_acc": 0.51734, "loss_cls": 4.22035, "loss": 4.22035, "time": 0.81612} +{"mode": "train", "epoch": 32, "iter": 2900, "lr": 0.08933, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27047, "top5_acc": 0.51922, "loss_cls": 4.2353, "loss": 4.2353, "time": 0.8204} +{"mode": "train", "epoch": 32, "iter": 3000, "lr": 0.08931, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26625, "top5_acc": 0.51344, "loss_cls": 4.26725, "loss": 4.26725, "time": 0.81325} +{"mode": "train", "epoch": 32, "iter": 3100, "lr": 0.0893, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27797, "top5_acc": 0.52375, "loss_cls": 4.17835, "loss": 4.17835, "time": 0.81306} +{"mode": "train", "epoch": 32, "iter": 3200, "lr": 0.08928, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26875, "top5_acc": 0.52016, "loss_cls": 4.24256, "loss": 4.24256, "time": 0.81132} +{"mode": "train", "epoch": 32, "iter": 3300, "lr": 0.08926, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27672, "top5_acc": 0.52562, "loss_cls": 4.21647, "loss": 4.21647, "time": 0.8103} +{"mode": "train", "epoch": 32, "iter": 3400, "lr": 0.08924, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27453, "top5_acc": 0.52531, "loss_cls": 4.19145, "loss": 4.19145, "time": 0.81864} +{"mode": "train", "epoch": 32, "iter": 3500, "lr": 0.08923, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26047, "top5_acc": 0.51375, "loss_cls": 4.26061, "loss": 4.26061, "time": 0.81865} +{"mode": "train", "epoch": 32, "iter": 3600, "lr": 0.08921, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26562, "top5_acc": 0.51547, "loss_cls": 4.22438, "loss": 4.22438, "time": 0.8153} +{"mode": "train", "epoch": 32, "iter": 3700, "lr": 0.08919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25859, "top5_acc": 0.52094, "loss_cls": 4.24819, "loss": 4.24819, "time": 0.81276} +{"mode": "val", "epoch": 32, "iter": 309, "lr": 0.08918, "top1_acc": 0.2101, "top5_acc": 0.44041, "mean_class_accuracy": 0.20977} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.08917, "memory": 15990, "data_time": 1.28955, "top1_acc": 0.27953, "top5_acc": 0.52625, "loss_cls": 4.16108, "loss": 4.16108, "time": 2.26277} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.08915, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26875, "top5_acc": 0.52078, "loss_cls": 4.20798, "loss": 4.20798, "time": 0.81786} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.08913, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27797, "top5_acc": 0.53078, "loss_cls": 4.16141, "loss": 4.16141, "time": 0.82393} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.08912, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26875, "top5_acc": 0.52281, "loss_cls": 4.21953, "loss": 4.21953, "time": 0.82142} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.0891, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26625, "top5_acc": 0.52438, "loss_cls": 4.17918, "loss": 4.17918, "time": 0.82291} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.08908, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27078, "top5_acc": 0.53359, "loss_cls": 4.16755, "loss": 4.16755, "time": 0.81927} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.08906, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.28125, "top5_acc": 0.52688, "loss_cls": 4.15857, "loss": 4.15857, "time": 0.82973} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.08905, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26734, "top5_acc": 0.52266, "loss_cls": 4.19296, "loss": 4.19296, "time": 0.8214} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.08903, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27062, "top5_acc": 0.51703, "loss_cls": 4.23695, "loss": 4.23695, "time": 0.82036} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.08901, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26578, "top5_acc": 0.52188, "loss_cls": 4.19582, "loss": 4.19582, "time": 0.81516} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.08899, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27266, "top5_acc": 0.52, "loss_cls": 4.2544, "loss": 4.2544, "time": 0.81848} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.08898, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27047, "top5_acc": 0.52141, "loss_cls": 4.21823, "loss": 4.21823, "time": 0.82157} +{"mode": "train", "epoch": 33, "iter": 1300, "lr": 0.08896, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26703, "top5_acc": 0.51828, "loss_cls": 4.2228, "loss": 4.2228, "time": 0.81366} +{"mode": "train", "epoch": 33, "iter": 1400, "lr": 0.08894, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27891, "top5_acc": 0.52125, "loss_cls": 4.18972, "loss": 4.18972, "time": 0.81639} +{"mode": "train", "epoch": 33, "iter": 1500, "lr": 0.08892, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2725, "top5_acc": 0.51609, "loss_cls": 4.23952, "loss": 4.23952, "time": 0.81343} +{"mode": "train", "epoch": 33, "iter": 1600, "lr": 0.08891, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26906, "top5_acc": 0.52266, "loss_cls": 4.21092, "loss": 4.21092, "time": 0.81475} +{"mode": "train", "epoch": 33, "iter": 1700, "lr": 0.08889, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26438, "top5_acc": 0.51141, "loss_cls": 4.23668, "loss": 4.23668, "time": 0.81593} +{"mode": "train", "epoch": 33, "iter": 1800, "lr": 0.08887, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26766, "top5_acc": 0.52625, "loss_cls": 4.2282, "loss": 4.2282, "time": 0.81403} +{"mode": "train", "epoch": 33, "iter": 1900, "lr": 0.08885, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27094, "top5_acc": 0.52141, "loss_cls": 4.23543, "loss": 4.23543, "time": 0.81925} +{"mode": "train", "epoch": 33, "iter": 2000, "lr": 0.08884, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27172, "top5_acc": 0.52562, "loss_cls": 4.22753, "loss": 4.22753, "time": 0.81753} +{"mode": "train", "epoch": 33, "iter": 2100, "lr": 0.08882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27359, "top5_acc": 0.52594, "loss_cls": 4.1981, "loss": 4.1981, "time": 0.81609} +{"mode": "train", "epoch": 33, "iter": 2200, "lr": 0.0888, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27422, "top5_acc": 0.51891, "loss_cls": 4.18903, "loss": 4.18903, "time": 0.81897} +{"mode": "train", "epoch": 33, "iter": 2300, "lr": 0.08878, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27281, "top5_acc": 0.51922, "loss_cls": 4.2064, "loss": 4.2064, "time": 0.8172} +{"mode": "train", "epoch": 33, "iter": 2400, "lr": 0.08876, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26984, "top5_acc": 0.51344, "loss_cls": 4.22547, "loss": 4.22547, "time": 0.82051} +{"mode": "train", "epoch": 33, "iter": 2500, "lr": 0.08875, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27297, "top5_acc": 0.52641, "loss_cls": 4.19227, "loss": 4.19227, "time": 0.81784} +{"mode": "train", "epoch": 33, "iter": 2600, "lr": 0.08873, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27266, "top5_acc": 0.52156, "loss_cls": 4.18097, "loss": 4.18097, "time": 0.82133} +{"mode": "train", "epoch": 33, "iter": 2700, "lr": 0.08871, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26516, "top5_acc": 0.51375, "loss_cls": 4.26011, "loss": 4.26011, "time": 0.81975} +{"mode": "train", "epoch": 33, "iter": 2800, "lr": 0.08869, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26906, "top5_acc": 0.52078, "loss_cls": 4.21109, "loss": 4.21109, "time": 0.81861} +{"mode": "train", "epoch": 33, "iter": 2900, "lr": 0.08868, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27062, "top5_acc": 0.52109, "loss_cls": 4.20842, "loss": 4.20842, "time": 0.82394} +{"mode": "train", "epoch": 33, "iter": 3000, "lr": 0.08866, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27312, "top5_acc": 0.52328, "loss_cls": 4.20738, "loss": 4.20738, "time": 0.81607} +{"mode": "train", "epoch": 33, "iter": 3100, "lr": 0.08864, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27031, "top5_acc": 0.52812, "loss_cls": 4.18828, "loss": 4.18828, "time": 0.81468} +{"mode": "train", "epoch": 33, "iter": 3200, "lr": 0.08862, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26625, "top5_acc": 0.52062, "loss_cls": 4.23352, "loss": 4.23352, "time": 0.81638} +{"mode": "train", "epoch": 33, "iter": 3300, "lr": 0.08861, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27109, "top5_acc": 0.52531, "loss_cls": 4.17181, "loss": 4.17181, "time": 0.81172} +{"mode": "train", "epoch": 33, "iter": 3400, "lr": 0.08859, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26547, "top5_acc": 0.51766, "loss_cls": 4.2154, "loss": 4.2154, "time": 0.81583} +{"mode": "train", "epoch": 33, "iter": 3500, "lr": 0.08857, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26578, "top5_acc": 0.51688, "loss_cls": 4.21202, "loss": 4.21202, "time": 0.81632} +{"mode": "train", "epoch": 33, "iter": 3600, "lr": 0.08855, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26234, "top5_acc": 0.50813, "loss_cls": 4.26172, "loss": 4.26172, "time": 0.81742} +{"mode": "train", "epoch": 33, "iter": 3700, "lr": 0.08853, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26781, "top5_acc": 0.52141, "loss_cls": 4.22239, "loss": 4.22239, "time": 0.81987} +{"mode": "val", "epoch": 33, "iter": 309, "lr": 0.08853, "top1_acc": 0.21121, "top5_acc": 0.44552, "mean_class_accuracy": 0.21101} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.08851, "memory": 15990, "data_time": 1.33693, "top1_acc": 0.27812, "top5_acc": 0.5325, "loss_cls": 4.15914, "loss": 4.15914, "time": 2.31338} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.08849, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28234, "top5_acc": 0.525, "loss_cls": 4.13817, "loss": 4.13817, "time": 0.81558} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.08847, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27156, "top5_acc": 0.52844, "loss_cls": 4.14637, "loss": 4.14637, "time": 0.82533} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.08845, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26391, "top5_acc": 0.52422, "loss_cls": 4.19949, "loss": 4.19949, "time": 0.81883} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.08844, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27422, "top5_acc": 0.51516, "loss_cls": 4.22348, "loss": 4.22348, "time": 0.8202} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.08842, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27625, "top5_acc": 0.52797, "loss_cls": 4.19268, "loss": 4.19268, "time": 0.8258} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.0884, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27031, "top5_acc": 0.52125, "loss_cls": 4.19213, "loss": 4.19213, "time": 0.83122} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.08838, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27516, "top5_acc": 0.52703, "loss_cls": 4.18826, "loss": 4.18826, "time": 0.82245} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.08836, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26656, "top5_acc": 0.51812, "loss_cls": 4.2344, "loss": 4.2344, "time": 0.82522} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.08835, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27141, "top5_acc": 0.52062, "loss_cls": 4.2001, "loss": 4.2001, "time": 0.81796} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.08833, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26906, "top5_acc": 0.51828, "loss_cls": 4.22298, "loss": 4.22298, "time": 0.81637} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.08831, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27125, "top5_acc": 0.52406, "loss_cls": 4.18868, "loss": 4.18868, "time": 0.81869} +{"mode": "train", "epoch": 34, "iter": 1300, "lr": 0.08829, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26453, "top5_acc": 0.51188, "loss_cls": 4.2337, "loss": 4.2337, "time": 0.81698} +{"mode": "train", "epoch": 34, "iter": 1400, "lr": 0.08828, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27219, "top5_acc": 0.51953, "loss_cls": 4.19514, "loss": 4.19514, "time": 0.81374} +{"mode": "train", "epoch": 34, "iter": 1500, "lr": 0.08826, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27344, "top5_acc": 0.52422, "loss_cls": 4.17717, "loss": 4.17717, "time": 0.81464} +{"mode": "train", "epoch": 34, "iter": 1600, "lr": 0.08824, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27187, "top5_acc": 0.52109, "loss_cls": 4.20511, "loss": 4.20511, "time": 0.81308} +{"mode": "train", "epoch": 34, "iter": 1700, "lr": 0.08822, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27156, "top5_acc": 0.52, "loss_cls": 4.21963, "loss": 4.21963, "time": 0.81285} +{"mode": "train", "epoch": 34, "iter": 1800, "lr": 0.0882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28266, "top5_acc": 0.53109, "loss_cls": 4.16332, "loss": 4.16332, "time": 0.81619} +{"mode": "train", "epoch": 34, "iter": 1900, "lr": 0.08819, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27078, "top5_acc": 0.52453, "loss_cls": 4.19051, "loss": 4.19051, "time": 0.81736} +{"mode": "train", "epoch": 34, "iter": 2000, "lr": 0.08817, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26906, "top5_acc": 0.525, "loss_cls": 4.17411, "loss": 4.17411, "time": 0.81895} +{"mode": "train", "epoch": 34, "iter": 2100, "lr": 0.08815, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26516, "top5_acc": 0.51031, "loss_cls": 4.26299, "loss": 4.26299, "time": 0.81421} +{"mode": "train", "epoch": 34, "iter": 2200, "lr": 0.08813, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27734, "top5_acc": 0.52031, "loss_cls": 4.20806, "loss": 4.20806, "time": 0.81498} +{"mode": "train", "epoch": 34, "iter": 2300, "lr": 0.08811, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27078, "top5_acc": 0.52406, "loss_cls": 4.19093, "loss": 4.19093, "time": 0.81896} +{"mode": "train", "epoch": 34, "iter": 2400, "lr": 0.08809, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26469, "top5_acc": 0.51781, "loss_cls": 4.23226, "loss": 4.23226, "time": 0.81664} +{"mode": "train", "epoch": 34, "iter": 2500, "lr": 0.08808, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28281, "top5_acc": 0.5275, "loss_cls": 4.171, "loss": 4.171, "time": 0.81495} +{"mode": "train", "epoch": 34, "iter": 2600, "lr": 0.08806, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26312, "top5_acc": 0.51406, "loss_cls": 4.24621, "loss": 4.24621, "time": 0.81518} +{"mode": "train", "epoch": 34, "iter": 2700, "lr": 0.08804, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26344, "top5_acc": 0.51469, "loss_cls": 4.22559, "loss": 4.22559, "time": 0.81658} +{"mode": "train", "epoch": 34, "iter": 2800, "lr": 0.08802, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27219, "top5_acc": 0.52078, "loss_cls": 4.20564, "loss": 4.20564, "time": 0.81347} +{"mode": "train", "epoch": 34, "iter": 2900, "lr": 0.088, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27422, "top5_acc": 0.51922, "loss_cls": 4.20127, "loss": 4.20127, "time": 0.8162} +{"mode": "train", "epoch": 34, "iter": 3000, "lr": 0.08799, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27062, "top5_acc": 0.52547, "loss_cls": 4.2108, "loss": 4.2108, "time": 0.81515} +{"mode": "train", "epoch": 34, "iter": 3100, "lr": 0.08797, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27031, "top5_acc": 0.52672, "loss_cls": 4.20195, "loss": 4.20195, "time": 0.81661} +{"mode": "train", "epoch": 34, "iter": 3200, "lr": 0.08795, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26781, "top5_acc": 0.51953, "loss_cls": 4.21065, "loss": 4.21065, "time": 0.81355} +{"mode": "train", "epoch": 34, "iter": 3300, "lr": 0.08793, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27016, "top5_acc": 0.52203, "loss_cls": 4.21032, "loss": 4.21032, "time": 0.81387} +{"mode": "train", "epoch": 34, "iter": 3400, "lr": 0.08791, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27062, "top5_acc": 0.52109, "loss_cls": 4.18363, "loss": 4.18363, "time": 0.81626} +{"mode": "train", "epoch": 34, "iter": 3500, "lr": 0.08789, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26719, "top5_acc": 0.51219, "loss_cls": 4.21565, "loss": 4.21565, "time": 0.81442} +{"mode": "train", "epoch": 34, "iter": 3600, "lr": 0.08788, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26594, "top5_acc": 0.51281, "loss_cls": 4.25595, "loss": 4.25595, "time": 0.81634} +{"mode": "train", "epoch": 34, "iter": 3700, "lr": 0.08786, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27203, "top5_acc": 0.52516, "loss_cls": 4.1883, "loss": 4.1883, "time": 0.81187} +{"mode": "val", "epoch": 34, "iter": 309, "lr": 0.08785, "top1_acc": 0.19222, "top5_acc": 0.42207, "mean_class_accuracy": 0.19189} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.08783, "memory": 15990, "data_time": 1.2957, "top1_acc": 0.26609, "top5_acc": 0.52078, "loss_cls": 4.16555, "loss": 4.16555, "time": 2.27067} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.08781, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27938, "top5_acc": 0.52641, "loss_cls": 4.16039, "loss": 4.16039, "time": 0.81905} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.0878, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27375, "top5_acc": 0.52797, "loss_cls": 4.17644, "loss": 4.17644, "time": 0.81596} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.08778, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26641, "top5_acc": 0.51625, "loss_cls": 4.2211, "loss": 4.2211, "time": 0.83069} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.08776, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28172, "top5_acc": 0.52812, "loss_cls": 4.13924, "loss": 4.13924, "time": 0.82307} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.08774, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27094, "top5_acc": 0.51688, "loss_cls": 4.22001, "loss": 4.22001, "time": 0.82697} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.08772, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27297, "top5_acc": 0.52328, "loss_cls": 4.18016, "loss": 4.18016, "time": 0.82998} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.0877, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27672, "top5_acc": 0.52547, "loss_cls": 4.20458, "loss": 4.20458, "time": 0.82329} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.08769, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2725, "top5_acc": 0.51844, "loss_cls": 4.20743, "loss": 4.20743, "time": 0.82703} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.08767, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27656, "top5_acc": 0.52359, "loss_cls": 4.21532, "loss": 4.21532, "time": 0.81381} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.08765, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27312, "top5_acc": 0.52859, "loss_cls": 4.16905, "loss": 4.16905, "time": 0.81808} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.08763, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26641, "top5_acc": 0.52875, "loss_cls": 4.1708, "loss": 4.1708, "time": 0.81485} +{"mode": "train", "epoch": 35, "iter": 1300, "lr": 0.08761, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2675, "top5_acc": 0.52062, "loss_cls": 4.2345, "loss": 4.2345, "time": 0.81856} +{"mode": "train", "epoch": 35, "iter": 1400, "lr": 0.08759, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26828, "top5_acc": 0.52031, "loss_cls": 4.22513, "loss": 4.22513, "time": 0.81482} +{"mode": "train", "epoch": 35, "iter": 1500, "lr": 0.08757, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27312, "top5_acc": 0.52078, "loss_cls": 4.21944, "loss": 4.21944, "time": 0.8195} +{"mode": "train", "epoch": 35, "iter": 1600, "lr": 0.08756, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27375, "top5_acc": 0.51938, "loss_cls": 4.21294, "loss": 4.21294, "time": 0.81605} +{"mode": "train", "epoch": 35, "iter": 1700, "lr": 0.08754, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27172, "top5_acc": 0.515, "loss_cls": 4.20547, "loss": 4.20547, "time": 0.82346} +{"mode": "train", "epoch": 35, "iter": 1800, "lr": 0.08752, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2675, "top5_acc": 0.5175, "loss_cls": 4.20476, "loss": 4.20476, "time": 0.81405} +{"mode": "train", "epoch": 35, "iter": 1900, "lr": 0.0875, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26734, "top5_acc": 0.52734, "loss_cls": 4.15241, "loss": 4.15241, "time": 0.81605} +{"mode": "train", "epoch": 35, "iter": 2000, "lr": 0.08748, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27484, "top5_acc": 0.52438, "loss_cls": 4.17468, "loss": 4.17468, "time": 0.81754} +{"mode": "train", "epoch": 35, "iter": 2100, "lr": 0.08746, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27656, "top5_acc": 0.52297, "loss_cls": 4.19473, "loss": 4.19473, "time": 0.81871} +{"mode": "train", "epoch": 35, "iter": 2200, "lr": 0.08745, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27281, "top5_acc": 0.52328, "loss_cls": 4.2186, "loss": 4.2186, "time": 0.81358} +{"mode": "train", "epoch": 35, "iter": 2300, "lr": 0.08743, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27312, "top5_acc": 0.53562, "loss_cls": 4.16101, "loss": 4.16101, "time": 0.81699} +{"mode": "train", "epoch": 35, "iter": 2400, "lr": 0.08741, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27266, "top5_acc": 0.53234, "loss_cls": 4.18786, "loss": 4.18786, "time": 0.82183} +{"mode": "train", "epoch": 35, "iter": 2500, "lr": 0.08739, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26797, "top5_acc": 0.51719, "loss_cls": 4.20802, "loss": 4.20802, "time": 0.81578} +{"mode": "train", "epoch": 35, "iter": 2600, "lr": 0.08737, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26641, "top5_acc": 0.52297, "loss_cls": 4.19832, "loss": 4.19832, "time": 0.81979} +{"mode": "train", "epoch": 35, "iter": 2700, "lr": 0.08735, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.275, "top5_acc": 0.5275, "loss_cls": 4.17289, "loss": 4.17289, "time": 0.81562} +{"mode": "train", "epoch": 35, "iter": 2800, "lr": 0.08733, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26531, "top5_acc": 0.51453, "loss_cls": 4.24999, "loss": 4.24999, "time": 0.81839} +{"mode": "train", "epoch": 35, "iter": 2900, "lr": 0.08732, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27109, "top5_acc": 0.52141, "loss_cls": 4.19429, "loss": 4.19429, "time": 0.81127} +{"mode": "train", "epoch": 35, "iter": 3000, "lr": 0.0873, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27422, "top5_acc": 0.52484, "loss_cls": 4.19381, "loss": 4.19381, "time": 0.81456} +{"mode": "train", "epoch": 35, "iter": 3100, "lr": 0.08728, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26031, "top5_acc": 0.51141, "loss_cls": 4.25201, "loss": 4.25201, "time": 0.8267} +{"mode": "train", "epoch": 35, "iter": 3200, "lr": 0.08726, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27453, "top5_acc": 0.52281, "loss_cls": 4.22363, "loss": 4.22363, "time": 0.82367} +{"mode": "train", "epoch": 35, "iter": 3300, "lr": 0.08724, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27484, "top5_acc": 0.52297, "loss_cls": 4.21235, "loss": 4.21235, "time": 0.81869} +{"mode": "train", "epoch": 35, "iter": 3400, "lr": 0.08722, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26641, "top5_acc": 0.525, "loss_cls": 4.222, "loss": 4.222, "time": 0.82461} +{"mode": "train", "epoch": 35, "iter": 3500, "lr": 0.0872, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27266, "top5_acc": 0.51406, "loss_cls": 4.21896, "loss": 4.21896, "time": 0.82065} +{"mode": "train", "epoch": 35, "iter": 3600, "lr": 0.08718, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28297, "top5_acc": 0.53172, "loss_cls": 4.17028, "loss": 4.17028, "time": 0.81682} +{"mode": "train", "epoch": 35, "iter": 3700, "lr": 0.08717, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26078, "top5_acc": 0.52203, "loss_cls": 4.19166, "loss": 4.19166, "time": 0.82042} +{"mode": "val", "epoch": 35, "iter": 309, "lr": 0.08716, "top1_acc": 0.16811, "top5_acc": 0.37745, "mean_class_accuracy": 0.16783} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.08714, "memory": 15990, "data_time": 1.28959, "top1_acc": 0.27844, "top5_acc": 0.54188, "loss_cls": 4.11809, "loss": 4.11809, "time": 2.26202} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.08712, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26469, "top5_acc": 0.51703, "loss_cls": 4.20672, "loss": 4.20672, "time": 0.81776} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.0871, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26891, "top5_acc": 0.53031, "loss_cls": 4.16895, "loss": 4.16895, "time": 0.8155} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.08708, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.265, "top5_acc": 0.53047, "loss_cls": 4.2068, "loss": 4.2068, "time": 0.82181} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.08706, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27719, "top5_acc": 0.53672, "loss_cls": 4.13962, "loss": 4.13962, "time": 0.81989} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.08704, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27547, "top5_acc": 0.52656, "loss_cls": 4.16563, "loss": 4.16563, "time": 0.82607} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.08703, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26984, "top5_acc": 0.51828, "loss_cls": 4.21171, "loss": 4.21171, "time": 0.82445} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.08701, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27672, "top5_acc": 0.52203, "loss_cls": 4.2067, "loss": 4.2067, "time": 0.82385} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.08699, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27203, "top5_acc": 0.53516, "loss_cls": 4.16029, "loss": 4.16029, "time": 0.82286} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.08697, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27531, "top5_acc": 0.52312, "loss_cls": 4.16369, "loss": 4.16369, "time": 0.81937} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.08695, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27484, "top5_acc": 0.52516, "loss_cls": 4.18533, "loss": 4.18533, "time": 0.81683} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.08693, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2775, "top5_acc": 0.53, "loss_cls": 4.1375, "loss": 4.1375, "time": 0.81876} +{"mode": "train", "epoch": 36, "iter": 1300, "lr": 0.08691, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27703, "top5_acc": 0.52688, "loss_cls": 4.15541, "loss": 4.15541, "time": 0.81336} +{"mode": "train", "epoch": 36, "iter": 1400, "lr": 0.08689, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26844, "top5_acc": 0.51406, "loss_cls": 4.24111, "loss": 4.24111, "time": 0.81866} +{"mode": "train", "epoch": 36, "iter": 1500, "lr": 0.08688, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27453, "top5_acc": 0.52, "loss_cls": 4.17407, "loss": 4.17407, "time": 0.8187} +{"mode": "train", "epoch": 36, "iter": 1600, "lr": 0.08686, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27609, "top5_acc": 0.51938, "loss_cls": 4.21701, "loss": 4.21701, "time": 0.81372} +{"mode": "train", "epoch": 36, "iter": 1700, "lr": 0.08684, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27281, "top5_acc": 0.51984, "loss_cls": 4.20431, "loss": 4.20431, "time": 0.81881} +{"mode": "train", "epoch": 36, "iter": 1800, "lr": 0.08682, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28016, "top5_acc": 0.53719, "loss_cls": 4.13017, "loss": 4.13017, "time": 0.82007} +{"mode": "train", "epoch": 36, "iter": 1900, "lr": 0.0868, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27891, "top5_acc": 0.52984, "loss_cls": 4.1598, "loss": 4.1598, "time": 0.82049} +{"mode": "train", "epoch": 36, "iter": 2000, "lr": 0.08678, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26859, "top5_acc": 0.52656, "loss_cls": 4.1819, "loss": 4.1819, "time": 0.81492} +{"mode": "train", "epoch": 36, "iter": 2100, "lr": 0.08676, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26469, "top5_acc": 0.51031, "loss_cls": 4.2557, "loss": 4.2557, "time": 0.81873} +{"mode": "train", "epoch": 36, "iter": 2200, "lr": 0.08674, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26141, "top5_acc": 0.52125, "loss_cls": 4.22954, "loss": 4.22954, "time": 0.81271} +{"mode": "train", "epoch": 36, "iter": 2300, "lr": 0.08672, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26969, "top5_acc": 0.52281, "loss_cls": 4.17004, "loss": 4.17004, "time": 0.81706} +{"mode": "train", "epoch": 36, "iter": 2400, "lr": 0.08671, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27312, "top5_acc": 0.52562, "loss_cls": 4.18931, "loss": 4.18931, "time": 0.81617} +{"mode": "train", "epoch": 36, "iter": 2500, "lr": 0.08669, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27703, "top5_acc": 0.52453, "loss_cls": 4.19025, "loss": 4.19025, "time": 0.82414} +{"mode": "train", "epoch": 36, "iter": 2600, "lr": 0.08667, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26828, "top5_acc": 0.51656, "loss_cls": 4.19806, "loss": 4.19806, "time": 0.8169} +{"mode": "train", "epoch": 36, "iter": 2700, "lr": 0.08665, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27266, "top5_acc": 0.52312, "loss_cls": 4.18285, "loss": 4.18285, "time": 0.81727} +{"mode": "train", "epoch": 36, "iter": 2800, "lr": 0.08663, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25656, "top5_acc": 0.50875, "loss_cls": 4.27067, "loss": 4.27067, "time": 0.8174} +{"mode": "train", "epoch": 36, "iter": 2900, "lr": 0.08661, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26875, "top5_acc": 0.51281, "loss_cls": 4.23733, "loss": 4.23733, "time": 0.82027} +{"mode": "train", "epoch": 36, "iter": 3000, "lr": 0.08659, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2775, "top5_acc": 0.52828, "loss_cls": 4.17137, "loss": 4.17137, "time": 0.81702} +{"mode": "train", "epoch": 36, "iter": 3100, "lr": 0.08657, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26922, "top5_acc": 0.51812, "loss_cls": 4.21498, "loss": 4.21498, "time": 0.81641} +{"mode": "train", "epoch": 36, "iter": 3200, "lr": 0.08655, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.52984, "loss_cls": 4.1799, "loss": 4.1799, "time": 0.81634} +{"mode": "train", "epoch": 36, "iter": 3300, "lr": 0.08653, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26984, "top5_acc": 0.5275, "loss_cls": 4.18659, "loss": 4.18659, "time": 0.81872} +{"mode": "train", "epoch": 36, "iter": 3400, "lr": 0.08651, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27828, "top5_acc": 0.52328, "loss_cls": 4.19823, "loss": 4.19823, "time": 0.81909} +{"mode": "train", "epoch": 36, "iter": 3500, "lr": 0.0865, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27156, "top5_acc": 0.52219, "loss_cls": 4.2114, "loss": 4.2114, "time": 0.82241} +{"mode": "train", "epoch": 36, "iter": 3600, "lr": 0.08648, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27906, "top5_acc": 0.52078, "loss_cls": 4.21354, "loss": 4.21354, "time": 0.81345} +{"mode": "train", "epoch": 36, "iter": 3700, "lr": 0.08646, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27984, "top5_acc": 0.53328, "loss_cls": 4.1533, "loss": 4.1533, "time": 0.8158} +{"mode": "val", "epoch": 36, "iter": 309, "lr": 0.08645, "top1_acc": 0.19653, "top5_acc": 0.42871, "mean_class_accuracy": 0.19651} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.08643, "memory": 15990, "data_time": 1.28329, "top1_acc": 0.27297, "top5_acc": 0.5225, "loss_cls": 4.18385, "loss": 4.18385, "time": 2.25949} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.08641, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28031, "top5_acc": 0.53844, "loss_cls": 4.15252, "loss": 4.15252, "time": 0.81122} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.08639, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27219, "top5_acc": 0.52891, "loss_cls": 4.18027, "loss": 4.18027, "time": 0.82014} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.08637, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28125, "top5_acc": 0.53172, "loss_cls": 4.1649, "loss": 4.1649, "time": 0.81426} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.08635, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27547, "top5_acc": 0.53109, "loss_cls": 4.16999, "loss": 4.16999, "time": 0.8203} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.08633, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26953, "top5_acc": 0.53156, "loss_cls": 4.17277, "loss": 4.17277, "time": 0.81374} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.08631, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.275, "top5_acc": 0.52828, "loss_cls": 4.19261, "loss": 4.19261, "time": 0.83114} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0863, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27266, "top5_acc": 0.52453, "loss_cls": 4.20564, "loss": 4.20564, "time": 0.8207} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.08628, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27578, "top5_acc": 0.52469, "loss_cls": 4.16025, "loss": 4.16025, "time": 0.82615} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.08626, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28141, "top5_acc": 0.53078, "loss_cls": 4.15964, "loss": 4.15964, "time": 0.81975} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.08624, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27141, "top5_acc": 0.53406, "loss_cls": 4.15452, "loss": 4.15452, "time": 0.82} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.08622, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27125, "top5_acc": 0.50969, "loss_cls": 4.24279, "loss": 4.24279, "time": 0.81843} +{"mode": "train", "epoch": 37, "iter": 1300, "lr": 0.0862, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26953, "top5_acc": 0.52328, "loss_cls": 4.20354, "loss": 4.20354, "time": 0.81587} +{"mode": "train", "epoch": 37, "iter": 1400, "lr": 0.08618, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27516, "top5_acc": 0.5225, "loss_cls": 4.18821, "loss": 4.18821, "time": 0.82113} +{"mode": "train", "epoch": 37, "iter": 1500, "lr": 0.08616, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27156, "top5_acc": 0.51984, "loss_cls": 4.20156, "loss": 4.20156, "time": 0.81685} +{"mode": "train", "epoch": 37, "iter": 1600, "lr": 0.08614, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.275, "top5_acc": 0.53281, "loss_cls": 4.18254, "loss": 4.18254, "time": 0.81777} +{"mode": "train", "epoch": 37, "iter": 1700, "lr": 0.08612, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28422, "top5_acc": 0.53234, "loss_cls": 4.14854, "loss": 4.14854, "time": 0.81162} +{"mode": "train", "epoch": 37, "iter": 1800, "lr": 0.0861, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27359, "top5_acc": 0.52547, "loss_cls": 4.16958, "loss": 4.16958, "time": 0.81585} +{"mode": "train", "epoch": 37, "iter": 1900, "lr": 0.08608, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27453, "top5_acc": 0.52734, "loss_cls": 4.16661, "loss": 4.16661, "time": 0.82004} +{"mode": "train", "epoch": 37, "iter": 2000, "lr": 0.08606, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26281, "top5_acc": 0.51359, "loss_cls": 4.23392, "loss": 4.23392, "time": 0.81735} +{"mode": "train", "epoch": 37, "iter": 2100, "lr": 0.08604, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26969, "top5_acc": 0.52906, "loss_cls": 4.17924, "loss": 4.17924, "time": 0.81888} +{"mode": "train", "epoch": 37, "iter": 2200, "lr": 0.08602, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27437, "top5_acc": 0.525, "loss_cls": 4.18223, "loss": 4.18223, "time": 0.8192} +{"mode": "train", "epoch": 37, "iter": 2300, "lr": 0.08601, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26609, "top5_acc": 0.52484, "loss_cls": 4.2149, "loss": 4.2149, "time": 0.81607} +{"mode": "train", "epoch": 37, "iter": 2400, "lr": 0.08599, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26891, "top5_acc": 0.51266, "loss_cls": 4.24212, "loss": 4.24212, "time": 0.81691} +{"mode": "train", "epoch": 37, "iter": 2500, "lr": 0.08597, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28859, "top5_acc": 0.52953, "loss_cls": 4.14963, "loss": 4.14963, "time": 0.81996} +{"mode": "train", "epoch": 37, "iter": 2600, "lr": 0.08595, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27641, "top5_acc": 0.52203, "loss_cls": 4.18527, "loss": 4.18527, "time": 0.82045} +{"mode": "train", "epoch": 37, "iter": 2700, "lr": 0.08593, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26422, "top5_acc": 0.51875, "loss_cls": 4.20887, "loss": 4.20887, "time": 0.81493} +{"mode": "train", "epoch": 37, "iter": 2800, "lr": 0.08591, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26672, "top5_acc": 0.51688, "loss_cls": 4.23847, "loss": 4.23847, "time": 0.81371} +{"mode": "train", "epoch": 37, "iter": 2900, "lr": 0.08589, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27156, "top5_acc": 0.51734, "loss_cls": 4.2104, "loss": 4.2104, "time": 0.81991} +{"mode": "train", "epoch": 37, "iter": 3000, "lr": 0.08587, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26953, "top5_acc": 0.51922, "loss_cls": 4.183, "loss": 4.183, "time": 0.81137} +{"mode": "train", "epoch": 37, "iter": 3100, "lr": 0.08585, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27703, "top5_acc": 0.52484, "loss_cls": 4.19423, "loss": 4.19423, "time": 0.82283} +{"mode": "train", "epoch": 37, "iter": 3200, "lr": 0.08583, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27875, "top5_acc": 0.53438, "loss_cls": 4.13652, "loss": 4.13652, "time": 0.81551} +{"mode": "train", "epoch": 37, "iter": 3300, "lr": 0.08581, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28203, "top5_acc": 0.53422, "loss_cls": 4.12799, "loss": 4.12799, "time": 0.82039} +{"mode": "train", "epoch": 37, "iter": 3400, "lr": 0.08579, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27078, "top5_acc": 0.51656, "loss_cls": 4.19048, "loss": 4.19048, "time": 0.81687} +{"mode": "train", "epoch": 37, "iter": 3500, "lr": 0.08577, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27797, "top5_acc": 0.53, "loss_cls": 4.16487, "loss": 4.16487, "time": 0.81531} +{"mode": "train", "epoch": 37, "iter": 3600, "lr": 0.08575, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28312, "top5_acc": 0.53, "loss_cls": 4.16539, "loss": 4.16539, "time": 0.8181} +{"mode": "train", "epoch": 37, "iter": 3700, "lr": 0.08573, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26484, "top5_acc": 0.52109, "loss_cls": 4.23614, "loss": 4.23614, "time": 0.81917} +{"mode": "val", "epoch": 37, "iter": 309, "lr": 0.08572, "top1_acc": 0.19587, "top5_acc": 0.4199, "mean_class_accuracy": 0.19559} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.0857, "memory": 15990, "data_time": 1.26642, "top1_acc": 0.27547, "top5_acc": 0.52703, "loss_cls": 4.17592, "loss": 4.17592, "time": 2.24133} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.08568, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28984, "top5_acc": 0.53781, "loss_cls": 4.10336, "loss": 4.10336, "time": 0.82026} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.08567, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28016, "top5_acc": 0.53453, "loss_cls": 4.15504, "loss": 4.15504, "time": 0.81562} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.08565, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27187, "top5_acc": 0.53391, "loss_cls": 4.15366, "loss": 4.15366, "time": 0.81603} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.08563, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28172, "top5_acc": 0.53453, "loss_cls": 4.13645, "loss": 4.13645, "time": 0.81846} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.08561, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27406, "top5_acc": 0.52625, "loss_cls": 4.20543, "loss": 4.20543, "time": 0.83416} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.08559, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27047, "top5_acc": 0.52953, "loss_cls": 4.17305, "loss": 4.17305, "time": 0.8322} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.08557, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27703, "top5_acc": 0.525, "loss_cls": 4.1873, "loss": 4.1873, "time": 0.83182} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.08555, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.26672, "top5_acc": 0.52281, "loss_cls": 4.22464, "loss": 4.22464, "time": 0.82475} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.08553, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27469, "top5_acc": 0.51969, "loss_cls": 4.18258, "loss": 4.18258, "time": 0.82398} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.08551, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27828, "top5_acc": 0.53641, "loss_cls": 4.14939, "loss": 4.14939, "time": 0.81911} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.08549, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26844, "top5_acc": 0.52812, "loss_cls": 4.17357, "loss": 4.17357, "time": 0.81851} +{"mode": "train", "epoch": 38, "iter": 1300, "lr": 0.08547, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28453, "top5_acc": 0.53, "loss_cls": 4.15606, "loss": 4.15606, "time": 0.81824} +{"mode": "train", "epoch": 38, "iter": 1400, "lr": 0.08545, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27094, "top5_acc": 0.52953, "loss_cls": 4.2023, "loss": 4.2023, "time": 0.81613} +{"mode": "train", "epoch": 38, "iter": 1500, "lr": 0.08543, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26844, "top5_acc": 0.51891, "loss_cls": 4.21182, "loss": 4.21182, "time": 0.82665} +{"mode": "train", "epoch": 38, "iter": 1600, "lr": 0.08541, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28219, "top5_acc": 0.53453, "loss_cls": 4.13947, "loss": 4.13947, "time": 0.82953} +{"mode": "train", "epoch": 38, "iter": 1700, "lr": 0.08539, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27516, "top5_acc": 0.51969, "loss_cls": 4.18093, "loss": 4.18093, "time": 0.81699} +{"mode": "train", "epoch": 38, "iter": 1800, "lr": 0.08537, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2775, "top5_acc": 0.51062, "loss_cls": 4.21776, "loss": 4.21776, "time": 0.8203} +{"mode": "train", "epoch": 38, "iter": 1900, "lr": 0.08535, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27266, "top5_acc": 0.52469, "loss_cls": 4.18494, "loss": 4.18494, "time": 0.81591} +{"mode": "train", "epoch": 38, "iter": 2000, "lr": 0.08533, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26812, "top5_acc": 0.52141, "loss_cls": 4.18774, "loss": 4.18774, "time": 0.81509} +{"mode": "train", "epoch": 38, "iter": 2100, "lr": 0.08531, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27078, "top5_acc": 0.53, "loss_cls": 4.16553, "loss": 4.16553, "time": 0.82285} +{"mode": "train", "epoch": 38, "iter": 2200, "lr": 0.08529, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27437, "top5_acc": 0.52203, "loss_cls": 4.1996, "loss": 4.1996, "time": 0.81553} +{"mode": "train", "epoch": 38, "iter": 2300, "lr": 0.08527, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28188, "top5_acc": 0.52859, "loss_cls": 4.14175, "loss": 4.14175, "time": 0.81609} +{"mode": "train", "epoch": 38, "iter": 2400, "lr": 0.08525, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28219, "top5_acc": 0.52562, "loss_cls": 4.15651, "loss": 4.15651, "time": 0.81926} +{"mode": "train", "epoch": 38, "iter": 2500, "lr": 0.08523, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28, "top5_acc": 0.5275, "loss_cls": 4.18349, "loss": 4.18349, "time": 0.81832} +{"mode": "train", "epoch": 38, "iter": 2600, "lr": 0.08521, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27859, "top5_acc": 0.52906, "loss_cls": 4.15202, "loss": 4.15202, "time": 0.8143} +{"mode": "train", "epoch": 38, "iter": 2700, "lr": 0.08519, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27562, "top5_acc": 0.52234, "loss_cls": 4.20045, "loss": 4.20045, "time": 0.81457} +{"mode": "train", "epoch": 38, "iter": 2800, "lr": 0.08517, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26906, "top5_acc": 0.52922, "loss_cls": 4.1975, "loss": 4.1975, "time": 0.81484} +{"mode": "train", "epoch": 38, "iter": 2900, "lr": 0.08515, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27219, "top5_acc": 0.51281, "loss_cls": 4.2102, "loss": 4.2102, "time": 0.8153} +{"mode": "train", "epoch": 38, "iter": 3000, "lr": 0.08513, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27891, "top5_acc": 0.53047, "loss_cls": 4.16152, "loss": 4.16152, "time": 0.81288} +{"mode": "train", "epoch": 38, "iter": 3100, "lr": 0.08511, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26672, "top5_acc": 0.52062, "loss_cls": 4.19346, "loss": 4.19346, "time": 0.81999} +{"mode": "train", "epoch": 38, "iter": 3200, "lr": 0.08509, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27484, "top5_acc": 0.52297, "loss_cls": 4.205, "loss": 4.205, "time": 0.8186} +{"mode": "train", "epoch": 38, "iter": 3300, "lr": 0.08507, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28359, "top5_acc": 0.53109, "loss_cls": 4.13651, "loss": 4.13651, "time": 0.82178} +{"mode": "train", "epoch": 38, "iter": 3400, "lr": 0.08505, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27312, "top5_acc": 0.52375, "loss_cls": 4.18938, "loss": 4.18938, "time": 0.81295} +{"mode": "train", "epoch": 38, "iter": 3500, "lr": 0.08503, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27203, "top5_acc": 0.52, "loss_cls": 4.15949, "loss": 4.15949, "time": 0.81408} +{"mode": "train", "epoch": 38, "iter": 3600, "lr": 0.08501, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27297, "top5_acc": 0.52328, "loss_cls": 4.21979, "loss": 4.21979, "time": 0.81841} +{"mode": "train", "epoch": 38, "iter": 3700, "lr": 0.08499, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26578, "top5_acc": 0.51719, "loss_cls": 4.21093, "loss": 4.21093, "time": 0.8258} +{"mode": "val", "epoch": 38, "iter": 309, "lr": 0.08498, "top1_acc": 0.21937, "top5_acc": 0.45687, "mean_class_accuracy": 0.21905} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.08496, "memory": 15990, "data_time": 1.2693, "top1_acc": 0.27109, "top5_acc": 0.52984, "loss_cls": 4.15355, "loss": 4.15355, "time": 2.28983} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.08494, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27266, "top5_acc": 0.525, "loss_cls": 4.171, "loss": 4.171, "time": 0.8301} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.08492, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26922, "top5_acc": 0.52219, "loss_cls": 4.18363, "loss": 4.18363, "time": 0.81945} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.0849, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28625, "top5_acc": 0.52844, "loss_cls": 4.16489, "loss": 4.16489, "time": 0.82176} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.08488, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27703, "top5_acc": 0.54031, "loss_cls": 4.14224, "loss": 4.14224, "time": 0.82243} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.08486, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.28188, "top5_acc": 0.53156, "loss_cls": 4.14585, "loss": 4.14585, "time": 0.83072} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.08484, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.28375, "top5_acc": 0.53625, "loss_cls": 4.14348, "loss": 4.14348, "time": 0.83581} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.08482, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.28, "top5_acc": 0.52828, "loss_cls": 4.17068, "loss": 4.17068, "time": 0.82773} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.0848, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27938, "top5_acc": 0.52562, "loss_cls": 4.13698, "loss": 4.13698, "time": 0.82232} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.08478, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26953, "top5_acc": 0.5225, "loss_cls": 4.20097, "loss": 4.20097, "time": 0.81827} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.08476, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27516, "top5_acc": 0.51781, "loss_cls": 4.19986, "loss": 4.19986, "time": 0.81512} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.08474, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27656, "top5_acc": 0.52938, "loss_cls": 4.17132, "loss": 4.17132, "time": 0.81562} +{"mode": "train", "epoch": 39, "iter": 1300, "lr": 0.08472, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28203, "top5_acc": 0.54531, "loss_cls": 4.12362, "loss": 4.12362, "time": 0.81666} +{"mode": "train", "epoch": 39, "iter": 1400, "lr": 0.0847, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28297, "top5_acc": 0.52516, "loss_cls": 4.15239, "loss": 4.15239, "time": 0.82194} +{"mode": "train", "epoch": 39, "iter": 1500, "lr": 0.08468, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27234, "top5_acc": 0.52656, "loss_cls": 4.19582, "loss": 4.19582, "time": 0.81845} +{"mode": "train", "epoch": 39, "iter": 1600, "lr": 0.08466, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27984, "top5_acc": 0.52547, "loss_cls": 4.19943, "loss": 4.19943, "time": 0.8197} +{"mode": "train", "epoch": 39, "iter": 1700, "lr": 0.08464, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.52547, "loss_cls": 4.18829, "loss": 4.18829, "time": 0.82068} +{"mode": "train", "epoch": 39, "iter": 1800, "lr": 0.08462, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27672, "top5_acc": 0.53016, "loss_cls": 4.17703, "loss": 4.17703, "time": 0.81898} +{"mode": "train", "epoch": 39, "iter": 1900, "lr": 0.0846, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.52172, "loss_cls": 4.19018, "loss": 4.19018, "time": 0.8156} +{"mode": "train", "epoch": 39, "iter": 2000, "lr": 0.08458, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27625, "top5_acc": 0.52453, "loss_cls": 4.17044, "loss": 4.17044, "time": 0.82806} +{"mode": "train", "epoch": 39, "iter": 2100, "lr": 0.08456, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27812, "top5_acc": 0.52797, "loss_cls": 4.16191, "loss": 4.16191, "time": 0.8173} +{"mode": "train", "epoch": 39, "iter": 2200, "lr": 0.08454, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27406, "top5_acc": 0.52641, "loss_cls": 4.19257, "loss": 4.19257, "time": 0.81907} +{"mode": "train", "epoch": 39, "iter": 2300, "lr": 0.08452, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28625, "top5_acc": 0.53859, "loss_cls": 4.10902, "loss": 4.10902, "time": 0.81861} +{"mode": "train", "epoch": 39, "iter": 2400, "lr": 0.0845, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27391, "top5_acc": 0.53, "loss_cls": 4.16296, "loss": 4.16296, "time": 0.82192} +{"mode": "train", "epoch": 39, "iter": 2500, "lr": 0.08448, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26875, "top5_acc": 0.52156, "loss_cls": 4.22034, "loss": 4.22034, "time": 0.81663} +{"mode": "train", "epoch": 39, "iter": 2600, "lr": 0.08446, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28359, "top5_acc": 0.52156, "loss_cls": 4.16927, "loss": 4.16927, "time": 0.81556} +{"mode": "train", "epoch": 39, "iter": 2700, "lr": 0.08444, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28203, "top5_acc": 0.52922, "loss_cls": 4.17864, "loss": 4.17864, "time": 0.82089} +{"mode": "train", "epoch": 39, "iter": 2800, "lr": 0.08442, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27312, "top5_acc": 0.52219, "loss_cls": 4.18005, "loss": 4.18005, "time": 0.81359} +{"mode": "train", "epoch": 39, "iter": 2900, "lr": 0.0844, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27625, "top5_acc": 0.5325, "loss_cls": 4.15268, "loss": 4.15268, "time": 0.81583} +{"mode": "train", "epoch": 39, "iter": 3000, "lr": 0.08438, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26922, "top5_acc": 0.52125, "loss_cls": 4.23376, "loss": 4.23376, "time": 0.82194} +{"mode": "train", "epoch": 39, "iter": 3100, "lr": 0.08436, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26719, "top5_acc": 0.52516, "loss_cls": 4.20372, "loss": 4.20372, "time": 0.82102} +{"mode": "train", "epoch": 39, "iter": 3200, "lr": 0.08434, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2675, "top5_acc": 0.52016, "loss_cls": 4.17628, "loss": 4.17628, "time": 0.8289} +{"mode": "train", "epoch": 39, "iter": 3300, "lr": 0.08432, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26469, "top5_acc": 0.51438, "loss_cls": 4.2325, "loss": 4.2325, "time": 0.82338} +{"mode": "train", "epoch": 39, "iter": 3400, "lr": 0.0843, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27672, "top5_acc": 0.52469, "loss_cls": 4.17196, "loss": 4.17196, "time": 0.8171} +{"mode": "train", "epoch": 39, "iter": 3500, "lr": 0.08428, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27547, "top5_acc": 0.53219, "loss_cls": 4.14748, "loss": 4.14748, "time": 0.82247} +{"mode": "train", "epoch": 39, "iter": 3600, "lr": 0.08426, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2775, "top5_acc": 0.53, "loss_cls": 4.16391, "loss": 4.16391, "time": 0.81823} +{"mode": "train", "epoch": 39, "iter": 3700, "lr": 0.08424, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27187, "top5_acc": 0.52016, "loss_cls": 4.18484, "loss": 4.18484, "time": 0.82317} +{"mode": "val", "epoch": 39, "iter": 309, "lr": 0.08423, "top1_acc": 0.20392, "top5_acc": 0.43489, "mean_class_accuracy": 0.20372} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.08421, "memory": 15990, "data_time": 1.28484, "top1_acc": 0.27938, "top5_acc": 0.53781, "loss_cls": 4.13472, "loss": 4.13472, "time": 2.30515} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.08419, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27859, "top5_acc": 0.52453, "loss_cls": 4.16219, "loss": 4.16219, "time": 0.81628} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.08417, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27797, "top5_acc": 0.5275, "loss_cls": 4.14854, "loss": 4.14854, "time": 0.81718} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.08415, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27562, "top5_acc": 0.52984, "loss_cls": 4.16914, "loss": 4.16914, "time": 0.82348} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.08413, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2875, "top5_acc": 0.54016, "loss_cls": 4.10609, "loss": 4.10609, "time": 0.81852} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.08411, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27062, "top5_acc": 0.52797, "loss_cls": 4.1788, "loss": 4.1788, "time": 0.82148} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.08408, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26625, "top5_acc": 0.52609, "loss_cls": 4.1772, "loss": 4.1772, "time": 0.83783} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.08406, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27531, "top5_acc": 0.52219, "loss_cls": 4.22284, "loss": 4.22284, "time": 0.82856} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.08404, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27891, "top5_acc": 0.52922, "loss_cls": 4.1413, "loss": 4.1413, "time": 0.82834} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.08402, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28359, "top5_acc": 0.53359, "loss_cls": 4.11045, "loss": 4.11045, "time": 0.82032} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.084, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.285, "top5_acc": 0.53203, "loss_cls": 4.13907, "loss": 4.13907, "time": 0.81899} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.08398, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29516, "top5_acc": 0.54828, "loss_cls": 4.05692, "loss": 4.05692, "time": 0.82126} +{"mode": "train", "epoch": 40, "iter": 1300, "lr": 0.08396, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27031, "top5_acc": 0.52, "loss_cls": 4.18396, "loss": 4.18396, "time": 0.82337} +{"mode": "train", "epoch": 40, "iter": 1400, "lr": 0.08394, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27562, "top5_acc": 0.53172, "loss_cls": 4.14332, "loss": 4.14332, "time": 0.81805} +{"mode": "train", "epoch": 40, "iter": 1500, "lr": 0.08392, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28547, "top5_acc": 0.52734, "loss_cls": 4.16337, "loss": 4.16337, "time": 0.81916} +{"mode": "train", "epoch": 40, "iter": 1600, "lr": 0.0839, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28172, "top5_acc": 0.52906, "loss_cls": 4.15957, "loss": 4.15957, "time": 0.82144} +{"mode": "train", "epoch": 40, "iter": 1700, "lr": 0.08388, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27859, "top5_acc": 0.53172, "loss_cls": 4.1386, "loss": 4.1386, "time": 0.81581} +{"mode": "train", "epoch": 40, "iter": 1800, "lr": 0.08386, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27516, "top5_acc": 0.52766, "loss_cls": 4.15603, "loss": 4.15603, "time": 0.81787} +{"mode": "train", "epoch": 40, "iter": 1900, "lr": 0.08384, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27391, "top5_acc": 0.53391, "loss_cls": 4.16752, "loss": 4.16752, "time": 0.82478} +{"mode": "train", "epoch": 40, "iter": 2000, "lr": 0.08382, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27109, "top5_acc": 0.52312, "loss_cls": 4.18833, "loss": 4.18833, "time": 0.82164} +{"mode": "train", "epoch": 40, "iter": 2100, "lr": 0.0838, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27812, "top5_acc": 0.52969, "loss_cls": 4.18019, "loss": 4.18019, "time": 0.81458} +{"mode": "train", "epoch": 40, "iter": 2200, "lr": 0.08378, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27, "top5_acc": 0.51203, "loss_cls": 4.1995, "loss": 4.1995, "time": 0.81938} +{"mode": "train", "epoch": 40, "iter": 2300, "lr": 0.08376, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27016, "top5_acc": 0.51922, "loss_cls": 4.20441, "loss": 4.20441, "time": 0.82012} +{"mode": "train", "epoch": 40, "iter": 2400, "lr": 0.08374, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28594, "top5_acc": 0.53141, "loss_cls": 4.17243, "loss": 4.17243, "time": 0.82769} +{"mode": "train", "epoch": 40, "iter": 2500, "lr": 0.08371, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27859, "top5_acc": 0.54, "loss_cls": 4.11494, "loss": 4.11494, "time": 0.8189} +{"mode": "train", "epoch": 40, "iter": 2600, "lr": 0.08369, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27266, "top5_acc": 0.52891, "loss_cls": 4.16724, "loss": 4.16724, "time": 0.81918} +{"mode": "train", "epoch": 40, "iter": 2700, "lr": 0.08367, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28031, "top5_acc": 0.53516, "loss_cls": 4.13595, "loss": 4.13595, "time": 0.81441} +{"mode": "train", "epoch": 40, "iter": 2800, "lr": 0.08365, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27047, "top5_acc": 0.52656, "loss_cls": 4.17449, "loss": 4.17449, "time": 0.82109} +{"mode": "train", "epoch": 40, "iter": 2900, "lr": 0.08363, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.53047, "loss_cls": 4.17319, "loss": 4.17319, "time": 0.8189} +{"mode": "train", "epoch": 40, "iter": 3000, "lr": 0.08361, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27859, "top5_acc": 0.52312, "loss_cls": 4.18357, "loss": 4.18357, "time": 0.82318} +{"mode": "train", "epoch": 40, "iter": 3100, "lr": 0.08359, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28234, "top5_acc": 0.52609, "loss_cls": 4.13114, "loss": 4.13114, "time": 0.83085} +{"mode": "train", "epoch": 40, "iter": 3200, "lr": 0.08357, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28547, "top5_acc": 0.53438, "loss_cls": 4.14169, "loss": 4.14169, "time": 0.82457} +{"mode": "train", "epoch": 40, "iter": 3300, "lr": 0.08355, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27359, "top5_acc": 0.51953, "loss_cls": 4.19815, "loss": 4.19815, "time": 0.8243} +{"mode": "train", "epoch": 40, "iter": 3400, "lr": 0.08353, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.52422, "loss_cls": 4.19891, "loss": 4.19891, "time": 0.8197} +{"mode": "train", "epoch": 40, "iter": 3500, "lr": 0.08351, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27062, "top5_acc": 0.525, "loss_cls": 4.18459, "loss": 4.18459, "time": 0.81765} +{"mode": "train", "epoch": 40, "iter": 3600, "lr": 0.08349, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27875, "top5_acc": 0.52125, "loss_cls": 4.16777, "loss": 4.16777, "time": 0.81495} +{"mode": "train", "epoch": 40, "iter": 3700, "lr": 0.08347, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27375, "top5_acc": 0.52859, "loss_cls": 4.1685, "loss": 4.1685, "time": 0.8193} +{"mode": "val", "epoch": 40, "iter": 309, "lr": 0.08346, "top1_acc": 0.20402, "top5_acc": 0.43651, "mean_class_accuracy": 0.20382} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.08344, "memory": 15990, "data_time": 1.26584, "top1_acc": 0.29031, "top5_acc": 0.54516, "loss_cls": 4.07186, "loss": 4.07186, "time": 2.24983} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.08342, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28391, "top5_acc": 0.54141, "loss_cls": 4.11068, "loss": 4.11068, "time": 0.81721} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.08339, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27203, "top5_acc": 0.52969, "loss_cls": 4.16423, "loss": 4.16423, "time": 0.8231} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.08337, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27672, "top5_acc": 0.53297, "loss_cls": 4.16019, "loss": 4.16019, "time": 0.82103} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.08335, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28562, "top5_acc": 0.53016, "loss_cls": 4.13761, "loss": 4.13761, "time": 0.81993} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.08333, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28016, "top5_acc": 0.52688, "loss_cls": 4.15155, "loss": 4.15155, "time": 0.82205} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.08331, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27812, "top5_acc": 0.52984, "loss_cls": 4.1469, "loss": 4.1469, "time": 0.82432} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.08329, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29094, "top5_acc": 0.52969, "loss_cls": 4.12854, "loss": 4.12854, "time": 0.83613} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.08327, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28578, "top5_acc": 0.53828, "loss_cls": 4.10383, "loss": 4.10383, "time": 0.82167} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.08325, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27406, "top5_acc": 0.52375, "loss_cls": 4.17255, "loss": 4.17255, "time": 0.82172} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.08323, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27406, "top5_acc": 0.52469, "loss_cls": 4.18818, "loss": 4.18818, "time": 0.81893} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.08321, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27312, "top5_acc": 0.53531, "loss_cls": 4.15, "loss": 4.15, "time": 0.81646} +{"mode": "train", "epoch": 41, "iter": 1300, "lr": 0.08319, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27078, "top5_acc": 0.52094, "loss_cls": 4.22097, "loss": 4.22097, "time": 0.82224} +{"mode": "train", "epoch": 41, "iter": 1400, "lr": 0.08316, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27297, "top5_acc": 0.52031, "loss_cls": 4.18067, "loss": 4.18067, "time": 0.81843} +{"mode": "train", "epoch": 41, "iter": 1500, "lr": 0.08314, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27891, "top5_acc": 0.53062, "loss_cls": 4.13657, "loss": 4.13657, "time": 0.81871} +{"mode": "train", "epoch": 41, "iter": 1600, "lr": 0.08312, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27766, "top5_acc": 0.52562, "loss_cls": 4.15086, "loss": 4.15086, "time": 0.81372} +{"mode": "train", "epoch": 41, "iter": 1700, "lr": 0.0831, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27734, "top5_acc": 0.53516, "loss_cls": 4.13645, "loss": 4.13645, "time": 0.81747} +{"mode": "train", "epoch": 41, "iter": 1800, "lr": 0.08308, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26828, "top5_acc": 0.52797, "loss_cls": 4.18687, "loss": 4.18687, "time": 0.82351} +{"mode": "train", "epoch": 41, "iter": 1900, "lr": 0.08306, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27469, "top5_acc": 0.52562, "loss_cls": 4.16275, "loss": 4.16275, "time": 0.81467} +{"mode": "train", "epoch": 41, "iter": 2000, "lr": 0.08304, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27187, "top5_acc": 0.52469, "loss_cls": 4.16837, "loss": 4.16837, "time": 0.82068} +{"mode": "train", "epoch": 41, "iter": 2100, "lr": 0.08302, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27594, "top5_acc": 0.53422, "loss_cls": 4.1497, "loss": 4.1497, "time": 0.8283} +{"mode": "train", "epoch": 41, "iter": 2200, "lr": 0.083, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28391, "top5_acc": 0.53328, "loss_cls": 4.11563, "loss": 4.11563, "time": 0.81657} +{"mode": "train", "epoch": 41, "iter": 2300, "lr": 0.08298, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27625, "top5_acc": 0.52812, "loss_cls": 4.17135, "loss": 4.17135, "time": 0.81628} +{"mode": "train", "epoch": 41, "iter": 2400, "lr": 0.08296, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27562, "top5_acc": 0.53812, "loss_cls": 4.14334, "loss": 4.14334, "time": 0.82259} +{"mode": "train", "epoch": 41, "iter": 2500, "lr": 0.08293, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27656, "top5_acc": 0.53594, "loss_cls": 4.15414, "loss": 4.15414, "time": 0.81614} +{"mode": "train", "epoch": 41, "iter": 2600, "lr": 0.08291, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28, "top5_acc": 0.53297, "loss_cls": 4.15655, "loss": 4.15655, "time": 0.81652} +{"mode": "train", "epoch": 41, "iter": 2700, "lr": 0.08289, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26734, "top5_acc": 0.52062, "loss_cls": 4.20631, "loss": 4.20631, "time": 0.8139} +{"mode": "train", "epoch": 41, "iter": 2800, "lr": 0.08287, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28813, "top5_acc": 0.53594, "loss_cls": 4.12702, "loss": 4.12702, "time": 0.81866} +{"mode": "train", "epoch": 41, "iter": 2900, "lr": 0.08285, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26703, "top5_acc": 0.52344, "loss_cls": 4.18437, "loss": 4.18437, "time": 0.82145} +{"mode": "train", "epoch": 41, "iter": 3000, "lr": 0.08283, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27578, "top5_acc": 0.52344, "loss_cls": 4.19156, "loss": 4.19156, "time": 0.8185} +{"mode": "train", "epoch": 41, "iter": 3100, "lr": 0.08281, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27453, "top5_acc": 0.52875, "loss_cls": 4.18481, "loss": 4.18481, "time": 0.81384} +{"mode": "train", "epoch": 41, "iter": 3200, "lr": 0.08279, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27922, "top5_acc": 0.54062, "loss_cls": 4.13166, "loss": 4.13166, "time": 0.81924} +{"mode": "train", "epoch": 41, "iter": 3300, "lr": 0.08277, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27125, "top5_acc": 0.52688, "loss_cls": 4.19671, "loss": 4.19671, "time": 0.81766} +{"mode": "train", "epoch": 41, "iter": 3400, "lr": 0.08274, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27109, "top5_acc": 0.53672, "loss_cls": 4.15187, "loss": 4.15187, "time": 0.81139} +{"mode": "train", "epoch": 41, "iter": 3500, "lr": 0.08272, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28266, "top5_acc": 0.53656, "loss_cls": 4.12752, "loss": 4.12752, "time": 0.81787} +{"mode": "train", "epoch": 41, "iter": 3600, "lr": 0.0827, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27891, "top5_acc": 0.52594, "loss_cls": 4.1443, "loss": 4.1443, "time": 0.81849} +{"mode": "train", "epoch": 41, "iter": 3700, "lr": 0.08268, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27219, "top5_acc": 0.52188, "loss_cls": 4.19796, "loss": 4.19796, "time": 0.81545} +{"mode": "val", "epoch": 41, "iter": 309, "lr": 0.08267, "top1_acc": 0.18705, "top5_acc": 0.41909, "mean_class_accuracy": 0.18686} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.08265, "memory": 15990, "data_time": 1.27491, "top1_acc": 0.28578, "top5_acc": 0.52938, "loss_cls": 4.12204, "loss": 4.12204, "time": 2.25211} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.08263, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27953, "top5_acc": 0.55016, "loss_cls": 4.07074, "loss": 4.07074, "time": 0.82299} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.08261, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2775, "top5_acc": 0.53891, "loss_cls": 4.12442, "loss": 4.12442, "time": 0.81903} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.08259, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28172, "top5_acc": 0.535, "loss_cls": 4.13563, "loss": 4.13563, "time": 0.81231} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.08257, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28141, "top5_acc": 0.53281, "loss_cls": 4.13609, "loss": 4.13609, "time": 0.80993} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.08254, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27547, "top5_acc": 0.53891, "loss_cls": 4.14322, "loss": 4.14322, "time": 0.82053} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.08252, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27656, "top5_acc": 0.53484, "loss_cls": 4.10287, "loss": 4.10287, "time": 0.81831} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.0825, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28047, "top5_acc": 0.53594, "loss_cls": 4.09676, "loss": 4.09676, "time": 0.824} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.08248, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27703, "top5_acc": 0.53031, "loss_cls": 4.16694, "loss": 4.16694, "time": 0.82176} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.08246, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28609, "top5_acc": 0.53828, "loss_cls": 4.08203, "loss": 4.08203, "time": 0.81955} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.08244, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27672, "top5_acc": 0.52844, "loss_cls": 4.15701, "loss": 4.15701, "time": 0.82322} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.08242, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2775, "top5_acc": 0.53438, "loss_cls": 4.11929, "loss": 4.11929, "time": 0.82008} +{"mode": "train", "epoch": 42, "iter": 1300, "lr": 0.0824, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28141, "top5_acc": 0.54078, "loss_cls": 4.10953, "loss": 4.10953, "time": 0.81824} +{"mode": "train", "epoch": 42, "iter": 1400, "lr": 0.08237, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28203, "top5_acc": 0.54031, "loss_cls": 4.12879, "loss": 4.12879, "time": 0.81651} +{"mode": "train", "epoch": 42, "iter": 1500, "lr": 0.08235, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28125, "top5_acc": 0.52766, "loss_cls": 4.13471, "loss": 4.13471, "time": 0.81438} +{"mode": "train", "epoch": 42, "iter": 1600, "lr": 0.08233, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27859, "top5_acc": 0.52969, "loss_cls": 4.15114, "loss": 4.15114, "time": 0.81335} +{"mode": "train", "epoch": 42, "iter": 1700, "lr": 0.08231, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28125, "top5_acc": 0.52188, "loss_cls": 4.17547, "loss": 4.17547, "time": 0.81529} +{"mode": "train", "epoch": 42, "iter": 1800, "lr": 0.08229, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27688, "top5_acc": 0.52875, "loss_cls": 4.15902, "loss": 4.15902, "time": 0.81863} +{"mode": "train", "epoch": 42, "iter": 1900, "lr": 0.08227, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27797, "top5_acc": 0.53703, "loss_cls": 4.12313, "loss": 4.12313, "time": 0.81893} +{"mode": "train", "epoch": 42, "iter": 2000, "lr": 0.08225, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27297, "top5_acc": 0.52859, "loss_cls": 4.18446, "loss": 4.18446, "time": 0.81871} +{"mode": "train", "epoch": 42, "iter": 2100, "lr": 0.08222, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27797, "top5_acc": 0.52734, "loss_cls": 4.16857, "loss": 4.16857, "time": 0.815} +{"mode": "train", "epoch": 42, "iter": 2200, "lr": 0.0822, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27516, "top5_acc": 0.53266, "loss_cls": 4.16582, "loss": 4.16582, "time": 0.8146} +{"mode": "train", "epoch": 42, "iter": 2300, "lr": 0.08218, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26359, "top5_acc": 0.52391, "loss_cls": 4.20667, "loss": 4.20667, "time": 0.81574} +{"mode": "train", "epoch": 42, "iter": 2400, "lr": 0.08216, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27297, "top5_acc": 0.52812, "loss_cls": 4.16396, "loss": 4.16396, "time": 0.82111} +{"mode": "train", "epoch": 42, "iter": 2500, "lr": 0.08214, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28266, "top5_acc": 0.52859, "loss_cls": 4.14118, "loss": 4.14118, "time": 0.81635} +{"mode": "train", "epoch": 42, "iter": 2600, "lr": 0.08212, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28344, "top5_acc": 0.53453, "loss_cls": 4.11265, "loss": 4.11265, "time": 0.81883} +{"mode": "train", "epoch": 42, "iter": 2700, "lr": 0.0821, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28266, "top5_acc": 0.53406, "loss_cls": 4.13319, "loss": 4.13319, "time": 0.81561} +{"mode": "train", "epoch": 42, "iter": 2800, "lr": 0.08207, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28078, "top5_acc": 0.53281, "loss_cls": 4.14672, "loss": 4.14672, "time": 0.81931} +{"mode": "train", "epoch": 42, "iter": 2900, "lr": 0.08205, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28016, "top5_acc": 0.52578, "loss_cls": 4.17671, "loss": 4.17671, "time": 0.81412} +{"mode": "train", "epoch": 42, "iter": 3000, "lr": 0.08203, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26547, "top5_acc": 0.52375, "loss_cls": 4.22725, "loss": 4.22725, "time": 0.81829} +{"mode": "train", "epoch": 42, "iter": 3100, "lr": 0.08201, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28016, "top5_acc": 0.52453, "loss_cls": 4.19729, "loss": 4.19729, "time": 0.82297} +{"mode": "train", "epoch": 42, "iter": 3200, "lr": 0.08199, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27156, "top5_acc": 0.51641, "loss_cls": 4.20914, "loss": 4.20914, "time": 0.81942} +{"mode": "train", "epoch": 42, "iter": 3300, "lr": 0.08197, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27156, "top5_acc": 0.52094, "loss_cls": 4.18521, "loss": 4.18521, "time": 0.81468} +{"mode": "train", "epoch": 42, "iter": 3400, "lr": 0.08195, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28547, "top5_acc": 0.53703, "loss_cls": 4.14498, "loss": 4.14498, "time": 0.81529} +{"mode": "train", "epoch": 42, "iter": 3500, "lr": 0.08192, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26828, "top5_acc": 0.5225, "loss_cls": 4.20006, "loss": 4.20006, "time": 0.81766} +{"mode": "train", "epoch": 42, "iter": 3600, "lr": 0.0819, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26875, "top5_acc": 0.52578, "loss_cls": 4.20022, "loss": 4.20022, "time": 0.81791} +{"mode": "train", "epoch": 42, "iter": 3700, "lr": 0.08188, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28891, "top5_acc": 0.54172, "loss_cls": 4.11252, "loss": 4.11252, "time": 0.81588} +{"mode": "val", "epoch": 42, "iter": 309, "lr": 0.08187, "top1_acc": 0.2025, "top5_acc": 0.43124, "mean_class_accuracy": 0.20225} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.08185, "memory": 15990, "data_time": 1.25144, "top1_acc": 0.28234, "top5_acc": 0.54062, "loss_cls": 4.09685, "loss": 4.09685, "time": 2.22425} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.08183, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28141, "top5_acc": 0.54531, "loss_cls": 4.09795, "loss": 4.09795, "time": 0.81819} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.08181, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28906, "top5_acc": 0.52797, "loss_cls": 4.1387, "loss": 4.1387, "time": 0.82629} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.08179, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28141, "top5_acc": 0.53562, "loss_cls": 4.1151, "loss": 4.1151, "time": 0.82078} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.08176, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27328, "top5_acc": 0.52859, "loss_cls": 4.15317, "loss": 4.15317, "time": 0.81549} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.08174, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27766, "top5_acc": 0.535, "loss_cls": 4.15536, "loss": 4.15536, "time": 0.81642} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.08172, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28016, "top5_acc": 0.5325, "loss_cls": 4.11729, "loss": 4.11729, "time": 0.82041} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.0817, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27594, "top5_acc": 0.53594, "loss_cls": 4.12934, "loss": 4.12934, "time": 0.82161} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.08168, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28422, "top5_acc": 0.53359, "loss_cls": 4.14204, "loss": 4.14204, "time": 0.8301} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.08166, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27422, "top5_acc": 0.53266, "loss_cls": 4.15759, "loss": 4.15759, "time": 0.81506} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.08163, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28453, "top5_acc": 0.53578, "loss_cls": 4.11364, "loss": 4.11364, "time": 0.82417} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.08161, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27875, "top5_acc": 0.52453, "loss_cls": 4.16517, "loss": 4.16517, "time": 0.81695} +{"mode": "train", "epoch": 43, "iter": 1300, "lr": 0.08159, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27453, "top5_acc": 0.52391, "loss_cls": 4.18986, "loss": 4.18986, "time": 0.81426} +{"mode": "train", "epoch": 43, "iter": 1400, "lr": 0.08157, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28266, "top5_acc": 0.53094, "loss_cls": 4.13522, "loss": 4.13522, "time": 0.81732} +{"mode": "train", "epoch": 43, "iter": 1500, "lr": 0.08155, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28438, "top5_acc": 0.54156, "loss_cls": 4.12274, "loss": 4.12274, "time": 0.8131} +{"mode": "train", "epoch": 43, "iter": 1600, "lr": 0.08153, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28422, "top5_acc": 0.53953, "loss_cls": 4.11102, "loss": 4.11102, "time": 0.81869} +{"mode": "train", "epoch": 43, "iter": 1700, "lr": 0.0815, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27828, "top5_acc": 0.5275, "loss_cls": 4.15942, "loss": 4.15942, "time": 0.81636} +{"mode": "train", "epoch": 43, "iter": 1800, "lr": 0.08148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27734, "top5_acc": 0.53172, "loss_cls": 4.15025, "loss": 4.15025, "time": 0.82074} +{"mode": "train", "epoch": 43, "iter": 1900, "lr": 0.08146, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29344, "top5_acc": 0.54641, "loss_cls": 4.06261, "loss": 4.06261, "time": 0.81803} +{"mode": "train", "epoch": 43, "iter": 2000, "lr": 0.08144, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27375, "top5_acc": 0.52734, "loss_cls": 4.17086, "loss": 4.17086, "time": 0.82013} +{"mode": "train", "epoch": 43, "iter": 2100, "lr": 0.08142, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27359, "top5_acc": 0.53422, "loss_cls": 4.16305, "loss": 4.16305, "time": 0.81495} +{"mode": "train", "epoch": 43, "iter": 2200, "lr": 0.0814, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28594, "top5_acc": 0.53766, "loss_cls": 4.12672, "loss": 4.12672, "time": 0.81666} +{"mode": "train", "epoch": 43, "iter": 2300, "lr": 0.08137, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26547, "top5_acc": 0.52141, "loss_cls": 4.21551, "loss": 4.21551, "time": 0.81788} +{"mode": "train", "epoch": 43, "iter": 2400, "lr": 0.08135, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27547, "top5_acc": 0.52562, "loss_cls": 4.17191, "loss": 4.17191, "time": 0.81748} +{"mode": "train", "epoch": 43, "iter": 2500, "lr": 0.08133, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.52906, "loss_cls": 4.15538, "loss": 4.15538, "time": 0.81702} +{"mode": "train", "epoch": 43, "iter": 2600, "lr": 0.08131, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28047, "top5_acc": 0.52578, "loss_cls": 4.16954, "loss": 4.16954, "time": 0.81446} +{"mode": "train", "epoch": 43, "iter": 2700, "lr": 0.08129, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27844, "top5_acc": 0.53375, "loss_cls": 4.15265, "loss": 4.15265, "time": 0.8156} +{"mode": "train", "epoch": 43, "iter": 2800, "lr": 0.08126, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28344, "top5_acc": 0.53781, "loss_cls": 4.14428, "loss": 4.14428, "time": 0.81464} +{"mode": "train", "epoch": 43, "iter": 2900, "lr": 0.08124, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27187, "top5_acc": 0.52188, "loss_cls": 4.18216, "loss": 4.18216, "time": 0.81614} +{"mode": "train", "epoch": 43, "iter": 3000, "lr": 0.08122, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28641, "top5_acc": 0.54266, "loss_cls": 4.0919, "loss": 4.0919, "time": 0.82038} +{"mode": "train", "epoch": 43, "iter": 3100, "lr": 0.0812, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28344, "top5_acc": 0.53609, "loss_cls": 4.15228, "loss": 4.15228, "time": 0.81915} +{"mode": "train", "epoch": 43, "iter": 3200, "lr": 0.08118, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28016, "top5_acc": 0.53484, "loss_cls": 4.13713, "loss": 4.13713, "time": 0.81272} +{"mode": "train", "epoch": 43, "iter": 3300, "lr": 0.08116, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27719, "top5_acc": 0.53141, "loss_cls": 4.14339, "loss": 4.14339, "time": 0.81678} +{"mode": "train", "epoch": 43, "iter": 3400, "lr": 0.08113, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28094, "top5_acc": 0.53156, "loss_cls": 4.13459, "loss": 4.13459, "time": 0.81571} +{"mode": "train", "epoch": 43, "iter": 3500, "lr": 0.08111, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28375, "top5_acc": 0.53125, "loss_cls": 4.14467, "loss": 4.14467, "time": 0.82326} +{"mode": "train", "epoch": 43, "iter": 3600, "lr": 0.08109, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27969, "top5_acc": 0.53516, "loss_cls": 4.10532, "loss": 4.10532, "time": 0.81946} +{"mode": "train", "epoch": 43, "iter": 3700, "lr": 0.08107, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28234, "top5_acc": 0.53953, "loss_cls": 4.12291, "loss": 4.12291, "time": 0.82004} +{"mode": "val", "epoch": 43, "iter": 309, "lr": 0.08106, "top1_acc": 0.20985, "top5_acc": 0.45049, "mean_class_accuracy": 0.20973} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.08104, "memory": 15990, "data_time": 1.26431, "top1_acc": 0.27594, "top5_acc": 0.54047, "loss_cls": 4.13612, "loss": 4.13612, "time": 2.23557} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.08101, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27828, "top5_acc": 0.53344, "loss_cls": 4.13799, "loss": 4.13799, "time": 0.81483} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.08099, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28219, "top5_acc": 0.53234, "loss_cls": 4.12839, "loss": 4.12839, "time": 0.82342} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.08097, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27437, "top5_acc": 0.535, "loss_cls": 4.14084, "loss": 4.14084, "time": 0.81768} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.08095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26953, "top5_acc": 0.53312, "loss_cls": 4.13589, "loss": 4.13589, "time": 0.81743} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.08093, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28875, "top5_acc": 0.54344, "loss_cls": 4.10716, "loss": 4.10716, "time": 0.82316} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.0809, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2825, "top5_acc": 0.5375, "loss_cls": 4.09811, "loss": 4.09811, "time": 0.82123} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.08088, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28813, "top5_acc": 0.53547, "loss_cls": 4.0874, "loss": 4.0874, "time": 0.82229} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.08086, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28969, "top5_acc": 0.54453, "loss_cls": 4.10619, "loss": 4.10619, "time": 0.81897} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.08084, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28547, "top5_acc": 0.53641, "loss_cls": 4.12492, "loss": 4.12492, "time": 0.83188} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.08082, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28516, "top5_acc": 0.53922, "loss_cls": 4.1256, "loss": 4.1256, "time": 0.81734} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.08079, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28813, "top5_acc": 0.52938, "loss_cls": 4.14631, "loss": 4.14631, "time": 0.81888} +{"mode": "train", "epoch": 44, "iter": 1300, "lr": 0.08077, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27641, "top5_acc": 0.52984, "loss_cls": 4.14972, "loss": 4.14972, "time": 0.81806} +{"mode": "train", "epoch": 44, "iter": 1400, "lr": 0.08075, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27641, "top5_acc": 0.52984, "loss_cls": 4.11214, "loss": 4.11214, "time": 0.81489} +{"mode": "train", "epoch": 44, "iter": 1500, "lr": 0.08073, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27609, "top5_acc": 0.53125, "loss_cls": 4.15673, "loss": 4.15673, "time": 0.81421} +{"mode": "train", "epoch": 44, "iter": 1600, "lr": 0.08071, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.285, "top5_acc": 0.53859, "loss_cls": 4.12344, "loss": 4.12344, "time": 0.81186} +{"mode": "train", "epoch": 44, "iter": 1700, "lr": 0.08068, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27984, "top5_acc": 0.545, "loss_cls": 4.10584, "loss": 4.10584, "time": 0.81598} +{"mode": "train", "epoch": 44, "iter": 1800, "lr": 0.08066, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27906, "top5_acc": 0.53312, "loss_cls": 4.13967, "loss": 4.13967, "time": 0.81254} +{"mode": "train", "epoch": 44, "iter": 1900, "lr": 0.08064, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27797, "top5_acc": 0.53656, "loss_cls": 4.14209, "loss": 4.14209, "time": 0.8176} +{"mode": "train", "epoch": 44, "iter": 2000, "lr": 0.08062, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28797, "top5_acc": 0.54109, "loss_cls": 4.11048, "loss": 4.11048, "time": 0.81614} +{"mode": "train", "epoch": 44, "iter": 2100, "lr": 0.0806, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27297, "top5_acc": 0.52656, "loss_cls": 4.16204, "loss": 4.16204, "time": 0.81783} +{"mode": "train", "epoch": 44, "iter": 2200, "lr": 0.08057, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27281, "top5_acc": 0.52234, "loss_cls": 4.1842, "loss": 4.1842, "time": 0.81391} +{"mode": "train", "epoch": 44, "iter": 2300, "lr": 0.08055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28109, "top5_acc": 0.53625, "loss_cls": 4.13859, "loss": 4.13859, "time": 0.81867} +{"mode": "train", "epoch": 44, "iter": 2400, "lr": 0.08053, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26422, "top5_acc": 0.51969, "loss_cls": 4.19225, "loss": 4.19225, "time": 0.81028} +{"mode": "train", "epoch": 44, "iter": 2500, "lr": 0.08051, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28703, "top5_acc": 0.5375, "loss_cls": 4.13334, "loss": 4.13334, "time": 0.81791} +{"mode": "train", "epoch": 44, "iter": 2600, "lr": 0.08048, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29266, "top5_acc": 0.54281, "loss_cls": 4.08733, "loss": 4.08733, "time": 0.81722} +{"mode": "train", "epoch": 44, "iter": 2700, "lr": 0.08046, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28422, "top5_acc": 0.53672, "loss_cls": 4.12436, "loss": 4.12436, "time": 0.82542} +{"mode": "train", "epoch": 44, "iter": 2800, "lr": 0.08044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27969, "top5_acc": 0.51969, "loss_cls": 4.17073, "loss": 4.17073, "time": 0.8191} +{"mode": "train", "epoch": 44, "iter": 2900, "lr": 0.08042, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28031, "top5_acc": 0.53703, "loss_cls": 4.12779, "loss": 4.12779, "time": 0.82336} +{"mode": "train", "epoch": 44, "iter": 3000, "lr": 0.0804, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28625, "top5_acc": 0.54078, "loss_cls": 4.05936, "loss": 4.05936, "time": 0.81655} +{"mode": "train", "epoch": 44, "iter": 3100, "lr": 0.08037, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27375, "top5_acc": 0.52969, "loss_cls": 4.18859, "loss": 4.18859, "time": 0.81377} +{"mode": "train", "epoch": 44, "iter": 3200, "lr": 0.08035, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28203, "top5_acc": 0.52906, "loss_cls": 4.16779, "loss": 4.16779, "time": 0.81541} +{"mode": "train", "epoch": 44, "iter": 3300, "lr": 0.08033, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27453, "top5_acc": 0.52516, "loss_cls": 4.18855, "loss": 4.18855, "time": 0.8162} +{"mode": "train", "epoch": 44, "iter": 3400, "lr": 0.08031, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28078, "top5_acc": 0.54125, "loss_cls": 4.11573, "loss": 4.11573, "time": 0.81745} +{"mode": "train", "epoch": 44, "iter": 3500, "lr": 0.08028, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28359, "top5_acc": 0.53453, "loss_cls": 4.13487, "loss": 4.13487, "time": 0.81468} +{"mode": "train", "epoch": 44, "iter": 3600, "lr": 0.08026, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28281, "top5_acc": 0.52562, "loss_cls": 4.15962, "loss": 4.15962, "time": 0.82321} +{"mode": "train", "epoch": 44, "iter": 3700, "lr": 0.08024, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28141, "top5_acc": 0.52609, "loss_cls": 4.14993, "loss": 4.14993, "time": 0.81616} +{"mode": "val", "epoch": 44, "iter": 309, "lr": 0.08023, "top1_acc": 0.20645, "top5_acc": 0.43464, "mean_class_accuracy": 0.20626} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.08021, "memory": 15990, "data_time": 1.26828, "top1_acc": 0.28875, "top5_acc": 0.54891, "loss_cls": 4.04946, "loss": 4.04946, "time": 2.27889} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.08019, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28531, "top5_acc": 0.53969, "loss_cls": 4.10294, "loss": 4.10294, "time": 0.81694} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.08016, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28266, "top5_acc": 0.54547, "loss_cls": 4.07691, "loss": 4.07691, "time": 0.82216} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.08014, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28641, "top5_acc": 0.54234, "loss_cls": 4.09072, "loss": 4.09072, "time": 0.81956} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.08012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28781, "top5_acc": 0.53766, "loss_cls": 4.10586, "loss": 4.10586, "time": 0.81945} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.0801, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27812, "top5_acc": 0.53391, "loss_cls": 4.15774, "loss": 4.15774, "time": 0.82072} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.08007, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28625, "top5_acc": 0.53812, "loss_cls": 4.11571, "loss": 4.11571, "time": 0.82627} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.08005, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28531, "top5_acc": 0.53219, "loss_cls": 4.1269, "loss": 4.1269, "time": 0.81511} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.08003, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28703, "top5_acc": 0.54562, "loss_cls": 4.09903, "loss": 4.09903, "time": 0.81811} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.08001, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28641, "top5_acc": 0.53844, "loss_cls": 4.10456, "loss": 4.10456, "time": 0.82506} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.07998, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28813, "top5_acc": 0.53234, "loss_cls": 4.11347, "loss": 4.11347, "time": 0.82466} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.07996, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28031, "top5_acc": 0.5325, "loss_cls": 4.14159, "loss": 4.14159, "time": 0.83059} +{"mode": "train", "epoch": 45, "iter": 1300, "lr": 0.07994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28312, "top5_acc": 0.53109, "loss_cls": 4.11927, "loss": 4.11927, "time": 0.82039} +{"mode": "train", "epoch": 45, "iter": 1400, "lr": 0.07992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28328, "top5_acc": 0.53078, "loss_cls": 4.12899, "loss": 4.12899, "time": 0.81834} +{"mode": "train", "epoch": 45, "iter": 1500, "lr": 0.0799, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27391, "top5_acc": 0.51406, "loss_cls": 4.19645, "loss": 4.19645, "time": 0.81643} +{"mode": "train", "epoch": 45, "iter": 1600, "lr": 0.07987, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27594, "top5_acc": 0.53453, "loss_cls": 4.14139, "loss": 4.14139, "time": 0.81466} +{"mode": "train", "epoch": 45, "iter": 1700, "lr": 0.07985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28813, "top5_acc": 0.53703, "loss_cls": 4.11599, "loss": 4.11599, "time": 0.81366} +{"mode": "train", "epoch": 45, "iter": 1800, "lr": 0.07983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28719, "top5_acc": 0.54141, "loss_cls": 4.10662, "loss": 4.10662, "time": 0.82133} +{"mode": "train", "epoch": 45, "iter": 1900, "lr": 0.07981, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27484, "top5_acc": 0.52828, "loss_cls": 4.15626, "loss": 4.15626, "time": 0.82024} +{"mode": "train", "epoch": 45, "iter": 2000, "lr": 0.07978, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27984, "top5_acc": 0.53, "loss_cls": 4.15341, "loss": 4.15341, "time": 0.82204} +{"mode": "train", "epoch": 45, "iter": 2100, "lr": 0.07976, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27859, "top5_acc": 0.53078, "loss_cls": 4.17062, "loss": 4.17062, "time": 0.81385} +{"mode": "train", "epoch": 45, "iter": 2200, "lr": 0.07974, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28375, "top5_acc": 0.53172, "loss_cls": 4.13107, "loss": 4.13107, "time": 0.82169} +{"mode": "train", "epoch": 45, "iter": 2300, "lr": 0.07972, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28, "top5_acc": 0.53453, "loss_cls": 4.14834, "loss": 4.14834, "time": 0.81439} +{"mode": "train", "epoch": 45, "iter": 2400, "lr": 0.07969, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28062, "top5_acc": 0.52828, "loss_cls": 4.14066, "loss": 4.14066, "time": 0.81426} +{"mode": "train", "epoch": 45, "iter": 2500, "lr": 0.07967, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27672, "top5_acc": 0.52359, "loss_cls": 4.16488, "loss": 4.16488, "time": 0.81852} +{"mode": "train", "epoch": 45, "iter": 2600, "lr": 0.07965, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28719, "top5_acc": 0.53422, "loss_cls": 4.10264, "loss": 4.10264, "time": 0.81076} +{"mode": "train", "epoch": 45, "iter": 2700, "lr": 0.07963, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29609, "top5_acc": 0.53922, "loss_cls": 4.09495, "loss": 4.09495, "time": 0.8167} +{"mode": "train", "epoch": 45, "iter": 2800, "lr": 0.0796, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27609, "top5_acc": 0.52906, "loss_cls": 4.16917, "loss": 4.16917, "time": 0.81512} +{"mode": "train", "epoch": 45, "iter": 2900, "lr": 0.07958, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27484, "top5_acc": 0.52531, "loss_cls": 4.15831, "loss": 4.15831, "time": 0.82009} +{"mode": "train", "epoch": 45, "iter": 3000, "lr": 0.07956, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28578, "top5_acc": 0.53609, "loss_cls": 4.15206, "loss": 4.15206, "time": 0.81504} +{"mode": "train", "epoch": 45, "iter": 3100, "lr": 0.07954, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27859, "top5_acc": 0.52172, "loss_cls": 4.19138, "loss": 4.19138, "time": 0.81707} +{"mode": "train", "epoch": 45, "iter": 3200, "lr": 0.07951, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27406, "top5_acc": 0.52547, "loss_cls": 4.14765, "loss": 4.14765, "time": 0.81582} +{"mode": "train", "epoch": 45, "iter": 3300, "lr": 0.07949, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28422, "top5_acc": 0.53469, "loss_cls": 4.1164, "loss": 4.1164, "time": 0.81474} +{"mode": "train", "epoch": 45, "iter": 3400, "lr": 0.07947, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27984, "top5_acc": 0.53922, "loss_cls": 4.09343, "loss": 4.09343, "time": 0.81481} +{"mode": "train", "epoch": 45, "iter": 3500, "lr": 0.07945, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28297, "top5_acc": 0.53875, "loss_cls": 4.09323, "loss": 4.09323, "time": 0.8145} +{"mode": "train", "epoch": 45, "iter": 3600, "lr": 0.07942, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27844, "top5_acc": 0.53219, "loss_cls": 4.14269, "loss": 4.14269, "time": 0.81946} +{"mode": "train", "epoch": 45, "iter": 3700, "lr": 0.0794, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27766, "top5_acc": 0.535, "loss_cls": 4.14662, "loss": 4.14662, "time": 0.81588} +{"mode": "val", "epoch": 45, "iter": 309, "lr": 0.07939, "top1_acc": 0.20833, "top5_acc": 0.43529, "mean_class_accuracy": 0.20826} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.07937, "memory": 15990, "data_time": 1.26541, "top1_acc": 0.28844, "top5_acc": 0.53469, "loss_cls": 4.11389, "loss": 4.11389, "time": 2.24071} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.07934, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29609, "top5_acc": 0.54797, "loss_cls": 4.07477, "loss": 4.07477, "time": 0.8246} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.07932, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28516, "top5_acc": 0.53875, "loss_cls": 4.11112, "loss": 4.11112, "time": 0.81764} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.0793, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28406, "top5_acc": 0.54547, "loss_cls": 4.11443, "loss": 4.11443, "time": 0.82236} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.07928, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27875, "top5_acc": 0.54062, "loss_cls": 4.11236, "loss": 4.11236, "time": 0.81962} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.07925, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29219, "top5_acc": 0.54891, "loss_cls": 4.0543, "loss": 4.0543, "time": 0.81939} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.07923, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28453, "top5_acc": 0.53906, "loss_cls": 4.09089, "loss": 4.09089, "time": 0.81516} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.07921, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27891, "top5_acc": 0.53312, "loss_cls": 4.14646, "loss": 4.14646, "time": 0.82388} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.07919, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29047, "top5_acc": 0.54594, "loss_cls": 4.06686, "loss": 4.06686, "time": 0.8195} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.07916, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28453, "top5_acc": 0.52781, "loss_cls": 4.1389, "loss": 4.1389, "time": 0.82229} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.07914, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27891, "top5_acc": 0.52828, "loss_cls": 4.15175, "loss": 4.15175, "time": 0.82603} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.07912, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28234, "top5_acc": 0.52188, "loss_cls": 4.19213, "loss": 4.19213, "time": 0.82541} +{"mode": "train", "epoch": 46, "iter": 1300, "lr": 0.07909, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27578, "top5_acc": 0.53219, "loss_cls": 4.1393, "loss": 4.1393, "time": 0.81861} +{"mode": "train", "epoch": 46, "iter": 1400, "lr": 0.07907, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28609, "top5_acc": 0.54281, "loss_cls": 4.10829, "loss": 4.10829, "time": 0.81716} +{"mode": "train", "epoch": 46, "iter": 1500, "lr": 0.07905, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28, "top5_acc": 0.53312, "loss_cls": 4.14328, "loss": 4.14328, "time": 0.8169} +{"mode": "train", "epoch": 46, "iter": 1600, "lr": 0.07903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28109, "top5_acc": 0.53188, "loss_cls": 4.12279, "loss": 4.12279, "time": 0.81717} +{"mode": "train", "epoch": 46, "iter": 1700, "lr": 0.079, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27844, "top5_acc": 0.53328, "loss_cls": 4.14843, "loss": 4.14843, "time": 0.81864} +{"mode": "train", "epoch": 46, "iter": 1800, "lr": 0.07898, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27953, "top5_acc": 0.53438, "loss_cls": 4.15068, "loss": 4.15068, "time": 0.81931} +{"mode": "train", "epoch": 46, "iter": 1900, "lr": 0.07896, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28813, "top5_acc": 0.53969, "loss_cls": 4.11621, "loss": 4.11621, "time": 0.81625} +{"mode": "train", "epoch": 46, "iter": 2000, "lr": 0.07894, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29406, "top5_acc": 0.54281, "loss_cls": 4.09467, "loss": 4.09467, "time": 0.81986} +{"mode": "train", "epoch": 46, "iter": 2100, "lr": 0.07891, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27406, "top5_acc": 0.5225, "loss_cls": 4.17572, "loss": 4.17572, "time": 0.81262} +{"mode": "train", "epoch": 46, "iter": 2200, "lr": 0.07889, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28094, "top5_acc": 0.54859, "loss_cls": 4.09481, "loss": 4.09481, "time": 0.81844} +{"mode": "train", "epoch": 46, "iter": 2300, "lr": 0.07887, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28016, "top5_acc": 0.53297, "loss_cls": 4.16301, "loss": 4.16301, "time": 0.81393} +{"mode": "train", "epoch": 46, "iter": 2400, "lr": 0.07884, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28484, "top5_acc": 0.53438, "loss_cls": 4.14905, "loss": 4.14905, "time": 0.81933} +{"mode": "train", "epoch": 46, "iter": 2500, "lr": 0.07882, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28625, "top5_acc": 0.54172, "loss_cls": 4.08976, "loss": 4.08976, "time": 0.81716} +{"mode": "train", "epoch": 46, "iter": 2600, "lr": 0.0788, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28734, "top5_acc": 0.53766, "loss_cls": 4.12604, "loss": 4.12604, "time": 0.81672} +{"mode": "train", "epoch": 46, "iter": 2700, "lr": 0.07878, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27938, "top5_acc": 0.53344, "loss_cls": 4.14698, "loss": 4.14698, "time": 0.81792} +{"mode": "train", "epoch": 46, "iter": 2800, "lr": 0.07875, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28766, "top5_acc": 0.54594, "loss_cls": 4.07834, "loss": 4.07834, "time": 0.81396} +{"mode": "train", "epoch": 46, "iter": 2900, "lr": 0.07873, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28438, "top5_acc": 0.52766, "loss_cls": 4.14404, "loss": 4.14404, "time": 0.81877} +{"mode": "train", "epoch": 46, "iter": 3000, "lr": 0.07871, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28562, "top5_acc": 0.53203, "loss_cls": 4.13877, "loss": 4.13877, "time": 0.81665} +{"mode": "train", "epoch": 46, "iter": 3100, "lr": 0.07868, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27672, "top5_acc": 0.53078, "loss_cls": 4.13307, "loss": 4.13307, "time": 0.81196} +{"mode": "train", "epoch": 46, "iter": 3200, "lr": 0.07866, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27641, "top5_acc": 0.52844, "loss_cls": 4.15955, "loss": 4.15955, "time": 0.81804} +{"mode": "train", "epoch": 46, "iter": 3300, "lr": 0.07864, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29266, "top5_acc": 0.54234, "loss_cls": 4.08736, "loss": 4.08736, "time": 0.81694} +{"mode": "train", "epoch": 46, "iter": 3400, "lr": 0.07862, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27031, "top5_acc": 0.52969, "loss_cls": 4.18614, "loss": 4.18614, "time": 0.81303} +{"mode": "train", "epoch": 46, "iter": 3500, "lr": 0.07859, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27672, "top5_acc": 0.54031, "loss_cls": 4.13068, "loss": 4.13068, "time": 0.81468} +{"mode": "train", "epoch": 46, "iter": 3600, "lr": 0.07857, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27484, "top5_acc": 0.52781, "loss_cls": 4.17576, "loss": 4.17576, "time": 0.81818} +{"mode": "train", "epoch": 46, "iter": 3700, "lr": 0.07855, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27859, "top5_acc": 0.54188, "loss_cls": 4.11021, "loss": 4.11021, "time": 0.81983} +{"mode": "val", "epoch": 46, "iter": 309, "lr": 0.07854, "top1_acc": 0.21694, "top5_acc": 0.45753, "mean_class_accuracy": 0.21672} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.07851, "memory": 15990, "data_time": 1.27978, "top1_acc": 0.28203, "top5_acc": 0.54172, "loss_cls": 4.0928, "loss": 4.0928, "time": 2.25552} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.07849, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28109, "top5_acc": 0.53906, "loss_cls": 4.11308, "loss": 4.11308, "time": 0.81388} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.07847, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29047, "top5_acc": 0.55234, "loss_cls": 4.04116, "loss": 4.04116, "time": 0.81928} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.07844, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28312, "top5_acc": 0.53422, "loss_cls": 4.11749, "loss": 4.11749, "time": 0.81808} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.07842, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27469, "top5_acc": 0.53719, "loss_cls": 4.12069, "loss": 4.12069, "time": 0.81502} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.0784, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27953, "top5_acc": 0.54, "loss_cls": 4.1034, "loss": 4.1034, "time": 0.82738} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.07838, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27984, "top5_acc": 0.54078, "loss_cls": 4.12877, "loss": 4.12877, "time": 0.81663} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.07835, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28859, "top5_acc": 0.5475, "loss_cls": 4.06499, "loss": 4.06499, "time": 0.82271} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.07833, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28688, "top5_acc": 0.53016, "loss_cls": 4.09725, "loss": 4.09725, "time": 0.82396} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.07831, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29125, "top5_acc": 0.53922, "loss_cls": 4.0731, "loss": 4.0731, "time": 0.81883} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.07828, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28625, "top5_acc": 0.53016, "loss_cls": 4.133, "loss": 4.133, "time": 0.82047} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.07826, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28656, "top5_acc": 0.53438, "loss_cls": 4.09293, "loss": 4.09293, "time": 0.82634} +{"mode": "train", "epoch": 47, "iter": 1300, "lr": 0.07824, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28109, "top5_acc": 0.53344, "loss_cls": 4.14564, "loss": 4.14564, "time": 0.8223} +{"mode": "train", "epoch": 47, "iter": 1400, "lr": 0.07821, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28734, "top5_acc": 0.53922, "loss_cls": 4.11315, "loss": 4.11315, "time": 0.81602} +{"mode": "train", "epoch": 47, "iter": 1500, "lr": 0.07819, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29219, "top5_acc": 0.52812, "loss_cls": 4.12808, "loss": 4.12808, "time": 0.81381} +{"mode": "train", "epoch": 47, "iter": 1600, "lr": 0.07817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28359, "top5_acc": 0.53172, "loss_cls": 4.13226, "loss": 4.13226, "time": 0.81278} +{"mode": "train", "epoch": 47, "iter": 1700, "lr": 0.07814, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28078, "top5_acc": 0.54062, "loss_cls": 4.12094, "loss": 4.12094, "time": 0.81591} +{"mode": "train", "epoch": 47, "iter": 1800, "lr": 0.07812, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28203, "top5_acc": 0.53469, "loss_cls": 4.12681, "loss": 4.12681, "time": 0.81635} +{"mode": "train", "epoch": 47, "iter": 1900, "lr": 0.0781, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27938, "top5_acc": 0.53859, "loss_cls": 4.12157, "loss": 4.12157, "time": 0.81972} +{"mode": "train", "epoch": 47, "iter": 2000, "lr": 0.07808, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28, "top5_acc": 0.54609, "loss_cls": 4.12953, "loss": 4.12953, "time": 0.82} +{"mode": "train", "epoch": 47, "iter": 2100, "lr": 0.07805, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28547, "top5_acc": 0.54062, "loss_cls": 4.09554, "loss": 4.09554, "time": 0.81277} +{"mode": "train", "epoch": 47, "iter": 2200, "lr": 0.07803, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28469, "top5_acc": 0.54141, "loss_cls": 4.11867, "loss": 4.11867, "time": 0.82052} +{"mode": "train", "epoch": 47, "iter": 2300, "lr": 0.07801, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27516, "top5_acc": 0.53656, "loss_cls": 4.13584, "loss": 4.13584, "time": 0.82502} +{"mode": "train", "epoch": 47, "iter": 2400, "lr": 0.07798, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28203, "top5_acc": 0.53109, "loss_cls": 4.16072, "loss": 4.16072, "time": 0.81921} +{"mode": "train", "epoch": 47, "iter": 2500, "lr": 0.07796, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29422, "top5_acc": 0.53312, "loss_cls": 4.11105, "loss": 4.11105, "time": 0.81991} +{"mode": "train", "epoch": 47, "iter": 2600, "lr": 0.07794, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27922, "top5_acc": 0.54172, "loss_cls": 4.12081, "loss": 4.12081, "time": 0.81521} +{"mode": "train", "epoch": 47, "iter": 2700, "lr": 0.07791, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28609, "top5_acc": 0.53344, "loss_cls": 4.1182, "loss": 4.1182, "time": 0.81785} +{"mode": "train", "epoch": 47, "iter": 2800, "lr": 0.07789, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28406, "top5_acc": 0.52875, "loss_cls": 4.14697, "loss": 4.14697, "time": 0.81613} +{"mode": "train", "epoch": 47, "iter": 2900, "lr": 0.07787, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28703, "top5_acc": 0.53344, "loss_cls": 4.12527, "loss": 4.12527, "time": 0.8143} +{"mode": "train", "epoch": 47, "iter": 3000, "lr": 0.07784, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28609, "top5_acc": 0.53875, "loss_cls": 4.10775, "loss": 4.10775, "time": 0.81823} +{"mode": "train", "epoch": 47, "iter": 3100, "lr": 0.07782, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29094, "top5_acc": 0.53016, "loss_cls": 4.11456, "loss": 4.11456, "time": 0.81582} +{"mode": "train", "epoch": 47, "iter": 3200, "lr": 0.0778, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28172, "top5_acc": 0.53781, "loss_cls": 4.11937, "loss": 4.11937, "time": 0.81722} +{"mode": "train", "epoch": 47, "iter": 3300, "lr": 0.07777, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28062, "top5_acc": 0.53312, "loss_cls": 4.12667, "loss": 4.12667, "time": 0.81162} +{"mode": "train", "epoch": 47, "iter": 3400, "lr": 0.07775, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28406, "top5_acc": 0.53, "loss_cls": 4.15172, "loss": 4.15172, "time": 0.81662} +{"mode": "train", "epoch": 47, "iter": 3500, "lr": 0.07773, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27703, "top5_acc": 0.52984, "loss_cls": 4.12744, "loss": 4.12744, "time": 0.8152} +{"mode": "train", "epoch": 47, "iter": 3600, "lr": 0.0777, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28641, "top5_acc": 0.53922, "loss_cls": 4.10093, "loss": 4.10093, "time": 0.81313} +{"mode": "train", "epoch": 47, "iter": 3700, "lr": 0.07768, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.275, "top5_acc": 0.52375, "loss_cls": 4.18564, "loss": 4.18564, "time": 0.81393} +{"mode": "val", "epoch": 47, "iter": 309, "lr": 0.07767, "top1_acc": 0.21527, "top5_acc": 0.45474, "mean_class_accuracy": 0.21533} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.07765, "memory": 15990, "data_time": 1.31169, "top1_acc": 0.29109, "top5_acc": 0.54688, "loss_cls": 4.0765, "loss": 4.0765, "time": 2.28961} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.07762, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28844, "top5_acc": 0.5475, "loss_cls": 4.05273, "loss": 4.05273, "time": 0.82002} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.0776, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29594, "top5_acc": 0.545, "loss_cls": 4.05625, "loss": 4.05625, "time": 0.81876} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.07758, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28578, "top5_acc": 0.53859, "loss_cls": 4.10776, "loss": 4.10776, "time": 0.81611} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.07755, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29109, "top5_acc": 0.54656, "loss_cls": 4.08537, "loss": 4.08537, "time": 0.81531} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.07753, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29594, "top5_acc": 0.54484, "loss_cls": 4.06647, "loss": 4.06647, "time": 0.81921} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.07751, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28953, "top5_acc": 0.53359, "loss_cls": 4.10445, "loss": 4.10445, "time": 0.81777} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.07748, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30188, "top5_acc": 0.5525, "loss_cls": 4.0573, "loss": 4.0573, "time": 0.82295} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.07746, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28766, "top5_acc": 0.53531, "loss_cls": 4.10115, "loss": 4.10115, "time": 0.81579} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.07744, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27766, "top5_acc": 0.53891, "loss_cls": 4.12536, "loss": 4.12536, "time": 0.82367} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.07741, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28375, "top5_acc": 0.53906, "loss_cls": 4.1056, "loss": 4.1056, "time": 0.82625} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.07739, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28078, "top5_acc": 0.52422, "loss_cls": 4.1549, "loss": 4.1549, "time": 0.82591} +{"mode": "train", "epoch": 48, "iter": 1300, "lr": 0.07737, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28719, "top5_acc": 0.53703, "loss_cls": 4.11261, "loss": 4.11261, "time": 0.82067} +{"mode": "train", "epoch": 48, "iter": 1400, "lr": 0.07734, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28188, "top5_acc": 0.53812, "loss_cls": 4.11426, "loss": 4.11426, "time": 0.81802} +{"mode": "train", "epoch": 48, "iter": 1500, "lr": 0.07732, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28, "top5_acc": 0.53969, "loss_cls": 4.13686, "loss": 4.13686, "time": 0.82058} +{"mode": "train", "epoch": 48, "iter": 1600, "lr": 0.0773, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28031, "top5_acc": 0.53297, "loss_cls": 4.14135, "loss": 4.14135, "time": 0.81447} +{"mode": "train", "epoch": 48, "iter": 1700, "lr": 0.07727, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28641, "top5_acc": 0.5375, "loss_cls": 4.10781, "loss": 4.10781, "time": 0.82309} +{"mode": "train", "epoch": 48, "iter": 1800, "lr": 0.07725, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28766, "top5_acc": 0.53734, "loss_cls": 4.09355, "loss": 4.09355, "time": 0.81789} +{"mode": "train", "epoch": 48, "iter": 1900, "lr": 0.07723, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28156, "top5_acc": 0.54062, "loss_cls": 4.11619, "loss": 4.11619, "time": 0.82246} +{"mode": "train", "epoch": 48, "iter": 2000, "lr": 0.0772, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29422, "top5_acc": 0.54297, "loss_cls": 4.06763, "loss": 4.06763, "time": 0.81826} +{"mode": "train", "epoch": 48, "iter": 2100, "lr": 0.07718, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28797, "top5_acc": 0.53578, "loss_cls": 4.10024, "loss": 4.10024, "time": 0.8209} +{"mode": "train", "epoch": 48, "iter": 2200, "lr": 0.07716, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29234, "top5_acc": 0.53828, "loss_cls": 4.11178, "loss": 4.11178, "time": 0.81264} +{"mode": "train", "epoch": 48, "iter": 2300, "lr": 0.07713, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28203, "top5_acc": 0.53094, "loss_cls": 4.12851, "loss": 4.12851, "time": 0.81757} +{"mode": "train", "epoch": 48, "iter": 2400, "lr": 0.07711, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27734, "top5_acc": 0.53469, "loss_cls": 4.11634, "loss": 4.11634, "time": 0.81688} +{"mode": "train", "epoch": 48, "iter": 2500, "lr": 0.07709, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28688, "top5_acc": 0.54219, "loss_cls": 4.08902, "loss": 4.08902, "time": 0.81312} +{"mode": "train", "epoch": 48, "iter": 2600, "lr": 0.07706, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28578, "top5_acc": 0.53438, "loss_cls": 4.15095, "loss": 4.15095, "time": 0.81868} +{"mode": "train", "epoch": 48, "iter": 2700, "lr": 0.07704, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28438, "top5_acc": 0.53219, "loss_cls": 4.12819, "loss": 4.12819, "time": 0.815} +{"mode": "train", "epoch": 48, "iter": 2800, "lr": 0.07701, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28672, "top5_acc": 0.53594, "loss_cls": 4.12659, "loss": 4.12659, "time": 0.81416} +{"mode": "train", "epoch": 48, "iter": 2900, "lr": 0.07699, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29156, "top5_acc": 0.54672, "loss_cls": 4.07658, "loss": 4.07658, "time": 0.81574} +{"mode": "train", "epoch": 48, "iter": 3000, "lr": 0.07697, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28312, "top5_acc": 0.53953, "loss_cls": 4.13092, "loss": 4.13092, "time": 0.81694} +{"mode": "train", "epoch": 48, "iter": 3100, "lr": 0.07694, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28656, "top5_acc": 0.53375, "loss_cls": 4.11418, "loss": 4.11418, "time": 0.81619} +{"mode": "train", "epoch": 48, "iter": 3200, "lr": 0.07692, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.285, "top5_acc": 0.53328, "loss_cls": 4.116, "loss": 4.116, "time": 0.8176} +{"mode": "train", "epoch": 48, "iter": 3300, "lr": 0.0769, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28625, "top5_acc": 0.53688, "loss_cls": 4.11645, "loss": 4.11645, "time": 0.81792} +{"mode": "train", "epoch": 48, "iter": 3400, "lr": 0.07687, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28234, "top5_acc": 0.53, "loss_cls": 4.1362, "loss": 4.1362, "time": 0.81534} +{"mode": "train", "epoch": 48, "iter": 3500, "lr": 0.07685, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27609, "top5_acc": 0.53828, "loss_cls": 4.11532, "loss": 4.11532, "time": 0.8223} +{"mode": "train", "epoch": 48, "iter": 3600, "lr": 0.07683, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29219, "top5_acc": 0.53391, "loss_cls": 4.10529, "loss": 4.10529, "time": 0.81522} +{"mode": "train", "epoch": 48, "iter": 3700, "lr": 0.0768, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28125, "top5_acc": 0.54, "loss_cls": 4.11191, "loss": 4.11191, "time": 0.81543} +{"mode": "val", "epoch": 48, "iter": 309, "lr": 0.07679, "top1_acc": 0.21805, "top5_acc": 0.45758, "mean_class_accuracy": 0.21789} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.07677, "memory": 15990, "data_time": 1.34397, "top1_acc": 0.29531, "top5_acc": 0.55188, "loss_cls": 4.02351, "loss": 4.02351, "time": 2.32197} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.07674, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29406, "top5_acc": 0.54484, "loss_cls": 4.09691, "loss": 4.09691, "time": 0.81456} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.07672, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29375, "top5_acc": 0.55297, "loss_cls": 4.03462, "loss": 4.03462, "time": 0.81674} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.0767, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28141, "top5_acc": 0.53562, "loss_cls": 4.10538, "loss": 4.10538, "time": 0.82352} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.07667, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28484, "top5_acc": 0.53266, "loss_cls": 4.12109, "loss": 4.12109, "time": 0.81974} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.07665, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28234, "top5_acc": 0.53609, "loss_cls": 4.10985, "loss": 4.10985, "time": 0.82053} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.07663, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27797, "top5_acc": 0.54109, "loss_cls": 4.12088, "loss": 4.12088, "time": 0.81957} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.0766, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27922, "top5_acc": 0.53344, "loss_cls": 4.13474, "loss": 4.13474, "time": 0.82153} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.07658, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29609, "top5_acc": 0.54469, "loss_cls": 4.07622, "loss": 4.07622, "time": 0.81968} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.07656, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28094, "top5_acc": 0.53766, "loss_cls": 4.11956, "loss": 4.11956, "time": 0.82693} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.07653, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29375, "top5_acc": 0.55609, "loss_cls": 4.05295, "loss": 4.05295, "time": 0.82244} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.07651, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28469, "top5_acc": 0.54531, "loss_cls": 4.06461, "loss": 4.06461, "time": 0.83531} +{"mode": "train", "epoch": 49, "iter": 1300, "lr": 0.07648, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28344, "top5_acc": 0.53469, "loss_cls": 4.13985, "loss": 4.13985, "time": 0.8205} +{"mode": "train", "epoch": 49, "iter": 1400, "lr": 0.07646, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28156, "top5_acc": 0.53672, "loss_cls": 4.13248, "loss": 4.13248, "time": 0.81611} +{"mode": "train", "epoch": 49, "iter": 1500, "lr": 0.07644, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28719, "top5_acc": 0.53875, "loss_cls": 4.11374, "loss": 4.11374, "time": 0.81791} +{"mode": "train", "epoch": 49, "iter": 1600, "lr": 0.07641, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28625, "top5_acc": 0.53703, "loss_cls": 4.12254, "loss": 4.12254, "time": 0.8161} +{"mode": "train", "epoch": 49, "iter": 1700, "lr": 0.07639, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27688, "top5_acc": 0.54141, "loss_cls": 4.09257, "loss": 4.09257, "time": 0.81821} +{"mode": "train", "epoch": 49, "iter": 1800, "lr": 0.07637, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28406, "top5_acc": 0.5425, "loss_cls": 4.11397, "loss": 4.11397, "time": 0.8167} +{"mode": "train", "epoch": 49, "iter": 1900, "lr": 0.07634, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28625, "top5_acc": 0.54406, "loss_cls": 4.07297, "loss": 4.07297, "time": 0.82081} +{"mode": "train", "epoch": 49, "iter": 2000, "lr": 0.07632, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29344, "top5_acc": 0.55266, "loss_cls": 4.06172, "loss": 4.06172, "time": 0.81745} +{"mode": "train", "epoch": 49, "iter": 2100, "lr": 0.07629, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28109, "top5_acc": 0.53703, "loss_cls": 4.13221, "loss": 4.13221, "time": 0.8208} +{"mode": "train", "epoch": 49, "iter": 2200, "lr": 0.07627, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28031, "top5_acc": 0.53391, "loss_cls": 4.11372, "loss": 4.11372, "time": 0.81264} +{"mode": "train", "epoch": 49, "iter": 2300, "lr": 0.07625, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28172, "top5_acc": 0.5275, "loss_cls": 4.1221, "loss": 4.1221, "time": 0.82162} +{"mode": "train", "epoch": 49, "iter": 2400, "lr": 0.07622, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28203, "top5_acc": 0.53, "loss_cls": 4.11906, "loss": 4.11906, "time": 0.81604} +{"mode": "train", "epoch": 49, "iter": 2500, "lr": 0.0762, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27781, "top5_acc": 0.53656, "loss_cls": 4.12461, "loss": 4.12461, "time": 0.82143} +{"mode": "train", "epoch": 49, "iter": 2600, "lr": 0.07618, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28844, "top5_acc": 0.53875, "loss_cls": 4.13701, "loss": 4.13701, "time": 0.81561} +{"mode": "train", "epoch": 49, "iter": 2700, "lr": 0.07615, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27625, "top5_acc": 0.53516, "loss_cls": 4.10698, "loss": 4.10698, "time": 0.81551} +{"mode": "train", "epoch": 49, "iter": 2800, "lr": 0.07613, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28703, "top5_acc": 0.54125, "loss_cls": 4.06756, "loss": 4.06756, "time": 0.81613} +{"mode": "train", "epoch": 49, "iter": 2900, "lr": 0.0761, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28766, "top5_acc": 0.54109, "loss_cls": 4.08813, "loss": 4.08813, "time": 0.82149} +{"mode": "train", "epoch": 49, "iter": 3000, "lr": 0.07608, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29375, "top5_acc": 0.53984, "loss_cls": 4.08854, "loss": 4.08854, "time": 0.81354} +{"mode": "train", "epoch": 49, "iter": 3100, "lr": 0.07606, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28422, "top5_acc": 0.53078, "loss_cls": 4.15189, "loss": 4.15189, "time": 0.81725} +{"mode": "train", "epoch": 49, "iter": 3200, "lr": 0.07603, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28844, "top5_acc": 0.54281, "loss_cls": 4.08504, "loss": 4.08504, "time": 0.8147} +{"mode": "train", "epoch": 49, "iter": 3300, "lr": 0.07601, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29281, "top5_acc": 0.54109, "loss_cls": 4.07878, "loss": 4.07878, "time": 0.81621} +{"mode": "train", "epoch": 49, "iter": 3400, "lr": 0.07598, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27844, "top5_acc": 0.53266, "loss_cls": 4.14277, "loss": 4.14277, "time": 0.81546} +{"mode": "train", "epoch": 49, "iter": 3500, "lr": 0.07596, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28891, "top5_acc": 0.54266, "loss_cls": 4.10173, "loss": 4.10173, "time": 0.81398} +{"mode": "train", "epoch": 49, "iter": 3600, "lr": 0.07594, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28016, "top5_acc": 0.52656, "loss_cls": 4.16085, "loss": 4.16085, "time": 0.81557} +{"mode": "train", "epoch": 49, "iter": 3700, "lr": 0.07591, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29016, "top5_acc": 0.53812, "loss_cls": 4.09613, "loss": 4.09613, "time": 0.81582} +{"mode": "val", "epoch": 49, "iter": 309, "lr": 0.0759, "top1_acc": 0.22499, "top5_acc": 0.45287, "mean_class_accuracy": 0.2248} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.07588, "memory": 15990, "data_time": 1.30744, "top1_acc": 0.29266, "top5_acc": 0.54766, "loss_cls": 4.05346, "loss": 4.05346, "time": 2.28458} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.07585, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29078, "top5_acc": 0.54188, "loss_cls": 4.05658, "loss": 4.05658, "time": 0.82344} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.07583, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29094, "top5_acc": 0.54906, "loss_cls": 4.04151, "loss": 4.04151, "time": 0.81469} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.07581, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30453, "top5_acc": 0.55, "loss_cls": 4.02241, "loss": 4.02241, "time": 0.81303} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.07578, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2875, "top5_acc": 0.5425, "loss_cls": 4.1023, "loss": 4.1023, "time": 0.81589} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.07576, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29812, "top5_acc": 0.54828, "loss_cls": 4.04059, "loss": 4.04059, "time": 0.81798} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.07573, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28422, "top5_acc": 0.54156, "loss_cls": 4.12275, "loss": 4.12275, "time": 0.82047} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.07571, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28922, "top5_acc": 0.54609, "loss_cls": 4.08579, "loss": 4.08579, "time": 0.8181} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.07569, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29375, "top5_acc": 0.54953, "loss_cls": 4.04472, "loss": 4.04472, "time": 0.82057} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.07566, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28172, "top5_acc": 0.53984, "loss_cls": 4.1257, "loss": 4.1257, "time": 0.82069} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.07564, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28359, "top5_acc": 0.54234, "loss_cls": 4.07386, "loss": 4.07386, "time": 0.81374} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.07561, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28531, "top5_acc": 0.53891, "loss_cls": 4.11319, "loss": 4.11319, "time": 0.82812} +{"mode": "train", "epoch": 50, "iter": 1300, "lr": 0.07559, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28391, "top5_acc": 0.54203, "loss_cls": 4.10294, "loss": 4.10294, "time": 0.82251} +{"mode": "train", "epoch": 50, "iter": 1400, "lr": 0.07557, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28406, "top5_acc": 0.54609, "loss_cls": 4.06227, "loss": 4.06227, "time": 0.82319} +{"mode": "train", "epoch": 50, "iter": 1500, "lr": 0.07554, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27469, "top5_acc": 0.53141, "loss_cls": 4.1502, "loss": 4.1502, "time": 0.81496} +{"mode": "train", "epoch": 50, "iter": 1600, "lr": 0.07552, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29203, "top5_acc": 0.54703, "loss_cls": 4.07281, "loss": 4.07281, "time": 0.82096} +{"mode": "train", "epoch": 50, "iter": 1700, "lr": 0.07549, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28203, "top5_acc": 0.53688, "loss_cls": 4.12453, "loss": 4.12453, "time": 0.8181} +{"mode": "train", "epoch": 50, "iter": 1800, "lr": 0.07547, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28922, "top5_acc": 0.54375, "loss_cls": 4.13434, "loss": 4.13434, "time": 0.81562} +{"mode": "train", "epoch": 50, "iter": 1900, "lr": 0.07545, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28359, "top5_acc": 0.5275, "loss_cls": 4.11609, "loss": 4.11609, "time": 0.81739} +{"mode": "train", "epoch": 50, "iter": 2000, "lr": 0.07542, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28406, "top5_acc": 0.53484, "loss_cls": 4.1055, "loss": 4.1055, "time": 0.81573} +{"mode": "train", "epoch": 50, "iter": 2100, "lr": 0.0754, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28156, "top5_acc": 0.53688, "loss_cls": 4.12115, "loss": 4.12115, "time": 0.81572} +{"mode": "train", "epoch": 50, "iter": 2200, "lr": 0.07537, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28156, "top5_acc": 0.53625, "loss_cls": 4.1168, "loss": 4.1168, "time": 0.81485} +{"mode": "train", "epoch": 50, "iter": 2300, "lr": 0.07535, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28859, "top5_acc": 0.53812, "loss_cls": 4.12246, "loss": 4.12246, "time": 0.82366} +{"mode": "train", "epoch": 50, "iter": 2400, "lr": 0.07533, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29469, "top5_acc": 0.55406, "loss_cls": 4.04331, "loss": 4.04331, "time": 0.81289} +{"mode": "train", "epoch": 50, "iter": 2500, "lr": 0.0753, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28266, "top5_acc": 0.53406, "loss_cls": 4.11333, "loss": 4.11333, "time": 0.82126} +{"mode": "train", "epoch": 50, "iter": 2600, "lr": 0.07528, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29438, "top5_acc": 0.54469, "loss_cls": 4.07213, "loss": 4.07213, "time": 0.8151} +{"mode": "train", "epoch": 50, "iter": 2700, "lr": 0.07525, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29703, "top5_acc": 0.54766, "loss_cls": 4.06785, "loss": 4.06785, "time": 0.81752} +{"mode": "train", "epoch": 50, "iter": 2800, "lr": 0.07523, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28109, "top5_acc": 0.52891, "loss_cls": 4.11901, "loss": 4.11901, "time": 0.81877} +{"mode": "train", "epoch": 50, "iter": 2900, "lr": 0.0752, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28172, "top5_acc": 0.53688, "loss_cls": 4.11203, "loss": 4.11203, "time": 0.81982} +{"mode": "train", "epoch": 50, "iter": 3000, "lr": 0.07518, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28828, "top5_acc": 0.55141, "loss_cls": 4.06956, "loss": 4.06956, "time": 0.81564} +{"mode": "train", "epoch": 50, "iter": 3100, "lr": 0.07516, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28562, "top5_acc": 0.53734, "loss_cls": 4.12229, "loss": 4.12229, "time": 0.81717} +{"mode": "train", "epoch": 50, "iter": 3200, "lr": 0.07513, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28672, "top5_acc": 0.53797, "loss_cls": 4.09957, "loss": 4.09957, "time": 0.81901} +{"mode": "train", "epoch": 50, "iter": 3300, "lr": 0.07511, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28141, "top5_acc": 0.53391, "loss_cls": 4.11004, "loss": 4.11004, "time": 0.81243} +{"mode": "train", "epoch": 50, "iter": 3400, "lr": 0.07508, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28094, "top5_acc": 0.53344, "loss_cls": 4.1077, "loss": 4.1077, "time": 0.81861} +{"mode": "train", "epoch": 50, "iter": 3500, "lr": 0.07506, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28312, "top5_acc": 0.53984, "loss_cls": 4.11492, "loss": 4.11492, "time": 0.81456} +{"mode": "train", "epoch": 50, "iter": 3600, "lr": 0.07504, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29031, "top5_acc": 0.54016, "loss_cls": 4.09145, "loss": 4.09145, "time": 0.81726} +{"mode": "train", "epoch": 50, "iter": 3700, "lr": 0.07501, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.53531, "loss_cls": 4.1347, "loss": 4.1347, "time": 0.81673} +{"mode": "val", "epoch": 50, "iter": 309, "lr": 0.075, "top1_acc": 0.23006, "top5_acc": 0.47146, "mean_class_accuracy": 0.22993} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.07498, "memory": 15990, "data_time": 1.3311, "top1_acc": 0.29, "top5_acc": 0.55234, "loss_cls": 4.03189, "loss": 4.03189, "time": 2.30802} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.07495, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29172, "top5_acc": 0.54937, "loss_cls": 4.02374, "loss": 4.02374, "time": 0.81762} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.07493, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28875, "top5_acc": 0.54594, "loss_cls": 4.06398, "loss": 4.06398, "time": 0.82097} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.0749, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29141, "top5_acc": 0.545, "loss_cls": 4.07938, "loss": 4.07938, "time": 0.816} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.07488, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28891, "top5_acc": 0.54234, "loss_cls": 4.04776, "loss": 4.04776, "time": 0.81829} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.07485, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29547, "top5_acc": 0.54734, "loss_cls": 4.0655, "loss": 4.0655, "time": 0.81912} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.07483, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.285, "top5_acc": 0.53734, "loss_cls": 4.1137, "loss": 4.1137, "time": 0.81813} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.07481, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28328, "top5_acc": 0.53938, "loss_cls": 4.10962, "loss": 4.10962, "time": 0.82089} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.07478, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27484, "top5_acc": 0.53797, "loss_cls": 4.10517, "loss": 4.10517, "time": 0.82032} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.07476, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28297, "top5_acc": 0.54188, "loss_cls": 4.10428, "loss": 4.10428, "time": 0.82516} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.07473, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28359, "top5_acc": 0.54484, "loss_cls": 4.09502, "loss": 4.09502, "time": 0.81768} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.07471, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29422, "top5_acc": 0.54531, "loss_cls": 4.06844, "loss": 4.06844, "time": 0.82836} +{"mode": "train", "epoch": 51, "iter": 1300, "lr": 0.07468, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28312, "top5_acc": 0.54016, "loss_cls": 4.11795, "loss": 4.11795, "time": 0.82012} +{"mode": "train", "epoch": 51, "iter": 1400, "lr": 0.07466, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28953, "top5_acc": 0.53844, "loss_cls": 4.07088, "loss": 4.07088, "time": 0.82032} +{"mode": "train", "epoch": 51, "iter": 1500, "lr": 0.07464, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27688, "top5_acc": 0.53469, "loss_cls": 4.11686, "loss": 4.11686, "time": 0.82049} +{"mode": "train", "epoch": 51, "iter": 1600, "lr": 0.07461, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28547, "top5_acc": 0.54141, "loss_cls": 4.0756, "loss": 4.0756, "time": 0.81498} +{"mode": "train", "epoch": 51, "iter": 1700, "lr": 0.07459, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28062, "top5_acc": 0.52891, "loss_cls": 4.13593, "loss": 4.13593, "time": 0.81392} +{"mode": "train", "epoch": 51, "iter": 1800, "lr": 0.07456, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28125, "top5_acc": 0.53938, "loss_cls": 4.11003, "loss": 4.11003, "time": 0.81609} +{"mode": "train", "epoch": 51, "iter": 1900, "lr": 0.07454, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29953, "top5_acc": 0.55078, "loss_cls": 4.05486, "loss": 4.05486, "time": 0.8147} +{"mode": "train", "epoch": 51, "iter": 2000, "lr": 0.07451, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28453, "top5_acc": 0.53938, "loss_cls": 4.08954, "loss": 4.08954, "time": 0.81564} +{"mode": "train", "epoch": 51, "iter": 2100, "lr": 0.07449, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29047, "top5_acc": 0.53625, "loss_cls": 4.09864, "loss": 4.09864, "time": 0.8229} +{"mode": "train", "epoch": 51, "iter": 2200, "lr": 0.07447, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30438, "top5_acc": 0.54781, "loss_cls": 4.06391, "loss": 4.06391, "time": 0.81724} +{"mode": "train", "epoch": 51, "iter": 2300, "lr": 0.07444, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28016, "top5_acc": 0.53812, "loss_cls": 4.11272, "loss": 4.11272, "time": 0.81997} +{"mode": "train", "epoch": 51, "iter": 2400, "lr": 0.07442, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28562, "top5_acc": 0.53375, "loss_cls": 4.10871, "loss": 4.10871, "time": 0.8154} +{"mode": "train", "epoch": 51, "iter": 2500, "lr": 0.07439, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28625, "top5_acc": 0.53969, "loss_cls": 4.08547, "loss": 4.08547, "time": 0.8148} +{"mode": "train", "epoch": 51, "iter": 2600, "lr": 0.07437, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29297, "top5_acc": 0.54781, "loss_cls": 4.06786, "loss": 4.06786, "time": 0.81488} +{"mode": "train", "epoch": 51, "iter": 2700, "lr": 0.07434, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29125, "top5_acc": 0.5425, "loss_cls": 4.05465, "loss": 4.05465, "time": 0.81463} +{"mode": "train", "epoch": 51, "iter": 2800, "lr": 0.07432, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28703, "top5_acc": 0.54, "loss_cls": 4.09711, "loss": 4.09711, "time": 0.81948} +{"mode": "train", "epoch": 51, "iter": 2900, "lr": 0.07429, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29594, "top5_acc": 0.55844, "loss_cls": 4.02553, "loss": 4.02553, "time": 0.81585} +{"mode": "train", "epoch": 51, "iter": 3000, "lr": 0.07427, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29125, "top5_acc": 0.54922, "loss_cls": 4.05226, "loss": 4.05226, "time": 0.8134} +{"mode": "train", "epoch": 51, "iter": 3100, "lr": 0.07425, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28391, "top5_acc": 0.5325, "loss_cls": 4.10344, "loss": 4.10344, "time": 0.8128} +{"mode": "train", "epoch": 51, "iter": 3200, "lr": 0.07422, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2875, "top5_acc": 0.53609, "loss_cls": 4.13619, "loss": 4.13619, "time": 0.81724} +{"mode": "train", "epoch": 51, "iter": 3300, "lr": 0.0742, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29688, "top5_acc": 0.54703, "loss_cls": 4.06436, "loss": 4.06436, "time": 0.81494} +{"mode": "train", "epoch": 51, "iter": 3400, "lr": 0.07417, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28875, "top5_acc": 0.53734, "loss_cls": 4.09671, "loss": 4.09671, "time": 0.81314} +{"mode": "train", "epoch": 51, "iter": 3500, "lr": 0.07415, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28297, "top5_acc": 0.54, "loss_cls": 4.10026, "loss": 4.10026, "time": 0.81838} +{"mode": "train", "epoch": 51, "iter": 3600, "lr": 0.07412, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28438, "top5_acc": 0.53969, "loss_cls": 4.09055, "loss": 4.09055, "time": 0.81519} +{"mode": "train", "epoch": 51, "iter": 3700, "lr": 0.0741, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28031, "top5_acc": 0.53578, "loss_cls": 4.11711, "loss": 4.11711, "time": 0.81588} +{"mode": "val", "epoch": 51, "iter": 309, "lr": 0.07409, "top1_acc": 0.21491, "top5_acc": 0.45419, "mean_class_accuracy": 0.21472} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.07406, "memory": 15990, "data_time": 1.32576, "top1_acc": 0.29219, "top5_acc": 0.56625, "loss_cls": 4.01188, "loss": 4.01188, "time": 2.30938} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.07404, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28984, "top5_acc": 0.54125, "loss_cls": 4.10064, "loss": 4.10064, "time": 0.81968} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.07401, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29219, "top5_acc": 0.55328, "loss_cls": 4.03501, "loss": 4.03501, "time": 0.81671} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.07399, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29313, "top5_acc": 0.55312, "loss_cls": 4.04969, "loss": 4.04969, "time": 0.81591} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.07397, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29391, "top5_acc": 0.54531, "loss_cls": 4.08678, "loss": 4.08678, "time": 0.81767} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.07394, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29516, "top5_acc": 0.5475, "loss_cls": 4.05821, "loss": 4.05821, "time": 0.81783} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.07392, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28672, "top5_acc": 0.5475, "loss_cls": 4.09237, "loss": 4.09237, "time": 0.81569} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.07389, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28031, "top5_acc": 0.53078, "loss_cls": 4.11633, "loss": 4.11633, "time": 0.81519} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.07387, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28031, "top5_acc": 0.54047, "loss_cls": 4.10848, "loss": 4.10848, "time": 0.82166} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.07384, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28547, "top5_acc": 0.54234, "loss_cls": 4.07764, "loss": 4.07764, "time": 0.82122} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.07382, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28719, "top5_acc": 0.54562, "loss_cls": 4.05946, "loss": 4.05946, "time": 0.8192} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.07379, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28766, "top5_acc": 0.54562, "loss_cls": 4.09282, "loss": 4.09282, "time": 0.83012} +{"mode": "train", "epoch": 52, "iter": 1300, "lr": 0.07377, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29297, "top5_acc": 0.54969, "loss_cls": 4.05379, "loss": 4.05379, "time": 0.82109} +{"mode": "train", "epoch": 52, "iter": 1400, "lr": 0.07374, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28547, "top5_acc": 0.54062, "loss_cls": 4.07797, "loss": 4.07797, "time": 0.822} +{"mode": "train", "epoch": 52, "iter": 1500, "lr": 0.07372, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2825, "top5_acc": 0.53766, "loss_cls": 4.08276, "loss": 4.08276, "time": 0.8168} +{"mode": "train", "epoch": 52, "iter": 1600, "lr": 0.0737, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28578, "top5_acc": 0.54125, "loss_cls": 4.10564, "loss": 4.10564, "time": 0.81727} +{"mode": "train", "epoch": 52, "iter": 1700, "lr": 0.07367, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29734, "top5_acc": 0.53516, "loss_cls": 4.07511, "loss": 4.07511, "time": 0.81614} +{"mode": "train", "epoch": 52, "iter": 1800, "lr": 0.07365, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29313, "top5_acc": 0.55203, "loss_cls": 4.04696, "loss": 4.04696, "time": 0.81503} +{"mode": "train", "epoch": 52, "iter": 1900, "lr": 0.07362, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.285, "top5_acc": 0.53109, "loss_cls": 4.1443, "loss": 4.1443, "time": 0.81775} +{"mode": "train", "epoch": 52, "iter": 2000, "lr": 0.0736, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29938, "top5_acc": 0.54078, "loss_cls": 4.06527, "loss": 4.06527, "time": 0.81751} +{"mode": "train", "epoch": 52, "iter": 2100, "lr": 0.07357, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28391, "top5_acc": 0.53922, "loss_cls": 4.09522, "loss": 4.09522, "time": 0.82} +{"mode": "train", "epoch": 52, "iter": 2200, "lr": 0.07355, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30062, "top5_acc": 0.55688, "loss_cls": 4.04126, "loss": 4.04126, "time": 0.81582} +{"mode": "train", "epoch": 52, "iter": 2300, "lr": 0.07352, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28312, "top5_acc": 0.53359, "loss_cls": 4.1446, "loss": 4.1446, "time": 0.81785} +{"mode": "train", "epoch": 52, "iter": 2400, "lr": 0.0735, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28281, "top5_acc": 0.53453, "loss_cls": 4.13108, "loss": 4.13108, "time": 0.81186} +{"mode": "train", "epoch": 52, "iter": 2500, "lr": 0.07347, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2875, "top5_acc": 0.53953, "loss_cls": 4.07312, "loss": 4.07312, "time": 0.81648} +{"mode": "train", "epoch": 52, "iter": 2600, "lr": 0.07345, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29031, "top5_acc": 0.53875, "loss_cls": 4.10779, "loss": 4.10779, "time": 0.81689} +{"mode": "train", "epoch": 52, "iter": 2700, "lr": 0.07342, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29594, "top5_acc": 0.5375, "loss_cls": 4.07692, "loss": 4.07692, "time": 0.81649} +{"mode": "train", "epoch": 52, "iter": 2800, "lr": 0.0734, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29047, "top5_acc": 0.54375, "loss_cls": 4.06608, "loss": 4.06608, "time": 0.82742} +{"mode": "train", "epoch": 52, "iter": 2900, "lr": 0.07337, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28562, "top5_acc": 0.54031, "loss_cls": 4.09728, "loss": 4.09728, "time": 0.82171} +{"mode": "train", "epoch": 52, "iter": 3000, "lr": 0.07335, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27781, "top5_acc": 0.53953, "loss_cls": 4.11208, "loss": 4.11208, "time": 0.81213} +{"mode": "train", "epoch": 52, "iter": 3100, "lr": 0.07332, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29812, "top5_acc": 0.55391, "loss_cls": 4.0518, "loss": 4.0518, "time": 0.81494} +{"mode": "train", "epoch": 52, "iter": 3200, "lr": 0.0733, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30062, "top5_acc": 0.54719, "loss_cls": 4.05811, "loss": 4.05811, "time": 0.81359} +{"mode": "train", "epoch": 52, "iter": 3300, "lr": 0.07328, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28813, "top5_acc": 0.53516, "loss_cls": 4.12079, "loss": 4.12079, "time": 0.81586} +{"mode": "train", "epoch": 52, "iter": 3400, "lr": 0.07325, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28578, "top5_acc": 0.53547, "loss_cls": 4.08196, "loss": 4.08196, "time": 0.81723} +{"mode": "train", "epoch": 52, "iter": 3500, "lr": 0.07323, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28906, "top5_acc": 0.53641, "loss_cls": 4.09086, "loss": 4.09086, "time": 0.81999} +{"mode": "train", "epoch": 52, "iter": 3600, "lr": 0.0732, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28922, "top5_acc": 0.54375, "loss_cls": 4.08146, "loss": 4.08146, "time": 0.81577} +{"mode": "train", "epoch": 52, "iter": 3700, "lr": 0.07318, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29109, "top5_acc": 0.54766, "loss_cls": 4.07361, "loss": 4.07361, "time": 0.81356} +{"mode": "val", "epoch": 52, "iter": 309, "lr": 0.07317, "top1_acc": 0.20326, "top5_acc": 0.43803, "mean_class_accuracy": 0.20299} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.07314, "memory": 15990, "data_time": 1.31696, "top1_acc": 0.29531, "top5_acc": 0.54641, "loss_cls": 4.04149, "loss": 4.04149, "time": 2.29483} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.07312, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29109, "top5_acc": 0.55812, "loss_cls": 4.02717, "loss": 4.02717, "time": 0.8188} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.07309, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29578, "top5_acc": 0.54219, "loss_cls": 4.0602, "loss": 4.0602, "time": 0.81898} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.07307, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29609, "top5_acc": 0.55688, "loss_cls": 4.02264, "loss": 4.02264, "time": 0.81587} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.07304, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29328, "top5_acc": 0.55188, "loss_cls": 4.01785, "loss": 4.01785, "time": 0.81403} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.07302, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29078, "top5_acc": 0.54156, "loss_cls": 4.09618, "loss": 4.09618, "time": 0.82212} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.07299, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28813, "top5_acc": 0.54156, "loss_cls": 4.09041, "loss": 4.09041, "time": 0.82087} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.07297, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28438, "top5_acc": 0.54578, "loss_cls": 4.09347, "loss": 4.09347, "time": 0.81672} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.07294, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29344, "top5_acc": 0.55531, "loss_cls": 4.05184, "loss": 4.05184, "time": 0.8167} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.07292, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28688, "top5_acc": 0.55219, "loss_cls": 4.06352, "loss": 4.06352, "time": 0.83241} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.07289, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29016, "top5_acc": 0.55312, "loss_cls": 4.06422, "loss": 4.06422, "time": 0.82045} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.07287, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29141, "top5_acc": 0.54047, "loss_cls": 4.08442, "loss": 4.08442, "time": 0.82718} +{"mode": "train", "epoch": 53, "iter": 1300, "lr": 0.07284, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28625, "top5_acc": 0.54062, "loss_cls": 4.1, "loss": 4.1, "time": 0.82333} +{"mode": "train", "epoch": 53, "iter": 1400, "lr": 0.07282, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28844, "top5_acc": 0.54812, "loss_cls": 4.06198, "loss": 4.06198, "time": 0.8257} +{"mode": "train", "epoch": 53, "iter": 1500, "lr": 0.07279, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28656, "top5_acc": 0.54031, "loss_cls": 4.08303, "loss": 4.08303, "time": 0.81981} +{"mode": "train", "epoch": 53, "iter": 1600, "lr": 0.07277, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30016, "top5_acc": 0.54734, "loss_cls": 4.04875, "loss": 4.04875, "time": 0.80953} +{"mode": "train", "epoch": 53, "iter": 1700, "lr": 0.07274, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2875, "top5_acc": 0.53797, "loss_cls": 4.10089, "loss": 4.10089, "time": 0.81494} +{"mode": "train", "epoch": 53, "iter": 1800, "lr": 0.07272, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28219, "top5_acc": 0.55406, "loss_cls": 4.07169, "loss": 4.07169, "time": 0.82241} +{"mode": "train", "epoch": 53, "iter": 1900, "lr": 0.07269, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29156, "top5_acc": 0.54469, "loss_cls": 4.07025, "loss": 4.07025, "time": 0.81794} +{"mode": "train", "epoch": 53, "iter": 2000, "lr": 0.07267, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28984, "top5_acc": 0.55172, "loss_cls": 4.05236, "loss": 4.05236, "time": 0.81963} +{"mode": "train", "epoch": 53, "iter": 2100, "lr": 0.07264, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28875, "top5_acc": 0.54594, "loss_cls": 4.06994, "loss": 4.06994, "time": 0.8179} +{"mode": "train", "epoch": 53, "iter": 2200, "lr": 0.07262, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29406, "top5_acc": 0.54547, "loss_cls": 4.04532, "loss": 4.04532, "time": 0.81823} +{"mode": "train", "epoch": 53, "iter": 2300, "lr": 0.07259, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29609, "top5_acc": 0.55172, "loss_cls": 4.03714, "loss": 4.03714, "time": 0.8117} +{"mode": "train", "epoch": 53, "iter": 2400, "lr": 0.07257, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29469, "top5_acc": 0.54812, "loss_cls": 4.04022, "loss": 4.04022, "time": 0.8145} +{"mode": "train", "epoch": 53, "iter": 2500, "lr": 0.07254, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28844, "top5_acc": 0.54562, "loss_cls": 4.05394, "loss": 4.05394, "time": 0.8186} +{"mode": "train", "epoch": 53, "iter": 2600, "lr": 0.07252, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28078, "top5_acc": 0.5275, "loss_cls": 4.13336, "loss": 4.13336, "time": 0.8145} +{"mode": "train", "epoch": 53, "iter": 2700, "lr": 0.07249, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28484, "top5_acc": 0.54188, "loss_cls": 4.10839, "loss": 4.10839, "time": 0.81395} +{"mode": "train", "epoch": 53, "iter": 2800, "lr": 0.07247, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28406, "top5_acc": 0.54281, "loss_cls": 4.07446, "loss": 4.07446, "time": 0.81728} +{"mode": "train", "epoch": 53, "iter": 2900, "lr": 0.07244, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28078, "top5_acc": 0.53047, "loss_cls": 4.11859, "loss": 4.11859, "time": 0.81861} +{"mode": "train", "epoch": 53, "iter": 3000, "lr": 0.07242, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28844, "top5_acc": 0.53953, "loss_cls": 4.06897, "loss": 4.06897, "time": 0.82042} +{"mode": "train", "epoch": 53, "iter": 3100, "lr": 0.07239, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28016, "top5_acc": 0.54266, "loss_cls": 4.11083, "loss": 4.11083, "time": 0.81631} +{"mode": "train", "epoch": 53, "iter": 3200, "lr": 0.07237, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28844, "top5_acc": 0.545, "loss_cls": 4.08242, "loss": 4.08242, "time": 0.82097} +{"mode": "train", "epoch": 53, "iter": 3300, "lr": 0.07234, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29891, "top5_acc": 0.55094, "loss_cls": 4.06346, "loss": 4.06346, "time": 0.81844} +{"mode": "train", "epoch": 53, "iter": 3400, "lr": 0.07232, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28969, "top5_acc": 0.54828, "loss_cls": 4.09015, "loss": 4.09015, "time": 0.81854} +{"mode": "train", "epoch": 53, "iter": 3500, "lr": 0.07229, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29234, "top5_acc": 0.55062, "loss_cls": 4.0612, "loss": 4.0612, "time": 0.81499} +{"mode": "train", "epoch": 53, "iter": 3600, "lr": 0.07227, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28875, "top5_acc": 0.54344, "loss_cls": 4.09464, "loss": 4.09464, "time": 0.81454} +{"mode": "train", "epoch": 53, "iter": 3700, "lr": 0.07224, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29156, "top5_acc": 0.54156, "loss_cls": 4.07017, "loss": 4.07017, "time": 0.8185} +{"mode": "val", "epoch": 53, "iter": 309, "lr": 0.07223, "top1_acc": 0.22631, "top5_acc": 0.46756, "mean_class_accuracy": 0.22613} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.07221, "memory": 15990, "data_time": 1.32266, "top1_acc": 0.30453, "top5_acc": 0.55312, "loss_cls": 4.00429, "loss": 4.00429, "time": 2.30271} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.07218, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28766, "top5_acc": 0.555, "loss_cls": 4.07335, "loss": 4.07335, "time": 0.81897} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.07216, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28672, "top5_acc": 0.54078, "loss_cls": 4.0652, "loss": 4.0652, "time": 0.81449} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.07213, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30531, "top5_acc": 0.55281, "loss_cls": 4.0317, "loss": 4.0317, "time": 0.81557} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.07211, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28406, "top5_acc": 0.545, "loss_cls": 4.06677, "loss": 4.06677, "time": 0.82117} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.07208, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28906, "top5_acc": 0.54875, "loss_cls": 4.07555, "loss": 4.07555, "time": 0.82315} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.07206, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29531, "top5_acc": 0.54828, "loss_cls": 4.03479, "loss": 4.03479, "time": 0.82208} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.07203, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2925, "top5_acc": 0.54766, "loss_cls": 4.03675, "loss": 4.03675, "time": 0.81754} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.07201, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29297, "top5_acc": 0.54625, "loss_cls": 4.07039, "loss": 4.07039, "time": 0.81749} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.07198, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29406, "top5_acc": 0.53906, "loss_cls": 4.05629, "loss": 4.05629, "time": 0.82214} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.07196, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28922, "top5_acc": 0.54656, "loss_cls": 4.08232, "loss": 4.08232, "time": 0.81862} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.07193, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30141, "top5_acc": 0.55719, "loss_cls": 4.00069, "loss": 4.00069, "time": 0.82131} +{"mode": "train", "epoch": 54, "iter": 1300, "lr": 0.07191, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28203, "top5_acc": 0.54422, "loss_cls": 4.07647, "loss": 4.07647, "time": 0.82448} +{"mode": "train", "epoch": 54, "iter": 1400, "lr": 0.07188, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29063, "top5_acc": 0.53641, "loss_cls": 4.08185, "loss": 4.08185, "time": 0.82053} +{"mode": "train", "epoch": 54, "iter": 1500, "lr": 0.07186, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29094, "top5_acc": 0.53953, "loss_cls": 4.09282, "loss": 4.09282, "time": 0.8239} +{"mode": "train", "epoch": 54, "iter": 1600, "lr": 0.07183, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29125, "top5_acc": 0.54328, "loss_cls": 4.04364, "loss": 4.04364, "time": 0.81924} +{"mode": "train", "epoch": 54, "iter": 1700, "lr": 0.07181, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28609, "top5_acc": 0.55016, "loss_cls": 4.05474, "loss": 4.05474, "time": 0.82328} +{"mode": "train", "epoch": 54, "iter": 1800, "lr": 0.07178, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30109, "top5_acc": 0.54438, "loss_cls": 4.04195, "loss": 4.04195, "time": 0.81534} +{"mode": "train", "epoch": 54, "iter": 1900, "lr": 0.07176, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28953, "top5_acc": 0.53891, "loss_cls": 4.0707, "loss": 4.0707, "time": 0.81507} +{"mode": "train", "epoch": 54, "iter": 2000, "lr": 0.07173, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29391, "top5_acc": 0.5475, "loss_cls": 4.0926, "loss": 4.0926, "time": 0.81471} +{"mode": "train", "epoch": 54, "iter": 2100, "lr": 0.0717, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27953, "top5_acc": 0.53438, "loss_cls": 4.11919, "loss": 4.11919, "time": 0.81913} +{"mode": "train", "epoch": 54, "iter": 2200, "lr": 0.07168, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29109, "top5_acc": 0.54375, "loss_cls": 4.07691, "loss": 4.07691, "time": 0.81608} +{"mode": "train", "epoch": 54, "iter": 2300, "lr": 0.07165, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29938, "top5_acc": 0.56, "loss_cls": 4.00806, "loss": 4.00806, "time": 0.82257} +{"mode": "train", "epoch": 54, "iter": 2400, "lr": 0.07163, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28547, "top5_acc": 0.54672, "loss_cls": 4.0907, "loss": 4.0907, "time": 0.81358} +{"mode": "train", "epoch": 54, "iter": 2500, "lr": 0.0716, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29297, "top5_acc": 0.55719, "loss_cls": 4.05756, "loss": 4.05756, "time": 0.81096} +{"mode": "train", "epoch": 54, "iter": 2600, "lr": 0.07158, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29563, "top5_acc": 0.54094, "loss_cls": 4.06147, "loss": 4.06147, "time": 0.81253} +{"mode": "train", "epoch": 54, "iter": 2700, "lr": 0.07155, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28719, "top5_acc": 0.54141, "loss_cls": 4.06534, "loss": 4.06534, "time": 0.81676} +{"mode": "train", "epoch": 54, "iter": 2800, "lr": 0.07153, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29188, "top5_acc": 0.5475, "loss_cls": 4.06653, "loss": 4.06653, "time": 0.81323} +{"mode": "train", "epoch": 54, "iter": 2900, "lr": 0.0715, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28078, "top5_acc": 0.53719, "loss_cls": 4.12066, "loss": 4.12066, "time": 0.81656} +{"mode": "train", "epoch": 54, "iter": 3000, "lr": 0.07148, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2975, "top5_acc": 0.54719, "loss_cls": 4.04673, "loss": 4.04673, "time": 0.81747} +{"mode": "train", "epoch": 54, "iter": 3100, "lr": 0.07145, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29563, "top5_acc": 0.54516, "loss_cls": 4.04419, "loss": 4.04419, "time": 0.81682} +{"mode": "train", "epoch": 54, "iter": 3200, "lr": 0.07143, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28969, "top5_acc": 0.54766, "loss_cls": 4.05679, "loss": 4.05679, "time": 0.81775} +{"mode": "train", "epoch": 54, "iter": 3300, "lr": 0.0714, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27891, "top5_acc": 0.53328, "loss_cls": 4.14494, "loss": 4.14494, "time": 0.81566} +{"mode": "train", "epoch": 54, "iter": 3400, "lr": 0.07138, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29219, "top5_acc": 0.54719, "loss_cls": 4.06213, "loss": 4.06213, "time": 0.81452} +{"mode": "train", "epoch": 54, "iter": 3500, "lr": 0.07135, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29281, "top5_acc": 0.54141, "loss_cls": 4.06939, "loss": 4.06939, "time": 0.81244} +{"mode": "train", "epoch": 54, "iter": 3600, "lr": 0.07133, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.295, "top5_acc": 0.54828, "loss_cls": 4.02147, "loss": 4.02147, "time": 0.81436} +{"mode": "train", "epoch": 54, "iter": 3700, "lr": 0.0713, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29078, "top5_acc": 0.545, "loss_cls": 4.09047, "loss": 4.09047, "time": 0.8225} +{"mode": "val", "epoch": 54, "iter": 309, "lr": 0.07129, "top1_acc": 0.20539, "top5_acc": 0.43438, "mean_class_accuracy": 0.20517} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.07126, "memory": 15990, "data_time": 1.33283, "top1_acc": 0.29406, "top5_acc": 0.54953, "loss_cls": 4.01159, "loss": 4.01159, "time": 2.30525} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.07124, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29375, "top5_acc": 0.54953, "loss_cls": 4.02645, "loss": 4.02645, "time": 0.82032} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.07121, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29156, "top5_acc": 0.5475, "loss_cls": 4.05401, "loss": 4.05401, "time": 0.82033} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.07119, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29719, "top5_acc": 0.55391, "loss_cls": 4.02216, "loss": 4.02216, "time": 0.823} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.07116, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28813, "top5_acc": 0.54234, "loss_cls": 4.06642, "loss": 4.06642, "time": 0.81876} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.07114, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29219, "top5_acc": 0.54641, "loss_cls": 4.05765, "loss": 4.05765, "time": 0.81313} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.07111, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3, "top5_acc": 0.55859, "loss_cls": 4.00777, "loss": 4.00777, "time": 0.8184} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.07109, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2875, "top5_acc": 0.54875, "loss_cls": 4.04687, "loss": 4.04687, "time": 0.82142} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.07106, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28734, "top5_acc": 0.55547, "loss_cls": 4.0091, "loss": 4.0091, "time": 0.81985} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.07104, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29188, "top5_acc": 0.55375, "loss_cls": 4.04093, "loss": 4.04093, "time": 0.82319} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.07101, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28906, "top5_acc": 0.54531, "loss_cls": 4.0607, "loss": 4.0607, "time": 0.81759} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.07099, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29047, "top5_acc": 0.54688, "loss_cls": 4.03195, "loss": 4.03195, "time": 0.82396} +{"mode": "train", "epoch": 55, "iter": 1300, "lr": 0.07096, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29875, "top5_acc": 0.55188, "loss_cls": 4.03827, "loss": 4.03827, "time": 0.82421} +{"mode": "train", "epoch": 55, "iter": 1400, "lr": 0.07093, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28875, "top5_acc": 0.53797, "loss_cls": 4.09959, "loss": 4.09959, "time": 0.82552} +{"mode": "train", "epoch": 55, "iter": 1500, "lr": 0.07091, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29688, "top5_acc": 0.55, "loss_cls": 4.03907, "loss": 4.03907, "time": 0.81813} +{"mode": "train", "epoch": 55, "iter": 1600, "lr": 0.07088, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29781, "top5_acc": 0.55797, "loss_cls": 3.99054, "loss": 3.99054, "time": 0.81586} +{"mode": "train", "epoch": 55, "iter": 1700, "lr": 0.07086, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28562, "top5_acc": 0.54641, "loss_cls": 4.08041, "loss": 4.08041, "time": 0.81598} +{"mode": "train", "epoch": 55, "iter": 1800, "lr": 0.07083, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28781, "top5_acc": 0.53641, "loss_cls": 4.07732, "loss": 4.07732, "time": 0.81147} +{"mode": "train", "epoch": 55, "iter": 1900, "lr": 0.07081, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29141, "top5_acc": 0.54328, "loss_cls": 4.05178, "loss": 4.05178, "time": 0.81352} +{"mode": "train", "epoch": 55, "iter": 2000, "lr": 0.07078, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28891, "top5_acc": 0.54062, "loss_cls": 4.11152, "loss": 4.11152, "time": 0.81674} +{"mode": "train", "epoch": 55, "iter": 2100, "lr": 0.07076, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29, "top5_acc": 0.55594, "loss_cls": 4.06261, "loss": 4.06261, "time": 0.81816} +{"mode": "train", "epoch": 55, "iter": 2200, "lr": 0.07073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28328, "top5_acc": 0.54359, "loss_cls": 4.07839, "loss": 4.07839, "time": 0.81858} +{"mode": "train", "epoch": 55, "iter": 2300, "lr": 0.07071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29094, "top5_acc": 0.55062, "loss_cls": 4.04485, "loss": 4.04485, "time": 0.81542} +{"mode": "train", "epoch": 55, "iter": 2400, "lr": 0.07068, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29297, "top5_acc": 0.53875, "loss_cls": 4.07389, "loss": 4.07389, "time": 0.8175} +{"mode": "train", "epoch": 55, "iter": 2500, "lr": 0.07065, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28625, "top5_acc": 0.54375, "loss_cls": 4.08527, "loss": 4.08527, "time": 0.81441} +{"mode": "train", "epoch": 55, "iter": 2600, "lr": 0.07063, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29656, "top5_acc": 0.54937, "loss_cls": 4.03757, "loss": 4.03757, "time": 0.81529} +{"mode": "train", "epoch": 55, "iter": 2700, "lr": 0.0706, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29297, "top5_acc": 0.55219, "loss_cls": 4.03358, "loss": 4.03358, "time": 0.81582} +{"mode": "train", "epoch": 55, "iter": 2800, "lr": 0.07058, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30047, "top5_acc": 0.55953, "loss_cls": 4.02565, "loss": 4.02565, "time": 0.81421} +{"mode": "train", "epoch": 55, "iter": 2900, "lr": 0.07055, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29344, "top5_acc": 0.54188, "loss_cls": 4.09283, "loss": 4.09283, "time": 0.81433} +{"mode": "train", "epoch": 55, "iter": 3000, "lr": 0.07053, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29438, "top5_acc": 0.55547, "loss_cls": 4.03939, "loss": 4.03939, "time": 0.81707} +{"mode": "train", "epoch": 55, "iter": 3100, "lr": 0.0705, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.295, "top5_acc": 0.55437, "loss_cls": 4.04811, "loss": 4.04811, "time": 0.81535} +{"mode": "train", "epoch": 55, "iter": 3200, "lr": 0.07048, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28797, "top5_acc": 0.54266, "loss_cls": 4.07166, "loss": 4.07166, "time": 0.81305} +{"mode": "train", "epoch": 55, "iter": 3300, "lr": 0.07045, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28516, "top5_acc": 0.53531, "loss_cls": 4.09905, "loss": 4.09905, "time": 0.81474} +{"mode": "train", "epoch": 55, "iter": 3400, "lr": 0.07043, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28906, "top5_acc": 0.54203, "loss_cls": 4.09744, "loss": 4.09744, "time": 0.81849} +{"mode": "train", "epoch": 55, "iter": 3500, "lr": 0.0704, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28938, "top5_acc": 0.54453, "loss_cls": 4.075, "loss": 4.075, "time": 0.81605} +{"mode": "train", "epoch": 55, "iter": 3600, "lr": 0.07037, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28984, "top5_acc": 0.54281, "loss_cls": 4.07256, "loss": 4.07256, "time": 0.81351} +{"mode": "train", "epoch": 55, "iter": 3700, "lr": 0.07035, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30109, "top5_acc": 0.54312, "loss_cls": 4.05109, "loss": 4.05109, "time": 0.81964} +{"mode": "val", "epoch": 55, "iter": 309, "lr": 0.07034, "top1_acc": 0.22666, "top5_acc": 0.46391, "mean_class_accuracy": 0.22633} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.07031, "memory": 15990, "data_time": 1.32126, "top1_acc": 0.30016, "top5_acc": 0.56344, "loss_cls": 3.97, "loss": 3.97, "time": 2.30701} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.07029, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3, "top5_acc": 0.55328, "loss_cls": 4.02701, "loss": 4.02701, "time": 0.81808} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.07026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28781, "top5_acc": 0.54609, "loss_cls": 4.07667, "loss": 4.07667, "time": 0.81462} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.07023, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29609, "top5_acc": 0.55328, "loss_cls": 4.0272, "loss": 4.0272, "time": 0.81758} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.07021, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29391, "top5_acc": 0.55375, "loss_cls": 4.02631, "loss": 4.02631, "time": 0.81935} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.07018, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29063, "top5_acc": 0.55125, "loss_cls": 4.02951, "loss": 4.02951, "time": 0.81541} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.07016, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30828, "top5_acc": 0.56188, "loss_cls": 3.99047, "loss": 3.99047, "time": 0.82329} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.07013, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30781, "top5_acc": 0.55781, "loss_cls": 3.99521, "loss": 3.99521, "time": 0.82288} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.07011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29516, "top5_acc": 0.54953, "loss_cls": 4.04006, "loss": 4.04006, "time": 0.81795} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.07008, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30031, "top5_acc": 0.55281, "loss_cls": 4.0115, "loss": 4.0115, "time": 0.81858} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.07006, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29016, "top5_acc": 0.54703, "loss_cls": 4.04743, "loss": 4.04743, "time": 0.81714} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.07003, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29359, "top5_acc": 0.53953, "loss_cls": 4.06391, "loss": 4.06391, "time": 0.82236} +{"mode": "train", "epoch": 56, "iter": 1300, "lr": 0.07, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29766, "top5_acc": 0.55047, "loss_cls": 4.0158, "loss": 4.0158, "time": 0.82341} +{"mode": "train", "epoch": 56, "iter": 1400, "lr": 0.06998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29219, "top5_acc": 0.555, "loss_cls": 4.04364, "loss": 4.04364, "time": 0.82085} +{"mode": "train", "epoch": 56, "iter": 1500, "lr": 0.06995, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28891, "top5_acc": 0.555, "loss_cls": 4.02422, "loss": 4.02422, "time": 0.82512} +{"mode": "train", "epoch": 56, "iter": 1600, "lr": 0.06993, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28609, "top5_acc": 0.53969, "loss_cls": 4.08882, "loss": 4.08882, "time": 0.81908} +{"mode": "train", "epoch": 56, "iter": 1700, "lr": 0.0699, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29422, "top5_acc": 0.55219, "loss_cls": 4.01351, "loss": 4.01351, "time": 0.82327} +{"mode": "train", "epoch": 56, "iter": 1800, "lr": 0.06988, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3, "top5_acc": 0.55078, "loss_cls": 4.05409, "loss": 4.05409, "time": 0.81761} +{"mode": "train", "epoch": 56, "iter": 1900, "lr": 0.06985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30172, "top5_acc": 0.56, "loss_cls": 4.01978, "loss": 4.01978, "time": 0.81707} +{"mode": "train", "epoch": 56, "iter": 2000, "lr": 0.06983, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29234, "top5_acc": 0.53641, "loss_cls": 4.08753, "loss": 4.08753, "time": 0.81509} +{"mode": "train", "epoch": 56, "iter": 2100, "lr": 0.0698, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28844, "top5_acc": 0.55312, "loss_cls": 4.04202, "loss": 4.04202, "time": 0.81459} +{"mode": "train", "epoch": 56, "iter": 2200, "lr": 0.06977, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29766, "top5_acc": 0.55562, "loss_cls": 4.01113, "loss": 4.01113, "time": 0.82038} +{"mode": "train", "epoch": 56, "iter": 2300, "lr": 0.06975, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29359, "top5_acc": 0.54547, "loss_cls": 4.08468, "loss": 4.08468, "time": 0.81842} +{"mode": "train", "epoch": 56, "iter": 2400, "lr": 0.06972, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29828, "top5_acc": 0.55062, "loss_cls": 4.017, "loss": 4.017, "time": 0.81391} +{"mode": "train", "epoch": 56, "iter": 2500, "lr": 0.0697, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29531, "top5_acc": 0.54937, "loss_cls": 4.03597, "loss": 4.03597, "time": 0.82116} +{"mode": "train", "epoch": 56, "iter": 2600, "lr": 0.06967, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29688, "top5_acc": 0.54438, "loss_cls": 4.05039, "loss": 4.05039, "time": 0.81906} +{"mode": "train", "epoch": 56, "iter": 2700, "lr": 0.06965, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29625, "top5_acc": 0.54594, "loss_cls": 4.06125, "loss": 4.06125, "time": 0.81693} +{"mode": "train", "epoch": 56, "iter": 2800, "lr": 0.06962, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29797, "top5_acc": 0.54047, "loss_cls": 4.05271, "loss": 4.05271, "time": 0.8211} +{"mode": "train", "epoch": 56, "iter": 2900, "lr": 0.06959, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29609, "top5_acc": 0.54312, "loss_cls": 4.04898, "loss": 4.04898, "time": 0.81405} +{"mode": "train", "epoch": 56, "iter": 3000, "lr": 0.06957, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2975, "top5_acc": 0.54438, "loss_cls": 4.07679, "loss": 4.07679, "time": 0.81093} +{"mode": "train", "epoch": 56, "iter": 3100, "lr": 0.06954, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28297, "top5_acc": 0.53094, "loss_cls": 4.1261, "loss": 4.1261, "time": 0.81669} +{"mode": "train", "epoch": 56, "iter": 3200, "lr": 0.06952, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28797, "top5_acc": 0.54469, "loss_cls": 4.06292, "loss": 4.06292, "time": 0.81498} +{"mode": "train", "epoch": 56, "iter": 3300, "lr": 0.06949, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30141, "top5_acc": 0.54812, "loss_cls": 4.01566, "loss": 4.01566, "time": 0.81378} +{"mode": "train", "epoch": 56, "iter": 3400, "lr": 0.06947, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29281, "top5_acc": 0.53906, "loss_cls": 4.07825, "loss": 4.07825, "time": 0.81674} +{"mode": "train", "epoch": 56, "iter": 3500, "lr": 0.06944, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29609, "top5_acc": 0.54438, "loss_cls": 4.06754, "loss": 4.06754, "time": 0.81709} +{"mode": "train", "epoch": 56, "iter": 3600, "lr": 0.06941, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29422, "top5_acc": 0.55141, "loss_cls": 4.05058, "loss": 4.05058, "time": 0.81383} +{"mode": "train", "epoch": 56, "iter": 3700, "lr": 0.06939, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29922, "top5_acc": 0.54656, "loss_cls": 4.0408, "loss": 4.0408, "time": 0.81648} +{"mode": "val", "epoch": 56, "iter": 309, "lr": 0.06938, "top1_acc": 0.22844, "top5_acc": 0.46958, "mean_class_accuracy": 0.22817} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.06935, "memory": 15990, "data_time": 1.31172, "top1_acc": 0.29, "top5_acc": 0.54453, "loss_cls": 4.03815, "loss": 4.03815, "time": 2.28738} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.06932, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30547, "top5_acc": 0.55891, "loss_cls": 3.95613, "loss": 3.95613, "time": 0.81928} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.0693, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29688, "top5_acc": 0.55828, "loss_cls": 3.99922, "loss": 3.99922, "time": 0.81695} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.06927, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30328, "top5_acc": 0.55625, "loss_cls": 4.0133, "loss": 4.0133, "time": 0.82237} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.06925, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29797, "top5_acc": 0.55828, "loss_cls": 3.99947, "loss": 3.99947, "time": 0.82097} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.06922, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30891, "top5_acc": 0.56281, "loss_cls": 3.95468, "loss": 3.95468, "time": 0.82055} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.0692, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29875, "top5_acc": 0.5575, "loss_cls": 3.98973, "loss": 3.98973, "time": 0.82019} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.06917, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29625, "top5_acc": 0.55125, "loss_cls": 4.02237, "loss": 4.02237, "time": 0.82273} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.06914, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30125, "top5_acc": 0.55594, "loss_cls": 4.00921, "loss": 4.00921, "time": 0.81946} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.06912, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29, "top5_acc": 0.54375, "loss_cls": 4.07949, "loss": 4.07949, "time": 0.81886} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.06909, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28656, "top5_acc": 0.55328, "loss_cls": 4.06392, "loss": 4.06392, "time": 0.81829} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.06907, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29328, "top5_acc": 0.55172, "loss_cls": 4.05838, "loss": 4.05838, "time": 0.82637} +{"mode": "train", "epoch": 57, "iter": 1300, "lr": 0.06904, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29891, "top5_acc": 0.55125, "loss_cls": 4.03804, "loss": 4.03804, "time": 0.83299} +{"mode": "train", "epoch": 57, "iter": 1400, "lr": 0.06901, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29109, "top5_acc": 0.54109, "loss_cls": 4.07756, "loss": 4.07756, "time": 0.81932} +{"mode": "train", "epoch": 57, "iter": 1500, "lr": 0.06899, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29672, "top5_acc": 0.54625, "loss_cls": 4.0513, "loss": 4.0513, "time": 0.81963} +{"mode": "train", "epoch": 57, "iter": 1600, "lr": 0.06896, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30344, "top5_acc": 0.55656, "loss_cls": 3.97312, "loss": 3.97312, "time": 0.8152} +{"mode": "train", "epoch": 57, "iter": 1700, "lr": 0.06894, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29453, "top5_acc": 0.54297, "loss_cls": 4.06189, "loss": 4.06189, "time": 0.81856} +{"mode": "train", "epoch": 57, "iter": 1800, "lr": 0.06891, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30281, "top5_acc": 0.55703, "loss_cls": 4.01806, "loss": 4.01806, "time": 0.81862} +{"mode": "train", "epoch": 57, "iter": 1900, "lr": 0.06889, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28203, "top5_acc": 0.54484, "loss_cls": 4.0878, "loss": 4.0878, "time": 0.81675} +{"mode": "train", "epoch": 57, "iter": 2000, "lr": 0.06886, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29422, "top5_acc": 0.54953, "loss_cls": 4.03825, "loss": 4.03825, "time": 0.81627} +{"mode": "train", "epoch": 57, "iter": 2100, "lr": 0.06883, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3, "top5_acc": 0.55391, "loss_cls": 4.04036, "loss": 4.04036, "time": 0.81913} +{"mode": "train", "epoch": 57, "iter": 2200, "lr": 0.06881, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28953, "top5_acc": 0.55, "loss_cls": 4.08618, "loss": 4.08618, "time": 0.81321} +{"mode": "train", "epoch": 57, "iter": 2300, "lr": 0.06878, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29953, "top5_acc": 0.55031, "loss_cls": 4.02876, "loss": 4.02876, "time": 0.81338} +{"mode": "train", "epoch": 57, "iter": 2400, "lr": 0.06876, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29672, "top5_acc": 0.53797, "loss_cls": 4.0789, "loss": 4.0789, "time": 0.81329} +{"mode": "train", "epoch": 57, "iter": 2500, "lr": 0.06873, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29219, "top5_acc": 0.54703, "loss_cls": 4.07633, "loss": 4.07633, "time": 0.81835} +{"mode": "train", "epoch": 57, "iter": 2600, "lr": 0.0687, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29078, "top5_acc": 0.54469, "loss_cls": 4.08066, "loss": 4.08066, "time": 0.8167} +{"mode": "train", "epoch": 57, "iter": 2700, "lr": 0.06868, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28312, "top5_acc": 0.55172, "loss_cls": 4.07729, "loss": 4.07729, "time": 0.81934} +{"mode": "train", "epoch": 57, "iter": 2800, "lr": 0.06865, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29453, "top5_acc": 0.55016, "loss_cls": 4.06598, "loss": 4.06598, "time": 0.81543} +{"mode": "train", "epoch": 57, "iter": 2900, "lr": 0.06863, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29984, "top5_acc": 0.55469, "loss_cls": 3.99652, "loss": 3.99652, "time": 0.81892} +{"mode": "train", "epoch": 57, "iter": 3000, "lr": 0.0686, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28906, "top5_acc": 0.54375, "loss_cls": 4.06678, "loss": 4.06678, "time": 0.8138} +{"mode": "train", "epoch": 57, "iter": 3100, "lr": 0.06857, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29484, "top5_acc": 0.55016, "loss_cls": 4.05717, "loss": 4.05717, "time": 0.81248} +{"mode": "train", "epoch": 57, "iter": 3200, "lr": 0.06855, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28766, "top5_acc": 0.5425, "loss_cls": 4.06713, "loss": 4.06713, "time": 0.81494} +{"mode": "train", "epoch": 57, "iter": 3300, "lr": 0.06852, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29781, "top5_acc": 0.55078, "loss_cls": 4.01019, "loss": 4.01019, "time": 0.81667} +{"mode": "train", "epoch": 57, "iter": 3400, "lr": 0.0685, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29031, "top5_acc": 0.54844, "loss_cls": 4.06042, "loss": 4.06042, "time": 0.82215} +{"mode": "train", "epoch": 57, "iter": 3500, "lr": 0.06847, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29672, "top5_acc": 0.53953, "loss_cls": 4.06028, "loss": 4.06028, "time": 0.81955} +{"mode": "train", "epoch": 57, "iter": 3600, "lr": 0.06844, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29094, "top5_acc": 0.54016, "loss_cls": 4.0723, "loss": 4.0723, "time": 0.81956} +{"mode": "train", "epoch": 57, "iter": 3700, "lr": 0.06842, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29672, "top5_acc": 0.54625, "loss_cls": 4.03845, "loss": 4.03845, "time": 0.8166} +{"mode": "val", "epoch": 57, "iter": 309, "lr": 0.06841, "top1_acc": 0.2216, "top5_acc": 0.46584, "mean_class_accuracy": 0.22116} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.06838, "memory": 15990, "data_time": 1.32121, "top1_acc": 0.29656, "top5_acc": 0.55891, "loss_cls": 4.02464, "loss": 4.02464, "time": 2.30476} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.06835, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29484, "top5_acc": 0.55937, "loss_cls": 4.03064, "loss": 4.03064, "time": 0.81893} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.06833, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29984, "top5_acc": 0.54844, "loss_cls": 4.04313, "loss": 4.04313, "time": 0.8209} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.0683, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29219, "top5_acc": 0.55234, "loss_cls": 4.04203, "loss": 4.04203, "time": 0.81524} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.06828, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29953, "top5_acc": 0.55703, "loss_cls": 3.99868, "loss": 3.99868, "time": 0.82197} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.06825, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30969, "top5_acc": 0.55094, "loss_cls": 4.00817, "loss": 4.00817, "time": 0.82013} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.06822, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29906, "top5_acc": 0.54891, "loss_cls": 4.0233, "loss": 4.0233, "time": 0.81753} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.0682, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29438, "top5_acc": 0.55516, "loss_cls": 4.03031, "loss": 4.03031, "time": 0.82566} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.06817, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30172, "top5_acc": 0.55625, "loss_cls": 3.97756, "loss": 3.97756, "time": 0.82134} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.06815, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29156, "top5_acc": 0.54703, "loss_cls": 4.06192, "loss": 4.06192, "time": 0.82137} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.06812, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29531, "top5_acc": 0.54609, "loss_cls": 4.02895, "loss": 4.02895, "time": 0.82047} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.06809, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29641, "top5_acc": 0.54937, "loss_cls": 4.01301, "loss": 4.01301, "time": 0.82152} +{"mode": "train", "epoch": 58, "iter": 1300, "lr": 0.06807, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29797, "top5_acc": 0.55797, "loss_cls": 4.00234, "loss": 4.00234, "time": 0.82933} +{"mode": "train", "epoch": 58, "iter": 1400, "lr": 0.06804, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29672, "top5_acc": 0.55188, "loss_cls": 4.05269, "loss": 4.05269, "time": 0.81728} +{"mode": "train", "epoch": 58, "iter": 1500, "lr": 0.06802, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30016, "top5_acc": 0.56766, "loss_cls": 4.00601, "loss": 4.00601, "time": 0.82291} +{"mode": "train", "epoch": 58, "iter": 1600, "lr": 0.06799, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29422, "top5_acc": 0.54141, "loss_cls": 4.07588, "loss": 4.07588, "time": 0.82345} +{"mode": "train", "epoch": 58, "iter": 1700, "lr": 0.06796, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.295, "top5_acc": 0.55781, "loss_cls": 4.01322, "loss": 4.01322, "time": 0.81687} +{"mode": "train", "epoch": 58, "iter": 1800, "lr": 0.06794, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29719, "top5_acc": 0.55594, "loss_cls": 4.0304, "loss": 4.0304, "time": 0.81805} +{"mode": "train", "epoch": 58, "iter": 1900, "lr": 0.06791, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29766, "top5_acc": 0.55359, "loss_cls": 4.02503, "loss": 4.02503, "time": 0.8189} +{"mode": "train", "epoch": 58, "iter": 2000, "lr": 0.06789, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27922, "top5_acc": 0.52891, "loss_cls": 4.12743, "loss": 4.12743, "time": 0.8143} +{"mode": "train", "epoch": 58, "iter": 2100, "lr": 0.06786, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2975, "top5_acc": 0.55375, "loss_cls": 4.0568, "loss": 4.0568, "time": 0.81545} +{"mode": "train", "epoch": 58, "iter": 2200, "lr": 0.06783, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29469, "top5_acc": 0.54797, "loss_cls": 4.05784, "loss": 4.05784, "time": 0.82018} +{"mode": "train", "epoch": 58, "iter": 2300, "lr": 0.06781, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29563, "top5_acc": 0.54734, "loss_cls": 4.04485, "loss": 4.04485, "time": 0.81648} +{"mode": "train", "epoch": 58, "iter": 2400, "lr": 0.06778, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29828, "top5_acc": 0.55672, "loss_cls": 4.02483, "loss": 4.02483, "time": 0.81949} +{"mode": "train", "epoch": 58, "iter": 2500, "lr": 0.06775, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30312, "top5_acc": 0.55734, "loss_cls": 3.9999, "loss": 3.9999, "time": 0.82358} +{"mode": "train", "epoch": 58, "iter": 2600, "lr": 0.06773, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29859, "top5_acc": 0.54984, "loss_cls": 4.00016, "loss": 4.00016, "time": 0.82164} +{"mode": "train", "epoch": 58, "iter": 2700, "lr": 0.0677, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29656, "top5_acc": 0.54391, "loss_cls": 4.05044, "loss": 4.05044, "time": 0.8186} +{"mode": "train", "epoch": 58, "iter": 2800, "lr": 0.06768, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29484, "top5_acc": 0.55047, "loss_cls": 4.05578, "loss": 4.05578, "time": 0.81998} +{"mode": "train", "epoch": 58, "iter": 2900, "lr": 0.06765, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30469, "top5_acc": 0.55969, "loss_cls": 3.9951, "loss": 3.9951, "time": 0.81213} +{"mode": "train", "epoch": 58, "iter": 3000, "lr": 0.06762, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28875, "top5_acc": 0.54375, "loss_cls": 4.06608, "loss": 4.06608, "time": 0.81791} +{"mode": "train", "epoch": 58, "iter": 3100, "lr": 0.0676, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29688, "top5_acc": 0.55125, "loss_cls": 4.05548, "loss": 4.05548, "time": 0.8194} +{"mode": "train", "epoch": 58, "iter": 3200, "lr": 0.06757, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30391, "top5_acc": 0.55172, "loss_cls": 4.03958, "loss": 4.03958, "time": 0.81962} +{"mode": "train", "epoch": 58, "iter": 3300, "lr": 0.06755, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29484, "top5_acc": 0.55656, "loss_cls": 4.00789, "loss": 4.00789, "time": 0.81529} +{"mode": "train", "epoch": 58, "iter": 3400, "lr": 0.06752, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29625, "top5_acc": 0.54391, "loss_cls": 4.05257, "loss": 4.05257, "time": 0.81582} +{"mode": "train", "epoch": 58, "iter": 3500, "lr": 0.06749, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29469, "top5_acc": 0.55906, "loss_cls": 4.01848, "loss": 4.01848, "time": 0.81091} +{"mode": "train", "epoch": 58, "iter": 3600, "lr": 0.06747, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29031, "top5_acc": 0.54484, "loss_cls": 4.05731, "loss": 4.05731, "time": 0.81655} +{"mode": "train", "epoch": 58, "iter": 3700, "lr": 0.06744, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29297, "top5_acc": 0.54594, "loss_cls": 4.06264, "loss": 4.06264, "time": 0.81727} +{"mode": "val", "epoch": 58, "iter": 309, "lr": 0.06743, "top1_acc": 0.21562, "top5_acc": 0.45302, "mean_class_accuracy": 0.21547} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.0674, "memory": 15990, "data_time": 1.30977, "top1_acc": 0.29969, "top5_acc": 0.55937, "loss_cls": 3.9734, "loss": 3.9734, "time": 2.28726} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.06738, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29672, "top5_acc": 0.54906, "loss_cls": 4.03548, "loss": 4.03548, "time": 0.81738} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.06735, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30688, "top5_acc": 0.55734, "loss_cls": 3.99078, "loss": 3.99078, "time": 0.81803} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.06732, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30375, "top5_acc": 0.57125, "loss_cls": 3.97447, "loss": 3.97447, "time": 0.81886} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.0673, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29578, "top5_acc": 0.54734, "loss_cls": 4.0663, "loss": 4.0663, "time": 0.81181} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.06727, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29859, "top5_acc": 0.55469, "loss_cls": 3.99659, "loss": 3.99659, "time": 0.81543} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.06725, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30969, "top5_acc": 0.55859, "loss_cls": 3.99941, "loss": 3.99941, "time": 0.82161} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.06722, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30484, "top5_acc": 0.55562, "loss_cls": 3.98706, "loss": 3.98706, "time": 0.81419} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.06719, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30062, "top5_acc": 0.55125, "loss_cls": 4.00846, "loss": 4.00846, "time": 0.82361} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.06717, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29594, "top5_acc": 0.54812, "loss_cls": 4.04521, "loss": 4.04521, "time": 0.81483} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.06714, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29672, "top5_acc": 0.55125, "loss_cls": 4.03109, "loss": 4.03109, "time": 0.82428} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.06711, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28422, "top5_acc": 0.54703, "loss_cls": 4.05531, "loss": 4.05531, "time": 0.82023} +{"mode": "train", "epoch": 59, "iter": 1300, "lr": 0.06709, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29219, "top5_acc": 0.54734, "loss_cls": 4.06577, "loss": 4.06577, "time": 0.82669} +{"mode": "train", "epoch": 59, "iter": 1400, "lr": 0.06706, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29875, "top5_acc": 0.54922, "loss_cls": 4.04285, "loss": 4.04285, "time": 0.82422} +{"mode": "train", "epoch": 59, "iter": 1500, "lr": 0.06704, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30109, "top5_acc": 0.56094, "loss_cls": 4.00791, "loss": 4.00791, "time": 0.82753} +{"mode": "train", "epoch": 59, "iter": 1600, "lr": 0.06701, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29172, "top5_acc": 0.53516, "loss_cls": 4.07536, "loss": 4.07536, "time": 0.8207} +{"mode": "train", "epoch": 59, "iter": 1700, "lr": 0.06698, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28328, "top5_acc": 0.53844, "loss_cls": 4.05963, "loss": 4.05963, "time": 0.81644} +{"mode": "train", "epoch": 59, "iter": 1800, "lr": 0.06696, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29609, "top5_acc": 0.55344, "loss_cls": 4.0426, "loss": 4.0426, "time": 0.81902} +{"mode": "train", "epoch": 59, "iter": 1900, "lr": 0.06693, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29266, "top5_acc": 0.54844, "loss_cls": 4.06174, "loss": 4.06174, "time": 0.81387} +{"mode": "train", "epoch": 59, "iter": 2000, "lr": 0.0669, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29266, "top5_acc": 0.55078, "loss_cls": 4.0458, "loss": 4.0458, "time": 0.81834} +{"mode": "train", "epoch": 59, "iter": 2100, "lr": 0.06688, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30562, "top5_acc": 0.55297, "loss_cls": 3.9729, "loss": 3.9729, "time": 0.82464} +{"mode": "train", "epoch": 59, "iter": 2200, "lr": 0.06685, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29234, "top5_acc": 0.55156, "loss_cls": 4.03232, "loss": 4.03232, "time": 0.81657} +{"mode": "train", "epoch": 59, "iter": 2300, "lr": 0.06682, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.3025, "top5_acc": 0.55531, "loss_cls": 3.98736, "loss": 3.98736, "time": 0.81364} +{"mode": "train", "epoch": 59, "iter": 2400, "lr": 0.0668, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30219, "top5_acc": 0.56078, "loss_cls": 4.01951, "loss": 4.01951, "time": 0.82218} +{"mode": "train", "epoch": 59, "iter": 2500, "lr": 0.06677, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30453, "top5_acc": 0.56031, "loss_cls": 3.94927, "loss": 3.94927, "time": 0.81729} +{"mode": "train", "epoch": 59, "iter": 2600, "lr": 0.06675, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29875, "top5_acc": 0.55781, "loss_cls": 4.0058, "loss": 4.0058, "time": 0.81283} +{"mode": "train", "epoch": 59, "iter": 2700, "lr": 0.06672, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30297, "top5_acc": 0.55828, "loss_cls": 4.00782, "loss": 4.00782, "time": 0.81738} +{"mode": "train", "epoch": 59, "iter": 2800, "lr": 0.06669, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30734, "top5_acc": 0.56328, "loss_cls": 4.013, "loss": 4.013, "time": 0.81238} +{"mode": "train", "epoch": 59, "iter": 2900, "lr": 0.06667, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29641, "top5_acc": 0.55453, "loss_cls": 4.04522, "loss": 4.04522, "time": 0.81575} +{"mode": "train", "epoch": 59, "iter": 3000, "lr": 0.06664, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29547, "top5_acc": 0.55281, "loss_cls": 4.02117, "loss": 4.02117, "time": 0.81219} +{"mode": "train", "epoch": 59, "iter": 3100, "lr": 0.06661, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30484, "top5_acc": 0.55953, "loss_cls": 3.99582, "loss": 3.99582, "time": 0.81539} +{"mode": "train", "epoch": 59, "iter": 3200, "lr": 0.06659, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29406, "top5_acc": 0.56, "loss_cls": 4.03027, "loss": 4.03027, "time": 0.81825} +{"mode": "train", "epoch": 59, "iter": 3300, "lr": 0.06656, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29219, "top5_acc": 0.55516, "loss_cls": 4.03077, "loss": 4.03077, "time": 0.81628} +{"mode": "train", "epoch": 59, "iter": 3400, "lr": 0.06653, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29766, "top5_acc": 0.55109, "loss_cls": 4.02925, "loss": 4.02925, "time": 0.81682} +{"mode": "train", "epoch": 59, "iter": 3500, "lr": 0.06651, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30422, "top5_acc": 0.56188, "loss_cls": 3.97915, "loss": 3.97915, "time": 0.81691} +{"mode": "train", "epoch": 59, "iter": 3600, "lr": 0.06648, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29766, "top5_acc": 0.54984, "loss_cls": 4.04421, "loss": 4.04421, "time": 0.81771} +{"mode": "train", "epoch": 59, "iter": 3700, "lr": 0.06646, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29938, "top5_acc": 0.54562, "loss_cls": 4.04432, "loss": 4.04432, "time": 0.81935} +{"mode": "val", "epoch": 59, "iter": 309, "lr": 0.06644, "top1_acc": 0.23304, "top5_acc": 0.47045, "mean_class_accuracy": 0.23287} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.06642, "memory": 15990, "data_time": 1.29162, "top1_acc": 0.29859, "top5_acc": 0.56141, "loss_cls": 3.98107, "loss": 3.98107, "time": 2.29338} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.06639, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30656, "top5_acc": 0.56078, "loss_cls": 3.96816, "loss": 3.96816, "time": 0.82294} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.06636, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31469, "top5_acc": 0.56891, "loss_cls": 3.9117, "loss": 3.9117, "time": 0.81676} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.06634, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29688, "top5_acc": 0.55859, "loss_cls": 3.98344, "loss": 3.98344, "time": 0.81968} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.06631, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30344, "top5_acc": 0.55453, "loss_cls": 4.00764, "loss": 4.00764, "time": 0.81709} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.06629, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29344, "top5_acc": 0.56, "loss_cls": 4.01283, "loss": 4.01283, "time": 0.81681} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.06626, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30828, "top5_acc": 0.57672, "loss_cls": 3.94261, "loss": 3.94261, "time": 0.82528} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.06623, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32016, "top5_acc": 0.56516, "loss_cls": 3.95374, "loss": 3.95374, "time": 0.82057} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.06621, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31094, "top5_acc": 0.56344, "loss_cls": 3.96655, "loss": 3.96655, "time": 0.82029} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.06618, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30672, "top5_acc": 0.55891, "loss_cls": 4.00644, "loss": 4.00644, "time": 0.8155} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.06615, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29766, "top5_acc": 0.54172, "loss_cls": 4.04509, "loss": 4.04509, "time": 0.82232} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.06613, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29703, "top5_acc": 0.555, "loss_cls": 4.01452, "loss": 4.01452, "time": 0.82713} +{"mode": "train", "epoch": 60, "iter": 1300, "lr": 0.0661, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29109, "top5_acc": 0.54281, "loss_cls": 4.08186, "loss": 4.08186, "time": 0.81945} +{"mode": "train", "epoch": 60, "iter": 1400, "lr": 0.06607, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29563, "top5_acc": 0.55703, "loss_cls": 3.99186, "loss": 3.99186, "time": 0.82444} +{"mode": "train", "epoch": 60, "iter": 1500, "lr": 0.06605, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28719, "top5_acc": 0.55422, "loss_cls": 4.04736, "loss": 4.04736, "time": 0.82468} +{"mode": "train", "epoch": 60, "iter": 1600, "lr": 0.06602, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30703, "top5_acc": 0.55625, "loss_cls": 3.97012, "loss": 3.97012, "time": 0.82165} +{"mode": "train", "epoch": 60, "iter": 1700, "lr": 0.06599, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29094, "top5_acc": 0.54828, "loss_cls": 4.02417, "loss": 4.02417, "time": 0.81683} +{"mode": "train", "epoch": 60, "iter": 1800, "lr": 0.06597, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30656, "top5_acc": 0.56297, "loss_cls": 3.98071, "loss": 3.98071, "time": 0.81598} +{"mode": "train", "epoch": 60, "iter": 1900, "lr": 0.06594, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29734, "top5_acc": 0.55109, "loss_cls": 4.06585, "loss": 4.06585, "time": 0.81956} +{"mode": "train", "epoch": 60, "iter": 2000, "lr": 0.06591, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29156, "top5_acc": 0.54188, "loss_cls": 4.06351, "loss": 4.06351, "time": 0.81654} +{"mode": "train", "epoch": 60, "iter": 2100, "lr": 0.06589, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29063, "top5_acc": 0.54922, "loss_cls": 4.04329, "loss": 4.04329, "time": 0.81732} +{"mode": "train", "epoch": 60, "iter": 2200, "lr": 0.06586, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29656, "top5_acc": 0.54531, "loss_cls": 4.05763, "loss": 4.05763, "time": 0.8167} +{"mode": "train", "epoch": 60, "iter": 2300, "lr": 0.06584, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29328, "top5_acc": 0.54125, "loss_cls": 4.07801, "loss": 4.07801, "time": 0.81526} +{"mode": "train", "epoch": 60, "iter": 2400, "lr": 0.06581, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29969, "top5_acc": 0.55391, "loss_cls": 4.00458, "loss": 4.00458, "time": 0.8147} +{"mode": "train", "epoch": 60, "iter": 2500, "lr": 0.06578, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29828, "top5_acc": 0.55484, "loss_cls": 4.00788, "loss": 4.00788, "time": 0.8163} +{"mode": "train", "epoch": 60, "iter": 2600, "lr": 0.06576, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.295, "top5_acc": 0.55906, "loss_cls": 3.98769, "loss": 3.98769, "time": 0.81559} +{"mode": "train", "epoch": 60, "iter": 2700, "lr": 0.06573, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29625, "top5_acc": 0.545, "loss_cls": 4.06612, "loss": 4.06612, "time": 0.8142} +{"mode": "train", "epoch": 60, "iter": 2800, "lr": 0.0657, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3, "top5_acc": 0.55125, "loss_cls": 4.02643, "loss": 4.02643, "time": 0.82404} +{"mode": "train", "epoch": 60, "iter": 2900, "lr": 0.06568, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30578, "top5_acc": 0.55891, "loss_cls": 3.96945, "loss": 3.96945, "time": 0.81497} +{"mode": "train", "epoch": 60, "iter": 3000, "lr": 0.06565, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30078, "top5_acc": 0.55516, "loss_cls": 4.02929, "loss": 4.02929, "time": 0.82493} +{"mode": "train", "epoch": 60, "iter": 3100, "lr": 0.06562, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28875, "top5_acc": 0.54203, "loss_cls": 4.07216, "loss": 4.07216, "time": 0.82238} +{"mode": "train", "epoch": 60, "iter": 3200, "lr": 0.0656, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30172, "top5_acc": 0.56016, "loss_cls": 3.98684, "loss": 3.98684, "time": 0.82003} +{"mode": "train", "epoch": 60, "iter": 3300, "lr": 0.06557, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30203, "top5_acc": 0.55891, "loss_cls": 4.00458, "loss": 4.00458, "time": 0.81882} +{"mode": "train", "epoch": 60, "iter": 3400, "lr": 0.06554, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30391, "top5_acc": 0.54828, "loss_cls": 4.03862, "loss": 4.03862, "time": 0.81476} +{"mode": "train", "epoch": 60, "iter": 3500, "lr": 0.06552, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30562, "top5_acc": 0.56484, "loss_cls": 3.94371, "loss": 3.94371, "time": 0.82044} +{"mode": "train", "epoch": 60, "iter": 3600, "lr": 0.06549, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29953, "top5_acc": 0.54797, "loss_cls": 4.05776, "loss": 4.05776, "time": 0.82357} +{"mode": "train", "epoch": 60, "iter": 3700, "lr": 0.06546, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28984, "top5_acc": 0.55031, "loss_cls": 4.0371, "loss": 4.0371, "time": 0.81726} +{"mode": "val", "epoch": 60, "iter": 309, "lr": 0.06545, "top1_acc": 0.2413, "top5_acc": 0.48402, "mean_class_accuracy": 0.24101} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.06542, "memory": 15990, "data_time": 1.30279, "top1_acc": 0.31406, "top5_acc": 0.57203, "loss_cls": 3.92808, "loss": 3.92808, "time": 2.28104} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.0654, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30281, "top5_acc": 0.56688, "loss_cls": 3.94397, "loss": 3.94397, "time": 0.82504} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.06537, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30156, "top5_acc": 0.55844, "loss_cls": 3.97053, "loss": 3.97053, "time": 0.82262} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.06534, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.295, "top5_acc": 0.55641, "loss_cls": 4.00009, "loss": 4.00009, "time": 0.81563} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.06532, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30219, "top5_acc": 0.56188, "loss_cls": 3.99729, "loss": 3.99729, "time": 0.81596} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.06529, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31484, "top5_acc": 0.55516, "loss_cls": 3.97568, "loss": 3.97568, "time": 0.81453} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.06526, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30266, "top5_acc": 0.55656, "loss_cls": 3.98386, "loss": 3.98386, "time": 0.81705} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.06524, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30812, "top5_acc": 0.56922, "loss_cls": 3.94925, "loss": 3.94925, "time": 0.82217} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.06521, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30359, "top5_acc": 0.55766, "loss_cls": 3.98581, "loss": 3.98581, "time": 0.81926} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.06519, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30281, "top5_acc": 0.55797, "loss_cls": 3.99925, "loss": 3.99925, "time": 0.81053} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.06516, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30172, "top5_acc": 0.55266, "loss_cls": 4.01998, "loss": 4.01998, "time": 0.81535} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.06513, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29719, "top5_acc": 0.55141, "loss_cls": 4.01942, "loss": 4.01942, "time": 0.82144} +{"mode": "train", "epoch": 61, "iter": 1300, "lr": 0.06511, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.305, "top5_acc": 0.55781, "loss_cls": 4.00872, "loss": 4.00872, "time": 0.82172} +{"mode": "train", "epoch": 61, "iter": 1400, "lr": 0.06508, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30703, "top5_acc": 0.55906, "loss_cls": 3.97701, "loss": 3.97701, "time": 0.83217} +{"mode": "train", "epoch": 61, "iter": 1500, "lr": 0.06505, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29578, "top5_acc": 0.56156, "loss_cls": 4.00443, "loss": 4.00443, "time": 0.82237} +{"mode": "train", "epoch": 61, "iter": 1600, "lr": 0.06503, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29625, "top5_acc": 0.55047, "loss_cls": 4.02891, "loss": 4.02891, "time": 0.81973} +{"mode": "train", "epoch": 61, "iter": 1700, "lr": 0.065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30656, "top5_acc": 0.55703, "loss_cls": 3.99933, "loss": 3.99933, "time": 0.82109} +{"mode": "train", "epoch": 61, "iter": 1800, "lr": 0.06497, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29656, "top5_acc": 0.55062, "loss_cls": 4.03165, "loss": 4.03165, "time": 0.81611} +{"mode": "train", "epoch": 61, "iter": 1900, "lr": 0.06495, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29875, "top5_acc": 0.555, "loss_cls": 4.01148, "loss": 4.01148, "time": 0.81968} +{"mode": "train", "epoch": 61, "iter": 2000, "lr": 0.06492, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30531, "top5_acc": 0.56609, "loss_cls": 3.97813, "loss": 3.97813, "time": 0.81557} +{"mode": "train", "epoch": 61, "iter": 2100, "lr": 0.06489, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29734, "top5_acc": 0.55422, "loss_cls": 4.00837, "loss": 4.00837, "time": 0.82022} +{"mode": "train", "epoch": 61, "iter": 2200, "lr": 0.06487, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30641, "top5_acc": 0.55328, "loss_cls": 4.0106, "loss": 4.0106, "time": 0.82493} +{"mode": "train", "epoch": 61, "iter": 2300, "lr": 0.06484, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29984, "top5_acc": 0.55688, "loss_cls": 4.00308, "loss": 4.00308, "time": 0.81858} +{"mode": "train", "epoch": 61, "iter": 2400, "lr": 0.06481, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30078, "top5_acc": 0.55359, "loss_cls": 4.00549, "loss": 4.00549, "time": 0.81741} +{"mode": "train", "epoch": 61, "iter": 2500, "lr": 0.06478, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30578, "top5_acc": 0.55469, "loss_cls": 3.98481, "loss": 3.98481, "time": 0.81793} +{"mode": "train", "epoch": 61, "iter": 2600, "lr": 0.06476, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30641, "top5_acc": 0.54984, "loss_cls": 4.02675, "loss": 4.02675, "time": 0.81946} +{"mode": "train", "epoch": 61, "iter": 2700, "lr": 0.06473, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29953, "top5_acc": 0.56484, "loss_cls": 4.00925, "loss": 4.00925, "time": 0.81428} +{"mode": "train", "epoch": 61, "iter": 2800, "lr": 0.0647, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29219, "top5_acc": 0.55281, "loss_cls": 4.01801, "loss": 4.01801, "time": 0.81352} +{"mode": "train", "epoch": 61, "iter": 2900, "lr": 0.06468, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29313, "top5_acc": 0.55922, "loss_cls": 4.00259, "loss": 4.00259, "time": 0.8162} +{"mode": "train", "epoch": 61, "iter": 3000, "lr": 0.06465, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29719, "top5_acc": 0.56125, "loss_cls": 4.00831, "loss": 4.00831, "time": 0.81547} +{"mode": "train", "epoch": 61, "iter": 3100, "lr": 0.06462, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29328, "top5_acc": 0.54031, "loss_cls": 4.09543, "loss": 4.09543, "time": 0.81131} +{"mode": "train", "epoch": 61, "iter": 3200, "lr": 0.0646, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30328, "top5_acc": 0.55656, "loss_cls": 4.00683, "loss": 4.00683, "time": 0.81593} +{"mode": "train", "epoch": 61, "iter": 3300, "lr": 0.06457, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30328, "top5_acc": 0.55609, "loss_cls": 3.98486, "loss": 3.98486, "time": 0.81514} +{"mode": "train", "epoch": 61, "iter": 3400, "lr": 0.06454, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29688, "top5_acc": 0.55797, "loss_cls": 4.02273, "loss": 4.02273, "time": 0.81923} +{"mode": "train", "epoch": 61, "iter": 3500, "lr": 0.06452, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30844, "top5_acc": 0.55953, "loss_cls": 3.99639, "loss": 3.99639, "time": 0.81378} +{"mode": "train", "epoch": 61, "iter": 3600, "lr": 0.06449, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30312, "top5_acc": 0.55328, "loss_cls": 4.01755, "loss": 4.01755, "time": 0.81781} +{"mode": "train", "epoch": 61, "iter": 3700, "lr": 0.06446, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29594, "top5_acc": 0.545, "loss_cls": 4.06424, "loss": 4.06424, "time": 0.81457} +{"mode": "val", "epoch": 61, "iter": 309, "lr": 0.06445, "top1_acc": 0.23786, "top5_acc": 0.48002, "mean_class_accuracy": 0.23755} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.06443, "memory": 15990, "data_time": 1.2992, "top1_acc": 0.30812, "top5_acc": 0.55937, "loss_cls": 3.9935, "loss": 3.9935, "time": 2.28527} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.0644, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31312, "top5_acc": 0.56641, "loss_cls": 3.90337, "loss": 3.90337, "time": 0.81796} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.06437, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30078, "top5_acc": 0.56063, "loss_cls": 3.9737, "loss": 3.9737, "time": 0.82189} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.06434, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30922, "top5_acc": 0.56141, "loss_cls": 3.97969, "loss": 3.97969, "time": 0.81799} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.06432, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30688, "top5_acc": 0.57, "loss_cls": 3.94757, "loss": 3.94757, "time": 0.81556} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.06429, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30703, "top5_acc": 0.56734, "loss_cls": 3.97668, "loss": 3.97668, "time": 0.81721} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.06426, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30891, "top5_acc": 0.55906, "loss_cls": 3.97488, "loss": 3.97488, "time": 0.82326} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.06424, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30484, "top5_acc": 0.56297, "loss_cls": 3.97241, "loss": 3.97241, "time": 0.81967} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.06421, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30219, "top5_acc": 0.55984, "loss_cls": 3.98989, "loss": 3.98989, "time": 0.8158} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.06418, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29469, "top5_acc": 0.55688, "loss_cls": 4.01529, "loss": 4.01529, "time": 0.81956} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.06416, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3, "top5_acc": 0.54266, "loss_cls": 4.02831, "loss": 4.02831, "time": 0.81223} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.06413, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29703, "top5_acc": 0.55016, "loss_cls": 4.0168, "loss": 4.0168, "time": 0.81697} +{"mode": "train", "epoch": 62, "iter": 1300, "lr": 0.0641, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30234, "top5_acc": 0.555, "loss_cls": 4.00384, "loss": 4.00384, "time": 0.82228} +{"mode": "train", "epoch": 62, "iter": 1400, "lr": 0.06408, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29672, "top5_acc": 0.55281, "loss_cls": 4.01214, "loss": 4.01214, "time": 0.82502} +{"mode": "train", "epoch": 62, "iter": 1500, "lr": 0.06405, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30516, "top5_acc": 0.56, "loss_cls": 4.01157, "loss": 4.01157, "time": 0.82913} +{"mode": "train", "epoch": 62, "iter": 1600, "lr": 0.06402, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31141, "top5_acc": 0.56219, "loss_cls": 3.95522, "loss": 3.95522, "time": 0.82266} +{"mode": "train", "epoch": 62, "iter": 1700, "lr": 0.064, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30906, "top5_acc": 0.55891, "loss_cls": 3.99982, "loss": 3.99982, "time": 0.81624} +{"mode": "train", "epoch": 62, "iter": 1800, "lr": 0.06397, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31641, "top5_acc": 0.56094, "loss_cls": 3.93685, "loss": 3.93685, "time": 0.8187} +{"mode": "train", "epoch": 62, "iter": 1900, "lr": 0.06394, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3, "top5_acc": 0.55734, "loss_cls": 3.9861, "loss": 3.9861, "time": 0.8156} +{"mode": "train", "epoch": 62, "iter": 2000, "lr": 0.06392, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30516, "top5_acc": 0.56437, "loss_cls": 3.96279, "loss": 3.96279, "time": 0.81657} +{"mode": "train", "epoch": 62, "iter": 2100, "lr": 0.06389, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29094, "top5_acc": 0.55234, "loss_cls": 4.05068, "loss": 4.05068, "time": 0.81454} +{"mode": "train", "epoch": 62, "iter": 2200, "lr": 0.06386, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29797, "top5_acc": 0.55, "loss_cls": 4.02659, "loss": 4.02659, "time": 0.8138} +{"mode": "train", "epoch": 62, "iter": 2300, "lr": 0.06384, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.305, "top5_acc": 0.55922, "loss_cls": 3.99711, "loss": 3.99711, "time": 0.81084} +{"mode": "train", "epoch": 62, "iter": 2400, "lr": 0.06381, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30859, "top5_acc": 0.55937, "loss_cls": 3.98182, "loss": 3.98182, "time": 0.81948} +{"mode": "train", "epoch": 62, "iter": 2500, "lr": 0.06378, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29672, "top5_acc": 0.56063, "loss_cls": 4.00781, "loss": 4.00781, "time": 0.81837} +{"mode": "train", "epoch": 62, "iter": 2600, "lr": 0.06375, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30359, "top5_acc": 0.56031, "loss_cls": 3.98492, "loss": 3.98492, "time": 0.81147} +{"mode": "train", "epoch": 62, "iter": 2700, "lr": 0.06373, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29703, "top5_acc": 0.54812, "loss_cls": 4.02293, "loss": 4.02293, "time": 0.81431} +{"mode": "train", "epoch": 62, "iter": 2800, "lr": 0.0637, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29672, "top5_acc": 0.55484, "loss_cls": 4.00661, "loss": 4.00661, "time": 0.81354} +{"mode": "train", "epoch": 62, "iter": 2900, "lr": 0.06367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29656, "top5_acc": 0.55609, "loss_cls": 4.01604, "loss": 4.01604, "time": 0.8209} +{"mode": "train", "epoch": 62, "iter": 3000, "lr": 0.06365, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29781, "top5_acc": 0.56047, "loss_cls": 4.00735, "loss": 4.00735, "time": 0.81833} +{"mode": "train", "epoch": 62, "iter": 3100, "lr": 0.06362, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29812, "top5_acc": 0.55094, "loss_cls": 4.03213, "loss": 4.03213, "time": 0.81846} +{"mode": "train", "epoch": 62, "iter": 3200, "lr": 0.06359, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29938, "top5_acc": 0.5575, "loss_cls": 3.99472, "loss": 3.99472, "time": 0.81649} +{"mode": "train", "epoch": 62, "iter": 3300, "lr": 0.06357, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28172, "top5_acc": 0.54297, "loss_cls": 4.06625, "loss": 4.06625, "time": 0.81769} +{"mode": "train", "epoch": 62, "iter": 3400, "lr": 0.06354, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30516, "top5_acc": 0.56703, "loss_cls": 3.95835, "loss": 3.95835, "time": 0.81293} +{"mode": "train", "epoch": 62, "iter": 3500, "lr": 0.06351, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29984, "top5_acc": 0.56094, "loss_cls": 3.99751, "loss": 3.99751, "time": 0.81531} +{"mode": "train", "epoch": 62, "iter": 3600, "lr": 0.06349, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30734, "top5_acc": 0.55109, "loss_cls": 4.0156, "loss": 4.0156, "time": 0.81695} +{"mode": "train", "epoch": 62, "iter": 3700, "lr": 0.06346, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30141, "top5_acc": 0.55391, "loss_cls": 4.01172, "loss": 4.01172, "time": 0.81256} +{"mode": "val", "epoch": 62, "iter": 309, "lr": 0.06345, "top1_acc": 0.24323, "top5_acc": 0.48549, "mean_class_accuracy": 0.24297} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.06342, "memory": 15990, "data_time": 1.32791, "top1_acc": 0.3025, "top5_acc": 0.56703, "loss_cls": 3.96016, "loss": 3.96016, "time": 2.29823} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.06339, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30938, "top5_acc": 0.56516, "loss_cls": 3.93997, "loss": 3.93997, "time": 0.82008} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.06337, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30141, "top5_acc": 0.55766, "loss_cls": 3.99495, "loss": 3.99495, "time": 0.82262} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.06334, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30281, "top5_acc": 0.56422, "loss_cls": 3.96756, "loss": 3.96756, "time": 0.8193} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.06331, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29594, "top5_acc": 0.56031, "loss_cls": 3.97477, "loss": 3.97477, "time": 0.81389} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.06328, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30641, "top5_acc": 0.56, "loss_cls": 3.98395, "loss": 3.98395, "time": 0.81868} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.06326, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30562, "top5_acc": 0.56859, "loss_cls": 3.95319, "loss": 3.95319, "time": 0.81858} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.06323, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30562, "top5_acc": 0.54797, "loss_cls": 4.01327, "loss": 4.01327, "time": 0.81777} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.0632, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30516, "top5_acc": 0.56531, "loss_cls": 3.93846, "loss": 3.93846, "time": 0.82224} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.06318, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30125, "top5_acc": 0.55094, "loss_cls": 3.99686, "loss": 3.99686, "time": 0.8237} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.06315, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30703, "top5_acc": 0.55891, "loss_cls": 3.97374, "loss": 3.97374, "time": 0.81303} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.06312, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30328, "top5_acc": 0.55406, "loss_cls": 4.00371, "loss": 4.00371, "time": 0.8168} +{"mode": "train", "epoch": 63, "iter": 1300, "lr": 0.0631, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30688, "top5_acc": 0.56453, "loss_cls": 3.9776, "loss": 3.9776, "time": 0.82467} +{"mode": "train", "epoch": 63, "iter": 1400, "lr": 0.06307, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30562, "top5_acc": 0.55906, "loss_cls": 3.97787, "loss": 3.97787, "time": 0.82634} +{"mode": "train", "epoch": 63, "iter": 1500, "lr": 0.06304, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31516, "top5_acc": 0.56547, "loss_cls": 3.97448, "loss": 3.97448, "time": 0.82229} +{"mode": "train", "epoch": 63, "iter": 1600, "lr": 0.06301, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30609, "top5_acc": 0.56203, "loss_cls": 3.98255, "loss": 3.98255, "time": 0.82646} +{"mode": "train", "epoch": 63, "iter": 1700, "lr": 0.06299, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30172, "top5_acc": 0.55719, "loss_cls": 4.01613, "loss": 4.01613, "time": 0.81626} +{"mode": "train", "epoch": 63, "iter": 1800, "lr": 0.06296, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30578, "top5_acc": 0.56766, "loss_cls": 3.93297, "loss": 3.93297, "time": 0.81562} +{"mode": "train", "epoch": 63, "iter": 1900, "lr": 0.06293, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29094, "top5_acc": 0.55297, "loss_cls": 4.03964, "loss": 4.03964, "time": 0.81163} +{"mode": "train", "epoch": 63, "iter": 2000, "lr": 0.06291, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31312, "top5_acc": 0.56703, "loss_cls": 3.96548, "loss": 3.96548, "time": 0.82341} +{"mode": "train", "epoch": 63, "iter": 2100, "lr": 0.06288, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.295, "top5_acc": 0.54516, "loss_cls": 4.05216, "loss": 4.05216, "time": 0.81972} +{"mode": "train", "epoch": 63, "iter": 2200, "lr": 0.06285, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31703, "top5_acc": 0.57344, "loss_cls": 3.90161, "loss": 3.90161, "time": 0.8211} +{"mode": "train", "epoch": 63, "iter": 2300, "lr": 0.06283, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30891, "top5_acc": 0.56953, "loss_cls": 3.99047, "loss": 3.99047, "time": 0.81947} +{"mode": "train", "epoch": 63, "iter": 2400, "lr": 0.0628, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30688, "top5_acc": 0.56328, "loss_cls": 3.98369, "loss": 3.98369, "time": 0.81972} +{"mode": "train", "epoch": 63, "iter": 2500, "lr": 0.06277, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30812, "top5_acc": 0.55891, "loss_cls": 3.99266, "loss": 3.99266, "time": 0.81649} +{"mode": "train", "epoch": 63, "iter": 2600, "lr": 0.06274, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30625, "top5_acc": 0.56203, "loss_cls": 3.96942, "loss": 3.96942, "time": 0.81826} +{"mode": "train", "epoch": 63, "iter": 2700, "lr": 0.06272, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29563, "top5_acc": 0.54891, "loss_cls": 4.05946, "loss": 4.05946, "time": 0.81653} +{"mode": "train", "epoch": 63, "iter": 2800, "lr": 0.06269, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2975, "top5_acc": 0.55062, "loss_cls": 4.02372, "loss": 4.02372, "time": 0.81196} +{"mode": "train", "epoch": 63, "iter": 2900, "lr": 0.06266, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29969, "top5_acc": 0.55672, "loss_cls": 4.00355, "loss": 4.00355, "time": 0.82327} +{"mode": "train", "epoch": 63, "iter": 3000, "lr": 0.06264, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30359, "top5_acc": 0.55688, "loss_cls": 3.99949, "loss": 3.99949, "time": 0.816} +{"mode": "train", "epoch": 63, "iter": 3100, "lr": 0.06261, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29938, "top5_acc": 0.5575, "loss_cls": 4.00914, "loss": 4.00914, "time": 0.81989} +{"mode": "train", "epoch": 63, "iter": 3200, "lr": 0.06258, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30406, "top5_acc": 0.56016, "loss_cls": 3.96153, "loss": 3.96153, "time": 0.81406} +{"mode": "train", "epoch": 63, "iter": 3300, "lr": 0.06256, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29781, "top5_acc": 0.55312, "loss_cls": 4.02282, "loss": 4.02282, "time": 0.82041} +{"mode": "train", "epoch": 63, "iter": 3400, "lr": 0.06253, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30875, "top5_acc": 0.55484, "loss_cls": 3.9666, "loss": 3.9666, "time": 0.81855} +{"mode": "train", "epoch": 63, "iter": 3500, "lr": 0.0625, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30438, "top5_acc": 0.54984, "loss_cls": 4.00475, "loss": 4.00475, "time": 0.81449} +{"mode": "train", "epoch": 63, "iter": 3600, "lr": 0.06247, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29953, "top5_acc": 0.54922, "loss_cls": 4.04456, "loss": 4.04456, "time": 0.81293} +{"mode": "train", "epoch": 63, "iter": 3700, "lr": 0.06245, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30172, "top5_acc": 0.55531, "loss_cls": 4.02767, "loss": 4.02767, "time": 0.81677} +{"mode": "val", "epoch": 63, "iter": 309, "lr": 0.06243, "top1_acc": 0.23933, "top5_acc": 0.48757, "mean_class_accuracy": 0.23892} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.06241, "memory": 15990, "data_time": 1.30645, "top1_acc": 0.31297, "top5_acc": 0.56578, "loss_cls": 3.93695, "loss": 3.93695, "time": 2.29262} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.06238, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31328, "top5_acc": 0.56766, "loss_cls": 3.91851, "loss": 3.91851, "time": 0.81989} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.06235, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31031, "top5_acc": 0.5725, "loss_cls": 3.92052, "loss": 3.92052, "time": 0.81482} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.06233, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30094, "top5_acc": 0.55922, "loss_cls": 3.98818, "loss": 3.98818, "time": 0.81706} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.0623, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30922, "top5_acc": 0.56281, "loss_cls": 3.96521, "loss": 3.96521, "time": 0.81113} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.06227, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29797, "top5_acc": 0.56797, "loss_cls": 3.96657, "loss": 3.96657, "time": 0.81288} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.06225, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30594, "top5_acc": 0.55547, "loss_cls": 3.97689, "loss": 3.97689, "time": 0.81625} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.06222, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30641, "top5_acc": 0.55453, "loss_cls": 3.98434, "loss": 3.98434, "time": 0.82112} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.06219, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30453, "top5_acc": 0.56812, "loss_cls": 3.96461, "loss": 3.96461, "time": 0.82646} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.06216, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3075, "top5_acc": 0.56094, "loss_cls": 3.95528, "loss": 3.95528, "time": 0.82244} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.06214, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30281, "top5_acc": 0.56234, "loss_cls": 3.97597, "loss": 3.97597, "time": 0.81669} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.06211, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31328, "top5_acc": 0.57156, "loss_cls": 3.94897, "loss": 3.94897, "time": 0.81833} +{"mode": "train", "epoch": 64, "iter": 1300, "lr": 0.06208, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30422, "top5_acc": 0.56234, "loss_cls": 4.00725, "loss": 4.00725, "time": 0.81575} +{"mode": "train", "epoch": 64, "iter": 1400, "lr": 0.06206, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30984, "top5_acc": 0.56391, "loss_cls": 3.95897, "loss": 3.95897, "time": 0.82709} +{"mode": "train", "epoch": 64, "iter": 1500, "lr": 0.06203, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30359, "top5_acc": 0.55828, "loss_cls": 3.99156, "loss": 3.99156, "time": 0.83237} +{"mode": "train", "epoch": 64, "iter": 1600, "lr": 0.062, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31125, "top5_acc": 0.5625, "loss_cls": 3.97253, "loss": 3.97253, "time": 0.82446} +{"mode": "train", "epoch": 64, "iter": 1700, "lr": 0.06197, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29812, "top5_acc": 0.545, "loss_cls": 4.03914, "loss": 4.03914, "time": 0.82218} +{"mode": "train", "epoch": 64, "iter": 1800, "lr": 0.06195, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30094, "top5_acc": 0.55625, "loss_cls": 3.98364, "loss": 3.98364, "time": 0.81693} +{"mode": "train", "epoch": 64, "iter": 1900, "lr": 0.06192, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31109, "top5_acc": 0.56672, "loss_cls": 3.9708, "loss": 3.9708, "time": 0.8161} +{"mode": "train", "epoch": 64, "iter": 2000, "lr": 0.06189, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30828, "top5_acc": 0.57172, "loss_cls": 3.951, "loss": 3.951, "time": 0.81305} +{"mode": "train", "epoch": 64, "iter": 2100, "lr": 0.06187, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30703, "top5_acc": 0.56547, "loss_cls": 3.96542, "loss": 3.96542, "time": 0.81537} +{"mode": "train", "epoch": 64, "iter": 2200, "lr": 0.06184, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30516, "top5_acc": 0.56547, "loss_cls": 3.9664, "loss": 3.9664, "time": 0.81673} +{"mode": "train", "epoch": 64, "iter": 2300, "lr": 0.06181, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30703, "top5_acc": 0.57094, "loss_cls": 3.96039, "loss": 3.96039, "time": 0.81856} +{"mode": "train", "epoch": 64, "iter": 2400, "lr": 0.06178, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30219, "top5_acc": 0.55406, "loss_cls": 4.01293, "loss": 4.01293, "time": 0.81739} +{"mode": "train", "epoch": 64, "iter": 2500, "lr": 0.06176, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31094, "top5_acc": 0.56594, "loss_cls": 3.98151, "loss": 3.98151, "time": 0.81725} +{"mode": "train", "epoch": 64, "iter": 2600, "lr": 0.06173, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30438, "top5_acc": 0.55359, "loss_cls": 3.99986, "loss": 3.99986, "time": 0.8246} +{"mode": "train", "epoch": 64, "iter": 2700, "lr": 0.0617, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30266, "top5_acc": 0.56328, "loss_cls": 3.96903, "loss": 3.96903, "time": 0.81449} +{"mode": "train", "epoch": 64, "iter": 2800, "lr": 0.06168, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30375, "top5_acc": 0.55734, "loss_cls": 4.00008, "loss": 4.00008, "time": 0.81059} +{"mode": "train", "epoch": 64, "iter": 2900, "lr": 0.06165, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30594, "top5_acc": 0.55609, "loss_cls": 3.9801, "loss": 3.9801, "time": 0.81799} +{"mode": "train", "epoch": 64, "iter": 3000, "lr": 0.06162, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29906, "top5_acc": 0.55578, "loss_cls": 4.02317, "loss": 4.02317, "time": 0.81339} +{"mode": "train", "epoch": 64, "iter": 3100, "lr": 0.06159, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29953, "top5_acc": 0.54969, "loss_cls": 4.00673, "loss": 4.00673, "time": 0.81692} +{"mode": "train", "epoch": 64, "iter": 3200, "lr": 0.06157, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30984, "top5_acc": 0.56641, "loss_cls": 3.95894, "loss": 3.95894, "time": 0.81799} +{"mode": "train", "epoch": 64, "iter": 3300, "lr": 0.06154, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3025, "top5_acc": 0.55625, "loss_cls": 3.96402, "loss": 3.96402, "time": 0.81411} +{"mode": "train", "epoch": 64, "iter": 3400, "lr": 0.06151, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30281, "top5_acc": 0.55453, "loss_cls": 3.99143, "loss": 3.99143, "time": 0.81471} +{"mode": "train", "epoch": 64, "iter": 3500, "lr": 0.06148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30141, "top5_acc": 0.55469, "loss_cls": 3.99365, "loss": 3.99365, "time": 0.81862} +{"mode": "train", "epoch": 64, "iter": 3600, "lr": 0.06146, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30297, "top5_acc": 0.55969, "loss_cls": 4.00374, "loss": 4.00374, "time": 0.81145} +{"mode": "train", "epoch": 64, "iter": 3700, "lr": 0.06143, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31297, "top5_acc": 0.56312, "loss_cls": 3.97978, "loss": 3.97978, "time": 0.82112} +{"mode": "val", "epoch": 64, "iter": 309, "lr": 0.06142, "top1_acc": 0.24206, "top5_acc": 0.48696, "mean_class_accuracy": 0.24201} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.06139, "memory": 15990, "data_time": 1.29757, "top1_acc": 0.31625, "top5_acc": 0.57688, "loss_cls": 3.88046, "loss": 3.88046, "time": 2.27328} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.06136, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31031, "top5_acc": 0.57125, "loss_cls": 3.91373, "loss": 3.91373, "time": 0.81892} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.06134, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31297, "top5_acc": 0.56375, "loss_cls": 3.93136, "loss": 3.93136, "time": 0.81705} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.06131, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30391, "top5_acc": 0.57016, "loss_cls": 3.96006, "loss": 3.96006, "time": 0.82817} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.06128, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30891, "top5_acc": 0.57156, "loss_cls": 3.93373, "loss": 3.93373, "time": 0.8185} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.06125, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30016, "top5_acc": 0.56031, "loss_cls": 3.9787, "loss": 3.9787, "time": 0.81767} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.06123, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29578, "top5_acc": 0.55828, "loss_cls": 3.95811, "loss": 3.95811, "time": 0.82364} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0612, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30578, "top5_acc": 0.55906, "loss_cls": 3.97009, "loss": 3.97009, "time": 0.81799} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.06117, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30266, "top5_acc": 0.55875, "loss_cls": 3.97132, "loss": 3.97132, "time": 0.82545} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.06115, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29891, "top5_acc": 0.55703, "loss_cls": 3.98106, "loss": 3.98106, "time": 0.82249} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.06112, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31906, "top5_acc": 0.56781, "loss_cls": 3.91348, "loss": 3.91348, "time": 0.81787} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.06109, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29922, "top5_acc": 0.55125, "loss_cls": 4.01219, "loss": 4.01219, "time": 0.81394} +{"mode": "train", "epoch": 65, "iter": 1300, "lr": 0.06106, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29781, "top5_acc": 0.54844, "loss_cls": 4.03748, "loss": 4.03748, "time": 0.81388} +{"mode": "train", "epoch": 65, "iter": 1400, "lr": 0.06104, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29781, "top5_acc": 0.56297, "loss_cls": 3.99112, "loss": 3.99112, "time": 0.81811} +{"mode": "train", "epoch": 65, "iter": 1500, "lr": 0.06101, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30328, "top5_acc": 0.56453, "loss_cls": 3.96607, "loss": 3.96607, "time": 0.82355} +{"mode": "train", "epoch": 65, "iter": 1600, "lr": 0.06098, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31156, "top5_acc": 0.56344, "loss_cls": 3.95854, "loss": 3.95854, "time": 0.83039} +{"mode": "train", "epoch": 65, "iter": 1700, "lr": 0.06095, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30859, "top5_acc": 0.56281, "loss_cls": 3.99455, "loss": 3.99455, "time": 0.81744} +{"mode": "train", "epoch": 65, "iter": 1800, "lr": 0.06093, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30766, "top5_acc": 0.57422, "loss_cls": 3.93828, "loss": 3.93828, "time": 0.81965} +{"mode": "train", "epoch": 65, "iter": 1900, "lr": 0.0609, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31094, "top5_acc": 0.56031, "loss_cls": 3.96723, "loss": 3.96723, "time": 0.81579} +{"mode": "train", "epoch": 65, "iter": 2000, "lr": 0.06087, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30891, "top5_acc": 0.56578, "loss_cls": 3.95188, "loss": 3.95188, "time": 0.81726} +{"mode": "train", "epoch": 65, "iter": 2100, "lr": 0.06085, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30828, "top5_acc": 0.56688, "loss_cls": 3.96969, "loss": 3.96969, "time": 0.81378} +{"mode": "train", "epoch": 65, "iter": 2200, "lr": 0.06082, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30656, "top5_acc": 0.5625, "loss_cls": 3.93514, "loss": 3.93514, "time": 0.82554} +{"mode": "train", "epoch": 65, "iter": 2300, "lr": 0.06079, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30922, "top5_acc": 0.56406, "loss_cls": 3.96735, "loss": 3.96735, "time": 0.82305} +{"mode": "train", "epoch": 65, "iter": 2400, "lr": 0.06076, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31016, "top5_acc": 0.57141, "loss_cls": 3.94382, "loss": 3.94382, "time": 0.82315} +{"mode": "train", "epoch": 65, "iter": 2500, "lr": 0.06074, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31266, "top5_acc": 0.56453, "loss_cls": 3.94396, "loss": 3.94396, "time": 0.81518} +{"mode": "train", "epoch": 65, "iter": 2600, "lr": 0.06071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31125, "top5_acc": 0.56375, "loss_cls": 3.96361, "loss": 3.96361, "time": 0.81868} +{"mode": "train", "epoch": 65, "iter": 2700, "lr": 0.06068, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29781, "top5_acc": 0.56125, "loss_cls": 3.98601, "loss": 3.98601, "time": 0.81492} +{"mode": "train", "epoch": 65, "iter": 2800, "lr": 0.06065, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29578, "top5_acc": 0.56031, "loss_cls": 4.00973, "loss": 4.00973, "time": 0.81767} +{"mode": "train", "epoch": 65, "iter": 2900, "lr": 0.06063, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30016, "top5_acc": 0.55891, "loss_cls": 3.99431, "loss": 3.99431, "time": 0.82305} +{"mode": "train", "epoch": 65, "iter": 3000, "lr": 0.0606, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29578, "top5_acc": 0.55844, "loss_cls": 3.99302, "loss": 3.99302, "time": 0.81373} +{"mode": "train", "epoch": 65, "iter": 3100, "lr": 0.06057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30719, "top5_acc": 0.57047, "loss_cls": 3.94935, "loss": 3.94935, "time": 0.81364} +{"mode": "train", "epoch": 65, "iter": 3200, "lr": 0.06055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30516, "top5_acc": 0.56172, "loss_cls": 3.99282, "loss": 3.99282, "time": 0.81464} +{"mode": "train", "epoch": 65, "iter": 3300, "lr": 0.06052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30219, "top5_acc": 0.56078, "loss_cls": 4.00108, "loss": 4.00108, "time": 0.81322} +{"mode": "train", "epoch": 65, "iter": 3400, "lr": 0.06049, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29875, "top5_acc": 0.56141, "loss_cls": 3.97853, "loss": 3.97853, "time": 0.8118} +{"mode": "train", "epoch": 65, "iter": 3500, "lr": 0.06046, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30625, "top5_acc": 0.56188, "loss_cls": 3.98627, "loss": 3.98627, "time": 0.81438} +{"mode": "train", "epoch": 65, "iter": 3600, "lr": 0.06044, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30875, "top5_acc": 0.56312, "loss_cls": 3.97183, "loss": 3.97183, "time": 0.81641} +{"mode": "train", "epoch": 65, "iter": 3700, "lr": 0.06041, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31469, "top5_acc": 0.56547, "loss_cls": 3.9418, "loss": 3.9418, "time": 0.81595} +{"mode": "val", "epoch": 65, "iter": 309, "lr": 0.0604, "top1_acc": 0.23011, "top5_acc": 0.47531, "mean_class_accuracy": 0.22988} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.06037, "memory": 15990, "data_time": 1.31126, "top1_acc": 0.30484, "top5_acc": 0.57078, "loss_cls": 3.91424, "loss": 3.91424, "time": 2.30528} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.06034, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30594, "top5_acc": 0.57203, "loss_cls": 3.93979, "loss": 3.93979, "time": 0.81819} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.06031, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31312, "top5_acc": 0.56922, "loss_cls": 3.95266, "loss": 3.95266, "time": 0.81702} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.06029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30406, "top5_acc": 0.55875, "loss_cls": 4.0017, "loss": 4.0017, "time": 0.8161} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.06026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31578, "top5_acc": 0.57578, "loss_cls": 3.90016, "loss": 3.90016, "time": 0.81883} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.06023, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31016, "top5_acc": 0.56672, "loss_cls": 3.9165, "loss": 3.9165, "time": 0.81979} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.0602, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31172, "top5_acc": 0.56188, "loss_cls": 3.95573, "loss": 3.95573, "time": 0.81662} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.06018, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31016, "top5_acc": 0.56844, "loss_cls": 3.94182, "loss": 3.94182, "time": 0.82255} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.06015, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30719, "top5_acc": 0.56797, "loss_cls": 3.96546, "loss": 3.96546, "time": 0.82333} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.06012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31188, "top5_acc": 0.57031, "loss_cls": 3.92567, "loss": 3.92567, "time": 0.8145} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.06009, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3075, "top5_acc": 0.56531, "loss_cls": 3.95781, "loss": 3.95781, "time": 0.81997} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.06007, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30203, "top5_acc": 0.57359, "loss_cls": 3.92965, "loss": 3.92965, "time": 0.81368} +{"mode": "train", "epoch": 66, "iter": 1300, "lr": 0.06004, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3075, "top5_acc": 0.55875, "loss_cls": 3.96899, "loss": 3.96899, "time": 0.81992} +{"mode": "train", "epoch": 66, "iter": 1400, "lr": 0.06001, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30984, "top5_acc": 0.55734, "loss_cls": 3.95884, "loss": 3.95884, "time": 0.82471} +{"mode": "train", "epoch": 66, "iter": 1500, "lr": 0.05999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30641, "top5_acc": 0.55625, "loss_cls": 3.99281, "loss": 3.99281, "time": 0.8231} +{"mode": "train", "epoch": 66, "iter": 1600, "lr": 0.05996, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30812, "top5_acc": 0.56156, "loss_cls": 3.97197, "loss": 3.97197, "time": 0.82703} +{"mode": "train", "epoch": 66, "iter": 1700, "lr": 0.05993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30172, "top5_acc": 0.56359, "loss_cls": 3.97878, "loss": 3.97878, "time": 0.82041} +{"mode": "train", "epoch": 66, "iter": 1800, "lr": 0.0599, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31641, "top5_acc": 0.5775, "loss_cls": 3.91551, "loss": 3.91551, "time": 0.81766} +{"mode": "train", "epoch": 66, "iter": 1900, "lr": 0.05988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29438, "top5_acc": 0.55672, "loss_cls": 4.00419, "loss": 4.00419, "time": 0.82106} +{"mode": "train", "epoch": 66, "iter": 2000, "lr": 0.05985, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30516, "top5_acc": 0.56297, "loss_cls": 3.97799, "loss": 3.97799, "time": 0.81558} +{"mode": "train", "epoch": 66, "iter": 2100, "lr": 0.05982, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30266, "top5_acc": 0.55937, "loss_cls": 3.97175, "loss": 3.97175, "time": 0.81543} +{"mode": "train", "epoch": 66, "iter": 2200, "lr": 0.05979, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31312, "top5_acc": 0.56219, "loss_cls": 3.95091, "loss": 3.95091, "time": 0.82068} +{"mode": "train", "epoch": 66, "iter": 2300, "lr": 0.05977, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30203, "top5_acc": 0.56063, "loss_cls": 3.96223, "loss": 3.96223, "time": 0.81749} +{"mode": "train", "epoch": 66, "iter": 2400, "lr": 0.05974, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32297, "top5_acc": 0.57219, "loss_cls": 3.89736, "loss": 3.89736, "time": 0.82339} +{"mode": "train", "epoch": 66, "iter": 2500, "lr": 0.05971, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3225, "top5_acc": 0.57, "loss_cls": 3.94712, "loss": 3.94712, "time": 0.81583} +{"mode": "train", "epoch": 66, "iter": 2600, "lr": 0.05968, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29875, "top5_acc": 0.55562, "loss_cls": 3.97936, "loss": 3.97936, "time": 0.81628} +{"mode": "train", "epoch": 66, "iter": 2700, "lr": 0.05966, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29938, "top5_acc": 0.56625, "loss_cls": 3.98825, "loss": 3.98825, "time": 0.81953} +{"mode": "train", "epoch": 66, "iter": 2800, "lr": 0.05963, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29297, "top5_acc": 0.54656, "loss_cls": 4.04478, "loss": 4.04478, "time": 0.8161} +{"mode": "train", "epoch": 66, "iter": 2900, "lr": 0.0596, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30922, "top5_acc": 0.55969, "loss_cls": 3.96238, "loss": 3.96238, "time": 0.82101} +{"mode": "train", "epoch": 66, "iter": 3000, "lr": 0.05957, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31094, "top5_acc": 0.57766, "loss_cls": 3.9144, "loss": 3.9144, "time": 0.82021} +{"mode": "train", "epoch": 66, "iter": 3100, "lr": 0.05955, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30578, "top5_acc": 0.55766, "loss_cls": 3.99795, "loss": 3.99795, "time": 0.81513} +{"mode": "train", "epoch": 66, "iter": 3200, "lr": 0.05952, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31203, "top5_acc": 0.56563, "loss_cls": 3.95718, "loss": 3.95718, "time": 0.81627} +{"mode": "train", "epoch": 66, "iter": 3300, "lr": 0.05949, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29516, "top5_acc": 0.55359, "loss_cls": 4.02854, "loss": 4.02854, "time": 0.81132} +{"mode": "train", "epoch": 66, "iter": 3400, "lr": 0.05946, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30609, "top5_acc": 0.56344, "loss_cls": 3.98603, "loss": 3.98603, "time": 0.81384} +{"mode": "train", "epoch": 66, "iter": 3500, "lr": 0.05944, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.3225, "top5_acc": 0.57281, "loss_cls": 3.89798, "loss": 3.89798, "time": 0.82126} +{"mode": "train", "epoch": 66, "iter": 3600, "lr": 0.05941, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30547, "top5_acc": 0.55703, "loss_cls": 3.98134, "loss": 3.98134, "time": 0.81372} +{"mode": "train", "epoch": 66, "iter": 3700, "lr": 0.05938, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30703, "top5_acc": 0.56609, "loss_cls": 3.92395, "loss": 3.92395, "time": 0.81641} +{"mode": "val", "epoch": 66, "iter": 309, "lr": 0.05937, "top1_acc": 0.24961, "top5_acc": 0.49972, "mean_class_accuracy": 0.24932} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.05934, "memory": 15990, "data_time": 1.34366, "top1_acc": 0.31312, "top5_acc": 0.57688, "loss_cls": 3.90282, "loss": 3.90282, "time": 2.33197} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.05931, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31641, "top5_acc": 0.56859, "loss_cls": 3.93275, "loss": 3.93275, "time": 0.82396} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.05929, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.30797, "top5_acc": 0.56359, "loss_cls": 3.96294, "loss": 3.96294, "time": 0.82047} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.05926, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30391, "top5_acc": 0.57891, "loss_cls": 3.95875, "loss": 3.95875, "time": 0.81813} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.05923, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30453, "top5_acc": 0.56359, "loss_cls": 3.95716, "loss": 3.95716, "time": 0.8201} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.0592, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32109, "top5_acc": 0.57531, "loss_cls": 3.90809, "loss": 3.90809, "time": 0.82362} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.05918, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31922, "top5_acc": 0.57875, "loss_cls": 3.86854, "loss": 3.86854, "time": 0.82098} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.05915, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31797, "top5_acc": 0.57, "loss_cls": 3.89125, "loss": 3.89125, "time": 0.81989} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.05912, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31547, "top5_acc": 0.56969, "loss_cls": 3.92581, "loss": 3.92581, "time": 0.82271} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.05909, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30516, "top5_acc": 0.56453, "loss_cls": 3.94331, "loss": 3.94331, "time": 0.81447} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.05907, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30969, "top5_acc": 0.56984, "loss_cls": 3.9459, "loss": 3.9459, "time": 0.81748} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.05904, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32047, "top5_acc": 0.57203, "loss_cls": 3.89913, "loss": 3.89913, "time": 0.816} +{"mode": "train", "epoch": 67, "iter": 1300, "lr": 0.05901, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32156, "top5_acc": 0.56828, "loss_cls": 3.92004, "loss": 3.92004, "time": 0.81566} +{"mode": "train", "epoch": 67, "iter": 1400, "lr": 0.05898, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31641, "top5_acc": 0.57703, "loss_cls": 3.91742, "loss": 3.91742, "time": 0.82264} +{"mode": "train", "epoch": 67, "iter": 1500, "lr": 0.05896, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31234, "top5_acc": 0.57172, "loss_cls": 3.93692, "loss": 3.93692, "time": 0.82249} +{"mode": "train", "epoch": 67, "iter": 1600, "lr": 0.05893, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29578, "top5_acc": 0.56141, "loss_cls": 3.9963, "loss": 3.9963, "time": 0.82695} +{"mode": "train", "epoch": 67, "iter": 1700, "lr": 0.0589, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31812, "top5_acc": 0.5675, "loss_cls": 3.92128, "loss": 3.92128, "time": 0.81712} +{"mode": "train", "epoch": 67, "iter": 1800, "lr": 0.05887, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31219, "top5_acc": 0.56781, "loss_cls": 3.95575, "loss": 3.95575, "time": 0.82246} +{"mode": "train", "epoch": 67, "iter": 1900, "lr": 0.05885, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30578, "top5_acc": 0.56063, "loss_cls": 3.9881, "loss": 3.9881, "time": 0.82544} +{"mode": "train", "epoch": 67, "iter": 2000, "lr": 0.05882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31016, "top5_acc": 0.56734, "loss_cls": 3.94769, "loss": 3.94769, "time": 0.81872} +{"mode": "train", "epoch": 67, "iter": 2100, "lr": 0.05879, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30469, "top5_acc": 0.55922, "loss_cls": 3.9824, "loss": 3.9824, "time": 0.81498} +{"mode": "train", "epoch": 67, "iter": 2200, "lr": 0.05876, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30891, "top5_acc": 0.55328, "loss_cls": 3.99777, "loss": 3.99777, "time": 0.81564} +{"mode": "train", "epoch": 67, "iter": 2300, "lr": 0.05874, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30281, "top5_acc": 0.56406, "loss_cls": 3.96229, "loss": 3.96229, "time": 0.81705} +{"mode": "train", "epoch": 67, "iter": 2400, "lr": 0.05871, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30562, "top5_acc": 0.55828, "loss_cls": 3.99463, "loss": 3.99463, "time": 0.81774} +{"mode": "train", "epoch": 67, "iter": 2500, "lr": 0.05868, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28703, "top5_acc": 0.54937, "loss_cls": 4.04268, "loss": 4.04268, "time": 0.8154} +{"mode": "train", "epoch": 67, "iter": 2600, "lr": 0.05865, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31531, "top5_acc": 0.56125, "loss_cls": 3.94399, "loss": 3.94399, "time": 0.81361} +{"mode": "train", "epoch": 67, "iter": 2700, "lr": 0.05863, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3125, "top5_acc": 0.56672, "loss_cls": 3.9322, "loss": 3.9322, "time": 0.8168} +{"mode": "train", "epoch": 67, "iter": 2800, "lr": 0.0586, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30031, "top5_acc": 0.55562, "loss_cls": 3.99924, "loss": 3.99924, "time": 0.8168} +{"mode": "train", "epoch": 67, "iter": 2900, "lr": 0.05857, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30562, "top5_acc": 0.56375, "loss_cls": 3.94467, "loss": 3.94467, "time": 0.81637} +{"mode": "train", "epoch": 67, "iter": 3000, "lr": 0.05854, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30797, "top5_acc": 0.56188, "loss_cls": 3.97262, "loss": 3.97262, "time": 0.81639} +{"mode": "train", "epoch": 67, "iter": 3100, "lr": 0.05852, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30344, "top5_acc": 0.55969, "loss_cls": 3.9973, "loss": 3.9973, "time": 0.81729} +{"mode": "train", "epoch": 67, "iter": 3200, "lr": 0.05849, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30031, "top5_acc": 0.55047, "loss_cls": 4.01488, "loss": 4.01488, "time": 0.81727} +{"mode": "train", "epoch": 67, "iter": 3300, "lr": 0.05846, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30328, "top5_acc": 0.56156, "loss_cls": 3.991, "loss": 3.991, "time": 0.81476} +{"mode": "train", "epoch": 67, "iter": 3400, "lr": 0.05843, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30438, "top5_acc": 0.56281, "loss_cls": 3.97672, "loss": 3.97672, "time": 0.8154} +{"mode": "train", "epoch": 67, "iter": 3500, "lr": 0.05841, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31141, "top5_acc": 0.57531, "loss_cls": 3.90015, "loss": 3.90015, "time": 0.8152} +{"mode": "train", "epoch": 67, "iter": 3600, "lr": 0.05838, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31328, "top5_acc": 0.56516, "loss_cls": 3.91387, "loss": 3.91387, "time": 0.81792} +{"mode": "train", "epoch": 67, "iter": 3700, "lr": 0.05835, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31344, "top5_acc": 0.57203, "loss_cls": 3.90318, "loss": 3.90318, "time": 0.81198} +{"mode": "val", "epoch": 67, "iter": 309, "lr": 0.05834, "top1_acc": 0.24905, "top5_acc": 0.49157, "mean_class_accuracy": 0.24871} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.05831, "memory": 15990, "data_time": 1.3619, "top1_acc": 0.31625, "top5_acc": 0.57938, "loss_cls": 3.906, "loss": 3.906, "time": 2.33946} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.05828, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30938, "top5_acc": 0.56984, "loss_cls": 3.91183, "loss": 3.91183, "time": 0.82318} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.05826, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30953, "top5_acc": 0.56797, "loss_cls": 3.95137, "loss": 3.95137, "time": 0.81782} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.05823, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31812, "top5_acc": 0.58, "loss_cls": 3.88373, "loss": 3.88373, "time": 0.81932} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.0582, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32422, "top5_acc": 0.57688, "loss_cls": 3.88199, "loss": 3.88199, "time": 0.81956} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.05817, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32078, "top5_acc": 0.57344, "loss_cls": 3.91333, "loss": 3.91333, "time": 0.82096} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.05815, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30781, "top5_acc": 0.57125, "loss_cls": 3.92403, "loss": 3.92403, "time": 0.81995} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.05812, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31078, "top5_acc": 0.57109, "loss_cls": 3.93254, "loss": 3.93254, "time": 0.81973} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.05809, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32172, "top5_acc": 0.58016, "loss_cls": 3.85397, "loss": 3.85397, "time": 0.82617} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.05806, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30891, "top5_acc": 0.57, "loss_cls": 3.94272, "loss": 3.94272, "time": 0.8135} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.05804, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.305, "top5_acc": 0.55984, "loss_cls": 3.96647, "loss": 3.96647, "time": 0.81507} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.05801, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31672, "top5_acc": 0.56688, "loss_cls": 3.93027, "loss": 3.93027, "time": 0.82024} +{"mode": "train", "epoch": 68, "iter": 1300, "lr": 0.05798, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3075, "top5_acc": 0.56219, "loss_cls": 3.9504, "loss": 3.9504, "time": 0.81464} +{"mode": "train", "epoch": 68, "iter": 1400, "lr": 0.05795, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3125, "top5_acc": 0.56078, "loss_cls": 3.9712, "loss": 3.9712, "time": 0.82143} +{"mode": "train", "epoch": 68, "iter": 1500, "lr": 0.05792, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32234, "top5_acc": 0.57078, "loss_cls": 3.90768, "loss": 3.90768, "time": 0.81883} +{"mode": "train", "epoch": 68, "iter": 1600, "lr": 0.0579, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3125, "top5_acc": 0.56766, "loss_cls": 3.94448, "loss": 3.94448, "time": 0.83172} +{"mode": "train", "epoch": 68, "iter": 1700, "lr": 0.05787, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31594, "top5_acc": 0.57453, "loss_cls": 3.90333, "loss": 3.90333, "time": 0.8154} +{"mode": "train", "epoch": 68, "iter": 1800, "lr": 0.05784, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3075, "top5_acc": 0.56656, "loss_cls": 3.94699, "loss": 3.94699, "time": 0.82049} +{"mode": "train", "epoch": 68, "iter": 1900, "lr": 0.05781, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3075, "top5_acc": 0.56125, "loss_cls": 3.96182, "loss": 3.96182, "time": 0.82134} +{"mode": "train", "epoch": 68, "iter": 2000, "lr": 0.05779, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32016, "top5_acc": 0.57688, "loss_cls": 3.89849, "loss": 3.89849, "time": 0.81766} +{"mode": "train", "epoch": 68, "iter": 2100, "lr": 0.05776, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30578, "top5_acc": 0.56125, "loss_cls": 3.9758, "loss": 3.9758, "time": 0.8192} +{"mode": "train", "epoch": 68, "iter": 2200, "lr": 0.05773, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31047, "top5_acc": 0.56328, "loss_cls": 3.93463, "loss": 3.93463, "time": 0.81695} +{"mode": "train", "epoch": 68, "iter": 2300, "lr": 0.0577, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30969, "top5_acc": 0.56141, "loss_cls": 3.94705, "loss": 3.94705, "time": 0.81687} +{"mode": "train", "epoch": 68, "iter": 2400, "lr": 0.05768, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31984, "top5_acc": 0.57219, "loss_cls": 3.92839, "loss": 3.92839, "time": 0.82239} +{"mode": "train", "epoch": 68, "iter": 2500, "lr": 0.05765, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31094, "top5_acc": 0.55172, "loss_cls": 3.97904, "loss": 3.97904, "time": 0.8192} +{"mode": "train", "epoch": 68, "iter": 2600, "lr": 0.05762, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31141, "top5_acc": 0.57328, "loss_cls": 3.92692, "loss": 3.92692, "time": 0.81316} +{"mode": "train", "epoch": 68, "iter": 2700, "lr": 0.05759, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31312, "top5_acc": 0.56969, "loss_cls": 3.92458, "loss": 3.92458, "time": 0.81218} +{"mode": "train", "epoch": 68, "iter": 2800, "lr": 0.05757, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30812, "top5_acc": 0.57125, "loss_cls": 3.96311, "loss": 3.96311, "time": 0.81463} +{"mode": "train", "epoch": 68, "iter": 2900, "lr": 0.05754, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31719, "top5_acc": 0.57188, "loss_cls": 3.90348, "loss": 3.90348, "time": 0.81926} +{"mode": "train", "epoch": 68, "iter": 3000, "lr": 0.05751, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30703, "top5_acc": 0.55562, "loss_cls": 3.97735, "loss": 3.97735, "time": 0.81336} +{"mode": "train", "epoch": 68, "iter": 3100, "lr": 0.05748, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31375, "top5_acc": 0.5675, "loss_cls": 3.9371, "loss": 3.9371, "time": 0.81431} +{"mode": "train", "epoch": 68, "iter": 3200, "lr": 0.05746, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30438, "top5_acc": 0.55766, "loss_cls": 3.97406, "loss": 3.97406, "time": 0.81318} +{"mode": "train", "epoch": 68, "iter": 3300, "lr": 0.05743, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31438, "top5_acc": 0.56266, "loss_cls": 3.94188, "loss": 3.94188, "time": 0.82268} +{"mode": "train", "epoch": 68, "iter": 3400, "lr": 0.0574, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30562, "top5_acc": 0.56266, "loss_cls": 3.97786, "loss": 3.97786, "time": 0.81769} +{"mode": "train", "epoch": 68, "iter": 3500, "lr": 0.05737, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30891, "top5_acc": 0.57094, "loss_cls": 3.9472, "loss": 3.9472, "time": 0.81558} +{"mode": "train", "epoch": 68, "iter": 3600, "lr": 0.05734, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31188, "top5_acc": 0.56594, "loss_cls": 3.94638, "loss": 3.94638, "time": 0.8162} +{"mode": "train", "epoch": 68, "iter": 3700, "lr": 0.05732, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31641, "top5_acc": 0.56484, "loss_cls": 3.9376, "loss": 3.9376, "time": 0.81708} +{"mode": "val", "epoch": 68, "iter": 309, "lr": 0.0573, "top1_acc": 0.24353, "top5_acc": 0.48645, "mean_class_accuracy": 0.24345} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.05728, "memory": 15990, "data_time": 1.30884, "top1_acc": 0.33047, "top5_acc": 0.59031, "loss_cls": 3.81044, "loss": 3.81044, "time": 2.30893} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.05725, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32188, "top5_acc": 0.58406, "loss_cls": 3.84421, "loss": 3.84421, "time": 0.82463} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.05722, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30891, "top5_acc": 0.57953, "loss_cls": 3.91469, "loss": 3.91469, "time": 0.81296} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.05719, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31109, "top5_acc": 0.57453, "loss_cls": 3.90478, "loss": 3.90478, "time": 0.82106} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.05717, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31469, "top5_acc": 0.57141, "loss_cls": 3.9008, "loss": 3.9008, "time": 0.82019} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.05714, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30469, "top5_acc": 0.56641, "loss_cls": 3.93378, "loss": 3.93378, "time": 0.81298} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.05711, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30859, "top5_acc": 0.57547, "loss_cls": 3.92663, "loss": 3.92663, "time": 0.82142} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.05708, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31469, "top5_acc": 0.56781, "loss_cls": 3.92699, "loss": 3.92699, "time": 0.81684} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.05706, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32141, "top5_acc": 0.57812, "loss_cls": 3.8697, "loss": 3.8697, "time": 0.81806} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.05703, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31391, "top5_acc": 0.56, "loss_cls": 3.94527, "loss": 3.94527, "time": 0.82052} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32031, "top5_acc": 0.56828, "loss_cls": 3.91794, "loss": 3.91794, "time": 0.81577} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.05697, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30797, "top5_acc": 0.56812, "loss_cls": 3.9312, "loss": 3.9312, "time": 0.81562} +{"mode": "train", "epoch": 69, "iter": 1300, "lr": 0.05694, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31688, "top5_acc": 0.58297, "loss_cls": 3.89476, "loss": 3.89476, "time": 0.81202} +{"mode": "train", "epoch": 69, "iter": 1400, "lr": 0.05692, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30484, "top5_acc": 0.57125, "loss_cls": 3.95214, "loss": 3.95214, "time": 0.82391} +{"mode": "train", "epoch": 69, "iter": 1500, "lr": 0.05689, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31375, "top5_acc": 0.56297, "loss_cls": 3.9418, "loss": 3.9418, "time": 0.82038} +{"mode": "train", "epoch": 69, "iter": 1600, "lr": 0.05686, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32062, "top5_acc": 0.57734, "loss_cls": 3.90715, "loss": 3.90715, "time": 0.83552} +{"mode": "train", "epoch": 69, "iter": 1700, "lr": 0.05683, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30812, "top5_acc": 0.56437, "loss_cls": 3.97081, "loss": 3.97081, "time": 0.81916} +{"mode": "train", "epoch": 69, "iter": 1800, "lr": 0.05681, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31, "top5_acc": 0.56453, "loss_cls": 3.94378, "loss": 3.94378, "time": 0.82436} +{"mode": "train", "epoch": 69, "iter": 1900, "lr": 0.05678, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31656, "top5_acc": 0.56719, "loss_cls": 3.93832, "loss": 3.93832, "time": 0.81485} +{"mode": "train", "epoch": 69, "iter": 2000, "lr": 0.05675, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32, "top5_acc": 0.57328, "loss_cls": 3.9104, "loss": 3.9104, "time": 0.81914} +{"mode": "train", "epoch": 69, "iter": 2100, "lr": 0.05672, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31266, "top5_acc": 0.57188, "loss_cls": 3.92319, "loss": 3.92319, "time": 0.81802} +{"mode": "train", "epoch": 69, "iter": 2200, "lr": 0.0567, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.3175, "top5_acc": 0.56391, "loss_cls": 3.96259, "loss": 3.96259, "time": 0.81268} +{"mode": "train", "epoch": 69, "iter": 2300, "lr": 0.05667, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31359, "top5_acc": 0.55328, "loss_cls": 4.00596, "loss": 4.00596, "time": 0.81351} +{"mode": "train", "epoch": 69, "iter": 2400, "lr": 0.05664, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30109, "top5_acc": 0.55516, "loss_cls": 3.98854, "loss": 3.98854, "time": 0.81522} +{"mode": "train", "epoch": 69, "iter": 2500, "lr": 0.05661, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30281, "top5_acc": 0.57, "loss_cls": 3.95712, "loss": 3.95712, "time": 0.81711} +{"mode": "train", "epoch": 69, "iter": 2600, "lr": 0.05658, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31016, "top5_acc": 0.56844, "loss_cls": 3.93932, "loss": 3.93932, "time": 0.81523} +{"mode": "train", "epoch": 69, "iter": 2700, "lr": 0.05656, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31031, "top5_acc": 0.57359, "loss_cls": 3.89709, "loss": 3.89709, "time": 0.81394} +{"mode": "train", "epoch": 69, "iter": 2800, "lr": 0.05653, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30625, "top5_acc": 0.57141, "loss_cls": 3.92743, "loss": 3.92743, "time": 0.81398} +{"mode": "train", "epoch": 69, "iter": 2900, "lr": 0.0565, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31406, "top5_acc": 0.56969, "loss_cls": 3.93579, "loss": 3.93579, "time": 0.81262} +{"mode": "train", "epoch": 69, "iter": 3000, "lr": 0.05647, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30719, "top5_acc": 0.57812, "loss_cls": 3.88882, "loss": 3.88882, "time": 0.81902} +{"mode": "train", "epoch": 69, "iter": 3100, "lr": 0.05645, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30703, "top5_acc": 0.56828, "loss_cls": 3.94846, "loss": 3.94846, "time": 0.81252} +{"mode": "train", "epoch": 69, "iter": 3200, "lr": 0.05642, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31391, "top5_acc": 0.57266, "loss_cls": 3.9417, "loss": 3.9417, "time": 0.81649} +{"mode": "train", "epoch": 69, "iter": 3300, "lr": 0.05639, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31734, "top5_acc": 0.57531, "loss_cls": 3.89442, "loss": 3.89442, "time": 0.81599} +{"mode": "train", "epoch": 69, "iter": 3400, "lr": 0.05636, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30609, "top5_acc": 0.56453, "loss_cls": 3.98747, "loss": 3.98747, "time": 0.81765} +{"mode": "train", "epoch": 69, "iter": 3500, "lr": 0.05634, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31391, "top5_acc": 0.57266, "loss_cls": 3.92557, "loss": 3.92557, "time": 0.81548} +{"mode": "train", "epoch": 69, "iter": 3600, "lr": 0.05631, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30531, "top5_acc": 0.56812, "loss_cls": 3.96402, "loss": 3.96402, "time": 0.82153} +{"mode": "train", "epoch": 69, "iter": 3700, "lr": 0.05628, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30891, "top5_acc": 0.56078, "loss_cls": 3.95704, "loss": 3.95704, "time": 0.81448} +{"mode": "val", "epoch": 69, "iter": 309, "lr": 0.05627, "top1_acc": 0.23527, "top5_acc": 0.47541, "mean_class_accuracy": 0.23499} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.05624, "memory": 15990, "data_time": 1.36015, "top1_acc": 0.31703, "top5_acc": 0.57906, "loss_cls": 3.88399, "loss": 3.88399, "time": 2.34392} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.05621, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32312, "top5_acc": 0.57328, "loss_cls": 3.88916, "loss": 3.88916, "time": 0.82029} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.05618, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31453, "top5_acc": 0.57609, "loss_cls": 3.89395, "loss": 3.89395, "time": 0.81997} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.05616, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30609, "top5_acc": 0.56859, "loss_cls": 3.94322, "loss": 3.94322, "time": 0.81686} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.05613, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30953, "top5_acc": 0.57328, "loss_cls": 3.90535, "loss": 3.90535, "time": 0.81344} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.0561, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31906, "top5_acc": 0.57563, "loss_cls": 3.88111, "loss": 3.88111, "time": 0.81736} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.05607, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31016, "top5_acc": 0.57328, "loss_cls": 3.92012, "loss": 3.92012, "time": 0.81625} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.05605, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32125, "top5_acc": 0.58047, "loss_cls": 3.86895, "loss": 3.86895, "time": 0.82847} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.05602, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3175, "top5_acc": 0.57969, "loss_cls": 3.87504, "loss": 3.87504, "time": 0.81208} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.05599, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31609, "top5_acc": 0.57312, "loss_cls": 3.9072, "loss": 3.9072, "time": 0.81751} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.05596, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30922, "top5_acc": 0.56328, "loss_cls": 3.96621, "loss": 3.96621, "time": 0.81621} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.05593, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31609, "top5_acc": 0.57203, "loss_cls": 3.87104, "loss": 3.87104, "time": 0.82069} +{"mode": "train", "epoch": 70, "iter": 1300, "lr": 0.05591, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32344, "top5_acc": 0.57016, "loss_cls": 3.92542, "loss": 3.92542, "time": 0.82631} +{"mode": "train", "epoch": 70, "iter": 1400, "lr": 0.05588, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31422, "top5_acc": 0.57641, "loss_cls": 3.89913, "loss": 3.89913, "time": 0.82429} +{"mode": "train", "epoch": 70, "iter": 1500, "lr": 0.05585, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31062, "top5_acc": 0.56219, "loss_cls": 3.96047, "loss": 3.96047, "time": 0.81934} +{"mode": "train", "epoch": 70, "iter": 1600, "lr": 0.05582, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31297, "top5_acc": 0.57016, "loss_cls": 3.91761, "loss": 3.91761, "time": 0.83297} +{"mode": "train", "epoch": 70, "iter": 1700, "lr": 0.0558, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31078, "top5_acc": 0.57094, "loss_cls": 3.91365, "loss": 3.91365, "time": 0.81981} +{"mode": "train", "epoch": 70, "iter": 1800, "lr": 0.05577, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30984, "top5_acc": 0.57828, "loss_cls": 3.90453, "loss": 3.90453, "time": 0.82146} +{"mode": "train", "epoch": 70, "iter": 1900, "lr": 0.05574, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32156, "top5_acc": 0.56531, "loss_cls": 3.90235, "loss": 3.90235, "time": 0.8176} +{"mode": "train", "epoch": 70, "iter": 2000, "lr": 0.05571, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31406, "top5_acc": 0.57344, "loss_cls": 3.91652, "loss": 3.91652, "time": 0.81667} +{"mode": "train", "epoch": 70, "iter": 2100, "lr": 0.05568, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31297, "top5_acc": 0.56828, "loss_cls": 3.94405, "loss": 3.94405, "time": 0.82079} +{"mode": "train", "epoch": 70, "iter": 2200, "lr": 0.05566, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31359, "top5_acc": 0.56859, "loss_cls": 3.92238, "loss": 3.92238, "time": 0.81885} +{"mode": "train", "epoch": 70, "iter": 2300, "lr": 0.05563, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31391, "top5_acc": 0.57109, "loss_cls": 3.92645, "loss": 3.92645, "time": 0.81686} +{"mode": "train", "epoch": 70, "iter": 2400, "lr": 0.0556, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3275, "top5_acc": 0.58062, "loss_cls": 3.85939, "loss": 3.85939, "time": 0.8186} +{"mode": "train", "epoch": 70, "iter": 2500, "lr": 0.05557, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31688, "top5_acc": 0.57266, "loss_cls": 3.87776, "loss": 3.87776, "time": 0.81865} +{"mode": "train", "epoch": 70, "iter": 2600, "lr": 0.05555, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31562, "top5_acc": 0.57219, "loss_cls": 3.88584, "loss": 3.88584, "time": 0.81329} +{"mode": "train", "epoch": 70, "iter": 2700, "lr": 0.05552, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.3125, "top5_acc": 0.56516, "loss_cls": 3.95844, "loss": 3.95844, "time": 0.81622} +{"mode": "train", "epoch": 70, "iter": 2800, "lr": 0.05549, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31031, "top5_acc": 0.5575, "loss_cls": 3.97248, "loss": 3.97248, "time": 0.81892} +{"mode": "train", "epoch": 70, "iter": 2900, "lr": 0.05546, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31578, "top5_acc": 0.56531, "loss_cls": 3.93049, "loss": 3.93049, "time": 0.81919} +{"mode": "train", "epoch": 70, "iter": 3000, "lr": 0.05543, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30547, "top5_acc": 0.57109, "loss_cls": 3.9259, "loss": 3.9259, "time": 0.81565} +{"mode": "train", "epoch": 70, "iter": 3100, "lr": 0.05541, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31297, "top5_acc": 0.56953, "loss_cls": 3.9428, "loss": 3.9428, "time": 0.81706} +{"mode": "train", "epoch": 70, "iter": 3200, "lr": 0.05538, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31203, "top5_acc": 0.56844, "loss_cls": 3.92668, "loss": 3.92668, "time": 0.81859} +{"mode": "train", "epoch": 70, "iter": 3300, "lr": 0.05535, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31953, "top5_acc": 0.56109, "loss_cls": 3.92589, "loss": 3.92589, "time": 0.8162} +{"mode": "train", "epoch": 70, "iter": 3400, "lr": 0.05532, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30797, "top5_acc": 0.56203, "loss_cls": 3.96203, "loss": 3.96203, "time": 0.81543} +{"mode": "train", "epoch": 70, "iter": 3500, "lr": 0.0553, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30484, "top5_acc": 0.56312, "loss_cls": 3.96707, "loss": 3.96707, "time": 0.81566} +{"mode": "train", "epoch": 70, "iter": 3600, "lr": 0.05527, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31422, "top5_acc": 0.56609, "loss_cls": 3.9383, "loss": 3.9383, "time": 0.81499} +{"mode": "train", "epoch": 70, "iter": 3700, "lr": 0.05524, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31391, "top5_acc": 0.57547, "loss_cls": 3.91933, "loss": 3.91933, "time": 0.81294} +{"mode": "val", "epoch": 70, "iter": 309, "lr": 0.05523, "top1_acc": 0.2566, "top5_acc": 0.50322, "mean_class_accuracy": 0.25622} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.0552, "memory": 15990, "data_time": 1.36022, "top1_acc": 0.31234, "top5_acc": 0.5625, "loss_cls": 3.94641, "loss": 3.94641, "time": 2.34337} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.05517, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32297, "top5_acc": 0.58, "loss_cls": 3.87448, "loss": 3.87448, "time": 0.8295} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.05514, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32078, "top5_acc": 0.57656, "loss_cls": 3.876, "loss": 3.876, "time": 0.82316} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.05512, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31859, "top5_acc": 0.57172, "loss_cls": 3.88437, "loss": 3.88437, "time": 0.81479} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.05509, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31812, "top5_acc": 0.57516, "loss_cls": 3.89936, "loss": 3.89936, "time": 0.8171} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.05506, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31234, "top5_acc": 0.56672, "loss_cls": 3.93143, "loss": 3.93143, "time": 0.81645} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.05503, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31266, "top5_acc": 0.57281, "loss_cls": 3.90378, "loss": 3.90378, "time": 0.82031} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.055, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32266, "top5_acc": 0.58141, "loss_cls": 3.88228, "loss": 3.88228, "time": 0.82048} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.05498, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31766, "top5_acc": 0.57484, "loss_cls": 3.92311, "loss": 3.92311, "time": 0.81945} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.05495, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31422, "top5_acc": 0.58156, "loss_cls": 3.88945, "loss": 3.88945, "time": 0.8257} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.05492, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31406, "top5_acc": 0.57172, "loss_cls": 3.91597, "loss": 3.91597, "time": 0.82346} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.05489, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31844, "top5_acc": 0.57891, "loss_cls": 3.88109, "loss": 3.88109, "time": 0.82302} +{"mode": "train", "epoch": 71, "iter": 1300, "lr": 0.05487, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32422, "top5_acc": 0.57531, "loss_cls": 3.87467, "loss": 3.87467, "time": 0.81841} +{"mode": "train", "epoch": 71, "iter": 1400, "lr": 0.05484, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31172, "top5_acc": 0.57344, "loss_cls": 3.91897, "loss": 3.91897, "time": 0.82117} +{"mode": "train", "epoch": 71, "iter": 1500, "lr": 0.05481, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31172, "top5_acc": 0.56906, "loss_cls": 3.90799, "loss": 3.90799, "time": 0.81733} +{"mode": "train", "epoch": 71, "iter": 1600, "lr": 0.05478, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31188, "top5_acc": 0.57031, "loss_cls": 3.90293, "loss": 3.90293, "time": 0.82975} +{"mode": "train", "epoch": 71, "iter": 1700, "lr": 0.05475, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30438, "top5_acc": 0.57078, "loss_cls": 3.96017, "loss": 3.96017, "time": 0.82686} +{"mode": "train", "epoch": 71, "iter": 1800, "lr": 0.05473, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31062, "top5_acc": 0.57484, "loss_cls": 3.90732, "loss": 3.90732, "time": 0.8234} +{"mode": "train", "epoch": 71, "iter": 1900, "lr": 0.0547, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31922, "top5_acc": 0.5825, "loss_cls": 3.87431, "loss": 3.87431, "time": 0.82372} +{"mode": "train", "epoch": 71, "iter": 2000, "lr": 0.05467, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31094, "top5_acc": 0.56984, "loss_cls": 3.9259, "loss": 3.9259, "time": 0.8157} +{"mode": "train", "epoch": 71, "iter": 2100, "lr": 0.05464, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31781, "top5_acc": 0.57125, "loss_cls": 3.91136, "loss": 3.91136, "time": 0.816} +{"mode": "train", "epoch": 71, "iter": 2200, "lr": 0.05461, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31047, "top5_acc": 0.56125, "loss_cls": 3.96043, "loss": 3.96043, "time": 0.8144} +{"mode": "train", "epoch": 71, "iter": 2300, "lr": 0.05459, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30609, "top5_acc": 0.57297, "loss_cls": 3.94226, "loss": 3.94226, "time": 0.8174} +{"mode": "train", "epoch": 71, "iter": 2400, "lr": 0.05456, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32125, "top5_acc": 0.57234, "loss_cls": 3.8914, "loss": 3.8914, "time": 0.82348} +{"mode": "train", "epoch": 71, "iter": 2500, "lr": 0.05453, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30688, "top5_acc": 0.56563, "loss_cls": 3.93679, "loss": 3.93679, "time": 0.81646} +{"mode": "train", "epoch": 71, "iter": 2600, "lr": 0.0545, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31266, "top5_acc": 0.57781, "loss_cls": 3.91287, "loss": 3.91287, "time": 0.82016} +{"mode": "train", "epoch": 71, "iter": 2700, "lr": 0.05448, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32219, "top5_acc": 0.56781, "loss_cls": 3.88991, "loss": 3.88991, "time": 0.81009} +{"mode": "train", "epoch": 71, "iter": 2800, "lr": 0.05445, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30734, "top5_acc": 0.56391, "loss_cls": 3.95975, "loss": 3.95975, "time": 0.82047} +{"mode": "train", "epoch": 71, "iter": 2900, "lr": 0.05442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31797, "top5_acc": 0.57328, "loss_cls": 3.90455, "loss": 3.90455, "time": 0.81817} +{"mode": "train", "epoch": 71, "iter": 3000, "lr": 0.05439, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32406, "top5_acc": 0.58328, "loss_cls": 3.88119, "loss": 3.88119, "time": 0.818} +{"mode": "train", "epoch": 71, "iter": 3100, "lr": 0.05436, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31594, "top5_acc": 0.56531, "loss_cls": 3.94533, "loss": 3.94533, "time": 0.81253} +{"mode": "train", "epoch": 71, "iter": 3200, "lr": 0.05434, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31422, "top5_acc": 0.56766, "loss_cls": 3.9228, "loss": 3.9228, "time": 0.81456} +{"mode": "train", "epoch": 71, "iter": 3300, "lr": 0.05431, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31875, "top5_acc": 0.57188, "loss_cls": 3.90988, "loss": 3.90988, "time": 0.81402} +{"mode": "train", "epoch": 71, "iter": 3400, "lr": 0.05428, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.305, "top5_acc": 0.56484, "loss_cls": 3.95247, "loss": 3.95247, "time": 0.81615} +{"mode": "train", "epoch": 71, "iter": 3500, "lr": 0.05425, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31094, "top5_acc": 0.56797, "loss_cls": 3.9226, "loss": 3.9226, "time": 0.81279} +{"mode": "train", "epoch": 71, "iter": 3600, "lr": 0.05422, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30938, "top5_acc": 0.57438, "loss_cls": 3.90252, "loss": 3.90252, "time": 0.81342} +{"mode": "train", "epoch": 71, "iter": 3700, "lr": 0.0542, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31297, "top5_acc": 0.56781, "loss_cls": 3.93877, "loss": 3.93877, "time": 0.81416} +{"mode": "val", "epoch": 71, "iter": 309, "lr": 0.05418, "top1_acc": 0.24115, "top5_acc": 0.4862, "mean_class_accuracy": 0.24106} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.05416, "memory": 15990, "data_time": 1.35496, "top1_acc": 0.33266, "top5_acc": 0.58891, "loss_cls": 3.85751, "loss": 3.85751, "time": 2.33192} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.05413, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31547, "top5_acc": 0.57672, "loss_cls": 3.89421, "loss": 3.89421, "time": 0.81727} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.0541, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32266, "top5_acc": 0.58297, "loss_cls": 3.8519, "loss": 3.8519, "time": 0.81651} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.05407, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31812, "top5_acc": 0.58859, "loss_cls": 3.85185, "loss": 3.85185, "time": 0.81541} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.05404, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31734, "top5_acc": 0.57047, "loss_cls": 3.89963, "loss": 3.89963, "time": 0.81403} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.05402, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32203, "top5_acc": 0.57906, "loss_cls": 3.88927, "loss": 3.88927, "time": 0.81512} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.05399, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31828, "top5_acc": 0.56875, "loss_cls": 3.91901, "loss": 3.91901, "time": 0.82125} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.05396, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32031, "top5_acc": 0.57547, "loss_cls": 3.88652, "loss": 3.88652, "time": 0.82086} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.05393, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32594, "top5_acc": 0.58219, "loss_cls": 3.8295, "loss": 3.8295, "time": 0.81984} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.05391, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31781, "top5_acc": 0.57656, "loss_cls": 3.90312, "loss": 3.90312, "time": 0.81984} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.05388, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31797, "top5_acc": 0.56938, "loss_cls": 3.90162, "loss": 3.90162, "time": 0.82196} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.05385, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32438, "top5_acc": 0.56953, "loss_cls": 3.895, "loss": 3.895, "time": 0.81594} +{"mode": "train", "epoch": 72, "iter": 1300, "lr": 0.05382, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31094, "top5_acc": 0.58172, "loss_cls": 3.88855, "loss": 3.88855, "time": 0.82514} +{"mode": "train", "epoch": 72, "iter": 1400, "lr": 0.05379, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30562, "top5_acc": 0.56797, "loss_cls": 3.93968, "loss": 3.93968, "time": 0.82377} +{"mode": "train", "epoch": 72, "iter": 1500, "lr": 0.05377, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31609, "top5_acc": 0.57859, "loss_cls": 3.87885, "loss": 3.87885, "time": 0.81848} +{"mode": "train", "epoch": 72, "iter": 1600, "lr": 0.05374, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.3175, "top5_acc": 0.57156, "loss_cls": 3.89161, "loss": 3.89161, "time": 0.82884} +{"mode": "train", "epoch": 72, "iter": 1700, "lr": 0.05371, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31516, "top5_acc": 0.57922, "loss_cls": 3.90091, "loss": 3.90091, "time": 0.82085} +{"mode": "train", "epoch": 72, "iter": 1800, "lr": 0.05368, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30359, "top5_acc": 0.55875, "loss_cls": 3.97222, "loss": 3.97222, "time": 0.82444} +{"mode": "train", "epoch": 72, "iter": 1900, "lr": 0.05365, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.315, "top5_acc": 0.57359, "loss_cls": 3.90338, "loss": 3.90338, "time": 0.81546} +{"mode": "train", "epoch": 72, "iter": 2000, "lr": 0.05363, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32109, "top5_acc": 0.5825, "loss_cls": 3.8908, "loss": 3.8908, "time": 0.81686} +{"mode": "train", "epoch": 72, "iter": 2100, "lr": 0.0536, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33328, "top5_acc": 0.58375, "loss_cls": 3.84014, "loss": 3.84014, "time": 0.81865} +{"mode": "train", "epoch": 72, "iter": 2200, "lr": 0.05357, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30922, "top5_acc": 0.57156, "loss_cls": 3.93175, "loss": 3.93175, "time": 0.81664} +{"mode": "train", "epoch": 72, "iter": 2300, "lr": 0.05354, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31781, "top5_acc": 0.57344, "loss_cls": 3.91631, "loss": 3.91631, "time": 0.81531} +{"mode": "train", "epoch": 72, "iter": 2400, "lr": 0.05352, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33078, "top5_acc": 0.59641, "loss_cls": 3.80713, "loss": 3.80713, "time": 0.82165} +{"mode": "train", "epoch": 72, "iter": 2500, "lr": 0.05349, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32062, "top5_acc": 0.57312, "loss_cls": 3.90393, "loss": 3.90393, "time": 0.81498} +{"mode": "train", "epoch": 72, "iter": 2600, "lr": 0.05346, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31797, "top5_acc": 0.57594, "loss_cls": 3.90169, "loss": 3.90169, "time": 0.81865} +{"mode": "train", "epoch": 72, "iter": 2700, "lr": 0.05343, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31734, "top5_acc": 0.57484, "loss_cls": 3.90977, "loss": 3.90977, "time": 0.81621} +{"mode": "train", "epoch": 72, "iter": 2800, "lr": 0.0534, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31172, "top5_acc": 0.57516, "loss_cls": 3.92394, "loss": 3.92394, "time": 0.81484} +{"mode": "train", "epoch": 72, "iter": 2900, "lr": 0.05338, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31375, "top5_acc": 0.57844, "loss_cls": 3.91111, "loss": 3.91111, "time": 0.81709} +{"mode": "train", "epoch": 72, "iter": 3000, "lr": 0.05335, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32469, "top5_acc": 0.57875, "loss_cls": 3.88922, "loss": 3.88922, "time": 0.81563} +{"mode": "train", "epoch": 72, "iter": 3100, "lr": 0.05332, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32078, "top5_acc": 0.57094, "loss_cls": 3.91074, "loss": 3.91074, "time": 0.8191} +{"mode": "train", "epoch": 72, "iter": 3200, "lr": 0.05329, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31438, "top5_acc": 0.57172, "loss_cls": 3.91305, "loss": 3.91305, "time": 0.81499} +{"mode": "train", "epoch": 72, "iter": 3300, "lr": 0.05326, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31469, "top5_acc": 0.57703, "loss_cls": 3.90713, "loss": 3.90713, "time": 0.81779} +{"mode": "train", "epoch": 72, "iter": 3400, "lr": 0.05324, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32188, "top5_acc": 0.57922, "loss_cls": 3.89185, "loss": 3.89185, "time": 0.81557} +{"mode": "train", "epoch": 72, "iter": 3500, "lr": 0.05321, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30203, "top5_acc": 0.55703, "loss_cls": 3.99535, "loss": 3.99535, "time": 0.81813} +{"mode": "train", "epoch": 72, "iter": 3600, "lr": 0.05318, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31891, "top5_acc": 0.57641, "loss_cls": 3.89365, "loss": 3.89365, "time": 0.81232} +{"mode": "train", "epoch": 72, "iter": 3700, "lr": 0.05315, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31703, "top5_acc": 0.56719, "loss_cls": 3.91837, "loss": 3.91837, "time": 0.81401} +{"mode": "val", "epoch": 72, "iter": 309, "lr": 0.05314, "top1_acc": 0.22737, "top5_acc": 0.4632, "mean_class_accuracy": 0.22717} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.05311, "memory": 15990, "data_time": 1.34625, "top1_acc": 0.32156, "top5_acc": 0.5825, "loss_cls": 3.8506, "loss": 3.8506, "time": 2.3239} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.05308, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32328, "top5_acc": 0.58438, "loss_cls": 3.84574, "loss": 3.84574, "time": 0.81964} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.05306, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32281, "top5_acc": 0.575, "loss_cls": 3.86879, "loss": 3.86879, "time": 0.81531} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.05303, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32578, "top5_acc": 0.58359, "loss_cls": 3.84149, "loss": 3.84149, "time": 0.81627} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.053, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31812, "top5_acc": 0.56969, "loss_cls": 3.90235, "loss": 3.90235, "time": 0.81413} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.05297, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32359, "top5_acc": 0.58031, "loss_cls": 3.86524, "loss": 3.86524, "time": 0.82286} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.05294, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32172, "top5_acc": 0.57359, "loss_cls": 3.87044, "loss": 3.87044, "time": 0.81921} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.05292, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31125, "top5_acc": 0.57688, "loss_cls": 3.89037, "loss": 3.89037, "time": 0.82161} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.05289, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31672, "top5_acc": 0.57594, "loss_cls": 3.89351, "loss": 3.89351, "time": 0.81858} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.05286, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31797, "top5_acc": 0.57797, "loss_cls": 3.90876, "loss": 3.90876, "time": 0.82351} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.05283, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33297, "top5_acc": 0.59125, "loss_cls": 3.83418, "loss": 3.83418, "time": 0.82101} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.0528, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32047, "top5_acc": 0.57812, "loss_cls": 3.86324, "loss": 3.86324, "time": 0.81742} +{"mode": "train", "epoch": 73, "iter": 1300, "lr": 0.05278, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31812, "top5_acc": 0.58172, "loss_cls": 3.88359, "loss": 3.88359, "time": 0.81545} +{"mode": "train", "epoch": 73, "iter": 1400, "lr": 0.05275, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31266, "top5_acc": 0.57328, "loss_cls": 3.88517, "loss": 3.88517, "time": 0.81776} +{"mode": "train", "epoch": 73, "iter": 1500, "lr": 0.05272, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31438, "top5_acc": 0.56766, "loss_cls": 3.94045, "loss": 3.94045, "time": 0.82125} +{"mode": "train", "epoch": 73, "iter": 1600, "lr": 0.05269, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3175, "top5_acc": 0.57109, "loss_cls": 3.87718, "loss": 3.87718, "time": 0.82408} +{"mode": "train", "epoch": 73, "iter": 1700, "lr": 0.05267, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32734, "top5_acc": 0.56969, "loss_cls": 3.89, "loss": 3.89, "time": 0.82859} +{"mode": "train", "epoch": 73, "iter": 1800, "lr": 0.05264, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30828, "top5_acc": 0.57125, "loss_cls": 3.92867, "loss": 3.92867, "time": 0.82343} +{"mode": "train", "epoch": 73, "iter": 1900, "lr": 0.05261, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31469, "top5_acc": 0.57109, "loss_cls": 3.89505, "loss": 3.89505, "time": 0.81741} +{"mode": "train", "epoch": 73, "iter": 2000, "lr": 0.05258, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31547, "top5_acc": 0.57141, "loss_cls": 3.92151, "loss": 3.92151, "time": 0.81453} +{"mode": "train", "epoch": 73, "iter": 2100, "lr": 0.05255, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31438, "top5_acc": 0.57297, "loss_cls": 3.93067, "loss": 3.93067, "time": 0.81435} +{"mode": "train", "epoch": 73, "iter": 2200, "lr": 0.05253, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31875, "top5_acc": 0.58156, "loss_cls": 3.88423, "loss": 3.88423, "time": 0.8183} +{"mode": "train", "epoch": 73, "iter": 2300, "lr": 0.0525, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32312, "top5_acc": 0.56891, "loss_cls": 3.8919, "loss": 3.8919, "time": 0.81816} +{"mode": "train", "epoch": 73, "iter": 2400, "lr": 0.05247, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31766, "top5_acc": 0.57531, "loss_cls": 3.9118, "loss": 3.9118, "time": 0.81575} +{"mode": "train", "epoch": 73, "iter": 2500, "lr": 0.05244, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32562, "top5_acc": 0.58688, "loss_cls": 3.84942, "loss": 3.84942, "time": 0.81857} +{"mode": "train", "epoch": 73, "iter": 2600, "lr": 0.05241, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30359, "top5_acc": 0.56844, "loss_cls": 3.94341, "loss": 3.94341, "time": 0.81238} +{"mode": "train", "epoch": 73, "iter": 2700, "lr": 0.05239, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32984, "top5_acc": 0.57953, "loss_cls": 3.86364, "loss": 3.86364, "time": 0.81854} +{"mode": "train", "epoch": 73, "iter": 2800, "lr": 0.05236, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32297, "top5_acc": 0.58125, "loss_cls": 3.86975, "loss": 3.86975, "time": 0.81085} +{"mode": "train", "epoch": 73, "iter": 2900, "lr": 0.05233, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31609, "top5_acc": 0.57875, "loss_cls": 3.87701, "loss": 3.87701, "time": 0.81454} +{"mode": "train", "epoch": 73, "iter": 3000, "lr": 0.0523, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31578, "top5_acc": 0.57219, "loss_cls": 3.91627, "loss": 3.91627, "time": 0.81798} +{"mode": "train", "epoch": 73, "iter": 3100, "lr": 0.05227, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31859, "top5_acc": 0.57797, "loss_cls": 3.88981, "loss": 3.88981, "time": 0.81634} +{"mode": "train", "epoch": 73, "iter": 3200, "lr": 0.05225, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31438, "top5_acc": 0.57812, "loss_cls": 3.92188, "loss": 3.92188, "time": 0.81944} +{"mode": "train", "epoch": 73, "iter": 3300, "lr": 0.05222, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31375, "top5_acc": 0.57953, "loss_cls": 3.90957, "loss": 3.90957, "time": 0.8203} +{"mode": "train", "epoch": 73, "iter": 3400, "lr": 0.05219, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.325, "top5_acc": 0.59156, "loss_cls": 3.82604, "loss": 3.82604, "time": 0.81632} +{"mode": "train", "epoch": 73, "iter": 3500, "lr": 0.05216, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31969, "top5_acc": 0.56984, "loss_cls": 3.89466, "loss": 3.89466, "time": 0.81669} +{"mode": "train", "epoch": 73, "iter": 3600, "lr": 0.05213, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31766, "top5_acc": 0.56969, "loss_cls": 3.92233, "loss": 3.92233, "time": 0.81484} +{"mode": "train", "epoch": 73, "iter": 3700, "lr": 0.05211, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31766, "top5_acc": 0.58016, "loss_cls": 3.84644, "loss": 3.84644, "time": 0.81898} +{"mode": "val", "epoch": 73, "iter": 309, "lr": 0.05209, "top1_acc": 0.23234, "top5_acc": 0.47657, "mean_class_accuracy": 0.23207} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.05207, "memory": 15990, "data_time": 1.35461, "top1_acc": 0.33, "top5_acc": 0.59266, "loss_cls": 3.80747, "loss": 3.80747, "time": 2.34452} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.05204, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32156, "top5_acc": 0.58328, "loss_cls": 3.8462, "loss": 3.8462, "time": 0.82393} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.05201, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32672, "top5_acc": 0.57906, "loss_cls": 3.84025, "loss": 3.84025, "time": 0.81977} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.05198, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32016, "top5_acc": 0.58438, "loss_cls": 3.85617, "loss": 3.85617, "time": 0.81789} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.05195, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31703, "top5_acc": 0.57641, "loss_cls": 3.88531, "loss": 3.88531, "time": 0.81906} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.05193, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32688, "top5_acc": 0.58688, "loss_cls": 3.84069, "loss": 3.84069, "time": 0.8185} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.0519, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33578, "top5_acc": 0.58484, "loss_cls": 3.80129, "loss": 3.80129, "time": 0.82399} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.05187, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32078, "top5_acc": 0.58, "loss_cls": 3.85105, "loss": 3.85105, "time": 0.82285} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.05184, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32984, "top5_acc": 0.58594, "loss_cls": 3.83765, "loss": 3.83765, "time": 0.81449} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.05181, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32953, "top5_acc": 0.58469, "loss_cls": 3.87092, "loss": 3.87092, "time": 0.81951} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.05179, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32031, "top5_acc": 0.58141, "loss_cls": 3.89058, "loss": 3.89058, "time": 0.81745} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.05176, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32297, "top5_acc": 0.57703, "loss_cls": 3.88662, "loss": 3.88662, "time": 0.81467} +{"mode": "train", "epoch": 74, "iter": 1300, "lr": 0.05173, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33234, "top5_acc": 0.59047, "loss_cls": 3.84487, "loss": 3.84487, "time": 0.81548} +{"mode": "train", "epoch": 74, "iter": 1400, "lr": 0.0517, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31828, "top5_acc": 0.57516, "loss_cls": 3.85577, "loss": 3.85577, "time": 0.81978} +{"mode": "train", "epoch": 74, "iter": 1500, "lr": 0.05168, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31469, "top5_acc": 0.57641, "loss_cls": 3.89128, "loss": 3.89128, "time": 0.82022} +{"mode": "train", "epoch": 74, "iter": 1600, "lr": 0.05165, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30875, "top5_acc": 0.57531, "loss_cls": 3.91784, "loss": 3.91784, "time": 0.82344} +{"mode": "train", "epoch": 74, "iter": 1700, "lr": 0.05162, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.32281, "top5_acc": 0.57797, "loss_cls": 3.86541, "loss": 3.86541, "time": 0.82888} +{"mode": "train", "epoch": 74, "iter": 1800, "lr": 0.05159, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31734, "top5_acc": 0.58203, "loss_cls": 3.87081, "loss": 3.87081, "time": 0.81752} +{"mode": "train", "epoch": 74, "iter": 1900, "lr": 0.05156, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32938, "top5_acc": 0.58594, "loss_cls": 3.85504, "loss": 3.85504, "time": 0.81712} +{"mode": "train", "epoch": 74, "iter": 2000, "lr": 0.05154, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30828, "top5_acc": 0.5775, "loss_cls": 3.89026, "loss": 3.89026, "time": 0.81878} +{"mode": "train", "epoch": 74, "iter": 2100, "lr": 0.05151, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32172, "top5_acc": 0.58391, "loss_cls": 3.85756, "loss": 3.85756, "time": 0.81709} +{"mode": "train", "epoch": 74, "iter": 2200, "lr": 0.05148, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32188, "top5_acc": 0.57688, "loss_cls": 3.90353, "loss": 3.90353, "time": 0.8128} +{"mode": "train", "epoch": 74, "iter": 2300, "lr": 0.05145, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31406, "top5_acc": 0.57672, "loss_cls": 3.88879, "loss": 3.88879, "time": 0.81653} +{"mode": "train", "epoch": 74, "iter": 2400, "lr": 0.05142, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31625, "top5_acc": 0.56844, "loss_cls": 3.92634, "loss": 3.92634, "time": 0.81905} +{"mode": "train", "epoch": 74, "iter": 2500, "lr": 0.0514, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30625, "top5_acc": 0.56719, "loss_cls": 3.94805, "loss": 3.94805, "time": 0.81555} +{"mode": "train", "epoch": 74, "iter": 2600, "lr": 0.05137, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32, "top5_acc": 0.57141, "loss_cls": 3.8891, "loss": 3.8891, "time": 0.81386} +{"mode": "train", "epoch": 74, "iter": 2700, "lr": 0.05134, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30969, "top5_acc": 0.57297, "loss_cls": 3.92454, "loss": 3.92454, "time": 0.81717} +{"mode": "train", "epoch": 74, "iter": 2800, "lr": 0.05131, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32766, "top5_acc": 0.57422, "loss_cls": 3.89853, "loss": 3.89853, "time": 0.81693} +{"mode": "train", "epoch": 74, "iter": 2900, "lr": 0.05128, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32828, "top5_acc": 0.58562, "loss_cls": 3.82808, "loss": 3.82808, "time": 0.81611} +{"mode": "train", "epoch": 74, "iter": 3000, "lr": 0.05126, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32391, "top5_acc": 0.58047, "loss_cls": 3.87227, "loss": 3.87227, "time": 0.81245} +{"mode": "train", "epoch": 74, "iter": 3100, "lr": 0.05123, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31656, "top5_acc": 0.57359, "loss_cls": 3.91763, "loss": 3.91763, "time": 0.81692} +{"mode": "train", "epoch": 74, "iter": 3200, "lr": 0.0512, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32125, "top5_acc": 0.58172, "loss_cls": 3.84937, "loss": 3.84937, "time": 0.81756} +{"mode": "train", "epoch": 74, "iter": 3300, "lr": 0.05117, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30906, "top5_acc": 0.56984, "loss_cls": 3.92569, "loss": 3.92569, "time": 0.81461} +{"mode": "train", "epoch": 74, "iter": 3400, "lr": 0.05114, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32516, "top5_acc": 0.57484, "loss_cls": 3.89716, "loss": 3.89716, "time": 0.81097} +{"mode": "train", "epoch": 74, "iter": 3500, "lr": 0.05112, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32, "top5_acc": 0.56812, "loss_cls": 3.87877, "loss": 3.87877, "time": 0.81461} +{"mode": "train", "epoch": 74, "iter": 3600, "lr": 0.05109, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31406, "top5_acc": 0.57609, "loss_cls": 3.88607, "loss": 3.88607, "time": 0.81342} +{"mode": "train", "epoch": 74, "iter": 3700, "lr": 0.05106, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32297, "top5_acc": 0.57609, "loss_cls": 3.86924, "loss": 3.86924, "time": 0.81583} +{"mode": "val", "epoch": 74, "iter": 309, "lr": 0.05105, "top1_acc": 0.25087, "top5_acc": 0.48407, "mean_class_accuracy": 0.25075} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.05102, "memory": 15990, "data_time": 1.36241, "top1_acc": 0.33375, "top5_acc": 0.58953, "loss_cls": 3.80987, "loss": 3.80987, "time": 2.34277} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.05099, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32875, "top5_acc": 0.59875, "loss_cls": 3.77321, "loss": 3.77321, "time": 0.8199} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.05096, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33047, "top5_acc": 0.58734, "loss_cls": 3.82958, "loss": 3.82958, "time": 0.82226} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.05094, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32156, "top5_acc": 0.58859, "loss_cls": 3.82639, "loss": 3.82639, "time": 0.81511} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.05091, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32969, "top5_acc": 0.58625, "loss_cls": 3.83976, "loss": 3.83976, "time": 0.81452} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.05088, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31188, "top5_acc": 0.57, "loss_cls": 3.90229, "loss": 3.90229, "time": 0.81674} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.05085, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32375, "top5_acc": 0.57797, "loss_cls": 3.86648, "loss": 3.86648, "time": 0.8119} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.05082, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32484, "top5_acc": 0.58266, "loss_cls": 3.84584, "loss": 3.84584, "time": 0.81934} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.0508, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3225, "top5_acc": 0.57875, "loss_cls": 3.86464, "loss": 3.86464, "time": 0.8174} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.05077, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31562, "top5_acc": 0.57984, "loss_cls": 3.88202, "loss": 3.88202, "time": 0.82097} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.05074, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31906, "top5_acc": 0.57063, "loss_cls": 3.88479, "loss": 3.88479, "time": 0.8201} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.05071, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31766, "top5_acc": 0.57547, "loss_cls": 3.88955, "loss": 3.88955, "time": 0.81424} +{"mode": "train", "epoch": 75, "iter": 1300, "lr": 0.05068, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32562, "top5_acc": 0.58047, "loss_cls": 3.85743, "loss": 3.85743, "time": 0.81791} +{"mode": "train", "epoch": 75, "iter": 1400, "lr": 0.05066, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31906, "top5_acc": 0.58188, "loss_cls": 3.85234, "loss": 3.85234, "time": 0.82} +{"mode": "train", "epoch": 75, "iter": 1500, "lr": 0.05063, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33422, "top5_acc": 0.58531, "loss_cls": 3.83785, "loss": 3.83785, "time": 0.82176} +{"mode": "train", "epoch": 75, "iter": 1600, "lr": 0.0506, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32047, "top5_acc": 0.58203, "loss_cls": 3.85856, "loss": 3.85856, "time": 0.82493} +{"mode": "train", "epoch": 75, "iter": 1700, "lr": 0.05057, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32547, "top5_acc": 0.58359, "loss_cls": 3.85786, "loss": 3.85786, "time": 0.82972} +{"mode": "train", "epoch": 75, "iter": 1800, "lr": 0.05054, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32344, "top5_acc": 0.58359, "loss_cls": 3.85975, "loss": 3.85975, "time": 0.82613} +{"mode": "train", "epoch": 75, "iter": 1900, "lr": 0.05052, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32844, "top5_acc": 0.58234, "loss_cls": 3.86727, "loss": 3.86727, "time": 0.82006} +{"mode": "train", "epoch": 75, "iter": 2000, "lr": 0.05049, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31531, "top5_acc": 0.56969, "loss_cls": 3.91867, "loss": 3.91867, "time": 0.81857} +{"mode": "train", "epoch": 75, "iter": 2100, "lr": 0.05046, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32109, "top5_acc": 0.57609, "loss_cls": 3.87626, "loss": 3.87626, "time": 0.81801} +{"mode": "train", "epoch": 75, "iter": 2200, "lr": 0.05043, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31719, "top5_acc": 0.56578, "loss_cls": 3.90672, "loss": 3.90672, "time": 0.81983} +{"mode": "train", "epoch": 75, "iter": 2300, "lr": 0.0504, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32281, "top5_acc": 0.58422, "loss_cls": 3.85378, "loss": 3.85378, "time": 0.81462} +{"mode": "train", "epoch": 75, "iter": 2400, "lr": 0.05038, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31812, "top5_acc": 0.5625, "loss_cls": 3.9295, "loss": 3.9295, "time": 0.81638} +{"mode": "train", "epoch": 75, "iter": 2500, "lr": 0.05035, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32953, "top5_acc": 0.5825, "loss_cls": 3.8542, "loss": 3.8542, "time": 0.81857} +{"mode": "train", "epoch": 75, "iter": 2600, "lr": 0.05032, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3175, "top5_acc": 0.56328, "loss_cls": 3.91548, "loss": 3.91548, "time": 0.82362} +{"mode": "train", "epoch": 75, "iter": 2700, "lr": 0.05029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31906, "top5_acc": 0.58766, "loss_cls": 3.86061, "loss": 3.86061, "time": 0.81652} +{"mode": "train", "epoch": 75, "iter": 2800, "lr": 0.05026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31766, "top5_acc": 0.5775, "loss_cls": 3.89718, "loss": 3.89718, "time": 0.81643} +{"mode": "train", "epoch": 75, "iter": 2900, "lr": 0.05024, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30812, "top5_acc": 0.56812, "loss_cls": 3.92661, "loss": 3.92661, "time": 0.81556} +{"mode": "train", "epoch": 75, "iter": 3000, "lr": 0.05021, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31766, "top5_acc": 0.57484, "loss_cls": 3.90248, "loss": 3.90248, "time": 0.81803} +{"mode": "train", "epoch": 75, "iter": 3100, "lr": 0.05018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32516, "top5_acc": 0.58453, "loss_cls": 3.86532, "loss": 3.86532, "time": 0.81853} +{"mode": "train", "epoch": 75, "iter": 3200, "lr": 0.05015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31484, "top5_acc": 0.57484, "loss_cls": 3.88101, "loss": 3.88101, "time": 0.81953} +{"mode": "train", "epoch": 75, "iter": 3300, "lr": 0.05012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31812, "top5_acc": 0.57469, "loss_cls": 3.91724, "loss": 3.91724, "time": 0.81906} +{"mode": "train", "epoch": 75, "iter": 3400, "lr": 0.0501, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.325, "top5_acc": 0.58812, "loss_cls": 3.83079, "loss": 3.83079, "time": 0.81283} +{"mode": "train", "epoch": 75, "iter": 3500, "lr": 0.05007, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31625, "top5_acc": 0.57453, "loss_cls": 3.90072, "loss": 3.90072, "time": 0.81876} +{"mode": "train", "epoch": 75, "iter": 3600, "lr": 0.05004, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31109, "top5_acc": 0.57516, "loss_cls": 3.89017, "loss": 3.89017, "time": 0.81686} +{"mode": "train", "epoch": 75, "iter": 3700, "lr": 0.05001, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33797, "top5_acc": 0.59328, "loss_cls": 3.80313, "loss": 3.80313, "time": 0.81251} +{"mode": "val", "epoch": 75, "iter": 309, "lr": 0.05, "top1_acc": 0.25062, "top5_acc": 0.49466, "mean_class_accuracy": 0.25041} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.04997, "memory": 15990, "data_time": 1.34865, "top1_acc": 0.33516, "top5_acc": 0.59125, "loss_cls": 3.8209, "loss": 3.8209, "time": 2.33076} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.04994, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33, "top5_acc": 0.59172, "loss_cls": 3.80487, "loss": 3.80487, "time": 0.81971} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.04992, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31844, "top5_acc": 0.58203, "loss_cls": 3.8489, "loss": 3.8489, "time": 0.82493} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.04989, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32688, "top5_acc": 0.58688, "loss_cls": 3.82447, "loss": 3.82447, "time": 0.81495} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.04986, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33375, "top5_acc": 0.58594, "loss_cls": 3.80117, "loss": 3.80117, "time": 0.81979} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.04983, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32453, "top5_acc": 0.57609, "loss_cls": 3.86455, "loss": 3.86455, "time": 0.81899} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.0498, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32422, "top5_acc": 0.58141, "loss_cls": 3.8635, "loss": 3.8635, "time": 0.8125} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.04978, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32141, "top5_acc": 0.57969, "loss_cls": 3.86361, "loss": 3.86361, "time": 0.82028} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.04975, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33234, "top5_acc": 0.58844, "loss_cls": 3.83335, "loss": 3.83335, "time": 0.81846} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.04972, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32281, "top5_acc": 0.57938, "loss_cls": 3.86701, "loss": 3.86701, "time": 0.81931} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.04969, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33078, "top5_acc": 0.58422, "loss_cls": 3.85516, "loss": 3.85516, "time": 0.81203} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.04966, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32578, "top5_acc": 0.57859, "loss_cls": 3.84269, "loss": 3.84269, "time": 0.81806} +{"mode": "train", "epoch": 76, "iter": 1300, "lr": 0.04964, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32734, "top5_acc": 0.58578, "loss_cls": 3.82704, "loss": 3.82704, "time": 0.81835} +{"mode": "train", "epoch": 76, "iter": 1400, "lr": 0.04961, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33031, "top5_acc": 0.59609, "loss_cls": 3.80302, "loss": 3.80302, "time": 0.81401} +{"mode": "train", "epoch": 76, "iter": 1500, "lr": 0.04958, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32094, "top5_acc": 0.57406, "loss_cls": 3.90182, "loss": 3.90182, "time": 0.81947} +{"mode": "train", "epoch": 76, "iter": 1600, "lr": 0.04955, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33438, "top5_acc": 0.58781, "loss_cls": 3.83565, "loss": 3.83565, "time": 0.82762} +{"mode": "train", "epoch": 76, "iter": 1700, "lr": 0.04953, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32859, "top5_acc": 0.58031, "loss_cls": 3.84803, "loss": 3.84803, "time": 0.82611} +{"mode": "train", "epoch": 76, "iter": 1800, "lr": 0.0495, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3375, "top5_acc": 0.59172, "loss_cls": 3.79512, "loss": 3.79512, "time": 0.82499} +{"mode": "train", "epoch": 76, "iter": 1900, "lr": 0.04947, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30547, "top5_acc": 0.56781, "loss_cls": 3.91369, "loss": 3.91369, "time": 0.82235} +{"mode": "train", "epoch": 76, "iter": 2000, "lr": 0.04944, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32531, "top5_acc": 0.58031, "loss_cls": 3.86642, "loss": 3.86642, "time": 0.81548} +{"mode": "train", "epoch": 76, "iter": 2100, "lr": 0.04941, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33016, "top5_acc": 0.5875, "loss_cls": 3.81805, "loss": 3.81805, "time": 0.81388} +{"mode": "train", "epoch": 76, "iter": 2200, "lr": 0.04939, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31547, "top5_acc": 0.57078, "loss_cls": 3.92095, "loss": 3.92095, "time": 0.82134} +{"mode": "train", "epoch": 76, "iter": 2300, "lr": 0.04936, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33125, "top5_acc": 0.59203, "loss_cls": 3.8254, "loss": 3.8254, "time": 0.81784} +{"mode": "train", "epoch": 76, "iter": 2400, "lr": 0.04933, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33125, "top5_acc": 0.58391, "loss_cls": 3.81344, "loss": 3.81344, "time": 0.8154} +{"mode": "train", "epoch": 76, "iter": 2500, "lr": 0.0493, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32875, "top5_acc": 0.58797, "loss_cls": 3.84332, "loss": 3.84332, "time": 0.8147} +{"mode": "train", "epoch": 76, "iter": 2600, "lr": 0.04927, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31484, "top5_acc": 0.56797, "loss_cls": 3.90717, "loss": 3.90717, "time": 0.81173} +{"mode": "train", "epoch": 76, "iter": 2700, "lr": 0.04925, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31516, "top5_acc": 0.57828, "loss_cls": 3.87933, "loss": 3.87933, "time": 0.81438} +{"mode": "train", "epoch": 76, "iter": 2800, "lr": 0.04922, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32375, "top5_acc": 0.58375, "loss_cls": 3.85062, "loss": 3.85062, "time": 0.81439} +{"mode": "train", "epoch": 76, "iter": 2900, "lr": 0.04919, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31938, "top5_acc": 0.58031, "loss_cls": 3.88191, "loss": 3.88191, "time": 0.81353} +{"mode": "train", "epoch": 76, "iter": 3000, "lr": 0.04916, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33047, "top5_acc": 0.58469, "loss_cls": 3.81068, "loss": 3.81068, "time": 0.81671} +{"mode": "train", "epoch": 76, "iter": 3100, "lr": 0.04913, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31594, "top5_acc": 0.58016, "loss_cls": 3.85722, "loss": 3.85722, "time": 0.8157} +{"mode": "train", "epoch": 76, "iter": 3200, "lr": 0.04911, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32078, "top5_acc": 0.57266, "loss_cls": 3.85703, "loss": 3.85703, "time": 0.81579} +{"mode": "train", "epoch": 76, "iter": 3300, "lr": 0.04908, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32781, "top5_acc": 0.58953, "loss_cls": 3.80737, "loss": 3.80737, "time": 0.81916} +{"mode": "train", "epoch": 76, "iter": 3400, "lr": 0.04905, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31578, "top5_acc": 0.57156, "loss_cls": 3.8884, "loss": 3.8884, "time": 0.81697} +{"mode": "train", "epoch": 76, "iter": 3500, "lr": 0.04902, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31891, "top5_acc": 0.57516, "loss_cls": 3.88009, "loss": 3.88009, "time": 0.81415} +{"mode": "train", "epoch": 76, "iter": 3600, "lr": 0.04899, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31578, "top5_acc": 0.57422, "loss_cls": 3.88354, "loss": 3.88354, "time": 0.81393} +{"mode": "train", "epoch": 76, "iter": 3700, "lr": 0.04897, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32797, "top5_acc": 0.59016, "loss_cls": 3.8355, "loss": 3.8355, "time": 0.81772} +{"mode": "val", "epoch": 76, "iter": 309, "lr": 0.04895, "top1_acc": 0.24849, "top5_acc": 0.49521, "mean_class_accuracy": 0.24827} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.04893, "memory": 15990, "data_time": 1.31647, "top1_acc": 0.33562, "top5_acc": 0.59453, "loss_cls": 3.77623, "loss": 3.77623, "time": 2.30225} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0489, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33172, "top5_acc": 0.59484, "loss_cls": 3.80351, "loss": 3.80351, "time": 0.82041} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.04887, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32703, "top5_acc": 0.59156, "loss_cls": 3.82459, "loss": 3.82459, "time": 0.81886} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.04884, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32812, "top5_acc": 0.58516, "loss_cls": 3.8246, "loss": 3.8246, "time": 0.82361} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.04881, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32188, "top5_acc": 0.59406, "loss_cls": 3.82963, "loss": 3.82963, "time": 0.82064} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.04879, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33109, "top5_acc": 0.58969, "loss_cls": 3.79711, "loss": 3.79711, "time": 0.81298} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.04876, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33984, "top5_acc": 0.58938, "loss_cls": 3.77227, "loss": 3.77227, "time": 0.82203} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.04873, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33078, "top5_acc": 0.58047, "loss_cls": 3.851, "loss": 3.851, "time": 0.81935} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.0487, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32688, "top5_acc": 0.58906, "loss_cls": 3.83388, "loss": 3.83388, "time": 0.81952} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.04867, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32734, "top5_acc": 0.59156, "loss_cls": 3.80565, "loss": 3.80565, "time": 0.82218} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.04865, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33047, "top5_acc": 0.58547, "loss_cls": 3.83362, "loss": 3.83362, "time": 0.82038} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.04862, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32359, "top5_acc": 0.58, "loss_cls": 3.89728, "loss": 3.89728, "time": 0.81482} +{"mode": "train", "epoch": 77, "iter": 1300, "lr": 0.04859, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32266, "top5_acc": 0.58078, "loss_cls": 3.838, "loss": 3.838, "time": 0.81289} +{"mode": "train", "epoch": 77, "iter": 1400, "lr": 0.04856, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32594, "top5_acc": 0.58359, "loss_cls": 3.87061, "loss": 3.87061, "time": 0.81669} +{"mode": "train", "epoch": 77, "iter": 1500, "lr": 0.04853, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31984, "top5_acc": 0.57812, "loss_cls": 3.85695, "loss": 3.85695, "time": 0.81757} +{"mode": "train", "epoch": 77, "iter": 1600, "lr": 0.04851, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32953, "top5_acc": 0.59016, "loss_cls": 3.79259, "loss": 3.79259, "time": 0.82182} +{"mode": "train", "epoch": 77, "iter": 1700, "lr": 0.04848, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32859, "top5_acc": 0.58859, "loss_cls": 3.81067, "loss": 3.81067, "time": 0.82618} +{"mode": "train", "epoch": 77, "iter": 1800, "lr": 0.04845, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.315, "top5_acc": 0.58125, "loss_cls": 3.84953, "loss": 3.84953, "time": 0.82461} +{"mode": "train", "epoch": 77, "iter": 1900, "lr": 0.04842, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32531, "top5_acc": 0.58453, "loss_cls": 3.81912, "loss": 3.81912, "time": 0.82432} +{"mode": "train", "epoch": 77, "iter": 2000, "lr": 0.04839, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33734, "top5_acc": 0.58469, "loss_cls": 3.83536, "loss": 3.83536, "time": 0.81682} +{"mode": "train", "epoch": 77, "iter": 2100, "lr": 0.04837, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32953, "top5_acc": 0.58531, "loss_cls": 3.82288, "loss": 3.82288, "time": 0.82146} +{"mode": "train", "epoch": 77, "iter": 2200, "lr": 0.04834, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32203, "top5_acc": 0.58516, "loss_cls": 3.84935, "loss": 3.84935, "time": 0.81394} +{"mode": "train", "epoch": 77, "iter": 2300, "lr": 0.04831, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33484, "top5_acc": 0.59438, "loss_cls": 3.794, "loss": 3.794, "time": 0.81837} +{"mode": "train", "epoch": 77, "iter": 2400, "lr": 0.04828, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32875, "top5_acc": 0.58406, "loss_cls": 3.83431, "loss": 3.83431, "time": 0.8161} +{"mode": "train", "epoch": 77, "iter": 2500, "lr": 0.04825, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33719, "top5_acc": 0.58812, "loss_cls": 3.83253, "loss": 3.83253, "time": 0.81808} +{"mode": "train", "epoch": 77, "iter": 2600, "lr": 0.04823, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32812, "top5_acc": 0.58188, "loss_cls": 3.84728, "loss": 3.84728, "time": 0.81919} +{"mode": "train", "epoch": 77, "iter": 2700, "lr": 0.0482, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32453, "top5_acc": 0.59, "loss_cls": 3.82532, "loss": 3.82532, "time": 0.82417} +{"mode": "train", "epoch": 77, "iter": 2800, "lr": 0.04817, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32375, "top5_acc": 0.58172, "loss_cls": 3.86494, "loss": 3.86494, "time": 0.81904} +{"mode": "train", "epoch": 77, "iter": 2900, "lr": 0.04814, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32266, "top5_acc": 0.57156, "loss_cls": 3.90605, "loss": 3.90605, "time": 0.81598} +{"mode": "train", "epoch": 77, "iter": 3000, "lr": 0.04811, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31672, "top5_acc": 0.57516, "loss_cls": 3.88599, "loss": 3.88599, "time": 0.82117} +{"mode": "train", "epoch": 77, "iter": 3100, "lr": 0.04809, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32906, "top5_acc": 0.58062, "loss_cls": 3.83768, "loss": 3.83768, "time": 0.82505} +{"mode": "train", "epoch": 77, "iter": 3200, "lr": 0.04806, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.325, "top5_acc": 0.57906, "loss_cls": 3.84966, "loss": 3.84966, "time": 0.81492} +{"mode": "train", "epoch": 77, "iter": 3300, "lr": 0.04803, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31547, "top5_acc": 0.5725, "loss_cls": 3.89653, "loss": 3.89653, "time": 0.81738} +{"mode": "train", "epoch": 77, "iter": 3400, "lr": 0.048, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32219, "top5_acc": 0.57906, "loss_cls": 3.8749, "loss": 3.8749, "time": 0.82043} +{"mode": "train", "epoch": 77, "iter": 3500, "lr": 0.04798, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31781, "top5_acc": 0.57219, "loss_cls": 3.91391, "loss": 3.91391, "time": 0.81713} +{"mode": "train", "epoch": 77, "iter": 3600, "lr": 0.04795, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31938, "top5_acc": 0.57922, "loss_cls": 3.85583, "loss": 3.85583, "time": 0.81556} +{"mode": "train", "epoch": 77, "iter": 3700, "lr": 0.04792, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31828, "top5_acc": 0.58688, "loss_cls": 3.85658, "loss": 3.85658, "time": 0.81941} +{"mode": "val", "epoch": 77, "iter": 309, "lr": 0.04791, "top1_acc": 0.25239, "top5_acc": 0.48691, "mean_class_accuracy": 0.25215} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.04788, "memory": 15990, "data_time": 1.29432, "top1_acc": 0.33031, "top5_acc": 0.59969, "loss_cls": 3.76624, "loss": 3.76624, "time": 2.26833} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.04785, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3475, "top5_acc": 0.60094, "loss_cls": 3.74442, "loss": 3.74442, "time": 0.81963} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.04782, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33312, "top5_acc": 0.59219, "loss_cls": 3.78407, "loss": 3.78407, "time": 0.81795} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.04779, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33672, "top5_acc": 0.59125, "loss_cls": 3.81771, "loss": 3.81771, "time": 0.81416} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.04777, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33047, "top5_acc": 0.58062, "loss_cls": 3.82764, "loss": 3.82764, "time": 0.81062} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.04774, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32922, "top5_acc": 0.58, "loss_cls": 3.83763, "loss": 3.83763, "time": 0.81496} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.04771, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32453, "top5_acc": 0.58672, "loss_cls": 3.81505, "loss": 3.81505, "time": 0.81405} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.04768, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33688, "top5_acc": 0.60344, "loss_cls": 3.75144, "loss": 3.75144, "time": 0.81888} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.04766, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33078, "top5_acc": 0.58797, "loss_cls": 3.80041, "loss": 3.80041, "time": 0.81899} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.04763, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34297, "top5_acc": 0.59172, "loss_cls": 3.79804, "loss": 3.79804, "time": 0.82284} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.0476, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32281, "top5_acc": 0.58672, "loss_cls": 3.83536, "loss": 3.83536, "time": 0.81619} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.04757, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32203, "top5_acc": 0.57688, "loss_cls": 3.85207, "loss": 3.85207, "time": 0.82019} +{"mode": "train", "epoch": 78, "iter": 1300, "lr": 0.04754, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33516, "top5_acc": 0.58656, "loss_cls": 3.83075, "loss": 3.83075, "time": 0.81634} +{"mode": "train", "epoch": 78, "iter": 1400, "lr": 0.04752, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32406, "top5_acc": 0.58656, "loss_cls": 3.83901, "loss": 3.83901, "time": 0.81805} +{"mode": "train", "epoch": 78, "iter": 1500, "lr": 0.04749, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32125, "top5_acc": 0.58031, "loss_cls": 3.88737, "loss": 3.88737, "time": 0.81995} +{"mode": "train", "epoch": 78, "iter": 1600, "lr": 0.04746, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31391, "top5_acc": 0.58453, "loss_cls": 3.85649, "loss": 3.85649, "time": 0.82049} +{"mode": "train", "epoch": 78, "iter": 1700, "lr": 0.04743, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31625, "top5_acc": 0.57547, "loss_cls": 3.84084, "loss": 3.84084, "time": 0.82712} +{"mode": "train", "epoch": 78, "iter": 1800, "lr": 0.0474, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32109, "top5_acc": 0.57906, "loss_cls": 3.869, "loss": 3.869, "time": 0.8235} +{"mode": "train", "epoch": 78, "iter": 1900, "lr": 0.04738, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32344, "top5_acc": 0.58078, "loss_cls": 3.85397, "loss": 3.85397, "time": 0.82033} +{"mode": "train", "epoch": 78, "iter": 2000, "lr": 0.04735, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31938, "top5_acc": 0.5725, "loss_cls": 3.88369, "loss": 3.88369, "time": 0.81794} +{"mode": "train", "epoch": 78, "iter": 2100, "lr": 0.04732, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33875, "top5_acc": 0.58953, "loss_cls": 3.81483, "loss": 3.81483, "time": 0.82027} +{"mode": "train", "epoch": 78, "iter": 2200, "lr": 0.04729, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33109, "top5_acc": 0.58969, "loss_cls": 3.81155, "loss": 3.81155, "time": 0.81779} +{"mode": "train", "epoch": 78, "iter": 2300, "lr": 0.04726, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30875, "top5_acc": 0.57188, "loss_cls": 3.91833, "loss": 3.91833, "time": 0.81337} +{"mode": "train", "epoch": 78, "iter": 2400, "lr": 0.04724, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31922, "top5_acc": 0.57578, "loss_cls": 3.86586, "loss": 3.86586, "time": 0.81643} +{"mode": "train", "epoch": 78, "iter": 2500, "lr": 0.04721, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32203, "top5_acc": 0.58672, "loss_cls": 3.82053, "loss": 3.82053, "time": 0.82084} +{"mode": "train", "epoch": 78, "iter": 2600, "lr": 0.04718, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33328, "top5_acc": 0.60188, "loss_cls": 3.77627, "loss": 3.77627, "time": 0.82136} +{"mode": "train", "epoch": 78, "iter": 2700, "lr": 0.04715, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32797, "top5_acc": 0.58703, "loss_cls": 3.86408, "loss": 3.86408, "time": 0.81656} +{"mode": "train", "epoch": 78, "iter": 2800, "lr": 0.04712, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33906, "top5_acc": 0.59312, "loss_cls": 3.80548, "loss": 3.80548, "time": 0.81987} +{"mode": "train", "epoch": 78, "iter": 2900, "lr": 0.0471, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32828, "top5_acc": 0.58797, "loss_cls": 3.84325, "loss": 3.84325, "time": 0.82074} +{"mode": "train", "epoch": 78, "iter": 3000, "lr": 0.04707, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33531, "top5_acc": 0.58609, "loss_cls": 3.82414, "loss": 3.82414, "time": 0.82145} +{"mode": "train", "epoch": 78, "iter": 3100, "lr": 0.04704, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31906, "top5_acc": 0.58297, "loss_cls": 3.83392, "loss": 3.83392, "time": 0.81656} +{"mode": "train", "epoch": 78, "iter": 3200, "lr": 0.04701, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32547, "top5_acc": 0.5875, "loss_cls": 3.84402, "loss": 3.84402, "time": 0.81695} +{"mode": "train", "epoch": 78, "iter": 3300, "lr": 0.04699, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32875, "top5_acc": 0.57875, "loss_cls": 3.85582, "loss": 3.85582, "time": 0.81443} +{"mode": "train", "epoch": 78, "iter": 3400, "lr": 0.04696, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34219, "top5_acc": 0.58875, "loss_cls": 3.79912, "loss": 3.79912, "time": 0.81552} +{"mode": "train", "epoch": 78, "iter": 3500, "lr": 0.04693, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32078, "top5_acc": 0.57969, "loss_cls": 3.84622, "loss": 3.84622, "time": 0.82004} +{"mode": "train", "epoch": 78, "iter": 3600, "lr": 0.0469, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33156, "top5_acc": 0.59703, "loss_cls": 3.78963, "loss": 3.78963, "time": 0.81276} +{"mode": "train", "epoch": 78, "iter": 3700, "lr": 0.04687, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34266, "top5_acc": 0.5975, "loss_cls": 3.7868, "loss": 3.7868, "time": 0.81485} +{"mode": "val", "epoch": 78, "iter": 309, "lr": 0.04686, "top1_acc": 0.25356, "top5_acc": 0.5017, "mean_class_accuracy": 0.25328} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.04683, "memory": 15990, "data_time": 1.33609, "top1_acc": 0.32719, "top5_acc": 0.5925, "loss_cls": 3.77338, "loss": 3.77338, "time": 2.31806} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.0468, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32797, "top5_acc": 0.58719, "loss_cls": 3.81716, "loss": 3.81716, "time": 0.82127} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.04678, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33078, "top5_acc": 0.59828, "loss_cls": 3.7933, "loss": 3.7933, "time": 0.82319} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.04675, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34391, "top5_acc": 0.59547, "loss_cls": 3.77584, "loss": 3.77584, "time": 0.81898} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.04672, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32906, "top5_acc": 0.57703, "loss_cls": 3.86588, "loss": 3.86588, "time": 0.81625} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.04669, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33016, "top5_acc": 0.58375, "loss_cls": 3.83683, "loss": 3.83683, "time": 0.8133} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.04667, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33344, "top5_acc": 0.59266, "loss_cls": 3.77303, "loss": 3.77303, "time": 0.81731} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.04664, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33047, "top5_acc": 0.59953, "loss_cls": 3.78428, "loss": 3.78428, "time": 0.82094} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.04661, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33469, "top5_acc": 0.58891, "loss_cls": 3.80172, "loss": 3.80172, "time": 0.81953} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.04658, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33625, "top5_acc": 0.60188, "loss_cls": 3.76826, "loss": 3.76826, "time": 0.81881} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.04655, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33812, "top5_acc": 0.59406, "loss_cls": 3.78676, "loss": 3.78676, "time": 0.81631} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.04653, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32281, "top5_acc": 0.58531, "loss_cls": 3.84924, "loss": 3.84924, "time": 0.81709} +{"mode": "train", "epoch": 79, "iter": 1300, "lr": 0.0465, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32922, "top5_acc": 0.58719, "loss_cls": 3.82995, "loss": 3.82995, "time": 0.8224} +{"mode": "train", "epoch": 79, "iter": 1400, "lr": 0.04647, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34016, "top5_acc": 0.59172, "loss_cls": 3.82029, "loss": 3.82029, "time": 0.81773} +{"mode": "train", "epoch": 79, "iter": 1500, "lr": 0.04644, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33281, "top5_acc": 0.59266, "loss_cls": 3.80562, "loss": 3.80562, "time": 0.81968} +{"mode": "train", "epoch": 79, "iter": 1600, "lr": 0.04641, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32656, "top5_acc": 0.58578, "loss_cls": 3.82985, "loss": 3.82985, "time": 0.81755} +{"mode": "train", "epoch": 79, "iter": 1700, "lr": 0.04639, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33906, "top5_acc": 0.59672, "loss_cls": 3.76509, "loss": 3.76509, "time": 0.827} +{"mode": "train", "epoch": 79, "iter": 1800, "lr": 0.04636, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33047, "top5_acc": 0.58969, "loss_cls": 3.81034, "loss": 3.81034, "time": 0.82732} +{"mode": "train", "epoch": 79, "iter": 1900, "lr": 0.04633, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33969, "top5_acc": 0.59078, "loss_cls": 3.77453, "loss": 3.77453, "time": 0.82659} +{"mode": "train", "epoch": 79, "iter": 2000, "lr": 0.0463, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33641, "top5_acc": 0.59188, "loss_cls": 3.81166, "loss": 3.81166, "time": 0.81501} +{"mode": "train", "epoch": 79, "iter": 2100, "lr": 0.04628, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33875, "top5_acc": 0.59016, "loss_cls": 3.8066, "loss": 3.8066, "time": 0.81919} +{"mode": "train", "epoch": 79, "iter": 2200, "lr": 0.04625, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32953, "top5_acc": 0.585, "loss_cls": 3.85212, "loss": 3.85212, "time": 0.81407} +{"mode": "train", "epoch": 79, "iter": 2300, "lr": 0.04622, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32906, "top5_acc": 0.59203, "loss_cls": 3.82978, "loss": 3.82978, "time": 0.8144} +{"mode": "train", "epoch": 79, "iter": 2400, "lr": 0.04619, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32953, "top5_acc": 0.58672, "loss_cls": 3.83434, "loss": 3.83434, "time": 0.81957} +{"mode": "train", "epoch": 79, "iter": 2500, "lr": 0.04616, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32375, "top5_acc": 0.58562, "loss_cls": 3.82648, "loss": 3.82648, "time": 0.81904} +{"mode": "train", "epoch": 79, "iter": 2600, "lr": 0.04614, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33125, "top5_acc": 0.58734, "loss_cls": 3.80651, "loss": 3.80651, "time": 0.81827} +{"mode": "train", "epoch": 79, "iter": 2700, "lr": 0.04611, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33266, "top5_acc": 0.57844, "loss_cls": 3.8471, "loss": 3.8471, "time": 0.81491} +{"mode": "train", "epoch": 79, "iter": 2800, "lr": 0.04608, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33281, "top5_acc": 0.59094, "loss_cls": 3.80493, "loss": 3.80493, "time": 0.81633} +{"mode": "train", "epoch": 79, "iter": 2900, "lr": 0.04605, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32172, "top5_acc": 0.57688, "loss_cls": 3.86563, "loss": 3.86563, "time": 0.81756} +{"mode": "train", "epoch": 79, "iter": 3000, "lr": 0.04602, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3375, "top5_acc": 0.58906, "loss_cls": 3.82343, "loss": 3.82343, "time": 0.81445} +{"mode": "train", "epoch": 79, "iter": 3100, "lr": 0.046, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33406, "top5_acc": 0.58281, "loss_cls": 3.81076, "loss": 3.81076, "time": 0.81863} +{"mode": "train", "epoch": 79, "iter": 3200, "lr": 0.04597, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32078, "top5_acc": 0.58047, "loss_cls": 3.87476, "loss": 3.87476, "time": 0.82453} +{"mode": "train", "epoch": 79, "iter": 3300, "lr": 0.04594, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32562, "top5_acc": 0.57938, "loss_cls": 3.84414, "loss": 3.84414, "time": 0.81502} +{"mode": "train", "epoch": 79, "iter": 3400, "lr": 0.04591, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32281, "top5_acc": 0.59, "loss_cls": 3.85548, "loss": 3.85548, "time": 0.81734} +{"mode": "train", "epoch": 79, "iter": 3500, "lr": 0.04588, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31812, "top5_acc": 0.57906, "loss_cls": 3.86547, "loss": 3.86547, "time": 0.81631} +{"mode": "train", "epoch": 79, "iter": 3600, "lr": 0.04586, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32578, "top5_acc": 0.58078, "loss_cls": 3.84645, "loss": 3.84645, "time": 0.81416} +{"mode": "train", "epoch": 79, "iter": 3700, "lr": 0.04583, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32438, "top5_acc": 0.58312, "loss_cls": 3.83405, "loss": 3.83405, "time": 0.8153} +{"mode": "val", "epoch": 79, "iter": 309, "lr": 0.04582, "top1_acc": 0.24677, "top5_acc": 0.49932, "mean_class_accuracy": 0.24664} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.04579, "memory": 15990, "data_time": 1.34915, "top1_acc": 0.33062, "top5_acc": 0.58766, "loss_cls": 3.79842, "loss": 3.79842, "time": 2.35768} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.04576, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33797, "top5_acc": 0.61078, "loss_cls": 3.70842, "loss": 3.70842, "time": 0.82678} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.04573, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33062, "top5_acc": 0.58812, "loss_cls": 3.78585, "loss": 3.78585, "time": 0.81879} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.0457, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33203, "top5_acc": 0.58484, "loss_cls": 3.84945, "loss": 3.84945, "time": 0.816} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.04568, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33609, "top5_acc": 0.59516, "loss_cls": 3.796, "loss": 3.796, "time": 0.81409} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.04565, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33688, "top5_acc": 0.59234, "loss_cls": 3.78051, "loss": 3.78051, "time": 0.81587} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.04562, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33, "top5_acc": 0.595, "loss_cls": 3.75735, "loss": 3.75735, "time": 0.81679} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.04559, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32312, "top5_acc": 0.58797, "loss_cls": 3.86499, "loss": 3.86499, "time": 0.82479} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.04557, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33516, "top5_acc": 0.59984, "loss_cls": 3.79423, "loss": 3.79423, "time": 0.82556} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.04554, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32969, "top5_acc": 0.58875, "loss_cls": 3.7943, "loss": 3.7943, "time": 0.82016} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.04551, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32781, "top5_acc": 0.5925, "loss_cls": 3.81864, "loss": 3.81864, "time": 0.82168} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.04548, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33094, "top5_acc": 0.59125, "loss_cls": 3.79829, "loss": 3.79829, "time": 0.81322} +{"mode": "train", "epoch": 80, "iter": 1300, "lr": 0.04545, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33875, "top5_acc": 0.59781, "loss_cls": 3.77908, "loss": 3.77908, "time": 0.82625} +{"mode": "train", "epoch": 80, "iter": 1400, "lr": 0.04543, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3325, "top5_acc": 0.59328, "loss_cls": 3.80596, "loss": 3.80596, "time": 0.81491} +{"mode": "train", "epoch": 80, "iter": 1500, "lr": 0.0454, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33688, "top5_acc": 0.59672, "loss_cls": 3.7738, "loss": 3.7738, "time": 0.81598} +{"mode": "train", "epoch": 80, "iter": 1600, "lr": 0.04537, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33703, "top5_acc": 0.59969, "loss_cls": 3.76035, "loss": 3.76035, "time": 0.82072} +{"mode": "train", "epoch": 80, "iter": 1700, "lr": 0.04534, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32484, "top5_acc": 0.57922, "loss_cls": 3.85049, "loss": 3.85049, "time": 0.82231} +{"mode": "train", "epoch": 80, "iter": 1800, "lr": 0.04532, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33141, "top5_acc": 0.58953, "loss_cls": 3.79529, "loss": 3.79529, "time": 0.82362} +{"mode": "train", "epoch": 80, "iter": 1900, "lr": 0.04529, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33734, "top5_acc": 0.59703, "loss_cls": 3.77059, "loss": 3.77059, "time": 0.81407} +{"mode": "train", "epoch": 80, "iter": 2000, "lr": 0.04526, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33062, "top5_acc": 0.59172, "loss_cls": 3.81227, "loss": 3.81227, "time": 0.81816} +{"mode": "train", "epoch": 80, "iter": 2100, "lr": 0.04523, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34266, "top5_acc": 0.59297, "loss_cls": 3.76527, "loss": 3.76527, "time": 0.82071} +{"mode": "train", "epoch": 80, "iter": 2200, "lr": 0.0452, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32781, "top5_acc": 0.59391, "loss_cls": 3.80102, "loss": 3.80102, "time": 0.81898} +{"mode": "train", "epoch": 80, "iter": 2300, "lr": 0.04518, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33422, "top5_acc": 0.59641, "loss_cls": 3.80766, "loss": 3.80766, "time": 0.81663} +{"mode": "train", "epoch": 80, "iter": 2400, "lr": 0.04515, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32578, "top5_acc": 0.58219, "loss_cls": 3.84263, "loss": 3.84263, "time": 0.8127} +{"mode": "train", "epoch": 80, "iter": 2500, "lr": 0.04512, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33312, "top5_acc": 0.60062, "loss_cls": 3.78522, "loss": 3.78522, "time": 0.812} +{"mode": "train", "epoch": 80, "iter": 2600, "lr": 0.04509, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32594, "top5_acc": 0.58062, "loss_cls": 3.84313, "loss": 3.84313, "time": 0.81435} +{"mode": "train", "epoch": 80, "iter": 2700, "lr": 0.04506, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32984, "top5_acc": 0.59078, "loss_cls": 3.82151, "loss": 3.82151, "time": 0.81696} +{"mode": "train", "epoch": 80, "iter": 2800, "lr": 0.04504, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32719, "top5_acc": 0.58625, "loss_cls": 3.83972, "loss": 3.83972, "time": 0.82136} +{"mode": "train", "epoch": 80, "iter": 2900, "lr": 0.04501, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32547, "top5_acc": 0.58609, "loss_cls": 3.81964, "loss": 3.81964, "time": 0.81565} +{"mode": "train", "epoch": 80, "iter": 3000, "lr": 0.04498, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32938, "top5_acc": 0.59641, "loss_cls": 3.75932, "loss": 3.75932, "time": 0.81312} +{"mode": "train", "epoch": 80, "iter": 3100, "lr": 0.04495, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33656, "top5_acc": 0.59531, "loss_cls": 3.7695, "loss": 3.7695, "time": 0.81761} +{"mode": "train", "epoch": 80, "iter": 3200, "lr": 0.04493, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32812, "top5_acc": 0.58, "loss_cls": 3.85356, "loss": 3.85356, "time": 0.8207} +{"mode": "train", "epoch": 80, "iter": 3300, "lr": 0.0449, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32719, "top5_acc": 0.58094, "loss_cls": 3.82488, "loss": 3.82488, "time": 0.81488} +{"mode": "train", "epoch": 80, "iter": 3400, "lr": 0.04487, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32891, "top5_acc": 0.59359, "loss_cls": 3.82042, "loss": 3.82042, "time": 0.81845} +{"mode": "train", "epoch": 80, "iter": 3500, "lr": 0.04484, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32, "top5_acc": 0.58031, "loss_cls": 3.8676, "loss": 3.8676, "time": 0.824} +{"mode": "train", "epoch": 80, "iter": 3600, "lr": 0.04481, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33172, "top5_acc": 0.58938, "loss_cls": 3.80256, "loss": 3.80256, "time": 0.81757} +{"mode": "train", "epoch": 80, "iter": 3700, "lr": 0.04479, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33375, "top5_acc": 0.58391, "loss_cls": 3.82653, "loss": 3.82653, "time": 0.81879} +{"mode": "val", "epoch": 80, "iter": 309, "lr": 0.04477, "top1_acc": 0.27215, "top5_acc": 0.51446, "mean_class_accuracy": 0.27173} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.04475, "memory": 15990, "data_time": 1.36675, "top1_acc": 0.34109, "top5_acc": 0.59969, "loss_cls": 3.75392, "loss": 3.75392, "time": 2.34299} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.04472, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33375, "top5_acc": 0.58922, "loss_cls": 3.78433, "loss": 3.78433, "time": 0.82785} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.04469, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33422, "top5_acc": 0.59766, "loss_cls": 3.78251, "loss": 3.78251, "time": 0.82775} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.04466, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33109, "top5_acc": 0.60234, "loss_cls": 3.75382, "loss": 3.75382, "time": 0.81382} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.04463, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33, "top5_acc": 0.59375, "loss_cls": 3.79276, "loss": 3.79276, "time": 0.81848} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.04461, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33594, "top5_acc": 0.58859, "loss_cls": 3.79419, "loss": 3.79419, "time": 0.81829} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.04458, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33203, "top5_acc": 0.585, "loss_cls": 3.79925, "loss": 3.79925, "time": 0.81461} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.04455, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33391, "top5_acc": 0.59734, "loss_cls": 3.76566, "loss": 3.76566, "time": 0.82258} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.04452, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34172, "top5_acc": 0.60484, "loss_cls": 3.75794, "loss": 3.75794, "time": 0.81775} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.0445, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33516, "top5_acc": 0.59219, "loss_cls": 3.78781, "loss": 3.78781, "time": 0.82138} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.04447, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34078, "top5_acc": 0.59375, "loss_cls": 3.78163, "loss": 3.78163, "time": 0.81453} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.04444, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34172, "top5_acc": 0.60125, "loss_cls": 3.76196, "loss": 3.76196, "time": 0.81842} +{"mode": "train", "epoch": 81, "iter": 1300, "lr": 0.04441, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33469, "top5_acc": 0.58797, "loss_cls": 3.81611, "loss": 3.81611, "time": 0.81543} +{"mode": "train", "epoch": 81, "iter": 1400, "lr": 0.04438, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32812, "top5_acc": 0.58297, "loss_cls": 3.85682, "loss": 3.85682, "time": 0.81461} +{"mode": "train", "epoch": 81, "iter": 1500, "lr": 0.04436, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33203, "top5_acc": 0.59484, "loss_cls": 3.7977, "loss": 3.7977, "time": 0.81891} +{"mode": "train", "epoch": 81, "iter": 1600, "lr": 0.04433, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32734, "top5_acc": 0.58562, "loss_cls": 3.81076, "loss": 3.81076, "time": 0.82188} +{"mode": "train", "epoch": 81, "iter": 1700, "lr": 0.0443, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33188, "top5_acc": 0.58953, "loss_cls": 3.81099, "loss": 3.81099, "time": 0.81823} +{"mode": "train", "epoch": 81, "iter": 1800, "lr": 0.04427, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32578, "top5_acc": 0.57828, "loss_cls": 3.85713, "loss": 3.85713, "time": 0.8293} +{"mode": "train", "epoch": 81, "iter": 1900, "lr": 0.04425, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33156, "top5_acc": 0.5875, "loss_cls": 3.81263, "loss": 3.81263, "time": 0.81905} +{"mode": "train", "epoch": 81, "iter": 2000, "lr": 0.04422, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32969, "top5_acc": 0.59359, "loss_cls": 3.79341, "loss": 3.79341, "time": 0.81794} +{"mode": "train", "epoch": 81, "iter": 2100, "lr": 0.04419, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32906, "top5_acc": 0.59453, "loss_cls": 3.79839, "loss": 3.79839, "time": 0.81608} +{"mode": "train", "epoch": 81, "iter": 2200, "lr": 0.04416, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33234, "top5_acc": 0.58906, "loss_cls": 3.83878, "loss": 3.83878, "time": 0.81507} +{"mode": "train", "epoch": 81, "iter": 2300, "lr": 0.04413, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33922, "top5_acc": 0.6, "loss_cls": 3.73716, "loss": 3.73716, "time": 0.81745} +{"mode": "train", "epoch": 81, "iter": 2400, "lr": 0.04411, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34031, "top5_acc": 0.59547, "loss_cls": 3.77187, "loss": 3.77187, "time": 0.81679} +{"mode": "train", "epoch": 81, "iter": 2500, "lr": 0.04408, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33391, "top5_acc": 0.59047, "loss_cls": 3.78243, "loss": 3.78243, "time": 0.8214} +{"mode": "train", "epoch": 81, "iter": 2600, "lr": 0.04405, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33031, "top5_acc": 0.60078, "loss_cls": 3.76065, "loss": 3.76065, "time": 0.81655} +{"mode": "train", "epoch": 81, "iter": 2700, "lr": 0.04402, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32969, "top5_acc": 0.58875, "loss_cls": 3.79268, "loss": 3.79268, "time": 0.81906} +{"mode": "train", "epoch": 81, "iter": 2800, "lr": 0.044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33688, "top5_acc": 0.59234, "loss_cls": 3.78855, "loss": 3.78855, "time": 0.81485} +{"mode": "train", "epoch": 81, "iter": 2900, "lr": 0.04397, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32516, "top5_acc": 0.59609, "loss_cls": 3.79526, "loss": 3.79526, "time": 0.81901} +{"mode": "train", "epoch": 81, "iter": 3000, "lr": 0.04394, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32469, "top5_acc": 0.58266, "loss_cls": 3.8015, "loss": 3.8015, "time": 0.81945} +{"mode": "train", "epoch": 81, "iter": 3100, "lr": 0.04391, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32938, "top5_acc": 0.5975, "loss_cls": 3.78408, "loss": 3.78408, "time": 0.81356} +{"mode": "train", "epoch": 81, "iter": 3200, "lr": 0.04389, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33797, "top5_acc": 0.58922, "loss_cls": 3.78498, "loss": 3.78498, "time": 0.82068} +{"mode": "train", "epoch": 81, "iter": 3300, "lr": 0.04386, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34125, "top5_acc": 0.59531, "loss_cls": 3.78627, "loss": 3.78627, "time": 0.81879} +{"mode": "train", "epoch": 81, "iter": 3400, "lr": 0.04383, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32766, "top5_acc": 0.58109, "loss_cls": 3.82091, "loss": 3.82091, "time": 0.816} +{"mode": "train", "epoch": 81, "iter": 3500, "lr": 0.0438, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33609, "top5_acc": 0.60031, "loss_cls": 3.78272, "loss": 3.78272, "time": 0.81747} +{"mode": "train", "epoch": 81, "iter": 3600, "lr": 0.04377, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32359, "top5_acc": 0.57516, "loss_cls": 3.855, "loss": 3.855, "time": 0.81852} +{"mode": "train", "epoch": 81, "iter": 3700, "lr": 0.04375, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.335, "top5_acc": 0.59484, "loss_cls": 3.78476, "loss": 3.78476, "time": 0.81298} +{"mode": "val", "epoch": 81, "iter": 309, "lr": 0.04373, "top1_acc": 0.2685, "top5_acc": 0.52029, "mean_class_accuracy": 0.26831} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.04371, "memory": 15990, "data_time": 1.34629, "top1_acc": 0.33859, "top5_acc": 0.60453, "loss_cls": 3.73265, "loss": 3.73265, "time": 2.32791} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.04368, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34047, "top5_acc": 0.59969, "loss_cls": 3.77043, "loss": 3.77043, "time": 0.82155} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.04365, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34266, "top5_acc": 0.6025, "loss_cls": 3.73659, "loss": 3.73659, "time": 0.82605} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.04362, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34203, "top5_acc": 0.60406, "loss_cls": 3.73751, "loss": 3.73751, "time": 0.82262} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.04359, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33062, "top5_acc": 0.58719, "loss_cls": 3.80859, "loss": 3.80859, "time": 0.8133} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.04357, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33531, "top5_acc": 0.58688, "loss_cls": 3.7911, "loss": 3.7911, "time": 0.81515} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.04354, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34312, "top5_acc": 0.6025, "loss_cls": 3.73805, "loss": 3.73805, "time": 0.81964} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.04351, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33578, "top5_acc": 0.59953, "loss_cls": 3.73894, "loss": 3.73894, "time": 0.82847} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.04348, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34094, "top5_acc": 0.59516, "loss_cls": 3.78206, "loss": 3.78206, "time": 0.82162} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.04346, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33828, "top5_acc": 0.58641, "loss_cls": 3.78316, "loss": 3.78316, "time": 0.81733} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.04343, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33625, "top5_acc": 0.59344, "loss_cls": 3.77836, "loss": 3.77836, "time": 0.81631} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.0434, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32906, "top5_acc": 0.59875, "loss_cls": 3.78592, "loss": 3.78592, "time": 0.81313} +{"mode": "train", "epoch": 82, "iter": 1300, "lr": 0.04337, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33719, "top5_acc": 0.60297, "loss_cls": 3.75301, "loss": 3.75301, "time": 0.82026} +{"mode": "train", "epoch": 82, "iter": 1400, "lr": 0.04335, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32844, "top5_acc": 0.59172, "loss_cls": 3.78117, "loss": 3.78117, "time": 0.82126} +{"mode": "train", "epoch": 82, "iter": 1500, "lr": 0.04332, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32594, "top5_acc": 0.58766, "loss_cls": 3.80776, "loss": 3.80776, "time": 0.81824} +{"mode": "train", "epoch": 82, "iter": 1600, "lr": 0.04329, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32875, "top5_acc": 0.57938, "loss_cls": 3.86042, "loss": 3.86042, "time": 0.8249} +{"mode": "train", "epoch": 82, "iter": 1700, "lr": 0.04326, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33, "top5_acc": 0.58766, "loss_cls": 3.8389, "loss": 3.8389, "time": 0.8227} +{"mode": "train", "epoch": 82, "iter": 1800, "lr": 0.04323, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34516, "top5_acc": 0.59312, "loss_cls": 3.75719, "loss": 3.75719, "time": 0.8241} +{"mode": "train", "epoch": 82, "iter": 1900, "lr": 0.04321, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3325, "top5_acc": 0.59797, "loss_cls": 3.77612, "loss": 3.77612, "time": 0.82539} +{"mode": "train", "epoch": 82, "iter": 2000, "lr": 0.04318, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33906, "top5_acc": 0.60531, "loss_cls": 3.76124, "loss": 3.76124, "time": 0.82175} +{"mode": "train", "epoch": 82, "iter": 2100, "lr": 0.04315, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33953, "top5_acc": 0.60203, "loss_cls": 3.73048, "loss": 3.73048, "time": 0.82038} +{"mode": "train", "epoch": 82, "iter": 2200, "lr": 0.04312, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33125, "top5_acc": 0.58812, "loss_cls": 3.79296, "loss": 3.79296, "time": 0.81404} +{"mode": "train", "epoch": 82, "iter": 2300, "lr": 0.0431, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34, "top5_acc": 0.59875, "loss_cls": 3.79308, "loss": 3.79308, "time": 0.81926} +{"mode": "train", "epoch": 82, "iter": 2400, "lr": 0.04307, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33734, "top5_acc": 0.59844, "loss_cls": 3.76178, "loss": 3.76178, "time": 0.81849} +{"mode": "train", "epoch": 82, "iter": 2500, "lr": 0.04304, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33156, "top5_acc": 0.5925, "loss_cls": 3.78726, "loss": 3.78726, "time": 0.81597} +{"mode": "train", "epoch": 82, "iter": 2600, "lr": 0.04301, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34688, "top5_acc": 0.60344, "loss_cls": 3.73866, "loss": 3.73866, "time": 0.81857} +{"mode": "train", "epoch": 82, "iter": 2700, "lr": 0.04299, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33375, "top5_acc": 0.60625, "loss_cls": 3.77951, "loss": 3.77951, "time": 0.81782} +{"mode": "train", "epoch": 82, "iter": 2800, "lr": 0.04296, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3375, "top5_acc": 0.59625, "loss_cls": 3.78395, "loss": 3.78395, "time": 0.81379} +{"mode": "train", "epoch": 82, "iter": 2900, "lr": 0.04293, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33125, "top5_acc": 0.59688, "loss_cls": 3.78632, "loss": 3.78632, "time": 0.81128} +{"mode": "train", "epoch": 82, "iter": 3000, "lr": 0.0429, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32453, "top5_acc": 0.58141, "loss_cls": 3.86403, "loss": 3.86403, "time": 0.81373} +{"mode": "train", "epoch": 82, "iter": 3100, "lr": 0.04287, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34141, "top5_acc": 0.59422, "loss_cls": 3.79058, "loss": 3.79058, "time": 0.81366} +{"mode": "train", "epoch": 82, "iter": 3200, "lr": 0.04285, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33281, "top5_acc": 0.6, "loss_cls": 3.77769, "loss": 3.77769, "time": 0.8201} +{"mode": "train", "epoch": 82, "iter": 3300, "lr": 0.04282, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33047, "top5_acc": 0.5875, "loss_cls": 3.8282, "loss": 3.8282, "time": 0.81289} +{"mode": "train", "epoch": 82, "iter": 3400, "lr": 0.04279, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32938, "top5_acc": 0.58922, "loss_cls": 3.79544, "loss": 3.79544, "time": 0.81743} +{"mode": "train", "epoch": 82, "iter": 3500, "lr": 0.04276, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33875, "top5_acc": 0.59016, "loss_cls": 3.81313, "loss": 3.81313, "time": 0.81315} +{"mode": "train", "epoch": 82, "iter": 3600, "lr": 0.04274, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33594, "top5_acc": 0.59078, "loss_cls": 3.79609, "loss": 3.79609, "time": 0.8153} +{"mode": "train", "epoch": 82, "iter": 3700, "lr": 0.04271, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33688, "top5_acc": 0.59984, "loss_cls": 3.7519, "loss": 3.7519, "time": 0.81713} +{"mode": "val", "epoch": 82, "iter": 309, "lr": 0.0427, "top1_acc": 0.26728, "top5_acc": 0.51856, "mean_class_accuracy": 0.26715} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.04267, "memory": 15990, "data_time": 1.36321, "top1_acc": 0.3425, "top5_acc": 0.59859, "loss_cls": 3.74976, "loss": 3.74976, "time": 2.34174} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.04264, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33594, "top5_acc": 0.60078, "loss_cls": 3.73487, "loss": 3.73487, "time": 0.83175} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.04261, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3375, "top5_acc": 0.59984, "loss_cls": 3.76867, "loss": 3.76867, "time": 0.84173} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.04259, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35, "top5_acc": 0.59844, "loss_cls": 3.72775, "loss": 3.72775, "time": 0.83695} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.04256, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34203, "top5_acc": 0.60438, "loss_cls": 3.73577, "loss": 3.73577, "time": 0.83561} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.04253, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34469, "top5_acc": 0.59922, "loss_cls": 3.75918, "loss": 3.75918, "time": 0.84066} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.0425, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34188, "top5_acc": 0.59047, "loss_cls": 3.77634, "loss": 3.77634, "time": 0.83777} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.04247, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34781, "top5_acc": 0.60484, "loss_cls": 3.73835, "loss": 3.73835, "time": 0.83348} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.04245, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34016, "top5_acc": 0.60734, "loss_cls": 3.70661, "loss": 3.70661, "time": 0.82216} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.04242, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34422, "top5_acc": 0.60562, "loss_cls": 3.70988, "loss": 3.70988, "time": 0.8277} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.04239, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33531, "top5_acc": 0.60078, "loss_cls": 3.77089, "loss": 3.77089, "time": 0.83114} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.04236, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33578, "top5_acc": 0.59578, "loss_cls": 3.78238, "loss": 3.78238, "time": 0.83373} +{"mode": "train", "epoch": 83, "iter": 1300, "lr": 0.04234, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33797, "top5_acc": 0.59328, "loss_cls": 3.75391, "loss": 3.75391, "time": 0.83472} +{"mode": "train", "epoch": 83, "iter": 1400, "lr": 0.04231, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33266, "top5_acc": 0.59984, "loss_cls": 3.76646, "loss": 3.76646, "time": 0.83366} +{"mode": "train", "epoch": 83, "iter": 1500, "lr": 0.04228, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33422, "top5_acc": 0.58719, "loss_cls": 3.77874, "loss": 3.77874, "time": 0.83426} +{"mode": "train", "epoch": 83, "iter": 1600, "lr": 0.04225, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34219, "top5_acc": 0.59906, "loss_cls": 3.74614, "loss": 3.74614, "time": 0.83483} +{"mode": "train", "epoch": 83, "iter": 1700, "lr": 0.04223, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33297, "top5_acc": 0.59969, "loss_cls": 3.76531, "loss": 3.76531, "time": 0.82689} +{"mode": "train", "epoch": 83, "iter": 1800, "lr": 0.0422, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33953, "top5_acc": 0.59906, "loss_cls": 3.7418, "loss": 3.7418, "time": 0.82803} +{"mode": "train", "epoch": 83, "iter": 1900, "lr": 0.04217, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34297, "top5_acc": 0.59, "loss_cls": 3.79457, "loss": 3.79457, "time": 0.8381} +{"mode": "train", "epoch": 83, "iter": 2000, "lr": 0.04214, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33328, "top5_acc": 0.59234, "loss_cls": 3.77771, "loss": 3.77771, "time": 0.83084} +{"mode": "train", "epoch": 83, "iter": 2100, "lr": 0.04212, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32812, "top5_acc": 0.58812, "loss_cls": 3.79978, "loss": 3.79978, "time": 0.83149} +{"mode": "train", "epoch": 83, "iter": 2200, "lr": 0.04209, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33938, "top5_acc": 0.60078, "loss_cls": 3.74915, "loss": 3.74915, "time": 0.82684} +{"mode": "train", "epoch": 83, "iter": 2300, "lr": 0.04206, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34609, "top5_acc": 0.59688, "loss_cls": 3.74001, "loss": 3.74001, "time": 0.83422} +{"mode": "train", "epoch": 83, "iter": 2400, "lr": 0.04203, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.3375, "top5_acc": 0.5975, "loss_cls": 3.76337, "loss": 3.76337, "time": 0.8263} +{"mode": "train", "epoch": 83, "iter": 2500, "lr": 0.04201, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33391, "top5_acc": 0.59547, "loss_cls": 3.76243, "loss": 3.76243, "time": 0.82848} +{"mode": "train", "epoch": 83, "iter": 2600, "lr": 0.04198, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33078, "top5_acc": 0.59859, "loss_cls": 3.78805, "loss": 3.78805, "time": 0.83466} +{"mode": "train", "epoch": 83, "iter": 2700, "lr": 0.04195, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33422, "top5_acc": 0.59938, "loss_cls": 3.7648, "loss": 3.7648, "time": 0.83308} +{"mode": "train", "epoch": 83, "iter": 2800, "lr": 0.04192, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33031, "top5_acc": 0.58547, "loss_cls": 3.81135, "loss": 3.81135, "time": 0.82944} +{"mode": "train", "epoch": 83, "iter": 2900, "lr": 0.0419, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34703, "top5_acc": 0.60547, "loss_cls": 3.72316, "loss": 3.72316, "time": 0.83219} +{"mode": "train", "epoch": 83, "iter": 3000, "lr": 0.04187, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33844, "top5_acc": 0.58516, "loss_cls": 3.80583, "loss": 3.80583, "time": 0.82602} +{"mode": "train", "epoch": 83, "iter": 3100, "lr": 0.04184, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3375, "top5_acc": 0.59406, "loss_cls": 3.78069, "loss": 3.78069, "time": 0.82094} +{"mode": "train", "epoch": 83, "iter": 3200, "lr": 0.04181, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35062, "top5_acc": 0.605, "loss_cls": 3.72971, "loss": 3.72971, "time": 0.81808} +{"mode": "train", "epoch": 83, "iter": 3300, "lr": 0.04178, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33797, "top5_acc": 0.59406, "loss_cls": 3.77884, "loss": 3.77884, "time": 0.81246} +{"mode": "train", "epoch": 83, "iter": 3400, "lr": 0.04176, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32547, "top5_acc": 0.58344, "loss_cls": 3.84232, "loss": 3.84232, "time": 0.81405} +{"mode": "train", "epoch": 83, "iter": 3500, "lr": 0.04173, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33203, "top5_acc": 0.59375, "loss_cls": 3.7806, "loss": 3.7806, "time": 0.8181} +{"mode": "train", "epoch": 83, "iter": 3600, "lr": 0.0417, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34094, "top5_acc": 0.59516, "loss_cls": 3.81172, "loss": 3.81172, "time": 0.81773} +{"mode": "train", "epoch": 83, "iter": 3700, "lr": 0.04167, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33266, "top5_acc": 0.58891, "loss_cls": 3.80834, "loss": 3.80834, "time": 0.81724} +{"mode": "val", "epoch": 83, "iter": 309, "lr": 0.04166, "top1_acc": 0.28866, "top5_acc": 0.53766, "mean_class_accuracy": 0.28843} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.04163, "memory": 15990, "data_time": 1.33662, "top1_acc": 0.34891, "top5_acc": 0.60391, "loss_cls": 3.71758, "loss": 3.71758, "time": 2.31772} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.04161, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35125, "top5_acc": 0.6025, "loss_cls": 3.70727, "loss": 3.70727, "time": 0.82629} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.04158, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34828, "top5_acc": 0.60516, "loss_cls": 3.71936, "loss": 3.71936, "time": 0.82029} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.04155, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34578, "top5_acc": 0.59594, "loss_cls": 3.74291, "loss": 3.74291, "time": 0.81728} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.04152, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33375, "top5_acc": 0.59203, "loss_cls": 3.78668, "loss": 3.78668, "time": 0.8161} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.0415, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34234, "top5_acc": 0.59375, "loss_cls": 3.77489, "loss": 3.77489, "time": 0.81621} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.04147, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34297, "top5_acc": 0.60391, "loss_cls": 3.74505, "loss": 3.74505, "time": 0.82479} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.04144, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33391, "top5_acc": 0.59797, "loss_cls": 3.76763, "loss": 3.76763, "time": 0.819} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.04141, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34359, "top5_acc": 0.6025, "loss_cls": 3.75543, "loss": 3.75543, "time": 0.82005} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.04139, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33469, "top5_acc": 0.59547, "loss_cls": 3.76389, "loss": 3.76389, "time": 0.82033} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.04136, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35344, "top5_acc": 0.60984, "loss_cls": 3.6972, "loss": 3.6972, "time": 0.81708} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.04133, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3325, "top5_acc": 0.59672, "loss_cls": 3.77, "loss": 3.77, "time": 0.81763} +{"mode": "train", "epoch": 84, "iter": 1300, "lr": 0.0413, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33406, "top5_acc": 0.59781, "loss_cls": 3.76483, "loss": 3.76483, "time": 0.81645} +{"mode": "train", "epoch": 84, "iter": 1400, "lr": 0.04128, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34031, "top5_acc": 0.59375, "loss_cls": 3.75566, "loss": 3.75566, "time": 0.81767} +{"mode": "train", "epoch": 84, "iter": 1500, "lr": 0.04125, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33219, "top5_acc": 0.59375, "loss_cls": 3.77153, "loss": 3.77153, "time": 0.82304} +{"mode": "train", "epoch": 84, "iter": 1600, "lr": 0.04122, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34438, "top5_acc": 0.60297, "loss_cls": 3.71199, "loss": 3.71199, "time": 0.8198} +{"mode": "train", "epoch": 84, "iter": 1700, "lr": 0.04119, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34219, "top5_acc": 0.59719, "loss_cls": 3.77294, "loss": 3.77294, "time": 0.82494} +{"mode": "train", "epoch": 84, "iter": 1800, "lr": 0.04117, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33547, "top5_acc": 0.60078, "loss_cls": 3.75866, "loss": 3.75866, "time": 0.83022} +{"mode": "train", "epoch": 84, "iter": 1900, "lr": 0.04114, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34047, "top5_acc": 0.59938, "loss_cls": 3.77336, "loss": 3.77336, "time": 0.822} +{"mode": "train", "epoch": 84, "iter": 2000, "lr": 0.04111, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33359, "top5_acc": 0.59594, "loss_cls": 3.78267, "loss": 3.78267, "time": 0.81853} +{"mode": "train", "epoch": 84, "iter": 2100, "lr": 0.04108, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32375, "top5_acc": 0.59, "loss_cls": 3.81441, "loss": 3.81441, "time": 0.82101} +{"mode": "train", "epoch": 84, "iter": 2200, "lr": 0.04106, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33922, "top5_acc": 0.59984, "loss_cls": 3.75969, "loss": 3.75969, "time": 0.81543} +{"mode": "train", "epoch": 84, "iter": 2300, "lr": 0.04103, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32984, "top5_acc": 0.59, "loss_cls": 3.81781, "loss": 3.81781, "time": 0.81528} +{"mode": "train", "epoch": 84, "iter": 2400, "lr": 0.041, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34328, "top5_acc": 0.60516, "loss_cls": 3.7142, "loss": 3.7142, "time": 0.82082} +{"mode": "train", "epoch": 84, "iter": 2500, "lr": 0.04097, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33766, "top5_acc": 0.59438, "loss_cls": 3.76351, "loss": 3.76351, "time": 0.81649} +{"mode": "train", "epoch": 84, "iter": 2600, "lr": 0.04095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34734, "top5_acc": 0.60219, "loss_cls": 3.73024, "loss": 3.73024, "time": 0.81533} +{"mode": "train", "epoch": 84, "iter": 2700, "lr": 0.04092, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34234, "top5_acc": 0.60594, "loss_cls": 3.7283, "loss": 3.7283, "time": 0.81968} +{"mode": "train", "epoch": 84, "iter": 2800, "lr": 0.04089, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33172, "top5_acc": 0.59266, "loss_cls": 3.77039, "loss": 3.77039, "time": 0.81824} +{"mode": "train", "epoch": 84, "iter": 2900, "lr": 0.04086, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32906, "top5_acc": 0.59203, "loss_cls": 3.80193, "loss": 3.80193, "time": 0.8183} +{"mode": "train", "epoch": 84, "iter": 3000, "lr": 0.04084, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34078, "top5_acc": 0.59719, "loss_cls": 3.74776, "loss": 3.74776, "time": 0.81851} +{"mode": "train", "epoch": 84, "iter": 3100, "lr": 0.04081, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34078, "top5_acc": 0.59344, "loss_cls": 3.76561, "loss": 3.76561, "time": 0.81607} +{"mode": "train", "epoch": 84, "iter": 3200, "lr": 0.04078, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33469, "top5_acc": 0.59406, "loss_cls": 3.78802, "loss": 3.78802, "time": 0.81865} +{"mode": "train", "epoch": 84, "iter": 3300, "lr": 0.04075, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34797, "top5_acc": 0.61656, "loss_cls": 3.68599, "loss": 3.68599, "time": 0.81544} +{"mode": "train", "epoch": 84, "iter": 3400, "lr": 0.04073, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33609, "top5_acc": 0.58906, "loss_cls": 3.8055, "loss": 3.8055, "time": 0.81581} +{"mode": "train", "epoch": 84, "iter": 3500, "lr": 0.0407, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33703, "top5_acc": 0.59906, "loss_cls": 3.77347, "loss": 3.77347, "time": 0.81906} +{"mode": "train", "epoch": 84, "iter": 3600, "lr": 0.04067, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34906, "top5_acc": 0.60391, "loss_cls": 3.72417, "loss": 3.72417, "time": 0.81515} +{"mode": "train", "epoch": 84, "iter": 3700, "lr": 0.04064, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33859, "top5_acc": 0.60125, "loss_cls": 3.74873, "loss": 3.74873, "time": 0.82086} +{"mode": "val", "epoch": 84, "iter": 309, "lr": 0.04063, "top1_acc": 0.26511, "top5_acc": 0.50717, "mean_class_accuracy": 0.26493} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.0406, "memory": 15990, "data_time": 1.30951, "top1_acc": 0.35844, "top5_acc": 0.61391, "loss_cls": 3.66135, "loss": 3.66135, "time": 2.29028} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.04058, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34359, "top5_acc": 0.61219, "loss_cls": 3.65636, "loss": 3.65636, "time": 0.8214} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.04055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34875, "top5_acc": 0.60516, "loss_cls": 3.69419, "loss": 3.69419, "time": 0.82108} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.04052, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34531, "top5_acc": 0.60344, "loss_cls": 3.70568, "loss": 3.70568, "time": 0.8147} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.04049, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35219, "top5_acc": 0.60953, "loss_cls": 3.69437, "loss": 3.69437, "time": 0.82129} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.04047, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34328, "top5_acc": 0.60344, "loss_cls": 3.7221, "loss": 3.7221, "time": 0.82321} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.04044, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35203, "top5_acc": 0.60484, "loss_cls": 3.67175, "loss": 3.67175, "time": 0.82354} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.04041, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34203, "top5_acc": 0.59328, "loss_cls": 3.73801, "loss": 3.73801, "time": 0.81947} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.04038, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34344, "top5_acc": 0.59391, "loss_cls": 3.76721, "loss": 3.76721, "time": 0.81616} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.04036, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33562, "top5_acc": 0.60734, "loss_cls": 3.74121, "loss": 3.74121, "time": 0.82021} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.04033, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35016, "top5_acc": 0.59688, "loss_cls": 3.72673, "loss": 3.72673, "time": 0.81434} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.0403, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34672, "top5_acc": 0.60188, "loss_cls": 3.7396, "loss": 3.7396, "time": 0.81966} +{"mode": "train", "epoch": 85, "iter": 1300, "lr": 0.04027, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33734, "top5_acc": 0.59094, "loss_cls": 3.77875, "loss": 3.77875, "time": 0.81769} +{"mode": "train", "epoch": 85, "iter": 1400, "lr": 0.04025, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34406, "top5_acc": 0.60344, "loss_cls": 3.75921, "loss": 3.75921, "time": 0.81773} +{"mode": "train", "epoch": 85, "iter": 1500, "lr": 0.04022, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34812, "top5_acc": 0.60453, "loss_cls": 3.69445, "loss": 3.69445, "time": 0.83051} +{"mode": "train", "epoch": 85, "iter": 1600, "lr": 0.04019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34703, "top5_acc": 0.60172, "loss_cls": 3.72371, "loss": 3.72371, "time": 0.81708} +{"mode": "train", "epoch": 85, "iter": 1700, "lr": 0.04016, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33906, "top5_acc": 0.59625, "loss_cls": 3.74961, "loss": 3.74961, "time": 0.82284} +{"mode": "train", "epoch": 85, "iter": 1800, "lr": 0.04014, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34438, "top5_acc": 0.59984, "loss_cls": 3.7633, "loss": 3.7633, "time": 0.82618} +{"mode": "train", "epoch": 85, "iter": 1900, "lr": 0.04011, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33531, "top5_acc": 0.59781, "loss_cls": 3.77464, "loss": 3.77464, "time": 0.82845} +{"mode": "train", "epoch": 85, "iter": 2000, "lr": 0.04008, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33703, "top5_acc": 0.59969, "loss_cls": 3.73091, "loss": 3.73091, "time": 0.81771} +{"mode": "train", "epoch": 85, "iter": 2100, "lr": 0.04006, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33266, "top5_acc": 0.59328, "loss_cls": 3.77372, "loss": 3.77372, "time": 0.815} +{"mode": "train", "epoch": 85, "iter": 2200, "lr": 0.04003, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33062, "top5_acc": 0.60469, "loss_cls": 3.75443, "loss": 3.75443, "time": 0.82168} +{"mode": "train", "epoch": 85, "iter": 2300, "lr": 0.04, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34469, "top5_acc": 0.60391, "loss_cls": 3.72924, "loss": 3.72924, "time": 0.81067} +{"mode": "train", "epoch": 85, "iter": 2400, "lr": 0.03997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34125, "top5_acc": 0.60094, "loss_cls": 3.76355, "loss": 3.76355, "time": 0.81373} +{"mode": "train", "epoch": 85, "iter": 2500, "lr": 0.03995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33188, "top5_acc": 0.58281, "loss_cls": 3.822, "loss": 3.822, "time": 0.81841} +{"mode": "train", "epoch": 85, "iter": 2600, "lr": 0.03992, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34109, "top5_acc": 0.59344, "loss_cls": 3.76878, "loss": 3.76878, "time": 0.81125} +{"mode": "train", "epoch": 85, "iter": 2700, "lr": 0.03989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34109, "top5_acc": 0.60109, "loss_cls": 3.74907, "loss": 3.74907, "time": 0.8133} +{"mode": "train", "epoch": 85, "iter": 2800, "lr": 0.03986, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34453, "top5_acc": 0.60547, "loss_cls": 3.73247, "loss": 3.73247, "time": 0.81292} +{"mode": "train", "epoch": 85, "iter": 2900, "lr": 0.03984, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34297, "top5_acc": 0.59359, "loss_cls": 3.76647, "loss": 3.76647, "time": 0.81547} +{"mode": "train", "epoch": 85, "iter": 3000, "lr": 0.03981, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32656, "top5_acc": 0.59391, "loss_cls": 3.80907, "loss": 3.80907, "time": 0.82238} +{"mode": "train", "epoch": 85, "iter": 3100, "lr": 0.03978, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35, "top5_acc": 0.60609, "loss_cls": 3.71955, "loss": 3.71955, "time": 0.81957} +{"mode": "train", "epoch": 85, "iter": 3200, "lr": 0.03975, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33359, "top5_acc": 0.59391, "loss_cls": 3.76666, "loss": 3.76666, "time": 0.81584} +{"mode": "train", "epoch": 85, "iter": 3300, "lr": 0.03973, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34469, "top5_acc": 0.59625, "loss_cls": 3.73692, "loss": 3.73692, "time": 0.82002} +{"mode": "train", "epoch": 85, "iter": 3400, "lr": 0.0397, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34266, "top5_acc": 0.59609, "loss_cls": 3.77926, "loss": 3.77926, "time": 0.81603} +{"mode": "train", "epoch": 85, "iter": 3500, "lr": 0.03967, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34172, "top5_acc": 0.6025, "loss_cls": 3.74868, "loss": 3.74868, "time": 0.81795} +{"mode": "train", "epoch": 85, "iter": 3600, "lr": 0.03964, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33625, "top5_acc": 0.60594, "loss_cls": 3.74861, "loss": 3.74861, "time": 0.8161} +{"mode": "train", "epoch": 85, "iter": 3700, "lr": 0.03962, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35344, "top5_acc": 0.60688, "loss_cls": 3.706, "loss": 3.706, "time": 0.81529} +{"mode": "val", "epoch": 85, "iter": 309, "lr": 0.0396, "top1_acc": 0.28603, "top5_acc": 0.54186, "mean_class_accuracy": 0.28573} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.03958, "memory": 15990, "data_time": 1.36727, "top1_acc": 0.35422, "top5_acc": 0.62219, "loss_cls": 3.63363, "loss": 3.63363, "time": 2.36478} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.03955, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35609, "top5_acc": 0.60938, "loss_cls": 3.66338, "loss": 3.66338, "time": 0.82878} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.03952, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33891, "top5_acc": 0.61219, "loss_cls": 3.74498, "loss": 3.74498, "time": 0.82623} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.0395, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34422, "top5_acc": 0.60125, "loss_cls": 3.73618, "loss": 3.73618, "time": 0.8203} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.03947, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34469, "top5_acc": 0.59984, "loss_cls": 3.73551, "loss": 3.73551, "time": 0.82061} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.03944, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.345, "top5_acc": 0.61234, "loss_cls": 3.67553, "loss": 3.67553, "time": 0.81511} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.03941, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34797, "top5_acc": 0.60703, "loss_cls": 3.72173, "loss": 3.72173, "time": 0.81928} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.03939, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34469, "top5_acc": 0.61172, "loss_cls": 3.71247, "loss": 3.71247, "time": 0.81993} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.03936, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34203, "top5_acc": 0.60766, "loss_cls": 3.69911, "loss": 3.69911, "time": 0.82344} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.03933, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34594, "top5_acc": 0.60797, "loss_cls": 3.72337, "loss": 3.72337, "time": 0.81607} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.0393, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3375, "top5_acc": 0.60062, "loss_cls": 3.75128, "loss": 3.75128, "time": 0.81332} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.03928, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34109, "top5_acc": 0.60469, "loss_cls": 3.69703, "loss": 3.69703, "time": 0.82094} +{"mode": "train", "epoch": 86, "iter": 1300, "lr": 0.03925, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3475, "top5_acc": 0.60953, "loss_cls": 3.69892, "loss": 3.69892, "time": 0.8186} +{"mode": "train", "epoch": 86, "iter": 1400, "lr": 0.03922, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35703, "top5_acc": 0.62109, "loss_cls": 3.64121, "loss": 3.64121, "time": 0.81704} +{"mode": "train", "epoch": 86, "iter": 1500, "lr": 0.03919, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33609, "top5_acc": 0.59531, "loss_cls": 3.76983, "loss": 3.76983, "time": 0.81802} +{"mode": "train", "epoch": 86, "iter": 1600, "lr": 0.03917, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33594, "top5_acc": 0.59578, "loss_cls": 3.76014, "loss": 3.76014, "time": 0.81624} +{"mode": "train", "epoch": 86, "iter": 1700, "lr": 0.03914, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34406, "top5_acc": 0.60203, "loss_cls": 3.73086, "loss": 3.73086, "time": 0.81854} +{"mode": "train", "epoch": 86, "iter": 1800, "lr": 0.03911, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33156, "top5_acc": 0.59234, "loss_cls": 3.7433, "loss": 3.7433, "time": 0.82915} +{"mode": "train", "epoch": 86, "iter": 1900, "lr": 0.03909, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34531, "top5_acc": 0.60406, "loss_cls": 3.73665, "loss": 3.73665, "time": 0.81686} +{"mode": "train", "epoch": 86, "iter": 2000, "lr": 0.03906, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33625, "top5_acc": 0.60141, "loss_cls": 3.75474, "loss": 3.75474, "time": 0.82062} +{"mode": "train", "epoch": 86, "iter": 2100, "lr": 0.03903, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3525, "top5_acc": 0.60828, "loss_cls": 3.70457, "loss": 3.70457, "time": 0.82045} +{"mode": "train", "epoch": 86, "iter": 2200, "lr": 0.039, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34266, "top5_acc": 0.60141, "loss_cls": 3.74837, "loss": 3.74837, "time": 0.81541} +{"mode": "train", "epoch": 86, "iter": 2300, "lr": 0.03898, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34297, "top5_acc": 0.59969, "loss_cls": 3.7303, "loss": 3.7303, "time": 0.82072} +{"mode": "train", "epoch": 86, "iter": 2400, "lr": 0.03895, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34047, "top5_acc": 0.59469, "loss_cls": 3.76164, "loss": 3.76164, "time": 0.81583} +{"mode": "train", "epoch": 86, "iter": 2500, "lr": 0.03892, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34359, "top5_acc": 0.60484, "loss_cls": 3.7126, "loss": 3.7126, "time": 0.8182} +{"mode": "train", "epoch": 86, "iter": 2600, "lr": 0.03889, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34453, "top5_acc": 0.60375, "loss_cls": 3.71327, "loss": 3.71327, "time": 0.82151} +{"mode": "train", "epoch": 86, "iter": 2700, "lr": 0.03887, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34203, "top5_acc": 0.60344, "loss_cls": 3.74867, "loss": 3.74867, "time": 0.81661} +{"mode": "train", "epoch": 86, "iter": 2800, "lr": 0.03884, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33562, "top5_acc": 0.60266, "loss_cls": 3.72822, "loss": 3.72822, "time": 0.81657} +{"mode": "train", "epoch": 86, "iter": 2900, "lr": 0.03881, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35031, "top5_acc": 0.60688, "loss_cls": 3.70652, "loss": 3.70652, "time": 0.81627} +{"mode": "train", "epoch": 86, "iter": 3000, "lr": 0.03879, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33688, "top5_acc": 0.59156, "loss_cls": 3.7658, "loss": 3.7658, "time": 0.81531} +{"mode": "train", "epoch": 86, "iter": 3100, "lr": 0.03876, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34625, "top5_acc": 0.60391, "loss_cls": 3.72386, "loss": 3.72386, "time": 0.81607} +{"mode": "train", "epoch": 86, "iter": 3200, "lr": 0.03873, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34156, "top5_acc": 0.6, "loss_cls": 3.75432, "loss": 3.75432, "time": 0.81222} +{"mode": "train", "epoch": 86, "iter": 3300, "lr": 0.0387, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33781, "top5_acc": 0.60094, "loss_cls": 3.76671, "loss": 3.76671, "time": 0.81452} +{"mode": "train", "epoch": 86, "iter": 3400, "lr": 0.03868, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34781, "top5_acc": 0.60375, "loss_cls": 3.72673, "loss": 3.72673, "time": 0.81596} +{"mode": "train", "epoch": 86, "iter": 3500, "lr": 0.03865, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33875, "top5_acc": 0.59938, "loss_cls": 3.74254, "loss": 3.74254, "time": 0.82154} +{"mode": "train", "epoch": 86, "iter": 3600, "lr": 0.03862, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33969, "top5_acc": 0.59781, "loss_cls": 3.73922, "loss": 3.73922, "time": 0.81649} +{"mode": "train", "epoch": 86, "iter": 3700, "lr": 0.0386, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33312, "top5_acc": 0.59781, "loss_cls": 3.76403, "loss": 3.76403, "time": 0.81694} +{"mode": "val", "epoch": 86, "iter": 309, "lr": 0.03858, "top1_acc": 0.27888, "top5_acc": 0.5334, "mean_class_accuracy": 0.27877} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.03856, "memory": 15990, "data_time": 1.30146, "top1_acc": 0.35188, "top5_acc": 0.61922, "loss_cls": 3.64537, "loss": 3.64537, "time": 2.27999} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.03853, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35172, "top5_acc": 0.61438, "loss_cls": 3.66818, "loss": 3.66818, "time": 0.8217} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.0385, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33906, "top5_acc": 0.60281, "loss_cls": 3.729, "loss": 3.729, "time": 0.81428} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.03847, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35953, "top5_acc": 0.62172, "loss_cls": 3.62975, "loss": 3.62975, "time": 0.81969} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.03845, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34953, "top5_acc": 0.61734, "loss_cls": 3.67712, "loss": 3.67712, "time": 0.81809} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.03842, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3375, "top5_acc": 0.60234, "loss_cls": 3.74033, "loss": 3.74033, "time": 0.82526} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.03839, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35062, "top5_acc": 0.61047, "loss_cls": 3.70451, "loss": 3.70451, "time": 0.83116} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.03837, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34609, "top5_acc": 0.60719, "loss_cls": 3.70592, "loss": 3.70592, "time": 0.81947} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.03834, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33719, "top5_acc": 0.59922, "loss_cls": 3.75014, "loss": 3.75014, "time": 0.82459} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.03831, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34328, "top5_acc": 0.61047, "loss_cls": 3.71091, "loss": 3.71091, "time": 0.81514} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.03828, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34328, "top5_acc": 0.60594, "loss_cls": 3.71198, "loss": 3.71198, "time": 0.8181} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.03826, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34391, "top5_acc": 0.60406, "loss_cls": 3.73849, "loss": 3.73849, "time": 0.81721} +{"mode": "train", "epoch": 87, "iter": 1300, "lr": 0.03823, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33656, "top5_acc": 0.58891, "loss_cls": 3.77118, "loss": 3.77118, "time": 0.81316} +{"mode": "train", "epoch": 87, "iter": 1400, "lr": 0.0382, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34906, "top5_acc": 0.60656, "loss_cls": 3.71643, "loss": 3.71643, "time": 0.8153} +{"mode": "train", "epoch": 87, "iter": 1500, "lr": 0.03817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33672, "top5_acc": 0.60422, "loss_cls": 3.73035, "loss": 3.73035, "time": 0.81652} +{"mode": "train", "epoch": 87, "iter": 1600, "lr": 0.03815, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35266, "top5_acc": 0.60953, "loss_cls": 3.70069, "loss": 3.70069, "time": 0.82175} +{"mode": "train", "epoch": 87, "iter": 1700, "lr": 0.03812, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34172, "top5_acc": 0.60609, "loss_cls": 3.71634, "loss": 3.71634, "time": 0.82078} +{"mode": "train", "epoch": 87, "iter": 1800, "lr": 0.03809, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34969, "top5_acc": 0.60906, "loss_cls": 3.69604, "loss": 3.69604, "time": 0.82173} +{"mode": "train", "epoch": 87, "iter": 1900, "lr": 0.03807, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34359, "top5_acc": 0.60406, "loss_cls": 3.73196, "loss": 3.73196, "time": 0.82888} +{"mode": "train", "epoch": 87, "iter": 2000, "lr": 0.03804, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34188, "top5_acc": 0.59469, "loss_cls": 3.75751, "loss": 3.75751, "time": 0.82594} +{"mode": "train", "epoch": 87, "iter": 2100, "lr": 0.03801, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.345, "top5_acc": 0.60312, "loss_cls": 3.71347, "loss": 3.71347, "time": 0.81406} +{"mode": "train", "epoch": 87, "iter": 2200, "lr": 0.03798, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35328, "top5_acc": 0.61016, "loss_cls": 3.70912, "loss": 3.70912, "time": 0.82201} +{"mode": "train", "epoch": 87, "iter": 2300, "lr": 0.03796, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34594, "top5_acc": 0.60844, "loss_cls": 3.71227, "loss": 3.71227, "time": 0.82106} +{"mode": "train", "epoch": 87, "iter": 2400, "lr": 0.03793, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35391, "top5_acc": 0.60531, "loss_cls": 3.71175, "loss": 3.71175, "time": 0.81735} +{"mode": "train", "epoch": 87, "iter": 2500, "lr": 0.0379, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34031, "top5_acc": 0.59531, "loss_cls": 3.7588, "loss": 3.7588, "time": 0.81524} +{"mode": "train", "epoch": 87, "iter": 2600, "lr": 0.03788, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3475, "top5_acc": 0.59891, "loss_cls": 3.75483, "loss": 3.75483, "time": 0.82013} +{"mode": "train", "epoch": 87, "iter": 2700, "lr": 0.03785, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34891, "top5_acc": 0.60297, "loss_cls": 3.75182, "loss": 3.75182, "time": 0.82197} +{"mode": "train", "epoch": 87, "iter": 2800, "lr": 0.03782, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34984, "top5_acc": 0.59641, "loss_cls": 3.72883, "loss": 3.72883, "time": 0.81475} +{"mode": "train", "epoch": 87, "iter": 2900, "lr": 0.03779, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34969, "top5_acc": 0.60844, "loss_cls": 3.69368, "loss": 3.69368, "time": 0.81611} +{"mode": "train", "epoch": 87, "iter": 3000, "lr": 0.03777, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34531, "top5_acc": 0.60359, "loss_cls": 3.73051, "loss": 3.73051, "time": 0.81728} +{"mode": "train", "epoch": 87, "iter": 3100, "lr": 0.03774, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34219, "top5_acc": 0.60625, "loss_cls": 3.75157, "loss": 3.75157, "time": 0.81726} +{"mode": "train", "epoch": 87, "iter": 3200, "lr": 0.03771, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35438, "top5_acc": 0.60625, "loss_cls": 3.69582, "loss": 3.69582, "time": 0.8183} +{"mode": "train", "epoch": 87, "iter": 3300, "lr": 0.03769, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34516, "top5_acc": 0.60281, "loss_cls": 3.72729, "loss": 3.72729, "time": 0.81687} +{"mode": "train", "epoch": 87, "iter": 3400, "lr": 0.03766, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34359, "top5_acc": 0.59969, "loss_cls": 3.73235, "loss": 3.73235, "time": 0.81917} +{"mode": "train", "epoch": 87, "iter": 3500, "lr": 0.03763, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33984, "top5_acc": 0.60562, "loss_cls": 3.71929, "loss": 3.71929, "time": 0.81549} +{"mode": "train", "epoch": 87, "iter": 3600, "lr": 0.03761, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34797, "top5_acc": 0.61016, "loss_cls": 3.69886, "loss": 3.69886, "time": 0.81799} +{"mode": "train", "epoch": 87, "iter": 3700, "lr": 0.03758, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34125, "top5_acc": 0.60422, "loss_cls": 3.71191, "loss": 3.71191, "time": 0.81696} +{"mode": "val", "epoch": 87, "iter": 309, "lr": 0.03757, "top1_acc": 0.2882, "top5_acc": 0.5409, "mean_class_accuracy": 0.28804} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.03754, "memory": 15990, "data_time": 1.28978, "top1_acc": 0.34844, "top5_acc": 0.60953, "loss_cls": 3.68183, "loss": 3.68183, "time": 2.26803} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.03751, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36422, "top5_acc": 0.61656, "loss_cls": 3.60204, "loss": 3.60204, "time": 0.83038} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.03748, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35203, "top5_acc": 0.60922, "loss_cls": 3.68409, "loss": 3.68409, "time": 0.8265} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.03746, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35141, "top5_acc": 0.61453, "loss_cls": 3.66113, "loss": 3.66113, "time": 0.82279} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.03743, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34969, "top5_acc": 0.6125, "loss_cls": 3.6822, "loss": 3.6822, "time": 0.81545} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.0374, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35234, "top5_acc": 0.61453, "loss_cls": 3.68852, "loss": 3.68852, "time": 0.81735} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.03738, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36078, "top5_acc": 0.61516, "loss_cls": 3.67149, "loss": 3.67149, "time": 0.8258} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.03735, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34609, "top5_acc": 0.60344, "loss_cls": 3.71213, "loss": 3.71213, "time": 0.81987} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.03732, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35312, "top5_acc": 0.61172, "loss_cls": 3.68283, "loss": 3.68283, "time": 0.82514} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.0373, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35031, "top5_acc": 0.61187, "loss_cls": 3.69997, "loss": 3.69997, "time": 0.8179} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.03727, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34141, "top5_acc": 0.60609, "loss_cls": 3.72346, "loss": 3.72346, "time": 0.81676} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.03724, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34656, "top5_acc": 0.60594, "loss_cls": 3.72192, "loss": 3.72192, "time": 0.81363} +{"mode": "train", "epoch": 88, "iter": 1300, "lr": 0.03721, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34938, "top5_acc": 0.61234, "loss_cls": 3.66602, "loss": 3.66602, "time": 0.81725} +{"mode": "train", "epoch": 88, "iter": 1400, "lr": 0.03719, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35203, "top5_acc": 0.61359, "loss_cls": 3.67506, "loss": 3.67506, "time": 0.8177} +{"mode": "train", "epoch": 88, "iter": 1500, "lr": 0.03716, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35281, "top5_acc": 0.60984, "loss_cls": 3.71282, "loss": 3.71282, "time": 0.81908} +{"mode": "train", "epoch": 88, "iter": 1600, "lr": 0.03713, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34562, "top5_acc": 0.60438, "loss_cls": 3.72997, "loss": 3.72997, "time": 0.82176} +{"mode": "train", "epoch": 88, "iter": 1700, "lr": 0.03711, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33359, "top5_acc": 0.60172, "loss_cls": 3.73115, "loss": 3.73115, "time": 0.8201} +{"mode": "train", "epoch": 88, "iter": 1800, "lr": 0.03708, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34844, "top5_acc": 0.60562, "loss_cls": 3.69442, "loss": 3.69442, "time": 0.8262} +{"mode": "train", "epoch": 88, "iter": 1900, "lr": 0.03705, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3475, "top5_acc": 0.60219, "loss_cls": 3.71815, "loss": 3.71815, "time": 0.8256} +{"mode": "train", "epoch": 88, "iter": 2000, "lr": 0.03703, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35344, "top5_acc": 0.61328, "loss_cls": 3.65748, "loss": 3.65748, "time": 0.82242} +{"mode": "train", "epoch": 88, "iter": 2100, "lr": 0.037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34281, "top5_acc": 0.61297, "loss_cls": 3.69659, "loss": 3.69659, "time": 0.81827} +{"mode": "train", "epoch": 88, "iter": 2200, "lr": 0.03697, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34531, "top5_acc": 0.60812, "loss_cls": 3.70493, "loss": 3.70493, "time": 0.82239} +{"mode": "train", "epoch": 88, "iter": 2300, "lr": 0.03694, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34422, "top5_acc": 0.60703, "loss_cls": 3.72549, "loss": 3.72549, "time": 0.81661} +{"mode": "train", "epoch": 88, "iter": 2400, "lr": 0.03692, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35266, "top5_acc": 0.61438, "loss_cls": 3.70706, "loss": 3.70706, "time": 0.81376} +{"mode": "train", "epoch": 88, "iter": 2500, "lr": 0.03689, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34953, "top5_acc": 0.60953, "loss_cls": 3.71163, "loss": 3.71163, "time": 0.82229} +{"mode": "train", "epoch": 88, "iter": 2600, "lr": 0.03686, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34766, "top5_acc": 0.60922, "loss_cls": 3.69704, "loss": 3.69704, "time": 0.81355} +{"mode": "train", "epoch": 88, "iter": 2700, "lr": 0.03684, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34656, "top5_acc": 0.60922, "loss_cls": 3.71355, "loss": 3.71355, "time": 0.81763} +{"mode": "train", "epoch": 88, "iter": 2800, "lr": 0.03681, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35203, "top5_acc": 0.61094, "loss_cls": 3.70928, "loss": 3.70928, "time": 0.81683} +{"mode": "train", "epoch": 88, "iter": 2900, "lr": 0.03678, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34188, "top5_acc": 0.60516, "loss_cls": 3.7381, "loss": 3.7381, "time": 0.81864} +{"mode": "train", "epoch": 88, "iter": 3000, "lr": 0.03676, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35469, "top5_acc": 0.61469, "loss_cls": 3.67348, "loss": 3.67348, "time": 0.81795} +{"mode": "train", "epoch": 88, "iter": 3100, "lr": 0.03673, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34828, "top5_acc": 0.60562, "loss_cls": 3.71036, "loss": 3.71036, "time": 0.81716} +{"mode": "train", "epoch": 88, "iter": 3200, "lr": 0.0367, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34391, "top5_acc": 0.59734, "loss_cls": 3.7436, "loss": 3.7436, "time": 0.81695} +{"mode": "train", "epoch": 88, "iter": 3300, "lr": 0.03667, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34359, "top5_acc": 0.60016, "loss_cls": 3.74602, "loss": 3.74602, "time": 0.8163} +{"mode": "train", "epoch": 88, "iter": 3400, "lr": 0.03665, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34219, "top5_acc": 0.60344, "loss_cls": 3.71151, "loss": 3.71151, "time": 0.81939} +{"mode": "train", "epoch": 88, "iter": 3500, "lr": 0.03662, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34188, "top5_acc": 0.60656, "loss_cls": 3.71308, "loss": 3.71308, "time": 0.81556} +{"mode": "train", "epoch": 88, "iter": 3600, "lr": 0.03659, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34297, "top5_acc": 0.605, "loss_cls": 3.72399, "loss": 3.72399, "time": 0.81371} +{"mode": "train", "epoch": 88, "iter": 3700, "lr": 0.03657, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34531, "top5_acc": 0.60344, "loss_cls": 3.73313, "loss": 3.73313, "time": 0.81684} +{"mode": "val", "epoch": 88, "iter": 309, "lr": 0.03655, "top1_acc": 0.29585, "top5_acc": 0.55042, "mean_class_accuracy": 0.29567} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.03653, "memory": 15990, "data_time": 1.27425, "top1_acc": 0.36656, "top5_acc": 0.61969, "loss_cls": 3.60485, "loss": 3.60485, "time": 2.24388} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0365, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34781, "top5_acc": 0.60516, "loss_cls": 3.68973, "loss": 3.68973, "time": 0.81573} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.03647, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35859, "top5_acc": 0.61703, "loss_cls": 3.64779, "loss": 3.64779, "time": 0.8171} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.03645, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3575, "top5_acc": 0.61578, "loss_cls": 3.66167, "loss": 3.66167, "time": 0.81771} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.03642, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35438, "top5_acc": 0.61609, "loss_cls": 3.65129, "loss": 3.65129, "time": 0.81345} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.03639, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35016, "top5_acc": 0.62109, "loss_cls": 3.63671, "loss": 3.63671, "time": 0.81687} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.03637, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34984, "top5_acc": 0.60594, "loss_cls": 3.69019, "loss": 3.69019, "time": 0.82488} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.03634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34828, "top5_acc": 0.61422, "loss_cls": 3.67495, "loss": 3.67495, "time": 0.82033} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.03631, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34938, "top5_acc": 0.60656, "loss_cls": 3.67697, "loss": 3.67697, "time": 0.82007} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.03629, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35812, "top5_acc": 0.61016, "loss_cls": 3.68122, "loss": 3.68122, "time": 0.8158} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.03626, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34859, "top5_acc": 0.60906, "loss_cls": 3.69204, "loss": 3.69204, "time": 0.81511} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.03623, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35375, "top5_acc": 0.59797, "loss_cls": 3.72087, "loss": 3.72087, "time": 0.81522} +{"mode": "train", "epoch": 89, "iter": 1300, "lr": 0.0362, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35062, "top5_acc": 0.61125, "loss_cls": 3.69264, "loss": 3.69264, "time": 0.81181} +{"mode": "train", "epoch": 89, "iter": 1400, "lr": 0.03618, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34203, "top5_acc": 0.60703, "loss_cls": 3.70662, "loss": 3.70662, "time": 0.81561} +{"mode": "train", "epoch": 89, "iter": 1500, "lr": 0.03615, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35922, "top5_acc": 0.61594, "loss_cls": 3.65791, "loss": 3.65791, "time": 0.81872} +{"mode": "train", "epoch": 89, "iter": 1600, "lr": 0.03612, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35812, "top5_acc": 0.61781, "loss_cls": 3.6413, "loss": 3.6413, "time": 0.818} +{"mode": "train", "epoch": 89, "iter": 1700, "lr": 0.0361, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34609, "top5_acc": 0.61328, "loss_cls": 3.69043, "loss": 3.69043, "time": 0.81845} +{"mode": "train", "epoch": 89, "iter": 1800, "lr": 0.03607, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33828, "top5_acc": 0.61469, "loss_cls": 3.70972, "loss": 3.70972, "time": 0.8207} +{"mode": "train", "epoch": 89, "iter": 1900, "lr": 0.03604, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33953, "top5_acc": 0.60641, "loss_cls": 3.70627, "loss": 3.70627, "time": 0.82323} +{"mode": "train", "epoch": 89, "iter": 2000, "lr": 0.03602, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34609, "top5_acc": 0.60781, "loss_cls": 3.71729, "loss": 3.71729, "time": 0.82592} +{"mode": "train", "epoch": 89, "iter": 2100, "lr": 0.03599, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35719, "top5_acc": 0.61078, "loss_cls": 3.69443, "loss": 3.69443, "time": 0.82262} +{"mode": "train", "epoch": 89, "iter": 2200, "lr": 0.03596, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34875, "top5_acc": 0.60266, "loss_cls": 3.71998, "loss": 3.71998, "time": 0.8204} +{"mode": "train", "epoch": 89, "iter": 2300, "lr": 0.03594, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35453, "top5_acc": 0.61687, "loss_cls": 3.68138, "loss": 3.68138, "time": 0.8164} +{"mode": "train", "epoch": 89, "iter": 2400, "lr": 0.03591, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34031, "top5_acc": 0.60062, "loss_cls": 3.73627, "loss": 3.73627, "time": 0.8152} +{"mode": "train", "epoch": 89, "iter": 2500, "lr": 0.03588, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35469, "top5_acc": 0.61234, "loss_cls": 3.65006, "loss": 3.65006, "time": 0.81272} +{"mode": "train", "epoch": 89, "iter": 2600, "lr": 0.03586, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35172, "top5_acc": 0.60719, "loss_cls": 3.69362, "loss": 3.69362, "time": 0.81691} +{"mode": "train", "epoch": 89, "iter": 2700, "lr": 0.03583, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34672, "top5_acc": 0.60188, "loss_cls": 3.71415, "loss": 3.71415, "time": 0.81355} +{"mode": "train", "epoch": 89, "iter": 2800, "lr": 0.0358, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34875, "top5_acc": 0.60219, "loss_cls": 3.72673, "loss": 3.72673, "time": 0.8157} +{"mode": "train", "epoch": 89, "iter": 2900, "lr": 0.03578, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35281, "top5_acc": 0.61391, "loss_cls": 3.68411, "loss": 3.68411, "time": 0.81923} +{"mode": "train", "epoch": 89, "iter": 3000, "lr": 0.03575, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35719, "top5_acc": 0.61547, "loss_cls": 3.66701, "loss": 3.66701, "time": 0.82221} +{"mode": "train", "epoch": 89, "iter": 3100, "lr": 0.03572, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34703, "top5_acc": 0.60875, "loss_cls": 3.70013, "loss": 3.70013, "time": 0.81502} +{"mode": "train", "epoch": 89, "iter": 3200, "lr": 0.03569, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35203, "top5_acc": 0.61187, "loss_cls": 3.67643, "loss": 3.67643, "time": 0.82102} +{"mode": "train", "epoch": 89, "iter": 3300, "lr": 0.03567, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34797, "top5_acc": 0.60812, "loss_cls": 3.70391, "loss": 3.70391, "time": 0.81969} +{"mode": "train", "epoch": 89, "iter": 3400, "lr": 0.03564, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35734, "top5_acc": 0.61516, "loss_cls": 3.67031, "loss": 3.67031, "time": 0.81777} +{"mode": "train", "epoch": 89, "iter": 3500, "lr": 0.03561, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35984, "top5_acc": 0.61391, "loss_cls": 3.657, "loss": 3.657, "time": 0.81821} +{"mode": "train", "epoch": 89, "iter": 3600, "lr": 0.03559, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35156, "top5_acc": 0.6075, "loss_cls": 3.68334, "loss": 3.68334, "time": 0.81834} +{"mode": "train", "epoch": 89, "iter": 3700, "lr": 0.03556, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35109, "top5_acc": 0.60891, "loss_cls": 3.68817, "loss": 3.68817, "time": 0.81266} +{"mode": "val", "epoch": 89, "iter": 309, "lr": 0.03555, "top1_acc": 0.27964, "top5_acc": 0.53183, "mean_class_accuracy": 0.27942} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.03552, "memory": 15990, "data_time": 1.33127, "top1_acc": 0.36797, "top5_acc": 0.62297, "loss_cls": 3.61225, "loss": 3.61225, "time": 2.30979} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.0355, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35859, "top5_acc": 0.61422, "loss_cls": 3.65232, "loss": 3.65232, "time": 0.81599} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.03547, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35656, "top5_acc": 0.61828, "loss_cls": 3.65931, "loss": 3.65931, "time": 0.81926} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.03544, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36984, "top5_acc": 0.62016, "loss_cls": 3.5954, "loss": 3.5954, "time": 0.81406} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.03541, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35, "top5_acc": 0.61094, "loss_cls": 3.68669, "loss": 3.68669, "time": 0.81624} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.03539, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35062, "top5_acc": 0.61234, "loss_cls": 3.66776, "loss": 3.66776, "time": 0.81435} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.03536, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34688, "top5_acc": 0.61328, "loss_cls": 3.66954, "loss": 3.66954, "time": 0.82083} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.03533, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35172, "top5_acc": 0.615, "loss_cls": 3.68131, "loss": 3.68131, "time": 0.81813} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.03531, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35328, "top5_acc": 0.61687, "loss_cls": 3.67629, "loss": 3.67629, "time": 0.81887} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.03528, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35047, "top5_acc": 0.61359, "loss_cls": 3.67127, "loss": 3.67127, "time": 0.81023} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.03525, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35031, "top5_acc": 0.615, "loss_cls": 3.65984, "loss": 3.65984, "time": 0.81482} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.03523, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34078, "top5_acc": 0.61187, "loss_cls": 3.70878, "loss": 3.70878, "time": 0.81712} +{"mode": "train", "epoch": 90, "iter": 1300, "lr": 0.0352, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35594, "top5_acc": 0.61219, "loss_cls": 3.65702, "loss": 3.65702, "time": 0.81477} +{"mode": "train", "epoch": 90, "iter": 1400, "lr": 0.03517, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35531, "top5_acc": 0.60797, "loss_cls": 3.6687, "loss": 3.6687, "time": 0.81459} +{"mode": "train", "epoch": 90, "iter": 1500, "lr": 0.03515, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35312, "top5_acc": 0.61234, "loss_cls": 3.66212, "loss": 3.66212, "time": 0.81289} +{"mode": "train", "epoch": 90, "iter": 1600, "lr": 0.03512, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35844, "top5_acc": 0.61031, "loss_cls": 3.65234, "loss": 3.65234, "time": 0.81515} +{"mode": "train", "epoch": 90, "iter": 1700, "lr": 0.03509, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35109, "top5_acc": 0.60797, "loss_cls": 3.68598, "loss": 3.68598, "time": 0.8227} +{"mode": "train", "epoch": 90, "iter": 1800, "lr": 0.03507, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.365, "top5_acc": 0.61594, "loss_cls": 3.66721, "loss": 3.66721, "time": 0.82231} +{"mode": "train", "epoch": 90, "iter": 1900, "lr": 0.03504, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34891, "top5_acc": 0.61031, "loss_cls": 3.68336, "loss": 3.68336, "time": 0.81935} +{"mode": "train", "epoch": 90, "iter": 2000, "lr": 0.03501, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35047, "top5_acc": 0.61828, "loss_cls": 3.66285, "loss": 3.66285, "time": 0.8352} +{"mode": "train", "epoch": 90, "iter": 2100, "lr": 0.03499, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35156, "top5_acc": 0.61203, "loss_cls": 3.68998, "loss": 3.68998, "time": 0.82823} +{"mode": "train", "epoch": 90, "iter": 2200, "lr": 0.03496, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35016, "top5_acc": 0.60922, "loss_cls": 3.68997, "loss": 3.68997, "time": 0.81718} +{"mode": "train", "epoch": 90, "iter": 2300, "lr": 0.03493, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35312, "top5_acc": 0.61125, "loss_cls": 3.66948, "loss": 3.66948, "time": 0.81393} +{"mode": "train", "epoch": 90, "iter": 2400, "lr": 0.03491, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34812, "top5_acc": 0.59984, "loss_cls": 3.70238, "loss": 3.70238, "time": 0.81404} +{"mode": "train", "epoch": 90, "iter": 2500, "lr": 0.03488, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.345, "top5_acc": 0.61469, "loss_cls": 3.6799, "loss": 3.6799, "time": 0.81948} +{"mode": "train", "epoch": 90, "iter": 2600, "lr": 0.03485, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34078, "top5_acc": 0.59688, "loss_cls": 3.73169, "loss": 3.73169, "time": 0.81907} +{"mode": "train", "epoch": 90, "iter": 2700, "lr": 0.03483, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35422, "top5_acc": 0.61625, "loss_cls": 3.68121, "loss": 3.68121, "time": 0.81211} +{"mode": "train", "epoch": 90, "iter": 2800, "lr": 0.0348, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34922, "top5_acc": 0.61656, "loss_cls": 3.67505, "loss": 3.67505, "time": 0.82265} +{"mode": "train", "epoch": 90, "iter": 2900, "lr": 0.03477, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33906, "top5_acc": 0.60781, "loss_cls": 3.72795, "loss": 3.72795, "time": 0.81419} +{"mode": "train", "epoch": 90, "iter": 3000, "lr": 0.03475, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35359, "top5_acc": 0.60797, "loss_cls": 3.70387, "loss": 3.70387, "time": 0.81759} +{"mode": "train", "epoch": 90, "iter": 3100, "lr": 0.03472, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34688, "top5_acc": 0.61125, "loss_cls": 3.66146, "loss": 3.66146, "time": 0.81368} +{"mode": "train", "epoch": 90, "iter": 3200, "lr": 0.03469, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35312, "top5_acc": 0.62047, "loss_cls": 3.64454, "loss": 3.64454, "time": 0.81695} +{"mode": "train", "epoch": 90, "iter": 3300, "lr": 0.03467, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35828, "top5_acc": 0.61531, "loss_cls": 3.66512, "loss": 3.66512, "time": 0.81527} +{"mode": "train", "epoch": 90, "iter": 3400, "lr": 0.03464, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35312, "top5_acc": 0.60906, "loss_cls": 3.69909, "loss": 3.69909, "time": 0.81527} +{"mode": "train", "epoch": 90, "iter": 3500, "lr": 0.03461, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34672, "top5_acc": 0.60547, "loss_cls": 3.70741, "loss": 3.70741, "time": 0.81517} +{"mode": "train", "epoch": 90, "iter": 3600, "lr": 0.03459, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35469, "top5_acc": 0.61609, "loss_cls": 3.65949, "loss": 3.65949, "time": 0.81633} +{"mode": "train", "epoch": 90, "iter": 3700, "lr": 0.03456, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35609, "top5_acc": 0.61547, "loss_cls": 3.65549, "loss": 3.65549, "time": 0.81497} +{"mode": "val", "epoch": 90, "iter": 309, "lr": 0.03455, "top1_acc": 0.29388, "top5_acc": 0.54906, "mean_class_accuracy": 0.29361} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.03452, "memory": 15990, "data_time": 1.28975, "top1_acc": 0.36672, "top5_acc": 0.62562, "loss_cls": 3.58193, "loss": 3.58193, "time": 2.27176} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0345, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35969, "top5_acc": 0.61906, "loss_cls": 3.59283, "loss": 3.59283, "time": 0.81395} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.03447, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.355, "top5_acc": 0.61422, "loss_cls": 3.67466, "loss": 3.67466, "time": 0.82325} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.03444, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36766, "top5_acc": 0.62391, "loss_cls": 3.62421, "loss": 3.62421, "time": 0.8206} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.03442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35094, "top5_acc": 0.61453, "loss_cls": 3.66463, "loss": 3.66463, "time": 0.82104} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.03439, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34641, "top5_acc": 0.61516, "loss_cls": 3.67747, "loss": 3.67747, "time": 0.82895} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.03436, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36438, "top5_acc": 0.61828, "loss_cls": 3.64209, "loss": 3.64209, "time": 0.82253} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.03434, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34719, "top5_acc": 0.61328, "loss_cls": 3.66791, "loss": 3.66791, "time": 0.82382} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.03431, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35594, "top5_acc": 0.61266, "loss_cls": 3.68917, "loss": 3.68917, "time": 0.8231} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.03428, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35375, "top5_acc": 0.61469, "loss_cls": 3.66803, "loss": 3.66803, "time": 0.81823} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.03426, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36547, "top5_acc": 0.62109, "loss_cls": 3.61119, "loss": 3.61119, "time": 0.8178} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.03423, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35938, "top5_acc": 0.61656, "loss_cls": 3.65433, "loss": 3.65433, "time": 0.81543} +{"mode": "train", "epoch": 91, "iter": 1300, "lr": 0.0342, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35281, "top5_acc": 0.60984, "loss_cls": 3.69256, "loss": 3.69256, "time": 0.81859} +{"mode": "train", "epoch": 91, "iter": 1400, "lr": 0.03418, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35422, "top5_acc": 0.61047, "loss_cls": 3.68601, "loss": 3.68601, "time": 0.81831} +{"mode": "train", "epoch": 91, "iter": 1500, "lr": 0.03415, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35984, "top5_acc": 0.61703, "loss_cls": 3.65468, "loss": 3.65468, "time": 0.81612} +{"mode": "train", "epoch": 91, "iter": 1600, "lr": 0.03412, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36188, "top5_acc": 0.61297, "loss_cls": 3.66665, "loss": 3.66665, "time": 0.81403} +{"mode": "train", "epoch": 91, "iter": 1700, "lr": 0.0341, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.355, "top5_acc": 0.60688, "loss_cls": 3.68869, "loss": 3.68869, "time": 0.82454} +{"mode": "train", "epoch": 91, "iter": 1800, "lr": 0.03407, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35844, "top5_acc": 0.62562, "loss_cls": 3.61115, "loss": 3.61115, "time": 0.82299} +{"mode": "train", "epoch": 91, "iter": 1900, "lr": 0.03405, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37109, "top5_acc": 0.62875, "loss_cls": 3.57493, "loss": 3.57493, "time": 0.81909} +{"mode": "train", "epoch": 91, "iter": 2000, "lr": 0.03402, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35438, "top5_acc": 0.62109, "loss_cls": 3.65656, "loss": 3.65656, "time": 0.81873} +{"mode": "train", "epoch": 91, "iter": 2100, "lr": 0.03399, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3475, "top5_acc": 0.60641, "loss_cls": 3.71814, "loss": 3.71814, "time": 0.82671} +{"mode": "train", "epoch": 91, "iter": 2200, "lr": 0.03397, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3475, "top5_acc": 0.60953, "loss_cls": 3.68668, "loss": 3.68668, "time": 0.81863} +{"mode": "train", "epoch": 91, "iter": 2300, "lr": 0.03394, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35266, "top5_acc": 0.61312, "loss_cls": 3.66374, "loss": 3.66374, "time": 0.8144} +{"mode": "train", "epoch": 91, "iter": 2400, "lr": 0.03391, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3575, "top5_acc": 0.60453, "loss_cls": 3.68781, "loss": 3.68781, "time": 0.81209} +{"mode": "train", "epoch": 91, "iter": 2500, "lr": 0.03389, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36031, "top5_acc": 0.61422, "loss_cls": 3.65858, "loss": 3.65858, "time": 0.81547} +{"mode": "train", "epoch": 91, "iter": 2600, "lr": 0.03386, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34219, "top5_acc": 0.60625, "loss_cls": 3.70397, "loss": 3.70397, "time": 0.82082} +{"mode": "train", "epoch": 91, "iter": 2700, "lr": 0.03383, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36594, "top5_acc": 0.61328, "loss_cls": 3.64771, "loss": 3.64771, "time": 0.81243} +{"mode": "train", "epoch": 91, "iter": 2800, "lr": 0.03381, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35516, "top5_acc": 0.59844, "loss_cls": 3.72018, "loss": 3.72018, "time": 0.82126} +{"mode": "train", "epoch": 91, "iter": 2900, "lr": 0.03378, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34766, "top5_acc": 0.61828, "loss_cls": 3.6577, "loss": 3.6577, "time": 0.81667} +{"mode": "train", "epoch": 91, "iter": 3000, "lr": 0.03375, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35578, "top5_acc": 0.61453, "loss_cls": 3.65492, "loss": 3.65492, "time": 0.82072} +{"mode": "train", "epoch": 91, "iter": 3100, "lr": 0.03373, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34969, "top5_acc": 0.60109, "loss_cls": 3.69842, "loss": 3.69842, "time": 0.81412} +{"mode": "train", "epoch": 91, "iter": 3200, "lr": 0.0337, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35688, "top5_acc": 0.61453, "loss_cls": 3.65862, "loss": 3.65862, "time": 0.81868} +{"mode": "train", "epoch": 91, "iter": 3300, "lr": 0.03367, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36297, "top5_acc": 0.62109, "loss_cls": 3.62798, "loss": 3.62798, "time": 0.81519} +{"mode": "train", "epoch": 91, "iter": 3400, "lr": 0.03365, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35641, "top5_acc": 0.6175, "loss_cls": 3.67461, "loss": 3.67461, "time": 0.81861} +{"mode": "train", "epoch": 91, "iter": 3500, "lr": 0.03362, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36094, "top5_acc": 0.61781, "loss_cls": 3.65722, "loss": 3.65722, "time": 0.81557} +{"mode": "train", "epoch": 91, "iter": 3600, "lr": 0.0336, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35953, "top5_acc": 0.61891, "loss_cls": 3.63351, "loss": 3.63351, "time": 0.81025} +{"mode": "train", "epoch": 91, "iter": 3700, "lr": 0.03357, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35047, "top5_acc": 0.61312, "loss_cls": 3.69644, "loss": 3.69644, "time": 0.81259} +{"mode": "val", "epoch": 91, "iter": 309, "lr": 0.03356, "top1_acc": 0.2843, "top5_acc": 0.53315, "mean_class_accuracy": 0.28399} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.03353, "memory": 15990, "data_time": 1.31551, "top1_acc": 0.35266, "top5_acc": 0.62313, "loss_cls": 3.61058, "loss": 3.61058, "time": 2.29378} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.0335, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36266, "top5_acc": 0.62313, "loss_cls": 3.6073, "loss": 3.6073, "time": 0.81235} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.03348, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35719, "top5_acc": 0.62219, "loss_cls": 3.6231, "loss": 3.6231, "time": 0.82289} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.03345, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35531, "top5_acc": 0.62813, "loss_cls": 3.61404, "loss": 3.61404, "time": 0.82103} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.03342, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35828, "top5_acc": 0.62344, "loss_cls": 3.61552, "loss": 3.61552, "time": 0.82475} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.0334, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35562, "top5_acc": 0.62047, "loss_cls": 3.62904, "loss": 3.62904, "time": 0.81578} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.03337, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36156, "top5_acc": 0.61969, "loss_cls": 3.59588, "loss": 3.59588, "time": 0.82461} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.03335, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35719, "top5_acc": 0.61938, "loss_cls": 3.63601, "loss": 3.63601, "time": 0.81746} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.03332, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36344, "top5_acc": 0.63062, "loss_cls": 3.59612, "loss": 3.59612, "time": 0.81432} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.03329, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35734, "top5_acc": 0.61797, "loss_cls": 3.62262, "loss": 3.62262, "time": 0.81472} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.03327, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35719, "top5_acc": 0.61266, "loss_cls": 3.65165, "loss": 3.65165, "time": 0.81531} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.03324, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36031, "top5_acc": 0.62078, "loss_cls": 3.61412, "loss": 3.61412, "time": 0.81986} +{"mode": "train", "epoch": 92, "iter": 1300, "lr": 0.03321, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35984, "top5_acc": 0.61547, "loss_cls": 3.65069, "loss": 3.65069, "time": 0.81367} +{"mode": "train", "epoch": 92, "iter": 1400, "lr": 0.03319, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35625, "top5_acc": 0.61984, "loss_cls": 3.63379, "loss": 3.63379, "time": 0.81995} +{"mode": "train", "epoch": 92, "iter": 1500, "lr": 0.03316, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35219, "top5_acc": 0.61703, "loss_cls": 3.66884, "loss": 3.66884, "time": 0.82516} +{"mode": "train", "epoch": 92, "iter": 1600, "lr": 0.03314, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35531, "top5_acc": 0.62156, "loss_cls": 3.65311, "loss": 3.65311, "time": 0.8182} +{"mode": "train", "epoch": 92, "iter": 1700, "lr": 0.03311, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35516, "top5_acc": 0.62297, "loss_cls": 3.61405, "loss": 3.61405, "time": 0.81875} +{"mode": "train", "epoch": 92, "iter": 1800, "lr": 0.03308, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35141, "top5_acc": 0.61391, "loss_cls": 3.67483, "loss": 3.67483, "time": 0.82668} +{"mode": "train", "epoch": 92, "iter": 1900, "lr": 0.03306, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35328, "top5_acc": 0.60938, "loss_cls": 3.66239, "loss": 3.66239, "time": 0.82212} +{"mode": "train", "epoch": 92, "iter": 2000, "lr": 0.03303, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34891, "top5_acc": 0.60953, "loss_cls": 3.678, "loss": 3.678, "time": 0.82474} +{"mode": "train", "epoch": 92, "iter": 2100, "lr": 0.033, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35844, "top5_acc": 0.61281, "loss_cls": 3.65552, "loss": 3.65552, "time": 0.82159} +{"mode": "train", "epoch": 92, "iter": 2200, "lr": 0.03298, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35844, "top5_acc": 0.62094, "loss_cls": 3.63905, "loss": 3.63905, "time": 0.82514} +{"mode": "train", "epoch": 92, "iter": 2300, "lr": 0.03295, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35516, "top5_acc": 0.61109, "loss_cls": 3.68035, "loss": 3.68035, "time": 0.82202} +{"mode": "train", "epoch": 92, "iter": 2400, "lr": 0.03292, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35797, "top5_acc": 0.61469, "loss_cls": 3.65322, "loss": 3.65322, "time": 0.82287} +{"mode": "train", "epoch": 92, "iter": 2500, "lr": 0.0329, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36141, "top5_acc": 0.61578, "loss_cls": 3.63157, "loss": 3.63157, "time": 0.81485} +{"mode": "train", "epoch": 92, "iter": 2600, "lr": 0.03287, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35344, "top5_acc": 0.61172, "loss_cls": 3.68388, "loss": 3.68388, "time": 0.81512} +{"mode": "train", "epoch": 92, "iter": 2700, "lr": 0.03285, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36062, "top5_acc": 0.62687, "loss_cls": 3.61361, "loss": 3.61361, "time": 0.82049} +{"mode": "train", "epoch": 92, "iter": 2800, "lr": 0.03282, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36234, "top5_acc": 0.60688, "loss_cls": 3.63211, "loss": 3.63211, "time": 0.8141} +{"mode": "train", "epoch": 92, "iter": 2900, "lr": 0.03279, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36016, "top5_acc": 0.61047, "loss_cls": 3.67104, "loss": 3.67104, "time": 0.81538} +{"mode": "train", "epoch": 92, "iter": 3000, "lr": 0.03277, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36422, "top5_acc": 0.62031, "loss_cls": 3.62783, "loss": 3.62783, "time": 0.81694} +{"mode": "train", "epoch": 92, "iter": 3100, "lr": 0.03274, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35812, "top5_acc": 0.61375, "loss_cls": 3.65954, "loss": 3.65954, "time": 0.8154} +{"mode": "train", "epoch": 92, "iter": 3200, "lr": 0.03271, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35188, "top5_acc": 0.60812, "loss_cls": 3.66991, "loss": 3.66991, "time": 0.81363} +{"mode": "train", "epoch": 92, "iter": 3300, "lr": 0.03269, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34844, "top5_acc": 0.61016, "loss_cls": 3.67128, "loss": 3.67128, "time": 0.81502} +{"mode": "train", "epoch": 92, "iter": 3400, "lr": 0.03266, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36141, "top5_acc": 0.61797, "loss_cls": 3.63668, "loss": 3.63668, "time": 0.81806} +{"mode": "train", "epoch": 92, "iter": 3500, "lr": 0.03264, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35188, "top5_acc": 0.61875, "loss_cls": 3.64172, "loss": 3.64172, "time": 0.81737} +{"mode": "train", "epoch": 92, "iter": 3600, "lr": 0.03261, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35219, "top5_acc": 0.60469, "loss_cls": 3.69169, "loss": 3.69169, "time": 0.81228} +{"mode": "train", "epoch": 92, "iter": 3700, "lr": 0.03258, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36047, "top5_acc": 0.62687, "loss_cls": 3.62244, "loss": 3.62244, "time": 0.81502} +{"mode": "val", "epoch": 92, "iter": 309, "lr": 0.03257, "top1_acc": 0.31074, "top5_acc": 0.55964, "mean_class_accuracy": 0.31062} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.03255, "memory": 15990, "data_time": 1.37509, "top1_acc": 0.36453, "top5_acc": 0.63078, "loss_cls": 3.56802, "loss": 3.56802, "time": 2.35396} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.03252, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35641, "top5_acc": 0.62516, "loss_cls": 3.61431, "loss": 3.61431, "time": 0.82044} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.03249, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36375, "top5_acc": 0.61828, "loss_cls": 3.61836, "loss": 3.61836, "time": 0.82158} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.03247, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37609, "top5_acc": 0.63031, "loss_cls": 3.57396, "loss": 3.57396, "time": 0.81805} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.03244, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35906, "top5_acc": 0.62578, "loss_cls": 3.59487, "loss": 3.59487, "time": 0.8214} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.03241, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36641, "top5_acc": 0.62734, "loss_cls": 3.60323, "loss": 3.60323, "time": 0.82247} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.03239, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36469, "top5_acc": 0.62406, "loss_cls": 3.59099, "loss": 3.59099, "time": 0.82252} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.03236, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35812, "top5_acc": 0.61766, "loss_cls": 3.63798, "loss": 3.63798, "time": 0.81741} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.03234, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34859, "top5_acc": 0.61687, "loss_cls": 3.67618, "loss": 3.67618, "time": 0.82531} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.03231, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36516, "top5_acc": 0.62531, "loss_cls": 3.59054, "loss": 3.59054, "time": 0.81561} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.03228, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36812, "top5_acc": 0.62734, "loss_cls": 3.60899, "loss": 3.60899, "time": 0.81652} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.03226, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36375, "top5_acc": 0.62078, "loss_cls": 3.62641, "loss": 3.62641, "time": 0.81997} +{"mode": "train", "epoch": 93, "iter": 1300, "lr": 0.03223, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35906, "top5_acc": 0.61219, "loss_cls": 3.64536, "loss": 3.64536, "time": 0.81631} +{"mode": "train", "epoch": 93, "iter": 1400, "lr": 0.03221, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36016, "top5_acc": 0.62266, "loss_cls": 3.58725, "loss": 3.58725, "time": 0.81511} +{"mode": "train", "epoch": 93, "iter": 1500, "lr": 0.03218, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35203, "top5_acc": 0.61438, "loss_cls": 3.66995, "loss": 3.66995, "time": 0.81857} +{"mode": "train", "epoch": 93, "iter": 1600, "lr": 0.03215, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36203, "top5_acc": 0.62672, "loss_cls": 3.59672, "loss": 3.59672, "time": 0.8159} +{"mode": "train", "epoch": 93, "iter": 1700, "lr": 0.03213, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36406, "top5_acc": 0.62906, "loss_cls": 3.59432, "loss": 3.59432, "time": 0.81913} +{"mode": "train", "epoch": 93, "iter": 1800, "lr": 0.0321, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36609, "top5_acc": 0.62484, "loss_cls": 3.58838, "loss": 3.58838, "time": 0.82542} +{"mode": "train", "epoch": 93, "iter": 1900, "lr": 0.03207, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35594, "top5_acc": 0.61125, "loss_cls": 3.69135, "loss": 3.69135, "time": 0.823} +{"mode": "train", "epoch": 93, "iter": 2000, "lr": 0.03205, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.35531, "top5_acc": 0.60812, "loss_cls": 3.6725, "loss": 3.6725, "time": 0.8254} +{"mode": "train", "epoch": 93, "iter": 2100, "lr": 0.03202, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36125, "top5_acc": 0.62969, "loss_cls": 3.61324, "loss": 3.61324, "time": 0.82346} +{"mode": "train", "epoch": 93, "iter": 2200, "lr": 0.032, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36375, "top5_acc": 0.62062, "loss_cls": 3.62678, "loss": 3.62678, "time": 0.82144} +{"mode": "train", "epoch": 93, "iter": 2300, "lr": 0.03197, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35734, "top5_acc": 0.62266, "loss_cls": 3.64285, "loss": 3.64285, "time": 0.81828} +{"mode": "train", "epoch": 93, "iter": 2400, "lr": 0.03194, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36078, "top5_acc": 0.61969, "loss_cls": 3.64836, "loss": 3.64836, "time": 0.81987} +{"mode": "train", "epoch": 93, "iter": 2500, "lr": 0.03192, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35891, "top5_acc": 0.61328, "loss_cls": 3.63989, "loss": 3.63989, "time": 0.81616} +{"mode": "train", "epoch": 93, "iter": 2600, "lr": 0.03189, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35188, "top5_acc": 0.61687, "loss_cls": 3.67997, "loss": 3.67997, "time": 0.82184} +{"mode": "train", "epoch": 93, "iter": 2700, "lr": 0.03187, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36094, "top5_acc": 0.61953, "loss_cls": 3.61022, "loss": 3.61022, "time": 0.8161} +{"mode": "train", "epoch": 93, "iter": 2800, "lr": 0.03184, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36953, "top5_acc": 0.61812, "loss_cls": 3.63858, "loss": 3.63858, "time": 0.81782} +{"mode": "train", "epoch": 93, "iter": 2900, "lr": 0.03181, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35469, "top5_acc": 0.61078, "loss_cls": 3.63421, "loss": 3.63421, "time": 0.81381} +{"mode": "train", "epoch": 93, "iter": 3000, "lr": 0.03179, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36281, "top5_acc": 0.61812, "loss_cls": 3.60713, "loss": 3.60713, "time": 0.81493} +{"mode": "train", "epoch": 93, "iter": 3100, "lr": 0.03176, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36422, "top5_acc": 0.62156, "loss_cls": 3.6178, "loss": 3.6178, "time": 0.81592} +{"mode": "train", "epoch": 93, "iter": 3200, "lr": 0.03174, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35891, "top5_acc": 0.61922, "loss_cls": 3.6525, "loss": 3.6525, "time": 0.81496} +{"mode": "train", "epoch": 93, "iter": 3300, "lr": 0.03171, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35906, "top5_acc": 0.61938, "loss_cls": 3.6391, "loss": 3.6391, "time": 0.81772} +{"mode": "train", "epoch": 93, "iter": 3400, "lr": 0.03168, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35297, "top5_acc": 0.60406, "loss_cls": 3.66798, "loss": 3.66798, "time": 0.8178} +{"mode": "train", "epoch": 93, "iter": 3500, "lr": 0.03166, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35984, "top5_acc": 0.62375, "loss_cls": 3.6343, "loss": 3.6343, "time": 0.81611} +{"mode": "train", "epoch": 93, "iter": 3600, "lr": 0.03163, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35594, "top5_acc": 0.60938, "loss_cls": 3.66526, "loss": 3.66526, "time": 0.81234} +{"mode": "train", "epoch": 93, "iter": 3700, "lr": 0.03161, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35875, "top5_acc": 0.61328, "loss_cls": 3.63934, "loss": 3.63934, "time": 0.81324} +{"mode": "val", "epoch": 93, "iter": 309, "lr": 0.03159, "top1_acc": 0.2921, "top5_acc": 0.54602, "mean_class_accuracy": 0.29181} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.03157, "memory": 15990, "data_time": 1.34485, "top1_acc": 0.36984, "top5_acc": 0.63172, "loss_cls": 3.56126, "loss": 3.56126, "time": 2.32091} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.03154, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3625, "top5_acc": 0.63031, "loss_cls": 3.59966, "loss": 3.59966, "time": 0.81693} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.03152, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37031, "top5_acc": 0.63172, "loss_cls": 3.56865, "loss": 3.56865, "time": 0.81955} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.03149, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36828, "top5_acc": 0.62656, "loss_cls": 3.59783, "loss": 3.59783, "time": 0.8172} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.03146, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36484, "top5_acc": 0.62703, "loss_cls": 3.59927, "loss": 3.59927, "time": 0.81403} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.03144, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35125, "top5_acc": 0.61297, "loss_cls": 3.63548, "loss": 3.63548, "time": 0.81729} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.03141, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36, "top5_acc": 0.61953, "loss_cls": 3.61461, "loss": 3.61461, "time": 0.82323} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.03139, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36266, "top5_acc": 0.62578, "loss_cls": 3.58706, "loss": 3.58706, "time": 0.81987} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.03136, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36656, "top5_acc": 0.62781, "loss_cls": 3.56218, "loss": 3.56218, "time": 0.82043} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.03133, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36672, "top5_acc": 0.62734, "loss_cls": 3.60764, "loss": 3.60764, "time": 0.82169} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.03131, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35844, "top5_acc": 0.6225, "loss_cls": 3.60397, "loss": 3.60397, "time": 0.81572} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.03128, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36234, "top5_acc": 0.61656, "loss_cls": 3.64628, "loss": 3.64628, "time": 0.81873} +{"mode": "train", "epoch": 94, "iter": 1300, "lr": 0.03126, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35984, "top5_acc": 0.62578, "loss_cls": 3.60341, "loss": 3.60341, "time": 0.82199} +{"mode": "train", "epoch": 94, "iter": 1400, "lr": 0.03123, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36891, "top5_acc": 0.62531, "loss_cls": 3.57982, "loss": 3.57982, "time": 0.81653} +{"mode": "train", "epoch": 94, "iter": 1500, "lr": 0.0312, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36891, "top5_acc": 0.62891, "loss_cls": 3.55905, "loss": 3.55905, "time": 0.81931} +{"mode": "train", "epoch": 94, "iter": 1600, "lr": 0.03118, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36078, "top5_acc": 0.62062, "loss_cls": 3.62879, "loss": 3.62879, "time": 0.81986} +{"mode": "train", "epoch": 94, "iter": 1700, "lr": 0.03115, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34828, "top5_acc": 0.60453, "loss_cls": 3.69595, "loss": 3.69595, "time": 0.82437} +{"mode": "train", "epoch": 94, "iter": 1800, "lr": 0.03113, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35938, "top5_acc": 0.62047, "loss_cls": 3.63071, "loss": 3.63071, "time": 0.82837} +{"mode": "train", "epoch": 94, "iter": 1900, "lr": 0.0311, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3625, "top5_acc": 0.62641, "loss_cls": 3.60733, "loss": 3.60733, "time": 0.82165} +{"mode": "train", "epoch": 94, "iter": 2000, "lr": 0.03108, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36875, "top5_acc": 0.62391, "loss_cls": 3.58773, "loss": 3.58773, "time": 0.82687} +{"mode": "train", "epoch": 94, "iter": 2100, "lr": 0.03105, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36438, "top5_acc": 0.62594, "loss_cls": 3.61235, "loss": 3.61235, "time": 0.8244} +{"mode": "train", "epoch": 94, "iter": 2200, "lr": 0.03102, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34672, "top5_acc": 0.60859, "loss_cls": 3.68353, "loss": 3.68353, "time": 0.81643} +{"mode": "train", "epoch": 94, "iter": 2300, "lr": 0.031, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37594, "top5_acc": 0.63188, "loss_cls": 3.58477, "loss": 3.58477, "time": 0.81764} +{"mode": "train", "epoch": 94, "iter": 2400, "lr": 0.03097, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36594, "top5_acc": 0.63609, "loss_cls": 3.59461, "loss": 3.59461, "time": 0.81818} +{"mode": "train", "epoch": 94, "iter": 2500, "lr": 0.03095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34562, "top5_acc": 0.61812, "loss_cls": 3.68581, "loss": 3.68581, "time": 0.81464} +{"mode": "train", "epoch": 94, "iter": 2600, "lr": 0.03092, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37656, "top5_acc": 0.62891, "loss_cls": 3.57929, "loss": 3.57929, "time": 0.82184} +{"mode": "train", "epoch": 94, "iter": 2700, "lr": 0.03089, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35594, "top5_acc": 0.62266, "loss_cls": 3.64384, "loss": 3.64384, "time": 0.81727} +{"mode": "train", "epoch": 94, "iter": 2800, "lr": 0.03087, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36672, "top5_acc": 0.62141, "loss_cls": 3.59664, "loss": 3.59664, "time": 0.8189} +{"mode": "train", "epoch": 94, "iter": 2900, "lr": 0.03084, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36547, "top5_acc": 0.625, "loss_cls": 3.58884, "loss": 3.58884, "time": 0.81772} +{"mode": "train", "epoch": 94, "iter": 3000, "lr": 0.03082, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36156, "top5_acc": 0.61766, "loss_cls": 3.62036, "loss": 3.62036, "time": 0.81507} +{"mode": "train", "epoch": 94, "iter": 3100, "lr": 0.03079, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35828, "top5_acc": 0.62094, "loss_cls": 3.62039, "loss": 3.62039, "time": 0.82741} +{"mode": "train", "epoch": 94, "iter": 3200, "lr": 0.03077, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36328, "top5_acc": 0.62516, "loss_cls": 3.61356, "loss": 3.61356, "time": 0.81427} +{"mode": "train", "epoch": 94, "iter": 3300, "lr": 0.03074, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36047, "top5_acc": 0.62016, "loss_cls": 3.6214, "loss": 3.6214, "time": 0.81394} +{"mode": "train", "epoch": 94, "iter": 3400, "lr": 0.03071, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34344, "top5_acc": 0.61406, "loss_cls": 3.66664, "loss": 3.66664, "time": 0.8168} +{"mode": "train", "epoch": 94, "iter": 3500, "lr": 0.03069, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36453, "top5_acc": 0.62219, "loss_cls": 3.60177, "loss": 3.60177, "time": 0.8162} +{"mode": "train", "epoch": 94, "iter": 3600, "lr": 0.03066, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35375, "top5_acc": 0.61781, "loss_cls": 3.67404, "loss": 3.67404, "time": 0.81579} +{"mode": "train", "epoch": 94, "iter": 3700, "lr": 0.03064, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36094, "top5_acc": 0.62719, "loss_cls": 3.6124, "loss": 3.6124, "time": 0.81672} +{"mode": "val", "epoch": 94, "iter": 309, "lr": 0.03062, "top1_acc": 0.29864, "top5_acc": 0.55934, "mean_class_accuracy": 0.2985} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.0306, "memory": 15990, "data_time": 1.34314, "top1_acc": 0.37531, "top5_acc": 0.63125, "loss_cls": 3.53935, "loss": 3.53935, "time": 2.32325} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.03057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37172, "top5_acc": 0.63281, "loss_cls": 3.5372, "loss": 3.5372, "time": 0.81214} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.03055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37344, "top5_acc": 0.63422, "loss_cls": 3.53032, "loss": 3.53032, "time": 0.81371} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.03052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38375, "top5_acc": 0.63781, "loss_cls": 3.49252, "loss": 3.49252, "time": 0.81801} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.0305, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37031, "top5_acc": 0.62656, "loss_cls": 3.56643, "loss": 3.56643, "time": 0.82065} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.03047, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36297, "top5_acc": 0.62391, "loss_cls": 3.60795, "loss": 3.60795, "time": 0.81495} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.03044, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37344, "top5_acc": 0.63719, "loss_cls": 3.55834, "loss": 3.55834, "time": 0.82455} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.03042, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36609, "top5_acc": 0.63719, "loss_cls": 3.57268, "loss": 3.57268, "time": 0.81415} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.03039, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36781, "top5_acc": 0.62719, "loss_cls": 3.56381, "loss": 3.56381, "time": 0.82183} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.03037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3625, "top5_acc": 0.61641, "loss_cls": 3.65671, "loss": 3.65671, "time": 0.81934} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.03034, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36406, "top5_acc": 0.62469, "loss_cls": 3.6033, "loss": 3.6033, "time": 0.81664} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.03032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35438, "top5_acc": 0.61781, "loss_cls": 3.61113, "loss": 3.61113, "time": 0.81697} +{"mode": "train", "epoch": 95, "iter": 1300, "lr": 0.03029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35031, "top5_acc": 0.61641, "loss_cls": 3.61052, "loss": 3.61052, "time": 0.82108} +{"mode": "train", "epoch": 95, "iter": 1400, "lr": 0.03026, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36109, "top5_acc": 0.62016, "loss_cls": 3.59533, "loss": 3.59533, "time": 0.81123} +{"mode": "train", "epoch": 95, "iter": 1500, "lr": 0.03024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35312, "top5_acc": 0.61922, "loss_cls": 3.61662, "loss": 3.61662, "time": 0.81705} +{"mode": "train", "epoch": 95, "iter": 1600, "lr": 0.03021, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36375, "top5_acc": 0.61969, "loss_cls": 3.60889, "loss": 3.60889, "time": 0.81747} +{"mode": "train", "epoch": 95, "iter": 1700, "lr": 0.03019, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36172, "top5_acc": 0.62297, "loss_cls": 3.61555, "loss": 3.61555, "time": 0.81654} +{"mode": "train", "epoch": 95, "iter": 1800, "lr": 0.03016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36391, "top5_acc": 0.62297, "loss_cls": 3.60368, "loss": 3.60368, "time": 0.82263} +{"mode": "train", "epoch": 95, "iter": 1900, "lr": 0.03014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35094, "top5_acc": 0.61625, "loss_cls": 3.65178, "loss": 3.65178, "time": 0.82232} +{"mode": "train", "epoch": 95, "iter": 2000, "lr": 0.03011, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35453, "top5_acc": 0.61859, "loss_cls": 3.67208, "loss": 3.67208, "time": 0.82957} +{"mode": "train", "epoch": 95, "iter": 2100, "lr": 0.03008, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36906, "top5_acc": 0.62813, "loss_cls": 3.57244, "loss": 3.57244, "time": 0.81783} +{"mode": "train", "epoch": 95, "iter": 2200, "lr": 0.03006, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36062, "top5_acc": 0.62969, "loss_cls": 3.58951, "loss": 3.58951, "time": 0.81985} +{"mode": "train", "epoch": 95, "iter": 2300, "lr": 0.03003, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38125, "top5_acc": 0.63609, "loss_cls": 3.51203, "loss": 3.51203, "time": 0.82193} +{"mode": "train", "epoch": 95, "iter": 2400, "lr": 0.03001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36953, "top5_acc": 0.62641, "loss_cls": 3.60508, "loss": 3.60508, "time": 0.81764} +{"mode": "train", "epoch": 95, "iter": 2500, "lr": 0.02998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36656, "top5_acc": 0.61875, "loss_cls": 3.60093, "loss": 3.60093, "time": 0.81483} +{"mode": "train", "epoch": 95, "iter": 2600, "lr": 0.02996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36125, "top5_acc": 0.62656, "loss_cls": 3.59691, "loss": 3.59691, "time": 0.8189} +{"mode": "train", "epoch": 95, "iter": 2700, "lr": 0.02993, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36047, "top5_acc": 0.61938, "loss_cls": 3.61919, "loss": 3.61919, "time": 0.81556} +{"mode": "train", "epoch": 95, "iter": 2800, "lr": 0.02991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36172, "top5_acc": 0.625, "loss_cls": 3.63099, "loss": 3.63099, "time": 0.81393} +{"mode": "train", "epoch": 95, "iter": 2900, "lr": 0.02988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35516, "top5_acc": 0.61562, "loss_cls": 3.66137, "loss": 3.66137, "time": 0.81672} +{"mode": "train", "epoch": 95, "iter": 3000, "lr": 0.02985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36094, "top5_acc": 0.62891, "loss_cls": 3.58823, "loss": 3.58823, "time": 0.81625} +{"mode": "train", "epoch": 95, "iter": 3100, "lr": 0.02983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35562, "top5_acc": 0.61328, "loss_cls": 3.6448, "loss": 3.6448, "time": 0.81699} +{"mode": "train", "epoch": 95, "iter": 3200, "lr": 0.0298, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36578, "top5_acc": 0.62125, "loss_cls": 3.62731, "loss": 3.62731, "time": 0.81804} +{"mode": "train", "epoch": 95, "iter": 3300, "lr": 0.02978, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35922, "top5_acc": 0.61922, "loss_cls": 3.60913, "loss": 3.60913, "time": 0.82068} +{"mode": "train", "epoch": 95, "iter": 3400, "lr": 0.02975, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36078, "top5_acc": 0.62531, "loss_cls": 3.62997, "loss": 3.62997, "time": 0.82123} +{"mode": "train", "epoch": 95, "iter": 3500, "lr": 0.02973, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36234, "top5_acc": 0.62297, "loss_cls": 3.60972, "loss": 3.60972, "time": 0.82107} +{"mode": "train", "epoch": 95, "iter": 3600, "lr": 0.0297, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.365, "top5_acc": 0.62141, "loss_cls": 3.58616, "loss": 3.58616, "time": 0.81698} +{"mode": "train", "epoch": 95, "iter": 3700, "lr": 0.02968, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.365, "top5_acc": 0.62391, "loss_cls": 3.62302, "loss": 3.62302, "time": 0.81321} +{"mode": "val", "epoch": 95, "iter": 309, "lr": 0.02966, "top1_acc": 0.29453, "top5_acc": 0.5487, "mean_class_accuracy": 0.29425} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.02964, "memory": 15990, "data_time": 1.33294, "top1_acc": 0.37672, "top5_acc": 0.6375, "loss_cls": 3.49579, "loss": 3.49579, "time": 2.31379} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.02961, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37562, "top5_acc": 0.63484, "loss_cls": 3.53837, "loss": 3.53837, "time": 0.81886} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.02959, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36969, "top5_acc": 0.63609, "loss_cls": 3.54156, "loss": 3.54156, "time": 0.81524} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.02956, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36625, "top5_acc": 0.63391, "loss_cls": 3.54153, "loss": 3.54153, "time": 0.82115} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.02954, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37703, "top5_acc": 0.63984, "loss_cls": 3.51811, "loss": 3.51811, "time": 0.82079} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.02951, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36359, "top5_acc": 0.63766, "loss_cls": 3.5272, "loss": 3.5272, "time": 0.8225} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.02948, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37188, "top5_acc": 0.63031, "loss_cls": 3.55511, "loss": 3.55511, "time": 0.82306} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.02946, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37125, "top5_acc": 0.62984, "loss_cls": 3.55652, "loss": 3.55652, "time": 0.81696} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.02943, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36734, "top5_acc": 0.63359, "loss_cls": 3.56799, "loss": 3.56799, "time": 0.82103} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.02941, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35688, "top5_acc": 0.62969, "loss_cls": 3.61456, "loss": 3.61456, "time": 0.81719} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.02938, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36172, "top5_acc": 0.62828, "loss_cls": 3.61892, "loss": 3.61892, "time": 0.8203} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.02936, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37047, "top5_acc": 0.63, "loss_cls": 3.58213, "loss": 3.58213, "time": 0.8163} +{"mode": "train", "epoch": 96, "iter": 1300, "lr": 0.02933, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36359, "top5_acc": 0.62266, "loss_cls": 3.6152, "loss": 3.6152, "time": 0.81132} +{"mode": "train", "epoch": 96, "iter": 1400, "lr": 0.02931, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35703, "top5_acc": 0.61469, "loss_cls": 3.64057, "loss": 3.64057, "time": 0.81692} +{"mode": "train", "epoch": 96, "iter": 1500, "lr": 0.02928, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37281, "top5_acc": 0.62906, "loss_cls": 3.56227, "loss": 3.56227, "time": 0.82106} +{"mode": "train", "epoch": 96, "iter": 1600, "lr": 0.02926, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36969, "top5_acc": 0.63781, "loss_cls": 3.54332, "loss": 3.54332, "time": 0.825} +{"mode": "train", "epoch": 96, "iter": 1700, "lr": 0.02923, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37281, "top5_acc": 0.63, "loss_cls": 3.57172, "loss": 3.57172, "time": 0.82278} +{"mode": "train", "epoch": 96, "iter": 1800, "lr": 0.0292, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35906, "top5_acc": 0.62313, "loss_cls": 3.63357, "loss": 3.63357, "time": 0.82548} +{"mode": "train", "epoch": 96, "iter": 1900, "lr": 0.02918, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35953, "top5_acc": 0.62047, "loss_cls": 3.61077, "loss": 3.61077, "time": 0.81677} +{"mode": "train", "epoch": 96, "iter": 2000, "lr": 0.02915, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36641, "top5_acc": 0.63359, "loss_cls": 3.55756, "loss": 3.55756, "time": 0.83435} +{"mode": "train", "epoch": 96, "iter": 2100, "lr": 0.02913, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35469, "top5_acc": 0.62281, "loss_cls": 3.59561, "loss": 3.59561, "time": 0.81316} +{"mode": "train", "epoch": 96, "iter": 2200, "lr": 0.0291, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37016, "top5_acc": 0.62375, "loss_cls": 3.58894, "loss": 3.58894, "time": 0.81925} +{"mode": "train", "epoch": 96, "iter": 2300, "lr": 0.02908, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36859, "top5_acc": 0.62938, "loss_cls": 3.57997, "loss": 3.57997, "time": 0.812} +{"mode": "train", "epoch": 96, "iter": 2400, "lr": 0.02905, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36828, "top5_acc": 0.62219, "loss_cls": 3.5975, "loss": 3.5975, "time": 0.81943} +{"mode": "train", "epoch": 96, "iter": 2500, "lr": 0.02903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37328, "top5_acc": 0.63094, "loss_cls": 3.56398, "loss": 3.56398, "time": 0.81895} +{"mode": "train", "epoch": 96, "iter": 2600, "lr": 0.029, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36766, "top5_acc": 0.62859, "loss_cls": 3.58329, "loss": 3.58329, "time": 0.81952} +{"mode": "train", "epoch": 96, "iter": 2700, "lr": 0.02898, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36781, "top5_acc": 0.62938, "loss_cls": 3.57366, "loss": 3.57366, "time": 0.8133} +{"mode": "train", "epoch": 96, "iter": 2800, "lr": 0.02895, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35953, "top5_acc": 0.62813, "loss_cls": 3.59162, "loss": 3.59162, "time": 0.81519} +{"mode": "train", "epoch": 96, "iter": 2900, "lr": 0.02893, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36656, "top5_acc": 0.62734, "loss_cls": 3.59133, "loss": 3.59133, "time": 0.81863} +{"mode": "train", "epoch": 96, "iter": 3000, "lr": 0.0289, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36969, "top5_acc": 0.63156, "loss_cls": 3.56666, "loss": 3.56666, "time": 0.81528} +{"mode": "train", "epoch": 96, "iter": 3100, "lr": 0.02887, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3675, "top5_acc": 0.62328, "loss_cls": 3.57454, "loss": 3.57454, "time": 0.81556} +{"mode": "train", "epoch": 96, "iter": 3200, "lr": 0.02885, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36953, "top5_acc": 0.63172, "loss_cls": 3.58638, "loss": 3.58638, "time": 0.81741} +{"mode": "train", "epoch": 96, "iter": 3300, "lr": 0.02882, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35891, "top5_acc": 0.62766, "loss_cls": 3.63128, "loss": 3.63128, "time": 0.81497} +{"mode": "train", "epoch": 96, "iter": 3400, "lr": 0.0288, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36469, "top5_acc": 0.61453, "loss_cls": 3.61934, "loss": 3.61934, "time": 0.81687} +{"mode": "train", "epoch": 96, "iter": 3500, "lr": 0.02877, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36094, "top5_acc": 0.61828, "loss_cls": 3.63722, "loss": 3.63722, "time": 0.813} +{"mode": "train", "epoch": 96, "iter": 3600, "lr": 0.02875, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35641, "top5_acc": 0.61031, "loss_cls": 3.66477, "loss": 3.66477, "time": 0.81416} +{"mode": "train", "epoch": 96, "iter": 3700, "lr": 0.02872, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3625, "top5_acc": 0.62687, "loss_cls": 3.5814, "loss": 3.5814, "time": 0.81777} +{"mode": "val", "epoch": 96, "iter": 309, "lr": 0.02871, "top1_acc": 0.30304, "top5_acc": 0.55544, "mean_class_accuracy": 0.30265} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.02869, "memory": 15990, "data_time": 1.29282, "top1_acc": 0.37641, "top5_acc": 0.63094, "loss_cls": 3.54517, "loss": 3.54517, "time": 2.26979} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.02866, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37469, "top5_acc": 0.63891, "loss_cls": 3.53525, "loss": 3.53525, "time": 0.82437} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.02864, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36375, "top5_acc": 0.63062, "loss_cls": 3.55551, "loss": 3.55551, "time": 0.81882} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.02861, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37469, "top5_acc": 0.64969, "loss_cls": 3.49774, "loss": 3.49774, "time": 0.81688} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.02858, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37359, "top5_acc": 0.63969, "loss_cls": 3.527, "loss": 3.527, "time": 0.81939} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.02856, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37391, "top5_acc": 0.63391, "loss_cls": 3.57281, "loss": 3.57281, "time": 0.81456} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.02853, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37422, "top5_acc": 0.63375, "loss_cls": 3.55875, "loss": 3.55875, "time": 0.82499} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.02851, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37766, "top5_acc": 0.64141, "loss_cls": 3.52815, "loss": 3.52815, "time": 0.8224} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.02848, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36172, "top5_acc": 0.62078, "loss_cls": 3.60922, "loss": 3.60922, "time": 0.81991} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.02846, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36438, "top5_acc": 0.62875, "loss_cls": 3.54567, "loss": 3.54567, "time": 0.82024} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.02843, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37297, "top5_acc": 0.63391, "loss_cls": 3.54291, "loss": 3.54291, "time": 0.81564} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.02841, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37594, "top5_acc": 0.64266, "loss_cls": 3.52956, "loss": 3.52956, "time": 0.81919} +{"mode": "train", "epoch": 97, "iter": 1300, "lr": 0.02838, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36234, "top5_acc": 0.62406, "loss_cls": 3.60324, "loss": 3.60324, "time": 0.82296} +{"mode": "train", "epoch": 97, "iter": 1400, "lr": 0.02836, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38281, "top5_acc": 0.63469, "loss_cls": 3.53037, "loss": 3.53037, "time": 0.81704} +{"mode": "train", "epoch": 97, "iter": 1500, "lr": 0.02833, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36234, "top5_acc": 0.61312, "loss_cls": 3.6128, "loss": 3.6128, "time": 0.81469} +{"mode": "train", "epoch": 97, "iter": 1600, "lr": 0.02831, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38094, "top5_acc": 0.63078, "loss_cls": 3.55815, "loss": 3.55815, "time": 0.81305} +{"mode": "train", "epoch": 97, "iter": 1700, "lr": 0.02828, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35281, "top5_acc": 0.61438, "loss_cls": 3.65492, "loss": 3.65492, "time": 0.81688} +{"mode": "train", "epoch": 97, "iter": 1800, "lr": 0.02826, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36312, "top5_acc": 0.63719, "loss_cls": 3.57039, "loss": 3.57039, "time": 0.81823} +{"mode": "train", "epoch": 97, "iter": 1900, "lr": 0.02823, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37375, "top5_acc": 0.63031, "loss_cls": 3.54724, "loss": 3.54724, "time": 0.83107} +{"mode": "train", "epoch": 97, "iter": 2000, "lr": 0.02821, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37406, "top5_acc": 0.62672, "loss_cls": 3.56843, "loss": 3.56843, "time": 0.81936} +{"mode": "train", "epoch": 97, "iter": 2100, "lr": 0.02818, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36781, "top5_acc": 0.62859, "loss_cls": 3.56167, "loss": 3.56167, "time": 0.82014} +{"mode": "train", "epoch": 97, "iter": 2200, "lr": 0.02816, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36594, "top5_acc": 0.62078, "loss_cls": 3.56365, "loss": 3.56365, "time": 0.82133} +{"mode": "train", "epoch": 97, "iter": 2300, "lr": 0.02813, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36516, "top5_acc": 0.62109, "loss_cls": 3.59172, "loss": 3.59172, "time": 0.82131} +{"mode": "train", "epoch": 97, "iter": 2400, "lr": 0.02811, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36703, "top5_acc": 0.62781, "loss_cls": 3.58334, "loss": 3.58334, "time": 0.82081} +{"mode": "train", "epoch": 97, "iter": 2500, "lr": 0.02808, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36734, "top5_acc": 0.62172, "loss_cls": 3.60858, "loss": 3.60858, "time": 0.8201} +{"mode": "train", "epoch": 97, "iter": 2600, "lr": 0.02806, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37297, "top5_acc": 0.6325, "loss_cls": 3.53193, "loss": 3.53193, "time": 0.81661} +{"mode": "train", "epoch": 97, "iter": 2700, "lr": 0.02803, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37891, "top5_acc": 0.63875, "loss_cls": 3.53171, "loss": 3.53171, "time": 0.81937} +{"mode": "train", "epoch": 97, "iter": 2800, "lr": 0.02801, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3775, "top5_acc": 0.63719, "loss_cls": 3.5069, "loss": 3.5069, "time": 0.81603} +{"mode": "train", "epoch": 97, "iter": 2900, "lr": 0.02798, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36438, "top5_acc": 0.62031, "loss_cls": 3.60931, "loss": 3.60931, "time": 0.82213} +{"mode": "train", "epoch": 97, "iter": 3000, "lr": 0.02796, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36844, "top5_acc": 0.62687, "loss_cls": 3.5521, "loss": 3.5521, "time": 0.81686} +{"mode": "train", "epoch": 97, "iter": 3100, "lr": 0.02793, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36172, "top5_acc": 0.62562, "loss_cls": 3.60102, "loss": 3.60102, "time": 0.81849} +{"mode": "train", "epoch": 97, "iter": 3200, "lr": 0.02791, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37438, "top5_acc": 0.63469, "loss_cls": 3.54296, "loss": 3.54296, "time": 0.81809} +{"mode": "train", "epoch": 97, "iter": 3300, "lr": 0.02788, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36, "top5_acc": 0.62922, "loss_cls": 3.57782, "loss": 3.57782, "time": 0.81193} +{"mode": "train", "epoch": 97, "iter": 3400, "lr": 0.02786, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37172, "top5_acc": 0.62797, "loss_cls": 3.5729, "loss": 3.5729, "time": 0.81409} +{"mode": "train", "epoch": 97, "iter": 3500, "lr": 0.02783, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37469, "top5_acc": 0.63047, "loss_cls": 3.55901, "loss": 3.55901, "time": 0.81405} +{"mode": "train", "epoch": 97, "iter": 3600, "lr": 0.02781, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36172, "top5_acc": 0.62125, "loss_cls": 3.63359, "loss": 3.63359, "time": 0.81983} +{"mode": "train", "epoch": 97, "iter": 3700, "lr": 0.02778, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37609, "top5_acc": 0.63125, "loss_cls": 3.53879, "loss": 3.53879, "time": 0.82196} +{"mode": "val", "epoch": 97, "iter": 309, "lr": 0.02777, "top1_acc": 0.31702, "top5_acc": 0.57084, "mean_class_accuracy": 0.31671} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.02774, "memory": 15990, "data_time": 1.32882, "top1_acc": 0.38234, "top5_acc": 0.63828, "loss_cls": 3.50542, "loss": 3.50542, "time": 2.30464} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.02772, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38281, "top5_acc": 0.63438, "loss_cls": 3.49032, "loss": 3.49032, "time": 0.81166} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.02769, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38172, "top5_acc": 0.64375, "loss_cls": 3.49534, "loss": 3.49534, "time": 0.81397} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.02767, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37125, "top5_acc": 0.63828, "loss_cls": 3.50836, "loss": 3.50836, "time": 0.81846} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.02764, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37969, "top5_acc": 0.63594, "loss_cls": 3.51307, "loss": 3.51307, "time": 0.82071} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.02762, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38125, "top5_acc": 0.63203, "loss_cls": 3.51133, "loss": 3.51133, "time": 0.81916} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.02759, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38484, "top5_acc": 0.64156, "loss_cls": 3.50836, "loss": 3.50836, "time": 0.82546} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.02757, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36531, "top5_acc": 0.62594, "loss_cls": 3.59613, "loss": 3.59613, "time": 0.81362} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.02754, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37578, "top5_acc": 0.63516, "loss_cls": 3.54042, "loss": 3.54042, "time": 0.82156} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.02752, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37141, "top5_acc": 0.635, "loss_cls": 3.54182, "loss": 3.54182, "time": 0.82081} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.02749, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37484, "top5_acc": 0.63234, "loss_cls": 3.51837, "loss": 3.51837, "time": 0.82218} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.02747, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37359, "top5_acc": 0.62438, "loss_cls": 3.56885, "loss": 3.56885, "time": 0.81974} +{"mode": "train", "epoch": 98, "iter": 1300, "lr": 0.02744, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37656, "top5_acc": 0.63625, "loss_cls": 3.54622, "loss": 3.54622, "time": 0.8199} +{"mode": "train", "epoch": 98, "iter": 1400, "lr": 0.02742, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37219, "top5_acc": 0.63938, "loss_cls": 3.55112, "loss": 3.55112, "time": 0.81362} +{"mode": "train", "epoch": 98, "iter": 1500, "lr": 0.02739, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36609, "top5_acc": 0.62516, "loss_cls": 3.576, "loss": 3.576, "time": 0.81485} +{"mode": "train", "epoch": 98, "iter": 1600, "lr": 0.02737, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36906, "top5_acc": 0.63219, "loss_cls": 3.55026, "loss": 3.55026, "time": 0.81678} +{"mode": "train", "epoch": 98, "iter": 1700, "lr": 0.02734, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37719, "top5_acc": 0.63078, "loss_cls": 3.55218, "loss": 3.55218, "time": 0.8169} +{"mode": "train", "epoch": 98, "iter": 1800, "lr": 0.02732, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36484, "top5_acc": 0.63125, "loss_cls": 3.59435, "loss": 3.59435, "time": 0.82137} +{"mode": "train", "epoch": 98, "iter": 1900, "lr": 0.02729, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36734, "top5_acc": 0.63406, "loss_cls": 3.5481, "loss": 3.5481, "time": 0.82731} +{"mode": "train", "epoch": 98, "iter": 2000, "lr": 0.02727, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38, "top5_acc": 0.63359, "loss_cls": 3.5307, "loss": 3.5307, "time": 0.82316} +{"mode": "train", "epoch": 98, "iter": 2100, "lr": 0.02724, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36672, "top5_acc": 0.62578, "loss_cls": 3.577, "loss": 3.577, "time": 0.82261} +{"mode": "train", "epoch": 98, "iter": 2200, "lr": 0.02722, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37328, "top5_acc": 0.63344, "loss_cls": 3.51874, "loss": 3.51874, "time": 0.82094} +{"mode": "train", "epoch": 98, "iter": 2300, "lr": 0.02719, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37906, "top5_acc": 0.63125, "loss_cls": 3.53671, "loss": 3.53671, "time": 0.81592} +{"mode": "train", "epoch": 98, "iter": 2400, "lr": 0.02717, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3625, "top5_acc": 0.63047, "loss_cls": 3.56488, "loss": 3.56488, "time": 0.82007} +{"mode": "train", "epoch": 98, "iter": 2500, "lr": 0.02714, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36562, "top5_acc": 0.63672, "loss_cls": 3.55525, "loss": 3.55525, "time": 0.81376} +{"mode": "train", "epoch": 98, "iter": 2600, "lr": 0.02712, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37312, "top5_acc": 0.63297, "loss_cls": 3.54646, "loss": 3.54646, "time": 0.81461} +{"mode": "train", "epoch": 98, "iter": 2700, "lr": 0.02709, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.3675, "top5_acc": 0.63062, "loss_cls": 3.55811, "loss": 3.55811, "time": 0.81526} +{"mode": "train", "epoch": 98, "iter": 2800, "lr": 0.02707, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36594, "top5_acc": 0.62703, "loss_cls": 3.58115, "loss": 3.58115, "time": 0.81385} +{"mode": "train", "epoch": 98, "iter": 2900, "lr": 0.02705, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36984, "top5_acc": 0.62938, "loss_cls": 3.55848, "loss": 3.55848, "time": 0.81672} +{"mode": "train", "epoch": 98, "iter": 3000, "lr": 0.02702, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37016, "top5_acc": 0.6275, "loss_cls": 3.57785, "loss": 3.57785, "time": 0.81777} +{"mode": "train", "epoch": 98, "iter": 3100, "lr": 0.027, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36469, "top5_acc": 0.62406, "loss_cls": 3.57604, "loss": 3.57604, "time": 0.81183} +{"mode": "train", "epoch": 98, "iter": 3200, "lr": 0.02697, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37672, "top5_acc": 0.63609, "loss_cls": 3.55796, "loss": 3.55796, "time": 0.81527} +{"mode": "train", "epoch": 98, "iter": 3300, "lr": 0.02695, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37484, "top5_acc": 0.63156, "loss_cls": 3.54209, "loss": 3.54209, "time": 0.81604} +{"mode": "train", "epoch": 98, "iter": 3400, "lr": 0.02692, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37188, "top5_acc": 0.63031, "loss_cls": 3.59976, "loss": 3.59976, "time": 0.81833} +{"mode": "train", "epoch": 98, "iter": 3500, "lr": 0.0269, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37562, "top5_acc": 0.63391, "loss_cls": 3.55163, "loss": 3.55163, "time": 0.81424} +{"mode": "train", "epoch": 98, "iter": 3600, "lr": 0.02687, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37641, "top5_acc": 0.63375, "loss_cls": 3.54241, "loss": 3.54241, "time": 0.81409} +{"mode": "train", "epoch": 98, "iter": 3700, "lr": 0.02685, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37047, "top5_acc": 0.63453, "loss_cls": 3.55425, "loss": 3.55425, "time": 0.82002} +{"mode": "val", "epoch": 98, "iter": 309, "lr": 0.02684, "top1_acc": 0.31657, "top5_acc": 0.56764, "mean_class_accuracy": 0.31633} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.02681, "memory": 15990, "data_time": 1.31435, "top1_acc": 0.38891, "top5_acc": 0.64344, "loss_cls": 3.47147, "loss": 3.47147, "time": 2.28333} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.02679, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38781, "top5_acc": 0.64672, "loss_cls": 3.47872, "loss": 3.47872, "time": 0.8123} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.02676, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37219, "top5_acc": 0.63578, "loss_cls": 3.50945, "loss": 3.50945, "time": 0.81174} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.02674, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37797, "top5_acc": 0.63828, "loss_cls": 3.49367, "loss": 3.49367, "time": 0.81701} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.02671, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38281, "top5_acc": 0.64391, "loss_cls": 3.48495, "loss": 3.48495, "time": 0.81252} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.02669, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38328, "top5_acc": 0.64141, "loss_cls": 3.51412, "loss": 3.51412, "time": 0.81837} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.02666, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38016, "top5_acc": 0.63859, "loss_cls": 3.50708, "loss": 3.50708, "time": 0.82123} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.02664, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37938, "top5_acc": 0.64359, "loss_cls": 3.47977, "loss": 3.47977, "time": 0.82013} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.02661, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37734, "top5_acc": 0.63922, "loss_cls": 3.51529, "loss": 3.51529, "time": 0.82109} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.02659, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36375, "top5_acc": 0.63359, "loss_cls": 3.55126, "loss": 3.55126, "time": 0.81948} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.02656, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37328, "top5_acc": 0.63781, "loss_cls": 3.53533, "loss": 3.53533, "time": 0.81598} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.02654, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37578, "top5_acc": 0.63719, "loss_cls": 3.53827, "loss": 3.53827, "time": 0.81308} +{"mode": "train", "epoch": 99, "iter": 1300, "lr": 0.02651, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36547, "top5_acc": 0.62953, "loss_cls": 3.56249, "loss": 3.56249, "time": 0.81816} +{"mode": "train", "epoch": 99, "iter": 1400, "lr": 0.02649, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36844, "top5_acc": 0.62406, "loss_cls": 3.56877, "loss": 3.56877, "time": 0.81559} +{"mode": "train", "epoch": 99, "iter": 1500, "lr": 0.02646, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36188, "top5_acc": 0.62672, "loss_cls": 3.59524, "loss": 3.59524, "time": 0.81539} +{"mode": "train", "epoch": 99, "iter": 1600, "lr": 0.02644, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38125, "top5_acc": 0.63625, "loss_cls": 3.53023, "loss": 3.53023, "time": 0.82043} +{"mode": "train", "epoch": 99, "iter": 1700, "lr": 0.02642, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3675, "top5_acc": 0.63141, "loss_cls": 3.55926, "loss": 3.55926, "time": 0.81473} +{"mode": "train", "epoch": 99, "iter": 1800, "lr": 0.02639, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38797, "top5_acc": 0.635, "loss_cls": 3.51749, "loss": 3.51749, "time": 0.81712} +{"mode": "train", "epoch": 99, "iter": 1900, "lr": 0.02637, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36734, "top5_acc": 0.63469, "loss_cls": 3.55088, "loss": 3.55088, "time": 0.82966} +{"mode": "train", "epoch": 99, "iter": 2000, "lr": 0.02634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37562, "top5_acc": 0.63234, "loss_cls": 3.55153, "loss": 3.55153, "time": 0.82087} +{"mode": "train", "epoch": 99, "iter": 2100, "lr": 0.02632, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37953, "top5_acc": 0.6325, "loss_cls": 3.52475, "loss": 3.52475, "time": 0.82778} +{"mode": "train", "epoch": 99, "iter": 2200, "lr": 0.02629, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38109, "top5_acc": 0.63656, "loss_cls": 3.52848, "loss": 3.52848, "time": 0.81753} +{"mode": "train", "epoch": 99, "iter": 2300, "lr": 0.02627, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37953, "top5_acc": 0.64547, "loss_cls": 3.50465, "loss": 3.50465, "time": 0.82514} +{"mode": "train", "epoch": 99, "iter": 2400, "lr": 0.02624, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35812, "top5_acc": 0.6275, "loss_cls": 3.60146, "loss": 3.60146, "time": 0.82251} +{"mode": "train", "epoch": 99, "iter": 2500, "lr": 0.02622, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37797, "top5_acc": 0.64234, "loss_cls": 3.53786, "loss": 3.53786, "time": 0.82173} +{"mode": "train", "epoch": 99, "iter": 2600, "lr": 0.02619, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36531, "top5_acc": 0.63625, "loss_cls": 3.55658, "loss": 3.55658, "time": 0.81873} +{"mode": "train", "epoch": 99, "iter": 2700, "lr": 0.02617, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38656, "top5_acc": 0.64, "loss_cls": 3.47937, "loss": 3.47937, "time": 0.81499} +{"mode": "train", "epoch": 99, "iter": 2800, "lr": 0.02614, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36234, "top5_acc": 0.62578, "loss_cls": 3.58602, "loss": 3.58602, "time": 0.81795} +{"mode": "train", "epoch": 99, "iter": 2900, "lr": 0.02612, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37359, "top5_acc": 0.63734, "loss_cls": 3.5461, "loss": 3.5461, "time": 0.81673} +{"mode": "train", "epoch": 99, "iter": 3000, "lr": 0.0261, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37406, "top5_acc": 0.63781, "loss_cls": 3.5029, "loss": 3.5029, "time": 0.81819} +{"mode": "train", "epoch": 99, "iter": 3100, "lr": 0.02607, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36547, "top5_acc": 0.62344, "loss_cls": 3.58464, "loss": 3.58464, "time": 0.81611} +{"mode": "train", "epoch": 99, "iter": 3200, "lr": 0.02605, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37422, "top5_acc": 0.63172, "loss_cls": 3.53277, "loss": 3.53277, "time": 0.81876} +{"mode": "train", "epoch": 99, "iter": 3300, "lr": 0.02602, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37531, "top5_acc": 0.63547, "loss_cls": 3.51953, "loss": 3.51953, "time": 0.81419} +{"mode": "train", "epoch": 99, "iter": 3400, "lr": 0.026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36828, "top5_acc": 0.63125, "loss_cls": 3.57299, "loss": 3.57299, "time": 0.81449} +{"mode": "train", "epoch": 99, "iter": 3500, "lr": 0.02597, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.375, "top5_acc": 0.63734, "loss_cls": 3.53548, "loss": 3.53548, "time": 0.81288} +{"mode": "train", "epoch": 99, "iter": 3600, "lr": 0.02595, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37109, "top5_acc": 0.63719, "loss_cls": 3.54319, "loss": 3.54319, "time": 0.81973} +{"mode": "train", "epoch": 99, "iter": 3700, "lr": 0.02592, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37938, "top5_acc": 0.63594, "loss_cls": 3.52482, "loss": 3.52482, "time": 0.81654} +{"mode": "val", "epoch": 99, "iter": 309, "lr": 0.02591, "top1_acc": 0.31844, "top5_acc": 0.57671, "mean_class_accuracy": 0.31815} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.02589, "memory": 15990, "data_time": 1.27685, "top1_acc": 0.39156, "top5_acc": 0.6525, "loss_cls": 3.43034, "loss": 3.43034, "time": 2.25014} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.02586, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38641, "top5_acc": 0.64812, "loss_cls": 3.46339, "loss": 3.46339, "time": 0.82313} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.02584, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38562, "top5_acc": 0.64047, "loss_cls": 3.47743, "loss": 3.47743, "time": 0.81485} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.02581, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38547, "top5_acc": 0.63734, "loss_cls": 3.49231, "loss": 3.49231, "time": 0.8211} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.02579, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37469, "top5_acc": 0.64266, "loss_cls": 3.52657, "loss": 3.52657, "time": 0.81525} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.02577, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37031, "top5_acc": 0.63313, "loss_cls": 3.52627, "loss": 3.52627, "time": 0.81769} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.02574, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37141, "top5_acc": 0.64359, "loss_cls": 3.50695, "loss": 3.50695, "time": 0.82804} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.02572, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36797, "top5_acc": 0.62266, "loss_cls": 3.57493, "loss": 3.57493, "time": 0.81889} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.02569, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38219, "top5_acc": 0.6425, "loss_cls": 3.4702, "loss": 3.4702, "time": 0.81982} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.02567, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38703, "top5_acc": 0.64453, "loss_cls": 3.48743, "loss": 3.48743, "time": 0.81287} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.02564, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37609, "top5_acc": 0.63984, "loss_cls": 3.52206, "loss": 3.52206, "time": 0.81733} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.02562, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38391, "top5_acc": 0.64938, "loss_cls": 3.45951, "loss": 3.45951, "time": 0.81186} +{"mode": "train", "epoch": 100, "iter": 1300, "lr": 0.02559, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37672, "top5_acc": 0.6375, "loss_cls": 3.53006, "loss": 3.53006, "time": 0.8182} +{"mode": "train", "epoch": 100, "iter": 1400, "lr": 0.02557, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38797, "top5_acc": 0.64141, "loss_cls": 3.45737, "loss": 3.45737, "time": 0.81846} +{"mode": "train", "epoch": 100, "iter": 1500, "lr": 0.02555, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37422, "top5_acc": 0.62766, "loss_cls": 3.55915, "loss": 3.55915, "time": 0.80994} +{"mode": "train", "epoch": 100, "iter": 1600, "lr": 0.02552, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37406, "top5_acc": 0.63516, "loss_cls": 3.52793, "loss": 3.52793, "time": 0.81816} +{"mode": "train", "epoch": 100, "iter": 1700, "lr": 0.0255, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36844, "top5_acc": 0.63219, "loss_cls": 3.54899, "loss": 3.54899, "time": 0.81393} +{"mode": "train", "epoch": 100, "iter": 1800, "lr": 0.02547, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38453, "top5_acc": 0.63797, "loss_cls": 3.51278, "loss": 3.51278, "time": 0.81675} +{"mode": "train", "epoch": 100, "iter": 1900, "lr": 0.02545, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38172, "top5_acc": 0.63938, "loss_cls": 3.49181, "loss": 3.49181, "time": 0.82315} +{"mode": "train", "epoch": 100, "iter": 2000, "lr": 0.02542, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38656, "top5_acc": 0.63797, "loss_cls": 3.50605, "loss": 3.50605, "time": 0.8256} +{"mode": "train", "epoch": 100, "iter": 2100, "lr": 0.0254, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37359, "top5_acc": 0.64156, "loss_cls": 3.51866, "loss": 3.51866, "time": 0.82968} +{"mode": "train", "epoch": 100, "iter": 2200, "lr": 0.02538, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38656, "top5_acc": 0.63547, "loss_cls": 3.50436, "loss": 3.50436, "time": 0.82077} +{"mode": "train", "epoch": 100, "iter": 2300, "lr": 0.02535, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38188, "top5_acc": 0.63688, "loss_cls": 3.51837, "loss": 3.51837, "time": 0.82003} +{"mode": "train", "epoch": 100, "iter": 2400, "lr": 0.02533, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37688, "top5_acc": 0.6325, "loss_cls": 3.54558, "loss": 3.54558, "time": 0.82198} +{"mode": "train", "epoch": 100, "iter": 2500, "lr": 0.0253, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37359, "top5_acc": 0.63594, "loss_cls": 3.52952, "loss": 3.52952, "time": 0.81998} +{"mode": "train", "epoch": 100, "iter": 2600, "lr": 0.02528, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37656, "top5_acc": 0.64562, "loss_cls": 3.51358, "loss": 3.51358, "time": 0.81672} +{"mode": "train", "epoch": 100, "iter": 2700, "lr": 0.02525, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38016, "top5_acc": 0.63281, "loss_cls": 3.53239, "loss": 3.53239, "time": 0.81758} +{"mode": "train", "epoch": 100, "iter": 2800, "lr": 0.02523, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37172, "top5_acc": 0.62891, "loss_cls": 3.54501, "loss": 3.54501, "time": 0.81615} +{"mode": "train", "epoch": 100, "iter": 2900, "lr": 0.02521, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37078, "top5_acc": 0.62766, "loss_cls": 3.5522, "loss": 3.5522, "time": 0.81757} +{"mode": "train", "epoch": 100, "iter": 3000, "lr": 0.02518, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37453, "top5_acc": 0.63703, "loss_cls": 3.51277, "loss": 3.51277, "time": 0.8162} +{"mode": "train", "epoch": 100, "iter": 3100, "lr": 0.02516, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37922, "top5_acc": 0.64422, "loss_cls": 3.48156, "loss": 3.48156, "time": 0.8179} +{"mode": "train", "epoch": 100, "iter": 3200, "lr": 0.02513, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.365, "top5_acc": 0.62938, "loss_cls": 3.56344, "loss": 3.56344, "time": 0.81334} +{"mode": "train", "epoch": 100, "iter": 3300, "lr": 0.02511, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37875, "top5_acc": 0.63453, "loss_cls": 3.53172, "loss": 3.53172, "time": 0.81603} +{"mode": "train", "epoch": 100, "iter": 3400, "lr": 0.02508, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38406, "top5_acc": 0.64594, "loss_cls": 3.49497, "loss": 3.49497, "time": 0.82274} +{"mode": "train", "epoch": 100, "iter": 3500, "lr": 0.02506, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37219, "top5_acc": 0.63375, "loss_cls": 3.53329, "loss": 3.53329, "time": 0.81333} +{"mode": "train", "epoch": 100, "iter": 3600, "lr": 0.02504, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37766, "top5_acc": 0.64172, "loss_cls": 3.51908, "loss": 3.51908, "time": 0.82116} +{"mode": "train", "epoch": 100, "iter": 3700, "lr": 0.02501, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36219, "top5_acc": 0.62891, "loss_cls": 3.57907, "loss": 3.57907, "time": 0.81696} +{"mode": "val", "epoch": 100, "iter": 309, "lr": 0.025, "top1_acc": 0.3072, "top5_acc": 0.56709, "mean_class_accuracy": 0.30685} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.02498, "memory": 15990, "data_time": 1.36325, "top1_acc": 0.39625, "top5_acc": 0.65297, "loss_cls": 3.40221, "loss": 3.40221, "time": 2.33518} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.02495, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39094, "top5_acc": 0.64891, "loss_cls": 3.45524, "loss": 3.45524, "time": 0.81503} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.02493, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38391, "top5_acc": 0.65375, "loss_cls": 3.45789, "loss": 3.45789, "time": 0.81651} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.0249, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39266, "top5_acc": 0.65547, "loss_cls": 3.42873, "loss": 3.42873, "time": 0.81555} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.02488, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39, "top5_acc": 0.64891, "loss_cls": 3.45672, "loss": 3.45672, "time": 0.81695} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.02486, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39891, "top5_acc": 0.64703, "loss_cls": 3.42134, "loss": 3.42134, "time": 0.81637} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.02483, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37969, "top5_acc": 0.64828, "loss_cls": 3.45858, "loss": 3.45858, "time": 0.82671} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.02481, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36375, "top5_acc": 0.6325, "loss_cls": 3.55362, "loss": 3.55362, "time": 0.82068} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.02478, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4, "top5_acc": 0.65078, "loss_cls": 3.42991, "loss": 3.42991, "time": 0.81746} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.02476, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38188, "top5_acc": 0.64328, "loss_cls": 3.48209, "loss": 3.48209, "time": 0.81486} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.02473, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37562, "top5_acc": 0.64469, "loss_cls": 3.49533, "loss": 3.49533, "time": 0.81507} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.02471, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37547, "top5_acc": 0.63859, "loss_cls": 3.50594, "loss": 3.50594, "time": 0.81659} +{"mode": "train", "epoch": 101, "iter": 1300, "lr": 0.02469, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36844, "top5_acc": 0.62859, "loss_cls": 3.56191, "loss": 3.56191, "time": 0.81376} +{"mode": "train", "epoch": 101, "iter": 1400, "lr": 0.02466, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37891, "top5_acc": 0.63781, "loss_cls": 3.49874, "loss": 3.49874, "time": 0.82085} +{"mode": "train", "epoch": 101, "iter": 1500, "lr": 0.02464, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38469, "top5_acc": 0.64891, "loss_cls": 3.44679, "loss": 3.44679, "time": 0.81455} +{"mode": "train", "epoch": 101, "iter": 1600, "lr": 0.02461, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38906, "top5_acc": 0.65016, "loss_cls": 3.46155, "loss": 3.46155, "time": 0.81576} +{"mode": "train", "epoch": 101, "iter": 1700, "lr": 0.02459, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37719, "top5_acc": 0.63781, "loss_cls": 3.55609, "loss": 3.55609, "time": 0.81622} +{"mode": "train", "epoch": 101, "iter": 1800, "lr": 0.02457, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37375, "top5_acc": 0.63656, "loss_cls": 3.54475, "loss": 3.54475, "time": 0.82198} +{"mode": "train", "epoch": 101, "iter": 1900, "lr": 0.02454, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37953, "top5_acc": 0.63016, "loss_cls": 3.51561, "loss": 3.51561, "time": 0.81735} +{"mode": "train", "epoch": 101, "iter": 2000, "lr": 0.02452, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38438, "top5_acc": 0.64172, "loss_cls": 3.49185, "loss": 3.49185, "time": 0.82567} +{"mode": "train", "epoch": 101, "iter": 2100, "lr": 0.02449, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39078, "top5_acc": 0.64469, "loss_cls": 3.46495, "loss": 3.46495, "time": 0.82113} +{"mode": "train", "epoch": 101, "iter": 2200, "lr": 0.02447, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38109, "top5_acc": 0.64219, "loss_cls": 3.49591, "loss": 3.49591, "time": 0.81968} +{"mode": "train", "epoch": 101, "iter": 2300, "lr": 0.02445, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37641, "top5_acc": 0.63594, "loss_cls": 3.52698, "loss": 3.52698, "time": 0.81813} +{"mode": "train", "epoch": 101, "iter": 2400, "lr": 0.02442, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38297, "top5_acc": 0.64812, "loss_cls": 3.45976, "loss": 3.45976, "time": 0.81853} +{"mode": "train", "epoch": 101, "iter": 2500, "lr": 0.0244, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37812, "top5_acc": 0.64234, "loss_cls": 3.49174, "loss": 3.49174, "time": 0.81511} +{"mode": "train", "epoch": 101, "iter": 2600, "lr": 0.02437, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38047, "top5_acc": 0.65312, "loss_cls": 3.47098, "loss": 3.47098, "time": 0.81665} +{"mode": "train", "epoch": 101, "iter": 2700, "lr": 0.02435, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38703, "top5_acc": 0.64406, "loss_cls": 3.46664, "loss": 3.46664, "time": 0.81344} +{"mode": "train", "epoch": 101, "iter": 2800, "lr": 0.02433, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37094, "top5_acc": 0.63516, "loss_cls": 3.53326, "loss": 3.53326, "time": 0.81585} +{"mode": "train", "epoch": 101, "iter": 2900, "lr": 0.0243, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37703, "top5_acc": 0.62828, "loss_cls": 3.55434, "loss": 3.55434, "time": 0.8122} +{"mode": "train", "epoch": 101, "iter": 3000, "lr": 0.02428, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38562, "top5_acc": 0.63797, "loss_cls": 3.50432, "loss": 3.50432, "time": 0.8137} +{"mode": "train", "epoch": 101, "iter": 3100, "lr": 0.02425, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37594, "top5_acc": 0.62813, "loss_cls": 3.54226, "loss": 3.54226, "time": 0.82036} +{"mode": "train", "epoch": 101, "iter": 3200, "lr": 0.02423, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37672, "top5_acc": 0.64094, "loss_cls": 3.51379, "loss": 3.51379, "time": 0.8184} +{"mode": "train", "epoch": 101, "iter": 3300, "lr": 0.02421, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37531, "top5_acc": 0.62641, "loss_cls": 3.54974, "loss": 3.54974, "time": 0.81585} +{"mode": "train", "epoch": 101, "iter": 3400, "lr": 0.02418, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36547, "top5_acc": 0.63234, "loss_cls": 3.57507, "loss": 3.57507, "time": 0.81672} +{"mode": "train", "epoch": 101, "iter": 3500, "lr": 0.02416, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37797, "top5_acc": 0.63891, "loss_cls": 3.53087, "loss": 3.53087, "time": 0.81395} +{"mode": "train", "epoch": 101, "iter": 3600, "lr": 0.02413, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38172, "top5_acc": 0.64453, "loss_cls": 3.48092, "loss": 3.48092, "time": 0.81344} +{"mode": "train", "epoch": 101, "iter": 3700, "lr": 0.02411, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37219, "top5_acc": 0.63266, "loss_cls": 3.528, "loss": 3.528, "time": 0.82012} +{"mode": "val", "epoch": 101, "iter": 309, "lr": 0.0241, "top1_acc": 0.31961, "top5_acc": 0.57443, "mean_class_accuracy": 0.31925} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.02407, "memory": 15990, "data_time": 1.29285, "top1_acc": 0.39672, "top5_acc": 0.65578, "loss_cls": 3.4183, "loss": 3.4183, "time": 2.30767} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.02405, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38953, "top5_acc": 0.66109, "loss_cls": 3.44347, "loss": 3.44347, "time": 0.81756} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.02403, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39562, "top5_acc": 0.64844, "loss_cls": 3.42988, "loss": 3.42988, "time": 0.81276} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39406, "top5_acc": 0.65422, "loss_cls": 3.41389, "loss": 3.41389, "time": 0.81328} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.02398, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38906, "top5_acc": 0.6525, "loss_cls": 3.40927, "loss": 3.40927, "time": 0.81594} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.02396, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38406, "top5_acc": 0.64156, "loss_cls": 3.51338, "loss": 3.51338, "time": 0.81617} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.02393, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38688, "top5_acc": 0.64828, "loss_cls": 3.47766, "loss": 3.47766, "time": 0.81955} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.02391, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38703, "top5_acc": 0.64859, "loss_cls": 3.45567, "loss": 3.45567, "time": 0.81911} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.02388, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37203, "top5_acc": 0.64219, "loss_cls": 3.52451, "loss": 3.52451, "time": 0.82016} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.02386, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39312, "top5_acc": 0.64719, "loss_cls": 3.43697, "loss": 3.43697, "time": 0.81776} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.02384, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38016, "top5_acc": 0.64422, "loss_cls": 3.46555, "loss": 3.46555, "time": 0.8191} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.02381, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39344, "top5_acc": 0.65422, "loss_cls": 3.41302, "loss": 3.41302, "time": 0.8188} +{"mode": "train", "epoch": 102, "iter": 1300, "lr": 0.02379, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38281, "top5_acc": 0.63469, "loss_cls": 3.5079, "loss": 3.5079, "time": 0.8176} +{"mode": "train", "epoch": 102, "iter": 1400, "lr": 0.02376, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40016, "top5_acc": 0.65797, "loss_cls": 3.38718, "loss": 3.38718, "time": 0.81729} +{"mode": "train", "epoch": 102, "iter": 1500, "lr": 0.02374, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39094, "top5_acc": 0.64984, "loss_cls": 3.45792, "loss": 3.45792, "time": 0.81826} +{"mode": "train", "epoch": 102, "iter": 1600, "lr": 0.02372, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38609, "top5_acc": 0.64734, "loss_cls": 3.47183, "loss": 3.47183, "time": 0.81186} +{"mode": "train", "epoch": 102, "iter": 1700, "lr": 0.02369, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37578, "top5_acc": 0.64094, "loss_cls": 3.51008, "loss": 3.51008, "time": 0.81127} +{"mode": "train", "epoch": 102, "iter": 1800, "lr": 0.02367, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38766, "top5_acc": 0.64672, "loss_cls": 3.48417, "loss": 3.48417, "time": 0.81235} +{"mode": "train", "epoch": 102, "iter": 1900, "lr": 0.02365, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37906, "top5_acc": 0.63031, "loss_cls": 3.52097, "loss": 3.52097, "time": 0.81366} +{"mode": "train", "epoch": 102, "iter": 2000, "lr": 0.02362, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37359, "top5_acc": 0.63141, "loss_cls": 3.51302, "loss": 3.51302, "time": 0.81877} +{"mode": "train", "epoch": 102, "iter": 2100, "lr": 0.0236, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38422, "top5_acc": 0.64281, "loss_cls": 3.47411, "loss": 3.47411, "time": 0.82122} +{"mode": "train", "epoch": 102, "iter": 2200, "lr": 0.02357, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38, "top5_acc": 0.64375, "loss_cls": 3.50343, "loss": 3.50343, "time": 0.81859} +{"mode": "train", "epoch": 102, "iter": 2300, "lr": 0.02355, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38844, "top5_acc": 0.64766, "loss_cls": 3.44431, "loss": 3.44431, "time": 0.82602} +{"mode": "train", "epoch": 102, "iter": 2400, "lr": 0.02353, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37469, "top5_acc": 0.64328, "loss_cls": 3.49151, "loss": 3.49151, "time": 0.81572} +{"mode": "train", "epoch": 102, "iter": 2500, "lr": 0.0235, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38234, "top5_acc": 0.63906, "loss_cls": 3.50445, "loss": 3.50445, "time": 0.81682} +{"mode": "train", "epoch": 102, "iter": 2600, "lr": 0.02348, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38531, "top5_acc": 0.65047, "loss_cls": 3.46833, "loss": 3.46833, "time": 0.81464} +{"mode": "train", "epoch": 102, "iter": 2700, "lr": 0.02346, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38, "top5_acc": 0.64719, "loss_cls": 3.46226, "loss": 3.46226, "time": 0.81557} +{"mode": "train", "epoch": 102, "iter": 2800, "lr": 0.02343, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38453, "top5_acc": 0.64922, "loss_cls": 3.49014, "loss": 3.49014, "time": 0.81555} +{"mode": "train", "epoch": 102, "iter": 2900, "lr": 0.02341, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37078, "top5_acc": 0.62828, "loss_cls": 3.57671, "loss": 3.57671, "time": 0.81008} +{"mode": "train", "epoch": 102, "iter": 3000, "lr": 0.02339, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38625, "top5_acc": 0.63922, "loss_cls": 3.47821, "loss": 3.47821, "time": 0.81372} +{"mode": "train", "epoch": 102, "iter": 3100, "lr": 0.02336, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38453, "top5_acc": 0.63891, "loss_cls": 3.50326, "loss": 3.50326, "time": 0.8159} +{"mode": "train", "epoch": 102, "iter": 3200, "lr": 0.02334, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38469, "top5_acc": 0.6375, "loss_cls": 3.48488, "loss": 3.48488, "time": 0.81362} +{"mode": "train", "epoch": 102, "iter": 3300, "lr": 0.02331, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3825, "top5_acc": 0.64547, "loss_cls": 3.48256, "loss": 3.48256, "time": 0.81618} +{"mode": "train", "epoch": 102, "iter": 3400, "lr": 0.02329, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38781, "top5_acc": 0.63453, "loss_cls": 3.52072, "loss": 3.52072, "time": 0.8159} +{"mode": "train", "epoch": 102, "iter": 3500, "lr": 0.02327, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37328, "top5_acc": 0.63547, "loss_cls": 3.52985, "loss": 3.52985, "time": 0.81946} +{"mode": "train", "epoch": 102, "iter": 3600, "lr": 0.02324, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37969, "top5_acc": 0.63516, "loss_cls": 3.52357, "loss": 3.52357, "time": 0.81652} +{"mode": "train", "epoch": 102, "iter": 3700, "lr": 0.02322, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.38125, "top5_acc": 0.64516, "loss_cls": 3.45421, "loss": 3.45421, "time": 0.8125} +{"mode": "val", "epoch": 102, "iter": 309, "lr": 0.02321, "top1_acc": 0.319, "top5_acc": 0.57433, "mean_class_accuracy": 0.31863} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.02319, "memory": 15990, "data_time": 1.31196, "top1_acc": 0.39688, "top5_acc": 0.66156, "loss_cls": 3.4091, "loss": 3.4091, "time": 2.30075} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.02316, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39594, "top5_acc": 0.65484, "loss_cls": 3.42984, "loss": 3.42984, "time": 0.81637} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.02314, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39172, "top5_acc": 0.65734, "loss_cls": 3.42219, "loss": 3.42219, "time": 0.81875} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.02311, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39, "top5_acc": 0.6525, "loss_cls": 3.44647, "loss": 3.44647, "time": 0.81661} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.02309, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40703, "top5_acc": 0.66031, "loss_cls": 3.37549, "loss": 3.37549, "time": 0.81362} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.02307, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3925, "top5_acc": 0.6525, "loss_cls": 3.42847, "loss": 3.42847, "time": 0.81922} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.02304, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39422, "top5_acc": 0.64453, "loss_cls": 3.43872, "loss": 3.43872, "time": 0.82775} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.02302, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39391, "top5_acc": 0.65109, "loss_cls": 3.41038, "loss": 3.41038, "time": 0.81905} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.023, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37953, "top5_acc": 0.64859, "loss_cls": 3.46849, "loss": 3.46849, "time": 0.81718} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.02297, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39172, "top5_acc": 0.65156, "loss_cls": 3.42303, "loss": 3.42303, "time": 0.81735} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.02295, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3975, "top5_acc": 0.65406, "loss_cls": 3.39647, "loss": 3.39647, "time": 0.81995} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.02293, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38688, "top5_acc": 0.64703, "loss_cls": 3.43211, "loss": 3.43211, "time": 0.81731} +{"mode": "train", "epoch": 103, "iter": 1300, "lr": 0.0229, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38016, "top5_acc": 0.64703, "loss_cls": 3.46956, "loss": 3.46956, "time": 0.8148} +{"mode": "train", "epoch": 103, "iter": 1400, "lr": 0.02288, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38422, "top5_acc": 0.63734, "loss_cls": 3.49988, "loss": 3.49988, "time": 0.81394} +{"mode": "train", "epoch": 103, "iter": 1500, "lr": 0.02286, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39547, "top5_acc": 0.65156, "loss_cls": 3.42692, "loss": 3.42692, "time": 0.81447} +{"mode": "train", "epoch": 103, "iter": 1600, "lr": 0.02283, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37359, "top5_acc": 0.64891, "loss_cls": 3.50746, "loss": 3.50746, "time": 0.82214} +{"mode": "train", "epoch": 103, "iter": 1700, "lr": 0.02281, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38, "top5_acc": 0.63859, "loss_cls": 3.50232, "loss": 3.50232, "time": 0.81365} +{"mode": "train", "epoch": 103, "iter": 1800, "lr": 0.02279, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38203, "top5_acc": 0.64109, "loss_cls": 3.48097, "loss": 3.48097, "time": 0.8114} +{"mode": "train", "epoch": 103, "iter": 1900, "lr": 0.02276, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38547, "top5_acc": 0.64172, "loss_cls": 3.47219, "loss": 3.47219, "time": 0.81623} +{"mode": "train", "epoch": 103, "iter": 2000, "lr": 0.02274, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38359, "top5_acc": 0.63828, "loss_cls": 3.47386, "loss": 3.47386, "time": 0.81282} +{"mode": "train", "epoch": 103, "iter": 2100, "lr": 0.02272, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38859, "top5_acc": 0.64203, "loss_cls": 3.5018, "loss": 3.5018, "time": 0.83489} +{"mode": "train", "epoch": 103, "iter": 2200, "lr": 0.02269, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39062, "top5_acc": 0.64484, "loss_cls": 3.45929, "loss": 3.45929, "time": 0.82148} +{"mode": "train", "epoch": 103, "iter": 2300, "lr": 0.02267, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39234, "top5_acc": 0.64734, "loss_cls": 3.44296, "loss": 3.44296, "time": 0.82697} +{"mode": "train", "epoch": 103, "iter": 2400, "lr": 0.02264, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38828, "top5_acc": 0.65375, "loss_cls": 3.4429, "loss": 3.4429, "time": 0.82018} +{"mode": "train", "epoch": 103, "iter": 2500, "lr": 0.02262, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37875, "top5_acc": 0.63984, "loss_cls": 3.49522, "loss": 3.49522, "time": 0.82245} +{"mode": "train", "epoch": 103, "iter": 2600, "lr": 0.0226, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38906, "top5_acc": 0.64812, "loss_cls": 3.4299, "loss": 3.4299, "time": 0.82291} +{"mode": "train", "epoch": 103, "iter": 2700, "lr": 0.02257, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36953, "top5_acc": 0.63672, "loss_cls": 3.54308, "loss": 3.54308, "time": 0.8108} +{"mode": "train", "epoch": 103, "iter": 2800, "lr": 0.02255, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38109, "top5_acc": 0.64266, "loss_cls": 3.48203, "loss": 3.48203, "time": 0.81414} +{"mode": "train", "epoch": 103, "iter": 2900, "lr": 0.02253, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37438, "top5_acc": 0.62859, "loss_cls": 3.55052, "loss": 3.55052, "time": 0.81005} +{"mode": "train", "epoch": 103, "iter": 3000, "lr": 0.0225, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3875, "top5_acc": 0.65047, "loss_cls": 3.47974, "loss": 3.47974, "time": 0.81414} +{"mode": "train", "epoch": 103, "iter": 3100, "lr": 0.02248, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37812, "top5_acc": 0.64375, "loss_cls": 3.49708, "loss": 3.49708, "time": 0.815} +{"mode": "train", "epoch": 103, "iter": 3200, "lr": 0.02246, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39625, "top5_acc": 0.65172, "loss_cls": 3.4242, "loss": 3.4242, "time": 0.81638} +{"mode": "train", "epoch": 103, "iter": 3300, "lr": 0.02243, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37969, "top5_acc": 0.63531, "loss_cls": 3.50248, "loss": 3.50248, "time": 0.81035} +{"mode": "train", "epoch": 103, "iter": 3400, "lr": 0.02241, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39375, "top5_acc": 0.64875, "loss_cls": 3.43052, "loss": 3.43052, "time": 0.81213} +{"mode": "train", "epoch": 103, "iter": 3500, "lr": 0.02239, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.38672, "top5_acc": 0.64875, "loss_cls": 3.44716, "loss": 3.44716, "time": 0.81112} +{"mode": "train", "epoch": 103, "iter": 3600, "lr": 0.02236, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38984, "top5_acc": 0.64391, "loss_cls": 3.4626, "loss": 3.4626, "time": 0.8153} +{"mode": "train", "epoch": 103, "iter": 3700, "lr": 0.02234, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.38734, "top5_acc": 0.64938, "loss_cls": 3.45411, "loss": 3.45411, "time": 0.81124} +{"mode": "val", "epoch": 103, "iter": 309, "lr": 0.02233, "top1_acc": 0.32776, "top5_acc": 0.58578, "mean_class_accuracy": 0.32751} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.02231, "memory": 15990, "data_time": 1.30939, "top1_acc": 0.40516, "top5_acc": 0.66859, "loss_cls": 3.33797, "loss": 3.33797, "time": 2.30815} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.02228, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3925, "top5_acc": 0.65328, "loss_cls": 3.42708, "loss": 3.42708, "time": 0.81777} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.02226, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39484, "top5_acc": 0.65688, "loss_cls": 3.40247, "loss": 3.40247, "time": 0.81602} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.02224, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39016, "top5_acc": 0.65031, "loss_cls": 3.43193, "loss": 3.43193, "time": 0.81706} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.02221, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38828, "top5_acc": 0.65766, "loss_cls": 3.39038, "loss": 3.39038, "time": 0.81171} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.02219, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3925, "top5_acc": 0.64625, "loss_cls": 3.43043, "loss": 3.43043, "time": 0.81098} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.02217, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38609, "top5_acc": 0.65422, "loss_cls": 3.4282, "loss": 3.4282, "time": 0.8244} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.02214, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38609, "top5_acc": 0.64641, "loss_cls": 3.43113, "loss": 3.43113, "time": 0.81785} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.02212, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39188, "top5_acc": 0.65438, "loss_cls": 3.42071, "loss": 3.42071, "time": 0.8132} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.0221, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38828, "top5_acc": 0.64672, "loss_cls": 3.44206, "loss": 3.44206, "time": 0.82333} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.02208, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39781, "top5_acc": 0.65297, "loss_cls": 3.39074, "loss": 3.39074, "time": 0.81659} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.02205, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.40234, "top5_acc": 0.65312, "loss_cls": 3.39767, "loss": 3.39767, "time": 0.81224} +{"mode": "train", "epoch": 104, "iter": 1300, "lr": 0.02203, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40078, "top5_acc": 0.6575, "loss_cls": 3.36907, "loss": 3.36907, "time": 0.81677} +{"mode": "train", "epoch": 104, "iter": 1400, "lr": 0.02201, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38281, "top5_acc": 0.64469, "loss_cls": 3.459, "loss": 3.459, "time": 0.81456} +{"mode": "train", "epoch": 104, "iter": 1500, "lr": 0.02198, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38438, "top5_acc": 0.64312, "loss_cls": 3.49583, "loss": 3.49583, "time": 0.81986} +{"mode": "train", "epoch": 104, "iter": 1600, "lr": 0.02196, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39016, "top5_acc": 0.64844, "loss_cls": 3.45946, "loss": 3.45946, "time": 0.81253} +{"mode": "train", "epoch": 104, "iter": 1700, "lr": 0.02194, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37672, "top5_acc": 0.64422, "loss_cls": 3.46879, "loss": 3.46879, "time": 0.81718} +{"mode": "train", "epoch": 104, "iter": 1800, "lr": 0.02191, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39125, "top5_acc": 0.65141, "loss_cls": 3.44769, "loss": 3.44769, "time": 0.81183} +{"mode": "train", "epoch": 104, "iter": 1900, "lr": 0.02189, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.39734, "top5_acc": 0.65734, "loss_cls": 3.41477, "loss": 3.41477, "time": 0.81254} +{"mode": "train", "epoch": 104, "iter": 2000, "lr": 0.02187, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39047, "top5_acc": 0.65188, "loss_cls": 3.42295, "loss": 3.42295, "time": 0.81405} +{"mode": "train", "epoch": 104, "iter": 2100, "lr": 0.02184, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39016, "top5_acc": 0.64219, "loss_cls": 3.45855, "loss": 3.45855, "time": 0.82966} +{"mode": "train", "epoch": 104, "iter": 2200, "lr": 0.02182, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39344, "top5_acc": 0.64812, "loss_cls": 3.4445, "loss": 3.4445, "time": 0.82341} +{"mode": "train", "epoch": 104, "iter": 2300, "lr": 0.0218, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39047, "top5_acc": 0.6525, "loss_cls": 3.43176, "loss": 3.43176, "time": 0.82272} +{"mode": "train", "epoch": 104, "iter": 2400, "lr": 0.02177, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38891, "top5_acc": 0.6525, "loss_cls": 3.45563, "loss": 3.45563, "time": 0.82258} +{"mode": "train", "epoch": 104, "iter": 2500, "lr": 0.02175, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38734, "top5_acc": 0.64656, "loss_cls": 3.48338, "loss": 3.48338, "time": 0.81972} +{"mode": "train", "epoch": 104, "iter": 2600, "lr": 0.02173, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38797, "top5_acc": 0.64812, "loss_cls": 3.41403, "loss": 3.41403, "time": 0.81898} +{"mode": "train", "epoch": 104, "iter": 2700, "lr": 0.02171, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39031, "top5_acc": 0.64859, "loss_cls": 3.42854, "loss": 3.42854, "time": 0.81166} +{"mode": "train", "epoch": 104, "iter": 2800, "lr": 0.02168, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38734, "top5_acc": 0.64922, "loss_cls": 3.46783, "loss": 3.46783, "time": 0.81465} +{"mode": "train", "epoch": 104, "iter": 2900, "lr": 0.02166, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38531, "top5_acc": 0.64312, "loss_cls": 3.48712, "loss": 3.48712, "time": 0.81846} +{"mode": "train", "epoch": 104, "iter": 3000, "lr": 0.02164, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.39422, "top5_acc": 0.65312, "loss_cls": 3.43146, "loss": 3.43146, "time": 0.81152} +{"mode": "train", "epoch": 104, "iter": 3100, "lr": 0.02161, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39469, "top5_acc": 0.65578, "loss_cls": 3.42038, "loss": 3.42038, "time": 0.81607} +{"mode": "train", "epoch": 104, "iter": 3200, "lr": 0.02159, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38406, "top5_acc": 0.64906, "loss_cls": 3.45922, "loss": 3.45922, "time": 0.81521} +{"mode": "train", "epoch": 104, "iter": 3300, "lr": 0.02157, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38281, "top5_acc": 0.64719, "loss_cls": 3.4806, "loss": 3.4806, "time": 0.82215} +{"mode": "train", "epoch": 104, "iter": 3400, "lr": 0.02154, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38906, "top5_acc": 0.65406, "loss_cls": 3.42522, "loss": 3.42522, "time": 0.81911} +{"mode": "train", "epoch": 104, "iter": 3500, "lr": 0.02152, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39062, "top5_acc": 0.63313, "loss_cls": 3.47565, "loss": 3.47565, "time": 0.8152} +{"mode": "train", "epoch": 104, "iter": 3600, "lr": 0.0215, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38062, "top5_acc": 0.65422, "loss_cls": 3.45963, "loss": 3.45963, "time": 0.81378} +{"mode": "train", "epoch": 104, "iter": 3700, "lr": 0.02148, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.38016, "top5_acc": 0.63969, "loss_cls": 3.48612, "loss": 3.48612, "time": 0.81275} +{"mode": "val", "epoch": 104, "iter": 309, "lr": 0.02146, "top1_acc": 0.30061, "top5_acc": 0.55017, "mean_class_accuracy": 0.30038} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.02144, "memory": 15990, "data_time": 1.28937, "top1_acc": 0.41125, "top5_acc": 0.66766, "loss_cls": 3.31946, "loss": 3.31946, "time": 2.26277} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.02142, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41266, "top5_acc": 0.66594, "loss_cls": 3.33192, "loss": 3.33192, "time": 0.8194} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.0214, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39547, "top5_acc": 0.66094, "loss_cls": 3.37376, "loss": 3.37376, "time": 0.81486} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.02137, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39141, "top5_acc": 0.64812, "loss_cls": 3.41496, "loss": 3.41496, "time": 0.81274} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.02135, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39188, "top5_acc": 0.65062, "loss_cls": 3.41623, "loss": 3.41623, "time": 0.81184} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.02133, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39594, "top5_acc": 0.64922, "loss_cls": 3.3969, "loss": 3.3969, "time": 0.81568} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.0213, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38906, "top5_acc": 0.65422, "loss_cls": 3.42982, "loss": 3.42982, "time": 0.81999} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.02128, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3925, "top5_acc": 0.65453, "loss_cls": 3.39927, "loss": 3.39927, "time": 0.8148} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.02126, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39812, "top5_acc": 0.65094, "loss_cls": 3.40919, "loss": 3.40919, "time": 0.81628} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.02124, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40094, "top5_acc": 0.65312, "loss_cls": 3.38399, "loss": 3.38399, "time": 0.82012} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.02121, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38156, "top5_acc": 0.64516, "loss_cls": 3.46276, "loss": 3.46276, "time": 0.81829} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.02119, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39656, "top5_acc": 0.66266, "loss_cls": 3.38931, "loss": 3.38931, "time": 0.81874} +{"mode": "train", "epoch": 105, "iter": 1300, "lr": 0.02117, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38859, "top5_acc": 0.65578, "loss_cls": 3.41576, "loss": 3.41576, "time": 0.81885} +{"mode": "train", "epoch": 105, "iter": 1400, "lr": 0.02114, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39656, "top5_acc": 0.65266, "loss_cls": 3.38417, "loss": 3.38417, "time": 0.81909} +{"mode": "train", "epoch": 105, "iter": 1500, "lr": 0.02112, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39734, "top5_acc": 0.65656, "loss_cls": 3.41207, "loss": 3.41207, "time": 0.8163} +{"mode": "train", "epoch": 105, "iter": 1600, "lr": 0.0211, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39219, "top5_acc": 0.65094, "loss_cls": 3.41772, "loss": 3.41772, "time": 0.8191} +{"mode": "train", "epoch": 105, "iter": 1700, "lr": 0.02108, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38484, "top5_acc": 0.6475, "loss_cls": 3.44254, "loss": 3.44254, "time": 0.81738} +{"mode": "train", "epoch": 105, "iter": 1800, "lr": 0.02105, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40016, "top5_acc": 0.65828, "loss_cls": 3.37165, "loss": 3.37165, "time": 0.81688} +{"mode": "train", "epoch": 105, "iter": 1900, "lr": 0.02103, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39953, "top5_acc": 0.65641, "loss_cls": 3.37849, "loss": 3.37849, "time": 0.81658} +{"mode": "train", "epoch": 105, "iter": 2000, "lr": 0.02101, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39344, "top5_acc": 0.64938, "loss_cls": 3.43495, "loss": 3.43495, "time": 0.81532} +{"mode": "train", "epoch": 105, "iter": 2100, "lr": 0.02098, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39266, "top5_acc": 0.65922, "loss_cls": 3.38084, "loss": 3.38084, "time": 0.82071} +{"mode": "train", "epoch": 105, "iter": 2200, "lr": 0.02096, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38781, "top5_acc": 0.64078, "loss_cls": 3.47101, "loss": 3.47101, "time": 0.8261} +{"mode": "train", "epoch": 105, "iter": 2300, "lr": 0.02094, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38875, "top5_acc": 0.655, "loss_cls": 3.42813, "loss": 3.42813, "time": 0.82112} +{"mode": "train", "epoch": 105, "iter": 2400, "lr": 0.02092, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38609, "top5_acc": 0.64484, "loss_cls": 3.44, "loss": 3.44, "time": 0.82725} +{"mode": "train", "epoch": 105, "iter": 2500, "lr": 0.02089, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39297, "top5_acc": 0.64797, "loss_cls": 3.4203, "loss": 3.4203, "time": 0.82155} +{"mode": "train", "epoch": 105, "iter": 2600, "lr": 0.02087, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39328, "top5_acc": 0.64812, "loss_cls": 3.41505, "loss": 3.41505, "time": 0.81369} +{"mode": "train", "epoch": 105, "iter": 2700, "lr": 0.02085, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39375, "top5_acc": 0.65734, "loss_cls": 3.42361, "loss": 3.42361, "time": 0.81303} +{"mode": "train", "epoch": 105, "iter": 2800, "lr": 0.02083, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38266, "top5_acc": 0.64312, "loss_cls": 3.46703, "loss": 3.46703, "time": 0.81477} +{"mode": "train", "epoch": 105, "iter": 2900, "lr": 0.0208, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38953, "top5_acc": 0.65641, "loss_cls": 3.4353, "loss": 3.4353, "time": 0.81984} +{"mode": "train", "epoch": 105, "iter": 3000, "lr": 0.02078, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38047, "top5_acc": 0.64, "loss_cls": 3.49036, "loss": 3.49036, "time": 0.8132} +{"mode": "train", "epoch": 105, "iter": 3100, "lr": 0.02076, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39219, "top5_acc": 0.65094, "loss_cls": 3.43891, "loss": 3.43891, "time": 0.81634} +{"mode": "train", "epoch": 105, "iter": 3200, "lr": 0.02073, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38375, "top5_acc": 0.63875, "loss_cls": 3.48387, "loss": 3.48387, "time": 0.81832} +{"mode": "train", "epoch": 105, "iter": 3300, "lr": 0.02071, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38969, "top5_acc": 0.65156, "loss_cls": 3.45501, "loss": 3.45501, "time": 0.81055} +{"mode": "train", "epoch": 105, "iter": 3400, "lr": 0.02069, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3825, "top5_acc": 0.6475, "loss_cls": 3.45696, "loss": 3.45696, "time": 0.81384} +{"mode": "train", "epoch": 105, "iter": 3500, "lr": 0.02067, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38938, "top5_acc": 0.64641, "loss_cls": 3.45784, "loss": 3.45784, "time": 0.81531} +{"mode": "train", "epoch": 105, "iter": 3600, "lr": 0.02064, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39125, "top5_acc": 0.64953, "loss_cls": 3.45932, "loss": 3.45932, "time": 0.81879} +{"mode": "train", "epoch": 105, "iter": 3700, "lr": 0.02062, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.405, "top5_acc": 0.64766, "loss_cls": 3.41695, "loss": 3.41695, "time": 0.81371} +{"mode": "val", "epoch": 105, "iter": 309, "lr": 0.02061, "top1_acc": 0.33126, "top5_acc": 0.58851, "mean_class_accuracy": 0.33094} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.02059, "memory": 15990, "data_time": 1.29489, "top1_acc": 0.41156, "top5_acc": 0.67125, "loss_cls": 3.33337, "loss": 3.33337, "time": 2.26475} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.02057, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40609, "top5_acc": 0.66734, "loss_cls": 3.32453, "loss": 3.32453, "time": 0.81626} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.02054, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40922, "top5_acc": 0.66625, "loss_cls": 3.33868, "loss": 3.33868, "time": 0.8167} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.02052, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39188, "top5_acc": 0.65812, "loss_cls": 3.38421, "loss": 3.38421, "time": 0.8174} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.0205, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40469, "top5_acc": 0.6625, "loss_cls": 3.35196, "loss": 3.35196, "time": 0.8126} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.02048, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39875, "top5_acc": 0.65594, "loss_cls": 3.38028, "loss": 3.38028, "time": 0.81879} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.02045, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39828, "top5_acc": 0.66281, "loss_cls": 3.37419, "loss": 3.37419, "time": 0.82032} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.02043, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4, "top5_acc": 0.65859, "loss_cls": 3.3993, "loss": 3.3993, "time": 0.81764} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.02041, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39, "top5_acc": 0.65188, "loss_cls": 3.41358, "loss": 3.41358, "time": 0.81642} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.02039, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39594, "top5_acc": 0.65156, "loss_cls": 3.39793, "loss": 3.39793, "time": 0.81973} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.02036, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39688, "top5_acc": 0.65766, "loss_cls": 3.3751, "loss": 3.3751, "time": 0.81565} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.02034, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38703, "top5_acc": 0.655, "loss_cls": 3.44027, "loss": 3.44027, "time": 0.81621} +{"mode": "train", "epoch": 106, "iter": 1300, "lr": 0.02032, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39641, "top5_acc": 0.65828, "loss_cls": 3.39522, "loss": 3.39522, "time": 0.81612} +{"mode": "train", "epoch": 106, "iter": 1400, "lr": 0.0203, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39141, "top5_acc": 0.65438, "loss_cls": 3.40956, "loss": 3.40956, "time": 0.81785} +{"mode": "train", "epoch": 106, "iter": 1500, "lr": 0.02027, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39562, "top5_acc": 0.65766, "loss_cls": 3.41517, "loss": 3.41517, "time": 0.81897} +{"mode": "train", "epoch": 106, "iter": 1600, "lr": 0.02025, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38938, "top5_acc": 0.64625, "loss_cls": 3.43379, "loss": 3.43379, "time": 0.82266} +{"mode": "train", "epoch": 106, "iter": 1700, "lr": 0.02023, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40219, "top5_acc": 0.66344, "loss_cls": 3.38445, "loss": 3.38445, "time": 0.82084} +{"mode": "train", "epoch": 106, "iter": 1800, "lr": 0.02021, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3925, "top5_acc": 0.65594, "loss_cls": 3.42396, "loss": 3.42396, "time": 0.81814} +{"mode": "train", "epoch": 106, "iter": 1900, "lr": 0.02018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4075, "top5_acc": 0.65938, "loss_cls": 3.37833, "loss": 3.37833, "time": 0.81833} +{"mode": "train", "epoch": 106, "iter": 2000, "lr": 0.02016, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39891, "top5_acc": 0.65984, "loss_cls": 3.37229, "loss": 3.37229, "time": 0.81212} +{"mode": "train", "epoch": 106, "iter": 2100, "lr": 0.02014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40391, "top5_acc": 0.66266, "loss_cls": 3.38331, "loss": 3.38331, "time": 0.82285} +{"mode": "train", "epoch": 106, "iter": 2200, "lr": 0.02012, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40203, "top5_acc": 0.66547, "loss_cls": 3.36115, "loss": 3.36115, "time": 0.83241} +{"mode": "train", "epoch": 106, "iter": 2300, "lr": 0.02009, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.395, "top5_acc": 0.65547, "loss_cls": 3.4024, "loss": 3.4024, "time": 0.82322} +{"mode": "train", "epoch": 106, "iter": 2400, "lr": 0.02007, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40172, "top5_acc": 0.65312, "loss_cls": 3.41281, "loss": 3.41281, "time": 0.8263} +{"mode": "train", "epoch": 106, "iter": 2500, "lr": 0.02005, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39281, "top5_acc": 0.65578, "loss_cls": 3.3845, "loss": 3.3845, "time": 0.81904} +{"mode": "train", "epoch": 106, "iter": 2600, "lr": 0.02003, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39375, "top5_acc": 0.65422, "loss_cls": 3.4279, "loss": 3.4279, "time": 0.81343} +{"mode": "train", "epoch": 106, "iter": 2700, "lr": 0.02, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39109, "top5_acc": 0.64344, "loss_cls": 3.47781, "loss": 3.47781, "time": 0.81785} +{"mode": "train", "epoch": 106, "iter": 2800, "lr": 0.01998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39688, "top5_acc": 0.65141, "loss_cls": 3.43942, "loss": 3.43942, "time": 0.81311} +{"mode": "train", "epoch": 106, "iter": 2900, "lr": 0.01996, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39859, "top5_acc": 0.65688, "loss_cls": 3.39158, "loss": 3.39158, "time": 0.81276} +{"mode": "train", "epoch": 106, "iter": 3000, "lr": 0.01994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39234, "top5_acc": 0.65875, "loss_cls": 3.39921, "loss": 3.39921, "time": 0.81394} +{"mode": "train", "epoch": 106, "iter": 3100, "lr": 0.01991, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40188, "top5_acc": 0.66031, "loss_cls": 3.37014, "loss": 3.37014, "time": 0.81702} +{"mode": "train", "epoch": 106, "iter": 3200, "lr": 0.01989, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.39344, "top5_acc": 0.65703, "loss_cls": 3.39836, "loss": 3.39836, "time": 0.81725} +{"mode": "train", "epoch": 106, "iter": 3300, "lr": 0.01987, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37188, "top5_acc": 0.64031, "loss_cls": 3.52143, "loss": 3.52143, "time": 0.81615} +{"mode": "train", "epoch": 106, "iter": 3400, "lr": 0.01985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39391, "top5_acc": 0.65281, "loss_cls": 3.41392, "loss": 3.41392, "time": 0.82418} +{"mode": "train", "epoch": 106, "iter": 3500, "lr": 0.01983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38625, "top5_acc": 0.64828, "loss_cls": 3.44961, "loss": 3.44961, "time": 0.81125} +{"mode": "train", "epoch": 106, "iter": 3600, "lr": 0.0198, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39359, "top5_acc": 0.65438, "loss_cls": 3.36904, "loss": 3.36904, "time": 0.81412} +{"mode": "train", "epoch": 106, "iter": 3700, "lr": 0.01978, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40188, "top5_acc": 0.65438, "loss_cls": 3.39805, "loss": 3.39805, "time": 0.81569} +{"mode": "val", "epoch": 106, "iter": 309, "lr": 0.01977, "top1_acc": 0.3311, "top5_acc": 0.59413, "mean_class_accuracy": 0.33096} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.01975, "memory": 15990, "data_time": 1.31596, "top1_acc": 0.40734, "top5_acc": 0.66828, "loss_cls": 3.33183, "loss": 3.33183, "time": 2.29126} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.01973, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40578, "top5_acc": 0.66281, "loss_cls": 3.36746, "loss": 3.36746, "time": 0.81401} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.0197, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40781, "top5_acc": 0.67406, "loss_cls": 3.29549, "loss": 3.29549, "time": 0.81531} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.01968, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40234, "top5_acc": 0.66641, "loss_cls": 3.33228, "loss": 3.33228, "time": 0.82193} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.01966, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40453, "top5_acc": 0.6675, "loss_cls": 3.33328, "loss": 3.33328, "time": 0.82115} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.01964, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40312, "top5_acc": 0.66297, "loss_cls": 3.36193, "loss": 3.36193, "time": 0.82126} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.01961, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39547, "top5_acc": 0.66047, "loss_cls": 3.36916, "loss": 3.36916, "time": 0.81967} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.01959, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39875, "top5_acc": 0.65641, "loss_cls": 3.38564, "loss": 3.38564, "time": 0.81645} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.01957, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.40641, "top5_acc": 0.66797, "loss_cls": 3.32425, "loss": 3.32425, "time": 0.82514} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.01955, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39375, "top5_acc": 0.65297, "loss_cls": 3.40456, "loss": 3.40456, "time": 0.81852} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.01953, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39422, "top5_acc": 0.65656, "loss_cls": 3.40827, "loss": 3.40827, "time": 0.81947} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.0195, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39844, "top5_acc": 0.65688, "loss_cls": 3.40751, "loss": 3.40751, "time": 0.81651} +{"mode": "train", "epoch": 107, "iter": 1300, "lr": 0.01948, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40812, "top5_acc": 0.65844, "loss_cls": 3.37012, "loss": 3.37012, "time": 0.82184} +{"mode": "train", "epoch": 107, "iter": 1400, "lr": 0.01946, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39406, "top5_acc": 0.67109, "loss_cls": 3.37623, "loss": 3.37623, "time": 0.81758} +{"mode": "train", "epoch": 107, "iter": 1500, "lr": 0.01944, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39906, "top5_acc": 0.66188, "loss_cls": 3.37404, "loss": 3.37404, "time": 0.81932} +{"mode": "train", "epoch": 107, "iter": 1600, "lr": 0.01942, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.40219, "top5_acc": 0.66406, "loss_cls": 3.35866, "loss": 3.35866, "time": 0.81356} +{"mode": "train", "epoch": 107, "iter": 1700, "lr": 0.01939, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40391, "top5_acc": 0.66219, "loss_cls": 3.38443, "loss": 3.38443, "time": 0.81673} +{"mode": "train", "epoch": 107, "iter": 1800, "lr": 0.01937, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39891, "top5_acc": 0.65766, "loss_cls": 3.4086, "loss": 3.4086, "time": 0.81811} +{"mode": "train", "epoch": 107, "iter": 1900, "lr": 0.01935, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39672, "top5_acc": 0.66141, "loss_cls": 3.36537, "loss": 3.36537, "time": 0.82137} +{"mode": "train", "epoch": 107, "iter": 2000, "lr": 0.01933, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40141, "top5_acc": 0.65438, "loss_cls": 3.40017, "loss": 3.40017, "time": 0.81643} +{"mode": "train", "epoch": 107, "iter": 2100, "lr": 0.0193, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39906, "top5_acc": 0.65609, "loss_cls": 3.37159, "loss": 3.37159, "time": 0.81948} +{"mode": "train", "epoch": 107, "iter": 2200, "lr": 0.01928, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40453, "top5_acc": 0.65781, "loss_cls": 3.38169, "loss": 3.38169, "time": 0.83092} +{"mode": "train", "epoch": 107, "iter": 2300, "lr": 0.01926, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39953, "top5_acc": 0.66297, "loss_cls": 3.35745, "loss": 3.35745, "time": 0.8201} +{"mode": "train", "epoch": 107, "iter": 2400, "lr": 0.01924, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39156, "top5_acc": 0.655, "loss_cls": 3.40995, "loss": 3.40995, "time": 0.82386} +{"mode": "train", "epoch": 107, "iter": 2500, "lr": 0.01922, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3875, "top5_acc": 0.65547, "loss_cls": 3.38401, "loss": 3.38401, "time": 0.81916} +{"mode": "train", "epoch": 107, "iter": 2600, "lr": 0.01919, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39438, "top5_acc": 0.65922, "loss_cls": 3.38628, "loss": 3.38628, "time": 0.82017} +{"mode": "train", "epoch": 107, "iter": 2700, "lr": 0.01917, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39531, "top5_acc": 0.66031, "loss_cls": 3.40334, "loss": 3.40334, "time": 0.81649} +{"mode": "train", "epoch": 107, "iter": 2800, "lr": 0.01915, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.39719, "top5_acc": 0.67109, "loss_cls": 3.36566, "loss": 3.36566, "time": 0.81414} +{"mode": "train", "epoch": 107, "iter": 2900, "lr": 0.01913, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40109, "top5_acc": 0.65766, "loss_cls": 3.37403, "loss": 3.37403, "time": 0.81394} +{"mode": "train", "epoch": 107, "iter": 3000, "lr": 0.01911, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38891, "top5_acc": 0.64719, "loss_cls": 3.44586, "loss": 3.44586, "time": 0.8131} +{"mode": "train", "epoch": 107, "iter": 3100, "lr": 0.01908, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38719, "top5_acc": 0.65188, "loss_cls": 3.43172, "loss": 3.43172, "time": 0.81846} +{"mode": "train", "epoch": 107, "iter": 3200, "lr": 0.01906, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39, "top5_acc": 0.64672, "loss_cls": 3.44112, "loss": 3.44112, "time": 0.81279} +{"mode": "train", "epoch": 107, "iter": 3300, "lr": 0.01904, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39781, "top5_acc": 0.65906, "loss_cls": 3.36279, "loss": 3.36279, "time": 0.81745} +{"mode": "train", "epoch": 107, "iter": 3400, "lr": 0.01902, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40016, "top5_acc": 0.65516, "loss_cls": 3.38971, "loss": 3.38971, "time": 0.81728} +{"mode": "train", "epoch": 107, "iter": 3500, "lr": 0.019, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40078, "top5_acc": 0.65844, "loss_cls": 3.38433, "loss": 3.38433, "time": 0.81394} +{"mode": "train", "epoch": 107, "iter": 3600, "lr": 0.01897, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39344, "top5_acc": 0.65219, "loss_cls": 3.38275, "loss": 3.38275, "time": 0.81472} +{"mode": "train", "epoch": 107, "iter": 3700, "lr": 0.01895, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39312, "top5_acc": 0.66516, "loss_cls": 3.3862, "loss": 3.3862, "time": 0.81499} +{"mode": "val", "epoch": 107, "iter": 309, "lr": 0.01894, "top1_acc": 0.34417, "top5_acc": 0.59505, "mean_class_accuracy": 0.34394} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.01892, "memory": 15990, "data_time": 1.38183, "top1_acc": 0.39703, "top5_acc": 0.66141, "loss_cls": 3.38397, "loss": 3.38397, "time": 2.35461} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0189, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41625, "top5_acc": 0.67906, "loss_cls": 3.25859, "loss": 3.25859, "time": 0.81595} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.01888, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41234, "top5_acc": 0.67359, "loss_cls": 3.30382, "loss": 3.30382, "time": 0.8135} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.01886, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40484, "top5_acc": 0.67219, "loss_cls": 3.32264, "loss": 3.32264, "time": 0.81844} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.01883, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39953, "top5_acc": 0.66516, "loss_cls": 3.37, "loss": 3.37, "time": 0.81566} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.01881, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.40469, "top5_acc": 0.66172, "loss_cls": 3.36236, "loss": 3.36236, "time": 0.8181} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.01879, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40672, "top5_acc": 0.66516, "loss_cls": 3.3481, "loss": 3.3481, "time": 0.82222} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.01877, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41094, "top5_acc": 0.66328, "loss_cls": 3.31959, "loss": 3.31959, "time": 0.81875} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.01875, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39297, "top5_acc": 0.65984, "loss_cls": 3.342, "loss": 3.342, "time": 0.822} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.01872, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40531, "top5_acc": 0.66844, "loss_cls": 3.34134, "loss": 3.34134, "time": 0.81757} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.0187, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40406, "top5_acc": 0.66266, "loss_cls": 3.36118, "loss": 3.36118, "time": 0.81402} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.01868, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40219, "top5_acc": 0.65875, "loss_cls": 3.38391, "loss": 3.38391, "time": 0.82174} +{"mode": "train", "epoch": 108, "iter": 1300, "lr": 0.01866, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39984, "top5_acc": 0.67031, "loss_cls": 3.33448, "loss": 3.33448, "time": 0.81804} +{"mode": "train", "epoch": 108, "iter": 1400, "lr": 0.01864, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40016, "top5_acc": 0.66703, "loss_cls": 3.37739, "loss": 3.37739, "time": 0.81497} +{"mode": "train", "epoch": 108, "iter": 1500, "lr": 0.01862, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42266, "top5_acc": 0.67406, "loss_cls": 3.27324, "loss": 3.27324, "time": 0.81907} +{"mode": "train", "epoch": 108, "iter": 1600, "lr": 0.01859, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40109, "top5_acc": 0.6575, "loss_cls": 3.38584, "loss": 3.38584, "time": 0.81844} +{"mode": "train", "epoch": 108, "iter": 1700, "lr": 0.01857, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39594, "top5_acc": 0.66703, "loss_cls": 3.38023, "loss": 3.38023, "time": 0.81504} +{"mode": "train", "epoch": 108, "iter": 1800, "lr": 0.01855, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.39609, "top5_acc": 0.65906, "loss_cls": 3.38154, "loss": 3.38154, "time": 0.81382} +{"mode": "train", "epoch": 108, "iter": 1900, "lr": 0.01853, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40016, "top5_acc": 0.66188, "loss_cls": 3.37432, "loss": 3.37432, "time": 0.81199} +{"mode": "train", "epoch": 108, "iter": 2000, "lr": 0.01851, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39984, "top5_acc": 0.65891, "loss_cls": 3.37476, "loss": 3.37476, "time": 0.81129} +{"mode": "train", "epoch": 108, "iter": 2100, "lr": 0.01848, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39641, "top5_acc": 0.65078, "loss_cls": 3.38914, "loss": 3.38914, "time": 0.81799} +{"mode": "train", "epoch": 108, "iter": 2200, "lr": 0.01846, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40016, "top5_acc": 0.66062, "loss_cls": 3.38269, "loss": 3.38269, "time": 0.82724} +{"mode": "train", "epoch": 108, "iter": 2300, "lr": 0.01844, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39609, "top5_acc": 0.65266, "loss_cls": 3.40346, "loss": 3.40346, "time": 0.82777} +{"mode": "train", "epoch": 108, "iter": 2400, "lr": 0.01842, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40656, "top5_acc": 0.66531, "loss_cls": 3.34843, "loss": 3.34843, "time": 0.82906} +{"mode": "train", "epoch": 108, "iter": 2500, "lr": 0.0184, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40219, "top5_acc": 0.65609, "loss_cls": 3.37764, "loss": 3.37764, "time": 0.8198} +{"mode": "train", "epoch": 108, "iter": 2600, "lr": 0.01838, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.38969, "top5_acc": 0.66312, "loss_cls": 3.37868, "loss": 3.37868, "time": 0.81152} +{"mode": "train", "epoch": 108, "iter": 2700, "lr": 0.01835, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39156, "top5_acc": 0.66016, "loss_cls": 3.38048, "loss": 3.38048, "time": 0.81568} +{"mode": "train", "epoch": 108, "iter": 2800, "lr": 0.01833, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40375, "top5_acc": 0.66766, "loss_cls": 3.37118, "loss": 3.37118, "time": 0.81546} +{"mode": "train", "epoch": 108, "iter": 2900, "lr": 0.01831, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40078, "top5_acc": 0.65562, "loss_cls": 3.40004, "loss": 3.40004, "time": 0.81589} +{"mode": "train", "epoch": 108, "iter": 3000, "lr": 0.01829, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4075, "top5_acc": 0.66391, "loss_cls": 3.3436, "loss": 3.3436, "time": 0.81518} +{"mode": "train", "epoch": 108, "iter": 3100, "lr": 0.01827, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39953, "top5_acc": 0.66203, "loss_cls": 3.37807, "loss": 3.37807, "time": 0.81798} +{"mode": "train", "epoch": 108, "iter": 3200, "lr": 0.01825, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40297, "top5_acc": 0.65688, "loss_cls": 3.3499, "loss": 3.3499, "time": 0.81501} +{"mode": "train", "epoch": 108, "iter": 3300, "lr": 0.01823, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41438, "top5_acc": 0.67422, "loss_cls": 3.30707, "loss": 3.30707, "time": 0.81431} +{"mode": "train", "epoch": 108, "iter": 3400, "lr": 0.0182, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40719, "top5_acc": 0.66109, "loss_cls": 3.34204, "loss": 3.34204, "time": 0.8148} +{"mode": "train", "epoch": 108, "iter": 3500, "lr": 0.01818, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40047, "top5_acc": 0.67141, "loss_cls": 3.3612, "loss": 3.3612, "time": 0.82168} +{"mode": "train", "epoch": 108, "iter": 3600, "lr": 0.01816, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40406, "top5_acc": 0.65797, "loss_cls": 3.37971, "loss": 3.37971, "time": 0.81693} +{"mode": "train", "epoch": 108, "iter": 3700, "lr": 0.01814, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39734, "top5_acc": 0.65406, "loss_cls": 3.39729, "loss": 3.39729, "time": 0.81042} +{"mode": "val", "epoch": 108, "iter": 309, "lr": 0.01813, "top1_acc": 0.3424, "top5_acc": 0.60275, "mean_class_accuracy": 0.34229} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.01811, "memory": 15990, "data_time": 1.35403, "top1_acc": 0.42703, "top5_acc": 0.67766, "loss_cls": 3.26356, "loss": 3.26356, "time": 2.3372} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.01809, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41797, "top5_acc": 0.67438, "loss_cls": 3.28272, "loss": 3.28272, "time": 0.82335} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.01806, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41266, "top5_acc": 0.67203, "loss_cls": 3.29026, "loss": 3.29026, "time": 0.82356} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.01804, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41391, "top5_acc": 0.66859, "loss_cls": 3.27382, "loss": 3.27382, "time": 0.8192} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.01802, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41453, "top5_acc": 0.68266, "loss_cls": 3.24111, "loss": 3.24111, "time": 0.81809} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.018, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42234, "top5_acc": 0.68406, "loss_cls": 3.24075, "loss": 3.24075, "time": 0.82108} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.01798, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41859, "top5_acc": 0.68078, "loss_cls": 3.27264, "loss": 3.27264, "time": 0.81967} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.01796, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41516, "top5_acc": 0.66656, "loss_cls": 3.32642, "loss": 3.32642, "time": 0.81753} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.01794, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40344, "top5_acc": 0.66453, "loss_cls": 3.3489, "loss": 3.3489, "time": 0.82574} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.01791, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.415, "top5_acc": 0.66703, "loss_cls": 3.32707, "loss": 3.32707, "time": 0.8162} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.01789, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40516, "top5_acc": 0.67516, "loss_cls": 3.31713, "loss": 3.31713, "time": 0.81075} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.01787, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39766, "top5_acc": 0.65578, "loss_cls": 3.39618, "loss": 3.39618, "time": 0.81675} +{"mode": "train", "epoch": 109, "iter": 1300, "lr": 0.01785, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40328, "top5_acc": 0.67078, "loss_cls": 3.3109, "loss": 3.3109, "time": 0.81627} +{"mode": "train", "epoch": 109, "iter": 1400, "lr": 0.01783, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40391, "top5_acc": 0.66281, "loss_cls": 3.35845, "loss": 3.35845, "time": 0.81877} +{"mode": "train", "epoch": 109, "iter": 1500, "lr": 0.01781, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40359, "top5_acc": 0.66109, "loss_cls": 3.36694, "loss": 3.36694, "time": 0.81518} +{"mode": "train", "epoch": 109, "iter": 1600, "lr": 0.01779, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39844, "top5_acc": 0.65438, "loss_cls": 3.41218, "loss": 3.41218, "time": 0.81143} +{"mode": "train", "epoch": 109, "iter": 1700, "lr": 0.01776, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41484, "top5_acc": 0.67203, "loss_cls": 3.28889, "loss": 3.28889, "time": 0.81463} +{"mode": "train", "epoch": 109, "iter": 1800, "lr": 0.01774, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40719, "top5_acc": 0.67156, "loss_cls": 3.32031, "loss": 3.32031, "time": 0.81719} +{"mode": "train", "epoch": 109, "iter": 1900, "lr": 0.01772, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40578, "top5_acc": 0.65953, "loss_cls": 3.35649, "loss": 3.35649, "time": 0.81735} +{"mode": "train", "epoch": 109, "iter": 2000, "lr": 0.0177, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39484, "top5_acc": 0.65609, "loss_cls": 3.38575, "loss": 3.38575, "time": 0.81912} +{"mode": "train", "epoch": 109, "iter": 2100, "lr": 0.01768, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40797, "top5_acc": 0.66344, "loss_cls": 3.35055, "loss": 3.35055, "time": 0.81331} +{"mode": "train", "epoch": 109, "iter": 2200, "lr": 0.01766, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40234, "top5_acc": 0.66391, "loss_cls": 3.33701, "loss": 3.33701, "time": 0.83221} +{"mode": "train", "epoch": 109, "iter": 2300, "lr": 0.01764, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40641, "top5_acc": 0.6625, "loss_cls": 3.34806, "loss": 3.34806, "time": 0.82547} +{"mode": "train", "epoch": 109, "iter": 2400, "lr": 0.01761, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40703, "top5_acc": 0.66734, "loss_cls": 3.32874, "loss": 3.32874, "time": 0.83161} +{"mode": "train", "epoch": 109, "iter": 2500, "lr": 0.01759, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40219, "top5_acc": 0.66547, "loss_cls": 3.33162, "loss": 3.33162, "time": 0.81759} +{"mode": "train", "epoch": 109, "iter": 2600, "lr": 0.01757, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.395, "top5_acc": 0.64906, "loss_cls": 3.41618, "loss": 3.41618, "time": 0.81632} +{"mode": "train", "epoch": 109, "iter": 2700, "lr": 0.01755, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39984, "top5_acc": 0.65812, "loss_cls": 3.38316, "loss": 3.38316, "time": 0.81358} +{"mode": "train", "epoch": 109, "iter": 2800, "lr": 0.01753, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40734, "top5_acc": 0.66297, "loss_cls": 3.34292, "loss": 3.34292, "time": 0.81644} +{"mode": "train", "epoch": 109, "iter": 2900, "lr": 0.01751, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38891, "top5_acc": 0.6675, "loss_cls": 3.34913, "loss": 3.34913, "time": 0.81399} +{"mode": "train", "epoch": 109, "iter": 3000, "lr": 0.01749, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40953, "top5_acc": 0.66781, "loss_cls": 3.33397, "loss": 3.33397, "time": 0.81247} +{"mode": "train", "epoch": 109, "iter": 3100, "lr": 0.01747, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40281, "top5_acc": 0.66547, "loss_cls": 3.36182, "loss": 3.36182, "time": 0.81268} +{"mode": "train", "epoch": 109, "iter": 3200, "lr": 0.01744, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39812, "top5_acc": 0.64766, "loss_cls": 3.41634, "loss": 3.41634, "time": 0.81515} +{"mode": "train", "epoch": 109, "iter": 3300, "lr": 0.01742, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40281, "top5_acc": 0.66547, "loss_cls": 3.35871, "loss": 3.35871, "time": 0.81411} +{"mode": "train", "epoch": 109, "iter": 3400, "lr": 0.0174, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40297, "top5_acc": 0.66344, "loss_cls": 3.36385, "loss": 3.36385, "time": 0.81801} +{"mode": "train", "epoch": 109, "iter": 3500, "lr": 0.01738, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39688, "top5_acc": 0.65672, "loss_cls": 3.38965, "loss": 3.38965, "time": 0.81212} +{"mode": "train", "epoch": 109, "iter": 3600, "lr": 0.01736, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40281, "top5_acc": 0.66219, "loss_cls": 3.35868, "loss": 3.35868, "time": 0.82285} +{"mode": "train", "epoch": 109, "iter": 3700, "lr": 0.01734, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39391, "top5_acc": 0.65781, "loss_cls": 3.406, "loss": 3.406, "time": 0.82034} +{"mode": "val", "epoch": 109, "iter": 309, "lr": 0.01733, "top1_acc": 0.32984, "top5_acc": 0.59054, "mean_class_accuracy": 0.32964} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.01731, "memory": 15990, "data_time": 1.35296, "top1_acc": 0.42422, "top5_acc": 0.68266, "loss_cls": 3.22779, "loss": 3.22779, "time": 2.33502} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.01729, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41266, "top5_acc": 0.67547, "loss_cls": 3.30522, "loss": 3.30522, "time": 0.81524} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.01727, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42344, "top5_acc": 0.67672, "loss_cls": 3.26259, "loss": 3.26259, "time": 0.82595} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.01724, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42406, "top5_acc": 0.68188, "loss_cls": 3.25089, "loss": 3.25089, "time": 0.8138} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.01722, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41016, "top5_acc": 0.66922, "loss_cls": 3.31061, "loss": 3.31061, "time": 0.81487} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.0172, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42328, "top5_acc": 0.68578, "loss_cls": 3.23809, "loss": 3.23809, "time": 0.81804} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.01718, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41469, "top5_acc": 0.67188, "loss_cls": 3.29501, "loss": 3.29501, "time": 0.82023} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.01716, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41375, "top5_acc": 0.67, "loss_cls": 3.30184, "loss": 3.30184, "time": 0.81828} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.01714, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41547, "top5_acc": 0.67188, "loss_cls": 3.29849, "loss": 3.29849, "time": 0.81988} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.01712, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.415, "top5_acc": 0.66938, "loss_cls": 3.30099, "loss": 3.30099, "time": 0.8198} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.0171, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39562, "top5_acc": 0.65797, "loss_cls": 3.39151, "loss": 3.39151, "time": 0.81792} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.01708, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40609, "top5_acc": 0.67125, "loss_cls": 3.31349, "loss": 3.31349, "time": 0.81749} +{"mode": "train", "epoch": 110, "iter": 1300, "lr": 0.01705, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40469, "top5_acc": 0.66688, "loss_cls": 3.31312, "loss": 3.31312, "time": 0.81209} +{"mode": "train", "epoch": 110, "iter": 1400, "lr": 0.01703, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40312, "top5_acc": 0.67047, "loss_cls": 3.33782, "loss": 3.33782, "time": 0.8121} +{"mode": "train", "epoch": 110, "iter": 1500, "lr": 0.01701, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42219, "top5_acc": 0.67609, "loss_cls": 3.26491, "loss": 3.26491, "time": 0.82107} +{"mode": "train", "epoch": 110, "iter": 1600, "lr": 0.01699, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39922, "top5_acc": 0.66141, "loss_cls": 3.37266, "loss": 3.37266, "time": 0.82139} +{"mode": "train", "epoch": 110, "iter": 1700, "lr": 0.01697, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42016, "top5_acc": 0.67375, "loss_cls": 3.29059, "loss": 3.29059, "time": 0.81756} +{"mode": "train", "epoch": 110, "iter": 1800, "lr": 0.01695, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40953, "top5_acc": 0.66344, "loss_cls": 3.32188, "loss": 3.32188, "time": 0.81595} +{"mode": "train", "epoch": 110, "iter": 1900, "lr": 0.01693, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42031, "top5_acc": 0.67562, "loss_cls": 3.29309, "loss": 3.29309, "time": 0.81574} +{"mode": "train", "epoch": 110, "iter": 2000, "lr": 0.01691, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39969, "top5_acc": 0.66109, "loss_cls": 3.36997, "loss": 3.36997, "time": 0.81846} +{"mode": "train", "epoch": 110, "iter": 2100, "lr": 0.01689, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41062, "top5_acc": 0.65953, "loss_cls": 3.32996, "loss": 3.32996, "time": 0.8203} +{"mode": "train", "epoch": 110, "iter": 2200, "lr": 0.01687, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40359, "top5_acc": 0.66766, "loss_cls": 3.34532, "loss": 3.34532, "time": 0.82562} +{"mode": "train", "epoch": 110, "iter": 2300, "lr": 0.01685, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40672, "top5_acc": 0.66812, "loss_cls": 3.34438, "loss": 3.34438, "time": 0.82238} +{"mode": "train", "epoch": 110, "iter": 2400, "lr": 0.01682, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41953, "top5_acc": 0.67734, "loss_cls": 3.27594, "loss": 3.27594, "time": 0.82833} +{"mode": "train", "epoch": 110, "iter": 2500, "lr": 0.0168, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41031, "top5_acc": 0.66516, "loss_cls": 3.31845, "loss": 3.31845, "time": 0.8182} +{"mode": "train", "epoch": 110, "iter": 2600, "lr": 0.01678, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40719, "top5_acc": 0.65875, "loss_cls": 3.35992, "loss": 3.35992, "time": 0.82182} +{"mode": "train", "epoch": 110, "iter": 2700, "lr": 0.01676, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40453, "top5_acc": 0.66016, "loss_cls": 3.36971, "loss": 3.36971, "time": 0.82243} +{"mode": "train", "epoch": 110, "iter": 2800, "lr": 0.01674, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40688, "top5_acc": 0.6525, "loss_cls": 3.36642, "loss": 3.36642, "time": 0.81269} +{"mode": "train", "epoch": 110, "iter": 2900, "lr": 0.01672, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40641, "top5_acc": 0.65453, "loss_cls": 3.34889, "loss": 3.34889, "time": 0.81428} +{"mode": "train", "epoch": 110, "iter": 3000, "lr": 0.0167, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40328, "top5_acc": 0.66078, "loss_cls": 3.3381, "loss": 3.3381, "time": 0.81663} +{"mode": "train", "epoch": 110, "iter": 3100, "lr": 0.01668, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41156, "top5_acc": 0.66812, "loss_cls": 3.30585, "loss": 3.30585, "time": 0.82} +{"mode": "train", "epoch": 110, "iter": 3200, "lr": 0.01666, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4125, "top5_acc": 0.67078, "loss_cls": 3.3238, "loss": 3.3238, "time": 0.81265} +{"mode": "train", "epoch": 110, "iter": 3300, "lr": 0.01664, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41344, "top5_acc": 0.67125, "loss_cls": 3.29876, "loss": 3.29876, "time": 0.81806} +{"mode": "train", "epoch": 110, "iter": 3400, "lr": 0.01662, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40609, "top5_acc": 0.6775, "loss_cls": 3.29057, "loss": 3.29057, "time": 0.81451} +{"mode": "train", "epoch": 110, "iter": 3500, "lr": 0.01659, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41906, "top5_acc": 0.66578, "loss_cls": 3.30471, "loss": 3.30471, "time": 0.8141} +{"mode": "train", "epoch": 110, "iter": 3600, "lr": 0.01657, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40109, "top5_acc": 0.66312, "loss_cls": 3.37915, "loss": 3.37915, "time": 0.81404} +{"mode": "train", "epoch": 110, "iter": 3700, "lr": 0.01655, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4175, "top5_acc": 0.67172, "loss_cls": 3.31141, "loss": 3.31141, "time": 0.81833} +{"mode": "val", "epoch": 110, "iter": 309, "lr": 0.01654, "top1_acc": 0.34974, "top5_acc": 0.60178, "mean_class_accuracy": 0.34959} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.01652, "memory": 15990, "data_time": 1.36309, "top1_acc": 0.41953, "top5_acc": 0.68016, "loss_cls": 3.22008, "loss": 3.22008, "time": 2.33705} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.0165, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42328, "top5_acc": 0.68047, "loss_cls": 3.24951, "loss": 3.24951, "time": 0.82327} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.01648, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42797, "top5_acc": 0.68516, "loss_cls": 3.20781, "loss": 3.20781, "time": 0.82432} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.01646, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43297, "top5_acc": 0.68422, "loss_cls": 3.18339, "loss": 3.18339, "time": 0.81898} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.01644, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42203, "top5_acc": 0.68328, "loss_cls": 3.24526, "loss": 3.24526, "time": 0.82115} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.01642, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42109, "top5_acc": 0.67969, "loss_cls": 3.2785, "loss": 3.2785, "time": 0.81847} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.0164, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42359, "top5_acc": 0.6775, "loss_cls": 3.2469, "loss": 3.2469, "time": 0.82531} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.01638, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41422, "top5_acc": 0.67109, "loss_cls": 3.27635, "loss": 3.27635, "time": 0.81715} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.01636, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41734, "top5_acc": 0.68359, "loss_cls": 3.22857, "loss": 3.22857, "time": 0.82646} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.01634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41453, "top5_acc": 0.68, "loss_cls": 3.27951, "loss": 3.27951, "time": 0.81531} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.01632, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.415, "top5_acc": 0.67672, "loss_cls": 3.26534, "loss": 3.26534, "time": 0.82063} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.0163, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41484, "top5_acc": 0.67266, "loss_cls": 3.29306, "loss": 3.29306, "time": 0.81503} +{"mode": "train", "epoch": 111, "iter": 1300, "lr": 0.01627, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40938, "top5_acc": 0.67406, "loss_cls": 3.32159, "loss": 3.32159, "time": 0.81787} +{"mode": "train", "epoch": 111, "iter": 1400, "lr": 0.01625, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42516, "top5_acc": 0.67781, "loss_cls": 3.26843, "loss": 3.26843, "time": 0.81186} +{"mode": "train", "epoch": 111, "iter": 1500, "lr": 0.01623, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40094, "top5_acc": 0.67078, "loss_cls": 3.33682, "loss": 3.33682, "time": 0.81727} +{"mode": "train", "epoch": 111, "iter": 1600, "lr": 0.01621, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40984, "top5_acc": 0.66531, "loss_cls": 3.32317, "loss": 3.32317, "time": 0.81952} +{"mode": "train", "epoch": 111, "iter": 1700, "lr": 0.01619, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41266, "top5_acc": 0.67062, "loss_cls": 3.30829, "loss": 3.30829, "time": 0.82113} +{"mode": "train", "epoch": 111, "iter": 1800, "lr": 0.01617, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41031, "top5_acc": 0.66984, "loss_cls": 3.30735, "loss": 3.30735, "time": 0.81587} +{"mode": "train", "epoch": 111, "iter": 1900, "lr": 0.01615, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41281, "top5_acc": 0.66859, "loss_cls": 3.30176, "loss": 3.30176, "time": 0.81323} +{"mode": "train", "epoch": 111, "iter": 2000, "lr": 0.01613, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41594, "top5_acc": 0.67062, "loss_cls": 3.31768, "loss": 3.31768, "time": 0.81582} +{"mode": "train", "epoch": 111, "iter": 2100, "lr": 0.01611, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41688, "top5_acc": 0.66875, "loss_cls": 3.31581, "loss": 3.31581, "time": 0.82344} +{"mode": "train", "epoch": 111, "iter": 2200, "lr": 0.01609, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41094, "top5_acc": 0.66359, "loss_cls": 3.34044, "loss": 3.34044, "time": 0.82334} +{"mode": "train", "epoch": 111, "iter": 2300, "lr": 0.01607, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41078, "top5_acc": 0.66984, "loss_cls": 3.3137, "loss": 3.3137, "time": 0.82397} +{"mode": "train", "epoch": 111, "iter": 2400, "lr": 0.01605, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40578, "top5_acc": 0.66406, "loss_cls": 3.33954, "loss": 3.33954, "time": 0.82309} +{"mode": "train", "epoch": 111, "iter": 2500, "lr": 0.01603, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4225, "top5_acc": 0.67672, "loss_cls": 3.27541, "loss": 3.27541, "time": 0.82368} +{"mode": "train", "epoch": 111, "iter": 2600, "lr": 0.01601, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42094, "top5_acc": 0.67203, "loss_cls": 3.29056, "loss": 3.29056, "time": 0.81922} +{"mode": "train", "epoch": 111, "iter": 2700, "lr": 0.01599, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40609, "top5_acc": 0.66938, "loss_cls": 3.33693, "loss": 3.33693, "time": 0.82292} +{"mode": "train", "epoch": 111, "iter": 2800, "lr": 0.01597, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.41688, "top5_acc": 0.67391, "loss_cls": 3.32651, "loss": 3.32651, "time": 0.81503} +{"mode": "train", "epoch": 111, "iter": 2900, "lr": 0.01595, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42031, "top5_acc": 0.67141, "loss_cls": 3.28421, "loss": 3.28421, "time": 0.8158} +{"mode": "train", "epoch": 111, "iter": 3000, "lr": 0.01593, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40828, "top5_acc": 0.67297, "loss_cls": 3.29856, "loss": 3.29856, "time": 0.81979} +{"mode": "train", "epoch": 111, "iter": 3100, "lr": 0.0159, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41016, "top5_acc": 0.66891, "loss_cls": 3.31927, "loss": 3.31927, "time": 0.8179} +{"mode": "train", "epoch": 111, "iter": 3200, "lr": 0.01588, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41047, "top5_acc": 0.675, "loss_cls": 3.29112, "loss": 3.29112, "time": 0.81669} +{"mode": "train", "epoch": 111, "iter": 3300, "lr": 0.01586, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40125, "top5_acc": 0.65812, "loss_cls": 3.36618, "loss": 3.36618, "time": 0.81594} +{"mode": "train", "epoch": 111, "iter": 3400, "lr": 0.01584, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40375, "top5_acc": 0.66125, "loss_cls": 3.3524, "loss": 3.3524, "time": 0.81234} +{"mode": "train", "epoch": 111, "iter": 3500, "lr": 0.01582, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41219, "top5_acc": 0.66203, "loss_cls": 3.32749, "loss": 3.32749, "time": 0.81578} +{"mode": "train", "epoch": 111, "iter": 3600, "lr": 0.0158, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41141, "top5_acc": 0.67547, "loss_cls": 3.31446, "loss": 3.31446, "time": 0.81437} +{"mode": "train", "epoch": 111, "iter": 3700, "lr": 0.01578, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40406, "top5_acc": 0.66328, "loss_cls": 3.37302, "loss": 3.37302, "time": 0.81531} +{"mode": "val", "epoch": 111, "iter": 309, "lr": 0.01577, "top1_acc": 0.35116, "top5_acc": 0.60872, "mean_class_accuracy": 0.35088} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.01575, "memory": 15990, "data_time": 1.3956, "top1_acc": 0.43234, "top5_acc": 0.68469, "loss_cls": 3.21145, "loss": 3.21145, "time": 2.37691} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.01573, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42375, "top5_acc": 0.6825, "loss_cls": 3.23694, "loss": 3.23694, "time": 0.81956} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.01571, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41922, "top5_acc": 0.68031, "loss_cls": 3.24613, "loss": 3.24613, "time": 0.82521} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.01569, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42625, "top5_acc": 0.68594, "loss_cls": 3.21894, "loss": 3.21894, "time": 0.81728} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.01567, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42859, "top5_acc": 0.67781, "loss_cls": 3.23877, "loss": 3.23877, "time": 0.81937} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.01565, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40594, "top5_acc": 0.66688, "loss_cls": 3.30098, "loss": 3.30098, "time": 0.81917} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.01563, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41594, "top5_acc": 0.67719, "loss_cls": 3.26861, "loss": 3.26861, "time": 0.83003} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.01561, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42078, "top5_acc": 0.67844, "loss_cls": 3.25013, "loss": 3.25013, "time": 0.81801} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.01559, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42672, "top5_acc": 0.68656, "loss_cls": 3.23651, "loss": 3.23651, "time": 0.82207} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.01557, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41984, "top5_acc": 0.67453, "loss_cls": 3.2613, "loss": 3.2613, "time": 0.82084} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.01555, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42062, "top5_acc": 0.68062, "loss_cls": 3.26571, "loss": 3.26571, "time": 0.81503} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.01553, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41109, "top5_acc": 0.67719, "loss_cls": 3.25573, "loss": 3.25573, "time": 0.81725} +{"mode": "train", "epoch": 112, "iter": 1300, "lr": 0.01551, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41875, "top5_acc": 0.6725, "loss_cls": 3.2627, "loss": 3.2627, "time": 0.81351} +{"mode": "train", "epoch": 112, "iter": 1400, "lr": 0.01549, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43172, "top5_acc": 0.67812, "loss_cls": 3.21165, "loss": 3.21165, "time": 0.8163} +{"mode": "train", "epoch": 112, "iter": 1500, "lr": 0.01547, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4225, "top5_acc": 0.67938, "loss_cls": 3.25981, "loss": 3.25981, "time": 0.81768} +{"mode": "train", "epoch": 112, "iter": 1600, "lr": 0.01545, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40281, "top5_acc": 0.67328, "loss_cls": 3.30518, "loss": 3.30518, "time": 0.81335} +{"mode": "train", "epoch": 112, "iter": 1700, "lr": 0.01543, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.41656, "top5_acc": 0.67891, "loss_cls": 3.27207, "loss": 3.27207, "time": 0.81094} +{"mode": "train", "epoch": 112, "iter": 1800, "lr": 0.01541, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41438, "top5_acc": 0.67031, "loss_cls": 3.28529, "loss": 3.28529, "time": 0.81583} +{"mode": "train", "epoch": 112, "iter": 1900, "lr": 0.01539, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41641, "top5_acc": 0.67453, "loss_cls": 3.29151, "loss": 3.29151, "time": 0.81521} +{"mode": "train", "epoch": 112, "iter": 2000, "lr": 0.01537, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42859, "top5_acc": 0.67672, "loss_cls": 3.24411, "loss": 3.24411, "time": 0.81957} +{"mode": "train", "epoch": 112, "iter": 2100, "lr": 0.01535, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40406, "top5_acc": 0.66594, "loss_cls": 3.33566, "loss": 3.33566, "time": 0.8249} +{"mode": "train", "epoch": 112, "iter": 2200, "lr": 0.01533, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40969, "top5_acc": 0.66859, "loss_cls": 3.31107, "loss": 3.31107, "time": 0.83066} +{"mode": "train", "epoch": 112, "iter": 2300, "lr": 0.01531, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41219, "top5_acc": 0.67094, "loss_cls": 3.3041, "loss": 3.3041, "time": 0.81877} +{"mode": "train", "epoch": 112, "iter": 2400, "lr": 0.01529, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41516, "top5_acc": 0.67984, "loss_cls": 3.28405, "loss": 3.28405, "time": 0.81891} +{"mode": "train", "epoch": 112, "iter": 2500, "lr": 0.01527, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41406, "top5_acc": 0.67641, "loss_cls": 3.29085, "loss": 3.29085, "time": 0.8166} +{"mode": "train", "epoch": 112, "iter": 2600, "lr": 0.01525, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41625, "top5_acc": 0.67031, "loss_cls": 3.30723, "loss": 3.30723, "time": 0.81926} +{"mode": "train", "epoch": 112, "iter": 2700, "lr": 0.01523, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41172, "top5_acc": 0.66812, "loss_cls": 3.30534, "loss": 3.30534, "time": 0.81075} +{"mode": "train", "epoch": 112, "iter": 2800, "lr": 0.01521, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41, "top5_acc": 0.67328, "loss_cls": 3.27699, "loss": 3.27699, "time": 0.80933} +{"mode": "train", "epoch": 112, "iter": 2900, "lr": 0.01519, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40453, "top5_acc": 0.66125, "loss_cls": 3.33439, "loss": 3.33439, "time": 0.81504} +{"mode": "train", "epoch": 112, "iter": 3000, "lr": 0.01517, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.42156, "top5_acc": 0.68172, "loss_cls": 3.23517, "loss": 3.23517, "time": 0.81141} +{"mode": "train", "epoch": 112, "iter": 3100, "lr": 0.01515, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42562, "top5_acc": 0.67906, "loss_cls": 3.2487, "loss": 3.2487, "time": 0.81158} +{"mode": "train", "epoch": 112, "iter": 3200, "lr": 0.01513, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41859, "top5_acc": 0.67641, "loss_cls": 3.27863, "loss": 3.27863, "time": 0.81853} +{"mode": "train", "epoch": 112, "iter": 3300, "lr": 0.01511, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41297, "top5_acc": 0.67125, "loss_cls": 3.28132, "loss": 3.28132, "time": 0.81612} +{"mode": "train", "epoch": 112, "iter": 3400, "lr": 0.01509, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40297, "top5_acc": 0.66391, "loss_cls": 3.34733, "loss": 3.34733, "time": 0.81916} +{"mode": "train", "epoch": 112, "iter": 3500, "lr": 0.01507, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41234, "top5_acc": 0.68203, "loss_cls": 3.28552, "loss": 3.28552, "time": 0.81153} +{"mode": "train", "epoch": 112, "iter": 3600, "lr": 0.01505, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41953, "top5_acc": 0.68281, "loss_cls": 3.22921, "loss": 3.22921, "time": 0.81899} +{"mode": "train", "epoch": 112, "iter": 3700, "lr": 0.01503, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41062, "top5_acc": 0.67859, "loss_cls": 3.28691, "loss": 3.28691, "time": 0.81425} +{"mode": "val", "epoch": 112, "iter": 309, "lr": 0.01502, "top1_acc": 0.35851, "top5_acc": 0.61931, "mean_class_accuracy": 0.35825} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.015, "memory": 15990, "data_time": 1.30715, "top1_acc": 0.43297, "top5_acc": 0.68828, "loss_cls": 3.16765, "loss": 3.16765, "time": 2.28046} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.01498, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43562, "top5_acc": 0.69094, "loss_cls": 3.17477, "loss": 3.17477, "time": 0.81711} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.01496, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42188, "top5_acc": 0.68359, "loss_cls": 3.20888, "loss": 3.20888, "time": 0.81958} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.01494, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.43125, "top5_acc": 0.69094, "loss_cls": 3.1842, "loss": 3.1842, "time": 0.81731} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.01492, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41578, "top5_acc": 0.68172, "loss_cls": 3.26158, "loss": 3.26158, "time": 0.81599} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.0149, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.43156, "top5_acc": 0.69062, "loss_cls": 3.20027, "loss": 3.20027, "time": 0.81402} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.01488, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42703, "top5_acc": 0.67828, "loss_cls": 3.20271, "loss": 3.20271, "time": 0.83127} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.01486, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42609, "top5_acc": 0.67984, "loss_cls": 3.22919, "loss": 3.22919, "time": 0.8199} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.01484, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42734, "top5_acc": 0.68734, "loss_cls": 3.19813, "loss": 3.19813, "time": 0.819} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.01482, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42734, "top5_acc": 0.6875, "loss_cls": 3.20985, "loss": 3.20985, "time": 0.81916} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0148, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42297, "top5_acc": 0.68109, "loss_cls": 3.26796, "loss": 3.26796, "time": 0.81756} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.01478, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42188, "top5_acc": 0.68109, "loss_cls": 3.2503, "loss": 3.2503, "time": 0.8148} +{"mode": "train", "epoch": 113, "iter": 1300, "lr": 0.01476, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43188, "top5_acc": 0.69516, "loss_cls": 3.18373, "loss": 3.18373, "time": 0.81959} +{"mode": "train", "epoch": 113, "iter": 1400, "lr": 0.01474, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42016, "top5_acc": 0.68766, "loss_cls": 3.22144, "loss": 3.22144, "time": 0.81785} +{"mode": "train", "epoch": 113, "iter": 1500, "lr": 0.01472, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41312, "top5_acc": 0.66766, "loss_cls": 3.29617, "loss": 3.29617, "time": 0.81292} +{"mode": "train", "epoch": 113, "iter": 1600, "lr": 0.0147, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42453, "top5_acc": 0.68891, "loss_cls": 3.21923, "loss": 3.21923, "time": 0.81983} +{"mode": "train", "epoch": 113, "iter": 1700, "lr": 0.01468, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42734, "top5_acc": 0.68453, "loss_cls": 3.22647, "loss": 3.22647, "time": 0.81754} +{"mode": "train", "epoch": 113, "iter": 1800, "lr": 0.01466, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41828, "top5_acc": 0.67375, "loss_cls": 3.24852, "loss": 3.24852, "time": 0.81676} +{"mode": "train", "epoch": 113, "iter": 1900, "lr": 0.01464, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42234, "top5_acc": 0.67625, "loss_cls": 3.2735, "loss": 3.2735, "time": 0.81738} +{"mode": "train", "epoch": 113, "iter": 2000, "lr": 0.01462, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41859, "top5_acc": 0.68125, "loss_cls": 3.25613, "loss": 3.25613, "time": 0.81526} +{"mode": "train", "epoch": 113, "iter": 2100, "lr": 0.0146, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41953, "top5_acc": 0.67875, "loss_cls": 3.24723, "loss": 3.24723, "time": 0.81859} +{"mode": "train", "epoch": 113, "iter": 2200, "lr": 0.01458, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42172, "top5_acc": 0.67812, "loss_cls": 3.25857, "loss": 3.25857, "time": 0.82428} +{"mode": "train", "epoch": 113, "iter": 2300, "lr": 0.01456, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40844, "top5_acc": 0.67766, "loss_cls": 3.31635, "loss": 3.31635, "time": 0.83065} +{"mode": "train", "epoch": 113, "iter": 2400, "lr": 0.01454, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42078, "top5_acc": 0.66844, "loss_cls": 3.28516, "loss": 3.28516, "time": 0.81979} +{"mode": "train", "epoch": 113, "iter": 2500, "lr": 0.01452, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41328, "top5_acc": 0.67375, "loss_cls": 3.29632, "loss": 3.29632, "time": 0.83593} +{"mode": "train", "epoch": 113, "iter": 2600, "lr": 0.0145, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42688, "top5_acc": 0.68156, "loss_cls": 3.22785, "loss": 3.22785, "time": 0.81411} +{"mode": "train", "epoch": 113, "iter": 2700, "lr": 0.01448, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41922, "top5_acc": 0.68062, "loss_cls": 3.24796, "loss": 3.24796, "time": 0.81624} +{"mode": "train", "epoch": 113, "iter": 2800, "lr": 0.01446, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4175, "top5_acc": 0.6725, "loss_cls": 3.27412, "loss": 3.27412, "time": 0.81636} +{"mode": "train", "epoch": 113, "iter": 2900, "lr": 0.01444, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41844, "top5_acc": 0.66781, "loss_cls": 3.31094, "loss": 3.31094, "time": 0.81361} +{"mode": "train", "epoch": 113, "iter": 3000, "lr": 0.01442, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.415, "top5_acc": 0.67312, "loss_cls": 3.27165, "loss": 3.27165, "time": 0.81878} +{"mode": "train", "epoch": 113, "iter": 3100, "lr": 0.0144, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42, "top5_acc": 0.67594, "loss_cls": 3.27604, "loss": 3.27604, "time": 0.81545} +{"mode": "train", "epoch": 113, "iter": 3200, "lr": 0.01438, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42812, "top5_acc": 0.67812, "loss_cls": 3.26165, "loss": 3.26165, "time": 0.81745} +{"mode": "train", "epoch": 113, "iter": 3300, "lr": 0.01436, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41156, "top5_acc": 0.66078, "loss_cls": 3.32, "loss": 3.32, "time": 0.81232} +{"mode": "train", "epoch": 113, "iter": 3400, "lr": 0.01434, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41875, "top5_acc": 0.67062, "loss_cls": 3.26432, "loss": 3.26432, "time": 0.81634} +{"mode": "train", "epoch": 113, "iter": 3500, "lr": 0.01432, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42172, "top5_acc": 0.68625, "loss_cls": 3.23076, "loss": 3.23076, "time": 0.81215} +{"mode": "train", "epoch": 113, "iter": 3600, "lr": 0.01431, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41859, "top5_acc": 0.67125, "loss_cls": 3.27835, "loss": 3.27835, "time": 0.81292} +{"mode": "train", "epoch": 113, "iter": 3700, "lr": 0.01429, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41062, "top5_acc": 0.67688, "loss_cls": 3.26964, "loss": 3.26964, "time": 0.81822} +{"mode": "val", "epoch": 113, "iter": 309, "lr": 0.01428, "top1_acc": 0.35233, "top5_acc": 0.61414, "mean_class_accuracy": 0.35215} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.01426, "memory": 15990, "data_time": 1.30117, "top1_acc": 0.42797, "top5_acc": 0.68453, "loss_cls": 3.21016, "loss": 3.21016, "time": 2.27483} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.01424, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42531, "top5_acc": 0.69281, "loss_cls": 3.17367, "loss": 3.17367, "time": 0.81506} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.01422, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43797, "top5_acc": 0.69406, "loss_cls": 3.16016, "loss": 3.16016, "time": 0.82177} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.0142, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.43453, "top5_acc": 0.69594, "loss_cls": 3.1668, "loss": 3.1668, "time": 0.81332} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.01418, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4275, "top5_acc": 0.69391, "loss_cls": 3.19957, "loss": 3.19957, "time": 0.81487} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.01416, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42281, "top5_acc": 0.68922, "loss_cls": 3.20639, "loss": 3.20639, "time": 0.81878} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.01414, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41406, "top5_acc": 0.6775, "loss_cls": 3.25094, "loss": 3.25094, "time": 0.82519} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.01412, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43891, "top5_acc": 0.68797, "loss_cls": 3.16717, "loss": 3.16717, "time": 0.81712} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.0141, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41969, "top5_acc": 0.68016, "loss_cls": 3.23094, "loss": 3.23094, "time": 0.81458} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.01408, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42484, "top5_acc": 0.68641, "loss_cls": 3.21014, "loss": 3.21014, "time": 0.81393} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.01406, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42531, "top5_acc": 0.69, "loss_cls": 3.19107, "loss": 3.19107, "time": 0.81677} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.01404, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42516, "top5_acc": 0.68531, "loss_cls": 3.21669, "loss": 3.21669, "time": 0.81488} +{"mode": "train", "epoch": 114, "iter": 1300, "lr": 0.01402, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43469, "top5_acc": 0.69312, "loss_cls": 3.20135, "loss": 3.20135, "time": 0.81536} +{"mode": "train", "epoch": 114, "iter": 1400, "lr": 0.014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41859, "top5_acc": 0.67719, "loss_cls": 3.26149, "loss": 3.26149, "time": 0.81241} +{"mode": "train", "epoch": 114, "iter": 1500, "lr": 0.01398, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42641, "top5_acc": 0.68469, "loss_cls": 3.21412, "loss": 3.21412, "time": 0.82013} +{"mode": "train", "epoch": 114, "iter": 1600, "lr": 0.01397, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43438, "top5_acc": 0.68156, "loss_cls": 3.22376, "loss": 3.22376, "time": 0.8142} +{"mode": "train", "epoch": 114, "iter": 1700, "lr": 0.01395, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42828, "top5_acc": 0.67969, "loss_cls": 3.19833, "loss": 3.19833, "time": 0.81682} +{"mode": "train", "epoch": 114, "iter": 1800, "lr": 0.01393, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4275, "top5_acc": 0.69891, "loss_cls": 3.16761, "loss": 3.16761, "time": 0.81775} +{"mode": "train", "epoch": 114, "iter": 1900, "lr": 0.01391, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42812, "top5_acc": 0.68781, "loss_cls": 3.21157, "loss": 3.21157, "time": 0.81502} +{"mode": "train", "epoch": 114, "iter": 2000, "lr": 0.01389, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.41875, "top5_acc": 0.68188, "loss_cls": 3.22071, "loss": 3.22071, "time": 0.81492} +{"mode": "train", "epoch": 114, "iter": 2100, "lr": 0.01387, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42656, "top5_acc": 0.68188, "loss_cls": 3.22522, "loss": 3.22522, "time": 0.81494} +{"mode": "train", "epoch": 114, "iter": 2200, "lr": 0.01385, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42688, "top5_acc": 0.68375, "loss_cls": 3.21023, "loss": 3.21023, "time": 0.82081} +{"mode": "train", "epoch": 114, "iter": 2300, "lr": 0.01383, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42344, "top5_acc": 0.685, "loss_cls": 3.19477, "loss": 3.19477, "time": 0.82768} +{"mode": "train", "epoch": 114, "iter": 2400, "lr": 0.01381, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4225, "top5_acc": 0.68375, "loss_cls": 3.20729, "loss": 3.20729, "time": 0.82297} +{"mode": "train", "epoch": 114, "iter": 2500, "lr": 0.01379, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41062, "top5_acc": 0.6725, "loss_cls": 3.27718, "loss": 3.27718, "time": 0.81788} +{"mode": "train", "epoch": 114, "iter": 2600, "lr": 0.01377, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41938, "top5_acc": 0.67828, "loss_cls": 3.23993, "loss": 3.23993, "time": 0.82379} +{"mode": "train", "epoch": 114, "iter": 2700, "lr": 0.01375, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.415, "top5_acc": 0.66828, "loss_cls": 3.31082, "loss": 3.31082, "time": 0.81659} +{"mode": "train", "epoch": 114, "iter": 2800, "lr": 0.01373, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41312, "top5_acc": 0.67906, "loss_cls": 3.26194, "loss": 3.26194, "time": 0.81189} +{"mode": "train", "epoch": 114, "iter": 2900, "lr": 0.01371, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41688, "top5_acc": 0.66969, "loss_cls": 3.28624, "loss": 3.28624, "time": 0.81822} +{"mode": "train", "epoch": 114, "iter": 3000, "lr": 0.01369, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42938, "top5_acc": 0.68703, "loss_cls": 3.2138, "loss": 3.2138, "time": 0.81763} +{"mode": "train", "epoch": 114, "iter": 3100, "lr": 0.01368, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41922, "top5_acc": 0.67391, "loss_cls": 3.25864, "loss": 3.25864, "time": 0.81684} +{"mode": "train", "epoch": 114, "iter": 3200, "lr": 0.01366, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42219, "top5_acc": 0.68688, "loss_cls": 3.21118, "loss": 3.21118, "time": 0.81788} +{"mode": "train", "epoch": 114, "iter": 3300, "lr": 0.01364, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4275, "top5_acc": 0.68391, "loss_cls": 3.23676, "loss": 3.23676, "time": 0.81732} +{"mode": "train", "epoch": 114, "iter": 3400, "lr": 0.01362, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42094, "top5_acc": 0.67641, "loss_cls": 3.26188, "loss": 3.26188, "time": 0.81765} +{"mode": "train", "epoch": 114, "iter": 3500, "lr": 0.0136, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41781, "top5_acc": 0.67797, "loss_cls": 3.24948, "loss": 3.24948, "time": 0.81307} +{"mode": "train", "epoch": 114, "iter": 3600, "lr": 0.01358, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.41594, "top5_acc": 0.6725, "loss_cls": 3.28916, "loss": 3.28916, "time": 0.81528} +{"mode": "train", "epoch": 114, "iter": 3700, "lr": 0.01356, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41219, "top5_acc": 0.67953, "loss_cls": 3.27329, "loss": 3.27329, "time": 0.81415} +{"mode": "val", "epoch": 114, "iter": 309, "lr": 0.01355, "top1_acc": 0.36555, "top5_acc": 0.62057, "mean_class_accuracy": 0.36516} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.01353, "memory": 15990, "data_time": 1.2985, "top1_acc": 0.44469, "top5_acc": 0.69922, "loss_cls": 3.09987, "loss": 3.09987, "time": 2.27037} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.01351, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43516, "top5_acc": 0.69219, "loss_cls": 3.14971, "loss": 3.14971, "time": 0.81758} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.01349, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42703, "top5_acc": 0.69234, "loss_cls": 3.1922, "loss": 3.1922, "time": 0.81696} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.01348, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42875, "top5_acc": 0.68828, "loss_cls": 3.2127, "loss": 3.2127, "time": 0.82003} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.01346, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44766, "top5_acc": 0.70156, "loss_cls": 3.09527, "loss": 3.09527, "time": 0.81511} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.01344, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44062, "top5_acc": 0.69516, "loss_cls": 3.17272, "loss": 3.17272, "time": 0.81535} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.01342, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43797, "top5_acc": 0.69594, "loss_cls": 3.1725, "loss": 3.1725, "time": 0.82043} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.0134, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.43328, "top5_acc": 0.69453, "loss_cls": 3.17506, "loss": 3.17506, "time": 0.81775} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.01338, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44109, "top5_acc": 0.69938, "loss_cls": 3.13072, "loss": 3.13072, "time": 0.8201} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.01336, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42891, "top5_acc": 0.68531, "loss_cls": 3.20157, "loss": 3.20157, "time": 0.81879} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.01334, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43672, "top5_acc": 0.68344, "loss_cls": 3.20089, "loss": 3.20089, "time": 0.80975} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.01332, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43875, "top5_acc": 0.68781, "loss_cls": 3.16913, "loss": 3.16913, "time": 0.81346} +{"mode": "train", "epoch": 115, "iter": 1300, "lr": 0.0133, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.41828, "top5_acc": 0.68391, "loss_cls": 3.22194, "loss": 3.22194, "time": 0.81327} +{"mode": "train", "epoch": 115, "iter": 1400, "lr": 0.01328, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42875, "top5_acc": 0.68344, "loss_cls": 3.19546, "loss": 3.19546, "time": 0.81437} +{"mode": "train", "epoch": 115, "iter": 1500, "lr": 0.01327, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42062, "top5_acc": 0.67562, "loss_cls": 3.2442, "loss": 3.2442, "time": 0.81306} +{"mode": "train", "epoch": 115, "iter": 1600, "lr": 0.01325, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43188, "top5_acc": 0.69109, "loss_cls": 3.20131, "loss": 3.20131, "time": 0.81359} +{"mode": "train", "epoch": 115, "iter": 1700, "lr": 0.01323, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43375, "top5_acc": 0.69297, "loss_cls": 3.16523, "loss": 3.16523, "time": 0.81585} +{"mode": "train", "epoch": 115, "iter": 1800, "lr": 0.01321, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42375, "top5_acc": 0.68328, "loss_cls": 3.22956, "loss": 3.22956, "time": 0.81379} +{"mode": "train", "epoch": 115, "iter": 1900, "lr": 0.01319, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42312, "top5_acc": 0.68609, "loss_cls": 3.208, "loss": 3.208, "time": 0.82057} +{"mode": "train", "epoch": 115, "iter": 2000, "lr": 0.01317, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43391, "top5_acc": 0.6825, "loss_cls": 3.2085, "loss": 3.2085, "time": 0.81323} +{"mode": "train", "epoch": 115, "iter": 2100, "lr": 0.01315, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41484, "top5_acc": 0.68234, "loss_cls": 3.26986, "loss": 3.26986, "time": 0.81989} +{"mode": "train", "epoch": 115, "iter": 2200, "lr": 0.01313, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42922, "top5_acc": 0.68562, "loss_cls": 3.17956, "loss": 3.17956, "time": 0.81611} +{"mode": "train", "epoch": 115, "iter": 2300, "lr": 0.01311, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42828, "top5_acc": 0.68094, "loss_cls": 3.20113, "loss": 3.20113, "time": 0.81863} +{"mode": "train", "epoch": 115, "iter": 2400, "lr": 0.0131, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42047, "top5_acc": 0.68547, "loss_cls": 3.22512, "loss": 3.22512, "time": 0.83227} +{"mode": "train", "epoch": 115, "iter": 2500, "lr": 0.01308, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41438, "top5_acc": 0.67891, "loss_cls": 3.25072, "loss": 3.25072, "time": 0.81872} +{"mode": "train", "epoch": 115, "iter": 2600, "lr": 0.01306, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42672, "top5_acc": 0.68016, "loss_cls": 3.22064, "loss": 3.22064, "time": 0.8192} +{"mode": "train", "epoch": 115, "iter": 2700, "lr": 0.01304, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.43047, "top5_acc": 0.68891, "loss_cls": 3.18949, "loss": 3.18949, "time": 0.8141} +{"mode": "train", "epoch": 115, "iter": 2800, "lr": 0.01302, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.42578, "top5_acc": 0.67375, "loss_cls": 3.23508, "loss": 3.23508, "time": 0.81369} +{"mode": "train", "epoch": 115, "iter": 2900, "lr": 0.013, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42781, "top5_acc": 0.68469, "loss_cls": 3.19388, "loss": 3.19388, "time": 0.81397} +{"mode": "train", "epoch": 115, "iter": 3000, "lr": 0.01298, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42781, "top5_acc": 0.68219, "loss_cls": 3.22631, "loss": 3.22631, "time": 0.81928} +{"mode": "train", "epoch": 115, "iter": 3100, "lr": 0.01296, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43188, "top5_acc": 0.68438, "loss_cls": 3.21489, "loss": 3.21489, "time": 0.81661} +{"mode": "train", "epoch": 115, "iter": 3200, "lr": 0.01295, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.42109, "top5_acc": 0.68328, "loss_cls": 3.21672, "loss": 3.21672, "time": 0.81521} +{"mode": "train", "epoch": 115, "iter": 3300, "lr": 0.01293, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42828, "top5_acc": 0.68234, "loss_cls": 3.23864, "loss": 3.23864, "time": 0.81287} +{"mode": "train", "epoch": 115, "iter": 3400, "lr": 0.01291, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4225, "top5_acc": 0.68828, "loss_cls": 3.21031, "loss": 3.21031, "time": 0.81461} +{"mode": "train", "epoch": 115, "iter": 3500, "lr": 0.01289, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42469, "top5_acc": 0.67719, "loss_cls": 3.24595, "loss": 3.24595, "time": 0.81666} +{"mode": "train", "epoch": 115, "iter": 3600, "lr": 0.01287, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42812, "top5_acc": 0.68031, "loss_cls": 3.23463, "loss": 3.23463, "time": 0.81676} +{"mode": "train", "epoch": 115, "iter": 3700, "lr": 0.01285, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43281, "top5_acc": 0.67781, "loss_cls": 3.21469, "loss": 3.21469, "time": 0.81965} +{"mode": "val", "epoch": 115, "iter": 309, "lr": 0.01284, "top1_acc": 0.36479, "top5_acc": 0.62088, "mean_class_accuracy": 0.36457} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.01282, "memory": 15990, "data_time": 1.35999, "top1_acc": 0.4525, "top5_acc": 0.70812, "loss_cls": 3.07191, "loss": 3.07191, "time": 2.33952} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.01281, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43781, "top5_acc": 0.69219, "loss_cls": 3.16352, "loss": 3.16352, "time": 0.82093} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.01279, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43812, "top5_acc": 0.69516, "loss_cls": 3.15904, "loss": 3.15904, "time": 0.81458} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.01277, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43172, "top5_acc": 0.69016, "loss_cls": 3.16473, "loss": 3.16473, "time": 0.81878} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.01275, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.435, "top5_acc": 0.68938, "loss_cls": 3.16483, "loss": 3.16483, "time": 0.81368} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.01273, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44109, "top5_acc": 0.69844, "loss_cls": 3.12576, "loss": 3.12576, "time": 0.81573} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.01271, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43594, "top5_acc": 0.69422, "loss_cls": 3.15942, "loss": 3.15942, "time": 0.8234} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.01269, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43062, "top5_acc": 0.69812, "loss_cls": 3.16157, "loss": 3.16157, "time": 0.81786} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.01268, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43719, "top5_acc": 0.69531, "loss_cls": 3.14537, "loss": 3.14537, "time": 0.8208} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.01266, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43594, "top5_acc": 0.69391, "loss_cls": 3.17553, "loss": 3.17553, "time": 0.81587} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.01264, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44281, "top5_acc": 0.70766, "loss_cls": 3.11189, "loss": 3.11189, "time": 0.81653} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.01262, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43016, "top5_acc": 0.68812, "loss_cls": 3.1905, "loss": 3.1905, "time": 0.81342} +{"mode": "train", "epoch": 116, "iter": 1300, "lr": 0.0126, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43203, "top5_acc": 0.69859, "loss_cls": 3.14713, "loss": 3.14713, "time": 0.81638} +{"mode": "train", "epoch": 116, "iter": 1400, "lr": 0.01258, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43266, "top5_acc": 0.68344, "loss_cls": 3.19028, "loss": 3.19028, "time": 0.81487} +{"mode": "train", "epoch": 116, "iter": 1500, "lr": 0.01256, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43781, "top5_acc": 0.69391, "loss_cls": 3.14521, "loss": 3.14521, "time": 0.81924} +{"mode": "train", "epoch": 116, "iter": 1600, "lr": 0.01255, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42422, "top5_acc": 0.68234, "loss_cls": 3.20356, "loss": 3.20356, "time": 0.81667} +{"mode": "train", "epoch": 116, "iter": 1700, "lr": 0.01253, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43453, "top5_acc": 0.69484, "loss_cls": 3.15235, "loss": 3.15235, "time": 0.82131} +{"mode": "train", "epoch": 116, "iter": 1800, "lr": 0.01251, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43188, "top5_acc": 0.69031, "loss_cls": 3.18142, "loss": 3.18142, "time": 0.81643} +{"mode": "train", "epoch": 116, "iter": 1900, "lr": 0.01249, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43016, "top5_acc": 0.69297, "loss_cls": 3.20134, "loss": 3.20134, "time": 0.81447} +{"mode": "train", "epoch": 116, "iter": 2000, "lr": 0.01247, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43828, "top5_acc": 0.69344, "loss_cls": 3.15231, "loss": 3.15231, "time": 0.81639} +{"mode": "train", "epoch": 116, "iter": 2100, "lr": 0.01245, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43672, "top5_acc": 0.68734, "loss_cls": 3.16503, "loss": 3.16503, "time": 0.82115} +{"mode": "train", "epoch": 116, "iter": 2200, "lr": 0.01243, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42328, "top5_acc": 0.68516, "loss_cls": 3.22495, "loss": 3.22495, "time": 0.82309} +{"mode": "train", "epoch": 116, "iter": 2300, "lr": 0.01242, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43141, "top5_acc": 0.68984, "loss_cls": 3.20158, "loss": 3.20158, "time": 0.82607} +{"mode": "train", "epoch": 116, "iter": 2400, "lr": 0.0124, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.42938, "top5_acc": 0.68125, "loss_cls": 3.20595, "loss": 3.20595, "time": 0.8323} +{"mode": "train", "epoch": 116, "iter": 2500, "lr": 0.01238, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43094, "top5_acc": 0.69875, "loss_cls": 3.16932, "loss": 3.16932, "time": 0.82071} +{"mode": "train", "epoch": 116, "iter": 2600, "lr": 0.01236, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44156, "top5_acc": 0.68578, "loss_cls": 3.15318, "loss": 3.15318, "time": 0.82145} +{"mode": "train", "epoch": 116, "iter": 2700, "lr": 0.01234, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43516, "top5_acc": 0.69297, "loss_cls": 3.16278, "loss": 3.16278, "time": 0.82265} +{"mode": "train", "epoch": 116, "iter": 2800, "lr": 0.01232, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43375, "top5_acc": 0.68859, "loss_cls": 3.20054, "loss": 3.20054, "time": 0.81818} +{"mode": "train", "epoch": 116, "iter": 2900, "lr": 0.01231, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43328, "top5_acc": 0.68969, "loss_cls": 3.19391, "loss": 3.19391, "time": 0.81451} +{"mode": "train", "epoch": 116, "iter": 3000, "lr": 0.01229, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44047, "top5_acc": 0.695, "loss_cls": 3.14636, "loss": 3.14636, "time": 0.81687} +{"mode": "train", "epoch": 116, "iter": 3100, "lr": 0.01227, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42609, "top5_acc": 0.68859, "loss_cls": 3.19945, "loss": 3.19945, "time": 0.81524} +{"mode": "train", "epoch": 116, "iter": 3200, "lr": 0.01225, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42688, "top5_acc": 0.68438, "loss_cls": 3.20744, "loss": 3.20744, "time": 0.81595} +{"mode": "train", "epoch": 116, "iter": 3300, "lr": 0.01223, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.4275, "top5_acc": 0.68109, "loss_cls": 3.23646, "loss": 3.23646, "time": 0.81356} +{"mode": "train", "epoch": 116, "iter": 3400, "lr": 0.01221, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42984, "top5_acc": 0.67781, "loss_cls": 3.22389, "loss": 3.22389, "time": 0.82259} +{"mode": "train", "epoch": 116, "iter": 3500, "lr": 0.0122, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42359, "top5_acc": 0.68719, "loss_cls": 3.21448, "loss": 3.21448, "time": 0.81616} +{"mode": "train", "epoch": 116, "iter": 3600, "lr": 0.01218, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42516, "top5_acc": 0.68062, "loss_cls": 3.22434, "loss": 3.22434, "time": 0.81545} +{"mode": "train", "epoch": 116, "iter": 3700, "lr": 0.01216, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42969, "top5_acc": 0.68109, "loss_cls": 3.20147, "loss": 3.20147, "time": 0.82055} +{"mode": "val", "epoch": 116, "iter": 309, "lr": 0.01215, "top1_acc": 0.36843, "top5_acc": 0.62123, "mean_class_accuracy": 0.3682} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.01213, "memory": 15990, "data_time": 1.37344, "top1_acc": 0.44906, "top5_acc": 0.71328, "loss_cls": 3.04514, "loss": 3.04514, "time": 2.34952} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.01211, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44188, "top5_acc": 0.69484, "loss_cls": 3.13646, "loss": 3.13646, "time": 0.81835} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.0121, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45109, "top5_acc": 0.70672, "loss_cls": 3.05595, "loss": 3.05595, "time": 0.81268} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.01208, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43203, "top5_acc": 0.69906, "loss_cls": 3.11695, "loss": 3.11695, "time": 0.81995} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.01206, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44578, "top5_acc": 0.695, "loss_cls": 3.11251, "loss": 3.11251, "time": 0.81571} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.01204, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44688, "top5_acc": 0.70516, "loss_cls": 3.10143, "loss": 3.10143, "time": 0.81925} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.01202, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44375, "top5_acc": 0.69594, "loss_cls": 3.1359, "loss": 3.1359, "time": 0.82154} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43141, "top5_acc": 0.69203, "loss_cls": 3.15007, "loss": 3.15007, "time": 0.81789} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.01199, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43984, "top5_acc": 0.69453, "loss_cls": 3.13499, "loss": 3.13499, "time": 0.82613} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.01197, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43031, "top5_acc": 0.69203, "loss_cls": 3.16168, "loss": 3.16168, "time": 0.81527} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.01195, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44156, "top5_acc": 0.69734, "loss_cls": 3.13744, "loss": 3.13744, "time": 0.8192} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.01193, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45062, "top5_acc": 0.70719, "loss_cls": 3.07195, "loss": 3.07195, "time": 0.82106} +{"mode": "train", "epoch": 117, "iter": 1300, "lr": 0.01191, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43984, "top5_acc": 0.69391, "loss_cls": 3.12561, "loss": 3.12561, "time": 0.81736} +{"mode": "train", "epoch": 117, "iter": 1400, "lr": 0.0119, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44359, "top5_acc": 0.69688, "loss_cls": 3.13556, "loss": 3.13556, "time": 0.8264} +{"mode": "train", "epoch": 117, "iter": 1500, "lr": 0.01188, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44062, "top5_acc": 0.69609, "loss_cls": 3.15186, "loss": 3.15186, "time": 0.815} +{"mode": "train", "epoch": 117, "iter": 1600, "lr": 0.01186, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42391, "top5_acc": 0.69484, "loss_cls": 3.17808, "loss": 3.17808, "time": 0.81358} +{"mode": "train", "epoch": 117, "iter": 1700, "lr": 0.01184, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43953, "top5_acc": 0.6975, "loss_cls": 3.12078, "loss": 3.12078, "time": 0.81512} +{"mode": "train", "epoch": 117, "iter": 1800, "lr": 0.01182, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44594, "top5_acc": 0.69219, "loss_cls": 3.14411, "loss": 3.14411, "time": 0.81625} +{"mode": "train", "epoch": 117, "iter": 1900, "lr": 0.01181, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44469, "top5_acc": 0.70438, "loss_cls": 3.11711, "loss": 3.11711, "time": 0.81917} +{"mode": "train", "epoch": 117, "iter": 2000, "lr": 0.01179, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42969, "top5_acc": 0.70062, "loss_cls": 3.14688, "loss": 3.14688, "time": 0.81674} +{"mode": "train", "epoch": 117, "iter": 2100, "lr": 0.01177, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43328, "top5_acc": 0.68422, "loss_cls": 3.21928, "loss": 3.21928, "time": 0.81362} +{"mode": "train", "epoch": 117, "iter": 2200, "lr": 0.01175, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44875, "top5_acc": 0.70047, "loss_cls": 3.10958, "loss": 3.10958, "time": 0.81892} +{"mode": "train", "epoch": 117, "iter": 2300, "lr": 0.01173, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42875, "top5_acc": 0.69203, "loss_cls": 3.19086, "loss": 3.19086, "time": 0.82093} +{"mode": "train", "epoch": 117, "iter": 2400, "lr": 0.01172, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43125, "top5_acc": 0.67859, "loss_cls": 3.19484, "loss": 3.19484, "time": 0.825} +{"mode": "train", "epoch": 117, "iter": 2500, "lr": 0.0117, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43484, "top5_acc": 0.69438, "loss_cls": 3.17638, "loss": 3.17638, "time": 0.81845} +{"mode": "train", "epoch": 117, "iter": 2600, "lr": 0.01168, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42906, "top5_acc": 0.68922, "loss_cls": 3.18, "loss": 3.18, "time": 0.82055} +{"mode": "train", "epoch": 117, "iter": 2700, "lr": 0.01166, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43297, "top5_acc": 0.68453, "loss_cls": 3.18339, "loss": 3.18339, "time": 0.81752} +{"mode": "train", "epoch": 117, "iter": 2800, "lr": 0.01164, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43406, "top5_acc": 0.68938, "loss_cls": 3.17104, "loss": 3.17104, "time": 0.81648} +{"mode": "train", "epoch": 117, "iter": 2900, "lr": 0.01163, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43156, "top5_acc": 0.69266, "loss_cls": 3.15211, "loss": 3.15211, "time": 0.81485} +{"mode": "train", "epoch": 117, "iter": 3000, "lr": 0.01161, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43188, "top5_acc": 0.69203, "loss_cls": 3.17791, "loss": 3.17791, "time": 0.81731} +{"mode": "train", "epoch": 117, "iter": 3100, "lr": 0.01159, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43922, "top5_acc": 0.69562, "loss_cls": 3.13154, "loss": 3.13154, "time": 0.81804} +{"mode": "train", "epoch": 117, "iter": 3200, "lr": 0.01157, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43328, "top5_acc": 0.68516, "loss_cls": 3.20635, "loss": 3.20635, "time": 0.81675} +{"mode": "train", "epoch": 117, "iter": 3300, "lr": 0.01155, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44297, "top5_acc": 0.7025, "loss_cls": 3.11188, "loss": 3.11188, "time": 0.81224} +{"mode": "train", "epoch": 117, "iter": 3400, "lr": 0.01154, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43453, "top5_acc": 0.69359, "loss_cls": 3.15778, "loss": 3.15778, "time": 0.81582} +{"mode": "train", "epoch": 117, "iter": 3500, "lr": 0.01152, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41906, "top5_acc": 0.67984, "loss_cls": 3.25146, "loss": 3.25146, "time": 0.82022} +{"mode": "train", "epoch": 117, "iter": 3600, "lr": 0.0115, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41984, "top5_acc": 0.68656, "loss_cls": 3.2077, "loss": 3.2077, "time": 0.81837} +{"mode": "train", "epoch": 117, "iter": 3700, "lr": 0.01148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42797, "top5_acc": 0.68891, "loss_cls": 3.17486, "loss": 3.17486, "time": 0.81885} +{"mode": "val", "epoch": 117, "iter": 309, "lr": 0.01147, "top1_acc": 0.37066, "top5_acc": 0.62736, "mean_class_accuracy": 0.37037} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.01146, "memory": 15990, "data_time": 1.37747, "top1_acc": 0.45609, "top5_acc": 0.71438, "loss_cls": 3.03252, "loss": 3.03252, "time": 2.35932} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.01144, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45922, "top5_acc": 0.71375, "loss_cls": 3.037, "loss": 3.037, "time": 0.82227} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.01142, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45656, "top5_acc": 0.71438, "loss_cls": 3.05181, "loss": 3.05181, "time": 0.82805} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.0114, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44609, "top5_acc": 0.70562, "loss_cls": 3.0823, "loss": 3.0823, "time": 0.82543} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.01139, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45, "top5_acc": 0.70375, "loss_cls": 3.10927, "loss": 3.10927, "time": 0.82675} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.01137, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44062, "top5_acc": 0.70016, "loss_cls": 3.13245, "loss": 3.13245, "time": 0.82136} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.01135, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.44141, "top5_acc": 0.70953, "loss_cls": 3.07897, "loss": 3.07897, "time": 0.83219} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.01133, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45875, "top5_acc": 0.70797, "loss_cls": 3.05276, "loss": 3.05276, "time": 0.82953} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.01131, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43297, "top5_acc": 0.70406, "loss_cls": 3.12065, "loss": 3.12065, "time": 0.82488} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.0113, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45109, "top5_acc": 0.70641, "loss_cls": 3.04323, "loss": 3.04323, "time": 0.82984} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.01128, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45266, "top5_acc": 0.70547, "loss_cls": 3.08264, "loss": 3.08264, "time": 0.82693} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.01126, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44391, "top5_acc": 0.70359, "loss_cls": 3.10337, "loss": 3.10337, "time": 0.8292} +{"mode": "train", "epoch": 118, "iter": 1300, "lr": 0.01124, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42594, "top5_acc": 0.68406, "loss_cls": 3.2043, "loss": 3.2043, "time": 0.82635} +{"mode": "train", "epoch": 118, "iter": 1400, "lr": 0.01123, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45156, "top5_acc": 0.70375, "loss_cls": 3.10278, "loss": 3.10278, "time": 0.81978} +{"mode": "train", "epoch": 118, "iter": 1500, "lr": 0.01121, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43891, "top5_acc": 0.69594, "loss_cls": 3.13243, "loss": 3.13243, "time": 0.82062} +{"mode": "train", "epoch": 118, "iter": 1600, "lr": 0.01119, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42688, "top5_acc": 0.68062, "loss_cls": 3.20124, "loss": 3.20124, "time": 0.82141} +{"mode": "train", "epoch": 118, "iter": 1700, "lr": 0.01117, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44734, "top5_acc": 0.69984, "loss_cls": 3.09592, "loss": 3.09592, "time": 0.82282} +{"mode": "train", "epoch": 118, "iter": 1800, "lr": 0.01116, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.445, "top5_acc": 0.69641, "loss_cls": 3.13057, "loss": 3.13057, "time": 0.82838} +{"mode": "train", "epoch": 118, "iter": 1900, "lr": 0.01114, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43125, "top5_acc": 0.68984, "loss_cls": 3.15397, "loss": 3.15397, "time": 0.81952} +{"mode": "train", "epoch": 118, "iter": 2000, "lr": 0.01112, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42641, "top5_acc": 0.68734, "loss_cls": 3.20201, "loss": 3.20201, "time": 0.81618} +{"mode": "train", "epoch": 118, "iter": 2100, "lr": 0.0111, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43797, "top5_acc": 0.68828, "loss_cls": 3.16818, "loss": 3.16818, "time": 0.82275} +{"mode": "train", "epoch": 118, "iter": 2200, "lr": 0.01109, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45156, "top5_acc": 0.70625, "loss_cls": 3.09417, "loss": 3.09417, "time": 0.82283} +{"mode": "train", "epoch": 118, "iter": 2300, "lr": 0.01107, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44047, "top5_acc": 0.68828, "loss_cls": 3.1561, "loss": 3.1561, "time": 0.83344} +{"mode": "train", "epoch": 118, "iter": 2400, "lr": 0.01105, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.43422, "top5_acc": 0.69156, "loss_cls": 3.17097, "loss": 3.17097, "time": 0.8387} +{"mode": "train", "epoch": 118, "iter": 2500, "lr": 0.01103, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44578, "top5_acc": 0.69797, "loss_cls": 3.12929, "loss": 3.12929, "time": 0.8262} +{"mode": "train", "epoch": 118, "iter": 2600, "lr": 0.01102, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.44344, "top5_acc": 0.69875, "loss_cls": 3.1456, "loss": 3.1456, "time": 0.82541} +{"mode": "train", "epoch": 118, "iter": 2700, "lr": 0.011, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43094, "top5_acc": 0.7, "loss_cls": 3.16039, "loss": 3.16039, "time": 0.83179} +{"mode": "train", "epoch": 118, "iter": 2800, "lr": 0.01098, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43297, "top5_acc": 0.6975, "loss_cls": 3.1498, "loss": 3.1498, "time": 0.82678} +{"mode": "train", "epoch": 118, "iter": 2900, "lr": 0.01096, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43891, "top5_acc": 0.69172, "loss_cls": 3.15317, "loss": 3.15317, "time": 0.83162} +{"mode": "train", "epoch": 118, "iter": 3000, "lr": 0.01095, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44188, "top5_acc": 0.70391, "loss_cls": 3.13381, "loss": 3.13381, "time": 0.82375} +{"mode": "train", "epoch": 118, "iter": 3100, "lr": 0.01093, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44047, "top5_acc": 0.69859, "loss_cls": 3.1133, "loss": 3.1133, "time": 0.82577} +{"mode": "train", "epoch": 118, "iter": 3200, "lr": 0.01091, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43406, "top5_acc": 0.69422, "loss_cls": 3.1622, "loss": 3.1622, "time": 0.82476} +{"mode": "train", "epoch": 118, "iter": 3300, "lr": 0.01089, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44094, "top5_acc": 0.69594, "loss_cls": 3.12437, "loss": 3.12437, "time": 0.8275} +{"mode": "train", "epoch": 118, "iter": 3400, "lr": 0.01088, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43297, "top5_acc": 0.69578, "loss_cls": 3.13883, "loss": 3.13883, "time": 0.82582} +{"mode": "train", "epoch": 118, "iter": 3500, "lr": 0.01086, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44266, "top5_acc": 0.69688, "loss_cls": 3.11838, "loss": 3.11838, "time": 0.82509} +{"mode": "train", "epoch": 118, "iter": 3600, "lr": 0.01084, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43406, "top5_acc": 0.69859, "loss_cls": 3.14559, "loss": 3.14559, "time": 0.82303} +{"mode": "train", "epoch": 118, "iter": 3700, "lr": 0.01082, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44203, "top5_acc": 0.68906, "loss_cls": 3.13667, "loss": 3.13667, "time": 0.81778} +{"mode": "val", "epoch": 118, "iter": 309, "lr": 0.01082, "top1_acc": 0.37239, "top5_acc": 0.62655, "mean_class_accuracy": 0.37212} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.0108, "memory": 15990, "data_time": 1.3631, "top1_acc": 0.45844, "top5_acc": 0.71594, "loss_cls": 3.03202, "loss": 3.03202, "time": 2.34218} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.01078, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45359, "top5_acc": 0.71031, "loss_cls": 3.04309, "loss": 3.04309, "time": 0.82609} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.01076, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45, "top5_acc": 0.70656, "loss_cls": 3.0656, "loss": 3.0656, "time": 0.82356} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.01075, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45219, "top5_acc": 0.70312, "loss_cls": 3.08759, "loss": 3.08759, "time": 0.82205} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.01073, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45016, "top5_acc": 0.705, "loss_cls": 3.05784, "loss": 3.05784, "time": 0.81536} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.01071, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45188, "top5_acc": 0.70391, "loss_cls": 3.05095, "loss": 3.05095, "time": 0.81974} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.01069, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44922, "top5_acc": 0.70844, "loss_cls": 3.05478, "loss": 3.05478, "time": 0.81725} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.01068, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44531, "top5_acc": 0.69781, "loss_cls": 3.10624, "loss": 3.10624, "time": 0.82268} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.01066, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45016, "top5_acc": 0.69359, "loss_cls": 3.10336, "loss": 3.10336, "time": 0.81412} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.01064, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44781, "top5_acc": 0.70484, "loss_cls": 3.10122, "loss": 3.10122, "time": 0.81746} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.01063, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45031, "top5_acc": 0.70734, "loss_cls": 3.06736, "loss": 3.06736, "time": 0.81882} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.01061, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45062, "top5_acc": 0.70625, "loss_cls": 3.08139, "loss": 3.08139, "time": 0.81494} +{"mode": "train", "epoch": 119, "iter": 1300, "lr": 0.01059, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44062, "top5_acc": 0.69641, "loss_cls": 3.12587, "loss": 3.12587, "time": 0.81988} +{"mode": "train", "epoch": 119, "iter": 1400, "lr": 0.01057, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45, "top5_acc": 0.70891, "loss_cls": 3.0731, "loss": 3.0731, "time": 0.82275} +{"mode": "train", "epoch": 119, "iter": 1500, "lr": 0.01056, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44141, "top5_acc": 0.7025, "loss_cls": 3.12883, "loss": 3.12883, "time": 0.81185} +{"mode": "train", "epoch": 119, "iter": 1600, "lr": 0.01054, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44859, "top5_acc": 0.70562, "loss_cls": 3.07192, "loss": 3.07192, "time": 0.81728} +{"mode": "train", "epoch": 119, "iter": 1700, "lr": 0.01052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44156, "top5_acc": 0.70312, "loss_cls": 3.09648, "loss": 3.09648, "time": 0.81588} +{"mode": "train", "epoch": 119, "iter": 1800, "lr": 0.0105, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45141, "top5_acc": 0.70234, "loss_cls": 3.08788, "loss": 3.08788, "time": 0.81617} +{"mode": "train", "epoch": 119, "iter": 1900, "lr": 0.01049, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43875, "top5_acc": 0.69891, "loss_cls": 3.14848, "loss": 3.14848, "time": 0.81849} +{"mode": "train", "epoch": 119, "iter": 2000, "lr": 0.01047, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44656, "top5_acc": 0.70078, "loss_cls": 3.10865, "loss": 3.10865, "time": 0.81567} +{"mode": "train", "epoch": 119, "iter": 2100, "lr": 0.01045, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44766, "top5_acc": 0.70422, "loss_cls": 3.1093, "loss": 3.1093, "time": 0.81857} +{"mode": "train", "epoch": 119, "iter": 2200, "lr": 0.01044, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45141, "top5_acc": 0.70641, "loss_cls": 3.08627, "loss": 3.08627, "time": 0.82916} +{"mode": "train", "epoch": 119, "iter": 2300, "lr": 0.01042, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44906, "top5_acc": 0.70422, "loss_cls": 3.10645, "loss": 3.10645, "time": 0.81936} +{"mode": "train", "epoch": 119, "iter": 2400, "lr": 0.0104, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44203, "top5_acc": 0.70609, "loss_cls": 3.09537, "loss": 3.09537, "time": 0.82984} +{"mode": "train", "epoch": 119, "iter": 2500, "lr": 0.01039, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43453, "top5_acc": 0.69719, "loss_cls": 3.12509, "loss": 3.12509, "time": 0.81797} +{"mode": "train", "epoch": 119, "iter": 2600, "lr": 0.01037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44266, "top5_acc": 0.7, "loss_cls": 3.11628, "loss": 3.11628, "time": 0.81748} +{"mode": "train", "epoch": 119, "iter": 2700, "lr": 0.01035, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45656, "top5_acc": 0.70312, "loss_cls": 3.06341, "loss": 3.06341, "time": 0.8162} +{"mode": "train", "epoch": 119, "iter": 2800, "lr": 0.01033, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44219, "top5_acc": 0.69984, "loss_cls": 3.1097, "loss": 3.1097, "time": 0.8196} +{"mode": "train", "epoch": 119, "iter": 2900, "lr": 0.01032, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45078, "top5_acc": 0.70266, "loss_cls": 3.07665, "loss": 3.07665, "time": 0.81448} +{"mode": "train", "epoch": 119, "iter": 3000, "lr": 0.0103, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43688, "top5_acc": 0.69125, "loss_cls": 3.16023, "loss": 3.16023, "time": 0.81622} +{"mode": "train", "epoch": 119, "iter": 3100, "lr": 0.01028, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44672, "top5_acc": 0.70422, "loss_cls": 3.11392, "loss": 3.11392, "time": 0.81715} +{"mode": "train", "epoch": 119, "iter": 3200, "lr": 0.01027, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44516, "top5_acc": 0.70453, "loss_cls": 3.05974, "loss": 3.05974, "time": 0.81204} +{"mode": "train", "epoch": 119, "iter": 3300, "lr": 0.01025, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44094, "top5_acc": 0.69953, "loss_cls": 3.12602, "loss": 3.12602, "time": 0.81266} +{"mode": "train", "epoch": 119, "iter": 3400, "lr": 0.01023, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43641, "top5_acc": 0.69984, "loss_cls": 3.12081, "loss": 3.12081, "time": 0.81325} +{"mode": "train", "epoch": 119, "iter": 3500, "lr": 0.01022, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44719, "top5_acc": 0.69812, "loss_cls": 3.127, "loss": 3.127, "time": 0.81686} +{"mode": "train", "epoch": 119, "iter": 3600, "lr": 0.0102, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44609, "top5_acc": 0.69891, "loss_cls": 3.11381, "loss": 3.11381, "time": 0.81423} +{"mode": "train", "epoch": 119, "iter": 3700, "lr": 0.01018, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44, "top5_acc": 0.68875, "loss_cls": 3.16415, "loss": 3.16415, "time": 0.81292} +{"mode": "val", "epoch": 119, "iter": 309, "lr": 0.01017, "top1_acc": 0.38672, "top5_acc": 0.64043, "mean_class_accuracy": 0.38657} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.01016, "memory": 15990, "data_time": 1.30862, "top1_acc": 0.46891, "top5_acc": 0.72141, "loss_cls": 2.96865, "loss": 2.96865, "time": 2.29072} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.01014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47, "top5_acc": 0.72484, "loss_cls": 2.96549, "loss": 2.96549, "time": 0.82082} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.01012, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46, "top5_acc": 0.71141, "loss_cls": 3.02389, "loss": 3.02389, "time": 0.81348} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.01011, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46172, "top5_acc": 0.70797, "loss_cls": 3.01585, "loss": 3.01585, "time": 0.81295} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.01009, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44609, "top5_acc": 0.71656, "loss_cls": 3.05271, "loss": 3.05271, "time": 0.81641} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.01007, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46172, "top5_acc": 0.71, "loss_cls": 3.05368, "loss": 3.05368, "time": 0.81859} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.01006, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45219, "top5_acc": 0.71344, "loss_cls": 3.04703, "loss": 3.04703, "time": 0.82152} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.01004, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44922, "top5_acc": 0.71422, "loss_cls": 3.05064, "loss": 3.05064, "time": 0.81934} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.01002, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45578, "top5_acc": 0.71469, "loss_cls": 3.02351, "loss": 3.02351, "time": 0.81919} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.01001, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46, "top5_acc": 0.72422, "loss_cls": 2.99976, "loss": 2.99976, "time": 0.81533} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45875, "top5_acc": 0.70531, "loss_cls": 3.0788, "loss": 3.0788, "time": 0.8169} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.00997, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44328, "top5_acc": 0.69594, "loss_cls": 3.10566, "loss": 3.10566, "time": 0.81489} +{"mode": "train", "epoch": 120, "iter": 1300, "lr": 0.00996, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43953, "top5_acc": 0.70938, "loss_cls": 3.07978, "loss": 3.07978, "time": 0.81546} +{"mode": "train", "epoch": 120, "iter": 1400, "lr": 0.00994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44781, "top5_acc": 0.69953, "loss_cls": 3.11352, "loss": 3.11352, "time": 0.81337} +{"mode": "train", "epoch": 120, "iter": 1500, "lr": 0.00992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44422, "top5_acc": 0.70078, "loss_cls": 3.10258, "loss": 3.10258, "time": 0.81603} +{"mode": "train", "epoch": 120, "iter": 1600, "lr": 0.0099, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45078, "top5_acc": 0.70312, "loss_cls": 3.09516, "loss": 3.09516, "time": 0.81803} +{"mode": "train", "epoch": 120, "iter": 1700, "lr": 0.00989, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45438, "top5_acc": 0.70875, "loss_cls": 3.04606, "loss": 3.04606, "time": 0.81615} +{"mode": "train", "epoch": 120, "iter": 1800, "lr": 0.00987, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44484, "top5_acc": 0.71141, "loss_cls": 3.07116, "loss": 3.07116, "time": 0.81662} +{"mode": "train", "epoch": 120, "iter": 1900, "lr": 0.00985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44391, "top5_acc": 0.69781, "loss_cls": 3.10637, "loss": 3.10637, "time": 0.81541} +{"mode": "train", "epoch": 120, "iter": 2000, "lr": 0.00984, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45062, "top5_acc": 0.70484, "loss_cls": 3.09341, "loss": 3.09341, "time": 0.81142} +{"mode": "train", "epoch": 120, "iter": 2100, "lr": 0.00982, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45641, "top5_acc": 0.7125, "loss_cls": 3.03649, "loss": 3.03649, "time": 0.81496} +{"mode": "train", "epoch": 120, "iter": 2200, "lr": 0.0098, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.445, "top5_acc": 0.69891, "loss_cls": 3.11727, "loss": 3.11727, "time": 0.82184} +{"mode": "train", "epoch": 120, "iter": 2300, "lr": 0.00979, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44703, "top5_acc": 0.71219, "loss_cls": 3.04978, "loss": 3.04978, "time": 0.81952} +{"mode": "train", "epoch": 120, "iter": 2400, "lr": 0.00977, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45391, "top5_acc": 0.70359, "loss_cls": 3.09027, "loss": 3.09027, "time": 0.82901} +{"mode": "train", "epoch": 120, "iter": 2500, "lr": 0.00976, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44359, "top5_acc": 0.70078, "loss_cls": 3.0856, "loss": 3.0856, "time": 0.81984} +{"mode": "train", "epoch": 120, "iter": 2600, "lr": 0.00974, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44234, "top5_acc": 0.69781, "loss_cls": 3.11697, "loss": 3.11697, "time": 0.81969} +{"mode": "train", "epoch": 120, "iter": 2700, "lr": 0.00972, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44969, "top5_acc": 0.70438, "loss_cls": 3.10028, "loss": 3.10028, "time": 0.81371} +{"mode": "train", "epoch": 120, "iter": 2800, "lr": 0.00971, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45031, "top5_acc": 0.71172, "loss_cls": 3.04931, "loss": 3.04931, "time": 0.81351} +{"mode": "train", "epoch": 120, "iter": 2900, "lr": 0.00969, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45344, "top5_acc": 0.71531, "loss_cls": 3.03837, "loss": 3.03837, "time": 0.81364} +{"mode": "train", "epoch": 120, "iter": 3000, "lr": 0.00967, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44469, "top5_acc": 0.6975, "loss_cls": 3.10006, "loss": 3.10006, "time": 0.81314} +{"mode": "train", "epoch": 120, "iter": 3100, "lr": 0.00966, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44656, "top5_acc": 0.70531, "loss_cls": 3.05647, "loss": 3.05647, "time": 0.81419} +{"mode": "train", "epoch": 120, "iter": 3200, "lr": 0.00964, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44609, "top5_acc": 0.69281, "loss_cls": 3.12735, "loss": 3.12735, "time": 0.81413} +{"mode": "train", "epoch": 120, "iter": 3300, "lr": 0.00962, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45125, "top5_acc": 0.70672, "loss_cls": 3.04753, "loss": 3.04753, "time": 0.81849} +{"mode": "train", "epoch": 120, "iter": 3400, "lr": 0.00961, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44531, "top5_acc": 0.69141, "loss_cls": 3.12499, "loss": 3.12499, "time": 0.81651} +{"mode": "train", "epoch": 120, "iter": 3500, "lr": 0.00959, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.455, "top5_acc": 0.71297, "loss_cls": 3.04861, "loss": 3.04861, "time": 0.81417} +{"mode": "train", "epoch": 120, "iter": 3600, "lr": 0.00957, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44984, "top5_acc": 0.70156, "loss_cls": 3.09866, "loss": 3.09866, "time": 0.81298} +{"mode": "train", "epoch": 120, "iter": 3700, "lr": 0.00956, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44547, "top5_acc": 0.69469, "loss_cls": 3.12278, "loss": 3.12278, "time": 0.81988} +{"mode": "val", "epoch": 120, "iter": 309, "lr": 0.00955, "top1_acc": 0.37907, "top5_acc": 0.63146, "mean_class_accuracy": 0.37887} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00953, "memory": 15990, "data_time": 1.29688, "top1_acc": 0.46297, "top5_acc": 0.72297, "loss_cls": 2.98393, "loss": 2.98393, "time": 2.26889} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00952, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46594, "top5_acc": 0.71312, "loss_cls": 3.00381, "loss": 3.00381, "time": 0.81609} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.0095, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46688, "top5_acc": 0.72938, "loss_cls": 2.95988, "loss": 2.95988, "time": 0.81543} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00948, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46344, "top5_acc": 0.715, "loss_cls": 3.01045, "loss": 3.01045, "time": 0.81602} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00947, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46, "top5_acc": 0.71859, "loss_cls": 2.98687, "loss": 2.98687, "time": 0.81243} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00945, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46141, "top5_acc": 0.70734, "loss_cls": 3.03551, "loss": 3.03551, "time": 0.81932} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.00943, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45703, "top5_acc": 0.70953, "loss_cls": 3.03615, "loss": 3.03615, "time": 0.81697} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00942, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45922, "top5_acc": 0.71438, "loss_cls": 3.03325, "loss": 3.03325, "time": 0.81604} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.0094, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47094, "top5_acc": 0.71797, "loss_cls": 2.98496, "loss": 2.98496, "time": 0.81805} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00939, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45438, "top5_acc": 0.71328, "loss_cls": 3.03825, "loss": 3.03825, "time": 0.8141} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00937, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45828, "top5_acc": 0.72047, "loss_cls": 2.98426, "loss": 2.98426, "time": 0.81443} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00935, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44266, "top5_acc": 0.70625, "loss_cls": 3.08234, "loss": 3.08234, "time": 0.81627} +{"mode": "train", "epoch": 121, "iter": 1300, "lr": 0.00934, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45969, "top5_acc": 0.71703, "loss_cls": 3.02166, "loss": 3.02166, "time": 0.81886} +{"mode": "train", "epoch": 121, "iter": 1400, "lr": 0.00932, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.4525, "top5_acc": 0.70328, "loss_cls": 3.06751, "loss": 3.06751, "time": 0.81121} +{"mode": "train", "epoch": 121, "iter": 1500, "lr": 0.0093, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45734, "top5_acc": 0.70844, "loss_cls": 3.07146, "loss": 3.07146, "time": 0.81482} +{"mode": "train", "epoch": 121, "iter": 1600, "lr": 0.00929, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46344, "top5_acc": 0.71078, "loss_cls": 3.02204, "loss": 3.02204, "time": 0.81548} +{"mode": "train", "epoch": 121, "iter": 1700, "lr": 0.00927, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44328, "top5_acc": 0.70859, "loss_cls": 3.07354, "loss": 3.07354, "time": 0.81746} +{"mode": "train", "epoch": 121, "iter": 1800, "lr": 0.00926, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4575, "top5_acc": 0.71156, "loss_cls": 3.04363, "loss": 3.04363, "time": 0.81844} +{"mode": "train", "epoch": 121, "iter": 1900, "lr": 0.00924, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45203, "top5_acc": 0.71047, "loss_cls": 3.03622, "loss": 3.03622, "time": 0.81422} +{"mode": "train", "epoch": 121, "iter": 2000, "lr": 0.00922, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45859, "top5_acc": 0.71297, "loss_cls": 3.02599, "loss": 3.02599, "time": 0.81662} +{"mode": "train", "epoch": 121, "iter": 2100, "lr": 0.00921, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45141, "top5_acc": 0.70672, "loss_cls": 3.06413, "loss": 3.06413, "time": 0.81677} +{"mode": "train", "epoch": 121, "iter": 2200, "lr": 0.00919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46172, "top5_acc": 0.70766, "loss_cls": 3.02175, "loss": 3.02175, "time": 0.81462} +{"mode": "train", "epoch": 121, "iter": 2300, "lr": 0.00917, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44672, "top5_acc": 0.70812, "loss_cls": 3.06662, "loss": 3.06662, "time": 0.81734} +{"mode": "train", "epoch": 121, "iter": 2400, "lr": 0.00916, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45031, "top5_acc": 0.70281, "loss_cls": 3.06415, "loss": 3.06415, "time": 0.82589} +{"mode": "train", "epoch": 121, "iter": 2500, "lr": 0.00914, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45344, "top5_acc": 0.71469, "loss_cls": 3.033, "loss": 3.033, "time": 0.81621} +{"mode": "train", "epoch": 121, "iter": 2600, "lr": 0.00913, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44484, "top5_acc": 0.70078, "loss_cls": 3.10313, "loss": 3.10313, "time": 0.81793} +{"mode": "train", "epoch": 121, "iter": 2700, "lr": 0.00911, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44891, "top5_acc": 0.70328, "loss_cls": 3.09781, "loss": 3.09781, "time": 0.81604} +{"mode": "train", "epoch": 121, "iter": 2800, "lr": 0.00909, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44969, "top5_acc": 0.71078, "loss_cls": 3.0471, "loss": 3.0471, "time": 0.81499} +{"mode": "train", "epoch": 121, "iter": 2900, "lr": 0.00908, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45688, "top5_acc": 0.70812, "loss_cls": 3.04176, "loss": 3.04176, "time": 0.82149} +{"mode": "train", "epoch": 121, "iter": 3000, "lr": 0.00906, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45266, "top5_acc": 0.70375, "loss_cls": 3.07366, "loss": 3.07366, "time": 0.81687} +{"mode": "train", "epoch": 121, "iter": 3100, "lr": 0.00905, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45609, "top5_acc": 0.70297, "loss_cls": 3.07086, "loss": 3.07086, "time": 0.81426} +{"mode": "train", "epoch": 121, "iter": 3200, "lr": 0.00903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45328, "top5_acc": 0.70672, "loss_cls": 3.06549, "loss": 3.06549, "time": 0.80957} +{"mode": "train", "epoch": 121, "iter": 3300, "lr": 0.00901, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45375, "top5_acc": 0.71359, "loss_cls": 3.05023, "loss": 3.05023, "time": 0.8168} +{"mode": "train", "epoch": 121, "iter": 3400, "lr": 0.009, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44906, "top5_acc": 0.70484, "loss_cls": 3.0896, "loss": 3.0896, "time": 0.81322} +{"mode": "train", "epoch": 121, "iter": 3500, "lr": 0.00898, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45531, "top5_acc": 0.70812, "loss_cls": 3.05027, "loss": 3.05027, "time": 0.81461} +{"mode": "train", "epoch": 121, "iter": 3600, "lr": 0.00897, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46219, "top5_acc": 0.71484, "loss_cls": 3.01402, "loss": 3.01402, "time": 0.81586} +{"mode": "train", "epoch": 121, "iter": 3700, "lr": 0.00895, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45656, "top5_acc": 0.71172, "loss_cls": 3.02266, "loss": 3.02266, "time": 0.81454} +{"mode": "val", "epoch": 121, "iter": 309, "lr": 0.00894, "top1_acc": 0.3894, "top5_acc": 0.64129, "mean_class_accuracy": 0.38907} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00893, "memory": 15990, "data_time": 1.34951, "top1_acc": 0.46156, "top5_acc": 0.71281, "loss_cls": 2.98757, "loss": 2.98757, "time": 2.3345} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00891, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.46953, "top5_acc": 0.72766, "loss_cls": 2.95558, "loss": 2.95558, "time": 0.82616} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.00889, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46766, "top5_acc": 0.72484, "loss_cls": 2.98501, "loss": 2.98501, "time": 0.82969} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00888, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46969, "top5_acc": 0.72594, "loss_cls": 2.96929, "loss": 2.96929, "time": 0.82357} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00886, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46984, "top5_acc": 0.72031, "loss_cls": 2.95868, "loss": 2.95868, "time": 0.82094} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00885, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46, "top5_acc": 0.71766, "loss_cls": 3.0222, "loss": 3.0222, "time": 0.82835} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00883, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46, "top5_acc": 0.71016, "loss_cls": 3.01943, "loss": 3.01943, "time": 0.82925} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00882, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46438, "top5_acc": 0.71969, "loss_cls": 2.98348, "loss": 2.98348, "time": 0.81836} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.0088, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45594, "top5_acc": 0.71797, "loss_cls": 3.03235, "loss": 3.03235, "time": 0.82439} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00878, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46781, "top5_acc": 0.7225, "loss_cls": 2.97007, "loss": 2.97007, "time": 0.82824} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00877, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46266, "top5_acc": 0.72078, "loss_cls": 2.97617, "loss": 2.97617, "time": 0.8217} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.00875, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46516, "top5_acc": 0.71672, "loss_cls": 3.00598, "loss": 3.00598, "time": 0.81998} +{"mode": "train", "epoch": 122, "iter": 1300, "lr": 0.00874, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45891, "top5_acc": 0.7175, "loss_cls": 3.01272, "loss": 3.01272, "time": 0.81954} +{"mode": "train", "epoch": 122, "iter": 1400, "lr": 0.00872, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46328, "top5_acc": 0.71, "loss_cls": 3.02011, "loss": 3.02011, "time": 0.82369} +{"mode": "train", "epoch": 122, "iter": 1500, "lr": 0.0087, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45812, "top5_acc": 0.70906, "loss_cls": 3.02289, "loss": 3.02289, "time": 0.81833} +{"mode": "train", "epoch": 122, "iter": 1600, "lr": 0.00869, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46281, "top5_acc": 0.72062, "loss_cls": 3.01047, "loss": 3.01047, "time": 0.81609} +{"mode": "train", "epoch": 122, "iter": 1700, "lr": 0.00867, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46047, "top5_acc": 0.71719, "loss_cls": 3.01395, "loss": 3.01395, "time": 0.81724} +{"mode": "train", "epoch": 122, "iter": 1800, "lr": 0.00866, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45375, "top5_acc": 0.71438, "loss_cls": 3.06171, "loss": 3.06171, "time": 0.81675} +{"mode": "train", "epoch": 122, "iter": 1900, "lr": 0.00864, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44453, "top5_acc": 0.70562, "loss_cls": 3.07722, "loss": 3.07722, "time": 0.81715} +{"mode": "train", "epoch": 122, "iter": 2000, "lr": 0.00863, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45359, "top5_acc": 0.71891, "loss_cls": 3.03591, "loss": 3.03591, "time": 0.82049} +{"mode": "train", "epoch": 122, "iter": 2100, "lr": 0.00861, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43875, "top5_acc": 0.70844, "loss_cls": 3.07983, "loss": 3.07983, "time": 0.81704} +{"mode": "train", "epoch": 122, "iter": 2200, "lr": 0.00859, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.45219, "top5_acc": 0.70719, "loss_cls": 3.06009, "loss": 3.06009, "time": 0.81886} +{"mode": "train", "epoch": 122, "iter": 2300, "lr": 0.00858, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47359, "top5_acc": 0.71719, "loss_cls": 2.99706, "loss": 2.99706, "time": 0.82579} +{"mode": "train", "epoch": 122, "iter": 2400, "lr": 0.00856, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.46, "top5_acc": 0.7175, "loss_cls": 3.0046, "loss": 3.0046, "time": 0.82808} +{"mode": "train", "epoch": 122, "iter": 2500, "lr": 0.00855, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46359, "top5_acc": 0.71422, "loss_cls": 3.00929, "loss": 3.00929, "time": 0.83931} +{"mode": "train", "epoch": 122, "iter": 2600, "lr": 0.00853, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46281, "top5_acc": 0.72219, "loss_cls": 2.97175, "loss": 2.97175, "time": 0.84201} +{"mode": "train", "epoch": 122, "iter": 2700, "lr": 0.00852, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46391, "top5_acc": 0.70609, "loss_cls": 3.00175, "loss": 3.00175, "time": 0.83922} +{"mode": "train", "epoch": 122, "iter": 2800, "lr": 0.0085, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.47156, "top5_acc": 0.71562, "loss_cls": 3.00298, "loss": 3.00298, "time": 0.83164} +{"mode": "train", "epoch": 122, "iter": 2900, "lr": 0.00849, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46234, "top5_acc": 0.71375, "loss_cls": 3.01699, "loss": 3.01699, "time": 0.82653} +{"mode": "train", "epoch": 122, "iter": 3000, "lr": 0.00847, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45344, "top5_acc": 0.71766, "loss_cls": 3.03564, "loss": 3.03564, "time": 0.83357} +{"mode": "train", "epoch": 122, "iter": 3100, "lr": 0.00845, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44812, "top5_acc": 0.70609, "loss_cls": 3.05524, "loss": 3.05524, "time": 0.82236} +{"mode": "train", "epoch": 122, "iter": 3200, "lr": 0.00844, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45781, "top5_acc": 0.71344, "loss_cls": 3.01668, "loss": 3.01668, "time": 0.82334} +{"mode": "train", "epoch": 122, "iter": 3300, "lr": 0.00842, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44953, "top5_acc": 0.70391, "loss_cls": 3.0753, "loss": 3.0753, "time": 0.82117} +{"mode": "train", "epoch": 122, "iter": 3400, "lr": 0.00841, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4575, "top5_acc": 0.71109, "loss_cls": 2.99628, "loss": 2.99628, "time": 0.8185} +{"mode": "train", "epoch": 122, "iter": 3500, "lr": 0.00839, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45469, "top5_acc": 0.70781, "loss_cls": 3.04807, "loss": 3.04807, "time": 0.81838} +{"mode": "train", "epoch": 122, "iter": 3600, "lr": 0.00838, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45734, "top5_acc": 0.71016, "loss_cls": 3.02538, "loss": 3.02538, "time": 0.82004} +{"mode": "train", "epoch": 122, "iter": 3700, "lr": 0.00836, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46016, "top5_acc": 0.70984, "loss_cls": 3.01197, "loss": 3.01197, "time": 0.82388} +{"mode": "val", "epoch": 122, "iter": 309, "lr": 0.00835, "top1_acc": 0.39265, "top5_acc": 0.64327, "mean_class_accuracy": 0.3924} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00834, "memory": 15990, "data_time": 1.36944, "top1_acc": 0.47266, "top5_acc": 0.72625, "loss_cls": 2.93963, "loss": 2.93963, "time": 2.3604} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00832, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48344, "top5_acc": 0.73344, "loss_cls": 2.90535, "loss": 2.90535, "time": 0.82139} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00831, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47344, "top5_acc": 0.72906, "loss_cls": 2.91655, "loss": 2.91655, "time": 0.81368} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00829, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47953, "top5_acc": 0.73125, "loss_cls": 2.93961, "loss": 2.93961, "time": 0.81519} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00828, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4625, "top5_acc": 0.71781, "loss_cls": 2.98807, "loss": 2.98807, "time": 0.81956} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00826, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46969, "top5_acc": 0.72406, "loss_cls": 2.9538, "loss": 2.9538, "time": 0.82366} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00825, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46844, "top5_acc": 0.72219, "loss_cls": 2.94662, "loss": 2.94662, "time": 0.81606} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.00823, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45875, "top5_acc": 0.72234, "loss_cls": 2.96987, "loss": 2.96987, "time": 0.81533} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00822, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45969, "top5_acc": 0.71406, "loss_cls": 2.99969, "loss": 2.99969, "time": 0.81497} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.0082, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46469, "top5_acc": 0.71562, "loss_cls": 2.96907, "loss": 2.96907, "time": 0.81498} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00818, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46625, "top5_acc": 0.7175, "loss_cls": 2.96978, "loss": 2.96978, "time": 0.81304} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00817, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46812, "top5_acc": 0.71938, "loss_cls": 2.98527, "loss": 2.98527, "time": 0.8137} +{"mode": "train", "epoch": 123, "iter": 1300, "lr": 0.00815, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46234, "top5_acc": 0.72328, "loss_cls": 2.97292, "loss": 2.97292, "time": 0.81756} +{"mode": "train", "epoch": 123, "iter": 1400, "lr": 0.00814, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46109, "top5_acc": 0.72156, "loss_cls": 2.98811, "loss": 2.98811, "time": 0.81473} +{"mode": "train", "epoch": 123, "iter": 1500, "lr": 0.00812, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45156, "top5_acc": 0.70703, "loss_cls": 3.05091, "loss": 3.05091, "time": 0.8184} +{"mode": "train", "epoch": 123, "iter": 1600, "lr": 0.00811, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47484, "top5_acc": 0.72266, "loss_cls": 2.94689, "loss": 2.94689, "time": 0.8148} +{"mode": "train", "epoch": 123, "iter": 1700, "lr": 0.00809, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46406, "top5_acc": 0.71453, "loss_cls": 3.00114, "loss": 3.00114, "time": 0.81248} +{"mode": "train", "epoch": 123, "iter": 1800, "lr": 0.00808, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45469, "top5_acc": 0.71125, "loss_cls": 3.03422, "loss": 3.03422, "time": 0.81578} +{"mode": "train", "epoch": 123, "iter": 1900, "lr": 0.00806, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47562, "top5_acc": 0.72328, "loss_cls": 2.94291, "loss": 2.94291, "time": 0.81459} +{"mode": "train", "epoch": 123, "iter": 2000, "lr": 0.00805, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45156, "top5_acc": 0.71422, "loss_cls": 3.03162, "loss": 3.03162, "time": 0.81082} +{"mode": "train", "epoch": 123, "iter": 2100, "lr": 0.00803, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47516, "top5_acc": 0.71922, "loss_cls": 2.94197, "loss": 2.94197, "time": 0.82198} +{"mode": "train", "epoch": 123, "iter": 2200, "lr": 0.00802, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4675, "top5_acc": 0.70609, "loss_cls": 3.00918, "loss": 3.00918, "time": 0.81718} +{"mode": "train", "epoch": 123, "iter": 2300, "lr": 0.008, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46406, "top5_acc": 0.71688, "loss_cls": 3.00288, "loss": 3.00288, "time": 0.81602} +{"mode": "train", "epoch": 123, "iter": 2400, "lr": 0.00799, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.46672, "top5_acc": 0.72219, "loss_cls": 2.99101, "loss": 2.99101, "time": 0.82808} +{"mode": "train", "epoch": 123, "iter": 2500, "lr": 0.00797, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46344, "top5_acc": 0.71656, "loss_cls": 2.98038, "loss": 2.98038, "time": 0.81852} +{"mode": "train", "epoch": 123, "iter": 2600, "lr": 0.00796, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46328, "top5_acc": 0.71594, "loss_cls": 2.99134, "loss": 2.99134, "time": 0.82013} +{"mode": "train", "epoch": 123, "iter": 2700, "lr": 0.00794, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46031, "top5_acc": 0.72047, "loss_cls": 2.99775, "loss": 2.99775, "time": 0.81571} +{"mode": "train", "epoch": 123, "iter": 2800, "lr": 0.00793, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46391, "top5_acc": 0.71828, "loss_cls": 2.99428, "loss": 2.99428, "time": 0.81201} +{"mode": "train", "epoch": 123, "iter": 2900, "lr": 0.00791, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47328, "top5_acc": 0.72188, "loss_cls": 2.96725, "loss": 2.96725, "time": 0.81373} +{"mode": "train", "epoch": 123, "iter": 3000, "lr": 0.0079, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4525, "top5_acc": 0.70891, "loss_cls": 3.06393, "loss": 3.06393, "time": 0.81262} +{"mode": "train", "epoch": 123, "iter": 3100, "lr": 0.00788, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46172, "top5_acc": 0.715, "loss_cls": 3.03244, "loss": 3.03244, "time": 0.82053} +{"mode": "train", "epoch": 123, "iter": 3200, "lr": 0.00787, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46031, "top5_acc": 0.71344, "loss_cls": 2.98881, "loss": 2.98881, "time": 0.81436} +{"mode": "train", "epoch": 123, "iter": 3300, "lr": 0.00785, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47531, "top5_acc": 0.72906, "loss_cls": 2.95104, "loss": 2.95104, "time": 0.81522} +{"mode": "train", "epoch": 123, "iter": 3400, "lr": 0.00784, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46078, "top5_acc": 0.71656, "loss_cls": 3.00752, "loss": 3.00752, "time": 0.81573} +{"mode": "train", "epoch": 123, "iter": 3500, "lr": 0.00782, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46844, "top5_acc": 0.71875, "loss_cls": 2.98919, "loss": 2.98919, "time": 0.81406} +{"mode": "train", "epoch": 123, "iter": 3600, "lr": 0.00781, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47688, "top5_acc": 0.72859, "loss_cls": 2.9238, "loss": 2.9238, "time": 0.81651} +{"mode": "train", "epoch": 123, "iter": 3700, "lr": 0.00779, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46578, "top5_acc": 0.71062, "loss_cls": 3.01895, "loss": 3.01895, "time": 0.81187} +{"mode": "val", "epoch": 123, "iter": 309, "lr": 0.00778, "top1_acc": 0.39234, "top5_acc": 0.64869, "mean_class_accuracy": 0.39213} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00777, "memory": 15990, "data_time": 1.35879, "top1_acc": 0.48125, "top5_acc": 0.74078, "loss_cls": 2.87987, "loss": 2.87987, "time": 2.33914} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00775, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49203, "top5_acc": 0.73766, "loss_cls": 2.83455, "loss": 2.83455, "time": 0.82019} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00774, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48391, "top5_acc": 0.72828, "loss_cls": 2.89166, "loss": 2.89166, "time": 0.82197} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.00772, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47594, "top5_acc": 0.73094, "loss_cls": 2.9023, "loss": 2.9023, "time": 0.81876} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00771, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46609, "top5_acc": 0.73172, "loss_cls": 2.93522, "loss": 2.93522, "time": 0.8153} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00769, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47562, "top5_acc": 0.72906, "loss_cls": 2.94146, "loss": 2.94146, "time": 0.82282} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00768, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48031, "top5_acc": 0.72875, "loss_cls": 2.91297, "loss": 2.91297, "time": 0.81746} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00766, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46234, "top5_acc": 0.71906, "loss_cls": 2.98922, "loss": 2.98922, "time": 0.82117} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00765, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47, "top5_acc": 0.72312, "loss_cls": 2.95156, "loss": 2.95156, "time": 0.81545} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00763, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46922, "top5_acc": 0.72672, "loss_cls": 2.94971, "loss": 2.94971, "time": 0.81893} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00762, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47703, "top5_acc": 0.72625, "loss_cls": 2.93549, "loss": 2.93549, "time": 0.81549} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.0076, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47188, "top5_acc": 0.72531, "loss_cls": 2.94973, "loss": 2.94973, "time": 0.8129} +{"mode": "train", "epoch": 124, "iter": 1300, "lr": 0.00759, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46906, "top5_acc": 0.71875, "loss_cls": 2.95773, "loss": 2.95773, "time": 0.81408} +{"mode": "train", "epoch": 124, "iter": 1400, "lr": 0.00758, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47672, "top5_acc": 0.73516, "loss_cls": 2.91721, "loss": 2.91721, "time": 0.81239} +{"mode": "train", "epoch": 124, "iter": 1500, "lr": 0.00756, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46875, "top5_acc": 0.72172, "loss_cls": 2.96644, "loss": 2.96644, "time": 0.81581} +{"mode": "train", "epoch": 124, "iter": 1600, "lr": 0.00755, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46375, "top5_acc": 0.72, "loss_cls": 2.98704, "loss": 2.98704, "time": 0.81587} +{"mode": "train", "epoch": 124, "iter": 1700, "lr": 0.00753, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47547, "top5_acc": 0.73078, "loss_cls": 2.92975, "loss": 2.92975, "time": 0.81634} +{"mode": "train", "epoch": 124, "iter": 1800, "lr": 0.00752, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47281, "top5_acc": 0.72781, "loss_cls": 2.92203, "loss": 2.92203, "time": 0.8102} +{"mode": "train", "epoch": 124, "iter": 1900, "lr": 0.0075, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.455, "top5_acc": 0.71125, "loss_cls": 3.03622, "loss": 3.03622, "time": 0.81423} +{"mode": "train", "epoch": 124, "iter": 2000, "lr": 0.00749, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47516, "top5_acc": 0.71828, "loss_cls": 2.95189, "loss": 2.95189, "time": 0.81638} +{"mode": "train", "epoch": 124, "iter": 2100, "lr": 0.00747, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47391, "top5_acc": 0.72156, "loss_cls": 2.9545, "loss": 2.9545, "time": 0.82083} +{"mode": "train", "epoch": 124, "iter": 2200, "lr": 0.00746, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47531, "top5_acc": 0.72609, "loss_cls": 2.93449, "loss": 2.93449, "time": 0.81978} +{"mode": "train", "epoch": 124, "iter": 2300, "lr": 0.00744, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46625, "top5_acc": 0.72031, "loss_cls": 2.96149, "loss": 2.96149, "time": 0.81574} +{"mode": "train", "epoch": 124, "iter": 2400, "lr": 0.00743, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47453, "top5_acc": 0.72594, "loss_cls": 2.9596, "loss": 2.9596, "time": 0.83497} +{"mode": "train", "epoch": 124, "iter": 2500, "lr": 0.00741, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45797, "top5_acc": 0.72062, "loss_cls": 2.99069, "loss": 2.99069, "time": 0.82299} +{"mode": "train", "epoch": 124, "iter": 2600, "lr": 0.0074, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45953, "top5_acc": 0.70781, "loss_cls": 3.03, "loss": 3.03, "time": 0.8195} +{"mode": "train", "epoch": 124, "iter": 2700, "lr": 0.00738, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47812, "top5_acc": 0.72797, "loss_cls": 2.92835, "loss": 2.92835, "time": 0.81409} +{"mode": "train", "epoch": 124, "iter": 2800, "lr": 0.00737, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48219, "top5_acc": 0.73016, "loss_cls": 2.89743, "loss": 2.89743, "time": 0.81352} +{"mode": "train", "epoch": 124, "iter": 2900, "lr": 0.00735, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46156, "top5_acc": 0.71219, "loss_cls": 2.99234, "loss": 2.99234, "time": 0.81375} +{"mode": "train", "epoch": 124, "iter": 3000, "lr": 0.00734, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46344, "top5_acc": 0.71344, "loss_cls": 3.00727, "loss": 3.00727, "time": 0.81447} +{"mode": "train", "epoch": 124, "iter": 3100, "lr": 0.00733, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46812, "top5_acc": 0.71641, "loss_cls": 2.98794, "loss": 2.98794, "time": 0.81348} +{"mode": "train", "epoch": 124, "iter": 3200, "lr": 0.00731, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46594, "top5_acc": 0.72203, "loss_cls": 2.9716, "loss": 2.9716, "time": 0.81503} +{"mode": "train", "epoch": 124, "iter": 3300, "lr": 0.0073, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46344, "top5_acc": 0.71828, "loss_cls": 2.98802, "loss": 2.98802, "time": 0.81557} +{"mode": "train", "epoch": 124, "iter": 3400, "lr": 0.00728, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47516, "top5_acc": 0.73562, "loss_cls": 2.92508, "loss": 2.92508, "time": 0.81802} +{"mode": "train", "epoch": 124, "iter": 3500, "lr": 0.00727, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46953, "top5_acc": 0.7175, "loss_cls": 2.93196, "loss": 2.93196, "time": 0.81567} +{"mode": "train", "epoch": 124, "iter": 3600, "lr": 0.00725, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46234, "top5_acc": 0.72219, "loss_cls": 2.9777, "loss": 2.9777, "time": 0.8206} +{"mode": "train", "epoch": 124, "iter": 3700, "lr": 0.00724, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46484, "top5_acc": 0.72125, "loss_cls": 2.98495, "loss": 2.98495, "time": 0.8129} +{"mode": "val", "epoch": 124, "iter": 309, "lr": 0.00723, "top1_acc": 0.39888, "top5_acc": 0.65274, "mean_class_accuracy": 0.39865} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.00722, "memory": 15990, "data_time": 1.32378, "top1_acc": 0.49531, "top5_acc": 0.74766, "loss_cls": 2.82178, "loss": 2.82178, "time": 2.29859} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.0072, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47453, "top5_acc": 0.72891, "loss_cls": 2.92565, "loss": 2.92565, "time": 0.81992} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00719, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48328, "top5_acc": 0.74281, "loss_cls": 2.85349, "loss": 2.85349, "time": 0.81916} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00717, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47234, "top5_acc": 0.72891, "loss_cls": 2.93527, "loss": 2.93527, "time": 0.8141} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00716, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49156, "top5_acc": 0.73781, "loss_cls": 2.84947, "loss": 2.84947, "time": 0.81598} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00715, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48125, "top5_acc": 0.73844, "loss_cls": 2.87533, "loss": 2.87533, "time": 0.82527} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00713, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49172, "top5_acc": 0.74266, "loss_cls": 2.85377, "loss": 2.85377, "time": 0.82106} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00712, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48719, "top5_acc": 0.73969, "loss_cls": 2.85881, "loss": 2.85881, "time": 0.82147} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.0071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48047, "top5_acc": 0.72828, "loss_cls": 2.9093, "loss": 2.9093, "time": 0.82217} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.00709, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46906, "top5_acc": 0.72781, "loss_cls": 2.94153, "loss": 2.94153, "time": 0.81413} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00707, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4775, "top5_acc": 0.72406, "loss_cls": 2.90469, "loss": 2.90469, "time": 0.81591} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00706, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47219, "top5_acc": 0.72656, "loss_cls": 2.93809, "loss": 2.93809, "time": 0.81554} +{"mode": "train", "epoch": 125, "iter": 1300, "lr": 0.00704, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4825, "top5_acc": 0.73203, "loss_cls": 2.87911, "loss": 2.87911, "time": 0.81446} +{"mode": "train", "epoch": 125, "iter": 1400, "lr": 0.00703, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47531, "top5_acc": 0.72547, "loss_cls": 2.93439, "loss": 2.93439, "time": 0.81611} +{"mode": "train", "epoch": 125, "iter": 1500, "lr": 0.00702, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47594, "top5_acc": 0.72297, "loss_cls": 2.93709, "loss": 2.93709, "time": 0.81447} +{"mode": "train", "epoch": 125, "iter": 1600, "lr": 0.007, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48609, "top5_acc": 0.73438, "loss_cls": 2.88469, "loss": 2.88469, "time": 0.81815} +{"mode": "train", "epoch": 125, "iter": 1700, "lr": 0.00699, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.47531, "top5_acc": 0.73203, "loss_cls": 2.91823, "loss": 2.91823, "time": 0.81321} +{"mode": "train", "epoch": 125, "iter": 1800, "lr": 0.00697, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.47656, "top5_acc": 0.72859, "loss_cls": 2.9092, "loss": 2.9092, "time": 0.81288} +{"mode": "train", "epoch": 125, "iter": 1900, "lr": 0.00696, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46578, "top5_acc": 0.72469, "loss_cls": 2.94286, "loss": 2.94286, "time": 0.81778} +{"mode": "train", "epoch": 125, "iter": 2000, "lr": 0.00694, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46922, "top5_acc": 0.72688, "loss_cls": 2.93734, "loss": 2.93734, "time": 0.81884} +{"mode": "train", "epoch": 125, "iter": 2100, "lr": 0.00693, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46797, "top5_acc": 0.72562, "loss_cls": 2.94487, "loss": 2.94487, "time": 0.81558} +{"mode": "train", "epoch": 125, "iter": 2200, "lr": 0.00692, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47562, "top5_acc": 0.73422, "loss_cls": 2.87114, "loss": 2.87114, "time": 0.8175} +{"mode": "train", "epoch": 125, "iter": 2300, "lr": 0.0069, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47469, "top5_acc": 0.72609, "loss_cls": 2.94504, "loss": 2.94504, "time": 0.81941} +{"mode": "train", "epoch": 125, "iter": 2400, "lr": 0.00689, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47219, "top5_acc": 0.73219, "loss_cls": 2.91201, "loss": 2.91201, "time": 0.82832} +{"mode": "train", "epoch": 125, "iter": 2500, "lr": 0.00687, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47062, "top5_acc": 0.72422, "loss_cls": 2.95069, "loss": 2.95069, "time": 0.82673} +{"mode": "train", "epoch": 125, "iter": 2600, "lr": 0.00686, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46875, "top5_acc": 0.73422, "loss_cls": 2.90932, "loss": 2.90932, "time": 0.81736} +{"mode": "train", "epoch": 125, "iter": 2700, "lr": 0.00685, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47625, "top5_acc": 0.72781, "loss_cls": 2.93122, "loss": 2.93122, "time": 0.8224} +{"mode": "train", "epoch": 125, "iter": 2800, "lr": 0.00683, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47234, "top5_acc": 0.72406, "loss_cls": 2.96683, "loss": 2.96683, "time": 0.81797} +{"mode": "train", "epoch": 125, "iter": 2900, "lr": 0.00682, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46828, "top5_acc": 0.73, "loss_cls": 2.92496, "loss": 2.92496, "time": 0.8159} +{"mode": "train", "epoch": 125, "iter": 3000, "lr": 0.0068, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47562, "top5_acc": 0.7275, "loss_cls": 2.93864, "loss": 2.93864, "time": 0.81246} +{"mode": "train", "epoch": 125, "iter": 3100, "lr": 0.00679, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46672, "top5_acc": 0.7275, "loss_cls": 2.95944, "loss": 2.95944, "time": 0.81714} +{"mode": "train", "epoch": 125, "iter": 3200, "lr": 0.00678, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48188, "top5_acc": 0.72766, "loss_cls": 2.90686, "loss": 2.90686, "time": 0.81505} +{"mode": "train", "epoch": 125, "iter": 3300, "lr": 0.00676, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46578, "top5_acc": 0.71734, "loss_cls": 2.98835, "loss": 2.98835, "time": 0.81353} +{"mode": "train", "epoch": 125, "iter": 3400, "lr": 0.00675, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46609, "top5_acc": 0.73312, "loss_cls": 2.94018, "loss": 2.94018, "time": 0.81178} +{"mode": "train", "epoch": 125, "iter": 3500, "lr": 0.00673, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46672, "top5_acc": 0.72562, "loss_cls": 2.95886, "loss": 2.95886, "time": 0.81548} +{"mode": "train", "epoch": 125, "iter": 3600, "lr": 0.00672, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47312, "top5_acc": 0.72312, "loss_cls": 2.96879, "loss": 2.96879, "time": 0.81906} +{"mode": "train", "epoch": 125, "iter": 3700, "lr": 0.00671, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46734, "top5_acc": 0.7275, "loss_cls": 2.92308, "loss": 2.92308, "time": 0.81599} +{"mode": "val", "epoch": 125, "iter": 309, "lr": 0.0067, "top1_acc": 0.40754, "top5_acc": 0.6611, "mean_class_accuracy": 0.40732} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00668, "memory": 15990, "data_time": 1.37145, "top1_acc": 0.49, "top5_acc": 0.75156, "loss_cls": 2.7839, "loss": 2.7839, "time": 2.3553} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00667, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49484, "top5_acc": 0.73859, "loss_cls": 2.83062, "loss": 2.83062, "time": 0.8197} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00666, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50391, "top5_acc": 0.74219, "loss_cls": 2.81302, "loss": 2.81302, "time": 0.82393} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00664, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48656, "top5_acc": 0.74484, "loss_cls": 2.83524, "loss": 2.83524, "time": 0.81996} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00663, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48766, "top5_acc": 0.74328, "loss_cls": 2.82219, "loss": 2.82219, "time": 0.81375} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00662, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47328, "top5_acc": 0.72469, "loss_cls": 2.9088, "loss": 2.9088, "time": 0.82478} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0066, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48891, "top5_acc": 0.73625, "loss_cls": 2.83191, "loss": 2.83191, "time": 0.81397} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00659, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48281, "top5_acc": 0.73484, "loss_cls": 2.87777, "loss": 2.87777, "time": 0.82456} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00657, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.485, "top5_acc": 0.72859, "loss_cls": 2.87209, "loss": 2.87209, "time": 0.81826} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00656, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49578, "top5_acc": 0.73812, "loss_cls": 2.8456, "loss": 2.8456, "time": 0.81841} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00655, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47375, "top5_acc": 0.73219, "loss_cls": 2.89282, "loss": 2.89282, "time": 0.81841} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00653, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49719, "top5_acc": 0.73844, "loss_cls": 2.81051, "loss": 2.81051, "time": 0.81122} +{"mode": "train", "epoch": 126, "iter": 1300, "lr": 0.00652, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47953, "top5_acc": 0.73172, "loss_cls": 2.8959, "loss": 2.8959, "time": 0.81572} +{"mode": "train", "epoch": 126, "iter": 1400, "lr": 0.0065, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47641, "top5_acc": 0.72969, "loss_cls": 2.92934, "loss": 2.92934, "time": 0.81405} +{"mode": "train", "epoch": 126, "iter": 1500, "lr": 0.00649, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47641, "top5_acc": 0.73156, "loss_cls": 2.89989, "loss": 2.89989, "time": 0.81435} +{"mode": "train", "epoch": 126, "iter": 1600, "lr": 0.00648, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47859, "top5_acc": 0.73047, "loss_cls": 2.88081, "loss": 2.88081, "time": 0.81773} +{"mode": "train", "epoch": 126, "iter": 1700, "lr": 0.00646, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48328, "top5_acc": 0.73891, "loss_cls": 2.83066, "loss": 2.83066, "time": 0.81279} +{"mode": "train", "epoch": 126, "iter": 1800, "lr": 0.00645, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48422, "top5_acc": 0.73672, "loss_cls": 2.89859, "loss": 2.89859, "time": 0.82303} +{"mode": "train", "epoch": 126, "iter": 1900, "lr": 0.00644, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48312, "top5_acc": 0.74156, "loss_cls": 2.87977, "loss": 2.87977, "time": 0.81972} +{"mode": "train", "epoch": 126, "iter": 2000, "lr": 0.00642, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46625, "top5_acc": 0.72875, "loss_cls": 2.95506, "loss": 2.95506, "time": 0.82006} +{"mode": "train", "epoch": 126, "iter": 2100, "lr": 0.00641, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47781, "top5_acc": 0.72734, "loss_cls": 2.91161, "loss": 2.91161, "time": 0.81522} +{"mode": "train", "epoch": 126, "iter": 2200, "lr": 0.00639, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49531, "top5_acc": 0.74078, "loss_cls": 2.84381, "loss": 2.84381, "time": 0.81768} +{"mode": "train", "epoch": 126, "iter": 2300, "lr": 0.00638, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47688, "top5_acc": 0.73297, "loss_cls": 2.90198, "loss": 2.90198, "time": 0.82369} +{"mode": "train", "epoch": 126, "iter": 2400, "lr": 0.00637, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47609, "top5_acc": 0.73812, "loss_cls": 2.91492, "loss": 2.91492, "time": 0.82501} +{"mode": "train", "epoch": 126, "iter": 2500, "lr": 0.00635, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.47703, "top5_acc": 0.72516, "loss_cls": 2.94073, "loss": 2.94073, "time": 0.82921} +{"mode": "train", "epoch": 126, "iter": 2600, "lr": 0.00634, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47438, "top5_acc": 0.72547, "loss_cls": 2.92531, "loss": 2.92531, "time": 0.82438} +{"mode": "train", "epoch": 126, "iter": 2700, "lr": 0.00633, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47359, "top5_acc": 0.72969, "loss_cls": 2.89565, "loss": 2.89565, "time": 0.82338} +{"mode": "train", "epoch": 126, "iter": 2800, "lr": 0.00631, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48812, "top5_acc": 0.73078, "loss_cls": 2.90254, "loss": 2.90254, "time": 0.81599} +{"mode": "train", "epoch": 126, "iter": 2900, "lr": 0.0063, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48109, "top5_acc": 0.72797, "loss_cls": 2.88942, "loss": 2.88942, "time": 0.81463} +{"mode": "train", "epoch": 126, "iter": 3000, "lr": 0.00629, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48859, "top5_acc": 0.72844, "loss_cls": 2.88753, "loss": 2.88753, "time": 0.8141} +{"mode": "train", "epoch": 126, "iter": 3100, "lr": 0.00627, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48297, "top5_acc": 0.72812, "loss_cls": 2.89516, "loss": 2.89516, "time": 0.81819} +{"mode": "train", "epoch": 126, "iter": 3200, "lr": 0.00626, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46875, "top5_acc": 0.71969, "loss_cls": 2.96288, "loss": 2.96288, "time": 0.81786} +{"mode": "train", "epoch": 126, "iter": 3300, "lr": 0.00625, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47828, "top5_acc": 0.73109, "loss_cls": 2.90313, "loss": 2.90313, "time": 0.82024} +{"mode": "train", "epoch": 126, "iter": 3400, "lr": 0.00623, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48578, "top5_acc": 0.73875, "loss_cls": 2.85302, "loss": 2.85302, "time": 0.81503} +{"mode": "train", "epoch": 126, "iter": 3500, "lr": 0.00622, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4775, "top5_acc": 0.73469, "loss_cls": 2.92522, "loss": 2.92522, "time": 0.81635} +{"mode": "train", "epoch": 126, "iter": 3600, "lr": 0.0062, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47203, "top5_acc": 0.72469, "loss_cls": 2.94342, "loss": 2.94342, "time": 0.81081} +{"mode": "train", "epoch": 126, "iter": 3700, "lr": 0.00619, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.4775, "top5_acc": 0.72641, "loss_cls": 2.93828, "loss": 2.93828, "time": 0.81771} +{"mode": "val", "epoch": 126, "iter": 309, "lr": 0.00618, "top1_acc": 0.40977, "top5_acc": 0.6651, "mean_class_accuracy": 0.40952} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00617, "memory": 15990, "data_time": 1.34159, "top1_acc": 0.5225, "top5_acc": 0.76266, "loss_cls": 2.69482, "loss": 2.69482, "time": 2.32142} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00616, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49703, "top5_acc": 0.74938, "loss_cls": 2.77956, "loss": 2.77956, "time": 0.82175} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00614, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5075, "top5_acc": 0.75578, "loss_cls": 2.76607, "loss": 2.76607, "time": 0.82231} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00613, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49781, "top5_acc": 0.74984, "loss_cls": 2.79478, "loss": 2.79478, "time": 0.82083} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.00612, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47531, "top5_acc": 0.73875, "loss_cls": 2.88128, "loss": 2.88128, "time": 0.81903} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.0061, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49969, "top5_acc": 0.74266, "loss_cls": 2.82164, "loss": 2.82164, "time": 0.82656} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00609, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48594, "top5_acc": 0.74375, "loss_cls": 2.85156, "loss": 2.85156, "time": 0.81817} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00608, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49281, "top5_acc": 0.75312, "loss_cls": 2.78652, "loss": 2.78652, "time": 0.81876} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00606, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49812, "top5_acc": 0.74844, "loss_cls": 2.79247, "loss": 2.79247, "time": 0.81716} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00605, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48984, "top5_acc": 0.73594, "loss_cls": 2.86021, "loss": 2.86021, "time": 0.81714} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00604, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48641, "top5_acc": 0.73266, "loss_cls": 2.86441, "loss": 2.86441, "time": 0.81406} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00602, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50094, "top5_acc": 0.75141, "loss_cls": 2.79492, "loss": 2.79492, "time": 0.81158} +{"mode": "train", "epoch": 127, "iter": 1300, "lr": 0.00601, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49641, "top5_acc": 0.74203, "loss_cls": 2.82291, "loss": 2.82291, "time": 0.81625} +{"mode": "train", "epoch": 127, "iter": 1400, "lr": 0.006, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49328, "top5_acc": 0.74609, "loss_cls": 2.80978, "loss": 2.80978, "time": 0.8178} +{"mode": "train", "epoch": 127, "iter": 1500, "lr": 0.00598, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48641, "top5_acc": 0.72766, "loss_cls": 2.88238, "loss": 2.88238, "time": 0.81881} +{"mode": "train", "epoch": 127, "iter": 1600, "lr": 0.00597, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48, "top5_acc": 0.73922, "loss_cls": 2.86626, "loss": 2.86626, "time": 0.81107} +{"mode": "train", "epoch": 127, "iter": 1700, "lr": 0.00596, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49, "top5_acc": 0.73297, "loss_cls": 2.87631, "loss": 2.87631, "time": 0.819} +{"mode": "train", "epoch": 127, "iter": 1800, "lr": 0.00594, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48281, "top5_acc": 0.73859, "loss_cls": 2.86524, "loss": 2.86524, "time": 0.8108} +{"mode": "train", "epoch": 127, "iter": 1900, "lr": 0.00593, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48922, "top5_acc": 0.74547, "loss_cls": 2.81022, "loss": 2.81022, "time": 0.81424} +{"mode": "train", "epoch": 127, "iter": 2000, "lr": 0.00592, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47875, "top5_acc": 0.73641, "loss_cls": 2.83528, "loss": 2.83528, "time": 0.8184} +{"mode": "train", "epoch": 127, "iter": 2100, "lr": 0.00591, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48109, "top5_acc": 0.73734, "loss_cls": 2.84871, "loss": 2.84871, "time": 0.82189} +{"mode": "train", "epoch": 127, "iter": 2200, "lr": 0.00589, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48266, "top5_acc": 0.73031, "loss_cls": 2.89688, "loss": 2.89688, "time": 0.81511} +{"mode": "train", "epoch": 127, "iter": 2300, "lr": 0.00588, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48469, "top5_acc": 0.73469, "loss_cls": 2.89078, "loss": 2.89078, "time": 0.82245} +{"mode": "train", "epoch": 127, "iter": 2400, "lr": 0.00587, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48062, "top5_acc": 0.72453, "loss_cls": 2.90163, "loss": 2.90163, "time": 0.81669} +{"mode": "train", "epoch": 127, "iter": 2500, "lr": 0.00585, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48094, "top5_acc": 0.72734, "loss_cls": 2.8919, "loss": 2.8919, "time": 0.8257} +{"mode": "train", "epoch": 127, "iter": 2600, "lr": 0.00584, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47844, "top5_acc": 0.73469, "loss_cls": 2.89534, "loss": 2.89534, "time": 0.82177} +{"mode": "train", "epoch": 127, "iter": 2700, "lr": 0.00583, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48516, "top5_acc": 0.72969, "loss_cls": 2.89387, "loss": 2.89387, "time": 0.82368} +{"mode": "train", "epoch": 127, "iter": 2800, "lr": 0.00581, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47719, "top5_acc": 0.73281, "loss_cls": 2.86622, "loss": 2.86622, "time": 0.8177} +{"mode": "train", "epoch": 127, "iter": 2900, "lr": 0.0058, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48781, "top5_acc": 0.73969, "loss_cls": 2.85488, "loss": 2.85488, "time": 0.81744} +{"mode": "train", "epoch": 127, "iter": 3000, "lr": 0.00579, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48359, "top5_acc": 0.73125, "loss_cls": 2.89185, "loss": 2.89185, "time": 0.82096} +{"mode": "train", "epoch": 127, "iter": 3100, "lr": 0.00577, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47781, "top5_acc": 0.72406, "loss_cls": 2.92034, "loss": 2.92034, "time": 0.81602} +{"mode": "train", "epoch": 127, "iter": 3200, "lr": 0.00576, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49, "top5_acc": 0.74156, "loss_cls": 2.84799, "loss": 2.84799, "time": 0.81515} +{"mode": "train", "epoch": 127, "iter": 3300, "lr": 0.00575, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48016, "top5_acc": 0.74016, "loss_cls": 2.87907, "loss": 2.87907, "time": 0.81568} +{"mode": "train", "epoch": 127, "iter": 3400, "lr": 0.00573, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47781, "top5_acc": 0.72562, "loss_cls": 2.91245, "loss": 2.91245, "time": 0.8168} +{"mode": "train", "epoch": 127, "iter": 3500, "lr": 0.00572, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48297, "top5_acc": 0.73828, "loss_cls": 2.85648, "loss": 2.85648, "time": 0.81735} +{"mode": "train", "epoch": 127, "iter": 3600, "lr": 0.00571, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47938, "top5_acc": 0.74031, "loss_cls": 2.85737, "loss": 2.85737, "time": 0.81469} +{"mode": "train", "epoch": 127, "iter": 3700, "lr": 0.0057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47844, "top5_acc": 0.73984, "loss_cls": 2.86963, "loss": 2.86963, "time": 0.81575} +{"mode": "val", "epoch": 127, "iter": 309, "lr": 0.00569, "top1_acc": 0.41108, "top5_acc": 0.65993, "mean_class_accuracy": 0.41073} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00568, "memory": 15990, "data_time": 1.3197, "top1_acc": 0.51344, "top5_acc": 0.75922, "loss_cls": 2.70396, "loss": 2.70396, "time": 2.29231} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.00566, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50016, "top5_acc": 0.75328, "loss_cls": 2.78026, "loss": 2.78026, "time": 0.80957} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00565, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50625, "top5_acc": 0.75781, "loss_cls": 2.73351, "loss": 2.73351, "time": 0.81562} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00564, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49188, "top5_acc": 0.74891, "loss_cls": 2.78001, "loss": 2.78001, "time": 0.81879} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00563, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.49578, "top5_acc": 0.75297, "loss_cls": 2.76346, "loss": 2.76346, "time": 0.8101} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00561, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50047, "top5_acc": 0.755, "loss_cls": 2.78293, "loss": 2.78293, "time": 0.82378} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.0056, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49406, "top5_acc": 0.74391, "loss_cls": 2.81381, "loss": 2.81381, "time": 0.81369} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00559, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48562, "top5_acc": 0.74, "loss_cls": 2.82694, "loss": 2.82694, "time": 0.82018} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00557, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.49609, "top5_acc": 0.74562, "loss_cls": 2.80259, "loss": 2.80259, "time": 0.8155} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00556, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50844, "top5_acc": 0.75938, "loss_cls": 2.73062, "loss": 2.73062, "time": 0.81592} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00555, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48453, "top5_acc": 0.74375, "loss_cls": 2.83898, "loss": 2.83898, "time": 0.81756} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00554, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49562, "top5_acc": 0.74453, "loss_cls": 2.81559, "loss": 2.81559, "time": 0.81646} +{"mode": "train", "epoch": 128, "iter": 1300, "lr": 0.00552, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50359, "top5_acc": 0.74156, "loss_cls": 2.79908, "loss": 2.79908, "time": 0.81335} +{"mode": "train", "epoch": 128, "iter": 1400, "lr": 0.00551, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49516, "top5_acc": 0.74531, "loss_cls": 2.79831, "loss": 2.79831, "time": 0.8144} +{"mode": "train", "epoch": 128, "iter": 1500, "lr": 0.0055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48516, "top5_acc": 0.73531, "loss_cls": 2.86627, "loss": 2.86627, "time": 0.82168} +{"mode": "train", "epoch": 128, "iter": 1600, "lr": 0.00548, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49656, "top5_acc": 0.7475, "loss_cls": 2.79607, "loss": 2.79607, "time": 0.81721} +{"mode": "train", "epoch": 128, "iter": 1700, "lr": 0.00547, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48562, "top5_acc": 0.74281, "loss_cls": 2.83023, "loss": 2.83023, "time": 0.81169} +{"mode": "train", "epoch": 128, "iter": 1800, "lr": 0.00546, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49203, "top5_acc": 0.74859, "loss_cls": 2.79322, "loss": 2.79322, "time": 0.81297} +{"mode": "train", "epoch": 128, "iter": 1900, "lr": 0.00545, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48766, "top5_acc": 0.74219, "loss_cls": 2.84719, "loss": 2.84719, "time": 0.81677} +{"mode": "train", "epoch": 128, "iter": 2000, "lr": 0.00543, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49188, "top5_acc": 0.74047, "loss_cls": 2.83431, "loss": 2.83431, "time": 0.81579} +{"mode": "train", "epoch": 128, "iter": 2100, "lr": 0.00542, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49453, "top5_acc": 0.74125, "loss_cls": 2.82182, "loss": 2.82182, "time": 0.81204} +{"mode": "train", "epoch": 128, "iter": 2200, "lr": 0.00541, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48688, "top5_acc": 0.74391, "loss_cls": 2.8363, "loss": 2.8363, "time": 0.81697} +{"mode": "train", "epoch": 128, "iter": 2300, "lr": 0.0054, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48875, "top5_acc": 0.74609, "loss_cls": 2.83262, "loss": 2.83262, "time": 0.81963} +{"mode": "train", "epoch": 128, "iter": 2400, "lr": 0.00538, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49594, "top5_acc": 0.74531, "loss_cls": 2.82308, "loss": 2.82308, "time": 0.83218} +{"mode": "train", "epoch": 128, "iter": 2500, "lr": 0.00537, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.48672, "top5_acc": 0.73781, "loss_cls": 2.84214, "loss": 2.84214, "time": 0.82695} +{"mode": "train", "epoch": 128, "iter": 2600, "lr": 0.00536, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.48484, "top5_acc": 0.74141, "loss_cls": 2.84431, "loss": 2.84431, "time": 0.8214} +{"mode": "train", "epoch": 128, "iter": 2700, "lr": 0.00535, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48562, "top5_acc": 0.74031, "loss_cls": 2.86176, "loss": 2.86176, "time": 0.81888} +{"mode": "train", "epoch": 128, "iter": 2800, "lr": 0.00533, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49297, "top5_acc": 0.74922, "loss_cls": 2.79006, "loss": 2.79006, "time": 0.81372} +{"mode": "train", "epoch": 128, "iter": 2900, "lr": 0.00532, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49109, "top5_acc": 0.73984, "loss_cls": 2.82289, "loss": 2.82289, "time": 0.81495} +{"mode": "train", "epoch": 128, "iter": 3000, "lr": 0.00531, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48734, "top5_acc": 0.74219, "loss_cls": 2.86331, "loss": 2.86331, "time": 0.81687} +{"mode": "train", "epoch": 128, "iter": 3100, "lr": 0.0053, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48922, "top5_acc": 0.74703, "loss_cls": 2.81986, "loss": 2.81986, "time": 0.81142} +{"mode": "train", "epoch": 128, "iter": 3200, "lr": 0.00528, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49656, "top5_acc": 0.73672, "loss_cls": 2.85704, "loss": 2.85704, "time": 0.81703} +{"mode": "train", "epoch": 128, "iter": 3300, "lr": 0.00527, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48656, "top5_acc": 0.73672, "loss_cls": 2.85661, "loss": 2.85661, "time": 0.81733} +{"mode": "train", "epoch": 128, "iter": 3400, "lr": 0.00526, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49312, "top5_acc": 0.73812, "loss_cls": 2.84946, "loss": 2.84946, "time": 0.81283} +{"mode": "train", "epoch": 128, "iter": 3500, "lr": 0.00525, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48531, "top5_acc": 0.73875, "loss_cls": 2.83114, "loss": 2.83114, "time": 0.81586} +{"mode": "train", "epoch": 128, "iter": 3600, "lr": 0.00523, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48406, "top5_acc": 0.73312, "loss_cls": 2.86115, "loss": 2.86115, "time": 0.81407} +{"mode": "train", "epoch": 128, "iter": 3700, "lr": 0.00522, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49328, "top5_acc": 0.75, "loss_cls": 2.8081, "loss": 2.8081, "time": 0.82038} +{"mode": "val", "epoch": 128, "iter": 309, "lr": 0.00521, "top1_acc": 0.41534, "top5_acc": 0.66966, "mean_class_accuracy": 0.415} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.0052, "memory": 15990, "data_time": 1.34905, "top1_acc": 0.50609, "top5_acc": 0.75391, "loss_cls": 2.74012, "loss": 2.74012, "time": 2.32538} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00519, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50453, "top5_acc": 0.7575, "loss_cls": 2.74158, "loss": 2.74158, "time": 0.81865} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00518, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50688, "top5_acc": 0.75891, "loss_cls": 2.73896, "loss": 2.73896, "time": 0.817} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00516, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51203, "top5_acc": 0.77016, "loss_cls": 2.68283, "loss": 2.68283, "time": 0.81722} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00515, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51547, "top5_acc": 0.76734, "loss_cls": 2.69672, "loss": 2.69672, "time": 0.81762} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00514, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50625, "top5_acc": 0.75688, "loss_cls": 2.73904, "loss": 2.73904, "time": 0.82392} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00513, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50391, "top5_acc": 0.75531, "loss_cls": 2.73797, "loss": 2.73797, "time": 0.8215} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00512, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49938, "top5_acc": 0.74734, "loss_cls": 2.77158, "loss": 2.77158, "time": 0.82586} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.0051, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50219, "top5_acc": 0.75344, "loss_cls": 2.76297, "loss": 2.76297, "time": 0.82053} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00509, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50156, "top5_acc": 0.74734, "loss_cls": 2.77791, "loss": 2.77791, "time": 0.81542} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00508, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49422, "top5_acc": 0.75375, "loss_cls": 2.75866, "loss": 2.75866, "time": 0.81518} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.00507, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50172, "top5_acc": 0.74781, "loss_cls": 2.76298, "loss": 2.76298, "time": 0.81408} +{"mode": "train", "epoch": 129, "iter": 1300, "lr": 0.00505, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49016, "top5_acc": 0.74297, "loss_cls": 2.79169, "loss": 2.79169, "time": 0.81362} +{"mode": "train", "epoch": 129, "iter": 1400, "lr": 0.00504, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50875, "top5_acc": 0.75578, "loss_cls": 2.74247, "loss": 2.74247, "time": 0.81757} +{"mode": "train", "epoch": 129, "iter": 1500, "lr": 0.00503, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49375, "top5_acc": 0.74578, "loss_cls": 2.81327, "loss": 2.81327, "time": 0.81748} +{"mode": "train", "epoch": 129, "iter": 1600, "lr": 0.00502, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49109, "top5_acc": 0.75312, "loss_cls": 2.77205, "loss": 2.77205, "time": 0.81542} +{"mode": "train", "epoch": 129, "iter": 1700, "lr": 0.00501, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50609, "top5_acc": 0.75062, "loss_cls": 2.76738, "loss": 2.76738, "time": 0.81646} +{"mode": "train", "epoch": 129, "iter": 1800, "lr": 0.00499, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.5075, "top5_acc": 0.75328, "loss_cls": 2.75956, "loss": 2.75956, "time": 0.81391} +{"mode": "train", "epoch": 129, "iter": 1900, "lr": 0.00498, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49891, "top5_acc": 0.74781, "loss_cls": 2.79943, "loss": 2.79943, "time": 0.81729} +{"mode": "train", "epoch": 129, "iter": 2000, "lr": 0.00497, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50672, "top5_acc": 0.75281, "loss_cls": 2.74272, "loss": 2.74272, "time": 0.81217} +{"mode": "train", "epoch": 129, "iter": 2100, "lr": 0.00496, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49812, "top5_acc": 0.74875, "loss_cls": 2.79158, "loss": 2.79158, "time": 0.8199} +{"mode": "train", "epoch": 129, "iter": 2200, "lr": 0.00494, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49516, "top5_acc": 0.75, "loss_cls": 2.79974, "loss": 2.79974, "time": 0.81735} +{"mode": "train", "epoch": 129, "iter": 2300, "lr": 0.00493, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49438, "top5_acc": 0.74609, "loss_cls": 2.79235, "loss": 2.79235, "time": 0.81317} +{"mode": "train", "epoch": 129, "iter": 2400, "lr": 0.00492, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49594, "top5_acc": 0.74, "loss_cls": 2.8225, "loss": 2.8225, "time": 0.82269} +{"mode": "train", "epoch": 129, "iter": 2500, "lr": 0.00491, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50187, "top5_acc": 0.75078, "loss_cls": 2.78638, "loss": 2.78638, "time": 0.83105} +{"mode": "train", "epoch": 129, "iter": 2600, "lr": 0.0049, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.49688, "top5_acc": 0.74953, "loss_cls": 2.8053, "loss": 2.8053, "time": 0.81751} +{"mode": "train", "epoch": 129, "iter": 2700, "lr": 0.00488, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49516, "top5_acc": 0.75234, "loss_cls": 2.79056, "loss": 2.79056, "time": 0.82286} +{"mode": "train", "epoch": 129, "iter": 2800, "lr": 0.00487, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49281, "top5_acc": 0.74359, "loss_cls": 2.81046, "loss": 2.81046, "time": 0.81696} +{"mode": "train", "epoch": 129, "iter": 2900, "lr": 0.00486, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50469, "top5_acc": 0.75828, "loss_cls": 2.75484, "loss": 2.75484, "time": 0.81669} +{"mode": "train", "epoch": 129, "iter": 3000, "lr": 0.00485, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49531, "top5_acc": 0.74219, "loss_cls": 2.83909, "loss": 2.83909, "time": 0.81056} +{"mode": "train", "epoch": 129, "iter": 3100, "lr": 0.00484, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49578, "top5_acc": 0.74609, "loss_cls": 2.81796, "loss": 2.81796, "time": 0.81557} +{"mode": "train", "epoch": 129, "iter": 3200, "lr": 0.00482, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49031, "top5_acc": 0.74031, "loss_cls": 2.82356, "loss": 2.82356, "time": 0.81344} +{"mode": "train", "epoch": 129, "iter": 3300, "lr": 0.00481, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49734, "top5_acc": 0.74641, "loss_cls": 2.80972, "loss": 2.80972, "time": 0.81444} +{"mode": "train", "epoch": 129, "iter": 3400, "lr": 0.0048, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49516, "top5_acc": 0.74688, "loss_cls": 2.79661, "loss": 2.79661, "time": 0.81823} +{"mode": "train", "epoch": 129, "iter": 3500, "lr": 0.00479, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49906, "top5_acc": 0.74672, "loss_cls": 2.7955, "loss": 2.7955, "time": 0.8207} +{"mode": "train", "epoch": 129, "iter": 3600, "lr": 0.00478, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50156, "top5_acc": 0.76, "loss_cls": 2.73555, "loss": 2.73555, "time": 0.82166} +{"mode": "train", "epoch": 129, "iter": 3700, "lr": 0.00476, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51094, "top5_acc": 0.76156, "loss_cls": 2.73894, "loss": 2.73894, "time": 0.81595} +{"mode": "val", "epoch": 129, "iter": 309, "lr": 0.00476, "top1_acc": 0.41594, "top5_acc": 0.66525, "mean_class_accuracy": 0.41571} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00475, "memory": 15990, "data_time": 1.34022, "top1_acc": 0.52062, "top5_acc": 0.76938, "loss_cls": 2.68453, "loss": 2.68453, "time": 2.3344} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00473, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52188, "top5_acc": 0.76031, "loss_cls": 2.70514, "loss": 2.70514, "time": 0.82204} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00472, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51141, "top5_acc": 0.75609, "loss_cls": 2.72804, "loss": 2.72804, "time": 0.81818} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00471, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51313, "top5_acc": 0.76094, "loss_cls": 2.70408, "loss": 2.70408, "time": 0.81548} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.0047, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52219, "top5_acc": 0.76953, "loss_cls": 2.63495, "loss": 2.63495, "time": 0.81721} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00469, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.52391, "top5_acc": 0.77219, "loss_cls": 2.64885, "loss": 2.64885, "time": 0.82108} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00468, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50422, "top5_acc": 0.75438, "loss_cls": 2.72581, "loss": 2.72581, "time": 0.81752} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00466, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51359, "top5_acc": 0.76125, "loss_cls": 2.72809, "loss": 2.72809, "time": 0.82299} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00465, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50953, "top5_acc": 0.76938, "loss_cls": 2.70256, "loss": 2.70256, "time": 0.81369} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.00464, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51672, "top5_acc": 0.75219, "loss_cls": 2.74103, "loss": 2.74103, "time": 0.81846} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.00463, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.52031, "top5_acc": 0.76578, "loss_cls": 2.67772, "loss": 2.67772, "time": 0.81335} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00462, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51828, "top5_acc": 0.76375, "loss_cls": 2.70615, "loss": 2.70615, "time": 0.8166} +{"mode": "train", "epoch": 130, "iter": 1300, "lr": 0.00461, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50484, "top5_acc": 0.75203, "loss_cls": 2.72929, "loss": 2.72929, "time": 0.81592} +{"mode": "train", "epoch": 130, "iter": 1400, "lr": 0.00459, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.51609, "top5_acc": 0.76172, "loss_cls": 2.69936, "loss": 2.69936, "time": 0.81216} +{"mode": "train", "epoch": 130, "iter": 1500, "lr": 0.00458, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51156, "top5_acc": 0.75766, "loss_cls": 2.72242, "loss": 2.72242, "time": 0.81486} +{"mode": "train", "epoch": 130, "iter": 1600, "lr": 0.00457, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51219, "top5_acc": 0.75312, "loss_cls": 2.74964, "loss": 2.74964, "time": 0.82269} +{"mode": "train", "epoch": 130, "iter": 1700, "lr": 0.00456, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51, "top5_acc": 0.75891, "loss_cls": 2.74804, "loss": 2.74804, "time": 0.81878} +{"mode": "train", "epoch": 130, "iter": 1800, "lr": 0.00455, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50578, "top5_acc": 0.76781, "loss_cls": 2.70954, "loss": 2.70954, "time": 0.81513} +{"mode": "train", "epoch": 130, "iter": 1900, "lr": 0.00454, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5025, "top5_acc": 0.74531, "loss_cls": 2.75754, "loss": 2.75754, "time": 0.81668} +{"mode": "train", "epoch": 130, "iter": 2000, "lr": 0.00452, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50875, "top5_acc": 0.7625, "loss_cls": 2.70677, "loss": 2.70677, "time": 0.81539} +{"mode": "train", "epoch": 130, "iter": 2100, "lr": 0.00451, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49719, "top5_acc": 0.74531, "loss_cls": 2.81423, "loss": 2.81423, "time": 0.82447} +{"mode": "train", "epoch": 130, "iter": 2200, "lr": 0.0045, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50844, "top5_acc": 0.75766, "loss_cls": 2.7363, "loss": 2.7363, "time": 0.81935} +{"mode": "train", "epoch": 130, "iter": 2300, "lr": 0.00449, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50125, "top5_acc": 0.75594, "loss_cls": 2.75472, "loss": 2.75472, "time": 0.81229} +{"mode": "train", "epoch": 130, "iter": 2400, "lr": 0.00448, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51484, "top5_acc": 0.75766, "loss_cls": 2.70739, "loss": 2.70739, "time": 0.82583} +{"mode": "train", "epoch": 130, "iter": 2500, "lr": 0.00447, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.49609, "top5_acc": 0.74578, "loss_cls": 2.788, "loss": 2.788, "time": 0.82601} +{"mode": "train", "epoch": 130, "iter": 2600, "lr": 0.00445, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50266, "top5_acc": 0.74844, "loss_cls": 2.78697, "loss": 2.78697, "time": 0.82377} +{"mode": "train", "epoch": 130, "iter": 2700, "lr": 0.00444, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51125, "top5_acc": 0.75609, "loss_cls": 2.76365, "loss": 2.76365, "time": 0.82324} +{"mode": "train", "epoch": 130, "iter": 2800, "lr": 0.00443, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50406, "top5_acc": 0.75297, "loss_cls": 2.7639, "loss": 2.7639, "time": 0.8211} +{"mode": "train", "epoch": 130, "iter": 2900, "lr": 0.00442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49688, "top5_acc": 0.74812, "loss_cls": 2.81079, "loss": 2.81079, "time": 0.82327} +{"mode": "train", "epoch": 130, "iter": 3000, "lr": 0.00441, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50594, "top5_acc": 0.75266, "loss_cls": 2.74467, "loss": 2.74467, "time": 0.81599} +{"mode": "train", "epoch": 130, "iter": 3100, "lr": 0.0044, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.5, "top5_acc": 0.74891, "loss_cls": 2.7798, "loss": 2.7798, "time": 0.81399} +{"mode": "train", "epoch": 130, "iter": 3200, "lr": 0.00439, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49891, "top5_acc": 0.74828, "loss_cls": 2.78004, "loss": 2.78004, "time": 0.81614} +{"mode": "train", "epoch": 130, "iter": 3300, "lr": 0.00437, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.50078, "top5_acc": 0.74844, "loss_cls": 2.79424, "loss": 2.79424, "time": 0.81599} +{"mode": "train", "epoch": 130, "iter": 3400, "lr": 0.00436, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50156, "top5_acc": 0.7475, "loss_cls": 2.82222, "loss": 2.82222, "time": 0.8243} +{"mode": "train", "epoch": 130, "iter": 3500, "lr": 0.00435, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50688, "top5_acc": 0.75609, "loss_cls": 2.75017, "loss": 2.75017, "time": 0.81808} +{"mode": "train", "epoch": 130, "iter": 3600, "lr": 0.00434, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51219, "top5_acc": 0.74859, "loss_cls": 2.75343, "loss": 2.75343, "time": 0.81226} +{"mode": "train", "epoch": 130, "iter": 3700, "lr": 0.00433, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51344, "top5_acc": 0.76453, "loss_cls": 2.70802, "loss": 2.70802, "time": 0.81502} +{"mode": "val", "epoch": 130, "iter": 309, "lr": 0.00432, "top1_acc": 0.41559, "top5_acc": 0.67001, "mean_class_accuracy": 0.41525} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00431, "memory": 15990, "data_time": 1.29623, "top1_acc": 0.51922, "top5_acc": 0.76812, "loss_cls": 2.6428, "loss": 2.6428, "time": 2.26995} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.0043, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52281, "top5_acc": 0.77359, "loss_cls": 2.64294, "loss": 2.64294, "time": 0.82036} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00429, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51453, "top5_acc": 0.76812, "loss_cls": 2.67517, "loss": 2.67517, "time": 0.82336} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00428, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53062, "top5_acc": 0.77188, "loss_cls": 2.6103, "loss": 2.6103, "time": 0.81935} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00427, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52641, "top5_acc": 0.77094, "loss_cls": 2.6184, "loss": 2.6184, "time": 0.81571} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00425, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.52281, "top5_acc": 0.76734, "loss_cls": 2.66825, "loss": 2.66825, "time": 0.82453} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00424, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52688, "top5_acc": 0.77094, "loss_cls": 2.63448, "loss": 2.63448, "time": 0.817} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00423, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51094, "top5_acc": 0.76297, "loss_cls": 2.70035, "loss": 2.70035, "time": 0.81842} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00422, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50375, "top5_acc": 0.75469, "loss_cls": 2.73249, "loss": 2.73249, "time": 0.81735} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.00421, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53094, "top5_acc": 0.77094, "loss_cls": 2.64182, "loss": 2.64182, "time": 0.8149} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.0042, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50953, "top5_acc": 0.7575, "loss_cls": 2.71184, "loss": 2.71184, "time": 0.81868} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00419, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.50891, "top5_acc": 0.75469, "loss_cls": 2.72609, "loss": 2.72609, "time": 0.81215} +{"mode": "train", "epoch": 131, "iter": 1300, "lr": 0.00418, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52062, "top5_acc": 0.76188, "loss_cls": 2.67806, "loss": 2.67806, "time": 0.81233} +{"mode": "train", "epoch": 131, "iter": 1400, "lr": 0.00417, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52703, "top5_acc": 0.76031, "loss_cls": 2.65759, "loss": 2.65759, "time": 0.81272} +{"mode": "train", "epoch": 131, "iter": 1500, "lr": 0.00415, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51031, "top5_acc": 0.75938, "loss_cls": 2.68715, "loss": 2.68715, "time": 0.81582} +{"mode": "train", "epoch": 131, "iter": 1600, "lr": 0.00414, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51703, "top5_acc": 0.77234, "loss_cls": 2.68143, "loss": 2.68143, "time": 0.81878} +{"mode": "train", "epoch": 131, "iter": 1700, "lr": 0.00413, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50344, "top5_acc": 0.75844, "loss_cls": 2.70791, "loss": 2.70791, "time": 0.81574} +{"mode": "train", "epoch": 131, "iter": 1800, "lr": 0.00412, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51609, "top5_acc": 0.76578, "loss_cls": 2.7041, "loss": 2.7041, "time": 0.81738} +{"mode": "train", "epoch": 131, "iter": 1900, "lr": 0.00411, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51969, "top5_acc": 0.76203, "loss_cls": 2.673, "loss": 2.673, "time": 0.81593} +{"mode": "train", "epoch": 131, "iter": 2000, "lr": 0.0041, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51625, "top5_acc": 0.76109, "loss_cls": 2.70644, "loss": 2.70644, "time": 0.81789} +{"mode": "train", "epoch": 131, "iter": 2100, "lr": 0.00409, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51297, "top5_acc": 0.75906, "loss_cls": 2.69001, "loss": 2.69001, "time": 0.81806} +{"mode": "train", "epoch": 131, "iter": 2200, "lr": 0.00408, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51719, "top5_acc": 0.76781, "loss_cls": 2.69894, "loss": 2.69894, "time": 0.81551} +{"mode": "train", "epoch": 131, "iter": 2300, "lr": 0.00407, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50844, "top5_acc": 0.76328, "loss_cls": 2.71543, "loss": 2.71543, "time": 0.81186} +{"mode": "train", "epoch": 131, "iter": 2400, "lr": 0.00405, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51375, "top5_acc": 0.75719, "loss_cls": 2.70206, "loss": 2.70206, "time": 0.82211} +{"mode": "train", "epoch": 131, "iter": 2500, "lr": 0.00404, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50969, "top5_acc": 0.75141, "loss_cls": 2.76184, "loss": 2.76184, "time": 0.81659} +{"mode": "train", "epoch": 131, "iter": 2600, "lr": 0.00403, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.50594, "top5_acc": 0.75125, "loss_cls": 2.75189, "loss": 2.75189, "time": 0.82605} +{"mode": "train", "epoch": 131, "iter": 2700, "lr": 0.00402, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52641, "top5_acc": 0.76016, "loss_cls": 2.68271, "loss": 2.68271, "time": 0.81639} +{"mode": "train", "epoch": 131, "iter": 2800, "lr": 0.00401, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51062, "top5_acc": 0.75641, "loss_cls": 2.74366, "loss": 2.74366, "time": 0.81927} +{"mode": "train", "epoch": 131, "iter": 2900, "lr": 0.004, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52172, "top5_acc": 0.76828, "loss_cls": 2.6755, "loss": 2.6755, "time": 0.82147} +{"mode": "train", "epoch": 131, "iter": 3000, "lr": 0.00399, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.50859, "top5_acc": 0.75594, "loss_cls": 2.72318, "loss": 2.72318, "time": 0.81476} +{"mode": "train", "epoch": 131, "iter": 3100, "lr": 0.00398, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51641, "top5_acc": 0.75719, "loss_cls": 2.71524, "loss": 2.71524, "time": 0.81459} +{"mode": "train", "epoch": 131, "iter": 3200, "lr": 0.00397, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51469, "top5_acc": 0.76031, "loss_cls": 2.69093, "loss": 2.69093, "time": 0.81911} +{"mode": "train", "epoch": 131, "iter": 3300, "lr": 0.00396, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51516, "top5_acc": 0.76297, "loss_cls": 2.68968, "loss": 2.68968, "time": 0.81417} +{"mode": "train", "epoch": 131, "iter": 3400, "lr": 0.00394, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50719, "top5_acc": 0.75922, "loss_cls": 2.72904, "loss": 2.72904, "time": 0.81269} +{"mode": "train", "epoch": 131, "iter": 3500, "lr": 0.00393, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.50781, "top5_acc": 0.75906, "loss_cls": 2.72565, "loss": 2.72565, "time": 0.81597} +{"mode": "train", "epoch": 131, "iter": 3600, "lr": 0.00392, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49375, "top5_acc": 0.74875, "loss_cls": 2.79519, "loss": 2.79519, "time": 0.81802} +{"mode": "train", "epoch": 131, "iter": 3700, "lr": 0.00391, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50281, "top5_acc": 0.75078, "loss_cls": 2.76265, "loss": 2.76265, "time": 0.81173} +{"mode": "val", "epoch": 131, "iter": 309, "lr": 0.00391, "top1_acc": 0.42086, "top5_acc": 0.67305, "mean_class_accuracy": 0.42063} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.0039, "memory": 15990, "data_time": 1.32253, "top1_acc": 0.525, "top5_acc": 0.77312, "loss_cls": 2.62264, "loss": 2.62264, "time": 2.30135} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00389, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53281, "top5_acc": 0.77625, "loss_cls": 2.60055, "loss": 2.60055, "time": 0.82132} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00387, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53281, "top5_acc": 0.77859, "loss_cls": 2.58841, "loss": 2.58841, "time": 0.81655} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00386, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52781, "top5_acc": 0.7775, "loss_cls": 2.60773, "loss": 2.60773, "time": 0.81805} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00385, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53203, "top5_acc": 0.77953, "loss_cls": 2.59984, "loss": 2.59984, "time": 0.81418} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00384, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52656, "top5_acc": 0.77391, "loss_cls": 2.61602, "loss": 2.61602, "time": 0.82258} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00383, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53531, "top5_acc": 0.775, "loss_cls": 2.60901, "loss": 2.60901, "time": 0.81293} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00382, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51703, "top5_acc": 0.77203, "loss_cls": 2.65027, "loss": 2.65027, "time": 0.81532} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00381, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52922, "top5_acc": 0.77172, "loss_cls": 2.62948, "loss": 2.62948, "time": 0.81335} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0038, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51922, "top5_acc": 0.76938, "loss_cls": 2.6726, "loss": 2.6726, "time": 0.8193} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00379, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51531, "top5_acc": 0.755, "loss_cls": 2.71518, "loss": 2.71518, "time": 0.81404} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00378, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51875, "top5_acc": 0.77266, "loss_cls": 2.67593, "loss": 2.67593, "time": 0.81946} +{"mode": "train", "epoch": 132, "iter": 1300, "lr": 0.00377, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.50953, "top5_acc": 0.76016, "loss_cls": 2.70583, "loss": 2.70583, "time": 0.81559} +{"mode": "train", "epoch": 132, "iter": 1400, "lr": 0.00376, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50969, "top5_acc": 0.76266, "loss_cls": 2.67817, "loss": 2.67817, "time": 0.81373} +{"mode": "train", "epoch": 132, "iter": 1500, "lr": 0.00375, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.53281, "top5_acc": 0.77328, "loss_cls": 2.599, "loss": 2.599, "time": 0.81196} +{"mode": "train", "epoch": 132, "iter": 1600, "lr": 0.00374, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51906, "top5_acc": 0.76609, "loss_cls": 2.63744, "loss": 2.63744, "time": 0.81302} +{"mode": "train", "epoch": 132, "iter": 1700, "lr": 0.00372, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53078, "top5_acc": 0.77375, "loss_cls": 2.62048, "loss": 2.62048, "time": 0.81758} +{"mode": "train", "epoch": 132, "iter": 1800, "lr": 0.00371, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52672, "top5_acc": 0.77, "loss_cls": 2.64209, "loss": 2.64209, "time": 0.81712} +{"mode": "train", "epoch": 132, "iter": 1900, "lr": 0.0037, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52984, "top5_acc": 0.77094, "loss_cls": 2.62677, "loss": 2.62677, "time": 0.81102} +{"mode": "train", "epoch": 132, "iter": 2000, "lr": 0.00369, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52875, "top5_acc": 0.775, "loss_cls": 2.64697, "loss": 2.64697, "time": 0.81559} +{"mode": "train", "epoch": 132, "iter": 2100, "lr": 0.00368, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52297, "top5_acc": 0.76875, "loss_cls": 2.64993, "loss": 2.64993, "time": 0.81169} +{"mode": "train", "epoch": 132, "iter": 2200, "lr": 0.00367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51984, "top5_acc": 0.76438, "loss_cls": 2.66738, "loss": 2.66738, "time": 0.8144} +{"mode": "train", "epoch": 132, "iter": 2300, "lr": 0.00366, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51047, "top5_acc": 0.75297, "loss_cls": 2.73419, "loss": 2.73419, "time": 0.81376} +{"mode": "train", "epoch": 132, "iter": 2400, "lr": 0.00365, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52203, "top5_acc": 0.76828, "loss_cls": 2.65429, "loss": 2.65429, "time": 0.81674} +{"mode": "train", "epoch": 132, "iter": 2500, "lr": 0.00364, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53062, "top5_acc": 0.765, "loss_cls": 2.63839, "loss": 2.63839, "time": 0.8167} +{"mode": "train", "epoch": 132, "iter": 2600, "lr": 0.00363, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51531, "top5_acc": 0.75969, "loss_cls": 2.68129, "loss": 2.68129, "time": 0.82565} +{"mode": "train", "epoch": 132, "iter": 2700, "lr": 0.00362, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51812, "top5_acc": 0.76609, "loss_cls": 2.66462, "loss": 2.66462, "time": 0.81876} +{"mode": "train", "epoch": 132, "iter": 2800, "lr": 0.00361, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.51594, "top5_acc": 0.76734, "loss_cls": 2.66475, "loss": 2.66475, "time": 0.81732} +{"mode": "train", "epoch": 132, "iter": 2900, "lr": 0.0036, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50328, "top5_acc": 0.75016, "loss_cls": 2.77237, "loss": 2.77237, "time": 0.82199} +{"mode": "train", "epoch": 132, "iter": 3000, "lr": 0.00359, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51938, "top5_acc": 0.76453, "loss_cls": 2.68954, "loss": 2.68954, "time": 0.81652} +{"mode": "train", "epoch": 132, "iter": 3100, "lr": 0.00358, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51719, "top5_acc": 0.76172, "loss_cls": 2.68357, "loss": 2.68357, "time": 0.81134} +{"mode": "train", "epoch": 132, "iter": 3200, "lr": 0.00357, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51938, "top5_acc": 0.76594, "loss_cls": 2.66153, "loss": 2.66153, "time": 0.81638} +{"mode": "train", "epoch": 132, "iter": 3300, "lr": 0.00356, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51781, "top5_acc": 0.76266, "loss_cls": 2.67284, "loss": 2.67284, "time": 0.81179} +{"mode": "train", "epoch": 132, "iter": 3400, "lr": 0.00355, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52094, "top5_acc": 0.76375, "loss_cls": 2.66605, "loss": 2.66605, "time": 0.81292} +{"mode": "train", "epoch": 132, "iter": 3500, "lr": 0.00354, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51344, "top5_acc": 0.75141, "loss_cls": 2.72614, "loss": 2.72614, "time": 0.81167} +{"mode": "train", "epoch": 132, "iter": 3600, "lr": 0.00353, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51156, "top5_acc": 0.76328, "loss_cls": 2.6935, "loss": 2.6935, "time": 0.8127} +{"mode": "train", "epoch": 132, "iter": 3700, "lr": 0.00352, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52547, "top5_acc": 0.76797, "loss_cls": 2.66783, "loss": 2.66783, "time": 0.81222} +{"mode": "val", "epoch": 132, "iter": 309, "lr": 0.00351, "top1_acc": 0.4277, "top5_acc": 0.68252, "mean_class_accuracy": 0.42732} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.0035, "memory": 15990, "data_time": 1.28756, "top1_acc": 0.5375, "top5_acc": 0.78266, "loss_cls": 2.54628, "loss": 2.54628, "time": 2.27302} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00349, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54953, "top5_acc": 0.78828, "loss_cls": 2.50172, "loss": 2.50172, "time": 0.82491} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00348, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.5375, "top5_acc": 0.77984, "loss_cls": 2.56324, "loss": 2.56324, "time": 0.81654} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00347, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54688, "top5_acc": 0.78281, "loss_cls": 2.545, "loss": 2.545, "time": 0.81601} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00346, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54656, "top5_acc": 0.78094, "loss_cls": 2.55283, "loss": 2.55283, "time": 0.81314} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00345, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53812, "top5_acc": 0.77938, "loss_cls": 2.58407, "loss": 2.58407, "time": 0.81259} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00344, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52891, "top5_acc": 0.77906, "loss_cls": 2.59811, "loss": 2.59811, "time": 0.81406} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00343, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53297, "top5_acc": 0.78328, "loss_cls": 2.57785, "loss": 2.57785, "time": 0.82129} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00342, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53797, "top5_acc": 0.7875, "loss_cls": 2.51738, "loss": 2.51738, "time": 0.81673} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.00341, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53188, "top5_acc": 0.77422, "loss_cls": 2.58673, "loss": 2.58673, "time": 0.81987} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0034, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54, "top5_acc": 0.78172, "loss_cls": 2.55705, "loss": 2.55705, "time": 0.81597} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00339, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53375, "top5_acc": 0.78016, "loss_cls": 2.58742, "loss": 2.58742, "time": 0.81176} +{"mode": "train", "epoch": 133, "iter": 1300, "lr": 0.00338, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52188, "top5_acc": 0.775, "loss_cls": 2.62499, "loss": 2.62499, "time": 0.82008} +{"mode": "train", "epoch": 133, "iter": 1400, "lr": 0.00337, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53656, "top5_acc": 0.77328, "loss_cls": 2.58725, "loss": 2.58725, "time": 0.81394} +{"mode": "train", "epoch": 133, "iter": 1500, "lr": 0.00336, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53297, "top5_acc": 0.77828, "loss_cls": 2.58469, "loss": 2.58469, "time": 0.81634} +{"mode": "train", "epoch": 133, "iter": 1600, "lr": 0.00335, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51969, "top5_acc": 0.76875, "loss_cls": 2.66259, "loss": 2.66259, "time": 0.82051} +{"mode": "train", "epoch": 133, "iter": 1700, "lr": 0.00334, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5225, "top5_acc": 0.77859, "loss_cls": 2.60921, "loss": 2.60921, "time": 0.81877} +{"mode": "train", "epoch": 133, "iter": 1800, "lr": 0.00333, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52797, "top5_acc": 0.76875, "loss_cls": 2.65095, "loss": 2.65095, "time": 0.81655} +{"mode": "train", "epoch": 133, "iter": 1900, "lr": 0.00332, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53078, "top5_acc": 0.76828, "loss_cls": 2.62097, "loss": 2.62097, "time": 0.81537} +{"mode": "train", "epoch": 133, "iter": 2000, "lr": 0.00331, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51328, "top5_acc": 0.76844, "loss_cls": 2.65988, "loss": 2.65988, "time": 0.81178} +{"mode": "train", "epoch": 133, "iter": 2100, "lr": 0.0033, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52797, "top5_acc": 0.76938, "loss_cls": 2.62289, "loss": 2.62289, "time": 0.81496} +{"mode": "train", "epoch": 133, "iter": 2200, "lr": 0.00329, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53422, "top5_acc": 0.76734, "loss_cls": 2.6217, "loss": 2.6217, "time": 0.81535} +{"mode": "train", "epoch": 133, "iter": 2300, "lr": 0.00328, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52281, "top5_acc": 0.76688, "loss_cls": 2.67427, "loss": 2.67427, "time": 0.81438} +{"mode": "train", "epoch": 133, "iter": 2400, "lr": 0.00327, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52125, "top5_acc": 0.765, "loss_cls": 2.65647, "loss": 2.65647, "time": 0.8225} +{"mode": "train", "epoch": 133, "iter": 2500, "lr": 0.00326, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51938, "top5_acc": 0.77031, "loss_cls": 2.68198, "loss": 2.68198, "time": 0.81947} +{"mode": "train", "epoch": 133, "iter": 2600, "lr": 0.00325, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52719, "top5_acc": 0.77312, "loss_cls": 2.617, "loss": 2.617, "time": 0.82417} +{"mode": "train", "epoch": 133, "iter": 2700, "lr": 0.00324, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52672, "top5_acc": 0.77078, "loss_cls": 2.64025, "loss": 2.64025, "time": 0.82005} +{"mode": "train", "epoch": 133, "iter": 2800, "lr": 0.00323, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51594, "top5_acc": 0.76906, "loss_cls": 2.66292, "loss": 2.66292, "time": 0.82015} +{"mode": "train", "epoch": 133, "iter": 2900, "lr": 0.00322, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.52062, "top5_acc": 0.76062, "loss_cls": 2.67854, "loss": 2.67854, "time": 0.81851} +{"mode": "train", "epoch": 133, "iter": 3000, "lr": 0.00321, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5225, "top5_acc": 0.76922, "loss_cls": 2.62132, "loss": 2.62132, "time": 0.81811} +{"mode": "train", "epoch": 133, "iter": 3100, "lr": 0.0032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53172, "top5_acc": 0.77109, "loss_cls": 2.62303, "loss": 2.62303, "time": 0.81324} +{"mode": "train", "epoch": 133, "iter": 3200, "lr": 0.00319, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52469, "top5_acc": 0.76953, "loss_cls": 2.61768, "loss": 2.61768, "time": 0.82445} +{"mode": "train", "epoch": 133, "iter": 3300, "lr": 0.00318, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.52281, "top5_acc": 0.76391, "loss_cls": 2.65316, "loss": 2.65316, "time": 0.81302} +{"mode": "train", "epoch": 133, "iter": 3400, "lr": 0.00317, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52078, "top5_acc": 0.76969, "loss_cls": 2.66105, "loss": 2.66105, "time": 0.81794} +{"mode": "train", "epoch": 133, "iter": 3500, "lr": 0.00316, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52047, "top5_acc": 0.76266, "loss_cls": 2.6772, "loss": 2.6772, "time": 0.81346} +{"mode": "train", "epoch": 133, "iter": 3600, "lr": 0.00315, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.53234, "top5_acc": 0.77469, "loss_cls": 2.6049, "loss": 2.6049, "time": 0.8103} +{"mode": "train", "epoch": 133, "iter": 3700, "lr": 0.00314, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51953, "top5_acc": 0.76734, "loss_cls": 2.66236, "loss": 2.66236, "time": 0.81378} +{"mode": "val", "epoch": 133, "iter": 309, "lr": 0.00314, "top1_acc": 0.43124, "top5_acc": 0.67882, "mean_class_accuracy": 0.43087} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00313, "memory": 15990, "data_time": 1.29036, "top1_acc": 0.55312, "top5_acc": 0.78984, "loss_cls": 2.52616, "loss": 2.52616, "time": 2.27062} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00312, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.565, "top5_acc": 0.79953, "loss_cls": 2.4501, "loss": 2.4501, "time": 0.81989} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00311, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55344, "top5_acc": 0.79734, "loss_cls": 2.48078, "loss": 2.48078, "time": 0.81729} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.0031, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54453, "top5_acc": 0.78219, "loss_cls": 2.52334, "loss": 2.52334, "time": 0.82301} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00309, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53594, "top5_acc": 0.78422, "loss_cls": 2.53569, "loss": 2.53569, "time": 0.8149} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00308, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.54203, "top5_acc": 0.78125, "loss_cls": 2.54419, "loss": 2.54419, "time": 0.82032} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00307, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53688, "top5_acc": 0.78109, "loss_cls": 2.56185, "loss": 2.56185, "time": 0.82021} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00306, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54656, "top5_acc": 0.79266, "loss_cls": 2.51708, "loss": 2.51708, "time": 0.82224} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00305, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54484, "top5_acc": 0.78266, "loss_cls": 2.54235, "loss": 2.54235, "time": 0.8165} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00304, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53109, "top5_acc": 0.78375, "loss_cls": 2.56609, "loss": 2.56609, "time": 0.82472} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00303, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54172, "top5_acc": 0.79016, "loss_cls": 2.5149, "loss": 2.5149, "time": 0.81256} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.00302, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53469, "top5_acc": 0.77297, "loss_cls": 2.61174, "loss": 2.61174, "time": 0.81758} +{"mode": "train", "epoch": 134, "iter": 1300, "lr": 0.00301, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53797, "top5_acc": 0.78156, "loss_cls": 2.60295, "loss": 2.60295, "time": 0.81251} +{"mode": "train", "epoch": 134, "iter": 1400, "lr": 0.003, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52266, "top5_acc": 0.77516, "loss_cls": 2.59919, "loss": 2.59919, "time": 0.81464} +{"mode": "train", "epoch": 134, "iter": 1500, "lr": 0.00299, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.5375, "top5_acc": 0.78156, "loss_cls": 2.58933, "loss": 2.58933, "time": 0.8187} +{"mode": "train", "epoch": 134, "iter": 1600, "lr": 0.00298, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52984, "top5_acc": 0.77797, "loss_cls": 2.60648, "loss": 2.60648, "time": 0.82156} +{"mode": "train", "epoch": 134, "iter": 1700, "lr": 0.00297, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53312, "top5_acc": 0.78266, "loss_cls": 2.59558, "loss": 2.59558, "time": 0.81418} +{"mode": "train", "epoch": 134, "iter": 1800, "lr": 0.00296, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54797, "top5_acc": 0.78281, "loss_cls": 2.52881, "loss": 2.52881, "time": 0.81702} +{"mode": "train", "epoch": 134, "iter": 1900, "lr": 0.00295, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.545, "top5_acc": 0.77703, "loss_cls": 2.56946, "loss": 2.56946, "time": 0.8179} +{"mode": "train", "epoch": 134, "iter": 2000, "lr": 0.00294, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53281, "top5_acc": 0.77625, "loss_cls": 2.58549, "loss": 2.58549, "time": 0.81631} +{"mode": "train", "epoch": 134, "iter": 2100, "lr": 0.00293, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54047, "top5_acc": 0.78312, "loss_cls": 2.55466, "loss": 2.55466, "time": 0.81488} +{"mode": "train", "epoch": 134, "iter": 2200, "lr": 0.00293, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.535, "top5_acc": 0.77719, "loss_cls": 2.57407, "loss": 2.57407, "time": 0.81472} +{"mode": "train", "epoch": 134, "iter": 2300, "lr": 0.00292, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52781, "top5_acc": 0.77406, "loss_cls": 2.61714, "loss": 2.61714, "time": 0.82273} +{"mode": "train", "epoch": 134, "iter": 2400, "lr": 0.00291, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53062, "top5_acc": 0.77781, "loss_cls": 2.58851, "loss": 2.58851, "time": 0.82645} +{"mode": "train", "epoch": 134, "iter": 2500, "lr": 0.0029, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.52828, "top5_acc": 0.77828, "loss_cls": 2.61, "loss": 2.61, "time": 0.81573} +{"mode": "train", "epoch": 134, "iter": 2600, "lr": 0.00289, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53562, "top5_acc": 0.78047, "loss_cls": 2.56772, "loss": 2.56772, "time": 0.81927} +{"mode": "train", "epoch": 134, "iter": 2700, "lr": 0.00288, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52953, "top5_acc": 0.77562, "loss_cls": 2.59702, "loss": 2.59702, "time": 0.83357} +{"mode": "train", "epoch": 134, "iter": 2800, "lr": 0.00287, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.53188, "top5_acc": 0.77219, "loss_cls": 2.59576, "loss": 2.59576, "time": 0.81384} +{"mode": "train", "epoch": 134, "iter": 2900, "lr": 0.00286, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52438, "top5_acc": 0.78391, "loss_cls": 2.59747, "loss": 2.59747, "time": 0.82216} +{"mode": "train", "epoch": 134, "iter": 3000, "lr": 0.00285, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.54, "top5_acc": 0.77672, "loss_cls": 2.57555, "loss": 2.57555, "time": 0.81297} +{"mode": "train", "epoch": 134, "iter": 3100, "lr": 0.00284, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53484, "top5_acc": 0.77547, "loss_cls": 2.58612, "loss": 2.58612, "time": 0.81527} +{"mode": "train", "epoch": 134, "iter": 3200, "lr": 0.00283, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53812, "top5_acc": 0.77953, "loss_cls": 2.55712, "loss": 2.55712, "time": 0.81473} +{"mode": "train", "epoch": 134, "iter": 3300, "lr": 0.00282, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53391, "top5_acc": 0.77344, "loss_cls": 2.60166, "loss": 2.60166, "time": 0.81691} +{"mode": "train", "epoch": 134, "iter": 3400, "lr": 0.00281, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53453, "top5_acc": 0.78031, "loss_cls": 2.55936, "loss": 2.55936, "time": 0.81874} +{"mode": "train", "epoch": 134, "iter": 3500, "lr": 0.0028, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52875, "top5_acc": 0.77359, "loss_cls": 2.6232, "loss": 2.6232, "time": 0.82017} +{"mode": "train", "epoch": 134, "iter": 3600, "lr": 0.00279, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.5275, "top5_acc": 0.77453, "loss_cls": 2.614, "loss": 2.614, "time": 0.81228} +{"mode": "train", "epoch": 134, "iter": 3700, "lr": 0.00279, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.535, "top5_acc": 0.78625, "loss_cls": 2.55052, "loss": 2.55052, "time": 0.81442} +{"mode": "val", "epoch": 134, "iter": 309, "lr": 0.00278, "top1_acc": 0.43443, "top5_acc": 0.68515, "mean_class_accuracy": 0.43404} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00277, "memory": 15990, "data_time": 1.28757, "top1_acc": 0.56594, "top5_acc": 0.80375, "loss_cls": 2.42096, "loss": 2.42096, "time": 2.26351} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00276, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.5575, "top5_acc": 0.79844, "loss_cls": 2.44271, "loss": 2.44271, "time": 0.82331} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00275, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55172, "top5_acc": 0.78984, "loss_cls": 2.49032, "loss": 2.49032, "time": 0.81414} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00274, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54969, "top5_acc": 0.79094, "loss_cls": 2.50989, "loss": 2.50989, "time": 0.81633} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00274, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.55906, "top5_acc": 0.79109, "loss_cls": 2.47247, "loss": 2.47247, "time": 0.81895} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00273, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56031, "top5_acc": 0.79203, "loss_cls": 2.47328, "loss": 2.47328, "time": 0.81792} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00272, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54719, "top5_acc": 0.78703, "loss_cls": 2.52153, "loss": 2.52153, "time": 0.81231} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00271, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.54937, "top5_acc": 0.79453, "loss_cls": 2.49265, "loss": 2.49265, "time": 0.82451} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.0027, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53438, "top5_acc": 0.78641, "loss_cls": 2.54101, "loss": 2.54101, "time": 0.82622} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00269, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54547, "top5_acc": 0.78562, "loss_cls": 2.52211, "loss": 2.52211, "time": 0.81058} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00268, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54969, "top5_acc": 0.78875, "loss_cls": 2.49969, "loss": 2.49969, "time": 0.81409} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00267, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55875, "top5_acc": 0.79469, "loss_cls": 2.46195, "loss": 2.46195, "time": 0.81952} +{"mode": "train", "epoch": 135, "iter": 1300, "lr": 0.00266, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54531, "top5_acc": 0.78672, "loss_cls": 2.50951, "loss": 2.50951, "time": 0.81456} +{"mode": "train", "epoch": 135, "iter": 1400, "lr": 0.00265, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55406, "top5_acc": 0.78891, "loss_cls": 2.49558, "loss": 2.49558, "time": 0.81511} +{"mode": "train", "epoch": 135, "iter": 1500, "lr": 0.00265, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.54312, "top5_acc": 0.78406, "loss_cls": 2.52803, "loss": 2.52803, "time": 0.81397} +{"mode": "train", "epoch": 135, "iter": 1600, "lr": 0.00264, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.5475, "top5_acc": 0.79547, "loss_cls": 2.50844, "loss": 2.50844, "time": 0.81612} +{"mode": "train", "epoch": 135, "iter": 1700, "lr": 0.00263, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55219, "top5_acc": 0.78969, "loss_cls": 2.47745, "loss": 2.47745, "time": 0.81611} +{"mode": "train", "epoch": 135, "iter": 1800, "lr": 0.00262, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54516, "top5_acc": 0.78391, "loss_cls": 2.52423, "loss": 2.52423, "time": 0.81674} +{"mode": "train", "epoch": 135, "iter": 1900, "lr": 0.00261, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54031, "top5_acc": 0.77953, "loss_cls": 2.54057, "loss": 2.54057, "time": 0.81903} +{"mode": "train", "epoch": 135, "iter": 2000, "lr": 0.0026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54172, "top5_acc": 0.78141, "loss_cls": 2.5251, "loss": 2.5251, "time": 0.81553} +{"mode": "train", "epoch": 135, "iter": 2100, "lr": 0.00259, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.54156, "top5_acc": 0.78906, "loss_cls": 2.51386, "loss": 2.51386, "time": 0.81123} +{"mode": "train", "epoch": 135, "iter": 2200, "lr": 0.00258, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55109, "top5_acc": 0.78469, "loss_cls": 2.507, "loss": 2.507, "time": 0.81599} +{"mode": "train", "epoch": 135, "iter": 2300, "lr": 0.00257, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53875, "top5_acc": 0.79016, "loss_cls": 2.53111, "loss": 2.53111, "time": 0.81595} +{"mode": "train", "epoch": 135, "iter": 2400, "lr": 0.00256, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55328, "top5_acc": 0.78938, "loss_cls": 2.49187, "loss": 2.49187, "time": 0.81499} +{"mode": "train", "epoch": 135, "iter": 2500, "lr": 0.00256, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53766, "top5_acc": 0.78328, "loss_cls": 2.53715, "loss": 2.53715, "time": 0.81187} +{"mode": "train", "epoch": 135, "iter": 2600, "lr": 0.00255, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54219, "top5_acc": 0.77562, "loss_cls": 2.55806, "loss": 2.55806, "time": 0.81438} +{"mode": "train", "epoch": 135, "iter": 2700, "lr": 0.00254, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54312, "top5_acc": 0.77938, "loss_cls": 2.57231, "loss": 2.57231, "time": 0.82174} +{"mode": "train", "epoch": 135, "iter": 2800, "lr": 0.00253, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54906, "top5_acc": 0.78688, "loss_cls": 2.50926, "loss": 2.50926, "time": 0.81369} +{"mode": "train", "epoch": 135, "iter": 2900, "lr": 0.00252, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53375, "top5_acc": 0.78094, "loss_cls": 2.57694, "loss": 2.57694, "time": 0.81841} +{"mode": "train", "epoch": 135, "iter": 3000, "lr": 0.00251, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53234, "top5_acc": 0.78078, "loss_cls": 2.58983, "loss": 2.58983, "time": 0.81577} +{"mode": "train", "epoch": 135, "iter": 3100, "lr": 0.0025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54828, "top5_acc": 0.78438, "loss_cls": 2.53894, "loss": 2.53894, "time": 0.81924} +{"mode": "train", "epoch": 135, "iter": 3200, "lr": 0.00249, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53922, "top5_acc": 0.785, "loss_cls": 2.54608, "loss": 2.54608, "time": 0.81835} +{"mode": "train", "epoch": 135, "iter": 3300, "lr": 0.00249, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53516, "top5_acc": 0.78266, "loss_cls": 2.56369, "loss": 2.56369, "time": 0.81435} +{"mode": "train", "epoch": 135, "iter": 3400, "lr": 0.00248, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53219, "top5_acc": 0.78312, "loss_cls": 2.57711, "loss": 2.57711, "time": 0.81569} +{"mode": "train", "epoch": 135, "iter": 3500, "lr": 0.00247, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.53344, "top5_acc": 0.77625, "loss_cls": 2.6086, "loss": 2.6086, "time": 0.81293} +{"mode": "train", "epoch": 135, "iter": 3600, "lr": 0.00246, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54156, "top5_acc": 0.78641, "loss_cls": 2.52258, "loss": 2.52258, "time": 0.81418} +{"mode": "train", "epoch": 135, "iter": 3700, "lr": 0.00245, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54672, "top5_acc": 0.77438, "loss_cls": 2.55791, "loss": 2.55791, "time": 0.81282} +{"mode": "val", "epoch": 135, "iter": 309, "lr": 0.00245, "top1_acc": 0.43681, "top5_acc": 0.68728, "mean_class_accuracy": 0.43654} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00244, "memory": 15990, "data_time": 1.31112, "top1_acc": 0.56781, "top5_acc": 0.81453, "loss_cls": 2.37518, "loss": 2.37518, "time": 2.29133} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.00243, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55859, "top5_acc": 0.79422, "loss_cls": 2.44382, "loss": 2.44382, "time": 0.81775} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00242, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55734, "top5_acc": 0.79594, "loss_cls": 2.46876, "loss": 2.46876, "time": 0.81768} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00241, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56719, "top5_acc": 0.8025, "loss_cls": 2.40373, "loss": 2.40373, "time": 0.81462} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.0024, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56734, "top5_acc": 0.81, "loss_cls": 2.38327, "loss": 2.38327, "time": 0.82204} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.0024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56188, "top5_acc": 0.80609, "loss_cls": 2.41737, "loss": 2.41737, "time": 0.81665} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00239, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55531, "top5_acc": 0.79906, "loss_cls": 2.45383, "loss": 2.45383, "time": 0.82167} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00238, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.55781, "top5_acc": 0.80016, "loss_cls": 2.45098, "loss": 2.45098, "time": 0.81774} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00237, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57, "top5_acc": 0.81016, "loss_cls": 2.37562, "loss": 2.37562, "time": 0.82129} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00236, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54969, "top5_acc": 0.7925, "loss_cls": 2.48737, "loss": 2.48737, "time": 0.81622} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00235, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54766, "top5_acc": 0.78969, "loss_cls": 2.49721, "loss": 2.49721, "time": 0.81629} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00234, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54844, "top5_acc": 0.79609, "loss_cls": 2.47947, "loss": 2.47947, "time": 0.81315} +{"mode": "train", "epoch": 136, "iter": 1300, "lr": 0.00234, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.55047, "top5_acc": 0.79, "loss_cls": 2.48227, "loss": 2.48227, "time": 0.8171} +{"mode": "train", "epoch": 136, "iter": 1400, "lr": 0.00233, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.55812, "top5_acc": 0.78875, "loss_cls": 2.46602, "loss": 2.46602, "time": 0.8138} +{"mode": "train", "epoch": 136, "iter": 1500, "lr": 0.00232, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55562, "top5_acc": 0.79391, "loss_cls": 2.47796, "loss": 2.47796, "time": 0.81944} +{"mode": "train", "epoch": 136, "iter": 1600, "lr": 0.00231, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55062, "top5_acc": 0.79312, "loss_cls": 2.47376, "loss": 2.47376, "time": 0.81141} +{"mode": "train", "epoch": 136, "iter": 1700, "lr": 0.0023, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55656, "top5_acc": 0.79953, "loss_cls": 2.45265, "loss": 2.45265, "time": 0.81867} +{"mode": "train", "epoch": 136, "iter": 1800, "lr": 0.00229, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55031, "top5_acc": 0.7925, "loss_cls": 2.48648, "loss": 2.48648, "time": 0.81468} +{"mode": "train", "epoch": 136, "iter": 1900, "lr": 0.00229, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55594, "top5_acc": 0.79422, "loss_cls": 2.49927, "loss": 2.49927, "time": 0.81446} +{"mode": "train", "epoch": 136, "iter": 2000, "lr": 0.00228, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55016, "top5_acc": 0.79031, "loss_cls": 2.47925, "loss": 2.47925, "time": 0.8228} +{"mode": "train", "epoch": 136, "iter": 2100, "lr": 0.00227, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54266, "top5_acc": 0.78406, "loss_cls": 2.52737, "loss": 2.52737, "time": 0.81528} +{"mode": "train", "epoch": 136, "iter": 2200, "lr": 0.00226, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.56109, "top5_acc": 0.79672, "loss_cls": 2.44701, "loss": 2.44701, "time": 0.81188} +{"mode": "train", "epoch": 136, "iter": 2300, "lr": 0.00225, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55516, "top5_acc": 0.79625, "loss_cls": 2.45419, "loss": 2.45419, "time": 0.81612} +{"mode": "train", "epoch": 136, "iter": 2400, "lr": 0.00224, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55078, "top5_acc": 0.79, "loss_cls": 2.46191, "loss": 2.46191, "time": 0.81882} +{"mode": "train", "epoch": 136, "iter": 2500, "lr": 0.00224, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56969, "top5_acc": 0.80094, "loss_cls": 2.42026, "loss": 2.42026, "time": 0.81246} +{"mode": "train", "epoch": 136, "iter": 2600, "lr": 0.00223, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54844, "top5_acc": 0.79375, "loss_cls": 2.49055, "loss": 2.49055, "time": 0.82229} +{"mode": "train", "epoch": 136, "iter": 2700, "lr": 0.00222, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55141, "top5_acc": 0.79406, "loss_cls": 2.473, "loss": 2.473, "time": 0.81687} +{"mode": "train", "epoch": 136, "iter": 2800, "lr": 0.00221, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54453, "top5_acc": 0.78781, "loss_cls": 2.53715, "loss": 2.53715, "time": 0.82456} +{"mode": "train", "epoch": 136, "iter": 2900, "lr": 0.0022, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55844, "top5_acc": 0.79219, "loss_cls": 2.47307, "loss": 2.47307, "time": 0.82176} +{"mode": "train", "epoch": 136, "iter": 3000, "lr": 0.00219, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55188, "top5_acc": 0.78734, "loss_cls": 2.48655, "loss": 2.48655, "time": 0.8264} +{"mode": "train", "epoch": 136, "iter": 3100, "lr": 0.00219, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55672, "top5_acc": 0.78812, "loss_cls": 2.48565, "loss": 2.48565, "time": 0.81821} +{"mode": "train", "epoch": 136, "iter": 3200, "lr": 0.00218, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54969, "top5_acc": 0.78438, "loss_cls": 2.50405, "loss": 2.50405, "time": 0.81632} +{"mode": "train", "epoch": 136, "iter": 3300, "lr": 0.00217, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.55375, "top5_acc": 0.78641, "loss_cls": 2.499, "loss": 2.499, "time": 0.81337} +{"mode": "train", "epoch": 136, "iter": 3400, "lr": 0.00216, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55875, "top5_acc": 0.79312, "loss_cls": 2.46325, "loss": 2.46325, "time": 0.81229} +{"mode": "train", "epoch": 136, "iter": 3500, "lr": 0.00215, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54578, "top5_acc": 0.77938, "loss_cls": 2.53206, "loss": 2.53206, "time": 0.8214} +{"mode": "train", "epoch": 136, "iter": 3600, "lr": 0.00215, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55688, "top5_acc": 0.79484, "loss_cls": 2.48116, "loss": 2.48116, "time": 0.81898} +{"mode": "train", "epoch": 136, "iter": 3700, "lr": 0.00214, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54562, "top5_acc": 0.78672, "loss_cls": 2.51624, "loss": 2.51624, "time": 0.81574} +{"mode": "val", "epoch": 136, "iter": 309, "lr": 0.00213, "top1_acc": 0.43296, "top5_acc": 0.68546, "mean_class_accuracy": 0.43265} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00213, "memory": 15990, "data_time": 1.34479, "top1_acc": 0.57391, "top5_acc": 0.81203, "loss_cls": 2.36902, "loss": 2.36902, "time": 2.3263} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00212, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58875, "top5_acc": 0.81891, "loss_cls": 2.30367, "loss": 2.30367, "time": 0.81488} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00211, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57703, "top5_acc": 0.80141, "loss_cls": 2.38775, "loss": 2.38775, "time": 0.81799} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.0021, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56016, "top5_acc": 0.80672, "loss_cls": 2.37801, "loss": 2.37801, "time": 0.81562} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.00209, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.56734, "top5_acc": 0.80844, "loss_cls": 2.41082, "loss": 2.41082, "time": 0.81358} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.00209, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57812, "top5_acc": 0.81328, "loss_cls": 2.3654, "loss": 2.3654, "time": 0.81671} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00208, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57781, "top5_acc": 0.81203, "loss_cls": 2.36289, "loss": 2.36289, "time": 0.81578} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00207, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55969, "top5_acc": 0.8025, "loss_cls": 2.4223, "loss": 2.4223, "time": 0.81541} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00206, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56688, "top5_acc": 0.80562, "loss_cls": 2.39459, "loss": 2.39459, "time": 0.81509} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00205, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57656, "top5_acc": 0.81219, "loss_cls": 2.34409, "loss": 2.34409, "time": 0.8172} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00205, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.56594, "top5_acc": 0.79266, "loss_cls": 2.42299, "loss": 2.42299, "time": 0.81418} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00204, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56516, "top5_acc": 0.79328, "loss_cls": 2.43729, "loss": 2.43729, "time": 0.81195} +{"mode": "train", "epoch": 137, "iter": 1300, "lr": 0.00203, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56672, "top5_acc": 0.80469, "loss_cls": 2.40036, "loss": 2.40036, "time": 0.81327} +{"mode": "train", "epoch": 137, "iter": 1400, "lr": 0.00202, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55, "top5_acc": 0.79547, "loss_cls": 2.47716, "loss": 2.47716, "time": 0.81452} +{"mode": "train", "epoch": 137, "iter": 1500, "lr": 0.00201, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56125, "top5_acc": 0.80562, "loss_cls": 2.41618, "loss": 2.41618, "time": 0.81082} +{"mode": "train", "epoch": 137, "iter": 1600, "lr": 0.00201, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57125, "top5_acc": 0.80781, "loss_cls": 2.39521, "loss": 2.39521, "time": 0.82211} +{"mode": "train", "epoch": 137, "iter": 1700, "lr": 0.002, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.56422, "top5_acc": 0.80578, "loss_cls": 2.39899, "loss": 2.39899, "time": 0.81363} +{"mode": "train", "epoch": 137, "iter": 1800, "lr": 0.00199, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56484, "top5_acc": 0.80688, "loss_cls": 2.38914, "loss": 2.38914, "time": 0.81395} +{"mode": "train", "epoch": 137, "iter": 1900, "lr": 0.00198, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.5625, "top5_acc": 0.80781, "loss_cls": 2.40482, "loss": 2.40482, "time": 0.81634} +{"mode": "train", "epoch": 137, "iter": 2000, "lr": 0.00198, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54484, "top5_acc": 0.79562, "loss_cls": 2.49401, "loss": 2.49401, "time": 0.81343} +{"mode": "train", "epoch": 137, "iter": 2100, "lr": 0.00197, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55516, "top5_acc": 0.79328, "loss_cls": 2.47202, "loss": 2.47202, "time": 0.81699} +{"mode": "train", "epoch": 137, "iter": 2200, "lr": 0.00196, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56531, "top5_acc": 0.80594, "loss_cls": 2.39262, "loss": 2.39262, "time": 0.81445} +{"mode": "train", "epoch": 137, "iter": 2300, "lr": 0.00195, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56969, "top5_acc": 0.80547, "loss_cls": 2.38421, "loss": 2.38421, "time": 0.81472} +{"mode": "train", "epoch": 137, "iter": 2400, "lr": 0.00194, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57734, "top5_acc": 0.80141, "loss_cls": 2.37277, "loss": 2.37277, "time": 0.82096} +{"mode": "train", "epoch": 137, "iter": 2500, "lr": 0.00194, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.56484, "top5_acc": 0.80172, "loss_cls": 2.42407, "loss": 2.42407, "time": 0.81837} +{"mode": "train", "epoch": 137, "iter": 2600, "lr": 0.00193, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55719, "top5_acc": 0.795, "loss_cls": 2.47512, "loss": 2.47512, "time": 0.82094} +{"mode": "train", "epoch": 137, "iter": 2700, "lr": 0.00192, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55453, "top5_acc": 0.79281, "loss_cls": 2.46975, "loss": 2.46975, "time": 0.8176} +{"mode": "train", "epoch": 137, "iter": 2800, "lr": 0.00191, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55437, "top5_acc": 0.79859, "loss_cls": 2.46957, "loss": 2.46957, "time": 0.82644} +{"mode": "train", "epoch": 137, "iter": 2900, "lr": 0.00191, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55453, "top5_acc": 0.78938, "loss_cls": 2.48034, "loss": 2.48034, "time": 0.82102} +{"mode": "train", "epoch": 137, "iter": 3000, "lr": 0.0019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55719, "top5_acc": 0.79188, "loss_cls": 2.44421, "loss": 2.44421, "time": 0.82117} +{"mode": "train", "epoch": 137, "iter": 3100, "lr": 0.00189, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55922, "top5_acc": 0.79516, "loss_cls": 2.42955, "loss": 2.42955, "time": 0.81723} +{"mode": "train", "epoch": 137, "iter": 3200, "lr": 0.00188, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55547, "top5_acc": 0.7975, "loss_cls": 2.46397, "loss": 2.46397, "time": 0.8162} +{"mode": "train", "epoch": 137, "iter": 3300, "lr": 0.00188, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54656, "top5_acc": 0.79234, "loss_cls": 2.47228, "loss": 2.47228, "time": 0.81566} +{"mode": "train", "epoch": 137, "iter": 3400, "lr": 0.00187, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.55359, "top5_acc": 0.78734, "loss_cls": 2.48518, "loss": 2.48518, "time": 0.81821} +{"mode": "train", "epoch": 137, "iter": 3500, "lr": 0.00186, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56141, "top5_acc": 0.79781, "loss_cls": 2.43385, "loss": 2.43385, "time": 0.81464} +{"mode": "train", "epoch": 137, "iter": 3600, "lr": 0.00185, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55656, "top5_acc": 0.79844, "loss_cls": 2.45941, "loss": 2.45941, "time": 0.81446} +{"mode": "train", "epoch": 137, "iter": 3700, "lr": 0.00185, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55672, "top5_acc": 0.80062, "loss_cls": 2.43802, "loss": 2.43802, "time": 0.8169} +{"mode": "val", "epoch": 137, "iter": 309, "lr": 0.00184, "top1_acc": 0.44157, "top5_acc": 0.69098, "mean_class_accuracy": 0.44134} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00183, "memory": 15990, "data_time": 1.29879, "top1_acc": 0.59016, "top5_acc": 0.82344, "loss_cls": 2.26845, "loss": 2.26845, "time": 2.28727} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00183, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58453, "top5_acc": 0.8225, "loss_cls": 2.28224, "loss": 2.28224, "time": 0.81766} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00182, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57422, "top5_acc": 0.80859, "loss_cls": 2.37478, "loss": 2.37478, "time": 0.81879} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00181, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58094, "top5_acc": 0.81125, "loss_cls": 2.33793, "loss": 2.33793, "time": 0.82502} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.0018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57531, "top5_acc": 0.80828, "loss_cls": 2.34931, "loss": 2.34931, "time": 0.82059} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.0018, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57938, "top5_acc": 0.81734, "loss_cls": 2.31504, "loss": 2.31504, "time": 0.81898} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00179, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57359, "top5_acc": 0.81188, "loss_cls": 2.37278, "loss": 2.37278, "time": 0.81998} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00178, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.5725, "top5_acc": 0.80594, "loss_cls": 2.36876, "loss": 2.36876, "time": 0.81543} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00177, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57672, "top5_acc": 0.81141, "loss_cls": 2.32865, "loss": 2.32865, "time": 0.81916} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00177, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57203, "top5_acc": 0.80969, "loss_cls": 2.35251, "loss": 2.35251, "time": 0.81304} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.00176, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57453, "top5_acc": 0.80672, "loss_cls": 2.37487, "loss": 2.37487, "time": 0.81521} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.00175, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56984, "top5_acc": 0.80062, "loss_cls": 2.41025, "loss": 2.41025, "time": 0.81336} +{"mode": "train", "epoch": 138, "iter": 1300, "lr": 0.00175, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56859, "top5_acc": 0.81234, "loss_cls": 2.35335, "loss": 2.35335, "time": 0.813} +{"mode": "train", "epoch": 138, "iter": 1400, "lr": 0.00174, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57891, "top5_acc": 0.80938, "loss_cls": 2.33491, "loss": 2.33491, "time": 0.81776} +{"mode": "train", "epoch": 138, "iter": 1500, "lr": 0.00173, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58016, "top5_acc": 0.80141, "loss_cls": 2.36279, "loss": 2.36279, "time": 0.81615} +{"mode": "train", "epoch": 138, "iter": 1600, "lr": 0.00172, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58, "top5_acc": 0.81266, "loss_cls": 2.33735, "loss": 2.33735, "time": 0.81492} +{"mode": "train", "epoch": 138, "iter": 1700, "lr": 0.00172, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.57859, "top5_acc": 0.81594, "loss_cls": 2.32859, "loss": 2.32859, "time": 0.81244} +{"mode": "train", "epoch": 138, "iter": 1800, "lr": 0.00171, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.58266, "top5_acc": 0.81016, "loss_cls": 2.35008, "loss": 2.35008, "time": 0.81233} +{"mode": "train", "epoch": 138, "iter": 1900, "lr": 0.0017, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57375, "top5_acc": 0.81062, "loss_cls": 2.35998, "loss": 2.35998, "time": 0.81655} +{"mode": "train", "epoch": 138, "iter": 2000, "lr": 0.00169, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.58219, "top5_acc": 0.80922, "loss_cls": 2.33866, "loss": 2.33866, "time": 0.81342} +{"mode": "train", "epoch": 138, "iter": 2100, "lr": 0.00169, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56922, "top5_acc": 0.80484, "loss_cls": 2.39039, "loss": 2.39039, "time": 0.81508} +{"mode": "train", "epoch": 138, "iter": 2200, "lr": 0.00168, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.57328, "top5_acc": 0.80969, "loss_cls": 2.37541, "loss": 2.37541, "time": 0.80983} +{"mode": "train", "epoch": 138, "iter": 2300, "lr": 0.00167, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56797, "top5_acc": 0.80641, "loss_cls": 2.38743, "loss": 2.38743, "time": 0.81608} +{"mode": "train", "epoch": 138, "iter": 2400, "lr": 0.00167, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56, "top5_acc": 0.8025, "loss_cls": 2.3878, "loss": 2.3878, "time": 0.81812} +{"mode": "train", "epoch": 138, "iter": 2500, "lr": 0.00166, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56375, "top5_acc": 0.80562, "loss_cls": 2.4284, "loss": 2.4284, "time": 0.81363} +{"mode": "train", "epoch": 138, "iter": 2600, "lr": 0.00165, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.56719, "top5_acc": 0.80531, "loss_cls": 2.40271, "loss": 2.40271, "time": 0.82019} +{"mode": "train", "epoch": 138, "iter": 2700, "lr": 0.00164, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57094, "top5_acc": 0.80562, "loss_cls": 2.39202, "loss": 2.39202, "time": 0.81904} +{"mode": "train", "epoch": 138, "iter": 2800, "lr": 0.00164, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58094, "top5_acc": 0.80625, "loss_cls": 2.34767, "loss": 2.34767, "time": 0.82385} +{"mode": "train", "epoch": 138, "iter": 2900, "lr": 0.00163, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.56766, "top5_acc": 0.80562, "loss_cls": 2.38166, "loss": 2.38166, "time": 0.8193} +{"mode": "train", "epoch": 138, "iter": 3000, "lr": 0.00162, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56844, "top5_acc": 0.80609, "loss_cls": 2.38508, "loss": 2.38508, "time": 0.82336} +{"mode": "train", "epoch": 138, "iter": 3100, "lr": 0.00162, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57266, "top5_acc": 0.80516, "loss_cls": 2.37691, "loss": 2.37691, "time": 0.81726} +{"mode": "train", "epoch": 138, "iter": 3200, "lr": 0.00161, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57922, "top5_acc": 0.80469, "loss_cls": 2.34686, "loss": 2.34686, "time": 0.81465} +{"mode": "train", "epoch": 138, "iter": 3300, "lr": 0.0016, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55844, "top5_acc": 0.79484, "loss_cls": 2.44491, "loss": 2.44491, "time": 0.81656} +{"mode": "train", "epoch": 138, "iter": 3400, "lr": 0.0016, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56656, "top5_acc": 0.80109, "loss_cls": 2.38956, "loss": 2.38956, "time": 0.81679} +{"mode": "train", "epoch": 138, "iter": 3500, "lr": 0.00159, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56016, "top5_acc": 0.79656, "loss_cls": 2.42861, "loss": 2.42861, "time": 0.81719} +{"mode": "train", "epoch": 138, "iter": 3600, "lr": 0.00158, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57453, "top5_acc": 0.80703, "loss_cls": 2.37167, "loss": 2.37167, "time": 0.8175} +{"mode": "train", "epoch": 138, "iter": 3700, "lr": 0.00157, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55953, "top5_acc": 0.80297, "loss_cls": 2.41582, "loss": 2.41582, "time": 0.81487} +{"mode": "val", "epoch": 138, "iter": 309, "lr": 0.00157, "top1_acc": 0.44482, "top5_acc": 0.68926, "mean_class_accuracy": 0.44458} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00156, "memory": 15990, "data_time": 1.27812, "top1_acc": 0.59109, "top5_acc": 0.82688, "loss_cls": 2.27022, "loss": 2.27022, "time": 2.25727} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00156, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60188, "top5_acc": 0.82594, "loss_cls": 2.26307, "loss": 2.26307, "time": 0.81676} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00155, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.60156, "top5_acc": 0.82797, "loss_cls": 2.24884, "loss": 2.24884, "time": 0.81381} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00154, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59469, "top5_acc": 0.81781, "loss_cls": 2.27196, "loss": 2.27196, "time": 0.81231} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00154, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.59719, "top5_acc": 0.82375, "loss_cls": 2.25508, "loss": 2.25508, "time": 0.81646} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00153, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59641, "top5_acc": 0.81547, "loss_cls": 2.28863, "loss": 2.28863, "time": 0.81728} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00152, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58812, "top5_acc": 0.81766, "loss_cls": 2.31499, "loss": 2.31499, "time": 0.81736} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00152, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59359, "top5_acc": 0.82312, "loss_cls": 2.26093, "loss": 2.26093, "time": 0.81691} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00151, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58547, "top5_acc": 0.81656, "loss_cls": 2.30596, "loss": 2.30596, "time": 0.82565} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.0015, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59078, "top5_acc": 0.81672, "loss_cls": 2.29332, "loss": 2.29332, "time": 0.8182} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.0015, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58219, "top5_acc": 0.81781, "loss_cls": 2.28768, "loss": 2.28768, "time": 0.81745} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00149, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58219, "top5_acc": 0.82016, "loss_cls": 2.32376, "loss": 2.32376, "time": 0.81656} +{"mode": "train", "epoch": 139, "iter": 1300, "lr": 0.00148, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59031, "top5_acc": 0.82031, "loss_cls": 2.27512, "loss": 2.27512, "time": 0.81686} +{"mode": "train", "epoch": 139, "iter": 1400, "lr": 0.00148, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58422, "top5_acc": 0.82172, "loss_cls": 2.29822, "loss": 2.29822, "time": 0.8172} +{"mode": "train", "epoch": 139, "iter": 1500, "lr": 0.00147, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58172, "top5_acc": 0.80797, "loss_cls": 2.35784, "loss": 2.35784, "time": 0.8154} +{"mode": "train", "epoch": 139, "iter": 1600, "lr": 0.00146, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58141, "top5_acc": 0.815, "loss_cls": 2.31434, "loss": 2.31434, "time": 0.81772} +{"mode": "train", "epoch": 139, "iter": 1700, "lr": 0.00145, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58078, "top5_acc": 0.81875, "loss_cls": 2.33286, "loss": 2.33286, "time": 0.82067} +{"mode": "train", "epoch": 139, "iter": 1800, "lr": 0.00145, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58484, "top5_acc": 0.81969, "loss_cls": 2.30231, "loss": 2.30231, "time": 0.8128} +{"mode": "train", "epoch": 139, "iter": 1900, "lr": 0.00144, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58531, "top5_acc": 0.81594, "loss_cls": 2.29769, "loss": 2.29769, "time": 0.81508} +{"mode": "train", "epoch": 139, "iter": 2000, "lr": 0.00143, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58688, "top5_acc": 0.81359, "loss_cls": 2.33715, "loss": 2.33715, "time": 0.81374} +{"mode": "train", "epoch": 139, "iter": 2100, "lr": 0.00143, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58547, "top5_acc": 0.81031, "loss_cls": 2.31375, "loss": 2.31375, "time": 0.8147} +{"mode": "train", "epoch": 139, "iter": 2200, "lr": 0.00142, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58828, "top5_acc": 0.82172, "loss_cls": 2.29702, "loss": 2.29702, "time": 0.81807} +{"mode": "train", "epoch": 139, "iter": 2300, "lr": 0.00142, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.5775, "top5_acc": 0.81219, "loss_cls": 2.32238, "loss": 2.32238, "time": 0.81642} +{"mode": "train", "epoch": 139, "iter": 2400, "lr": 0.00141, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58234, "top5_acc": 0.81375, "loss_cls": 2.32328, "loss": 2.32328, "time": 0.8189} +{"mode": "train", "epoch": 139, "iter": 2500, "lr": 0.0014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57641, "top5_acc": 0.81734, "loss_cls": 2.32077, "loss": 2.32077, "time": 0.81957} +{"mode": "train", "epoch": 139, "iter": 2600, "lr": 0.0014, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57656, "top5_acc": 0.81422, "loss_cls": 2.33047, "loss": 2.33047, "time": 0.81398} +{"mode": "train", "epoch": 139, "iter": 2700, "lr": 0.00139, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58516, "top5_acc": 0.82234, "loss_cls": 2.29586, "loss": 2.29586, "time": 0.82012} +{"mode": "train", "epoch": 139, "iter": 2800, "lr": 0.00138, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58734, "top5_acc": 0.815, "loss_cls": 2.31035, "loss": 2.31035, "time": 0.8175} +{"mode": "train", "epoch": 139, "iter": 2900, "lr": 0.00138, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57563, "top5_acc": 0.80656, "loss_cls": 2.36983, "loss": 2.36983, "time": 0.81928} +{"mode": "train", "epoch": 139, "iter": 3000, "lr": 0.00137, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58375, "top5_acc": 0.81531, "loss_cls": 2.31639, "loss": 2.31639, "time": 0.81866} +{"mode": "train", "epoch": 139, "iter": 3100, "lr": 0.00136, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58844, "top5_acc": 0.82016, "loss_cls": 2.27444, "loss": 2.27444, "time": 0.82055} +{"mode": "train", "epoch": 139, "iter": 3200, "lr": 0.00136, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58344, "top5_acc": 0.81984, "loss_cls": 2.28726, "loss": 2.28726, "time": 0.81671} +{"mode": "train", "epoch": 139, "iter": 3300, "lr": 0.00135, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57688, "top5_acc": 0.80812, "loss_cls": 2.34289, "loss": 2.34289, "time": 0.81243} +{"mode": "train", "epoch": 139, "iter": 3400, "lr": 0.00134, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57734, "top5_acc": 0.81156, "loss_cls": 2.33445, "loss": 2.33445, "time": 0.82068} +{"mode": "train", "epoch": 139, "iter": 3500, "lr": 0.00134, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58312, "top5_acc": 0.81156, "loss_cls": 2.31943, "loss": 2.31943, "time": 0.81705} +{"mode": "train", "epoch": 139, "iter": 3600, "lr": 0.00133, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57594, "top5_acc": 0.80953, "loss_cls": 2.3465, "loss": 2.3465, "time": 0.81531} +{"mode": "train", "epoch": 139, "iter": 3700, "lr": 0.00132, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56781, "top5_acc": 0.80453, "loss_cls": 2.38658, "loss": 2.38658, "time": 0.81243} +{"mode": "val", "epoch": 139, "iter": 309, "lr": 0.00132, "top1_acc": 0.44861, "top5_acc": 0.69802, "mean_class_accuracy": 0.44833} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00131, "memory": 15990, "data_time": 1.30405, "top1_acc": 0.59875, "top5_acc": 0.83312, "loss_cls": 2.21842, "loss": 2.21842, "time": 2.29153} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00131, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60547, "top5_acc": 0.83203, "loss_cls": 2.20097, "loss": 2.20097, "time": 0.81453} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.0013, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60984, "top5_acc": 0.82344, "loss_cls": 2.22756, "loss": 2.22756, "time": 0.82094} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.0013, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59406, "top5_acc": 0.81953, "loss_cls": 2.26513, "loss": 2.26513, "time": 0.81705} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00129, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59672, "top5_acc": 0.82125, "loss_cls": 2.25642, "loss": 2.25642, "time": 0.81595} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.00128, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59938, "top5_acc": 0.83219, "loss_cls": 2.2053, "loss": 2.2053, "time": 0.81762} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.00128, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59562, "top5_acc": 0.82734, "loss_cls": 2.2453, "loss": 2.2453, "time": 0.82168} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00127, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60031, "top5_acc": 0.82453, "loss_cls": 2.24623, "loss": 2.24623, "time": 0.81899} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00126, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.60828, "top5_acc": 0.82609, "loss_cls": 2.22642, "loss": 2.22642, "time": 0.81084} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00126, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60016, "top5_acc": 0.82828, "loss_cls": 2.22929, "loss": 2.22929, "time": 0.8154} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00125, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59312, "top5_acc": 0.81984, "loss_cls": 2.27638, "loss": 2.27638, "time": 0.81402} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00125, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59812, "top5_acc": 0.82891, "loss_cls": 2.21831, "loss": 2.21831, "time": 0.81378} +{"mode": "train", "epoch": 140, "iter": 1300, "lr": 0.00124, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60672, "top5_acc": 0.83078, "loss_cls": 2.20835, "loss": 2.20835, "time": 0.81357} +{"mode": "train", "epoch": 140, "iter": 1400, "lr": 0.00123, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59594, "top5_acc": 0.8275, "loss_cls": 2.23969, "loss": 2.23969, "time": 0.81265} +{"mode": "train", "epoch": 140, "iter": 1500, "lr": 0.00123, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59922, "top5_acc": 0.8275, "loss_cls": 2.2351, "loss": 2.2351, "time": 0.81452} +{"mode": "train", "epoch": 140, "iter": 1600, "lr": 0.00122, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58953, "top5_acc": 0.82281, "loss_cls": 2.28075, "loss": 2.28075, "time": 0.81691} +{"mode": "train", "epoch": 140, "iter": 1700, "lr": 0.00121, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59656, "top5_acc": 0.82062, "loss_cls": 2.26728, "loss": 2.26728, "time": 0.81535} +{"mode": "train", "epoch": 140, "iter": 1800, "lr": 0.00121, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59656, "top5_acc": 0.82969, "loss_cls": 2.22748, "loss": 2.22748, "time": 0.81541} +{"mode": "train", "epoch": 140, "iter": 1900, "lr": 0.0012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59688, "top5_acc": 0.82828, "loss_cls": 2.23487, "loss": 2.23487, "time": 0.81424} +{"mode": "train", "epoch": 140, "iter": 2000, "lr": 0.0012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58953, "top5_acc": 0.81625, "loss_cls": 2.28358, "loss": 2.28358, "time": 0.81403} +{"mode": "train", "epoch": 140, "iter": 2100, "lr": 0.00119, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60094, "top5_acc": 0.82969, "loss_cls": 2.21083, "loss": 2.21083, "time": 0.8174} +{"mode": "train", "epoch": 140, "iter": 2200, "lr": 0.00118, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59734, "top5_acc": 0.82547, "loss_cls": 2.23638, "loss": 2.23638, "time": 0.8114} +{"mode": "train", "epoch": 140, "iter": 2300, "lr": 0.00118, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59781, "top5_acc": 0.82641, "loss_cls": 2.2311, "loss": 2.2311, "time": 0.81494} +{"mode": "train", "epoch": 140, "iter": 2400, "lr": 0.00117, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.59422, "top5_acc": 0.82234, "loss_cls": 2.2595, "loss": 2.2595, "time": 0.8178} +{"mode": "train", "epoch": 140, "iter": 2500, "lr": 0.00117, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5875, "top5_acc": 0.81531, "loss_cls": 2.2886, "loss": 2.2886, "time": 0.826} +{"mode": "train", "epoch": 140, "iter": 2600, "lr": 0.00116, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58594, "top5_acc": 0.81609, "loss_cls": 2.30589, "loss": 2.30589, "time": 0.81779} +{"mode": "train", "epoch": 140, "iter": 2700, "lr": 0.00115, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59188, "top5_acc": 0.81859, "loss_cls": 2.26734, "loss": 2.26734, "time": 0.81792} +{"mode": "train", "epoch": 140, "iter": 2800, "lr": 0.00115, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60172, "top5_acc": 0.82562, "loss_cls": 2.246, "loss": 2.246, "time": 0.81611} +{"mode": "train", "epoch": 140, "iter": 2900, "lr": 0.00114, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.59156, "top5_acc": 0.82062, "loss_cls": 2.27269, "loss": 2.27269, "time": 0.82366} +{"mode": "train", "epoch": 140, "iter": 3000, "lr": 0.00114, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58844, "top5_acc": 0.81734, "loss_cls": 2.28913, "loss": 2.28913, "time": 0.81825} +{"mode": "train", "epoch": 140, "iter": 3100, "lr": 0.00113, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59656, "top5_acc": 0.8275, "loss_cls": 2.23722, "loss": 2.23722, "time": 0.81912} +{"mode": "train", "epoch": 140, "iter": 3200, "lr": 0.00112, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59906, "top5_acc": 0.82141, "loss_cls": 2.26722, "loss": 2.26722, "time": 0.81604} +{"mode": "train", "epoch": 140, "iter": 3300, "lr": 0.00112, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59797, "top5_acc": 0.82359, "loss_cls": 2.25024, "loss": 2.25024, "time": 0.82285} +{"mode": "train", "epoch": 140, "iter": 3400, "lr": 0.00111, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5775, "top5_acc": 0.81391, "loss_cls": 2.29392, "loss": 2.29392, "time": 0.81503} +{"mode": "train", "epoch": 140, "iter": 3500, "lr": 0.00111, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59062, "top5_acc": 0.81891, "loss_cls": 2.26178, "loss": 2.26178, "time": 0.81454} +{"mode": "train", "epoch": 140, "iter": 3600, "lr": 0.0011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59562, "top5_acc": 0.81828, "loss_cls": 2.26234, "loss": 2.26234, "time": 0.8175} +{"mode": "train", "epoch": 140, "iter": 3700, "lr": 0.0011, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59594, "top5_acc": 0.8225, "loss_cls": 2.24148, "loss": 2.24148, "time": 0.8161} +{"mode": "val", "epoch": 140, "iter": 309, "lr": 0.00109, "top1_acc": 0.45383, "top5_acc": 0.7006, "mean_class_accuracy": 0.45351} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00109, "memory": 15990, "data_time": 1.29328, "top1_acc": 0.62531, "top5_acc": 0.83891, "loss_cls": 2.1243, "loss": 2.1243, "time": 2.27142} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00108, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61219, "top5_acc": 0.83828, "loss_cls": 2.17099, "loss": 2.17099, "time": 0.81628} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00108, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61906, "top5_acc": 0.83812, "loss_cls": 2.16775, "loss": 2.16775, "time": 0.81786} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00107, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62938, "top5_acc": 0.83984, "loss_cls": 2.10333, "loss": 2.10333, "time": 0.81822} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00106, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61766, "top5_acc": 0.83828, "loss_cls": 2.16396, "loss": 2.16396, "time": 0.81442} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00106, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.61672, "top5_acc": 0.84141, "loss_cls": 2.13734, "loss": 2.13734, "time": 0.82481} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00105, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60969, "top5_acc": 0.83219, "loss_cls": 2.17865, "loss": 2.17865, "time": 0.82029} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00105, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60531, "top5_acc": 0.83219, "loss_cls": 2.19218, "loss": 2.19218, "time": 0.82077} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00104, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60844, "top5_acc": 0.83078, "loss_cls": 2.2047, "loss": 2.2047, "time": 0.81506} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00104, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61484, "top5_acc": 0.83406, "loss_cls": 2.15989, "loss": 2.15989, "time": 0.81971} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00103, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61078, "top5_acc": 0.83078, "loss_cls": 2.18347, "loss": 2.18347, "time": 0.81931} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00102, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61781, "top5_acc": 0.84078, "loss_cls": 2.14019, "loss": 2.14019, "time": 0.82089} +{"mode": "train", "epoch": 141, "iter": 1300, "lr": 0.00102, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.60328, "top5_acc": 0.83203, "loss_cls": 2.18889, "loss": 2.18889, "time": 0.81601} +{"mode": "train", "epoch": 141, "iter": 1400, "lr": 0.00101, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61625, "top5_acc": 0.83969, "loss_cls": 2.16311, "loss": 2.16311, "time": 0.82222} +{"mode": "train", "epoch": 141, "iter": 1500, "lr": 0.00101, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60359, "top5_acc": 0.84188, "loss_cls": 2.16498, "loss": 2.16498, "time": 0.8151} +{"mode": "train", "epoch": 141, "iter": 1600, "lr": 0.001, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59062, "top5_acc": 0.82203, "loss_cls": 2.26911, "loss": 2.26911, "time": 0.81637} +{"mode": "train", "epoch": 141, "iter": 1700, "lr": 0.001, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60609, "top5_acc": 0.83234, "loss_cls": 2.21085, "loss": 2.21085, "time": 0.8144} +{"mode": "train", "epoch": 141, "iter": 1800, "lr": 0.00099, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60875, "top5_acc": 0.83812, "loss_cls": 2.17902, "loss": 2.17902, "time": 0.81339} +{"mode": "train", "epoch": 141, "iter": 1900, "lr": 0.00099, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60688, "top5_acc": 0.83469, "loss_cls": 2.16188, "loss": 2.16188, "time": 0.81259} +{"mode": "train", "epoch": 141, "iter": 2000, "lr": 0.00098, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60953, "top5_acc": 0.82984, "loss_cls": 2.1907, "loss": 2.1907, "time": 0.81696} +{"mode": "train", "epoch": 141, "iter": 2100, "lr": 0.00097, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60984, "top5_acc": 0.83484, "loss_cls": 2.18402, "loss": 2.18402, "time": 0.81622} +{"mode": "train", "epoch": 141, "iter": 2200, "lr": 0.00097, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59672, "top5_acc": 0.82719, "loss_cls": 2.23907, "loss": 2.23907, "time": 0.8179} +{"mode": "train", "epoch": 141, "iter": 2300, "lr": 0.00096, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61266, "top5_acc": 0.83125, "loss_cls": 2.1851, "loss": 2.1851, "time": 0.8172} +{"mode": "train", "epoch": 141, "iter": 2400, "lr": 0.00096, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.61094, "top5_acc": 0.82844, "loss_cls": 2.21068, "loss": 2.21068, "time": 0.81862} +{"mode": "train", "epoch": 141, "iter": 2500, "lr": 0.00095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60609, "top5_acc": 0.82797, "loss_cls": 2.20321, "loss": 2.20321, "time": 0.81712} +{"mode": "train", "epoch": 141, "iter": 2600, "lr": 0.00095, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.61078, "top5_acc": 0.83781, "loss_cls": 2.18051, "loss": 2.18051, "time": 0.82348} +{"mode": "train", "epoch": 141, "iter": 2700, "lr": 0.00094, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60125, "top5_acc": 0.83266, "loss_cls": 2.1972, "loss": 2.1972, "time": 0.81592} +{"mode": "train", "epoch": 141, "iter": 2800, "lr": 0.00094, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60812, "top5_acc": 0.82609, "loss_cls": 2.19631, "loss": 2.19631, "time": 0.81655} +{"mode": "train", "epoch": 141, "iter": 2900, "lr": 0.00093, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61766, "top5_acc": 0.83609, "loss_cls": 2.15386, "loss": 2.15386, "time": 0.81575} +{"mode": "train", "epoch": 141, "iter": 3000, "lr": 0.00093, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60344, "top5_acc": 0.83, "loss_cls": 2.20643, "loss": 2.20643, "time": 0.82727} +{"mode": "train", "epoch": 141, "iter": 3100, "lr": 0.00092, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.60234, "top5_acc": 0.83062, "loss_cls": 2.21035, "loss": 2.21035, "time": 0.81969} +{"mode": "train", "epoch": 141, "iter": 3200, "lr": 0.00091, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59188, "top5_acc": 0.8125, "loss_cls": 2.2843, "loss": 2.2843, "time": 0.81797} +{"mode": "train", "epoch": 141, "iter": 3300, "lr": 0.00091, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60891, "top5_acc": 0.83828, "loss_cls": 2.15218, "loss": 2.15218, "time": 0.81456} +{"mode": "train", "epoch": 141, "iter": 3400, "lr": 0.0009, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60938, "top5_acc": 0.83188, "loss_cls": 2.20641, "loss": 2.20641, "time": 0.81076} +{"mode": "train", "epoch": 141, "iter": 3500, "lr": 0.0009, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60703, "top5_acc": 0.82391, "loss_cls": 2.21689, "loss": 2.21689, "time": 0.81694} +{"mode": "train", "epoch": 141, "iter": 3600, "lr": 0.00089, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59641, "top5_acc": 0.82141, "loss_cls": 2.25826, "loss": 2.25826, "time": 0.81494} +{"mode": "train", "epoch": 141, "iter": 3700, "lr": 0.00089, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60922, "top5_acc": 0.82484, "loss_cls": 2.21213, "loss": 2.21213, "time": 0.8155} +{"mode": "val", "epoch": 141, "iter": 309, "lr": 0.00089, "top1_acc": 0.45348, "top5_acc": 0.7005, "mean_class_accuracy": 0.45322} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00088, "memory": 15990, "data_time": 1.32472, "top1_acc": 0.62516, "top5_acc": 0.84562, "loss_cls": 2.10176, "loss": 2.10176, "time": 2.30992} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00088, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63078, "top5_acc": 0.84797, "loss_cls": 2.06567, "loss": 2.06567, "time": 0.82612} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00087, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62562, "top5_acc": 0.84422, "loss_cls": 2.09962, "loss": 2.09962, "time": 0.81916} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00086, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62703, "top5_acc": 0.84266, "loss_cls": 2.09931, "loss": 2.09931, "time": 0.81341} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.00086, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62313, "top5_acc": 0.84828, "loss_cls": 2.08989, "loss": 2.08989, "time": 0.819} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.00085, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.62422, "top5_acc": 0.84297, "loss_cls": 2.12065, "loss": 2.12065, "time": 0.82217} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.00085, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62734, "top5_acc": 0.84516, "loss_cls": 2.09883, "loss": 2.09883, "time": 0.81883} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00084, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.61875, "top5_acc": 0.83812, "loss_cls": 2.14824, "loss": 2.14824, "time": 0.82125} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00084, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61812, "top5_acc": 0.83641, "loss_cls": 2.14611, "loss": 2.14611, "time": 0.82333} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00083, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61453, "top5_acc": 0.84438, "loss_cls": 2.14681, "loss": 2.14681, "time": 0.81786} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00083, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61672, "top5_acc": 0.84109, "loss_cls": 2.14416, "loss": 2.14416, "time": 0.82045} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00082, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61266, "top5_acc": 0.84141, "loss_cls": 2.1196, "loss": 2.1196, "time": 0.81039} +{"mode": "train", "epoch": 142, "iter": 1300, "lr": 0.00082, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61906, "top5_acc": 0.84062, "loss_cls": 2.13425, "loss": 2.13425, "time": 0.81575} +{"mode": "train", "epoch": 142, "iter": 1400, "lr": 0.00081, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62266, "top5_acc": 0.83328, "loss_cls": 2.12719, "loss": 2.12719, "time": 0.81588} +{"mode": "train", "epoch": 142, "iter": 1500, "lr": 0.00081, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.625, "top5_acc": 0.83781, "loss_cls": 2.12298, "loss": 2.12298, "time": 0.81456} +{"mode": "train", "epoch": 142, "iter": 1600, "lr": 0.0008, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.62031, "top5_acc": 0.83406, "loss_cls": 2.1458, "loss": 2.1458, "time": 0.81175} +{"mode": "train", "epoch": 142, "iter": 1700, "lr": 0.0008, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60734, "top5_acc": 0.83406, "loss_cls": 2.16273, "loss": 2.16273, "time": 0.81585} +{"mode": "train", "epoch": 142, "iter": 1800, "lr": 0.00079, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61687, "top5_acc": 0.84031, "loss_cls": 2.13271, "loss": 2.13271, "time": 0.82032} +{"mode": "train", "epoch": 142, "iter": 1900, "lr": 0.00079, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61656, "top5_acc": 0.83297, "loss_cls": 2.13565, "loss": 2.13565, "time": 0.82023} +{"mode": "train", "epoch": 142, "iter": 2000, "lr": 0.00078, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.61422, "top5_acc": 0.83609, "loss_cls": 2.14842, "loss": 2.14842, "time": 0.81325} +{"mode": "train", "epoch": 142, "iter": 2100, "lr": 0.00078, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62094, "top5_acc": 0.83891, "loss_cls": 2.12773, "loss": 2.12773, "time": 0.81772} +{"mode": "train", "epoch": 142, "iter": 2200, "lr": 0.00077, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62266, "top5_acc": 0.84516, "loss_cls": 2.11284, "loss": 2.11284, "time": 0.815} +{"mode": "train", "epoch": 142, "iter": 2300, "lr": 0.00077, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62031, "top5_acc": 0.83672, "loss_cls": 2.12749, "loss": 2.12749, "time": 0.82029} +{"mode": "train", "epoch": 142, "iter": 2400, "lr": 0.00076, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60938, "top5_acc": 0.83266, "loss_cls": 2.19441, "loss": 2.19441, "time": 0.8246} +{"mode": "train", "epoch": 142, "iter": 2500, "lr": 0.00076, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62469, "top5_acc": 0.84266, "loss_cls": 2.10673, "loss": 2.10673, "time": 0.81256} +{"mode": "train", "epoch": 142, "iter": 2600, "lr": 0.00075, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.61469, "top5_acc": 0.83922, "loss_cls": 2.14349, "loss": 2.14349, "time": 0.82479} +{"mode": "train", "epoch": 142, "iter": 2700, "lr": 0.00075, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62453, "top5_acc": 0.84078, "loss_cls": 2.10107, "loss": 2.10107, "time": 0.82003} +{"mode": "train", "epoch": 142, "iter": 2800, "lr": 0.00075, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61859, "top5_acc": 0.83906, "loss_cls": 2.13885, "loss": 2.13885, "time": 0.81894} +{"mode": "train", "epoch": 142, "iter": 2900, "lr": 0.00074, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62031, "top5_acc": 0.84359, "loss_cls": 2.11849, "loss": 2.11849, "time": 0.81712} +{"mode": "train", "epoch": 142, "iter": 3000, "lr": 0.00074, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61453, "top5_acc": 0.84141, "loss_cls": 2.12426, "loss": 2.12426, "time": 0.82936} +{"mode": "train", "epoch": 142, "iter": 3100, "lr": 0.00073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61078, "top5_acc": 0.83438, "loss_cls": 2.18131, "loss": 2.18131, "time": 0.81588} +{"mode": "train", "epoch": 142, "iter": 3200, "lr": 0.00073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62078, "top5_acc": 0.84016, "loss_cls": 2.13804, "loss": 2.13804, "time": 0.81864} +{"mode": "train", "epoch": 142, "iter": 3300, "lr": 0.00072, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.61109, "top5_acc": 0.83625, "loss_cls": 2.14185, "loss": 2.14185, "time": 0.81633} +{"mode": "train", "epoch": 142, "iter": 3400, "lr": 0.00072, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61828, "top5_acc": 0.84328, "loss_cls": 2.11039, "loss": 2.11039, "time": 0.81521} +{"mode": "train", "epoch": 142, "iter": 3500, "lr": 0.00071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61922, "top5_acc": 0.84172, "loss_cls": 2.12272, "loss": 2.12272, "time": 0.81657} +{"mode": "train", "epoch": 142, "iter": 3600, "lr": 0.00071, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.6225, "top5_acc": 0.84172, "loss_cls": 2.10786, "loss": 2.10786, "time": 0.80903} +{"mode": "train", "epoch": 142, "iter": 3700, "lr": 0.0007, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61687, "top5_acc": 0.84078, "loss_cls": 2.14982, "loss": 2.14982, "time": 0.8141} +{"mode": "val", "epoch": 142, "iter": 309, "lr": 0.0007, "top1_acc": 0.4549, "top5_acc": 0.70172, "mean_class_accuracy": 0.45471} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.0007, "memory": 15990, "data_time": 1.30393, "top1_acc": 0.64453, "top5_acc": 0.85172, "loss_cls": 2.01577, "loss": 2.01577, "time": 2.28516} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00069, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63891, "top5_acc": 0.85062, "loss_cls": 2.03248, "loss": 2.03248, "time": 0.81673} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00069, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.64406, "top5_acc": 0.86062, "loss_cls": 1.99549, "loss": 1.99549, "time": 0.81617} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00068, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63, "top5_acc": 0.84906, "loss_cls": 2.07213, "loss": 2.07213, "time": 0.81572} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00068, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63297, "top5_acc": 0.84984, "loss_cls": 2.05197, "loss": 2.05197, "time": 0.81496} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00067, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63469, "top5_acc": 0.85484, "loss_cls": 2.03835, "loss": 2.03835, "time": 0.82152} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00067, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63031, "top5_acc": 0.85375, "loss_cls": 2.0518, "loss": 2.0518, "time": 0.82088} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00066, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63438, "top5_acc": 0.85406, "loss_cls": 2.04427, "loss": 2.04427, "time": 0.82139} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00066, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64125, "top5_acc": 0.85344, "loss_cls": 2.03627, "loss": 2.03627, "time": 0.81475} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00065, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62875, "top5_acc": 0.84672, "loss_cls": 2.07558, "loss": 2.07558, "time": 0.81478} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63656, "top5_acc": 0.85406, "loss_cls": 2.0498, "loss": 2.0498, "time": 0.81492} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63344, "top5_acc": 0.85141, "loss_cls": 2.06227, "loss": 2.06227, "time": 0.81738} +{"mode": "train", "epoch": 143, "iter": 1300, "lr": 0.00064, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63062, "top5_acc": 0.84828, "loss_cls": 2.0787, "loss": 2.0787, "time": 0.81478} +{"mode": "train", "epoch": 143, "iter": 1400, "lr": 0.00064, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62094, "top5_acc": 0.84656, "loss_cls": 2.09104, "loss": 2.09104, "time": 0.81587} +{"mode": "train", "epoch": 143, "iter": 1500, "lr": 0.00063, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62734, "top5_acc": 0.85422, "loss_cls": 2.05281, "loss": 2.05281, "time": 0.81263} +{"mode": "train", "epoch": 143, "iter": 1600, "lr": 0.00063, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63156, "top5_acc": 0.84953, "loss_cls": 2.07044, "loss": 2.07044, "time": 0.81593} +{"mode": "train", "epoch": 143, "iter": 1700, "lr": 0.00062, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62531, "top5_acc": 0.84828, "loss_cls": 2.09952, "loss": 2.09952, "time": 0.80905} +{"mode": "train", "epoch": 143, "iter": 1800, "lr": 0.00062, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62625, "top5_acc": 0.84234, "loss_cls": 2.10733, "loss": 2.10733, "time": 0.82005} +{"mode": "train", "epoch": 143, "iter": 1900, "lr": 0.00061, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.6325, "top5_acc": 0.84938, "loss_cls": 2.0628, "loss": 2.0628, "time": 0.81513} +{"mode": "train", "epoch": 143, "iter": 2000, "lr": 0.00061, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62141, "top5_acc": 0.84578, "loss_cls": 2.10114, "loss": 2.10114, "time": 0.81587} +{"mode": "train", "epoch": 143, "iter": 2100, "lr": 0.00061, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64391, "top5_acc": 0.84656, "loss_cls": 2.06675, "loss": 2.06675, "time": 0.81416} +{"mode": "train", "epoch": 143, "iter": 2200, "lr": 0.0006, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62875, "top5_acc": 0.84531, "loss_cls": 2.07391, "loss": 2.07391, "time": 0.81699} +{"mode": "train", "epoch": 143, "iter": 2300, "lr": 0.0006, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62656, "top5_acc": 0.84016, "loss_cls": 2.09857, "loss": 2.09857, "time": 0.81044} +{"mode": "train", "epoch": 143, "iter": 2400, "lr": 0.00059, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.62734, "top5_acc": 0.84766, "loss_cls": 2.08194, "loss": 2.08194, "time": 0.81717} +{"mode": "train", "epoch": 143, "iter": 2500, "lr": 0.00059, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.62797, "top5_acc": 0.84156, "loss_cls": 2.10389, "loss": 2.10389, "time": 0.81862} +{"mode": "train", "epoch": 143, "iter": 2600, "lr": 0.00058, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.63109, "top5_acc": 0.845, "loss_cls": 2.08961, "loss": 2.08961, "time": 0.81754} +{"mode": "train", "epoch": 143, "iter": 2700, "lr": 0.00058, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62828, "top5_acc": 0.84453, "loss_cls": 2.08791, "loss": 2.08791, "time": 0.81868} +{"mode": "train", "epoch": 143, "iter": 2800, "lr": 0.00058, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63281, "top5_acc": 0.85141, "loss_cls": 2.04864, "loss": 2.04864, "time": 0.81785} +{"mode": "train", "epoch": 143, "iter": 2900, "lr": 0.00057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62641, "top5_acc": 0.84797, "loss_cls": 2.07096, "loss": 2.07096, "time": 0.81894} +{"mode": "train", "epoch": 143, "iter": 3000, "lr": 0.00057, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.63266, "top5_acc": 0.84594, "loss_cls": 2.06218, "loss": 2.06218, "time": 0.81971} +{"mode": "train", "epoch": 143, "iter": 3100, "lr": 0.00056, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63484, "top5_acc": 0.85031, "loss_cls": 2.06252, "loss": 2.06252, "time": 0.81989} +{"mode": "train", "epoch": 143, "iter": 3200, "lr": 0.00056, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62859, "top5_acc": 0.85406, "loss_cls": 2.06561, "loss": 2.06561, "time": 0.82335} +{"mode": "train", "epoch": 143, "iter": 3300, "lr": 0.00055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63516, "top5_acc": 0.84828, "loss_cls": 2.05435, "loss": 2.05435, "time": 0.81894} +{"mode": "train", "epoch": 143, "iter": 3400, "lr": 0.00055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62578, "top5_acc": 0.84781, "loss_cls": 2.08493, "loss": 2.08493, "time": 0.81499} +{"mode": "train", "epoch": 143, "iter": 3500, "lr": 0.00055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61562, "top5_acc": 0.83609, "loss_cls": 2.14598, "loss": 2.14598, "time": 0.82254} +{"mode": "train", "epoch": 143, "iter": 3600, "lr": 0.00054, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63781, "top5_acc": 0.8525, "loss_cls": 2.0537, "loss": 2.0537, "time": 0.81324} +{"mode": "train", "epoch": 143, "iter": 3700, "lr": 0.00054, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63094, "top5_acc": 0.85094, "loss_cls": 2.07807, "loss": 2.07807, "time": 0.8136} +{"mode": "val", "epoch": 143, "iter": 309, "lr": 0.00054, "top1_acc": 0.45393, "top5_acc": 0.70025, "mean_class_accuracy": 0.45375} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00053, "memory": 15990, "data_time": 1.29016, "top1_acc": 0.64609, "top5_acc": 0.86062, "loss_cls": 1.99225, "loss": 1.99225, "time": 2.28074} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00053, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65297, "top5_acc": 0.86625, "loss_cls": 1.94935, "loss": 1.94935, "time": 0.81709} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64484, "top5_acc": 0.85609, "loss_cls": 1.99838, "loss": 1.99838, "time": 0.81638} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00052, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63797, "top5_acc": 0.85094, "loss_cls": 2.03673, "loss": 2.03673, "time": 0.81491} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00052, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64469, "top5_acc": 0.85828, "loss_cls": 1.98215, "loss": 1.98215, "time": 0.81302} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00051, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.64891, "top5_acc": 0.85484, "loss_cls": 1.9851, "loss": 1.9851, "time": 0.82155} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00051, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65141, "top5_acc": 0.86406, "loss_cls": 1.97282, "loss": 1.97282, "time": 0.81742} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.0005, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65125, "top5_acc": 0.85953, "loss_cls": 1.96569, "loss": 1.96569, "time": 0.824} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.0005, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65078, "top5_acc": 0.85797, "loss_cls": 1.97862, "loss": 1.97862, "time": 0.81856} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.0005, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64328, "top5_acc": 0.85203, "loss_cls": 2.01862, "loss": 2.01862, "time": 0.81308} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.00049, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.65469, "top5_acc": 0.86281, "loss_cls": 1.96419, "loss": 1.96419, "time": 0.80957} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.00049, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.63516, "top5_acc": 0.85047, "loss_cls": 2.03028, "loss": 2.03028, "time": 0.81611} +{"mode": "train", "epoch": 144, "iter": 1300, "lr": 0.00048, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.64109, "top5_acc": 0.86328, "loss_cls": 2.00017, "loss": 2.00017, "time": 0.81462} +{"mode": "train", "epoch": 144, "iter": 1400, "lr": 0.00048, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64438, "top5_acc": 0.85562, "loss_cls": 2.01543, "loss": 2.01543, "time": 0.81119} +{"mode": "train", "epoch": 144, "iter": 1500, "lr": 0.00048, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65109, "top5_acc": 0.86016, "loss_cls": 1.98783, "loss": 1.98783, "time": 0.81635} +{"mode": "train", "epoch": 144, "iter": 1600, "lr": 0.00047, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64766, "top5_acc": 0.86203, "loss_cls": 1.98747, "loss": 1.98747, "time": 0.81121} +{"mode": "train", "epoch": 144, "iter": 1700, "lr": 0.00047, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.64578, "top5_acc": 0.85625, "loss_cls": 2.01451, "loss": 2.01451, "time": 0.81539} +{"mode": "train", "epoch": 144, "iter": 1800, "lr": 0.00047, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63844, "top5_acc": 0.85328, "loss_cls": 2.03325, "loss": 2.03325, "time": 0.81542} +{"mode": "train", "epoch": 144, "iter": 1900, "lr": 0.00046, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64281, "top5_acc": 0.85094, "loss_cls": 2.0181, "loss": 2.0181, "time": 0.81395} +{"mode": "train", "epoch": 144, "iter": 2000, "lr": 0.00046, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64438, "top5_acc": 0.85344, "loss_cls": 2.00744, "loss": 2.00744, "time": 0.81512} +{"mode": "train", "epoch": 144, "iter": 2100, "lr": 0.00045, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63188, "top5_acc": 0.84766, "loss_cls": 2.07253, "loss": 2.07253, "time": 0.82403} +{"mode": "train", "epoch": 144, "iter": 2200, "lr": 0.00045, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65156, "top5_acc": 0.85547, "loss_cls": 1.97189, "loss": 1.97189, "time": 0.81437} +{"mode": "train", "epoch": 144, "iter": 2300, "lr": 0.00045, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63594, "top5_acc": 0.85469, "loss_cls": 2.03055, "loss": 2.03055, "time": 0.81458} +{"mode": "train", "epoch": 144, "iter": 2400, "lr": 0.00044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64922, "top5_acc": 0.85781, "loss_cls": 1.97154, "loss": 1.97154, "time": 0.82278} +{"mode": "train", "epoch": 144, "iter": 2500, "lr": 0.00044, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63359, "top5_acc": 0.84688, "loss_cls": 2.05612, "loss": 2.05612, "time": 0.81635} +{"mode": "train", "epoch": 144, "iter": 2600, "lr": 0.00044, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.63688, "top5_acc": 0.85453, "loss_cls": 2.03587, "loss": 2.03587, "time": 0.8258} +{"mode": "train", "epoch": 144, "iter": 2700, "lr": 0.00043, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63828, "top5_acc": 0.85, "loss_cls": 2.05616, "loss": 2.05616, "time": 0.81513} +{"mode": "train", "epoch": 144, "iter": 2800, "lr": 0.00043, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64594, "top5_acc": 0.85859, "loss_cls": 2.01304, "loss": 2.01304, "time": 0.81361} +{"mode": "train", "epoch": 144, "iter": 2900, "lr": 0.00042, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63562, "top5_acc": 0.85375, "loss_cls": 2.01966, "loss": 2.01966, "time": 0.81186} +{"mode": "train", "epoch": 144, "iter": 3000, "lr": 0.00042, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64797, "top5_acc": 0.85938, "loss_cls": 1.97508, "loss": 1.97508, "time": 0.81497} +{"mode": "train", "epoch": 144, "iter": 3100, "lr": 0.00042, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63344, "top5_acc": 0.85359, "loss_cls": 2.03748, "loss": 2.03748, "time": 0.82964} +{"mode": "train", "epoch": 144, "iter": 3200, "lr": 0.00041, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64312, "top5_acc": 0.85344, "loss_cls": 2.02235, "loss": 2.02235, "time": 0.81428} +{"mode": "train", "epoch": 144, "iter": 3300, "lr": 0.00041, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64219, "top5_acc": 0.85188, "loss_cls": 2.01947, "loss": 2.01947, "time": 0.82014} +{"mode": "train", "epoch": 144, "iter": 3400, "lr": 0.00041, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.63313, "top5_acc": 0.85469, "loss_cls": 2.04006, "loss": 2.04006, "time": 0.81216} +{"mode": "train", "epoch": 144, "iter": 3500, "lr": 0.0004, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63766, "top5_acc": 0.84891, "loss_cls": 2.03554, "loss": 2.03554, "time": 0.81772} +{"mode": "train", "epoch": 144, "iter": 3600, "lr": 0.0004, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65203, "top5_acc": 0.86156, "loss_cls": 1.9689, "loss": 1.9689, "time": 0.81078} +{"mode": "train", "epoch": 144, "iter": 3700, "lr": 0.0004, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63438, "top5_acc": 0.84953, "loss_cls": 2.07513, "loss": 2.07513, "time": 0.81109} +{"mode": "val", "epoch": 144, "iter": 309, "lr": 0.00039, "top1_acc": 0.45707, "top5_acc": 0.70435, "mean_class_accuracy": 0.45681} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.00039, "memory": 15990, "data_time": 1.28985, "top1_acc": 0.66312, "top5_acc": 0.86484, "loss_cls": 1.9401, "loss": 1.9401, "time": 2.28533} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 0.00039, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.65359, "top5_acc": 0.86422, "loss_cls": 1.92671, "loss": 1.92671, "time": 0.81567} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 0.00038, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66406, "top5_acc": 0.86688, "loss_cls": 1.90844, "loss": 1.90844, "time": 0.81279} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 0.00038, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64672, "top5_acc": 0.86531, "loss_cls": 1.9726, "loss": 1.9726, "time": 0.81723} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 0.00038, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66344, "top5_acc": 0.86375, "loss_cls": 1.93867, "loss": 1.93867, "time": 0.81704} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 0.00037, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.64531, "top5_acc": 0.86359, "loss_cls": 1.95228, "loss": 1.95228, "time": 0.82456} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 0.00037, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65781, "top5_acc": 0.86203, "loss_cls": 1.95338, "loss": 1.95338, "time": 0.81973} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 0.00037, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.65391, "top5_acc": 0.85656, "loss_cls": 1.97383, "loss": 1.97383, "time": 0.8181} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 0.00036, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65875, "top5_acc": 0.86047, "loss_cls": 1.94148, "loss": 1.94148, "time": 0.8187} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 0.00036, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66188, "top5_acc": 0.8725, "loss_cls": 1.90987, "loss": 1.90987, "time": 0.81517} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 0.00036, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64312, "top5_acc": 0.85688, "loss_cls": 1.99513, "loss": 1.99513, "time": 0.8165} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 0.00035, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65797, "top5_acc": 0.86781, "loss_cls": 1.94559, "loss": 1.94559, "time": 0.81405} +{"mode": "train", "epoch": 145, "iter": 1300, "lr": 0.00035, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64828, "top5_acc": 0.85406, "loss_cls": 1.97674, "loss": 1.97674, "time": 0.81479} +{"mode": "train", "epoch": 145, "iter": 1400, "lr": 0.00035, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64875, "top5_acc": 0.86531, "loss_cls": 1.94971, "loss": 1.94971, "time": 0.81069} +{"mode": "train", "epoch": 145, "iter": 1500, "lr": 0.00034, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65469, "top5_acc": 0.86484, "loss_cls": 1.94055, "loss": 1.94055, "time": 0.81598} +{"mode": "train", "epoch": 145, "iter": 1600, "lr": 0.00034, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64359, "top5_acc": 0.85844, "loss_cls": 1.98641, "loss": 1.98641, "time": 0.81439} +{"mode": "train", "epoch": 145, "iter": 1700, "lr": 0.00034, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65453, "top5_acc": 0.86219, "loss_cls": 1.95791, "loss": 1.95791, "time": 0.81642} +{"mode": "train", "epoch": 145, "iter": 1800, "lr": 0.00033, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.6575, "top5_acc": 0.86078, "loss_cls": 1.96052, "loss": 1.96052, "time": 0.81185} +{"mode": "train", "epoch": 145, "iter": 1900, "lr": 0.00033, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64641, "top5_acc": 0.86078, "loss_cls": 1.99946, "loss": 1.99946, "time": 0.81628} +{"mode": "train", "epoch": 145, "iter": 2000, "lr": 0.00033, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64562, "top5_acc": 0.85984, "loss_cls": 1.99763, "loss": 1.99763, "time": 0.8169} +{"mode": "train", "epoch": 145, "iter": 2100, "lr": 0.00032, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64734, "top5_acc": 0.85578, "loss_cls": 1.9961, "loss": 1.9961, "time": 0.81414} +{"mode": "train", "epoch": 145, "iter": 2200, "lr": 0.00032, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65188, "top5_acc": 0.85578, "loss_cls": 1.97344, "loss": 1.97344, "time": 0.81564} +{"mode": "train", "epoch": 145, "iter": 2300, "lr": 0.00032, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65969, "top5_acc": 0.86531, "loss_cls": 1.95304, "loss": 1.95304, "time": 0.81908} +{"mode": "train", "epoch": 145, "iter": 2400, "lr": 0.00031, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.65391, "top5_acc": 0.86078, "loss_cls": 1.94121, "loss": 1.94121, "time": 0.82361} +{"mode": "train", "epoch": 145, "iter": 2500, "lr": 0.00031, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64359, "top5_acc": 0.85547, "loss_cls": 1.99111, "loss": 1.99111, "time": 0.81985} +{"mode": "train", "epoch": 145, "iter": 2600, "lr": 0.00031, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.65109, "top5_acc": 0.86281, "loss_cls": 1.96864, "loss": 1.96864, "time": 0.82216} +{"mode": "train", "epoch": 145, "iter": 2700, "lr": 0.00031, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65672, "top5_acc": 0.8675, "loss_cls": 1.94259, "loss": 1.94259, "time": 0.82314} +{"mode": "train", "epoch": 145, "iter": 2800, "lr": 0.0003, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.6475, "top5_acc": 0.86297, "loss_cls": 1.96658, "loss": 1.96658, "time": 0.81407} +{"mode": "train", "epoch": 145, "iter": 2900, "lr": 0.0003, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65594, "top5_acc": 0.86281, "loss_cls": 1.95734, "loss": 1.95734, "time": 0.81476} +{"mode": "train", "epoch": 145, "iter": 3000, "lr": 0.0003, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66047, "top5_acc": 0.8625, "loss_cls": 1.94966, "loss": 1.94966, "time": 0.81769} +{"mode": "train", "epoch": 145, "iter": 3100, "lr": 0.00029, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.64281, "top5_acc": 0.86578, "loss_cls": 1.97537, "loss": 1.97537, "time": 0.82078} +{"mode": "train", "epoch": 145, "iter": 3200, "lr": 0.00029, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.64094, "top5_acc": 0.85906, "loss_cls": 1.96941, "loss": 1.96941, "time": 0.81701} +{"mode": "train", "epoch": 145, "iter": 3300, "lr": 0.00029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65047, "top5_acc": 0.85938, "loss_cls": 1.97303, "loss": 1.97303, "time": 0.8164} +{"mode": "train", "epoch": 145, "iter": 3400, "lr": 0.00028, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66875, "top5_acc": 0.86719, "loss_cls": 1.88813, "loss": 1.88813, "time": 0.81509} +{"mode": "train", "epoch": 145, "iter": 3500, "lr": 0.00028, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65609, "top5_acc": 0.86234, "loss_cls": 1.93774, "loss": 1.93774, "time": 0.82129} +{"mode": "train", "epoch": 145, "iter": 3600, "lr": 0.00028, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65453, "top5_acc": 0.86219, "loss_cls": 1.94883, "loss": 1.94883, "time": 0.81314} +{"mode": "train", "epoch": 145, "iter": 3700, "lr": 0.00028, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.64609, "top5_acc": 0.86078, "loss_cls": 1.98445, "loss": 1.98445, "time": 0.81423} +{"mode": "val", "epoch": 145, "iter": 309, "lr": 0.00027, "top1_acc": 0.45834, "top5_acc": 0.70319, "mean_class_accuracy": 0.45809} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 0.00027, "memory": 15990, "data_time": 1.27721, "top1_acc": 0.66281, "top5_acc": 0.86562, "loss_cls": 1.91618, "loss": 1.91618, "time": 2.25293} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 0.00027, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66141, "top5_acc": 0.86969, "loss_cls": 1.88514, "loss": 1.88514, "time": 0.81463} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 0.00027, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.6625, "top5_acc": 0.86484, "loss_cls": 1.91675, "loss": 1.91675, "time": 0.81108} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 0.00026, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65078, "top5_acc": 0.85891, "loss_cls": 1.95038, "loss": 1.95038, "time": 0.81864} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 0.00026, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65938, "top5_acc": 0.86516, "loss_cls": 1.92592, "loss": 1.92592, "time": 0.8171} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 0.00026, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.66531, "top5_acc": 0.87234, "loss_cls": 1.90086, "loss": 1.90086, "time": 0.81727} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 0.00025, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65969, "top5_acc": 0.86906, "loss_cls": 1.92102, "loss": 1.92102, "time": 0.81711} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 0.00025, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66688, "top5_acc": 0.87328, "loss_cls": 1.88362, "loss": 1.88362, "time": 0.82162} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 0.00025, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67016, "top5_acc": 0.87266, "loss_cls": 1.87299, "loss": 1.87299, "time": 0.82166} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 0.00025, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66719, "top5_acc": 0.86812, "loss_cls": 1.9114, "loss": 1.9114, "time": 0.81455} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 0.00024, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66031, "top5_acc": 0.86938, "loss_cls": 1.90894, "loss": 1.90894, "time": 0.81578} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 0.00024, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.6625, "top5_acc": 0.86594, "loss_cls": 1.9154, "loss": 1.9154, "time": 0.81321} +{"mode": "train", "epoch": 146, "iter": 1300, "lr": 0.00024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.6625, "top5_acc": 0.87281, "loss_cls": 1.90585, "loss": 1.90585, "time": 0.8176} +{"mode": "train", "epoch": 146, "iter": 1400, "lr": 0.00023, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66047, "top5_acc": 0.86859, "loss_cls": 1.91439, "loss": 1.91439, "time": 0.81905} +{"mode": "train", "epoch": 146, "iter": 1500, "lr": 0.00023, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66469, "top5_acc": 0.86812, "loss_cls": 1.9065, "loss": 1.9065, "time": 0.81498} +{"mode": "train", "epoch": 146, "iter": 1600, "lr": 0.00023, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66125, "top5_acc": 0.87297, "loss_cls": 1.91456, "loss": 1.91456, "time": 0.82191} +{"mode": "train", "epoch": 146, "iter": 1700, "lr": 0.00023, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67453, "top5_acc": 0.87, "loss_cls": 1.87928, "loss": 1.87928, "time": 0.81296} +{"mode": "train", "epoch": 146, "iter": 1800, "lr": 0.00022, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66797, "top5_acc": 0.86938, "loss_cls": 1.88698, "loss": 1.88698, "time": 0.81691} +{"mode": "train", "epoch": 146, "iter": 1900, "lr": 0.00022, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66312, "top5_acc": 0.86672, "loss_cls": 1.92553, "loss": 1.92553, "time": 0.81516} +{"mode": "train", "epoch": 146, "iter": 2000, "lr": 0.00022, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66422, "top5_acc": 0.86922, "loss_cls": 1.91665, "loss": 1.91665, "time": 0.81482} +{"mode": "train", "epoch": 146, "iter": 2100, "lr": 0.00022, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66359, "top5_acc": 0.86812, "loss_cls": 1.92681, "loss": 1.92681, "time": 0.8175} +{"mode": "train", "epoch": 146, "iter": 2200, "lr": 0.00021, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65094, "top5_acc": 0.86141, "loss_cls": 1.96394, "loss": 1.96394, "time": 0.81295} +{"mode": "train", "epoch": 146, "iter": 2300, "lr": 0.00021, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.65625, "top5_acc": 0.85516, "loss_cls": 1.96776, "loss": 1.96776, "time": 0.81894} +{"mode": "train", "epoch": 146, "iter": 2400, "lr": 0.00021, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66219, "top5_acc": 0.86625, "loss_cls": 1.89962, "loss": 1.89962, "time": 0.82116} +{"mode": "train", "epoch": 146, "iter": 2500, "lr": 0.00021, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66047, "top5_acc": 0.87047, "loss_cls": 1.90901, "loss": 1.90901, "time": 0.81506} +{"mode": "train", "epoch": 146, "iter": 2600, "lr": 0.0002, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66641, "top5_acc": 0.87578, "loss_cls": 1.87118, "loss": 1.87118, "time": 0.81616} +{"mode": "train", "epoch": 146, "iter": 2700, "lr": 0.0002, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66922, "top5_acc": 0.87094, "loss_cls": 1.88596, "loss": 1.88596, "time": 0.82204} +{"mode": "train", "epoch": 146, "iter": 2800, "lr": 0.0002, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66156, "top5_acc": 0.87609, "loss_cls": 1.90729, "loss": 1.90729, "time": 0.81813} +{"mode": "train", "epoch": 146, "iter": 2900, "lr": 0.0002, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66312, "top5_acc": 0.8675, "loss_cls": 1.90451, "loss": 1.90451, "time": 0.81341} +{"mode": "train", "epoch": 146, "iter": 3000, "lr": 0.00019, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65625, "top5_acc": 0.86203, "loss_cls": 1.94275, "loss": 1.94275, "time": 0.81425} +{"mode": "train", "epoch": 146, "iter": 3100, "lr": 0.00019, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67156, "top5_acc": 0.87203, "loss_cls": 1.87226, "loss": 1.87226, "time": 0.81895} +{"mode": "train", "epoch": 146, "iter": 3200, "lr": 0.00019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65922, "top5_acc": 0.86234, "loss_cls": 1.94739, "loss": 1.94739, "time": 0.83008} +{"mode": "train", "epoch": 146, "iter": 3300, "lr": 0.00019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66094, "top5_acc": 0.87172, "loss_cls": 1.89751, "loss": 1.89751, "time": 0.82248} +{"mode": "train", "epoch": 146, "iter": 3400, "lr": 0.00018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67266, "top5_acc": 0.87031, "loss_cls": 1.87695, "loss": 1.87695, "time": 0.82754} +{"mode": "train", "epoch": 146, "iter": 3500, "lr": 0.00018, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66953, "top5_acc": 0.87422, "loss_cls": 1.88296, "loss": 1.88296, "time": 0.81507} +{"mode": "train", "epoch": 146, "iter": 3600, "lr": 0.00018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66672, "top5_acc": 0.87531, "loss_cls": 1.89275, "loss": 1.89275, "time": 0.81879} +{"mode": "train", "epoch": 146, "iter": 3700, "lr": 0.00018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.665, "top5_acc": 0.86938, "loss_cls": 1.90433, "loss": 1.90433, "time": 0.81923} +{"mode": "val", "epoch": 146, "iter": 309, "lr": 0.00018, "top1_acc": 0.45834, "top5_acc": 0.7042, "mean_class_accuracy": 0.45811} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 0.00017, "memory": 15990, "data_time": 1.30907, "top1_acc": 0.66766, "top5_acc": 0.87438, "loss_cls": 1.8961, "loss": 1.8961, "time": 2.29651} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 0.00017, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.67359, "top5_acc": 0.87156, "loss_cls": 1.87552, "loss": 1.87552, "time": 0.81558} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 0.00017, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66547, "top5_acc": 0.86922, "loss_cls": 1.91138, "loss": 1.91138, "time": 0.81703} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 0.00017, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66766, "top5_acc": 0.86953, "loss_cls": 1.88154, "loss": 1.88154, "time": 0.82289} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 0.00016, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67297, "top5_acc": 0.86734, "loss_cls": 1.8852, "loss": 1.8852, "time": 0.81366} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 0.00016, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.68125, "top5_acc": 0.87672, "loss_cls": 1.84211, "loss": 1.84211, "time": 0.82288} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 0.00016, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.68391, "top5_acc": 0.87766, "loss_cls": 1.8122, "loss": 1.8122, "time": 0.81705} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 0.00016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66922, "top5_acc": 0.87422, "loss_cls": 1.87686, "loss": 1.87686, "time": 0.81487} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 0.00015, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.67641, "top5_acc": 0.87047, "loss_cls": 1.87655, "loss": 1.87655, "time": 0.81459} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 0.00015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67047, "top5_acc": 0.87172, "loss_cls": 1.86677, "loss": 1.86677, "time": 0.81588} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 0.00015, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67703, "top5_acc": 0.88047, "loss_cls": 1.85335, "loss": 1.85335, "time": 0.81819} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 0.00015, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.6825, "top5_acc": 0.875, "loss_cls": 1.83515, "loss": 1.83515, "time": 0.82188} +{"mode": "train", "epoch": 147, "iter": 1300, "lr": 0.00015, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67641, "top5_acc": 0.87531, "loss_cls": 1.86593, "loss": 1.86593, "time": 0.81856} +{"mode": "train", "epoch": 147, "iter": 1400, "lr": 0.00014, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67406, "top5_acc": 0.86969, "loss_cls": 1.86132, "loss": 1.86132, "time": 0.81765} +{"mode": "train", "epoch": 147, "iter": 1500, "lr": 0.00014, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67547, "top5_acc": 0.87672, "loss_cls": 1.86067, "loss": 1.86067, "time": 0.81381} +{"mode": "train", "epoch": 147, "iter": 1600, "lr": 0.00014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66406, "top5_acc": 0.86859, "loss_cls": 1.90359, "loss": 1.90359, "time": 0.81787} +{"mode": "train", "epoch": 147, "iter": 1700, "lr": 0.00014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67516, "top5_acc": 0.87344, "loss_cls": 1.8648, "loss": 1.8648, "time": 0.81382} +{"mode": "train", "epoch": 147, "iter": 1800, "lr": 0.00014, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67078, "top5_acc": 0.87266, "loss_cls": 1.87999, "loss": 1.87999, "time": 0.81674} +{"mode": "train", "epoch": 147, "iter": 1900, "lr": 0.00013, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67703, "top5_acc": 0.87641, "loss_cls": 1.84272, "loss": 1.84272, "time": 0.81778} +{"mode": "train", "epoch": 147, "iter": 2000, "lr": 0.00013, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66469, "top5_acc": 0.87156, "loss_cls": 1.88204, "loss": 1.88204, "time": 0.81575} +{"mode": "train", "epoch": 147, "iter": 2100, "lr": 0.00013, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68562, "top5_acc": 0.87719, "loss_cls": 1.84195, "loss": 1.84195, "time": 0.81843} +{"mode": "train", "epoch": 147, "iter": 2200, "lr": 0.00013, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67594, "top5_acc": 0.87234, "loss_cls": 1.85985, "loss": 1.85985, "time": 0.81625} +{"mode": "train", "epoch": 147, "iter": 2300, "lr": 0.00013, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.68219, "top5_acc": 0.88047, "loss_cls": 1.82539, "loss": 1.82539, "time": 0.81382} +{"mode": "train", "epoch": 147, "iter": 2400, "lr": 0.00012, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67625, "top5_acc": 0.87422, "loss_cls": 1.84821, "loss": 1.84821, "time": 0.82404} +{"mode": "train", "epoch": 147, "iter": 2500, "lr": 0.00012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67172, "top5_acc": 0.86875, "loss_cls": 1.86647, "loss": 1.86647, "time": 0.82084} +{"mode": "train", "epoch": 147, "iter": 2600, "lr": 0.00012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67562, "top5_acc": 0.87562, "loss_cls": 1.84875, "loss": 1.84875, "time": 0.81888} +{"mode": "train", "epoch": 147, "iter": 2700, "lr": 0.00012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67312, "top5_acc": 0.87, "loss_cls": 1.88738, "loss": 1.88738, "time": 0.81815} +{"mode": "train", "epoch": 147, "iter": 2800, "lr": 0.00012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67078, "top5_acc": 0.8775, "loss_cls": 1.85207, "loss": 1.85207, "time": 0.81701} +{"mode": "train", "epoch": 147, "iter": 2900, "lr": 0.00011, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.67953, "top5_acc": 0.88109, "loss_cls": 1.84439, "loss": 1.84439, "time": 0.81141} +{"mode": "train", "epoch": 147, "iter": 3000, "lr": 0.00011, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67906, "top5_acc": 0.87453, "loss_cls": 1.84695, "loss": 1.84695, "time": 0.81568} +{"mode": "train", "epoch": 147, "iter": 3100, "lr": 0.00011, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66047, "top5_acc": 0.86875, "loss_cls": 1.91608, "loss": 1.91608, "time": 0.81473} +{"mode": "train", "epoch": 147, "iter": 3200, "lr": 0.00011, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.67094, "top5_acc": 0.87, "loss_cls": 1.87036, "loss": 1.87036, "time": 0.8273} +{"mode": "train", "epoch": 147, "iter": 3300, "lr": 0.00011, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67656, "top5_acc": 0.87312, "loss_cls": 1.86343, "loss": 1.86343, "time": 0.81661} +{"mode": "train", "epoch": 147, "iter": 3400, "lr": 0.0001, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67672, "top5_acc": 0.87469, "loss_cls": 1.8696, "loss": 1.8696, "time": 0.82892} +{"mode": "train", "epoch": 147, "iter": 3500, "lr": 0.0001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66906, "top5_acc": 0.87031, "loss_cls": 1.88575, "loss": 1.88575, "time": 0.81569} +{"mode": "train", "epoch": 147, "iter": 3600, "lr": 0.0001, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67266, "top5_acc": 0.87906, "loss_cls": 1.86766, "loss": 1.86766, "time": 0.81434} +{"mode": "train", "epoch": 147, "iter": 3700, "lr": 0.0001, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67516, "top5_acc": 0.87516, "loss_cls": 1.86153, "loss": 1.86153, "time": 0.8171} +{"mode": "val", "epoch": 147, "iter": 309, "lr": 0.0001, "top1_acc": 0.46057, "top5_acc": 0.7046, "mean_class_accuracy": 0.46036} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 0.0001, "memory": 15990, "data_time": 1.29636, "top1_acc": 0.67609, "top5_acc": 0.87734, "loss_cls": 1.84789, "loss": 1.84789, "time": 2.2766} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 0.0001, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.68062, "top5_acc": 0.87969, "loss_cls": 1.83199, "loss": 1.83199, "time": 0.8234} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 9e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67125, "top5_acc": 0.87609, "loss_cls": 1.8761, "loss": 1.8761, "time": 0.81879} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 9e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68969, "top5_acc": 0.87938, "loss_cls": 1.79526, "loss": 1.79526, "time": 0.81594} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 9e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68672, "top5_acc": 0.87969, "loss_cls": 1.80534, "loss": 1.80534, "time": 0.81699} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 9e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68031, "top5_acc": 0.87594, "loss_cls": 1.85306, "loss": 1.85306, "time": 0.82736} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 9e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68797, "top5_acc": 0.87391, "loss_cls": 1.81749, "loss": 1.81749, "time": 0.82048} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 9e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.69234, "top5_acc": 0.88281, "loss_cls": 1.81741, "loss": 1.81741, "time": 0.81754} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 8e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.68062, "top5_acc": 0.88266, "loss_cls": 1.84213, "loss": 1.84213, "time": 0.81544} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 8e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68094, "top5_acc": 0.87688, "loss_cls": 1.82938, "loss": 1.82938, "time": 0.81548} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 8e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67703, "top5_acc": 0.88141, "loss_cls": 1.83007, "loss": 1.83007, "time": 0.81576} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 8e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68203, "top5_acc": 0.875, "loss_cls": 1.84659, "loss": 1.84659, "time": 0.81454} +{"mode": "train", "epoch": 148, "iter": 1300, "lr": 8e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67953, "top5_acc": 0.87547, "loss_cls": 1.83968, "loss": 1.83968, "time": 0.81629} +{"mode": "train", "epoch": 148, "iter": 1400, "lr": 8e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68156, "top5_acc": 0.87406, "loss_cls": 1.84441, "loss": 1.84441, "time": 0.82242} +{"mode": "train", "epoch": 148, "iter": 1500, "lr": 7e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.6825, "top5_acc": 0.87859, "loss_cls": 1.81211, "loss": 1.81211, "time": 0.8135} +{"mode": "train", "epoch": 148, "iter": 1600, "lr": 7e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67516, "top5_acc": 0.87578, "loss_cls": 1.8468, "loss": 1.8468, "time": 0.81658} +{"mode": "train", "epoch": 148, "iter": 1700, "lr": 7e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67375, "top5_acc": 0.87125, "loss_cls": 1.87111, "loss": 1.87111, "time": 0.8153} +{"mode": "train", "epoch": 148, "iter": 1800, "lr": 7e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67656, "top5_acc": 0.87844, "loss_cls": 1.83325, "loss": 1.83325, "time": 0.81498} +{"mode": "train", "epoch": 148, "iter": 1900, "lr": 7e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.69281, "top5_acc": 0.87844, "loss_cls": 1.80106, "loss": 1.80106, "time": 0.81487} +{"mode": "train", "epoch": 148, "iter": 2000, "lr": 7e-05, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.67484, "top5_acc": 0.88156, "loss_cls": 1.84859, "loss": 1.84859, "time": 0.81067} +{"mode": "train", "epoch": 148, "iter": 2100, "lr": 7e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.68359, "top5_acc": 0.87625, "loss_cls": 1.84636, "loss": 1.84636, "time": 0.8128} +{"mode": "train", "epoch": 148, "iter": 2200, "lr": 6e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68156, "top5_acc": 0.87531, "loss_cls": 1.84721, "loss": 1.84721, "time": 0.81606} +{"mode": "train", "epoch": 148, "iter": 2300, "lr": 6e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67797, "top5_acc": 0.87484, "loss_cls": 1.83514, "loss": 1.83514, "time": 0.81685} +{"mode": "train", "epoch": 148, "iter": 2400, "lr": 6e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67875, "top5_acc": 0.87906, "loss_cls": 1.83076, "loss": 1.83076, "time": 0.82201} +{"mode": "train", "epoch": 148, "iter": 2500, "lr": 6e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66844, "top5_acc": 0.87672, "loss_cls": 1.87526, "loss": 1.87526, "time": 0.8168} +{"mode": "train", "epoch": 148, "iter": 2600, "lr": 6e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.67156, "top5_acc": 0.87562, "loss_cls": 1.86383, "loss": 1.86383, "time": 0.81457} +{"mode": "train", "epoch": 148, "iter": 2700, "lr": 6e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67297, "top5_acc": 0.88016, "loss_cls": 1.84862, "loss": 1.84862, "time": 0.8204} +{"mode": "train", "epoch": 148, "iter": 2800, "lr": 6e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.68906, "top5_acc": 0.87453, "loss_cls": 1.82389, "loss": 1.82389, "time": 0.81455} +{"mode": "train", "epoch": 148, "iter": 2900, "lr": 5e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68, "top5_acc": 0.88375, "loss_cls": 1.82253, "loss": 1.82253, "time": 0.81724} +{"mode": "train", "epoch": 148, "iter": 3000, "lr": 5e-05, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.68578, "top5_acc": 0.88266, "loss_cls": 1.80927, "loss": 1.80927, "time": 0.81147} +{"mode": "train", "epoch": 148, "iter": 3100, "lr": 5e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67906, "top5_acc": 0.87891, "loss_cls": 1.82016, "loss": 1.82016, "time": 0.81533} +{"mode": "train", "epoch": 148, "iter": 3200, "lr": 5e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.68297, "top5_acc": 0.87891, "loss_cls": 1.81846, "loss": 1.81846, "time": 0.8323} +{"mode": "train", "epoch": 148, "iter": 3300, "lr": 5e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67688, "top5_acc": 0.87844, "loss_cls": 1.82765, "loss": 1.82765, "time": 0.81923} +{"mode": "train", "epoch": 148, "iter": 3400, "lr": 5e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.68016, "top5_acc": 0.87422, "loss_cls": 1.86794, "loss": 1.86794, "time": 0.82129} +{"mode": "train", "epoch": 148, "iter": 3500, "lr": 5e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67734, "top5_acc": 0.87344, "loss_cls": 1.85904, "loss": 1.85904, "time": 0.81827} +{"mode": "train", "epoch": 148, "iter": 3600, "lr": 5e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68859, "top5_acc": 0.88188, "loss_cls": 1.82061, "loss": 1.82061, "time": 0.81969} +{"mode": "train", "epoch": 148, "iter": 3700, "lr": 4e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67875, "top5_acc": 0.88375, "loss_cls": 1.83497, "loss": 1.83497, "time": 0.81578} +{"mode": "val", "epoch": 148, "iter": 309, "lr": 4e-05, "top1_acc": 0.45849, "top5_acc": 0.70263, "mean_class_accuracy": 0.45822} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 4e-05, "memory": 15990, "data_time": 1.26946, "top1_acc": 0.67844, "top5_acc": 0.87391, "loss_cls": 1.84305, "loss": 1.84305, "time": 2.24436} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 4e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67344, "top5_acc": 0.87688, "loss_cls": 1.83175, "loss": 1.83175, "time": 0.81552} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 4e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.69156, "top5_acc": 0.88312, "loss_cls": 1.80428, "loss": 1.80428, "time": 0.8172} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 4e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67906, "top5_acc": 0.87484, "loss_cls": 1.8399, "loss": 1.8399, "time": 0.81406} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 4e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68234, "top5_acc": 0.88734, "loss_cls": 1.80542, "loss": 1.80542, "time": 0.82021} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 4e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67547, "top5_acc": 0.88141, "loss_cls": 1.83033, "loss": 1.83033, "time": 0.8234} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 4e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.69109, "top5_acc": 0.88703, "loss_cls": 1.7934, "loss": 1.7934, "time": 0.82297} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 4e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.68234, "top5_acc": 0.88094, "loss_cls": 1.82119, "loss": 1.82119, "time": 0.82019} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 3e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.68141, "top5_acc": 0.87766, "loss_cls": 1.83185, "loss": 1.83185, "time": 0.81494} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 3e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.68734, "top5_acc": 0.87812, "loss_cls": 1.80993, "loss": 1.80993, "time": 0.82436} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 3e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.6825, "top5_acc": 0.88359, "loss_cls": 1.79979, "loss": 1.79979, "time": 0.81654} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 3e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.68578, "top5_acc": 0.87672, "loss_cls": 1.8328, "loss": 1.8328, "time": 0.81411} +{"mode": "train", "epoch": 149, "iter": 1300, "lr": 3e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.69156, "top5_acc": 0.88312, "loss_cls": 1.80692, "loss": 1.80692, "time": 0.8143} +{"mode": "train", "epoch": 149, "iter": 1400, "lr": 3e-05, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.68938, "top5_acc": 0.88312, "loss_cls": 1.79008, "loss": 1.79008, "time": 0.81429} +{"mode": "train", "epoch": 149, "iter": 1500, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68156, "top5_acc": 0.88281, "loss_cls": 1.83679, "loss": 1.83679, "time": 0.81142} +{"mode": "train", "epoch": 149, "iter": 1600, "lr": 3e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.69688, "top5_acc": 0.88547, "loss_cls": 1.75365, "loss": 1.75365, "time": 0.81284} +{"mode": "train", "epoch": 149, "iter": 1700, "lr": 3e-05, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.68469, "top5_acc": 0.87844, "loss_cls": 1.83411, "loss": 1.83411, "time": 0.81139} +{"mode": "train", "epoch": 149, "iter": 1800, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68703, "top5_acc": 0.87703, "loss_cls": 1.81878, "loss": 1.81878, "time": 0.81352} +{"mode": "train", "epoch": 149, "iter": 1900, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.68594, "top5_acc": 0.88188, "loss_cls": 1.80408, "loss": 1.80408, "time": 0.81362} +{"mode": "train", "epoch": 149, "iter": 2000, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.68844, "top5_acc": 0.88359, "loss_cls": 1.78833, "loss": 1.78833, "time": 0.81535} +{"mode": "train", "epoch": 149, "iter": 2100, "lr": 2e-05, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.68344, "top5_acc": 0.88172, "loss_cls": 1.78539, "loss": 1.78539, "time": 0.81479} +{"mode": "train", "epoch": 149, "iter": 2200, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.68156, "top5_acc": 0.88672, "loss_cls": 1.80816, "loss": 1.80816, "time": 0.81887} +{"mode": "train", "epoch": 149, "iter": 2300, "lr": 2e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68344, "top5_acc": 0.88234, "loss_cls": 1.8109, "loss": 1.8109, "time": 0.81551} +{"mode": "train", "epoch": 149, "iter": 2400, "lr": 2e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68594, "top5_acc": 0.87562, "loss_cls": 1.81492, "loss": 1.81492, "time": 0.818} +{"mode": "train", "epoch": 149, "iter": 2500, "lr": 2e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68906, "top5_acc": 0.88828, "loss_cls": 1.77626, "loss": 1.77626, "time": 0.82397} +{"mode": "train", "epoch": 149, "iter": 2600, "lr": 2e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.68328, "top5_acc": 0.87484, "loss_cls": 1.85053, "loss": 1.85053, "time": 0.81897} +{"mode": "train", "epoch": 149, "iter": 2700, "lr": 2e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67438, "top5_acc": 0.87453, "loss_cls": 1.84648, "loss": 1.84648, "time": 0.81425} +{"mode": "train", "epoch": 149, "iter": 2800, "lr": 2e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.69047, "top5_acc": 0.88297, "loss_cls": 1.80199, "loss": 1.80199, "time": 0.82141} +{"mode": "train", "epoch": 149, "iter": 2900, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.68172, "top5_acc": 0.88562, "loss_cls": 1.77877, "loss": 1.77877, "time": 0.82092} +{"mode": "train", "epoch": 149, "iter": 3000, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.68406, "top5_acc": 0.87656, "loss_cls": 1.83264, "loss": 1.83264, "time": 0.8094} +{"mode": "train", "epoch": 149, "iter": 3100, "lr": 2e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68172, "top5_acc": 0.87969, "loss_cls": 1.81432, "loss": 1.81432, "time": 0.81525} +{"mode": "train", "epoch": 149, "iter": 3200, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68172, "top5_acc": 0.88016, "loss_cls": 1.8191, "loss": 1.8191, "time": 0.81886} +{"mode": "train", "epoch": 149, "iter": 3300, "lr": 1e-05, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.68516, "top5_acc": 0.87969, "loss_cls": 1.82857, "loss": 1.82857, "time": 0.82883} +{"mode": "train", "epoch": 149, "iter": 3400, "lr": 1e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.67844, "top5_acc": 0.87859, "loss_cls": 1.84331, "loss": 1.84331, "time": 0.81458} +{"mode": "train", "epoch": 149, "iter": 3500, "lr": 1e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.67281, "top5_acc": 0.87219, "loss_cls": 1.88387, "loss": 1.88387, "time": 0.81911} +{"mode": "train", "epoch": 149, "iter": 3600, "lr": 1e-05, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.69125, "top5_acc": 0.88781, "loss_cls": 1.77753, "loss": 1.77753, "time": 0.81603} +{"mode": "train", "epoch": 149, "iter": 3700, "lr": 1e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.68797, "top5_acc": 0.87562, "loss_cls": 1.8301, "loss": 1.8301, "time": 0.81393} +{"mode": "val", "epoch": 149, "iter": 309, "lr": 1e-05, "top1_acc": 0.45966, "top5_acc": 0.70359, "mean_class_accuracy": 0.45942} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 1e-05, "memory": 15990, "data_time": 1.30504, "top1_acc": 0.68797, "top5_acc": 0.88, "loss_cls": 1.81462, "loss": 1.81462, "time": 2.28073} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 1e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67688, "top5_acc": 0.8775, "loss_cls": 1.84072, "loss": 1.84072, "time": 0.81992} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68953, "top5_acc": 0.88438, "loss_cls": 1.7878, "loss": 1.7878, "time": 0.8163} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68016, "top5_acc": 0.87703, "loss_cls": 1.84416, "loss": 1.84416, "time": 0.81549} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68234, "top5_acc": 0.88453, "loss_cls": 1.82002, "loss": 1.82002, "time": 0.81431} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 1e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67969, "top5_acc": 0.88156, "loss_cls": 1.83681, "loss": 1.83681, "time": 0.82338} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 1e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.69578, "top5_acc": 0.88156, "loss_cls": 1.79691, "loss": 1.79691, "time": 0.81982} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 1e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.68875, "top5_acc": 0.88641, "loss_cls": 1.80857, "loss": 1.80857, "time": 0.81778} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.69219, "top5_acc": 0.88562, "loss_cls": 1.77435, "loss": 1.77435, "time": 0.8161} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.69375, "top5_acc": 0.89016, "loss_cls": 1.77888, "loss": 1.77888, "time": 0.82051} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 1e-05, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.69, "top5_acc": 0.8875, "loss_cls": 1.77943, "loss": 1.77943, "time": 0.81113} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 1e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.68219, "top5_acc": 0.87969, "loss_cls": 1.82584, "loss": 1.82584, "time": 0.81582} +{"mode": "train", "epoch": 150, "iter": 1300, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68312, "top5_acc": 0.87391, "loss_cls": 1.82616, "loss": 1.82616, "time": 0.81524} +{"mode": "train", "epoch": 150, "iter": 1400, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68547, "top5_acc": 0.87812, "loss_cls": 1.81726, "loss": 1.81726, "time": 0.82468} +{"mode": "train", "epoch": 150, "iter": 1500, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67609, "top5_acc": 0.88016, "loss_cls": 1.85524, "loss": 1.85524, "time": 0.8203} +{"mode": "train", "epoch": 150, "iter": 1600, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.69578, "top5_acc": 0.88641, "loss_cls": 1.7661, "loss": 1.7661, "time": 0.81664} +{"mode": "train", "epoch": 150, "iter": 1700, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.69578, "top5_acc": 0.88062, "loss_cls": 1.78894, "loss": 1.78894, "time": 0.81806} +{"mode": "train", "epoch": 150, "iter": 1800, "lr": 0.0, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.68375, "top5_acc": 0.88812, "loss_cls": 1.78914, "loss": 1.78914, "time": 0.81309} +{"mode": "train", "epoch": 150, "iter": 1900, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68188, "top5_acc": 0.87828, "loss_cls": 1.81535, "loss": 1.81535, "time": 0.81605} +{"mode": "train", "epoch": 150, "iter": 2000, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68891, "top5_acc": 0.87938, "loss_cls": 1.82504, "loss": 1.82504, "time": 0.81481} +{"mode": "train", "epoch": 150, "iter": 2100, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67578, "top5_acc": 0.87938, "loss_cls": 1.8225, "loss": 1.8225, "time": 0.81244} +{"mode": "train", "epoch": 150, "iter": 2200, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68875, "top5_acc": 0.88438, "loss_cls": 1.80652, "loss": 1.80652, "time": 0.81192} +{"mode": "train", "epoch": 150, "iter": 2300, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.69047, "top5_acc": 0.88453, "loss_cls": 1.80788, "loss": 1.80788, "time": 0.8121} +{"mode": "train", "epoch": 150, "iter": 2400, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68547, "top5_acc": 0.87719, "loss_cls": 1.83921, "loss": 1.83921, "time": 0.81736} +{"mode": "train", "epoch": 150, "iter": 2500, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68375, "top5_acc": 0.88, "loss_cls": 1.80924, "loss": 1.80924, "time": 0.81734} +{"mode": "train", "epoch": 150, "iter": 2600, "lr": 0.0, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.68203, "top5_acc": 0.87578, "loss_cls": 1.8294, "loss": 1.8294, "time": 0.81897} +{"mode": "train", "epoch": 150, "iter": 2700, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68969, "top5_acc": 0.88891, "loss_cls": 1.77398, "loss": 1.77398, "time": 0.81842} +{"mode": "train", "epoch": 150, "iter": 2800, "lr": 0.0, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.68766, "top5_acc": 0.88828, "loss_cls": 1.797, "loss": 1.797, "time": 0.81389} +{"mode": "train", "epoch": 150, "iter": 2900, "lr": 0.0, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.68375, "top5_acc": 0.88562, "loss_cls": 1.82027, "loss": 1.82027, "time": 0.81933} +{"mode": "train", "epoch": 150, "iter": 3000, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68953, "top5_acc": 0.88391, "loss_cls": 1.7697, "loss": 1.7697, "time": 0.81521} +{"mode": "train", "epoch": 150, "iter": 3100, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.69328, "top5_acc": 0.88156, "loss_cls": 1.76713, "loss": 1.76713, "time": 0.81207} +{"mode": "train", "epoch": 150, "iter": 3200, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68891, "top5_acc": 0.88109, "loss_cls": 1.80844, "loss": 1.80844, "time": 0.81334} +{"mode": "train", "epoch": 150, "iter": 3300, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.69422, "top5_acc": 0.88453, "loss_cls": 1.79727, "loss": 1.79727, "time": 0.82758} +{"mode": "train", "epoch": 150, "iter": 3400, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68938, "top5_acc": 0.88453, "loss_cls": 1.79363, "loss": 1.79363, "time": 0.8139} +{"mode": "train", "epoch": 150, "iter": 3500, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.69609, "top5_acc": 0.88922, "loss_cls": 1.75264, "loss": 1.75264, "time": 0.82313} +{"mode": "train", "epoch": 150, "iter": 3600, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68906, "top5_acc": 0.88594, "loss_cls": 1.79842, "loss": 1.79842, "time": 0.81546} +{"mode": "train", "epoch": 150, "iter": 3700, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.69, "top5_acc": 0.87953, "loss_cls": 1.77851, "loss": 1.77851, "time": 0.81379} +{"mode": "val", "epoch": 150, "iter": 309, "lr": 0.0, "top1_acc": 0.45778, "top5_acc": 0.70162, "mean_class_accuracy": 0.45755} diff --git a/k400/b_1/b_1.py b/k400/b_1/b_1.py new file mode 100644 index 0000000000000000000000000000000000000000..a4bde1b80845610fe9d9cb4e960378ba3b7f0e4d --- /dev/null +++ b/k400/b_1/b_1.py @@ -0,0 +1,133 @@ +modality = 'b' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/b_2' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/k400/b_1/best_pred.pkl b/k400/b_1/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..d74c14865727b2ab89cb9957d253e53b2f2f153f --- /dev/null +++ b/k400/b_1/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:07e4ddf9f8e3d51dbf491ccab8eca2293cb3c76bd26e5b3ada32b80d8de6671b +size 44888023 diff --git a/k400/b_1/best_top1_acc_epoch_147.pth b/k400/b_1/best_top1_acc_epoch_147.pth new file mode 100644 index 0000000000000000000000000000000000000000..d7210dfcf574e12040e1d005e68e61dd57b8d0d9 --- /dev/null +++ b/k400/b_1/best_top1_acc_epoch_147.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:341d27241dc38d1be6d28b253dd98480747c768ae1f2ddca25c48ea6796e6106 +size 32926705 diff --git a/k400/b_2/20240722_022809.log b/k400/b_2/20240722_022809.log new file mode 100644 index 0000000000000000000000000000000000000000..8cfbd1ba7b04c9b4e41cc9899d5b1b99be960034 --- /dev/null +++ b/k400/b_2/20240722_022809.log @@ -0,0 +1,7286 @@ +2024-07-22 02:28:09,437 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2024-07-22 02:28:09,752 - pyskl - INFO - Config: modality = 'b' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/b_2' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2024-07-22 02:28:09,753 - pyskl - INFO - Set random seed to 684653118, deterministic: False +2024-07-22 02:28:19,849 - pyskl - INFO - 239737 videos remain after valid thresholding +2024-07-22 02:28:33,732 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-07-22 02:28:33,733 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2 +2024-07-22 02:28:33,736 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2024-07-22 02:28:33,759 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2024-07-22 02:28:33,764 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2 by HardDiskBackend. +2024-07-22 02:31:51,819 - pyskl - INFO - Epoch [1][100/3746] lr: 1.000e-01, eta: 12 days, 21:03:37, time: 1.980, data_time: 1.268, memory: 15990, top1_acc: 0.0081, top5_acc: 0.0380, loss_cls: 6.4006, loss: 6.4006 +2024-07-22 02:33:02,194 - pyskl - INFO - Epoch [1][200/3746] lr: 1.000e-01, eta: 8 days, 17:24:14, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0172, top5_acc: 0.0669, loss_cls: 6.2476, loss: 6.2476 +2024-07-22 02:34:12,455 - pyskl - INFO - Epoch [1][300/3746] lr: 1.000e-01, eta: 7 days, 8:06:48, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0177, top5_acc: 0.0830, loss_cls: 6.0773, loss: 6.0773 +2024-07-22 02:35:22,693 - pyskl - INFO - Epoch [1][400/3746] lr: 1.000e-01, eta: 6 days, 15:26:57, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0266, top5_acc: 0.1030, loss_cls: 5.9443, loss: 5.9443 +2024-07-22 02:36:32,873 - pyskl - INFO - Epoch [1][500/3746] lr: 1.000e-01, eta: 6 days, 5:25:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0270, top5_acc: 0.1050, loss_cls: 5.8917, loss: 5.8917 +2024-07-22 02:37:43,185 - pyskl - INFO - Epoch [1][600/3746] lr: 1.000e-01, eta: 5 days, 22:46:10, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0372, top5_acc: 0.1258, loss_cls: 5.8177, loss: 5.8177 +2024-07-22 02:38:53,128 - pyskl - INFO - Epoch [1][700/3746] lr: 1.000e-01, eta: 5 days, 17:55:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0359, top5_acc: 0.1277, loss_cls: 5.7933, loss: 5.7933 +2024-07-22 02:40:03,592 - pyskl - INFO - Epoch [1][800/3746] lr: 1.000e-01, eta: 5 days, 14:23:36, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0387, top5_acc: 0.1333, loss_cls: 5.7389, loss: 5.7389 +2024-07-22 02:41:13,384 - pyskl - INFO - Epoch [1][900/3746] lr: 1.000e-01, eta: 5 days, 11:31:26, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.0427, top5_acc: 0.1420, loss_cls: 5.7035, loss: 5.7035 +2024-07-22 02:42:23,367 - pyskl - INFO - Epoch [1][1000/3746] lr: 1.000e-01, eta: 5 days, 9:15:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0389, top5_acc: 0.1484, loss_cls: 5.6641, loss: 5.6641 +2024-07-22 02:43:33,632 - pyskl - INFO - Epoch [1][1100/3746] lr: 1.000e-01, eta: 5 days, 7:25:59, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0433, top5_acc: 0.1530, loss_cls: 5.6448, loss: 5.6448 +2024-07-22 02:44:43,875 - pyskl - INFO - Epoch [1][1200/3746] lr: 1.000e-01, eta: 5 days, 5:54:35, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0497, top5_acc: 0.1589, loss_cls: 5.6149, loss: 5.6149 +2024-07-22 02:45:53,962 - pyskl - INFO - Epoch [1][1300/3746] lr: 1.000e-01, eta: 5 days, 4:35:56, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0478, top5_acc: 0.1737, loss_cls: 5.5865, loss: 5.5865 +2024-07-22 02:47:04,028 - pyskl - INFO - Epoch [1][1400/3746] lr: 1.000e-01, eta: 5 days, 3:28:13, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0553, top5_acc: 0.1777, loss_cls: 5.5587, loss: 5.5587 +2024-07-22 02:48:14,207 - pyskl - INFO - Epoch [1][1500/3746] lr: 1.000e-01, eta: 5 days, 2:30:05, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0587, top5_acc: 0.1884, loss_cls: 5.5032, loss: 5.5032 +2024-07-22 02:49:24,225 - pyskl - INFO - Epoch [1][1600/3746] lr: 1.000e-01, eta: 5 days, 1:38:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0609, top5_acc: 0.1961, loss_cls: 5.4723, loss: 5.4723 +2024-07-22 02:50:34,288 - pyskl - INFO - Epoch [1][1700/3746] lr: 1.000e-01, eta: 5 days, 0:52:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0638, top5_acc: 0.2006, loss_cls: 5.4466, loss: 5.4466 +2024-07-22 02:51:44,822 - pyskl - INFO - Epoch [1][1800/3746] lr: 1.000e-01, eta: 5 days, 0:14:03, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0650, top5_acc: 0.2055, loss_cls: 5.4502, loss: 5.4502 +2024-07-22 02:52:54,852 - pyskl - INFO - Epoch [1][1900/3746] lr: 1.000e-01, eta: 4 days, 23:37:09, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0622, top5_acc: 0.2111, loss_cls: 5.4325, loss: 5.4325 +2024-07-22 02:54:05,149 - pyskl - INFO - Epoch [1][2000/3746] lr: 1.000e-01, eta: 4 days, 23:05:03, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0731, top5_acc: 0.2150, loss_cls: 5.4096, loss: 5.4096 +2024-07-22 02:55:17,523 - pyskl - INFO - Epoch [1][2100/3746] lr: 1.000e-01, eta: 4 days, 22:45:09, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.0722, top5_acc: 0.2102, loss_cls: 5.3951, loss: 5.3951 +2024-07-22 02:56:29,374 - pyskl - INFO - Epoch [1][2200/3746] lr: 1.000e-01, eta: 4 days, 22:24:43, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.0731, top5_acc: 0.2238, loss_cls: 5.3464, loss: 5.3464 +2024-07-22 02:57:39,638 - pyskl - INFO - Epoch [1][2300/3746] lr: 1.000e-01, eta: 4 days, 21:59:31, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0813, top5_acc: 0.2364, loss_cls: 5.3223, loss: 5.3223 +2024-07-22 02:58:49,710 - pyskl - INFO - Epoch [1][2400/3746] lr: 1.000e-01, eta: 4 days, 21:35:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0753, top5_acc: 0.2277, loss_cls: 5.3416, loss: 5.3416 +2024-07-22 02:59:59,954 - pyskl - INFO - Epoch [1][2500/3746] lr: 1.000e-01, eta: 4 days, 21:14:07, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0803, top5_acc: 0.2341, loss_cls: 5.3116, loss: 5.3116 +2024-07-22 03:01:09,850 - pyskl - INFO - Epoch [1][2600/3746] lr: 9.999e-02, eta: 4 days, 20:52:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0798, top5_acc: 0.2334, loss_cls: 5.3214, loss: 5.3214 +2024-07-22 03:02:20,082 - pyskl - INFO - Epoch [1][2700/3746] lr: 9.999e-02, eta: 4 days, 20:34:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0844, top5_acc: 0.2358, loss_cls: 5.2828, loss: 5.2828 +2024-07-22 03:03:30,125 - pyskl - INFO - Epoch [1][2800/3746] lr: 9.999e-02, eta: 4 days, 20:16:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0864, top5_acc: 0.2425, loss_cls: 5.2641, loss: 5.2641 +2024-07-22 03:04:40,117 - pyskl - INFO - Epoch [1][2900/3746] lr: 9.999e-02, eta: 4 days, 19:59:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0861, top5_acc: 0.2428, loss_cls: 5.2744, loss: 5.2744 +2024-07-22 03:05:50,263 - pyskl - INFO - Epoch [1][3000/3746] lr: 9.999e-02, eta: 4 days, 19:44:13, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0852, top5_acc: 0.2478, loss_cls: 5.2361, loss: 5.2361 +2024-07-22 03:06:59,972 - pyskl - INFO - Epoch [1][3100/3746] lr: 9.999e-02, eta: 4 days, 19:28:26, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.0973, top5_acc: 0.2637, loss_cls: 5.1862, loss: 5.1862 +2024-07-22 03:08:09,985 - pyskl - INFO - Epoch [1][3200/3746] lr: 9.999e-02, eta: 4 days, 19:14:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0978, top5_acc: 0.2673, loss_cls: 5.1779, loss: 5.1779 +2024-07-22 03:09:19,799 - pyskl - INFO - Epoch [1][3300/3746] lr: 9.999e-02, eta: 4 days, 19:00:40, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.0977, top5_acc: 0.2587, loss_cls: 5.1942, loss: 5.1942 +2024-07-22 03:10:30,011 - pyskl - INFO - Epoch [1][3400/3746] lr: 9.999e-02, eta: 4 days, 18:48:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0952, top5_acc: 0.2580, loss_cls: 5.1787, loss: 5.1787 +2024-07-22 03:11:40,286 - pyskl - INFO - Epoch [1][3500/3746] lr: 9.999e-02, eta: 4 days, 18:37:35, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1033, top5_acc: 0.2787, loss_cls: 5.1267, loss: 5.1267 +2024-07-22 03:12:50,549 - pyskl - INFO - Epoch [1][3600/3746] lr: 9.999e-02, eta: 4 days, 18:26:57, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0928, top5_acc: 0.2683, loss_cls: 5.1641, loss: 5.1641 +2024-07-22 03:14:00,782 - pyskl - INFO - Epoch [1][3700/3746] lr: 9.999e-02, eta: 4 days, 18:16:45, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0998, top5_acc: 0.2741, loss_cls: 5.1224, loss: 5.1224 +2024-07-22 03:14:35,416 - pyskl - INFO - Saving checkpoint at 1 epochs +2024-07-22 03:16:28,134 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 03:16:28,796 - pyskl - INFO - +top1_acc 0.0700 +top5_acc 0.2054 +2024-07-22 03:16:28,796 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 03:16:28,834 - pyskl - INFO - +mean_acc 0.0700 +2024-07-22 03:16:29,086 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2024-07-22 03:16:29,086 - pyskl - INFO - Best top1_acc is 0.0700 at 1 epoch. +2024-07-22 03:16:29,100 - pyskl - INFO - Epoch(val) [1][309] top1_acc: 0.0700, top5_acc: 0.2054, mean_class_accuracy: 0.0700 +2024-07-22 03:19:46,797 - pyskl - INFO - Epoch [2][100/3746] lr: 9.999e-02, eta: 4 days, 21:52:48, time: 1.977, data_time: 1.269, memory: 15990, top1_acc: 0.1056, top5_acc: 0.2878, loss_cls: 5.1004, loss: 5.1004 +2024-07-22 03:20:56,735 - pyskl - INFO - Epoch [2][200/3746] lr: 9.999e-02, eta: 4 days, 21:37:08, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1127, top5_acc: 0.2891, loss_cls: 5.0728, loss: 5.0728 +2024-07-22 03:22:07,056 - pyskl - INFO - Epoch [2][300/3746] lr: 9.999e-02, eta: 4 days, 21:23:05, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1055, top5_acc: 0.2877, loss_cls: 5.0880, loss: 5.0880 +2024-07-22 03:23:16,960 - pyskl - INFO - Epoch [2][400/3746] lr: 9.999e-02, eta: 4 days, 21:08:42, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1125, top5_acc: 0.2867, loss_cls: 5.0607, loss: 5.0607 +2024-07-22 03:24:26,919 - pyskl - INFO - Epoch [2][500/3746] lr: 9.999e-02, eta: 4 days, 20:55:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1136, top5_acc: 0.2978, loss_cls: 5.0405, loss: 5.0405 +2024-07-22 03:25:36,491 - pyskl - INFO - Epoch [2][600/3746] lr: 9.999e-02, eta: 4 days, 20:41:11, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1184, top5_acc: 0.3041, loss_cls: 5.0334, loss: 5.0334 +2024-07-22 03:26:46,585 - pyskl - INFO - Epoch [2][700/3746] lr: 9.998e-02, eta: 4 days, 20:28:57, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1211, top5_acc: 0.3052, loss_cls: 5.0237, loss: 5.0237 +2024-07-22 03:27:56,549 - pyskl - INFO - Epoch [2][800/3746] lr: 9.998e-02, eta: 4 days, 20:16:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1209, top5_acc: 0.3139, loss_cls: 5.0115, loss: 5.0115 +2024-07-22 03:29:06,362 - pyskl - INFO - Epoch [2][900/3746] lr: 9.998e-02, eta: 4 days, 20:05:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1266, top5_acc: 0.3002, loss_cls: 4.9761, loss: 4.9761 +2024-07-22 03:30:16,338 - pyskl - INFO - Epoch [2][1000/3746] lr: 9.998e-02, eta: 4 days, 19:54:03, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1153, top5_acc: 0.3128, loss_cls: 4.9843, loss: 4.9843 +2024-07-22 03:31:26,237 - pyskl - INFO - Epoch [2][1100/3746] lr: 9.998e-02, eta: 4 days, 19:43:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1237, top5_acc: 0.3198, loss_cls: 4.9646, loss: 4.9646 +2024-07-22 03:32:36,209 - pyskl - INFO - Epoch [2][1200/3746] lr: 9.998e-02, eta: 4 days, 19:32:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1289, top5_acc: 0.3220, loss_cls: 4.9619, loss: 4.9619 +2024-07-22 03:33:46,153 - pyskl - INFO - Epoch [2][1300/3746] lr: 9.998e-02, eta: 4 days, 19:22:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1305, top5_acc: 0.3187, loss_cls: 4.9705, loss: 4.9705 +2024-07-22 03:34:56,111 - pyskl - INFO - Epoch [2][1400/3746] lr: 9.998e-02, eta: 4 days, 19:13:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1278, top5_acc: 0.3258, loss_cls: 4.9188, loss: 4.9188 +2024-07-22 03:36:06,091 - pyskl - INFO - Epoch [2][1500/3746] lr: 9.998e-02, eta: 4 days, 19:04:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1295, top5_acc: 0.3223, loss_cls: 4.9264, loss: 4.9264 +2024-07-22 03:37:15,890 - pyskl - INFO - Epoch [2][1600/3746] lr: 9.998e-02, eta: 4 days, 18:54:53, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1358, top5_acc: 0.3323, loss_cls: 4.9334, loss: 4.9334 +2024-07-22 03:38:25,783 - pyskl - INFO - Epoch [2][1700/3746] lr: 9.998e-02, eta: 4 days, 18:46:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1328, top5_acc: 0.3283, loss_cls: 4.8810, loss: 4.8810 +2024-07-22 03:39:35,673 - pyskl - INFO - Epoch [2][1800/3746] lr: 9.998e-02, eta: 4 days, 18:37:33, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1339, top5_acc: 0.3323, loss_cls: 4.8801, loss: 4.8801 +2024-07-22 03:40:45,490 - pyskl - INFO - Epoch [2][1900/3746] lr: 9.998e-02, eta: 4 days, 18:29:10, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1431, top5_acc: 0.3394, loss_cls: 4.8679, loss: 4.8679 +2024-07-22 03:41:55,359 - pyskl - INFO - Epoch [2][2000/3746] lr: 9.997e-02, eta: 4 days, 18:21:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1420, top5_acc: 0.3422, loss_cls: 4.8858, loss: 4.8858 +2024-07-22 03:43:05,279 - pyskl - INFO - Epoch [2][2100/3746] lr: 9.997e-02, eta: 4 days, 18:13:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1398, top5_acc: 0.3503, loss_cls: 4.8448, loss: 4.8448 +2024-07-22 03:44:14,977 - pyskl - INFO - Epoch [2][2200/3746] lr: 9.997e-02, eta: 4 days, 18:05:31, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1425, top5_acc: 0.3395, loss_cls: 4.8461, loss: 4.8461 +2024-07-22 03:45:24,763 - pyskl - INFO - Epoch [2][2300/3746] lr: 9.997e-02, eta: 4 days, 17:58:01, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1353, top5_acc: 0.3425, loss_cls: 4.8607, loss: 4.8607 +2024-07-22 03:46:34,575 - pyskl - INFO - Epoch [2][2400/3746] lr: 9.997e-02, eta: 4 days, 17:50:45, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1313, top5_acc: 0.3380, loss_cls: 4.9014, loss: 4.9014 +2024-07-22 03:47:44,269 - pyskl - INFO - Epoch [2][2500/3746] lr: 9.997e-02, eta: 4 days, 17:43:31, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1450, top5_acc: 0.3514, loss_cls: 4.8053, loss: 4.8053 +2024-07-22 03:48:53,910 - pyskl - INFO - Epoch [2][2600/3746] lr: 9.997e-02, eta: 4 days, 17:36:24, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1464, top5_acc: 0.3478, loss_cls: 4.8276, loss: 4.8276 +2024-07-22 03:50:03,983 - pyskl - INFO - Epoch [2][2700/3746] lr: 9.997e-02, eta: 4 days, 17:30:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1466, top5_acc: 0.3467, loss_cls: 4.8567, loss: 4.8567 +2024-07-22 03:51:13,825 - pyskl - INFO - Epoch [2][2800/3746] lr: 9.997e-02, eta: 4 days, 17:23:35, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1372, top5_acc: 0.3362, loss_cls: 4.8534, loss: 4.8534 +2024-07-22 03:52:23,726 - pyskl - INFO - Epoch [2][2900/3746] lr: 9.997e-02, eta: 4 days, 17:17:21, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1453, top5_acc: 0.3539, loss_cls: 4.8154, loss: 4.8154 +2024-07-22 03:53:33,412 - pyskl - INFO - Epoch [2][3000/3746] lr: 9.996e-02, eta: 4 days, 17:10:57, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1466, top5_acc: 0.3588, loss_cls: 4.7870, loss: 4.7870 +2024-07-22 03:54:43,595 - pyskl - INFO - Epoch [2][3100/3746] lr: 9.996e-02, eta: 4 days, 17:05:23, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1528, top5_acc: 0.3597, loss_cls: 4.7842, loss: 4.7842 +2024-07-22 03:55:53,427 - pyskl - INFO - Epoch [2][3200/3746] lr: 9.996e-02, eta: 4 days, 16:59:29, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1411, top5_acc: 0.3552, loss_cls: 4.8218, loss: 4.8218 +2024-07-22 03:57:03,529 - pyskl - INFO - Epoch [2][3300/3746] lr: 9.996e-02, eta: 4 days, 16:54:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1534, top5_acc: 0.3584, loss_cls: 4.7886, loss: 4.7886 +2024-07-22 03:58:13,713 - pyskl - INFO - Epoch [2][3400/3746] lr: 9.996e-02, eta: 4 days, 16:48:52, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1486, top5_acc: 0.3584, loss_cls: 4.7890, loss: 4.7890 +2024-07-22 03:59:23,748 - pyskl - INFO - Epoch [2][3500/3746] lr: 9.996e-02, eta: 4 days, 16:43:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1573, top5_acc: 0.3762, loss_cls: 4.7069, loss: 4.7069 +2024-07-22 04:00:33,738 - pyskl - INFO - Epoch [2][3600/3746] lr: 9.996e-02, eta: 4 days, 16:38:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1461, top5_acc: 0.3656, loss_cls: 4.7828, loss: 4.7828 +2024-07-22 04:01:43,583 - pyskl - INFO - Epoch [2][3700/3746] lr: 9.996e-02, eta: 4 days, 16:33:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1531, top5_acc: 0.3716, loss_cls: 4.7225, loss: 4.7225 +2024-07-22 04:02:18,195 - pyskl - INFO - Saving checkpoint at 2 epochs +2024-07-22 04:04:10,925 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 04:04:11,595 - pyskl - INFO - +top1_acc 0.1017 +top5_acc 0.2768 +2024-07-22 04:04:11,596 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 04:04:11,634 - pyskl - INFO - +mean_acc 0.1016 +2024-07-22 04:04:11,638 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_1.pth was removed +2024-07-22 04:04:11,881 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2024-07-22 04:04:11,882 - pyskl - INFO - Best top1_acc is 0.1017 at 2 epoch. +2024-07-22 04:04:11,892 - pyskl - INFO - Epoch(val) [2][309] top1_acc: 0.1017, top5_acc: 0.2768, mean_class_accuracy: 0.1016 +2024-07-22 04:07:30,120 - pyskl - INFO - Epoch [3][100/3746] lr: 9.995e-02, eta: 4 days, 18:22:42, time: 1.982, data_time: 1.275, memory: 15990, top1_acc: 0.1625, top5_acc: 0.3733, loss_cls: 4.6820, loss: 4.6820 +2024-07-22 04:08:40,151 - pyskl - INFO - Epoch [3][200/3746] lr: 9.995e-02, eta: 4 days, 18:16:21, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1622, top5_acc: 0.3800, loss_cls: 4.7020, loss: 4.7020 +2024-07-22 04:09:50,559 - pyskl - INFO - Epoch [3][300/3746] lr: 9.995e-02, eta: 4 days, 18:10:35, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1480, top5_acc: 0.3652, loss_cls: 4.7607, loss: 4.7607 +2024-07-22 04:11:00,378 - pyskl - INFO - Epoch [3][400/3746] lr: 9.995e-02, eta: 4 days, 18:04:15, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1509, top5_acc: 0.3647, loss_cls: 4.7551, loss: 4.7551 +2024-07-22 04:12:10,555 - pyskl - INFO - Epoch [3][500/3746] lr: 9.995e-02, eta: 4 days, 17:58:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1514, top5_acc: 0.3702, loss_cls: 4.7162, loss: 4.7162 +2024-07-22 04:13:20,478 - pyskl - INFO - Epoch [3][600/3746] lr: 9.995e-02, eta: 4 days, 17:52:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1581, top5_acc: 0.3747, loss_cls: 4.7049, loss: 4.7049 +2024-07-22 04:14:30,383 - pyskl - INFO - Epoch [3][700/3746] lr: 9.995e-02, eta: 4 days, 17:46:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1598, top5_acc: 0.3783, loss_cls: 4.6841, loss: 4.6841 +2024-07-22 04:15:40,177 - pyskl - INFO - Epoch [3][800/3746] lr: 9.995e-02, eta: 4 days, 17:40:43, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1669, top5_acc: 0.3802, loss_cls: 4.6993, loss: 4.6993 +2024-07-22 04:16:49,937 - pyskl - INFO - Epoch [3][900/3746] lr: 9.994e-02, eta: 4 days, 17:34:54, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1695, top5_acc: 0.3825, loss_cls: 4.6770, loss: 4.6770 +2024-07-22 04:18:00,132 - pyskl - INFO - Epoch [3][1000/3746] lr: 9.994e-02, eta: 4 days, 17:29:41, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1611, top5_acc: 0.3809, loss_cls: 4.7151, loss: 4.7151 +2024-07-22 04:19:09,907 - pyskl - INFO - Epoch [3][1100/3746] lr: 9.994e-02, eta: 4 days, 17:24:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1659, top5_acc: 0.3823, loss_cls: 4.6807, loss: 4.6807 +2024-07-22 04:20:19,955 - pyskl - INFO - Epoch [3][1200/3746] lr: 9.994e-02, eta: 4 days, 17:18:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1625, top5_acc: 0.3777, loss_cls: 4.6845, loss: 4.6845 +2024-07-22 04:21:29,732 - pyskl - INFO - Epoch [3][1300/3746] lr: 9.994e-02, eta: 4 days, 17:13:31, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1714, top5_acc: 0.3864, loss_cls: 4.6621, loss: 4.6621 +2024-07-22 04:22:39,620 - pyskl - INFO - Epoch [3][1400/3746] lr: 9.994e-02, eta: 4 days, 17:08:21, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1689, top5_acc: 0.3912, loss_cls: 4.6495, loss: 4.6495 +2024-07-22 04:23:49,609 - pyskl - INFO - Epoch [3][1500/3746] lr: 9.994e-02, eta: 4 days, 17:03:22, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1683, top5_acc: 0.3923, loss_cls: 4.6400, loss: 4.6400 +2024-07-22 04:24:59,792 - pyskl - INFO - Epoch [3][1600/3746] lr: 9.994e-02, eta: 4 days, 16:58:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1716, top5_acc: 0.3892, loss_cls: 4.6139, loss: 4.6139 +2024-07-22 04:26:09,755 - pyskl - INFO - Epoch [3][1700/3746] lr: 9.993e-02, eta: 4 days, 16:53:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1747, top5_acc: 0.3934, loss_cls: 4.6297, loss: 4.6297 +2024-07-22 04:27:19,406 - pyskl - INFO - Epoch [3][1800/3746] lr: 9.993e-02, eta: 4 days, 16:48:44, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1695, top5_acc: 0.3975, loss_cls: 4.6070, loss: 4.6070 +2024-07-22 04:28:29,167 - pyskl - INFO - Epoch [3][1900/3746] lr: 9.993e-02, eta: 4 days, 16:43:51, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1677, top5_acc: 0.3952, loss_cls: 4.6521, loss: 4.6521 +2024-07-22 04:29:38,852 - pyskl - INFO - Epoch [3][2000/3746] lr: 9.993e-02, eta: 4 days, 16:38:59, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1780, top5_acc: 0.3980, loss_cls: 4.5759, loss: 4.5759 +2024-07-22 04:30:48,711 - pyskl - INFO - Epoch [3][2100/3746] lr: 9.993e-02, eta: 4 days, 16:34:20, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1794, top5_acc: 0.3947, loss_cls: 4.6136, loss: 4.6136 +2024-07-22 04:31:58,683 - pyskl - INFO - Epoch [3][2200/3746] lr: 9.993e-02, eta: 4 days, 16:29:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1766, top5_acc: 0.3931, loss_cls: 4.5867, loss: 4.5867 +2024-07-22 04:33:08,444 - pyskl - INFO - Epoch [3][2300/3746] lr: 9.993e-02, eta: 4 days, 16:25:18, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1809, top5_acc: 0.4072, loss_cls: 4.5647, loss: 4.5647 +2024-07-22 04:34:18,516 - pyskl - INFO - Epoch [3][2400/3746] lr: 9.992e-02, eta: 4 days, 16:21:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1825, top5_acc: 0.3994, loss_cls: 4.6086, loss: 4.6086 +2024-07-22 04:35:28,253 - pyskl - INFO - Epoch [3][2500/3746] lr: 9.992e-02, eta: 4 days, 16:16:36, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1789, top5_acc: 0.4120, loss_cls: 4.5776, loss: 4.5776 +2024-07-22 04:36:37,983 - pyskl - INFO - Epoch [3][2600/3746] lr: 9.992e-02, eta: 4 days, 16:12:11, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1781, top5_acc: 0.3966, loss_cls: 4.5777, loss: 4.5777 +2024-07-22 04:37:47,841 - pyskl - INFO - Epoch [3][2700/3746] lr: 9.992e-02, eta: 4 days, 16:07:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1777, top5_acc: 0.4102, loss_cls: 4.5634, loss: 4.5634 +2024-07-22 04:38:57,939 - pyskl - INFO - Epoch [3][2800/3746] lr: 9.992e-02, eta: 4 days, 16:03:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1850, top5_acc: 0.3998, loss_cls: 4.5577, loss: 4.5577 +2024-07-22 04:40:07,766 - pyskl - INFO - Epoch [3][2900/3746] lr: 9.992e-02, eta: 4 days, 15:59:50, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1897, top5_acc: 0.4158, loss_cls: 4.5259, loss: 4.5259 +2024-07-22 04:41:17,764 - pyskl - INFO - Epoch [3][3000/3746] lr: 9.991e-02, eta: 4 days, 15:55:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1828, top5_acc: 0.4066, loss_cls: 4.5291, loss: 4.5291 +2024-07-22 04:42:27,639 - pyskl - INFO - Epoch [3][3100/3746] lr: 9.991e-02, eta: 4 days, 15:51:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1722, top5_acc: 0.3997, loss_cls: 4.6176, loss: 4.6176 +2024-07-22 04:43:37,330 - pyskl - INFO - Epoch [3][3200/3746] lr: 9.991e-02, eta: 4 days, 15:47:47, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1797, top5_acc: 0.4075, loss_cls: 4.5598, loss: 4.5598 +2024-07-22 04:44:47,187 - pyskl - INFO - Epoch [3][3300/3746] lr: 9.991e-02, eta: 4 days, 15:43:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1920, top5_acc: 0.4216, loss_cls: 4.5190, loss: 4.5190 +2024-07-22 04:45:57,127 - pyskl - INFO - Epoch [3][3400/3746] lr: 9.991e-02, eta: 4 days, 15:40:06, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1770, top5_acc: 0.3994, loss_cls: 4.5715, loss: 4.5715 +2024-07-22 04:47:07,340 - pyskl - INFO - Epoch [3][3500/3746] lr: 9.991e-02, eta: 4 days, 15:36:35, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1941, top5_acc: 0.4220, loss_cls: 4.5034, loss: 4.5034 +2024-07-22 04:48:17,384 - pyskl - INFO - Epoch [3][3600/3746] lr: 9.990e-02, eta: 4 days, 15:32:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1811, top5_acc: 0.4103, loss_cls: 4.5492, loss: 4.5492 +2024-07-22 04:49:27,297 - pyskl - INFO - Epoch [3][3700/3746] lr: 9.990e-02, eta: 4 days, 15:29:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1858, top5_acc: 0.4080, loss_cls: 4.5788, loss: 4.5788 +2024-07-22 04:50:02,025 - pyskl - INFO - Saving checkpoint at 3 epochs +2024-07-22 04:51:54,612 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 04:51:55,287 - pyskl - INFO - +top1_acc 0.1047 +top5_acc 0.2741 +2024-07-22 04:51:55,288 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 04:51:55,331 - pyskl - INFO - +mean_acc 0.1045 +2024-07-22 04:51:55,335 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_2.pth was removed +2024-07-22 04:51:55,578 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2024-07-22 04:51:55,579 - pyskl - INFO - Best top1_acc is 0.1047 at 3 epoch. +2024-07-22 04:51:55,590 - pyskl - INFO - Epoch(val) [3][309] top1_acc: 0.1047, top5_acc: 0.2741, mean_class_accuracy: 0.1045 +2024-07-22 04:55:11,949 - pyskl - INFO - Epoch [4][100/3746] lr: 9.990e-02, eta: 4 days, 16:40:20, time: 1.964, data_time: 1.256, memory: 15990, top1_acc: 0.1875, top5_acc: 0.4252, loss_cls: 4.4884, loss: 4.4884 +2024-07-22 04:56:22,159 - pyskl - INFO - Epoch [4][200/3746] lr: 9.990e-02, eta: 4 days, 16:36:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1908, top5_acc: 0.4206, loss_cls: 4.5085, loss: 4.5085 +2024-07-22 04:57:32,440 - pyskl - INFO - Epoch [4][300/3746] lr: 9.990e-02, eta: 4 days, 16:32:25, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1773, top5_acc: 0.4080, loss_cls: 4.5590, loss: 4.5590 +2024-07-22 04:58:42,463 - pyskl - INFO - Epoch [4][400/3746] lr: 9.989e-02, eta: 4 days, 16:28:22, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1939, top5_acc: 0.4192, loss_cls: 4.4907, loss: 4.4907 +2024-07-22 04:59:52,423 - pyskl - INFO - Epoch [4][500/3746] lr: 9.989e-02, eta: 4 days, 16:24:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1875, top5_acc: 0.4109, loss_cls: 4.5360, loss: 4.5360 +2024-07-22 05:01:02,780 - pyskl - INFO - Epoch [4][600/3746] lr: 9.989e-02, eta: 4 days, 16:20:36, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1823, top5_acc: 0.4072, loss_cls: 4.5627, loss: 4.5627 +2024-07-22 05:02:12,937 - pyskl - INFO - Epoch [4][700/3746] lr: 9.989e-02, eta: 4 days, 16:16:47, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1941, top5_acc: 0.4152, loss_cls: 4.5217, loss: 4.5217 +2024-07-22 05:03:23,281 - pyskl - INFO - Epoch [4][800/3746] lr: 9.989e-02, eta: 4 days, 16:13:10, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1894, top5_acc: 0.4150, loss_cls: 4.4966, loss: 4.4966 +2024-07-22 05:04:33,720 - pyskl - INFO - Epoch [4][900/3746] lr: 9.988e-02, eta: 4 days, 16:09:39, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1983, top5_acc: 0.4370, loss_cls: 4.4402, loss: 4.4402 +2024-07-22 05:05:43,752 - pyskl - INFO - Epoch [4][1000/3746] lr: 9.988e-02, eta: 4 days, 16:05:52, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1920, top5_acc: 0.4127, loss_cls: 4.5028, loss: 4.5028 +2024-07-22 05:06:53,858 - pyskl - INFO - Epoch [4][1100/3746] lr: 9.988e-02, eta: 4 days, 16:02:11, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4313, loss_cls: 4.4716, loss: 4.4716 +2024-07-22 05:08:04,355 - pyskl - INFO - Epoch [4][1200/3746] lr: 9.988e-02, eta: 4 days, 15:58:50, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2045, top5_acc: 0.4395, loss_cls: 4.4264, loss: 4.4264 +2024-07-22 05:09:14,912 - pyskl - INFO - Epoch [4][1300/3746] lr: 9.988e-02, eta: 4 days, 15:55:33, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1845, top5_acc: 0.4209, loss_cls: 4.4944, loss: 4.4944 +2024-07-22 05:10:25,481 - pyskl - INFO - Epoch [4][1400/3746] lr: 9.988e-02, eta: 4 days, 15:52:19, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4261, loss_cls: 4.4840, loss: 4.4840 +2024-07-22 05:11:35,590 - pyskl - INFO - Epoch [4][1500/3746] lr: 9.987e-02, eta: 4 days, 15:48:47, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4234, loss_cls: 4.4944, loss: 4.4944 +2024-07-22 05:12:45,621 - pyskl - INFO - Epoch [4][1600/3746] lr: 9.987e-02, eta: 4 days, 15:45:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1984, top5_acc: 0.4289, loss_cls: 4.5073, loss: 4.5073 +2024-07-22 05:13:55,842 - pyskl - INFO - Epoch [4][1700/3746] lr: 9.987e-02, eta: 4 days, 15:41:51, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1973, top5_acc: 0.4200, loss_cls: 4.5025, loss: 4.5025 +2024-07-22 05:15:06,295 - pyskl - INFO - Epoch [4][1800/3746] lr: 9.987e-02, eta: 4 days, 15:38:40, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4359, loss_cls: 4.4406, loss: 4.4406 +2024-07-22 05:16:16,448 - pyskl - INFO - Epoch [4][1900/3746] lr: 9.987e-02, eta: 4 days, 15:35:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4292, loss_cls: 4.4102, loss: 4.4102 +2024-07-22 05:17:26,444 - pyskl - INFO - Epoch [4][2000/3746] lr: 9.986e-02, eta: 4 days, 15:31:52, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1928, top5_acc: 0.4234, loss_cls: 4.4673, loss: 4.4673 +2024-07-22 05:18:36,487 - pyskl - INFO - Epoch [4][2100/3746] lr: 9.986e-02, eta: 4 days, 15:28:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4336, loss_cls: 4.4590, loss: 4.4590 +2024-07-22 05:19:46,479 - pyskl - INFO - Epoch [4][2200/3746] lr: 9.986e-02, eta: 4 days, 15:25:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2030, top5_acc: 0.4319, loss_cls: 4.4582, loss: 4.4582 +2024-07-22 05:20:56,980 - pyskl - INFO - Epoch [4][2300/3746] lr: 9.986e-02, eta: 4 days, 15:22:08, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2000, top5_acc: 0.4323, loss_cls: 4.4536, loss: 4.4536 +2024-07-22 05:22:07,215 - pyskl - INFO - Epoch [4][2400/3746] lr: 9.985e-02, eta: 4 days, 15:18:59, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4327, loss_cls: 4.4360, loss: 4.4360 +2024-07-22 05:23:17,189 - pyskl - INFO - Epoch [4][2500/3746] lr: 9.985e-02, eta: 4 days, 15:15:42, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2033, top5_acc: 0.4428, loss_cls: 4.4147, loss: 4.4147 +2024-07-22 05:24:27,494 - pyskl - INFO - Epoch [4][2600/3746] lr: 9.985e-02, eta: 4 days, 15:12:39, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4384, loss_cls: 4.4115, loss: 4.4115 +2024-07-22 05:25:37,693 - pyskl - INFO - Epoch [4][2700/3746] lr: 9.985e-02, eta: 4 days, 15:09:34, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1961, top5_acc: 0.4281, loss_cls: 4.4658, loss: 4.4658 +2024-07-22 05:26:47,530 - pyskl - INFO - Epoch [4][2800/3746] lr: 9.985e-02, eta: 4 days, 15:06:16, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4386, loss_cls: 4.4211, loss: 4.4211 +2024-07-22 05:27:57,566 - pyskl - INFO - Epoch [4][2900/3746] lr: 9.984e-02, eta: 4 days, 15:03:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2008, top5_acc: 0.4423, loss_cls: 4.4365, loss: 4.4365 +2024-07-22 05:29:07,430 - pyskl - INFO - Epoch [4][3000/3746] lr: 9.984e-02, eta: 4 days, 14:59:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2056, top5_acc: 0.4420, loss_cls: 4.3915, loss: 4.3915 +2024-07-22 05:30:17,558 - pyskl - INFO - Epoch [4][3100/3746] lr: 9.984e-02, eta: 4 days, 14:56:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1984, top5_acc: 0.4397, loss_cls: 4.3999, loss: 4.3999 +2024-07-22 05:31:27,554 - pyskl - INFO - Epoch [4][3200/3746] lr: 9.984e-02, eta: 4 days, 14:53:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4444, loss_cls: 4.3979, loss: 4.3979 +2024-07-22 05:32:37,726 - pyskl - INFO - Epoch [4][3300/3746] lr: 9.983e-02, eta: 4 days, 14:50:52, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4384, loss_cls: 4.4060, loss: 4.4060 +2024-07-22 05:33:47,561 - pyskl - INFO - Epoch [4][3400/3746] lr: 9.983e-02, eta: 4 days, 14:47:45, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2056, top5_acc: 0.4383, loss_cls: 4.3933, loss: 4.3933 +2024-07-22 05:34:58,232 - pyskl - INFO - Epoch [4][3500/3746] lr: 9.983e-02, eta: 4 days, 14:45:10, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4384, loss_cls: 4.4187, loss: 4.4187 +2024-07-22 05:36:08,462 - pyskl - INFO - Epoch [4][3600/3746] lr: 9.983e-02, eta: 4 days, 14:42:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4356, loss_cls: 4.3965, loss: 4.3965 +2024-07-22 05:37:18,776 - pyskl - INFO - Epoch [4][3700/3746] lr: 9.983e-02, eta: 4 days, 14:39:34, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2045, top5_acc: 0.4327, loss_cls: 4.4052, loss: 4.4052 +2024-07-22 05:37:53,256 - pyskl - INFO - Saving checkpoint at 4 epochs +2024-07-22 05:39:45,410 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 05:39:46,076 - pyskl - INFO - +top1_acc 0.1391 +top5_acc 0.3215 +2024-07-22 05:39:46,077 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 05:39:46,114 - pyskl - INFO - +mean_acc 0.1388 +2024-07-22 05:39:46,119 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_3.pth was removed +2024-07-22 05:39:46,372 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2024-07-22 05:39:46,373 - pyskl - INFO - Best top1_acc is 0.1391 at 4 epoch. +2024-07-22 05:39:46,384 - pyskl - INFO - Epoch(val) [4][309] top1_acc: 0.1391, top5_acc: 0.3215, mean_class_accuracy: 0.1388 +2024-07-22 05:43:03,820 - pyskl - INFO - Epoch [5][100/3746] lr: 9.982e-02, eta: 4 days, 15:32:49, time: 1.974, data_time: 1.267, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4480, loss_cls: 4.3479, loss: 4.3479 +2024-07-22 05:44:14,034 - pyskl - INFO - Epoch [5][200/3746] lr: 9.982e-02, eta: 4 days, 15:29:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4364, loss_cls: 4.3943, loss: 4.3943 +2024-07-22 05:45:24,369 - pyskl - INFO - Epoch [5][300/3746] lr: 9.982e-02, eta: 4 days, 15:26:36, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2078, top5_acc: 0.4503, loss_cls: 4.3694, loss: 4.3694 +2024-07-22 05:46:34,384 - pyskl - INFO - Epoch [5][400/3746] lr: 9.982e-02, eta: 4 days, 15:23:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4514, loss_cls: 4.3325, loss: 4.3325 +2024-07-22 05:47:44,541 - pyskl - INFO - Epoch [5][500/3746] lr: 9.981e-02, eta: 4 days, 15:20:16, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2092, top5_acc: 0.4402, loss_cls: 4.3933, loss: 4.3933 +2024-07-22 05:48:54,723 - pyskl - INFO - Epoch [5][600/3746] lr: 9.981e-02, eta: 4 days, 15:17:11, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4436, loss_cls: 4.3899, loss: 4.3899 +2024-07-22 05:50:05,005 - pyskl - INFO - Epoch [5][700/3746] lr: 9.981e-02, eta: 4 days, 15:14:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4389, loss_cls: 4.3574, loss: 4.3574 +2024-07-22 05:51:15,060 - pyskl - INFO - Epoch [5][800/3746] lr: 9.981e-02, eta: 4 days, 15:11:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4447, loss_cls: 4.3815, loss: 4.3815 +2024-07-22 05:52:25,276 - pyskl - INFO - Epoch [5][900/3746] lr: 9.980e-02, eta: 4 days, 15:08:06, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4497, loss_cls: 4.3806, loss: 4.3806 +2024-07-22 05:53:35,838 - pyskl - INFO - Epoch [5][1000/3746] lr: 9.980e-02, eta: 4 days, 15:05:20, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4503, loss_cls: 4.3420, loss: 4.3420 +2024-07-22 05:54:46,254 - pyskl - INFO - Epoch [5][1100/3746] lr: 9.980e-02, eta: 4 days, 15:02:31, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4366, loss_cls: 4.3976, loss: 4.3976 +2024-07-22 05:55:56,221 - pyskl - INFO - Epoch [5][1200/3746] lr: 9.980e-02, eta: 4 days, 14:59:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4497, loss_cls: 4.3838, loss: 4.3838 +2024-07-22 05:57:06,555 - pyskl - INFO - Epoch [5][1300/3746] lr: 9.979e-02, eta: 4 days, 14:56:37, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4494, loss_cls: 4.3679, loss: 4.3679 +2024-07-22 05:58:16,591 - pyskl - INFO - Epoch [5][1400/3746] lr: 9.979e-02, eta: 4 days, 14:53:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4492, loss_cls: 4.3592, loss: 4.3592 +2024-07-22 05:59:26,612 - pyskl - INFO - Epoch [5][1500/3746] lr: 9.979e-02, eta: 4 days, 14:50:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4511, loss_cls: 4.3663, loss: 4.3663 +2024-07-22 06:00:36,724 - pyskl - INFO - Epoch [5][1600/3746] lr: 9.979e-02, eta: 4 days, 14:47:47, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4489, loss_cls: 4.3572, loss: 4.3572 +2024-07-22 06:01:46,713 - pyskl - INFO - Epoch [5][1700/3746] lr: 9.978e-02, eta: 4 days, 14:44:51, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2131, top5_acc: 0.4469, loss_cls: 4.3636, loss: 4.3636 +2024-07-22 06:02:56,821 - pyskl - INFO - Epoch [5][1800/3746] lr: 9.978e-02, eta: 4 days, 14:42:00, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2002, top5_acc: 0.4377, loss_cls: 4.4073, loss: 4.4073 +2024-07-22 06:04:07,032 - pyskl - INFO - Epoch [5][1900/3746] lr: 9.978e-02, eta: 4 days, 14:39:13, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4512, loss_cls: 4.3361, loss: 4.3361 +2024-07-22 06:05:17,279 - pyskl - INFO - Epoch [5][2000/3746] lr: 9.977e-02, eta: 4 days, 14:36:29, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2194, top5_acc: 0.4491, loss_cls: 4.3529, loss: 4.3529 +2024-07-22 06:06:27,167 - pyskl - INFO - Epoch [5][2100/3746] lr: 9.977e-02, eta: 4 days, 14:33:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4550, loss_cls: 4.3188, loss: 4.3188 +2024-07-22 06:07:37,195 - pyskl - INFO - Epoch [5][2200/3746] lr: 9.977e-02, eta: 4 days, 14:30:45, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4647, loss_cls: 4.3100, loss: 4.3100 +2024-07-22 06:08:47,658 - pyskl - INFO - Epoch [5][2300/3746] lr: 9.977e-02, eta: 4 days, 14:28:11, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4562, loss_cls: 4.3339, loss: 4.3339 +2024-07-22 06:09:57,999 - pyskl - INFO - Epoch [5][2400/3746] lr: 9.976e-02, eta: 4 days, 14:25:34, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4580, loss_cls: 4.3221, loss: 4.3221 +2024-07-22 06:11:08,050 - pyskl - INFO - Epoch [5][2500/3746] lr: 9.976e-02, eta: 4 days, 14:22:49, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4556, loss_cls: 4.3204, loss: 4.3204 +2024-07-22 06:12:18,216 - pyskl - INFO - Epoch [5][2600/3746] lr: 9.976e-02, eta: 4 days, 14:20:08, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4533, loss_cls: 4.3259, loss: 4.3259 +2024-07-22 06:13:28,311 - pyskl - INFO - Epoch [5][2700/3746] lr: 9.976e-02, eta: 4 days, 14:17:27, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4511, loss_cls: 4.3366, loss: 4.3366 +2024-07-22 06:14:38,453 - pyskl - INFO - Epoch [5][2800/3746] lr: 9.975e-02, eta: 4 days, 14:14:48, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4619, loss_cls: 4.2964, loss: 4.2964 +2024-07-22 06:15:48,283 - pyskl - INFO - Epoch [5][2900/3746] lr: 9.975e-02, eta: 4 days, 14:12:00, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4509, loss_cls: 4.3150, loss: 4.3150 +2024-07-22 06:16:58,211 - pyskl - INFO - Epoch [5][3000/3746] lr: 9.975e-02, eta: 4 days, 14:09:16, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4617, loss_cls: 4.2901, loss: 4.2901 +2024-07-22 06:18:08,711 - pyskl - INFO - Epoch [5][3100/3746] lr: 9.974e-02, eta: 4 days, 14:06:51, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4689, loss_cls: 4.2801, loss: 4.2801 +2024-07-22 06:19:18,454 - pyskl - INFO - Epoch [5][3200/3746] lr: 9.974e-02, eta: 4 days, 14:04:04, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4477, loss_cls: 4.3435, loss: 4.3435 +2024-07-22 06:20:28,397 - pyskl - INFO - Epoch [5][3300/3746] lr: 9.974e-02, eta: 4 days, 14:01:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4628, loss_cls: 4.3087, loss: 4.3087 +2024-07-22 06:21:38,874 - pyskl - INFO - Epoch [5][3400/3746] lr: 9.974e-02, eta: 4 days, 13:59:00, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4664, loss_cls: 4.3076, loss: 4.3076 +2024-07-22 06:22:49,301 - pyskl - INFO - Epoch [5][3500/3746] lr: 9.973e-02, eta: 4 days, 13:56:36, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4669, loss_cls: 4.2721, loss: 4.2721 +2024-07-22 06:23:59,382 - pyskl - INFO - Epoch [5][3600/3746] lr: 9.973e-02, eta: 4 days, 13:54:03, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4525, loss_cls: 4.3405, loss: 4.3405 +2024-07-22 06:25:09,360 - pyskl - INFO - Epoch [5][3700/3746] lr: 9.973e-02, eta: 4 days, 13:51:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4547, loss_cls: 4.3156, loss: 4.3156 +2024-07-22 06:25:44,373 - pyskl - INFO - Saving checkpoint at 5 epochs +2024-07-22 06:27:37,044 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 06:27:37,733 - pyskl - INFO - +top1_acc 0.1554 +top5_acc 0.3693 +2024-07-22 06:27:37,733 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 06:27:37,771 - pyskl - INFO - +mean_acc 0.1554 +2024-07-22 06:27:37,776 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_4.pth was removed +2024-07-22 06:27:38,039 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2024-07-22 06:27:38,040 - pyskl - INFO - Best top1_acc is 0.1554 at 5 epoch. +2024-07-22 06:27:38,057 - pyskl - INFO - Epoch(val) [5][309] top1_acc: 0.1554, top5_acc: 0.3693, mean_class_accuracy: 0.1554 +2024-07-22 06:30:57,034 - pyskl - INFO - Epoch [6][100/3746] lr: 9.972e-02, eta: 4 days, 14:34:14, time: 1.990, data_time: 1.285, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4697, loss_cls: 4.2394, loss: 4.2394 +2024-07-22 06:32:07,696 - pyskl - INFO - Epoch [6][200/3746] lr: 9.972e-02, eta: 4 days, 14:31:45, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4722, loss_cls: 4.2623, loss: 4.2623 +2024-07-22 06:33:17,928 - pyskl - INFO - Epoch [6][300/3746] lr: 9.972e-02, eta: 4 days, 14:29:05, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4628, loss_cls: 4.2948, loss: 4.2948 +2024-07-22 06:34:27,998 - pyskl - INFO - Epoch [6][400/3746] lr: 9.971e-02, eta: 4 days, 14:26:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4594, loss_cls: 4.3040, loss: 4.3040 +2024-07-22 06:35:38,132 - pyskl - INFO - Epoch [6][500/3746] lr: 9.971e-02, eta: 4 days, 14:23:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4558, loss_cls: 4.3244, loss: 4.3244 +2024-07-22 06:36:48,528 - pyskl - INFO - Epoch [6][600/3746] lr: 9.971e-02, eta: 4 days, 14:21:07, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4594, loss_cls: 4.2745, loss: 4.2745 +2024-07-22 06:37:58,531 - pyskl - INFO - Epoch [6][700/3746] lr: 9.971e-02, eta: 4 days, 14:18:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4619, loss_cls: 4.2728, loss: 4.2728 +2024-07-22 06:39:08,387 - pyskl - INFO - Epoch [6][800/3746] lr: 9.970e-02, eta: 4 days, 14:15:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4611, loss_cls: 4.2959, loss: 4.2959 +2024-07-22 06:40:18,495 - pyskl - INFO - Epoch [6][900/3746] lr: 9.970e-02, eta: 4 days, 14:12:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4572, loss_cls: 4.3147, loss: 4.3147 +2024-07-22 06:41:28,783 - pyskl - INFO - Epoch [6][1000/3746] lr: 9.970e-02, eta: 4 days, 14:10:27, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4711, loss_cls: 4.2551, loss: 4.2551 +2024-07-22 06:42:38,919 - pyskl - INFO - Epoch [6][1100/3746] lr: 9.969e-02, eta: 4 days, 14:07:51, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4683, loss_cls: 4.2519, loss: 4.2519 +2024-07-22 06:43:48,803 - pyskl - INFO - Epoch [6][1200/3746] lr: 9.969e-02, eta: 4 days, 14:05:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4591, loss_cls: 4.3163, loss: 4.3163 +2024-07-22 06:44:59,251 - pyskl - INFO - Epoch [6][1300/3746] lr: 9.969e-02, eta: 4 days, 14:02:44, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4680, loss_cls: 4.2663, loss: 4.2663 +2024-07-22 06:46:09,166 - pyskl - INFO - Epoch [6][1400/3746] lr: 9.968e-02, eta: 4 days, 14:00:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4644, loss_cls: 4.2787, loss: 4.2787 +2024-07-22 06:47:19,330 - pyskl - INFO - Epoch [6][1500/3746] lr: 9.968e-02, eta: 4 days, 13:57:33, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4655, loss_cls: 4.2868, loss: 4.2868 +2024-07-22 06:48:29,436 - pyskl - INFO - Epoch [6][1600/3746] lr: 9.968e-02, eta: 4 days, 13:55:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4606, loss_cls: 4.2875, loss: 4.2875 +2024-07-22 06:49:39,345 - pyskl - INFO - Epoch [6][1700/3746] lr: 9.967e-02, eta: 4 days, 13:52:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4597, loss_cls: 4.2867, loss: 4.2867 +2024-07-22 06:50:49,449 - pyskl - INFO - Epoch [6][1800/3746] lr: 9.967e-02, eta: 4 days, 13:49:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4716, loss_cls: 4.2791, loss: 4.2791 +2024-07-22 06:51:59,631 - pyskl - INFO - Epoch [6][1900/3746] lr: 9.967e-02, eta: 4 days, 13:47:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4714, loss_cls: 4.2803, loss: 4.2803 +2024-07-22 06:53:09,762 - pyskl - INFO - Epoch [6][2000/3746] lr: 9.966e-02, eta: 4 days, 13:44:57, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4595, loss_cls: 4.2990, loss: 4.2990 +2024-07-22 06:54:19,629 - pyskl - INFO - Epoch [6][2100/3746] lr: 9.966e-02, eta: 4 days, 13:42:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4728, loss_cls: 4.2428, loss: 4.2428 +2024-07-22 06:55:29,705 - pyskl - INFO - Epoch [6][2200/3746] lr: 9.966e-02, eta: 4 days, 13:39:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4623, loss_cls: 4.2769, loss: 4.2769 +2024-07-22 06:56:40,000 - pyskl - INFO - Epoch [6][2300/3746] lr: 9.965e-02, eta: 4 days, 13:37:32, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4664, loss_cls: 4.2970, loss: 4.2970 +2024-07-22 06:57:50,035 - pyskl - INFO - Epoch [6][2400/3746] lr: 9.965e-02, eta: 4 days, 13:35:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4714, loss_cls: 4.2569, loss: 4.2569 +2024-07-22 06:59:00,216 - pyskl - INFO - Epoch [6][2500/3746] lr: 9.965e-02, eta: 4 days, 13:32:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4669, loss_cls: 4.2768, loss: 4.2768 +2024-07-22 07:00:10,369 - pyskl - INFO - Epoch [6][2600/3746] lr: 9.964e-02, eta: 4 days, 13:30:17, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4727, loss_cls: 4.2102, loss: 4.2102 +2024-07-22 07:01:20,502 - pyskl - INFO - Epoch [6][2700/3746] lr: 9.964e-02, eta: 4 days, 13:27:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4775, loss_cls: 4.2424, loss: 4.2424 +2024-07-22 07:02:30,492 - pyskl - INFO - Epoch [6][2800/3746] lr: 9.964e-02, eta: 4 days, 13:25:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4744, loss_cls: 4.2414, loss: 4.2414 +2024-07-22 07:03:40,400 - pyskl - INFO - Epoch [6][2900/3746] lr: 9.963e-02, eta: 4 days, 13:22:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4736, loss_cls: 4.2762, loss: 4.2762 +2024-07-22 07:04:50,419 - pyskl - INFO - Epoch [6][3000/3746] lr: 9.963e-02, eta: 4 days, 13:20:35, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4745, loss_cls: 4.2185, loss: 4.2185 +2024-07-22 07:06:00,822 - pyskl - INFO - Epoch [6][3100/3746] lr: 9.963e-02, eta: 4 days, 13:18:21, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4717, loss_cls: 4.2530, loss: 4.2530 +2024-07-22 07:07:10,806 - pyskl - INFO - Epoch [6][3200/3746] lr: 9.962e-02, eta: 4 days, 13:15:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4711, loss_cls: 4.2511, loss: 4.2511 +2024-07-22 07:08:21,020 - pyskl - INFO - Epoch [6][3300/3746] lr: 9.962e-02, eta: 4 days, 13:13:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4789, loss_cls: 4.2394, loss: 4.2394 +2024-07-22 07:09:30,812 - pyskl - INFO - Epoch [6][3400/3746] lr: 9.962e-02, eta: 4 days, 13:11:13, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4706, loss_cls: 4.2840, loss: 4.2840 +2024-07-22 07:10:41,309 - pyskl - INFO - Epoch [6][3500/3746] lr: 9.961e-02, eta: 4 days, 13:09:04, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4666, loss_cls: 4.2449, loss: 4.2449 +2024-07-22 07:11:51,424 - pyskl - INFO - Epoch [6][3600/3746] lr: 9.961e-02, eta: 4 days, 13:06:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4720, loss_cls: 4.2424, loss: 4.2424 +2024-07-22 07:13:01,651 - pyskl - INFO - Epoch [6][3700/3746] lr: 9.961e-02, eta: 4 days, 13:04:31, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4700, loss_cls: 4.2302, loss: 4.2302 +2024-07-22 07:13:36,357 - pyskl - INFO - Saving checkpoint at 6 epochs +2024-07-22 07:15:29,886 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 07:15:30,561 - pyskl - INFO - +top1_acc 0.1758 +top5_acc 0.3870 +2024-07-22 07:15:30,561 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 07:15:30,600 - pyskl - INFO - +mean_acc 0.1757 +2024-07-22 07:15:30,604 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_5.pth was removed +2024-07-22 07:15:30,848 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_6.pth. +2024-07-22 07:15:30,849 - pyskl - INFO - Best top1_acc is 0.1758 at 6 epoch. +2024-07-22 07:15:30,859 - pyskl - INFO - Epoch(val) [6][309] top1_acc: 0.1758, top5_acc: 0.3870, mean_class_accuracy: 0.1757 +2024-07-22 07:18:50,078 - pyskl - INFO - Epoch [7][100/3746] lr: 9.960e-02, eta: 4 days, 13:39:45, time: 1.992, data_time: 1.283, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4819, loss_cls: 4.1509, loss: 4.1509 +2024-07-22 07:20:00,697 - pyskl - INFO - Epoch [7][200/3746] lr: 9.960e-02, eta: 4 days, 13:37:30, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4764, loss_cls: 4.2179, loss: 4.2179 +2024-07-22 07:21:11,575 - pyskl - INFO - Epoch [7][300/3746] lr: 9.960e-02, eta: 4 days, 13:35:22, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4788, loss_cls: 4.2248, loss: 4.2248 +2024-07-22 07:22:22,360 - pyskl - INFO - Epoch [7][400/3746] lr: 9.959e-02, eta: 4 days, 13:33:13, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4688, loss_cls: 4.2586, loss: 4.2586 +2024-07-22 07:23:32,428 - pyskl - INFO - Epoch [7][500/3746] lr: 9.959e-02, eta: 4 days, 13:30:47, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4686, loss_cls: 4.2258, loss: 4.2258 +2024-07-22 07:24:42,448 - pyskl - INFO - Epoch [7][600/3746] lr: 9.958e-02, eta: 4 days, 13:28:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4714, loss_cls: 4.2339, loss: 4.2339 +2024-07-22 07:25:52,614 - pyskl - INFO - Epoch [7][700/3746] lr: 9.958e-02, eta: 4 days, 13:25:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4828, loss_cls: 4.1756, loss: 4.1756 +2024-07-22 07:27:02,995 - pyskl - INFO - Epoch [7][800/3746] lr: 9.958e-02, eta: 4 days, 13:23:41, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4813, loss_cls: 4.1907, loss: 4.1907 +2024-07-22 07:28:13,310 - pyskl - INFO - Epoch [7][900/3746] lr: 9.957e-02, eta: 4 days, 13:21:24, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4747, loss_cls: 4.2162, loss: 4.2162 +2024-07-22 07:29:23,870 - pyskl - INFO - Epoch [7][1000/3746] lr: 9.957e-02, eta: 4 days, 13:19:12, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4675, loss_cls: 4.2862, loss: 4.2862 +2024-07-22 07:30:34,147 - pyskl - INFO - Epoch [7][1100/3746] lr: 9.957e-02, eta: 4 days, 13:16:55, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4766, loss_cls: 4.2227, loss: 4.2227 +2024-07-22 07:31:44,434 - pyskl - INFO - Epoch [7][1200/3746] lr: 9.956e-02, eta: 4 days, 13:14:38, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4759, loss_cls: 4.2266, loss: 4.2266 +2024-07-22 07:32:54,775 - pyskl - INFO - Epoch [7][1300/3746] lr: 9.956e-02, eta: 4 days, 13:12:23, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4764, loss_cls: 4.2042, loss: 4.2042 +2024-07-22 07:34:05,431 - pyskl - INFO - Epoch [7][1400/3746] lr: 9.956e-02, eta: 4 days, 13:10:16, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4736, loss_cls: 4.2359, loss: 4.2359 +2024-07-22 07:35:15,445 - pyskl - INFO - Epoch [7][1500/3746] lr: 9.955e-02, eta: 4 days, 13:07:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4825, loss_cls: 4.2083, loss: 4.2083 +2024-07-22 07:36:25,966 - pyskl - INFO - Epoch [7][1600/3746] lr: 9.955e-02, eta: 4 days, 13:05:46, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4769, loss_cls: 4.1960, loss: 4.1960 +2024-07-22 07:37:35,920 - pyskl - INFO - Epoch [7][1700/3746] lr: 9.954e-02, eta: 4 days, 13:03:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4703, loss_cls: 4.2203, loss: 4.2203 +2024-07-22 07:38:45,905 - pyskl - INFO - Epoch [7][1800/3746] lr: 9.954e-02, eta: 4 days, 13:01:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4713, loss_cls: 4.2489, loss: 4.2489 +2024-07-22 07:39:55,807 - pyskl - INFO - Epoch [7][1900/3746] lr: 9.954e-02, eta: 4 days, 12:58:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4747, loss_cls: 4.2241, loss: 4.2241 +2024-07-22 07:41:05,872 - pyskl - INFO - Epoch [7][2000/3746] lr: 9.953e-02, eta: 4 days, 12:56:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4655, loss_cls: 4.2816, loss: 4.2816 +2024-07-22 07:42:15,893 - pyskl - INFO - Epoch [7][2100/3746] lr: 9.953e-02, eta: 4 days, 12:54:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4780, loss_cls: 4.2455, loss: 4.2455 +2024-07-22 07:43:26,222 - pyskl - INFO - Epoch [7][2200/3746] lr: 9.952e-02, eta: 4 days, 12:51:58, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4700, loss_cls: 4.2190, loss: 4.2190 +2024-07-22 07:44:36,724 - pyskl - INFO - Epoch [7][2300/3746] lr: 9.952e-02, eta: 4 days, 12:49:52, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4797, loss_cls: 4.2154, loss: 4.2154 +2024-07-22 07:45:47,248 - pyskl - INFO - Epoch [7][2400/3746] lr: 9.952e-02, eta: 4 days, 12:47:47, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4831, loss_cls: 4.1745, loss: 4.1745 +2024-07-22 07:46:57,415 - pyskl - INFO - Epoch [7][2500/3746] lr: 9.951e-02, eta: 4 days, 12:45:34, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4594, loss_cls: 4.2840, loss: 4.2840 +2024-07-22 07:48:07,852 - pyskl - INFO - Epoch [7][2600/3746] lr: 9.951e-02, eta: 4 days, 12:43:28, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4809, loss_cls: 4.2226, loss: 4.2226 +2024-07-22 07:49:18,251 - pyskl - INFO - Epoch [7][2700/3746] lr: 9.951e-02, eta: 4 days, 12:41:22, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4706, loss_cls: 4.2474, loss: 4.2474 +2024-07-22 07:50:28,671 - pyskl - INFO - Epoch [7][2800/3746] lr: 9.950e-02, eta: 4 days, 12:39:16, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4642, loss_cls: 4.2843, loss: 4.2843 +2024-07-22 07:51:39,044 - pyskl - INFO - Epoch [7][2900/3746] lr: 9.950e-02, eta: 4 days, 12:37:10, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4745, loss_cls: 4.2192, loss: 4.2192 +2024-07-22 07:52:49,235 - pyskl - INFO - Epoch [7][3000/3746] lr: 9.949e-02, eta: 4 days, 12:35:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4897, loss_cls: 4.1748, loss: 4.1748 +2024-07-22 07:53:59,415 - pyskl - INFO - Epoch [7][3100/3746] lr: 9.949e-02, eta: 4 days, 12:32:51, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4913, loss_cls: 4.1713, loss: 4.1713 +2024-07-22 07:55:09,549 - pyskl - INFO - Epoch [7][3200/3746] lr: 9.949e-02, eta: 4 days, 12:30:41, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4720, loss_cls: 4.2404, loss: 4.2404 +2024-07-22 07:56:19,659 - pyskl - INFO - Epoch [7][3300/3746] lr: 9.948e-02, eta: 4 days, 12:28:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4677, loss_cls: 4.2282, loss: 4.2282 +2024-07-22 07:57:30,355 - pyskl - INFO - Epoch [7][3400/3746] lr: 9.948e-02, eta: 4 days, 12:26:34, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4750, loss_cls: 4.2356, loss: 4.2356 +2024-07-22 07:58:40,747 - pyskl - INFO - Epoch [7][3500/3746] lr: 9.947e-02, eta: 4 days, 12:24:31, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4698, loss_cls: 4.2583, loss: 4.2583 +2024-07-22 07:59:51,165 - pyskl - INFO - Epoch [7][3600/3746] lr: 9.947e-02, eta: 4 days, 12:22:29, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4825, loss_cls: 4.1844, loss: 4.1844 +2024-07-22 08:01:01,337 - pyskl - INFO - Epoch [7][3700/3746] lr: 9.947e-02, eta: 4 days, 12:20:22, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4947, loss_cls: 4.1599, loss: 4.1599 +2024-07-22 08:01:35,969 - pyskl - INFO - Saving checkpoint at 7 epochs +2024-07-22 08:03:28,668 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 08:03:29,364 - pyskl - INFO - +top1_acc 0.1357 +top5_acc 0.3263 +2024-07-22 08:03:29,364 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 08:03:29,404 - pyskl - INFO - +mean_acc 0.1356 +2024-07-22 08:03:29,416 - pyskl - INFO - Epoch(val) [7][309] top1_acc: 0.1357, top5_acc: 0.3263, mean_class_accuracy: 0.1356 +2024-07-22 08:06:48,503 - pyskl - INFO - Epoch [8][100/3746] lr: 9.946e-02, eta: 4 days, 12:50:03, time: 1.991, data_time: 1.284, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4898, loss_cls: 4.1753, loss: 4.1753 +2024-07-22 08:07:59,386 - pyskl - INFO - Epoch [8][200/3746] lr: 9.946e-02, eta: 4 days, 12:48:04, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4880, loss_cls: 4.1865, loss: 4.1865 +2024-07-22 08:09:09,808 - pyskl - INFO - Epoch [8][300/3746] lr: 9.945e-02, eta: 4 days, 12:45:56, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4778, loss_cls: 4.1622, loss: 4.1622 +2024-07-22 08:10:19,832 - pyskl - INFO - Epoch [8][400/3746] lr: 9.945e-02, eta: 4 days, 12:43:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4789, loss_cls: 4.2150, loss: 4.2150 +2024-07-22 08:11:29,866 - pyskl - INFO - Epoch [8][500/3746] lr: 9.944e-02, eta: 4 days, 12:41:25, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4920, loss_cls: 4.1529, loss: 4.1529 +2024-07-22 08:12:40,464 - pyskl - INFO - Epoch [8][600/3746] lr: 9.944e-02, eta: 4 days, 12:39:22, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4741, loss_cls: 4.1978, loss: 4.1978 +2024-07-22 08:13:50,985 - pyskl - INFO - Epoch [8][700/3746] lr: 9.943e-02, eta: 4 days, 12:37:17, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4789, loss_cls: 4.1931, loss: 4.1931 +2024-07-22 08:15:01,269 - pyskl - INFO - Epoch [8][800/3746] lr: 9.943e-02, eta: 4 days, 12:35:09, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4844, loss_cls: 4.1797, loss: 4.1797 +2024-07-22 08:16:11,373 - pyskl - INFO - Epoch [8][900/3746] lr: 9.943e-02, eta: 4 days, 12:32:57, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4853, loss_cls: 4.1853, loss: 4.1853 +2024-07-22 08:17:21,246 - pyskl - INFO - Epoch [8][1000/3746] lr: 9.942e-02, eta: 4 days, 12:30:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4863, loss_cls: 4.1593, loss: 4.1593 +2024-07-22 08:18:31,247 - pyskl - INFO - Epoch [8][1100/3746] lr: 9.942e-02, eta: 4 days, 12:28:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4855, loss_cls: 4.1861, loss: 4.1861 +2024-07-22 08:19:41,523 - pyskl - INFO - Epoch [8][1200/3746] lr: 9.941e-02, eta: 4 days, 12:26:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4723, loss_cls: 4.2088, loss: 4.2088 +2024-07-22 08:20:51,727 - pyskl - INFO - Epoch [8][1300/3746] lr: 9.941e-02, eta: 4 days, 12:24:13, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4820, loss_cls: 4.2013, loss: 4.2013 +2024-07-22 08:22:01,752 - pyskl - INFO - Epoch [8][1400/3746] lr: 9.940e-02, eta: 4 days, 12:22:02, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4809, loss_cls: 4.1988, loss: 4.1988 +2024-07-22 08:23:11,602 - pyskl - INFO - Epoch [8][1500/3746] lr: 9.940e-02, eta: 4 days, 12:19:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4916, loss_cls: 4.1745, loss: 4.1745 +2024-07-22 08:24:21,815 - pyskl - INFO - Epoch [8][1600/3746] lr: 9.940e-02, eta: 4 days, 12:17:41, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4822, loss_cls: 4.1705, loss: 4.1705 +2024-07-22 08:25:31,789 - pyskl - INFO - Epoch [8][1700/3746] lr: 9.939e-02, eta: 4 days, 12:15:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4800, loss_cls: 4.2121, loss: 4.2121 +2024-07-22 08:26:41,796 - pyskl - INFO - Epoch [8][1800/3746] lr: 9.939e-02, eta: 4 days, 12:13:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4903, loss_cls: 4.1757, loss: 4.1757 +2024-07-22 08:27:52,107 - pyskl - INFO - Epoch [8][1900/3746] lr: 9.938e-02, eta: 4 days, 12:11:17, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4802, loss_cls: 4.2201, loss: 4.2201 +2024-07-22 08:29:02,487 - pyskl - INFO - Epoch [8][2000/3746] lr: 9.938e-02, eta: 4 days, 12:09:15, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4777, loss_cls: 4.2083, loss: 4.2083 +2024-07-22 08:30:12,515 - pyskl - INFO - Epoch [8][2100/3746] lr: 9.937e-02, eta: 4 days, 12:07:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4917, loss_cls: 4.1462, loss: 4.1462 +2024-07-22 08:31:22,747 - pyskl - INFO - Epoch [8][2200/3746] lr: 9.937e-02, eta: 4 days, 12:05:03, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4861, loss_cls: 4.1901, loss: 4.1901 +2024-07-22 08:32:33,220 - pyskl - INFO - Epoch [8][2300/3746] lr: 9.937e-02, eta: 4 days, 12:03:04, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4877, loss_cls: 4.2029, loss: 4.2029 +2024-07-22 08:33:43,234 - pyskl - INFO - Epoch [8][2400/3746] lr: 9.936e-02, eta: 4 days, 12:00:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4767, loss_cls: 4.2282, loss: 4.2282 +2024-07-22 08:34:53,562 - pyskl - INFO - Epoch [8][2500/3746] lr: 9.936e-02, eta: 4 days, 11:58:55, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4842, loss_cls: 4.1877, loss: 4.1877 +2024-07-22 08:36:03,661 - pyskl - INFO - Epoch [8][2600/3746] lr: 9.935e-02, eta: 4 days, 11:56:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4902, loss_cls: 4.1766, loss: 4.1766 +2024-07-22 08:37:13,578 - pyskl - INFO - Epoch [8][2700/3746] lr: 9.935e-02, eta: 4 days, 11:54:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4870, loss_cls: 4.1716, loss: 4.1716 +2024-07-22 08:38:23,626 - pyskl - INFO - Epoch [8][2800/3746] lr: 9.934e-02, eta: 4 days, 11:52:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4941, loss_cls: 4.1667, loss: 4.1667 +2024-07-22 08:39:33,682 - pyskl - INFO - Epoch [8][2900/3746] lr: 9.934e-02, eta: 4 days, 11:50:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4803, loss_cls: 4.1850, loss: 4.1850 +2024-07-22 08:40:43,816 - pyskl - INFO - Epoch [8][3000/3746] lr: 9.933e-02, eta: 4 days, 11:48:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4905, loss_cls: 4.1791, loss: 4.1791 +2024-07-22 08:41:53,749 - pyskl - INFO - Epoch [8][3100/3746] lr: 9.933e-02, eta: 4 days, 11:46:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4772, loss_cls: 4.2030, loss: 4.2030 +2024-07-22 08:43:04,193 - pyskl - INFO - Epoch [8][3200/3746] lr: 9.933e-02, eta: 4 days, 11:44:28, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4897, loss_cls: 4.1740, loss: 4.1740 +2024-07-22 08:44:14,152 - pyskl - INFO - Epoch [8][3300/3746] lr: 9.932e-02, eta: 4 days, 11:42:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4713, loss_cls: 4.2330, loss: 4.2330 +2024-07-22 08:45:24,253 - pyskl - INFO - Epoch [8][3400/3746] lr: 9.932e-02, eta: 4 days, 11:40:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4945, loss_cls: 4.1616, loss: 4.1616 +2024-07-22 08:46:34,840 - pyskl - INFO - Epoch [8][3500/3746] lr: 9.931e-02, eta: 4 days, 11:38:28, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4864, loss_cls: 4.1478, loss: 4.1478 +2024-07-22 08:47:44,749 - pyskl - INFO - Epoch [8][3600/3746] lr: 9.931e-02, eta: 4 days, 11:36:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4894, loss_cls: 4.1582, loss: 4.1582 +2024-07-22 08:48:55,289 - pyskl - INFO - Epoch [8][3700/3746] lr: 9.930e-02, eta: 4 days, 11:34:30, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4872, loss_cls: 4.1836, loss: 4.1836 +2024-07-22 08:49:30,041 - pyskl - INFO - Saving checkpoint at 8 epochs +2024-07-22 08:51:23,143 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 08:51:23,804 - pyskl - INFO - +top1_acc 0.1729 +top5_acc 0.3938 +2024-07-22 08:51:23,804 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 08:51:23,842 - pyskl - INFO - +mean_acc 0.1726 +2024-07-22 08:51:23,853 - pyskl - INFO - Epoch(val) [8][309] top1_acc: 0.1729, top5_acc: 0.3938, mean_class_accuracy: 0.1726 +2024-07-22 08:54:42,209 - pyskl - INFO - Epoch [9][100/3746] lr: 9.930e-02, eta: 4 days, 11:59:52, time: 1.983, data_time: 1.272, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4953, loss_cls: 4.1367, loss: 4.1367 +2024-07-22 08:55:52,806 - pyskl - INFO - Epoch [9][200/3746] lr: 9.929e-02, eta: 4 days, 11:57:55, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4875, loss_cls: 4.1512, loss: 4.1512 +2024-07-22 08:57:03,762 - pyskl - INFO - Epoch [9][300/3746] lr: 9.929e-02, eta: 4 days, 11:56:04, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.5006, loss_cls: 4.1232, loss: 4.1232 +2024-07-22 08:58:14,002 - pyskl - INFO - Epoch [9][400/3746] lr: 9.928e-02, eta: 4 days, 11:54:01, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4900, loss_cls: 4.1312, loss: 4.1312 +2024-07-22 08:59:24,345 - pyskl - INFO - Epoch [9][500/3746] lr: 9.928e-02, eta: 4 days, 11:52:01, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4919, loss_cls: 4.1453, loss: 4.1453 +2024-07-22 09:00:34,565 - pyskl - INFO - Epoch [9][600/3746] lr: 9.927e-02, eta: 4 days, 11:49:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5039, loss_cls: 4.1015, loss: 4.1015 +2024-07-22 09:01:44,628 - pyskl - INFO - Epoch [9][700/3746] lr: 9.927e-02, eta: 4 days, 11:47:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4903, loss_cls: 4.1839, loss: 4.1839 +2024-07-22 09:02:54,645 - pyskl - INFO - Epoch [9][800/3746] lr: 9.926e-02, eta: 4 days, 11:45:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4858, loss_cls: 4.1774, loss: 4.1774 +2024-07-22 09:04:04,487 - pyskl - INFO - Epoch [9][900/3746] lr: 9.926e-02, eta: 4 days, 11:43:39, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4797, loss_cls: 4.1943, loss: 4.1943 +2024-07-22 09:05:14,744 - pyskl - INFO - Epoch [9][1000/3746] lr: 9.925e-02, eta: 4 days, 11:41:39, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4755, loss_cls: 4.2110, loss: 4.2110 +2024-07-22 09:06:24,724 - pyskl - INFO - Epoch [9][1100/3746] lr: 9.925e-02, eta: 4 days, 11:39:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4909, loss_cls: 4.1309, loss: 4.1309 +2024-07-22 09:07:34,809 - pyskl - INFO - Epoch [9][1200/3746] lr: 9.924e-02, eta: 4 days, 11:37:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4906, loss_cls: 4.1660, loss: 4.1660 +2024-07-22 09:08:45,071 - pyskl - INFO - Epoch [9][1300/3746] lr: 9.924e-02, eta: 4 days, 11:35:31, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4934, loss_cls: 4.1457, loss: 4.1457 +2024-07-22 09:09:55,031 - pyskl - INFO - Epoch [9][1400/3746] lr: 9.923e-02, eta: 4 days, 11:33:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4933, loss_cls: 4.1458, loss: 4.1458 +2024-07-22 09:11:05,114 - pyskl - INFO - Epoch [9][1500/3746] lr: 9.923e-02, eta: 4 days, 11:31:25, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4913, loss_cls: 4.1591, loss: 4.1591 +2024-07-22 09:12:15,220 - pyskl - INFO - Epoch [9][1600/3746] lr: 9.922e-02, eta: 4 days, 11:29:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4955, loss_cls: 4.1277, loss: 4.1277 +2024-07-22 09:13:25,197 - pyskl - INFO - Epoch [9][1700/3746] lr: 9.922e-02, eta: 4 days, 11:27:21, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4813, loss_cls: 4.2082, loss: 4.2082 +2024-07-22 09:14:35,463 - pyskl - INFO - Epoch [9][1800/3746] lr: 9.921e-02, eta: 4 days, 11:25:23, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4831, loss_cls: 4.1830, loss: 4.1830 +2024-07-22 09:15:45,672 - pyskl - INFO - Epoch [9][1900/3746] lr: 9.921e-02, eta: 4 days, 11:23:24, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4925, loss_cls: 4.1244, loss: 4.1244 +2024-07-22 09:16:55,494 - pyskl - INFO - Epoch [9][2000/3746] lr: 9.920e-02, eta: 4 days, 11:21:20, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4778, loss_cls: 4.2144, loss: 4.2144 +2024-07-22 09:18:05,748 - pyskl - INFO - Epoch [9][2100/3746] lr: 9.920e-02, eta: 4 days, 11:19:23, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4988, loss_cls: 4.1242, loss: 4.1242 +2024-07-22 09:19:15,695 - pyskl - INFO - Epoch [9][2200/3746] lr: 9.919e-02, eta: 4 days, 11:17:21, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4845, loss_cls: 4.1774, loss: 4.1774 +2024-07-22 09:20:26,430 - pyskl - INFO - Epoch [9][2300/3746] lr: 9.919e-02, eta: 4 days, 11:15:32, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4872, loss_cls: 4.1864, loss: 4.1864 +2024-07-22 09:21:36,418 - pyskl - INFO - Epoch [9][2400/3746] lr: 9.918e-02, eta: 4 days, 11:13:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4877, loss_cls: 4.1400, loss: 4.1400 +2024-07-22 09:22:46,602 - pyskl - INFO - Epoch [9][2500/3746] lr: 9.918e-02, eta: 4 days, 11:11:34, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4916, loss_cls: 4.1434, loss: 4.1434 +2024-07-22 09:23:56,968 - pyskl - INFO - Epoch [9][2600/3746] lr: 9.917e-02, eta: 4 days, 11:09:40, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4891, loss_cls: 4.1750, loss: 4.1750 +2024-07-22 09:25:06,906 - pyskl - INFO - Epoch [9][2700/3746] lr: 9.917e-02, eta: 4 days, 11:07:40, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4917, loss_cls: 4.1560, loss: 4.1560 +2024-07-22 09:26:16,976 - pyskl - INFO - Epoch [9][2800/3746] lr: 9.916e-02, eta: 4 days, 11:05:42, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4948, loss_cls: 4.1382, loss: 4.1382 +2024-07-22 09:27:26,847 - pyskl - INFO - Epoch [9][2900/3746] lr: 9.916e-02, eta: 4 days, 11:03:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4811, loss_cls: 4.1657, loss: 4.1657 +2024-07-22 09:28:36,974 - pyskl - INFO - Epoch [9][3000/3746] lr: 9.915e-02, eta: 4 days, 11:01:44, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4922, loss_cls: 4.1657, loss: 4.1657 +2024-07-22 09:29:46,984 - pyskl - INFO - Epoch [9][3100/3746] lr: 9.915e-02, eta: 4 days, 10:59:46, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4864, loss_cls: 4.1834, loss: 4.1834 +2024-07-22 09:30:56,885 - pyskl - INFO - Epoch [9][3200/3746] lr: 9.914e-02, eta: 4 days, 10:57:46, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4797, loss_cls: 4.2000, loss: 4.2000 +2024-07-22 09:32:07,143 - pyskl - INFO - Epoch [9][3300/3746] lr: 9.914e-02, eta: 4 days, 10:55:52, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4958, loss_cls: 4.1384, loss: 4.1384 +2024-07-22 09:33:17,115 - pyskl - INFO - Epoch [9][3400/3746] lr: 9.913e-02, eta: 4 days, 10:53:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4881, loss_cls: 4.1600, loss: 4.1600 +2024-07-22 09:34:27,672 - pyskl - INFO - Epoch [9][3500/3746] lr: 9.913e-02, eta: 4 days, 10:52:06, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4819, loss_cls: 4.1747, loss: 4.1747 +2024-07-22 09:35:37,803 - pyskl - INFO - Epoch [9][3600/3746] lr: 9.912e-02, eta: 4 days, 10:50:11, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4888, loss_cls: 4.1689, loss: 4.1689 +2024-07-22 09:36:48,151 - pyskl - INFO - Epoch [9][3700/3746] lr: 9.912e-02, eta: 4 days, 10:48:20, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4869, loss_cls: 4.1672, loss: 4.1672 +2024-07-22 09:37:22,643 - pyskl - INFO - Saving checkpoint at 9 epochs +2024-07-22 09:39:15,117 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 09:39:15,775 - pyskl - INFO - +top1_acc 0.1722 +top5_acc 0.3940 +2024-07-22 09:39:15,775 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 09:39:15,814 - pyskl - INFO - +mean_acc 0.1723 +2024-07-22 09:39:15,824 - pyskl - INFO - Epoch(val) [9][309] top1_acc: 0.1722, top5_acc: 0.3940, mean_class_accuracy: 0.1723 +2024-07-22 09:42:34,109 - pyskl - INFO - Epoch [10][100/3746] lr: 9.911e-02, eta: 4 days, 11:10:31, time: 1.983, data_time: 1.275, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4983, loss_cls: 4.1159, loss: 4.1159 +2024-07-22 09:43:44,670 - pyskl - INFO - Epoch [10][200/3746] lr: 9.910e-02, eta: 4 days, 11:08:38, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4975, loss_cls: 4.1176, loss: 4.1176 +2024-07-22 09:44:55,106 - pyskl - INFO - Epoch [10][300/3746] lr: 9.910e-02, eta: 4 days, 11:06:45, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4838, loss_cls: 4.1686, loss: 4.1686 +2024-07-22 09:46:05,755 - pyskl - INFO - Epoch [10][400/3746] lr: 9.909e-02, eta: 4 days, 11:04:55, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4841, loss_cls: 4.1885, loss: 4.1885 +2024-07-22 09:47:15,880 - pyskl - INFO - Epoch [10][500/3746] lr: 9.909e-02, eta: 4 days, 11:02:57, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4894, loss_cls: 4.1677, loss: 4.1677 +2024-07-22 09:48:25,866 - pyskl - INFO - Epoch [10][600/3746] lr: 9.908e-02, eta: 4 days, 11:00:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4958, loss_cls: 4.1321, loss: 4.1321 +2024-07-22 09:49:36,059 - pyskl - INFO - Epoch [10][700/3746] lr: 9.908e-02, eta: 4 days, 10:59:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5022, loss_cls: 4.1011, loss: 4.1011 +2024-07-22 09:50:46,469 - pyskl - INFO - Epoch [10][800/3746] lr: 9.907e-02, eta: 4 days, 10:57:07, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4928, loss_cls: 4.1570, loss: 4.1570 +2024-07-22 09:51:56,456 - pyskl - INFO - Epoch [10][900/3746] lr: 9.907e-02, eta: 4 days, 10:55:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4881, loss_cls: 4.1599, loss: 4.1599 +2024-07-22 09:53:06,407 - pyskl - INFO - Epoch [10][1000/3746] lr: 9.906e-02, eta: 4 days, 10:53:09, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5003, loss_cls: 4.1326, loss: 4.1326 +2024-07-22 09:54:16,420 - pyskl - INFO - Epoch [10][1100/3746] lr: 9.906e-02, eta: 4 days, 10:51:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4852, loss_cls: 4.1420, loss: 4.1420 +2024-07-22 09:55:26,576 - pyskl - INFO - Epoch [10][1200/3746] lr: 9.905e-02, eta: 4 days, 10:49:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4892, loss_cls: 4.1452, loss: 4.1452 +2024-07-22 09:56:36,286 - pyskl - INFO - Epoch [10][1300/3746] lr: 9.905e-02, eta: 4 days, 10:47:13, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4842, loss_cls: 4.1841, loss: 4.1841 +2024-07-22 09:57:45,970 - pyskl - INFO - Epoch [10][1400/3746] lr: 9.904e-02, eta: 4 days, 10:45:11, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4772, loss_cls: 4.1710, loss: 4.1710 +2024-07-22 09:58:55,991 - pyskl - INFO - Epoch [10][1500/3746] lr: 9.903e-02, eta: 4 days, 10:43:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4909, loss_cls: 4.1477, loss: 4.1477 +2024-07-22 10:00:06,230 - pyskl - INFO - Epoch [10][1600/3746] lr: 9.903e-02, eta: 4 days, 10:41:21, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4791, loss_cls: 4.1698, loss: 4.1698 +2024-07-22 10:01:16,003 - pyskl - INFO - Epoch [10][1700/3746] lr: 9.902e-02, eta: 4 days, 10:39:21, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4928, loss_cls: 4.1372, loss: 4.1372 +2024-07-22 10:02:26,390 - pyskl - INFO - Epoch [10][1800/3746] lr: 9.902e-02, eta: 4 days, 10:37:30, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4861, loss_cls: 4.1603, loss: 4.1603 +2024-07-22 10:03:36,701 - pyskl - INFO - Epoch [10][1900/3746] lr: 9.901e-02, eta: 4 days, 10:35:39, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4911, loss_cls: 4.1791, loss: 4.1791 +2024-07-22 10:04:46,789 - pyskl - INFO - Epoch [10][2000/3746] lr: 9.901e-02, eta: 4 days, 10:33:44, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5012, loss_cls: 4.1099, loss: 4.1099 +2024-07-22 10:05:57,092 - pyskl - INFO - Epoch [10][2100/3746] lr: 9.900e-02, eta: 4 days, 10:31:53, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4881, loss_cls: 4.1433, loss: 4.1433 +2024-07-22 10:07:07,380 - pyskl - INFO - Epoch [10][2200/3746] lr: 9.900e-02, eta: 4 days, 10:30:02, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4906, loss_cls: 4.1357, loss: 4.1357 +2024-07-22 10:08:17,669 - pyskl - INFO - Epoch [10][2300/3746] lr: 9.899e-02, eta: 4 days, 10:28:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4959, loss_cls: 4.1440, loss: 4.1440 +2024-07-22 10:09:27,653 - pyskl - INFO - Epoch [10][2400/3746] lr: 9.898e-02, eta: 4 days, 10:26:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5045, loss_cls: 4.0823, loss: 4.0823 +2024-07-22 10:10:37,855 - pyskl - INFO - Epoch [10][2500/3746] lr: 9.898e-02, eta: 4 days, 10:24:24, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4913, loss_cls: 4.1695, loss: 4.1695 +2024-07-22 10:11:47,926 - pyskl - INFO - Epoch [10][2600/3746] lr: 9.897e-02, eta: 4 days, 10:22:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4930, loss_cls: 4.1314, loss: 4.1314 +2024-07-22 10:12:57,870 - pyskl - INFO - Epoch [10][2700/3746] lr: 9.897e-02, eta: 4 days, 10:20:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4919, loss_cls: 4.1155, loss: 4.1155 +2024-07-22 10:14:07,787 - pyskl - INFO - Epoch [10][2800/3746] lr: 9.896e-02, eta: 4 days, 10:18:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4920, loss_cls: 4.1204, loss: 4.1204 +2024-07-22 10:15:17,496 - pyskl - INFO - Epoch [10][2900/3746] lr: 9.896e-02, eta: 4 days, 10:16:43, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4909, loss_cls: 4.1375, loss: 4.1375 +2024-07-22 10:16:27,254 - pyskl - INFO - Epoch [10][3000/3746] lr: 9.895e-02, eta: 4 days, 10:14:46, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4978, loss_cls: 4.1315, loss: 4.1315 +2024-07-22 10:17:37,179 - pyskl - INFO - Epoch [10][3100/3746] lr: 9.894e-02, eta: 4 days, 10:12:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4867, loss_cls: 4.1420, loss: 4.1420 +2024-07-22 10:18:47,072 - pyskl - INFO - Epoch [10][3200/3746] lr: 9.894e-02, eta: 4 days, 10:10:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4848, loss_cls: 4.1767, loss: 4.1767 +2024-07-22 10:19:57,461 - pyskl - INFO - Epoch [10][3300/3746] lr: 9.893e-02, eta: 4 days, 10:09:10, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4955, loss_cls: 4.1417, loss: 4.1417 +2024-07-22 10:21:07,345 - pyskl - INFO - Epoch [10][3400/3746] lr: 9.893e-02, eta: 4 days, 10:07:16, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4848, loss_cls: 4.1581, loss: 4.1581 +2024-07-22 10:22:17,992 - pyskl - INFO - Epoch [10][3500/3746] lr: 9.892e-02, eta: 4 days, 10:05:33, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.4942, loss_cls: 4.1227, loss: 4.1227 +2024-07-22 10:23:27,994 - pyskl - INFO - Epoch [10][3600/3746] lr: 9.892e-02, eta: 4 days, 10:03:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.4967, loss_cls: 4.1045, loss: 4.1045 +2024-07-22 10:24:38,484 - pyskl - INFO - Epoch [10][3700/3746] lr: 9.891e-02, eta: 4 days, 10:01:56, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4928, loss_cls: 4.1099, loss: 4.1099 +2024-07-22 10:25:13,000 - pyskl - INFO - Saving checkpoint at 10 epochs +2024-07-22 10:27:05,300 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 10:27:05,959 - pyskl - INFO - +top1_acc 0.1677 +top5_acc 0.3770 +2024-07-22 10:27:05,959 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 10:27:05,998 - pyskl - INFO - +mean_acc 0.1677 +2024-07-22 10:27:06,009 - pyskl - INFO - Epoch(val) [10][309] top1_acc: 0.1677, top5_acc: 0.3770, mean_class_accuracy: 0.1677 +2024-07-22 10:30:26,283 - pyskl - INFO - Epoch [11][100/3746] lr: 9.890e-02, eta: 4 days, 10:22:02, time: 2.003, data_time: 1.297, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5059, loss_cls: 4.0656, loss: 4.0656 +2024-07-22 10:31:37,226 - pyskl - INFO - Epoch [11][200/3746] lr: 9.890e-02, eta: 4 days, 10:20:20, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4856, loss_cls: 4.1788, loss: 4.1788 +2024-07-22 10:32:47,570 - pyskl - INFO - Epoch [11][300/3746] lr: 9.889e-02, eta: 4 days, 10:18:30, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5169, loss_cls: 4.0281, loss: 4.0281 +2024-07-22 10:33:57,783 - pyskl - INFO - Epoch [11][400/3746] lr: 9.888e-02, eta: 4 days, 10:16:38, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4945, loss_cls: 4.1164, loss: 4.1164 +2024-07-22 10:35:07,999 - pyskl - INFO - Epoch [11][500/3746] lr: 9.888e-02, eta: 4 days, 10:14:46, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5031, loss_cls: 4.1073, loss: 4.1073 +2024-07-22 10:36:18,206 - pyskl - INFO - Epoch [11][600/3746] lr: 9.887e-02, eta: 4 days, 10:12:55, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4964, loss_cls: 4.1240, loss: 4.1240 +2024-07-22 10:37:28,513 - pyskl - INFO - Epoch [11][700/3746] lr: 9.887e-02, eta: 4 days, 10:11:05, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5125, loss_cls: 4.0980, loss: 4.0980 +2024-07-22 10:38:38,600 - pyskl - INFO - Epoch [11][800/3746] lr: 9.886e-02, eta: 4 days, 10:09:12, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4878, loss_cls: 4.1378, loss: 4.1378 +2024-07-22 10:39:48,468 - pyskl - INFO - Epoch [11][900/3746] lr: 9.885e-02, eta: 4 days, 10:07:17, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4914, loss_cls: 4.1400, loss: 4.1400 +2024-07-22 10:40:58,824 - pyskl - INFO - Epoch [11][1000/3746] lr: 9.885e-02, eta: 4 days, 10:05:28, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4920, loss_cls: 4.1133, loss: 4.1133 +2024-07-22 10:42:08,904 - pyskl - INFO - Epoch [11][1100/3746] lr: 9.884e-02, eta: 4 days, 10:03:36, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5106, loss_cls: 4.0491, loss: 4.0491 +2024-07-22 10:43:18,656 - pyskl - INFO - Epoch [11][1200/3746] lr: 9.884e-02, eta: 4 days, 10:01:40, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5000, loss_cls: 4.1300, loss: 4.1300 +2024-07-22 10:44:28,755 - pyskl - INFO - Epoch [11][1300/3746] lr: 9.883e-02, eta: 4 days, 9:59:48, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4864, loss_cls: 4.1904, loss: 4.1904 +2024-07-22 10:45:38,795 - pyskl - INFO - Epoch [11][1400/3746] lr: 9.882e-02, eta: 4 days, 9:57:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4936, loss_cls: 4.1345, loss: 4.1345 +2024-07-22 10:46:48,516 - pyskl - INFO - Epoch [11][1500/3746] lr: 9.882e-02, eta: 4 days, 9:56:00, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.4933, loss_cls: 4.1350, loss: 4.1350 +2024-07-22 10:47:58,498 - pyskl - INFO - Epoch [11][1600/3746] lr: 9.881e-02, eta: 4 days, 9:54:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4908, loss_cls: 4.1238, loss: 4.1238 +2024-07-22 10:49:08,919 - pyskl - INFO - Epoch [11][1700/3746] lr: 9.881e-02, eta: 4 days, 9:52:21, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4795, loss_cls: 4.2023, loss: 4.2023 +2024-07-22 10:50:18,829 - pyskl - INFO - Epoch [11][1800/3746] lr: 9.880e-02, eta: 4 days, 9:50:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4955, loss_cls: 4.1534, loss: 4.1534 +2024-07-22 10:51:29,015 - pyskl - INFO - Epoch [11][1900/3746] lr: 9.879e-02, eta: 4 days, 9:48:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5017, loss_cls: 4.1205, loss: 4.1205 +2024-07-22 10:52:39,176 - pyskl - INFO - Epoch [11][2000/3746] lr: 9.879e-02, eta: 4 days, 9:46:50, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4900, loss_cls: 4.1337, loss: 4.1337 +2024-07-22 10:53:49,381 - pyskl - INFO - Epoch [11][2100/3746] lr: 9.878e-02, eta: 4 days, 9:45:02, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4853, loss_cls: 4.1771, loss: 4.1771 +2024-07-22 10:54:59,410 - pyskl - INFO - Epoch [11][2200/3746] lr: 9.878e-02, eta: 4 days, 9:43:12, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4995, loss_cls: 4.1174, loss: 4.1174 +2024-07-22 10:56:09,451 - pyskl - INFO - Epoch [11][2300/3746] lr: 9.877e-02, eta: 4 days, 9:41:21, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4941, loss_cls: 4.1284, loss: 4.1284 +2024-07-22 10:57:19,567 - pyskl - INFO - Epoch [11][2400/3746] lr: 9.876e-02, eta: 4 days, 9:39:32, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.4952, loss_cls: 4.1271, loss: 4.1271 +2024-07-22 10:58:29,683 - pyskl - INFO - Epoch [11][2500/3746] lr: 9.876e-02, eta: 4 days, 9:37:44, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4967, loss_cls: 4.1019, loss: 4.1019 +2024-07-22 10:59:39,516 - pyskl - INFO - Epoch [11][2600/3746] lr: 9.875e-02, eta: 4 days, 9:35:51, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4850, loss_cls: 4.1586, loss: 4.1586 +2024-07-22 11:00:49,397 - pyskl - INFO - Epoch [11][2700/3746] lr: 9.874e-02, eta: 4 days, 9:34:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4898, loss_cls: 4.1694, loss: 4.1694 +2024-07-22 11:01:59,812 - pyskl - INFO - Epoch [11][2800/3746] lr: 9.874e-02, eta: 4 days, 9:32:16, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4947, loss_cls: 4.1469, loss: 4.1469 +2024-07-22 11:03:09,982 - pyskl - INFO - Epoch [11][2900/3746] lr: 9.873e-02, eta: 4 days, 9:30:28, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.5053, loss_cls: 4.0960, loss: 4.0960 +2024-07-22 11:04:19,976 - pyskl - INFO - Epoch [11][3000/3746] lr: 9.873e-02, eta: 4 days, 9:28:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4903, loss_cls: 4.1487, loss: 4.1487 +2024-07-22 11:05:29,716 - pyskl - INFO - Epoch [11][3100/3746] lr: 9.872e-02, eta: 4 days, 9:26:47, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.4956, loss_cls: 4.1036, loss: 4.1036 +2024-07-22 11:06:39,539 - pyskl - INFO - Epoch [11][3200/3746] lr: 9.871e-02, eta: 4 days, 9:24:55, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5006, loss_cls: 4.1158, loss: 4.1158 +2024-07-22 11:07:49,469 - pyskl - INFO - Epoch [11][3300/3746] lr: 9.871e-02, eta: 4 days, 9:23:06, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.4995, loss_cls: 4.1030, loss: 4.1030 +2024-07-22 11:08:59,551 - pyskl - INFO - Epoch [11][3400/3746] lr: 9.870e-02, eta: 4 days, 9:21:18, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4975, loss_cls: 4.1028, loss: 4.1028 +2024-07-22 11:10:10,303 - pyskl - INFO - Epoch [11][3500/3746] lr: 9.869e-02, eta: 4 days, 9:19:40, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4888, loss_cls: 4.1494, loss: 4.1494 +2024-07-22 11:11:20,410 - pyskl - INFO - Epoch [11][3600/3746] lr: 9.869e-02, eta: 4 days, 9:17:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4945, loss_cls: 4.1421, loss: 4.1421 +2024-07-22 11:12:30,277 - pyskl - INFO - Epoch [11][3700/3746] lr: 9.868e-02, eta: 4 days, 9:16:03, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4938, loss_cls: 4.1276, loss: 4.1276 +2024-07-22 11:13:04,843 - pyskl - INFO - Saving checkpoint at 11 epochs +2024-07-22 11:14:57,264 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 11:14:57,943 - pyskl - INFO - +top1_acc 0.1715 +top5_acc 0.3849 +2024-07-22 11:14:57,943 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 11:14:57,982 - pyskl - INFO - +mean_acc 0.1713 +2024-07-22 11:14:57,992 - pyskl - INFO - Epoch(val) [11][309] top1_acc: 0.1715, top5_acc: 0.3849, mean_class_accuracy: 0.1713 +2024-07-22 11:18:17,782 - pyskl - INFO - Epoch [12][100/3746] lr: 9.867e-02, eta: 4 days, 9:33:56, time: 1.998, data_time: 1.292, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4969, loss_cls: 4.1457, loss: 4.1457 +2024-07-22 11:19:28,392 - pyskl - INFO - Epoch [12][200/3746] lr: 9.867e-02, eta: 4 days, 9:32:13, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.4991, loss_cls: 4.1267, loss: 4.1267 +2024-07-22 11:20:38,892 - pyskl - INFO - Epoch [12][300/3746] lr: 9.866e-02, eta: 4 days, 9:30:28, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4975, loss_cls: 4.1028, loss: 4.1028 +2024-07-22 11:21:49,471 - pyskl - INFO - Epoch [12][400/3746] lr: 9.865e-02, eta: 4 days, 9:28:45, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5031, loss_cls: 4.0749, loss: 4.0749 +2024-07-22 11:22:59,505 - pyskl - INFO - Epoch [12][500/3746] lr: 9.865e-02, eta: 4 days, 9:26:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4970, loss_cls: 4.1048, loss: 4.1048 +2024-07-22 11:24:09,544 - pyskl - INFO - Epoch [12][600/3746] lr: 9.864e-02, eta: 4 days, 9:25:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.4989, loss_cls: 4.0566, loss: 4.0566 +2024-07-22 11:25:19,827 - pyskl - INFO - Epoch [12][700/3746] lr: 9.863e-02, eta: 4 days, 9:23:20, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.4967, loss_cls: 4.0842, loss: 4.0842 +2024-07-22 11:26:29,903 - pyskl - INFO - Epoch [12][800/3746] lr: 9.863e-02, eta: 4 days, 9:21:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4939, loss_cls: 4.1241, loss: 4.1241 +2024-07-22 11:27:39,808 - pyskl - INFO - Epoch [12][900/3746] lr: 9.862e-02, eta: 4 days, 9:19:40, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5038, loss_cls: 4.0741, loss: 4.0741 +2024-07-22 11:28:50,178 - pyskl - INFO - Epoch [12][1000/3746] lr: 9.861e-02, eta: 4 days, 9:17:56, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4989, loss_cls: 4.1200, loss: 4.1200 +2024-07-22 11:30:00,286 - pyskl - INFO - Epoch [12][1100/3746] lr: 9.861e-02, eta: 4 days, 9:16:08, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4845, loss_cls: 4.1677, loss: 4.1677 +2024-07-22 11:31:10,438 - pyskl - INFO - Epoch [12][1200/3746] lr: 9.860e-02, eta: 4 days, 9:14:21, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5055, loss_cls: 4.1039, loss: 4.1039 +2024-07-22 11:32:20,475 - pyskl - INFO - Epoch [12][1300/3746] lr: 9.859e-02, eta: 4 days, 9:12:33, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4892, loss_cls: 4.1320, loss: 4.1320 +2024-07-22 11:33:30,637 - pyskl - INFO - Epoch [12][1400/3746] lr: 9.859e-02, eta: 4 days, 9:10:46, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.4925, loss_cls: 4.1262, loss: 4.1262 +2024-07-22 11:34:40,679 - pyskl - INFO - Epoch [12][1500/3746] lr: 9.858e-02, eta: 4 days, 9:08:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.4948, loss_cls: 4.1297, loss: 4.1297 +2024-07-22 11:35:50,788 - pyskl - INFO - Epoch [12][1600/3746] lr: 9.857e-02, eta: 4 days, 9:07:11, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.4916, loss_cls: 4.1503, loss: 4.1503 +2024-07-22 11:37:00,903 - pyskl - INFO - Epoch [12][1700/3746] lr: 9.857e-02, eta: 4 days, 9:05:25, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5095, loss_cls: 4.0920, loss: 4.0920 +2024-07-22 11:38:11,230 - pyskl - INFO - Epoch [12][1800/3746] lr: 9.856e-02, eta: 4 days, 9:03:41, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4967, loss_cls: 4.1246, loss: 4.1246 +2024-07-22 11:39:21,238 - pyskl - INFO - Epoch [12][1900/3746] lr: 9.855e-02, eta: 4 days, 9:01:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4891, loss_cls: 4.1334, loss: 4.1334 +2024-07-22 11:40:31,242 - pyskl - INFO - Epoch [12][2000/3746] lr: 9.855e-02, eta: 4 days, 9:00:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4986, loss_cls: 4.1199, loss: 4.1199 +2024-07-22 11:41:41,650 - pyskl - INFO - Epoch [12][2100/3746] lr: 9.854e-02, eta: 4 days, 8:58:23, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.4997, loss_cls: 4.0803, loss: 4.0803 +2024-07-22 11:42:51,811 - pyskl - INFO - Epoch [12][2200/3746] lr: 9.853e-02, eta: 4 days, 8:56:38, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.4958, loss_cls: 4.0981, loss: 4.0981 +2024-07-22 11:44:01,981 - pyskl - INFO - Epoch [12][2300/3746] lr: 9.853e-02, eta: 4 days, 8:54:53, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5050, loss_cls: 4.0986, loss: 4.0986 +2024-07-22 11:45:12,113 - pyskl - INFO - Epoch [12][2400/3746] lr: 9.852e-02, eta: 4 days, 8:53:08, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.5073, loss_cls: 4.0909, loss: 4.0909 +2024-07-22 11:46:22,225 - pyskl - INFO - Epoch [12][2500/3746] lr: 9.851e-02, eta: 4 days, 8:51:22, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.4973, loss_cls: 4.1270, loss: 4.1270 +2024-07-22 11:47:32,696 - pyskl - INFO - Epoch [12][2600/3746] lr: 9.851e-02, eta: 4 days, 8:49:41, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4888, loss_cls: 4.1334, loss: 4.1334 +2024-07-22 11:48:42,676 - pyskl - INFO - Epoch [12][2700/3746] lr: 9.850e-02, eta: 4 days, 8:47:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5109, loss_cls: 4.0715, loss: 4.0715 +2024-07-22 11:49:52,950 - pyskl - INFO - Epoch [12][2800/3746] lr: 9.849e-02, eta: 4 days, 8:46:12, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4880, loss_cls: 4.1621, loss: 4.1621 +2024-07-22 11:51:03,237 - pyskl - INFO - Epoch [12][2900/3746] lr: 9.849e-02, eta: 4 days, 8:44:29, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4991, loss_cls: 4.1207, loss: 4.1207 +2024-07-22 11:52:13,087 - pyskl - INFO - Epoch [12][3000/3746] lr: 9.848e-02, eta: 4 days, 8:42:42, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4863, loss_cls: 4.1687, loss: 4.1687 +2024-07-22 11:53:23,491 - pyskl - INFO - Epoch [12][3100/3746] lr: 9.847e-02, eta: 4 days, 8:41:01, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4964, loss_cls: 4.1375, loss: 4.1375 +2024-07-22 11:54:33,612 - pyskl - INFO - Epoch [12][3200/3746] lr: 9.847e-02, eta: 4 days, 8:39:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4955, loss_cls: 4.1293, loss: 4.1293 +2024-07-22 11:55:43,747 - pyskl - INFO - Epoch [12][3300/3746] lr: 9.846e-02, eta: 4 days, 8:37:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4930, loss_cls: 4.1161, loss: 4.1161 +2024-07-22 11:56:53,747 - pyskl - INFO - Epoch [12][3400/3746] lr: 9.845e-02, eta: 4 days, 8:35:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5006, loss_cls: 4.1032, loss: 4.1032 +2024-07-22 11:58:04,342 - pyskl - INFO - Epoch [12][3500/3746] lr: 9.845e-02, eta: 4 days, 8:34:09, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5050, loss_cls: 4.0965, loss: 4.0965 +2024-07-22 11:59:14,582 - pyskl - INFO - Epoch [12][3600/3746] lr: 9.844e-02, eta: 4 days, 8:32:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5070, loss_cls: 4.0623, loss: 4.0623 +2024-07-22 12:00:24,958 - pyskl - INFO - Epoch [12][3700/3746] lr: 9.843e-02, eta: 4 days, 8:30:46, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4903, loss_cls: 4.1385, loss: 4.1385 +2024-07-22 12:00:59,461 - pyskl - INFO - Saving checkpoint at 12 epochs +2024-07-22 12:02:52,552 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 12:02:53,228 - pyskl - INFO - +top1_acc 0.1915 +top5_acc 0.4149 +2024-07-22 12:02:53,228 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 12:02:53,265 - pyskl - INFO - +mean_acc 0.1912 +2024-07-22 12:02:53,270 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_6.pth was removed +2024-07-22 12:02:53,516 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_12.pth. +2024-07-22 12:02:53,517 - pyskl - INFO - Best top1_acc is 0.1915 at 12 epoch. +2024-07-22 12:02:53,528 - pyskl - INFO - Epoch(val) [12][309] top1_acc: 0.1915, top5_acc: 0.4149, mean_class_accuracy: 0.1912 +2024-07-22 12:06:11,055 - pyskl - INFO - Epoch [13][100/3746] lr: 9.842e-02, eta: 4 days, 8:46:27, time: 1.975, data_time: 1.263, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5044, loss_cls: 4.0683, loss: 4.0683 +2024-07-22 12:07:21,303 - pyskl - INFO - Epoch [13][200/3746] lr: 9.842e-02, eta: 4 days, 8:44:43, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5109, loss_cls: 4.0361, loss: 4.0361 +2024-07-22 12:08:32,146 - pyskl - INFO - Epoch [13][300/3746] lr: 9.841e-02, eta: 4 days, 8:43:06, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5042, loss_cls: 4.0906, loss: 4.0906 +2024-07-22 12:09:42,601 - pyskl - INFO - Epoch [13][400/3746] lr: 9.840e-02, eta: 4 days, 8:41:24, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5139, loss_cls: 4.0393, loss: 4.0393 +2024-07-22 12:10:52,693 - pyskl - INFO - Epoch [13][500/3746] lr: 9.839e-02, eta: 4 days, 8:39:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4955, loss_cls: 4.0883, loss: 4.0883 +2024-07-22 12:12:03,207 - pyskl - INFO - Epoch [13][600/3746] lr: 9.839e-02, eta: 4 days, 8:37:58, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4989, loss_cls: 4.1134, loss: 4.1134 +2024-07-22 12:13:13,500 - pyskl - INFO - Epoch [13][700/3746] lr: 9.838e-02, eta: 4 days, 8:36:15, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.5006, loss_cls: 4.1062, loss: 4.1062 +2024-07-22 12:14:23,467 - pyskl - INFO - Epoch [13][800/3746] lr: 9.837e-02, eta: 4 days, 8:34:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4925, loss_cls: 4.1175, loss: 4.1175 +2024-07-22 12:15:33,642 - pyskl - INFO - Epoch [13][900/3746] lr: 9.837e-02, eta: 4 days, 8:32:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4972, loss_cls: 4.1247, loss: 4.1247 +2024-07-22 12:16:43,601 - pyskl - INFO - Epoch [13][1000/3746] lr: 9.836e-02, eta: 4 days, 8:30:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5048, loss_cls: 4.1011, loss: 4.1011 +2024-07-22 12:17:54,300 - pyskl - INFO - Epoch [13][1100/3746] lr: 9.835e-02, eta: 4 days, 8:29:20, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.4977, loss_cls: 4.1292, loss: 4.1292 +2024-07-22 12:19:04,539 - pyskl - INFO - Epoch [13][1200/3746] lr: 9.834e-02, eta: 4 days, 8:27:37, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.4938, loss_cls: 4.0909, loss: 4.0909 +2024-07-22 12:20:14,684 - pyskl - INFO - Epoch [13][1300/3746] lr: 9.834e-02, eta: 4 days, 8:25:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5047, loss_cls: 4.0744, loss: 4.0744 +2024-07-22 12:21:24,978 - pyskl - INFO - Epoch [13][1400/3746] lr: 9.833e-02, eta: 4 days, 8:24:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4980, loss_cls: 4.1268, loss: 4.1268 +2024-07-22 12:22:34,894 - pyskl - INFO - Epoch [13][1500/3746] lr: 9.832e-02, eta: 4 days, 8:22:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5023, loss_cls: 4.0896, loss: 4.0896 +2024-07-22 12:23:44,712 - pyskl - INFO - Epoch [13][1600/3746] lr: 9.832e-02, eta: 4 days, 8:20:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.5022, loss_cls: 4.0968, loss: 4.0968 +2024-07-22 12:24:55,175 - pyskl - INFO - Epoch [13][1700/3746] lr: 9.831e-02, eta: 4 days, 8:18:59, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5005, loss_cls: 4.1089, loss: 4.1089 +2024-07-22 12:26:05,084 - pyskl - INFO - Epoch [13][1800/3746] lr: 9.830e-02, eta: 4 days, 8:17:13, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5066, loss_cls: 4.0798, loss: 4.0798 +2024-07-22 12:27:15,022 - pyskl - INFO - Epoch [13][1900/3746] lr: 9.829e-02, eta: 4 days, 8:15:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4909, loss_cls: 4.1299, loss: 4.1299 +2024-07-22 12:28:25,394 - pyskl - INFO - Epoch [13][2000/3746] lr: 9.829e-02, eta: 4 days, 8:13:47, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4992, loss_cls: 4.1651, loss: 4.1651 +2024-07-22 12:29:35,186 - pyskl - INFO - Epoch [13][2100/3746] lr: 9.828e-02, eta: 4 days, 8:12:01, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4809, loss_cls: 4.1827, loss: 4.1827 +2024-07-22 12:30:45,283 - pyskl - INFO - Epoch [13][2200/3746] lr: 9.827e-02, eta: 4 days, 8:10:18, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4938, loss_cls: 4.1367, loss: 4.1367 +2024-07-22 12:31:55,182 - pyskl - INFO - Epoch [13][2300/3746] lr: 9.827e-02, eta: 4 days, 8:08:33, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4991, loss_cls: 4.1427, loss: 4.1427 +2024-07-22 12:33:05,071 - pyskl - INFO - Epoch [13][2400/3746] lr: 9.826e-02, eta: 4 days, 8:06:48, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4950, loss_cls: 4.1385, loss: 4.1385 +2024-07-22 12:34:15,350 - pyskl - INFO - Epoch [13][2500/3746] lr: 9.825e-02, eta: 4 days, 8:05:07, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4945, loss_cls: 4.1095, loss: 4.1095 +2024-07-22 12:35:25,469 - pyskl - INFO - Epoch [13][2600/3746] lr: 9.824e-02, eta: 4 days, 8:03:25, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4997, loss_cls: 4.0968, loss: 4.0968 +2024-07-22 12:36:35,443 - pyskl - INFO - Epoch [13][2700/3746] lr: 9.824e-02, eta: 4 days, 8:01:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5012, loss_cls: 4.1089, loss: 4.1089 +2024-07-22 12:37:45,374 - pyskl - INFO - Epoch [13][2800/3746] lr: 9.823e-02, eta: 4 days, 7:59:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4967, loss_cls: 4.1178, loss: 4.1178 +2024-07-22 12:38:55,504 - pyskl - INFO - Epoch [13][2900/3746] lr: 9.822e-02, eta: 4 days, 7:58:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4986, loss_cls: 4.1016, loss: 4.1016 +2024-07-22 12:40:06,215 - pyskl - INFO - Epoch [13][3000/3746] lr: 9.821e-02, eta: 4 days, 7:56:40, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5072, loss_cls: 4.0667, loss: 4.0667 +2024-07-22 12:41:16,472 - pyskl - INFO - Epoch [13][3100/3746] lr: 9.821e-02, eta: 4 days, 7:55:00, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.4977, loss_cls: 4.0993, loss: 4.0993 +2024-07-22 12:42:27,021 - pyskl - INFO - Epoch [13][3200/3746] lr: 9.820e-02, eta: 4 days, 7:53:23, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4998, loss_cls: 4.1129, loss: 4.1129 +2024-07-22 12:43:37,211 - pyskl - INFO - Epoch [13][3300/3746] lr: 9.819e-02, eta: 4 days, 7:51:43, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5005, loss_cls: 4.0990, loss: 4.0990 +2024-07-22 12:44:47,268 - pyskl - INFO - Epoch [13][3400/3746] lr: 9.818e-02, eta: 4 days, 7:50:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5081, loss_cls: 4.0905, loss: 4.0905 +2024-07-22 12:45:57,664 - pyskl - INFO - Epoch [13][3500/3746] lr: 9.818e-02, eta: 4 days, 7:48:23, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.4989, loss_cls: 4.0911, loss: 4.0911 +2024-07-22 12:47:07,836 - pyskl - INFO - Epoch [13][3600/3746] lr: 9.817e-02, eta: 4 days, 7:46:42, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4841, loss_cls: 4.1514, loss: 4.1514 +2024-07-22 12:48:18,119 - pyskl - INFO - Epoch [13][3700/3746] lr: 9.816e-02, eta: 4 days, 7:45:03, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4913, loss_cls: 4.1065, loss: 4.1065 +2024-07-22 12:48:52,903 - pyskl - INFO - Saving checkpoint at 13 epochs +2024-07-22 12:50:47,233 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 12:50:47,912 - pyskl - INFO - +top1_acc 0.1843 +top5_acc 0.4078 +2024-07-22 12:50:47,912 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 12:50:47,954 - pyskl - INFO - +mean_acc 0.1841 +2024-07-22 12:50:47,966 - pyskl - INFO - Epoch(val) [13][309] top1_acc: 0.1843, top5_acc: 0.4078, mean_class_accuracy: 0.1841 +2024-07-22 12:54:06,362 - pyskl - INFO - Epoch [14][100/3746] lr: 9.815e-02, eta: 4 days, 7:59:26, time: 1.984, data_time: 1.280, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5005, loss_cls: 4.0461, loss: 4.0461 +2024-07-22 12:55:17,259 - pyskl - INFO - Epoch [14][200/3746] lr: 9.814e-02, eta: 4 days, 7:57:51, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5056, loss_cls: 4.0633, loss: 4.0633 +2024-07-22 12:56:27,820 - pyskl - INFO - Epoch [14][300/3746] lr: 9.814e-02, eta: 4 days, 7:56:13, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5142, loss_cls: 4.0545, loss: 4.0545 +2024-07-22 12:57:38,347 - pyskl - INFO - Epoch [14][400/3746] lr: 9.813e-02, eta: 4 days, 7:54:35, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4944, loss_cls: 4.1149, loss: 4.1149 +2024-07-22 12:58:48,588 - pyskl - INFO - Epoch [14][500/3746] lr: 9.812e-02, eta: 4 days, 7:52:53, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5038, loss_cls: 4.0918, loss: 4.0918 +2024-07-22 12:59:58,409 - pyskl - INFO - Epoch [14][600/3746] lr: 9.811e-02, eta: 4 days, 7:51:08, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5030, loss_cls: 4.1134, loss: 4.1134 +2024-07-22 13:01:08,451 - pyskl - INFO - Epoch [14][700/3746] lr: 9.811e-02, eta: 4 days, 7:49:25, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5069, loss_cls: 4.1029, loss: 4.1029 +2024-07-22 13:02:18,641 - pyskl - INFO - Epoch [14][800/3746] lr: 9.810e-02, eta: 4 days, 7:47:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4913, loss_cls: 4.1164, loss: 4.1164 +2024-07-22 13:03:28,963 - pyskl - INFO - Epoch [14][900/3746] lr: 9.809e-02, eta: 4 days, 7:46:04, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5091, loss_cls: 4.0637, loss: 4.0637 +2024-07-22 13:04:39,270 - pyskl - INFO - Epoch [14][1000/3746] lr: 9.808e-02, eta: 4 days, 7:44:24, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5073, loss_cls: 4.0584, loss: 4.0584 +2024-07-22 13:05:49,398 - pyskl - INFO - Epoch [14][1100/3746] lr: 9.807e-02, eta: 4 days, 7:42:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5009, loss_cls: 4.0859, loss: 4.0859 +2024-07-22 13:06:59,679 - pyskl - INFO - Epoch [14][1200/3746] lr: 9.807e-02, eta: 4 days, 7:41:03, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5039, loss_cls: 4.1037, loss: 4.1037 +2024-07-22 13:08:09,768 - pyskl - INFO - Epoch [14][1300/3746] lr: 9.806e-02, eta: 4 days, 7:39:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5033, loss_cls: 4.1076, loss: 4.1076 +2024-07-22 13:09:20,031 - pyskl - INFO - Epoch [14][1400/3746] lr: 9.805e-02, eta: 4 days, 7:37:41, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4942, loss_cls: 4.1274, loss: 4.1274 +2024-07-22 13:10:30,110 - pyskl - INFO - Epoch [14][1500/3746] lr: 9.804e-02, eta: 4 days, 7:36:00, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5016, loss_cls: 4.0907, loss: 4.0907 +2024-07-22 13:11:40,234 - pyskl - INFO - Epoch [14][1600/3746] lr: 9.804e-02, eta: 4 days, 7:34:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.5045, loss_cls: 4.0834, loss: 4.0834 +2024-07-22 13:12:50,284 - pyskl - INFO - Epoch [14][1700/3746] lr: 9.803e-02, eta: 4 days, 7:32:37, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5069, loss_cls: 4.0906, loss: 4.0906 +2024-07-22 13:14:00,723 - pyskl - INFO - Epoch [14][1800/3746] lr: 9.802e-02, eta: 4 days, 7:30:59, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4967, loss_cls: 4.1020, loss: 4.1020 +2024-07-22 13:15:11,076 - pyskl - INFO - Epoch [14][1900/3746] lr: 9.801e-02, eta: 4 days, 7:29:21, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5106, loss_cls: 4.0734, loss: 4.0734 +2024-07-22 13:16:21,168 - pyskl - INFO - Epoch [14][2000/3746] lr: 9.800e-02, eta: 4 days, 7:27:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5005, loss_cls: 4.0834, loss: 4.0834 +2024-07-22 13:17:31,508 - pyskl - INFO - Epoch [14][2100/3746] lr: 9.800e-02, eta: 4 days, 7:26:02, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5011, loss_cls: 4.1299, loss: 4.1299 +2024-07-22 13:18:41,507 - pyskl - INFO - Epoch [14][2200/3746] lr: 9.799e-02, eta: 4 days, 7:24:21, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4942, loss_cls: 4.0982, loss: 4.0982 +2024-07-22 13:19:51,238 - pyskl - INFO - Epoch [14][2300/3746] lr: 9.798e-02, eta: 4 days, 7:22:36, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5084, loss_cls: 4.0869, loss: 4.0869 +2024-07-22 13:21:01,777 - pyskl - INFO - Epoch [14][2400/3746] lr: 9.797e-02, eta: 4 days, 7:21:01, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5053, loss_cls: 4.0938, loss: 4.0938 +2024-07-22 13:22:12,149 - pyskl - INFO - Epoch [14][2500/3746] lr: 9.797e-02, eta: 4 days, 7:19:23, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.4984, loss_cls: 4.0732, loss: 4.0732 +2024-07-22 13:23:22,012 - pyskl - INFO - Epoch [14][2600/3746] lr: 9.796e-02, eta: 4 days, 7:17:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5089, loss_cls: 4.0723, loss: 4.0723 +2024-07-22 13:24:32,111 - pyskl - INFO - Epoch [14][2700/3746] lr: 9.795e-02, eta: 4 days, 7:16:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4916, loss_cls: 4.1022, loss: 4.1022 +2024-07-22 13:25:42,428 - pyskl - INFO - Epoch [14][2800/3746] lr: 9.794e-02, eta: 4 days, 7:14:23, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.4988, loss_cls: 4.1095, loss: 4.1095 +2024-07-22 13:26:52,412 - pyskl - INFO - Epoch [14][2900/3746] lr: 9.793e-02, eta: 4 days, 7:12:42, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.4938, loss_cls: 4.0938, loss: 4.0938 +2024-07-22 13:28:02,529 - pyskl - INFO - Epoch [14][3000/3746] lr: 9.793e-02, eta: 4 days, 7:11:03, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4972, loss_cls: 4.1165, loss: 4.1165 +2024-07-22 13:29:12,410 - pyskl - INFO - Epoch [14][3100/3746] lr: 9.792e-02, eta: 4 days, 7:09:21, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5012, loss_cls: 4.0890, loss: 4.0890 +2024-07-22 13:30:22,576 - pyskl - INFO - Epoch [14][3200/3746] lr: 9.791e-02, eta: 4 days, 7:07:42, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.5061, loss_cls: 4.0952, loss: 4.0952 +2024-07-22 13:31:32,749 - pyskl - INFO - Epoch [14][3300/3746] lr: 9.790e-02, eta: 4 days, 7:06:04, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5008, loss_cls: 4.1039, loss: 4.1039 +2024-07-22 13:32:43,000 - pyskl - INFO - Epoch [14][3400/3746] lr: 9.789e-02, eta: 4 days, 7:04:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5028, loss_cls: 4.0699, loss: 4.0699 +2024-07-22 13:33:53,482 - pyskl - INFO - Epoch [14][3500/3746] lr: 9.789e-02, eta: 4 days, 7:02:51, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.4981, loss_cls: 4.1169, loss: 4.1169 +2024-07-22 13:35:03,792 - pyskl - INFO - Epoch [14][3600/3746] lr: 9.788e-02, eta: 4 days, 7:01:14, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4963, loss_cls: 4.1080, loss: 4.1080 +2024-07-22 13:36:13,821 - pyskl - INFO - Epoch [14][3700/3746] lr: 9.787e-02, eta: 4 days, 6:59:35, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5011, loss_cls: 4.0778, loss: 4.0778 +2024-07-22 13:36:48,440 - pyskl - INFO - Saving checkpoint at 14 epochs +2024-07-22 13:38:41,075 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 13:38:41,741 - pyskl - INFO - +top1_acc 0.1812 +top5_acc 0.4060 +2024-07-22 13:38:41,741 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 13:38:41,782 - pyskl - INFO - +mean_acc 0.1810 +2024-07-22 13:38:41,792 - pyskl - INFO - Epoch(val) [14][309] top1_acc: 0.1812, top5_acc: 0.4060, mean_class_accuracy: 0.1810 +2024-07-22 13:42:00,031 - pyskl - INFO - Epoch [15][100/3746] lr: 9.786e-02, eta: 4 days, 7:12:40, time: 1.982, data_time: 1.278, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5042, loss_cls: 4.0915, loss: 4.0915 +2024-07-22 13:43:10,288 - pyskl - INFO - Epoch [15][200/3746] lr: 9.785e-02, eta: 4 days, 7:11:01, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4966, loss_cls: 4.1207, loss: 4.1207 +2024-07-22 13:44:21,728 - pyskl - INFO - Epoch [15][300/3746] lr: 9.784e-02, eta: 4 days, 7:09:34, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5050, loss_cls: 4.0610, loss: 4.0610 +2024-07-22 13:45:32,176 - pyskl - INFO - Epoch [15][400/3746] lr: 9.783e-02, eta: 4 days, 7:07:57, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5066, loss_cls: 4.0486, loss: 4.0486 +2024-07-22 13:46:42,489 - pyskl - INFO - Epoch [15][500/3746] lr: 9.783e-02, eta: 4 days, 7:06:18, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.4945, loss_cls: 4.0894, loss: 4.0894 +2024-07-22 13:47:52,745 - pyskl - INFO - Epoch [15][600/3746] lr: 9.782e-02, eta: 4 days, 7:04:40, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5028, loss_cls: 4.0844, loss: 4.0844 +2024-07-22 13:49:02,675 - pyskl - INFO - Epoch [15][700/3746] lr: 9.781e-02, eta: 4 days, 7:02:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.4986, loss_cls: 4.1243, loss: 4.1243 +2024-07-22 13:50:13,291 - pyskl - INFO - Epoch [15][800/3746] lr: 9.780e-02, eta: 4 days, 7:01:23, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5000, loss_cls: 4.0717, loss: 4.0717 +2024-07-22 13:51:23,071 - pyskl - INFO - Epoch [15][900/3746] lr: 9.779e-02, eta: 4 days, 6:59:41, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5141, loss_cls: 4.0639, loss: 4.0639 +2024-07-22 13:52:32,999 - pyskl - INFO - Epoch [15][1000/3746] lr: 9.778e-02, eta: 4 days, 6:57:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4978, loss_cls: 4.1180, loss: 4.1180 +2024-07-22 13:53:42,978 - pyskl - INFO - Epoch [15][1100/3746] lr: 9.778e-02, eta: 4 days, 6:56:19, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5061, loss_cls: 4.0865, loss: 4.0865 +2024-07-22 13:54:52,901 - pyskl - INFO - Epoch [15][1200/3746] lr: 9.777e-02, eta: 4 days, 6:54:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5059, loss_cls: 4.0955, loss: 4.0955 +2024-07-22 13:56:02,574 - pyskl - INFO - Epoch [15][1300/3746] lr: 9.776e-02, eta: 4 days, 6:52:54, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4845, loss_cls: 4.1582, loss: 4.1582 +2024-07-22 13:57:12,715 - pyskl - INFO - Epoch [15][1400/3746] lr: 9.775e-02, eta: 4 days, 6:51:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5000, loss_cls: 4.0894, loss: 4.0894 +2024-07-22 13:58:22,577 - pyskl - INFO - Epoch [15][1500/3746] lr: 9.774e-02, eta: 4 days, 6:49:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5069, loss_cls: 4.0784, loss: 4.0784 +2024-07-22 13:59:32,514 - pyskl - INFO - Epoch [15][1600/3746] lr: 9.773e-02, eta: 4 days, 6:47:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5059, loss_cls: 4.0647, loss: 4.0647 +2024-07-22 14:00:42,571 - pyskl - INFO - Epoch [15][1700/3746] lr: 9.773e-02, eta: 4 days, 6:46:14, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4964, loss_cls: 4.1275, loss: 4.1275 +2024-07-22 14:01:52,536 - pyskl - INFO - Epoch [15][1800/3746] lr: 9.772e-02, eta: 4 days, 6:44:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.4978, loss_cls: 4.1080, loss: 4.1080 +2024-07-22 14:03:02,416 - pyskl - INFO - Epoch [15][1900/3746] lr: 9.771e-02, eta: 4 days, 6:42:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5003, loss_cls: 4.1150, loss: 4.1150 +2024-07-22 14:04:12,240 - pyskl - INFO - Epoch [15][2000/3746] lr: 9.770e-02, eta: 4 days, 6:41:13, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5044, loss_cls: 4.0968, loss: 4.0968 +2024-07-22 14:05:22,074 - pyskl - INFO - Epoch [15][2100/3746] lr: 9.769e-02, eta: 4 days, 6:39:32, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4964, loss_cls: 4.0946, loss: 4.0946 +2024-07-22 14:06:31,781 - pyskl - INFO - Epoch [15][2200/3746] lr: 9.768e-02, eta: 4 days, 6:37:50, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5045, loss_cls: 4.0880, loss: 4.0880 +2024-07-22 14:07:41,887 - pyskl - INFO - Epoch [15][2300/3746] lr: 9.768e-02, eta: 4 days, 6:36:12, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4905, loss_cls: 4.1312, loss: 4.1312 +2024-07-22 14:08:51,957 - pyskl - INFO - Epoch [15][2400/3746] lr: 9.767e-02, eta: 4 days, 6:34:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5019, loss_cls: 4.1029, loss: 4.1029 +2024-07-22 14:10:01,984 - pyskl - INFO - Epoch [15][2500/3746] lr: 9.766e-02, eta: 4 days, 6:32:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4906, loss_cls: 4.1298, loss: 4.1298 +2024-07-22 14:11:12,011 - pyskl - INFO - Epoch [15][2600/3746] lr: 9.765e-02, eta: 4 days, 6:31:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5039, loss_cls: 4.0603, loss: 4.0603 +2024-07-22 14:12:21,918 - pyskl - INFO - Epoch [15][2700/3746] lr: 9.764e-02, eta: 4 days, 6:29:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5056, loss_cls: 4.0856, loss: 4.0856 +2024-07-22 14:13:31,702 - pyskl - INFO - Epoch [15][2800/3746] lr: 9.763e-02, eta: 4 days, 6:27:56, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5075, loss_cls: 4.0587, loss: 4.0587 +2024-07-22 14:14:41,669 - pyskl - INFO - Epoch [15][2900/3746] lr: 9.763e-02, eta: 4 days, 6:26:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5153, loss_cls: 4.0597, loss: 4.0597 +2024-07-22 14:15:52,017 - pyskl - INFO - Epoch [15][3000/3746] lr: 9.762e-02, eta: 4 days, 6:24:42, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.5019, loss_cls: 4.0947, loss: 4.0947 +2024-07-22 14:17:01,691 - pyskl - INFO - Epoch [15][3100/3746] lr: 9.761e-02, eta: 4 days, 6:23:00, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4972, loss_cls: 4.1130, loss: 4.1130 +2024-07-22 14:18:11,755 - pyskl - INFO - Epoch [15][3200/3746] lr: 9.760e-02, eta: 4 days, 6:21:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4991, loss_cls: 4.1189, loss: 4.1189 +2024-07-22 14:19:21,580 - pyskl - INFO - Epoch [15][3300/3746] lr: 9.759e-02, eta: 4 days, 6:19:43, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.4970, loss_cls: 4.0736, loss: 4.0736 +2024-07-22 14:20:31,529 - pyskl - INFO - Epoch [15][3400/3746] lr: 9.758e-02, eta: 4 days, 6:18:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5014, loss_cls: 4.0822, loss: 4.0822 +2024-07-22 14:21:41,853 - pyskl - INFO - Epoch [15][3500/3746] lr: 9.757e-02, eta: 4 days, 6:16:30, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.4969, loss_cls: 4.1089, loss: 4.1089 +2024-07-22 14:22:51,773 - pyskl - INFO - Epoch [15][3600/3746] lr: 9.757e-02, eta: 4 days, 6:14:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5020, loss_cls: 4.0634, loss: 4.0634 +2024-07-22 14:24:01,700 - pyskl - INFO - Epoch [15][3700/3746] lr: 9.756e-02, eta: 4 days, 6:13:13, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5017, loss_cls: 4.0974, loss: 4.0974 +2024-07-22 14:24:36,198 - pyskl - INFO - Saving checkpoint at 15 epochs +2024-07-22 14:26:28,327 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 14:26:29,009 - pyskl - INFO - +top1_acc 0.1728 +top5_acc 0.3920 +2024-07-22 14:26:29,009 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 14:26:29,047 - pyskl - INFO - +mean_acc 0.1725 +2024-07-22 14:26:29,057 - pyskl - INFO - Epoch(val) [15][309] top1_acc: 0.1728, top5_acc: 0.3920, mean_class_accuracy: 0.1725 +2024-07-22 14:29:51,027 - pyskl - INFO - Epoch [16][100/3746] lr: 9.754e-02, eta: 4 days, 6:25:47, time: 2.020, data_time: 1.312, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5200, loss_cls: 4.0440, loss: 4.0440 +2024-07-22 14:31:01,359 - pyskl - INFO - Epoch [16][200/3746] lr: 9.754e-02, eta: 4 days, 6:24:10, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5145, loss_cls: 4.0278, loss: 4.0278 +2024-07-22 14:32:12,500 - pyskl - INFO - Epoch [16][300/3746] lr: 9.753e-02, eta: 4 days, 6:22:41, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5067, loss_cls: 4.0691, loss: 4.0691 +2024-07-22 14:33:23,300 - pyskl - INFO - Epoch [16][400/3746] lr: 9.752e-02, eta: 4 days, 6:21:10, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5092, loss_cls: 4.0508, loss: 4.0508 +2024-07-22 14:34:33,986 - pyskl - INFO - Epoch [16][500/3746] lr: 9.751e-02, eta: 4 days, 6:19:37, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.4950, loss_cls: 4.0452, loss: 4.0452 +2024-07-22 14:35:44,047 - pyskl - INFO - Epoch [16][600/3746] lr: 9.750e-02, eta: 4 days, 6:17:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5058, loss_cls: 4.0897, loss: 4.0897 +2024-07-22 14:36:54,069 - pyskl - INFO - Epoch [16][700/3746] lr: 9.749e-02, eta: 4 days, 6:16:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5081, loss_cls: 4.0552, loss: 4.0552 +2024-07-22 14:38:04,039 - pyskl - INFO - Epoch [16][800/3746] lr: 9.748e-02, eta: 4 days, 6:14:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4952, loss_cls: 4.0940, loss: 4.0940 +2024-07-22 14:39:13,969 - pyskl - INFO - Epoch [16][900/3746] lr: 9.747e-02, eta: 4 days, 6:13:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.5023, loss_cls: 4.1097, loss: 4.1097 +2024-07-22 14:40:23,974 - pyskl - INFO - Epoch [16][1000/3746] lr: 9.747e-02, eta: 4 days, 6:11:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5069, loss_cls: 4.0682, loss: 4.0682 +2024-07-22 14:41:34,064 - pyskl - INFO - Epoch [16][1100/3746] lr: 9.746e-02, eta: 4 days, 6:09:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5008, loss_cls: 4.0726, loss: 4.0726 +2024-07-22 14:42:44,189 - pyskl - INFO - Epoch [16][1200/3746] lr: 9.745e-02, eta: 4 days, 6:08:09, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5009, loss_cls: 4.0740, loss: 4.0740 +2024-07-22 14:43:53,959 - pyskl - INFO - Epoch [16][1300/3746] lr: 9.744e-02, eta: 4 days, 6:06:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4956, loss_cls: 4.1198, loss: 4.1198 +2024-07-22 14:45:03,579 - pyskl - INFO - Epoch [16][1400/3746] lr: 9.743e-02, eta: 4 days, 6:04:47, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5072, loss_cls: 4.0743, loss: 4.0743 +2024-07-22 14:46:13,511 - pyskl - INFO - Epoch [16][1500/3746] lr: 9.742e-02, eta: 4 days, 6:03:08, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5038, loss_cls: 4.0686, loss: 4.0686 +2024-07-22 14:47:23,290 - pyskl - INFO - Epoch [16][1600/3746] lr: 9.741e-02, eta: 4 days, 6:01:29, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4986, loss_cls: 4.1007, loss: 4.1007 +2024-07-22 14:48:33,029 - pyskl - INFO - Epoch [16][1700/3746] lr: 9.740e-02, eta: 4 days, 5:59:49, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5103, loss_cls: 4.0682, loss: 4.0682 +2024-07-22 14:49:42,764 - pyskl - INFO - Epoch [16][1800/3746] lr: 9.740e-02, eta: 4 days, 5:58:09, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5003, loss_cls: 4.0678, loss: 4.0678 +2024-07-22 14:50:53,008 - pyskl - INFO - Epoch [16][1900/3746] lr: 9.739e-02, eta: 4 days, 5:56:33, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5014, loss_cls: 4.1032, loss: 4.1032 +2024-07-22 14:52:02,786 - pyskl - INFO - Epoch [16][2000/3746] lr: 9.738e-02, eta: 4 days, 5:54:54, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5045, loss_cls: 4.1106, loss: 4.1106 +2024-07-22 14:53:12,336 - pyskl - INFO - Epoch [16][2100/3746] lr: 9.737e-02, eta: 4 days, 5:53:13, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5056, loss_cls: 4.0659, loss: 4.0659 +2024-07-22 14:54:22,240 - pyskl - INFO - Epoch [16][2200/3746] lr: 9.736e-02, eta: 4 days, 5:51:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5048, loss_cls: 4.1020, loss: 4.1020 +2024-07-22 14:55:32,124 - pyskl - INFO - Epoch [16][2300/3746] lr: 9.735e-02, eta: 4 days, 5:49:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5005, loss_cls: 4.1147, loss: 4.1147 +2024-07-22 14:56:41,727 - pyskl - INFO - Epoch [16][2400/3746] lr: 9.734e-02, eta: 4 days, 5:48:16, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4878, loss_cls: 4.1366, loss: 4.1366 +2024-07-22 14:57:51,604 - pyskl - INFO - Epoch [16][2500/3746] lr: 9.733e-02, eta: 4 days, 5:46:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5045, loss_cls: 4.0706, loss: 4.0706 +2024-07-22 14:59:01,892 - pyskl - INFO - Epoch [16][2600/3746] lr: 9.732e-02, eta: 4 days, 5:45:03, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5002, loss_cls: 4.1029, loss: 4.1029 +2024-07-22 15:00:11,820 - pyskl - INFO - Epoch [16][2700/3746] lr: 9.731e-02, eta: 4 days, 5:43:26, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5023, loss_cls: 4.0889, loss: 4.0889 +2024-07-22 15:01:21,585 - pyskl - INFO - Epoch [16][2800/3746] lr: 9.731e-02, eta: 4 days, 5:41:47, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.4925, loss_cls: 4.0801, loss: 4.0801 +2024-07-22 15:02:31,281 - pyskl - INFO - Epoch [16][2900/3746] lr: 9.730e-02, eta: 4 days, 5:40:08, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5011, loss_cls: 4.0938, loss: 4.0938 +2024-07-22 15:03:41,244 - pyskl - INFO - Epoch [16][3000/3746] lr: 9.729e-02, eta: 4 days, 5:38:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5055, loss_cls: 4.0944, loss: 4.0944 +2024-07-22 15:04:51,415 - pyskl - INFO - Epoch [16][3100/3746] lr: 9.728e-02, eta: 4 days, 5:36:56, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4863, loss_cls: 4.1510, loss: 4.1510 +2024-07-22 15:06:01,298 - pyskl - INFO - Epoch [16][3200/3746] lr: 9.727e-02, eta: 4 days, 5:35:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5033, loss_cls: 4.0776, loss: 4.0776 +2024-07-22 15:07:11,139 - pyskl - INFO - Epoch [16][3300/3746] lr: 9.726e-02, eta: 4 days, 5:33:41, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5045, loss_cls: 4.1001, loss: 4.1001 +2024-07-22 15:08:21,142 - pyskl - INFO - Epoch [16][3400/3746] lr: 9.725e-02, eta: 4 days, 5:32:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5050, loss_cls: 4.0699, loss: 4.0699 +2024-07-22 15:09:31,460 - pyskl - INFO - Epoch [16][3500/3746] lr: 9.724e-02, eta: 4 days, 5:30:32, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.4970, loss_cls: 4.1083, loss: 4.1083 +2024-07-22 15:10:41,379 - pyskl - INFO - Epoch [16][3600/3746] lr: 9.723e-02, eta: 4 days, 5:28:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5170, loss_cls: 4.0573, loss: 4.0573 +2024-07-22 15:11:51,476 - pyskl - INFO - Epoch [16][3700/3746] lr: 9.722e-02, eta: 4 days, 5:27:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.4956, loss_cls: 4.0924, loss: 4.0924 +2024-07-22 15:12:26,017 - pyskl - INFO - Saving checkpoint at 16 epochs +2024-07-22 15:14:18,498 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 15:14:19,168 - pyskl - INFO - +top1_acc 0.1673 +top5_acc 0.3803 +2024-07-22 15:14:19,168 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 15:14:19,208 - pyskl - INFO - +mean_acc 0.1671 +2024-07-22 15:14:19,219 - pyskl - INFO - Epoch(val) [16][309] top1_acc: 0.1673, top5_acc: 0.3803, mean_class_accuracy: 0.1671 +2024-07-22 15:17:38,441 - pyskl - INFO - Epoch [17][100/3746] lr: 9.721e-02, eta: 4 days, 5:38:31, time: 1.992, data_time: 1.289, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5089, loss_cls: 4.0649, loss: 4.0649 +2024-07-22 15:18:49,124 - pyskl - INFO - Epoch [17][200/3746] lr: 9.720e-02, eta: 4 days, 5:37:00, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.5020, loss_cls: 4.0989, loss: 4.0989 +2024-07-22 15:20:00,404 - pyskl - INFO - Epoch [17][300/3746] lr: 9.719e-02, eta: 4 days, 5:35:33, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5197, loss_cls: 4.0011, loss: 4.0011 +2024-07-22 15:21:10,700 - pyskl - INFO - Epoch [17][400/3746] lr: 9.718e-02, eta: 4 days, 5:33:59, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.5011, loss_cls: 4.1090, loss: 4.1090 +2024-07-22 15:22:21,133 - pyskl - INFO - Epoch [17][500/3746] lr: 9.717e-02, eta: 4 days, 5:32:25, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.5028, loss_cls: 4.0657, loss: 4.0657 +2024-07-22 15:23:31,461 - pyskl - INFO - Epoch [17][600/3746] lr: 9.716e-02, eta: 4 days, 5:30:51, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5105, loss_cls: 4.0610, loss: 4.0610 +2024-07-22 15:24:41,641 - pyskl - INFO - Epoch [17][700/3746] lr: 9.715e-02, eta: 4 days, 5:29:16, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5073, loss_cls: 4.0692, loss: 4.0692 +2024-07-22 15:25:51,566 - pyskl - INFO - Epoch [17][800/3746] lr: 9.714e-02, eta: 4 days, 5:27:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5205, loss_cls: 4.0063, loss: 4.0063 +2024-07-22 15:27:01,780 - pyskl - INFO - Epoch [17][900/3746] lr: 9.714e-02, eta: 4 days, 5:26:04, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5103, loss_cls: 4.0659, loss: 4.0659 +2024-07-22 15:28:11,551 - pyskl - INFO - Epoch [17][1000/3746] lr: 9.713e-02, eta: 4 days, 5:24:25, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5103, loss_cls: 4.0578, loss: 4.0578 +2024-07-22 15:29:21,317 - pyskl - INFO - Epoch [17][1100/3746] lr: 9.712e-02, eta: 4 days, 5:22:47, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5041, loss_cls: 4.0750, loss: 4.0750 +2024-07-22 15:30:31,148 - pyskl - INFO - Epoch [17][1200/3746] lr: 9.711e-02, eta: 4 days, 5:21:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5047, loss_cls: 4.0818, loss: 4.0818 +2024-07-22 15:31:40,884 - pyskl - INFO - Epoch [17][1300/3746] lr: 9.710e-02, eta: 4 days, 5:19:31, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5077, loss_cls: 4.0867, loss: 4.0867 +2024-07-22 15:32:50,894 - pyskl - INFO - Epoch [17][1400/3746] lr: 9.709e-02, eta: 4 days, 5:17:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5084, loss_cls: 4.0469, loss: 4.0469 +2024-07-22 15:34:00,789 - pyskl - INFO - Epoch [17][1500/3746] lr: 9.708e-02, eta: 4 days, 5:16:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5055, loss_cls: 4.0526, loss: 4.0526 +2024-07-22 15:35:10,519 - pyskl - INFO - Epoch [17][1600/3746] lr: 9.707e-02, eta: 4 days, 5:14:39, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4941, loss_cls: 4.1237, loss: 4.1237 +2024-07-22 15:36:20,659 - pyskl - INFO - Epoch [17][1700/3746] lr: 9.706e-02, eta: 4 days, 5:13:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5216, loss_cls: 4.0193, loss: 4.0193 +2024-07-22 15:37:30,494 - pyskl - INFO - Epoch [17][1800/3746] lr: 9.705e-02, eta: 4 days, 5:11:27, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.4970, loss_cls: 4.1052, loss: 4.1052 +2024-07-22 15:38:40,064 - pyskl - INFO - Epoch [17][1900/3746] lr: 9.704e-02, eta: 4 days, 5:09:48, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4947, loss_cls: 4.0970, loss: 4.0970 +2024-07-22 15:39:50,049 - pyskl - INFO - Epoch [17][2000/3746] lr: 9.703e-02, eta: 4 days, 5:08:12, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5091, loss_cls: 4.0717, loss: 4.0717 +2024-07-22 15:40:59,829 - pyskl - INFO - Epoch [17][2100/3746] lr: 9.702e-02, eta: 4 days, 5:06:35, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4928, loss_cls: 4.1264, loss: 4.1264 +2024-07-22 15:42:09,626 - pyskl - INFO - Epoch [17][2200/3746] lr: 9.701e-02, eta: 4 days, 5:04:58, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5067, loss_cls: 4.0616, loss: 4.0616 +2024-07-22 15:43:20,148 - pyskl - INFO - Epoch [17][2300/3746] lr: 9.700e-02, eta: 4 days, 5:03:27, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5033, loss_cls: 4.0895, loss: 4.0895 +2024-07-22 15:44:29,885 - pyskl - INFO - Epoch [17][2400/3746] lr: 9.699e-02, eta: 4 days, 5:01:49, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5144, loss_cls: 4.0379, loss: 4.0379 +2024-07-22 15:45:39,791 - pyskl - INFO - Epoch [17][2500/3746] lr: 9.698e-02, eta: 4 days, 5:00:13, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.4991, loss_cls: 4.1181, loss: 4.1181 +2024-07-22 15:46:49,788 - pyskl - INFO - Epoch [17][2600/3746] lr: 9.697e-02, eta: 4 days, 4:58:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5130, loss_cls: 4.0241, loss: 4.0241 +2024-07-22 15:47:59,689 - pyskl - INFO - Epoch [17][2700/3746] lr: 9.697e-02, eta: 4 days, 4:57:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5108, loss_cls: 4.0554, loss: 4.0554 +2024-07-22 15:49:09,896 - pyskl - INFO - Epoch [17][2800/3746] lr: 9.696e-02, eta: 4 days, 4:55:29, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5112, loss_cls: 4.0337, loss: 4.0337 +2024-07-22 15:50:19,913 - pyskl - INFO - Epoch [17][2900/3746] lr: 9.695e-02, eta: 4 days, 4:53:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5045, loss_cls: 4.0413, loss: 4.0413 +2024-07-22 15:51:29,794 - pyskl - INFO - Epoch [17][3000/3746] lr: 9.694e-02, eta: 4 days, 4:52:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5012, loss_cls: 4.0656, loss: 4.0656 +2024-07-22 15:52:39,645 - pyskl - INFO - Epoch [17][3100/3746] lr: 9.693e-02, eta: 4 days, 4:50:42, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5008, loss_cls: 4.0752, loss: 4.0752 +2024-07-22 15:53:49,775 - pyskl - INFO - Epoch [17][3200/3746] lr: 9.692e-02, eta: 4 days, 4:49:09, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.5023, loss_cls: 4.0997, loss: 4.0997 +2024-07-22 15:54:59,423 - pyskl - INFO - Epoch [17][3300/3746] lr: 9.691e-02, eta: 4 days, 4:47:31, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4880, loss_cls: 4.1501, loss: 4.1501 +2024-07-22 15:56:09,665 - pyskl - INFO - Epoch [17][3400/3746] lr: 9.690e-02, eta: 4 days, 4:45:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5028, loss_cls: 4.0836, loss: 4.0836 +2024-07-22 15:57:20,128 - pyskl - INFO - Epoch [17][3500/3746] lr: 9.689e-02, eta: 4 days, 4:44:28, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.5030, loss_cls: 4.1190, loss: 4.1190 +2024-07-22 15:58:30,475 - pyskl - INFO - Epoch [17][3600/3746] lr: 9.688e-02, eta: 4 days, 4:42:56, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5042, loss_cls: 4.0904, loss: 4.0904 +2024-07-22 15:59:40,765 - pyskl - INFO - Epoch [17][3700/3746] lr: 9.687e-02, eta: 4 days, 4:41:24, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5103, loss_cls: 4.0643, loss: 4.0643 +2024-07-22 16:00:15,206 - pyskl - INFO - Saving checkpoint at 17 epochs +2024-07-22 16:02:07,747 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 16:02:08,415 - pyskl - INFO - +top1_acc 0.1654 +top5_acc 0.3647 +2024-07-22 16:02:08,415 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 16:02:08,457 - pyskl - INFO - +mean_acc 0.1652 +2024-07-22 16:02:08,468 - pyskl - INFO - Epoch(val) [17][309] top1_acc: 0.1654, top5_acc: 0.3647, mean_class_accuracy: 0.1652 +2024-07-22 16:05:27,226 - pyskl - INFO - Epoch [18][100/3746] lr: 9.685e-02, eta: 4 days, 4:51:40, time: 1.987, data_time: 1.283, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5180, loss_cls: 4.0274, loss: 4.0274 +2024-07-22 16:06:37,639 - pyskl - INFO - Epoch [18][200/3746] lr: 9.684e-02, eta: 4 days, 4:50:08, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5125, loss_cls: 4.0243, loss: 4.0243 +2024-07-22 16:07:48,938 - pyskl - INFO - Epoch [18][300/3746] lr: 9.683e-02, eta: 4 days, 4:48:43, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5038, loss_cls: 4.0842, loss: 4.0842 +2024-07-22 16:08:59,455 - pyskl - INFO - Epoch [18][400/3746] lr: 9.683e-02, eta: 4 days, 4:47:12, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5128, loss_cls: 4.0550, loss: 4.0550 +2024-07-22 16:10:10,176 - pyskl - INFO - Epoch [18][500/3746] lr: 9.682e-02, eta: 4 days, 4:45:42, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5084, loss_cls: 4.0603, loss: 4.0603 +2024-07-22 16:11:20,671 - pyskl - INFO - Epoch [18][600/3746] lr: 9.681e-02, eta: 4 days, 4:44:11, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5033, loss_cls: 4.0741, loss: 4.0741 +2024-07-22 16:12:30,793 - pyskl - INFO - Epoch [18][700/3746] lr: 9.680e-02, eta: 4 days, 4:42:36, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4977, loss_cls: 4.0866, loss: 4.0866 +2024-07-22 16:13:40,898 - pyskl - INFO - Epoch [18][800/3746] lr: 9.679e-02, eta: 4 days, 4:41:02, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5088, loss_cls: 4.0842, loss: 4.0842 +2024-07-22 16:14:50,979 - pyskl - INFO - Epoch [18][900/3746] lr: 9.678e-02, eta: 4 days, 4:39:28, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5084, loss_cls: 4.0802, loss: 4.0802 +2024-07-22 16:16:01,112 - pyskl - INFO - Epoch [18][1000/3746] lr: 9.677e-02, eta: 4 days, 4:37:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5142, loss_cls: 4.0455, loss: 4.0455 +2024-07-22 16:17:11,035 - pyskl - INFO - Epoch [18][1100/3746] lr: 9.676e-02, eta: 4 days, 4:36:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5081, loss_cls: 4.0781, loss: 4.0781 +2024-07-22 16:18:21,168 - pyskl - INFO - Epoch [18][1200/3746] lr: 9.675e-02, eta: 4 days, 4:34:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5100, loss_cls: 4.0567, loss: 4.0567 +2024-07-22 16:19:31,014 - pyskl - INFO - Epoch [18][1300/3746] lr: 9.674e-02, eta: 4 days, 4:33:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5000, loss_cls: 4.0750, loss: 4.0750 +2024-07-22 16:20:40,931 - pyskl - INFO - Epoch [18][1400/3746] lr: 9.673e-02, eta: 4 days, 4:31:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5064, loss_cls: 4.0726, loss: 4.0726 +2024-07-22 16:21:50,745 - pyskl - INFO - Epoch [18][1500/3746] lr: 9.672e-02, eta: 4 days, 4:29:58, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5009, loss_cls: 4.0937, loss: 4.0937 +2024-07-22 16:23:00,790 - pyskl - INFO - Epoch [18][1600/3746] lr: 9.671e-02, eta: 4 days, 4:28:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5094, loss_cls: 4.0217, loss: 4.0217 +2024-07-22 16:24:10,788 - pyskl - INFO - Epoch [18][1700/3746] lr: 9.670e-02, eta: 4 days, 4:26:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5078, loss_cls: 4.0568, loss: 4.0568 +2024-07-22 16:25:20,717 - pyskl - INFO - Epoch [18][1800/3746] lr: 9.669e-02, eta: 4 days, 4:25:15, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5094, loss_cls: 4.0442, loss: 4.0442 +2024-07-22 16:26:30,404 - pyskl - INFO - Epoch [18][1900/3746] lr: 9.668e-02, eta: 4 days, 4:23:38, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5089, loss_cls: 4.0592, loss: 4.0592 +2024-07-22 16:27:40,227 - pyskl - INFO - Epoch [18][2000/3746] lr: 9.667e-02, eta: 4 days, 4:22:02, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5083, loss_cls: 4.0877, loss: 4.0877 +2024-07-22 16:28:50,057 - pyskl - INFO - Epoch [18][2100/3746] lr: 9.666e-02, eta: 4 days, 4:20:27, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5073, loss_cls: 4.0577, loss: 4.0577 +2024-07-22 16:29:59,965 - pyskl - INFO - Epoch [18][2200/3746] lr: 9.665e-02, eta: 4 days, 4:18:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5048, loss_cls: 4.0657, loss: 4.0657 +2024-07-22 16:31:09,910 - pyskl - INFO - Epoch [18][2300/3746] lr: 9.664e-02, eta: 4 days, 4:17:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4978, loss_cls: 4.1032, loss: 4.1032 +2024-07-22 16:32:19,848 - pyskl - INFO - Epoch [18][2400/3746] lr: 9.663e-02, eta: 4 days, 4:15:44, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4966, loss_cls: 4.1066, loss: 4.1066 +2024-07-22 16:33:29,763 - pyskl - INFO - Epoch [18][2500/3746] lr: 9.662e-02, eta: 4 days, 4:14:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.4988, loss_cls: 4.1121, loss: 4.1121 +2024-07-22 16:34:39,485 - pyskl - INFO - Epoch [18][2600/3746] lr: 9.661e-02, eta: 4 days, 4:12:34, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5127, loss_cls: 4.0280, loss: 4.0280 +2024-07-22 16:35:49,394 - pyskl - INFO - Epoch [18][2700/3746] lr: 9.660e-02, eta: 4 days, 4:10:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5073, loss_cls: 4.0681, loss: 4.0681 +2024-07-22 16:36:59,293 - pyskl - INFO - Epoch [18][2800/3746] lr: 9.659e-02, eta: 4 days, 4:09:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.4953, loss_cls: 4.0842, loss: 4.0842 +2024-07-22 16:38:09,029 - pyskl - INFO - Epoch [18][2900/3746] lr: 9.658e-02, eta: 4 days, 4:07:50, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5017, loss_cls: 4.0832, loss: 4.0832 +2024-07-22 16:39:18,922 - pyskl - INFO - Epoch [18][3000/3746] lr: 9.657e-02, eta: 4 days, 4:06:15, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5084, loss_cls: 4.0675, loss: 4.0675 +2024-07-22 16:40:28,695 - pyskl - INFO - Epoch [18][3100/3746] lr: 9.656e-02, eta: 4 days, 4:04:40, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5069, loss_cls: 4.0530, loss: 4.0530 +2024-07-22 16:41:38,683 - pyskl - INFO - Epoch [18][3200/3746] lr: 9.654e-02, eta: 4 days, 4:03:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5017, loss_cls: 4.1034, loss: 4.1034 +2024-07-22 16:42:48,759 - pyskl - INFO - Epoch [18][3300/3746] lr: 9.653e-02, eta: 4 days, 4:01:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5128, loss_cls: 4.0332, loss: 4.0332 +2024-07-22 16:43:58,726 - pyskl - INFO - Epoch [18][3400/3746] lr: 9.652e-02, eta: 4 days, 4:00:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4986, loss_cls: 4.0836, loss: 4.0836 +2024-07-22 16:45:09,100 - pyskl - INFO - Epoch [18][3500/3746] lr: 9.651e-02, eta: 4 days, 3:58:31, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5078, loss_cls: 4.0599, loss: 4.0599 +2024-07-22 16:46:19,178 - pyskl - INFO - Epoch [18][3600/3746] lr: 9.650e-02, eta: 4 days, 3:56:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.4947, loss_cls: 4.1336, loss: 4.1336 +2024-07-22 16:47:29,262 - pyskl - INFO - Epoch [18][3700/3746] lr: 9.649e-02, eta: 4 days, 3:55:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5038, loss_cls: 4.0798, loss: 4.0798 +2024-07-22 16:48:03,850 - pyskl - INFO - Saving checkpoint at 18 epochs +2024-07-22 16:49:56,436 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 16:49:57,109 - pyskl - INFO - +top1_acc 0.1795 +top5_acc 0.4076 +2024-07-22 16:49:57,109 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 16:49:57,152 - pyskl - INFO - +mean_acc 0.1792 +2024-07-22 16:49:57,162 - pyskl - INFO - Epoch(val) [18][309] top1_acc: 0.1795, top5_acc: 0.4076, mean_class_accuracy: 0.1792 +2024-07-22 16:53:16,156 - pyskl - INFO - Epoch [19][100/3746] lr: 9.648e-02, eta: 4 days, 4:04:59, time: 1.990, data_time: 1.286, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5114, loss_cls: 4.0313, loss: 4.0313 +2024-07-22 16:54:26,837 - pyskl - INFO - Epoch [19][200/3746] lr: 9.647e-02, eta: 4 days, 4:03:30, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5139, loss_cls: 4.0580, loss: 4.0580 +2024-07-22 16:55:37,926 - pyskl - INFO - Epoch [19][300/3746] lr: 9.646e-02, eta: 4 days, 4:02:04, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5112, loss_cls: 4.0467, loss: 4.0467 +2024-07-22 16:56:48,319 - pyskl - INFO - Epoch [19][400/3746] lr: 9.645e-02, eta: 4 days, 4:00:33, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5081, loss_cls: 4.0531, loss: 4.0531 +2024-07-22 16:57:58,940 - pyskl - INFO - Epoch [19][500/3746] lr: 9.644e-02, eta: 4 days, 3:59:04, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5166, loss_cls: 4.0366, loss: 4.0366 +2024-07-22 16:59:09,735 - pyskl - INFO - Epoch [19][600/3746] lr: 9.643e-02, eta: 4 days, 3:57:36, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5077, loss_cls: 4.0669, loss: 4.0669 +2024-07-22 17:00:19,997 - pyskl - INFO - Epoch [19][700/3746] lr: 9.642e-02, eta: 4 days, 3:56:04, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5084, loss_cls: 4.0527, loss: 4.0527 +2024-07-22 17:01:30,161 - pyskl - INFO - Epoch [19][800/3746] lr: 9.641e-02, eta: 4 days, 3:54:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5105, loss_cls: 4.0395, loss: 4.0395 +2024-07-22 17:02:39,874 - pyskl - INFO - Epoch [19][900/3746] lr: 9.640e-02, eta: 4 days, 3:52:57, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5180, loss_cls: 3.9939, loss: 3.9939 +2024-07-22 17:03:49,926 - pyskl - INFO - Epoch [19][1000/3746] lr: 9.639e-02, eta: 4 days, 3:51:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5014, loss_cls: 4.0721, loss: 4.0721 +2024-07-22 17:04:59,694 - pyskl - INFO - Epoch [19][1100/3746] lr: 9.637e-02, eta: 4 days, 3:49:48, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5153, loss_cls: 4.0046, loss: 4.0046 +2024-07-22 17:06:09,306 - pyskl - INFO - Epoch [19][1200/3746] lr: 9.636e-02, eta: 4 days, 3:48:12, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5122, loss_cls: 4.0402, loss: 4.0402 +2024-07-22 17:07:19,243 - pyskl - INFO - Epoch [19][1300/3746] lr: 9.635e-02, eta: 4 days, 3:46:39, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5048, loss_cls: 4.0937, loss: 4.0937 +2024-07-22 17:08:29,177 - pyskl - INFO - Epoch [19][1400/3746] lr: 9.634e-02, eta: 4 days, 3:45:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5059, loss_cls: 4.0537, loss: 4.0537 +2024-07-22 17:09:39,072 - pyskl - INFO - Epoch [19][1500/3746] lr: 9.633e-02, eta: 4 days, 3:43:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5089, loss_cls: 4.0489, loss: 4.0489 +2024-07-22 17:10:48,796 - pyskl - INFO - Epoch [19][1600/3746] lr: 9.632e-02, eta: 4 days, 3:41:56, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4945, loss_cls: 4.1316, loss: 4.1316 +2024-07-22 17:11:58,531 - pyskl - INFO - Epoch [19][1700/3746] lr: 9.631e-02, eta: 4 days, 3:40:21, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5053, loss_cls: 4.0383, loss: 4.0383 +2024-07-22 17:13:08,371 - pyskl - INFO - Epoch [19][1800/3746] lr: 9.630e-02, eta: 4 days, 3:38:47, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5147, loss_cls: 4.0170, loss: 4.0170 +2024-07-22 17:14:18,219 - pyskl - INFO - Epoch [19][1900/3746] lr: 9.629e-02, eta: 4 days, 3:37:14, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5241, loss_cls: 4.0089, loss: 4.0089 +2024-07-22 17:15:27,951 - pyskl - INFO - Epoch [19][2000/3746] lr: 9.628e-02, eta: 4 days, 3:35:39, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5011, loss_cls: 4.0793, loss: 4.0793 +2024-07-22 17:16:38,064 - pyskl - INFO - Epoch [19][2100/3746] lr: 9.627e-02, eta: 4 days, 3:34:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5089, loss_cls: 4.0570, loss: 4.0570 +2024-07-22 17:17:47,782 - pyskl - INFO - Epoch [19][2200/3746] lr: 9.626e-02, eta: 4 days, 3:32:32, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5138, loss_cls: 4.0303, loss: 4.0303 +2024-07-22 17:18:57,963 - pyskl - INFO - Epoch [19][2300/3746] lr: 9.625e-02, eta: 4 days, 3:31:01, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5012, loss_cls: 4.1014, loss: 4.1014 +2024-07-22 17:20:07,727 - pyskl - INFO - Epoch [19][2400/3746] lr: 9.624e-02, eta: 4 days, 3:29:27, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5014, loss_cls: 4.1031, loss: 4.1031 +2024-07-22 17:21:17,673 - pyskl - INFO - Epoch [19][2500/3746] lr: 9.623e-02, eta: 4 days, 3:27:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5098, loss_cls: 4.0667, loss: 4.0667 +2024-07-22 17:22:27,627 - pyskl - INFO - Epoch [19][2600/3746] lr: 9.622e-02, eta: 4 days, 3:26:22, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5147, loss_cls: 4.0291, loss: 4.0291 +2024-07-22 17:23:37,622 - pyskl - INFO - Epoch [19][2700/3746] lr: 9.621e-02, eta: 4 days, 3:24:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5017, loss_cls: 4.0676, loss: 4.0676 +2024-07-22 17:24:47,414 - pyskl - INFO - Epoch [19][2800/3746] lr: 9.620e-02, eta: 4 days, 3:23:16, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5125, loss_cls: 4.0372, loss: 4.0372 +2024-07-22 17:25:57,293 - pyskl - INFO - Epoch [19][2900/3746] lr: 9.618e-02, eta: 4 days, 3:21:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5139, loss_cls: 4.0217, loss: 4.0217 +2024-07-22 17:27:07,202 - pyskl - INFO - Epoch [19][3000/3746] lr: 9.617e-02, eta: 4 days, 3:20:10, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5023, loss_cls: 4.0892, loss: 4.0892 +2024-07-22 17:28:17,350 - pyskl - INFO - Epoch [19][3100/3746] lr: 9.616e-02, eta: 4 days, 3:18:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5014, loss_cls: 4.0882, loss: 4.0882 +2024-07-22 17:29:27,130 - pyskl - INFO - Epoch [19][3200/3746] lr: 9.615e-02, eta: 4 days, 3:17:05, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5025, loss_cls: 4.0968, loss: 4.0968 +2024-07-22 17:30:37,145 - pyskl - INFO - Epoch [19][3300/3746] lr: 9.614e-02, eta: 4 days, 3:15:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5061, loss_cls: 4.0966, loss: 4.0966 +2024-07-22 17:31:47,359 - pyskl - INFO - Epoch [19][3400/3746] lr: 9.613e-02, eta: 4 days, 3:14:03, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5145, loss_cls: 4.0509, loss: 4.0509 +2024-07-22 17:32:57,547 - pyskl - INFO - Epoch [19][3500/3746] lr: 9.612e-02, eta: 4 days, 3:12:33, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5147, loss_cls: 4.0452, loss: 4.0452 +2024-07-22 17:34:07,392 - pyskl - INFO - Epoch [19][3600/3746] lr: 9.611e-02, eta: 4 days, 3:11:00, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4997, loss_cls: 4.0844, loss: 4.0844 +2024-07-22 17:35:17,373 - pyskl - INFO - Epoch [19][3700/3746] lr: 9.610e-02, eta: 4 days, 3:09:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5045, loss_cls: 4.0702, loss: 4.0702 +2024-07-22 17:35:51,988 - pyskl - INFO - Saving checkpoint at 19 epochs +2024-07-22 17:37:44,057 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 17:37:44,721 - pyskl - INFO - +top1_acc 0.1727 +top5_acc 0.3988 +2024-07-22 17:37:44,722 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 17:37:44,759 - pyskl - INFO - +mean_acc 0.1724 +2024-07-22 17:37:44,769 - pyskl - INFO - Epoch(val) [19][309] top1_acc: 0.1727, top5_acc: 0.3988, mean_class_accuracy: 0.1724 +2024-07-22 17:41:03,254 - pyskl - INFO - Epoch [20][100/3746] lr: 9.608e-02, eta: 4 days, 3:18:17, time: 1.985, data_time: 1.280, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5103, loss_cls: 4.0087, loss: 4.0087 +2024-07-22 17:42:13,727 - pyskl - INFO - Epoch [20][200/3746] lr: 9.607e-02, eta: 4 days, 3:16:48, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5100, loss_cls: 4.0292, loss: 4.0292 +2024-07-22 17:43:24,731 - pyskl - INFO - Epoch [20][300/3746] lr: 9.606e-02, eta: 4 days, 3:15:22, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5261, loss_cls: 4.0024, loss: 4.0024 +2024-07-22 17:44:35,117 - pyskl - INFO - Epoch [20][400/3746] lr: 9.605e-02, eta: 4 days, 3:13:53, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5162, loss_cls: 4.0041, loss: 4.0041 +2024-07-22 17:45:45,482 - pyskl - INFO - Epoch [20][500/3746] lr: 9.604e-02, eta: 4 days, 3:12:23, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5103, loss_cls: 4.0453, loss: 4.0453 +2024-07-22 17:46:55,742 - pyskl - INFO - Epoch [20][600/3746] lr: 9.603e-02, eta: 4 days, 3:10:52, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5095, loss_cls: 4.0591, loss: 4.0591 +2024-07-22 17:48:05,774 - pyskl - INFO - Epoch [20][700/3746] lr: 9.602e-02, eta: 4 days, 3:09:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5061, loss_cls: 4.0570, loss: 4.0570 +2024-07-22 17:49:15,711 - pyskl - INFO - Epoch [20][800/3746] lr: 9.601e-02, eta: 4 days, 3:07:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4938, loss_cls: 4.1147, loss: 4.1147 +2024-07-22 17:50:25,545 - pyskl - INFO - Epoch [20][900/3746] lr: 9.600e-02, eta: 4 days, 3:06:14, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5095, loss_cls: 4.0215, loss: 4.0215 +2024-07-22 17:51:35,309 - pyskl - INFO - Epoch [20][1000/3746] lr: 9.598e-02, eta: 4 days, 3:04:40, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5159, loss_cls: 4.0217, loss: 4.0217 +2024-07-22 17:52:45,197 - pyskl - INFO - Epoch [20][1100/3746] lr: 9.597e-02, eta: 4 days, 3:03:08, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5045, loss_cls: 4.0658, loss: 4.0658 +2024-07-22 17:53:55,183 - pyskl - INFO - Epoch [20][1200/3746] lr: 9.596e-02, eta: 4 days, 3:01:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5005, loss_cls: 4.0726, loss: 4.0726 +2024-07-22 17:55:04,997 - pyskl - INFO - Epoch [20][1300/3746] lr: 9.595e-02, eta: 4 days, 3:00:02, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5109, loss_cls: 4.0363, loss: 4.0363 +2024-07-22 17:56:14,863 - pyskl - INFO - Epoch [20][1400/3746] lr: 9.594e-02, eta: 4 days, 2:58:30, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5102, loss_cls: 4.0434, loss: 4.0434 +2024-07-22 17:57:24,839 - pyskl - INFO - Epoch [20][1500/3746] lr: 9.593e-02, eta: 4 days, 2:56:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5153, loss_cls: 4.0134, loss: 4.0134 +2024-07-22 17:58:34,562 - pyskl - INFO - Epoch [20][1600/3746] lr: 9.592e-02, eta: 4 days, 2:55:24, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5016, loss_cls: 4.0827, loss: 4.0827 +2024-07-22 17:59:44,363 - pyskl - INFO - Epoch [20][1700/3746] lr: 9.591e-02, eta: 4 days, 2:53:51, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5166, loss_cls: 4.0108, loss: 4.0108 +2024-07-22 18:00:54,279 - pyskl - INFO - Epoch [20][1800/3746] lr: 9.590e-02, eta: 4 days, 2:52:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5073, loss_cls: 4.0733, loss: 4.0733 +2024-07-22 18:02:03,983 - pyskl - INFO - Epoch [20][1900/3746] lr: 9.588e-02, eta: 4 days, 2:50:45, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5139, loss_cls: 4.0368, loss: 4.0368 +2024-07-22 18:03:13,625 - pyskl - INFO - Epoch [20][2000/3746] lr: 9.587e-02, eta: 4 days, 2:49:12, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5020, loss_cls: 4.0910, loss: 4.0910 +2024-07-22 18:04:23,312 - pyskl - INFO - Epoch [20][2100/3746] lr: 9.586e-02, eta: 4 days, 2:47:38, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5066, loss_cls: 4.0680, loss: 4.0680 +2024-07-22 18:05:33,151 - pyskl - INFO - Epoch [20][2200/3746] lr: 9.585e-02, eta: 4 days, 2:46:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5152, loss_cls: 4.0676, loss: 4.0676 +2024-07-22 18:06:43,120 - pyskl - INFO - Epoch [20][2300/3746] lr: 9.584e-02, eta: 4 days, 2:44:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5059, loss_cls: 4.0820, loss: 4.0820 +2024-07-22 18:07:53,116 - pyskl - INFO - Epoch [20][2400/3746] lr: 9.583e-02, eta: 4 days, 2:43:03, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5152, loss_cls: 4.0161, loss: 4.0161 +2024-07-22 18:09:02,817 - pyskl - INFO - Epoch [20][2500/3746] lr: 9.582e-02, eta: 4 days, 2:41:30, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5120, loss_cls: 4.0282, loss: 4.0282 +2024-07-22 18:10:12,953 - pyskl - INFO - Epoch [20][2600/3746] lr: 9.581e-02, eta: 4 days, 2:40:00, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5083, loss_cls: 4.0679, loss: 4.0679 +2024-07-22 18:11:22,831 - pyskl - INFO - Epoch [20][2700/3746] lr: 9.580e-02, eta: 4 days, 2:38:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5083, loss_cls: 4.0637, loss: 4.0637 +2024-07-22 18:12:32,585 - pyskl - INFO - Epoch [20][2800/3746] lr: 9.578e-02, eta: 4 days, 2:36:55, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5081, loss_cls: 4.0561, loss: 4.0561 +2024-07-22 18:13:42,598 - pyskl - INFO - Epoch [20][2900/3746] lr: 9.577e-02, eta: 4 days, 2:35:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5138, loss_cls: 4.0668, loss: 4.0668 +2024-07-22 18:14:52,555 - pyskl - INFO - Epoch [20][3000/3746] lr: 9.576e-02, eta: 4 days, 2:33:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5053, loss_cls: 4.0750, loss: 4.0750 +2024-07-22 18:16:02,503 - pyskl - INFO - Epoch [20][3100/3746] lr: 9.575e-02, eta: 4 days, 2:32:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5111, loss_cls: 4.0471, loss: 4.0471 +2024-07-22 18:17:12,329 - pyskl - INFO - Epoch [20][3200/3746] lr: 9.574e-02, eta: 4 days, 2:30:50, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5030, loss_cls: 4.0804, loss: 4.0804 +2024-07-22 18:18:22,273 - pyskl - INFO - Epoch [20][3300/3746] lr: 9.573e-02, eta: 4 days, 2:29:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5223, loss_cls: 4.0249, loss: 4.0249 +2024-07-22 18:19:32,492 - pyskl - INFO - Epoch [20][3400/3746] lr: 9.572e-02, eta: 4 days, 2:27:50, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.5008, loss_cls: 4.1163, loss: 4.1163 +2024-07-22 18:20:42,745 - pyskl - INFO - Epoch [20][3500/3746] lr: 9.571e-02, eta: 4 days, 2:26:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5050, loss_cls: 4.0495, loss: 4.0495 +2024-07-22 18:21:52,633 - pyskl - INFO - Epoch [20][3600/3746] lr: 9.569e-02, eta: 4 days, 2:24:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5011, loss_cls: 4.0770, loss: 4.0770 +2024-07-22 18:23:02,640 - pyskl - INFO - Epoch [20][3700/3746] lr: 9.568e-02, eta: 4 days, 2:23:19, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5119, loss_cls: 4.0456, loss: 4.0456 +2024-07-22 18:23:37,058 - pyskl - INFO - Saving checkpoint at 20 epochs +2024-07-22 18:25:29,318 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 18:25:29,992 - pyskl - INFO - +top1_acc 0.1855 +top5_acc 0.4022 +2024-07-22 18:25:29,993 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 18:25:30,034 - pyskl - INFO - +mean_acc 0.1853 +2024-07-22 18:25:30,044 - pyskl - INFO - Epoch(val) [20][309] top1_acc: 0.1855, top5_acc: 0.4022, mean_class_accuracy: 0.1853 +2024-07-22 18:28:49,086 - pyskl - INFO - Epoch [21][100/3746] lr: 9.567e-02, eta: 4 days, 2:31:35, time: 1.990, data_time: 1.284, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5309, loss_cls: 3.9625, loss: 3.9625 +2024-07-22 18:29:59,452 - pyskl - INFO - Epoch [21][200/3746] lr: 9.565e-02, eta: 4 days, 2:30:06, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5120, loss_cls: 4.0375, loss: 4.0375 +2024-07-22 18:31:10,222 - pyskl - INFO - Epoch [21][300/3746] lr: 9.564e-02, eta: 4 days, 2:28:40, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5158, loss_cls: 4.0450, loss: 4.0450 +2024-07-22 18:32:20,534 - pyskl - INFO - Epoch [21][400/3746] lr: 9.563e-02, eta: 4 days, 2:27:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5208, loss_cls: 3.9994, loss: 3.9994 +2024-07-22 18:33:31,128 - pyskl - INFO - Epoch [21][500/3746] lr: 9.562e-02, eta: 4 days, 2:25:44, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5077, loss_cls: 4.0680, loss: 4.0680 +2024-07-22 18:34:41,414 - pyskl - INFO - Epoch [21][600/3746] lr: 9.561e-02, eta: 4 days, 2:24:14, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5086, loss_cls: 4.0539, loss: 4.0539 +2024-07-22 18:35:51,618 - pyskl - INFO - Epoch [21][700/3746] lr: 9.560e-02, eta: 4 days, 2:22:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5012, loss_cls: 4.0665, loss: 4.0665 +2024-07-22 18:37:01,458 - pyskl - INFO - Epoch [21][800/3746] lr: 9.559e-02, eta: 4 days, 2:21:12, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5205, loss_cls: 4.0339, loss: 4.0339 +2024-07-22 18:38:11,538 - pyskl - INFO - Epoch [21][900/3746] lr: 9.557e-02, eta: 4 days, 2:19:42, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5061, loss_cls: 4.0752, loss: 4.0752 +2024-07-22 18:39:21,227 - pyskl - INFO - Epoch [21][1000/3746] lr: 9.556e-02, eta: 4 days, 2:18:09, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5019, loss_cls: 4.0841, loss: 4.0841 +2024-07-22 18:40:31,353 - pyskl - INFO - Epoch [21][1100/3746] lr: 9.555e-02, eta: 4 days, 2:16:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5134, loss_cls: 4.0280, loss: 4.0280 +2024-07-22 18:41:41,361 - pyskl - INFO - Epoch [21][1200/3746] lr: 9.554e-02, eta: 4 days, 2:15:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5109, loss_cls: 4.0303, loss: 4.0303 +2024-07-22 18:42:51,316 - pyskl - INFO - Epoch [21][1300/3746] lr: 9.553e-02, eta: 4 days, 2:13:37, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5095, loss_cls: 4.0592, loss: 4.0592 +2024-07-22 18:44:01,065 - pyskl - INFO - Epoch [21][1400/3746] lr: 9.552e-02, eta: 4 days, 2:12:05, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5020, loss_cls: 4.0683, loss: 4.0683 +2024-07-22 18:45:10,780 - pyskl - INFO - Epoch [21][1500/3746] lr: 9.551e-02, eta: 4 days, 2:10:33, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5009, loss_cls: 4.0575, loss: 4.0575 +2024-07-22 18:46:20,575 - pyskl - INFO - Epoch [21][1600/3746] lr: 9.549e-02, eta: 4 days, 2:09:01, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5138, loss_cls: 4.0259, loss: 4.0259 +2024-07-22 18:47:30,346 - pyskl - INFO - Epoch [21][1700/3746] lr: 9.548e-02, eta: 4 days, 2:07:29, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5061, loss_cls: 4.1034, loss: 4.1034 +2024-07-22 18:48:40,374 - pyskl - INFO - Epoch [21][1800/3746] lr: 9.547e-02, eta: 4 days, 2:05:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5041, loss_cls: 4.0917, loss: 4.0917 +2024-07-22 18:49:50,099 - pyskl - INFO - Epoch [21][1900/3746] lr: 9.546e-02, eta: 4 days, 2:04:26, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5130, loss_cls: 4.0298, loss: 4.0298 +2024-07-22 18:51:00,017 - pyskl - INFO - Epoch [21][2000/3746] lr: 9.545e-02, eta: 4 days, 2:02:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5091, loss_cls: 4.0516, loss: 4.0516 +2024-07-22 18:52:09,882 - pyskl - INFO - Epoch [21][2100/3746] lr: 9.544e-02, eta: 4 days, 2:01:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5072, loss_cls: 4.0521, loss: 4.0521 +2024-07-22 18:53:19,710 - pyskl - INFO - Epoch [21][2200/3746] lr: 9.542e-02, eta: 4 days, 1:59:53, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5031, loss_cls: 4.0986, loss: 4.0986 +2024-07-22 18:54:29,403 - pyskl - INFO - Epoch [21][2300/3746] lr: 9.541e-02, eta: 4 days, 1:58:21, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4973, loss_cls: 4.0990, loss: 4.0990 +2024-07-22 18:55:39,134 - pyskl - INFO - Epoch [21][2400/3746] lr: 9.540e-02, eta: 4 days, 1:56:49, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.4991, loss_cls: 4.0735, loss: 4.0735 +2024-07-22 18:56:49,213 - pyskl - INFO - Epoch [21][2500/3746] lr: 9.539e-02, eta: 4 days, 1:55:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5052, loss_cls: 4.0762, loss: 4.0762 +2024-07-22 18:57:58,967 - pyskl - INFO - Epoch [21][2600/3746] lr: 9.538e-02, eta: 4 days, 1:53:48, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5098, loss_cls: 4.0556, loss: 4.0556 +2024-07-22 18:59:08,948 - pyskl - INFO - Epoch [21][2700/3746] lr: 9.537e-02, eta: 4 days, 1:52:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5092, loss_cls: 4.0791, loss: 4.0791 +2024-07-22 19:00:18,929 - pyskl - INFO - Epoch [21][2800/3746] lr: 9.535e-02, eta: 4 days, 1:50:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5088, loss_cls: 4.0661, loss: 4.0661 +2024-07-22 19:01:28,979 - pyskl - INFO - Epoch [21][2900/3746] lr: 9.534e-02, eta: 4 days, 1:49:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5175, loss_cls: 4.0113, loss: 4.0113 +2024-07-22 19:02:38,725 - pyskl - INFO - Epoch [21][3000/3746] lr: 9.533e-02, eta: 4 days, 1:47:47, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5130, loss_cls: 4.0516, loss: 4.0516 +2024-07-22 19:03:48,854 - pyskl - INFO - Epoch [21][3100/3746] lr: 9.532e-02, eta: 4 days, 1:46:18, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5086, loss_cls: 4.0566, loss: 4.0566 +2024-07-22 19:04:58,494 - pyskl - INFO - Epoch [21][3200/3746] lr: 9.531e-02, eta: 4 days, 1:44:46, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5028, loss_cls: 4.0925, loss: 4.0925 +2024-07-22 19:06:08,592 - pyskl - INFO - Epoch [21][3300/3746] lr: 9.529e-02, eta: 4 days, 1:43:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5039, loss_cls: 4.0546, loss: 4.0546 +2024-07-22 19:07:18,553 - pyskl - INFO - Epoch [21][3400/3746] lr: 9.528e-02, eta: 4 days, 1:41:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5116, loss_cls: 4.0465, loss: 4.0465 +2024-07-22 19:08:28,765 - pyskl - INFO - Epoch [21][3500/3746] lr: 9.527e-02, eta: 4 days, 1:40:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5077, loss_cls: 4.0719, loss: 4.0719 +2024-07-22 19:09:38,554 - pyskl - INFO - Epoch [21][3600/3746] lr: 9.526e-02, eta: 4 days, 1:38:48, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5108, loss_cls: 4.0358, loss: 4.0358 +2024-07-22 19:10:48,754 - pyskl - INFO - Epoch [21][3700/3746] lr: 9.525e-02, eta: 4 days, 1:37:20, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5119, loss_cls: 4.0534, loss: 4.0534 +2024-07-22 19:11:23,065 - pyskl - INFO - Saving checkpoint at 21 epochs +2024-07-22 19:13:15,350 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 19:13:16,022 - pyskl - INFO - +top1_acc 0.1632 +top5_acc 0.3699 +2024-07-22 19:13:16,022 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 19:13:16,061 - pyskl - INFO - +mean_acc 0.1631 +2024-07-22 19:13:16,071 - pyskl - INFO - Epoch(val) [21][309] top1_acc: 0.1632, top5_acc: 0.3699, mean_class_accuracy: 0.1631 +2024-07-22 19:16:36,781 - pyskl - INFO - Epoch [22][100/3746] lr: 9.523e-02, eta: 4 days, 1:45:13, time: 2.007, data_time: 1.299, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5230, loss_cls: 4.0022, loss: 4.0022 +2024-07-22 19:17:47,152 - pyskl - INFO - Epoch [22][200/3746] lr: 9.522e-02, eta: 4 days, 1:43:45, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5205, loss_cls: 4.0189, loss: 4.0189 +2024-07-22 19:18:57,907 - pyskl - INFO - Epoch [22][300/3746] lr: 9.521e-02, eta: 4 days, 1:42:20, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5127, loss_cls: 4.0281, loss: 4.0281 +2024-07-22 19:20:07,930 - pyskl - INFO - Epoch [22][400/3746] lr: 9.519e-02, eta: 4 days, 1:40:50, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5044, loss_cls: 4.0540, loss: 4.0540 +2024-07-22 19:21:18,484 - pyskl - INFO - Epoch [22][500/3746] lr: 9.518e-02, eta: 4 days, 1:39:23, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5078, loss_cls: 4.0575, loss: 4.0575 +2024-07-22 19:22:29,155 - pyskl - INFO - Epoch [22][600/3746] lr: 9.517e-02, eta: 4 days, 1:37:57, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5081, loss_cls: 4.0631, loss: 4.0631 +2024-07-22 19:23:38,987 - pyskl - INFO - Epoch [22][700/3746] lr: 9.516e-02, eta: 4 days, 1:36:26, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5152, loss_cls: 4.0286, loss: 4.0286 +2024-07-22 19:24:49,094 - pyskl - INFO - Epoch [22][800/3746] lr: 9.515e-02, eta: 4 days, 1:34:57, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5084, loss_cls: 4.0589, loss: 4.0589 +2024-07-22 19:25:59,055 - pyskl - INFO - Epoch [22][900/3746] lr: 9.513e-02, eta: 4 days, 1:33:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5088, loss_cls: 4.0602, loss: 4.0602 +2024-07-22 19:27:09,230 - pyskl - INFO - Epoch [22][1000/3746] lr: 9.512e-02, eta: 4 days, 1:31:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5084, loss_cls: 4.0778, loss: 4.0778 +2024-07-22 19:28:19,349 - pyskl - INFO - Epoch [22][1100/3746] lr: 9.511e-02, eta: 4 days, 1:30:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5105, loss_cls: 4.0553, loss: 4.0553 +2024-07-22 19:29:29,252 - pyskl - INFO - Epoch [22][1200/3746] lr: 9.510e-02, eta: 4 days, 1:28:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5192, loss_cls: 4.0206, loss: 4.0206 +2024-07-22 19:30:39,252 - pyskl - INFO - Epoch [22][1300/3746] lr: 9.509e-02, eta: 4 days, 1:27:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5136, loss_cls: 4.0457, loss: 4.0457 +2024-07-22 19:31:49,416 - pyskl - INFO - Epoch [22][1400/3746] lr: 9.507e-02, eta: 4 days, 1:26:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5266, loss_cls: 3.9680, loss: 3.9680 +2024-07-22 19:32:59,198 - pyskl - INFO - Epoch [22][1500/3746] lr: 9.506e-02, eta: 4 days, 1:24:29, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5150, loss_cls: 4.0426, loss: 4.0426 +2024-07-22 19:34:09,413 - pyskl - INFO - Epoch [22][1600/3746] lr: 9.505e-02, eta: 4 days, 1:23:01, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5136, loss_cls: 4.0494, loss: 4.0494 +2024-07-22 19:35:19,139 - pyskl - INFO - Epoch [22][1700/3746] lr: 9.504e-02, eta: 4 days, 1:21:30, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5083, loss_cls: 4.0628, loss: 4.0628 +2024-07-22 19:36:29,246 - pyskl - INFO - Epoch [22][1800/3746] lr: 9.502e-02, eta: 4 days, 1:20:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5048, loss_cls: 4.0562, loss: 4.0562 +2024-07-22 19:37:38,855 - pyskl - INFO - Epoch [22][1900/3746] lr: 9.501e-02, eta: 4 days, 1:18:29, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5056, loss_cls: 4.0531, loss: 4.0531 +2024-07-22 19:38:48,923 - pyskl - INFO - Epoch [22][2000/3746] lr: 9.500e-02, eta: 4 days, 1:17:00, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5236, loss_cls: 4.0038, loss: 4.0038 +2024-07-22 19:39:59,026 - pyskl - INFO - Epoch [22][2100/3746] lr: 9.499e-02, eta: 4 days, 1:15:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5139, loss_cls: 4.0201, loss: 4.0201 +2024-07-22 19:41:09,051 - pyskl - INFO - Epoch [22][2200/3746] lr: 9.498e-02, eta: 4 days, 1:14:02, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5084, loss_cls: 4.0463, loss: 4.0463 +2024-07-22 19:42:19,082 - pyskl - INFO - Epoch [22][2300/3746] lr: 9.496e-02, eta: 4 days, 1:12:33, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5186, loss_cls: 3.9962, loss: 3.9962 +2024-07-22 19:43:28,918 - pyskl - INFO - Epoch [22][2400/3746] lr: 9.495e-02, eta: 4 days, 1:11:03, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5134, loss_cls: 4.0458, loss: 4.0458 +2024-07-22 19:44:39,146 - pyskl - INFO - Epoch [22][2500/3746] lr: 9.494e-02, eta: 4 days, 1:09:35, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5072, loss_cls: 4.0592, loss: 4.0592 +2024-07-22 19:45:49,108 - pyskl - INFO - Epoch [22][2600/3746] lr: 9.493e-02, eta: 4 days, 1:08:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5067, loss_cls: 4.0756, loss: 4.0756 +2024-07-22 19:46:59,273 - pyskl - INFO - Epoch [22][2700/3746] lr: 9.491e-02, eta: 4 days, 1:06:38, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4877, loss_cls: 4.1223, loss: 4.1223 +2024-07-22 19:48:09,272 - pyskl - INFO - Epoch [22][2800/3746] lr: 9.490e-02, eta: 4 days, 1:05:09, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5050, loss_cls: 4.0878, loss: 4.0878 +2024-07-22 19:49:19,108 - pyskl - INFO - Epoch [22][2900/3746] lr: 9.489e-02, eta: 4 days, 1:03:39, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5066, loss_cls: 4.0555, loss: 4.0555 +2024-07-22 19:50:28,942 - pyskl - INFO - Epoch [22][3000/3746] lr: 9.488e-02, eta: 4 days, 1:02:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5181, loss_cls: 4.0034, loss: 4.0034 +2024-07-22 19:51:38,670 - pyskl - INFO - Epoch [22][3100/3746] lr: 9.487e-02, eta: 4 days, 1:00:39, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5162, loss_cls: 4.0166, loss: 4.0166 +2024-07-22 19:52:48,673 - pyskl - INFO - Epoch [22][3200/3746] lr: 9.485e-02, eta: 4 days, 0:59:10, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5034, loss_cls: 4.0699, loss: 4.0699 +2024-07-22 19:53:58,577 - pyskl - INFO - Epoch [22][3300/3746] lr: 9.484e-02, eta: 4 days, 0:57:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5002, loss_cls: 4.1098, loss: 4.1098 +2024-07-22 19:55:08,852 - pyskl - INFO - Epoch [22][3400/3746] lr: 9.483e-02, eta: 4 days, 0:56:14, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5106, loss_cls: 4.0339, loss: 4.0339 +2024-07-22 19:56:19,150 - pyskl - INFO - Epoch [22][3500/3746] lr: 9.482e-02, eta: 4 days, 0:54:47, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.4988, loss_cls: 4.0532, loss: 4.0532 +2024-07-22 19:57:29,384 - pyskl - INFO - Epoch [22][3600/3746] lr: 9.480e-02, eta: 4 days, 0:53:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5134, loss_cls: 4.0161, loss: 4.0161 +2024-07-22 19:58:40,387 - pyskl - INFO - Epoch [22][3700/3746] lr: 9.479e-02, eta: 4 days, 0:51:57, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5139, loss_cls: 4.0543, loss: 4.0543 +2024-07-22 19:59:14,929 - pyskl - INFO - Saving checkpoint at 22 epochs +2024-07-22 20:01:07,584 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 20:01:08,255 - pyskl - INFO - +top1_acc 0.1992 +top5_acc 0.4258 +2024-07-22 20:01:08,255 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 20:01:08,293 - pyskl - INFO - +mean_acc 0.1989 +2024-07-22 20:01:08,298 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_12.pth was removed +2024-07-22 20:01:08,545 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_22.pth. +2024-07-22 20:01:08,545 - pyskl - INFO - Best top1_acc is 0.1992 at 22 epoch. +2024-07-22 20:01:08,556 - pyskl - INFO - Epoch(val) [22][309] top1_acc: 0.1992, top5_acc: 0.4258, mean_class_accuracy: 0.1989 +2024-07-22 20:04:30,156 - pyskl - INFO - Epoch [23][100/3746] lr: 9.477e-02, eta: 4 days, 0:59:25, time: 2.016, data_time: 1.310, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5184, loss_cls: 3.9979, loss: 3.9979 +2024-07-22 20:05:41,179 - pyskl - INFO - Epoch [23][200/3746] lr: 9.476e-02, eta: 4 days, 0:58:02, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5350, loss_cls: 3.9536, loss: 3.9536 +2024-07-22 20:06:52,159 - pyskl - INFO - Epoch [23][300/3746] lr: 9.475e-02, eta: 4 days, 0:56:38, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5194, loss_cls: 4.0008, loss: 4.0008 +2024-07-22 20:08:02,554 - pyskl - INFO - Epoch [23][400/3746] lr: 9.474e-02, eta: 4 days, 0:55:11, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5202, loss_cls: 3.9799, loss: 3.9799 +2024-07-22 20:09:13,360 - pyskl - INFO - Epoch [23][500/3746] lr: 9.472e-02, eta: 4 days, 0:53:46, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5188, loss_cls: 4.0183, loss: 4.0183 +2024-07-22 20:10:23,907 - pyskl - INFO - Epoch [23][600/3746] lr: 9.471e-02, eta: 4 days, 0:52:21, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5150, loss_cls: 4.0308, loss: 4.0308 +2024-07-22 20:11:33,671 - pyskl - INFO - Epoch [23][700/3746] lr: 9.470e-02, eta: 4 days, 0:50:50, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5042, loss_cls: 4.0414, loss: 4.0414 +2024-07-22 20:12:43,570 - pyskl - INFO - Epoch [23][800/3746] lr: 9.469e-02, eta: 4 days, 0:49:20, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5131, loss_cls: 4.0583, loss: 4.0583 +2024-07-22 20:13:53,487 - pyskl - INFO - Epoch [23][900/3746] lr: 9.467e-02, eta: 4 days, 0:47:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5031, loss_cls: 4.0440, loss: 4.0440 +2024-07-22 20:15:03,359 - pyskl - INFO - Epoch [23][1000/3746] lr: 9.466e-02, eta: 4 days, 0:46:21, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5069, loss_cls: 4.0605, loss: 4.0605 +2024-07-22 20:16:13,304 - pyskl - INFO - Epoch [23][1100/3746] lr: 9.465e-02, eta: 4 days, 0:44:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5028, loss_cls: 4.0651, loss: 4.0651 +2024-07-22 20:17:23,216 - pyskl - INFO - Epoch [23][1200/3746] lr: 9.464e-02, eta: 4 days, 0:43:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5077, loss_cls: 4.0469, loss: 4.0469 +2024-07-22 20:18:33,243 - pyskl - INFO - Epoch [23][1300/3746] lr: 9.462e-02, eta: 4 days, 0:41:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4963, loss_cls: 4.0796, loss: 4.0796 +2024-07-22 20:19:43,420 - pyskl - INFO - Epoch [23][1400/3746] lr: 9.461e-02, eta: 4 days, 0:40:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5052, loss_cls: 4.0230, loss: 4.0230 +2024-07-22 20:20:53,512 - pyskl - INFO - Epoch [23][1500/3746] lr: 9.460e-02, eta: 4 days, 0:38:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5184, loss_cls: 4.0204, loss: 4.0204 +2024-07-22 20:22:03,331 - pyskl - INFO - Epoch [23][1600/3746] lr: 9.459e-02, eta: 4 days, 0:37:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5127, loss_cls: 4.0195, loss: 4.0195 +2024-07-22 20:23:13,197 - pyskl - INFO - Epoch [23][1700/3746] lr: 9.457e-02, eta: 4 days, 0:35:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5138, loss_cls: 4.0438, loss: 4.0438 +2024-07-22 20:24:23,287 - pyskl - INFO - Epoch [23][1800/3746] lr: 9.456e-02, eta: 4 days, 0:34:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.4988, loss_cls: 4.0717, loss: 4.0717 +2024-07-22 20:25:33,140 - pyskl - INFO - Epoch [23][1900/3746] lr: 9.455e-02, eta: 4 days, 0:33:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5086, loss_cls: 4.0640, loss: 4.0640 +2024-07-22 20:26:43,000 - pyskl - INFO - Epoch [23][2000/3746] lr: 9.453e-02, eta: 4 days, 0:31:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5233, loss_cls: 3.9725, loss: 3.9725 +2024-07-22 20:27:52,949 - pyskl - INFO - Epoch [23][2100/3746] lr: 9.452e-02, eta: 4 days, 0:30:03, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4952, loss_cls: 4.1284, loss: 4.1284 +2024-07-22 20:29:02,596 - pyskl - INFO - Epoch [23][2200/3746] lr: 9.451e-02, eta: 4 days, 0:28:33, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.4973, loss_cls: 4.0810, loss: 4.0810 +2024-07-22 20:30:12,527 - pyskl - INFO - Epoch [23][2300/3746] lr: 9.450e-02, eta: 4 days, 0:27:04, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5052, loss_cls: 4.0600, loss: 4.0600 +2024-07-22 20:31:22,526 - pyskl - INFO - Epoch [23][2400/3746] lr: 9.448e-02, eta: 4 days, 0:25:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5127, loss_cls: 4.0482, loss: 4.0482 +2024-07-22 20:32:32,208 - pyskl - INFO - Epoch [23][2500/3746] lr: 9.447e-02, eta: 4 days, 0:24:06, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5097, loss_cls: 4.0292, loss: 4.0292 +2024-07-22 20:33:42,155 - pyskl - INFO - Epoch [23][2600/3746] lr: 9.446e-02, eta: 4 days, 0:22:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5062, loss_cls: 4.0504, loss: 4.0504 +2024-07-22 20:34:52,069 - pyskl - INFO - Epoch [23][2700/3746] lr: 9.445e-02, eta: 4 days, 0:21:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5047, loss_cls: 4.0911, loss: 4.0911 +2024-07-22 20:36:02,180 - pyskl - INFO - Epoch [23][2800/3746] lr: 9.443e-02, eta: 4 days, 0:19:41, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4973, loss_cls: 4.0769, loss: 4.0769 +2024-07-22 20:37:11,943 - pyskl - INFO - Epoch [23][2900/3746] lr: 9.442e-02, eta: 4 days, 0:18:12, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5173, loss_cls: 4.0228, loss: 4.0228 +2024-07-22 20:38:21,786 - pyskl - INFO - Epoch [23][3000/3746] lr: 9.441e-02, eta: 4 days, 0:16:43, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5039, loss_cls: 4.0842, loss: 4.0842 +2024-07-22 20:39:31,600 - pyskl - INFO - Epoch [23][3100/3746] lr: 9.439e-02, eta: 4 days, 0:15:14, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5027, loss_cls: 4.0659, loss: 4.0659 +2024-07-22 20:40:41,423 - pyskl - INFO - Epoch [23][3200/3746] lr: 9.438e-02, eta: 4 days, 0:13:45, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5119, loss_cls: 4.0239, loss: 4.0239 +2024-07-22 20:41:51,419 - pyskl - INFO - Epoch [23][3300/3746] lr: 9.437e-02, eta: 4 days, 0:12:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5108, loss_cls: 4.0312, loss: 4.0312 +2024-07-22 20:43:01,232 - pyskl - INFO - Epoch [23][3400/3746] lr: 9.436e-02, eta: 4 days, 0:10:48, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5169, loss_cls: 4.0360, loss: 4.0360 +2024-07-22 20:44:11,489 - pyskl - INFO - Epoch [23][3500/3746] lr: 9.434e-02, eta: 4 days, 0:09:22, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5180, loss_cls: 4.0030, loss: 4.0030 +2024-07-22 20:45:21,698 - pyskl - INFO - Epoch [23][3600/3746] lr: 9.433e-02, eta: 4 days, 0:07:55, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5172, loss_cls: 4.0304, loss: 4.0304 +2024-07-22 20:46:31,698 - pyskl - INFO - Epoch [23][3700/3746] lr: 9.432e-02, eta: 4 days, 0:06:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5194, loss_cls: 4.0423, loss: 4.0423 +2024-07-22 20:47:06,217 - pyskl - INFO - Saving checkpoint at 23 epochs +2024-07-22 20:48:58,293 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 20:48:58,957 - pyskl - INFO - +top1_acc 0.1755 +top5_acc 0.3999 +2024-07-22 20:48:58,957 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 20:48:58,996 - pyskl - INFO - +mean_acc 0.1753 +2024-07-22 20:48:59,006 - pyskl - INFO - Epoch(val) [23][309] top1_acc: 0.1755, top5_acc: 0.3999, mean_class_accuracy: 0.1753 +2024-07-22 20:52:19,903 - pyskl - INFO - Epoch [24][100/3746] lr: 9.430e-02, eta: 4 days, 0:13:23, time: 2.009, data_time: 1.299, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5214, loss_cls: 3.9943, loss: 3.9943 +2024-07-22 20:53:30,372 - pyskl - INFO - Epoch [24][200/3746] lr: 9.428e-02, eta: 4 days, 0:11:57, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5148, loss_cls: 4.0287, loss: 4.0287 +2024-07-22 20:54:41,176 - pyskl - INFO - Epoch [24][300/3746] lr: 9.427e-02, eta: 4 days, 0:10:33, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5122, loss_cls: 4.0166, loss: 4.0166 +2024-07-22 20:55:52,147 - pyskl - INFO - Epoch [24][400/3746] lr: 9.426e-02, eta: 4 days, 0:09:10, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5184, loss_cls: 3.9938, loss: 3.9938 +2024-07-22 20:57:02,675 - pyskl - INFO - Epoch [24][500/3746] lr: 9.425e-02, eta: 4 days, 0:07:45, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5095, loss_cls: 4.0308, loss: 4.0308 +2024-07-22 20:58:13,310 - pyskl - INFO - Epoch [24][600/3746] lr: 9.423e-02, eta: 4 days, 0:06:20, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4994, loss_cls: 4.0940, loss: 4.0940 +2024-07-22 20:59:23,520 - pyskl - INFO - Epoch [24][700/3746] lr: 9.422e-02, eta: 4 days, 0:04:53, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5261, loss_cls: 4.0005, loss: 4.0005 +2024-07-22 21:00:33,283 - pyskl - INFO - Epoch [24][800/3746] lr: 9.421e-02, eta: 4 days, 0:03:24, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5047, loss_cls: 4.0809, loss: 4.0809 +2024-07-22 21:01:43,264 - pyskl - INFO - Epoch [24][900/3746] lr: 9.419e-02, eta: 4 days, 0:01:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5136, loss_cls: 4.0383, loss: 4.0383 +2024-07-22 21:02:53,209 - pyskl - INFO - Epoch [24][1000/3746] lr: 9.418e-02, eta: 4 days, 0:00:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5069, loss_cls: 4.0603, loss: 4.0603 +2024-07-22 21:04:03,140 - pyskl - INFO - Epoch [24][1100/3746] lr: 9.417e-02, eta: 3 days, 23:58:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5177, loss_cls: 3.9941, loss: 3.9941 +2024-07-22 21:05:13,058 - pyskl - INFO - Epoch [24][1200/3746] lr: 9.415e-02, eta: 3 days, 23:57:30, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5194, loss_cls: 3.9756, loss: 3.9756 +2024-07-22 21:06:22,937 - pyskl - INFO - Epoch [24][1300/3746] lr: 9.414e-02, eta: 3 days, 23:56:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5152, loss_cls: 4.0429, loss: 4.0429 +2024-07-22 21:07:32,820 - pyskl - INFO - Epoch [24][1400/3746] lr: 9.413e-02, eta: 3 days, 23:54:33, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5209, loss_cls: 4.0200, loss: 4.0200 +2024-07-22 21:08:42,766 - pyskl - INFO - Epoch [24][1500/3746] lr: 9.411e-02, eta: 3 days, 23:53:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5236, loss_cls: 4.0052, loss: 4.0052 +2024-07-22 21:09:52,912 - pyskl - INFO - Epoch [24][1600/3746] lr: 9.410e-02, eta: 3 days, 23:51:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5008, loss_cls: 4.0765, loss: 4.0765 +2024-07-22 21:11:02,975 - pyskl - INFO - Epoch [24][1700/3746] lr: 9.409e-02, eta: 3 days, 23:50:10, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5095, loss_cls: 4.0431, loss: 4.0431 +2024-07-22 21:12:13,025 - pyskl - INFO - Epoch [24][1800/3746] lr: 9.407e-02, eta: 3 days, 23:48:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5153, loss_cls: 4.0403, loss: 4.0403 +2024-07-22 21:13:23,109 - pyskl - INFO - Epoch [24][1900/3746] lr: 9.406e-02, eta: 3 days, 23:47:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5128, loss_cls: 4.0282, loss: 4.0282 +2024-07-22 21:14:33,064 - pyskl - INFO - Epoch [24][2000/3746] lr: 9.405e-02, eta: 3 days, 23:45:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5102, loss_cls: 4.0527, loss: 4.0527 +2024-07-22 21:15:42,758 - pyskl - INFO - Epoch [24][2100/3746] lr: 9.404e-02, eta: 3 days, 23:44:18, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5092, loss_cls: 4.0251, loss: 4.0251 +2024-07-22 21:16:52,443 - pyskl - INFO - Epoch [24][2200/3746] lr: 9.402e-02, eta: 3 days, 23:42:49, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5278, loss_cls: 3.9794, loss: 3.9794 +2024-07-22 21:18:02,374 - pyskl - INFO - Epoch [24][2300/3746] lr: 9.401e-02, eta: 3 days, 23:41:21, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5078, loss_cls: 4.0501, loss: 4.0501 +2024-07-22 21:19:12,267 - pyskl - INFO - Epoch [24][2400/3746] lr: 9.400e-02, eta: 3 days, 23:39:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5103, loss_cls: 4.0496, loss: 4.0496 +2024-07-22 21:20:22,253 - pyskl - INFO - Epoch [24][2500/3746] lr: 9.398e-02, eta: 3 days, 23:38:25, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5188, loss_cls: 3.9860, loss: 3.9860 +2024-07-22 21:21:32,287 - pyskl - INFO - Epoch [24][2600/3746] lr: 9.397e-02, eta: 3 days, 23:36:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5067, loss_cls: 4.0549, loss: 4.0549 +2024-07-22 21:22:42,304 - pyskl - INFO - Epoch [24][2700/3746] lr: 9.396e-02, eta: 3 days, 23:35:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5025, loss_cls: 4.0531, loss: 4.0531 +2024-07-22 21:23:51,978 - pyskl - INFO - Epoch [24][2800/3746] lr: 9.394e-02, eta: 3 days, 23:34:02, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.4967, loss_cls: 4.1133, loss: 4.1133 +2024-07-22 21:25:01,957 - pyskl - INFO - Epoch [24][2900/3746] lr: 9.393e-02, eta: 3 days, 23:32:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5145, loss_cls: 4.0308, loss: 4.0308 +2024-07-22 21:26:11,696 - pyskl - INFO - Epoch [24][3000/3746] lr: 9.392e-02, eta: 3 days, 23:31:06, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5123, loss_cls: 4.0567, loss: 4.0567 +2024-07-22 21:27:21,413 - pyskl - INFO - Epoch [24][3100/3746] lr: 9.390e-02, eta: 3 days, 23:29:37, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5023, loss_cls: 4.0935, loss: 4.0935 +2024-07-22 21:28:31,269 - pyskl - INFO - Epoch [24][3200/3746] lr: 9.389e-02, eta: 3 days, 23:28:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5195, loss_cls: 3.9994, loss: 3.9994 +2024-07-22 21:29:41,148 - pyskl - INFO - Epoch [24][3300/3746] lr: 9.388e-02, eta: 3 days, 23:26:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5091, loss_cls: 4.0336, loss: 4.0336 +2024-07-22 21:30:51,495 - pyskl - INFO - Epoch [24][3400/3746] lr: 9.386e-02, eta: 3 days, 23:25:16, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5214, loss_cls: 3.9766, loss: 3.9766 +2024-07-22 21:32:01,678 - pyskl - INFO - Epoch [24][3500/3746] lr: 9.385e-02, eta: 3 days, 23:23:50, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5080, loss_cls: 4.0596, loss: 4.0596 +2024-07-22 21:33:12,107 - pyskl - INFO - Epoch [24][3600/3746] lr: 9.384e-02, eta: 3 days, 23:22:25, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.4988, loss_cls: 4.0569, loss: 4.0569 +2024-07-22 21:34:22,410 - pyskl - INFO - Epoch [24][3700/3746] lr: 9.382e-02, eta: 3 days, 23:20:59, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5061, loss_cls: 4.0506, loss: 4.0506 +2024-07-22 21:34:57,049 - pyskl - INFO - Saving checkpoint at 24 epochs +2024-07-22 21:36:48,695 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 21:36:49,385 - pyskl - INFO - +top1_acc 0.1891 +top5_acc 0.4108 +2024-07-22 21:36:49,385 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 21:36:49,426 - pyskl - INFO - +mean_acc 0.1890 +2024-07-22 21:36:49,442 - pyskl - INFO - Epoch(val) [24][309] top1_acc: 0.1891, top5_acc: 0.4108, mean_class_accuracy: 0.1890 +2024-07-22 21:40:09,645 - pyskl - INFO - Epoch [25][100/3746] lr: 9.380e-02, eta: 3 days, 23:27:26, time: 2.002, data_time: 1.297, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5258, loss_cls: 3.9796, loss: 3.9796 +2024-07-22 21:41:19,736 - pyskl - INFO - Epoch [25][200/3746] lr: 9.379e-02, eta: 3 days, 23:25:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5119, loss_cls: 3.9955, loss: 3.9955 +2024-07-22 21:42:30,724 - pyskl - INFO - Epoch [25][300/3746] lr: 9.378e-02, eta: 3 days, 23:24:37, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5178, loss_cls: 4.0138, loss: 4.0138 +2024-07-22 21:43:41,120 - pyskl - INFO - Epoch [25][400/3746] lr: 9.376e-02, eta: 3 days, 23:23:11, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5122, loss_cls: 3.9969, loss: 3.9969 +2024-07-22 21:44:51,888 - pyskl - INFO - Epoch [25][500/3746] lr: 9.375e-02, eta: 3 days, 23:21:48, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5030, loss_cls: 4.0668, loss: 4.0668 +2024-07-22 21:46:02,031 - pyskl - INFO - Epoch [25][600/3746] lr: 9.373e-02, eta: 3 days, 23:20:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5136, loss_cls: 4.0096, loss: 4.0096 +2024-07-22 21:47:12,182 - pyskl - INFO - Epoch [25][700/3746] lr: 9.372e-02, eta: 3 days, 23:18:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5133, loss_cls: 4.0188, loss: 4.0188 +2024-07-22 21:48:22,314 - pyskl - INFO - Epoch [25][800/3746] lr: 9.371e-02, eta: 3 days, 23:17:28, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5164, loss_cls: 4.0167, loss: 4.0167 +2024-07-22 21:49:32,437 - pyskl - INFO - Epoch [25][900/3746] lr: 9.369e-02, eta: 3 days, 23:16:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5255, loss_cls: 3.9986, loss: 3.9986 +2024-07-22 21:50:42,597 - pyskl - INFO - Epoch [25][1000/3746] lr: 9.368e-02, eta: 3 days, 23:14:34, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5064, loss_cls: 4.0555, loss: 4.0555 +2024-07-22 21:51:52,592 - pyskl - INFO - Epoch [25][1100/3746] lr: 9.367e-02, eta: 3 days, 23:13:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5108, loss_cls: 4.0566, loss: 4.0566 +2024-07-22 21:53:02,402 - pyskl - INFO - Epoch [25][1200/3746] lr: 9.365e-02, eta: 3 days, 23:11:39, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5172, loss_cls: 3.9939, loss: 3.9939 +2024-07-22 21:54:12,226 - pyskl - INFO - Epoch [25][1300/3746] lr: 9.364e-02, eta: 3 days, 23:10:11, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5181, loss_cls: 4.0280, loss: 4.0280 +2024-07-22 21:55:21,999 - pyskl - INFO - Epoch [25][1400/3746] lr: 9.363e-02, eta: 3 days, 23:08:43, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5136, loss_cls: 4.0184, loss: 4.0184 +2024-07-22 21:56:32,025 - pyskl - INFO - Epoch [25][1500/3746] lr: 9.361e-02, eta: 3 days, 23:07:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5091, loss_cls: 4.0403, loss: 4.0403 +2024-07-22 21:57:41,845 - pyskl - INFO - Epoch [25][1600/3746] lr: 9.360e-02, eta: 3 days, 23:05:48, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5166, loss_cls: 4.0147, loss: 4.0147 +2024-07-22 21:58:51,570 - pyskl - INFO - Epoch [25][1700/3746] lr: 9.358e-02, eta: 3 days, 23:04:19, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5023, loss_cls: 4.0831, loss: 4.0831 +2024-07-22 22:00:01,358 - pyskl - INFO - Epoch [25][1800/3746] lr: 9.357e-02, eta: 3 days, 23:02:51, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5073, loss_cls: 4.0736, loss: 4.0736 +2024-07-22 22:01:11,300 - pyskl - INFO - Epoch [25][1900/3746] lr: 9.356e-02, eta: 3 days, 23:01:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5184, loss_cls: 4.0147, loss: 4.0147 +2024-07-22 22:02:21,465 - pyskl - INFO - Epoch [25][2000/3746] lr: 9.354e-02, eta: 3 days, 22:59:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5159, loss_cls: 4.0323, loss: 4.0323 +2024-07-22 22:03:31,387 - pyskl - INFO - Epoch [25][2100/3746] lr: 9.353e-02, eta: 3 days, 22:58:30, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5175, loss_cls: 3.9932, loss: 3.9932 +2024-07-22 22:04:41,697 - pyskl - INFO - Epoch [25][2200/3746] lr: 9.352e-02, eta: 3 days, 22:57:05, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5161, loss_cls: 4.0145, loss: 4.0145 +2024-07-22 22:05:51,560 - pyskl - INFO - Epoch [25][2300/3746] lr: 9.350e-02, eta: 3 days, 22:55:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5134, loss_cls: 4.0262, loss: 4.0262 +2024-07-22 22:07:01,486 - pyskl - INFO - Epoch [25][2400/3746] lr: 9.349e-02, eta: 3 days, 22:54:10, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5103, loss_cls: 4.0444, loss: 4.0444 +2024-07-22 22:08:11,205 - pyskl - INFO - Epoch [25][2500/3746] lr: 9.347e-02, eta: 3 days, 22:52:42, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5222, loss_cls: 4.0059, loss: 4.0059 +2024-07-22 22:09:20,860 - pyskl - INFO - Epoch [25][2600/3746] lr: 9.346e-02, eta: 3 days, 22:51:14, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5120, loss_cls: 4.0572, loss: 4.0572 +2024-07-22 22:10:30,901 - pyskl - INFO - Epoch [25][2700/3746] lr: 9.345e-02, eta: 3 days, 22:49:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5127, loss_cls: 4.0316, loss: 4.0316 +2024-07-22 22:11:40,738 - pyskl - INFO - Epoch [25][2800/3746] lr: 9.343e-02, eta: 3 days, 22:48:20, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5117, loss_cls: 4.0319, loss: 4.0319 +2024-07-22 22:12:50,544 - pyskl - INFO - Epoch [25][2900/3746] lr: 9.342e-02, eta: 3 days, 22:46:52, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5105, loss_cls: 4.0328, loss: 4.0328 +2024-07-22 22:14:00,497 - pyskl - INFO - Epoch [25][3000/3746] lr: 9.341e-02, eta: 3 days, 22:45:25, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5169, loss_cls: 4.0259, loss: 4.0259 +2024-07-22 22:15:10,456 - pyskl - INFO - Epoch [25][3100/3746] lr: 9.339e-02, eta: 3 days, 22:43:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5141, loss_cls: 4.0300, loss: 4.0300 +2024-07-22 22:16:20,339 - pyskl - INFO - Epoch [25][3200/3746] lr: 9.338e-02, eta: 3 days, 22:42:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5039, loss_cls: 4.0295, loss: 4.0295 +2024-07-22 22:17:30,244 - pyskl - INFO - Epoch [25][3300/3746] lr: 9.336e-02, eta: 3 days, 22:41:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5098, loss_cls: 4.0384, loss: 4.0384 +2024-07-22 22:18:40,176 - pyskl - INFO - Epoch [25][3400/3746] lr: 9.335e-02, eta: 3 days, 22:39:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5150, loss_cls: 4.0269, loss: 4.0269 +2024-07-22 22:19:50,330 - pyskl - INFO - Epoch [25][3500/3746] lr: 9.334e-02, eta: 3 days, 22:38:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5084, loss_cls: 4.0420, loss: 4.0420 +2024-07-22 22:21:00,298 - pyskl - INFO - Epoch [25][3600/3746] lr: 9.332e-02, eta: 3 days, 22:36:46, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5109, loss_cls: 4.0411, loss: 4.0411 +2024-07-22 22:22:10,502 - pyskl - INFO - Epoch [25][3700/3746] lr: 9.331e-02, eta: 3 days, 22:35:20, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5166, loss_cls: 4.0588, loss: 4.0588 +2024-07-22 22:22:45,016 - pyskl - INFO - Saving checkpoint at 25 epochs +2024-07-22 22:24:37,327 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 22:24:38,129 - pyskl - INFO - +top1_acc 0.1562 +top5_acc 0.3687 +2024-07-22 22:24:38,129 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 22:24:38,171 - pyskl - INFO - +mean_acc 0.1561 +2024-07-22 22:24:38,182 - pyskl - INFO - Epoch(val) [25][309] top1_acc: 0.1562, top5_acc: 0.3687, mean_class_accuracy: 0.1561 +2024-07-22 22:27:57,684 - pyskl - INFO - Epoch [26][100/3746] lr: 9.329e-02, eta: 3 days, 22:41:20, time: 1.995, data_time: 1.289, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5156, loss_cls: 4.0014, loss: 4.0014 +2024-07-22 22:29:08,388 - pyskl - INFO - Epoch [26][200/3746] lr: 9.327e-02, eta: 3 days, 22:39:57, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5089, loss_cls: 4.0219, loss: 4.0219 +2024-07-22 22:30:19,050 - pyskl - INFO - Epoch [26][300/3746] lr: 9.326e-02, eta: 3 days, 22:38:33, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5075, loss_cls: 4.0330, loss: 4.0330 +2024-07-22 22:31:30,076 - pyskl - INFO - Epoch [26][400/3746] lr: 9.325e-02, eta: 3 days, 22:37:12, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5148, loss_cls: 4.0165, loss: 4.0165 +2024-07-22 22:32:40,570 - pyskl - INFO - Epoch [26][500/3746] lr: 9.323e-02, eta: 3 days, 22:35:47, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5220, loss_cls: 3.9803, loss: 3.9803 +2024-07-22 22:33:50,799 - pyskl - INFO - Epoch [26][600/3746] lr: 9.322e-02, eta: 3 days, 22:34:22, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5220, loss_cls: 3.9850, loss: 3.9850 +2024-07-22 22:35:00,869 - pyskl - INFO - Epoch [26][700/3746] lr: 9.320e-02, eta: 3 days, 22:32:56, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5009, loss_cls: 4.0927, loss: 4.0927 +2024-07-22 22:36:11,155 - pyskl - INFO - Epoch [26][800/3746] lr: 9.319e-02, eta: 3 days, 22:31:30, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5112, loss_cls: 4.0444, loss: 4.0444 +2024-07-22 22:37:21,273 - pyskl - INFO - Epoch [26][900/3746] lr: 9.318e-02, eta: 3 days, 22:30:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5131, loss_cls: 4.0485, loss: 4.0485 +2024-07-22 22:38:31,283 - pyskl - INFO - Epoch [26][1000/3746] lr: 9.316e-02, eta: 3 days, 22:28:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5133, loss_cls: 4.0406, loss: 4.0406 +2024-07-22 22:39:41,101 - pyskl - INFO - Epoch [26][1100/3746] lr: 9.315e-02, eta: 3 days, 22:27:10, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5077, loss_cls: 4.0470, loss: 4.0470 +2024-07-22 22:40:51,095 - pyskl - INFO - Epoch [26][1200/3746] lr: 9.313e-02, eta: 3 days, 22:25:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5197, loss_cls: 4.0098, loss: 4.0098 +2024-07-22 22:42:00,965 - pyskl - INFO - Epoch [26][1300/3746] lr: 9.312e-02, eta: 3 days, 22:24:17, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5139, loss_cls: 4.0256, loss: 4.0256 +2024-07-22 22:43:10,891 - pyskl - INFO - Epoch [26][1400/3746] lr: 9.310e-02, eta: 3 days, 22:22:50, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5159, loss_cls: 4.0108, loss: 4.0108 +2024-07-22 22:44:20,876 - pyskl - INFO - Epoch [26][1500/3746] lr: 9.309e-02, eta: 3 days, 22:21:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5205, loss_cls: 4.0145, loss: 4.0145 +2024-07-22 22:45:30,647 - pyskl - INFO - Epoch [26][1600/3746] lr: 9.308e-02, eta: 3 days, 22:19:56, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5152, loss_cls: 3.9965, loss: 3.9965 +2024-07-22 22:46:40,597 - pyskl - INFO - Epoch [26][1700/3746] lr: 9.306e-02, eta: 3 days, 22:18:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5028, loss_cls: 4.0548, loss: 4.0548 +2024-07-22 22:47:50,497 - pyskl - INFO - Epoch [26][1800/3746] lr: 9.305e-02, eta: 3 days, 22:17:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5098, loss_cls: 4.0331, loss: 4.0331 +2024-07-22 22:49:00,235 - pyskl - INFO - Epoch [26][1900/3746] lr: 9.303e-02, eta: 3 days, 22:15:35, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5156, loss_cls: 4.0435, loss: 4.0435 +2024-07-22 22:50:10,174 - pyskl - INFO - Epoch [26][2000/3746] lr: 9.302e-02, eta: 3 days, 22:14:08, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5184, loss_cls: 4.0259, loss: 4.0259 +2024-07-22 22:51:20,006 - pyskl - INFO - Epoch [26][2100/3746] lr: 9.300e-02, eta: 3 days, 22:12:41, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5158, loss_cls: 4.0235, loss: 4.0235 +2024-07-22 22:52:29,914 - pyskl - INFO - Epoch [26][2200/3746] lr: 9.299e-02, eta: 3 days, 22:11:15, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5105, loss_cls: 4.0273, loss: 4.0273 +2024-07-22 22:53:39,878 - pyskl - INFO - Epoch [26][2300/3746] lr: 9.298e-02, eta: 3 days, 22:09:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5336, loss_cls: 3.9594, loss: 3.9594 +2024-07-22 22:54:50,033 - pyskl - INFO - Epoch [26][2400/3746] lr: 9.296e-02, eta: 3 days, 22:08:23, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5223, loss_cls: 3.9836, loss: 3.9836 +2024-07-22 22:55:59,782 - pyskl - INFO - Epoch [26][2500/3746] lr: 9.295e-02, eta: 3 days, 22:06:56, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5008, loss_cls: 4.0761, loss: 4.0761 +2024-07-22 22:57:09,555 - pyskl - INFO - Epoch [26][2600/3746] lr: 9.293e-02, eta: 3 days, 22:05:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5080, loss_cls: 4.0521, loss: 4.0521 +2024-07-22 22:58:19,717 - pyskl - INFO - Epoch [26][2700/3746] lr: 9.292e-02, eta: 3 days, 22:04:03, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.4988, loss_cls: 4.0983, loss: 4.0983 +2024-07-22 22:59:29,658 - pyskl - INFO - Epoch [26][2800/3746] lr: 9.290e-02, eta: 3 days, 22:02:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5155, loss_cls: 4.0143, loss: 4.0143 +2024-07-22 23:00:39,866 - pyskl - INFO - Epoch [26][2900/3746] lr: 9.289e-02, eta: 3 days, 22:01:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5014, loss_cls: 4.0917, loss: 4.0917 +2024-07-22 23:01:49,703 - pyskl - INFO - Epoch [26][3000/3746] lr: 9.288e-02, eta: 3 days, 21:59:45, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5181, loss_cls: 4.0263, loss: 4.0263 +2024-07-22 23:02:59,725 - pyskl - INFO - Epoch [26][3100/3746] lr: 9.286e-02, eta: 3 days, 21:58:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5036, loss_cls: 4.0732, loss: 4.0732 +2024-07-22 23:04:09,348 - pyskl - INFO - Epoch [26][3200/3746] lr: 9.285e-02, eta: 3 days, 21:56:52, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5041, loss_cls: 4.0425, loss: 4.0425 +2024-07-22 23:05:19,396 - pyskl - INFO - Epoch [26][3300/3746] lr: 9.283e-02, eta: 3 days, 21:55:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5014, loss_cls: 4.0823, loss: 4.0823 +2024-07-22 23:06:29,339 - pyskl - INFO - Epoch [26][3400/3746] lr: 9.282e-02, eta: 3 days, 21:54:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5119, loss_cls: 4.0189, loss: 4.0189 +2024-07-22 23:07:39,437 - pyskl - INFO - Epoch [26][3500/3746] lr: 9.280e-02, eta: 3 days, 21:52:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5216, loss_cls: 3.9978, loss: 3.9978 +2024-07-22 23:08:49,653 - pyskl - INFO - Epoch [26][3600/3746] lr: 9.279e-02, eta: 3 days, 21:51:10, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5172, loss_cls: 4.0396, loss: 4.0396 +2024-07-22 23:09:59,643 - pyskl - INFO - Epoch [26][3700/3746] lr: 9.278e-02, eta: 3 days, 21:49:45, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5111, loss_cls: 4.0216, loss: 4.0216 +2024-07-22 23:10:34,256 - pyskl - INFO - Saving checkpoint at 26 epochs +2024-07-22 23:12:25,909 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 23:12:26,576 - pyskl - INFO - +top1_acc 0.1870 +top5_acc 0.4080 +2024-07-22 23:12:26,576 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 23:12:26,627 - pyskl - INFO - +mean_acc 0.1870 +2024-07-22 23:12:26,643 - pyskl - INFO - Epoch(val) [26][309] top1_acc: 0.1870, top5_acc: 0.4080, mean_class_accuracy: 0.1870 +2024-07-22 23:15:44,704 - pyskl - INFO - Epoch [27][100/3746] lr: 9.275e-02, eta: 3 days, 21:55:16, time: 1.980, data_time: 1.276, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5269, loss_cls: 3.9734, loss: 3.9734 +2024-07-22 23:16:55,153 - pyskl - INFO - Epoch [27][200/3746] lr: 9.274e-02, eta: 3 days, 21:53:52, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5120, loss_cls: 4.0284, loss: 4.0284 +2024-07-22 23:18:05,545 - pyskl - INFO - Epoch [27][300/3746] lr: 9.272e-02, eta: 3 days, 21:52:28, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5188, loss_cls: 4.0157, loss: 4.0157 +2024-07-22 23:19:16,308 - pyskl - INFO - Epoch [27][400/3746] lr: 9.271e-02, eta: 3 days, 21:51:05, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5178, loss_cls: 4.0027, loss: 4.0027 +2024-07-22 23:20:26,989 - pyskl - INFO - Epoch [27][500/3746] lr: 9.270e-02, eta: 3 days, 21:49:43, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5186, loss_cls: 4.0103, loss: 4.0103 +2024-07-22 23:21:37,347 - pyskl - INFO - Epoch [27][600/3746] lr: 9.268e-02, eta: 3 days, 21:48:18, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5142, loss_cls: 4.0006, loss: 4.0006 +2024-07-22 23:22:47,403 - pyskl - INFO - Epoch [27][700/3746] lr: 9.267e-02, eta: 3 days, 21:46:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5106, loss_cls: 4.0604, loss: 4.0604 +2024-07-22 23:23:57,314 - pyskl - INFO - Epoch [27][800/3746] lr: 9.265e-02, eta: 3 days, 21:45:26, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5173, loss_cls: 4.0036, loss: 4.0036 +2024-07-22 23:25:07,523 - pyskl - INFO - Epoch [27][900/3746] lr: 9.264e-02, eta: 3 days, 21:44:01, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5072, loss_cls: 4.0468, loss: 4.0468 +2024-07-22 23:26:17,583 - pyskl - INFO - Epoch [27][1000/3746] lr: 9.262e-02, eta: 3 days, 21:42:36, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5095, loss_cls: 4.0512, loss: 4.0512 +2024-07-22 23:27:27,402 - pyskl - INFO - Epoch [27][1100/3746] lr: 9.261e-02, eta: 3 days, 21:41:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5097, loss_cls: 4.0398, loss: 4.0398 +2024-07-22 23:28:37,196 - pyskl - INFO - Epoch [27][1200/3746] lr: 9.259e-02, eta: 3 days, 21:39:42, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5136, loss_cls: 4.0127, loss: 4.0127 +2024-07-22 23:29:47,157 - pyskl - INFO - Epoch [27][1300/3746] lr: 9.258e-02, eta: 3 days, 21:38:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5139, loss_cls: 4.0031, loss: 4.0031 +2024-07-22 23:30:57,206 - pyskl - INFO - Epoch [27][1400/3746] lr: 9.256e-02, eta: 3 days, 21:36:50, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5119, loss_cls: 4.0407, loss: 4.0407 +2024-07-22 23:32:06,991 - pyskl - INFO - Epoch [27][1500/3746] lr: 9.255e-02, eta: 3 days, 21:35:24, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5139, loss_cls: 4.0217, loss: 4.0217 +2024-07-22 23:33:16,845 - pyskl - INFO - Epoch [27][1600/3746] lr: 9.253e-02, eta: 3 days, 21:33:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5131, loss_cls: 3.9999, loss: 3.9999 +2024-07-22 23:34:26,736 - pyskl - INFO - Epoch [27][1700/3746] lr: 9.252e-02, eta: 3 days, 21:32:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5212, loss_cls: 4.0083, loss: 4.0083 +2024-07-22 23:35:36,748 - pyskl - INFO - Epoch [27][1800/3746] lr: 9.251e-02, eta: 3 days, 21:31:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5145, loss_cls: 4.0177, loss: 4.0177 +2024-07-22 23:36:46,376 - pyskl - INFO - Epoch [27][1900/3746] lr: 9.249e-02, eta: 3 days, 21:29:38, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5186, loss_cls: 4.0076, loss: 4.0076 +2024-07-22 23:37:56,416 - pyskl - INFO - Epoch [27][2000/3746] lr: 9.248e-02, eta: 3 days, 21:28:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5086, loss_cls: 4.0460, loss: 4.0460 +2024-07-22 23:39:06,352 - pyskl - INFO - Epoch [27][2100/3746] lr: 9.246e-02, eta: 3 days, 21:26:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5150, loss_cls: 4.0083, loss: 4.0083 +2024-07-22 23:40:16,241 - pyskl - INFO - Epoch [27][2200/3746] lr: 9.245e-02, eta: 3 days, 21:25:21, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5156, loss_cls: 4.0133, loss: 4.0133 +2024-07-22 23:41:26,333 - pyskl - INFO - Epoch [27][2300/3746] lr: 9.243e-02, eta: 3 days, 21:23:56, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5239, loss_cls: 3.9877, loss: 3.9877 +2024-07-22 23:42:36,146 - pyskl - INFO - Epoch [27][2400/3746] lr: 9.242e-02, eta: 3 days, 21:22:29, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5097, loss_cls: 4.0552, loss: 4.0552 +2024-07-22 23:43:45,861 - pyskl - INFO - Epoch [27][2500/3746] lr: 9.240e-02, eta: 3 days, 21:21:03, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5133, loss_cls: 4.0314, loss: 4.0314 +2024-07-22 23:44:55,767 - pyskl - INFO - Epoch [27][2600/3746] lr: 9.239e-02, eta: 3 days, 21:19:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5105, loss_cls: 4.0619, loss: 4.0619 +2024-07-22 23:46:05,775 - pyskl - INFO - Epoch [27][2700/3746] lr: 9.237e-02, eta: 3 days, 21:18:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5136, loss_cls: 4.0201, loss: 4.0201 +2024-07-22 23:47:15,658 - pyskl - INFO - Epoch [27][2800/3746] lr: 9.236e-02, eta: 3 days, 21:16:46, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5167, loss_cls: 4.0273, loss: 4.0273 +2024-07-22 23:48:25,582 - pyskl - INFO - Epoch [27][2900/3746] lr: 9.234e-02, eta: 3 days, 21:15:20, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5177, loss_cls: 4.0222, loss: 4.0222 +2024-07-22 23:49:35,438 - pyskl - INFO - Epoch [27][3000/3746] lr: 9.233e-02, eta: 3 days, 21:13:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5038, loss_cls: 4.0687, loss: 4.0687 +2024-07-22 23:50:45,175 - pyskl - INFO - Epoch [27][3100/3746] lr: 9.231e-02, eta: 3 days, 21:12:28, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5094, loss_cls: 4.0414, loss: 4.0414 +2024-07-22 23:51:55,327 - pyskl - INFO - Epoch [27][3200/3746] lr: 9.230e-02, eta: 3 days, 21:11:03, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5072, loss_cls: 4.0524, loss: 4.0524 +2024-07-22 23:53:05,125 - pyskl - INFO - Epoch [27][3300/3746] lr: 9.228e-02, eta: 3 days, 21:09:37, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5180, loss_cls: 4.0543, loss: 4.0543 +2024-07-22 23:54:15,506 - pyskl - INFO - Epoch [27][3400/3746] lr: 9.227e-02, eta: 3 days, 21:08:13, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5216, loss_cls: 4.0130, loss: 4.0130 +2024-07-22 23:55:25,914 - pyskl - INFO - Epoch [27][3500/3746] lr: 9.225e-02, eta: 3 days, 21:06:50, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5266, loss_cls: 3.9630, loss: 3.9630 +2024-07-22 23:56:36,118 - pyskl - INFO - Epoch [27][3600/3746] lr: 9.224e-02, eta: 3 days, 21:05:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5103, loss_cls: 4.0146, loss: 4.0146 +2024-07-22 23:57:45,932 - pyskl - INFO - Epoch [27][3700/3746] lr: 9.222e-02, eta: 3 days, 21:04:00, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5161, loss_cls: 4.0278, loss: 4.0278 +2024-07-22 23:58:20,296 - pyskl - INFO - Saving checkpoint at 27 epochs +2024-07-23 00:00:12,699 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 00:00:13,361 - pyskl - INFO - +top1_acc 0.1663 +top5_acc 0.3639 +2024-07-23 00:00:13,361 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 00:00:13,400 - pyskl - INFO - +mean_acc 0.1663 +2024-07-23 00:00:13,412 - pyskl - INFO - Epoch(val) [27][309] top1_acc: 0.1663, top5_acc: 0.3639, mean_class_accuracy: 0.1663 +2024-07-23 00:03:34,013 - pyskl - INFO - Epoch [28][100/3746] lr: 9.220e-02, eta: 3 days, 21:09:24, time: 2.006, data_time: 1.300, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5225, loss_cls: 3.9637, loss: 3.9637 +2024-07-23 00:04:43,950 - pyskl - INFO - Epoch [28][200/3746] lr: 9.219e-02, eta: 3 days, 21:07:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5236, loss_cls: 3.9987, loss: 3.9987 +2024-07-23 00:05:53,976 - pyskl - INFO - Epoch [28][300/3746] lr: 9.217e-02, eta: 3 days, 21:06:32, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5256, loss_cls: 3.9768, loss: 3.9768 +2024-07-23 00:07:04,705 - pyskl - INFO - Epoch [28][400/3746] lr: 9.216e-02, eta: 3 days, 21:05:10, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5144, loss_cls: 3.9939, loss: 3.9939 +2024-07-23 00:08:15,017 - pyskl - INFO - Epoch [28][500/3746] lr: 9.214e-02, eta: 3 days, 21:03:46, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5094, loss_cls: 4.0275, loss: 4.0275 +2024-07-23 00:09:25,567 - pyskl - INFO - Epoch [28][600/3746] lr: 9.213e-02, eta: 3 days, 21:02:23, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5183, loss_cls: 3.9859, loss: 3.9859 +2024-07-23 00:10:35,546 - pyskl - INFO - Epoch [28][700/3746] lr: 9.211e-02, eta: 3 days, 21:00:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5048, loss_cls: 4.0682, loss: 4.0682 +2024-07-23 00:11:45,632 - pyskl - INFO - Epoch [28][800/3746] lr: 9.210e-02, eta: 3 days, 20:59:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5114, loss_cls: 4.0370, loss: 4.0370 +2024-07-23 00:12:55,567 - pyskl - INFO - Epoch [28][900/3746] lr: 9.208e-02, eta: 3 days, 20:58:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5211, loss_cls: 4.0205, loss: 4.0205 +2024-07-23 00:14:05,225 - pyskl - INFO - Epoch [28][1000/3746] lr: 9.207e-02, eta: 3 days, 20:56:40, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5103, loss_cls: 4.0177, loss: 4.0177 +2024-07-23 00:15:15,182 - pyskl - INFO - Epoch [28][1100/3746] lr: 9.205e-02, eta: 3 days, 20:55:15, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5131, loss_cls: 4.0238, loss: 4.0238 +2024-07-23 00:16:24,970 - pyskl - INFO - Epoch [28][1200/3746] lr: 9.204e-02, eta: 3 days, 20:53:49, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5272, loss_cls: 4.0084, loss: 4.0084 +2024-07-23 00:17:34,883 - pyskl - INFO - Epoch [28][1300/3746] lr: 9.202e-02, eta: 3 days, 20:52:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5142, loss_cls: 4.0425, loss: 4.0425 +2024-07-23 00:18:44,881 - pyskl - INFO - Epoch [28][1400/3746] lr: 9.201e-02, eta: 3 days, 20:50:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5239, loss_cls: 3.9920, loss: 3.9920 +2024-07-23 00:19:54,529 - pyskl - INFO - Epoch [28][1500/3746] lr: 9.199e-02, eta: 3 days, 20:49:31, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5144, loss_cls: 4.0245, loss: 4.0245 +2024-07-23 00:21:04,391 - pyskl - INFO - Epoch [28][1600/3746] lr: 9.198e-02, eta: 3 days, 20:48:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5191, loss_cls: 4.0099, loss: 4.0099 +2024-07-23 00:22:14,415 - pyskl - INFO - Epoch [28][1700/3746] lr: 9.196e-02, eta: 3 days, 20:46:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5067, loss_cls: 4.0404, loss: 4.0404 +2024-07-23 00:23:24,461 - pyskl - INFO - Epoch [28][1800/3746] lr: 9.194e-02, eta: 3 days, 20:45:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5202, loss_cls: 4.0047, loss: 4.0047 +2024-07-23 00:24:34,175 - pyskl - INFO - Epoch [28][1900/3746] lr: 9.193e-02, eta: 3 days, 20:43:49, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5155, loss_cls: 4.0183, loss: 4.0183 +2024-07-23 00:25:43,919 - pyskl - INFO - Epoch [28][2000/3746] lr: 9.191e-02, eta: 3 days, 20:42:23, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5044, loss_cls: 4.0627, loss: 4.0627 +2024-07-23 00:26:53,505 - pyskl - INFO - Epoch [28][2100/3746] lr: 9.190e-02, eta: 3 days, 20:40:56, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5144, loss_cls: 4.0165, loss: 4.0165 +2024-07-23 00:28:03,434 - pyskl - INFO - Epoch [28][2200/3746] lr: 9.188e-02, eta: 3 days, 20:39:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5164, loss_cls: 4.0375, loss: 4.0375 +2024-07-23 00:29:13,355 - pyskl - INFO - Epoch [28][2300/3746] lr: 9.187e-02, eta: 3 days, 20:38:06, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5142, loss_cls: 4.0474, loss: 4.0474 +2024-07-23 00:30:23,110 - pyskl - INFO - Epoch [28][2400/3746] lr: 9.185e-02, eta: 3 days, 20:36:40, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5041, loss_cls: 4.0505, loss: 4.0505 +2024-07-23 00:31:32,991 - pyskl - INFO - Epoch [28][2500/3746] lr: 9.184e-02, eta: 3 days, 20:35:15, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5084, loss_cls: 4.0638, loss: 4.0638 +2024-07-23 00:32:42,909 - pyskl - INFO - Epoch [28][2600/3746] lr: 9.182e-02, eta: 3 days, 20:33:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5116, loss_cls: 4.0592, loss: 4.0592 +2024-07-23 00:33:52,715 - pyskl - INFO - Epoch [28][2700/3746] lr: 9.181e-02, eta: 3 days, 20:32:24, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5122, loss_cls: 4.0306, loss: 4.0306 +2024-07-23 00:35:02,397 - pyskl - INFO - Epoch [28][2800/3746] lr: 9.179e-02, eta: 3 days, 20:30:58, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5159, loss_cls: 4.0204, loss: 4.0204 +2024-07-23 00:36:12,437 - pyskl - INFO - Epoch [28][2900/3746] lr: 9.178e-02, eta: 3 days, 20:29:33, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5127, loss_cls: 4.0238, loss: 4.0238 +2024-07-23 00:37:22,147 - pyskl - INFO - Epoch [28][3000/3746] lr: 9.176e-02, eta: 3 days, 20:28:07, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5088, loss_cls: 4.0494, loss: 4.0494 +2024-07-23 00:38:31,828 - pyskl - INFO - Epoch [28][3100/3746] lr: 9.175e-02, eta: 3 days, 20:26:41, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5267, loss_cls: 3.9736, loss: 3.9736 +2024-07-23 00:39:42,134 - pyskl - INFO - Epoch [28][3200/3746] lr: 9.173e-02, eta: 3 days, 20:25:18, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5162, loss_cls: 4.0052, loss: 4.0052 +2024-07-23 00:40:51,992 - pyskl - INFO - Epoch [28][3300/3746] lr: 9.172e-02, eta: 3 days, 20:23:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5172, loss_cls: 4.0266, loss: 4.0266 +2024-07-23 00:42:02,185 - pyskl - INFO - Epoch [28][3400/3746] lr: 9.170e-02, eta: 3 days, 20:22:29, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5136, loss_cls: 4.0710, loss: 4.0710 +2024-07-23 00:43:12,167 - pyskl - INFO - Epoch [28][3500/3746] lr: 9.168e-02, eta: 3 days, 20:21:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5344, loss_cls: 3.9749, loss: 3.9749 +2024-07-23 00:44:22,242 - pyskl - INFO - Epoch [28][3600/3746] lr: 9.167e-02, eta: 3 days, 20:19:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5125, loss_cls: 4.0209, loss: 4.0209 +2024-07-23 00:45:32,128 - pyskl - INFO - Epoch [28][3700/3746] lr: 9.165e-02, eta: 3 days, 20:18:15, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5086, loss_cls: 4.0385, loss: 4.0385 +2024-07-23 00:46:06,695 - pyskl - INFO - Saving checkpoint at 28 epochs +2024-07-23 00:47:59,144 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 00:47:59,807 - pyskl - INFO - +top1_acc 0.1931 +top5_acc 0.4135 +2024-07-23 00:47:59,807 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 00:47:59,846 - pyskl - INFO - +mean_acc 0.1929 +2024-07-23 00:47:59,858 - pyskl - INFO - Epoch(val) [28][309] top1_acc: 0.1931, top5_acc: 0.4135, mean_class_accuracy: 0.1929 +2024-07-23 00:51:18,649 - pyskl - INFO - Epoch [29][100/3746] lr: 9.163e-02, eta: 3 days, 20:23:12, time: 1.988, data_time: 1.282, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5256, loss_cls: 3.9517, loss: 3.9517 +2024-07-23 00:52:28,890 - pyskl - INFO - Epoch [29][200/3746] lr: 9.162e-02, eta: 3 days, 20:21:48, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5222, loss_cls: 3.9708, loss: 3.9708 +2024-07-23 00:53:39,264 - pyskl - INFO - Epoch [29][300/3746] lr: 9.160e-02, eta: 3 days, 20:20:25, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5164, loss_cls: 4.0221, loss: 4.0221 +2024-07-23 00:54:50,195 - pyskl - INFO - Epoch [29][400/3746] lr: 9.158e-02, eta: 3 days, 20:19:04, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5086, loss_cls: 4.0257, loss: 4.0257 +2024-07-23 00:56:00,216 - pyskl - INFO - Epoch [29][500/3746] lr: 9.157e-02, eta: 3 days, 20:17:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5286, loss_cls: 3.9698, loss: 3.9698 +2024-07-23 00:57:10,619 - pyskl - INFO - Epoch [29][600/3746] lr: 9.155e-02, eta: 3 days, 20:16:16, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5028, loss_cls: 4.0654, loss: 4.0654 +2024-07-23 00:58:20,809 - pyskl - INFO - Epoch [29][700/3746] lr: 9.154e-02, eta: 3 days, 20:14:52, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5220, loss_cls: 3.9989, loss: 3.9989 +2024-07-23 00:59:30,822 - pyskl - INFO - Epoch [29][800/3746] lr: 9.152e-02, eta: 3 days, 20:13:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5197, loss_cls: 4.0116, loss: 4.0116 +2024-07-23 01:00:40,955 - pyskl - INFO - Epoch [29][900/3746] lr: 9.151e-02, eta: 3 days, 20:12:03, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5225, loss_cls: 3.9893, loss: 3.9893 +2024-07-23 01:01:51,030 - pyskl - INFO - Epoch [29][1000/3746] lr: 9.149e-02, eta: 3 days, 20:10:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5059, loss_cls: 4.0392, loss: 4.0392 +2024-07-23 01:03:01,155 - pyskl - INFO - Epoch [29][1100/3746] lr: 9.148e-02, eta: 3 days, 20:09:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5277, loss_cls: 3.9817, loss: 3.9817 +2024-07-23 01:04:10,878 - pyskl - INFO - Epoch [29][1200/3746] lr: 9.146e-02, eta: 3 days, 20:07:49, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5114, loss_cls: 4.0392, loss: 4.0392 +2024-07-23 01:05:20,758 - pyskl - INFO - Epoch [29][1300/3746] lr: 9.144e-02, eta: 3 days, 20:06:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5211, loss_cls: 4.0070, loss: 4.0070 +2024-07-23 01:06:30,521 - pyskl - INFO - Epoch [29][1400/3746] lr: 9.143e-02, eta: 3 days, 20:04:58, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5106, loss_cls: 4.0301, loss: 4.0301 +2024-07-23 01:07:40,290 - pyskl - INFO - Epoch [29][1500/3746] lr: 9.141e-02, eta: 3 days, 20:03:32, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5220, loss_cls: 3.9954, loss: 3.9954 +2024-07-23 01:08:50,451 - pyskl - INFO - Epoch [29][1600/3746] lr: 9.140e-02, eta: 3 days, 20:02:09, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5062, loss_cls: 4.0552, loss: 4.0552 +2024-07-23 01:10:00,488 - pyskl - INFO - Epoch [29][1700/3746] lr: 9.138e-02, eta: 3 days, 20:00:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5100, loss_cls: 4.0411, loss: 4.0411 +2024-07-23 01:11:10,387 - pyskl - INFO - Epoch [29][1800/3746] lr: 9.137e-02, eta: 3 days, 19:59:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5208, loss_cls: 3.9955, loss: 3.9955 +2024-07-23 01:12:20,703 - pyskl - INFO - Epoch [29][1900/3746] lr: 9.135e-02, eta: 3 days, 19:57:56, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5155, loss_cls: 4.0304, loss: 4.0304 +2024-07-23 01:13:30,507 - pyskl - INFO - Epoch [29][2000/3746] lr: 9.133e-02, eta: 3 days, 19:56:31, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5138, loss_cls: 4.0225, loss: 4.0225 +2024-07-23 01:14:40,469 - pyskl - INFO - Epoch [29][2100/3746] lr: 9.132e-02, eta: 3 days, 19:55:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5195, loss_cls: 3.9863, loss: 3.9863 +2024-07-23 01:15:50,323 - pyskl - INFO - Epoch [29][2200/3746] lr: 9.130e-02, eta: 3 days, 19:53:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5134, loss_cls: 4.0168, loss: 4.0168 +2024-07-23 01:17:00,116 - pyskl - INFO - Epoch [29][2300/3746] lr: 9.129e-02, eta: 3 days, 19:52:16, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5152, loss_cls: 4.0367, loss: 4.0367 +2024-07-23 01:18:09,927 - pyskl - INFO - Epoch [29][2400/3746] lr: 9.127e-02, eta: 3 days, 19:50:51, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5133, loss_cls: 4.0344, loss: 4.0344 +2024-07-23 01:19:19,846 - pyskl - INFO - Epoch [29][2500/3746] lr: 9.126e-02, eta: 3 days, 19:49:26, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5180, loss_cls: 4.0127, loss: 4.0127 +2024-07-23 01:20:29,751 - pyskl - INFO - Epoch [29][2600/3746] lr: 9.124e-02, eta: 3 days, 19:48:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5092, loss_cls: 4.0328, loss: 4.0328 +2024-07-23 01:21:39,662 - pyskl - INFO - Epoch [29][2700/3746] lr: 9.122e-02, eta: 3 days, 19:46:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5022, loss_cls: 4.0665, loss: 4.0665 +2024-07-23 01:22:49,334 - pyskl - INFO - Epoch [29][2800/3746] lr: 9.121e-02, eta: 3 days, 19:45:11, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5203, loss_cls: 3.9851, loss: 3.9851 +2024-07-23 01:23:59,351 - pyskl - INFO - Epoch [29][2900/3746] lr: 9.119e-02, eta: 3 days, 19:43:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5116, loss_cls: 4.0379, loss: 4.0379 +2024-07-23 01:25:09,171 - pyskl - INFO - Epoch [29][3000/3746] lr: 9.118e-02, eta: 3 days, 19:42:22, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5111, loss_cls: 4.0246, loss: 4.0246 +2024-07-23 01:26:19,384 - pyskl - INFO - Epoch [29][3100/3746] lr: 9.116e-02, eta: 3 days, 19:40:59, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5059, loss_cls: 4.0395, loss: 4.0395 +2024-07-23 01:27:29,328 - pyskl - INFO - Epoch [29][3200/3746] lr: 9.114e-02, eta: 3 days, 19:39:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5083, loss_cls: 4.0638, loss: 4.0638 +2024-07-23 01:28:39,308 - pyskl - INFO - Epoch [29][3300/3746] lr: 9.113e-02, eta: 3 days, 19:38:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5258, loss_cls: 3.9623, loss: 3.9623 +2024-07-23 01:29:49,951 - pyskl - INFO - Epoch [29][3400/3746] lr: 9.111e-02, eta: 3 days, 19:36:49, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5186, loss_cls: 4.0202, loss: 4.0202 +2024-07-23 01:31:00,073 - pyskl - INFO - Epoch [29][3500/3746] lr: 9.110e-02, eta: 3 days, 19:35:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5167, loss_cls: 4.0205, loss: 4.0205 +2024-07-23 01:32:10,100 - pyskl - INFO - Epoch [29][3600/3746] lr: 9.108e-02, eta: 3 days, 19:34:02, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5205, loss_cls: 3.9946, loss: 3.9946 +2024-07-23 01:33:20,188 - pyskl - INFO - Epoch [29][3700/3746] lr: 9.106e-02, eta: 3 days, 19:32:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5084, loss_cls: 4.0525, loss: 4.0525 +2024-07-23 01:33:54,332 - pyskl - INFO - Saving checkpoint at 29 epochs +2024-07-23 01:35:46,449 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 01:35:47,107 - pyskl - INFO - +top1_acc 0.1823 +top5_acc 0.4001 +2024-07-23 01:35:47,108 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 01:35:47,147 - pyskl - INFO - +mean_acc 0.1822 +2024-07-23 01:35:47,158 - pyskl - INFO - Epoch(val) [29][309] top1_acc: 0.1823, top5_acc: 0.4001, mean_class_accuracy: 0.1822 +2024-07-23 01:39:16,895 - pyskl - INFO - Epoch [30][100/3746] lr: 9.104e-02, eta: 3 days, 19:38:04, time: 2.097, data_time: 1.293, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5356, loss_cls: 3.9030, loss: 3.9030 +2024-07-23 01:40:37,239 - pyskl - INFO - Epoch [30][200/3746] lr: 9.103e-02, eta: 3 days, 19:37:22, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5288, loss_cls: 3.9276, loss: 3.9276 +2024-07-23 01:41:57,937 - pyskl - INFO - Epoch [30][300/3746] lr: 9.101e-02, eta: 3 days, 19:36:42, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5180, loss_cls: 3.9766, loss: 3.9766 +2024-07-23 01:43:18,880 - pyskl - INFO - Epoch [30][400/3746] lr: 9.099e-02, eta: 3 days, 19:36:03, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5208, loss_cls: 3.9761, loss: 3.9761 +2024-07-23 01:44:39,448 - pyskl - INFO - Epoch [30][500/3746] lr: 9.098e-02, eta: 3 days, 19:35:23, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5172, loss_cls: 3.9927, loss: 3.9927 +2024-07-23 01:46:00,191 - pyskl - INFO - Epoch [30][600/3746] lr: 9.096e-02, eta: 3 days, 19:34:43, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5159, loss_cls: 4.0551, loss: 4.0551 +2024-07-23 01:47:20,326 - pyskl - INFO - Epoch [30][700/3746] lr: 9.095e-02, eta: 3 days, 19:34:00, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5206, loss_cls: 3.9877, loss: 3.9877 +2024-07-23 01:48:40,268 - pyskl - INFO - Epoch [30][800/3746] lr: 9.093e-02, eta: 3 days, 19:33:17, time: 0.799, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5170, loss_cls: 4.0193, loss: 4.0193 +2024-07-23 01:49:59,981 - pyskl - INFO - Epoch [30][900/3746] lr: 9.091e-02, eta: 3 days, 19:32:32, time: 0.797, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5147, loss_cls: 4.0506, loss: 4.0506 +2024-07-23 01:51:20,724 - pyskl - INFO - Epoch [30][1000/3746] lr: 9.090e-02, eta: 3 days, 19:31:52, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5144, loss_cls: 4.0423, loss: 4.0423 +2024-07-23 01:52:41,035 - pyskl - INFO - Epoch [30][1100/3746] lr: 9.088e-02, eta: 3 days, 19:31:10, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5144, loss_cls: 4.0114, loss: 4.0114 +2024-07-23 01:54:01,655 - pyskl - INFO - Epoch [30][1200/3746] lr: 9.087e-02, eta: 3 days, 19:30:29, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5181, loss_cls: 3.9951, loss: 3.9951 +2024-07-23 01:55:21,964 - pyskl - INFO - Epoch [30][1300/3746] lr: 9.085e-02, eta: 3 days, 19:29:46, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5166, loss_cls: 4.0247, loss: 4.0247 +2024-07-23 01:56:41,889 - pyskl - INFO - Epoch [30][1400/3746] lr: 9.083e-02, eta: 3 days, 19:29:02, time: 0.799, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5248, loss_cls: 3.9783, loss: 3.9783 +2024-07-23 01:58:02,118 - pyskl - INFO - Epoch [30][1500/3746] lr: 9.082e-02, eta: 3 days, 19:28:20, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5141, loss_cls: 4.0453, loss: 4.0453 +2024-07-23 01:59:22,106 - pyskl - INFO - Epoch [30][1600/3746] lr: 9.080e-02, eta: 3 days, 19:27:36, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5152, loss_cls: 4.0329, loss: 4.0329 +2024-07-23 02:00:42,478 - pyskl - INFO - Epoch [30][1700/3746] lr: 9.078e-02, eta: 3 days, 19:26:54, time: 0.804, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5128, loss_cls: 3.9888, loss: 3.9888 +2024-07-23 02:02:02,289 - pyskl - INFO - Epoch [30][1800/3746] lr: 9.077e-02, eta: 3 days, 19:26:09, time: 0.798, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5062, loss_cls: 4.0401, loss: 4.0401 +2024-07-23 02:03:22,399 - pyskl - INFO - Epoch [30][1900/3746] lr: 9.075e-02, eta: 3 days, 19:25:25, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5141, loss_cls: 4.0190, loss: 4.0190 +2024-07-23 02:04:42,234 - pyskl - INFO - Epoch [30][2000/3746] lr: 9.074e-02, eta: 3 days, 19:24:41, time: 0.798, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5217, loss_cls: 3.9861, loss: 3.9861 +2024-07-23 02:06:02,287 - pyskl - INFO - Epoch [30][2100/3746] lr: 9.072e-02, eta: 3 days, 19:23:57, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5145, loss_cls: 3.9846, loss: 3.9846 +2024-07-23 02:07:22,512 - pyskl - INFO - Epoch [30][2200/3746] lr: 9.070e-02, eta: 3 days, 19:23:14, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5141, loss_cls: 3.9926, loss: 3.9926 +2024-07-23 02:08:42,107 - pyskl - INFO - Epoch [30][2300/3746] lr: 9.069e-02, eta: 3 days, 19:22:28, time: 0.796, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5084, loss_cls: 4.0258, loss: 4.0258 +2024-07-23 02:10:02,023 - pyskl - INFO - Epoch [30][2400/3746] lr: 9.067e-02, eta: 3 days, 19:21:43, time: 0.799, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5208, loss_cls: 3.9946, loss: 3.9946 +2024-07-23 02:11:22,102 - pyskl - INFO - Epoch [30][2500/3746] lr: 9.065e-02, eta: 3 days, 19:20:59, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5159, loss_cls: 3.9887, loss: 3.9887 +2024-07-23 02:12:42,080 - pyskl - INFO - Epoch [30][2600/3746] lr: 9.064e-02, eta: 3 days, 19:20:15, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5228, loss_cls: 4.0011, loss: 4.0011 +2024-07-23 02:14:02,054 - pyskl - INFO - Epoch [30][2700/3746] lr: 9.062e-02, eta: 3 days, 19:19:30, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5230, loss_cls: 3.9843, loss: 3.9843 +2024-07-23 02:15:22,316 - pyskl - INFO - Epoch [30][2800/3746] lr: 9.061e-02, eta: 3 days, 19:18:46, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5192, loss_cls: 4.0258, loss: 4.0258 +2024-07-23 02:16:42,274 - pyskl - INFO - Epoch [30][2900/3746] lr: 9.059e-02, eta: 3 days, 19:18:02, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5206, loss_cls: 3.9980, loss: 3.9980 +2024-07-23 02:18:02,806 - pyskl - INFO - Epoch [30][3000/3746] lr: 9.057e-02, eta: 3 days, 19:17:19, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5173, loss_cls: 4.0175, loss: 4.0175 +2024-07-23 02:19:22,428 - pyskl - INFO - Epoch [30][3100/3746] lr: 9.056e-02, eta: 3 days, 19:16:33, time: 0.796, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5114, loss_cls: 4.0497, loss: 4.0497 +2024-07-23 02:20:42,328 - pyskl - INFO - Epoch [30][3200/3746] lr: 9.054e-02, eta: 3 days, 19:15:48, time: 0.799, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5162, loss_cls: 4.0373, loss: 4.0373 +2024-07-23 02:22:02,681 - pyskl - INFO - Epoch [30][3300/3746] lr: 9.052e-02, eta: 3 days, 19:15:04, time: 0.804, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5131, loss_cls: 4.0246, loss: 4.0246 +2024-07-23 02:23:22,592 - pyskl - INFO - Epoch [30][3400/3746] lr: 9.051e-02, eta: 3 days, 19:14:19, time: 0.799, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5147, loss_cls: 4.0188, loss: 4.0188 +2024-07-23 02:24:43,230 - pyskl - INFO - Epoch [30][3500/3746] lr: 9.049e-02, eta: 3 days, 19:13:36, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5142, loss_cls: 4.0505, loss: 4.0505 +2024-07-23 02:26:03,675 - pyskl - INFO - Epoch [30][3600/3746] lr: 9.047e-02, eta: 3 days, 19:12:53, time: 0.804, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5130, loss_cls: 4.0354, loss: 4.0354 +2024-07-23 02:27:23,367 - pyskl - INFO - Epoch [30][3700/3746] lr: 9.046e-02, eta: 3 days, 19:12:07, time: 0.797, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5183, loss_cls: 3.9974, loss: 3.9974 +2024-07-23 02:28:02,438 - pyskl - INFO - Saving checkpoint at 30 epochs +2024-07-23 02:29:55,354 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 02:29:56,160 - pyskl - INFO - +top1_acc 0.2068 +top5_acc 0.4427 +2024-07-23 02:29:56,160 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 02:29:56,207 - pyskl - INFO - +mean_acc 0.2066 +2024-07-23 02:29:56,212 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_22.pth was removed +2024-07-23 02:29:56,567 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_30.pth. +2024-07-23 02:29:56,568 - pyskl - INFO - Best top1_acc is 0.2068 at 30 epoch. +2024-07-23 02:29:56,587 - pyskl - INFO - Epoch(val) [30][309] top1_acc: 0.2068, top5_acc: 0.4427, mean_class_accuracy: 0.2066 +2024-07-23 02:33:47,630 - pyskl - INFO - Epoch [31][100/3746] lr: 9.043e-02, eta: 3 days, 19:18:37, time: 2.310, data_time: 1.336, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5269, loss_cls: 4.1838, loss: 4.1838 +2024-07-23 02:35:09,595 - pyskl - INFO - Epoch [31][200/3746] lr: 9.042e-02, eta: 3 days, 19:17:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5206, loss_cls: 4.1839, loss: 4.1839 +2024-07-23 02:36:32,850 - pyskl - INFO - Epoch [31][300/3746] lr: 9.040e-02, eta: 3 days, 19:17:27, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5164, loss_cls: 4.2160, loss: 4.2160 +2024-07-23 02:37:55,192 - pyskl - INFO - Epoch [31][400/3746] lr: 9.039e-02, eta: 3 days, 19:16:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5178, loss_cls: 4.2599, loss: 4.2599 +2024-07-23 02:39:18,497 - pyskl - INFO - Epoch [31][500/3746] lr: 9.037e-02, eta: 3 days, 19:16:17, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5158, loss_cls: 4.2083, loss: 4.2083 +2024-07-23 02:40:40,550 - pyskl - INFO - Epoch [31][600/3746] lr: 9.035e-02, eta: 3 days, 19:15:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5078, loss_cls: 4.2831, loss: 4.2831 +2024-07-23 02:42:02,747 - pyskl - INFO - Epoch [31][700/3746] lr: 9.034e-02, eta: 3 days, 19:15:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5200, loss_cls: 4.2167, loss: 4.2167 +2024-07-23 02:43:24,477 - pyskl - INFO - Epoch [31][800/3746] lr: 9.032e-02, eta: 3 days, 19:14:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5316, loss_cls: 4.2019, loss: 4.2019 +2024-07-23 02:44:46,316 - pyskl - INFO - Epoch [31][900/3746] lr: 9.030e-02, eta: 3 days, 19:13:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5203, loss_cls: 4.2193, loss: 4.2193 +2024-07-23 02:46:07,789 - pyskl - INFO - Epoch [31][1000/3746] lr: 9.029e-02, eta: 3 days, 19:13:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5183, loss_cls: 4.1949, loss: 4.1949 +2024-07-23 02:47:29,308 - pyskl - INFO - Epoch [31][1100/3746] lr: 9.027e-02, eta: 3 days, 19:12:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5152, loss_cls: 4.2054, loss: 4.2054 +2024-07-23 02:48:51,090 - pyskl - INFO - Epoch [31][1200/3746] lr: 9.025e-02, eta: 3 days, 19:11:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5311, loss_cls: 4.1616, loss: 4.1616 +2024-07-23 02:50:12,450 - pyskl - INFO - Epoch [31][1300/3746] lr: 9.024e-02, eta: 3 days, 19:11:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5239, loss_cls: 4.1861, loss: 4.1861 +2024-07-23 02:51:33,909 - pyskl - INFO - Epoch [31][1400/3746] lr: 9.022e-02, eta: 3 days, 19:10:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5105, loss_cls: 4.2546, loss: 4.2546 +2024-07-23 02:52:55,286 - pyskl - INFO - Epoch [31][1500/3746] lr: 9.020e-02, eta: 3 days, 19:09:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5159, loss_cls: 4.2481, loss: 4.2481 +2024-07-23 02:54:16,689 - pyskl - INFO - Epoch [31][1600/3746] lr: 9.019e-02, eta: 3 days, 19:08:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5233, loss_cls: 4.1990, loss: 4.1990 +2024-07-23 02:55:37,752 - pyskl - INFO - Epoch [31][1700/3746] lr: 9.017e-02, eta: 3 days, 19:08:16, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5109, loss_cls: 4.2762, loss: 4.2762 +2024-07-23 02:56:59,782 - pyskl - INFO - Epoch [31][1800/3746] lr: 9.015e-02, eta: 3 days, 19:07:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5050, loss_cls: 4.2885, loss: 4.2885 +2024-07-23 02:58:20,900 - pyskl - INFO - Epoch [31][1900/3746] lr: 9.014e-02, eta: 3 days, 19:06:54, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5161, loss_cls: 4.2460, loss: 4.2460 +2024-07-23 02:59:42,285 - pyskl - INFO - Epoch [31][2000/3746] lr: 9.012e-02, eta: 3 days, 19:06:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5167, loss_cls: 4.2250, loss: 4.2250 +2024-07-23 03:01:03,667 - pyskl - INFO - Epoch [31][2100/3746] lr: 9.010e-02, eta: 3 days, 19:05:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5158, loss_cls: 4.2467, loss: 4.2467 +2024-07-23 03:02:25,286 - pyskl - INFO - Epoch [31][2200/3746] lr: 9.009e-02, eta: 3 days, 19:04:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5220, loss_cls: 4.2083, loss: 4.2083 +2024-07-23 03:03:46,863 - pyskl - INFO - Epoch [31][2300/3746] lr: 9.007e-02, eta: 3 days, 19:04:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5116, loss_cls: 4.2656, loss: 4.2656 +2024-07-23 03:05:08,431 - pyskl - INFO - Epoch [31][2400/3746] lr: 9.005e-02, eta: 3 days, 19:03:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5206, loss_cls: 4.2285, loss: 4.2285 +2024-07-23 03:06:29,718 - pyskl - INFO - Epoch [31][2500/3746] lr: 9.004e-02, eta: 3 days, 19:02:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5078, loss_cls: 4.2642, loss: 4.2642 +2024-07-23 03:07:51,054 - pyskl - INFO - Epoch [31][2600/3746] lr: 9.002e-02, eta: 3 days, 19:02:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5138, loss_cls: 4.2465, loss: 4.2465 +2024-07-23 03:09:12,676 - pyskl - INFO - Epoch [31][2700/3746] lr: 9.000e-02, eta: 3 days, 19:01:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5130, loss_cls: 4.2379, loss: 4.2379 +2024-07-23 03:10:33,683 - pyskl - INFO - Epoch [31][2800/3746] lr: 8.999e-02, eta: 3 days, 19:00:39, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5241, loss_cls: 4.2219, loss: 4.2219 +2024-07-23 03:11:55,682 - pyskl - INFO - Epoch [31][2900/3746] lr: 8.997e-02, eta: 3 days, 18:59:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5095, loss_cls: 4.2552, loss: 4.2552 +2024-07-23 03:13:17,007 - pyskl - INFO - Epoch [31][3000/3746] lr: 8.995e-02, eta: 3 days, 18:59:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5066, loss_cls: 4.2795, loss: 4.2795 +2024-07-23 03:14:38,191 - pyskl - INFO - Epoch [31][3100/3746] lr: 8.994e-02, eta: 3 days, 18:58:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5139, loss_cls: 4.2498, loss: 4.2498 +2024-07-23 03:15:59,786 - pyskl - INFO - Epoch [31][3200/3746] lr: 8.992e-02, eta: 3 days, 18:57:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5181, loss_cls: 4.2494, loss: 4.2494 +2024-07-23 03:17:21,521 - pyskl - INFO - Epoch [31][3300/3746] lr: 8.990e-02, eta: 3 days, 18:57:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5145, loss_cls: 4.2470, loss: 4.2470 +2024-07-23 03:18:43,784 - pyskl - INFO - Epoch [31][3400/3746] lr: 8.989e-02, eta: 3 days, 18:56:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5238, loss_cls: 4.2077, loss: 4.2077 +2024-07-23 03:20:05,831 - pyskl - INFO - Epoch [31][3500/3746] lr: 8.987e-02, eta: 3 days, 18:55:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5242, loss_cls: 4.2295, loss: 4.2295 +2024-07-23 03:21:27,240 - pyskl - INFO - Epoch [31][3600/3746] lr: 8.985e-02, eta: 3 days, 18:55:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5230, loss_cls: 4.2030, loss: 4.2030 +2024-07-23 03:22:48,664 - pyskl - INFO - Epoch [31][3700/3746] lr: 8.983e-02, eta: 3 days, 18:54:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5291, loss_cls: 4.1950, loss: 4.1950 +2024-07-23 03:23:28,557 - pyskl - INFO - Saving checkpoint at 31 epochs +2024-07-23 03:25:20,677 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 03:25:21,328 - pyskl - INFO - +top1_acc 0.1675 +top5_acc 0.3954 +2024-07-23 03:25:21,329 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 03:25:21,367 - pyskl - INFO - +mean_acc 0.1674 +2024-07-23 03:25:21,378 - pyskl - INFO - Epoch(val) [31][309] top1_acc: 0.1675, top5_acc: 0.3954, mean_class_accuracy: 0.1674 +2024-07-23 03:29:07,785 - pyskl - INFO - Epoch [32][100/3746] lr: 8.981e-02, eta: 3 days, 19:00:17, time: 2.264, data_time: 1.284, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5363, loss_cls: 4.1667, loss: 4.1667 +2024-07-23 03:30:29,314 - pyskl - INFO - Epoch [32][200/3746] lr: 8.979e-02, eta: 3 days, 18:59:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5156, loss_cls: 4.2288, loss: 4.2288 +2024-07-23 03:31:51,444 - pyskl - INFO - Epoch [32][300/3746] lr: 8.978e-02, eta: 3 days, 18:58:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5214, loss_cls: 4.1822, loss: 4.1822 +2024-07-23 03:33:13,679 - pyskl - INFO - Epoch [32][400/3746] lr: 8.976e-02, eta: 3 days, 18:58:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5275, loss_cls: 4.1920, loss: 4.1920 +2024-07-23 03:34:35,926 - pyskl - INFO - Epoch [32][500/3746] lr: 8.974e-02, eta: 3 days, 18:57:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5250, loss_cls: 4.1950, loss: 4.1950 +2024-07-23 03:35:57,989 - pyskl - INFO - Epoch [32][600/3746] lr: 8.973e-02, eta: 3 days, 18:56:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5192, loss_cls: 4.2090, loss: 4.2090 +2024-07-23 03:37:19,355 - pyskl - INFO - Epoch [32][700/3746] lr: 8.971e-02, eta: 3 days, 18:56:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5227, loss_cls: 4.2105, loss: 4.2105 +2024-07-23 03:38:41,020 - pyskl - INFO - Epoch [32][800/3746] lr: 8.969e-02, eta: 3 days, 18:55:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5311, loss_cls: 4.1944, loss: 4.1944 +2024-07-23 03:40:02,608 - pyskl - INFO - Epoch [32][900/3746] lr: 8.967e-02, eta: 3 days, 18:54:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5150, loss_cls: 4.2228, loss: 4.2228 +2024-07-23 03:41:24,296 - pyskl - INFO - Epoch [32][1000/3746] lr: 8.966e-02, eta: 3 days, 18:53:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5127, loss_cls: 4.2625, loss: 4.2625 +2024-07-23 03:42:45,770 - pyskl - INFO - Epoch [32][1100/3746] lr: 8.964e-02, eta: 3 days, 18:53:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5095, loss_cls: 4.2617, loss: 4.2617 +2024-07-23 03:44:07,052 - pyskl - INFO - Epoch [32][1200/3746] lr: 8.962e-02, eta: 3 days, 18:52:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5302, loss_cls: 4.2009, loss: 4.2009 +2024-07-23 03:45:28,355 - pyskl - INFO - Epoch [32][1300/3746] lr: 8.961e-02, eta: 3 days, 18:51:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5244, loss_cls: 4.2171, loss: 4.2171 +2024-07-23 03:46:50,494 - pyskl - INFO - Epoch [32][1400/3746] lr: 8.959e-02, eta: 3 days, 18:51:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5134, loss_cls: 4.2461, loss: 4.2461 +2024-07-23 03:48:12,139 - pyskl - INFO - Epoch [32][1500/3746] lr: 8.957e-02, eta: 3 days, 18:50:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5220, loss_cls: 4.2239, loss: 4.2239 +2024-07-23 03:49:33,345 - pyskl - INFO - Epoch [32][1600/3746] lr: 8.955e-02, eta: 3 days, 18:49:38, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5111, loss_cls: 4.2452, loss: 4.2452 +2024-07-23 03:50:54,954 - pyskl - INFO - Epoch [32][1700/3746] lr: 8.954e-02, eta: 3 days, 18:48:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5086, loss_cls: 4.2426, loss: 4.2426 +2024-07-23 03:52:16,504 - pyskl - INFO - Epoch [32][1800/3746] lr: 8.952e-02, eta: 3 days, 18:48:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5188, loss_cls: 4.2068, loss: 4.2068 +2024-07-23 03:53:38,149 - pyskl - INFO - Epoch [32][1900/3746] lr: 8.950e-02, eta: 3 days, 18:47:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5102, loss_cls: 4.2463, loss: 4.2463 +2024-07-23 03:54:59,670 - pyskl - INFO - Epoch [32][2000/3746] lr: 8.949e-02, eta: 3 days, 18:46:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5227, loss_cls: 4.2015, loss: 4.2015 +2024-07-23 03:56:21,112 - pyskl - INFO - Epoch [32][2100/3746] lr: 8.947e-02, eta: 3 days, 18:45:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5308, loss_cls: 4.1775, loss: 4.1775 +2024-07-23 03:57:42,886 - pyskl - INFO - Epoch [32][2200/3746] lr: 8.945e-02, eta: 3 days, 18:45:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5103, loss_cls: 4.2529, loss: 4.2529 +2024-07-23 03:59:04,896 - pyskl - INFO - Epoch [32][2300/3746] lr: 8.943e-02, eta: 3 days, 18:44:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5172, loss_cls: 4.2390, loss: 4.2390 +2024-07-23 04:00:26,279 - pyskl - INFO - Epoch [32][2400/3746] lr: 8.942e-02, eta: 3 days, 18:43:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5203, loss_cls: 4.2421, loss: 4.2421 +2024-07-23 04:01:48,107 - pyskl - INFO - Epoch [32][2500/3746] lr: 8.940e-02, eta: 3 days, 18:43:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5123, loss_cls: 4.2139, loss: 4.2139 +2024-07-23 04:03:09,461 - pyskl - INFO - Epoch [32][2600/3746] lr: 8.938e-02, eta: 3 days, 18:42:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5117, loss_cls: 4.2409, loss: 4.2409 +2024-07-23 04:04:31,331 - pyskl - INFO - Epoch [32][2700/3746] lr: 8.937e-02, eta: 3 days, 18:41:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5205, loss_cls: 4.2259, loss: 4.2259 +2024-07-23 04:05:53,511 - pyskl - INFO - Epoch [32][2800/3746] lr: 8.935e-02, eta: 3 days, 18:40:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5141, loss_cls: 4.2514, loss: 4.2514 +2024-07-23 04:07:14,926 - pyskl - INFO - Epoch [32][2900/3746] lr: 8.933e-02, eta: 3 days, 18:40:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5230, loss_cls: 4.1922, loss: 4.1922 +2024-07-23 04:08:36,384 - pyskl - INFO - Epoch [32][3000/3746] lr: 8.931e-02, eta: 3 days, 18:39:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5214, loss_cls: 4.2021, loss: 4.2021 +2024-07-23 04:09:58,409 - pyskl - INFO - Epoch [32][3100/3746] lr: 8.930e-02, eta: 3 days, 18:38:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5355, loss_cls: 4.1575, loss: 4.1575 +2024-07-23 04:11:19,914 - pyskl - INFO - Epoch [32][3200/3746] lr: 8.928e-02, eta: 3 days, 18:37:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5178, loss_cls: 4.2361, loss: 4.2361 +2024-07-23 04:12:41,193 - pyskl - INFO - Epoch [32][3300/3746] lr: 8.926e-02, eta: 3 days, 18:37:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5097, loss_cls: 4.2701, loss: 4.2701 +2024-07-23 04:14:02,800 - pyskl - INFO - Epoch [32][3400/3746] lr: 8.924e-02, eta: 3 days, 18:36:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5141, loss_cls: 4.2667, loss: 4.2667 +2024-07-23 04:15:24,300 - pyskl - INFO - Epoch [32][3500/3746] lr: 8.923e-02, eta: 3 days, 18:35:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5186, loss_cls: 4.2174, loss: 4.2174 +2024-07-23 04:16:45,813 - pyskl - INFO - Epoch [32][3600/3746] lr: 8.921e-02, eta: 3 days, 18:34:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5169, loss_cls: 4.2208, loss: 4.2208 +2024-07-23 04:18:08,053 - pyskl - INFO - Epoch [32][3700/3746] lr: 8.919e-02, eta: 3 days, 18:34:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5366, loss_cls: 4.1472, loss: 4.1472 +2024-07-23 04:18:47,868 - pyskl - INFO - Saving checkpoint at 32 epochs +2024-07-23 04:20:40,327 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 04:20:40,989 - pyskl - INFO - +top1_acc 0.1972 +top5_acc 0.4159 +2024-07-23 04:20:40,989 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 04:20:41,028 - pyskl - INFO - +mean_acc 0.1970 +2024-07-23 04:20:41,039 - pyskl - INFO - Epoch(val) [32][309] top1_acc: 0.1972, top5_acc: 0.4159, mean_class_accuracy: 0.1970 +2024-07-23 04:24:27,774 - pyskl - INFO - Epoch [33][100/3746] lr: 8.917e-02, eta: 3 days, 18:39:45, time: 2.267, data_time: 1.291, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5242, loss_cls: 4.2121, loss: 4.2121 +2024-07-23 04:25:49,645 - pyskl - INFO - Epoch [33][200/3746] lr: 8.915e-02, eta: 3 days, 18:39:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5398, loss_cls: 4.1568, loss: 4.1568 +2024-07-23 04:27:12,555 - pyskl - INFO - Epoch [33][300/3746] lr: 8.913e-02, eta: 3 days, 18:38:20, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5373, loss_cls: 4.1626, loss: 4.1626 +2024-07-23 04:28:34,786 - pyskl - INFO - Epoch [33][400/3746] lr: 8.912e-02, eta: 3 days, 18:37:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5177, loss_cls: 4.2268, loss: 4.2268 +2024-07-23 04:29:57,477 - pyskl - INFO - Epoch [33][500/3746] lr: 8.910e-02, eta: 3 days, 18:36:55, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5202, loss_cls: 4.2273, loss: 4.2273 +2024-07-23 04:31:19,641 - pyskl - INFO - Epoch [33][600/3746] lr: 8.908e-02, eta: 3 days, 18:36:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5164, loss_cls: 4.2258, loss: 4.2258 +2024-07-23 04:32:41,553 - pyskl - INFO - Epoch [33][700/3746] lr: 8.906e-02, eta: 3 days, 18:35:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5197, loss_cls: 4.2207, loss: 4.2207 +2024-07-23 04:34:03,792 - pyskl - INFO - Epoch [33][800/3746] lr: 8.905e-02, eta: 3 days, 18:34:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5280, loss_cls: 4.1865, loss: 4.1865 +2024-07-23 04:35:25,371 - pyskl - INFO - Epoch [33][900/3746] lr: 8.903e-02, eta: 3 days, 18:33:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5255, loss_cls: 4.2210, loss: 4.2210 +2024-07-23 04:36:46,765 - pyskl - INFO - Epoch [33][1000/3746] lr: 8.901e-02, eta: 3 days, 18:33:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5162, loss_cls: 4.2420, loss: 4.2420 +2024-07-23 04:38:08,477 - pyskl - INFO - Epoch [33][1100/3746] lr: 8.899e-02, eta: 3 days, 18:32:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5170, loss_cls: 4.2250, loss: 4.2250 +2024-07-23 04:39:29,817 - pyskl - INFO - Epoch [33][1200/3746] lr: 8.898e-02, eta: 3 days, 18:31:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5247, loss_cls: 4.1941, loss: 4.1941 +2024-07-23 04:40:51,136 - pyskl - INFO - Epoch [33][1300/3746] lr: 8.896e-02, eta: 3 days, 18:30:52, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5217, loss_cls: 4.2171, loss: 4.2171 +2024-07-23 04:42:12,446 - pyskl - INFO - Epoch [33][1400/3746] lr: 8.894e-02, eta: 3 days, 18:30:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5178, loss_cls: 4.2352, loss: 4.2352 +2024-07-23 04:43:33,684 - pyskl - INFO - Epoch [33][1500/3746] lr: 8.892e-02, eta: 3 days, 18:29:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5142, loss_cls: 4.2498, loss: 4.2498 +2024-07-23 04:44:55,121 - pyskl - INFO - Epoch [33][1600/3746] lr: 8.891e-02, eta: 3 days, 18:28:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5297, loss_cls: 4.1963, loss: 4.1963 +2024-07-23 04:46:16,817 - pyskl - INFO - Epoch [33][1700/3746] lr: 8.889e-02, eta: 3 days, 18:27:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5266, loss_cls: 4.1726, loss: 4.1726 +2024-07-23 04:47:38,619 - pyskl - INFO - Epoch [33][1800/3746] lr: 8.887e-02, eta: 3 days, 18:26:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5259, loss_cls: 4.2005, loss: 4.2005 +2024-07-23 04:49:00,134 - pyskl - INFO - Epoch [33][1900/3746] lr: 8.885e-02, eta: 3 days, 18:26:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5155, loss_cls: 4.2267, loss: 4.2267 +2024-07-23 04:50:21,775 - pyskl - INFO - Epoch [33][2000/3746] lr: 8.884e-02, eta: 3 days, 18:25:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5103, loss_cls: 4.2812, loss: 4.2812 +2024-07-23 04:51:43,097 - pyskl - INFO - Epoch [33][2100/3746] lr: 8.882e-02, eta: 3 days, 18:24:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5153, loss_cls: 4.2171, loss: 4.2171 +2024-07-23 04:53:04,480 - pyskl - INFO - Epoch [33][2200/3746] lr: 8.880e-02, eta: 3 days, 18:23:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5152, loss_cls: 4.2615, loss: 4.2615 +2024-07-23 04:54:26,188 - pyskl - INFO - Epoch [33][2300/3746] lr: 8.878e-02, eta: 3 days, 18:23:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5145, loss_cls: 4.2610, loss: 4.2610 +2024-07-23 04:55:48,260 - pyskl - INFO - Epoch [33][2400/3746] lr: 8.876e-02, eta: 3 days, 18:22:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5088, loss_cls: 4.2531, loss: 4.2531 +2024-07-23 04:57:10,779 - pyskl - INFO - Epoch [33][2500/3746] lr: 8.875e-02, eta: 3 days, 18:21:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5188, loss_cls: 4.1882, loss: 4.1882 +2024-07-23 04:58:31,818 - pyskl - INFO - Epoch [33][2600/3746] lr: 8.873e-02, eta: 3 days, 18:20:48, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5216, loss_cls: 4.2101, loss: 4.2101 +2024-07-23 04:59:53,139 - pyskl - INFO - Epoch [33][2700/3746] lr: 8.871e-02, eta: 3 days, 18:20:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5302, loss_cls: 4.1651, loss: 4.1651 +2024-07-23 05:01:14,385 - pyskl - INFO - Epoch [33][2800/3746] lr: 8.869e-02, eta: 3 days, 18:19:12, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5102, loss_cls: 4.2791, loss: 4.2791 +2024-07-23 05:02:35,834 - pyskl - INFO - Epoch [33][2900/3746] lr: 8.868e-02, eta: 3 days, 18:18:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5286, loss_cls: 4.1610, loss: 4.1610 +2024-07-23 05:03:57,369 - pyskl - INFO - Epoch [33][3000/3746] lr: 8.866e-02, eta: 3 days, 18:17:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5239, loss_cls: 4.1930, loss: 4.1930 +2024-07-23 05:05:19,494 - pyskl - INFO - Epoch [33][3100/3746] lr: 8.864e-02, eta: 3 days, 18:16:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5108, loss_cls: 4.2708, loss: 4.2708 +2024-07-23 05:06:40,887 - pyskl - INFO - Epoch [33][3200/3746] lr: 8.862e-02, eta: 3 days, 18:16:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5222, loss_cls: 4.2287, loss: 4.2287 +2024-07-23 05:08:02,715 - pyskl - INFO - Epoch [33][3300/3746] lr: 8.861e-02, eta: 3 days, 18:15:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5200, loss_cls: 4.2131, loss: 4.2131 +2024-07-23 05:09:24,170 - pyskl - INFO - Epoch [33][3400/3746] lr: 8.859e-02, eta: 3 days, 18:14:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5130, loss_cls: 4.2271, loss: 4.2271 +2024-07-23 05:10:46,202 - pyskl - INFO - Epoch [33][3500/3746] lr: 8.857e-02, eta: 3 days, 18:13:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5167, loss_cls: 4.2454, loss: 4.2454 +2024-07-23 05:12:07,475 - pyskl - INFO - Epoch [33][3600/3746] lr: 8.855e-02, eta: 3 days, 18:12:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5138, loss_cls: 4.2272, loss: 4.2272 +2024-07-23 05:13:28,920 - pyskl - INFO - Epoch [33][3700/3746] lr: 8.853e-02, eta: 3 days, 18:12:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5269, loss_cls: 4.2204, loss: 4.2204 +2024-07-23 05:14:08,729 - pyskl - INFO - Saving checkpoint at 33 epochs +2024-07-23 05:16:00,856 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 05:16:01,530 - pyskl - INFO - +top1_acc 0.1954 +top5_acc 0.4235 +2024-07-23 05:16:01,530 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 05:16:01,571 - pyskl - INFO - +mean_acc 0.1950 +2024-07-23 05:16:01,583 - pyskl - INFO - Epoch(val) [33][309] top1_acc: 0.1954, top5_acc: 0.4235, mean_class_accuracy: 0.1950 +2024-07-23 05:19:56,312 - pyskl - INFO - Epoch [34][100/3746] lr: 8.851e-02, eta: 3 days, 18:17:48, time: 2.347, data_time: 1.353, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5463, loss_cls: 4.1080, loss: 4.1080 +2024-07-23 05:21:18,912 - pyskl - INFO - Epoch [34][200/3746] lr: 8.849e-02, eta: 3 days, 18:17:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5231, loss_cls: 4.1973, loss: 4.1973 +2024-07-23 05:22:41,894 - pyskl - INFO - Epoch [34][300/3746] lr: 8.847e-02, eta: 3 days, 18:16:20, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5314, loss_cls: 4.1805, loss: 4.1805 +2024-07-23 05:24:04,510 - pyskl - INFO - Epoch [34][400/3746] lr: 8.845e-02, eta: 3 days, 18:15:36, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5212, loss_cls: 4.2116, loss: 4.2116 +2024-07-23 05:25:26,421 - pyskl - INFO - Epoch [34][500/3746] lr: 8.844e-02, eta: 3 days, 18:14:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5311, loss_cls: 4.1631, loss: 4.1631 +2024-07-23 05:26:47,983 - pyskl - INFO - Epoch [34][600/3746] lr: 8.842e-02, eta: 3 days, 18:14:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5219, loss_cls: 4.2304, loss: 4.2304 +2024-07-23 05:28:09,555 - pyskl - INFO - Epoch [34][700/3746] lr: 8.840e-02, eta: 3 days, 18:13:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5172, loss_cls: 4.2366, loss: 4.2366 +2024-07-23 05:29:30,563 - pyskl - INFO - Epoch [34][800/3746] lr: 8.838e-02, eta: 3 days, 18:12:22, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5173, loss_cls: 4.2329, loss: 4.2329 +2024-07-23 05:30:52,300 - pyskl - INFO - Epoch [34][900/3746] lr: 8.836e-02, eta: 3 days, 18:11:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5281, loss_cls: 4.1689, loss: 4.1689 +2024-07-23 05:32:13,744 - pyskl - INFO - Epoch [34][1000/3746] lr: 8.835e-02, eta: 3 days, 18:10:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5225, loss_cls: 4.2177, loss: 4.2177 +2024-07-23 05:33:35,271 - pyskl - INFO - Epoch [34][1100/3746] lr: 8.833e-02, eta: 3 days, 18:09:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5150, loss_cls: 4.2231, loss: 4.2231 +2024-07-23 05:34:56,785 - pyskl - INFO - Epoch [34][1200/3746] lr: 8.831e-02, eta: 3 days, 18:09:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5252, loss_cls: 4.1918, loss: 4.1918 +2024-07-23 05:36:17,925 - pyskl - INFO - Epoch [34][1300/3746] lr: 8.829e-02, eta: 3 days, 18:08:17, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5209, loss_cls: 4.2266, loss: 4.2266 +2024-07-23 05:37:39,400 - pyskl - INFO - Epoch [34][1400/3746] lr: 8.828e-02, eta: 3 days, 18:07:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5194, loss_cls: 4.2092, loss: 4.2092 +2024-07-23 05:39:01,162 - pyskl - INFO - Epoch [34][1500/3746] lr: 8.826e-02, eta: 3 days, 18:06:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5219, loss_cls: 4.2441, loss: 4.2441 +2024-07-23 05:40:23,139 - pyskl - INFO - Epoch [34][1600/3746] lr: 8.824e-02, eta: 3 days, 18:05:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5284, loss_cls: 4.1620, loss: 4.1620 +2024-07-23 05:41:44,343 - pyskl - INFO - Epoch [34][1700/3746] lr: 8.822e-02, eta: 3 days, 18:05:03, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5131, loss_cls: 4.2663, loss: 4.2663 +2024-07-23 05:43:05,415 - pyskl - INFO - Epoch [34][1800/3746] lr: 8.820e-02, eta: 3 days, 18:04:12, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5136, loss_cls: 4.2329, loss: 4.2329 +2024-07-23 05:44:27,860 - pyskl - INFO - Epoch [34][1900/3746] lr: 8.819e-02, eta: 3 days, 18:03:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5180, loss_cls: 4.2448, loss: 4.2448 +2024-07-23 05:45:49,673 - pyskl - INFO - Epoch [34][2000/3746] lr: 8.817e-02, eta: 3 days, 18:02:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5297, loss_cls: 4.1749, loss: 4.1749 +2024-07-23 05:47:11,108 - pyskl - INFO - Epoch [34][2100/3746] lr: 8.815e-02, eta: 3 days, 18:01:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5173, loss_cls: 4.2430, loss: 4.2430 +2024-07-23 05:48:33,001 - pyskl - INFO - Epoch [34][2200/3746] lr: 8.813e-02, eta: 3 days, 18:01:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5148, loss_cls: 4.2373, loss: 4.2373 +2024-07-23 05:49:54,142 - pyskl - INFO - Epoch [34][2300/3746] lr: 8.811e-02, eta: 3 days, 18:00:10, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5206, loss_cls: 4.2159, loss: 4.2159 +2024-07-23 05:51:15,097 - pyskl - INFO - Epoch [34][2400/3746] lr: 8.809e-02, eta: 3 days, 17:59:18, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5339, loss_cls: 4.1089, loss: 4.1089 +2024-07-23 05:52:36,244 - pyskl - INFO - Epoch [34][2500/3746] lr: 8.808e-02, eta: 3 days, 17:58:28, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5273, loss_cls: 4.1722, loss: 4.1722 +2024-07-23 05:53:57,239 - pyskl - INFO - Epoch [34][2600/3746] lr: 8.806e-02, eta: 3 days, 17:57:36, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5164, loss_cls: 4.2120, loss: 4.2120 +2024-07-23 05:55:18,657 - pyskl - INFO - Epoch [34][2700/3746] lr: 8.804e-02, eta: 3 days, 17:56:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5209, loss_cls: 4.2113, loss: 4.2113 +2024-07-23 05:56:40,393 - pyskl - INFO - Epoch [34][2800/3746] lr: 8.802e-02, eta: 3 days, 17:55:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5214, loss_cls: 4.2247, loss: 4.2247 +2024-07-23 05:58:01,838 - pyskl - INFO - Epoch [34][2900/3746] lr: 8.800e-02, eta: 3 days, 17:55:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5175, loss_cls: 4.2340, loss: 4.2340 +2024-07-23 05:59:23,219 - pyskl - INFO - Epoch [34][3000/3746] lr: 8.799e-02, eta: 3 days, 17:54:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5194, loss_cls: 4.2297, loss: 4.2297 +2024-07-23 06:00:44,490 - pyskl - INFO - Epoch [34][3100/3746] lr: 8.797e-02, eta: 3 days, 17:53:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5244, loss_cls: 4.1993, loss: 4.1993 +2024-07-23 06:02:06,131 - pyskl - INFO - Epoch [34][3200/3746] lr: 8.795e-02, eta: 3 days, 17:52:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5241, loss_cls: 4.2064, loss: 4.2064 +2024-07-23 06:03:27,537 - pyskl - INFO - Epoch [34][3300/3746] lr: 8.793e-02, eta: 3 days, 17:51:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5180, loss_cls: 4.1987, loss: 4.1987 +2024-07-23 06:04:49,440 - pyskl - INFO - Epoch [34][3400/3746] lr: 8.791e-02, eta: 3 days, 17:50:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5117, loss_cls: 4.2442, loss: 4.2442 +2024-07-23 06:06:11,082 - pyskl - INFO - Epoch [34][3500/3746] lr: 8.789e-02, eta: 3 days, 17:50:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5202, loss_cls: 4.2373, loss: 4.2373 +2024-07-23 06:07:32,554 - pyskl - INFO - Epoch [34][3600/3746] lr: 8.788e-02, eta: 3 days, 17:49:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5214, loss_cls: 4.2092, loss: 4.2092 +2024-07-23 06:08:54,236 - pyskl - INFO - Epoch [34][3700/3746] lr: 8.786e-02, eta: 3 days, 17:48:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5155, loss_cls: 4.2184, loss: 4.2184 +2024-07-23 06:09:33,805 - pyskl - INFO - Saving checkpoint at 34 epochs +2024-07-23 06:11:26,851 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 06:11:27,520 - pyskl - INFO - +top1_acc 0.2066 +top5_acc 0.4395 +2024-07-23 06:11:27,520 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 06:11:27,561 - pyskl - INFO - +mean_acc 0.2061 +2024-07-23 06:11:27,580 - pyskl - INFO - Epoch(val) [34][309] top1_acc: 0.2066, top5_acc: 0.4395, mean_class_accuracy: 0.2061 +2024-07-23 06:15:21,404 - pyskl - INFO - Epoch [35][100/3746] lr: 8.783e-02, eta: 3 days, 17:53:47, time: 2.338, data_time: 1.360, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5217, loss_cls: 4.1756, loss: 4.1756 +2024-07-23 06:16:43,718 - pyskl - INFO - Epoch [35][200/3746] lr: 8.781e-02, eta: 3 days, 17:53:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5259, loss_cls: 4.1809, loss: 4.1809 +2024-07-23 06:18:06,057 - pyskl - INFO - Epoch [35][300/3746] lr: 8.780e-02, eta: 3 days, 17:52:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5262, loss_cls: 4.1741, loss: 4.1741 +2024-07-23 06:19:27,965 - pyskl - INFO - Epoch [35][400/3746] lr: 8.778e-02, eta: 3 days, 17:51:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5256, loss_cls: 4.2047, loss: 4.2047 +2024-07-23 06:20:51,211 - pyskl - INFO - Epoch [35][500/3746] lr: 8.776e-02, eta: 3 days, 17:50:37, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5312, loss_cls: 4.1481, loss: 4.1481 +2024-07-23 06:22:12,760 - pyskl - INFO - Epoch [35][600/3746] lr: 8.774e-02, eta: 3 days, 17:49:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5244, loss_cls: 4.2107, loss: 4.2107 +2024-07-23 06:23:35,307 - pyskl - INFO - Epoch [35][700/3746] lr: 8.772e-02, eta: 3 days, 17:48:59, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5194, loss_cls: 4.1919, loss: 4.1919 +2024-07-23 06:24:57,596 - pyskl - INFO - Epoch [35][800/3746] lr: 8.770e-02, eta: 3 days, 17:48:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5311, loss_cls: 4.1707, loss: 4.1707 +2024-07-23 06:26:19,490 - pyskl - INFO - Epoch [35][900/3746] lr: 8.769e-02, eta: 3 days, 17:47:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5175, loss_cls: 4.2563, loss: 4.2563 +2024-07-23 06:27:41,418 - pyskl - INFO - Epoch [35][1000/3746] lr: 8.767e-02, eta: 3 days, 17:46:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5239, loss_cls: 4.1733, loss: 4.1733 +2024-07-23 06:29:02,934 - pyskl - INFO - Epoch [35][1100/3746] lr: 8.765e-02, eta: 3 days, 17:45:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5189, loss_cls: 4.2116, loss: 4.2116 +2024-07-23 06:30:25,261 - pyskl - INFO - Epoch [35][1200/3746] lr: 8.763e-02, eta: 3 days, 17:44:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5264, loss_cls: 4.2094, loss: 4.2094 +2024-07-23 06:31:47,097 - pyskl - INFO - Epoch [35][1300/3746] lr: 8.761e-02, eta: 3 days, 17:44:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5188, loss_cls: 4.2124, loss: 4.2124 +2024-07-23 06:33:08,805 - pyskl - INFO - Epoch [35][1400/3746] lr: 8.759e-02, eta: 3 days, 17:43:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5181, loss_cls: 4.2317, loss: 4.2317 +2024-07-23 06:34:30,272 - pyskl - INFO - Epoch [35][1500/3746] lr: 8.757e-02, eta: 3 days, 17:42:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5312, loss_cls: 4.1940, loss: 4.1940 +2024-07-23 06:35:51,511 - pyskl - INFO - Epoch [35][1600/3746] lr: 8.756e-02, eta: 3 days, 17:41:28, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5155, loss_cls: 4.2211, loss: 4.2211 +2024-07-23 06:37:13,531 - pyskl - INFO - Epoch [35][1700/3746] lr: 8.754e-02, eta: 3 days, 17:40:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5188, loss_cls: 4.2264, loss: 4.2264 +2024-07-23 06:38:34,884 - pyskl - INFO - Epoch [35][1800/3746] lr: 8.752e-02, eta: 3 days, 17:39:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5223, loss_cls: 4.2041, loss: 4.2041 +2024-07-23 06:39:56,769 - pyskl - INFO - Epoch [35][1900/3746] lr: 8.750e-02, eta: 3 days, 17:38:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5333, loss_cls: 4.1419, loss: 4.1419 +2024-07-23 06:41:18,166 - pyskl - INFO - Epoch [35][2000/3746] lr: 8.748e-02, eta: 3 days, 17:38:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5297, loss_cls: 4.2183, loss: 4.2183 +2024-07-23 06:42:39,231 - pyskl - INFO - Epoch [35][2100/3746] lr: 8.746e-02, eta: 3 days, 17:37:12, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5206, loss_cls: 4.1907, loss: 4.1907 +2024-07-23 06:44:00,870 - pyskl - INFO - Epoch [35][2200/3746] lr: 8.745e-02, eta: 3 days, 17:36:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5277, loss_cls: 4.1829, loss: 4.1829 +2024-07-23 06:45:22,400 - pyskl - INFO - Epoch [35][2300/3746] lr: 8.743e-02, eta: 3 days, 17:35:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5269, loss_cls: 4.1892, loss: 4.1892 +2024-07-23 06:46:44,110 - pyskl - INFO - Epoch [35][2400/3746] lr: 8.741e-02, eta: 3 days, 17:34:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5172, loss_cls: 4.2677, loss: 4.2677 +2024-07-23 06:48:06,186 - pyskl - INFO - Epoch [35][2500/3746] lr: 8.739e-02, eta: 3 days, 17:33:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5178, loss_cls: 4.2123, loss: 4.2123 +2024-07-23 06:49:27,513 - pyskl - INFO - Epoch [35][2600/3746] lr: 8.737e-02, eta: 3 days, 17:32:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5234, loss_cls: 4.1836, loss: 4.1836 +2024-07-23 06:50:48,983 - pyskl - INFO - Epoch [35][2700/3746] lr: 8.735e-02, eta: 3 days, 17:32:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5169, loss_cls: 4.2427, loss: 4.2427 +2024-07-23 06:52:10,359 - pyskl - INFO - Epoch [35][2800/3746] lr: 8.733e-02, eta: 3 days, 17:31:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5148, loss_cls: 4.2438, loss: 4.2438 +2024-07-23 06:53:31,538 - pyskl - INFO - Epoch [35][2900/3746] lr: 8.732e-02, eta: 3 days, 17:30:18, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5220, loss_cls: 4.1880, loss: 4.1880 +2024-07-23 06:54:53,022 - pyskl - INFO - Epoch [35][3000/3746] lr: 8.730e-02, eta: 3 days, 17:29:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5286, loss_cls: 4.1832, loss: 4.1832 +2024-07-23 06:56:14,246 - pyskl - INFO - Epoch [35][3100/3746] lr: 8.728e-02, eta: 3 days, 17:28:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5337, loss_cls: 4.1236, loss: 4.1236 +2024-07-23 06:57:35,881 - pyskl - INFO - Epoch [35][3200/3746] lr: 8.726e-02, eta: 3 days, 17:27:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5145, loss_cls: 4.2523, loss: 4.2523 +2024-07-23 06:58:57,993 - pyskl - INFO - Epoch [35][3300/3746] lr: 8.724e-02, eta: 3 days, 17:26:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5167, loss_cls: 4.2104, loss: 4.2104 +2024-07-23 07:00:19,412 - pyskl - INFO - Epoch [35][3400/3746] lr: 8.722e-02, eta: 3 days, 17:25:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5091, loss_cls: 4.2885, loss: 4.2885 +2024-07-23 07:01:41,627 - pyskl - INFO - Epoch [35][3500/3746] lr: 8.720e-02, eta: 3 days, 17:25:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5212, loss_cls: 4.2033, loss: 4.2033 +2024-07-23 07:03:03,421 - pyskl - INFO - Epoch [35][3600/3746] lr: 8.718e-02, eta: 3 days, 17:24:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5138, loss_cls: 4.2447, loss: 4.2447 +2024-07-23 07:04:25,057 - pyskl - INFO - Epoch [35][3700/3746] lr: 8.717e-02, eta: 3 days, 17:23:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5222, loss_cls: 4.2036, loss: 4.2036 +2024-07-23 07:05:04,632 - pyskl - INFO - Saving checkpoint at 35 epochs +2024-07-23 07:06:56,509 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 07:06:57,160 - pyskl - INFO - +top1_acc 0.2172 +top5_acc 0.4419 +2024-07-23 07:06:57,161 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 07:06:57,202 - pyskl - INFO - +mean_acc 0.2171 +2024-07-23 07:06:57,208 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_30.pth was removed +2024-07-23 07:06:57,469 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_35.pth. +2024-07-23 07:06:57,470 - pyskl - INFO - Best top1_acc is 0.2172 at 35 epoch. +2024-07-23 07:06:57,480 - pyskl - INFO - Epoch(val) [35][309] top1_acc: 0.2172, top5_acc: 0.4419, mean_class_accuracy: 0.2171 +2024-07-23 07:10:42,034 - pyskl - INFO - Epoch [36][100/3746] lr: 8.714e-02, eta: 3 days, 17:27:56, time: 2.245, data_time: 1.261, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5230, loss_cls: 4.1909, loss: 4.1909 +2024-07-23 07:12:04,066 - pyskl - INFO - Epoch [36][200/3746] lr: 8.712e-02, eta: 3 days, 17:27:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5345, loss_cls: 4.1717, loss: 4.1717 +2024-07-23 07:13:26,957 - pyskl - INFO - Epoch [36][300/3746] lr: 8.710e-02, eta: 3 days, 17:26:17, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5245, loss_cls: 4.1967, loss: 4.1967 +2024-07-23 07:14:48,843 - pyskl - INFO - Epoch [36][400/3746] lr: 8.708e-02, eta: 3 days, 17:25:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5363, loss_cls: 4.1347, loss: 4.1347 +2024-07-23 07:16:11,657 - pyskl - INFO - Epoch [36][500/3746] lr: 8.706e-02, eta: 3 days, 17:24:37, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5277, loss_cls: 4.1819, loss: 4.1819 +2024-07-23 07:17:34,435 - pyskl - INFO - Epoch [36][600/3746] lr: 8.704e-02, eta: 3 days, 17:23:48, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5211, loss_cls: 4.1847, loss: 4.1847 +2024-07-23 07:18:56,426 - pyskl - INFO - Epoch [36][700/3746] lr: 8.703e-02, eta: 3 days, 17:22:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5130, loss_cls: 4.2400, loss: 4.2400 +2024-07-23 07:20:18,503 - pyskl - INFO - Epoch [36][800/3746] lr: 8.701e-02, eta: 3 days, 17:22:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5167, loss_cls: 4.2027, loss: 4.2027 +2024-07-23 07:21:40,115 - pyskl - INFO - Epoch [36][900/3746] lr: 8.699e-02, eta: 3 days, 17:21:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5363, loss_cls: 4.1192, loss: 4.1192 +2024-07-23 07:23:01,692 - pyskl - INFO - Epoch [36][1000/3746] lr: 8.697e-02, eta: 3 days, 17:20:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5136, loss_cls: 4.2303, loss: 4.2303 +2024-07-23 07:24:23,731 - pyskl - INFO - Epoch [36][1100/3746] lr: 8.695e-02, eta: 3 days, 17:19:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5169, loss_cls: 4.2321, loss: 4.2321 +2024-07-23 07:25:45,071 - pyskl - INFO - Epoch [36][1200/3746] lr: 8.693e-02, eta: 3 days, 17:18:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5209, loss_cls: 4.2124, loss: 4.2124 +2024-07-23 07:27:06,193 - pyskl - INFO - Epoch [36][1300/3746] lr: 8.691e-02, eta: 3 days, 17:17:40, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5191, loss_cls: 4.2083, loss: 4.2083 +2024-07-23 07:28:27,511 - pyskl - INFO - Epoch [36][1400/3746] lr: 8.689e-02, eta: 3 days, 17:16:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5233, loss_cls: 4.2158, loss: 4.2158 +2024-07-23 07:29:48,717 - pyskl - INFO - Epoch [36][1500/3746] lr: 8.688e-02, eta: 3 days, 17:15:52, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5128, loss_cls: 4.2638, loss: 4.2638 +2024-07-23 07:31:10,229 - pyskl - INFO - Epoch [36][1600/3746] lr: 8.686e-02, eta: 3 days, 17:14:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5262, loss_cls: 4.1883, loss: 4.1883 +2024-07-23 07:32:31,899 - pyskl - INFO - Epoch [36][1700/3746] lr: 8.684e-02, eta: 3 days, 17:14:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5181, loss_cls: 4.2384, loss: 4.2384 +2024-07-23 07:33:53,413 - pyskl - INFO - Epoch [36][1800/3746] lr: 8.682e-02, eta: 3 days, 17:13:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5209, loss_cls: 4.2450, loss: 4.2450 +2024-07-23 07:35:14,958 - pyskl - INFO - Epoch [36][1900/3746] lr: 8.680e-02, eta: 3 days, 17:12:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5188, loss_cls: 4.2379, loss: 4.2379 +2024-07-23 07:36:36,632 - pyskl - INFO - Epoch [36][2000/3746] lr: 8.678e-02, eta: 3 days, 17:11:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5256, loss_cls: 4.1990, loss: 4.1990 +2024-07-23 07:37:58,321 - pyskl - INFO - Epoch [36][2100/3746] lr: 8.676e-02, eta: 3 days, 17:10:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5242, loss_cls: 4.2430, loss: 4.2430 +2024-07-23 07:39:20,349 - pyskl - INFO - Epoch [36][2200/3746] lr: 8.674e-02, eta: 3 days, 17:09:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5311, loss_cls: 4.1510, loss: 4.1510 +2024-07-23 07:40:41,565 - pyskl - INFO - Epoch [36][2300/3746] lr: 8.672e-02, eta: 3 days, 17:08:47, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5250, loss_cls: 4.2077, loss: 4.2077 +2024-07-23 07:42:03,817 - pyskl - INFO - Epoch [36][2400/3746] lr: 8.671e-02, eta: 3 days, 17:07:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5236, loss_cls: 4.1848, loss: 4.1848 +2024-07-23 07:43:25,679 - pyskl - INFO - Epoch [36][2500/3746] lr: 8.669e-02, eta: 3 days, 17:07:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5142, loss_cls: 4.2316, loss: 4.2316 +2024-07-23 07:44:47,795 - pyskl - INFO - Epoch [36][2600/3746] lr: 8.667e-02, eta: 3 days, 17:06:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5331, loss_cls: 4.1466, loss: 4.1466 +2024-07-23 07:46:09,381 - pyskl - INFO - Epoch [36][2700/3746] lr: 8.665e-02, eta: 3 days, 17:05:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5242, loss_cls: 4.2065, loss: 4.2065 +2024-07-23 07:47:31,048 - pyskl - INFO - Epoch [36][2800/3746] lr: 8.663e-02, eta: 3 days, 17:04:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5216, loss_cls: 4.2320, loss: 4.2320 +2024-07-23 07:48:52,861 - pyskl - INFO - Epoch [36][2900/3746] lr: 8.661e-02, eta: 3 days, 17:03:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5180, loss_cls: 4.2286, loss: 4.2286 +2024-07-23 07:50:14,184 - pyskl - INFO - Epoch [36][3000/3746] lr: 8.659e-02, eta: 3 days, 17:02:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5127, loss_cls: 4.2127, loss: 4.2127 +2024-07-23 07:51:35,931 - pyskl - INFO - Epoch [36][3100/3746] lr: 8.657e-02, eta: 3 days, 17:01:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5194, loss_cls: 4.2340, loss: 4.2340 +2024-07-23 07:52:57,796 - pyskl - INFO - Epoch [36][3200/3746] lr: 8.655e-02, eta: 3 days, 17:00:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5209, loss_cls: 4.2331, loss: 4.2331 +2024-07-23 07:54:19,851 - pyskl - INFO - Epoch [36][3300/3746] lr: 8.653e-02, eta: 3 days, 16:59:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5170, loss_cls: 4.2362, loss: 4.2362 +2024-07-23 07:55:41,061 - pyskl - INFO - Epoch [36][3400/3746] lr: 8.651e-02, eta: 3 days, 16:59:03, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5222, loss_cls: 4.1974, loss: 4.1974 +2024-07-23 07:57:02,992 - pyskl - INFO - Epoch [36][3500/3746] lr: 8.650e-02, eta: 3 days, 16:58:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5180, loss_cls: 4.2250, loss: 4.2250 +2024-07-23 07:58:24,399 - pyskl - INFO - Epoch [36][3600/3746] lr: 8.648e-02, eta: 3 days, 16:57:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5169, loss_cls: 4.2178, loss: 4.2178 +2024-07-23 07:59:46,493 - pyskl - INFO - Epoch [36][3700/3746] lr: 8.646e-02, eta: 3 days, 16:56:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5267, loss_cls: 4.2162, loss: 4.2162 +2024-07-23 08:00:26,101 - pyskl - INFO - Saving checkpoint at 36 epochs +2024-07-23 08:02:18,682 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 08:02:19,334 - pyskl - INFO - +top1_acc 0.1925 +top5_acc 0.4278 +2024-07-23 08:02:19,334 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 08:02:19,373 - pyskl - INFO - +mean_acc 0.1924 +2024-07-23 08:02:19,383 - pyskl - INFO - Epoch(val) [36][309] top1_acc: 0.1925, top5_acc: 0.4278, mean_class_accuracy: 0.1924 +2024-07-23 08:06:06,004 - pyskl - INFO - Epoch [37][100/3746] lr: 8.643e-02, eta: 3 days, 17:00:45, time: 2.266, data_time: 1.267, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5269, loss_cls: 4.1742, loss: 4.1742 +2024-07-23 08:07:27,541 - pyskl - INFO - Epoch [37][200/3746] lr: 8.641e-02, eta: 3 days, 16:59:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5216, loss_cls: 4.2377, loss: 4.2377 +2024-07-23 08:08:51,156 - pyskl - INFO - Epoch [37][300/3746] lr: 8.639e-02, eta: 3 days, 16:59:02, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5212, loss_cls: 4.2082, loss: 4.2082 +2024-07-23 08:10:13,704 - pyskl - INFO - Epoch [37][400/3746] lr: 8.637e-02, eta: 3 days, 16:58:11, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5277, loss_cls: 4.1766, loss: 4.1766 +2024-07-23 08:11:35,484 - pyskl - INFO - Epoch [37][500/3746] lr: 8.635e-02, eta: 3 days, 16:57:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5227, loss_cls: 4.1838, loss: 4.1838 +2024-07-23 08:12:57,640 - pyskl - INFO - Epoch [37][600/3746] lr: 8.633e-02, eta: 3 days, 16:56:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5244, loss_cls: 4.2256, loss: 4.2256 +2024-07-23 08:14:19,319 - pyskl - INFO - Epoch [37][700/3746] lr: 8.631e-02, eta: 3 days, 16:55:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5192, loss_cls: 4.1899, loss: 4.1899 +2024-07-23 08:15:40,718 - pyskl - INFO - Epoch [37][800/3746] lr: 8.630e-02, eta: 3 days, 16:54:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5344, loss_cls: 4.1924, loss: 4.1924 +2024-07-23 08:17:02,229 - pyskl - INFO - Epoch [37][900/3746] lr: 8.628e-02, eta: 3 days, 16:53:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5322, loss_cls: 4.1502, loss: 4.1502 +2024-07-23 08:18:24,051 - pyskl - INFO - Epoch [37][1000/3746] lr: 8.626e-02, eta: 3 days, 16:52:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5241, loss_cls: 4.1757, loss: 4.1757 +2024-07-23 08:19:46,118 - pyskl - INFO - Epoch [37][1100/3746] lr: 8.624e-02, eta: 3 days, 16:51:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5169, loss_cls: 4.2334, loss: 4.2334 +2024-07-23 08:21:07,916 - pyskl - INFO - Epoch [37][1200/3746] lr: 8.622e-02, eta: 3 days, 16:50:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5152, loss_cls: 4.2199, loss: 4.2199 +2024-07-23 08:22:29,419 - pyskl - INFO - Epoch [37][1300/3746] lr: 8.620e-02, eta: 3 days, 16:50:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5206, loss_cls: 4.2159, loss: 4.2159 +2024-07-23 08:23:50,578 - pyskl - INFO - Epoch [37][1400/3746] lr: 8.618e-02, eta: 3 days, 16:49:07, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5159, loss_cls: 4.2037, loss: 4.2037 +2024-07-23 08:25:11,925 - pyskl - INFO - Epoch [37][1500/3746] lr: 8.616e-02, eta: 3 days, 16:48:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5161, loss_cls: 4.2190, loss: 4.2190 +2024-07-23 08:26:33,683 - pyskl - INFO - Epoch [37][1600/3746] lr: 8.614e-02, eta: 3 days, 16:47:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5222, loss_cls: 4.2061, loss: 4.2061 +2024-07-23 08:27:55,642 - pyskl - INFO - Epoch [37][1700/3746] lr: 8.612e-02, eta: 3 days, 16:46:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5281, loss_cls: 4.1844, loss: 4.1844 +2024-07-23 08:29:17,152 - pyskl - INFO - Epoch [37][1800/3746] lr: 8.610e-02, eta: 3 days, 16:45:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5202, loss_cls: 4.2326, loss: 4.2326 +2024-07-23 08:30:39,048 - pyskl - INFO - Epoch [37][1900/3746] lr: 8.608e-02, eta: 3 days, 16:44:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5308, loss_cls: 4.1902, loss: 4.1902 +2024-07-23 08:32:00,528 - pyskl - INFO - Epoch [37][2000/3746] lr: 8.606e-02, eta: 3 days, 16:43:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5234, loss_cls: 4.1950, loss: 4.1950 +2024-07-23 08:33:22,009 - pyskl - INFO - Epoch [37][2100/3746] lr: 8.604e-02, eta: 3 days, 16:42:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5253, loss_cls: 4.1867, loss: 4.1867 +2024-07-23 08:34:43,478 - pyskl - INFO - Epoch [37][2200/3746] lr: 8.602e-02, eta: 3 days, 16:41:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5278, loss_cls: 4.1974, loss: 4.1974 +2024-07-23 08:36:04,610 - pyskl - INFO - Epoch [37][2300/3746] lr: 8.601e-02, eta: 3 days, 16:40:50, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5180, loss_cls: 4.2181, loss: 4.2181 +2024-07-23 08:37:26,268 - pyskl - INFO - Epoch [37][2400/3746] lr: 8.599e-02, eta: 3 days, 16:39:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5175, loss_cls: 4.2202, loss: 4.2202 +2024-07-23 08:38:48,011 - pyskl - INFO - Epoch [37][2500/3746] lr: 8.597e-02, eta: 3 days, 16:39:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5139, loss_cls: 4.2244, loss: 4.2244 +2024-07-23 08:40:09,248 - pyskl - INFO - Epoch [37][2600/3746] lr: 8.595e-02, eta: 3 days, 16:38:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5283, loss_cls: 4.1822, loss: 4.1822 +2024-07-23 08:41:31,147 - pyskl - INFO - Epoch [37][2700/3746] lr: 8.593e-02, eta: 3 days, 16:37:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5273, loss_cls: 4.2133, loss: 4.2133 +2024-07-23 08:42:52,326 - pyskl - INFO - Epoch [37][2800/3746] lr: 8.591e-02, eta: 3 days, 16:36:12, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5169, loss_cls: 4.2156, loss: 4.2156 +2024-07-23 08:44:13,967 - pyskl - INFO - Epoch [37][2900/3746] lr: 8.589e-02, eta: 3 days, 16:35:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5169, loss_cls: 4.2297, loss: 4.2297 +2024-07-23 08:45:35,094 - pyskl - INFO - Epoch [37][3000/3746] lr: 8.587e-02, eta: 3 days, 16:34:20, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5355, loss_cls: 4.1622, loss: 4.1622 +2024-07-23 08:46:56,749 - pyskl - INFO - Epoch [37][3100/3746] lr: 8.585e-02, eta: 3 days, 16:33:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5222, loss_cls: 4.2296, loss: 4.2296 +2024-07-23 08:48:18,738 - pyskl - INFO - Epoch [37][3200/3746] lr: 8.583e-02, eta: 3 days, 16:32:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5219, loss_cls: 4.2619, loss: 4.2619 +2024-07-23 08:49:40,397 - pyskl - INFO - Epoch [37][3300/3746] lr: 8.581e-02, eta: 3 days, 16:31:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5284, loss_cls: 4.1577, loss: 4.1577 +2024-07-23 08:51:01,747 - pyskl - INFO - Epoch [37][3400/3746] lr: 8.579e-02, eta: 3 days, 16:30:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5078, loss_cls: 4.2458, loss: 4.2458 +2024-07-23 08:52:23,616 - pyskl - INFO - Epoch [37][3500/3746] lr: 8.577e-02, eta: 3 days, 16:29:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5203, loss_cls: 4.1777, loss: 4.1777 +2024-07-23 08:53:44,698 - pyskl - INFO - Epoch [37][3600/3746] lr: 8.575e-02, eta: 3 days, 16:28:46, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5217, loss_cls: 4.1916, loss: 4.1916 +2024-07-23 08:55:06,628 - pyskl - INFO - Epoch [37][3700/3746] lr: 8.573e-02, eta: 3 days, 16:27:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5144, loss_cls: 4.2371, loss: 4.2371 +2024-07-23 08:55:46,753 - pyskl - INFO - Saving checkpoint at 37 epochs +2024-07-23 08:57:39,507 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 08:57:40,157 - pyskl - INFO - +top1_acc 0.1871 +top5_acc 0.4270 +2024-07-23 08:57:40,158 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 08:57:40,196 - pyskl - INFO - +mean_acc 0.1871 +2024-07-23 08:57:40,205 - pyskl - INFO - Epoch(val) [37][309] top1_acc: 0.1871, top5_acc: 0.4270, mean_class_accuracy: 0.1871 +2024-07-23 09:01:24,549 - pyskl - INFO - Epoch [38][100/3746] lr: 8.570e-02, eta: 3 days, 16:31:51, time: 2.243, data_time: 1.271, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5448, loss_cls: 4.1135, loss: 4.1135 +2024-07-23 09:02:46,619 - pyskl - INFO - Epoch [38][200/3746] lr: 8.568e-02, eta: 3 days, 16:30:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5222, loss_cls: 4.2002, loss: 4.2002 +2024-07-23 09:04:09,556 - pyskl - INFO - Epoch [38][300/3746] lr: 8.567e-02, eta: 3 days, 16:30:04, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5244, loss_cls: 4.1954, loss: 4.1954 +2024-07-23 09:05:32,274 - pyskl - INFO - Epoch [38][400/3746] lr: 8.565e-02, eta: 3 days, 16:29:12, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5300, loss_cls: 4.1601, loss: 4.1601 +2024-07-23 09:06:55,310 - pyskl - INFO - Epoch [38][500/3746] lr: 8.563e-02, eta: 3 days, 16:28:20, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5203, loss_cls: 4.2127, loss: 4.2127 +2024-07-23 09:08:17,561 - pyskl - INFO - Epoch [38][600/3746] lr: 8.561e-02, eta: 3 days, 16:27:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5241, loss_cls: 4.2268, loss: 4.2268 +2024-07-23 09:09:39,239 - pyskl - INFO - Epoch [38][700/3746] lr: 8.559e-02, eta: 3 days, 16:26:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5186, loss_cls: 4.2128, loss: 4.2128 +2024-07-23 09:11:01,072 - pyskl - INFO - Epoch [38][800/3746] lr: 8.557e-02, eta: 3 days, 16:25:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5306, loss_cls: 4.1688, loss: 4.1688 +2024-07-23 09:12:22,733 - pyskl - INFO - Epoch [38][900/3746] lr: 8.555e-02, eta: 3 days, 16:24:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5144, loss_cls: 4.2261, loss: 4.2261 +2024-07-23 09:13:44,444 - pyskl - INFO - Epoch [38][1000/3746] lr: 8.553e-02, eta: 3 days, 16:23:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5184, loss_cls: 4.2075, loss: 4.2075 +2024-07-23 09:15:06,206 - pyskl - INFO - Epoch [38][1100/3746] lr: 8.551e-02, eta: 3 days, 16:22:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5375, loss_cls: 4.1181, loss: 4.1181 +2024-07-23 09:16:27,606 - pyskl - INFO - Epoch [38][1200/3746] lr: 8.549e-02, eta: 3 days, 16:21:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5155, loss_cls: 4.2144, loss: 4.2144 +2024-07-23 09:17:48,984 - pyskl - INFO - Epoch [38][1300/3746] lr: 8.547e-02, eta: 3 days, 16:20:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5275, loss_cls: 4.1758, loss: 4.1758 +2024-07-23 09:19:11,125 - pyskl - INFO - Epoch [38][1400/3746] lr: 8.545e-02, eta: 3 days, 16:19:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5323, loss_cls: 4.1771, loss: 4.1771 +2024-07-23 09:20:33,045 - pyskl - INFO - Epoch [38][1500/3746] lr: 8.543e-02, eta: 3 days, 16:19:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5264, loss_cls: 4.2009, loss: 4.2009 +2024-07-23 09:21:55,105 - pyskl - INFO - Epoch [38][1600/3746] lr: 8.541e-02, eta: 3 days, 16:18:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5217, loss_cls: 4.1989, loss: 4.1989 +2024-07-23 09:23:16,648 - pyskl - INFO - Epoch [38][1700/3746] lr: 8.539e-02, eta: 3 days, 16:17:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5212, loss_cls: 4.1993, loss: 4.1993 +2024-07-23 09:24:37,837 - pyskl - INFO - Epoch [38][1800/3746] lr: 8.537e-02, eta: 3 days, 16:16:10, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5225, loss_cls: 4.2061, loss: 4.2061 +2024-07-23 09:25:59,892 - pyskl - INFO - Epoch [38][1900/3746] lr: 8.535e-02, eta: 3 days, 16:15:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5291, loss_cls: 4.1733, loss: 4.1733 +2024-07-23 09:27:21,886 - pyskl - INFO - Epoch [38][2000/3746] lr: 8.533e-02, eta: 3 days, 16:14:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5289, loss_cls: 4.1784, loss: 4.1784 +2024-07-23 09:28:43,483 - pyskl - INFO - Epoch [38][2100/3746] lr: 8.531e-02, eta: 3 days, 16:13:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5336, loss_cls: 4.1528, loss: 4.1528 +2024-07-23 09:30:05,188 - pyskl - INFO - Epoch [38][2200/3746] lr: 8.529e-02, eta: 3 days, 16:12:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5212, loss_cls: 4.2138, loss: 4.2138 +2024-07-23 09:31:26,927 - pyskl - INFO - Epoch [38][2300/3746] lr: 8.527e-02, eta: 3 days, 16:11:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5192, loss_cls: 4.1723, loss: 4.1723 +2024-07-23 09:32:48,168 - pyskl - INFO - Epoch [38][2400/3746] lr: 8.525e-02, eta: 3 days, 16:10:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5159, loss_cls: 4.2175, loss: 4.2175 +2024-07-23 09:34:09,983 - pyskl - INFO - Epoch [38][2500/3746] lr: 8.523e-02, eta: 3 days, 16:09:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5208, loss_cls: 4.2069, loss: 4.2069 +2024-07-23 09:35:31,335 - pyskl - INFO - Epoch [38][2600/3746] lr: 8.521e-02, eta: 3 days, 16:08:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5258, loss_cls: 4.1519, loss: 4.1519 +2024-07-23 09:36:52,586 - pyskl - INFO - Epoch [38][2700/3746] lr: 8.519e-02, eta: 3 days, 16:07:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5208, loss_cls: 4.2317, loss: 4.2317 +2024-07-23 09:38:14,147 - pyskl - INFO - Epoch [38][2800/3746] lr: 8.517e-02, eta: 3 days, 16:06:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5253, loss_cls: 4.1855, loss: 4.1855 +2024-07-23 09:39:35,486 - pyskl - INFO - Epoch [38][2900/3746] lr: 8.515e-02, eta: 3 days, 16:05:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5177, loss_cls: 4.2208, loss: 4.2208 +2024-07-23 09:40:57,186 - pyskl - INFO - Epoch [38][3000/3746] lr: 8.513e-02, eta: 3 days, 16:04:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5316, loss_cls: 4.1894, loss: 4.1894 +2024-07-23 09:42:19,052 - pyskl - INFO - Epoch [38][3100/3746] lr: 8.511e-02, eta: 3 days, 16:03:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5194, loss_cls: 4.2065, loss: 4.2065 +2024-07-23 09:43:40,477 - pyskl - INFO - Epoch [38][3200/3746] lr: 8.509e-02, eta: 3 days, 16:02:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5170, loss_cls: 4.2235, loss: 4.2235 +2024-07-23 09:45:01,879 - pyskl - INFO - Epoch [38][3300/3746] lr: 8.507e-02, eta: 3 days, 16:01:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5277, loss_cls: 4.1622, loss: 4.1622 +2024-07-23 09:46:23,129 - pyskl - INFO - Epoch [38][3400/3746] lr: 8.505e-02, eta: 3 days, 16:00:57, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5267, loss_cls: 4.1930, loss: 4.1930 +2024-07-23 09:47:45,086 - pyskl - INFO - Epoch [38][3500/3746] lr: 8.503e-02, eta: 3 days, 16:00:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5116, loss_cls: 4.2346, loss: 4.2346 +2024-07-23 09:49:06,498 - pyskl - INFO - Epoch [38][3600/3746] lr: 8.501e-02, eta: 3 days, 15:59:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5350, loss_cls: 4.1666, loss: 4.1666 +2024-07-23 09:50:28,611 - pyskl - INFO - Epoch [38][3700/3746] lr: 8.499e-02, eta: 3 days, 15:58:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5319, loss_cls: 4.1639, loss: 4.1639 +2024-07-23 09:51:07,847 - pyskl - INFO - Saving checkpoint at 38 epochs +2024-07-23 09:53:00,404 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 09:53:01,063 - pyskl - INFO - +top1_acc 0.2066 +top5_acc 0.4366 +2024-07-23 09:53:01,063 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 09:53:01,102 - pyskl - INFO - +mean_acc 0.2064 +2024-07-23 09:53:01,112 - pyskl - INFO - Epoch(val) [38][309] top1_acc: 0.2066, top5_acc: 0.4366, mean_class_accuracy: 0.2064 +2024-07-23 09:56:45,688 - pyskl - INFO - Epoch [39][100/3746] lr: 8.496e-02, eta: 3 days, 16:01:53, time: 2.246, data_time: 1.279, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5384, loss_cls: 4.1361, loss: 4.1361 +2024-07-23 09:58:07,049 - pyskl - INFO - Epoch [39][200/3746] lr: 8.494e-02, eta: 3 days, 16:00:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5361, loss_cls: 4.1273, loss: 4.1273 +2024-07-23 09:59:31,197 - pyskl - INFO - Epoch [39][300/3746] lr: 8.492e-02, eta: 3 days, 16:00:05, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5341, loss_cls: 4.1418, loss: 4.1418 +2024-07-23 10:00:53,966 - pyskl - INFO - Epoch [39][400/3746] lr: 8.490e-02, eta: 3 days, 15:59:10, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5387, loss_cls: 4.1454, loss: 4.1454 +2024-07-23 10:02:16,755 - pyskl - INFO - Epoch [39][500/3746] lr: 8.488e-02, eta: 3 days, 15:58:16, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5262, loss_cls: 4.1720, loss: 4.1720 +2024-07-23 10:03:38,659 - pyskl - INFO - Epoch [39][600/3746] lr: 8.486e-02, eta: 3 days, 15:57:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5264, loss_cls: 4.1810, loss: 4.1810 +2024-07-23 10:04:59,934 - pyskl - INFO - Epoch [39][700/3746] lr: 8.484e-02, eta: 3 days, 15:56:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5225, loss_cls: 4.2144, loss: 4.2144 +2024-07-23 10:06:22,500 - pyskl - INFO - Epoch [39][800/3746] lr: 8.482e-02, eta: 3 days, 15:55:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5241, loss_cls: 4.2067, loss: 4.2067 +2024-07-23 10:07:43,748 - pyskl - INFO - Epoch [39][900/3746] lr: 8.480e-02, eta: 3 days, 15:54:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5264, loss_cls: 4.1814, loss: 4.1814 +2024-07-23 10:09:04,988 - pyskl - INFO - Epoch [39][1000/3746] lr: 8.478e-02, eta: 3 days, 15:53:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5170, loss_cls: 4.2176, loss: 4.2176 +2024-07-23 10:10:26,539 - pyskl - INFO - Epoch [39][1100/3746] lr: 8.476e-02, eta: 3 days, 15:52:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5314, loss_cls: 4.1540, loss: 4.1540 +2024-07-23 10:11:48,448 - pyskl - INFO - Epoch [39][1200/3746] lr: 8.474e-02, eta: 3 days, 15:51:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5375, loss_cls: 4.1625, loss: 4.1625 +2024-07-23 10:13:10,705 - pyskl - INFO - Epoch [39][1300/3746] lr: 8.472e-02, eta: 3 days, 15:50:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5256, loss_cls: 4.2002, loss: 4.2002 +2024-07-23 10:14:32,070 - pyskl - INFO - Epoch [39][1400/3746] lr: 8.470e-02, eta: 3 days, 15:49:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5277, loss_cls: 4.2027, loss: 4.2027 +2024-07-23 10:15:53,556 - pyskl - INFO - Epoch [39][1500/3746] lr: 8.468e-02, eta: 3 days, 15:48:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5236, loss_cls: 4.1780, loss: 4.1780 +2024-07-23 10:17:15,249 - pyskl - INFO - Epoch [39][1600/3746] lr: 8.466e-02, eta: 3 days, 15:47:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5303, loss_cls: 4.1415, loss: 4.1415 +2024-07-23 10:18:36,795 - pyskl - INFO - Epoch [39][1700/3746] lr: 8.464e-02, eta: 3 days, 15:46:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5319, loss_cls: 4.1431, loss: 4.1431 +2024-07-23 10:19:58,078 - pyskl - INFO - Epoch [39][1800/3746] lr: 8.462e-02, eta: 3 days, 15:45:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5195, loss_cls: 4.2223, loss: 4.2223 +2024-07-23 10:21:20,191 - pyskl - INFO - Epoch [39][1900/3746] lr: 8.460e-02, eta: 3 days, 15:44:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5164, loss_cls: 4.2345, loss: 4.2345 +2024-07-23 10:22:41,672 - pyskl - INFO - Epoch [39][2000/3746] lr: 8.458e-02, eta: 3 days, 15:43:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5127, loss_cls: 4.2597, loss: 4.2597 +2024-07-23 10:24:03,051 - pyskl - INFO - Epoch [39][2100/3746] lr: 8.456e-02, eta: 3 days, 15:42:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5177, loss_cls: 4.2085, loss: 4.2085 +2024-07-23 10:25:25,038 - pyskl - INFO - Epoch [39][2200/3746] lr: 8.454e-02, eta: 3 days, 15:41:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5202, loss_cls: 4.2471, loss: 4.2471 +2024-07-23 10:26:46,318 - pyskl - INFO - Epoch [39][2300/3746] lr: 8.452e-02, eta: 3 days, 15:40:50, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5144, loss_cls: 4.2162, loss: 4.2162 +2024-07-23 10:28:07,855 - pyskl - INFO - Epoch [39][2400/3746] lr: 8.450e-02, eta: 3 days, 15:39:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5147, loss_cls: 4.2202, loss: 4.2202 +2024-07-23 10:29:29,182 - pyskl - INFO - Epoch [39][2500/3746] lr: 8.448e-02, eta: 3 days, 15:38:52, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5247, loss_cls: 4.1852, loss: 4.1852 +2024-07-23 10:30:50,771 - pyskl - INFO - Epoch [39][2600/3746] lr: 8.446e-02, eta: 3 days, 15:37:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5273, loss_cls: 4.2315, loss: 4.2315 +2024-07-23 10:32:12,395 - pyskl - INFO - Epoch [39][2700/3746] lr: 8.444e-02, eta: 3 days, 15:36:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5272, loss_cls: 4.1953, loss: 4.1953 +2024-07-23 10:33:34,413 - pyskl - INFO - Epoch [39][2800/3746] lr: 8.442e-02, eta: 3 days, 15:35:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5297, loss_cls: 4.1867, loss: 4.1867 +2024-07-23 10:34:55,865 - pyskl - INFO - Epoch [39][2900/3746] lr: 8.440e-02, eta: 3 days, 15:34:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5148, loss_cls: 4.2267, loss: 4.2267 +2024-07-23 10:36:17,330 - pyskl - INFO - Epoch [39][3000/3746] lr: 8.438e-02, eta: 3 days, 15:34:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5298, loss_cls: 4.1591, loss: 4.1591 +2024-07-23 10:37:39,066 - pyskl - INFO - Epoch [39][3100/3746] lr: 8.436e-02, eta: 3 days, 15:33:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5184, loss_cls: 4.2087, loss: 4.2087 +2024-07-23 10:39:00,894 - pyskl - INFO - Epoch [39][3200/3746] lr: 8.434e-02, eta: 3 days, 15:32:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5273, loss_cls: 4.1815, loss: 4.1815 +2024-07-23 10:40:22,898 - pyskl - INFO - Epoch [39][3300/3746] lr: 8.432e-02, eta: 3 days, 15:31:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5230, loss_cls: 4.2062, loss: 4.2062 +2024-07-23 10:41:44,254 - pyskl - INFO - Epoch [39][3400/3746] lr: 8.430e-02, eta: 3 days, 15:30:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5117, loss_cls: 4.2363, loss: 4.2363 +2024-07-23 10:43:06,035 - pyskl - INFO - Epoch [39][3500/3746] lr: 8.428e-02, eta: 3 days, 15:29:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5342, loss_cls: 4.1580, loss: 4.1580 +2024-07-23 10:44:27,697 - pyskl - INFO - Epoch [39][3600/3746] lr: 8.426e-02, eta: 3 days, 15:28:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5347, loss_cls: 4.1468, loss: 4.1468 +2024-07-23 10:45:49,829 - pyskl - INFO - Epoch [39][3700/3746] lr: 8.424e-02, eta: 3 days, 15:27:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5283, loss_cls: 4.1840, loss: 4.1840 +2024-07-23 10:46:29,269 - pyskl - INFO - Saving checkpoint at 39 epochs +2024-07-23 10:48:21,622 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 10:48:22,276 - pyskl - INFO - +top1_acc 0.2028 +top5_acc 0.4288 +2024-07-23 10:48:22,276 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 10:48:22,315 - pyskl - INFO - +mean_acc 0.2025 +2024-07-23 10:48:22,326 - pyskl - INFO - Epoch(val) [39][309] top1_acc: 0.2028, top5_acc: 0.4288, mean_class_accuracy: 0.2025 +2024-07-23 10:52:07,483 - pyskl - INFO - Epoch [40][100/3746] lr: 8.421e-02, eta: 3 days, 15:30:47, time: 2.251, data_time: 1.281, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5387, loss_cls: 4.1229, loss: 4.1229 +2024-07-23 10:53:29,257 - pyskl - INFO - Epoch [40][200/3746] lr: 8.419e-02, eta: 3 days, 15:29:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5330, loss_cls: 4.1279, loss: 4.1279 +2024-07-23 10:54:51,079 - pyskl - INFO - Epoch [40][300/3746] lr: 8.417e-02, eta: 3 days, 15:28:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5284, loss_cls: 4.1670, loss: 4.1670 +2024-07-23 10:56:14,878 - pyskl - INFO - Epoch [40][400/3746] lr: 8.415e-02, eta: 3 days, 15:27:56, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5194, loss_cls: 4.1931, loss: 4.1931 +2024-07-23 10:57:37,346 - pyskl - INFO - Epoch [40][500/3746] lr: 8.413e-02, eta: 3 days, 15:27:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5267, loss_cls: 4.1717, loss: 4.1717 +2024-07-23 10:58:59,658 - pyskl - INFO - Epoch [40][600/3746] lr: 8.411e-02, eta: 3 days, 15:26:02, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5314, loss_cls: 4.1785, loss: 4.1785 +2024-07-23 11:00:21,980 - pyskl - INFO - Epoch [40][700/3746] lr: 8.408e-02, eta: 3 days, 15:25:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5256, loss_cls: 4.1900, loss: 4.1900 +2024-07-23 11:01:43,878 - pyskl - INFO - Epoch [40][800/3746] lr: 8.406e-02, eta: 3 days, 15:24:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5180, loss_cls: 4.2065, loss: 4.2065 +2024-07-23 11:03:05,429 - pyskl - INFO - Epoch [40][900/3746] lr: 8.404e-02, eta: 3 days, 15:23:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5255, loss_cls: 4.1781, loss: 4.1781 +2024-07-23 11:04:26,881 - pyskl - INFO - Epoch [40][1000/3746] lr: 8.402e-02, eta: 3 days, 15:22:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5320, loss_cls: 4.1643, loss: 4.1643 +2024-07-23 11:05:48,743 - pyskl - INFO - Epoch [40][1100/3746] lr: 8.400e-02, eta: 3 days, 15:21:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5353, loss_cls: 4.1357, loss: 4.1357 +2024-07-23 11:07:10,882 - pyskl - INFO - Epoch [40][1200/3746] lr: 8.398e-02, eta: 3 days, 15:20:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5258, loss_cls: 4.1898, loss: 4.1898 +2024-07-23 11:08:32,738 - pyskl - INFO - Epoch [40][1300/3746] lr: 8.396e-02, eta: 3 days, 15:19:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5233, loss_cls: 4.1531, loss: 4.1531 +2024-07-23 11:09:55,211 - pyskl - INFO - Epoch [40][1400/3746] lr: 8.394e-02, eta: 3 days, 15:18:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5317, loss_cls: 4.1714, loss: 4.1714 +2024-07-23 11:11:17,344 - pyskl - INFO - Epoch [40][1500/3746] lr: 8.392e-02, eta: 3 days, 15:17:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5262, loss_cls: 4.1884, loss: 4.1884 +2024-07-23 11:12:39,363 - pyskl - INFO - Epoch [40][1600/3746] lr: 8.390e-02, eta: 3 days, 15:16:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5175, loss_cls: 4.2114, loss: 4.2114 +2024-07-23 11:14:01,122 - pyskl - INFO - Epoch [40][1700/3746] lr: 8.388e-02, eta: 3 days, 15:15:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5247, loss_cls: 4.1837, loss: 4.1837 +2024-07-23 11:15:23,215 - pyskl - INFO - Epoch [40][1800/3746] lr: 8.386e-02, eta: 3 days, 15:14:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5128, loss_cls: 4.2329, loss: 4.2329 +2024-07-23 11:16:45,241 - pyskl - INFO - Epoch [40][1900/3746] lr: 8.384e-02, eta: 3 days, 15:13:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5298, loss_cls: 4.1527, loss: 4.1527 +2024-07-23 11:18:07,088 - pyskl - INFO - Epoch [40][2000/3746] lr: 8.382e-02, eta: 3 days, 15:12:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5233, loss_cls: 4.2038, loss: 4.2038 +2024-07-23 11:19:28,938 - pyskl - INFO - Epoch [40][2100/3746] lr: 8.380e-02, eta: 3 days, 15:11:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5247, loss_cls: 4.1865, loss: 4.1865 +2024-07-23 11:20:50,843 - pyskl - INFO - Epoch [40][2200/3746] lr: 8.378e-02, eta: 3 days, 15:10:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5222, loss_cls: 4.2207, loss: 4.2207 +2024-07-23 11:22:13,033 - pyskl - INFO - Epoch [40][2300/3746] lr: 8.376e-02, eta: 3 days, 15:09:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5278, loss_cls: 4.1620, loss: 4.1620 +2024-07-23 11:23:35,038 - pyskl - INFO - Epoch [40][2400/3746] lr: 8.374e-02, eta: 3 days, 15:08:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5169, loss_cls: 4.2228, loss: 4.2228 +2024-07-23 11:24:56,636 - pyskl - INFO - Epoch [40][2500/3746] lr: 8.371e-02, eta: 3 days, 15:07:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5219, loss_cls: 4.1961, loss: 4.1961 +2024-07-23 11:26:18,965 - pyskl - INFO - Epoch [40][2600/3746] lr: 8.369e-02, eta: 3 days, 15:06:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5233, loss_cls: 4.1879, loss: 4.1879 +2024-07-23 11:27:40,904 - pyskl - INFO - Epoch [40][2700/3746] lr: 8.367e-02, eta: 3 days, 15:05:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5286, loss_cls: 4.1584, loss: 4.1584 +2024-07-23 11:29:02,831 - pyskl - INFO - Epoch [40][2800/3746] lr: 8.365e-02, eta: 3 days, 15:04:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5228, loss_cls: 4.1927, loss: 4.1927 +2024-07-23 11:30:25,180 - pyskl - INFO - Epoch [40][2900/3746] lr: 8.363e-02, eta: 3 days, 15:03:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5303, loss_cls: 4.1900, loss: 4.1900 +2024-07-23 11:31:47,283 - pyskl - INFO - Epoch [40][3000/3746] lr: 8.361e-02, eta: 3 days, 15:02:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5189, loss_cls: 4.2080, loss: 4.2080 +2024-07-23 11:33:08,947 - pyskl - INFO - Epoch [40][3100/3746] lr: 8.359e-02, eta: 3 days, 15:01:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5244, loss_cls: 4.1846, loss: 4.1846 +2024-07-23 11:34:30,658 - pyskl - INFO - Epoch [40][3200/3746] lr: 8.357e-02, eta: 3 days, 15:00:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5273, loss_cls: 4.1821, loss: 4.1821 +2024-07-23 11:35:52,320 - pyskl - INFO - Epoch [40][3300/3746] lr: 8.355e-02, eta: 3 days, 14:59:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5145, loss_cls: 4.1896, loss: 4.1896 +2024-07-23 11:37:13,965 - pyskl - INFO - Epoch [40][3400/3746] lr: 8.353e-02, eta: 3 days, 14:58:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5267, loss_cls: 4.1990, loss: 4.1990 +2024-07-23 11:38:36,128 - pyskl - INFO - Epoch [40][3500/3746] lr: 8.351e-02, eta: 3 days, 14:57:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5191, loss_cls: 4.2381, loss: 4.2381 +2024-07-23 11:39:57,937 - pyskl - INFO - Epoch [40][3600/3746] lr: 8.349e-02, eta: 3 days, 14:56:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5300, loss_cls: 4.1683, loss: 4.1683 +2024-07-23 11:41:20,952 - pyskl - INFO - Epoch [40][3700/3746] lr: 8.347e-02, eta: 3 days, 14:55:38, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5300, loss_cls: 4.1671, loss: 4.1671 +2024-07-23 11:42:00,767 - pyskl - INFO - Saving checkpoint at 40 epochs +2024-07-23 11:43:53,023 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 11:43:53,677 - pyskl - INFO - +top1_acc 0.2261 +top5_acc 0.4559 +2024-07-23 11:43:53,677 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 11:43:53,716 - pyskl - INFO - +mean_acc 0.2259 +2024-07-23 11:43:53,720 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_35.pth was removed +2024-07-23 11:43:53,965 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_40.pth. +2024-07-23 11:43:53,966 - pyskl - INFO - Best top1_acc is 0.2261 at 40 epoch. +2024-07-23 11:43:53,976 - pyskl - INFO - Epoch(val) [40][309] top1_acc: 0.2261, top5_acc: 0.4559, mean_class_accuracy: 0.2259 +2024-07-23 11:47:35,630 - pyskl - INFO - Epoch [41][100/3746] lr: 8.344e-02, eta: 3 days, 14:58:52, time: 2.216, data_time: 1.242, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5316, loss_cls: 4.1094, loss: 4.1094 +2024-07-23 11:48:57,050 - pyskl - INFO - Epoch [41][200/3746] lr: 8.342e-02, eta: 3 days, 14:57:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5348, loss_cls: 4.1475, loss: 4.1475 +2024-07-23 11:50:19,200 - pyskl - INFO - Epoch [41][300/3746] lr: 8.339e-02, eta: 3 days, 14:56:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5398, loss_cls: 4.1326, loss: 4.1326 +2024-07-23 11:51:40,998 - pyskl - INFO - Epoch [41][400/3746] lr: 8.337e-02, eta: 3 days, 14:55:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5300, loss_cls: 4.1739, loss: 4.1739 +2024-07-23 11:53:03,999 - pyskl - INFO - Epoch [41][500/3746] lr: 8.335e-02, eta: 3 days, 14:54:54, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5245, loss_cls: 4.1589, loss: 4.1589 +2024-07-23 11:54:26,409 - pyskl - INFO - Epoch [41][600/3746] lr: 8.333e-02, eta: 3 days, 14:53:56, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5327, loss_cls: 4.1473, loss: 4.1473 +2024-07-23 11:55:48,598 - pyskl - INFO - Epoch [41][700/3746] lr: 8.331e-02, eta: 3 days, 14:52:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5275, loss_cls: 4.1785, loss: 4.1785 +2024-07-23 11:57:10,170 - pyskl - INFO - Epoch [41][800/3746] lr: 8.329e-02, eta: 3 days, 14:51:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5311, loss_cls: 4.1867, loss: 4.1867 +2024-07-23 11:58:32,910 - pyskl - INFO - Epoch [41][900/3746] lr: 8.327e-02, eta: 3 days, 14:50:58, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5248, loss_cls: 4.1712, loss: 4.1712 +2024-07-23 11:59:54,575 - pyskl - INFO - Epoch [41][1000/3746] lr: 8.325e-02, eta: 3 days, 14:49:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5363, loss_cls: 4.1254, loss: 4.1254 +2024-07-23 12:01:16,506 - pyskl - INFO - Epoch [41][1100/3746] lr: 8.323e-02, eta: 3 days, 14:48:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5311, loss_cls: 4.1159, loss: 4.1159 +2024-07-23 12:02:38,232 - pyskl - INFO - Epoch [41][1200/3746] lr: 8.321e-02, eta: 3 days, 14:47:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5197, loss_cls: 4.2186, loss: 4.2186 +2024-07-23 12:04:00,611 - pyskl - INFO - Epoch [41][1300/3746] lr: 8.319e-02, eta: 3 days, 14:46:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5303, loss_cls: 4.1665, loss: 4.1665 +2024-07-23 12:05:22,078 - pyskl - INFO - Epoch [41][1400/3746] lr: 8.316e-02, eta: 3 days, 14:45:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5286, loss_cls: 4.1499, loss: 4.1499 +2024-07-23 12:06:43,999 - pyskl - INFO - Epoch [41][1500/3746] lr: 8.314e-02, eta: 3 days, 14:44:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5205, loss_cls: 4.2101, loss: 4.2101 +2024-07-23 12:08:06,234 - pyskl - INFO - Epoch [41][1600/3746] lr: 8.312e-02, eta: 3 days, 14:43:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5152, loss_cls: 4.2271, loss: 4.2271 +2024-07-23 12:09:27,283 - pyskl - INFO - Epoch [41][1700/3746] lr: 8.310e-02, eta: 3 days, 14:42:54, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5308, loss_cls: 4.1876, loss: 4.1876 +2024-07-23 12:10:49,197 - pyskl - INFO - Epoch [41][1800/3746] lr: 8.308e-02, eta: 3 days, 14:41:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5341, loss_cls: 4.1556, loss: 4.1556 +2024-07-23 12:12:10,790 - pyskl - INFO - Epoch [41][1900/3746] lr: 8.306e-02, eta: 3 days, 14:40:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5337, loss_cls: 4.1497, loss: 4.1497 +2024-07-23 12:13:32,401 - pyskl - INFO - Epoch [41][2000/3746] lr: 8.304e-02, eta: 3 days, 14:39:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5259, loss_cls: 4.1682, loss: 4.1682 +2024-07-23 12:14:53,863 - pyskl - INFO - Epoch [41][2100/3746] lr: 8.302e-02, eta: 3 days, 14:38:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5280, loss_cls: 4.1543, loss: 4.1543 +2024-07-23 12:16:15,702 - pyskl - INFO - Epoch [41][2200/3746] lr: 8.300e-02, eta: 3 days, 14:37:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5280, loss_cls: 4.1948, loss: 4.1948 +2024-07-23 12:17:38,079 - pyskl - INFO - Epoch [41][2300/3746] lr: 8.298e-02, eta: 3 days, 14:36:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5262, loss_cls: 4.1813, loss: 4.1813 +2024-07-23 12:18:59,670 - pyskl - INFO - Epoch [41][2400/3746] lr: 8.296e-02, eta: 3 days, 14:35:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5309, loss_cls: 4.1497, loss: 4.1497 +2024-07-23 12:20:21,260 - pyskl - INFO - Epoch [41][2500/3746] lr: 8.293e-02, eta: 3 days, 14:34:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5278, loss_cls: 4.1597, loss: 4.1597 +2024-07-23 12:21:42,693 - pyskl - INFO - Epoch [41][2600/3746] lr: 8.291e-02, eta: 3 days, 14:33:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5288, loss_cls: 4.1657, loss: 4.1657 +2024-07-23 12:23:04,028 - pyskl - INFO - Epoch [41][2700/3746] lr: 8.289e-02, eta: 3 days, 14:32:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5152, loss_cls: 4.2216, loss: 4.2216 +2024-07-23 12:24:25,486 - pyskl - INFO - Epoch [41][2800/3746] lr: 8.287e-02, eta: 3 days, 14:31:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5147, loss_cls: 4.2205, loss: 4.2205 +2024-07-23 12:25:47,454 - pyskl - INFO - Epoch [41][2900/3746] lr: 8.285e-02, eta: 3 days, 14:30:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5320, loss_cls: 4.1345, loss: 4.1345 +2024-07-23 12:27:09,537 - pyskl - INFO - Epoch [41][3000/3746] lr: 8.283e-02, eta: 3 days, 14:29:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5200, loss_cls: 4.2092, loss: 4.2092 +2024-07-23 12:28:31,093 - pyskl - INFO - Epoch [41][3100/3746] lr: 8.281e-02, eta: 3 days, 14:28:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5195, loss_cls: 4.2022, loss: 4.2022 +2024-07-23 12:29:52,747 - pyskl - INFO - Epoch [41][3200/3746] lr: 8.279e-02, eta: 3 days, 14:27:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5183, loss_cls: 4.2288, loss: 4.2288 +2024-07-23 12:31:14,352 - pyskl - INFO - Epoch [41][3300/3746] lr: 8.277e-02, eta: 3 days, 14:26:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5350, loss_cls: 4.1331, loss: 4.1331 +2024-07-23 12:32:36,012 - pyskl - INFO - Epoch [41][3400/3746] lr: 8.274e-02, eta: 3 days, 14:25:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5239, loss_cls: 4.1934, loss: 4.1934 +2024-07-23 12:33:57,874 - pyskl - INFO - Epoch [41][3500/3746] lr: 8.272e-02, eta: 3 days, 14:24:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5339, loss_cls: 4.1691, loss: 4.1691 +2024-07-23 12:35:19,349 - pyskl - INFO - Epoch [41][3600/3746] lr: 8.270e-02, eta: 3 days, 14:23:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5058, loss_cls: 4.2876, loss: 4.2876 +2024-07-23 12:36:41,100 - pyskl - INFO - Epoch [41][3700/3746] lr: 8.268e-02, eta: 3 days, 14:22:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5277, loss_cls: 4.2166, loss: 4.2166 +2024-07-23 12:37:20,777 - pyskl - INFO - Saving checkpoint at 41 epochs +2024-07-23 12:39:12,828 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 12:39:13,481 - pyskl - INFO - +top1_acc 0.1803 +top5_acc 0.3968 +2024-07-23 12:39:13,481 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 12:39:13,520 - pyskl - INFO - +mean_acc 0.1801 +2024-07-23 12:39:13,531 - pyskl - INFO - Epoch(val) [41][309] top1_acc: 0.1803, top5_acc: 0.3968, mean_class_accuracy: 0.1801 +2024-07-23 12:43:01,656 - pyskl - INFO - Epoch [42][100/3746] lr: 8.265e-02, eta: 3 days, 14:25:51, time: 2.281, data_time: 1.263, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5370, loss_cls: 4.1386, loss: 4.1386 +2024-07-23 12:44:22,956 - pyskl - INFO - Epoch [42][200/3746] lr: 8.263e-02, eta: 3 days, 14:24:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5356, loss_cls: 4.1283, loss: 4.1283 +2024-07-23 12:45:45,430 - pyskl - INFO - Epoch [42][300/3746] lr: 8.261e-02, eta: 3 days, 14:23:48, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5202, loss_cls: 4.1770, loss: 4.1770 +2024-07-23 12:47:07,095 - pyskl - INFO - Epoch [42][400/3746] lr: 8.259e-02, eta: 3 days, 14:22:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5352, loss_cls: 4.1180, loss: 4.1180 +2024-07-23 12:48:29,126 - pyskl - INFO - Epoch [42][500/3746] lr: 8.257e-02, eta: 3 days, 14:21:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5317, loss_cls: 4.1608, loss: 4.1608 +2024-07-23 12:49:51,618 - pyskl - INFO - Epoch [42][600/3746] lr: 8.254e-02, eta: 3 days, 14:20:46, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5342, loss_cls: 4.1652, loss: 4.1652 +2024-07-23 12:51:13,724 - pyskl - INFO - Epoch [42][700/3746] lr: 8.252e-02, eta: 3 days, 14:19:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5334, loss_cls: 4.1294, loss: 4.1294 +2024-07-23 12:52:35,669 - pyskl - INFO - Epoch [42][800/3746] lr: 8.250e-02, eta: 3 days, 14:18:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5309, loss_cls: 4.1612, loss: 4.1612 +2024-07-23 12:53:57,575 - pyskl - INFO - Epoch [42][900/3746] lr: 8.248e-02, eta: 3 days, 14:17:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5264, loss_cls: 4.1856, loss: 4.1856 +2024-07-23 12:55:18,841 - pyskl - INFO - Epoch [42][1000/3746] lr: 8.246e-02, eta: 3 days, 14:16:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5275, loss_cls: 4.1587, loss: 4.1587 +2024-07-23 12:56:40,066 - pyskl - INFO - Epoch [42][1100/3746] lr: 8.244e-02, eta: 3 days, 14:15:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5186, loss_cls: 4.2074, loss: 4.2074 +2024-07-23 12:58:01,695 - pyskl - INFO - Epoch [42][1200/3746] lr: 8.242e-02, eta: 3 days, 14:14:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5356, loss_cls: 4.1449, loss: 4.1449 +2024-07-23 12:59:23,133 - pyskl - INFO - Epoch [42][1300/3746] lr: 8.240e-02, eta: 3 days, 14:13:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5353, loss_cls: 4.1357, loss: 4.1357 +2024-07-23 13:00:44,797 - pyskl - INFO - Epoch [42][1400/3746] lr: 8.237e-02, eta: 3 days, 14:12:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5228, loss_cls: 4.1706, loss: 4.1706 +2024-07-23 13:02:06,434 - pyskl - INFO - Epoch [42][1500/3746] lr: 8.235e-02, eta: 3 days, 14:11:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5311, loss_cls: 4.1746, loss: 4.1746 +2024-07-23 13:03:27,853 - pyskl - INFO - Epoch [42][1600/3746] lr: 8.233e-02, eta: 3 days, 14:10:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5298, loss_cls: 4.1342, loss: 4.1342 +2024-07-23 13:04:49,930 - pyskl - INFO - Epoch [42][1700/3746] lr: 8.231e-02, eta: 3 days, 14:09:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5328, loss_cls: 4.1482, loss: 4.1482 +2024-07-23 13:06:11,583 - pyskl - INFO - Epoch [42][1800/3746] lr: 8.229e-02, eta: 3 days, 14:08:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5348, loss_cls: 4.1370, loss: 4.1370 +2024-07-23 13:07:33,620 - pyskl - INFO - Epoch [42][1900/3746] lr: 8.227e-02, eta: 3 days, 14:07:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5308, loss_cls: 4.1723, loss: 4.1723 +2024-07-23 13:08:55,015 - pyskl - INFO - Epoch [42][2000/3746] lr: 8.225e-02, eta: 3 days, 14:06:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5225, loss_cls: 4.1934, loss: 4.1934 +2024-07-23 13:10:16,388 - pyskl - INFO - Epoch [42][2100/3746] lr: 8.222e-02, eta: 3 days, 14:05:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5255, loss_cls: 4.1816, loss: 4.1816 +2024-07-23 13:11:38,203 - pyskl - INFO - Epoch [42][2200/3746] lr: 8.220e-02, eta: 3 days, 14:04:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5242, loss_cls: 4.1736, loss: 4.1736 +2024-07-23 13:12:59,808 - pyskl - INFO - Epoch [42][2300/3746] lr: 8.218e-02, eta: 3 days, 14:03:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5269, loss_cls: 4.1669, loss: 4.1669 +2024-07-23 13:14:21,518 - pyskl - INFO - Epoch [42][2400/3746] lr: 8.216e-02, eta: 3 days, 14:02:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5286, loss_cls: 4.1632, loss: 4.1632 +2024-07-23 13:15:43,202 - pyskl - INFO - Epoch [42][2500/3746] lr: 8.214e-02, eta: 3 days, 14:01:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5253, loss_cls: 4.1956, loss: 4.1956 +2024-07-23 13:17:04,992 - pyskl - INFO - Epoch [42][2600/3746] lr: 8.212e-02, eta: 3 days, 14:00:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5356, loss_cls: 4.1398, loss: 4.1398 +2024-07-23 13:18:26,454 - pyskl - INFO - Epoch [42][2700/3746] lr: 8.210e-02, eta: 3 days, 13:59:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5212, loss_cls: 4.2096, loss: 4.2096 +2024-07-23 13:19:47,988 - pyskl - INFO - Epoch [42][2800/3746] lr: 8.207e-02, eta: 3 days, 13:57:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5131, loss_cls: 4.2075, loss: 4.2075 +2024-07-23 13:21:09,606 - pyskl - INFO - Epoch [42][2900/3746] lr: 8.205e-02, eta: 3 days, 13:56:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5277, loss_cls: 4.2046, loss: 4.2046 +2024-07-23 13:22:31,146 - pyskl - INFO - Epoch [42][3000/3746] lr: 8.203e-02, eta: 3 days, 13:55:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5253, loss_cls: 4.1901, loss: 4.1901 +2024-07-23 13:23:52,606 - pyskl - INFO - Epoch [42][3100/3746] lr: 8.201e-02, eta: 3 days, 13:54:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5275, loss_cls: 4.2025, loss: 4.2025 +2024-07-23 13:25:14,668 - pyskl - INFO - Epoch [42][3200/3746] lr: 8.199e-02, eta: 3 days, 13:53:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5352, loss_cls: 4.1617, loss: 4.1617 +2024-07-23 13:26:36,404 - pyskl - INFO - Epoch [42][3300/3746] lr: 8.197e-02, eta: 3 days, 13:52:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5180, loss_cls: 4.2039, loss: 4.2039 +2024-07-23 13:27:57,688 - pyskl - INFO - Epoch [42][3400/3746] lr: 8.195e-02, eta: 3 days, 13:51:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5248, loss_cls: 4.2100, loss: 4.2100 +2024-07-23 13:29:19,591 - pyskl - INFO - Epoch [42][3500/3746] lr: 8.192e-02, eta: 3 days, 13:50:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5228, loss_cls: 4.1979, loss: 4.1979 +2024-07-23 13:30:41,094 - pyskl - INFO - Epoch [42][3600/3746] lr: 8.190e-02, eta: 3 days, 13:49:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5288, loss_cls: 4.1680, loss: 4.1680 +2024-07-23 13:32:02,653 - pyskl - INFO - Epoch [42][3700/3746] lr: 8.188e-02, eta: 3 days, 13:48:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5234, loss_cls: 4.1972, loss: 4.1972 +2024-07-23 13:32:41,935 - pyskl - INFO - Saving checkpoint at 42 epochs +2024-07-23 13:34:34,511 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 13:34:35,171 - pyskl - INFO - +top1_acc 0.1985 +top5_acc 0.4221 +2024-07-23 13:34:35,171 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 13:34:35,208 - pyskl - INFO - +mean_acc 0.1983 +2024-07-23 13:34:35,218 - pyskl - INFO - Epoch(val) [42][309] top1_acc: 0.1985, top5_acc: 0.4221, mean_class_accuracy: 0.1983 +2024-07-23 13:38:17,525 - pyskl - INFO - Epoch [43][100/3746] lr: 8.185e-02, eta: 3 days, 13:51:27, time: 2.223, data_time: 1.256, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5508, loss_cls: 4.0867, loss: 4.0867 +2024-07-23 13:39:38,910 - pyskl - INFO - Epoch [43][200/3746] lr: 8.183e-02, eta: 3 days, 13:50:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5325, loss_cls: 4.1410, loss: 4.1410 +2024-07-23 13:41:00,752 - pyskl - INFO - Epoch [43][300/3746] lr: 8.181e-02, eta: 3 days, 13:49:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5441, loss_cls: 4.1065, loss: 4.1065 +2024-07-23 13:42:22,839 - pyskl - INFO - Epoch [43][400/3746] lr: 8.179e-02, eta: 3 days, 13:48:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5342, loss_cls: 4.1455, loss: 4.1455 +2024-07-23 13:43:44,702 - pyskl - INFO - Epoch [43][500/3746] lr: 8.176e-02, eta: 3 days, 13:47:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5320, loss_cls: 4.1521, loss: 4.1521 +2024-07-23 13:45:08,138 - pyskl - INFO - Epoch [43][600/3746] lr: 8.174e-02, eta: 3 days, 13:46:17, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5305, loss_cls: 4.1389, loss: 4.1389 +2024-07-23 13:46:29,990 - pyskl - INFO - Epoch [43][700/3746] lr: 8.172e-02, eta: 3 days, 13:45:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5394, loss_cls: 4.1333, loss: 4.1333 +2024-07-23 13:47:52,592 - pyskl - INFO - Epoch [43][800/3746] lr: 8.170e-02, eta: 3 days, 13:44:14, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5170, loss_cls: 4.2124, loss: 4.2124 +2024-07-23 13:49:14,365 - pyskl - INFO - Epoch [43][900/3746] lr: 8.168e-02, eta: 3 days, 13:43:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5250, loss_cls: 4.1850, loss: 4.1850 +2024-07-23 13:50:35,651 - pyskl - INFO - Epoch [43][1000/3746] lr: 8.166e-02, eta: 3 days, 13:42:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5386, loss_cls: 4.1233, loss: 4.1233 +2024-07-23 13:51:57,319 - pyskl - INFO - Epoch [43][1100/3746] lr: 8.163e-02, eta: 3 days, 13:41:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5303, loss_cls: 4.1544, loss: 4.1544 +2024-07-23 13:53:18,741 - pyskl - INFO - Epoch [43][1200/3746] lr: 8.161e-02, eta: 3 days, 13:40:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5141, loss_cls: 4.2246, loss: 4.2246 +2024-07-23 13:54:40,873 - pyskl - INFO - Epoch [43][1300/3746] lr: 8.159e-02, eta: 3 days, 13:38:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5350, loss_cls: 4.1807, loss: 4.1807 +2024-07-23 13:56:02,029 - pyskl - INFO - Epoch [43][1400/3746] lr: 8.157e-02, eta: 3 days, 13:37:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5205, loss_cls: 4.1886, loss: 4.1886 +2024-07-23 13:57:23,680 - pyskl - INFO - Epoch [43][1500/3746] lr: 8.155e-02, eta: 3 days, 13:36:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5439, loss_cls: 4.1269, loss: 4.1269 +2024-07-23 13:58:45,413 - pyskl - INFO - Epoch [43][1600/3746] lr: 8.153e-02, eta: 3 days, 13:35:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5336, loss_cls: 4.1382, loss: 4.1382 +2024-07-23 14:00:07,194 - pyskl - INFO - Epoch [43][1700/3746] lr: 8.150e-02, eta: 3 days, 13:34:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5345, loss_cls: 4.1036, loss: 4.1036 +2024-07-23 14:01:28,995 - pyskl - INFO - Epoch [43][1800/3746] lr: 8.148e-02, eta: 3 days, 13:33:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5209, loss_cls: 4.2045, loss: 4.2045 +2024-07-23 14:02:50,681 - pyskl - INFO - Epoch [43][1900/3746] lr: 8.146e-02, eta: 3 days, 13:32:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5261, loss_cls: 4.1992, loss: 4.1992 +2024-07-23 14:04:12,454 - pyskl - INFO - Epoch [43][2000/3746] lr: 8.144e-02, eta: 3 days, 13:31:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5327, loss_cls: 4.1543, loss: 4.1543 +2024-07-23 14:05:34,079 - pyskl - INFO - Epoch [43][2100/3746] lr: 8.142e-02, eta: 3 days, 13:30:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5188, loss_cls: 4.2233, loss: 4.2233 +2024-07-23 14:06:55,679 - pyskl - INFO - Epoch [43][2200/3746] lr: 8.140e-02, eta: 3 days, 13:29:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5227, loss_cls: 4.1890, loss: 4.1890 +2024-07-23 14:08:17,120 - pyskl - INFO - Epoch [43][2300/3746] lr: 8.137e-02, eta: 3 days, 13:28:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5233, loss_cls: 4.1615, loss: 4.1615 +2024-07-23 14:09:38,506 - pyskl - INFO - Epoch [43][2400/3746] lr: 8.135e-02, eta: 3 days, 13:27:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5303, loss_cls: 4.1662, loss: 4.1662 +2024-07-23 14:10:59,794 - pyskl - INFO - Epoch [43][2500/3746] lr: 8.133e-02, eta: 3 days, 13:26:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5222, loss_cls: 4.1912, loss: 4.1912 +2024-07-23 14:12:21,023 - pyskl - INFO - Epoch [43][2600/3746] lr: 8.131e-02, eta: 3 days, 13:25:10, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5275, loss_cls: 4.1922, loss: 4.1922 +2024-07-23 14:13:42,941 - pyskl - INFO - Epoch [43][2700/3746] lr: 8.129e-02, eta: 3 days, 13:24:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5269, loss_cls: 4.1739, loss: 4.1739 +2024-07-23 14:15:04,547 - pyskl - INFO - Epoch [43][2800/3746] lr: 8.126e-02, eta: 3 days, 13:23:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5202, loss_cls: 4.1918, loss: 4.1918 +2024-07-23 14:16:26,814 - pyskl - INFO - Epoch [43][2900/3746] lr: 8.124e-02, eta: 3 days, 13:22:02, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5347, loss_cls: 4.1301, loss: 4.1301 +2024-07-23 14:17:48,454 - pyskl - INFO - Epoch [43][3000/3746] lr: 8.122e-02, eta: 3 days, 13:20:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5300, loss_cls: 4.1622, loss: 4.1622 +2024-07-23 14:19:10,409 - pyskl - INFO - Epoch [43][3100/3746] lr: 8.120e-02, eta: 3 days, 13:19:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5320, loss_cls: 4.1499, loss: 4.1499 +2024-07-23 14:20:32,121 - pyskl - INFO - Epoch [43][3200/3746] lr: 8.118e-02, eta: 3 days, 13:18:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5216, loss_cls: 4.2121, loss: 4.2121 +2024-07-23 14:21:53,804 - pyskl - INFO - Epoch [43][3300/3746] lr: 8.116e-02, eta: 3 days, 13:17:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5331, loss_cls: 4.1448, loss: 4.1448 +2024-07-23 14:23:15,566 - pyskl - INFO - Epoch [43][3400/3746] lr: 8.113e-02, eta: 3 days, 13:16:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5278, loss_cls: 4.1439, loss: 4.1439 +2024-07-23 14:24:37,514 - pyskl - INFO - Epoch [43][3500/3746] lr: 8.111e-02, eta: 3 days, 13:15:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5323, loss_cls: 4.1681, loss: 4.1681 +2024-07-23 14:25:59,000 - pyskl - INFO - Epoch [43][3600/3746] lr: 8.109e-02, eta: 3 days, 13:14:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5359, loss_cls: 4.1178, loss: 4.1178 +2024-07-23 14:27:20,066 - pyskl - INFO - Epoch [43][3700/3746] lr: 8.107e-02, eta: 3 days, 13:13:32, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5136, loss_cls: 4.2420, loss: 4.2420 +2024-07-23 14:27:59,987 - pyskl - INFO - Saving checkpoint at 43 epochs +2024-07-23 14:29:52,765 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 14:29:53,418 - pyskl - INFO - +top1_acc 0.2191 +top5_acc 0.4541 +2024-07-23 14:29:53,419 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 14:29:53,456 - pyskl - INFO - +mean_acc 0.2189 +2024-07-23 14:29:53,466 - pyskl - INFO - Epoch(val) [43][309] top1_acc: 0.2191, top5_acc: 0.4541, mean_class_accuracy: 0.2189 +2024-07-23 14:33:36,647 - pyskl - INFO - Epoch [44][100/3746] lr: 8.104e-02, eta: 3 days, 13:16:17, time: 2.232, data_time: 1.264, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5330, loss_cls: 4.1227, loss: 4.1227 +2024-07-23 14:34:58,383 - pyskl - INFO - Epoch [44][200/3746] lr: 8.101e-02, eta: 3 days, 13:15:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5352, loss_cls: 4.1260, loss: 4.1260 +2024-07-23 14:36:20,476 - pyskl - INFO - Epoch [44][300/3746] lr: 8.099e-02, eta: 3 days, 13:14:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5442, loss_cls: 4.0971, loss: 4.0971 +2024-07-23 14:37:42,620 - pyskl - INFO - Epoch [44][400/3746] lr: 8.097e-02, eta: 3 days, 13:13:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5302, loss_cls: 4.1291, loss: 4.1291 +2024-07-23 14:39:04,322 - pyskl - INFO - Epoch [44][500/3746] lr: 8.095e-02, eta: 3 days, 13:12:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5264, loss_cls: 4.1637, loss: 4.1637 +2024-07-23 14:40:25,881 - pyskl - INFO - Epoch [44][600/3746] lr: 8.093e-02, eta: 3 days, 13:10:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5358, loss_cls: 4.0977, loss: 4.0977 +2024-07-23 14:41:48,695 - pyskl - INFO - Epoch [44][700/3746] lr: 8.090e-02, eta: 3 days, 13:09:58, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5292, loss_cls: 4.1701, loss: 4.1701 +2024-07-23 14:43:10,559 - pyskl - INFO - Epoch [44][800/3746] lr: 8.088e-02, eta: 3 days, 13:08:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5292, loss_cls: 4.1500, loss: 4.1500 +2024-07-23 14:44:32,540 - pyskl - INFO - Epoch [44][900/3746] lr: 8.086e-02, eta: 3 days, 13:07:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5167, loss_cls: 4.2322, loss: 4.2322 +2024-07-23 14:45:54,533 - pyskl - INFO - Epoch [44][1000/3746] lr: 8.084e-02, eta: 3 days, 13:06:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5023, loss_cls: 4.2575, loss: 4.2575 +2024-07-23 14:47:16,352 - pyskl - INFO - Epoch [44][1100/3746] lr: 8.082e-02, eta: 3 days, 13:05:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5211, loss_cls: 4.1787, loss: 4.1787 +2024-07-23 14:48:38,555 - pyskl - INFO - Epoch [44][1200/3746] lr: 8.079e-02, eta: 3 days, 13:04:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5294, loss_cls: 4.1346, loss: 4.1346 +2024-07-23 14:49:59,981 - pyskl - INFO - Epoch [44][1300/3746] lr: 8.077e-02, eta: 3 days, 13:03:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5297, loss_cls: 4.1592, loss: 4.1592 +2024-07-23 14:51:21,383 - pyskl - INFO - Epoch [44][1400/3746] lr: 8.075e-02, eta: 3 days, 13:02:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5372, loss_cls: 4.1214, loss: 4.1214 +2024-07-23 14:52:43,439 - pyskl - INFO - Epoch [44][1500/3746] lr: 8.073e-02, eta: 3 days, 13:01:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5363, loss_cls: 4.1392, loss: 4.1392 +2024-07-23 14:54:05,022 - pyskl - INFO - Epoch [44][1600/3746] lr: 8.071e-02, eta: 3 days, 13:00:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5372, loss_cls: 4.1458, loss: 4.1458 +2024-07-23 14:55:26,642 - pyskl - INFO - Epoch [44][1700/3746] lr: 8.068e-02, eta: 3 days, 12:59:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5367, loss_cls: 4.1336, loss: 4.1336 +2024-07-23 14:56:48,314 - pyskl - INFO - Epoch [44][1800/3746] lr: 8.066e-02, eta: 3 days, 12:58:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5253, loss_cls: 4.1938, loss: 4.1938 +2024-07-23 14:58:09,494 - pyskl - INFO - Epoch [44][1900/3746] lr: 8.064e-02, eta: 3 days, 12:57:08, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5261, loss_cls: 4.1747, loss: 4.1747 +2024-07-23 14:59:30,695 - pyskl - INFO - Epoch [44][2000/3746] lr: 8.062e-02, eta: 3 days, 12:56:03, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5267, loss_cls: 4.1683, loss: 4.1683 +2024-07-23 15:00:52,214 - pyskl - INFO - Epoch [44][2100/3746] lr: 8.060e-02, eta: 3 days, 12:54:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5327, loss_cls: 4.1193, loss: 4.1193 +2024-07-23 15:02:13,758 - pyskl - INFO - Epoch [44][2200/3746] lr: 8.057e-02, eta: 3 days, 12:53:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5300, loss_cls: 4.1715, loss: 4.1715 +2024-07-23 15:03:35,858 - pyskl - INFO - Epoch [44][2300/3746] lr: 8.055e-02, eta: 3 days, 12:52:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5231, loss_cls: 4.1727, loss: 4.1727 +2024-07-23 15:04:57,797 - pyskl - INFO - Epoch [44][2400/3746] lr: 8.053e-02, eta: 3 days, 12:51:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5463, loss_cls: 4.1111, loss: 4.1111 +2024-07-23 15:06:19,713 - pyskl - INFO - Epoch [44][2500/3746] lr: 8.051e-02, eta: 3 days, 12:50:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5253, loss_cls: 4.1943, loss: 4.1943 +2024-07-23 15:07:40,745 - pyskl - INFO - Epoch [44][2600/3746] lr: 8.048e-02, eta: 3 days, 12:49:36, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5308, loss_cls: 4.1703, loss: 4.1703 +2024-07-23 15:09:02,148 - pyskl - INFO - Epoch [44][2700/3746] lr: 8.046e-02, eta: 3 days, 12:48:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5342, loss_cls: 4.1306, loss: 4.1306 +2024-07-23 15:10:23,702 - pyskl - INFO - Epoch [44][2800/3746] lr: 8.044e-02, eta: 3 days, 12:47:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5233, loss_cls: 4.1942, loss: 4.1942 +2024-07-23 15:11:45,617 - pyskl - INFO - Epoch [44][2900/3746] lr: 8.042e-02, eta: 3 days, 12:46:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5359, loss_cls: 4.1300, loss: 4.1300 +2024-07-23 15:13:07,267 - pyskl - INFO - Epoch [44][3000/3746] lr: 8.040e-02, eta: 3 days, 12:45:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5280, loss_cls: 4.1397, loss: 4.1397 +2024-07-23 15:14:28,603 - pyskl - INFO - Epoch [44][3100/3746] lr: 8.037e-02, eta: 3 days, 12:44:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5203, loss_cls: 4.2122, loss: 4.2122 +2024-07-23 15:15:50,327 - pyskl - INFO - Epoch [44][3200/3746] lr: 8.035e-02, eta: 3 days, 12:43:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5225, loss_cls: 4.1932, loss: 4.1932 +2024-07-23 15:17:12,088 - pyskl - INFO - Epoch [44][3300/3746] lr: 8.033e-02, eta: 3 days, 12:42:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5256, loss_cls: 4.1837, loss: 4.1837 +2024-07-23 15:18:34,460 - pyskl - INFO - Epoch [44][3400/3746] lr: 8.031e-02, eta: 3 days, 12:41:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5275, loss_cls: 4.1644, loss: 4.1644 +2024-07-23 15:19:56,640 - pyskl - INFO - Epoch [44][3500/3746] lr: 8.028e-02, eta: 3 days, 12:39:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5292, loss_cls: 4.1617, loss: 4.1617 +2024-07-23 15:21:18,364 - pyskl - INFO - Epoch [44][3600/3746] lr: 8.026e-02, eta: 3 days, 12:38:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5212, loss_cls: 4.1822, loss: 4.1822 +2024-07-23 15:22:39,930 - pyskl - INFO - Epoch [44][3700/3746] lr: 8.024e-02, eta: 3 days, 12:37:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5227, loss_cls: 4.1884, loss: 4.1884 +2024-07-23 15:23:19,308 - pyskl - INFO - Saving checkpoint at 44 epochs +2024-07-23 15:25:11,699 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 15:25:12,352 - pyskl - INFO - +top1_acc 0.1766 +top5_acc 0.3834 +2024-07-23 15:25:12,352 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 15:25:12,390 - pyskl - INFO - +mean_acc 0.1764 +2024-07-23 15:25:12,401 - pyskl - INFO - Epoch(val) [44][309] top1_acc: 0.1766, top5_acc: 0.3834, mean_class_accuracy: 0.1764 +2024-07-23 15:28:56,123 - pyskl - INFO - Epoch [45][100/3746] lr: 8.021e-02, eta: 3 days, 12:40:23, time: 2.237, data_time: 1.263, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5292, loss_cls: 4.1683, loss: 4.1683 +2024-07-23 15:30:18,189 - pyskl - INFO - Epoch [45][200/3746] lr: 8.019e-02, eta: 3 days, 12:39:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5333, loss_cls: 4.1699, loss: 4.1699 +2024-07-23 15:31:40,122 - pyskl - INFO - Epoch [45][300/3746] lr: 8.016e-02, eta: 3 days, 12:38:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5348, loss_cls: 4.1261, loss: 4.1261 +2024-07-23 15:33:02,369 - pyskl - INFO - Epoch [45][400/3746] lr: 8.014e-02, eta: 3 days, 12:37:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5383, loss_cls: 4.1243, loss: 4.1243 +2024-07-23 15:34:23,898 - pyskl - INFO - Epoch [45][500/3746] lr: 8.012e-02, eta: 3 days, 12:36:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5406, loss_cls: 4.1108, loss: 4.1108 +2024-07-23 15:35:45,928 - pyskl - INFO - Epoch [45][600/3746] lr: 8.010e-02, eta: 3 days, 12:35:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5367, loss_cls: 4.1233, loss: 4.1233 +2024-07-23 15:37:08,493 - pyskl - INFO - Epoch [45][700/3746] lr: 8.007e-02, eta: 3 days, 12:33:58, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5345, loss_cls: 4.1406, loss: 4.1406 +2024-07-23 15:38:30,087 - pyskl - INFO - Epoch [45][800/3746] lr: 8.005e-02, eta: 3 days, 12:32:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5255, loss_cls: 4.1672, loss: 4.1672 +2024-07-23 15:39:52,979 - pyskl - INFO - Epoch [45][900/3746] lr: 8.003e-02, eta: 3 days, 12:31:51, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5323, loss_cls: 4.1543, loss: 4.1543 +2024-07-23 15:41:14,738 - pyskl - INFO - Epoch [45][1000/3746] lr: 8.001e-02, eta: 3 days, 12:30:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5264, loss_cls: 4.1626, loss: 4.1626 +2024-07-23 15:42:36,216 - pyskl - INFO - Epoch [45][1100/3746] lr: 7.998e-02, eta: 3 days, 12:29:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5288, loss_cls: 4.1418, loss: 4.1418 +2024-07-23 15:43:57,636 - pyskl - INFO - Epoch [45][1200/3746] lr: 7.996e-02, eta: 3 days, 12:28:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5348, loss_cls: 4.1235, loss: 4.1235 +2024-07-23 15:45:19,093 - pyskl - INFO - Epoch [45][1300/3746] lr: 7.994e-02, eta: 3 days, 12:27:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5209, loss_cls: 4.1748, loss: 4.1748 +2024-07-23 15:46:40,789 - pyskl - INFO - Epoch [45][1400/3746] lr: 7.992e-02, eta: 3 days, 12:26:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5353, loss_cls: 4.1423, loss: 4.1423 +2024-07-23 15:48:02,568 - pyskl - INFO - Epoch [45][1500/3746] lr: 7.990e-02, eta: 3 days, 12:25:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5386, loss_cls: 4.1307, loss: 4.1307 +2024-07-23 15:49:24,278 - pyskl - INFO - Epoch [45][1600/3746] lr: 7.987e-02, eta: 3 days, 12:24:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5356, loss_cls: 4.1196, loss: 4.1196 +2024-07-23 15:50:46,360 - pyskl - INFO - Epoch [45][1700/3746] lr: 7.985e-02, eta: 3 days, 12:23:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5309, loss_cls: 4.1407, loss: 4.1407 +2024-07-23 15:52:08,241 - pyskl - INFO - Epoch [45][1800/3746] lr: 7.983e-02, eta: 3 days, 12:22:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5347, loss_cls: 4.1551, loss: 4.1551 +2024-07-23 15:53:30,436 - pyskl - INFO - Epoch [45][1900/3746] lr: 7.981e-02, eta: 3 days, 12:21:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5245, loss_cls: 4.1536, loss: 4.1536 +2024-07-23 15:54:51,876 - pyskl - INFO - Epoch [45][2000/3746] lr: 7.978e-02, eta: 3 days, 12:19:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5278, loss_cls: 4.1800, loss: 4.1800 +2024-07-23 15:56:14,514 - pyskl - INFO - Epoch [45][2100/3746] lr: 7.976e-02, eta: 3 days, 12:18:50, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5363, loss_cls: 4.1343, loss: 4.1343 +2024-07-23 15:57:35,513 - pyskl - INFO - Epoch [45][2200/3746] lr: 7.974e-02, eta: 3 days, 12:17:43, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5278, loss_cls: 4.1525, loss: 4.1525 +2024-07-23 15:58:57,235 - pyskl - INFO - Epoch [45][2300/3746] lr: 7.972e-02, eta: 3 days, 12:16:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5186, loss_cls: 4.2433, loss: 4.2433 +2024-07-23 16:00:18,722 - pyskl - INFO - Epoch [45][2400/3746] lr: 7.969e-02, eta: 3 days, 12:15:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5216, loss_cls: 4.2008, loss: 4.2008 +2024-07-23 16:01:40,050 - pyskl - INFO - Epoch [45][2500/3746] lr: 7.967e-02, eta: 3 days, 12:14:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5272, loss_cls: 4.1628, loss: 4.1628 +2024-07-23 16:03:01,512 - pyskl - INFO - Epoch [45][2600/3746] lr: 7.965e-02, eta: 3 days, 12:13:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5320, loss_cls: 4.1675, loss: 4.1675 +2024-07-23 16:04:23,128 - pyskl - INFO - Epoch [45][2700/3746] lr: 7.963e-02, eta: 3 days, 12:12:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5256, loss_cls: 4.1546, loss: 4.1546 +2024-07-23 16:05:44,868 - pyskl - INFO - Epoch [45][2800/3746] lr: 7.960e-02, eta: 3 days, 12:11:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5242, loss_cls: 4.1911, loss: 4.1911 +2024-07-23 16:07:06,462 - pyskl - INFO - Epoch [45][2900/3746] lr: 7.958e-02, eta: 3 days, 12:10:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5294, loss_cls: 4.1659, loss: 4.1659 +2024-07-23 16:08:27,815 - pyskl - INFO - Epoch [45][3000/3746] lr: 7.956e-02, eta: 3 days, 12:08:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5241, loss_cls: 4.1750, loss: 4.1750 +2024-07-23 16:09:49,199 - pyskl - INFO - Epoch [45][3100/3746] lr: 7.954e-02, eta: 3 days, 12:07:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5395, loss_cls: 4.1329, loss: 4.1329 +2024-07-23 16:11:10,974 - pyskl - INFO - Epoch [45][3200/3746] lr: 7.951e-02, eta: 3 days, 12:06:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5320, loss_cls: 4.1323, loss: 4.1323 +2024-07-23 16:12:31,904 - pyskl - INFO - Epoch [45][3300/3746] lr: 7.949e-02, eta: 3 days, 12:05:38, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5302, loss_cls: 4.1428, loss: 4.1428 +2024-07-23 16:13:53,176 - pyskl - INFO - Epoch [45][3400/3746] lr: 7.947e-02, eta: 3 days, 12:04:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5328, loss_cls: 4.1575, loss: 4.1575 +2024-07-23 16:15:14,691 - pyskl - INFO - Epoch [45][3500/3746] lr: 7.945e-02, eta: 3 days, 12:03:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5262, loss_cls: 4.1779, loss: 4.1779 +2024-07-23 16:16:36,023 - pyskl - INFO - Epoch [45][3600/3746] lr: 7.942e-02, eta: 3 days, 12:02:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5320, loss_cls: 4.1373, loss: 4.1373 +2024-07-23 16:17:56,951 - pyskl - INFO - Epoch [45][3700/3746] lr: 7.940e-02, eta: 3 days, 12:01:11, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5289, loss_cls: 4.1393, loss: 4.1393 +2024-07-23 16:18:36,574 - pyskl - INFO - Saving checkpoint at 45 epochs +2024-07-23 16:20:27,760 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 16:20:28,415 - pyskl - INFO - +top1_acc 0.2197 +top5_acc 0.4498 +2024-07-23 16:20:28,415 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 16:20:28,456 - pyskl - INFO - +mean_acc 0.2196 +2024-07-23 16:20:28,466 - pyskl - INFO - Epoch(val) [45][309] top1_acc: 0.2197, top5_acc: 0.4498, mean_class_accuracy: 0.2196 +2024-07-23 16:24:13,255 - pyskl - INFO - Epoch [46][100/3746] lr: 7.937e-02, eta: 3 days, 12:03:41, time: 2.248, data_time: 1.271, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5245, loss_cls: 4.1389, loss: 4.1389 +2024-07-23 16:25:34,991 - pyskl - INFO - Epoch [46][200/3746] lr: 7.934e-02, eta: 3 days, 12:02:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5444, loss_cls: 4.1051, loss: 4.1051 +2024-07-23 16:26:56,852 - pyskl - INFO - Epoch [46][300/3746] lr: 7.932e-02, eta: 3 days, 12:01:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5397, loss_cls: 4.0853, loss: 4.0853 +2024-07-23 16:28:19,270 - pyskl - INFO - Epoch [46][400/3746] lr: 7.930e-02, eta: 3 days, 12:00:25, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5211, loss_cls: 4.1914, loss: 4.1914 +2024-07-23 16:29:40,964 - pyskl - INFO - Epoch [46][500/3746] lr: 7.928e-02, eta: 3 days, 11:59:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5323, loss_cls: 4.1660, loss: 4.1660 +2024-07-23 16:31:03,300 - pyskl - INFO - Epoch [46][600/3746] lr: 7.925e-02, eta: 3 days, 11:58:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5281, loss_cls: 4.1313, loss: 4.1313 +2024-07-23 16:32:25,572 - pyskl - INFO - Epoch [46][700/3746] lr: 7.923e-02, eta: 3 days, 11:57:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5363, loss_cls: 4.1419, loss: 4.1419 +2024-07-23 16:33:48,428 - pyskl - INFO - Epoch [46][800/3746] lr: 7.921e-02, eta: 3 days, 11:56:07, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5309, loss_cls: 4.1233, loss: 4.1233 +2024-07-23 16:35:10,199 - pyskl - INFO - Epoch [46][900/3746] lr: 7.919e-02, eta: 3 days, 11:55:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5291, loss_cls: 4.1655, loss: 4.1655 +2024-07-23 16:36:32,711 - pyskl - INFO - Epoch [46][1000/3746] lr: 7.916e-02, eta: 3 days, 11:53:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5400, loss_cls: 4.1240, loss: 4.1240 +2024-07-23 16:37:54,160 - pyskl - INFO - Epoch [46][1100/3746] lr: 7.914e-02, eta: 3 days, 11:52:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5375, loss_cls: 4.1401, loss: 4.1401 +2024-07-23 16:39:15,956 - pyskl - INFO - Epoch [46][1200/3746] lr: 7.912e-02, eta: 3 days, 11:51:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5272, loss_cls: 4.1710, loss: 4.1710 +2024-07-23 16:40:37,570 - pyskl - INFO - Epoch [46][1300/3746] lr: 7.909e-02, eta: 3 days, 11:50:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5302, loss_cls: 4.1399, loss: 4.1399 +2024-07-23 16:41:58,781 - pyskl - INFO - Epoch [46][1400/3746] lr: 7.907e-02, eta: 3 days, 11:49:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5306, loss_cls: 4.1542, loss: 4.1542 +2024-07-23 16:43:20,600 - pyskl - INFO - Epoch [46][1500/3746] lr: 7.905e-02, eta: 3 days, 11:48:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5344, loss_cls: 4.1309, loss: 4.1309 +2024-07-23 16:44:43,003 - pyskl - INFO - Epoch [46][1600/3746] lr: 7.903e-02, eta: 3 days, 11:47:20, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5269, loss_cls: 4.1616, loss: 4.1616 +2024-07-23 16:46:04,559 - pyskl - INFO - Epoch [46][1700/3746] lr: 7.900e-02, eta: 3 days, 11:46:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5302, loss_cls: 4.1280, loss: 4.1280 +2024-07-23 16:47:25,743 - pyskl - INFO - Epoch [46][1800/3746] lr: 7.898e-02, eta: 3 days, 11:45:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5259, loss_cls: 4.2073, loss: 4.2073 +2024-07-23 16:48:47,018 - pyskl - INFO - Epoch [46][1900/3746] lr: 7.896e-02, eta: 3 days, 11:43:59, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5298, loss_cls: 4.1440, loss: 4.1440 +2024-07-23 16:50:08,256 - pyskl - INFO - Epoch [46][2000/3746] lr: 7.894e-02, eta: 3 days, 11:42:52, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5183, loss_cls: 4.1676, loss: 4.1676 +2024-07-23 16:51:29,713 - pyskl - INFO - Epoch [46][2100/3746] lr: 7.891e-02, eta: 3 days, 11:41:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5181, loss_cls: 4.1688, loss: 4.1688 +2024-07-23 16:52:51,571 - pyskl - INFO - Epoch [46][2200/3746] lr: 7.889e-02, eta: 3 days, 11:40:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5433, loss_cls: 4.1097, loss: 4.1097 +2024-07-23 16:54:13,374 - pyskl - INFO - Epoch [46][2300/3746] lr: 7.887e-02, eta: 3 days, 11:39:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5350, loss_cls: 4.1471, loss: 4.1471 +2024-07-23 16:55:35,017 - pyskl - INFO - Epoch [46][2400/3746] lr: 7.884e-02, eta: 3 days, 11:38:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5259, loss_cls: 4.1630, loss: 4.1630 +2024-07-23 16:56:56,500 - pyskl - INFO - Epoch [46][2500/3746] lr: 7.882e-02, eta: 3 days, 11:37:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5359, loss_cls: 4.1473, loss: 4.1473 +2024-07-23 16:58:18,403 - pyskl - INFO - Epoch [46][2600/3746] lr: 7.880e-02, eta: 3 days, 11:36:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5330, loss_cls: 4.1216, loss: 4.1216 +2024-07-23 16:59:40,426 - pyskl - INFO - Epoch [46][2700/3746] lr: 7.878e-02, eta: 3 days, 11:35:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5434, loss_cls: 4.1211, loss: 4.1211 +2024-07-23 17:01:02,028 - pyskl - INFO - Epoch [46][2800/3746] lr: 7.875e-02, eta: 3 days, 11:34:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5320, loss_cls: 4.1192, loss: 4.1192 +2024-07-23 17:02:23,093 - pyskl - INFO - Epoch [46][2900/3746] lr: 7.873e-02, eta: 3 days, 11:32:54, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5345, loss_cls: 4.1427, loss: 4.1427 +2024-07-23 17:03:44,503 - pyskl - INFO - Epoch [46][3000/3746] lr: 7.871e-02, eta: 3 days, 11:31:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5345, loss_cls: 4.1408, loss: 4.1408 +2024-07-23 17:05:06,284 - pyskl - INFO - Epoch [46][3100/3746] lr: 7.868e-02, eta: 3 days, 11:30:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5311, loss_cls: 4.1694, loss: 4.1694 +2024-07-23 17:06:27,347 - pyskl - INFO - Epoch [46][3200/3746] lr: 7.866e-02, eta: 3 days, 11:29:32, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5352, loss_cls: 4.1493, loss: 4.1493 +2024-07-23 17:07:49,106 - pyskl - INFO - Epoch [46][3300/3746] lr: 7.864e-02, eta: 3 days, 11:28:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5248, loss_cls: 4.1772, loss: 4.1772 +2024-07-23 17:09:10,976 - pyskl - INFO - Epoch [46][3400/3746] lr: 7.862e-02, eta: 3 days, 11:27:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5489, loss_cls: 4.0736, loss: 4.0736 +2024-07-23 17:10:33,094 - pyskl - INFO - Epoch [46][3500/3746] lr: 7.859e-02, eta: 3 days, 11:26:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5387, loss_cls: 4.1379, loss: 4.1379 +2024-07-23 17:11:54,531 - pyskl - INFO - Epoch [46][3600/3746] lr: 7.857e-02, eta: 3 days, 11:25:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5370, loss_cls: 4.1279, loss: 4.1279 +2024-07-23 17:13:16,143 - pyskl - INFO - Epoch [46][3700/3746] lr: 7.855e-02, eta: 3 days, 11:24:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5416, loss_cls: 4.1129, loss: 4.1129 +2024-07-23 17:13:55,923 - pyskl - INFO - Saving checkpoint at 46 epochs +2024-07-23 17:15:47,882 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 17:15:48,541 - pyskl - INFO - +top1_acc 0.1907 +top5_acc 0.4187 +2024-07-23 17:15:48,541 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 17:15:48,581 - pyskl - INFO - +mean_acc 0.1906 +2024-07-23 17:15:48,593 - pyskl - INFO - Epoch(val) [46][309] top1_acc: 0.1907, top5_acc: 0.4187, mean_class_accuracy: 0.1906 +2024-07-23 17:19:35,882 - pyskl - INFO - Epoch [47][100/3746] lr: 7.851e-02, eta: 3 days, 11:26:27, time: 2.273, data_time: 1.294, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5484, loss_cls: 4.0628, loss: 4.0628 +2024-07-23 17:20:58,076 - pyskl - INFO - Epoch [47][200/3746] lr: 7.849e-02, eta: 3 days, 11:25:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5384, loss_cls: 4.1233, loss: 4.1233 +2024-07-23 17:22:20,008 - pyskl - INFO - Epoch [47][300/3746] lr: 7.847e-02, eta: 3 days, 11:24:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5306, loss_cls: 4.1321, loss: 4.1321 +2024-07-23 17:23:41,666 - pyskl - INFO - Epoch [47][400/3746] lr: 7.844e-02, eta: 3 days, 11:23:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5330, loss_cls: 4.1321, loss: 4.1321 +2024-07-23 17:25:03,651 - pyskl - INFO - Epoch [47][500/3746] lr: 7.842e-02, eta: 3 days, 11:22:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5275, loss_cls: 4.1623, loss: 4.1623 +2024-07-23 17:26:25,609 - pyskl - INFO - Epoch [47][600/3746] lr: 7.840e-02, eta: 3 days, 11:20:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5466, loss_cls: 4.0976, loss: 4.0976 +2024-07-23 17:27:48,206 - pyskl - INFO - Epoch [47][700/3746] lr: 7.838e-02, eta: 3 days, 11:19:51, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5323, loss_cls: 4.1418, loss: 4.1418 +2024-07-23 17:29:10,428 - pyskl - INFO - Epoch [47][800/3746] lr: 7.835e-02, eta: 3 days, 11:18:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5353, loss_cls: 4.1128, loss: 4.1128 +2024-07-23 17:30:33,170 - pyskl - INFO - Epoch [47][900/3746] lr: 7.833e-02, eta: 3 days, 11:17:41, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5452, loss_cls: 4.0945, loss: 4.0945 +2024-07-23 17:31:55,962 - pyskl - INFO - Epoch [47][1000/3746] lr: 7.831e-02, eta: 3 days, 11:16:36, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5417, loss_cls: 4.0887, loss: 4.0887 +2024-07-23 17:33:17,393 - pyskl - INFO - Epoch [47][1100/3746] lr: 7.828e-02, eta: 3 days, 11:15:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5272, loss_cls: 4.2125, loss: 4.2125 +2024-07-23 17:34:38,769 - pyskl - INFO - Epoch [47][1200/3746] lr: 7.826e-02, eta: 3 days, 11:14:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5441, loss_cls: 4.0996, loss: 4.0996 +2024-07-23 17:36:00,308 - pyskl - INFO - Epoch [47][1300/3746] lr: 7.824e-02, eta: 3 days, 11:13:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5417, loss_cls: 4.0999, loss: 4.0999 +2024-07-23 17:37:22,034 - pyskl - INFO - Epoch [47][1400/3746] lr: 7.821e-02, eta: 3 days, 11:12:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5256, loss_cls: 4.1650, loss: 4.1650 +2024-07-23 17:38:43,297 - pyskl - INFO - Epoch [47][1500/3746] lr: 7.819e-02, eta: 3 days, 11:10:59, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5472, loss_cls: 4.0996, loss: 4.0996 +2024-07-23 17:40:04,648 - pyskl - INFO - Epoch [47][1600/3746] lr: 7.817e-02, eta: 3 days, 11:09:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5389, loss_cls: 4.1035, loss: 4.1035 +2024-07-23 17:41:25,707 - pyskl - INFO - Epoch [47][1700/3746] lr: 7.814e-02, eta: 3 days, 11:08:42, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5348, loss_cls: 4.1065, loss: 4.1065 +2024-07-23 17:42:47,064 - pyskl - INFO - Epoch [47][1800/3746] lr: 7.812e-02, eta: 3 days, 11:07:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5341, loss_cls: 4.1169, loss: 4.1169 +2024-07-23 17:44:08,872 - pyskl - INFO - Epoch [47][1900/3746] lr: 7.810e-02, eta: 3 days, 11:06:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5305, loss_cls: 4.1463, loss: 4.1463 +2024-07-23 17:45:30,239 - pyskl - INFO - Epoch [47][2000/3746] lr: 7.808e-02, eta: 3 days, 11:05:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5195, loss_cls: 4.1440, loss: 4.1440 +2024-07-23 17:46:51,895 - pyskl - INFO - Epoch [47][2100/3746] lr: 7.805e-02, eta: 3 days, 11:04:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5336, loss_cls: 4.1100, loss: 4.1100 +2024-07-23 17:48:13,033 - pyskl - INFO - Epoch [47][2200/3746] lr: 7.803e-02, eta: 3 days, 11:03:04, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5308, loss_cls: 4.1738, loss: 4.1738 +2024-07-23 17:49:34,797 - pyskl - INFO - Epoch [47][2300/3746] lr: 7.801e-02, eta: 3 days, 11:01:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5319, loss_cls: 4.1348, loss: 4.1348 +2024-07-23 17:50:56,518 - pyskl - INFO - Epoch [47][2400/3746] lr: 7.798e-02, eta: 3 days, 11:00:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5309, loss_cls: 4.1565, loss: 4.1565 +2024-07-23 17:52:17,720 - pyskl - INFO - Epoch [47][2500/3746] lr: 7.796e-02, eta: 3 days, 10:59:42, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5411, loss_cls: 4.1107, loss: 4.1107 +2024-07-23 17:53:39,219 - pyskl - INFO - Epoch [47][2600/3746] lr: 7.794e-02, eta: 3 days, 10:58:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5291, loss_cls: 4.1758, loss: 4.1758 +2024-07-23 17:55:00,872 - pyskl - INFO - Epoch [47][2700/3746] lr: 7.791e-02, eta: 3 days, 10:57:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5312, loss_cls: 4.1514, loss: 4.1514 +2024-07-23 17:56:22,355 - pyskl - INFO - Epoch [47][2800/3746] lr: 7.789e-02, eta: 3 days, 10:56:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5439, loss_cls: 4.0833, loss: 4.0833 +2024-07-23 17:57:43,574 - pyskl - INFO - Epoch [47][2900/3746] lr: 7.787e-02, eta: 3 days, 10:55:11, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5283, loss_cls: 4.1515, loss: 4.1515 +2024-07-23 17:59:05,157 - pyskl - INFO - Epoch [47][3000/3746] lr: 7.784e-02, eta: 3 days, 10:54:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5198, loss_cls: 4.1696, loss: 4.1696 +2024-07-23 18:00:26,520 - pyskl - INFO - Epoch [47][3100/3746] lr: 7.782e-02, eta: 3 days, 10:52:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5342, loss_cls: 4.1503, loss: 4.1503 +2024-07-23 18:01:48,084 - pyskl - INFO - Epoch [47][3200/3746] lr: 7.780e-02, eta: 3 days, 10:51:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5305, loss_cls: 4.1403, loss: 4.1403 +2024-07-23 18:03:09,346 - pyskl - INFO - Epoch [47][3300/3746] lr: 7.777e-02, eta: 3 days, 10:50:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5252, loss_cls: 4.2067, loss: 4.2067 +2024-07-23 18:04:30,959 - pyskl - INFO - Epoch [47][3400/3746] lr: 7.775e-02, eta: 3 days, 10:49:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5300, loss_cls: 4.1541, loss: 4.1541 +2024-07-23 18:05:52,404 - pyskl - INFO - Epoch [47][3500/3746] lr: 7.773e-02, eta: 3 days, 10:48:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5328, loss_cls: 4.1445, loss: 4.1445 +2024-07-23 18:07:14,064 - pyskl - INFO - Epoch [47][3600/3746] lr: 7.770e-02, eta: 3 days, 10:47:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5300, loss_cls: 4.1748, loss: 4.1748 +2024-07-23 18:08:35,599 - pyskl - INFO - Epoch [47][3700/3746] lr: 7.768e-02, eta: 3 days, 10:46:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5314, loss_cls: 4.1417, loss: 4.1417 +2024-07-23 18:09:15,067 - pyskl - INFO - Saving checkpoint at 47 epochs +2024-07-23 18:11:08,100 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 18:11:08,758 - pyskl - INFO - +top1_acc 0.1879 +top5_acc 0.3983 +2024-07-23 18:11:08,758 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 18:11:08,797 - pyskl - INFO - +mean_acc 0.1877 +2024-07-23 18:11:08,808 - pyskl - INFO - Epoch(val) [47][309] top1_acc: 0.1879, top5_acc: 0.3983, mean_class_accuracy: 0.1877 +2024-07-23 18:14:54,396 - pyskl - INFO - Epoch [48][100/3746] lr: 7.765e-02, eta: 3 days, 10:48:23, time: 2.256, data_time: 1.276, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5434, loss_cls: 4.0676, loss: 4.0676 +2024-07-23 18:16:16,938 - pyskl - INFO - Epoch [48][200/3746] lr: 7.762e-02, eta: 3 days, 10:47:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5475, loss_cls: 4.0375, loss: 4.0375 +2024-07-23 18:17:39,271 - pyskl - INFO - Epoch [48][300/3746] lr: 7.760e-02, eta: 3 days, 10:46:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5284, loss_cls: 4.1516, loss: 4.1516 +2024-07-23 18:19:01,272 - pyskl - INFO - Epoch [48][400/3746] lr: 7.758e-02, eta: 3 days, 10:45:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5325, loss_cls: 4.1315, loss: 4.1315 +2024-07-23 18:20:23,098 - pyskl - INFO - Epoch [48][500/3746] lr: 7.755e-02, eta: 3 days, 10:43:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5248, loss_cls: 4.1403, loss: 4.1403 +2024-07-23 18:21:44,904 - pyskl - INFO - Epoch [48][600/3746] lr: 7.753e-02, eta: 3 days, 10:42:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5409, loss_cls: 4.0827, loss: 4.0827 +2024-07-23 18:23:07,504 - pyskl - INFO - Epoch [48][700/3746] lr: 7.751e-02, eta: 3 days, 10:41:43, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5317, loss_cls: 4.1237, loss: 4.1237 +2024-07-23 18:24:29,571 - pyskl - INFO - Epoch [48][800/3746] lr: 7.748e-02, eta: 3 days, 10:40:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5330, loss_cls: 4.1467, loss: 4.1467 +2024-07-23 18:25:52,236 - pyskl - INFO - Epoch [48][900/3746] lr: 7.746e-02, eta: 3 days, 10:39:31, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5281, loss_cls: 4.1346, loss: 4.1346 +2024-07-23 18:27:14,466 - pyskl - INFO - Epoch [48][1000/3746] lr: 7.744e-02, eta: 3 days, 10:38:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5291, loss_cls: 4.1801, loss: 4.1801 +2024-07-23 18:28:36,642 - pyskl - INFO - Epoch [48][1100/3746] lr: 7.741e-02, eta: 3 days, 10:37:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5277, loss_cls: 4.1587, loss: 4.1587 +2024-07-23 18:29:58,186 - pyskl - INFO - Epoch [48][1200/3746] lr: 7.739e-02, eta: 3 days, 10:36:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5400, loss_cls: 4.0984, loss: 4.0984 +2024-07-23 18:31:19,813 - pyskl - INFO - Epoch [48][1300/3746] lr: 7.737e-02, eta: 3 days, 10:35:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5391, loss_cls: 4.0962, loss: 4.0962 +2024-07-23 18:32:41,667 - pyskl - INFO - Epoch [48][1400/3746] lr: 7.734e-02, eta: 3 days, 10:33:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5305, loss_cls: 4.0963, loss: 4.0963 +2024-07-23 18:34:03,087 - pyskl - INFO - Epoch [48][1500/3746] lr: 7.732e-02, eta: 3 days, 10:32:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5363, loss_cls: 4.1193, loss: 4.1193 +2024-07-23 18:35:24,988 - pyskl - INFO - Epoch [48][1600/3746] lr: 7.730e-02, eta: 3 days, 10:31:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5238, loss_cls: 4.1819, loss: 4.1819 +2024-07-23 18:36:46,483 - pyskl - INFO - Epoch [48][1700/3746] lr: 7.727e-02, eta: 3 days, 10:30:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5344, loss_cls: 4.1565, loss: 4.1565 +2024-07-23 18:38:07,988 - pyskl - INFO - Epoch [48][1800/3746] lr: 7.725e-02, eta: 3 days, 10:29:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5228, loss_cls: 4.2295, loss: 4.2295 +2024-07-23 18:39:29,355 - pyskl - INFO - Epoch [48][1900/3746] lr: 7.723e-02, eta: 3 days, 10:28:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5220, loss_cls: 4.1639, loss: 4.1639 +2024-07-23 18:40:51,546 - pyskl - INFO - Epoch [48][2000/3746] lr: 7.720e-02, eta: 3 days, 10:27:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5323, loss_cls: 4.1308, loss: 4.1308 +2024-07-23 18:42:12,936 - pyskl - INFO - Epoch [48][2100/3746] lr: 7.718e-02, eta: 3 days, 10:25:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5392, loss_cls: 4.1464, loss: 4.1464 +2024-07-23 18:43:34,094 - pyskl - INFO - Epoch [48][2200/3746] lr: 7.716e-02, eta: 3 days, 10:24:48, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5405, loss_cls: 4.0967, loss: 4.0967 +2024-07-23 18:44:55,379 - pyskl - INFO - Epoch [48][2300/3746] lr: 7.713e-02, eta: 3 days, 10:23:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5352, loss_cls: 4.1370, loss: 4.1370 +2024-07-23 18:46:16,723 - pyskl - INFO - Epoch [48][2400/3746] lr: 7.711e-02, eta: 3 days, 10:22:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5383, loss_cls: 4.0991, loss: 4.0991 +2024-07-23 18:47:37,865 - pyskl - INFO - Epoch [48][2500/3746] lr: 7.709e-02, eta: 3 days, 10:21:21, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5483, loss_cls: 4.0816, loss: 4.0816 +2024-07-23 18:48:59,388 - pyskl - INFO - Epoch [48][2600/3746] lr: 7.706e-02, eta: 3 days, 10:20:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5339, loss_cls: 4.1463, loss: 4.1463 +2024-07-23 18:50:20,666 - pyskl - INFO - Epoch [48][2700/3746] lr: 7.704e-02, eta: 3 days, 10:19:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5300, loss_cls: 4.1595, loss: 4.1595 +2024-07-23 18:51:41,837 - pyskl - INFO - Epoch [48][2800/3746] lr: 7.701e-02, eta: 3 days, 10:17:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5272, loss_cls: 4.1717, loss: 4.1717 +2024-07-23 18:53:03,389 - pyskl - INFO - Epoch [48][2900/3746] lr: 7.699e-02, eta: 3 days, 10:16:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5267, loss_cls: 4.1600, loss: 4.1600 +2024-07-23 18:54:25,546 - pyskl - INFO - Epoch [48][3000/3746] lr: 7.697e-02, eta: 3 days, 10:15:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5392, loss_cls: 4.1081, loss: 4.1081 +2024-07-23 18:55:47,036 - pyskl - INFO - Epoch [48][3100/3746] lr: 7.694e-02, eta: 3 days, 10:14:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5294, loss_cls: 4.1839, loss: 4.1839 +2024-07-23 18:57:09,066 - pyskl - INFO - Epoch [48][3200/3746] lr: 7.692e-02, eta: 3 days, 10:13:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5353, loss_cls: 4.1284, loss: 4.1284 +2024-07-23 18:58:30,533 - pyskl - INFO - Epoch [48][3300/3746] lr: 7.690e-02, eta: 3 days, 10:12:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5316, loss_cls: 4.1380, loss: 4.1380 +2024-07-23 18:59:52,075 - pyskl - INFO - Epoch [48][3400/3746] lr: 7.687e-02, eta: 3 days, 10:11:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5475, loss_cls: 4.0564, loss: 4.0564 +2024-07-23 19:01:13,858 - pyskl - INFO - Epoch [48][3500/3746] lr: 7.685e-02, eta: 3 days, 10:09:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5344, loss_cls: 4.1035, loss: 4.1035 +2024-07-23 19:02:35,431 - pyskl - INFO - Epoch [48][3600/3746] lr: 7.683e-02, eta: 3 days, 10:08:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5278, loss_cls: 4.1533, loss: 4.1533 +2024-07-23 19:03:57,262 - pyskl - INFO - Epoch [48][3700/3746] lr: 7.680e-02, eta: 3 days, 10:07:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5308, loss_cls: 4.1608, loss: 4.1608 +2024-07-23 19:04:36,927 - pyskl - INFO - Saving checkpoint at 48 epochs +2024-07-23 19:06:28,449 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 19:06:29,118 - pyskl - INFO - +top1_acc 0.2047 +top5_acc 0.4320 +2024-07-23 19:06:29,118 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 19:06:29,157 - pyskl - INFO - +mean_acc 0.2046 +2024-07-23 19:06:29,168 - pyskl - INFO - Epoch(val) [48][309] top1_acc: 0.2047, top5_acc: 0.4320, mean_class_accuracy: 0.2046 +2024-07-23 19:10:19,506 - pyskl - INFO - Epoch [49][100/3746] lr: 7.677e-02, eta: 3 days, 10:09:59, time: 2.303, data_time: 1.315, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5456, loss_cls: 4.0693, loss: 4.0693 +2024-07-23 19:11:41,730 - pyskl - INFO - Epoch [49][200/3746] lr: 7.674e-02, eta: 3 days, 10:08:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5430, loss_cls: 4.0950, loss: 4.0950 +2024-07-23 19:13:03,427 - pyskl - INFO - Epoch [49][300/3746] lr: 7.672e-02, eta: 3 days, 10:07:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5386, loss_cls: 4.1274, loss: 4.1274 +2024-07-23 19:14:25,464 - pyskl - INFO - Epoch [49][400/3746] lr: 7.670e-02, eta: 3 days, 10:06:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5469, loss_cls: 4.0835, loss: 4.0835 +2024-07-23 19:15:47,577 - pyskl - INFO - Epoch [49][500/3746] lr: 7.667e-02, eta: 3 days, 10:05:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5506, loss_cls: 4.0573, loss: 4.0573 +2024-07-23 19:17:09,285 - pyskl - INFO - Epoch [49][600/3746] lr: 7.665e-02, eta: 3 days, 10:04:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5434, loss_cls: 4.0645, loss: 4.0645 +2024-07-23 19:18:31,870 - pyskl - INFO - Epoch [49][700/3746] lr: 7.663e-02, eta: 3 days, 10:03:13, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5370, loss_cls: 4.1050, loss: 4.1050 +2024-07-23 19:19:54,041 - pyskl - INFO - Epoch [49][800/3746] lr: 7.660e-02, eta: 3 days, 10:02:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5334, loss_cls: 4.1281, loss: 4.1281 +2024-07-23 19:21:16,664 - pyskl - INFO - Epoch [49][900/3746] lr: 7.658e-02, eta: 3 days, 10:00:59, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5384, loss_cls: 4.1160, loss: 4.1160 +2024-07-23 19:22:38,453 - pyskl - INFO - Epoch [49][1000/3746] lr: 7.656e-02, eta: 3 days, 9:59:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5309, loss_cls: 4.1360, loss: 4.1360 +2024-07-23 19:24:00,475 - pyskl - INFO - Epoch [49][1100/3746] lr: 7.653e-02, eta: 3 days, 9:58:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5269, loss_cls: 4.1870, loss: 4.1870 +2024-07-23 19:25:22,213 - pyskl - INFO - Epoch [49][1200/3746] lr: 7.651e-02, eta: 3 days, 9:57:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5394, loss_cls: 4.0853, loss: 4.0853 +2024-07-23 19:26:44,407 - pyskl - INFO - Epoch [49][1300/3746] lr: 7.648e-02, eta: 3 days, 9:56:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5295, loss_cls: 4.1300, loss: 4.1300 +2024-07-23 19:28:05,849 - pyskl - INFO - Epoch [49][1400/3746] lr: 7.646e-02, eta: 3 days, 9:55:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5364, loss_cls: 4.1103, loss: 4.1103 +2024-07-23 19:29:27,726 - pyskl - INFO - Epoch [49][1500/3746] lr: 7.644e-02, eta: 3 days, 9:54:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5387, loss_cls: 4.1281, loss: 4.1281 +2024-07-23 19:30:49,121 - pyskl - INFO - Epoch [49][1600/3746] lr: 7.641e-02, eta: 3 days, 9:53:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5297, loss_cls: 4.1209, loss: 4.1209 +2024-07-23 19:32:10,708 - pyskl - INFO - Epoch [49][1700/3746] lr: 7.639e-02, eta: 3 days, 9:51:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5356, loss_cls: 4.1100, loss: 4.1100 +2024-07-23 19:33:32,244 - pyskl - INFO - Epoch [49][1800/3746] lr: 7.637e-02, eta: 3 days, 9:50:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5172, loss_cls: 4.1895, loss: 4.1895 +2024-07-23 19:34:54,122 - pyskl - INFO - Epoch [49][1900/3746] lr: 7.634e-02, eta: 3 days, 9:49:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5297, loss_cls: 4.1793, loss: 4.1793 +2024-07-23 19:36:15,961 - pyskl - INFO - Epoch [49][2000/3746] lr: 7.632e-02, eta: 3 days, 9:48:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5477, loss_cls: 4.0976, loss: 4.0976 +2024-07-23 19:37:37,423 - pyskl - INFO - Epoch [49][2100/3746] lr: 7.629e-02, eta: 3 days, 9:47:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5372, loss_cls: 4.1055, loss: 4.1055 +2024-07-23 19:38:59,298 - pyskl - INFO - Epoch [49][2200/3746] lr: 7.627e-02, eta: 3 days, 9:46:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5337, loss_cls: 4.1220, loss: 4.1220 +2024-07-23 19:40:20,204 - pyskl - INFO - Epoch [49][2300/3746] lr: 7.625e-02, eta: 3 days, 9:44:57, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5328, loss_cls: 4.1364, loss: 4.1364 +2024-07-23 19:41:41,872 - pyskl - INFO - Epoch [49][2400/3746] lr: 7.622e-02, eta: 3 days, 9:43:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5394, loss_cls: 4.0932, loss: 4.0932 +2024-07-23 19:43:03,467 - pyskl - INFO - Epoch [49][2500/3746] lr: 7.620e-02, eta: 3 days, 9:42:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5231, loss_cls: 4.1993, loss: 4.1993 +2024-07-23 19:44:24,664 - pyskl - INFO - Epoch [49][2600/3746] lr: 7.618e-02, eta: 3 days, 9:41:30, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5369, loss_cls: 4.1372, loss: 4.1372 +2024-07-23 19:45:46,356 - pyskl - INFO - Epoch [49][2700/3746] lr: 7.615e-02, eta: 3 days, 9:40:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5202, loss_cls: 4.1691, loss: 4.1691 +2024-07-23 19:47:07,958 - pyskl - INFO - Epoch [49][2800/3746] lr: 7.613e-02, eta: 3 days, 9:39:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5448, loss_cls: 4.0706, loss: 4.0706 +2024-07-23 19:48:29,488 - pyskl - INFO - Epoch [49][2900/3746] lr: 7.610e-02, eta: 3 days, 9:38:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5383, loss_cls: 4.1131, loss: 4.1131 +2024-07-23 19:49:50,779 - pyskl - INFO - Epoch [49][3000/3746] lr: 7.608e-02, eta: 3 days, 9:36:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5353, loss_cls: 4.1482, loss: 4.1482 +2024-07-23 19:51:12,536 - pyskl - INFO - Epoch [49][3100/3746] lr: 7.606e-02, eta: 3 days, 9:35:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5378, loss_cls: 4.1319, loss: 4.1319 +2024-07-23 19:52:33,910 - pyskl - INFO - Epoch [49][3200/3746] lr: 7.603e-02, eta: 3 days, 9:34:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5428, loss_cls: 4.0853, loss: 4.0853 +2024-07-23 19:53:55,543 - pyskl - INFO - Epoch [49][3300/3746] lr: 7.601e-02, eta: 3 days, 9:33:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5414, loss_cls: 4.1221, loss: 4.1221 +2024-07-23 19:55:16,425 - pyskl - INFO - Epoch [49][3400/3746] lr: 7.598e-02, eta: 3 days, 9:32:16, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5387, loss_cls: 4.1091, loss: 4.1091 +2024-07-23 19:56:37,916 - pyskl - INFO - Epoch [49][3500/3746] lr: 7.596e-02, eta: 3 days, 9:31:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5322, loss_cls: 4.1076, loss: 4.1076 +2024-07-23 19:57:59,339 - pyskl - INFO - Epoch [49][3600/3746] lr: 7.594e-02, eta: 3 days, 9:29:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5366, loss_cls: 4.1556, loss: 4.1556 +2024-07-23 19:59:20,765 - pyskl - INFO - Epoch [49][3700/3746] lr: 7.591e-02, eta: 3 days, 9:28:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5275, loss_cls: 4.1729, loss: 4.1729 +2024-07-23 19:59:59,978 - pyskl - INFO - Saving checkpoint at 49 epochs +2024-07-23 20:01:52,638 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 20:01:53,369 - pyskl - INFO - +top1_acc 0.1981 +top5_acc 0.4285 +2024-07-23 20:01:53,369 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 20:01:53,408 - pyskl - INFO - +mean_acc 0.1980 +2024-07-23 20:01:53,420 - pyskl - INFO - Epoch(val) [49][309] top1_acc: 0.1981, top5_acc: 0.4285, mean_class_accuracy: 0.1980 +2024-07-23 20:05:42,565 - pyskl - INFO - Epoch [50][100/3746] lr: 7.588e-02, eta: 3 days, 9:30:53, time: 2.291, data_time: 1.316, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5536, loss_cls: 4.0232, loss: 4.0232 +2024-07-23 20:07:04,188 - pyskl - INFO - Epoch [50][200/3746] lr: 7.585e-02, eta: 3 days, 9:29:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5455, loss_cls: 4.0854, loss: 4.0854 +2024-07-23 20:08:25,731 - pyskl - INFO - Epoch [50][300/3746] lr: 7.583e-02, eta: 3 days, 9:28:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5494, loss_cls: 4.0921, loss: 4.0921 +2024-07-23 20:09:48,370 - pyskl - INFO - Epoch [50][400/3746] lr: 7.581e-02, eta: 3 days, 9:27:27, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5547, loss_cls: 4.0379, loss: 4.0379 +2024-07-23 20:11:10,628 - pyskl - INFO - Epoch [50][500/3746] lr: 7.578e-02, eta: 3 days, 9:26:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5345, loss_cls: 4.1157, loss: 4.1157 +2024-07-23 20:12:33,052 - pyskl - INFO - Epoch [50][600/3746] lr: 7.576e-02, eta: 3 days, 9:25:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5392, loss_cls: 4.1266, loss: 4.1266 +2024-07-23 20:13:55,430 - pyskl - INFO - Epoch [50][700/3746] lr: 7.573e-02, eta: 3 days, 9:24:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5367, loss_cls: 4.1188, loss: 4.1188 +2024-07-23 20:15:17,048 - pyskl - INFO - Epoch [50][800/3746] lr: 7.571e-02, eta: 3 days, 9:22:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5378, loss_cls: 4.1152, loss: 4.1152 +2024-07-23 20:16:40,210 - pyskl - INFO - Epoch [50][900/3746] lr: 7.569e-02, eta: 3 days, 9:21:48, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5413, loss_cls: 4.1224, loss: 4.1224 +2024-07-23 20:18:02,676 - pyskl - INFO - Epoch [50][1000/3746] lr: 7.566e-02, eta: 3 days, 9:20:40, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5411, loss_cls: 4.1200, loss: 4.1200 +2024-07-23 20:19:24,987 - pyskl - INFO - Epoch [50][1100/3746] lr: 7.564e-02, eta: 3 days, 9:19:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5403, loss_cls: 4.1016, loss: 4.1016 +2024-07-23 20:20:47,438 - pyskl - INFO - Epoch [50][1200/3746] lr: 7.561e-02, eta: 3 days, 9:18:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5342, loss_cls: 4.1459, loss: 4.1459 +2024-07-23 20:22:08,852 - pyskl - INFO - Epoch [50][1300/3746] lr: 7.559e-02, eta: 3 days, 9:17:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5461, loss_cls: 4.0911, loss: 4.0911 +2024-07-23 20:23:30,917 - pyskl - INFO - Epoch [50][1400/3746] lr: 7.557e-02, eta: 3 days, 9:16:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5398, loss_cls: 4.1104, loss: 4.1104 +2024-07-23 20:24:52,745 - pyskl - INFO - Epoch [50][1500/3746] lr: 7.554e-02, eta: 3 days, 9:14:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5314, loss_cls: 4.1205, loss: 4.1205 +2024-07-23 20:26:13,983 - pyskl - INFO - Epoch [50][1600/3746] lr: 7.552e-02, eta: 3 days, 9:13:47, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5375, loss_cls: 4.1163, loss: 4.1163 +2024-07-23 20:27:35,361 - pyskl - INFO - Epoch [50][1700/3746] lr: 7.549e-02, eta: 3 days, 9:12:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5400, loss_cls: 4.1092, loss: 4.1092 +2024-07-23 20:28:57,760 - pyskl - INFO - Epoch [50][1800/3746] lr: 7.547e-02, eta: 3 days, 9:11:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5397, loss_cls: 4.1345, loss: 4.1345 +2024-07-23 20:30:19,552 - pyskl - INFO - Epoch [50][1900/3746] lr: 7.545e-02, eta: 3 days, 9:10:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5319, loss_cls: 4.1551, loss: 4.1551 +2024-07-23 20:31:41,244 - pyskl - INFO - Epoch [50][2000/3746] lr: 7.542e-02, eta: 3 days, 9:09:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5267, loss_cls: 4.1618, loss: 4.1618 +2024-07-23 20:33:02,282 - pyskl - INFO - Epoch [50][2100/3746] lr: 7.540e-02, eta: 3 days, 9:08:00, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5323, loss_cls: 4.1436, loss: 4.1436 +2024-07-23 20:34:24,594 - pyskl - INFO - Epoch [50][2200/3746] lr: 7.537e-02, eta: 3 days, 9:06:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5377, loss_cls: 4.1208, loss: 4.1208 +2024-07-23 20:35:45,900 - pyskl - INFO - Epoch [50][2300/3746] lr: 7.535e-02, eta: 3 days, 9:05:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5311, loss_cls: 4.1549, loss: 4.1549 +2024-07-23 20:37:07,868 - pyskl - INFO - Epoch [50][2400/3746] lr: 7.533e-02, eta: 3 days, 9:04:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5380, loss_cls: 4.1049, loss: 4.1049 +2024-07-23 20:38:29,494 - pyskl - INFO - Epoch [50][2500/3746] lr: 7.530e-02, eta: 3 days, 9:03:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5430, loss_cls: 4.1034, loss: 4.1034 +2024-07-23 20:39:51,001 - pyskl - INFO - Epoch [50][2600/3746] lr: 7.528e-02, eta: 3 days, 9:02:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5387, loss_cls: 4.1042, loss: 4.1042 +2024-07-23 20:41:12,576 - pyskl - INFO - Epoch [50][2700/3746] lr: 7.525e-02, eta: 3 days, 9:01:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5184, loss_cls: 4.2007, loss: 4.2007 +2024-07-23 20:42:34,048 - pyskl - INFO - Epoch [50][2800/3746] lr: 7.523e-02, eta: 3 days, 8:59:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5356, loss_cls: 4.1209, loss: 4.1209 +2024-07-23 20:43:55,488 - pyskl - INFO - Epoch [50][2900/3746] lr: 7.520e-02, eta: 3 days, 8:58:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5383, loss_cls: 4.0995, loss: 4.0995 +2024-07-23 20:45:16,921 - pyskl - INFO - Epoch [50][3000/3746] lr: 7.518e-02, eta: 3 days, 8:57:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5352, loss_cls: 4.1189, loss: 4.1189 +2024-07-23 20:46:38,379 - pyskl - INFO - Epoch [50][3100/3746] lr: 7.516e-02, eta: 3 days, 8:56:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5353, loss_cls: 4.1174, loss: 4.1174 +2024-07-23 20:48:00,432 - pyskl - INFO - Epoch [50][3200/3746] lr: 7.513e-02, eta: 3 days, 8:55:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5323, loss_cls: 4.1659, loss: 4.1659 +2024-07-23 20:49:22,051 - pyskl - INFO - Epoch [50][3300/3746] lr: 7.511e-02, eta: 3 days, 8:54:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5422, loss_cls: 4.0978, loss: 4.0978 +2024-07-23 20:50:43,551 - pyskl - INFO - Epoch [50][3400/3746] lr: 7.508e-02, eta: 3 days, 8:52:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5278, loss_cls: 4.1156, loss: 4.1156 +2024-07-23 20:52:05,124 - pyskl - INFO - Epoch [50][3500/3746] lr: 7.506e-02, eta: 3 days, 8:51:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5433, loss_cls: 4.0822, loss: 4.0822 +2024-07-23 20:53:27,489 - pyskl - INFO - Epoch [50][3600/3746] lr: 7.504e-02, eta: 3 days, 8:50:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5331, loss_cls: 4.1179, loss: 4.1179 +2024-07-23 20:54:49,682 - pyskl - INFO - Epoch [50][3700/3746] lr: 7.501e-02, eta: 3 days, 8:49:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5300, loss_cls: 4.1302, loss: 4.1302 +2024-07-23 20:55:29,380 - pyskl - INFO - Saving checkpoint at 50 epochs +2024-07-23 20:57:22,086 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 20:57:22,747 - pyskl - INFO - +top1_acc 0.2002 +top5_acc 0.4335 +2024-07-23 20:57:22,747 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 20:57:22,787 - pyskl - INFO - +mean_acc 0.2000 +2024-07-23 20:57:22,798 - pyskl - INFO - Epoch(val) [50][309] top1_acc: 0.2002, top5_acc: 0.4335, mean_class_accuracy: 0.2000 +2024-07-23 21:01:09,004 - pyskl - INFO - Epoch [51][100/3746] lr: 7.498e-02, eta: 3 days, 8:51:21, time: 2.262, data_time: 1.287, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5408, loss_cls: 4.0834, loss: 4.0834 +2024-07-23 21:02:30,770 - pyskl - INFO - Epoch [51][200/3746] lr: 7.495e-02, eta: 3 days, 8:50:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5552, loss_cls: 4.0641, loss: 4.0641 +2024-07-23 21:03:52,570 - pyskl - INFO - Epoch [51][300/3746] lr: 7.493e-02, eta: 3 days, 8:49:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5391, loss_cls: 4.0847, loss: 4.0847 +2024-07-23 21:05:14,804 - pyskl - INFO - Epoch [51][400/3746] lr: 7.490e-02, eta: 3 days, 8:47:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5419, loss_cls: 4.0910, loss: 4.0910 +2024-07-23 21:06:36,766 - pyskl - INFO - Epoch [51][500/3746] lr: 7.488e-02, eta: 3 days, 8:46:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5302, loss_cls: 4.1267, loss: 4.1267 +2024-07-23 21:07:59,420 - pyskl - INFO - Epoch [51][600/3746] lr: 7.485e-02, eta: 3 days, 8:45:35, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5420, loss_cls: 4.1014, loss: 4.1014 +2024-07-23 21:09:22,245 - pyskl - INFO - Epoch [51][700/3746] lr: 7.483e-02, eta: 3 days, 8:44:28, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5394, loss_cls: 4.1248, loss: 4.1248 +2024-07-23 21:10:43,657 - pyskl - INFO - Epoch [51][800/3746] lr: 7.481e-02, eta: 3 days, 8:43:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5416, loss_cls: 4.0746, loss: 4.0746 +2024-07-23 21:12:06,551 - pyskl - INFO - Epoch [51][900/3746] lr: 7.478e-02, eta: 3 days, 8:42:10, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5397, loss_cls: 4.1365, loss: 4.1365 +2024-07-23 21:13:28,643 - pyskl - INFO - Epoch [51][1000/3746] lr: 7.476e-02, eta: 3 days, 8:41:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5428, loss_cls: 4.0886, loss: 4.0886 +2024-07-23 21:14:50,533 - pyskl - INFO - Epoch [51][1100/3746] lr: 7.473e-02, eta: 3 days, 8:39:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5442, loss_cls: 4.0962, loss: 4.0962 +2024-07-23 21:16:12,390 - pyskl - INFO - Epoch [51][1200/3746] lr: 7.471e-02, eta: 3 days, 8:38:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5461, loss_cls: 4.0972, loss: 4.0972 +2024-07-23 21:17:34,197 - pyskl - INFO - Epoch [51][1300/3746] lr: 7.468e-02, eta: 3 days, 8:37:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5366, loss_cls: 4.1036, loss: 4.1036 +2024-07-23 21:18:56,122 - pyskl - INFO - Epoch [51][1400/3746] lr: 7.466e-02, eta: 3 days, 8:36:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5422, loss_cls: 4.0928, loss: 4.0928 +2024-07-23 21:20:17,901 - pyskl - INFO - Epoch [51][1500/3746] lr: 7.464e-02, eta: 3 days, 8:35:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5325, loss_cls: 4.1342, loss: 4.1342 +2024-07-23 21:21:39,662 - pyskl - INFO - Epoch [51][1600/3746] lr: 7.461e-02, eta: 3 days, 8:34:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5397, loss_cls: 4.1134, loss: 4.1134 +2024-07-23 21:23:01,247 - pyskl - INFO - Epoch [51][1700/3746] lr: 7.459e-02, eta: 3 days, 8:32:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5319, loss_cls: 4.1126, loss: 4.1126 +2024-07-23 21:24:22,616 - pyskl - INFO - Epoch [51][1800/3746] lr: 7.456e-02, eta: 3 days, 8:31:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5431, loss_cls: 4.1041, loss: 4.1041 +2024-07-23 21:25:44,274 - pyskl - INFO - Epoch [51][1900/3746] lr: 7.454e-02, eta: 3 days, 8:30:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5255, loss_cls: 4.1807, loss: 4.1807 +2024-07-23 21:27:06,535 - pyskl - INFO - Epoch [51][2000/3746] lr: 7.451e-02, eta: 3 days, 8:29:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5428, loss_cls: 4.0991, loss: 4.0991 +2024-07-23 21:28:27,630 - pyskl - INFO - Epoch [51][2100/3746] lr: 7.449e-02, eta: 3 days, 8:28:11, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5391, loss_cls: 4.1257, loss: 4.1257 +2024-07-23 21:29:49,171 - pyskl - INFO - Epoch [51][2200/3746] lr: 7.447e-02, eta: 3 days, 8:27:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5456, loss_cls: 4.0978, loss: 4.0978 +2024-07-23 21:31:10,808 - pyskl - INFO - Epoch [51][2300/3746] lr: 7.444e-02, eta: 3 days, 8:25:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5470, loss_cls: 4.0842, loss: 4.0842 +2024-07-23 21:32:32,342 - pyskl - INFO - Epoch [51][2400/3746] lr: 7.442e-02, eta: 3 days, 8:24:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5434, loss_cls: 4.0872, loss: 4.0872 +2024-07-23 21:33:53,388 - pyskl - INFO - Epoch [51][2500/3746] lr: 7.439e-02, eta: 3 days, 8:23:29, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5420, loss_cls: 4.1055, loss: 4.1055 +2024-07-23 21:35:14,954 - pyskl - INFO - Epoch [51][2600/3746] lr: 7.437e-02, eta: 3 days, 8:22:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5356, loss_cls: 4.1481, loss: 4.1481 +2024-07-23 21:36:36,504 - pyskl - INFO - Epoch [51][2700/3746] lr: 7.434e-02, eta: 3 days, 8:21:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5413, loss_cls: 4.0960, loss: 4.0960 +2024-07-23 21:37:58,064 - pyskl - INFO - Epoch [51][2800/3746] lr: 7.432e-02, eta: 3 days, 8:19:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5470, loss_cls: 4.0833, loss: 4.0833 +2024-07-23 21:39:19,504 - pyskl - INFO - Epoch [51][2900/3746] lr: 7.429e-02, eta: 3 days, 8:18:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5373, loss_cls: 4.1243, loss: 4.1243 +2024-07-23 21:40:41,035 - pyskl - INFO - Epoch [51][3000/3746] lr: 7.427e-02, eta: 3 days, 8:17:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5413, loss_cls: 4.0832, loss: 4.0832 +2024-07-23 21:42:02,188 - pyskl - INFO - Epoch [51][3100/3746] lr: 7.425e-02, eta: 3 days, 8:16:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5434, loss_cls: 4.1318, loss: 4.1318 +2024-07-23 21:43:23,937 - pyskl - INFO - Epoch [51][3200/3746] lr: 7.422e-02, eta: 3 days, 8:15:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5284, loss_cls: 4.1604, loss: 4.1604 +2024-07-23 21:44:45,644 - pyskl - INFO - Epoch [51][3300/3746] lr: 7.420e-02, eta: 3 days, 8:14:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5270, loss_cls: 4.1380, loss: 4.1380 +2024-07-23 21:46:07,193 - pyskl - INFO - Epoch [51][3400/3746] lr: 7.417e-02, eta: 3 days, 8:12:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5252, loss_cls: 4.1491, loss: 4.1491 +2024-07-23 21:47:28,647 - pyskl - INFO - Epoch [51][3500/3746] lr: 7.415e-02, eta: 3 days, 8:11:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5306, loss_cls: 4.1681, loss: 4.1681 +2024-07-23 21:48:50,128 - pyskl - INFO - Epoch [51][3600/3746] lr: 7.412e-02, eta: 3 days, 8:10:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5361, loss_cls: 4.1210, loss: 4.1210 +2024-07-23 21:50:11,724 - pyskl - INFO - Epoch [51][3700/3746] lr: 7.410e-02, eta: 3 days, 8:09:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5344, loss_cls: 4.1314, loss: 4.1314 +2024-07-23 21:50:51,765 - pyskl - INFO - Saving checkpoint at 51 epochs +2024-07-23 21:52:43,887 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 21:52:44,550 - pyskl - INFO - +top1_acc 0.1692 +top5_acc 0.3817 +2024-07-23 21:52:44,551 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 21:52:44,597 - pyskl - INFO - +mean_acc 0.1691 +2024-07-23 21:52:44,608 - pyskl - INFO - Epoch(val) [51][309] top1_acc: 0.1692, top5_acc: 0.3817, mean_class_accuracy: 0.1691 +2024-07-23 21:56:34,282 - pyskl - INFO - Epoch [52][100/3746] lr: 7.406e-02, eta: 3 days, 8:11:15, time: 2.297, data_time: 1.310, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5487, loss_cls: 4.0502, loss: 4.0502 +2024-07-23 21:57:56,551 - pyskl - INFO - Epoch [52][200/3746] lr: 7.404e-02, eta: 3 days, 8:10:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5492, loss_cls: 4.0983, loss: 4.0983 +2024-07-23 21:59:19,177 - pyskl - INFO - Epoch [52][300/3746] lr: 7.401e-02, eta: 3 days, 8:08:57, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5453, loss_cls: 4.0780, loss: 4.0780 +2024-07-23 22:00:41,581 - pyskl - INFO - Epoch [52][400/3746] lr: 7.399e-02, eta: 3 days, 8:07:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5458, loss_cls: 4.0710, loss: 4.0710 +2024-07-23 22:02:03,427 - pyskl - INFO - Epoch [52][500/3746] lr: 7.397e-02, eta: 3 days, 8:06:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5417, loss_cls: 4.0783, loss: 4.0783 +2024-07-23 22:03:25,339 - pyskl - INFO - Epoch [52][600/3746] lr: 7.394e-02, eta: 3 days, 8:05:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5361, loss_cls: 4.0861, loss: 4.0861 +2024-07-23 22:04:47,562 - pyskl - INFO - Epoch [52][700/3746] lr: 7.392e-02, eta: 3 days, 8:04:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5359, loss_cls: 4.1084, loss: 4.1084 +2024-07-23 22:06:09,582 - pyskl - INFO - Epoch [52][800/3746] lr: 7.389e-02, eta: 3 days, 8:03:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5361, loss_cls: 4.0872, loss: 4.0872 +2024-07-23 22:07:32,309 - pyskl - INFO - Epoch [52][900/3746] lr: 7.387e-02, eta: 3 days, 8:02:00, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5403, loss_cls: 4.1189, loss: 4.1189 +2024-07-23 22:08:54,780 - pyskl - INFO - Epoch [52][1000/3746] lr: 7.384e-02, eta: 3 days, 8:00:50, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5361, loss_cls: 4.1036, loss: 4.1036 +2024-07-23 22:10:16,753 - pyskl - INFO - Epoch [52][1100/3746] lr: 7.382e-02, eta: 3 days, 7:59:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5278, loss_cls: 4.1815, loss: 4.1815 +2024-07-23 22:11:38,641 - pyskl - INFO - Epoch [52][1200/3746] lr: 7.379e-02, eta: 3 days, 7:58:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5409, loss_cls: 4.0591, loss: 4.0591 +2024-07-23 22:13:00,282 - pyskl - INFO - Epoch [52][1300/3746] lr: 7.377e-02, eta: 3 days, 7:57:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5402, loss_cls: 4.1123, loss: 4.1123 +2024-07-23 22:14:22,257 - pyskl - INFO - Epoch [52][1400/3746] lr: 7.374e-02, eta: 3 days, 7:56:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5445, loss_cls: 4.0859, loss: 4.0859 +2024-07-23 22:15:43,663 - pyskl - INFO - Epoch [52][1500/3746] lr: 7.372e-02, eta: 3 days, 7:54:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5305, loss_cls: 4.1252, loss: 4.1252 +2024-07-23 22:17:05,348 - pyskl - INFO - Epoch [52][1600/3746] lr: 7.370e-02, eta: 3 days, 7:53:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5411, loss_cls: 4.0878, loss: 4.0878 +2024-07-23 22:18:26,987 - pyskl - INFO - Epoch [52][1700/3746] lr: 7.367e-02, eta: 3 days, 7:52:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5434, loss_cls: 4.0720, loss: 4.0720 +2024-07-23 22:19:48,804 - pyskl - INFO - Epoch [52][1800/3746] lr: 7.365e-02, eta: 3 days, 7:51:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5361, loss_cls: 4.1045, loss: 4.1045 +2024-07-23 22:21:10,864 - pyskl - INFO - Epoch [52][1900/3746] lr: 7.362e-02, eta: 3 days, 7:50:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5380, loss_cls: 4.0980, loss: 4.0980 +2024-07-23 22:22:32,136 - pyskl - INFO - Epoch [52][2000/3746] lr: 7.360e-02, eta: 3 days, 7:49:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5486, loss_cls: 4.0482, loss: 4.0482 +2024-07-23 22:23:53,322 - pyskl - INFO - Epoch [52][2100/3746] lr: 7.357e-02, eta: 3 days, 7:47:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5492, loss_cls: 4.0682, loss: 4.0682 +2024-07-23 22:25:14,990 - pyskl - INFO - Epoch [52][2200/3746] lr: 7.355e-02, eta: 3 days, 7:46:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5419, loss_cls: 4.0986, loss: 4.0986 +2024-07-23 22:26:36,859 - pyskl - INFO - Epoch [52][2300/3746] lr: 7.352e-02, eta: 3 days, 7:45:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5433, loss_cls: 4.1023, loss: 4.1023 +2024-07-23 22:27:58,686 - pyskl - INFO - Epoch [52][2400/3746] lr: 7.350e-02, eta: 3 days, 7:44:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5408, loss_cls: 4.1049, loss: 4.1049 +2024-07-23 22:29:20,304 - pyskl - INFO - Epoch [52][2500/3746] lr: 7.347e-02, eta: 3 days, 7:43:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5408, loss_cls: 4.1122, loss: 4.1122 +2024-07-23 22:30:41,682 - pyskl - INFO - Epoch [52][2600/3746] lr: 7.345e-02, eta: 3 days, 7:42:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5331, loss_cls: 4.1364, loss: 4.1364 +2024-07-23 22:32:03,150 - pyskl - INFO - Epoch [52][2700/3746] lr: 7.342e-02, eta: 3 days, 7:40:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5386, loss_cls: 4.1230, loss: 4.1230 +2024-07-23 22:33:24,583 - pyskl - INFO - Epoch [52][2800/3746] lr: 7.340e-02, eta: 3 days, 7:39:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5508, loss_cls: 4.0832, loss: 4.0832 +2024-07-23 22:34:45,800 - pyskl - INFO - Epoch [52][2900/3746] lr: 7.337e-02, eta: 3 days, 7:38:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5386, loss_cls: 4.1075, loss: 4.1075 +2024-07-23 22:36:07,182 - pyskl - INFO - Epoch [52][3000/3746] lr: 7.335e-02, eta: 3 days, 7:37:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5375, loss_cls: 4.1238, loss: 4.1238 +2024-07-23 22:37:28,704 - pyskl - INFO - Epoch [52][3100/3746] lr: 7.332e-02, eta: 3 days, 7:36:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5291, loss_cls: 4.1580, loss: 4.1580 +2024-07-23 22:38:50,067 - pyskl - INFO - Epoch [52][3200/3746] lr: 7.330e-02, eta: 3 days, 7:34:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5445, loss_cls: 4.0692, loss: 4.0692 +2024-07-23 22:40:11,929 - pyskl - INFO - Epoch [52][3300/3746] lr: 7.328e-02, eta: 3 days, 7:33:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5461, loss_cls: 4.1055, loss: 4.1055 +2024-07-23 22:41:33,307 - pyskl - INFO - Epoch [52][3400/3746] lr: 7.325e-02, eta: 3 days, 7:32:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5395, loss_cls: 4.1006, loss: 4.1006 +2024-07-23 22:42:55,478 - pyskl - INFO - Epoch [52][3500/3746] lr: 7.323e-02, eta: 3 days, 7:31:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5373, loss_cls: 4.1163, loss: 4.1163 +2024-07-23 22:44:17,314 - pyskl - INFO - Epoch [52][3600/3746] lr: 7.320e-02, eta: 3 days, 7:30:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5444, loss_cls: 4.1037, loss: 4.1037 +2024-07-23 22:45:39,090 - pyskl - INFO - Epoch [52][3700/3746] lr: 7.318e-02, eta: 3 days, 7:28:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5312, loss_cls: 4.1293, loss: 4.1293 +2024-07-23 22:46:19,159 - pyskl - INFO - Saving checkpoint at 52 epochs +2024-07-23 22:48:11,792 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 22:48:12,470 - pyskl - INFO - +top1_acc 0.2283 +top5_acc 0.4617 +2024-07-23 22:48:12,470 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 22:48:12,515 - pyskl - INFO - +mean_acc 0.2281 +2024-07-23 22:48:12,519 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_40.pth was removed +2024-07-23 22:48:12,783 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_52.pth. +2024-07-23 22:48:12,784 - pyskl - INFO - Best top1_acc is 0.2283 at 52 epoch. +2024-07-23 22:48:12,802 - pyskl - INFO - Epoch(val) [52][309] top1_acc: 0.2283, top5_acc: 0.4617, mean_class_accuracy: 0.2281 +2024-07-23 22:52:02,206 - pyskl - INFO - Epoch [53][100/3746] lr: 7.314e-02, eta: 3 days, 7:30:43, time: 2.294, data_time: 1.317, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5439, loss_cls: 4.0462, loss: 4.0462 +2024-07-23 22:53:24,359 - pyskl - INFO - Epoch [53][200/3746] lr: 7.312e-02, eta: 3 days, 7:29:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5420, loss_cls: 4.0630, loss: 4.0630 +2024-07-23 22:54:46,849 - pyskl - INFO - Epoch [53][300/3746] lr: 7.309e-02, eta: 3 days, 7:28:23, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5430, loss_cls: 4.0834, loss: 4.0834 +2024-07-23 22:56:09,901 - pyskl - INFO - Epoch [53][400/3746] lr: 7.307e-02, eta: 3 days, 7:27:15, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5431, loss_cls: 4.0722, loss: 4.0722 +2024-07-23 22:57:32,302 - pyskl - INFO - Epoch [53][500/3746] lr: 7.304e-02, eta: 3 days, 7:26:05, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5431, loss_cls: 4.0555, loss: 4.0555 +2024-07-23 22:58:54,331 - pyskl - INFO - Epoch [53][600/3746] lr: 7.302e-02, eta: 3 days, 7:24:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5333, loss_cls: 4.1083, loss: 4.1083 +2024-07-23 23:00:16,367 - pyskl - INFO - Epoch [53][700/3746] lr: 7.299e-02, eta: 3 days, 7:23:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5453, loss_cls: 4.0718, loss: 4.0718 +2024-07-23 23:01:38,631 - pyskl - INFO - Epoch [53][800/3746] lr: 7.297e-02, eta: 3 days, 7:22:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5461, loss_cls: 4.0785, loss: 4.0785 +2024-07-23 23:03:00,939 - pyskl - INFO - Epoch [53][900/3746] lr: 7.294e-02, eta: 3 days, 7:21:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5522, loss_cls: 4.0616, loss: 4.0616 +2024-07-23 23:04:23,535 - pyskl - INFO - Epoch [53][1000/3746] lr: 7.292e-02, eta: 3 days, 7:20:15, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5417, loss_cls: 4.1002, loss: 4.1002 +2024-07-23 23:05:45,372 - pyskl - INFO - Epoch [53][1100/3746] lr: 7.289e-02, eta: 3 days, 7:19:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5448, loss_cls: 4.0645, loss: 4.0645 +2024-07-23 23:07:07,160 - pyskl - INFO - Epoch [53][1200/3746] lr: 7.287e-02, eta: 3 days, 7:17:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5444, loss_cls: 4.0742, loss: 4.0742 +2024-07-23 23:08:29,155 - pyskl - INFO - Epoch [53][1300/3746] lr: 7.284e-02, eta: 3 days, 7:16:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5492, loss_cls: 4.0675, loss: 4.0675 +2024-07-23 23:09:50,934 - pyskl - INFO - Epoch [53][1400/3746] lr: 7.282e-02, eta: 3 days, 7:15:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5434, loss_cls: 4.1027, loss: 4.1027 +2024-07-23 23:11:13,396 - pyskl - INFO - Epoch [53][1500/3746] lr: 7.279e-02, eta: 3 days, 7:14:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5456, loss_cls: 4.0963, loss: 4.0963 +2024-07-23 23:12:34,496 - pyskl - INFO - Epoch [53][1600/3746] lr: 7.277e-02, eta: 3 days, 7:13:09, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5300, loss_cls: 4.1148, loss: 4.1148 +2024-07-23 23:13:55,813 - pyskl - INFO - Epoch [53][1700/3746] lr: 7.274e-02, eta: 3 days, 7:11:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5344, loss_cls: 4.1048, loss: 4.1048 +2024-07-23 23:15:17,498 - pyskl - INFO - Epoch [53][1800/3746] lr: 7.272e-02, eta: 3 days, 7:10:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5305, loss_cls: 4.1541, loss: 4.1541 +2024-07-23 23:16:39,164 - pyskl - INFO - Epoch [53][1900/3746] lr: 7.269e-02, eta: 3 days, 7:09:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5406, loss_cls: 4.1036, loss: 4.1036 +2024-07-23 23:18:01,543 - pyskl - INFO - Epoch [53][2000/3746] lr: 7.267e-02, eta: 3 days, 7:08:25, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5436, loss_cls: 4.0774, loss: 4.0774 +2024-07-23 23:19:23,186 - pyskl - INFO - Epoch [53][2100/3746] lr: 7.264e-02, eta: 3 days, 7:07:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5486, loss_cls: 4.0979, loss: 4.0979 +2024-07-23 23:20:44,785 - pyskl - INFO - Epoch [53][2200/3746] lr: 7.262e-02, eta: 3 days, 7:06:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5555, loss_cls: 4.0634, loss: 4.0634 +2024-07-23 23:22:06,291 - pyskl - INFO - Epoch [53][2300/3746] lr: 7.259e-02, eta: 3 days, 7:04:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5491, loss_cls: 4.0654, loss: 4.0654 +2024-07-23 23:23:27,346 - pyskl - INFO - Epoch [53][2400/3746] lr: 7.257e-02, eta: 3 days, 7:03:38, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5400, loss_cls: 4.1139, loss: 4.1139 +2024-07-23 23:24:48,656 - pyskl - INFO - Epoch [53][2500/3746] lr: 7.254e-02, eta: 3 days, 7:02:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5391, loss_cls: 4.0877, loss: 4.0877 +2024-07-23 23:26:10,059 - pyskl - INFO - Epoch [53][2600/3746] lr: 7.252e-02, eta: 3 days, 7:01:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5311, loss_cls: 4.1516, loss: 4.1516 +2024-07-23 23:27:31,418 - pyskl - INFO - Epoch [53][2700/3746] lr: 7.249e-02, eta: 3 days, 7:00:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5477, loss_cls: 4.0784, loss: 4.0784 +2024-07-23 23:28:53,297 - pyskl - INFO - Epoch [53][2800/3746] lr: 7.247e-02, eta: 3 days, 6:58:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5334, loss_cls: 4.1392, loss: 4.1392 +2024-07-23 23:30:14,581 - pyskl - INFO - Epoch [53][2900/3746] lr: 7.244e-02, eta: 3 days, 6:57:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5394, loss_cls: 4.1244, loss: 4.1244 +2024-07-23 23:31:36,382 - pyskl - INFO - Epoch [53][3000/3746] lr: 7.242e-02, eta: 3 days, 6:56:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5420, loss_cls: 4.0817, loss: 4.0817 +2024-07-23 23:32:58,152 - pyskl - INFO - Epoch [53][3100/3746] lr: 7.239e-02, eta: 3 days, 6:55:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5427, loss_cls: 4.0751, loss: 4.0751 +2024-07-23 23:34:19,912 - pyskl - INFO - Epoch [53][3200/3746] lr: 7.237e-02, eta: 3 days, 6:54:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5384, loss_cls: 4.1166, loss: 4.1166 +2024-07-23 23:35:41,586 - pyskl - INFO - Epoch [53][3300/3746] lr: 7.234e-02, eta: 3 days, 6:52:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5353, loss_cls: 4.1335, loss: 4.1335 +2024-07-23 23:37:03,119 - pyskl - INFO - Epoch [53][3400/3746] lr: 7.232e-02, eta: 3 days, 6:51:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5333, loss_cls: 4.1489, loss: 4.1489 +2024-07-23 23:38:24,805 - pyskl - INFO - Epoch [53][3500/3746] lr: 7.229e-02, eta: 3 days, 6:50:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5420, loss_cls: 4.0889, loss: 4.0889 +2024-07-23 23:39:46,227 - pyskl - INFO - Epoch [53][3600/3746] lr: 7.227e-02, eta: 3 days, 6:49:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5452, loss_cls: 4.1163, loss: 4.1163 +2024-07-23 23:41:08,342 - pyskl - INFO - Epoch [53][3700/3746] lr: 7.224e-02, eta: 3 days, 6:48:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5348, loss_cls: 4.1295, loss: 4.1295 +2024-07-23 23:41:48,399 - pyskl - INFO - Saving checkpoint at 53 epochs +2024-07-23 23:43:40,986 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 23:43:41,648 - pyskl - INFO - +top1_acc 0.2092 +top5_acc 0.4512 +2024-07-23 23:43:41,648 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 23:43:41,689 - pyskl - INFO - +mean_acc 0.2090 +2024-07-23 23:43:41,700 - pyskl - INFO - Epoch(val) [53][309] top1_acc: 0.2092, top5_acc: 0.4512, mean_class_accuracy: 0.2090 +2024-07-23 23:47:31,650 - pyskl - INFO - Epoch [54][100/3746] lr: 7.221e-02, eta: 3 days, 6:49:46, time: 2.299, data_time: 1.320, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5616, loss_cls: 4.0016, loss: 4.0016 +2024-07-23 23:48:54,145 - pyskl - INFO - Epoch [54][200/3746] lr: 7.218e-02, eta: 3 days, 6:48:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5525, loss_cls: 4.0556, loss: 4.0556 +2024-07-23 23:50:16,353 - pyskl - INFO - Epoch [54][300/3746] lr: 7.216e-02, eta: 3 days, 6:47:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5455, loss_cls: 4.0868, loss: 4.0868 +2024-07-23 23:51:38,059 - pyskl - INFO - Epoch [54][400/3746] lr: 7.213e-02, eta: 3 days, 6:46:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5406, loss_cls: 4.0937, loss: 4.0937 +2024-07-23 23:52:59,796 - pyskl - INFO - Epoch [54][500/3746] lr: 7.211e-02, eta: 3 days, 6:45:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5563, loss_cls: 4.0329, loss: 4.0329 +2024-07-23 23:54:21,590 - pyskl - INFO - Epoch [54][600/3746] lr: 7.208e-02, eta: 3 days, 6:43:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5425, loss_cls: 4.0667, loss: 4.0667 +2024-07-23 23:55:43,655 - pyskl - INFO - Epoch [54][700/3746] lr: 7.206e-02, eta: 3 days, 6:42:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5544, loss_cls: 4.0244, loss: 4.0244 +2024-07-23 23:57:05,264 - pyskl - INFO - Epoch [54][800/3746] lr: 7.203e-02, eta: 3 days, 6:41:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5225, loss_cls: 4.1456, loss: 4.1456 +2024-07-23 23:58:27,448 - pyskl - INFO - Epoch [54][900/3746] lr: 7.201e-02, eta: 3 days, 6:40:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5437, loss_cls: 4.0862, loss: 4.0862 +2024-07-23 23:59:50,024 - pyskl - INFO - Epoch [54][1000/3746] lr: 7.198e-02, eta: 3 days, 6:39:07, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5464, loss_cls: 4.0667, loss: 4.0667 +2024-07-24 00:01:12,105 - pyskl - INFO - Epoch [54][1100/3746] lr: 7.196e-02, eta: 3 days, 6:37:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5433, loss_cls: 4.1011, loss: 4.1011 +2024-07-24 00:02:33,697 - pyskl - INFO - Epoch [54][1200/3746] lr: 7.193e-02, eta: 3 days, 6:36:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5363, loss_cls: 4.0831, loss: 4.0831 +2024-07-24 00:03:55,081 - pyskl - INFO - Epoch [54][1300/3746] lr: 7.191e-02, eta: 3 days, 6:35:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5359, loss_cls: 4.0859, loss: 4.0859 +2024-07-24 00:05:17,166 - pyskl - INFO - Epoch [54][1400/3746] lr: 7.188e-02, eta: 3 days, 6:34:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5445, loss_cls: 4.0585, loss: 4.0585 +2024-07-24 00:06:39,504 - pyskl - INFO - Epoch [54][1500/3746] lr: 7.186e-02, eta: 3 days, 6:33:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5523, loss_cls: 4.0409, loss: 4.0409 +2024-07-24 00:08:01,300 - pyskl - INFO - Epoch [54][1600/3746] lr: 7.183e-02, eta: 3 days, 6:31:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5422, loss_cls: 4.0857, loss: 4.0857 +2024-07-24 00:09:23,157 - pyskl - INFO - Epoch [54][1700/3746] lr: 7.181e-02, eta: 3 days, 6:30:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5437, loss_cls: 4.1116, loss: 4.1116 +2024-07-24 00:10:45,044 - pyskl - INFO - Epoch [54][1800/3746] lr: 7.178e-02, eta: 3 days, 6:29:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5352, loss_cls: 4.0961, loss: 4.0961 +2024-07-24 00:12:06,594 - pyskl - INFO - Epoch [54][1900/3746] lr: 7.176e-02, eta: 3 days, 6:28:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5372, loss_cls: 4.1078, loss: 4.1078 +2024-07-24 00:13:27,890 - pyskl - INFO - Epoch [54][2000/3746] lr: 7.173e-02, eta: 3 days, 6:27:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5444, loss_cls: 4.0862, loss: 4.0862 +2024-07-24 00:14:49,708 - pyskl - INFO - Epoch [54][2100/3746] lr: 7.170e-02, eta: 3 days, 6:26:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5255, loss_cls: 4.1671, loss: 4.1671 +2024-07-24 00:16:10,880 - pyskl - INFO - Epoch [54][2200/3746] lr: 7.168e-02, eta: 3 days, 6:24:47, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5456, loss_cls: 4.0876, loss: 4.0876 +2024-07-24 00:17:32,144 - pyskl - INFO - Epoch [54][2300/3746] lr: 7.165e-02, eta: 3 days, 6:23:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5456, loss_cls: 4.0895, loss: 4.0895 +2024-07-24 00:18:53,788 - pyskl - INFO - Epoch [54][2400/3746] lr: 7.163e-02, eta: 3 days, 6:22:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5423, loss_cls: 4.1018, loss: 4.1018 +2024-07-24 00:20:15,271 - pyskl - INFO - Epoch [54][2500/3746] lr: 7.160e-02, eta: 3 days, 6:21:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5420, loss_cls: 4.1037, loss: 4.1037 +2024-07-24 00:21:36,866 - pyskl - INFO - Epoch [54][2600/3746] lr: 7.158e-02, eta: 3 days, 6:19:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5391, loss_cls: 4.1660, loss: 4.1660 +2024-07-24 00:22:58,217 - pyskl - INFO - Epoch [54][2700/3746] lr: 7.155e-02, eta: 3 days, 6:18:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5491, loss_cls: 4.0566, loss: 4.0566 +2024-07-24 00:24:19,328 - pyskl - INFO - Epoch [54][2800/3746] lr: 7.153e-02, eta: 3 days, 6:17:33, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5431, loss_cls: 4.0917, loss: 4.0917 +2024-07-24 00:25:40,837 - pyskl - INFO - Epoch [54][2900/3746] lr: 7.150e-02, eta: 3 days, 6:16:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5450, loss_cls: 4.0948, loss: 4.0948 +2024-07-24 00:27:02,492 - pyskl - INFO - Epoch [54][3000/3746] lr: 7.148e-02, eta: 3 days, 6:15:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5445, loss_cls: 4.0886, loss: 4.0886 +2024-07-24 00:28:24,131 - pyskl - INFO - Epoch [54][3100/3746] lr: 7.145e-02, eta: 3 days, 6:13:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5375, loss_cls: 4.1162, loss: 4.1162 +2024-07-24 00:29:45,702 - pyskl - INFO - Epoch [54][3200/3746] lr: 7.143e-02, eta: 3 days, 6:12:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5367, loss_cls: 4.1165, loss: 4.1165 +2024-07-24 00:31:06,925 - pyskl - INFO - Epoch [54][3300/3746] lr: 7.140e-02, eta: 3 days, 6:11:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5417, loss_cls: 4.0778, loss: 4.0778 +2024-07-24 00:32:28,611 - pyskl - INFO - Epoch [54][3400/3746] lr: 7.138e-02, eta: 3 days, 6:10:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5384, loss_cls: 4.1035, loss: 4.1035 +2024-07-24 00:33:49,732 - pyskl - INFO - Epoch [54][3500/3746] lr: 7.135e-02, eta: 3 days, 6:09:07, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5358, loss_cls: 4.1180, loss: 4.1180 +2024-07-24 00:35:11,241 - pyskl - INFO - Epoch [54][3600/3746] lr: 7.133e-02, eta: 3 days, 6:07:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5434, loss_cls: 4.0763, loss: 4.0763 +2024-07-24 00:36:33,207 - pyskl - INFO - Epoch [54][3700/3746] lr: 7.130e-02, eta: 3 days, 6:06:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5403, loss_cls: 4.0834, loss: 4.0834 +2024-07-24 00:37:12,619 - pyskl - INFO - Saving checkpoint at 54 epochs +2024-07-24 00:39:05,409 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 00:39:06,071 - pyskl - INFO - +top1_acc 0.1890 +top5_acc 0.4134 +2024-07-24 00:39:06,072 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 00:39:06,112 - pyskl - INFO - +mean_acc 0.1885 +2024-07-24 00:39:06,123 - pyskl - INFO - Epoch(val) [54][309] top1_acc: 0.1890, top5_acc: 0.4134, mean_class_accuracy: 0.1885 +2024-07-24 00:42:56,354 - pyskl - INFO - Epoch [55][100/3746] lr: 7.126e-02, eta: 3 days, 6:08:16, time: 2.302, data_time: 1.320, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5580, loss_cls: 4.0269, loss: 4.0269 +2024-07-24 00:44:18,704 - pyskl - INFO - Epoch [55][200/3746] lr: 7.124e-02, eta: 3 days, 6:07:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5398, loss_cls: 4.0905, loss: 4.0905 +2024-07-24 00:45:40,924 - pyskl - INFO - Epoch [55][300/3746] lr: 7.121e-02, eta: 3 days, 6:05:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5552, loss_cls: 4.0362, loss: 4.0362 +2024-07-24 00:47:02,941 - pyskl - INFO - Epoch [55][400/3746] lr: 7.119e-02, eta: 3 days, 6:04:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5397, loss_cls: 4.1146, loss: 4.1146 +2024-07-24 00:48:24,667 - pyskl - INFO - Epoch [55][500/3746] lr: 7.116e-02, eta: 3 days, 6:03:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5513, loss_cls: 4.0287, loss: 4.0287 +2024-07-24 00:49:46,495 - pyskl - INFO - Epoch [55][600/3746] lr: 7.114e-02, eta: 3 days, 6:02:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5502, loss_cls: 4.0610, loss: 4.0610 +2024-07-24 00:51:08,375 - pyskl - INFO - Epoch [55][700/3746] lr: 7.111e-02, eta: 3 days, 6:01:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5484, loss_cls: 4.0925, loss: 4.0925 +2024-07-24 00:52:30,850 - pyskl - INFO - Epoch [55][800/3746] lr: 7.109e-02, eta: 3 days, 5:59:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5467, loss_cls: 4.0952, loss: 4.0952 +2024-07-24 00:53:53,013 - pyskl - INFO - Epoch [55][900/3746] lr: 7.106e-02, eta: 3 days, 5:58:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5564, loss_cls: 4.0203, loss: 4.0203 +2024-07-24 00:55:14,918 - pyskl - INFO - Epoch [55][1000/3746] lr: 7.104e-02, eta: 3 days, 5:57:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5498, loss_cls: 4.0599, loss: 4.0599 +2024-07-24 00:56:37,080 - pyskl - INFO - Epoch [55][1100/3746] lr: 7.101e-02, eta: 3 days, 5:56:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5453, loss_cls: 4.0507, loss: 4.0507 +2024-07-24 00:57:59,138 - pyskl - INFO - Epoch [55][1200/3746] lr: 7.099e-02, eta: 3 days, 5:55:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5344, loss_cls: 4.1012, loss: 4.1012 +2024-07-24 00:59:20,423 - pyskl - INFO - Epoch [55][1300/3746] lr: 7.096e-02, eta: 3 days, 5:53:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5511, loss_cls: 4.0293, loss: 4.0293 +2024-07-24 01:00:41,816 - pyskl - INFO - Epoch [55][1400/3746] lr: 7.093e-02, eta: 3 days, 5:52:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5398, loss_cls: 4.0944, loss: 4.0944 +2024-07-24 01:02:03,559 - pyskl - INFO - Epoch [55][1500/3746] lr: 7.091e-02, eta: 3 days, 5:51:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5461, loss_cls: 4.0581, loss: 4.0581 +2024-07-24 01:03:24,941 - pyskl - INFO - Epoch [55][1600/3746] lr: 7.088e-02, eta: 3 days, 5:50:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5411, loss_cls: 4.0890, loss: 4.0890 +2024-07-24 01:04:46,017 - pyskl - INFO - Epoch [55][1700/3746] lr: 7.086e-02, eta: 3 days, 5:49:05, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5395, loss_cls: 4.0688, loss: 4.0688 +2024-07-24 01:06:07,384 - pyskl - INFO - Epoch [55][1800/3746] lr: 7.083e-02, eta: 3 days, 5:47:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5363, loss_cls: 4.1239, loss: 4.1239 +2024-07-24 01:07:28,910 - pyskl - INFO - Epoch [55][1900/3746] lr: 7.081e-02, eta: 3 days, 5:46:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5466, loss_cls: 4.0750, loss: 4.0750 +2024-07-24 01:08:50,991 - pyskl - INFO - Epoch [55][2000/3746] lr: 7.078e-02, eta: 3 days, 5:45:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5481, loss_cls: 4.0522, loss: 4.0522 +2024-07-24 01:10:13,144 - pyskl - INFO - Epoch [55][2100/3746] lr: 7.076e-02, eta: 3 days, 5:44:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5387, loss_cls: 4.0920, loss: 4.0920 +2024-07-24 01:11:34,690 - pyskl - INFO - Epoch [55][2200/3746] lr: 7.073e-02, eta: 3 days, 5:43:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5433, loss_cls: 4.0642, loss: 4.0642 +2024-07-24 01:12:56,295 - pyskl - INFO - Epoch [55][2300/3746] lr: 7.071e-02, eta: 3 days, 5:41:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5442, loss_cls: 4.0700, loss: 4.0700 +2024-07-24 01:14:17,676 - pyskl - INFO - Epoch [55][2400/3746] lr: 7.068e-02, eta: 3 days, 5:40:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5373, loss_cls: 4.0782, loss: 4.0782 +2024-07-24 01:15:39,353 - pyskl - INFO - Epoch [55][2500/3746] lr: 7.065e-02, eta: 3 days, 5:39:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5392, loss_cls: 4.1255, loss: 4.1255 +2024-07-24 01:17:00,783 - pyskl - INFO - Epoch [55][2600/3746] lr: 7.063e-02, eta: 3 days, 5:38:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5389, loss_cls: 4.0965, loss: 4.0965 +2024-07-24 01:18:22,171 - pyskl - INFO - Epoch [55][2700/3746] lr: 7.060e-02, eta: 3 days, 5:37:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5563, loss_cls: 4.0355, loss: 4.0355 +2024-07-24 01:19:44,092 - pyskl - INFO - Epoch [55][2800/3746] lr: 7.058e-02, eta: 3 days, 5:35:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5539, loss_cls: 4.0939, loss: 4.0939 +2024-07-24 01:21:05,605 - pyskl - INFO - Epoch [55][2900/3746] lr: 7.055e-02, eta: 3 days, 5:34:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5427, loss_cls: 4.0584, loss: 4.0584 +2024-07-24 01:22:26,989 - pyskl - INFO - Epoch [55][3000/3746] lr: 7.053e-02, eta: 3 days, 5:33:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5408, loss_cls: 4.0976, loss: 4.0976 +2024-07-24 01:23:48,452 - pyskl - INFO - Epoch [55][3100/3746] lr: 7.050e-02, eta: 3 days, 5:32:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5377, loss_cls: 4.1002, loss: 4.1002 +2024-07-24 01:25:09,937 - pyskl - INFO - Epoch [55][3200/3746] lr: 7.048e-02, eta: 3 days, 5:30:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5391, loss_cls: 4.1150, loss: 4.1150 +2024-07-24 01:26:31,440 - pyskl - INFO - Epoch [55][3300/3746] lr: 7.045e-02, eta: 3 days, 5:29:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5434, loss_cls: 4.0769, loss: 4.0769 +2024-07-24 01:27:53,298 - pyskl - INFO - Epoch [55][3400/3746] lr: 7.043e-02, eta: 3 days, 5:28:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5247, loss_cls: 4.1355, loss: 4.1355 +2024-07-24 01:29:14,769 - pyskl - INFO - Epoch [55][3500/3746] lr: 7.040e-02, eta: 3 days, 5:27:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5431, loss_cls: 4.1268, loss: 4.1268 +2024-07-24 01:30:36,630 - pyskl - INFO - Epoch [55][3600/3746] lr: 7.037e-02, eta: 3 days, 5:26:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5320, loss_cls: 4.1153, loss: 4.1153 +2024-07-24 01:31:58,315 - pyskl - INFO - Epoch [55][3700/3746] lr: 7.035e-02, eta: 3 days, 5:24:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5447, loss_cls: 4.0723, loss: 4.0723 +2024-07-24 01:32:38,075 - pyskl - INFO - Saving checkpoint at 55 epochs +2024-07-24 01:34:30,896 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 01:34:31,573 - pyskl - INFO - +top1_acc 0.1953 +top5_acc 0.4312 +2024-07-24 01:34:31,574 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 01:34:31,616 - pyskl - INFO - +mean_acc 0.1952 +2024-07-24 01:34:31,630 - pyskl - INFO - Epoch(val) [55][309] top1_acc: 0.1953, top5_acc: 0.4312, mean_class_accuracy: 0.1952 +2024-07-24 01:38:21,241 - pyskl - INFO - Epoch [56][100/3746] lr: 7.031e-02, eta: 3 days, 5:26:20, time: 2.296, data_time: 1.313, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5503, loss_cls: 4.0359, loss: 4.0359 +2024-07-24 01:39:43,675 - pyskl - INFO - Epoch [56][200/3746] lr: 7.029e-02, eta: 3 days, 5:25:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5623, loss_cls: 4.0025, loss: 4.0025 +2024-07-24 01:41:05,606 - pyskl - INFO - Epoch [56][300/3746] lr: 7.026e-02, eta: 3 days, 5:23:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5455, loss_cls: 4.0703, loss: 4.0703 +2024-07-24 01:42:28,104 - pyskl - INFO - Epoch [56][400/3746] lr: 7.023e-02, eta: 3 days, 5:22:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5523, loss_cls: 4.0545, loss: 4.0545 +2024-07-24 01:43:50,109 - pyskl - INFO - Epoch [56][500/3746] lr: 7.021e-02, eta: 3 days, 5:21:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5331, loss_cls: 4.1293, loss: 4.1293 +2024-07-24 01:45:12,031 - pyskl - INFO - Epoch [56][600/3746] lr: 7.018e-02, eta: 3 days, 5:20:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5434, loss_cls: 4.0784, loss: 4.0784 +2024-07-24 01:46:34,276 - pyskl - INFO - Epoch [56][700/3746] lr: 7.016e-02, eta: 3 days, 5:19:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5489, loss_cls: 4.0533, loss: 4.0533 +2024-07-24 01:47:56,207 - pyskl - INFO - Epoch [56][800/3746] lr: 7.013e-02, eta: 3 days, 5:17:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5447, loss_cls: 4.0665, loss: 4.0665 +2024-07-24 01:49:18,126 - pyskl - INFO - Epoch [56][900/3746] lr: 7.011e-02, eta: 3 days, 5:16:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5494, loss_cls: 4.0312, loss: 4.0312 +2024-07-24 01:50:41,015 - pyskl - INFO - Epoch [56][1000/3746] lr: 7.008e-02, eta: 3 days, 5:15:34, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5402, loss_cls: 4.1013, loss: 4.1013 +2024-07-24 01:52:03,414 - pyskl - INFO - Epoch [56][1100/3746] lr: 7.006e-02, eta: 3 days, 5:14:22, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5405, loss_cls: 4.0571, loss: 4.0571 +2024-07-24 01:53:25,649 - pyskl - INFO - Epoch [56][1200/3746] lr: 7.003e-02, eta: 3 days, 5:13:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5464, loss_cls: 4.0802, loss: 4.0802 +2024-07-24 01:54:47,304 - pyskl - INFO - Epoch [56][1300/3746] lr: 7.000e-02, eta: 3 days, 5:11:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5527, loss_cls: 4.0459, loss: 4.0459 +2024-07-24 01:56:09,212 - pyskl - INFO - Epoch [56][1400/3746] lr: 6.998e-02, eta: 3 days, 5:10:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5427, loss_cls: 4.0897, loss: 4.0897 +2024-07-24 01:57:31,150 - pyskl - INFO - Epoch [56][1500/3746] lr: 6.995e-02, eta: 3 days, 5:09:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5445, loss_cls: 4.0921, loss: 4.0921 +2024-07-24 01:58:52,774 - pyskl - INFO - Epoch [56][1600/3746] lr: 6.993e-02, eta: 3 days, 5:08:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5367, loss_cls: 4.1138, loss: 4.1138 +2024-07-24 02:00:14,093 - pyskl - INFO - Epoch [56][1700/3746] lr: 6.990e-02, eta: 3 days, 5:07:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5377, loss_cls: 4.0761, loss: 4.0761 +2024-07-24 02:01:35,136 - pyskl - INFO - Epoch [56][1800/3746] lr: 6.988e-02, eta: 3 days, 5:05:53, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5463, loss_cls: 4.0834, loss: 4.0834 +2024-07-24 02:02:56,818 - pyskl - INFO - Epoch [56][1900/3746] lr: 6.985e-02, eta: 3 days, 5:04:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5530, loss_cls: 4.0348, loss: 4.0348 +2024-07-24 02:04:18,735 - pyskl - INFO - Epoch [56][2000/3746] lr: 6.983e-02, eta: 3 days, 5:03:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5392, loss_cls: 4.1198, loss: 4.1198 +2024-07-24 02:05:40,303 - pyskl - INFO - Epoch [56][2100/3746] lr: 6.980e-02, eta: 3 days, 5:02:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5411, loss_cls: 4.1010, loss: 4.1010 +2024-07-24 02:07:02,350 - pyskl - INFO - Epoch [56][2200/3746] lr: 6.977e-02, eta: 3 days, 5:01:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5502, loss_cls: 4.0641, loss: 4.0641 +2024-07-24 02:08:23,570 - pyskl - INFO - Epoch [56][2300/3746] lr: 6.975e-02, eta: 3 days, 4:59:49, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5417, loss_cls: 4.0757, loss: 4.0757 +2024-07-24 02:09:44,817 - pyskl - INFO - Epoch [56][2400/3746] lr: 6.972e-02, eta: 3 days, 4:58:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5417, loss_cls: 4.0774, loss: 4.0774 +2024-07-24 02:11:06,445 - pyskl - INFO - Epoch [56][2500/3746] lr: 6.970e-02, eta: 3 days, 4:57:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5511, loss_cls: 4.0493, loss: 4.0493 +2024-07-24 02:12:27,696 - pyskl - INFO - Epoch [56][2600/3746] lr: 6.967e-02, eta: 3 days, 4:56:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5367, loss_cls: 4.0982, loss: 4.0982 +2024-07-24 02:13:48,915 - pyskl - INFO - Epoch [56][2700/3746] lr: 6.965e-02, eta: 3 days, 4:54:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5467, loss_cls: 4.0704, loss: 4.0704 +2024-07-24 02:15:10,521 - pyskl - INFO - Epoch [56][2800/3746] lr: 6.962e-02, eta: 3 days, 4:53:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5503, loss_cls: 4.0125, loss: 4.0125 +2024-07-24 02:16:32,405 - pyskl - INFO - Epoch [56][2900/3746] lr: 6.959e-02, eta: 3 days, 4:52:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5437, loss_cls: 4.0905, loss: 4.0905 +2024-07-24 02:17:54,183 - pyskl - INFO - Epoch [56][3000/3746] lr: 6.957e-02, eta: 3 days, 4:51:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5463, loss_cls: 4.0681, loss: 4.0681 +2024-07-24 02:19:16,166 - pyskl - INFO - Epoch [56][3100/3746] lr: 6.954e-02, eta: 3 days, 4:50:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5441, loss_cls: 4.0545, loss: 4.0545 +2024-07-24 02:20:37,647 - pyskl - INFO - Epoch [56][3200/3746] lr: 6.952e-02, eta: 3 days, 4:48:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5514, loss_cls: 4.0423, loss: 4.0423 +2024-07-24 02:21:59,033 - pyskl - INFO - Epoch [56][3300/3746] lr: 6.949e-02, eta: 3 days, 4:47:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5545, loss_cls: 4.0524, loss: 4.0524 +2024-07-24 02:23:20,888 - pyskl - INFO - Epoch [56][3400/3746] lr: 6.947e-02, eta: 3 days, 4:46:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5369, loss_cls: 4.1154, loss: 4.1154 +2024-07-24 02:24:42,798 - pyskl - INFO - Epoch [56][3500/3746] lr: 6.944e-02, eta: 3 days, 4:45:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5553, loss_cls: 4.0503, loss: 4.0503 +2024-07-24 02:26:04,721 - pyskl - INFO - Epoch [56][3600/3746] lr: 6.941e-02, eta: 3 days, 4:44:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5350, loss_cls: 4.1029, loss: 4.1029 +2024-07-24 02:27:26,263 - pyskl - INFO - Epoch [56][3700/3746] lr: 6.939e-02, eta: 3 days, 4:42:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5328, loss_cls: 4.1270, loss: 4.1270 +2024-07-24 02:28:05,645 - pyskl - INFO - Saving checkpoint at 56 epochs +2024-07-24 02:29:58,298 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 02:29:58,960 - pyskl - INFO - +top1_acc 0.2083 +top5_acc 0.4438 +2024-07-24 02:29:58,960 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 02:29:59,002 - pyskl - INFO - +mean_acc 0.2082 +2024-07-24 02:29:59,014 - pyskl - INFO - Epoch(val) [56][309] top1_acc: 0.2083, top5_acc: 0.4438, mean_class_accuracy: 0.2082 +2024-07-24 02:33:46,798 - pyskl - INFO - Epoch [57][100/3746] lr: 6.935e-02, eta: 3 days, 4:44:03, time: 2.278, data_time: 1.300, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5663, loss_cls: 3.9183, loss: 3.9183 +2024-07-24 02:35:09,130 - pyskl - INFO - Epoch [57][200/3746] lr: 6.932e-02, eta: 3 days, 4:42:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5483, loss_cls: 4.0403, loss: 4.0403 +2024-07-24 02:36:30,846 - pyskl - INFO - Epoch [57][300/3746] lr: 6.930e-02, eta: 3 days, 4:41:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5402, loss_cls: 4.1250, loss: 4.1250 +2024-07-24 02:37:53,112 - pyskl - INFO - Epoch [57][400/3746] lr: 6.927e-02, eta: 3 days, 4:40:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5445, loss_cls: 4.0439, loss: 4.0439 +2024-07-24 02:39:14,924 - pyskl - INFO - Epoch [57][500/3746] lr: 6.925e-02, eta: 3 days, 4:39:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5489, loss_cls: 4.0752, loss: 4.0752 +2024-07-24 02:40:37,145 - pyskl - INFO - Epoch [57][600/3746] lr: 6.922e-02, eta: 3 days, 4:38:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5502, loss_cls: 4.0280, loss: 4.0280 +2024-07-24 02:41:58,687 - pyskl - INFO - Epoch [57][700/3746] lr: 6.920e-02, eta: 3 days, 4:36:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5441, loss_cls: 4.0848, loss: 4.0848 +2024-07-24 02:43:20,844 - pyskl - INFO - Epoch [57][800/3746] lr: 6.917e-02, eta: 3 days, 4:35:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5423, loss_cls: 4.0471, loss: 4.0471 +2024-07-24 02:44:42,942 - pyskl - INFO - Epoch [57][900/3746] lr: 6.914e-02, eta: 3 days, 4:34:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5452, loss_cls: 4.0564, loss: 4.0564 +2024-07-24 02:46:05,662 - pyskl - INFO - Epoch [57][1000/3746] lr: 6.912e-02, eta: 3 days, 4:33:11, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5578, loss_cls: 4.0379, loss: 4.0379 +2024-07-24 02:47:27,980 - pyskl - INFO - Epoch [57][1100/3746] lr: 6.909e-02, eta: 3 days, 4:31:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5508, loss_cls: 4.0165, loss: 4.0165 +2024-07-24 02:48:49,898 - pyskl - INFO - Epoch [57][1200/3746] lr: 6.907e-02, eta: 3 days, 4:30:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5495, loss_cls: 4.0575, loss: 4.0575 +2024-07-24 02:50:11,457 - pyskl - INFO - Epoch [57][1300/3746] lr: 6.904e-02, eta: 3 days, 4:29:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5419, loss_cls: 4.0733, loss: 4.0733 +2024-07-24 02:51:33,208 - pyskl - INFO - Epoch [57][1400/3746] lr: 6.901e-02, eta: 3 days, 4:28:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5561, loss_cls: 4.0618, loss: 4.0618 +2024-07-24 02:52:55,181 - pyskl - INFO - Epoch [57][1500/3746] lr: 6.899e-02, eta: 3 days, 4:27:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5566, loss_cls: 4.0375, loss: 4.0375 +2024-07-24 02:54:16,704 - pyskl - INFO - Epoch [57][1600/3746] lr: 6.896e-02, eta: 3 days, 4:25:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5581, loss_cls: 4.0627, loss: 4.0627 +2024-07-24 02:55:38,318 - pyskl - INFO - Epoch [57][1700/3746] lr: 6.894e-02, eta: 3 days, 4:24:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5400, loss_cls: 4.0635, loss: 4.0635 +2024-07-24 02:56:59,518 - pyskl - INFO - Epoch [57][1800/3746] lr: 6.891e-02, eta: 3 days, 4:23:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5478, loss_cls: 4.0500, loss: 4.0500 +2024-07-24 02:58:21,082 - pyskl - INFO - Epoch [57][1900/3746] lr: 6.889e-02, eta: 3 days, 4:22:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5345, loss_cls: 4.0715, loss: 4.0715 +2024-07-24 02:59:42,526 - pyskl - INFO - Epoch [57][2000/3746] lr: 6.886e-02, eta: 3 days, 4:20:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5523, loss_cls: 4.0607, loss: 4.0607 +2024-07-24 03:01:03,711 - pyskl - INFO - Epoch [57][2100/3746] lr: 6.883e-02, eta: 3 days, 4:19:45, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5400, loss_cls: 4.0900, loss: 4.0900 +2024-07-24 03:02:25,318 - pyskl - INFO - Epoch [57][2200/3746] lr: 6.881e-02, eta: 3 days, 4:18:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5483, loss_cls: 4.0935, loss: 4.0935 +2024-07-24 03:03:47,145 - pyskl - INFO - Epoch [57][2300/3746] lr: 6.878e-02, eta: 3 days, 4:17:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5441, loss_cls: 4.0649, loss: 4.0649 +2024-07-24 03:05:09,293 - pyskl - INFO - Epoch [57][2400/3746] lr: 6.876e-02, eta: 3 days, 4:16:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5455, loss_cls: 4.0834, loss: 4.0834 +2024-07-24 03:06:31,012 - pyskl - INFO - Epoch [57][2500/3746] lr: 6.873e-02, eta: 3 days, 4:14:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5541, loss_cls: 4.0558, loss: 4.0558 +2024-07-24 03:07:52,553 - pyskl - INFO - Epoch [57][2600/3746] lr: 6.870e-02, eta: 3 days, 4:13:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5397, loss_cls: 4.0854, loss: 4.0854 +2024-07-24 03:09:13,913 - pyskl - INFO - Epoch [57][2700/3746] lr: 6.868e-02, eta: 3 days, 4:12:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5409, loss_cls: 4.0875, loss: 4.0875 +2024-07-24 03:10:35,379 - pyskl - INFO - Epoch [57][2800/3746] lr: 6.865e-02, eta: 3 days, 4:11:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5497, loss_cls: 4.0450, loss: 4.0450 +2024-07-24 03:11:56,908 - pyskl - INFO - Epoch [57][2900/3746] lr: 6.863e-02, eta: 3 days, 4:09:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5398, loss_cls: 4.0926, loss: 4.0926 +2024-07-24 03:13:18,356 - pyskl - INFO - Epoch [57][3000/3746] lr: 6.860e-02, eta: 3 days, 4:08:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5573, loss_cls: 4.0503, loss: 4.0503 +2024-07-24 03:14:40,042 - pyskl - INFO - Epoch [57][3100/3746] lr: 6.857e-02, eta: 3 days, 4:07:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5516, loss_cls: 4.0445, loss: 4.0445 +2024-07-24 03:16:01,470 - pyskl - INFO - Epoch [57][3200/3746] lr: 6.855e-02, eta: 3 days, 4:06:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5298, loss_cls: 4.1351, loss: 4.1351 +2024-07-24 03:17:22,955 - pyskl - INFO - Epoch [57][3300/3746] lr: 6.852e-02, eta: 3 days, 4:05:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5511, loss_cls: 4.0655, loss: 4.0655 +2024-07-24 03:18:44,555 - pyskl - INFO - Epoch [57][3400/3746] lr: 6.850e-02, eta: 3 days, 4:03:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5442, loss_cls: 4.0702, loss: 4.0702 +2024-07-24 03:20:06,346 - pyskl - INFO - Epoch [57][3500/3746] lr: 6.847e-02, eta: 3 days, 4:02:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5363, loss_cls: 4.1170, loss: 4.1170 +2024-07-24 03:21:27,830 - pyskl - INFO - Epoch [57][3600/3746] lr: 6.844e-02, eta: 3 days, 4:01:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5359, loss_cls: 4.0870, loss: 4.0870 +2024-07-24 03:22:49,196 - pyskl - INFO - Epoch [57][3700/3746] lr: 6.842e-02, eta: 3 days, 4:00:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5522, loss_cls: 4.0505, loss: 4.0505 +2024-07-24 03:23:29,049 - pyskl - INFO - Saving checkpoint at 57 epochs +2024-07-24 03:25:20,773 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 03:25:21,429 - pyskl - INFO - +top1_acc 0.2137 +top5_acc 0.4443 +2024-07-24 03:25:21,429 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 03:25:21,468 - pyskl - INFO - +mean_acc 0.2135 +2024-07-24 03:25:21,479 - pyskl - INFO - Epoch(val) [57][309] top1_acc: 0.2137, top5_acc: 0.4443, mean_class_accuracy: 0.2135 +2024-07-24 03:29:10,158 - pyskl - INFO - Epoch [58][100/3746] lr: 6.838e-02, eta: 3 days, 4:01:21, time: 2.287, data_time: 1.310, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5603, loss_cls: 3.9893, loss: 3.9893 +2024-07-24 03:30:32,362 - pyskl - INFO - Epoch [58][200/3746] lr: 6.835e-02, eta: 3 days, 4:00:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5478, loss_cls: 4.0342, loss: 4.0342 +2024-07-24 03:31:54,152 - pyskl - INFO - Epoch [58][300/3746] lr: 6.833e-02, eta: 3 days, 3:58:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5487, loss_cls: 4.0677, loss: 4.0677 +2024-07-24 03:33:16,492 - pyskl - INFO - Epoch [58][400/3746] lr: 6.830e-02, eta: 3 days, 3:57:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5534, loss_cls: 4.0339, loss: 4.0339 +2024-07-24 03:34:37,976 - pyskl - INFO - Epoch [58][500/3746] lr: 6.828e-02, eta: 3 days, 3:56:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5541, loss_cls: 4.0341, loss: 4.0341 +2024-07-24 03:36:00,237 - pyskl - INFO - Epoch [58][600/3746] lr: 6.825e-02, eta: 3 days, 3:55:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5545, loss_cls: 4.0300, loss: 4.0300 +2024-07-24 03:37:22,110 - pyskl - INFO - Epoch [58][700/3746] lr: 6.822e-02, eta: 3 days, 3:54:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5472, loss_cls: 4.0629, loss: 4.0629 +2024-07-24 03:38:44,221 - pyskl - INFO - Epoch [58][800/3746] lr: 6.820e-02, eta: 3 days, 3:52:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5514, loss_cls: 4.0284, loss: 4.0284 +2024-07-24 03:40:06,047 - pyskl - INFO - Epoch [58][900/3746] lr: 6.817e-02, eta: 3 days, 3:51:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5441, loss_cls: 4.0866, loss: 4.0866 +2024-07-24 03:41:28,634 - pyskl - INFO - Epoch [58][1000/3746] lr: 6.815e-02, eta: 3 days, 3:50:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5473, loss_cls: 4.0678, loss: 4.0678 +2024-07-24 03:42:50,706 - pyskl - INFO - Epoch [58][1100/3746] lr: 6.812e-02, eta: 3 days, 3:49:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5416, loss_cls: 4.0973, loss: 4.0973 +2024-07-24 03:44:12,618 - pyskl - INFO - Epoch [58][1200/3746] lr: 6.809e-02, eta: 3 days, 3:47:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5459, loss_cls: 4.0442, loss: 4.0442 +2024-07-24 03:45:34,398 - pyskl - INFO - Epoch [58][1300/3746] lr: 6.807e-02, eta: 3 days, 3:46:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5487, loss_cls: 4.0456, loss: 4.0456 +2024-07-24 03:46:55,800 - pyskl - INFO - Epoch [58][1400/3746] lr: 6.804e-02, eta: 3 days, 3:45:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5533, loss_cls: 4.0259, loss: 4.0259 +2024-07-24 03:48:17,473 - pyskl - INFO - Epoch [58][1500/3746] lr: 6.802e-02, eta: 3 days, 3:44:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5516, loss_cls: 4.0460, loss: 4.0460 +2024-07-24 03:49:38,828 - pyskl - INFO - Epoch [58][1600/3746] lr: 6.799e-02, eta: 3 days, 3:43:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5436, loss_cls: 4.0713, loss: 4.0713 +2024-07-24 03:51:00,744 - pyskl - INFO - Epoch [58][1700/3746] lr: 6.796e-02, eta: 3 days, 3:41:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5550, loss_cls: 3.9778, loss: 3.9778 +2024-07-24 03:52:22,714 - pyskl - INFO - Epoch [58][1800/3746] lr: 6.794e-02, eta: 3 days, 3:40:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5509, loss_cls: 4.0656, loss: 4.0656 +2024-07-24 03:53:44,546 - pyskl - INFO - Epoch [58][1900/3746] lr: 6.791e-02, eta: 3 days, 3:39:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5461, loss_cls: 4.0619, loss: 4.0619 +2024-07-24 03:55:06,285 - pyskl - INFO - Epoch [58][2000/3746] lr: 6.789e-02, eta: 3 days, 3:38:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5389, loss_cls: 4.0856, loss: 4.0856 +2024-07-24 03:56:27,690 - pyskl - INFO - Epoch [58][2100/3746] lr: 6.786e-02, eta: 3 days, 3:36:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5444, loss_cls: 4.0601, loss: 4.0601 +2024-07-24 03:57:49,351 - pyskl - INFO - Epoch [58][2200/3746] lr: 6.783e-02, eta: 3 days, 3:35:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5444, loss_cls: 4.0696, loss: 4.0696 +2024-07-24 03:59:10,891 - pyskl - INFO - Epoch [58][2300/3746] lr: 6.781e-02, eta: 3 days, 3:34:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5377, loss_cls: 4.1115, loss: 4.1115 +2024-07-24 04:00:32,786 - pyskl - INFO - Epoch [58][2400/3746] lr: 6.778e-02, eta: 3 days, 3:33:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5563, loss_cls: 4.0687, loss: 4.0687 +2024-07-24 04:01:54,556 - pyskl - INFO - Epoch [58][2500/3746] lr: 6.775e-02, eta: 3 days, 3:32:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5475, loss_cls: 4.0466, loss: 4.0466 +2024-07-24 04:03:15,628 - pyskl - INFO - Epoch [58][2600/3746] lr: 6.773e-02, eta: 3 days, 3:30:45, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5475, loss_cls: 4.0595, loss: 4.0595 +2024-07-24 04:04:36,765 - pyskl - INFO - Epoch [58][2700/3746] lr: 6.770e-02, eta: 3 days, 3:29:31, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5548, loss_cls: 4.0223, loss: 4.0223 +2024-07-24 04:05:58,396 - pyskl - INFO - Epoch [58][2800/3746] lr: 6.768e-02, eta: 3 days, 3:28:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5559, loss_cls: 4.0508, loss: 4.0508 +2024-07-24 04:07:19,737 - pyskl - INFO - Epoch [58][2900/3746] lr: 6.765e-02, eta: 3 days, 3:27:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5433, loss_cls: 4.0332, loss: 4.0332 +2024-07-24 04:08:41,479 - pyskl - INFO - Epoch [58][3000/3746] lr: 6.762e-02, eta: 3 days, 3:25:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5530, loss_cls: 4.0530, loss: 4.0530 +2024-07-24 04:10:03,132 - pyskl - INFO - Epoch [58][3100/3746] lr: 6.760e-02, eta: 3 days, 3:24:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5452, loss_cls: 4.0441, loss: 4.0441 +2024-07-24 04:11:24,555 - pyskl - INFO - Epoch [58][3200/3746] lr: 6.757e-02, eta: 3 days, 3:23:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5441, loss_cls: 4.0628, loss: 4.0628 +2024-07-24 04:12:45,959 - pyskl - INFO - Epoch [58][3300/3746] lr: 6.755e-02, eta: 3 days, 3:22:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5378, loss_cls: 4.1075, loss: 4.1075 +2024-07-24 04:14:07,315 - pyskl - INFO - Epoch [58][3400/3746] lr: 6.752e-02, eta: 3 days, 3:20:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5581, loss_cls: 4.0229, loss: 4.0229 +2024-07-24 04:15:28,689 - pyskl - INFO - Epoch [58][3500/3746] lr: 6.749e-02, eta: 3 days, 3:19:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5405, loss_cls: 4.0551, loss: 4.0551 +2024-07-24 04:16:50,856 - pyskl - INFO - Epoch [58][3600/3746] lr: 6.747e-02, eta: 3 days, 3:18:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5456, loss_cls: 4.0650, loss: 4.0650 +2024-07-24 04:18:12,145 - pyskl - INFO - Epoch [58][3700/3746] lr: 6.744e-02, eta: 3 days, 3:17:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5489, loss_cls: 4.0203, loss: 4.0203 +2024-07-24 04:18:51,635 - pyskl - INFO - Saving checkpoint at 58 epochs +2024-07-24 04:20:42,398 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 04:20:43,068 - pyskl - INFO - +top1_acc 0.2183 +top5_acc 0.4489 +2024-07-24 04:20:43,068 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 04:20:43,110 - pyskl - INFO - +mean_acc 0.2181 +2024-07-24 04:20:43,122 - pyskl - INFO - Epoch(val) [58][309] top1_acc: 0.2183, top5_acc: 0.4489, mean_class_accuracy: 0.2181 +2024-07-24 04:24:29,284 - pyskl - INFO - Epoch [59][100/3746] lr: 6.740e-02, eta: 3 days, 3:18:13, time: 2.262, data_time: 1.283, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5539, loss_cls: 4.0137, loss: 4.0137 +2024-07-24 04:25:50,982 - pyskl - INFO - Epoch [59][200/3746] lr: 6.738e-02, eta: 3 days, 3:16:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5637, loss_cls: 3.9950, loss: 3.9950 +2024-07-24 04:27:13,553 - pyskl - INFO - Epoch [59][300/3746] lr: 6.735e-02, eta: 3 days, 3:15:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5413, loss_cls: 4.0830, loss: 4.0830 +2024-07-24 04:28:36,462 - pyskl - INFO - Epoch [59][400/3746] lr: 6.732e-02, eta: 3 days, 3:14:34, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5609, loss_cls: 3.9735, loss: 3.9735 +2024-07-24 04:29:58,139 - pyskl - INFO - Epoch [59][500/3746] lr: 6.730e-02, eta: 3 days, 3:13:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5403, loss_cls: 4.0767, loss: 4.0767 +2024-07-24 04:31:19,967 - pyskl - INFO - Epoch [59][600/3746] lr: 6.727e-02, eta: 3 days, 3:12:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5505, loss_cls: 4.0731, loss: 4.0731 +2024-07-24 04:32:41,848 - pyskl - INFO - Epoch [59][700/3746] lr: 6.725e-02, eta: 3 days, 3:10:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5487, loss_cls: 4.0295, loss: 4.0295 +2024-07-24 04:34:03,787 - pyskl - INFO - Epoch [59][800/3746] lr: 6.722e-02, eta: 3 days, 3:09:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5442, loss_cls: 4.0742, loss: 4.0742 +2024-07-24 04:35:26,318 - pyskl - INFO - Epoch [59][900/3746] lr: 6.719e-02, eta: 3 days, 3:08:27, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5556, loss_cls: 4.0253, loss: 4.0253 +2024-07-24 04:36:47,589 - pyskl - INFO - Epoch [59][1000/3746] lr: 6.717e-02, eta: 3 days, 3:07:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5556, loss_cls: 4.0038, loss: 4.0038 +2024-07-24 04:38:10,194 - pyskl - INFO - Epoch [59][1100/3746] lr: 6.714e-02, eta: 3 days, 3:05:59, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5583, loss_cls: 4.0025, loss: 4.0025 +2024-07-24 04:39:31,852 - pyskl - INFO - Epoch [59][1200/3746] lr: 6.711e-02, eta: 3 days, 3:04:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5491, loss_cls: 4.0682, loss: 4.0682 +2024-07-24 04:40:53,695 - pyskl - INFO - Epoch [59][1300/3746] lr: 6.709e-02, eta: 3 days, 3:03:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5469, loss_cls: 4.0720, loss: 4.0720 +2024-07-24 04:42:15,498 - pyskl - INFO - Epoch [59][1400/3746] lr: 6.706e-02, eta: 3 days, 3:02:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5578, loss_cls: 4.0122, loss: 4.0122 +2024-07-24 04:43:36,710 - pyskl - INFO - Epoch [59][1500/3746] lr: 6.704e-02, eta: 3 days, 3:01:03, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5445, loss_cls: 4.0869, loss: 4.0869 +2024-07-24 04:44:58,214 - pyskl - INFO - Epoch [59][1600/3746] lr: 6.701e-02, eta: 3 days, 2:59:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5384, loss_cls: 4.0879, loss: 4.0879 +2024-07-24 04:46:19,659 - pyskl - INFO - Epoch [59][1700/3746] lr: 6.698e-02, eta: 3 days, 2:58:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5511, loss_cls: 4.0397, loss: 4.0397 +2024-07-24 04:47:41,004 - pyskl - INFO - Epoch [59][1800/3746] lr: 6.696e-02, eta: 3 days, 2:57:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5509, loss_cls: 4.0470, loss: 4.0470 +2024-07-24 04:49:02,918 - pyskl - INFO - Epoch [59][1900/3746] lr: 6.693e-02, eta: 3 days, 2:56:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5539, loss_cls: 4.0235, loss: 4.0235 +2024-07-24 04:50:24,119 - pyskl - INFO - Epoch [59][2000/3746] lr: 6.690e-02, eta: 3 days, 2:54:51, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5356, loss_cls: 4.1049, loss: 4.1049 +2024-07-24 04:51:46,328 - pyskl - INFO - Epoch [59][2100/3746] lr: 6.688e-02, eta: 3 days, 2:53:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5492, loss_cls: 4.0468, loss: 4.0468 +2024-07-24 04:53:08,101 - pyskl - INFO - Epoch [59][2200/3746] lr: 6.685e-02, eta: 3 days, 2:52:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5570, loss_cls: 4.0177, loss: 4.0177 +2024-07-24 04:54:29,942 - pyskl - INFO - Epoch [59][2300/3746] lr: 6.682e-02, eta: 3 days, 2:51:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5481, loss_cls: 4.0746, loss: 4.0746 +2024-07-24 04:55:51,430 - pyskl - INFO - Epoch [59][2400/3746] lr: 6.680e-02, eta: 3 days, 2:49:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5431, loss_cls: 4.0439, loss: 4.0439 +2024-07-24 04:57:12,598 - pyskl - INFO - Epoch [59][2500/3746] lr: 6.677e-02, eta: 3 days, 2:48:41, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5544, loss_cls: 4.0088, loss: 4.0088 +2024-07-24 04:58:34,242 - pyskl - INFO - Epoch [59][2600/3746] lr: 6.675e-02, eta: 3 days, 2:47:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5502, loss_cls: 4.0604, loss: 4.0604 +2024-07-24 04:59:55,783 - pyskl - INFO - Epoch [59][2700/3746] lr: 6.672e-02, eta: 3 days, 2:46:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5552, loss_cls: 4.0234, loss: 4.0234 +2024-07-24 05:01:17,162 - pyskl - INFO - Epoch [59][2800/3746] lr: 6.669e-02, eta: 3 days, 2:44:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5431, loss_cls: 4.0780, loss: 4.0780 +2024-07-24 05:02:38,296 - pyskl - INFO - Epoch [59][2900/3746] lr: 6.667e-02, eta: 3 days, 2:43:43, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5466, loss_cls: 4.0683, loss: 4.0683 +2024-07-24 05:03:59,407 - pyskl - INFO - Epoch [59][3000/3746] lr: 6.664e-02, eta: 3 days, 2:42:28, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5492, loss_cls: 4.0486, loss: 4.0486 +2024-07-24 05:05:21,055 - pyskl - INFO - Epoch [59][3100/3746] lr: 6.661e-02, eta: 3 days, 2:41:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5583, loss_cls: 4.0103, loss: 4.0103 +2024-07-24 05:06:42,255 - pyskl - INFO - Epoch [59][3200/3746] lr: 6.659e-02, eta: 3 days, 2:39:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5375, loss_cls: 4.1126, loss: 4.1126 +2024-07-24 05:08:03,861 - pyskl - INFO - Epoch [59][3300/3746] lr: 6.656e-02, eta: 3 days, 2:38:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5469, loss_cls: 4.0535, loss: 4.0535 +2024-07-24 05:09:25,582 - pyskl - INFO - Epoch [59][3400/3746] lr: 6.653e-02, eta: 3 days, 2:37:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5506, loss_cls: 4.0472, loss: 4.0472 +2024-07-24 05:10:46,943 - pyskl - INFO - Epoch [59][3500/3746] lr: 6.651e-02, eta: 3 days, 2:36:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5519, loss_cls: 4.0294, loss: 4.0294 +2024-07-24 05:12:08,723 - pyskl - INFO - Epoch [59][3600/3746] lr: 6.648e-02, eta: 3 days, 2:35:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5497, loss_cls: 4.0271, loss: 4.0271 +2024-07-24 05:13:30,269 - pyskl - INFO - Epoch [59][3700/3746] lr: 6.646e-02, eta: 3 days, 2:33:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5469, loss_cls: 4.0695, loss: 4.0695 +2024-07-24 05:14:10,292 - pyskl - INFO - Saving checkpoint at 59 epochs +2024-07-24 05:16:02,090 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 05:16:02,753 - pyskl - INFO - +top1_acc 0.2248 +top5_acc 0.4602 +2024-07-24 05:16:02,754 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 05:16:02,794 - pyskl - INFO - +mean_acc 0.2248 +2024-07-24 05:16:02,807 - pyskl - INFO - Epoch(val) [59][309] top1_acc: 0.2248, top5_acc: 0.4602, mean_class_accuracy: 0.2248 +2024-07-24 05:19:55,194 - pyskl - INFO - Epoch [60][100/3746] lr: 6.642e-02, eta: 3 days, 2:34:53, time: 2.324, data_time: 1.319, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5659, loss_cls: 3.9802, loss: 3.9802 +2024-07-24 05:21:16,938 - pyskl - INFO - Epoch [60][200/3746] lr: 6.639e-02, eta: 3 days, 2:33:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5522, loss_cls: 4.0306, loss: 4.0306 +2024-07-24 05:22:38,827 - pyskl - INFO - Epoch [60][300/3746] lr: 6.636e-02, eta: 3 days, 2:32:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5506, loss_cls: 4.0238, loss: 4.0238 +2024-07-24 05:24:01,186 - pyskl - INFO - Epoch [60][400/3746] lr: 6.634e-02, eta: 3 days, 2:31:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5427, loss_cls: 4.0334, loss: 4.0334 +2024-07-24 05:25:23,038 - pyskl - INFO - Epoch [60][500/3746] lr: 6.631e-02, eta: 3 days, 2:29:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5592, loss_cls: 3.9740, loss: 3.9740 +2024-07-24 05:26:44,475 - pyskl - INFO - Epoch [60][600/3746] lr: 6.629e-02, eta: 3 days, 2:28:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5605, loss_cls: 3.9811, loss: 3.9811 +2024-07-24 05:28:06,670 - pyskl - INFO - Epoch [60][700/3746] lr: 6.626e-02, eta: 3 days, 2:27:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5553, loss_cls: 4.0438, loss: 4.0438 +2024-07-24 05:29:28,194 - pyskl - INFO - Epoch [60][800/3746] lr: 6.623e-02, eta: 3 days, 2:26:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5572, loss_cls: 4.0037, loss: 4.0037 +2024-07-24 05:30:50,012 - pyskl - INFO - Epoch [60][900/3746] lr: 6.621e-02, eta: 3 days, 2:25:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5437, loss_cls: 4.1055, loss: 4.1055 +2024-07-24 05:32:12,494 - pyskl - INFO - Epoch [60][1000/3746] lr: 6.618e-02, eta: 3 days, 2:23:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5505, loss_cls: 4.0789, loss: 4.0789 +2024-07-24 05:33:35,749 - pyskl - INFO - Epoch [60][1100/3746] lr: 6.615e-02, eta: 3 days, 2:22:35, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5584, loss_cls: 4.0099, loss: 4.0099 +2024-07-24 05:34:57,779 - pyskl - INFO - Epoch [60][1200/3746] lr: 6.613e-02, eta: 3 days, 2:21:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5583, loss_cls: 3.9904, loss: 3.9904 +2024-07-24 05:36:20,025 - pyskl - INFO - Epoch [60][1300/3746] lr: 6.610e-02, eta: 3 days, 2:20:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5491, loss_cls: 4.0676, loss: 4.0676 +2024-07-24 05:37:41,755 - pyskl - INFO - Epoch [60][1400/3746] lr: 6.607e-02, eta: 3 days, 2:18:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5467, loss_cls: 4.0470, loss: 4.0470 +2024-07-24 05:39:03,422 - pyskl - INFO - Epoch [60][1500/3746] lr: 6.605e-02, eta: 3 days, 2:17:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5497, loss_cls: 4.0416, loss: 4.0416 +2024-07-24 05:40:25,239 - pyskl - INFO - Epoch [60][1600/3746] lr: 6.602e-02, eta: 3 days, 2:16:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5459, loss_cls: 4.0579, loss: 4.0579 +2024-07-24 05:41:46,807 - pyskl - INFO - Epoch [60][1700/3746] lr: 6.599e-02, eta: 3 days, 2:15:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5414, loss_cls: 4.0823, loss: 4.0823 +2024-07-24 05:43:08,721 - pyskl - INFO - Epoch [60][1800/3746] lr: 6.597e-02, eta: 3 days, 2:13:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5633, loss_cls: 4.0171, loss: 4.0171 +2024-07-24 05:44:30,472 - pyskl - INFO - Epoch [60][1900/3746] lr: 6.594e-02, eta: 3 days, 2:12:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5533, loss_cls: 4.0120, loss: 4.0120 +2024-07-24 05:45:52,008 - pyskl - INFO - Epoch [60][2000/3746] lr: 6.591e-02, eta: 3 days, 2:11:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5497, loss_cls: 4.0348, loss: 4.0348 +2024-07-24 05:47:13,506 - pyskl - INFO - Epoch [60][2100/3746] lr: 6.589e-02, eta: 3 days, 2:10:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5577, loss_cls: 3.9957, loss: 3.9957 +2024-07-24 05:48:35,176 - pyskl - INFO - Epoch [60][2200/3746] lr: 6.586e-02, eta: 3 days, 2:08:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5502, loss_cls: 4.0651, loss: 4.0651 +2024-07-24 05:49:56,515 - pyskl - INFO - Epoch [60][2300/3746] lr: 6.584e-02, eta: 3 days, 2:07:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5505, loss_cls: 4.0664, loss: 4.0664 +2024-07-24 05:51:18,520 - pyskl - INFO - Epoch [60][2400/3746] lr: 6.581e-02, eta: 3 days, 2:06:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5539, loss_cls: 4.0138, loss: 4.0138 +2024-07-24 05:52:40,489 - pyskl - INFO - Epoch [60][2500/3746] lr: 6.578e-02, eta: 3 days, 2:05:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5484, loss_cls: 4.0591, loss: 4.0591 +2024-07-24 05:54:02,022 - pyskl - INFO - Epoch [60][2600/3746] lr: 6.576e-02, eta: 3 days, 2:04:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5623, loss_cls: 4.0066, loss: 4.0066 +2024-07-24 05:55:23,406 - pyskl - INFO - Epoch [60][2700/3746] lr: 6.573e-02, eta: 3 days, 2:02:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5427, loss_cls: 4.0752, loss: 4.0752 +2024-07-24 05:56:45,282 - pyskl - INFO - Epoch [60][2800/3746] lr: 6.570e-02, eta: 3 days, 2:01:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5539, loss_cls: 4.0176, loss: 4.0176 +2024-07-24 05:58:06,559 - pyskl - INFO - Epoch [60][2900/3746] lr: 6.568e-02, eta: 3 days, 2:00:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5377, loss_cls: 4.0741, loss: 4.0741 +2024-07-24 05:59:28,142 - pyskl - INFO - Epoch [60][3000/3746] lr: 6.565e-02, eta: 3 days, 1:59:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5444, loss_cls: 4.0535, loss: 4.0535 +2024-07-24 06:00:49,617 - pyskl - INFO - Epoch [60][3100/3746] lr: 6.562e-02, eta: 3 days, 1:57:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5544, loss_cls: 4.0606, loss: 4.0606 +2024-07-24 06:02:11,732 - pyskl - INFO - Epoch [60][3200/3746] lr: 6.560e-02, eta: 3 days, 1:56:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5511, loss_cls: 4.0405, loss: 4.0405 +2024-07-24 06:03:33,244 - pyskl - INFO - Epoch [60][3300/3746] lr: 6.557e-02, eta: 3 days, 1:55:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5455, loss_cls: 4.0884, loss: 4.0884 +2024-07-24 06:04:55,534 - pyskl - INFO - Epoch [60][3400/3746] lr: 6.554e-02, eta: 3 days, 1:54:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5592, loss_cls: 4.0149, loss: 4.0149 +2024-07-24 06:06:17,267 - pyskl - INFO - Epoch [60][3500/3746] lr: 6.552e-02, eta: 3 days, 1:52:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5572, loss_cls: 4.0094, loss: 4.0094 +2024-07-24 06:07:39,094 - pyskl - INFO - Epoch [60][3600/3746] lr: 6.549e-02, eta: 3 days, 1:51:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5548, loss_cls: 4.0161, loss: 4.0161 +2024-07-24 06:09:00,449 - pyskl - INFO - Epoch [60][3700/3746] lr: 6.546e-02, eta: 3 days, 1:50:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5459, loss_cls: 4.0695, loss: 4.0695 +2024-07-24 06:09:40,923 - pyskl - INFO - Saving checkpoint at 60 epochs +2024-07-24 06:11:32,812 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 06:11:33,566 - pyskl - INFO - +top1_acc 0.2310 +top5_acc 0.4708 +2024-07-24 06:11:33,567 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 06:11:33,617 - pyskl - INFO - +mean_acc 0.2305 +2024-07-24 06:11:33,622 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_52.pth was removed +2024-07-24 06:11:33,914 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_60.pth. +2024-07-24 06:11:33,915 - pyskl - INFO - Best top1_acc is 0.2310 at 60 epoch. +2024-07-24 06:11:33,929 - pyskl - INFO - Epoch(val) [60][309] top1_acc: 0.2310, top5_acc: 0.4708, mean_class_accuracy: 0.2305 +2024-07-24 06:15:27,553 - pyskl - INFO - Epoch [61][100/3746] lr: 6.542e-02, eta: 3 days, 1:51:23, time: 2.336, data_time: 1.355, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5545, loss_cls: 4.0273, loss: 4.0273 +2024-07-24 06:16:49,963 - pyskl - INFO - Epoch [61][200/3746] lr: 6.540e-02, eta: 3 days, 1:50:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5625, loss_cls: 3.9778, loss: 3.9778 +2024-07-24 06:18:12,250 - pyskl - INFO - Epoch [61][300/3746] lr: 6.537e-02, eta: 3 days, 1:48:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5487, loss_cls: 4.0344, loss: 4.0344 +2024-07-24 06:19:34,081 - pyskl - INFO - Epoch [61][400/3746] lr: 6.534e-02, eta: 3 days, 1:47:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5591, loss_cls: 3.9630, loss: 3.9630 +2024-07-24 06:20:55,713 - pyskl - INFO - Epoch [61][500/3746] lr: 6.532e-02, eta: 3 days, 1:46:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5558, loss_cls: 4.0196, loss: 4.0196 +2024-07-24 06:22:17,962 - pyskl - INFO - Epoch [61][600/3746] lr: 6.529e-02, eta: 3 days, 1:45:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5609, loss_cls: 3.9863, loss: 3.9863 +2024-07-24 06:23:40,053 - pyskl - INFO - Epoch [61][700/3746] lr: 6.526e-02, eta: 3 days, 1:43:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5630, loss_cls: 3.9914, loss: 3.9914 +2024-07-24 06:25:01,772 - pyskl - INFO - Epoch [61][800/3746] lr: 6.524e-02, eta: 3 days, 1:42:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5673, loss_cls: 3.9238, loss: 3.9238 +2024-07-24 06:26:23,869 - pyskl - INFO - Epoch [61][900/3746] lr: 6.521e-02, eta: 3 days, 1:41:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5413, loss_cls: 4.0934, loss: 4.0934 +2024-07-24 06:27:45,366 - pyskl - INFO - Epoch [61][1000/3746] lr: 6.519e-02, eta: 3 days, 1:40:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5461, loss_cls: 4.0504, loss: 4.0504 +2024-07-24 06:29:08,197 - pyskl - INFO - Epoch [61][1100/3746] lr: 6.516e-02, eta: 3 days, 1:39:01, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5491, loss_cls: 4.0327, loss: 4.0327 +2024-07-24 06:30:30,017 - pyskl - INFO - Epoch [61][1200/3746] lr: 6.513e-02, eta: 3 days, 1:37:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5544, loss_cls: 4.0306, loss: 4.0306 +2024-07-24 06:31:52,024 - pyskl - INFO - Epoch [61][1300/3746] lr: 6.511e-02, eta: 3 days, 1:36:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5469, loss_cls: 4.0500, loss: 4.0500 +2024-07-24 06:33:13,751 - pyskl - INFO - Epoch [61][1400/3746] lr: 6.508e-02, eta: 3 days, 1:35:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5453, loss_cls: 4.0511, loss: 4.0511 +2024-07-24 06:34:35,341 - pyskl - INFO - Epoch [61][1500/3746] lr: 6.505e-02, eta: 3 days, 1:34:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5589, loss_cls: 4.0358, loss: 4.0358 +2024-07-24 06:35:56,856 - pyskl - INFO - Epoch [61][1600/3746] lr: 6.503e-02, eta: 3 days, 1:32:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5550, loss_cls: 4.0431, loss: 4.0431 +2024-07-24 06:37:18,255 - pyskl - INFO - Epoch [61][1700/3746] lr: 6.500e-02, eta: 3 days, 1:31:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5570, loss_cls: 3.9794, loss: 3.9794 +2024-07-24 06:38:39,947 - pyskl - INFO - Epoch [61][1800/3746] lr: 6.497e-02, eta: 3 days, 1:30:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5486, loss_cls: 4.0298, loss: 4.0298 +2024-07-24 06:40:02,220 - pyskl - INFO - Epoch [61][1900/3746] lr: 6.495e-02, eta: 3 days, 1:29:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5530, loss_cls: 3.9961, loss: 3.9961 +2024-07-24 06:41:23,662 - pyskl - INFO - Epoch [61][2000/3746] lr: 6.492e-02, eta: 3 days, 1:27:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5472, loss_cls: 4.0229, loss: 4.0229 +2024-07-24 06:42:45,146 - pyskl - INFO - Epoch [61][2100/3746] lr: 6.489e-02, eta: 3 days, 1:26:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5466, loss_cls: 4.0521, loss: 4.0521 +2024-07-24 06:44:07,129 - pyskl - INFO - Epoch [61][2200/3746] lr: 6.487e-02, eta: 3 days, 1:25:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5534, loss_cls: 4.0473, loss: 4.0473 +2024-07-24 06:45:28,549 - pyskl - INFO - Epoch [61][2300/3746] lr: 6.484e-02, eta: 3 days, 1:24:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5463, loss_cls: 4.0577, loss: 4.0577 +2024-07-24 06:46:49,911 - pyskl - INFO - Epoch [61][2400/3746] lr: 6.481e-02, eta: 3 days, 1:22:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5616, loss_cls: 3.9890, loss: 3.9890 +2024-07-24 06:48:11,105 - pyskl - INFO - Epoch [61][2500/3746] lr: 6.478e-02, eta: 3 days, 1:21:33, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5545, loss_cls: 4.0127, loss: 4.0127 +2024-07-24 06:49:33,245 - pyskl - INFO - Epoch [61][2600/3746] lr: 6.476e-02, eta: 3 days, 1:20:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5502, loss_cls: 4.0132, loss: 4.0132 +2024-07-24 06:50:54,804 - pyskl - INFO - Epoch [61][2700/3746] lr: 6.473e-02, eta: 3 days, 1:19:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5595, loss_cls: 3.9847, loss: 3.9847 +2024-07-24 06:52:16,358 - pyskl - INFO - Epoch [61][2800/3746] lr: 6.470e-02, eta: 3 days, 1:17:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5523, loss_cls: 4.0340, loss: 4.0340 +2024-07-24 06:53:38,043 - pyskl - INFO - Epoch [61][2900/3746] lr: 6.468e-02, eta: 3 days, 1:16:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5422, loss_cls: 4.0656, loss: 4.0656 +2024-07-24 06:54:59,562 - pyskl - INFO - Epoch [61][3000/3746] lr: 6.465e-02, eta: 3 days, 1:15:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5373, loss_cls: 4.1220, loss: 4.1220 +2024-07-24 06:56:21,052 - pyskl - INFO - Epoch [61][3100/3746] lr: 6.462e-02, eta: 3 days, 1:14:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5511, loss_cls: 4.0399, loss: 4.0399 +2024-07-24 06:57:42,511 - pyskl - INFO - Epoch [61][3200/3746] lr: 6.460e-02, eta: 3 days, 1:12:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5533, loss_cls: 4.0266, loss: 4.0266 +2024-07-24 06:59:04,383 - pyskl - INFO - Epoch [61][3300/3746] lr: 6.457e-02, eta: 3 days, 1:11:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5558, loss_cls: 4.0361, loss: 4.0361 +2024-07-24 07:00:25,948 - pyskl - INFO - Epoch [61][3400/3746] lr: 6.454e-02, eta: 3 days, 1:10:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5527, loss_cls: 4.0308, loss: 4.0308 +2024-07-24 07:01:47,888 - pyskl - INFO - Epoch [61][3500/3746] lr: 6.452e-02, eta: 3 days, 1:09:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5506, loss_cls: 4.0603, loss: 4.0603 +2024-07-24 07:03:09,351 - pyskl - INFO - Epoch [61][3600/3746] lr: 6.449e-02, eta: 3 days, 1:07:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5453, loss_cls: 4.0534, loss: 4.0534 +2024-07-24 07:04:30,894 - pyskl - INFO - Epoch [61][3700/3746] lr: 6.446e-02, eta: 3 days, 1:06:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5455, loss_cls: 4.0609, loss: 4.0609 +2024-07-24 07:05:10,178 - pyskl - INFO - Saving checkpoint at 61 epochs +2024-07-24 07:07:01,732 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 07:07:02,391 - pyskl - INFO - +top1_acc 0.2243 +top5_acc 0.4618 +2024-07-24 07:07:02,391 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 07:07:02,432 - pyskl - INFO - +mean_acc 0.2240 +2024-07-24 07:07:02,444 - pyskl - INFO - Epoch(val) [61][309] top1_acc: 0.2243, top5_acc: 0.4618, mean_class_accuracy: 0.2240 +2024-07-24 07:10:51,126 - pyskl - INFO - Epoch [62][100/3746] lr: 6.443e-02, eta: 3 days, 1:07:24, time: 2.287, data_time: 1.309, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5569, loss_cls: 3.9883, loss: 3.9883 +2024-07-24 07:12:12,764 - pyskl - INFO - Epoch [62][200/3746] lr: 6.440e-02, eta: 3 days, 1:06:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5639, loss_cls: 3.9848, loss: 3.9848 +2024-07-24 07:13:33,999 - pyskl - INFO - Epoch [62][300/3746] lr: 6.437e-02, eta: 3 days, 1:04:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5436, loss_cls: 4.0677, loss: 4.0677 +2024-07-24 07:14:56,451 - pyskl - INFO - Epoch [62][400/3746] lr: 6.434e-02, eta: 3 days, 1:03:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5616, loss_cls: 3.9730, loss: 3.9730 +2024-07-24 07:16:18,171 - pyskl - INFO - Epoch [62][500/3746] lr: 6.432e-02, eta: 3 days, 1:02:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5597, loss_cls: 3.9952, loss: 3.9952 +2024-07-24 07:17:40,240 - pyskl - INFO - Epoch [62][600/3746] lr: 6.429e-02, eta: 3 days, 1:01:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5619, loss_cls: 3.9980, loss: 3.9980 +2024-07-24 07:19:02,680 - pyskl - INFO - Epoch [62][700/3746] lr: 6.426e-02, eta: 3 days, 0:59:56, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5523, loss_cls: 4.0229, loss: 4.0229 +2024-07-24 07:20:23,999 - pyskl - INFO - Epoch [62][800/3746] lr: 6.424e-02, eta: 3 days, 0:58:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5555, loss_cls: 3.9854, loss: 3.9854 +2024-07-24 07:21:45,515 - pyskl - INFO - Epoch [62][900/3746] lr: 6.421e-02, eta: 3 days, 0:57:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5563, loss_cls: 3.9969, loss: 3.9969 +2024-07-24 07:23:07,205 - pyskl - INFO - Epoch [62][1000/3746] lr: 6.418e-02, eta: 3 days, 0:56:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5569, loss_cls: 4.0002, loss: 4.0002 +2024-07-24 07:24:30,169 - pyskl - INFO - Epoch [62][1100/3746] lr: 6.416e-02, eta: 3 days, 0:54:56, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5487, loss_cls: 4.0453, loss: 4.0453 +2024-07-24 07:25:52,155 - pyskl - INFO - Epoch [62][1200/3746] lr: 6.413e-02, eta: 3 days, 0:53:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5475, loss_cls: 4.0647, loss: 4.0647 +2024-07-24 07:27:15,004 - pyskl - INFO - Epoch [62][1300/3746] lr: 6.410e-02, eta: 3 days, 0:52:28, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5503, loss_cls: 4.0374, loss: 4.0374 +2024-07-24 07:28:37,282 - pyskl - INFO - Epoch [62][1400/3746] lr: 6.408e-02, eta: 3 days, 0:51:14, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5630, loss_cls: 3.9985, loss: 3.9985 +2024-07-24 07:29:59,176 - pyskl - INFO - Epoch [62][1500/3746] lr: 6.405e-02, eta: 3 days, 0:49:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5544, loss_cls: 4.0641, loss: 4.0641 +2024-07-24 07:31:20,617 - pyskl - INFO - Epoch [62][1600/3746] lr: 6.402e-02, eta: 3 days, 0:48:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5531, loss_cls: 4.0502, loss: 4.0502 +2024-07-24 07:32:42,329 - pyskl - INFO - Epoch [62][1700/3746] lr: 6.400e-02, eta: 3 days, 0:47:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5513, loss_cls: 3.9988, loss: 3.9988 +2024-07-24 07:34:03,846 - pyskl - INFO - Epoch [62][1800/3746] lr: 6.397e-02, eta: 3 days, 0:46:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5556, loss_cls: 4.0182, loss: 4.0182 +2024-07-24 07:35:25,046 - pyskl - INFO - Epoch [62][1900/3746] lr: 6.394e-02, eta: 3 days, 0:44:57, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5441, loss_cls: 4.0772, loss: 4.0772 +2024-07-24 07:36:46,511 - pyskl - INFO - Epoch [62][2000/3746] lr: 6.392e-02, eta: 3 days, 0:43:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5452, loss_cls: 4.0685, loss: 4.0685 +2024-07-24 07:38:07,824 - pyskl - INFO - Epoch [62][2100/3746] lr: 6.389e-02, eta: 3 days, 0:42:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5467, loss_cls: 4.0639, loss: 4.0639 +2024-07-24 07:39:29,636 - pyskl - INFO - Epoch [62][2200/3746] lr: 6.386e-02, eta: 3 days, 0:41:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5584, loss_cls: 4.0404, loss: 4.0404 +2024-07-24 07:40:51,103 - pyskl - INFO - Epoch [62][2300/3746] lr: 6.384e-02, eta: 3 days, 0:39:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5509, loss_cls: 4.0430, loss: 4.0430 +2024-07-24 07:42:13,317 - pyskl - INFO - Epoch [62][2400/3746] lr: 6.381e-02, eta: 3 days, 0:38:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5558, loss_cls: 4.0463, loss: 4.0463 +2024-07-24 07:43:35,070 - pyskl - INFO - Epoch [62][2500/3746] lr: 6.378e-02, eta: 3 days, 0:37:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5573, loss_cls: 4.0171, loss: 4.0171 +2024-07-24 07:44:56,344 - pyskl - INFO - Epoch [62][2600/3746] lr: 6.375e-02, eta: 3 days, 0:36:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5581, loss_cls: 4.0361, loss: 4.0361 +2024-07-24 07:46:17,814 - pyskl - INFO - Epoch [62][2700/3746] lr: 6.373e-02, eta: 3 days, 0:34:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5592, loss_cls: 3.9685, loss: 3.9685 +2024-07-24 07:47:38,957 - pyskl - INFO - Epoch [62][2800/3746] lr: 6.370e-02, eta: 3 days, 0:33:39, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5564, loss_cls: 4.0017, loss: 4.0017 +2024-07-24 07:49:00,592 - pyskl - INFO - Epoch [62][2900/3746] lr: 6.367e-02, eta: 3 days, 0:32:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5537, loss_cls: 4.0109, loss: 4.0109 +2024-07-24 07:50:22,527 - pyskl - INFO - Epoch [62][3000/3746] lr: 6.365e-02, eta: 3 days, 0:31:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5559, loss_cls: 4.0028, loss: 4.0028 +2024-07-24 07:51:43,803 - pyskl - INFO - Epoch [62][3100/3746] lr: 6.362e-02, eta: 3 days, 0:29:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5486, loss_cls: 4.0285, loss: 4.0285 +2024-07-24 07:53:05,455 - pyskl - INFO - Epoch [62][3200/3746] lr: 6.359e-02, eta: 3 days, 0:28:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5584, loss_cls: 4.0340, loss: 4.0340 +2024-07-24 07:54:27,177 - pyskl - INFO - Epoch [62][3300/3746] lr: 6.357e-02, eta: 3 days, 0:27:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5542, loss_cls: 4.0199, loss: 4.0199 +2024-07-24 07:55:48,588 - pyskl - INFO - Epoch [62][3400/3746] lr: 6.354e-02, eta: 3 days, 0:26:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5448, loss_cls: 4.0613, loss: 4.0613 +2024-07-24 07:57:10,424 - pyskl - INFO - Epoch [62][3500/3746] lr: 6.351e-02, eta: 3 days, 0:24:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5441, loss_cls: 4.0482, loss: 4.0482 +2024-07-24 07:58:33,120 - pyskl - INFO - Epoch [62][3600/3746] lr: 6.349e-02, eta: 3 days, 0:23:38, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5702, loss_cls: 3.9696, loss: 3.9696 +2024-07-24 07:59:54,719 - pyskl - INFO - Epoch [62][3700/3746] lr: 6.346e-02, eta: 3 days, 0:22:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5669, loss_cls: 3.9838, loss: 3.9838 +2024-07-24 08:00:34,377 - pyskl - INFO - Saving checkpoint at 62 epochs +2024-07-24 08:02:26,817 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 08:02:27,660 - pyskl - INFO - +top1_acc 0.2369 +top5_acc 0.4718 +2024-07-24 08:02:27,660 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 08:02:27,712 - pyskl - INFO - +mean_acc 0.2366 +2024-07-24 08:02:27,718 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_60.pth was removed +2024-07-24 08:02:28,022 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_62.pth. +2024-07-24 08:02:28,022 - pyskl - INFO - Best top1_acc is 0.2369 at 62 epoch. +2024-07-24 08:02:28,036 - pyskl - INFO - Epoch(val) [62][309] top1_acc: 0.2369, top5_acc: 0.4718, mean_class_accuracy: 0.2366 +2024-07-24 08:06:22,370 - pyskl - INFO - Epoch [63][100/3746] lr: 6.342e-02, eta: 3 days, 0:23:16, time: 2.343, data_time: 1.352, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5639, loss_cls: 3.9666, loss: 3.9666 +2024-07-24 08:07:44,052 - pyskl - INFO - Epoch [63][200/3746] lr: 6.339e-02, eta: 3 days, 0:22:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5558, loss_cls: 4.0181, loss: 4.0181 +2024-07-24 08:09:05,559 - pyskl - INFO - Epoch [63][300/3746] lr: 6.337e-02, eta: 3 days, 0:20:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5492, loss_cls: 4.0069, loss: 4.0069 +2024-07-24 08:10:28,487 - pyskl - INFO - Epoch [63][400/3746] lr: 6.334e-02, eta: 3 days, 0:19:31, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5586, loss_cls: 3.9618, loss: 3.9618 +2024-07-24 08:11:50,149 - pyskl - INFO - Epoch [63][500/3746] lr: 6.331e-02, eta: 3 days, 0:18:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5569, loss_cls: 4.0315, loss: 4.0315 +2024-07-24 08:13:12,276 - pyskl - INFO - Epoch [63][600/3746] lr: 6.328e-02, eta: 3 days, 0:17:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5450, loss_cls: 4.0672, loss: 4.0672 +2024-07-24 08:14:34,494 - pyskl - INFO - Epoch [63][700/3746] lr: 6.326e-02, eta: 3 days, 0:15:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5537, loss_cls: 4.0252, loss: 4.0252 +2024-07-24 08:15:55,945 - pyskl - INFO - Epoch [63][800/3746] lr: 6.323e-02, eta: 3 days, 0:14:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5641, loss_cls: 3.9697, loss: 3.9697 +2024-07-24 08:17:17,572 - pyskl - INFO - Epoch [63][900/3746] lr: 6.320e-02, eta: 3 days, 0:13:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5681, loss_cls: 3.9424, loss: 3.9424 +2024-07-24 08:18:38,763 - pyskl - INFO - Epoch [63][1000/3746] lr: 6.318e-02, eta: 3 days, 0:11:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5637, loss_cls: 3.9568, loss: 3.9568 +2024-07-24 08:20:01,351 - pyskl - INFO - Epoch [63][1100/3746] lr: 6.315e-02, eta: 3 days, 0:10:45, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5536, loss_cls: 4.0122, loss: 4.0122 +2024-07-24 08:21:23,878 - pyskl - INFO - Epoch [63][1200/3746] lr: 6.312e-02, eta: 3 days, 0:09:31, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5453, loss_cls: 4.0764, loss: 4.0764 +2024-07-24 08:22:46,104 - pyskl - INFO - Epoch [63][1300/3746] lr: 6.310e-02, eta: 3 days, 0:08:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5617, loss_cls: 3.9584, loss: 3.9584 +2024-07-24 08:24:07,724 - pyskl - INFO - Epoch [63][1400/3746] lr: 6.307e-02, eta: 3 days, 0:07:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5591, loss_cls: 3.9825, loss: 3.9825 +2024-07-24 08:25:29,079 - pyskl - INFO - Epoch [63][1500/3746] lr: 6.304e-02, eta: 3 days, 0:05:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5627, loss_cls: 3.9793, loss: 3.9793 +2024-07-24 08:26:50,755 - pyskl - INFO - Epoch [63][1600/3746] lr: 6.301e-02, eta: 3 days, 0:04:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5611, loss_cls: 3.9744, loss: 3.9744 +2024-07-24 08:28:12,578 - pyskl - INFO - Epoch [63][1700/3746] lr: 6.299e-02, eta: 3 days, 0:03:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5745, loss_cls: 3.9546, loss: 3.9546 +2024-07-24 08:29:33,897 - pyskl - INFO - Epoch [63][1800/3746] lr: 6.296e-02, eta: 3 days, 0:01:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5467, loss_cls: 4.0392, loss: 4.0392 +2024-07-24 08:30:55,634 - pyskl - INFO - Epoch [63][1900/3746] lr: 6.293e-02, eta: 3 days, 0:00:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5528, loss_cls: 4.0504, loss: 4.0504 +2024-07-24 08:32:17,302 - pyskl - INFO - Epoch [63][2000/3746] lr: 6.291e-02, eta: 2 days, 23:59:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5478, loss_cls: 4.0211, loss: 4.0211 +2024-07-24 08:33:39,813 - pyskl - INFO - Epoch [63][2100/3746] lr: 6.288e-02, eta: 2 days, 23:58:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5475, loss_cls: 4.0362, loss: 4.0362 +2024-07-24 08:35:00,914 - pyskl - INFO - Epoch [63][2200/3746] lr: 6.285e-02, eta: 2 days, 23:56:56, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5555, loss_cls: 3.9996, loss: 3.9996 +2024-07-24 08:36:22,762 - pyskl - INFO - Epoch [63][2300/3746] lr: 6.283e-02, eta: 2 days, 23:55:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5480, loss_cls: 4.0468, loss: 4.0468 +2024-07-24 08:37:44,233 - pyskl - INFO - Epoch [63][2400/3746] lr: 6.280e-02, eta: 2 days, 23:54:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5448, loss_cls: 4.0743, loss: 4.0743 +2024-07-24 08:39:05,926 - pyskl - INFO - Epoch [63][2500/3746] lr: 6.277e-02, eta: 2 days, 23:53:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5559, loss_cls: 4.0344, loss: 4.0344 +2024-07-24 08:40:28,124 - pyskl - INFO - Epoch [63][2600/3746] lr: 6.274e-02, eta: 2 days, 23:51:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5625, loss_cls: 3.9825, loss: 3.9825 +2024-07-24 08:41:50,108 - pyskl - INFO - Epoch [63][2700/3746] lr: 6.272e-02, eta: 2 days, 23:50:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5500, loss_cls: 4.0018, loss: 4.0018 +2024-07-24 08:43:11,983 - pyskl - INFO - Epoch [63][2800/3746] lr: 6.269e-02, eta: 2 days, 23:49:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5470, loss_cls: 4.0365, loss: 4.0365 +2024-07-24 08:44:33,142 - pyskl - INFO - Epoch [63][2900/3746] lr: 6.266e-02, eta: 2 days, 23:48:08, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5602, loss_cls: 3.9852, loss: 3.9852 +2024-07-24 08:45:54,804 - pyskl - INFO - Epoch [63][3000/3746] lr: 6.264e-02, eta: 2 days, 23:46:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5498, loss_cls: 4.0508, loss: 4.0508 +2024-07-24 08:47:16,081 - pyskl - INFO - Epoch [63][3100/3746] lr: 6.261e-02, eta: 2 days, 23:45:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5647, loss_cls: 3.9720, loss: 3.9720 +2024-07-24 08:48:37,371 - pyskl - INFO - Epoch [63][3200/3746] lr: 6.258e-02, eta: 2 days, 23:44:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5392, loss_cls: 4.0797, loss: 4.0797 +2024-07-24 08:49:59,320 - pyskl - INFO - Epoch [63][3300/3746] lr: 6.256e-02, eta: 2 days, 23:43:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5566, loss_cls: 4.0109, loss: 4.0109 +2024-07-24 08:51:21,124 - pyskl - INFO - Epoch [63][3400/3746] lr: 6.253e-02, eta: 2 days, 23:41:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5514, loss_cls: 4.0210, loss: 4.0210 +2024-07-24 08:52:42,699 - pyskl - INFO - Epoch [63][3500/3746] lr: 6.250e-02, eta: 2 days, 23:40:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5548, loss_cls: 4.0184, loss: 4.0184 +2024-07-24 08:54:05,299 - pyskl - INFO - Epoch [63][3600/3746] lr: 6.247e-02, eta: 2 days, 23:39:20, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5519, loss_cls: 4.0551, loss: 4.0551 +2024-07-24 08:55:26,832 - pyskl - INFO - Epoch [63][3700/3746] lr: 6.245e-02, eta: 2 days, 23:38:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5569, loss_cls: 3.9845, loss: 3.9845 +2024-07-24 08:56:06,323 - pyskl - INFO - Saving checkpoint at 63 epochs +2024-07-24 08:57:57,615 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 08:57:58,341 - pyskl - INFO - +top1_acc 0.2358 +top5_acc 0.4801 +2024-07-24 08:57:58,342 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 08:57:58,389 - pyskl - INFO - +mean_acc 0.2357 +2024-07-24 08:57:58,403 - pyskl - INFO - Epoch(val) [63][309] top1_acc: 0.2358, top5_acc: 0.4801, mean_class_accuracy: 0.2357 +2024-07-24 09:01:48,597 - pyskl - INFO - Epoch [64][100/3746] lr: 6.241e-02, eta: 2 days, 23:38:47, time: 2.302, data_time: 1.316, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5705, loss_cls: 3.9257, loss: 3.9257 +2024-07-24 09:03:10,402 - pyskl - INFO - Epoch [64][200/3746] lr: 6.238e-02, eta: 2 days, 23:37:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5659, loss_cls: 3.9679, loss: 3.9679 +2024-07-24 09:04:32,347 - pyskl - INFO - Epoch [64][300/3746] lr: 6.235e-02, eta: 2 days, 23:36:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5647, loss_cls: 3.9478, loss: 3.9478 +2024-07-24 09:05:54,469 - pyskl - INFO - Epoch [64][400/3746] lr: 6.233e-02, eta: 2 days, 23:35:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5484, loss_cls: 4.0300, loss: 4.0300 +2024-07-24 09:07:16,542 - pyskl - INFO - Epoch [64][500/3746] lr: 6.230e-02, eta: 2 days, 23:33:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5591, loss_cls: 4.0053, loss: 4.0053 +2024-07-24 09:08:38,413 - pyskl - INFO - Epoch [64][600/3746] lr: 6.227e-02, eta: 2 days, 23:32:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5478, loss_cls: 4.0149, loss: 4.0149 +2024-07-24 09:09:59,900 - pyskl - INFO - Epoch [64][700/3746] lr: 6.225e-02, eta: 2 days, 23:31:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5522, loss_cls: 4.0221, loss: 4.0221 +2024-07-24 09:11:22,394 - pyskl - INFO - Epoch [64][800/3746] lr: 6.222e-02, eta: 2 days, 23:30:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5728, loss_cls: 3.9052, loss: 3.9052 +2024-07-24 09:12:44,103 - pyskl - INFO - Epoch [64][900/3746] lr: 6.219e-02, eta: 2 days, 23:28:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5561, loss_cls: 4.0353, loss: 4.0353 +2024-07-24 09:14:05,702 - pyskl - INFO - Epoch [64][1000/3746] lr: 6.216e-02, eta: 2 days, 23:27:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5525, loss_cls: 4.0410, loss: 4.0410 +2024-07-24 09:15:27,703 - pyskl - INFO - Epoch [64][1100/3746] lr: 6.214e-02, eta: 2 days, 23:26:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5597, loss_cls: 4.0141, loss: 4.0141 +2024-07-24 09:16:50,604 - pyskl - INFO - Epoch [64][1200/3746] lr: 6.211e-02, eta: 2 days, 23:24:59, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5587, loss_cls: 3.9717, loss: 3.9717 +2024-07-24 09:18:13,046 - pyskl - INFO - Epoch [64][1300/3746] lr: 6.208e-02, eta: 2 days, 23:23:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5486, loss_cls: 4.0108, loss: 4.0108 +2024-07-24 09:19:35,247 - pyskl - INFO - Epoch [64][1400/3746] lr: 6.206e-02, eta: 2 days, 23:22:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5639, loss_cls: 3.9885, loss: 3.9885 +2024-07-24 09:20:57,060 - pyskl - INFO - Epoch [64][1500/3746] lr: 6.203e-02, eta: 2 days, 23:21:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5461, loss_cls: 4.0235, loss: 4.0235 +2024-07-24 09:22:19,160 - pyskl - INFO - Epoch [64][1600/3746] lr: 6.200e-02, eta: 2 days, 23:19:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5539, loss_cls: 4.0590, loss: 4.0590 +2024-07-24 09:23:40,651 - pyskl - INFO - Epoch [64][1700/3746] lr: 6.197e-02, eta: 2 days, 23:18:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5503, loss_cls: 4.0201, loss: 4.0201 +2024-07-24 09:25:02,493 - pyskl - INFO - Epoch [64][1800/3746] lr: 6.195e-02, eta: 2 days, 23:17:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5572, loss_cls: 4.0133, loss: 4.0133 +2024-07-24 09:26:24,427 - pyskl - INFO - Epoch [64][1900/3746] lr: 6.192e-02, eta: 2 days, 23:16:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5567, loss_cls: 3.9896, loss: 3.9896 +2024-07-24 09:27:45,965 - pyskl - INFO - Epoch [64][2000/3746] lr: 6.189e-02, eta: 2 days, 23:14:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5508, loss_cls: 4.0150, loss: 4.0150 +2024-07-24 09:29:07,128 - pyskl - INFO - Epoch [64][2100/3746] lr: 6.187e-02, eta: 2 days, 23:13:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5633, loss_cls: 3.9865, loss: 3.9865 +2024-07-24 09:30:28,538 - pyskl - INFO - Epoch [64][2200/3746] lr: 6.184e-02, eta: 2 days, 23:12:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5628, loss_cls: 4.0111, loss: 4.0111 +2024-07-24 09:31:50,257 - pyskl - INFO - Epoch [64][2300/3746] lr: 6.181e-02, eta: 2 days, 23:11:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5509, loss_cls: 4.0239, loss: 4.0239 +2024-07-24 09:33:11,941 - pyskl - INFO - Epoch [64][2400/3746] lr: 6.178e-02, eta: 2 days, 23:09:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5552, loss_cls: 4.0404, loss: 4.0404 +2024-07-24 09:34:33,607 - pyskl - INFO - Epoch [64][2500/3746] lr: 6.176e-02, eta: 2 days, 23:08:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5630, loss_cls: 3.9678, loss: 3.9678 +2024-07-24 09:35:55,106 - pyskl - INFO - Epoch [64][2600/3746] lr: 6.173e-02, eta: 2 days, 23:07:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5595, loss_cls: 3.9703, loss: 3.9703 +2024-07-24 09:37:16,654 - pyskl - INFO - Epoch [64][2700/3746] lr: 6.170e-02, eta: 2 days, 23:06:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5648, loss_cls: 3.9572, loss: 3.9572 +2024-07-24 09:38:38,436 - pyskl - INFO - Epoch [64][2800/3746] lr: 6.168e-02, eta: 2 days, 23:04:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5425, loss_cls: 4.0603, loss: 4.0603 +2024-07-24 09:40:00,341 - pyskl - INFO - Epoch [64][2900/3746] lr: 6.165e-02, eta: 2 days, 23:03:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5534, loss_cls: 4.0109, loss: 4.0109 +2024-07-24 09:41:22,579 - pyskl - INFO - Epoch [64][3000/3746] lr: 6.162e-02, eta: 2 days, 23:02:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5534, loss_cls: 4.0101, loss: 4.0101 +2024-07-24 09:42:44,083 - pyskl - INFO - Epoch [64][3100/3746] lr: 6.159e-02, eta: 2 days, 23:01:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5545, loss_cls: 4.0140, loss: 4.0140 +2024-07-24 09:44:05,707 - pyskl - INFO - Epoch [64][3200/3746] lr: 6.157e-02, eta: 2 days, 22:59:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5511, loss_cls: 4.0461, loss: 4.0461 +2024-07-24 09:45:27,161 - pyskl - INFO - Epoch [64][3300/3746] lr: 6.154e-02, eta: 2 days, 22:58:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5423, loss_cls: 4.0639, loss: 4.0639 +2024-07-24 09:46:48,514 - pyskl - INFO - Epoch [64][3400/3746] lr: 6.151e-02, eta: 2 days, 22:57:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5652, loss_cls: 3.9892, loss: 3.9892 +2024-07-24 09:48:10,063 - pyskl - INFO - Epoch [64][3500/3746] lr: 6.148e-02, eta: 2 days, 22:55:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5687, loss_cls: 3.9779, loss: 3.9779 +2024-07-24 09:49:31,909 - pyskl - INFO - Epoch [64][3600/3746] lr: 6.146e-02, eta: 2 days, 22:54:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5600, loss_cls: 4.0082, loss: 4.0082 +2024-07-24 09:50:53,932 - pyskl - INFO - Epoch [64][3700/3746] lr: 6.143e-02, eta: 2 days, 22:53:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5527, loss_cls: 3.9824, loss: 3.9824 +2024-07-24 09:51:33,662 - pyskl - INFO - Saving checkpoint at 64 epochs +2024-07-24 09:53:25,523 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 09:53:26,183 - pyskl - INFO - +top1_acc 0.2370 +top5_acc 0.4714 +2024-07-24 09:53:26,183 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 09:53:26,226 - pyskl - INFO - +mean_acc 0.2367 +2024-07-24 09:53:26,231 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_62.pth was removed +2024-07-24 09:53:26,492 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_64.pth. +2024-07-24 09:53:26,493 - pyskl - INFO - Best top1_acc is 0.2370 at 64 epoch. +2024-07-24 09:53:26,505 - pyskl - INFO - Epoch(val) [64][309] top1_acc: 0.2370, top5_acc: 0.4714, mean_class_accuracy: 0.2367 +2024-07-24 09:57:15,576 - pyskl - INFO - Epoch [65][100/3746] lr: 6.139e-02, eta: 2 days, 22:54:02, time: 2.291, data_time: 1.318, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5806, loss_cls: 3.8990, loss: 3.8990 +2024-07-24 09:58:36,700 - pyskl - INFO - Epoch [65][200/3746] lr: 6.136e-02, eta: 2 days, 22:52:45, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5730, loss_cls: 3.9728, loss: 3.9728 +2024-07-24 09:59:58,267 - pyskl - INFO - Epoch [65][300/3746] lr: 6.134e-02, eta: 2 days, 22:51:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5667, loss_cls: 3.9332, loss: 3.9332 +2024-07-24 10:01:20,023 - pyskl - INFO - Epoch [65][400/3746] lr: 6.131e-02, eta: 2 days, 22:50:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5553, loss_cls: 3.9815, loss: 3.9815 +2024-07-24 10:02:41,484 - pyskl - INFO - Epoch [65][500/3746] lr: 6.128e-02, eta: 2 days, 22:48:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5644, loss_cls: 3.9640, loss: 3.9640 +2024-07-24 10:04:03,793 - pyskl - INFO - Epoch [65][600/3746] lr: 6.125e-02, eta: 2 days, 22:47:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5664, loss_cls: 3.9644, loss: 3.9644 +2024-07-24 10:05:25,595 - pyskl - INFO - Epoch [65][700/3746] lr: 6.123e-02, eta: 2 days, 22:46:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5556, loss_cls: 3.9958, loss: 3.9958 +2024-07-24 10:06:46,854 - pyskl - INFO - Epoch [65][800/3746] lr: 6.120e-02, eta: 2 days, 22:45:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5552, loss_cls: 3.9885, loss: 3.9885 +2024-07-24 10:08:08,468 - pyskl - INFO - Epoch [65][900/3746] lr: 6.117e-02, eta: 2 days, 22:43:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5548, loss_cls: 4.0292, loss: 4.0292 +2024-07-24 10:09:30,532 - pyskl - INFO - Epoch [65][1000/3746] lr: 6.115e-02, eta: 2 days, 22:42:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5613, loss_cls: 4.0022, loss: 4.0022 +2024-07-24 10:10:53,616 - pyskl - INFO - Epoch [65][1100/3746] lr: 6.112e-02, eta: 2 days, 22:41:23, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5631, loss_cls: 3.9640, loss: 3.9640 +2024-07-24 10:12:15,450 - pyskl - INFO - Epoch [65][1200/3746] lr: 6.109e-02, eta: 2 days, 22:40:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5630, loss_cls: 3.9904, loss: 3.9904 +2024-07-24 10:13:38,659 - pyskl - INFO - Epoch [65][1300/3746] lr: 6.106e-02, eta: 2 days, 22:38:53, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5513, loss_cls: 4.0394, loss: 4.0394 +2024-07-24 10:15:00,115 - pyskl - INFO - Epoch [65][1400/3746] lr: 6.104e-02, eta: 2 days, 22:37:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5664, loss_cls: 3.9834, loss: 3.9834 +2024-07-24 10:16:21,756 - pyskl - INFO - Epoch [65][1500/3746] lr: 6.101e-02, eta: 2 days, 22:36:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5569, loss_cls: 3.9973, loss: 3.9973 +2024-07-24 10:17:43,580 - pyskl - INFO - Epoch [65][1600/3746] lr: 6.098e-02, eta: 2 days, 22:35:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5669, loss_cls: 3.9437, loss: 3.9437 +2024-07-24 10:19:04,819 - pyskl - INFO - Epoch [65][1700/3746] lr: 6.095e-02, eta: 2 days, 22:33:48, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5586, loss_cls: 3.9962, loss: 3.9962 +2024-07-24 10:20:26,363 - pyskl - INFO - Epoch [65][1800/3746] lr: 6.093e-02, eta: 2 days, 22:32:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5634, loss_cls: 3.9390, loss: 3.9390 +2024-07-24 10:21:47,786 - pyskl - INFO - Epoch [65][1900/3746] lr: 6.090e-02, eta: 2 days, 22:31:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5531, loss_cls: 4.0242, loss: 4.0242 +2024-07-24 10:23:09,981 - pyskl - INFO - Epoch [65][2000/3746] lr: 6.087e-02, eta: 2 days, 22:30:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5647, loss_cls: 3.9739, loss: 3.9739 +2024-07-24 10:24:31,620 - pyskl - INFO - Epoch [65][2100/3746] lr: 6.085e-02, eta: 2 days, 22:28:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5519, loss_cls: 4.0119, loss: 4.0119 +2024-07-24 10:25:53,241 - pyskl - INFO - Epoch [65][2200/3746] lr: 6.082e-02, eta: 2 days, 22:27:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5589, loss_cls: 3.9972, loss: 3.9972 +2024-07-24 10:27:14,727 - pyskl - INFO - Epoch [65][2300/3746] lr: 6.079e-02, eta: 2 days, 22:26:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5563, loss_cls: 4.0106, loss: 4.0106 +2024-07-24 10:28:36,468 - pyskl - INFO - Epoch [65][2400/3746] lr: 6.076e-02, eta: 2 days, 22:24:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5544, loss_cls: 4.0137, loss: 4.0137 +2024-07-24 10:29:58,239 - pyskl - INFO - Epoch [65][2500/3746] lr: 6.074e-02, eta: 2 days, 22:23:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5583, loss_cls: 3.9801, loss: 3.9801 +2024-07-24 10:31:19,955 - pyskl - INFO - Epoch [65][2600/3746] lr: 6.071e-02, eta: 2 days, 22:22:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5564, loss_cls: 4.0252, loss: 4.0252 +2024-07-24 10:32:41,388 - pyskl - INFO - Epoch [65][2700/3746] lr: 6.068e-02, eta: 2 days, 22:21:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5541, loss_cls: 3.9975, loss: 3.9975 +2024-07-24 10:34:02,922 - pyskl - INFO - Epoch [65][2800/3746] lr: 6.065e-02, eta: 2 days, 22:19:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5497, loss_cls: 4.0479, loss: 4.0479 +2024-07-24 10:35:24,188 - pyskl - INFO - Epoch [65][2900/3746] lr: 6.063e-02, eta: 2 days, 22:18:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5564, loss_cls: 3.9935, loss: 3.9935 +2024-07-24 10:36:45,854 - pyskl - INFO - Epoch [65][3000/3746] lr: 6.060e-02, eta: 2 days, 22:17:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5591, loss_cls: 4.0186, loss: 4.0186 +2024-07-24 10:38:07,751 - pyskl - INFO - Epoch [65][3100/3746] lr: 6.057e-02, eta: 2 days, 22:16:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5544, loss_cls: 4.0423, loss: 4.0423 +2024-07-24 10:39:29,198 - pyskl - INFO - Epoch [65][3200/3746] lr: 6.055e-02, eta: 2 days, 22:14:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5614, loss_cls: 4.0096, loss: 4.0096 +2024-07-24 10:40:50,422 - pyskl - INFO - Epoch [65][3300/3746] lr: 6.052e-02, eta: 2 days, 22:13:29, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5537, loss_cls: 4.0397, loss: 4.0397 +2024-07-24 10:42:11,933 - pyskl - INFO - Epoch [65][3400/3746] lr: 6.049e-02, eta: 2 days, 22:12:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5592, loss_cls: 4.0267, loss: 4.0267 +2024-07-24 10:43:33,948 - pyskl - INFO - Epoch [65][3500/3746] lr: 6.046e-02, eta: 2 days, 22:10:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5558, loss_cls: 3.9904, loss: 3.9904 +2024-07-24 10:44:55,764 - pyskl - INFO - Epoch [65][3600/3746] lr: 6.044e-02, eta: 2 days, 22:09:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5625, loss_cls: 3.9889, loss: 3.9889 +2024-07-24 10:46:17,110 - pyskl - INFO - Epoch [65][3700/3746] lr: 6.041e-02, eta: 2 days, 22:08:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5691, loss_cls: 3.9376, loss: 3.9376 +2024-07-24 10:46:57,166 - pyskl - INFO - Saving checkpoint at 65 epochs +2024-07-24 10:48:49,455 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 10:48:50,128 - pyskl - INFO - +top1_acc 0.2510 +top5_acc 0.4910 +2024-07-24 10:48:50,128 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 10:48:50,171 - pyskl - INFO - +mean_acc 0.2508 +2024-07-24 10:48:50,176 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_64.pth was removed +2024-07-24 10:48:50,438 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_65.pth. +2024-07-24 10:48:50,438 - pyskl - INFO - Best top1_acc is 0.2510 at 65 epoch. +2024-07-24 10:48:50,455 - pyskl - INFO - Epoch(val) [65][309] top1_acc: 0.2510, top5_acc: 0.4910, mean_class_accuracy: 0.2508 +2024-07-24 10:52:40,539 - pyskl - INFO - Epoch [66][100/3746] lr: 6.037e-02, eta: 2 days, 22:08:58, time: 2.301, data_time: 1.314, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5734, loss_cls: 3.9146, loss: 3.9146 +2024-07-24 10:54:02,764 - pyskl - INFO - Epoch [66][200/3746] lr: 6.034e-02, eta: 2 days, 22:07:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5727, loss_cls: 3.9342, loss: 3.9342 +2024-07-24 10:55:24,368 - pyskl - INFO - Epoch [66][300/3746] lr: 6.031e-02, eta: 2 days, 22:06:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5702, loss_cls: 3.9341, loss: 3.9341 +2024-07-24 10:56:46,439 - pyskl - INFO - Epoch [66][400/3746] lr: 6.029e-02, eta: 2 days, 22:05:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5613, loss_cls: 3.9537, loss: 3.9537 +2024-07-24 10:58:07,979 - pyskl - INFO - Epoch [66][500/3746] lr: 6.026e-02, eta: 2 days, 22:03:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5616, loss_cls: 3.9823, loss: 3.9823 +2024-07-24 10:59:30,509 - pyskl - INFO - Epoch [66][600/3746] lr: 6.023e-02, eta: 2 days, 22:02:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5613, loss_cls: 3.9947, loss: 3.9947 +2024-07-24 11:00:52,280 - pyskl - INFO - Epoch [66][700/3746] lr: 6.020e-02, eta: 2 days, 22:01:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5561, loss_cls: 4.0007, loss: 4.0007 +2024-07-24 11:02:13,663 - pyskl - INFO - Epoch [66][800/3746] lr: 6.018e-02, eta: 2 days, 22:00:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5642, loss_cls: 3.9772, loss: 3.9772 +2024-07-24 11:03:34,973 - pyskl - INFO - Epoch [66][900/3746] lr: 6.015e-02, eta: 2 days, 21:58:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5564, loss_cls: 4.0078, loss: 4.0078 +2024-07-24 11:04:56,411 - pyskl - INFO - Epoch [66][1000/3746] lr: 6.012e-02, eta: 2 days, 21:57:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5773, loss_cls: 3.9004, loss: 3.9004 +2024-07-24 11:06:19,016 - pyskl - INFO - Epoch [66][1100/3746] lr: 6.009e-02, eta: 2 days, 21:56:17, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5697, loss_cls: 3.9554, loss: 3.9554 +2024-07-24 11:07:40,701 - pyskl - INFO - Epoch [66][1200/3746] lr: 6.007e-02, eta: 2 days, 21:55:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5691, loss_cls: 3.9338, loss: 3.9338 +2024-07-24 11:09:03,226 - pyskl - INFO - Epoch [66][1300/3746] lr: 6.004e-02, eta: 2 days, 21:53:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5677, loss_cls: 3.9552, loss: 3.9552 +2024-07-24 11:10:25,579 - pyskl - INFO - Epoch [66][1400/3746] lr: 6.001e-02, eta: 2 days, 21:52:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5650, loss_cls: 3.9789, loss: 3.9789 +2024-07-24 11:11:47,136 - pyskl - INFO - Epoch [66][1500/3746] lr: 5.999e-02, eta: 2 days, 21:51:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5683, loss_cls: 3.9702, loss: 3.9702 +2024-07-24 11:13:09,271 - pyskl - INFO - Epoch [66][1600/3746] lr: 5.996e-02, eta: 2 days, 21:49:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5519, loss_cls: 3.9907, loss: 3.9907 +2024-07-24 11:14:30,996 - pyskl - INFO - Epoch [66][1700/3746] lr: 5.993e-02, eta: 2 days, 21:48:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5659, loss_cls: 3.9567, loss: 3.9567 +2024-07-24 11:15:52,816 - pyskl - INFO - Epoch [66][1800/3746] lr: 5.990e-02, eta: 2 days, 21:47:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5608, loss_cls: 3.9892, loss: 3.9892 +2024-07-24 11:17:14,450 - pyskl - INFO - Epoch [66][1900/3746] lr: 5.988e-02, eta: 2 days, 21:46:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5530, loss_cls: 4.0107, loss: 4.0107 +2024-07-24 11:18:36,125 - pyskl - INFO - Epoch [66][2000/3746] lr: 5.985e-02, eta: 2 days, 21:44:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5530, loss_cls: 4.0332, loss: 4.0332 +2024-07-24 11:19:57,348 - pyskl - INFO - Epoch [66][2100/3746] lr: 5.982e-02, eta: 2 days, 21:43:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5561, loss_cls: 4.0084, loss: 4.0084 +2024-07-24 11:21:18,537 - pyskl - INFO - Epoch [66][2200/3746] lr: 5.979e-02, eta: 2 days, 21:42:18, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5550, loss_cls: 4.0268, loss: 4.0268 +2024-07-24 11:22:40,384 - pyskl - INFO - Epoch [66][2300/3746] lr: 5.977e-02, eta: 2 days, 21:41:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5570, loss_cls: 4.0117, loss: 4.0117 +2024-07-24 11:24:01,706 - pyskl - INFO - Epoch [66][2400/3746] lr: 5.974e-02, eta: 2 days, 21:39:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5630, loss_cls: 4.0066, loss: 4.0066 +2024-07-24 11:25:22,883 - pyskl - INFO - Epoch [66][2500/3746] lr: 5.971e-02, eta: 2 days, 21:38:28, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5555, loss_cls: 4.0163, loss: 4.0163 +2024-07-24 11:26:44,385 - pyskl - INFO - Epoch [66][2600/3746] lr: 5.968e-02, eta: 2 days, 21:37:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5645, loss_cls: 3.9683, loss: 3.9683 +2024-07-24 11:28:06,045 - pyskl - INFO - Epoch [66][2700/3746] lr: 5.966e-02, eta: 2 days, 21:35:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5472, loss_cls: 4.0262, loss: 4.0262 +2024-07-24 11:29:27,851 - pyskl - INFO - Epoch [66][2800/3746] lr: 5.963e-02, eta: 2 days, 21:34:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5561, loss_cls: 4.0090, loss: 4.0090 +2024-07-24 11:30:49,259 - pyskl - INFO - Epoch [66][2900/3746] lr: 5.960e-02, eta: 2 days, 21:33:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5603, loss_cls: 4.0206, loss: 4.0206 +2024-07-24 11:32:10,704 - pyskl - INFO - Epoch [66][3000/3746] lr: 5.957e-02, eta: 2 days, 21:32:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5545, loss_cls: 3.9944, loss: 3.9944 +2024-07-24 11:33:32,249 - pyskl - INFO - Epoch [66][3100/3746] lr: 5.955e-02, eta: 2 days, 21:30:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5616, loss_cls: 4.0004, loss: 4.0004 +2024-07-24 11:34:53,535 - pyskl - INFO - Epoch [66][3200/3746] lr: 5.952e-02, eta: 2 days, 21:29:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5550, loss_cls: 4.0020, loss: 4.0020 +2024-07-24 11:36:14,939 - pyskl - INFO - Epoch [66][3300/3746] lr: 5.949e-02, eta: 2 days, 21:28:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5575, loss_cls: 3.9909, loss: 3.9909 +2024-07-24 11:37:36,303 - pyskl - INFO - Epoch [66][3400/3746] lr: 5.946e-02, eta: 2 days, 21:26:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5539, loss_cls: 4.0080, loss: 4.0080 +2024-07-24 11:38:57,804 - pyskl - INFO - Epoch [66][3500/3746] lr: 5.944e-02, eta: 2 days, 21:25:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5661, loss_cls: 3.9566, loss: 3.9566 +2024-07-24 11:40:19,535 - pyskl - INFO - Epoch [66][3600/3746] lr: 5.941e-02, eta: 2 days, 21:24:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5578, loss_cls: 4.0113, loss: 4.0113 +2024-07-24 11:41:40,584 - pyskl - INFO - Epoch [66][3700/3746] lr: 5.938e-02, eta: 2 days, 21:23:08, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5622, loss_cls: 3.9741, loss: 3.9741 +2024-07-24 11:42:20,268 - pyskl - INFO - Saving checkpoint at 66 epochs +2024-07-24 11:44:12,837 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 11:44:13,497 - pyskl - INFO - +top1_acc 0.2239 +top5_acc 0.4656 +2024-07-24 11:44:13,498 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 11:44:13,537 - pyskl - INFO - +mean_acc 0.2238 +2024-07-24 11:44:13,549 - pyskl - INFO - Epoch(val) [66][309] top1_acc: 0.2239, top5_acc: 0.4656, mean_class_accuracy: 0.2238 +2024-07-24 11:48:07,787 - pyskl - INFO - Epoch [67][100/3746] lr: 5.934e-02, eta: 2 days, 21:23:43, time: 2.342, data_time: 1.364, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5789, loss_cls: 3.9083, loss: 3.9083 +2024-07-24 11:49:30,427 - pyskl - INFO - Epoch [67][200/3746] lr: 5.931e-02, eta: 2 days, 21:22:28, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5727, loss_cls: 3.9151, loss: 3.9151 +2024-07-24 11:50:52,316 - pyskl - INFO - Epoch [67][300/3746] lr: 5.929e-02, eta: 2 days, 21:21:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5536, loss_cls: 4.0116, loss: 4.0116 +2024-07-24 11:52:14,841 - pyskl - INFO - Epoch [67][400/3746] lr: 5.926e-02, eta: 2 days, 21:19:56, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5734, loss_cls: 3.9383, loss: 3.9383 +2024-07-24 11:53:36,767 - pyskl - INFO - Epoch [67][500/3746] lr: 5.923e-02, eta: 2 days, 21:18:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5722, loss_cls: 3.9363, loss: 3.9363 +2024-07-24 11:54:58,496 - pyskl - INFO - Epoch [67][600/3746] lr: 5.920e-02, eta: 2 days, 21:17:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5502, loss_cls: 4.0093, loss: 4.0093 +2024-07-24 11:56:19,841 - pyskl - INFO - Epoch [67][700/3746] lr: 5.918e-02, eta: 2 days, 21:16:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5608, loss_cls: 4.0073, loss: 4.0073 +2024-07-24 11:57:41,557 - pyskl - INFO - Epoch [67][800/3746] lr: 5.915e-02, eta: 2 days, 21:14:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5642, loss_cls: 3.9685, loss: 3.9685 +2024-07-24 11:59:03,956 - pyskl - INFO - Epoch [67][900/3746] lr: 5.912e-02, eta: 2 days, 21:13:34, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5614, loss_cls: 3.9589, loss: 3.9589 +2024-07-24 12:00:25,572 - pyskl - INFO - Epoch [67][1000/3746] lr: 5.909e-02, eta: 2 days, 21:12:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5627, loss_cls: 3.9650, loss: 3.9650 +2024-07-24 12:01:47,501 - pyskl - INFO - Epoch [67][1100/3746] lr: 5.907e-02, eta: 2 days, 21:11:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5583, loss_cls: 3.9816, loss: 3.9816 +2024-07-24 12:03:09,417 - pyskl - INFO - Epoch [67][1200/3746] lr: 5.904e-02, eta: 2 days, 21:09:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5528, loss_cls: 4.0114, loss: 4.0114 +2024-07-24 12:04:31,942 - pyskl - INFO - Epoch [67][1300/3746] lr: 5.901e-02, eta: 2 days, 21:08:29, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5617, loss_cls: 3.9577, loss: 3.9577 +2024-07-24 12:05:53,865 - pyskl - INFO - Epoch [67][1400/3746] lr: 5.898e-02, eta: 2 days, 21:07:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5775, loss_cls: 3.9065, loss: 3.9065 +2024-07-24 12:07:16,304 - pyskl - INFO - Epoch [67][1500/3746] lr: 5.896e-02, eta: 2 days, 21:05:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5717, loss_cls: 3.9029, loss: 3.9029 +2024-07-24 12:08:38,600 - pyskl - INFO - Epoch [67][1600/3746] lr: 5.893e-02, eta: 2 days, 21:04:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5502, loss_cls: 4.0341, loss: 4.0341 +2024-07-24 12:10:00,721 - pyskl - INFO - Epoch [67][1700/3746] lr: 5.890e-02, eta: 2 days, 21:03:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5613, loss_cls: 3.9742, loss: 3.9742 +2024-07-24 12:11:22,253 - pyskl - INFO - Epoch [67][1800/3746] lr: 5.887e-02, eta: 2 days, 21:02:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5689, loss_cls: 3.9635, loss: 3.9635 +2024-07-24 12:12:44,465 - pyskl - INFO - Epoch [67][1900/3746] lr: 5.885e-02, eta: 2 days, 21:00:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5533, loss_cls: 3.9979, loss: 3.9979 +2024-07-24 12:14:06,235 - pyskl - INFO - Epoch [67][2000/3746] lr: 5.882e-02, eta: 2 days, 20:59:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5695, loss_cls: 3.9405, loss: 3.9405 +2024-07-24 12:15:27,371 - pyskl - INFO - Epoch [67][2100/3746] lr: 5.879e-02, eta: 2 days, 20:58:19, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5592, loss_cls: 3.9728, loss: 3.9728 +2024-07-24 12:16:49,101 - pyskl - INFO - Epoch [67][2200/3746] lr: 5.876e-02, eta: 2 days, 20:57:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5595, loss_cls: 4.0004, loss: 4.0004 +2024-07-24 12:18:10,726 - pyskl - INFO - Epoch [67][2300/3746] lr: 5.874e-02, eta: 2 days, 20:55:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5550, loss_cls: 3.9935, loss: 3.9935 +2024-07-24 12:19:32,821 - pyskl - INFO - Epoch [67][2400/3746] lr: 5.871e-02, eta: 2 days, 20:54:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5673, loss_cls: 3.9398, loss: 3.9398 +2024-07-24 12:20:54,388 - pyskl - INFO - Epoch [67][2500/3746] lr: 5.868e-02, eta: 2 days, 20:53:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5567, loss_cls: 4.0194, loss: 4.0194 +2024-07-24 12:22:16,533 - pyskl - INFO - Epoch [67][2600/3746] lr: 5.865e-02, eta: 2 days, 20:51:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5533, loss_cls: 4.0232, loss: 4.0232 +2024-07-24 12:23:37,891 - pyskl - INFO - Epoch [67][2700/3746] lr: 5.863e-02, eta: 2 days, 20:50:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5608, loss_cls: 3.9629, loss: 3.9629 +2024-07-24 12:24:59,621 - pyskl - INFO - Epoch [67][2800/3746] lr: 5.860e-02, eta: 2 days, 20:49:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5675, loss_cls: 3.9714, loss: 3.9714 +2024-07-24 12:26:21,196 - pyskl - INFO - Epoch [67][2900/3746] lr: 5.857e-02, eta: 2 days, 20:48:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5494, loss_cls: 4.0002, loss: 4.0002 +2024-07-24 12:27:42,156 - pyskl - INFO - Epoch [67][3000/3746] lr: 5.854e-02, eta: 2 days, 20:46:48, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5587, loss_cls: 3.9877, loss: 3.9877 +2024-07-24 12:29:03,896 - pyskl - INFO - Epoch [67][3100/3746] lr: 5.852e-02, eta: 2 days, 20:45:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5614, loss_cls: 3.9815, loss: 3.9815 +2024-07-24 12:30:25,429 - pyskl - INFO - Epoch [67][3200/3746] lr: 5.849e-02, eta: 2 days, 20:44:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5572, loss_cls: 4.0212, loss: 4.0212 +2024-07-24 12:31:46,987 - pyskl - INFO - Epoch [67][3300/3746] lr: 5.846e-02, eta: 2 days, 20:42:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5606, loss_cls: 3.9963, loss: 3.9963 +2024-07-24 12:33:08,681 - pyskl - INFO - Epoch [67][3400/3746] lr: 5.843e-02, eta: 2 days, 20:41:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5616, loss_cls: 4.0140, loss: 4.0140 +2024-07-24 12:34:29,996 - pyskl - INFO - Epoch [67][3500/3746] lr: 5.841e-02, eta: 2 days, 20:40:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5548, loss_cls: 4.0002, loss: 4.0002 +2024-07-24 12:35:52,114 - pyskl - INFO - Epoch [67][3600/3746] lr: 5.838e-02, eta: 2 days, 20:39:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5592, loss_cls: 3.9885, loss: 3.9885 +2024-07-24 12:37:13,721 - pyskl - INFO - Epoch [67][3700/3746] lr: 5.835e-02, eta: 2 days, 20:37:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5613, loss_cls: 3.9553, loss: 3.9553 +2024-07-24 12:37:53,321 - pyskl - INFO - Saving checkpoint at 67 epochs +2024-07-24 12:39:46,568 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 12:39:47,232 - pyskl - INFO - +top1_acc 0.2369 +top5_acc 0.4791 +2024-07-24 12:39:47,232 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 12:39:47,273 - pyskl - INFO - +mean_acc 0.2368 +2024-07-24 12:39:47,285 - pyskl - INFO - Epoch(val) [67][309] top1_acc: 0.2369, top5_acc: 0.4791, mean_class_accuracy: 0.2368 +2024-07-24 12:43:35,759 - pyskl - INFO - Epoch [68][100/3746] lr: 5.831e-02, eta: 2 days, 20:38:15, time: 2.285, data_time: 1.308, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5594, loss_cls: 3.9681, loss: 3.9681 +2024-07-24 12:44:57,784 - pyskl - INFO - Epoch [68][200/3746] lr: 5.828e-02, eta: 2 days, 20:36:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5727, loss_cls: 3.8864, loss: 3.8864 +2024-07-24 12:46:19,226 - pyskl - INFO - Epoch [68][300/3746] lr: 5.826e-02, eta: 2 days, 20:35:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5808, loss_cls: 3.8814, loss: 3.8814 +2024-07-24 12:47:42,007 - pyskl - INFO - Epoch [68][400/3746] lr: 5.823e-02, eta: 2 days, 20:34:26, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5672, loss_cls: 3.9341, loss: 3.9341 +2024-07-24 12:49:03,667 - pyskl - INFO - Epoch [68][500/3746] lr: 5.820e-02, eta: 2 days, 20:33:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5605, loss_cls: 3.9693, loss: 3.9693 +2024-07-24 12:50:25,358 - pyskl - INFO - Epoch [68][600/3746] lr: 5.817e-02, eta: 2 days, 20:31:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5631, loss_cls: 3.9624, loss: 3.9624 +2024-07-24 12:51:46,752 - pyskl - INFO - Epoch [68][700/3746] lr: 5.815e-02, eta: 2 days, 20:30:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5694, loss_cls: 3.9359, loss: 3.9359 +2024-07-24 12:53:08,136 - pyskl - INFO - Epoch [68][800/3746] lr: 5.812e-02, eta: 2 days, 20:29:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5623, loss_cls: 3.9381, loss: 3.9381 +2024-07-24 12:54:29,219 - pyskl - INFO - Epoch [68][900/3746] lr: 5.809e-02, eta: 2 days, 20:28:00, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5598, loss_cls: 3.9762, loss: 3.9762 +2024-07-24 12:55:50,760 - pyskl - INFO - Epoch [68][1000/3746] lr: 5.806e-02, eta: 2 days, 20:26:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5556, loss_cls: 3.9891, loss: 3.9891 +2024-07-24 12:57:12,724 - pyskl - INFO - Epoch [68][1100/3746] lr: 5.804e-02, eta: 2 days, 20:25:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5663, loss_cls: 4.0106, loss: 4.0106 +2024-07-24 12:58:34,579 - pyskl - INFO - Epoch [68][1200/3746] lr: 5.801e-02, eta: 2 days, 20:24:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5750, loss_cls: 3.9092, loss: 3.9092 +2024-07-24 12:59:56,551 - pyskl - INFO - Epoch [68][1300/3746] lr: 5.798e-02, eta: 2 days, 20:22:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5575, loss_cls: 3.9672, loss: 3.9672 +2024-07-24 13:01:18,630 - pyskl - INFO - Epoch [68][1400/3746] lr: 5.795e-02, eta: 2 days, 20:21:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5580, loss_cls: 3.9831, loss: 3.9831 +2024-07-24 13:02:40,308 - pyskl - INFO - Epoch [68][1500/3746] lr: 5.792e-02, eta: 2 days, 20:20:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5591, loss_cls: 3.9856, loss: 3.9856 +2024-07-24 13:04:01,952 - pyskl - INFO - Epoch [68][1600/3746] lr: 5.790e-02, eta: 2 days, 20:19:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5683, loss_cls: 3.9528, loss: 3.9528 +2024-07-24 13:05:24,056 - pyskl - INFO - Epoch [68][1700/3746] lr: 5.787e-02, eta: 2 days, 20:17:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5586, loss_cls: 4.0000, loss: 4.0000 +2024-07-24 13:06:45,737 - pyskl - INFO - Epoch [68][1800/3746] lr: 5.784e-02, eta: 2 days, 20:16:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5622, loss_cls: 3.9472, loss: 3.9472 +2024-07-24 13:08:06,966 - pyskl - INFO - Epoch [68][1900/3746] lr: 5.781e-02, eta: 2 days, 20:15:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5736, loss_cls: 3.9200, loss: 3.9200 +2024-07-24 13:09:28,907 - pyskl - INFO - Epoch [68][2000/3746] lr: 5.779e-02, eta: 2 days, 20:13:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5641, loss_cls: 3.9446, loss: 3.9446 +2024-07-24 13:10:50,256 - pyskl - INFO - Epoch [68][2100/3746] lr: 5.776e-02, eta: 2 days, 20:12:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5631, loss_cls: 3.9670, loss: 3.9670 +2024-07-24 13:12:11,680 - pyskl - INFO - Epoch [68][2200/3746] lr: 5.773e-02, eta: 2 days, 20:11:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5552, loss_cls: 3.9943, loss: 3.9943 +2024-07-24 13:13:33,094 - pyskl - INFO - Epoch [68][2300/3746] lr: 5.770e-02, eta: 2 days, 20:10:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5642, loss_cls: 3.9575, loss: 3.9575 +2024-07-24 13:14:55,029 - pyskl - INFO - Epoch [68][2400/3746] lr: 5.768e-02, eta: 2 days, 20:08:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5706, loss_cls: 3.9379, loss: 3.9379 +2024-07-24 13:16:16,335 - pyskl - INFO - Epoch [68][2500/3746] lr: 5.765e-02, eta: 2 days, 20:07:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5616, loss_cls: 3.9762, loss: 3.9762 +2024-07-24 13:17:37,591 - pyskl - INFO - Epoch [68][2600/3746] lr: 5.762e-02, eta: 2 days, 20:06:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5708, loss_cls: 3.9446, loss: 3.9446 +2024-07-24 13:18:59,145 - pyskl - INFO - Epoch [68][2700/3746] lr: 5.759e-02, eta: 2 days, 20:04:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5619, loss_cls: 3.9922, loss: 3.9922 +2024-07-24 13:20:20,802 - pyskl - INFO - Epoch [68][2800/3746] lr: 5.757e-02, eta: 2 days, 20:03:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5559, loss_cls: 4.0150, loss: 4.0150 +2024-07-24 13:21:42,847 - pyskl - INFO - Epoch [68][2900/3746] lr: 5.754e-02, eta: 2 days, 20:02:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5525, loss_cls: 4.0164, loss: 4.0164 +2024-07-24 13:23:04,425 - pyskl - INFO - Epoch [68][3000/3746] lr: 5.751e-02, eta: 2 days, 20:01:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5602, loss_cls: 3.9761, loss: 3.9761 +2024-07-24 13:24:25,962 - pyskl - INFO - Epoch [68][3100/3746] lr: 5.748e-02, eta: 2 days, 19:59:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5581, loss_cls: 4.0219, loss: 4.0219 +2024-07-24 13:25:46,929 - pyskl - INFO - Epoch [68][3200/3746] lr: 5.746e-02, eta: 2 days, 19:58:31, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5663, loss_cls: 3.9541, loss: 3.9541 +2024-07-24 13:27:08,294 - pyskl - INFO - Epoch [68][3300/3746] lr: 5.743e-02, eta: 2 days, 19:57:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5520, loss_cls: 3.9999, loss: 3.9999 +2024-07-24 13:28:29,602 - pyskl - INFO - Epoch [68][3400/3746] lr: 5.740e-02, eta: 2 days, 19:55:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5552, loss_cls: 4.0003, loss: 4.0003 +2024-07-24 13:29:50,948 - pyskl - INFO - Epoch [68][3500/3746] lr: 5.737e-02, eta: 2 days, 19:54:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5567, loss_cls: 4.0069, loss: 4.0069 +2024-07-24 13:31:13,035 - pyskl - INFO - Epoch [68][3600/3746] lr: 5.734e-02, eta: 2 days, 19:53:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5548, loss_cls: 4.0089, loss: 4.0089 +2024-07-24 13:32:34,503 - pyskl - INFO - Epoch [68][3700/3746] lr: 5.732e-02, eta: 2 days, 19:52:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5683, loss_cls: 3.9676, loss: 3.9676 +2024-07-24 13:33:13,977 - pyskl - INFO - Saving checkpoint at 68 epochs +2024-07-24 13:35:05,635 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 13:35:06,292 - pyskl - INFO - +top1_acc 0.2364 +top5_acc 0.4784 +2024-07-24 13:35:06,293 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 13:35:06,332 - pyskl - INFO - +mean_acc 0.2362 +2024-07-24 13:35:06,344 - pyskl - INFO - Epoch(val) [68][309] top1_acc: 0.2364, top5_acc: 0.4784, mean_class_accuracy: 0.2362 +2024-07-24 13:38:53,911 - pyskl - INFO - Epoch [69][100/3746] lr: 5.728e-02, eta: 2 days, 19:52:23, time: 2.276, data_time: 1.293, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5777, loss_cls: 3.9107, loss: 3.9107 +2024-07-24 13:40:15,645 - pyskl - INFO - Epoch [69][200/3746] lr: 5.725e-02, eta: 2 days, 19:51:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5689, loss_cls: 3.9085, loss: 3.9085 +2024-07-24 13:41:37,009 - pyskl - INFO - Epoch [69][300/3746] lr: 5.722e-02, eta: 2 days, 19:49:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5714, loss_cls: 3.9105, loss: 3.9105 +2024-07-24 13:42:58,353 - pyskl - INFO - Epoch [69][400/3746] lr: 5.719e-02, eta: 2 days, 19:48:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5731, loss_cls: 3.9212, loss: 3.9212 +2024-07-24 13:44:20,845 - pyskl - INFO - Epoch [69][500/3746] lr: 5.717e-02, eta: 2 days, 19:47:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5636, loss_cls: 3.9750, loss: 3.9750 +2024-07-24 13:45:42,284 - pyskl - INFO - Epoch [69][600/3746] lr: 5.714e-02, eta: 2 days, 19:45:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5722, loss_cls: 3.9208, loss: 3.9208 +2024-07-24 13:47:03,909 - pyskl - INFO - Epoch [69][700/3746] lr: 5.711e-02, eta: 2 days, 19:44:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5686, loss_cls: 3.9201, loss: 3.9201 +2024-07-24 13:48:25,811 - pyskl - INFO - Epoch [69][800/3746] lr: 5.708e-02, eta: 2 days, 19:43:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5591, loss_cls: 3.9696, loss: 3.9696 +2024-07-24 13:49:47,524 - pyskl - INFO - Epoch [69][900/3746] lr: 5.706e-02, eta: 2 days, 19:42:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5742, loss_cls: 3.9285, loss: 3.9285 +2024-07-24 13:51:08,568 - pyskl - INFO - Epoch [69][1000/3746] lr: 5.703e-02, eta: 2 days, 19:40:49, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5627, loss_cls: 3.9528, loss: 3.9528 +2024-07-24 13:52:29,983 - pyskl - INFO - Epoch [69][1100/3746] lr: 5.700e-02, eta: 2 days, 19:39:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5586, loss_cls: 3.9706, loss: 3.9706 +2024-07-24 13:53:52,574 - pyskl - INFO - Epoch [69][1200/3746] lr: 5.697e-02, eta: 2 days, 19:38:16, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5598, loss_cls: 3.9595, loss: 3.9595 +2024-07-24 13:55:14,711 - pyskl - INFO - Epoch [69][1300/3746] lr: 5.694e-02, eta: 2 days, 19:37:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5652, loss_cls: 3.9531, loss: 3.9531 +2024-07-24 13:56:37,413 - pyskl - INFO - Epoch [69][1400/3746] lr: 5.692e-02, eta: 2 days, 19:35:44, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5680, loss_cls: 3.9231, loss: 3.9231 +2024-07-24 13:57:59,753 - pyskl - INFO - Epoch [69][1500/3746] lr: 5.689e-02, eta: 2 days, 19:34:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5577, loss_cls: 3.9838, loss: 3.9838 +2024-07-24 13:59:21,445 - pyskl - INFO - Epoch [69][1600/3746] lr: 5.686e-02, eta: 2 days, 19:33:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5566, loss_cls: 4.0014, loss: 4.0014 +2024-07-24 14:00:42,843 - pyskl - INFO - Epoch [69][1700/3746] lr: 5.683e-02, eta: 2 days, 19:31:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5737, loss_cls: 3.9329, loss: 3.9329 +2024-07-24 14:02:04,287 - pyskl - INFO - Epoch [69][1800/3746] lr: 5.681e-02, eta: 2 days, 19:30:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5672, loss_cls: 3.9640, loss: 3.9640 +2024-07-24 14:03:25,529 - pyskl - INFO - Epoch [69][1900/3746] lr: 5.678e-02, eta: 2 days, 19:29:18, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5589, loss_cls: 3.9767, loss: 3.9767 +2024-07-24 14:04:46,713 - pyskl - INFO - Epoch [69][2000/3746] lr: 5.675e-02, eta: 2 days, 19:28:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5513, loss_cls: 4.0111, loss: 4.0111 +2024-07-24 14:06:08,185 - pyskl - INFO - Epoch [69][2100/3746] lr: 5.672e-02, eta: 2 days, 19:26:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5661, loss_cls: 3.9629, loss: 3.9629 +2024-07-24 14:07:29,636 - pyskl - INFO - Epoch [69][2200/3746] lr: 5.670e-02, eta: 2 days, 19:25:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5583, loss_cls: 4.0074, loss: 4.0074 +2024-07-24 14:08:50,849 - pyskl - INFO - Epoch [69][2300/3746] lr: 5.667e-02, eta: 2 days, 19:24:08, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5713, loss_cls: 3.9112, loss: 3.9112 +2024-07-24 14:10:12,639 - pyskl - INFO - Epoch [69][2400/3746] lr: 5.664e-02, eta: 2 days, 19:22:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5586, loss_cls: 3.9984, loss: 3.9984 +2024-07-24 14:11:34,077 - pyskl - INFO - Epoch [69][2500/3746] lr: 5.661e-02, eta: 2 days, 19:21:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5647, loss_cls: 3.9481, loss: 3.9481 +2024-07-24 14:12:55,315 - pyskl - INFO - Epoch [69][2600/3746] lr: 5.658e-02, eta: 2 days, 19:20:16, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5613, loss_cls: 3.9531, loss: 3.9531 +2024-07-24 14:14:16,963 - pyskl - INFO - Epoch [69][2700/3746] lr: 5.656e-02, eta: 2 days, 19:18:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5627, loss_cls: 3.9797, loss: 3.9797 +2024-07-24 14:15:38,423 - pyskl - INFO - Epoch [69][2800/3746] lr: 5.653e-02, eta: 2 days, 19:17:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5719, loss_cls: 3.9378, loss: 3.9378 +2024-07-24 14:16:59,796 - pyskl - INFO - Epoch [69][2900/3746] lr: 5.650e-02, eta: 2 days, 19:16:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5770, loss_cls: 3.9126, loss: 3.9126 +2024-07-24 14:18:21,669 - pyskl - INFO - Epoch [69][3000/3746] lr: 5.647e-02, eta: 2 days, 19:15:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5616, loss_cls: 3.9604, loss: 3.9604 +2024-07-24 14:19:43,215 - pyskl - INFO - Epoch [69][3100/3746] lr: 5.645e-02, eta: 2 days, 19:13:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5561, loss_cls: 4.0019, loss: 4.0019 +2024-07-24 14:21:04,949 - pyskl - INFO - Epoch [69][3200/3746] lr: 5.642e-02, eta: 2 days, 19:12:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5619, loss_cls: 3.9792, loss: 3.9792 +2024-07-24 14:22:26,389 - pyskl - INFO - Epoch [69][3300/3746] lr: 5.639e-02, eta: 2 days, 19:11:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5650, loss_cls: 3.9514, loss: 3.9514 +2024-07-24 14:23:47,714 - pyskl - INFO - Epoch [69][3400/3746] lr: 5.636e-02, eta: 2 days, 19:09:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5666, loss_cls: 3.9535, loss: 3.9535 +2024-07-24 14:25:09,258 - pyskl - INFO - Epoch [69][3500/3746] lr: 5.634e-02, eta: 2 days, 19:08:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5592, loss_cls: 3.9619, loss: 3.9619 +2024-07-24 14:26:31,217 - pyskl - INFO - Epoch [69][3600/3746] lr: 5.631e-02, eta: 2 days, 19:07:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5667, loss_cls: 3.9432, loss: 3.9432 +2024-07-24 14:27:53,000 - pyskl - INFO - Epoch [69][3700/3746] lr: 5.628e-02, eta: 2 days, 19:06:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5703, loss_cls: 3.9357, loss: 3.9357 +2024-07-24 14:28:32,478 - pyskl - INFO - Saving checkpoint at 69 epochs +2024-07-24 14:30:25,013 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 14:30:25,690 - pyskl - INFO - +top1_acc 0.2457 +top5_acc 0.4946 +2024-07-24 14:30:25,690 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 14:30:25,729 - pyskl - INFO - +mean_acc 0.2454 +2024-07-24 14:30:25,740 - pyskl - INFO - Epoch(val) [69][309] top1_acc: 0.2457, top5_acc: 0.4946, mean_class_accuracy: 0.2454 +2024-07-24 14:34:16,376 - pyskl - INFO - Epoch [70][100/3746] lr: 5.624e-02, eta: 2 days, 19:06:24, time: 2.306, data_time: 1.325, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5720, loss_cls: 3.9152, loss: 3.9152 +2024-07-24 14:35:38,450 - pyskl - INFO - Epoch [70][200/3746] lr: 5.621e-02, eta: 2 days, 19:05:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5614, loss_cls: 3.9510, loss: 3.9510 +2024-07-24 14:37:00,559 - pyskl - INFO - Epoch [70][300/3746] lr: 5.618e-02, eta: 2 days, 19:03:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5705, loss_cls: 3.9070, loss: 3.9070 +2024-07-24 14:38:22,223 - pyskl - INFO - Epoch [70][400/3746] lr: 5.616e-02, eta: 2 days, 19:02:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5772, loss_cls: 3.8736, loss: 3.8736 +2024-07-24 14:39:43,984 - pyskl - INFO - Epoch [70][500/3746] lr: 5.613e-02, eta: 2 days, 19:01:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5708, loss_cls: 3.9350, loss: 3.9350 +2024-07-24 14:41:06,025 - pyskl - INFO - Epoch [70][600/3746] lr: 5.610e-02, eta: 2 days, 18:59:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5666, loss_cls: 3.9414, loss: 3.9414 +2024-07-24 14:42:27,744 - pyskl - INFO - Epoch [70][700/3746] lr: 5.607e-02, eta: 2 days, 18:58:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5741, loss_cls: 3.9436, loss: 3.9436 +2024-07-24 14:43:49,136 - pyskl - INFO - Epoch [70][800/3746] lr: 5.605e-02, eta: 2 days, 18:57:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5655, loss_cls: 3.9370, loss: 3.9370 +2024-07-24 14:45:10,692 - pyskl - INFO - Epoch [70][900/3746] lr: 5.602e-02, eta: 2 days, 18:56:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5556, loss_cls: 3.9801, loss: 3.9801 +2024-07-24 14:46:31,993 - pyskl - INFO - Epoch [70][1000/3746] lr: 5.599e-02, eta: 2 days, 18:54:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5787, loss_cls: 3.9022, loss: 3.9022 +2024-07-24 14:47:53,642 - pyskl - INFO - Epoch [70][1100/3746] lr: 5.596e-02, eta: 2 days, 18:53:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5653, loss_cls: 3.9621, loss: 3.9621 +2024-07-24 14:49:15,413 - pyskl - INFO - Epoch [70][1200/3746] lr: 5.593e-02, eta: 2 days, 18:52:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5766, loss_cls: 3.8819, loss: 3.8819 +2024-07-24 14:50:37,710 - pyskl - INFO - Epoch [70][1300/3746] lr: 5.591e-02, eta: 2 days, 18:50:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5564, loss_cls: 3.9868, loss: 3.9868 +2024-07-24 14:52:00,487 - pyskl - INFO - Epoch [70][1400/3746] lr: 5.588e-02, eta: 2 days, 18:49:42, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5727, loss_cls: 3.9411, loss: 3.9411 +2024-07-24 14:53:22,623 - pyskl - INFO - Epoch [70][1500/3746] lr: 5.585e-02, eta: 2 days, 18:48:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5537, loss_cls: 3.9775, loss: 3.9775 +2024-07-24 14:54:45,267 - pyskl - INFO - Epoch [70][1600/3746] lr: 5.582e-02, eta: 2 days, 18:47:09, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5659, loss_cls: 3.9495, loss: 3.9495 +2024-07-24 14:56:06,864 - pyskl - INFO - Epoch [70][1700/3746] lr: 5.580e-02, eta: 2 days, 18:45:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5706, loss_cls: 3.9356, loss: 3.9356 +2024-07-24 14:57:28,712 - pyskl - INFO - Epoch [70][1800/3746] lr: 5.577e-02, eta: 2 days, 18:44:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5516, loss_cls: 4.0115, loss: 4.0115 +2024-07-24 14:58:50,963 - pyskl - INFO - Epoch [70][1900/3746] lr: 5.574e-02, eta: 2 days, 18:43:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5616, loss_cls: 3.9582, loss: 3.9582 +2024-07-24 15:00:12,826 - pyskl - INFO - Epoch [70][2000/3746] lr: 5.571e-02, eta: 2 days, 18:42:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5741, loss_cls: 3.9200, loss: 3.9200 +2024-07-24 15:01:34,339 - pyskl - INFO - Epoch [70][2100/3746] lr: 5.568e-02, eta: 2 days, 18:40:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5700, loss_cls: 3.9839, loss: 3.9839 +2024-07-24 15:02:55,735 - pyskl - INFO - Epoch [70][2200/3746] lr: 5.566e-02, eta: 2 days, 18:39:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5573, loss_cls: 3.9971, loss: 3.9971 +2024-07-24 15:04:17,102 - pyskl - INFO - Epoch [70][2300/3746] lr: 5.563e-02, eta: 2 days, 18:38:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5709, loss_cls: 3.9573, loss: 3.9573 +2024-07-24 15:05:38,689 - pyskl - INFO - Epoch [70][2400/3746] lr: 5.560e-02, eta: 2 days, 18:36:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5645, loss_cls: 3.9350, loss: 3.9350 +2024-07-24 15:07:00,212 - pyskl - INFO - Epoch [70][2500/3746] lr: 5.557e-02, eta: 2 days, 18:35:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5694, loss_cls: 3.9481, loss: 3.9481 +2024-07-24 15:08:22,108 - pyskl - INFO - Epoch [70][2600/3746] lr: 5.555e-02, eta: 2 days, 18:34:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5675, loss_cls: 3.9542, loss: 3.9542 +2024-07-24 15:09:43,946 - pyskl - INFO - Epoch [70][2700/3746] lr: 5.552e-02, eta: 2 days, 18:32:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5623, loss_cls: 3.9656, loss: 3.9656 +2024-07-24 15:11:05,661 - pyskl - INFO - Epoch [70][2800/3746] lr: 5.549e-02, eta: 2 days, 18:31:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5695, loss_cls: 3.9314, loss: 3.9314 +2024-07-24 15:12:27,476 - pyskl - INFO - Epoch [70][2900/3746] lr: 5.546e-02, eta: 2 days, 18:30:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5616, loss_cls: 4.0053, loss: 4.0053 +2024-07-24 15:13:49,038 - pyskl - INFO - Epoch [70][3000/3746] lr: 5.543e-02, eta: 2 days, 18:29:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5605, loss_cls: 3.9482, loss: 3.9482 +2024-07-24 15:15:10,720 - pyskl - INFO - Epoch [70][3100/3746] lr: 5.541e-02, eta: 2 days, 18:27:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5633, loss_cls: 3.9715, loss: 3.9715 +2024-07-24 15:16:32,244 - pyskl - INFO - Epoch [70][3200/3746] lr: 5.538e-02, eta: 2 days, 18:26:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5675, loss_cls: 3.9726, loss: 3.9726 +2024-07-24 15:17:53,823 - pyskl - INFO - Epoch [70][3300/3746] lr: 5.535e-02, eta: 2 days, 18:25:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5752, loss_cls: 3.9211, loss: 3.9211 +2024-07-24 15:19:15,241 - pyskl - INFO - Epoch [70][3400/3746] lr: 5.532e-02, eta: 2 days, 18:23:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5606, loss_cls: 3.9973, loss: 3.9973 +2024-07-24 15:20:36,491 - pyskl - INFO - Epoch [70][3500/3746] lr: 5.530e-02, eta: 2 days, 18:22:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5806, loss_cls: 3.8725, loss: 3.8725 +2024-07-24 15:21:58,867 - pyskl - INFO - Epoch [70][3600/3746] lr: 5.527e-02, eta: 2 days, 18:21:22, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5695, loss_cls: 3.9174, loss: 3.9174 +2024-07-24 15:23:19,970 - pyskl - INFO - Epoch [70][3700/3746] lr: 5.524e-02, eta: 2 days, 18:20:04, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5552, loss_cls: 3.9655, loss: 3.9655 +2024-07-24 15:23:59,834 - pyskl - INFO - Saving checkpoint at 70 epochs +2024-07-24 15:25:51,686 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 15:25:52,506 - pyskl - INFO - +top1_acc 0.2416 +top5_acc 0.4915 +2024-07-24 15:25:52,507 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 15:25:52,557 - pyskl - INFO - +mean_acc 0.2416 +2024-07-24 15:25:52,571 - pyskl - INFO - Epoch(val) [70][309] top1_acc: 0.2416, top5_acc: 0.4915, mean_class_accuracy: 0.2416 +2024-07-24 15:29:43,829 - pyskl - INFO - Epoch [71][100/3746] lr: 5.520e-02, eta: 2 days, 18:20:19, time: 2.312, data_time: 1.331, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5691, loss_cls: 3.9450, loss: 3.9450 +2024-07-24 15:31:05,742 - pyskl - INFO - Epoch [71][200/3746] lr: 5.517e-02, eta: 2 days, 18:19:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5753, loss_cls: 3.8859, loss: 3.8859 +2024-07-24 15:32:27,678 - pyskl - INFO - Epoch [71][300/3746] lr: 5.514e-02, eta: 2 days, 18:17:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5677, loss_cls: 3.9213, loss: 3.9213 +2024-07-24 15:33:49,614 - pyskl - INFO - Epoch [71][400/3746] lr: 5.512e-02, eta: 2 days, 18:16:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5830, loss_cls: 3.8673, loss: 3.8673 +2024-07-24 15:35:11,391 - pyskl - INFO - Epoch [71][500/3746] lr: 5.509e-02, eta: 2 days, 18:15:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5820, loss_cls: 3.8589, loss: 3.8589 +2024-07-24 15:36:33,234 - pyskl - INFO - Epoch [71][600/3746] lr: 5.506e-02, eta: 2 days, 18:13:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5673, loss_cls: 3.9474, loss: 3.9474 +2024-07-24 15:37:55,058 - pyskl - INFO - Epoch [71][700/3746] lr: 5.503e-02, eta: 2 days, 18:12:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5719, loss_cls: 3.9334, loss: 3.9334 +2024-07-24 15:39:17,149 - pyskl - INFO - Epoch [71][800/3746] lr: 5.500e-02, eta: 2 days, 18:11:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5739, loss_cls: 3.9049, loss: 3.9049 +2024-07-24 15:40:38,746 - pyskl - INFO - Epoch [71][900/3746] lr: 5.498e-02, eta: 2 days, 18:10:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5814, loss_cls: 3.8803, loss: 3.8803 +2024-07-24 15:42:00,020 - pyskl - INFO - Epoch [71][1000/3746] lr: 5.495e-02, eta: 2 days, 18:08:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5622, loss_cls: 3.9779, loss: 3.9779 +2024-07-24 15:43:21,633 - pyskl - INFO - Epoch [71][1100/3746] lr: 5.492e-02, eta: 2 days, 18:07:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5797, loss_cls: 3.8989, loss: 3.8989 +2024-07-24 15:44:43,743 - pyskl - INFO - Epoch [71][1200/3746] lr: 5.489e-02, eta: 2 days, 18:06:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5725, loss_cls: 3.9117, loss: 3.9117 +2024-07-24 15:46:06,144 - pyskl - INFO - Epoch [71][1300/3746] lr: 5.487e-02, eta: 2 days, 18:04:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5603, loss_cls: 3.9717, loss: 3.9717 +2024-07-24 15:47:28,551 - pyskl - INFO - Epoch [71][1400/3746] lr: 5.484e-02, eta: 2 days, 18:03:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5677, loss_cls: 3.9266, loss: 3.9266 +2024-07-24 15:48:50,498 - pyskl - INFO - Epoch [71][1500/3746] lr: 5.481e-02, eta: 2 days, 18:02:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5609, loss_cls: 3.9415, loss: 3.9415 +2024-07-24 15:50:13,337 - pyskl - INFO - Epoch [71][1600/3746] lr: 5.478e-02, eta: 2 days, 18:01:02, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5639, loss_cls: 3.9704, loss: 3.9704 +2024-07-24 15:51:35,311 - pyskl - INFO - Epoch [71][1700/3746] lr: 5.475e-02, eta: 2 days, 17:59:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5695, loss_cls: 3.9423, loss: 3.9423 +2024-07-24 15:52:56,723 - pyskl - INFO - Epoch [71][1800/3746] lr: 5.473e-02, eta: 2 days, 17:58:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5667, loss_cls: 3.9493, loss: 3.9493 +2024-07-24 15:54:18,247 - pyskl - INFO - Epoch [71][1900/3746] lr: 5.470e-02, eta: 2 days, 17:57:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5658, loss_cls: 3.9361, loss: 3.9361 +2024-07-24 15:55:39,936 - pyskl - INFO - Epoch [71][2000/3746] lr: 5.467e-02, eta: 2 days, 17:55:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5675, loss_cls: 3.9487, loss: 3.9487 +2024-07-24 15:57:01,474 - pyskl - INFO - Epoch [71][2100/3746] lr: 5.464e-02, eta: 2 days, 17:54:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5681, loss_cls: 3.9312, loss: 3.9312 +2024-07-24 15:58:23,058 - pyskl - INFO - Epoch [71][2200/3746] lr: 5.461e-02, eta: 2 days, 17:53:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5756, loss_cls: 3.8941, loss: 3.8941 +2024-07-24 15:59:44,628 - pyskl - INFO - Epoch [71][2300/3746] lr: 5.459e-02, eta: 2 days, 17:51:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5680, loss_cls: 3.9470, loss: 3.9470 +2024-07-24 16:01:06,604 - pyskl - INFO - Epoch [71][2400/3746] lr: 5.456e-02, eta: 2 days, 17:50:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5606, loss_cls: 3.9251, loss: 3.9251 +2024-07-24 16:02:28,857 - pyskl - INFO - Epoch [71][2500/3746] lr: 5.453e-02, eta: 2 days, 17:49:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5686, loss_cls: 3.9313, loss: 3.9313 +2024-07-24 16:03:50,648 - pyskl - INFO - Epoch [71][2600/3746] lr: 5.450e-02, eta: 2 days, 17:48:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5752, loss_cls: 3.8678, loss: 3.8678 +2024-07-24 16:05:12,129 - pyskl - INFO - Epoch [71][2700/3746] lr: 5.448e-02, eta: 2 days, 17:46:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5667, loss_cls: 3.9593, loss: 3.9593 +2024-07-24 16:06:33,944 - pyskl - INFO - Epoch [71][2800/3746] lr: 5.445e-02, eta: 2 days, 17:45:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5703, loss_cls: 3.9363, loss: 3.9363 +2024-07-24 16:07:55,526 - pyskl - INFO - Epoch [71][2900/3746] lr: 5.442e-02, eta: 2 days, 17:44:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5689, loss_cls: 3.9544, loss: 3.9544 +2024-07-24 16:09:17,022 - pyskl - INFO - Epoch [71][3000/3746] lr: 5.439e-02, eta: 2 days, 17:42:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5637, loss_cls: 3.9748, loss: 3.9748 +2024-07-24 16:10:38,565 - pyskl - INFO - Epoch [71][3100/3746] lr: 5.436e-02, eta: 2 days, 17:41:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5711, loss_cls: 3.9075, loss: 3.9075 +2024-07-24 16:11:59,848 - pyskl - INFO - Epoch [71][3200/3746] lr: 5.434e-02, eta: 2 days, 17:40:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5598, loss_cls: 3.9795, loss: 3.9795 +2024-07-24 16:13:21,160 - pyskl - INFO - Epoch [71][3300/3746] lr: 5.431e-02, eta: 2 days, 17:39:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5578, loss_cls: 3.9969, loss: 3.9969 +2024-07-24 16:14:42,485 - pyskl - INFO - Epoch [71][3400/3746] lr: 5.428e-02, eta: 2 days, 17:37:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5692, loss_cls: 3.9392, loss: 3.9392 +2024-07-24 16:16:04,109 - pyskl - INFO - Epoch [71][3500/3746] lr: 5.425e-02, eta: 2 days, 17:36:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5637, loss_cls: 3.9500, loss: 3.9500 +2024-07-24 16:17:26,305 - pyskl - INFO - Epoch [71][3600/3746] lr: 5.422e-02, eta: 2 days, 17:35:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5555, loss_cls: 3.9800, loss: 3.9800 +2024-07-24 16:18:47,857 - pyskl - INFO - Epoch [71][3700/3746] lr: 5.420e-02, eta: 2 days, 17:33:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5684, loss_cls: 3.9058, loss: 3.9058 +2024-07-24 16:19:27,205 - pyskl - INFO - Saving checkpoint at 71 epochs +2024-07-24 16:21:19,817 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 16:21:20,612 - pyskl - INFO - +top1_acc 0.2554 +top5_acc 0.4958 +2024-07-24 16:21:20,612 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 16:21:20,657 - pyskl - INFO - +mean_acc 0.2553 +2024-07-24 16:21:20,662 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_65.pth was removed +2024-07-24 16:21:20,940 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_71.pth. +2024-07-24 16:21:20,941 - pyskl - INFO - Best top1_acc is 0.2554 at 71 epoch. +2024-07-24 16:21:20,957 - pyskl - INFO - Epoch(val) [71][309] top1_acc: 0.2554, top5_acc: 0.4958, mean_class_accuracy: 0.2553 +2024-07-24 16:25:19,408 - pyskl - INFO - Epoch [72][100/3746] lr: 5.416e-02, eta: 2 days, 17:34:11, time: 2.384, data_time: 1.391, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5859, loss_cls: 3.8454, loss: 3.8454 +2024-07-24 16:26:42,853 - pyskl - INFO - Epoch [72][200/3746] lr: 5.413e-02, eta: 2 days, 17:32:55, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5803, loss_cls: 3.8630, loss: 3.8630 +2024-07-24 16:28:06,554 - pyskl - INFO - Epoch [72][300/3746] lr: 5.410e-02, eta: 2 days, 17:31:40, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5823, loss_cls: 3.8192, loss: 3.8192 +2024-07-24 16:29:29,084 - pyskl - INFO - Epoch [72][400/3746] lr: 5.407e-02, eta: 2 days, 17:30:23, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5683, loss_cls: 3.9346, loss: 3.9346 +2024-07-24 16:30:51,345 - pyskl - INFO - Epoch [72][500/3746] lr: 5.404e-02, eta: 2 days, 17:29:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5728, loss_cls: 3.9360, loss: 3.9360 +2024-07-24 16:32:13,375 - pyskl - INFO - Epoch [72][600/3746] lr: 5.402e-02, eta: 2 days, 17:27:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5686, loss_cls: 3.9456, loss: 3.9456 +2024-07-24 16:33:35,208 - pyskl - INFO - Epoch [72][700/3746] lr: 5.399e-02, eta: 2 days, 17:26:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5727, loss_cls: 3.9176, loss: 3.9176 +2024-07-24 16:34:57,376 - pyskl - INFO - Epoch [72][800/3746] lr: 5.396e-02, eta: 2 days, 17:25:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5678, loss_cls: 3.9264, loss: 3.9264 +2024-07-24 16:36:18,662 - pyskl - INFO - Epoch [72][900/3746] lr: 5.393e-02, eta: 2 days, 17:23:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5741, loss_cls: 3.9282, loss: 3.9282 +2024-07-24 16:37:40,731 - pyskl - INFO - Epoch [72][1000/3746] lr: 5.391e-02, eta: 2 days, 17:22:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5756, loss_cls: 3.8812, loss: 3.8812 +2024-07-24 16:39:02,893 - pyskl - INFO - Epoch [72][1100/3746] lr: 5.388e-02, eta: 2 days, 17:21:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5637, loss_cls: 3.9874, loss: 3.9874 +2024-07-24 16:40:24,501 - pyskl - INFO - Epoch [72][1200/3746] lr: 5.385e-02, eta: 2 days, 17:20:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5859, loss_cls: 3.8704, loss: 3.8704 +2024-07-24 16:41:46,574 - pyskl - INFO - Epoch [72][1300/3746] lr: 5.382e-02, eta: 2 days, 17:18:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5730, loss_cls: 3.9101, loss: 3.9101 +2024-07-24 16:43:09,057 - pyskl - INFO - Epoch [72][1400/3746] lr: 5.379e-02, eta: 2 days, 17:17:30, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5697, loss_cls: 3.9309, loss: 3.9309 +2024-07-24 16:44:31,118 - pyskl - INFO - Epoch [72][1500/3746] lr: 5.377e-02, eta: 2 days, 17:16:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5817, loss_cls: 3.8585, loss: 3.8585 +2024-07-24 16:45:52,985 - pyskl - INFO - Epoch [72][1600/3746] lr: 5.374e-02, eta: 2 days, 17:14:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5766, loss_cls: 3.8900, loss: 3.8900 +2024-07-24 16:47:14,531 - pyskl - INFO - Epoch [72][1700/3746] lr: 5.371e-02, eta: 2 days, 17:13:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5755, loss_cls: 3.9237, loss: 3.9237 +2024-07-24 16:48:36,224 - pyskl - INFO - Epoch [72][1800/3746] lr: 5.368e-02, eta: 2 days, 17:12:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5605, loss_cls: 3.9468, loss: 3.9468 +2024-07-24 16:49:58,640 - pyskl - INFO - Epoch [72][1900/3746] lr: 5.365e-02, eta: 2 days, 17:11:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5691, loss_cls: 3.9380, loss: 3.9380 +2024-07-24 16:51:20,530 - pyskl - INFO - Epoch [72][2000/3746] lr: 5.363e-02, eta: 2 days, 17:09:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5631, loss_cls: 3.9576, loss: 3.9576 +2024-07-24 16:52:42,033 - pyskl - INFO - Epoch [72][2100/3746] lr: 5.360e-02, eta: 2 days, 17:08:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5681, loss_cls: 3.9416, loss: 3.9416 +2024-07-24 16:54:04,162 - pyskl - INFO - Epoch [72][2200/3746] lr: 5.357e-02, eta: 2 days, 17:07:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5606, loss_cls: 3.9494, loss: 3.9494 +2024-07-24 16:55:25,459 - pyskl - INFO - Epoch [72][2300/3746] lr: 5.354e-02, eta: 2 days, 17:05:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5534, loss_cls: 4.0116, loss: 4.0116 +2024-07-24 16:56:47,357 - pyskl - INFO - Epoch [72][2400/3746] lr: 5.352e-02, eta: 2 days, 17:04:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5739, loss_cls: 3.8886, loss: 3.8886 +2024-07-24 16:58:09,192 - pyskl - INFO - Epoch [72][2500/3746] lr: 5.349e-02, eta: 2 days, 17:03:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5809, loss_cls: 3.9203, loss: 3.9203 +2024-07-24 16:59:31,320 - pyskl - INFO - Epoch [72][2600/3746] lr: 5.346e-02, eta: 2 days, 17:01:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5600, loss_cls: 3.9622, loss: 3.9622 +2024-07-24 17:00:53,681 - pyskl - INFO - Epoch [72][2700/3746] lr: 5.343e-02, eta: 2 days, 17:00:42, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5817, loss_cls: 3.8936, loss: 3.8936 +2024-07-24 17:02:14,948 - pyskl - INFO - Epoch [72][2800/3746] lr: 5.340e-02, eta: 2 days, 16:59:23, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5584, loss_cls: 3.9937, loss: 3.9937 +2024-07-24 17:03:36,279 - pyskl - INFO - Epoch [72][2900/3746] lr: 5.338e-02, eta: 2 days, 16:58:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5692, loss_cls: 3.9670, loss: 3.9670 +2024-07-24 17:04:58,693 - pyskl - INFO - Epoch [72][3000/3746] lr: 5.335e-02, eta: 2 days, 16:56:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5766, loss_cls: 3.9042, loss: 3.9042 +2024-07-24 17:06:20,756 - pyskl - INFO - Epoch [72][3100/3746] lr: 5.332e-02, eta: 2 days, 16:55:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5595, loss_cls: 3.9670, loss: 3.9670 +2024-07-24 17:07:42,242 - pyskl - INFO - Epoch [72][3200/3746] lr: 5.329e-02, eta: 2 days, 16:54:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5572, loss_cls: 3.9488, loss: 3.9488 +2024-07-24 17:09:03,840 - pyskl - INFO - Epoch [72][3300/3746] lr: 5.326e-02, eta: 2 days, 16:52:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5752, loss_cls: 3.8678, loss: 3.8678 +2024-07-24 17:10:25,654 - pyskl - INFO - Epoch [72][3400/3746] lr: 5.324e-02, eta: 2 days, 16:51:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5716, loss_cls: 3.9223, loss: 3.9223 +2024-07-24 17:11:47,627 - pyskl - INFO - Epoch [72][3500/3746] lr: 5.321e-02, eta: 2 days, 16:50:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5817, loss_cls: 3.8590, loss: 3.8590 +2024-07-24 17:13:09,849 - pyskl - INFO - Epoch [72][3600/3746] lr: 5.318e-02, eta: 2 days, 16:49:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5761, loss_cls: 3.9430, loss: 3.9430 +2024-07-24 17:14:32,124 - pyskl - INFO - Epoch [72][3700/3746] lr: 5.315e-02, eta: 2 days, 16:47:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5539, loss_cls: 4.0067, loss: 4.0067 +2024-07-24 17:15:11,636 - pyskl - INFO - Saving checkpoint at 72 epochs +2024-07-24 17:17:04,818 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 17:17:05,597 - pyskl - INFO - +top1_acc 0.2472 +top5_acc 0.4849 +2024-07-24 17:17:05,597 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 17:17:05,637 - pyskl - INFO - +mean_acc 0.2470 +2024-07-24 17:17:05,649 - pyskl - INFO - Epoch(val) [72][309] top1_acc: 0.2472, top5_acc: 0.4849, mean_class_accuracy: 0.2470 +2024-07-24 17:21:02,090 - pyskl - INFO - Epoch [73][100/3746] lr: 5.311e-02, eta: 2 days, 16:47:59, time: 2.364, data_time: 1.368, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5916, loss_cls: 3.8385, loss: 3.8385 +2024-07-24 17:22:24,264 - pyskl - INFO - Epoch [73][200/3746] lr: 5.308e-02, eta: 2 days, 16:46:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5680, loss_cls: 3.9111, loss: 3.9111 +2024-07-24 17:23:46,392 - pyskl - INFO - Epoch [73][300/3746] lr: 5.306e-02, eta: 2 days, 16:45:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5814, loss_cls: 3.8549, loss: 3.8549 +2024-07-24 17:25:08,343 - pyskl - INFO - Epoch [73][400/3746] lr: 5.303e-02, eta: 2 days, 16:44:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5764, loss_cls: 3.8806, loss: 3.8806 +2024-07-24 17:26:30,273 - pyskl - INFO - Epoch [73][500/3746] lr: 5.300e-02, eta: 2 days, 16:42:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5681, loss_cls: 3.9398, loss: 3.9398 +2024-07-24 17:27:52,001 - pyskl - INFO - Epoch [73][600/3746] lr: 5.297e-02, eta: 2 days, 16:41:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5737, loss_cls: 3.8983, loss: 3.8983 +2024-07-24 17:29:14,383 - pyskl - INFO - Epoch [73][700/3746] lr: 5.294e-02, eta: 2 days, 16:40:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5730, loss_cls: 3.9193, loss: 3.9193 +2024-07-24 17:30:36,780 - pyskl - INFO - Epoch [73][800/3746] lr: 5.292e-02, eta: 2 days, 16:38:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5681, loss_cls: 3.9200, loss: 3.9200 +2024-07-24 17:31:58,590 - pyskl - INFO - Epoch [73][900/3746] lr: 5.289e-02, eta: 2 days, 16:37:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5778, loss_cls: 3.9032, loss: 3.9032 +2024-07-24 17:33:20,305 - pyskl - INFO - Epoch [73][1000/3746] lr: 5.286e-02, eta: 2 days, 16:36:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5637, loss_cls: 3.9344, loss: 3.9344 +2024-07-24 17:34:41,522 - pyskl - INFO - Epoch [73][1100/3746] lr: 5.283e-02, eta: 2 days, 16:35:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5706, loss_cls: 3.9127, loss: 3.9127 +2024-07-24 17:36:03,715 - pyskl - INFO - Epoch [73][1200/3746] lr: 5.280e-02, eta: 2 days, 16:33:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5661, loss_cls: 3.9320, loss: 3.9320 +2024-07-24 17:37:25,267 - pyskl - INFO - Epoch [73][1300/3746] lr: 5.278e-02, eta: 2 days, 16:32:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5653, loss_cls: 3.9567, loss: 3.9567 +2024-07-24 17:38:47,920 - pyskl - INFO - Epoch [73][1400/3746] lr: 5.275e-02, eta: 2 days, 16:31:10, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5789, loss_cls: 3.8752, loss: 3.8752 +2024-07-24 17:40:10,857 - pyskl - INFO - Epoch [73][1500/3746] lr: 5.272e-02, eta: 2 days, 16:29:54, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5719, loss_cls: 3.9360, loss: 3.9360 +2024-07-24 17:41:33,175 - pyskl - INFO - Epoch [73][1600/3746] lr: 5.269e-02, eta: 2 days, 16:28:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5737, loss_cls: 3.9013, loss: 3.9013 +2024-07-24 17:42:55,211 - pyskl - INFO - Epoch [73][1700/3746] lr: 5.267e-02, eta: 2 days, 16:27:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5677, loss_cls: 3.9472, loss: 3.9472 +2024-07-24 17:44:16,959 - pyskl - INFO - Epoch [73][1800/3746] lr: 5.264e-02, eta: 2 days, 16:26:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5678, loss_cls: 3.9391, loss: 3.9391 +2024-07-24 17:45:38,996 - pyskl - INFO - Epoch [73][1900/3746] lr: 5.261e-02, eta: 2 days, 16:24:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5802, loss_cls: 3.9051, loss: 3.9051 +2024-07-24 17:47:00,623 - pyskl - INFO - Epoch [73][2000/3746] lr: 5.258e-02, eta: 2 days, 16:23:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5567, loss_cls: 4.0055, loss: 4.0055 +2024-07-24 17:48:22,490 - pyskl - INFO - Epoch [73][2100/3746] lr: 5.255e-02, eta: 2 days, 16:22:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5698, loss_cls: 3.9255, loss: 3.9255 +2024-07-24 17:49:44,201 - pyskl - INFO - Epoch [73][2200/3746] lr: 5.253e-02, eta: 2 days, 16:20:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5802, loss_cls: 3.8881, loss: 3.8881 +2024-07-24 17:51:05,316 - pyskl - INFO - Epoch [73][2300/3746] lr: 5.250e-02, eta: 2 days, 16:19:31, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5737, loss_cls: 3.9066, loss: 3.9066 +2024-07-24 17:52:26,888 - pyskl - INFO - Epoch [73][2400/3746] lr: 5.247e-02, eta: 2 days, 16:18:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5663, loss_cls: 3.9175, loss: 3.9175 +2024-07-24 17:53:48,522 - pyskl - INFO - Epoch [73][2500/3746] lr: 5.244e-02, eta: 2 days, 16:16:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5716, loss_cls: 3.9219, loss: 3.9219 +2024-07-24 17:55:10,474 - pyskl - INFO - Epoch [73][2600/3746] lr: 5.241e-02, eta: 2 days, 16:15:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5803, loss_cls: 3.8576, loss: 3.8576 +2024-07-24 17:56:32,344 - pyskl - INFO - Epoch [73][2700/3746] lr: 5.239e-02, eta: 2 days, 16:14:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5813, loss_cls: 3.9031, loss: 3.9031 +2024-07-24 17:57:53,710 - pyskl - INFO - Epoch [73][2800/3746] lr: 5.236e-02, eta: 2 days, 16:13:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5720, loss_cls: 3.9044, loss: 3.9044 +2024-07-24 17:59:15,536 - pyskl - INFO - Epoch [73][2900/3746] lr: 5.233e-02, eta: 2 days, 16:11:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5630, loss_cls: 3.9633, loss: 3.9633 +2024-07-24 18:00:37,142 - pyskl - INFO - Epoch [73][3000/3746] lr: 5.230e-02, eta: 2 days, 16:10:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5748, loss_cls: 3.9329, loss: 3.9329 +2024-07-24 18:01:59,066 - pyskl - INFO - Epoch [73][3100/3746] lr: 5.227e-02, eta: 2 days, 16:09:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5714, loss_cls: 3.9070, loss: 3.9070 +2024-07-24 18:03:20,509 - pyskl - INFO - Epoch [73][3200/3746] lr: 5.225e-02, eta: 2 days, 16:07:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5741, loss_cls: 3.9352, loss: 3.9352 +2024-07-24 18:04:42,177 - pyskl - INFO - Epoch [73][3300/3746] lr: 5.222e-02, eta: 2 days, 16:06:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5656, loss_cls: 3.9225, loss: 3.9225 +2024-07-24 18:06:03,631 - pyskl - INFO - Epoch [73][3400/3746] lr: 5.219e-02, eta: 2 days, 16:05:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5784, loss_cls: 3.9058, loss: 3.9058 +2024-07-24 18:07:25,359 - pyskl - INFO - Epoch [73][3500/3746] lr: 5.216e-02, eta: 2 days, 16:03:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5747, loss_cls: 3.8854, loss: 3.8854 +2024-07-24 18:08:47,260 - pyskl - INFO - Epoch [73][3600/3746] lr: 5.213e-02, eta: 2 days, 16:02:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5656, loss_cls: 3.9562, loss: 3.9562 +2024-07-24 18:10:08,666 - pyskl - INFO - Epoch [73][3700/3746] lr: 5.211e-02, eta: 2 days, 16:01:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5728, loss_cls: 3.9419, loss: 3.9419 +2024-07-24 18:10:48,177 - pyskl - INFO - Saving checkpoint at 73 epochs +2024-07-24 18:12:41,239 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 18:12:41,924 - pyskl - INFO - +top1_acc 0.2315 +top5_acc 0.4812 +2024-07-24 18:12:41,924 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 18:12:41,969 - pyskl - INFO - +mean_acc 0.2312 +2024-07-24 18:12:41,983 - pyskl - INFO - Epoch(val) [73][309] top1_acc: 0.2315, top5_acc: 0.4812, mean_class_accuracy: 0.2312 +2024-07-24 18:16:37,775 - pyskl - INFO - Epoch [74][100/3746] lr: 5.207e-02, eta: 2 days, 16:01:28, time: 2.358, data_time: 1.383, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5827, loss_cls: 3.8527, loss: 3.8527 +2024-07-24 18:18:00,143 - pyskl - INFO - Epoch [74][200/3746] lr: 5.204e-02, eta: 2 days, 16:00:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5764, loss_cls: 3.8957, loss: 3.8957 +2024-07-24 18:19:22,184 - pyskl - INFO - Epoch [74][300/3746] lr: 5.201e-02, eta: 2 days, 15:58:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5711, loss_cls: 3.9033, loss: 3.9033 +2024-07-24 18:20:43,752 - pyskl - INFO - Epoch [74][400/3746] lr: 5.198e-02, eta: 2 days, 15:57:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5789, loss_cls: 3.8834, loss: 3.8834 +2024-07-24 18:22:05,874 - pyskl - INFO - Epoch [74][500/3746] lr: 5.195e-02, eta: 2 days, 15:56:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5742, loss_cls: 3.9469, loss: 3.9469 +2024-07-24 18:23:27,872 - pyskl - INFO - Epoch [74][600/3746] lr: 5.193e-02, eta: 2 days, 15:54:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5837, loss_cls: 3.8677, loss: 3.8677 +2024-07-24 18:24:50,184 - pyskl - INFO - Epoch [74][700/3746] lr: 5.190e-02, eta: 2 days, 15:53:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5713, loss_cls: 3.9225, loss: 3.9225 +2024-07-24 18:26:12,285 - pyskl - INFO - Epoch [74][800/3746] lr: 5.187e-02, eta: 2 days, 15:52:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5569, loss_cls: 3.9507, loss: 3.9507 +2024-07-24 18:27:33,666 - pyskl - INFO - Epoch [74][900/3746] lr: 5.184e-02, eta: 2 days, 15:51:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5861, loss_cls: 3.8313, loss: 3.8313 +2024-07-24 18:28:55,328 - pyskl - INFO - Epoch [74][1000/3746] lr: 5.181e-02, eta: 2 days, 15:49:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5775, loss_cls: 3.8974, loss: 3.8974 +2024-07-24 18:30:16,943 - pyskl - INFO - Epoch [74][1100/3746] lr: 5.179e-02, eta: 2 days, 15:48:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5852, loss_cls: 3.8577, loss: 3.8577 +2024-07-24 18:31:39,267 - pyskl - INFO - Epoch [74][1200/3746] lr: 5.176e-02, eta: 2 days, 15:47:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5706, loss_cls: 3.9250, loss: 3.9250 +2024-07-24 18:33:01,310 - pyskl - INFO - Epoch [74][1300/3746] lr: 5.173e-02, eta: 2 days, 15:45:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5813, loss_cls: 3.8429, loss: 3.8429 +2024-07-24 18:34:23,426 - pyskl - INFO - Epoch [74][1400/3746] lr: 5.170e-02, eta: 2 days, 15:44:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5714, loss_cls: 3.9149, loss: 3.9149 +2024-07-24 18:35:45,772 - pyskl - INFO - Epoch [74][1500/3746] lr: 5.168e-02, eta: 2 days, 15:43:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5617, loss_cls: 3.9408, loss: 3.9408 +2024-07-24 18:37:07,741 - pyskl - INFO - Epoch [74][1600/3746] lr: 5.165e-02, eta: 2 days, 15:42:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5756, loss_cls: 3.8824, loss: 3.8824 +2024-07-24 18:38:29,709 - pyskl - INFO - Epoch [74][1700/3746] lr: 5.162e-02, eta: 2 days, 15:40:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5852, loss_cls: 3.8504, loss: 3.8504 +2024-07-24 18:39:51,556 - pyskl - INFO - Epoch [74][1800/3746] lr: 5.159e-02, eta: 2 days, 15:39:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5769, loss_cls: 3.8833, loss: 3.8833 +2024-07-24 18:41:13,769 - pyskl - INFO - Epoch [74][1900/3746] lr: 5.156e-02, eta: 2 days, 15:38:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5778, loss_cls: 3.9005, loss: 3.9005 +2024-07-24 18:42:35,279 - pyskl - INFO - Epoch [74][2000/3746] lr: 5.154e-02, eta: 2 days, 15:36:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5848, loss_cls: 3.8311, loss: 3.8311 +2024-07-24 18:43:56,982 - pyskl - INFO - Epoch [74][2100/3746] lr: 5.151e-02, eta: 2 days, 15:35:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5731, loss_cls: 3.9245, loss: 3.9245 +2024-07-24 18:45:18,816 - pyskl - INFO - Epoch [74][2200/3746] lr: 5.148e-02, eta: 2 days, 15:34:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5637, loss_cls: 3.9510, loss: 3.9510 +2024-07-24 18:46:40,226 - pyskl - INFO - Epoch [74][2300/3746] lr: 5.145e-02, eta: 2 days, 15:32:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5728, loss_cls: 3.9239, loss: 3.9239 +2024-07-24 18:48:02,253 - pyskl - INFO - Epoch [74][2400/3746] lr: 5.142e-02, eta: 2 days, 15:31:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5806, loss_cls: 3.8777, loss: 3.8777 +2024-07-24 18:49:23,718 - pyskl - INFO - Epoch [74][2500/3746] lr: 5.140e-02, eta: 2 days, 15:30:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5716, loss_cls: 3.9154, loss: 3.9154 +2024-07-24 18:50:45,252 - pyskl - INFO - Epoch [74][2600/3746] lr: 5.137e-02, eta: 2 days, 15:29:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5761, loss_cls: 3.8695, loss: 3.8695 +2024-07-24 18:52:06,753 - pyskl - INFO - Epoch [74][2700/3746] lr: 5.134e-02, eta: 2 days, 15:27:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5637, loss_cls: 3.8913, loss: 3.8913 +2024-07-24 18:53:28,163 - pyskl - INFO - Epoch [74][2800/3746] lr: 5.131e-02, eta: 2 days, 15:26:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5816, loss_cls: 3.9020, loss: 3.9020 +2024-07-24 18:54:49,793 - pyskl - INFO - Epoch [74][2900/3746] lr: 5.128e-02, eta: 2 days, 15:25:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5748, loss_cls: 3.9173, loss: 3.9173 +2024-07-24 18:56:11,522 - pyskl - INFO - Epoch [74][3000/3746] lr: 5.126e-02, eta: 2 days, 15:23:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5634, loss_cls: 3.9575, loss: 3.9575 +2024-07-24 18:57:33,393 - pyskl - INFO - Epoch [74][3100/3746] lr: 5.123e-02, eta: 2 days, 15:22:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5736, loss_cls: 3.8959, loss: 3.8959 +2024-07-24 18:58:55,320 - pyskl - INFO - Epoch [74][3200/3746] lr: 5.120e-02, eta: 2 days, 15:21:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5637, loss_cls: 3.9792, loss: 3.9792 +2024-07-24 19:00:16,753 - pyskl - INFO - Epoch [74][3300/3746] lr: 5.117e-02, eta: 2 days, 15:19:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5656, loss_cls: 3.9696, loss: 3.9696 +2024-07-24 19:01:38,370 - pyskl - INFO - Epoch [74][3400/3746] lr: 5.114e-02, eta: 2 days, 15:18:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5761, loss_cls: 3.8919, loss: 3.8919 +2024-07-24 19:03:00,131 - pyskl - INFO - Epoch [74][3500/3746] lr: 5.112e-02, eta: 2 days, 15:17:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5844, loss_cls: 3.8537, loss: 3.8537 +2024-07-24 19:04:22,428 - pyskl - INFO - Epoch [74][3600/3746] lr: 5.109e-02, eta: 2 days, 15:15:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5672, loss_cls: 3.9289, loss: 3.9289 +2024-07-24 19:05:44,749 - pyskl - INFO - Epoch [74][3700/3746] lr: 5.106e-02, eta: 2 days, 15:14:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5633, loss_cls: 3.9594, loss: 3.9594 +2024-07-24 19:06:24,216 - pyskl - INFO - Saving checkpoint at 74 epochs +2024-07-24 19:08:16,539 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 19:08:17,273 - pyskl - INFO - +top1_acc 0.2627 +top5_acc 0.5093 +2024-07-24 19:08:17,273 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 19:08:17,325 - pyskl - INFO - +mean_acc 0.2625 +2024-07-24 19:08:17,330 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_71.pth was removed +2024-07-24 19:08:17,630 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_74.pth. +2024-07-24 19:08:17,631 - pyskl - INFO - Best top1_acc is 0.2627 at 74 epoch. +2024-07-24 19:08:17,649 - pyskl - INFO - Epoch(val) [74][309] top1_acc: 0.2627, top5_acc: 0.5093, mean_class_accuracy: 0.2625 +2024-07-24 19:12:11,057 - pyskl - INFO - Epoch [75][100/3746] lr: 5.102e-02, eta: 2 days, 15:14:44, time: 2.334, data_time: 1.352, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5823, loss_cls: 3.8684, loss: 3.8684 +2024-07-24 19:13:32,404 - pyskl - INFO - Epoch [75][200/3746] lr: 5.099e-02, eta: 2 days, 15:13:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5933, loss_cls: 3.8231, loss: 3.8231 +2024-07-24 19:14:53,959 - pyskl - INFO - Epoch [75][300/3746] lr: 5.096e-02, eta: 2 days, 15:12:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5867, loss_cls: 3.8238, loss: 3.8238 +2024-07-24 19:16:15,236 - pyskl - INFO - Epoch [75][400/3746] lr: 5.094e-02, eta: 2 days, 15:10:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5792, loss_cls: 3.9008, loss: 3.9008 +2024-07-24 19:17:36,770 - pyskl - INFO - Epoch [75][500/3746] lr: 5.091e-02, eta: 2 days, 15:09:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5880, loss_cls: 3.8509, loss: 3.8509 +2024-07-24 19:18:58,556 - pyskl - INFO - Epoch [75][600/3746] lr: 5.088e-02, eta: 2 days, 15:08:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5895, loss_cls: 3.8420, loss: 3.8420 +2024-07-24 19:20:20,655 - pyskl - INFO - Epoch [75][700/3746] lr: 5.085e-02, eta: 2 days, 15:06:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5802, loss_cls: 3.8801, loss: 3.8801 +2024-07-24 19:21:42,712 - pyskl - INFO - Epoch [75][800/3746] lr: 5.082e-02, eta: 2 days, 15:05:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5695, loss_cls: 3.9103, loss: 3.9103 +2024-07-24 19:23:04,517 - pyskl - INFO - Epoch [75][900/3746] lr: 5.080e-02, eta: 2 days, 15:04:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5767, loss_cls: 3.9178, loss: 3.9178 +2024-07-24 19:24:26,039 - pyskl - INFO - Epoch [75][1000/3746] lr: 5.077e-02, eta: 2 days, 15:03:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5748, loss_cls: 3.8967, loss: 3.8967 +2024-07-24 19:25:47,557 - pyskl - INFO - Epoch [75][1100/3746] lr: 5.074e-02, eta: 2 days, 15:01:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5806, loss_cls: 3.9152, loss: 3.9152 +2024-07-24 19:27:09,448 - pyskl - INFO - Epoch [75][1200/3746] lr: 5.071e-02, eta: 2 days, 15:00:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5748, loss_cls: 3.8829, loss: 3.8829 +2024-07-24 19:28:31,308 - pyskl - INFO - Epoch [75][1300/3746] lr: 5.068e-02, eta: 2 days, 14:59:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5647, loss_cls: 3.9169, loss: 3.9169 +2024-07-24 19:29:53,795 - pyskl - INFO - Epoch [75][1400/3746] lr: 5.066e-02, eta: 2 days, 14:57:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5730, loss_cls: 3.9182, loss: 3.9182 +2024-07-24 19:31:15,827 - pyskl - INFO - Epoch [75][1500/3746] lr: 5.063e-02, eta: 2 days, 14:56:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5716, loss_cls: 3.8656, loss: 3.8656 +2024-07-24 19:32:38,600 - pyskl - INFO - Epoch [75][1600/3746] lr: 5.060e-02, eta: 2 days, 14:55:12, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5747, loss_cls: 3.8671, loss: 3.8671 +2024-07-24 19:34:00,539 - pyskl - INFO - Epoch [75][1700/3746] lr: 5.057e-02, eta: 2 days, 14:53:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5669, loss_cls: 3.9398, loss: 3.9398 +2024-07-24 19:35:22,171 - pyskl - INFO - Epoch [75][1800/3746] lr: 5.054e-02, eta: 2 days, 14:52:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5814, loss_cls: 3.8849, loss: 3.8849 +2024-07-24 19:36:44,323 - pyskl - INFO - Epoch [75][1900/3746] lr: 5.052e-02, eta: 2 days, 14:51:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5841, loss_cls: 3.8759, loss: 3.8759 +2024-07-24 19:38:05,703 - pyskl - INFO - Epoch [75][2000/3746] lr: 5.049e-02, eta: 2 days, 14:49:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5744, loss_cls: 3.9031, loss: 3.9031 +2024-07-24 19:39:28,280 - pyskl - INFO - Epoch [75][2100/3746] lr: 5.046e-02, eta: 2 days, 14:48:42, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5742, loss_cls: 3.8900, loss: 3.8900 +2024-07-24 19:40:49,917 - pyskl - INFO - Epoch [75][2200/3746] lr: 5.043e-02, eta: 2 days, 14:47:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5736, loss_cls: 3.8885, loss: 3.8885 +2024-07-24 19:42:11,622 - pyskl - INFO - Epoch [75][2300/3746] lr: 5.040e-02, eta: 2 days, 14:46:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5845, loss_cls: 3.8540, loss: 3.8540 +2024-07-24 19:43:32,797 - pyskl - INFO - Epoch [75][2400/3746] lr: 5.038e-02, eta: 2 days, 14:44:46, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5820, loss_cls: 3.8755, loss: 3.8755 +2024-07-24 19:44:54,163 - pyskl - INFO - Epoch [75][2500/3746] lr: 5.035e-02, eta: 2 days, 14:43:28, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5664, loss_cls: 3.9259, loss: 3.9259 +2024-07-24 19:46:15,395 - pyskl - INFO - Epoch [75][2600/3746] lr: 5.032e-02, eta: 2 days, 14:42:09, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5806, loss_cls: 3.8861, loss: 3.8861 +2024-07-24 19:47:37,530 - pyskl - INFO - Epoch [75][2700/3746] lr: 5.029e-02, eta: 2 days, 14:40:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5691, loss_cls: 3.9325, loss: 3.9325 +2024-07-24 19:48:58,844 - pyskl - INFO - Epoch [75][2800/3746] lr: 5.026e-02, eta: 2 days, 14:39:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5742, loss_cls: 3.9313, loss: 3.9313 +2024-07-24 19:50:20,570 - pyskl - INFO - Epoch [75][2900/3746] lr: 5.024e-02, eta: 2 days, 14:38:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5777, loss_cls: 3.9304, loss: 3.9304 +2024-07-24 19:51:42,490 - pyskl - INFO - Epoch [75][3000/3746] lr: 5.021e-02, eta: 2 days, 14:36:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5800, loss_cls: 3.9035, loss: 3.9035 +2024-07-24 19:53:04,051 - pyskl - INFO - Epoch [75][3100/3746] lr: 5.018e-02, eta: 2 days, 14:35:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5811, loss_cls: 3.8693, loss: 3.8693 +2024-07-24 19:54:25,481 - pyskl - INFO - Epoch [75][3200/3746] lr: 5.015e-02, eta: 2 days, 14:34:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5631, loss_cls: 3.9326, loss: 3.9326 +2024-07-24 19:55:47,074 - pyskl - INFO - Epoch [75][3300/3746] lr: 5.012e-02, eta: 2 days, 14:33:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5683, loss_cls: 3.9037, loss: 3.9037 +2024-07-24 19:57:08,767 - pyskl - INFO - Epoch [75][3400/3746] lr: 5.010e-02, eta: 2 days, 14:31:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5806, loss_cls: 3.8874, loss: 3.8874 +2024-07-24 19:58:30,191 - pyskl - INFO - Epoch [75][3500/3746] lr: 5.007e-02, eta: 2 days, 14:30:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5758, loss_cls: 3.8844, loss: 3.8844 +2024-07-24 19:59:52,279 - pyskl - INFO - Epoch [75][3600/3746] lr: 5.004e-02, eta: 2 days, 14:29:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5758, loss_cls: 3.9150, loss: 3.9150 +2024-07-24 20:01:13,906 - pyskl - INFO - Epoch [75][3700/3746] lr: 5.001e-02, eta: 2 days, 14:27:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5748, loss_cls: 3.9246, loss: 3.9246 +2024-07-24 20:01:53,407 - pyskl - INFO - Saving checkpoint at 75 epochs +2024-07-24 20:03:46,241 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 20:03:46,910 - pyskl - INFO - +top1_acc 0.2564 +top5_acc 0.4954 +2024-07-24 20:03:46,910 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 20:03:46,950 - pyskl - INFO - +mean_acc 0.2563 +2024-07-24 20:03:46,962 - pyskl - INFO - Epoch(val) [75][309] top1_acc: 0.2564, top5_acc: 0.4954, mean_class_accuracy: 0.2563 +2024-07-24 20:07:37,685 - pyskl - INFO - Epoch [76][100/3746] lr: 4.997e-02, eta: 2 days, 14:27:44, time: 2.307, data_time: 1.326, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5806, loss_cls: 3.8399, loss: 3.8399 +2024-07-24 20:09:00,052 - pyskl - INFO - Epoch [76][200/3746] lr: 4.994e-02, eta: 2 days, 14:26:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5806, loss_cls: 3.8867, loss: 3.8867 +2024-07-24 20:10:21,721 - pyskl - INFO - Epoch [76][300/3746] lr: 4.992e-02, eta: 2 days, 14:25:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5725, loss_cls: 3.8459, loss: 3.8459 +2024-07-24 20:11:43,120 - pyskl - INFO - Epoch [76][400/3746] lr: 4.989e-02, eta: 2 days, 14:23:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5859, loss_cls: 3.8274, loss: 3.8274 +2024-07-24 20:13:04,822 - pyskl - INFO - Epoch [76][500/3746] lr: 4.986e-02, eta: 2 days, 14:22:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5914, loss_cls: 3.8354, loss: 3.8354 +2024-07-24 20:14:26,712 - pyskl - INFO - Epoch [76][600/3746] lr: 4.983e-02, eta: 2 days, 14:21:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5891, loss_cls: 3.8225, loss: 3.8225 +2024-07-24 20:15:48,906 - pyskl - INFO - Epoch [76][700/3746] lr: 4.980e-02, eta: 2 days, 14:19:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5700, loss_cls: 3.9479, loss: 3.9479 +2024-07-24 20:17:10,351 - pyskl - INFO - Epoch [76][800/3746] lr: 4.978e-02, eta: 2 days, 14:18:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5714, loss_cls: 3.9004, loss: 3.9004 +2024-07-24 20:18:32,104 - pyskl - INFO - Epoch [76][900/3746] lr: 4.975e-02, eta: 2 days, 14:17:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5706, loss_cls: 3.8832, loss: 3.8832 +2024-07-24 20:19:53,484 - pyskl - INFO - Epoch [76][1000/3746] lr: 4.972e-02, eta: 2 days, 14:15:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5733, loss_cls: 3.8693, loss: 3.8693 +2024-07-24 20:21:15,244 - pyskl - INFO - Epoch [76][1100/3746] lr: 4.969e-02, eta: 2 days, 14:14:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5836, loss_cls: 3.8478, loss: 3.8478 +2024-07-24 20:22:37,556 - pyskl - INFO - Epoch [76][1200/3746] lr: 4.966e-02, eta: 2 days, 14:13:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5867, loss_cls: 3.8443, loss: 3.8443 +2024-07-24 20:23:58,888 - pyskl - INFO - Epoch [76][1300/3746] lr: 4.964e-02, eta: 2 days, 14:12:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5919, loss_cls: 3.8122, loss: 3.8122 +2024-07-24 20:25:21,041 - pyskl - INFO - Epoch [76][1400/3746] lr: 4.961e-02, eta: 2 days, 14:10:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5795, loss_cls: 3.8503, loss: 3.8503 +2024-07-24 20:26:42,907 - pyskl - INFO - Epoch [76][1500/3746] lr: 4.958e-02, eta: 2 days, 14:09:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5827, loss_cls: 3.8728, loss: 3.8728 +2024-07-24 20:28:05,127 - pyskl - INFO - Epoch [76][1600/3746] lr: 4.955e-02, eta: 2 days, 14:08:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5789, loss_cls: 3.8802, loss: 3.8802 +2024-07-24 20:29:27,212 - pyskl - INFO - Epoch [76][1700/3746] lr: 4.953e-02, eta: 2 days, 14:06:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5822, loss_cls: 3.8973, loss: 3.8973 +2024-07-24 20:30:48,484 - pyskl - INFO - Epoch [76][1800/3746] lr: 4.950e-02, eta: 2 days, 14:05:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5773, loss_cls: 3.8790, loss: 3.8790 +2024-07-24 20:32:10,114 - pyskl - INFO - Epoch [76][1900/3746] lr: 4.947e-02, eta: 2 days, 14:04:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5813, loss_cls: 3.8483, loss: 3.8483 +2024-07-24 20:33:31,781 - pyskl - INFO - Epoch [76][2000/3746] lr: 4.944e-02, eta: 2 days, 14:02:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5809, loss_cls: 3.8933, loss: 3.8933 +2024-07-24 20:34:53,241 - pyskl - INFO - Epoch [76][2100/3746] lr: 4.941e-02, eta: 2 days, 14:01:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5727, loss_cls: 3.9449, loss: 3.9449 +2024-07-24 20:36:15,551 - pyskl - INFO - Epoch [76][2200/3746] lr: 4.939e-02, eta: 2 days, 14:00:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5798, loss_cls: 3.8872, loss: 3.8872 +2024-07-24 20:37:37,122 - pyskl - INFO - Epoch [76][2300/3746] lr: 4.936e-02, eta: 2 days, 13:59:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5741, loss_cls: 3.9028, loss: 3.9028 +2024-07-24 20:38:58,579 - pyskl - INFO - Epoch [76][2400/3746] lr: 4.933e-02, eta: 2 days, 13:57:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5798, loss_cls: 3.8782, loss: 3.8782 +2024-07-24 20:40:20,183 - pyskl - INFO - Epoch [76][2500/3746] lr: 4.930e-02, eta: 2 days, 13:56:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5753, loss_cls: 3.9053, loss: 3.9053 +2024-07-24 20:41:41,777 - pyskl - INFO - Epoch [76][2600/3746] lr: 4.927e-02, eta: 2 days, 13:55:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5725, loss_cls: 3.9432, loss: 3.9432 +2024-07-24 20:43:03,549 - pyskl - INFO - Epoch [76][2700/3746] lr: 4.925e-02, eta: 2 days, 13:53:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5777, loss_cls: 3.8772, loss: 3.8772 +2024-07-24 20:44:25,011 - pyskl - INFO - Epoch [76][2800/3746] lr: 4.922e-02, eta: 2 days, 13:52:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5884, loss_cls: 3.8451, loss: 3.8451 +2024-07-24 20:45:46,935 - pyskl - INFO - Epoch [76][2900/3746] lr: 4.919e-02, eta: 2 days, 13:51:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5791, loss_cls: 3.8523, loss: 3.8523 +2024-07-24 20:47:08,853 - pyskl - INFO - Epoch [76][3000/3746] lr: 4.916e-02, eta: 2 days, 13:49:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5725, loss_cls: 3.8694, loss: 3.8694 +2024-07-24 20:48:30,847 - pyskl - INFO - Epoch [76][3100/3746] lr: 4.913e-02, eta: 2 days, 13:48:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5741, loss_cls: 3.9132, loss: 3.9132 +2024-07-24 20:49:52,590 - pyskl - INFO - Epoch [76][3200/3746] lr: 4.911e-02, eta: 2 days, 13:47:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5750, loss_cls: 3.9017, loss: 3.9017 +2024-07-24 20:51:13,972 - pyskl - INFO - Epoch [76][3300/3746] lr: 4.908e-02, eta: 2 days, 13:45:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5775, loss_cls: 3.9007, loss: 3.9007 +2024-07-24 20:52:35,496 - pyskl - INFO - Epoch [76][3400/3746] lr: 4.905e-02, eta: 2 days, 13:44:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5753, loss_cls: 3.9126, loss: 3.9126 +2024-07-24 20:53:56,667 - pyskl - INFO - Epoch [76][3500/3746] lr: 4.902e-02, eta: 2 days, 13:43:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5783, loss_cls: 3.9015, loss: 3.9015 +2024-07-24 20:55:18,926 - pyskl - INFO - Epoch [76][3600/3746] lr: 4.899e-02, eta: 2 days, 13:41:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5745, loss_cls: 3.9118, loss: 3.9118 +2024-07-24 20:56:40,358 - pyskl - INFO - Epoch [76][3700/3746] lr: 4.897e-02, eta: 2 days, 13:40:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5680, loss_cls: 3.9522, loss: 3.9522 +2024-07-24 20:57:19,648 - pyskl - INFO - Saving checkpoint at 76 epochs +2024-07-24 20:59:11,555 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 20:59:12,216 - pyskl - INFO - +top1_acc 0.2545 +top5_acc 0.4926 +2024-07-24 20:59:12,216 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 20:59:12,256 - pyskl - INFO - +mean_acc 0.2543 +2024-07-24 20:59:12,268 - pyskl - INFO - Epoch(val) [76][309] top1_acc: 0.2545, top5_acc: 0.4926, mean_class_accuracy: 0.2543 +2024-07-24 21:03:07,033 - pyskl - INFO - Epoch [77][100/3746] lr: 4.893e-02, eta: 2 days, 13:40:38, time: 2.348, data_time: 1.364, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6006, loss_cls: 3.7600, loss: 3.7600 +2024-07-24 21:04:29,187 - pyskl - INFO - Epoch [77][200/3746] lr: 4.890e-02, eta: 2 days, 13:39:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5972, loss_cls: 3.8118, loss: 3.8118 +2024-07-24 21:05:50,932 - pyskl - INFO - Epoch [77][300/3746] lr: 4.887e-02, eta: 2 days, 13:38:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5806, loss_cls: 3.8300, loss: 3.8300 +2024-07-24 21:07:12,312 - pyskl - INFO - Epoch [77][400/3746] lr: 4.884e-02, eta: 2 days, 13:36:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5823, loss_cls: 3.8405, loss: 3.8405 +2024-07-24 21:08:34,116 - pyskl - INFO - Epoch [77][500/3746] lr: 4.881e-02, eta: 2 days, 13:35:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5723, loss_cls: 3.8910, loss: 3.8910 +2024-07-24 21:09:55,635 - pyskl - INFO - Epoch [77][600/3746] lr: 4.879e-02, eta: 2 days, 13:34:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.5891, loss_cls: 3.8054, loss: 3.8054 +2024-07-24 21:11:18,016 - pyskl - INFO - Epoch [77][700/3746] lr: 4.876e-02, eta: 2 days, 13:32:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5938, loss_cls: 3.8213, loss: 3.8213 +2024-07-24 21:12:39,836 - pyskl - INFO - Epoch [77][800/3746] lr: 4.873e-02, eta: 2 days, 13:31:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5830, loss_cls: 3.8717, loss: 3.8717 +2024-07-24 21:14:00,838 - pyskl - INFO - Epoch [77][900/3746] lr: 4.870e-02, eta: 2 days, 13:30:09, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5814, loss_cls: 3.8848, loss: 3.8848 +2024-07-24 21:15:22,131 - pyskl - INFO - Epoch [77][1000/3746] lr: 4.867e-02, eta: 2 days, 13:28:50, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5802, loss_cls: 3.8494, loss: 3.8494 +2024-07-24 21:16:43,405 - pyskl - INFO - Epoch [77][1100/3746] lr: 4.865e-02, eta: 2 days, 13:27:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5714, loss_cls: 3.9157, loss: 3.9157 +2024-07-24 21:18:06,037 - pyskl - INFO - Epoch [77][1200/3746] lr: 4.862e-02, eta: 2 days, 13:26:14, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5823, loss_cls: 3.8499, loss: 3.8499 +2024-07-24 21:19:28,151 - pyskl - INFO - Epoch [77][1300/3746] lr: 4.859e-02, eta: 2 days, 13:24:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5691, loss_cls: 3.9148, loss: 3.9148 +2024-07-24 21:20:50,647 - pyskl - INFO - Epoch [77][1400/3746] lr: 4.856e-02, eta: 2 days, 13:23:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5814, loss_cls: 3.8250, loss: 3.8250 +2024-07-24 21:22:12,755 - pyskl - INFO - Epoch [77][1500/3746] lr: 4.853e-02, eta: 2 days, 13:22:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5816, loss_cls: 3.8466, loss: 3.8466 +2024-07-24 21:23:34,744 - pyskl - INFO - Epoch [77][1600/3746] lr: 4.851e-02, eta: 2 days, 13:21:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5705, loss_cls: 3.8780, loss: 3.8780 +2024-07-24 21:24:56,352 - pyskl - INFO - Epoch [77][1700/3746] lr: 4.848e-02, eta: 2 days, 13:19:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5833, loss_cls: 3.8227, loss: 3.8227 +2024-07-24 21:26:18,415 - pyskl - INFO - Epoch [77][1800/3746] lr: 4.845e-02, eta: 2 days, 13:18:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5784, loss_cls: 3.8819, loss: 3.8819 +2024-07-24 21:27:40,167 - pyskl - INFO - Epoch [77][1900/3746] lr: 4.842e-02, eta: 2 days, 13:17:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5705, loss_cls: 3.9378, loss: 3.9378 +2024-07-24 21:29:02,118 - pyskl - INFO - Epoch [77][2000/3746] lr: 4.839e-02, eta: 2 days, 13:15:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5748, loss_cls: 3.8969, loss: 3.8969 +2024-07-24 21:30:23,666 - pyskl - INFO - Epoch [77][2100/3746] lr: 4.837e-02, eta: 2 days, 13:14:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5780, loss_cls: 3.8832, loss: 3.8832 +2024-07-24 21:31:45,800 - pyskl - INFO - Epoch [77][2200/3746] lr: 4.834e-02, eta: 2 days, 13:13:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.5928, loss_cls: 3.7994, loss: 3.7994 +2024-07-24 21:33:07,567 - pyskl - INFO - Epoch [77][2300/3746] lr: 4.831e-02, eta: 2 days, 13:11:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5831, loss_cls: 3.8905, loss: 3.8905 +2024-07-24 21:34:29,894 - pyskl - INFO - Epoch [77][2400/3746] lr: 4.828e-02, eta: 2 days, 13:10:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5761, loss_cls: 3.8960, loss: 3.8960 +2024-07-24 21:35:51,414 - pyskl - INFO - Epoch [77][2500/3746] lr: 4.825e-02, eta: 2 days, 13:09:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5830, loss_cls: 3.8354, loss: 3.8354 +2024-07-24 21:37:12,935 - pyskl - INFO - Epoch [77][2600/3746] lr: 4.823e-02, eta: 2 days, 13:07:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5737, loss_cls: 3.9109, loss: 3.9109 +2024-07-24 21:38:34,502 - pyskl - INFO - Epoch [77][2700/3746] lr: 4.820e-02, eta: 2 days, 13:06:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5783, loss_cls: 3.8614, loss: 3.8614 +2024-07-24 21:39:55,751 - pyskl - INFO - Epoch [77][2800/3746] lr: 4.817e-02, eta: 2 days, 13:05:18, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5767, loss_cls: 3.9063, loss: 3.9063 +2024-07-24 21:41:17,151 - pyskl - INFO - Epoch [77][2900/3746] lr: 4.814e-02, eta: 2 days, 13:03:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5714, loss_cls: 3.9258, loss: 3.9258 +2024-07-24 21:42:39,409 - pyskl - INFO - Epoch [77][3000/3746] lr: 4.811e-02, eta: 2 days, 13:02:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5767, loss_cls: 3.8736, loss: 3.8736 +2024-07-24 21:44:01,351 - pyskl - INFO - Epoch [77][3100/3746] lr: 4.809e-02, eta: 2 days, 13:01:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5775, loss_cls: 3.8869, loss: 3.8869 +2024-07-24 21:45:22,869 - pyskl - INFO - Epoch [77][3200/3746] lr: 4.806e-02, eta: 2 days, 13:00:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5698, loss_cls: 3.9307, loss: 3.9307 +2024-07-24 21:46:44,544 - pyskl - INFO - Epoch [77][3300/3746] lr: 4.803e-02, eta: 2 days, 12:58:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5728, loss_cls: 3.9115, loss: 3.9115 +2024-07-24 21:48:06,140 - pyskl - INFO - Epoch [77][3400/3746] lr: 4.800e-02, eta: 2 days, 12:57:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5853, loss_cls: 3.8631, loss: 3.8631 +2024-07-24 21:49:27,693 - pyskl - INFO - Epoch [77][3500/3746] lr: 4.798e-02, eta: 2 days, 12:56:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5825, loss_cls: 3.8485, loss: 3.8485 +2024-07-24 21:50:49,747 - pyskl - INFO - Epoch [77][3600/3746] lr: 4.795e-02, eta: 2 days, 12:54:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5873, loss_cls: 3.8580, loss: 3.8580 +2024-07-24 21:52:11,461 - pyskl - INFO - Epoch [77][3700/3746] lr: 4.792e-02, eta: 2 days, 12:53:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5873, loss_cls: 3.8308, loss: 3.8308 +2024-07-24 21:52:50,708 - pyskl - INFO - Saving checkpoint at 77 epochs +2024-07-24 21:54:42,788 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 21:54:43,445 - pyskl - INFO - +top1_acc 0.2721 +top5_acc 0.5162 +2024-07-24 21:54:43,445 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 21:54:43,483 - pyskl - INFO - +mean_acc 0.2718 +2024-07-24 21:54:43,488 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_74.pth was removed +2024-07-24 21:54:43,750 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_77.pth. +2024-07-24 21:54:43,750 - pyskl - INFO - Best top1_acc is 0.2721 at 77 epoch. +2024-07-24 21:54:43,763 - pyskl - INFO - Epoch(val) [77][309] top1_acc: 0.2721, top5_acc: 0.5162, mean_class_accuracy: 0.2718 +2024-07-24 21:58:30,548 - pyskl - INFO - Epoch [78][100/3746] lr: 4.788e-02, eta: 2 days, 12:53:17, time: 2.268, data_time: 1.287, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5934, loss_cls: 3.7885, loss: 3.7885 +2024-07-24 21:59:52,007 - pyskl - INFO - Epoch [78][200/3746] lr: 4.785e-02, eta: 2 days, 12:51:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5789, loss_cls: 3.8469, loss: 3.8469 +2024-07-24 22:01:13,702 - pyskl - INFO - Epoch [78][300/3746] lr: 4.782e-02, eta: 2 days, 12:50:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5895, loss_cls: 3.7900, loss: 3.7900 +2024-07-24 22:02:35,424 - pyskl - INFO - Epoch [78][400/3746] lr: 4.779e-02, eta: 2 days, 12:49:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5852, loss_cls: 3.8288, loss: 3.8288 +2024-07-24 22:03:56,903 - pyskl - INFO - Epoch [78][500/3746] lr: 4.777e-02, eta: 2 days, 12:48:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5930, loss_cls: 3.8067, loss: 3.8067 +2024-07-24 22:05:18,780 - pyskl - INFO - Epoch [78][600/3746] lr: 4.774e-02, eta: 2 days, 12:46:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5813, loss_cls: 3.8671, loss: 3.8671 +2024-07-24 22:06:40,491 - pyskl - INFO - Epoch [78][700/3746] lr: 4.771e-02, eta: 2 days, 12:45:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5872, loss_cls: 3.8329, loss: 3.8329 +2024-07-24 22:08:02,224 - pyskl - INFO - Epoch [78][800/3746] lr: 4.768e-02, eta: 2 days, 12:44:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5791, loss_cls: 3.8634, loss: 3.8634 +2024-07-24 22:09:23,764 - pyskl - INFO - Epoch [78][900/3746] lr: 4.766e-02, eta: 2 days, 12:42:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5723, loss_cls: 3.9083, loss: 3.9083 +2024-07-24 22:10:45,599 - pyskl - INFO - Epoch [78][1000/3746] lr: 4.763e-02, eta: 2 days, 12:41:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5856, loss_cls: 3.8443, loss: 3.8443 +2024-07-24 22:12:07,210 - pyskl - INFO - Epoch [78][1100/3746] lr: 4.760e-02, eta: 2 days, 12:40:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5748, loss_cls: 3.8884, loss: 3.8884 +2024-07-24 22:13:28,955 - pyskl - INFO - Epoch [78][1200/3746] lr: 4.757e-02, eta: 2 days, 12:38:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5823, loss_cls: 3.8417, loss: 3.8417 +2024-07-24 22:14:50,403 - pyskl - INFO - Epoch [78][1300/3746] lr: 4.754e-02, eta: 2 days, 12:37:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5837, loss_cls: 3.8634, loss: 3.8634 +2024-07-24 22:16:12,578 - pyskl - INFO - Epoch [78][1400/3746] lr: 4.752e-02, eta: 2 days, 12:36:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5811, loss_cls: 3.8469, loss: 3.8469 +2024-07-24 22:17:35,247 - pyskl - INFO - Epoch [78][1500/3746] lr: 4.749e-02, eta: 2 days, 12:34:55, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5886, loss_cls: 3.8034, loss: 3.8034 +2024-07-24 22:18:57,319 - pyskl - INFO - Epoch [78][1600/3746] lr: 4.746e-02, eta: 2 days, 12:33:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5814, loss_cls: 3.8724, loss: 3.8724 +2024-07-24 22:20:19,358 - pyskl - INFO - Epoch [78][1700/3746] lr: 4.743e-02, eta: 2 days, 12:32:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5770, loss_cls: 3.9238, loss: 3.9238 +2024-07-24 22:21:40,846 - pyskl - INFO - Epoch [78][1800/3746] lr: 4.740e-02, eta: 2 days, 12:30:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5730, loss_cls: 3.9018, loss: 3.9018 +2024-07-24 22:23:03,034 - pyskl - INFO - Epoch [78][1900/3746] lr: 4.738e-02, eta: 2 days, 12:29:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5837, loss_cls: 3.8614, loss: 3.8614 +2024-07-24 22:24:24,825 - pyskl - INFO - Epoch [78][2000/3746] lr: 4.735e-02, eta: 2 days, 12:28:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5733, loss_cls: 3.9154, loss: 3.9154 +2024-07-24 22:25:46,123 - pyskl - INFO - Epoch [78][2100/3746] lr: 4.732e-02, eta: 2 days, 12:27:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5869, loss_cls: 3.8413, loss: 3.8413 +2024-07-24 22:27:07,784 - pyskl - INFO - Epoch [78][2200/3746] lr: 4.729e-02, eta: 2 days, 12:25:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5863, loss_cls: 3.8455, loss: 3.8455 +2024-07-24 22:28:29,632 - pyskl - INFO - Epoch [78][2300/3746] lr: 4.726e-02, eta: 2 days, 12:24:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5784, loss_cls: 3.8396, loss: 3.8396 +2024-07-24 22:29:50,874 - pyskl - INFO - Epoch [78][2400/3746] lr: 4.724e-02, eta: 2 days, 12:23:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5744, loss_cls: 3.8994, loss: 3.8994 +2024-07-24 22:31:12,212 - pyskl - INFO - Epoch [78][2500/3746] lr: 4.721e-02, eta: 2 days, 12:21:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5819, loss_cls: 3.8902, loss: 3.8902 +2024-07-24 22:32:34,170 - pyskl - INFO - Epoch [78][2600/3746] lr: 4.718e-02, eta: 2 days, 12:20:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5875, loss_cls: 3.8509, loss: 3.8509 +2024-07-24 22:33:55,842 - pyskl - INFO - Epoch [78][2700/3746] lr: 4.715e-02, eta: 2 days, 12:19:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5816, loss_cls: 3.8550, loss: 3.8550 +2024-07-24 22:35:17,415 - pyskl - INFO - Epoch [78][2800/3746] lr: 4.712e-02, eta: 2 days, 12:17:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5950, loss_cls: 3.7984, loss: 3.7984 +2024-07-24 22:36:38,607 - pyskl - INFO - Epoch [78][2900/3746] lr: 4.710e-02, eta: 2 days, 12:16:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5825, loss_cls: 3.8423, loss: 3.8423 +2024-07-24 22:38:00,248 - pyskl - INFO - Epoch [78][3000/3746] lr: 4.707e-02, eta: 2 days, 12:15:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5775, loss_cls: 3.8768, loss: 3.8768 +2024-07-24 22:39:21,616 - pyskl - INFO - Epoch [78][3100/3746] lr: 4.704e-02, eta: 2 days, 12:13:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5861, loss_cls: 3.8231, loss: 3.8231 +2024-07-24 22:40:43,381 - pyskl - INFO - Epoch [78][3200/3746] lr: 4.701e-02, eta: 2 days, 12:12:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5769, loss_cls: 3.8785, loss: 3.8785 +2024-07-24 22:42:04,733 - pyskl - INFO - Epoch [78][3300/3746] lr: 4.699e-02, eta: 2 days, 12:11:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5877, loss_cls: 3.8575, loss: 3.8575 +2024-07-24 22:43:26,236 - pyskl - INFO - Epoch [78][3400/3746] lr: 4.696e-02, eta: 2 days, 12:09:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5870, loss_cls: 3.8258, loss: 3.8258 +2024-07-24 22:44:48,005 - pyskl - INFO - Epoch [78][3500/3746] lr: 4.693e-02, eta: 2 days, 12:08:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5798, loss_cls: 3.8649, loss: 3.8649 +2024-07-24 22:46:09,890 - pyskl - INFO - Epoch [78][3600/3746] lr: 4.690e-02, eta: 2 days, 12:07:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5836, loss_cls: 3.8443, loss: 3.8443 +2024-07-24 22:47:31,124 - pyskl - INFO - Epoch [78][3700/3746] lr: 4.687e-02, eta: 2 days, 12:06:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5775, loss_cls: 3.8936, loss: 3.8936 +2024-07-24 22:48:10,635 - pyskl - INFO - Saving checkpoint at 78 epochs +2024-07-24 22:50:02,220 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 22:50:02,905 - pyskl - INFO - +top1_acc 0.2651 +top5_acc 0.5097 +2024-07-24 22:50:02,905 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 22:50:02,945 - pyskl - INFO - +mean_acc 0.2648 +2024-07-24 22:50:02,957 - pyskl - INFO - Epoch(val) [78][309] top1_acc: 0.2651, top5_acc: 0.5097, mean_class_accuracy: 0.2648 +2024-07-24 22:53:50,580 - pyskl - INFO - Epoch [79][100/3746] lr: 4.683e-02, eta: 2 days, 12:05:45, time: 2.276, data_time: 1.299, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5898, loss_cls: 3.7856, loss: 3.7856 +2024-07-24 22:55:12,471 - pyskl - INFO - Epoch [79][200/3746] lr: 4.680e-02, eta: 2 days, 12:04:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5819, loss_cls: 3.8314, loss: 3.8314 +2024-07-24 22:56:33,624 - pyskl - INFO - Epoch [79][300/3746] lr: 4.678e-02, eta: 2 days, 12:03:07, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5839, loss_cls: 3.8159, loss: 3.8159 +2024-07-24 22:57:55,727 - pyskl - INFO - Epoch [79][400/3746] lr: 4.675e-02, eta: 2 days, 12:01:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5913, loss_cls: 3.8224, loss: 3.8224 +2024-07-24 22:59:18,002 - pyskl - INFO - Epoch [79][500/3746] lr: 4.672e-02, eta: 2 days, 12:00:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5839, loss_cls: 3.8356, loss: 3.8356 +2024-07-24 23:00:39,700 - pyskl - INFO - Epoch [79][600/3746] lr: 4.669e-02, eta: 2 days, 11:59:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5919, loss_cls: 3.8123, loss: 3.8123 +2024-07-24 23:02:01,182 - pyskl - INFO - Epoch [79][700/3746] lr: 4.667e-02, eta: 2 days, 11:57:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5828, loss_cls: 3.8742, loss: 3.8742 +2024-07-24 23:03:23,313 - pyskl - INFO - Epoch [79][800/3746] lr: 4.664e-02, eta: 2 days, 11:56:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5916, loss_cls: 3.7934, loss: 3.7934 +2024-07-24 23:04:45,460 - pyskl - INFO - Epoch [79][900/3746] lr: 4.661e-02, eta: 2 days, 11:55:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5736, loss_cls: 3.8786, loss: 3.8786 +2024-07-24 23:06:07,105 - pyskl - INFO - Epoch [79][1000/3746] lr: 4.658e-02, eta: 2 days, 11:53:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5875, loss_cls: 3.8221, loss: 3.8221 +2024-07-24 23:07:28,718 - pyskl - INFO - Epoch [79][1100/3746] lr: 4.655e-02, eta: 2 days, 11:52:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5820, loss_cls: 3.8515, loss: 3.8515 +2024-07-24 23:08:50,787 - pyskl - INFO - Epoch [79][1200/3746] lr: 4.653e-02, eta: 2 days, 11:51:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5822, loss_cls: 3.8467, loss: 3.8467 +2024-07-24 23:10:12,802 - pyskl - INFO - Epoch [79][1300/3746] lr: 4.650e-02, eta: 2 days, 11:50:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5855, loss_cls: 3.8637, loss: 3.8637 +2024-07-24 23:11:34,313 - pyskl - INFO - Epoch [79][1400/3746] lr: 4.647e-02, eta: 2 days, 11:48:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5841, loss_cls: 3.8331, loss: 3.8331 +2024-07-24 23:12:56,444 - pyskl - INFO - Epoch [79][1500/3746] lr: 4.644e-02, eta: 2 days, 11:47:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5803, loss_cls: 3.8583, loss: 3.8583 +2024-07-24 23:14:19,174 - pyskl - INFO - Epoch [79][1600/3746] lr: 4.641e-02, eta: 2 days, 11:46:04, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5878, loss_cls: 3.8317, loss: 3.8317 +2024-07-24 23:15:41,286 - pyskl - INFO - Epoch [79][1700/3746] lr: 4.639e-02, eta: 2 days, 11:44:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5763, loss_cls: 3.8569, loss: 3.8569 +2024-07-24 23:17:02,769 - pyskl - INFO - Epoch [79][1800/3746] lr: 4.636e-02, eta: 2 days, 11:43:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5905, loss_cls: 3.8104, loss: 3.8104 +2024-07-24 23:18:24,125 - pyskl - INFO - Epoch [79][1900/3746] lr: 4.633e-02, eta: 2 days, 11:42:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5861, loss_cls: 3.8202, loss: 3.8202 +2024-07-24 23:19:46,849 - pyskl - INFO - Epoch [79][2000/3746] lr: 4.630e-02, eta: 2 days, 11:40:49, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5722, loss_cls: 3.9086, loss: 3.9086 +2024-07-24 23:21:08,080 - pyskl - INFO - Epoch [79][2100/3746] lr: 4.628e-02, eta: 2 days, 11:39:30, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5845, loss_cls: 3.8760, loss: 3.8760 +2024-07-24 23:22:30,132 - pyskl - INFO - Epoch [79][2200/3746] lr: 4.625e-02, eta: 2 days, 11:38:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5752, loss_cls: 3.9148, loss: 3.9148 +2024-07-24 23:23:51,771 - pyskl - INFO - Epoch [79][2300/3746] lr: 4.622e-02, eta: 2 days, 11:36:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.6000, loss_cls: 3.7913, loss: 3.7913 +2024-07-24 23:25:13,259 - pyskl - INFO - Epoch [79][2400/3746] lr: 4.619e-02, eta: 2 days, 11:35:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5841, loss_cls: 3.8634, loss: 3.8634 +2024-07-24 23:26:34,774 - pyskl - INFO - Epoch [79][2500/3746] lr: 4.616e-02, eta: 2 days, 11:34:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5881, loss_cls: 3.7935, loss: 3.7935 +2024-07-24 23:27:56,380 - pyskl - INFO - Epoch [79][2600/3746] lr: 4.614e-02, eta: 2 days, 11:32:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5866, loss_cls: 3.8391, loss: 3.8391 +2024-07-24 23:29:17,676 - pyskl - INFO - Epoch [79][2700/3746] lr: 4.611e-02, eta: 2 days, 11:31:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5759, loss_cls: 3.8937, loss: 3.8937 +2024-07-24 23:30:39,267 - pyskl - INFO - Epoch [79][2800/3746] lr: 4.608e-02, eta: 2 days, 11:30:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5822, loss_cls: 3.8245, loss: 3.8245 +2024-07-24 23:32:01,247 - pyskl - INFO - Epoch [79][2900/3746] lr: 4.605e-02, eta: 2 days, 11:28:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5767, loss_cls: 3.9040, loss: 3.9040 +2024-07-24 23:33:22,950 - pyskl - INFO - Epoch [79][3000/3746] lr: 4.602e-02, eta: 2 days, 11:27:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5891, loss_cls: 3.8527, loss: 3.8527 +2024-07-24 23:34:44,534 - pyskl - INFO - Epoch [79][3100/3746] lr: 4.600e-02, eta: 2 days, 11:26:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5819, loss_cls: 3.8866, loss: 3.8866 +2024-07-24 23:36:05,744 - pyskl - INFO - Epoch [79][3200/3746] lr: 4.597e-02, eta: 2 days, 11:25:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5825, loss_cls: 3.8535, loss: 3.8535 +2024-07-24 23:37:27,334 - pyskl - INFO - Epoch [79][3300/3746] lr: 4.594e-02, eta: 2 days, 11:23:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5787, loss_cls: 3.8668, loss: 3.8668 +2024-07-24 23:38:48,977 - pyskl - INFO - Epoch [79][3400/3746] lr: 4.591e-02, eta: 2 days, 11:22:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5766, loss_cls: 3.8847, loss: 3.8847 +2024-07-24 23:40:10,515 - pyskl - INFO - Epoch [79][3500/3746] lr: 4.588e-02, eta: 2 days, 11:21:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5875, loss_cls: 3.8485, loss: 3.8485 +2024-07-24 23:41:32,892 - pyskl - INFO - Epoch [79][3600/3746] lr: 4.586e-02, eta: 2 days, 11:19:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5911, loss_cls: 3.8270, loss: 3.8270 +2024-07-24 23:42:54,752 - pyskl - INFO - Epoch [79][3700/3746] lr: 4.583e-02, eta: 2 days, 11:18:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5922, loss_cls: 3.8307, loss: 3.8307 +2024-07-24 23:43:34,522 - pyskl - INFO - Saving checkpoint at 79 epochs +2024-07-24 23:45:27,444 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 23:45:28,225 - pyskl - INFO - +top1_acc 0.2755 +top5_acc 0.5275 +2024-07-24 23:45:28,226 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 23:45:28,266 - pyskl - INFO - +mean_acc 0.2752 +2024-07-24 23:45:28,271 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_77.pth was removed +2024-07-24 23:45:28,522 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_79.pth. +2024-07-24 23:45:28,523 - pyskl - INFO - Best top1_acc is 0.2755 at 79 epoch. +2024-07-24 23:45:28,534 - pyskl - INFO - Epoch(val) [79][309] top1_acc: 0.2755, top5_acc: 0.5275, mean_class_accuracy: 0.2752 +2024-07-24 23:49:16,919 - pyskl - INFO - Epoch [80][100/3746] lr: 4.579e-02, eta: 2 days, 11:18:09, time: 2.284, data_time: 1.304, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5911, loss_cls: 3.8243, loss: 3.8243 +2024-07-24 23:50:38,791 - pyskl - INFO - Epoch [80][200/3746] lr: 4.576e-02, eta: 2 days, 11:16:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5900, loss_cls: 3.8023, loss: 3.8023 +2024-07-24 23:52:00,494 - pyskl - INFO - Epoch [80][300/3746] lr: 4.573e-02, eta: 2 days, 11:15:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5891, loss_cls: 3.8353, loss: 3.8353 +2024-07-24 23:53:21,864 - pyskl - INFO - Epoch [80][400/3746] lr: 4.570e-02, eta: 2 days, 11:14:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5841, loss_cls: 3.8400, loss: 3.8400 +2024-07-24 23:54:43,784 - pyskl - INFO - Epoch [80][500/3746] lr: 4.568e-02, eta: 2 days, 11:12:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5917, loss_cls: 3.8087, loss: 3.8087 +2024-07-24 23:56:06,080 - pyskl - INFO - Epoch [80][600/3746] lr: 4.565e-02, eta: 2 days, 11:11:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5983, loss_cls: 3.7901, loss: 3.7901 +2024-07-24 23:57:27,896 - pyskl - INFO - Epoch [80][700/3746] lr: 4.562e-02, eta: 2 days, 11:10:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5884, loss_cls: 3.7987, loss: 3.7987 +2024-07-24 23:58:49,384 - pyskl - INFO - Epoch [80][800/3746] lr: 4.559e-02, eta: 2 days, 11:08:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5958, loss_cls: 3.8011, loss: 3.8011 +2024-07-25 00:00:11,191 - pyskl - INFO - Epoch [80][900/3746] lr: 4.557e-02, eta: 2 days, 11:07:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5800, loss_cls: 3.8320, loss: 3.8320 +2024-07-25 00:01:32,898 - pyskl - INFO - Epoch [80][1000/3746] lr: 4.554e-02, eta: 2 days, 11:06:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5873, loss_cls: 3.8323, loss: 3.8323 +2024-07-25 00:02:54,465 - pyskl - INFO - Epoch [80][1100/3746] lr: 4.551e-02, eta: 2 days, 11:04:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5845, loss_cls: 3.8209, loss: 3.8209 +2024-07-25 00:04:16,631 - pyskl - INFO - Epoch [80][1200/3746] lr: 4.548e-02, eta: 2 days, 11:03:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5883, loss_cls: 3.8371, loss: 3.8371 +2024-07-25 00:05:39,098 - pyskl - INFO - Epoch [80][1300/3746] lr: 4.545e-02, eta: 2 days, 11:02:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5881, loss_cls: 3.8131, loss: 3.8131 +2024-07-25 00:07:00,978 - pyskl - INFO - Epoch [80][1400/3746] lr: 4.543e-02, eta: 2 days, 11:01:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5848, loss_cls: 3.8434, loss: 3.8434 +2024-07-25 00:08:23,095 - pyskl - INFO - Epoch [80][1500/3746] lr: 4.540e-02, eta: 2 days, 10:59:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5863, loss_cls: 3.7863, loss: 3.7863 +2024-07-25 00:09:45,556 - pyskl - INFO - Epoch [80][1600/3746] lr: 4.537e-02, eta: 2 days, 10:58:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5805, loss_cls: 3.8682, loss: 3.8682 +2024-07-25 00:11:07,363 - pyskl - INFO - Epoch [80][1700/3746] lr: 4.534e-02, eta: 2 days, 10:57:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5900, loss_cls: 3.8494, loss: 3.8494 +2024-07-25 00:12:29,211 - pyskl - INFO - Epoch [80][1800/3746] lr: 4.532e-02, eta: 2 days, 10:55:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5986, loss_cls: 3.8052, loss: 3.8052 +2024-07-25 00:13:51,082 - pyskl - INFO - Epoch [80][1900/3746] lr: 4.529e-02, eta: 2 days, 10:54:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5895, loss_cls: 3.8178, loss: 3.8178 +2024-07-25 00:15:12,728 - pyskl - INFO - Epoch [80][2000/3746] lr: 4.526e-02, eta: 2 days, 10:53:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5878, loss_cls: 3.8507, loss: 3.8507 +2024-07-25 00:16:34,340 - pyskl - INFO - Epoch [80][2100/3746] lr: 4.523e-02, eta: 2 days, 10:51:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5758, loss_cls: 3.8971, loss: 3.8971 +2024-07-25 00:17:56,202 - pyskl - INFO - Epoch [80][2200/3746] lr: 4.520e-02, eta: 2 days, 10:50:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5970, loss_cls: 3.7807, loss: 3.7807 +2024-07-25 00:19:18,021 - pyskl - INFO - Epoch [80][2300/3746] lr: 4.518e-02, eta: 2 days, 10:49:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5742, loss_cls: 3.8748, loss: 3.8748 +2024-07-25 00:20:39,334 - pyskl - INFO - Epoch [80][2400/3746] lr: 4.515e-02, eta: 2 days, 10:47:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5833, loss_cls: 3.8263, loss: 3.8263 +2024-07-25 00:22:00,869 - pyskl - INFO - Epoch [80][2500/3746] lr: 4.512e-02, eta: 2 days, 10:46:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5795, loss_cls: 3.8849, loss: 3.8849 +2024-07-25 00:23:22,428 - pyskl - INFO - Epoch [80][2600/3746] lr: 4.509e-02, eta: 2 days, 10:45:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5800, loss_cls: 3.8369, loss: 3.8369 +2024-07-25 00:24:44,342 - pyskl - INFO - Epoch [80][2700/3746] lr: 4.506e-02, eta: 2 days, 10:43:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5830, loss_cls: 3.8629, loss: 3.8629 +2024-07-25 00:26:06,022 - pyskl - INFO - Epoch [80][2800/3746] lr: 4.504e-02, eta: 2 days, 10:42:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5819, loss_cls: 3.8662, loss: 3.8662 +2024-07-25 00:27:27,961 - pyskl - INFO - Epoch [80][2900/3746] lr: 4.501e-02, eta: 2 days, 10:41:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5848, loss_cls: 3.8447, loss: 3.8447 +2024-07-25 00:28:49,747 - pyskl - INFO - Epoch [80][3000/3746] lr: 4.498e-02, eta: 2 days, 10:39:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5809, loss_cls: 3.8720, loss: 3.8720 +2024-07-25 00:30:10,920 - pyskl - INFO - Epoch [80][3100/3746] lr: 4.495e-02, eta: 2 days, 10:38:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5806, loss_cls: 3.8580, loss: 3.8580 +2024-07-25 00:31:32,357 - pyskl - INFO - Epoch [80][3200/3746] lr: 4.493e-02, eta: 2 days, 10:37:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5934, loss_cls: 3.7960, loss: 3.7960 +2024-07-25 00:32:54,147 - pyskl - INFO - Epoch [80][3300/3746] lr: 4.490e-02, eta: 2 days, 10:36:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5883, loss_cls: 3.7809, loss: 3.7809 +2024-07-25 00:34:15,646 - pyskl - INFO - Epoch [80][3400/3746] lr: 4.487e-02, eta: 2 days, 10:34:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5731, loss_cls: 3.9186, loss: 3.9186 +2024-07-25 00:35:37,631 - pyskl - INFO - Epoch [80][3500/3746] lr: 4.484e-02, eta: 2 days, 10:33:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5903, loss_cls: 3.7949, loss: 3.7949 +2024-07-25 00:36:59,309 - pyskl - INFO - Epoch [80][3600/3746] lr: 4.481e-02, eta: 2 days, 10:32:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5887, loss_cls: 3.8295, loss: 3.8295 +2024-07-25 00:38:20,598 - pyskl - INFO - Epoch [80][3700/3746] lr: 4.479e-02, eta: 2 days, 10:30:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5827, loss_cls: 3.8696, loss: 3.8696 +2024-07-25 00:39:00,313 - pyskl - INFO - Saving checkpoint at 80 epochs +2024-07-25 00:40:53,161 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 00:40:53,835 - pyskl - INFO - +top1_acc 0.2572 +top5_acc 0.5007 +2024-07-25 00:40:53,835 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 00:40:53,877 - pyskl - INFO - +mean_acc 0.2570 +2024-07-25 00:40:53,891 - pyskl - INFO - Epoch(val) [80][309] top1_acc: 0.2572, top5_acc: 0.5007, mean_class_accuracy: 0.2570 +2024-07-25 00:44:41,368 - pyskl - INFO - Epoch [81][100/3746] lr: 4.475e-02, eta: 2 days, 10:30:23, time: 2.275, data_time: 1.298, memory: 15990, top1_acc: 0.3456, top5_acc: 0.5952, loss_cls: 3.7481, loss: 3.7481 +2024-07-25 00:46:02,932 - pyskl - INFO - Epoch [81][200/3746] lr: 4.472e-02, eta: 2 days, 10:29:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.6027, loss_cls: 3.7648, loss: 3.7648 +2024-07-25 00:47:24,582 - pyskl - INFO - Epoch [81][300/3746] lr: 4.469e-02, eta: 2 days, 10:27:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5962, loss_cls: 3.7781, loss: 3.7781 +2024-07-25 00:48:46,268 - pyskl - INFO - Epoch [81][400/3746] lr: 4.466e-02, eta: 2 days, 10:26:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5923, loss_cls: 3.7972, loss: 3.7972 +2024-07-25 00:50:07,838 - pyskl - INFO - Epoch [81][500/3746] lr: 4.463e-02, eta: 2 days, 10:25:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6008, loss_cls: 3.7639, loss: 3.7639 +2024-07-25 00:51:30,282 - pyskl - INFO - Epoch [81][600/3746] lr: 4.461e-02, eta: 2 days, 10:23:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5847, loss_cls: 3.8280, loss: 3.8280 +2024-07-25 00:52:52,256 - pyskl - INFO - Epoch [81][700/3746] lr: 4.458e-02, eta: 2 days, 10:22:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5978, loss_cls: 3.7542, loss: 3.7542 +2024-07-25 00:54:13,894 - pyskl - INFO - Epoch [81][800/3746] lr: 4.455e-02, eta: 2 days, 10:21:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5836, loss_cls: 3.8414, loss: 3.8414 +2024-07-25 00:55:35,724 - pyskl - INFO - Epoch [81][900/3746] lr: 4.452e-02, eta: 2 days, 10:19:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5944, loss_cls: 3.8162, loss: 3.8162 +2024-07-25 00:56:57,503 - pyskl - INFO - Epoch [81][1000/3746] lr: 4.450e-02, eta: 2 days, 10:18:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5837, loss_cls: 3.8139, loss: 3.8139 +2024-07-25 00:58:19,047 - pyskl - INFO - Epoch [81][1100/3746] lr: 4.447e-02, eta: 2 days, 10:17:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5853, loss_cls: 3.8552, loss: 3.8552 +2024-07-25 00:59:40,913 - pyskl - INFO - Epoch [81][1200/3746] lr: 4.444e-02, eta: 2 days, 10:15:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5913, loss_cls: 3.7902, loss: 3.7902 +2024-07-25 01:01:03,413 - pyskl - INFO - Epoch [81][1300/3746] lr: 4.441e-02, eta: 2 days, 10:14:34, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5906, loss_cls: 3.7956, loss: 3.7956 +2024-07-25 01:02:25,330 - pyskl - INFO - Epoch [81][1400/3746] lr: 4.438e-02, eta: 2 days, 10:13:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.5978, loss_cls: 3.7740, loss: 3.7740 +2024-07-25 01:03:47,199 - pyskl - INFO - Epoch [81][1500/3746] lr: 4.436e-02, eta: 2 days, 10:11:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5827, loss_cls: 3.8553, loss: 3.8553 +2024-07-25 01:05:09,844 - pyskl - INFO - Epoch [81][1600/3746] lr: 4.433e-02, eta: 2 days, 10:10:38, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5883, loss_cls: 3.8213, loss: 3.8213 +2024-07-25 01:06:31,716 - pyskl - INFO - Epoch [81][1700/3746] lr: 4.430e-02, eta: 2 days, 10:09:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5919, loss_cls: 3.7885, loss: 3.7885 +2024-07-25 01:07:53,577 - pyskl - INFO - Epoch [81][1800/3746] lr: 4.427e-02, eta: 2 days, 10:08:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5866, loss_cls: 3.8407, loss: 3.8407 +2024-07-25 01:09:14,982 - pyskl - INFO - Epoch [81][1900/3746] lr: 4.425e-02, eta: 2 days, 10:06:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5794, loss_cls: 3.8628, loss: 3.8628 +2024-07-25 01:10:36,392 - pyskl - INFO - Epoch [81][2000/3746] lr: 4.422e-02, eta: 2 days, 10:05:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5866, loss_cls: 3.8178, loss: 3.8178 +2024-07-25 01:11:57,543 - pyskl - INFO - Epoch [81][2100/3746] lr: 4.419e-02, eta: 2 days, 10:04:01, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5825, loss_cls: 3.9016, loss: 3.9016 +2024-07-25 01:13:19,454 - pyskl - INFO - Epoch [81][2200/3746] lr: 4.416e-02, eta: 2 days, 10:02:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5953, loss_cls: 3.8118, loss: 3.8118 +2024-07-25 01:14:40,717 - pyskl - INFO - Epoch [81][2300/3746] lr: 4.413e-02, eta: 2 days, 10:01:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5881, loss_cls: 3.8408, loss: 3.8408 +2024-07-25 01:16:02,628 - pyskl - INFO - Epoch [81][2400/3746] lr: 4.411e-02, eta: 2 days, 10:00:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5828, loss_cls: 3.8471, loss: 3.8471 +2024-07-25 01:17:24,579 - pyskl - INFO - Epoch [81][2500/3746] lr: 4.408e-02, eta: 2 days, 9:58:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5870, loss_cls: 3.8374, loss: 3.8374 +2024-07-25 01:18:46,003 - pyskl - INFO - Epoch [81][2600/3746] lr: 4.405e-02, eta: 2 days, 9:57:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5744, loss_cls: 3.8830, loss: 3.8830 +2024-07-25 01:20:07,479 - pyskl - INFO - Epoch [81][2700/3746] lr: 4.402e-02, eta: 2 days, 9:56:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.5867, loss_cls: 3.8140, loss: 3.8140 +2024-07-25 01:21:28,832 - pyskl - INFO - Epoch [81][2800/3746] lr: 4.400e-02, eta: 2 days, 9:54:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5934, loss_cls: 3.8189, loss: 3.8189 +2024-07-25 01:22:50,424 - pyskl - INFO - Epoch [81][2900/3746] lr: 4.397e-02, eta: 2 days, 9:53:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5880, loss_cls: 3.8387, loss: 3.8387 +2024-07-25 01:24:11,655 - pyskl - INFO - Epoch [81][3000/3746] lr: 4.394e-02, eta: 2 days, 9:52:07, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5975, loss_cls: 3.7713, loss: 3.7713 +2024-07-25 01:25:33,439 - pyskl - INFO - Epoch [81][3100/3746] lr: 4.391e-02, eta: 2 days, 9:50:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5906, loss_cls: 3.8078, loss: 3.8078 +2024-07-25 01:26:55,339 - pyskl - INFO - Epoch [81][3200/3746] lr: 4.389e-02, eta: 2 days, 9:49:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5828, loss_cls: 3.8142, loss: 3.8142 +2024-07-25 01:28:16,960 - pyskl - INFO - Epoch [81][3300/3746] lr: 4.386e-02, eta: 2 days, 9:48:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5850, loss_cls: 3.8302, loss: 3.8302 +2024-07-25 01:29:38,234 - pyskl - INFO - Epoch [81][3400/3746] lr: 4.383e-02, eta: 2 days, 9:46:50, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5792, loss_cls: 3.8741, loss: 3.8741 +2024-07-25 01:30:59,791 - pyskl - INFO - Epoch [81][3500/3746] lr: 4.380e-02, eta: 2 days, 9:45:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5777, loss_cls: 3.8678, loss: 3.8678 +2024-07-25 01:32:21,850 - pyskl - INFO - Epoch [81][3600/3746] lr: 4.377e-02, eta: 2 days, 9:44:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5803, loss_cls: 3.8244, loss: 3.8244 +2024-07-25 01:33:43,714 - pyskl - INFO - Epoch [81][3700/3746] lr: 4.375e-02, eta: 2 days, 9:42:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5892, loss_cls: 3.7974, loss: 3.7974 +2024-07-25 01:34:23,147 - pyskl - INFO - Saving checkpoint at 81 epochs +2024-07-25 01:36:14,794 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 01:36:15,512 - pyskl - INFO - +top1_acc 0.2596 +top5_acc 0.5021 +2024-07-25 01:36:15,513 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 01:36:15,560 - pyskl - INFO - +mean_acc 0.2593 +2024-07-25 01:36:15,575 - pyskl - INFO - Epoch(val) [81][309] top1_acc: 0.2596, top5_acc: 0.5021, mean_class_accuracy: 0.2593 +2024-07-25 01:40:04,365 - pyskl - INFO - Epoch [82][100/3746] lr: 4.371e-02, eta: 2 days, 9:42:30, time: 2.288, data_time: 1.309, memory: 15990, top1_acc: 0.3416, top5_acc: 0.6064, loss_cls: 3.7271, loss: 3.7271 +2024-07-25 01:41:26,912 - pyskl - INFO - Epoch [82][200/3746] lr: 4.368e-02, eta: 2 days, 9:41:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5947, loss_cls: 3.7763, loss: 3.7763 +2024-07-25 01:42:49,281 - pyskl - INFO - Epoch [82][300/3746] lr: 4.365e-02, eta: 2 days, 9:39:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6027, loss_cls: 3.7489, loss: 3.7489 +2024-07-25 01:44:10,659 - pyskl - INFO - Epoch [82][400/3746] lr: 4.362e-02, eta: 2 days, 9:38:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5948, loss_cls: 3.7789, loss: 3.7789 +2024-07-25 01:45:32,259 - pyskl - INFO - Epoch [82][500/3746] lr: 4.359e-02, eta: 2 days, 9:37:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5797, loss_cls: 3.8306, loss: 3.8306 +2024-07-25 01:46:54,180 - pyskl - INFO - Epoch [82][600/3746] lr: 4.357e-02, eta: 2 days, 9:35:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5975, loss_cls: 3.7663, loss: 3.7663 +2024-07-25 01:48:15,844 - pyskl - INFO - Epoch [82][700/3746] lr: 4.354e-02, eta: 2 days, 9:34:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5934, loss_cls: 3.7975, loss: 3.7975 +2024-07-25 01:49:38,190 - pyskl - INFO - Epoch [82][800/3746] lr: 4.351e-02, eta: 2 days, 9:33:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6119, loss_cls: 3.7261, loss: 3.7261 +2024-07-25 01:50:59,859 - pyskl - INFO - Epoch [82][900/3746] lr: 4.348e-02, eta: 2 days, 9:31:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5917, loss_cls: 3.8176, loss: 3.8176 +2024-07-25 01:52:21,885 - pyskl - INFO - Epoch [82][1000/3746] lr: 4.346e-02, eta: 2 days, 9:30:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5863, loss_cls: 3.8543, loss: 3.8543 +2024-07-25 01:53:43,478 - pyskl - INFO - Epoch [82][1100/3746] lr: 4.343e-02, eta: 2 days, 9:29:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5878, loss_cls: 3.7890, loss: 3.7890 +2024-07-25 01:55:05,272 - pyskl - INFO - Epoch [82][1200/3746] lr: 4.340e-02, eta: 2 days, 9:28:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5952, loss_cls: 3.7839, loss: 3.7839 +2024-07-25 01:56:26,907 - pyskl - INFO - Epoch [82][1300/3746] lr: 4.337e-02, eta: 2 days, 9:26:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5923, loss_cls: 3.7789, loss: 3.7789 +2024-07-25 01:57:48,782 - pyskl - INFO - Epoch [82][1400/3746] lr: 4.335e-02, eta: 2 days, 9:25:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5828, loss_cls: 3.8316, loss: 3.8316 +2024-07-25 01:59:10,958 - pyskl - INFO - Epoch [82][1500/3746] lr: 4.332e-02, eta: 2 days, 9:24:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.5863, loss_cls: 3.8066, loss: 3.8066 +2024-07-25 02:00:33,767 - pyskl - INFO - Epoch [82][1600/3746] lr: 4.329e-02, eta: 2 days, 9:22:44, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5869, loss_cls: 3.8322, loss: 3.8322 +2024-07-25 02:01:55,963 - pyskl - INFO - Epoch [82][1700/3746] lr: 4.326e-02, eta: 2 days, 9:21:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5936, loss_cls: 3.7820, loss: 3.7820 +2024-07-25 02:03:17,979 - pyskl - INFO - Epoch [82][1800/3746] lr: 4.323e-02, eta: 2 days, 9:20:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5842, loss_cls: 3.8476, loss: 3.8476 +2024-07-25 02:04:39,463 - pyskl - INFO - Epoch [82][1900/3746] lr: 4.321e-02, eta: 2 days, 9:18:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5889, loss_cls: 3.8031, loss: 3.8031 +2024-07-25 02:06:00,876 - pyskl - INFO - Epoch [82][2000/3746] lr: 4.318e-02, eta: 2 days, 9:17:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.5931, loss_cls: 3.8044, loss: 3.8044 +2024-07-25 02:07:22,867 - pyskl - INFO - Epoch [82][2100/3746] lr: 4.315e-02, eta: 2 days, 9:16:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5808, loss_cls: 3.8445, loss: 3.8445 +2024-07-25 02:08:44,766 - pyskl - INFO - Epoch [82][2200/3746] lr: 4.312e-02, eta: 2 days, 9:14:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5831, loss_cls: 3.8431, loss: 3.8431 +2024-07-25 02:10:06,201 - pyskl - INFO - Epoch [82][2300/3746] lr: 4.310e-02, eta: 2 days, 9:13:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5933, loss_cls: 3.8052, loss: 3.8052 +2024-07-25 02:11:27,954 - pyskl - INFO - Epoch [82][2400/3746] lr: 4.307e-02, eta: 2 days, 9:12:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5847, loss_cls: 3.8308, loss: 3.8308 +2024-07-25 02:12:49,578 - pyskl - INFO - Epoch [82][2500/3746] lr: 4.304e-02, eta: 2 days, 9:10:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5928, loss_cls: 3.8113, loss: 3.8113 +2024-07-25 02:14:10,375 - pyskl - INFO - Epoch [82][2600/3746] lr: 4.301e-02, eta: 2 days, 9:09:30, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5920, loss_cls: 3.8132, loss: 3.8132 +2024-07-25 02:15:31,631 - pyskl - INFO - Epoch [82][2700/3746] lr: 4.299e-02, eta: 2 days, 9:08:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5887, loss_cls: 3.7971, loss: 3.7971 +2024-07-25 02:16:53,134 - pyskl - INFO - Epoch [82][2800/3746] lr: 4.296e-02, eta: 2 days, 9:06:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5920, loss_cls: 3.8099, loss: 3.8099 +2024-07-25 02:18:14,433 - pyskl - INFO - Epoch [82][2900/3746] lr: 4.293e-02, eta: 2 days, 9:05:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5878, loss_cls: 3.8371, loss: 3.8371 +2024-07-25 02:19:36,308 - pyskl - INFO - Epoch [82][3000/3746] lr: 4.290e-02, eta: 2 days, 9:04:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5794, loss_cls: 3.8505, loss: 3.8505 +2024-07-25 02:20:57,619 - pyskl - INFO - Epoch [82][3100/3746] lr: 4.287e-02, eta: 2 days, 9:02:52, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5892, loss_cls: 3.8171, loss: 3.8171 +2024-07-25 02:22:19,478 - pyskl - INFO - Epoch [82][3200/3746] lr: 4.285e-02, eta: 2 days, 9:01:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5777, loss_cls: 3.8538, loss: 3.8538 +2024-07-25 02:23:41,329 - pyskl - INFO - Epoch [82][3300/3746] lr: 4.282e-02, eta: 2 days, 9:00:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5877, loss_cls: 3.8167, loss: 3.8167 +2024-07-25 02:25:02,639 - pyskl - INFO - Epoch [82][3400/3746] lr: 4.279e-02, eta: 2 days, 8:58:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5842, loss_cls: 3.7920, loss: 3.7920 +2024-07-25 02:26:24,347 - pyskl - INFO - Epoch [82][3500/3746] lr: 4.276e-02, eta: 2 days, 8:57:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5972, loss_cls: 3.7896, loss: 3.7896 +2024-07-25 02:27:46,109 - pyskl - INFO - Epoch [82][3600/3746] lr: 4.274e-02, eta: 2 days, 8:56:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5852, loss_cls: 3.7971, loss: 3.7971 +2024-07-25 02:29:08,185 - pyskl - INFO - Epoch [82][3700/3746] lr: 4.271e-02, eta: 2 days, 8:54:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5847, loss_cls: 3.8272, loss: 3.8272 +2024-07-25 02:29:47,741 - pyskl - INFO - Saving checkpoint at 82 epochs +2024-07-25 02:31:40,861 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 02:31:41,541 - pyskl - INFO - +top1_acc 0.2676 +top5_acc 0.5208 +2024-07-25 02:31:41,541 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 02:31:41,590 - pyskl - INFO - +mean_acc 0.2672 +2024-07-25 02:31:41,603 - pyskl - INFO - Epoch(val) [82][309] top1_acc: 0.2676, top5_acc: 0.5208, mean_class_accuracy: 0.2672 +2024-07-25 02:35:42,172 - pyskl - INFO - Epoch [83][100/3746] lr: 4.267e-02, eta: 2 days, 8:54:41, time: 2.406, data_time: 1.418, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6089, loss_cls: 3.7221, loss: 3.7221 +2024-07-25 02:37:04,782 - pyskl - INFO - Epoch [83][200/3746] lr: 4.264e-02, eta: 2 days, 8:53:22, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6003, loss_cls: 3.7676, loss: 3.7676 +2024-07-25 02:38:26,422 - pyskl - INFO - Epoch [83][300/3746] lr: 4.261e-02, eta: 2 days, 8:52:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6103, loss_cls: 3.7182, loss: 3.7182 +2024-07-25 02:39:48,628 - pyskl - INFO - Epoch [83][400/3746] lr: 4.259e-02, eta: 2 days, 8:50:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.5995, loss_cls: 3.7369, loss: 3.7369 +2024-07-25 02:41:10,621 - pyskl - INFO - Epoch [83][500/3746] lr: 4.256e-02, eta: 2 days, 8:49:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5959, loss_cls: 3.7768, loss: 3.7768 +2024-07-25 02:42:32,733 - pyskl - INFO - Epoch [83][600/3746] lr: 4.253e-02, eta: 2 days, 8:48:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5831, loss_cls: 3.8187, loss: 3.8187 +2024-07-25 02:43:54,276 - pyskl - INFO - Epoch [83][700/3746] lr: 4.250e-02, eta: 2 days, 8:46:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6031, loss_cls: 3.7464, loss: 3.7464 +2024-07-25 02:45:15,739 - pyskl - INFO - Epoch [83][800/3746] lr: 4.247e-02, eta: 2 days, 8:45:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5717, loss_cls: 3.8583, loss: 3.8583 +2024-07-25 02:46:37,499 - pyskl - INFO - Epoch [83][900/3746] lr: 4.245e-02, eta: 2 days, 8:44:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6058, loss_cls: 3.7193, loss: 3.7193 +2024-07-25 02:47:58,915 - pyskl - INFO - Epoch [83][1000/3746] lr: 4.242e-02, eta: 2 days, 8:42:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.5984, loss_cls: 3.7511, loss: 3.7511 +2024-07-25 02:49:20,808 - pyskl - INFO - Epoch [83][1100/3746] lr: 4.239e-02, eta: 2 days, 8:41:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6064, loss_cls: 3.7667, loss: 3.7667 +2024-07-25 02:50:42,013 - pyskl - INFO - Epoch [83][1200/3746] lr: 4.236e-02, eta: 2 days, 8:40:08, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.6016, loss_cls: 3.7285, loss: 3.7285 +2024-07-25 02:52:03,705 - pyskl - INFO - Epoch [83][1300/3746] lr: 4.234e-02, eta: 2 days, 8:38:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5900, loss_cls: 3.8271, loss: 3.8271 +2024-07-25 02:53:25,576 - pyskl - INFO - Epoch [83][1400/3746] lr: 4.231e-02, eta: 2 days, 8:37:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5914, loss_cls: 3.7864, loss: 3.7864 +2024-07-25 02:54:47,123 - pyskl - INFO - Epoch [83][1500/3746] lr: 4.228e-02, eta: 2 days, 8:36:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5855, loss_cls: 3.8137, loss: 3.8137 +2024-07-25 02:56:09,800 - pyskl - INFO - Epoch [83][1600/3746] lr: 4.225e-02, eta: 2 days, 8:34:51, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5884, loss_cls: 3.7872, loss: 3.7872 +2024-07-25 02:57:31,693 - pyskl - INFO - Epoch [83][1700/3746] lr: 4.223e-02, eta: 2 days, 8:33:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.5973, loss_cls: 3.7599, loss: 3.7599 +2024-07-25 02:58:53,372 - pyskl - INFO - Epoch [83][1800/3746] lr: 4.220e-02, eta: 2 days, 8:32:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6047, loss_cls: 3.7351, loss: 3.7351 +2024-07-25 03:00:14,941 - pyskl - INFO - Epoch [83][1900/3746] lr: 4.217e-02, eta: 2 days, 8:30:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5948, loss_cls: 3.7936, loss: 3.7936 +2024-07-25 03:01:37,210 - pyskl - INFO - Epoch [83][2000/3746] lr: 4.214e-02, eta: 2 days, 8:29:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5891, loss_cls: 3.8211, loss: 3.8211 +2024-07-25 03:02:58,388 - pyskl - INFO - Epoch [83][2100/3746] lr: 4.212e-02, eta: 2 days, 8:28:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5795, loss_cls: 3.8649, loss: 3.8649 +2024-07-25 03:04:19,734 - pyskl - INFO - Epoch [83][2200/3746] lr: 4.209e-02, eta: 2 days, 8:26:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5925, loss_cls: 3.8026, loss: 3.8026 +2024-07-25 03:05:40,807 - pyskl - INFO - Epoch [83][2300/3746] lr: 4.206e-02, eta: 2 days, 8:25:34, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5853, loss_cls: 3.7922, loss: 3.7922 +2024-07-25 03:07:01,977 - pyskl - INFO - Epoch [83][2400/3746] lr: 4.203e-02, eta: 2 days, 8:24:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5991, loss_cls: 3.7843, loss: 3.7843 +2024-07-25 03:08:23,342 - pyskl - INFO - Epoch [83][2500/3746] lr: 4.201e-02, eta: 2 days, 8:22:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5837, loss_cls: 3.8374, loss: 3.8374 +2024-07-25 03:09:44,787 - pyskl - INFO - Epoch [83][2600/3746] lr: 4.198e-02, eta: 2 days, 8:21:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5911, loss_cls: 3.7893, loss: 3.7893 +2024-07-25 03:11:06,956 - pyskl - INFO - Epoch [83][2700/3746] lr: 4.195e-02, eta: 2 days, 8:20:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5945, loss_cls: 3.8006, loss: 3.8006 +2024-07-25 03:12:28,364 - pyskl - INFO - Epoch [83][2800/3746] lr: 4.192e-02, eta: 2 days, 8:18:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5945, loss_cls: 3.7913, loss: 3.7913 +2024-07-25 03:13:49,417 - pyskl - INFO - Epoch [83][2900/3746] lr: 4.190e-02, eta: 2 days, 8:17:36, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5834, loss_cls: 3.8164, loss: 3.8164 +2024-07-25 03:15:11,251 - pyskl - INFO - Epoch [83][3000/3746] lr: 4.187e-02, eta: 2 days, 8:16:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5892, loss_cls: 3.8059, loss: 3.8059 +2024-07-25 03:16:32,938 - pyskl - INFO - Epoch [83][3100/3746] lr: 4.184e-02, eta: 2 days, 8:14:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5906, loss_cls: 3.8239, loss: 3.8239 +2024-07-25 03:17:54,699 - pyskl - INFO - Epoch [83][3200/3746] lr: 4.181e-02, eta: 2 days, 8:13:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5916, loss_cls: 3.8039, loss: 3.8039 +2024-07-25 03:19:16,062 - pyskl - INFO - Epoch [83][3300/3746] lr: 4.178e-02, eta: 2 days, 8:12:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5855, loss_cls: 3.8247, loss: 3.8247 +2024-07-25 03:20:37,666 - pyskl - INFO - Epoch [83][3400/3746] lr: 4.176e-02, eta: 2 days, 8:10:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5939, loss_cls: 3.7812, loss: 3.7812 +2024-07-25 03:21:58,885 - pyskl - INFO - Epoch [83][3500/3746] lr: 4.173e-02, eta: 2 days, 8:09:38, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5806, loss_cls: 3.8532, loss: 3.8532 +2024-07-25 03:23:20,922 - pyskl - INFO - Epoch [83][3600/3746] lr: 4.170e-02, eta: 2 days, 8:08:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5903, loss_cls: 3.8194, loss: 3.8194 +2024-07-25 03:24:43,189 - pyskl - INFO - Epoch [83][3700/3746] lr: 4.167e-02, eta: 2 days, 8:07:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5972, loss_cls: 3.7799, loss: 3.7799 +2024-07-25 03:25:22,488 - pyskl - INFO - Saving checkpoint at 83 epochs +2024-07-25 03:27:15,914 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 03:27:16,570 - pyskl - INFO - +top1_acc 0.2317 +top5_acc 0.4767 +2024-07-25 03:27:16,570 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 03:27:16,610 - pyskl - INFO - +mean_acc 0.2316 +2024-07-25 03:27:16,623 - pyskl - INFO - Epoch(val) [83][309] top1_acc: 0.2317, top5_acc: 0.4767, mean_class_accuracy: 0.2316 +2024-07-25 03:31:08,682 - pyskl - INFO - Epoch [84][100/3746] lr: 4.163e-02, eta: 2 days, 8:06:35, time: 2.320, data_time: 1.344, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6058, loss_cls: 3.7289, loss: 3.7289 +2024-07-25 03:32:30,396 - pyskl - INFO - Epoch [84][200/3746] lr: 4.161e-02, eta: 2 days, 8:05:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.5989, loss_cls: 3.7358, loss: 3.7358 +2024-07-25 03:33:52,292 - pyskl - INFO - Epoch [84][300/3746] lr: 4.158e-02, eta: 2 days, 8:03:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6019, loss_cls: 3.7292, loss: 3.7292 +2024-07-25 03:35:14,021 - pyskl - INFO - Epoch [84][400/3746] lr: 4.155e-02, eta: 2 days, 8:02:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5952, loss_cls: 3.7948, loss: 3.7948 +2024-07-25 03:36:35,531 - pyskl - INFO - Epoch [84][500/3746] lr: 4.152e-02, eta: 2 days, 8:01:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.6023, loss_cls: 3.7441, loss: 3.7441 +2024-07-25 03:37:57,678 - pyskl - INFO - Epoch [84][600/3746] lr: 4.150e-02, eta: 2 days, 7:59:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5945, loss_cls: 3.7608, loss: 3.7608 +2024-07-25 03:39:19,772 - pyskl - INFO - Epoch [84][700/3746] lr: 4.147e-02, eta: 2 days, 7:58:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5894, loss_cls: 3.7900, loss: 3.7900 +2024-07-25 03:40:41,785 - pyskl - INFO - Epoch [84][800/3746] lr: 4.144e-02, eta: 2 days, 7:57:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.5986, loss_cls: 3.7315, loss: 3.7315 +2024-07-25 03:42:03,772 - pyskl - INFO - Epoch [84][900/3746] lr: 4.141e-02, eta: 2 days, 7:56:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5992, loss_cls: 3.7522, loss: 3.7522 +2024-07-25 03:43:25,355 - pyskl - INFO - Epoch [84][1000/3746] lr: 4.139e-02, eta: 2 days, 7:54:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.6075, loss_cls: 3.7338, loss: 3.7338 +2024-07-25 03:44:47,463 - pyskl - INFO - Epoch [84][1100/3746] lr: 4.136e-02, eta: 2 days, 7:53:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.5969, loss_cls: 3.7565, loss: 3.7565 +2024-07-25 03:46:08,704 - pyskl - INFO - Epoch [84][1200/3746] lr: 4.133e-02, eta: 2 days, 7:52:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5941, loss_cls: 3.7845, loss: 3.7845 +2024-07-25 03:47:30,630 - pyskl - INFO - Epoch [84][1300/3746] lr: 4.130e-02, eta: 2 days, 7:50:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5909, loss_cls: 3.7804, loss: 3.7804 +2024-07-25 03:48:53,004 - pyskl - INFO - Epoch [84][1400/3746] lr: 4.128e-02, eta: 2 days, 7:49:22, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5883, loss_cls: 3.7832, loss: 3.7832 +2024-07-25 03:50:15,184 - pyskl - INFO - Epoch [84][1500/3746] lr: 4.125e-02, eta: 2 days, 7:48:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5873, loss_cls: 3.7876, loss: 3.7876 +2024-07-25 03:51:38,283 - pyskl - INFO - Epoch [84][1600/3746] lr: 4.122e-02, eta: 2 days, 7:46:45, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5933, loss_cls: 3.7649, loss: 3.7649 +2024-07-25 03:53:00,184 - pyskl - INFO - Epoch [84][1700/3746] lr: 4.119e-02, eta: 2 days, 7:45:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.5989, loss_cls: 3.7617, loss: 3.7617 +2024-07-25 03:54:21,883 - pyskl - INFO - Epoch [84][1800/3746] lr: 4.117e-02, eta: 2 days, 7:44:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5913, loss_cls: 3.8006, loss: 3.8006 +2024-07-25 03:55:43,586 - pyskl - INFO - Epoch [84][1900/3746] lr: 4.114e-02, eta: 2 days, 7:42:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5986, loss_cls: 3.7883, loss: 3.7883 +2024-07-25 03:57:04,687 - pyskl - INFO - Epoch [84][2000/3746] lr: 4.111e-02, eta: 2 days, 7:41:26, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5950, loss_cls: 3.7845, loss: 3.7845 +2024-07-25 03:58:26,517 - pyskl - INFO - Epoch [84][2100/3746] lr: 4.108e-02, eta: 2 days, 7:40:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5873, loss_cls: 3.8199, loss: 3.8199 +2024-07-25 03:59:48,337 - pyskl - INFO - Epoch [84][2200/3746] lr: 4.106e-02, eta: 2 days, 7:38:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6062, loss_cls: 3.7296, loss: 3.7296 +2024-07-25 04:01:09,661 - pyskl - INFO - Epoch [84][2300/3746] lr: 4.103e-02, eta: 2 days, 7:37:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5931, loss_cls: 3.7909, loss: 3.7909 +2024-07-25 04:02:30,908 - pyskl - INFO - Epoch [84][2400/3746] lr: 4.100e-02, eta: 2 days, 7:36:07, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5878, loss_cls: 3.8008, loss: 3.8008 +2024-07-25 04:03:52,634 - pyskl - INFO - Epoch [84][2500/3746] lr: 4.097e-02, eta: 2 days, 7:34:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5927, loss_cls: 3.7971, loss: 3.7971 +2024-07-25 04:05:14,541 - pyskl - INFO - Epoch [84][2600/3746] lr: 4.095e-02, eta: 2 days, 7:33:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.5939, loss_cls: 3.7761, loss: 3.7761 +2024-07-25 04:06:36,082 - pyskl - INFO - Epoch [84][2700/3746] lr: 4.092e-02, eta: 2 days, 7:32:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5928, loss_cls: 3.8118, loss: 3.8118 +2024-07-25 04:07:57,221 - pyskl - INFO - Epoch [84][2800/3746] lr: 4.089e-02, eta: 2 days, 7:30:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.5983, loss_cls: 3.7801, loss: 3.7801 +2024-07-25 04:09:18,337 - pyskl - INFO - Epoch [84][2900/3746] lr: 4.086e-02, eta: 2 days, 7:29:28, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5928, loss_cls: 3.7768, loss: 3.7768 +2024-07-25 04:10:40,048 - pyskl - INFO - Epoch [84][3000/3746] lr: 4.084e-02, eta: 2 days, 7:28:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5861, loss_cls: 3.8193, loss: 3.8193 +2024-07-25 04:12:01,598 - pyskl - INFO - Epoch [84][3100/3746] lr: 4.081e-02, eta: 2 days, 7:26:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.5981, loss_cls: 3.7580, loss: 3.7580 +2024-07-25 04:13:23,397 - pyskl - INFO - Epoch [84][3200/3746] lr: 4.078e-02, eta: 2 days, 7:25:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5917, loss_cls: 3.8116, loss: 3.8116 +2024-07-25 04:14:45,168 - pyskl - INFO - Epoch [84][3300/3746] lr: 4.075e-02, eta: 2 days, 7:24:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6041, loss_cls: 3.7442, loss: 3.7442 +2024-07-25 04:16:06,611 - pyskl - INFO - Epoch [84][3400/3746] lr: 4.073e-02, eta: 2 days, 7:22:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5863, loss_cls: 3.8133, loss: 3.8133 +2024-07-25 04:17:28,198 - pyskl - INFO - Epoch [84][3500/3746] lr: 4.070e-02, eta: 2 days, 7:21:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5964, loss_cls: 3.7767, loss: 3.7767 +2024-07-25 04:18:49,815 - pyskl - INFO - Epoch [84][3600/3746] lr: 4.067e-02, eta: 2 days, 7:20:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5803, loss_cls: 3.8694, loss: 3.8694 +2024-07-25 04:20:12,132 - pyskl - INFO - Epoch [84][3700/3746] lr: 4.064e-02, eta: 2 days, 7:18:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.5895, loss_cls: 3.7983, loss: 3.7983 +2024-07-25 04:20:51,662 - pyskl - INFO - Saving checkpoint at 84 epochs +2024-07-25 04:22:44,148 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 04:22:44,859 - pyskl - INFO - +top1_acc 0.2758 +top5_acc 0.5250 +2024-07-25 04:22:44,859 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 04:22:44,902 - pyskl - INFO - +mean_acc 0.2756 +2024-07-25 04:22:44,907 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_79.pth was removed +2024-07-25 04:22:45,168 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_84.pth. +2024-07-25 04:22:45,169 - pyskl - INFO - Best top1_acc is 0.2758 at 84 epoch. +2024-07-25 04:22:45,180 - pyskl - INFO - Epoch(val) [84][309] top1_acc: 0.2758, top5_acc: 0.5250, mean_class_accuracy: 0.2756 +2024-07-25 04:26:43,417 - pyskl - INFO - Epoch [85][100/3746] lr: 4.060e-02, eta: 2 days, 7:18:29, time: 2.382, data_time: 1.386, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6097, loss_cls: 3.7158, loss: 3.7158 +2024-07-25 04:28:07,078 - pyskl - INFO - Epoch [85][200/3746] lr: 4.058e-02, eta: 2 days, 7:17:11, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.5981, loss_cls: 3.7134, loss: 3.7134 +2024-07-25 04:29:29,261 - pyskl - INFO - Epoch [85][300/3746] lr: 4.055e-02, eta: 2 days, 7:15:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6014, loss_cls: 3.7185, loss: 3.7185 +2024-07-25 04:30:51,783 - pyskl - INFO - Epoch [85][400/3746] lr: 4.052e-02, eta: 2 days, 7:14:32, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5980, loss_cls: 3.7512, loss: 3.7512 +2024-07-25 04:32:14,348 - pyskl - INFO - Epoch [85][500/3746] lr: 4.049e-02, eta: 2 days, 7:13:13, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5887, loss_cls: 3.7807, loss: 3.7807 +2024-07-25 04:33:35,894 - pyskl - INFO - Epoch [85][600/3746] lr: 4.047e-02, eta: 2 days, 7:11:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.5969, loss_cls: 3.7672, loss: 3.7672 +2024-07-25 04:34:58,132 - pyskl - INFO - Epoch [85][700/3746] lr: 4.044e-02, eta: 2 days, 7:10:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6081, loss_cls: 3.6543, loss: 3.6543 +2024-07-25 04:36:20,562 - pyskl - INFO - Epoch [85][800/3746] lr: 4.041e-02, eta: 2 days, 7:09:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5941, loss_cls: 3.7720, loss: 3.7720 +2024-07-25 04:37:42,339 - pyskl - INFO - Epoch [85][900/3746] lr: 4.038e-02, eta: 2 days, 7:07:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5884, loss_cls: 3.8225, loss: 3.8225 +2024-07-25 04:39:03,754 - pyskl - INFO - Epoch [85][1000/3746] lr: 4.036e-02, eta: 2 days, 7:06:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5986, loss_cls: 3.7942, loss: 3.7942 +2024-07-25 04:40:25,536 - pyskl - INFO - Epoch [85][1100/3746] lr: 4.033e-02, eta: 2 days, 7:05:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.5970, loss_cls: 3.7556, loss: 3.7556 +2024-07-25 04:41:46,811 - pyskl - INFO - Epoch [85][1200/3746] lr: 4.030e-02, eta: 2 days, 7:03:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5917, loss_cls: 3.7856, loss: 3.7856 +2024-07-25 04:43:08,650 - pyskl - INFO - Epoch [85][1300/3746] lr: 4.027e-02, eta: 2 days, 7:02:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5944, loss_cls: 3.7432, loss: 3.7432 +2024-07-25 04:44:30,792 - pyskl - INFO - Epoch [85][1400/3746] lr: 4.025e-02, eta: 2 days, 7:01:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5952, loss_cls: 3.7525, loss: 3.7525 +2024-07-25 04:45:52,400 - pyskl - INFO - Epoch [85][1500/3746] lr: 4.022e-02, eta: 2 days, 6:59:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5917, loss_cls: 3.7848, loss: 3.7848 +2024-07-25 04:47:14,551 - pyskl - INFO - Epoch [85][1600/3746] lr: 4.019e-02, eta: 2 days, 6:58:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.5978, loss_cls: 3.7583, loss: 3.7583 +2024-07-25 04:48:36,463 - pyskl - INFO - Epoch [85][1700/3746] lr: 4.016e-02, eta: 2 days, 6:57:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.5942, loss_cls: 3.7790, loss: 3.7790 +2024-07-25 04:49:58,181 - pyskl - INFO - Epoch [85][1800/3746] lr: 4.014e-02, eta: 2 days, 6:55:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6131, loss_cls: 3.7137, loss: 3.7137 +2024-07-25 04:51:19,744 - pyskl - INFO - Epoch [85][1900/3746] lr: 4.011e-02, eta: 2 days, 6:54:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5913, loss_cls: 3.7942, loss: 3.7942 +2024-07-25 04:52:41,041 - pyskl - INFO - Epoch [85][2000/3746] lr: 4.008e-02, eta: 2 days, 6:53:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.5983, loss_cls: 3.7573, loss: 3.7573 +2024-07-25 04:54:02,405 - pyskl - INFO - Epoch [85][2100/3746] lr: 4.006e-02, eta: 2 days, 6:51:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5992, loss_cls: 3.7847, loss: 3.7847 +2024-07-25 04:55:24,651 - pyskl - INFO - Epoch [85][2200/3746] lr: 4.003e-02, eta: 2 days, 6:50:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5902, loss_cls: 3.7537, loss: 3.7537 +2024-07-25 04:56:45,996 - pyskl - INFO - Epoch [85][2300/3746] lr: 4.000e-02, eta: 2 days, 6:49:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5936, loss_cls: 3.7834, loss: 3.7834 +2024-07-25 04:58:07,189 - pyskl - INFO - Epoch [85][2400/3746] lr: 3.997e-02, eta: 2 days, 6:48:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5913, loss_cls: 3.7957, loss: 3.7957 +2024-07-25 04:59:28,448 - pyskl - INFO - Epoch [85][2500/3746] lr: 3.995e-02, eta: 2 days, 6:46:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5928, loss_cls: 3.8007, loss: 3.8007 +2024-07-25 05:00:50,309 - pyskl - INFO - Epoch [85][2600/3746] lr: 3.992e-02, eta: 2 days, 6:45:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5887, loss_cls: 3.8319, loss: 3.8319 +2024-07-25 05:02:12,020 - pyskl - INFO - Epoch [85][2700/3746] lr: 3.989e-02, eta: 2 days, 6:44:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5863, loss_cls: 3.8269, loss: 3.8269 +2024-07-25 05:03:33,383 - pyskl - INFO - Epoch [85][2800/3746] lr: 3.986e-02, eta: 2 days, 6:42:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.5992, loss_cls: 3.7850, loss: 3.7850 +2024-07-25 05:04:54,758 - pyskl - INFO - Epoch [85][2900/3746] lr: 3.984e-02, eta: 2 days, 6:41:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5950, loss_cls: 3.7932, loss: 3.7932 +2024-07-25 05:06:16,047 - pyskl - INFO - Epoch [85][3000/3746] lr: 3.981e-02, eta: 2 days, 6:40:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5897, loss_cls: 3.7773, loss: 3.7773 +2024-07-25 05:07:37,055 - pyskl - INFO - Epoch [85][3100/3746] lr: 3.978e-02, eta: 2 days, 6:38:40, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.5995, loss_cls: 3.7495, loss: 3.7495 +2024-07-25 05:08:58,322 - pyskl - INFO - Epoch [85][3200/3746] lr: 3.975e-02, eta: 2 days, 6:37:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5962, loss_cls: 3.7768, loss: 3.7768 +2024-07-25 05:10:19,592 - pyskl - INFO - Epoch [85][3300/3746] lr: 3.973e-02, eta: 2 days, 6:36:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.6106, loss_cls: 3.7426, loss: 3.7426 +2024-07-25 05:11:40,804 - pyskl - INFO - Epoch [85][3400/3746] lr: 3.970e-02, eta: 2 days, 6:34:40, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.5970, loss_cls: 3.7719, loss: 3.7719 +2024-07-25 05:13:01,984 - pyskl - INFO - Epoch [85][3500/3746] lr: 3.967e-02, eta: 2 days, 6:33:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.6009, loss_cls: 3.7587, loss: 3.7587 +2024-07-25 05:14:23,336 - pyskl - INFO - Epoch [85][3600/3746] lr: 3.964e-02, eta: 2 days, 6:32:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.5955, loss_cls: 3.7886, loss: 3.7886 +2024-07-25 05:15:45,775 - pyskl - INFO - Epoch [85][3700/3746] lr: 3.962e-02, eta: 2 days, 6:30:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5972, loss_cls: 3.7434, loss: 3.7434 +2024-07-25 05:16:25,209 - pyskl - INFO - Saving checkpoint at 85 epochs +2024-07-25 05:18:17,903 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 05:18:18,637 - pyskl - INFO - +top1_acc 0.2578 +top5_acc 0.5039 +2024-07-25 05:18:18,637 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 05:18:18,678 - pyskl - INFO - +mean_acc 0.2575 +2024-07-25 05:18:18,690 - pyskl - INFO - Epoch(val) [85][309] top1_acc: 0.2578, top5_acc: 0.5039, mean_class_accuracy: 0.2575 +2024-07-25 05:22:16,424 - pyskl - INFO - Epoch [86][100/3746] lr: 3.958e-02, eta: 2 days, 6:30:15, time: 2.377, data_time: 1.394, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6125, loss_cls: 3.6921, loss: 3.6921 +2024-07-25 05:23:38,965 - pyskl - INFO - Epoch [86][200/3746] lr: 3.955e-02, eta: 2 days, 6:28:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6108, loss_cls: 3.6863, loss: 3.6863 +2024-07-25 05:25:01,397 - pyskl - INFO - Epoch [86][300/3746] lr: 3.952e-02, eta: 2 days, 6:27:36, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6153, loss_cls: 3.6846, loss: 3.6846 +2024-07-25 05:26:23,798 - pyskl - INFO - Epoch [86][400/3746] lr: 3.950e-02, eta: 2 days, 6:26:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.6011, loss_cls: 3.7770, loss: 3.7770 +2024-07-25 05:27:45,894 - pyskl - INFO - Epoch [86][500/3746] lr: 3.947e-02, eta: 2 days, 6:24:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6016, loss_cls: 3.7154, loss: 3.7154 +2024-07-25 05:29:07,689 - pyskl - INFO - Epoch [86][600/3746] lr: 3.944e-02, eta: 2 days, 6:23:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.6025, loss_cls: 3.7249, loss: 3.7249 +2024-07-25 05:30:29,807 - pyskl - INFO - Epoch [86][700/3746] lr: 3.941e-02, eta: 2 days, 6:22:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.5959, loss_cls: 3.7808, loss: 3.7808 +2024-07-25 05:31:51,049 - pyskl - INFO - Epoch [86][800/3746] lr: 3.939e-02, eta: 2 days, 6:20:58, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6059, loss_cls: 3.7153, loss: 3.7153 +2024-07-25 05:33:12,603 - pyskl - INFO - Epoch [86][900/3746] lr: 3.936e-02, eta: 2 days, 6:19:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5922, loss_cls: 3.7950, loss: 3.7950 +2024-07-25 05:34:34,176 - pyskl - INFO - Epoch [86][1000/3746] lr: 3.933e-02, eta: 2 days, 6:18:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5892, loss_cls: 3.8061, loss: 3.8061 +2024-07-25 05:35:55,822 - pyskl - INFO - Epoch [86][1100/3746] lr: 3.930e-02, eta: 2 days, 6:16:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5962, loss_cls: 3.7482, loss: 3.7482 +2024-07-25 05:37:17,230 - pyskl - INFO - Epoch [86][1200/3746] lr: 3.928e-02, eta: 2 days, 6:15:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6030, loss_cls: 3.7195, loss: 3.7195 +2024-07-25 05:38:39,251 - pyskl - INFO - Epoch [86][1300/3746] lr: 3.925e-02, eta: 2 days, 6:14:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.6045, loss_cls: 3.7427, loss: 3.7427 +2024-07-25 05:40:01,790 - pyskl - INFO - Epoch [86][1400/3746] lr: 3.922e-02, eta: 2 days, 6:13:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5933, loss_cls: 3.7346, loss: 3.7346 +2024-07-25 05:41:23,358 - pyskl - INFO - Epoch [86][1500/3746] lr: 3.919e-02, eta: 2 days, 6:11:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5936, loss_cls: 3.7737, loss: 3.7737 +2024-07-25 05:42:45,642 - pyskl - INFO - Epoch [86][1600/3746] lr: 3.917e-02, eta: 2 days, 6:10:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.5928, loss_cls: 3.7551, loss: 3.7551 +2024-07-25 05:44:07,693 - pyskl - INFO - Epoch [86][1700/3746] lr: 3.914e-02, eta: 2 days, 6:09:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.5995, loss_cls: 3.7319, loss: 3.7319 +2024-07-25 05:45:30,093 - pyskl - INFO - Epoch [86][1800/3746] lr: 3.911e-02, eta: 2 days, 6:07:42, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.5998, loss_cls: 3.7467, loss: 3.7467 +2024-07-25 05:46:52,281 - pyskl - INFO - Epoch [86][1900/3746] lr: 3.909e-02, eta: 2 days, 6:06:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5945, loss_cls: 3.7821, loss: 3.7821 +2024-07-25 05:48:13,727 - pyskl - INFO - Epoch [86][2000/3746] lr: 3.906e-02, eta: 2 days, 6:05:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.5919, loss_cls: 3.7614, loss: 3.7614 +2024-07-25 05:49:35,003 - pyskl - INFO - Epoch [86][2100/3746] lr: 3.903e-02, eta: 2 days, 6:03:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5922, loss_cls: 3.7878, loss: 3.7878 +2024-07-25 05:50:56,625 - pyskl - INFO - Epoch [86][2200/3746] lr: 3.900e-02, eta: 2 days, 6:02:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6086, loss_cls: 3.7043, loss: 3.7043 +2024-07-25 05:52:18,223 - pyskl - INFO - Epoch [86][2300/3746] lr: 3.898e-02, eta: 2 days, 6:01:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.5989, loss_cls: 3.7575, loss: 3.7575 +2024-07-25 05:53:39,831 - pyskl - INFO - Epoch [86][2400/3746] lr: 3.895e-02, eta: 2 days, 5:59:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5891, loss_cls: 3.8014, loss: 3.8014 +2024-07-25 05:55:01,867 - pyskl - INFO - Epoch [86][2500/3746] lr: 3.892e-02, eta: 2 days, 5:58:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5916, loss_cls: 3.7614, loss: 3.7614 +2024-07-25 05:56:23,592 - pyskl - INFO - Epoch [86][2600/3746] lr: 3.889e-02, eta: 2 days, 5:57:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5923, loss_cls: 3.7773, loss: 3.7773 +2024-07-25 05:57:44,737 - pyskl - INFO - Epoch [86][2700/3746] lr: 3.887e-02, eta: 2 days, 5:55:43, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5989, loss_cls: 3.7284, loss: 3.7284 +2024-07-25 05:59:05,981 - pyskl - INFO - Epoch [86][2800/3746] lr: 3.884e-02, eta: 2 days, 5:54:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5964, loss_cls: 3.7838, loss: 3.7838 +2024-07-25 06:00:27,606 - pyskl - INFO - Epoch [86][2900/3746] lr: 3.881e-02, eta: 2 days, 5:53:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.6011, loss_cls: 3.7551, loss: 3.7551 +2024-07-25 06:01:49,099 - pyskl - INFO - Epoch [86][3000/3746] lr: 3.879e-02, eta: 2 days, 5:51:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5977, loss_cls: 3.7426, loss: 3.7426 +2024-07-25 06:03:10,703 - pyskl - INFO - Epoch [86][3100/3746] lr: 3.876e-02, eta: 2 days, 5:50:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5917, loss_cls: 3.7897, loss: 3.7897 +2024-07-25 06:04:32,173 - pyskl - INFO - Epoch [86][3200/3746] lr: 3.873e-02, eta: 2 days, 5:49:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6019, loss_cls: 3.7493, loss: 3.7493 +2024-07-25 06:05:53,724 - pyskl - INFO - Epoch [86][3300/3746] lr: 3.870e-02, eta: 2 days, 5:47:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5962, loss_cls: 3.7802, loss: 3.7802 +2024-07-25 06:07:15,337 - pyskl - INFO - Epoch [86][3400/3746] lr: 3.868e-02, eta: 2 days, 5:46:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6084, loss_cls: 3.7073, loss: 3.7073 +2024-07-25 06:08:36,805 - pyskl - INFO - Epoch [86][3500/3746] lr: 3.865e-02, eta: 2 days, 5:45:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5931, loss_cls: 3.7864, loss: 3.7864 +2024-07-25 06:09:58,905 - pyskl - INFO - Epoch [86][3600/3746] lr: 3.862e-02, eta: 2 days, 5:43:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.5988, loss_cls: 3.7232, loss: 3.7232 +2024-07-25 06:11:20,770 - pyskl - INFO - Epoch [86][3700/3746] lr: 3.860e-02, eta: 2 days, 5:42:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.5934, loss_cls: 3.7772, loss: 3.7772 +2024-07-25 06:11:59,853 - pyskl - INFO - Saving checkpoint at 86 epochs +2024-07-25 06:13:52,687 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 06:13:53,366 - pyskl - INFO - +top1_acc 0.2709 +top5_acc 0.5138 +2024-07-25 06:13:53,366 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 06:13:53,407 - pyskl - INFO - +mean_acc 0.2707 +2024-07-25 06:13:53,419 - pyskl - INFO - Epoch(val) [86][309] top1_acc: 0.2709, top5_acc: 0.5138, mean_class_accuracy: 0.2707 +2024-07-25 06:17:48,939 - pyskl - INFO - Epoch [87][100/3746] lr: 3.856e-02, eta: 2 days, 5:41:54, time: 2.355, data_time: 1.368, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6053, loss_cls: 3.7001, loss: 3.7001 +2024-07-25 06:19:10,900 - pyskl - INFO - Epoch [87][200/3746] lr: 3.853e-02, eta: 2 days, 5:40:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6062, loss_cls: 3.7048, loss: 3.7048 +2024-07-25 06:20:32,683 - pyskl - INFO - Epoch [87][300/3746] lr: 3.850e-02, eta: 2 days, 5:39:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.5936, loss_cls: 3.7498, loss: 3.7498 +2024-07-25 06:21:54,300 - pyskl - INFO - Epoch [87][400/3746] lr: 3.847e-02, eta: 2 days, 5:37:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6031, loss_cls: 3.7230, loss: 3.7230 +2024-07-25 06:23:16,736 - pyskl - INFO - Epoch [87][500/3746] lr: 3.845e-02, eta: 2 days, 5:36:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6073, loss_cls: 3.7216, loss: 3.7216 +2024-07-25 06:24:38,465 - pyskl - INFO - Epoch [87][600/3746] lr: 3.842e-02, eta: 2 days, 5:35:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6044, loss_cls: 3.6975, loss: 3.6975 +2024-07-25 06:26:00,640 - pyskl - INFO - Epoch [87][700/3746] lr: 3.839e-02, eta: 2 days, 5:33:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6062, loss_cls: 3.7152, loss: 3.7152 +2024-07-25 06:27:22,623 - pyskl - INFO - Epoch [87][800/3746] lr: 3.837e-02, eta: 2 days, 5:32:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5964, loss_cls: 3.7562, loss: 3.7562 +2024-07-25 06:28:44,607 - pyskl - INFO - Epoch [87][900/3746] lr: 3.834e-02, eta: 2 days, 5:31:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.6022, loss_cls: 3.7360, loss: 3.7360 +2024-07-25 06:30:06,200 - pyskl - INFO - Epoch [87][1000/3746] lr: 3.831e-02, eta: 2 days, 5:29:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.5992, loss_cls: 3.7031, loss: 3.7031 +2024-07-25 06:31:27,860 - pyskl - INFO - Epoch [87][1100/3746] lr: 3.828e-02, eta: 2 days, 5:28:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6019, loss_cls: 3.7462, loss: 3.7462 +2024-07-25 06:32:49,302 - pyskl - INFO - Epoch [87][1200/3746] lr: 3.826e-02, eta: 2 days, 5:27:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.6006, loss_cls: 3.7516, loss: 3.7516 +2024-07-25 06:34:10,622 - pyskl - INFO - Epoch [87][1300/3746] lr: 3.823e-02, eta: 2 days, 5:25:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6094, loss_cls: 3.6723, loss: 3.6723 +2024-07-25 06:35:32,894 - pyskl - INFO - Epoch [87][1400/3746] lr: 3.820e-02, eta: 2 days, 5:24:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6025, loss_cls: 3.7248, loss: 3.7248 +2024-07-25 06:36:54,024 - pyskl - INFO - Epoch [87][1500/3746] lr: 3.817e-02, eta: 2 days, 5:23:16, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6041, loss_cls: 3.7563, loss: 3.7563 +2024-07-25 06:38:16,405 - pyskl - INFO - Epoch [87][1600/3746] lr: 3.815e-02, eta: 2 days, 5:21:56, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.6034, loss_cls: 3.7497, loss: 3.7497 +2024-07-25 06:39:38,753 - pyskl - INFO - Epoch [87][1700/3746] lr: 3.812e-02, eta: 2 days, 5:20:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6081, loss_cls: 3.7236, loss: 3.7236 +2024-07-25 06:41:00,585 - pyskl - INFO - Epoch [87][1800/3746] lr: 3.809e-02, eta: 2 days, 5:19:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6009, loss_cls: 3.7421, loss: 3.7421 +2024-07-25 06:42:23,044 - pyskl - INFO - Epoch [87][1900/3746] lr: 3.807e-02, eta: 2 days, 5:17:58, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.5989, loss_cls: 3.7543, loss: 3.7543 +2024-07-25 06:43:45,134 - pyskl - INFO - Epoch [87][2000/3746] lr: 3.804e-02, eta: 2 days, 5:16:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6000, loss_cls: 3.7331, loss: 3.7331 +2024-07-25 06:45:07,543 - pyskl - INFO - Epoch [87][2100/3746] lr: 3.801e-02, eta: 2 days, 5:15:19, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5914, loss_cls: 3.7842, loss: 3.7842 +2024-07-25 06:46:29,009 - pyskl - INFO - Epoch [87][2200/3746] lr: 3.798e-02, eta: 2 days, 5:13:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5970, loss_cls: 3.7844, loss: 3.7844 +2024-07-25 06:47:50,541 - pyskl - INFO - Epoch [87][2300/3746] lr: 3.796e-02, eta: 2 days, 5:12:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6058, loss_cls: 3.7262, loss: 3.7262 +2024-07-25 06:49:12,582 - pyskl - INFO - Epoch [87][2400/3746] lr: 3.793e-02, eta: 2 days, 5:11:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5966, loss_cls: 3.8080, loss: 3.8080 +2024-07-25 06:50:34,729 - pyskl - INFO - Epoch [87][2500/3746] lr: 3.790e-02, eta: 2 days, 5:09:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.6034, loss_cls: 3.7303, loss: 3.7303 +2024-07-25 06:51:56,557 - pyskl - INFO - Epoch [87][2600/3746] lr: 3.788e-02, eta: 2 days, 5:08:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6070, loss_cls: 3.7230, loss: 3.7230 +2024-07-25 06:53:18,100 - pyskl - INFO - Epoch [87][2700/3746] lr: 3.785e-02, eta: 2 days, 5:07:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.5942, loss_cls: 3.7862, loss: 3.7862 +2024-07-25 06:54:39,671 - pyskl - INFO - Epoch [87][2800/3746] lr: 3.782e-02, eta: 2 days, 5:05:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5952, loss_cls: 3.7299, loss: 3.7299 +2024-07-25 06:56:01,120 - pyskl - INFO - Epoch [87][2900/3746] lr: 3.779e-02, eta: 2 days, 5:04:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6033, loss_cls: 3.7384, loss: 3.7384 +2024-07-25 06:57:22,935 - pyskl - INFO - Epoch [87][3000/3746] lr: 3.777e-02, eta: 2 days, 5:03:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6056, loss_cls: 3.7283, loss: 3.7283 +2024-07-25 06:58:44,311 - pyskl - INFO - Epoch [87][3100/3746] lr: 3.774e-02, eta: 2 days, 5:01:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6036, loss_cls: 3.7387, loss: 3.7387 +2024-07-25 07:00:06,202 - pyskl - INFO - Epoch [87][3200/3746] lr: 3.771e-02, eta: 2 days, 5:00:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.5889, loss_cls: 3.7856, loss: 3.7856 +2024-07-25 07:01:27,654 - pyskl - INFO - Epoch [87][3300/3746] lr: 3.769e-02, eta: 2 days, 4:59:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.5961, loss_cls: 3.7206, loss: 3.7206 +2024-07-25 07:02:48,817 - pyskl - INFO - Epoch [87][3400/3746] lr: 3.766e-02, eta: 2 days, 4:57:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5905, loss_cls: 3.7893, loss: 3.7893 +2024-07-25 07:04:10,217 - pyskl - INFO - Epoch [87][3500/3746] lr: 3.763e-02, eta: 2 days, 4:56:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5984, loss_cls: 3.7779, loss: 3.7779 +2024-07-25 07:05:31,876 - pyskl - INFO - Epoch [87][3600/3746] lr: 3.761e-02, eta: 2 days, 4:55:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.5934, loss_cls: 3.7515, loss: 3.7515 +2024-07-25 07:06:53,755 - pyskl - INFO - Epoch [87][3700/3746] lr: 3.758e-02, eta: 2 days, 4:53:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.5955, loss_cls: 3.7598, loss: 3.7598 +2024-07-25 07:07:33,021 - pyskl - INFO - Saving checkpoint at 87 epochs +2024-07-25 07:09:25,469 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 07:09:26,266 - pyskl - INFO - +top1_acc 0.2686 +top5_acc 0.5153 +2024-07-25 07:09:26,267 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 07:09:26,315 - pyskl - INFO - +mean_acc 0.2683 +2024-07-25 07:09:26,329 - pyskl - INFO - Epoch(val) [87][309] top1_acc: 0.2686, top5_acc: 0.5153, mean_class_accuracy: 0.2683 +2024-07-25 07:13:20,815 - pyskl - INFO - Epoch [88][100/3746] lr: 3.754e-02, eta: 2 days, 4:53:26, time: 2.345, data_time: 1.350, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6139, loss_cls: 3.6471, loss: 3.6471 +2024-07-25 07:14:44,148 - pyskl - INFO - Epoch [88][200/3746] lr: 3.751e-02, eta: 2 days, 4:52:07, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6016, loss_cls: 3.7266, loss: 3.7266 +2024-07-25 07:16:06,878 - pyskl - INFO - Epoch [88][300/3746] lr: 3.748e-02, eta: 2 days, 4:50:48, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6230, loss_cls: 3.6277, loss: 3.6277 +2024-07-25 07:17:29,232 - pyskl - INFO - Epoch [88][400/3746] lr: 3.746e-02, eta: 2 days, 4:49:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.5995, loss_cls: 3.7142, loss: 3.7142 +2024-07-25 07:18:51,752 - pyskl - INFO - Epoch [88][500/3746] lr: 3.743e-02, eta: 2 days, 4:48:09, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5947, loss_cls: 3.7839, loss: 3.7839 +2024-07-25 07:20:14,027 - pyskl - INFO - Epoch [88][600/3746] lr: 3.740e-02, eta: 2 days, 4:46:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5975, loss_cls: 3.7303, loss: 3.7303 +2024-07-25 07:21:35,962 - pyskl - INFO - Epoch [88][700/3746] lr: 3.738e-02, eta: 2 days, 4:45:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6058, loss_cls: 3.6978, loss: 3.6978 +2024-07-25 07:22:58,066 - pyskl - INFO - Epoch [88][800/3746] lr: 3.735e-02, eta: 2 days, 4:44:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5989, loss_cls: 3.7486, loss: 3.7486 +2024-07-25 07:24:20,300 - pyskl - INFO - Epoch [88][900/3746] lr: 3.732e-02, eta: 2 days, 4:42:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6073, loss_cls: 3.7111, loss: 3.7111 +2024-07-25 07:25:42,136 - pyskl - INFO - Epoch [88][1000/3746] lr: 3.730e-02, eta: 2 days, 4:41:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6062, loss_cls: 3.7115, loss: 3.7115 +2024-07-25 07:27:04,369 - pyskl - INFO - Epoch [88][1100/3746] lr: 3.727e-02, eta: 2 days, 4:40:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6177, loss_cls: 3.6719, loss: 3.6719 +2024-07-25 07:28:26,014 - pyskl - INFO - Epoch [88][1200/3746] lr: 3.724e-02, eta: 2 days, 4:38:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.6042, loss_cls: 3.7231, loss: 3.7231 +2024-07-25 07:29:47,784 - pyskl - INFO - Epoch [88][1300/3746] lr: 3.721e-02, eta: 2 days, 4:37:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6134, loss_cls: 3.6588, loss: 3.6588 +2024-07-25 07:31:10,230 - pyskl - INFO - Epoch [88][1400/3746] lr: 3.719e-02, eta: 2 days, 4:36:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6080, loss_cls: 3.7242, loss: 3.7242 +2024-07-25 07:32:31,377 - pyskl - INFO - Epoch [88][1500/3746] lr: 3.716e-02, eta: 2 days, 4:34:50, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6136, loss_cls: 3.7029, loss: 3.7029 +2024-07-25 07:33:54,205 - pyskl - INFO - Epoch [88][1600/3746] lr: 3.713e-02, eta: 2 days, 4:33:31, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6031, loss_cls: 3.7360, loss: 3.7360 +2024-07-25 07:35:16,378 - pyskl - INFO - Epoch [88][1700/3746] lr: 3.711e-02, eta: 2 days, 4:32:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.6050, loss_cls: 3.7630, loss: 3.7630 +2024-07-25 07:36:38,337 - pyskl - INFO - Epoch [88][1800/3746] lr: 3.708e-02, eta: 2 days, 4:30:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3541, top5_acc: 0.6103, loss_cls: 3.6936, loss: 3.6936 +2024-07-25 07:38:00,638 - pyskl - INFO - Epoch [88][1900/3746] lr: 3.705e-02, eta: 2 days, 4:29:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.6033, loss_cls: 3.7295, loss: 3.7295 +2024-07-25 07:39:22,003 - pyskl - INFO - Epoch [88][2000/3746] lr: 3.703e-02, eta: 2 days, 4:28:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5953, loss_cls: 3.7608, loss: 3.7608 +2024-07-25 07:40:43,473 - pyskl - INFO - Epoch [88][2100/3746] lr: 3.700e-02, eta: 2 days, 4:26:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.6008, loss_cls: 3.7474, loss: 3.7474 +2024-07-25 07:42:05,203 - pyskl - INFO - Epoch [88][2200/3746] lr: 3.697e-02, eta: 2 days, 4:25:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.5959, loss_cls: 3.7576, loss: 3.7576 +2024-07-25 07:43:26,365 - pyskl - INFO - Epoch [88][2300/3746] lr: 3.694e-02, eta: 2 days, 4:24:11, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5986, loss_cls: 3.7386, loss: 3.7386 +2024-07-25 07:44:47,795 - pyskl - INFO - Epoch [88][2400/3746] lr: 3.692e-02, eta: 2 days, 4:22:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.5994, loss_cls: 3.7512, loss: 3.7512 +2024-07-25 07:46:09,496 - pyskl - INFO - Epoch [88][2500/3746] lr: 3.689e-02, eta: 2 days, 4:21:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.5994, loss_cls: 3.7330, loss: 3.7330 +2024-07-25 07:47:30,501 - pyskl - INFO - Epoch [88][2600/3746] lr: 3.686e-02, eta: 2 days, 4:20:10, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.6084, loss_cls: 3.7495, loss: 3.7495 +2024-07-25 07:48:51,858 - pyskl - INFO - Epoch [88][2700/3746] lr: 3.684e-02, eta: 2 days, 4:18:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5983, loss_cls: 3.7643, loss: 3.7643 +2024-07-25 07:50:13,481 - pyskl - INFO - Epoch [88][2800/3746] lr: 3.681e-02, eta: 2 days, 4:17:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6097, loss_cls: 3.7131, loss: 3.7131 +2024-07-25 07:51:34,692 - pyskl - INFO - Epoch [88][2900/3746] lr: 3.678e-02, eta: 2 days, 4:16:10, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.5983, loss_cls: 3.7435, loss: 3.7435 +2024-07-25 07:52:56,647 - pyskl - INFO - Epoch [88][3000/3746] lr: 3.676e-02, eta: 2 days, 4:14:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6091, loss_cls: 3.7238, loss: 3.7238 +2024-07-25 07:54:18,146 - pyskl - INFO - Epoch [88][3100/3746] lr: 3.673e-02, eta: 2 days, 4:13:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5853, loss_cls: 3.8199, loss: 3.8199 +2024-07-25 07:55:39,718 - pyskl - INFO - Epoch [88][3200/3746] lr: 3.670e-02, eta: 2 days, 4:12:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6073, loss_cls: 3.7189, loss: 3.7189 +2024-07-25 07:57:01,326 - pyskl - INFO - Epoch [88][3300/3746] lr: 3.667e-02, eta: 2 days, 4:10:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5938, loss_cls: 3.7717, loss: 3.7717 +2024-07-25 07:58:23,386 - pyskl - INFO - Epoch [88][3400/3746] lr: 3.665e-02, eta: 2 days, 4:09:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6034, loss_cls: 3.7436, loss: 3.7436 +2024-07-25 07:59:45,345 - pyskl - INFO - Epoch [88][3500/3746] lr: 3.662e-02, eta: 2 days, 4:08:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6044, loss_cls: 3.7512, loss: 3.7512 +2024-07-25 08:01:08,239 - pyskl - INFO - Epoch [88][3600/3746] lr: 3.659e-02, eta: 2 days, 4:06:51, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.6064, loss_cls: 3.7254, loss: 3.7254 +2024-07-25 08:02:29,809 - pyskl - INFO - Epoch [88][3700/3746] lr: 3.657e-02, eta: 2 days, 4:05:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6036, loss_cls: 3.7197, loss: 3.7197 +2024-07-25 08:03:08,940 - pyskl - INFO - Saving checkpoint at 88 epochs +2024-07-25 08:05:01,999 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 08:05:02,705 - pyskl - INFO - +top1_acc 0.2778 +top5_acc 0.5241 +2024-07-25 08:05:02,705 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 08:05:02,757 - pyskl - INFO - +mean_acc 0.2775 +2024-07-25 08:05:02,762 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_84.pth was removed +2024-07-25 08:05:03,078 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_88.pth. +2024-07-25 08:05:03,079 - pyskl - INFO - Best top1_acc is 0.2778 at 88 epoch. +2024-07-25 08:05:03,103 - pyskl - INFO - Epoch(val) [88][309] top1_acc: 0.2778, top5_acc: 0.5241, mean_class_accuracy: 0.2775 +2024-07-25 08:08:57,624 - pyskl - INFO - Epoch [89][100/3746] lr: 3.653e-02, eta: 2 days, 4:04:55, time: 2.345, data_time: 1.343, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6280, loss_cls: 3.5855, loss: 3.5855 +2024-07-25 08:10:19,503 - pyskl - INFO - Epoch [89][200/3746] lr: 3.650e-02, eta: 2 days, 4:03:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.6048, loss_cls: 3.7149, loss: 3.7149 +2024-07-25 08:11:40,786 - pyskl - INFO - Epoch [89][300/3746] lr: 3.647e-02, eta: 2 days, 4:02:14, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6258, loss_cls: 3.6537, loss: 3.6537 +2024-07-25 08:13:02,322 - pyskl - INFO - Epoch [89][400/3746] lr: 3.645e-02, eta: 2 days, 4:00:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6084, loss_cls: 3.6796, loss: 3.6796 +2024-07-25 08:14:23,890 - pyskl - INFO - Epoch [89][500/3746] lr: 3.642e-02, eta: 2 days, 3:59:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6170, loss_cls: 3.6836, loss: 3.6836 +2024-07-25 08:15:45,438 - pyskl - INFO - Epoch [89][600/3746] lr: 3.639e-02, eta: 2 days, 3:58:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6175, loss_cls: 3.6506, loss: 3.6506 +2024-07-25 08:17:06,996 - pyskl - INFO - Epoch [89][700/3746] lr: 3.637e-02, eta: 2 days, 3:56:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.6025, loss_cls: 3.6870, loss: 3.6870 +2024-07-25 08:18:28,866 - pyskl - INFO - Epoch [89][800/3746] lr: 3.634e-02, eta: 2 days, 3:55:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6094, loss_cls: 3.6865, loss: 3.6865 +2024-07-25 08:19:50,076 - pyskl - INFO - Epoch [89][900/3746] lr: 3.631e-02, eta: 2 days, 3:54:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6148, loss_cls: 3.6883, loss: 3.6883 +2024-07-25 08:21:11,408 - pyskl - INFO - Epoch [89][1000/3746] lr: 3.629e-02, eta: 2 days, 3:52:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.5956, loss_cls: 3.6975, loss: 3.6975 +2024-07-25 08:22:32,966 - pyskl - INFO - Epoch [89][1100/3746] lr: 3.626e-02, eta: 2 days, 3:51:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6008, loss_cls: 3.7207, loss: 3.7207 +2024-07-25 08:23:53,979 - pyskl - INFO - Epoch [89][1200/3746] lr: 3.623e-02, eta: 2 days, 3:50:12, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.5969, loss_cls: 3.7405, loss: 3.7405 +2024-07-25 08:25:15,977 - pyskl - INFO - Epoch [89][1300/3746] lr: 3.620e-02, eta: 2 days, 3:48:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6152, loss_cls: 3.6722, loss: 3.6722 +2024-07-25 08:26:37,256 - pyskl - INFO - Epoch [89][1400/3746] lr: 3.618e-02, eta: 2 days, 3:47:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6036, loss_cls: 3.7208, loss: 3.7208 +2024-07-25 08:27:59,306 - pyskl - INFO - Epoch [89][1500/3746] lr: 3.615e-02, eta: 2 days, 3:46:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6084, loss_cls: 3.7057, loss: 3.7057 +2024-07-25 08:29:21,542 - pyskl - INFO - Epoch [89][1600/3746] lr: 3.612e-02, eta: 2 days, 3:44:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.5997, loss_cls: 3.7368, loss: 3.7368 +2024-07-25 08:30:43,349 - pyskl - INFO - Epoch [89][1700/3746] lr: 3.610e-02, eta: 2 days, 3:43:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6067, loss_cls: 3.7477, loss: 3.7477 +2024-07-25 08:32:05,928 - pyskl - INFO - Epoch [89][1800/3746] lr: 3.607e-02, eta: 2 days, 3:42:12, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6022, loss_cls: 3.7264, loss: 3.7264 +2024-07-25 08:33:27,886 - pyskl - INFO - Epoch [89][1900/3746] lr: 3.604e-02, eta: 2 days, 3:40:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6053, loss_cls: 3.7390, loss: 3.7390 +2024-07-25 08:34:49,609 - pyskl - INFO - Epoch [89][2000/3746] lr: 3.602e-02, eta: 2 days, 3:39:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.5997, loss_cls: 3.7118, loss: 3.7118 +2024-07-25 08:36:11,458 - pyskl - INFO - Epoch [89][2100/3746] lr: 3.599e-02, eta: 2 days, 3:38:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6044, loss_cls: 3.7570, loss: 3.7570 +2024-07-25 08:37:33,482 - pyskl - INFO - Epoch [89][2200/3746] lr: 3.596e-02, eta: 2 days, 3:36:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.6027, loss_cls: 3.7399, loss: 3.7399 +2024-07-25 08:38:55,200 - pyskl - INFO - Epoch [89][2300/3746] lr: 3.594e-02, eta: 2 days, 3:35:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6136, loss_cls: 3.6713, loss: 3.6713 +2024-07-25 08:40:16,891 - pyskl - INFO - Epoch [89][2400/3746] lr: 3.591e-02, eta: 2 days, 3:34:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6039, loss_cls: 3.7197, loss: 3.7197 +2024-07-25 08:41:38,502 - pyskl - INFO - Epoch [89][2500/3746] lr: 3.588e-02, eta: 2 days, 3:32:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6033, loss_cls: 3.7240, loss: 3.7240 +2024-07-25 08:42:59,734 - pyskl - INFO - Epoch [89][2600/3746] lr: 3.586e-02, eta: 2 days, 3:31:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6000, loss_cls: 3.7314, loss: 3.7314 +2024-07-25 08:44:21,057 - pyskl - INFO - Epoch [89][2700/3746] lr: 3.583e-02, eta: 2 days, 3:30:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6047, loss_cls: 3.7138, loss: 3.7138 +2024-07-25 08:45:42,874 - pyskl - INFO - Epoch [89][2800/3746] lr: 3.580e-02, eta: 2 days, 3:28:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.5945, loss_cls: 3.7732, loss: 3.7732 +2024-07-25 08:47:04,307 - pyskl - INFO - Epoch [89][2900/3746] lr: 3.578e-02, eta: 2 days, 3:27:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5964, loss_cls: 3.7286, loss: 3.7286 +2024-07-25 08:48:25,808 - pyskl - INFO - Epoch [89][3000/3746] lr: 3.575e-02, eta: 2 days, 3:26:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.6059, loss_cls: 3.7364, loss: 3.7364 +2024-07-25 08:49:47,589 - pyskl - INFO - Epoch [89][3100/3746] lr: 3.572e-02, eta: 2 days, 3:24:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6053, loss_cls: 3.7119, loss: 3.7119 +2024-07-25 08:51:09,560 - pyskl - INFO - Epoch [89][3200/3746] lr: 3.569e-02, eta: 2 days, 3:23:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.6019, loss_cls: 3.7408, loss: 3.7408 +2024-07-25 08:52:30,989 - pyskl - INFO - Epoch [89][3300/3746] lr: 3.567e-02, eta: 2 days, 3:22:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6095, loss_cls: 3.6916, loss: 3.6916 +2024-07-25 08:53:52,538 - pyskl - INFO - Epoch [89][3400/3746] lr: 3.564e-02, eta: 2 days, 3:20:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6009, loss_cls: 3.7373, loss: 3.7373 +2024-07-25 08:55:14,452 - pyskl - INFO - Epoch [89][3500/3746] lr: 3.561e-02, eta: 2 days, 3:19:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6073, loss_cls: 3.7372, loss: 3.7372 +2024-07-25 08:56:36,074 - pyskl - INFO - Epoch [89][3600/3746] lr: 3.559e-02, eta: 2 days, 3:18:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6117, loss_cls: 3.7004, loss: 3.7004 +2024-07-25 08:57:58,053 - pyskl - INFO - Epoch [89][3700/3746] lr: 3.556e-02, eta: 2 days, 3:16:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.5969, loss_cls: 3.7557, loss: 3.7557 +2024-07-25 08:58:37,562 - pyskl - INFO - Saving checkpoint at 89 epochs +2024-07-25 09:00:30,445 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 09:00:31,262 - pyskl - INFO - +top1_acc 0.2866 +top5_acc 0.5352 +2024-07-25 09:00:31,262 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 09:00:31,307 - pyskl - INFO - +mean_acc 0.2861 +2024-07-25 09:00:31,312 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_88.pth was removed +2024-07-25 09:00:31,568 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_89.pth. +2024-07-25 09:00:31,569 - pyskl - INFO - Best top1_acc is 0.2866 at 89 epoch. +2024-07-25 09:00:31,582 - pyskl - INFO - Epoch(val) [89][309] top1_acc: 0.2866, top5_acc: 0.5352, mean_class_accuracy: 0.2861 +2024-07-25 09:04:30,370 - pyskl - INFO - Epoch [90][100/3746] lr: 3.552e-02, eta: 2 days, 3:16:15, time: 2.388, data_time: 1.394, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6211, loss_cls: 3.6219, loss: 3.6219 +2024-07-25 09:05:52,995 - pyskl - INFO - Epoch [90][200/3746] lr: 3.550e-02, eta: 2 days, 3:14:55, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.6134, loss_cls: 3.6962, loss: 3.6962 +2024-07-25 09:07:14,817 - pyskl - INFO - Epoch [90][300/3746] lr: 3.547e-02, eta: 2 days, 3:13:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6145, loss_cls: 3.6836, loss: 3.6836 +2024-07-25 09:08:36,990 - pyskl - INFO - Epoch [90][400/3746] lr: 3.544e-02, eta: 2 days, 3:12:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6209, loss_cls: 3.6381, loss: 3.6381 +2024-07-25 09:09:58,947 - pyskl - INFO - Epoch [90][500/3746] lr: 3.541e-02, eta: 2 days, 3:10:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6136, loss_cls: 3.6766, loss: 3.6766 +2024-07-25 09:11:20,564 - pyskl - INFO - Epoch [90][600/3746] lr: 3.539e-02, eta: 2 days, 3:09:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6131, loss_cls: 3.6752, loss: 3.6752 +2024-07-25 09:12:42,828 - pyskl - INFO - Epoch [90][700/3746] lr: 3.536e-02, eta: 2 days, 3:08:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6095, loss_cls: 3.6863, loss: 3.6863 +2024-07-25 09:14:04,488 - pyskl - INFO - Epoch [90][800/3746] lr: 3.533e-02, eta: 2 days, 3:06:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6164, loss_cls: 3.6752, loss: 3.6752 +2024-07-25 09:15:25,718 - pyskl - INFO - Epoch [90][900/3746] lr: 3.531e-02, eta: 2 days, 3:05:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6098, loss_cls: 3.6825, loss: 3.6825 +2024-07-25 09:16:47,535 - pyskl - INFO - Epoch [90][1000/3746] lr: 3.528e-02, eta: 2 days, 3:04:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.6053, loss_cls: 3.7207, loss: 3.7207 +2024-07-25 09:18:08,790 - pyskl - INFO - Epoch [90][1100/3746] lr: 3.525e-02, eta: 2 days, 3:02:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6062, loss_cls: 3.7150, loss: 3.7150 +2024-07-25 09:19:30,099 - pyskl - INFO - Epoch [90][1200/3746] lr: 3.523e-02, eta: 2 days, 3:01:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6033, loss_cls: 3.7206, loss: 3.7206 +2024-07-25 09:20:51,532 - pyskl - INFO - Epoch [90][1300/3746] lr: 3.520e-02, eta: 2 days, 3:00:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6148, loss_cls: 3.6826, loss: 3.6826 +2024-07-25 09:22:13,294 - pyskl - INFO - Epoch [90][1400/3746] lr: 3.517e-02, eta: 2 days, 2:58:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6052, loss_cls: 3.7428, loss: 3.7428 +2024-07-25 09:23:35,197 - pyskl - INFO - Epoch [90][1500/3746] lr: 3.515e-02, eta: 2 days, 2:57:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6189, loss_cls: 3.6498, loss: 3.6498 +2024-07-25 09:24:57,733 - pyskl - INFO - Epoch [90][1600/3746] lr: 3.512e-02, eta: 2 days, 2:56:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6059, loss_cls: 3.7119, loss: 3.7119 +2024-07-25 09:26:19,115 - pyskl - INFO - Epoch [90][1700/3746] lr: 3.509e-02, eta: 2 days, 2:54:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6086, loss_cls: 3.6978, loss: 3.6978 +2024-07-25 09:27:41,353 - pyskl - INFO - Epoch [90][1800/3746] lr: 3.507e-02, eta: 2 days, 2:53:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6023, loss_cls: 3.7586, loss: 3.7586 +2024-07-25 09:29:03,522 - pyskl - INFO - Epoch [90][1900/3746] lr: 3.504e-02, eta: 2 days, 2:52:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6116, loss_cls: 3.7047, loss: 3.7047 +2024-07-25 09:30:25,550 - pyskl - INFO - Epoch [90][2000/3746] lr: 3.501e-02, eta: 2 days, 2:50:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6078, loss_cls: 3.6935, loss: 3.6935 +2024-07-25 09:31:47,256 - pyskl - INFO - Epoch [90][2100/3746] lr: 3.499e-02, eta: 2 days, 2:49:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6061, loss_cls: 3.7155, loss: 3.7155 +2024-07-25 09:33:08,568 - pyskl - INFO - Epoch [90][2200/3746] lr: 3.496e-02, eta: 2 days, 2:48:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6109, loss_cls: 3.6904, loss: 3.6904 +2024-07-25 09:34:30,160 - pyskl - INFO - Epoch [90][2300/3746] lr: 3.493e-02, eta: 2 days, 2:46:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5950, loss_cls: 3.7426, loss: 3.7426 +2024-07-25 09:35:51,731 - pyskl - INFO - Epoch [90][2400/3746] lr: 3.491e-02, eta: 2 days, 2:45:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6039, loss_cls: 3.7029, loss: 3.7029 +2024-07-25 09:37:13,492 - pyskl - INFO - Epoch [90][2500/3746] lr: 3.488e-02, eta: 2 days, 2:44:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6016, loss_cls: 3.7285, loss: 3.7285 +2024-07-25 09:38:34,854 - pyskl - INFO - Epoch [90][2600/3746] lr: 3.485e-02, eta: 2 days, 2:42:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6062, loss_cls: 3.6792, loss: 3.6792 +2024-07-25 09:39:57,487 - pyskl - INFO - Epoch [90][2700/3746] lr: 3.483e-02, eta: 2 days, 2:41:31, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6131, loss_cls: 3.7130, loss: 3.7130 +2024-07-25 09:41:19,323 - pyskl - INFO - Epoch [90][2800/3746] lr: 3.480e-02, eta: 2 days, 2:40:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6125, loss_cls: 3.7014, loss: 3.7014 +2024-07-25 09:42:40,882 - pyskl - INFO - Epoch [90][2900/3746] lr: 3.477e-02, eta: 2 days, 2:38:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.5988, loss_cls: 3.7100, loss: 3.7100 +2024-07-25 09:44:02,275 - pyskl - INFO - Epoch [90][3000/3746] lr: 3.475e-02, eta: 2 days, 2:37:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6002, loss_cls: 3.7506, loss: 3.7506 +2024-07-25 09:45:23,836 - pyskl - INFO - Epoch [90][3100/3746] lr: 3.472e-02, eta: 2 days, 2:36:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6048, loss_cls: 3.6958, loss: 3.6958 +2024-07-25 09:46:45,504 - pyskl - INFO - Epoch [90][3200/3746] lr: 3.469e-02, eta: 2 days, 2:34:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6062, loss_cls: 3.7135, loss: 3.7135 +2024-07-25 09:48:06,878 - pyskl - INFO - Epoch [90][3300/3746] lr: 3.467e-02, eta: 2 days, 2:33:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6114, loss_cls: 3.6604, loss: 3.6604 +2024-07-25 09:49:28,721 - pyskl - INFO - Epoch [90][3400/3746] lr: 3.464e-02, eta: 2 days, 2:32:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.5994, loss_cls: 3.7150, loss: 3.7150 +2024-07-25 09:50:50,629 - pyskl - INFO - Epoch [90][3500/3746] lr: 3.461e-02, eta: 2 days, 2:30:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6058, loss_cls: 3.7063, loss: 3.7063 +2024-07-25 09:52:12,424 - pyskl - INFO - Epoch [90][3600/3746] lr: 3.459e-02, eta: 2 days, 2:29:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6091, loss_cls: 3.6779, loss: 3.6779 +2024-07-25 09:53:33,774 - pyskl - INFO - Epoch [90][3700/3746] lr: 3.456e-02, eta: 2 days, 2:28:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6023, loss_cls: 3.7417, loss: 3.7417 +2024-07-25 09:54:13,241 - pyskl - INFO - Saving checkpoint at 90 epochs +2024-07-25 09:56:06,876 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 09:56:07,776 - pyskl - INFO - +top1_acc 0.2884 +top5_acc 0.5452 +2024-07-25 09:56:07,777 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 09:56:07,827 - pyskl - INFO - +mean_acc 0.2881 +2024-07-25 09:56:07,832 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_89.pth was removed +2024-07-25 09:56:08,113 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_90.pth. +2024-07-25 09:56:08,114 - pyskl - INFO - Best top1_acc is 0.2884 at 90 epoch. +2024-07-25 09:56:08,127 - pyskl - INFO - Epoch(val) [90][309] top1_acc: 0.2884, top5_acc: 0.5452, mean_class_accuracy: 0.2881 +2024-07-25 10:00:05,785 - pyskl - INFO - Epoch [91][100/3746] lr: 3.452e-02, eta: 2 days, 2:27:30, time: 2.376, data_time: 1.390, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6275, loss_cls: 3.5815, loss: 3.5815 +2024-07-25 10:01:27,804 - pyskl - INFO - Epoch [91][200/3746] lr: 3.450e-02, eta: 2 days, 2:26:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6197, loss_cls: 3.6350, loss: 3.6350 +2024-07-25 10:02:50,203 - pyskl - INFO - Epoch [91][300/3746] lr: 3.447e-02, eta: 2 days, 2:24:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6211, loss_cls: 3.6321, loss: 3.6321 +2024-07-25 10:04:12,133 - pyskl - INFO - Epoch [91][400/3746] lr: 3.444e-02, eta: 2 days, 2:23:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6122, loss_cls: 3.6598, loss: 3.6598 +2024-07-25 10:05:33,998 - pyskl - INFO - Epoch [91][500/3746] lr: 3.442e-02, eta: 2 days, 2:22:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6131, loss_cls: 3.6640, loss: 3.6640 +2024-07-25 10:06:56,247 - pyskl - INFO - Epoch [91][600/3746] lr: 3.439e-02, eta: 2 days, 2:20:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6217, loss_cls: 3.6410, loss: 3.6410 +2024-07-25 10:08:18,520 - pyskl - INFO - Epoch [91][700/3746] lr: 3.436e-02, eta: 2 days, 2:19:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6148, loss_cls: 3.6841, loss: 3.6841 +2024-07-25 10:09:40,231 - pyskl - INFO - Epoch [91][800/3746] lr: 3.434e-02, eta: 2 days, 2:18:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.6120, loss_cls: 3.7029, loss: 3.7029 +2024-07-25 10:11:01,841 - pyskl - INFO - Epoch [91][900/3746] lr: 3.431e-02, eta: 2 days, 2:16:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6127, loss_cls: 3.6942, loss: 3.6942 +2024-07-25 10:12:23,391 - pyskl - INFO - Epoch [91][1000/3746] lr: 3.428e-02, eta: 2 days, 2:15:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6102, loss_cls: 3.6649, loss: 3.6649 +2024-07-25 10:13:45,629 - pyskl - INFO - Epoch [91][1100/3746] lr: 3.426e-02, eta: 2 days, 2:14:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6105, loss_cls: 3.6816, loss: 3.6816 +2024-07-25 10:15:07,470 - pyskl - INFO - Epoch [91][1200/3746] lr: 3.423e-02, eta: 2 days, 2:12:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6089, loss_cls: 3.7024, loss: 3.7024 +2024-07-25 10:16:28,700 - pyskl - INFO - Epoch [91][1300/3746] lr: 3.420e-02, eta: 2 days, 2:11:28, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6042, loss_cls: 3.7329, loss: 3.7329 +2024-07-25 10:17:50,055 - pyskl - INFO - Epoch [91][1400/3746] lr: 3.418e-02, eta: 2 days, 2:10:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6095, loss_cls: 3.6645, loss: 3.6645 +2024-07-25 10:19:12,456 - pyskl - INFO - Epoch [91][1500/3746] lr: 3.415e-02, eta: 2 days, 2:08:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6075, loss_cls: 3.7328, loss: 3.7328 +2024-07-25 10:20:35,135 - pyskl - INFO - Epoch [91][1600/3746] lr: 3.412e-02, eta: 2 days, 2:07:28, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6183, loss_cls: 3.6576, loss: 3.6576 +2024-07-25 10:21:57,287 - pyskl - INFO - Epoch [91][1700/3746] lr: 3.410e-02, eta: 2 days, 2:06:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6147, loss_cls: 3.6649, loss: 3.6649 +2024-07-25 10:23:19,215 - pyskl - INFO - Epoch [91][1800/3746] lr: 3.407e-02, eta: 2 days, 2:04:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6173, loss_cls: 3.6555, loss: 3.6555 +2024-07-25 10:24:40,895 - pyskl - INFO - Epoch [91][1900/3746] lr: 3.405e-02, eta: 2 days, 2:03:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6075, loss_cls: 3.7015, loss: 3.7015 +2024-07-25 10:26:02,413 - pyskl - INFO - Epoch [91][2000/3746] lr: 3.402e-02, eta: 2 days, 2:02:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6042, loss_cls: 3.7055, loss: 3.7055 +2024-07-25 10:27:23,888 - pyskl - INFO - Epoch [91][2100/3746] lr: 3.399e-02, eta: 2 days, 2:00:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6245, loss_cls: 3.6401, loss: 3.6401 +2024-07-25 10:28:45,168 - pyskl - INFO - Epoch [91][2200/3746] lr: 3.397e-02, eta: 2 days, 1:59:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6109, loss_cls: 3.6757, loss: 3.6757 +2024-07-25 10:30:06,925 - pyskl - INFO - Epoch [91][2300/3746] lr: 3.394e-02, eta: 2 days, 1:58:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6139, loss_cls: 3.6619, loss: 3.6619 +2024-07-25 10:31:28,591 - pyskl - INFO - Epoch [91][2400/3746] lr: 3.391e-02, eta: 2 days, 1:56:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6033, loss_cls: 3.7246, loss: 3.7246 +2024-07-25 10:32:49,975 - pyskl - INFO - Epoch [91][2500/3746] lr: 3.389e-02, eta: 2 days, 1:55:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6088, loss_cls: 3.7008, loss: 3.7008 +2024-07-25 10:34:11,936 - pyskl - INFO - Epoch [91][2600/3746] lr: 3.386e-02, eta: 2 days, 1:54:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6170, loss_cls: 3.6544, loss: 3.6544 +2024-07-25 10:35:33,827 - pyskl - INFO - Epoch [91][2700/3746] lr: 3.383e-02, eta: 2 days, 1:52:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6062, loss_cls: 3.7228, loss: 3.7228 +2024-07-25 10:36:55,136 - pyskl - INFO - Epoch [91][2800/3746] lr: 3.381e-02, eta: 2 days, 1:51:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6044, loss_cls: 3.7206, loss: 3.7206 +2024-07-25 10:38:16,678 - pyskl - INFO - Epoch [91][2900/3746] lr: 3.378e-02, eta: 2 days, 1:50:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6127, loss_cls: 3.6657, loss: 3.6657 +2024-07-25 10:39:38,181 - pyskl - INFO - Epoch [91][3000/3746] lr: 3.375e-02, eta: 2 days, 1:48:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6027, loss_cls: 3.7304, loss: 3.7304 +2024-07-25 10:41:00,263 - pyskl - INFO - Epoch [91][3100/3746] lr: 3.373e-02, eta: 2 days, 1:47:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6206, loss_cls: 3.6578, loss: 3.6578 +2024-07-25 10:42:21,901 - pyskl - INFO - Epoch [91][3200/3746] lr: 3.370e-02, eta: 2 days, 1:46:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.5966, loss_cls: 3.7497, loss: 3.7497 +2024-07-25 10:43:43,240 - pyskl - INFO - Epoch [91][3300/3746] lr: 3.367e-02, eta: 2 days, 1:44:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6098, loss_cls: 3.6853, loss: 3.6853 +2024-07-25 10:45:04,411 - pyskl - INFO - Epoch [91][3400/3746] lr: 3.365e-02, eta: 2 days, 1:43:21, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6081, loss_cls: 3.6992, loss: 3.6992 +2024-07-25 10:46:25,844 - pyskl - INFO - Epoch [91][3500/3746] lr: 3.362e-02, eta: 2 days, 1:42:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6147, loss_cls: 3.6333, loss: 3.6333 +2024-07-25 10:47:48,049 - pyskl - INFO - Epoch [91][3600/3746] lr: 3.360e-02, eta: 2 days, 1:40:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6028, loss_cls: 3.6868, loss: 3.6868 +2024-07-25 10:49:09,564 - pyskl - INFO - Epoch [91][3700/3746] lr: 3.357e-02, eta: 2 days, 1:39:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5984, loss_cls: 3.7375, loss: 3.7375 +2024-07-25 10:49:49,318 - pyskl - INFO - Saving checkpoint at 91 epochs +2024-07-25 10:51:41,717 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 10:51:42,462 - pyskl - INFO - +top1_acc 0.2943 +top5_acc 0.5434 +2024-07-25 10:51:42,462 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 10:51:42,513 - pyskl - INFO - +mean_acc 0.2941 +2024-07-25 10:51:42,518 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_90.pth was removed +2024-07-25 10:51:42,793 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_91.pth. +2024-07-25 10:51:42,794 - pyskl - INFO - Best top1_acc is 0.2943 at 91 epoch. +2024-07-25 10:51:42,807 - pyskl - INFO - Epoch(val) [91][309] top1_acc: 0.2943, top5_acc: 0.5434, mean_class_accuracy: 0.2941 +2024-07-25 10:55:38,517 - pyskl - INFO - Epoch [92][100/3746] lr: 3.353e-02, eta: 2 days, 1:38:39, time: 2.357, data_time: 1.379, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6261, loss_cls: 3.6136, loss: 3.6136 +2024-07-25 10:57:00,999 - pyskl - INFO - Epoch [92][200/3746] lr: 3.350e-02, eta: 2 days, 1:37:19, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6250, loss_cls: 3.5952, loss: 3.5952 +2024-07-25 10:58:22,977 - pyskl - INFO - Epoch [92][300/3746] lr: 3.348e-02, eta: 2 days, 1:35:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6133, loss_cls: 3.6495, loss: 3.6495 +2024-07-25 10:59:44,488 - pyskl - INFO - Epoch [92][400/3746] lr: 3.345e-02, eta: 2 days, 1:34:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6177, loss_cls: 3.6246, loss: 3.6246 +2024-07-25 11:01:06,883 - pyskl - INFO - Epoch [92][500/3746] lr: 3.342e-02, eta: 2 days, 1:33:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6156, loss_cls: 3.6617, loss: 3.6617 +2024-07-25 11:02:28,446 - pyskl - INFO - Epoch [92][600/3746] lr: 3.340e-02, eta: 2 days, 1:31:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6106, loss_cls: 3.6827, loss: 3.6827 +2024-07-25 11:03:50,219 - pyskl - INFO - Epoch [92][700/3746] lr: 3.337e-02, eta: 2 days, 1:30:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3594, top5_acc: 0.6189, loss_cls: 3.6482, loss: 3.6482 +2024-07-25 11:05:11,686 - pyskl - INFO - Epoch [92][800/3746] lr: 3.335e-02, eta: 2 days, 1:29:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6230, loss_cls: 3.6117, loss: 3.6117 +2024-07-25 11:06:33,016 - pyskl - INFO - Epoch [92][900/3746] lr: 3.332e-02, eta: 2 days, 1:27:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6109, loss_cls: 3.6937, loss: 3.6937 +2024-07-25 11:07:54,339 - pyskl - INFO - Epoch [92][1000/3746] lr: 3.329e-02, eta: 2 days, 1:26:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6136, loss_cls: 3.6618, loss: 3.6618 +2024-07-25 11:09:15,636 - pyskl - INFO - Epoch [92][1100/3746] lr: 3.327e-02, eta: 2 days, 1:25:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6139, loss_cls: 3.6739, loss: 3.6739 +2024-07-25 11:10:37,139 - pyskl - INFO - Epoch [92][1200/3746] lr: 3.324e-02, eta: 2 days, 1:23:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3541, top5_acc: 0.6105, loss_cls: 3.6571, loss: 3.6571 +2024-07-25 11:11:59,021 - pyskl - INFO - Epoch [92][1300/3746] lr: 3.321e-02, eta: 2 days, 1:22:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6106, loss_cls: 3.6776, loss: 3.6776 +2024-07-25 11:13:20,122 - pyskl - INFO - Epoch [92][1400/3746] lr: 3.319e-02, eta: 2 days, 1:21:13, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6089, loss_cls: 3.6668, loss: 3.6668 +2024-07-25 11:14:41,747 - pyskl - INFO - Epoch [92][1500/3746] lr: 3.316e-02, eta: 2 days, 1:19:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6128, loss_cls: 3.6724, loss: 3.6724 +2024-07-25 11:16:04,252 - pyskl - INFO - Epoch [92][1600/3746] lr: 3.314e-02, eta: 2 days, 1:18:33, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6144, loss_cls: 3.6366, loss: 3.6366 +2024-07-25 11:17:26,258 - pyskl - INFO - Epoch [92][1700/3746] lr: 3.311e-02, eta: 2 days, 1:17:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6088, loss_cls: 3.6813, loss: 3.6813 +2024-07-25 11:18:48,885 - pyskl - INFO - Epoch [92][1800/3746] lr: 3.308e-02, eta: 2 days, 1:15:53, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6150, loss_cls: 3.6779, loss: 3.6779 +2024-07-25 11:20:11,251 - pyskl - INFO - Epoch [92][1900/3746] lr: 3.306e-02, eta: 2 days, 1:14:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6130, loss_cls: 3.6993, loss: 3.6993 +2024-07-25 11:21:33,317 - pyskl - INFO - Epoch [92][2000/3746] lr: 3.303e-02, eta: 2 days, 1:13:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6030, loss_cls: 3.7274, loss: 3.7274 +2024-07-25 11:22:55,659 - pyskl - INFO - Epoch [92][2100/3746] lr: 3.300e-02, eta: 2 days, 1:11:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6116, loss_cls: 3.6896, loss: 3.6896 +2024-07-25 11:24:18,140 - pyskl - INFO - Epoch [92][2200/3746] lr: 3.298e-02, eta: 2 days, 1:10:33, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6081, loss_cls: 3.6597, loss: 3.6597 +2024-07-25 11:25:39,713 - pyskl - INFO - Epoch [92][2300/3746] lr: 3.295e-02, eta: 2 days, 1:09:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6089, loss_cls: 3.6830, loss: 3.6830 +2024-07-25 11:27:01,134 - pyskl - INFO - Epoch [92][2400/3746] lr: 3.292e-02, eta: 2 days, 1:07:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6191, loss_cls: 3.6582, loss: 3.6582 +2024-07-25 11:28:22,642 - pyskl - INFO - Epoch [92][2500/3746] lr: 3.290e-02, eta: 2 days, 1:06:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6080, loss_cls: 3.6819, loss: 3.6819 +2024-07-25 11:29:43,707 - pyskl - INFO - Epoch [92][2600/3746] lr: 3.287e-02, eta: 2 days, 1:05:11, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6159, loss_cls: 3.6994, loss: 3.6994 +2024-07-25 11:31:05,216 - pyskl - INFO - Epoch [92][2700/3746] lr: 3.285e-02, eta: 2 days, 1:03:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6036, loss_cls: 3.6951, loss: 3.6951 +2024-07-25 11:32:27,034 - pyskl - INFO - Epoch [92][2800/3746] lr: 3.282e-02, eta: 2 days, 1:02:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.6005, loss_cls: 3.7326, loss: 3.7326 +2024-07-25 11:33:48,305 - pyskl - INFO - Epoch [92][2900/3746] lr: 3.279e-02, eta: 2 days, 1:01:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6156, loss_cls: 3.6361, loss: 3.6361 +2024-07-25 11:35:09,676 - pyskl - INFO - Epoch [92][3000/3746] lr: 3.277e-02, eta: 2 days, 0:59:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6177, loss_cls: 3.6396, loss: 3.6396 +2024-07-25 11:36:31,781 - pyskl - INFO - Epoch [92][3100/3746] lr: 3.274e-02, eta: 2 days, 0:58:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6134, loss_cls: 3.6699, loss: 3.6699 +2024-07-25 11:37:53,479 - pyskl - INFO - Epoch [92][3200/3746] lr: 3.271e-02, eta: 2 days, 0:57:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6019, loss_cls: 3.7159, loss: 3.7159 +2024-07-25 11:39:14,655 - pyskl - INFO - Epoch [92][3300/3746] lr: 3.269e-02, eta: 2 days, 0:55:47, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6112, loss_cls: 3.6731, loss: 3.6731 +2024-07-25 11:40:36,147 - pyskl - INFO - Epoch [92][3400/3746] lr: 3.266e-02, eta: 2 days, 0:54:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6112, loss_cls: 3.6775, loss: 3.6775 +2024-07-25 11:41:57,686 - pyskl - INFO - Epoch [92][3500/3746] lr: 3.264e-02, eta: 2 days, 0:53:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6181, loss_cls: 3.6596, loss: 3.6596 +2024-07-25 11:43:19,247 - pyskl - INFO - Epoch [92][3600/3746] lr: 3.261e-02, eta: 2 days, 0:51:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6094, loss_cls: 3.6727, loss: 3.6727 +2024-07-25 11:44:40,778 - pyskl - INFO - Epoch [92][3700/3746] lr: 3.258e-02, eta: 2 days, 0:50:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6044, loss_cls: 3.6906, loss: 3.6906 +2024-07-25 11:45:20,268 - pyskl - INFO - Saving checkpoint at 92 epochs +2024-07-25 11:47:14,225 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 11:47:14,951 - pyskl - INFO - +top1_acc 0.3085 +top5_acc 0.5633 +2024-07-25 11:47:14,951 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 11:47:14,995 - pyskl - INFO - +mean_acc 0.3082 +2024-07-25 11:47:15,000 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_91.pth was removed +2024-07-25 11:47:15,275 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_92.pth. +2024-07-25 11:47:15,275 - pyskl - INFO - Best top1_acc is 0.3085 at 92 epoch. +2024-07-25 11:47:15,289 - pyskl - INFO - Epoch(val) [92][309] top1_acc: 0.3085, top5_acc: 0.5633, mean_class_accuracy: 0.3082 +2024-07-25 11:51:11,593 - pyskl - INFO - Epoch [93][100/3746] lr: 3.255e-02, eta: 2 days, 0:49:41, time: 2.363, data_time: 1.377, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6178, loss_cls: 3.6096, loss: 3.6096 +2024-07-25 11:52:34,012 - pyskl - INFO - Epoch [93][200/3746] lr: 3.252e-02, eta: 2 days, 0:48:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6219, loss_cls: 3.5935, loss: 3.5935 +2024-07-25 11:53:56,062 - pyskl - INFO - Epoch [93][300/3746] lr: 3.249e-02, eta: 2 days, 0:47:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6233, loss_cls: 3.5986, loss: 3.5986 +2024-07-25 11:55:17,854 - pyskl - INFO - Epoch [93][400/3746] lr: 3.247e-02, eta: 2 days, 0:45:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6198, loss_cls: 3.6379, loss: 3.6379 +2024-07-25 11:56:40,551 - pyskl - INFO - Epoch [93][500/3746] lr: 3.244e-02, eta: 2 days, 0:44:21, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6128, loss_cls: 3.6566, loss: 3.6566 +2024-07-25 11:58:02,106 - pyskl - INFO - Epoch [93][600/3746] lr: 3.241e-02, eta: 2 days, 0:43:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6095, loss_cls: 3.6761, loss: 3.6761 +2024-07-25 11:59:24,098 - pyskl - INFO - Epoch [93][700/3746] lr: 3.239e-02, eta: 2 days, 0:41:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6155, loss_cls: 3.6305, loss: 3.6305 +2024-07-25 12:00:45,557 - pyskl - INFO - Epoch [93][800/3746] lr: 3.236e-02, eta: 2 days, 0:40:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6194, loss_cls: 3.6172, loss: 3.6172 +2024-07-25 12:02:07,066 - pyskl - INFO - Epoch [93][900/3746] lr: 3.234e-02, eta: 2 days, 0:38:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6222, loss_cls: 3.6037, loss: 3.6037 +2024-07-25 12:03:28,818 - pyskl - INFO - Epoch [93][1000/3746] lr: 3.231e-02, eta: 2 days, 0:37:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6139, loss_cls: 3.6465, loss: 3.6465 +2024-07-25 12:04:50,104 - pyskl - INFO - Epoch [93][1100/3746] lr: 3.228e-02, eta: 2 days, 0:36:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6131, loss_cls: 3.6799, loss: 3.6799 +2024-07-25 12:06:11,538 - pyskl - INFO - Epoch [93][1200/3746] lr: 3.226e-02, eta: 2 days, 0:34:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6156, loss_cls: 3.6619, loss: 3.6619 +2024-07-25 12:07:33,202 - pyskl - INFO - Epoch [93][1300/3746] lr: 3.223e-02, eta: 2 days, 0:33:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6275, loss_cls: 3.6005, loss: 3.6005 +2024-07-25 12:08:54,944 - pyskl - INFO - Epoch [93][1400/3746] lr: 3.221e-02, eta: 2 days, 0:32:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6177, loss_cls: 3.6426, loss: 3.6426 +2024-07-25 12:10:16,940 - pyskl - INFO - Epoch [93][1500/3746] lr: 3.218e-02, eta: 2 days, 0:30:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6103, loss_cls: 3.6819, loss: 3.6819 +2024-07-25 12:11:39,238 - pyskl - INFO - Epoch [93][1600/3746] lr: 3.215e-02, eta: 2 days, 0:29:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3594, top5_acc: 0.6178, loss_cls: 3.6412, loss: 3.6412 +2024-07-25 12:13:00,935 - pyskl - INFO - Epoch [93][1700/3746] lr: 3.213e-02, eta: 2 days, 0:28:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6058, loss_cls: 3.6832, loss: 3.6832 +2024-07-25 12:14:23,074 - pyskl - INFO - Epoch [93][1800/3746] lr: 3.210e-02, eta: 2 days, 0:26:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6128, loss_cls: 3.6581, loss: 3.6581 +2024-07-25 12:15:44,727 - pyskl - INFO - Epoch [93][1900/3746] lr: 3.207e-02, eta: 2 days, 0:25:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6072, loss_cls: 3.7104, loss: 3.7104 +2024-07-25 12:17:06,451 - pyskl - INFO - Epoch [93][2000/3746] lr: 3.205e-02, eta: 2 days, 0:24:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6256, loss_cls: 3.6068, loss: 3.6068 +2024-07-25 12:18:28,489 - pyskl - INFO - Epoch [93][2100/3746] lr: 3.202e-02, eta: 2 days, 0:22:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6070, loss_cls: 3.6633, loss: 3.6633 +2024-07-25 12:19:50,244 - pyskl - INFO - Epoch [93][2200/3746] lr: 3.200e-02, eta: 2 days, 0:21:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6134, loss_cls: 3.6784, loss: 3.6784 +2024-07-25 12:21:11,767 - pyskl - INFO - Epoch [93][2300/3746] lr: 3.197e-02, eta: 2 days, 0:20:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6222, loss_cls: 3.6197, loss: 3.6197 +2024-07-25 12:22:32,864 - pyskl - INFO - Epoch [93][2400/3746] lr: 3.194e-02, eta: 2 days, 0:18:52, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6108, loss_cls: 3.6600, loss: 3.6600 +2024-07-25 12:23:54,719 - pyskl - INFO - Epoch [93][2500/3746] lr: 3.192e-02, eta: 2 days, 0:17:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6234, loss_cls: 3.5984, loss: 3.5984 +2024-07-25 12:25:15,862 - pyskl - INFO - Epoch [93][2600/3746] lr: 3.189e-02, eta: 2 days, 0:16:11, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6202, loss_cls: 3.6310, loss: 3.6310 +2024-07-25 12:26:36,899 - pyskl - INFO - Epoch [93][2700/3746] lr: 3.187e-02, eta: 2 days, 0:14:50, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6122, loss_cls: 3.6607, loss: 3.6607 +2024-07-25 12:27:58,449 - pyskl - INFO - Epoch [93][2800/3746] lr: 3.184e-02, eta: 2 days, 0:13:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6119, loss_cls: 3.6597, loss: 3.6597 +2024-07-25 12:29:20,026 - pyskl - INFO - Epoch [93][2900/3746] lr: 3.181e-02, eta: 2 days, 0:12:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6164, loss_cls: 3.6624, loss: 3.6624 +2024-07-25 12:30:41,657 - pyskl - INFO - Epoch [93][3000/3746] lr: 3.179e-02, eta: 2 days, 0:10:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6123, loss_cls: 3.6846, loss: 3.6846 +2024-07-25 12:32:03,218 - pyskl - INFO - Epoch [93][3100/3746] lr: 3.176e-02, eta: 2 days, 0:09:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6159, loss_cls: 3.6592, loss: 3.6592 +2024-07-25 12:33:24,505 - pyskl - INFO - Epoch [93][3200/3746] lr: 3.174e-02, eta: 2 days, 0:08:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6067, loss_cls: 3.7193, loss: 3.7193 +2024-07-25 12:34:45,780 - pyskl - INFO - Epoch [93][3300/3746] lr: 3.171e-02, eta: 2 days, 0:06:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6252, loss_cls: 3.6420, loss: 3.6420 +2024-07-25 12:36:07,446 - pyskl - INFO - Epoch [93][3400/3746] lr: 3.168e-02, eta: 2 days, 0:05:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6112, loss_cls: 3.6748, loss: 3.6748 +2024-07-25 12:37:29,285 - pyskl - INFO - Epoch [93][3500/3746] lr: 3.166e-02, eta: 2 days, 0:04:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6075, loss_cls: 3.7062, loss: 3.7062 +2024-07-25 12:38:51,014 - pyskl - INFO - Epoch [93][3600/3746] lr: 3.163e-02, eta: 2 days, 0:02:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6197, loss_cls: 3.5949, loss: 3.5949 +2024-07-25 12:40:12,398 - pyskl - INFO - Epoch [93][3700/3746] lr: 3.161e-02, eta: 2 days, 0:01:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6106, loss_cls: 3.6752, loss: 3.6752 +2024-07-25 12:40:51,676 - pyskl - INFO - Saving checkpoint at 93 epochs +2024-07-25 12:42:44,236 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 12:42:45,216 - pyskl - INFO - +top1_acc 0.3043 +top5_acc 0.5549 +2024-07-25 12:42:45,216 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 12:42:45,261 - pyskl - INFO - +mean_acc 0.3040 +2024-07-25 12:42:45,283 - pyskl - INFO - Epoch(val) [93][309] top1_acc: 0.3043, top5_acc: 0.5549, mean_class_accuracy: 0.3040 +2024-07-25 12:46:39,234 - pyskl - INFO - Epoch [94][100/3746] lr: 3.157e-02, eta: 2 days, 0:00:37, time: 2.339, data_time: 1.350, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6212, loss_cls: 3.6258, loss: 3.6258 +2024-07-25 12:48:01,063 - pyskl - INFO - Epoch [94][200/3746] lr: 3.154e-02, eta: 1 day, 23:59:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6252, loss_cls: 3.6168, loss: 3.6168 +2024-07-25 12:49:22,735 - pyskl - INFO - Epoch [94][300/3746] lr: 3.152e-02, eta: 1 day, 23:57:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6309, loss_cls: 3.5797, loss: 3.5797 +2024-07-25 12:50:45,058 - pyskl - INFO - Epoch [94][400/3746] lr: 3.149e-02, eta: 1 day, 23:56:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3708, top5_acc: 0.6311, loss_cls: 3.5855, loss: 3.5855 +2024-07-25 12:52:07,273 - pyskl - INFO - Epoch [94][500/3746] lr: 3.146e-02, eta: 1 day, 23:55:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6234, loss_cls: 3.5812, loss: 3.5812 +2024-07-25 12:53:29,526 - pyskl - INFO - Epoch [94][600/3746] lr: 3.144e-02, eta: 1 day, 23:53:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6181, loss_cls: 3.6008, loss: 3.6008 +2024-07-25 12:54:51,540 - pyskl - INFO - Epoch [94][700/3746] lr: 3.141e-02, eta: 1 day, 23:52:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6255, loss_cls: 3.5943, loss: 3.5943 +2024-07-25 12:56:13,815 - pyskl - INFO - Epoch [94][800/3746] lr: 3.139e-02, eta: 1 day, 23:51:14, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6239, loss_cls: 3.6213, loss: 3.6213 +2024-07-25 12:57:35,519 - pyskl - INFO - Epoch [94][900/3746] lr: 3.136e-02, eta: 1 day, 23:49:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6159, loss_cls: 3.6557, loss: 3.6557 +2024-07-25 12:58:57,203 - pyskl - INFO - Epoch [94][1000/3746] lr: 3.133e-02, eta: 1 day, 23:48:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6178, loss_cls: 3.6031, loss: 3.6031 +2024-07-25 13:00:19,188 - pyskl - INFO - Epoch [94][1100/3746] lr: 3.131e-02, eta: 1 day, 23:47:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6211, loss_cls: 3.6039, loss: 3.6039 +2024-07-25 13:01:40,317 - pyskl - INFO - Epoch [94][1200/3746] lr: 3.128e-02, eta: 1 day, 23:45:52, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6238, loss_cls: 3.5873, loss: 3.5873 +2024-07-25 13:03:02,063 - pyskl - INFO - Epoch [94][1300/3746] lr: 3.126e-02, eta: 1 day, 23:44:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6130, loss_cls: 3.6598, loss: 3.6598 +2024-07-25 13:04:23,290 - pyskl - INFO - Epoch [94][1400/3746] lr: 3.123e-02, eta: 1 day, 23:43:11, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6216, loss_cls: 3.6190, loss: 3.6190 +2024-07-25 13:05:45,475 - pyskl - INFO - Epoch [94][1500/3746] lr: 3.120e-02, eta: 1 day, 23:41:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6128, loss_cls: 3.6394, loss: 3.6394 +2024-07-25 13:07:07,551 - pyskl - INFO - Epoch [94][1600/3746] lr: 3.118e-02, eta: 1 day, 23:40:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6122, loss_cls: 3.6444, loss: 3.6444 +2024-07-25 13:08:29,955 - pyskl - INFO - Epoch [94][1700/3746] lr: 3.115e-02, eta: 1 day, 23:39:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6136, loss_cls: 3.6540, loss: 3.6540 +2024-07-25 13:09:52,269 - pyskl - INFO - Epoch [94][1800/3746] lr: 3.113e-02, eta: 1 day, 23:37:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6119, loss_cls: 3.6917, loss: 3.6917 +2024-07-25 13:11:14,292 - pyskl - INFO - Epoch [94][1900/3746] lr: 3.110e-02, eta: 1 day, 23:36:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6147, loss_cls: 3.6981, loss: 3.6981 +2024-07-25 13:12:35,803 - pyskl - INFO - Epoch [94][2000/3746] lr: 3.108e-02, eta: 1 day, 23:35:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6261, loss_cls: 3.6008, loss: 3.6008 +2024-07-25 13:13:57,888 - pyskl - INFO - Epoch [94][2100/3746] lr: 3.105e-02, eta: 1 day, 23:33:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6200, loss_cls: 3.6318, loss: 3.6318 +2024-07-25 13:15:19,698 - pyskl - INFO - Epoch [94][2200/3746] lr: 3.102e-02, eta: 1 day, 23:32:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6217, loss_cls: 3.6340, loss: 3.6340 +2024-07-25 13:16:41,211 - pyskl - INFO - Epoch [94][2300/3746] lr: 3.100e-02, eta: 1 day, 23:31:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6081, loss_cls: 3.6463, loss: 3.6463 +2024-07-25 13:18:03,080 - pyskl - INFO - Epoch [94][2400/3746] lr: 3.097e-02, eta: 1 day, 23:29:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6075, loss_cls: 3.6858, loss: 3.6858 +2024-07-25 13:19:25,166 - pyskl - INFO - Epoch [94][2500/3746] lr: 3.095e-02, eta: 1 day, 23:28:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6212, loss_cls: 3.6352, loss: 3.6352 +2024-07-25 13:20:46,877 - pyskl - INFO - Epoch [94][2600/3746] lr: 3.092e-02, eta: 1 day, 23:27:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6089, loss_cls: 3.6839, loss: 3.6839 +2024-07-25 13:22:08,444 - pyskl - INFO - Epoch [94][2700/3746] lr: 3.089e-02, eta: 1 day, 23:25:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6298, loss_cls: 3.5837, loss: 3.5837 +2024-07-25 13:23:30,061 - pyskl - INFO - Epoch [94][2800/3746] lr: 3.087e-02, eta: 1 day, 23:24:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6222, loss_cls: 3.6384, loss: 3.6384 +2024-07-25 13:24:51,593 - pyskl - INFO - Epoch [94][2900/3746] lr: 3.084e-02, eta: 1 day, 23:23:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6034, loss_cls: 3.7177, loss: 3.7177 +2024-07-25 13:26:12,634 - pyskl - INFO - Epoch [94][3000/3746] lr: 3.082e-02, eta: 1 day, 23:21:43, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6212, loss_cls: 3.6276, loss: 3.6276 +2024-07-25 13:27:34,668 - pyskl - INFO - Epoch [94][3100/3746] lr: 3.079e-02, eta: 1 day, 23:20:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6158, loss_cls: 3.6703, loss: 3.6703 +2024-07-25 13:28:56,554 - pyskl - INFO - Epoch [94][3200/3746] lr: 3.077e-02, eta: 1 day, 23:19:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6088, loss_cls: 3.6511, loss: 3.6511 +2024-07-25 13:30:17,916 - pyskl - INFO - Epoch [94][3300/3746] lr: 3.074e-02, eta: 1 day, 23:17:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6045, loss_cls: 3.6743, loss: 3.6743 +2024-07-25 13:31:38,802 - pyskl - INFO - Epoch [94][3400/3746] lr: 3.071e-02, eta: 1 day, 23:16:20, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6212, loss_cls: 3.6477, loss: 3.6477 +2024-07-25 13:33:00,304 - pyskl - INFO - Epoch [94][3500/3746] lr: 3.069e-02, eta: 1 day, 23:15:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6139, loss_cls: 3.6926, loss: 3.6926 +2024-07-25 13:34:21,816 - pyskl - INFO - Epoch [94][3600/3746] lr: 3.066e-02, eta: 1 day, 23:13:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6206, loss_cls: 3.6140, loss: 3.6140 +2024-07-25 13:35:43,171 - pyskl - INFO - Epoch [94][3700/3746] lr: 3.064e-02, eta: 1 day, 23:12:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6147, loss_cls: 3.6589, loss: 3.6589 +2024-07-25 13:36:22,608 - pyskl - INFO - Saving checkpoint at 94 epochs +2024-07-25 13:38:15,672 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 13:38:16,543 - pyskl - INFO - +top1_acc 0.2905 +top5_acc 0.5493 +2024-07-25 13:38:16,543 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 13:38:16,586 - pyskl - INFO - +mean_acc 0.2905 +2024-07-25 13:38:16,599 - pyskl - INFO - Epoch(val) [94][309] top1_acc: 0.2905, top5_acc: 0.5493, mean_class_accuracy: 0.2905 +2024-07-25 13:42:10,901 - pyskl - INFO - Epoch [95][100/3746] lr: 3.060e-02, eta: 1 day, 23:11:29, time: 2.343, data_time: 1.348, memory: 15990, top1_acc: 0.3686, top5_acc: 0.6314, loss_cls: 3.5474, loss: 3.5474 +2024-07-25 13:43:33,220 - pyskl - INFO - Epoch [95][200/3746] lr: 3.057e-02, eta: 1 day, 23:10:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6225, loss_cls: 3.5703, loss: 3.5703 +2024-07-25 13:44:55,081 - pyskl - INFO - Epoch [95][300/3746] lr: 3.055e-02, eta: 1 day, 23:08:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6230, loss_cls: 3.6186, loss: 3.6186 +2024-07-25 13:46:16,880 - pyskl - INFO - Epoch [95][400/3746] lr: 3.052e-02, eta: 1 day, 23:07:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6212, loss_cls: 3.6118, loss: 3.6118 +2024-07-25 13:47:39,016 - pyskl - INFO - Epoch [95][500/3746] lr: 3.050e-02, eta: 1 day, 23:06:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6203, loss_cls: 3.6340, loss: 3.6340 +2024-07-25 13:49:00,948 - pyskl - INFO - Epoch [95][600/3746] lr: 3.047e-02, eta: 1 day, 23:04:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6244, loss_cls: 3.5905, loss: 3.5905 +2024-07-25 13:50:23,025 - pyskl - INFO - Epoch [95][700/3746] lr: 3.044e-02, eta: 1 day, 23:03:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6233, loss_cls: 3.6050, loss: 3.6050 +2024-07-25 13:51:44,830 - pyskl - INFO - Epoch [95][800/3746] lr: 3.042e-02, eta: 1 day, 23:02:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6284, loss_cls: 3.5737, loss: 3.5737 +2024-07-25 13:53:05,834 - pyskl - INFO - Epoch [95][900/3746] lr: 3.039e-02, eta: 1 day, 23:00:45, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6233, loss_cls: 3.5980, loss: 3.5980 +2024-07-25 13:54:27,411 - pyskl - INFO - Epoch [95][1000/3746] lr: 3.037e-02, eta: 1 day, 22:59:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6214, loss_cls: 3.5838, loss: 3.5838 +2024-07-25 13:55:49,268 - pyskl - INFO - Epoch [95][1100/3746] lr: 3.034e-02, eta: 1 day, 22:58:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6203, loss_cls: 3.6131, loss: 3.6131 +2024-07-25 13:57:10,515 - pyskl - INFO - Epoch [95][1200/3746] lr: 3.032e-02, eta: 1 day, 22:56:43, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3588, top5_acc: 0.6086, loss_cls: 3.6993, loss: 3.6993 +2024-07-25 13:58:31,781 - pyskl - INFO - Epoch [95][1300/3746] lr: 3.029e-02, eta: 1 day, 22:55:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6295, loss_cls: 3.5789, loss: 3.5789 +2024-07-25 13:59:52,888 - pyskl - INFO - Epoch [95][1400/3746] lr: 3.026e-02, eta: 1 day, 22:54:01, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6211, loss_cls: 3.6438, loss: 3.6438 +2024-07-25 14:01:14,224 - pyskl - INFO - Epoch [95][1500/3746] lr: 3.024e-02, eta: 1 day, 22:52:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6244, loss_cls: 3.5834, loss: 3.5834 +2024-07-25 14:02:37,267 - pyskl - INFO - Epoch [95][1600/3746] lr: 3.021e-02, eta: 1 day, 22:51:20, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6098, loss_cls: 3.6645, loss: 3.6645 +2024-07-25 14:03:59,019 - pyskl - INFO - Epoch [95][1700/3746] lr: 3.019e-02, eta: 1 day, 22:49:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6206, loss_cls: 3.6103, loss: 3.6103 +2024-07-25 14:05:20,889 - pyskl - INFO - Epoch [95][1800/3746] lr: 3.016e-02, eta: 1 day, 22:48:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6112, loss_cls: 3.6653, loss: 3.6653 +2024-07-25 14:06:43,427 - pyskl - INFO - Epoch [95][1900/3746] lr: 3.014e-02, eta: 1 day, 22:47:19, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6247, loss_cls: 3.6092, loss: 3.6092 +2024-07-25 14:08:05,101 - pyskl - INFO - Epoch [95][2000/3746] lr: 3.011e-02, eta: 1 day, 22:45:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6183, loss_cls: 3.5991, loss: 3.5991 +2024-07-25 14:09:26,827 - pyskl - INFO - Epoch [95][2100/3746] lr: 3.008e-02, eta: 1 day, 22:44:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6067, loss_cls: 3.6552, loss: 3.6552 +2024-07-25 14:10:48,401 - pyskl - INFO - Epoch [95][2200/3746] lr: 3.006e-02, eta: 1 day, 22:43:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6103, loss_cls: 3.6690, loss: 3.6690 +2024-07-25 14:12:10,366 - pyskl - INFO - Epoch [95][2300/3746] lr: 3.003e-02, eta: 1 day, 22:41:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3731, top5_acc: 0.6344, loss_cls: 3.5444, loss: 3.5444 +2024-07-25 14:13:31,822 - pyskl - INFO - Epoch [95][2400/3746] lr: 3.001e-02, eta: 1 day, 22:40:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6252, loss_cls: 3.6008, loss: 3.6008 +2024-07-25 14:14:53,522 - pyskl - INFO - Epoch [95][2500/3746] lr: 2.998e-02, eta: 1 day, 22:39:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6258, loss_cls: 3.5997, loss: 3.5997 +2024-07-25 14:16:14,731 - pyskl - INFO - Epoch [95][2600/3746] lr: 2.996e-02, eta: 1 day, 22:37:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6234, loss_cls: 3.5948, loss: 3.5948 +2024-07-25 14:17:37,192 - pyskl - INFO - Epoch [95][2700/3746] lr: 2.993e-02, eta: 1 day, 22:36:34, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6111, loss_cls: 3.6572, loss: 3.6572 +2024-07-25 14:18:59,386 - pyskl - INFO - Epoch [95][2800/3746] lr: 2.991e-02, eta: 1 day, 22:35:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6228, loss_cls: 3.6401, loss: 3.6401 +2024-07-25 14:20:20,644 - pyskl - INFO - Epoch [95][2900/3746] lr: 2.988e-02, eta: 1 day, 22:33:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6169, loss_cls: 3.6438, loss: 3.6438 +2024-07-25 14:21:42,237 - pyskl - INFO - Epoch [95][3000/3746] lr: 2.985e-02, eta: 1 day, 22:32:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6205, loss_cls: 3.6353, loss: 3.6353 +2024-07-25 14:23:03,627 - pyskl - INFO - Epoch [95][3100/3746] lr: 2.983e-02, eta: 1 day, 22:31:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6159, loss_cls: 3.6606, loss: 3.6606 +2024-07-25 14:24:25,558 - pyskl - INFO - Epoch [95][3200/3746] lr: 2.980e-02, eta: 1 day, 22:29:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6225, loss_cls: 3.6246, loss: 3.6246 +2024-07-25 14:25:46,395 - pyskl - INFO - Epoch [95][3300/3746] lr: 2.978e-02, eta: 1 day, 22:28:29, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6188, loss_cls: 3.6556, loss: 3.6556 +2024-07-25 14:27:08,026 - pyskl - INFO - Epoch [95][3400/3746] lr: 2.975e-02, eta: 1 day, 22:27:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3588, top5_acc: 0.6128, loss_cls: 3.6468, loss: 3.6468 +2024-07-25 14:28:29,309 - pyskl - INFO - Epoch [95][3500/3746] lr: 2.973e-02, eta: 1 day, 22:25:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6148, loss_cls: 3.6628, loss: 3.6628 +2024-07-25 14:29:51,717 - pyskl - INFO - Epoch [95][3600/3746] lr: 2.970e-02, eta: 1 day, 22:24:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3686, top5_acc: 0.6256, loss_cls: 3.5984, loss: 3.5984 +2024-07-25 14:31:13,221 - pyskl - INFO - Epoch [95][3700/3746] lr: 2.968e-02, eta: 1 day, 22:23:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6069, loss_cls: 3.6993, loss: 3.6993 +2024-07-25 14:31:53,037 - pyskl - INFO - Saving checkpoint at 95 epochs +2024-07-25 14:33:46,374 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 14:33:47,037 - pyskl - INFO - +top1_acc 0.2960 +top5_acc 0.5526 +2024-07-25 14:33:47,037 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 14:33:47,078 - pyskl - INFO - +mean_acc 0.2956 +2024-07-25 14:33:47,090 - pyskl - INFO - Epoch(val) [95][309] top1_acc: 0.2960, top5_acc: 0.5526, mean_class_accuracy: 0.2956 +2024-07-25 14:37:41,314 - pyskl - INFO - Epoch [96][100/3746] lr: 2.964e-02, eta: 1 day, 22:22:16, time: 2.342, data_time: 1.351, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6323, loss_cls: 3.5430, loss: 3.5430 +2024-07-25 14:39:03,466 - pyskl - INFO - Epoch [96][200/3746] lr: 2.961e-02, eta: 1 day, 22:20:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6230, loss_cls: 3.5872, loss: 3.5872 +2024-07-25 14:40:25,245 - pyskl - INFO - Epoch [96][300/3746] lr: 2.959e-02, eta: 1 day, 22:19:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6253, loss_cls: 3.5668, loss: 3.5668 +2024-07-25 14:41:47,303 - pyskl - INFO - Epoch [96][400/3746] lr: 2.956e-02, eta: 1 day, 22:18:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6245, loss_cls: 3.5878, loss: 3.5878 +2024-07-25 14:43:09,899 - pyskl - INFO - Epoch [96][500/3746] lr: 2.954e-02, eta: 1 day, 22:16:54, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6220, loss_cls: 3.6026, loss: 3.6026 +2024-07-25 14:44:31,391 - pyskl - INFO - Epoch [96][600/3746] lr: 2.951e-02, eta: 1 day, 22:15:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6261, loss_cls: 3.5920, loss: 3.5920 +2024-07-25 14:45:53,811 - pyskl - INFO - Epoch [96][700/3746] lr: 2.948e-02, eta: 1 day, 22:14:13, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6267, loss_cls: 3.5911, loss: 3.5911 +2024-07-25 14:47:15,282 - pyskl - INFO - Epoch [96][800/3746] lr: 2.946e-02, eta: 1 day, 22:12:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6317, loss_cls: 3.5486, loss: 3.5486 +2024-07-25 14:48:37,135 - pyskl - INFO - Epoch [96][900/3746] lr: 2.943e-02, eta: 1 day, 22:11:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6342, loss_cls: 3.5654, loss: 3.5654 +2024-07-25 14:49:58,557 - pyskl - INFO - Epoch [96][1000/3746] lr: 2.941e-02, eta: 1 day, 22:10:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3720, top5_acc: 0.6316, loss_cls: 3.5513, loss: 3.5513 +2024-07-25 14:51:19,991 - pyskl - INFO - Epoch [96][1100/3746] lr: 2.938e-02, eta: 1 day, 22:08:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6280, loss_cls: 3.5618, loss: 3.5618 +2024-07-25 14:52:41,840 - pyskl - INFO - Epoch [96][1200/3746] lr: 2.936e-02, eta: 1 day, 22:07:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6203, loss_cls: 3.6004, loss: 3.6004 +2024-07-25 14:54:03,381 - pyskl - INFO - Epoch [96][1300/3746] lr: 2.933e-02, eta: 1 day, 22:06:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6159, loss_cls: 3.6324, loss: 3.6324 +2024-07-25 14:55:24,800 - pyskl - INFO - Epoch [96][1400/3746] lr: 2.931e-02, eta: 1 day, 22:04:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6172, loss_cls: 3.6004, loss: 3.6004 +2024-07-25 14:56:46,726 - pyskl - INFO - Epoch [96][1500/3746] lr: 2.928e-02, eta: 1 day, 22:03:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6333, loss_cls: 3.5767, loss: 3.5767 +2024-07-25 14:58:09,510 - pyskl - INFO - Epoch [96][1600/3746] lr: 2.926e-02, eta: 1 day, 22:02:07, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6325, loss_cls: 3.5876, loss: 3.5876 +2024-07-25 14:59:31,230 - pyskl - INFO - Epoch [96][1700/3746] lr: 2.923e-02, eta: 1 day, 22:00:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6200, loss_cls: 3.6390, loss: 3.6390 +2024-07-25 15:00:53,361 - pyskl - INFO - Epoch [96][1800/3746] lr: 2.920e-02, eta: 1 day, 21:59:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6261, loss_cls: 3.5894, loss: 3.5894 +2024-07-25 15:02:14,949 - pyskl - INFO - Epoch [96][1900/3746] lr: 2.918e-02, eta: 1 day, 21:58:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6242, loss_cls: 3.6455, loss: 3.6455 +2024-07-25 15:03:36,855 - pyskl - INFO - Epoch [96][2000/3746] lr: 2.915e-02, eta: 1 day, 21:56:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6236, loss_cls: 3.5818, loss: 3.5818 +2024-07-25 15:04:59,182 - pyskl - INFO - Epoch [96][2100/3746] lr: 2.913e-02, eta: 1 day, 21:55:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3594, top5_acc: 0.6195, loss_cls: 3.6041, loss: 3.6041 +2024-07-25 15:06:21,077 - pyskl - INFO - Epoch [96][2200/3746] lr: 2.910e-02, eta: 1 day, 21:54:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6270, loss_cls: 3.5890, loss: 3.5890 +2024-07-25 15:07:42,423 - pyskl - INFO - Epoch [96][2300/3746] lr: 2.908e-02, eta: 1 day, 21:52:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6327, loss_cls: 3.5914, loss: 3.5914 +2024-07-25 15:09:03,289 - pyskl - INFO - Epoch [96][2400/3746] lr: 2.905e-02, eta: 1 day, 21:51:21, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6236, loss_cls: 3.6025, loss: 3.6025 +2024-07-25 15:10:24,792 - pyskl - INFO - Epoch [96][2500/3746] lr: 2.903e-02, eta: 1 day, 21:50:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6191, loss_cls: 3.6050, loss: 3.6050 +2024-07-25 15:11:46,129 - pyskl - INFO - Epoch [96][2600/3746] lr: 2.900e-02, eta: 1 day, 21:48:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6333, loss_cls: 3.5961, loss: 3.5961 +2024-07-25 15:13:07,903 - pyskl - INFO - Epoch [96][2700/3746] lr: 2.898e-02, eta: 1 day, 21:47:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6183, loss_cls: 3.6485, loss: 3.6485 +2024-07-25 15:14:29,372 - pyskl - INFO - Epoch [96][2800/3746] lr: 2.895e-02, eta: 1 day, 21:45:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6133, loss_cls: 3.6406, loss: 3.6406 +2024-07-25 15:15:51,507 - pyskl - INFO - Epoch [96][2900/3746] lr: 2.893e-02, eta: 1 day, 21:44:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6086, loss_cls: 3.6842, loss: 3.6842 +2024-07-25 15:17:13,452 - pyskl - INFO - Epoch [96][3000/3746] lr: 2.890e-02, eta: 1 day, 21:43:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6198, loss_cls: 3.6393, loss: 3.6393 +2024-07-25 15:18:35,177 - pyskl - INFO - Epoch [96][3100/3746] lr: 2.887e-02, eta: 1 day, 21:41:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6211, loss_cls: 3.5994, loss: 3.5994 +2024-07-25 15:19:56,735 - pyskl - INFO - Epoch [96][3200/3746] lr: 2.885e-02, eta: 1 day, 21:40:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6272, loss_cls: 3.5906, loss: 3.5906 +2024-07-25 15:21:18,570 - pyskl - INFO - Epoch [96][3300/3746] lr: 2.882e-02, eta: 1 day, 21:39:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6219, loss_cls: 3.6512, loss: 3.6512 +2024-07-25 15:22:40,087 - pyskl - INFO - Epoch [96][3400/3746] lr: 2.880e-02, eta: 1 day, 21:37:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6169, loss_cls: 3.6201, loss: 3.6201 +2024-07-25 15:24:02,239 - pyskl - INFO - Epoch [96][3500/3746] lr: 2.877e-02, eta: 1 day, 21:36:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6209, loss_cls: 3.6108, loss: 3.6108 +2024-07-25 15:25:23,870 - pyskl - INFO - Epoch [96][3600/3746] lr: 2.875e-02, eta: 1 day, 21:35:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6117, loss_cls: 3.6475, loss: 3.6475 +2024-07-25 15:26:45,504 - pyskl - INFO - Epoch [96][3700/3746] lr: 2.872e-02, eta: 1 day, 21:33:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6295, loss_cls: 3.5735, loss: 3.5735 +2024-07-25 15:27:24,582 - pyskl - INFO - Saving checkpoint at 96 epochs +2024-07-25 15:29:17,279 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 15:29:18,172 - pyskl - INFO - +top1_acc 0.3055 +top5_acc 0.5663 +2024-07-25 15:29:18,173 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 15:29:18,215 - pyskl - INFO - +mean_acc 0.3051 +2024-07-25 15:29:18,228 - pyskl - INFO - Epoch(val) [96][309] top1_acc: 0.3055, top5_acc: 0.5663, mean_class_accuracy: 0.3051 +2024-07-25 15:33:15,061 - pyskl - INFO - Epoch [97][100/3746] lr: 2.869e-02, eta: 1 day, 21:33:00, time: 2.368, data_time: 1.368, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6508, loss_cls: 3.4592, loss: 3.4592 +2024-07-25 15:34:38,732 - pyskl - INFO - Epoch [97][200/3746] lr: 2.866e-02, eta: 1 day, 21:31:40, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6284, loss_cls: 3.5494, loss: 3.5494 +2024-07-25 15:36:01,627 - pyskl - INFO - Epoch [97][300/3746] lr: 2.864e-02, eta: 1 day, 21:30:20, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6280, loss_cls: 3.5722, loss: 3.5722 +2024-07-25 15:37:23,818 - pyskl - INFO - Epoch [97][400/3746] lr: 2.861e-02, eta: 1 day, 21:29:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6288, loss_cls: 3.5740, loss: 3.5740 +2024-07-25 15:38:46,108 - pyskl - INFO - Epoch [97][500/3746] lr: 2.858e-02, eta: 1 day, 21:27:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6338, loss_cls: 3.5362, loss: 3.5362 +2024-07-25 15:40:08,139 - pyskl - INFO - Epoch [97][600/3746] lr: 2.856e-02, eta: 1 day, 21:26:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6281, loss_cls: 3.5858, loss: 3.5858 +2024-07-25 15:41:30,827 - pyskl - INFO - Epoch [97][700/3746] lr: 2.853e-02, eta: 1 day, 21:24:58, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6205, loss_cls: 3.5729, loss: 3.5729 +2024-07-25 15:42:52,335 - pyskl - INFO - Epoch [97][800/3746] lr: 2.851e-02, eta: 1 day, 21:23:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6302, loss_cls: 3.5873, loss: 3.5873 +2024-07-25 15:44:13,401 - pyskl - INFO - Epoch [97][900/3746] lr: 2.848e-02, eta: 1 day, 21:22:16, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6405, loss_cls: 3.5229, loss: 3.5229 +2024-07-25 15:45:35,042 - pyskl - INFO - Epoch [97][1000/3746] lr: 2.846e-02, eta: 1 day, 21:20:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6234, loss_cls: 3.5773, loss: 3.5773 +2024-07-25 15:46:56,984 - pyskl - INFO - Epoch [97][1100/3746] lr: 2.843e-02, eta: 1 day, 21:19:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6214, loss_cls: 3.5911, loss: 3.5911 +2024-07-25 15:48:18,769 - pyskl - INFO - Epoch [97][1200/3746] lr: 2.841e-02, eta: 1 day, 21:18:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6300, loss_cls: 3.5505, loss: 3.5505 +2024-07-25 15:49:40,277 - pyskl - INFO - Epoch [97][1300/3746] lr: 2.838e-02, eta: 1 day, 21:16:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6303, loss_cls: 3.5664, loss: 3.5664 +2024-07-25 15:51:01,758 - pyskl - INFO - Epoch [97][1400/3746] lr: 2.836e-02, eta: 1 day, 21:15:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6350, loss_cls: 3.5478, loss: 3.5478 +2024-07-25 15:52:23,187 - pyskl - INFO - Epoch [97][1500/3746] lr: 2.833e-02, eta: 1 day, 21:14:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6348, loss_cls: 3.5634, loss: 3.5634 +2024-07-25 15:53:45,039 - pyskl - INFO - Epoch [97][1600/3746] lr: 2.831e-02, eta: 1 day, 21:12:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6238, loss_cls: 3.6126, loss: 3.6126 +2024-07-25 15:55:07,752 - pyskl - INFO - Epoch [97][1700/3746] lr: 2.828e-02, eta: 1 day, 21:11:31, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6209, loss_cls: 3.6203, loss: 3.6203 +2024-07-25 15:56:29,770 - pyskl - INFO - Epoch [97][1800/3746] lr: 2.826e-02, eta: 1 day, 21:10:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6266, loss_cls: 3.6153, loss: 3.6153 +2024-07-25 15:57:52,633 - pyskl - INFO - Epoch [97][1900/3746] lr: 2.823e-02, eta: 1 day, 21:08:50, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6353, loss_cls: 3.5663, loss: 3.5663 +2024-07-25 15:59:15,015 - pyskl - INFO - Epoch [97][2000/3746] lr: 2.821e-02, eta: 1 day, 21:07:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6286, loss_cls: 3.5794, loss: 3.5794 +2024-07-25 16:00:37,291 - pyskl - INFO - Epoch [97][2100/3746] lr: 2.818e-02, eta: 1 day, 21:06:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6181, loss_cls: 3.6282, loss: 3.6282 +2024-07-25 16:01:58,829 - pyskl - INFO - Epoch [97][2200/3746] lr: 2.816e-02, eta: 1 day, 21:04:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6283, loss_cls: 3.5815, loss: 3.5815 +2024-07-25 16:03:20,380 - pyskl - INFO - Epoch [97][2300/3746] lr: 2.813e-02, eta: 1 day, 21:03:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6166, loss_cls: 3.6615, loss: 3.6615 +2024-07-25 16:04:42,070 - pyskl - INFO - Epoch [97][2400/3746] lr: 2.811e-02, eta: 1 day, 21:02:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6230, loss_cls: 3.5818, loss: 3.5818 +2024-07-25 16:06:03,222 - pyskl - INFO - Epoch [97][2500/3746] lr: 2.808e-02, eta: 1 day, 21:00:45, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6178, loss_cls: 3.6347, loss: 3.6347 +2024-07-25 16:07:24,809 - pyskl - INFO - Epoch [97][2600/3746] lr: 2.806e-02, eta: 1 day, 20:59:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6308, loss_cls: 3.5530, loss: 3.5530 +2024-07-25 16:08:46,253 - pyskl - INFO - Epoch [97][2700/3746] lr: 2.803e-02, eta: 1 day, 20:58:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3588, top5_acc: 0.6189, loss_cls: 3.6315, loss: 3.6315 +2024-07-25 16:10:07,643 - pyskl - INFO - Epoch [97][2800/3746] lr: 2.801e-02, eta: 1 day, 20:56:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6233, loss_cls: 3.5744, loss: 3.5744 +2024-07-25 16:11:28,851 - pyskl - INFO - Epoch [97][2900/3746] lr: 2.798e-02, eta: 1 day, 20:55:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6192, loss_cls: 3.6256, loss: 3.6256 +2024-07-25 16:12:50,273 - pyskl - INFO - Epoch [97][3000/3746] lr: 2.796e-02, eta: 1 day, 20:54:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6166, loss_cls: 3.6387, loss: 3.6387 +2024-07-25 16:14:11,321 - pyskl - INFO - Epoch [97][3100/3746] lr: 2.793e-02, eta: 1 day, 20:52:40, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6186, loss_cls: 3.6069, loss: 3.6069 +2024-07-25 16:15:32,671 - pyskl - INFO - Epoch [97][3200/3746] lr: 2.791e-02, eta: 1 day, 20:51:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6238, loss_cls: 3.5758, loss: 3.5758 +2024-07-25 16:16:53,838 - pyskl - INFO - Epoch [97][3300/3746] lr: 2.788e-02, eta: 1 day, 20:49:57, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6175, loss_cls: 3.6321, loss: 3.6321 +2024-07-25 16:18:15,986 - pyskl - INFO - Epoch [97][3400/3746] lr: 2.786e-02, eta: 1 day, 20:48:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6300, loss_cls: 3.5805, loss: 3.5805 +2024-07-25 16:19:37,203 - pyskl - INFO - Epoch [97][3500/3746] lr: 2.783e-02, eta: 1 day, 20:47:16, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6242, loss_cls: 3.6084, loss: 3.6084 +2024-07-25 16:20:58,587 - pyskl - INFO - Epoch [97][3600/3746] lr: 2.781e-02, eta: 1 day, 20:45:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6284, loss_cls: 3.5966, loss: 3.5966 +2024-07-25 16:22:20,115 - pyskl - INFO - Epoch [97][3700/3746] lr: 2.778e-02, eta: 1 day, 20:44:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6172, loss_cls: 3.6287, loss: 3.6287 +2024-07-25 16:22:59,580 - pyskl - INFO - Saving checkpoint at 97 epochs +2024-07-25 16:24:53,316 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 16:24:53,993 - pyskl - INFO - +top1_acc 0.3129 +top5_acc 0.5716 +2024-07-25 16:24:53,993 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 16:24:54,040 - pyskl - INFO - +mean_acc 0.3126 +2024-07-25 16:24:54,045 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_92.pth was removed +2024-07-25 16:24:54,315 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_97.pth. +2024-07-25 16:24:54,316 - pyskl - INFO - Best top1_acc is 0.3129 at 97 epoch. +2024-07-25 16:24:54,328 - pyskl - INFO - Epoch(val) [97][309] top1_acc: 0.3129, top5_acc: 0.5716, mean_class_accuracy: 0.3126 +2024-07-25 16:28:45,949 - pyskl - INFO - Epoch [98][100/3746] lr: 2.774e-02, eta: 1 day, 20:43:37, time: 2.316, data_time: 1.324, memory: 15990, top1_acc: 0.3819, top5_acc: 0.6434, loss_cls: 3.5131, loss: 3.5131 +2024-07-25 16:30:08,053 - pyskl - INFO - Epoch [98][200/3746] lr: 2.772e-02, eta: 1 day, 20:42:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3848, top5_acc: 0.6509, loss_cls: 3.4584, loss: 3.4584 +2024-07-25 16:31:29,922 - pyskl - INFO - Epoch [98][300/3746] lr: 2.769e-02, eta: 1 day, 20:40:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6169, loss_cls: 3.6247, loss: 3.6247 +2024-07-25 16:32:52,003 - pyskl - INFO - Epoch [98][400/3746] lr: 2.767e-02, eta: 1 day, 20:39:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6358, loss_cls: 3.5735, loss: 3.5735 +2024-07-25 16:34:14,266 - pyskl - INFO - Epoch [98][500/3746] lr: 2.764e-02, eta: 1 day, 20:38:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6267, loss_cls: 3.5706, loss: 3.5706 +2024-07-25 16:35:36,070 - pyskl - INFO - Epoch [98][600/3746] lr: 2.762e-02, eta: 1 day, 20:36:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6316, loss_cls: 3.5584, loss: 3.5584 +2024-07-25 16:36:58,066 - pyskl - INFO - Epoch [98][700/3746] lr: 2.759e-02, eta: 1 day, 20:35:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6453, loss_cls: 3.4847, loss: 3.4847 +2024-07-25 16:38:20,118 - pyskl - INFO - Epoch [98][800/3746] lr: 2.757e-02, eta: 1 day, 20:34:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6350, loss_cls: 3.5490, loss: 3.5490 +2024-07-25 16:39:41,573 - pyskl - INFO - Epoch [98][900/3746] lr: 2.754e-02, eta: 1 day, 20:32:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6264, loss_cls: 3.5840, loss: 3.5840 +2024-07-25 16:41:03,275 - pyskl - INFO - Epoch [98][1000/3746] lr: 2.752e-02, eta: 1 day, 20:31:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6262, loss_cls: 3.5767, loss: 3.5767 +2024-07-25 16:42:24,994 - pyskl - INFO - Epoch [98][1100/3746] lr: 2.749e-02, eta: 1 day, 20:30:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6211, loss_cls: 3.6181, loss: 3.6181 +2024-07-25 16:43:46,367 - pyskl - INFO - Epoch [98][1200/3746] lr: 2.747e-02, eta: 1 day, 20:28:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6292, loss_cls: 3.5633, loss: 3.5633 +2024-07-25 16:45:07,700 - pyskl - INFO - Epoch [98][1300/3746] lr: 2.744e-02, eta: 1 day, 20:27:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6198, loss_cls: 3.6206, loss: 3.6206 +2024-07-25 16:46:29,226 - pyskl - INFO - Epoch [98][1400/3746] lr: 2.742e-02, eta: 1 day, 20:26:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6253, loss_cls: 3.6189, loss: 3.6189 +2024-07-25 16:47:50,899 - pyskl - INFO - Epoch [98][1500/3746] lr: 2.739e-02, eta: 1 day, 20:24:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6328, loss_cls: 3.5564, loss: 3.5564 +2024-07-25 16:49:12,646 - pyskl - INFO - Epoch [98][1600/3746] lr: 2.737e-02, eta: 1 day, 20:23:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6320, loss_cls: 3.5490, loss: 3.5490 +2024-07-25 16:50:35,821 - pyskl - INFO - Epoch [98][1700/3746] lr: 2.734e-02, eta: 1 day, 20:22:05, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6273, loss_cls: 3.5776, loss: 3.5776 +2024-07-25 16:51:58,177 - pyskl - INFO - Epoch [98][1800/3746] lr: 2.732e-02, eta: 1 day, 20:20:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6188, loss_cls: 3.5928, loss: 3.5928 +2024-07-25 16:53:19,972 - pyskl - INFO - Epoch [98][1900/3746] lr: 2.729e-02, eta: 1 day, 20:19:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6227, loss_cls: 3.5659, loss: 3.5659 +2024-07-25 16:54:41,951 - pyskl - INFO - Epoch [98][2000/3746] lr: 2.727e-02, eta: 1 day, 20:18:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6322, loss_cls: 3.5400, loss: 3.5400 +2024-07-25 16:56:03,265 - pyskl - INFO - Epoch [98][2100/3746] lr: 2.724e-02, eta: 1 day, 20:16:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6284, loss_cls: 3.5502, loss: 3.5502 +2024-07-25 16:57:24,792 - pyskl - INFO - Epoch [98][2200/3746] lr: 2.722e-02, eta: 1 day, 20:15:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6234, loss_cls: 3.5782, loss: 3.5782 +2024-07-25 16:58:46,544 - pyskl - INFO - Epoch [98][2300/3746] lr: 2.719e-02, eta: 1 day, 20:14:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6198, loss_cls: 3.5996, loss: 3.5996 +2024-07-25 17:00:08,414 - pyskl - INFO - Epoch [98][2400/3746] lr: 2.717e-02, eta: 1 day, 20:12:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6309, loss_cls: 3.5620, loss: 3.5620 +2024-07-25 17:01:30,363 - pyskl - INFO - Epoch [98][2500/3746] lr: 2.714e-02, eta: 1 day, 20:11:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6270, loss_cls: 3.5790, loss: 3.5790 +2024-07-25 17:02:51,959 - pyskl - INFO - Epoch [98][2600/3746] lr: 2.712e-02, eta: 1 day, 20:09:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6386, loss_cls: 3.5467, loss: 3.5467 +2024-07-25 17:04:13,237 - pyskl - INFO - Epoch [98][2700/3746] lr: 2.709e-02, eta: 1 day, 20:08:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6309, loss_cls: 3.5645, loss: 3.5645 +2024-07-25 17:05:35,011 - pyskl - INFO - Epoch [98][2800/3746] lr: 2.707e-02, eta: 1 day, 20:07:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6233, loss_cls: 3.5963, loss: 3.5963 +2024-07-25 17:06:56,754 - pyskl - INFO - Epoch [98][2900/3746] lr: 2.705e-02, eta: 1 day, 20:05:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6238, loss_cls: 3.5976, loss: 3.5976 +2024-07-25 17:08:18,043 - pyskl - INFO - Epoch [98][3000/3746] lr: 2.702e-02, eta: 1 day, 20:04:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6245, loss_cls: 3.6138, loss: 3.6138 +2024-07-25 17:09:39,601 - pyskl - INFO - Epoch [98][3100/3746] lr: 2.700e-02, eta: 1 day, 20:03:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6281, loss_cls: 3.5844, loss: 3.5844 +2024-07-25 17:11:01,312 - pyskl - INFO - Epoch [98][3200/3746] lr: 2.697e-02, eta: 1 day, 20:01:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6261, loss_cls: 3.5859, loss: 3.5859 +2024-07-25 17:12:22,667 - pyskl - INFO - Epoch [98][3300/3746] lr: 2.695e-02, eta: 1 day, 20:00:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6316, loss_cls: 3.5889, loss: 3.5889 +2024-07-25 17:13:44,110 - pyskl - INFO - Epoch [98][3400/3746] lr: 2.692e-02, eta: 1 day, 19:59:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6300, loss_cls: 3.5678, loss: 3.5678 +2024-07-25 17:15:06,138 - pyskl - INFO - Epoch [98][3500/3746] lr: 2.690e-02, eta: 1 day, 19:57:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6302, loss_cls: 3.6245, loss: 3.6245 +2024-07-25 17:16:29,145 - pyskl - INFO - Epoch [98][3600/3746] lr: 2.687e-02, eta: 1 day, 19:56:30, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6256, loss_cls: 3.5923, loss: 3.5923 +2024-07-25 17:17:50,961 - pyskl - INFO - Epoch [98][3700/3746] lr: 2.685e-02, eta: 1 day, 19:55:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6228, loss_cls: 3.5893, loss: 3.5893 +2024-07-25 17:18:30,233 - pyskl - INFO - Saving checkpoint at 98 epochs +2024-07-25 17:20:23,391 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 17:20:24,114 - pyskl - INFO - +top1_acc 0.3010 +top5_acc 0.5591 +2024-07-25 17:20:24,114 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 17:20:24,165 - pyskl - INFO - +mean_acc 0.3008 +2024-07-25 17:20:24,181 - pyskl - INFO - Epoch(val) [98][309] top1_acc: 0.3010, top5_acc: 0.5591, mean_class_accuracy: 0.3008 +2024-07-25 17:24:16,761 - pyskl - INFO - Epoch [99][100/3746] lr: 2.681e-02, eta: 1 day, 19:54:11, time: 2.326, data_time: 1.337, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6492, loss_cls: 3.4658, loss: 3.4658 +2024-07-25 17:25:38,935 - pyskl - INFO - Epoch [99][200/3746] lr: 2.679e-02, eta: 1 day, 19:52:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6395, loss_cls: 3.4940, loss: 3.4940 +2024-07-25 17:27:00,669 - pyskl - INFO - Epoch [99][300/3746] lr: 2.676e-02, eta: 1 day, 19:51:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6295, loss_cls: 3.5798, loss: 3.5798 +2024-07-25 17:28:22,570 - pyskl - INFO - Epoch [99][400/3746] lr: 2.674e-02, eta: 1 day, 19:50:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6422, loss_cls: 3.5158, loss: 3.5158 +2024-07-25 17:29:44,470 - pyskl - INFO - Epoch [99][500/3746] lr: 2.671e-02, eta: 1 day, 19:48:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6294, loss_cls: 3.5767, loss: 3.5767 +2024-07-25 17:31:06,219 - pyskl - INFO - Epoch [99][600/3746] lr: 2.669e-02, eta: 1 day, 19:47:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6358, loss_cls: 3.5290, loss: 3.5290 +2024-07-25 17:32:27,725 - pyskl - INFO - Epoch [99][700/3746] lr: 2.666e-02, eta: 1 day, 19:46:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3731, top5_acc: 0.6325, loss_cls: 3.5353, loss: 3.5353 +2024-07-25 17:33:49,292 - pyskl - INFO - Epoch [99][800/3746] lr: 2.664e-02, eta: 1 day, 19:44:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6202, loss_cls: 3.6005, loss: 3.6005 +2024-07-25 17:35:10,424 - pyskl - INFO - Epoch [99][900/3746] lr: 2.661e-02, eta: 1 day, 19:43:24, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6264, loss_cls: 3.5819, loss: 3.5819 +2024-07-25 17:36:31,958 - pyskl - INFO - Epoch [99][1000/3746] lr: 2.659e-02, eta: 1 day, 19:42:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6439, loss_cls: 3.4892, loss: 3.4892 +2024-07-25 17:37:53,868 - pyskl - INFO - Epoch [99][1100/3746] lr: 2.656e-02, eta: 1 day, 19:40:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6219, loss_cls: 3.5823, loss: 3.5823 +2024-07-25 17:39:14,998 - pyskl - INFO - Epoch [99][1200/3746] lr: 2.654e-02, eta: 1 day, 19:39:21, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6389, loss_cls: 3.5407, loss: 3.5407 +2024-07-25 17:40:36,691 - pyskl - INFO - Epoch [99][1300/3746] lr: 2.651e-02, eta: 1 day, 19:38:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6316, loss_cls: 3.5436, loss: 3.5436 +2024-07-25 17:41:58,611 - pyskl - INFO - Epoch [99][1400/3746] lr: 2.649e-02, eta: 1 day, 19:36:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3723, top5_acc: 0.6311, loss_cls: 3.5568, loss: 3.5568 +2024-07-25 17:43:20,236 - pyskl - INFO - Epoch [99][1500/3746] lr: 2.646e-02, eta: 1 day, 19:35:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6350, loss_cls: 3.5374, loss: 3.5374 +2024-07-25 17:44:41,832 - pyskl - INFO - Epoch [99][1600/3746] lr: 2.644e-02, eta: 1 day, 19:33:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6223, loss_cls: 3.5757, loss: 3.5757 +2024-07-25 17:46:05,069 - pyskl - INFO - Epoch [99][1700/3746] lr: 2.642e-02, eta: 1 day, 19:32:37, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6436, loss_cls: 3.4950, loss: 3.4950 +2024-07-25 17:47:26,649 - pyskl - INFO - Epoch [99][1800/3746] lr: 2.639e-02, eta: 1 day, 19:31:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6353, loss_cls: 3.5352, loss: 3.5352 +2024-07-25 17:48:49,571 - pyskl - INFO - Epoch [99][1900/3746] lr: 2.637e-02, eta: 1 day, 19:29:56, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3739, top5_acc: 0.6309, loss_cls: 3.5408, loss: 3.5408 +2024-07-25 17:50:11,237 - pyskl - INFO - Epoch [99][2000/3746] lr: 2.634e-02, eta: 1 day, 19:28:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6302, loss_cls: 3.5511, loss: 3.5511 +2024-07-25 17:51:33,290 - pyskl - INFO - Epoch [99][2100/3746] lr: 2.632e-02, eta: 1 day, 19:27:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6262, loss_cls: 3.5954, loss: 3.5954 +2024-07-25 17:52:55,115 - pyskl - INFO - Epoch [99][2200/3746] lr: 2.629e-02, eta: 1 day, 19:25:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6322, loss_cls: 3.5466, loss: 3.5466 +2024-07-25 17:54:16,657 - pyskl - INFO - Epoch [99][2300/3746] lr: 2.627e-02, eta: 1 day, 19:24:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6212, loss_cls: 3.5939, loss: 3.5939 +2024-07-25 17:55:38,636 - pyskl - INFO - Epoch [99][2400/3746] lr: 2.624e-02, eta: 1 day, 19:23:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6242, loss_cls: 3.6009, loss: 3.6009 +2024-07-25 17:57:00,054 - pyskl - INFO - Epoch [99][2500/3746] lr: 2.622e-02, eta: 1 day, 19:21:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6317, loss_cls: 3.5385, loss: 3.5385 +2024-07-25 17:58:22,129 - pyskl - INFO - Epoch [99][2600/3746] lr: 2.619e-02, eta: 1 day, 19:20:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6334, loss_cls: 3.5813, loss: 3.5813 +2024-07-25 17:59:43,520 - pyskl - INFO - Epoch [99][2700/3746] lr: 2.617e-02, eta: 1 day, 19:19:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6353, loss_cls: 3.5689, loss: 3.5689 +2024-07-25 18:01:05,342 - pyskl - INFO - Epoch [99][2800/3746] lr: 2.614e-02, eta: 1 day, 19:17:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6209, loss_cls: 3.5890, loss: 3.5890 +2024-07-25 18:02:26,945 - pyskl - INFO - Epoch [99][2900/3746] lr: 2.612e-02, eta: 1 day, 19:16:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6222, loss_cls: 3.5724, loss: 3.5724 +2024-07-25 18:03:48,939 - pyskl - INFO - Epoch [99][3000/3746] lr: 2.610e-02, eta: 1 day, 19:15:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6344, loss_cls: 3.5335, loss: 3.5335 +2024-07-25 18:05:10,268 - pyskl - INFO - Epoch [99][3100/3746] lr: 2.607e-02, eta: 1 day, 19:13:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6264, loss_cls: 3.6144, loss: 3.6144 +2024-07-25 18:06:31,789 - pyskl - INFO - Epoch [99][3200/3746] lr: 2.605e-02, eta: 1 day, 19:12:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3708, top5_acc: 0.6281, loss_cls: 3.5563, loss: 3.5563 +2024-07-25 18:07:53,763 - pyskl - INFO - Epoch [99][3300/3746] lr: 2.602e-02, eta: 1 day, 19:11:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6206, loss_cls: 3.5971, loss: 3.5971 +2024-07-25 18:09:15,795 - pyskl - INFO - Epoch [99][3400/3746] lr: 2.600e-02, eta: 1 day, 19:09:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6328, loss_cls: 3.5314, loss: 3.5314 +2024-07-25 18:10:37,443 - pyskl - INFO - Epoch [99][3500/3746] lr: 2.597e-02, eta: 1 day, 19:08:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6286, loss_cls: 3.5621, loss: 3.5621 +2024-07-25 18:11:59,363 - pyskl - INFO - Epoch [99][3600/3746] lr: 2.595e-02, eta: 1 day, 19:07:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6314, loss_cls: 3.5269, loss: 3.5269 +2024-07-25 18:13:21,634 - pyskl - INFO - Epoch [99][3700/3746] lr: 2.592e-02, eta: 1 day, 19:05:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6203, loss_cls: 3.5784, loss: 3.5784 +2024-07-25 18:14:01,114 - pyskl - INFO - Saving checkpoint at 99 epochs +2024-07-25 18:15:54,088 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 18:15:54,789 - pyskl - INFO - +top1_acc 0.2997 +top5_acc 0.5480 +2024-07-25 18:15:54,790 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 18:15:54,835 - pyskl - INFO - +mean_acc 0.2994 +2024-07-25 18:15:54,849 - pyskl - INFO - Epoch(val) [99][309] top1_acc: 0.2997, top5_acc: 0.5480, mean_class_accuracy: 0.2994 +2024-07-25 18:19:54,215 - pyskl - INFO - Epoch [100][100/3746] lr: 2.589e-02, eta: 1 day, 19:04:44, time: 2.394, data_time: 1.388, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6497, loss_cls: 3.4541, loss: 3.4541 +2024-07-25 18:21:18,125 - pyskl - INFO - Epoch [100][200/3746] lr: 2.586e-02, eta: 1 day, 19:03:24, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6353, loss_cls: 3.5001, loss: 3.5001 +2024-07-25 18:22:40,407 - pyskl - INFO - Epoch [100][300/3746] lr: 2.584e-02, eta: 1 day, 19:02:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6431, loss_cls: 3.4963, loss: 3.4963 +2024-07-25 18:24:03,284 - pyskl - INFO - Epoch [100][400/3746] lr: 2.581e-02, eta: 1 day, 19:00:43, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6280, loss_cls: 3.5240, loss: 3.5240 +2024-07-25 18:25:26,342 - pyskl - INFO - Epoch [100][500/3746] lr: 2.579e-02, eta: 1 day, 18:59:22, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6453, loss_cls: 3.4891, loss: 3.4891 +2024-07-25 18:26:48,218 - pyskl - INFO - Epoch [100][600/3746] lr: 2.577e-02, eta: 1 day, 18:58:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6397, loss_cls: 3.5112, loss: 3.5112 +2024-07-25 18:28:09,928 - pyskl - INFO - Epoch [100][700/3746] lr: 2.574e-02, eta: 1 day, 18:56:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6356, loss_cls: 3.5201, loss: 3.5201 +2024-07-25 18:29:32,217 - pyskl - INFO - Epoch [100][800/3746] lr: 2.572e-02, eta: 1 day, 18:55:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3691, top5_acc: 0.6244, loss_cls: 3.5858, loss: 3.5858 +2024-07-25 18:30:53,468 - pyskl - INFO - Epoch [100][900/3746] lr: 2.569e-02, eta: 1 day, 18:53:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6427, loss_cls: 3.4845, loss: 3.4845 +2024-07-25 18:32:15,210 - pyskl - INFO - Epoch [100][1000/3746] lr: 2.567e-02, eta: 1 day, 18:52:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6359, loss_cls: 3.5341, loss: 3.5341 +2024-07-25 18:33:37,060 - pyskl - INFO - Epoch [100][1100/3746] lr: 2.564e-02, eta: 1 day, 18:51:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6317, loss_cls: 3.5432, loss: 3.5432 +2024-07-25 18:34:58,365 - pyskl - INFO - Epoch [100][1200/3746] lr: 2.562e-02, eta: 1 day, 18:49:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6391, loss_cls: 3.5207, loss: 3.5207 +2024-07-25 18:36:19,786 - pyskl - INFO - Epoch [100][1300/3746] lr: 2.559e-02, eta: 1 day, 18:48:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6383, loss_cls: 3.4902, loss: 3.4902 +2024-07-25 18:37:41,904 - pyskl - INFO - Epoch [100][1400/3746] lr: 2.557e-02, eta: 1 day, 18:47:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6339, loss_cls: 3.5218, loss: 3.5218 +2024-07-25 18:39:03,264 - pyskl - INFO - Epoch [100][1500/3746] lr: 2.555e-02, eta: 1 day, 18:45:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6255, loss_cls: 3.5543, loss: 3.5543 +2024-07-25 18:40:25,030 - pyskl - INFO - Epoch [100][1600/3746] lr: 2.552e-02, eta: 1 day, 18:44:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6377, loss_cls: 3.5478, loss: 3.5478 +2024-07-25 18:41:46,962 - pyskl - INFO - Epoch [100][1700/3746] lr: 2.550e-02, eta: 1 day, 18:43:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6320, loss_cls: 3.5824, loss: 3.5824 +2024-07-25 18:43:08,837 - pyskl - INFO - Epoch [100][1800/3746] lr: 2.547e-02, eta: 1 day, 18:41:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6323, loss_cls: 3.5226, loss: 3.5226 +2024-07-25 18:44:31,161 - pyskl - INFO - Epoch [100][1900/3746] lr: 2.545e-02, eta: 1 day, 18:40:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3812, top5_acc: 0.6344, loss_cls: 3.5336, loss: 3.5336 +2024-07-25 18:45:53,309 - pyskl - INFO - Epoch [100][2000/3746] lr: 2.542e-02, eta: 1 day, 18:39:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6284, loss_cls: 3.5631, loss: 3.5631 +2024-07-25 18:47:15,112 - pyskl - INFO - Epoch [100][2100/3746] lr: 2.540e-02, eta: 1 day, 18:37:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6302, loss_cls: 3.6009, loss: 3.6009 +2024-07-25 18:48:36,336 - pyskl - INFO - Epoch [100][2200/3746] lr: 2.538e-02, eta: 1 day, 18:36:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6336, loss_cls: 3.5565, loss: 3.5565 +2024-07-25 18:49:57,996 - pyskl - INFO - Epoch [100][2300/3746] lr: 2.535e-02, eta: 1 day, 18:35:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6391, loss_cls: 3.5250, loss: 3.5250 +2024-07-25 18:51:19,402 - pyskl - INFO - Epoch [100][2400/3746] lr: 2.533e-02, eta: 1 day, 18:33:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6294, loss_cls: 3.5471, loss: 3.5471 +2024-07-25 18:52:41,025 - pyskl - INFO - Epoch [100][2500/3746] lr: 2.530e-02, eta: 1 day, 18:32:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6248, loss_cls: 3.5560, loss: 3.5560 +2024-07-25 18:54:03,308 - pyskl - INFO - Epoch [100][2600/3746] lr: 2.528e-02, eta: 1 day, 18:31:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6338, loss_cls: 3.5408, loss: 3.5408 +2024-07-25 18:55:25,235 - pyskl - INFO - Epoch [100][2700/3746] lr: 2.525e-02, eta: 1 day, 18:29:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6292, loss_cls: 3.5440, loss: 3.5440 +2024-07-25 18:56:46,711 - pyskl - INFO - Epoch [100][2800/3746] lr: 2.523e-02, eta: 1 day, 18:28:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6322, loss_cls: 3.5540, loss: 3.5540 +2024-07-25 18:58:08,548 - pyskl - INFO - Epoch [100][2900/3746] lr: 2.521e-02, eta: 1 day, 18:27:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6261, loss_cls: 3.5621, loss: 3.5621 +2024-07-25 18:59:29,919 - pyskl - INFO - Epoch [100][3000/3746] lr: 2.518e-02, eta: 1 day, 18:25:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6206, loss_cls: 3.5936, loss: 3.5936 +2024-07-25 19:00:51,364 - pyskl - INFO - Epoch [100][3100/3746] lr: 2.516e-02, eta: 1 day, 18:24:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6222, loss_cls: 3.5946, loss: 3.5946 +2024-07-25 19:02:12,819 - pyskl - INFO - Epoch [100][3200/3746] lr: 2.513e-02, eta: 1 day, 18:22:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6312, loss_cls: 3.5425, loss: 3.5425 +2024-07-25 19:03:34,088 - pyskl - INFO - Epoch [100][3300/3746] lr: 2.511e-02, eta: 1 day, 18:21:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6295, loss_cls: 3.5635, loss: 3.5635 +2024-07-25 19:04:55,958 - pyskl - INFO - Epoch [100][3400/3746] lr: 2.508e-02, eta: 1 day, 18:20:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6334, loss_cls: 3.5424, loss: 3.5424 +2024-07-25 19:06:17,315 - pyskl - INFO - Epoch [100][3500/3746] lr: 2.506e-02, eta: 1 day, 18:18:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6317, loss_cls: 3.5422, loss: 3.5422 +2024-07-25 19:07:39,447 - pyskl - INFO - Epoch [100][3600/3746] lr: 2.504e-02, eta: 1 day, 18:17:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6325, loss_cls: 3.5500, loss: 3.5500 +2024-07-25 19:09:00,449 - pyskl - INFO - Epoch [100][3700/3746] lr: 2.501e-02, eta: 1 day, 18:16:11, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6306, loss_cls: 3.5221, loss: 3.5221 +2024-07-25 19:09:39,799 - pyskl - INFO - Saving checkpoint at 100 epochs +2024-07-25 19:11:32,937 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 19:11:33,606 - pyskl - INFO - +top1_acc 0.3246 +top5_acc 0.5718 +2024-07-25 19:11:33,606 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 19:11:33,648 - pyskl - INFO - +mean_acc 0.3242 +2024-07-25 19:11:33,653 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_97.pth was removed +2024-07-25 19:11:33,926 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_100.pth. +2024-07-25 19:11:33,927 - pyskl - INFO - Best top1_acc is 0.3246 at 100 epoch. +2024-07-25 19:11:33,940 - pyskl - INFO - Epoch(val) [100][309] top1_acc: 0.3246, top5_acc: 0.5718, mean_class_accuracy: 0.3242 +2024-07-25 19:15:28,620 - pyskl - INFO - Epoch [101][100/3746] lr: 2.498e-02, eta: 1 day, 18:15:10, time: 2.347, data_time: 1.364, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6434, loss_cls: 3.4887, loss: 3.4887 +2024-07-25 19:16:50,617 - pyskl - INFO - Epoch [101][200/3746] lr: 2.495e-02, eta: 1 day, 18:13:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6462, loss_cls: 3.4634, loss: 3.4634 +2024-07-25 19:18:12,747 - pyskl - INFO - Epoch [101][300/3746] lr: 2.493e-02, eta: 1 day, 18:12:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3914, top5_acc: 0.6509, loss_cls: 3.4257, loss: 3.4257 +2024-07-25 19:19:34,390 - pyskl - INFO - Epoch [101][400/3746] lr: 2.490e-02, eta: 1 day, 18:11:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6323, loss_cls: 3.5262, loss: 3.5262 +2024-07-25 19:20:56,191 - pyskl - INFO - Epoch [101][500/3746] lr: 2.488e-02, eta: 1 day, 18:09:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6370, loss_cls: 3.5123, loss: 3.5123 +2024-07-25 19:22:17,809 - pyskl - INFO - Epoch [101][600/3746] lr: 2.486e-02, eta: 1 day, 18:08:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6445, loss_cls: 3.5063, loss: 3.5063 +2024-07-25 19:23:39,448 - pyskl - INFO - Epoch [101][700/3746] lr: 2.483e-02, eta: 1 day, 18:07:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6483, loss_cls: 3.4634, loss: 3.4634 +2024-07-25 19:25:00,742 - pyskl - INFO - Epoch [101][800/3746] lr: 2.481e-02, eta: 1 day, 18:05:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6381, loss_cls: 3.5007, loss: 3.5007 +2024-07-25 19:26:22,719 - pyskl - INFO - Epoch [101][900/3746] lr: 2.478e-02, eta: 1 day, 18:04:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6522, loss_cls: 3.4571, loss: 3.4571 +2024-07-25 19:27:44,226 - pyskl - INFO - Epoch [101][1000/3746] lr: 2.476e-02, eta: 1 day, 18:03:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6452, loss_cls: 3.4765, loss: 3.4765 +2024-07-25 19:29:05,863 - pyskl - INFO - Epoch [101][1100/3746] lr: 2.473e-02, eta: 1 day, 18:01:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6366, loss_cls: 3.4956, loss: 3.4956 +2024-07-25 19:30:27,117 - pyskl - INFO - Epoch [101][1200/3746] lr: 2.471e-02, eta: 1 day, 18:00:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3731, top5_acc: 0.6338, loss_cls: 3.5324, loss: 3.5324 +2024-07-25 19:31:49,025 - pyskl - INFO - Epoch [101][1300/3746] lr: 2.469e-02, eta: 1 day, 17:58:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6475, loss_cls: 3.4848, loss: 3.4848 +2024-07-25 19:33:10,927 - pyskl - INFO - Epoch [101][1400/3746] lr: 2.466e-02, eta: 1 day, 17:57:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6334, loss_cls: 3.5435, loss: 3.5435 +2024-07-25 19:34:32,444 - pyskl - INFO - Epoch [101][1500/3746] lr: 2.464e-02, eta: 1 day, 17:56:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6383, loss_cls: 3.4739, loss: 3.4739 +2024-07-25 19:35:53,681 - pyskl - INFO - Epoch [101][1600/3746] lr: 2.461e-02, eta: 1 day, 17:54:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6436, loss_cls: 3.4986, loss: 3.4986 +2024-07-25 19:37:15,998 - pyskl - INFO - Epoch [101][1700/3746] lr: 2.459e-02, eta: 1 day, 17:53:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6223, loss_cls: 3.5540, loss: 3.5540 +2024-07-25 19:38:38,142 - pyskl - INFO - Epoch [101][1800/3746] lr: 2.457e-02, eta: 1 day, 17:52:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6414, loss_cls: 3.4831, loss: 3.4831 +2024-07-25 19:40:01,280 - pyskl - INFO - Epoch [101][1900/3746] lr: 2.454e-02, eta: 1 day, 17:50:53, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6355, loss_cls: 3.5332, loss: 3.5332 +2024-07-25 19:41:23,448 - pyskl - INFO - Epoch [101][2000/3746] lr: 2.452e-02, eta: 1 day, 17:49:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6300, loss_cls: 3.5557, loss: 3.5557 +2024-07-25 19:42:46,086 - pyskl - INFO - Epoch [101][2100/3746] lr: 2.449e-02, eta: 1 day, 17:48:11, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6300, loss_cls: 3.5824, loss: 3.5824 +2024-07-25 19:44:08,293 - pyskl - INFO - Epoch [101][2200/3746] lr: 2.447e-02, eta: 1 day, 17:46:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6275, loss_cls: 3.5721, loss: 3.5721 +2024-07-25 19:45:30,738 - pyskl - INFO - Epoch [101][2300/3746] lr: 2.445e-02, eta: 1 day, 17:45:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6372, loss_cls: 3.5121, loss: 3.5121 +2024-07-25 19:46:52,220 - pyskl - INFO - Epoch [101][2400/3746] lr: 2.442e-02, eta: 1 day, 17:44:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6288, loss_cls: 3.5542, loss: 3.5542 +2024-07-25 19:48:13,759 - pyskl - INFO - Epoch [101][2500/3746] lr: 2.440e-02, eta: 1 day, 17:42:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6339, loss_cls: 3.5097, loss: 3.5097 +2024-07-25 19:49:35,256 - pyskl - INFO - Epoch [101][2600/3746] lr: 2.437e-02, eta: 1 day, 17:41:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3739, top5_acc: 0.6311, loss_cls: 3.5389, loss: 3.5389 +2024-07-25 19:50:56,884 - pyskl - INFO - Epoch [101][2700/3746] lr: 2.435e-02, eta: 1 day, 17:40:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6267, loss_cls: 3.5867, loss: 3.5867 +2024-07-25 19:52:18,503 - pyskl - INFO - Epoch [101][2800/3746] lr: 2.433e-02, eta: 1 day, 17:38:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6344, loss_cls: 3.5133, loss: 3.5133 +2024-07-25 19:53:39,958 - pyskl - INFO - Epoch [101][2900/3746] lr: 2.430e-02, eta: 1 day, 17:37:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6408, loss_cls: 3.5124, loss: 3.5124 +2024-07-25 19:55:01,392 - pyskl - INFO - Epoch [101][3000/3746] lr: 2.428e-02, eta: 1 day, 17:36:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6389, loss_cls: 3.5305, loss: 3.5305 +2024-07-25 19:56:22,555 - pyskl - INFO - Epoch [101][3100/3746] lr: 2.425e-02, eta: 1 day, 17:34:41, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6305, loss_cls: 3.5588, loss: 3.5588 +2024-07-25 19:57:44,624 - pyskl - INFO - Epoch [101][3200/3746] lr: 2.423e-02, eta: 1 day, 17:33:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6330, loss_cls: 3.5199, loss: 3.5199 +2024-07-25 19:59:06,901 - pyskl - INFO - Epoch [101][3300/3746] lr: 2.421e-02, eta: 1 day, 17:31:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6430, loss_cls: 3.4903, loss: 3.4903 +2024-07-25 20:00:28,742 - pyskl - INFO - Epoch [101][3400/3746] lr: 2.418e-02, eta: 1 day, 17:30:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6338, loss_cls: 3.5506, loss: 3.5506 +2024-07-25 20:01:50,270 - pyskl - INFO - Epoch [101][3500/3746] lr: 2.416e-02, eta: 1 day, 17:29:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6388, loss_cls: 3.5069, loss: 3.5069 +2024-07-25 20:03:11,986 - pyskl - INFO - Epoch [101][3600/3746] lr: 2.413e-02, eta: 1 day, 17:27:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6197, loss_cls: 3.5937, loss: 3.5937 +2024-07-25 20:04:33,926 - pyskl - INFO - Epoch [101][3700/3746] lr: 2.411e-02, eta: 1 day, 17:26:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6338, loss_cls: 3.5542, loss: 3.5542 +2024-07-25 20:05:13,431 - pyskl - INFO - Saving checkpoint at 101 epochs +2024-07-25 20:07:05,605 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 20:07:06,257 - pyskl - INFO - +top1_acc 0.3200 +top5_acc 0.5754 +2024-07-25 20:07:06,257 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 20:07:06,309 - pyskl - INFO - +mean_acc 0.3197 +2024-07-25 20:07:06,323 - pyskl - INFO - Epoch(val) [101][309] top1_acc: 0.3200, top5_acc: 0.5754, mean_class_accuracy: 0.3197 +2024-07-25 20:10:54,345 - pyskl - INFO - Epoch [102][100/3746] lr: 2.407e-02, eta: 1 day, 17:25:29, time: 2.280, data_time: 1.306, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6495, loss_cls: 3.4392, loss: 3.4392 +2024-07-25 20:12:15,963 - pyskl - INFO - Epoch [102][200/3746] lr: 2.405e-02, eta: 1 day, 17:24:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6591, loss_cls: 3.3891, loss: 3.3891 +2024-07-25 20:13:37,737 - pyskl - INFO - Epoch [102][300/3746] lr: 2.403e-02, eta: 1 day, 17:22:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6472, loss_cls: 3.4341, loss: 3.4341 +2024-07-25 20:14:59,578 - pyskl - INFO - Epoch [102][400/3746] lr: 2.400e-02, eta: 1 day, 17:21:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6531, loss_cls: 3.4473, loss: 3.4473 +2024-07-25 20:16:21,855 - pyskl - INFO - Epoch [102][500/3746] lr: 2.398e-02, eta: 1 day, 17:20:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6412, loss_cls: 3.4720, loss: 3.4720 +2024-07-25 20:17:43,400 - pyskl - INFO - Epoch [102][600/3746] lr: 2.396e-02, eta: 1 day, 17:18:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6406, loss_cls: 3.5083, loss: 3.5083 +2024-07-25 20:19:05,246 - pyskl - INFO - Epoch [102][700/3746] lr: 2.393e-02, eta: 1 day, 17:17:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6495, loss_cls: 3.4460, loss: 3.4460 +2024-07-25 20:20:27,244 - pyskl - INFO - Epoch [102][800/3746] lr: 2.391e-02, eta: 1 day, 17:16:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6500, loss_cls: 3.4421, loss: 3.4421 +2024-07-25 20:21:49,084 - pyskl - INFO - Epoch [102][900/3746] lr: 2.388e-02, eta: 1 day, 17:14:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6419, loss_cls: 3.4665, loss: 3.4665 +2024-07-25 20:23:10,694 - pyskl - INFO - Epoch [102][1000/3746] lr: 2.386e-02, eta: 1 day, 17:13:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6419, loss_cls: 3.4729, loss: 3.4729 +2024-07-25 20:24:31,971 - pyskl - INFO - Epoch [102][1100/3746] lr: 2.384e-02, eta: 1 day, 17:11:59, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6377, loss_cls: 3.5018, loss: 3.5018 +2024-07-25 20:25:53,345 - pyskl - INFO - Epoch [102][1200/3746] lr: 2.381e-02, eta: 1 day, 17:10:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3686, top5_acc: 0.6425, loss_cls: 3.5125, loss: 3.5125 +2024-07-25 20:27:15,084 - pyskl - INFO - Epoch [102][1300/3746] lr: 2.379e-02, eta: 1 day, 17:09:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6328, loss_cls: 3.5144, loss: 3.5144 +2024-07-25 20:28:36,933 - pyskl - INFO - Epoch [102][1400/3746] lr: 2.376e-02, eta: 1 day, 17:07:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6384, loss_cls: 3.5001, loss: 3.5001 +2024-07-25 20:29:57,942 - pyskl - INFO - Epoch [102][1500/3746] lr: 2.374e-02, eta: 1 day, 17:06:34, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6414, loss_cls: 3.4920, loss: 3.4920 +2024-07-25 20:31:19,551 - pyskl - INFO - Epoch [102][1600/3746] lr: 2.372e-02, eta: 1 day, 17:05:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6320, loss_cls: 3.5330, loss: 3.5330 +2024-07-25 20:32:41,251 - pyskl - INFO - Epoch [102][1700/3746] lr: 2.369e-02, eta: 1 day, 17:03:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6283, loss_cls: 3.5381, loss: 3.5381 +2024-07-25 20:34:04,187 - pyskl - INFO - Epoch [102][1800/3746] lr: 2.367e-02, eta: 1 day, 17:02:31, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6350, loss_cls: 3.5072, loss: 3.5072 +2024-07-25 20:35:26,135 - pyskl - INFO - Epoch [102][1900/3746] lr: 2.365e-02, eta: 1 day, 17:01:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6362, loss_cls: 3.5188, loss: 3.5188 +2024-07-25 20:36:49,038 - pyskl - INFO - Epoch [102][2000/3746] lr: 2.362e-02, eta: 1 day, 16:59:50, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6361, loss_cls: 3.4912, loss: 3.4912 +2024-07-25 20:38:10,984 - pyskl - INFO - Epoch [102][2100/3746] lr: 2.360e-02, eta: 1 day, 16:58:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6395, loss_cls: 3.4917, loss: 3.4917 +2024-07-25 20:39:32,700 - pyskl - INFO - Epoch [102][2200/3746] lr: 2.357e-02, eta: 1 day, 16:57:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6322, loss_cls: 3.4986, loss: 3.4986 +2024-07-25 20:40:53,957 - pyskl - INFO - Epoch [102][2300/3746] lr: 2.355e-02, eta: 1 day, 16:55:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6366, loss_cls: 3.5237, loss: 3.5237 +2024-07-25 20:42:16,306 - pyskl - INFO - Epoch [102][2400/3746] lr: 2.353e-02, eta: 1 day, 16:54:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6398, loss_cls: 3.4824, loss: 3.4824 +2024-07-25 20:43:37,885 - pyskl - INFO - Epoch [102][2500/3746] lr: 2.350e-02, eta: 1 day, 16:53:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6378, loss_cls: 3.5203, loss: 3.5203 +2024-07-25 20:44:59,495 - pyskl - INFO - Epoch [102][2600/3746] lr: 2.348e-02, eta: 1 day, 16:51:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6423, loss_cls: 3.5048, loss: 3.5048 +2024-07-25 20:46:21,129 - pyskl - INFO - Epoch [102][2700/3746] lr: 2.346e-02, eta: 1 day, 16:50:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6281, loss_cls: 3.5645, loss: 3.5645 +2024-07-25 20:47:42,707 - pyskl - INFO - Epoch [102][2800/3746] lr: 2.343e-02, eta: 1 day, 16:49:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6316, loss_cls: 3.5399, loss: 3.5399 +2024-07-25 20:49:04,472 - pyskl - INFO - Epoch [102][2900/3746] lr: 2.341e-02, eta: 1 day, 16:47:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6358, loss_cls: 3.5472, loss: 3.5472 +2024-07-25 20:50:25,792 - pyskl - INFO - Epoch [102][3000/3746] lr: 2.339e-02, eta: 1 day, 16:46:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6411, loss_cls: 3.5121, loss: 3.5121 +2024-07-25 20:51:48,007 - pyskl - INFO - Epoch [102][3100/3746] lr: 2.336e-02, eta: 1 day, 16:44:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6394, loss_cls: 3.5228, loss: 3.5228 +2024-07-25 20:53:09,111 - pyskl - INFO - Epoch [102][3200/3746] lr: 2.334e-02, eta: 1 day, 16:43:36, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6373, loss_cls: 3.4986, loss: 3.4986 +2024-07-25 20:54:30,987 - pyskl - INFO - Epoch [102][3300/3746] lr: 2.331e-02, eta: 1 day, 16:42:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6284, loss_cls: 3.5743, loss: 3.5743 +2024-07-25 20:55:52,242 - pyskl - INFO - Epoch [102][3400/3746] lr: 2.329e-02, eta: 1 day, 16:40:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6284, loss_cls: 3.5800, loss: 3.5800 +2024-07-25 20:57:13,404 - pyskl - INFO - Epoch [102][3500/3746] lr: 2.327e-02, eta: 1 day, 16:39:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3844, top5_acc: 0.6506, loss_cls: 3.4704, loss: 3.4704 +2024-07-25 20:58:35,179 - pyskl - INFO - Epoch [102][3600/3746] lr: 2.324e-02, eta: 1 day, 16:38:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6350, loss_cls: 3.5237, loss: 3.5237 +2024-07-25 20:59:56,377 - pyskl - INFO - Epoch [102][3700/3746] lr: 2.322e-02, eta: 1 day, 16:36:50, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6403, loss_cls: 3.5092, loss: 3.5092 +2024-07-25 21:00:35,981 - pyskl - INFO - Saving checkpoint at 102 epochs +2024-07-25 21:02:27,987 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 21:02:28,651 - pyskl - INFO - +top1_acc 0.3197 +top5_acc 0.5683 +2024-07-25 21:02:28,651 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 21:02:28,692 - pyskl - INFO - +mean_acc 0.3195 +2024-07-25 21:02:28,705 - pyskl - INFO - Epoch(val) [102][309] top1_acc: 0.3197, top5_acc: 0.5683, mean_class_accuracy: 0.3195 +2024-07-25 21:06:17,353 - pyskl - INFO - Epoch [103][100/3746] lr: 2.319e-02, eta: 1 day, 16:35:43, time: 2.286, data_time: 1.306, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6616, loss_cls: 3.3675, loss: 3.3675 +2024-07-25 21:07:38,938 - pyskl - INFO - Epoch [103][200/3746] lr: 2.316e-02, eta: 1 day, 16:34:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6406, loss_cls: 3.4607, loss: 3.4607 +2024-07-25 21:09:00,571 - pyskl - INFO - Epoch [103][300/3746] lr: 2.314e-02, eta: 1 day, 16:33:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6548, loss_cls: 3.4284, loss: 3.4284 +2024-07-25 21:10:22,317 - pyskl - INFO - Epoch [103][400/3746] lr: 2.311e-02, eta: 1 day, 16:31:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6441, loss_cls: 3.4524, loss: 3.4524 +2024-07-25 21:11:43,683 - pyskl - INFO - Epoch [103][500/3746] lr: 2.309e-02, eta: 1 day, 16:30:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6484, loss_cls: 3.4233, loss: 3.4233 +2024-07-25 21:13:05,777 - pyskl - INFO - Epoch [103][600/3746] lr: 2.307e-02, eta: 1 day, 16:28:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6412, loss_cls: 3.4557, loss: 3.4557 +2024-07-25 21:14:27,246 - pyskl - INFO - Epoch [103][700/3746] lr: 2.304e-02, eta: 1 day, 16:27:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6430, loss_cls: 3.4744, loss: 3.4744 +2024-07-25 21:15:48,572 - pyskl - INFO - Epoch [103][800/3746] lr: 2.302e-02, eta: 1 day, 16:26:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6497, loss_cls: 3.4534, loss: 3.4534 +2024-07-25 21:17:10,055 - pyskl - INFO - Epoch [103][900/3746] lr: 2.300e-02, eta: 1 day, 16:24:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6495, loss_cls: 3.4350, loss: 3.4350 +2024-07-25 21:18:32,108 - pyskl - INFO - Epoch [103][1000/3746] lr: 2.297e-02, eta: 1 day, 16:23:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6431, loss_cls: 3.5002, loss: 3.5002 +2024-07-25 21:19:53,321 - pyskl - INFO - Epoch [103][1100/3746] lr: 2.295e-02, eta: 1 day, 16:22:11, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6441, loss_cls: 3.4863, loss: 3.4863 +2024-07-25 21:21:15,611 - pyskl - INFO - Epoch [103][1200/3746] lr: 2.293e-02, eta: 1 day, 16:20:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6508, loss_cls: 3.4450, loss: 3.4450 +2024-07-25 21:22:37,279 - pyskl - INFO - Epoch [103][1300/3746] lr: 2.290e-02, eta: 1 day, 16:19:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6391, loss_cls: 3.5086, loss: 3.5086 +2024-07-25 21:23:58,446 - pyskl - INFO - Epoch [103][1400/3746] lr: 2.288e-02, eta: 1 day, 16:18:08, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6508, loss_cls: 3.4585, loss: 3.4585 +2024-07-25 21:25:19,759 - pyskl - INFO - Epoch [103][1500/3746] lr: 2.286e-02, eta: 1 day, 16:16:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3819, top5_acc: 0.6420, loss_cls: 3.4614, loss: 3.4614 +2024-07-25 21:26:40,957 - pyskl - INFO - Epoch [103][1600/3746] lr: 2.283e-02, eta: 1 day, 16:15:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6377, loss_cls: 3.4896, loss: 3.4896 +2024-07-25 21:28:02,539 - pyskl - INFO - Epoch [103][1700/3746] lr: 2.281e-02, eta: 1 day, 16:14:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6397, loss_cls: 3.4970, loss: 3.4970 +2024-07-25 21:29:24,463 - pyskl - INFO - Epoch [103][1800/3746] lr: 2.279e-02, eta: 1 day, 16:12:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6366, loss_cls: 3.5041, loss: 3.5041 +2024-07-25 21:30:47,729 - pyskl - INFO - Epoch [103][1900/3746] lr: 2.276e-02, eta: 1 day, 16:11:22, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6455, loss_cls: 3.5007, loss: 3.5007 +2024-07-25 21:32:09,140 - pyskl - INFO - Epoch [103][2000/3746] lr: 2.274e-02, eta: 1 day, 16:10:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6425, loss_cls: 3.4753, loss: 3.4753 +2024-07-25 21:33:30,678 - pyskl - INFO - Epoch [103][2100/3746] lr: 2.272e-02, eta: 1 day, 16:08:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6381, loss_cls: 3.4999, loss: 3.4999 +2024-07-25 21:34:52,610 - pyskl - INFO - Epoch [103][2200/3746] lr: 2.269e-02, eta: 1 day, 16:07:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3822, top5_acc: 0.6395, loss_cls: 3.4957, loss: 3.4957 +2024-07-25 21:36:13,923 - pyskl - INFO - Epoch [103][2300/3746] lr: 2.267e-02, eta: 1 day, 16:05:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6328, loss_cls: 3.5122, loss: 3.5122 +2024-07-25 21:37:35,581 - pyskl - INFO - Epoch [103][2400/3746] lr: 2.264e-02, eta: 1 day, 16:04:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3748, top5_acc: 0.6388, loss_cls: 3.5053, loss: 3.5053 +2024-07-25 21:38:57,039 - pyskl - INFO - Epoch [103][2500/3746] lr: 2.262e-02, eta: 1 day, 16:03:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3748, top5_acc: 0.6347, loss_cls: 3.5230, loss: 3.5230 +2024-07-25 21:40:18,184 - pyskl - INFO - Epoch [103][2600/3746] lr: 2.260e-02, eta: 1 day, 16:01:54, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6445, loss_cls: 3.4766, loss: 3.4766 +2024-07-25 21:41:39,390 - pyskl - INFO - Epoch [103][2700/3746] lr: 2.257e-02, eta: 1 day, 16:00:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6359, loss_cls: 3.4977, loss: 3.4977 +2024-07-25 21:43:01,391 - pyskl - INFO - Epoch [103][2800/3746] lr: 2.255e-02, eta: 1 day, 15:59:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6370, loss_cls: 3.5084, loss: 3.5084 +2024-07-25 21:44:22,867 - pyskl - INFO - Epoch [103][2900/3746] lr: 2.253e-02, eta: 1 day, 15:57:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6384, loss_cls: 3.4968, loss: 3.4968 +2024-07-25 21:45:43,937 - pyskl - INFO - Epoch [103][3000/3746] lr: 2.250e-02, eta: 1 day, 15:56:29, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6384, loss_cls: 3.5317, loss: 3.5317 +2024-07-25 21:47:05,314 - pyskl - INFO - Epoch [103][3100/3746] lr: 2.248e-02, eta: 1 day, 15:55:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6375, loss_cls: 3.4958, loss: 3.4958 +2024-07-25 21:48:27,002 - pyskl - INFO - Epoch [103][3200/3746] lr: 2.246e-02, eta: 1 day, 15:53:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6420, loss_cls: 3.4861, loss: 3.4861 +2024-07-25 21:49:48,562 - pyskl - INFO - Epoch [103][3300/3746] lr: 2.243e-02, eta: 1 day, 15:52:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6406, loss_cls: 3.4914, loss: 3.4914 +2024-07-25 21:51:10,069 - pyskl - INFO - Epoch [103][3400/3746] lr: 2.241e-02, eta: 1 day, 15:51:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6441, loss_cls: 3.4546, loss: 3.4546 +2024-07-25 21:52:31,786 - pyskl - INFO - Epoch [103][3500/3746] lr: 2.239e-02, eta: 1 day, 15:49:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6381, loss_cls: 3.5060, loss: 3.5060 +2024-07-25 21:53:53,444 - pyskl - INFO - Epoch [103][3600/3746] lr: 2.236e-02, eta: 1 day, 15:48:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3723, top5_acc: 0.6314, loss_cls: 3.5390, loss: 3.5390 +2024-07-25 21:55:15,029 - pyskl - INFO - Epoch [103][3700/3746] lr: 2.234e-02, eta: 1 day, 15:47:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6398, loss_cls: 3.4752, loss: 3.4752 +2024-07-25 21:55:54,935 - pyskl - INFO - Saving checkpoint at 103 epochs +2024-07-25 21:57:46,041 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 21:57:46,826 - pyskl - INFO - +top1_acc 0.3202 +top5_acc 0.5750 +2024-07-25 21:57:46,826 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 21:57:46,870 - pyskl - INFO - +mean_acc 0.3198 +2024-07-25 21:57:46,881 - pyskl - INFO - Epoch(val) [103][309] top1_acc: 0.3202, top5_acc: 0.5750, mean_class_accuracy: 0.3198 +2024-07-25 22:01:35,486 - pyskl - INFO - Epoch [104][100/3746] lr: 2.231e-02, eta: 1 day, 15:45:51, time: 2.286, data_time: 1.294, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6523, loss_cls: 3.3947, loss: 3.3947 +2024-07-25 22:02:57,186 - pyskl - INFO - Epoch [104][200/3746] lr: 2.228e-02, eta: 1 day, 15:44:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6491, loss_cls: 3.4198, loss: 3.4198 +2024-07-25 22:04:19,631 - pyskl - INFO - Epoch [104][300/3746] lr: 2.226e-02, eta: 1 day, 15:43:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6473, loss_cls: 3.4779, loss: 3.4779 +2024-07-25 22:05:41,733 - pyskl - INFO - Epoch [104][400/3746] lr: 2.224e-02, eta: 1 day, 15:41:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6373, loss_cls: 3.4820, loss: 3.4820 +2024-07-25 22:07:04,137 - pyskl - INFO - Epoch [104][500/3746] lr: 2.221e-02, eta: 1 day, 15:40:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6459, loss_cls: 3.4525, loss: 3.4525 +2024-07-25 22:08:25,676 - pyskl - INFO - Epoch [104][600/3746] lr: 2.219e-02, eta: 1 day, 15:39:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6423, loss_cls: 3.4734, loss: 3.4734 +2024-07-25 22:09:47,458 - pyskl - INFO - Epoch [104][700/3746] lr: 2.217e-02, eta: 1 day, 15:37:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6417, loss_cls: 3.4713, loss: 3.4713 +2024-07-25 22:11:08,858 - pyskl - INFO - Epoch [104][800/3746] lr: 2.214e-02, eta: 1 day, 15:36:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6514, loss_cls: 3.4484, loss: 3.4484 +2024-07-25 22:12:30,057 - pyskl - INFO - Epoch [104][900/3746] lr: 2.212e-02, eta: 1 day, 15:35:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6462, loss_cls: 3.4264, loss: 3.4264 +2024-07-25 22:13:51,320 - pyskl - INFO - Epoch [104][1000/3746] lr: 2.210e-02, eta: 1 day, 15:33:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3923, top5_acc: 0.6544, loss_cls: 3.4118, loss: 3.4118 +2024-07-25 22:15:12,961 - pyskl - INFO - Epoch [104][1100/3746] lr: 2.208e-02, eta: 1 day, 15:32:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6517, loss_cls: 3.4222, loss: 3.4222 +2024-07-25 22:16:34,645 - pyskl - INFO - Epoch [104][1200/3746] lr: 2.205e-02, eta: 1 day, 15:30:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6509, loss_cls: 3.4398, loss: 3.4398 +2024-07-25 22:17:55,672 - pyskl - INFO - Epoch [104][1300/3746] lr: 2.203e-02, eta: 1 day, 15:29:37, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3989, top5_acc: 0.6556, loss_cls: 3.3980, loss: 3.3980 +2024-07-25 22:19:16,961 - pyskl - INFO - Epoch [104][1400/3746] lr: 2.201e-02, eta: 1 day, 15:28:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6483, loss_cls: 3.4494, loss: 3.4494 +2024-07-25 22:20:38,161 - pyskl - INFO - Epoch [104][1500/3746] lr: 2.198e-02, eta: 1 day, 15:26:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6497, loss_cls: 3.4828, loss: 3.4828 +2024-07-25 22:22:00,021 - pyskl - INFO - Epoch [104][1600/3746] lr: 2.196e-02, eta: 1 day, 15:25:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6486, loss_cls: 3.4915, loss: 3.4915 +2024-07-25 22:23:21,563 - pyskl - INFO - Epoch [104][1700/3746] lr: 2.194e-02, eta: 1 day, 15:24:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3848, top5_acc: 0.6439, loss_cls: 3.4656, loss: 3.4656 +2024-07-25 22:24:43,767 - pyskl - INFO - Epoch [104][1800/3746] lr: 2.191e-02, eta: 1 day, 15:22:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6366, loss_cls: 3.5206, loss: 3.5206 +2024-07-25 22:26:06,484 - pyskl - INFO - Epoch [104][1900/3746] lr: 2.189e-02, eta: 1 day, 15:21:30, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6409, loss_cls: 3.5113, loss: 3.5113 +2024-07-25 22:27:28,714 - pyskl - INFO - Epoch [104][2000/3746] lr: 2.187e-02, eta: 1 day, 15:20:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6570, loss_cls: 3.4142, loss: 3.4142 +2024-07-25 22:28:50,671 - pyskl - INFO - Epoch [104][2100/3746] lr: 2.184e-02, eta: 1 day, 15:18:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3933, top5_acc: 0.6491, loss_cls: 3.4073, loss: 3.4073 +2024-07-25 22:30:12,193 - pyskl - INFO - Epoch [104][2200/3746] lr: 2.182e-02, eta: 1 day, 15:17:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6455, loss_cls: 3.4673, loss: 3.4673 +2024-07-25 22:31:33,560 - pyskl - INFO - Epoch [104][2300/3746] lr: 2.180e-02, eta: 1 day, 15:16:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6483, loss_cls: 3.4469, loss: 3.4469 +2024-07-25 22:32:54,805 - pyskl - INFO - Epoch [104][2400/3746] lr: 2.177e-02, eta: 1 day, 15:14:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6483, loss_cls: 3.5049, loss: 3.5049 +2024-07-25 22:34:16,250 - pyskl - INFO - Epoch [104][2500/3746] lr: 2.175e-02, eta: 1 day, 15:13:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6419, loss_cls: 3.4875, loss: 3.4875 +2024-07-25 22:35:37,923 - pyskl - INFO - Epoch [104][2600/3746] lr: 2.173e-02, eta: 1 day, 15:12:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6406, loss_cls: 3.4820, loss: 3.4820 +2024-07-25 22:36:59,441 - pyskl - INFO - Epoch [104][2700/3746] lr: 2.171e-02, eta: 1 day, 15:10:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6520, loss_cls: 3.4308, loss: 3.4308 +2024-07-25 22:38:20,739 - pyskl - INFO - Epoch [104][2800/3746] lr: 2.168e-02, eta: 1 day, 15:09:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6462, loss_cls: 3.4854, loss: 3.4854 +2024-07-25 22:39:42,367 - pyskl - INFO - Epoch [104][2900/3746] lr: 2.166e-02, eta: 1 day, 15:07:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6406, loss_cls: 3.4744, loss: 3.4744 +2024-07-25 22:41:04,099 - pyskl - INFO - Epoch [104][3000/3746] lr: 2.164e-02, eta: 1 day, 15:06:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6383, loss_cls: 3.5070, loss: 3.5070 +2024-07-25 22:42:25,269 - pyskl - INFO - Epoch [104][3100/3746] lr: 2.161e-02, eta: 1 day, 15:05:15, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6442, loss_cls: 3.4587, loss: 3.4587 +2024-07-25 22:43:46,242 - pyskl - INFO - Epoch [104][3200/3746] lr: 2.159e-02, eta: 1 day, 15:03:53, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6481, loss_cls: 3.4371, loss: 3.4371 +2024-07-25 22:45:08,003 - pyskl - INFO - Epoch [104][3300/3746] lr: 2.157e-02, eta: 1 day, 15:02:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6328, loss_cls: 3.4957, loss: 3.4957 +2024-07-25 22:46:29,260 - pyskl - INFO - Epoch [104][3400/3746] lr: 2.154e-02, eta: 1 day, 15:01:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6450, loss_cls: 3.4615, loss: 3.4615 +2024-07-25 22:47:50,680 - pyskl - INFO - Epoch [104][3500/3746] lr: 2.152e-02, eta: 1 day, 14:59:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6425, loss_cls: 3.4834, loss: 3.4834 +2024-07-25 22:49:12,529 - pyskl - INFO - Epoch [104][3600/3746] lr: 2.150e-02, eta: 1 day, 14:58:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6438, loss_cls: 3.4935, loss: 3.4935 +2024-07-25 22:50:33,892 - pyskl - INFO - Epoch [104][3700/3746] lr: 2.148e-02, eta: 1 day, 14:57:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6359, loss_cls: 3.5131, loss: 3.5131 +2024-07-25 22:51:13,225 - pyskl - INFO - Saving checkpoint at 104 epochs +2024-07-25 22:53:04,859 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 22:53:05,535 - pyskl - INFO - +top1_acc 0.3273 +top5_acc 0.5863 +2024-07-25 22:53:05,536 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 22:53:05,578 - pyskl - INFO - +mean_acc 0.3270 +2024-07-25 22:53:05,583 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_100.pth was removed +2024-07-25 22:53:05,853 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_104.pth. +2024-07-25 22:53:05,854 - pyskl - INFO - Best top1_acc is 0.3273 at 104 epoch. +2024-07-25 22:53:05,872 - pyskl - INFO - Epoch(val) [104][309] top1_acc: 0.3273, top5_acc: 0.5863, mean_class_accuracy: 0.3270 +2024-07-25 22:56:53,090 - pyskl - INFO - Epoch [105][100/3746] lr: 2.144e-02, eta: 1 day, 14:55:56, time: 2.272, data_time: 1.293, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6534, loss_cls: 3.4301, loss: 3.4301 +2024-07-25 22:58:14,523 - pyskl - INFO - Epoch [105][200/3746] lr: 2.142e-02, eta: 1 day, 14:54:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6595, loss_cls: 3.4037, loss: 3.4037 +2024-07-25 22:59:35,985 - pyskl - INFO - Epoch [105][300/3746] lr: 2.140e-02, eta: 1 day, 14:53:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6500, loss_cls: 3.4235, loss: 3.4235 +2024-07-25 23:00:57,509 - pyskl - INFO - Epoch [105][400/3746] lr: 2.137e-02, eta: 1 day, 14:51:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4033, top5_acc: 0.6614, loss_cls: 3.3759, loss: 3.3759 +2024-07-25 23:02:19,519 - pyskl - INFO - Epoch [105][500/3746] lr: 2.135e-02, eta: 1 day, 14:50:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6562, loss_cls: 3.4206, loss: 3.4206 +2024-07-25 23:03:41,488 - pyskl - INFO - Epoch [105][600/3746] lr: 2.133e-02, eta: 1 day, 14:49:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6534, loss_cls: 3.4190, loss: 3.4190 +2024-07-25 23:05:03,055 - pyskl - INFO - Epoch [105][700/3746] lr: 2.130e-02, eta: 1 day, 14:47:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6486, loss_cls: 3.4222, loss: 3.4222 +2024-07-25 23:06:24,199 - pyskl - INFO - Epoch [105][800/3746] lr: 2.128e-02, eta: 1 day, 14:46:27, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6367, loss_cls: 3.4956, loss: 3.4956 +2024-07-25 23:07:45,583 - pyskl - INFO - Epoch [105][900/3746] lr: 2.126e-02, eta: 1 day, 14:45:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3941, top5_acc: 0.6608, loss_cls: 3.4103, loss: 3.4103 +2024-07-25 23:09:07,159 - pyskl - INFO - Epoch [105][1000/3746] lr: 2.124e-02, eta: 1 day, 14:43:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6430, loss_cls: 3.4569, loss: 3.4569 +2024-07-25 23:10:28,050 - pyskl - INFO - Epoch [105][1100/3746] lr: 2.121e-02, eta: 1 day, 14:42:23, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4072, top5_acc: 0.6517, loss_cls: 3.3951, loss: 3.3951 +2024-07-25 23:11:49,447 - pyskl - INFO - Epoch [105][1200/3746] lr: 2.119e-02, eta: 1 day, 14:41:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6411, loss_cls: 3.4372, loss: 3.4372 +2024-07-25 23:13:11,037 - pyskl - INFO - Epoch [105][1300/3746] lr: 2.117e-02, eta: 1 day, 14:39:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6467, loss_cls: 3.4703, loss: 3.4703 +2024-07-25 23:14:32,202 - pyskl - INFO - Epoch [105][1400/3746] lr: 2.114e-02, eta: 1 day, 14:38:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6608, loss_cls: 3.3876, loss: 3.3876 +2024-07-25 23:15:53,459 - pyskl - INFO - Epoch [105][1500/3746] lr: 2.112e-02, eta: 1 day, 14:36:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3848, top5_acc: 0.6423, loss_cls: 3.4725, loss: 3.4725 +2024-07-25 23:17:14,461 - pyskl - INFO - Epoch [105][1600/3746] lr: 2.110e-02, eta: 1 day, 14:35:36, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6497, loss_cls: 3.4468, loss: 3.4468 +2024-07-25 23:18:36,321 - pyskl - INFO - Epoch [105][1700/3746] lr: 2.108e-02, eta: 1 day, 14:34:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3903, top5_acc: 0.6450, loss_cls: 3.4628, loss: 3.4628 +2024-07-25 23:19:57,543 - pyskl - INFO - Epoch [105][1800/3746] lr: 2.105e-02, eta: 1 day, 14:32:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6431, loss_cls: 3.4321, loss: 3.4321 +2024-07-25 23:21:19,718 - pyskl - INFO - Epoch [105][1900/3746] lr: 2.103e-02, eta: 1 day, 14:31:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6442, loss_cls: 3.4845, loss: 3.4845 +2024-07-25 23:22:42,360 - pyskl - INFO - Epoch [105][2000/3746] lr: 2.101e-02, eta: 1 day, 14:30:11, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3927, top5_acc: 0.6547, loss_cls: 3.4474, loss: 3.4474 +2024-07-25 23:24:04,462 - pyskl - INFO - Epoch [105][2100/3746] lr: 2.098e-02, eta: 1 day, 14:28:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6488, loss_cls: 3.4504, loss: 3.4504 +2024-07-25 23:25:26,651 - pyskl - INFO - Epoch [105][2200/3746] lr: 2.096e-02, eta: 1 day, 14:27:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3945, top5_acc: 0.6467, loss_cls: 3.4565, loss: 3.4565 +2024-07-25 23:26:48,228 - pyskl - INFO - Epoch [105][2300/3746] lr: 2.094e-02, eta: 1 day, 14:26:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3831, top5_acc: 0.6495, loss_cls: 3.4723, loss: 3.4723 +2024-07-25 23:28:09,519 - pyskl - INFO - Epoch [105][2400/3746] lr: 2.092e-02, eta: 1 day, 14:24:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6452, loss_cls: 3.4638, loss: 3.4638 +2024-07-25 23:29:30,812 - pyskl - INFO - Epoch [105][2500/3746] lr: 2.089e-02, eta: 1 day, 14:23:25, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6506, loss_cls: 3.4257, loss: 3.4257 +2024-07-25 23:30:52,320 - pyskl - INFO - Epoch [105][2600/3746] lr: 2.087e-02, eta: 1 day, 14:22:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6519, loss_cls: 3.4407, loss: 3.4407 +2024-07-25 23:32:13,532 - pyskl - INFO - Epoch [105][2700/3746] lr: 2.085e-02, eta: 1 day, 14:20:42, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6461, loss_cls: 3.4340, loss: 3.4340 +2024-07-25 23:33:35,099 - pyskl - INFO - Epoch [105][2800/3746] lr: 2.083e-02, eta: 1 day, 14:19:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6559, loss_cls: 3.4364, loss: 3.4364 +2024-07-25 23:34:56,843 - pyskl - INFO - Epoch [105][2900/3746] lr: 2.080e-02, eta: 1 day, 14:17:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6519, loss_cls: 3.4426, loss: 3.4426 +2024-07-25 23:36:18,207 - pyskl - INFO - Epoch [105][3000/3746] lr: 2.078e-02, eta: 1 day, 14:16:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6448, loss_cls: 3.4811, loss: 3.4811 +2024-07-25 23:37:39,356 - pyskl - INFO - Epoch [105][3100/3746] lr: 2.076e-02, eta: 1 day, 14:15:17, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6466, loss_cls: 3.4516, loss: 3.4516 +2024-07-25 23:39:01,467 - pyskl - INFO - Epoch [105][3200/3746] lr: 2.073e-02, eta: 1 day, 14:13:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3989, top5_acc: 0.6538, loss_cls: 3.4076, loss: 3.4076 +2024-07-25 23:40:23,256 - pyskl - INFO - Epoch [105][3300/3746] lr: 2.071e-02, eta: 1 day, 14:12:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6555, loss_cls: 3.4371, loss: 3.4371 +2024-07-25 23:41:45,118 - pyskl - INFO - Epoch [105][3400/3746] lr: 2.069e-02, eta: 1 day, 14:11:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6430, loss_cls: 3.4744, loss: 3.4744 +2024-07-25 23:43:06,733 - pyskl - INFO - Epoch [105][3500/3746] lr: 2.067e-02, eta: 1 day, 14:09:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6430, loss_cls: 3.4887, loss: 3.4887 +2024-07-25 23:44:28,223 - pyskl - INFO - Epoch [105][3600/3746] lr: 2.064e-02, eta: 1 day, 14:08:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6430, loss_cls: 3.4449, loss: 3.4449 +2024-07-25 23:45:49,841 - pyskl - INFO - Epoch [105][3700/3746] lr: 2.062e-02, eta: 1 day, 14:07:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6466, loss_cls: 3.4440, loss: 3.4440 +2024-07-25 23:46:29,490 - pyskl - INFO - Saving checkpoint at 105 epochs +2024-07-25 23:48:22,085 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 23:48:22,782 - pyskl - INFO - +top1_acc 0.3219 +top5_acc 0.5811 +2024-07-25 23:48:22,782 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 23:48:22,831 - pyskl - INFO - +mean_acc 0.3217 +2024-07-25 23:48:22,847 - pyskl - INFO - Epoch(val) [105][309] top1_acc: 0.3219, top5_acc: 0.5811, mean_class_accuracy: 0.3217 +2024-07-25 23:52:22,173 - pyskl - INFO - Epoch [106][100/3746] lr: 2.059e-02, eta: 1 day, 14:06:02, time: 2.393, data_time: 1.405, memory: 15990, top1_acc: 0.4042, top5_acc: 0.6700, loss_cls: 3.3449, loss: 3.3449 +2024-07-25 23:53:44,162 - pyskl - INFO - Epoch [106][200/3746] lr: 2.057e-02, eta: 1 day, 14:04:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6670, loss_cls: 3.3483, loss: 3.3483 +2024-07-25 23:55:05,872 - pyskl - INFO - Epoch [106][300/3746] lr: 2.054e-02, eta: 1 day, 14:03:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6520, loss_cls: 3.4026, loss: 3.4026 +2024-07-25 23:56:27,973 - pyskl - INFO - Epoch [106][400/3746] lr: 2.052e-02, eta: 1 day, 14:01:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4059, top5_acc: 0.6637, loss_cls: 3.3700, loss: 3.3700 +2024-07-25 23:57:49,556 - pyskl - INFO - Epoch [106][500/3746] lr: 2.050e-02, eta: 1 day, 14:00:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6555, loss_cls: 3.3982, loss: 3.3982 +2024-07-25 23:59:11,524 - pyskl - INFO - Epoch [106][600/3746] lr: 2.048e-02, eta: 1 day, 13:59:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6534, loss_cls: 3.4145, loss: 3.4145 +2024-07-26 00:00:33,072 - pyskl - INFO - Epoch [106][700/3746] lr: 2.045e-02, eta: 1 day, 13:57:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3992, top5_acc: 0.6520, loss_cls: 3.4073, loss: 3.4073 +2024-07-26 00:01:54,919 - pyskl - INFO - Epoch [106][800/3746] lr: 2.043e-02, eta: 1 day, 13:56:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6423, loss_cls: 3.4523, loss: 3.4523 +2024-07-26 00:03:16,667 - pyskl - INFO - Epoch [106][900/3746] lr: 2.041e-02, eta: 1 day, 13:55:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6538, loss_cls: 3.4175, loss: 3.4175 +2024-07-26 00:04:38,157 - pyskl - INFO - Epoch [106][1000/3746] lr: 2.039e-02, eta: 1 day, 13:53:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6594, loss_cls: 3.4126, loss: 3.4126 +2024-07-26 00:05:59,983 - pyskl - INFO - Epoch [106][1100/3746] lr: 2.036e-02, eta: 1 day, 13:52:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6533, loss_cls: 3.4064, loss: 3.4064 +2024-07-26 00:07:21,711 - pyskl - INFO - Epoch [106][1200/3746] lr: 2.034e-02, eta: 1 day, 13:51:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3970, top5_acc: 0.6572, loss_cls: 3.3706, loss: 3.3706 +2024-07-26 00:08:43,271 - pyskl - INFO - Epoch [106][1300/3746] lr: 2.032e-02, eta: 1 day, 13:49:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6531, loss_cls: 3.4553, loss: 3.4553 +2024-07-26 00:10:04,930 - pyskl - INFO - Epoch [106][1400/3746] lr: 2.030e-02, eta: 1 day, 13:48:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6383, loss_cls: 3.4758, loss: 3.4758 +2024-07-26 00:11:27,346 - pyskl - INFO - Epoch [106][1500/3746] lr: 2.027e-02, eta: 1 day, 13:47:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6439, loss_cls: 3.4602, loss: 3.4602 +2024-07-26 00:12:48,780 - pyskl - INFO - Epoch [106][1600/3746] lr: 2.025e-02, eta: 1 day, 13:45:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3914, top5_acc: 0.6509, loss_cls: 3.4452, loss: 3.4452 +2024-07-26 00:14:10,517 - pyskl - INFO - Epoch [106][1700/3746] lr: 2.023e-02, eta: 1 day, 13:44:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6642, loss_cls: 3.3645, loss: 3.3645 +2024-07-26 00:15:32,416 - pyskl - INFO - Epoch [106][1800/3746] lr: 2.021e-02, eta: 1 day, 13:43:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6503, loss_cls: 3.3934, loss: 3.3934 +2024-07-26 00:16:55,083 - pyskl - INFO - Epoch [106][1900/3746] lr: 2.018e-02, eta: 1 day, 13:41:40, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6492, loss_cls: 3.4159, loss: 3.4159 +2024-07-26 00:18:17,553 - pyskl - INFO - Epoch [106][2000/3746] lr: 2.016e-02, eta: 1 day, 13:40:19, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6416, loss_cls: 3.5052, loss: 3.5052 +2024-07-26 00:19:40,239 - pyskl - INFO - Epoch [106][2100/3746] lr: 2.014e-02, eta: 1 day, 13:38:58, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6477, loss_cls: 3.4531, loss: 3.4531 +2024-07-26 00:21:02,332 - pyskl - INFO - Epoch [106][2200/3746] lr: 2.012e-02, eta: 1 day, 13:37:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6509, loss_cls: 3.4022, loss: 3.4022 +2024-07-26 00:22:24,015 - pyskl - INFO - Epoch [106][2300/3746] lr: 2.009e-02, eta: 1 day, 13:36:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6517, loss_cls: 3.4019, loss: 3.4019 +2024-07-26 00:23:45,314 - pyskl - INFO - Epoch [106][2400/3746] lr: 2.007e-02, eta: 1 day, 13:34:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6531, loss_cls: 3.4142, loss: 3.4142 +2024-07-26 00:25:06,394 - pyskl - INFO - Epoch [106][2500/3746] lr: 2.005e-02, eta: 1 day, 13:33:32, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6575, loss_cls: 3.3991, loss: 3.3991 +2024-07-26 00:26:27,778 - pyskl - INFO - Epoch [106][2600/3746] lr: 2.003e-02, eta: 1 day, 13:32:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6473, loss_cls: 3.4615, loss: 3.4615 +2024-07-26 00:27:49,141 - pyskl - INFO - Epoch [106][2700/3746] lr: 2.000e-02, eta: 1 day, 13:30:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3914, top5_acc: 0.6587, loss_cls: 3.4057, loss: 3.4057 +2024-07-26 00:29:10,582 - pyskl - INFO - Epoch [106][2800/3746] lr: 1.998e-02, eta: 1 day, 13:29:28, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6550, loss_cls: 3.4220, loss: 3.4220 +2024-07-26 00:30:32,377 - pyskl - INFO - Epoch [106][2900/3746] lr: 1.996e-02, eta: 1 day, 13:28:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6470, loss_cls: 3.4548, loss: 3.4548 +2024-07-26 00:31:54,033 - pyskl - INFO - Epoch [106][3000/3746] lr: 1.994e-02, eta: 1 day, 13:26:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3831, top5_acc: 0.6530, loss_cls: 3.4631, loss: 3.4631 +2024-07-26 00:33:15,783 - pyskl - INFO - Epoch [106][3100/3746] lr: 1.991e-02, eta: 1 day, 13:25:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6558, loss_cls: 3.4042, loss: 3.4042 +2024-07-26 00:34:37,547 - pyskl - INFO - Epoch [106][3200/3746] lr: 1.989e-02, eta: 1 day, 13:24:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6434, loss_cls: 3.4653, loss: 3.4653 +2024-07-26 00:35:59,159 - pyskl - INFO - Epoch [106][3300/3746] lr: 1.987e-02, eta: 1 day, 13:22:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6564, loss_cls: 3.4230, loss: 3.4230 +2024-07-26 00:37:20,631 - pyskl - INFO - Epoch [106][3400/3746] lr: 1.985e-02, eta: 1 day, 13:21:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6470, loss_cls: 3.4381, loss: 3.4381 +2024-07-26 00:38:42,289 - pyskl - INFO - Epoch [106][3500/3746] lr: 1.983e-02, eta: 1 day, 13:19:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3908, top5_acc: 0.6484, loss_cls: 3.4371, loss: 3.4371 +2024-07-26 00:40:04,393 - pyskl - INFO - Epoch [106][3600/3746] lr: 1.980e-02, eta: 1 day, 13:18:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6466, loss_cls: 3.4559, loss: 3.4559 +2024-07-26 00:41:25,646 - pyskl - INFO - Epoch [106][3700/3746] lr: 1.978e-02, eta: 1 day, 13:17:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6492, loss_cls: 3.4399, loss: 3.4399 +2024-07-26 00:42:04,973 - pyskl - INFO - Saving checkpoint at 106 epochs +2024-07-26 00:43:58,443 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 00:43:59,188 - pyskl - INFO - +top1_acc 0.3328 +top5_acc 0.5907 +2024-07-26 00:43:59,188 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 00:43:59,236 - pyskl - INFO - +mean_acc 0.3325 +2024-07-26 00:43:59,241 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_104.pth was removed +2024-07-26 00:43:59,505 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_106.pth. +2024-07-26 00:43:59,506 - pyskl - INFO - Best top1_acc is 0.3328 at 106 epoch. +2024-07-26 00:43:59,524 - pyskl - INFO - Epoch(val) [106][309] top1_acc: 0.3328, top5_acc: 0.5907, mean_class_accuracy: 0.3325 +2024-07-26 00:47:56,228 - pyskl - INFO - Epoch [107][100/3746] lr: 1.975e-02, eta: 1 day, 13:16:06, time: 2.367, data_time: 1.381, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6723, loss_cls: 3.3250, loss: 3.3250 +2024-07-26 00:49:18,598 - pyskl - INFO - Epoch [107][200/3746] lr: 1.973e-02, eta: 1 day, 13:14:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6564, loss_cls: 3.4003, loss: 3.4003 +2024-07-26 00:50:40,729 - pyskl - INFO - Epoch [107][300/3746] lr: 1.970e-02, eta: 1 day, 13:13:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6598, loss_cls: 3.3766, loss: 3.3766 +2024-07-26 00:52:03,205 - pyskl - INFO - Epoch [107][400/3746] lr: 1.968e-02, eta: 1 day, 13:12:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6633, loss_cls: 3.3654, loss: 3.3654 +2024-07-26 00:53:24,873 - pyskl - INFO - Epoch [107][500/3746] lr: 1.966e-02, eta: 1 day, 13:10:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6633, loss_cls: 3.3418, loss: 3.3418 +2024-07-26 00:54:47,066 - pyskl - INFO - Epoch [107][600/3746] lr: 1.964e-02, eta: 1 day, 13:09:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6478, loss_cls: 3.4475, loss: 3.4475 +2024-07-26 00:56:08,520 - pyskl - INFO - Epoch [107][700/3746] lr: 1.961e-02, eta: 1 day, 13:07:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6497, loss_cls: 3.4152, loss: 3.4152 +2024-07-26 00:57:30,211 - pyskl - INFO - Epoch [107][800/3746] lr: 1.959e-02, eta: 1 day, 13:06:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6595, loss_cls: 3.3767, loss: 3.3767 +2024-07-26 00:58:51,783 - pyskl - INFO - Epoch [107][900/3746] lr: 1.957e-02, eta: 1 day, 13:05:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6534, loss_cls: 3.4242, loss: 3.4242 +2024-07-26 01:00:13,218 - pyskl - INFO - Epoch [107][1000/3746] lr: 1.955e-02, eta: 1 day, 13:03:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3992, top5_acc: 0.6566, loss_cls: 3.3796, loss: 3.3796 +2024-07-26 01:01:34,905 - pyskl - INFO - Epoch [107][1100/3746] lr: 1.953e-02, eta: 1 day, 13:02:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6630, loss_cls: 3.3940, loss: 3.3940 +2024-07-26 01:02:56,829 - pyskl - INFO - Epoch [107][1200/3746] lr: 1.950e-02, eta: 1 day, 13:01:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6587, loss_cls: 3.3657, loss: 3.3657 +2024-07-26 01:04:18,945 - pyskl - INFO - Epoch [107][1300/3746] lr: 1.948e-02, eta: 1 day, 12:59:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6497, loss_cls: 3.4469, loss: 3.4469 +2024-07-26 01:05:40,465 - pyskl - INFO - Epoch [107][1400/3746] lr: 1.946e-02, eta: 1 day, 12:58:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6442, loss_cls: 3.4624, loss: 3.4624 +2024-07-26 01:07:02,272 - pyskl - INFO - Epoch [107][1500/3746] lr: 1.944e-02, eta: 1 day, 12:57:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6656, loss_cls: 3.3572, loss: 3.3572 +2024-07-26 01:08:23,549 - pyskl - INFO - Epoch [107][1600/3746] lr: 1.942e-02, eta: 1 day, 12:55:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4072, top5_acc: 0.6645, loss_cls: 3.3609, loss: 3.3609 +2024-07-26 01:09:45,176 - pyskl - INFO - Epoch [107][1700/3746] lr: 1.939e-02, eta: 1 day, 12:54:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6597, loss_cls: 3.3678, loss: 3.3678 +2024-07-26 01:11:06,575 - pyskl - INFO - Epoch [107][1800/3746] lr: 1.937e-02, eta: 1 day, 12:53:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6602, loss_cls: 3.3789, loss: 3.3789 +2024-07-26 01:12:28,792 - pyskl - INFO - Epoch [107][1900/3746] lr: 1.935e-02, eta: 1 day, 12:51:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6459, loss_cls: 3.4968, loss: 3.4968 +2024-07-26 01:13:51,109 - pyskl - INFO - Epoch [107][2000/3746] lr: 1.933e-02, eta: 1 day, 12:50:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6598, loss_cls: 3.3915, loss: 3.3915 +2024-07-26 01:15:13,213 - pyskl - INFO - Epoch [107][2100/3746] lr: 1.930e-02, eta: 1 day, 12:49:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6547, loss_cls: 3.4096, loss: 3.4096 +2024-07-26 01:16:35,228 - pyskl - INFO - Epoch [107][2200/3746] lr: 1.928e-02, eta: 1 day, 12:47:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6584, loss_cls: 3.3671, loss: 3.3671 +2024-07-26 01:17:56,816 - pyskl - INFO - Epoch [107][2300/3746] lr: 1.926e-02, eta: 1 day, 12:46:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6608, loss_cls: 3.3981, loss: 3.3981 +2024-07-26 01:19:18,122 - pyskl - INFO - Epoch [107][2400/3746] lr: 1.924e-02, eta: 1 day, 12:44:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6539, loss_cls: 3.4112, loss: 3.4112 +2024-07-26 01:20:39,464 - pyskl - INFO - Epoch [107][2500/3746] lr: 1.922e-02, eta: 1 day, 12:43:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6538, loss_cls: 3.4420, loss: 3.4420 +2024-07-26 01:22:00,915 - pyskl - INFO - Epoch [107][2600/3746] lr: 1.919e-02, eta: 1 day, 12:42:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6592, loss_cls: 3.3989, loss: 3.3989 +2024-07-26 01:23:22,033 - pyskl - INFO - Epoch [107][2700/3746] lr: 1.917e-02, eta: 1 day, 12:40:52, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6492, loss_cls: 3.4277, loss: 3.4277 +2024-07-26 01:24:43,907 - pyskl - INFO - Epoch [107][2800/3746] lr: 1.915e-02, eta: 1 day, 12:39:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6542, loss_cls: 3.4013, loss: 3.4013 +2024-07-26 01:26:05,231 - pyskl - INFO - Epoch [107][2900/3746] lr: 1.913e-02, eta: 1 day, 12:38:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6472, loss_cls: 3.4740, loss: 3.4740 +2024-07-26 01:27:26,888 - pyskl - INFO - Epoch [107][3000/3746] lr: 1.911e-02, eta: 1 day, 12:36:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6397, loss_cls: 3.4588, loss: 3.4588 +2024-07-26 01:28:48,167 - pyskl - INFO - Epoch [107][3100/3746] lr: 1.908e-02, eta: 1 day, 12:35:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6575, loss_cls: 3.4199, loss: 3.4199 +2024-07-26 01:30:10,164 - pyskl - INFO - Epoch [107][3200/3746] lr: 1.906e-02, eta: 1 day, 12:34:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6519, loss_cls: 3.4352, loss: 3.4352 +2024-07-26 01:31:31,491 - pyskl - INFO - Epoch [107][3300/3746] lr: 1.904e-02, eta: 1 day, 12:32:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6613, loss_cls: 3.3648, loss: 3.3648 +2024-07-26 01:32:52,685 - pyskl - INFO - Epoch [107][3400/3746] lr: 1.902e-02, eta: 1 day, 12:31:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3903, top5_acc: 0.6553, loss_cls: 3.4276, loss: 3.4276 +2024-07-26 01:34:13,979 - pyskl - INFO - Epoch [107][3500/3746] lr: 1.900e-02, eta: 1 day, 12:30:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6539, loss_cls: 3.4330, loss: 3.4330 +2024-07-26 01:35:35,730 - pyskl - INFO - Epoch [107][3600/3746] lr: 1.897e-02, eta: 1 day, 12:28:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6520, loss_cls: 3.4195, loss: 3.4195 +2024-07-26 01:36:56,800 - pyskl - INFO - Epoch [107][3700/3746] lr: 1.895e-02, eta: 1 day, 12:27:17, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6506, loss_cls: 3.4211, loss: 3.4211 +2024-07-26 01:37:35,871 - pyskl - INFO - Saving checkpoint at 107 epochs +2024-07-26 01:39:28,753 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 01:39:29,470 - pyskl - INFO - +top1_acc 0.3320 +top5_acc 0.5938 +2024-07-26 01:39:29,470 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 01:39:29,517 - pyskl - INFO - +mean_acc 0.3317 +2024-07-26 01:39:29,532 - pyskl - INFO - Epoch(val) [107][309] top1_acc: 0.3320, top5_acc: 0.5938, mean_class_accuracy: 0.3317 +2024-07-26 01:43:25,508 - pyskl - INFO - Epoch [108][100/3746] lr: 1.892e-02, eta: 1 day, 12:26:05, time: 2.360, data_time: 1.372, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6766, loss_cls: 3.2775, loss: 3.2775 +2024-07-26 01:44:47,581 - pyskl - INFO - Epoch [108][200/3746] lr: 1.890e-02, eta: 1 day, 12:24:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6722, loss_cls: 3.3093, loss: 3.3093 +2024-07-26 01:46:09,505 - pyskl - INFO - Epoch [108][300/3746] lr: 1.888e-02, eta: 1 day, 12:23:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6611, loss_cls: 3.3516, loss: 3.3516 +2024-07-26 01:47:31,961 - pyskl - INFO - Epoch [108][400/3746] lr: 1.886e-02, eta: 1 day, 12:22:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6559, loss_cls: 3.3888, loss: 3.3888 +2024-07-26 01:48:53,348 - pyskl - INFO - Epoch [108][500/3746] lr: 1.883e-02, eta: 1 day, 12:20:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6595, loss_cls: 3.3854, loss: 3.3854 +2024-07-26 01:50:15,301 - pyskl - INFO - Epoch [108][600/3746] lr: 1.881e-02, eta: 1 day, 12:19:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6609, loss_cls: 3.4011, loss: 3.4011 +2024-07-26 01:51:37,166 - pyskl - INFO - Epoch [108][700/3746] lr: 1.879e-02, eta: 1 day, 12:17:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6642, loss_cls: 3.3653, loss: 3.3653 +2024-07-26 01:52:58,936 - pyskl - INFO - Epoch [108][800/3746] lr: 1.877e-02, eta: 1 day, 12:16:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6597, loss_cls: 3.3646, loss: 3.3646 +2024-07-26 01:54:20,027 - pyskl - INFO - Epoch [108][900/3746] lr: 1.875e-02, eta: 1 day, 12:15:15, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6583, loss_cls: 3.3674, loss: 3.3674 +2024-07-26 01:55:41,183 - pyskl - INFO - Epoch [108][1000/3746] lr: 1.872e-02, eta: 1 day, 12:13:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6680, loss_cls: 3.3264, loss: 3.3264 +2024-07-26 01:57:02,611 - pyskl - INFO - Epoch [108][1100/3746] lr: 1.870e-02, eta: 1 day, 12:12:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4042, top5_acc: 0.6644, loss_cls: 3.3576, loss: 3.3576 +2024-07-26 01:58:24,466 - pyskl - INFO - Epoch [108][1200/3746] lr: 1.868e-02, eta: 1 day, 12:11:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6627, loss_cls: 3.3860, loss: 3.3860 +2024-07-26 01:59:46,284 - pyskl - INFO - Epoch [108][1300/3746] lr: 1.866e-02, eta: 1 day, 12:09:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6628, loss_cls: 3.3679, loss: 3.3679 +2024-07-26 02:01:07,888 - pyskl - INFO - Epoch [108][1400/3746] lr: 1.864e-02, eta: 1 day, 12:08:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6509, loss_cls: 3.3879, loss: 3.3879 +2024-07-26 02:02:29,311 - pyskl - INFO - Epoch [108][1500/3746] lr: 1.862e-02, eta: 1 day, 12:07:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6580, loss_cls: 3.3897, loss: 3.3897 +2024-07-26 02:03:50,525 - pyskl - INFO - Epoch [108][1600/3746] lr: 1.859e-02, eta: 1 day, 12:05:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3972, top5_acc: 0.6528, loss_cls: 3.4239, loss: 3.4239 +2024-07-26 02:05:12,092 - pyskl - INFO - Epoch [108][1700/3746] lr: 1.857e-02, eta: 1 day, 12:04:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6639, loss_cls: 3.3449, loss: 3.3449 +2024-07-26 02:06:33,340 - pyskl - INFO - Epoch [108][1800/3746] lr: 1.855e-02, eta: 1 day, 12:03:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6681, loss_cls: 3.3461, loss: 3.3461 +2024-07-26 02:07:55,136 - pyskl - INFO - Epoch [108][1900/3746] lr: 1.853e-02, eta: 1 day, 12:01:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6528, loss_cls: 3.4248, loss: 3.4248 +2024-07-26 02:09:18,359 - pyskl - INFO - Epoch [108][2000/3746] lr: 1.851e-02, eta: 1 day, 12:00:19, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6634, loss_cls: 3.3750, loss: 3.3750 +2024-07-26 02:10:40,434 - pyskl - INFO - Epoch [108][2100/3746] lr: 1.848e-02, eta: 1 day, 11:58:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6502, loss_cls: 3.4148, loss: 3.4148 +2024-07-26 02:12:02,151 - pyskl - INFO - Epoch [108][2200/3746] lr: 1.846e-02, eta: 1 day, 11:57:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6519, loss_cls: 3.4510, loss: 3.4510 +2024-07-26 02:13:23,945 - pyskl - INFO - Epoch [108][2300/3746] lr: 1.844e-02, eta: 1 day, 11:56:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6539, loss_cls: 3.4028, loss: 3.4028 +2024-07-26 02:14:45,416 - pyskl - INFO - Epoch [108][2400/3746] lr: 1.842e-02, eta: 1 day, 11:54:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3994, top5_acc: 0.6619, loss_cls: 3.3726, loss: 3.3726 +2024-07-26 02:16:07,317 - pyskl - INFO - Epoch [108][2500/3746] lr: 1.840e-02, eta: 1 day, 11:53:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6562, loss_cls: 3.4056, loss: 3.4056 +2024-07-26 02:17:28,900 - pyskl - INFO - Epoch [108][2600/3746] lr: 1.838e-02, eta: 1 day, 11:52:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3909, top5_acc: 0.6494, loss_cls: 3.4220, loss: 3.4220 +2024-07-26 02:18:50,328 - pyskl - INFO - Epoch [108][2700/3746] lr: 1.835e-02, eta: 1 day, 11:50:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3992, top5_acc: 0.6600, loss_cls: 3.3791, loss: 3.3791 +2024-07-26 02:20:12,098 - pyskl - INFO - Epoch [108][2800/3746] lr: 1.833e-02, eta: 1 day, 11:49:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4042, top5_acc: 0.6631, loss_cls: 3.3493, loss: 3.3493 +2024-07-26 02:21:33,790 - pyskl - INFO - Epoch [108][2900/3746] lr: 1.831e-02, eta: 1 day, 11:48:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6508, loss_cls: 3.3878, loss: 3.3878 +2024-07-26 02:22:55,233 - pyskl - INFO - Epoch [108][3000/3746] lr: 1.829e-02, eta: 1 day, 11:46:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6594, loss_cls: 3.3553, loss: 3.3553 +2024-07-26 02:24:16,766 - pyskl - INFO - Epoch [108][3100/3746] lr: 1.827e-02, eta: 1 day, 11:45:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6514, loss_cls: 3.4229, loss: 3.4229 +2024-07-26 02:25:37,964 - pyskl - INFO - Epoch [108][3200/3746] lr: 1.825e-02, eta: 1 day, 11:44:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6566, loss_cls: 3.3900, loss: 3.3900 +2024-07-26 02:26:59,644 - pyskl - INFO - Epoch [108][3300/3746] lr: 1.823e-02, eta: 1 day, 11:42:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6503, loss_cls: 3.4793, loss: 3.4793 +2024-07-26 02:28:20,835 - pyskl - INFO - Epoch [108][3400/3746] lr: 1.820e-02, eta: 1 day, 11:41:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6506, loss_cls: 3.4190, loss: 3.4190 +2024-07-26 02:29:42,848 - pyskl - INFO - Epoch [108][3500/3746] lr: 1.818e-02, eta: 1 day, 11:39:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6502, loss_cls: 3.4559, loss: 3.4559 +2024-07-26 02:31:05,231 - pyskl - INFO - Epoch [108][3600/3746] lr: 1.816e-02, eta: 1 day, 11:38:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3989, top5_acc: 0.6670, loss_cls: 3.3475, loss: 3.3475 +2024-07-26 02:32:26,419 - pyskl - INFO - Epoch [108][3700/3746] lr: 1.814e-02, eta: 1 day, 11:37:15, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6442, loss_cls: 3.4593, loss: 3.4593 +2024-07-26 02:33:05,624 - pyskl - INFO - Saving checkpoint at 108 epochs +2024-07-26 02:34:59,832 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 02:35:00,493 - pyskl - INFO - +top1_acc 0.3419 +top5_acc 0.5920 +2024-07-26 02:35:00,494 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 02:35:00,535 - pyskl - INFO - +mean_acc 0.3416 +2024-07-26 02:35:00,540 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_106.pth was removed +2024-07-26 02:35:00,802 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_108.pth. +2024-07-26 02:35:00,803 - pyskl - INFO - Best top1_acc is 0.3419 at 108 epoch. +2024-07-26 02:35:00,815 - pyskl - INFO - Epoch(val) [108][309] top1_acc: 0.3419, top5_acc: 0.5920, mean_class_accuracy: 0.3416 +2024-07-26 02:38:49,886 - pyskl - INFO - Epoch [109][100/3746] lr: 1.811e-02, eta: 1 day, 11:35:59, time: 2.291, data_time: 1.311, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6806, loss_cls: 3.2575, loss: 3.2575 +2024-07-26 02:40:11,992 - pyskl - INFO - Epoch [109][200/3746] lr: 1.809e-02, eta: 1 day, 11:34:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4197, top5_acc: 0.6831, loss_cls: 3.2463, loss: 3.2463 +2024-07-26 02:41:33,712 - pyskl - INFO - Epoch [109][300/3746] lr: 1.806e-02, eta: 1 day, 11:33:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6727, loss_cls: 3.3309, loss: 3.3309 +2024-07-26 02:42:56,011 - pyskl - INFO - Epoch [109][400/3746] lr: 1.804e-02, eta: 1 day, 11:31:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6725, loss_cls: 3.2691, loss: 3.2691 +2024-07-26 02:44:18,031 - pyskl - INFO - Epoch [109][500/3746] lr: 1.802e-02, eta: 1 day, 11:30:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6556, loss_cls: 3.3881, loss: 3.3881 +2024-07-26 02:45:40,101 - pyskl - INFO - Epoch [109][600/3746] lr: 1.800e-02, eta: 1 day, 11:29:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6669, loss_cls: 3.3177, loss: 3.3177 +2024-07-26 02:47:02,470 - pyskl - INFO - Epoch [109][700/3746] lr: 1.798e-02, eta: 1 day, 11:27:51, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6617, loss_cls: 3.3591, loss: 3.3591 +2024-07-26 02:48:23,903 - pyskl - INFO - Epoch [109][800/3746] lr: 1.796e-02, eta: 1 day, 11:26:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6550, loss_cls: 3.3672, loss: 3.3672 +2024-07-26 02:49:45,540 - pyskl - INFO - Epoch [109][900/3746] lr: 1.794e-02, eta: 1 day, 11:25:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6592, loss_cls: 3.3771, loss: 3.3771 +2024-07-26 02:51:06,982 - pyskl - INFO - Epoch [109][1000/3746] lr: 1.791e-02, eta: 1 day, 11:23:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6527, loss_cls: 3.3706, loss: 3.3706 +2024-07-26 02:52:29,097 - pyskl - INFO - Epoch [109][1100/3746] lr: 1.789e-02, eta: 1 day, 11:22:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4119, top5_acc: 0.6666, loss_cls: 3.3395, loss: 3.3395 +2024-07-26 02:53:50,565 - pyskl - INFO - Epoch [109][1200/3746] lr: 1.787e-02, eta: 1 day, 11:21:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6680, loss_cls: 3.3545, loss: 3.3545 +2024-07-26 02:55:11,861 - pyskl - INFO - Epoch [109][1300/3746] lr: 1.785e-02, eta: 1 day, 11:19:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3844, top5_acc: 0.6442, loss_cls: 3.4447, loss: 3.4447 +2024-07-26 02:56:33,865 - pyskl - INFO - Epoch [109][1400/3746] lr: 1.783e-02, eta: 1 day, 11:18:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4016, top5_acc: 0.6539, loss_cls: 3.3908, loss: 3.3908 +2024-07-26 02:57:55,369 - pyskl - INFO - Epoch [109][1500/3746] lr: 1.781e-02, eta: 1 day, 11:16:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3898, top5_acc: 0.6536, loss_cls: 3.4266, loss: 3.4266 +2024-07-26 02:59:17,058 - pyskl - INFO - Epoch [109][1600/3746] lr: 1.779e-02, eta: 1 day, 11:15:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6639, loss_cls: 3.3480, loss: 3.3480 +2024-07-26 03:00:38,239 - pyskl - INFO - Epoch [109][1700/3746] lr: 1.776e-02, eta: 1 day, 11:14:16, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6622, loss_cls: 3.3737, loss: 3.3737 +2024-07-26 03:01:59,405 - pyskl - INFO - Epoch [109][1800/3746] lr: 1.774e-02, eta: 1 day, 11:12:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6592, loss_cls: 3.3803, loss: 3.3803 +2024-07-26 03:03:21,665 - pyskl - INFO - Epoch [109][1900/3746] lr: 1.772e-02, eta: 1 day, 11:11:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6613, loss_cls: 3.3706, loss: 3.3706 +2024-07-26 03:04:44,771 - pyskl - INFO - Epoch [109][2000/3746] lr: 1.770e-02, eta: 1 day, 11:10:13, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6602, loss_cls: 3.3941, loss: 3.3941 +2024-07-26 03:06:07,806 - pyskl - INFO - Epoch [109][2100/3746] lr: 1.768e-02, eta: 1 day, 11:08:52, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6597, loss_cls: 3.3547, loss: 3.3547 +2024-07-26 03:07:30,325 - pyskl - INFO - Epoch [109][2200/3746] lr: 1.766e-02, eta: 1 day, 11:07:30, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6556, loss_cls: 3.4078, loss: 3.4078 +2024-07-26 03:08:52,285 - pyskl - INFO - Epoch [109][2300/3746] lr: 1.764e-02, eta: 1 day, 11:06:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6619, loss_cls: 3.3829, loss: 3.3829 +2024-07-26 03:10:13,935 - pyskl - INFO - Epoch [109][2400/3746] lr: 1.761e-02, eta: 1 day, 11:04:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6711, loss_cls: 3.3308, loss: 3.3308 +2024-07-26 03:11:35,711 - pyskl - INFO - Epoch [109][2500/3746] lr: 1.759e-02, eta: 1 day, 11:03:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6562, loss_cls: 3.3802, loss: 3.3802 +2024-07-26 03:12:57,280 - pyskl - INFO - Epoch [109][2600/3746] lr: 1.757e-02, eta: 1 day, 11:02:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4125, top5_acc: 0.6541, loss_cls: 3.3405, loss: 3.3405 +2024-07-26 03:14:18,552 - pyskl - INFO - Epoch [109][2700/3746] lr: 1.755e-02, eta: 1 day, 11:00:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6548, loss_cls: 3.4040, loss: 3.4040 +2024-07-26 03:15:40,045 - pyskl - INFO - Epoch [109][2800/3746] lr: 1.753e-02, eta: 1 day, 10:59:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3859, top5_acc: 0.6453, loss_cls: 3.4377, loss: 3.4377 +2024-07-26 03:17:02,092 - pyskl - INFO - Epoch [109][2900/3746] lr: 1.751e-02, eta: 1 day, 10:58:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6659, loss_cls: 3.3840, loss: 3.3840 +2024-07-26 03:18:23,675 - pyskl - INFO - Epoch [109][3000/3746] lr: 1.749e-02, eta: 1 day, 10:56:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6597, loss_cls: 3.3726, loss: 3.3726 +2024-07-26 03:19:45,339 - pyskl - INFO - Epoch [109][3100/3746] lr: 1.747e-02, eta: 1 day, 10:55:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6656, loss_cls: 3.3642, loss: 3.3642 +2024-07-26 03:21:06,786 - pyskl - INFO - Epoch [109][3200/3746] lr: 1.744e-02, eta: 1 day, 10:53:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6606, loss_cls: 3.3550, loss: 3.3550 +2024-07-26 03:22:28,420 - pyskl - INFO - Epoch [109][3300/3746] lr: 1.742e-02, eta: 1 day, 10:52:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6530, loss_cls: 3.3958, loss: 3.3958 +2024-07-26 03:23:49,633 - pyskl - INFO - Epoch [109][3400/3746] lr: 1.740e-02, eta: 1 day, 10:51:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4008, top5_acc: 0.6675, loss_cls: 3.3760, loss: 3.3760 +2024-07-26 03:25:11,284 - pyskl - INFO - Epoch [109][3500/3746] lr: 1.738e-02, eta: 1 day, 10:49:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4062, top5_acc: 0.6602, loss_cls: 3.3347, loss: 3.3347 +2024-07-26 03:26:33,831 - pyskl - INFO - Epoch [109][3600/3746] lr: 1.736e-02, eta: 1 day, 10:48:30, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6572, loss_cls: 3.3653, loss: 3.3653 +2024-07-26 03:27:55,344 - pyskl - INFO - Epoch [109][3700/3746] lr: 1.734e-02, eta: 1 day, 10:47:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4008, top5_acc: 0.6653, loss_cls: 3.3641, loss: 3.3641 +2024-07-26 03:28:35,456 - pyskl - INFO - Saving checkpoint at 109 epochs +2024-07-26 03:30:27,650 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 03:30:28,310 - pyskl - INFO - +top1_acc 0.3499 +top5_acc 0.6025 +2024-07-26 03:30:28,310 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 03:30:28,350 - pyskl - INFO - +mean_acc 0.3496 +2024-07-26 03:30:28,355 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_108.pth was removed +2024-07-26 03:30:28,616 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_109.pth. +2024-07-26 03:30:28,617 - pyskl - INFO - Best top1_acc is 0.3499 at 109 epoch. +2024-07-26 03:30:28,628 - pyskl - INFO - Epoch(val) [109][309] top1_acc: 0.3499, top5_acc: 0.6025, mean_class_accuracy: 0.3496 +2024-07-26 03:34:15,606 - pyskl - INFO - Epoch [110][100/3746] lr: 1.731e-02, eta: 1 day, 10:45:50, time: 2.270, data_time: 1.284, memory: 15990, top1_acc: 0.4189, top5_acc: 0.6728, loss_cls: 3.2901, loss: 3.2901 +2024-07-26 03:35:37,735 - pyskl - INFO - Epoch [110][200/3746] lr: 1.729e-02, eta: 1 day, 10:44:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4152, top5_acc: 0.6730, loss_cls: 3.2805, loss: 3.2805 +2024-07-26 03:36:59,696 - pyskl - INFO - Epoch [110][300/3746] lr: 1.727e-02, eta: 1 day, 10:43:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6622, loss_cls: 3.3520, loss: 3.3520 +2024-07-26 03:38:20,869 - pyskl - INFO - Epoch [110][400/3746] lr: 1.724e-02, eta: 1 day, 10:41:46, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6753, loss_cls: 3.2852, loss: 3.2852 +2024-07-26 03:39:43,466 - pyskl - INFO - Epoch [110][500/3746] lr: 1.722e-02, eta: 1 day, 10:40:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6687, loss_cls: 3.3612, loss: 3.3612 +2024-07-26 03:41:04,947 - pyskl - INFO - Epoch [110][600/3746] lr: 1.720e-02, eta: 1 day, 10:39:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6719, loss_cls: 3.3158, loss: 3.3158 +2024-07-26 03:42:27,201 - pyskl - INFO - Epoch [110][700/3746] lr: 1.718e-02, eta: 1 day, 10:37:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6648, loss_cls: 3.3280, loss: 3.3280 +2024-07-26 03:43:49,000 - pyskl - INFO - Epoch [110][800/3746] lr: 1.716e-02, eta: 1 day, 10:36:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6622, loss_cls: 3.3578, loss: 3.3578 +2024-07-26 03:45:10,849 - pyskl - INFO - Epoch [110][900/3746] lr: 1.714e-02, eta: 1 day, 10:34:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4145, top5_acc: 0.6714, loss_cls: 3.3163, loss: 3.3163 +2024-07-26 03:46:32,088 - pyskl - INFO - Epoch [110][1000/3746] lr: 1.712e-02, eta: 1 day, 10:33:37, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6636, loss_cls: 3.3315, loss: 3.3315 +2024-07-26 03:47:53,253 - pyskl - INFO - Epoch [110][1100/3746] lr: 1.710e-02, eta: 1 day, 10:32:16, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4091, top5_acc: 0.6673, loss_cls: 3.3101, loss: 3.3101 +2024-07-26 03:49:14,646 - pyskl - INFO - Epoch [110][1200/3746] lr: 1.708e-02, eta: 1 day, 10:30:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6725, loss_cls: 3.2930, loss: 3.2930 +2024-07-26 03:50:36,578 - pyskl - INFO - Epoch [110][1300/3746] lr: 1.705e-02, eta: 1 day, 10:29:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6578, loss_cls: 3.3715, loss: 3.3715 +2024-07-26 03:51:58,229 - pyskl - INFO - Epoch [110][1400/3746] lr: 1.703e-02, eta: 1 day, 10:28:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4059, top5_acc: 0.6667, loss_cls: 3.3462, loss: 3.3462 +2024-07-26 03:53:19,709 - pyskl - INFO - Epoch [110][1500/3746] lr: 1.701e-02, eta: 1 day, 10:26:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4008, top5_acc: 0.6556, loss_cls: 3.3876, loss: 3.3876 +2024-07-26 03:54:41,155 - pyskl - INFO - Epoch [110][1600/3746] lr: 1.699e-02, eta: 1 day, 10:25:28, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6727, loss_cls: 3.3082, loss: 3.3082 +2024-07-26 03:56:02,388 - pyskl - INFO - Epoch [110][1700/3746] lr: 1.697e-02, eta: 1 day, 10:24:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6637, loss_cls: 3.3559, loss: 3.3559 +2024-07-26 03:57:23,843 - pyskl - INFO - Epoch [110][1800/3746] lr: 1.695e-02, eta: 1 day, 10:22:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6705, loss_cls: 3.2940, loss: 3.2940 +2024-07-26 03:58:45,902 - pyskl - INFO - Epoch [110][1900/3746] lr: 1.693e-02, eta: 1 day, 10:21:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6681, loss_cls: 3.3201, loss: 3.3201 +2024-07-26 04:00:08,278 - pyskl - INFO - Epoch [110][2000/3746] lr: 1.691e-02, eta: 1 day, 10:20:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6655, loss_cls: 3.3497, loss: 3.3497 +2024-07-26 04:01:30,135 - pyskl - INFO - Epoch [110][2100/3746] lr: 1.689e-02, eta: 1 day, 10:18:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6533, loss_cls: 3.3952, loss: 3.3952 +2024-07-26 04:02:52,247 - pyskl - INFO - Epoch [110][2200/3746] lr: 1.687e-02, eta: 1 day, 10:17:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6625, loss_cls: 3.3562, loss: 3.3562 +2024-07-26 04:04:13,899 - pyskl - INFO - Epoch [110][2300/3746] lr: 1.685e-02, eta: 1 day, 10:15:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4088, top5_acc: 0.6603, loss_cls: 3.3489, loss: 3.3489 +2024-07-26 04:05:36,208 - pyskl - INFO - Epoch [110][2400/3746] lr: 1.682e-02, eta: 1 day, 10:14:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6603, loss_cls: 3.3802, loss: 3.3802 +2024-07-26 04:06:57,620 - pyskl - INFO - Epoch [110][2500/3746] lr: 1.680e-02, eta: 1 day, 10:13:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6644, loss_cls: 3.3214, loss: 3.3214 +2024-07-26 04:08:19,309 - pyskl - INFO - Epoch [110][2600/3746] lr: 1.678e-02, eta: 1 day, 10:11:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6737, loss_cls: 3.3421, loss: 3.3421 +2024-07-26 04:09:40,932 - pyskl - INFO - Epoch [110][2700/3746] lr: 1.676e-02, eta: 1 day, 10:10:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3977, top5_acc: 0.6583, loss_cls: 3.4104, loss: 3.4104 +2024-07-26 04:11:02,700 - pyskl - INFO - Epoch [110][2800/3746] lr: 1.674e-02, eta: 1 day, 10:09:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6566, loss_cls: 3.3949, loss: 3.3949 +2024-07-26 04:12:24,303 - pyskl - INFO - Epoch [110][2900/3746] lr: 1.672e-02, eta: 1 day, 10:07:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6473, loss_cls: 3.4203, loss: 3.4203 +2024-07-26 04:13:45,995 - pyskl - INFO - Epoch [110][3000/3746] lr: 1.670e-02, eta: 1 day, 10:06:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4005, top5_acc: 0.6539, loss_cls: 3.3814, loss: 3.3814 +2024-07-26 04:15:07,503 - pyskl - INFO - Epoch [110][3100/3746] lr: 1.668e-02, eta: 1 day, 10:05:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6737, loss_cls: 3.3316, loss: 3.3316 +2024-07-26 04:16:28,874 - pyskl - INFO - Epoch [110][3200/3746] lr: 1.666e-02, eta: 1 day, 10:03:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6586, loss_cls: 3.3672, loss: 3.3672 +2024-07-26 04:17:49,899 - pyskl - INFO - Epoch [110][3300/3746] lr: 1.664e-02, eta: 1 day, 10:02:23, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6645, loss_cls: 3.3286, loss: 3.3286 +2024-07-26 04:19:11,429 - pyskl - INFO - Epoch [110][3400/3746] lr: 1.662e-02, eta: 1 day, 10:01:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4095, top5_acc: 0.6594, loss_cls: 3.3637, loss: 3.3637 +2024-07-26 04:20:32,875 - pyskl - INFO - Epoch [110][3500/3746] lr: 1.659e-02, eta: 1 day, 9:59:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4095, top5_acc: 0.6650, loss_cls: 3.3092, loss: 3.3092 +2024-07-26 04:21:54,691 - pyskl - INFO - Epoch [110][3600/3746] lr: 1.657e-02, eta: 1 day, 9:58:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6652, loss_cls: 3.3323, loss: 3.3323 +2024-07-26 04:23:16,392 - pyskl - INFO - Epoch [110][3700/3746] lr: 1.655e-02, eta: 1 day, 9:56:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6591, loss_cls: 3.3519, loss: 3.3519 +2024-07-26 04:23:56,011 - pyskl - INFO - Saving checkpoint at 110 epochs +2024-07-26 04:25:48,401 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 04:25:49,062 - pyskl - INFO - +top1_acc 0.3490 +top5_acc 0.6025 +2024-07-26 04:25:49,062 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 04:25:49,101 - pyskl - INFO - +mean_acc 0.3488 +2024-07-26 04:25:49,112 - pyskl - INFO - Epoch(val) [110][309] top1_acc: 0.3490, top5_acc: 0.6025, mean_class_accuracy: 0.3488 +2024-07-26 04:29:35,792 - pyskl - INFO - Epoch [111][100/3746] lr: 1.652e-02, eta: 1 day, 9:55:37, time: 2.267, data_time: 1.294, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6847, loss_cls: 3.2189, loss: 3.2189 +2024-07-26 04:30:57,317 - pyskl - INFO - Epoch [111][200/3746] lr: 1.650e-02, eta: 1 day, 9:54:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4266, top5_acc: 0.6861, loss_cls: 3.2491, loss: 3.2491 +2024-07-26 04:32:19,046 - pyskl - INFO - Epoch [111][300/3746] lr: 1.648e-02, eta: 1 day, 9:52:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6769, loss_cls: 3.2874, loss: 3.2874 +2024-07-26 04:33:40,574 - pyskl - INFO - Epoch [111][400/3746] lr: 1.646e-02, eta: 1 day, 9:51:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6834, loss_cls: 3.2202, loss: 3.2202 +2024-07-26 04:35:02,570 - pyskl - INFO - Epoch [111][500/3746] lr: 1.644e-02, eta: 1 day, 9:50:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6727, loss_cls: 3.3162, loss: 3.3162 +2024-07-26 04:36:23,828 - pyskl - INFO - Epoch [111][600/3746] lr: 1.642e-02, eta: 1 day, 9:48:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4211, top5_acc: 0.6708, loss_cls: 3.3241, loss: 3.3241 +2024-07-26 04:37:45,624 - pyskl - INFO - Epoch [111][700/3746] lr: 1.640e-02, eta: 1 day, 9:47:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4264, top5_acc: 0.6881, loss_cls: 3.2319, loss: 3.2319 +2024-07-26 04:39:06,978 - pyskl - INFO - Epoch [111][800/3746] lr: 1.638e-02, eta: 1 day, 9:46:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6683, loss_cls: 3.3508, loss: 3.3508 +2024-07-26 04:40:28,427 - pyskl - INFO - Epoch [111][900/3746] lr: 1.636e-02, eta: 1 day, 9:44:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4139, top5_acc: 0.6681, loss_cls: 3.3147, loss: 3.3147 +2024-07-26 04:41:50,464 - pyskl - INFO - Epoch [111][1000/3746] lr: 1.634e-02, eta: 1 day, 9:43:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4044, top5_acc: 0.6677, loss_cls: 3.3220, loss: 3.3220 +2024-07-26 04:43:11,950 - pyskl - INFO - Epoch [111][1100/3746] lr: 1.632e-02, eta: 1 day, 9:42:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6670, loss_cls: 3.3260, loss: 3.3260 +2024-07-26 04:44:33,260 - pyskl - INFO - Epoch [111][1200/3746] lr: 1.630e-02, eta: 1 day, 9:40:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6767, loss_cls: 3.2687, loss: 3.2687 +2024-07-26 04:45:54,426 - pyskl - INFO - Epoch [111][1300/3746] lr: 1.627e-02, eta: 1 day, 9:39:18, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6694, loss_cls: 3.3140, loss: 3.3140 +2024-07-26 04:47:16,121 - pyskl - INFO - Epoch [111][1400/3746] lr: 1.625e-02, eta: 1 day, 9:37:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4070, top5_acc: 0.6670, loss_cls: 3.3058, loss: 3.3058 +2024-07-26 04:48:37,287 - pyskl - INFO - Epoch [111][1500/3746] lr: 1.623e-02, eta: 1 day, 9:36:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6702, loss_cls: 3.3182, loss: 3.3182 +2024-07-26 04:49:59,010 - pyskl - INFO - Epoch [111][1600/3746] lr: 1.621e-02, eta: 1 day, 9:35:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6677, loss_cls: 3.3187, loss: 3.3187 +2024-07-26 04:51:20,223 - pyskl - INFO - Epoch [111][1700/3746] lr: 1.619e-02, eta: 1 day, 9:33:52, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6656, loss_cls: 3.3315, loss: 3.3315 +2024-07-26 04:52:41,463 - pyskl - INFO - Epoch [111][1800/3746] lr: 1.617e-02, eta: 1 day, 9:32:30, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6672, loss_cls: 3.3227, loss: 3.3227 +2024-07-26 04:54:03,515 - pyskl - INFO - Epoch [111][1900/3746] lr: 1.615e-02, eta: 1 day, 9:31:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6728, loss_cls: 3.2987, loss: 3.2987 +2024-07-26 04:55:25,960 - pyskl - INFO - Epoch [111][2000/3746] lr: 1.613e-02, eta: 1 day, 9:29:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6580, loss_cls: 3.3775, loss: 3.3775 +2024-07-26 04:56:47,763 - pyskl - INFO - Epoch [111][2100/3746] lr: 1.611e-02, eta: 1 day, 9:28:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4025, top5_acc: 0.6594, loss_cls: 3.3786, loss: 3.3786 +2024-07-26 04:58:09,983 - pyskl - INFO - Epoch [111][2200/3746] lr: 1.609e-02, eta: 1 day, 9:27:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4130, top5_acc: 0.6684, loss_cls: 3.3071, loss: 3.3071 +2024-07-26 04:59:31,999 - pyskl - INFO - Epoch [111][2300/3746] lr: 1.607e-02, eta: 1 day, 9:25:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6670, loss_cls: 3.3541, loss: 3.3541 +2024-07-26 05:00:53,826 - pyskl - INFO - Epoch [111][2400/3746] lr: 1.605e-02, eta: 1 day, 9:24:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4006, top5_acc: 0.6641, loss_cls: 3.3570, loss: 3.3570 +2024-07-26 05:02:15,194 - pyskl - INFO - Epoch [111][2500/3746] lr: 1.603e-02, eta: 1 day, 9:23:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6548, loss_cls: 3.3810, loss: 3.3810 +2024-07-26 05:03:36,326 - pyskl - INFO - Epoch [111][2600/3746] lr: 1.601e-02, eta: 1 day, 9:21:38, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6589, loss_cls: 3.3557, loss: 3.3557 +2024-07-26 05:04:57,957 - pyskl - INFO - Epoch [111][2700/3746] lr: 1.599e-02, eta: 1 day, 9:20:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6687, loss_cls: 3.3288, loss: 3.3288 +2024-07-26 05:06:19,574 - pyskl - INFO - Epoch [111][2800/3746] lr: 1.597e-02, eta: 1 day, 9:18:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6667, loss_cls: 3.3409, loss: 3.3409 +2024-07-26 05:07:41,299 - pyskl - INFO - Epoch [111][2900/3746] lr: 1.595e-02, eta: 1 day, 9:17:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6720, loss_cls: 3.2943, loss: 3.2943 +2024-07-26 05:09:02,471 - pyskl - INFO - Epoch [111][3000/3746] lr: 1.593e-02, eta: 1 day, 9:16:12, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4059, top5_acc: 0.6620, loss_cls: 3.3312, loss: 3.3312 +2024-07-26 05:10:24,170 - pyskl - INFO - Epoch [111][3100/3746] lr: 1.590e-02, eta: 1 day, 9:14:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6581, loss_cls: 3.3559, loss: 3.3559 +2024-07-26 05:11:45,418 - pyskl - INFO - Epoch [111][3200/3746] lr: 1.588e-02, eta: 1 day, 9:13:29, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6614, loss_cls: 3.3660, loss: 3.3660 +2024-07-26 05:13:06,990 - pyskl - INFO - Epoch [111][3300/3746] lr: 1.586e-02, eta: 1 day, 9:12:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4008, top5_acc: 0.6586, loss_cls: 3.3532, loss: 3.3532 +2024-07-26 05:14:28,202 - pyskl - INFO - Epoch [111][3400/3746] lr: 1.584e-02, eta: 1 day, 9:10:45, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6675, loss_cls: 3.3277, loss: 3.3277 +2024-07-26 05:15:49,823 - pyskl - INFO - Epoch [111][3500/3746] lr: 1.582e-02, eta: 1 day, 9:09:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6552, loss_cls: 3.3807, loss: 3.3807 +2024-07-26 05:17:11,497 - pyskl - INFO - Epoch [111][3600/3746] lr: 1.580e-02, eta: 1 day, 9:08:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4163, top5_acc: 0.6717, loss_cls: 3.3149, loss: 3.3149 +2024-07-26 05:18:32,870 - pyskl - INFO - Epoch [111][3700/3746] lr: 1.578e-02, eta: 1 day, 9:06:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6672, loss_cls: 3.3368, loss: 3.3368 +2024-07-26 05:19:12,239 - pyskl - INFO - Saving checkpoint at 111 epochs +2024-07-26 05:21:04,306 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 05:21:05,013 - pyskl - INFO - +top1_acc 0.3441 +top5_acc 0.6013 +2024-07-26 05:21:05,013 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 05:21:05,052 - pyskl - INFO - +mean_acc 0.3437 +2024-07-26 05:21:05,063 - pyskl - INFO - Epoch(val) [111][309] top1_acc: 0.3441, top5_acc: 0.6013, mean_class_accuracy: 0.3437 +2024-07-26 05:24:55,178 - pyskl - INFO - Epoch [112][100/3746] lr: 1.575e-02, eta: 1 day, 9:05:21, time: 2.301, data_time: 1.293, memory: 15990, top1_acc: 0.4300, top5_acc: 0.6886, loss_cls: 3.2019, loss: 3.2019 +2024-07-26 05:26:17,000 - pyskl - INFO - Epoch [112][200/3746] lr: 1.573e-02, eta: 1 day, 9:03:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4095, top5_acc: 0.6773, loss_cls: 3.2680, loss: 3.2680 +2024-07-26 05:27:38,903 - pyskl - INFO - Epoch [112][300/3746] lr: 1.571e-02, eta: 1 day, 9:02:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4241, top5_acc: 0.6833, loss_cls: 3.2121, loss: 3.2121 +2024-07-26 05:29:00,368 - pyskl - INFO - Epoch [112][400/3746] lr: 1.569e-02, eta: 1 day, 9:01:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4167, top5_acc: 0.6737, loss_cls: 3.2887, loss: 3.2887 +2024-07-26 05:30:22,458 - pyskl - INFO - Epoch [112][500/3746] lr: 1.567e-02, eta: 1 day, 8:59:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4292, top5_acc: 0.6803, loss_cls: 3.2540, loss: 3.2540 +2024-07-26 05:31:44,177 - pyskl - INFO - Epoch [112][600/3746] lr: 1.565e-02, eta: 1 day, 8:58:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4147, top5_acc: 0.6809, loss_cls: 3.2592, loss: 3.2592 +2024-07-26 05:33:05,829 - pyskl - INFO - Epoch [112][700/3746] lr: 1.563e-02, eta: 1 day, 8:57:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6698, loss_cls: 3.3084, loss: 3.3084 +2024-07-26 05:34:27,378 - pyskl - INFO - Epoch [112][800/3746] lr: 1.561e-02, eta: 1 day, 8:55:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4109, top5_acc: 0.6731, loss_cls: 3.3011, loss: 3.3011 +2024-07-26 05:35:48,989 - pyskl - INFO - Epoch [112][900/3746] lr: 1.559e-02, eta: 1 day, 8:54:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4205, top5_acc: 0.6787, loss_cls: 3.2645, loss: 3.2645 +2024-07-26 05:37:10,746 - pyskl - INFO - Epoch [112][1000/3746] lr: 1.557e-02, eta: 1 day, 8:53:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6708, loss_cls: 3.3222, loss: 3.3222 +2024-07-26 05:38:32,293 - pyskl - INFO - Epoch [112][1100/3746] lr: 1.555e-02, eta: 1 day, 8:51:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6650, loss_cls: 3.3142, loss: 3.3142 +2024-07-26 05:39:53,764 - pyskl - INFO - Epoch [112][1200/3746] lr: 1.553e-02, eta: 1 day, 8:50:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4109, top5_acc: 0.6733, loss_cls: 3.2974, loss: 3.2974 +2024-07-26 05:41:15,368 - pyskl - INFO - Epoch [112][1300/3746] lr: 1.551e-02, eta: 1 day, 8:49:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4091, top5_acc: 0.6611, loss_cls: 3.3186, loss: 3.3186 +2024-07-26 05:42:38,151 - pyskl - INFO - Epoch [112][1400/3746] lr: 1.549e-02, eta: 1 day, 8:47:41, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6583, loss_cls: 3.3329, loss: 3.3329 +2024-07-26 05:43:59,400 - pyskl - INFO - Epoch [112][1500/3746] lr: 1.547e-02, eta: 1 day, 8:46:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6766, loss_cls: 3.2993, loss: 3.2993 +2024-07-26 05:45:21,585 - pyskl - INFO - Epoch [112][1600/3746] lr: 1.545e-02, eta: 1 day, 8:44:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6639, loss_cls: 3.3426, loss: 3.3426 +2024-07-26 05:46:43,700 - pyskl - INFO - Epoch [112][1700/3746] lr: 1.543e-02, eta: 1 day, 8:43:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6739, loss_cls: 3.3039, loss: 3.3039 +2024-07-26 05:48:05,191 - pyskl - INFO - Epoch [112][1800/3746] lr: 1.541e-02, eta: 1 day, 8:42:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6777, loss_cls: 3.2563, loss: 3.2563 +2024-07-26 05:49:27,042 - pyskl - INFO - Epoch [112][1900/3746] lr: 1.539e-02, eta: 1 day, 8:40:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4086, top5_acc: 0.6739, loss_cls: 3.3100, loss: 3.3100 +2024-07-26 05:50:49,091 - pyskl - INFO - Epoch [112][2000/3746] lr: 1.537e-02, eta: 1 day, 8:39:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6689, loss_cls: 3.3011, loss: 3.3011 +2024-07-26 05:52:11,702 - pyskl - INFO - Epoch [112][2100/3746] lr: 1.535e-02, eta: 1 day, 8:38:10, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6711, loss_cls: 3.2903, loss: 3.2903 +2024-07-26 05:53:34,333 - pyskl - INFO - Epoch [112][2200/3746] lr: 1.533e-02, eta: 1 day, 8:36:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6781, loss_cls: 3.2482, loss: 3.2482 +2024-07-26 05:54:55,726 - pyskl - INFO - Epoch [112][2300/3746] lr: 1.531e-02, eta: 1 day, 8:35:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6683, loss_cls: 3.3103, loss: 3.3103 +2024-07-26 05:56:16,976 - pyskl - INFO - Epoch [112][2400/3746] lr: 1.529e-02, eta: 1 day, 8:34:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4005, top5_acc: 0.6655, loss_cls: 3.3234, loss: 3.3234 +2024-07-26 05:57:38,396 - pyskl - INFO - Epoch [112][2500/3746] lr: 1.527e-02, eta: 1 day, 8:32:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6656, loss_cls: 3.3359, loss: 3.3359 +2024-07-26 05:58:59,640 - pyskl - INFO - Epoch [112][2600/3746] lr: 1.525e-02, eta: 1 day, 8:31:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6777, loss_cls: 3.2931, loss: 3.2931 +2024-07-26 06:00:21,412 - pyskl - INFO - Epoch [112][2700/3746] lr: 1.523e-02, eta: 1 day, 8:30:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6647, loss_cls: 3.3434, loss: 3.3434 +2024-07-26 06:01:42,304 - pyskl - INFO - Epoch [112][2800/3746] lr: 1.521e-02, eta: 1 day, 8:28:39, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4017, top5_acc: 0.6597, loss_cls: 3.3650, loss: 3.3650 +2024-07-26 06:03:03,786 - pyskl - INFO - Epoch [112][2900/3746] lr: 1.519e-02, eta: 1 day, 8:27:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6664, loss_cls: 3.3307, loss: 3.3307 +2024-07-26 06:04:25,347 - pyskl - INFO - Epoch [112][3000/3746] lr: 1.517e-02, eta: 1 day, 8:25:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6652, loss_cls: 3.3360, loss: 3.3360 +2024-07-26 06:05:46,700 - pyskl - INFO - Epoch [112][3100/3746] lr: 1.515e-02, eta: 1 day, 8:24:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6691, loss_cls: 3.3079, loss: 3.3079 +2024-07-26 06:07:08,563 - pyskl - INFO - Epoch [112][3200/3746] lr: 1.513e-02, eta: 1 day, 8:23:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6678, loss_cls: 3.3141, loss: 3.3141 +2024-07-26 06:08:30,185 - pyskl - INFO - Epoch [112][3300/3746] lr: 1.511e-02, eta: 1 day, 8:21:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4177, top5_acc: 0.6777, loss_cls: 3.2890, loss: 3.2890 +2024-07-26 06:09:51,575 - pyskl - INFO - Epoch [112][3400/3746] lr: 1.509e-02, eta: 1 day, 8:20:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4173, top5_acc: 0.6739, loss_cls: 3.2754, loss: 3.2754 +2024-07-26 06:11:13,050 - pyskl - INFO - Epoch [112][3500/3746] lr: 1.507e-02, eta: 1 day, 8:19:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4134, top5_acc: 0.6702, loss_cls: 3.2990, loss: 3.2990 +2024-07-26 06:12:34,944 - pyskl - INFO - Epoch [112][3600/3746] lr: 1.505e-02, eta: 1 day, 8:17:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4202, top5_acc: 0.6697, loss_cls: 3.2920, loss: 3.2920 +2024-07-26 06:13:56,650 - pyskl - INFO - Epoch [112][3700/3746] lr: 1.503e-02, eta: 1 day, 8:16:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6669, loss_cls: 3.3286, loss: 3.3286 +2024-07-26 06:14:36,129 - pyskl - INFO - Saving checkpoint at 112 epochs +2024-07-26 06:16:27,392 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 06:16:28,079 - pyskl - INFO - +top1_acc 0.3456 +top5_acc 0.5990 +2024-07-26 06:16:28,079 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 06:16:28,131 - pyskl - INFO - +mean_acc 0.3455 +2024-07-26 06:16:28,146 - pyskl - INFO - Epoch(val) [112][309] top1_acc: 0.3456, top5_acc: 0.5990, mean_class_accuracy: 0.3455 +2024-07-26 06:20:20,037 - pyskl - INFO - Epoch [113][100/3746] lr: 1.500e-02, eta: 1 day, 8:15:04, time: 2.319, data_time: 1.311, memory: 15990, top1_acc: 0.4258, top5_acc: 0.6773, loss_cls: 3.2401, loss: 3.2401 +2024-07-26 06:21:41,456 - pyskl - INFO - Epoch [113][200/3746] lr: 1.498e-02, eta: 1 day, 8:13:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4219, top5_acc: 0.6877, loss_cls: 3.2079, loss: 3.2079 +2024-07-26 06:23:03,160 - pyskl - INFO - Epoch [113][300/3746] lr: 1.496e-02, eta: 1 day, 8:12:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4311, top5_acc: 0.6894, loss_cls: 3.1805, loss: 3.1805 +2024-07-26 06:24:24,884 - pyskl - INFO - Epoch [113][400/3746] lr: 1.494e-02, eta: 1 day, 8:10:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6773, loss_cls: 3.2619, loss: 3.2619 +2024-07-26 06:25:46,704 - pyskl - INFO - Epoch [113][500/3746] lr: 1.492e-02, eta: 1 day, 8:09:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6852, loss_cls: 3.1964, loss: 3.1964 +2024-07-26 06:27:09,049 - pyskl - INFO - Epoch [113][600/3746] lr: 1.490e-02, eta: 1 day, 8:08:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6778, loss_cls: 3.2739, loss: 3.2739 +2024-07-26 06:28:30,648 - pyskl - INFO - Epoch [113][700/3746] lr: 1.488e-02, eta: 1 day, 8:06:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4183, top5_acc: 0.6802, loss_cls: 3.2455, loss: 3.2455 +2024-07-26 06:29:51,932 - pyskl - INFO - Epoch [113][800/3746] lr: 1.486e-02, eta: 1 day, 8:05:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6767, loss_cls: 3.2678, loss: 3.2678 +2024-07-26 06:31:13,624 - pyskl - INFO - Epoch [113][900/3746] lr: 1.484e-02, eta: 1 day, 8:04:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4158, top5_acc: 0.6809, loss_cls: 3.2663, loss: 3.2663 +2024-07-26 06:32:35,952 - pyskl - INFO - Epoch [113][1000/3746] lr: 1.482e-02, eta: 1 day, 8:02:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6702, loss_cls: 3.3161, loss: 3.3161 +2024-07-26 06:33:57,502 - pyskl - INFO - Epoch [113][1100/3746] lr: 1.480e-02, eta: 1 day, 8:01:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6817, loss_cls: 3.2572, loss: 3.2572 +2024-07-26 06:35:18,762 - pyskl - INFO - Epoch [113][1200/3746] lr: 1.478e-02, eta: 1 day, 8:00:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6722, loss_cls: 3.3137, loss: 3.3137 +2024-07-26 06:36:40,230 - pyskl - INFO - Epoch [113][1300/3746] lr: 1.476e-02, eta: 1 day, 7:58:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4114, top5_acc: 0.6758, loss_cls: 3.2952, loss: 3.2952 +2024-07-26 06:38:01,596 - pyskl - INFO - Epoch [113][1400/3746] lr: 1.474e-02, eta: 1 day, 7:57:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6780, loss_cls: 3.2709, loss: 3.2709 +2024-07-26 06:39:22,792 - pyskl - INFO - Epoch [113][1500/3746] lr: 1.472e-02, eta: 1 day, 7:56:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4269, top5_acc: 0.6858, loss_cls: 3.2161, loss: 3.2161 +2024-07-26 06:40:44,037 - pyskl - INFO - Epoch [113][1600/3746] lr: 1.470e-02, eta: 1 day, 7:54:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4213, top5_acc: 0.6731, loss_cls: 3.2492, loss: 3.2492 +2024-07-26 06:42:05,556 - pyskl - INFO - Epoch [113][1700/3746] lr: 1.468e-02, eta: 1 day, 7:53:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6802, loss_cls: 3.2535, loss: 3.2535 +2024-07-26 06:43:27,284 - pyskl - INFO - Epoch [113][1800/3746] lr: 1.466e-02, eta: 1 day, 7:51:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4153, top5_acc: 0.6761, loss_cls: 3.2709, loss: 3.2709 +2024-07-26 06:44:49,063 - pyskl - INFO - Epoch [113][1900/3746] lr: 1.464e-02, eta: 1 day, 7:50:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6722, loss_cls: 3.2606, loss: 3.2606 +2024-07-26 06:46:10,827 - pyskl - INFO - Epoch [113][2000/3746] lr: 1.462e-02, eta: 1 day, 7:49:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6853, loss_cls: 3.2492, loss: 3.2492 +2024-07-26 06:47:33,685 - pyskl - INFO - Epoch [113][2100/3746] lr: 1.460e-02, eta: 1 day, 7:47:52, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4114, top5_acc: 0.6717, loss_cls: 3.2844, loss: 3.2844 +2024-07-26 06:48:55,616 - pyskl - INFO - Epoch [113][2200/3746] lr: 1.458e-02, eta: 1 day, 7:46:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4147, top5_acc: 0.6672, loss_cls: 3.3134, loss: 3.3134 +2024-07-26 06:50:18,969 - pyskl - INFO - Epoch [113][2300/3746] lr: 1.456e-02, eta: 1 day, 7:45:09, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4125, top5_acc: 0.6750, loss_cls: 3.3007, loss: 3.3007 +2024-07-26 06:51:40,920 - pyskl - INFO - Epoch [113][2400/3746] lr: 1.454e-02, eta: 1 day, 7:43:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4109, top5_acc: 0.6727, loss_cls: 3.2872, loss: 3.2872 +2024-07-26 06:53:02,477 - pyskl - INFO - Epoch [113][2500/3746] lr: 1.452e-02, eta: 1 day, 7:42:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4220, top5_acc: 0.6748, loss_cls: 3.2659, loss: 3.2659 +2024-07-26 06:54:24,157 - pyskl - INFO - Epoch [113][2600/3746] lr: 1.450e-02, eta: 1 day, 7:41:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6570, loss_cls: 3.3571, loss: 3.3571 +2024-07-26 06:55:45,661 - pyskl - INFO - Epoch [113][2700/3746] lr: 1.448e-02, eta: 1 day, 7:39:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4153, top5_acc: 0.6784, loss_cls: 3.2628, loss: 3.2628 +2024-07-26 06:57:06,596 - pyskl - INFO - Epoch [113][2800/3746] lr: 1.446e-02, eta: 1 day, 7:38:21, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4228, top5_acc: 0.6781, loss_cls: 3.2581, loss: 3.2581 +2024-07-26 06:58:27,729 - pyskl - INFO - Epoch [113][2900/3746] lr: 1.444e-02, eta: 1 day, 7:36:59, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6711, loss_cls: 3.2900, loss: 3.2900 +2024-07-26 06:59:48,969 - pyskl - INFO - Epoch [113][3000/3746] lr: 1.442e-02, eta: 1 day, 7:35:37, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4078, top5_acc: 0.6714, loss_cls: 3.2978, loss: 3.2978 +2024-07-26 07:01:10,411 - pyskl - INFO - Epoch [113][3100/3746] lr: 1.440e-02, eta: 1 day, 7:34:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4136, top5_acc: 0.6736, loss_cls: 3.3054, loss: 3.3054 +2024-07-26 07:02:31,751 - pyskl - INFO - Epoch [113][3200/3746] lr: 1.438e-02, eta: 1 day, 7:32:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6730, loss_cls: 3.2885, loss: 3.2885 +2024-07-26 07:03:52,869 - pyskl - INFO - Epoch [113][3300/3746] lr: 1.436e-02, eta: 1 day, 7:31:32, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6770, loss_cls: 3.2897, loss: 3.2897 +2024-07-26 07:05:14,415 - pyskl - INFO - Epoch [113][3400/3746] lr: 1.434e-02, eta: 1 day, 7:30:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6706, loss_cls: 3.3064, loss: 3.3064 +2024-07-26 07:06:36,232 - pyskl - INFO - Epoch [113][3500/3746] lr: 1.432e-02, eta: 1 day, 7:28:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6697, loss_cls: 3.2931, loss: 3.2931 +2024-07-26 07:07:57,388 - pyskl - INFO - Epoch [113][3600/3746] lr: 1.431e-02, eta: 1 day, 7:27:27, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6677, loss_cls: 3.3350, loss: 3.3350 +2024-07-26 07:09:19,807 - pyskl - INFO - Epoch [113][3700/3746] lr: 1.429e-02, eta: 1 day, 7:26:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3992, top5_acc: 0.6578, loss_cls: 3.3355, loss: 3.3355 +2024-07-26 07:09:59,110 - pyskl - INFO - Saving checkpoint at 113 epochs +2024-07-26 07:11:50,204 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 07:11:50,867 - pyskl - INFO - +top1_acc 0.3614 +top5_acc 0.6116 +2024-07-26 07:11:50,867 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 07:11:50,906 - pyskl - INFO - +mean_acc 0.3611 +2024-07-26 07:11:50,911 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_109.pth was removed +2024-07-26 07:11:51,174 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_113.pth. +2024-07-26 07:11:51,175 - pyskl - INFO - Best top1_acc is 0.3614 at 113 epoch. +2024-07-26 07:11:51,186 - pyskl - INFO - Epoch(val) [113][309] top1_acc: 0.3614, top5_acc: 0.6116, mean_class_accuracy: 0.3611 +2024-07-26 07:15:45,173 - pyskl - INFO - Epoch [114][100/3746] lr: 1.426e-02, eta: 1 day, 7:24:44, time: 2.340, data_time: 1.323, memory: 15990, top1_acc: 0.4316, top5_acc: 0.6998, loss_cls: 3.1654, loss: 3.1654 +2024-07-26 07:17:06,703 - pyskl - INFO - Epoch [114][200/3746] lr: 1.424e-02, eta: 1 day, 7:23:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6778, loss_cls: 3.2490, loss: 3.2490 +2024-07-26 07:18:28,120 - pyskl - INFO - Epoch [114][300/3746] lr: 1.422e-02, eta: 1 day, 7:22:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4313, top5_acc: 0.6886, loss_cls: 3.1906, loss: 3.1906 +2024-07-26 07:19:49,850 - pyskl - INFO - Epoch [114][400/3746] lr: 1.420e-02, eta: 1 day, 7:20:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4339, top5_acc: 0.6936, loss_cls: 3.1747, loss: 3.1747 +2024-07-26 07:21:11,567 - pyskl - INFO - Epoch [114][500/3746] lr: 1.418e-02, eta: 1 day, 7:19:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4302, top5_acc: 0.6845, loss_cls: 3.2123, loss: 3.2123 +2024-07-26 07:22:33,434 - pyskl - INFO - Epoch [114][600/3746] lr: 1.416e-02, eta: 1 day, 7:17:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4247, top5_acc: 0.6795, loss_cls: 3.2354, loss: 3.2354 +2024-07-26 07:23:55,357 - pyskl - INFO - Epoch [114][700/3746] lr: 1.414e-02, eta: 1 day, 7:16:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4295, top5_acc: 0.6920, loss_cls: 3.1828, loss: 3.1828 +2024-07-26 07:25:16,749 - pyskl - INFO - Epoch [114][800/3746] lr: 1.412e-02, eta: 1 day, 7:15:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4247, top5_acc: 0.6764, loss_cls: 3.2294, loss: 3.2294 +2024-07-26 07:26:38,355 - pyskl - INFO - Epoch [114][900/3746] lr: 1.410e-02, eta: 1 day, 7:13:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4334, top5_acc: 0.6895, loss_cls: 3.1915, loss: 3.1915 +2024-07-26 07:27:59,705 - pyskl - INFO - Epoch [114][1000/3746] lr: 1.408e-02, eta: 1 day, 7:12:29, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4153, top5_acc: 0.6745, loss_cls: 3.2767, loss: 3.2767 +2024-07-26 07:29:20,788 - pyskl - INFO - Epoch [114][1100/3746] lr: 1.406e-02, eta: 1 day, 7:11:07, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4125, top5_acc: 0.6761, loss_cls: 3.2693, loss: 3.2693 +2024-07-26 07:30:42,452 - pyskl - INFO - Epoch [114][1200/3746] lr: 1.404e-02, eta: 1 day, 7:09:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6789, loss_cls: 3.2593, loss: 3.2593 +2024-07-26 07:32:04,330 - pyskl - INFO - Epoch [114][1300/3746] lr: 1.402e-02, eta: 1 day, 7:08:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4106, top5_acc: 0.6689, loss_cls: 3.3076, loss: 3.3076 +2024-07-26 07:33:25,844 - pyskl - INFO - Epoch [114][1400/3746] lr: 1.400e-02, eta: 1 day, 7:07:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6859, loss_cls: 3.2092, loss: 3.2092 +2024-07-26 07:34:47,814 - pyskl - INFO - Epoch [114][1500/3746] lr: 1.398e-02, eta: 1 day, 7:05:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6777, loss_cls: 3.2656, loss: 3.2656 +2024-07-26 07:36:09,352 - pyskl - INFO - Epoch [114][1600/3746] lr: 1.397e-02, eta: 1 day, 7:04:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4320, top5_acc: 0.6870, loss_cls: 3.2143, loss: 3.2143 +2024-07-26 07:37:30,636 - pyskl - INFO - Epoch [114][1700/3746] lr: 1.395e-02, eta: 1 day, 7:02:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4163, top5_acc: 0.6719, loss_cls: 3.2854, loss: 3.2854 +2024-07-26 07:38:51,644 - pyskl - INFO - Epoch [114][1800/3746] lr: 1.393e-02, eta: 1 day, 7:01:36, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4155, top5_acc: 0.6867, loss_cls: 3.2319, loss: 3.2319 +2024-07-26 07:40:13,005 - pyskl - INFO - Epoch [114][1900/3746] lr: 1.391e-02, eta: 1 day, 7:00:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6808, loss_cls: 3.2455, loss: 3.2455 +2024-07-26 07:41:34,912 - pyskl - INFO - Epoch [114][2000/3746] lr: 1.389e-02, eta: 1 day, 6:58:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4245, top5_acc: 0.6733, loss_cls: 3.2552, loss: 3.2552 +2024-07-26 07:42:56,965 - pyskl - INFO - Epoch [114][2100/3746] lr: 1.387e-02, eta: 1 day, 6:57:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4214, top5_acc: 0.6750, loss_cls: 3.2723, loss: 3.2723 +2024-07-26 07:44:18,986 - pyskl - INFO - Epoch [114][2200/3746] lr: 1.385e-02, eta: 1 day, 6:56:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6734, loss_cls: 3.3004, loss: 3.3004 +2024-07-26 07:45:40,893 - pyskl - INFO - Epoch [114][2300/3746] lr: 1.383e-02, eta: 1 day, 6:54:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4197, top5_acc: 0.6858, loss_cls: 3.2202, loss: 3.2202 +2024-07-26 07:47:03,006 - pyskl - INFO - Epoch [114][2400/3746] lr: 1.381e-02, eta: 1 day, 6:53:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4181, top5_acc: 0.6786, loss_cls: 3.2658, loss: 3.2658 +2024-07-26 07:48:24,073 - pyskl - INFO - Epoch [114][2500/3746] lr: 1.379e-02, eta: 1 day, 6:52:04, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4141, top5_acc: 0.6791, loss_cls: 3.2841, loss: 3.2841 +2024-07-26 07:49:46,107 - pyskl - INFO - Epoch [114][2600/3746] lr: 1.377e-02, eta: 1 day, 6:50:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4178, top5_acc: 0.6748, loss_cls: 3.2647, loss: 3.2647 +2024-07-26 07:51:07,514 - pyskl - INFO - Epoch [114][2700/3746] lr: 1.375e-02, eta: 1 day, 6:49:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4252, top5_acc: 0.6705, loss_cls: 3.2795, loss: 3.2795 +2024-07-26 07:52:29,041 - pyskl - INFO - Epoch [114][2800/3746] lr: 1.373e-02, eta: 1 day, 6:47:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6620, loss_cls: 3.3432, loss: 3.3432 +2024-07-26 07:53:50,576 - pyskl - INFO - Epoch [114][2900/3746] lr: 1.371e-02, eta: 1 day, 6:46:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4164, top5_acc: 0.6775, loss_cls: 3.2955, loss: 3.2955 +2024-07-26 07:55:12,048 - pyskl - INFO - Epoch [114][3000/3746] lr: 1.369e-02, eta: 1 day, 6:45:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4142, top5_acc: 0.6783, loss_cls: 3.2597, loss: 3.2597 +2024-07-26 07:56:33,403 - pyskl - INFO - Epoch [114][3100/3746] lr: 1.368e-02, eta: 1 day, 6:43:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6661, loss_cls: 3.3293, loss: 3.3293 +2024-07-26 07:57:55,648 - pyskl - INFO - Epoch [114][3200/3746] lr: 1.366e-02, eta: 1 day, 6:42:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4273, top5_acc: 0.6761, loss_cls: 3.2450, loss: 3.2450 +2024-07-26 07:59:16,873 - pyskl - INFO - Epoch [114][3300/3746] lr: 1.364e-02, eta: 1 day, 6:41:11, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6672, loss_cls: 3.2885, loss: 3.2885 +2024-07-26 08:00:38,290 - pyskl - INFO - Epoch [114][3400/3746] lr: 1.362e-02, eta: 1 day, 6:39:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4109, top5_acc: 0.6678, loss_cls: 3.3095, loss: 3.3095 +2024-07-26 08:02:00,055 - pyskl - INFO - Epoch [114][3500/3746] lr: 1.360e-02, eta: 1 day, 6:38:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6752, loss_cls: 3.2683, loss: 3.2683 +2024-07-26 08:03:21,447 - pyskl - INFO - Epoch [114][3600/3746] lr: 1.358e-02, eta: 1 day, 6:37:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6784, loss_cls: 3.2305, loss: 3.2305 +2024-07-26 08:04:42,975 - pyskl - INFO - Epoch [114][3700/3746] lr: 1.356e-02, eta: 1 day, 6:35:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6869, loss_cls: 3.2404, loss: 3.2404 +2024-07-26 08:05:22,233 - pyskl - INFO - Saving checkpoint at 114 epochs +2024-07-26 08:07:14,039 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 08:07:14,696 - pyskl - INFO - +top1_acc 0.3545 +top5_acc 0.6124 +2024-07-26 08:07:14,696 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 08:07:14,737 - pyskl - INFO - +mean_acc 0.3541 +2024-07-26 08:07:14,750 - pyskl - INFO - Epoch(val) [114][309] top1_acc: 0.3545, top5_acc: 0.6124, mean_class_accuracy: 0.3541 +2024-07-26 08:11:05,484 - pyskl - INFO - Epoch [115][100/3746] lr: 1.353e-02, eta: 1 day, 6:34:20, time: 2.307, data_time: 1.331, memory: 15990, top1_acc: 0.4416, top5_acc: 0.6964, loss_cls: 3.1258, loss: 3.1258 +2024-07-26 08:12:26,879 - pyskl - INFO - Epoch [115][200/3746] lr: 1.351e-02, eta: 1 day, 6:32:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4320, top5_acc: 0.6941, loss_cls: 3.1855, loss: 3.1855 +2024-07-26 08:13:48,480 - pyskl - INFO - Epoch [115][300/3746] lr: 1.349e-02, eta: 1 day, 6:31:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4298, top5_acc: 0.6909, loss_cls: 3.1866, loss: 3.1866 +2024-07-26 08:15:10,041 - pyskl - INFO - Epoch [115][400/3746] lr: 1.348e-02, eta: 1 day, 6:30:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4289, top5_acc: 0.6845, loss_cls: 3.2025, loss: 3.2025 +2024-07-26 08:16:31,987 - pyskl - INFO - Epoch [115][500/3746] lr: 1.346e-02, eta: 1 day, 6:28:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6827, loss_cls: 3.2438, loss: 3.2438 +2024-07-26 08:17:53,427 - pyskl - INFO - Epoch [115][600/3746] lr: 1.344e-02, eta: 1 day, 6:27:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.6836, loss_cls: 3.1981, loss: 3.1981 +2024-07-26 08:19:15,152 - pyskl - INFO - Epoch [115][700/3746] lr: 1.342e-02, eta: 1 day, 6:26:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4370, top5_acc: 0.6970, loss_cls: 3.1643, loss: 3.1643 +2024-07-26 08:20:36,344 - pyskl - INFO - Epoch [115][800/3746] lr: 1.340e-02, eta: 1 day, 6:24:48, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6847, loss_cls: 3.2131, loss: 3.2131 +2024-07-26 08:21:57,470 - pyskl - INFO - Epoch [115][900/3746] lr: 1.338e-02, eta: 1 day, 6:23:26, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6842, loss_cls: 3.2473, loss: 3.2473 +2024-07-26 08:23:18,790 - pyskl - INFO - Epoch [115][1000/3746] lr: 1.336e-02, eta: 1 day, 6:22:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4280, top5_acc: 0.6798, loss_cls: 3.2192, loss: 3.2192 +2024-07-26 08:24:40,144 - pyskl - INFO - Epoch [115][1100/3746] lr: 1.334e-02, eta: 1 day, 6:20:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4273, top5_acc: 0.6859, loss_cls: 3.2088, loss: 3.2088 +2024-07-26 08:26:01,382 - pyskl - INFO - Epoch [115][1200/3746] lr: 1.332e-02, eta: 1 day, 6:19:21, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4202, top5_acc: 0.6745, loss_cls: 3.2795, loss: 3.2795 +2024-07-26 08:27:22,362 - pyskl - INFO - Epoch [115][1300/3746] lr: 1.330e-02, eta: 1 day, 6:17:59, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6756, loss_cls: 3.2807, loss: 3.2807 +2024-07-26 08:28:43,544 - pyskl - INFO - Epoch [115][1400/3746] lr: 1.328e-02, eta: 1 day, 6:16:37, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4328, top5_acc: 0.6869, loss_cls: 3.1875, loss: 3.1875 +2024-07-26 08:30:04,905 - pyskl - INFO - Epoch [115][1500/3746] lr: 1.327e-02, eta: 1 day, 6:15:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4219, top5_acc: 0.6823, loss_cls: 3.2188, loss: 3.2188 +2024-07-26 08:31:26,734 - pyskl - INFO - Epoch [115][1600/3746] lr: 1.325e-02, eta: 1 day, 6:13:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4263, top5_acc: 0.6794, loss_cls: 3.2142, loss: 3.2142 +2024-07-26 08:32:48,425 - pyskl - INFO - Epoch [115][1700/3746] lr: 1.323e-02, eta: 1 day, 6:12:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6841, loss_cls: 3.2270, loss: 3.2270 +2024-07-26 08:34:10,066 - pyskl - INFO - Epoch [115][1800/3746] lr: 1.321e-02, eta: 1 day, 6:11:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6795, loss_cls: 3.2446, loss: 3.2446 +2024-07-26 08:35:32,105 - pyskl - INFO - Epoch [115][1900/3746] lr: 1.319e-02, eta: 1 day, 6:09:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6800, loss_cls: 3.2589, loss: 3.2589 +2024-07-26 08:36:53,942 - pyskl - INFO - Epoch [115][2000/3746] lr: 1.317e-02, eta: 1 day, 6:08:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6820, loss_cls: 3.2551, loss: 3.2551 +2024-07-26 08:38:15,766 - pyskl - INFO - Epoch [115][2100/3746] lr: 1.315e-02, eta: 1 day, 6:07:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6827, loss_cls: 3.2373, loss: 3.2373 +2024-07-26 08:39:38,613 - pyskl - INFO - Epoch [115][2200/3746] lr: 1.313e-02, eta: 1 day, 6:05:45, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4266, top5_acc: 0.6852, loss_cls: 3.2195, loss: 3.2195 +2024-07-26 08:41:00,405 - pyskl - INFO - Epoch [115][2300/3746] lr: 1.311e-02, eta: 1 day, 6:04:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4308, top5_acc: 0.6875, loss_cls: 3.2218, loss: 3.2218 +2024-07-26 08:42:22,385 - pyskl - INFO - Epoch [115][2400/3746] lr: 1.310e-02, eta: 1 day, 6:03:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6878, loss_cls: 3.2230, loss: 3.2230 +2024-07-26 08:43:43,914 - pyskl - INFO - Epoch [115][2500/3746] lr: 1.308e-02, eta: 1 day, 6:01:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6698, loss_cls: 3.2824, loss: 3.2824 +2024-07-26 08:45:05,262 - pyskl - INFO - Epoch [115][2600/3746] lr: 1.306e-02, eta: 1 day, 6:00:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4219, top5_acc: 0.6806, loss_cls: 3.2385, loss: 3.2385 +2024-07-26 08:46:26,453 - pyskl - INFO - Epoch [115][2700/3746] lr: 1.304e-02, eta: 1 day, 5:58:56, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4259, top5_acc: 0.6781, loss_cls: 3.2264, loss: 3.2264 +2024-07-26 08:47:47,503 - pyskl - INFO - Epoch [115][2800/3746] lr: 1.302e-02, eta: 1 day, 5:57:34, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4181, top5_acc: 0.6725, loss_cls: 3.2901, loss: 3.2901 +2024-07-26 08:49:08,839 - pyskl - INFO - Epoch [115][2900/3746] lr: 1.300e-02, eta: 1 day, 5:56:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6881, loss_cls: 3.2135, loss: 3.2135 +2024-07-26 08:50:30,110 - pyskl - INFO - Epoch [115][3000/3746] lr: 1.298e-02, eta: 1 day, 5:54:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6814, loss_cls: 3.2418, loss: 3.2418 +2024-07-26 08:51:51,346 - pyskl - INFO - Epoch [115][3100/3746] lr: 1.296e-02, eta: 1 day, 5:53:29, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6822, loss_cls: 3.2112, loss: 3.2112 +2024-07-26 08:53:12,960 - pyskl - INFO - Epoch [115][3200/3746] lr: 1.295e-02, eta: 1 day, 5:52:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6730, loss_cls: 3.2863, loss: 3.2863 +2024-07-26 08:54:34,383 - pyskl - INFO - Epoch [115][3300/3746] lr: 1.293e-02, eta: 1 day, 5:50:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6730, loss_cls: 3.2817, loss: 3.2817 +2024-07-26 08:55:55,493 - pyskl - INFO - Epoch [115][3400/3746] lr: 1.291e-02, eta: 1 day, 5:49:23, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6802, loss_cls: 3.2460, loss: 3.2460 +2024-07-26 08:57:16,933 - pyskl - INFO - Epoch [115][3500/3746] lr: 1.289e-02, eta: 1 day, 5:48:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4130, top5_acc: 0.6830, loss_cls: 3.2489, loss: 3.2489 +2024-07-26 08:58:38,589 - pyskl - INFO - Epoch [115][3600/3746] lr: 1.287e-02, eta: 1 day, 5:46:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4248, top5_acc: 0.6794, loss_cls: 3.2324, loss: 3.2324 +2024-07-26 09:00:00,017 - pyskl - INFO - Epoch [115][3700/3746] lr: 1.285e-02, eta: 1 day, 5:45:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4283, top5_acc: 0.6777, loss_cls: 3.2486, loss: 3.2486 +2024-07-26 09:00:39,567 - pyskl - INFO - Saving checkpoint at 115 epochs +2024-07-26 09:02:32,782 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 09:02:33,472 - pyskl - INFO - +top1_acc 0.3595 +top5_acc 0.6188 +2024-07-26 09:02:33,472 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 09:02:33,517 - pyskl - INFO - +mean_acc 0.3592 +2024-07-26 09:02:33,530 - pyskl - INFO - Epoch(val) [115][309] top1_acc: 0.3595, top5_acc: 0.6188, mean_class_accuracy: 0.3592 +2024-07-26 09:06:35,676 - pyskl - INFO - Epoch [116][100/3746] lr: 1.282e-02, eta: 1 day, 5:43:56, time: 2.421, data_time: 1.427, memory: 15990, top1_acc: 0.4411, top5_acc: 0.6936, loss_cls: 3.1258, loss: 3.1258 +2024-07-26 09:07:58,316 - pyskl - INFO - Epoch [116][200/3746] lr: 1.281e-02, eta: 1 day, 5:42:35, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.6953, loss_cls: 3.1210, loss: 3.1210 +2024-07-26 09:09:20,757 - pyskl - INFO - Epoch [116][300/3746] lr: 1.279e-02, eta: 1 day, 5:41:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4342, top5_acc: 0.6855, loss_cls: 3.1876, loss: 3.1876 +2024-07-26 09:10:42,985 - pyskl - INFO - Epoch [116][400/3746] lr: 1.277e-02, eta: 1 day, 5:39:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4339, top5_acc: 0.6939, loss_cls: 3.1671, loss: 3.1671 +2024-07-26 09:12:05,228 - pyskl - INFO - Epoch [116][500/3746] lr: 1.275e-02, eta: 1 day, 5:38:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4252, top5_acc: 0.6861, loss_cls: 3.1679, loss: 3.1679 +2024-07-26 09:13:26,772 - pyskl - INFO - Epoch [116][600/3746] lr: 1.273e-02, eta: 1 day, 5:37:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6827, loss_cls: 3.2243, loss: 3.2243 +2024-07-26 09:14:49,276 - pyskl - INFO - Epoch [116][700/3746] lr: 1.271e-02, eta: 1 day, 5:35:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4311, top5_acc: 0.6919, loss_cls: 3.1912, loss: 3.1912 +2024-07-26 09:16:11,219 - pyskl - INFO - Epoch [116][800/3746] lr: 1.269e-02, eta: 1 day, 5:34:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4361, top5_acc: 0.6950, loss_cls: 3.1607, loss: 3.1607 +2024-07-26 09:17:33,008 - pyskl - INFO - Epoch [116][900/3746] lr: 1.268e-02, eta: 1 day, 5:33:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4273, top5_acc: 0.6950, loss_cls: 3.1820, loss: 3.1820 +2024-07-26 09:18:54,445 - pyskl - INFO - Epoch [116][1000/3746] lr: 1.266e-02, eta: 1 day, 5:31:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4339, top5_acc: 0.6881, loss_cls: 3.2013, loss: 3.2013 +2024-07-26 09:20:15,828 - pyskl - INFO - Epoch [116][1100/3746] lr: 1.264e-02, eta: 1 day, 5:30:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6767, loss_cls: 3.2633, loss: 3.2633 +2024-07-26 09:21:37,470 - pyskl - INFO - Epoch [116][1200/3746] lr: 1.262e-02, eta: 1 day, 5:28:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6870, loss_cls: 3.2215, loss: 3.2215 +2024-07-26 09:22:59,465 - pyskl - INFO - Epoch [116][1300/3746] lr: 1.260e-02, eta: 1 day, 5:27:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4316, top5_acc: 0.6828, loss_cls: 3.2273, loss: 3.2273 +2024-07-26 09:24:20,805 - pyskl - INFO - Epoch [116][1400/3746] lr: 1.258e-02, eta: 1 day, 5:26:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4358, top5_acc: 0.6942, loss_cls: 3.1420, loss: 3.1420 +2024-07-26 09:25:42,365 - pyskl - INFO - Epoch [116][1500/3746] lr: 1.256e-02, eta: 1 day, 5:24:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4341, top5_acc: 0.6958, loss_cls: 3.1765, loss: 3.1765 +2024-07-26 09:27:03,522 - pyskl - INFO - Epoch [116][1600/3746] lr: 1.255e-02, eta: 1 day, 5:23:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4291, top5_acc: 0.6878, loss_cls: 3.1998, loss: 3.1998 +2024-07-26 09:28:24,633 - pyskl - INFO - Epoch [116][1700/3746] lr: 1.253e-02, eta: 1 day, 5:22:10, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4309, top5_acc: 0.6881, loss_cls: 3.2130, loss: 3.2130 +2024-07-26 09:29:46,167 - pyskl - INFO - Epoch [116][1800/3746] lr: 1.251e-02, eta: 1 day, 5:20:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4317, top5_acc: 0.6883, loss_cls: 3.1761, loss: 3.1761 +2024-07-26 09:31:07,602 - pyskl - INFO - Epoch [116][1900/3746] lr: 1.249e-02, eta: 1 day, 5:19:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6783, loss_cls: 3.2517, loss: 3.2517 +2024-07-26 09:32:29,779 - pyskl - INFO - Epoch [116][2000/3746] lr: 1.247e-02, eta: 1 day, 5:18:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4323, top5_acc: 0.6937, loss_cls: 3.1817, loss: 3.1817 +2024-07-26 09:33:51,578 - pyskl - INFO - Epoch [116][2100/3746] lr: 1.245e-02, eta: 1 day, 5:16:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4344, top5_acc: 0.6858, loss_cls: 3.2176, loss: 3.2176 +2024-07-26 09:35:14,091 - pyskl - INFO - Epoch [116][2200/3746] lr: 1.243e-02, eta: 1 day, 5:15:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4369, top5_acc: 0.6842, loss_cls: 3.1772, loss: 3.1772 +2024-07-26 09:36:35,924 - pyskl - INFO - Epoch [116][2300/3746] lr: 1.242e-02, eta: 1 day, 5:14:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4313, top5_acc: 0.6841, loss_cls: 3.2336, loss: 3.2336 +2024-07-26 09:37:57,943 - pyskl - INFO - Epoch [116][2400/3746] lr: 1.240e-02, eta: 1 day, 5:12:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4116, top5_acc: 0.6744, loss_cls: 3.2647, loss: 3.2647 +2024-07-26 09:39:19,777 - pyskl - INFO - Epoch [116][2500/3746] lr: 1.238e-02, eta: 1 day, 5:11:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4228, top5_acc: 0.6797, loss_cls: 3.2226, loss: 3.2226 +2024-07-26 09:40:41,438 - pyskl - INFO - Epoch [116][2600/3746] lr: 1.236e-02, eta: 1 day, 5:09:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4252, top5_acc: 0.6867, loss_cls: 3.2137, loss: 3.2137 +2024-07-26 09:42:03,400 - pyskl - INFO - Epoch [116][2700/3746] lr: 1.234e-02, eta: 1 day, 5:08:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4170, top5_acc: 0.6758, loss_cls: 3.2622, loss: 3.2622 +2024-07-26 09:43:24,653 - pyskl - INFO - Epoch [116][2800/3746] lr: 1.232e-02, eta: 1 day, 5:07:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4213, top5_acc: 0.6781, loss_cls: 3.2449, loss: 3.2449 +2024-07-26 09:44:45,976 - pyskl - INFO - Epoch [116][2900/3746] lr: 1.231e-02, eta: 1 day, 5:05:50, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6906, loss_cls: 3.1925, loss: 3.1925 +2024-07-26 09:46:07,840 - pyskl - INFO - Epoch [116][3000/3746] lr: 1.229e-02, eta: 1 day, 5:04:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4245, top5_acc: 0.6675, loss_cls: 3.2749, loss: 3.2749 +2024-07-26 09:47:29,609 - pyskl - INFO - Epoch [116][3100/3746] lr: 1.227e-02, eta: 1 day, 5:03:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6834, loss_cls: 3.2136, loss: 3.2136 +2024-07-26 09:48:50,702 - pyskl - INFO - Epoch [116][3200/3746] lr: 1.225e-02, eta: 1 day, 5:01:44, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4350, top5_acc: 0.6909, loss_cls: 3.1895, loss: 3.1895 +2024-07-26 09:50:12,247 - pyskl - INFO - Epoch [116][3300/3746] lr: 1.223e-02, eta: 1 day, 5:00:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6756, loss_cls: 3.2527, loss: 3.2527 +2024-07-26 09:51:33,699 - pyskl - INFO - Epoch [116][3400/3746] lr: 1.221e-02, eta: 1 day, 4:59:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4213, top5_acc: 0.6794, loss_cls: 3.2276, loss: 3.2276 +2024-07-26 09:52:55,005 - pyskl - INFO - Epoch [116][3500/3746] lr: 1.220e-02, eta: 1 day, 4:57:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4214, top5_acc: 0.6823, loss_cls: 3.2319, loss: 3.2319 +2024-07-26 09:54:16,742 - pyskl - INFO - Epoch [116][3600/3746] lr: 1.218e-02, eta: 1 day, 4:56:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4241, top5_acc: 0.6850, loss_cls: 3.2245, loss: 3.2245 +2024-07-26 09:55:38,954 - pyskl - INFO - Epoch [116][3700/3746] lr: 1.216e-02, eta: 1 day, 4:54:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4384, top5_acc: 0.6897, loss_cls: 3.1873, loss: 3.1873 +2024-07-26 09:56:18,817 - pyskl - INFO - Saving checkpoint at 116 epochs +2024-07-26 09:58:11,239 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 09:58:12,143 - pyskl - INFO - +top1_acc 0.3616 +top5_acc 0.6128 +2024-07-26 09:58:12,143 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 09:58:12,200 - pyskl - INFO - +mean_acc 0.3612 +2024-07-26 09:58:12,205 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_113.pth was removed +2024-07-26 09:58:12,505 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_116.pth. +2024-07-26 09:58:12,505 - pyskl - INFO - Best top1_acc is 0.3616 at 116 epoch. +2024-07-26 09:58:12,527 - pyskl - INFO - Epoch(val) [116][309] top1_acc: 0.3616, top5_acc: 0.6128, mean_class_accuracy: 0.3612 +2024-07-26 10:02:06,849 - pyskl - INFO - Epoch [117][100/3746] lr: 1.213e-02, eta: 1 day, 4:53:30, time: 2.343, data_time: 1.351, memory: 15990, top1_acc: 0.4395, top5_acc: 0.7070, loss_cls: 3.1277, loss: 3.1277 +2024-07-26 10:03:29,559 - pyskl - INFO - Epoch [117][200/3746] lr: 1.211e-02, eta: 1 day, 4:52:09, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4370, top5_acc: 0.7008, loss_cls: 3.1470, loss: 3.1470 +2024-07-26 10:04:51,267 - pyskl - INFO - Epoch [117][300/3746] lr: 1.210e-02, eta: 1 day, 4:50:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4341, top5_acc: 0.6920, loss_cls: 3.1632, loss: 3.1632 +2024-07-26 10:06:12,990 - pyskl - INFO - Epoch [117][400/3746] lr: 1.208e-02, eta: 1 day, 4:49:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.6944, loss_cls: 3.1271, loss: 3.1271 +2024-07-26 10:07:35,235 - pyskl - INFO - Epoch [117][500/3746] lr: 1.206e-02, eta: 1 day, 4:48:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4311, top5_acc: 0.6919, loss_cls: 3.1626, loss: 3.1626 +2024-07-26 10:08:57,001 - pyskl - INFO - Epoch [117][600/3746] lr: 1.204e-02, eta: 1 day, 4:46:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4291, top5_acc: 0.6927, loss_cls: 3.1895, loss: 3.1895 +2024-07-26 10:10:18,701 - pyskl - INFO - Epoch [117][700/3746] lr: 1.202e-02, eta: 1 day, 4:45:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6941, loss_cls: 3.1711, loss: 3.1711 +2024-07-26 10:11:40,069 - pyskl - INFO - Epoch [117][800/3746] lr: 1.200e-02, eta: 1 day, 4:43:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.6894, loss_cls: 3.1724, loss: 3.1724 +2024-07-26 10:13:01,478 - pyskl - INFO - Epoch [117][900/3746] lr: 1.199e-02, eta: 1 day, 4:42:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4370, top5_acc: 0.7023, loss_cls: 3.1371, loss: 3.1371 +2024-07-26 10:14:22,483 - pyskl - INFO - Epoch [117][1000/3746] lr: 1.197e-02, eta: 1 day, 4:41:15, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4197, top5_acc: 0.6908, loss_cls: 3.2156, loss: 3.2156 +2024-07-26 10:15:43,520 - pyskl - INFO - Epoch [117][1100/3746] lr: 1.195e-02, eta: 1 day, 4:39:53, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4322, top5_acc: 0.6967, loss_cls: 3.1738, loss: 3.1738 +2024-07-26 10:17:04,806 - pyskl - INFO - Epoch [117][1200/3746] lr: 1.193e-02, eta: 1 day, 4:38:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.6942, loss_cls: 3.1403, loss: 3.1403 +2024-07-26 10:18:26,381 - pyskl - INFO - Epoch [117][1300/3746] lr: 1.191e-02, eta: 1 day, 4:37:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6894, loss_cls: 3.2107, loss: 3.2107 +2024-07-26 10:19:48,306 - pyskl - INFO - Epoch [117][1400/3746] lr: 1.190e-02, eta: 1 day, 4:35:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4259, top5_acc: 0.6769, loss_cls: 3.2259, loss: 3.2259 +2024-07-26 10:21:09,732 - pyskl - INFO - Epoch [117][1500/3746] lr: 1.188e-02, eta: 1 day, 4:34:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4277, top5_acc: 0.6877, loss_cls: 3.2008, loss: 3.2008 +2024-07-26 10:22:31,811 - pyskl - INFO - Epoch [117][1600/3746] lr: 1.186e-02, eta: 1 day, 4:33:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4345, top5_acc: 0.6889, loss_cls: 3.1568, loss: 3.1568 +2024-07-26 10:23:52,941 - pyskl - INFO - Epoch [117][1700/3746] lr: 1.184e-02, eta: 1 day, 4:31:42, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4272, top5_acc: 0.6894, loss_cls: 3.2186, loss: 3.2186 +2024-07-26 10:25:14,623 - pyskl - INFO - Epoch [117][1800/3746] lr: 1.182e-02, eta: 1 day, 4:30:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4289, top5_acc: 0.6839, loss_cls: 3.1983, loss: 3.1983 +2024-07-26 10:26:36,320 - pyskl - INFO - Epoch [117][1900/3746] lr: 1.181e-02, eta: 1 day, 4:28:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6964, loss_cls: 3.1529, loss: 3.1529 +2024-07-26 10:27:58,072 - pyskl - INFO - Epoch [117][2000/3746] lr: 1.179e-02, eta: 1 day, 4:27:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4359, top5_acc: 0.6884, loss_cls: 3.1762, loss: 3.1762 +2024-07-26 10:29:19,679 - pyskl - INFO - Epoch [117][2100/3746] lr: 1.177e-02, eta: 1 day, 4:26:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4462, top5_acc: 0.7013, loss_cls: 3.1217, loss: 3.1217 +2024-07-26 10:30:42,372 - pyskl - INFO - Epoch [117][2200/3746] lr: 1.175e-02, eta: 1 day, 4:24:54, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6894, loss_cls: 3.1999, loss: 3.1999 +2024-07-26 10:32:03,768 - pyskl - INFO - Epoch [117][2300/3746] lr: 1.173e-02, eta: 1 day, 4:23:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4350, top5_acc: 0.6900, loss_cls: 3.1715, loss: 3.1715 +2024-07-26 10:33:25,704 - pyskl - INFO - Epoch [117][2400/3746] lr: 1.172e-02, eta: 1 day, 4:22:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4428, top5_acc: 0.6905, loss_cls: 3.1445, loss: 3.1445 +2024-07-26 10:34:47,952 - pyskl - INFO - Epoch [117][2500/3746] lr: 1.170e-02, eta: 1 day, 4:20:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4303, top5_acc: 0.6933, loss_cls: 3.1938, loss: 3.1938 +2024-07-26 10:36:09,349 - pyskl - INFO - Epoch [117][2600/3746] lr: 1.168e-02, eta: 1 day, 4:19:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4387, top5_acc: 0.6942, loss_cls: 3.1508, loss: 3.1508 +2024-07-26 10:37:30,819 - pyskl - INFO - Epoch [117][2700/3746] lr: 1.166e-02, eta: 1 day, 4:18:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6798, loss_cls: 3.2243, loss: 3.2243 +2024-07-26 10:38:52,557 - pyskl - INFO - Epoch [117][2800/3746] lr: 1.164e-02, eta: 1 day, 4:16:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4205, top5_acc: 0.6937, loss_cls: 3.1916, loss: 3.1916 +2024-07-26 10:40:14,076 - pyskl - INFO - Epoch [117][2900/3746] lr: 1.163e-02, eta: 1 day, 4:15:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4356, top5_acc: 0.6948, loss_cls: 3.1628, loss: 3.1628 +2024-07-26 10:41:35,642 - pyskl - INFO - Epoch [117][3000/3746] lr: 1.161e-02, eta: 1 day, 4:14:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4289, top5_acc: 0.6800, loss_cls: 3.2117, loss: 3.2117 +2024-07-26 10:42:57,091 - pyskl - INFO - Epoch [117][3100/3746] lr: 1.159e-02, eta: 1 day, 4:12:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4309, top5_acc: 0.6756, loss_cls: 3.2262, loss: 3.2262 +2024-07-26 10:44:18,719 - pyskl - INFO - Epoch [117][3200/3746] lr: 1.157e-02, eta: 1 day, 4:11:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4394, top5_acc: 0.6939, loss_cls: 3.1606, loss: 3.1606 +2024-07-26 10:45:40,662 - pyskl - INFO - Epoch [117][3300/3746] lr: 1.155e-02, eta: 1 day, 4:09:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4342, top5_acc: 0.6928, loss_cls: 3.1670, loss: 3.1670 +2024-07-26 10:47:02,132 - pyskl - INFO - Epoch [117][3400/3746] lr: 1.154e-02, eta: 1 day, 4:08:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4238, top5_acc: 0.6866, loss_cls: 3.1968, loss: 3.1968 +2024-07-26 10:48:23,449 - pyskl - INFO - Epoch [117][3500/3746] lr: 1.152e-02, eta: 1 day, 4:07:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.6763, loss_cls: 3.1998, loss: 3.1998 +2024-07-26 10:49:45,152 - pyskl - INFO - Epoch [117][3600/3746] lr: 1.150e-02, eta: 1 day, 4:05:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6814, loss_cls: 3.2355, loss: 3.2355 +2024-07-26 10:51:06,553 - pyskl - INFO - Epoch [117][3700/3746] lr: 1.148e-02, eta: 1 day, 4:04:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4300, top5_acc: 0.6761, loss_cls: 3.2470, loss: 3.2470 +2024-07-26 10:51:46,299 - pyskl - INFO - Saving checkpoint at 117 epochs +2024-07-26 10:53:38,393 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 10:53:39,056 - pyskl - INFO - +top1_acc 0.3591 +top5_acc 0.6193 +2024-07-26 10:53:39,056 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 10:53:39,099 - pyskl - INFO - +mean_acc 0.3589 +2024-07-26 10:53:39,112 - pyskl - INFO - Epoch(val) [117][309] top1_acc: 0.3591, top5_acc: 0.6193, mean_class_accuracy: 0.3589 +2024-07-26 10:57:36,281 - pyskl - INFO - Epoch [118][100/3746] lr: 1.146e-02, eta: 1 day, 4:03:01, time: 2.372, data_time: 1.393, memory: 15990, top1_acc: 0.4452, top5_acc: 0.7008, loss_cls: 3.1185, loss: 3.1185 +2024-07-26 10:58:58,245 - pyskl - INFO - Epoch [118][200/3746] lr: 1.144e-02, eta: 1 day, 4:01:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4375, top5_acc: 0.7047, loss_cls: 3.1120, loss: 3.1120 +2024-07-26 11:00:19,949 - pyskl - INFO - Epoch [118][300/3746] lr: 1.142e-02, eta: 1 day, 4:00:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.6953, loss_cls: 3.1368, loss: 3.1368 +2024-07-26 11:01:41,532 - pyskl - INFO - Epoch [118][400/3746] lr: 1.140e-02, eta: 1 day, 3:58:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4344, top5_acc: 0.6934, loss_cls: 3.1482, loss: 3.1482 +2024-07-26 11:03:03,470 - pyskl - INFO - Epoch [118][500/3746] lr: 1.139e-02, eta: 1 day, 3:57:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4412, top5_acc: 0.6953, loss_cls: 3.1284, loss: 3.1284 +2024-07-26 11:04:24,721 - pyskl - INFO - Epoch [118][600/3746] lr: 1.137e-02, eta: 1 day, 3:56:12, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4405, top5_acc: 0.7017, loss_cls: 3.1278, loss: 3.1278 +2024-07-26 11:05:46,666 - pyskl - INFO - Epoch [118][700/3746] lr: 1.135e-02, eta: 1 day, 3:54:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4542, top5_acc: 0.7148, loss_cls: 3.0732, loss: 3.0732 +2024-07-26 11:07:07,844 - pyskl - INFO - Epoch [118][800/3746] lr: 1.133e-02, eta: 1 day, 3:53:29, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4369, top5_acc: 0.6948, loss_cls: 3.1459, loss: 3.1459 +2024-07-26 11:08:30,063 - pyskl - INFO - Epoch [118][900/3746] lr: 1.131e-02, eta: 1 day, 3:52:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4380, top5_acc: 0.6953, loss_cls: 3.1562, loss: 3.1562 +2024-07-26 11:09:51,584 - pyskl - INFO - Epoch [118][1000/3746] lr: 1.130e-02, eta: 1 day, 3:50:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4366, top5_acc: 0.6955, loss_cls: 3.1628, loss: 3.1628 +2024-07-26 11:11:13,317 - pyskl - INFO - Epoch [118][1100/3746] lr: 1.128e-02, eta: 1 day, 3:49:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6898, loss_cls: 3.1805, loss: 3.1805 +2024-07-26 11:12:34,736 - pyskl - INFO - Epoch [118][1200/3746] lr: 1.126e-02, eta: 1 day, 3:48:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.7023, loss_cls: 3.1368, loss: 3.1368 +2024-07-26 11:13:56,302 - pyskl - INFO - Epoch [118][1300/3746] lr: 1.124e-02, eta: 1 day, 3:46:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4405, top5_acc: 0.7002, loss_cls: 3.0925, loss: 3.0925 +2024-07-26 11:15:17,898 - pyskl - INFO - Epoch [118][1400/3746] lr: 1.123e-02, eta: 1 day, 3:45:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.6956, loss_cls: 3.1340, loss: 3.1340 +2024-07-26 11:16:39,831 - pyskl - INFO - Epoch [118][1500/3746] lr: 1.121e-02, eta: 1 day, 3:43:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4377, top5_acc: 0.6980, loss_cls: 3.1589, loss: 3.1589 +2024-07-26 11:18:01,328 - pyskl - INFO - Epoch [118][1600/3746] lr: 1.119e-02, eta: 1 day, 3:42:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4331, top5_acc: 0.6883, loss_cls: 3.1809, loss: 3.1809 +2024-07-26 11:19:23,120 - pyskl - INFO - Epoch [118][1700/3746] lr: 1.117e-02, eta: 1 day, 3:41:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4406, top5_acc: 0.6923, loss_cls: 3.1419, loss: 3.1419 +2024-07-26 11:20:44,787 - pyskl - INFO - Epoch [118][1800/3746] lr: 1.116e-02, eta: 1 day, 3:39:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4378, top5_acc: 0.6911, loss_cls: 3.1697, loss: 3.1697 +2024-07-26 11:22:06,462 - pyskl - INFO - Epoch [118][1900/3746] lr: 1.114e-02, eta: 1 day, 3:38:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4505, top5_acc: 0.6934, loss_cls: 3.1109, loss: 3.1109 +2024-07-26 11:23:28,232 - pyskl - INFO - Epoch [118][2000/3746] lr: 1.112e-02, eta: 1 day, 3:37:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4323, top5_acc: 0.6859, loss_cls: 3.1773, loss: 3.1773 +2024-07-26 11:24:49,934 - pyskl - INFO - Epoch [118][2100/3746] lr: 1.110e-02, eta: 1 day, 3:35:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4414, top5_acc: 0.6987, loss_cls: 3.1336, loss: 3.1336 +2024-07-26 11:26:13,710 - pyskl - INFO - Epoch [118][2200/3746] lr: 1.109e-02, eta: 1 day, 3:34:24, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4433, top5_acc: 0.7069, loss_cls: 3.1112, loss: 3.1112 +2024-07-26 11:27:35,593 - pyskl - INFO - Epoch [118][2300/3746] lr: 1.107e-02, eta: 1 day, 3:33:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4314, top5_acc: 0.6914, loss_cls: 3.1859, loss: 3.1859 +2024-07-26 11:28:58,180 - pyskl - INFO - Epoch [118][2400/3746] lr: 1.105e-02, eta: 1 day, 3:31:41, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.6958, loss_cls: 3.1470, loss: 3.1470 +2024-07-26 11:30:20,268 - pyskl - INFO - Epoch [118][2500/3746] lr: 1.103e-02, eta: 1 day, 3:30:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6858, loss_cls: 3.2107, loss: 3.2107 +2024-07-26 11:31:41,795 - pyskl - INFO - Epoch [118][2600/3746] lr: 1.102e-02, eta: 1 day, 3:28:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.6933, loss_cls: 3.1546, loss: 3.1546 +2024-07-26 11:33:03,740 - pyskl - INFO - Epoch [118][2700/3746] lr: 1.100e-02, eta: 1 day, 3:27:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4369, top5_acc: 0.6897, loss_cls: 3.1532, loss: 3.1532 +2024-07-26 11:34:25,226 - pyskl - INFO - Epoch [118][2800/3746] lr: 1.098e-02, eta: 1 day, 3:26:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4386, top5_acc: 0.6972, loss_cls: 3.1589, loss: 3.1589 +2024-07-26 11:35:47,662 - pyskl - INFO - Epoch [118][2900/3746] lr: 1.096e-02, eta: 1 day, 3:24:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6761, loss_cls: 3.2560, loss: 3.2560 +2024-07-26 11:37:09,077 - pyskl - INFO - Epoch [118][3000/3746] lr: 1.095e-02, eta: 1 day, 3:23:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4369, top5_acc: 0.6956, loss_cls: 3.1600, loss: 3.1600 +2024-07-26 11:38:31,218 - pyskl - INFO - Epoch [118][3100/3746] lr: 1.093e-02, eta: 1 day, 3:22:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4345, top5_acc: 0.6877, loss_cls: 3.1992, loss: 3.1992 +2024-07-26 11:39:52,984 - pyskl - INFO - Epoch [118][3200/3746] lr: 1.091e-02, eta: 1 day, 3:20:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4320, top5_acc: 0.6841, loss_cls: 3.2063, loss: 3.2063 +2024-07-26 11:41:14,307 - pyskl - INFO - Epoch [118][3300/3746] lr: 1.089e-02, eta: 1 day, 3:19:25, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4273, top5_acc: 0.6823, loss_cls: 3.2046, loss: 3.2046 +2024-07-26 11:42:35,816 - pyskl - INFO - Epoch [118][3400/3746] lr: 1.088e-02, eta: 1 day, 3:18:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4405, top5_acc: 0.6948, loss_cls: 3.1518, loss: 3.1518 +2024-07-26 11:43:57,305 - pyskl - INFO - Epoch [118][3500/3746] lr: 1.086e-02, eta: 1 day, 3:16:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4363, top5_acc: 0.6850, loss_cls: 3.1482, loss: 3.1482 +2024-07-26 11:45:18,606 - pyskl - INFO - Epoch [118][3600/3746] lr: 1.084e-02, eta: 1 day, 3:15:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.6902, loss_cls: 3.1744, loss: 3.1744 +2024-07-26 11:46:40,230 - pyskl - INFO - Epoch [118][3700/3746] lr: 1.082e-02, eta: 1 day, 3:13:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4334, top5_acc: 0.6891, loss_cls: 3.1952, loss: 3.1952 +2024-07-26 11:47:19,657 - pyskl - INFO - Saving checkpoint at 118 epochs +2024-07-26 11:49:10,951 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 11:49:11,691 - pyskl - INFO - +top1_acc 0.3700 +top5_acc 0.6248 +2024-07-26 11:49:11,692 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 11:49:11,732 - pyskl - INFO - +mean_acc 0.3695 +2024-07-26 11:49:11,737 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_116.pth was removed +2024-07-26 11:49:11,998 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2024-07-26 11:49:11,998 - pyskl - INFO - Best top1_acc is 0.3700 at 118 epoch. +2024-07-26 11:49:12,011 - pyskl - INFO - Epoch(val) [118][309] top1_acc: 0.3700, top5_acc: 0.6248, mean_class_accuracy: 0.3695 +2024-07-26 11:53:00,325 - pyskl - INFO - Epoch [119][100/3746] lr: 1.080e-02, eta: 1 day, 3:12:28, time: 2.283, data_time: 1.308, memory: 15990, top1_acc: 0.4569, top5_acc: 0.7198, loss_cls: 3.0201, loss: 3.0201 +2024-07-26 11:54:22,464 - pyskl - INFO - Epoch [119][200/3746] lr: 1.078e-02, eta: 1 day, 3:11:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4559, top5_acc: 0.7036, loss_cls: 3.0916, loss: 3.0916 +2024-07-26 11:55:44,130 - pyskl - INFO - Epoch [119][300/3746] lr: 1.076e-02, eta: 1 day, 3:09:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4439, top5_acc: 0.7016, loss_cls: 3.0980, loss: 3.0980 +2024-07-26 11:57:05,966 - pyskl - INFO - Epoch [119][400/3746] lr: 1.075e-02, eta: 1 day, 3:08:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.6987, loss_cls: 3.1086, loss: 3.1086 +2024-07-26 11:58:27,977 - pyskl - INFO - Epoch [119][500/3746] lr: 1.073e-02, eta: 1 day, 3:07:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7050, loss_cls: 3.0579, loss: 3.0579 +2024-07-26 11:59:50,005 - pyskl - INFO - Epoch [119][600/3746] lr: 1.071e-02, eta: 1 day, 3:05:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4422, top5_acc: 0.7041, loss_cls: 3.0991, loss: 3.0991 +2024-07-26 12:01:11,771 - pyskl - INFO - Epoch [119][700/3746] lr: 1.069e-02, eta: 1 day, 3:04:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.7016, loss_cls: 3.1343, loss: 3.1343 +2024-07-26 12:02:33,524 - pyskl - INFO - Epoch [119][800/3746] lr: 1.068e-02, eta: 1 day, 3:02:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4422, top5_acc: 0.6947, loss_cls: 3.1392, loss: 3.1392 +2024-07-26 12:03:55,200 - pyskl - INFO - Epoch [119][900/3746] lr: 1.066e-02, eta: 1 day, 3:01:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.6972, loss_cls: 3.1369, loss: 3.1369 +2024-07-26 12:05:16,587 - pyskl - INFO - Epoch [119][1000/3746] lr: 1.064e-02, eta: 1 day, 3:00:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4398, top5_acc: 0.7109, loss_cls: 3.0892, loss: 3.0892 +2024-07-26 12:06:38,879 - pyskl - INFO - Epoch [119][1100/3746] lr: 1.063e-02, eta: 1 day, 2:58:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4400, top5_acc: 0.6989, loss_cls: 3.1393, loss: 3.1393 +2024-07-26 12:08:00,788 - pyskl - INFO - Epoch [119][1200/3746] lr: 1.061e-02, eta: 1 day, 2:57:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4452, top5_acc: 0.6977, loss_cls: 3.1100, loss: 3.1100 +2024-07-26 12:09:22,343 - pyskl - INFO - Epoch [119][1300/3746] lr: 1.059e-02, eta: 1 day, 2:56:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4369, top5_acc: 0.6983, loss_cls: 3.1146, loss: 3.1146 +2024-07-26 12:10:43,600 - pyskl - INFO - Epoch [119][1400/3746] lr: 1.057e-02, eta: 1 day, 2:54:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4359, top5_acc: 0.6877, loss_cls: 3.1663, loss: 3.1663 +2024-07-26 12:12:05,900 - pyskl - INFO - Epoch [119][1500/3746] lr: 1.056e-02, eta: 1 day, 2:53:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4495, top5_acc: 0.7025, loss_cls: 3.1164, loss: 3.1164 +2024-07-26 12:13:27,110 - pyskl - INFO - Epoch [119][1600/3746] lr: 1.054e-02, eta: 1 day, 2:52:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4422, top5_acc: 0.7013, loss_cls: 3.0977, loss: 3.0977 +2024-07-26 12:14:48,441 - pyskl - INFO - Epoch [119][1700/3746] lr: 1.052e-02, eta: 1 day, 2:50:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4341, top5_acc: 0.7002, loss_cls: 3.1451, loss: 3.1451 +2024-07-26 12:16:10,023 - pyskl - INFO - Epoch [119][1800/3746] lr: 1.050e-02, eta: 1 day, 2:49:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4387, top5_acc: 0.6963, loss_cls: 3.1182, loss: 3.1182 +2024-07-26 12:17:31,442 - pyskl - INFO - Epoch [119][1900/3746] lr: 1.049e-02, eta: 1 day, 2:47:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4448, top5_acc: 0.6889, loss_cls: 3.1503, loss: 3.1503 +2024-07-26 12:18:53,081 - pyskl - INFO - Epoch [119][2000/3746] lr: 1.047e-02, eta: 1 day, 2:46:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4330, top5_acc: 0.7020, loss_cls: 3.1218, loss: 3.1218 +2024-07-26 12:20:14,631 - pyskl - INFO - Epoch [119][2100/3746] lr: 1.045e-02, eta: 1 day, 2:45:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4456, top5_acc: 0.6995, loss_cls: 3.0934, loss: 3.0934 +2024-07-26 12:21:37,562 - pyskl - INFO - Epoch [119][2200/3746] lr: 1.044e-02, eta: 1 day, 2:43:51, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4381, top5_acc: 0.7053, loss_cls: 3.1062, loss: 3.1062 +2024-07-26 12:22:59,592 - pyskl - INFO - Epoch [119][2300/3746] lr: 1.042e-02, eta: 1 day, 2:42:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4370, top5_acc: 0.6956, loss_cls: 3.1456, loss: 3.1456 +2024-07-26 12:24:21,827 - pyskl - INFO - Epoch [119][2400/3746] lr: 1.040e-02, eta: 1 day, 2:41:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4502, top5_acc: 0.6967, loss_cls: 3.1177, loss: 3.1177 +2024-07-26 12:25:43,030 - pyskl - INFO - Epoch [119][2500/3746] lr: 1.039e-02, eta: 1 day, 2:39:46, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4422, top5_acc: 0.6920, loss_cls: 3.1205, loss: 3.1205 +2024-07-26 12:27:04,996 - pyskl - INFO - Epoch [119][2600/3746] lr: 1.037e-02, eta: 1 day, 2:38:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4406, top5_acc: 0.6947, loss_cls: 3.1749, loss: 3.1749 +2024-07-26 12:28:26,894 - pyskl - INFO - Epoch [119][2700/3746] lr: 1.035e-02, eta: 1 day, 2:37:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.6881, loss_cls: 3.1740, loss: 3.1740 +2024-07-26 12:29:48,096 - pyskl - INFO - Epoch [119][2800/3746] lr: 1.033e-02, eta: 1 day, 2:35:40, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.6975, loss_cls: 3.1339, loss: 3.1339 +2024-07-26 12:31:09,612 - pyskl - INFO - Epoch [119][2900/3746] lr: 1.032e-02, eta: 1 day, 2:34:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4347, top5_acc: 0.6955, loss_cls: 3.1411, loss: 3.1411 +2024-07-26 12:32:30,807 - pyskl - INFO - Epoch [119][3000/3746] lr: 1.030e-02, eta: 1 day, 2:32:56, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.6911, loss_cls: 3.1312, loss: 3.1312 +2024-07-26 12:33:52,187 - pyskl - INFO - Epoch [119][3100/3746] lr: 1.028e-02, eta: 1 day, 2:31:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4322, top5_acc: 0.6872, loss_cls: 3.1594, loss: 3.1594 +2024-07-26 12:35:13,227 - pyskl - INFO - Epoch [119][3200/3746] lr: 1.027e-02, eta: 1 day, 2:30:13, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.6883, loss_cls: 3.1757, loss: 3.1757 +2024-07-26 12:36:34,480 - pyskl - INFO - Epoch [119][3300/3746] lr: 1.025e-02, eta: 1 day, 2:28:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4377, top5_acc: 0.6937, loss_cls: 3.1460, loss: 3.1460 +2024-07-26 12:37:56,013 - pyskl - INFO - Epoch [119][3400/3746] lr: 1.023e-02, eta: 1 day, 2:27:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4425, top5_acc: 0.6977, loss_cls: 3.1172, loss: 3.1172 +2024-07-26 12:39:17,374 - pyskl - INFO - Epoch [119][3500/3746] lr: 1.022e-02, eta: 1 day, 2:26:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4366, top5_acc: 0.7036, loss_cls: 3.1322, loss: 3.1322 +2024-07-26 12:40:38,755 - pyskl - INFO - Epoch [119][3600/3746] lr: 1.020e-02, eta: 1 day, 2:24:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4422, top5_acc: 0.6941, loss_cls: 3.1398, loss: 3.1398 +2024-07-26 12:42:00,906 - pyskl - INFO - Epoch [119][3700/3746] lr: 1.018e-02, eta: 1 day, 2:23:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4330, top5_acc: 0.6852, loss_cls: 3.1990, loss: 3.1990 +2024-07-26 12:42:40,288 - pyskl - INFO - Saving checkpoint at 119 epochs +2024-07-26 12:44:31,166 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 12:44:31,832 - pyskl - INFO - +top1_acc 0.3794 +top5_acc 0.6350 +2024-07-26 12:44:31,832 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 12:44:31,873 - pyskl - INFO - +mean_acc 0.3792 +2024-07-26 12:44:31,878 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_118.pth was removed +2024-07-26 12:44:32,125 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_119.pth. +2024-07-26 12:44:32,126 - pyskl - INFO - Best top1_acc is 0.3794 at 119 epoch. +2024-07-26 12:44:32,138 - pyskl - INFO - Epoch(val) [119][309] top1_acc: 0.3794, top5_acc: 0.6350, mean_class_accuracy: 0.3792 +2024-07-26 12:48:26,526 - pyskl - INFO - Epoch [120][100/3746] lr: 1.016e-02, eta: 1 day, 2:21:54, time: 2.344, data_time: 1.349, memory: 15990, top1_acc: 0.4637, top5_acc: 0.7180, loss_cls: 3.0292, loss: 3.0292 +2024-07-26 12:49:49,392 - pyskl - INFO - Epoch [120][200/3746] lr: 1.014e-02, eta: 1 day, 2:20:32, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4400, top5_acc: 0.7008, loss_cls: 3.0997, loss: 3.0997 +2024-07-26 12:51:12,037 - pyskl - INFO - Epoch [120][300/3746] lr: 1.012e-02, eta: 1 day, 2:19:11, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4584, top5_acc: 0.7172, loss_cls: 3.0183, loss: 3.0183 +2024-07-26 12:52:34,643 - pyskl - INFO - Epoch [120][400/3746] lr: 1.011e-02, eta: 1 day, 2:17:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4506, top5_acc: 0.7127, loss_cls: 3.0655, loss: 3.0655 +2024-07-26 12:53:57,727 - pyskl - INFO - Epoch [120][500/3746] lr: 1.009e-02, eta: 1 day, 2:16:28, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.6995, loss_cls: 3.0863, loss: 3.0863 +2024-07-26 12:55:20,545 - pyskl - INFO - Epoch [120][600/3746] lr: 1.007e-02, eta: 1 day, 2:15:06, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4500, top5_acc: 0.7117, loss_cls: 3.0659, loss: 3.0659 +2024-07-26 12:56:42,971 - pyskl - INFO - Epoch [120][700/3746] lr: 1.006e-02, eta: 1 day, 2:13:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.7025, loss_cls: 3.0849, loss: 3.0849 +2024-07-26 12:58:05,977 - pyskl - INFO - Epoch [120][800/3746] lr: 1.004e-02, eta: 1 day, 2:12:23, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4534, top5_acc: 0.7092, loss_cls: 3.0930, loss: 3.0930 +2024-07-26 12:59:29,023 - pyskl - INFO - Epoch [120][900/3746] lr: 1.002e-02, eta: 1 day, 2:11:01, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4544, top5_acc: 0.7030, loss_cls: 3.0737, loss: 3.0737 +2024-07-26 13:00:51,443 - pyskl - INFO - Epoch [120][1000/3746] lr: 1.001e-02, eta: 1 day, 2:09:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4495, top5_acc: 0.7031, loss_cls: 3.0947, loss: 3.0947 +2024-07-26 13:02:14,466 - pyskl - INFO - Epoch [120][1100/3746] lr: 9.989e-03, eta: 1 day, 2:08:18, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4469, top5_acc: 0.7103, loss_cls: 3.0885, loss: 3.0885 +2024-07-26 13:03:37,259 - pyskl - INFO - Epoch [120][1200/3746] lr: 9.972e-03, eta: 1 day, 2:06:57, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4534, top5_acc: 0.7108, loss_cls: 3.0683, loss: 3.0683 +2024-07-26 13:04:59,553 - pyskl - INFO - Epoch [120][1300/3746] lr: 9.955e-03, eta: 1 day, 2:05:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4436, top5_acc: 0.7044, loss_cls: 3.1036, loss: 3.1036 +2024-07-26 13:06:22,666 - pyskl - INFO - Epoch [120][1400/3746] lr: 9.938e-03, eta: 1 day, 2:04:14, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4464, top5_acc: 0.7016, loss_cls: 3.0907, loss: 3.0907 +2024-07-26 13:07:45,324 - pyskl - INFO - Epoch [120][1500/3746] lr: 9.922e-03, eta: 1 day, 2:02:52, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4437, top5_acc: 0.7072, loss_cls: 3.0803, loss: 3.0803 +2024-07-26 13:09:08,484 - pyskl - INFO - Epoch [120][1600/3746] lr: 9.905e-03, eta: 1 day, 2:01:31, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4558, top5_acc: 0.7044, loss_cls: 3.0718, loss: 3.0718 +2024-07-26 13:10:31,474 - pyskl - INFO - Epoch [120][1700/3746] lr: 9.888e-03, eta: 1 day, 2:00:09, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.6963, loss_cls: 3.1382, loss: 3.1382 +2024-07-26 13:11:54,316 - pyskl - INFO - Epoch [120][1800/3746] lr: 9.871e-03, eta: 1 day, 1:58:48, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4520, top5_acc: 0.7041, loss_cls: 3.1011, loss: 3.1011 +2024-07-26 13:13:16,964 - pyskl - INFO - Epoch [120][1900/3746] lr: 9.855e-03, eta: 1 day, 1:57:26, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.6977, loss_cls: 3.1365, loss: 3.1365 +2024-07-26 13:14:40,396 - pyskl - INFO - Epoch [120][2000/3746] lr: 9.838e-03, eta: 1 day, 1:56:05, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4445, top5_acc: 0.6959, loss_cls: 3.1379, loss: 3.1379 +2024-07-26 13:16:03,949 - pyskl - INFO - Epoch [120][2100/3746] lr: 9.821e-03, eta: 1 day, 1:54:43, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4453, top5_acc: 0.7031, loss_cls: 3.0950, loss: 3.0950 +2024-07-26 13:17:27,283 - pyskl - INFO - Epoch [120][2200/3746] lr: 9.805e-03, eta: 1 day, 1:53:22, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4494, top5_acc: 0.7055, loss_cls: 3.0953, loss: 3.0953 +2024-07-26 13:18:50,763 - pyskl - INFO - Epoch [120][2300/3746] lr: 9.788e-03, eta: 1 day, 1:52:00, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4408, top5_acc: 0.7005, loss_cls: 3.1132, loss: 3.1132 +2024-07-26 13:20:14,182 - pyskl - INFO - Epoch [120][2400/3746] lr: 9.772e-03, eta: 1 day, 1:50:39, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4483, top5_acc: 0.7064, loss_cls: 3.0912, loss: 3.0912 +2024-07-26 13:21:37,689 - pyskl - INFO - Epoch [120][2500/3746] lr: 9.755e-03, eta: 1 day, 1:49:18, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4492, top5_acc: 0.7052, loss_cls: 3.0973, loss: 3.0973 +2024-07-26 13:23:01,200 - pyskl - INFO - Epoch [120][2600/3746] lr: 9.738e-03, eta: 1 day, 1:47:56, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4384, top5_acc: 0.6941, loss_cls: 3.1399, loss: 3.1399 +2024-07-26 13:24:24,635 - pyskl - INFO - Epoch [120][2700/3746] lr: 9.722e-03, eta: 1 day, 1:46:35, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.6884, loss_cls: 3.1699, loss: 3.1699 +2024-07-26 13:25:47,498 - pyskl - INFO - Epoch [120][2800/3746] lr: 9.705e-03, eta: 1 day, 1:45:13, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4345, top5_acc: 0.6987, loss_cls: 3.1315, loss: 3.1315 +2024-07-26 13:27:10,682 - pyskl - INFO - Epoch [120][2900/3746] lr: 9.689e-03, eta: 1 day, 1:43:52, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4356, top5_acc: 0.6919, loss_cls: 3.1645, loss: 3.1645 +2024-07-26 13:28:32,684 - pyskl - INFO - Epoch [120][3000/3746] lr: 9.672e-03, eta: 1 day, 1:42:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.7017, loss_cls: 3.1054, loss: 3.1054 +2024-07-26 13:29:55,441 - pyskl - INFO - Epoch [120][3100/3746] lr: 9.656e-03, eta: 1 day, 1:41:08, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4434, top5_acc: 0.6994, loss_cls: 3.1251, loss: 3.1251 +2024-07-26 13:31:17,626 - pyskl - INFO - Epoch [120][3200/3746] lr: 9.639e-03, eta: 1 day, 1:39:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4536, top5_acc: 0.7084, loss_cls: 3.0533, loss: 3.0533 +2024-07-26 13:32:39,868 - pyskl - INFO - Epoch [120][3300/3746] lr: 9.623e-03, eta: 1 day, 1:38:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4414, top5_acc: 0.6947, loss_cls: 3.1334, loss: 3.1334 +2024-07-26 13:34:02,408 - pyskl - INFO - Epoch [120][3400/3746] lr: 9.606e-03, eta: 1 day, 1:37:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.7006, loss_cls: 3.1218, loss: 3.1218 +2024-07-26 13:35:25,066 - pyskl - INFO - Epoch [120][3500/3746] lr: 9.590e-03, eta: 1 day, 1:35:42, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4433, top5_acc: 0.7019, loss_cls: 3.1213, loss: 3.1213 +2024-07-26 13:36:47,427 - pyskl - INFO - Epoch [120][3600/3746] lr: 9.573e-03, eta: 1 day, 1:34:20, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4406, top5_acc: 0.6955, loss_cls: 3.1299, loss: 3.1299 +2024-07-26 13:38:10,019 - pyskl - INFO - Epoch [120][3700/3746] lr: 9.557e-03, eta: 1 day, 1:32:59, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.6872, loss_cls: 3.1536, loss: 3.1536 +2024-07-26 13:38:49,549 - pyskl - INFO - Saving checkpoint at 120 epochs +2024-07-26 13:40:42,945 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 13:40:43,708 - pyskl - INFO - +top1_acc 0.3766 +top5_acc 0.6330 +2024-07-26 13:40:43,708 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 13:40:43,751 - pyskl - INFO - +mean_acc 0.3763 +2024-07-26 13:40:43,764 - pyskl - INFO - Epoch(val) [120][309] top1_acc: 0.3766, top5_acc: 0.6330, mean_class_accuracy: 0.3763 +2024-07-26 13:44:36,952 - pyskl - INFO - Epoch [121][100/3746] lr: 9.533e-03, eta: 1 day, 1:31:27, time: 2.332, data_time: 1.336, memory: 15990, top1_acc: 0.4584, top5_acc: 0.7166, loss_cls: 3.0251, loss: 3.0251 +2024-07-26 13:46:00,059 - pyskl - INFO - Epoch [121][200/3746] lr: 9.516e-03, eta: 1 day, 1:30:06, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4672, top5_acc: 0.7164, loss_cls: 3.0041, loss: 3.0041 +2024-07-26 13:47:22,434 - pyskl - INFO - Epoch [121][300/3746] lr: 9.500e-03, eta: 1 day, 1:28:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4592, top5_acc: 0.7119, loss_cls: 3.0389, loss: 3.0389 +2024-07-26 13:48:45,058 - pyskl - INFO - Epoch [121][400/3746] lr: 9.484e-03, eta: 1 day, 1:27:23, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.7056, loss_cls: 3.0749, loss: 3.0749 +2024-07-26 13:50:08,245 - pyskl - INFO - Epoch [121][500/3746] lr: 9.467e-03, eta: 1 day, 1:26:01, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4536, top5_acc: 0.7073, loss_cls: 3.0564, loss: 3.0564 +2024-07-26 13:51:31,091 - pyskl - INFO - Epoch [121][600/3746] lr: 9.451e-03, eta: 1 day, 1:24:39, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4491, top5_acc: 0.7111, loss_cls: 3.0839, loss: 3.0839 +2024-07-26 13:52:54,523 - pyskl - INFO - Epoch [121][700/3746] lr: 9.435e-03, eta: 1 day, 1:23:18, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4502, top5_acc: 0.7106, loss_cls: 3.0534, loss: 3.0534 +2024-07-26 13:54:17,917 - pyskl - INFO - Epoch [121][800/3746] lr: 9.418e-03, eta: 1 day, 1:21:57, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4580, top5_acc: 0.7128, loss_cls: 3.0411, loss: 3.0411 +2024-07-26 13:55:41,058 - pyskl - INFO - Epoch [121][900/3746] lr: 9.402e-03, eta: 1 day, 1:20:35, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4548, top5_acc: 0.7106, loss_cls: 3.0575, loss: 3.0575 +2024-07-26 13:57:04,447 - pyskl - INFO - Epoch [121][1000/3746] lr: 9.386e-03, eta: 1 day, 1:19:14, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.7047, loss_cls: 3.1258, loss: 3.1258 +2024-07-26 13:58:27,397 - pyskl - INFO - Epoch [121][1100/3746] lr: 9.369e-03, eta: 1 day, 1:17:52, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4520, top5_acc: 0.7078, loss_cls: 3.0602, loss: 3.0602 +2024-07-26 13:59:49,484 - pyskl - INFO - Epoch [121][1200/3746] lr: 9.353e-03, eta: 1 day, 1:16:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4542, top5_acc: 0.7048, loss_cls: 3.0479, loss: 3.0479 +2024-07-26 14:01:12,638 - pyskl - INFO - Epoch [121][1300/3746] lr: 9.337e-03, eta: 1 day, 1:15:09, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.7028, loss_cls: 3.0853, loss: 3.0853 +2024-07-26 14:02:35,311 - pyskl - INFO - Epoch [121][1400/3746] lr: 9.321e-03, eta: 1 day, 1:13:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4419, top5_acc: 0.6977, loss_cls: 3.1097, loss: 3.1097 +2024-07-26 14:03:57,953 - pyskl - INFO - Epoch [121][1500/3746] lr: 9.304e-03, eta: 1 day, 1:12:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4422, top5_acc: 0.7089, loss_cls: 3.0783, loss: 3.0783 +2024-07-26 14:05:20,325 - pyskl - INFO - Epoch [121][1600/3746] lr: 9.288e-03, eta: 1 day, 1:11:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4491, top5_acc: 0.7078, loss_cls: 3.0702, loss: 3.0702 +2024-07-26 14:06:43,257 - pyskl - INFO - Epoch [121][1700/3746] lr: 9.272e-03, eta: 1 day, 1:09:42, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4533, top5_acc: 0.7055, loss_cls: 3.0787, loss: 3.0787 +2024-07-26 14:08:05,831 - pyskl - INFO - Epoch [121][1800/3746] lr: 9.256e-03, eta: 1 day, 1:08:21, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4578, top5_acc: 0.7145, loss_cls: 3.0176, loss: 3.0176 +2024-07-26 14:09:28,008 - pyskl - INFO - Epoch [121][1900/3746] lr: 9.239e-03, eta: 1 day, 1:06:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4533, top5_acc: 0.7072, loss_cls: 3.0665, loss: 3.0665 +2024-07-26 14:10:51,309 - pyskl - INFO - Epoch [121][2000/3746] lr: 9.223e-03, eta: 1 day, 1:05:37, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4466, top5_acc: 0.7059, loss_cls: 3.1032, loss: 3.1032 +2024-07-26 14:12:13,744 - pyskl - INFO - Epoch [121][2100/3746] lr: 9.207e-03, eta: 1 day, 1:04:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4402, top5_acc: 0.6997, loss_cls: 3.1251, loss: 3.1251 +2024-07-26 14:13:37,655 - pyskl - INFO - Epoch [121][2200/3746] lr: 9.191e-03, eta: 1 day, 1:02:54, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4602, top5_acc: 0.7091, loss_cls: 3.0524, loss: 3.0524 +2024-07-26 14:15:01,031 - pyskl - INFO - Epoch [121][2300/3746] lr: 9.175e-03, eta: 1 day, 1:01:33, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4392, top5_acc: 0.7005, loss_cls: 3.1182, loss: 3.1182 +2024-07-26 14:16:24,411 - pyskl - INFO - Epoch [121][2400/3746] lr: 9.159e-03, eta: 1 day, 1:00:11, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4452, top5_acc: 0.7084, loss_cls: 3.0900, loss: 3.0900 +2024-07-26 14:17:47,263 - pyskl - INFO - Epoch [121][2500/3746] lr: 9.142e-03, eta: 1 day, 0:58:50, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4461, top5_acc: 0.6928, loss_cls: 3.1140, loss: 3.1140 +2024-07-26 14:19:10,358 - pyskl - INFO - Epoch [121][2600/3746] lr: 9.126e-03, eta: 1 day, 0:57:28, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4506, top5_acc: 0.7125, loss_cls: 3.0749, loss: 3.0749 +2024-07-26 14:20:33,062 - pyskl - INFO - Epoch [121][2700/3746] lr: 9.110e-03, eta: 1 day, 0:56:07, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4414, top5_acc: 0.6986, loss_cls: 3.1170, loss: 3.1170 +2024-07-26 14:21:56,207 - pyskl - INFO - Epoch [121][2800/3746] lr: 9.094e-03, eta: 1 day, 0:54:45, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4475, top5_acc: 0.7034, loss_cls: 3.0966, loss: 3.0966 +2024-07-26 14:23:18,738 - pyskl - INFO - Epoch [121][2900/3746] lr: 9.078e-03, eta: 1 day, 0:53:23, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4595, top5_acc: 0.7156, loss_cls: 3.0250, loss: 3.0250 +2024-07-26 14:24:41,389 - pyskl - INFO - Epoch [121][3000/3746] lr: 9.062e-03, eta: 1 day, 0:52:02, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4453, top5_acc: 0.7005, loss_cls: 3.1179, loss: 3.1179 +2024-07-26 14:26:03,797 - pyskl - INFO - Epoch [121][3100/3746] lr: 9.046e-03, eta: 1 day, 0:50:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7066, loss_cls: 3.0765, loss: 3.0765 +2024-07-26 14:27:26,613 - pyskl - INFO - Epoch [121][3200/3746] lr: 9.030e-03, eta: 1 day, 0:49:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4517, top5_acc: 0.7077, loss_cls: 3.0512, loss: 3.0512 +2024-07-26 14:28:49,383 - pyskl - INFO - Epoch [121][3300/3746] lr: 9.014e-03, eta: 1 day, 0:47:57, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4511, top5_acc: 0.7108, loss_cls: 3.0380, loss: 3.0380 +2024-07-26 14:30:11,337 - pyskl - INFO - Epoch [121][3400/3746] lr: 8.998e-03, eta: 1 day, 0:46:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4462, top5_acc: 0.6997, loss_cls: 3.0996, loss: 3.0996 +2024-07-26 14:31:33,680 - pyskl - INFO - Epoch [121][3500/3746] lr: 8.982e-03, eta: 1 day, 0:45:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.7013, loss_cls: 3.1292, loss: 3.1292 +2024-07-26 14:32:56,129 - pyskl - INFO - Epoch [121][3600/3746] lr: 8.966e-03, eta: 1 day, 0:43:51, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4575, top5_acc: 0.7172, loss_cls: 3.0157, loss: 3.0157 +2024-07-26 14:34:18,316 - pyskl - INFO - Epoch [121][3700/3746] lr: 8.950e-03, eta: 1 day, 0:42:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4505, top5_acc: 0.7025, loss_cls: 3.0868, loss: 3.0868 +2024-07-26 14:34:57,414 - pyskl - INFO - Saving checkpoint at 121 epochs +2024-07-26 14:36:48,847 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 14:36:49,724 - pyskl - INFO - +top1_acc 0.3894 +top5_acc 0.6449 +2024-07-26 14:36:49,724 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 14:36:49,767 - pyskl - INFO - +mean_acc 0.3892 +2024-07-26 14:36:49,772 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_119.pth was removed +2024-07-26 14:36:50,016 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_121.pth. +2024-07-26 14:36:50,017 - pyskl - INFO - Best top1_acc is 0.3894 at 121 epoch. +2024-07-26 14:36:50,029 - pyskl - INFO - Epoch(val) [121][309] top1_acc: 0.3894, top5_acc: 0.6449, mean_class_accuracy: 0.3892 +2024-07-26 14:40:42,671 - pyskl - INFO - Epoch [122][100/3746] lr: 8.927e-03, eta: 1 day, 0:40:57, time: 2.326, data_time: 1.340, memory: 15990, top1_acc: 0.4764, top5_acc: 0.7227, loss_cls: 2.9679, loss: 2.9679 +2024-07-26 14:42:05,188 - pyskl - INFO - Epoch [122][200/3746] lr: 8.911e-03, eta: 1 day, 0:39:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4558, top5_acc: 0.7159, loss_cls: 3.0078, loss: 3.0078 +2024-07-26 14:43:26,957 - pyskl - INFO - Epoch [122][300/3746] lr: 8.895e-03, eta: 1 day, 0:38:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4628, top5_acc: 0.7206, loss_cls: 3.0051, loss: 3.0051 +2024-07-26 14:44:48,923 - pyskl - INFO - Epoch [122][400/3746] lr: 8.879e-03, eta: 1 day, 0:36:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4541, top5_acc: 0.7169, loss_cls: 3.0208, loss: 3.0208 +2024-07-26 14:46:10,725 - pyskl - INFO - Epoch [122][500/3746] lr: 8.863e-03, eta: 1 day, 0:35:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4648, top5_acc: 0.7220, loss_cls: 2.9949, loss: 2.9949 +2024-07-26 14:47:33,049 - pyskl - INFO - Epoch [122][600/3746] lr: 8.847e-03, eta: 1 day, 0:34:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4661, top5_acc: 0.7167, loss_cls: 3.0183, loss: 3.0183 +2024-07-26 14:48:54,804 - pyskl - INFO - Epoch [122][700/3746] lr: 8.831e-03, eta: 1 day, 0:32:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4578, top5_acc: 0.7209, loss_cls: 3.0045, loss: 3.0045 +2024-07-26 14:50:17,087 - pyskl - INFO - Epoch [122][800/3746] lr: 8.815e-03, eta: 1 day, 0:31:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4652, top5_acc: 0.7200, loss_cls: 2.9864, loss: 2.9864 +2024-07-26 14:51:38,554 - pyskl - INFO - Epoch [122][900/3746] lr: 8.800e-03, eta: 1 day, 0:30:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4616, top5_acc: 0.7191, loss_cls: 3.0177, loss: 3.0177 +2024-07-26 14:53:00,567 - pyskl - INFO - Epoch [122][1000/3746] lr: 8.784e-03, eta: 1 day, 0:28:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4612, top5_acc: 0.7178, loss_cls: 3.0104, loss: 3.0104 +2024-07-26 14:54:21,987 - pyskl - INFO - Epoch [122][1100/3746] lr: 8.768e-03, eta: 1 day, 0:27:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4536, top5_acc: 0.7137, loss_cls: 3.0326, loss: 3.0326 +2024-07-26 14:55:43,764 - pyskl - INFO - Epoch [122][1200/3746] lr: 8.752e-03, eta: 1 day, 0:25:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4561, top5_acc: 0.7053, loss_cls: 3.0512, loss: 3.0512 +2024-07-26 14:57:06,178 - pyskl - INFO - Epoch [122][1300/3746] lr: 8.736e-03, eta: 1 day, 0:24:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4598, top5_acc: 0.7108, loss_cls: 3.0405, loss: 3.0405 +2024-07-26 14:58:27,496 - pyskl - INFO - Epoch [122][1400/3746] lr: 8.721e-03, eta: 1 day, 0:23:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4589, top5_acc: 0.7067, loss_cls: 3.0580, loss: 3.0580 +2024-07-26 14:59:49,133 - pyskl - INFO - Epoch [122][1500/3746] lr: 8.705e-03, eta: 1 day, 0:21:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4509, top5_acc: 0.7100, loss_cls: 3.0384, loss: 3.0384 +2024-07-26 15:01:11,119 - pyskl - INFO - Epoch [122][1600/3746] lr: 8.689e-03, eta: 1 day, 0:20:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4444, top5_acc: 0.7127, loss_cls: 3.0674, loss: 3.0674 +2024-07-26 15:02:32,516 - pyskl - INFO - Epoch [122][1700/3746] lr: 8.673e-03, eta: 1 day, 0:19:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.7113, loss_cls: 3.0603, loss: 3.0603 +2024-07-26 15:03:54,607 - pyskl - INFO - Epoch [122][1800/3746] lr: 8.658e-03, eta: 1 day, 0:17:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4537, top5_acc: 0.7059, loss_cls: 3.0370, loss: 3.0370 +2024-07-26 15:05:16,317 - pyskl - INFO - Epoch [122][1900/3746] lr: 8.642e-03, eta: 1 day, 0:16:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4450, top5_acc: 0.7027, loss_cls: 3.0801, loss: 3.0801 +2024-07-26 15:06:38,367 - pyskl - INFO - Epoch [122][2000/3746] lr: 8.626e-03, eta: 1 day, 0:15:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4462, top5_acc: 0.7050, loss_cls: 3.0803, loss: 3.0803 +2024-07-26 15:08:01,069 - pyskl - INFO - Epoch [122][2100/3746] lr: 8.610e-03, eta: 1 day, 0:13:40, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4433, top5_acc: 0.7122, loss_cls: 3.0845, loss: 3.0845 +2024-07-26 15:09:23,949 - pyskl - INFO - Epoch [122][2200/3746] lr: 8.595e-03, eta: 1 day, 0:12:18, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4408, top5_acc: 0.6941, loss_cls: 3.1440, loss: 3.1440 +2024-07-26 15:10:45,983 - pyskl - INFO - Epoch [122][2300/3746] lr: 8.579e-03, eta: 1 day, 0:10:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4530, top5_acc: 0.7086, loss_cls: 3.0676, loss: 3.0676 +2024-07-26 15:12:07,469 - pyskl - INFO - Epoch [122][2400/3746] lr: 8.563e-03, eta: 1 day, 0:09:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4567, top5_acc: 0.7123, loss_cls: 3.0490, loss: 3.0490 +2024-07-26 15:13:29,243 - pyskl - INFO - Epoch [122][2500/3746] lr: 8.548e-03, eta: 1 day, 0:08:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4569, top5_acc: 0.7139, loss_cls: 3.0586, loss: 3.0586 +2024-07-26 15:14:50,309 - pyskl - INFO - Epoch [122][2600/3746] lr: 8.532e-03, eta: 1 day, 0:06:51, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4555, top5_acc: 0.7134, loss_cls: 3.0335, loss: 3.0335 +2024-07-26 15:16:11,850 - pyskl - INFO - Epoch [122][2700/3746] lr: 8.517e-03, eta: 1 day, 0:05:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7141, loss_cls: 3.0624, loss: 3.0624 +2024-07-26 15:17:33,731 - pyskl - INFO - Epoch [122][2800/3746] lr: 8.501e-03, eta: 1 day, 0:04:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4545, top5_acc: 0.7100, loss_cls: 3.0638, loss: 3.0638 +2024-07-26 15:18:55,594 - pyskl - INFO - Epoch [122][2900/3746] lr: 8.485e-03, eta: 1 day, 0:02:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4555, top5_acc: 0.7042, loss_cls: 3.0651, loss: 3.0651 +2024-07-26 15:20:17,129 - pyskl - INFO - Epoch [122][3000/3746] lr: 8.470e-03, eta: 1 day, 0:01:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4580, top5_acc: 0.7105, loss_cls: 3.0190, loss: 3.0190 +2024-07-26 15:21:38,553 - pyskl - INFO - Epoch [122][3100/3746] lr: 8.454e-03, eta: 1 day, 0:00:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4536, top5_acc: 0.7087, loss_cls: 3.0409, loss: 3.0409 +2024-07-26 15:22:59,639 - pyskl - INFO - Epoch [122][3200/3746] lr: 8.439e-03, eta: 23:58:39, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4425, top5_acc: 0.6994, loss_cls: 3.0907, loss: 3.0907 +2024-07-26 15:24:21,192 - pyskl - INFO - Epoch [122][3300/3746] lr: 8.423e-03, eta: 23:57:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4492, top5_acc: 0.7144, loss_cls: 3.0504, loss: 3.0504 +2024-07-26 15:25:42,631 - pyskl - INFO - Epoch [122][3400/3746] lr: 8.408e-03, eta: 23:55:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4564, top5_acc: 0.7045, loss_cls: 3.0690, loss: 3.0690 +2024-07-26 15:27:03,760 - pyskl - INFO - Epoch [122][3500/3746] lr: 8.392e-03, eta: 23:54:33, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4555, top5_acc: 0.7125, loss_cls: 3.0318, loss: 3.0318 +2024-07-26 15:28:26,267 - pyskl - INFO - Epoch [122][3600/3746] lr: 8.377e-03, eta: 23:53:11, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4609, top5_acc: 0.7145, loss_cls: 3.0410, loss: 3.0410 +2024-07-26 15:29:47,983 - pyskl - INFO - Epoch [122][3700/3746] lr: 8.361e-03, eta: 23:51:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7056, loss_cls: 3.0472, loss: 3.0472 +2024-07-26 15:30:27,551 - pyskl - INFO - Saving checkpoint at 122 epochs +2024-07-26 15:32:20,342 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 15:32:21,004 - pyskl - INFO - +top1_acc 0.3812 +top5_acc 0.6344 +2024-07-26 15:32:21,005 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 15:32:21,045 - pyskl - INFO - +mean_acc 0.3811 +2024-07-26 15:32:21,057 - pyskl - INFO - Epoch(val) [122][309] top1_acc: 0.3812, top5_acc: 0.6344, mean_class_accuracy: 0.3811 +2024-07-26 15:36:13,331 - pyskl - INFO - Epoch [123][100/3746] lr: 8.339e-03, eta: 23:50:16, time: 2.323, data_time: 1.318, memory: 15990, top1_acc: 0.4738, top5_acc: 0.7202, loss_cls: 2.9658, loss: 2.9658 +2024-07-26 15:37:35,180 - pyskl - INFO - Epoch [123][200/3746] lr: 8.323e-03, eta: 23:48:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4770, top5_acc: 0.7267, loss_cls: 2.9155, loss: 2.9155 +2024-07-26 15:38:57,431 - pyskl - INFO - Epoch [123][300/3746] lr: 8.308e-03, eta: 23:47:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4777, top5_acc: 0.7275, loss_cls: 2.9343, loss: 2.9343 +2024-07-26 15:40:19,474 - pyskl - INFO - Epoch [123][400/3746] lr: 8.292e-03, eta: 23:46:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4711, top5_acc: 0.7262, loss_cls: 2.9476, loss: 2.9476 +2024-07-26 15:41:41,125 - pyskl - INFO - Epoch [123][500/3746] lr: 8.277e-03, eta: 23:44:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4598, top5_acc: 0.7208, loss_cls: 2.9783, loss: 2.9783 +2024-07-26 15:43:03,149 - pyskl - INFO - Epoch [123][600/3746] lr: 8.262e-03, eta: 23:43:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4745, top5_acc: 0.7294, loss_cls: 2.9326, loss: 2.9326 +2024-07-26 15:44:24,729 - pyskl - INFO - Epoch [123][700/3746] lr: 8.246e-03, eta: 23:42:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4750, top5_acc: 0.7242, loss_cls: 2.9738, loss: 2.9738 +2024-07-26 15:45:46,265 - pyskl - INFO - Epoch [123][800/3746] lr: 8.231e-03, eta: 23:40:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4583, top5_acc: 0.7166, loss_cls: 2.9956, loss: 2.9956 +2024-07-26 15:47:07,761 - pyskl - INFO - Epoch [123][900/3746] lr: 8.215e-03, eta: 23:39:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4597, top5_acc: 0.7195, loss_cls: 2.9844, loss: 2.9844 +2024-07-26 15:48:29,072 - pyskl - INFO - Epoch [123][1000/3746] lr: 8.200e-03, eta: 23:37:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4537, top5_acc: 0.7173, loss_cls: 3.0181, loss: 3.0181 +2024-07-26 15:49:50,621 - pyskl - INFO - Epoch [123][1100/3746] lr: 8.185e-03, eta: 23:36:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4592, top5_acc: 0.7205, loss_cls: 2.9982, loss: 2.9982 +2024-07-26 15:51:12,594 - pyskl - INFO - Epoch [123][1200/3746] lr: 8.169e-03, eta: 23:35:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4591, top5_acc: 0.7236, loss_cls: 2.9790, loss: 2.9790 +2024-07-26 15:52:34,304 - pyskl - INFO - Epoch [123][1300/3746] lr: 8.154e-03, eta: 23:33:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4659, top5_acc: 0.7202, loss_cls: 2.9884, loss: 2.9884 +2024-07-26 15:53:55,610 - pyskl - INFO - Epoch [123][1400/3746] lr: 8.139e-03, eta: 23:32:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4573, top5_acc: 0.7103, loss_cls: 3.0394, loss: 3.0394 +2024-07-26 15:55:17,533 - pyskl - INFO - Epoch [123][1500/3746] lr: 8.124e-03, eta: 23:31:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4606, top5_acc: 0.7111, loss_cls: 3.0345, loss: 3.0345 +2024-07-26 15:56:39,258 - pyskl - INFO - Epoch [123][1600/3746] lr: 8.108e-03, eta: 23:29:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4481, top5_acc: 0.7031, loss_cls: 3.0608, loss: 3.0608 +2024-07-26 15:58:01,325 - pyskl - INFO - Epoch [123][1700/3746] lr: 8.093e-03, eta: 23:28:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4586, top5_acc: 0.7105, loss_cls: 3.0267, loss: 3.0267 +2024-07-26 15:59:22,323 - pyskl - INFO - Epoch [123][1800/3746] lr: 8.078e-03, eta: 23:27:03, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4525, top5_acc: 0.7058, loss_cls: 3.0661, loss: 3.0661 +2024-07-26 16:00:43,580 - pyskl - INFO - Epoch [123][1900/3746] lr: 8.063e-03, eta: 23:25:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4561, top5_acc: 0.7092, loss_cls: 3.0312, loss: 3.0312 +2024-07-26 16:02:05,517 - pyskl - INFO - Epoch [123][2000/3746] lr: 8.047e-03, eta: 23:24:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4406, top5_acc: 0.6980, loss_cls: 3.1155, loss: 3.1155 +2024-07-26 16:03:26,737 - pyskl - INFO - Epoch [123][2100/3746] lr: 8.032e-03, eta: 23:22:57, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.7066, loss_cls: 3.0878, loss: 3.0878 +2024-07-26 16:04:49,524 - pyskl - INFO - Epoch [123][2200/3746] lr: 8.017e-03, eta: 23:21:35, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4580, top5_acc: 0.7188, loss_cls: 3.0083, loss: 3.0083 +2024-07-26 16:06:11,280 - pyskl - INFO - Epoch [123][2300/3746] lr: 8.002e-03, eta: 23:20:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4550, top5_acc: 0.7078, loss_cls: 3.0353, loss: 3.0353 +2024-07-26 16:07:33,316 - pyskl - INFO - Epoch [123][2400/3746] lr: 7.987e-03, eta: 23:18:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7145, loss_cls: 3.0312, loss: 3.0312 +2024-07-26 16:08:54,976 - pyskl - INFO - Epoch [123][2500/3746] lr: 7.971e-03, eta: 23:17:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4580, top5_acc: 0.7152, loss_cls: 3.0227, loss: 3.0227 +2024-07-26 16:10:16,434 - pyskl - INFO - Epoch [123][2600/3746] lr: 7.956e-03, eta: 23:16:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4492, top5_acc: 0.7047, loss_cls: 3.0848, loss: 3.0848 +2024-07-26 16:11:37,775 - pyskl - INFO - Epoch [123][2700/3746] lr: 7.941e-03, eta: 23:14:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7155, loss_cls: 3.0140, loss: 3.0140 +2024-07-26 16:12:58,983 - pyskl - INFO - Epoch [123][2800/3746] lr: 7.926e-03, eta: 23:13:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4655, top5_acc: 0.7148, loss_cls: 3.0321, loss: 3.0321 +2024-07-26 16:14:20,213 - pyskl - INFO - Epoch [123][2900/3746] lr: 7.911e-03, eta: 23:12:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4572, top5_acc: 0.7148, loss_cls: 3.0485, loss: 3.0485 +2024-07-26 16:15:41,106 - pyskl - INFO - Epoch [123][3000/3746] lr: 7.896e-03, eta: 23:10:39, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4609, top5_acc: 0.7161, loss_cls: 3.0129, loss: 3.0129 +2024-07-26 16:17:03,021 - pyskl - INFO - Epoch [123][3100/3746] lr: 7.881e-03, eta: 23:09:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4608, top5_acc: 0.7183, loss_cls: 3.0091, loss: 3.0091 +2024-07-26 16:18:24,323 - pyskl - INFO - Epoch [123][3200/3746] lr: 7.866e-03, eta: 23:07:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4580, top5_acc: 0.7102, loss_cls: 3.0488, loss: 3.0488 +2024-07-26 16:19:46,198 - pyskl - INFO - Epoch [123][3300/3746] lr: 7.851e-03, eta: 23:06:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4534, top5_acc: 0.7091, loss_cls: 3.0521, loss: 3.0521 +2024-07-26 16:21:07,305 - pyskl - INFO - Epoch [123][3400/3746] lr: 7.836e-03, eta: 23:05:11, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4717, top5_acc: 0.7148, loss_cls: 3.0144, loss: 3.0144 +2024-07-26 16:22:28,755 - pyskl - INFO - Epoch [123][3500/3746] lr: 7.821e-03, eta: 23:03:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4539, top5_acc: 0.7056, loss_cls: 3.0772, loss: 3.0772 +2024-07-26 16:23:50,711 - pyskl - INFO - Epoch [123][3600/3746] lr: 7.806e-03, eta: 23:02:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7103, loss_cls: 3.0394, loss: 3.0394 +2024-07-26 16:25:12,192 - pyskl - INFO - Epoch [123][3700/3746] lr: 7.791e-03, eta: 23:01:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4512, top5_acc: 0.7100, loss_cls: 3.0624, loss: 3.0624 +2024-07-26 16:25:52,025 - pyskl - INFO - Saving checkpoint at 123 epochs +2024-07-26 16:27:45,163 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 16:27:45,831 - pyskl - INFO - +top1_acc 0.3863 +top5_acc 0.6382 +2024-07-26 16:27:45,831 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 16:27:45,877 - pyskl - INFO - +mean_acc 0.3860 +2024-07-26 16:27:45,888 - pyskl - INFO - Epoch(val) [123][309] top1_acc: 0.3863, top5_acc: 0.6382, mean_class_accuracy: 0.3860 +2024-07-26 16:31:34,525 - pyskl - INFO - Epoch [124][100/3746] lr: 7.769e-03, eta: 22:59:30, time: 2.286, data_time: 1.307, memory: 15990, top1_acc: 0.4708, top5_acc: 0.7306, loss_cls: 2.9262, loss: 2.9262 +2024-07-26 16:32:56,565 - pyskl - INFO - Epoch [124][200/3746] lr: 7.754e-03, eta: 22:58:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4838, top5_acc: 0.7425, loss_cls: 2.8723, loss: 2.8723 +2024-07-26 16:34:18,602 - pyskl - INFO - Epoch [124][300/3746] lr: 7.739e-03, eta: 22:56:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4686, top5_acc: 0.7206, loss_cls: 2.9656, loss: 2.9656 +2024-07-26 16:35:40,544 - pyskl - INFO - Epoch [124][400/3746] lr: 7.724e-03, eta: 22:55:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4662, top5_acc: 0.7305, loss_cls: 2.9879, loss: 2.9879 +2024-07-26 16:37:02,475 - pyskl - INFO - Epoch [124][500/3746] lr: 7.709e-03, eta: 22:54:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4861, top5_acc: 0.7345, loss_cls: 2.8924, loss: 2.8924 +2024-07-26 16:38:24,092 - pyskl - INFO - Epoch [124][600/3746] lr: 7.694e-03, eta: 22:52:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4683, top5_acc: 0.7228, loss_cls: 2.9540, loss: 2.9540 +2024-07-26 16:39:45,528 - pyskl - INFO - Epoch [124][700/3746] lr: 7.679e-03, eta: 22:51:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4755, top5_acc: 0.7256, loss_cls: 2.9290, loss: 2.9290 +2024-07-26 16:41:06,718 - pyskl - INFO - Epoch [124][800/3746] lr: 7.664e-03, eta: 22:49:56, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4736, top5_acc: 0.7297, loss_cls: 2.9415, loss: 2.9415 +2024-07-26 16:42:27,946 - pyskl - INFO - Epoch [124][900/3746] lr: 7.649e-03, eta: 22:48:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4655, top5_acc: 0.7177, loss_cls: 2.9755, loss: 2.9755 +2024-07-26 16:43:49,325 - pyskl - INFO - Epoch [124][1000/3746] lr: 7.635e-03, eta: 22:47:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4636, top5_acc: 0.7244, loss_cls: 2.9960, loss: 2.9960 +2024-07-26 16:45:10,723 - pyskl - INFO - Epoch [124][1100/3746] lr: 7.620e-03, eta: 22:45:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4636, top5_acc: 0.7192, loss_cls: 3.0107, loss: 3.0107 +2024-07-26 16:46:32,957 - pyskl - INFO - Epoch [124][1200/3746] lr: 7.605e-03, eta: 22:44:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4631, top5_acc: 0.7273, loss_cls: 2.9588, loss: 2.9588 +2024-07-26 16:47:54,563 - pyskl - INFO - Epoch [124][1300/3746] lr: 7.590e-03, eta: 22:43:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4603, top5_acc: 0.7141, loss_cls: 3.0124, loss: 3.0124 +2024-07-26 16:49:16,849 - pyskl - INFO - Epoch [124][1400/3746] lr: 7.575e-03, eta: 22:41:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4586, top5_acc: 0.7236, loss_cls: 2.9957, loss: 2.9957 +2024-07-26 16:50:38,936 - pyskl - INFO - Epoch [124][1500/3746] lr: 7.561e-03, eta: 22:40:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7173, loss_cls: 3.0056, loss: 3.0056 +2024-07-26 16:52:00,201 - pyskl - INFO - Epoch [124][1600/3746] lr: 7.546e-03, eta: 22:39:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4612, top5_acc: 0.7148, loss_cls: 3.0384, loss: 3.0384 +2024-07-26 16:53:21,715 - pyskl - INFO - Epoch [124][1700/3746] lr: 7.531e-03, eta: 22:37:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4591, top5_acc: 0.7219, loss_cls: 2.9799, loss: 2.9799 +2024-07-26 16:54:43,138 - pyskl - INFO - Epoch [124][1800/3746] lr: 7.516e-03, eta: 22:36:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4717, top5_acc: 0.7250, loss_cls: 2.9461, loss: 2.9461 +2024-07-26 16:56:04,531 - pyskl - INFO - Epoch [124][1900/3746] lr: 7.502e-03, eta: 22:34:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4602, top5_acc: 0.7178, loss_cls: 3.0111, loss: 3.0111 +2024-07-26 16:57:26,492 - pyskl - INFO - Epoch [124][2000/3746] lr: 7.487e-03, eta: 22:33:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4677, top5_acc: 0.7205, loss_cls: 2.9582, loss: 2.9582 +2024-07-26 16:58:48,592 - pyskl - INFO - Epoch [124][2100/3746] lr: 7.472e-03, eta: 22:32:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4656, top5_acc: 0.7128, loss_cls: 2.9943, loss: 2.9943 +2024-07-26 17:00:11,728 - pyskl - INFO - Epoch [124][2200/3746] lr: 7.457e-03, eta: 22:30:49, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4542, top5_acc: 0.7161, loss_cls: 2.9945, loss: 2.9945 +2024-07-26 17:01:33,063 - pyskl - INFO - Epoch [124][2300/3746] lr: 7.443e-03, eta: 22:29:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4703, top5_acc: 0.7223, loss_cls: 2.9681, loss: 2.9681 +2024-07-26 17:02:54,724 - pyskl - INFO - Epoch [124][2400/3746] lr: 7.428e-03, eta: 22:28:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4633, top5_acc: 0.7064, loss_cls: 3.0382, loss: 3.0382 +2024-07-26 17:04:16,253 - pyskl - INFO - Epoch [124][2500/3746] lr: 7.413e-03, eta: 22:26:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4572, top5_acc: 0.7108, loss_cls: 3.0371, loss: 3.0371 +2024-07-26 17:05:37,344 - pyskl - INFO - Epoch [124][2600/3746] lr: 7.399e-03, eta: 22:25:21, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4589, top5_acc: 0.7152, loss_cls: 2.9995, loss: 2.9995 +2024-07-26 17:06:58,470 - pyskl - INFO - Epoch [124][2700/3746] lr: 7.384e-03, eta: 22:23:59, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4698, top5_acc: 0.7238, loss_cls: 2.9564, loss: 2.9564 +2024-07-26 17:08:20,101 - pyskl - INFO - Epoch [124][2800/3746] lr: 7.370e-03, eta: 22:22:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4606, top5_acc: 0.7161, loss_cls: 2.9940, loss: 2.9940 +2024-07-26 17:09:42,023 - pyskl - INFO - Epoch [124][2900/3746] lr: 7.355e-03, eta: 22:21:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4702, top5_acc: 0.7159, loss_cls: 2.9764, loss: 2.9764 +2024-07-26 17:11:03,650 - pyskl - INFO - Epoch [124][3000/3746] lr: 7.340e-03, eta: 22:19:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4591, top5_acc: 0.7094, loss_cls: 3.0295, loss: 3.0295 +2024-07-26 17:12:24,768 - pyskl - INFO - Epoch [124][3100/3746] lr: 7.326e-03, eta: 22:18:31, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4573, top5_acc: 0.7134, loss_cls: 3.0359, loss: 3.0359 +2024-07-26 17:13:45,837 - pyskl - INFO - Epoch [124][3200/3746] lr: 7.311e-03, eta: 22:17:09, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4627, top5_acc: 0.7137, loss_cls: 3.0377, loss: 3.0377 +2024-07-26 17:15:07,380 - pyskl - INFO - Epoch [124][3300/3746] lr: 7.297e-03, eta: 22:15:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4692, top5_acc: 0.7200, loss_cls: 2.9946, loss: 2.9946 +2024-07-26 17:16:28,841 - pyskl - INFO - Epoch [124][3400/3746] lr: 7.282e-03, eta: 22:14:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7159, loss_cls: 2.9918, loss: 2.9918 +2024-07-26 17:17:50,410 - pyskl - INFO - Epoch [124][3500/3746] lr: 7.268e-03, eta: 22:13:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4572, top5_acc: 0.7100, loss_cls: 3.0269, loss: 3.0269 +2024-07-26 17:19:12,253 - pyskl - INFO - Epoch [124][3600/3746] lr: 7.253e-03, eta: 22:11:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4567, top5_acc: 0.7120, loss_cls: 3.0273, loss: 3.0273 +2024-07-26 17:20:34,043 - pyskl - INFO - Epoch [124][3700/3746] lr: 7.239e-03, eta: 22:10:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4562, top5_acc: 0.7100, loss_cls: 3.0251, loss: 3.0251 +2024-07-26 17:21:13,659 - pyskl - INFO - Saving checkpoint at 124 epochs +2024-07-26 17:23:06,284 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 17:23:06,992 - pyskl - INFO - +top1_acc 0.3884 +top5_acc 0.6470 +2024-07-26 17:23:06,993 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 17:23:07,051 - pyskl - INFO - +mean_acc 0.3880 +2024-07-26 17:23:07,071 - pyskl - INFO - Epoch(val) [124][309] top1_acc: 0.3884, top5_acc: 0.6470, mean_class_accuracy: 0.3880 +2024-07-26 17:26:57,376 - pyskl - INFO - Epoch [125][100/3746] lr: 7.217e-03, eta: 22:08:42, time: 2.303, data_time: 1.323, memory: 15990, top1_acc: 0.4925, top5_acc: 0.7416, loss_cls: 2.8626, loss: 2.8626 +2024-07-26 17:28:19,269 - pyskl - INFO - Epoch [125][200/3746] lr: 7.203e-03, eta: 22:07:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4839, top5_acc: 0.7331, loss_cls: 2.9050, loss: 2.9050 +2024-07-26 17:29:40,738 - pyskl - INFO - Epoch [125][300/3746] lr: 7.189e-03, eta: 22:05:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4853, top5_acc: 0.7341, loss_cls: 2.8775, loss: 2.8775 +2024-07-26 17:31:03,364 - pyskl - INFO - Epoch [125][400/3746] lr: 7.174e-03, eta: 22:04:37, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4763, top5_acc: 0.7297, loss_cls: 2.9096, loss: 2.9096 +2024-07-26 17:32:25,090 - pyskl - INFO - Epoch [125][500/3746] lr: 7.160e-03, eta: 22:03:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4859, top5_acc: 0.7352, loss_cls: 2.8866, loss: 2.8866 +2024-07-26 17:33:47,301 - pyskl - INFO - Epoch [125][600/3746] lr: 7.145e-03, eta: 22:01:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4756, top5_acc: 0.7412, loss_cls: 2.8776, loss: 2.8776 +2024-07-26 17:35:08,955 - pyskl - INFO - Epoch [125][700/3746] lr: 7.131e-03, eta: 22:00:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4697, top5_acc: 0.7255, loss_cls: 2.9534, loss: 2.9534 +2024-07-26 17:36:30,295 - pyskl - INFO - Epoch [125][800/3746] lr: 7.117e-03, eta: 21:59:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4755, top5_acc: 0.7291, loss_cls: 2.9263, loss: 2.9263 +2024-07-26 17:37:52,015 - pyskl - INFO - Epoch [125][900/3746] lr: 7.102e-03, eta: 21:57:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4730, top5_acc: 0.7241, loss_cls: 2.9600, loss: 2.9600 +2024-07-26 17:39:13,578 - pyskl - INFO - Epoch [125][1000/3746] lr: 7.088e-03, eta: 21:56:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4791, top5_acc: 0.7309, loss_cls: 2.9327, loss: 2.9327 +2024-07-26 17:40:34,953 - pyskl - INFO - Epoch [125][1100/3746] lr: 7.073e-03, eta: 21:55:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4706, top5_acc: 0.7280, loss_cls: 2.9449, loss: 2.9449 +2024-07-26 17:41:56,261 - pyskl - INFO - Epoch [125][1200/3746] lr: 7.059e-03, eta: 21:53:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4695, top5_acc: 0.7166, loss_cls: 2.9949, loss: 2.9949 +2024-07-26 17:43:18,018 - pyskl - INFO - Epoch [125][1300/3746] lr: 7.045e-03, eta: 21:52:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4777, top5_acc: 0.7238, loss_cls: 2.9360, loss: 2.9360 +2024-07-26 17:44:39,863 - pyskl - INFO - Epoch [125][1400/3746] lr: 7.031e-03, eta: 21:50:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4666, top5_acc: 0.7291, loss_cls: 2.9575, loss: 2.9575 +2024-07-26 17:46:01,052 - pyskl - INFO - Epoch [125][1500/3746] lr: 7.016e-03, eta: 21:49:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4697, top5_acc: 0.7303, loss_cls: 2.9465, loss: 2.9465 +2024-07-26 17:47:22,467 - pyskl - INFO - Epoch [125][1600/3746] lr: 7.002e-03, eta: 21:48:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4720, top5_acc: 0.7209, loss_cls: 2.9672, loss: 2.9672 +2024-07-26 17:48:44,064 - pyskl - INFO - Epoch [125][1700/3746] lr: 6.988e-03, eta: 21:46:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4767, top5_acc: 0.7239, loss_cls: 2.9469, loss: 2.9469 +2024-07-26 17:50:05,902 - pyskl - INFO - Epoch [125][1800/3746] lr: 6.973e-03, eta: 21:45:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7137, loss_cls: 2.9708, loss: 2.9708 +2024-07-26 17:51:28,001 - pyskl - INFO - Epoch [125][1900/3746] lr: 6.959e-03, eta: 21:44:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4627, top5_acc: 0.7208, loss_cls: 2.9971, loss: 2.9971 +2024-07-26 17:52:49,576 - pyskl - INFO - Epoch [125][2000/3746] lr: 6.945e-03, eta: 21:42:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4705, top5_acc: 0.7281, loss_cls: 2.9439, loss: 2.9439 +2024-07-26 17:54:10,868 - pyskl - INFO - Epoch [125][2100/3746] lr: 6.931e-03, eta: 21:41:23, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4603, top5_acc: 0.7241, loss_cls: 2.9815, loss: 2.9815 +2024-07-26 17:55:33,267 - pyskl - INFO - Epoch [125][2200/3746] lr: 6.917e-03, eta: 21:40:01, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4772, top5_acc: 0.7258, loss_cls: 2.9239, loss: 2.9239 +2024-07-26 17:56:55,664 - pyskl - INFO - Epoch [125][2300/3746] lr: 6.902e-03, eta: 21:38:39, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4695, top5_acc: 0.7314, loss_cls: 2.9526, loss: 2.9526 +2024-07-26 17:58:17,227 - pyskl - INFO - Epoch [125][2400/3746] lr: 6.888e-03, eta: 21:37:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4630, top5_acc: 0.7216, loss_cls: 2.9579, loss: 2.9579 +2024-07-26 17:59:39,084 - pyskl - INFO - Epoch [125][2500/3746] lr: 6.874e-03, eta: 21:35:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4648, top5_acc: 0.7161, loss_cls: 2.9491, loss: 2.9491 +2024-07-26 18:01:00,369 - pyskl - INFO - Epoch [125][2600/3746] lr: 6.860e-03, eta: 21:34:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7203, loss_cls: 2.9997, loss: 2.9997 +2024-07-26 18:02:21,491 - pyskl - INFO - Epoch [125][2700/3746] lr: 6.846e-03, eta: 21:33:11, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7217, loss_cls: 2.9741, loss: 2.9741 +2024-07-26 18:03:43,041 - pyskl - INFO - Epoch [125][2800/3746] lr: 6.832e-03, eta: 21:31:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4681, top5_acc: 0.7244, loss_cls: 2.9710, loss: 2.9710 +2024-07-26 18:05:04,028 - pyskl - INFO - Epoch [125][2900/3746] lr: 6.818e-03, eta: 21:30:27, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4713, top5_acc: 0.7314, loss_cls: 2.9232, loss: 2.9232 +2024-07-26 18:06:25,551 - pyskl - INFO - Epoch [125][3000/3746] lr: 6.804e-03, eta: 21:29:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4706, top5_acc: 0.7206, loss_cls: 2.9585, loss: 2.9585 +2024-07-26 18:07:47,443 - pyskl - INFO - Epoch [125][3100/3746] lr: 6.789e-03, eta: 21:27:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4677, top5_acc: 0.7217, loss_cls: 2.9674, loss: 2.9674 +2024-07-26 18:09:08,480 - pyskl - INFO - Epoch [125][3200/3746] lr: 6.775e-03, eta: 21:26:21, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4675, top5_acc: 0.7181, loss_cls: 2.9722, loss: 2.9722 +2024-07-26 18:10:30,332 - pyskl - INFO - Epoch [125][3300/3746] lr: 6.761e-03, eta: 21:24:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4684, top5_acc: 0.7266, loss_cls: 2.9500, loss: 2.9500 +2024-07-26 18:11:51,590 - pyskl - INFO - Epoch [125][3400/3746] lr: 6.747e-03, eta: 21:23:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4775, top5_acc: 0.7219, loss_cls: 2.9396, loss: 2.9396 +2024-07-26 18:13:13,105 - pyskl - INFO - Epoch [125][3500/3746] lr: 6.733e-03, eta: 21:22:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4691, top5_acc: 0.7191, loss_cls: 3.0009, loss: 3.0009 +2024-07-26 18:14:34,847 - pyskl - INFO - Epoch [125][3600/3746] lr: 6.719e-03, eta: 21:20:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4580, top5_acc: 0.7169, loss_cls: 3.0320, loss: 3.0320 +2024-07-26 18:15:56,144 - pyskl - INFO - Epoch [125][3700/3746] lr: 6.705e-03, eta: 21:19:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7131, loss_cls: 3.0239, loss: 3.0239 +2024-07-26 18:16:35,787 - pyskl - INFO - Saving checkpoint at 125 epochs +2024-07-26 18:18:28,380 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 18:18:29,047 - pyskl - INFO - +top1_acc 0.4021 +top5_acc 0.6528 +2024-07-26 18:18:29,047 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 18:18:29,087 - pyskl - INFO - +mean_acc 0.4019 +2024-07-26 18:18:29,092 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_121.pth was removed +2024-07-26 18:18:29,367 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_125.pth. +2024-07-26 18:18:29,367 - pyskl - INFO - Best top1_acc is 0.4021 at 125 epoch. +2024-07-26 18:18:29,379 - pyskl - INFO - Epoch(val) [125][309] top1_acc: 0.4021, top5_acc: 0.6528, mean_class_accuracy: 0.4019 +2024-07-26 18:22:18,216 - pyskl - INFO - Epoch [126][100/3746] lr: 6.685e-03, eta: 21:17:53, time: 2.288, data_time: 1.306, memory: 15990, top1_acc: 0.4931, top5_acc: 0.7430, loss_cls: 2.8293, loss: 2.8293 +2024-07-26 18:23:40,467 - pyskl - INFO - Epoch [126][200/3746] lr: 6.671e-03, eta: 21:16:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4950, top5_acc: 0.7366, loss_cls: 2.8574, loss: 2.8574 +2024-07-26 18:25:02,164 - pyskl - INFO - Epoch [126][300/3746] lr: 6.657e-03, eta: 21:15:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4784, top5_acc: 0.7253, loss_cls: 2.9096, loss: 2.9096 +2024-07-26 18:26:23,724 - pyskl - INFO - Epoch [126][400/3746] lr: 6.643e-03, eta: 21:13:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4927, top5_acc: 0.7420, loss_cls: 2.8590, loss: 2.8590 +2024-07-26 18:27:45,729 - pyskl - INFO - Epoch [126][500/3746] lr: 6.629e-03, eta: 21:12:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4934, top5_acc: 0.7503, loss_cls: 2.8131, loss: 2.8131 +2024-07-26 18:29:08,188 - pyskl - INFO - Epoch [126][600/3746] lr: 6.615e-03, eta: 21:11:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4773, top5_acc: 0.7358, loss_cls: 2.8720, loss: 2.8720 +2024-07-26 18:30:30,069 - pyskl - INFO - Epoch [126][700/3746] lr: 6.601e-03, eta: 21:09:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4734, top5_acc: 0.7355, loss_cls: 2.9075, loss: 2.9075 +2024-07-26 18:31:52,204 - pyskl - INFO - Epoch [126][800/3746] lr: 6.587e-03, eta: 21:08:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4756, top5_acc: 0.7367, loss_cls: 2.8999, loss: 2.8999 +2024-07-26 18:33:13,621 - pyskl - INFO - Epoch [126][900/3746] lr: 6.574e-03, eta: 21:06:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4858, top5_acc: 0.7392, loss_cls: 2.8648, loss: 2.8648 +2024-07-26 18:34:34,824 - pyskl - INFO - Epoch [126][1000/3746] lr: 6.560e-03, eta: 21:05:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4706, top5_acc: 0.7278, loss_cls: 2.9760, loss: 2.9760 +2024-07-26 18:35:56,317 - pyskl - INFO - Epoch [126][1100/3746] lr: 6.546e-03, eta: 21:04:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7333, loss_cls: 2.9167, loss: 2.9167 +2024-07-26 18:37:17,953 - pyskl - INFO - Epoch [126][1200/3746] lr: 6.532e-03, eta: 21:02:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4706, top5_acc: 0.7272, loss_cls: 2.9243, loss: 2.9243 +2024-07-26 18:38:39,518 - pyskl - INFO - Epoch [126][1300/3746] lr: 6.518e-03, eta: 21:01:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4723, top5_acc: 0.7302, loss_cls: 2.9212, loss: 2.9212 +2024-07-26 18:40:00,905 - pyskl - INFO - Epoch [126][1400/3746] lr: 6.505e-03, eta: 21:00:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4752, top5_acc: 0.7323, loss_cls: 2.9188, loss: 2.9188 +2024-07-26 18:41:22,142 - pyskl - INFO - Epoch [126][1500/3746] lr: 6.491e-03, eta: 20:58:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4683, top5_acc: 0.7248, loss_cls: 2.9463, loss: 2.9463 +2024-07-26 18:42:43,611 - pyskl - INFO - Epoch [126][1600/3746] lr: 6.477e-03, eta: 20:57:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4773, top5_acc: 0.7320, loss_cls: 2.9242, loss: 2.9242 +2024-07-26 18:44:05,034 - pyskl - INFO - Epoch [126][1700/3746] lr: 6.463e-03, eta: 20:56:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4752, top5_acc: 0.7314, loss_cls: 2.9444, loss: 2.9444 +2024-07-26 18:45:26,130 - pyskl - INFO - Epoch [126][1800/3746] lr: 6.449e-03, eta: 20:54:38, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4706, top5_acc: 0.7239, loss_cls: 2.9716, loss: 2.9716 +2024-07-26 18:46:47,680 - pyskl - INFO - Epoch [126][1900/3746] lr: 6.436e-03, eta: 20:53:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4680, top5_acc: 0.7253, loss_cls: 2.9439, loss: 2.9439 +2024-07-26 18:48:09,609 - pyskl - INFO - Epoch [126][2000/3746] lr: 6.422e-03, eta: 20:51:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4789, top5_acc: 0.7230, loss_cls: 2.9316, loss: 2.9316 +2024-07-26 18:49:31,834 - pyskl - INFO - Epoch [126][2100/3746] lr: 6.408e-03, eta: 20:50:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4681, top5_acc: 0.7181, loss_cls: 2.9398, loss: 2.9398 +2024-07-26 18:50:54,691 - pyskl - INFO - Epoch [126][2200/3746] lr: 6.395e-03, eta: 20:49:11, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4730, top5_acc: 0.7259, loss_cls: 2.9231, loss: 2.9231 +2024-07-26 18:52:17,399 - pyskl - INFO - Epoch [126][2300/3746] lr: 6.381e-03, eta: 20:47:49, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4833, top5_acc: 0.7344, loss_cls: 2.9062, loss: 2.9062 +2024-07-26 18:53:40,196 - pyskl - INFO - Epoch [126][2400/3746] lr: 6.367e-03, eta: 20:46:27, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4703, top5_acc: 0.7203, loss_cls: 2.9792, loss: 2.9792 +2024-07-26 18:55:01,990 - pyskl - INFO - Epoch [126][2500/3746] lr: 6.354e-03, eta: 20:45:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4688, top5_acc: 0.7312, loss_cls: 2.9452, loss: 2.9452 +2024-07-26 18:56:23,710 - pyskl - INFO - Epoch [126][2600/3746] lr: 6.340e-03, eta: 20:43:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4666, top5_acc: 0.7200, loss_cls: 2.9543, loss: 2.9543 +2024-07-26 18:57:45,063 - pyskl - INFO - Epoch [126][2700/3746] lr: 6.326e-03, eta: 20:42:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4622, top5_acc: 0.7220, loss_cls: 2.9622, loss: 2.9622 +2024-07-26 18:59:06,687 - pyskl - INFO - Epoch [126][2800/3746] lr: 6.313e-03, eta: 20:40:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4705, top5_acc: 0.7183, loss_cls: 2.9601, loss: 2.9601 +2024-07-26 19:00:28,345 - pyskl - INFO - Epoch [126][2900/3746] lr: 6.299e-03, eta: 20:39:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4594, top5_acc: 0.7203, loss_cls: 2.9704, loss: 2.9704 +2024-07-26 19:01:49,437 - pyskl - INFO - Epoch [126][3000/3746] lr: 6.286e-03, eta: 20:38:15, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4738, top5_acc: 0.7228, loss_cls: 2.9427, loss: 2.9427 +2024-07-26 19:03:10,940 - pyskl - INFO - Epoch [126][3100/3746] lr: 6.272e-03, eta: 20:36:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4794, top5_acc: 0.7273, loss_cls: 2.9338, loss: 2.9338 +2024-07-26 19:04:32,869 - pyskl - INFO - Epoch [126][3200/3746] lr: 6.259e-03, eta: 20:35:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4769, top5_acc: 0.7292, loss_cls: 2.9271, loss: 2.9271 +2024-07-26 19:05:54,245 - pyskl - INFO - Epoch [126][3300/3746] lr: 6.245e-03, eta: 20:34:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4728, top5_acc: 0.7241, loss_cls: 2.9245, loss: 2.9245 +2024-07-26 19:07:15,768 - pyskl - INFO - Epoch [126][3400/3746] lr: 6.231e-03, eta: 20:32:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4716, top5_acc: 0.7219, loss_cls: 2.9474, loss: 2.9474 +2024-07-26 19:08:37,028 - pyskl - INFO - Epoch [126][3500/3746] lr: 6.218e-03, eta: 20:31:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4742, top5_acc: 0.7173, loss_cls: 2.9417, loss: 2.9417 +2024-07-26 19:09:58,896 - pyskl - INFO - Epoch [126][3600/3746] lr: 6.204e-03, eta: 20:30:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4941, top5_acc: 0.7447, loss_cls: 2.8368, loss: 2.8368 +2024-07-26 19:11:20,075 - pyskl - INFO - Epoch [126][3700/3746] lr: 6.191e-03, eta: 20:28:40, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7191, loss_cls: 2.9846, loss: 2.9846 +2024-07-26 19:11:59,950 - pyskl - INFO - Saving checkpoint at 126 epochs +2024-07-26 19:13:51,672 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 19:13:52,368 - pyskl - INFO - +top1_acc 0.3951 +top5_acc 0.6552 +2024-07-26 19:13:52,369 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 19:13:52,412 - pyskl - INFO - +mean_acc 0.3949 +2024-07-26 19:13:52,427 - pyskl - INFO - Epoch(val) [126][309] top1_acc: 0.3951, top5_acc: 0.6552, mean_class_accuracy: 0.3949 +2024-07-26 19:17:46,144 - pyskl - INFO - Epoch [127][100/3746] lr: 6.171e-03, eta: 20:27:02, time: 2.337, data_time: 1.346, memory: 15990, top1_acc: 0.5011, top5_acc: 0.7444, loss_cls: 2.8128, loss: 2.8128 +2024-07-26 19:19:09,283 - pyskl - INFO - Epoch [127][200/3746] lr: 6.158e-03, eta: 20:25:41, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5048, top5_acc: 0.7570, loss_cls: 2.7877, loss: 2.7877 +2024-07-26 19:20:31,851 - pyskl - INFO - Epoch [127][300/3746] lr: 6.144e-03, eta: 20:24:19, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4955, top5_acc: 0.7478, loss_cls: 2.8090, loss: 2.8090 +2024-07-26 19:21:55,889 - pyskl - INFO - Epoch [127][400/3746] lr: 6.131e-03, eta: 20:22:57, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.4859, top5_acc: 0.7414, loss_cls: 2.8270, loss: 2.8270 +2024-07-26 19:23:18,889 - pyskl - INFO - Epoch [127][500/3746] lr: 6.118e-03, eta: 20:21:35, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4947, top5_acc: 0.7373, loss_cls: 2.8452, loss: 2.8452 +2024-07-26 19:24:41,547 - pyskl - INFO - Epoch [127][600/3746] lr: 6.104e-03, eta: 20:20:14, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4766, top5_acc: 0.7312, loss_cls: 2.8872, loss: 2.8872 +2024-07-26 19:26:05,221 - pyskl - INFO - Epoch [127][700/3746] lr: 6.091e-03, eta: 20:18:52, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4919, top5_acc: 0.7411, loss_cls: 2.8334, loss: 2.8334 +2024-07-26 19:27:28,759 - pyskl - INFO - Epoch [127][800/3746] lr: 6.077e-03, eta: 20:17:30, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4947, top5_acc: 0.7403, loss_cls: 2.8404, loss: 2.8404 +2024-07-26 19:28:52,439 - pyskl - INFO - Epoch [127][900/3746] lr: 6.064e-03, eta: 20:16:09, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4883, top5_acc: 0.7375, loss_cls: 2.8434, loss: 2.8434 +2024-07-26 19:30:15,468 - pyskl - INFO - Epoch [127][1000/3746] lr: 6.051e-03, eta: 20:14:47, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4953, top5_acc: 0.7466, loss_cls: 2.8147, loss: 2.8147 +2024-07-26 19:31:38,250 - pyskl - INFO - Epoch [127][1100/3746] lr: 6.037e-03, eta: 20:13:25, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4780, top5_acc: 0.7308, loss_cls: 2.9255, loss: 2.9255 +2024-07-26 19:33:00,864 - pyskl - INFO - Epoch [127][1200/3746] lr: 6.024e-03, eta: 20:12:03, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4902, top5_acc: 0.7416, loss_cls: 2.8737, loss: 2.8737 +2024-07-26 19:34:23,799 - pyskl - INFO - Epoch [127][1300/3746] lr: 6.011e-03, eta: 20:10:41, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4834, top5_acc: 0.7348, loss_cls: 2.8995, loss: 2.8995 +2024-07-26 19:35:46,609 - pyskl - INFO - Epoch [127][1400/3746] lr: 5.998e-03, eta: 20:09:19, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4908, top5_acc: 0.7377, loss_cls: 2.8817, loss: 2.8817 +2024-07-26 19:37:09,243 - pyskl - INFO - Epoch [127][1500/3746] lr: 5.984e-03, eta: 20:07:58, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4730, top5_acc: 0.7370, loss_cls: 2.8935, loss: 2.8935 +2024-07-26 19:38:30,798 - pyskl - INFO - Epoch [127][1600/3746] lr: 5.971e-03, eta: 20:06:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4900, top5_acc: 0.7372, loss_cls: 2.8702, loss: 2.8702 +2024-07-26 19:39:52,449 - pyskl - INFO - Epoch [127][1700/3746] lr: 5.958e-03, eta: 20:05:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7356, loss_cls: 2.8845, loss: 2.8845 +2024-07-26 19:41:14,053 - pyskl - INFO - Epoch [127][1800/3746] lr: 5.945e-03, eta: 20:03:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4717, top5_acc: 0.7275, loss_cls: 2.9356, loss: 2.9356 +2024-07-26 19:42:35,490 - pyskl - INFO - Epoch [127][1900/3746] lr: 5.931e-03, eta: 20:02:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4742, top5_acc: 0.7305, loss_cls: 2.9211, loss: 2.9211 +2024-07-26 19:43:57,175 - pyskl - INFO - Epoch [127][2000/3746] lr: 5.918e-03, eta: 20:01:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7402, loss_cls: 2.8644, loss: 2.8644 +2024-07-26 19:45:19,634 - pyskl - INFO - Epoch [127][2100/3746] lr: 5.905e-03, eta: 19:59:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7277, loss_cls: 2.9129, loss: 2.9129 +2024-07-26 19:46:42,551 - pyskl - INFO - Epoch [127][2200/3746] lr: 5.892e-03, eta: 19:58:24, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4769, top5_acc: 0.7300, loss_cls: 2.9109, loss: 2.9109 +2024-07-26 19:48:04,802 - pyskl - INFO - Epoch [127][2300/3746] lr: 5.879e-03, eta: 19:57:02, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4814, top5_acc: 0.7356, loss_cls: 2.8784, loss: 2.8784 +2024-07-26 19:49:27,369 - pyskl - INFO - Epoch [127][2400/3746] lr: 5.866e-03, eta: 19:55:40, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4881, top5_acc: 0.7283, loss_cls: 2.9129, loss: 2.9129 +2024-07-26 19:50:49,075 - pyskl - INFO - Epoch [127][2500/3746] lr: 5.852e-03, eta: 19:54:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4733, top5_acc: 0.7220, loss_cls: 2.9475, loss: 2.9475 +2024-07-26 19:52:10,445 - pyskl - INFO - Epoch [127][2600/3746] lr: 5.839e-03, eta: 19:52:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4658, top5_acc: 0.7231, loss_cls: 2.9536, loss: 2.9536 +2024-07-26 19:53:32,383 - pyskl - INFO - Epoch [127][2700/3746] lr: 5.826e-03, eta: 19:51:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4806, top5_acc: 0.7327, loss_cls: 2.9251, loss: 2.9251 +2024-07-26 19:54:54,197 - pyskl - INFO - Epoch [127][2800/3746] lr: 5.813e-03, eta: 19:50:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4841, top5_acc: 0.7392, loss_cls: 2.8714, loss: 2.8714 +2024-07-26 19:56:15,691 - pyskl - INFO - Epoch [127][2900/3746] lr: 5.800e-03, eta: 19:48:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4877, top5_acc: 0.7359, loss_cls: 2.8634, loss: 2.8634 +2024-07-26 19:57:37,243 - pyskl - INFO - Epoch [127][3000/3746] lr: 5.787e-03, eta: 19:47:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4711, top5_acc: 0.7261, loss_cls: 2.9480, loss: 2.9480 +2024-07-26 19:58:58,827 - pyskl - INFO - Epoch [127][3100/3746] lr: 5.774e-03, eta: 19:46:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4670, top5_acc: 0.7219, loss_cls: 2.9680, loss: 2.9680 +2024-07-26 20:00:20,536 - pyskl - INFO - Epoch [127][3200/3746] lr: 5.761e-03, eta: 19:44:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7391, loss_cls: 2.8834, loss: 2.8834 +2024-07-26 20:01:42,289 - pyskl - INFO - Epoch [127][3300/3746] lr: 5.748e-03, eta: 19:43:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4744, top5_acc: 0.7289, loss_cls: 2.9329, loss: 2.9329 +2024-07-26 20:03:03,795 - pyskl - INFO - Epoch [127][3400/3746] lr: 5.735e-03, eta: 19:41:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7292, loss_cls: 2.9355, loss: 2.9355 +2024-07-26 20:04:25,866 - pyskl - INFO - Epoch [127][3500/3746] lr: 5.722e-03, eta: 19:40:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4731, top5_acc: 0.7266, loss_cls: 2.9478, loss: 2.9478 +2024-07-26 20:05:47,816 - pyskl - INFO - Epoch [127][3600/3746] lr: 5.709e-03, eta: 19:39:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4733, top5_acc: 0.7309, loss_cls: 2.9324, loss: 2.9324 +2024-07-26 20:07:09,283 - pyskl - INFO - Epoch [127][3700/3746] lr: 5.696e-03, eta: 19:37:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4716, top5_acc: 0.7291, loss_cls: 2.9224, loss: 2.9224 +2024-07-26 20:07:48,646 - pyskl - INFO - Saving checkpoint at 127 epochs +2024-07-26 20:09:41,787 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 20:09:42,510 - pyskl - INFO - +top1_acc 0.4046 +top5_acc 0.6590 +2024-07-26 20:09:42,510 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 20:09:42,562 - pyskl - INFO - +mean_acc 0.4044 +2024-07-26 20:09:42,566 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_125.pth was removed +2024-07-26 20:09:42,842 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_127.pth. +2024-07-26 20:09:42,842 - pyskl - INFO - Best top1_acc is 0.4046 at 127 epoch. +2024-07-26 20:09:42,861 - pyskl - INFO - Epoch(val) [127][309] top1_acc: 0.4046, top5_acc: 0.6590, mean_class_accuracy: 0.4044 +2024-07-26 20:13:38,963 - pyskl - INFO - Epoch [128][100/3746] lr: 5.677e-03, eta: 19:36:15, time: 2.361, data_time: 1.368, memory: 15990, top1_acc: 0.5166, top5_acc: 0.7645, loss_cls: 2.7101, loss: 2.7101 +2024-07-26 20:15:02,639 - pyskl - INFO - Epoch [128][200/3746] lr: 5.664e-03, eta: 19:34:53, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4931, top5_acc: 0.7447, loss_cls: 2.8080, loss: 2.8080 +2024-07-26 20:16:25,954 - pyskl - INFO - Epoch [128][300/3746] lr: 5.651e-03, eta: 19:33:31, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4928, top5_acc: 0.7466, loss_cls: 2.7964, loss: 2.7964 +2024-07-26 20:17:49,450 - pyskl - INFO - Epoch [128][400/3746] lr: 5.638e-03, eta: 19:32:09, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4959, top5_acc: 0.7531, loss_cls: 2.7994, loss: 2.7994 +2024-07-26 20:19:11,960 - pyskl - INFO - Epoch [128][500/3746] lr: 5.625e-03, eta: 19:30:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4939, top5_acc: 0.7441, loss_cls: 2.8277, loss: 2.8277 +2024-07-26 20:20:33,947 - pyskl - INFO - Epoch [128][600/3746] lr: 5.612e-03, eta: 19:29:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4914, top5_acc: 0.7491, loss_cls: 2.8051, loss: 2.8051 +2024-07-26 20:21:56,131 - pyskl - INFO - Epoch [128][700/3746] lr: 5.600e-03, eta: 19:28:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4884, top5_acc: 0.7433, loss_cls: 2.8423, loss: 2.8423 +2024-07-26 20:23:17,401 - pyskl - INFO - Epoch [128][800/3746] lr: 5.587e-03, eta: 19:26:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4992, top5_acc: 0.7519, loss_cls: 2.7654, loss: 2.7654 +2024-07-26 20:24:38,866 - pyskl - INFO - Epoch [128][900/3746] lr: 5.574e-03, eta: 19:25:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4855, top5_acc: 0.7408, loss_cls: 2.8543, loss: 2.8543 +2024-07-26 20:26:00,419 - pyskl - INFO - Epoch [128][1000/3746] lr: 5.561e-03, eta: 19:23:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4898, top5_acc: 0.7372, loss_cls: 2.8449, loss: 2.8449 +2024-07-26 20:27:22,397 - pyskl - INFO - Epoch [128][1100/3746] lr: 5.548e-03, eta: 19:22:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4880, top5_acc: 0.7400, loss_cls: 2.8384, loss: 2.8384 +2024-07-26 20:28:43,914 - pyskl - INFO - Epoch [128][1200/3746] lr: 5.536e-03, eta: 19:21:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4913, top5_acc: 0.7359, loss_cls: 2.8468, loss: 2.8468 +2024-07-26 20:30:05,309 - pyskl - INFO - Epoch [128][1300/3746] lr: 5.523e-03, eta: 19:19:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4870, top5_acc: 0.7353, loss_cls: 2.8795, loss: 2.8795 +2024-07-26 20:31:26,343 - pyskl - INFO - Epoch [128][1400/3746] lr: 5.510e-03, eta: 19:18:29, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4820, top5_acc: 0.7417, loss_cls: 2.8617, loss: 2.8617 +2024-07-26 20:32:47,866 - pyskl - INFO - Epoch [128][1500/3746] lr: 5.497e-03, eta: 19:17:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4803, top5_acc: 0.7344, loss_cls: 2.8809, loss: 2.8809 +2024-07-26 20:34:09,121 - pyskl - INFO - Epoch [128][1600/3746] lr: 5.485e-03, eta: 19:15:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4778, top5_acc: 0.7342, loss_cls: 2.9139, loss: 2.9139 +2024-07-26 20:35:30,360 - pyskl - INFO - Epoch [128][1700/3746] lr: 5.472e-03, eta: 19:14:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4803, top5_acc: 0.7325, loss_cls: 2.8651, loss: 2.8651 +2024-07-26 20:36:52,014 - pyskl - INFO - Epoch [128][1800/3746] lr: 5.459e-03, eta: 19:13:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4922, top5_acc: 0.7395, loss_cls: 2.8421, loss: 2.8421 +2024-07-26 20:38:13,580 - pyskl - INFO - Epoch [128][1900/3746] lr: 5.446e-03, eta: 19:11:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7317, loss_cls: 2.8935, loss: 2.8935 +2024-07-26 20:39:34,957 - pyskl - INFO - Epoch [128][2000/3746] lr: 5.434e-03, eta: 19:10:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4933, top5_acc: 0.7378, loss_cls: 2.8457, loss: 2.8457 +2024-07-26 20:40:56,335 - pyskl - INFO - Epoch [128][2100/3746] lr: 5.421e-03, eta: 19:08:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4809, top5_acc: 0.7395, loss_cls: 2.8573, loss: 2.8573 +2024-07-26 20:42:19,292 - pyskl - INFO - Epoch [128][2200/3746] lr: 5.408e-03, eta: 19:07:32, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4817, top5_acc: 0.7334, loss_cls: 2.8822, loss: 2.8822 +2024-07-26 20:43:41,549 - pyskl - INFO - Epoch [128][2300/3746] lr: 5.396e-03, eta: 19:06:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7334, loss_cls: 2.8678, loss: 2.8678 +2024-07-26 20:45:03,417 - pyskl - INFO - Epoch [128][2400/3746] lr: 5.383e-03, eta: 19:04:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4842, top5_acc: 0.7333, loss_cls: 2.8816, loss: 2.8816 +2024-07-26 20:46:25,067 - pyskl - INFO - Epoch [128][2500/3746] lr: 5.370e-03, eta: 19:03:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4827, top5_acc: 0.7359, loss_cls: 2.8781, loss: 2.8781 +2024-07-26 20:47:46,615 - pyskl - INFO - Epoch [128][2600/3746] lr: 5.358e-03, eta: 19:02:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7442, loss_cls: 2.8163, loss: 2.8163 +2024-07-26 20:49:08,223 - pyskl - INFO - Epoch [128][2700/3746] lr: 5.345e-03, eta: 19:00:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4725, top5_acc: 0.7344, loss_cls: 2.9074, loss: 2.9074 +2024-07-26 20:50:29,473 - pyskl - INFO - Epoch [128][2800/3746] lr: 5.333e-03, eta: 18:59:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4866, top5_acc: 0.7464, loss_cls: 2.8215, loss: 2.8215 +2024-07-26 20:51:51,036 - pyskl - INFO - Epoch [128][2900/3746] lr: 5.320e-03, eta: 18:57:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7372, loss_cls: 2.9084, loss: 2.9084 +2024-07-26 20:53:12,553 - pyskl - INFO - Epoch [128][3000/3746] lr: 5.308e-03, eta: 18:56:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4916, top5_acc: 0.7367, loss_cls: 2.8753, loss: 2.8753 +2024-07-26 20:54:34,298 - pyskl - INFO - Epoch [128][3100/3746] lr: 5.295e-03, eta: 18:55:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4850, top5_acc: 0.7319, loss_cls: 2.9075, loss: 2.9075 +2024-07-26 20:55:56,289 - pyskl - INFO - Epoch [128][3200/3746] lr: 5.283e-03, eta: 18:53:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4845, top5_acc: 0.7238, loss_cls: 2.9080, loss: 2.9080 +2024-07-26 20:57:17,816 - pyskl - INFO - Epoch [128][3300/3746] lr: 5.270e-03, eta: 18:52:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4819, top5_acc: 0.7372, loss_cls: 2.8865, loss: 2.8865 +2024-07-26 20:58:39,177 - pyskl - INFO - Epoch [128][3400/3746] lr: 5.258e-03, eta: 18:51:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4795, top5_acc: 0.7350, loss_cls: 2.8900, loss: 2.8900 +2024-07-26 21:00:00,493 - pyskl - INFO - Epoch [128][3500/3746] lr: 5.245e-03, eta: 18:49:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4719, top5_acc: 0.7334, loss_cls: 2.9215, loss: 2.9215 +2024-07-26 21:01:22,122 - pyskl - INFO - Epoch [128][3600/3746] lr: 5.233e-03, eta: 18:48:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4777, top5_acc: 0.7311, loss_cls: 2.9125, loss: 2.9125 +2024-07-26 21:02:43,362 - pyskl - INFO - Epoch [128][3700/3746] lr: 5.220e-03, eta: 18:47:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4913, top5_acc: 0.7431, loss_cls: 2.8241, loss: 2.8241 +2024-07-26 21:03:23,029 - pyskl - INFO - Saving checkpoint at 128 epochs +2024-07-26 21:05:15,623 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 21:05:16,299 - pyskl - INFO - +top1_acc 0.4062 +top5_acc 0.6604 +2024-07-26 21:05:16,299 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 21:05:16,342 - pyskl - INFO - +mean_acc 0.4059 +2024-07-26 21:05:16,346 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_127.pth was removed +2024-07-26 21:05:16,622 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_128.pth. +2024-07-26 21:05:16,623 - pyskl - INFO - Best top1_acc is 0.4062 at 128 epoch. +2024-07-26 21:05:16,636 - pyskl - INFO - Epoch(val) [128][309] top1_acc: 0.4062, top5_acc: 0.6604, mean_class_accuracy: 0.4059 +2024-07-26 21:09:06,321 - pyskl - INFO - Epoch [129][100/3746] lr: 5.202e-03, eta: 18:45:20, time: 2.297, data_time: 1.322, memory: 15990, top1_acc: 0.5048, top5_acc: 0.7627, loss_cls: 2.7294, loss: 2.7294 +2024-07-26 21:10:28,552 - pyskl - INFO - Epoch [129][200/3746] lr: 5.190e-03, eta: 18:43:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4984, top5_acc: 0.7617, loss_cls: 2.7618, loss: 2.7618 +2024-07-26 21:11:50,966 - pyskl - INFO - Epoch [129][300/3746] lr: 5.177e-03, eta: 18:42:36, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4995, top5_acc: 0.7467, loss_cls: 2.7914, loss: 2.7914 +2024-07-26 21:13:12,399 - pyskl - INFO - Epoch [129][400/3746] lr: 5.165e-03, eta: 18:41:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5011, top5_acc: 0.7611, loss_cls: 2.7517, loss: 2.7517 +2024-07-26 21:14:34,624 - pyskl - INFO - Epoch [129][500/3746] lr: 5.153e-03, eta: 18:39:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4964, top5_acc: 0.7567, loss_cls: 2.7474, loss: 2.7474 +2024-07-26 21:15:56,857 - pyskl - INFO - Epoch [129][600/3746] lr: 5.140e-03, eta: 18:38:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5089, top5_acc: 0.7489, loss_cls: 2.7653, loss: 2.7653 +2024-07-26 21:17:18,550 - pyskl - INFO - Epoch [129][700/3746] lr: 5.128e-03, eta: 18:37:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4953, top5_acc: 0.7462, loss_cls: 2.8239, loss: 2.8239 +2024-07-26 21:18:40,116 - pyskl - INFO - Epoch [129][800/3746] lr: 5.116e-03, eta: 18:35:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4994, top5_acc: 0.7514, loss_cls: 2.7763, loss: 2.7763 +2024-07-26 21:20:01,626 - pyskl - INFO - Epoch [129][900/3746] lr: 5.103e-03, eta: 18:34:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4964, top5_acc: 0.7466, loss_cls: 2.7863, loss: 2.7863 +2024-07-26 21:21:22,922 - pyskl - INFO - Epoch [129][1000/3746] lr: 5.091e-03, eta: 18:33:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4883, top5_acc: 0.7342, loss_cls: 2.8534, loss: 2.8534 +2024-07-26 21:22:44,217 - pyskl - INFO - Epoch [129][1100/3746] lr: 5.079e-03, eta: 18:31:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4986, top5_acc: 0.7406, loss_cls: 2.8251, loss: 2.8251 +2024-07-26 21:24:06,185 - pyskl - INFO - Epoch [129][1200/3746] lr: 5.066e-03, eta: 18:30:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4903, top5_acc: 0.7458, loss_cls: 2.8213, loss: 2.8213 +2024-07-26 21:25:27,908 - pyskl - INFO - Epoch [129][1300/3746] lr: 5.054e-03, eta: 18:28:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5053, top5_acc: 0.7484, loss_cls: 2.7782, loss: 2.7782 +2024-07-26 21:26:49,901 - pyskl - INFO - Epoch [129][1400/3746] lr: 5.042e-03, eta: 18:27:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5095, top5_acc: 0.7556, loss_cls: 2.7431, loss: 2.7431 +2024-07-26 21:28:11,002 - pyskl - INFO - Epoch [129][1500/3746] lr: 5.030e-03, eta: 18:26:11, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4917, top5_acc: 0.7405, loss_cls: 2.8306, loss: 2.8306 +2024-07-26 21:29:32,450 - pyskl - INFO - Epoch [129][1600/3746] lr: 5.017e-03, eta: 18:24:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4845, top5_acc: 0.7327, loss_cls: 2.8561, loss: 2.8561 +2024-07-26 21:30:53,797 - pyskl - INFO - Epoch [129][1700/3746] lr: 5.005e-03, eta: 18:23:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4945, top5_acc: 0.7427, loss_cls: 2.8376, loss: 2.8376 +2024-07-26 21:32:15,486 - pyskl - INFO - Epoch [129][1800/3746] lr: 4.993e-03, eta: 18:22:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4983, top5_acc: 0.7397, loss_cls: 2.8087, loss: 2.8087 +2024-07-26 21:33:36,756 - pyskl - INFO - Epoch [129][1900/3746] lr: 4.981e-03, eta: 18:20:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7498, loss_cls: 2.8026, loss: 2.8026 +2024-07-26 21:34:58,292 - pyskl - INFO - Epoch [129][2000/3746] lr: 4.969e-03, eta: 18:19:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4905, top5_acc: 0.7380, loss_cls: 2.8417, loss: 2.8417 +2024-07-26 21:36:20,439 - pyskl - INFO - Epoch [129][2100/3746] lr: 4.957e-03, eta: 18:17:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4939, top5_acc: 0.7430, loss_cls: 2.8620, loss: 2.8620 +2024-07-26 21:37:43,175 - pyskl - INFO - Epoch [129][2200/3746] lr: 4.944e-03, eta: 18:16:36, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4863, top5_acc: 0.7411, loss_cls: 2.8486, loss: 2.8486 +2024-07-26 21:39:05,407 - pyskl - INFO - Epoch [129][2300/3746] lr: 4.932e-03, eta: 18:15:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4764, top5_acc: 0.7319, loss_cls: 2.9080, loss: 2.9080 +2024-07-26 21:40:27,071 - pyskl - INFO - Epoch [129][2400/3746] lr: 4.920e-03, eta: 18:13:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5027, top5_acc: 0.7534, loss_cls: 2.7908, loss: 2.7908 +2024-07-26 21:41:48,690 - pyskl - INFO - Epoch [129][2500/3746] lr: 4.908e-03, eta: 18:12:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4859, top5_acc: 0.7414, loss_cls: 2.8295, loss: 2.8295 +2024-07-26 21:43:10,402 - pyskl - INFO - Epoch [129][2600/3746] lr: 4.896e-03, eta: 18:11:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4953, top5_acc: 0.7491, loss_cls: 2.7837, loss: 2.7837 +2024-07-26 21:44:31,793 - pyskl - INFO - Epoch [129][2700/3746] lr: 4.884e-03, eta: 18:09:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5009, top5_acc: 0.7453, loss_cls: 2.8007, loss: 2.8007 +2024-07-26 21:45:53,994 - pyskl - INFO - Epoch [129][2800/3746] lr: 4.872e-03, eta: 18:08:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4892, top5_acc: 0.7398, loss_cls: 2.8554, loss: 2.8554 +2024-07-26 21:47:15,419 - pyskl - INFO - Epoch [129][2900/3746] lr: 4.860e-03, eta: 18:07:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4863, top5_acc: 0.7386, loss_cls: 2.8614, loss: 2.8614 +2024-07-26 21:48:37,535 - pyskl - INFO - Epoch [129][3000/3746] lr: 4.848e-03, eta: 18:05:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4916, top5_acc: 0.7498, loss_cls: 2.8147, loss: 2.8147 +2024-07-26 21:49:58,815 - pyskl - INFO - Epoch [129][3100/3746] lr: 4.836e-03, eta: 18:04:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5070, top5_acc: 0.7466, loss_cls: 2.7927, loss: 2.7927 +2024-07-26 21:51:20,802 - pyskl - INFO - Epoch [129][3200/3746] lr: 4.824e-03, eta: 18:02:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4855, top5_acc: 0.7372, loss_cls: 2.8516, loss: 2.8516 +2024-07-26 21:52:42,152 - pyskl - INFO - Epoch [129][3300/3746] lr: 4.812e-03, eta: 18:01:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4727, top5_acc: 0.7280, loss_cls: 2.9026, loss: 2.9026 +2024-07-26 21:54:03,905 - pyskl - INFO - Epoch [129][3400/3746] lr: 4.800e-03, eta: 18:00:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4872, top5_acc: 0.7488, loss_cls: 2.8249, loss: 2.8249 +2024-07-26 21:55:25,560 - pyskl - INFO - Epoch [129][3500/3746] lr: 4.788e-03, eta: 17:58:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4734, top5_acc: 0.7356, loss_cls: 2.8861, loss: 2.8861 +2024-07-26 21:56:47,758 - pyskl - INFO - Epoch [129][3600/3746] lr: 4.776e-03, eta: 17:57:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4827, top5_acc: 0.7395, loss_cls: 2.8849, loss: 2.8849 +2024-07-26 21:58:09,157 - pyskl - INFO - Epoch [129][3700/3746] lr: 4.764e-03, eta: 17:56:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4953, top5_acc: 0.7427, loss_cls: 2.8422, loss: 2.8422 +2024-07-26 21:58:48,194 - pyskl - INFO - Saving checkpoint at 129 epochs +2024-07-26 22:00:40,294 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 22:00:40,959 - pyskl - INFO - +top1_acc 0.4101 +top5_acc 0.6645 +2024-07-26 22:00:40,959 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 22:00:41,000 - pyskl - INFO - +mean_acc 0.4098 +2024-07-26 22:00:41,004 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_128.pth was removed +2024-07-26 22:00:41,266 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_129.pth. +2024-07-26 22:00:41,267 - pyskl - INFO - Best top1_acc is 0.4101 at 129 epoch. +2024-07-26 22:00:41,278 - pyskl - INFO - Epoch(val) [129][309] top1_acc: 0.4101, top5_acc: 0.6645, mean_class_accuracy: 0.4098 +2024-07-26 22:04:30,631 - pyskl - INFO - Epoch [130][100/3746] lr: 4.747e-03, eta: 17:54:23, time: 2.293, data_time: 1.313, memory: 15990, top1_acc: 0.5197, top5_acc: 0.7625, loss_cls: 2.6996, loss: 2.6996 +2024-07-26 22:05:52,169 - pyskl - INFO - Epoch [130][200/3746] lr: 4.735e-03, eta: 17:53:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5028, top5_acc: 0.7536, loss_cls: 2.7404, loss: 2.7404 +2024-07-26 22:07:14,140 - pyskl - INFO - Epoch [130][300/3746] lr: 4.723e-03, eta: 17:51:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4991, top5_acc: 0.7584, loss_cls: 2.7305, loss: 2.7305 +2024-07-26 22:08:35,919 - pyskl - INFO - Epoch [130][400/3746] lr: 4.711e-03, eta: 17:50:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5112, top5_acc: 0.7533, loss_cls: 2.7255, loss: 2.7255 +2024-07-26 22:09:58,251 - pyskl - INFO - Epoch [130][500/3746] lr: 4.699e-03, eta: 17:48:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5123, top5_acc: 0.7586, loss_cls: 2.7292, loss: 2.7292 +2024-07-26 22:11:19,723 - pyskl - INFO - Epoch [130][600/3746] lr: 4.688e-03, eta: 17:47:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7506, loss_cls: 2.7864, loss: 2.7864 +2024-07-26 22:12:40,976 - pyskl - INFO - Epoch [130][700/3746] lr: 4.676e-03, eta: 17:46:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4955, top5_acc: 0.7481, loss_cls: 2.7958, loss: 2.7958 +2024-07-26 22:14:02,396 - pyskl - INFO - Epoch [130][800/3746] lr: 4.664e-03, eta: 17:44:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4988, top5_acc: 0.7536, loss_cls: 2.7519, loss: 2.7519 +2024-07-26 22:15:24,137 - pyskl - INFO - Epoch [130][900/3746] lr: 4.652e-03, eta: 17:43:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4991, top5_acc: 0.7438, loss_cls: 2.8105, loss: 2.8105 +2024-07-26 22:16:45,697 - pyskl - INFO - Epoch [130][1000/3746] lr: 4.640e-03, eta: 17:42:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5025, top5_acc: 0.7514, loss_cls: 2.7780, loss: 2.7780 +2024-07-26 22:18:07,235 - pyskl - INFO - Epoch [130][1100/3746] lr: 4.629e-03, eta: 17:40:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4998, top5_acc: 0.7486, loss_cls: 2.7693, loss: 2.7693 +2024-07-26 22:19:28,345 - pyskl - INFO - Epoch [130][1200/3746] lr: 4.617e-03, eta: 17:39:20, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5086, top5_acc: 0.7508, loss_cls: 2.7612, loss: 2.7612 +2024-07-26 22:20:49,592 - pyskl - INFO - Epoch [130][1300/3746] lr: 4.605e-03, eta: 17:37:58, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4845, top5_acc: 0.7431, loss_cls: 2.8201, loss: 2.8201 +2024-07-26 22:22:11,293 - pyskl - INFO - Epoch [130][1400/3746] lr: 4.594e-03, eta: 17:36:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5034, top5_acc: 0.7509, loss_cls: 2.7702, loss: 2.7702 +2024-07-26 22:23:32,990 - pyskl - INFO - Epoch [130][1500/3746] lr: 4.582e-03, eta: 17:35:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4986, top5_acc: 0.7569, loss_cls: 2.7749, loss: 2.7749 +2024-07-26 22:24:54,459 - pyskl - INFO - Epoch [130][1600/3746] lr: 4.570e-03, eta: 17:33:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4905, top5_acc: 0.7445, loss_cls: 2.8156, loss: 2.8156 +2024-07-26 22:26:15,803 - pyskl - INFO - Epoch [130][1700/3746] lr: 4.558e-03, eta: 17:32:29, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4972, top5_acc: 0.7405, loss_cls: 2.8310, loss: 2.8310 +2024-07-26 22:27:37,614 - pyskl - INFO - Epoch [130][1800/3746] lr: 4.547e-03, eta: 17:31:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4931, top5_acc: 0.7478, loss_cls: 2.8114, loss: 2.8114 +2024-07-26 22:28:58,891 - pyskl - INFO - Epoch [130][1900/3746] lr: 4.535e-03, eta: 17:29:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4977, top5_acc: 0.7477, loss_cls: 2.7743, loss: 2.7743 +2024-07-26 22:30:20,450 - pyskl - INFO - Epoch [130][2000/3746] lr: 4.524e-03, eta: 17:28:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5008, top5_acc: 0.7562, loss_cls: 2.7668, loss: 2.7668 +2024-07-26 22:31:41,846 - pyskl - INFO - Epoch [130][2100/3746] lr: 4.512e-03, eta: 17:27:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4992, top5_acc: 0.7472, loss_cls: 2.7985, loss: 2.7985 +2024-07-26 22:33:03,406 - pyskl - INFO - Epoch [130][2200/3746] lr: 4.500e-03, eta: 17:25:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5008, top5_acc: 0.7527, loss_cls: 2.7837, loss: 2.7837 +2024-07-26 22:34:26,573 - pyskl - INFO - Epoch [130][2300/3746] lr: 4.489e-03, eta: 17:24:17, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4944, top5_acc: 0.7483, loss_cls: 2.7999, loss: 2.7999 +2024-07-26 22:35:48,635 - pyskl - INFO - Epoch [130][2400/3746] lr: 4.477e-03, eta: 17:22:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5019, top5_acc: 0.7486, loss_cls: 2.7887, loss: 2.7887 +2024-07-26 22:37:10,414 - pyskl - INFO - Epoch [130][2500/3746] lr: 4.466e-03, eta: 17:21:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5056, top5_acc: 0.7570, loss_cls: 2.7559, loss: 2.7559 +2024-07-26 22:38:31,890 - pyskl - INFO - Epoch [130][2600/3746] lr: 4.454e-03, eta: 17:20:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4964, top5_acc: 0.7555, loss_cls: 2.7845, loss: 2.7845 +2024-07-26 22:39:53,544 - pyskl - INFO - Epoch [130][2700/3746] lr: 4.443e-03, eta: 17:18:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4933, top5_acc: 0.7495, loss_cls: 2.8045, loss: 2.8045 +2024-07-26 22:41:15,282 - pyskl - INFO - Epoch [130][2800/3746] lr: 4.431e-03, eta: 17:17:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4975, top5_acc: 0.7472, loss_cls: 2.7930, loss: 2.7930 +2024-07-26 22:42:36,756 - pyskl - INFO - Epoch [130][2900/3746] lr: 4.420e-03, eta: 17:16:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4917, top5_acc: 0.7427, loss_cls: 2.8275, loss: 2.8275 +2024-07-26 22:43:58,472 - pyskl - INFO - Epoch [130][3000/3746] lr: 4.408e-03, eta: 17:14:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4920, top5_acc: 0.7414, loss_cls: 2.8223, loss: 2.8223 +2024-07-26 22:45:19,944 - pyskl - INFO - Epoch [130][3100/3746] lr: 4.397e-03, eta: 17:13:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4995, top5_acc: 0.7502, loss_cls: 2.7905, loss: 2.7905 +2024-07-26 22:46:41,307 - pyskl - INFO - Epoch [130][3200/3746] lr: 4.385e-03, eta: 17:11:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4953, top5_acc: 0.7480, loss_cls: 2.8072, loss: 2.8072 +2024-07-26 22:48:02,331 - pyskl - INFO - Epoch [130][3300/3746] lr: 4.374e-03, eta: 17:10:36, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4892, top5_acc: 0.7439, loss_cls: 2.8415, loss: 2.8415 +2024-07-26 22:49:23,934 - pyskl - INFO - Epoch [130][3400/3746] lr: 4.362e-03, eta: 17:09:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5086, top5_acc: 0.7609, loss_cls: 2.7368, loss: 2.7368 +2024-07-26 22:50:45,283 - pyskl - INFO - Epoch [130][3500/3746] lr: 4.351e-03, eta: 17:07:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5052, top5_acc: 0.7498, loss_cls: 2.7592, loss: 2.7592 +2024-07-26 22:52:07,246 - pyskl - INFO - Epoch [130][3600/3746] lr: 4.339e-03, eta: 17:06:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4880, top5_acc: 0.7405, loss_cls: 2.8469, loss: 2.8469 +2024-07-26 22:53:28,143 - pyskl - INFO - Epoch [130][3700/3746] lr: 4.328e-03, eta: 17:05:07, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5000, top5_acc: 0.7548, loss_cls: 2.7655, loss: 2.7655 +2024-07-26 22:54:07,576 - pyskl - INFO - Saving checkpoint at 130 epochs +2024-07-26 22:55:58,674 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 22:55:59,342 - pyskl - INFO - +top1_acc 0.4070 +top5_acc 0.6634 +2024-07-26 22:55:59,343 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 22:55:59,389 - pyskl - INFO - +mean_acc 0.4068 +2024-07-26 22:55:59,402 - pyskl - INFO - Epoch(val) [130][309] top1_acc: 0.4070, top5_acc: 0.6634, mean_class_accuracy: 0.4068 +2024-07-26 22:59:46,723 - pyskl - INFO - Epoch [131][100/3746] lr: 4.311e-03, eta: 17:03:24, time: 2.273, data_time: 1.296, memory: 15990, top1_acc: 0.5169, top5_acc: 0.7609, loss_cls: 2.7010, loss: 2.7010 +2024-07-26 23:01:07,946 - pyskl - INFO - Epoch [131][200/3746] lr: 4.300e-03, eta: 17:02:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5258, top5_acc: 0.7645, loss_cls: 2.6662, loss: 2.6662 +2024-07-26 23:02:29,594 - pyskl - INFO - Epoch [131][300/3746] lr: 4.289e-03, eta: 17:00:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5211, top5_acc: 0.7695, loss_cls: 2.6485, loss: 2.6485 +2024-07-26 23:03:52,051 - pyskl - INFO - Epoch [131][400/3746] lr: 4.277e-03, eta: 16:59:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5153, top5_acc: 0.7650, loss_cls: 2.6973, loss: 2.6973 +2024-07-26 23:05:13,445 - pyskl - INFO - Epoch [131][500/3746] lr: 4.266e-03, eta: 16:57:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5019, top5_acc: 0.7548, loss_cls: 2.7581, loss: 2.7581 +2024-07-26 23:06:35,212 - pyskl - INFO - Epoch [131][600/3746] lr: 4.255e-03, eta: 16:56:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5114, top5_acc: 0.7516, loss_cls: 2.7548, loss: 2.7548 +2024-07-26 23:07:56,851 - pyskl - INFO - Epoch [131][700/3746] lr: 4.244e-03, eta: 16:55:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5036, top5_acc: 0.7648, loss_cls: 2.7325, loss: 2.7325 +2024-07-26 23:09:18,295 - pyskl - INFO - Epoch [131][800/3746] lr: 4.232e-03, eta: 16:53:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5192, top5_acc: 0.7733, loss_cls: 2.6632, loss: 2.6632 +2024-07-26 23:10:39,768 - pyskl - INFO - Epoch [131][900/3746] lr: 4.221e-03, eta: 16:52:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5106, top5_acc: 0.7528, loss_cls: 2.7325, loss: 2.7325 +2024-07-26 23:12:01,019 - pyskl - INFO - Epoch [131][1000/3746] lr: 4.210e-03, eta: 16:51:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5092, top5_acc: 0.7567, loss_cls: 2.7340, loss: 2.7340 +2024-07-26 23:13:22,558 - pyskl - INFO - Epoch [131][1100/3746] lr: 4.199e-03, eta: 16:49:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5150, top5_acc: 0.7686, loss_cls: 2.6804, loss: 2.6804 +2024-07-26 23:14:44,029 - pyskl - INFO - Epoch [131][1200/3746] lr: 4.187e-03, eta: 16:48:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5105, top5_acc: 0.7650, loss_cls: 2.7067, loss: 2.7067 +2024-07-26 23:16:05,475 - pyskl - INFO - Epoch [131][1300/3746] lr: 4.176e-03, eta: 16:46:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5159, top5_acc: 0.7656, loss_cls: 2.7061, loss: 2.7061 +2024-07-26 23:17:26,636 - pyskl - INFO - Epoch [131][1400/3746] lr: 4.165e-03, eta: 16:45:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5070, top5_acc: 0.7491, loss_cls: 2.7763, loss: 2.7763 +2024-07-26 23:18:48,494 - pyskl - INFO - Epoch [131][1500/3746] lr: 4.154e-03, eta: 16:44:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5047, top5_acc: 0.7589, loss_cls: 2.7282, loss: 2.7282 +2024-07-26 23:20:09,898 - pyskl - INFO - Epoch [131][1600/3746] lr: 4.143e-03, eta: 16:42:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4920, top5_acc: 0.7459, loss_cls: 2.8006, loss: 2.8006 +2024-07-26 23:21:31,520 - pyskl - INFO - Epoch [131][1700/3746] lr: 4.132e-03, eta: 16:41:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5130, top5_acc: 0.7584, loss_cls: 2.6844, loss: 2.6844 +2024-07-26 23:22:53,240 - pyskl - INFO - Epoch [131][1800/3746] lr: 4.120e-03, eta: 16:40:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5141, top5_acc: 0.7538, loss_cls: 2.7440, loss: 2.7440 +2024-07-26 23:24:14,369 - pyskl - INFO - Epoch [131][1900/3746] lr: 4.109e-03, eta: 16:38:45, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5081, top5_acc: 0.7627, loss_cls: 2.7216, loss: 2.7216 +2024-07-26 23:25:35,423 - pyskl - INFO - Epoch [131][2000/3746] lr: 4.098e-03, eta: 16:37:23, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4991, top5_acc: 0.7441, loss_cls: 2.8018, loss: 2.8018 +2024-07-26 23:26:56,761 - pyskl - INFO - Epoch [131][2100/3746] lr: 4.087e-03, eta: 16:36:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4966, top5_acc: 0.7467, loss_cls: 2.7935, loss: 2.7935 +2024-07-26 23:28:18,761 - pyskl - INFO - Epoch [131][2200/3746] lr: 4.076e-03, eta: 16:34:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5045, top5_acc: 0.7609, loss_cls: 2.7702, loss: 2.7702 +2024-07-26 23:29:41,084 - pyskl - INFO - Epoch [131][2300/3746] lr: 4.065e-03, eta: 16:33:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5019, top5_acc: 0.7533, loss_cls: 2.7860, loss: 2.7860 +2024-07-26 23:31:03,341 - pyskl - INFO - Epoch [131][2400/3746] lr: 4.054e-03, eta: 16:31:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5130, top5_acc: 0.7612, loss_cls: 2.7262, loss: 2.7262 +2024-07-26 23:32:25,429 - pyskl - INFO - Epoch [131][2500/3746] lr: 4.043e-03, eta: 16:30:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5117, top5_acc: 0.7544, loss_cls: 2.7511, loss: 2.7511 +2024-07-26 23:33:47,168 - pyskl - INFO - Epoch [131][2600/3746] lr: 4.032e-03, eta: 16:29:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5014, top5_acc: 0.7489, loss_cls: 2.7909, loss: 2.7909 +2024-07-26 23:35:08,707 - pyskl - INFO - Epoch [131][2700/3746] lr: 4.021e-03, eta: 16:27:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5042, top5_acc: 0.7502, loss_cls: 2.7675, loss: 2.7675 +2024-07-26 23:36:29,824 - pyskl - INFO - Epoch [131][2800/3746] lr: 4.010e-03, eta: 16:26:26, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4955, top5_acc: 0.7422, loss_cls: 2.8028, loss: 2.8028 +2024-07-26 23:37:51,313 - pyskl - INFO - Epoch [131][2900/3746] lr: 3.999e-03, eta: 16:25:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5050, top5_acc: 0.7622, loss_cls: 2.7330, loss: 2.7330 +2024-07-26 23:39:13,016 - pyskl - INFO - Epoch [131][3000/3746] lr: 3.988e-03, eta: 16:23:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5069, top5_acc: 0.7605, loss_cls: 2.7289, loss: 2.7289 +2024-07-26 23:40:34,962 - pyskl - INFO - Epoch [131][3100/3746] lr: 3.977e-03, eta: 16:22:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4977, top5_acc: 0.7447, loss_cls: 2.7975, loss: 2.7975 +2024-07-26 23:41:56,095 - pyskl - INFO - Epoch [131][3200/3746] lr: 3.966e-03, eta: 16:20:57, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4967, top5_acc: 0.7422, loss_cls: 2.8303, loss: 2.8303 +2024-07-26 23:43:17,436 - pyskl - INFO - Epoch [131][3300/3746] lr: 3.955e-03, eta: 16:19:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5111, top5_acc: 0.7612, loss_cls: 2.7072, loss: 2.7072 +2024-07-26 23:44:38,997 - pyskl - INFO - Epoch [131][3400/3746] lr: 3.945e-03, eta: 16:18:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4973, top5_acc: 0.7466, loss_cls: 2.7903, loss: 2.7903 +2024-07-26 23:46:00,014 - pyskl - INFO - Epoch [131][3500/3746] lr: 3.934e-03, eta: 16:16:51, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5112, top5_acc: 0.7594, loss_cls: 2.7246, loss: 2.7246 +2024-07-26 23:47:21,929 - pyskl - INFO - Epoch [131][3600/3746] lr: 3.923e-03, eta: 16:15:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5030, top5_acc: 0.7497, loss_cls: 2.7327, loss: 2.7327 +2024-07-26 23:48:43,589 - pyskl - INFO - Epoch [131][3700/3746] lr: 3.912e-03, eta: 16:14:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5064, top5_acc: 0.7603, loss_cls: 2.7334, loss: 2.7334 +2024-07-26 23:49:23,152 - pyskl - INFO - Saving checkpoint at 131 epochs +2024-07-26 23:51:15,408 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 23:51:16,075 - pyskl - INFO - +top1_acc 0.4191 +top5_acc 0.6716 +2024-07-26 23:51:16,075 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 23:51:16,115 - pyskl - INFO - +mean_acc 0.4189 +2024-07-26 23:51:16,119 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_129.pth was removed +2024-07-26 23:51:16,382 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_131.pth. +2024-07-26 23:51:16,383 - pyskl - INFO - Best top1_acc is 0.4191 at 131 epoch. +2024-07-26 23:51:16,397 - pyskl - INFO - Epoch(val) [131][309] top1_acc: 0.4191, top5_acc: 0.6716, mean_class_accuracy: 0.4189 +2024-07-26 23:55:06,461 - pyskl - INFO - Epoch [132][100/3746] lr: 3.896e-03, eta: 16:12:23, time: 2.301, data_time: 1.322, memory: 15990, top1_acc: 0.5234, top5_acc: 0.7677, loss_cls: 2.6455, loss: 2.6455 +2024-07-26 23:56:28,382 - pyskl - INFO - Epoch [132][200/3746] lr: 3.885e-03, eta: 16:11:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5223, top5_acc: 0.7719, loss_cls: 2.6716, loss: 2.6716 +2024-07-26 23:57:50,375 - pyskl - INFO - Epoch [132][300/3746] lr: 3.875e-03, eta: 16:09:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5256, top5_acc: 0.7705, loss_cls: 2.6223, loss: 2.6223 +2024-07-26 23:59:12,266 - pyskl - INFO - Epoch [132][400/3746] lr: 3.864e-03, eta: 16:08:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5139, top5_acc: 0.7658, loss_cls: 2.6859, loss: 2.6859 +2024-07-27 00:00:34,148 - pyskl - INFO - Epoch [132][500/3746] lr: 3.853e-03, eta: 16:06:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5250, top5_acc: 0.7702, loss_cls: 2.6420, loss: 2.6420 +2024-07-27 00:01:55,693 - pyskl - INFO - Epoch [132][600/3746] lr: 3.842e-03, eta: 16:05:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5211, top5_acc: 0.7673, loss_cls: 2.6593, loss: 2.6593 +2024-07-27 00:03:16,923 - pyskl - INFO - Epoch [132][700/3746] lr: 3.831e-03, eta: 16:04:10, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5198, top5_acc: 0.7652, loss_cls: 2.6885, loss: 2.6885 +2024-07-27 00:04:38,583 - pyskl - INFO - Epoch [132][800/3746] lr: 3.821e-03, eta: 16:02:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5231, top5_acc: 0.7662, loss_cls: 2.6678, loss: 2.6678 +2024-07-27 00:06:00,734 - pyskl - INFO - Epoch [132][900/3746] lr: 3.810e-03, eta: 16:01:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5133, top5_acc: 0.7589, loss_cls: 2.7332, loss: 2.7332 +2024-07-27 00:07:22,778 - pyskl - INFO - Epoch [132][1000/3746] lr: 3.799e-03, eta: 16:00:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5173, top5_acc: 0.7738, loss_cls: 2.6768, loss: 2.6768 +2024-07-27 00:08:44,419 - pyskl - INFO - Epoch [132][1100/3746] lr: 3.789e-03, eta: 15:58:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5122, top5_acc: 0.7542, loss_cls: 2.7200, loss: 2.7200 +2024-07-27 00:10:05,797 - pyskl - INFO - Epoch [132][1200/3746] lr: 3.778e-03, eta: 15:57:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5220, top5_acc: 0.7605, loss_cls: 2.7130, loss: 2.7130 +2024-07-27 00:11:27,391 - pyskl - INFO - Epoch [132][1300/3746] lr: 3.767e-03, eta: 15:55:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5033, top5_acc: 0.7575, loss_cls: 2.7418, loss: 2.7418 +2024-07-27 00:12:48,985 - pyskl - INFO - Epoch [132][1400/3746] lr: 3.757e-03, eta: 15:54:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7700, loss_cls: 2.6522, loss: 2.6522 +2024-07-27 00:14:10,786 - pyskl - INFO - Epoch [132][1500/3746] lr: 3.746e-03, eta: 15:53:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5100, top5_acc: 0.7719, loss_cls: 2.6861, loss: 2.6861 +2024-07-27 00:15:32,127 - pyskl - INFO - Epoch [132][1600/3746] lr: 3.735e-03, eta: 15:51:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5081, top5_acc: 0.7581, loss_cls: 2.7248, loss: 2.7248 +2024-07-27 00:16:53,421 - pyskl - INFO - Epoch [132][1700/3746] lr: 3.725e-03, eta: 15:50:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5103, top5_acc: 0.7661, loss_cls: 2.7102, loss: 2.7102 +2024-07-27 00:18:15,000 - pyskl - INFO - Epoch [132][1800/3746] lr: 3.714e-03, eta: 15:49:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5184, top5_acc: 0.7644, loss_cls: 2.6930, loss: 2.6930 +2024-07-27 00:19:36,381 - pyskl - INFO - Epoch [132][1900/3746] lr: 3.704e-03, eta: 15:47:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5133, top5_acc: 0.7616, loss_cls: 2.7082, loss: 2.7082 +2024-07-27 00:20:57,527 - pyskl - INFO - Epoch [132][2000/3746] lr: 3.693e-03, eta: 15:46:22, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5208, top5_acc: 0.7602, loss_cls: 2.6960, loss: 2.6960 +2024-07-27 00:22:19,514 - pyskl - INFO - Epoch [132][2100/3746] lr: 3.683e-03, eta: 15:45:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5189, top5_acc: 0.7611, loss_cls: 2.7013, loss: 2.7013 +2024-07-27 00:23:42,258 - pyskl - INFO - Epoch [132][2200/3746] lr: 3.672e-03, eta: 15:43:38, time: 0.827, data_time: 0.001, memory: 15990, top1_acc: 0.5336, top5_acc: 0.7667, loss_cls: 2.6349, loss: 2.6349 +2024-07-27 00:25:04,457 - pyskl - INFO - Epoch [132][2300/3746] lr: 3.662e-03, eta: 15:42:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4941, top5_acc: 0.7514, loss_cls: 2.7741, loss: 2.7741 +2024-07-27 00:26:27,688 - pyskl - INFO - Epoch [132][2400/3746] lr: 3.651e-03, eta: 15:40:54, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5011, top5_acc: 0.7536, loss_cls: 2.7444, loss: 2.7444 +2024-07-27 00:27:49,727 - pyskl - INFO - Epoch [132][2500/3746] lr: 3.641e-03, eta: 15:39:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5220, top5_acc: 0.7645, loss_cls: 2.6855, loss: 2.6855 +2024-07-27 00:29:11,917 - pyskl - INFO - Epoch [132][2600/3746] lr: 3.630e-03, eta: 15:38:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5175, top5_acc: 0.7619, loss_cls: 2.6957, loss: 2.6957 +2024-07-27 00:30:34,025 - pyskl - INFO - Epoch [132][2700/3746] lr: 3.620e-03, eta: 15:36:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5069, top5_acc: 0.7592, loss_cls: 2.7290, loss: 2.7290 +2024-07-27 00:31:55,563 - pyskl - INFO - Epoch [132][2800/3746] lr: 3.609e-03, eta: 15:35:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5005, top5_acc: 0.7509, loss_cls: 2.7419, loss: 2.7419 +2024-07-27 00:33:16,794 - pyskl - INFO - Epoch [132][2900/3746] lr: 3.599e-03, eta: 15:34:03, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5214, top5_acc: 0.7612, loss_cls: 2.7048, loss: 2.7048 +2024-07-27 00:34:38,677 - pyskl - INFO - Epoch [132][3000/3746] lr: 3.588e-03, eta: 15:32:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5194, top5_acc: 0.7552, loss_cls: 2.7442, loss: 2.7442 +2024-07-27 00:36:00,371 - pyskl - INFO - Epoch [132][3100/3746] lr: 3.578e-03, eta: 15:31:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5164, top5_acc: 0.7642, loss_cls: 2.7059, loss: 2.7059 +2024-07-27 00:37:21,603 - pyskl - INFO - Epoch [132][3200/3746] lr: 3.568e-03, eta: 15:29:57, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5109, top5_acc: 0.7566, loss_cls: 2.7168, loss: 2.7168 +2024-07-27 00:38:42,937 - pyskl - INFO - Epoch [132][3300/3746] lr: 3.557e-03, eta: 15:28:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5039, top5_acc: 0.7495, loss_cls: 2.7630, loss: 2.7630 +2024-07-27 00:40:03,862 - pyskl - INFO - Epoch [132][3400/3746] lr: 3.547e-03, eta: 15:27:12, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5103, top5_acc: 0.7655, loss_cls: 2.6984, loss: 2.6984 +2024-07-27 00:41:25,460 - pyskl - INFO - Epoch [132][3500/3746] lr: 3.537e-03, eta: 15:25:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5162, top5_acc: 0.7553, loss_cls: 2.7130, loss: 2.7130 +2024-07-27 00:42:47,120 - pyskl - INFO - Epoch [132][3600/3746] lr: 3.526e-03, eta: 15:24:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5066, top5_acc: 0.7608, loss_cls: 2.7120, loss: 2.7120 +2024-07-27 00:44:08,669 - pyskl - INFO - Epoch [132][3700/3746] lr: 3.516e-03, eta: 15:23:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5095, top5_acc: 0.7542, loss_cls: 2.7373, loss: 2.7373 +2024-07-27 00:44:47,684 - pyskl - INFO - Saving checkpoint at 132 epochs +2024-07-27 00:46:39,822 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 00:46:40,477 - pyskl - INFO - +top1_acc 0.4162 +top5_acc 0.6712 +2024-07-27 00:46:40,477 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 00:46:40,517 - pyskl - INFO - +mean_acc 0.4160 +2024-07-27 00:46:40,528 - pyskl - INFO - Epoch(val) [132][309] top1_acc: 0.4162, top5_acc: 0.6712, mean_class_accuracy: 0.4160 +2024-07-27 00:50:26,838 - pyskl - INFO - Epoch [133][100/3746] lr: 3.501e-03, eta: 15:21:21, time: 2.263, data_time: 1.290, memory: 15990, top1_acc: 0.5434, top5_acc: 0.7791, loss_cls: 2.5805, loss: 2.5805 +2024-07-27 00:51:48,161 - pyskl - INFO - Epoch [133][200/3746] lr: 3.491e-03, eta: 15:19:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5431, top5_acc: 0.7864, loss_cls: 2.5610, loss: 2.5610 +2024-07-27 00:53:09,389 - pyskl - INFO - Epoch [133][300/3746] lr: 3.480e-03, eta: 15:18:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5325, top5_acc: 0.7802, loss_cls: 2.5978, loss: 2.5978 +2024-07-27 00:54:32,169 - pyskl - INFO - Epoch [133][400/3746] lr: 3.470e-03, eta: 15:17:14, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5284, top5_acc: 0.7738, loss_cls: 2.6238, loss: 2.6238 +2024-07-27 00:55:53,624 - pyskl - INFO - Epoch [133][500/3746] lr: 3.460e-03, eta: 15:15:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5339, top5_acc: 0.7742, loss_cls: 2.5887, loss: 2.5887 +2024-07-27 00:57:15,318 - pyskl - INFO - Epoch [133][600/3746] lr: 3.450e-03, eta: 15:14:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5230, top5_acc: 0.7728, loss_cls: 2.6343, loss: 2.6343 +2024-07-27 00:58:36,967 - pyskl - INFO - Epoch [133][700/3746] lr: 3.440e-03, eta: 15:13:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5255, top5_acc: 0.7631, loss_cls: 2.6517, loss: 2.6517 +2024-07-27 00:59:58,093 - pyskl - INFO - Epoch [133][800/3746] lr: 3.429e-03, eta: 15:11:45, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5222, top5_acc: 0.7648, loss_cls: 2.6529, loss: 2.6529 +2024-07-27 01:01:19,471 - pyskl - INFO - Epoch [133][900/3746] lr: 3.419e-03, eta: 15:10:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5161, top5_acc: 0.7664, loss_cls: 2.6704, loss: 2.6704 +2024-07-27 01:02:41,154 - pyskl - INFO - Epoch [133][1000/3746] lr: 3.409e-03, eta: 15:09:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5219, top5_acc: 0.7733, loss_cls: 2.6384, loss: 2.6384 +2024-07-27 01:04:02,964 - pyskl - INFO - Epoch [133][1100/3746] lr: 3.399e-03, eta: 15:07:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5277, top5_acc: 0.7688, loss_cls: 2.6181, loss: 2.6181 +2024-07-27 01:05:24,256 - pyskl - INFO - Epoch [133][1200/3746] lr: 3.389e-03, eta: 15:06:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5256, top5_acc: 0.7742, loss_cls: 2.6430, loss: 2.6430 +2024-07-27 01:06:46,073 - pyskl - INFO - Epoch [133][1300/3746] lr: 3.379e-03, eta: 15:04:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5186, top5_acc: 0.7606, loss_cls: 2.6754, loss: 2.6754 +2024-07-27 01:08:07,271 - pyskl - INFO - Epoch [133][1400/3746] lr: 3.369e-03, eta: 15:03:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5208, top5_acc: 0.7725, loss_cls: 2.6500, loss: 2.6500 +2024-07-27 01:09:28,382 - pyskl - INFO - Epoch [133][1500/3746] lr: 3.359e-03, eta: 15:02:10, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5236, top5_acc: 0.7678, loss_cls: 2.6456, loss: 2.6456 +2024-07-27 01:10:49,946 - pyskl - INFO - Epoch [133][1600/3746] lr: 3.348e-03, eta: 15:00:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5145, top5_acc: 0.7617, loss_cls: 2.7006, loss: 2.7006 +2024-07-27 01:12:11,165 - pyskl - INFO - Epoch [133][1700/3746] lr: 3.338e-03, eta: 14:59:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5117, top5_acc: 0.7662, loss_cls: 2.6912, loss: 2.6912 +2024-07-27 01:13:32,462 - pyskl - INFO - Epoch [133][1800/3746] lr: 3.328e-03, eta: 14:58:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5092, top5_acc: 0.7641, loss_cls: 2.7275, loss: 2.7275 +2024-07-27 01:14:53,936 - pyskl - INFO - Epoch [133][1900/3746] lr: 3.318e-03, eta: 14:56:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5186, top5_acc: 0.7653, loss_cls: 2.6703, loss: 2.6703 +2024-07-27 01:16:14,867 - pyskl - INFO - Epoch [133][2000/3746] lr: 3.308e-03, eta: 14:55:19, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5239, top5_acc: 0.7738, loss_cls: 2.6252, loss: 2.6252 +2024-07-27 01:17:36,304 - pyskl - INFO - Epoch [133][2100/3746] lr: 3.298e-03, eta: 14:53:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5214, top5_acc: 0.7795, loss_cls: 2.6261, loss: 2.6261 +2024-07-27 01:18:58,313 - pyskl - INFO - Epoch [133][2200/3746] lr: 3.288e-03, eta: 14:52:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5122, top5_acc: 0.7612, loss_cls: 2.6977, loss: 2.6977 +2024-07-27 01:20:20,040 - pyskl - INFO - Epoch [133][2300/3746] lr: 3.278e-03, eta: 14:51:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5322, top5_acc: 0.7647, loss_cls: 2.6369, loss: 2.6369 +2024-07-27 01:21:42,752 - pyskl - INFO - Epoch [133][2400/3746] lr: 3.268e-03, eta: 14:49:50, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5239, top5_acc: 0.7728, loss_cls: 2.6518, loss: 2.6518 +2024-07-27 01:23:04,376 - pyskl - INFO - Epoch [133][2500/3746] lr: 3.259e-03, eta: 14:48:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5223, top5_acc: 0.7714, loss_cls: 2.6649, loss: 2.6649 +2024-07-27 01:24:26,556 - pyskl - INFO - Epoch [133][2600/3746] lr: 3.249e-03, eta: 14:47:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5209, top5_acc: 0.7667, loss_cls: 2.6637, loss: 2.6637 +2024-07-27 01:25:47,892 - pyskl - INFO - Epoch [133][2700/3746] lr: 3.239e-03, eta: 14:45:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5056, top5_acc: 0.7555, loss_cls: 2.7382, loss: 2.7382 +2024-07-27 01:27:09,928 - pyskl - INFO - Epoch [133][2800/3746] lr: 3.229e-03, eta: 14:44:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5045, top5_acc: 0.7612, loss_cls: 2.7224, loss: 2.7224 +2024-07-27 01:28:31,202 - pyskl - INFO - Epoch [133][2900/3746] lr: 3.219e-03, eta: 14:43:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5153, top5_acc: 0.7625, loss_cls: 2.6771, loss: 2.6771 +2024-07-27 01:29:52,682 - pyskl - INFO - Epoch [133][3000/3746] lr: 3.209e-03, eta: 14:41:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5148, top5_acc: 0.7728, loss_cls: 2.6683, loss: 2.6683 +2024-07-27 01:31:13,859 - pyskl - INFO - Epoch [133][3100/3746] lr: 3.199e-03, eta: 14:40:15, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5147, top5_acc: 0.7705, loss_cls: 2.6652, loss: 2.6652 +2024-07-27 01:32:35,667 - pyskl - INFO - Epoch [133][3200/3746] lr: 3.189e-03, eta: 14:38:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5150, top5_acc: 0.7602, loss_cls: 2.7061, loss: 2.7061 +2024-07-27 01:33:57,166 - pyskl - INFO - Epoch [133][3300/3746] lr: 3.180e-03, eta: 14:37:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5130, top5_acc: 0.7606, loss_cls: 2.7025, loss: 2.7025 +2024-07-27 01:35:18,128 - pyskl - INFO - Epoch [133][3400/3746] lr: 3.170e-03, eta: 14:36:09, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5075, top5_acc: 0.7586, loss_cls: 2.7188, loss: 2.7188 +2024-07-27 01:36:39,799 - pyskl - INFO - Epoch [133][3500/3746] lr: 3.160e-03, eta: 14:34:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5188, top5_acc: 0.7688, loss_cls: 2.6764, loss: 2.6764 +2024-07-27 01:38:01,724 - pyskl - INFO - Epoch [133][3600/3746] lr: 3.150e-03, eta: 14:33:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5261, top5_acc: 0.7694, loss_cls: 2.6676, loss: 2.6676 +2024-07-27 01:39:22,986 - pyskl - INFO - Epoch [133][3700/3746] lr: 3.140e-03, eta: 14:32:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5280, top5_acc: 0.7612, loss_cls: 2.6665, loss: 2.6665 +2024-07-27 01:40:02,758 - pyskl - INFO - Saving checkpoint at 133 epochs +2024-07-27 01:41:55,148 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 01:41:55,810 - pyskl - INFO - +top1_acc 0.4232 +top5_acc 0.6749 +2024-07-27 01:41:55,810 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 01:41:55,857 - pyskl - INFO - +mean_acc 0.4230 +2024-07-27 01:41:55,862 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_131.pth was removed +2024-07-27 01:41:56,122 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_133.pth. +2024-07-27 01:41:56,123 - pyskl - INFO - Best top1_acc is 0.4232 at 133 epoch. +2024-07-27 01:41:56,135 - pyskl - INFO - Epoch(val) [133][309] top1_acc: 0.4232, top5_acc: 0.6749, mean_class_accuracy: 0.4230 +2024-07-27 01:45:47,606 - pyskl - INFO - Epoch [134][100/3746] lr: 3.126e-03, eta: 14:30:17, time: 2.315, data_time: 1.335, memory: 15990, top1_acc: 0.5411, top5_acc: 0.7894, loss_cls: 2.5274, loss: 2.5274 +2024-07-27 01:47:09,096 - pyskl - INFO - Epoch [134][200/3746] lr: 3.117e-03, eta: 14:28:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5489, top5_acc: 0.7820, loss_cls: 2.5290, loss: 2.5290 +2024-07-27 01:48:30,673 - pyskl - INFO - Epoch [134][300/3746] lr: 3.107e-03, eta: 14:27:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5353, top5_acc: 0.7819, loss_cls: 2.5786, loss: 2.5786 +2024-07-27 01:49:52,638 - pyskl - INFO - Epoch [134][400/3746] lr: 3.097e-03, eta: 14:26:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5267, top5_acc: 0.7773, loss_cls: 2.6060, loss: 2.6060 +2024-07-27 01:51:14,499 - pyskl - INFO - Epoch [134][500/3746] lr: 3.087e-03, eta: 14:24:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5397, top5_acc: 0.7894, loss_cls: 2.5516, loss: 2.5516 +2024-07-27 01:52:36,243 - pyskl - INFO - Epoch [134][600/3746] lr: 3.078e-03, eta: 14:23:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5322, top5_acc: 0.7772, loss_cls: 2.5980, loss: 2.5980 +2024-07-27 01:53:57,585 - pyskl - INFO - Epoch [134][700/3746] lr: 3.068e-03, eta: 14:22:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5359, top5_acc: 0.7805, loss_cls: 2.5863, loss: 2.5863 +2024-07-27 01:55:18,699 - pyskl - INFO - Epoch [134][800/3746] lr: 3.059e-03, eta: 14:20:41, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5270, top5_acc: 0.7772, loss_cls: 2.5906, loss: 2.5906 +2024-07-27 01:56:40,270 - pyskl - INFO - Epoch [134][900/3746] lr: 3.049e-03, eta: 14:19:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7677, loss_cls: 2.6305, loss: 2.6305 +2024-07-27 01:58:01,607 - pyskl - INFO - Epoch [134][1000/3746] lr: 3.039e-03, eta: 14:17:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5403, top5_acc: 0.7823, loss_cls: 2.5567, loss: 2.5567 +2024-07-27 01:59:22,451 - pyskl - INFO - Epoch [134][1100/3746] lr: 3.030e-03, eta: 14:16:34, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.5319, top5_acc: 0.7744, loss_cls: 2.6158, loss: 2.6158 +2024-07-27 02:00:44,155 - pyskl - INFO - Epoch [134][1200/3746] lr: 3.020e-03, eta: 14:15:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5233, top5_acc: 0.7773, loss_cls: 2.6095, loss: 2.6095 +2024-07-27 02:02:05,563 - pyskl - INFO - Epoch [134][1300/3746] lr: 3.011e-03, eta: 14:13:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5312, top5_acc: 0.7733, loss_cls: 2.6241, loss: 2.6241 +2024-07-27 02:03:27,196 - pyskl - INFO - Epoch [134][1400/3746] lr: 3.001e-03, eta: 14:12:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5220, top5_acc: 0.7759, loss_cls: 2.6365, loss: 2.6365 +2024-07-27 02:04:48,707 - pyskl - INFO - Epoch [134][1500/3746] lr: 2.991e-03, eta: 14:11:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5227, top5_acc: 0.7738, loss_cls: 2.6320, loss: 2.6320 +2024-07-27 02:06:10,355 - pyskl - INFO - Epoch [134][1600/3746] lr: 2.982e-03, eta: 14:09:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5297, top5_acc: 0.7747, loss_cls: 2.6136, loss: 2.6136 +2024-07-27 02:07:31,926 - pyskl - INFO - Epoch [134][1700/3746] lr: 2.972e-03, eta: 14:08:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5250, top5_acc: 0.7744, loss_cls: 2.6156, loss: 2.6156 +2024-07-27 02:08:53,642 - pyskl - INFO - Epoch [134][1800/3746] lr: 2.963e-03, eta: 14:06:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5264, top5_acc: 0.7689, loss_cls: 2.6701, loss: 2.6701 +2024-07-27 02:10:15,047 - pyskl - INFO - Epoch [134][1900/3746] lr: 2.953e-03, eta: 14:05:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5302, top5_acc: 0.7731, loss_cls: 2.6244, loss: 2.6244 +2024-07-27 02:11:36,531 - pyskl - INFO - Epoch [134][2000/3746] lr: 2.944e-03, eta: 14:04:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5337, top5_acc: 0.7731, loss_cls: 2.5938, loss: 2.5938 +2024-07-27 02:12:58,378 - pyskl - INFO - Epoch [134][2100/3746] lr: 2.935e-03, eta: 14:02:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5234, top5_acc: 0.7673, loss_cls: 2.6480, loss: 2.6480 +2024-07-27 02:14:20,830 - pyskl - INFO - Epoch [134][2200/3746] lr: 2.925e-03, eta: 14:01:30, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5222, top5_acc: 0.7702, loss_cls: 2.6463, loss: 2.6463 +2024-07-27 02:15:42,416 - pyskl - INFO - Epoch [134][2300/3746] lr: 2.916e-03, eta: 14:00:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5269, top5_acc: 0.7747, loss_cls: 2.5986, loss: 2.5986 +2024-07-27 02:17:04,606 - pyskl - INFO - Epoch [134][2400/3746] lr: 2.906e-03, eta: 13:58:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5347, top5_acc: 0.7783, loss_cls: 2.5997, loss: 2.5997 +2024-07-27 02:18:27,207 - pyskl - INFO - Epoch [134][2500/3746] lr: 2.897e-03, eta: 13:57:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5353, top5_acc: 0.7739, loss_cls: 2.6018, loss: 2.6018 +2024-07-27 02:19:49,407 - pyskl - INFO - Epoch [134][2600/3746] lr: 2.888e-03, eta: 13:56:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5311, top5_acc: 0.7775, loss_cls: 2.5940, loss: 2.5940 +2024-07-27 02:21:10,662 - pyskl - INFO - Epoch [134][2700/3746] lr: 2.878e-03, eta: 13:54:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5208, top5_acc: 0.7627, loss_cls: 2.6774, loss: 2.6774 +2024-07-27 02:22:31,724 - pyskl - INFO - Epoch [134][2800/3746] lr: 2.869e-03, eta: 13:53:18, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5231, top5_acc: 0.7650, loss_cls: 2.6754, loss: 2.6754 +2024-07-27 02:23:53,344 - pyskl - INFO - Epoch [134][2900/3746] lr: 2.860e-03, eta: 13:51:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5194, top5_acc: 0.7658, loss_cls: 2.6789, loss: 2.6789 +2024-07-27 02:25:15,604 - pyskl - INFO - Epoch [134][3000/3746] lr: 2.850e-03, eta: 13:50:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5302, top5_acc: 0.7703, loss_cls: 2.6500, loss: 2.6500 +2024-07-27 02:26:37,631 - pyskl - INFO - Epoch [134][3100/3746] lr: 2.841e-03, eta: 13:49:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5147, top5_acc: 0.7583, loss_cls: 2.6864, loss: 2.6864 +2024-07-27 02:27:59,243 - pyskl - INFO - Epoch [134][3200/3746] lr: 2.832e-03, eta: 13:47:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5248, top5_acc: 0.7722, loss_cls: 2.6183, loss: 2.6183 +2024-07-27 02:29:20,576 - pyskl - INFO - Epoch [134][3300/3746] lr: 2.822e-03, eta: 13:46:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5267, top5_acc: 0.7766, loss_cls: 2.6227, loss: 2.6227 +2024-07-27 02:30:42,171 - pyskl - INFO - Epoch [134][3400/3746] lr: 2.813e-03, eta: 13:45:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5223, top5_acc: 0.7708, loss_cls: 2.6475, loss: 2.6475 +2024-07-27 02:32:03,553 - pyskl - INFO - Epoch [134][3500/3746] lr: 2.804e-03, eta: 13:43:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5405, top5_acc: 0.7777, loss_cls: 2.5876, loss: 2.5876 +2024-07-27 02:33:25,798 - pyskl - INFO - Epoch [134][3600/3746] lr: 2.795e-03, eta: 13:42:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5238, top5_acc: 0.7617, loss_cls: 2.6444, loss: 2.6444 +2024-07-27 02:34:46,893 - pyskl - INFO - Epoch [134][3700/3746] lr: 2.786e-03, eta: 13:40:58, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5305, top5_acc: 0.7720, loss_cls: 2.6425, loss: 2.6425 +2024-07-27 02:35:26,378 - pyskl - INFO - Saving checkpoint at 134 epochs +2024-07-27 02:37:18,857 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 02:37:19,520 - pyskl - INFO - +top1_acc 0.4297 +top5_acc 0.6776 +2024-07-27 02:37:19,521 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 02:37:19,563 - pyskl - INFO - +mean_acc 0.4294 +2024-07-27 02:37:19,568 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_133.pth was removed +2024-07-27 02:37:19,848 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_134.pth. +2024-07-27 02:37:19,848 - pyskl - INFO - Best top1_acc is 0.4297 at 134 epoch. +2024-07-27 02:37:19,861 - pyskl - INFO - Epoch(val) [134][309] top1_acc: 0.4297, top5_acc: 0.6776, mean_class_accuracy: 0.4294 +2024-07-27 02:41:09,505 - pyskl - INFO - Epoch [135][100/3746] lr: 2.772e-03, eta: 13:39:11, time: 2.296, data_time: 1.323, memory: 15990, top1_acc: 0.5511, top5_acc: 0.7905, loss_cls: 2.5332, loss: 2.5332 +2024-07-27 02:42:31,383 - pyskl - INFO - Epoch [135][200/3746] lr: 2.763e-03, eta: 13:37:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5544, top5_acc: 0.7927, loss_cls: 2.5056, loss: 2.5056 +2024-07-27 02:43:53,319 - pyskl - INFO - Epoch [135][300/3746] lr: 2.754e-03, eta: 13:36:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5455, top5_acc: 0.7900, loss_cls: 2.5073, loss: 2.5073 +2024-07-27 02:45:14,649 - pyskl - INFO - Epoch [135][400/3746] lr: 2.745e-03, eta: 13:35:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5539, top5_acc: 0.7966, loss_cls: 2.4791, loss: 2.4791 +2024-07-27 02:46:36,428 - pyskl - INFO - Epoch [135][500/3746] lr: 2.735e-03, eta: 13:33:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5381, top5_acc: 0.7806, loss_cls: 2.5821, loss: 2.5821 +2024-07-27 02:47:58,589 - pyskl - INFO - Epoch [135][600/3746] lr: 2.726e-03, eta: 13:32:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5358, top5_acc: 0.7831, loss_cls: 2.5407, loss: 2.5407 +2024-07-27 02:49:19,768 - pyskl - INFO - Epoch [135][700/3746] lr: 2.717e-03, eta: 13:30:58, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5333, top5_acc: 0.7895, loss_cls: 2.5521, loss: 2.5521 +2024-07-27 02:50:41,132 - pyskl - INFO - Epoch [135][800/3746] lr: 2.708e-03, eta: 13:29:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5350, top5_acc: 0.7823, loss_cls: 2.5558, loss: 2.5558 +2024-07-27 02:52:02,891 - pyskl - INFO - Epoch [135][900/3746] lr: 2.699e-03, eta: 13:28:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5375, top5_acc: 0.7853, loss_cls: 2.5304, loss: 2.5304 +2024-07-27 02:53:23,976 - pyskl - INFO - Epoch [135][1000/3746] lr: 2.690e-03, eta: 13:26:51, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5363, top5_acc: 0.7770, loss_cls: 2.5868, loss: 2.5868 +2024-07-27 02:54:45,601 - pyskl - INFO - Epoch [135][1100/3746] lr: 2.681e-03, eta: 13:25:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5477, top5_acc: 0.7867, loss_cls: 2.5542, loss: 2.5542 +2024-07-27 02:56:07,168 - pyskl - INFO - Epoch [135][1200/3746] lr: 2.672e-03, eta: 13:24:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5411, top5_acc: 0.7811, loss_cls: 2.5494, loss: 2.5494 +2024-07-27 02:57:28,477 - pyskl - INFO - Epoch [135][1300/3746] lr: 2.663e-03, eta: 13:22:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5361, top5_acc: 0.7784, loss_cls: 2.5974, loss: 2.5974 +2024-07-27 02:58:49,631 - pyskl - INFO - Epoch [135][1400/3746] lr: 2.654e-03, eta: 13:21:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5414, top5_acc: 0.7789, loss_cls: 2.5587, loss: 2.5587 +2024-07-27 03:00:10,930 - pyskl - INFO - Epoch [135][1500/3746] lr: 2.645e-03, eta: 13:20:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5397, top5_acc: 0.7797, loss_cls: 2.5766, loss: 2.5766 +2024-07-27 03:01:32,580 - pyskl - INFO - Epoch [135][1600/3746] lr: 2.636e-03, eta: 13:18:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5291, top5_acc: 0.7783, loss_cls: 2.5968, loss: 2.5968 +2024-07-27 03:02:54,468 - pyskl - INFO - Epoch [135][1700/3746] lr: 2.627e-03, eta: 13:17:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5270, top5_acc: 0.7800, loss_cls: 2.6174, loss: 2.6174 +2024-07-27 03:04:16,404 - pyskl - INFO - Epoch [135][1800/3746] lr: 2.618e-03, eta: 13:15:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5395, top5_acc: 0.7833, loss_cls: 2.5554, loss: 2.5554 +2024-07-27 03:05:37,762 - pyskl - INFO - Epoch [135][1900/3746] lr: 2.609e-03, eta: 13:14:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5369, top5_acc: 0.7837, loss_cls: 2.5484, loss: 2.5484 +2024-07-27 03:06:58,741 - pyskl - INFO - Epoch [135][2000/3746] lr: 2.600e-03, eta: 13:13:09, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5394, top5_acc: 0.7822, loss_cls: 2.5440, loss: 2.5440 +2024-07-27 03:08:20,068 - pyskl - INFO - Epoch [135][2100/3746] lr: 2.591e-03, eta: 13:11:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5487, top5_acc: 0.7881, loss_cls: 2.5165, loss: 2.5165 +2024-07-27 03:09:42,241 - pyskl - INFO - Epoch [135][2200/3746] lr: 2.583e-03, eta: 13:10:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5320, top5_acc: 0.7802, loss_cls: 2.5871, loss: 2.5871 +2024-07-27 03:11:04,165 - pyskl - INFO - Epoch [135][2300/3746] lr: 2.574e-03, eta: 13:09:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5458, top5_acc: 0.7791, loss_cls: 2.5724, loss: 2.5724 +2024-07-27 03:12:26,008 - pyskl - INFO - Epoch [135][2400/3746] lr: 2.565e-03, eta: 13:07:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5395, top5_acc: 0.7786, loss_cls: 2.5690, loss: 2.5690 +2024-07-27 03:13:48,825 - pyskl - INFO - Epoch [135][2500/3746] lr: 2.556e-03, eta: 13:06:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5292, top5_acc: 0.7786, loss_cls: 2.5872, loss: 2.5872 +2024-07-27 03:15:10,859 - pyskl - INFO - Epoch [135][2600/3746] lr: 2.547e-03, eta: 13:04:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5411, top5_acc: 0.7773, loss_cls: 2.5699, loss: 2.5699 +2024-07-27 03:16:32,906 - pyskl - INFO - Epoch [135][2700/3746] lr: 2.538e-03, eta: 13:03:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5445, top5_acc: 0.7788, loss_cls: 2.5515, loss: 2.5515 +2024-07-27 03:17:54,288 - pyskl - INFO - Epoch [135][2800/3746] lr: 2.530e-03, eta: 13:02:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5225, top5_acc: 0.7722, loss_cls: 2.6430, loss: 2.6430 +2024-07-27 03:19:15,931 - pyskl - INFO - Epoch [135][2900/3746] lr: 2.521e-03, eta: 13:00:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5298, top5_acc: 0.7777, loss_cls: 2.5936, loss: 2.5936 +2024-07-27 03:20:37,399 - pyskl - INFO - Epoch [135][3000/3746] lr: 2.512e-03, eta: 12:59:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5359, top5_acc: 0.7755, loss_cls: 2.6224, loss: 2.6224 +2024-07-27 03:21:59,092 - pyskl - INFO - Epoch [135][3100/3746] lr: 2.503e-03, eta: 12:58:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5341, top5_acc: 0.7750, loss_cls: 2.5982, loss: 2.5982 +2024-07-27 03:23:20,871 - pyskl - INFO - Epoch [135][3200/3746] lr: 2.495e-03, eta: 12:56:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5350, top5_acc: 0.7712, loss_cls: 2.6102, loss: 2.6102 +2024-07-27 03:24:42,535 - pyskl - INFO - Epoch [135][3300/3746] lr: 2.486e-03, eta: 12:55:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5284, top5_acc: 0.7789, loss_cls: 2.5799, loss: 2.5799 +2024-07-27 03:26:03,989 - pyskl - INFO - Epoch [135][3400/3746] lr: 2.477e-03, eta: 12:53:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5302, top5_acc: 0.7778, loss_cls: 2.6149, loss: 2.6149 +2024-07-27 03:27:25,348 - pyskl - INFO - Epoch [135][3500/3746] lr: 2.469e-03, eta: 12:52:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5316, top5_acc: 0.7803, loss_cls: 2.5763, loss: 2.5763 +2024-07-27 03:28:46,750 - pyskl - INFO - Epoch [135][3600/3746] lr: 2.460e-03, eta: 12:51:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5214, top5_acc: 0.7720, loss_cls: 2.6447, loss: 2.6447 +2024-07-27 03:30:08,360 - pyskl - INFO - Epoch [135][3700/3746] lr: 2.451e-03, eta: 12:49:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5294, top5_acc: 0.7739, loss_cls: 2.5858, loss: 2.5858 +2024-07-27 03:30:47,783 - pyskl - INFO - Saving checkpoint at 135 epochs +2024-07-27 03:32:39,829 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 03:32:40,495 - pyskl - INFO - +top1_acc 0.4287 +top5_acc 0.6846 +2024-07-27 03:32:40,495 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 03:32:40,538 - pyskl - INFO - +mean_acc 0.4284 +2024-07-27 03:32:40,550 - pyskl - INFO - Epoch(val) [135][309] top1_acc: 0.4287, top5_acc: 0.6846, mean_class_accuracy: 0.4284 +2024-07-27 03:36:32,427 - pyskl - INFO - Epoch [136][100/3746] lr: 2.439e-03, eta: 12:48:04, time: 2.319, data_time: 1.345, memory: 15990, top1_acc: 0.5645, top5_acc: 0.8047, loss_cls: 2.4180, loss: 2.4180 +2024-07-27 03:37:53,634 - pyskl - INFO - Epoch [136][200/3746] lr: 2.430e-03, eta: 12:46:42, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5483, top5_acc: 0.7875, loss_cls: 2.5109, loss: 2.5109 +2024-07-27 03:39:15,465 - pyskl - INFO - Epoch [136][300/3746] lr: 2.421e-03, eta: 12:45:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5469, top5_acc: 0.7950, loss_cls: 2.4646, loss: 2.4646 +2024-07-27 03:40:37,635 - pyskl - INFO - Epoch [136][400/3746] lr: 2.413e-03, eta: 12:43:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5561, top5_acc: 0.7964, loss_cls: 2.4559, loss: 2.4559 +2024-07-27 03:41:59,316 - pyskl - INFO - Epoch [136][500/3746] lr: 2.404e-03, eta: 12:42:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5542, top5_acc: 0.7930, loss_cls: 2.4643, loss: 2.4643 +2024-07-27 03:43:21,784 - pyskl - INFO - Epoch [136][600/3746] lr: 2.396e-03, eta: 12:41:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5544, top5_acc: 0.7950, loss_cls: 2.4799, loss: 2.4799 +2024-07-27 03:44:43,401 - pyskl - INFO - Epoch [136][700/3746] lr: 2.387e-03, eta: 12:39:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5516, top5_acc: 0.7819, loss_cls: 2.5115, loss: 2.5115 +2024-07-27 03:46:05,127 - pyskl - INFO - Epoch [136][800/3746] lr: 2.379e-03, eta: 12:38:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5594, top5_acc: 0.7950, loss_cls: 2.4629, loss: 2.4629 +2024-07-27 03:47:26,716 - pyskl - INFO - Epoch [136][900/3746] lr: 2.370e-03, eta: 12:37:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5600, top5_acc: 0.7972, loss_cls: 2.4752, loss: 2.4752 +2024-07-27 03:48:49,372 - pyskl - INFO - Epoch [136][1000/3746] lr: 2.362e-03, eta: 12:35:45, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5456, top5_acc: 0.7845, loss_cls: 2.5271, loss: 2.5271 +2024-07-27 03:50:10,820 - pyskl - INFO - Epoch [136][1100/3746] lr: 2.353e-03, eta: 12:34:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5505, top5_acc: 0.7903, loss_cls: 2.5231, loss: 2.5231 +2024-07-27 03:51:32,746 - pyskl - INFO - Epoch [136][1200/3746] lr: 2.345e-03, eta: 12:33:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5436, top5_acc: 0.7841, loss_cls: 2.5206, loss: 2.5206 +2024-07-27 03:52:54,467 - pyskl - INFO - Epoch [136][1300/3746] lr: 2.336e-03, eta: 12:31:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5394, top5_acc: 0.7880, loss_cls: 2.5273, loss: 2.5273 +2024-07-27 03:54:15,663 - pyskl - INFO - Epoch [136][1400/3746] lr: 2.328e-03, eta: 12:30:16, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5487, top5_acc: 0.7969, loss_cls: 2.4761, loss: 2.4761 +2024-07-27 03:55:37,302 - pyskl - INFO - Epoch [136][1500/3746] lr: 2.319e-03, eta: 12:28:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5619, top5_acc: 0.7961, loss_cls: 2.4612, loss: 2.4612 +2024-07-27 03:56:58,325 - pyskl - INFO - Epoch [136][1600/3746] lr: 2.311e-03, eta: 12:27:31, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5448, top5_acc: 0.7842, loss_cls: 2.5540, loss: 2.5540 +2024-07-27 03:58:19,702 - pyskl - INFO - Epoch [136][1700/3746] lr: 2.303e-03, eta: 12:26:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5286, top5_acc: 0.7720, loss_cls: 2.5935, loss: 2.5935 +2024-07-27 03:59:40,990 - pyskl - INFO - Epoch [136][1800/3746] lr: 2.294e-03, eta: 12:24:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5497, top5_acc: 0.7900, loss_cls: 2.5265, loss: 2.5265 +2024-07-27 04:01:02,594 - pyskl - INFO - Epoch [136][1900/3746] lr: 2.286e-03, eta: 12:23:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5431, top5_acc: 0.7853, loss_cls: 2.5231, loss: 2.5231 +2024-07-27 04:02:23,957 - pyskl - INFO - Epoch [136][2000/3746] lr: 2.277e-03, eta: 12:22:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5405, top5_acc: 0.7872, loss_cls: 2.5323, loss: 2.5323 +2024-07-27 04:03:45,249 - pyskl - INFO - Epoch [136][2100/3746] lr: 2.269e-03, eta: 12:20:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5383, top5_acc: 0.7814, loss_cls: 2.5513, loss: 2.5513 +2024-07-27 04:05:07,078 - pyskl - INFO - Epoch [136][2200/3746] lr: 2.261e-03, eta: 12:19:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5416, top5_acc: 0.7827, loss_cls: 2.5665, loss: 2.5665 +2024-07-27 04:06:28,811 - pyskl - INFO - Epoch [136][2300/3746] lr: 2.253e-03, eta: 12:17:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5406, top5_acc: 0.7880, loss_cls: 2.5356, loss: 2.5356 +2024-07-27 04:07:50,840 - pyskl - INFO - Epoch [136][2400/3746] lr: 2.244e-03, eta: 12:16:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5514, top5_acc: 0.7922, loss_cls: 2.4982, loss: 2.4982 +2024-07-27 04:09:12,929 - pyskl - INFO - Epoch [136][2500/3746] lr: 2.236e-03, eta: 12:15:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5544, top5_acc: 0.7948, loss_cls: 2.4826, loss: 2.4826 +2024-07-27 04:10:34,898 - pyskl - INFO - Epoch [136][2600/3746] lr: 2.228e-03, eta: 12:13:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5414, top5_acc: 0.7731, loss_cls: 2.5905, loss: 2.5905 +2024-07-27 04:11:56,304 - pyskl - INFO - Epoch [136][2700/3746] lr: 2.219e-03, eta: 12:12:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5447, top5_acc: 0.7845, loss_cls: 2.5261, loss: 2.5261 +2024-07-27 04:13:18,155 - pyskl - INFO - Epoch [136][2800/3746] lr: 2.211e-03, eta: 12:11:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5367, top5_acc: 0.7853, loss_cls: 2.5452, loss: 2.5452 +2024-07-27 04:14:39,483 - pyskl - INFO - Epoch [136][2900/3746] lr: 2.203e-03, eta: 12:09:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5342, top5_acc: 0.7809, loss_cls: 2.5741, loss: 2.5741 +2024-07-27 04:16:00,694 - pyskl - INFO - Epoch [136][3000/3746] lr: 2.195e-03, eta: 12:08:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5470, top5_acc: 0.7887, loss_cls: 2.5259, loss: 2.5259 +2024-07-27 04:17:22,179 - pyskl - INFO - Epoch [136][3100/3746] lr: 2.187e-03, eta: 12:06:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5389, top5_acc: 0.7861, loss_cls: 2.5363, loss: 2.5363 +2024-07-27 04:18:43,225 - pyskl - INFO - Epoch [136][3200/3746] lr: 2.178e-03, eta: 12:05:36, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5381, top5_acc: 0.7800, loss_cls: 2.5737, loss: 2.5737 +2024-07-27 04:20:04,827 - pyskl - INFO - Epoch [136][3300/3746] lr: 2.170e-03, eta: 12:04:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5417, top5_acc: 0.7845, loss_cls: 2.5516, loss: 2.5516 +2024-07-27 04:21:26,772 - pyskl - INFO - Epoch [136][3400/3746] lr: 2.162e-03, eta: 12:02:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5342, top5_acc: 0.7762, loss_cls: 2.5962, loss: 2.5962 +2024-07-27 04:22:48,225 - pyskl - INFO - Epoch [136][3500/3746] lr: 2.154e-03, eta: 12:01:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5400, top5_acc: 0.7875, loss_cls: 2.5317, loss: 2.5317 +2024-07-27 04:24:09,627 - pyskl - INFO - Epoch [136][3600/3746] lr: 2.146e-03, eta: 12:00:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5461, top5_acc: 0.7767, loss_cls: 2.5441, loss: 2.5441 +2024-07-27 04:25:31,874 - pyskl - INFO - Epoch [136][3700/3746] lr: 2.138e-03, eta: 11:58:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5380, top5_acc: 0.7808, loss_cls: 2.5477, loss: 2.5477 +2024-07-27 04:26:11,379 - pyskl - INFO - Saving checkpoint at 136 epochs +2024-07-27 04:28:04,074 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 04:28:04,787 - pyskl - INFO - +top1_acc 0.4317 +top5_acc 0.6868 +2024-07-27 04:28:04,788 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 04:28:04,843 - pyskl - INFO - +mean_acc 0.4314 +2024-07-27 04:28:04,850 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_134.pth was removed +2024-07-27 04:28:05,150 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_136.pth. +2024-07-27 04:28:05,150 - pyskl - INFO - Best top1_acc is 0.4317 at 136 epoch. +2024-07-27 04:28:05,171 - pyskl - INFO - Epoch(val) [136][309] top1_acc: 0.4317, top5_acc: 0.6868, mean_class_accuracy: 0.4314 +2024-07-27 04:31:57,644 - pyskl - INFO - Epoch [137][100/3746] lr: 2.126e-03, eta: 11:56:56, time: 2.325, data_time: 1.343, memory: 15990, top1_acc: 0.5714, top5_acc: 0.8025, loss_cls: 2.3991, loss: 2.3991 +2024-07-27 04:33:20,980 - pyskl - INFO - Epoch [137][200/3746] lr: 2.118e-03, eta: 11:55:34, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5500, top5_acc: 0.7878, loss_cls: 2.5217, loss: 2.5217 +2024-07-27 04:34:43,856 - pyskl - INFO - Epoch [137][300/3746] lr: 2.110e-03, eta: 11:54:12, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5667, top5_acc: 0.8006, loss_cls: 2.4265, loss: 2.4265 +2024-07-27 04:36:06,849 - pyskl - INFO - Epoch [137][400/3746] lr: 2.102e-03, eta: 11:52:50, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5527, top5_acc: 0.7944, loss_cls: 2.4800, loss: 2.4800 +2024-07-27 04:37:28,941 - pyskl - INFO - Epoch [137][500/3746] lr: 2.094e-03, eta: 11:51:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5677, top5_acc: 0.8025, loss_cls: 2.4077, loss: 2.4077 +2024-07-27 04:38:52,020 - pyskl - INFO - Epoch [137][600/3746] lr: 2.086e-03, eta: 11:50:06, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5648, top5_acc: 0.7973, loss_cls: 2.4321, loss: 2.4321 +2024-07-27 04:40:15,651 - pyskl - INFO - Epoch [137][700/3746] lr: 2.078e-03, eta: 11:48:44, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5677, top5_acc: 0.7958, loss_cls: 2.4424, loss: 2.4424 +2024-07-27 04:41:39,193 - pyskl - INFO - Epoch [137][800/3746] lr: 2.070e-03, eta: 11:47:22, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5564, top5_acc: 0.7973, loss_cls: 2.4878, loss: 2.4878 +2024-07-27 04:43:02,018 - pyskl - INFO - Epoch [137][900/3746] lr: 2.062e-03, eta: 11:45:59, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5539, top5_acc: 0.7950, loss_cls: 2.4638, loss: 2.4638 +2024-07-27 04:44:25,061 - pyskl - INFO - Epoch [137][1000/3746] lr: 2.054e-03, eta: 11:44:37, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5616, top5_acc: 0.7986, loss_cls: 2.4324, loss: 2.4324 +2024-07-27 04:45:47,620 - pyskl - INFO - Epoch [137][1100/3746] lr: 2.046e-03, eta: 11:43:15, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5598, top5_acc: 0.7916, loss_cls: 2.4621, loss: 2.4621 +2024-07-27 04:47:10,854 - pyskl - INFO - Epoch [137][1200/3746] lr: 2.038e-03, eta: 11:41:53, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5556, top5_acc: 0.7917, loss_cls: 2.4660, loss: 2.4660 +2024-07-27 04:48:33,352 - pyskl - INFO - Epoch [137][1300/3746] lr: 2.030e-03, eta: 11:40:31, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5494, top5_acc: 0.7987, loss_cls: 2.4814, loss: 2.4814 +2024-07-27 04:49:55,784 - pyskl - INFO - Epoch [137][1400/3746] lr: 2.022e-03, eta: 11:39:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5528, top5_acc: 0.7884, loss_cls: 2.4944, loss: 2.4944 +2024-07-27 04:51:18,482 - pyskl - INFO - Epoch [137][1500/3746] lr: 2.015e-03, eta: 11:37:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5448, top5_acc: 0.7894, loss_cls: 2.4970, loss: 2.4970 +2024-07-27 04:52:41,288 - pyskl - INFO - Epoch [137][1600/3746] lr: 2.007e-03, eta: 11:36:25, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5603, top5_acc: 0.7966, loss_cls: 2.4516, loss: 2.4516 +2024-07-27 04:54:03,719 - pyskl - INFO - Epoch [137][1700/3746] lr: 1.999e-03, eta: 11:35:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5580, top5_acc: 0.7998, loss_cls: 2.4617, loss: 2.4617 +2024-07-27 04:55:26,111 - pyskl - INFO - Epoch [137][1800/3746] lr: 1.991e-03, eta: 11:33:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5573, top5_acc: 0.7980, loss_cls: 2.4770, loss: 2.4770 +2024-07-27 04:56:49,667 - pyskl - INFO - Epoch [137][1900/3746] lr: 1.983e-03, eta: 11:32:18, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5544, top5_acc: 0.7931, loss_cls: 2.4647, loss: 2.4647 +2024-07-27 04:58:11,593 - pyskl - INFO - Epoch [137][2000/3746] lr: 1.976e-03, eta: 11:30:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5592, top5_acc: 0.7970, loss_cls: 2.4597, loss: 2.4597 +2024-07-27 04:59:33,202 - pyskl - INFO - Epoch [137][2100/3746] lr: 1.968e-03, eta: 11:29:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5458, top5_acc: 0.7861, loss_cls: 2.4871, loss: 2.4871 +2024-07-27 05:00:55,265 - pyskl - INFO - Epoch [137][2200/3746] lr: 1.960e-03, eta: 11:28:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5452, top5_acc: 0.7906, loss_cls: 2.4932, loss: 2.4932 +2024-07-27 05:02:17,206 - pyskl - INFO - Epoch [137][2300/3746] lr: 1.952e-03, eta: 11:26:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5534, top5_acc: 0.7900, loss_cls: 2.5047, loss: 2.5047 +2024-07-27 05:03:38,903 - pyskl - INFO - Epoch [137][2400/3746] lr: 1.944e-03, eta: 11:25:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5502, top5_acc: 0.7928, loss_cls: 2.5079, loss: 2.5079 +2024-07-27 05:05:01,463 - pyskl - INFO - Epoch [137][2500/3746] lr: 1.937e-03, eta: 11:24:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5514, top5_acc: 0.7878, loss_cls: 2.4970, loss: 2.4970 +2024-07-27 05:06:23,516 - pyskl - INFO - Epoch [137][2600/3746] lr: 1.929e-03, eta: 11:22:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5508, top5_acc: 0.7998, loss_cls: 2.4764, loss: 2.4764 +2024-07-27 05:07:45,278 - pyskl - INFO - Epoch [137][2700/3746] lr: 1.921e-03, eta: 11:21:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5502, top5_acc: 0.7853, loss_cls: 2.5047, loss: 2.5047 +2024-07-27 05:09:06,811 - pyskl - INFO - Epoch [137][2800/3746] lr: 1.914e-03, eta: 11:19:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5569, top5_acc: 0.7948, loss_cls: 2.4652, loss: 2.4652 +2024-07-27 05:10:28,427 - pyskl - INFO - Epoch [137][2900/3746] lr: 1.906e-03, eta: 11:18:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5464, top5_acc: 0.7880, loss_cls: 2.4949, loss: 2.4949 +2024-07-27 05:11:50,860 - pyskl - INFO - Epoch [137][3000/3746] lr: 1.898e-03, eta: 11:17:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5523, top5_acc: 0.7798, loss_cls: 2.5003, loss: 2.5003 +2024-07-27 05:13:12,965 - pyskl - INFO - Epoch [137][3100/3746] lr: 1.891e-03, eta: 11:15:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5506, top5_acc: 0.7944, loss_cls: 2.4799, loss: 2.4799 +2024-07-27 05:14:34,247 - pyskl - INFO - Epoch [137][3200/3746] lr: 1.883e-03, eta: 11:14:29, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5552, top5_acc: 0.7983, loss_cls: 2.4728, loss: 2.4728 +2024-07-27 05:15:55,950 - pyskl - INFO - Epoch [137][3300/3746] lr: 1.876e-03, eta: 11:13:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5544, top5_acc: 0.7930, loss_cls: 2.4768, loss: 2.4768 +2024-07-27 05:17:17,320 - pyskl - INFO - Epoch [137][3400/3746] lr: 1.868e-03, eta: 11:11:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5464, top5_acc: 0.7920, loss_cls: 2.4876, loss: 2.4876 +2024-07-27 05:18:38,929 - pyskl - INFO - Epoch [137][3500/3746] lr: 1.860e-03, eta: 11:10:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5486, top5_acc: 0.7894, loss_cls: 2.4893, loss: 2.4893 +2024-07-27 05:20:00,328 - pyskl - INFO - Epoch [137][3600/3746] lr: 1.853e-03, eta: 11:09:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5481, top5_acc: 0.7973, loss_cls: 2.4722, loss: 2.4722 +2024-07-27 05:21:22,032 - pyskl - INFO - Epoch [137][3700/3746] lr: 1.845e-03, eta: 11:07:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5406, top5_acc: 0.7884, loss_cls: 2.5352, loss: 2.5352 +2024-07-27 05:22:01,985 - pyskl - INFO - Saving checkpoint at 137 epochs +2024-07-27 05:23:53,330 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 05:23:53,991 - pyskl - INFO - +top1_acc 0.4399 +top5_acc 0.6885 +2024-07-27 05:23:53,991 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 05:23:54,032 - pyskl - INFO - +mean_acc 0.4395 +2024-07-27 05:23:54,036 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_136.pth was removed +2024-07-27 05:23:54,310 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_137.pth. +2024-07-27 05:23:54,311 - pyskl - INFO - Best top1_acc is 0.4399 at 137 epoch. +2024-07-27 05:23:54,322 - pyskl - INFO - Epoch(val) [137][309] top1_acc: 0.4399, top5_acc: 0.6885, mean_class_accuracy: 0.4395 +2024-07-27 05:27:44,190 - pyskl - INFO - Epoch [138][100/3746] lr: 1.834e-03, eta: 11:05:49, time: 2.299, data_time: 1.324, memory: 15990, top1_acc: 0.5650, top5_acc: 0.8058, loss_cls: 2.4057, loss: 2.4057 +2024-07-27 05:29:05,555 - pyskl - INFO - Epoch [138][200/3746] lr: 1.827e-03, eta: 11:04:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5702, top5_acc: 0.8081, loss_cls: 2.3708, loss: 2.3708 +2024-07-27 05:30:27,416 - pyskl - INFO - Epoch [138][300/3746] lr: 1.819e-03, eta: 11:03:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5852, top5_acc: 0.8134, loss_cls: 2.3273, loss: 2.3273 +2024-07-27 05:31:49,186 - pyskl - INFO - Epoch [138][400/3746] lr: 1.812e-03, eta: 11:01:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5745, top5_acc: 0.8044, loss_cls: 2.3760, loss: 2.3760 +2024-07-27 05:33:11,450 - pyskl - INFO - Epoch [138][500/3746] lr: 1.805e-03, eta: 11:00:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5705, top5_acc: 0.8077, loss_cls: 2.3738, loss: 2.3738 +2024-07-27 05:34:33,149 - pyskl - INFO - Epoch [138][600/3746] lr: 1.797e-03, eta: 10:58:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5692, top5_acc: 0.8108, loss_cls: 2.3775, loss: 2.3775 +2024-07-27 05:35:54,426 - pyskl - INFO - Epoch [138][700/3746] lr: 1.790e-03, eta: 10:57:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5708, top5_acc: 0.8075, loss_cls: 2.3824, loss: 2.3824 +2024-07-27 05:37:15,504 - pyskl - INFO - Epoch [138][800/3746] lr: 1.782e-03, eta: 10:56:13, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5678, top5_acc: 0.8075, loss_cls: 2.4037, loss: 2.4037 +2024-07-27 05:38:37,088 - pyskl - INFO - Epoch [138][900/3746] lr: 1.775e-03, eta: 10:54:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5702, top5_acc: 0.8084, loss_cls: 2.3736, loss: 2.3736 +2024-07-27 05:39:58,637 - pyskl - INFO - Epoch [138][1000/3746] lr: 1.768e-03, eta: 10:53:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5666, top5_acc: 0.7984, loss_cls: 2.4079, loss: 2.4079 +2024-07-27 05:41:20,042 - pyskl - INFO - Epoch [138][1100/3746] lr: 1.760e-03, eta: 10:52:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5600, top5_acc: 0.8013, loss_cls: 2.4397, loss: 2.4397 +2024-07-27 05:42:41,729 - pyskl - INFO - Epoch [138][1200/3746] lr: 1.753e-03, eta: 10:50:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5558, top5_acc: 0.8016, loss_cls: 2.4296, loss: 2.4296 +2024-07-27 05:44:03,003 - pyskl - INFO - Epoch [138][1300/3746] lr: 1.745e-03, eta: 10:49:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5686, top5_acc: 0.8067, loss_cls: 2.4003, loss: 2.4003 +2024-07-27 05:45:24,124 - pyskl - INFO - Epoch [138][1400/3746] lr: 1.738e-03, eta: 10:47:59, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5611, top5_acc: 0.7961, loss_cls: 2.4643, loss: 2.4643 +2024-07-27 05:46:46,198 - pyskl - INFO - Epoch [138][1500/3746] lr: 1.731e-03, eta: 10:46:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5731, top5_acc: 0.8066, loss_cls: 2.3831, loss: 2.3831 +2024-07-27 05:48:07,401 - pyskl - INFO - Epoch [138][1600/3746] lr: 1.724e-03, eta: 10:45:15, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5603, top5_acc: 0.8044, loss_cls: 2.4336, loss: 2.4336 +2024-07-27 05:49:28,887 - pyskl - INFO - Epoch [138][1700/3746] lr: 1.716e-03, eta: 10:43:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5513, top5_acc: 0.7977, loss_cls: 2.4515, loss: 2.4515 +2024-07-27 05:50:50,632 - pyskl - INFO - Epoch [138][1800/3746] lr: 1.709e-03, eta: 10:42:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5673, top5_acc: 0.8045, loss_cls: 2.4054, loss: 2.4054 +2024-07-27 05:52:12,342 - pyskl - INFO - Epoch [138][1900/3746] lr: 1.702e-03, eta: 10:41:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5664, top5_acc: 0.7958, loss_cls: 2.4274, loss: 2.4274 +2024-07-27 05:53:33,578 - pyskl - INFO - Epoch [138][2000/3746] lr: 1.695e-03, eta: 10:39:46, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5641, top5_acc: 0.8036, loss_cls: 2.4132, loss: 2.4132 +2024-07-27 05:54:55,312 - pyskl - INFO - Epoch [138][2100/3746] lr: 1.687e-03, eta: 10:38:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5667, top5_acc: 0.7995, loss_cls: 2.4250, loss: 2.4250 +2024-07-27 05:56:17,024 - pyskl - INFO - Epoch [138][2200/3746] lr: 1.680e-03, eta: 10:37:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5694, top5_acc: 0.8006, loss_cls: 2.4229, loss: 2.4229 +2024-07-27 05:57:39,645 - pyskl - INFO - Epoch [138][2300/3746] lr: 1.673e-03, eta: 10:35:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5569, top5_acc: 0.8019, loss_cls: 2.4442, loss: 2.4442 +2024-07-27 05:59:01,389 - pyskl - INFO - Epoch [138][2400/3746] lr: 1.666e-03, eta: 10:34:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5684, top5_acc: 0.8009, loss_cls: 2.4095, loss: 2.4095 +2024-07-27 06:00:23,371 - pyskl - INFO - Epoch [138][2500/3746] lr: 1.659e-03, eta: 10:32:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5637, top5_acc: 0.7983, loss_cls: 2.4426, loss: 2.4426 +2024-07-27 06:01:45,245 - pyskl - INFO - Epoch [138][2600/3746] lr: 1.652e-03, eta: 10:31:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5536, top5_acc: 0.7994, loss_cls: 2.4513, loss: 2.4513 +2024-07-27 06:03:07,124 - pyskl - INFO - Epoch [138][2700/3746] lr: 1.644e-03, eta: 10:30:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5598, top5_acc: 0.7947, loss_cls: 2.4480, loss: 2.4480 +2024-07-27 06:04:29,285 - pyskl - INFO - Epoch [138][2800/3746] lr: 1.637e-03, eta: 10:28:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5595, top5_acc: 0.7945, loss_cls: 2.4596, loss: 2.4596 +2024-07-27 06:05:50,624 - pyskl - INFO - Epoch [138][2900/3746] lr: 1.630e-03, eta: 10:27:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5591, top5_acc: 0.8008, loss_cls: 2.4216, loss: 2.4216 +2024-07-27 06:07:12,391 - pyskl - INFO - Epoch [138][3000/3746] lr: 1.623e-03, eta: 10:26:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5536, top5_acc: 0.7931, loss_cls: 2.4818, loss: 2.4818 +2024-07-27 06:08:33,864 - pyskl - INFO - Epoch [138][3100/3746] lr: 1.616e-03, eta: 10:24:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5667, top5_acc: 0.7967, loss_cls: 2.4451, loss: 2.4451 +2024-07-27 06:09:55,217 - pyskl - INFO - Epoch [138][3200/3746] lr: 1.609e-03, eta: 10:23:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5591, top5_acc: 0.8020, loss_cls: 2.4465, loss: 2.4465 +2024-07-27 06:11:16,327 - pyskl - INFO - Epoch [138][3300/3746] lr: 1.602e-03, eta: 10:21:57, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5647, top5_acc: 0.7978, loss_cls: 2.4343, loss: 2.4343 +2024-07-27 06:12:37,946 - pyskl - INFO - Epoch [138][3400/3746] lr: 1.595e-03, eta: 10:20:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5513, top5_acc: 0.7967, loss_cls: 2.4694, loss: 2.4694 +2024-07-27 06:13:59,361 - pyskl - INFO - Epoch [138][3500/3746] lr: 1.588e-03, eta: 10:19:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5686, top5_acc: 0.7967, loss_cls: 2.4473, loss: 2.4473 +2024-07-27 06:15:20,498 - pyskl - INFO - Epoch [138][3600/3746] lr: 1.581e-03, eta: 10:17:50, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5603, top5_acc: 0.7966, loss_cls: 2.4451, loss: 2.4451 +2024-07-27 06:16:41,858 - pyskl - INFO - Epoch [138][3700/3746] lr: 1.574e-03, eta: 10:16:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5561, top5_acc: 0.7961, loss_cls: 2.4589, loss: 2.4589 +2024-07-27 06:17:21,106 - pyskl - INFO - Saving checkpoint at 138 epochs +2024-07-27 06:19:13,684 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 06:19:14,343 - pyskl - INFO - +top1_acc 0.4414 +top5_acc 0.6902 +2024-07-27 06:19:14,343 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 06:19:14,381 - pyskl - INFO - +mean_acc 0.4411 +2024-07-27 06:19:14,386 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_137.pth was removed +2024-07-27 06:19:14,664 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_138.pth. +2024-07-27 06:19:14,665 - pyskl - INFO - Best top1_acc is 0.4414 at 138 epoch. +2024-07-27 06:19:14,677 - pyskl - INFO - Epoch(val) [138][309] top1_acc: 0.4414, top5_acc: 0.6902, mean_class_accuracy: 0.4411 +2024-07-27 06:23:00,409 - pyskl - INFO - Epoch [139][100/3746] lr: 1.564e-03, eta: 10:14:37, time: 2.257, data_time: 1.288, memory: 15990, top1_acc: 0.5945, top5_acc: 0.8200, loss_cls: 2.2986, loss: 2.2986 +2024-07-27 06:24:22,142 - pyskl - INFO - Epoch [139][200/3746] lr: 1.557e-03, eta: 10:13:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5892, top5_acc: 0.8217, loss_cls: 2.3039, loss: 2.3039 +2024-07-27 06:25:44,204 - pyskl - INFO - Epoch [139][300/3746] lr: 1.550e-03, eta: 10:11:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5813, top5_acc: 0.8130, loss_cls: 2.3219, loss: 2.3219 +2024-07-27 06:27:05,983 - pyskl - INFO - Epoch [139][400/3746] lr: 1.543e-03, eta: 10:10:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5803, top5_acc: 0.8194, loss_cls: 2.3108, loss: 2.3108 +2024-07-27 06:28:27,739 - pyskl - INFO - Epoch [139][500/3746] lr: 1.536e-03, eta: 10:09:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5691, top5_acc: 0.8050, loss_cls: 2.3955, loss: 2.3955 +2024-07-27 06:29:49,378 - pyskl - INFO - Epoch [139][600/3746] lr: 1.529e-03, eta: 10:07:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5814, top5_acc: 0.8228, loss_cls: 2.3138, loss: 2.3138 +2024-07-27 06:31:10,569 - pyskl - INFO - Epoch [139][700/3746] lr: 1.523e-03, eta: 10:06:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5813, top5_acc: 0.8109, loss_cls: 2.3325, loss: 2.3325 +2024-07-27 06:32:31,685 - pyskl - INFO - Epoch [139][800/3746] lr: 1.516e-03, eta: 10:05:01, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5822, top5_acc: 0.8105, loss_cls: 2.3498, loss: 2.3498 +2024-07-27 06:33:53,420 - pyskl - INFO - Epoch [139][900/3746] lr: 1.509e-03, eta: 10:03:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5770, top5_acc: 0.8136, loss_cls: 2.3587, loss: 2.3587 +2024-07-27 06:35:14,418 - pyskl - INFO - Epoch [139][1000/3746] lr: 1.502e-03, eta: 10:02:16, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5772, top5_acc: 0.8089, loss_cls: 2.3662, loss: 2.3662 +2024-07-27 06:36:35,783 - pyskl - INFO - Epoch [139][1100/3746] lr: 1.495e-03, eta: 10:00:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5848, top5_acc: 0.8163, loss_cls: 2.3275, loss: 2.3275 +2024-07-27 06:37:57,184 - pyskl - INFO - Epoch [139][1200/3746] lr: 1.489e-03, eta: 9:59:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5642, top5_acc: 0.8106, loss_cls: 2.3893, loss: 2.3893 +2024-07-27 06:39:18,941 - pyskl - INFO - Epoch [139][1300/3746] lr: 1.482e-03, eta: 9:58:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5727, top5_acc: 0.7995, loss_cls: 2.3800, loss: 2.3800 +2024-07-27 06:40:40,637 - pyskl - INFO - Epoch [139][1400/3746] lr: 1.475e-03, eta: 9:56:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5734, top5_acc: 0.8116, loss_cls: 2.3636, loss: 2.3636 +2024-07-27 06:42:02,279 - pyskl - INFO - Epoch [139][1500/3746] lr: 1.468e-03, eta: 9:55:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5795, top5_acc: 0.8144, loss_cls: 2.3422, loss: 2.3422 +2024-07-27 06:43:23,856 - pyskl - INFO - Epoch [139][1600/3746] lr: 1.462e-03, eta: 9:54:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5814, top5_acc: 0.8158, loss_cls: 2.3257, loss: 2.3257 +2024-07-27 06:44:45,247 - pyskl - INFO - Epoch [139][1700/3746] lr: 1.455e-03, eta: 9:52:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5736, top5_acc: 0.8117, loss_cls: 2.3627, loss: 2.3627 +2024-07-27 06:46:06,914 - pyskl - INFO - Epoch [139][1800/3746] lr: 1.448e-03, eta: 9:51:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5711, top5_acc: 0.8036, loss_cls: 2.3880, loss: 2.3880 +2024-07-27 06:47:28,347 - pyskl - INFO - Epoch [139][1900/3746] lr: 1.442e-03, eta: 9:49:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5695, top5_acc: 0.8119, loss_cls: 2.3775, loss: 2.3775 +2024-07-27 06:48:49,295 - pyskl - INFO - Epoch [139][2000/3746] lr: 1.435e-03, eta: 9:48:33, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5725, top5_acc: 0.8106, loss_cls: 2.3598, loss: 2.3598 +2024-07-27 06:50:10,790 - pyskl - INFO - Epoch [139][2100/3746] lr: 1.428e-03, eta: 9:47:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5687, top5_acc: 0.8019, loss_cls: 2.4060, loss: 2.4060 +2024-07-27 06:51:32,932 - pyskl - INFO - Epoch [139][2200/3746] lr: 1.422e-03, eta: 9:45:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5714, top5_acc: 0.8011, loss_cls: 2.4137, loss: 2.4137 +2024-07-27 06:52:54,455 - pyskl - INFO - Epoch [139][2300/3746] lr: 1.415e-03, eta: 9:44:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5792, top5_acc: 0.8150, loss_cls: 2.3212, loss: 2.3212 +2024-07-27 06:54:16,540 - pyskl - INFO - Epoch [139][2400/3746] lr: 1.408e-03, eta: 9:43:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5769, top5_acc: 0.8098, loss_cls: 2.3728, loss: 2.3728 +2024-07-27 06:55:38,163 - pyskl - INFO - Epoch [139][2500/3746] lr: 1.402e-03, eta: 9:41:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5650, top5_acc: 0.8056, loss_cls: 2.3992, loss: 2.3992 +2024-07-27 06:57:00,596 - pyskl - INFO - Epoch [139][2600/3746] lr: 1.395e-03, eta: 9:40:20, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5697, top5_acc: 0.8055, loss_cls: 2.3930, loss: 2.3930 +2024-07-27 06:58:22,333 - pyskl - INFO - Epoch [139][2700/3746] lr: 1.389e-03, eta: 9:38:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5714, top5_acc: 0.8122, loss_cls: 2.3919, loss: 2.3919 +2024-07-27 06:59:44,493 - pyskl - INFO - Epoch [139][2800/3746] lr: 1.382e-03, eta: 9:37:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5705, top5_acc: 0.8034, loss_cls: 2.4161, loss: 2.4161 +2024-07-27 07:01:06,488 - pyskl - INFO - Epoch [139][2900/3746] lr: 1.376e-03, eta: 9:36:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5670, top5_acc: 0.8030, loss_cls: 2.3890, loss: 2.3890 +2024-07-27 07:02:28,174 - pyskl - INFO - Epoch [139][3000/3746] lr: 1.369e-03, eta: 9:34:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5672, top5_acc: 0.8092, loss_cls: 2.3832, loss: 2.3832 +2024-07-27 07:03:49,194 - pyskl - INFO - Epoch [139][3100/3746] lr: 1.363e-03, eta: 9:33:29, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5713, top5_acc: 0.8013, loss_cls: 2.4098, loss: 2.4098 +2024-07-27 07:05:10,698 - pyskl - INFO - Epoch [139][3200/3746] lr: 1.356e-03, eta: 9:32:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5702, top5_acc: 0.8050, loss_cls: 2.3902, loss: 2.3902 +2024-07-27 07:06:32,460 - pyskl - INFO - Epoch [139][3300/3746] lr: 1.350e-03, eta: 9:30:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5636, top5_acc: 0.7987, loss_cls: 2.4152, loss: 2.4152 +2024-07-27 07:07:53,677 - pyskl - INFO - Epoch [139][3400/3746] lr: 1.343e-03, eta: 9:29:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5613, top5_acc: 0.7994, loss_cls: 2.4456, loss: 2.4456 +2024-07-27 07:09:15,682 - pyskl - INFO - Epoch [139][3500/3746] lr: 1.337e-03, eta: 9:28:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5659, top5_acc: 0.7987, loss_cls: 2.3960, loss: 2.3960 +2024-07-27 07:10:37,493 - pyskl - INFO - Epoch [139][3600/3746] lr: 1.330e-03, eta: 9:26:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5569, top5_acc: 0.7975, loss_cls: 2.4463, loss: 2.4463 +2024-07-27 07:11:59,221 - pyskl - INFO - Epoch [139][3700/3746] lr: 1.324e-03, eta: 9:25:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5727, top5_acc: 0.8098, loss_cls: 2.3633, loss: 2.3633 +2024-07-27 07:12:38,779 - pyskl - INFO - Saving checkpoint at 139 epochs +2024-07-27 07:14:30,795 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 07:14:31,468 - pyskl - INFO - +top1_acc 0.4480 +top5_acc 0.6924 +2024-07-27 07:14:31,468 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 07:14:31,507 - pyskl - INFO - +mean_acc 0.4477 +2024-07-27 07:14:31,512 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_138.pth was removed +2024-07-27 07:14:31,770 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_139.pth. +2024-07-27 07:14:31,770 - pyskl - INFO - Best top1_acc is 0.4480 at 139 epoch. +2024-07-27 07:14:31,782 - pyskl - INFO - Epoch(val) [139][309] top1_acc: 0.4480, top5_acc: 0.6924, mean_class_accuracy: 0.4477 +2024-07-27 07:18:18,112 - pyskl - INFO - Epoch [140][100/3746] lr: 1.315e-03, eta: 9:23:23, time: 2.263, data_time: 1.288, memory: 15990, top1_acc: 0.6064, top5_acc: 0.8339, loss_cls: 2.2139, loss: 2.2139 +2024-07-27 07:19:39,831 - pyskl - INFO - Epoch [140][200/3746] lr: 1.308e-03, eta: 9:22:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6025, top5_acc: 0.8252, loss_cls: 2.2445, loss: 2.2445 +2024-07-27 07:21:01,649 - pyskl - INFO - Epoch [140][300/3746] lr: 1.302e-03, eta: 9:20:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5842, top5_acc: 0.8180, loss_cls: 2.3032, loss: 2.3032 +2024-07-27 07:22:23,740 - pyskl - INFO - Epoch [140][400/3746] lr: 1.296e-03, eta: 9:19:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5870, top5_acc: 0.8158, loss_cls: 2.3275, loss: 2.3275 +2024-07-27 07:23:45,870 - pyskl - INFO - Epoch [140][500/3746] lr: 1.289e-03, eta: 9:17:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5878, top5_acc: 0.8125, loss_cls: 2.3430, loss: 2.3430 +2024-07-27 07:25:07,809 - pyskl - INFO - Epoch [140][600/3746] lr: 1.283e-03, eta: 9:16:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5988, top5_acc: 0.8245, loss_cls: 2.2603, loss: 2.2603 +2024-07-27 07:26:29,856 - pyskl - INFO - Epoch [140][700/3746] lr: 1.277e-03, eta: 9:15:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5902, top5_acc: 0.8195, loss_cls: 2.2935, loss: 2.2935 +2024-07-27 07:27:51,440 - pyskl - INFO - Epoch [140][800/3746] lr: 1.271e-03, eta: 9:13:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5894, top5_acc: 0.8184, loss_cls: 2.2858, loss: 2.2858 +2024-07-27 07:29:13,367 - pyskl - INFO - Epoch [140][900/3746] lr: 1.264e-03, eta: 9:12:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5800, top5_acc: 0.8159, loss_cls: 2.3093, loss: 2.3093 +2024-07-27 07:30:34,829 - pyskl - INFO - Epoch [140][1000/3746] lr: 1.258e-03, eta: 9:11:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5816, top5_acc: 0.8194, loss_cls: 2.3285, loss: 2.3285 +2024-07-27 07:31:56,520 - pyskl - INFO - Epoch [140][1100/3746] lr: 1.252e-03, eta: 9:09:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5908, top5_acc: 0.8234, loss_cls: 2.2911, loss: 2.2911 +2024-07-27 07:33:17,680 - pyskl - INFO - Epoch [140][1200/3746] lr: 1.246e-03, eta: 9:08:18, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5895, top5_acc: 0.8231, loss_cls: 2.2946, loss: 2.2946 +2024-07-27 07:34:39,109 - pyskl - INFO - Epoch [140][1300/3746] lr: 1.239e-03, eta: 9:06:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5783, top5_acc: 0.8150, loss_cls: 2.3367, loss: 2.3367 +2024-07-27 07:36:00,847 - pyskl - INFO - Epoch [140][1400/3746] lr: 1.233e-03, eta: 9:05:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5792, top5_acc: 0.8148, loss_cls: 2.3269, loss: 2.3269 +2024-07-27 07:37:22,179 - pyskl - INFO - Epoch [140][1500/3746] lr: 1.227e-03, eta: 9:04:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5955, top5_acc: 0.8214, loss_cls: 2.2664, loss: 2.2664 +2024-07-27 07:38:43,660 - pyskl - INFO - Epoch [140][1600/3746] lr: 1.221e-03, eta: 9:02:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5856, top5_acc: 0.8167, loss_cls: 2.3060, loss: 2.3060 +2024-07-27 07:40:05,760 - pyskl - INFO - Epoch [140][1700/3746] lr: 1.215e-03, eta: 9:01:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5913, top5_acc: 0.8186, loss_cls: 2.2733, loss: 2.2733 +2024-07-27 07:41:27,307 - pyskl - INFO - Epoch [140][1800/3746] lr: 1.209e-03, eta: 9:00:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5733, top5_acc: 0.8208, loss_cls: 2.3261, loss: 2.3261 +2024-07-27 07:42:49,204 - pyskl - INFO - Epoch [140][1900/3746] lr: 1.203e-03, eta: 8:58:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5902, top5_acc: 0.8252, loss_cls: 2.2621, loss: 2.2621 +2024-07-27 07:44:10,206 - pyskl - INFO - Epoch [140][2000/3746] lr: 1.196e-03, eta: 8:57:20, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5837, top5_acc: 0.8167, loss_cls: 2.3297, loss: 2.3297 +2024-07-27 07:45:31,841 - pyskl - INFO - Epoch [140][2100/3746] lr: 1.190e-03, eta: 8:55:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5855, top5_acc: 0.8181, loss_cls: 2.2857, loss: 2.2857 +2024-07-27 07:46:53,777 - pyskl - INFO - Epoch [140][2200/3746] lr: 1.184e-03, eta: 8:54:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5789, top5_acc: 0.8147, loss_cls: 2.3181, loss: 2.3181 +2024-07-27 07:48:16,318 - pyskl - INFO - Epoch [140][2300/3746] lr: 1.178e-03, eta: 8:53:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5736, top5_acc: 0.8116, loss_cls: 2.3614, loss: 2.3614 +2024-07-27 07:49:37,845 - pyskl - INFO - Epoch [140][2400/3746] lr: 1.172e-03, eta: 8:51:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5898, top5_acc: 0.8139, loss_cls: 2.2941, loss: 2.2941 +2024-07-27 07:50:59,908 - pyskl - INFO - Epoch [140][2500/3746] lr: 1.166e-03, eta: 8:50:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5764, top5_acc: 0.8059, loss_cls: 2.3607, loss: 2.3607 +2024-07-27 07:52:21,993 - pyskl - INFO - Epoch [140][2600/3746] lr: 1.160e-03, eta: 8:49:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5714, top5_acc: 0.8028, loss_cls: 2.3881, loss: 2.3881 +2024-07-27 07:53:44,388 - pyskl - INFO - Epoch [140][2700/3746] lr: 1.154e-03, eta: 8:47:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5823, top5_acc: 0.8161, loss_cls: 2.3046, loss: 2.3046 +2024-07-27 07:55:06,130 - pyskl - INFO - Epoch [140][2800/3746] lr: 1.148e-03, eta: 8:46:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5684, top5_acc: 0.8028, loss_cls: 2.3973, loss: 2.3973 +2024-07-27 07:56:27,923 - pyskl - INFO - Epoch [140][2900/3746] lr: 1.142e-03, eta: 8:45:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5837, top5_acc: 0.8145, loss_cls: 2.3057, loss: 2.3057 +2024-07-27 07:57:49,327 - pyskl - INFO - Epoch [140][3000/3746] lr: 1.136e-03, eta: 8:43:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5709, top5_acc: 0.8102, loss_cls: 2.3561, loss: 2.3561 +2024-07-27 07:59:10,516 - pyskl - INFO - Epoch [140][3100/3746] lr: 1.131e-03, eta: 8:42:15, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5803, top5_acc: 0.8152, loss_cls: 2.3519, loss: 2.3519 +2024-07-27 08:00:32,121 - pyskl - INFO - Epoch [140][3200/3746] lr: 1.125e-03, eta: 8:40:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5837, top5_acc: 0.8186, loss_cls: 2.3075, loss: 2.3075 +2024-07-27 08:01:53,833 - pyskl - INFO - Epoch [140][3300/3746] lr: 1.119e-03, eta: 8:39:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5822, top5_acc: 0.8170, loss_cls: 2.3229, loss: 2.3229 +2024-07-27 08:03:15,563 - pyskl - INFO - Epoch [140][3400/3746] lr: 1.113e-03, eta: 8:38:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5702, top5_acc: 0.8073, loss_cls: 2.3504, loss: 2.3504 +2024-07-27 08:04:36,964 - pyskl - INFO - Epoch [140][3500/3746] lr: 1.107e-03, eta: 8:36:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5787, top5_acc: 0.8127, loss_cls: 2.3325, loss: 2.3325 +2024-07-27 08:05:59,382 - pyskl - INFO - Epoch [140][3600/3746] lr: 1.101e-03, eta: 8:35:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5889, top5_acc: 0.8172, loss_cls: 2.3161, loss: 2.3161 +2024-07-27 08:07:21,085 - pyskl - INFO - Epoch [140][3700/3746] lr: 1.095e-03, eta: 8:34:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5831, top5_acc: 0.8081, loss_cls: 2.3496, loss: 2.3496 +2024-07-27 08:08:00,874 - pyskl - INFO - Saving checkpoint at 140 epochs +2024-07-27 08:09:52,900 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 08:09:53,555 - pyskl - INFO - +top1_acc 0.4465 +top5_acc 0.6928 +2024-07-27 08:09:53,555 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 08:09:53,594 - pyskl - INFO - +mean_acc 0.4463 +2024-07-27 08:09:53,606 - pyskl - INFO - Epoch(val) [140][309] top1_acc: 0.4465, top5_acc: 0.6928, mean_class_accuracy: 0.4463 +2024-07-27 08:13:39,346 - pyskl - INFO - Epoch [141][100/3746] lr: 1.087e-03, eta: 8:32:09, time: 2.257, data_time: 1.282, memory: 15990, top1_acc: 0.6031, top5_acc: 0.8341, loss_cls: 2.2096, loss: 2.2096 +2024-07-27 08:15:00,594 - pyskl - INFO - Epoch [141][200/3746] lr: 1.081e-03, eta: 8:30:47, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6025, top5_acc: 0.8255, loss_cls: 2.2336, loss: 2.2336 +2024-07-27 08:16:22,658 - pyskl - INFO - Epoch [141][300/3746] lr: 1.075e-03, eta: 8:29:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6064, top5_acc: 0.8255, loss_cls: 2.2049, loss: 2.2049 +2024-07-27 08:17:44,536 - pyskl - INFO - Epoch [141][400/3746] lr: 1.070e-03, eta: 8:28:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5953, top5_acc: 0.8266, loss_cls: 2.2656, loss: 2.2656 +2024-07-27 08:19:06,889 - pyskl - INFO - Epoch [141][500/3746] lr: 1.064e-03, eta: 8:26:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5966, top5_acc: 0.8294, loss_cls: 2.2400, loss: 2.2400 +2024-07-27 08:20:28,601 - pyskl - INFO - Epoch [141][600/3746] lr: 1.058e-03, eta: 8:25:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5966, top5_acc: 0.8302, loss_cls: 2.2287, loss: 2.2287 +2024-07-27 08:21:49,888 - pyskl - INFO - Epoch [141][700/3746] lr: 1.052e-03, eta: 8:23:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6088, top5_acc: 0.8303, loss_cls: 2.2144, loss: 2.2144 +2024-07-27 08:23:10,919 - pyskl - INFO - Epoch [141][800/3746] lr: 1.047e-03, eta: 8:22:33, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5917, top5_acc: 0.8181, loss_cls: 2.2744, loss: 2.2744 +2024-07-27 08:24:31,898 - pyskl - INFO - Epoch [141][900/3746] lr: 1.041e-03, eta: 8:21:11, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5986, top5_acc: 0.8225, loss_cls: 2.2432, loss: 2.2432 +2024-07-27 08:25:53,479 - pyskl - INFO - Epoch [141][1000/3746] lr: 1.035e-03, eta: 8:19:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5934, top5_acc: 0.8242, loss_cls: 2.2757, loss: 2.2757 +2024-07-27 08:27:14,861 - pyskl - INFO - Epoch [141][1100/3746] lr: 1.030e-03, eta: 8:18:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5872, top5_acc: 0.8186, loss_cls: 2.2758, loss: 2.2758 +2024-07-27 08:28:36,238 - pyskl - INFO - Epoch [141][1200/3746] lr: 1.024e-03, eta: 8:17:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5950, top5_acc: 0.8202, loss_cls: 2.2599, loss: 2.2599 +2024-07-27 08:29:57,470 - pyskl - INFO - Epoch [141][1300/3746] lr: 1.018e-03, eta: 8:15:41, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5908, top5_acc: 0.8253, loss_cls: 2.2816, loss: 2.2816 +2024-07-27 08:31:18,757 - pyskl - INFO - Epoch [141][1400/3746] lr: 1.013e-03, eta: 8:14:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5994, top5_acc: 0.8253, loss_cls: 2.2421, loss: 2.2421 +2024-07-27 08:32:40,137 - pyskl - INFO - Epoch [141][1500/3746] lr: 1.007e-03, eta: 8:12:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5947, top5_acc: 0.8287, loss_cls: 2.2418, loss: 2.2418 +2024-07-27 08:34:01,161 - pyskl - INFO - Epoch [141][1600/3746] lr: 1.002e-03, eta: 8:11:35, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5906, top5_acc: 0.8213, loss_cls: 2.2638, loss: 2.2638 +2024-07-27 08:35:22,979 - pyskl - INFO - Epoch [141][1700/3746] lr: 9.961e-04, eta: 8:10:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5950, top5_acc: 0.8264, loss_cls: 2.2482, loss: 2.2482 +2024-07-27 08:36:44,890 - pyskl - INFO - Epoch [141][1800/3746] lr: 9.905e-04, eta: 8:08:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5894, top5_acc: 0.8217, loss_cls: 2.2644, loss: 2.2644 +2024-07-27 08:38:06,373 - pyskl - INFO - Epoch [141][1900/3746] lr: 9.850e-04, eta: 8:07:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5959, top5_acc: 0.8295, loss_cls: 2.2415, loss: 2.2415 +2024-07-27 08:39:27,889 - pyskl - INFO - Epoch [141][2000/3746] lr: 9.795e-04, eta: 8:06:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5998, top5_acc: 0.8306, loss_cls: 2.2297, loss: 2.2297 +2024-07-27 08:40:50,297 - pyskl - INFO - Epoch [141][2100/3746] lr: 9.740e-04, eta: 8:04:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5973, top5_acc: 0.8228, loss_cls: 2.2738, loss: 2.2738 +2024-07-27 08:42:12,512 - pyskl - INFO - Epoch [141][2200/3746] lr: 9.685e-04, eta: 8:03:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5905, top5_acc: 0.8203, loss_cls: 2.2654, loss: 2.2654 +2024-07-27 08:43:34,707 - pyskl - INFO - Epoch [141][2300/3746] lr: 9.630e-04, eta: 8:01:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5908, top5_acc: 0.8236, loss_cls: 2.2507, loss: 2.2507 +2024-07-27 08:44:56,630 - pyskl - INFO - Epoch [141][2400/3746] lr: 9.576e-04, eta: 8:00:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5962, top5_acc: 0.8161, loss_cls: 2.2779, loss: 2.2779 +2024-07-27 08:46:18,536 - pyskl - INFO - Epoch [141][2500/3746] lr: 9.522e-04, eta: 7:59:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5889, top5_acc: 0.8156, loss_cls: 2.2928, loss: 2.2928 +2024-07-27 08:47:39,660 - pyskl - INFO - Epoch [141][2600/3746] lr: 9.467e-04, eta: 7:57:52, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5830, top5_acc: 0.8245, loss_cls: 2.2921, loss: 2.2921 +2024-07-27 08:49:02,253 - pyskl - INFO - Epoch [141][2700/3746] lr: 9.413e-04, eta: 7:56:30, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5908, top5_acc: 0.8191, loss_cls: 2.2825, loss: 2.2825 +2024-07-27 08:50:23,544 - pyskl - INFO - Epoch [141][2800/3746] lr: 9.359e-04, eta: 7:55:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5925, top5_acc: 0.8248, loss_cls: 2.2688, loss: 2.2688 +2024-07-27 08:51:45,474 - pyskl - INFO - Epoch [141][2900/3746] lr: 9.306e-04, eta: 7:53:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5958, top5_acc: 0.8261, loss_cls: 2.2495, loss: 2.2495 +2024-07-27 08:53:06,952 - pyskl - INFO - Epoch [141][3000/3746] lr: 9.252e-04, eta: 7:52:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6014, top5_acc: 0.8211, loss_cls: 2.2494, loss: 2.2494 +2024-07-27 08:54:28,341 - pyskl - INFO - Epoch [141][3100/3746] lr: 9.199e-04, eta: 7:51:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5858, top5_acc: 0.8167, loss_cls: 2.2819, loss: 2.2819 +2024-07-27 08:55:49,852 - pyskl - INFO - Epoch [141][3200/3746] lr: 9.145e-04, eta: 7:49:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5873, top5_acc: 0.8247, loss_cls: 2.2625, loss: 2.2625 +2024-07-27 08:57:11,041 - pyskl - INFO - Epoch [141][3300/3746] lr: 9.092e-04, eta: 7:48:16, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5900, top5_acc: 0.8195, loss_cls: 2.2576, loss: 2.2576 +2024-07-27 08:58:32,929 - pyskl - INFO - Epoch [141][3400/3746] lr: 9.039e-04, eta: 7:46:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5984, top5_acc: 0.8252, loss_cls: 2.2624, loss: 2.2624 +2024-07-27 08:59:54,834 - pyskl - INFO - Epoch [141][3500/3746] lr: 8.986e-04, eta: 7:45:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5884, top5_acc: 0.8145, loss_cls: 2.3094, loss: 2.3094 +2024-07-27 09:01:16,241 - pyskl - INFO - Epoch [141][3600/3746] lr: 8.934e-04, eta: 7:44:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5903, top5_acc: 0.8217, loss_cls: 2.2795, loss: 2.2795 +2024-07-27 09:02:37,289 - pyskl - INFO - Epoch [141][3700/3746] lr: 8.881e-04, eta: 7:42:47, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5939, top5_acc: 0.8206, loss_cls: 2.2549, loss: 2.2549 +2024-07-27 09:03:16,677 - pyskl - INFO - Saving checkpoint at 141 epochs +2024-07-27 09:05:09,136 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 09:05:09,796 - pyskl - INFO - +top1_acc 0.4477 +top5_acc 0.7003 +2024-07-27 09:05:09,797 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 09:05:09,835 - pyskl - INFO - +mean_acc 0.4474 +2024-07-27 09:05:09,846 - pyskl - INFO - Epoch(val) [141][309] top1_acc: 0.4477, top5_acc: 0.7003, mean_class_accuracy: 0.4474 +2024-07-27 09:08:58,390 - pyskl - INFO - Epoch [142][100/3746] lr: 8.805e-04, eta: 7:40:53, time: 2.285, data_time: 1.307, memory: 15990, top1_acc: 0.6114, top5_acc: 0.8414, loss_cls: 2.1461, loss: 2.1461 +2024-07-27 09:10:20,037 - pyskl - INFO - Epoch [142][200/3746] lr: 8.752e-04, eta: 7:39:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6162, top5_acc: 0.8364, loss_cls: 2.1522, loss: 2.1522 +2024-07-27 09:11:42,136 - pyskl - INFO - Epoch [142][300/3746] lr: 8.700e-04, eta: 7:38:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6147, top5_acc: 0.8405, loss_cls: 2.1546, loss: 2.1546 +2024-07-27 09:13:03,962 - pyskl - INFO - Epoch [142][400/3746] lr: 8.649e-04, eta: 7:36:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6184, top5_acc: 0.8369, loss_cls: 2.1510, loss: 2.1510 +2024-07-27 09:14:26,043 - pyskl - INFO - Epoch [142][500/3746] lr: 8.597e-04, eta: 7:35:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6098, top5_acc: 0.8323, loss_cls: 2.1834, loss: 2.1834 +2024-07-27 09:15:48,037 - pyskl - INFO - Epoch [142][600/3746] lr: 8.545e-04, eta: 7:34:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6098, top5_acc: 0.8336, loss_cls: 2.1801, loss: 2.1801 +2024-07-27 09:17:09,656 - pyskl - INFO - Epoch [142][700/3746] lr: 8.494e-04, eta: 7:32:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6225, top5_acc: 0.8397, loss_cls: 2.1582, loss: 2.1582 +2024-07-27 09:18:31,307 - pyskl - INFO - Epoch [142][800/3746] lr: 8.443e-04, eta: 7:31:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6130, top5_acc: 0.8397, loss_cls: 2.1471, loss: 2.1471 +2024-07-27 09:19:52,685 - pyskl - INFO - Epoch [142][900/3746] lr: 8.392e-04, eta: 7:29:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6053, top5_acc: 0.8380, loss_cls: 2.2006, loss: 2.2006 +2024-07-27 09:21:14,105 - pyskl - INFO - Epoch [142][1000/3746] lr: 8.341e-04, eta: 7:28:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6100, top5_acc: 0.8356, loss_cls: 2.1810, loss: 2.1810 +2024-07-27 09:22:35,170 - pyskl - INFO - Epoch [142][1100/3746] lr: 8.290e-04, eta: 7:27:10, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6144, top5_acc: 0.8370, loss_cls: 2.1732, loss: 2.1732 +2024-07-27 09:23:57,257 - pyskl - INFO - Epoch [142][1200/3746] lr: 8.239e-04, eta: 7:25:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6058, top5_acc: 0.8322, loss_cls: 2.2008, loss: 2.2008 +2024-07-27 09:25:18,552 - pyskl - INFO - Epoch [142][1300/3746] lr: 8.189e-04, eta: 7:24:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5938, top5_acc: 0.8261, loss_cls: 2.2462, loss: 2.2462 +2024-07-27 09:26:39,969 - pyskl - INFO - Epoch [142][1400/3746] lr: 8.139e-04, eta: 7:23:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6091, top5_acc: 0.8364, loss_cls: 2.1882, loss: 2.1882 +2024-07-27 09:28:01,448 - pyskl - INFO - Epoch [142][1500/3746] lr: 8.088e-04, eta: 7:21:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5939, top5_acc: 0.8247, loss_cls: 2.2483, loss: 2.2483 +2024-07-27 09:29:23,013 - pyskl - INFO - Epoch [142][1600/3746] lr: 8.038e-04, eta: 7:20:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6067, top5_acc: 0.8273, loss_cls: 2.2144, loss: 2.2144 +2024-07-27 09:30:44,551 - pyskl - INFO - Epoch [142][1700/3746] lr: 7.989e-04, eta: 7:18:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6233, top5_acc: 0.8356, loss_cls: 2.1375, loss: 2.1375 +2024-07-27 09:32:06,183 - pyskl - INFO - Epoch [142][1800/3746] lr: 7.939e-04, eta: 7:17:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5995, top5_acc: 0.8245, loss_cls: 2.2459, loss: 2.2459 +2024-07-27 09:33:27,483 - pyskl - INFO - Epoch [142][1900/3746] lr: 7.889e-04, eta: 7:16:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6048, top5_acc: 0.8295, loss_cls: 2.2044, loss: 2.2044 +2024-07-27 09:34:49,522 - pyskl - INFO - Epoch [142][2000/3746] lr: 7.840e-04, eta: 7:14:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6023, top5_acc: 0.8263, loss_cls: 2.2333, loss: 2.2333 +2024-07-27 09:36:11,054 - pyskl - INFO - Epoch [142][2100/3746] lr: 7.791e-04, eta: 7:13:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6041, top5_acc: 0.8330, loss_cls: 2.2014, loss: 2.2014 +2024-07-27 09:37:32,794 - pyskl - INFO - Epoch [142][2200/3746] lr: 7.742e-04, eta: 7:12:05, time: 0.817, data_time: 0.001, memory: 15990, top1_acc: 0.6097, top5_acc: 0.8391, loss_cls: 2.1810, loss: 2.1810 +2024-07-27 09:38:54,207 - pyskl - INFO - Epoch [142][2300/3746] lr: 7.693e-04, eta: 7:10:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6056, top5_acc: 0.8272, loss_cls: 2.2167, loss: 2.2167 +2024-07-27 09:40:16,263 - pyskl - INFO - Epoch [142][2400/3746] lr: 7.644e-04, eta: 7:09:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5988, top5_acc: 0.8239, loss_cls: 2.2250, loss: 2.2250 +2024-07-27 09:41:37,750 - pyskl - INFO - Epoch [142][2500/3746] lr: 7.595e-04, eta: 7:07:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5973, top5_acc: 0.8266, loss_cls: 2.2439, loss: 2.2439 +2024-07-27 09:42:58,970 - pyskl - INFO - Epoch [142][2600/3746] lr: 7.547e-04, eta: 7:06:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6036, top5_acc: 0.8309, loss_cls: 2.2094, loss: 2.2094 +2024-07-27 09:44:21,919 - pyskl - INFO - Epoch [142][2700/3746] lr: 7.499e-04, eta: 7:05:13, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6069, top5_acc: 0.8252, loss_cls: 2.2360, loss: 2.2360 +2024-07-27 09:45:43,737 - pyskl - INFO - Epoch [142][2800/3746] lr: 7.450e-04, eta: 7:03:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5953, top5_acc: 0.8214, loss_cls: 2.2407, loss: 2.2407 +2024-07-27 09:47:05,490 - pyskl - INFO - Epoch [142][2900/3746] lr: 7.402e-04, eta: 7:02:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6011, top5_acc: 0.8277, loss_cls: 2.2101, loss: 2.2101 +2024-07-27 09:48:27,191 - pyskl - INFO - Epoch [142][3000/3746] lr: 7.355e-04, eta: 7:01:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6159, top5_acc: 0.8367, loss_cls: 2.1673, loss: 2.1673 +2024-07-27 09:49:48,703 - pyskl - INFO - Epoch [142][3100/3746] lr: 7.307e-04, eta: 6:59:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6020, top5_acc: 0.8278, loss_cls: 2.2240, loss: 2.2240 +2024-07-27 09:51:09,664 - pyskl - INFO - Epoch [142][3200/3746] lr: 7.259e-04, eta: 6:58:22, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5953, top5_acc: 0.8253, loss_cls: 2.2330, loss: 2.2330 +2024-07-27 09:52:30,678 - pyskl - INFO - Epoch [142][3300/3746] lr: 7.212e-04, eta: 6:57:00, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6020, top5_acc: 0.8295, loss_cls: 2.2009, loss: 2.2009 +2024-07-27 09:53:52,118 - pyskl - INFO - Epoch [142][3400/3746] lr: 7.165e-04, eta: 6:55:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6117, top5_acc: 0.8341, loss_cls: 2.1648, loss: 2.1648 +2024-07-27 09:55:13,457 - pyskl - INFO - Epoch [142][3500/3746] lr: 7.118e-04, eta: 6:54:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6055, top5_acc: 0.8252, loss_cls: 2.2266, loss: 2.2266 +2024-07-27 09:56:35,630 - pyskl - INFO - Epoch [142][3600/3746] lr: 7.071e-04, eta: 6:52:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5920, top5_acc: 0.8230, loss_cls: 2.2334, loss: 2.2334 +2024-07-27 09:57:57,099 - pyskl - INFO - Epoch [142][3700/3746] lr: 7.024e-04, eta: 6:51:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5934, top5_acc: 0.8216, loss_cls: 2.2574, loss: 2.2574 +2024-07-27 09:58:36,538 - pyskl - INFO - Saving checkpoint at 142 epochs +2024-07-27 10:00:28,346 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 10:00:29,017 - pyskl - INFO - +top1_acc 0.4536 +top5_acc 0.6986 +2024-07-27 10:00:29,017 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 10:00:29,061 - pyskl - INFO - +mean_acc 0.4533 +2024-07-27 10:00:29,066 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_139.pth was removed +2024-07-27 10:00:29,328 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_142.pth. +2024-07-27 10:00:29,329 - pyskl - INFO - Best top1_acc is 0.4536 at 142 epoch. +2024-07-27 10:00:29,342 - pyskl - INFO - Epoch(val) [142][309] top1_acc: 0.4536, top5_acc: 0.6986, mean_class_accuracy: 0.4533 +2024-07-27 10:04:20,537 - pyskl - INFO - Epoch [143][100/3746] lr: 6.956e-04, eta: 6:49:37, time: 2.312, data_time: 1.335, memory: 15990, top1_acc: 0.6314, top5_acc: 0.8489, loss_cls: 2.0694, loss: 2.0694 +2024-07-27 10:05:42,117 - pyskl - INFO - Epoch [143][200/3746] lr: 6.910e-04, eta: 6:48:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6288, top5_acc: 0.8452, loss_cls: 2.0921, loss: 2.0921 +2024-07-27 10:07:03,681 - pyskl - INFO - Epoch [143][300/3746] lr: 6.863e-04, eta: 6:46:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6186, top5_acc: 0.8422, loss_cls: 2.1216, loss: 2.1216 +2024-07-27 10:08:25,712 - pyskl - INFO - Epoch [143][400/3746] lr: 6.817e-04, eta: 6:45:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6228, top5_acc: 0.8462, loss_cls: 2.1025, loss: 2.1025 +2024-07-27 10:09:47,266 - pyskl - INFO - Epoch [143][500/3746] lr: 6.771e-04, eta: 6:44:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6138, top5_acc: 0.8363, loss_cls: 2.1693, loss: 2.1693 +2024-07-27 10:11:08,967 - pyskl - INFO - Epoch [143][600/3746] lr: 6.725e-04, eta: 6:42:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6183, top5_acc: 0.8325, loss_cls: 2.1527, loss: 2.1527 +2024-07-27 10:12:30,466 - pyskl - INFO - Epoch [143][700/3746] lr: 6.680e-04, eta: 6:41:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6261, top5_acc: 0.8450, loss_cls: 2.1218, loss: 2.1218 +2024-07-27 10:13:52,031 - pyskl - INFO - Epoch [143][800/3746] lr: 6.634e-04, eta: 6:40:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6183, top5_acc: 0.8486, loss_cls: 2.1156, loss: 2.1156 +2024-07-27 10:15:13,672 - pyskl - INFO - Epoch [143][900/3746] lr: 6.589e-04, eta: 6:38:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6138, top5_acc: 0.8364, loss_cls: 2.1621, loss: 2.1621 +2024-07-27 10:16:35,064 - pyskl - INFO - Epoch [143][1000/3746] lr: 6.544e-04, eta: 6:37:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6078, top5_acc: 0.8361, loss_cls: 2.1791, loss: 2.1791 +2024-07-27 10:17:56,464 - pyskl - INFO - Epoch [143][1100/3746] lr: 6.499e-04, eta: 6:35:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6177, top5_acc: 0.8381, loss_cls: 2.1448, loss: 2.1448 +2024-07-27 10:19:19,177 - pyskl - INFO - Epoch [143][1200/3746] lr: 6.454e-04, eta: 6:34:31, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6067, top5_acc: 0.8378, loss_cls: 2.1694, loss: 2.1694 +2024-07-27 10:20:40,889 - pyskl - INFO - Epoch [143][1300/3746] lr: 6.409e-04, eta: 6:33:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6144, top5_acc: 0.8395, loss_cls: 2.1234, loss: 2.1234 +2024-07-27 10:22:01,883 - pyskl - INFO - Epoch [143][1400/3746] lr: 6.365e-04, eta: 6:31:46, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6233, top5_acc: 0.8477, loss_cls: 2.1216, loss: 2.1216 +2024-07-27 10:23:23,399 - pyskl - INFO - Epoch [143][1500/3746] lr: 6.320e-04, eta: 6:30:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6289, top5_acc: 0.8502, loss_cls: 2.0836, loss: 2.0836 +2024-07-27 10:24:44,662 - pyskl - INFO - Epoch [143][1600/3746] lr: 6.276e-04, eta: 6:29:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6098, top5_acc: 0.8348, loss_cls: 2.1772, loss: 2.1772 +2024-07-27 10:26:06,173 - pyskl - INFO - Epoch [143][1700/3746] lr: 6.232e-04, eta: 6:27:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6100, top5_acc: 0.8414, loss_cls: 2.1608, loss: 2.1608 +2024-07-27 10:27:27,667 - pyskl - INFO - Epoch [143][1800/3746] lr: 6.188e-04, eta: 6:26:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6056, top5_acc: 0.8356, loss_cls: 2.1786, loss: 2.1786 +2024-07-27 10:28:49,302 - pyskl - INFO - Epoch [143][1900/3746] lr: 6.144e-04, eta: 6:24:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6166, top5_acc: 0.8327, loss_cls: 2.1474, loss: 2.1474 +2024-07-27 10:30:10,457 - pyskl - INFO - Epoch [143][2000/3746] lr: 6.101e-04, eta: 6:23:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6181, top5_acc: 0.8425, loss_cls: 2.1328, loss: 2.1328 +2024-07-27 10:31:32,135 - pyskl - INFO - Epoch [143][2100/3746] lr: 6.057e-04, eta: 6:22:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6253, top5_acc: 0.8347, loss_cls: 2.1485, loss: 2.1485 +2024-07-27 10:32:54,154 - pyskl - INFO - Epoch [143][2200/3746] lr: 6.014e-04, eta: 6:20:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6059, top5_acc: 0.8280, loss_cls: 2.1996, loss: 2.1996 +2024-07-27 10:34:15,569 - pyskl - INFO - Epoch [143][2300/3746] lr: 5.971e-04, eta: 6:19:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6159, top5_acc: 0.8469, loss_cls: 2.1253, loss: 2.1253 +2024-07-27 10:35:37,749 - pyskl - INFO - Epoch [143][2400/3746] lr: 5.928e-04, eta: 6:18:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6089, top5_acc: 0.8305, loss_cls: 2.1565, loss: 2.1565 +2024-07-27 10:36:59,267 - pyskl - INFO - Epoch [143][2500/3746] lr: 5.885e-04, eta: 6:16:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6192, top5_acc: 0.8439, loss_cls: 2.1430, loss: 2.1430 +2024-07-27 10:38:20,388 - pyskl - INFO - Epoch [143][2600/3746] lr: 5.842e-04, eta: 6:15:19, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6206, top5_acc: 0.8377, loss_cls: 2.1493, loss: 2.1493 +2024-07-27 10:39:42,352 - pyskl - INFO - Epoch [143][2700/3746] lr: 5.800e-04, eta: 6:13:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6117, top5_acc: 0.8331, loss_cls: 2.1820, loss: 2.1820 +2024-07-27 10:41:04,521 - pyskl - INFO - Epoch [143][2800/3746] lr: 5.757e-04, eta: 6:12:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6141, top5_acc: 0.8334, loss_cls: 2.1768, loss: 2.1768 +2024-07-27 10:42:25,774 - pyskl - INFO - Epoch [143][2900/3746] lr: 5.715e-04, eta: 6:11:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6197, top5_acc: 0.8438, loss_cls: 2.1204, loss: 2.1204 +2024-07-27 10:43:47,030 - pyskl - INFO - Epoch [143][3000/3746] lr: 5.673e-04, eta: 6:09:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6294, top5_acc: 0.8481, loss_cls: 2.0872, loss: 2.0872 +2024-07-27 10:45:08,347 - pyskl - INFO - Epoch [143][3100/3746] lr: 5.631e-04, eta: 6:08:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6103, top5_acc: 0.8389, loss_cls: 2.1679, loss: 2.1679 +2024-07-27 10:46:30,062 - pyskl - INFO - Epoch [143][3200/3746] lr: 5.590e-04, eta: 6:07:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6048, top5_acc: 0.8353, loss_cls: 2.1696, loss: 2.1696 +2024-07-27 10:47:51,269 - pyskl - INFO - Epoch [143][3300/3746] lr: 5.548e-04, eta: 6:05:42, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6033, top5_acc: 0.8275, loss_cls: 2.2287, loss: 2.2287 +2024-07-27 10:49:13,284 - pyskl - INFO - Epoch [143][3400/3746] lr: 5.506e-04, eta: 6:04:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6158, top5_acc: 0.8397, loss_cls: 2.1216, loss: 2.1216 +2024-07-27 10:50:34,912 - pyskl - INFO - Epoch [143][3500/3746] lr: 5.465e-04, eta: 6:02:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6178, top5_acc: 0.8473, loss_cls: 2.1174, loss: 2.1174 +2024-07-27 10:51:56,121 - pyskl - INFO - Epoch [143][3600/3746] lr: 5.424e-04, eta: 6:01:35, time: 0.812, data_time: 0.001, memory: 15990, top1_acc: 0.6136, top5_acc: 0.8367, loss_cls: 2.1736, loss: 2.1736 +2024-07-27 10:53:18,173 - pyskl - INFO - Epoch [143][3700/3746] lr: 5.383e-04, eta: 6:00:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6162, top5_acc: 0.8345, loss_cls: 2.1475, loss: 2.1475 +2024-07-27 10:53:57,548 - pyskl - INFO - Saving checkpoint at 143 epochs +2024-07-27 10:55:49,595 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 10:55:50,265 - pyskl - INFO - +top1_acc 0.4552 +top5_acc 0.6994 +2024-07-27 10:55:50,265 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 10:55:50,307 - pyskl - INFO - +mean_acc 0.4550 +2024-07-27 10:55:50,311 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_142.pth was removed +2024-07-27 10:55:50,574 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_143.pth. +2024-07-27 10:55:50,575 - pyskl - INFO - Best top1_acc is 0.4552 at 143 epoch. +2024-07-27 10:55:50,588 - pyskl - INFO - Epoch(val) [143][309] top1_acc: 0.4552, top5_acc: 0.6994, mean_class_accuracy: 0.4550 +2024-07-27 10:59:41,616 - pyskl - INFO - Epoch [144][100/3746] lr: 5.323e-04, eta: 5:58:18, time: 2.310, data_time: 1.330, memory: 15990, top1_acc: 0.6241, top5_acc: 0.8525, loss_cls: 2.0721, loss: 2.0721 +2024-07-27 11:01:03,376 - pyskl - INFO - Epoch [144][200/3746] lr: 5.283e-04, eta: 5:56:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6305, top5_acc: 0.8505, loss_cls: 2.0799, loss: 2.0799 +2024-07-27 11:02:25,302 - pyskl - INFO - Epoch [144][300/3746] lr: 5.242e-04, eta: 5:55:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6341, top5_acc: 0.8488, loss_cls: 2.0844, loss: 2.0844 +2024-07-27 11:03:46,815 - pyskl - INFO - Epoch [144][400/3746] lr: 5.202e-04, eta: 5:54:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6309, top5_acc: 0.8431, loss_cls: 2.0960, loss: 2.0960 +2024-07-27 11:05:08,585 - pyskl - INFO - Epoch [144][500/3746] lr: 5.162e-04, eta: 5:52:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6292, top5_acc: 0.8481, loss_cls: 2.0951, loss: 2.0951 +2024-07-27 11:06:30,121 - pyskl - INFO - Epoch [144][600/3746] lr: 5.122e-04, eta: 5:51:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6220, top5_acc: 0.8475, loss_cls: 2.1051, loss: 2.1051 +2024-07-27 11:07:51,816 - pyskl - INFO - Epoch [144][700/3746] lr: 5.082e-04, eta: 5:50:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6241, top5_acc: 0.8427, loss_cls: 2.1028, loss: 2.1028 +2024-07-27 11:09:12,946 - pyskl - INFO - Epoch [144][800/3746] lr: 5.042e-04, eta: 5:48:42, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6220, top5_acc: 0.8427, loss_cls: 2.1126, loss: 2.1126 +2024-07-27 11:10:34,042 - pyskl - INFO - Epoch [144][900/3746] lr: 5.003e-04, eta: 5:47:20, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6341, top5_acc: 0.8505, loss_cls: 2.0677, loss: 2.0677 +2024-07-27 11:11:55,611 - pyskl - INFO - Epoch [144][1000/3746] lr: 4.964e-04, eta: 5:45:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6327, top5_acc: 0.8509, loss_cls: 2.0597, loss: 2.0597 +2024-07-27 11:13:17,316 - pyskl - INFO - Epoch [144][1100/3746] lr: 4.924e-04, eta: 5:44:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6361, top5_acc: 0.8467, loss_cls: 2.0576, loss: 2.0576 +2024-07-27 11:14:38,515 - pyskl - INFO - Epoch [144][1200/3746] lr: 4.885e-04, eta: 5:43:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6348, top5_acc: 0.8534, loss_cls: 2.0407, loss: 2.0407 +2024-07-27 11:16:00,113 - pyskl - INFO - Epoch [144][1300/3746] lr: 4.846e-04, eta: 5:41:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6231, top5_acc: 0.8394, loss_cls: 2.1155, loss: 2.1155 +2024-07-27 11:17:21,852 - pyskl - INFO - Epoch [144][1400/3746] lr: 4.808e-04, eta: 5:40:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6303, top5_acc: 0.8375, loss_cls: 2.1033, loss: 2.1033 +2024-07-27 11:18:43,217 - pyskl - INFO - Epoch [144][1500/3746] lr: 4.769e-04, eta: 5:39:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6270, top5_acc: 0.8478, loss_cls: 2.0929, loss: 2.0929 +2024-07-27 11:20:04,482 - pyskl - INFO - Epoch [144][1600/3746] lr: 4.731e-04, eta: 5:37:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6314, top5_acc: 0.8455, loss_cls: 2.0591, loss: 2.0591 +2024-07-27 11:21:25,854 - pyskl - INFO - Epoch [144][1700/3746] lr: 4.692e-04, eta: 5:36:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6302, top5_acc: 0.8459, loss_cls: 2.0904, loss: 2.0904 +2024-07-27 11:22:47,354 - pyskl - INFO - Epoch [144][1800/3746] lr: 4.654e-04, eta: 5:34:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6275, top5_acc: 0.8464, loss_cls: 2.1085, loss: 2.1085 +2024-07-27 11:24:08,978 - pyskl - INFO - Epoch [144][1900/3746] lr: 4.616e-04, eta: 5:33:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6344, top5_acc: 0.8500, loss_cls: 2.0545, loss: 2.0545 +2024-07-27 11:25:31,340 - pyskl - INFO - Epoch [144][2000/3746] lr: 4.578e-04, eta: 5:32:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6284, top5_acc: 0.8462, loss_cls: 2.0732, loss: 2.0732 +2024-07-27 11:26:53,020 - pyskl - INFO - Epoch [144][2100/3746] lr: 4.541e-04, eta: 5:30:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6291, top5_acc: 0.8472, loss_cls: 2.0866, loss: 2.0866 +2024-07-27 11:28:15,084 - pyskl - INFO - Epoch [144][2200/3746] lr: 4.503e-04, eta: 5:29:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6245, top5_acc: 0.8402, loss_cls: 2.1267, loss: 2.1267 +2024-07-27 11:29:37,407 - pyskl - INFO - Epoch [144][2300/3746] lr: 4.466e-04, eta: 5:28:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6231, top5_acc: 0.8456, loss_cls: 2.1012, loss: 2.1012 +2024-07-27 11:30:59,057 - pyskl - INFO - Epoch [144][2400/3746] lr: 4.429e-04, eta: 5:26:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6320, top5_acc: 0.8402, loss_cls: 2.0950, loss: 2.0950 +2024-07-27 11:32:21,258 - pyskl - INFO - Epoch [144][2500/3746] lr: 4.392e-04, eta: 5:25:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6300, top5_acc: 0.8461, loss_cls: 2.0904, loss: 2.0904 +2024-07-27 11:33:42,668 - pyskl - INFO - Epoch [144][2600/3746] lr: 4.355e-04, eta: 5:24:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6261, top5_acc: 0.8488, loss_cls: 2.0646, loss: 2.0646 +2024-07-27 11:35:04,023 - pyskl - INFO - Epoch [144][2700/3746] lr: 4.318e-04, eta: 5:22:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6269, top5_acc: 0.8516, loss_cls: 2.0758, loss: 2.0758 +2024-07-27 11:36:26,927 - pyskl - INFO - Epoch [144][2800/3746] lr: 4.281e-04, eta: 5:21:15, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6241, top5_acc: 0.8478, loss_cls: 2.0762, loss: 2.0762 +2024-07-27 11:37:48,499 - pyskl - INFO - Epoch [144][2900/3746] lr: 4.245e-04, eta: 5:19:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6195, top5_acc: 0.8409, loss_cls: 2.1352, loss: 2.1352 +2024-07-27 11:39:10,503 - pyskl - INFO - Epoch [144][3000/3746] lr: 4.209e-04, eta: 5:18:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6245, top5_acc: 0.8458, loss_cls: 2.1051, loss: 2.1051 +2024-07-27 11:40:32,106 - pyskl - INFO - Epoch [144][3100/3746] lr: 4.173e-04, eta: 5:17:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6203, top5_acc: 0.8473, loss_cls: 2.1205, loss: 2.1205 +2024-07-27 11:41:53,504 - pyskl - INFO - Epoch [144][3200/3746] lr: 4.137e-04, eta: 5:15:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6105, top5_acc: 0.8406, loss_cls: 2.1477, loss: 2.1477 +2024-07-27 11:43:15,265 - pyskl - INFO - Epoch [144][3300/3746] lr: 4.101e-04, eta: 5:14:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6211, top5_acc: 0.8353, loss_cls: 2.1101, loss: 2.1101 +2024-07-27 11:44:36,644 - pyskl - INFO - Epoch [144][3400/3746] lr: 4.065e-04, eta: 5:13:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6108, top5_acc: 0.8419, loss_cls: 2.1425, loss: 2.1425 +2024-07-27 11:45:57,805 - pyskl - INFO - Epoch [144][3500/3746] lr: 4.030e-04, eta: 5:11:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6308, top5_acc: 0.8420, loss_cls: 2.1074, loss: 2.1074 +2024-07-27 11:47:19,907 - pyskl - INFO - Epoch [144][3600/3746] lr: 3.994e-04, eta: 5:10:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6198, top5_acc: 0.8391, loss_cls: 2.1134, loss: 2.1134 +2024-07-27 11:48:41,522 - pyskl - INFO - Epoch [144][3700/3746] lr: 3.959e-04, eta: 5:08:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6306, top5_acc: 0.8447, loss_cls: 2.0679, loss: 2.0679 +2024-07-27 11:49:20,702 - pyskl - INFO - Saving checkpoint at 144 epochs +2024-07-27 11:51:13,079 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 11:51:13,745 - pyskl - INFO - +top1_acc 0.4575 +top5_acc 0.6985 +2024-07-27 11:51:13,745 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 11:51:13,785 - pyskl - INFO - +mean_acc 0.4572 +2024-07-27 11:51:13,790 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_143.pth was removed +2024-07-27 11:51:14,053 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_144.pth. +2024-07-27 11:51:14,053 - pyskl - INFO - Best top1_acc is 0.4575 at 144 epoch. +2024-07-27 11:51:14,066 - pyskl - INFO - Epoch(val) [144][309] top1_acc: 0.4575, top5_acc: 0.6985, mean_class_accuracy: 0.4572 +2024-07-27 11:55:04,673 - pyskl - INFO - Epoch [145][100/3746] lr: 3.908e-04, eta: 5:06:59, time: 2.306, data_time: 1.329, memory: 15990, top1_acc: 0.6480, top5_acc: 0.8603, loss_cls: 1.9888, loss: 1.9888 +2024-07-27 11:56:26,395 - pyskl - INFO - Epoch [145][200/3746] lr: 3.873e-04, eta: 5:05:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6472, top5_acc: 0.8634, loss_cls: 1.9954, loss: 1.9954 +2024-07-27 11:57:48,584 - pyskl - INFO - Epoch [145][300/3746] lr: 3.839e-04, eta: 5:04:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6328, top5_acc: 0.8509, loss_cls: 2.0529, loss: 2.0529 +2024-07-27 11:59:10,956 - pyskl - INFO - Epoch [145][400/3746] lr: 3.804e-04, eta: 5:02:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6455, top5_acc: 0.8577, loss_cls: 2.0091, loss: 2.0091 +2024-07-27 12:00:32,673 - pyskl - INFO - Epoch [145][500/3746] lr: 3.770e-04, eta: 5:01:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6406, top5_acc: 0.8561, loss_cls: 2.0370, loss: 2.0370 +2024-07-27 12:01:54,566 - pyskl - INFO - Epoch [145][600/3746] lr: 3.736e-04, eta: 5:00:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6469, top5_acc: 0.8559, loss_cls: 2.0025, loss: 2.0025 +2024-07-27 12:03:17,229 - pyskl - INFO - Epoch [145][700/3746] lr: 3.702e-04, eta: 4:58:45, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6388, top5_acc: 0.8505, loss_cls: 2.0431, loss: 2.0431 +2024-07-27 12:04:38,948 - pyskl - INFO - Epoch [145][800/3746] lr: 3.668e-04, eta: 4:57:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6450, top5_acc: 0.8555, loss_cls: 1.9944, loss: 1.9944 +2024-07-27 12:06:00,171 - pyskl - INFO - Epoch [145][900/3746] lr: 3.634e-04, eta: 4:56:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6347, top5_acc: 0.8520, loss_cls: 2.0196, loss: 2.0196 +2024-07-27 12:07:21,314 - pyskl - INFO - Epoch [145][1000/3746] lr: 3.600e-04, eta: 4:54:38, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6352, top5_acc: 0.8536, loss_cls: 2.0429, loss: 2.0429 +2024-07-27 12:08:42,563 - pyskl - INFO - Epoch [145][1100/3746] lr: 3.567e-04, eta: 4:53:16, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6408, top5_acc: 0.8539, loss_cls: 2.0428, loss: 2.0428 +2024-07-27 12:10:04,171 - pyskl - INFO - Epoch [145][1200/3746] lr: 3.534e-04, eta: 4:51:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6384, top5_acc: 0.8572, loss_cls: 2.0149, loss: 2.0149 +2024-07-27 12:11:25,702 - pyskl - INFO - Epoch [145][1300/3746] lr: 3.501e-04, eta: 4:50:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6339, top5_acc: 0.8566, loss_cls: 2.0243, loss: 2.0243 +2024-07-27 12:12:47,531 - pyskl - INFO - Epoch [145][1400/3746] lr: 3.468e-04, eta: 4:49:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6384, top5_acc: 0.8541, loss_cls: 2.0424, loss: 2.0424 +2024-07-27 12:14:08,987 - pyskl - INFO - Epoch [145][1500/3746] lr: 3.435e-04, eta: 4:47:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6428, top5_acc: 0.8533, loss_cls: 2.0091, loss: 2.0091 +2024-07-27 12:15:30,361 - pyskl - INFO - Epoch [145][1600/3746] lr: 3.402e-04, eta: 4:46:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6336, top5_acc: 0.8456, loss_cls: 2.0738, loss: 2.0738 +2024-07-27 12:16:51,702 - pyskl - INFO - Epoch [145][1700/3746] lr: 3.370e-04, eta: 4:45:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6297, top5_acc: 0.8477, loss_cls: 2.0748, loss: 2.0748 +2024-07-27 12:18:13,006 - pyskl - INFO - Epoch [145][1800/3746] lr: 3.337e-04, eta: 4:43:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6309, top5_acc: 0.8431, loss_cls: 2.0730, loss: 2.0730 +2024-07-27 12:19:34,540 - pyskl - INFO - Epoch [145][1900/3746] lr: 3.305e-04, eta: 4:42:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6383, top5_acc: 0.8462, loss_cls: 2.0608, loss: 2.0608 +2024-07-27 12:20:56,138 - pyskl - INFO - Epoch [145][2000/3746] lr: 3.273e-04, eta: 4:40:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6362, top5_acc: 0.8536, loss_cls: 2.0382, loss: 2.0382 +2024-07-27 12:22:17,969 - pyskl - INFO - Epoch [145][2100/3746] lr: 3.241e-04, eta: 4:39:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6378, top5_acc: 0.8578, loss_cls: 2.0339, loss: 2.0339 +2024-07-27 12:23:39,827 - pyskl - INFO - Epoch [145][2200/3746] lr: 3.210e-04, eta: 4:38:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6362, top5_acc: 0.8509, loss_cls: 2.0365, loss: 2.0365 +2024-07-27 12:25:01,585 - pyskl - INFO - Epoch [145][2300/3746] lr: 3.178e-04, eta: 4:36:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6358, top5_acc: 0.8500, loss_cls: 2.0517, loss: 2.0517 +2024-07-27 12:26:23,125 - pyskl - INFO - Epoch [145][2400/3746] lr: 3.147e-04, eta: 4:35:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6319, top5_acc: 0.8533, loss_cls: 2.0450, loss: 2.0450 +2024-07-27 12:27:45,402 - pyskl - INFO - Epoch [145][2500/3746] lr: 3.116e-04, eta: 4:34:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6356, top5_acc: 0.8541, loss_cls: 2.0308, loss: 2.0308 +2024-07-27 12:29:06,980 - pyskl - INFO - Epoch [145][2600/3746] lr: 3.084e-04, eta: 4:32:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6417, top5_acc: 0.8508, loss_cls: 2.0164, loss: 2.0164 +2024-07-27 12:30:28,487 - pyskl - INFO - Epoch [145][2700/3746] lr: 3.054e-04, eta: 4:31:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6400, top5_acc: 0.8520, loss_cls: 2.0464, loss: 2.0464 +2024-07-27 12:31:49,968 - pyskl - INFO - Epoch [145][2800/3746] lr: 3.023e-04, eta: 4:29:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6366, top5_acc: 0.8578, loss_cls: 2.0155, loss: 2.0155 +2024-07-27 12:33:12,307 - pyskl - INFO - Epoch [145][2900/3746] lr: 2.992e-04, eta: 4:28:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6350, top5_acc: 0.8497, loss_cls: 2.0580, loss: 2.0580 +2024-07-27 12:34:34,150 - pyskl - INFO - Epoch [145][3000/3746] lr: 2.962e-04, eta: 4:27:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6425, top5_acc: 0.8538, loss_cls: 2.0358, loss: 2.0358 +2024-07-27 12:35:55,797 - pyskl - INFO - Epoch [145][3100/3746] lr: 2.931e-04, eta: 4:25:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6317, top5_acc: 0.8441, loss_cls: 2.0775, loss: 2.0775 +2024-07-27 12:37:16,919 - pyskl - INFO - Epoch [145][3200/3746] lr: 2.901e-04, eta: 4:24:26, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6364, top5_acc: 0.8541, loss_cls: 2.0459, loss: 2.0459 +2024-07-27 12:38:38,394 - pyskl - INFO - Epoch [145][3300/3746] lr: 2.871e-04, eta: 4:23:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6322, top5_acc: 0.8425, loss_cls: 2.1107, loss: 2.1107 +2024-07-27 12:39:59,612 - pyskl - INFO - Epoch [145][3400/3746] lr: 2.841e-04, eta: 4:21:42, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6348, top5_acc: 0.8461, loss_cls: 2.0545, loss: 2.0545 +2024-07-27 12:41:20,825 - pyskl - INFO - Epoch [145][3500/3746] lr: 2.812e-04, eta: 4:20:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6361, top5_acc: 0.8561, loss_cls: 2.0435, loss: 2.0435 +2024-07-27 12:42:42,223 - pyskl - INFO - Epoch [145][3600/3746] lr: 2.782e-04, eta: 4:18:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6397, top5_acc: 0.8542, loss_cls: 2.0274, loss: 2.0274 +2024-07-27 12:44:03,434 - pyskl - INFO - Epoch [145][3700/3746] lr: 2.753e-04, eta: 4:17:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6230, top5_acc: 0.8458, loss_cls: 2.0959, loss: 2.0959 +2024-07-27 12:44:43,254 - pyskl - INFO - Saving checkpoint at 145 epochs +2024-07-27 12:46:35,932 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 12:46:36,603 - pyskl - INFO - +top1_acc 0.4547 +top5_acc 0.7009 +2024-07-27 12:46:36,603 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 12:46:36,644 - pyskl - INFO - +mean_acc 0.4544 +2024-07-27 12:46:36,656 - pyskl - INFO - Epoch(val) [145][309] top1_acc: 0.4547, top5_acc: 0.7009, mean_class_accuracy: 0.4544 +2024-07-27 12:50:26,035 - pyskl - INFO - Epoch [146][100/3746] lr: 2.710e-04, eta: 4:15:38, time: 2.294, data_time: 1.314, memory: 15990, top1_acc: 0.6411, top5_acc: 0.8605, loss_cls: 2.0165, loss: 2.0165 +2024-07-27 12:51:47,773 - pyskl - INFO - Epoch [146][200/3746] lr: 2.681e-04, eta: 4:14:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6500, top5_acc: 0.8595, loss_cls: 1.9701, loss: 1.9701 +2024-07-27 12:53:09,878 - pyskl - INFO - Epoch [146][300/3746] lr: 2.652e-04, eta: 4:12:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6538, top5_acc: 0.8555, loss_cls: 1.9865, loss: 1.9865 +2024-07-27 12:54:31,231 - pyskl - INFO - Epoch [146][400/3746] lr: 2.624e-04, eta: 4:11:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6512, top5_acc: 0.8633, loss_cls: 1.9722, loss: 1.9722 +2024-07-27 12:55:53,302 - pyskl - INFO - Epoch [146][500/3746] lr: 2.595e-04, eta: 4:10:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6519, top5_acc: 0.8664, loss_cls: 1.9687, loss: 1.9687 +2024-07-27 12:57:15,206 - pyskl - INFO - Epoch [146][600/3746] lr: 2.567e-04, eta: 4:08:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6550, top5_acc: 0.8583, loss_cls: 1.9910, loss: 1.9910 +2024-07-27 12:58:36,227 - pyskl - INFO - Epoch [146][700/3746] lr: 2.539e-04, eta: 4:07:24, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6467, top5_acc: 0.8561, loss_cls: 1.9954, loss: 1.9954 +2024-07-27 12:59:58,170 - pyskl - INFO - Epoch [146][800/3746] lr: 2.511e-04, eta: 4:06:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6533, top5_acc: 0.8556, loss_cls: 1.9926, loss: 1.9926 +2024-07-27 13:01:19,280 - pyskl - INFO - Epoch [146][900/3746] lr: 2.483e-04, eta: 4:04:39, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6369, top5_acc: 0.8589, loss_cls: 2.0347, loss: 2.0347 +2024-07-27 13:02:40,773 - pyskl - INFO - Epoch [146][1000/3746] lr: 2.455e-04, eta: 4:03:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6339, top5_acc: 0.8536, loss_cls: 2.0131, loss: 2.0131 +2024-07-27 13:04:02,544 - pyskl - INFO - Epoch [146][1100/3746] lr: 2.427e-04, eta: 4:01:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6327, top5_acc: 0.8489, loss_cls: 2.0364, loss: 2.0364 +2024-07-27 13:05:24,024 - pyskl - INFO - Epoch [146][1200/3746] lr: 2.400e-04, eta: 4:00:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6547, top5_acc: 0.8636, loss_cls: 1.9600, loss: 1.9600 +2024-07-27 13:06:45,404 - pyskl - INFO - Epoch [146][1300/3746] lr: 2.373e-04, eta: 3:59:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6370, top5_acc: 0.8502, loss_cls: 2.0505, loss: 2.0505 +2024-07-27 13:08:06,808 - pyskl - INFO - Epoch [146][1400/3746] lr: 2.345e-04, eta: 3:57:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6538, top5_acc: 0.8612, loss_cls: 1.9723, loss: 1.9723 +2024-07-27 13:09:28,412 - pyskl - INFO - Epoch [146][1500/3746] lr: 2.318e-04, eta: 3:56:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6438, top5_acc: 0.8552, loss_cls: 2.0187, loss: 2.0187 +2024-07-27 13:10:50,273 - pyskl - INFO - Epoch [146][1600/3746] lr: 2.292e-04, eta: 3:55:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6473, top5_acc: 0.8611, loss_cls: 1.9979, loss: 1.9979 +2024-07-27 13:12:11,533 - pyskl - INFO - Epoch [146][1700/3746] lr: 2.265e-04, eta: 3:53:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6530, top5_acc: 0.8605, loss_cls: 1.9872, loss: 1.9872 +2024-07-27 13:13:33,167 - pyskl - INFO - Epoch [146][1800/3746] lr: 2.239e-04, eta: 3:52:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6494, top5_acc: 0.8580, loss_cls: 2.0025, loss: 2.0025 +2024-07-27 13:14:54,761 - pyskl - INFO - Epoch [146][1900/3746] lr: 2.212e-04, eta: 3:50:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6494, top5_acc: 0.8609, loss_cls: 2.0079, loss: 2.0079 +2024-07-27 13:16:16,031 - pyskl - INFO - Epoch [146][2000/3746] lr: 2.186e-04, eta: 3:49:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6461, top5_acc: 0.8620, loss_cls: 1.9995, loss: 1.9995 +2024-07-27 13:17:37,402 - pyskl - INFO - Epoch [146][2100/3746] lr: 2.160e-04, eta: 3:48:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6425, top5_acc: 0.8566, loss_cls: 2.0205, loss: 2.0205 +2024-07-27 13:18:59,388 - pyskl - INFO - Epoch [146][2200/3746] lr: 2.134e-04, eta: 3:46:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6502, top5_acc: 0.8647, loss_cls: 1.9488, loss: 1.9488 +2024-07-27 13:20:21,118 - pyskl - INFO - Epoch [146][2300/3746] lr: 2.108e-04, eta: 3:45:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6506, top5_acc: 0.8614, loss_cls: 1.9668, loss: 1.9668 +2024-07-27 13:21:42,931 - pyskl - INFO - Epoch [146][2400/3746] lr: 2.083e-04, eta: 3:44:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6469, top5_acc: 0.8581, loss_cls: 2.0020, loss: 2.0020 +2024-07-27 13:23:04,967 - pyskl - INFO - Epoch [146][2500/3746] lr: 2.057e-04, eta: 3:42:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6423, top5_acc: 0.8536, loss_cls: 2.0191, loss: 2.0191 +2024-07-27 13:24:26,694 - pyskl - INFO - Epoch [146][2600/3746] lr: 2.032e-04, eta: 3:41:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6434, top5_acc: 0.8650, loss_cls: 1.9855, loss: 1.9855 +2024-07-27 13:25:48,407 - pyskl - INFO - Epoch [146][2700/3746] lr: 2.007e-04, eta: 3:39:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6448, top5_acc: 0.8503, loss_cls: 2.0306, loss: 2.0306 +2024-07-27 13:27:10,385 - pyskl - INFO - Epoch [146][2800/3746] lr: 1.982e-04, eta: 3:38:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6461, top5_acc: 0.8528, loss_cls: 1.9951, loss: 1.9951 +2024-07-27 13:28:33,504 - pyskl - INFO - Epoch [146][2900/3746] lr: 1.957e-04, eta: 3:37:12, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6509, top5_acc: 0.8644, loss_cls: 1.9854, loss: 1.9854 +2024-07-27 13:29:55,375 - pyskl - INFO - Epoch [146][3000/3746] lr: 1.933e-04, eta: 3:35:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6531, top5_acc: 0.8633, loss_cls: 1.9509, loss: 1.9509 +2024-07-27 13:31:18,289 - pyskl - INFO - Epoch [146][3100/3746] lr: 1.908e-04, eta: 3:34:28, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6461, top5_acc: 0.8611, loss_cls: 2.0012, loss: 2.0012 +2024-07-27 13:32:40,335 - pyskl - INFO - Epoch [146][3200/3746] lr: 1.884e-04, eta: 3:33:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6616, top5_acc: 0.8650, loss_cls: 1.9404, loss: 1.9404 +2024-07-27 13:34:01,800 - pyskl - INFO - Epoch [146][3300/3746] lr: 1.860e-04, eta: 3:31:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6539, top5_acc: 0.8548, loss_cls: 1.9907, loss: 1.9907 +2024-07-27 13:35:23,284 - pyskl - INFO - Epoch [146][3400/3746] lr: 1.836e-04, eta: 3:30:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6575, top5_acc: 0.8616, loss_cls: 1.9599, loss: 1.9599 +2024-07-27 13:36:45,176 - pyskl - INFO - Epoch [146][3500/3746] lr: 1.812e-04, eta: 3:28:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6431, top5_acc: 0.8584, loss_cls: 2.0049, loss: 2.0049 +2024-07-27 13:38:07,123 - pyskl - INFO - Epoch [146][3600/3746] lr: 1.788e-04, eta: 3:27:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6480, top5_acc: 0.8583, loss_cls: 1.9881, loss: 1.9881 +2024-07-27 13:39:28,783 - pyskl - INFO - Epoch [146][3700/3746] lr: 1.765e-04, eta: 3:26:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6517, top5_acc: 0.8641, loss_cls: 1.9774, loss: 1.9774 +2024-07-27 13:40:08,214 - pyskl - INFO - Saving checkpoint at 146 epochs +2024-07-27 13:42:01,015 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 13:42:01,678 - pyskl - INFO - +top1_acc 0.4560 +top5_acc 0.6999 +2024-07-27 13:42:01,679 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 13:42:01,719 - pyskl - INFO - +mean_acc 0.4557 +2024-07-27 13:42:01,732 - pyskl - INFO - Epoch(val) [146][309] top1_acc: 0.4560, top5_acc: 0.6999, mean_class_accuracy: 0.4557 +2024-07-27 13:45:53,911 - pyskl - INFO - Epoch [147][100/3746] lr: 1.730e-04, eta: 3:24:16, time: 2.322, data_time: 1.333, memory: 15990, top1_acc: 0.6586, top5_acc: 0.8623, loss_cls: 1.9489, loss: 1.9489 +2024-07-27 13:47:15,590 - pyskl - INFO - Epoch [147][200/3746] lr: 1.707e-04, eta: 3:22:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6600, top5_acc: 0.8669, loss_cls: 1.9330, loss: 1.9330 +2024-07-27 13:48:37,448 - pyskl - INFO - Epoch [147][300/3746] lr: 1.684e-04, eta: 3:21:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6567, top5_acc: 0.8577, loss_cls: 1.9572, loss: 1.9572 +2024-07-27 13:49:58,993 - pyskl - INFO - Epoch [147][400/3746] lr: 1.661e-04, eta: 3:20:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6531, top5_acc: 0.8669, loss_cls: 1.9305, loss: 1.9305 +2024-07-27 13:51:20,805 - pyskl - INFO - Epoch [147][500/3746] lr: 1.639e-04, eta: 3:18:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6506, top5_acc: 0.8633, loss_cls: 1.9746, loss: 1.9746 +2024-07-27 13:52:42,340 - pyskl - INFO - Epoch [147][600/3746] lr: 1.616e-04, eta: 3:17:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6611, top5_acc: 0.8683, loss_cls: 1.9330, loss: 1.9330 +2024-07-27 13:54:03,814 - pyskl - INFO - Epoch [147][700/3746] lr: 1.594e-04, eta: 3:16:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6500, top5_acc: 0.8544, loss_cls: 1.9952, loss: 1.9952 +2024-07-27 13:55:25,415 - pyskl - INFO - Epoch [147][800/3746] lr: 1.572e-04, eta: 3:14:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6645, top5_acc: 0.8692, loss_cls: 1.9151, loss: 1.9151 +2024-07-27 13:56:47,257 - pyskl - INFO - Epoch [147][900/3746] lr: 1.550e-04, eta: 3:13:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6622, top5_acc: 0.8602, loss_cls: 1.9704, loss: 1.9704 +2024-07-27 13:58:08,715 - pyskl - INFO - Epoch [147][1000/3746] lr: 1.528e-04, eta: 3:11:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6466, top5_acc: 0.8561, loss_cls: 2.0021, loss: 2.0021 +2024-07-27 13:59:30,140 - pyskl - INFO - Epoch [147][1100/3746] lr: 1.506e-04, eta: 3:10:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6595, top5_acc: 0.8673, loss_cls: 1.9585, loss: 1.9585 +2024-07-27 14:00:51,729 - pyskl - INFO - Epoch [147][1200/3746] lr: 1.484e-04, eta: 3:09:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6684, top5_acc: 0.8706, loss_cls: 1.9071, loss: 1.9071 +2024-07-27 14:02:13,246 - pyskl - INFO - Epoch [147][1300/3746] lr: 1.463e-04, eta: 3:07:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6570, top5_acc: 0.8666, loss_cls: 1.9207, loss: 1.9207 +2024-07-27 14:03:34,605 - pyskl - INFO - Epoch [147][1400/3746] lr: 1.442e-04, eta: 3:06:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6594, top5_acc: 0.8642, loss_cls: 1.9642, loss: 1.9642 +2024-07-27 14:04:56,624 - pyskl - INFO - Epoch [147][1500/3746] lr: 1.420e-04, eta: 3:05:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6483, top5_acc: 0.8605, loss_cls: 1.9913, loss: 1.9913 +2024-07-27 14:06:18,324 - pyskl - INFO - Epoch [147][1600/3746] lr: 1.399e-04, eta: 3:03:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6480, top5_acc: 0.8620, loss_cls: 1.9873, loss: 1.9873 +2024-07-27 14:07:39,294 - pyskl - INFO - Epoch [147][1700/3746] lr: 1.379e-04, eta: 3:02:19, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6573, top5_acc: 0.8616, loss_cls: 1.9597, loss: 1.9597 +2024-07-27 14:09:01,076 - pyskl - INFO - Epoch [147][1800/3746] lr: 1.358e-04, eta: 3:00:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6605, top5_acc: 0.8642, loss_cls: 1.9447, loss: 1.9447 +2024-07-27 14:10:22,747 - pyskl - INFO - Epoch [147][1900/3746] lr: 1.337e-04, eta: 2:59:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6583, top5_acc: 0.8639, loss_cls: 1.9541, loss: 1.9541 +2024-07-27 14:11:44,324 - pyskl - INFO - Epoch [147][2000/3746] lr: 1.317e-04, eta: 2:58:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6597, top5_acc: 0.8658, loss_cls: 1.9443, loss: 1.9443 +2024-07-27 14:13:05,802 - pyskl - INFO - Epoch [147][2100/3746] lr: 1.297e-04, eta: 2:56:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6575, top5_acc: 0.8605, loss_cls: 1.9604, loss: 1.9604 +2024-07-27 14:14:27,711 - pyskl - INFO - Epoch [147][2200/3746] lr: 1.277e-04, eta: 2:55:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6528, top5_acc: 0.8645, loss_cls: 1.9466, loss: 1.9466 +2024-07-27 14:15:49,095 - pyskl - INFO - Epoch [147][2300/3746] lr: 1.257e-04, eta: 2:54:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6598, top5_acc: 0.8647, loss_cls: 1.9357, loss: 1.9357 +2024-07-27 14:17:11,440 - pyskl - INFO - Epoch [147][2400/3746] lr: 1.237e-04, eta: 2:52:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6561, top5_acc: 0.8609, loss_cls: 1.9433, loss: 1.9433 +2024-07-27 14:18:32,785 - pyskl - INFO - Epoch [147][2500/3746] lr: 1.218e-04, eta: 2:51:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6559, top5_acc: 0.8609, loss_cls: 1.9592, loss: 1.9592 +2024-07-27 14:19:54,213 - pyskl - INFO - Epoch [147][2600/3746] lr: 1.198e-04, eta: 2:49:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6464, top5_acc: 0.8591, loss_cls: 1.9774, loss: 1.9774 +2024-07-27 14:21:15,381 - pyskl - INFO - Epoch [147][2700/3746] lr: 1.179e-04, eta: 2:48:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6500, top5_acc: 0.8584, loss_cls: 1.9788, loss: 1.9788 +2024-07-27 14:22:36,644 - pyskl - INFO - Epoch [147][2800/3746] lr: 1.160e-04, eta: 2:47:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6491, top5_acc: 0.8647, loss_cls: 1.9781, loss: 1.9781 +2024-07-27 14:23:59,781 - pyskl - INFO - Epoch [147][2900/3746] lr: 1.141e-04, eta: 2:45:50, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6592, top5_acc: 0.8677, loss_cls: 1.9179, loss: 1.9179 +2024-07-27 14:25:21,351 - pyskl - INFO - Epoch [147][3000/3746] lr: 1.122e-04, eta: 2:44:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6473, top5_acc: 0.8591, loss_cls: 1.9819, loss: 1.9819 +2024-07-27 14:26:43,732 - pyskl - INFO - Epoch [147][3100/3746] lr: 1.103e-04, eta: 2:43:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6553, top5_acc: 0.8594, loss_cls: 1.9642, loss: 1.9642 +2024-07-27 14:28:05,452 - pyskl - INFO - Epoch [147][3200/3746] lr: 1.085e-04, eta: 2:41:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6553, top5_acc: 0.8628, loss_cls: 1.9550, loss: 1.9550 +2024-07-27 14:29:27,325 - pyskl - INFO - Epoch [147][3300/3746] lr: 1.067e-04, eta: 2:40:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6531, top5_acc: 0.8677, loss_cls: 1.9469, loss: 1.9469 +2024-07-27 14:30:48,707 - pyskl - INFO - Epoch [147][3400/3746] lr: 1.048e-04, eta: 2:38:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6619, top5_acc: 0.8700, loss_cls: 1.9343, loss: 1.9343 +2024-07-27 14:32:10,350 - pyskl - INFO - Epoch [147][3500/3746] lr: 1.030e-04, eta: 2:37:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6606, top5_acc: 0.8720, loss_cls: 1.9295, loss: 1.9295 +2024-07-27 14:33:32,077 - pyskl - INFO - Epoch [147][3600/3746] lr: 1.013e-04, eta: 2:36:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6538, top5_acc: 0.8598, loss_cls: 1.9780, loss: 1.9780 +2024-07-27 14:34:53,385 - pyskl - INFO - Epoch [147][3700/3746] lr: 9.949e-05, eta: 2:34:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6564, top5_acc: 0.8722, loss_cls: 1.9194, loss: 1.9194 +2024-07-27 14:35:32,662 - pyskl - INFO - Saving checkpoint at 147 epochs +2024-07-27 14:37:25,682 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 14:37:26,350 - pyskl - INFO - +top1_acc 0.4579 +top5_acc 0.7030 +2024-07-27 14:37:26,350 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 14:37:26,391 - pyskl - INFO - +mean_acc 0.4576 +2024-07-27 14:37:26,395 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_144.pth was removed +2024-07-27 14:37:26,652 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_147.pth. +2024-07-27 14:37:26,653 - pyskl - INFO - Best top1_acc is 0.4579 at 147 epoch. +2024-07-27 14:37:26,673 - pyskl - INFO - Epoch(val) [147][309] top1_acc: 0.4579, top5_acc: 0.7030, mean_class_accuracy: 0.4576 +2024-07-27 14:41:14,762 - pyskl - INFO - Epoch [148][100/3746] lr: 9.693e-05, eta: 2:32:53, time: 2.281, data_time: 1.291, memory: 15990, top1_acc: 0.6548, top5_acc: 0.8714, loss_cls: 1.9321, loss: 1.9321 +2024-07-27 14:42:36,812 - pyskl - INFO - Epoch [148][200/3746] lr: 9.520e-05, eta: 2:31:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6691, top5_acc: 0.8695, loss_cls: 1.9172, loss: 1.9172 +2024-07-27 14:43:58,762 - pyskl - INFO - Epoch [148][300/3746] lr: 9.348e-05, eta: 2:30:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6642, top5_acc: 0.8678, loss_cls: 1.9156, loss: 1.9156 +2024-07-27 14:45:20,364 - pyskl - INFO - Epoch [148][400/3746] lr: 9.178e-05, eta: 2:28:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6589, top5_acc: 0.8580, loss_cls: 1.9633, loss: 1.9633 +2024-07-27 14:46:42,067 - pyskl - INFO - Epoch [148][500/3746] lr: 9.010e-05, eta: 2:27:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6606, top5_acc: 0.8602, loss_cls: 1.9420, loss: 1.9420 +2024-07-27 14:48:04,050 - pyskl - INFO - Epoch [148][600/3746] lr: 8.843e-05, eta: 2:26:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6575, top5_acc: 0.8678, loss_cls: 1.9282, loss: 1.9282 +2024-07-27 14:49:25,995 - pyskl - INFO - Epoch [148][700/3746] lr: 8.678e-05, eta: 2:24:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6602, top5_acc: 0.8709, loss_cls: 1.9224, loss: 1.9224 +2024-07-27 14:50:47,775 - pyskl - INFO - Epoch [148][800/3746] lr: 8.514e-05, eta: 2:23:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6619, top5_acc: 0.8692, loss_cls: 1.9141, loss: 1.9141 +2024-07-27 14:52:09,377 - pyskl - INFO - Epoch [148][900/3746] lr: 8.351e-05, eta: 2:21:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6586, top5_acc: 0.8681, loss_cls: 1.9263, loss: 1.9263 +2024-07-27 14:53:30,753 - pyskl - INFO - Epoch [148][1000/3746] lr: 8.191e-05, eta: 2:20:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6623, top5_acc: 0.8714, loss_cls: 1.9327, loss: 1.9327 +2024-07-27 14:54:51,835 - pyskl - INFO - Epoch [148][1100/3746] lr: 8.031e-05, eta: 2:19:10, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6603, top5_acc: 0.8691, loss_cls: 1.9121, loss: 1.9121 +2024-07-27 14:56:13,807 - pyskl - INFO - Epoch [148][1200/3746] lr: 7.874e-05, eta: 2:17:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6569, top5_acc: 0.8664, loss_cls: 1.9469, loss: 1.9469 +2024-07-27 14:57:34,978 - pyskl - INFO - Epoch [148][1300/3746] lr: 7.718e-05, eta: 2:16:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6572, top5_acc: 0.8686, loss_cls: 1.9385, loss: 1.9385 +2024-07-27 14:58:56,235 - pyskl - INFO - Epoch [148][1400/3746] lr: 7.563e-05, eta: 2:15:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6586, top5_acc: 0.8642, loss_cls: 1.9518, loss: 1.9518 +2024-07-27 15:00:17,462 - pyskl - INFO - Epoch [148][1500/3746] lr: 7.410e-05, eta: 2:13:40, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6644, top5_acc: 0.8608, loss_cls: 1.9510, loss: 1.9510 +2024-07-27 15:01:39,783 - pyskl - INFO - Epoch [148][1600/3746] lr: 7.259e-05, eta: 2:12:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6614, top5_acc: 0.8692, loss_cls: 1.9380, loss: 1.9380 +2024-07-27 15:03:01,059 - pyskl - INFO - Epoch [148][1700/3746] lr: 7.109e-05, eta: 2:10:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6581, top5_acc: 0.8688, loss_cls: 1.9277, loss: 1.9277 +2024-07-27 15:04:22,860 - pyskl - INFO - Epoch [148][1800/3746] lr: 6.961e-05, eta: 2:09:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6697, top5_acc: 0.8808, loss_cls: 1.8745, loss: 1.8745 +2024-07-27 15:05:44,393 - pyskl - INFO - Epoch [148][1900/3746] lr: 6.814e-05, eta: 2:08:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6583, top5_acc: 0.8675, loss_cls: 1.9347, loss: 1.9347 +2024-07-27 15:07:06,083 - pyskl - INFO - Epoch [148][2000/3746] lr: 6.669e-05, eta: 2:06:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6661, top5_acc: 0.8648, loss_cls: 1.9043, loss: 1.9043 +2024-07-27 15:08:28,210 - pyskl - INFO - Epoch [148][2100/3746] lr: 6.526e-05, eta: 2:05:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6584, top5_acc: 0.8639, loss_cls: 1.9347, loss: 1.9347 +2024-07-27 15:09:50,081 - pyskl - INFO - Epoch [148][2200/3746] lr: 6.384e-05, eta: 2:04:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6533, top5_acc: 0.8678, loss_cls: 1.9346, loss: 1.9346 +2024-07-27 15:11:12,224 - pyskl - INFO - Epoch [148][2300/3746] lr: 6.243e-05, eta: 2:02:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6609, top5_acc: 0.8664, loss_cls: 1.9154, loss: 1.9154 +2024-07-27 15:12:34,293 - pyskl - INFO - Epoch [148][2400/3746] lr: 6.104e-05, eta: 2:01:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6591, top5_acc: 0.8655, loss_cls: 1.9332, loss: 1.9332 +2024-07-27 15:13:56,137 - pyskl - INFO - Epoch [148][2500/3746] lr: 5.967e-05, eta: 1:59:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6519, top5_acc: 0.8662, loss_cls: 1.9504, loss: 1.9504 +2024-07-27 15:15:17,526 - pyskl - INFO - Epoch [148][2600/3746] lr: 5.831e-05, eta: 1:58:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6677, top5_acc: 0.8641, loss_cls: 1.9400, loss: 1.9400 +2024-07-27 15:16:39,555 - pyskl - INFO - Epoch [148][2700/3746] lr: 5.697e-05, eta: 1:57:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6600, top5_acc: 0.8652, loss_cls: 1.9417, loss: 1.9417 +2024-07-27 15:18:00,984 - pyskl - INFO - Epoch [148][2800/3746] lr: 5.564e-05, eta: 1:55:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6681, top5_acc: 0.8642, loss_cls: 1.9094, loss: 1.9094 +2024-07-27 15:19:23,065 - pyskl - INFO - Epoch [148][2900/3746] lr: 5.433e-05, eta: 1:54:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6727, top5_acc: 0.8734, loss_cls: 1.8712, loss: 1.8712 +2024-07-27 15:20:45,050 - pyskl - INFO - Epoch [148][3000/3746] lr: 5.304e-05, eta: 1:53:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6797, top5_acc: 0.8775, loss_cls: 1.8570, loss: 1.8570 +2024-07-27 15:22:07,268 - pyskl - INFO - Epoch [148][3100/3746] lr: 5.176e-05, eta: 1:51:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6656, top5_acc: 0.8712, loss_cls: 1.8933, loss: 1.8933 +2024-07-27 15:23:28,642 - pyskl - INFO - Epoch [148][3200/3746] lr: 5.050e-05, eta: 1:50:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6602, top5_acc: 0.8664, loss_cls: 1.9304, loss: 1.9304 +2024-07-27 15:24:49,667 - pyskl - INFO - Epoch [148][3300/3746] lr: 4.925e-05, eta: 1:48:57, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6592, top5_acc: 0.8677, loss_cls: 1.9446, loss: 1.9446 +2024-07-27 15:26:11,982 - pyskl - INFO - Epoch [148][3400/3746] lr: 4.801e-05, eta: 1:47:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6614, top5_acc: 0.8681, loss_cls: 1.9330, loss: 1.9330 +2024-07-27 15:27:33,371 - pyskl - INFO - Epoch [148][3500/3746] lr: 4.680e-05, eta: 1:46:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6647, top5_acc: 0.8694, loss_cls: 1.9150, loss: 1.9150 +2024-07-27 15:28:55,199 - pyskl - INFO - Epoch [148][3600/3746] lr: 4.560e-05, eta: 1:44:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6566, top5_acc: 0.8638, loss_cls: 1.9532, loss: 1.9532 +2024-07-27 15:30:17,147 - pyskl - INFO - Epoch [148][3700/3746] lr: 4.441e-05, eta: 1:43:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6675, top5_acc: 0.8716, loss_cls: 1.9120, loss: 1.9120 +2024-07-27 15:30:56,598 - pyskl - INFO - Saving checkpoint at 148 epochs +2024-07-27 15:32:49,501 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 15:32:50,162 - pyskl - INFO - +top1_acc 0.4574 +top5_acc 0.7022 +2024-07-27 15:32:50,162 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 15:32:50,206 - pyskl - INFO - +mean_acc 0.4571 +2024-07-27 15:32:50,218 - pyskl - INFO - Epoch(val) [148][309] top1_acc: 0.4574, top5_acc: 0.7022, mean_class_accuracy: 0.4571 +2024-07-27 15:36:37,113 - pyskl - INFO - Epoch [149][100/3746] lr: 4.271e-05, eta: 1:41:29, time: 2.269, data_time: 1.285, memory: 15990, top1_acc: 0.6633, top5_acc: 0.8708, loss_cls: 1.9208, loss: 1.9208 +2024-07-27 15:37:58,481 - pyskl - INFO - Epoch [149][200/3746] lr: 4.156e-05, eta: 1:40:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6659, top5_acc: 0.8759, loss_cls: 1.9054, loss: 1.9054 +2024-07-27 15:39:20,668 - pyskl - INFO - Epoch [149][300/3746] lr: 4.043e-05, eta: 1:38:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6687, top5_acc: 0.8709, loss_cls: 1.8944, loss: 1.8944 +2024-07-27 15:40:42,970 - pyskl - INFO - Epoch [149][400/3746] lr: 3.931e-05, eta: 1:37:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6500, top5_acc: 0.8620, loss_cls: 1.9848, loss: 1.9848 +2024-07-27 15:42:04,994 - pyskl - INFO - Epoch [149][500/3746] lr: 3.821e-05, eta: 1:35:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6719, top5_acc: 0.8725, loss_cls: 1.8904, loss: 1.8904 +2024-07-27 15:43:26,643 - pyskl - INFO - Epoch [149][600/3746] lr: 3.713e-05, eta: 1:34:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6647, top5_acc: 0.8678, loss_cls: 1.9089, loss: 1.9089 +2024-07-27 15:44:48,248 - pyskl - INFO - Epoch [149][700/3746] lr: 3.606e-05, eta: 1:33:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6663, top5_acc: 0.8675, loss_cls: 1.9064, loss: 1.9064 +2024-07-27 15:46:10,568 - pyskl - INFO - Epoch [149][800/3746] lr: 3.500e-05, eta: 1:31:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6622, top5_acc: 0.8666, loss_cls: 1.9049, loss: 1.9049 +2024-07-27 15:47:31,607 - pyskl - INFO - Epoch [149][900/3746] lr: 3.397e-05, eta: 1:30:30, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6706, top5_acc: 0.8762, loss_cls: 1.8869, loss: 1.8869 +2024-07-27 15:48:53,440 - pyskl - INFO - Epoch [149][1000/3746] lr: 3.294e-05, eta: 1:29:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6633, top5_acc: 0.8739, loss_cls: 1.8975, loss: 1.8975 +2024-07-27 15:50:15,408 - pyskl - INFO - Epoch [149][1100/3746] lr: 3.194e-05, eta: 1:27:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6703, top5_acc: 0.8694, loss_cls: 1.8830, loss: 1.8830 +2024-07-27 15:51:37,170 - pyskl - INFO - Epoch [149][1200/3746] lr: 3.095e-05, eta: 1:26:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6692, top5_acc: 0.8716, loss_cls: 1.9015, loss: 1.9015 +2024-07-27 15:52:58,965 - pyskl - INFO - Epoch [149][1300/3746] lr: 2.997e-05, eta: 1:25:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6677, top5_acc: 0.8722, loss_cls: 1.9007, loss: 1.9007 +2024-07-27 15:54:20,623 - pyskl - INFO - Epoch [149][1400/3746] lr: 2.901e-05, eta: 1:23:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6734, top5_acc: 0.8714, loss_cls: 1.8826, loss: 1.8826 +2024-07-27 15:55:41,860 - pyskl - INFO - Epoch [149][1500/3746] lr: 2.807e-05, eta: 1:22:16, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6667, top5_acc: 0.8744, loss_cls: 1.8793, loss: 1.8793 +2024-07-27 15:57:03,503 - pyskl - INFO - Epoch [149][1600/3746] lr: 2.714e-05, eta: 1:20:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6589, top5_acc: 0.8689, loss_cls: 1.9239, loss: 1.9239 +2024-07-27 15:58:24,882 - pyskl - INFO - Epoch [149][1700/3746] lr: 2.622e-05, eta: 1:19:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6580, top5_acc: 0.8725, loss_cls: 1.9153, loss: 1.9153 +2024-07-27 15:59:46,387 - pyskl - INFO - Epoch [149][1800/3746] lr: 2.533e-05, eta: 1:18:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6650, top5_acc: 0.8669, loss_cls: 1.9272, loss: 1.9272 +2024-07-27 16:01:07,971 - pyskl - INFO - Epoch [149][1900/3746] lr: 2.444e-05, eta: 1:16:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6667, top5_acc: 0.8714, loss_cls: 1.9138, loss: 1.9138 +2024-07-27 16:02:29,569 - pyskl - INFO - Epoch [149][2000/3746] lr: 2.358e-05, eta: 1:15:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6750, top5_acc: 0.8744, loss_cls: 1.8800, loss: 1.8800 +2024-07-27 16:03:51,154 - pyskl - INFO - Epoch [149][2100/3746] lr: 2.273e-05, eta: 1:14:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6622, top5_acc: 0.8727, loss_cls: 1.9112, loss: 1.9112 +2024-07-27 16:05:12,767 - pyskl - INFO - Epoch [149][2200/3746] lr: 2.189e-05, eta: 1:12:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6706, top5_acc: 0.8708, loss_cls: 1.9232, loss: 1.9232 +2024-07-27 16:06:34,415 - pyskl - INFO - Epoch [149][2300/3746] lr: 2.107e-05, eta: 1:11:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6698, top5_acc: 0.8678, loss_cls: 1.9177, loss: 1.9177 +2024-07-27 16:07:56,755 - pyskl - INFO - Epoch [149][2400/3746] lr: 2.027e-05, eta: 1:09:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6708, top5_acc: 0.8725, loss_cls: 1.8680, loss: 1.8680 +2024-07-27 16:09:18,236 - pyskl - INFO - Epoch [149][2500/3746] lr: 1.948e-05, eta: 1:08:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6666, top5_acc: 0.8692, loss_cls: 1.9149, loss: 1.9149 +2024-07-27 16:10:39,972 - pyskl - INFO - Epoch [149][2600/3746] lr: 1.871e-05, eta: 1:07:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6625, top5_acc: 0.8673, loss_cls: 1.9147, loss: 1.9147 +2024-07-27 16:12:01,293 - pyskl - INFO - Epoch [149][2700/3746] lr: 1.795e-05, eta: 1:05:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6586, top5_acc: 0.8614, loss_cls: 1.9468, loss: 1.9468 +2024-07-27 16:13:22,993 - pyskl - INFO - Epoch [149][2800/3746] lr: 1.721e-05, eta: 1:04:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6758, top5_acc: 0.8781, loss_cls: 1.8695, loss: 1.8695 +2024-07-27 16:14:44,455 - pyskl - INFO - Epoch [149][2900/3746] lr: 1.649e-05, eta: 1:03:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6686, top5_acc: 0.8689, loss_cls: 1.9137, loss: 1.9137 +2024-07-27 16:16:07,178 - pyskl - INFO - Epoch [149][3000/3746] lr: 1.578e-05, eta: 1:01:40, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6686, top5_acc: 0.8670, loss_cls: 1.9165, loss: 1.9165 +2024-07-27 16:17:28,766 - pyskl - INFO - Epoch [149][3100/3746] lr: 1.508e-05, eta: 1:00:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6737, top5_acc: 0.8692, loss_cls: 1.8994, loss: 1.8994 +2024-07-27 16:18:51,071 - pyskl - INFO - Epoch [149][3200/3746] lr: 1.440e-05, eta: 0:58:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6636, top5_acc: 0.8669, loss_cls: 1.9222, loss: 1.9222 +2024-07-27 16:20:13,101 - pyskl - INFO - Epoch [149][3300/3746] lr: 1.374e-05, eta: 0:57:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6739, top5_acc: 0.8830, loss_cls: 1.8667, loss: 1.8667 +2024-07-27 16:21:34,093 - pyskl - INFO - Epoch [149][3400/3746] lr: 1.309e-05, eta: 0:56:10, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6677, top5_acc: 0.8664, loss_cls: 1.9377, loss: 1.9377 +2024-07-27 16:22:55,519 - pyskl - INFO - Epoch [149][3500/3746] lr: 1.246e-05, eta: 0:54:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6673, top5_acc: 0.8711, loss_cls: 1.9130, loss: 1.9130 +2024-07-27 16:24:16,852 - pyskl - INFO - Epoch [149][3600/3746] lr: 1.184e-05, eta: 0:53:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6613, top5_acc: 0.8700, loss_cls: 1.9177, loss: 1.9177 +2024-07-27 16:25:37,996 - pyskl - INFO - Epoch [149][3700/3746] lr: 1.124e-05, eta: 0:52:03, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6677, top5_acc: 0.8689, loss_cls: 1.9034, loss: 1.9034 +2024-07-27 16:26:17,133 - pyskl - INFO - Saving checkpoint at 149 epochs +2024-07-27 16:28:09,618 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 16:28:10,295 - pyskl - INFO - +top1_acc 0.4564 +top5_acc 0.7016 +2024-07-27 16:28:10,295 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 16:28:10,334 - pyskl - INFO - +mean_acc 0.4561 +2024-07-27 16:28:10,346 - pyskl - INFO - Epoch(val) [149][309] top1_acc: 0.4564, top5_acc: 0.7016, mean_class_accuracy: 0.4561 +2024-07-27 16:31:55,809 - pyskl - INFO - Epoch [150][100/3746] lr: 1.039e-05, eta: 0:50:04, time: 2.255, data_time: 1.278, memory: 15990, top1_acc: 0.6752, top5_acc: 0.8738, loss_cls: 1.8780, loss: 1.8780 +2024-07-27 16:33:17,264 - pyskl - INFO - Epoch [150][200/3746] lr: 9.832e-06, eta: 0:48:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6734, top5_acc: 0.8716, loss_cls: 1.8688, loss: 1.8688 +2024-07-27 16:34:39,336 - pyskl - INFO - Epoch [150][300/3746] lr: 9.285e-06, eta: 0:47:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6797, top5_acc: 0.8739, loss_cls: 1.8757, loss: 1.8757 +2024-07-27 16:36:00,893 - pyskl - INFO - Epoch [150][400/3746] lr: 8.754e-06, eta: 0:45:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6758, top5_acc: 0.8769, loss_cls: 1.8763, loss: 1.8763 +2024-07-27 16:37:23,364 - pyskl - INFO - Epoch [150][500/3746] lr: 8.239e-06, eta: 0:44:34, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6619, top5_acc: 0.8633, loss_cls: 1.9334, loss: 1.9334 +2024-07-27 16:38:44,990 - pyskl - INFO - Epoch [150][600/3746] lr: 7.739e-06, eta: 0:43:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6717, top5_acc: 0.8784, loss_cls: 1.8705, loss: 1.8705 +2024-07-27 16:40:06,968 - pyskl - INFO - Epoch [150][700/3746] lr: 7.255e-06, eta: 0:41:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6623, top5_acc: 0.8689, loss_cls: 1.9035, loss: 1.9035 +2024-07-27 16:41:28,275 - pyskl - INFO - Epoch [150][800/3746] lr: 6.787e-06, eta: 0:40:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6773, top5_acc: 0.8767, loss_cls: 1.8627, loss: 1.8627 +2024-07-27 16:42:49,474 - pyskl - INFO - Epoch [150][900/3746] lr: 6.334e-06, eta: 0:39:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6567, top5_acc: 0.8688, loss_cls: 1.9348, loss: 1.9348 +2024-07-27 16:44:10,901 - pyskl - INFO - Epoch [150][1000/3746] lr: 5.897e-06, eta: 0:37:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6584, top5_acc: 0.8642, loss_cls: 1.9274, loss: 1.9274 +2024-07-27 16:45:32,251 - pyskl - INFO - Epoch [150][1100/3746] lr: 5.475e-06, eta: 0:36:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6816, top5_acc: 0.8802, loss_cls: 1.8509, loss: 1.8509 +2024-07-27 16:46:53,582 - pyskl - INFO - Epoch [150][1200/3746] lr: 5.070e-06, eta: 0:34:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6673, top5_acc: 0.8722, loss_cls: 1.9026, loss: 1.9026 +2024-07-27 16:48:14,970 - pyskl - INFO - Epoch [150][1300/3746] lr: 4.679e-06, eta: 0:33:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6633, top5_acc: 0.8659, loss_cls: 1.9199, loss: 1.9199 +2024-07-27 16:49:36,305 - pyskl - INFO - Epoch [150][1400/3746] lr: 4.305e-06, eta: 0:32:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6722, top5_acc: 0.8708, loss_cls: 1.9109, loss: 1.9109 +2024-07-27 16:50:57,805 - pyskl - INFO - Epoch [150][1500/3746] lr: 3.946e-06, eta: 0:30:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6670, top5_acc: 0.8675, loss_cls: 1.8921, loss: 1.8921 +2024-07-27 16:52:18,615 - pyskl - INFO - Epoch [150][1600/3746] lr: 3.602e-06, eta: 0:29:28, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.6706, top5_acc: 0.8784, loss_cls: 1.8946, loss: 1.8946 +2024-07-27 16:53:40,141 - pyskl - INFO - Epoch [150][1700/3746] lr: 3.275e-06, eta: 0:28:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6695, top5_acc: 0.8725, loss_cls: 1.8806, loss: 1.8806 +2024-07-27 16:55:02,273 - pyskl - INFO - Epoch [150][1800/3746] lr: 2.962e-06, eta: 0:26:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6616, top5_acc: 0.8638, loss_cls: 1.9260, loss: 1.9260 +2024-07-27 16:56:23,581 - pyskl - INFO - Epoch [150][1900/3746] lr: 2.666e-06, eta: 0:25:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6694, top5_acc: 0.8712, loss_cls: 1.8782, loss: 1.8782 +2024-07-27 16:57:44,885 - pyskl - INFO - Epoch [150][2000/3746] lr: 2.385e-06, eta: 0:23:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6581, top5_acc: 0.8648, loss_cls: 1.9557, loss: 1.9557 +2024-07-27 16:59:06,300 - pyskl - INFO - Epoch [150][2100/3746] lr: 2.120e-06, eta: 0:22:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6669, top5_acc: 0.8697, loss_cls: 1.9137, loss: 1.9137 +2024-07-27 17:00:28,082 - pyskl - INFO - Epoch [150][2200/3746] lr: 1.870e-06, eta: 0:21:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6666, top5_acc: 0.8733, loss_cls: 1.8954, loss: 1.8954 +2024-07-27 17:01:49,860 - pyskl - INFO - Epoch [150][2300/3746] lr: 1.636e-06, eta: 0:19:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6653, top5_acc: 0.8723, loss_cls: 1.9175, loss: 1.9175 +2024-07-27 17:03:11,503 - pyskl - INFO - Epoch [150][2400/3746] lr: 1.418e-06, eta: 0:18:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6594, top5_acc: 0.8705, loss_cls: 1.8996, loss: 1.8996 +2024-07-27 17:04:33,147 - pyskl - INFO - Epoch [150][2500/3746] lr: 1.215e-06, eta: 0:17:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6733, top5_acc: 0.8758, loss_cls: 1.8693, loss: 1.8693 +2024-07-27 17:05:54,678 - pyskl - INFO - Epoch [150][2600/3746] lr: 1.028e-06, eta: 0:15:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6742, top5_acc: 0.8780, loss_cls: 1.8614, loss: 1.8614 +2024-07-27 17:07:15,976 - pyskl - INFO - Epoch [150][2700/3746] lr: 8.567e-07, eta: 0:14:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6736, top5_acc: 0.8686, loss_cls: 1.8935, loss: 1.8935 +2024-07-27 17:08:37,095 - pyskl - INFO - Epoch [150][2800/3746] lr: 7.008e-07, eta: 0:12:59, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6652, top5_acc: 0.8661, loss_cls: 1.9154, loss: 1.9154 +2024-07-27 17:09:58,543 - pyskl - INFO - Epoch [150][2900/3746] lr: 5.606e-07, eta: 0:11:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6698, top5_acc: 0.8712, loss_cls: 1.8708, loss: 1.8708 +2024-07-27 17:11:20,512 - pyskl - INFO - Epoch [150][3000/3746] lr: 4.361e-07, eta: 0:10:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6623, top5_acc: 0.8680, loss_cls: 1.9166, loss: 1.9166 +2024-07-27 17:12:42,633 - pyskl - INFO - Epoch [150][3100/3746] lr: 3.271e-07, eta: 0:08:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6737, top5_acc: 0.8766, loss_cls: 1.8857, loss: 1.8857 +2024-07-27 17:14:04,470 - pyskl - INFO - Epoch [150][3200/3746] lr: 2.338e-07, eta: 0:07:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6658, top5_acc: 0.8733, loss_cls: 1.9145, loss: 1.9145 +2024-07-27 17:15:26,302 - pyskl - INFO - Epoch [150][3300/3746] lr: 1.561e-07, eta: 0:06:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6611, top5_acc: 0.8681, loss_cls: 1.9251, loss: 1.9251 +2024-07-27 17:16:47,870 - pyskl - INFO - Epoch [150][3400/3746] lr: 9.410e-08, eta: 0:04:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6683, top5_acc: 0.8733, loss_cls: 1.8973, loss: 1.8973 +2024-07-27 17:18:09,803 - pyskl - INFO - Epoch [150][3500/3746] lr: 4.768e-08, eta: 0:03:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6641, top5_acc: 0.8750, loss_cls: 1.8911, loss: 1.8911 +2024-07-27 17:19:31,267 - pyskl - INFO - Epoch [150][3600/3746] lr: 1.689e-08, eta: 0:02:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6645, top5_acc: 0.8672, loss_cls: 1.8982, loss: 1.8982 +2024-07-27 17:20:53,423 - pyskl - INFO - Epoch [150][3700/3746] lr: 1.726e-09, eta: 0:00:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6687, top5_acc: 0.8742, loss_cls: 1.9074, loss: 1.9074 +2024-07-27 17:21:32,446 - pyskl - INFO - Saving checkpoint at 150 epochs +2024-07-27 17:23:23,658 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 17:23:24,328 - pyskl - INFO - +top1_acc 0.4571 +top5_acc 0.7026 +2024-07-27 17:23:24,328 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 17:23:24,368 - pyskl - INFO - +mean_acc 0.4568 +2024-07-27 17:23:24,380 - pyskl - INFO - Epoch(val) [150][309] top1_acc: 0.4571, top5_acc: 0.7026, mean_class_accuracy: 0.4568 +2024-07-27 17:23:38,750 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-07-27 17:35:45,084 - pyskl - INFO - Testing results of the last checkpoint +2024-07-27 17:35:45,085 - pyskl - INFO - top1_acc: 0.4690 +2024-07-27 17:35:45,085 - pyskl - INFO - top5_acc: 0.7108 +2024-07-27 17:35:45,085 - pyskl - INFO - mean_class_accuracy: 0.4688 +2024-07-27 17:35:45,085 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/k400/b_2/best_top1_acc_epoch_147.pth +2024-07-27 17:47:42,754 - pyskl - INFO - Testing results of the best checkpoint +2024-07-27 17:47:42,754 - pyskl - INFO - top1_acc: 0.4696 +2024-07-27 17:47:42,754 - pyskl - INFO - top5_acc: 0.7110 +2024-07-27 17:47:42,754 - pyskl - INFO - mean_class_accuracy: 0.4694 diff --git a/k400/b_2/20240722_022809.log.json b/k400/b_2/20240722_022809.log.json new file mode 100644 index 0000000000000000000000000000000000000000..8f76535cf66f3d6b556d408d800f57fecaf98058 --- /dev/null +++ b/k400/b_2/20240722_022809.log.json @@ -0,0 +1,5701 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 684653118, "config_name": "b_2.py", "work_dir": "b_2", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.1, "memory": 15990, "data_time": 1.26841, "top1_acc": 0.00812, "top5_acc": 0.03797, "loss_cls": 6.40056, "loss": 6.40056, "time": 1.98045} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.1, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.01719, "top5_acc": 0.06688, "loss_cls": 6.24755, "loss": 6.24755, "time": 0.70374} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.01766, "top5_acc": 0.08297, "loss_cls": 6.07732, "loss": 6.07732, "time": 0.70261} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.02656, "top5_acc": 0.10297, "loss_cls": 5.94429, "loss": 5.94429, "time": 0.70237} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.02703, "top5_acc": 0.105, "loss_cls": 5.89172, "loss": 5.89172, "time": 0.70178} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.03719, "top5_acc": 0.12578, "loss_cls": 5.81772, "loss": 5.81772, "time": 0.70312} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.03594, "top5_acc": 0.12766, "loss_cls": 5.79327, "loss": 5.79327, "time": 0.69943} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.1, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.03875, "top5_acc": 0.13328, "loss_cls": 5.73886, "loss": 5.73886, "time": 0.70463} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.04266, "top5_acc": 0.14203, "loss_cls": 5.70352, "loss": 5.70352, "time": 0.69792} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.1, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.03891, "top5_acc": 0.14844, "loss_cls": 5.66409, "loss": 5.66409, "time": 0.69982} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.04328, "top5_acc": 0.15297, "loss_cls": 5.64479, "loss": 5.64479, "time": 0.70264} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.04969, "top5_acc": 0.15891, "loss_cls": 5.61486, "loss": 5.61486, "time": 0.70241} +{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.04781, "top5_acc": 0.17375, "loss_cls": 5.58653, "loss": 5.58653, "time": 0.70086} +{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.05531, "top5_acc": 0.17766, "loss_cls": 5.55873, "loss": 5.55873, "time": 0.70065} +{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.05875, "top5_acc": 0.18844, "loss_cls": 5.50315, "loss": 5.50315, "time": 0.70178} +{"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.06094, "top5_acc": 0.19609, "loss_cls": 5.47228, "loss": 5.47228, "time": 0.70018} +{"mode": "train", "epoch": 1, "iter": 1700, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.06375, "top5_acc": 0.20062, "loss_cls": 5.44663, "loss": 5.44663, "time": 0.70062} +{"mode": "train", "epoch": 1, "iter": 1800, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.065, "top5_acc": 0.20547, "loss_cls": 5.45021, "loss": 5.45021, "time": 0.70533} +{"mode": "train", "epoch": 1, "iter": 1900, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.06219, "top5_acc": 0.21109, "loss_cls": 5.43254, "loss": 5.43254, "time": 0.70029} +{"mode": "train", "epoch": 1, "iter": 2000, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.07312, "top5_acc": 0.215, "loss_cls": 5.40961, "loss": 5.40961, "time": 0.70296} +{"mode": "train", "epoch": 1, "iter": 2100, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.07219, "top5_acc": 0.21016, "loss_cls": 5.39515, "loss": 5.39515, "time": 0.72373} +{"mode": "train", "epoch": 1, "iter": 2200, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.07312, "top5_acc": 0.22375, "loss_cls": 5.34645, "loss": 5.34645, "time": 0.7185} +{"mode": "train", "epoch": 1, "iter": 2300, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.08125, "top5_acc": 0.23641, "loss_cls": 5.3223, "loss": 5.3223, "time": 0.70263} +{"mode": "train", "epoch": 1, "iter": 2400, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.07531, "top5_acc": 0.22766, "loss_cls": 5.34159, "loss": 5.34159, "time": 0.70071} +{"mode": "train", "epoch": 1, "iter": 2500, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.08031, "top5_acc": 0.23406, "loss_cls": 5.31162, "loss": 5.31162, "time": 0.70244} +{"mode": "train", "epoch": 1, "iter": 2600, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.07984, "top5_acc": 0.23344, "loss_cls": 5.32138, "loss": 5.32138, "time": 0.69895} +{"mode": "train", "epoch": 1, "iter": 2700, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.08438, "top5_acc": 0.23578, "loss_cls": 5.2828, "loss": 5.2828, "time": 0.70231} +{"mode": "train", "epoch": 1, "iter": 2800, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.08641, "top5_acc": 0.2425, "loss_cls": 5.26408, "loss": 5.26408, "time": 0.70043} +{"mode": "train", "epoch": 1, "iter": 2900, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.08609, "top5_acc": 0.24281, "loss_cls": 5.27441, "loss": 5.27441, "time": 0.69991} +{"mode": "train", "epoch": 1, "iter": 3000, "lr": 0.09999, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.08516, "top5_acc": 0.24781, "loss_cls": 5.23613, "loss": 5.23613, "time": 0.70145} +{"mode": "train", "epoch": 1, "iter": 3100, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.09734, "top5_acc": 0.26375, "loss_cls": 5.18617, "loss": 5.18617, "time": 0.69708} +{"mode": "train", "epoch": 1, "iter": 3200, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.09781, "top5_acc": 0.26734, "loss_cls": 5.17791, "loss": 5.17791, "time": 0.70012} +{"mode": "train", "epoch": 1, "iter": 3300, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.09766, "top5_acc": 0.25875, "loss_cls": 5.19417, "loss": 5.19417, "time": 0.69814} +{"mode": "train", "epoch": 1, "iter": 3400, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.09516, "top5_acc": 0.25797, "loss_cls": 5.17866, "loss": 5.17866, "time": 0.70211} +{"mode": "train", "epoch": 1, "iter": 3500, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.10328, "top5_acc": 0.27875, "loss_cls": 5.12674, "loss": 5.12674, "time": 0.70274} +{"mode": "train", "epoch": 1, "iter": 3600, "lr": 0.09999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.09281, "top5_acc": 0.26828, "loss_cls": 5.16414, "loss": 5.16414, "time": 0.70262} +{"mode": "train", "epoch": 1, "iter": 3700, "lr": 0.09999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.09984, "top5_acc": 0.27406, "loss_cls": 5.12241, "loss": 5.12241, "time": 0.70233} +{"mode": "val", "epoch": 1, "iter": 309, "lr": 0.09999, "top1_acc": 0.07, "top5_acc": 0.20544, "mean_class_accuracy": 0.07003} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.09999, "memory": 15990, "data_time": 1.26853, "top1_acc": 0.10562, "top5_acc": 0.28781, "loss_cls": 5.10045, "loss": 5.10045, "time": 1.97688} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.11266, "top5_acc": 0.28906, "loss_cls": 5.07278, "loss": 5.07278, "time": 0.69936} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.10547, "top5_acc": 0.28766, "loss_cls": 5.08803, "loss": 5.08803, "time": 0.7032} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.1125, "top5_acc": 0.28672, "loss_cls": 5.06075, "loss": 5.06075, "time": 0.69903} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.11359, "top5_acc": 0.29781, "loss_cls": 5.04049, "loss": 5.04049, "time": 0.69958} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.11844, "top5_acc": 0.30406, "loss_cls": 5.03336, "loss": 5.03336, "time": 0.69572} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.12109, "top5_acc": 0.30516, "loss_cls": 5.02367, "loss": 5.02367, "time": 0.70092} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.12094, "top5_acc": 0.31391, "loss_cls": 5.01154, "loss": 5.01154, "time": 0.69964} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.12656, "top5_acc": 0.30016, "loss_cls": 4.97613, "loss": 4.97613, "time": 0.69812} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.11531, "top5_acc": 0.31281, "loss_cls": 4.9843, "loss": 4.9843, "time": 0.69976} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.12375, "top5_acc": 0.31984, "loss_cls": 4.96456, "loss": 4.96456, "time": 0.69898} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.12891, "top5_acc": 0.32203, "loss_cls": 4.96194, "loss": 4.96194, "time": 0.69971} +{"mode": "train", "epoch": 2, "iter": 1300, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.13047, "top5_acc": 0.31875, "loss_cls": 4.97054, "loss": 4.97054, "time": 0.69944} +{"mode": "train", "epoch": 2, "iter": 1400, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.12781, "top5_acc": 0.32578, "loss_cls": 4.91881, "loss": 4.91881, "time": 0.69957} +{"mode": "train", "epoch": 2, "iter": 1500, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.12953, "top5_acc": 0.32234, "loss_cls": 4.92637, "loss": 4.92637, "time": 0.69979} +{"mode": "train", "epoch": 2, "iter": 1600, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.13578, "top5_acc": 0.33234, "loss_cls": 4.93337, "loss": 4.93337, "time": 0.69798} +{"mode": "train", "epoch": 2, "iter": 1700, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.13281, "top5_acc": 0.32828, "loss_cls": 4.88102, "loss": 4.88102, "time": 0.69892} +{"mode": "train", "epoch": 2, "iter": 1800, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.13391, "top5_acc": 0.33234, "loss_cls": 4.88014, "loss": 4.88014, "time": 0.69889} +{"mode": "train", "epoch": 2, "iter": 1900, "lr": 0.09998, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.14312, "top5_acc": 0.33938, "loss_cls": 4.86792, "loss": 4.86792, "time": 0.69816} +{"mode": "train", "epoch": 2, "iter": 2000, "lr": 0.09997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.14203, "top5_acc": 0.34219, "loss_cls": 4.88583, "loss": 4.88583, "time": 0.69868} +{"mode": "train", "epoch": 2, "iter": 2100, "lr": 0.09997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.13984, "top5_acc": 0.35031, "loss_cls": 4.8448, "loss": 4.8448, "time": 0.69919} +{"mode": "train", "epoch": 2, "iter": 2200, "lr": 0.09997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.1425, "top5_acc": 0.33953, "loss_cls": 4.84614, "loss": 4.84614, "time": 0.69698} +{"mode": "train", "epoch": 2, "iter": 2300, "lr": 0.09997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.13531, "top5_acc": 0.3425, "loss_cls": 4.86067, "loss": 4.86067, "time": 0.69785} +{"mode": "train", "epoch": 2, "iter": 2400, "lr": 0.09997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.13125, "top5_acc": 0.33797, "loss_cls": 4.90143, "loss": 4.90143, "time": 0.6981} +{"mode": "train", "epoch": 2, "iter": 2500, "lr": 0.09997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.145, "top5_acc": 0.35141, "loss_cls": 4.80526, "loss": 4.80526, "time": 0.69694} +{"mode": "train", "epoch": 2, "iter": 2600, "lr": 0.09997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.14641, "top5_acc": 0.34781, "loss_cls": 4.82764, "loss": 4.82764, "time": 0.6964} +{"mode": "train", "epoch": 2, "iter": 2700, "lr": 0.09997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.14656, "top5_acc": 0.34672, "loss_cls": 4.85666, "loss": 4.85666, "time": 0.70072} +{"mode": "train", "epoch": 2, "iter": 2800, "lr": 0.09997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.13719, "top5_acc": 0.33625, "loss_cls": 4.85341, "loss": 4.85341, "time": 0.69842} +{"mode": "train", "epoch": 2, "iter": 2900, "lr": 0.09997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.14531, "top5_acc": 0.35391, "loss_cls": 4.81543, "loss": 4.81543, "time": 0.699} +{"mode": "train", "epoch": 2, "iter": 3000, "lr": 0.09996, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.14656, "top5_acc": 0.35875, "loss_cls": 4.78695, "loss": 4.78695, "time": 0.69685} +{"mode": "train", "epoch": 2, "iter": 3100, "lr": 0.09996, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15281, "top5_acc": 0.35969, "loss_cls": 4.78415, "loss": 4.78415, "time": 0.70183} +{"mode": "train", "epoch": 2, "iter": 3200, "lr": 0.09996, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.14109, "top5_acc": 0.35516, "loss_cls": 4.82184, "loss": 4.82184, "time": 0.69831} +{"mode": "train", "epoch": 2, "iter": 3300, "lr": 0.09996, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.15344, "top5_acc": 0.35844, "loss_cls": 4.78856, "loss": 4.78856, "time": 0.70102} +{"mode": "train", "epoch": 2, "iter": 3400, "lr": 0.09996, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.14859, "top5_acc": 0.35844, "loss_cls": 4.78901, "loss": 4.78901, "time": 0.70183} +{"mode": "train", "epoch": 2, "iter": 3500, "lr": 0.09996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.15734, "top5_acc": 0.37625, "loss_cls": 4.70694, "loss": 4.70694, "time": 0.70034} +{"mode": "train", "epoch": 2, "iter": 3600, "lr": 0.09996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.14609, "top5_acc": 0.36562, "loss_cls": 4.78279, "loss": 4.78279, "time": 0.69989} +{"mode": "train", "epoch": 2, "iter": 3700, "lr": 0.09996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.15313, "top5_acc": 0.37156, "loss_cls": 4.72251, "loss": 4.72251, "time": 0.69844} +{"mode": "val", "epoch": 2, "iter": 309, "lr": 0.09996, "top1_acc": 0.10166, "top5_acc": 0.27676, "mean_class_accuracy": 0.10156} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.09995, "memory": 15990, "data_time": 1.275, "top1_acc": 0.1625, "top5_acc": 0.37328, "loss_cls": 4.68198, "loss": 4.68198, "time": 1.98219} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.09995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16219, "top5_acc": 0.38, "loss_cls": 4.70198, "loss": 4.70198, "time": 0.7003} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.09995, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.14797, "top5_acc": 0.36516, "loss_cls": 4.76074, "loss": 4.76074, "time": 0.70407} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.09995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15094, "top5_acc": 0.36469, "loss_cls": 4.75508, "loss": 4.75508, "time": 0.69818} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.09995, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.15141, "top5_acc": 0.37016, "loss_cls": 4.71622, "loss": 4.71622, "time": 0.70176} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.09995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15812, "top5_acc": 0.37469, "loss_cls": 4.7049, "loss": 4.7049, "time": 0.69922} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.09995, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.15984, "top5_acc": 0.37828, "loss_cls": 4.68406, "loss": 4.68406, "time": 0.69904} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.09995, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16688, "top5_acc": 0.38016, "loss_cls": 4.69928, "loss": 4.69928, "time": 0.69792} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.09994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16953, "top5_acc": 0.3825, "loss_cls": 4.67697, "loss": 4.67697, "time": 0.6976} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.09994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16109, "top5_acc": 0.38094, "loss_cls": 4.71514, "loss": 4.71514, "time": 0.70195} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.09994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16594, "top5_acc": 0.38234, "loss_cls": 4.68065, "loss": 4.68065, "time": 0.69773} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.09994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.1625, "top5_acc": 0.37766, "loss_cls": 4.68447, "loss": 4.68447, "time": 0.70048} +{"mode": "train", "epoch": 3, "iter": 1300, "lr": 0.09994, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17141, "top5_acc": 0.38641, "loss_cls": 4.66212, "loss": 4.66212, "time": 0.69776} +{"mode": "train", "epoch": 3, "iter": 1400, "lr": 0.09994, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16891, "top5_acc": 0.39125, "loss_cls": 4.64947, "loss": 4.64947, "time": 0.69887} +{"mode": "train", "epoch": 3, "iter": 1500, "lr": 0.09994, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.16828, "top5_acc": 0.39234, "loss_cls": 4.64003, "loss": 4.64003, "time": 0.69988} +{"mode": "train", "epoch": 3, "iter": 1600, "lr": 0.09994, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17156, "top5_acc": 0.38922, "loss_cls": 4.61389, "loss": 4.61389, "time": 0.70182} +{"mode": "train", "epoch": 3, "iter": 1700, "lr": 0.09993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17469, "top5_acc": 0.39344, "loss_cls": 4.62975, "loss": 4.62975, "time": 0.69963} +{"mode": "train", "epoch": 3, "iter": 1800, "lr": 0.09993, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.16953, "top5_acc": 0.3975, "loss_cls": 4.607, "loss": 4.607, "time": 0.69649} +{"mode": "train", "epoch": 3, "iter": 1900, "lr": 0.09993, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16766, "top5_acc": 0.39516, "loss_cls": 4.65206, "loss": 4.65206, "time": 0.69761} +{"mode": "train", "epoch": 3, "iter": 2000, "lr": 0.09993, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.17797, "top5_acc": 0.39797, "loss_cls": 4.57587, "loss": 4.57587, "time": 0.69684} +{"mode": "train", "epoch": 3, "iter": 2100, "lr": 0.09993, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17938, "top5_acc": 0.39469, "loss_cls": 4.61363, "loss": 4.61363, "time": 0.69858} +{"mode": "train", "epoch": 3, "iter": 2200, "lr": 0.09993, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17656, "top5_acc": 0.39312, "loss_cls": 4.58674, "loss": 4.58674, "time": 0.69972} +{"mode": "train", "epoch": 3, "iter": 2300, "lr": 0.09993, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.18094, "top5_acc": 0.40719, "loss_cls": 4.56475, "loss": 4.56475, "time": 0.6976} +{"mode": "train", "epoch": 3, "iter": 2400, "lr": 0.09992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.1825, "top5_acc": 0.39938, "loss_cls": 4.60855, "loss": 4.60855, "time": 0.70071} +{"mode": "train", "epoch": 3, "iter": 2500, "lr": 0.09992, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17891, "top5_acc": 0.41203, "loss_cls": 4.57764, "loss": 4.57764, "time": 0.69736} +{"mode": "train", "epoch": 3, "iter": 2600, "lr": 0.09992, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.17812, "top5_acc": 0.39656, "loss_cls": 4.57769, "loss": 4.57769, "time": 0.69729} +{"mode": "train", "epoch": 3, "iter": 2700, "lr": 0.09992, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.17766, "top5_acc": 0.41016, "loss_cls": 4.56342, "loss": 4.56342, "time": 0.69857} +{"mode": "train", "epoch": 3, "iter": 2800, "lr": 0.09992, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.185, "top5_acc": 0.39984, "loss_cls": 4.55772, "loss": 4.55772, "time": 0.70098} +{"mode": "train", "epoch": 3, "iter": 2900, "lr": 0.09992, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18969, "top5_acc": 0.41578, "loss_cls": 4.52593, "loss": 4.52593, "time": 0.69825} +{"mode": "train", "epoch": 3, "iter": 3000, "lr": 0.09991, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18281, "top5_acc": 0.40656, "loss_cls": 4.52913, "loss": 4.52913, "time": 0.69997} +{"mode": "train", "epoch": 3, "iter": 3100, "lr": 0.09991, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17219, "top5_acc": 0.39969, "loss_cls": 4.6176, "loss": 4.6176, "time": 0.69874} +{"mode": "train", "epoch": 3, "iter": 3200, "lr": 0.09991, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17969, "top5_acc": 0.4075, "loss_cls": 4.55984, "loss": 4.55984, "time": 0.69691} +{"mode": "train", "epoch": 3, "iter": 3300, "lr": 0.09991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19203, "top5_acc": 0.42156, "loss_cls": 4.51902, "loss": 4.51902, "time": 0.69855} +{"mode": "train", "epoch": 3, "iter": 3400, "lr": 0.09991, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.17703, "top5_acc": 0.39938, "loss_cls": 4.57153, "loss": 4.57153, "time": 0.69939} +{"mode": "train", "epoch": 3, "iter": 3500, "lr": 0.09991, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19406, "top5_acc": 0.42203, "loss_cls": 4.50343, "loss": 4.50343, "time": 0.70213} +{"mode": "train", "epoch": 3, "iter": 3600, "lr": 0.0999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18109, "top5_acc": 0.41031, "loss_cls": 4.54925, "loss": 4.54925, "time": 0.70043} +{"mode": "train", "epoch": 3, "iter": 3700, "lr": 0.0999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18578, "top5_acc": 0.40797, "loss_cls": 4.5788, "loss": 4.5788, "time": 0.69912} +{"mode": "val", "epoch": 3, "iter": 309, "lr": 0.0999, "top1_acc": 0.10475, "top5_acc": 0.27412, "mean_class_accuracy": 0.10445} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.0999, "memory": 15990, "data_time": 1.25599, "top1_acc": 0.1875, "top5_acc": 0.42516, "loss_cls": 4.48839, "loss": 4.48839, "time": 1.96351} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.0999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19078, "top5_acc": 0.42062, "loss_cls": 4.50851, "loss": 4.50851, "time": 0.70209} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.0999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17734, "top5_acc": 0.40797, "loss_cls": 4.55901, "loss": 4.55901, "time": 0.7028} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.09989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19391, "top5_acc": 0.41922, "loss_cls": 4.49068, "loss": 4.49068, "time": 0.70022} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.09989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.1875, "top5_acc": 0.41094, "loss_cls": 4.53603, "loss": 4.53603, "time": 0.69959} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.09989, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18234, "top5_acc": 0.40719, "loss_cls": 4.56266, "loss": 4.56266, "time": 0.70356} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.09989, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19406, "top5_acc": 0.41516, "loss_cls": 4.52168, "loss": 4.52168, "time": 0.70156} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.09989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18938, "top5_acc": 0.415, "loss_cls": 4.49659, "loss": 4.49659, "time": 0.70343} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.09988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19828, "top5_acc": 0.43703, "loss_cls": 4.4402, "loss": 4.4402, "time": 0.70437} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.09988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19203, "top5_acc": 0.41266, "loss_cls": 4.50277, "loss": 4.50277, "time": 0.70031} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.09988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19812, "top5_acc": 0.43125, "loss_cls": 4.47157, "loss": 4.47157, "time": 0.70106} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.09988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20453, "top5_acc": 0.43953, "loss_cls": 4.42636, "loss": 4.42636, "time": 0.70496} +{"mode": "train", "epoch": 4, "iter": 1300, "lr": 0.09988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18453, "top5_acc": 0.42094, "loss_cls": 4.49443, "loss": 4.49443, "time": 0.70556} +{"mode": "train", "epoch": 4, "iter": 1400, "lr": 0.09988, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19938, "top5_acc": 0.42609, "loss_cls": 4.48402, "loss": 4.48402, "time": 0.70569} +{"mode": "train", "epoch": 4, "iter": 1500, "lr": 0.09987, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19938, "top5_acc": 0.42344, "loss_cls": 4.49443, "loss": 4.49443, "time": 0.70108} +{"mode": "train", "epoch": 4, "iter": 1600, "lr": 0.09987, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19844, "top5_acc": 0.42891, "loss_cls": 4.50733, "loss": 4.50733, "time": 0.7003} +{"mode": "train", "epoch": 4, "iter": 1700, "lr": 0.09987, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19734, "top5_acc": 0.42, "loss_cls": 4.50251, "loss": 4.50251, "time": 0.7022} +{"mode": "train", "epoch": 4, "iter": 1800, "lr": 0.09987, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19859, "top5_acc": 0.43594, "loss_cls": 4.44061, "loss": 4.44061, "time": 0.70452} +{"mode": "train", "epoch": 4, "iter": 1900, "lr": 0.09987, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20484, "top5_acc": 0.42922, "loss_cls": 4.41025, "loss": 4.41025, "time": 0.70153} +{"mode": "train", "epoch": 4, "iter": 2000, "lr": 0.09986, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19281, "top5_acc": 0.42344, "loss_cls": 4.46729, "loss": 4.46729, "time": 0.69995} +{"mode": "train", "epoch": 4, "iter": 2100, "lr": 0.09986, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19875, "top5_acc": 0.43359, "loss_cls": 4.45897, "loss": 4.45897, "time": 0.70042} +{"mode": "train", "epoch": 4, "iter": 2200, "lr": 0.09986, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20297, "top5_acc": 0.43188, "loss_cls": 4.45821, "loss": 4.45821, "time": 0.6999} +{"mode": "train", "epoch": 4, "iter": 2300, "lr": 0.09986, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2, "top5_acc": 0.43234, "loss_cls": 4.45356, "loss": 4.45356, "time": 0.70501} +{"mode": "train", "epoch": 4, "iter": 2400, "lr": 0.09985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19859, "top5_acc": 0.43266, "loss_cls": 4.43597, "loss": 4.43597, "time": 0.70234} +{"mode": "train", "epoch": 4, "iter": 2500, "lr": 0.09985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20328, "top5_acc": 0.44281, "loss_cls": 4.4147, "loss": 4.4147, "time": 0.69972} +{"mode": "train", "epoch": 4, "iter": 2600, "lr": 0.09985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20531, "top5_acc": 0.43844, "loss_cls": 4.41148, "loss": 4.41148, "time": 0.70305} +{"mode": "train", "epoch": 4, "iter": 2700, "lr": 0.09985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19609, "top5_acc": 0.42812, "loss_cls": 4.46583, "loss": 4.46583, "time": 0.70198} +{"mode": "train", "epoch": 4, "iter": 2800, "lr": 0.09985, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20578, "top5_acc": 0.43859, "loss_cls": 4.42111, "loss": 4.42111, "time": 0.69836} +{"mode": "train", "epoch": 4, "iter": 2900, "lr": 0.09984, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20078, "top5_acc": 0.44234, "loss_cls": 4.43646, "loss": 4.43646, "time": 0.70035} +{"mode": "train", "epoch": 4, "iter": 3000, "lr": 0.09984, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.20562, "top5_acc": 0.44203, "loss_cls": 4.3915, "loss": 4.3915, "time": 0.69863} +{"mode": "train", "epoch": 4, "iter": 3100, "lr": 0.09984, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19844, "top5_acc": 0.43969, "loss_cls": 4.39987, "loss": 4.39987, "time": 0.70127} +{"mode": "train", "epoch": 4, "iter": 3200, "lr": 0.09984, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19797, "top5_acc": 0.44438, "loss_cls": 4.39793, "loss": 4.39793, "time": 0.69995} +{"mode": "train", "epoch": 4, "iter": 3300, "lr": 0.09983, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20797, "top5_acc": 0.43844, "loss_cls": 4.40598, "loss": 4.40598, "time": 0.70171} +{"mode": "train", "epoch": 4, "iter": 3400, "lr": 0.09983, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20562, "top5_acc": 0.43828, "loss_cls": 4.39326, "loss": 4.39326, "time": 0.69835} +{"mode": "train", "epoch": 4, "iter": 3500, "lr": 0.09983, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20438, "top5_acc": 0.43844, "loss_cls": 4.41871, "loss": 4.41871, "time": 0.7067} +{"mode": "train", "epoch": 4, "iter": 3600, "lr": 0.09983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20172, "top5_acc": 0.43562, "loss_cls": 4.39651, "loss": 4.39651, "time": 0.70229} +{"mode": "train", "epoch": 4, "iter": 3700, "lr": 0.09983, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20453, "top5_acc": 0.43266, "loss_cls": 4.40522, "loss": 4.40522, "time": 0.70314} +{"mode": "val", "epoch": 4, "iter": 309, "lr": 0.09982, "top1_acc": 0.13914, "top5_acc": 0.32148, "mean_class_accuracy": 0.13882} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.09982, "memory": 15990, "data_time": 1.26689, "top1_acc": 0.21641, "top5_acc": 0.44797, "loss_cls": 4.34789, "loss": 4.34789, "time": 1.97427} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.09982, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2075, "top5_acc": 0.43641, "loss_cls": 4.39427, "loss": 4.39427, "time": 0.70214} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.09982, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20781, "top5_acc": 0.45031, "loss_cls": 4.36938, "loss": 4.36938, "time": 0.70334} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.09982, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21859, "top5_acc": 0.45141, "loss_cls": 4.33248, "loss": 4.33248, "time": 0.70014} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.09981, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20922, "top5_acc": 0.44016, "loss_cls": 4.39326, "loss": 4.39326, "time": 0.70156} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.09981, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20797, "top5_acc": 0.44359, "loss_cls": 4.38987, "loss": 4.38987, "time": 0.70181} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.09981, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21, "top5_acc": 0.43891, "loss_cls": 4.35744, "loss": 4.35744, "time": 0.7028} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.09981, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21203, "top5_acc": 0.44469, "loss_cls": 4.38153, "loss": 4.38153, "time": 0.70055} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.0998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21328, "top5_acc": 0.44969, "loss_cls": 4.38061, "loss": 4.38061, "time": 0.70215} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.0998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21609, "top5_acc": 0.45031, "loss_cls": 4.34197, "loss": 4.34197, "time": 0.70561} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.0998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20812, "top5_acc": 0.43656, "loss_cls": 4.39761, "loss": 4.39761, "time": 0.70415} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.0998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21906, "top5_acc": 0.44969, "loss_cls": 4.38375, "loss": 4.38375, "time": 0.69966} +{"mode": "train", "epoch": 5, "iter": 1300, "lr": 0.09979, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21281, "top5_acc": 0.44938, "loss_cls": 4.36785, "loss": 4.36785, "time": 0.70333} +{"mode": "train", "epoch": 5, "iter": 1400, "lr": 0.09979, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21359, "top5_acc": 0.44922, "loss_cls": 4.35917, "loss": 4.35917, "time": 0.70035} +{"mode": "train", "epoch": 5, "iter": 1500, "lr": 0.09979, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21562, "top5_acc": 0.45109, "loss_cls": 4.36627, "loss": 4.36627, "time": 0.7002} +{"mode": "train", "epoch": 5, "iter": 1600, "lr": 0.09979, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21531, "top5_acc": 0.44891, "loss_cls": 4.35725, "loss": 4.35725, "time": 0.70111} +{"mode": "train", "epoch": 5, "iter": 1700, "lr": 0.09978, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21312, "top5_acc": 0.44688, "loss_cls": 4.36362, "loss": 4.36362, "time": 0.69988} +{"mode": "train", "epoch": 5, "iter": 1800, "lr": 0.09978, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20016, "top5_acc": 0.43766, "loss_cls": 4.40728, "loss": 4.40728, "time": 0.70107} +{"mode": "train", "epoch": 5, "iter": 1900, "lr": 0.09978, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21656, "top5_acc": 0.45125, "loss_cls": 4.33608, "loss": 4.33608, "time": 0.7021} +{"mode": "train", "epoch": 5, "iter": 2000, "lr": 0.09977, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21938, "top5_acc": 0.44906, "loss_cls": 4.35289, "loss": 4.35289, "time": 0.70246} +{"mode": "train", "epoch": 5, "iter": 2100, "lr": 0.09977, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21984, "top5_acc": 0.455, "loss_cls": 4.31884, "loss": 4.31884, "time": 0.69888} +{"mode": "train", "epoch": 5, "iter": 2200, "lr": 0.09977, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21984, "top5_acc": 0.46469, "loss_cls": 4.31002, "loss": 4.31002, "time": 0.70027} +{"mode": "train", "epoch": 5, "iter": 2300, "lr": 0.09977, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21828, "top5_acc": 0.45625, "loss_cls": 4.3339, "loss": 4.3339, "time": 0.70462} +{"mode": "train", "epoch": 5, "iter": 2400, "lr": 0.09976, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21625, "top5_acc": 0.45797, "loss_cls": 4.32211, "loss": 4.32211, "time": 0.7034} +{"mode": "train", "epoch": 5, "iter": 2500, "lr": 0.09976, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22266, "top5_acc": 0.45562, "loss_cls": 4.32036, "loss": 4.32036, "time": 0.70051} +{"mode": "train", "epoch": 5, "iter": 2600, "lr": 0.09976, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21953, "top5_acc": 0.45328, "loss_cls": 4.32591, "loss": 4.32591, "time": 0.70165} +{"mode": "train", "epoch": 5, "iter": 2700, "lr": 0.09976, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21703, "top5_acc": 0.45109, "loss_cls": 4.33655, "loss": 4.33655, "time": 0.70095} +{"mode": "train", "epoch": 5, "iter": 2800, "lr": 0.09975, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22297, "top5_acc": 0.46188, "loss_cls": 4.29643, "loss": 4.29643, "time": 0.70141} +{"mode": "train", "epoch": 5, "iter": 2900, "lr": 0.09975, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22359, "top5_acc": 0.45094, "loss_cls": 4.31496, "loss": 4.31496, "time": 0.69829} +{"mode": "train", "epoch": 5, "iter": 3000, "lr": 0.09975, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22297, "top5_acc": 0.46172, "loss_cls": 4.29007, "loss": 4.29007, "time": 0.69927} +{"mode": "train", "epoch": 5, "iter": 3100, "lr": 0.09974, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22125, "top5_acc": 0.46891, "loss_cls": 4.2801, "loss": 4.2801, "time": 0.705} +{"mode": "train", "epoch": 5, "iter": 3200, "lr": 0.09974, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22328, "top5_acc": 0.44766, "loss_cls": 4.34349, "loss": 4.34349, "time": 0.69742} +{"mode": "train", "epoch": 5, "iter": 3300, "lr": 0.09974, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22109, "top5_acc": 0.46281, "loss_cls": 4.30875, "loss": 4.30875, "time": 0.69942} +{"mode": "train", "epoch": 5, "iter": 3400, "lr": 0.09974, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22375, "top5_acc": 0.46641, "loss_cls": 4.30759, "loss": 4.30759, "time": 0.70476} +{"mode": "train", "epoch": 5, "iter": 3500, "lr": 0.09973, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2275, "top5_acc": 0.46688, "loss_cls": 4.27208, "loss": 4.27208, "time": 0.70426} +{"mode": "train", "epoch": 5, "iter": 3600, "lr": 0.09973, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21734, "top5_acc": 0.4525, "loss_cls": 4.34054, "loss": 4.34054, "time": 0.70079} +{"mode": "train", "epoch": 5, "iter": 3700, "lr": 0.09973, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22234, "top5_acc": 0.45469, "loss_cls": 4.31559, "loss": 4.31559, "time": 0.69978} +{"mode": "val", "epoch": 5, "iter": 309, "lr": 0.09973, "top1_acc": 0.15545, "top5_acc": 0.3693, "mean_class_accuracy": 0.15538} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.09972, "memory": 15990, "data_time": 1.28463, "top1_acc": 0.22766, "top5_acc": 0.46969, "loss_cls": 4.23939, "loss": 4.23939, "time": 1.98969} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.09972, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22984, "top5_acc": 0.47219, "loss_cls": 4.26229, "loss": 4.26229, "time": 0.7066} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.09972, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21984, "top5_acc": 0.46281, "loss_cls": 4.2948, "loss": 4.2948, "time": 0.70231} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.09971, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22266, "top5_acc": 0.45938, "loss_cls": 4.30402, "loss": 4.30402, "time": 0.70069} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.09971, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22328, "top5_acc": 0.45578, "loss_cls": 4.32443, "loss": 4.32443, "time": 0.70133} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.09971, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23188, "top5_acc": 0.45938, "loss_cls": 4.27449, "loss": 4.27449, "time": 0.70396} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.09971, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22453, "top5_acc": 0.46188, "loss_cls": 4.27281, "loss": 4.27281, "time": 0.70002} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.0997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22484, "top5_acc": 0.46109, "loss_cls": 4.29586, "loss": 4.29586, "time": 0.69855} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.0997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22297, "top5_acc": 0.45719, "loss_cls": 4.31468, "loss": 4.31468, "time": 0.70107} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.0997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23188, "top5_acc": 0.47109, "loss_cls": 4.25509, "loss": 4.25509, "time": 0.70287} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.09969, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23484, "top5_acc": 0.46828, "loss_cls": 4.25187, "loss": 4.25187, "time": 0.70135} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.09969, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22969, "top5_acc": 0.45906, "loss_cls": 4.31625, "loss": 4.31625, "time": 0.69883} +{"mode": "train", "epoch": 6, "iter": 1300, "lr": 0.09969, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22094, "top5_acc": 0.46797, "loss_cls": 4.26635, "loss": 4.26635, "time": 0.70446} +{"mode": "train", "epoch": 6, "iter": 1400, "lr": 0.09968, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22828, "top5_acc": 0.46438, "loss_cls": 4.27873, "loss": 4.27873, "time": 0.69915} +{"mode": "train", "epoch": 6, "iter": 1500, "lr": 0.09968, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22875, "top5_acc": 0.46547, "loss_cls": 4.28675, "loss": 4.28675, "time": 0.70163} +{"mode": "train", "epoch": 6, "iter": 1600, "lr": 0.09968, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22203, "top5_acc": 0.46062, "loss_cls": 4.28755, "loss": 4.28755, "time": 0.70105} +{"mode": "train", "epoch": 6, "iter": 1700, "lr": 0.09967, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22516, "top5_acc": 0.45969, "loss_cls": 4.28669, "loss": 4.28669, "time": 0.69908} +{"mode": "train", "epoch": 6, "iter": 1800, "lr": 0.09967, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22781, "top5_acc": 0.47156, "loss_cls": 4.27911, "loss": 4.27911, "time": 0.70103} +{"mode": "train", "epoch": 6, "iter": 1900, "lr": 0.09967, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23094, "top5_acc": 0.47141, "loss_cls": 4.28035, "loss": 4.28035, "time": 0.70181} +{"mode": "train", "epoch": 6, "iter": 2000, "lr": 0.09966, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22656, "top5_acc": 0.45953, "loss_cls": 4.29902, "loss": 4.29902, "time": 0.7013} +{"mode": "train", "epoch": 6, "iter": 2100, "lr": 0.09966, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23516, "top5_acc": 0.47281, "loss_cls": 4.24283, "loss": 4.24283, "time": 0.69866} +{"mode": "train", "epoch": 6, "iter": 2200, "lr": 0.09966, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22859, "top5_acc": 0.46234, "loss_cls": 4.27687, "loss": 4.27687, "time": 0.70076} +{"mode": "train", "epoch": 6, "iter": 2300, "lr": 0.09965, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22562, "top5_acc": 0.46641, "loss_cls": 4.29702, "loss": 4.29702, "time": 0.70294} +{"mode": "train", "epoch": 6, "iter": 2400, "lr": 0.09965, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23453, "top5_acc": 0.47141, "loss_cls": 4.25693, "loss": 4.25693, "time": 0.70033} +{"mode": "train", "epoch": 6, "iter": 2500, "lr": 0.09965, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22859, "top5_acc": 0.46688, "loss_cls": 4.27675, "loss": 4.27675, "time": 0.7018} +{"mode": "train", "epoch": 6, "iter": 2600, "lr": 0.09964, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23094, "top5_acc": 0.47266, "loss_cls": 4.21024, "loss": 4.21024, "time": 0.70153} +{"mode": "train", "epoch": 6, "iter": 2700, "lr": 0.09964, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23109, "top5_acc": 0.4775, "loss_cls": 4.24237, "loss": 4.24237, "time": 0.70132} +{"mode": "train", "epoch": 6, "iter": 2800, "lr": 0.09964, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23094, "top5_acc": 0.47438, "loss_cls": 4.24141, "loss": 4.24141, "time": 0.69989} +{"mode": "train", "epoch": 6, "iter": 2900, "lr": 0.09963, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22719, "top5_acc": 0.47359, "loss_cls": 4.27624, "loss": 4.27624, "time": 0.69908} +{"mode": "train", "epoch": 6, "iter": 3000, "lr": 0.09963, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22594, "top5_acc": 0.47453, "loss_cls": 4.2185, "loss": 4.2185, "time": 0.70018} +{"mode": "train", "epoch": 6, "iter": 3100, "lr": 0.09963, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22625, "top5_acc": 0.47172, "loss_cls": 4.25295, "loss": 4.25295, "time": 0.70403} +{"mode": "train", "epoch": 6, "iter": 3200, "lr": 0.09962, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23562, "top5_acc": 0.47109, "loss_cls": 4.25111, "loss": 4.25111, "time": 0.69983} +{"mode": "train", "epoch": 6, "iter": 3300, "lr": 0.09962, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22922, "top5_acc": 0.47891, "loss_cls": 4.23945, "loss": 4.23945, "time": 0.70213} +{"mode": "train", "epoch": 6, "iter": 3400, "lr": 0.09962, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22922, "top5_acc": 0.47062, "loss_cls": 4.28397, "loss": 4.28397, "time": 0.6979} +{"mode": "train", "epoch": 6, "iter": 3500, "lr": 0.09961, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23781, "top5_acc": 0.46656, "loss_cls": 4.2449, "loss": 4.2449, "time": 0.70496} +{"mode": "train", "epoch": 6, "iter": 3600, "lr": 0.09961, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23828, "top5_acc": 0.47203, "loss_cls": 4.24241, "loss": 4.24241, "time": 0.70115} +{"mode": "train", "epoch": 6, "iter": 3700, "lr": 0.09961, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22922, "top5_acc": 0.47, "loss_cls": 4.23022, "loss": 4.23022, "time": 0.70226} +{"mode": "val", "epoch": 6, "iter": 309, "lr": 0.09961, "top1_acc": 0.17581, "top5_acc": 0.38702, "mean_class_accuracy": 0.1757} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0996, "memory": 15990, "data_time": 1.2829, "top1_acc": 0.24578, "top5_acc": 0.48188, "loss_cls": 4.15094, "loss": 4.15094, "time": 1.99211} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0996, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23094, "top5_acc": 0.47641, "loss_cls": 4.21786, "loss": 4.21786, "time": 0.70617} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.0996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23844, "top5_acc": 0.47875, "loss_cls": 4.22482, "loss": 4.22482, "time": 0.70878} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.09959, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23016, "top5_acc": 0.46875, "loss_cls": 4.25864, "loss": 4.25864, "time": 0.70783} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.09959, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23266, "top5_acc": 0.46859, "loss_cls": 4.22582, "loss": 4.22582, "time": 0.70067} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.09958, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23078, "top5_acc": 0.47141, "loss_cls": 4.23387, "loss": 4.23387, "time": 0.7002} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.09958, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24219, "top5_acc": 0.48281, "loss_cls": 4.17565, "loss": 4.17565, "time": 0.70164} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.09958, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24656, "top5_acc": 0.48125, "loss_cls": 4.19069, "loss": 4.19069, "time": 0.7038} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.09957, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23922, "top5_acc": 0.47469, "loss_cls": 4.21625, "loss": 4.21625, "time": 0.70315} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.09957, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22531, "top5_acc": 0.4675, "loss_cls": 4.28624, "loss": 4.28624, "time": 0.70558} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.09957, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23609, "top5_acc": 0.47656, "loss_cls": 4.22268, "loss": 4.22268, "time": 0.70277} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.09956, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23344, "top5_acc": 0.47594, "loss_cls": 4.22657, "loss": 4.22657, "time": 0.70286} +{"mode": "train", "epoch": 7, "iter": 1300, "lr": 0.09956, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23969, "top5_acc": 0.47641, "loss_cls": 4.20421, "loss": 4.20421, "time": 0.7034} +{"mode": "train", "epoch": 7, "iter": 1400, "lr": 0.09956, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23547, "top5_acc": 0.47359, "loss_cls": 4.23588, "loss": 4.23588, "time": 0.70654} +{"mode": "train", "epoch": 7, "iter": 1500, "lr": 0.09955, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23547, "top5_acc": 0.4825, "loss_cls": 4.20825, "loss": 4.20825, "time": 0.70014} +{"mode": "train", "epoch": 7, "iter": 1600, "lr": 0.09955, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24031, "top5_acc": 0.47688, "loss_cls": 4.19604, "loss": 4.19604, "time": 0.7052} +{"mode": "train", "epoch": 7, "iter": 1700, "lr": 0.09954, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22844, "top5_acc": 0.47031, "loss_cls": 4.2203, "loss": 4.2203, "time": 0.69953} +{"mode": "train", "epoch": 7, "iter": 1800, "lr": 0.09954, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22547, "top5_acc": 0.47125, "loss_cls": 4.24891, "loss": 4.24891, "time": 0.69985} +{"mode": "train", "epoch": 7, "iter": 1900, "lr": 0.09954, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23297, "top5_acc": 0.47469, "loss_cls": 4.22414, "loss": 4.22414, "time": 0.69901} +{"mode": "train", "epoch": 7, "iter": 2000, "lr": 0.09953, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23188, "top5_acc": 0.46547, "loss_cls": 4.28165, "loss": 4.28165, "time": 0.70064} +{"mode": "train", "epoch": 7, "iter": 2100, "lr": 0.09953, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23938, "top5_acc": 0.47797, "loss_cls": 4.24555, "loss": 4.24555, "time": 0.7002} +{"mode": "train", "epoch": 7, "iter": 2200, "lr": 0.09952, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23844, "top5_acc": 0.47, "loss_cls": 4.219, "loss": 4.219, "time": 0.70328} +{"mode": "train", "epoch": 7, "iter": 2300, "lr": 0.09952, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23297, "top5_acc": 0.47969, "loss_cls": 4.21544, "loss": 4.21544, "time": 0.70501} +{"mode": "train", "epoch": 7, "iter": 2400, "lr": 0.09952, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24844, "top5_acc": 0.48312, "loss_cls": 4.1745, "loss": 4.1745, "time": 0.70523} +{"mode": "train", "epoch": 7, "iter": 2500, "lr": 0.09951, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23297, "top5_acc": 0.45938, "loss_cls": 4.28404, "loss": 4.28404, "time": 0.70167} +{"mode": "train", "epoch": 7, "iter": 2600, "lr": 0.09951, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23547, "top5_acc": 0.48094, "loss_cls": 4.22257, "loss": 4.22257, "time": 0.70435} +{"mode": "train", "epoch": 7, "iter": 2700, "lr": 0.09951, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23031, "top5_acc": 0.47062, "loss_cls": 4.24741, "loss": 4.24741, "time": 0.70398} +{"mode": "train", "epoch": 7, "iter": 2800, "lr": 0.0995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22047, "top5_acc": 0.46422, "loss_cls": 4.2843, "loss": 4.2843, "time": 0.7042} +{"mode": "train", "epoch": 7, "iter": 2900, "lr": 0.0995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23094, "top5_acc": 0.47453, "loss_cls": 4.21917, "loss": 4.21917, "time": 0.70372} +{"mode": "train", "epoch": 7, "iter": 3000, "lr": 0.09949, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24609, "top5_acc": 0.48969, "loss_cls": 4.17482, "loss": 4.17482, "time": 0.7019} +{"mode": "train", "epoch": 7, "iter": 3100, "lr": 0.09949, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24047, "top5_acc": 0.49125, "loss_cls": 4.17134, "loss": 4.17134, "time": 0.70179} +{"mode": "train", "epoch": 7, "iter": 3200, "lr": 0.09949, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23422, "top5_acc": 0.47203, "loss_cls": 4.24044, "loss": 4.24044, "time": 0.70133} +{"mode": "train", "epoch": 7, "iter": 3300, "lr": 0.09948, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2325, "top5_acc": 0.46766, "loss_cls": 4.22819, "loss": 4.22819, "time": 0.7011} +{"mode": "train", "epoch": 7, "iter": 3400, "lr": 0.09948, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23703, "top5_acc": 0.475, "loss_cls": 4.23561, "loss": 4.23561, "time": 0.70695} +{"mode": "train", "epoch": 7, "iter": 3500, "lr": 0.09947, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23406, "top5_acc": 0.46984, "loss_cls": 4.25826, "loss": 4.25826, "time": 0.70392} +{"mode": "train", "epoch": 7, "iter": 3600, "lr": 0.09947, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24016, "top5_acc": 0.4825, "loss_cls": 4.18439, "loss": 4.18439, "time": 0.70417} +{"mode": "train", "epoch": 7, "iter": 3700, "lr": 0.09947, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24844, "top5_acc": 0.49469, "loss_cls": 4.1599, "loss": 4.1599, "time": 0.70172} +{"mode": "val", "epoch": 7, "iter": 309, "lr": 0.09946, "top1_acc": 0.13569, "top5_acc": 0.32629, "mean_class_accuracy": 0.13555} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.09946, "memory": 15990, "data_time": 1.28421, "top1_acc": 0.24859, "top5_acc": 0.48984, "loss_cls": 4.17526, "loss": 4.17526, "time": 1.99078} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.09946, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22906, "top5_acc": 0.48797, "loss_cls": 4.1865, "loss": 4.1865, "time": 0.70883} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.09945, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23875, "top5_acc": 0.47781, "loss_cls": 4.1622, "loss": 4.1622, "time": 0.7042} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.09945, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23641, "top5_acc": 0.47891, "loss_cls": 4.21501, "loss": 4.21501, "time": 0.70023} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.09944, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24625, "top5_acc": 0.49203, "loss_cls": 4.15292, "loss": 4.15292, "time": 0.70033} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.09944, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23672, "top5_acc": 0.47406, "loss_cls": 4.19777, "loss": 4.19777, "time": 0.70597} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.09943, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24141, "top5_acc": 0.47891, "loss_cls": 4.19312, "loss": 4.19312, "time": 0.70521} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.09943, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24453, "top5_acc": 0.48438, "loss_cls": 4.17967, "loss": 4.17967, "time": 0.70283} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.09943, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23734, "top5_acc": 0.48531, "loss_cls": 4.18532, "loss": 4.18532, "time": 0.70102} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.09942, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24031, "top5_acc": 0.48625, "loss_cls": 4.15928, "loss": 4.15928, "time": 0.69873} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.09942, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24922, "top5_acc": 0.48547, "loss_cls": 4.18611, "loss": 4.18611, "time": 0.7} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.09941, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24203, "top5_acc": 0.47234, "loss_cls": 4.20882, "loss": 4.20882, "time": 0.70275} +{"mode": "train", "epoch": 8, "iter": 1300, "lr": 0.09941, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24078, "top5_acc": 0.48203, "loss_cls": 4.20126, "loss": 4.20126, "time": 0.70203} +{"mode": "train", "epoch": 8, "iter": 1400, "lr": 0.0994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24203, "top5_acc": 0.48094, "loss_cls": 4.19876, "loss": 4.19876, "time": 0.70024} +{"mode": "train", "epoch": 8, "iter": 1500, "lr": 0.0994, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24172, "top5_acc": 0.49156, "loss_cls": 4.17451, "loss": 4.17451, "time": 0.6985} +{"mode": "train", "epoch": 8, "iter": 1600, "lr": 0.0994, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23891, "top5_acc": 0.48219, "loss_cls": 4.17052, "loss": 4.17052, "time": 0.70212} +{"mode": "train", "epoch": 8, "iter": 1700, "lr": 0.09939, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23844, "top5_acc": 0.48, "loss_cls": 4.21211, "loss": 4.21211, "time": 0.69973} +{"mode": "train", "epoch": 8, "iter": 1800, "lr": 0.09939, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23984, "top5_acc": 0.49031, "loss_cls": 4.17572, "loss": 4.17572, "time": 0.70006} +{"mode": "train", "epoch": 8, "iter": 1900, "lr": 0.09938, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23219, "top5_acc": 0.48016, "loss_cls": 4.22009, "loss": 4.22009, "time": 0.7031} +{"mode": "train", "epoch": 8, "iter": 2000, "lr": 0.09938, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23719, "top5_acc": 0.47766, "loss_cls": 4.20829, "loss": 4.20829, "time": 0.70379} +{"mode": "train", "epoch": 8, "iter": 2100, "lr": 0.09937, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24406, "top5_acc": 0.49172, "loss_cls": 4.14617, "loss": 4.14617, "time": 0.70027} +{"mode": "train", "epoch": 8, "iter": 2200, "lr": 0.09937, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23812, "top5_acc": 0.48609, "loss_cls": 4.19011, "loss": 4.19011, "time": 0.70231} +{"mode": "train", "epoch": 8, "iter": 2300, "lr": 0.09937, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2425, "top5_acc": 0.48766, "loss_cls": 4.20285, "loss": 4.20285, "time": 0.70473} +{"mode": "train", "epoch": 8, "iter": 2400, "lr": 0.09936, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23281, "top5_acc": 0.47672, "loss_cls": 4.22823, "loss": 4.22823, "time": 0.70013} +{"mode": "train", "epoch": 8, "iter": 2500, "lr": 0.09936, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24234, "top5_acc": 0.48422, "loss_cls": 4.18774, "loss": 4.18774, "time": 0.70328} +{"mode": "train", "epoch": 8, "iter": 2600, "lr": 0.09935, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23922, "top5_acc": 0.49016, "loss_cls": 4.17658, "loss": 4.17658, "time": 0.70098} +{"mode": "train", "epoch": 8, "iter": 2700, "lr": 0.09935, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23891, "top5_acc": 0.48703, "loss_cls": 4.17162, "loss": 4.17162, "time": 0.69917} +{"mode": "train", "epoch": 8, "iter": 2800, "lr": 0.09934, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24344, "top5_acc": 0.49406, "loss_cls": 4.16669, "loss": 4.16669, "time": 0.70047} +{"mode": "train", "epoch": 8, "iter": 2900, "lr": 0.09934, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23609, "top5_acc": 0.48031, "loss_cls": 4.18504, "loss": 4.18504, "time": 0.70055} +{"mode": "train", "epoch": 8, "iter": 3000, "lr": 0.09933, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24141, "top5_acc": 0.49047, "loss_cls": 4.17914, "loss": 4.17914, "time": 0.70133} +{"mode": "train", "epoch": 8, "iter": 3100, "lr": 0.09933, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24156, "top5_acc": 0.47719, "loss_cls": 4.20303, "loss": 4.20303, "time": 0.69932} +{"mode": "train", "epoch": 8, "iter": 3200, "lr": 0.09933, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24141, "top5_acc": 0.48969, "loss_cls": 4.17397, "loss": 4.17397, "time": 0.70443} +{"mode": "train", "epoch": 8, "iter": 3300, "lr": 0.09932, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23625, "top5_acc": 0.47125, "loss_cls": 4.233, "loss": 4.233, "time": 0.69958} +{"mode": "train", "epoch": 8, "iter": 3400, "lr": 0.09932, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24953, "top5_acc": 0.49453, "loss_cls": 4.16159, "loss": 4.16159, "time": 0.701} +{"mode": "train", "epoch": 8, "iter": 3500, "lr": 0.09931, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24375, "top5_acc": 0.48641, "loss_cls": 4.14777, "loss": 4.14777, "time": 0.70586} +{"mode": "train", "epoch": 8, "iter": 3600, "lr": 0.09931, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23375, "top5_acc": 0.48938, "loss_cls": 4.15822, "loss": 4.15822, "time": 0.69908} +{"mode": "train", "epoch": 8, "iter": 3700, "lr": 0.0993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23609, "top5_acc": 0.48719, "loss_cls": 4.18355, "loss": 4.18355, "time": 0.70539} +{"mode": "val", "epoch": 8, "iter": 309, "lr": 0.0993, "top1_acc": 0.17287, "top5_acc": 0.39376, "mean_class_accuracy": 0.17262} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.0993, "memory": 15990, "data_time": 1.27153, "top1_acc": 0.24641, "top5_acc": 0.49531, "loss_cls": 4.13675, "loss": 4.13675, "time": 1.98348} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.09929, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24203, "top5_acc": 0.4875, "loss_cls": 4.15122, "loss": 4.15122, "time": 0.70596} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.09929, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24422, "top5_acc": 0.50062, "loss_cls": 4.12324, "loss": 4.12324, "time": 0.70956} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.09928, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25031, "top5_acc": 0.49, "loss_cls": 4.1312, "loss": 4.1312, "time": 0.70239} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.09928, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24141, "top5_acc": 0.49188, "loss_cls": 4.14529, "loss": 4.14529, "time": 0.70342} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.09927, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24781, "top5_acc": 0.50391, "loss_cls": 4.10153, "loss": 4.10153, "time": 0.70219} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.09927, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24562, "top5_acc": 0.49031, "loss_cls": 4.18389, "loss": 4.18389, "time": 0.70062} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.09926, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24719, "top5_acc": 0.48578, "loss_cls": 4.17738, "loss": 4.17738, "time": 0.70017} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.09926, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24, "top5_acc": 0.47969, "loss_cls": 4.19435, "loss": 4.19435, "time": 0.6984} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.09925, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24125, "top5_acc": 0.47547, "loss_cls": 4.21103, "loss": 4.21103, "time": 0.70257} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.09925, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24453, "top5_acc": 0.49094, "loss_cls": 4.1309, "loss": 4.1309, "time": 0.69979} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.09924, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24422, "top5_acc": 0.49062, "loss_cls": 4.16604, "loss": 4.16604, "time": 0.70084} +{"mode": "train", "epoch": 9, "iter": 1300, "lr": 0.09924, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24922, "top5_acc": 0.49344, "loss_cls": 4.14567, "loss": 4.14567, "time": 0.7026} +{"mode": "train", "epoch": 9, "iter": 1400, "lr": 0.09923, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24391, "top5_acc": 0.49328, "loss_cls": 4.14577, "loss": 4.14577, "time": 0.6996} +{"mode": "train", "epoch": 9, "iter": 1500, "lr": 0.09923, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24141, "top5_acc": 0.49125, "loss_cls": 4.15908, "loss": 4.15908, "time": 0.70082} +{"mode": "train", "epoch": 9, "iter": 1600, "lr": 0.09922, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24812, "top5_acc": 0.49547, "loss_cls": 4.12771, "loss": 4.12771, "time": 0.70105} +{"mode": "train", "epoch": 9, "iter": 1700, "lr": 0.09922, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.245, "top5_acc": 0.48125, "loss_cls": 4.20824, "loss": 4.20824, "time": 0.69977} +{"mode": "train", "epoch": 9, "iter": 1800, "lr": 0.09921, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24656, "top5_acc": 0.48312, "loss_cls": 4.18299, "loss": 4.18299, "time": 0.70265} +{"mode": "train", "epoch": 9, "iter": 1900, "lr": 0.09921, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25156, "top5_acc": 0.4925, "loss_cls": 4.12443, "loss": 4.12443, "time": 0.70208} +{"mode": "train", "epoch": 9, "iter": 2000, "lr": 0.0992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24109, "top5_acc": 0.47781, "loss_cls": 4.21437, "loss": 4.21437, "time": 0.69821} +{"mode": "train", "epoch": 9, "iter": 2100, "lr": 0.0992, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25297, "top5_acc": 0.49875, "loss_cls": 4.12423, "loss": 4.12423, "time": 0.70253} +{"mode": "train", "epoch": 9, "iter": 2200, "lr": 0.09919, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24547, "top5_acc": 0.48453, "loss_cls": 4.1774, "loss": 4.1774, "time": 0.69947} +{"mode": "train", "epoch": 9, "iter": 2300, "lr": 0.09919, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23797, "top5_acc": 0.48719, "loss_cls": 4.18637, "loss": 4.18637, "time": 0.70735} +{"mode": "train", "epoch": 9, "iter": 2400, "lr": 0.09918, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24891, "top5_acc": 0.48766, "loss_cls": 4.14004, "loss": 4.14004, "time": 0.69986} +{"mode": "train", "epoch": 9, "iter": 2500, "lr": 0.09918, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.245, "top5_acc": 0.49156, "loss_cls": 4.14342, "loss": 4.14342, "time": 0.70184} +{"mode": "train", "epoch": 9, "iter": 2600, "lr": 0.09917, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24297, "top5_acc": 0.48906, "loss_cls": 4.17497, "loss": 4.17497, "time": 0.70365} +{"mode": "train", "epoch": 9, "iter": 2700, "lr": 0.09917, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23953, "top5_acc": 0.49172, "loss_cls": 4.15602, "loss": 4.15602, "time": 0.69936} +{"mode": "train", "epoch": 9, "iter": 2800, "lr": 0.09916, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.245, "top5_acc": 0.49484, "loss_cls": 4.13823, "loss": 4.13823, "time": 0.70069} +{"mode": "train", "epoch": 9, "iter": 2900, "lr": 0.09916, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24516, "top5_acc": 0.48109, "loss_cls": 4.16568, "loss": 4.16568, "time": 0.6987} +{"mode": "train", "epoch": 9, "iter": 3000, "lr": 0.09915, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24562, "top5_acc": 0.49219, "loss_cls": 4.16572, "loss": 4.16572, "time": 0.70126} +{"mode": "train", "epoch": 9, "iter": 3100, "lr": 0.09915, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23938, "top5_acc": 0.48641, "loss_cls": 4.18336, "loss": 4.18336, "time": 0.7001} +{"mode": "train", "epoch": 9, "iter": 3200, "lr": 0.09914, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23562, "top5_acc": 0.47969, "loss_cls": 4.20002, "loss": 4.20002, "time": 0.699} +{"mode": "train", "epoch": 9, "iter": 3300, "lr": 0.09914, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24531, "top5_acc": 0.49578, "loss_cls": 4.13845, "loss": 4.13845, "time": 0.70258} +{"mode": "train", "epoch": 9, "iter": 3400, "lr": 0.09913, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24469, "top5_acc": 0.48812, "loss_cls": 4.16, "loss": 4.16, "time": 0.69971} +{"mode": "train", "epoch": 9, "iter": 3500, "lr": 0.09913, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24156, "top5_acc": 0.48188, "loss_cls": 4.17472, "loss": 4.17472, "time": 0.70556} +{"mode": "train", "epoch": 9, "iter": 3600, "lr": 0.09912, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2425, "top5_acc": 0.48875, "loss_cls": 4.16892, "loss": 4.16892, "time": 0.7013} +{"mode": "train", "epoch": 9, "iter": 3700, "lr": 0.09912, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24297, "top5_acc": 0.48688, "loss_cls": 4.16717, "loss": 4.16717, "time": 0.70347} +{"mode": "val", "epoch": 9, "iter": 309, "lr": 0.09911, "top1_acc": 0.17221, "top5_acc": 0.39401, "mean_class_accuracy": 0.17226} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.09911, "memory": 15990, "data_time": 1.27501, "top1_acc": 0.24641, "top5_acc": 0.49828, "loss_cls": 4.11586, "loss": 4.11586, "time": 1.98277} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.0991, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25375, "top5_acc": 0.4975, "loss_cls": 4.1176, "loss": 4.1176, "time": 0.7056} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.0991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24266, "top5_acc": 0.48375, "loss_cls": 4.16857, "loss": 4.16857, "time": 0.70435} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.09909, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23516, "top5_acc": 0.48406, "loss_cls": 4.18848, "loss": 4.18848, "time": 0.70648} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.09909, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24359, "top5_acc": 0.48938, "loss_cls": 4.16772, "loss": 4.16772, "time": 0.70124} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.09908, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24953, "top5_acc": 0.49578, "loss_cls": 4.13207, "loss": 4.13207, "time": 0.69985} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.09908, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25562, "top5_acc": 0.50219, "loss_cls": 4.10109, "loss": 4.10109, "time": 0.70193} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.09907, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24297, "top5_acc": 0.49281, "loss_cls": 4.15704, "loss": 4.15704, "time": 0.70408} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.09907, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24609, "top5_acc": 0.48812, "loss_cls": 4.15992, "loss": 4.15992, "time": 0.69987} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.09906, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24781, "top5_acc": 0.50031, "loss_cls": 4.13256, "loss": 4.13256, "time": 0.6995} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.09906, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24359, "top5_acc": 0.48516, "loss_cls": 4.14196, "loss": 4.14196, "time": 0.70012} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.09905, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24781, "top5_acc": 0.48922, "loss_cls": 4.14519, "loss": 4.14519, "time": 0.70156} +{"mode": "train", "epoch": 10, "iter": 1300, "lr": 0.09905, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23797, "top5_acc": 0.48422, "loss_cls": 4.18406, "loss": 4.18406, "time": 0.69709} +{"mode": "train", "epoch": 10, "iter": 1400, "lr": 0.09904, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23812, "top5_acc": 0.47719, "loss_cls": 4.17099, "loss": 4.17099, "time": 0.69683} +{"mode": "train", "epoch": 10, "iter": 1500, "lr": 0.09903, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.2475, "top5_acc": 0.49094, "loss_cls": 4.14767, "loss": 4.14767, "time": 0.7002} +{"mode": "train", "epoch": 10, "iter": 1600, "lr": 0.09903, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23984, "top5_acc": 0.47906, "loss_cls": 4.16982, "loss": 4.16982, "time": 0.70238} +{"mode": "train", "epoch": 10, "iter": 1700, "lr": 0.09902, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24391, "top5_acc": 0.49281, "loss_cls": 4.13717, "loss": 4.13717, "time": 0.69772} +{"mode": "train", "epoch": 10, "iter": 1800, "lr": 0.09902, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24016, "top5_acc": 0.48609, "loss_cls": 4.16034, "loss": 4.16034, "time": 0.70387} +{"mode": "train", "epoch": 10, "iter": 1900, "lr": 0.09901, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24984, "top5_acc": 0.49109, "loss_cls": 4.17907, "loss": 4.17907, "time": 0.7031} +{"mode": "train", "epoch": 10, "iter": 2000, "lr": 0.09901, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25547, "top5_acc": 0.50125, "loss_cls": 4.10986, "loss": 4.10986, "time": 0.70087} +{"mode": "train", "epoch": 10, "iter": 2100, "lr": 0.099, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24922, "top5_acc": 0.48812, "loss_cls": 4.14327, "loss": 4.14327, "time": 0.70303} +{"mode": "train", "epoch": 10, "iter": 2200, "lr": 0.099, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24781, "top5_acc": 0.49062, "loss_cls": 4.13568, "loss": 4.13568, "time": 0.70287} +{"mode": "train", "epoch": 10, "iter": 2300, "lr": 0.09899, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24125, "top5_acc": 0.49594, "loss_cls": 4.14404, "loss": 4.14404, "time": 0.70288} +{"mode": "train", "epoch": 10, "iter": 2400, "lr": 0.09898, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25344, "top5_acc": 0.50453, "loss_cls": 4.08225, "loss": 4.08225, "time": 0.69984} +{"mode": "train", "epoch": 10, "iter": 2500, "lr": 0.09898, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24438, "top5_acc": 0.49125, "loss_cls": 4.16946, "loss": 4.16946, "time": 0.702} +{"mode": "train", "epoch": 10, "iter": 2600, "lr": 0.09897, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24844, "top5_acc": 0.49297, "loss_cls": 4.13142, "loss": 4.13142, "time": 0.70071} +{"mode": "train", "epoch": 10, "iter": 2700, "lr": 0.09897, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24562, "top5_acc": 0.49188, "loss_cls": 4.11548, "loss": 4.11548, "time": 0.69942} +{"mode": "train", "epoch": 10, "iter": 2800, "lr": 0.09896, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25344, "top5_acc": 0.49203, "loss_cls": 4.12036, "loss": 4.12036, "time": 0.69916} +{"mode": "train", "epoch": 10, "iter": 2900, "lr": 0.09896, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24625, "top5_acc": 0.49094, "loss_cls": 4.13745, "loss": 4.13745, "time": 0.69708} +{"mode": "train", "epoch": 10, "iter": 3000, "lr": 0.09895, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2525, "top5_acc": 0.49781, "loss_cls": 4.13152, "loss": 4.13152, "time": 0.69757} +{"mode": "train", "epoch": 10, "iter": 3100, "lr": 0.09894, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25141, "top5_acc": 0.48672, "loss_cls": 4.14196, "loss": 4.14196, "time": 0.69925} +{"mode": "train", "epoch": 10, "iter": 3200, "lr": 0.09894, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24422, "top5_acc": 0.48484, "loss_cls": 4.1767, "loss": 4.1767, "time": 0.69891} +{"mode": "train", "epoch": 10, "iter": 3300, "lr": 0.09893, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24203, "top5_acc": 0.49547, "loss_cls": 4.14167, "loss": 4.14167, "time": 0.70389} +{"mode": "train", "epoch": 10, "iter": 3400, "lr": 0.09893, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23516, "top5_acc": 0.48484, "loss_cls": 4.15807, "loss": 4.15807, "time": 0.69882} +{"mode": "train", "epoch": 10, "iter": 3500, "lr": 0.09892, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25438, "top5_acc": 0.49422, "loss_cls": 4.12273, "loss": 4.12273, "time": 0.70646} +{"mode": "train", "epoch": 10, "iter": 3600, "lr": 0.09892, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25672, "top5_acc": 0.49672, "loss_cls": 4.10454, "loss": 4.10454, "time": 0.70002} +{"mode": "train", "epoch": 10, "iter": 3700, "lr": 0.09891, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24812, "top5_acc": 0.49281, "loss_cls": 4.1099, "loss": 4.1099, "time": 0.70489} +{"mode": "val", "epoch": 10, "iter": 309, "lr": 0.09891, "top1_acc": 0.16765, "top5_acc": 0.37699, "mean_class_accuracy": 0.16766} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.0989, "memory": 15990, "data_time": 1.29674, "top1_acc": 0.25375, "top5_acc": 0.50594, "loss_cls": 4.06561, "loss": 4.06561, "time": 2.00265} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.0989, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24531, "top5_acc": 0.48562, "loss_cls": 4.17881, "loss": 4.17881, "time": 0.70941} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.09889, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26312, "top5_acc": 0.51688, "loss_cls": 4.02805, "loss": 4.02805, "time": 0.70343} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.09888, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25156, "top5_acc": 0.49453, "loss_cls": 4.11636, "loss": 4.11636, "time": 0.70213} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.09888, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25141, "top5_acc": 0.50313, "loss_cls": 4.10725, "loss": 4.10725, "time": 0.70215} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.09887, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25016, "top5_acc": 0.49641, "loss_cls": 4.12398, "loss": 4.12398, "time": 0.70206} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.09887, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25188, "top5_acc": 0.5125, "loss_cls": 4.09801, "loss": 4.09801, "time": 0.70306} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.09886, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25172, "top5_acc": 0.48781, "loss_cls": 4.13778, "loss": 4.13778, "time": 0.70086} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.09885, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24406, "top5_acc": 0.49141, "loss_cls": 4.13998, "loss": 4.13998, "time": 0.69867} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.09885, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25109, "top5_acc": 0.49203, "loss_cls": 4.11328, "loss": 4.11328, "time": 0.70355} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.09884, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25641, "top5_acc": 0.51062, "loss_cls": 4.04907, "loss": 4.04907, "time": 0.70079} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.09884, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25547, "top5_acc": 0.5, "loss_cls": 4.13005, "loss": 4.13005, "time": 0.69751} +{"mode": "train", "epoch": 11, "iter": 1300, "lr": 0.09883, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23938, "top5_acc": 0.48641, "loss_cls": 4.19038, "loss": 4.19038, "time": 0.70098} +{"mode": "train", "epoch": 11, "iter": 1400, "lr": 0.09882, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24719, "top5_acc": 0.49359, "loss_cls": 4.13453, "loss": 4.13453, "time": 0.70039} +{"mode": "train", "epoch": 11, "iter": 1500, "lr": 0.09882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25312, "top5_acc": 0.49328, "loss_cls": 4.13496, "loss": 4.13496, "time": 0.6972} +{"mode": "train", "epoch": 11, "iter": 1600, "lr": 0.09881, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25094, "top5_acc": 0.49078, "loss_cls": 4.1238, "loss": 4.1238, "time": 0.69981} +{"mode": "train", "epoch": 11, "iter": 1700, "lr": 0.09881, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23688, "top5_acc": 0.47953, "loss_cls": 4.20232, "loss": 4.20232, "time": 0.7042} +{"mode": "train", "epoch": 11, "iter": 1800, "lr": 0.0988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25016, "top5_acc": 0.49547, "loss_cls": 4.15338, "loss": 4.15338, "time": 0.69909} +{"mode": "train", "epoch": 11, "iter": 1900, "lr": 0.09879, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25219, "top5_acc": 0.50172, "loss_cls": 4.12051, "loss": 4.12051, "time": 0.70186} +{"mode": "train", "epoch": 11, "iter": 2000, "lr": 0.09879, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24859, "top5_acc": 0.49, "loss_cls": 4.13368, "loss": 4.13368, "time": 0.7016} +{"mode": "train", "epoch": 11, "iter": 2100, "lr": 0.09878, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24266, "top5_acc": 0.48531, "loss_cls": 4.1771, "loss": 4.1771, "time": 0.70203} +{"mode": "train", "epoch": 11, "iter": 2200, "lr": 0.09878, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24656, "top5_acc": 0.49953, "loss_cls": 4.11744, "loss": 4.11744, "time": 0.70028} +{"mode": "train", "epoch": 11, "iter": 2300, "lr": 0.09877, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25062, "top5_acc": 0.49406, "loss_cls": 4.12837, "loss": 4.12837, "time": 0.7004} +{"mode": "train", "epoch": 11, "iter": 2400, "lr": 0.09876, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25281, "top5_acc": 0.49516, "loss_cls": 4.12707, "loss": 4.12707, "time": 0.70115} +{"mode": "train", "epoch": 11, "iter": 2500, "lr": 0.09876, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25156, "top5_acc": 0.49672, "loss_cls": 4.10193, "loss": 4.10193, "time": 0.70115} +{"mode": "train", "epoch": 11, "iter": 2600, "lr": 0.09875, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24859, "top5_acc": 0.485, "loss_cls": 4.15857, "loss": 4.15857, "time": 0.69832} +{"mode": "train", "epoch": 11, "iter": 2700, "lr": 0.09874, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24109, "top5_acc": 0.48984, "loss_cls": 4.16937, "loss": 4.16937, "time": 0.6988} +{"mode": "train", "epoch": 11, "iter": 2800, "lr": 0.09874, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24875, "top5_acc": 0.49469, "loss_cls": 4.14686, "loss": 4.14686, "time": 0.70415} +{"mode": "train", "epoch": 11, "iter": 2900, "lr": 0.09873, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24719, "top5_acc": 0.50531, "loss_cls": 4.09595, "loss": 4.09595, "time": 0.70168} +{"mode": "train", "epoch": 11, "iter": 3000, "lr": 0.09873, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24484, "top5_acc": 0.49031, "loss_cls": 4.14867, "loss": 4.14867, "time": 0.69994} +{"mode": "train", "epoch": 11, "iter": 3100, "lr": 0.09872, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25281, "top5_acc": 0.49562, "loss_cls": 4.1036, "loss": 4.1036, "time": 0.69739} +{"mode": "train", "epoch": 11, "iter": 3200, "lr": 0.09871, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25531, "top5_acc": 0.50062, "loss_cls": 4.11582, "loss": 4.11582, "time": 0.69822} +{"mode": "train", "epoch": 11, "iter": 3300, "lr": 0.09871, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25656, "top5_acc": 0.49953, "loss_cls": 4.10304, "loss": 4.10304, "time": 0.6993} +{"mode": "train", "epoch": 11, "iter": 3400, "lr": 0.0987, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24969, "top5_acc": 0.4975, "loss_cls": 4.10283, "loss": 4.10283, "time": 0.70081} +{"mode": "train", "epoch": 11, "iter": 3500, "lr": 0.09869, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24422, "top5_acc": 0.48875, "loss_cls": 4.1494, "loss": 4.1494, "time": 0.70751} +{"mode": "train", "epoch": 11, "iter": 3600, "lr": 0.09869, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25172, "top5_acc": 0.49453, "loss_cls": 4.1421, "loss": 4.1421, "time": 0.70107} +{"mode": "train", "epoch": 11, "iter": 3700, "lr": 0.09868, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25016, "top5_acc": 0.49375, "loss_cls": 4.12764, "loss": 4.12764, "time": 0.69866} +{"mode": "val", "epoch": 11, "iter": 309, "lr": 0.09868, "top1_acc": 0.17145, "top5_acc": 0.38495, "mean_class_accuracy": 0.17135} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.09867, "memory": 15990, "data_time": 1.292, "top1_acc": 0.24875, "top5_acc": 0.49688, "loss_cls": 4.14574, "loss": 4.14574, "time": 1.99781} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.09867, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25484, "top5_acc": 0.49906, "loss_cls": 4.12667, "loss": 4.12667, "time": 0.70609} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.09866, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25203, "top5_acc": 0.4975, "loss_cls": 4.10278, "loss": 4.10278, "time": 0.70499} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.09865, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25875, "top5_acc": 0.50313, "loss_cls": 4.07491, "loss": 4.07491, "time": 0.70577} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.09865, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25344, "top5_acc": 0.49703, "loss_cls": 4.10476, "loss": 4.10476, "time": 0.70034} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.09864, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25953, "top5_acc": 0.49891, "loss_cls": 4.05657, "loss": 4.05657, "time": 0.70038} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.09863, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25547, "top5_acc": 0.49672, "loss_cls": 4.08425, "loss": 4.08425, "time": 0.70282} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.09863, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25609, "top5_acc": 0.49391, "loss_cls": 4.1241, "loss": 4.1241, "time": 0.70076} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.09862, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25891, "top5_acc": 0.50375, "loss_cls": 4.07409, "loss": 4.07409, "time": 0.69903} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.09861, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24906, "top5_acc": 0.49891, "loss_cls": 4.11996, "loss": 4.11996, "time": 0.7037} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.09861, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24859, "top5_acc": 0.48453, "loss_cls": 4.16769, "loss": 4.16769, "time": 0.70107} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.0986, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25062, "top5_acc": 0.50547, "loss_cls": 4.10388, "loss": 4.10388, "time": 0.70151} +{"mode": "train", "epoch": 12, "iter": 1300, "lr": 0.09859, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25062, "top5_acc": 0.48922, "loss_cls": 4.13196, "loss": 4.13196, "time": 0.70035} +{"mode": "train", "epoch": 12, "iter": 1400, "lr": 0.09859, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25641, "top5_acc": 0.4925, "loss_cls": 4.12624, "loss": 4.12624, "time": 0.70161} +{"mode": "train", "epoch": 12, "iter": 1500, "lr": 0.09858, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25469, "top5_acc": 0.49484, "loss_cls": 4.12968, "loss": 4.12968, "time": 0.70042} +{"mode": "train", "epoch": 12, "iter": 1600, "lr": 0.09857, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25281, "top5_acc": 0.49156, "loss_cls": 4.15032, "loss": 4.15032, "time": 0.70108} +{"mode": "train", "epoch": 12, "iter": 1700, "lr": 0.09857, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26125, "top5_acc": 0.50953, "loss_cls": 4.09203, "loss": 4.09203, "time": 0.70114} +{"mode": "train", "epoch": 12, "iter": 1800, "lr": 0.09856, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25141, "top5_acc": 0.49672, "loss_cls": 4.12458, "loss": 4.12458, "time": 0.70326} +{"mode": "train", "epoch": 12, "iter": 1900, "lr": 0.09855, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24562, "top5_acc": 0.48906, "loss_cls": 4.1334, "loss": 4.1334, "time": 0.70007} +{"mode": "train", "epoch": 12, "iter": 2000, "lr": 0.09855, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24406, "top5_acc": 0.49859, "loss_cls": 4.11985, "loss": 4.11985, "time": 0.70002} +{"mode": "train", "epoch": 12, "iter": 2100, "lr": 0.09854, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25844, "top5_acc": 0.49969, "loss_cls": 4.08035, "loss": 4.08035, "time": 0.70408} +{"mode": "train", "epoch": 12, "iter": 2200, "lr": 0.09853, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25797, "top5_acc": 0.49578, "loss_cls": 4.09809, "loss": 4.09809, "time": 0.7016} +{"mode": "train", "epoch": 12, "iter": 2300, "lr": 0.09853, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25609, "top5_acc": 0.505, "loss_cls": 4.09857, "loss": 4.09857, "time": 0.7017} +{"mode": "train", "epoch": 12, "iter": 2400, "lr": 0.09852, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24766, "top5_acc": 0.50734, "loss_cls": 4.09091, "loss": 4.09091, "time": 0.70131} +{"mode": "train", "epoch": 12, "iter": 2500, "lr": 0.09851, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25875, "top5_acc": 0.49734, "loss_cls": 4.12699, "loss": 4.12699, "time": 0.70111} +{"mode": "train", "epoch": 12, "iter": 2600, "lr": 0.09851, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25109, "top5_acc": 0.48875, "loss_cls": 4.13339, "loss": 4.13339, "time": 0.7047} +{"mode": "train", "epoch": 12, "iter": 2700, "lr": 0.0985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25609, "top5_acc": 0.51094, "loss_cls": 4.07146, "loss": 4.07146, "time": 0.6998} +{"mode": "train", "epoch": 12, "iter": 2800, "lr": 0.09849, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2375, "top5_acc": 0.48797, "loss_cls": 4.16206, "loss": 4.16206, "time": 0.70272} +{"mode": "train", "epoch": 12, "iter": 2900, "lr": 0.09849, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24266, "top5_acc": 0.49906, "loss_cls": 4.1207, "loss": 4.1207, "time": 0.70287} +{"mode": "train", "epoch": 12, "iter": 3000, "lr": 0.09848, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24547, "top5_acc": 0.48625, "loss_cls": 4.16869, "loss": 4.16869, "time": 0.69848} +{"mode": "train", "epoch": 12, "iter": 3100, "lr": 0.09847, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23875, "top5_acc": 0.49641, "loss_cls": 4.13747, "loss": 4.13747, "time": 0.70403} +{"mode": "train", "epoch": 12, "iter": 3200, "lr": 0.09847, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24891, "top5_acc": 0.49547, "loss_cls": 4.12933, "loss": 4.12933, "time": 0.7012} +{"mode": "train", "epoch": 12, "iter": 3300, "lr": 0.09846, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25609, "top5_acc": 0.49297, "loss_cls": 4.11614, "loss": 4.11614, "time": 0.70134} +{"mode": "train", "epoch": 12, "iter": 3400, "lr": 0.09845, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25031, "top5_acc": 0.50062, "loss_cls": 4.1032, "loss": 4.1032, "time": 0.69999} +{"mode": "train", "epoch": 12, "iter": 3500, "lr": 0.09845, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26641, "top5_acc": 0.505, "loss_cls": 4.09651, "loss": 4.09651, "time": 0.70595} +{"mode": "train", "epoch": 12, "iter": 3600, "lr": 0.09844, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25453, "top5_acc": 0.50703, "loss_cls": 4.06234, "loss": 4.06234, "time": 0.70239} +{"mode": "train", "epoch": 12, "iter": 3700, "lr": 0.09843, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24312, "top5_acc": 0.49031, "loss_cls": 4.13852, "loss": 4.13852, "time": 0.70374} +{"mode": "val", "epoch": 12, "iter": 309, "lr": 0.09843, "top1_acc": 0.19146, "top5_acc": 0.41488, "mean_class_accuracy": 0.19119} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.09842, "memory": 15990, "data_time": 1.26341, "top1_acc": 0.25828, "top5_acc": 0.50438, "loss_cls": 4.06835, "loss": 4.06835, "time": 1.97519} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.09842, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26531, "top5_acc": 0.51094, "loss_cls": 4.03612, "loss": 4.03612, "time": 0.70247} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.09841, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25094, "top5_acc": 0.50422, "loss_cls": 4.09061, "loss": 4.09061, "time": 0.70842} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.0984, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26172, "top5_acc": 0.51391, "loss_cls": 4.03929, "loss": 4.03929, "time": 0.70454} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.09839, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25047, "top5_acc": 0.49547, "loss_cls": 4.08826, "loss": 4.08826, "time": 0.70091} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.09839, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25328, "top5_acc": 0.49891, "loss_cls": 4.11343, "loss": 4.11343, "time": 0.70512} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.09838, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24828, "top5_acc": 0.50062, "loss_cls": 4.10617, "loss": 4.10617, "time": 0.70292} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.09837, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25203, "top5_acc": 0.4925, "loss_cls": 4.11755, "loss": 4.11755, "time": 0.69966} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.09837, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24781, "top5_acc": 0.49719, "loss_cls": 4.12471, "loss": 4.12471, "time": 0.70175} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.09836, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25328, "top5_acc": 0.50484, "loss_cls": 4.10105, "loss": 4.10105, "time": 0.69958} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.09835, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25641, "top5_acc": 0.49766, "loss_cls": 4.12916, "loss": 4.12916, "time": 0.70698} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.09834, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25844, "top5_acc": 0.49375, "loss_cls": 4.09094, "loss": 4.09094, "time": 0.70238} +{"mode": "train", "epoch": 13, "iter": 1300, "lr": 0.09834, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26031, "top5_acc": 0.50469, "loss_cls": 4.07443, "loss": 4.07443, "time": 0.70145} +{"mode": "train", "epoch": 13, "iter": 1400, "lr": 0.09833, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2525, "top5_acc": 0.49797, "loss_cls": 4.12683, "loss": 4.12683, "time": 0.70293} +{"mode": "train", "epoch": 13, "iter": 1500, "lr": 0.09832, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25406, "top5_acc": 0.50234, "loss_cls": 4.08963, "loss": 4.08963, "time": 0.69915} +{"mode": "train", "epoch": 13, "iter": 1600, "lr": 0.09832, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2475, "top5_acc": 0.50219, "loss_cls": 4.09684, "loss": 4.09684, "time": 0.69817} +{"mode": "train", "epoch": 13, "iter": 1700, "lr": 0.09831, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25297, "top5_acc": 0.50047, "loss_cls": 4.10887, "loss": 4.10887, "time": 0.70462} +{"mode": "train", "epoch": 13, "iter": 1800, "lr": 0.0983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25828, "top5_acc": 0.50656, "loss_cls": 4.07982, "loss": 4.07982, "time": 0.69908} +{"mode": "train", "epoch": 13, "iter": 1900, "lr": 0.09829, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25125, "top5_acc": 0.49094, "loss_cls": 4.12992, "loss": 4.12992, "time": 0.69937} +{"mode": "train", "epoch": 13, "iter": 2000, "lr": 0.09829, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23672, "top5_acc": 0.49922, "loss_cls": 4.16514, "loss": 4.16514, "time": 0.70371} +{"mode": "train", "epoch": 13, "iter": 2100, "lr": 0.09828, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24469, "top5_acc": 0.48094, "loss_cls": 4.18267, "loss": 4.18267, "time": 0.69791} +{"mode": "train", "epoch": 13, "iter": 2200, "lr": 0.09827, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25047, "top5_acc": 0.49375, "loss_cls": 4.1367, "loss": 4.1367, "time": 0.70096} +{"mode": "train", "epoch": 13, "iter": 2300, "lr": 0.09827, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24922, "top5_acc": 0.49906, "loss_cls": 4.14268, "loss": 4.14268, "time": 0.69899} +{"mode": "train", "epoch": 13, "iter": 2400, "lr": 0.09826, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24734, "top5_acc": 0.495, "loss_cls": 4.13853, "loss": 4.13853, "time": 0.69888} +{"mode": "train", "epoch": 13, "iter": 2500, "lr": 0.09825, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24641, "top5_acc": 0.49453, "loss_cls": 4.10949, "loss": 4.10949, "time": 0.70278} +{"mode": "train", "epoch": 13, "iter": 2600, "lr": 0.09824, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24547, "top5_acc": 0.49969, "loss_cls": 4.09679, "loss": 4.09679, "time": 0.70119} +{"mode": "train", "epoch": 13, "iter": 2700, "lr": 0.09824, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25344, "top5_acc": 0.50125, "loss_cls": 4.10886, "loss": 4.10886, "time": 0.69973} +{"mode": "train", "epoch": 13, "iter": 2800, "lr": 0.09823, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25016, "top5_acc": 0.49672, "loss_cls": 4.11784, "loss": 4.11784, "time": 0.6993} +{"mode": "train", "epoch": 13, "iter": 2900, "lr": 0.09822, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25219, "top5_acc": 0.49859, "loss_cls": 4.10165, "loss": 4.10165, "time": 0.70129} +{"mode": "train", "epoch": 13, "iter": 3000, "lr": 0.09821, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25828, "top5_acc": 0.50719, "loss_cls": 4.06674, "loss": 4.06674, "time": 0.70711} +{"mode": "train", "epoch": 13, "iter": 3100, "lr": 0.09821, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25484, "top5_acc": 0.49766, "loss_cls": 4.09929, "loss": 4.09929, "time": 0.70256} +{"mode": "train", "epoch": 13, "iter": 3200, "lr": 0.0982, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25266, "top5_acc": 0.49984, "loss_cls": 4.11289, "loss": 4.11289, "time": 0.70548} +{"mode": "train", "epoch": 13, "iter": 3300, "lr": 0.09819, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25219, "top5_acc": 0.50047, "loss_cls": 4.099, "loss": 4.099, "time": 0.70189} +{"mode": "train", "epoch": 13, "iter": 3400, "lr": 0.09818, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26297, "top5_acc": 0.50813, "loss_cls": 4.0905, "loss": 4.0905, "time": 0.70056} +{"mode": "train", "epoch": 13, "iter": 3500, "lr": 0.09818, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25766, "top5_acc": 0.49891, "loss_cls": 4.09114, "loss": 4.09114, "time": 0.70395} +{"mode": "train", "epoch": 13, "iter": 3600, "lr": 0.09817, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24219, "top5_acc": 0.48406, "loss_cls": 4.15142, "loss": 4.15142, "time": 0.70172} +{"mode": "train", "epoch": 13, "iter": 3700, "lr": 0.09816, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24969, "top5_acc": 0.49125, "loss_cls": 4.10648, "loss": 4.10648, "time": 0.70282} +{"mode": "val", "epoch": 13, "iter": 309, "lr": 0.09816, "top1_acc": 0.18432, "top5_acc": 0.40779, "mean_class_accuracy": 0.18406} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.09815, "memory": 15990, "data_time": 1.27962, "top1_acc": 0.25891, "top5_acc": 0.50047, "loss_cls": 4.04609, "loss": 4.04609, "time": 1.98387} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.09814, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25922, "top5_acc": 0.50562, "loss_cls": 4.06326, "loss": 4.06326, "time": 0.70896} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.09814, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26016, "top5_acc": 0.51422, "loss_cls": 4.05454, "loss": 4.05454, "time": 0.7056} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.09813, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25172, "top5_acc": 0.49438, "loss_cls": 4.11486, "loss": 4.11486, "time": 0.70526} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.09812, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25172, "top5_acc": 0.50375, "loss_cls": 4.0918, "loss": 4.0918, "time": 0.7024} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.09811, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2525, "top5_acc": 0.50297, "loss_cls": 4.11342, "loss": 4.11342, "time": 0.6982} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.09811, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25359, "top5_acc": 0.50688, "loss_cls": 4.10292, "loss": 4.10292, "time": 0.70042} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.0981, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25203, "top5_acc": 0.49125, "loss_cls": 4.11644, "loss": 4.11644, "time": 0.70188} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.09809, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26562, "top5_acc": 0.50906, "loss_cls": 4.06374, "loss": 4.06374, "time": 0.70322} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.09808, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25703, "top5_acc": 0.50734, "loss_cls": 4.0584, "loss": 4.0584, "time": 0.70306} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.09807, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25422, "top5_acc": 0.50094, "loss_cls": 4.08587, "loss": 4.08587, "time": 0.70127} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.09807, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25078, "top5_acc": 0.50391, "loss_cls": 4.10365, "loss": 4.10365, "time": 0.70281} +{"mode": "train", "epoch": 14, "iter": 1300, "lr": 0.09806, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26016, "top5_acc": 0.50328, "loss_cls": 4.10758, "loss": 4.10758, "time": 0.70088} +{"mode": "train", "epoch": 14, "iter": 1400, "lr": 0.09805, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24531, "top5_acc": 0.49422, "loss_cls": 4.12738, "loss": 4.12738, "time": 0.70263} +{"mode": "train", "epoch": 14, "iter": 1500, "lr": 0.09804, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.255, "top5_acc": 0.50156, "loss_cls": 4.09071, "loss": 4.09071, "time": 0.70077} +{"mode": "train", "epoch": 14, "iter": 1600, "lr": 0.09804, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24922, "top5_acc": 0.50453, "loss_cls": 4.08343, "loss": 4.08343, "time": 0.70124} +{"mode": "train", "epoch": 14, "iter": 1700, "lr": 0.09803, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25422, "top5_acc": 0.50688, "loss_cls": 4.09057, "loss": 4.09057, "time": 0.70049} +{"mode": "train", "epoch": 14, "iter": 1800, "lr": 0.09802, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25109, "top5_acc": 0.49672, "loss_cls": 4.10195, "loss": 4.10195, "time": 0.70438} +{"mode": "train", "epoch": 14, "iter": 1900, "lr": 0.09801, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25797, "top5_acc": 0.51062, "loss_cls": 4.0734, "loss": 4.0734, "time": 0.70351} +{"mode": "train", "epoch": 14, "iter": 2000, "lr": 0.098, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25516, "top5_acc": 0.50047, "loss_cls": 4.08336, "loss": 4.08336, "time": 0.70092} +{"mode": "train", "epoch": 14, "iter": 2100, "lr": 0.098, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25484, "top5_acc": 0.50109, "loss_cls": 4.12989, "loss": 4.12989, "time": 0.70339} +{"mode": "train", "epoch": 14, "iter": 2200, "lr": 0.09799, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25094, "top5_acc": 0.49422, "loss_cls": 4.09818, "loss": 4.09818, "time": 0.69998} +{"mode": "train", "epoch": 14, "iter": 2300, "lr": 0.09798, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25391, "top5_acc": 0.50844, "loss_cls": 4.08693, "loss": 4.08693, "time": 0.6973} +{"mode": "train", "epoch": 14, "iter": 2400, "lr": 0.09797, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25109, "top5_acc": 0.50531, "loss_cls": 4.0938, "loss": 4.0938, "time": 0.70538} +{"mode": "train", "epoch": 14, "iter": 2500, "lr": 0.09797, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25453, "top5_acc": 0.49844, "loss_cls": 4.07316, "loss": 4.07316, "time": 0.70371} +{"mode": "train", "epoch": 14, "iter": 2600, "lr": 0.09796, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25719, "top5_acc": 0.50891, "loss_cls": 4.07226, "loss": 4.07226, "time": 0.69863} +{"mode": "train", "epoch": 14, "iter": 2700, "lr": 0.09795, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25188, "top5_acc": 0.49156, "loss_cls": 4.10223, "loss": 4.10223, "time": 0.70098} +{"mode": "train", "epoch": 14, "iter": 2800, "lr": 0.09794, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25531, "top5_acc": 0.49875, "loss_cls": 4.10949, "loss": 4.10949, "time": 0.70316} +{"mode": "train", "epoch": 14, "iter": 2900, "lr": 0.09793, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25703, "top5_acc": 0.49375, "loss_cls": 4.09385, "loss": 4.09385, "time": 0.69983} +{"mode": "train", "epoch": 14, "iter": 3000, "lr": 0.09793, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24906, "top5_acc": 0.49719, "loss_cls": 4.11653, "loss": 4.11653, "time": 0.70117} +{"mode": "train", "epoch": 14, "iter": 3100, "lr": 0.09792, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26359, "top5_acc": 0.50125, "loss_cls": 4.08897, "loss": 4.08897, "time": 0.6988} +{"mode": "train", "epoch": 14, "iter": 3200, "lr": 0.09791, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24875, "top5_acc": 0.50609, "loss_cls": 4.09519, "loss": 4.09519, "time": 0.70165} +{"mode": "train", "epoch": 14, "iter": 3300, "lr": 0.0979, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24781, "top5_acc": 0.50078, "loss_cls": 4.10387, "loss": 4.10387, "time": 0.70173} +{"mode": "train", "epoch": 14, "iter": 3400, "lr": 0.09789, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25516, "top5_acc": 0.50281, "loss_cls": 4.06991, "loss": 4.06991, "time": 0.7025} +{"mode": "train", "epoch": 14, "iter": 3500, "lr": 0.09789, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25734, "top5_acc": 0.49812, "loss_cls": 4.1169, "loss": 4.1169, "time": 0.70481} +{"mode": "train", "epoch": 14, "iter": 3600, "lr": 0.09788, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24594, "top5_acc": 0.49625, "loss_cls": 4.10803, "loss": 4.10803, "time": 0.7031} +{"mode": "train", "epoch": 14, "iter": 3700, "lr": 0.09787, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26047, "top5_acc": 0.50109, "loss_cls": 4.07776, "loss": 4.07776, "time": 0.70027} +{"mode": "val", "epoch": 14, "iter": 309, "lr": 0.09787, "top1_acc": 0.18123, "top5_acc": 0.40597, "mean_class_accuracy": 0.18103} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.09786, "memory": 15990, "data_time": 1.27763, "top1_acc": 0.25547, "top5_acc": 0.50422, "loss_cls": 4.09149, "loss": 4.09149, "time": 1.9823} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.09785, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25391, "top5_acc": 0.49656, "loss_cls": 4.12072, "loss": 4.12072, "time": 0.70256} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.09784, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25672, "top5_acc": 0.505, "loss_cls": 4.06099, "loss": 4.06099, "time": 0.71439} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.09783, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26203, "top5_acc": 0.50656, "loss_cls": 4.04859, "loss": 4.04859, "time": 0.70446} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.09783, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25859, "top5_acc": 0.49453, "loss_cls": 4.08944, "loss": 4.08944, "time": 0.70312} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.09782, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25422, "top5_acc": 0.50281, "loss_cls": 4.08438, "loss": 4.08438, "time": 0.70255} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.09781, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25453, "top5_acc": 0.49859, "loss_cls": 4.12431, "loss": 4.12431, "time": 0.69929} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.0978, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26484, "top5_acc": 0.5, "loss_cls": 4.07167, "loss": 4.07167, "time": 0.70615} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.09779, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26609, "top5_acc": 0.51406, "loss_cls": 4.06395, "loss": 4.06395, "time": 0.69779} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.09778, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2425, "top5_acc": 0.49781, "loss_cls": 4.11799, "loss": 4.11799, "time": 0.69927} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.09778, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25344, "top5_acc": 0.50609, "loss_cls": 4.08654, "loss": 4.08654, "time": 0.69978} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.09777, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25859, "top5_acc": 0.50594, "loss_cls": 4.0955, "loss": 4.0955, "time": 0.69922} +{"mode": "train", "epoch": 15, "iter": 1300, "lr": 0.09776, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24, "top5_acc": 0.48453, "loss_cls": 4.15816, "loss": 4.15816, "time": 0.69672} +{"mode": "train", "epoch": 15, "iter": 1400, "lr": 0.09775, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25, "top5_acc": 0.5, "loss_cls": 4.08936, "loss": 4.08936, "time": 0.7014} +{"mode": "train", "epoch": 15, "iter": 1500, "lr": 0.09774, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25812, "top5_acc": 0.50688, "loss_cls": 4.0784, "loss": 4.0784, "time": 0.69862} +{"mode": "train", "epoch": 15, "iter": 1600, "lr": 0.09773, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25438, "top5_acc": 0.50594, "loss_cls": 4.06471, "loss": 4.06471, "time": 0.69936} +{"mode": "train", "epoch": 15, "iter": 1700, "lr": 0.09773, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24969, "top5_acc": 0.49641, "loss_cls": 4.12748, "loss": 4.12748, "time": 0.70056} +{"mode": "train", "epoch": 15, "iter": 1800, "lr": 0.09772, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26062, "top5_acc": 0.49781, "loss_cls": 4.10802, "loss": 4.10802, "time": 0.69964} +{"mode": "train", "epoch": 15, "iter": 1900, "lr": 0.09771, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25516, "top5_acc": 0.50031, "loss_cls": 4.11497, "loss": 4.11497, "time": 0.69878} +{"mode": "train", "epoch": 15, "iter": 2000, "lr": 0.0977, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25516, "top5_acc": 0.50438, "loss_cls": 4.09684, "loss": 4.09684, "time": 0.69824} +{"mode": "train", "epoch": 15, "iter": 2100, "lr": 0.09769, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24828, "top5_acc": 0.49641, "loss_cls": 4.09462, "loss": 4.09462, "time": 0.69833} +{"mode": "train", "epoch": 15, "iter": 2200, "lr": 0.09768, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25609, "top5_acc": 0.50453, "loss_cls": 4.08799, "loss": 4.08799, "time": 0.69706} +{"mode": "train", "epoch": 15, "iter": 2300, "lr": 0.09768, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24641, "top5_acc": 0.49047, "loss_cls": 4.13123, "loss": 4.13123, "time": 0.70105} +{"mode": "train", "epoch": 15, "iter": 2400, "lr": 0.09767, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25438, "top5_acc": 0.50187, "loss_cls": 4.10291, "loss": 4.10291, "time": 0.70069} +{"mode": "train", "epoch": 15, "iter": 2500, "lr": 0.09766, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24688, "top5_acc": 0.49062, "loss_cls": 4.12984, "loss": 4.12984, "time": 0.70027} +{"mode": "train", "epoch": 15, "iter": 2600, "lr": 0.09765, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26188, "top5_acc": 0.50391, "loss_cls": 4.06029, "loss": 4.06029, "time": 0.70025} +{"mode": "train", "epoch": 15, "iter": 2700, "lr": 0.09764, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25625, "top5_acc": 0.50562, "loss_cls": 4.08565, "loss": 4.08565, "time": 0.69906} +{"mode": "train", "epoch": 15, "iter": 2800, "lr": 0.09763, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26438, "top5_acc": 0.5075, "loss_cls": 4.05867, "loss": 4.05867, "time": 0.69783} +{"mode": "train", "epoch": 15, "iter": 2900, "lr": 0.09763, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25797, "top5_acc": 0.51531, "loss_cls": 4.05975, "loss": 4.05975, "time": 0.69966} +{"mode": "train", "epoch": 15, "iter": 3000, "lr": 0.09762, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24906, "top5_acc": 0.50187, "loss_cls": 4.09466, "loss": 4.09466, "time": 0.70347} +{"mode": "train", "epoch": 15, "iter": 3100, "lr": 0.09761, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24734, "top5_acc": 0.49719, "loss_cls": 4.113, "loss": 4.113, "time": 0.69673} +{"mode": "train", "epoch": 15, "iter": 3200, "lr": 0.0976, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24812, "top5_acc": 0.49906, "loss_cls": 4.11888, "loss": 4.11888, "time": 0.70064} +{"mode": "train", "epoch": 15, "iter": 3300, "lr": 0.09759, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25922, "top5_acc": 0.49703, "loss_cls": 4.0736, "loss": 4.0736, "time": 0.69824} +{"mode": "train", "epoch": 15, "iter": 3400, "lr": 0.09758, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24969, "top5_acc": 0.50141, "loss_cls": 4.08219, "loss": 4.08219, "time": 0.69948} +{"mode": "train", "epoch": 15, "iter": 3500, "lr": 0.09757, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25562, "top5_acc": 0.49688, "loss_cls": 4.10886, "loss": 4.10886, "time": 0.70323} +{"mode": "train", "epoch": 15, "iter": 3600, "lr": 0.09757, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25344, "top5_acc": 0.50203, "loss_cls": 4.06345, "loss": 4.06345, "time": 0.6992} +{"mode": "train", "epoch": 15, "iter": 3700, "lr": 0.09756, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26031, "top5_acc": 0.50172, "loss_cls": 4.09742, "loss": 4.09742, "time": 0.69926} +{"mode": "val", "epoch": 15, "iter": 309, "lr": 0.09755, "top1_acc": 0.17277, "top5_acc": 0.39204, "mean_class_accuracy": 0.17252} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.09754, "memory": 15990, "data_time": 1.31242, "top1_acc": 0.26078, "top5_acc": 0.52, "loss_cls": 4.04403, "loss": 4.04403, "time": 2.01961} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.09754, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26062, "top5_acc": 0.51453, "loss_cls": 4.02776, "loss": 4.02776, "time": 0.70331} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.09753, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25609, "top5_acc": 0.50672, "loss_cls": 4.06911, "loss": 4.06911, "time": 0.7114} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.09752, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26344, "top5_acc": 0.50922, "loss_cls": 4.05076, "loss": 4.05076, "time": 0.70799} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.09751, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25984, "top5_acc": 0.495, "loss_cls": 4.04524, "loss": 4.04524, "time": 0.70685} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.0975, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25172, "top5_acc": 0.50578, "loss_cls": 4.08968, "loss": 4.08968, "time": 0.7006} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.09749, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.265, "top5_acc": 0.50813, "loss_cls": 4.05516, "loss": 4.05516, "time": 0.70021} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.09748, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24938, "top5_acc": 0.49516, "loss_cls": 4.094, "loss": 4.094, "time": 0.6997} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.09747, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24859, "top5_acc": 0.50234, "loss_cls": 4.10974, "loss": 4.10974, "time": 0.69929} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.09747, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26719, "top5_acc": 0.50688, "loss_cls": 4.06824, "loss": 4.06824, "time": 0.70004} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.09746, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25719, "top5_acc": 0.50078, "loss_cls": 4.07262, "loss": 4.07262, "time": 0.7009} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.09745, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25281, "top5_acc": 0.50094, "loss_cls": 4.07401, "loss": 4.07401, "time": 0.70124} +{"mode": "train", "epoch": 16, "iter": 1300, "lr": 0.09744, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24484, "top5_acc": 0.49562, "loss_cls": 4.11977, "loss": 4.11977, "time": 0.69769} +{"mode": "train", "epoch": 16, "iter": 1400, "lr": 0.09743, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25078, "top5_acc": 0.50719, "loss_cls": 4.07434, "loss": 4.07434, "time": 0.69619} +{"mode": "train", "epoch": 16, "iter": 1500, "lr": 0.09742, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25688, "top5_acc": 0.50375, "loss_cls": 4.06862, "loss": 4.06862, "time": 0.69932} +{"mode": "train", "epoch": 16, "iter": 1600, "lr": 0.09741, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25375, "top5_acc": 0.49859, "loss_cls": 4.10073, "loss": 4.10073, "time": 0.69778} +{"mode": "train", "epoch": 16, "iter": 1700, "lr": 0.0974, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25688, "top5_acc": 0.51031, "loss_cls": 4.06819, "loss": 4.06819, "time": 0.69739} +{"mode": "train", "epoch": 16, "iter": 1800, "lr": 0.0974, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25766, "top5_acc": 0.50031, "loss_cls": 4.06781, "loss": 4.06781, "time": 0.69734} +{"mode": "train", "epoch": 16, "iter": 1900, "lr": 0.09739, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.255, "top5_acc": 0.50141, "loss_cls": 4.10324, "loss": 4.10324, "time": 0.70243} +{"mode": "train", "epoch": 16, "iter": 2000, "lr": 0.09738, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24781, "top5_acc": 0.50453, "loss_cls": 4.11056, "loss": 4.11056, "time": 0.69778} +{"mode": "train", "epoch": 16, "iter": 2100, "lr": 0.09737, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25422, "top5_acc": 0.50562, "loss_cls": 4.06594, "loss": 4.06594, "time": 0.69549} +{"mode": "train", "epoch": 16, "iter": 2200, "lr": 0.09736, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25938, "top5_acc": 0.50484, "loss_cls": 4.10202, "loss": 4.10202, "time": 0.69904} +{"mode": "train", "epoch": 16, "iter": 2300, "lr": 0.09735, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.255, "top5_acc": 0.50047, "loss_cls": 4.11473, "loss": 4.11473, "time": 0.69883} +{"mode": "train", "epoch": 16, "iter": 2400, "lr": 0.09734, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25047, "top5_acc": 0.48781, "loss_cls": 4.13665, "loss": 4.13665, "time": 0.69602} +{"mode": "train", "epoch": 16, "iter": 2500, "lr": 0.09733, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26078, "top5_acc": 0.50453, "loss_cls": 4.07064, "loss": 4.07064, "time": 0.69876} +{"mode": "train", "epoch": 16, "iter": 2600, "lr": 0.09732, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25328, "top5_acc": 0.50016, "loss_cls": 4.10287, "loss": 4.10287, "time": 0.70286} +{"mode": "train", "epoch": 16, "iter": 2700, "lr": 0.09731, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26062, "top5_acc": 0.50234, "loss_cls": 4.0889, "loss": 4.0889, "time": 0.69928} +{"mode": "train", "epoch": 16, "iter": 2800, "lr": 0.09731, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26422, "top5_acc": 0.4925, "loss_cls": 4.08013, "loss": 4.08013, "time": 0.69764} +{"mode": "train", "epoch": 16, "iter": 2900, "lr": 0.0973, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26031, "top5_acc": 0.50109, "loss_cls": 4.09384, "loss": 4.09384, "time": 0.69695} +{"mode": "train", "epoch": 16, "iter": 3000, "lr": 0.09729, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25438, "top5_acc": 0.50547, "loss_cls": 4.09439, "loss": 4.09439, "time": 0.69962} +{"mode": "train", "epoch": 16, "iter": 3100, "lr": 0.09728, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25031, "top5_acc": 0.48625, "loss_cls": 4.15102, "loss": 4.15102, "time": 0.7017} +{"mode": "train", "epoch": 16, "iter": 3200, "lr": 0.09727, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26172, "top5_acc": 0.50328, "loss_cls": 4.07759, "loss": 4.07759, "time": 0.69883} +{"mode": "train", "epoch": 16, "iter": 3300, "lr": 0.09726, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24781, "top5_acc": 0.50453, "loss_cls": 4.1001, "loss": 4.1001, "time": 0.6984} +{"mode": "train", "epoch": 16, "iter": 3400, "lr": 0.09725, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26031, "top5_acc": 0.505, "loss_cls": 4.06995, "loss": 4.06995, "time": 0.70003} +{"mode": "train", "epoch": 16, "iter": 3500, "lr": 0.09724, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25438, "top5_acc": 0.49703, "loss_cls": 4.10825, "loss": 4.10825, "time": 0.70317} +{"mode": "train", "epoch": 16, "iter": 3600, "lr": 0.09723, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26375, "top5_acc": 0.51703, "loss_cls": 4.05726, "loss": 4.05726, "time": 0.69919} +{"mode": "train", "epoch": 16, "iter": 3700, "lr": 0.09722, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26062, "top5_acc": 0.49562, "loss_cls": 4.09235, "loss": 4.09235, "time": 0.70096} +{"mode": "val", "epoch": 16, "iter": 309, "lr": 0.09722, "top1_acc": 0.1673, "top5_acc": 0.38029, "mean_class_accuracy": 0.16714} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.09721, "memory": 15990, "data_time": 1.2886, "top1_acc": 0.26062, "top5_acc": 0.50891, "loss_cls": 4.06493, "loss": 4.06493, "time": 1.99213} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.0972, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24688, "top5_acc": 0.50203, "loss_cls": 4.09891, "loss": 4.09891, "time": 0.70682} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.09719, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26297, "top5_acc": 0.51969, "loss_cls": 4.00111, "loss": 4.00111, "time": 0.71279} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.09718, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24938, "top5_acc": 0.50109, "loss_cls": 4.10903, "loss": 4.10903, "time": 0.70295} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.09717, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2475, "top5_acc": 0.50281, "loss_cls": 4.06571, "loss": 4.06571, "time": 0.7043} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.09716, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26219, "top5_acc": 0.51047, "loss_cls": 4.06096, "loss": 4.06096, "time": 0.70329} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.09715, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25766, "top5_acc": 0.50734, "loss_cls": 4.06915, "loss": 4.06915, "time": 0.70179} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.09714, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26297, "top5_acc": 0.52047, "loss_cls": 4.00633, "loss": 4.00633, "time": 0.69924} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.09714, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26078, "top5_acc": 0.51031, "loss_cls": 4.06585, "loss": 4.06585, "time": 0.70213} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.09713, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26078, "top5_acc": 0.51031, "loss_cls": 4.05776, "loss": 4.05776, "time": 0.69771} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.09712, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25859, "top5_acc": 0.50406, "loss_cls": 4.07499, "loss": 4.07499, "time": 0.69766} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.09711, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25203, "top5_acc": 0.50469, "loss_cls": 4.08177, "loss": 4.08177, "time": 0.6983} +{"mode": "train", "epoch": 17, "iter": 1300, "lr": 0.0971, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25719, "top5_acc": 0.50766, "loss_cls": 4.08668, "loss": 4.08668, "time": 0.69735} +{"mode": "train", "epoch": 17, "iter": 1400, "lr": 0.09709, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25859, "top5_acc": 0.50844, "loss_cls": 4.04687, "loss": 4.04687, "time": 0.7001} +{"mode": "train", "epoch": 17, "iter": 1500, "lr": 0.09708, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26203, "top5_acc": 0.50547, "loss_cls": 4.05259, "loss": 4.05259, "time": 0.69894} +{"mode": "train", "epoch": 17, "iter": 1600, "lr": 0.09707, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24422, "top5_acc": 0.49406, "loss_cls": 4.12374, "loss": 4.12374, "time": 0.69729} +{"mode": "train", "epoch": 17, "iter": 1700, "lr": 0.09706, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26453, "top5_acc": 0.52156, "loss_cls": 4.01931, "loss": 4.01931, "time": 0.70139} +{"mode": "train", "epoch": 17, "iter": 1800, "lr": 0.09705, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25234, "top5_acc": 0.49703, "loss_cls": 4.10516, "loss": 4.10516, "time": 0.69834} +{"mode": "train", "epoch": 17, "iter": 1900, "lr": 0.09704, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.255, "top5_acc": 0.49469, "loss_cls": 4.09697, "loss": 4.09697, "time": 0.69569} +{"mode": "train", "epoch": 17, "iter": 2000, "lr": 0.09703, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26016, "top5_acc": 0.50906, "loss_cls": 4.07167, "loss": 4.07167, "time": 0.69985} +{"mode": "train", "epoch": 17, "iter": 2100, "lr": 0.09702, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25422, "top5_acc": 0.49281, "loss_cls": 4.12636, "loss": 4.12636, "time": 0.69779} +{"mode": "train", "epoch": 17, "iter": 2200, "lr": 0.09701, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26172, "top5_acc": 0.50672, "loss_cls": 4.0616, "loss": 4.0616, "time": 0.69797} +{"mode": "train", "epoch": 17, "iter": 2300, "lr": 0.097, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25828, "top5_acc": 0.50328, "loss_cls": 4.08954, "loss": 4.08954, "time": 0.70521} +{"mode": "train", "epoch": 17, "iter": 2400, "lr": 0.09699, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26781, "top5_acc": 0.51438, "loss_cls": 4.03792, "loss": 4.03792, "time": 0.69736} +{"mode": "train", "epoch": 17, "iter": 2500, "lr": 0.09698, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25359, "top5_acc": 0.49906, "loss_cls": 4.11815, "loss": 4.11815, "time": 0.69905} +{"mode": "train", "epoch": 17, "iter": 2600, "lr": 0.09697, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26469, "top5_acc": 0.51297, "loss_cls": 4.02406, "loss": 4.02406, "time": 0.69997} +{"mode": "train", "epoch": 17, "iter": 2700, "lr": 0.09697, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25734, "top5_acc": 0.51078, "loss_cls": 4.05539, "loss": 4.05539, "time": 0.699} +{"mode": "train", "epoch": 17, "iter": 2800, "lr": 0.09696, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26859, "top5_acc": 0.51125, "loss_cls": 4.03373, "loss": 4.03373, "time": 0.70205} +{"mode": "train", "epoch": 17, "iter": 2900, "lr": 0.09695, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25984, "top5_acc": 0.50453, "loss_cls": 4.04132, "loss": 4.04132, "time": 0.70018} +{"mode": "train", "epoch": 17, "iter": 3000, "lr": 0.09694, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26031, "top5_acc": 0.50125, "loss_cls": 4.06555, "loss": 4.06555, "time": 0.6988} +{"mode": "train", "epoch": 17, "iter": 3100, "lr": 0.09693, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26109, "top5_acc": 0.50078, "loss_cls": 4.07523, "loss": 4.07523, "time": 0.6985} +{"mode": "train", "epoch": 17, "iter": 3200, "lr": 0.09692, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24844, "top5_acc": 0.50234, "loss_cls": 4.09974, "loss": 4.09974, "time": 0.70129} +{"mode": "train", "epoch": 17, "iter": 3300, "lr": 0.09691, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24266, "top5_acc": 0.48797, "loss_cls": 4.15013, "loss": 4.15013, "time": 0.69648} +{"mode": "train", "epoch": 17, "iter": 3400, "lr": 0.0969, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25875, "top5_acc": 0.50281, "loss_cls": 4.08356, "loss": 4.08356, "time": 0.70241} +{"mode": "train", "epoch": 17, "iter": 3500, "lr": 0.09689, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24922, "top5_acc": 0.50297, "loss_cls": 4.11897, "loss": 4.11897, "time": 0.70462} +{"mode": "train", "epoch": 17, "iter": 3600, "lr": 0.09688, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25875, "top5_acc": 0.50422, "loss_cls": 4.0904, "loss": 4.0904, "time": 0.70346} +{"mode": "train", "epoch": 17, "iter": 3700, "lr": 0.09687, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26453, "top5_acc": 0.51031, "loss_cls": 4.06432, "loss": 4.06432, "time": 0.70289} +{"mode": "val", "epoch": 17, "iter": 309, "lr": 0.09686, "top1_acc": 0.16538, "top5_acc": 0.36469, "mean_class_accuracy": 0.16518} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.09685, "memory": 15990, "data_time": 1.2825, "top1_acc": 0.26328, "top5_acc": 0.51797, "loss_cls": 4.02742, "loss": 4.02742, "time": 1.9875} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.09684, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2675, "top5_acc": 0.5125, "loss_cls": 4.02431, "loss": 4.02431, "time": 0.70412} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.09683, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25172, "top5_acc": 0.50375, "loss_cls": 4.08421, "loss": 4.08421, "time": 0.71299} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.09683, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26219, "top5_acc": 0.51281, "loss_cls": 4.05497, "loss": 4.05497, "time": 0.70516} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.09682, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2575, "top5_acc": 0.50844, "loss_cls": 4.06031, "loss": 4.06031, "time": 0.70719} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.09681, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26094, "top5_acc": 0.50328, "loss_cls": 4.07411, "loss": 4.07411, "time": 0.70495} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.0968, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25422, "top5_acc": 0.49766, "loss_cls": 4.08657, "loss": 4.08657, "time": 0.70121} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.09679, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25297, "top5_acc": 0.50875, "loss_cls": 4.08425, "loss": 4.08425, "time": 0.70104} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.09678, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25453, "top5_acc": 0.50844, "loss_cls": 4.08024, "loss": 4.08024, "time": 0.7008} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.09677, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26672, "top5_acc": 0.51422, "loss_cls": 4.04547, "loss": 4.04547, "time": 0.70132} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.09676, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25891, "top5_acc": 0.50813, "loss_cls": 4.07813, "loss": 4.07813, "time": 0.69922} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.09675, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26453, "top5_acc": 0.51, "loss_cls": 4.05674, "loss": 4.05674, "time": 0.70132} +{"mode": "train", "epoch": 18, "iter": 1300, "lr": 0.09674, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26031, "top5_acc": 0.5, "loss_cls": 4.07497, "loss": 4.07497, "time": 0.69845} +{"mode": "train", "epoch": 18, "iter": 1400, "lr": 0.09673, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25984, "top5_acc": 0.50641, "loss_cls": 4.07255, "loss": 4.07255, "time": 0.69917} +{"mode": "train", "epoch": 18, "iter": 1500, "lr": 0.09672, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25422, "top5_acc": 0.50094, "loss_cls": 4.09371, "loss": 4.09371, "time": 0.69813} +{"mode": "train", "epoch": 18, "iter": 1600, "lr": 0.09671, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25953, "top5_acc": 0.50938, "loss_cls": 4.02171, "loss": 4.02171, "time": 0.70045} +{"mode": "train", "epoch": 18, "iter": 1700, "lr": 0.0967, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27, "top5_acc": 0.50781, "loss_cls": 4.0568, "loss": 4.0568, "time": 0.69997} +{"mode": "train", "epoch": 18, "iter": 1800, "lr": 0.09669, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26281, "top5_acc": 0.50938, "loss_cls": 4.04425, "loss": 4.04425, "time": 0.69928} +{"mode": "train", "epoch": 18, "iter": 1900, "lr": 0.09668, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25391, "top5_acc": 0.50891, "loss_cls": 4.05916, "loss": 4.05916, "time": 0.69686} +{"mode": "train", "epoch": 18, "iter": 2000, "lr": 0.09667, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25703, "top5_acc": 0.50828, "loss_cls": 4.0877, "loss": 4.0877, "time": 0.69822} +{"mode": "train", "epoch": 18, "iter": 2100, "lr": 0.09666, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25891, "top5_acc": 0.50734, "loss_cls": 4.05773, "loss": 4.05773, "time": 0.69829} +{"mode": "train", "epoch": 18, "iter": 2200, "lr": 0.09665, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26375, "top5_acc": 0.50484, "loss_cls": 4.06571, "loss": 4.06571, "time": 0.69908} +{"mode": "train", "epoch": 18, "iter": 2300, "lr": 0.09664, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25375, "top5_acc": 0.49781, "loss_cls": 4.10319, "loss": 4.10319, "time": 0.69944} +{"mode": "train", "epoch": 18, "iter": 2400, "lr": 0.09663, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25125, "top5_acc": 0.49656, "loss_cls": 4.10661, "loss": 4.10661, "time": 0.69938} +{"mode": "train", "epoch": 18, "iter": 2500, "lr": 0.09662, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25688, "top5_acc": 0.49875, "loss_cls": 4.11206, "loss": 4.11206, "time": 0.69914} +{"mode": "train", "epoch": 18, "iter": 2600, "lr": 0.09661, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26062, "top5_acc": 0.51266, "loss_cls": 4.02797, "loss": 4.02797, "time": 0.69721} +{"mode": "train", "epoch": 18, "iter": 2700, "lr": 0.0966, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27016, "top5_acc": 0.50734, "loss_cls": 4.06807, "loss": 4.06807, "time": 0.69908} +{"mode": "train", "epoch": 18, "iter": 2800, "lr": 0.09659, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25312, "top5_acc": 0.49531, "loss_cls": 4.08417, "loss": 4.08417, "time": 0.69899} +{"mode": "train", "epoch": 18, "iter": 2900, "lr": 0.09658, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25609, "top5_acc": 0.50172, "loss_cls": 4.08323, "loss": 4.08323, "time": 0.69735} +{"mode": "train", "epoch": 18, "iter": 3000, "lr": 0.09657, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26188, "top5_acc": 0.50844, "loss_cls": 4.06753, "loss": 4.06753, "time": 0.69892} +{"mode": "train", "epoch": 18, "iter": 3100, "lr": 0.09656, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25875, "top5_acc": 0.50688, "loss_cls": 4.05296, "loss": 4.05296, "time": 0.69772} +{"mode": "train", "epoch": 18, "iter": 3200, "lr": 0.09654, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25672, "top5_acc": 0.50172, "loss_cls": 4.10337, "loss": 4.10337, "time": 0.69988} +{"mode": "train", "epoch": 18, "iter": 3300, "lr": 0.09653, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26203, "top5_acc": 0.51281, "loss_cls": 4.03319, "loss": 4.03319, "time": 0.70075} +{"mode": "train", "epoch": 18, "iter": 3400, "lr": 0.09652, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25422, "top5_acc": 0.49859, "loss_cls": 4.08358, "loss": 4.08358, "time": 0.69967} +{"mode": "train", "epoch": 18, "iter": 3500, "lr": 0.09651, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26047, "top5_acc": 0.50781, "loss_cls": 4.05987, "loss": 4.05987, "time": 0.70373} +{"mode": "train", "epoch": 18, "iter": 3600, "lr": 0.0965, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25469, "top5_acc": 0.49469, "loss_cls": 4.13356, "loss": 4.13356, "time": 0.70077} +{"mode": "train", "epoch": 18, "iter": 3700, "lr": 0.09649, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25734, "top5_acc": 0.50375, "loss_cls": 4.07983, "loss": 4.07983, "time": 0.70083} +{"mode": "val", "epoch": 18, "iter": 309, "lr": 0.09649, "top1_acc": 0.17951, "top5_acc": 0.40759, "mean_class_accuracy": 0.17921} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.09648, "memory": 15990, "data_time": 1.28573, "top1_acc": 0.26812, "top5_acc": 0.51141, "loss_cls": 4.03134, "loss": 4.03134, "time": 1.98986} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.09647, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25828, "top5_acc": 0.51391, "loss_cls": 4.05796, "loss": 4.05796, "time": 0.7068} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.09646, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26281, "top5_acc": 0.51125, "loss_cls": 4.04667, "loss": 4.04667, "time": 0.71088} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.09645, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25422, "top5_acc": 0.50813, "loss_cls": 4.05307, "loss": 4.05307, "time": 0.70392} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.09644, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26516, "top5_acc": 0.51656, "loss_cls": 4.03655, "loss": 4.03655, "time": 0.70619} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.09643, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2625, "top5_acc": 0.50766, "loss_cls": 4.06688, "loss": 4.06688, "time": 0.70794} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.09642, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26516, "top5_acc": 0.50844, "loss_cls": 4.05266, "loss": 4.05266, "time": 0.70261} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.09641, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26969, "top5_acc": 0.51047, "loss_cls": 4.03953, "loss": 4.03953, "time": 0.70163} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.0964, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27625, "top5_acc": 0.51797, "loss_cls": 3.99387, "loss": 3.99387, "time": 0.69712} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.09639, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.255, "top5_acc": 0.50141, "loss_cls": 4.07215, "loss": 4.07215, "time": 0.70051} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.09637, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26234, "top5_acc": 0.51531, "loss_cls": 4.00456, "loss": 4.00456, "time": 0.69767} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.09636, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26078, "top5_acc": 0.51219, "loss_cls": 4.04016, "loss": 4.04016, "time": 0.69611} +{"mode": "train", "epoch": 19, "iter": 1300, "lr": 0.09635, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25297, "top5_acc": 0.50484, "loss_cls": 4.0937, "loss": 4.0937, "time": 0.69936} +{"mode": "train", "epoch": 19, "iter": 1400, "lr": 0.09634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25766, "top5_acc": 0.50594, "loss_cls": 4.05369, "loss": 4.05369, "time": 0.69934} +{"mode": "train", "epoch": 19, "iter": 1500, "lr": 0.09633, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25969, "top5_acc": 0.50891, "loss_cls": 4.04886, "loss": 4.04886, "time": 0.69894} +{"mode": "train", "epoch": 19, "iter": 1600, "lr": 0.09632, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24844, "top5_acc": 0.49453, "loss_cls": 4.1316, "loss": 4.1316, "time": 0.69723} +{"mode": "train", "epoch": 19, "iter": 1700, "lr": 0.09631, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26266, "top5_acc": 0.50531, "loss_cls": 4.03834, "loss": 4.03834, "time": 0.69734} +{"mode": "train", "epoch": 19, "iter": 1800, "lr": 0.0963, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26937, "top5_acc": 0.51469, "loss_cls": 4.01696, "loss": 4.01696, "time": 0.69839} +{"mode": "train", "epoch": 19, "iter": 1900, "lr": 0.09629, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27516, "top5_acc": 0.52406, "loss_cls": 4.00893, "loss": 4.00893, "time": 0.69848} +{"mode": "train", "epoch": 19, "iter": 2000, "lr": 0.09628, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25109, "top5_acc": 0.50109, "loss_cls": 4.07927, "loss": 4.07927, "time": 0.69731} +{"mode": "train", "epoch": 19, "iter": 2100, "lr": 0.09627, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26641, "top5_acc": 0.50891, "loss_cls": 4.05696, "loss": 4.05696, "time": 0.70113} +{"mode": "train", "epoch": 19, "iter": 2200, "lr": 0.09626, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26469, "top5_acc": 0.51375, "loss_cls": 4.03028, "loss": 4.03028, "time": 0.69717} +{"mode": "train", "epoch": 19, "iter": 2300, "lr": 0.09625, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25141, "top5_acc": 0.50125, "loss_cls": 4.1014, "loss": 4.1014, "time": 0.7018} +{"mode": "train", "epoch": 19, "iter": 2400, "lr": 0.09624, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25594, "top5_acc": 0.50141, "loss_cls": 4.10306, "loss": 4.10306, "time": 0.69763} +{"mode": "train", "epoch": 19, "iter": 2500, "lr": 0.09623, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26031, "top5_acc": 0.50984, "loss_cls": 4.06672, "loss": 4.06672, "time": 0.69945} +{"mode": "train", "epoch": 19, "iter": 2600, "lr": 0.09622, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26469, "top5_acc": 0.51469, "loss_cls": 4.02906, "loss": 4.02906, "time": 0.69954} +{"mode": "train", "epoch": 19, "iter": 2700, "lr": 0.09621, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25875, "top5_acc": 0.50172, "loss_cls": 4.0676, "loss": 4.0676, "time": 0.69994} +{"mode": "train", "epoch": 19, "iter": 2800, "lr": 0.0962, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26641, "top5_acc": 0.5125, "loss_cls": 4.03716, "loss": 4.03716, "time": 0.6979} +{"mode": "train", "epoch": 19, "iter": 2900, "lr": 0.09618, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26812, "top5_acc": 0.51391, "loss_cls": 4.0217, "loss": 4.0217, "time": 0.69878} +{"mode": "train", "epoch": 19, "iter": 3000, "lr": 0.09617, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26031, "top5_acc": 0.50234, "loss_cls": 4.08924, "loss": 4.08924, "time": 0.69908} +{"mode": "train", "epoch": 19, "iter": 3100, "lr": 0.09616, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25484, "top5_acc": 0.50141, "loss_cls": 4.08816, "loss": 4.08816, "time": 0.70147} +{"mode": "train", "epoch": 19, "iter": 3200, "lr": 0.09615, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26172, "top5_acc": 0.5025, "loss_cls": 4.09679, "loss": 4.09679, "time": 0.69779} +{"mode": "train", "epoch": 19, "iter": 3300, "lr": 0.09614, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25781, "top5_acc": 0.50609, "loss_cls": 4.0966, "loss": 4.0966, "time": 0.70014} +{"mode": "train", "epoch": 19, "iter": 3400, "lr": 0.09613, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26344, "top5_acc": 0.51453, "loss_cls": 4.05091, "loss": 4.05091, "time": 0.70213} +{"mode": "train", "epoch": 19, "iter": 3500, "lr": 0.09612, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25859, "top5_acc": 0.51469, "loss_cls": 4.04524, "loss": 4.04524, "time": 0.70187} +{"mode": "train", "epoch": 19, "iter": 3600, "lr": 0.09611, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.255, "top5_acc": 0.49969, "loss_cls": 4.08444, "loss": 4.08444, "time": 0.69844} +{"mode": "train", "epoch": 19, "iter": 3700, "lr": 0.0961, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26141, "top5_acc": 0.50453, "loss_cls": 4.0702, "loss": 4.0702, "time": 0.6998} +{"mode": "val", "epoch": 19, "iter": 309, "lr": 0.09609, "top1_acc": 0.17267, "top5_acc": 0.39877, "mean_class_accuracy": 0.17237} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.09608, "memory": 15990, "data_time": 1.27977, "top1_acc": 0.26531, "top5_acc": 0.51031, "loss_cls": 4.00873, "loss": 4.00873, "time": 1.98477} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.09607, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26359, "top5_acc": 0.51, "loss_cls": 4.02918, "loss": 4.02918, "time": 0.70472} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.09606, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27047, "top5_acc": 0.52609, "loss_cls": 4.00241, "loss": 4.00241, "time": 0.71003} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.09605, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26641, "top5_acc": 0.51625, "loss_cls": 4.00408, "loss": 4.00408, "time": 0.70385} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.09604, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25984, "top5_acc": 0.51031, "loss_cls": 4.04527, "loss": 4.04527, "time": 0.70364} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.09603, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26234, "top5_acc": 0.50953, "loss_cls": 4.05908, "loss": 4.05908, "time": 0.70258} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.09602, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25828, "top5_acc": 0.50609, "loss_cls": 4.057, "loss": 4.057, "time": 0.70031} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.09601, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25516, "top5_acc": 0.49375, "loss_cls": 4.11469, "loss": 4.11469, "time": 0.69937} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.096, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26438, "top5_acc": 0.50953, "loss_cls": 4.0215, "loss": 4.0215, "time": 0.69833} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.09598, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27062, "top5_acc": 0.51594, "loss_cls": 4.02169, "loss": 4.02169, "time": 0.69763} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.09597, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25781, "top5_acc": 0.50453, "loss_cls": 4.06578, "loss": 4.06578, "time": 0.69888} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.09596, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25891, "top5_acc": 0.50047, "loss_cls": 4.07261, "loss": 4.07261, "time": 0.69985} +{"mode": "train", "epoch": 20, "iter": 1300, "lr": 0.09595, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26359, "top5_acc": 0.51094, "loss_cls": 4.03626, "loss": 4.03626, "time": 0.69813} +{"mode": "train", "epoch": 20, "iter": 1400, "lr": 0.09594, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26453, "top5_acc": 0.51016, "loss_cls": 4.04343, "loss": 4.04343, "time": 0.69866} +{"mode": "train", "epoch": 20, "iter": 1500, "lr": 0.09593, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27047, "top5_acc": 0.51531, "loss_cls": 4.01342, "loss": 4.01342, "time": 0.69975} +{"mode": "train", "epoch": 20, "iter": 1600, "lr": 0.09592, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25328, "top5_acc": 0.50156, "loss_cls": 4.0827, "loss": 4.0827, "time": 0.69722} +{"mode": "train", "epoch": 20, "iter": 1700, "lr": 0.09591, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26297, "top5_acc": 0.51656, "loss_cls": 4.01082, "loss": 4.01082, "time": 0.698} +{"mode": "train", "epoch": 20, "iter": 1800, "lr": 0.0959, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26359, "top5_acc": 0.50734, "loss_cls": 4.07333, "loss": 4.07333, "time": 0.69915} +{"mode": "train", "epoch": 20, "iter": 1900, "lr": 0.09588, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26547, "top5_acc": 0.51391, "loss_cls": 4.03679, "loss": 4.03679, "time": 0.69704} +{"mode": "train", "epoch": 20, "iter": 2000, "lr": 0.09587, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25938, "top5_acc": 0.50203, "loss_cls": 4.09097, "loss": 4.09097, "time": 0.69641} +{"mode": "train", "epoch": 20, "iter": 2100, "lr": 0.09586, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25328, "top5_acc": 0.50656, "loss_cls": 4.06801, "loss": 4.06801, "time": 0.69686} +{"mode": "train", "epoch": 20, "iter": 2200, "lr": 0.09585, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26141, "top5_acc": 0.51516, "loss_cls": 4.06763, "loss": 4.06763, "time": 0.69839} +{"mode": "train", "epoch": 20, "iter": 2300, "lr": 0.09584, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25719, "top5_acc": 0.50594, "loss_cls": 4.08198, "loss": 4.08198, "time": 0.69968} +{"mode": "train", "epoch": 20, "iter": 2400, "lr": 0.09583, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27, "top5_acc": 0.51516, "loss_cls": 4.01611, "loss": 4.01611, "time": 0.69996} +{"mode": "train", "epoch": 20, "iter": 2500, "lr": 0.09582, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25984, "top5_acc": 0.51203, "loss_cls": 4.02816, "loss": 4.02816, "time": 0.697} +{"mode": "train", "epoch": 20, "iter": 2600, "lr": 0.09581, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25766, "top5_acc": 0.50828, "loss_cls": 4.06794, "loss": 4.06794, "time": 0.70135} +{"mode": "train", "epoch": 20, "iter": 2700, "lr": 0.0958, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25109, "top5_acc": 0.50828, "loss_cls": 4.06366, "loss": 4.06366, "time": 0.69878} +{"mode": "train", "epoch": 20, "iter": 2800, "lr": 0.09578, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25688, "top5_acc": 0.50813, "loss_cls": 4.05613, "loss": 4.05613, "time": 0.69753} +{"mode": "train", "epoch": 20, "iter": 2900, "lr": 0.09577, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26547, "top5_acc": 0.51375, "loss_cls": 4.06679, "loss": 4.06679, "time": 0.70012} +{"mode": "train", "epoch": 20, "iter": 3000, "lr": 0.09576, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26312, "top5_acc": 0.50531, "loss_cls": 4.07499, "loss": 4.07499, "time": 0.69956} +{"mode": "train", "epoch": 20, "iter": 3100, "lr": 0.09575, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27016, "top5_acc": 0.51109, "loss_cls": 4.04705, "loss": 4.04705, "time": 0.69948} +{"mode": "train", "epoch": 20, "iter": 3200, "lr": 0.09574, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25422, "top5_acc": 0.50297, "loss_cls": 4.0804, "loss": 4.0804, "time": 0.69825} +{"mode": "train", "epoch": 20, "iter": 3300, "lr": 0.09573, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26812, "top5_acc": 0.52234, "loss_cls": 4.02485, "loss": 4.02485, "time": 0.69943} +{"mode": "train", "epoch": 20, "iter": 3400, "lr": 0.09572, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.245, "top5_acc": 0.50078, "loss_cls": 4.11629, "loss": 4.11629, "time": 0.70218} +{"mode": "train", "epoch": 20, "iter": 3500, "lr": 0.09571, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26422, "top5_acc": 0.505, "loss_cls": 4.04953, "loss": 4.04953, "time": 0.70252} +{"mode": "train", "epoch": 20, "iter": 3600, "lr": 0.09569, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26172, "top5_acc": 0.50109, "loss_cls": 4.07703, "loss": 4.07703, "time": 0.69887} +{"mode": "train", "epoch": 20, "iter": 3700, "lr": 0.09568, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25938, "top5_acc": 0.51188, "loss_cls": 4.04564, "loss": 4.04564, "time": 0.70006} +{"mode": "val", "epoch": 20, "iter": 309, "lr": 0.09568, "top1_acc": 0.18548, "top5_acc": 0.40217, "mean_class_accuracy": 0.18527} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.09567, "memory": 15990, "data_time": 1.28376, "top1_acc": 0.27078, "top5_acc": 0.53094, "loss_cls": 3.96252, "loss": 3.96252, "time": 1.99032} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.09565, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26312, "top5_acc": 0.51203, "loss_cls": 4.03748, "loss": 4.03748, "time": 0.70365} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.09564, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25938, "top5_acc": 0.51578, "loss_cls": 4.04503, "loss": 4.04503, "time": 0.70769} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.09563, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26766, "top5_acc": 0.52078, "loss_cls": 3.99944, "loss": 3.99944, "time": 0.70311} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.09562, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25062, "top5_acc": 0.50766, "loss_cls": 4.068, "loss": 4.068, "time": 0.70593} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.09561, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26344, "top5_acc": 0.50859, "loss_cls": 4.05385, "loss": 4.05385, "time": 0.70285} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.0956, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25547, "top5_acc": 0.50125, "loss_cls": 4.06648, "loss": 4.06648, "time": 0.70203} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.09559, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2575, "top5_acc": 0.52047, "loss_cls": 4.03391, "loss": 4.03391, "time": 0.6984} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.09557, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26, "top5_acc": 0.50609, "loss_cls": 4.07525, "loss": 4.07525, "time": 0.70079} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.09556, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25203, "top5_acc": 0.50187, "loss_cls": 4.08411, "loss": 4.08411, "time": 0.69687} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.09555, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26281, "top5_acc": 0.51344, "loss_cls": 4.028, "loss": 4.028, "time": 0.70126} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.09554, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25844, "top5_acc": 0.51094, "loss_cls": 4.03033, "loss": 4.03033, "time": 0.70008} +{"mode": "train", "epoch": 21, "iter": 1300, "lr": 0.09553, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25547, "top5_acc": 0.50953, "loss_cls": 4.05918, "loss": 4.05918, "time": 0.69953} +{"mode": "train", "epoch": 21, "iter": 1400, "lr": 0.09552, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.265, "top5_acc": 0.50203, "loss_cls": 4.06828, "loss": 4.06828, "time": 0.69748} +{"mode": "train", "epoch": 21, "iter": 1500, "lr": 0.09551, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25766, "top5_acc": 0.50094, "loss_cls": 4.05747, "loss": 4.05747, "time": 0.69715} +{"mode": "train", "epoch": 21, "iter": 1600, "lr": 0.09549, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26875, "top5_acc": 0.51375, "loss_cls": 4.02588, "loss": 4.02588, "time": 0.69794} +{"mode": "train", "epoch": 21, "iter": 1700, "lr": 0.09548, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25156, "top5_acc": 0.50609, "loss_cls": 4.10342, "loss": 4.10342, "time": 0.6977} +{"mode": "train", "epoch": 21, "iter": 1800, "lr": 0.09547, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25766, "top5_acc": 0.50406, "loss_cls": 4.09172, "loss": 4.09172, "time": 0.70027} +{"mode": "train", "epoch": 21, "iter": 1900, "lr": 0.09546, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26703, "top5_acc": 0.51297, "loss_cls": 4.02984, "loss": 4.02984, "time": 0.69724} +{"mode": "train", "epoch": 21, "iter": 2000, "lr": 0.09545, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25734, "top5_acc": 0.50906, "loss_cls": 4.05163, "loss": 4.05163, "time": 0.69917} +{"mode": "train", "epoch": 21, "iter": 2100, "lr": 0.09544, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26125, "top5_acc": 0.50719, "loss_cls": 4.05214, "loss": 4.05214, "time": 0.69865} +{"mode": "train", "epoch": 21, "iter": 2200, "lr": 0.09542, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25266, "top5_acc": 0.50313, "loss_cls": 4.09856, "loss": 4.09856, "time": 0.69827} +{"mode": "train", "epoch": 21, "iter": 2300, "lr": 0.09541, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24453, "top5_acc": 0.49734, "loss_cls": 4.09899, "loss": 4.09899, "time": 0.69692} +{"mode": "train", "epoch": 21, "iter": 2400, "lr": 0.0954, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26062, "top5_acc": 0.49906, "loss_cls": 4.07346, "loss": 4.07346, "time": 0.6973} +{"mode": "train", "epoch": 21, "iter": 2500, "lr": 0.09539, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26625, "top5_acc": 0.50516, "loss_cls": 4.07623, "loss": 4.07623, "time": 0.70078} +{"mode": "train", "epoch": 21, "iter": 2600, "lr": 0.09538, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25625, "top5_acc": 0.50984, "loss_cls": 4.05564, "loss": 4.05564, "time": 0.69754} +{"mode": "train", "epoch": 21, "iter": 2700, "lr": 0.09537, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26062, "top5_acc": 0.50922, "loss_cls": 4.07905, "loss": 4.07905, "time": 0.6998} +{"mode": "train", "epoch": 21, "iter": 2800, "lr": 0.09535, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26656, "top5_acc": 0.50875, "loss_cls": 4.06611, "loss": 4.06611, "time": 0.69981} +{"mode": "train", "epoch": 21, "iter": 2900, "lr": 0.09534, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26172, "top5_acc": 0.5175, "loss_cls": 4.01126, "loss": 4.01126, "time": 0.70049} +{"mode": "train", "epoch": 21, "iter": 3000, "lr": 0.09533, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25953, "top5_acc": 0.51297, "loss_cls": 4.05156, "loss": 4.05156, "time": 0.69745} +{"mode": "train", "epoch": 21, "iter": 3100, "lr": 0.09532, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26406, "top5_acc": 0.50859, "loss_cls": 4.05663, "loss": 4.05663, "time": 0.70128} +{"mode": "train", "epoch": 21, "iter": 3200, "lr": 0.09531, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25797, "top5_acc": 0.50281, "loss_cls": 4.0925, "loss": 4.0925, "time": 0.69639} +{"mode": "train", "epoch": 21, "iter": 3300, "lr": 0.09529, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26219, "top5_acc": 0.50391, "loss_cls": 4.05463, "loss": 4.05463, "time": 0.70097} +{"mode": "train", "epoch": 21, "iter": 3400, "lr": 0.09528, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26562, "top5_acc": 0.51156, "loss_cls": 4.04655, "loss": 4.04655, "time": 0.6996} +{"mode": "train", "epoch": 21, "iter": 3500, "lr": 0.09527, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25781, "top5_acc": 0.50766, "loss_cls": 4.07193, "loss": 4.07193, "time": 0.70212} +{"mode": "train", "epoch": 21, "iter": 3600, "lr": 0.09526, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26281, "top5_acc": 0.51078, "loss_cls": 4.03581, "loss": 4.03581, "time": 0.69788} +{"mode": "train", "epoch": 21, "iter": 3700, "lr": 0.09525, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27312, "top5_acc": 0.51188, "loss_cls": 4.05335, "loss": 4.05335, "time": 0.70198} +{"mode": "val", "epoch": 21, "iter": 309, "lr": 0.09524, "top1_acc": 0.1632, "top5_acc": 0.36985, "mean_class_accuracy": 0.1631} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.09523, "memory": 15990, "data_time": 1.29899, "top1_acc": 0.27016, "top5_acc": 0.52297, "loss_cls": 4.00225, "loss": 4.00225, "time": 2.00701} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.09522, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27047, "top5_acc": 0.52047, "loss_cls": 4.0189, "loss": 4.0189, "time": 0.7037} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.09521, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26203, "top5_acc": 0.51266, "loss_cls": 4.02809, "loss": 4.02809, "time": 0.70754} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.09519, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25156, "top5_acc": 0.50438, "loss_cls": 4.05403, "loss": 4.05403, "time": 0.70022} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.09518, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25641, "top5_acc": 0.50781, "loss_cls": 4.0575, "loss": 4.0575, "time": 0.70552} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.09517, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25391, "top5_acc": 0.50813, "loss_cls": 4.06308, "loss": 4.06308, "time": 0.70671} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.09516, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26391, "top5_acc": 0.51516, "loss_cls": 4.02862, "loss": 4.02862, "time": 0.69831} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.09515, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26438, "top5_acc": 0.50844, "loss_cls": 4.05894, "loss": 4.05894, "time": 0.70106} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.09513, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25406, "top5_acc": 0.50875, "loss_cls": 4.06019, "loss": 4.06019, "time": 0.6996} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.09512, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25562, "top5_acc": 0.50844, "loss_cls": 4.0778, "loss": 4.0778, "time": 0.70175} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.09511, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26109, "top5_acc": 0.51047, "loss_cls": 4.05534, "loss": 4.05534, "time": 0.70118} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0951, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26719, "top5_acc": 0.51922, "loss_cls": 4.02056, "loss": 4.02056, "time": 0.69902} +{"mode": "train", "epoch": 22, "iter": 1300, "lr": 0.09509, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26016, "top5_acc": 0.51359, "loss_cls": 4.04574, "loss": 4.04574, "time": 0.7} +{"mode": "train", "epoch": 22, "iter": 1400, "lr": 0.09507, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27531, "top5_acc": 0.52656, "loss_cls": 3.96798, "loss": 3.96798, "time": 0.70163} +{"mode": "train", "epoch": 22, "iter": 1500, "lr": 0.09506, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25844, "top5_acc": 0.515, "loss_cls": 4.04262, "loss": 4.04262, "time": 0.69781} +{"mode": "train", "epoch": 22, "iter": 1600, "lr": 0.09505, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25953, "top5_acc": 0.51359, "loss_cls": 4.04936, "loss": 4.04936, "time": 0.70214} +{"mode": "train", "epoch": 22, "iter": 1700, "lr": 0.09504, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25766, "top5_acc": 0.50828, "loss_cls": 4.0628, "loss": 4.0628, "time": 0.69725} +{"mode": "train", "epoch": 22, "iter": 1800, "lr": 0.09502, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26203, "top5_acc": 0.50484, "loss_cls": 4.05615, "loss": 4.05615, "time": 0.70107} +{"mode": "train", "epoch": 22, "iter": 1900, "lr": 0.09501, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26094, "top5_acc": 0.50562, "loss_cls": 4.05305, "loss": 4.05305, "time": 0.69608} +{"mode": "train", "epoch": 22, "iter": 2000, "lr": 0.095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26297, "top5_acc": 0.52359, "loss_cls": 4.0038, "loss": 4.0038, "time": 0.70067} +{"mode": "train", "epoch": 22, "iter": 2100, "lr": 0.09499, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27, "top5_acc": 0.51391, "loss_cls": 4.0201, "loss": 4.0201, "time": 0.70102} +{"mode": "train", "epoch": 22, "iter": 2200, "lr": 0.09498, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26281, "top5_acc": 0.50844, "loss_cls": 4.04627, "loss": 4.04627, "time": 0.70024} +{"mode": "train", "epoch": 22, "iter": 2300, "lr": 0.09496, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26656, "top5_acc": 0.51859, "loss_cls": 3.99625, "loss": 3.99625, "time": 0.7003} +{"mode": "train", "epoch": 22, "iter": 2400, "lr": 0.09495, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25703, "top5_acc": 0.51344, "loss_cls": 4.04585, "loss": 4.04585, "time": 0.69835} +{"mode": "train", "epoch": 22, "iter": 2500, "lr": 0.09494, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25953, "top5_acc": 0.50719, "loss_cls": 4.05925, "loss": 4.05925, "time": 0.70228} +{"mode": "train", "epoch": 22, "iter": 2600, "lr": 0.09493, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25828, "top5_acc": 0.50672, "loss_cls": 4.07564, "loss": 4.07564, "time": 0.69961} +{"mode": "train", "epoch": 22, "iter": 2700, "lr": 0.09491, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24156, "top5_acc": 0.48766, "loss_cls": 4.12232, "loss": 4.12232, "time": 0.70164} +{"mode": "train", "epoch": 22, "iter": 2800, "lr": 0.0949, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25578, "top5_acc": 0.505, "loss_cls": 4.08779, "loss": 4.08779, "time": 0.69998} +{"mode": "train", "epoch": 22, "iter": 2900, "lr": 0.09489, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26344, "top5_acc": 0.50656, "loss_cls": 4.05555, "loss": 4.05555, "time": 0.69836} +{"mode": "train", "epoch": 22, "iter": 3000, "lr": 0.09488, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26578, "top5_acc": 0.51812, "loss_cls": 4.00345, "loss": 4.00345, "time": 0.69833} +{"mode": "train", "epoch": 22, "iter": 3100, "lr": 0.09487, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27141, "top5_acc": 0.51625, "loss_cls": 4.01657, "loss": 4.01657, "time": 0.69727} +{"mode": "train", "epoch": 22, "iter": 3200, "lr": 0.09485, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26516, "top5_acc": 0.50344, "loss_cls": 4.06989, "loss": 4.06989, "time": 0.70003} +{"mode": "train", "epoch": 22, "iter": 3300, "lr": 0.09484, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26156, "top5_acc": 0.50016, "loss_cls": 4.10982, "loss": 4.10982, "time": 0.69903} +{"mode": "train", "epoch": 22, "iter": 3400, "lr": 0.09483, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26438, "top5_acc": 0.51062, "loss_cls": 4.03387, "loss": 4.03387, "time": 0.70273} +{"mode": "train", "epoch": 22, "iter": 3500, "lr": 0.09482, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26344, "top5_acc": 0.49875, "loss_cls": 4.0532, "loss": 4.0532, "time": 0.70298} +{"mode": "train", "epoch": 22, "iter": 3600, "lr": 0.0948, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26609, "top5_acc": 0.51344, "loss_cls": 4.01615, "loss": 4.01615, "time": 0.70233} +{"mode": "train", "epoch": 22, "iter": 3700, "lr": 0.09479, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26375, "top5_acc": 0.51391, "loss_cls": 4.05426, "loss": 4.05426, "time": 0.71002} +{"mode": "val", "epoch": 22, "iter": 309, "lr": 0.09479, "top1_acc": 0.19921, "top5_acc": 0.42577, "mean_class_accuracy": 0.19894} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.09477, "memory": 15990, "data_time": 1.31027, "top1_acc": 0.26562, "top5_acc": 0.51844, "loss_cls": 3.99794, "loss": 3.99794, "time": 2.01592} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.09476, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27703, "top5_acc": 0.535, "loss_cls": 3.9536, "loss": 3.9536, "time": 0.71022} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.09475, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27016, "top5_acc": 0.51938, "loss_cls": 4.00079, "loss": 4.00079, "time": 0.70978} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.09474, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27156, "top5_acc": 0.52016, "loss_cls": 3.97992, "loss": 3.97992, "time": 0.70394} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.09472, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26969, "top5_acc": 0.51875, "loss_cls": 4.01835, "loss": 4.01835, "time": 0.70805} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.09471, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26188, "top5_acc": 0.515, "loss_cls": 4.03077, "loss": 4.03077, "time": 0.70547} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.0947, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26406, "top5_acc": 0.50422, "loss_cls": 4.04136, "loss": 4.04136, "time": 0.69763} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.09469, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25656, "top5_acc": 0.51313, "loss_cls": 4.05828, "loss": 4.05828, "time": 0.69899} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.09467, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26188, "top5_acc": 0.50313, "loss_cls": 4.04402, "loss": 4.04402, "time": 0.69916} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.09466, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26312, "top5_acc": 0.50688, "loss_cls": 4.06049, "loss": 4.06049, "time": 0.69871} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.09465, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25953, "top5_acc": 0.50281, "loss_cls": 4.06505, "loss": 4.06505, "time": 0.69944} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.09464, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26312, "top5_acc": 0.50766, "loss_cls": 4.04692, "loss": 4.04692, "time": 0.69911} +{"mode": "train", "epoch": 23, "iter": 1300, "lr": 0.09462, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25422, "top5_acc": 0.49625, "loss_cls": 4.07957, "loss": 4.07957, "time": 0.70027} +{"mode": "train", "epoch": 23, "iter": 1400, "lr": 0.09461, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26328, "top5_acc": 0.50516, "loss_cls": 4.023, "loss": 4.023, "time": 0.70175} +{"mode": "train", "epoch": 23, "iter": 1500, "lr": 0.0946, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26625, "top5_acc": 0.51844, "loss_cls": 4.02038, "loss": 4.02038, "time": 0.70092} +{"mode": "train", "epoch": 23, "iter": 1600, "lr": 0.09459, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27219, "top5_acc": 0.51266, "loss_cls": 4.01947, "loss": 4.01947, "time": 0.69818} +{"mode": "train", "epoch": 23, "iter": 1700, "lr": 0.09457, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.265, "top5_acc": 0.51375, "loss_cls": 4.0438, "loss": 4.0438, "time": 0.69866} +{"mode": "train", "epoch": 23, "iter": 1800, "lr": 0.09456, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25969, "top5_acc": 0.49875, "loss_cls": 4.07171, "loss": 4.07171, "time": 0.70089} +{"mode": "train", "epoch": 23, "iter": 1900, "lr": 0.09455, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.265, "top5_acc": 0.50859, "loss_cls": 4.06395, "loss": 4.06395, "time": 0.69852} +{"mode": "train", "epoch": 23, "iter": 2000, "lr": 0.09453, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27031, "top5_acc": 0.52328, "loss_cls": 3.97253, "loss": 3.97253, "time": 0.69859} +{"mode": "train", "epoch": 23, "iter": 2100, "lr": 0.09452, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24969, "top5_acc": 0.49516, "loss_cls": 4.1284, "loss": 4.1284, "time": 0.69948} +{"mode": "train", "epoch": 23, "iter": 2200, "lr": 0.09451, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25828, "top5_acc": 0.49734, "loss_cls": 4.08099, "loss": 4.08099, "time": 0.69647} +{"mode": "train", "epoch": 23, "iter": 2300, "lr": 0.0945, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25781, "top5_acc": 0.50516, "loss_cls": 4.06002, "loss": 4.06002, "time": 0.6993} +{"mode": "train", "epoch": 23, "iter": 2400, "lr": 0.09448, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26047, "top5_acc": 0.51266, "loss_cls": 4.04818, "loss": 4.04818, "time": 0.69998} +{"mode": "train", "epoch": 23, "iter": 2500, "lr": 0.09447, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26906, "top5_acc": 0.50969, "loss_cls": 4.02916, "loss": 4.02916, "time": 0.69681} +{"mode": "train", "epoch": 23, "iter": 2600, "lr": 0.09446, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26812, "top5_acc": 0.50625, "loss_cls": 4.0504, "loss": 4.0504, "time": 0.69946} +{"mode": "train", "epoch": 23, "iter": 2700, "lr": 0.09445, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25812, "top5_acc": 0.50469, "loss_cls": 4.09112, "loss": 4.09112, "time": 0.69913} +{"mode": "train", "epoch": 23, "iter": 2800, "lr": 0.09443, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25391, "top5_acc": 0.49734, "loss_cls": 4.07695, "loss": 4.07695, "time": 0.7011} +{"mode": "train", "epoch": 23, "iter": 2900, "lr": 0.09442, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26859, "top5_acc": 0.51734, "loss_cls": 4.02283, "loss": 4.02283, "time": 0.69763} +{"mode": "train", "epoch": 23, "iter": 3000, "lr": 0.09441, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25453, "top5_acc": 0.50391, "loss_cls": 4.08417, "loss": 4.08417, "time": 0.69842} +{"mode": "train", "epoch": 23, "iter": 3100, "lr": 0.09439, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26188, "top5_acc": 0.50266, "loss_cls": 4.06588, "loss": 4.06588, "time": 0.69813} +{"mode": "train", "epoch": 23, "iter": 3200, "lr": 0.09438, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26078, "top5_acc": 0.51188, "loss_cls": 4.02388, "loss": 4.02388, "time": 0.69823} +{"mode": "train", "epoch": 23, "iter": 3300, "lr": 0.09437, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26797, "top5_acc": 0.51078, "loss_cls": 4.03124, "loss": 4.03124, "time": 0.69995} +{"mode": "train", "epoch": 23, "iter": 3400, "lr": 0.09436, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26531, "top5_acc": 0.51688, "loss_cls": 4.03597, "loss": 4.03597, "time": 0.69812} +{"mode": "train", "epoch": 23, "iter": 3500, "lr": 0.09434, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27953, "top5_acc": 0.51797, "loss_cls": 4.00298, "loss": 4.00298, "time": 0.70256} +{"mode": "train", "epoch": 23, "iter": 3600, "lr": 0.09433, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26359, "top5_acc": 0.51719, "loss_cls": 4.03042, "loss": 4.03042, "time": 0.70207} +{"mode": "train", "epoch": 23, "iter": 3700, "lr": 0.09432, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26219, "top5_acc": 0.51938, "loss_cls": 4.04227, "loss": 4.04227, "time": 0.7} +{"mode": "val", "epoch": 23, "iter": 309, "lr": 0.09431, "top1_acc": 0.17545, "top5_acc": 0.39989, "mean_class_accuracy": 0.17528} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.0943, "memory": 15990, "data_time": 1.29897, "top1_acc": 0.27062, "top5_acc": 0.52141, "loss_cls": 3.9943, "loss": 3.9943, "time": 2.00888} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.09428, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25703, "top5_acc": 0.51484, "loss_cls": 4.0287, "loss": 4.0287, "time": 0.70467} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.09427, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26922, "top5_acc": 0.51219, "loss_cls": 4.01663, "loss": 4.01663, "time": 0.70803} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.09426, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27141, "top5_acc": 0.51844, "loss_cls": 3.99377, "loss": 3.99377, "time": 0.7097} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.09425, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26141, "top5_acc": 0.50953, "loss_cls": 4.03083, "loss": 4.03083, "time": 0.70527} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.09423, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25188, "top5_acc": 0.49938, "loss_cls": 4.09396, "loss": 4.09396, "time": 0.70634} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.09422, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26781, "top5_acc": 0.52609, "loss_cls": 4.00053, "loss": 4.00053, "time": 0.7021} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.09421, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25344, "top5_acc": 0.50469, "loss_cls": 4.08087, "loss": 4.08087, "time": 0.69762} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.09419, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26203, "top5_acc": 0.51359, "loss_cls": 4.03827, "loss": 4.03827, "time": 0.6998} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.09418, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26297, "top5_acc": 0.50688, "loss_cls": 4.06026, "loss": 4.06026, "time": 0.69944} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.09417, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27453, "top5_acc": 0.51766, "loss_cls": 3.9941, "loss": 3.9941, "time": 0.69931} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.09415, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27641, "top5_acc": 0.51938, "loss_cls": 3.97564, "loss": 3.97564, "time": 0.69917} +{"mode": "train", "epoch": 24, "iter": 1300, "lr": 0.09414, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26125, "top5_acc": 0.51516, "loss_cls": 4.04285, "loss": 4.04285, "time": 0.69878} +{"mode": "train", "epoch": 24, "iter": 1400, "lr": 0.09413, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27375, "top5_acc": 0.52094, "loss_cls": 4.02, "loss": 4.02, "time": 0.69883} +{"mode": "train", "epoch": 24, "iter": 1500, "lr": 0.09411, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26937, "top5_acc": 0.52359, "loss_cls": 4.00524, "loss": 4.00524, "time": 0.69945} +{"mode": "train", "epoch": 24, "iter": 1600, "lr": 0.0941, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26328, "top5_acc": 0.50078, "loss_cls": 4.07647, "loss": 4.07647, "time": 0.70145} +{"mode": "train", "epoch": 24, "iter": 1700, "lr": 0.09409, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25984, "top5_acc": 0.50953, "loss_cls": 4.04313, "loss": 4.04313, "time": 0.70061} +{"mode": "train", "epoch": 24, "iter": 1800, "lr": 0.09407, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26297, "top5_acc": 0.51531, "loss_cls": 4.04026, "loss": 4.04026, "time": 0.70049} +{"mode": "train", "epoch": 24, "iter": 1900, "lr": 0.09406, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26891, "top5_acc": 0.51281, "loss_cls": 4.02817, "loss": 4.02817, "time": 0.70083} +{"mode": "train", "epoch": 24, "iter": 2000, "lr": 0.09405, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26594, "top5_acc": 0.51016, "loss_cls": 4.0527, "loss": 4.0527, "time": 0.69955} +{"mode": "train", "epoch": 24, "iter": 2100, "lr": 0.09404, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26797, "top5_acc": 0.50922, "loss_cls": 4.02508, "loss": 4.02508, "time": 0.69692} +{"mode": "train", "epoch": 24, "iter": 2200, "lr": 0.09402, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27609, "top5_acc": 0.52781, "loss_cls": 3.97944, "loss": 3.97944, "time": 0.69684} +{"mode": "train", "epoch": 24, "iter": 2300, "lr": 0.09401, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25656, "top5_acc": 0.50781, "loss_cls": 4.05011, "loss": 4.05011, "time": 0.69931} +{"mode": "train", "epoch": 24, "iter": 2400, "lr": 0.094, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25891, "top5_acc": 0.51031, "loss_cls": 4.04964, "loss": 4.04964, "time": 0.69891} +{"mode": "train", "epoch": 24, "iter": 2500, "lr": 0.09398, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27453, "top5_acc": 0.51875, "loss_cls": 3.98604, "loss": 3.98604, "time": 0.69985} +{"mode": "train", "epoch": 24, "iter": 2600, "lr": 0.09397, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25594, "top5_acc": 0.50672, "loss_cls": 4.05493, "loss": 4.05493, "time": 0.70033} +{"mode": "train", "epoch": 24, "iter": 2700, "lr": 0.09396, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26359, "top5_acc": 0.5025, "loss_cls": 4.05307, "loss": 4.05307, "time": 0.70017} +{"mode": "train", "epoch": 24, "iter": 2800, "lr": 0.09394, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25656, "top5_acc": 0.49672, "loss_cls": 4.11327, "loss": 4.11327, "time": 0.69673} +{"mode": "train", "epoch": 24, "iter": 2900, "lr": 0.09393, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26172, "top5_acc": 0.51453, "loss_cls": 4.03075, "loss": 4.03075, "time": 0.69978} +{"mode": "train", "epoch": 24, "iter": 3000, "lr": 0.09392, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26391, "top5_acc": 0.51234, "loss_cls": 4.05667, "loss": 4.05667, "time": 0.69738} +{"mode": "train", "epoch": 24, "iter": 3100, "lr": 0.0939, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25828, "top5_acc": 0.50234, "loss_cls": 4.0935, "loss": 4.0935, "time": 0.69717} +{"mode": "train", "epoch": 24, "iter": 3200, "lr": 0.09389, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26438, "top5_acc": 0.51953, "loss_cls": 3.99941, "loss": 3.99941, "time": 0.69855} +{"mode": "train", "epoch": 24, "iter": 3300, "lr": 0.09388, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26438, "top5_acc": 0.50906, "loss_cls": 4.03363, "loss": 4.03363, "time": 0.69878} +{"mode": "train", "epoch": 24, "iter": 3400, "lr": 0.09386, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26891, "top5_acc": 0.52141, "loss_cls": 3.97658, "loss": 3.97658, "time": 0.70345} +{"mode": "train", "epoch": 24, "iter": 3500, "lr": 0.09385, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26047, "top5_acc": 0.50797, "loss_cls": 4.05955, "loss": 4.05955, "time": 0.70183} +{"mode": "train", "epoch": 24, "iter": 3600, "lr": 0.09384, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25766, "top5_acc": 0.49875, "loss_cls": 4.05688, "loss": 4.05688, "time": 0.70429} +{"mode": "train", "epoch": 24, "iter": 3700, "lr": 0.09382, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26328, "top5_acc": 0.50609, "loss_cls": 4.05059, "loss": 4.05059, "time": 0.70302} +{"mode": "val", "epoch": 24, "iter": 309, "lr": 0.09382, "top1_acc": 0.18908, "top5_acc": 0.41078, "mean_class_accuracy": 0.18902} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.0938, "memory": 15990, "data_time": 1.29663, "top1_acc": 0.27078, "top5_acc": 0.52578, "loss_cls": 3.9796, "loss": 3.9796, "time": 2.00194} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.09379, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27453, "top5_acc": 0.51188, "loss_cls": 3.9955, "loss": 3.9955, "time": 0.70089} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.09378, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26469, "top5_acc": 0.51781, "loss_cls": 4.01379, "loss": 4.01379, "time": 0.70987} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.09376, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26922, "top5_acc": 0.51219, "loss_cls": 3.99689, "loss": 3.99689, "time": 0.70396} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.09375, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26547, "top5_acc": 0.50297, "loss_cls": 4.06684, "loss": 4.06684, "time": 0.70767} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.09373, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26719, "top5_acc": 0.51359, "loss_cls": 4.00959, "loss": 4.00959, "time": 0.70142} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.09372, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26578, "top5_acc": 0.51328, "loss_cls": 4.01876, "loss": 4.01876, "time": 0.7015} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.09371, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26812, "top5_acc": 0.51641, "loss_cls": 4.0167, "loss": 4.0167, "time": 0.70131} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.09369, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27562, "top5_acc": 0.52547, "loss_cls": 3.99861, "loss": 3.99861, "time": 0.70122} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.09368, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25594, "top5_acc": 0.50641, "loss_cls": 4.05546, "loss": 4.05546, "time": 0.7016} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.09367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26828, "top5_acc": 0.51078, "loss_cls": 4.0566, "loss": 4.0566, "time": 0.69993} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.09365, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26891, "top5_acc": 0.51719, "loss_cls": 3.99386, "loss": 3.99386, "time": 0.6981} +{"mode": "train", "epoch": 25, "iter": 1300, "lr": 0.09364, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26625, "top5_acc": 0.51812, "loss_cls": 4.02795, "loss": 4.02795, "time": 0.69823} +{"mode": "train", "epoch": 25, "iter": 1400, "lr": 0.09363, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26672, "top5_acc": 0.51359, "loss_cls": 4.01841, "loss": 4.01841, "time": 0.69773} +{"mode": "train", "epoch": 25, "iter": 1500, "lr": 0.09361, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26797, "top5_acc": 0.50906, "loss_cls": 4.04031, "loss": 4.04031, "time": 0.70025} +{"mode": "train", "epoch": 25, "iter": 1600, "lr": 0.0936, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26203, "top5_acc": 0.51656, "loss_cls": 4.01468, "loss": 4.01468, "time": 0.69819} +{"mode": "train", "epoch": 25, "iter": 1700, "lr": 0.09358, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25953, "top5_acc": 0.50234, "loss_cls": 4.08309, "loss": 4.08309, "time": 0.69724} +{"mode": "train", "epoch": 25, "iter": 1800, "lr": 0.09357, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25625, "top5_acc": 0.50734, "loss_cls": 4.07355, "loss": 4.07355, "time": 0.69787} +{"mode": "train", "epoch": 25, "iter": 1900, "lr": 0.09356, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26937, "top5_acc": 0.51844, "loss_cls": 4.01469, "loss": 4.01469, "time": 0.69942} +{"mode": "train", "epoch": 25, "iter": 2000, "lr": 0.09354, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26672, "top5_acc": 0.51594, "loss_cls": 4.03231, "loss": 4.03231, "time": 0.70164} +{"mode": "train", "epoch": 25, "iter": 2100, "lr": 0.09353, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.28141, "top5_acc": 0.5175, "loss_cls": 3.99316, "loss": 3.99316, "time": 0.69921} +{"mode": "train", "epoch": 25, "iter": 2200, "lr": 0.09352, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26531, "top5_acc": 0.51609, "loss_cls": 4.01451, "loss": 4.01451, "time": 0.70309} +{"mode": "train", "epoch": 25, "iter": 2300, "lr": 0.0935, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26625, "top5_acc": 0.51344, "loss_cls": 4.02616, "loss": 4.02616, "time": 0.69863} +{"mode": "train", "epoch": 25, "iter": 2400, "lr": 0.09349, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26578, "top5_acc": 0.51031, "loss_cls": 4.04438, "loss": 4.04438, "time": 0.69925} +{"mode": "train", "epoch": 25, "iter": 2500, "lr": 0.09347, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26516, "top5_acc": 0.52219, "loss_cls": 4.00595, "loss": 4.00595, "time": 0.69718} +{"mode": "train", "epoch": 25, "iter": 2600, "lr": 0.09346, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25469, "top5_acc": 0.51203, "loss_cls": 4.05722, "loss": 4.05722, "time": 0.69654} +{"mode": "train", "epoch": 25, "iter": 2700, "lr": 0.09345, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26531, "top5_acc": 0.51266, "loss_cls": 4.03164, "loss": 4.03164, "time": 0.7004} +{"mode": "train", "epoch": 25, "iter": 2800, "lr": 0.09343, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26594, "top5_acc": 0.51172, "loss_cls": 4.03186, "loss": 4.03186, "time": 0.69837} +{"mode": "train", "epoch": 25, "iter": 2900, "lr": 0.09342, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26328, "top5_acc": 0.51047, "loss_cls": 4.03276, "loss": 4.03276, "time": 0.69806} +{"mode": "train", "epoch": 25, "iter": 3000, "lr": 0.09341, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26234, "top5_acc": 0.51688, "loss_cls": 4.02589, "loss": 4.02589, "time": 0.69952} +{"mode": "train", "epoch": 25, "iter": 3100, "lr": 0.09339, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26297, "top5_acc": 0.51406, "loss_cls": 4.02998, "loss": 4.02998, "time": 0.69958} +{"mode": "train", "epoch": 25, "iter": 3200, "lr": 0.09338, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26516, "top5_acc": 0.50391, "loss_cls": 4.02948, "loss": 4.02948, "time": 0.69883} +{"mode": "train", "epoch": 25, "iter": 3300, "lr": 0.09336, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26094, "top5_acc": 0.50984, "loss_cls": 4.03841, "loss": 4.03841, "time": 0.69904} +{"mode": "train", "epoch": 25, "iter": 3400, "lr": 0.09335, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26219, "top5_acc": 0.515, "loss_cls": 4.02687, "loss": 4.02687, "time": 0.69931} +{"mode": "train", "epoch": 25, "iter": 3500, "lr": 0.09334, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26328, "top5_acc": 0.50844, "loss_cls": 4.04196, "loss": 4.04196, "time": 0.70153} +{"mode": "train", "epoch": 25, "iter": 3600, "lr": 0.09332, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26672, "top5_acc": 0.51094, "loss_cls": 4.04113, "loss": 4.04113, "time": 0.69967} +{"mode": "train", "epoch": 25, "iter": 3700, "lr": 0.09331, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26125, "top5_acc": 0.51656, "loss_cls": 4.05878, "loss": 4.05878, "time": 0.70203} +{"mode": "val", "epoch": 25, "iter": 309, "lr": 0.0933, "top1_acc": 0.15616, "top5_acc": 0.36874, "mean_class_accuracy": 0.15615} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.09329, "memory": 15990, "data_time": 1.28915, "top1_acc": 0.27062, "top5_acc": 0.51562, "loss_cls": 4.00139, "loss": 4.00139, "time": 1.99493} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.09327, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26234, "top5_acc": 0.50891, "loss_cls": 4.02188, "loss": 4.02188, "time": 0.70703} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.09326, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26531, "top5_acc": 0.5075, "loss_cls": 4.03297, "loss": 4.03297, "time": 0.70661} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.09325, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26516, "top5_acc": 0.51484, "loss_cls": 4.01651, "loss": 4.01651, "time": 0.71025} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.09323, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27187, "top5_acc": 0.52203, "loss_cls": 3.98029, "loss": 3.98029, "time": 0.70493} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.09322, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26375, "top5_acc": 0.52203, "loss_cls": 3.98501, "loss": 3.98501, "time": 0.70228} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.0932, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25531, "top5_acc": 0.50094, "loss_cls": 4.09273, "loss": 4.09273, "time": 0.70069} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.09319, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26219, "top5_acc": 0.51125, "loss_cls": 4.04443, "loss": 4.04443, "time": 0.70284} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.09318, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26859, "top5_acc": 0.51313, "loss_cls": 4.04852, "loss": 4.04852, "time": 0.70118} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.09316, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26062, "top5_acc": 0.51328, "loss_cls": 4.04064, "loss": 4.04064, "time": 0.70009} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.09315, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26344, "top5_acc": 0.50766, "loss_cls": 4.04702, "loss": 4.04702, "time": 0.69818} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.09313, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27078, "top5_acc": 0.51969, "loss_cls": 4.00976, "loss": 4.00976, "time": 0.69993} +{"mode": "train", "epoch": 26, "iter": 1300, "lr": 0.09312, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26109, "top5_acc": 0.51391, "loss_cls": 4.02558, "loss": 4.02558, "time": 0.69869} +{"mode": "train", "epoch": 26, "iter": 1400, "lr": 0.0931, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26891, "top5_acc": 0.51594, "loss_cls": 4.01081, "loss": 4.01081, "time": 0.69926} +{"mode": "train", "epoch": 26, "iter": 1500, "lr": 0.09309, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27, "top5_acc": 0.52047, "loss_cls": 4.01452, "loss": 4.01452, "time": 0.69984} +{"mode": "train", "epoch": 26, "iter": 1600, "lr": 0.09308, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2625, "top5_acc": 0.51516, "loss_cls": 3.99651, "loss": 3.99651, "time": 0.6977} +{"mode": "train", "epoch": 26, "iter": 1700, "lr": 0.09306, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26141, "top5_acc": 0.50281, "loss_cls": 4.05484, "loss": 4.05484, "time": 0.69949} +{"mode": "train", "epoch": 26, "iter": 1800, "lr": 0.09305, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26297, "top5_acc": 0.50984, "loss_cls": 4.03307, "loss": 4.03307, "time": 0.699} +{"mode": "train", "epoch": 26, "iter": 1900, "lr": 0.09303, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2625, "top5_acc": 0.51562, "loss_cls": 4.0435, "loss": 4.0435, "time": 0.69736} +{"mode": "train", "epoch": 26, "iter": 2000, "lr": 0.09302, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26234, "top5_acc": 0.51844, "loss_cls": 4.02593, "loss": 4.02593, "time": 0.69939} +{"mode": "train", "epoch": 26, "iter": 2100, "lr": 0.093, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26734, "top5_acc": 0.51578, "loss_cls": 4.02352, "loss": 4.02352, "time": 0.6983} +{"mode": "train", "epoch": 26, "iter": 2200, "lr": 0.09299, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26078, "top5_acc": 0.51047, "loss_cls": 4.02735, "loss": 4.02735, "time": 0.69908} +{"mode": "train", "epoch": 26, "iter": 2300, "lr": 0.09298, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27484, "top5_acc": 0.53359, "loss_cls": 3.95939, "loss": 3.95939, "time": 0.69963} +{"mode": "train", "epoch": 26, "iter": 2400, "lr": 0.09296, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27281, "top5_acc": 0.52234, "loss_cls": 3.98356, "loss": 3.98356, "time": 0.70154} +{"mode": "train", "epoch": 26, "iter": 2500, "lr": 0.09295, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25812, "top5_acc": 0.50078, "loss_cls": 4.07609, "loss": 4.07609, "time": 0.69749} +{"mode": "train", "epoch": 26, "iter": 2600, "lr": 0.09293, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25188, "top5_acc": 0.50797, "loss_cls": 4.05209, "loss": 4.05209, "time": 0.69772} +{"mode": "train", "epoch": 26, "iter": 2700, "lr": 0.09292, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25688, "top5_acc": 0.49875, "loss_cls": 4.09832, "loss": 4.09832, "time": 0.70161} +{"mode": "train", "epoch": 26, "iter": 2800, "lr": 0.0929, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27469, "top5_acc": 0.51547, "loss_cls": 4.01434, "loss": 4.01434, "time": 0.69941} +{"mode": "train", "epoch": 26, "iter": 2900, "lr": 0.09289, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26141, "top5_acc": 0.50141, "loss_cls": 4.0917, "loss": 4.0917, "time": 0.70207} +{"mode": "train", "epoch": 26, "iter": 3000, "lr": 0.09288, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26438, "top5_acc": 0.51812, "loss_cls": 4.02631, "loss": 4.02631, "time": 0.69836} +{"mode": "train", "epoch": 26, "iter": 3100, "lr": 0.09286, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25859, "top5_acc": 0.50359, "loss_cls": 4.07317, "loss": 4.07317, "time": 0.70021} +{"mode": "train", "epoch": 26, "iter": 3200, "lr": 0.09285, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26219, "top5_acc": 0.50406, "loss_cls": 4.04251, "loss": 4.04251, "time": 0.69623} +{"mode": "train", "epoch": 26, "iter": 3300, "lr": 0.09283, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.2575, "top5_acc": 0.50141, "loss_cls": 4.08227, "loss": 4.08227, "time": 0.70047} +{"mode": "train", "epoch": 26, "iter": 3400, "lr": 0.09282, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27062, "top5_acc": 0.51188, "loss_cls": 4.01885, "loss": 4.01885, "time": 0.69942} +{"mode": "train", "epoch": 26, "iter": 3500, "lr": 0.0928, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27312, "top5_acc": 0.52156, "loss_cls": 3.99781, "loss": 3.99781, "time": 0.70097} +{"mode": "train", "epoch": 26, "iter": 3600, "lr": 0.09279, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27078, "top5_acc": 0.51719, "loss_cls": 4.03958, "loss": 4.03958, "time": 0.70215} +{"mode": "train", "epoch": 26, "iter": 3700, "lr": 0.09278, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27047, "top5_acc": 0.51109, "loss_cls": 4.02159, "loss": 4.02159, "time": 0.6999} +{"mode": "val", "epoch": 26, "iter": 309, "lr": 0.09277, "top1_acc": 0.187, "top5_acc": 0.40804, "mean_class_accuracy": 0.18703} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.09275, "memory": 15990, "data_time": 1.276, "top1_acc": 0.27516, "top5_acc": 0.52688, "loss_cls": 3.9734, "loss": 3.9734, "time": 1.98047} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.09274, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26406, "top5_acc": 0.51203, "loss_cls": 4.02841, "loss": 4.02841, "time": 0.70449} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.09272, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27031, "top5_acc": 0.51875, "loss_cls": 4.0157, "loss": 4.0157, "time": 0.70391} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.09271, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26375, "top5_acc": 0.51781, "loss_cls": 4.00275, "loss": 4.00275, "time": 0.70762} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.0927, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26859, "top5_acc": 0.51859, "loss_cls": 4.01028, "loss": 4.01028, "time": 0.7068} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.09268, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26953, "top5_acc": 0.51422, "loss_cls": 4.00061, "loss": 4.00061, "time": 0.70357} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.09267, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25828, "top5_acc": 0.51062, "loss_cls": 4.06041, "loss": 4.06041, "time": 0.70055} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.09265, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26094, "top5_acc": 0.51734, "loss_cls": 4.00357, "loss": 4.00357, "time": 0.69909} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.09264, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26531, "top5_acc": 0.50719, "loss_cls": 4.04678, "loss": 4.04678, "time": 0.70209} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.09262, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26156, "top5_acc": 0.50953, "loss_cls": 4.05117, "loss": 4.05117, "time": 0.70059} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.09261, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26125, "top5_acc": 0.50969, "loss_cls": 4.03977, "loss": 4.03977, "time": 0.69819} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.09259, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27016, "top5_acc": 0.51359, "loss_cls": 4.01272, "loss": 4.01272, "time": 0.69793} +{"mode": "train", "epoch": 27, "iter": 1300, "lr": 0.09258, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26859, "top5_acc": 0.51391, "loss_cls": 4.00307, "loss": 4.00307, "time": 0.69959} +{"mode": "train", "epoch": 27, "iter": 1400, "lr": 0.09256, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26438, "top5_acc": 0.51188, "loss_cls": 4.04065, "loss": 4.04065, "time": 0.70049} +{"mode": "train", "epoch": 27, "iter": 1500, "lr": 0.09255, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27, "top5_acc": 0.51391, "loss_cls": 4.02174, "loss": 4.02174, "time": 0.69784} +{"mode": "train", "epoch": 27, "iter": 1600, "lr": 0.09253, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27516, "top5_acc": 0.51313, "loss_cls": 3.99993, "loss": 3.99993, "time": 0.69853} +{"mode": "train", "epoch": 27, "iter": 1700, "lr": 0.09252, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27281, "top5_acc": 0.52125, "loss_cls": 4.00833, "loss": 4.00833, "time": 0.6989} +{"mode": "train", "epoch": 27, "iter": 1800, "lr": 0.09251, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26703, "top5_acc": 0.51453, "loss_cls": 4.01769, "loss": 4.01769, "time": 0.70011} +{"mode": "train", "epoch": 27, "iter": 1900, "lr": 0.09249, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26609, "top5_acc": 0.51859, "loss_cls": 4.00756, "loss": 4.00756, "time": 0.69627} +{"mode": "train", "epoch": 27, "iter": 2000, "lr": 0.09248, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2625, "top5_acc": 0.50859, "loss_cls": 4.04596, "loss": 4.04596, "time": 0.7004} +{"mode": "train", "epoch": 27, "iter": 2100, "lr": 0.09246, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26812, "top5_acc": 0.515, "loss_cls": 4.00826, "loss": 4.00826, "time": 0.69935} +{"mode": "train", "epoch": 27, "iter": 2200, "lr": 0.09245, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27, "top5_acc": 0.51562, "loss_cls": 4.01332, "loss": 4.01332, "time": 0.69888} +{"mode": "train", "epoch": 27, "iter": 2300, "lr": 0.09243, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2675, "top5_acc": 0.52391, "loss_cls": 3.98774, "loss": 3.98774, "time": 0.70092} +{"mode": "train", "epoch": 27, "iter": 2400, "lr": 0.09242, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25844, "top5_acc": 0.50969, "loss_cls": 4.05523, "loss": 4.05523, "time": 0.69811} +{"mode": "train", "epoch": 27, "iter": 2500, "lr": 0.0924, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26766, "top5_acc": 0.51328, "loss_cls": 4.03141, "loss": 4.03141, "time": 0.69715} +{"mode": "train", "epoch": 27, "iter": 2600, "lr": 0.09239, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26719, "top5_acc": 0.51047, "loss_cls": 4.06193, "loss": 4.06193, "time": 0.69905} +{"mode": "train", "epoch": 27, "iter": 2700, "lr": 0.09237, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26266, "top5_acc": 0.51359, "loss_cls": 4.02007, "loss": 4.02007, "time": 0.70008} +{"mode": "train", "epoch": 27, "iter": 2800, "lr": 0.09236, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26625, "top5_acc": 0.51672, "loss_cls": 4.02728, "loss": 4.02728, "time": 0.69882} +{"mode": "train", "epoch": 27, "iter": 2900, "lr": 0.09234, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26578, "top5_acc": 0.51766, "loss_cls": 4.02218, "loss": 4.02218, "time": 0.69923} +{"mode": "train", "epoch": 27, "iter": 3000, "lr": 0.09233, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25844, "top5_acc": 0.50375, "loss_cls": 4.06866, "loss": 4.06866, "time": 0.69854} +{"mode": "train", "epoch": 27, "iter": 3100, "lr": 0.09231, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2675, "top5_acc": 0.50938, "loss_cls": 4.04141, "loss": 4.04141, "time": 0.69736} +{"mode": "train", "epoch": 27, "iter": 3200, "lr": 0.0923, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25188, "top5_acc": 0.50719, "loss_cls": 4.05241, "loss": 4.05241, "time": 0.70152} +{"mode": "train", "epoch": 27, "iter": 3300, "lr": 0.09228, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25781, "top5_acc": 0.51797, "loss_cls": 4.05431, "loss": 4.05431, "time": 0.69797} +{"mode": "train", "epoch": 27, "iter": 3400, "lr": 0.09227, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26797, "top5_acc": 0.52156, "loss_cls": 4.013, "loss": 4.013, "time": 0.7038} +{"mode": "train", "epoch": 27, "iter": 3500, "lr": 0.09225, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27297, "top5_acc": 0.52656, "loss_cls": 3.96295, "loss": 3.96295, "time": 0.70408} +{"mode": "train", "epoch": 27, "iter": 3600, "lr": 0.09224, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26484, "top5_acc": 0.51031, "loss_cls": 4.01456, "loss": 4.01456, "time": 0.70203} +{"mode": "train", "epoch": 27, "iter": 3700, "lr": 0.09222, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26406, "top5_acc": 0.51609, "loss_cls": 4.02777, "loss": 4.02777, "time": 0.69813} +{"mode": "val", "epoch": 27, "iter": 309, "lr": 0.09222, "top1_acc": 0.16634, "top5_acc": 0.36393, "mean_class_accuracy": 0.1663} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.0922, "memory": 15990, "data_time": 1.30047, "top1_acc": 0.27156, "top5_acc": 0.5225, "loss_cls": 3.96367, "loss": 3.96367, "time": 2.00591} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.09219, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27078, "top5_acc": 0.52359, "loss_cls": 3.9987, "loss": 3.9987, "time": 0.69936} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.09217, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.265, "top5_acc": 0.52562, "loss_cls": 3.97678, "loss": 3.97678, "time": 0.70025} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.09216, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26906, "top5_acc": 0.51438, "loss_cls": 3.99389, "loss": 3.99389, "time": 0.70728} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.09214, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26766, "top5_acc": 0.50938, "loss_cls": 4.02755, "loss": 4.02755, "time": 0.7031} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.09213, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27344, "top5_acc": 0.51828, "loss_cls": 3.9859, "loss": 3.9859, "time": 0.70549} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.09211, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27062, "top5_acc": 0.50484, "loss_cls": 4.06816, "loss": 4.06816, "time": 0.69978} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.0921, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26078, "top5_acc": 0.51141, "loss_cls": 4.03703, "loss": 4.03703, "time": 0.70085} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.09208, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26453, "top5_acc": 0.52109, "loss_cls": 4.02046, "loss": 4.02046, "time": 0.69934} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.09207, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26328, "top5_acc": 0.51031, "loss_cls": 4.0177, "loss": 4.0177, "time": 0.69658} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.09205, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26141, "top5_acc": 0.51313, "loss_cls": 4.02376, "loss": 4.02376, "time": 0.69956} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.09204, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27094, "top5_acc": 0.52719, "loss_cls": 4.00845, "loss": 4.00845, "time": 0.69786} +{"mode": "train", "epoch": 28, "iter": 1300, "lr": 0.09202, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25609, "top5_acc": 0.51422, "loss_cls": 4.04254, "loss": 4.04254, "time": 0.69913} +{"mode": "train", "epoch": 28, "iter": 1400, "lr": 0.09201, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26797, "top5_acc": 0.52391, "loss_cls": 3.99197, "loss": 3.99197, "time": 0.69997} +{"mode": "train", "epoch": 28, "iter": 1500, "lr": 0.09199, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26141, "top5_acc": 0.51438, "loss_cls": 4.02446, "loss": 4.02446, "time": 0.69647} +{"mode": "train", "epoch": 28, "iter": 1600, "lr": 0.09198, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27031, "top5_acc": 0.51906, "loss_cls": 4.00991, "loss": 4.00991, "time": 0.69861} +{"mode": "train", "epoch": 28, "iter": 1700, "lr": 0.09196, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25844, "top5_acc": 0.50672, "loss_cls": 4.04043, "loss": 4.04043, "time": 0.70024} +{"mode": "train", "epoch": 28, "iter": 1800, "lr": 0.09194, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26672, "top5_acc": 0.52016, "loss_cls": 4.00475, "loss": 4.00475, "time": 0.70045} +{"mode": "train", "epoch": 28, "iter": 1900, "lr": 0.09193, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26281, "top5_acc": 0.51547, "loss_cls": 4.01834, "loss": 4.01834, "time": 0.69714} +{"mode": "train", "epoch": 28, "iter": 2000, "lr": 0.09191, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26062, "top5_acc": 0.50438, "loss_cls": 4.06274, "loss": 4.06274, "time": 0.69743} +{"mode": "train", "epoch": 28, "iter": 2100, "lr": 0.0919, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26203, "top5_acc": 0.51438, "loss_cls": 4.0165, "loss": 4.0165, "time": 0.69585} +{"mode": "train", "epoch": 28, "iter": 2200, "lr": 0.09188, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26438, "top5_acc": 0.51641, "loss_cls": 4.03751, "loss": 4.03751, "time": 0.69928} +{"mode": "train", "epoch": 28, "iter": 2300, "lr": 0.09187, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26609, "top5_acc": 0.51422, "loss_cls": 4.0474, "loss": 4.0474, "time": 0.69921} +{"mode": "train", "epoch": 28, "iter": 2400, "lr": 0.09185, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26062, "top5_acc": 0.50406, "loss_cls": 4.05051, "loss": 4.05051, "time": 0.69754} +{"mode": "train", "epoch": 28, "iter": 2500, "lr": 0.09184, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26469, "top5_acc": 0.50844, "loss_cls": 4.0638, "loss": 4.0638, "time": 0.6988} +{"mode": "train", "epoch": 28, "iter": 2600, "lr": 0.09182, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26406, "top5_acc": 0.51156, "loss_cls": 4.05924, "loss": 4.05924, "time": 0.69917} +{"mode": "train", "epoch": 28, "iter": 2700, "lr": 0.09181, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26609, "top5_acc": 0.51219, "loss_cls": 4.03056, "loss": 4.03056, "time": 0.69804} +{"mode": "train", "epoch": 28, "iter": 2800, "lr": 0.09179, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26453, "top5_acc": 0.51594, "loss_cls": 4.02041, "loss": 4.02041, "time": 0.69682} +{"mode": "train", "epoch": 28, "iter": 2900, "lr": 0.09178, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27047, "top5_acc": 0.51266, "loss_cls": 4.0238, "loss": 4.0238, "time": 0.70039} +{"mode": "train", "epoch": 28, "iter": 3000, "lr": 0.09176, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26062, "top5_acc": 0.50875, "loss_cls": 4.04938, "loss": 4.04938, "time": 0.69709} +{"mode": "train", "epoch": 28, "iter": 3100, "lr": 0.09175, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27688, "top5_acc": 0.52672, "loss_cls": 3.97365, "loss": 3.97365, "time": 0.6968} +{"mode": "train", "epoch": 28, "iter": 3200, "lr": 0.09173, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.51625, "loss_cls": 4.00517, "loss": 4.00517, "time": 0.70305} +{"mode": "train", "epoch": 28, "iter": 3300, "lr": 0.09172, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25953, "top5_acc": 0.51719, "loss_cls": 4.02655, "loss": 4.02655, "time": 0.69857} +{"mode": "train", "epoch": 28, "iter": 3400, "lr": 0.0917, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25828, "top5_acc": 0.51359, "loss_cls": 4.07098, "loss": 4.07098, "time": 0.70192} +{"mode": "train", "epoch": 28, "iter": 3500, "lr": 0.09168, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26766, "top5_acc": 0.53438, "loss_cls": 3.97491, "loss": 3.97491, "time": 0.69982} +{"mode": "train", "epoch": 28, "iter": 3600, "lr": 0.09167, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26328, "top5_acc": 0.5125, "loss_cls": 4.02089, "loss": 4.02089, "time": 0.70074} +{"mode": "train", "epoch": 28, "iter": 3700, "lr": 0.09165, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25688, "top5_acc": 0.50859, "loss_cls": 4.03852, "loss": 4.03852, "time": 0.69885} +{"mode": "val", "epoch": 28, "iter": 309, "lr": 0.09165, "top1_acc": 0.19308, "top5_acc": 0.41346, "mean_class_accuracy": 0.19287} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.09163, "memory": 15990, "data_time": 1.2816, "top1_acc": 0.27312, "top5_acc": 0.52562, "loss_cls": 3.95173, "loss": 3.95173, "time": 1.98783} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.09162, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27344, "top5_acc": 0.52219, "loss_cls": 3.97076, "loss": 3.97076, "time": 0.7024} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.0916, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26641, "top5_acc": 0.51641, "loss_cls": 4.0221, "loss": 4.0221, "time": 0.70373} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.09158, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27, "top5_acc": 0.50859, "loss_cls": 4.02567, "loss": 4.02567, "time": 0.7093} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.09157, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2775, "top5_acc": 0.52859, "loss_cls": 3.96976, "loss": 3.96976, "time": 0.7002} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.09155, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26031, "top5_acc": 0.50281, "loss_cls": 4.0654, "loss": 4.0654, "time": 0.70402} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.09154, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26937, "top5_acc": 0.52203, "loss_cls": 3.99885, "loss": 3.99885, "time": 0.70189} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.09152, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26875, "top5_acc": 0.51969, "loss_cls": 4.01161, "loss": 4.01161, "time": 0.70012} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.09151, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2675, "top5_acc": 0.5225, "loss_cls": 3.98935, "loss": 3.98935, "time": 0.70132} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.09149, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26609, "top5_acc": 0.50594, "loss_cls": 4.03924, "loss": 4.03924, "time": 0.70074} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.09148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27297, "top5_acc": 0.52766, "loss_cls": 3.98166, "loss": 3.98166, "time": 0.70124} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.09146, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26844, "top5_acc": 0.51141, "loss_cls": 4.03924, "loss": 4.03924, "time": 0.69723} +{"mode": "train", "epoch": 29, "iter": 1300, "lr": 0.09144, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27031, "top5_acc": 0.52109, "loss_cls": 4.007, "loss": 4.007, "time": 0.69878} +{"mode": "train", "epoch": 29, "iter": 1400, "lr": 0.09143, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26141, "top5_acc": 0.51062, "loss_cls": 4.0301, "loss": 4.0301, "time": 0.69763} +{"mode": "train", "epoch": 29, "iter": 1500, "lr": 0.09141, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27578, "top5_acc": 0.52203, "loss_cls": 3.9954, "loss": 3.9954, "time": 0.69768} +{"mode": "train", "epoch": 29, "iter": 1600, "lr": 0.0914, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25453, "top5_acc": 0.50625, "loss_cls": 4.05515, "loss": 4.05515, "time": 0.7016} +{"mode": "train", "epoch": 29, "iter": 1700, "lr": 0.09138, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.265, "top5_acc": 0.51, "loss_cls": 4.04115, "loss": 4.04115, "time": 0.70036} +{"mode": "train", "epoch": 29, "iter": 1800, "lr": 0.09137, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27219, "top5_acc": 0.52078, "loss_cls": 3.99552, "loss": 3.99552, "time": 0.69898} +{"mode": "train", "epoch": 29, "iter": 1900, "lr": 0.09135, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25844, "top5_acc": 0.51547, "loss_cls": 4.03035, "loss": 4.03035, "time": 0.70316} +{"mode": "train", "epoch": 29, "iter": 2000, "lr": 0.09133, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25875, "top5_acc": 0.51375, "loss_cls": 4.02248, "loss": 4.02248, "time": 0.69803} +{"mode": "train", "epoch": 29, "iter": 2100, "lr": 0.09132, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26781, "top5_acc": 0.51953, "loss_cls": 3.9863, "loss": 3.9863, "time": 0.69961} +{"mode": "train", "epoch": 29, "iter": 2200, "lr": 0.0913, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26609, "top5_acc": 0.51344, "loss_cls": 4.01681, "loss": 4.01681, "time": 0.69853} +{"mode": "train", "epoch": 29, "iter": 2300, "lr": 0.09129, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25938, "top5_acc": 0.51516, "loss_cls": 4.03667, "loss": 4.03667, "time": 0.69792} +{"mode": "train", "epoch": 29, "iter": 2400, "lr": 0.09127, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26156, "top5_acc": 0.51328, "loss_cls": 4.03445, "loss": 4.03445, "time": 0.6981} +{"mode": "train", "epoch": 29, "iter": 2500, "lr": 0.09126, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26531, "top5_acc": 0.51797, "loss_cls": 4.01268, "loss": 4.01268, "time": 0.69919} +{"mode": "train", "epoch": 29, "iter": 2600, "lr": 0.09124, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26547, "top5_acc": 0.50922, "loss_cls": 4.03279, "loss": 4.03279, "time": 0.69904} +{"mode": "train", "epoch": 29, "iter": 2700, "lr": 0.09122, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25984, "top5_acc": 0.50219, "loss_cls": 4.06654, "loss": 4.06654, "time": 0.6991} +{"mode": "train", "epoch": 29, "iter": 2800, "lr": 0.09121, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27391, "top5_acc": 0.52031, "loss_cls": 3.98506, "loss": 3.98506, "time": 0.69671} +{"mode": "train", "epoch": 29, "iter": 2900, "lr": 0.09119, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26391, "top5_acc": 0.51156, "loss_cls": 4.03789, "loss": 4.03789, "time": 0.70017} +{"mode": "train", "epoch": 29, "iter": 3000, "lr": 0.09118, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26734, "top5_acc": 0.51109, "loss_cls": 4.02462, "loss": 4.02462, "time": 0.69819} +{"mode": "train", "epoch": 29, "iter": 3100, "lr": 0.09116, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26, "top5_acc": 0.50594, "loss_cls": 4.03946, "loss": 4.03946, "time": 0.70212} +{"mode": "train", "epoch": 29, "iter": 3200, "lr": 0.09114, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26, "top5_acc": 0.50828, "loss_cls": 4.06377, "loss": 4.06377, "time": 0.69944} +{"mode": "train", "epoch": 29, "iter": 3300, "lr": 0.09113, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27766, "top5_acc": 0.52578, "loss_cls": 3.96228, "loss": 3.96228, "time": 0.69979} +{"mode": "train", "epoch": 29, "iter": 3400, "lr": 0.09111, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26328, "top5_acc": 0.51859, "loss_cls": 4.02018, "loss": 4.02018, "time": 0.70642} +{"mode": "train", "epoch": 29, "iter": 3500, "lr": 0.0911, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27125, "top5_acc": 0.51672, "loss_cls": 4.02049, "loss": 4.02049, "time": 0.70122} +{"mode": "train", "epoch": 29, "iter": 3600, "lr": 0.09108, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27484, "top5_acc": 0.52047, "loss_cls": 3.99456, "loss": 3.99456, "time": 0.70026} +{"mode": "train", "epoch": 29, "iter": 3700, "lr": 0.09106, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25406, "top5_acc": 0.50844, "loss_cls": 4.0525, "loss": 4.0525, "time": 0.70088} +{"mode": "val", "epoch": 29, "iter": 309, "lr": 0.09106, "top1_acc": 0.18234, "top5_acc": 0.40014, "mean_class_accuracy": 0.18225} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.09104, "memory": 15990, "data_time": 1.293, "top1_acc": 0.28391, "top5_acc": 0.53562, "loss_cls": 3.90301, "loss": 3.90301, "time": 2.09729} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.09103, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27281, "top5_acc": 0.52875, "loss_cls": 3.92759, "loss": 3.92759, "time": 0.80343} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.09101, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26781, "top5_acc": 0.51797, "loss_cls": 3.97657, "loss": 3.97657, "time": 0.80698} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.09099, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26781, "top5_acc": 0.52078, "loss_cls": 3.97611, "loss": 3.97611, "time": 0.80941} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.09098, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26984, "top5_acc": 0.51719, "loss_cls": 3.99266, "loss": 3.99266, "time": 0.80568} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.09096, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26594, "top5_acc": 0.51594, "loss_cls": 4.05508, "loss": 4.05508, "time": 0.80742} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.09095, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.275, "top5_acc": 0.52062, "loss_cls": 3.98765, "loss": 3.98765, "time": 0.80134} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.09093, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27281, "top5_acc": 0.51703, "loss_cls": 4.01932, "loss": 4.01932, "time": 0.79941} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.09091, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.265, "top5_acc": 0.51469, "loss_cls": 4.05062, "loss": 4.05062, "time": 0.79712} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.0909, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25906, "top5_acc": 0.51438, "loss_cls": 4.04226, "loss": 4.04226, "time": 0.80742} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.09088, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27531, "top5_acc": 0.51438, "loss_cls": 4.01139, "loss": 4.01139, "time": 0.80311} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.09087, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27062, "top5_acc": 0.51812, "loss_cls": 3.99508, "loss": 3.99508, "time": 0.80618} +{"mode": "train", "epoch": 30, "iter": 1300, "lr": 0.09085, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26687, "top5_acc": 0.51656, "loss_cls": 4.0247, "loss": 4.0247, "time": 0.80308} +{"mode": "train", "epoch": 30, "iter": 1400, "lr": 0.09083, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26984, "top5_acc": 0.52484, "loss_cls": 3.97831, "loss": 3.97831, "time": 0.79924} +{"mode": "train", "epoch": 30, "iter": 1500, "lr": 0.09082, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25844, "top5_acc": 0.51406, "loss_cls": 4.04533, "loss": 4.04533, "time": 0.80228} +{"mode": "train", "epoch": 30, "iter": 1600, "lr": 0.0908, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26062, "top5_acc": 0.51516, "loss_cls": 4.03292, "loss": 4.03292, "time": 0.79988} +{"mode": "train", "epoch": 30, "iter": 1700, "lr": 0.09078, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26766, "top5_acc": 0.51281, "loss_cls": 3.98876, "loss": 3.98876, "time": 0.8037} +{"mode": "train", "epoch": 30, "iter": 1800, "lr": 0.09077, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26406, "top5_acc": 0.50625, "loss_cls": 4.04014, "loss": 4.04014, "time": 0.79811} +{"mode": "train", "epoch": 30, "iter": 1900, "lr": 0.09075, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26906, "top5_acc": 0.51406, "loss_cls": 4.01902, "loss": 4.01902, "time": 0.80109} +{"mode": "train", "epoch": 30, "iter": 2000, "lr": 0.09074, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26953, "top5_acc": 0.52172, "loss_cls": 3.98611, "loss": 3.98611, "time": 0.79835} +{"mode": "train", "epoch": 30, "iter": 2100, "lr": 0.09072, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26859, "top5_acc": 0.51453, "loss_cls": 3.98458, "loss": 3.98458, "time": 0.80052} +{"mode": "train", "epoch": 30, "iter": 2200, "lr": 0.0907, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27062, "top5_acc": 0.51406, "loss_cls": 3.99261, "loss": 3.99261, "time": 0.80224} +{"mode": "train", "epoch": 30, "iter": 2300, "lr": 0.09069, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26109, "top5_acc": 0.50844, "loss_cls": 4.02578, "loss": 4.02578, "time": 0.79595} +{"mode": "train", "epoch": 30, "iter": 2400, "lr": 0.09067, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26859, "top5_acc": 0.52078, "loss_cls": 3.99456, "loss": 3.99456, "time": 0.79915} +{"mode": "train", "epoch": 30, "iter": 2500, "lr": 0.09065, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27062, "top5_acc": 0.51594, "loss_cls": 3.98872, "loss": 3.98872, "time": 0.80077} +{"mode": "train", "epoch": 30, "iter": 2600, "lr": 0.09064, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27141, "top5_acc": 0.52281, "loss_cls": 4.00111, "loss": 4.00111, "time": 0.79978} +{"mode": "train", "epoch": 30, "iter": 2700, "lr": 0.09062, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27641, "top5_acc": 0.52297, "loss_cls": 3.98433, "loss": 3.98433, "time": 0.79973} +{"mode": "train", "epoch": 30, "iter": 2800, "lr": 0.09061, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26937, "top5_acc": 0.51922, "loss_cls": 4.02584, "loss": 4.02584, "time": 0.80261} +{"mode": "train", "epoch": 30, "iter": 2900, "lr": 0.09059, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27391, "top5_acc": 0.52062, "loss_cls": 3.99797, "loss": 3.99797, "time": 0.79958} +{"mode": "train", "epoch": 30, "iter": 3000, "lr": 0.09057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26578, "top5_acc": 0.51734, "loss_cls": 4.01754, "loss": 4.01754, "time": 0.80531} +{"mode": "train", "epoch": 30, "iter": 3100, "lr": 0.09056, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26, "top5_acc": 0.51141, "loss_cls": 4.04971, "loss": 4.04971, "time": 0.79621} +{"mode": "train", "epoch": 30, "iter": 3200, "lr": 0.09054, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26141, "top5_acc": 0.51625, "loss_cls": 4.03727, "loss": 4.03727, "time": 0.79899} +{"mode": "train", "epoch": 30, "iter": 3300, "lr": 0.09052, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26359, "top5_acc": 0.51313, "loss_cls": 4.0246, "loss": 4.0246, "time": 0.80352} +{"mode": "train", "epoch": 30, "iter": 3400, "lr": 0.09051, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25766, "top5_acc": 0.51469, "loss_cls": 4.01885, "loss": 4.01885, "time": 0.79909} +{"mode": "train", "epoch": 30, "iter": 3500, "lr": 0.09049, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26016, "top5_acc": 0.51422, "loss_cls": 4.05053, "loss": 4.05053, "time": 0.80638} +{"mode": "train", "epoch": 30, "iter": 3600, "lr": 0.09047, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26672, "top5_acc": 0.51297, "loss_cls": 4.03541, "loss": 4.03541, "time": 0.80444} +{"mode": "train", "epoch": 30, "iter": 3700, "lr": 0.09046, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26828, "top5_acc": 0.51828, "loss_cls": 3.99738, "loss": 3.99738, "time": 0.79692} +{"mode": "val", "epoch": 30, "iter": 309, "lr": 0.09045, "top1_acc": 0.20681, "top5_acc": 0.44274, "mean_class_accuracy": 0.2066} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.09043, "memory": 15990, "data_time": 1.33565, "top1_acc": 0.26875, "top5_acc": 0.52688, "loss_cls": 4.18381, "loss": 4.18381, "time": 2.31033} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.09042, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26969, "top5_acc": 0.52062, "loss_cls": 4.18387, "loss": 4.18387, "time": 0.81963} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.0904, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26422, "top5_acc": 0.51641, "loss_cls": 4.21598, "loss": 4.21598, "time": 0.83254} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.09039, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25422, "top5_acc": 0.51781, "loss_cls": 4.25989, "loss": 4.25989, "time": 0.82341} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.09037, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26703, "top5_acc": 0.51578, "loss_cls": 4.20825, "loss": 4.20825, "time": 0.83303} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.09035, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26172, "top5_acc": 0.50781, "loss_cls": 4.28311, "loss": 4.28311, "time": 0.82052} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.09034, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26547, "top5_acc": 0.52, "loss_cls": 4.21675, "loss": 4.21675, "time": 0.82196} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.09032, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27297, "top5_acc": 0.53156, "loss_cls": 4.20193, "loss": 4.20193, "time": 0.81729} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0903, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26562, "top5_acc": 0.52031, "loss_cls": 4.2193, "loss": 4.2193, "time": 0.81838} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.09029, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26844, "top5_acc": 0.51828, "loss_cls": 4.19487, "loss": 4.19487, "time": 0.81473} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.09027, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27578, "top5_acc": 0.51516, "loss_cls": 4.20541, "loss": 4.20541, "time": 0.81517} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.09025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27688, "top5_acc": 0.53109, "loss_cls": 4.16158, "loss": 4.16158, "time": 0.81781} +{"mode": "train", "epoch": 31, "iter": 1300, "lr": 0.09024, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27688, "top5_acc": 0.52391, "loss_cls": 4.18615, "loss": 4.18615, "time": 0.8136} +{"mode": "train", "epoch": 31, "iter": 1400, "lr": 0.09022, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25859, "top5_acc": 0.51047, "loss_cls": 4.25463, "loss": 4.25463, "time": 0.81458} +{"mode": "train", "epoch": 31, "iter": 1500, "lr": 0.0902, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26875, "top5_acc": 0.51594, "loss_cls": 4.24806, "loss": 4.24806, "time": 0.81376} +{"mode": "train", "epoch": 31, "iter": 1600, "lr": 0.09019, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27219, "top5_acc": 0.52328, "loss_cls": 4.19897, "loss": 4.19897, "time": 0.81403} +{"mode": "train", "epoch": 31, "iter": 1700, "lr": 0.09017, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25984, "top5_acc": 0.51094, "loss_cls": 4.27617, "loss": 4.27617, "time": 0.81062} +{"mode": "train", "epoch": 31, "iter": 1800, "lr": 0.09015, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25766, "top5_acc": 0.505, "loss_cls": 4.28845, "loss": 4.28845, "time": 0.82029} +{"mode": "train", "epoch": 31, "iter": 1900, "lr": 0.09014, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26078, "top5_acc": 0.51609, "loss_cls": 4.24601, "loss": 4.24601, "time": 0.81117} +{"mode": "train", "epoch": 31, "iter": 2000, "lr": 0.09012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26625, "top5_acc": 0.51672, "loss_cls": 4.22503, "loss": 4.22503, "time": 0.81384} +{"mode": "train", "epoch": 31, "iter": 2100, "lr": 0.0901, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26812, "top5_acc": 0.51578, "loss_cls": 4.24669, "loss": 4.24669, "time": 0.81381} +{"mode": "train", "epoch": 31, "iter": 2200, "lr": 0.09009, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27281, "top5_acc": 0.52203, "loss_cls": 4.20829, "loss": 4.20829, "time": 0.81618} +{"mode": "train", "epoch": 31, "iter": 2300, "lr": 0.09007, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25547, "top5_acc": 0.51156, "loss_cls": 4.26557, "loss": 4.26557, "time": 0.81577} +{"mode": "train", "epoch": 31, "iter": 2400, "lr": 0.09005, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27219, "top5_acc": 0.52062, "loss_cls": 4.22854, "loss": 4.22854, "time": 0.81567} +{"mode": "train", "epoch": 31, "iter": 2500, "lr": 0.09004, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25875, "top5_acc": 0.50781, "loss_cls": 4.26416, "loss": 4.26416, "time": 0.81286} +{"mode": "train", "epoch": 31, "iter": 2600, "lr": 0.09002, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26547, "top5_acc": 0.51375, "loss_cls": 4.24648, "loss": 4.24648, "time": 0.81335} +{"mode": "train", "epoch": 31, "iter": 2700, "lr": 0.09, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26641, "top5_acc": 0.51297, "loss_cls": 4.23791, "loss": 4.23791, "time": 0.81621} +{"mode": "train", "epoch": 31, "iter": 2800, "lr": 0.08999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27703, "top5_acc": 0.52406, "loss_cls": 4.22185, "loss": 4.22185, "time": 0.81006} +{"mode": "train", "epoch": 31, "iter": 2900, "lr": 0.08997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2625, "top5_acc": 0.50953, "loss_cls": 4.25515, "loss": 4.25515, "time": 0.81998} +{"mode": "train", "epoch": 31, "iter": 3000, "lr": 0.08995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25875, "top5_acc": 0.50656, "loss_cls": 4.27955, "loss": 4.27955, "time": 0.81325} +{"mode": "train", "epoch": 31, "iter": 3100, "lr": 0.08994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26375, "top5_acc": 0.51391, "loss_cls": 4.24982, "loss": 4.24982, "time": 0.81183} +{"mode": "train", "epoch": 31, "iter": 3200, "lr": 0.08992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26594, "top5_acc": 0.51812, "loss_cls": 4.2494, "loss": 4.2494, "time": 0.81594} +{"mode": "train", "epoch": 31, "iter": 3300, "lr": 0.0899, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26375, "top5_acc": 0.51453, "loss_cls": 4.24704, "loss": 4.24704, "time": 0.81734} +{"mode": "train", "epoch": 31, "iter": 3400, "lr": 0.08989, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26734, "top5_acc": 0.52375, "loss_cls": 4.20771, "loss": 4.20771, "time": 0.82262} +{"mode": "train", "epoch": 31, "iter": 3500, "lr": 0.08987, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27109, "top5_acc": 0.52422, "loss_cls": 4.22953, "loss": 4.22953, "time": 0.82046} +{"mode": "train", "epoch": 31, "iter": 3600, "lr": 0.08985, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27609, "top5_acc": 0.52297, "loss_cls": 4.20304, "loss": 4.20304, "time": 0.81408} +{"mode": "train", "epoch": 31, "iter": 3700, "lr": 0.08983, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27266, "top5_acc": 0.52906, "loss_cls": 4.19496, "loss": 4.19496, "time": 0.81423} +{"mode": "val", "epoch": 31, "iter": 309, "lr": 0.08983, "top1_acc": 0.1675, "top5_acc": 0.39538, "mean_class_accuracy": 0.16743} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.08981, "memory": 15990, "data_time": 1.28378, "top1_acc": 0.27219, "top5_acc": 0.53625, "loss_cls": 4.16673, "loss": 4.16673, "time": 2.26399} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.08979, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2725, "top5_acc": 0.51562, "loss_cls": 4.22876, "loss": 4.22876, "time": 0.81528} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.08978, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27531, "top5_acc": 0.52141, "loss_cls": 4.18217, "loss": 4.18217, "time": 0.82129} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.08976, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26812, "top5_acc": 0.5275, "loss_cls": 4.19198, "loss": 4.19198, "time": 0.82234} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.08974, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27562, "top5_acc": 0.525, "loss_cls": 4.19497, "loss": 4.19497, "time": 0.82246} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.08973, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26406, "top5_acc": 0.51922, "loss_cls": 4.20902, "loss": 4.20902, "time": 0.82062} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.08971, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.275, "top5_acc": 0.52266, "loss_cls": 4.21052, "loss": 4.21052, "time": 0.81364} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.08969, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.53109, "loss_cls": 4.19444, "loss": 4.19444, "time": 0.81665} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.08967, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27109, "top5_acc": 0.515, "loss_cls": 4.22284, "loss": 4.22284, "time": 0.81587} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.08966, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26891, "top5_acc": 0.51266, "loss_cls": 4.26248, "loss": 4.26248, "time": 0.81688} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.08964, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26797, "top5_acc": 0.50953, "loss_cls": 4.26165, "loss": 4.26165, "time": 0.81473} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.08962, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27031, "top5_acc": 0.53016, "loss_cls": 4.20094, "loss": 4.20094, "time": 0.8128} +{"mode": "train", "epoch": 32, "iter": 1300, "lr": 0.08961, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27203, "top5_acc": 0.52438, "loss_cls": 4.21706, "loss": 4.21706, "time": 0.81303} +{"mode": "train", "epoch": 32, "iter": 1400, "lr": 0.08959, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25984, "top5_acc": 0.51344, "loss_cls": 4.24611, "loss": 4.24611, "time": 0.82138} +{"mode": "train", "epoch": 32, "iter": 1500, "lr": 0.08957, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26984, "top5_acc": 0.52203, "loss_cls": 4.22394, "loss": 4.22394, "time": 0.81644} +{"mode": "train", "epoch": 32, "iter": 1600, "lr": 0.08955, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26781, "top5_acc": 0.51109, "loss_cls": 4.24515, "loss": 4.24515, "time": 0.81204} +{"mode": "train", "epoch": 32, "iter": 1700, "lr": 0.08954, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26922, "top5_acc": 0.50859, "loss_cls": 4.24263, "loss": 4.24263, "time": 0.81609} +{"mode": "train", "epoch": 32, "iter": 1800, "lr": 0.08952, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27078, "top5_acc": 0.51875, "loss_cls": 4.20681, "loss": 4.20681, "time": 0.81549} +{"mode": "train", "epoch": 32, "iter": 1900, "lr": 0.0895, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26375, "top5_acc": 0.51016, "loss_cls": 4.24629, "loss": 4.24629, "time": 0.81644} +{"mode": "train", "epoch": 32, "iter": 2000, "lr": 0.08949, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27531, "top5_acc": 0.52266, "loss_cls": 4.2015, "loss": 4.2015, "time": 0.8152} +{"mode": "train", "epoch": 32, "iter": 2100, "lr": 0.08947, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27719, "top5_acc": 0.53078, "loss_cls": 4.17755, "loss": 4.17755, "time": 0.81441} +{"mode": "train", "epoch": 32, "iter": 2200, "lr": 0.08945, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26469, "top5_acc": 0.51031, "loss_cls": 4.25291, "loss": 4.25291, "time": 0.81774} +{"mode": "train", "epoch": 32, "iter": 2300, "lr": 0.08943, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26703, "top5_acc": 0.51719, "loss_cls": 4.239, "loss": 4.239, "time": 0.82009} +{"mode": "train", "epoch": 32, "iter": 2400, "lr": 0.08942, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26984, "top5_acc": 0.52031, "loss_cls": 4.24212, "loss": 4.24212, "time": 0.81382} +{"mode": "train", "epoch": 32, "iter": 2500, "lr": 0.0894, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27156, "top5_acc": 0.51234, "loss_cls": 4.2139, "loss": 4.2139, "time": 0.81827} +{"mode": "train", "epoch": 32, "iter": 2600, "lr": 0.08938, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26531, "top5_acc": 0.51172, "loss_cls": 4.24087, "loss": 4.24087, "time": 0.81354} +{"mode": "train", "epoch": 32, "iter": 2700, "lr": 0.08937, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27031, "top5_acc": 0.52047, "loss_cls": 4.22593, "loss": 4.22593, "time": 0.81869} +{"mode": "train", "epoch": 32, "iter": 2800, "lr": 0.08935, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26875, "top5_acc": 0.51406, "loss_cls": 4.25143, "loss": 4.25143, "time": 0.82179} +{"mode": "train", "epoch": 32, "iter": 2900, "lr": 0.08933, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27016, "top5_acc": 0.52297, "loss_cls": 4.19222, "loss": 4.19222, "time": 0.81414} +{"mode": "train", "epoch": 32, "iter": 3000, "lr": 0.08931, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27484, "top5_acc": 0.52141, "loss_cls": 4.20215, "loss": 4.20215, "time": 0.81457} +{"mode": "train", "epoch": 32, "iter": 3100, "lr": 0.0893, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28109, "top5_acc": 0.53547, "loss_cls": 4.15751, "loss": 4.15751, "time": 0.82025} +{"mode": "train", "epoch": 32, "iter": 3200, "lr": 0.08928, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26547, "top5_acc": 0.51781, "loss_cls": 4.23613, "loss": 4.23613, "time": 0.81504} +{"mode": "train", "epoch": 32, "iter": 3300, "lr": 0.08926, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25797, "top5_acc": 0.50969, "loss_cls": 4.27013, "loss": 4.27013, "time": 0.81278} +{"mode": "train", "epoch": 32, "iter": 3400, "lr": 0.08924, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26719, "top5_acc": 0.51406, "loss_cls": 4.26671, "loss": 4.26671, "time": 0.81606} +{"mode": "train", "epoch": 32, "iter": 3500, "lr": 0.08923, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27406, "top5_acc": 0.51859, "loss_cls": 4.21743, "loss": 4.21743, "time": 0.815} +{"mode": "train", "epoch": 32, "iter": 3600, "lr": 0.08921, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27562, "top5_acc": 0.51688, "loss_cls": 4.22077, "loss": 4.22077, "time": 0.81512} +{"mode": "train", "epoch": 32, "iter": 3700, "lr": 0.08919, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27969, "top5_acc": 0.53656, "loss_cls": 4.1472, "loss": 4.1472, "time": 0.82239} +{"mode": "val", "epoch": 32, "iter": 309, "lr": 0.08918, "top1_acc": 0.19718, "top5_acc": 0.41589, "mean_class_accuracy": 0.19697} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.08917, "memory": 15990, "data_time": 1.29141, "top1_acc": 0.27938, "top5_acc": 0.52422, "loss_cls": 4.21207, "loss": 4.21207, "time": 2.26726} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.08915, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27297, "top5_acc": 0.53984, "loss_cls": 4.15676, "loss": 4.15676, "time": 0.8187} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.08913, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28094, "top5_acc": 0.53734, "loss_cls": 4.1626, "loss": 4.1626, "time": 0.82909} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.08912, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26734, "top5_acc": 0.51766, "loss_cls": 4.22676, "loss": 4.22676, "time": 0.8223} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.0891, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26687, "top5_acc": 0.52016, "loss_cls": 4.22734, "loss": 4.22734, "time": 0.8269} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.08908, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2675, "top5_acc": 0.51641, "loss_cls": 4.22576, "loss": 4.22576, "time": 0.82163} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.08906, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26016, "top5_acc": 0.51969, "loss_cls": 4.22072, "loss": 4.22072, "time": 0.81911} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.08905, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27484, "top5_acc": 0.52797, "loss_cls": 4.1865, "loss": 4.1865, "time": 0.82238} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.08903, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27047, "top5_acc": 0.52547, "loss_cls": 4.22105, "loss": 4.22105, "time": 0.81579} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.08901, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27094, "top5_acc": 0.51625, "loss_cls": 4.24199, "loss": 4.24199, "time": 0.81392} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.08899, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26891, "top5_acc": 0.51703, "loss_cls": 4.22498, "loss": 4.22498, "time": 0.81711} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.08898, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27016, "top5_acc": 0.52469, "loss_cls": 4.19408, "loss": 4.19408, "time": 0.8134} +{"mode": "train", "epoch": 33, "iter": 1300, "lr": 0.08896, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27406, "top5_acc": 0.52172, "loss_cls": 4.21708, "loss": 4.21708, "time": 0.81318} +{"mode": "train", "epoch": 33, "iter": 1400, "lr": 0.08894, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26406, "top5_acc": 0.51781, "loss_cls": 4.23517, "loss": 4.23517, "time": 0.81309} +{"mode": "train", "epoch": 33, "iter": 1500, "lr": 0.08892, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26453, "top5_acc": 0.51422, "loss_cls": 4.24983, "loss": 4.24983, "time": 0.81238} +{"mode": "train", "epoch": 33, "iter": 1600, "lr": 0.08891, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28453, "top5_acc": 0.52969, "loss_cls": 4.19633, "loss": 4.19633, "time": 0.81436} +{"mode": "train", "epoch": 33, "iter": 1700, "lr": 0.08889, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27219, "top5_acc": 0.52656, "loss_cls": 4.1726, "loss": 4.1726, "time": 0.81695} +{"mode": "train", "epoch": 33, "iter": 1800, "lr": 0.08887, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27437, "top5_acc": 0.52594, "loss_cls": 4.20052, "loss": 4.20052, "time": 0.81802} +{"mode": "train", "epoch": 33, "iter": 1900, "lr": 0.08885, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26625, "top5_acc": 0.51547, "loss_cls": 4.22665, "loss": 4.22665, "time": 0.81514} +{"mode": "train", "epoch": 33, "iter": 2000, "lr": 0.08884, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26188, "top5_acc": 0.51031, "loss_cls": 4.28123, "loss": 4.28123, "time": 0.81639} +{"mode": "train", "epoch": 33, "iter": 2100, "lr": 0.08882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26594, "top5_acc": 0.51531, "loss_cls": 4.21714, "loss": 4.21714, "time": 0.81321} +{"mode": "train", "epoch": 33, "iter": 2200, "lr": 0.0888, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26656, "top5_acc": 0.51516, "loss_cls": 4.26146, "loss": 4.26146, "time": 0.81383} +{"mode": "train", "epoch": 33, "iter": 2300, "lr": 0.08878, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26344, "top5_acc": 0.51453, "loss_cls": 4.261, "loss": 4.261, "time": 0.81707} +{"mode": "train", "epoch": 33, "iter": 2400, "lr": 0.08876, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26687, "top5_acc": 0.50875, "loss_cls": 4.25315, "loss": 4.25315, "time": 0.82072} +{"mode": "train", "epoch": 33, "iter": 2500, "lr": 0.08875, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27531, "top5_acc": 0.51875, "loss_cls": 4.18821, "loss": 4.18821, "time": 0.82518} +{"mode": "train", "epoch": 33, "iter": 2600, "lr": 0.08873, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26953, "top5_acc": 0.52156, "loss_cls": 4.21009, "loss": 4.21009, "time": 0.81038} +{"mode": "train", "epoch": 33, "iter": 2700, "lr": 0.08871, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28328, "top5_acc": 0.53016, "loss_cls": 4.16506, "loss": 4.16506, "time": 0.8132} +{"mode": "train", "epoch": 33, "iter": 2800, "lr": 0.08869, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26328, "top5_acc": 0.51016, "loss_cls": 4.27909, "loss": 4.27909, "time": 0.81245} +{"mode": "train", "epoch": 33, "iter": 2900, "lr": 0.08868, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27766, "top5_acc": 0.52859, "loss_cls": 4.16097, "loss": 4.16097, "time": 0.81448} +{"mode": "train", "epoch": 33, "iter": 3000, "lr": 0.08866, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28141, "top5_acc": 0.52391, "loss_cls": 4.19299, "loss": 4.19299, "time": 0.81534} +{"mode": "train", "epoch": 33, "iter": 3100, "lr": 0.08864, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25406, "top5_acc": 0.51078, "loss_cls": 4.27082, "loss": 4.27082, "time": 0.82123} +{"mode": "train", "epoch": 33, "iter": 3200, "lr": 0.08862, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26937, "top5_acc": 0.52219, "loss_cls": 4.22872, "loss": 4.22872, "time": 0.81393} +{"mode": "train", "epoch": 33, "iter": 3300, "lr": 0.08861, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27219, "top5_acc": 0.52, "loss_cls": 4.21312, "loss": 4.21312, "time": 0.81827} +{"mode": "train", "epoch": 33, "iter": 3400, "lr": 0.08859, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27, "top5_acc": 0.51297, "loss_cls": 4.22711, "loss": 4.22711, "time": 0.81454} +{"mode": "train", "epoch": 33, "iter": 3500, "lr": 0.08857, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26672, "top5_acc": 0.51672, "loss_cls": 4.24545, "loss": 4.24545, "time": 0.82031} +{"mode": "train", "epoch": 33, "iter": 3600, "lr": 0.08855, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26656, "top5_acc": 0.51375, "loss_cls": 4.22721, "loss": 4.22721, "time": 0.81272} +{"mode": "train", "epoch": 33, "iter": 3700, "lr": 0.08853, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26641, "top5_acc": 0.52688, "loss_cls": 4.22035, "loss": 4.22035, "time": 0.81445} +{"mode": "val", "epoch": 33, "iter": 309, "lr": 0.08853, "top1_acc": 0.19536, "top5_acc": 0.42354, "mean_class_accuracy": 0.19501} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.08851, "memory": 15990, "data_time": 1.35339, "top1_acc": 0.28219, "top5_acc": 0.54625, "loss_cls": 4.10802, "loss": 4.10802, "time": 2.3472} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.08849, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26687, "top5_acc": 0.52312, "loss_cls": 4.19727, "loss": 4.19727, "time": 0.82599} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.08847, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27328, "top5_acc": 0.53141, "loss_cls": 4.1805, "loss": 4.1805, "time": 0.82981} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.08845, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26953, "top5_acc": 0.52125, "loss_cls": 4.21155, "loss": 4.21155, "time": 0.82614} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.08844, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27125, "top5_acc": 0.53109, "loss_cls": 4.16308, "loss": 4.16308, "time": 0.8191} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.08842, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26984, "top5_acc": 0.52188, "loss_cls": 4.23037, "loss": 4.23037, "time": 0.81562} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.0884, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27047, "top5_acc": 0.51719, "loss_cls": 4.23659, "loss": 4.23659, "time": 0.81571} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.08838, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26969, "top5_acc": 0.51734, "loss_cls": 4.23288, "loss": 4.23288, "time": 0.81007} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.08836, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28172, "top5_acc": 0.52812, "loss_cls": 4.1689, "loss": 4.1689, "time": 0.81736} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.08835, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26641, "top5_acc": 0.5225, "loss_cls": 4.21771, "loss": 4.21771, "time": 0.81443} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.08833, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27141, "top5_acc": 0.515, "loss_cls": 4.22315, "loss": 4.22315, "time": 0.81526} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.08831, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27672, "top5_acc": 0.52516, "loss_cls": 4.19179, "loss": 4.19179, "time": 0.81513} +{"mode": "train", "epoch": 34, "iter": 1300, "lr": 0.08829, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26937, "top5_acc": 0.52094, "loss_cls": 4.22664, "loss": 4.22664, "time": 0.81139} +{"mode": "train", "epoch": 34, "iter": 1400, "lr": 0.08828, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27062, "top5_acc": 0.51938, "loss_cls": 4.20925, "loss": 4.20925, "time": 0.81475} +{"mode": "train", "epoch": 34, "iter": 1500, "lr": 0.08826, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26937, "top5_acc": 0.52188, "loss_cls": 4.24407, "loss": 4.24407, "time": 0.81761} +{"mode": "train", "epoch": 34, "iter": 1600, "lr": 0.08824, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27078, "top5_acc": 0.52844, "loss_cls": 4.16198, "loss": 4.16198, "time": 0.81977} +{"mode": "train", "epoch": 34, "iter": 1700, "lr": 0.08822, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26469, "top5_acc": 0.51313, "loss_cls": 4.26631, "loss": 4.26631, "time": 0.81203} +{"mode": "train", "epoch": 34, "iter": 1800, "lr": 0.0882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26922, "top5_acc": 0.51359, "loss_cls": 4.23289, "loss": 4.23289, "time": 0.81071} +{"mode": "train", "epoch": 34, "iter": 1900, "lr": 0.08819, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26078, "top5_acc": 0.51797, "loss_cls": 4.24475, "loss": 4.24475, "time": 0.82444} +{"mode": "train", "epoch": 34, "iter": 2000, "lr": 0.08817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28234, "top5_acc": 0.52969, "loss_cls": 4.17494, "loss": 4.17494, "time": 0.81812} +{"mode": "train", "epoch": 34, "iter": 2100, "lr": 0.08815, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26453, "top5_acc": 0.51734, "loss_cls": 4.24304, "loss": 4.24304, "time": 0.81434} +{"mode": "train", "epoch": 34, "iter": 2200, "lr": 0.08813, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26531, "top5_acc": 0.51484, "loss_cls": 4.23729, "loss": 4.23729, "time": 0.81892} +{"mode": "train", "epoch": 34, "iter": 2300, "lr": 0.08811, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26906, "top5_acc": 0.52062, "loss_cls": 4.21595, "loss": 4.21595, "time": 0.81141} +{"mode": "train", "epoch": 34, "iter": 2400, "lr": 0.08809, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28422, "top5_acc": 0.53391, "loss_cls": 4.10886, "loss": 4.10886, "time": 0.80954} +{"mode": "train", "epoch": 34, "iter": 2500, "lr": 0.08808, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27422, "top5_acc": 0.52734, "loss_cls": 4.17219, "loss": 4.17219, "time": 0.81146} +{"mode": "train", "epoch": 34, "iter": 2600, "lr": 0.08806, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2675, "top5_acc": 0.51641, "loss_cls": 4.21203, "loss": 4.21203, "time": 0.80994} +{"mode": "train", "epoch": 34, "iter": 2700, "lr": 0.08804, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27094, "top5_acc": 0.52094, "loss_cls": 4.21128, "loss": 4.21128, "time": 0.81417} +{"mode": "train", "epoch": 34, "iter": 2800, "lr": 0.08802, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27562, "top5_acc": 0.52141, "loss_cls": 4.2247, "loss": 4.2247, "time": 0.81736} +{"mode": "train", "epoch": 34, "iter": 2900, "lr": 0.088, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26734, "top5_acc": 0.5175, "loss_cls": 4.23401, "loss": 4.23401, "time": 0.81444} +{"mode": "train", "epoch": 34, "iter": 3000, "lr": 0.08799, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27141, "top5_acc": 0.51938, "loss_cls": 4.22974, "loss": 4.22974, "time": 0.81381} +{"mode": "train", "epoch": 34, "iter": 3100, "lr": 0.08797, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27688, "top5_acc": 0.52438, "loss_cls": 4.19927, "loss": 4.19927, "time": 0.8127} +{"mode": "train", "epoch": 34, "iter": 3200, "lr": 0.08795, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26609, "top5_acc": 0.52406, "loss_cls": 4.20638, "loss": 4.20638, "time": 0.8164} +{"mode": "train", "epoch": 34, "iter": 3300, "lr": 0.08793, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.51797, "loss_cls": 4.1987, "loss": 4.1987, "time": 0.81406} +{"mode": "train", "epoch": 34, "iter": 3400, "lr": 0.08791, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26625, "top5_acc": 0.51172, "loss_cls": 4.24422, "loss": 4.24422, "time": 0.81901} +{"mode": "train", "epoch": 34, "iter": 3500, "lr": 0.08789, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26906, "top5_acc": 0.52016, "loss_cls": 4.23735, "loss": 4.23735, "time": 0.81641} +{"mode": "train", "epoch": 34, "iter": 3600, "lr": 0.08788, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26922, "top5_acc": 0.52141, "loss_cls": 4.20918, "loss": 4.20918, "time": 0.81471} +{"mode": "train", "epoch": 34, "iter": 3700, "lr": 0.08786, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26891, "top5_acc": 0.51547, "loss_cls": 4.21836, "loss": 4.21836, "time": 0.81681} +{"mode": "val", "epoch": 34, "iter": 309, "lr": 0.08785, "top1_acc": 0.20655, "top5_acc": 0.4395, "mean_class_accuracy": 0.20614} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.08783, "memory": 15990, "data_time": 1.3596, "top1_acc": 0.28312, "top5_acc": 0.52172, "loss_cls": 4.17562, "loss": 4.17562, "time": 2.33815} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.08781, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27344, "top5_acc": 0.52594, "loss_cls": 4.18087, "loss": 4.18087, "time": 0.82313} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.0878, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27297, "top5_acc": 0.52625, "loss_cls": 4.17409, "loss": 4.17409, "time": 0.82338} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.08778, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27078, "top5_acc": 0.52562, "loss_cls": 4.20469, "loss": 4.20469, "time": 0.81908} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.08776, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28328, "top5_acc": 0.53125, "loss_cls": 4.14808, "loss": 4.14808, "time": 0.83245} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.08774, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27047, "top5_acc": 0.52438, "loss_cls": 4.21071, "loss": 4.21071, "time": 0.81548} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.08772, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26656, "top5_acc": 0.51938, "loss_cls": 4.19195, "loss": 4.19195, "time": 0.82546} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.0877, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27141, "top5_acc": 0.53109, "loss_cls": 4.1707, "loss": 4.1707, "time": 0.82288} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.08769, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26391, "top5_acc": 0.5175, "loss_cls": 4.2563, "loss": 4.2563, "time": 0.81892} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.08767, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27938, "top5_acc": 0.52391, "loss_cls": 4.17334, "loss": 4.17334, "time": 0.81927} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.08765, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26578, "top5_acc": 0.51891, "loss_cls": 4.21156, "loss": 4.21156, "time": 0.81516} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.08763, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27078, "top5_acc": 0.52641, "loss_cls": 4.20938, "loss": 4.20938, "time": 0.82325} +{"mode": "train", "epoch": 35, "iter": 1300, "lr": 0.08761, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26781, "top5_acc": 0.51875, "loss_cls": 4.21241, "loss": 4.21241, "time": 0.81835} +{"mode": "train", "epoch": 35, "iter": 1400, "lr": 0.08759, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26937, "top5_acc": 0.51812, "loss_cls": 4.23169, "loss": 4.23169, "time": 0.81708} +{"mode": "train", "epoch": 35, "iter": 1500, "lr": 0.08757, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27719, "top5_acc": 0.53125, "loss_cls": 4.19404, "loss": 4.19404, "time": 0.81466} +{"mode": "train", "epoch": 35, "iter": 1600, "lr": 0.08756, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26844, "top5_acc": 0.51547, "loss_cls": 4.22115, "loss": 4.22115, "time": 0.81238} +{"mode": "train", "epoch": 35, "iter": 1700, "lr": 0.08754, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27484, "top5_acc": 0.51875, "loss_cls": 4.22637, "loss": 4.22637, "time": 0.8202} +{"mode": "train", "epoch": 35, "iter": 1800, "lr": 0.08752, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27344, "top5_acc": 0.52234, "loss_cls": 4.20407, "loss": 4.20407, "time": 0.81352} +{"mode": "train", "epoch": 35, "iter": 1900, "lr": 0.0875, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28312, "top5_acc": 0.53328, "loss_cls": 4.14194, "loss": 4.14194, "time": 0.81883} +{"mode": "train", "epoch": 35, "iter": 2000, "lr": 0.08748, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.265, "top5_acc": 0.52969, "loss_cls": 4.21834, "loss": 4.21834, "time": 0.81397} +{"mode": "train", "epoch": 35, "iter": 2100, "lr": 0.08746, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27609, "top5_acc": 0.52062, "loss_cls": 4.1907, "loss": 4.1907, "time": 0.81064} +{"mode": "train", "epoch": 35, "iter": 2200, "lr": 0.08745, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27047, "top5_acc": 0.52766, "loss_cls": 4.18291, "loss": 4.18291, "time": 0.81638} +{"mode": "train", "epoch": 35, "iter": 2300, "lr": 0.08743, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27984, "top5_acc": 0.52688, "loss_cls": 4.1892, "loss": 4.1892, "time": 0.81529} +{"mode": "train", "epoch": 35, "iter": 2400, "lr": 0.08741, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26062, "top5_acc": 0.51719, "loss_cls": 4.26772, "loss": 4.26772, "time": 0.8171} +{"mode": "train", "epoch": 35, "iter": 2500, "lr": 0.08739, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26156, "top5_acc": 0.51781, "loss_cls": 4.21233, "loss": 4.21233, "time": 0.82075} +{"mode": "train", "epoch": 35, "iter": 2600, "lr": 0.08737, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27938, "top5_acc": 0.52344, "loss_cls": 4.18364, "loss": 4.18364, "time": 0.81326} +{"mode": "train", "epoch": 35, "iter": 2700, "lr": 0.08735, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27281, "top5_acc": 0.51688, "loss_cls": 4.24271, "loss": 4.24271, "time": 0.81469} +{"mode": "train", "epoch": 35, "iter": 2800, "lr": 0.08733, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26984, "top5_acc": 0.51484, "loss_cls": 4.24378, "loss": 4.24378, "time": 0.81375} +{"mode": "train", "epoch": 35, "iter": 2900, "lr": 0.08732, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26922, "top5_acc": 0.52203, "loss_cls": 4.18798, "loss": 4.18798, "time": 0.81178} +{"mode": "train", "epoch": 35, "iter": 3000, "lr": 0.0873, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27578, "top5_acc": 0.52859, "loss_cls": 4.1832, "loss": 4.1832, "time": 0.81483} +{"mode": "train", "epoch": 35, "iter": 3100, "lr": 0.08728, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28672, "top5_acc": 0.53375, "loss_cls": 4.12359, "loss": 4.12359, "time": 0.81223} +{"mode": "train", "epoch": 35, "iter": 3200, "lr": 0.08726, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26719, "top5_acc": 0.51453, "loss_cls": 4.25233, "loss": 4.25233, "time": 0.81634} +{"mode": "train", "epoch": 35, "iter": 3300, "lr": 0.08724, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.51672, "loss_cls": 4.21037, "loss": 4.21037, "time": 0.82112} +{"mode": "train", "epoch": 35, "iter": 3400, "lr": 0.08722, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26359, "top5_acc": 0.50906, "loss_cls": 4.28845, "loss": 4.28845, "time": 0.81417} +{"mode": "train", "epoch": 35, "iter": 3500, "lr": 0.0872, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27187, "top5_acc": 0.52125, "loss_cls": 4.20332, "loss": 4.20332, "time": 0.82214} +{"mode": "train", "epoch": 35, "iter": 3600, "lr": 0.08718, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26438, "top5_acc": 0.51375, "loss_cls": 4.2447, "loss": 4.2447, "time": 0.81793} +{"mode": "train", "epoch": 35, "iter": 3700, "lr": 0.08717, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27109, "top5_acc": 0.52219, "loss_cls": 4.20363, "loss": 4.20363, "time": 0.81635} +{"mode": "val", "epoch": 35, "iter": 309, "lr": 0.08716, "top1_acc": 0.21724, "top5_acc": 0.44188, "mean_class_accuracy": 0.21713} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.08714, "memory": 15990, "data_time": 1.26137, "top1_acc": 0.27547, "top5_acc": 0.52297, "loss_cls": 4.19092, "loss": 4.19092, "time": 2.24545} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.08712, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27187, "top5_acc": 0.53453, "loss_cls": 4.17173, "loss": 4.17173, "time": 0.82031} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.0871, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27547, "top5_acc": 0.52453, "loss_cls": 4.19666, "loss": 4.19666, "time": 0.8289} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.08708, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28219, "top5_acc": 0.53625, "loss_cls": 4.13472, "loss": 4.13472, "time": 0.81885} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.08706, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28078, "top5_acc": 0.52766, "loss_cls": 4.18189, "loss": 4.18189, "time": 0.82812} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.08704, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27719, "top5_acc": 0.52109, "loss_cls": 4.18471, "loss": 4.18471, "time": 0.82777} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.08703, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2675, "top5_acc": 0.51297, "loss_cls": 4.24, "loss": 4.24, "time": 0.8199} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.08701, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26797, "top5_acc": 0.51672, "loss_cls": 4.20272, "loss": 4.20272, "time": 0.82076} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.08699, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27922, "top5_acc": 0.53625, "loss_cls": 4.11916, "loss": 4.11916, "time": 0.8161} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.08697, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26656, "top5_acc": 0.51359, "loss_cls": 4.23027, "loss": 4.23027, "time": 0.81576} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.08695, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26391, "top5_acc": 0.51688, "loss_cls": 4.23206, "loss": 4.23206, "time": 0.82038} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.08693, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27234, "top5_acc": 0.52094, "loss_cls": 4.21244, "loss": 4.21244, "time": 0.81339} +{"mode": "train", "epoch": 36, "iter": 1300, "lr": 0.08691, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2725, "top5_acc": 0.51906, "loss_cls": 4.20834, "loss": 4.20834, "time": 0.81121} +{"mode": "train", "epoch": 36, "iter": 1400, "lr": 0.08689, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27656, "top5_acc": 0.52328, "loss_cls": 4.21584, "loss": 4.21584, "time": 0.81318} +{"mode": "train", "epoch": 36, "iter": 1500, "lr": 0.08688, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26484, "top5_acc": 0.51281, "loss_cls": 4.26378, "loss": 4.26378, "time": 0.81205} +{"mode": "train", "epoch": 36, "iter": 1600, "lr": 0.08686, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27453, "top5_acc": 0.52625, "loss_cls": 4.18827, "loss": 4.18827, "time": 0.81511} +{"mode": "train", "epoch": 36, "iter": 1700, "lr": 0.08684, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26844, "top5_acc": 0.51812, "loss_cls": 4.23838, "loss": 4.23838, "time": 0.81669} +{"mode": "train", "epoch": 36, "iter": 1800, "lr": 0.08682, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26578, "top5_acc": 0.52094, "loss_cls": 4.24503, "loss": 4.24503, "time": 0.81514} +{"mode": "train", "epoch": 36, "iter": 1900, "lr": 0.0868, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26391, "top5_acc": 0.51875, "loss_cls": 4.23786, "loss": 4.23786, "time": 0.81544} +{"mode": "train", "epoch": 36, "iter": 2000, "lr": 0.08678, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26703, "top5_acc": 0.52562, "loss_cls": 4.19905, "loss": 4.19905, "time": 0.81674} +{"mode": "train", "epoch": 36, "iter": 2100, "lr": 0.08676, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25984, "top5_acc": 0.52422, "loss_cls": 4.24299, "loss": 4.24299, "time": 0.81688} +{"mode": "train", "epoch": 36, "iter": 2200, "lr": 0.08674, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28031, "top5_acc": 0.53109, "loss_cls": 4.15104, "loss": 4.15104, "time": 0.82027} +{"mode": "train", "epoch": 36, "iter": 2300, "lr": 0.08672, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27797, "top5_acc": 0.525, "loss_cls": 4.20766, "loss": 4.20766, "time": 0.81215} +{"mode": "train", "epoch": 36, "iter": 2400, "lr": 0.08671, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27734, "top5_acc": 0.52359, "loss_cls": 4.18476, "loss": 4.18476, "time": 0.82251} +{"mode": "train", "epoch": 36, "iter": 2500, "lr": 0.08669, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27187, "top5_acc": 0.51422, "loss_cls": 4.23158, "loss": 4.23158, "time": 0.81862} +{"mode": "train", "epoch": 36, "iter": 2600, "lr": 0.08667, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27766, "top5_acc": 0.53312, "loss_cls": 4.14659, "loss": 4.14659, "time": 0.82115} +{"mode": "train", "epoch": 36, "iter": 2700, "lr": 0.08665, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26469, "top5_acc": 0.52422, "loss_cls": 4.20648, "loss": 4.20648, "time": 0.81585} +{"mode": "train", "epoch": 36, "iter": 2800, "lr": 0.08663, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26719, "top5_acc": 0.52156, "loss_cls": 4.23197, "loss": 4.23197, "time": 0.81666} +{"mode": "train", "epoch": 36, "iter": 2900, "lr": 0.08661, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27375, "top5_acc": 0.51797, "loss_cls": 4.22858, "loss": 4.22858, "time": 0.81812} +{"mode": "train", "epoch": 36, "iter": 3000, "lr": 0.08659, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26734, "top5_acc": 0.51266, "loss_cls": 4.21269, "loss": 4.21269, "time": 0.81323} +{"mode": "train", "epoch": 36, "iter": 3100, "lr": 0.08657, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26375, "top5_acc": 0.51938, "loss_cls": 4.23398, "loss": 4.23398, "time": 0.81745} +{"mode": "train", "epoch": 36, "iter": 3200, "lr": 0.08655, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26797, "top5_acc": 0.52094, "loss_cls": 4.23307, "loss": 4.23307, "time": 0.81865} +{"mode": "train", "epoch": 36, "iter": 3300, "lr": 0.08653, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26406, "top5_acc": 0.51703, "loss_cls": 4.23622, "loss": 4.23622, "time": 0.82054} +{"mode": "train", "epoch": 36, "iter": 3400, "lr": 0.08651, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26969, "top5_acc": 0.52219, "loss_cls": 4.19743, "loss": 4.19743, "time": 0.81209} +{"mode": "train", "epoch": 36, "iter": 3500, "lr": 0.0865, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26703, "top5_acc": 0.51797, "loss_cls": 4.22502, "loss": 4.22502, "time": 0.8193} +{"mode": "train", "epoch": 36, "iter": 3600, "lr": 0.08648, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26922, "top5_acc": 0.51688, "loss_cls": 4.2178, "loss": 4.2178, "time": 0.81406} +{"mode": "train", "epoch": 36, "iter": 3700, "lr": 0.08646, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27484, "top5_acc": 0.52672, "loss_cls": 4.21625, "loss": 4.21625, "time": 0.82093} +{"mode": "val", "epoch": 36, "iter": 309, "lr": 0.08645, "top1_acc": 0.19252, "top5_acc": 0.42785, "mean_class_accuracy": 0.19241} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.08643, "memory": 15990, "data_time": 1.26688, "top1_acc": 0.27984, "top5_acc": 0.52688, "loss_cls": 4.1742, "loss": 4.1742, "time": 2.26613} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.08641, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27094, "top5_acc": 0.52156, "loss_cls": 4.23768, "loss": 4.23768, "time": 0.81536} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.08639, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27578, "top5_acc": 0.52125, "loss_cls": 4.20821, "loss": 4.20821, "time": 0.83614} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.08637, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2775, "top5_acc": 0.52766, "loss_cls": 4.17661, "loss": 4.17661, "time": 0.82547} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.08635, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27766, "top5_acc": 0.52266, "loss_cls": 4.18378, "loss": 4.18378, "time": 0.81779} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.08633, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26391, "top5_acc": 0.52438, "loss_cls": 4.22561, "loss": 4.22561, "time": 0.82154} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.08631, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27312, "top5_acc": 0.51922, "loss_cls": 4.18986, "loss": 4.18986, "time": 0.81678} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0863, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27531, "top5_acc": 0.53438, "loss_cls": 4.19237, "loss": 4.19237, "time": 0.81399} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.08628, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28266, "top5_acc": 0.53219, "loss_cls": 4.15021, "loss": 4.15021, "time": 0.81509} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.08626, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27875, "top5_acc": 0.52406, "loss_cls": 4.17575, "loss": 4.17575, "time": 0.81821} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.08624, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26094, "top5_acc": 0.51688, "loss_cls": 4.23343, "loss": 4.23343, "time": 0.82066} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.08622, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26578, "top5_acc": 0.51516, "loss_cls": 4.21986, "loss": 4.21986, "time": 0.81797} +{"mode": "train", "epoch": 37, "iter": 1300, "lr": 0.0862, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26719, "top5_acc": 0.52062, "loss_cls": 4.2159, "loss": 4.2159, "time": 0.81502} +{"mode": "train", "epoch": 37, "iter": 1400, "lr": 0.08618, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27016, "top5_acc": 0.51594, "loss_cls": 4.2037, "loss": 4.2037, "time": 0.81158} +{"mode": "train", "epoch": 37, "iter": 1500, "lr": 0.08616, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27328, "top5_acc": 0.51609, "loss_cls": 4.21904, "loss": 4.21904, "time": 0.81346} +{"mode": "train", "epoch": 37, "iter": 1600, "lr": 0.08614, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27531, "top5_acc": 0.52219, "loss_cls": 4.20614, "loss": 4.20614, "time": 0.81757} +{"mode": "train", "epoch": 37, "iter": 1700, "lr": 0.08612, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27344, "top5_acc": 0.52812, "loss_cls": 4.1844, "loss": 4.1844, "time": 0.81958} +{"mode": "train", "epoch": 37, "iter": 1800, "lr": 0.0861, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27031, "top5_acc": 0.52016, "loss_cls": 4.2326, "loss": 4.2326, "time": 0.8151} +{"mode": "train", "epoch": 37, "iter": 1900, "lr": 0.08608, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28203, "top5_acc": 0.53078, "loss_cls": 4.19015, "loss": 4.19015, "time": 0.81896} +{"mode": "train", "epoch": 37, "iter": 2000, "lr": 0.08606, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26953, "top5_acc": 0.52344, "loss_cls": 4.19504, "loss": 4.19504, "time": 0.81478} +{"mode": "train", "epoch": 37, "iter": 2100, "lr": 0.08604, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27219, "top5_acc": 0.52531, "loss_cls": 4.18669, "loss": 4.18669, "time": 0.81481} +{"mode": "train", "epoch": 37, "iter": 2200, "lr": 0.08602, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27109, "top5_acc": 0.52781, "loss_cls": 4.19745, "loss": 4.19745, "time": 0.81468} +{"mode": "train", "epoch": 37, "iter": 2300, "lr": 0.08601, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26906, "top5_acc": 0.51797, "loss_cls": 4.21806, "loss": 4.21806, "time": 0.81131} +{"mode": "train", "epoch": 37, "iter": 2400, "lr": 0.08599, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26953, "top5_acc": 0.5175, "loss_cls": 4.22019, "loss": 4.22019, "time": 0.81657} +{"mode": "train", "epoch": 37, "iter": 2500, "lr": 0.08597, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26562, "top5_acc": 0.51391, "loss_cls": 4.22444, "loss": 4.22444, "time": 0.81742} +{"mode": "train", "epoch": 37, "iter": 2600, "lr": 0.08595, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27562, "top5_acc": 0.52828, "loss_cls": 4.18217, "loss": 4.18217, "time": 0.81237} +{"mode": "train", "epoch": 37, "iter": 2700, "lr": 0.08593, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27187, "top5_acc": 0.52734, "loss_cls": 4.21327, "loss": 4.21327, "time": 0.81898} +{"mode": "train", "epoch": 37, "iter": 2800, "lr": 0.08591, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26922, "top5_acc": 0.51688, "loss_cls": 4.21556, "loss": 4.21556, "time": 0.81178} +{"mode": "train", "epoch": 37, "iter": 2900, "lr": 0.08589, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26984, "top5_acc": 0.51688, "loss_cls": 4.22965, "loss": 4.22965, "time": 0.8164} +{"mode": "train", "epoch": 37, "iter": 3000, "lr": 0.08587, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2775, "top5_acc": 0.53547, "loss_cls": 4.1622, "loss": 4.1622, "time": 0.81127} +{"mode": "train", "epoch": 37, "iter": 3100, "lr": 0.08585, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26578, "top5_acc": 0.52219, "loss_cls": 4.22964, "loss": 4.22964, "time": 0.81653} +{"mode": "train", "epoch": 37, "iter": 3200, "lr": 0.08583, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27016, "top5_acc": 0.52188, "loss_cls": 4.26195, "loss": 4.26195, "time": 0.81988} +{"mode": "train", "epoch": 37, "iter": 3300, "lr": 0.08581, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27688, "top5_acc": 0.52844, "loss_cls": 4.15773, "loss": 4.15773, "time": 0.81658} +{"mode": "train", "epoch": 37, "iter": 3400, "lr": 0.08579, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25859, "top5_acc": 0.50781, "loss_cls": 4.24583, "loss": 4.24583, "time": 0.81349} +{"mode": "train", "epoch": 37, "iter": 3500, "lr": 0.08577, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27469, "top5_acc": 0.52031, "loss_cls": 4.17775, "loss": 4.17775, "time": 0.81868} +{"mode": "train", "epoch": 37, "iter": 3600, "lr": 0.08575, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27656, "top5_acc": 0.52172, "loss_cls": 4.19155, "loss": 4.19155, "time": 0.81082} +{"mode": "train", "epoch": 37, "iter": 3700, "lr": 0.08573, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27906, "top5_acc": 0.51438, "loss_cls": 4.23709, "loss": 4.23709, "time": 0.81929} +{"mode": "val", "epoch": 37, "iter": 309, "lr": 0.08572, "top1_acc": 0.1871, "top5_acc": 0.42699, "mean_class_accuracy": 0.18713} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.0857, "memory": 15990, "data_time": 1.27054, "top1_acc": 0.28813, "top5_acc": 0.54484, "loss_cls": 4.11353, "loss": 4.11353, "time": 2.24335} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.08568, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26875, "top5_acc": 0.52219, "loss_cls": 4.20019, "loss": 4.20019, "time": 0.82069} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.08567, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27609, "top5_acc": 0.52438, "loss_cls": 4.19539, "loss": 4.19539, "time": 0.82937} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.08565, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27859, "top5_acc": 0.53, "loss_cls": 4.16005, "loss": 4.16005, "time": 0.82716} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.08563, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.265, "top5_acc": 0.52031, "loss_cls": 4.21272, "loss": 4.21272, "time": 0.83034} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.08561, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27062, "top5_acc": 0.52406, "loss_cls": 4.22682, "loss": 4.22682, "time": 0.8225} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.08559, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26625, "top5_acc": 0.51859, "loss_cls": 4.21275, "loss": 4.21275, "time": 0.81678} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.08557, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27453, "top5_acc": 0.53062, "loss_cls": 4.16881, "loss": 4.16881, "time": 0.81832} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.08555, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27484, "top5_acc": 0.51438, "loss_cls": 4.22608, "loss": 4.22608, "time": 0.8166} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.08553, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28047, "top5_acc": 0.51844, "loss_cls": 4.2075, "loss": 4.2075, "time": 0.8171} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.08551, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28516, "top5_acc": 0.5375, "loss_cls": 4.11805, "loss": 4.11805, "time": 0.81762} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.08549, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26734, "top5_acc": 0.51547, "loss_cls": 4.21443, "loss": 4.21443, "time": 0.81399} +{"mode": "train", "epoch": 38, "iter": 1300, "lr": 0.08547, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27672, "top5_acc": 0.5275, "loss_cls": 4.17579, "loss": 4.17579, "time": 0.81377} +{"mode": "train", "epoch": 38, "iter": 1400, "lr": 0.08545, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27547, "top5_acc": 0.53234, "loss_cls": 4.17708, "loss": 4.17708, "time": 0.8214} +{"mode": "train", "epoch": 38, "iter": 1500, "lr": 0.08543, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27547, "top5_acc": 0.52641, "loss_cls": 4.20093, "loss": 4.20093, "time": 0.81919} +{"mode": "train", "epoch": 38, "iter": 1600, "lr": 0.08541, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26641, "top5_acc": 0.52172, "loss_cls": 4.19891, "loss": 4.19891, "time": 0.82059} +{"mode": "train", "epoch": 38, "iter": 1700, "lr": 0.08539, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26547, "top5_acc": 0.52125, "loss_cls": 4.19931, "loss": 4.19931, "time": 0.81542} +{"mode": "train", "epoch": 38, "iter": 1800, "lr": 0.08537, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27656, "top5_acc": 0.5225, "loss_cls": 4.20613, "loss": 4.20613, "time": 0.81188} +{"mode": "train", "epoch": 38, "iter": 1900, "lr": 0.08535, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27719, "top5_acc": 0.52906, "loss_cls": 4.17329, "loss": 4.17329, "time": 0.82054} +{"mode": "train", "epoch": 38, "iter": 2000, "lr": 0.08533, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28141, "top5_acc": 0.52891, "loss_cls": 4.17839, "loss": 4.17839, "time": 0.81993} +{"mode": "train", "epoch": 38, "iter": 2100, "lr": 0.08531, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27203, "top5_acc": 0.53359, "loss_cls": 4.15284, "loss": 4.15284, "time": 0.81596} +{"mode": "train", "epoch": 38, "iter": 2200, "lr": 0.08529, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27531, "top5_acc": 0.52125, "loss_cls": 4.21384, "loss": 4.21384, "time": 0.81704} +{"mode": "train", "epoch": 38, "iter": 2300, "lr": 0.08527, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27187, "top5_acc": 0.51922, "loss_cls": 4.17233, "loss": 4.17233, "time": 0.81739} +{"mode": "train", "epoch": 38, "iter": 2400, "lr": 0.08525, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26875, "top5_acc": 0.51594, "loss_cls": 4.21754, "loss": 4.21754, "time": 0.8124} +{"mode": "train", "epoch": 38, "iter": 2500, "lr": 0.08523, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.52078, "loss_cls": 4.20691, "loss": 4.20691, "time": 0.81814} +{"mode": "train", "epoch": 38, "iter": 2600, "lr": 0.08521, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27516, "top5_acc": 0.52578, "loss_cls": 4.15186, "loss": 4.15186, "time": 0.81352} +{"mode": "train", "epoch": 38, "iter": 2700, "lr": 0.08519, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2675, "top5_acc": 0.52078, "loss_cls": 4.23167, "loss": 4.23167, "time": 0.8125} +{"mode": "train", "epoch": 38, "iter": 2800, "lr": 0.08517, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28, "top5_acc": 0.52531, "loss_cls": 4.18545, "loss": 4.18545, "time": 0.8156} +{"mode": "train", "epoch": 38, "iter": 2900, "lr": 0.08515, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2775, "top5_acc": 0.51766, "loss_cls": 4.22084, "loss": 4.22084, "time": 0.81338} +{"mode": "train", "epoch": 38, "iter": 3000, "lr": 0.08513, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26703, "top5_acc": 0.53156, "loss_cls": 4.18944, "loss": 4.18944, "time": 0.81699} +{"mode": "train", "epoch": 38, "iter": 3100, "lr": 0.08511, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26438, "top5_acc": 0.51938, "loss_cls": 4.20651, "loss": 4.20651, "time": 0.81865} +{"mode": "train", "epoch": 38, "iter": 3200, "lr": 0.08509, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26687, "top5_acc": 0.51703, "loss_cls": 4.22355, "loss": 4.22355, "time": 0.81424} +{"mode": "train", "epoch": 38, "iter": 3300, "lr": 0.08507, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27406, "top5_acc": 0.52766, "loss_cls": 4.1622, "loss": 4.1622, "time": 0.81401} +{"mode": "train", "epoch": 38, "iter": 3400, "lr": 0.08505, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27297, "top5_acc": 0.52672, "loss_cls": 4.19303, "loss": 4.19303, "time": 0.81249} +{"mode": "train", "epoch": 38, "iter": 3500, "lr": 0.08503, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26453, "top5_acc": 0.51156, "loss_cls": 4.23462, "loss": 4.23462, "time": 0.81956} +{"mode": "train", "epoch": 38, "iter": 3600, "lr": 0.08501, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28172, "top5_acc": 0.535, "loss_cls": 4.16664, "loss": 4.16664, "time": 0.81411} +{"mode": "train", "epoch": 38, "iter": 3700, "lr": 0.08499, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27625, "top5_acc": 0.53188, "loss_cls": 4.16393, "loss": 4.16393, "time": 0.82112} +{"mode": "val", "epoch": 38, "iter": 309, "lr": 0.08498, "top1_acc": 0.2066, "top5_acc": 0.43661, "mean_class_accuracy": 0.20639} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.08496, "memory": 15990, "data_time": 1.27895, "top1_acc": 0.28453, "top5_acc": 0.53844, "loss_cls": 4.13612, "loss": 4.13612, "time": 2.24567} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.08494, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27469, "top5_acc": 0.53609, "loss_cls": 4.12727, "loss": 4.12727, "time": 0.8136} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.08492, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.28359, "top5_acc": 0.53406, "loss_cls": 4.14181, "loss": 4.14181, "time": 0.84147} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.0849, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27797, "top5_acc": 0.53875, "loss_cls": 4.14536, "loss": 4.14536, "time": 0.82768} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.08488, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27984, "top5_acc": 0.52625, "loss_cls": 4.17198, "loss": 4.17198, "time": 0.82788} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.08486, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26625, "top5_acc": 0.52641, "loss_cls": 4.18095, "loss": 4.18095, "time": 0.81903} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.08484, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27688, "top5_acc": 0.5225, "loss_cls": 4.21439, "loss": 4.21439, "time": 0.81275} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.08482, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27625, "top5_acc": 0.52406, "loss_cls": 4.20665, "loss": 4.20665, "time": 0.82564} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.0848, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27469, "top5_acc": 0.52641, "loss_cls": 4.18145, "loss": 4.18145, "time": 0.81248} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.08478, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27219, "top5_acc": 0.51703, "loss_cls": 4.21757, "loss": 4.21757, "time": 0.81238} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.08476, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28109, "top5_acc": 0.53141, "loss_cls": 4.15399, "loss": 4.15399, "time": 0.8155} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.08474, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28016, "top5_acc": 0.5375, "loss_cls": 4.16254, "loss": 4.16254, "time": 0.81908} +{"mode": "train", "epoch": 39, "iter": 1300, "lr": 0.08472, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27203, "top5_acc": 0.52562, "loss_cls": 4.20018, "loss": 4.20018, "time": 0.82256} +{"mode": "train", "epoch": 39, "iter": 1400, "lr": 0.0847, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27766, "top5_acc": 0.52766, "loss_cls": 4.20273, "loss": 4.20273, "time": 0.81364} +{"mode": "train", "epoch": 39, "iter": 1500, "lr": 0.08468, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27828, "top5_acc": 0.52359, "loss_cls": 4.17802, "loss": 4.17802, "time": 0.81485} +{"mode": "train", "epoch": 39, "iter": 1600, "lr": 0.08466, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28109, "top5_acc": 0.53031, "loss_cls": 4.14146, "loss": 4.14146, "time": 0.81693} +{"mode": "train", "epoch": 39, "iter": 1700, "lr": 0.08464, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28375, "top5_acc": 0.53188, "loss_cls": 4.14306, "loss": 4.14306, "time": 0.81545} +{"mode": "train", "epoch": 39, "iter": 1800, "lr": 0.08462, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2725, "top5_acc": 0.51953, "loss_cls": 4.22229, "loss": 4.22229, "time": 0.81281} +{"mode": "train", "epoch": 39, "iter": 1900, "lr": 0.0846, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27437, "top5_acc": 0.51641, "loss_cls": 4.23447, "loss": 4.23447, "time": 0.82112} +{"mode": "train", "epoch": 39, "iter": 2000, "lr": 0.08458, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25234, "top5_acc": 0.51266, "loss_cls": 4.25969, "loss": 4.25969, "time": 0.81481} +{"mode": "train", "epoch": 39, "iter": 2100, "lr": 0.08456, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26594, "top5_acc": 0.51766, "loss_cls": 4.20853, "loss": 4.20853, "time": 0.81378} +{"mode": "train", "epoch": 39, "iter": 2200, "lr": 0.08454, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26578, "top5_acc": 0.52016, "loss_cls": 4.24708, "loss": 4.24708, "time": 0.81986} +{"mode": "train", "epoch": 39, "iter": 2300, "lr": 0.08452, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27859, "top5_acc": 0.51438, "loss_cls": 4.21617, "loss": 4.21617, "time": 0.8128} +{"mode": "train", "epoch": 39, "iter": 2400, "lr": 0.0845, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27047, "top5_acc": 0.51469, "loss_cls": 4.22022, "loss": 4.22022, "time": 0.81536} +{"mode": "train", "epoch": 39, "iter": 2500, "lr": 0.08448, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2775, "top5_acc": 0.52469, "loss_cls": 4.18515, "loss": 4.18515, "time": 0.81326} +{"mode": "train", "epoch": 39, "iter": 2600, "lr": 0.08446, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27031, "top5_acc": 0.52734, "loss_cls": 4.23151, "loss": 4.23151, "time": 0.81588} +{"mode": "train", "epoch": 39, "iter": 2700, "lr": 0.08444, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27109, "top5_acc": 0.52719, "loss_cls": 4.19527, "loss": 4.19527, "time": 0.81623} +{"mode": "train", "epoch": 39, "iter": 2800, "lr": 0.08442, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26734, "top5_acc": 0.52969, "loss_cls": 4.18675, "loss": 4.18675, "time": 0.82018} +{"mode": "train", "epoch": 39, "iter": 2900, "lr": 0.0844, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27203, "top5_acc": 0.51484, "loss_cls": 4.22672, "loss": 4.22672, "time": 0.81451} +{"mode": "train", "epoch": 39, "iter": 3000, "lr": 0.08438, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28453, "top5_acc": 0.52984, "loss_cls": 4.15909, "loss": 4.15909, "time": 0.81464} +{"mode": "train", "epoch": 39, "iter": 3100, "lr": 0.08436, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26047, "top5_acc": 0.51844, "loss_cls": 4.2087, "loss": 4.2087, "time": 0.81735} +{"mode": "train", "epoch": 39, "iter": 3200, "lr": 0.08434, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27234, "top5_acc": 0.52734, "loss_cls": 4.18147, "loss": 4.18147, "time": 0.81827} +{"mode": "train", "epoch": 39, "iter": 3300, "lr": 0.08432, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27344, "top5_acc": 0.52297, "loss_cls": 4.2062, "loss": 4.2062, "time": 0.82004} +{"mode": "train", "epoch": 39, "iter": 3400, "lr": 0.0843, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27234, "top5_acc": 0.51172, "loss_cls": 4.23633, "loss": 4.23633, "time": 0.81354} +{"mode": "train", "epoch": 39, "iter": 3500, "lr": 0.08428, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27828, "top5_acc": 0.53422, "loss_cls": 4.15803, "loss": 4.15803, "time": 0.81781} +{"mode": "train", "epoch": 39, "iter": 3600, "lr": 0.08426, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28797, "top5_acc": 0.53469, "loss_cls": 4.14675, "loss": 4.14675, "time": 0.8166} +{"mode": "train", "epoch": 39, "iter": 3700, "lr": 0.08424, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27844, "top5_acc": 0.52828, "loss_cls": 4.18401, "loss": 4.18401, "time": 0.82132} +{"mode": "val", "epoch": 39, "iter": 309, "lr": 0.08423, "top1_acc": 0.20276, "top5_acc": 0.42881, "mean_class_accuracy": 0.20251} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.08421, "memory": 15990, "data_time": 1.28128, "top1_acc": 0.28312, "top5_acc": 0.53875, "loss_cls": 4.1229, "loss": 4.1229, "time": 2.25149} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.08419, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28312, "top5_acc": 0.53297, "loss_cls": 4.12788, "loss": 4.12788, "time": 0.81773} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.08417, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27688, "top5_acc": 0.52844, "loss_cls": 4.16699, "loss": 4.16699, "time": 0.81821} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.08415, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27547, "top5_acc": 0.51938, "loss_cls": 4.19306, "loss": 4.19306, "time": 0.83798} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.08413, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28156, "top5_acc": 0.52672, "loss_cls": 4.17174, "loss": 4.17174, "time": 0.82466} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.08411, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27297, "top5_acc": 0.53141, "loss_cls": 4.17849, "loss": 4.17849, "time": 0.82311} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.08408, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27203, "top5_acc": 0.52562, "loss_cls": 4.19, "loss": 4.19, "time": 0.82321} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.08406, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27266, "top5_acc": 0.51797, "loss_cls": 4.20645, "loss": 4.20645, "time": 0.81897} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.08404, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27766, "top5_acc": 0.52547, "loss_cls": 4.17814, "loss": 4.17814, "time": 0.8155} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.08402, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27312, "top5_acc": 0.53203, "loss_cls": 4.16425, "loss": 4.16425, "time": 0.81451} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.084, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27766, "top5_acc": 0.53531, "loss_cls": 4.13574, "loss": 4.13574, "time": 0.81862} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.08398, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27281, "top5_acc": 0.52578, "loss_cls": 4.18978, "loss": 4.18978, "time": 0.82138} +{"mode": "train", "epoch": 40, "iter": 1300, "lr": 0.08396, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27719, "top5_acc": 0.52328, "loss_cls": 4.15311, "loss": 4.15311, "time": 0.81855} +{"mode": "train", "epoch": 40, "iter": 1400, "lr": 0.08394, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27109, "top5_acc": 0.53172, "loss_cls": 4.17142, "loss": 4.17142, "time": 0.82472} +{"mode": "train", "epoch": 40, "iter": 1500, "lr": 0.08392, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27156, "top5_acc": 0.52625, "loss_cls": 4.1884, "loss": 4.1884, "time": 0.82132} +{"mode": "train", "epoch": 40, "iter": 1600, "lr": 0.0839, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28188, "top5_acc": 0.5175, "loss_cls": 4.21137, "loss": 4.21137, "time": 0.82018} +{"mode": "train", "epoch": 40, "iter": 1700, "lr": 0.08388, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27719, "top5_acc": 0.52469, "loss_cls": 4.1837, "loss": 4.1837, "time": 0.81758} +{"mode": "train", "epoch": 40, "iter": 1800, "lr": 0.08386, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26422, "top5_acc": 0.51281, "loss_cls": 4.23285, "loss": 4.23285, "time": 0.82092} +{"mode": "train", "epoch": 40, "iter": 1900, "lr": 0.08384, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27906, "top5_acc": 0.52984, "loss_cls": 4.15273, "loss": 4.15273, "time": 0.82025} +{"mode": "train", "epoch": 40, "iter": 2000, "lr": 0.08382, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27437, "top5_acc": 0.52328, "loss_cls": 4.2038, "loss": 4.2038, "time": 0.81846} +{"mode": "train", "epoch": 40, "iter": 2100, "lr": 0.0838, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.52469, "loss_cls": 4.18646, "loss": 4.18646, "time": 0.81849} +{"mode": "train", "epoch": 40, "iter": 2200, "lr": 0.08378, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26312, "top5_acc": 0.52219, "loss_cls": 4.2207, "loss": 4.2207, "time": 0.81904} +{"mode": "train", "epoch": 40, "iter": 2300, "lr": 0.08376, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27172, "top5_acc": 0.52781, "loss_cls": 4.16202, "loss": 4.16202, "time": 0.82189} +{"mode": "train", "epoch": 40, "iter": 2400, "lr": 0.08374, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27078, "top5_acc": 0.51688, "loss_cls": 4.22281, "loss": 4.22281, "time": 0.82004} +{"mode": "train", "epoch": 40, "iter": 2500, "lr": 0.08371, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27266, "top5_acc": 0.52188, "loss_cls": 4.19606, "loss": 4.19606, "time": 0.81597} +{"mode": "train", "epoch": 40, "iter": 2600, "lr": 0.08369, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27359, "top5_acc": 0.52328, "loss_cls": 4.18785, "loss": 4.18785, "time": 0.82329} +{"mode": "train", "epoch": 40, "iter": 2700, "lr": 0.08367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27906, "top5_acc": 0.52859, "loss_cls": 4.15836, "loss": 4.15836, "time": 0.81938} +{"mode": "train", "epoch": 40, "iter": 2800, "lr": 0.08365, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27391, "top5_acc": 0.52281, "loss_cls": 4.19271, "loss": 4.19271, "time": 0.81925} +{"mode": "train", "epoch": 40, "iter": 2900, "lr": 0.08363, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27641, "top5_acc": 0.53031, "loss_cls": 4.19002, "loss": 4.19002, "time": 0.82349} +{"mode": "train", "epoch": 40, "iter": 3000, "lr": 0.08361, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27391, "top5_acc": 0.51891, "loss_cls": 4.20803, "loss": 4.20803, "time": 0.82102} +{"mode": "train", "epoch": 40, "iter": 3100, "lr": 0.08359, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27344, "top5_acc": 0.52438, "loss_cls": 4.18461, "loss": 4.18461, "time": 0.81663} +{"mode": "train", "epoch": 40, "iter": 3200, "lr": 0.08357, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27078, "top5_acc": 0.52734, "loss_cls": 4.18208, "loss": 4.18208, "time": 0.8171} +{"mode": "train", "epoch": 40, "iter": 3300, "lr": 0.08355, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27437, "top5_acc": 0.51453, "loss_cls": 4.18962, "loss": 4.18962, "time": 0.81662} +{"mode": "train", "epoch": 40, "iter": 3400, "lr": 0.08353, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27906, "top5_acc": 0.52672, "loss_cls": 4.19901, "loss": 4.19901, "time": 0.81644} +{"mode": "train", "epoch": 40, "iter": 3500, "lr": 0.08351, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26766, "top5_acc": 0.51906, "loss_cls": 4.23813, "loss": 4.23813, "time": 0.82162} +{"mode": "train", "epoch": 40, "iter": 3600, "lr": 0.08349, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27812, "top5_acc": 0.53, "loss_cls": 4.16825, "loss": 4.16825, "time": 0.81808} +{"mode": "train", "epoch": 40, "iter": 3700, "lr": 0.08347, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27938, "top5_acc": 0.53, "loss_cls": 4.16714, "loss": 4.16714, "time": 0.83014} +{"mode": "val", "epoch": 40, "iter": 309, "lr": 0.08346, "top1_acc": 0.22611, "top5_acc": 0.45591, "mean_class_accuracy": 0.22585} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.08344, "memory": 15990, "data_time": 1.2417, "top1_acc": 0.28906, "top5_acc": 0.53156, "loss_cls": 4.10943, "loss": 4.10943, "time": 2.21645} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.08342, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28203, "top5_acc": 0.53484, "loss_cls": 4.1475, "loss": 4.1475, "time": 0.81419} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.08339, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28125, "top5_acc": 0.53984, "loss_cls": 4.13261, "loss": 4.13261, "time": 0.82149} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.08337, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28031, "top5_acc": 0.53, "loss_cls": 4.1739, "loss": 4.1739, "time": 0.81797} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.08335, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2875, "top5_acc": 0.52453, "loss_cls": 4.1589, "loss": 4.1589, "time": 0.83} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.08333, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27578, "top5_acc": 0.53266, "loss_cls": 4.14728, "loss": 4.14728, "time": 0.82409} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.08331, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27437, "top5_acc": 0.5275, "loss_cls": 4.17851, "loss": 4.17851, "time": 0.82188} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.08329, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27031, "top5_acc": 0.53109, "loss_cls": 4.18666, "loss": 4.18666, "time": 0.81571} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.08327, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.52484, "loss_cls": 4.17124, "loss": 4.17124, "time": 0.82739} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.08325, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28031, "top5_acc": 0.53625, "loss_cls": 4.1254, "loss": 4.1254, "time": 0.81664} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.08323, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27859, "top5_acc": 0.53109, "loss_cls": 4.11587, "loss": 4.11587, "time": 0.8193} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.08321, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27031, "top5_acc": 0.51969, "loss_cls": 4.21859, "loss": 4.21859, "time": 0.81725} +{"mode": "train", "epoch": 41, "iter": 1300, "lr": 0.08319, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27656, "top5_acc": 0.53031, "loss_cls": 4.1665, "loss": 4.1665, "time": 0.82378} +{"mode": "train", "epoch": 41, "iter": 1400, "lr": 0.08316, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28172, "top5_acc": 0.52859, "loss_cls": 4.14987, "loss": 4.14987, "time": 0.81466} +{"mode": "train", "epoch": 41, "iter": 1500, "lr": 0.08314, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27109, "top5_acc": 0.52047, "loss_cls": 4.21006, "loss": 4.21006, "time": 0.8192} +{"mode": "train", "epoch": 41, "iter": 1600, "lr": 0.08312, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27391, "top5_acc": 0.51516, "loss_cls": 4.22708, "loss": 4.22708, "time": 0.82235} +{"mode": "train", "epoch": 41, "iter": 1700, "lr": 0.0831, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27547, "top5_acc": 0.53078, "loss_cls": 4.18762, "loss": 4.18762, "time": 0.81048} +{"mode": "train", "epoch": 41, "iter": 1800, "lr": 0.08308, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28156, "top5_acc": 0.53406, "loss_cls": 4.15562, "loss": 4.15562, "time": 0.81914} +{"mode": "train", "epoch": 41, "iter": 1900, "lr": 0.08306, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27609, "top5_acc": 0.53375, "loss_cls": 4.14972, "loss": 4.14972, "time": 0.81592} +{"mode": "train", "epoch": 41, "iter": 2000, "lr": 0.08304, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27656, "top5_acc": 0.52594, "loss_cls": 4.16821, "loss": 4.16821, "time": 0.8161} +{"mode": "train", "epoch": 41, "iter": 2100, "lr": 0.08302, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28188, "top5_acc": 0.52797, "loss_cls": 4.15426, "loss": 4.15426, "time": 0.81461} +{"mode": "train", "epoch": 41, "iter": 2200, "lr": 0.083, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27547, "top5_acc": 0.52797, "loss_cls": 4.1948, "loss": 4.1948, "time": 0.81838} +{"mode": "train", "epoch": 41, "iter": 2300, "lr": 0.08298, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27422, "top5_acc": 0.52625, "loss_cls": 4.18127, "loss": 4.18127, "time": 0.82376} +{"mode": "train", "epoch": 41, "iter": 2400, "lr": 0.08296, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27844, "top5_acc": 0.53094, "loss_cls": 4.14968, "loss": 4.14968, "time": 0.81591} +{"mode": "train", "epoch": 41, "iter": 2500, "lr": 0.08293, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28141, "top5_acc": 0.52781, "loss_cls": 4.15966, "loss": 4.15966, "time": 0.81589} +{"mode": "train", "epoch": 41, "iter": 2600, "lr": 0.08291, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27828, "top5_acc": 0.52875, "loss_cls": 4.16567, "loss": 4.16567, "time": 0.81433} +{"mode": "train", "epoch": 41, "iter": 2700, "lr": 0.08289, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27297, "top5_acc": 0.51516, "loss_cls": 4.22164, "loss": 4.22164, "time": 0.81334} +{"mode": "train", "epoch": 41, "iter": 2800, "lr": 0.08287, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27203, "top5_acc": 0.51469, "loss_cls": 4.22048, "loss": 4.22048, "time": 0.81457} +{"mode": "train", "epoch": 41, "iter": 2900, "lr": 0.08285, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28438, "top5_acc": 0.53203, "loss_cls": 4.13454, "loss": 4.13454, "time": 0.81967} +{"mode": "train", "epoch": 41, "iter": 3000, "lr": 0.08283, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27047, "top5_acc": 0.52, "loss_cls": 4.20917, "loss": 4.20917, "time": 0.8208} +{"mode": "train", "epoch": 41, "iter": 3100, "lr": 0.08281, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.275, "top5_acc": 0.51953, "loss_cls": 4.20224, "loss": 4.20224, "time": 0.81557} +{"mode": "train", "epoch": 41, "iter": 3200, "lr": 0.08279, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26984, "top5_acc": 0.51828, "loss_cls": 4.2288, "loss": 4.2288, "time": 0.81653} +{"mode": "train", "epoch": 41, "iter": 3300, "lr": 0.08277, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27953, "top5_acc": 0.535, "loss_cls": 4.13306, "loss": 4.13306, "time": 0.81604} +{"mode": "train", "epoch": 41, "iter": 3400, "lr": 0.08274, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26828, "top5_acc": 0.52391, "loss_cls": 4.19342, "loss": 4.19342, "time": 0.81659} +{"mode": "train", "epoch": 41, "iter": 3500, "lr": 0.08272, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27906, "top5_acc": 0.53391, "loss_cls": 4.1691, "loss": 4.1691, "time": 0.81861} +{"mode": "train", "epoch": 41, "iter": 3600, "lr": 0.0827, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2575, "top5_acc": 0.50578, "loss_cls": 4.28756, "loss": 4.28756, "time": 0.81474} +{"mode": "train", "epoch": 41, "iter": 3700, "lr": 0.08268, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27031, "top5_acc": 0.52766, "loss_cls": 4.21663, "loss": 4.21663, "time": 0.8175} +{"mode": "val", "epoch": 41, "iter": 309, "lr": 0.08267, "top1_acc": 0.18027, "top5_acc": 0.3968, "mean_class_accuracy": 0.18006} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.08265, "memory": 15990, "data_time": 1.26262, "top1_acc": 0.28422, "top5_acc": 0.53703, "loss_cls": 4.1386, "loss": 4.1386, "time": 2.28117} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.08263, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27969, "top5_acc": 0.53562, "loss_cls": 4.1283, "loss": 4.1283, "time": 0.81299} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.08261, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27187, "top5_acc": 0.52016, "loss_cls": 4.17702, "loss": 4.17702, "time": 0.82473} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.08259, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29234, "top5_acc": 0.53516, "loss_cls": 4.11797, "loss": 4.11797, "time": 0.81664} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.08257, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28328, "top5_acc": 0.53172, "loss_cls": 4.16081, "loss": 4.16081, "time": 0.8203} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.08254, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27578, "top5_acc": 0.53422, "loss_cls": 4.1652, "loss": 4.1652, "time": 0.82491} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.08252, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28266, "top5_acc": 0.53344, "loss_cls": 4.12943, "loss": 4.12943, "time": 0.82105} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.0825, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28391, "top5_acc": 0.53094, "loss_cls": 4.16122, "loss": 4.16122, "time": 0.81944} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.08248, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27766, "top5_acc": 0.52641, "loss_cls": 4.18558, "loss": 4.18558, "time": 0.81905} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.08246, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27844, "top5_acc": 0.5275, "loss_cls": 4.15871, "loss": 4.15871, "time": 0.81265} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.08244, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27109, "top5_acc": 0.51859, "loss_cls": 4.20744, "loss": 4.20744, "time": 0.81224} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.08242, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28219, "top5_acc": 0.53562, "loss_cls": 4.14487, "loss": 4.14487, "time": 0.81628} +{"mode": "train", "epoch": 42, "iter": 1300, "lr": 0.0824, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28516, "top5_acc": 0.53531, "loss_cls": 4.1357, "loss": 4.1357, "time": 0.81438} +{"mode": "train", "epoch": 42, "iter": 1400, "lr": 0.08237, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27656, "top5_acc": 0.52281, "loss_cls": 4.17059, "loss": 4.17059, "time": 0.81663} +{"mode": "train", "epoch": 42, "iter": 1500, "lr": 0.08235, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27312, "top5_acc": 0.53109, "loss_cls": 4.17461, "loss": 4.17461, "time": 0.81636} +{"mode": "train", "epoch": 42, "iter": 1600, "lr": 0.08233, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27797, "top5_acc": 0.52984, "loss_cls": 4.13425, "loss": 4.13425, "time": 0.81419} +{"mode": "train", "epoch": 42, "iter": 1700, "lr": 0.08231, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28656, "top5_acc": 0.53281, "loss_cls": 4.1482, "loss": 4.1482, "time": 0.82075} +{"mode": "train", "epoch": 42, "iter": 1800, "lr": 0.08229, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28109, "top5_acc": 0.53484, "loss_cls": 4.13701, "loss": 4.13701, "time": 0.81653} +{"mode": "train", "epoch": 42, "iter": 1900, "lr": 0.08227, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2825, "top5_acc": 0.53078, "loss_cls": 4.17232, "loss": 4.17232, "time": 0.82036} +{"mode": "train", "epoch": 42, "iter": 2000, "lr": 0.08225, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28531, "top5_acc": 0.5225, "loss_cls": 4.19341, "loss": 4.19341, "time": 0.81394} +{"mode": "train", "epoch": 42, "iter": 2100, "lr": 0.08222, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26953, "top5_acc": 0.52547, "loss_cls": 4.18164, "loss": 4.18164, "time": 0.81372} +{"mode": "train", "epoch": 42, "iter": 2200, "lr": 0.0822, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28094, "top5_acc": 0.52422, "loss_cls": 4.17361, "loss": 4.17361, "time": 0.81815} +{"mode": "train", "epoch": 42, "iter": 2300, "lr": 0.08218, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27859, "top5_acc": 0.52688, "loss_cls": 4.16686, "loss": 4.16686, "time": 0.81604} +{"mode": "train", "epoch": 42, "iter": 2400, "lr": 0.08216, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27766, "top5_acc": 0.52859, "loss_cls": 4.16324, "loss": 4.16324, "time": 0.81709} +{"mode": "train", "epoch": 42, "iter": 2500, "lr": 0.08214, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27391, "top5_acc": 0.52531, "loss_cls": 4.19556, "loss": 4.19556, "time": 0.81683} +{"mode": "train", "epoch": 42, "iter": 2600, "lr": 0.08212, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27688, "top5_acc": 0.53562, "loss_cls": 4.13985, "loss": 4.13985, "time": 0.8179} +{"mode": "train", "epoch": 42, "iter": 2700, "lr": 0.0821, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27172, "top5_acc": 0.52125, "loss_cls": 4.20964, "loss": 4.20964, "time": 0.81461} +{"mode": "train", "epoch": 42, "iter": 2800, "lr": 0.08207, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26781, "top5_acc": 0.51313, "loss_cls": 4.20748, "loss": 4.20748, "time": 0.81533} +{"mode": "train", "epoch": 42, "iter": 2900, "lr": 0.08205, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26547, "top5_acc": 0.52766, "loss_cls": 4.20462, "loss": 4.20462, "time": 0.81617} +{"mode": "train", "epoch": 42, "iter": 3000, "lr": 0.08203, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26891, "top5_acc": 0.52531, "loss_cls": 4.19006, "loss": 4.19006, "time": 0.8154} +{"mode": "train", "epoch": 42, "iter": 3100, "lr": 0.08201, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26953, "top5_acc": 0.5275, "loss_cls": 4.20252, "loss": 4.20252, "time": 0.81459} +{"mode": "train", "epoch": 42, "iter": 3200, "lr": 0.08199, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28328, "top5_acc": 0.53516, "loss_cls": 4.16174, "loss": 4.16174, "time": 0.82062} +{"mode": "train", "epoch": 42, "iter": 3300, "lr": 0.08197, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27297, "top5_acc": 0.51797, "loss_cls": 4.20391, "loss": 4.20391, "time": 0.81735} +{"mode": "train", "epoch": 42, "iter": 3400, "lr": 0.08195, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27437, "top5_acc": 0.52484, "loss_cls": 4.20996, "loss": 4.20996, "time": 0.81283} +{"mode": "train", "epoch": 42, "iter": 3500, "lr": 0.08192, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26797, "top5_acc": 0.52281, "loss_cls": 4.19794, "loss": 4.19794, "time": 0.81901} +{"mode": "train", "epoch": 42, "iter": 3600, "lr": 0.0819, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28016, "top5_acc": 0.52875, "loss_cls": 4.16804, "loss": 4.16804, "time": 0.81503} +{"mode": "train", "epoch": 42, "iter": 3700, "lr": 0.08188, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.275, "top5_acc": 0.52344, "loss_cls": 4.19724, "loss": 4.19724, "time": 0.81558} +{"mode": "val", "epoch": 42, "iter": 309, "lr": 0.08187, "top1_acc": 0.1985, "top5_acc": 0.42212, "mean_class_accuracy": 0.19832} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.08185, "memory": 15990, "data_time": 1.25619, "top1_acc": 0.29031, "top5_acc": 0.55078, "loss_cls": 4.08671, "loss": 4.08671, "time": 2.22297} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.08183, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27984, "top5_acc": 0.5325, "loss_cls": 4.14105, "loss": 4.14105, "time": 0.81384} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.08181, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28672, "top5_acc": 0.54406, "loss_cls": 4.10655, "loss": 4.10655, "time": 0.81841} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.08179, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2775, "top5_acc": 0.53422, "loss_cls": 4.14552, "loss": 4.14552, "time": 0.82086} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.08176, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27781, "top5_acc": 0.53203, "loss_cls": 4.1521, "loss": 4.1521, "time": 0.81861} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.08174, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28047, "top5_acc": 0.53047, "loss_cls": 4.13895, "loss": 4.13895, "time": 0.83435} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.08172, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28031, "top5_acc": 0.53938, "loss_cls": 4.13331, "loss": 4.13331, "time": 0.81852} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.0817, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27047, "top5_acc": 0.51703, "loss_cls": 4.21241, "loss": 4.21241, "time": 0.82601} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.08168, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27781, "top5_acc": 0.525, "loss_cls": 4.18501, "loss": 4.18501, "time": 0.81772} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.08166, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28703, "top5_acc": 0.53859, "loss_cls": 4.12326, "loss": 4.12326, "time": 0.81285} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.08163, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27187, "top5_acc": 0.53031, "loss_cls": 4.15442, "loss": 4.15442, "time": 0.81666} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.08161, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26844, "top5_acc": 0.51406, "loss_cls": 4.22464, "loss": 4.22464, "time": 0.81422} +{"mode": "train", "epoch": 43, "iter": 1300, "lr": 0.08159, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26875, "top5_acc": 0.535, "loss_cls": 4.18065, "loss": 4.18065, "time": 0.82131} +{"mode": "train", "epoch": 43, "iter": 1400, "lr": 0.08157, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26687, "top5_acc": 0.52047, "loss_cls": 4.18861, "loss": 4.18861, "time": 0.81154} +{"mode": "train", "epoch": 43, "iter": 1500, "lr": 0.08155, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.285, "top5_acc": 0.54391, "loss_cls": 4.12692, "loss": 4.12692, "time": 0.81651} +{"mode": "train", "epoch": 43, "iter": 1600, "lr": 0.08153, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27703, "top5_acc": 0.53359, "loss_cls": 4.13817, "loss": 4.13817, "time": 0.81732} +{"mode": "train", "epoch": 43, "iter": 1700, "lr": 0.0815, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28813, "top5_acc": 0.53453, "loss_cls": 4.10358, "loss": 4.10358, "time": 0.8178} +{"mode": "train", "epoch": 43, "iter": 1800, "lr": 0.08148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28234, "top5_acc": 0.52094, "loss_cls": 4.20452, "loss": 4.20452, "time": 0.818} +{"mode": "train", "epoch": 43, "iter": 1900, "lr": 0.08146, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27797, "top5_acc": 0.52609, "loss_cls": 4.19916, "loss": 4.19916, "time": 0.81686} +{"mode": "train", "epoch": 43, "iter": 2000, "lr": 0.08144, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27688, "top5_acc": 0.53266, "loss_cls": 4.15434, "loss": 4.15434, "time": 0.81772} +{"mode": "train", "epoch": 43, "iter": 2100, "lr": 0.08142, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26375, "top5_acc": 0.51875, "loss_cls": 4.22333, "loss": 4.22333, "time": 0.81624} +{"mode": "train", "epoch": 43, "iter": 2200, "lr": 0.0814, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28031, "top5_acc": 0.52266, "loss_cls": 4.189, "loss": 4.189, "time": 0.81599} +{"mode": "train", "epoch": 43, "iter": 2300, "lr": 0.08137, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28203, "top5_acc": 0.52328, "loss_cls": 4.16145, "loss": 4.16145, "time": 0.8144} +{"mode": "train", "epoch": 43, "iter": 2400, "lr": 0.08135, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28813, "top5_acc": 0.53031, "loss_cls": 4.16616, "loss": 4.16616, "time": 0.81385} +{"mode": "train", "epoch": 43, "iter": 2500, "lr": 0.08133, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28078, "top5_acc": 0.52219, "loss_cls": 4.19118, "loss": 4.19118, "time": 0.81287} +{"mode": "train", "epoch": 43, "iter": 2600, "lr": 0.08131, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27859, "top5_acc": 0.5275, "loss_cls": 4.19218, "loss": 4.19218, "time": 0.81229} +{"mode": "train", "epoch": 43, "iter": 2700, "lr": 0.08129, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27938, "top5_acc": 0.52688, "loss_cls": 4.17386, "loss": 4.17386, "time": 0.81917} +{"mode": "train", "epoch": 43, "iter": 2800, "lr": 0.08126, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27297, "top5_acc": 0.52016, "loss_cls": 4.19181, "loss": 4.19181, "time": 0.81606} +{"mode": "train", "epoch": 43, "iter": 2900, "lr": 0.08124, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28422, "top5_acc": 0.53469, "loss_cls": 4.13011, "loss": 4.13011, "time": 0.82267} +{"mode": "train", "epoch": 43, "iter": 3000, "lr": 0.08122, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2775, "top5_acc": 0.53, "loss_cls": 4.16215, "loss": 4.16215, "time": 0.81639} +{"mode": "train", "epoch": 43, "iter": 3100, "lr": 0.0812, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28234, "top5_acc": 0.53203, "loss_cls": 4.1499, "loss": 4.1499, "time": 0.81954} +{"mode": "train", "epoch": 43, "iter": 3200, "lr": 0.08118, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26734, "top5_acc": 0.52156, "loss_cls": 4.21211, "loss": 4.21211, "time": 0.81711} +{"mode": "train", "epoch": 43, "iter": 3300, "lr": 0.08116, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27891, "top5_acc": 0.53312, "loss_cls": 4.1448, "loss": 4.1448, "time": 0.81682} +{"mode": "train", "epoch": 43, "iter": 3400, "lr": 0.08113, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27938, "top5_acc": 0.52781, "loss_cls": 4.14393, "loss": 4.14393, "time": 0.81761} +{"mode": "train", "epoch": 43, "iter": 3500, "lr": 0.08111, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28125, "top5_acc": 0.53234, "loss_cls": 4.16814, "loss": 4.16814, "time": 0.81947} +{"mode": "train", "epoch": 43, "iter": 3600, "lr": 0.08109, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27875, "top5_acc": 0.53594, "loss_cls": 4.11783, "loss": 4.11783, "time": 0.81485} +{"mode": "train", "epoch": 43, "iter": 3700, "lr": 0.08107, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26828, "top5_acc": 0.51359, "loss_cls": 4.24195, "loss": 4.24195, "time": 0.81065} +{"mode": "val", "epoch": 43, "iter": 309, "lr": 0.08106, "top1_acc": 0.21906, "top5_acc": 0.45414, "mean_class_accuracy": 0.21887} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.08104, "memory": 15990, "data_time": 1.2644, "top1_acc": 0.28344, "top5_acc": 0.53297, "loss_cls": 4.12269, "loss": 4.12269, "time": 2.23172} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.08101, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.275, "top5_acc": 0.53516, "loss_cls": 4.12602, "loss": 4.12602, "time": 0.81736} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.08099, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28172, "top5_acc": 0.54422, "loss_cls": 4.09711, "loss": 4.09711, "time": 0.82092} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.08097, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28781, "top5_acc": 0.53016, "loss_cls": 4.12905, "loss": 4.12905, "time": 0.82143} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.08095, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27672, "top5_acc": 0.52641, "loss_cls": 4.16375, "loss": 4.16375, "time": 0.81701} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.08093, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29219, "top5_acc": 0.53578, "loss_cls": 4.09772, "loss": 4.09772, "time": 0.81558} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.0809, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27844, "top5_acc": 0.52922, "loss_cls": 4.17015, "loss": 4.17015, "time": 0.82814} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.08088, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28625, "top5_acc": 0.52922, "loss_cls": 4.14999, "loss": 4.14999, "time": 0.81862} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.08086, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26969, "top5_acc": 0.51672, "loss_cls": 4.23224, "loss": 4.23224, "time": 0.8198} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.08084, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25875, "top5_acc": 0.50234, "loss_cls": 4.25754, "loss": 4.25754, "time": 0.81992} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.08082, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27531, "top5_acc": 0.52109, "loss_cls": 4.17867, "loss": 4.17867, "time": 0.81819} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.08079, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26984, "top5_acc": 0.52938, "loss_cls": 4.13456, "loss": 4.13456, "time": 0.82202} +{"mode": "train", "epoch": 44, "iter": 1300, "lr": 0.08077, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27719, "top5_acc": 0.52969, "loss_cls": 4.1592, "loss": 4.1592, "time": 0.81425} +{"mode": "train", "epoch": 44, "iter": 1400, "lr": 0.08075, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29078, "top5_acc": 0.53719, "loss_cls": 4.12136, "loss": 4.12136, "time": 0.81401} +{"mode": "train", "epoch": 44, "iter": 1500, "lr": 0.08073, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27891, "top5_acc": 0.53625, "loss_cls": 4.13917, "loss": 4.13917, "time": 0.82056} +{"mode": "train", "epoch": 44, "iter": 1600, "lr": 0.08071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28359, "top5_acc": 0.53719, "loss_cls": 4.14585, "loss": 4.14585, "time": 0.81581} +{"mode": "train", "epoch": 44, "iter": 1700, "lr": 0.08068, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28406, "top5_acc": 0.53672, "loss_cls": 4.13355, "loss": 4.13355, "time": 0.81619} +{"mode": "train", "epoch": 44, "iter": 1800, "lr": 0.08066, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27281, "top5_acc": 0.52531, "loss_cls": 4.19379, "loss": 4.19379, "time": 0.81671} +{"mode": "train", "epoch": 44, "iter": 1900, "lr": 0.08064, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27406, "top5_acc": 0.52609, "loss_cls": 4.17469, "loss": 4.17469, "time": 0.81179} +{"mode": "train", "epoch": 44, "iter": 2000, "lr": 0.08062, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27906, "top5_acc": 0.52672, "loss_cls": 4.16829, "loss": 4.16829, "time": 0.812} +{"mode": "train", "epoch": 44, "iter": 2100, "lr": 0.0806, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28438, "top5_acc": 0.53266, "loss_cls": 4.11934, "loss": 4.11934, "time": 0.81518} +{"mode": "train", "epoch": 44, "iter": 2200, "lr": 0.08057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2825, "top5_acc": 0.53, "loss_cls": 4.17145, "loss": 4.17145, "time": 0.81544} +{"mode": "train", "epoch": 44, "iter": 2300, "lr": 0.08055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27734, "top5_acc": 0.52312, "loss_cls": 4.17268, "loss": 4.17268, "time": 0.82099} +{"mode": "train", "epoch": 44, "iter": 2400, "lr": 0.08053, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28609, "top5_acc": 0.54625, "loss_cls": 4.11113, "loss": 4.11113, "time": 0.81938} +{"mode": "train", "epoch": 44, "iter": 2500, "lr": 0.08051, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27219, "top5_acc": 0.52531, "loss_cls": 4.19431, "loss": 4.19431, "time": 0.81916} +{"mode": "train", "epoch": 44, "iter": 2600, "lr": 0.08048, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27938, "top5_acc": 0.53078, "loss_cls": 4.17035, "loss": 4.17035, "time": 0.81031} +{"mode": "train", "epoch": 44, "iter": 2700, "lr": 0.08046, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29109, "top5_acc": 0.53422, "loss_cls": 4.13057, "loss": 4.13057, "time": 0.81402} +{"mode": "train", "epoch": 44, "iter": 2800, "lr": 0.08044, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26594, "top5_acc": 0.52328, "loss_cls": 4.19417, "loss": 4.19417, "time": 0.81554} +{"mode": "train", "epoch": 44, "iter": 2900, "lr": 0.08042, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28, "top5_acc": 0.53594, "loss_cls": 4.12995, "loss": 4.12995, "time": 0.81914} +{"mode": "train", "epoch": 44, "iter": 3000, "lr": 0.0804, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27953, "top5_acc": 0.52797, "loss_cls": 4.13968, "loss": 4.13968, "time": 0.81649} +{"mode": "train", "epoch": 44, "iter": 3100, "lr": 0.08037, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27016, "top5_acc": 0.52031, "loss_cls": 4.21224, "loss": 4.21224, "time": 0.81335} +{"mode": "train", "epoch": 44, "iter": 3200, "lr": 0.08035, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27812, "top5_acc": 0.5225, "loss_cls": 4.19323, "loss": 4.19323, "time": 0.81723} +{"mode": "train", "epoch": 44, "iter": 3300, "lr": 0.08033, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27437, "top5_acc": 0.52562, "loss_cls": 4.18374, "loss": 4.18374, "time": 0.8176} +{"mode": "train", "epoch": 44, "iter": 3400, "lr": 0.08031, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27688, "top5_acc": 0.5275, "loss_cls": 4.16436, "loss": 4.16436, "time": 0.82372} +{"mode": "train", "epoch": 44, "iter": 3500, "lr": 0.08028, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28016, "top5_acc": 0.52922, "loss_cls": 4.16169, "loss": 4.16169, "time": 0.82179} +{"mode": "train", "epoch": 44, "iter": 3600, "lr": 0.08026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27594, "top5_acc": 0.52125, "loss_cls": 4.18224, "loss": 4.18224, "time": 0.81723} +{"mode": "train", "epoch": 44, "iter": 3700, "lr": 0.08024, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28094, "top5_acc": 0.52266, "loss_cls": 4.18844, "loss": 4.18844, "time": 0.81565} +{"mode": "val", "epoch": 44, "iter": 309, "lr": 0.08023, "top1_acc": 0.17662, "top5_acc": 0.38338, "mean_class_accuracy": 0.17645} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.08021, "memory": 15990, "data_time": 1.26322, "top1_acc": 0.27828, "top5_acc": 0.52922, "loss_cls": 4.16826, "loss": 4.16826, "time": 2.23714} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.08019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27484, "top5_acc": 0.53328, "loss_cls": 4.1699, "loss": 4.1699, "time": 0.82065} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.08016, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28703, "top5_acc": 0.53484, "loss_cls": 4.12613, "loss": 4.12613, "time": 0.81932} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.08014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28234, "top5_acc": 0.53828, "loss_cls": 4.12433, "loss": 4.12433, "time": 0.82246} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.08012, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28094, "top5_acc": 0.54062, "loss_cls": 4.11084, "loss": 4.11084, "time": 0.81528} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.0801, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28031, "top5_acc": 0.53672, "loss_cls": 4.12326, "loss": 4.12326, "time": 0.82029} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.08007, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28047, "top5_acc": 0.53453, "loss_cls": 4.14058, "loss": 4.14058, "time": 0.82564} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.08005, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2775, "top5_acc": 0.52547, "loss_cls": 4.16721, "loss": 4.16721, "time": 0.81593} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.08003, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2825, "top5_acc": 0.53234, "loss_cls": 4.15429, "loss": 4.15429, "time": 0.82891} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.08001, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27437, "top5_acc": 0.52641, "loss_cls": 4.16264, "loss": 4.16264, "time": 0.81758} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.07998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27766, "top5_acc": 0.52875, "loss_cls": 4.14184, "loss": 4.14184, "time": 0.81477} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.07996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28375, "top5_acc": 0.53484, "loss_cls": 4.12347, "loss": 4.12347, "time": 0.81419} +{"mode": "train", "epoch": 45, "iter": 1300, "lr": 0.07994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27375, "top5_acc": 0.52094, "loss_cls": 4.17475, "loss": 4.17475, "time": 0.81456} +{"mode": "train", "epoch": 45, "iter": 1400, "lr": 0.07992, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28266, "top5_acc": 0.53531, "loss_cls": 4.14225, "loss": 4.14225, "time": 0.81695} +{"mode": "train", "epoch": 45, "iter": 1500, "lr": 0.0799, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27766, "top5_acc": 0.53859, "loss_cls": 4.13072, "loss": 4.13072, "time": 0.81778} +{"mode": "train", "epoch": 45, "iter": 1600, "lr": 0.07987, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28656, "top5_acc": 0.53562, "loss_cls": 4.11957, "loss": 4.11957, "time": 0.81709} +{"mode": "train", "epoch": 45, "iter": 1700, "lr": 0.07985, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28531, "top5_acc": 0.53094, "loss_cls": 4.14068, "loss": 4.14068, "time": 0.82081} +{"mode": "train", "epoch": 45, "iter": 1800, "lr": 0.07983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28, "top5_acc": 0.53469, "loss_cls": 4.15507, "loss": 4.15507, "time": 0.8188} +{"mode": "train", "epoch": 45, "iter": 1900, "lr": 0.07981, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27672, "top5_acc": 0.52453, "loss_cls": 4.15358, "loss": 4.15358, "time": 0.82194} +{"mode": "train", "epoch": 45, "iter": 2000, "lr": 0.07978, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27938, "top5_acc": 0.52781, "loss_cls": 4.18002, "loss": 4.18002, "time": 0.8144} +{"mode": "train", "epoch": 45, "iter": 2100, "lr": 0.07976, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28016, "top5_acc": 0.53625, "loss_cls": 4.13432, "loss": 4.13432, "time": 0.82637} +{"mode": "train", "epoch": 45, "iter": 2200, "lr": 0.07974, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27578, "top5_acc": 0.52781, "loss_cls": 4.15252, "loss": 4.15252, "time": 0.80998} +{"mode": "train", "epoch": 45, "iter": 2300, "lr": 0.07972, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27266, "top5_acc": 0.51859, "loss_cls": 4.24333, "loss": 4.24333, "time": 0.81722} +{"mode": "train", "epoch": 45, "iter": 2400, "lr": 0.07969, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27281, "top5_acc": 0.52156, "loss_cls": 4.20078, "loss": 4.20078, "time": 0.81486} +{"mode": "train", "epoch": 45, "iter": 2500, "lr": 0.07967, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27766, "top5_acc": 0.52719, "loss_cls": 4.16277, "loss": 4.16277, "time": 0.81327} +{"mode": "train", "epoch": 45, "iter": 2600, "lr": 0.07965, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27781, "top5_acc": 0.53203, "loss_cls": 4.16746, "loss": 4.16746, "time": 0.81461} +{"mode": "train", "epoch": 45, "iter": 2700, "lr": 0.07963, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28344, "top5_acc": 0.52562, "loss_cls": 4.1546, "loss": 4.1546, "time": 0.81615} +{"mode": "train", "epoch": 45, "iter": 2800, "lr": 0.0796, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27391, "top5_acc": 0.52422, "loss_cls": 4.19114, "loss": 4.19114, "time": 0.81739} +{"mode": "train", "epoch": 45, "iter": 2900, "lr": 0.07958, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27953, "top5_acc": 0.52938, "loss_cls": 4.16587, "loss": 4.16587, "time": 0.81594} +{"mode": "train", "epoch": 45, "iter": 3000, "lr": 0.07956, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27625, "top5_acc": 0.52406, "loss_cls": 4.17495, "loss": 4.17495, "time": 0.81352} +{"mode": "train", "epoch": 45, "iter": 3100, "lr": 0.07954, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28031, "top5_acc": 0.53953, "loss_cls": 4.13293, "loss": 4.13293, "time": 0.81384} +{"mode": "train", "epoch": 45, "iter": 3200, "lr": 0.07951, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28078, "top5_acc": 0.53203, "loss_cls": 4.13235, "loss": 4.13235, "time": 0.81774} +{"mode": "train", "epoch": 45, "iter": 3300, "lr": 0.07949, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27625, "top5_acc": 0.53016, "loss_cls": 4.14283, "loss": 4.14283, "time": 0.8093} +{"mode": "train", "epoch": 45, "iter": 3400, "lr": 0.07947, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27516, "top5_acc": 0.53281, "loss_cls": 4.15752, "loss": 4.15752, "time": 0.81271} +{"mode": "train", "epoch": 45, "iter": 3500, "lr": 0.07945, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27984, "top5_acc": 0.52625, "loss_cls": 4.17788, "loss": 4.17788, "time": 0.81514} +{"mode": "train", "epoch": 45, "iter": 3600, "lr": 0.07942, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27781, "top5_acc": 0.53203, "loss_cls": 4.13727, "loss": 4.13727, "time": 0.81331} +{"mode": "train", "epoch": 45, "iter": 3700, "lr": 0.0794, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27672, "top5_acc": 0.52891, "loss_cls": 4.13925, "loss": 4.13925, "time": 0.80927} +{"mode": "val", "epoch": 45, "iter": 309, "lr": 0.07939, "top1_acc": 0.21967, "top5_acc": 0.44983, "mean_class_accuracy": 0.21955} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.07937, "memory": 15990, "data_time": 1.27149, "top1_acc": 0.28688, "top5_acc": 0.52453, "loss_cls": 4.1389, "loss": 4.1389, "time": 2.24781} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.07934, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28562, "top5_acc": 0.54438, "loss_cls": 4.10507, "loss": 4.10507, "time": 0.81735} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.07932, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30078, "top5_acc": 0.53969, "loss_cls": 4.08528, "loss": 4.08528, "time": 0.8186} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.0793, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27891, "top5_acc": 0.52109, "loss_cls": 4.1914, "loss": 4.1914, "time": 0.82417} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.07928, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28219, "top5_acc": 0.53234, "loss_cls": 4.16598, "loss": 4.16598, "time": 0.81693} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.07925, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28531, "top5_acc": 0.52812, "loss_cls": 4.13133, "loss": 4.13133, "time": 0.82334} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.07923, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28547, "top5_acc": 0.53625, "loss_cls": 4.14191, "loss": 4.14191, "time": 0.82272} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.07921, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29016, "top5_acc": 0.53094, "loss_cls": 4.12326, "loss": 4.12326, "time": 0.82855} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.07919, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27922, "top5_acc": 0.52906, "loss_cls": 4.16552, "loss": 4.16552, "time": 0.8177} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.07916, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.275, "top5_acc": 0.54, "loss_cls": 4.12397, "loss": 4.12397, "time": 0.82511} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.07914, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28062, "top5_acc": 0.5375, "loss_cls": 4.14007, "loss": 4.14007, "time": 0.81448} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.07912, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27297, "top5_acc": 0.52719, "loss_cls": 4.17097, "loss": 4.17097, "time": 0.81795} +{"mode": "train", "epoch": 46, "iter": 1300, "lr": 0.07909, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28297, "top5_acc": 0.53016, "loss_cls": 4.13986, "loss": 4.13986, "time": 0.81613} +{"mode": "train", "epoch": 46, "iter": 1400, "lr": 0.07907, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28406, "top5_acc": 0.53062, "loss_cls": 4.15425, "loss": 4.15425, "time": 0.8121} +{"mode": "train", "epoch": 46, "iter": 1500, "lr": 0.07905, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28297, "top5_acc": 0.53438, "loss_cls": 4.13092, "loss": 4.13092, "time": 0.81818} +{"mode": "train", "epoch": 46, "iter": 1600, "lr": 0.07903, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28016, "top5_acc": 0.52688, "loss_cls": 4.16157, "loss": 4.16157, "time": 0.82403} +{"mode": "train", "epoch": 46, "iter": 1700, "lr": 0.079, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28516, "top5_acc": 0.53016, "loss_cls": 4.12803, "loss": 4.12803, "time": 0.81555} +{"mode": "train", "epoch": 46, "iter": 1800, "lr": 0.07898, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27453, "top5_acc": 0.52594, "loss_cls": 4.20725, "loss": 4.20725, "time": 0.81183} +{"mode": "train", "epoch": 46, "iter": 1900, "lr": 0.07896, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27953, "top5_acc": 0.52984, "loss_cls": 4.14401, "loss": 4.14401, "time": 0.81274} +{"mode": "train", "epoch": 46, "iter": 2000, "lr": 0.07894, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27547, "top5_acc": 0.51828, "loss_cls": 4.16759, "loss": 4.16759, "time": 0.81237} +{"mode": "train", "epoch": 46, "iter": 2100, "lr": 0.07891, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27469, "top5_acc": 0.51812, "loss_cls": 4.16883, "loss": 4.16883, "time": 0.81455} +{"mode": "train", "epoch": 46, "iter": 2200, "lr": 0.07889, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28062, "top5_acc": 0.54328, "loss_cls": 4.10969, "loss": 4.10969, "time": 0.81857} +{"mode": "train", "epoch": 46, "iter": 2300, "lr": 0.07887, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27812, "top5_acc": 0.535, "loss_cls": 4.14715, "loss": 4.14715, "time": 0.81803} +{"mode": "train", "epoch": 46, "iter": 2400, "lr": 0.07884, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28609, "top5_acc": 0.52594, "loss_cls": 4.16296, "loss": 4.16296, "time": 0.81642} +{"mode": "train", "epoch": 46, "iter": 2500, "lr": 0.07882, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27797, "top5_acc": 0.53594, "loss_cls": 4.14731, "loss": 4.14731, "time": 0.81482} +{"mode": "train", "epoch": 46, "iter": 2600, "lr": 0.0788, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28234, "top5_acc": 0.53297, "loss_cls": 4.12155, "loss": 4.12155, "time": 0.81903} +{"mode": "train", "epoch": 46, "iter": 2700, "lr": 0.07878, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28547, "top5_acc": 0.54344, "loss_cls": 4.12108, "loss": 4.12108, "time": 0.82021} +{"mode": "train", "epoch": 46, "iter": 2800, "lr": 0.07875, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28516, "top5_acc": 0.53203, "loss_cls": 4.11919, "loss": 4.11919, "time": 0.81601} +{"mode": "train", "epoch": 46, "iter": 2900, "lr": 0.07873, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28, "top5_acc": 0.53453, "loss_cls": 4.14268, "loss": 4.14268, "time": 0.81064} +{"mode": "train", "epoch": 46, "iter": 3000, "lr": 0.07871, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27578, "top5_acc": 0.53453, "loss_cls": 4.14084, "loss": 4.14084, "time": 0.81409} +{"mode": "train", "epoch": 46, "iter": 3100, "lr": 0.07868, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27469, "top5_acc": 0.53109, "loss_cls": 4.16938, "loss": 4.16938, "time": 0.8178} +{"mode": "train", "epoch": 46, "iter": 3200, "lr": 0.07866, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27172, "top5_acc": 0.53516, "loss_cls": 4.14931, "loss": 4.14931, "time": 0.81063} +{"mode": "train", "epoch": 46, "iter": 3300, "lr": 0.07864, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26906, "top5_acc": 0.52484, "loss_cls": 4.17723, "loss": 4.17723, "time": 0.81758} +{"mode": "train", "epoch": 46, "iter": 3400, "lr": 0.07862, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29, "top5_acc": 0.54891, "loss_cls": 4.07363, "loss": 4.07363, "time": 0.81869} +{"mode": "train", "epoch": 46, "iter": 3500, "lr": 0.07859, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28859, "top5_acc": 0.53875, "loss_cls": 4.1379, "loss": 4.1379, "time": 0.82117} +{"mode": "train", "epoch": 46, "iter": 3600, "lr": 0.07857, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28484, "top5_acc": 0.53703, "loss_cls": 4.12786, "loss": 4.12786, "time": 0.81436} +{"mode": "train", "epoch": 46, "iter": 3700, "lr": 0.07855, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28516, "top5_acc": 0.54156, "loss_cls": 4.11288, "loss": 4.11288, "time": 0.81611} +{"mode": "val", "epoch": 46, "iter": 309, "lr": 0.07854, "top1_acc": 0.1907, "top5_acc": 0.41873, "mean_class_accuracy": 0.19061} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.07851, "memory": 15990, "data_time": 1.29426, "top1_acc": 0.29141, "top5_acc": 0.54844, "loss_cls": 4.06284, "loss": 4.06284, "time": 2.2728} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.07849, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27922, "top5_acc": 0.53844, "loss_cls": 4.12325, "loss": 4.12325, "time": 0.82193} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.07847, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27875, "top5_acc": 0.53062, "loss_cls": 4.13211, "loss": 4.13211, "time": 0.81931} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.07844, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28062, "top5_acc": 0.53297, "loss_cls": 4.13212, "loss": 4.13212, "time": 0.81657} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.07842, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28188, "top5_acc": 0.5275, "loss_cls": 4.16229, "loss": 4.16229, "time": 0.81984} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.0784, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28797, "top5_acc": 0.54656, "loss_cls": 4.09764, "loss": 4.09764, "time": 0.81957} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.07838, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28266, "top5_acc": 0.53234, "loss_cls": 4.14181, "loss": 4.14181, "time": 0.82596} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.07835, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2825, "top5_acc": 0.53531, "loss_cls": 4.1128, "loss": 4.1128, "time": 0.82221} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.07833, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28766, "top5_acc": 0.54516, "loss_cls": 4.09445, "loss": 4.09445, "time": 0.82741} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.07831, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29313, "top5_acc": 0.54172, "loss_cls": 4.08868, "loss": 4.08868, "time": 0.82791} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.07828, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27062, "top5_acc": 0.52719, "loss_cls": 4.21246, "loss": 4.21246, "time": 0.8143} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.07826, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28953, "top5_acc": 0.54406, "loss_cls": 4.09964, "loss": 4.09964, "time": 0.81376} +{"mode": "train", "epoch": 47, "iter": 1300, "lr": 0.07824, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28578, "top5_acc": 0.54172, "loss_cls": 4.09991, "loss": 4.09991, "time": 0.81538} +{"mode": "train", "epoch": 47, "iter": 1400, "lr": 0.07821, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27031, "top5_acc": 0.52562, "loss_cls": 4.16502, "loss": 4.16502, "time": 0.81726} +{"mode": "train", "epoch": 47, "iter": 1500, "lr": 0.07819, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28484, "top5_acc": 0.54719, "loss_cls": 4.09957, "loss": 4.09957, "time": 0.81262} +{"mode": "train", "epoch": 47, "iter": 1600, "lr": 0.07817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28484, "top5_acc": 0.53891, "loss_cls": 4.10353, "loss": 4.10353, "time": 0.8135} +{"mode": "train", "epoch": 47, "iter": 1700, "lr": 0.07814, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28391, "top5_acc": 0.53484, "loss_cls": 4.10645, "loss": 4.10645, "time": 0.81059} +{"mode": "train", "epoch": 47, "iter": 1800, "lr": 0.07812, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28234, "top5_acc": 0.53406, "loss_cls": 4.11694, "loss": 4.11694, "time": 0.81356} +{"mode": "train", "epoch": 47, "iter": 1900, "lr": 0.0781, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27781, "top5_acc": 0.53047, "loss_cls": 4.14634, "loss": 4.14634, "time": 0.81807} +{"mode": "train", "epoch": 47, "iter": 2000, "lr": 0.07808, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2775, "top5_acc": 0.51953, "loss_cls": 4.14404, "loss": 4.14404, "time": 0.81366} +{"mode": "train", "epoch": 47, "iter": 2100, "lr": 0.07805, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28703, "top5_acc": 0.53359, "loss_cls": 4.11002, "loss": 4.11002, "time": 0.81656} +{"mode": "train", "epoch": 47, "iter": 2200, "lr": 0.07803, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27469, "top5_acc": 0.53078, "loss_cls": 4.17382, "loss": 4.17382, "time": 0.81137} +{"mode": "train", "epoch": 47, "iter": 2300, "lr": 0.07801, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28562, "top5_acc": 0.53188, "loss_cls": 4.1348, "loss": 4.1348, "time": 0.81763} +{"mode": "train", "epoch": 47, "iter": 2400, "lr": 0.07798, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27719, "top5_acc": 0.53094, "loss_cls": 4.15649, "loss": 4.15649, "time": 0.8172} +{"mode": "train", "epoch": 47, "iter": 2500, "lr": 0.07796, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28266, "top5_acc": 0.54109, "loss_cls": 4.11073, "loss": 4.11073, "time": 0.81201} +{"mode": "train", "epoch": 47, "iter": 2600, "lr": 0.07794, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27656, "top5_acc": 0.52906, "loss_cls": 4.1758, "loss": 4.1758, "time": 0.81498} +{"mode": "train", "epoch": 47, "iter": 2700, "lr": 0.07791, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28297, "top5_acc": 0.53125, "loss_cls": 4.15139, "loss": 4.15139, "time": 0.81652} +{"mode": "train", "epoch": 47, "iter": 2800, "lr": 0.07789, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29078, "top5_acc": 0.54391, "loss_cls": 4.08334, "loss": 4.08334, "time": 0.81482} +{"mode": "train", "epoch": 47, "iter": 2900, "lr": 0.07787, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28, "top5_acc": 0.52828, "loss_cls": 4.15151, "loss": 4.15151, "time": 0.81219} +{"mode": "train", "epoch": 47, "iter": 3000, "lr": 0.07784, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28375, "top5_acc": 0.51984, "loss_cls": 4.16956, "loss": 4.16956, "time": 0.81582} +{"mode": "train", "epoch": 47, "iter": 3100, "lr": 0.07782, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27719, "top5_acc": 0.53422, "loss_cls": 4.15031, "loss": 4.15031, "time": 0.81362} +{"mode": "train", "epoch": 47, "iter": 3200, "lr": 0.0778, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27656, "top5_acc": 0.53047, "loss_cls": 4.14032, "loss": 4.14032, "time": 0.81563} +{"mode": "train", "epoch": 47, "iter": 3300, "lr": 0.07777, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27359, "top5_acc": 0.52516, "loss_cls": 4.20674, "loss": 4.20674, "time": 0.81262} +{"mode": "train", "epoch": 47, "iter": 3400, "lr": 0.07775, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27406, "top5_acc": 0.53, "loss_cls": 4.15414, "loss": 4.15414, "time": 0.81612} +{"mode": "train", "epoch": 47, "iter": 3500, "lr": 0.07773, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28156, "top5_acc": 0.53281, "loss_cls": 4.14448, "loss": 4.14448, "time": 0.81444} +{"mode": "train", "epoch": 47, "iter": 3600, "lr": 0.0777, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27141, "top5_acc": 0.53, "loss_cls": 4.17484, "loss": 4.17484, "time": 0.81659} +{"mode": "train", "epoch": 47, "iter": 3700, "lr": 0.07768, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28188, "top5_acc": 0.53141, "loss_cls": 4.1417, "loss": 4.1417, "time": 0.81535} +{"mode": "val", "epoch": 47, "iter": 309, "lr": 0.07767, "top1_acc": 0.18786, "top5_acc": 0.39827, "mean_class_accuracy": 0.18766} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.07765, "memory": 15990, "data_time": 1.27572, "top1_acc": 0.29391, "top5_acc": 0.54344, "loss_cls": 4.06755, "loss": 4.06755, "time": 2.2558} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.07762, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29781, "top5_acc": 0.5475, "loss_cls": 4.03747, "loss": 4.03747, "time": 0.82541} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.0776, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28203, "top5_acc": 0.52844, "loss_cls": 4.15162, "loss": 4.15162, "time": 0.82331} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.07758, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.275, "top5_acc": 0.5325, "loss_cls": 4.13148, "loss": 4.13148, "time": 0.82} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.07755, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28328, "top5_acc": 0.52484, "loss_cls": 4.14031, "loss": 4.14031, "time": 0.81825} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.07753, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29484, "top5_acc": 0.54094, "loss_cls": 4.08268, "loss": 4.08268, "time": 0.81805} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.07751, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28656, "top5_acc": 0.53172, "loss_cls": 4.12369, "loss": 4.12369, "time": 0.82598} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.07748, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28422, "top5_acc": 0.53297, "loss_cls": 4.14674, "loss": 4.14674, "time": 0.82066} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.07746, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28594, "top5_acc": 0.52812, "loss_cls": 4.13458, "loss": 4.13458, "time": 0.82665} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.07744, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27891, "top5_acc": 0.52906, "loss_cls": 4.18013, "loss": 4.18013, "time": 0.82229} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.07741, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28859, "top5_acc": 0.52766, "loss_cls": 4.15874, "loss": 4.15874, "time": 0.82175} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.07739, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29109, "top5_acc": 0.54, "loss_cls": 4.09844, "loss": 4.09844, "time": 0.81543} +{"mode": "train", "epoch": 48, "iter": 1300, "lr": 0.07737, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28703, "top5_acc": 0.53906, "loss_cls": 4.09617, "loss": 4.09617, "time": 0.81626} +{"mode": "train", "epoch": 48, "iter": 1400, "lr": 0.07734, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28953, "top5_acc": 0.53047, "loss_cls": 4.0963, "loss": 4.0963, "time": 0.81853} +{"mode": "train", "epoch": 48, "iter": 1500, "lr": 0.07732, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28766, "top5_acc": 0.53625, "loss_cls": 4.1193, "loss": 4.1193, "time": 0.81419} +{"mode": "train", "epoch": 48, "iter": 1600, "lr": 0.0773, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27734, "top5_acc": 0.52375, "loss_cls": 4.18186, "loss": 4.18186, "time": 0.819} +{"mode": "train", "epoch": 48, "iter": 1700, "lr": 0.07727, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27609, "top5_acc": 0.53438, "loss_cls": 4.15654, "loss": 4.15654, "time": 0.81494} +{"mode": "train", "epoch": 48, "iter": 1800, "lr": 0.07725, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26922, "top5_acc": 0.52281, "loss_cls": 4.22953, "loss": 4.22953, "time": 0.81504} +{"mode": "train", "epoch": 48, "iter": 1900, "lr": 0.07723, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28266, "top5_acc": 0.52203, "loss_cls": 4.16391, "loss": 4.16391, "time": 0.81366} +{"mode": "train", "epoch": 48, "iter": 2000, "lr": 0.0772, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29078, "top5_acc": 0.53234, "loss_cls": 4.13076, "loss": 4.13076, "time": 0.82191} +{"mode": "train", "epoch": 48, "iter": 2100, "lr": 0.07718, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27875, "top5_acc": 0.53922, "loss_cls": 4.14642, "loss": 4.14642, "time": 0.81389} +{"mode": "train", "epoch": 48, "iter": 2200, "lr": 0.07716, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2825, "top5_acc": 0.54047, "loss_cls": 4.09667, "loss": 4.09667, "time": 0.81156} +{"mode": "train", "epoch": 48, "iter": 2300, "lr": 0.07713, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27922, "top5_acc": 0.53516, "loss_cls": 4.13702, "loss": 4.13702, "time": 0.81285} +{"mode": "train", "epoch": 48, "iter": 2400, "lr": 0.07711, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28234, "top5_acc": 0.53828, "loss_cls": 4.09914, "loss": 4.09914, "time": 0.81343} +{"mode": "train", "epoch": 48, "iter": 2500, "lr": 0.07709, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28656, "top5_acc": 0.54828, "loss_cls": 4.08157, "loss": 4.08157, "time": 0.81141} +{"mode": "train", "epoch": 48, "iter": 2600, "lr": 0.07706, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27562, "top5_acc": 0.53391, "loss_cls": 4.14627, "loss": 4.14627, "time": 0.81523} +{"mode": "train", "epoch": 48, "iter": 2700, "lr": 0.07704, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27844, "top5_acc": 0.53, "loss_cls": 4.15945, "loss": 4.15945, "time": 0.81277} +{"mode": "train", "epoch": 48, "iter": 2800, "lr": 0.07701, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27516, "top5_acc": 0.52719, "loss_cls": 4.17169, "loss": 4.17169, "time": 0.8117} +{"mode": "train", "epoch": 48, "iter": 2900, "lr": 0.07699, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28781, "top5_acc": 0.52672, "loss_cls": 4.15996, "loss": 4.15996, "time": 0.81552} +{"mode": "train", "epoch": 48, "iter": 3000, "lr": 0.07697, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28953, "top5_acc": 0.53922, "loss_cls": 4.10809, "loss": 4.10809, "time": 0.82156} +{"mode": "train", "epoch": 48, "iter": 3100, "lr": 0.07694, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28422, "top5_acc": 0.52938, "loss_cls": 4.18389, "loss": 4.18389, "time": 0.81489} +{"mode": "train", "epoch": 48, "iter": 3200, "lr": 0.07692, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28422, "top5_acc": 0.53531, "loss_cls": 4.12839, "loss": 4.12839, "time": 0.8203} +{"mode": "train", "epoch": 48, "iter": 3300, "lr": 0.0769, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27922, "top5_acc": 0.53156, "loss_cls": 4.138, "loss": 4.138, "time": 0.81466} +{"mode": "train", "epoch": 48, "iter": 3400, "lr": 0.07687, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29359, "top5_acc": 0.5475, "loss_cls": 4.05641, "loss": 4.05641, "time": 0.81541} +{"mode": "train", "epoch": 48, "iter": 3500, "lr": 0.07685, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29203, "top5_acc": 0.53438, "loss_cls": 4.10345, "loss": 4.10345, "time": 0.81783} +{"mode": "train", "epoch": 48, "iter": 3600, "lr": 0.07683, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27781, "top5_acc": 0.52781, "loss_cls": 4.15332, "loss": 4.15332, "time": 0.81571} +{"mode": "train", "epoch": 48, "iter": 3700, "lr": 0.0768, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27625, "top5_acc": 0.53078, "loss_cls": 4.16077, "loss": 4.16077, "time": 0.81831} +{"mode": "val", "epoch": 48, "iter": 309, "lr": 0.07679, "top1_acc": 0.20473, "top5_acc": 0.43195, "mean_class_accuracy": 0.20459} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.07677, "memory": 15990, "data_time": 1.31513, "top1_acc": 0.29641, "top5_acc": 0.54562, "loss_cls": 4.06933, "loss": 4.06933, "time": 2.30328} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.07674, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29172, "top5_acc": 0.54297, "loss_cls": 4.09501, "loss": 4.09501, "time": 0.82224} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.07672, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28094, "top5_acc": 0.53859, "loss_cls": 4.12737, "loss": 4.12737, "time": 0.81695} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.0767, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27828, "top5_acc": 0.54688, "loss_cls": 4.08354, "loss": 4.08354, "time": 0.82037} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.07667, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29547, "top5_acc": 0.55062, "loss_cls": 4.05735, "loss": 4.05735, "time": 0.82111} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.07665, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29969, "top5_acc": 0.54344, "loss_cls": 4.06455, "loss": 4.06455, "time": 0.81708} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.07663, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27453, "top5_acc": 0.53703, "loss_cls": 4.105, "loss": 4.105, "time": 0.82583} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.0766, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2825, "top5_acc": 0.53344, "loss_cls": 4.12813, "loss": 4.12813, "time": 0.8217} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.07658, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27875, "top5_acc": 0.53844, "loss_cls": 4.11603, "loss": 4.11603, "time": 0.82623} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.07656, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2975, "top5_acc": 0.53094, "loss_cls": 4.13598, "loss": 4.13598, "time": 0.81787} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.07653, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26703, "top5_acc": 0.52688, "loss_cls": 4.18703, "loss": 4.18703, "time": 0.82022} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.07651, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28781, "top5_acc": 0.53938, "loss_cls": 4.08534, "loss": 4.08534, "time": 0.81737} +{"mode": "train", "epoch": 49, "iter": 1300, "lr": 0.07648, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28, "top5_acc": 0.52953, "loss_cls": 4.13002, "loss": 4.13002, "time": 0.82194} +{"mode": "train", "epoch": 49, "iter": 1400, "lr": 0.07646, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28719, "top5_acc": 0.53641, "loss_cls": 4.11026, "loss": 4.11026, "time": 0.81441} +{"mode": "train", "epoch": 49, "iter": 1500, "lr": 0.07644, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28203, "top5_acc": 0.53875, "loss_cls": 4.12807, "loss": 4.12807, "time": 0.81876} +{"mode": "train", "epoch": 49, "iter": 1600, "lr": 0.07641, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27953, "top5_acc": 0.52969, "loss_cls": 4.1209, "loss": 4.1209, "time": 0.81394} +{"mode": "train", "epoch": 49, "iter": 1700, "lr": 0.07639, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27938, "top5_acc": 0.53562, "loss_cls": 4.10997, "loss": 4.10997, "time": 0.81587} +{"mode": "train", "epoch": 49, "iter": 1800, "lr": 0.07637, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27812, "top5_acc": 0.51719, "loss_cls": 4.18946, "loss": 4.18946, "time": 0.81534} +{"mode": "train", "epoch": 49, "iter": 1900, "lr": 0.07634, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27266, "top5_acc": 0.52969, "loss_cls": 4.1793, "loss": 4.1793, "time": 0.81878} +{"mode": "train", "epoch": 49, "iter": 2000, "lr": 0.07632, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29156, "top5_acc": 0.54766, "loss_cls": 4.09761, "loss": 4.09761, "time": 0.81837} +{"mode": "train", "epoch": 49, "iter": 2100, "lr": 0.07629, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28609, "top5_acc": 0.53719, "loss_cls": 4.10545, "loss": 4.10545, "time": 0.81461} +{"mode": "train", "epoch": 49, "iter": 2200, "lr": 0.07627, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28312, "top5_acc": 0.53375, "loss_cls": 4.122, "loss": 4.122, "time": 0.81874} +{"mode": "train", "epoch": 49, "iter": 2300, "lr": 0.07625, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28359, "top5_acc": 0.53281, "loss_cls": 4.13641, "loss": 4.13641, "time": 0.80906} +{"mode": "train", "epoch": 49, "iter": 2400, "lr": 0.07622, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28547, "top5_acc": 0.53938, "loss_cls": 4.09319, "loss": 4.09319, "time": 0.81666} +{"mode": "train", "epoch": 49, "iter": 2500, "lr": 0.0762, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27094, "top5_acc": 0.52312, "loss_cls": 4.19931, "loss": 4.19931, "time": 0.81595} +{"mode": "train", "epoch": 49, "iter": 2600, "lr": 0.07618, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27766, "top5_acc": 0.53688, "loss_cls": 4.13722, "loss": 4.13722, "time": 0.81196} +{"mode": "train", "epoch": 49, "iter": 2700, "lr": 0.07615, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28391, "top5_acc": 0.52016, "loss_cls": 4.16909, "loss": 4.16909, "time": 0.81691} +{"mode": "train", "epoch": 49, "iter": 2800, "lr": 0.07613, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29422, "top5_acc": 0.54484, "loss_cls": 4.0706, "loss": 4.0706, "time": 0.81601} +{"mode": "train", "epoch": 49, "iter": 2900, "lr": 0.0761, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28609, "top5_acc": 0.53828, "loss_cls": 4.1131, "loss": 4.1131, "time": 0.81529} +{"mode": "train", "epoch": 49, "iter": 3000, "lr": 0.07608, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28531, "top5_acc": 0.53531, "loss_cls": 4.14822, "loss": 4.14822, "time": 0.8129} +{"mode": "train", "epoch": 49, "iter": 3100, "lr": 0.07606, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28125, "top5_acc": 0.53781, "loss_cls": 4.1319, "loss": 4.1319, "time": 0.81756} +{"mode": "train", "epoch": 49, "iter": 3200, "lr": 0.07603, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29359, "top5_acc": 0.54281, "loss_cls": 4.08527, "loss": 4.08527, "time": 0.81373} +{"mode": "train", "epoch": 49, "iter": 3300, "lr": 0.07601, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28469, "top5_acc": 0.54141, "loss_cls": 4.12207, "loss": 4.12207, "time": 0.81632} +{"mode": "train", "epoch": 49, "iter": 3400, "lr": 0.07598, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28797, "top5_acc": 0.53875, "loss_cls": 4.10915, "loss": 4.10915, "time": 0.80881} +{"mode": "train", "epoch": 49, "iter": 3500, "lr": 0.07596, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29359, "top5_acc": 0.53219, "loss_cls": 4.10755, "loss": 4.10755, "time": 0.81489} +{"mode": "train", "epoch": 49, "iter": 3600, "lr": 0.07594, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27969, "top5_acc": 0.53656, "loss_cls": 4.15563, "loss": 4.15563, "time": 0.81423} +{"mode": "train", "epoch": 49, "iter": 3700, "lr": 0.07591, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27969, "top5_acc": 0.5275, "loss_cls": 4.17291, "loss": 4.17291, "time": 0.81424} +{"mode": "val", "epoch": 49, "iter": 309, "lr": 0.0759, "top1_acc": 0.19815, "top5_acc": 0.42846, "mean_class_accuracy": 0.19805} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.07588, "memory": 15990, "data_time": 1.31596, "top1_acc": 0.30031, "top5_acc": 0.55359, "loss_cls": 4.02323, "loss": 4.02323, "time": 2.29136} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.07585, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29453, "top5_acc": 0.54547, "loss_cls": 4.08544, "loss": 4.08544, "time": 0.81623} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.07583, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28859, "top5_acc": 0.54937, "loss_cls": 4.09213, "loss": 4.09213, "time": 0.81541} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.07581, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29453, "top5_acc": 0.55469, "loss_cls": 4.03789, "loss": 4.03789, "time": 0.82638} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.07578, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28578, "top5_acc": 0.53453, "loss_cls": 4.11566, "loss": 4.11566, "time": 0.82256} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.07576, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28141, "top5_acc": 0.53922, "loss_cls": 4.12656, "loss": 4.12656, "time": 0.82423} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.07573, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.275, "top5_acc": 0.53672, "loss_cls": 4.11883, "loss": 4.11883, "time": 0.82377} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.07571, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28594, "top5_acc": 0.53781, "loss_cls": 4.11517, "loss": 4.11517, "time": 0.81617} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.07569, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28172, "top5_acc": 0.54125, "loss_cls": 4.12236, "loss": 4.12236, "time": 0.83161} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.07566, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28109, "top5_acc": 0.54109, "loss_cls": 4.11995, "loss": 4.11995, "time": 0.82465} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.07564, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28547, "top5_acc": 0.54031, "loss_cls": 4.10155, "loss": 4.10155, "time": 0.8231} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.07561, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29016, "top5_acc": 0.53422, "loss_cls": 4.14593, "loss": 4.14593, "time": 0.82451} +{"mode": "train", "epoch": 50, "iter": 1300, "lr": 0.07559, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29234, "top5_acc": 0.54609, "loss_cls": 4.09107, "loss": 4.09107, "time": 0.81412} +{"mode": "train", "epoch": 50, "iter": 1400, "lr": 0.07557, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28328, "top5_acc": 0.53984, "loss_cls": 4.11038, "loss": 4.11038, "time": 0.82065} +{"mode": "train", "epoch": 50, "iter": 1500, "lr": 0.07554, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28578, "top5_acc": 0.53141, "loss_cls": 4.12051, "loss": 4.12051, "time": 0.81827} +{"mode": "train", "epoch": 50, "iter": 1600, "lr": 0.07552, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28641, "top5_acc": 0.5375, "loss_cls": 4.11631, "loss": 4.11631, "time": 0.81237} +{"mode": "train", "epoch": 50, "iter": 1700, "lr": 0.07549, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28922, "top5_acc": 0.54, "loss_cls": 4.10922, "loss": 4.10922, "time": 0.81377} +{"mode": "train", "epoch": 50, "iter": 1800, "lr": 0.07547, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28, "top5_acc": 0.53969, "loss_cls": 4.13447, "loss": 4.13447, "time": 0.82398} +{"mode": "train", "epoch": 50, "iter": 1900, "lr": 0.07545, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27531, "top5_acc": 0.53188, "loss_cls": 4.15509, "loss": 4.15509, "time": 0.81791} +{"mode": "train", "epoch": 50, "iter": 2000, "lr": 0.07542, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27359, "top5_acc": 0.52672, "loss_cls": 4.16175, "loss": 4.16175, "time": 0.81691} +{"mode": "train", "epoch": 50, "iter": 2100, "lr": 0.0754, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28391, "top5_acc": 0.53234, "loss_cls": 4.14364, "loss": 4.14364, "time": 0.81037} +{"mode": "train", "epoch": 50, "iter": 2200, "lr": 0.07537, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28609, "top5_acc": 0.53766, "loss_cls": 4.12081, "loss": 4.12081, "time": 0.82311} +{"mode": "train", "epoch": 50, "iter": 2300, "lr": 0.07535, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28516, "top5_acc": 0.53109, "loss_cls": 4.15493, "loss": 4.15493, "time": 0.81305} +{"mode": "train", "epoch": 50, "iter": 2400, "lr": 0.07533, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29078, "top5_acc": 0.53797, "loss_cls": 4.10494, "loss": 4.10494, "time": 0.81967} +{"mode": "train", "epoch": 50, "iter": 2500, "lr": 0.0753, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28312, "top5_acc": 0.54297, "loss_cls": 4.10342, "loss": 4.10342, "time": 0.81625} +{"mode": "train", "epoch": 50, "iter": 2600, "lr": 0.07528, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28469, "top5_acc": 0.53875, "loss_cls": 4.10416, "loss": 4.10416, "time": 0.81506} +{"mode": "train", "epoch": 50, "iter": 2700, "lr": 0.07525, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27328, "top5_acc": 0.51844, "loss_cls": 4.20075, "loss": 4.20075, "time": 0.81574} +{"mode": "train", "epoch": 50, "iter": 2800, "lr": 0.07523, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28781, "top5_acc": 0.53562, "loss_cls": 4.12088, "loss": 4.12088, "time": 0.81472} +{"mode": "train", "epoch": 50, "iter": 2900, "lr": 0.0752, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28828, "top5_acc": 0.53828, "loss_cls": 4.09952, "loss": 4.09952, "time": 0.8144} +{"mode": "train", "epoch": 50, "iter": 3000, "lr": 0.07518, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28312, "top5_acc": 0.53516, "loss_cls": 4.11885, "loss": 4.11885, "time": 0.81431} +{"mode": "train", "epoch": 50, "iter": 3100, "lr": 0.07516, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28188, "top5_acc": 0.53531, "loss_cls": 4.11743, "loss": 4.11743, "time": 0.81457} +{"mode": "train", "epoch": 50, "iter": 3200, "lr": 0.07513, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27781, "top5_acc": 0.53234, "loss_cls": 4.16586, "loss": 4.16586, "time": 0.82052} +{"mode": "train", "epoch": 50, "iter": 3300, "lr": 0.07511, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29156, "top5_acc": 0.54219, "loss_cls": 4.09785, "loss": 4.09785, "time": 0.81618} +{"mode": "train", "epoch": 50, "iter": 3400, "lr": 0.07508, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27797, "top5_acc": 0.52781, "loss_cls": 4.1156, "loss": 4.1156, "time": 0.81499} +{"mode": "train", "epoch": 50, "iter": 3500, "lr": 0.07506, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2925, "top5_acc": 0.54328, "loss_cls": 4.08219, "loss": 4.08219, "time": 0.81573} +{"mode": "train", "epoch": 50, "iter": 3600, "lr": 0.07504, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28141, "top5_acc": 0.53312, "loss_cls": 4.11788, "loss": 4.11788, "time": 0.82365} +{"mode": "train", "epoch": 50, "iter": 3700, "lr": 0.07501, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28766, "top5_acc": 0.53, "loss_cls": 4.13021, "loss": 4.13021, "time": 0.82192} +{"mode": "val", "epoch": 50, "iter": 309, "lr": 0.075, "top1_acc": 0.20022, "top5_acc": 0.43347, "mean_class_accuracy": 0.19995} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.07498, "memory": 15990, "data_time": 1.28652, "top1_acc": 0.29172, "top5_acc": 0.54078, "loss_cls": 4.08341, "loss": 4.08341, "time": 2.26197} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.07495, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29266, "top5_acc": 0.55516, "loss_cls": 4.06409, "loss": 4.06409, "time": 0.81764} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.07493, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28406, "top5_acc": 0.53906, "loss_cls": 4.08475, "loss": 4.08475, "time": 0.81799} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.0749, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29016, "top5_acc": 0.54188, "loss_cls": 4.09098, "loss": 4.09098, "time": 0.82234} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.07488, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28078, "top5_acc": 0.53016, "loss_cls": 4.12673, "loss": 4.12673, "time": 0.8196} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.07485, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28516, "top5_acc": 0.54203, "loss_cls": 4.10135, "loss": 4.10135, "time": 0.82653} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.07483, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28625, "top5_acc": 0.53938, "loss_cls": 4.12481, "loss": 4.12481, "time": 0.82824} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.07481, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29859, "top5_acc": 0.54156, "loss_cls": 4.07463, "loss": 4.07463, "time": 0.81412} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.07478, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28984, "top5_acc": 0.53969, "loss_cls": 4.13648, "loss": 4.13648, "time": 0.82892} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.07476, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28688, "top5_acc": 0.54281, "loss_cls": 4.08862, "loss": 4.08862, "time": 0.82091} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.07473, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28656, "top5_acc": 0.54422, "loss_cls": 4.09621, "loss": 4.09621, "time": 0.8189} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.07471, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28922, "top5_acc": 0.54609, "loss_cls": 4.09715, "loss": 4.09715, "time": 0.81856} +{"mode": "train", "epoch": 51, "iter": 1300, "lr": 0.07468, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28031, "top5_acc": 0.53656, "loss_cls": 4.10357, "loss": 4.10357, "time": 0.81806} +{"mode": "train", "epoch": 51, "iter": 1400, "lr": 0.07466, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28844, "top5_acc": 0.54219, "loss_cls": 4.09283, "loss": 4.09283, "time": 0.81924} +{"mode": "train", "epoch": 51, "iter": 1500, "lr": 0.07464, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2775, "top5_acc": 0.5325, "loss_cls": 4.13417, "loss": 4.13417, "time": 0.81778} +{"mode": "train", "epoch": 51, "iter": 1600, "lr": 0.07461, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28875, "top5_acc": 0.53969, "loss_cls": 4.11336, "loss": 4.11336, "time": 0.8176} +{"mode": "train", "epoch": 51, "iter": 1700, "lr": 0.07459, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28531, "top5_acc": 0.53188, "loss_cls": 4.11261, "loss": 4.11261, "time": 0.81584} +{"mode": "train", "epoch": 51, "iter": 1800, "lr": 0.07456, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29031, "top5_acc": 0.54312, "loss_cls": 4.10408, "loss": 4.10408, "time": 0.81368} +{"mode": "train", "epoch": 51, "iter": 1900, "lr": 0.07454, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28344, "top5_acc": 0.52547, "loss_cls": 4.1807, "loss": 4.1807, "time": 0.81657} +{"mode": "train", "epoch": 51, "iter": 2000, "lr": 0.07451, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28328, "top5_acc": 0.54281, "loss_cls": 4.09905, "loss": 4.09905, "time": 0.8226} +{"mode": "train", "epoch": 51, "iter": 2100, "lr": 0.07449, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2925, "top5_acc": 0.53906, "loss_cls": 4.12571, "loss": 4.12571, "time": 0.81095} +{"mode": "train", "epoch": 51, "iter": 2200, "lr": 0.07447, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29172, "top5_acc": 0.54562, "loss_cls": 4.09784, "loss": 4.09784, "time": 0.81539} +{"mode": "train", "epoch": 51, "iter": 2300, "lr": 0.07444, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29516, "top5_acc": 0.54703, "loss_cls": 4.08425, "loss": 4.08425, "time": 0.81637} +{"mode": "train", "epoch": 51, "iter": 2400, "lr": 0.07442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29656, "top5_acc": 0.54344, "loss_cls": 4.08717, "loss": 4.08717, "time": 0.81532} +{"mode": "train", "epoch": 51, "iter": 2500, "lr": 0.07439, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28188, "top5_acc": 0.54203, "loss_cls": 4.10553, "loss": 4.10553, "time": 0.81046} +{"mode": "train", "epoch": 51, "iter": 2600, "lr": 0.07437, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.53562, "loss_cls": 4.14807, "loss": 4.14807, "time": 0.81566} +{"mode": "train", "epoch": 51, "iter": 2700, "lr": 0.07434, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28984, "top5_acc": 0.54125, "loss_cls": 4.09599, "loss": 4.09599, "time": 0.81549} +{"mode": "train", "epoch": 51, "iter": 2800, "lr": 0.07432, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28953, "top5_acc": 0.54703, "loss_cls": 4.08326, "loss": 4.08326, "time": 0.81559} +{"mode": "train", "epoch": 51, "iter": 2900, "lr": 0.07429, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27766, "top5_acc": 0.53734, "loss_cls": 4.12432, "loss": 4.12432, "time": 0.81439} +{"mode": "train", "epoch": 51, "iter": 3000, "lr": 0.07427, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28781, "top5_acc": 0.54125, "loss_cls": 4.08323, "loss": 4.08323, "time": 0.8153} +{"mode": "train", "epoch": 51, "iter": 3100, "lr": 0.07425, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28375, "top5_acc": 0.54344, "loss_cls": 4.13181, "loss": 4.13181, "time": 0.81152} +{"mode": "train", "epoch": 51, "iter": 3200, "lr": 0.07422, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28203, "top5_acc": 0.52844, "loss_cls": 4.1604, "loss": 4.1604, "time": 0.81749} +{"mode": "train", "epoch": 51, "iter": 3300, "lr": 0.0742, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28906, "top5_acc": 0.52703, "loss_cls": 4.138, "loss": 4.138, "time": 0.81706} +{"mode": "train", "epoch": 51, "iter": 3400, "lr": 0.07417, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28062, "top5_acc": 0.52516, "loss_cls": 4.14912, "loss": 4.14912, "time": 0.81548} +{"mode": "train", "epoch": 51, "iter": 3500, "lr": 0.07415, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27562, "top5_acc": 0.53062, "loss_cls": 4.1681, "loss": 4.1681, "time": 0.81453} +{"mode": "train", "epoch": 51, "iter": 3600, "lr": 0.07412, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28328, "top5_acc": 0.53609, "loss_cls": 4.121, "loss": 4.121, "time": 0.8148} +{"mode": "train", "epoch": 51, "iter": 3700, "lr": 0.0741, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28031, "top5_acc": 0.53438, "loss_cls": 4.13137, "loss": 4.13137, "time": 0.81595} +{"mode": "val", "epoch": 51, "iter": 309, "lr": 0.07409, "top1_acc": 0.16917, "top5_acc": 0.38165, "mean_class_accuracy": 0.16911} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.07406, "memory": 15990, "data_time": 1.30951, "top1_acc": 0.29281, "top5_acc": 0.54875, "loss_cls": 4.05019, "loss": 4.05019, "time": 2.29665} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.07404, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29125, "top5_acc": 0.54922, "loss_cls": 4.09827, "loss": 4.09827, "time": 0.82269} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.07401, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29203, "top5_acc": 0.54531, "loss_cls": 4.07796, "loss": 4.07796, "time": 0.82624} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.07399, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28656, "top5_acc": 0.54578, "loss_cls": 4.07097, "loss": 4.07097, "time": 0.82403} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.07397, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29563, "top5_acc": 0.54172, "loss_cls": 4.07826, "loss": 4.07826, "time": 0.81845} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.07394, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29484, "top5_acc": 0.53609, "loss_cls": 4.0861, "loss": 4.0861, "time": 0.81911} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.07392, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28547, "top5_acc": 0.53594, "loss_cls": 4.10844, "loss": 4.10844, "time": 0.8221} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.07389, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29125, "top5_acc": 0.53609, "loss_cls": 4.08722, "loss": 4.08722, "time": 0.8202} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.07387, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28625, "top5_acc": 0.54031, "loss_cls": 4.11887, "loss": 4.11887, "time": 0.82725} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.07384, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27922, "top5_acc": 0.53609, "loss_cls": 4.10358, "loss": 4.10358, "time": 0.82471} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.07382, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27688, "top5_acc": 0.52781, "loss_cls": 4.18147, "loss": 4.18147, "time": 0.81971} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.07379, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29234, "top5_acc": 0.54094, "loss_cls": 4.05907, "loss": 4.05907, "time": 0.81888} +{"mode": "train", "epoch": 52, "iter": 1300, "lr": 0.07377, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28016, "top5_acc": 0.54016, "loss_cls": 4.1123, "loss": 4.1123, "time": 0.81639} +{"mode": "train", "epoch": 52, "iter": 1400, "lr": 0.07374, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29063, "top5_acc": 0.54453, "loss_cls": 4.08588, "loss": 4.08588, "time": 0.81975} +{"mode": "train", "epoch": 52, "iter": 1500, "lr": 0.07372, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28516, "top5_acc": 0.53047, "loss_cls": 4.12519, "loss": 4.12519, "time": 0.81405} +{"mode": "train", "epoch": 52, "iter": 1600, "lr": 0.0737, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28953, "top5_acc": 0.54109, "loss_cls": 4.08777, "loss": 4.08777, "time": 0.81684} +{"mode": "train", "epoch": 52, "iter": 1700, "lr": 0.07367, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29672, "top5_acc": 0.54344, "loss_cls": 4.07198, "loss": 4.07198, "time": 0.81638} +{"mode": "train", "epoch": 52, "iter": 1800, "lr": 0.07365, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29125, "top5_acc": 0.53609, "loss_cls": 4.1045, "loss": 4.1045, "time": 0.81815} +{"mode": "train", "epoch": 52, "iter": 1900, "lr": 0.07362, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28844, "top5_acc": 0.53797, "loss_cls": 4.09799, "loss": 4.09799, "time": 0.8206} +{"mode": "train", "epoch": 52, "iter": 2000, "lr": 0.0736, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29563, "top5_acc": 0.54859, "loss_cls": 4.04821, "loss": 4.04821, "time": 0.81271} +{"mode": "train", "epoch": 52, "iter": 2100, "lr": 0.07357, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29203, "top5_acc": 0.54922, "loss_cls": 4.06819, "loss": 4.06819, "time": 0.81185} +{"mode": "train", "epoch": 52, "iter": 2200, "lr": 0.07355, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28703, "top5_acc": 0.54188, "loss_cls": 4.09861, "loss": 4.09861, "time": 0.81667} +{"mode": "train", "epoch": 52, "iter": 2300, "lr": 0.07352, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2875, "top5_acc": 0.54328, "loss_cls": 4.10227, "loss": 4.10227, "time": 0.81868} +{"mode": "train", "epoch": 52, "iter": 2400, "lr": 0.0735, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28484, "top5_acc": 0.54078, "loss_cls": 4.10489, "loss": 4.10489, "time": 0.81826} +{"mode": "train", "epoch": 52, "iter": 2500, "lr": 0.07347, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28969, "top5_acc": 0.54078, "loss_cls": 4.1122, "loss": 4.1122, "time": 0.81617} +{"mode": "train", "epoch": 52, "iter": 2600, "lr": 0.07345, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27406, "top5_acc": 0.53312, "loss_cls": 4.1364, "loss": 4.1364, "time": 0.81377} +{"mode": "train", "epoch": 52, "iter": 2700, "lr": 0.07342, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28516, "top5_acc": 0.53859, "loss_cls": 4.12297, "loss": 4.12297, "time": 0.81467} +{"mode": "train", "epoch": 52, "iter": 2800, "lr": 0.0734, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29844, "top5_acc": 0.55078, "loss_cls": 4.08315, "loss": 4.08315, "time": 0.81432} +{"mode": "train", "epoch": 52, "iter": 2900, "lr": 0.07337, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28875, "top5_acc": 0.53859, "loss_cls": 4.1075, "loss": 4.1075, "time": 0.81216} +{"mode": "train", "epoch": 52, "iter": 3000, "lr": 0.07335, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28562, "top5_acc": 0.5375, "loss_cls": 4.12385, "loss": 4.12385, "time": 0.81382} +{"mode": "train", "epoch": 52, "iter": 3100, "lr": 0.07332, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2825, "top5_acc": 0.52906, "loss_cls": 4.15796, "loss": 4.15796, "time": 0.81521} +{"mode": "train", "epoch": 52, "iter": 3200, "lr": 0.0733, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29422, "top5_acc": 0.54453, "loss_cls": 4.06924, "loss": 4.06924, "time": 0.81362} +{"mode": "train", "epoch": 52, "iter": 3300, "lr": 0.07328, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28516, "top5_acc": 0.54609, "loss_cls": 4.1055, "loss": 4.1055, "time": 0.81861} +{"mode": "train", "epoch": 52, "iter": 3400, "lr": 0.07325, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29375, "top5_acc": 0.53953, "loss_cls": 4.10065, "loss": 4.10065, "time": 0.81377} +{"mode": "train", "epoch": 52, "iter": 3500, "lr": 0.07323, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28641, "top5_acc": 0.53734, "loss_cls": 4.1163, "loss": 4.1163, "time": 0.8217} +{"mode": "train", "epoch": 52, "iter": 3600, "lr": 0.0732, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29703, "top5_acc": 0.54438, "loss_cls": 4.10369, "loss": 4.10369, "time": 0.81835} +{"mode": "train", "epoch": 52, "iter": 3700, "lr": 0.07318, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29203, "top5_acc": 0.53125, "loss_cls": 4.12934, "loss": 4.12934, "time": 0.81775} +{"mode": "val", "epoch": 52, "iter": 309, "lr": 0.07317, "top1_acc": 0.22833, "top5_acc": 0.46168, "mean_class_accuracy": 0.22814} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.07314, "memory": 15990, "data_time": 1.31705, "top1_acc": 0.2975, "top5_acc": 0.54391, "loss_cls": 4.04618, "loss": 4.04618, "time": 2.29394} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.07312, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29656, "top5_acc": 0.54203, "loss_cls": 4.06295, "loss": 4.06295, "time": 0.82152} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.07309, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29516, "top5_acc": 0.54297, "loss_cls": 4.08343, "loss": 4.08343, "time": 0.82489} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.07307, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29641, "top5_acc": 0.54312, "loss_cls": 4.07216, "loss": 4.07216, "time": 0.8305} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.07304, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30438, "top5_acc": 0.54312, "loss_cls": 4.05548, "loss": 4.05548, "time": 0.824} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.07302, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29063, "top5_acc": 0.53328, "loss_cls": 4.10835, "loss": 4.10835, "time": 0.82027} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.07299, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29203, "top5_acc": 0.54531, "loss_cls": 4.07183, "loss": 4.07183, "time": 0.82035} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.07297, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29328, "top5_acc": 0.54609, "loss_cls": 4.07849, "loss": 4.07849, "time": 0.82262} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.07294, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29453, "top5_acc": 0.55219, "loss_cls": 4.06163, "loss": 4.06163, "time": 0.82308} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.07292, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28734, "top5_acc": 0.54172, "loss_cls": 4.10017, "loss": 4.10017, "time": 0.82595} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.07289, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28188, "top5_acc": 0.54484, "loss_cls": 4.0645, "loss": 4.0645, "time": 0.81836} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.07287, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29078, "top5_acc": 0.54438, "loss_cls": 4.07424, "loss": 4.07424, "time": 0.81787} +{"mode": "train", "epoch": 53, "iter": 1300, "lr": 0.07284, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29359, "top5_acc": 0.54922, "loss_cls": 4.06751, "loss": 4.06751, "time": 0.81994} +{"mode": "train", "epoch": 53, "iter": 1400, "lr": 0.07282, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28406, "top5_acc": 0.54344, "loss_cls": 4.10268, "loss": 4.10268, "time": 0.81779} +{"mode": "train", "epoch": 53, "iter": 1500, "lr": 0.07279, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28844, "top5_acc": 0.54562, "loss_cls": 4.09628, "loss": 4.09628, "time": 0.82461} +{"mode": "train", "epoch": 53, "iter": 1600, "lr": 0.07277, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28422, "top5_acc": 0.53, "loss_cls": 4.11483, "loss": 4.11483, "time": 0.81099} +{"mode": "train", "epoch": 53, "iter": 1700, "lr": 0.07274, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28891, "top5_acc": 0.53438, "loss_cls": 4.10484, "loss": 4.10484, "time": 0.81316} +{"mode": "train", "epoch": 53, "iter": 1800, "lr": 0.07272, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27422, "top5_acc": 0.53047, "loss_cls": 4.15415, "loss": 4.15415, "time": 0.81684} +{"mode": "train", "epoch": 53, "iter": 1900, "lr": 0.07269, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28547, "top5_acc": 0.54062, "loss_cls": 4.10362, "loss": 4.10362, "time": 0.81665} +{"mode": "train", "epoch": 53, "iter": 2000, "lr": 0.07267, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28328, "top5_acc": 0.54359, "loss_cls": 4.07742, "loss": 4.07742, "time": 0.82378} +{"mode": "train", "epoch": 53, "iter": 2100, "lr": 0.07264, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28813, "top5_acc": 0.54859, "loss_cls": 4.09789, "loss": 4.09789, "time": 0.81643} +{"mode": "train", "epoch": 53, "iter": 2200, "lr": 0.07262, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2925, "top5_acc": 0.55547, "loss_cls": 4.06345, "loss": 4.06345, "time": 0.81598} +{"mode": "train", "epoch": 53, "iter": 2300, "lr": 0.07259, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28969, "top5_acc": 0.54906, "loss_cls": 4.06542, "loss": 4.06542, "time": 0.81505} +{"mode": "train", "epoch": 53, "iter": 2400, "lr": 0.07257, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28578, "top5_acc": 0.54, "loss_cls": 4.11389, "loss": 4.11389, "time": 0.81054} +{"mode": "train", "epoch": 53, "iter": 2500, "lr": 0.07254, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29109, "top5_acc": 0.53906, "loss_cls": 4.08772, "loss": 4.08772, "time": 0.8131} +{"mode": "train", "epoch": 53, "iter": 2600, "lr": 0.07252, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27453, "top5_acc": 0.53109, "loss_cls": 4.15162, "loss": 4.15162, "time": 0.81402} +{"mode": "train", "epoch": 53, "iter": 2700, "lr": 0.07249, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29203, "top5_acc": 0.54766, "loss_cls": 4.07844, "loss": 4.07844, "time": 0.81358} +{"mode": "train", "epoch": 53, "iter": 2800, "lr": 0.07247, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27703, "top5_acc": 0.53344, "loss_cls": 4.13923, "loss": 4.13923, "time": 0.81878} +{"mode": "train", "epoch": 53, "iter": 2900, "lr": 0.07244, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28984, "top5_acc": 0.53938, "loss_cls": 4.12436, "loss": 4.12436, "time": 0.81283} +{"mode": "train", "epoch": 53, "iter": 3000, "lr": 0.07242, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29406, "top5_acc": 0.54203, "loss_cls": 4.08171, "loss": 4.08171, "time": 0.81801} +{"mode": "train", "epoch": 53, "iter": 3100, "lr": 0.07239, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28578, "top5_acc": 0.54266, "loss_cls": 4.07514, "loss": 4.07514, "time": 0.81769} +{"mode": "train", "epoch": 53, "iter": 3200, "lr": 0.07237, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28094, "top5_acc": 0.53844, "loss_cls": 4.11655, "loss": 4.11655, "time": 0.81759} +{"mode": "train", "epoch": 53, "iter": 3300, "lr": 0.07234, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28422, "top5_acc": 0.53531, "loss_cls": 4.13348, "loss": 4.13348, "time": 0.81673} +{"mode": "train", "epoch": 53, "iter": 3400, "lr": 0.07232, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28141, "top5_acc": 0.53328, "loss_cls": 4.14894, "loss": 4.14894, "time": 0.81531} +{"mode": "train", "epoch": 53, "iter": 3500, "lr": 0.07229, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28406, "top5_acc": 0.54203, "loss_cls": 4.08892, "loss": 4.08892, "time": 0.81685} +{"mode": "train", "epoch": 53, "iter": 3600, "lr": 0.07227, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28516, "top5_acc": 0.54516, "loss_cls": 4.11628, "loss": 4.11628, "time": 0.8142} +{"mode": "train", "epoch": 53, "iter": 3700, "lr": 0.07224, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28141, "top5_acc": 0.53484, "loss_cls": 4.12945, "loss": 4.12945, "time": 0.82115} +{"mode": "val", "epoch": 53, "iter": 309, "lr": 0.07223, "top1_acc": 0.20919, "top5_acc": 0.4512, "mean_class_accuracy": 0.20902} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.07221, "memory": 15990, "data_time": 1.31988, "top1_acc": 0.30078, "top5_acc": 0.56156, "loss_cls": 4.00163, "loss": 4.00163, "time": 2.2994} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.07218, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29578, "top5_acc": 0.5525, "loss_cls": 4.05563, "loss": 4.05563, "time": 0.82493} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.07216, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29453, "top5_acc": 0.54547, "loss_cls": 4.08684, "loss": 4.08684, "time": 0.82207} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.07213, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29016, "top5_acc": 0.54062, "loss_cls": 4.09365, "loss": 4.09365, "time": 0.81705} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.07211, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30453, "top5_acc": 0.55625, "loss_cls": 4.03292, "loss": 4.03292, "time": 0.81736} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.07208, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28781, "top5_acc": 0.5425, "loss_cls": 4.06665, "loss": 4.06665, "time": 0.81793} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.07206, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30719, "top5_acc": 0.55437, "loss_cls": 4.02436, "loss": 4.02436, "time": 0.82064} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.07203, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27469, "top5_acc": 0.5225, "loss_cls": 4.14562, "loss": 4.14562, "time": 0.81608} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.07201, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29391, "top5_acc": 0.54375, "loss_cls": 4.08623, "loss": 4.08623, "time": 0.82183} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.07198, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29953, "top5_acc": 0.54641, "loss_cls": 4.06673, "loss": 4.06673, "time": 0.82575} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.07196, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28891, "top5_acc": 0.54328, "loss_cls": 4.10108, "loss": 4.10108, "time": 0.8208} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.07193, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2925, "top5_acc": 0.53625, "loss_cls": 4.08314, "loss": 4.08314, "time": 0.81591} +{"mode": "train", "epoch": 54, "iter": 1300, "lr": 0.07191, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28859, "top5_acc": 0.53594, "loss_cls": 4.08591, "loss": 4.08591, "time": 0.81383} +{"mode": "train", "epoch": 54, "iter": 1400, "lr": 0.07188, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29703, "top5_acc": 0.54453, "loss_cls": 4.05849, "loss": 4.05849, "time": 0.82083} +{"mode": "train", "epoch": 54, "iter": 1500, "lr": 0.07186, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29828, "top5_acc": 0.55234, "loss_cls": 4.04086, "loss": 4.04086, "time": 0.82338} +{"mode": "train", "epoch": 54, "iter": 1600, "lr": 0.07183, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28578, "top5_acc": 0.54219, "loss_cls": 4.08572, "loss": 4.08572, "time": 0.81796} +{"mode": "train", "epoch": 54, "iter": 1700, "lr": 0.07181, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.54375, "loss_cls": 4.11158, "loss": 4.11158, "time": 0.81856} +{"mode": "train", "epoch": 54, "iter": 1800, "lr": 0.07178, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28453, "top5_acc": 0.53516, "loss_cls": 4.09607, "loss": 4.09607, "time": 0.81886} +{"mode": "train", "epoch": 54, "iter": 1900, "lr": 0.07176, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28359, "top5_acc": 0.53719, "loss_cls": 4.1078, "loss": 4.1078, "time": 0.81548} +{"mode": "train", "epoch": 54, "iter": 2000, "lr": 0.07173, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28719, "top5_acc": 0.54438, "loss_cls": 4.08617, "loss": 4.08617, "time": 0.81295} +{"mode": "train", "epoch": 54, "iter": 2100, "lr": 0.0717, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28078, "top5_acc": 0.52547, "loss_cls": 4.16715, "loss": 4.16715, "time": 0.81817} +{"mode": "train", "epoch": 54, "iter": 2200, "lr": 0.07168, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28922, "top5_acc": 0.54562, "loss_cls": 4.08758, "loss": 4.08758, "time": 0.81171} +{"mode": "train", "epoch": 54, "iter": 2300, "lr": 0.07165, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29219, "top5_acc": 0.54562, "loss_cls": 4.08954, "loss": 4.08954, "time": 0.81264} +{"mode": "train", "epoch": 54, "iter": 2400, "lr": 0.07163, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28531, "top5_acc": 0.54234, "loss_cls": 4.10178, "loss": 4.10178, "time": 0.81643} +{"mode": "train", "epoch": 54, "iter": 2500, "lr": 0.0716, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28438, "top5_acc": 0.54203, "loss_cls": 4.10371, "loss": 4.10371, "time": 0.81482} +{"mode": "train", "epoch": 54, "iter": 2600, "lr": 0.07158, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27984, "top5_acc": 0.53906, "loss_cls": 4.16601, "loss": 4.16601, "time": 0.81595} +{"mode": "train", "epoch": 54, "iter": 2700, "lr": 0.07155, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28984, "top5_acc": 0.54906, "loss_cls": 4.05657, "loss": 4.05657, "time": 0.8135} +{"mode": "train", "epoch": 54, "iter": 2800, "lr": 0.07153, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28969, "top5_acc": 0.54312, "loss_cls": 4.09168, "loss": 4.09168, "time": 0.8111} +{"mode": "train", "epoch": 54, "iter": 2900, "lr": 0.0715, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28203, "top5_acc": 0.545, "loss_cls": 4.09483, "loss": 4.09483, "time": 0.81508} +{"mode": "train", "epoch": 54, "iter": 3000, "lr": 0.07148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28984, "top5_acc": 0.54453, "loss_cls": 4.08864, "loss": 4.08864, "time": 0.81654} +{"mode": "train", "epoch": 54, "iter": 3100, "lr": 0.07145, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27922, "top5_acc": 0.5375, "loss_cls": 4.11624, "loss": 4.11624, "time": 0.81639} +{"mode": "train", "epoch": 54, "iter": 3200, "lr": 0.07143, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2875, "top5_acc": 0.53672, "loss_cls": 4.11653, "loss": 4.11653, "time": 0.8157} +{"mode": "train", "epoch": 54, "iter": 3300, "lr": 0.0714, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28734, "top5_acc": 0.54172, "loss_cls": 4.07778, "loss": 4.07778, "time": 0.81222} +{"mode": "train", "epoch": 54, "iter": 3400, "lr": 0.07138, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28031, "top5_acc": 0.53844, "loss_cls": 4.10345, "loss": 4.10345, "time": 0.81686} +{"mode": "train", "epoch": 54, "iter": 3500, "lr": 0.07135, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28422, "top5_acc": 0.53578, "loss_cls": 4.11804, "loss": 4.11804, "time": 0.8112} +{"mode": "train", "epoch": 54, "iter": 3600, "lr": 0.07133, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28922, "top5_acc": 0.54344, "loss_cls": 4.07631, "loss": 4.07631, "time": 0.81508} +{"mode": "train", "epoch": 54, "iter": 3700, "lr": 0.0713, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29266, "top5_acc": 0.54031, "loss_cls": 4.08343, "loss": 4.08343, "time": 0.81965} +{"mode": "val", "epoch": 54, "iter": 309, "lr": 0.07129, "top1_acc": 0.18898, "top5_acc": 0.41341, "mean_class_accuracy": 0.18854} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.07126, "memory": 15990, "data_time": 1.32045, "top1_acc": 0.29578, "top5_acc": 0.55797, "loss_cls": 4.02687, "loss": 4.02687, "time": 2.30222} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.07124, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.295, "top5_acc": 0.53984, "loss_cls": 4.0905, "loss": 4.0905, "time": 0.82349} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.07121, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30203, "top5_acc": 0.55516, "loss_cls": 4.03619, "loss": 4.03619, "time": 0.82218} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.07119, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28625, "top5_acc": 0.53969, "loss_cls": 4.11456, "loss": 4.11456, "time": 0.82017} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.07116, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29266, "top5_acc": 0.55125, "loss_cls": 4.02866, "loss": 4.02866, "time": 0.81724} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.07114, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28703, "top5_acc": 0.55016, "loss_cls": 4.06097, "loss": 4.06097, "time": 0.81829} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.07111, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28766, "top5_acc": 0.54844, "loss_cls": 4.09246, "loss": 4.09246, "time": 0.81878} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.07109, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28016, "top5_acc": 0.54672, "loss_cls": 4.09519, "loss": 4.09519, "time": 0.82475} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.07106, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29938, "top5_acc": 0.55641, "loss_cls": 4.0203, "loss": 4.0203, "time": 0.82161} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.07104, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30062, "top5_acc": 0.54984, "loss_cls": 4.05993, "loss": 4.05993, "time": 0.81905} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.07101, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30312, "top5_acc": 0.54531, "loss_cls": 4.05069, "loss": 4.05069, "time": 0.82161} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.07099, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29516, "top5_acc": 0.53438, "loss_cls": 4.10121, "loss": 4.10121, "time": 0.82057} +{"mode": "train", "epoch": 55, "iter": 1300, "lr": 0.07096, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29609, "top5_acc": 0.55109, "loss_cls": 4.02927, "loss": 4.02927, "time": 0.81283} +{"mode": "train", "epoch": 55, "iter": 1400, "lr": 0.07093, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28703, "top5_acc": 0.53984, "loss_cls": 4.09437, "loss": 4.09437, "time": 0.81393} +{"mode": "train", "epoch": 55, "iter": 1500, "lr": 0.07091, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29219, "top5_acc": 0.54609, "loss_cls": 4.05809, "loss": 4.05809, "time": 0.81742} +{"mode": "train", "epoch": 55, "iter": 1600, "lr": 0.07088, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28828, "top5_acc": 0.54109, "loss_cls": 4.08899, "loss": 4.08899, "time": 0.81381} +{"mode": "train", "epoch": 55, "iter": 1700, "lr": 0.07086, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28422, "top5_acc": 0.53953, "loss_cls": 4.06877, "loss": 4.06877, "time": 0.81075} +{"mode": "train", "epoch": 55, "iter": 1800, "lr": 0.07083, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28813, "top5_acc": 0.53625, "loss_cls": 4.12393, "loss": 4.12393, "time": 0.81366} +{"mode": "train", "epoch": 55, "iter": 1900, "lr": 0.07081, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29078, "top5_acc": 0.54656, "loss_cls": 4.07505, "loss": 4.07505, "time": 0.81525} +{"mode": "train", "epoch": 55, "iter": 2000, "lr": 0.07078, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29219, "top5_acc": 0.54812, "loss_cls": 4.05215, "loss": 4.05215, "time": 0.8208} +{"mode": "train", "epoch": 55, "iter": 2100, "lr": 0.07076, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28516, "top5_acc": 0.53875, "loss_cls": 4.09204, "loss": 4.09204, "time": 0.82152} +{"mode": "train", "epoch": 55, "iter": 2200, "lr": 0.07073, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28641, "top5_acc": 0.54328, "loss_cls": 4.0642, "loss": 4.0642, "time": 0.81545} +{"mode": "train", "epoch": 55, "iter": 2300, "lr": 0.07071, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29375, "top5_acc": 0.54422, "loss_cls": 4.06997, "loss": 4.06997, "time": 0.81603} +{"mode": "train", "epoch": 55, "iter": 2400, "lr": 0.07068, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29125, "top5_acc": 0.53734, "loss_cls": 4.07819, "loss": 4.07819, "time": 0.81381} +{"mode": "train", "epoch": 55, "iter": 2500, "lr": 0.07065, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28375, "top5_acc": 0.53922, "loss_cls": 4.12546, "loss": 4.12546, "time": 0.81676} +{"mode": "train", "epoch": 55, "iter": 2600, "lr": 0.07063, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29188, "top5_acc": 0.53891, "loss_cls": 4.09652, "loss": 4.09652, "time": 0.8143} +{"mode": "train", "epoch": 55, "iter": 2700, "lr": 0.0706, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29641, "top5_acc": 0.55625, "loss_cls": 4.03549, "loss": 4.03549, "time": 0.81387} +{"mode": "train", "epoch": 55, "iter": 2800, "lr": 0.07058, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2875, "top5_acc": 0.55391, "loss_cls": 4.09392, "loss": 4.09392, "time": 0.81919} +{"mode": "train", "epoch": 55, "iter": 2900, "lr": 0.07055, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29422, "top5_acc": 0.54266, "loss_cls": 4.0584, "loss": 4.0584, "time": 0.81513} +{"mode": "train", "epoch": 55, "iter": 3000, "lr": 0.07053, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28297, "top5_acc": 0.54078, "loss_cls": 4.09759, "loss": 4.09759, "time": 0.81383} +{"mode": "train", "epoch": 55, "iter": 3100, "lr": 0.0705, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29047, "top5_acc": 0.53766, "loss_cls": 4.10022, "loss": 4.10022, "time": 0.81462} +{"mode": "train", "epoch": 55, "iter": 3200, "lr": 0.07048, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27891, "top5_acc": 0.53906, "loss_cls": 4.11495, "loss": 4.11495, "time": 0.81484} +{"mode": "train", "epoch": 55, "iter": 3300, "lr": 0.07045, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28734, "top5_acc": 0.54344, "loss_cls": 4.07687, "loss": 4.07687, "time": 0.81502} +{"mode": "train", "epoch": 55, "iter": 3400, "lr": 0.07043, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28156, "top5_acc": 0.52469, "loss_cls": 4.13548, "loss": 4.13548, "time": 0.81857} +{"mode": "train", "epoch": 55, "iter": 3500, "lr": 0.0704, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28156, "top5_acc": 0.54312, "loss_cls": 4.12682, "loss": 4.12682, "time": 0.8147} +{"mode": "train", "epoch": 55, "iter": 3600, "lr": 0.07037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2875, "top5_acc": 0.53203, "loss_cls": 4.11527, "loss": 4.11527, "time": 0.8186} +{"mode": "train", "epoch": 55, "iter": 3700, "lr": 0.07035, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28734, "top5_acc": 0.54469, "loss_cls": 4.07232, "loss": 4.07232, "time": 0.81684} +{"mode": "val", "epoch": 55, "iter": 309, "lr": 0.07034, "top1_acc": 0.19526, "top5_acc": 0.43119, "mean_class_accuracy": 0.19518} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.07031, "memory": 15990, "data_time": 1.31305, "top1_acc": 0.29375, "top5_acc": 0.55031, "loss_cls": 4.03592, "loss": 4.03592, "time": 2.29602} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.07029, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30297, "top5_acc": 0.56234, "loss_cls": 4.00252, "loss": 4.00252, "time": 0.82433} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.07026, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28969, "top5_acc": 0.54547, "loss_cls": 4.07025, "loss": 4.07025, "time": 0.8193} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.07023, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29203, "top5_acc": 0.55234, "loss_cls": 4.05449, "loss": 4.05449, "time": 0.82497} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.07021, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28391, "top5_acc": 0.53312, "loss_cls": 4.12927, "loss": 4.12927, "time": 0.82003} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.07018, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29109, "top5_acc": 0.54344, "loss_cls": 4.07839, "loss": 4.07839, "time": 0.81921} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.07016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29422, "top5_acc": 0.54891, "loss_cls": 4.05334, "loss": 4.05334, "time": 0.82245} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.07013, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29188, "top5_acc": 0.54469, "loss_cls": 4.06651, "loss": 4.06651, "time": 0.8193} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.07011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29797, "top5_acc": 0.54937, "loss_cls": 4.03116, "loss": 4.03116, "time": 0.81918} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.07008, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28844, "top5_acc": 0.54016, "loss_cls": 4.10129, "loss": 4.10129, "time": 0.82888} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.07006, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28688, "top5_acc": 0.54047, "loss_cls": 4.05713, "loss": 4.05713, "time": 0.82399} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.07003, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29922, "top5_acc": 0.54641, "loss_cls": 4.08024, "loss": 4.08024, "time": 0.82234} +{"mode": "train", "epoch": 56, "iter": 1300, "lr": 0.07, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29359, "top5_acc": 0.55266, "loss_cls": 4.0459, "loss": 4.0459, "time": 0.81654} +{"mode": "train", "epoch": 56, "iter": 1400, "lr": 0.06998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28219, "top5_acc": 0.54266, "loss_cls": 4.08973, "loss": 4.08973, "time": 0.81906} +{"mode": "train", "epoch": 56, "iter": 1500, "lr": 0.06995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28734, "top5_acc": 0.54453, "loss_cls": 4.09206, "loss": 4.09206, "time": 0.81937} +{"mode": "train", "epoch": 56, "iter": 1600, "lr": 0.06993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29547, "top5_acc": 0.53672, "loss_cls": 4.11376, "loss": 4.11376, "time": 0.81623} +{"mode": "train", "epoch": 56, "iter": 1700, "lr": 0.0699, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29328, "top5_acc": 0.53766, "loss_cls": 4.0761, "loss": 4.0761, "time": 0.81318} +{"mode": "train", "epoch": 56, "iter": 1800, "lr": 0.06988, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29203, "top5_acc": 0.54625, "loss_cls": 4.08343, "loss": 4.08343, "time": 0.81042} +{"mode": "train", "epoch": 56, "iter": 1900, "lr": 0.06985, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29703, "top5_acc": 0.55297, "loss_cls": 4.03476, "loss": 4.03476, "time": 0.81681} +{"mode": "train", "epoch": 56, "iter": 2000, "lr": 0.06983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28453, "top5_acc": 0.53922, "loss_cls": 4.11984, "loss": 4.11984, "time": 0.81916} +{"mode": "train", "epoch": 56, "iter": 2100, "lr": 0.0698, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29141, "top5_acc": 0.54109, "loss_cls": 4.10097, "loss": 4.10097, "time": 0.81567} +{"mode": "train", "epoch": 56, "iter": 2200, "lr": 0.06977, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29609, "top5_acc": 0.55016, "loss_cls": 4.0641, "loss": 4.0641, "time": 0.82047} +{"mode": "train", "epoch": 56, "iter": 2300, "lr": 0.06975, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29297, "top5_acc": 0.54172, "loss_cls": 4.07574, "loss": 4.07574, "time": 0.81219} +{"mode": "train", "epoch": 56, "iter": 2400, "lr": 0.06972, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29063, "top5_acc": 0.54172, "loss_cls": 4.07735, "loss": 4.07735, "time": 0.81246} +{"mode": "train", "epoch": 56, "iter": 2500, "lr": 0.0697, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29922, "top5_acc": 0.55109, "loss_cls": 4.04926, "loss": 4.04926, "time": 0.81627} +{"mode": "train", "epoch": 56, "iter": 2600, "lr": 0.06967, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28594, "top5_acc": 0.53672, "loss_cls": 4.09824, "loss": 4.09824, "time": 0.8125} +{"mode": "train", "epoch": 56, "iter": 2700, "lr": 0.06965, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29391, "top5_acc": 0.54672, "loss_cls": 4.07037, "loss": 4.07037, "time": 0.81218} +{"mode": "train", "epoch": 56, "iter": 2800, "lr": 0.06962, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30172, "top5_acc": 0.55031, "loss_cls": 4.01252, "loss": 4.01252, "time": 0.81605} +{"mode": "train", "epoch": 56, "iter": 2900, "lr": 0.06959, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28969, "top5_acc": 0.54375, "loss_cls": 4.0905, "loss": 4.0905, "time": 0.81883} +{"mode": "train", "epoch": 56, "iter": 3000, "lr": 0.06957, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29656, "top5_acc": 0.54625, "loss_cls": 4.06808, "loss": 4.06808, "time": 0.81778} +{"mode": "train", "epoch": 56, "iter": 3100, "lr": 0.06954, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30297, "top5_acc": 0.54406, "loss_cls": 4.05452, "loss": 4.05452, "time": 0.81982} +{"mode": "train", "epoch": 56, "iter": 3200, "lr": 0.06952, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30109, "top5_acc": 0.55141, "loss_cls": 4.04233, "loss": 4.04233, "time": 0.81481} +{"mode": "train", "epoch": 56, "iter": 3300, "lr": 0.06949, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28813, "top5_acc": 0.55453, "loss_cls": 4.05238, "loss": 4.05238, "time": 0.81385} +{"mode": "train", "epoch": 56, "iter": 3400, "lr": 0.06947, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28359, "top5_acc": 0.53688, "loss_cls": 4.11542, "loss": 4.11542, "time": 0.81854} +{"mode": "train", "epoch": 56, "iter": 3500, "lr": 0.06944, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29094, "top5_acc": 0.55531, "loss_cls": 4.05034, "loss": 4.05034, "time": 0.81909} +{"mode": "train", "epoch": 56, "iter": 3600, "lr": 0.06941, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28891, "top5_acc": 0.535, "loss_cls": 4.1029, "loss": 4.1029, "time": 0.81922} +{"mode": "train", "epoch": 56, "iter": 3700, "lr": 0.06939, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28859, "top5_acc": 0.53281, "loss_cls": 4.12703, "loss": 4.12703, "time": 0.81541} +{"mode": "val", "epoch": 56, "iter": 309, "lr": 0.06938, "top1_acc": 0.20828, "top5_acc": 0.44375, "mean_class_accuracy": 0.20819} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.06935, "memory": 15990, "data_time": 1.29975, "top1_acc": 0.30953, "top5_acc": 0.56625, "loss_cls": 3.91835, "loss": 3.91835, "time": 2.27776} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.06932, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29313, "top5_acc": 0.54828, "loss_cls": 4.04027, "loss": 4.04027, "time": 0.8233} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.0693, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27672, "top5_acc": 0.54016, "loss_cls": 4.12498, "loss": 4.12498, "time": 0.81715} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.06927, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29219, "top5_acc": 0.54453, "loss_cls": 4.04393, "loss": 4.04393, "time": 0.82266} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.06925, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29063, "top5_acc": 0.54891, "loss_cls": 4.07522, "loss": 4.07522, "time": 0.8181} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.06922, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2925, "top5_acc": 0.55016, "loss_cls": 4.02803, "loss": 4.02803, "time": 0.8222} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.0692, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29719, "top5_acc": 0.54406, "loss_cls": 4.08482, "loss": 4.08482, "time": 0.81541} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.06917, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29656, "top5_acc": 0.54234, "loss_cls": 4.0471, "loss": 4.0471, "time": 0.82156} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.06914, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29547, "top5_acc": 0.54516, "loss_cls": 4.05639, "loss": 4.05639, "time": 0.82097} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.06912, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29438, "top5_acc": 0.55781, "loss_cls": 4.03791, "loss": 4.03791, "time": 0.82719} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.06909, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29984, "top5_acc": 0.55078, "loss_cls": 4.01652, "loss": 4.01652, "time": 0.82317} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.06907, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2975, "top5_acc": 0.54953, "loss_cls": 4.05751, "loss": 4.05751, "time": 0.81917} +{"mode": "train", "epoch": 57, "iter": 1300, "lr": 0.06904, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28938, "top5_acc": 0.54188, "loss_cls": 4.07329, "loss": 4.07329, "time": 0.81559} +{"mode": "train", "epoch": 57, "iter": 1400, "lr": 0.06901, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29, "top5_acc": 0.55609, "loss_cls": 4.06177, "loss": 4.06177, "time": 0.8175} +{"mode": "train", "epoch": 57, "iter": 1500, "lr": 0.06899, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29984, "top5_acc": 0.55656, "loss_cls": 4.03751, "loss": 4.03751, "time": 0.81972} +{"mode": "train", "epoch": 57, "iter": 1600, "lr": 0.06896, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29609, "top5_acc": 0.55812, "loss_cls": 4.06272, "loss": 4.06272, "time": 0.81522} +{"mode": "train", "epoch": 57, "iter": 1700, "lr": 0.06894, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28906, "top5_acc": 0.54, "loss_cls": 4.06352, "loss": 4.06352, "time": 0.81613} +{"mode": "train", "epoch": 57, "iter": 1800, "lr": 0.06891, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29625, "top5_acc": 0.54781, "loss_cls": 4.04996, "loss": 4.04996, "time": 0.812} +{"mode": "train", "epoch": 57, "iter": 1900, "lr": 0.06889, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29297, "top5_acc": 0.53453, "loss_cls": 4.07147, "loss": 4.07147, "time": 0.81563} +{"mode": "train", "epoch": 57, "iter": 2000, "lr": 0.06886, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29234, "top5_acc": 0.55234, "loss_cls": 4.06071, "loss": 4.06071, "time": 0.81443} +{"mode": "train", "epoch": 57, "iter": 2100, "lr": 0.06883, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28281, "top5_acc": 0.54, "loss_cls": 4.08996, "loss": 4.08996, "time": 0.81184} +{"mode": "train", "epoch": 57, "iter": 2200, "lr": 0.06881, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29016, "top5_acc": 0.54828, "loss_cls": 4.0935, "loss": 4.0935, "time": 0.81606} +{"mode": "train", "epoch": 57, "iter": 2300, "lr": 0.06878, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29656, "top5_acc": 0.54406, "loss_cls": 4.06486, "loss": 4.06486, "time": 0.81826} +{"mode": "train", "epoch": 57, "iter": 2400, "lr": 0.06876, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29172, "top5_acc": 0.54547, "loss_cls": 4.08344, "loss": 4.08344, "time": 0.82148} +{"mode": "train", "epoch": 57, "iter": 2500, "lr": 0.06873, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29547, "top5_acc": 0.55406, "loss_cls": 4.05585, "loss": 4.05585, "time": 0.81718} +{"mode": "train", "epoch": 57, "iter": 2600, "lr": 0.0687, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29844, "top5_acc": 0.53969, "loss_cls": 4.08539, "loss": 4.08539, "time": 0.81541} +{"mode": "train", "epoch": 57, "iter": 2700, "lr": 0.06868, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29516, "top5_acc": 0.54094, "loss_cls": 4.0875, "loss": 4.0875, "time": 0.81359} +{"mode": "train", "epoch": 57, "iter": 2800, "lr": 0.06865, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29766, "top5_acc": 0.54969, "loss_cls": 4.04504, "loss": 4.04504, "time": 0.81465} +{"mode": "train", "epoch": 57, "iter": 2900, "lr": 0.06863, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29594, "top5_acc": 0.53984, "loss_cls": 4.09262, "loss": 4.09262, "time": 0.81528} +{"mode": "train", "epoch": 57, "iter": 3000, "lr": 0.0686, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29953, "top5_acc": 0.55734, "loss_cls": 4.05033, "loss": 4.05033, "time": 0.81447} +{"mode": "train", "epoch": 57, "iter": 3100, "lr": 0.06857, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29953, "top5_acc": 0.55156, "loss_cls": 4.04446, "loss": 4.04446, "time": 0.81685} +{"mode": "train", "epoch": 57, "iter": 3200, "lr": 0.06855, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.285, "top5_acc": 0.52984, "loss_cls": 4.13507, "loss": 4.13507, "time": 0.81427} +{"mode": "train", "epoch": 57, "iter": 3300, "lr": 0.06852, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29141, "top5_acc": 0.55109, "loss_cls": 4.06548, "loss": 4.06548, "time": 0.81483} +{"mode": "train", "epoch": 57, "iter": 3400, "lr": 0.0685, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29266, "top5_acc": 0.54422, "loss_cls": 4.0702, "loss": 4.0702, "time": 0.81599} +{"mode": "train", "epoch": 57, "iter": 3500, "lr": 0.06847, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28594, "top5_acc": 0.53625, "loss_cls": 4.11696, "loss": 4.11696, "time": 0.8179} +{"mode": "train", "epoch": 57, "iter": 3600, "lr": 0.06844, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29016, "top5_acc": 0.53594, "loss_cls": 4.08697, "loss": 4.08697, "time": 0.81484} +{"mode": "train", "epoch": 57, "iter": 3700, "lr": 0.06842, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29625, "top5_acc": 0.55219, "loss_cls": 4.05046, "loss": 4.05046, "time": 0.81365} +{"mode": "val", "epoch": 57, "iter": 309, "lr": 0.06841, "top1_acc": 0.2137, "top5_acc": 0.44426, "mean_class_accuracy": 0.21351} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.06838, "memory": 15990, "data_time": 1.30981, "top1_acc": 0.30516, "top5_acc": 0.56031, "loss_cls": 3.98925, "loss": 3.98925, "time": 2.2867} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.06835, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29672, "top5_acc": 0.54781, "loss_cls": 4.03418, "loss": 4.03418, "time": 0.82203} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.06833, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29281, "top5_acc": 0.54875, "loss_cls": 4.06774, "loss": 4.06774, "time": 0.81789} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.0683, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30438, "top5_acc": 0.55344, "loss_cls": 4.03393, "loss": 4.03393, "time": 0.82339} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.06828, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29875, "top5_acc": 0.55406, "loss_cls": 4.03408, "loss": 4.03408, "time": 0.81482} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.06825, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30109, "top5_acc": 0.55453, "loss_cls": 4.03003, "loss": 4.03003, "time": 0.8226} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.06822, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29172, "top5_acc": 0.54719, "loss_cls": 4.0629, "loss": 4.0629, "time": 0.81871} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.0682, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29281, "top5_acc": 0.55141, "loss_cls": 4.0284, "loss": 4.0284, "time": 0.8211} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.06817, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28844, "top5_acc": 0.54406, "loss_cls": 4.08659, "loss": 4.08659, "time": 0.81826} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.06815, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29266, "top5_acc": 0.54734, "loss_cls": 4.06777, "loss": 4.06777, "time": 0.82586} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.06812, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29094, "top5_acc": 0.54156, "loss_cls": 4.09732, "loss": 4.09732, "time": 0.82071} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.06809, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29406, "top5_acc": 0.54594, "loss_cls": 4.04423, "loss": 4.04423, "time": 0.81912} +{"mode": "train", "epoch": 58, "iter": 1300, "lr": 0.06807, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29672, "top5_acc": 0.54875, "loss_cls": 4.04558, "loss": 4.04558, "time": 0.81779} +{"mode": "train", "epoch": 58, "iter": 1400, "lr": 0.06804, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30172, "top5_acc": 0.55328, "loss_cls": 4.02594, "loss": 4.02594, "time": 0.814} +{"mode": "train", "epoch": 58, "iter": 1500, "lr": 0.06802, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29469, "top5_acc": 0.55156, "loss_cls": 4.046, "loss": 4.046, "time": 0.81673} +{"mode": "train", "epoch": 58, "iter": 1600, "lr": 0.06799, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29484, "top5_acc": 0.54359, "loss_cls": 4.0713, "loss": 4.0713, "time": 0.81353} +{"mode": "train", "epoch": 58, "iter": 1700, "lr": 0.06796, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30359, "top5_acc": 0.555, "loss_cls": 3.97781, "loss": 3.97781, "time": 0.81916} +{"mode": "train", "epoch": 58, "iter": 1800, "lr": 0.06794, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.285, "top5_acc": 0.55094, "loss_cls": 4.06555, "loss": 4.06555, "time": 0.81969} +{"mode": "train", "epoch": 58, "iter": 1900, "lr": 0.06791, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29125, "top5_acc": 0.54609, "loss_cls": 4.06193, "loss": 4.06193, "time": 0.81831} +{"mode": "train", "epoch": 58, "iter": 2000, "lr": 0.06789, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28813, "top5_acc": 0.53891, "loss_cls": 4.08561, "loss": 4.08561, "time": 0.81739} +{"mode": "train", "epoch": 58, "iter": 2100, "lr": 0.06786, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29328, "top5_acc": 0.54438, "loss_cls": 4.06011, "loss": 4.06011, "time": 0.81404} +{"mode": "train", "epoch": 58, "iter": 2200, "lr": 0.06783, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29641, "top5_acc": 0.54438, "loss_cls": 4.06962, "loss": 4.06962, "time": 0.8166} +{"mode": "train", "epoch": 58, "iter": 2300, "lr": 0.06781, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28734, "top5_acc": 0.53766, "loss_cls": 4.11145, "loss": 4.11145, "time": 0.8154} +{"mode": "train", "epoch": 58, "iter": 2400, "lr": 0.06778, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29266, "top5_acc": 0.55625, "loss_cls": 4.0687, "loss": 4.0687, "time": 0.81894} +{"mode": "train", "epoch": 58, "iter": 2500, "lr": 0.06775, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29406, "top5_acc": 0.5475, "loss_cls": 4.04664, "loss": 4.04664, "time": 0.81768} +{"mode": "train", "epoch": 58, "iter": 2600, "lr": 0.06773, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29, "top5_acc": 0.5475, "loss_cls": 4.05946, "loss": 4.05946, "time": 0.81072} +{"mode": "train", "epoch": 58, "iter": 2700, "lr": 0.0677, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29625, "top5_acc": 0.55484, "loss_cls": 4.02232, "loss": 4.02232, "time": 0.81135} +{"mode": "train", "epoch": 58, "iter": 2800, "lr": 0.06768, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29313, "top5_acc": 0.55594, "loss_cls": 4.05079, "loss": 4.05079, "time": 0.81631} +{"mode": "train", "epoch": 58, "iter": 2900, "lr": 0.06765, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29625, "top5_acc": 0.54328, "loss_cls": 4.03322, "loss": 4.03322, "time": 0.8134} +{"mode": "train", "epoch": 58, "iter": 3000, "lr": 0.06762, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29031, "top5_acc": 0.55297, "loss_cls": 4.05304, "loss": 4.05304, "time": 0.81741} +{"mode": "train", "epoch": 58, "iter": 3100, "lr": 0.0676, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29766, "top5_acc": 0.54516, "loss_cls": 4.04413, "loss": 4.04413, "time": 0.81652} +{"mode": "train", "epoch": 58, "iter": 3200, "lr": 0.06757, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28484, "top5_acc": 0.54406, "loss_cls": 4.0628, "loss": 4.0628, "time": 0.81422} +{"mode": "train", "epoch": 58, "iter": 3300, "lr": 0.06755, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28172, "top5_acc": 0.53781, "loss_cls": 4.10749, "loss": 4.10749, "time": 0.81404} +{"mode": "train", "epoch": 58, "iter": 3400, "lr": 0.06752, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29828, "top5_acc": 0.55812, "loss_cls": 4.02292, "loss": 4.02292, "time": 0.81356} +{"mode": "train", "epoch": 58, "iter": 3500, "lr": 0.06749, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29453, "top5_acc": 0.54047, "loss_cls": 4.05508, "loss": 4.05508, "time": 0.81373} +{"mode": "train", "epoch": 58, "iter": 3600, "lr": 0.06747, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29047, "top5_acc": 0.54562, "loss_cls": 4.06503, "loss": 4.06503, "time": 0.82166} +{"mode": "train", "epoch": 58, "iter": 3700, "lr": 0.06744, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30219, "top5_acc": 0.54891, "loss_cls": 4.02034, "loss": 4.02034, "time": 0.81289} +{"mode": "val", "epoch": 58, "iter": 309, "lr": 0.06743, "top1_acc": 0.21831, "top5_acc": 0.44887, "mean_class_accuracy": 0.21813} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.0674, "memory": 15990, "data_time": 1.28342, "top1_acc": 0.29875, "top5_acc": 0.55391, "loss_cls": 4.01372, "loss": 4.01372, "time": 2.26154} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.06738, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30578, "top5_acc": 0.56375, "loss_cls": 3.99497, "loss": 3.99497, "time": 0.81697} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.06735, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28703, "top5_acc": 0.54125, "loss_cls": 4.08299, "loss": 4.08299, "time": 0.8257} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.06732, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30328, "top5_acc": 0.56094, "loss_cls": 3.97351, "loss": 3.97351, "time": 0.82907} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.0673, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29313, "top5_acc": 0.54031, "loss_cls": 4.07671, "loss": 4.07671, "time": 0.81677} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.06727, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2925, "top5_acc": 0.55047, "loss_cls": 4.07311, "loss": 4.07311, "time": 0.81826} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.06725, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30406, "top5_acc": 0.54875, "loss_cls": 4.02951, "loss": 4.02951, "time": 0.8188} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.06722, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28969, "top5_acc": 0.54422, "loss_cls": 4.07415, "loss": 4.07415, "time": 0.81938} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.06719, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29625, "top5_acc": 0.55562, "loss_cls": 4.02529, "loss": 4.02529, "time": 0.8253} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.06717, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30625, "top5_acc": 0.55562, "loss_cls": 4.00381, "loss": 4.00381, "time": 0.8127} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.06714, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30094, "top5_acc": 0.55828, "loss_cls": 4.00255, "loss": 4.00255, "time": 0.82604} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.06711, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29188, "top5_acc": 0.54906, "loss_cls": 4.06821, "loss": 4.06821, "time": 0.81657} +{"mode": "train", "epoch": 59, "iter": 1300, "lr": 0.06709, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28953, "top5_acc": 0.54688, "loss_cls": 4.07202, "loss": 4.07202, "time": 0.81843} +{"mode": "train", "epoch": 59, "iter": 1400, "lr": 0.06706, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29719, "top5_acc": 0.55781, "loss_cls": 4.01216, "loss": 4.01216, "time": 0.81802} +{"mode": "train", "epoch": 59, "iter": 1500, "lr": 0.06704, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28688, "top5_acc": 0.54453, "loss_cls": 4.08687, "loss": 4.08687, "time": 0.81212} +{"mode": "train", "epoch": 59, "iter": 1600, "lr": 0.06701, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28844, "top5_acc": 0.53844, "loss_cls": 4.08793, "loss": 4.08793, "time": 0.81502} +{"mode": "train", "epoch": 59, "iter": 1700, "lr": 0.06698, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29953, "top5_acc": 0.55109, "loss_cls": 4.03965, "loss": 4.03965, "time": 0.81444} +{"mode": "train", "epoch": 59, "iter": 1800, "lr": 0.06696, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29547, "top5_acc": 0.55094, "loss_cls": 4.04698, "loss": 4.04698, "time": 0.81345} +{"mode": "train", "epoch": 59, "iter": 1900, "lr": 0.06693, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29641, "top5_acc": 0.55391, "loss_cls": 4.02352, "loss": 4.02352, "time": 0.81913} +{"mode": "train", "epoch": 59, "iter": 2000, "lr": 0.0669, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28625, "top5_acc": 0.53562, "loss_cls": 4.10492, "loss": 4.10492, "time": 0.812} +{"mode": "train", "epoch": 59, "iter": 2100, "lr": 0.06688, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29031, "top5_acc": 0.54922, "loss_cls": 4.04677, "loss": 4.04677, "time": 0.82208} +{"mode": "train", "epoch": 59, "iter": 2200, "lr": 0.06685, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29922, "top5_acc": 0.55703, "loss_cls": 4.01767, "loss": 4.01767, "time": 0.81772} +{"mode": "train", "epoch": 59, "iter": 2300, "lr": 0.06682, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29125, "top5_acc": 0.54812, "loss_cls": 4.07459, "loss": 4.07459, "time": 0.81841} +{"mode": "train", "epoch": 59, "iter": 2400, "lr": 0.0668, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3, "top5_acc": 0.54312, "loss_cls": 4.04385, "loss": 4.04385, "time": 0.81487} +{"mode": "train", "epoch": 59, "iter": 2500, "lr": 0.06677, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30422, "top5_acc": 0.55437, "loss_cls": 4.00877, "loss": 4.00877, "time": 0.81167} +{"mode": "train", "epoch": 59, "iter": 2600, "lr": 0.06675, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29734, "top5_acc": 0.55016, "loss_cls": 4.06042, "loss": 4.06042, "time": 0.81643} +{"mode": "train", "epoch": 59, "iter": 2700, "lr": 0.06672, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30016, "top5_acc": 0.55516, "loss_cls": 4.02339, "loss": 4.02339, "time": 0.8154} +{"mode": "train", "epoch": 59, "iter": 2800, "lr": 0.06669, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29703, "top5_acc": 0.54312, "loss_cls": 4.07804, "loss": 4.07804, "time": 0.81378} +{"mode": "train", "epoch": 59, "iter": 2900, "lr": 0.06667, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29047, "top5_acc": 0.54656, "loss_cls": 4.0683, "loss": 4.0683, "time": 0.81134} +{"mode": "train", "epoch": 59, "iter": 3000, "lr": 0.06664, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29609, "top5_acc": 0.54922, "loss_cls": 4.0486, "loss": 4.0486, "time": 0.8111} +{"mode": "train", "epoch": 59, "iter": 3100, "lr": 0.06661, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30125, "top5_acc": 0.55828, "loss_cls": 4.01028, "loss": 4.01028, "time": 0.81647} +{"mode": "train", "epoch": 59, "iter": 3200, "lr": 0.06659, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28594, "top5_acc": 0.5375, "loss_cls": 4.11264, "loss": 4.11264, "time": 0.812} +{"mode": "train", "epoch": 59, "iter": 3300, "lr": 0.06656, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29406, "top5_acc": 0.54688, "loss_cls": 4.05352, "loss": 4.05352, "time": 0.81605} +{"mode": "train", "epoch": 59, "iter": 3400, "lr": 0.06653, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29141, "top5_acc": 0.55062, "loss_cls": 4.04716, "loss": 4.04716, "time": 0.8172} +{"mode": "train", "epoch": 59, "iter": 3500, "lr": 0.06651, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29234, "top5_acc": 0.55188, "loss_cls": 4.02938, "loss": 4.02938, "time": 0.8136} +{"mode": "train", "epoch": 59, "iter": 3600, "lr": 0.06648, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29672, "top5_acc": 0.54969, "loss_cls": 4.02707, "loss": 4.02707, "time": 0.8178} +{"mode": "train", "epoch": 59, "iter": 3700, "lr": 0.06646, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29438, "top5_acc": 0.54688, "loss_cls": 4.06952, "loss": 4.06952, "time": 0.81545} +{"mode": "val", "epoch": 59, "iter": 309, "lr": 0.06644, "top1_acc": 0.22484, "top5_acc": 0.46016, "mean_class_accuracy": 0.22479} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.06642, "memory": 15990, "data_time": 1.31879, "top1_acc": 0.29641, "top5_acc": 0.56594, "loss_cls": 3.9802, "loss": 3.9802, "time": 2.32378} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.06639, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29359, "top5_acc": 0.55219, "loss_cls": 4.03065, "loss": 4.03065, "time": 0.81743} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.06636, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29828, "top5_acc": 0.55062, "loss_cls": 4.02378, "loss": 4.02378, "time": 0.81887} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.06634, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29891, "top5_acc": 0.54266, "loss_cls": 4.03339, "loss": 4.03339, "time": 0.82358} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.06631, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30406, "top5_acc": 0.55922, "loss_cls": 3.97402, "loss": 3.97402, "time": 0.81851} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.06629, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30188, "top5_acc": 0.56047, "loss_cls": 3.98109, "loss": 3.98109, "time": 0.81436} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.06626, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30453, "top5_acc": 0.55531, "loss_cls": 4.04385, "loss": 4.04385, "time": 0.82194} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.06623, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30125, "top5_acc": 0.55719, "loss_cls": 4.00371, "loss": 4.00371, "time": 0.81523} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.06621, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2875, "top5_acc": 0.54375, "loss_cls": 4.10551, "loss": 4.10551, "time": 0.81817} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.06618, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28844, "top5_acc": 0.55047, "loss_cls": 4.07888, "loss": 4.07888, "time": 0.82481} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.06615, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30172, "top5_acc": 0.55844, "loss_cls": 4.00993, "loss": 4.00993, "time": 0.83255} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.06613, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30141, "top5_acc": 0.55828, "loss_cls": 3.99042, "loss": 3.99042, "time": 0.82029} +{"mode": "train", "epoch": 60, "iter": 1300, "lr": 0.0661, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29328, "top5_acc": 0.54906, "loss_cls": 4.06756, "loss": 4.06756, "time": 0.82244} +{"mode": "train", "epoch": 60, "iter": 1400, "lr": 0.06607, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29453, "top5_acc": 0.54672, "loss_cls": 4.04697, "loss": 4.04697, "time": 0.8173} +{"mode": "train", "epoch": 60, "iter": 1500, "lr": 0.06605, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2975, "top5_acc": 0.54969, "loss_cls": 4.04162, "loss": 4.04162, "time": 0.81666} +{"mode": "train", "epoch": 60, "iter": 1600, "lr": 0.06602, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29172, "top5_acc": 0.54594, "loss_cls": 4.05786, "loss": 4.05786, "time": 0.81816} +{"mode": "train", "epoch": 60, "iter": 1700, "lr": 0.06599, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29, "top5_acc": 0.54141, "loss_cls": 4.0823, "loss": 4.0823, "time": 0.81567} +{"mode": "train", "epoch": 60, "iter": 1800, "lr": 0.06597, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30094, "top5_acc": 0.56328, "loss_cls": 4.01713, "loss": 4.01713, "time": 0.81914} +{"mode": "train", "epoch": 60, "iter": 1900, "lr": 0.06594, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29922, "top5_acc": 0.55328, "loss_cls": 4.01205, "loss": 4.01205, "time": 0.8175} +{"mode": "train", "epoch": 60, "iter": 2000, "lr": 0.06591, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29891, "top5_acc": 0.54969, "loss_cls": 4.03477, "loss": 4.03477, "time": 0.81535} +{"mode": "train", "epoch": 60, "iter": 2100, "lr": 0.06589, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3075, "top5_acc": 0.55766, "loss_cls": 3.99567, "loss": 3.99567, "time": 0.81497} +{"mode": "train", "epoch": 60, "iter": 2200, "lr": 0.06586, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29516, "top5_acc": 0.55016, "loss_cls": 4.06515, "loss": 4.06515, "time": 0.81669} +{"mode": "train", "epoch": 60, "iter": 2300, "lr": 0.06584, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29078, "top5_acc": 0.55047, "loss_cls": 4.06642, "loss": 4.06642, "time": 0.81338} +{"mode": "train", "epoch": 60, "iter": 2400, "lr": 0.06581, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30016, "top5_acc": 0.55391, "loss_cls": 4.01384, "loss": 4.01384, "time": 0.82004} +{"mode": "train", "epoch": 60, "iter": 2500, "lr": 0.06578, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29047, "top5_acc": 0.54844, "loss_cls": 4.05905, "loss": 4.05905, "time": 0.81968} +{"mode": "train", "epoch": 60, "iter": 2600, "lr": 0.06576, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30344, "top5_acc": 0.56234, "loss_cls": 4.00662, "loss": 4.00662, "time": 0.81533} +{"mode": "train", "epoch": 60, "iter": 2700, "lr": 0.06573, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29, "top5_acc": 0.54266, "loss_cls": 4.07519, "loss": 4.07519, "time": 0.81382} +{"mode": "train", "epoch": 60, "iter": 2800, "lr": 0.0657, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29797, "top5_acc": 0.55391, "loss_cls": 4.01756, "loss": 4.01756, "time": 0.81875} +{"mode": "train", "epoch": 60, "iter": 2900, "lr": 0.06568, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29359, "top5_acc": 0.53766, "loss_cls": 4.07409, "loss": 4.07409, "time": 0.81276} +{"mode": "train", "epoch": 60, "iter": 3000, "lr": 0.06565, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29188, "top5_acc": 0.54438, "loss_cls": 4.05351, "loss": 4.05351, "time": 0.81583} +{"mode": "train", "epoch": 60, "iter": 3100, "lr": 0.06562, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29406, "top5_acc": 0.55437, "loss_cls": 4.06064, "loss": 4.06064, "time": 0.81474} +{"mode": "train", "epoch": 60, "iter": 3200, "lr": 0.0656, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29594, "top5_acc": 0.55109, "loss_cls": 4.04052, "loss": 4.04052, "time": 0.82114} +{"mode": "train", "epoch": 60, "iter": 3300, "lr": 0.06557, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28875, "top5_acc": 0.54547, "loss_cls": 4.08839, "loss": 4.08839, "time": 0.81512} +{"mode": "train", "epoch": 60, "iter": 3400, "lr": 0.06554, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30109, "top5_acc": 0.55922, "loss_cls": 4.01487, "loss": 4.01487, "time": 0.82288} +{"mode": "train", "epoch": 60, "iter": 3500, "lr": 0.06552, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30062, "top5_acc": 0.55719, "loss_cls": 4.0094, "loss": 4.0094, "time": 0.81733} +{"mode": "train", "epoch": 60, "iter": 3600, "lr": 0.06549, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29594, "top5_acc": 0.55484, "loss_cls": 4.01609, "loss": 4.01609, "time": 0.81825} +{"mode": "train", "epoch": 60, "iter": 3700, "lr": 0.06546, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29203, "top5_acc": 0.54594, "loss_cls": 4.06949, "loss": 4.06949, "time": 0.81355} +{"mode": "val", "epoch": 60, "iter": 309, "lr": 0.06545, "top1_acc": 0.23097, "top5_acc": 0.4708, "mean_class_accuracy": 0.23054} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.06542, "memory": 15990, "data_time": 1.35529, "top1_acc": 0.30031, "top5_acc": 0.55453, "loss_cls": 4.02733, "loss": 4.02733, "time": 2.33615} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.0654, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30484, "top5_acc": 0.5625, "loss_cls": 3.97779, "loss": 3.97779, "time": 0.82408} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.06537, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29828, "top5_acc": 0.54875, "loss_cls": 4.03444, "loss": 4.03444, "time": 0.82286} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.06534, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31484, "top5_acc": 0.55906, "loss_cls": 3.96296, "loss": 3.96296, "time": 0.8183} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.06532, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29906, "top5_acc": 0.55578, "loss_cls": 4.01958, "loss": 4.01958, "time": 0.8163} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.06529, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30438, "top5_acc": 0.56094, "loss_cls": 3.98628, "loss": 3.98628, "time": 0.82249} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.06526, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30547, "top5_acc": 0.56297, "loss_cls": 3.99145, "loss": 3.99145, "time": 0.8209} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.06524, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31266, "top5_acc": 0.56734, "loss_cls": 3.92382, "loss": 3.92382, "time": 0.81718} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.06521, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28922, "top5_acc": 0.54125, "loss_cls": 4.09342, "loss": 4.09342, "time": 0.82097} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.06519, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29953, "top5_acc": 0.54609, "loss_cls": 4.05037, "loss": 4.05037, "time": 0.81496} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.06516, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29391, "top5_acc": 0.54906, "loss_cls": 4.03274, "loss": 4.03274, "time": 0.82829} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.06513, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29547, "top5_acc": 0.55437, "loss_cls": 4.03058, "loss": 4.03058, "time": 0.81819} +{"mode": "train", "epoch": 61, "iter": 1300, "lr": 0.06511, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29219, "top5_acc": 0.54688, "loss_cls": 4.04997, "loss": 4.04997, "time": 0.82006} +{"mode": "train", "epoch": 61, "iter": 1400, "lr": 0.06508, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29328, "top5_acc": 0.54531, "loss_cls": 4.05107, "loss": 4.05107, "time": 0.81727} +{"mode": "train", "epoch": 61, "iter": 1500, "lr": 0.06505, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29203, "top5_acc": 0.55891, "loss_cls": 4.03583, "loss": 4.03583, "time": 0.81588} +{"mode": "train", "epoch": 61, "iter": 1600, "lr": 0.06503, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30156, "top5_acc": 0.555, "loss_cls": 4.04306, "loss": 4.04306, "time": 0.81514} +{"mode": "train", "epoch": 61, "iter": 1700, "lr": 0.065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30719, "top5_acc": 0.55703, "loss_cls": 3.97938, "loss": 3.97938, "time": 0.81399} +{"mode": "train", "epoch": 61, "iter": 1800, "lr": 0.06497, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29109, "top5_acc": 0.54859, "loss_cls": 4.02979, "loss": 4.02979, "time": 0.81691} +{"mode": "train", "epoch": 61, "iter": 1900, "lr": 0.06495, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30938, "top5_acc": 0.55297, "loss_cls": 3.99607, "loss": 3.99607, "time": 0.82272} +{"mode": "train", "epoch": 61, "iter": 2000, "lr": 0.06492, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30266, "top5_acc": 0.54719, "loss_cls": 4.02286, "loss": 4.02286, "time": 0.81442} +{"mode": "train", "epoch": 61, "iter": 2100, "lr": 0.06489, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29266, "top5_acc": 0.54656, "loss_cls": 4.05207, "loss": 4.05207, "time": 0.81483} +{"mode": "train", "epoch": 61, "iter": 2200, "lr": 0.06487, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29797, "top5_acc": 0.55344, "loss_cls": 4.04731, "loss": 4.04731, "time": 0.81982} +{"mode": "train", "epoch": 61, "iter": 2300, "lr": 0.06484, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29578, "top5_acc": 0.54625, "loss_cls": 4.05773, "loss": 4.05773, "time": 0.81419} +{"mode": "train", "epoch": 61, "iter": 2400, "lr": 0.06481, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29688, "top5_acc": 0.56156, "loss_cls": 3.98896, "loss": 3.98896, "time": 0.81361} +{"mode": "train", "epoch": 61, "iter": 2500, "lr": 0.06478, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30141, "top5_acc": 0.55453, "loss_cls": 4.01268, "loss": 4.01268, "time": 0.81193} +{"mode": "train", "epoch": 61, "iter": 2600, "lr": 0.06476, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30125, "top5_acc": 0.55016, "loss_cls": 4.01322, "loss": 4.01322, "time": 0.82139} +{"mode": "train", "epoch": 61, "iter": 2700, "lr": 0.06473, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30734, "top5_acc": 0.55953, "loss_cls": 3.98467, "loss": 3.98467, "time": 0.81558} +{"mode": "train", "epoch": 61, "iter": 2800, "lr": 0.0647, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29844, "top5_acc": 0.55234, "loss_cls": 4.03396, "loss": 4.03396, "time": 0.81554} +{"mode": "train", "epoch": 61, "iter": 2900, "lr": 0.06468, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29297, "top5_acc": 0.54219, "loss_cls": 4.06556, "loss": 4.06556, "time": 0.81684} +{"mode": "train", "epoch": 61, "iter": 3000, "lr": 0.06465, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28219, "top5_acc": 0.53734, "loss_cls": 4.12204, "loss": 4.12204, "time": 0.81519} +{"mode": "train", "epoch": 61, "iter": 3100, "lr": 0.06462, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29359, "top5_acc": 0.55109, "loss_cls": 4.03991, "loss": 4.03991, "time": 0.81489} +{"mode": "train", "epoch": 61, "iter": 3200, "lr": 0.0646, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30047, "top5_acc": 0.55328, "loss_cls": 4.02664, "loss": 4.02664, "time": 0.81458} +{"mode": "train", "epoch": 61, "iter": 3300, "lr": 0.06457, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29469, "top5_acc": 0.55578, "loss_cls": 4.03607, "loss": 4.03607, "time": 0.81872} +{"mode": "train", "epoch": 61, "iter": 3400, "lr": 0.06454, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29313, "top5_acc": 0.55266, "loss_cls": 4.03076, "loss": 4.03076, "time": 0.81564} +{"mode": "train", "epoch": 61, "iter": 3500, "lr": 0.06452, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29547, "top5_acc": 0.55062, "loss_cls": 4.06031, "loss": 4.06031, "time": 0.8194} +{"mode": "train", "epoch": 61, "iter": 3600, "lr": 0.06449, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29922, "top5_acc": 0.54531, "loss_cls": 4.05339, "loss": 4.05339, "time": 0.81461} +{"mode": "train", "epoch": 61, "iter": 3700, "lr": 0.06446, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.295, "top5_acc": 0.54547, "loss_cls": 4.06088, "loss": 4.06088, "time": 0.81542} +{"mode": "val", "epoch": 61, "iter": 309, "lr": 0.06445, "top1_acc": 0.22428, "top5_acc": 0.46183, "mean_class_accuracy": 0.22403} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.06443, "memory": 15990, "data_time": 1.3089, "top1_acc": 0.29938, "top5_acc": 0.55688, "loss_cls": 3.98833, "loss": 3.98833, "time": 2.28673} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.0644, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30375, "top5_acc": 0.56391, "loss_cls": 3.98476, "loss": 3.98476, "time": 0.81637} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.06437, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29984, "top5_acc": 0.54359, "loss_cls": 4.06772, "loss": 4.06772, "time": 0.81234} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.06434, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30766, "top5_acc": 0.56156, "loss_cls": 3.97297, "loss": 3.97297, "time": 0.82451} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.06432, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30219, "top5_acc": 0.55969, "loss_cls": 3.99515, "loss": 3.99515, "time": 0.81719} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.06429, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30906, "top5_acc": 0.56188, "loss_cls": 3.99804, "loss": 3.99804, "time": 0.82067} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.06426, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30062, "top5_acc": 0.55234, "loss_cls": 4.02292, "loss": 4.02292, "time": 0.82439} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.06424, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30016, "top5_acc": 0.55547, "loss_cls": 3.9854, "loss": 3.9854, "time": 0.81319} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.06421, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30062, "top5_acc": 0.55625, "loss_cls": 3.99691, "loss": 3.99691, "time": 0.81515} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.06418, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29859, "top5_acc": 0.55688, "loss_cls": 4.00016, "loss": 4.00016, "time": 0.81689} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.06416, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30328, "top5_acc": 0.54875, "loss_cls": 4.04528, "loss": 4.04528, "time": 0.82963} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.06413, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29328, "top5_acc": 0.5475, "loss_cls": 4.06473, "loss": 4.06473, "time": 0.81985} +{"mode": "train", "epoch": 62, "iter": 1300, "lr": 0.0641, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2975, "top5_acc": 0.55031, "loss_cls": 4.0374, "loss": 4.0374, "time": 0.82847} +{"mode": "train", "epoch": 62, "iter": 1400, "lr": 0.06408, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30219, "top5_acc": 0.56297, "loss_cls": 3.99849, "loss": 3.99849, "time": 0.82277} +{"mode": "train", "epoch": 62, "iter": 1500, "lr": 0.06405, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28859, "top5_acc": 0.55437, "loss_cls": 4.06412, "loss": 4.06412, "time": 0.81893} +{"mode": "train", "epoch": 62, "iter": 1600, "lr": 0.06402, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29734, "top5_acc": 0.55312, "loss_cls": 4.05019, "loss": 4.05019, "time": 0.81441} +{"mode": "train", "epoch": 62, "iter": 1700, "lr": 0.064, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30016, "top5_acc": 0.55125, "loss_cls": 3.9988, "loss": 3.9988, "time": 0.8171} +{"mode": "train", "epoch": 62, "iter": 1800, "lr": 0.06397, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30234, "top5_acc": 0.55562, "loss_cls": 4.01817, "loss": 4.01817, "time": 0.81516} +{"mode": "train", "epoch": 62, "iter": 1900, "lr": 0.06394, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28813, "top5_acc": 0.54406, "loss_cls": 4.07719, "loss": 4.07719, "time": 0.81199} +{"mode": "train", "epoch": 62, "iter": 2000, "lr": 0.06392, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29094, "top5_acc": 0.54516, "loss_cls": 4.06854, "loss": 4.06854, "time": 0.81465} +{"mode": "train", "epoch": 62, "iter": 2100, "lr": 0.06389, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30547, "top5_acc": 0.54672, "loss_cls": 4.06393, "loss": 4.06393, "time": 0.81312} +{"mode": "train", "epoch": 62, "iter": 2200, "lr": 0.06386, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29906, "top5_acc": 0.55844, "loss_cls": 4.04043, "loss": 4.04043, "time": 0.81811} +{"mode": "train", "epoch": 62, "iter": 2300, "lr": 0.06384, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28953, "top5_acc": 0.55094, "loss_cls": 4.04296, "loss": 4.04296, "time": 0.81467} +{"mode": "train", "epoch": 62, "iter": 2400, "lr": 0.06381, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3, "top5_acc": 0.55578, "loss_cls": 4.04628, "loss": 4.04628, "time": 0.82212} +{"mode": "train", "epoch": 62, "iter": 2500, "lr": 0.06378, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30156, "top5_acc": 0.55734, "loss_cls": 4.01711, "loss": 4.01711, "time": 0.81753} +{"mode": "train", "epoch": 62, "iter": 2600, "lr": 0.06375, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30078, "top5_acc": 0.55812, "loss_cls": 4.03612, "loss": 4.03612, "time": 0.81273} +{"mode": "train", "epoch": 62, "iter": 2700, "lr": 0.06373, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3075, "top5_acc": 0.55922, "loss_cls": 3.96847, "loss": 3.96847, "time": 0.8147} +{"mode": "train", "epoch": 62, "iter": 2800, "lr": 0.0637, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30047, "top5_acc": 0.55641, "loss_cls": 4.00167, "loss": 4.00167, "time": 0.81142} +{"mode": "train", "epoch": 62, "iter": 2900, "lr": 0.06367, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30047, "top5_acc": 0.55375, "loss_cls": 4.01092, "loss": 4.01092, "time": 0.81634} +{"mode": "train", "epoch": 62, "iter": 3000, "lr": 0.06365, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30203, "top5_acc": 0.55594, "loss_cls": 4.00276, "loss": 4.00276, "time": 0.81934} +{"mode": "train", "epoch": 62, "iter": 3100, "lr": 0.06362, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29734, "top5_acc": 0.54859, "loss_cls": 4.02855, "loss": 4.02855, "time": 0.81275} +{"mode": "train", "epoch": 62, "iter": 3200, "lr": 0.06359, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29547, "top5_acc": 0.55844, "loss_cls": 4.03399, "loss": 4.03399, "time": 0.81651} +{"mode": "train", "epoch": 62, "iter": 3300, "lr": 0.06357, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30656, "top5_acc": 0.55422, "loss_cls": 4.01988, "loss": 4.01988, "time": 0.81721} +{"mode": "train", "epoch": 62, "iter": 3400, "lr": 0.06354, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29172, "top5_acc": 0.54484, "loss_cls": 4.06127, "loss": 4.06127, "time": 0.8141} +{"mode": "train", "epoch": 62, "iter": 3500, "lr": 0.06351, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30016, "top5_acc": 0.54406, "loss_cls": 4.04821, "loss": 4.04821, "time": 0.81835} +{"mode": "train", "epoch": 62, "iter": 3600, "lr": 0.06349, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30875, "top5_acc": 0.57016, "loss_cls": 3.96957, "loss": 3.96957, "time": 0.82695} +{"mode": "train", "epoch": 62, "iter": 3700, "lr": 0.06346, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3025, "top5_acc": 0.56688, "loss_cls": 3.98378, "loss": 3.98378, "time": 0.81599} +{"mode": "val", "epoch": 62, "iter": 309, "lr": 0.06345, "top1_acc": 0.23689, "top5_acc": 0.47176, "mean_class_accuracy": 0.23661} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.06342, "memory": 15990, "data_time": 1.35154, "top1_acc": 0.30484, "top5_acc": 0.56391, "loss_cls": 3.96661, "loss": 3.96661, "time": 2.34324} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.06339, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29578, "top5_acc": 0.55578, "loss_cls": 4.01813, "loss": 4.01813, "time": 0.81681} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.06337, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29469, "top5_acc": 0.54922, "loss_cls": 4.00691, "loss": 4.00691, "time": 0.81506} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.06334, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30547, "top5_acc": 0.55859, "loss_cls": 3.96179, "loss": 3.96179, "time": 0.82927} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.06331, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29609, "top5_acc": 0.55688, "loss_cls": 4.03145, "loss": 4.03145, "time": 0.81661} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.06328, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28953, "top5_acc": 0.545, "loss_cls": 4.06722, "loss": 4.06722, "time": 0.82126} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.06326, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29109, "top5_acc": 0.55375, "loss_cls": 4.02522, "loss": 4.02522, "time": 0.82217} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.06323, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30438, "top5_acc": 0.56406, "loss_cls": 3.96972, "loss": 3.96972, "time": 0.8145} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.0632, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31031, "top5_acc": 0.56812, "loss_cls": 3.94242, "loss": 3.94242, "time": 0.81626} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.06318, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.305, "top5_acc": 0.56375, "loss_cls": 3.95682, "loss": 3.95682, "time": 0.8119} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.06315, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29984, "top5_acc": 0.55359, "loss_cls": 4.01222, "loss": 4.01222, "time": 0.82587} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.06312, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29125, "top5_acc": 0.54531, "loss_cls": 4.07643, "loss": 4.07643, "time": 0.82526} +{"mode": "train", "epoch": 63, "iter": 1300, "lr": 0.0631, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29969, "top5_acc": 0.56172, "loss_cls": 3.9584, "loss": 3.9584, "time": 0.82225} +{"mode": "train", "epoch": 63, "iter": 1400, "lr": 0.06307, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29781, "top5_acc": 0.55906, "loss_cls": 3.98246, "loss": 3.98246, "time": 0.81619} +{"mode": "train", "epoch": 63, "iter": 1500, "lr": 0.06304, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30141, "top5_acc": 0.56266, "loss_cls": 3.97926, "loss": 3.97926, "time": 0.81354} +{"mode": "train", "epoch": 63, "iter": 1600, "lr": 0.06301, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30578, "top5_acc": 0.56109, "loss_cls": 3.97435, "loss": 3.97435, "time": 0.81675} +{"mode": "train", "epoch": 63, "iter": 1700, "lr": 0.06299, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30812, "top5_acc": 0.57453, "loss_cls": 3.95459, "loss": 3.95459, "time": 0.81823} +{"mode": "train", "epoch": 63, "iter": 1800, "lr": 0.06296, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.295, "top5_acc": 0.54672, "loss_cls": 4.03924, "loss": 4.03924, "time": 0.81318} +{"mode": "train", "epoch": 63, "iter": 1900, "lr": 0.06293, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29984, "top5_acc": 0.55281, "loss_cls": 4.05042, "loss": 4.05042, "time": 0.81737} +{"mode": "train", "epoch": 63, "iter": 2000, "lr": 0.06291, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29516, "top5_acc": 0.54781, "loss_cls": 4.02106, "loss": 4.02106, "time": 0.81667} +{"mode": "train", "epoch": 63, "iter": 2100, "lr": 0.06288, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29281, "top5_acc": 0.5475, "loss_cls": 4.03616, "loss": 4.03616, "time": 0.8251} +{"mode": "train", "epoch": 63, "iter": 2200, "lr": 0.06285, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29938, "top5_acc": 0.55547, "loss_cls": 3.99964, "loss": 3.99964, "time": 0.811} +{"mode": "train", "epoch": 63, "iter": 2300, "lr": 0.06283, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29938, "top5_acc": 0.54797, "loss_cls": 4.04685, "loss": 4.04685, "time": 0.81848} +{"mode": "train", "epoch": 63, "iter": 2400, "lr": 0.0628, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29344, "top5_acc": 0.54484, "loss_cls": 4.07431, "loss": 4.07431, "time": 0.8147} +{"mode": "train", "epoch": 63, "iter": 2500, "lr": 0.06277, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29672, "top5_acc": 0.55594, "loss_cls": 4.0344, "loss": 4.0344, "time": 0.81692} +{"mode": "train", "epoch": 63, "iter": 2600, "lr": 0.06274, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30328, "top5_acc": 0.5625, "loss_cls": 3.98253, "loss": 3.98253, "time": 0.82197} +{"mode": "train", "epoch": 63, "iter": 2700, "lr": 0.06272, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30828, "top5_acc": 0.55, "loss_cls": 4.00184, "loss": 4.00184, "time": 0.81983} +{"mode": "train", "epoch": 63, "iter": 2800, "lr": 0.06269, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29578, "top5_acc": 0.54703, "loss_cls": 4.03652, "loss": 4.03652, "time": 0.81874} +{"mode": "train", "epoch": 63, "iter": 2900, "lr": 0.06266, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30328, "top5_acc": 0.56016, "loss_cls": 3.98519, "loss": 3.98519, "time": 0.81159} +{"mode": "train", "epoch": 63, "iter": 3000, "lr": 0.06264, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29438, "top5_acc": 0.54984, "loss_cls": 4.05083, "loss": 4.05083, "time": 0.81661} +{"mode": "train", "epoch": 63, "iter": 3100, "lr": 0.06261, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30828, "top5_acc": 0.56469, "loss_cls": 3.972, "loss": 3.972, "time": 0.81277} +{"mode": "train", "epoch": 63, "iter": 3200, "lr": 0.06258, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28938, "top5_acc": 0.53922, "loss_cls": 4.07974, "loss": 4.07974, "time": 0.81289} +{"mode": "train", "epoch": 63, "iter": 3300, "lr": 0.06256, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30047, "top5_acc": 0.55656, "loss_cls": 4.01089, "loss": 4.01089, "time": 0.81948} +{"mode": "train", "epoch": 63, "iter": 3400, "lr": 0.06253, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29922, "top5_acc": 0.55141, "loss_cls": 4.02105, "loss": 4.02105, "time": 0.81803} +{"mode": "train", "epoch": 63, "iter": 3500, "lr": 0.0625, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29469, "top5_acc": 0.55484, "loss_cls": 4.01837, "loss": 4.01837, "time": 0.81574} +{"mode": "train", "epoch": 63, "iter": 3600, "lr": 0.06247, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2925, "top5_acc": 0.55188, "loss_cls": 4.05515, "loss": 4.05515, "time": 0.82599} +{"mode": "train", "epoch": 63, "iter": 3700, "lr": 0.06245, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30656, "top5_acc": 0.55688, "loss_cls": 3.98454, "loss": 3.98454, "time": 0.81532} +{"mode": "val", "epoch": 63, "iter": 309, "lr": 0.06243, "top1_acc": 0.23583, "top5_acc": 0.48007, "mean_class_accuracy": 0.23571} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.06241, "memory": 15990, "data_time": 1.31587, "top1_acc": 0.30688, "top5_acc": 0.57047, "loss_cls": 3.92573, "loss": 3.92573, "time": 2.30182} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.06238, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30562, "top5_acc": 0.56594, "loss_cls": 3.96785, "loss": 3.96785, "time": 0.81804} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.06235, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31906, "top5_acc": 0.56469, "loss_cls": 3.94782, "loss": 3.94782, "time": 0.81944} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.06233, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29891, "top5_acc": 0.54844, "loss_cls": 4.03, "loss": 4.03, "time": 0.82121} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.0623, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29828, "top5_acc": 0.55906, "loss_cls": 4.0053, "loss": 4.0053, "time": 0.82072} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.06227, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30047, "top5_acc": 0.54781, "loss_cls": 4.01489, "loss": 4.01489, "time": 0.8187} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.06225, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29688, "top5_acc": 0.55219, "loss_cls": 4.02206, "loss": 4.02206, "time": 0.81486} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.06222, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31344, "top5_acc": 0.57281, "loss_cls": 3.90521, "loss": 3.90521, "time": 0.82494} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.06219, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30266, "top5_acc": 0.55609, "loss_cls": 4.03526, "loss": 4.03526, "time": 0.81708} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.06216, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29625, "top5_acc": 0.5525, "loss_cls": 4.04102, "loss": 4.04102, "time": 0.81598} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.06214, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29547, "top5_acc": 0.55969, "loss_cls": 4.01405, "loss": 4.01405, "time": 0.82} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.06211, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30375, "top5_acc": 0.55875, "loss_cls": 3.97168, "loss": 3.97168, "time": 0.829} +{"mode": "train", "epoch": 64, "iter": 1300, "lr": 0.06208, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30203, "top5_acc": 0.54859, "loss_cls": 4.0108, "loss": 4.0108, "time": 0.8244} +{"mode": "train", "epoch": 64, "iter": 1400, "lr": 0.06206, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30031, "top5_acc": 0.56391, "loss_cls": 3.98847, "loss": 3.98847, "time": 0.82201} +{"mode": "train", "epoch": 64, "iter": 1500, "lr": 0.06203, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30391, "top5_acc": 0.54609, "loss_cls": 4.02345, "loss": 4.02345, "time": 0.81812} +{"mode": "train", "epoch": 64, "iter": 1600, "lr": 0.062, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29828, "top5_acc": 0.55391, "loss_cls": 4.059, "loss": 4.059, "time": 0.82099} +{"mode": "train", "epoch": 64, "iter": 1700, "lr": 0.06197, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29703, "top5_acc": 0.55031, "loss_cls": 4.02009, "loss": 4.02009, "time": 0.8149} +{"mode": "train", "epoch": 64, "iter": 1800, "lr": 0.06195, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29234, "top5_acc": 0.55719, "loss_cls": 4.01328, "loss": 4.01328, "time": 0.81841} +{"mode": "train", "epoch": 64, "iter": 1900, "lr": 0.06192, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29922, "top5_acc": 0.55672, "loss_cls": 3.98962, "loss": 3.98962, "time": 0.81933} +{"mode": "train", "epoch": 64, "iter": 2000, "lr": 0.06189, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30188, "top5_acc": 0.55078, "loss_cls": 4.01505, "loss": 4.01505, "time": 0.81537} +{"mode": "train", "epoch": 64, "iter": 2100, "lr": 0.06187, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30109, "top5_acc": 0.56328, "loss_cls": 3.98655, "loss": 3.98655, "time": 0.81163} +{"mode": "train", "epoch": 64, "iter": 2200, "lr": 0.06184, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29922, "top5_acc": 0.56281, "loss_cls": 4.01111, "loss": 4.01111, "time": 0.81409} +{"mode": "train", "epoch": 64, "iter": 2300, "lr": 0.06181, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29719, "top5_acc": 0.55094, "loss_cls": 4.02387, "loss": 4.02387, "time": 0.81718} +{"mode": "train", "epoch": 64, "iter": 2400, "lr": 0.06178, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29984, "top5_acc": 0.55516, "loss_cls": 4.0404, "loss": 4.0404, "time": 0.81683} +{"mode": "train", "epoch": 64, "iter": 2500, "lr": 0.06176, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31344, "top5_acc": 0.56297, "loss_cls": 3.96784, "loss": 3.96784, "time": 0.81665} +{"mode": "train", "epoch": 64, "iter": 2600, "lr": 0.06173, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31125, "top5_acc": 0.55953, "loss_cls": 3.97027, "loss": 3.97027, "time": 0.81499} +{"mode": "train", "epoch": 64, "iter": 2700, "lr": 0.0617, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30875, "top5_acc": 0.56484, "loss_cls": 3.95724, "loss": 3.95724, "time": 0.81547} +{"mode": "train", "epoch": 64, "iter": 2800, "lr": 0.06168, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29594, "top5_acc": 0.5425, "loss_cls": 4.06025, "loss": 4.06025, "time": 0.81781} +{"mode": "train", "epoch": 64, "iter": 2900, "lr": 0.06165, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29828, "top5_acc": 0.55344, "loss_cls": 4.01091, "loss": 4.01091, "time": 0.81904} +{"mode": "train", "epoch": 64, "iter": 3000, "lr": 0.06162, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30719, "top5_acc": 0.55344, "loss_cls": 4.01007, "loss": 4.01007, "time": 0.82237} +{"mode": "train", "epoch": 64, "iter": 3100, "lr": 0.06159, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29719, "top5_acc": 0.55453, "loss_cls": 4.014, "loss": 4.014, "time": 0.81503} +{"mode": "train", "epoch": 64, "iter": 3200, "lr": 0.06157, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28984, "top5_acc": 0.55109, "loss_cls": 4.04611, "loss": 4.04611, "time": 0.81622} +{"mode": "train", "epoch": 64, "iter": 3300, "lr": 0.06154, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29406, "top5_acc": 0.54234, "loss_cls": 4.06388, "loss": 4.06388, "time": 0.81454} +{"mode": "train", "epoch": 64, "iter": 3400, "lr": 0.06151, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30266, "top5_acc": 0.56516, "loss_cls": 3.98921, "loss": 3.98921, "time": 0.81352} +{"mode": "train", "epoch": 64, "iter": 3500, "lr": 0.06148, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30969, "top5_acc": 0.56875, "loss_cls": 3.97793, "loss": 3.97793, "time": 0.81549} +{"mode": "train", "epoch": 64, "iter": 3600, "lr": 0.06146, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30297, "top5_acc": 0.56, "loss_cls": 4.00823, "loss": 4.00823, "time": 0.81845} +{"mode": "train", "epoch": 64, "iter": 3700, "lr": 0.06143, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29547, "top5_acc": 0.55266, "loss_cls": 3.98242, "loss": 3.98242, "time": 0.82021} +{"mode": "val", "epoch": 64, "iter": 309, "lr": 0.06142, "top1_acc": 0.237, "top5_acc": 0.47136, "mean_class_accuracy": 0.23671} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.06139, "memory": 15990, "data_time": 1.31806, "top1_acc": 0.32359, "top5_acc": 0.58062, "loss_cls": 3.89897, "loss": 3.89897, "time": 2.29062} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.06136, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30938, "top5_acc": 0.57297, "loss_cls": 3.97283, "loss": 3.97283, "time": 0.81123} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.06134, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31078, "top5_acc": 0.56672, "loss_cls": 3.9332, "loss": 3.9332, "time": 0.81566} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.06131, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30641, "top5_acc": 0.55531, "loss_cls": 3.98151, "loss": 3.98151, "time": 0.81755} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.06128, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30656, "top5_acc": 0.56437, "loss_cls": 3.96401, "loss": 3.96401, "time": 0.81459} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.06125, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30359, "top5_acc": 0.56641, "loss_cls": 3.96436, "loss": 3.96436, "time": 0.82308} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.06123, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29844, "top5_acc": 0.55562, "loss_cls": 3.99582, "loss": 3.99582, "time": 0.81801} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0612, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30266, "top5_acc": 0.55516, "loss_cls": 3.98849, "loss": 3.98849, "time": 0.81258} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.06117, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29609, "top5_acc": 0.55484, "loss_cls": 4.0292, "loss": 4.0292, "time": 0.81612} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.06115, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30016, "top5_acc": 0.56125, "loss_cls": 4.00224, "loss": 4.00224, "time": 0.82063} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.06112, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30469, "top5_acc": 0.56312, "loss_cls": 3.964, "loss": 3.964, "time": 0.83083} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.06109, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30281, "top5_acc": 0.56297, "loss_cls": 3.99036, "loss": 3.99036, "time": 0.81833} +{"mode": "train", "epoch": 65, "iter": 1300, "lr": 0.06106, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29141, "top5_acc": 0.55125, "loss_cls": 4.03944, "loss": 4.03944, "time": 0.83207} +{"mode": "train", "epoch": 65, "iter": 1400, "lr": 0.06104, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30406, "top5_acc": 0.56641, "loss_cls": 3.98345, "loss": 3.98345, "time": 0.81455} +{"mode": "train", "epoch": 65, "iter": 1500, "lr": 0.06101, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30188, "top5_acc": 0.55688, "loss_cls": 3.99729, "loss": 3.99729, "time": 0.8164} +{"mode": "train", "epoch": 65, "iter": 1600, "lr": 0.06098, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31672, "top5_acc": 0.56688, "loss_cls": 3.94373, "loss": 3.94373, "time": 0.81824} +{"mode": "train", "epoch": 65, "iter": 1700, "lr": 0.06095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30188, "top5_acc": 0.55859, "loss_cls": 3.99624, "loss": 3.99624, "time": 0.81238} +{"mode": "train", "epoch": 65, "iter": 1800, "lr": 0.06093, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30703, "top5_acc": 0.56344, "loss_cls": 3.939, "loss": 3.939, "time": 0.81543} +{"mode": "train", "epoch": 65, "iter": 1900, "lr": 0.0609, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30172, "top5_acc": 0.55312, "loss_cls": 4.02425, "loss": 4.02425, "time": 0.81422} +{"mode": "train", "epoch": 65, "iter": 2000, "lr": 0.06087, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30938, "top5_acc": 0.56469, "loss_cls": 3.97395, "loss": 3.97395, "time": 0.82195} +{"mode": "train", "epoch": 65, "iter": 2100, "lr": 0.06085, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30344, "top5_acc": 0.55188, "loss_cls": 4.01187, "loss": 4.01187, "time": 0.81638} +{"mode": "train", "epoch": 65, "iter": 2200, "lr": 0.06082, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29906, "top5_acc": 0.55891, "loss_cls": 3.99724, "loss": 3.99724, "time": 0.81619} +{"mode": "train", "epoch": 65, "iter": 2300, "lr": 0.06079, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30156, "top5_acc": 0.55625, "loss_cls": 4.01061, "loss": 4.01061, "time": 0.81486} +{"mode": "train", "epoch": 65, "iter": 2400, "lr": 0.06076, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29719, "top5_acc": 0.55437, "loss_cls": 4.01373, "loss": 4.01373, "time": 0.8174} +{"mode": "train", "epoch": 65, "iter": 2500, "lr": 0.06074, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30219, "top5_acc": 0.55828, "loss_cls": 3.9801, "loss": 3.9801, "time": 0.81769} +{"mode": "train", "epoch": 65, "iter": 2600, "lr": 0.06071, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30609, "top5_acc": 0.55641, "loss_cls": 4.02517, "loss": 4.02517, "time": 0.81716} +{"mode": "train", "epoch": 65, "iter": 2700, "lr": 0.06068, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30469, "top5_acc": 0.55406, "loss_cls": 3.99748, "loss": 3.99748, "time": 0.81432} +{"mode": "train", "epoch": 65, "iter": 2800, "lr": 0.06065, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29391, "top5_acc": 0.54969, "loss_cls": 4.04792, "loss": 4.04792, "time": 0.81533} +{"mode": "train", "epoch": 65, "iter": 2900, "lr": 0.06063, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30266, "top5_acc": 0.55641, "loss_cls": 3.9935, "loss": 3.9935, "time": 0.81265} +{"mode": "train", "epoch": 65, "iter": 3000, "lr": 0.0606, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30484, "top5_acc": 0.55906, "loss_cls": 4.01857, "loss": 4.01857, "time": 0.81665} +{"mode": "train", "epoch": 65, "iter": 3100, "lr": 0.06057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30125, "top5_acc": 0.55437, "loss_cls": 4.0423, "loss": 4.0423, "time": 0.81896} +{"mode": "train", "epoch": 65, "iter": 3200, "lr": 0.06055, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29984, "top5_acc": 0.56141, "loss_cls": 4.00959, "loss": 4.00959, "time": 0.81447} +{"mode": "train", "epoch": 65, "iter": 3300, "lr": 0.06052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30016, "top5_acc": 0.55375, "loss_cls": 4.03969, "loss": 4.03969, "time": 0.81224} +{"mode": "train", "epoch": 65, "iter": 3400, "lr": 0.06049, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29625, "top5_acc": 0.55922, "loss_cls": 4.02673, "loss": 4.02673, "time": 0.81509} +{"mode": "train", "epoch": 65, "iter": 3500, "lr": 0.06046, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30312, "top5_acc": 0.55578, "loss_cls": 3.99042, "loss": 3.99042, "time": 0.82014} +{"mode": "train", "epoch": 65, "iter": 3600, "lr": 0.06044, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29953, "top5_acc": 0.5625, "loss_cls": 3.98886, "loss": 3.98886, "time": 0.81816} +{"mode": "train", "epoch": 65, "iter": 3700, "lr": 0.06041, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3125, "top5_acc": 0.56906, "loss_cls": 3.9376, "loss": 3.9376, "time": 0.81345} +{"mode": "val", "epoch": 65, "iter": 309, "lr": 0.0604, "top1_acc": 0.25103, "top5_acc": 0.49096, "mean_class_accuracy": 0.25082} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.06037, "memory": 15990, "data_time": 1.31354, "top1_acc": 0.31594, "top5_acc": 0.57344, "loss_cls": 3.91458, "loss": 3.91458, "time": 2.30075} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.06034, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30406, "top5_acc": 0.57266, "loss_cls": 3.93418, "loss": 3.93418, "time": 0.82223} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.06031, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31234, "top5_acc": 0.57016, "loss_cls": 3.93414, "loss": 3.93414, "time": 0.81603} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.06029, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30578, "top5_acc": 0.56125, "loss_cls": 3.95375, "loss": 3.95375, "time": 0.8207} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.06026, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30594, "top5_acc": 0.56156, "loss_cls": 3.98234, "loss": 3.98234, "time": 0.81539} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.06023, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30062, "top5_acc": 0.56125, "loss_cls": 3.99468, "loss": 3.99468, "time": 0.82528} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.0602, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30234, "top5_acc": 0.55609, "loss_cls": 4.00068, "loss": 4.00068, "time": 0.8177} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.06018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30484, "top5_acc": 0.56422, "loss_cls": 3.97724, "loss": 3.97724, "time": 0.81382} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.06015, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2975, "top5_acc": 0.55641, "loss_cls": 4.00783, "loss": 4.00783, "time": 0.81309} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.06012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32469, "top5_acc": 0.57734, "loss_cls": 3.9004, "loss": 3.9004, "time": 0.81437} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.06009, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30344, "top5_acc": 0.56969, "loss_cls": 3.95538, "loss": 3.95538, "time": 0.82604} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.06007, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31906, "top5_acc": 0.56906, "loss_cls": 3.93378, "loss": 3.93378, "time": 0.81683} +{"mode": "train", "epoch": 66, "iter": 1300, "lr": 0.06004, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30625, "top5_acc": 0.56766, "loss_cls": 3.95521, "loss": 3.95521, "time": 0.82524} +{"mode": "train", "epoch": 66, "iter": 1400, "lr": 0.06001, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3075, "top5_acc": 0.565, "loss_cls": 3.97895, "loss": 3.97895, "time": 0.82352} +{"mode": "train", "epoch": 66, "iter": 1500, "lr": 0.05999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30562, "top5_acc": 0.56828, "loss_cls": 3.9702, "loss": 3.9702, "time": 0.81556} +{"mode": "train", "epoch": 66, "iter": 1600, "lr": 0.05996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31359, "top5_acc": 0.55188, "loss_cls": 3.9907, "loss": 3.9907, "time": 0.82134} +{"mode": "train", "epoch": 66, "iter": 1700, "lr": 0.05993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30625, "top5_acc": 0.56594, "loss_cls": 3.95668, "loss": 3.95668, "time": 0.81724} +{"mode": "train", "epoch": 66, "iter": 1800, "lr": 0.0599, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30781, "top5_acc": 0.56078, "loss_cls": 3.98919, "loss": 3.98919, "time": 0.81819} +{"mode": "train", "epoch": 66, "iter": 1900, "lr": 0.05988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29984, "top5_acc": 0.55297, "loss_cls": 4.01066, "loss": 4.01066, "time": 0.81633} +{"mode": "train", "epoch": 66, "iter": 2000, "lr": 0.05985, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29594, "top5_acc": 0.55297, "loss_cls": 4.03324, "loss": 4.03324, "time": 0.81674} +{"mode": "train", "epoch": 66, "iter": 2100, "lr": 0.05982, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30172, "top5_acc": 0.55609, "loss_cls": 4.0084, "loss": 4.0084, "time": 0.81222} +{"mode": "train", "epoch": 66, "iter": 2200, "lr": 0.05979, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29594, "top5_acc": 0.555, "loss_cls": 4.02683, "loss": 4.02683, "time": 0.81188} +{"mode": "train", "epoch": 66, "iter": 2300, "lr": 0.05977, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30016, "top5_acc": 0.55703, "loss_cls": 4.01169, "loss": 4.01169, "time": 0.81847} +{"mode": "train", "epoch": 66, "iter": 2400, "lr": 0.05974, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29766, "top5_acc": 0.56297, "loss_cls": 4.00664, "loss": 4.00664, "time": 0.81321} +{"mode": "train", "epoch": 66, "iter": 2500, "lr": 0.05971, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30547, "top5_acc": 0.55547, "loss_cls": 4.01634, "loss": 4.01634, "time": 0.81176} +{"mode": "train", "epoch": 66, "iter": 2600, "lr": 0.05968, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31203, "top5_acc": 0.56453, "loss_cls": 3.9683, "loss": 3.9683, "time": 0.81501} +{"mode": "train", "epoch": 66, "iter": 2700, "lr": 0.05966, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29984, "top5_acc": 0.54719, "loss_cls": 4.0262, "loss": 4.0262, "time": 0.81659} +{"mode": "train", "epoch": 66, "iter": 2800, "lr": 0.05963, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30047, "top5_acc": 0.55609, "loss_cls": 4.00903, "loss": 4.00903, "time": 0.81805} +{"mode": "train", "epoch": 66, "iter": 2900, "lr": 0.0596, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29609, "top5_acc": 0.56031, "loss_cls": 4.02056, "loss": 4.02056, "time": 0.81408} +{"mode": "train", "epoch": 66, "iter": 3000, "lr": 0.05957, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30953, "top5_acc": 0.55453, "loss_cls": 3.99443, "loss": 3.99443, "time": 0.81443} +{"mode": "train", "epoch": 66, "iter": 3100, "lr": 0.05955, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30266, "top5_acc": 0.56156, "loss_cls": 4.00036, "loss": 4.00036, "time": 0.81544} +{"mode": "train", "epoch": 66, "iter": 3200, "lr": 0.05952, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31562, "top5_acc": 0.555, "loss_cls": 4.00197, "loss": 4.00197, "time": 0.81285} +{"mode": "train", "epoch": 66, "iter": 3300, "lr": 0.05949, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30672, "top5_acc": 0.5575, "loss_cls": 3.99091, "loss": 3.99091, "time": 0.81403} +{"mode": "train", "epoch": 66, "iter": 3400, "lr": 0.05946, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29531, "top5_acc": 0.55391, "loss_cls": 4.00802, "loss": 4.00802, "time": 0.81364} +{"mode": "train", "epoch": 66, "iter": 3500, "lr": 0.05944, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30297, "top5_acc": 0.56609, "loss_cls": 3.95663, "loss": 3.95663, "time": 0.815} +{"mode": "train", "epoch": 66, "iter": 3600, "lr": 0.05941, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29656, "top5_acc": 0.55781, "loss_cls": 4.01127, "loss": 4.01127, "time": 0.8173} +{"mode": "train", "epoch": 66, "iter": 3700, "lr": 0.05938, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30672, "top5_acc": 0.56219, "loss_cls": 3.97409, "loss": 3.97409, "time": 0.81047} +{"mode": "val", "epoch": 66, "iter": 309, "lr": 0.05937, "top1_acc": 0.22393, "top5_acc": 0.46558, "mean_class_accuracy": 0.22384} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.05934, "memory": 15990, "data_time": 1.3638, "top1_acc": 0.31766, "top5_acc": 0.57891, "loss_cls": 3.90832, "loss": 3.90832, "time": 2.34229} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.05931, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31109, "top5_acc": 0.57266, "loss_cls": 3.91508, "loss": 3.91508, "time": 0.82638} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.05929, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29859, "top5_acc": 0.55359, "loss_cls": 4.01158, "loss": 4.01158, "time": 0.81889} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.05926, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31234, "top5_acc": 0.57344, "loss_cls": 3.93827, "loss": 3.93827, "time": 0.82524} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.05923, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31016, "top5_acc": 0.57219, "loss_cls": 3.93634, "loss": 3.93634, "time": 0.81925} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.0592, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29688, "top5_acc": 0.55016, "loss_cls": 4.00926, "loss": 4.00926, "time": 0.81728} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.05918, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29594, "top5_acc": 0.56078, "loss_cls": 4.00728, "loss": 4.00728, "time": 0.81344} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.05915, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30203, "top5_acc": 0.56422, "loss_cls": 3.96854, "loss": 3.96854, "time": 0.81714} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.05912, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30594, "top5_acc": 0.56141, "loss_cls": 3.9589, "loss": 3.9589, "time": 0.82398} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.05909, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31297, "top5_acc": 0.56266, "loss_cls": 3.96501, "loss": 3.96501, "time": 0.81615} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.05907, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30219, "top5_acc": 0.55828, "loss_cls": 3.98164, "loss": 3.98164, "time": 0.81928} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.05904, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29875, "top5_acc": 0.55281, "loss_cls": 4.01137, "loss": 4.01137, "time": 0.81916} +{"mode": "train", "epoch": 67, "iter": 1300, "lr": 0.05901, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31609, "top5_acc": 0.56172, "loss_cls": 3.9577, "loss": 3.9577, "time": 0.82524} +{"mode": "train", "epoch": 67, "iter": 1400, "lr": 0.05898, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31297, "top5_acc": 0.5775, "loss_cls": 3.90649, "loss": 3.90649, "time": 0.81922} +{"mode": "train", "epoch": 67, "iter": 1500, "lr": 0.05896, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31453, "top5_acc": 0.57172, "loss_cls": 3.90287, "loss": 3.90287, "time": 0.82438} +{"mode": "train", "epoch": 67, "iter": 1600, "lr": 0.05893, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29703, "top5_acc": 0.55016, "loss_cls": 4.03412, "loss": 4.03412, "time": 0.82295} +{"mode": "train", "epoch": 67, "iter": 1700, "lr": 0.0589, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30969, "top5_acc": 0.56125, "loss_cls": 3.97416, "loss": 3.97416, "time": 0.8212} +{"mode": "train", "epoch": 67, "iter": 1800, "lr": 0.05887, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30484, "top5_acc": 0.56891, "loss_cls": 3.96346, "loss": 3.96346, "time": 0.81532} +{"mode": "train", "epoch": 67, "iter": 1900, "lr": 0.05885, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30031, "top5_acc": 0.55328, "loss_cls": 3.99786, "loss": 3.99786, "time": 0.82211} +{"mode": "train", "epoch": 67, "iter": 2000, "lr": 0.05882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31234, "top5_acc": 0.56953, "loss_cls": 3.94052, "loss": 3.94052, "time": 0.8177} +{"mode": "train", "epoch": 67, "iter": 2100, "lr": 0.05879, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30859, "top5_acc": 0.55922, "loss_cls": 3.9728, "loss": 3.9728, "time": 0.81134} +{"mode": "train", "epoch": 67, "iter": 2200, "lr": 0.05876, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30938, "top5_acc": 0.55953, "loss_cls": 4.00044, "loss": 4.00044, "time": 0.8173} +{"mode": "train", "epoch": 67, "iter": 2300, "lr": 0.05874, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3, "top5_acc": 0.555, "loss_cls": 3.99355, "loss": 3.99355, "time": 0.81624} +{"mode": "train", "epoch": 67, "iter": 2400, "lr": 0.05871, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31219, "top5_acc": 0.56734, "loss_cls": 3.9398, "loss": 3.9398, "time": 0.82094} +{"mode": "train", "epoch": 67, "iter": 2500, "lr": 0.05868, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30844, "top5_acc": 0.55672, "loss_cls": 4.01938, "loss": 4.01938, "time": 0.81566} +{"mode": "train", "epoch": 67, "iter": 2600, "lr": 0.05865, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30016, "top5_acc": 0.55328, "loss_cls": 4.02317, "loss": 4.02317, "time": 0.82143} +{"mode": "train", "epoch": 67, "iter": 2700, "lr": 0.05863, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30969, "top5_acc": 0.56078, "loss_cls": 3.96285, "loss": 3.96285, "time": 0.81357} +{"mode": "train", "epoch": 67, "iter": 2800, "lr": 0.0586, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30797, "top5_acc": 0.5675, "loss_cls": 3.9714, "loss": 3.9714, "time": 0.8173} +{"mode": "train", "epoch": 67, "iter": 2900, "lr": 0.05857, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30391, "top5_acc": 0.54937, "loss_cls": 4.00023, "loss": 4.00023, "time": 0.81573} +{"mode": "train", "epoch": 67, "iter": 3000, "lr": 0.05854, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30266, "top5_acc": 0.55875, "loss_cls": 3.98772, "loss": 3.98772, "time": 0.8096} +{"mode": "train", "epoch": 67, "iter": 3100, "lr": 0.05852, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30359, "top5_acc": 0.56141, "loss_cls": 3.98152, "loss": 3.98152, "time": 0.81738} +{"mode": "train", "epoch": 67, "iter": 3200, "lr": 0.05849, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29969, "top5_acc": 0.55719, "loss_cls": 4.0212, "loss": 4.0212, "time": 0.81532} +{"mode": "train", "epoch": 67, "iter": 3300, "lr": 0.05846, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30484, "top5_acc": 0.56063, "loss_cls": 3.99631, "loss": 3.99631, "time": 0.81557} +{"mode": "train", "epoch": 67, "iter": 3400, "lr": 0.05843, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29563, "top5_acc": 0.56156, "loss_cls": 4.01397, "loss": 4.01397, "time": 0.81692} +{"mode": "train", "epoch": 67, "iter": 3500, "lr": 0.05841, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29656, "top5_acc": 0.55484, "loss_cls": 4.00024, "loss": 4.00024, "time": 0.81315} +{"mode": "train", "epoch": 67, "iter": 3600, "lr": 0.05838, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30078, "top5_acc": 0.55922, "loss_cls": 3.98855, "loss": 3.98855, "time": 0.82117} +{"mode": "train", "epoch": 67, "iter": 3700, "lr": 0.05835, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30641, "top5_acc": 0.56125, "loss_cls": 3.95527, "loss": 3.95527, "time": 0.81606} +{"mode": "val", "epoch": 67, "iter": 309, "lr": 0.05834, "top1_acc": 0.23694, "top5_acc": 0.47906, "mean_class_accuracy": 0.23675} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.05831, "memory": 15990, "data_time": 1.30786, "top1_acc": 0.3025, "top5_acc": 0.55937, "loss_cls": 3.96811, "loss": 3.96811, "time": 2.28465} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.05828, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31625, "top5_acc": 0.57266, "loss_cls": 3.88636, "loss": 3.88636, "time": 0.82024} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.05826, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32469, "top5_acc": 0.58078, "loss_cls": 3.88143, "loss": 3.88143, "time": 0.81441} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.05823, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31594, "top5_acc": 0.56719, "loss_cls": 3.93408, "loss": 3.93408, "time": 0.8278} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.0582, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3075, "top5_acc": 0.56047, "loss_cls": 3.96926, "loss": 3.96926, "time": 0.81659} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.05817, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30844, "top5_acc": 0.56312, "loss_cls": 3.9624, "loss": 3.9624, "time": 0.8169} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.05815, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31516, "top5_acc": 0.56938, "loss_cls": 3.93585, "loss": 3.93585, "time": 0.81392} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.05812, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31641, "top5_acc": 0.56234, "loss_cls": 3.93811, "loss": 3.93811, "time": 0.81384} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.05809, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31406, "top5_acc": 0.55984, "loss_cls": 3.97624, "loss": 3.97624, "time": 0.81081} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.05806, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31062, "top5_acc": 0.55562, "loss_cls": 3.98911, "loss": 3.98911, "time": 0.8154} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.05804, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29953, "top5_acc": 0.56625, "loss_cls": 4.01058, "loss": 4.01058, "time": 0.81964} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.05801, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31547, "top5_acc": 0.575, "loss_cls": 3.90925, "loss": 3.90925, "time": 0.81854} +{"mode": "train", "epoch": 68, "iter": 1300, "lr": 0.05798, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30875, "top5_acc": 0.5575, "loss_cls": 3.96716, "loss": 3.96716, "time": 0.8197} +{"mode": "train", "epoch": 68, "iter": 1400, "lr": 0.05795, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30578, "top5_acc": 0.55797, "loss_cls": 3.98312, "loss": 3.98312, "time": 0.82078} +{"mode": "train", "epoch": 68, "iter": 1500, "lr": 0.05792, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30438, "top5_acc": 0.55906, "loss_cls": 3.98563, "loss": 3.98563, "time": 0.81678} +{"mode": "train", "epoch": 68, "iter": 1600, "lr": 0.0579, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30469, "top5_acc": 0.56828, "loss_cls": 3.95279, "loss": 3.95279, "time": 0.81643} +{"mode": "train", "epoch": 68, "iter": 1700, "lr": 0.05787, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30656, "top5_acc": 0.55859, "loss_cls": 4.00004, "loss": 4.00004, "time": 0.82103} +{"mode": "train", "epoch": 68, "iter": 1800, "lr": 0.05784, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31297, "top5_acc": 0.56219, "loss_cls": 3.94718, "loss": 3.94718, "time": 0.81681} +{"mode": "train", "epoch": 68, "iter": 1900, "lr": 0.05781, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31391, "top5_acc": 0.57359, "loss_cls": 3.91998, "loss": 3.91998, "time": 0.81227} +{"mode": "train", "epoch": 68, "iter": 2000, "lr": 0.05779, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31, "top5_acc": 0.56406, "loss_cls": 3.94459, "loss": 3.94459, "time": 0.81941} +{"mode": "train", "epoch": 68, "iter": 2100, "lr": 0.05776, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30875, "top5_acc": 0.56312, "loss_cls": 3.96704, "loss": 3.96704, "time": 0.81349} +{"mode": "train", "epoch": 68, "iter": 2200, "lr": 0.05773, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30344, "top5_acc": 0.55516, "loss_cls": 3.99432, "loss": 3.99432, "time": 0.81423} +{"mode": "train", "epoch": 68, "iter": 2300, "lr": 0.0577, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30812, "top5_acc": 0.56422, "loss_cls": 3.95752, "loss": 3.95752, "time": 0.81413} +{"mode": "train", "epoch": 68, "iter": 2400, "lr": 0.05768, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31406, "top5_acc": 0.57063, "loss_cls": 3.93795, "loss": 3.93795, "time": 0.81934} +{"mode": "train", "epoch": 68, "iter": 2500, "lr": 0.05765, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30438, "top5_acc": 0.56156, "loss_cls": 3.97624, "loss": 3.97624, "time": 0.81305} +{"mode": "train", "epoch": 68, "iter": 2600, "lr": 0.05762, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3075, "top5_acc": 0.57078, "loss_cls": 3.9446, "loss": 3.9446, "time": 0.81255} +{"mode": "train", "epoch": 68, "iter": 2700, "lr": 0.05759, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30297, "top5_acc": 0.56188, "loss_cls": 3.99216, "loss": 3.99216, "time": 0.81553} +{"mode": "train", "epoch": 68, "iter": 2800, "lr": 0.05757, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30391, "top5_acc": 0.55594, "loss_cls": 4.01497, "loss": 4.01497, "time": 0.81656} +{"mode": "train", "epoch": 68, "iter": 2900, "lr": 0.05754, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30203, "top5_acc": 0.5525, "loss_cls": 4.01639, "loss": 4.01639, "time": 0.82044} +{"mode": "train", "epoch": 68, "iter": 3000, "lr": 0.05751, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30109, "top5_acc": 0.56016, "loss_cls": 3.97608, "loss": 3.97608, "time": 0.81577} +{"mode": "train", "epoch": 68, "iter": 3100, "lr": 0.05748, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29438, "top5_acc": 0.55812, "loss_cls": 4.02188, "loss": 4.02188, "time": 0.81536} +{"mode": "train", "epoch": 68, "iter": 3200, "lr": 0.05746, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31047, "top5_acc": 0.56625, "loss_cls": 3.95412, "loss": 3.95412, "time": 0.80966} +{"mode": "train", "epoch": 68, "iter": 3300, "lr": 0.05743, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30594, "top5_acc": 0.55203, "loss_cls": 3.99991, "loss": 3.99991, "time": 0.81364} +{"mode": "train", "epoch": 68, "iter": 3400, "lr": 0.0574, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30625, "top5_acc": 0.55516, "loss_cls": 4.00029, "loss": 4.00029, "time": 0.81307} +{"mode": "train", "epoch": 68, "iter": 3500, "lr": 0.05737, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30234, "top5_acc": 0.55672, "loss_cls": 4.00687, "loss": 4.00687, "time": 0.81345} +{"mode": "train", "epoch": 68, "iter": 3600, "lr": 0.05734, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29828, "top5_acc": 0.55484, "loss_cls": 4.00886, "loss": 4.00886, "time": 0.82086} +{"mode": "train", "epoch": 68, "iter": 3700, "lr": 0.05732, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30844, "top5_acc": 0.56828, "loss_cls": 3.96764, "loss": 3.96764, "time": 0.81467} +{"mode": "val", "epoch": 68, "iter": 309, "lr": 0.0573, "top1_acc": 0.23644, "top5_acc": 0.4784, "mean_class_accuracy": 0.23618} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.05728, "memory": 15990, "data_time": 1.29324, "top1_acc": 0.31578, "top5_acc": 0.57766, "loss_cls": 3.91066, "loss": 3.91066, "time": 2.27557} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.05725, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31469, "top5_acc": 0.56891, "loss_cls": 3.90847, "loss": 3.90847, "time": 0.81733} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.05722, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31266, "top5_acc": 0.57141, "loss_cls": 3.91046, "loss": 3.91046, "time": 0.81364} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.05719, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3125, "top5_acc": 0.57312, "loss_cls": 3.92119, "loss": 3.92119, "time": 0.81342} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.05717, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30062, "top5_acc": 0.56359, "loss_cls": 3.975, "loss": 3.975, "time": 0.82491} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.05714, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31391, "top5_acc": 0.57219, "loss_cls": 3.92085, "loss": 3.92085, "time": 0.81437} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.05711, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31094, "top5_acc": 0.56859, "loss_cls": 3.92012, "loss": 3.92012, "time": 0.81625} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.05708, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30656, "top5_acc": 0.55906, "loss_cls": 3.96959, "loss": 3.96959, "time": 0.819} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.05706, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30875, "top5_acc": 0.57422, "loss_cls": 3.92849, "loss": 3.92849, "time": 0.81712} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.05703, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31, "top5_acc": 0.56266, "loss_cls": 3.95281, "loss": 3.95281, "time": 0.81043} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.057, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30547, "top5_acc": 0.55859, "loss_cls": 3.97055, "loss": 3.97055, "time": 0.81414} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.05697, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30812, "top5_acc": 0.55984, "loss_cls": 3.95948, "loss": 3.95948, "time": 0.8259} +{"mode": "train", "epoch": 69, "iter": 1300, "lr": 0.05694, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30312, "top5_acc": 0.56516, "loss_cls": 3.95309, "loss": 3.95309, "time": 0.82135} +{"mode": "train", "epoch": 69, "iter": 1400, "lr": 0.05692, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31969, "top5_acc": 0.56797, "loss_cls": 3.92308, "loss": 3.92308, "time": 0.82702} +{"mode": "train", "epoch": 69, "iter": 1500, "lr": 0.05689, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30219, "top5_acc": 0.55766, "loss_cls": 3.98378, "loss": 3.98378, "time": 0.82338} +{"mode": "train", "epoch": 69, "iter": 1600, "lr": 0.05686, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30344, "top5_acc": 0.55656, "loss_cls": 4.00136, "loss": 4.00136, "time": 0.81692} +{"mode": "train", "epoch": 69, "iter": 1700, "lr": 0.05683, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3025, "top5_acc": 0.57375, "loss_cls": 3.9329, "loss": 3.9329, "time": 0.81397} +{"mode": "train", "epoch": 69, "iter": 1800, "lr": 0.05681, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30438, "top5_acc": 0.56719, "loss_cls": 3.964, "loss": 3.964, "time": 0.81444} +{"mode": "train", "epoch": 69, "iter": 1900, "lr": 0.05678, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30797, "top5_acc": 0.55891, "loss_cls": 3.97666, "loss": 3.97666, "time": 0.81241} +{"mode": "train", "epoch": 69, "iter": 2000, "lr": 0.05675, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29969, "top5_acc": 0.55125, "loss_cls": 4.01106, "loss": 4.01106, "time": 0.81183} +{"mode": "train", "epoch": 69, "iter": 2100, "lr": 0.05672, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.305, "top5_acc": 0.56609, "loss_cls": 3.96291, "loss": 3.96291, "time": 0.81471} +{"mode": "train", "epoch": 69, "iter": 2200, "lr": 0.0567, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30594, "top5_acc": 0.55828, "loss_cls": 4.00736, "loss": 4.00736, "time": 0.8145} +{"mode": "train", "epoch": 69, "iter": 2300, "lr": 0.05667, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.325, "top5_acc": 0.57125, "loss_cls": 3.91125, "loss": 3.91125, "time": 0.81212} +{"mode": "train", "epoch": 69, "iter": 2400, "lr": 0.05664, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30031, "top5_acc": 0.55859, "loss_cls": 3.99841, "loss": 3.99841, "time": 0.81789} +{"mode": "train", "epoch": 69, "iter": 2500, "lr": 0.05661, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30703, "top5_acc": 0.56469, "loss_cls": 3.94811, "loss": 3.94811, "time": 0.81437} +{"mode": "train", "epoch": 69, "iter": 2600, "lr": 0.05658, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31406, "top5_acc": 0.56125, "loss_cls": 3.9531, "loss": 3.9531, "time": 0.81237} +{"mode": "train", "epoch": 69, "iter": 2700, "lr": 0.05656, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29969, "top5_acc": 0.56266, "loss_cls": 3.97966, "loss": 3.97966, "time": 0.81647} +{"mode": "train", "epoch": 69, "iter": 2800, "lr": 0.05653, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30844, "top5_acc": 0.57188, "loss_cls": 3.93777, "loss": 3.93777, "time": 0.81459} +{"mode": "train", "epoch": 69, "iter": 2900, "lr": 0.0565, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31766, "top5_acc": 0.57703, "loss_cls": 3.91263, "loss": 3.91263, "time": 0.81372} +{"mode": "train", "epoch": 69, "iter": 3000, "lr": 0.05647, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3175, "top5_acc": 0.56156, "loss_cls": 3.96042, "loss": 3.96042, "time": 0.81872} +{"mode": "train", "epoch": 69, "iter": 3100, "lr": 0.05645, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30203, "top5_acc": 0.55609, "loss_cls": 4.00188, "loss": 4.00188, "time": 0.81546} +{"mode": "train", "epoch": 69, "iter": 3200, "lr": 0.05642, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30125, "top5_acc": 0.56188, "loss_cls": 3.9792, "loss": 3.9792, "time": 0.81733} +{"mode": "train", "epoch": 69, "iter": 3300, "lr": 0.05639, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30938, "top5_acc": 0.565, "loss_cls": 3.95136, "loss": 3.95136, "time": 0.81439} +{"mode": "train", "epoch": 69, "iter": 3400, "lr": 0.05636, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31578, "top5_acc": 0.56656, "loss_cls": 3.95349, "loss": 3.95349, "time": 0.81324} +{"mode": "train", "epoch": 69, "iter": 3500, "lr": 0.05634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30484, "top5_acc": 0.55922, "loss_cls": 3.96188, "loss": 3.96188, "time": 0.81543} +{"mode": "train", "epoch": 69, "iter": 3600, "lr": 0.05631, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31938, "top5_acc": 0.56672, "loss_cls": 3.94316, "loss": 3.94316, "time": 0.81958} +{"mode": "train", "epoch": 69, "iter": 3700, "lr": 0.05628, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32094, "top5_acc": 0.57031, "loss_cls": 3.93567, "loss": 3.93567, "time": 0.81782} +{"mode": "val", "epoch": 69, "iter": 309, "lr": 0.05627, "top1_acc": 0.24566, "top5_acc": 0.49461, "mean_class_accuracy": 0.24541} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.05624, "memory": 15990, "data_time": 1.32517, "top1_acc": 0.30969, "top5_acc": 0.57203, "loss_cls": 3.9152, "loss": 3.9152, "time": 2.30626} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.05621, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31109, "top5_acc": 0.56141, "loss_cls": 3.95098, "loss": 3.95098, "time": 0.82073} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.05618, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31797, "top5_acc": 0.57047, "loss_cls": 3.90705, "loss": 3.90705, "time": 0.82108} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.05616, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32312, "top5_acc": 0.57719, "loss_cls": 3.87356, "loss": 3.87356, "time": 0.81663} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.05613, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31344, "top5_acc": 0.57078, "loss_cls": 3.93503, "loss": 3.93503, "time": 0.8176} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.0561, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30859, "top5_acc": 0.56656, "loss_cls": 3.94137, "loss": 3.94137, "time": 0.82039} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.05607, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30969, "top5_acc": 0.57406, "loss_cls": 3.94365, "loss": 3.94365, "time": 0.81718} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.05605, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31203, "top5_acc": 0.56547, "loss_cls": 3.937, "loss": 3.937, "time": 0.81391} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.05602, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30625, "top5_acc": 0.55562, "loss_cls": 3.98013, "loss": 3.98013, "time": 0.81555} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.05599, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32047, "top5_acc": 0.57875, "loss_cls": 3.90224, "loss": 3.90224, "time": 0.813} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.05596, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31047, "top5_acc": 0.56531, "loss_cls": 3.96211, "loss": 3.96211, "time": 0.81649} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.05593, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31391, "top5_acc": 0.57656, "loss_cls": 3.88191, "loss": 3.88191, "time": 0.8177} +{"mode": "train", "epoch": 70, "iter": 1300, "lr": 0.05591, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29906, "top5_acc": 0.55641, "loss_cls": 3.98676, "loss": 3.98676, "time": 0.82297} +{"mode": "train", "epoch": 70, "iter": 1400, "lr": 0.05588, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30922, "top5_acc": 0.57266, "loss_cls": 3.94108, "loss": 3.94108, "time": 0.82775} +{"mode": "train", "epoch": 70, "iter": 1500, "lr": 0.05585, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30438, "top5_acc": 0.55375, "loss_cls": 3.97746, "loss": 3.97746, "time": 0.82134} +{"mode": "train", "epoch": 70, "iter": 1600, "lr": 0.05582, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31375, "top5_acc": 0.56594, "loss_cls": 3.94947, "loss": 3.94947, "time": 0.82644} +{"mode": "train", "epoch": 70, "iter": 1700, "lr": 0.0558, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31656, "top5_acc": 0.57063, "loss_cls": 3.93557, "loss": 3.93557, "time": 0.81596} +{"mode": "train", "epoch": 70, "iter": 1800, "lr": 0.05577, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30016, "top5_acc": 0.55156, "loss_cls": 4.01154, "loss": 4.01154, "time": 0.81847} +{"mode": "train", "epoch": 70, "iter": 1900, "lr": 0.05574, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30922, "top5_acc": 0.56156, "loss_cls": 3.95823, "loss": 3.95823, "time": 0.8225} +{"mode": "train", "epoch": 70, "iter": 2000, "lr": 0.05571, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31547, "top5_acc": 0.57406, "loss_cls": 3.92003, "loss": 3.92003, "time": 0.81862} +{"mode": "train", "epoch": 70, "iter": 2100, "lr": 0.05568, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30109, "top5_acc": 0.57, "loss_cls": 3.98394, "loss": 3.98394, "time": 0.81512} +{"mode": "train", "epoch": 70, "iter": 2200, "lr": 0.05566, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30078, "top5_acc": 0.55734, "loss_cls": 3.99711, "loss": 3.99711, "time": 0.81395} +{"mode": "train", "epoch": 70, "iter": 2300, "lr": 0.05563, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30312, "top5_acc": 0.57094, "loss_cls": 3.95728, "loss": 3.95728, "time": 0.81367} +{"mode": "train", "epoch": 70, "iter": 2400, "lr": 0.0556, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31281, "top5_acc": 0.56453, "loss_cls": 3.935, "loss": 3.935, "time": 0.81586} +{"mode": "train", "epoch": 70, "iter": 2500, "lr": 0.05557, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30781, "top5_acc": 0.56938, "loss_cls": 3.94812, "loss": 3.94812, "time": 0.81522} +{"mode": "train", "epoch": 70, "iter": 2600, "lr": 0.05555, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3025, "top5_acc": 0.5675, "loss_cls": 3.95418, "loss": 3.95418, "time": 0.81895} +{"mode": "train", "epoch": 70, "iter": 2700, "lr": 0.05552, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31156, "top5_acc": 0.56234, "loss_cls": 3.96561, "loss": 3.96561, "time": 0.81837} +{"mode": "train", "epoch": 70, "iter": 2800, "lr": 0.05549, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31031, "top5_acc": 0.56953, "loss_cls": 3.93139, "loss": 3.93139, "time": 0.81714} +{"mode": "train", "epoch": 70, "iter": 2900, "lr": 0.05546, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31188, "top5_acc": 0.56156, "loss_cls": 4.00526, "loss": 4.00526, "time": 0.81815} +{"mode": "train", "epoch": 70, "iter": 3000, "lr": 0.05543, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31219, "top5_acc": 0.56047, "loss_cls": 3.94819, "loss": 3.94819, "time": 0.8156} +{"mode": "train", "epoch": 70, "iter": 3100, "lr": 0.05541, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30672, "top5_acc": 0.56328, "loss_cls": 3.97148, "loss": 3.97148, "time": 0.81681} +{"mode": "train", "epoch": 70, "iter": 3200, "lr": 0.05538, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31688, "top5_acc": 0.5675, "loss_cls": 3.97265, "loss": 3.97265, "time": 0.81524} +{"mode": "train", "epoch": 70, "iter": 3300, "lr": 0.05535, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31531, "top5_acc": 0.57516, "loss_cls": 3.92114, "loss": 3.92114, "time": 0.81578} +{"mode": "train", "epoch": 70, "iter": 3400, "lr": 0.05532, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30234, "top5_acc": 0.56063, "loss_cls": 3.99726, "loss": 3.99726, "time": 0.81417} +{"mode": "train", "epoch": 70, "iter": 3500, "lr": 0.0553, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31938, "top5_acc": 0.58062, "loss_cls": 3.87252, "loss": 3.87252, "time": 0.8125} +{"mode": "train", "epoch": 70, "iter": 3600, "lr": 0.05527, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31469, "top5_acc": 0.56953, "loss_cls": 3.91742, "loss": 3.91742, "time": 0.82376} +{"mode": "train", "epoch": 70, "iter": 3700, "lr": 0.05524, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30859, "top5_acc": 0.55516, "loss_cls": 3.96553, "loss": 3.96553, "time": 0.81102} +{"mode": "val", "epoch": 70, "iter": 309, "lr": 0.05523, "top1_acc": 0.2416, "top5_acc": 0.49152, "mean_class_accuracy": 0.24157} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.0552, "memory": 15990, "data_time": 1.3312, "top1_acc": 0.31781, "top5_acc": 0.56906, "loss_cls": 3.94505, "loss": 3.94505, "time": 2.31246} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.05517, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31984, "top5_acc": 0.57531, "loss_cls": 3.88587, "loss": 3.88587, "time": 0.81911} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.05514, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31875, "top5_acc": 0.56766, "loss_cls": 3.92135, "loss": 3.92135, "time": 0.81935} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.05512, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32078, "top5_acc": 0.58297, "loss_cls": 3.86734, "loss": 3.86734, "time": 0.81935} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.05509, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32016, "top5_acc": 0.58203, "loss_cls": 3.85889, "loss": 3.85889, "time": 0.81777} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.05506, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31047, "top5_acc": 0.56734, "loss_cls": 3.94743, "loss": 3.94743, "time": 0.81842} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.05503, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31406, "top5_acc": 0.57188, "loss_cls": 3.93336, "loss": 3.93336, "time": 0.81823} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.055, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.315, "top5_acc": 0.57391, "loss_cls": 3.90485, "loss": 3.90485, "time": 0.8209} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.05498, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31891, "top5_acc": 0.58141, "loss_cls": 3.88029, "loss": 3.88029, "time": 0.81597} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.05495, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30625, "top5_acc": 0.56219, "loss_cls": 3.97792, "loss": 3.97792, "time": 0.81272} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.05492, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32078, "top5_acc": 0.57969, "loss_cls": 3.89885, "loss": 3.89885, "time": 0.81612} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.05489, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31656, "top5_acc": 0.5725, "loss_cls": 3.91168, "loss": 3.91168, "time": 0.82109} +{"mode": "train", "epoch": 71, "iter": 1300, "lr": 0.05487, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30875, "top5_acc": 0.56031, "loss_cls": 3.97172, "loss": 3.97172, "time": 0.824} +{"mode": "train", "epoch": 71, "iter": 1400, "lr": 0.05484, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.315, "top5_acc": 0.56766, "loss_cls": 3.92664, "loss": 3.92664, "time": 0.82406} +{"mode": "train", "epoch": 71, "iter": 1500, "lr": 0.05481, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30688, "top5_acc": 0.56094, "loss_cls": 3.94146, "loss": 3.94146, "time": 0.81946} +{"mode": "train", "epoch": 71, "iter": 1600, "lr": 0.05478, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30516, "top5_acc": 0.56391, "loss_cls": 3.97036, "loss": 3.97036, "time": 0.82839} +{"mode": "train", "epoch": 71, "iter": 1700, "lr": 0.05475, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31094, "top5_acc": 0.56953, "loss_cls": 3.94227, "loss": 3.94227, "time": 0.81973} +{"mode": "train", "epoch": 71, "iter": 1800, "lr": 0.05473, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31109, "top5_acc": 0.56672, "loss_cls": 3.94925, "loss": 3.94925, "time": 0.81411} +{"mode": "train", "epoch": 71, "iter": 1900, "lr": 0.0547, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31797, "top5_acc": 0.56578, "loss_cls": 3.93611, "loss": 3.93611, "time": 0.81522} +{"mode": "train", "epoch": 71, "iter": 2000, "lr": 0.05467, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31094, "top5_acc": 0.5675, "loss_cls": 3.94867, "loss": 3.94867, "time": 0.81689} +{"mode": "train", "epoch": 71, "iter": 2100, "lr": 0.05464, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31047, "top5_acc": 0.56812, "loss_cls": 3.93117, "loss": 3.93117, "time": 0.81537} +{"mode": "train", "epoch": 71, "iter": 2200, "lr": 0.05461, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32, "top5_acc": 0.57563, "loss_cls": 3.89411, "loss": 3.89411, "time": 0.81583} +{"mode": "train", "epoch": 71, "iter": 2300, "lr": 0.05459, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31125, "top5_acc": 0.56797, "loss_cls": 3.94702, "loss": 3.94702, "time": 0.81569} +{"mode": "train", "epoch": 71, "iter": 2400, "lr": 0.05456, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32, "top5_acc": 0.56063, "loss_cls": 3.92507, "loss": 3.92507, "time": 0.81975} +{"mode": "train", "epoch": 71, "iter": 2500, "lr": 0.05453, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30719, "top5_acc": 0.56859, "loss_cls": 3.93128, "loss": 3.93128, "time": 0.82253} +{"mode": "train", "epoch": 71, "iter": 2600, "lr": 0.0545, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32109, "top5_acc": 0.57516, "loss_cls": 3.86776, "loss": 3.86776, "time": 0.81789} +{"mode": "train", "epoch": 71, "iter": 2700, "lr": 0.05448, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30484, "top5_acc": 0.56672, "loss_cls": 3.95932, "loss": 3.95932, "time": 0.81481} +{"mode": "train", "epoch": 71, "iter": 2800, "lr": 0.05445, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31141, "top5_acc": 0.57031, "loss_cls": 3.93632, "loss": 3.93632, "time": 0.81813} +{"mode": "train", "epoch": 71, "iter": 2900, "lr": 0.05442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30922, "top5_acc": 0.56891, "loss_cls": 3.95438, "loss": 3.95438, "time": 0.81582} +{"mode": "train", "epoch": 71, "iter": 3000, "lr": 0.05439, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30578, "top5_acc": 0.56375, "loss_cls": 3.97481, "loss": 3.97481, "time": 0.81495} +{"mode": "train", "epoch": 71, "iter": 3100, "lr": 0.05436, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31594, "top5_acc": 0.57109, "loss_cls": 3.90754, "loss": 3.90754, "time": 0.81542} +{"mode": "train", "epoch": 71, "iter": 3200, "lr": 0.05434, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30344, "top5_acc": 0.55984, "loss_cls": 3.97952, "loss": 3.97952, "time": 0.81282} +{"mode": "train", "epoch": 71, "iter": 3300, "lr": 0.05431, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30234, "top5_acc": 0.55781, "loss_cls": 3.99687, "loss": 3.99687, "time": 0.81312} +{"mode": "train", "epoch": 71, "iter": 3400, "lr": 0.05428, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31406, "top5_acc": 0.56922, "loss_cls": 3.93924, "loss": 3.93924, "time": 0.81323} +{"mode": "train", "epoch": 71, "iter": 3500, "lr": 0.05425, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30625, "top5_acc": 0.56375, "loss_cls": 3.95004, "loss": 3.95004, "time": 0.81624} +{"mode": "train", "epoch": 71, "iter": 3600, "lr": 0.05422, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30562, "top5_acc": 0.55547, "loss_cls": 3.98001, "loss": 3.98001, "time": 0.82195} +{"mode": "train", "epoch": 71, "iter": 3700, "lr": 0.0542, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31031, "top5_acc": 0.56844, "loss_cls": 3.90579, "loss": 3.90579, "time": 0.81552} +{"mode": "val", "epoch": 71, "iter": 309, "lr": 0.05418, "top1_acc": 0.25543, "top5_acc": 0.49582, "mean_class_accuracy": 0.25529} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.05416, "memory": 15990, "data_time": 1.39125, "top1_acc": 0.325, "top5_acc": 0.58594, "loss_cls": 3.84539, "loss": 3.84539, "time": 2.38441} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.05413, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31984, "top5_acc": 0.58031, "loss_cls": 3.863, "loss": 3.863, "time": 0.83443} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.0541, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33125, "top5_acc": 0.58234, "loss_cls": 3.81925, "loss": 3.81925, "time": 0.837} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.05407, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32359, "top5_acc": 0.56828, "loss_cls": 3.93458, "loss": 3.93458, "time": 0.82529} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.05404, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31344, "top5_acc": 0.57281, "loss_cls": 3.93596, "loss": 3.93596, "time": 0.82259} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.05402, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31094, "top5_acc": 0.56859, "loss_cls": 3.94557, "loss": 3.94557, "time": 0.82029} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.05399, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31406, "top5_acc": 0.57266, "loss_cls": 3.91762, "loss": 3.91762, "time": 0.81832} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.05396, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31531, "top5_acc": 0.56781, "loss_cls": 3.92642, "loss": 3.92642, "time": 0.82167} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.05393, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30844, "top5_acc": 0.57406, "loss_cls": 3.92823, "loss": 3.92823, "time": 0.81285} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.05391, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31766, "top5_acc": 0.57563, "loss_cls": 3.88122, "loss": 3.88122, "time": 0.82068} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.05388, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30312, "top5_acc": 0.56375, "loss_cls": 3.98738, "loss": 3.98738, "time": 0.82161} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.05385, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32078, "top5_acc": 0.58594, "loss_cls": 3.87043, "loss": 3.87043, "time": 0.81606} +{"mode": "train", "epoch": 72, "iter": 1300, "lr": 0.05382, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31062, "top5_acc": 0.57297, "loss_cls": 3.91011, "loss": 3.91011, "time": 0.82072} +{"mode": "train", "epoch": 72, "iter": 1400, "lr": 0.05379, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.30938, "top5_acc": 0.56969, "loss_cls": 3.93095, "loss": 3.93095, "time": 0.82483} +{"mode": "train", "epoch": 72, "iter": 1500, "lr": 0.05377, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31859, "top5_acc": 0.58172, "loss_cls": 3.85854, "loss": 3.85854, "time": 0.8206} +{"mode": "train", "epoch": 72, "iter": 1600, "lr": 0.05374, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32578, "top5_acc": 0.57656, "loss_cls": 3.89003, "loss": 3.89003, "time": 0.81866} +{"mode": "train", "epoch": 72, "iter": 1700, "lr": 0.05371, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32078, "top5_acc": 0.57547, "loss_cls": 3.92366, "loss": 3.92366, "time": 0.81546} +{"mode": "train", "epoch": 72, "iter": 1800, "lr": 0.05368, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31266, "top5_acc": 0.56047, "loss_cls": 3.94678, "loss": 3.94678, "time": 0.81692} +{"mode": "train", "epoch": 72, "iter": 1900, "lr": 0.05365, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31891, "top5_acc": 0.56906, "loss_cls": 3.93803, "loss": 3.93803, "time": 0.82415} +{"mode": "train", "epoch": 72, "iter": 2000, "lr": 0.05363, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30609, "top5_acc": 0.56312, "loss_cls": 3.95764, "loss": 3.95764, "time": 0.81888} +{"mode": "train", "epoch": 72, "iter": 2100, "lr": 0.0536, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30781, "top5_acc": 0.56812, "loss_cls": 3.94158, "loss": 3.94158, "time": 0.81502} +{"mode": "train", "epoch": 72, "iter": 2200, "lr": 0.05357, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30953, "top5_acc": 0.56063, "loss_cls": 3.94937, "loss": 3.94937, "time": 0.82128} +{"mode": "train", "epoch": 72, "iter": 2300, "lr": 0.05354, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30109, "top5_acc": 0.55344, "loss_cls": 4.01156, "loss": 4.01156, "time": 0.81297} +{"mode": "train", "epoch": 72, "iter": 2400, "lr": 0.05352, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30922, "top5_acc": 0.57391, "loss_cls": 3.88861, "loss": 3.88861, "time": 0.81897} +{"mode": "train", "epoch": 72, "iter": 2500, "lr": 0.05349, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31312, "top5_acc": 0.58094, "loss_cls": 3.92031, "loss": 3.92031, "time": 0.81835} +{"mode": "train", "epoch": 72, "iter": 2600, "lr": 0.05346, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31156, "top5_acc": 0.56, "loss_cls": 3.96216, "loss": 3.96216, "time": 0.82127} +{"mode": "train", "epoch": 72, "iter": 2700, "lr": 0.05343, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32047, "top5_acc": 0.58172, "loss_cls": 3.89363, "loss": 3.89363, "time": 0.8236} +{"mode": "train", "epoch": 72, "iter": 2800, "lr": 0.0534, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30906, "top5_acc": 0.55844, "loss_cls": 3.99368, "loss": 3.99368, "time": 0.81266} +{"mode": "train", "epoch": 72, "iter": 2900, "lr": 0.05338, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31078, "top5_acc": 0.56922, "loss_cls": 3.96696, "loss": 3.96696, "time": 0.81331} +{"mode": "train", "epoch": 72, "iter": 3000, "lr": 0.05335, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31469, "top5_acc": 0.57656, "loss_cls": 3.90425, "loss": 3.90425, "time": 0.82412} +{"mode": "train", "epoch": 72, "iter": 3100, "lr": 0.05332, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30953, "top5_acc": 0.55953, "loss_cls": 3.96701, "loss": 3.96701, "time": 0.82062} +{"mode": "train", "epoch": 72, "iter": 3200, "lr": 0.05329, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30922, "top5_acc": 0.55719, "loss_cls": 3.94877, "loss": 3.94877, "time": 0.81485} +{"mode": "train", "epoch": 72, "iter": 3300, "lr": 0.05326, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32156, "top5_acc": 0.57516, "loss_cls": 3.86783, "loss": 3.86783, "time": 0.81597} +{"mode": "train", "epoch": 72, "iter": 3400, "lr": 0.05324, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30719, "top5_acc": 0.57156, "loss_cls": 3.92235, "loss": 3.92235, "time": 0.81813} +{"mode": "train", "epoch": 72, "iter": 3500, "lr": 0.05321, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3275, "top5_acc": 0.58172, "loss_cls": 3.85901, "loss": 3.85901, "time": 0.81972} +{"mode": "train", "epoch": 72, "iter": 3600, "lr": 0.05318, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31828, "top5_acc": 0.57609, "loss_cls": 3.94298, "loss": 3.94298, "time": 0.82221} +{"mode": "train", "epoch": 72, "iter": 3700, "lr": 0.05315, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29906, "top5_acc": 0.55391, "loss_cls": 4.00675, "loss": 4.00675, "time": 0.82274} +{"mode": "val", "epoch": 72, "iter": 309, "lr": 0.05314, "top1_acc": 0.24723, "top5_acc": 0.48488, "mean_class_accuracy": 0.24703} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.05311, "memory": 15990, "data_time": 1.36806, "top1_acc": 0.3275, "top5_acc": 0.59156, "loss_cls": 3.83847, "loss": 3.83847, "time": 2.3643} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.05308, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31531, "top5_acc": 0.56797, "loss_cls": 3.91112, "loss": 3.91112, "time": 0.82173} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.05306, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32875, "top5_acc": 0.58141, "loss_cls": 3.85493, "loss": 3.85493, "time": 0.82127} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.05303, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32328, "top5_acc": 0.57641, "loss_cls": 3.88061, "loss": 3.88061, "time": 0.8195} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.053, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31406, "top5_acc": 0.56812, "loss_cls": 3.93984, "loss": 3.93984, "time": 0.81929} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.05297, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31344, "top5_acc": 0.57375, "loss_cls": 3.89826, "loss": 3.89826, "time": 0.81727} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.05294, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31625, "top5_acc": 0.57297, "loss_cls": 3.91927, "loss": 3.91927, "time": 0.8238} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.05292, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31922, "top5_acc": 0.56812, "loss_cls": 3.92001, "loss": 3.92001, "time": 0.82396} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.05289, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31453, "top5_acc": 0.57781, "loss_cls": 3.90316, "loss": 3.90316, "time": 0.81809} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.05286, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30766, "top5_acc": 0.56375, "loss_cls": 3.93443, "loss": 3.93443, "time": 0.81714} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.05283, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32312, "top5_acc": 0.57063, "loss_cls": 3.91266, "loss": 3.91266, "time": 0.81216} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.0528, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31328, "top5_acc": 0.56609, "loss_cls": 3.93204, "loss": 3.93204, "time": 0.82193} +{"mode": "train", "epoch": 73, "iter": 1300, "lr": 0.05278, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31109, "top5_acc": 0.56531, "loss_cls": 3.9567, "loss": 3.9567, "time": 0.8155} +{"mode": "train", "epoch": 73, "iter": 1400, "lr": 0.05275, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32234, "top5_acc": 0.57891, "loss_cls": 3.87524, "loss": 3.87524, "time": 0.82652} +{"mode": "train", "epoch": 73, "iter": 1500, "lr": 0.05272, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31516, "top5_acc": 0.57188, "loss_cls": 3.93597, "loss": 3.93597, "time": 0.82936} +{"mode": "train", "epoch": 73, "iter": 1600, "lr": 0.05269, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30781, "top5_acc": 0.57375, "loss_cls": 3.90134, "loss": 3.90134, "time": 0.82317} +{"mode": "train", "epoch": 73, "iter": 1700, "lr": 0.05267, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30766, "top5_acc": 0.56766, "loss_cls": 3.94724, "loss": 3.94724, "time": 0.82035} +{"mode": "train", "epoch": 73, "iter": 1800, "lr": 0.05264, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32172, "top5_acc": 0.56781, "loss_cls": 3.93915, "loss": 3.93915, "time": 0.81747} +{"mode": "train", "epoch": 73, "iter": 1900, "lr": 0.05261, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32031, "top5_acc": 0.58016, "loss_cls": 3.90514, "loss": 3.90514, "time": 0.82036} +{"mode": "train", "epoch": 73, "iter": 2000, "lr": 0.05258, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30469, "top5_acc": 0.55672, "loss_cls": 4.00548, "loss": 4.00548, "time": 0.81626} +{"mode": "train", "epoch": 73, "iter": 2100, "lr": 0.05255, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31266, "top5_acc": 0.56984, "loss_cls": 3.92555, "loss": 3.92555, "time": 0.81866} +{"mode": "train", "epoch": 73, "iter": 2200, "lr": 0.05253, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32766, "top5_acc": 0.58016, "loss_cls": 3.8881, "loss": 3.8881, "time": 0.8171} +{"mode": "train", "epoch": 73, "iter": 2300, "lr": 0.0525, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31625, "top5_acc": 0.57375, "loss_cls": 3.90655, "loss": 3.90655, "time": 0.81114} +{"mode": "train", "epoch": 73, "iter": 2400, "lr": 0.05247, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3125, "top5_acc": 0.56625, "loss_cls": 3.91755, "loss": 3.91755, "time": 0.81571} +{"mode": "train", "epoch": 73, "iter": 2500, "lr": 0.05244, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31953, "top5_acc": 0.57156, "loss_cls": 3.92195, "loss": 3.92195, "time": 0.81633} +{"mode": "train", "epoch": 73, "iter": 2600, "lr": 0.05241, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31797, "top5_acc": 0.58031, "loss_cls": 3.85762, "loss": 3.85762, "time": 0.81952} +{"mode": "train", "epoch": 73, "iter": 2700, "lr": 0.05239, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30594, "top5_acc": 0.58125, "loss_cls": 3.90313, "loss": 3.90313, "time": 0.81868} +{"mode": "train", "epoch": 73, "iter": 2800, "lr": 0.05236, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31094, "top5_acc": 0.57203, "loss_cls": 3.90445, "loss": 3.90445, "time": 0.81366} +{"mode": "train", "epoch": 73, "iter": 2900, "lr": 0.05233, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31094, "top5_acc": 0.56297, "loss_cls": 3.96326, "loss": 3.96326, "time": 0.81824} +{"mode": "train", "epoch": 73, "iter": 3000, "lr": 0.0523, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31844, "top5_acc": 0.57484, "loss_cls": 3.93286, "loss": 3.93286, "time": 0.81606} +{"mode": "train", "epoch": 73, "iter": 3100, "lr": 0.05227, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31141, "top5_acc": 0.57141, "loss_cls": 3.907, "loss": 3.907, "time": 0.81923} +{"mode": "train", "epoch": 73, "iter": 3200, "lr": 0.05225, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30797, "top5_acc": 0.57406, "loss_cls": 3.93519, "loss": 3.93519, "time": 0.81442} +{"mode": "train", "epoch": 73, "iter": 3300, "lr": 0.05222, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30938, "top5_acc": 0.56563, "loss_cls": 3.92252, "loss": 3.92252, "time": 0.81667} +{"mode": "train", "epoch": 73, "iter": 3400, "lr": 0.05219, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32031, "top5_acc": 0.57844, "loss_cls": 3.90576, "loss": 3.90576, "time": 0.81453} +{"mode": "train", "epoch": 73, "iter": 3500, "lr": 0.05216, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32547, "top5_acc": 0.57469, "loss_cls": 3.88545, "loss": 3.88545, "time": 0.81727} +{"mode": "train", "epoch": 73, "iter": 3600, "lr": 0.05213, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30828, "top5_acc": 0.56563, "loss_cls": 3.9562, "loss": 3.9562, "time": 0.819} +{"mode": "train", "epoch": 73, "iter": 3700, "lr": 0.05211, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31266, "top5_acc": 0.57281, "loss_cls": 3.94189, "loss": 3.94189, "time": 0.81405} +{"mode": "val", "epoch": 73, "iter": 309, "lr": 0.05209, "top1_acc": 0.23153, "top5_acc": 0.48123, "mean_class_accuracy": 0.23121} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.05207, "memory": 15990, "data_time": 1.38251, "top1_acc": 0.32922, "top5_acc": 0.58266, "loss_cls": 3.85272, "loss": 3.85272, "time": 2.35781} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.05204, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32422, "top5_acc": 0.57641, "loss_cls": 3.89573, "loss": 3.89573, "time": 0.82368} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.05201, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31484, "top5_acc": 0.57109, "loss_cls": 3.9033, "loss": 3.9033, "time": 0.82039} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.05198, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31984, "top5_acc": 0.57891, "loss_cls": 3.88335, "loss": 3.88335, "time": 0.81568} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.05195, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30609, "top5_acc": 0.57422, "loss_cls": 3.9469, "loss": 3.9469, "time": 0.8212} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.05193, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32172, "top5_acc": 0.58375, "loss_cls": 3.86771, "loss": 3.86771, "time": 0.81997} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.0519, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31828, "top5_acc": 0.57125, "loss_cls": 3.92253, "loss": 3.92253, "time": 0.82311} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.05187, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30281, "top5_acc": 0.55688, "loss_cls": 3.95073, "loss": 3.95073, "time": 0.82101} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.05184, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33359, "top5_acc": 0.58609, "loss_cls": 3.8313, "loss": 3.8313, "time": 0.8138} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.05181, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32016, "top5_acc": 0.5775, "loss_cls": 3.89743, "loss": 3.89743, "time": 0.81661} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.05179, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32453, "top5_acc": 0.58516, "loss_cls": 3.85765, "loss": 3.85765, "time": 0.81613} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.05176, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31047, "top5_acc": 0.57063, "loss_cls": 3.92499, "loss": 3.92499, "time": 0.82324} +{"mode": "train", "epoch": 74, "iter": 1300, "lr": 0.05173, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32812, "top5_acc": 0.58125, "loss_cls": 3.84294, "loss": 3.84294, "time": 0.82042} +{"mode": "train", "epoch": 74, "iter": 1400, "lr": 0.0517, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31266, "top5_acc": 0.57141, "loss_cls": 3.91491, "loss": 3.91491, "time": 0.82114} +{"mode": "train", "epoch": 74, "iter": 1500, "lr": 0.05168, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31531, "top5_acc": 0.56172, "loss_cls": 3.94076, "loss": 3.94076, "time": 0.82345} +{"mode": "train", "epoch": 74, "iter": 1600, "lr": 0.05165, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30797, "top5_acc": 0.57563, "loss_cls": 3.8824, "loss": 3.8824, "time": 0.81968} +{"mode": "train", "epoch": 74, "iter": 1700, "lr": 0.05162, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32531, "top5_acc": 0.58516, "loss_cls": 3.85036, "loss": 3.85036, "time": 0.81967} +{"mode": "train", "epoch": 74, "iter": 1800, "lr": 0.05159, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32312, "top5_acc": 0.57688, "loss_cls": 3.88327, "loss": 3.88327, "time": 0.81847} +{"mode": "train", "epoch": 74, "iter": 1900, "lr": 0.05156, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32031, "top5_acc": 0.57781, "loss_cls": 3.90052, "loss": 3.90052, "time": 0.82212} +{"mode": "train", "epoch": 74, "iter": 2000, "lr": 0.05154, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33031, "top5_acc": 0.58484, "loss_cls": 3.83109, "loss": 3.83109, "time": 0.81509} +{"mode": "train", "epoch": 74, "iter": 2100, "lr": 0.05151, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31453, "top5_acc": 0.57312, "loss_cls": 3.92449, "loss": 3.92449, "time": 0.81701} +{"mode": "train", "epoch": 74, "iter": 2200, "lr": 0.05148, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30828, "top5_acc": 0.56375, "loss_cls": 3.95102, "loss": 3.95102, "time": 0.81834} +{"mode": "train", "epoch": 74, "iter": 2300, "lr": 0.05145, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31812, "top5_acc": 0.57281, "loss_cls": 3.9239, "loss": 3.9239, "time": 0.81409} +{"mode": "train", "epoch": 74, "iter": 2400, "lr": 0.05142, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31562, "top5_acc": 0.58062, "loss_cls": 3.87772, "loss": 3.87772, "time": 0.82026} +{"mode": "train", "epoch": 74, "iter": 2500, "lr": 0.0514, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31281, "top5_acc": 0.57156, "loss_cls": 3.91538, "loss": 3.91538, "time": 0.81464} +{"mode": "train", "epoch": 74, "iter": 2600, "lr": 0.05137, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32641, "top5_acc": 0.57609, "loss_cls": 3.86947, "loss": 3.86947, "time": 0.81533} +{"mode": "train", "epoch": 74, "iter": 2700, "lr": 0.05134, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32094, "top5_acc": 0.56375, "loss_cls": 3.89127, "loss": 3.89127, "time": 0.815} +{"mode": "train", "epoch": 74, "iter": 2800, "lr": 0.05131, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32, "top5_acc": 0.58156, "loss_cls": 3.90201, "loss": 3.90201, "time": 0.8141} +{"mode": "train", "epoch": 74, "iter": 2900, "lr": 0.05128, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31672, "top5_acc": 0.57484, "loss_cls": 3.91732, "loss": 3.91732, "time": 0.81629} +{"mode": "train", "epoch": 74, "iter": 3000, "lr": 0.05126, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31078, "top5_acc": 0.56344, "loss_cls": 3.95754, "loss": 3.95754, "time": 0.81727} +{"mode": "train", "epoch": 74, "iter": 3100, "lr": 0.05123, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3275, "top5_acc": 0.57359, "loss_cls": 3.89588, "loss": 3.89588, "time": 0.8187} +{"mode": "train", "epoch": 74, "iter": 3200, "lr": 0.0512, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30609, "top5_acc": 0.56375, "loss_cls": 3.97915, "loss": 3.97915, "time": 0.81926} +{"mode": "train", "epoch": 74, "iter": 3300, "lr": 0.05117, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30938, "top5_acc": 0.56563, "loss_cls": 3.9696, "loss": 3.9696, "time": 0.81432} +{"mode": "train", "epoch": 74, "iter": 3400, "lr": 0.05114, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31609, "top5_acc": 0.57609, "loss_cls": 3.89186, "loss": 3.89186, "time": 0.81616} +{"mode": "train", "epoch": 74, "iter": 3500, "lr": 0.05112, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32734, "top5_acc": 0.58438, "loss_cls": 3.85369, "loss": 3.85369, "time": 0.8176} +{"mode": "train", "epoch": 74, "iter": 3600, "lr": 0.05109, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31141, "top5_acc": 0.56719, "loss_cls": 3.92886, "loss": 3.92886, "time": 0.82297} +{"mode": "train", "epoch": 74, "iter": 3700, "lr": 0.05106, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31578, "top5_acc": 0.56328, "loss_cls": 3.95944, "loss": 3.95944, "time": 0.8232} +{"mode": "val", "epoch": 74, "iter": 309, "lr": 0.05105, "top1_acc": 0.26268, "top5_acc": 0.50935, "mean_class_accuracy": 0.26247} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.05102, "memory": 15990, "data_time": 1.35228, "top1_acc": 0.32453, "top5_acc": 0.58234, "loss_cls": 3.86843, "loss": 3.86843, "time": 2.33398} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.05099, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33234, "top5_acc": 0.59328, "loss_cls": 3.8231, "loss": 3.8231, "time": 0.81346} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.05096, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32469, "top5_acc": 0.58672, "loss_cls": 3.82377, "loss": 3.82377, "time": 0.81555} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.05094, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32516, "top5_acc": 0.57922, "loss_cls": 3.90082, "loss": 3.90082, "time": 0.81275} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.05091, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32453, "top5_acc": 0.58797, "loss_cls": 3.8509, "loss": 3.8509, "time": 0.81534} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.05088, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3275, "top5_acc": 0.58953, "loss_cls": 3.84197, "loss": 3.84197, "time": 0.81785} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.05085, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31859, "top5_acc": 0.58016, "loss_cls": 3.88011, "loss": 3.88011, "time": 0.82097} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.05082, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31203, "top5_acc": 0.56953, "loss_cls": 3.91026, "loss": 3.91026, "time": 0.82056} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.0508, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30984, "top5_acc": 0.57672, "loss_cls": 3.91785, "loss": 3.91785, "time": 0.81804} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.05077, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32406, "top5_acc": 0.57484, "loss_cls": 3.89667, "loss": 3.89667, "time": 0.81521} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.05074, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31594, "top5_acc": 0.58062, "loss_cls": 3.91522, "loss": 3.91522, "time": 0.81517} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.05071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31969, "top5_acc": 0.57484, "loss_cls": 3.88289, "loss": 3.88289, "time": 0.8189} +{"mode": "train", "epoch": 75, "iter": 1300, "lr": 0.05068, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31516, "top5_acc": 0.56469, "loss_cls": 3.91687, "loss": 3.91687, "time": 0.81859} +{"mode": "train", "epoch": 75, "iter": 1400, "lr": 0.05066, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31578, "top5_acc": 0.57297, "loss_cls": 3.91816, "loss": 3.91816, "time": 0.82486} +{"mode": "train", "epoch": 75, "iter": 1500, "lr": 0.05063, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32422, "top5_acc": 0.57156, "loss_cls": 3.86556, "loss": 3.86556, "time": 0.82031} +{"mode": "train", "epoch": 75, "iter": 1600, "lr": 0.0506, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32859, "top5_acc": 0.57469, "loss_cls": 3.86709, "loss": 3.86709, "time": 0.82772} +{"mode": "train", "epoch": 75, "iter": 1700, "lr": 0.05057, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31031, "top5_acc": 0.56688, "loss_cls": 3.93984, "loss": 3.93984, "time": 0.81939} +{"mode": "train", "epoch": 75, "iter": 1800, "lr": 0.05054, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32156, "top5_acc": 0.58141, "loss_cls": 3.88492, "loss": 3.88492, "time": 0.81631} +{"mode": "train", "epoch": 75, "iter": 1900, "lr": 0.05052, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32438, "top5_acc": 0.58406, "loss_cls": 3.87586, "loss": 3.87586, "time": 0.82151} +{"mode": "train", "epoch": 75, "iter": 2000, "lr": 0.05049, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31938, "top5_acc": 0.57438, "loss_cls": 3.90313, "loss": 3.90313, "time": 0.8138} +{"mode": "train", "epoch": 75, "iter": 2100, "lr": 0.05046, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32047, "top5_acc": 0.57422, "loss_cls": 3.88996, "loss": 3.88996, "time": 0.82575} +{"mode": "train", "epoch": 75, "iter": 2200, "lr": 0.05043, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31766, "top5_acc": 0.57359, "loss_cls": 3.88848, "loss": 3.88848, "time": 0.81637} +{"mode": "train", "epoch": 75, "iter": 2300, "lr": 0.0504, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32766, "top5_acc": 0.58453, "loss_cls": 3.85398, "loss": 3.85398, "time": 0.81704} +{"mode": "train", "epoch": 75, "iter": 2400, "lr": 0.05038, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32219, "top5_acc": 0.58203, "loss_cls": 3.87546, "loss": 3.87546, "time": 0.81174} +{"mode": "train", "epoch": 75, "iter": 2500, "lr": 0.05035, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31234, "top5_acc": 0.56641, "loss_cls": 3.92586, "loss": 3.92586, "time": 0.81366} +{"mode": "train", "epoch": 75, "iter": 2600, "lr": 0.05032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31703, "top5_acc": 0.58062, "loss_cls": 3.88613, "loss": 3.88613, "time": 0.81231} +{"mode": "train", "epoch": 75, "iter": 2700, "lr": 0.05029, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31016, "top5_acc": 0.56906, "loss_cls": 3.93249, "loss": 3.93249, "time": 0.82134} +{"mode": "train", "epoch": 75, "iter": 2800, "lr": 0.05026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31547, "top5_acc": 0.57422, "loss_cls": 3.93134, "loss": 3.93134, "time": 0.81314} +{"mode": "train", "epoch": 75, "iter": 2900, "lr": 0.05024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31156, "top5_acc": 0.57766, "loss_cls": 3.93041, "loss": 3.93041, "time": 0.81725} +{"mode": "train", "epoch": 75, "iter": 3000, "lr": 0.05021, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31703, "top5_acc": 0.58, "loss_cls": 3.90346, "loss": 3.90346, "time": 0.81919} +{"mode": "train", "epoch": 75, "iter": 3100, "lr": 0.05018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32234, "top5_acc": 0.58109, "loss_cls": 3.86931, "loss": 3.86931, "time": 0.81561} +{"mode": "train", "epoch": 75, "iter": 3200, "lr": 0.05015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31891, "top5_acc": 0.56312, "loss_cls": 3.93259, "loss": 3.93259, "time": 0.81429} +{"mode": "train", "epoch": 75, "iter": 3300, "lr": 0.05012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32016, "top5_acc": 0.56828, "loss_cls": 3.90367, "loss": 3.90367, "time": 0.81592} +{"mode": "train", "epoch": 75, "iter": 3400, "lr": 0.0501, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32312, "top5_acc": 0.58062, "loss_cls": 3.88739, "loss": 3.88739, "time": 0.81692} +{"mode": "train", "epoch": 75, "iter": 3500, "lr": 0.05007, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32, "top5_acc": 0.57578, "loss_cls": 3.88444, "loss": 3.88444, "time": 0.81424} +{"mode": "train", "epoch": 75, "iter": 3600, "lr": 0.05004, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31297, "top5_acc": 0.57578, "loss_cls": 3.91497, "loss": 3.91497, "time": 0.82087} +{"mode": "train", "epoch": 75, "iter": 3700, "lr": 0.05001, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30906, "top5_acc": 0.57484, "loss_cls": 3.92464, "loss": 3.92464, "time": 0.81626} +{"mode": "val", "epoch": 75, "iter": 309, "lr": 0.05, "top1_acc": 0.25645, "top5_acc": 0.49542, "mean_class_accuracy": 0.25625} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.04997, "memory": 15990, "data_time": 1.32636, "top1_acc": 0.32344, "top5_acc": 0.58062, "loss_cls": 3.83989, "loss": 3.83989, "time": 2.30713} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.04994, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32094, "top5_acc": 0.58062, "loss_cls": 3.88665, "loss": 3.88665, "time": 0.82365} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.04992, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32531, "top5_acc": 0.5725, "loss_cls": 3.8459, "loss": 3.8459, "time": 0.81668} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.04989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33047, "top5_acc": 0.58594, "loss_cls": 3.82738, "loss": 3.82738, "time": 0.81398} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.04986, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32641, "top5_acc": 0.59141, "loss_cls": 3.83542, "loss": 3.83542, "time": 0.81701} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.04983, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32312, "top5_acc": 0.58906, "loss_cls": 3.82251, "loss": 3.82251, "time": 0.81888} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.0498, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31078, "top5_acc": 0.57, "loss_cls": 3.94789, "loss": 3.94789, "time": 0.82194} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.04978, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32188, "top5_acc": 0.57141, "loss_cls": 3.90038, "loss": 3.90038, "time": 0.81444} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.04975, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32219, "top5_acc": 0.57063, "loss_cls": 3.88324, "loss": 3.88324, "time": 0.81752} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.04972, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31703, "top5_acc": 0.57328, "loss_cls": 3.86933, "loss": 3.86933, "time": 0.81373} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.04969, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32797, "top5_acc": 0.58359, "loss_cls": 3.84778, "loss": 3.84778, "time": 0.81765} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.04966, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32531, "top5_acc": 0.58672, "loss_cls": 3.84429, "loss": 3.84429, "time": 0.82311} +{"mode": "train", "epoch": 76, "iter": 1300, "lr": 0.04964, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32875, "top5_acc": 0.59188, "loss_cls": 3.81215, "loss": 3.81215, "time": 0.81331} +{"mode": "train", "epoch": 76, "iter": 1400, "lr": 0.04961, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31906, "top5_acc": 0.57953, "loss_cls": 3.85029, "loss": 3.85029, "time": 0.82153} +{"mode": "train", "epoch": 76, "iter": 1500, "lr": 0.04958, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31484, "top5_acc": 0.58266, "loss_cls": 3.87281, "loss": 3.87281, "time": 0.81865} +{"mode": "train", "epoch": 76, "iter": 1600, "lr": 0.04955, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32406, "top5_acc": 0.57891, "loss_cls": 3.88023, "loss": 3.88023, "time": 0.82219} +{"mode": "train", "epoch": 76, "iter": 1700, "lr": 0.04953, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31969, "top5_acc": 0.58219, "loss_cls": 3.89733, "loss": 3.89733, "time": 0.82083} +{"mode": "train", "epoch": 76, "iter": 1800, "lr": 0.0495, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32031, "top5_acc": 0.57734, "loss_cls": 3.879, "loss": 3.879, "time": 0.81272} +{"mode": "train", "epoch": 76, "iter": 1900, "lr": 0.04947, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33406, "top5_acc": 0.58125, "loss_cls": 3.84835, "loss": 3.84835, "time": 0.81628} +{"mode": "train", "epoch": 76, "iter": 2000, "lr": 0.04944, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31391, "top5_acc": 0.58094, "loss_cls": 3.89328, "loss": 3.89328, "time": 0.81667} +{"mode": "train", "epoch": 76, "iter": 2100, "lr": 0.04941, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31656, "top5_acc": 0.57266, "loss_cls": 3.94492, "loss": 3.94492, "time": 0.81458} +{"mode": "train", "epoch": 76, "iter": 2200, "lr": 0.04939, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32969, "top5_acc": 0.57984, "loss_cls": 3.88716, "loss": 3.88716, "time": 0.8231} +{"mode": "train", "epoch": 76, "iter": 2300, "lr": 0.04936, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32125, "top5_acc": 0.57406, "loss_cls": 3.90279, "loss": 3.90279, "time": 0.8157} +{"mode": "train", "epoch": 76, "iter": 2400, "lr": 0.04933, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31922, "top5_acc": 0.57984, "loss_cls": 3.8782, "loss": 3.8782, "time": 0.81456} +{"mode": "train", "epoch": 76, "iter": 2500, "lr": 0.0493, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31516, "top5_acc": 0.57531, "loss_cls": 3.90525, "loss": 3.90525, "time": 0.81603} +{"mode": "train", "epoch": 76, "iter": 2600, "lr": 0.04927, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30766, "top5_acc": 0.5725, "loss_cls": 3.94317, "loss": 3.94317, "time": 0.81593} +{"mode": "train", "epoch": 76, "iter": 2700, "lr": 0.04925, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32344, "top5_acc": 0.57766, "loss_cls": 3.87715, "loss": 3.87715, "time": 0.81771} +{"mode": "train", "epoch": 76, "iter": 2800, "lr": 0.04922, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32891, "top5_acc": 0.58844, "loss_cls": 3.84513, "loss": 3.84513, "time": 0.81461} +{"mode": "train", "epoch": 76, "iter": 2900, "lr": 0.04919, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32766, "top5_acc": 0.57906, "loss_cls": 3.85232, "loss": 3.85232, "time": 0.81923} +{"mode": "train", "epoch": 76, "iter": 3000, "lr": 0.04916, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31641, "top5_acc": 0.5725, "loss_cls": 3.86945, "loss": 3.86945, "time": 0.81917} +{"mode": "train", "epoch": 76, "iter": 3100, "lr": 0.04913, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32, "top5_acc": 0.57406, "loss_cls": 3.91325, "loss": 3.91325, "time": 0.81993} +{"mode": "train", "epoch": 76, "iter": 3200, "lr": 0.04911, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3175, "top5_acc": 0.575, "loss_cls": 3.90173, "loss": 3.90173, "time": 0.81742} +{"mode": "train", "epoch": 76, "iter": 3300, "lr": 0.04908, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32328, "top5_acc": 0.5775, "loss_cls": 3.90067, "loss": 3.90067, "time": 0.81381} +{"mode": "train", "epoch": 76, "iter": 3400, "lr": 0.04905, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31438, "top5_acc": 0.57531, "loss_cls": 3.91264, "loss": 3.91264, "time": 0.81523} +{"mode": "train", "epoch": 76, "iter": 3500, "lr": 0.04902, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31375, "top5_acc": 0.57828, "loss_cls": 3.90152, "loss": 3.90152, "time": 0.8117} +{"mode": "train", "epoch": 76, "iter": 3600, "lr": 0.04899, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31453, "top5_acc": 0.57453, "loss_cls": 3.91182, "loss": 3.91182, "time": 0.82258} +{"mode": "train", "epoch": 76, "iter": 3700, "lr": 0.04897, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30531, "top5_acc": 0.56797, "loss_cls": 3.95216, "loss": 3.95216, "time": 0.81431} +{"mode": "val", "epoch": 76, "iter": 309, "lr": 0.04895, "top1_acc": 0.25452, "top5_acc": 0.49263, "mean_class_accuracy": 0.2543} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.04893, "memory": 15990, "data_time": 1.36361, "top1_acc": 0.33891, "top5_acc": 0.60062, "loss_cls": 3.75999, "loss": 3.75999, "time": 2.34756} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0489, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33266, "top5_acc": 0.59719, "loss_cls": 3.81178, "loss": 3.81178, "time": 0.82152} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.04887, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32922, "top5_acc": 0.58062, "loss_cls": 3.83002, "loss": 3.83002, "time": 0.81743} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.04884, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33047, "top5_acc": 0.58234, "loss_cls": 3.84054, "loss": 3.84054, "time": 0.81381} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.04881, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32, "top5_acc": 0.57234, "loss_cls": 3.89096, "loss": 3.89096, "time": 0.81802} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.04879, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34172, "top5_acc": 0.58906, "loss_cls": 3.80539, "loss": 3.80539, "time": 0.81518} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.04876, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32281, "top5_acc": 0.59375, "loss_cls": 3.82126, "loss": 3.82126, "time": 0.8238} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.04873, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32188, "top5_acc": 0.58297, "loss_cls": 3.87174, "loss": 3.87174, "time": 0.8182} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.0487, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31734, "top5_acc": 0.58141, "loss_cls": 3.88484, "loss": 3.88484, "time": 0.81001} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.04867, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32016, "top5_acc": 0.58016, "loss_cls": 3.84939, "loss": 3.84939, "time": 0.81292} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.04865, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31812, "top5_acc": 0.57141, "loss_cls": 3.9157, "loss": 3.9157, "time": 0.81273} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.04862, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32078, "top5_acc": 0.58234, "loss_cls": 3.84993, "loss": 3.84993, "time": 0.82632} +{"mode": "train", "epoch": 77, "iter": 1300, "lr": 0.04859, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31766, "top5_acc": 0.56906, "loss_cls": 3.91478, "loss": 3.91478, "time": 0.82112} +{"mode": "train", "epoch": 77, "iter": 1400, "lr": 0.04856, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33359, "top5_acc": 0.58141, "loss_cls": 3.82497, "loss": 3.82497, "time": 0.82495} +{"mode": "train", "epoch": 77, "iter": 1500, "lr": 0.04853, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32641, "top5_acc": 0.58156, "loss_cls": 3.84661, "loss": 3.84661, "time": 0.82107} +{"mode": "train", "epoch": 77, "iter": 1600, "lr": 0.04851, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31531, "top5_acc": 0.57047, "loss_cls": 3.87798, "loss": 3.87798, "time": 0.81988} +{"mode": "train", "epoch": 77, "iter": 1700, "lr": 0.04848, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32781, "top5_acc": 0.58328, "loss_cls": 3.82273, "loss": 3.82273, "time": 0.81608} +{"mode": "train", "epoch": 77, "iter": 1800, "lr": 0.04845, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31906, "top5_acc": 0.57844, "loss_cls": 3.8819, "loss": 3.8819, "time": 0.82061} +{"mode": "train", "epoch": 77, "iter": 1900, "lr": 0.04842, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31266, "top5_acc": 0.57047, "loss_cls": 3.93784, "loss": 3.93784, "time": 0.81752} +{"mode": "train", "epoch": 77, "iter": 2000, "lr": 0.04839, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31656, "top5_acc": 0.57484, "loss_cls": 3.89685, "loss": 3.89685, "time": 0.8195} +{"mode": "train", "epoch": 77, "iter": 2100, "lr": 0.04837, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31703, "top5_acc": 0.57797, "loss_cls": 3.88321, "loss": 3.88321, "time": 0.81547} +{"mode": "train", "epoch": 77, "iter": 2200, "lr": 0.04834, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33984, "top5_acc": 0.59281, "loss_cls": 3.79937, "loss": 3.79937, "time": 0.82133} +{"mode": "train", "epoch": 77, "iter": 2300, "lr": 0.04831, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33172, "top5_acc": 0.58312, "loss_cls": 3.89047, "loss": 3.89047, "time": 0.81766} +{"mode": "train", "epoch": 77, "iter": 2400, "lr": 0.04828, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32016, "top5_acc": 0.57609, "loss_cls": 3.89603, "loss": 3.89603, "time": 0.82326} +{"mode": "train", "epoch": 77, "iter": 2500, "lr": 0.04825, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31922, "top5_acc": 0.58297, "loss_cls": 3.83544, "loss": 3.83544, "time": 0.8152} +{"mode": "train", "epoch": 77, "iter": 2600, "lr": 0.04823, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31688, "top5_acc": 0.57375, "loss_cls": 3.91087, "loss": 3.91087, "time": 0.8152} +{"mode": "train", "epoch": 77, "iter": 2700, "lr": 0.0482, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3275, "top5_acc": 0.57828, "loss_cls": 3.86142, "loss": 3.86142, "time": 0.81566} +{"mode": "train", "epoch": 77, "iter": 2800, "lr": 0.04817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32156, "top5_acc": 0.57672, "loss_cls": 3.90628, "loss": 3.90628, "time": 0.81249} +{"mode": "train", "epoch": 77, "iter": 2900, "lr": 0.04814, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32297, "top5_acc": 0.57141, "loss_cls": 3.92578, "loss": 3.92578, "time": 0.814} +{"mode": "train", "epoch": 77, "iter": 3000, "lr": 0.04811, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32578, "top5_acc": 0.57672, "loss_cls": 3.87363, "loss": 3.87363, "time": 0.82257} +{"mode": "train", "epoch": 77, "iter": 3100, "lr": 0.04809, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31938, "top5_acc": 0.5775, "loss_cls": 3.88686, "loss": 3.88686, "time": 0.81941} +{"mode": "train", "epoch": 77, "iter": 3200, "lr": 0.04806, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31266, "top5_acc": 0.56984, "loss_cls": 3.93067, "loss": 3.93067, "time": 0.81517} +{"mode": "train", "epoch": 77, "iter": 3300, "lr": 0.04803, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32125, "top5_acc": 0.57281, "loss_cls": 3.91149, "loss": 3.91149, "time": 0.81675} +{"mode": "train", "epoch": 77, "iter": 3400, "lr": 0.048, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31984, "top5_acc": 0.58531, "loss_cls": 3.86311, "loss": 3.86311, "time": 0.81595} +{"mode": "train", "epoch": 77, "iter": 3500, "lr": 0.04798, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32391, "top5_acc": 0.5825, "loss_cls": 3.84849, "loss": 3.84849, "time": 0.81553} +{"mode": "train", "epoch": 77, "iter": 3600, "lr": 0.04795, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32688, "top5_acc": 0.58734, "loss_cls": 3.85799, "loss": 3.85799, "time": 0.82053} +{"mode": "train", "epoch": 77, "iter": 3700, "lr": 0.04792, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32781, "top5_acc": 0.58734, "loss_cls": 3.8308, "loss": 3.8308, "time": 0.81713} +{"mode": "val", "epoch": 77, "iter": 309, "lr": 0.04791, "top1_acc": 0.2721, "top5_acc": 0.51623, "mean_class_accuracy": 0.27179} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.04788, "memory": 15990, "data_time": 1.2872, "top1_acc": 0.3375, "top5_acc": 0.59344, "loss_cls": 3.78845, "loss": 3.78845, "time": 2.26776} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.04785, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32328, "top5_acc": 0.57891, "loss_cls": 3.84692, "loss": 3.84692, "time": 0.81458} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.04782, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3375, "top5_acc": 0.58953, "loss_cls": 3.79, "loss": 3.79, "time": 0.81694} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.04779, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32906, "top5_acc": 0.58516, "loss_cls": 3.82881, "loss": 3.82881, "time": 0.81721} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.04777, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33094, "top5_acc": 0.59297, "loss_cls": 3.80666, "loss": 3.80666, "time": 0.81478} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.04774, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32156, "top5_acc": 0.58125, "loss_cls": 3.8671, "loss": 3.8671, "time": 0.81876} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.04771, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32625, "top5_acc": 0.58719, "loss_cls": 3.83291, "loss": 3.83291, "time": 0.8171} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.04768, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31766, "top5_acc": 0.57906, "loss_cls": 3.86338, "loss": 3.86338, "time": 0.81732} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.04766, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31516, "top5_acc": 0.57234, "loss_cls": 3.90834, "loss": 3.90834, "time": 0.81539} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.04763, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32734, "top5_acc": 0.58562, "loss_cls": 3.84433, "loss": 3.84433, "time": 0.81835} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.0476, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31844, "top5_acc": 0.57484, "loss_cls": 3.88844, "loss": 3.88844, "time": 0.8161} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.04757, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32734, "top5_acc": 0.58234, "loss_cls": 3.84175, "loss": 3.84175, "time": 0.81745} +{"mode": "train", "epoch": 78, "iter": 1300, "lr": 0.04754, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32062, "top5_acc": 0.58375, "loss_cls": 3.86343, "loss": 3.86343, "time": 0.81447} +{"mode": "train", "epoch": 78, "iter": 1400, "lr": 0.04752, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32297, "top5_acc": 0.58109, "loss_cls": 3.84687, "loss": 3.84687, "time": 0.82174} +{"mode": "train", "epoch": 78, "iter": 1500, "lr": 0.04749, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34141, "top5_acc": 0.58859, "loss_cls": 3.8034, "loss": 3.8034, "time": 0.82668} +{"mode": "train", "epoch": 78, "iter": 1600, "lr": 0.04746, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32125, "top5_acc": 0.58141, "loss_cls": 3.87241, "loss": 3.87241, "time": 0.82071} +{"mode": "train", "epoch": 78, "iter": 1700, "lr": 0.04743, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31547, "top5_acc": 0.57703, "loss_cls": 3.92376, "loss": 3.92376, "time": 0.82039} +{"mode": "train", "epoch": 78, "iter": 1800, "lr": 0.0474, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31766, "top5_acc": 0.57297, "loss_cls": 3.90175, "loss": 3.90175, "time": 0.81487} +{"mode": "train", "epoch": 78, "iter": 1900, "lr": 0.04738, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32422, "top5_acc": 0.58375, "loss_cls": 3.86138, "loss": 3.86138, "time": 0.82187} +{"mode": "train", "epoch": 78, "iter": 2000, "lr": 0.04735, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31344, "top5_acc": 0.57328, "loss_cls": 3.91536, "loss": 3.91536, "time": 0.8179} +{"mode": "train", "epoch": 78, "iter": 2100, "lr": 0.04732, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32281, "top5_acc": 0.58688, "loss_cls": 3.84127, "loss": 3.84127, "time": 0.81297} +{"mode": "train", "epoch": 78, "iter": 2200, "lr": 0.04729, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.325, "top5_acc": 0.58625, "loss_cls": 3.8455, "loss": 3.8455, "time": 0.8166} +{"mode": "train", "epoch": 78, "iter": 2300, "lr": 0.04726, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32672, "top5_acc": 0.57844, "loss_cls": 3.83961, "loss": 3.83961, "time": 0.81848} +{"mode": "train", "epoch": 78, "iter": 2400, "lr": 0.04724, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31859, "top5_acc": 0.57438, "loss_cls": 3.89936, "loss": 3.89936, "time": 0.81241} +{"mode": "train", "epoch": 78, "iter": 2500, "lr": 0.04721, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32469, "top5_acc": 0.58188, "loss_cls": 3.89023, "loss": 3.89023, "time": 0.81337} +{"mode": "train", "epoch": 78, "iter": 2600, "lr": 0.04718, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32797, "top5_acc": 0.5875, "loss_cls": 3.85092, "loss": 3.85092, "time": 0.81958} +{"mode": "train", "epoch": 78, "iter": 2700, "lr": 0.04715, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.315, "top5_acc": 0.58156, "loss_cls": 3.85501, "loss": 3.85501, "time": 0.81671} +{"mode": "train", "epoch": 78, "iter": 2800, "lr": 0.04712, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33531, "top5_acc": 0.595, "loss_cls": 3.7984, "loss": 3.7984, "time": 0.81572} +{"mode": "train", "epoch": 78, "iter": 2900, "lr": 0.0471, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32078, "top5_acc": 0.5825, "loss_cls": 3.84226, "loss": 3.84226, "time": 0.81191} +{"mode": "train", "epoch": 78, "iter": 3000, "lr": 0.04707, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31547, "top5_acc": 0.5775, "loss_cls": 3.87684, "loss": 3.87684, "time": 0.8164} +{"mode": "train", "epoch": 78, "iter": 3100, "lr": 0.04704, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32781, "top5_acc": 0.58609, "loss_cls": 3.82311, "loss": 3.82311, "time": 0.81367} +{"mode": "train", "epoch": 78, "iter": 3200, "lr": 0.04701, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32656, "top5_acc": 0.57688, "loss_cls": 3.87851, "loss": 3.87851, "time": 0.81764} +{"mode": "train", "epoch": 78, "iter": 3300, "lr": 0.04699, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32688, "top5_acc": 0.58766, "loss_cls": 3.85748, "loss": 3.85748, "time": 0.81351} +{"mode": "train", "epoch": 78, "iter": 3400, "lr": 0.04696, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33484, "top5_acc": 0.58703, "loss_cls": 3.82585, "loss": 3.82585, "time": 0.81502} +{"mode": "train", "epoch": 78, "iter": 3500, "lr": 0.04693, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32688, "top5_acc": 0.57984, "loss_cls": 3.86486, "loss": 3.86486, "time": 0.81768} +{"mode": "train", "epoch": 78, "iter": 3600, "lr": 0.0469, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32703, "top5_acc": 0.58359, "loss_cls": 3.84427, "loss": 3.84427, "time": 0.81885} +{"mode": "train", "epoch": 78, "iter": 3700, "lr": 0.04687, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30969, "top5_acc": 0.5775, "loss_cls": 3.89361, "loss": 3.89361, "time": 0.81233} +{"mode": "val", "epoch": 78, "iter": 309, "lr": 0.04686, "top1_acc": 0.26511, "top5_acc": 0.5097, "mean_class_accuracy": 0.26481} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.04683, "memory": 15990, "data_time": 1.29867, "top1_acc": 0.33094, "top5_acc": 0.58984, "loss_cls": 3.78562, "loss": 3.78562, "time": 2.27614} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.0468, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33031, "top5_acc": 0.58188, "loss_cls": 3.8314, "loss": 3.8314, "time": 0.8189} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.04678, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33297, "top5_acc": 0.58391, "loss_cls": 3.81593, "loss": 3.81593, "time": 0.81151} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.04675, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32844, "top5_acc": 0.59125, "loss_cls": 3.8224, "loss": 3.8224, "time": 0.82102} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.04672, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32438, "top5_acc": 0.58391, "loss_cls": 3.8356, "loss": 3.8356, "time": 0.82275} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.04669, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32422, "top5_acc": 0.59188, "loss_cls": 3.81225, "loss": 3.81225, "time": 0.81696} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.04667, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3175, "top5_acc": 0.58281, "loss_cls": 3.87416, "loss": 3.87416, "time": 0.81481} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.04664, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33031, "top5_acc": 0.59156, "loss_cls": 3.79339, "loss": 3.79339, "time": 0.82131} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.04661, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31625, "top5_acc": 0.57359, "loss_cls": 3.87865, "loss": 3.87865, "time": 0.82146} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.04658, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33484, "top5_acc": 0.5875, "loss_cls": 3.82208, "loss": 3.82208, "time": 0.81644} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.04655, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32172, "top5_acc": 0.58203, "loss_cls": 3.85148, "loss": 3.85148, "time": 0.81612} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.04653, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32391, "top5_acc": 0.58219, "loss_cls": 3.84671, "loss": 3.84671, "time": 0.82067} +{"mode": "train", "epoch": 79, "iter": 1300, "lr": 0.0465, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32375, "top5_acc": 0.58547, "loss_cls": 3.8637, "loss": 3.8637, "time": 0.82014} +{"mode": "train", "epoch": 79, "iter": 1400, "lr": 0.04647, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32953, "top5_acc": 0.58406, "loss_cls": 3.83307, "loss": 3.83307, "time": 0.81511} +{"mode": "train", "epoch": 79, "iter": 1500, "lr": 0.04644, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33, "top5_acc": 0.58031, "loss_cls": 3.85832, "loss": 3.85832, "time": 0.8213} +{"mode": "train", "epoch": 79, "iter": 1600, "lr": 0.04641, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32625, "top5_acc": 0.58781, "loss_cls": 3.83172, "loss": 3.83172, "time": 0.82729} +{"mode": "train", "epoch": 79, "iter": 1700, "lr": 0.04639, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31984, "top5_acc": 0.57625, "loss_cls": 3.85692, "loss": 3.85692, "time": 0.82111} +{"mode": "train", "epoch": 79, "iter": 1800, "lr": 0.04636, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33031, "top5_acc": 0.59047, "loss_cls": 3.81044, "loss": 3.81044, "time": 0.81482} +{"mode": "train", "epoch": 79, "iter": 1900, "lr": 0.04633, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33234, "top5_acc": 0.58609, "loss_cls": 3.82024, "loss": 3.82024, "time": 0.81356} +{"mode": "train", "epoch": 79, "iter": 2000, "lr": 0.0463, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31578, "top5_acc": 0.57219, "loss_cls": 3.90858, "loss": 3.90858, "time": 0.82723} +{"mode": "train", "epoch": 79, "iter": 2100, "lr": 0.04628, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32906, "top5_acc": 0.58453, "loss_cls": 3.87596, "loss": 3.87596, "time": 0.81229} +{"mode": "train", "epoch": 79, "iter": 2200, "lr": 0.04625, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31109, "top5_acc": 0.57516, "loss_cls": 3.91479, "loss": 3.91479, "time": 0.82052} +{"mode": "train", "epoch": 79, "iter": 2300, "lr": 0.04622, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33531, "top5_acc": 0.6, "loss_cls": 3.79132, "loss": 3.79132, "time": 0.81638} +{"mode": "train", "epoch": 79, "iter": 2400, "lr": 0.04619, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32234, "top5_acc": 0.58406, "loss_cls": 3.86339, "loss": 3.86339, "time": 0.81486} +{"mode": "train", "epoch": 79, "iter": 2500, "lr": 0.04616, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32734, "top5_acc": 0.58812, "loss_cls": 3.79351, "loss": 3.79351, "time": 0.81514} +{"mode": "train", "epoch": 79, "iter": 2600, "lr": 0.04614, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32812, "top5_acc": 0.58656, "loss_cls": 3.8391, "loss": 3.8391, "time": 0.81606} +{"mode": "train", "epoch": 79, "iter": 2700, "lr": 0.04611, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32078, "top5_acc": 0.57594, "loss_cls": 3.89375, "loss": 3.89375, "time": 0.81294} +{"mode": "train", "epoch": 79, "iter": 2800, "lr": 0.04608, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32812, "top5_acc": 0.58219, "loss_cls": 3.82449, "loss": 3.82449, "time": 0.81591} +{"mode": "train", "epoch": 79, "iter": 2900, "lr": 0.04605, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32031, "top5_acc": 0.57672, "loss_cls": 3.90402, "loss": 3.90402, "time": 0.81979} +{"mode": "train", "epoch": 79, "iter": 3000, "lr": 0.04602, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32859, "top5_acc": 0.58906, "loss_cls": 3.85273, "loss": 3.85273, "time": 0.81702} +{"mode": "train", "epoch": 79, "iter": 3100, "lr": 0.046, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32125, "top5_acc": 0.58188, "loss_cls": 3.88663, "loss": 3.88663, "time": 0.81583} +{"mode": "train", "epoch": 79, "iter": 3200, "lr": 0.04597, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32078, "top5_acc": 0.5825, "loss_cls": 3.85345, "loss": 3.85345, "time": 0.8121} +{"mode": "train", "epoch": 79, "iter": 3300, "lr": 0.04594, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32188, "top5_acc": 0.57875, "loss_cls": 3.8668, "loss": 3.8668, "time": 0.81589} +{"mode": "train", "epoch": 79, "iter": 3400, "lr": 0.04591, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32219, "top5_acc": 0.57656, "loss_cls": 3.88473, "loss": 3.88473, "time": 0.81642} +{"mode": "train", "epoch": 79, "iter": 3500, "lr": 0.04588, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32094, "top5_acc": 0.5875, "loss_cls": 3.84846, "loss": 3.84846, "time": 0.81537} +{"mode": "train", "epoch": 79, "iter": 3600, "lr": 0.04586, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33703, "top5_acc": 0.59109, "loss_cls": 3.82703, "loss": 3.82703, "time": 0.82376} +{"mode": "train", "epoch": 79, "iter": 3700, "lr": 0.04583, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32734, "top5_acc": 0.59219, "loss_cls": 3.8307, "loss": 3.8307, "time": 0.81859} +{"mode": "val", "epoch": 79, "iter": 309, "lr": 0.04582, "top1_acc": 0.27549, "top5_acc": 0.52748, "mean_class_accuracy": 0.27524} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.04579, "memory": 15990, "data_time": 1.30389, "top1_acc": 0.32781, "top5_acc": 0.59109, "loss_cls": 3.8243, "loss": 3.8243, "time": 2.28376} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.04576, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32469, "top5_acc": 0.59, "loss_cls": 3.80226, "loss": 3.80226, "time": 0.81871} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.04573, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32719, "top5_acc": 0.58906, "loss_cls": 3.83529, "loss": 3.83529, "time": 0.81702} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.0457, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33172, "top5_acc": 0.58406, "loss_cls": 3.84002, "loss": 3.84002, "time": 0.81369} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.04568, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33078, "top5_acc": 0.59172, "loss_cls": 3.80868, "loss": 3.80868, "time": 0.81918} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.04565, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3325, "top5_acc": 0.59828, "loss_cls": 3.79011, "loss": 3.79011, "time": 0.82295} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.04562, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33531, "top5_acc": 0.58844, "loss_cls": 3.79867, "loss": 3.79867, "time": 0.81816} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.04559, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33062, "top5_acc": 0.59578, "loss_cls": 3.80114, "loss": 3.80114, "time": 0.81486} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.04557, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32797, "top5_acc": 0.58, "loss_cls": 3.83205, "loss": 3.83205, "time": 0.81806} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.04554, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33016, "top5_acc": 0.58734, "loss_cls": 3.83232, "loss": 3.83232, "time": 0.81706} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.04551, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32875, "top5_acc": 0.58453, "loss_cls": 3.82089, "loss": 3.82089, "time": 0.81567} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.04548, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33062, "top5_acc": 0.58828, "loss_cls": 3.83705, "loss": 3.83705, "time": 0.82165} +{"mode": "train", "epoch": 80, "iter": 1300, "lr": 0.04545, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33359, "top5_acc": 0.58812, "loss_cls": 3.81308, "loss": 3.81308, "time": 0.82465} +{"mode": "train", "epoch": 80, "iter": 1400, "lr": 0.04543, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31984, "top5_acc": 0.58484, "loss_cls": 3.84344, "loss": 3.84344, "time": 0.8188} +{"mode": "train", "epoch": 80, "iter": 1500, "lr": 0.0454, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33094, "top5_acc": 0.58625, "loss_cls": 3.78626, "loss": 3.78626, "time": 0.82116} +{"mode": "train", "epoch": 80, "iter": 1600, "lr": 0.04537, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32203, "top5_acc": 0.58047, "loss_cls": 3.86819, "loss": 3.86819, "time": 0.8246} +{"mode": "train", "epoch": 80, "iter": 1700, "lr": 0.04534, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32906, "top5_acc": 0.59, "loss_cls": 3.84941, "loss": 3.84941, "time": 0.81806} +{"mode": "train", "epoch": 80, "iter": 1800, "lr": 0.04532, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33516, "top5_acc": 0.59859, "loss_cls": 3.80523, "loss": 3.80523, "time": 0.81847} +{"mode": "train", "epoch": 80, "iter": 1900, "lr": 0.04529, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32672, "top5_acc": 0.58953, "loss_cls": 3.81775, "loss": 3.81775, "time": 0.81871} +{"mode": "train", "epoch": 80, "iter": 2000, "lr": 0.04526, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32453, "top5_acc": 0.58781, "loss_cls": 3.85065, "loss": 3.85065, "time": 0.81645} +{"mode": "train", "epoch": 80, "iter": 2100, "lr": 0.04523, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31375, "top5_acc": 0.57578, "loss_cls": 3.89707, "loss": 3.89707, "time": 0.8161} +{"mode": "train", "epoch": 80, "iter": 2200, "lr": 0.0452, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34031, "top5_acc": 0.59703, "loss_cls": 3.78067, "loss": 3.78067, "time": 0.81862} +{"mode": "train", "epoch": 80, "iter": 2300, "lr": 0.04518, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32203, "top5_acc": 0.57422, "loss_cls": 3.87476, "loss": 3.87476, "time": 0.81818} +{"mode": "train", "epoch": 80, "iter": 2400, "lr": 0.04515, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33359, "top5_acc": 0.58328, "loss_cls": 3.82628, "loss": 3.82628, "time": 0.81313} +{"mode": "train", "epoch": 80, "iter": 2500, "lr": 0.04512, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31969, "top5_acc": 0.57953, "loss_cls": 3.88494, "loss": 3.88494, "time": 0.81534} +{"mode": "train", "epoch": 80, "iter": 2600, "lr": 0.04509, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32641, "top5_acc": 0.58, "loss_cls": 3.83691, "loss": 3.83691, "time": 0.81558} +{"mode": "train", "epoch": 80, "iter": 2700, "lr": 0.04506, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32719, "top5_acc": 0.58297, "loss_cls": 3.86288, "loss": 3.86288, "time": 0.81912} +{"mode": "train", "epoch": 80, "iter": 2800, "lr": 0.04504, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3275, "top5_acc": 0.58188, "loss_cls": 3.8662, "loss": 3.8662, "time": 0.81679} +{"mode": "train", "epoch": 80, "iter": 2900, "lr": 0.04501, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33484, "top5_acc": 0.58484, "loss_cls": 3.84473, "loss": 3.84473, "time": 0.81938} +{"mode": "train", "epoch": 80, "iter": 3000, "lr": 0.04498, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32859, "top5_acc": 0.58094, "loss_cls": 3.87202, "loss": 3.87202, "time": 0.81785} +{"mode": "train", "epoch": 80, "iter": 3100, "lr": 0.04495, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32062, "top5_acc": 0.58062, "loss_cls": 3.858, "loss": 3.858, "time": 0.81172} +{"mode": "train", "epoch": 80, "iter": 3200, "lr": 0.04493, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33672, "top5_acc": 0.59344, "loss_cls": 3.79603, "loss": 3.79603, "time": 0.81437} +{"mode": "train", "epoch": 80, "iter": 3300, "lr": 0.0449, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33609, "top5_acc": 0.58828, "loss_cls": 3.78094, "loss": 3.78094, "time": 0.81789} +{"mode": "train", "epoch": 80, "iter": 3400, "lr": 0.04487, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31844, "top5_acc": 0.57312, "loss_cls": 3.91859, "loss": 3.91859, "time": 0.81498} +{"mode": "train", "epoch": 80, "iter": 3500, "lr": 0.04484, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33297, "top5_acc": 0.59031, "loss_cls": 3.79493, "loss": 3.79493, "time": 0.81985} +{"mode": "train", "epoch": 80, "iter": 3600, "lr": 0.04481, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3275, "top5_acc": 0.58875, "loss_cls": 3.82954, "loss": 3.82954, "time": 0.81677} +{"mode": "train", "epoch": 80, "iter": 3700, "lr": 0.04479, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32188, "top5_acc": 0.58266, "loss_cls": 3.86962, "loss": 3.86962, "time": 0.81288} +{"mode": "val", "epoch": 80, "iter": 309, "lr": 0.04477, "top1_acc": 0.25715, "top5_acc": 0.50068, "mean_class_accuracy": 0.25696} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.04475, "memory": 15990, "data_time": 1.29831, "top1_acc": 0.34562, "top5_acc": 0.59516, "loss_cls": 3.74806, "loss": 3.74806, "time": 2.27468} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.04472, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33438, "top5_acc": 0.60266, "loss_cls": 3.76475, "loss": 3.76475, "time": 0.81563} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.04469, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32281, "top5_acc": 0.59625, "loss_cls": 3.7781, "loss": 3.7781, "time": 0.81648} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.04466, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32625, "top5_acc": 0.59234, "loss_cls": 3.79716, "loss": 3.79716, "time": 0.81686} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.04463, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34359, "top5_acc": 0.60078, "loss_cls": 3.7639, "loss": 3.7639, "time": 0.81569} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.04461, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33312, "top5_acc": 0.58469, "loss_cls": 3.82799, "loss": 3.82799, "time": 0.82443} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.04458, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33359, "top5_acc": 0.59781, "loss_cls": 3.75417, "loss": 3.75417, "time": 0.81973} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.04455, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32359, "top5_acc": 0.58359, "loss_cls": 3.84142, "loss": 3.84142, "time": 0.81637} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.04452, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33672, "top5_acc": 0.59438, "loss_cls": 3.81616, "loss": 3.81616, "time": 0.81829} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.0445, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33156, "top5_acc": 0.58375, "loss_cls": 3.81386, "loss": 3.81386, "time": 0.81778} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.04447, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32344, "top5_acc": 0.58531, "loss_cls": 3.8552, "loss": 3.8552, "time": 0.81543} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.04444, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33625, "top5_acc": 0.59125, "loss_cls": 3.79022, "loss": 3.79022, "time": 0.81865} +{"mode": "train", "epoch": 81, "iter": 1300, "lr": 0.04441, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33812, "top5_acc": 0.59062, "loss_cls": 3.79555, "loss": 3.79555, "time": 0.82498} +{"mode": "train", "epoch": 81, "iter": 1400, "lr": 0.04438, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33953, "top5_acc": 0.59781, "loss_cls": 3.77404, "loss": 3.77404, "time": 0.81917} +{"mode": "train", "epoch": 81, "iter": 1500, "lr": 0.04436, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32641, "top5_acc": 0.58266, "loss_cls": 3.8553, "loss": 3.8553, "time": 0.81868} +{"mode": "train", "epoch": 81, "iter": 1600, "lr": 0.04433, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33062, "top5_acc": 0.58828, "loss_cls": 3.82135, "loss": 3.82135, "time": 0.82644} +{"mode": "train", "epoch": 81, "iter": 1700, "lr": 0.0443, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33062, "top5_acc": 0.59188, "loss_cls": 3.78853, "loss": 3.78853, "time": 0.81871} +{"mode": "train", "epoch": 81, "iter": 1800, "lr": 0.04427, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3325, "top5_acc": 0.58656, "loss_cls": 3.84065, "loss": 3.84065, "time": 0.81861} +{"mode": "train", "epoch": 81, "iter": 1900, "lr": 0.04425, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32312, "top5_acc": 0.57938, "loss_cls": 3.86285, "loss": 3.86285, "time": 0.81403} +{"mode": "train", "epoch": 81, "iter": 2000, "lr": 0.04422, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32719, "top5_acc": 0.58656, "loss_cls": 3.81782, "loss": 3.81782, "time": 0.8141} +{"mode": "train", "epoch": 81, "iter": 2100, "lr": 0.04419, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31844, "top5_acc": 0.5825, "loss_cls": 3.90156, "loss": 3.90156, "time": 0.8115} +{"mode": "train", "epoch": 81, "iter": 2200, "lr": 0.04416, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33391, "top5_acc": 0.59531, "loss_cls": 3.8118, "loss": 3.8118, "time": 0.8191} +{"mode": "train", "epoch": 81, "iter": 2300, "lr": 0.04413, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33266, "top5_acc": 0.58812, "loss_cls": 3.84081, "loss": 3.84081, "time": 0.81263} +{"mode": "train", "epoch": 81, "iter": 2400, "lr": 0.04411, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32703, "top5_acc": 0.58281, "loss_cls": 3.84712, "loss": 3.84712, "time": 0.8191} +{"mode": "train", "epoch": 81, "iter": 2500, "lr": 0.04408, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31922, "top5_acc": 0.58703, "loss_cls": 3.83736, "loss": 3.83736, "time": 0.81951} +{"mode": "train", "epoch": 81, "iter": 2600, "lr": 0.04405, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32109, "top5_acc": 0.57438, "loss_cls": 3.88302, "loss": 3.88302, "time": 0.81423} +{"mode": "train", "epoch": 81, "iter": 2700, "lr": 0.04402, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33766, "top5_acc": 0.58672, "loss_cls": 3.81396, "loss": 3.81396, "time": 0.81475} +{"mode": "train", "epoch": 81, "iter": 2800, "lr": 0.044, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33469, "top5_acc": 0.59344, "loss_cls": 3.81895, "loss": 3.81895, "time": 0.81353} +{"mode": "train", "epoch": 81, "iter": 2900, "lr": 0.04397, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32328, "top5_acc": 0.58797, "loss_cls": 3.8387, "loss": 3.8387, "time": 0.81591} +{"mode": "train", "epoch": 81, "iter": 3000, "lr": 0.04394, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33453, "top5_acc": 0.5975, "loss_cls": 3.77128, "loss": 3.77128, "time": 0.81229} +{"mode": "train", "epoch": 81, "iter": 3100, "lr": 0.04391, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33125, "top5_acc": 0.59062, "loss_cls": 3.80776, "loss": 3.80776, "time": 0.81783} +{"mode": "train", "epoch": 81, "iter": 3200, "lr": 0.04389, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33281, "top5_acc": 0.58281, "loss_cls": 3.81421, "loss": 3.81421, "time": 0.819} +{"mode": "train", "epoch": 81, "iter": 3300, "lr": 0.04386, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33484, "top5_acc": 0.585, "loss_cls": 3.83021, "loss": 3.83021, "time": 0.8162} +{"mode": "train", "epoch": 81, "iter": 3400, "lr": 0.04383, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32312, "top5_acc": 0.57922, "loss_cls": 3.87408, "loss": 3.87408, "time": 0.81274} +{"mode": "train", "epoch": 81, "iter": 3500, "lr": 0.0438, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31703, "top5_acc": 0.57766, "loss_cls": 3.86784, "loss": 3.86784, "time": 0.81556} +{"mode": "train", "epoch": 81, "iter": 3600, "lr": 0.04377, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32781, "top5_acc": 0.58031, "loss_cls": 3.82439, "loss": 3.82439, "time": 0.82058} +{"mode": "train", "epoch": 81, "iter": 3700, "lr": 0.04375, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33203, "top5_acc": 0.58922, "loss_cls": 3.79736, "loss": 3.79736, "time": 0.81863} +{"mode": "val", "epoch": 81, "iter": 309, "lr": 0.04373, "top1_acc": 0.25964, "top5_acc": 0.50205, "mean_class_accuracy": 0.25932} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.04371, "memory": 15990, "data_time": 1.30888, "top1_acc": 0.34156, "top5_acc": 0.60641, "loss_cls": 3.72708, "loss": 3.72708, "time": 2.28779} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.04368, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33828, "top5_acc": 0.59469, "loss_cls": 3.77629, "loss": 3.77629, "time": 0.82545} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.04365, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34438, "top5_acc": 0.60266, "loss_cls": 3.74889, "loss": 3.74889, "time": 0.82368} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.04362, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33703, "top5_acc": 0.59484, "loss_cls": 3.77894, "loss": 3.77894, "time": 0.81377} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.04359, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31984, "top5_acc": 0.57969, "loss_cls": 3.83055, "loss": 3.83055, "time": 0.81599} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.04357, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33828, "top5_acc": 0.5975, "loss_cls": 3.76627, "loss": 3.76627, "time": 0.81919} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.04354, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33734, "top5_acc": 0.59344, "loss_cls": 3.79754, "loss": 3.79754, "time": 0.81664} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.04351, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34781, "top5_acc": 0.61187, "loss_cls": 3.72611, "loss": 3.72611, "time": 0.82345} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.04348, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32766, "top5_acc": 0.59172, "loss_cls": 3.81761, "loss": 3.81761, "time": 0.81668} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.04346, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33422, "top5_acc": 0.58625, "loss_cls": 3.85434, "loss": 3.85434, "time": 0.82025} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.04343, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33, "top5_acc": 0.58781, "loss_cls": 3.78898, "loss": 3.78898, "time": 0.81592} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.0434, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33406, "top5_acc": 0.59516, "loss_cls": 3.78391, "loss": 3.78391, "time": 0.81792} +{"mode": "train", "epoch": 82, "iter": 1300, "lr": 0.04337, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33828, "top5_acc": 0.59234, "loss_cls": 3.77889, "loss": 3.77889, "time": 0.81635} +{"mode": "train", "epoch": 82, "iter": 1400, "lr": 0.04335, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32828, "top5_acc": 0.58281, "loss_cls": 3.83163, "loss": 3.83163, "time": 0.81874} +{"mode": "train", "epoch": 82, "iter": 1500, "lr": 0.04332, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33547, "top5_acc": 0.58625, "loss_cls": 3.80663, "loss": 3.80663, "time": 0.82174} +{"mode": "train", "epoch": 82, "iter": 1600, "lr": 0.04329, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32906, "top5_acc": 0.58688, "loss_cls": 3.83222, "loss": 3.83222, "time": 0.82809} +{"mode": "train", "epoch": 82, "iter": 1700, "lr": 0.04326, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33031, "top5_acc": 0.59359, "loss_cls": 3.78202, "loss": 3.78202, "time": 0.82195} +{"mode": "train", "epoch": 82, "iter": 1800, "lr": 0.04323, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32594, "top5_acc": 0.58422, "loss_cls": 3.84756, "loss": 3.84756, "time": 0.82015} +{"mode": "train", "epoch": 82, "iter": 1900, "lr": 0.04321, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32922, "top5_acc": 0.58891, "loss_cls": 3.80308, "loss": 3.80308, "time": 0.81483} +{"mode": "train", "epoch": 82, "iter": 2000, "lr": 0.04318, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33656, "top5_acc": 0.59312, "loss_cls": 3.80436, "loss": 3.80436, "time": 0.81412} +{"mode": "train", "epoch": 82, "iter": 2100, "lr": 0.04315, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32125, "top5_acc": 0.58078, "loss_cls": 3.84446, "loss": 3.84446, "time": 0.8199} +{"mode": "train", "epoch": 82, "iter": 2200, "lr": 0.04312, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32906, "top5_acc": 0.58312, "loss_cls": 3.84305, "loss": 3.84305, "time": 0.81898} +{"mode": "train", "epoch": 82, "iter": 2300, "lr": 0.0431, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33312, "top5_acc": 0.59328, "loss_cls": 3.80518, "loss": 3.80518, "time": 0.81435} +{"mode": "train", "epoch": 82, "iter": 2400, "lr": 0.04307, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33156, "top5_acc": 0.58469, "loss_cls": 3.83076, "loss": 3.83076, "time": 0.81752} +{"mode": "train", "epoch": 82, "iter": 2500, "lr": 0.04304, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32641, "top5_acc": 0.59281, "loss_cls": 3.81133, "loss": 3.81133, "time": 0.81623} +{"mode": "train", "epoch": 82, "iter": 2600, "lr": 0.04301, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33438, "top5_acc": 0.59203, "loss_cls": 3.81323, "loss": 3.81323, "time": 0.80796} +{"mode": "train", "epoch": 82, "iter": 2700, "lr": 0.04299, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33297, "top5_acc": 0.58875, "loss_cls": 3.79714, "loss": 3.79714, "time": 0.81255} +{"mode": "train", "epoch": 82, "iter": 2800, "lr": 0.04296, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33484, "top5_acc": 0.59203, "loss_cls": 3.80987, "loss": 3.80987, "time": 0.81502} +{"mode": "train", "epoch": 82, "iter": 2900, "lr": 0.04293, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33062, "top5_acc": 0.58781, "loss_cls": 3.83708, "loss": 3.83708, "time": 0.81299} +{"mode": "train", "epoch": 82, "iter": 3000, "lr": 0.0429, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32266, "top5_acc": 0.57938, "loss_cls": 3.85046, "loss": 3.85046, "time": 0.81875} +{"mode": "train", "epoch": 82, "iter": 3100, "lr": 0.04287, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32375, "top5_acc": 0.58922, "loss_cls": 3.81714, "loss": 3.81714, "time": 0.8131} +{"mode": "train", "epoch": 82, "iter": 3200, "lr": 0.04285, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32234, "top5_acc": 0.57766, "loss_cls": 3.85376, "loss": 3.85376, "time": 0.81858} +{"mode": "train", "epoch": 82, "iter": 3300, "lr": 0.04282, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33422, "top5_acc": 0.58766, "loss_cls": 3.81666, "loss": 3.81666, "time": 0.81851} +{"mode": "train", "epoch": 82, "iter": 3400, "lr": 0.04279, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33266, "top5_acc": 0.58422, "loss_cls": 3.79196, "loss": 3.79196, "time": 0.81308} +{"mode": "train", "epoch": 82, "iter": 3500, "lr": 0.04276, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33, "top5_acc": 0.59719, "loss_cls": 3.78959, "loss": 3.78959, "time": 0.81708} +{"mode": "train", "epoch": 82, "iter": 3600, "lr": 0.04274, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33281, "top5_acc": 0.58516, "loss_cls": 3.7971, "loss": 3.7971, "time": 0.81761} +{"mode": "train", "epoch": 82, "iter": 3700, "lr": 0.04271, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3275, "top5_acc": 0.58469, "loss_cls": 3.82719, "loss": 3.82719, "time": 0.82075} +{"mode": "val", "epoch": 82, "iter": 309, "lr": 0.0427, "top1_acc": 0.26759, "top5_acc": 0.52079, "mean_class_accuracy": 0.26718} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.04267, "memory": 15990, "data_time": 1.41825, "top1_acc": 0.35078, "top5_acc": 0.60891, "loss_cls": 3.72207, "loss": 3.72207, "time": 2.4056} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.04264, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33891, "top5_acc": 0.60031, "loss_cls": 3.76765, "loss": 3.76765, "time": 0.82609} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.04261, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34172, "top5_acc": 0.61031, "loss_cls": 3.71816, "loss": 3.71816, "time": 0.81639} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.04259, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34609, "top5_acc": 0.59953, "loss_cls": 3.73694, "loss": 3.73694, "time": 0.82205} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.04256, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.335, "top5_acc": 0.59594, "loss_cls": 3.7768, "loss": 3.7768, "time": 0.81992} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.04253, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33109, "top5_acc": 0.58312, "loss_cls": 3.8187, "loss": 3.8187, "time": 0.82111} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.0425, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34047, "top5_acc": 0.60312, "loss_cls": 3.74643, "loss": 3.74643, "time": 0.81542} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.04247, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31922, "top5_acc": 0.57172, "loss_cls": 3.85832, "loss": 3.85832, "time": 0.81462} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.04245, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35062, "top5_acc": 0.60578, "loss_cls": 3.71935, "loss": 3.71935, "time": 0.81759} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.04242, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34078, "top5_acc": 0.59844, "loss_cls": 3.75108, "loss": 3.75108, "time": 0.81415} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.04239, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34047, "top5_acc": 0.60641, "loss_cls": 3.76665, "loss": 3.76665, "time": 0.81892} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.04236, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33859, "top5_acc": 0.60156, "loss_cls": 3.72851, "loss": 3.72851, "time": 0.81204} +{"mode": "train", "epoch": 83, "iter": 1300, "lr": 0.04234, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32047, "top5_acc": 0.59, "loss_cls": 3.82713, "loss": 3.82713, "time": 0.81691} +{"mode": "train", "epoch": 83, "iter": 1400, "lr": 0.04231, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32703, "top5_acc": 0.59141, "loss_cls": 3.78645, "loss": 3.78645, "time": 0.8187} +{"mode": "train", "epoch": 83, "iter": 1500, "lr": 0.04228, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32688, "top5_acc": 0.58547, "loss_cls": 3.81367, "loss": 3.81367, "time": 0.81546} +{"mode": "train", "epoch": 83, "iter": 1600, "lr": 0.04225, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33156, "top5_acc": 0.58844, "loss_cls": 3.78719, "loss": 3.78719, "time": 0.82676} +{"mode": "train", "epoch": 83, "iter": 1700, "lr": 0.04223, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34719, "top5_acc": 0.59734, "loss_cls": 3.7599, "loss": 3.7599, "time": 0.81891} +{"mode": "train", "epoch": 83, "iter": 1800, "lr": 0.0422, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35172, "top5_acc": 0.60469, "loss_cls": 3.73514, "loss": 3.73514, "time": 0.81678} +{"mode": "train", "epoch": 83, "iter": 1900, "lr": 0.04217, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32953, "top5_acc": 0.59484, "loss_cls": 3.79363, "loss": 3.79363, "time": 0.81568} +{"mode": "train", "epoch": 83, "iter": 2000, "lr": 0.04214, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33078, "top5_acc": 0.58906, "loss_cls": 3.82107, "loss": 3.82107, "time": 0.82268} +{"mode": "train", "epoch": 83, "iter": 2100, "lr": 0.04212, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31938, "top5_acc": 0.57953, "loss_cls": 3.86486, "loss": 3.86486, "time": 0.81177} +{"mode": "train", "epoch": 83, "iter": 2200, "lr": 0.04209, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33562, "top5_acc": 0.5925, "loss_cls": 3.80257, "loss": 3.80257, "time": 0.81345} +{"mode": "train", "epoch": 83, "iter": 2300, "lr": 0.04206, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33234, "top5_acc": 0.58531, "loss_cls": 3.79217, "loss": 3.79217, "time": 0.81073} +{"mode": "train", "epoch": 83, "iter": 2400, "lr": 0.04203, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32984, "top5_acc": 0.59906, "loss_cls": 3.78435, "loss": 3.78435, "time": 0.81169} +{"mode": "train", "epoch": 83, "iter": 2500, "lr": 0.04201, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32953, "top5_acc": 0.58375, "loss_cls": 3.8374, "loss": 3.8374, "time": 0.81364} +{"mode": "train", "epoch": 83, "iter": 2600, "lr": 0.04198, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33469, "top5_acc": 0.59109, "loss_cls": 3.78929, "loss": 3.78929, "time": 0.81444} +{"mode": "train", "epoch": 83, "iter": 2700, "lr": 0.04195, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33891, "top5_acc": 0.59453, "loss_cls": 3.80055, "loss": 3.80055, "time": 0.82168} +{"mode": "train", "epoch": 83, "iter": 2800, "lr": 0.04192, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33828, "top5_acc": 0.59453, "loss_cls": 3.79125, "loss": 3.79125, "time": 0.81407} +{"mode": "train", "epoch": 83, "iter": 2900, "lr": 0.0419, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33422, "top5_acc": 0.58344, "loss_cls": 3.81644, "loss": 3.81644, "time": 0.81052} +{"mode": "train", "epoch": 83, "iter": 3000, "lr": 0.04187, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33734, "top5_acc": 0.58922, "loss_cls": 3.8059, "loss": 3.8059, "time": 0.81833} +{"mode": "train", "epoch": 83, "iter": 3100, "lr": 0.04184, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32375, "top5_acc": 0.59062, "loss_cls": 3.82387, "loss": 3.82387, "time": 0.81686} +{"mode": "train", "epoch": 83, "iter": 3200, "lr": 0.04181, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33453, "top5_acc": 0.59156, "loss_cls": 3.8039, "loss": 3.8039, "time": 0.8176} +{"mode": "train", "epoch": 83, "iter": 3300, "lr": 0.04178, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33453, "top5_acc": 0.58547, "loss_cls": 3.82465, "loss": 3.82465, "time": 0.81362} +{"mode": "train", "epoch": 83, "iter": 3400, "lr": 0.04176, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33875, "top5_acc": 0.59391, "loss_cls": 3.7812, "loss": 3.7812, "time": 0.81603} +{"mode": "train", "epoch": 83, "iter": 3500, "lr": 0.04173, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32938, "top5_acc": 0.58062, "loss_cls": 3.85319, "loss": 3.85319, "time": 0.81219} +{"mode": "train", "epoch": 83, "iter": 3600, "lr": 0.0417, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33047, "top5_acc": 0.59031, "loss_cls": 3.81942, "loss": 3.81942, "time": 0.82036} +{"mode": "train", "epoch": 83, "iter": 3700, "lr": 0.04167, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33703, "top5_acc": 0.59719, "loss_cls": 3.77993, "loss": 3.77993, "time": 0.82266} +{"mode": "val", "epoch": 83, "iter": 309, "lr": 0.04166, "top1_acc": 0.23173, "top5_acc": 0.47668, "mean_class_accuracy": 0.23156} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.04163, "memory": 15990, "data_time": 1.34363, "top1_acc": 0.34812, "top5_acc": 0.60578, "loss_cls": 3.7289, "loss": 3.7289, "time": 2.32049} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.04161, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34406, "top5_acc": 0.59891, "loss_cls": 3.73585, "loss": 3.73585, "time": 0.81714} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.04158, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34297, "top5_acc": 0.60188, "loss_cls": 3.72918, "loss": 3.72918, "time": 0.81894} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.04155, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33531, "top5_acc": 0.59516, "loss_cls": 3.79479, "loss": 3.79479, "time": 0.81729} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.04152, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33859, "top5_acc": 0.60234, "loss_cls": 3.74411, "loss": 3.74411, "time": 0.81508} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.0415, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33359, "top5_acc": 0.59453, "loss_cls": 3.7608, "loss": 3.7608, "time": 0.82147} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.04147, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33172, "top5_acc": 0.58938, "loss_cls": 3.78995, "loss": 3.78995, "time": 0.82093} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.04144, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35266, "top5_acc": 0.59859, "loss_cls": 3.7315, "loss": 3.7315, "time": 0.82011} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.04141, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33828, "top5_acc": 0.59922, "loss_cls": 3.75222, "loss": 3.75222, "time": 0.81987} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.04139, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33938, "top5_acc": 0.6075, "loss_cls": 3.73379, "loss": 3.73379, "time": 0.81581} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.04136, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33969, "top5_acc": 0.59688, "loss_cls": 3.75646, "loss": 3.75646, "time": 0.82107} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.04133, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3325, "top5_acc": 0.59406, "loss_cls": 3.78455, "loss": 3.78455, "time": 0.81241} +{"mode": "train", "epoch": 84, "iter": 1300, "lr": 0.0413, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33625, "top5_acc": 0.59094, "loss_cls": 3.78043, "loss": 3.78043, "time": 0.81925} +{"mode": "train", "epoch": 84, "iter": 1400, "lr": 0.04128, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33047, "top5_acc": 0.58828, "loss_cls": 3.78318, "loss": 3.78318, "time": 0.82373} +{"mode": "train", "epoch": 84, "iter": 1500, "lr": 0.04125, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33672, "top5_acc": 0.58734, "loss_cls": 3.78761, "loss": 3.78761, "time": 0.82178} +{"mode": "train", "epoch": 84, "iter": 1600, "lr": 0.04122, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33891, "top5_acc": 0.59328, "loss_cls": 3.76489, "loss": 3.76489, "time": 0.83099} +{"mode": "train", "epoch": 84, "iter": 1700, "lr": 0.04119, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34125, "top5_acc": 0.59891, "loss_cls": 3.76172, "loss": 3.76172, "time": 0.819} +{"mode": "train", "epoch": 84, "iter": 1800, "lr": 0.04117, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32344, "top5_acc": 0.59125, "loss_cls": 3.80058, "loss": 3.80058, "time": 0.81698} +{"mode": "train", "epoch": 84, "iter": 1900, "lr": 0.04114, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33125, "top5_acc": 0.59859, "loss_cls": 3.7883, "loss": 3.7883, "time": 0.81702} +{"mode": "train", "epoch": 84, "iter": 2000, "lr": 0.04111, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33312, "top5_acc": 0.595, "loss_cls": 3.78454, "loss": 3.78454, "time": 0.81101} +{"mode": "train", "epoch": 84, "iter": 2100, "lr": 0.04108, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33156, "top5_acc": 0.58734, "loss_cls": 3.81991, "loss": 3.81991, "time": 0.81829} +{"mode": "train", "epoch": 84, "iter": 2200, "lr": 0.04106, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34047, "top5_acc": 0.60625, "loss_cls": 3.72961, "loss": 3.72961, "time": 0.81819} +{"mode": "train", "epoch": 84, "iter": 2300, "lr": 0.04103, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3375, "top5_acc": 0.59312, "loss_cls": 3.79093, "loss": 3.79093, "time": 0.81323} +{"mode": "train", "epoch": 84, "iter": 2400, "lr": 0.041, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33797, "top5_acc": 0.58781, "loss_cls": 3.80084, "loss": 3.80084, "time": 0.81246} +{"mode": "train", "epoch": 84, "iter": 2500, "lr": 0.04097, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33281, "top5_acc": 0.59266, "loss_cls": 3.79711, "loss": 3.79711, "time": 0.81725} +{"mode": "train", "epoch": 84, "iter": 2600, "lr": 0.04095, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33547, "top5_acc": 0.59391, "loss_cls": 3.77607, "loss": 3.77607, "time": 0.81906} +{"mode": "train", "epoch": 84, "iter": 2700, "lr": 0.04092, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33328, "top5_acc": 0.59281, "loss_cls": 3.81175, "loss": 3.81175, "time": 0.8154} +{"mode": "train", "epoch": 84, "iter": 2800, "lr": 0.04089, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33547, "top5_acc": 0.59828, "loss_cls": 3.78012, "loss": 3.78012, "time": 0.81138} +{"mode": "train", "epoch": 84, "iter": 2900, "lr": 0.04086, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33359, "top5_acc": 0.59281, "loss_cls": 3.77685, "loss": 3.77685, "time": 0.81115} +{"mode": "train", "epoch": 84, "iter": 3000, "lr": 0.04084, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33391, "top5_acc": 0.58609, "loss_cls": 3.81931, "loss": 3.81931, "time": 0.8171} +{"mode": "train", "epoch": 84, "iter": 3100, "lr": 0.04081, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34062, "top5_acc": 0.59812, "loss_cls": 3.75798, "loss": 3.75798, "time": 0.8155} +{"mode": "train", "epoch": 84, "iter": 3200, "lr": 0.04078, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.325, "top5_acc": 0.59172, "loss_cls": 3.81161, "loss": 3.81161, "time": 0.81798} +{"mode": "train", "epoch": 84, "iter": 3300, "lr": 0.04075, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34016, "top5_acc": 0.60406, "loss_cls": 3.74419, "loss": 3.74419, "time": 0.8177} +{"mode": "train", "epoch": 84, "iter": 3400, "lr": 0.04073, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33078, "top5_acc": 0.58625, "loss_cls": 3.81334, "loss": 3.81334, "time": 0.81442} +{"mode": "train", "epoch": 84, "iter": 3500, "lr": 0.0407, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33641, "top5_acc": 0.59641, "loss_cls": 3.77668, "loss": 3.77668, "time": 0.81587} +{"mode": "train", "epoch": 84, "iter": 3600, "lr": 0.04067, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31828, "top5_acc": 0.58031, "loss_cls": 3.86942, "loss": 3.86942, "time": 0.81616} +{"mode": "train", "epoch": 84, "iter": 3700, "lr": 0.04064, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33656, "top5_acc": 0.58953, "loss_cls": 3.79832, "loss": 3.79832, "time": 0.82316} +{"mode": "val", "epoch": 84, "iter": 309, "lr": 0.04063, "top1_acc": 0.27579, "top5_acc": 0.525, "mean_class_accuracy": 0.27557} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.0406, "memory": 15990, "data_time": 1.38562, "top1_acc": 0.34594, "top5_acc": 0.60969, "loss_cls": 3.71581, "loss": 3.71581, "time": 2.38228} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.04058, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34906, "top5_acc": 0.59812, "loss_cls": 3.71341, "loss": 3.71341, "time": 0.8366} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.04055, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34562, "top5_acc": 0.60141, "loss_cls": 3.71849, "loss": 3.71849, "time": 0.82181} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.04052, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33797, "top5_acc": 0.59797, "loss_cls": 3.75124, "loss": 3.75124, "time": 0.82521} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.04049, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33031, "top5_acc": 0.58875, "loss_cls": 3.78073, "loss": 3.78073, "time": 0.82565} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.04047, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34062, "top5_acc": 0.59688, "loss_cls": 3.76723, "loss": 3.76723, "time": 0.81545} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.04044, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35688, "top5_acc": 0.60812, "loss_cls": 3.65432, "loss": 3.65432, "time": 0.82237} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.04041, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33125, "top5_acc": 0.59406, "loss_cls": 3.77199, "loss": 3.77199, "time": 0.82429} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.04038, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32641, "top5_acc": 0.58844, "loss_cls": 3.82246, "loss": 3.82246, "time": 0.81776} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.04036, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33531, "top5_acc": 0.59859, "loss_cls": 3.79416, "loss": 3.79416, "time": 0.81414} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.04033, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34406, "top5_acc": 0.59703, "loss_cls": 3.75564, "loss": 3.75564, "time": 0.81781} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.0403, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33688, "top5_acc": 0.59172, "loss_cls": 3.78558, "loss": 3.78558, "time": 0.81274} +{"mode": "train", "epoch": 85, "iter": 1300, "lr": 0.04027, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33625, "top5_acc": 0.59438, "loss_cls": 3.7432, "loss": 3.7432, "time": 0.81838} +{"mode": "train", "epoch": 85, "iter": 1400, "lr": 0.04025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33312, "top5_acc": 0.59516, "loss_cls": 3.7525, "loss": 3.7525, "time": 0.82141} +{"mode": "train", "epoch": 85, "iter": 1500, "lr": 0.04022, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33125, "top5_acc": 0.59172, "loss_cls": 3.78477, "loss": 3.78477, "time": 0.81606} +{"mode": "train", "epoch": 85, "iter": 1600, "lr": 0.04019, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.33922, "top5_acc": 0.59781, "loss_cls": 3.75825, "loss": 3.75825, "time": 0.8215} +{"mode": "train", "epoch": 85, "iter": 1700, "lr": 0.04016, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33781, "top5_acc": 0.59422, "loss_cls": 3.77896, "loss": 3.77896, "time": 0.81912} +{"mode": "train", "epoch": 85, "iter": 1800, "lr": 0.04014, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34891, "top5_acc": 0.61312, "loss_cls": 3.71366, "loss": 3.71366, "time": 0.81717} +{"mode": "train", "epoch": 85, "iter": 1900, "lr": 0.04011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33203, "top5_acc": 0.59125, "loss_cls": 3.79422, "loss": 3.79422, "time": 0.81562} +{"mode": "train", "epoch": 85, "iter": 2000, "lr": 0.04008, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33781, "top5_acc": 0.59828, "loss_cls": 3.75732, "loss": 3.75732, "time": 0.81296} +{"mode": "train", "epoch": 85, "iter": 2100, "lr": 0.04006, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33875, "top5_acc": 0.59922, "loss_cls": 3.78465, "loss": 3.78465, "time": 0.81364} +{"mode": "train", "epoch": 85, "iter": 2200, "lr": 0.04003, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34188, "top5_acc": 0.59016, "loss_cls": 3.75371, "loss": 3.75371, "time": 0.82245} +{"mode": "train", "epoch": 85, "iter": 2300, "lr": 0.04, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33156, "top5_acc": 0.59359, "loss_cls": 3.78342, "loss": 3.78342, "time": 0.81344} +{"mode": "train", "epoch": 85, "iter": 2400, "lr": 0.03997, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33891, "top5_acc": 0.59125, "loss_cls": 3.79572, "loss": 3.79572, "time": 0.81193} +{"mode": "train", "epoch": 85, "iter": 2500, "lr": 0.03995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33875, "top5_acc": 0.59281, "loss_cls": 3.80069, "loss": 3.80069, "time": 0.81258} +{"mode": "train", "epoch": 85, "iter": 2600, "lr": 0.03992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32688, "top5_acc": 0.58875, "loss_cls": 3.83195, "loss": 3.83195, "time": 0.8186} +{"mode": "train", "epoch": 85, "iter": 2700, "lr": 0.03989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32594, "top5_acc": 0.58625, "loss_cls": 3.82693, "loss": 3.82693, "time": 0.81711} +{"mode": "train", "epoch": 85, "iter": 2800, "lr": 0.03986, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34016, "top5_acc": 0.59922, "loss_cls": 3.78501, "loss": 3.78501, "time": 0.81362} +{"mode": "train", "epoch": 85, "iter": 2900, "lr": 0.03984, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32844, "top5_acc": 0.595, "loss_cls": 3.79317, "loss": 3.79317, "time": 0.81374} +{"mode": "train", "epoch": 85, "iter": 3000, "lr": 0.03981, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33672, "top5_acc": 0.58969, "loss_cls": 3.77727, "loss": 3.77727, "time": 0.81288} +{"mode": "train", "epoch": 85, "iter": 3100, "lr": 0.03978, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33953, "top5_acc": 0.59953, "loss_cls": 3.74955, "loss": 3.74955, "time": 0.81007} +{"mode": "train", "epoch": 85, "iter": 3200, "lr": 0.03975, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33375, "top5_acc": 0.59625, "loss_cls": 3.77679, "loss": 3.77679, "time": 0.81266} +{"mode": "train", "epoch": 85, "iter": 3300, "lr": 0.03973, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34125, "top5_acc": 0.61062, "loss_cls": 3.74256, "loss": 3.74256, "time": 0.81269} +{"mode": "train", "epoch": 85, "iter": 3400, "lr": 0.0397, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34203, "top5_acc": 0.59703, "loss_cls": 3.7719, "loss": 3.7719, "time": 0.81212} +{"mode": "train", "epoch": 85, "iter": 3500, "lr": 0.03967, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33531, "top5_acc": 0.60094, "loss_cls": 3.75875, "loss": 3.75875, "time": 0.81178} +{"mode": "train", "epoch": 85, "iter": 3600, "lr": 0.03964, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33781, "top5_acc": 0.59547, "loss_cls": 3.78855, "loss": 3.78855, "time": 0.81352} +{"mode": "train", "epoch": 85, "iter": 3700, "lr": 0.03962, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34109, "top5_acc": 0.59719, "loss_cls": 3.74345, "loss": 3.74345, "time": 0.82438} +{"mode": "val", "epoch": 85, "iter": 309, "lr": 0.0396, "top1_acc": 0.25781, "top5_acc": 0.50393, "mean_class_accuracy": 0.2575} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.03958, "memory": 15990, "data_time": 1.39449, "top1_acc": 0.34438, "top5_acc": 0.6125, "loss_cls": 3.69208, "loss": 3.69208, "time": 2.37724} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.03955, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35062, "top5_acc": 0.61078, "loss_cls": 3.68627, "loss": 3.68627, "time": 0.8254} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.03952, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35484, "top5_acc": 0.61531, "loss_cls": 3.68459, "loss": 3.68459, "time": 0.82431} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.0395, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33312, "top5_acc": 0.60109, "loss_cls": 3.77705, "loss": 3.77705, "time": 0.824} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.03947, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35438, "top5_acc": 0.60156, "loss_cls": 3.71537, "loss": 3.71537, "time": 0.82095} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.03944, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33922, "top5_acc": 0.6025, "loss_cls": 3.72485, "loss": 3.72485, "time": 0.81794} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.03941, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33844, "top5_acc": 0.59594, "loss_cls": 3.78078, "loss": 3.78078, "time": 0.82117} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.03939, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34109, "top5_acc": 0.60594, "loss_cls": 3.71528, "loss": 3.71528, "time": 0.81241} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.03936, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33156, "top5_acc": 0.59219, "loss_cls": 3.79503, "loss": 3.79503, "time": 0.81553} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.03933, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33453, "top5_acc": 0.58922, "loss_cls": 3.80609, "loss": 3.80609, "time": 0.81572} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.0393, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34141, "top5_acc": 0.59625, "loss_cls": 3.74817, "loss": 3.74817, "time": 0.81646} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.03928, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34953, "top5_acc": 0.60297, "loss_cls": 3.71948, "loss": 3.71948, "time": 0.81407} +{"mode": "train", "epoch": 86, "iter": 1300, "lr": 0.03925, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33844, "top5_acc": 0.60453, "loss_cls": 3.74266, "loss": 3.74266, "time": 0.82019} +{"mode": "train", "epoch": 86, "iter": 1400, "lr": 0.03922, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34047, "top5_acc": 0.59328, "loss_cls": 3.73464, "loss": 3.73464, "time": 0.82539} +{"mode": "train", "epoch": 86, "iter": 1500, "lr": 0.03919, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33594, "top5_acc": 0.59359, "loss_cls": 3.77365, "loss": 3.77365, "time": 0.81567} +{"mode": "train", "epoch": 86, "iter": 1600, "lr": 0.03917, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33922, "top5_acc": 0.59281, "loss_cls": 3.75513, "loss": 3.75513, "time": 0.82282} +{"mode": "train", "epoch": 86, "iter": 1700, "lr": 0.03914, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35297, "top5_acc": 0.59953, "loss_cls": 3.73186, "loss": 3.73186, "time": 0.8205} +{"mode": "train", "epoch": 86, "iter": 1800, "lr": 0.03911, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33938, "top5_acc": 0.59984, "loss_cls": 3.74672, "loss": 3.74672, "time": 0.82399} +{"mode": "train", "epoch": 86, "iter": 1900, "lr": 0.03909, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33109, "top5_acc": 0.59453, "loss_cls": 3.78214, "loss": 3.78214, "time": 0.82187} +{"mode": "train", "epoch": 86, "iter": 2000, "lr": 0.03906, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3475, "top5_acc": 0.59188, "loss_cls": 3.76142, "loss": 3.76142, "time": 0.81446} +{"mode": "train", "epoch": 86, "iter": 2100, "lr": 0.03903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33578, "top5_acc": 0.59219, "loss_cls": 3.78777, "loss": 3.78777, "time": 0.81274} +{"mode": "train", "epoch": 86, "iter": 2200, "lr": 0.039, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34766, "top5_acc": 0.60859, "loss_cls": 3.70426, "loss": 3.70426, "time": 0.81621} +{"mode": "train", "epoch": 86, "iter": 2300, "lr": 0.03898, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34391, "top5_acc": 0.59891, "loss_cls": 3.75745, "loss": 3.75745, "time": 0.81597} +{"mode": "train", "epoch": 86, "iter": 2400, "lr": 0.03895, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33484, "top5_acc": 0.58906, "loss_cls": 3.80141, "loss": 3.80141, "time": 0.81607} +{"mode": "train", "epoch": 86, "iter": 2500, "lr": 0.03892, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33625, "top5_acc": 0.59156, "loss_cls": 3.76143, "loss": 3.76143, "time": 0.82035} +{"mode": "train", "epoch": 86, "iter": 2600, "lr": 0.03889, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34047, "top5_acc": 0.59234, "loss_cls": 3.77728, "loss": 3.77728, "time": 0.81724} +{"mode": "train", "epoch": 86, "iter": 2700, "lr": 0.03887, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33734, "top5_acc": 0.59891, "loss_cls": 3.72837, "loss": 3.72837, "time": 0.81144} +{"mode": "train", "epoch": 86, "iter": 2800, "lr": 0.03884, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33203, "top5_acc": 0.59641, "loss_cls": 3.78378, "loss": 3.78378, "time": 0.81243} +{"mode": "train", "epoch": 86, "iter": 2900, "lr": 0.03881, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33672, "top5_acc": 0.60109, "loss_cls": 3.75515, "loss": 3.75515, "time": 0.81624} +{"mode": "train", "epoch": 86, "iter": 3000, "lr": 0.03879, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33875, "top5_acc": 0.59766, "loss_cls": 3.74261, "loss": 3.74261, "time": 0.81492} +{"mode": "train", "epoch": 86, "iter": 3100, "lr": 0.03876, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32828, "top5_acc": 0.59172, "loss_cls": 3.78971, "loss": 3.78971, "time": 0.81603} +{"mode": "train", "epoch": 86, "iter": 3200, "lr": 0.03873, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34047, "top5_acc": 0.60188, "loss_cls": 3.74931, "loss": 3.74931, "time": 0.81469} +{"mode": "train", "epoch": 86, "iter": 3300, "lr": 0.0387, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32922, "top5_acc": 0.59625, "loss_cls": 3.78025, "loss": 3.78025, "time": 0.8155} +{"mode": "train", "epoch": 86, "iter": 3400, "lr": 0.03868, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35266, "top5_acc": 0.60844, "loss_cls": 3.70728, "loss": 3.70728, "time": 0.81612} +{"mode": "train", "epoch": 86, "iter": 3500, "lr": 0.03865, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33156, "top5_acc": 0.59312, "loss_cls": 3.78644, "loss": 3.78644, "time": 0.81467} +{"mode": "train", "epoch": 86, "iter": 3600, "lr": 0.03862, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34312, "top5_acc": 0.59875, "loss_cls": 3.72322, "loss": 3.72322, "time": 0.821} +{"mode": "train", "epoch": 86, "iter": 3700, "lr": 0.0386, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34781, "top5_acc": 0.59344, "loss_cls": 3.77722, "loss": 3.77722, "time": 0.81864} +{"mode": "val", "epoch": 86, "iter": 309, "lr": 0.03858, "top1_acc": 0.27093, "top5_acc": 0.5138, "mean_class_accuracy": 0.2707} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.03856, "memory": 15990, "data_time": 1.36812, "top1_acc": 0.34797, "top5_acc": 0.60531, "loss_cls": 3.70014, "loss": 3.70014, "time": 2.35511} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.03853, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34781, "top5_acc": 0.60625, "loss_cls": 3.70478, "loss": 3.70478, "time": 0.81959} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.0385, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33969, "top5_acc": 0.59359, "loss_cls": 3.74981, "loss": 3.74981, "time": 0.81782} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.03847, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34656, "top5_acc": 0.60312, "loss_cls": 3.72302, "loss": 3.72302, "time": 0.81615} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.03845, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34594, "top5_acc": 0.60734, "loss_cls": 3.72162, "loss": 3.72162, "time": 0.82435} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.03842, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34891, "top5_acc": 0.60438, "loss_cls": 3.69751, "loss": 3.69751, "time": 0.81729} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.03839, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3525, "top5_acc": 0.60625, "loss_cls": 3.71516, "loss": 3.71516, "time": 0.82174} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.03837, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33906, "top5_acc": 0.59641, "loss_cls": 3.75619, "loss": 3.75619, "time": 0.81982} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.03834, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33922, "top5_acc": 0.60219, "loss_cls": 3.73596, "loss": 3.73596, "time": 0.81983} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.03831, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34281, "top5_acc": 0.59922, "loss_cls": 3.70306, "loss": 3.70306, "time": 0.81592} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.03828, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33906, "top5_acc": 0.60188, "loss_cls": 3.74624, "loss": 3.74624, "time": 0.81659} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.03826, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33688, "top5_acc": 0.60062, "loss_cls": 3.75164, "loss": 3.75164, "time": 0.8144} +{"mode": "train", "epoch": 87, "iter": 1300, "lr": 0.03823, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35328, "top5_acc": 0.60938, "loss_cls": 3.67226, "loss": 3.67226, "time": 0.81319} +{"mode": "train", "epoch": 87, "iter": 1400, "lr": 0.0382, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34312, "top5_acc": 0.6025, "loss_cls": 3.72475, "loss": 3.72475, "time": 0.82272} +{"mode": "train", "epoch": 87, "iter": 1500, "lr": 0.03817, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.345, "top5_acc": 0.60406, "loss_cls": 3.75633, "loss": 3.75633, "time": 0.81129} +{"mode": "train", "epoch": 87, "iter": 1600, "lr": 0.03815, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33281, "top5_acc": 0.60344, "loss_cls": 3.7497, "loss": 3.7497, "time": 0.8238} +{"mode": "train", "epoch": 87, "iter": 1700, "lr": 0.03812, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33906, "top5_acc": 0.60812, "loss_cls": 3.72356, "loss": 3.72356, "time": 0.82347} +{"mode": "train", "epoch": 87, "iter": 1800, "lr": 0.03809, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34453, "top5_acc": 0.60094, "loss_cls": 3.74211, "loss": 3.74211, "time": 0.81831} +{"mode": "train", "epoch": 87, "iter": 1900, "lr": 0.03807, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.34656, "top5_acc": 0.59891, "loss_cls": 3.75427, "loss": 3.75427, "time": 0.82458} +{"mode": "train", "epoch": 87, "iter": 2000, "lr": 0.03804, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34672, "top5_acc": 0.6, "loss_cls": 3.73312, "loss": 3.73312, "time": 0.82089} +{"mode": "train", "epoch": 87, "iter": 2100, "lr": 0.03801, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33406, "top5_acc": 0.59141, "loss_cls": 3.78416, "loss": 3.78416, "time": 0.82408} +{"mode": "train", "epoch": 87, "iter": 2200, "lr": 0.03798, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34109, "top5_acc": 0.59703, "loss_cls": 3.78441, "loss": 3.78441, "time": 0.81465} +{"mode": "train", "epoch": 87, "iter": 2300, "lr": 0.03796, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34609, "top5_acc": 0.60578, "loss_cls": 3.72616, "loss": 3.72616, "time": 0.81531} +{"mode": "train", "epoch": 87, "iter": 2400, "lr": 0.03793, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.335, "top5_acc": 0.59656, "loss_cls": 3.808, "loss": 3.808, "time": 0.8204} +{"mode": "train", "epoch": 87, "iter": 2500, "lr": 0.0379, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34062, "top5_acc": 0.60344, "loss_cls": 3.73029, "loss": 3.73029, "time": 0.82146} +{"mode": "train", "epoch": 87, "iter": 2600, "lr": 0.03788, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34484, "top5_acc": 0.60703, "loss_cls": 3.72303, "loss": 3.72303, "time": 0.81828} +{"mode": "train", "epoch": 87, "iter": 2700, "lr": 0.03785, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34344, "top5_acc": 0.59422, "loss_cls": 3.78624, "loss": 3.78624, "time": 0.81542} +{"mode": "train", "epoch": 87, "iter": 2800, "lr": 0.03782, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33891, "top5_acc": 0.59516, "loss_cls": 3.72995, "loss": 3.72995, "time": 0.81569} +{"mode": "train", "epoch": 87, "iter": 2900, "lr": 0.03779, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34766, "top5_acc": 0.60328, "loss_cls": 3.73836, "loss": 3.73836, "time": 0.81449} +{"mode": "train", "epoch": 87, "iter": 3000, "lr": 0.03777, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3475, "top5_acc": 0.60562, "loss_cls": 3.72832, "loss": 3.72832, "time": 0.81814} +{"mode": "train", "epoch": 87, "iter": 3100, "lr": 0.03774, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34609, "top5_acc": 0.60359, "loss_cls": 3.73867, "loss": 3.73867, "time": 0.81375} +{"mode": "train", "epoch": 87, "iter": 3200, "lr": 0.03771, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33656, "top5_acc": 0.58891, "loss_cls": 3.78559, "loss": 3.78559, "time": 0.8189} +{"mode": "train", "epoch": 87, "iter": 3300, "lr": 0.03769, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34359, "top5_acc": 0.59609, "loss_cls": 3.7206, "loss": 3.7206, "time": 0.81452} +{"mode": "train", "epoch": 87, "iter": 3400, "lr": 0.03766, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34047, "top5_acc": 0.59047, "loss_cls": 3.78925, "loss": 3.78925, "time": 0.81162} +{"mode": "train", "epoch": 87, "iter": 3500, "lr": 0.03763, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33344, "top5_acc": 0.59844, "loss_cls": 3.77794, "loss": 3.77794, "time": 0.81399} +{"mode": "train", "epoch": 87, "iter": 3600, "lr": 0.03761, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34156, "top5_acc": 0.59344, "loss_cls": 3.7515, "loss": 3.7515, "time": 0.81658} +{"mode": "train", "epoch": 87, "iter": 3700, "lr": 0.03758, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34391, "top5_acc": 0.59547, "loss_cls": 3.75977, "loss": 3.75977, "time": 0.81878} +{"mode": "val", "epoch": 87, "iter": 309, "lr": 0.03757, "top1_acc": 0.2686, "top5_acc": 0.51527, "mean_class_accuracy": 0.26833} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.03754, "memory": 15990, "data_time": 1.34993, "top1_acc": 0.35156, "top5_acc": 0.61391, "loss_cls": 3.64715, "loss": 3.64715, "time": 2.34475} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.03751, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34109, "top5_acc": 0.60156, "loss_cls": 3.72663, "loss": 3.72663, "time": 0.83332} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.03748, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35891, "top5_acc": 0.62297, "loss_cls": 3.62767, "loss": 3.62767, "time": 0.82729} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.03746, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34672, "top5_acc": 0.59953, "loss_cls": 3.7142, "loss": 3.7142, "time": 0.82352} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.03743, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33453, "top5_acc": 0.59469, "loss_cls": 3.78386, "loss": 3.78386, "time": 0.8252} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.0374, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34047, "top5_acc": 0.5975, "loss_cls": 3.73028, "loss": 3.73028, "time": 0.82273} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.03738, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34781, "top5_acc": 0.60578, "loss_cls": 3.69781, "loss": 3.69781, "time": 0.81935} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.03735, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34188, "top5_acc": 0.59891, "loss_cls": 3.74865, "loss": 3.74865, "time": 0.82103} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.03732, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35031, "top5_acc": 0.60734, "loss_cls": 3.71115, "loss": 3.71115, "time": 0.82233} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.0373, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3475, "top5_acc": 0.60625, "loss_cls": 3.71146, "loss": 3.71146, "time": 0.81834} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.03727, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35031, "top5_acc": 0.61766, "loss_cls": 3.67186, "loss": 3.67186, "time": 0.82233} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.03724, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33688, "top5_acc": 0.60422, "loss_cls": 3.72309, "loss": 3.72309, "time": 0.81644} +{"mode": "train", "epoch": 88, "iter": 1300, "lr": 0.03721, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34797, "top5_acc": 0.61344, "loss_cls": 3.65884, "loss": 3.65884, "time": 0.81769} +{"mode": "train", "epoch": 88, "iter": 1400, "lr": 0.03719, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34562, "top5_acc": 0.60797, "loss_cls": 3.7242, "loss": 3.7242, "time": 0.82446} +{"mode": "train", "epoch": 88, "iter": 1500, "lr": 0.03716, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34281, "top5_acc": 0.61359, "loss_cls": 3.70291, "loss": 3.70291, "time": 0.81146} +{"mode": "train", "epoch": 88, "iter": 1600, "lr": 0.03713, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34578, "top5_acc": 0.60312, "loss_cls": 3.73596, "loss": 3.73596, "time": 0.82828} +{"mode": "train", "epoch": 88, "iter": 1700, "lr": 0.03711, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33703, "top5_acc": 0.605, "loss_cls": 3.76301, "loss": 3.76301, "time": 0.82172} +{"mode": "train", "epoch": 88, "iter": 1800, "lr": 0.03708, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35406, "top5_acc": 0.61031, "loss_cls": 3.69363, "loss": 3.69363, "time": 0.81958} +{"mode": "train", "epoch": 88, "iter": 1900, "lr": 0.03705, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34094, "top5_acc": 0.60328, "loss_cls": 3.72951, "loss": 3.72951, "time": 0.82299} +{"mode": "train", "epoch": 88, "iter": 2000, "lr": 0.03703, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34031, "top5_acc": 0.59531, "loss_cls": 3.76076, "loss": 3.76076, "time": 0.81365} +{"mode": "train", "epoch": 88, "iter": 2100, "lr": 0.037, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33953, "top5_acc": 0.60078, "loss_cls": 3.7474, "loss": 3.7474, "time": 0.81469} +{"mode": "train", "epoch": 88, "iter": 2200, "lr": 0.03697, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33938, "top5_acc": 0.59594, "loss_cls": 3.75763, "loss": 3.75763, "time": 0.81729} +{"mode": "train", "epoch": 88, "iter": 2300, "lr": 0.03694, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33906, "top5_acc": 0.59859, "loss_cls": 3.73858, "loss": 3.73858, "time": 0.81162} +{"mode": "train", "epoch": 88, "iter": 2400, "lr": 0.03692, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33938, "top5_acc": 0.59938, "loss_cls": 3.75116, "loss": 3.75116, "time": 0.81429} +{"mode": "train", "epoch": 88, "iter": 2500, "lr": 0.03689, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33984, "top5_acc": 0.59938, "loss_cls": 3.73298, "loss": 3.73298, "time": 0.81699} +{"mode": "train", "epoch": 88, "iter": 2600, "lr": 0.03686, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33375, "top5_acc": 0.60844, "loss_cls": 3.74954, "loss": 3.74954, "time": 0.81004} +{"mode": "train", "epoch": 88, "iter": 2700, "lr": 0.03684, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33156, "top5_acc": 0.59828, "loss_cls": 3.76432, "loss": 3.76432, "time": 0.81356} +{"mode": "train", "epoch": 88, "iter": 2800, "lr": 0.03681, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35031, "top5_acc": 0.60969, "loss_cls": 3.71306, "loss": 3.71306, "time": 0.81623} +{"mode": "train", "epoch": 88, "iter": 2900, "lr": 0.03678, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34453, "top5_acc": 0.59828, "loss_cls": 3.74354, "loss": 3.74354, "time": 0.8121} +{"mode": "train", "epoch": 88, "iter": 3000, "lr": 0.03676, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34625, "top5_acc": 0.60906, "loss_cls": 3.7238, "loss": 3.7238, "time": 0.81953} +{"mode": "train", "epoch": 88, "iter": 3100, "lr": 0.03673, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32766, "top5_acc": 0.58531, "loss_cls": 3.81991, "loss": 3.81991, "time": 0.81499} +{"mode": "train", "epoch": 88, "iter": 3200, "lr": 0.0367, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35156, "top5_acc": 0.60734, "loss_cls": 3.71892, "loss": 3.71892, "time": 0.81571} +{"mode": "train", "epoch": 88, "iter": 3300, "lr": 0.03667, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33531, "top5_acc": 0.59375, "loss_cls": 3.7717, "loss": 3.7717, "time": 0.81607} +{"mode": "train", "epoch": 88, "iter": 3400, "lr": 0.03665, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34047, "top5_acc": 0.60344, "loss_cls": 3.74363, "loss": 3.74363, "time": 0.82059} +{"mode": "train", "epoch": 88, "iter": 3500, "lr": 0.03662, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34438, "top5_acc": 0.60438, "loss_cls": 3.75123, "loss": 3.75123, "time": 0.81958} +{"mode": "train", "epoch": 88, "iter": 3600, "lr": 0.03659, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34125, "top5_acc": 0.60641, "loss_cls": 3.72536, "loss": 3.72536, "time": 0.82893} +{"mode": "train", "epoch": 88, "iter": 3700, "lr": 0.03657, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34656, "top5_acc": 0.60359, "loss_cls": 3.71975, "loss": 3.71975, "time": 0.81569} +{"mode": "val", "epoch": 88, "iter": 309, "lr": 0.03655, "top1_acc": 0.27777, "top5_acc": 0.52414, "mean_class_accuracy": 0.2775} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.03653, "memory": 15990, "data_time": 1.34327, "top1_acc": 0.36172, "top5_acc": 0.62797, "loss_cls": 3.58554, "loss": 3.58554, "time": 2.34505} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0365, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33281, "top5_acc": 0.60484, "loss_cls": 3.71491, "loss": 3.71491, "time": 0.81877} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.03647, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35438, "top5_acc": 0.62578, "loss_cls": 3.65374, "loss": 3.65374, "time": 0.81282} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.03645, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35188, "top5_acc": 0.60844, "loss_cls": 3.67961, "loss": 3.67961, "time": 0.81534} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.03642, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34953, "top5_acc": 0.61703, "loss_cls": 3.68357, "loss": 3.68357, "time": 0.81568} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.03639, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36031, "top5_acc": 0.6175, "loss_cls": 3.65062, "loss": 3.65062, "time": 0.81546} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.03637, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34094, "top5_acc": 0.6025, "loss_cls": 3.68696, "loss": 3.68696, "time": 0.81558} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.03634, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34531, "top5_acc": 0.60938, "loss_cls": 3.68651, "loss": 3.68651, "time": 0.81868} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.03631, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34828, "top5_acc": 0.61484, "loss_cls": 3.68827, "loss": 3.68827, "time": 0.81209} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.03629, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34922, "top5_acc": 0.59562, "loss_cls": 3.69746, "loss": 3.69746, "time": 0.81332} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.03626, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34328, "top5_acc": 0.60078, "loss_cls": 3.72074, "loss": 3.72074, "time": 0.81557} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.03623, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34328, "top5_acc": 0.59688, "loss_cls": 3.74051, "loss": 3.74051, "time": 0.81012} +{"mode": "train", "epoch": 89, "iter": 1300, "lr": 0.0362, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34484, "top5_acc": 0.61516, "loss_cls": 3.67222, "loss": 3.67222, "time": 0.81997} +{"mode": "train", "epoch": 89, "iter": 1400, "lr": 0.03618, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34641, "top5_acc": 0.60359, "loss_cls": 3.72083, "loss": 3.72083, "time": 0.81278} +{"mode": "train", "epoch": 89, "iter": 1500, "lr": 0.03615, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34594, "top5_acc": 0.60844, "loss_cls": 3.70572, "loss": 3.70572, "time": 0.82049} +{"mode": "train", "epoch": 89, "iter": 1600, "lr": 0.03612, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35281, "top5_acc": 0.59969, "loss_cls": 3.73677, "loss": 3.73677, "time": 0.82235} +{"mode": "train", "epoch": 89, "iter": 1700, "lr": 0.0361, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.345, "top5_acc": 0.60672, "loss_cls": 3.74771, "loss": 3.74771, "time": 0.81807} +{"mode": "train", "epoch": 89, "iter": 1800, "lr": 0.03607, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34281, "top5_acc": 0.60219, "loss_cls": 3.72644, "loss": 3.72644, "time": 0.82578} +{"mode": "train", "epoch": 89, "iter": 1900, "lr": 0.03604, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34359, "top5_acc": 0.60531, "loss_cls": 3.73903, "loss": 3.73903, "time": 0.81957} +{"mode": "train", "epoch": 89, "iter": 2000, "lr": 0.03602, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3475, "top5_acc": 0.59969, "loss_cls": 3.7118, "loss": 3.7118, "time": 0.81723} +{"mode": "train", "epoch": 89, "iter": 2100, "lr": 0.03599, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34047, "top5_acc": 0.60438, "loss_cls": 3.75701, "loss": 3.75701, "time": 0.81847} +{"mode": "train", "epoch": 89, "iter": 2200, "lr": 0.03596, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33781, "top5_acc": 0.60266, "loss_cls": 3.73989, "loss": 3.73989, "time": 0.82024} +{"mode": "train", "epoch": 89, "iter": 2300, "lr": 0.03594, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34922, "top5_acc": 0.61359, "loss_cls": 3.67125, "loss": 3.67125, "time": 0.81717} +{"mode": "train", "epoch": 89, "iter": 2400, "lr": 0.03591, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34719, "top5_acc": 0.60391, "loss_cls": 3.71967, "loss": 3.71967, "time": 0.8169} +{"mode": "train", "epoch": 89, "iter": 2500, "lr": 0.03588, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34906, "top5_acc": 0.60328, "loss_cls": 3.72404, "loss": 3.72404, "time": 0.8161} +{"mode": "train", "epoch": 89, "iter": 2600, "lr": 0.03586, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35094, "top5_acc": 0.6, "loss_cls": 3.73138, "loss": 3.73138, "time": 0.81231} +{"mode": "train", "epoch": 89, "iter": 2700, "lr": 0.03583, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3525, "top5_acc": 0.60469, "loss_cls": 3.71382, "loss": 3.71382, "time": 0.81322} +{"mode": "train", "epoch": 89, "iter": 2800, "lr": 0.0358, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33938, "top5_acc": 0.59453, "loss_cls": 3.77325, "loss": 3.77325, "time": 0.81817} +{"mode": "train", "epoch": 89, "iter": 2900, "lr": 0.03578, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34188, "top5_acc": 0.59641, "loss_cls": 3.72863, "loss": 3.72863, "time": 0.81432} +{"mode": "train", "epoch": 89, "iter": 3000, "lr": 0.03575, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33516, "top5_acc": 0.60594, "loss_cls": 3.73643, "loss": 3.73643, "time": 0.815} +{"mode": "train", "epoch": 89, "iter": 3100, "lr": 0.03572, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.345, "top5_acc": 0.60531, "loss_cls": 3.71191, "loss": 3.71191, "time": 0.8178} +{"mode": "train", "epoch": 89, "iter": 3200, "lr": 0.03569, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33328, "top5_acc": 0.60188, "loss_cls": 3.74082, "loss": 3.74082, "time": 0.81971} +{"mode": "train", "epoch": 89, "iter": 3300, "lr": 0.03567, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3475, "top5_acc": 0.60953, "loss_cls": 3.69159, "loss": 3.69159, "time": 0.81429} +{"mode": "train", "epoch": 89, "iter": 3400, "lr": 0.03564, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34, "top5_acc": 0.60094, "loss_cls": 3.73729, "loss": 3.73729, "time": 0.81548} +{"mode": "train", "epoch": 89, "iter": 3500, "lr": 0.03561, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35156, "top5_acc": 0.60734, "loss_cls": 3.73723, "loss": 3.73723, "time": 0.81913} +{"mode": "train", "epoch": 89, "iter": 3600, "lr": 0.03559, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35031, "top5_acc": 0.61172, "loss_cls": 3.70037, "loss": 3.70037, "time": 0.81621} +{"mode": "train", "epoch": 89, "iter": 3700, "lr": 0.03556, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34359, "top5_acc": 0.59688, "loss_cls": 3.75574, "loss": 3.75574, "time": 0.81978} +{"mode": "val", "epoch": 89, "iter": 309, "lr": 0.03555, "top1_acc": 0.28658, "top5_acc": 0.53523, "mean_class_accuracy": 0.28608} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.03552, "memory": 15990, "data_time": 1.39438, "top1_acc": 0.35953, "top5_acc": 0.62109, "loss_cls": 3.62193, "loss": 3.62193, "time": 2.38778} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.0355, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34234, "top5_acc": 0.61344, "loss_cls": 3.69622, "loss": 3.69622, "time": 0.82623} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.03547, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34844, "top5_acc": 0.61453, "loss_cls": 3.68363, "loss": 3.68363, "time": 0.8182} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.03544, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34734, "top5_acc": 0.62094, "loss_cls": 3.63815, "loss": 3.63815, "time": 0.82172} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.03541, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35031, "top5_acc": 0.61359, "loss_cls": 3.67664, "loss": 3.67664, "time": 0.81957} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.03539, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3475, "top5_acc": 0.61312, "loss_cls": 3.67516, "loss": 3.67516, "time": 0.81615} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.03536, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35203, "top5_acc": 0.60953, "loss_cls": 3.68626, "loss": 3.68626, "time": 0.82264} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.03533, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34875, "top5_acc": 0.61641, "loss_cls": 3.67523, "loss": 3.67523, "time": 0.81658} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.03531, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35531, "top5_acc": 0.60984, "loss_cls": 3.68253, "loss": 3.68253, "time": 0.81229} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.03528, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34094, "top5_acc": 0.60531, "loss_cls": 3.72067, "loss": 3.72067, "time": 0.81816} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.03525, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34609, "top5_acc": 0.60625, "loss_cls": 3.71499, "loss": 3.71499, "time": 0.81254} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.03523, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34469, "top5_acc": 0.60328, "loss_cls": 3.72059, "loss": 3.72059, "time": 0.81308} +{"mode": "train", "epoch": 90, "iter": 1300, "lr": 0.0352, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34844, "top5_acc": 0.61484, "loss_cls": 3.68262, "loss": 3.68262, "time": 0.81432} +{"mode": "train", "epoch": 90, "iter": 1400, "lr": 0.03517, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34312, "top5_acc": 0.60516, "loss_cls": 3.74278, "loss": 3.74278, "time": 0.8176} +{"mode": "train", "epoch": 90, "iter": 1500, "lr": 0.03515, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35906, "top5_acc": 0.61891, "loss_cls": 3.64979, "loss": 3.64979, "time": 0.81903} +{"mode": "train", "epoch": 90, "iter": 1600, "lr": 0.03512, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35266, "top5_acc": 0.60594, "loss_cls": 3.71193, "loss": 3.71193, "time": 0.82536} +{"mode": "train", "epoch": 90, "iter": 1700, "lr": 0.03509, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34938, "top5_acc": 0.60859, "loss_cls": 3.69776, "loss": 3.69776, "time": 0.8138} +{"mode": "train", "epoch": 90, "iter": 1800, "lr": 0.03507, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34406, "top5_acc": 0.60234, "loss_cls": 3.75856, "loss": 3.75856, "time": 0.82237} +{"mode": "train", "epoch": 90, "iter": 1900, "lr": 0.03504, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34391, "top5_acc": 0.61156, "loss_cls": 3.70474, "loss": 3.70474, "time": 0.82168} +{"mode": "train", "epoch": 90, "iter": 2000, "lr": 0.03501, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34688, "top5_acc": 0.60781, "loss_cls": 3.69345, "loss": 3.69345, "time": 0.82027} +{"mode": "train", "epoch": 90, "iter": 2100, "lr": 0.03499, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34672, "top5_acc": 0.60609, "loss_cls": 3.71548, "loss": 3.71548, "time": 0.81706} +{"mode": "train", "epoch": 90, "iter": 2200, "lr": 0.03496, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35297, "top5_acc": 0.61094, "loss_cls": 3.69036, "loss": 3.69036, "time": 0.81311} +{"mode": "train", "epoch": 90, "iter": 2300, "lr": 0.03493, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33891, "top5_acc": 0.595, "loss_cls": 3.74263, "loss": 3.74263, "time": 0.81591} +{"mode": "train", "epoch": 90, "iter": 2400, "lr": 0.03491, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34844, "top5_acc": 0.60391, "loss_cls": 3.70289, "loss": 3.70289, "time": 0.8157} +{"mode": "train", "epoch": 90, "iter": 2500, "lr": 0.03488, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33906, "top5_acc": 0.60156, "loss_cls": 3.72852, "loss": 3.72852, "time": 0.8176} +{"mode": "train", "epoch": 90, "iter": 2600, "lr": 0.03485, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35719, "top5_acc": 0.60625, "loss_cls": 3.6792, "loss": 3.6792, "time": 0.81361} +{"mode": "train", "epoch": 90, "iter": 2700, "lr": 0.03483, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35625, "top5_acc": 0.61312, "loss_cls": 3.71303, "loss": 3.71303, "time": 0.82632} +{"mode": "train", "epoch": 90, "iter": 2800, "lr": 0.0348, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35359, "top5_acc": 0.6125, "loss_cls": 3.7014, "loss": 3.7014, "time": 0.81835} +{"mode": "train", "epoch": 90, "iter": 2900, "lr": 0.03477, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34312, "top5_acc": 0.59875, "loss_cls": 3.70997, "loss": 3.70997, "time": 0.81559} +{"mode": "train", "epoch": 90, "iter": 3000, "lr": 0.03475, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34375, "top5_acc": 0.60016, "loss_cls": 3.75062, "loss": 3.75062, "time": 0.81392} +{"mode": "train", "epoch": 90, "iter": 3100, "lr": 0.03472, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35453, "top5_acc": 0.60484, "loss_cls": 3.69583, "loss": 3.69583, "time": 0.8156} +{"mode": "train", "epoch": 90, "iter": 3200, "lr": 0.03469, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34484, "top5_acc": 0.60625, "loss_cls": 3.71347, "loss": 3.71347, "time": 0.81667} +{"mode": "train", "epoch": 90, "iter": 3300, "lr": 0.03467, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35281, "top5_acc": 0.61141, "loss_cls": 3.66043, "loss": 3.66043, "time": 0.81373} +{"mode": "train", "epoch": 90, "iter": 3400, "lr": 0.03464, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34797, "top5_acc": 0.59938, "loss_cls": 3.71503, "loss": 3.71503, "time": 0.81843} +{"mode": "train", "epoch": 90, "iter": 3500, "lr": 0.03461, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34766, "top5_acc": 0.60578, "loss_cls": 3.70629, "loss": 3.70629, "time": 0.81907} +{"mode": "train", "epoch": 90, "iter": 3600, "lr": 0.03459, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34484, "top5_acc": 0.60906, "loss_cls": 3.67789, "loss": 3.67789, "time": 0.81794} +{"mode": "train", "epoch": 90, "iter": 3700, "lr": 0.03456, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34344, "top5_acc": 0.60234, "loss_cls": 3.74173, "loss": 3.74173, "time": 0.81349} +{"mode": "val", "epoch": 90, "iter": 309, "lr": 0.03455, "top1_acc": 0.28841, "top5_acc": 0.54521, "mean_class_accuracy": 0.28813} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.03452, "memory": 15990, "data_time": 1.38961, "top1_acc": 0.36719, "top5_acc": 0.6275, "loss_cls": 3.58151, "loss": 3.58151, "time": 2.37649} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0345, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35812, "top5_acc": 0.61969, "loss_cls": 3.63498, "loss": 3.63498, "time": 0.82017} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.03447, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36047, "top5_acc": 0.62109, "loss_cls": 3.63213, "loss": 3.63213, "time": 0.82398} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.03444, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36094, "top5_acc": 0.61219, "loss_cls": 3.65982, "loss": 3.65982, "time": 0.81929} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.03442, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35391, "top5_acc": 0.61312, "loss_cls": 3.66402, "loss": 3.66402, "time": 0.81864} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.03439, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35281, "top5_acc": 0.62172, "loss_cls": 3.64103, "loss": 3.64103, "time": 0.82249} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.03436, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34844, "top5_acc": 0.61484, "loss_cls": 3.68407, "loss": 3.68407, "time": 0.82271} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.03434, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34141, "top5_acc": 0.61203, "loss_cls": 3.70286, "loss": 3.70286, "time": 0.8171} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.03431, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34828, "top5_acc": 0.61266, "loss_cls": 3.69424, "loss": 3.69424, "time": 0.81609} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.03428, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.355, "top5_acc": 0.61016, "loss_cls": 3.66493, "loss": 3.66493, "time": 0.81548} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.03426, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34688, "top5_acc": 0.61047, "loss_cls": 3.68162, "loss": 3.68162, "time": 0.82237} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.03423, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34484, "top5_acc": 0.60891, "loss_cls": 3.7024, "loss": 3.7024, "time": 0.81841} +{"mode": "train", "epoch": 91, "iter": 1300, "lr": 0.0342, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34047, "top5_acc": 0.60422, "loss_cls": 3.73292, "loss": 3.73292, "time": 0.81228} +{"mode": "train", "epoch": 91, "iter": 1400, "lr": 0.03418, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36047, "top5_acc": 0.60953, "loss_cls": 3.66449, "loss": 3.66449, "time": 0.81355} +{"mode": "train", "epoch": 91, "iter": 1500, "lr": 0.03415, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34609, "top5_acc": 0.6075, "loss_cls": 3.73278, "loss": 3.73278, "time": 0.82399} +{"mode": "train", "epoch": 91, "iter": 1600, "lr": 0.03412, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35297, "top5_acc": 0.61828, "loss_cls": 3.65763, "loss": 3.65763, "time": 0.82678} +{"mode": "train", "epoch": 91, "iter": 1700, "lr": 0.0341, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36516, "top5_acc": 0.61469, "loss_cls": 3.66493, "loss": 3.66493, "time": 0.82151} +{"mode": "train", "epoch": 91, "iter": 1800, "lr": 0.03407, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35906, "top5_acc": 0.61734, "loss_cls": 3.65547, "loss": 3.65547, "time": 0.81928} +{"mode": "train", "epoch": 91, "iter": 1900, "lr": 0.03405, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35141, "top5_acc": 0.6075, "loss_cls": 3.70146, "loss": 3.70146, "time": 0.81679} +{"mode": "train", "epoch": 91, "iter": 2000, "lr": 0.03402, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34578, "top5_acc": 0.60422, "loss_cls": 3.70547, "loss": 3.70547, "time": 0.81517} +{"mode": "train", "epoch": 91, "iter": 2100, "lr": 0.03399, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36109, "top5_acc": 0.62453, "loss_cls": 3.64011, "loss": 3.64011, "time": 0.81473} +{"mode": "train", "epoch": 91, "iter": 2200, "lr": 0.03397, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34938, "top5_acc": 0.61094, "loss_cls": 3.67573, "loss": 3.67573, "time": 0.81279} +{"mode": "train", "epoch": 91, "iter": 2300, "lr": 0.03394, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35125, "top5_acc": 0.61391, "loss_cls": 3.66185, "loss": 3.66185, "time": 0.81756} +{"mode": "train", "epoch": 91, "iter": 2400, "lr": 0.03391, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34344, "top5_acc": 0.60328, "loss_cls": 3.72457, "loss": 3.72457, "time": 0.81665} +{"mode": "train", "epoch": 91, "iter": 2500, "lr": 0.03389, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34719, "top5_acc": 0.60875, "loss_cls": 3.70076, "loss": 3.70076, "time": 0.81383} +{"mode": "train", "epoch": 91, "iter": 2600, "lr": 0.03386, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35516, "top5_acc": 0.61703, "loss_cls": 3.65444, "loss": 3.65444, "time": 0.8196} +{"mode": "train", "epoch": 91, "iter": 2700, "lr": 0.03383, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35391, "top5_acc": 0.60625, "loss_cls": 3.72278, "loss": 3.72278, "time": 0.8189} +{"mode": "train", "epoch": 91, "iter": 2800, "lr": 0.03381, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34203, "top5_acc": 0.60438, "loss_cls": 3.72062, "loss": 3.72062, "time": 0.81308} +{"mode": "train", "epoch": 91, "iter": 2900, "lr": 0.03378, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35359, "top5_acc": 0.61266, "loss_cls": 3.66569, "loss": 3.66569, "time": 0.81542} +{"mode": "train", "epoch": 91, "iter": 3000, "lr": 0.03375, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34188, "top5_acc": 0.60266, "loss_cls": 3.73044, "loss": 3.73044, "time": 0.81502} +{"mode": "train", "epoch": 91, "iter": 3100, "lr": 0.03373, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36359, "top5_acc": 0.62062, "loss_cls": 3.6578, "loss": 3.6578, "time": 0.82081} +{"mode": "train", "epoch": 91, "iter": 3200, "lr": 0.0337, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34359, "top5_acc": 0.59656, "loss_cls": 3.74968, "loss": 3.74968, "time": 0.81637} +{"mode": "train", "epoch": 91, "iter": 3300, "lr": 0.03367, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35234, "top5_acc": 0.60984, "loss_cls": 3.68527, "loss": 3.68527, "time": 0.81338} +{"mode": "train", "epoch": 91, "iter": 3400, "lr": 0.03365, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35016, "top5_acc": 0.60812, "loss_cls": 3.69923, "loss": 3.69923, "time": 0.81171} +{"mode": "train", "epoch": 91, "iter": 3500, "lr": 0.03362, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35688, "top5_acc": 0.61469, "loss_cls": 3.63328, "loss": 3.63328, "time": 0.81431} +{"mode": "train", "epoch": 91, "iter": 3600, "lr": 0.0336, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35281, "top5_acc": 0.60281, "loss_cls": 3.68679, "loss": 3.68679, "time": 0.82205} +{"mode": "train", "epoch": 91, "iter": 3700, "lr": 0.03357, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33812, "top5_acc": 0.59844, "loss_cls": 3.73746, "loss": 3.73746, "time": 0.81514} +{"mode": "val", "epoch": 91, "iter": 309, "lr": 0.03356, "top1_acc": 0.29428, "top5_acc": 0.54338, "mean_class_accuracy": 0.29413} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.03353, "memory": 15990, "data_time": 1.37875, "top1_acc": 0.36062, "top5_acc": 0.62609, "loss_cls": 3.61364, "loss": 3.61364, "time": 2.35699} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.0335, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36094, "top5_acc": 0.625, "loss_cls": 3.59516, "loss": 3.59516, "time": 0.82481} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.03348, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35547, "top5_acc": 0.61328, "loss_cls": 3.64948, "loss": 3.64948, "time": 0.81978} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.03345, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35438, "top5_acc": 0.61766, "loss_cls": 3.62462, "loss": 3.62462, "time": 0.81509} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.03342, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35531, "top5_acc": 0.61562, "loss_cls": 3.66175, "loss": 3.66175, "time": 0.82395} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.0334, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34484, "top5_acc": 0.61062, "loss_cls": 3.68273, "loss": 3.68273, "time": 0.81561} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.03337, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35938, "top5_acc": 0.61891, "loss_cls": 3.64817, "loss": 3.64817, "time": 0.81773} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.03335, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36203, "top5_acc": 0.62297, "loss_cls": 3.61175, "loss": 3.61175, "time": 0.81465} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.03332, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34812, "top5_acc": 0.61094, "loss_cls": 3.69369, "loss": 3.69369, "time": 0.81329} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.03329, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3525, "top5_acc": 0.61359, "loss_cls": 3.66176, "loss": 3.66176, "time": 0.81322} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.03327, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34891, "top5_acc": 0.61391, "loss_cls": 3.67388, "loss": 3.67388, "time": 0.81296} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.03324, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35406, "top5_acc": 0.61047, "loss_cls": 3.65714, "loss": 3.65714, "time": 0.81503} +{"mode": "train", "epoch": 92, "iter": 1300, "lr": 0.03321, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35125, "top5_acc": 0.61062, "loss_cls": 3.67765, "loss": 3.67765, "time": 0.81881} +{"mode": "train", "epoch": 92, "iter": 1400, "lr": 0.03319, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35109, "top5_acc": 0.60891, "loss_cls": 3.66684, "loss": 3.66684, "time": 0.81101} +{"mode": "train", "epoch": 92, "iter": 1500, "lr": 0.03316, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34953, "top5_acc": 0.61281, "loss_cls": 3.67245, "loss": 3.67245, "time": 0.81624} +{"mode": "train", "epoch": 92, "iter": 1600, "lr": 0.03314, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34578, "top5_acc": 0.61438, "loss_cls": 3.63663, "loss": 3.63663, "time": 0.82504} +{"mode": "train", "epoch": 92, "iter": 1700, "lr": 0.03311, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35203, "top5_acc": 0.60875, "loss_cls": 3.6813, "loss": 3.6813, "time": 0.82005} +{"mode": "train", "epoch": 92, "iter": 1800, "lr": 0.03308, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35562, "top5_acc": 0.615, "loss_cls": 3.67795, "loss": 3.67795, "time": 0.82627} +{"mode": "train", "epoch": 92, "iter": 1900, "lr": 0.03306, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35891, "top5_acc": 0.61297, "loss_cls": 3.69933, "loss": 3.69933, "time": 0.82364} +{"mode": "train", "epoch": 92, "iter": 2000, "lr": 0.03303, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34781, "top5_acc": 0.60297, "loss_cls": 3.72738, "loss": 3.72738, "time": 0.82066} +{"mode": "train", "epoch": 92, "iter": 2100, "lr": 0.033, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35344, "top5_acc": 0.61156, "loss_cls": 3.68958, "loss": 3.68958, "time": 0.8234} +{"mode": "train", "epoch": 92, "iter": 2200, "lr": 0.03298, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35281, "top5_acc": 0.60812, "loss_cls": 3.65967, "loss": 3.65967, "time": 0.82481} +{"mode": "train", "epoch": 92, "iter": 2300, "lr": 0.03295, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35094, "top5_acc": 0.60891, "loss_cls": 3.68298, "loss": 3.68298, "time": 0.81573} +{"mode": "train", "epoch": 92, "iter": 2400, "lr": 0.03292, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35094, "top5_acc": 0.61906, "loss_cls": 3.65818, "loss": 3.65818, "time": 0.8142} +{"mode": "train", "epoch": 92, "iter": 2500, "lr": 0.0329, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34719, "top5_acc": 0.60797, "loss_cls": 3.68187, "loss": 3.68187, "time": 0.81507} +{"mode": "train", "epoch": 92, "iter": 2600, "lr": 0.03287, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34422, "top5_acc": 0.61594, "loss_cls": 3.69941, "loss": 3.69941, "time": 0.81064} +{"mode": "train", "epoch": 92, "iter": 2700, "lr": 0.03285, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35328, "top5_acc": 0.60359, "loss_cls": 3.69509, "loss": 3.69509, "time": 0.81508} +{"mode": "train", "epoch": 92, "iter": 2800, "lr": 0.03282, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34125, "top5_acc": 0.60047, "loss_cls": 3.73256, "loss": 3.73256, "time": 0.81817} +{"mode": "train", "epoch": 92, "iter": 2900, "lr": 0.03279, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36406, "top5_acc": 0.61562, "loss_cls": 3.63612, "loss": 3.63612, "time": 0.81271} +{"mode": "train", "epoch": 92, "iter": 3000, "lr": 0.03277, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36109, "top5_acc": 0.61766, "loss_cls": 3.63962, "loss": 3.63962, "time": 0.81369} +{"mode": "train", "epoch": 92, "iter": 3100, "lr": 0.03274, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36156, "top5_acc": 0.61344, "loss_cls": 3.66991, "loss": 3.66991, "time": 0.82105} +{"mode": "train", "epoch": 92, "iter": 3200, "lr": 0.03271, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35328, "top5_acc": 0.60188, "loss_cls": 3.71587, "loss": 3.71587, "time": 0.81697} +{"mode": "train", "epoch": 92, "iter": 3300, "lr": 0.03269, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35266, "top5_acc": 0.61125, "loss_cls": 3.67305, "loss": 3.67305, "time": 0.81176} +{"mode": "train", "epoch": 92, "iter": 3400, "lr": 0.03266, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35438, "top5_acc": 0.61125, "loss_cls": 3.67748, "loss": 3.67748, "time": 0.8149} +{"mode": "train", "epoch": 92, "iter": 3500, "lr": 0.03264, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36234, "top5_acc": 0.61812, "loss_cls": 3.65957, "loss": 3.65957, "time": 0.81538} +{"mode": "train", "epoch": 92, "iter": 3600, "lr": 0.03261, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35, "top5_acc": 0.60938, "loss_cls": 3.6727, "loss": 3.6727, "time": 0.8156} +{"mode": "train", "epoch": 92, "iter": 3700, "lr": 0.03258, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34922, "top5_acc": 0.60438, "loss_cls": 3.69063, "loss": 3.69063, "time": 0.81531} +{"mode": "val", "epoch": 92, "iter": 309, "lr": 0.03257, "top1_acc": 0.30851, "top5_acc": 0.56329, "mean_class_accuracy": 0.3082} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.03255, "memory": 15990, "data_time": 1.37663, "top1_acc": 0.36094, "top5_acc": 0.61781, "loss_cls": 3.60965, "loss": 3.60965, "time": 2.36294} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.03252, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36578, "top5_acc": 0.62187, "loss_cls": 3.59347, "loss": 3.59347, "time": 0.82418} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.03249, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36406, "top5_acc": 0.62328, "loss_cls": 3.59865, "loss": 3.59865, "time": 0.82048} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.03247, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35953, "top5_acc": 0.61984, "loss_cls": 3.63785, "loss": 3.63785, "time": 0.81791} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.03244, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35375, "top5_acc": 0.61281, "loss_cls": 3.65665, "loss": 3.65665, "time": 0.82696} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.03241, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34844, "top5_acc": 0.60953, "loss_cls": 3.67613, "loss": 3.67613, "time": 0.81554} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.03239, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35859, "top5_acc": 0.61547, "loss_cls": 3.63052, "loss": 3.63052, "time": 0.81991} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.03236, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35828, "top5_acc": 0.61938, "loss_cls": 3.61723, "loss": 3.61723, "time": 0.81458} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.03234, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36688, "top5_acc": 0.62219, "loss_cls": 3.60372, "loss": 3.60372, "time": 0.81508} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.03231, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35297, "top5_acc": 0.61391, "loss_cls": 3.64653, "loss": 3.64653, "time": 0.81751} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.03228, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35797, "top5_acc": 0.61312, "loss_cls": 3.67991, "loss": 3.67991, "time": 0.81285} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.03226, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35844, "top5_acc": 0.61562, "loss_cls": 3.66186, "loss": 3.66186, "time": 0.81433} +{"mode": "train", "epoch": 93, "iter": 1300, "lr": 0.03223, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36828, "top5_acc": 0.6275, "loss_cls": 3.60049, "loss": 3.60049, "time": 0.81663} +{"mode": "train", "epoch": 93, "iter": 1400, "lr": 0.03221, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36281, "top5_acc": 0.61766, "loss_cls": 3.64263, "loss": 3.64263, "time": 0.81741} +{"mode": "train", "epoch": 93, "iter": 1500, "lr": 0.03218, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35188, "top5_acc": 0.61031, "loss_cls": 3.68192, "loss": 3.68192, "time": 0.81996} +{"mode": "train", "epoch": 93, "iter": 1600, "lr": 0.03215, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35938, "top5_acc": 0.61781, "loss_cls": 3.64125, "loss": 3.64125, "time": 0.82297} +{"mode": "train", "epoch": 93, "iter": 1700, "lr": 0.03213, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35375, "top5_acc": 0.60578, "loss_cls": 3.68323, "loss": 3.68323, "time": 0.81695} +{"mode": "train", "epoch": 93, "iter": 1800, "lr": 0.0321, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35594, "top5_acc": 0.61281, "loss_cls": 3.65808, "loss": 3.65808, "time": 0.82139} +{"mode": "train", "epoch": 93, "iter": 1900, "lr": 0.03207, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.345, "top5_acc": 0.60719, "loss_cls": 3.71036, "loss": 3.71036, "time": 0.81652} +{"mode": "train", "epoch": 93, "iter": 2000, "lr": 0.03205, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36344, "top5_acc": 0.62562, "loss_cls": 3.60684, "loss": 3.60684, "time": 0.81722} +{"mode": "train", "epoch": 93, "iter": 2100, "lr": 0.03202, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35359, "top5_acc": 0.60703, "loss_cls": 3.66332, "loss": 3.66332, "time": 0.82037} +{"mode": "train", "epoch": 93, "iter": 2200, "lr": 0.032, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35609, "top5_acc": 0.61344, "loss_cls": 3.67843, "loss": 3.67843, "time": 0.81754} +{"mode": "train", "epoch": 93, "iter": 2300, "lr": 0.03197, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36484, "top5_acc": 0.62219, "loss_cls": 3.61973, "loss": 3.61973, "time": 0.81522} +{"mode": "train", "epoch": 93, "iter": 2400, "lr": 0.03194, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35344, "top5_acc": 0.61078, "loss_cls": 3.65998, "loss": 3.65998, "time": 0.81097} +{"mode": "train", "epoch": 93, "iter": 2500, "lr": 0.03192, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36312, "top5_acc": 0.62344, "loss_cls": 3.59837, "loss": 3.59837, "time": 0.81854} +{"mode": "train", "epoch": 93, "iter": 2600, "lr": 0.03189, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35969, "top5_acc": 0.62016, "loss_cls": 3.63097, "loss": 3.63097, "time": 0.81142} +{"mode": "train", "epoch": 93, "iter": 2700, "lr": 0.03187, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35594, "top5_acc": 0.61219, "loss_cls": 3.66068, "loss": 3.66068, "time": 0.81036} +{"mode": "train", "epoch": 93, "iter": 2800, "lr": 0.03184, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36109, "top5_acc": 0.61187, "loss_cls": 3.65974, "loss": 3.65974, "time": 0.81549} +{"mode": "train", "epoch": 93, "iter": 2900, "lr": 0.03181, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36141, "top5_acc": 0.61641, "loss_cls": 3.66245, "loss": 3.66245, "time": 0.81577} +{"mode": "train", "epoch": 93, "iter": 3000, "lr": 0.03179, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34844, "top5_acc": 0.61234, "loss_cls": 3.68458, "loss": 3.68458, "time": 0.81629} +{"mode": "train", "epoch": 93, "iter": 3100, "lr": 0.03176, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34766, "top5_acc": 0.61594, "loss_cls": 3.65924, "loss": 3.65924, "time": 0.81561} +{"mode": "train", "epoch": 93, "iter": 3200, "lr": 0.03174, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35016, "top5_acc": 0.60672, "loss_cls": 3.71929, "loss": 3.71929, "time": 0.81286} +{"mode": "train", "epoch": 93, "iter": 3300, "lr": 0.03171, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35656, "top5_acc": 0.62516, "loss_cls": 3.64197, "loss": 3.64197, "time": 0.81274} +{"mode": "train", "epoch": 93, "iter": 3400, "lr": 0.03168, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3575, "top5_acc": 0.61125, "loss_cls": 3.67481, "loss": 3.67481, "time": 0.81665} +{"mode": "train", "epoch": 93, "iter": 3500, "lr": 0.03166, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34938, "top5_acc": 0.6075, "loss_cls": 3.70622, "loss": 3.70622, "time": 0.81838} +{"mode": "train", "epoch": 93, "iter": 3600, "lr": 0.03163, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36734, "top5_acc": 0.61969, "loss_cls": 3.59487, "loss": 3.59487, "time": 0.81728} +{"mode": "train", "epoch": 93, "iter": 3700, "lr": 0.03161, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35125, "top5_acc": 0.61062, "loss_cls": 3.67522, "loss": 3.67522, "time": 0.81383} +{"mode": "val", "epoch": 93, "iter": 309, "lr": 0.03159, "top1_acc": 0.30431, "top5_acc": 0.55493, "mean_class_accuracy": 0.30396} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.03157, "memory": 15990, "data_time": 1.35032, "top1_acc": 0.35422, "top5_acc": 0.62125, "loss_cls": 3.62581, "loss": 3.62581, "time": 2.33937} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.03154, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36328, "top5_acc": 0.62516, "loss_cls": 3.6168, "loss": 3.6168, "time": 0.81828} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.03152, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36766, "top5_acc": 0.63094, "loss_cls": 3.57968, "loss": 3.57968, "time": 0.8167} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.03149, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37078, "top5_acc": 0.63109, "loss_cls": 3.58552, "loss": 3.58552, "time": 0.82321} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.03146, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37, "top5_acc": 0.62344, "loss_cls": 3.58118, "loss": 3.58118, "time": 0.82214} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.03144, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36719, "top5_acc": 0.61812, "loss_cls": 3.60081, "loss": 3.60081, "time": 0.82252} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.03141, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37047, "top5_acc": 0.62547, "loss_cls": 3.59426, "loss": 3.59426, "time": 0.82013} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.03139, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35281, "top5_acc": 0.62391, "loss_cls": 3.62129, "loss": 3.62129, "time": 0.82274} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.03136, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35891, "top5_acc": 0.61594, "loss_cls": 3.6557, "loss": 3.6557, "time": 0.81703} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.03133, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35734, "top5_acc": 0.61781, "loss_cls": 3.60308, "loss": 3.60308, "time": 0.81683} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.03131, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36109, "top5_acc": 0.62109, "loss_cls": 3.60386, "loss": 3.60386, "time": 0.81985} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.03128, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36969, "top5_acc": 0.62375, "loss_cls": 3.5873, "loss": 3.5873, "time": 0.81128} +{"mode": "train", "epoch": 94, "iter": 1300, "lr": 0.03126, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35797, "top5_acc": 0.61297, "loss_cls": 3.65982, "loss": 3.65982, "time": 0.81745} +{"mode": "train", "epoch": 94, "iter": 1400, "lr": 0.03123, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36422, "top5_acc": 0.62156, "loss_cls": 3.61902, "loss": 3.61902, "time": 0.81226} +{"mode": "train", "epoch": 94, "iter": 1500, "lr": 0.0312, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36438, "top5_acc": 0.61281, "loss_cls": 3.63942, "loss": 3.63942, "time": 0.82184} +{"mode": "train", "epoch": 94, "iter": 1600, "lr": 0.03118, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35719, "top5_acc": 0.61219, "loss_cls": 3.64442, "loss": 3.64442, "time": 0.82076} +{"mode": "train", "epoch": 94, "iter": 1700, "lr": 0.03115, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35359, "top5_acc": 0.61359, "loss_cls": 3.65404, "loss": 3.65404, "time": 0.82402} +{"mode": "train", "epoch": 94, "iter": 1800, "lr": 0.03113, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34953, "top5_acc": 0.61187, "loss_cls": 3.69173, "loss": 3.69173, "time": 0.82313} +{"mode": "train", "epoch": 94, "iter": 1900, "lr": 0.0311, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35062, "top5_acc": 0.61469, "loss_cls": 3.69806, "loss": 3.69806, "time": 0.82022} +{"mode": "train", "epoch": 94, "iter": 2000, "lr": 0.03108, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36344, "top5_acc": 0.62609, "loss_cls": 3.60076, "loss": 3.60076, "time": 0.8151} +{"mode": "train", "epoch": 94, "iter": 2100, "lr": 0.03105, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35953, "top5_acc": 0.62, "loss_cls": 3.6318, "loss": 3.6318, "time": 0.82084} +{"mode": "train", "epoch": 94, "iter": 2200, "lr": 0.03102, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36062, "top5_acc": 0.62172, "loss_cls": 3.63396, "loss": 3.63396, "time": 0.81809} +{"mode": "train", "epoch": 94, "iter": 2300, "lr": 0.031, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35594, "top5_acc": 0.60812, "loss_cls": 3.6463, "loss": 3.6463, "time": 0.81513} +{"mode": "train", "epoch": 94, "iter": 2400, "lr": 0.03097, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35516, "top5_acc": 0.6075, "loss_cls": 3.68578, "loss": 3.68578, "time": 0.81868} +{"mode": "train", "epoch": 94, "iter": 2500, "lr": 0.03095, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36047, "top5_acc": 0.62125, "loss_cls": 3.63516, "loss": 3.63516, "time": 0.82085} +{"mode": "train", "epoch": 94, "iter": 2600, "lr": 0.03092, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35547, "top5_acc": 0.60891, "loss_cls": 3.68387, "loss": 3.68387, "time": 0.81709} +{"mode": "train", "epoch": 94, "iter": 2700, "lr": 0.03089, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36797, "top5_acc": 0.62984, "loss_cls": 3.58366, "loss": 3.58366, "time": 0.81566} +{"mode": "train", "epoch": 94, "iter": 2800, "lr": 0.03087, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35734, "top5_acc": 0.62219, "loss_cls": 3.63841, "loss": 3.63841, "time": 0.81616} +{"mode": "train", "epoch": 94, "iter": 2900, "lr": 0.03084, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35031, "top5_acc": 0.60344, "loss_cls": 3.71768, "loss": 3.71768, "time": 0.81532} +{"mode": "train", "epoch": 94, "iter": 3000, "lr": 0.03082, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35969, "top5_acc": 0.62125, "loss_cls": 3.62758, "loss": 3.62758, "time": 0.81039} +{"mode": "train", "epoch": 94, "iter": 3100, "lr": 0.03079, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35, "top5_acc": 0.61578, "loss_cls": 3.67031, "loss": 3.67031, "time": 0.82033} +{"mode": "train", "epoch": 94, "iter": 3200, "lr": 0.03077, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35375, "top5_acc": 0.60875, "loss_cls": 3.65105, "loss": 3.65105, "time": 0.81885} +{"mode": "train", "epoch": 94, "iter": 3300, "lr": 0.03074, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3525, "top5_acc": 0.60453, "loss_cls": 3.6743, "loss": 3.6743, "time": 0.8136} +{"mode": "train", "epoch": 94, "iter": 3400, "lr": 0.03071, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35422, "top5_acc": 0.62125, "loss_cls": 3.64768, "loss": 3.64768, "time": 0.80886} +{"mode": "train", "epoch": 94, "iter": 3500, "lr": 0.03069, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.345, "top5_acc": 0.61391, "loss_cls": 3.6926, "loss": 3.6926, "time": 0.81501} +{"mode": "train", "epoch": 94, "iter": 3600, "lr": 0.03066, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36125, "top5_acc": 0.62062, "loss_cls": 3.61395, "loss": 3.61395, "time": 0.81511} +{"mode": "train", "epoch": 94, "iter": 3700, "lr": 0.03064, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36031, "top5_acc": 0.61469, "loss_cls": 3.65894, "loss": 3.65894, "time": 0.81353} +{"mode": "val", "epoch": 94, "iter": 309, "lr": 0.03062, "top1_acc": 0.29048, "top5_acc": 0.54931, "mean_class_accuracy": 0.29052} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.0306, "memory": 15990, "data_time": 1.3479, "top1_acc": 0.36859, "top5_acc": 0.63141, "loss_cls": 3.5474, "loss": 3.5474, "time": 2.34291} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.03057, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36844, "top5_acc": 0.6225, "loss_cls": 3.5703, "loss": 3.5703, "time": 0.82318} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.03055, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36234, "top5_acc": 0.62297, "loss_cls": 3.61863, "loss": 3.61863, "time": 0.81861} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.03052, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35828, "top5_acc": 0.62125, "loss_cls": 3.61178, "loss": 3.61178, "time": 0.81797} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.0305, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35969, "top5_acc": 0.62031, "loss_cls": 3.63401, "loss": 3.63401, "time": 0.82135} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.03047, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36266, "top5_acc": 0.62438, "loss_cls": 3.59046, "loss": 3.59046, "time": 0.81931} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.03044, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35484, "top5_acc": 0.62328, "loss_cls": 3.60504, "loss": 3.60504, "time": 0.82075} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.03042, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36969, "top5_acc": 0.62844, "loss_cls": 3.5737, "loss": 3.5737, "time": 0.81804} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.03039, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36234, "top5_acc": 0.62328, "loss_cls": 3.59797, "loss": 3.59797, "time": 0.81004} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.03037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36734, "top5_acc": 0.62141, "loss_cls": 3.5838, "loss": 3.5838, "time": 0.81576} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.03034, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36156, "top5_acc": 0.62031, "loss_cls": 3.61307, "loss": 3.61307, "time": 0.81856} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.03032, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35875, "top5_acc": 0.60859, "loss_cls": 3.69933, "loss": 3.69933, "time": 0.81246} +{"mode": "train", "epoch": 95, "iter": 1300, "lr": 0.03029, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37266, "top5_acc": 0.62953, "loss_cls": 3.57895, "loss": 3.57895, "time": 0.81265} +{"mode": "train", "epoch": 95, "iter": 1400, "lr": 0.03026, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36344, "top5_acc": 0.62109, "loss_cls": 3.64383, "loss": 3.64383, "time": 0.81106} +{"mode": "train", "epoch": 95, "iter": 1500, "lr": 0.03024, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36875, "top5_acc": 0.62438, "loss_cls": 3.5834, "loss": 3.5834, "time": 0.81335} +{"mode": "train", "epoch": 95, "iter": 1600, "lr": 0.03021, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35016, "top5_acc": 0.60984, "loss_cls": 3.66446, "loss": 3.66446, "time": 0.83042} +{"mode": "train", "epoch": 95, "iter": 1700, "lr": 0.03019, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35797, "top5_acc": 0.62062, "loss_cls": 3.61027, "loss": 3.61027, "time": 0.81751} +{"mode": "train", "epoch": 95, "iter": 1800, "lr": 0.03016, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35125, "top5_acc": 0.61125, "loss_cls": 3.66526, "loss": 3.66526, "time": 0.81869} +{"mode": "train", "epoch": 95, "iter": 1900, "lr": 0.03014, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36531, "top5_acc": 0.62469, "loss_cls": 3.60921, "loss": 3.60921, "time": 0.82536} +{"mode": "train", "epoch": 95, "iter": 2000, "lr": 0.03011, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36891, "top5_acc": 0.61828, "loss_cls": 3.59908, "loss": 3.59908, "time": 0.81674} +{"mode": "train", "epoch": 95, "iter": 2100, "lr": 0.03008, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35438, "top5_acc": 0.60672, "loss_cls": 3.65524, "loss": 3.65524, "time": 0.81725} +{"mode": "train", "epoch": 95, "iter": 2200, "lr": 0.03006, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35203, "top5_acc": 0.61031, "loss_cls": 3.66902, "loss": 3.66902, "time": 0.81573} +{"mode": "train", "epoch": 95, "iter": 2300, "lr": 0.03003, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37312, "top5_acc": 0.63438, "loss_cls": 3.54438, "loss": 3.54438, "time": 0.81964} +{"mode": "train", "epoch": 95, "iter": 2400, "lr": 0.03001, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35781, "top5_acc": 0.62516, "loss_cls": 3.60076, "loss": 3.60076, "time": 0.81455} +{"mode": "train", "epoch": 95, "iter": 2500, "lr": 0.02998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36766, "top5_acc": 0.62578, "loss_cls": 3.59971, "loss": 3.59971, "time": 0.81699} +{"mode": "train", "epoch": 95, "iter": 2600, "lr": 0.02996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3625, "top5_acc": 0.62344, "loss_cls": 3.59483, "loss": 3.59483, "time": 0.81209} +{"mode": "train", "epoch": 95, "iter": 2700, "lr": 0.02993, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34938, "top5_acc": 0.61109, "loss_cls": 3.65721, "loss": 3.65721, "time": 0.8246} +{"mode": "train", "epoch": 95, "iter": 2800, "lr": 0.02991, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36266, "top5_acc": 0.62281, "loss_cls": 3.64009, "loss": 3.64009, "time": 0.82193} +{"mode": "train", "epoch": 95, "iter": 2900, "lr": 0.02988, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35078, "top5_acc": 0.61687, "loss_cls": 3.64377, "loss": 3.64377, "time": 0.81257} +{"mode": "train", "epoch": 95, "iter": 3000, "lr": 0.02985, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35797, "top5_acc": 0.62047, "loss_cls": 3.6353, "loss": 3.6353, "time": 0.81592} +{"mode": "train", "epoch": 95, "iter": 3100, "lr": 0.02983, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3575, "top5_acc": 0.61594, "loss_cls": 3.66056, "loss": 3.66056, "time": 0.8139} +{"mode": "train", "epoch": 95, "iter": 3200, "lr": 0.0298, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35594, "top5_acc": 0.6225, "loss_cls": 3.62461, "loss": 3.62461, "time": 0.8193} +{"mode": "train", "epoch": 95, "iter": 3300, "lr": 0.02978, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34969, "top5_acc": 0.61875, "loss_cls": 3.65559, "loss": 3.65559, "time": 0.80836} +{"mode": "train", "epoch": 95, "iter": 3400, "lr": 0.02975, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35875, "top5_acc": 0.61281, "loss_cls": 3.64676, "loss": 3.64676, "time": 0.81631} +{"mode": "train", "epoch": 95, "iter": 3500, "lr": 0.02973, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34734, "top5_acc": 0.61484, "loss_cls": 3.6628, "loss": 3.6628, "time": 0.81282} +{"mode": "train", "epoch": 95, "iter": 3600, "lr": 0.0297, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36859, "top5_acc": 0.62562, "loss_cls": 3.59841, "loss": 3.59841, "time": 0.82407} +{"mode": "train", "epoch": 95, "iter": 3700, "lr": 0.02968, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34766, "top5_acc": 0.60688, "loss_cls": 3.69929, "loss": 3.69929, "time": 0.81503} +{"mode": "val", "epoch": 95, "iter": 309, "lr": 0.02966, "top1_acc": 0.29595, "top5_acc": 0.55255, "mean_class_accuracy": 0.29563} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.02964, "memory": 15990, "data_time": 1.35126, "top1_acc": 0.37156, "top5_acc": 0.63234, "loss_cls": 3.54295, "loss": 3.54295, "time": 2.34215} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.02961, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37016, "top5_acc": 0.62297, "loss_cls": 3.58717, "loss": 3.58717, "time": 0.82151} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.02959, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37656, "top5_acc": 0.62531, "loss_cls": 3.56681, "loss": 3.56681, "time": 0.81777} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.02956, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35656, "top5_acc": 0.62453, "loss_cls": 3.58782, "loss": 3.58782, "time": 0.82057} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.02954, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36125, "top5_acc": 0.62203, "loss_cls": 3.60258, "loss": 3.60258, "time": 0.82594} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.02951, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36625, "top5_acc": 0.62609, "loss_cls": 3.59203, "loss": 3.59203, "time": 0.81491} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.02948, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36828, "top5_acc": 0.62672, "loss_cls": 3.59114, "loss": 3.59114, "time": 0.82419} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.02946, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37594, "top5_acc": 0.63172, "loss_cls": 3.54855, "loss": 3.54855, "time": 0.8147} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.02943, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36766, "top5_acc": 0.63422, "loss_cls": 3.56536, "loss": 3.56536, "time": 0.81852} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.02941, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37203, "top5_acc": 0.63156, "loss_cls": 3.55128, "loss": 3.55128, "time": 0.81421} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.02938, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3675, "top5_acc": 0.62797, "loss_cls": 3.56178, "loss": 3.56178, "time": 0.81433} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.02936, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36047, "top5_acc": 0.62031, "loss_cls": 3.60043, "loss": 3.60043, "time": 0.81848} +{"mode": "train", "epoch": 96, "iter": 1300, "lr": 0.02933, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36344, "top5_acc": 0.61594, "loss_cls": 3.63242, "loss": 3.63242, "time": 0.81541} +{"mode": "train", "epoch": 96, "iter": 1400, "lr": 0.02931, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36281, "top5_acc": 0.61719, "loss_cls": 3.60037, "loss": 3.60037, "time": 0.81418} +{"mode": "train", "epoch": 96, "iter": 1500, "lr": 0.02928, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37016, "top5_acc": 0.63328, "loss_cls": 3.57666, "loss": 3.57666, "time": 0.81925} +{"mode": "train", "epoch": 96, "iter": 1600, "lr": 0.02926, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35891, "top5_acc": 0.6325, "loss_cls": 3.58757, "loss": 3.58757, "time": 0.82783} +{"mode": "train", "epoch": 96, "iter": 1700, "lr": 0.02923, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36328, "top5_acc": 0.62, "loss_cls": 3.63899, "loss": 3.63899, "time": 0.81719} +{"mode": "train", "epoch": 96, "iter": 1800, "lr": 0.0292, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36844, "top5_acc": 0.62609, "loss_cls": 3.58944, "loss": 3.58944, "time": 0.8213} +{"mode": "train", "epoch": 96, "iter": 1900, "lr": 0.02918, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35859, "top5_acc": 0.62422, "loss_cls": 3.64545, "loss": 3.64545, "time": 0.81587} +{"mode": "train", "epoch": 96, "iter": 2000, "lr": 0.02915, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36578, "top5_acc": 0.62359, "loss_cls": 3.58181, "loss": 3.58181, "time": 0.81905} +{"mode": "train", "epoch": 96, "iter": 2100, "lr": 0.02913, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35938, "top5_acc": 0.61953, "loss_cls": 3.60411, "loss": 3.60411, "time": 0.82326} +{"mode": "train", "epoch": 96, "iter": 2200, "lr": 0.0291, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36516, "top5_acc": 0.62703, "loss_cls": 3.58899, "loss": 3.58899, "time": 0.81895} +{"mode": "train", "epoch": 96, "iter": 2300, "lr": 0.02908, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35656, "top5_acc": 0.63266, "loss_cls": 3.59138, "loss": 3.59138, "time": 0.81345} +{"mode": "train", "epoch": 96, "iter": 2400, "lr": 0.02905, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35625, "top5_acc": 0.62359, "loss_cls": 3.60245, "loss": 3.60245, "time": 0.80864} +{"mode": "train", "epoch": 96, "iter": 2500, "lr": 0.02903, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36812, "top5_acc": 0.61906, "loss_cls": 3.605, "loss": 3.605, "time": 0.81503} +{"mode": "train", "epoch": 96, "iter": 2600, "lr": 0.029, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36422, "top5_acc": 0.63328, "loss_cls": 3.59611, "loss": 3.59611, "time": 0.81335} +{"mode": "train", "epoch": 96, "iter": 2700, "lr": 0.02898, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35234, "top5_acc": 0.61828, "loss_cls": 3.64851, "loss": 3.64851, "time": 0.81773} +{"mode": "train", "epoch": 96, "iter": 2800, "lr": 0.02895, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3525, "top5_acc": 0.61328, "loss_cls": 3.64062, "loss": 3.64062, "time": 0.81468} +{"mode": "train", "epoch": 96, "iter": 2900, "lr": 0.02893, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35625, "top5_acc": 0.60859, "loss_cls": 3.68418, "loss": 3.68418, "time": 0.82135} +{"mode": "train", "epoch": 96, "iter": 3000, "lr": 0.0289, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35953, "top5_acc": 0.61984, "loss_cls": 3.63927, "loss": 3.63927, "time": 0.81943} +{"mode": "train", "epoch": 96, "iter": 3100, "lr": 0.02887, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36344, "top5_acc": 0.62109, "loss_cls": 3.59938, "loss": 3.59938, "time": 0.81725} +{"mode": "train", "epoch": 96, "iter": 3200, "lr": 0.02885, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36641, "top5_acc": 0.62719, "loss_cls": 3.5906, "loss": 3.5906, "time": 0.81557} +{"mode": "train", "epoch": 96, "iter": 3300, "lr": 0.02882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34875, "top5_acc": 0.62187, "loss_cls": 3.65115, "loss": 3.65115, "time": 0.81834} +{"mode": "train", "epoch": 96, "iter": 3400, "lr": 0.0288, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36156, "top5_acc": 0.61687, "loss_cls": 3.62013, "loss": 3.62013, "time": 0.81516} +{"mode": "train", "epoch": 96, "iter": 3500, "lr": 0.02877, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35766, "top5_acc": 0.62094, "loss_cls": 3.61083, "loss": 3.61083, "time": 0.82151} +{"mode": "train", "epoch": 96, "iter": 3600, "lr": 0.02875, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35484, "top5_acc": 0.61172, "loss_cls": 3.64748, "loss": 3.64748, "time": 0.81631} +{"mode": "train", "epoch": 96, "iter": 3700, "lr": 0.02872, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37672, "top5_acc": 0.62953, "loss_cls": 3.57353, "loss": 3.57353, "time": 0.81633} +{"mode": "val", "epoch": 96, "iter": 309, "lr": 0.02871, "top1_acc": 0.30548, "top5_acc": 0.56633, "mean_class_accuracy": 0.30515} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.02869, "memory": 15990, "data_time": 1.36837, "top1_acc": 0.38734, "top5_acc": 0.65078, "loss_cls": 3.4592, "loss": 3.4592, "time": 2.36822} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.02866, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36406, "top5_acc": 0.62844, "loss_cls": 3.54942, "loss": 3.54942, "time": 0.8367} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.02864, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37812, "top5_acc": 0.62797, "loss_cls": 3.57223, "loss": 3.57223, "time": 0.82894} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.02861, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.3675, "top5_acc": 0.62875, "loss_cls": 3.57401, "loss": 3.57401, "time": 0.82189} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.02858, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37344, "top5_acc": 0.63375, "loss_cls": 3.53618, "loss": 3.53618, "time": 0.82289} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.02856, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36391, "top5_acc": 0.62813, "loss_cls": 3.58579, "loss": 3.58579, "time": 0.82031} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.02853, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36984, "top5_acc": 0.62047, "loss_cls": 3.57294, "loss": 3.57294, "time": 0.82686} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.02851, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36844, "top5_acc": 0.63016, "loss_cls": 3.58733, "loss": 3.58733, "time": 0.81507} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.02848, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37797, "top5_acc": 0.64047, "loss_cls": 3.52285, "loss": 3.52285, "time": 0.81065} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.02846, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37, "top5_acc": 0.62344, "loss_cls": 3.5773, "loss": 3.5773, "time": 0.8164} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.02843, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36578, "top5_acc": 0.62141, "loss_cls": 3.59108, "loss": 3.59108, "time": 0.81941} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.02841, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37672, "top5_acc": 0.63, "loss_cls": 3.55049, "loss": 3.55049, "time": 0.81784} +{"mode": "train", "epoch": 97, "iter": 1300, "lr": 0.02838, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36594, "top5_acc": 0.63031, "loss_cls": 3.56636, "loss": 3.56636, "time": 0.81507} +{"mode": "train", "epoch": 97, "iter": 1400, "lr": 0.02836, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37141, "top5_acc": 0.635, "loss_cls": 3.54781, "loss": 3.54781, "time": 0.8148} +{"mode": "train", "epoch": 97, "iter": 1500, "lr": 0.02833, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37156, "top5_acc": 0.63484, "loss_cls": 3.56335, "loss": 3.56335, "time": 0.81429} +{"mode": "train", "epoch": 97, "iter": 1600, "lr": 0.02831, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3625, "top5_acc": 0.62375, "loss_cls": 3.61262, "loss": 3.61262, "time": 0.81851} +{"mode": "train", "epoch": 97, "iter": 1700, "lr": 0.02828, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36188, "top5_acc": 0.62094, "loss_cls": 3.62026, "loss": 3.62026, "time": 0.82712} +{"mode": "train", "epoch": 97, "iter": 1800, "lr": 0.02826, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35578, "top5_acc": 0.62656, "loss_cls": 3.61531, "loss": 3.61531, "time": 0.82017} +{"mode": "train", "epoch": 97, "iter": 1900, "lr": 0.02823, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37062, "top5_acc": 0.63531, "loss_cls": 3.56633, "loss": 3.56633, "time": 0.82861} +{"mode": "train", "epoch": 97, "iter": 2000, "lr": 0.02821, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36641, "top5_acc": 0.62859, "loss_cls": 3.57942, "loss": 3.57942, "time": 0.82382} +{"mode": "train", "epoch": 97, "iter": 2100, "lr": 0.02818, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36609, "top5_acc": 0.61812, "loss_cls": 3.62824, "loss": 3.62824, "time": 0.82274} +{"mode": "train", "epoch": 97, "iter": 2200, "lr": 0.02816, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36172, "top5_acc": 0.62828, "loss_cls": 3.58149, "loss": 3.58149, "time": 0.81538} +{"mode": "train", "epoch": 97, "iter": 2300, "lr": 0.02813, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35156, "top5_acc": 0.61656, "loss_cls": 3.66154, "loss": 3.66154, "time": 0.81549} +{"mode": "train", "epoch": 97, "iter": 2400, "lr": 0.02811, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37031, "top5_acc": 0.62297, "loss_cls": 3.58181, "loss": 3.58181, "time": 0.81689} +{"mode": "train", "epoch": 97, "iter": 2500, "lr": 0.02808, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3625, "top5_acc": 0.61781, "loss_cls": 3.63474, "loss": 3.63474, "time": 0.81151} +{"mode": "train", "epoch": 97, "iter": 2600, "lr": 0.02806, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37109, "top5_acc": 0.63078, "loss_cls": 3.55303, "loss": 3.55303, "time": 0.81586} +{"mode": "train", "epoch": 97, "iter": 2700, "lr": 0.02803, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35875, "top5_acc": 0.61891, "loss_cls": 3.63153, "loss": 3.63153, "time": 0.81443} +{"mode": "train", "epoch": 97, "iter": 2800, "lr": 0.02801, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36531, "top5_acc": 0.62328, "loss_cls": 3.5744, "loss": 3.5744, "time": 0.8139} +{"mode": "train", "epoch": 97, "iter": 2900, "lr": 0.02798, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36125, "top5_acc": 0.61922, "loss_cls": 3.62556, "loss": 3.62556, "time": 0.81207} +{"mode": "train", "epoch": 97, "iter": 3000, "lr": 0.02796, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36531, "top5_acc": 0.61656, "loss_cls": 3.63868, "loss": 3.63868, "time": 0.81421} +{"mode": "train", "epoch": 97, "iter": 3100, "lr": 0.02793, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3575, "top5_acc": 0.61859, "loss_cls": 3.60691, "loss": 3.60691, "time": 0.81047} +{"mode": "train", "epoch": 97, "iter": 3200, "lr": 0.02791, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36469, "top5_acc": 0.62375, "loss_cls": 3.57581, "loss": 3.57581, "time": 0.81349} +{"mode": "train", "epoch": 97, "iter": 3300, "lr": 0.02788, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.355, "top5_acc": 0.6175, "loss_cls": 3.63207, "loss": 3.63207, "time": 0.81167} +{"mode": "train", "epoch": 97, "iter": 3400, "lr": 0.02786, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37125, "top5_acc": 0.63, "loss_cls": 3.58048, "loss": 3.58048, "time": 0.82146} +{"mode": "train", "epoch": 97, "iter": 3500, "lr": 0.02783, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36531, "top5_acc": 0.62422, "loss_cls": 3.60841, "loss": 3.60841, "time": 0.81217} +{"mode": "train", "epoch": 97, "iter": 3600, "lr": 0.02781, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37062, "top5_acc": 0.62844, "loss_cls": 3.59661, "loss": 3.59661, "time": 0.81382} +{"mode": "train", "epoch": 97, "iter": 3700, "lr": 0.02778, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35656, "top5_acc": 0.61719, "loss_cls": 3.62867, "loss": 3.62867, "time": 0.81528} +{"mode": "val", "epoch": 97, "iter": 309, "lr": 0.02777, "top1_acc": 0.31292, "top5_acc": 0.57165, "mean_class_accuracy": 0.3126} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.02774, "memory": 15990, "data_time": 1.32364, "top1_acc": 0.38188, "top5_acc": 0.64344, "loss_cls": 3.51312, "loss": 3.51312, "time": 2.31609} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.02772, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38484, "top5_acc": 0.65094, "loss_cls": 3.45844, "loss": 3.45844, "time": 0.82103} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.02769, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36547, "top5_acc": 0.61687, "loss_cls": 3.62472, "loss": 3.62472, "time": 0.81868} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.02767, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36469, "top5_acc": 0.63578, "loss_cls": 3.57352, "loss": 3.57352, "time": 0.82079} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.02764, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38, "top5_acc": 0.62672, "loss_cls": 3.57063, "loss": 3.57063, "time": 0.82263} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.02762, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36875, "top5_acc": 0.63156, "loss_cls": 3.55836, "loss": 3.55836, "time": 0.81802} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.02759, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38547, "top5_acc": 0.64531, "loss_cls": 3.48466, "loss": 3.48466, "time": 0.81996} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.02757, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36938, "top5_acc": 0.635, "loss_cls": 3.54902, "loss": 3.54902, "time": 0.82051} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.02754, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36812, "top5_acc": 0.62641, "loss_cls": 3.58398, "loss": 3.58398, "time": 0.81453} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.02752, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36969, "top5_acc": 0.62625, "loss_cls": 3.57669, "loss": 3.57669, "time": 0.81701} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.02749, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35891, "top5_acc": 0.62109, "loss_cls": 3.61813, "loss": 3.61813, "time": 0.81719} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.02747, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37047, "top5_acc": 0.62922, "loss_cls": 3.56333, "loss": 3.56333, "time": 0.81372} +{"mode": "train", "epoch": 98, "iter": 1300, "lr": 0.02744, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35719, "top5_acc": 0.61984, "loss_cls": 3.62063, "loss": 3.62063, "time": 0.81332} +{"mode": "train", "epoch": 98, "iter": 1400, "lr": 0.02742, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36031, "top5_acc": 0.62531, "loss_cls": 3.61892, "loss": 3.61892, "time": 0.81525} +{"mode": "train", "epoch": 98, "iter": 1500, "lr": 0.02739, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37281, "top5_acc": 0.63281, "loss_cls": 3.55642, "loss": 3.55642, "time": 0.81673} +{"mode": "train", "epoch": 98, "iter": 1600, "lr": 0.02737, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37125, "top5_acc": 0.63203, "loss_cls": 3.54898, "loss": 3.54898, "time": 0.81745} +{"mode": "train", "epoch": 98, "iter": 1700, "lr": 0.02734, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36453, "top5_acc": 0.62734, "loss_cls": 3.57765, "loss": 3.57765, "time": 0.83174} +{"mode": "train", "epoch": 98, "iter": 1800, "lr": 0.02732, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36609, "top5_acc": 0.61875, "loss_cls": 3.59282, "loss": 3.59282, "time": 0.82355} +{"mode": "train", "epoch": 98, "iter": 1900, "lr": 0.02729, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36672, "top5_acc": 0.62266, "loss_cls": 3.56593, "loss": 3.56593, "time": 0.81794} +{"mode": "train", "epoch": 98, "iter": 2000, "lr": 0.02727, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37281, "top5_acc": 0.63219, "loss_cls": 3.54003, "loss": 3.54003, "time": 0.81979} +{"mode": "train", "epoch": 98, "iter": 2100, "lr": 0.02724, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36672, "top5_acc": 0.62844, "loss_cls": 3.55017, "loss": 3.55017, "time": 0.81312} +{"mode": "train", "epoch": 98, "iter": 2200, "lr": 0.02722, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36016, "top5_acc": 0.62344, "loss_cls": 3.57825, "loss": 3.57825, "time": 0.81527} +{"mode": "train", "epoch": 98, "iter": 2300, "lr": 0.02719, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36406, "top5_acc": 0.61984, "loss_cls": 3.5996, "loss": 3.5996, "time": 0.81751} +{"mode": "train", "epoch": 98, "iter": 2400, "lr": 0.02717, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.365, "top5_acc": 0.63094, "loss_cls": 3.56201, "loss": 3.56201, "time": 0.8187} +{"mode": "train", "epoch": 98, "iter": 2500, "lr": 0.02714, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37297, "top5_acc": 0.62703, "loss_cls": 3.57896, "loss": 3.57896, "time": 0.81948} +{"mode": "train", "epoch": 98, "iter": 2600, "lr": 0.02712, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38, "top5_acc": 0.63859, "loss_cls": 3.54667, "loss": 3.54667, "time": 0.81595} +{"mode": "train", "epoch": 98, "iter": 2700, "lr": 0.02709, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36719, "top5_acc": 0.63094, "loss_cls": 3.56453, "loss": 3.56453, "time": 0.81277} +{"mode": "train", "epoch": 98, "iter": 2800, "lr": 0.02707, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37125, "top5_acc": 0.62328, "loss_cls": 3.59635, "loss": 3.59635, "time": 0.81773} +{"mode": "train", "epoch": 98, "iter": 2900, "lr": 0.02705, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36594, "top5_acc": 0.62375, "loss_cls": 3.59756, "loss": 3.59756, "time": 0.81742} +{"mode": "train", "epoch": 98, "iter": 3000, "lr": 0.02702, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35984, "top5_acc": 0.62453, "loss_cls": 3.61379, "loss": 3.61379, "time": 0.81288} +{"mode": "train", "epoch": 98, "iter": 3100, "lr": 0.027, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36719, "top5_acc": 0.62813, "loss_cls": 3.58436, "loss": 3.58436, "time": 0.81558} +{"mode": "train", "epoch": 98, "iter": 3200, "lr": 0.02697, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36953, "top5_acc": 0.62609, "loss_cls": 3.58595, "loss": 3.58595, "time": 0.8171} +{"mode": "train", "epoch": 98, "iter": 3300, "lr": 0.02695, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36141, "top5_acc": 0.63156, "loss_cls": 3.58887, "loss": 3.58887, "time": 0.81353} +{"mode": "train", "epoch": 98, "iter": 3400, "lr": 0.02692, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36797, "top5_acc": 0.63, "loss_cls": 3.56779, "loss": 3.56779, "time": 0.81442} +{"mode": "train", "epoch": 98, "iter": 3500, "lr": 0.0269, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35953, "top5_acc": 0.63016, "loss_cls": 3.62447, "loss": 3.62447, "time": 0.82027} +{"mode": "train", "epoch": 98, "iter": 3600, "lr": 0.02687, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36844, "top5_acc": 0.62562, "loss_cls": 3.5923, "loss": 3.5923, "time": 0.83007} +{"mode": "train", "epoch": 98, "iter": 3700, "lr": 0.02685, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36344, "top5_acc": 0.62281, "loss_cls": 3.5893, "loss": 3.5893, "time": 0.81815} +{"mode": "val", "epoch": 98, "iter": 309, "lr": 0.02684, "top1_acc": 0.30097, "top5_acc": 0.55913, "mean_class_accuracy": 0.30081} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.02681, "memory": 15990, "data_time": 1.33693, "top1_acc": 0.37906, "top5_acc": 0.64922, "loss_cls": 3.46578, "loss": 3.46578, "time": 2.32569} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.02679, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38625, "top5_acc": 0.63953, "loss_cls": 3.49399, "loss": 3.49399, "time": 0.82173} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.02676, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36109, "top5_acc": 0.62953, "loss_cls": 3.57977, "loss": 3.57977, "time": 0.81733} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.02674, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36797, "top5_acc": 0.64219, "loss_cls": 3.51582, "loss": 3.51582, "time": 0.819} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.02671, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36688, "top5_acc": 0.62938, "loss_cls": 3.57669, "loss": 3.57669, "time": 0.81899} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.02669, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36922, "top5_acc": 0.63578, "loss_cls": 3.52898, "loss": 3.52898, "time": 0.81748} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.02666, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37312, "top5_acc": 0.6325, "loss_cls": 3.53534, "loss": 3.53534, "time": 0.81505} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.02664, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36438, "top5_acc": 0.62016, "loss_cls": 3.60049, "loss": 3.60049, "time": 0.81567} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.02661, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37266, "top5_acc": 0.62641, "loss_cls": 3.58187, "loss": 3.58187, "time": 0.81131} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.02659, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37828, "top5_acc": 0.64391, "loss_cls": 3.48922, "loss": 3.48922, "time": 0.81533} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.02656, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36875, "top5_acc": 0.62187, "loss_cls": 3.58232, "loss": 3.58232, "time": 0.81909} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.02654, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37688, "top5_acc": 0.63891, "loss_cls": 3.54069, "loss": 3.54069, "time": 0.81129} +{"mode": "train", "epoch": 99, "iter": 1300, "lr": 0.02651, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37188, "top5_acc": 0.63156, "loss_cls": 3.5436, "loss": 3.5436, "time": 0.81692} +{"mode": "train", "epoch": 99, "iter": 1400, "lr": 0.02649, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37234, "top5_acc": 0.63109, "loss_cls": 3.55685, "loss": 3.55685, "time": 0.81919} +{"mode": "train", "epoch": 99, "iter": 1500, "lr": 0.02646, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37453, "top5_acc": 0.635, "loss_cls": 3.53737, "loss": 3.53737, "time": 0.81624} +{"mode": "train", "epoch": 99, "iter": 1600, "lr": 0.02644, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36797, "top5_acc": 0.62234, "loss_cls": 3.57572, "loss": 3.57572, "time": 0.81595} +{"mode": "train", "epoch": 99, "iter": 1700, "lr": 0.02642, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38562, "top5_acc": 0.64359, "loss_cls": 3.49501, "loss": 3.49501, "time": 0.83236} +{"mode": "train", "epoch": 99, "iter": 1800, "lr": 0.02639, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37562, "top5_acc": 0.63531, "loss_cls": 3.53522, "loss": 3.53522, "time": 0.8158} +{"mode": "train", "epoch": 99, "iter": 1900, "lr": 0.02637, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37391, "top5_acc": 0.63094, "loss_cls": 3.5408, "loss": 3.5408, "time": 0.8292} +{"mode": "train", "epoch": 99, "iter": 2000, "lr": 0.02634, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37828, "top5_acc": 0.63016, "loss_cls": 3.55109, "loss": 3.55109, "time": 0.81665} +{"mode": "train", "epoch": 99, "iter": 2100, "lr": 0.02632, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36344, "top5_acc": 0.62625, "loss_cls": 3.59541, "loss": 3.59541, "time": 0.82052} +{"mode": "train", "epoch": 99, "iter": 2200, "lr": 0.02629, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37344, "top5_acc": 0.63219, "loss_cls": 3.54663, "loss": 3.54663, "time": 0.81825} +{"mode": "train", "epoch": 99, "iter": 2300, "lr": 0.02627, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36406, "top5_acc": 0.62125, "loss_cls": 3.59386, "loss": 3.59386, "time": 0.81541} +{"mode": "train", "epoch": 99, "iter": 2400, "lr": 0.02624, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37016, "top5_acc": 0.62422, "loss_cls": 3.6009, "loss": 3.6009, "time": 0.81978} +{"mode": "train", "epoch": 99, "iter": 2500, "lr": 0.02622, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37844, "top5_acc": 0.63172, "loss_cls": 3.53854, "loss": 3.53854, "time": 0.81417} +{"mode": "train", "epoch": 99, "iter": 2600, "lr": 0.02619, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36188, "top5_acc": 0.63344, "loss_cls": 3.58125, "loss": 3.58125, "time": 0.82074} +{"mode": "train", "epoch": 99, "iter": 2700, "lr": 0.02617, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36797, "top5_acc": 0.63531, "loss_cls": 3.56892, "loss": 3.56892, "time": 0.8139} +{"mode": "train", "epoch": 99, "iter": 2800, "lr": 0.02614, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36609, "top5_acc": 0.62094, "loss_cls": 3.58898, "loss": 3.58898, "time": 0.81821} +{"mode": "train", "epoch": 99, "iter": 2900, "lr": 0.02612, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37, "top5_acc": 0.62219, "loss_cls": 3.57245, "loss": 3.57245, "time": 0.81603} +{"mode": "train", "epoch": 99, "iter": 3000, "lr": 0.0261, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37375, "top5_acc": 0.63438, "loss_cls": 3.53349, "loss": 3.53349, "time": 0.81993} +{"mode": "train", "epoch": 99, "iter": 3100, "lr": 0.02607, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36578, "top5_acc": 0.62641, "loss_cls": 3.6144, "loss": 3.6144, "time": 0.81329} +{"mode": "train", "epoch": 99, "iter": 3200, "lr": 0.02605, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37078, "top5_acc": 0.62813, "loss_cls": 3.55627, "loss": 3.55627, "time": 0.8152} +{"mode": "train", "epoch": 99, "iter": 3300, "lr": 0.02602, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35703, "top5_acc": 0.62062, "loss_cls": 3.59708, "loss": 3.59708, "time": 0.81973} +{"mode": "train", "epoch": 99, "iter": 3400, "lr": 0.026, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.375, "top5_acc": 0.63281, "loss_cls": 3.53139, "loss": 3.53139, "time": 0.82031} +{"mode": "train", "epoch": 99, "iter": 3500, "lr": 0.02597, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36953, "top5_acc": 0.62859, "loss_cls": 3.56209, "loss": 3.56209, "time": 0.81647} +{"mode": "train", "epoch": 99, "iter": 3600, "lr": 0.02595, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36969, "top5_acc": 0.63141, "loss_cls": 3.5269, "loss": 3.5269, "time": 0.81919} +{"mode": "train", "epoch": 99, "iter": 3700, "lr": 0.02592, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37, "top5_acc": 0.62031, "loss_cls": 3.5784, "loss": 3.5784, "time": 0.82271} +{"mode": "val", "epoch": 99, "iter": 309, "lr": 0.02591, "top1_acc": 0.29965, "top5_acc": 0.54804, "mean_class_accuracy": 0.29942} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.02589, "memory": 15990, "data_time": 1.38787, "top1_acc": 0.38859, "top5_acc": 0.64969, "loss_cls": 3.45406, "loss": 3.45406, "time": 2.39356} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.02586, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38156, "top5_acc": 0.63531, "loss_cls": 3.50014, "loss": 3.50014, "time": 0.83909} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.02584, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37516, "top5_acc": 0.64312, "loss_cls": 3.49626, "loss": 3.49626, "time": 0.82281} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.02581, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38344, "top5_acc": 0.62797, "loss_cls": 3.52397, "loss": 3.52397, "time": 0.82875} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.02579, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37109, "top5_acc": 0.64531, "loss_cls": 3.48913, "loss": 3.48913, "time": 0.83057} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.02577, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37297, "top5_acc": 0.63969, "loss_cls": 3.51125, "loss": 3.51125, "time": 0.81875} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.02574, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37156, "top5_acc": 0.63562, "loss_cls": 3.52015, "loss": 3.52015, "time": 0.81709} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.02572, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36906, "top5_acc": 0.62438, "loss_cls": 3.58582, "loss": 3.58582, "time": 0.82289} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.02569, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38906, "top5_acc": 0.64266, "loss_cls": 3.48451, "loss": 3.48451, "time": 0.8125} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.02567, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37547, "top5_acc": 0.63594, "loss_cls": 3.53406, "loss": 3.53406, "time": 0.81741} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.02564, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37156, "top5_acc": 0.63172, "loss_cls": 3.54323, "loss": 3.54323, "time": 0.81849} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.02562, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37953, "top5_acc": 0.63906, "loss_cls": 3.52074, "loss": 3.52074, "time": 0.81304} +{"mode": "train", "epoch": 100, "iter": 1300, "lr": 0.02559, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37766, "top5_acc": 0.63828, "loss_cls": 3.49018, "loss": 3.49018, "time": 0.8142} +{"mode": "train", "epoch": 100, "iter": 1400, "lr": 0.02557, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37891, "top5_acc": 0.63391, "loss_cls": 3.52179, "loss": 3.52179, "time": 0.82116} +{"mode": "train", "epoch": 100, "iter": 1500, "lr": 0.02555, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37219, "top5_acc": 0.62547, "loss_cls": 3.55431, "loss": 3.55431, "time": 0.81359} +{"mode": "train", "epoch": 100, "iter": 1600, "lr": 0.02552, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37781, "top5_acc": 0.63766, "loss_cls": 3.54777, "loss": 3.54777, "time": 0.81765} +{"mode": "train", "epoch": 100, "iter": 1700, "lr": 0.0255, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36766, "top5_acc": 0.63203, "loss_cls": 3.58243, "loss": 3.58243, "time": 0.81931} +{"mode": "train", "epoch": 100, "iter": 1800, "lr": 0.02547, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37812, "top5_acc": 0.63234, "loss_cls": 3.52263, "loss": 3.52263, "time": 0.81875} +{"mode": "train", "epoch": 100, "iter": 1900, "lr": 0.02545, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38125, "top5_acc": 0.63438, "loss_cls": 3.53359, "loss": 3.53359, "time": 0.82323} +{"mode": "train", "epoch": 100, "iter": 2000, "lr": 0.02542, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36938, "top5_acc": 0.62844, "loss_cls": 3.56312, "loss": 3.56312, "time": 0.82147} +{"mode": "train", "epoch": 100, "iter": 2100, "lr": 0.0254, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37109, "top5_acc": 0.63016, "loss_cls": 3.60092, "loss": 3.60092, "time": 0.81802} +{"mode": "train", "epoch": 100, "iter": 2200, "lr": 0.02538, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36875, "top5_acc": 0.63359, "loss_cls": 3.55653, "loss": 3.55653, "time": 0.81224} +{"mode": "train", "epoch": 100, "iter": 2300, "lr": 0.02535, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37062, "top5_acc": 0.63906, "loss_cls": 3.52502, "loss": 3.52502, "time": 0.8166} +{"mode": "train", "epoch": 100, "iter": 2400, "lr": 0.02533, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37219, "top5_acc": 0.62938, "loss_cls": 3.54713, "loss": 3.54713, "time": 0.81404} +{"mode": "train", "epoch": 100, "iter": 2500, "lr": 0.0253, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36922, "top5_acc": 0.62484, "loss_cls": 3.55601, "loss": 3.55601, "time": 0.81622} +{"mode": "train", "epoch": 100, "iter": 2600, "lr": 0.02528, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36766, "top5_acc": 0.63375, "loss_cls": 3.54083, "loss": 3.54083, "time": 0.82283} +{"mode": "train", "epoch": 100, "iter": 2700, "lr": 0.02525, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37172, "top5_acc": 0.62922, "loss_cls": 3.54397, "loss": 3.54397, "time": 0.81926} +{"mode": "train", "epoch": 100, "iter": 2800, "lr": 0.02523, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36625, "top5_acc": 0.63219, "loss_cls": 3.55399, "loss": 3.55399, "time": 0.81475} +{"mode": "train", "epoch": 100, "iter": 2900, "lr": 0.02521, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37453, "top5_acc": 0.62609, "loss_cls": 3.56208, "loss": 3.56208, "time": 0.81837} +{"mode": "train", "epoch": 100, "iter": 3000, "lr": 0.02518, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37172, "top5_acc": 0.62062, "loss_cls": 3.59363, "loss": 3.59363, "time": 0.8137} +{"mode": "train", "epoch": 100, "iter": 3100, "lr": 0.02516, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36797, "top5_acc": 0.62219, "loss_cls": 3.59461, "loss": 3.59461, "time": 0.81444} +{"mode": "train", "epoch": 100, "iter": 3200, "lr": 0.02513, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37141, "top5_acc": 0.63125, "loss_cls": 3.54253, "loss": 3.54253, "time": 0.81454} +{"mode": "train", "epoch": 100, "iter": 3300, "lr": 0.02511, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37531, "top5_acc": 0.62953, "loss_cls": 3.56355, "loss": 3.56355, "time": 0.81267} +{"mode": "train", "epoch": 100, "iter": 3400, "lr": 0.02508, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37266, "top5_acc": 0.63344, "loss_cls": 3.54244, "loss": 3.54244, "time": 0.81869} +{"mode": "train", "epoch": 100, "iter": 3500, "lr": 0.02506, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37797, "top5_acc": 0.63172, "loss_cls": 3.5422, "loss": 3.5422, "time": 0.81357} +{"mode": "train", "epoch": 100, "iter": 3600, "lr": 0.02504, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36625, "top5_acc": 0.6325, "loss_cls": 3.55003, "loss": 3.55003, "time": 0.8213} +{"mode": "train", "epoch": 100, "iter": 3700, "lr": 0.02501, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38469, "top5_acc": 0.63062, "loss_cls": 3.52214, "loss": 3.52214, "time": 0.81001} +{"mode": "val", "epoch": 100, "iter": 309, "lr": 0.025, "top1_acc": 0.32457, "top5_acc": 0.57185, "mean_class_accuracy": 0.32423} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.02498, "memory": 15990, "data_time": 1.36403, "top1_acc": 0.38016, "top5_acc": 0.64344, "loss_cls": 3.48867, "loss": 3.48867, "time": 2.34672} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.02495, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38531, "top5_acc": 0.64625, "loss_cls": 3.46336, "loss": 3.46336, "time": 0.81995} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.02493, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39141, "top5_acc": 0.65094, "loss_cls": 3.42566, "loss": 3.42566, "time": 0.8213} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.0249, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37047, "top5_acc": 0.63234, "loss_cls": 3.52619, "loss": 3.52619, "time": 0.81641} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.02488, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37516, "top5_acc": 0.63703, "loss_cls": 3.51233, "loss": 3.51233, "time": 0.818} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.02486, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37797, "top5_acc": 0.64453, "loss_cls": 3.50625, "loss": 3.50625, "time": 0.81617} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.02483, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38688, "top5_acc": 0.64828, "loss_cls": 3.46345, "loss": 3.46345, "time": 0.81637} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.02481, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37797, "top5_acc": 0.63813, "loss_cls": 3.50068, "loss": 3.50068, "time": 0.81293} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.02478, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37859, "top5_acc": 0.65219, "loss_cls": 3.45714, "loss": 3.45714, "time": 0.81977} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.02476, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38562, "top5_acc": 0.64516, "loss_cls": 3.47652, "loss": 3.47652, "time": 0.81506} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.02473, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38031, "top5_acc": 0.63656, "loss_cls": 3.49559, "loss": 3.49559, "time": 0.81636} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.02471, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37312, "top5_acc": 0.63375, "loss_cls": 3.53238, "loss": 3.53238, "time": 0.81253} +{"mode": "train", "epoch": 101, "iter": 1300, "lr": 0.02469, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38031, "top5_acc": 0.6475, "loss_cls": 3.48477, "loss": 3.48477, "time": 0.81907} +{"mode": "train", "epoch": 101, "iter": 1400, "lr": 0.02466, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36469, "top5_acc": 0.63344, "loss_cls": 3.54349, "loss": 3.54349, "time": 0.81901} +{"mode": "train", "epoch": 101, "iter": 1500, "lr": 0.02464, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38812, "top5_acc": 0.63828, "loss_cls": 3.47391, "loss": 3.47391, "time": 0.81516} +{"mode": "train", "epoch": 101, "iter": 1600, "lr": 0.02461, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38328, "top5_acc": 0.64359, "loss_cls": 3.49862, "loss": 3.49862, "time": 0.81236} +{"mode": "train", "epoch": 101, "iter": 1700, "lr": 0.02459, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36375, "top5_acc": 0.62234, "loss_cls": 3.55397, "loss": 3.55397, "time": 0.82315} +{"mode": "train", "epoch": 101, "iter": 1800, "lr": 0.02457, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38078, "top5_acc": 0.64141, "loss_cls": 3.48315, "loss": 3.48315, "time": 0.82143} +{"mode": "train", "epoch": 101, "iter": 1900, "lr": 0.02454, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37844, "top5_acc": 0.63547, "loss_cls": 3.53318, "loss": 3.53318, "time": 0.83138} +{"mode": "train", "epoch": 101, "iter": 2000, "lr": 0.02452, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37141, "top5_acc": 0.63, "loss_cls": 3.55568, "loss": 3.55568, "time": 0.82167} +{"mode": "train", "epoch": 101, "iter": 2100, "lr": 0.02449, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36062, "top5_acc": 0.63, "loss_cls": 3.58239, "loss": 3.58239, "time": 0.82636} +{"mode": "train", "epoch": 101, "iter": 2200, "lr": 0.02447, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36625, "top5_acc": 0.6275, "loss_cls": 3.57208, "loss": 3.57208, "time": 0.82206} +{"mode": "train", "epoch": 101, "iter": 2300, "lr": 0.02445, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38703, "top5_acc": 0.63719, "loss_cls": 3.5121, "loss": 3.5121, "time": 0.82444} +{"mode": "train", "epoch": 101, "iter": 2400, "lr": 0.02442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37094, "top5_acc": 0.62875, "loss_cls": 3.55419, "loss": 3.55419, "time": 0.81481} +{"mode": "train", "epoch": 101, "iter": 2500, "lr": 0.0244, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37609, "top5_acc": 0.63391, "loss_cls": 3.50971, "loss": 3.50971, "time": 0.81538} +{"mode": "train", "epoch": 101, "iter": 2600, "lr": 0.02437, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37391, "top5_acc": 0.63109, "loss_cls": 3.53887, "loss": 3.53887, "time": 0.81496} +{"mode": "train", "epoch": 101, "iter": 2700, "lr": 0.02435, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36078, "top5_acc": 0.62672, "loss_cls": 3.5867, "loss": 3.5867, "time": 0.81627} +{"mode": "train", "epoch": 101, "iter": 2800, "lr": 0.02433, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.3825, "top5_acc": 0.63438, "loss_cls": 3.51334, "loss": 3.51334, "time": 0.81618} +{"mode": "train", "epoch": 101, "iter": 2900, "lr": 0.0243, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37672, "top5_acc": 0.64078, "loss_cls": 3.51241, "loss": 3.51241, "time": 0.81455} +{"mode": "train", "epoch": 101, "iter": 3000, "lr": 0.02428, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37656, "top5_acc": 0.63891, "loss_cls": 3.53054, "loss": 3.53054, "time": 0.81432} +{"mode": "train", "epoch": 101, "iter": 3100, "lr": 0.02425, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38, "top5_acc": 0.63047, "loss_cls": 3.55875, "loss": 3.55875, "time": 0.81163} +{"mode": "train", "epoch": 101, "iter": 3200, "lr": 0.02423, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37531, "top5_acc": 0.63297, "loss_cls": 3.51991, "loss": 3.51991, "time": 0.82068} +{"mode": "train", "epoch": 101, "iter": 3300, "lr": 0.02421, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38141, "top5_acc": 0.64297, "loss_cls": 3.4903, "loss": 3.4903, "time": 0.82277} +{"mode": "train", "epoch": 101, "iter": 3400, "lr": 0.02418, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37594, "top5_acc": 0.63375, "loss_cls": 3.55059, "loss": 3.55059, "time": 0.81839} +{"mode": "train", "epoch": 101, "iter": 3500, "lr": 0.02416, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38109, "top5_acc": 0.63875, "loss_cls": 3.50686, "loss": 3.50686, "time": 0.81528} +{"mode": "train", "epoch": 101, "iter": 3600, "lr": 0.02413, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36922, "top5_acc": 0.61969, "loss_cls": 3.59369, "loss": 3.59369, "time": 0.81714} +{"mode": "train", "epoch": 101, "iter": 3700, "lr": 0.02411, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37422, "top5_acc": 0.63375, "loss_cls": 3.55423, "loss": 3.55423, "time": 0.8194} +{"mode": "val", "epoch": 101, "iter": 309, "lr": 0.0241, "top1_acc": 0.32001, "top5_acc": 0.57544, "mean_class_accuracy": 0.31965} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.02407, "memory": 15990, "data_time": 1.30585, "top1_acc": 0.37969, "top5_acc": 0.64953, "loss_cls": 3.4392, "loss": 3.4392, "time": 2.28013} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.02405, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39375, "top5_acc": 0.65906, "loss_cls": 3.38908, "loss": 3.38908, "time": 0.81617} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.02403, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38859, "top5_acc": 0.64719, "loss_cls": 3.43405, "loss": 3.43405, "time": 0.81773} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.024, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38828, "top5_acc": 0.65312, "loss_cls": 3.44727, "loss": 3.44727, "time": 0.81839} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.02398, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38641, "top5_acc": 0.64125, "loss_cls": 3.47195, "loss": 3.47195, "time": 0.82276} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.02396, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37516, "top5_acc": 0.64062, "loss_cls": 3.5083, "loss": 3.5083, "time": 0.81544} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.02393, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39438, "top5_acc": 0.64953, "loss_cls": 3.44603, "loss": 3.44603, "time": 0.81845} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.02391, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38906, "top5_acc": 0.65, "loss_cls": 3.44206, "loss": 3.44206, "time": 0.81997} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.02388, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37844, "top5_acc": 0.64188, "loss_cls": 3.46652, "loss": 3.46652, "time": 0.81839} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.02386, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38344, "top5_acc": 0.64188, "loss_cls": 3.4729, "loss": 3.4729, "time": 0.8161} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.02384, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39172, "top5_acc": 0.63766, "loss_cls": 3.5018, "loss": 3.5018, "time": 0.81276} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.02381, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36859, "top5_acc": 0.6425, "loss_cls": 3.51248, "loss": 3.51248, "time": 0.81373} +{"mode": "train", "epoch": 102, "iter": 1300, "lr": 0.02379, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38016, "top5_acc": 0.63281, "loss_cls": 3.51441, "loss": 3.51441, "time": 0.81737} +{"mode": "train", "epoch": 102, "iter": 1400, "lr": 0.02376, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37844, "top5_acc": 0.63844, "loss_cls": 3.50006, "loss": 3.50006, "time": 0.81849} +{"mode": "train", "epoch": 102, "iter": 1500, "lr": 0.02374, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37688, "top5_acc": 0.64141, "loss_cls": 3.49196, "loss": 3.49196, "time": 0.81009} +{"mode": "train", "epoch": 102, "iter": 1600, "lr": 0.02372, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37609, "top5_acc": 0.63203, "loss_cls": 3.53304, "loss": 3.53304, "time": 0.81608} +{"mode": "train", "epoch": 102, "iter": 1700, "lr": 0.02369, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37656, "top5_acc": 0.62828, "loss_cls": 3.53814, "loss": 3.53814, "time": 0.81698} +{"mode": "train", "epoch": 102, "iter": 1800, "lr": 0.02367, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37766, "top5_acc": 0.635, "loss_cls": 3.50724, "loss": 3.50724, "time": 0.82935} +{"mode": "train", "epoch": 102, "iter": 1900, "lr": 0.02365, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38, "top5_acc": 0.63625, "loss_cls": 3.51878, "loss": 3.51878, "time": 0.81947} +{"mode": "train", "epoch": 102, "iter": 2000, "lr": 0.02362, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38406, "top5_acc": 0.63609, "loss_cls": 3.49121, "loss": 3.49121, "time": 0.82901} +{"mode": "train", "epoch": 102, "iter": 2100, "lr": 0.0236, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38609, "top5_acc": 0.63953, "loss_cls": 3.49169, "loss": 3.49169, "time": 0.81946} +{"mode": "train", "epoch": 102, "iter": 2200, "lr": 0.02357, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38406, "top5_acc": 0.63219, "loss_cls": 3.49861, "loss": 3.49861, "time": 0.81714} +{"mode": "train", "epoch": 102, "iter": 2300, "lr": 0.02355, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37344, "top5_acc": 0.63656, "loss_cls": 3.52368, "loss": 3.52368, "time": 0.81257} +{"mode": "train", "epoch": 102, "iter": 2400, "lr": 0.02353, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38391, "top5_acc": 0.63984, "loss_cls": 3.48238, "loss": 3.48238, "time": 0.82348} +{"mode": "train", "epoch": 102, "iter": 2500, "lr": 0.0235, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37875, "top5_acc": 0.63781, "loss_cls": 3.52031, "loss": 3.52031, "time": 0.81578} +{"mode": "train", "epoch": 102, "iter": 2600, "lr": 0.02348, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37828, "top5_acc": 0.64234, "loss_cls": 3.50476, "loss": 3.50476, "time": 0.81609} +{"mode": "train", "epoch": 102, "iter": 2700, "lr": 0.02346, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37141, "top5_acc": 0.62813, "loss_cls": 3.56449, "loss": 3.56449, "time": 0.81633} +{"mode": "train", "epoch": 102, "iter": 2800, "lr": 0.02343, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37672, "top5_acc": 0.63156, "loss_cls": 3.53995, "loss": 3.53995, "time": 0.81578} +{"mode": "train", "epoch": 102, "iter": 2900, "lr": 0.02341, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37438, "top5_acc": 0.63578, "loss_cls": 3.54719, "loss": 3.54719, "time": 0.81764} +{"mode": "train", "epoch": 102, "iter": 3000, "lr": 0.02339, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37859, "top5_acc": 0.64109, "loss_cls": 3.51214, "loss": 3.51214, "time": 0.8132} +{"mode": "train", "epoch": 102, "iter": 3100, "lr": 0.02336, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.375, "top5_acc": 0.63938, "loss_cls": 3.52284, "loss": 3.52284, "time": 0.82214} +{"mode": "train", "epoch": 102, "iter": 3200, "lr": 0.02334, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37828, "top5_acc": 0.63734, "loss_cls": 3.49859, "loss": 3.49859, "time": 0.81103} +{"mode": "train", "epoch": 102, "iter": 3300, "lr": 0.02331, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37047, "top5_acc": 0.62844, "loss_cls": 3.57427, "loss": 3.57427, "time": 0.81876} +{"mode": "train", "epoch": 102, "iter": 3400, "lr": 0.02329, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36359, "top5_acc": 0.62844, "loss_cls": 3.57999, "loss": 3.57999, "time": 0.81254} +{"mode": "train", "epoch": 102, "iter": 3500, "lr": 0.02327, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.38438, "top5_acc": 0.65062, "loss_cls": 3.47044, "loss": 3.47044, "time": 0.81161} +{"mode": "train", "epoch": 102, "iter": 3600, "lr": 0.02324, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37125, "top5_acc": 0.635, "loss_cls": 3.52368, "loss": 3.52368, "time": 0.81772} +{"mode": "train", "epoch": 102, "iter": 3700, "lr": 0.02322, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37531, "top5_acc": 0.64031, "loss_cls": 3.50922, "loss": 3.50922, "time": 0.81199} +{"mode": "val", "epoch": 102, "iter": 309, "lr": 0.02321, "top1_acc": 0.31971, "top5_acc": 0.5683, "mean_class_accuracy": 0.31953} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.02319, "memory": 15990, "data_time": 1.30579, "top1_acc": 0.40484, "top5_acc": 0.66156, "loss_cls": 3.36754, "loss": 3.36754, "time": 2.2864} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.02316, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39047, "top5_acc": 0.64062, "loss_cls": 3.4607, "loss": 3.4607, "time": 0.81584} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.02314, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38828, "top5_acc": 0.65484, "loss_cls": 3.42841, "loss": 3.42841, "time": 0.81633} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.02311, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37734, "top5_acc": 0.64406, "loss_cls": 3.45241, "loss": 3.45241, "time": 0.81744} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.02309, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38891, "top5_acc": 0.64844, "loss_cls": 3.42332, "loss": 3.42332, "time": 0.81365} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.02307, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38859, "top5_acc": 0.64125, "loss_cls": 3.45573, "loss": 3.45573, "time": 0.82093} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.02304, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37531, "top5_acc": 0.64297, "loss_cls": 3.4744, "loss": 3.4744, "time": 0.81468} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.02302, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38953, "top5_acc": 0.64969, "loss_cls": 3.45342, "loss": 3.45342, "time": 0.81326} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.023, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38531, "top5_acc": 0.64953, "loss_cls": 3.435, "loss": 3.435, "time": 0.81482} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.02297, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37922, "top5_acc": 0.64312, "loss_cls": 3.50015, "loss": 3.50015, "time": 0.82052} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.02295, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38266, "top5_acc": 0.64406, "loss_cls": 3.48629, "loss": 3.48629, "time": 0.81212} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.02293, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38703, "top5_acc": 0.65078, "loss_cls": 3.445, "loss": 3.445, "time": 0.82289} +{"mode": "train", "epoch": 103, "iter": 1300, "lr": 0.0229, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37781, "top5_acc": 0.63906, "loss_cls": 3.50864, "loss": 3.50864, "time": 0.81667} +{"mode": "train", "epoch": 103, "iter": 1400, "lr": 0.02288, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.38891, "top5_acc": 0.65078, "loss_cls": 3.45848, "loss": 3.45848, "time": 0.81166} +{"mode": "train", "epoch": 103, "iter": 1500, "lr": 0.02286, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38188, "top5_acc": 0.64203, "loss_cls": 3.4614, "loss": 3.4614, "time": 0.81312} +{"mode": "train", "epoch": 103, "iter": 1600, "lr": 0.02283, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37734, "top5_acc": 0.63766, "loss_cls": 3.48956, "loss": 3.48956, "time": 0.81198} +{"mode": "train", "epoch": 103, "iter": 1700, "lr": 0.02281, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37734, "top5_acc": 0.63969, "loss_cls": 3.49703, "loss": 3.49703, "time": 0.81581} +{"mode": "train", "epoch": 103, "iter": 1800, "lr": 0.02279, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37562, "top5_acc": 0.63656, "loss_cls": 3.50411, "loss": 3.50411, "time": 0.81922} +{"mode": "train", "epoch": 103, "iter": 1900, "lr": 0.02276, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37938, "top5_acc": 0.64547, "loss_cls": 3.50073, "loss": 3.50073, "time": 0.83265} +{"mode": "train", "epoch": 103, "iter": 2000, "lr": 0.02274, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3875, "top5_acc": 0.6425, "loss_cls": 3.47527, "loss": 3.47527, "time": 0.81411} +{"mode": "train", "epoch": 103, "iter": 2100, "lr": 0.02272, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37516, "top5_acc": 0.63813, "loss_cls": 3.49992, "loss": 3.49992, "time": 0.81536} +{"mode": "train", "epoch": 103, "iter": 2200, "lr": 0.02269, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38219, "top5_acc": 0.63953, "loss_cls": 3.49571, "loss": 3.49571, "time": 0.81931} +{"mode": "train", "epoch": 103, "iter": 2300, "lr": 0.02267, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37797, "top5_acc": 0.63281, "loss_cls": 3.51222, "loss": 3.51222, "time": 0.81312} +{"mode": "train", "epoch": 103, "iter": 2400, "lr": 0.02264, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37484, "top5_acc": 0.63875, "loss_cls": 3.50528, "loss": 3.50528, "time": 0.81657} +{"mode": "train", "epoch": 103, "iter": 2500, "lr": 0.02262, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37484, "top5_acc": 0.63469, "loss_cls": 3.52301, "loss": 3.52301, "time": 0.81458} +{"mode": "train", "epoch": 103, "iter": 2600, "lr": 0.0226, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38703, "top5_acc": 0.64453, "loss_cls": 3.47658, "loss": 3.47658, "time": 0.81144} +{"mode": "train", "epoch": 103, "iter": 2700, "lr": 0.02257, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38562, "top5_acc": 0.63594, "loss_cls": 3.49767, "loss": 3.49767, "time": 0.81205} +{"mode": "train", "epoch": 103, "iter": 2800, "lr": 0.02255, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37828, "top5_acc": 0.63703, "loss_cls": 3.50845, "loss": 3.50845, "time": 0.82} +{"mode": "train", "epoch": 103, "iter": 2900, "lr": 0.02253, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37953, "top5_acc": 0.63844, "loss_cls": 3.49681, "loss": 3.49681, "time": 0.81475} +{"mode": "train", "epoch": 103, "iter": 3000, "lr": 0.0225, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37938, "top5_acc": 0.63844, "loss_cls": 3.53167, "loss": 3.53167, "time": 0.8107} +{"mode": "train", "epoch": 103, "iter": 3100, "lr": 0.02248, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38344, "top5_acc": 0.6375, "loss_cls": 3.49576, "loss": 3.49576, "time": 0.81375} +{"mode": "train", "epoch": 103, "iter": 3200, "lr": 0.02246, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37984, "top5_acc": 0.64203, "loss_cls": 3.48615, "loss": 3.48615, "time": 0.81688} +{"mode": "train", "epoch": 103, "iter": 3300, "lr": 0.02243, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38031, "top5_acc": 0.64062, "loss_cls": 3.49137, "loss": 3.49137, "time": 0.81559} +{"mode": "train", "epoch": 103, "iter": 3400, "lr": 0.02241, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38531, "top5_acc": 0.64406, "loss_cls": 3.4546, "loss": 3.4546, "time": 0.81506} +{"mode": "train", "epoch": 103, "iter": 3500, "lr": 0.02239, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37266, "top5_acc": 0.63813, "loss_cls": 3.50603, "loss": 3.50603, "time": 0.81716} +{"mode": "train", "epoch": 103, "iter": 3600, "lr": 0.02236, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37234, "top5_acc": 0.63141, "loss_cls": 3.53898, "loss": 3.53898, "time": 0.81658} +{"mode": "train", "epoch": 103, "iter": 3700, "lr": 0.02234, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38359, "top5_acc": 0.63984, "loss_cls": 3.47518, "loss": 3.47518, "time": 0.81583} +{"mode": "val", "epoch": 103, "iter": 309, "lr": 0.02233, "top1_acc": 0.32021, "top5_acc": 0.57504, "mean_class_accuracy": 0.31985} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.02231, "memory": 15990, "data_time": 1.29434, "top1_acc": 0.39172, "top5_acc": 0.65234, "loss_cls": 3.39472, "loss": 3.39472, "time": 2.28596} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.02228, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39109, "top5_acc": 0.64906, "loss_cls": 3.41983, "loss": 3.41983, "time": 0.81699} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.02226, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37891, "top5_acc": 0.64734, "loss_cls": 3.47789, "loss": 3.47789, "time": 0.82444} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.02224, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38141, "top5_acc": 0.63734, "loss_cls": 3.48202, "loss": 3.48202, "time": 0.82101} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.02221, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38562, "top5_acc": 0.64594, "loss_cls": 3.45254, "loss": 3.45254, "time": 0.82403} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.02219, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38969, "top5_acc": 0.64234, "loss_cls": 3.47345, "loss": 3.47345, "time": 0.81538} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.02217, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3775, "top5_acc": 0.64172, "loss_cls": 3.47134, "loss": 3.47134, "time": 0.81782} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.02214, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38391, "top5_acc": 0.65141, "loss_cls": 3.44837, "loss": 3.44837, "time": 0.81398} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.02212, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40219, "top5_acc": 0.64625, "loss_cls": 3.42638, "loss": 3.42638, "time": 0.81198} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.0221, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39234, "top5_acc": 0.65438, "loss_cls": 3.4118, "loss": 3.4118, "time": 0.81262} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.02208, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38625, "top5_acc": 0.65172, "loss_cls": 3.42219, "loss": 3.42219, "time": 0.81641} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.02205, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38641, "top5_acc": 0.65094, "loss_cls": 3.43981, "loss": 3.43981, "time": 0.81682} +{"mode": "train", "epoch": 104, "iter": 1300, "lr": 0.02203, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39891, "top5_acc": 0.65562, "loss_cls": 3.39799, "loss": 3.39799, "time": 0.81026} +{"mode": "train", "epoch": 104, "iter": 1400, "lr": 0.02201, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38641, "top5_acc": 0.64828, "loss_cls": 3.44938, "loss": 3.44938, "time": 0.81289} +{"mode": "train", "epoch": 104, "iter": 1500, "lr": 0.02198, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38359, "top5_acc": 0.64969, "loss_cls": 3.48277, "loss": 3.48277, "time": 0.81199} +{"mode": "train", "epoch": 104, "iter": 1600, "lr": 0.02196, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38344, "top5_acc": 0.64859, "loss_cls": 3.49152, "loss": 3.49152, "time": 0.81859} +{"mode": "train", "epoch": 104, "iter": 1700, "lr": 0.02194, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38484, "top5_acc": 0.64391, "loss_cls": 3.46558, "loss": 3.46558, "time": 0.81541} +{"mode": "train", "epoch": 104, "iter": 1800, "lr": 0.02191, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37453, "top5_acc": 0.63656, "loss_cls": 3.52056, "loss": 3.52056, "time": 0.82203} +{"mode": "train", "epoch": 104, "iter": 1900, "lr": 0.02189, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37953, "top5_acc": 0.64094, "loss_cls": 3.51135, "loss": 3.51135, "time": 0.82716} +{"mode": "train", "epoch": 104, "iter": 2000, "lr": 0.02187, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39609, "top5_acc": 0.65703, "loss_cls": 3.41422, "loss": 3.41422, "time": 0.82229} +{"mode": "train", "epoch": 104, "iter": 2100, "lr": 0.02184, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39328, "top5_acc": 0.64906, "loss_cls": 3.40732, "loss": 3.40732, "time": 0.81956} +{"mode": "train", "epoch": 104, "iter": 2200, "lr": 0.02182, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39172, "top5_acc": 0.64547, "loss_cls": 3.46727, "loss": 3.46727, "time": 0.81521} +{"mode": "train", "epoch": 104, "iter": 2300, "lr": 0.0218, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39359, "top5_acc": 0.64828, "loss_cls": 3.44692, "loss": 3.44692, "time": 0.81366} +{"mode": "train", "epoch": 104, "iter": 2400, "lr": 0.02177, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37641, "top5_acc": 0.64828, "loss_cls": 3.50489, "loss": 3.50489, "time": 0.81244} +{"mode": "train", "epoch": 104, "iter": 2500, "lr": 0.02175, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38453, "top5_acc": 0.64188, "loss_cls": 3.48749, "loss": 3.48749, "time": 0.81445} +{"mode": "train", "epoch": 104, "iter": 2600, "lr": 0.02173, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37672, "top5_acc": 0.64062, "loss_cls": 3.48197, "loss": 3.48197, "time": 0.81672} +{"mode": "train", "epoch": 104, "iter": 2700, "lr": 0.02171, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39547, "top5_acc": 0.65203, "loss_cls": 3.43082, "loss": 3.43082, "time": 0.81517} +{"mode": "train", "epoch": 104, "iter": 2800, "lr": 0.02168, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38875, "top5_acc": 0.64625, "loss_cls": 3.48538, "loss": 3.48538, "time": 0.81298} +{"mode": "train", "epoch": 104, "iter": 2900, "lr": 0.02166, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38531, "top5_acc": 0.64062, "loss_cls": 3.47444, "loss": 3.47444, "time": 0.81627} +{"mode": "train", "epoch": 104, "iter": 3000, "lr": 0.02164, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37953, "top5_acc": 0.63828, "loss_cls": 3.507, "loss": 3.507, "time": 0.81731} +{"mode": "train", "epoch": 104, "iter": 3100, "lr": 0.02161, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38359, "top5_acc": 0.64422, "loss_cls": 3.45866, "loss": 3.45866, "time": 0.81169} +{"mode": "train", "epoch": 104, "iter": 3200, "lr": 0.02159, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39359, "top5_acc": 0.64812, "loss_cls": 3.43715, "loss": 3.43715, "time": 0.80973} +{"mode": "train", "epoch": 104, "iter": 3300, "lr": 0.02157, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38141, "top5_acc": 0.63281, "loss_cls": 3.49573, "loss": 3.49573, "time": 0.8176} +{"mode": "train", "epoch": 104, "iter": 3400, "lr": 0.02154, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38672, "top5_acc": 0.645, "loss_cls": 3.46149, "loss": 3.46149, "time": 0.81257} +{"mode": "train", "epoch": 104, "iter": 3500, "lr": 0.02152, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37938, "top5_acc": 0.6425, "loss_cls": 3.48343, "loss": 3.48343, "time": 0.81419} +{"mode": "train", "epoch": 104, "iter": 3600, "lr": 0.0215, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38359, "top5_acc": 0.64375, "loss_cls": 3.49349, "loss": 3.49349, "time": 0.81848} +{"mode": "train", "epoch": 104, "iter": 3700, "lr": 0.02148, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36797, "top5_acc": 0.63594, "loss_cls": 3.51308, "loss": 3.51308, "time": 0.81363} +{"mode": "val", "epoch": 104, "iter": 309, "lr": 0.02146, "top1_acc": 0.32726, "top5_acc": 0.58628, "mean_class_accuracy": 0.32698} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.02144, "memory": 15990, "data_time": 1.29286, "top1_acc": 0.39156, "top5_acc": 0.65344, "loss_cls": 3.43013, "loss": 3.43013, "time": 2.27208} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.02142, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38844, "top5_acc": 0.65953, "loss_cls": 3.40367, "loss": 3.40367, "time": 0.81433} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.0214, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38844, "top5_acc": 0.65, "loss_cls": 3.42354, "loss": 3.42354, "time": 0.81461} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.02137, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40328, "top5_acc": 0.66141, "loss_cls": 3.37595, "loss": 3.37595, "time": 0.81523} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.02135, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39688, "top5_acc": 0.65625, "loss_cls": 3.42059, "loss": 3.42059, "time": 0.82009} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.02133, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39203, "top5_acc": 0.65344, "loss_cls": 3.41903, "loss": 3.41903, "time": 0.81968} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.0213, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39125, "top5_acc": 0.64859, "loss_cls": 3.42224, "loss": 3.42224, "time": 0.81566} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.02128, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37828, "top5_acc": 0.63672, "loss_cls": 3.49561, "loss": 3.49561, "time": 0.81143} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.02126, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39406, "top5_acc": 0.66078, "loss_cls": 3.41031, "loss": 3.41031, "time": 0.81383} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.02124, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3925, "top5_acc": 0.64297, "loss_cls": 3.4569, "loss": 3.4569, "time": 0.81575} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.02121, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40719, "top5_acc": 0.65172, "loss_cls": 3.3951, "loss": 3.3951, "time": 0.8089} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.02119, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39578, "top5_acc": 0.64109, "loss_cls": 3.43718, "loss": 3.43718, "time": 0.81395} +{"mode": "train", "epoch": 105, "iter": 1300, "lr": 0.02117, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38453, "top5_acc": 0.64672, "loss_cls": 3.47026, "loss": 3.47026, "time": 0.8159} +{"mode": "train", "epoch": 105, "iter": 1400, "lr": 0.02114, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4, "top5_acc": 0.66078, "loss_cls": 3.38758, "loss": 3.38758, "time": 0.81164} +{"mode": "train", "epoch": 105, "iter": 1500, "lr": 0.02112, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38484, "top5_acc": 0.64234, "loss_cls": 3.47246, "loss": 3.47246, "time": 0.81255} +{"mode": "train", "epoch": 105, "iter": 1600, "lr": 0.0211, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39391, "top5_acc": 0.64969, "loss_cls": 3.44682, "loss": 3.44682, "time": 0.81001} +{"mode": "train", "epoch": 105, "iter": 1700, "lr": 0.02108, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39031, "top5_acc": 0.645, "loss_cls": 3.46277, "loss": 3.46277, "time": 0.81859} +{"mode": "train", "epoch": 105, "iter": 1800, "lr": 0.02105, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38875, "top5_acc": 0.64312, "loss_cls": 3.43209, "loss": 3.43209, "time": 0.81221} +{"mode": "train", "epoch": 105, "iter": 1900, "lr": 0.02103, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37641, "top5_acc": 0.64422, "loss_cls": 3.48446, "loss": 3.48446, "time": 0.82174} +{"mode": "train", "epoch": 105, "iter": 2000, "lr": 0.02101, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39266, "top5_acc": 0.65469, "loss_cls": 3.44735, "loss": 3.44735, "time": 0.82642} +{"mode": "train", "epoch": 105, "iter": 2100, "lr": 0.02098, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38781, "top5_acc": 0.64875, "loss_cls": 3.45036, "loss": 3.45036, "time": 0.82101} +{"mode": "train", "epoch": 105, "iter": 2200, "lr": 0.02096, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39453, "top5_acc": 0.64672, "loss_cls": 3.45651, "loss": 3.45651, "time": 0.82188} +{"mode": "train", "epoch": 105, "iter": 2300, "lr": 0.02094, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38312, "top5_acc": 0.64953, "loss_cls": 3.47234, "loss": 3.47234, "time": 0.81576} +{"mode": "train", "epoch": 105, "iter": 2400, "lr": 0.02092, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39219, "top5_acc": 0.64516, "loss_cls": 3.46375, "loss": 3.46375, "time": 0.81289} +{"mode": "train", "epoch": 105, "iter": 2500, "lr": 0.02089, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3875, "top5_acc": 0.65062, "loss_cls": 3.4257, "loss": 3.4257, "time": 0.81293} +{"mode": "train", "epoch": 105, "iter": 2600, "lr": 0.02087, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38297, "top5_acc": 0.65188, "loss_cls": 3.44075, "loss": 3.44075, "time": 0.81507} +{"mode": "train", "epoch": 105, "iter": 2700, "lr": 0.02085, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38828, "top5_acc": 0.64609, "loss_cls": 3.434, "loss": 3.434, "time": 0.81211} +{"mode": "train", "epoch": 105, "iter": 2800, "lr": 0.02083, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38172, "top5_acc": 0.65594, "loss_cls": 3.43636, "loss": 3.43636, "time": 0.81566} +{"mode": "train", "epoch": 105, "iter": 2900, "lr": 0.0208, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38844, "top5_acc": 0.65188, "loss_cls": 3.4426, "loss": 3.4426, "time": 0.81743} +{"mode": "train", "epoch": 105, "iter": 3000, "lr": 0.02078, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39125, "top5_acc": 0.64484, "loss_cls": 3.48113, "loss": 3.48113, "time": 0.81363} +{"mode": "train", "epoch": 105, "iter": 3100, "lr": 0.02076, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.385, "top5_acc": 0.64656, "loss_cls": 3.45165, "loss": 3.45165, "time": 0.81148} +{"mode": "train", "epoch": 105, "iter": 3200, "lr": 0.02073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39891, "top5_acc": 0.65375, "loss_cls": 3.40757, "loss": 3.40757, "time": 0.8211} +{"mode": "train", "epoch": 105, "iter": 3300, "lr": 0.02071, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38688, "top5_acc": 0.65547, "loss_cls": 3.43712, "loss": 3.43712, "time": 0.81788} +{"mode": "train", "epoch": 105, "iter": 3400, "lr": 0.02069, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38781, "top5_acc": 0.64297, "loss_cls": 3.47445, "loss": 3.47445, "time": 0.81861} +{"mode": "train", "epoch": 105, "iter": 3500, "lr": 0.02067, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38047, "top5_acc": 0.64297, "loss_cls": 3.48873, "loss": 3.48873, "time": 0.81615} +{"mode": "train", "epoch": 105, "iter": 3600, "lr": 0.02064, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3825, "top5_acc": 0.64297, "loss_cls": 3.44488, "loss": 3.44488, "time": 0.8149} +{"mode": "train", "epoch": 105, "iter": 3700, "lr": 0.02062, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38906, "top5_acc": 0.64656, "loss_cls": 3.44399, "loss": 3.44399, "time": 0.81616} +{"mode": "val", "epoch": 105, "iter": 309, "lr": 0.02061, "top1_acc": 0.32189, "top5_acc": 0.58107, "mean_class_accuracy": 0.32167} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.02059, "memory": 15990, "data_time": 1.40539, "top1_acc": 0.40422, "top5_acc": 0.67, "loss_cls": 3.34494, "loss": 3.34494, "time": 2.39316} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.02057, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40375, "top5_acc": 0.66703, "loss_cls": 3.34825, "loss": 3.34825, "time": 0.81988} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.02054, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39812, "top5_acc": 0.65203, "loss_cls": 3.40262, "loss": 3.40262, "time": 0.81709} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.02052, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40594, "top5_acc": 0.66375, "loss_cls": 3.37002, "loss": 3.37002, "time": 0.82099} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.0205, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40188, "top5_acc": 0.65547, "loss_cls": 3.39816, "loss": 3.39816, "time": 0.81583} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.02048, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.395, "top5_acc": 0.65344, "loss_cls": 3.41449, "loss": 3.41449, "time": 0.81966} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.02045, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39922, "top5_acc": 0.65203, "loss_cls": 3.40734, "loss": 3.40734, "time": 0.81547} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.02043, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37953, "top5_acc": 0.64234, "loss_cls": 3.45231, "loss": 3.45231, "time": 0.81846} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.02041, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38734, "top5_acc": 0.65375, "loss_cls": 3.41753, "loss": 3.41753, "time": 0.81747} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.02039, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39172, "top5_acc": 0.65938, "loss_cls": 3.41265, "loss": 3.41265, "time": 0.8149} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.02036, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39062, "top5_acc": 0.65328, "loss_cls": 3.40641, "loss": 3.40641, "time": 0.81825} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.02034, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39703, "top5_acc": 0.65719, "loss_cls": 3.3706, "loss": 3.3706, "time": 0.81727} +{"mode": "train", "epoch": 106, "iter": 1300, "lr": 0.02032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38641, "top5_acc": 0.65312, "loss_cls": 3.45534, "loss": 3.45534, "time": 0.81558} +{"mode": "train", "epoch": 106, "iter": 1400, "lr": 0.0203, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38391, "top5_acc": 0.63828, "loss_cls": 3.47583, "loss": 3.47583, "time": 0.81659} +{"mode": "train", "epoch": 106, "iter": 1500, "lr": 0.02027, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38547, "top5_acc": 0.64391, "loss_cls": 3.46016, "loss": 3.46016, "time": 0.82415} +{"mode": "train", "epoch": 106, "iter": 1600, "lr": 0.02025, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39141, "top5_acc": 0.65094, "loss_cls": 3.44525, "loss": 3.44525, "time": 0.81433} +{"mode": "train", "epoch": 106, "iter": 1700, "lr": 0.02023, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40297, "top5_acc": 0.66422, "loss_cls": 3.36453, "loss": 3.36453, "time": 0.81736} +{"mode": "train", "epoch": 106, "iter": 1800, "lr": 0.02021, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39297, "top5_acc": 0.65031, "loss_cls": 3.39341, "loss": 3.39341, "time": 0.81898} +{"mode": "train", "epoch": 106, "iter": 1900, "lr": 0.02018, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39047, "top5_acc": 0.64922, "loss_cls": 3.41587, "loss": 3.41587, "time": 0.82666} +{"mode": "train", "epoch": 106, "iter": 2000, "lr": 0.02016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38906, "top5_acc": 0.64156, "loss_cls": 3.5052, "loss": 3.5052, "time": 0.8247} +{"mode": "train", "epoch": 106, "iter": 2100, "lr": 0.02014, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38875, "top5_acc": 0.64766, "loss_cls": 3.4531, "loss": 3.4531, "time": 0.82685} +{"mode": "train", "epoch": 106, "iter": 2200, "lr": 0.02012, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39906, "top5_acc": 0.65094, "loss_cls": 3.40219, "loss": 3.40219, "time": 0.82092} +{"mode": "train", "epoch": 106, "iter": 2300, "lr": 0.02009, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39609, "top5_acc": 0.65172, "loss_cls": 3.40194, "loss": 3.40194, "time": 0.81682} +{"mode": "train", "epoch": 106, "iter": 2400, "lr": 0.02007, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38703, "top5_acc": 0.65312, "loss_cls": 3.41423, "loss": 3.41423, "time": 0.81299} +{"mode": "train", "epoch": 106, "iter": 2500, "lr": 0.02005, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39609, "top5_acc": 0.6575, "loss_cls": 3.39912, "loss": 3.39912, "time": 0.81079} +{"mode": "train", "epoch": 106, "iter": 2600, "lr": 0.02003, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38859, "top5_acc": 0.64734, "loss_cls": 3.46153, "loss": 3.46153, "time": 0.81383} +{"mode": "train", "epoch": 106, "iter": 2700, "lr": 0.02, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39141, "top5_acc": 0.65875, "loss_cls": 3.40569, "loss": 3.40569, "time": 0.81362} +{"mode": "train", "epoch": 106, "iter": 2800, "lr": 0.01998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39578, "top5_acc": 0.655, "loss_cls": 3.42203, "loss": 3.42203, "time": 0.81441} +{"mode": "train", "epoch": 106, "iter": 2900, "lr": 0.01996, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38453, "top5_acc": 0.64703, "loss_cls": 3.45484, "loss": 3.45484, "time": 0.81794} +{"mode": "train", "epoch": 106, "iter": 3000, "lr": 0.01994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38312, "top5_acc": 0.65297, "loss_cls": 3.46309, "loss": 3.46309, "time": 0.81655} +{"mode": "train", "epoch": 106, "iter": 3100, "lr": 0.01991, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3975, "top5_acc": 0.65578, "loss_cls": 3.40421, "loss": 3.40421, "time": 0.81749} +{"mode": "train", "epoch": 106, "iter": 3200, "lr": 0.01989, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38344, "top5_acc": 0.64344, "loss_cls": 3.46527, "loss": 3.46527, "time": 0.81763} +{"mode": "train", "epoch": 106, "iter": 3300, "lr": 0.01987, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39609, "top5_acc": 0.65641, "loss_cls": 3.42304, "loss": 3.42304, "time": 0.81612} +{"mode": "train", "epoch": 106, "iter": 3400, "lr": 0.01985, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39016, "top5_acc": 0.64703, "loss_cls": 3.4381, "loss": 3.4381, "time": 0.8147} +{"mode": "train", "epoch": 106, "iter": 3500, "lr": 0.01983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39078, "top5_acc": 0.64844, "loss_cls": 3.43706, "loss": 3.43706, "time": 0.81657} +{"mode": "train", "epoch": 106, "iter": 3600, "lr": 0.0198, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38281, "top5_acc": 0.64656, "loss_cls": 3.45589, "loss": 3.45589, "time": 0.82103} +{"mode": "train", "epoch": 106, "iter": 3700, "lr": 0.01978, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39547, "top5_acc": 0.64922, "loss_cls": 3.43991, "loss": 3.43991, "time": 0.81253} +{"mode": "val", "epoch": 106, "iter": 309, "lr": 0.01977, "top1_acc": 0.33278, "top5_acc": 0.59069, "mean_class_accuracy": 0.33254} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.01975, "memory": 15990, "data_time": 1.38053, "top1_acc": 0.41234, "top5_acc": 0.67234, "loss_cls": 3.32496, "loss": 3.32496, "time": 2.36695} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.01973, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39391, "top5_acc": 0.65641, "loss_cls": 3.40029, "loss": 3.40029, "time": 0.82368} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.0197, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40812, "top5_acc": 0.65984, "loss_cls": 3.37664, "loss": 3.37664, "time": 0.8213} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.01968, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40828, "top5_acc": 0.66328, "loss_cls": 3.36541, "loss": 3.36541, "time": 0.82475} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.01966, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40531, "top5_acc": 0.66328, "loss_cls": 3.34177, "loss": 3.34177, "time": 0.81667} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.01964, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39203, "top5_acc": 0.64781, "loss_cls": 3.44748, "loss": 3.44748, "time": 0.82192} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.01961, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39906, "top5_acc": 0.64969, "loss_cls": 3.41521, "loss": 3.41521, "time": 0.81453} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.01959, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39609, "top5_acc": 0.65953, "loss_cls": 3.37675, "loss": 3.37675, "time": 0.81691} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.01957, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38344, "top5_acc": 0.65344, "loss_cls": 3.42418, "loss": 3.42418, "time": 0.81571} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.01955, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39922, "top5_acc": 0.65656, "loss_cls": 3.37959, "loss": 3.37959, "time": 0.81434} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.01953, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38406, "top5_acc": 0.66297, "loss_cls": 3.39401, "loss": 3.39401, "time": 0.81686} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.0195, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40219, "top5_acc": 0.65875, "loss_cls": 3.36571, "loss": 3.36571, "time": 0.81923} +{"mode": "train", "epoch": 107, "iter": 1300, "lr": 0.01948, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38688, "top5_acc": 0.64969, "loss_cls": 3.44686, "loss": 3.44686, "time": 0.82114} +{"mode": "train", "epoch": 107, "iter": 1400, "lr": 0.01946, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38656, "top5_acc": 0.64422, "loss_cls": 3.46243, "loss": 3.46243, "time": 0.81519} +{"mode": "train", "epoch": 107, "iter": 1500, "lr": 0.01944, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39375, "top5_acc": 0.66562, "loss_cls": 3.35718, "loss": 3.35718, "time": 0.81806} +{"mode": "train", "epoch": 107, "iter": 1600, "lr": 0.01942, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40719, "top5_acc": 0.66453, "loss_cls": 3.36091, "loss": 3.36091, "time": 0.81276} +{"mode": "train", "epoch": 107, "iter": 1700, "lr": 0.01939, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39125, "top5_acc": 0.65969, "loss_cls": 3.36777, "loss": 3.36777, "time": 0.81626} +{"mode": "train", "epoch": 107, "iter": 1800, "lr": 0.01937, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39641, "top5_acc": 0.66016, "loss_cls": 3.37894, "loss": 3.37894, "time": 0.81398} +{"mode": "train", "epoch": 107, "iter": 1900, "lr": 0.01935, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38516, "top5_acc": 0.64594, "loss_cls": 3.4968, "loss": 3.4968, "time": 0.82216} +{"mode": "train", "epoch": 107, "iter": 2000, "lr": 0.01933, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39359, "top5_acc": 0.65984, "loss_cls": 3.39149, "loss": 3.39149, "time": 0.82316} +{"mode": "train", "epoch": 107, "iter": 2100, "lr": 0.0193, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39062, "top5_acc": 0.65469, "loss_cls": 3.40956, "loss": 3.40956, "time": 0.82102} +{"mode": "train", "epoch": 107, "iter": 2200, "lr": 0.01928, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40016, "top5_acc": 0.65844, "loss_cls": 3.36708, "loss": 3.36708, "time": 0.82014} +{"mode": "train", "epoch": 107, "iter": 2300, "lr": 0.01926, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39391, "top5_acc": 0.66078, "loss_cls": 3.39808, "loss": 3.39808, "time": 0.81587} +{"mode": "train", "epoch": 107, "iter": 2400, "lr": 0.01924, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40125, "top5_acc": 0.65391, "loss_cls": 3.41117, "loss": 3.41117, "time": 0.81306} +{"mode": "train", "epoch": 107, "iter": 2500, "lr": 0.01922, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38172, "top5_acc": 0.65375, "loss_cls": 3.44201, "loss": 3.44201, "time": 0.8134} +{"mode": "train", "epoch": 107, "iter": 2600, "lr": 0.01919, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38969, "top5_acc": 0.65922, "loss_cls": 3.3989, "loss": 3.3989, "time": 0.81451} +{"mode": "train", "epoch": 107, "iter": 2700, "lr": 0.01917, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38969, "top5_acc": 0.64922, "loss_cls": 3.42768, "loss": 3.42768, "time": 0.81116} +{"mode": "train", "epoch": 107, "iter": 2800, "lr": 0.01915, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39812, "top5_acc": 0.65422, "loss_cls": 3.4013, "loss": 3.4013, "time": 0.81873} +{"mode": "train", "epoch": 107, "iter": 2900, "lr": 0.01913, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38562, "top5_acc": 0.64719, "loss_cls": 3.47399, "loss": 3.47399, "time": 0.81323} +{"mode": "train", "epoch": 107, "iter": 3000, "lr": 0.01911, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38609, "top5_acc": 0.63969, "loss_cls": 3.45882, "loss": 3.45882, "time": 0.81656} +{"mode": "train", "epoch": 107, "iter": 3100, "lr": 0.01908, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39297, "top5_acc": 0.6575, "loss_cls": 3.41991, "loss": 3.41991, "time": 0.81278} +{"mode": "train", "epoch": 107, "iter": 3200, "lr": 0.01906, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38875, "top5_acc": 0.65188, "loss_cls": 3.43525, "loss": 3.43525, "time": 0.81996} +{"mode": "train", "epoch": 107, "iter": 3300, "lr": 0.01904, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39578, "top5_acc": 0.66125, "loss_cls": 3.36481, "loss": 3.36481, "time": 0.81326} +{"mode": "train", "epoch": 107, "iter": 3400, "lr": 0.01902, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39031, "top5_acc": 0.65531, "loss_cls": 3.42764, "loss": 3.42764, "time": 0.81193} +{"mode": "train", "epoch": 107, "iter": 3500, "lr": 0.019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38875, "top5_acc": 0.65391, "loss_cls": 3.43305, "loss": 3.43305, "time": 0.81293} +{"mode": "train", "epoch": 107, "iter": 3600, "lr": 0.01897, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39, "top5_acc": 0.65203, "loss_cls": 3.41948, "loss": 3.41948, "time": 0.8175} +{"mode": "train", "epoch": 107, "iter": 3700, "lr": 0.01895, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3925, "top5_acc": 0.65062, "loss_cls": 3.42106, "loss": 3.42106, "time": 0.81069} +{"mode": "val", "epoch": 107, "iter": 309, "lr": 0.01894, "top1_acc": 0.33197, "top5_acc": 0.59383, "mean_class_accuracy": 0.33167} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.01892, "memory": 15990, "data_time": 1.37179, "top1_acc": 0.41719, "top5_acc": 0.67656, "loss_cls": 3.27754, "loss": 3.27754, "time": 2.35965} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0189, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41438, "top5_acc": 0.67219, "loss_cls": 3.3093, "loss": 3.3093, "time": 0.82072} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.01888, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40234, "top5_acc": 0.66109, "loss_cls": 3.3516, "loss": 3.3516, "time": 0.81922} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.01886, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40234, "top5_acc": 0.65594, "loss_cls": 3.38875, "loss": 3.38875, "time": 0.82456} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.01883, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39641, "top5_acc": 0.65953, "loss_cls": 3.38541, "loss": 3.38541, "time": 0.81386} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.01881, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39312, "top5_acc": 0.66094, "loss_cls": 3.40107, "loss": 3.40107, "time": 0.81952} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.01879, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40188, "top5_acc": 0.66422, "loss_cls": 3.36529, "loss": 3.36529, "time": 0.81864} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.01877, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.395, "top5_acc": 0.65969, "loss_cls": 3.36464, "loss": 3.36464, "time": 0.81769} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.01875, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39531, "top5_acc": 0.65828, "loss_cls": 3.3674, "loss": 3.3674, "time": 0.81091} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.01872, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40188, "top5_acc": 0.66797, "loss_cls": 3.32641, "loss": 3.32641, "time": 0.81155} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.0187, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40422, "top5_acc": 0.66438, "loss_cls": 3.35762, "loss": 3.35762, "time": 0.81427} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.01868, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39812, "top5_acc": 0.66266, "loss_cls": 3.38602, "loss": 3.38602, "time": 0.81854} +{"mode": "train", "epoch": 108, "iter": 1300, "lr": 0.01866, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40688, "top5_acc": 0.66281, "loss_cls": 3.36789, "loss": 3.36789, "time": 0.81817} +{"mode": "train", "epoch": 108, "iter": 1400, "lr": 0.01864, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40141, "top5_acc": 0.65094, "loss_cls": 3.38792, "loss": 3.38792, "time": 0.81603} +{"mode": "train", "epoch": 108, "iter": 1500, "lr": 0.01862, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39047, "top5_acc": 0.65797, "loss_cls": 3.38975, "loss": 3.38975, "time": 0.81422} +{"mode": "train", "epoch": 108, "iter": 1600, "lr": 0.01859, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39719, "top5_acc": 0.65281, "loss_cls": 3.4239, "loss": 3.4239, "time": 0.81214} +{"mode": "train", "epoch": 108, "iter": 1700, "lr": 0.01857, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40281, "top5_acc": 0.66391, "loss_cls": 3.34494, "loss": 3.34494, "time": 0.81566} +{"mode": "train", "epoch": 108, "iter": 1800, "lr": 0.01855, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40016, "top5_acc": 0.66812, "loss_cls": 3.34612, "loss": 3.34612, "time": 0.81247} +{"mode": "train", "epoch": 108, "iter": 1900, "lr": 0.01853, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39562, "top5_acc": 0.65281, "loss_cls": 3.42481, "loss": 3.42481, "time": 0.81795} +{"mode": "train", "epoch": 108, "iter": 2000, "lr": 0.01851, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39375, "top5_acc": 0.66344, "loss_cls": 3.37503, "loss": 3.37503, "time": 0.83223} +{"mode": "train", "epoch": 108, "iter": 2100, "lr": 0.01848, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39219, "top5_acc": 0.65016, "loss_cls": 3.41476, "loss": 3.41476, "time": 0.82073} +{"mode": "train", "epoch": 108, "iter": 2200, "lr": 0.01846, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38562, "top5_acc": 0.65188, "loss_cls": 3.45102, "loss": 3.45102, "time": 0.81716} +{"mode": "train", "epoch": 108, "iter": 2300, "lr": 0.01844, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3925, "top5_acc": 0.65391, "loss_cls": 3.40284, "loss": 3.40284, "time": 0.81793} +{"mode": "train", "epoch": 108, "iter": 2400, "lr": 0.01842, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39938, "top5_acc": 0.66188, "loss_cls": 3.37258, "loss": 3.37258, "time": 0.81471} +{"mode": "train", "epoch": 108, "iter": 2500, "lr": 0.0184, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39609, "top5_acc": 0.65625, "loss_cls": 3.40564, "loss": 3.40564, "time": 0.819} +{"mode": "train", "epoch": 108, "iter": 2600, "lr": 0.01838, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39094, "top5_acc": 0.64938, "loss_cls": 3.42196, "loss": 3.42196, "time": 0.81583} +{"mode": "train", "epoch": 108, "iter": 2700, "lr": 0.01835, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39922, "top5_acc": 0.66, "loss_cls": 3.37913, "loss": 3.37913, "time": 0.81426} +{"mode": "train", "epoch": 108, "iter": 2800, "lr": 0.01833, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40422, "top5_acc": 0.66312, "loss_cls": 3.34933, "loss": 3.34933, "time": 0.81769} +{"mode": "train", "epoch": 108, "iter": 2900, "lr": 0.01831, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3975, "top5_acc": 0.65078, "loss_cls": 3.38779, "loss": 3.38779, "time": 0.81691} +{"mode": "train", "epoch": 108, "iter": 3000, "lr": 0.01829, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40922, "top5_acc": 0.65938, "loss_cls": 3.35527, "loss": 3.35527, "time": 0.81443} +{"mode": "train", "epoch": 108, "iter": 3100, "lr": 0.01827, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39625, "top5_acc": 0.65141, "loss_cls": 3.42293, "loss": 3.42293, "time": 0.81532} +{"mode": "train", "epoch": 108, "iter": 3200, "lr": 0.01825, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40219, "top5_acc": 0.65656, "loss_cls": 3.39001, "loss": 3.39001, "time": 0.81197} +{"mode": "train", "epoch": 108, "iter": 3300, "lr": 0.01823, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38562, "top5_acc": 0.65031, "loss_cls": 3.47931, "loss": 3.47931, "time": 0.81679} +{"mode": "train", "epoch": 108, "iter": 3400, "lr": 0.0182, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39625, "top5_acc": 0.65062, "loss_cls": 3.41898, "loss": 3.41898, "time": 0.8119} +{"mode": "train", "epoch": 108, "iter": 3500, "lr": 0.01818, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39297, "top5_acc": 0.65016, "loss_cls": 3.45595, "loss": 3.45595, "time": 0.82012} +{"mode": "train", "epoch": 108, "iter": 3600, "lr": 0.01816, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39891, "top5_acc": 0.66703, "loss_cls": 3.34753, "loss": 3.34753, "time": 0.82383} +{"mode": "train", "epoch": 108, "iter": 3700, "lr": 0.01814, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38391, "top5_acc": 0.64422, "loss_cls": 3.45926, "loss": 3.45926, "time": 0.81188} +{"mode": "val", "epoch": 108, "iter": 309, "lr": 0.01813, "top1_acc": 0.34194, "top5_acc": 0.59201, "mean_class_accuracy": 0.34162} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.01811, "memory": 15990, "data_time": 1.31052, "top1_acc": 0.41844, "top5_acc": 0.68062, "loss_cls": 3.25745, "loss": 3.25745, "time": 2.29062} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.01809, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41969, "top5_acc": 0.68312, "loss_cls": 3.24633, "loss": 3.24633, "time": 0.82105} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.01806, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40359, "top5_acc": 0.67266, "loss_cls": 3.33093, "loss": 3.33093, "time": 0.81719} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.01804, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41844, "top5_acc": 0.6725, "loss_cls": 3.26913, "loss": 3.26913, "time": 0.82298} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.01802, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40297, "top5_acc": 0.65562, "loss_cls": 3.38809, "loss": 3.38809, "time": 0.82019} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.018, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40938, "top5_acc": 0.66688, "loss_cls": 3.31771, "loss": 3.31771, "time": 0.82069} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.01798, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40203, "top5_acc": 0.66172, "loss_cls": 3.35913, "loss": 3.35913, "time": 0.82368} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.01796, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40125, "top5_acc": 0.655, "loss_cls": 3.36721, "loss": 3.36721, "time": 0.81432} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.01794, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39828, "top5_acc": 0.65922, "loss_cls": 3.37714, "loss": 3.37714, "time": 0.81636} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.01791, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40609, "top5_acc": 0.65266, "loss_cls": 3.37056, "loss": 3.37056, "time": 0.81442} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.01789, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41188, "top5_acc": 0.66656, "loss_cls": 3.33953, "loss": 3.33953, "time": 0.82114} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.01787, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40234, "top5_acc": 0.66797, "loss_cls": 3.35447, "loss": 3.35447, "time": 0.81467} +{"mode": "train", "epoch": 109, "iter": 1300, "lr": 0.01785, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38438, "top5_acc": 0.64422, "loss_cls": 3.44467, "loss": 3.44467, "time": 0.81295} +{"mode": "train", "epoch": 109, "iter": 1400, "lr": 0.01783, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40156, "top5_acc": 0.65391, "loss_cls": 3.39082, "loss": 3.39082, "time": 0.82003} +{"mode": "train", "epoch": 109, "iter": 1500, "lr": 0.01781, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38984, "top5_acc": 0.65359, "loss_cls": 3.42658, "loss": 3.42658, "time": 0.81503} +{"mode": "train", "epoch": 109, "iter": 1600, "lr": 0.01779, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40188, "top5_acc": 0.66391, "loss_cls": 3.34795, "loss": 3.34795, "time": 0.81689} +{"mode": "train", "epoch": 109, "iter": 1700, "lr": 0.01776, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39609, "top5_acc": 0.66219, "loss_cls": 3.37368, "loss": 3.37368, "time": 0.8118} +{"mode": "train", "epoch": 109, "iter": 1800, "lr": 0.01774, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39484, "top5_acc": 0.65922, "loss_cls": 3.38033, "loss": 3.38033, "time": 0.81165} +{"mode": "train", "epoch": 109, "iter": 1900, "lr": 0.01772, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40188, "top5_acc": 0.66125, "loss_cls": 3.37056, "loss": 3.37056, "time": 0.82259} +{"mode": "train", "epoch": 109, "iter": 2000, "lr": 0.0177, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39359, "top5_acc": 0.66016, "loss_cls": 3.3941, "loss": 3.3941, "time": 0.83105} +{"mode": "train", "epoch": 109, "iter": 2100, "lr": 0.01768, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40672, "top5_acc": 0.65969, "loss_cls": 3.35466, "loss": 3.35466, "time": 0.83034} +{"mode": "train", "epoch": 109, "iter": 2200, "lr": 0.01766, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40125, "top5_acc": 0.65562, "loss_cls": 3.40782, "loss": 3.40782, "time": 0.82518} +{"mode": "train", "epoch": 109, "iter": 2300, "lr": 0.01764, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39672, "top5_acc": 0.66188, "loss_cls": 3.38291, "loss": 3.38291, "time": 0.8196} +{"mode": "train", "epoch": 109, "iter": 2400, "lr": 0.01761, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41234, "top5_acc": 0.67109, "loss_cls": 3.33075, "loss": 3.33075, "time": 0.81648} +{"mode": "train", "epoch": 109, "iter": 2500, "lr": 0.01759, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40016, "top5_acc": 0.65625, "loss_cls": 3.38019, "loss": 3.38019, "time": 0.81775} +{"mode": "train", "epoch": 109, "iter": 2600, "lr": 0.01757, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4125, "top5_acc": 0.65406, "loss_cls": 3.34045, "loss": 3.34045, "time": 0.81569} +{"mode": "train", "epoch": 109, "iter": 2700, "lr": 0.01755, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39516, "top5_acc": 0.65484, "loss_cls": 3.40396, "loss": 3.40396, "time": 0.81271} +{"mode": "train", "epoch": 109, "iter": 2800, "lr": 0.01753, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38594, "top5_acc": 0.64531, "loss_cls": 3.43768, "loss": 3.43768, "time": 0.81492} +{"mode": "train", "epoch": 109, "iter": 2900, "lr": 0.01751, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39672, "top5_acc": 0.66594, "loss_cls": 3.38404, "loss": 3.38404, "time": 0.82046} +{"mode": "train", "epoch": 109, "iter": 3000, "lr": 0.01749, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40297, "top5_acc": 0.65969, "loss_cls": 3.37257, "loss": 3.37257, "time": 0.81582} +{"mode": "train", "epoch": 109, "iter": 3100, "lr": 0.01747, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39562, "top5_acc": 0.66562, "loss_cls": 3.36423, "loss": 3.36423, "time": 0.81663} +{"mode": "train", "epoch": 109, "iter": 3200, "lr": 0.01744, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40203, "top5_acc": 0.66062, "loss_cls": 3.355, "loss": 3.355, "time": 0.81446} +{"mode": "train", "epoch": 109, "iter": 3300, "lr": 0.01742, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39188, "top5_acc": 0.65297, "loss_cls": 3.39576, "loss": 3.39576, "time": 0.81634} +{"mode": "train", "epoch": 109, "iter": 3400, "lr": 0.0174, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40078, "top5_acc": 0.6675, "loss_cls": 3.37598, "loss": 3.37598, "time": 0.81212} +{"mode": "train", "epoch": 109, "iter": 3500, "lr": 0.01738, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40625, "top5_acc": 0.66016, "loss_cls": 3.33467, "loss": 3.33467, "time": 0.81649} +{"mode": "train", "epoch": 109, "iter": 3600, "lr": 0.01736, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40281, "top5_acc": 0.65719, "loss_cls": 3.36531, "loss": 3.36531, "time": 0.82547} +{"mode": "train", "epoch": 109, "iter": 3700, "lr": 0.01734, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40078, "top5_acc": 0.66531, "loss_cls": 3.36405, "loss": 3.36405, "time": 0.81512} +{"mode": "val", "epoch": 109, "iter": 309, "lr": 0.01733, "top1_acc": 0.3499, "top5_acc": 0.60249, "mean_class_accuracy": 0.34961} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.01731, "memory": 15990, "data_time": 1.28424, "top1_acc": 0.41891, "top5_acc": 0.67281, "loss_cls": 3.29012, "loss": 3.29012, "time": 2.2697} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.01729, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41516, "top5_acc": 0.67297, "loss_cls": 3.28046, "loss": 3.28046, "time": 0.82128} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.01727, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39875, "top5_acc": 0.66219, "loss_cls": 3.35196, "loss": 3.35196, "time": 0.8196} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.01724, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41844, "top5_acc": 0.67531, "loss_cls": 3.28517, "loss": 3.28517, "time": 0.81172} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.01722, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40641, "top5_acc": 0.66875, "loss_cls": 3.36115, "loss": 3.36115, "time": 0.82597} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.0172, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40562, "top5_acc": 0.67188, "loss_cls": 3.3158, "loss": 3.3158, "time": 0.8148} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.01718, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4075, "top5_acc": 0.66484, "loss_cls": 3.32801, "loss": 3.32801, "time": 0.82253} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.01716, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40672, "top5_acc": 0.66219, "loss_cls": 3.35783, "loss": 3.35783, "time": 0.81798} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.01714, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41453, "top5_acc": 0.67141, "loss_cls": 3.31627, "loss": 3.31627, "time": 0.81848} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.01712, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40578, "top5_acc": 0.66359, "loss_cls": 3.33153, "loss": 3.33153, "time": 0.81238} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.0171, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40906, "top5_acc": 0.66734, "loss_cls": 3.31005, "loss": 3.31005, "time": 0.81165} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.01708, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41234, "top5_acc": 0.6725, "loss_cls": 3.29302, "loss": 3.29302, "time": 0.81392} +{"mode": "train", "epoch": 110, "iter": 1300, "lr": 0.01705, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40125, "top5_acc": 0.65781, "loss_cls": 3.37151, "loss": 3.37151, "time": 0.81931} +{"mode": "train", "epoch": 110, "iter": 1400, "lr": 0.01703, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40594, "top5_acc": 0.66672, "loss_cls": 3.34625, "loss": 3.34625, "time": 0.8165} +{"mode": "train", "epoch": 110, "iter": 1500, "lr": 0.01701, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40078, "top5_acc": 0.65562, "loss_cls": 3.38757, "loss": 3.38757, "time": 0.81479} +{"mode": "train", "epoch": 110, "iter": 1600, "lr": 0.01699, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40891, "top5_acc": 0.67266, "loss_cls": 3.30815, "loss": 3.30815, "time": 0.81445} +{"mode": "train", "epoch": 110, "iter": 1700, "lr": 0.01697, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.40281, "top5_acc": 0.66375, "loss_cls": 3.35594, "loss": 3.35594, "time": 0.81232} +{"mode": "train", "epoch": 110, "iter": 1800, "lr": 0.01695, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41266, "top5_acc": 0.67047, "loss_cls": 3.29399, "loss": 3.29399, "time": 0.81454} +{"mode": "train", "epoch": 110, "iter": 1900, "lr": 0.01693, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.4075, "top5_acc": 0.66812, "loss_cls": 3.32009, "loss": 3.32009, "time": 0.82058} +{"mode": "train", "epoch": 110, "iter": 2000, "lr": 0.01691, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40141, "top5_acc": 0.66547, "loss_cls": 3.34966, "loss": 3.34966, "time": 0.82375} +{"mode": "train", "epoch": 110, "iter": 2100, "lr": 0.01689, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39516, "top5_acc": 0.65328, "loss_cls": 3.39523, "loss": 3.39523, "time": 0.81855} +{"mode": "train", "epoch": 110, "iter": 2200, "lr": 0.01687, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41078, "top5_acc": 0.6625, "loss_cls": 3.35623, "loss": 3.35623, "time": 0.82112} +{"mode": "train", "epoch": 110, "iter": 2300, "lr": 0.01685, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40875, "top5_acc": 0.66031, "loss_cls": 3.34889, "loss": 3.34889, "time": 0.81652} +{"mode": "train", "epoch": 110, "iter": 2400, "lr": 0.01682, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40312, "top5_acc": 0.66031, "loss_cls": 3.38017, "loss": 3.38017, "time": 0.82307} +{"mode": "train", "epoch": 110, "iter": 2500, "lr": 0.0168, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40219, "top5_acc": 0.66438, "loss_cls": 3.32144, "loss": 3.32144, "time": 0.81411} +{"mode": "train", "epoch": 110, "iter": 2600, "lr": 0.01678, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41047, "top5_acc": 0.67375, "loss_cls": 3.3421, "loss": 3.3421, "time": 0.81689} +{"mode": "train", "epoch": 110, "iter": 2700, "lr": 0.01676, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39766, "top5_acc": 0.65828, "loss_cls": 3.41042, "loss": 3.41042, "time": 0.81622} +{"mode": "train", "epoch": 110, "iter": 2800, "lr": 0.01674, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.39984, "top5_acc": 0.65656, "loss_cls": 3.39493, "loss": 3.39493, "time": 0.81767} +{"mode": "train", "epoch": 110, "iter": 2900, "lr": 0.01672, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39047, "top5_acc": 0.64734, "loss_cls": 3.42032, "loss": 3.42032, "time": 0.81602} +{"mode": "train", "epoch": 110, "iter": 3000, "lr": 0.0167, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40047, "top5_acc": 0.65391, "loss_cls": 3.38138, "loss": 3.38138, "time": 0.8169} +{"mode": "train", "epoch": 110, "iter": 3100, "lr": 0.01668, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4, "top5_acc": 0.67375, "loss_cls": 3.3316, "loss": 3.3316, "time": 0.81508} +{"mode": "train", "epoch": 110, "iter": 3200, "lr": 0.01666, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40234, "top5_acc": 0.65859, "loss_cls": 3.36716, "loss": 3.36716, "time": 0.8137} +{"mode": "train", "epoch": 110, "iter": 3300, "lr": 0.01664, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40469, "top5_acc": 0.66453, "loss_cls": 3.32858, "loss": 3.32858, "time": 0.81025} +{"mode": "train", "epoch": 110, "iter": 3400, "lr": 0.01662, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40953, "top5_acc": 0.65938, "loss_cls": 3.36366, "loss": 3.36366, "time": 0.81529} +{"mode": "train", "epoch": 110, "iter": 3500, "lr": 0.01659, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40953, "top5_acc": 0.665, "loss_cls": 3.30919, "loss": 3.30919, "time": 0.81445} +{"mode": "train", "epoch": 110, "iter": 3600, "lr": 0.01657, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40844, "top5_acc": 0.66516, "loss_cls": 3.33225, "loss": 3.33225, "time": 0.81815} +{"mode": "train", "epoch": 110, "iter": 3700, "lr": 0.01655, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.405, "top5_acc": 0.65906, "loss_cls": 3.35188, "loss": 3.35188, "time": 0.81701} +{"mode": "val", "epoch": 110, "iter": 309, "lr": 0.01654, "top1_acc": 0.34898, "top5_acc": 0.60254, "mean_class_accuracy": 0.34879} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.01652, "memory": 15990, "data_time": 1.29418, "top1_acc": 0.42391, "top5_acc": 0.68469, "loss_cls": 3.2189, "loss": 3.2189, "time": 2.2667} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.0165, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42656, "top5_acc": 0.68609, "loss_cls": 3.24906, "loss": 3.24906, "time": 0.81525} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.01648, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41859, "top5_acc": 0.67688, "loss_cls": 3.28744, "loss": 3.28744, "time": 0.81728} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.01646, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42781, "top5_acc": 0.68344, "loss_cls": 3.22017, "loss": 3.22017, "time": 0.81526} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.01644, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40828, "top5_acc": 0.67266, "loss_cls": 3.31625, "loss": 3.31625, "time": 0.81995} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.01642, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42109, "top5_acc": 0.67078, "loss_cls": 3.32407, "loss": 3.32407, "time": 0.81257} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.0164, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42641, "top5_acc": 0.68812, "loss_cls": 3.23188, "loss": 3.23188, "time": 0.81795} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.01638, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40391, "top5_acc": 0.66828, "loss_cls": 3.35078, "loss": 3.35078, "time": 0.81353} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.01636, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41391, "top5_acc": 0.66812, "loss_cls": 3.31471, "loss": 3.31471, "time": 0.81448} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.01634, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40438, "top5_acc": 0.66766, "loss_cls": 3.32203, "loss": 3.32203, "time": 0.82036} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.01632, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40297, "top5_acc": 0.66703, "loss_cls": 3.326, "loss": 3.326, "time": 0.81486} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.0163, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41484, "top5_acc": 0.67672, "loss_cls": 3.26868, "loss": 3.26868, "time": 0.81308} +{"mode": "train", "epoch": 111, "iter": 1300, "lr": 0.01627, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40969, "top5_acc": 0.66938, "loss_cls": 3.31395, "loss": 3.31395, "time": 0.81166} +{"mode": "train", "epoch": 111, "iter": 1400, "lr": 0.01625, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40703, "top5_acc": 0.66703, "loss_cls": 3.3058, "loss": 3.3058, "time": 0.81694} +{"mode": "train", "epoch": 111, "iter": 1500, "lr": 0.01623, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40922, "top5_acc": 0.67016, "loss_cls": 3.31823, "loss": 3.31823, "time": 0.81165} +{"mode": "train", "epoch": 111, "iter": 1600, "lr": 0.01621, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.405, "top5_acc": 0.66766, "loss_cls": 3.31874, "loss": 3.31874, "time": 0.81722} +{"mode": "train", "epoch": 111, "iter": 1700, "lr": 0.01619, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39844, "top5_acc": 0.66562, "loss_cls": 3.33151, "loss": 3.33151, "time": 0.81212} +{"mode": "train", "epoch": 111, "iter": 1800, "lr": 0.01617, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41328, "top5_acc": 0.66719, "loss_cls": 3.32271, "loss": 3.32271, "time": 0.81239} +{"mode": "train", "epoch": 111, "iter": 1900, "lr": 0.01615, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41203, "top5_acc": 0.67281, "loss_cls": 3.29871, "loss": 3.29871, "time": 0.82051} +{"mode": "train", "epoch": 111, "iter": 2000, "lr": 0.01613, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39484, "top5_acc": 0.65797, "loss_cls": 3.37752, "loss": 3.37752, "time": 0.82444} +{"mode": "train", "epoch": 111, "iter": 2100, "lr": 0.01611, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.4025, "top5_acc": 0.65938, "loss_cls": 3.37864, "loss": 3.37864, "time": 0.81801} +{"mode": "train", "epoch": 111, "iter": 2200, "lr": 0.01609, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41297, "top5_acc": 0.66844, "loss_cls": 3.30711, "loss": 3.30711, "time": 0.82219} +{"mode": "train", "epoch": 111, "iter": 2300, "lr": 0.01607, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40109, "top5_acc": 0.66703, "loss_cls": 3.35408, "loss": 3.35408, "time": 0.82016} +{"mode": "train", "epoch": 111, "iter": 2400, "lr": 0.01605, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40062, "top5_acc": 0.66406, "loss_cls": 3.35703, "loss": 3.35703, "time": 0.81826} +{"mode": "train", "epoch": 111, "iter": 2500, "lr": 0.01603, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.39594, "top5_acc": 0.65484, "loss_cls": 3.38104, "loss": 3.38104, "time": 0.81367} +{"mode": "train", "epoch": 111, "iter": 2600, "lr": 0.01601, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40547, "top5_acc": 0.65891, "loss_cls": 3.35565, "loss": 3.35565, "time": 0.81131} +{"mode": "train", "epoch": 111, "iter": 2700, "lr": 0.01599, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40891, "top5_acc": 0.66875, "loss_cls": 3.32876, "loss": 3.32876, "time": 0.8163} +{"mode": "train", "epoch": 111, "iter": 2800, "lr": 0.01597, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40266, "top5_acc": 0.66672, "loss_cls": 3.34089, "loss": 3.34089, "time": 0.81617} +{"mode": "train", "epoch": 111, "iter": 2900, "lr": 0.01595, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42547, "top5_acc": 0.67203, "loss_cls": 3.2943, "loss": 3.2943, "time": 0.81724} +{"mode": "train", "epoch": 111, "iter": 3000, "lr": 0.01593, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40594, "top5_acc": 0.66203, "loss_cls": 3.33119, "loss": 3.33119, "time": 0.81171} +{"mode": "train", "epoch": 111, "iter": 3100, "lr": 0.0159, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40344, "top5_acc": 0.65812, "loss_cls": 3.35586, "loss": 3.35586, "time": 0.81698} +{"mode": "train", "epoch": 111, "iter": 3200, "lr": 0.01588, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39953, "top5_acc": 0.66141, "loss_cls": 3.36604, "loss": 3.36604, "time": 0.81247} +{"mode": "train", "epoch": 111, "iter": 3300, "lr": 0.01586, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40078, "top5_acc": 0.65859, "loss_cls": 3.35321, "loss": 3.35321, "time": 0.81572} +{"mode": "train", "epoch": 111, "iter": 3400, "lr": 0.01584, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40469, "top5_acc": 0.6675, "loss_cls": 3.32765, "loss": 3.32765, "time": 0.81211} +{"mode": "train", "epoch": 111, "iter": 3500, "lr": 0.01582, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39953, "top5_acc": 0.65516, "loss_cls": 3.3807, "loss": 3.3807, "time": 0.81621} +{"mode": "train", "epoch": 111, "iter": 3600, "lr": 0.0158, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41625, "top5_acc": 0.67172, "loss_cls": 3.31491, "loss": 3.31491, "time": 0.81673} +{"mode": "train", "epoch": 111, "iter": 3700, "lr": 0.01578, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40281, "top5_acc": 0.66719, "loss_cls": 3.3368, "loss": 3.3368, "time": 0.81372} +{"mode": "val", "epoch": 111, "iter": 309, "lr": 0.01577, "top1_acc": 0.34407, "top5_acc": 0.60128, "mean_class_accuracy": 0.34369} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.01575, "memory": 15990, "data_time": 1.29283, "top1_acc": 0.43, "top5_acc": 0.68859, "loss_cls": 3.20193, "loss": 3.20193, "time": 2.30106} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.01573, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40953, "top5_acc": 0.67734, "loss_cls": 3.26801, "loss": 3.26801, "time": 0.81821} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.01571, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42406, "top5_acc": 0.68328, "loss_cls": 3.21206, "loss": 3.21206, "time": 0.81902} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.01569, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41672, "top5_acc": 0.67375, "loss_cls": 3.28871, "loss": 3.28871, "time": 0.81463} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.01567, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42922, "top5_acc": 0.68031, "loss_cls": 3.25398, "loss": 3.25398, "time": 0.8209} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.01565, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41469, "top5_acc": 0.68094, "loss_cls": 3.2592, "loss": 3.2592, "time": 0.81717} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.01563, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40578, "top5_acc": 0.66984, "loss_cls": 3.30841, "loss": 3.30841, "time": 0.81648} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.01561, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41094, "top5_acc": 0.67312, "loss_cls": 3.30112, "loss": 3.30112, "time": 0.81551} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.01559, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42047, "top5_acc": 0.67875, "loss_cls": 3.26451, "loss": 3.26451, "time": 0.8161} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.01557, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40516, "top5_acc": 0.67078, "loss_cls": 3.32221, "loss": 3.32221, "time": 0.81756} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.01555, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41203, "top5_acc": 0.665, "loss_cls": 3.31415, "loss": 3.31415, "time": 0.81546} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.01553, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41094, "top5_acc": 0.67328, "loss_cls": 3.29738, "loss": 3.29738, "time": 0.81471} +{"mode": "train", "epoch": 112, "iter": 1300, "lr": 0.01551, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40906, "top5_acc": 0.66109, "loss_cls": 3.31858, "loss": 3.31858, "time": 0.81603} +{"mode": "train", "epoch": 112, "iter": 1400, "lr": 0.01549, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40234, "top5_acc": 0.65828, "loss_cls": 3.33288, "loss": 3.33288, "time": 0.82782} +{"mode": "train", "epoch": 112, "iter": 1500, "lr": 0.01547, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41266, "top5_acc": 0.67656, "loss_cls": 3.2993, "loss": 3.2993, "time": 0.81248} +{"mode": "train", "epoch": 112, "iter": 1600, "lr": 0.01545, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40797, "top5_acc": 0.66391, "loss_cls": 3.34256, "loss": 3.34256, "time": 0.82185} +{"mode": "train", "epoch": 112, "iter": 1700, "lr": 0.01543, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40406, "top5_acc": 0.67391, "loss_cls": 3.3039, "loss": 3.3039, "time": 0.82114} +{"mode": "train", "epoch": 112, "iter": 1800, "lr": 0.01541, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41859, "top5_acc": 0.67766, "loss_cls": 3.25629, "loss": 3.25629, "time": 0.8149} +{"mode": "train", "epoch": 112, "iter": 1900, "lr": 0.01539, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40859, "top5_acc": 0.67391, "loss_cls": 3.31002, "loss": 3.31002, "time": 0.8185} +{"mode": "train", "epoch": 112, "iter": 2000, "lr": 0.01537, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41922, "top5_acc": 0.66891, "loss_cls": 3.30108, "loss": 3.30108, "time": 0.82048} +{"mode": "train", "epoch": 112, "iter": 2100, "lr": 0.01535, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41109, "top5_acc": 0.67109, "loss_cls": 3.29027, "loss": 3.29027, "time": 0.82611} +{"mode": "train", "epoch": 112, "iter": 2200, "lr": 0.01533, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42031, "top5_acc": 0.67812, "loss_cls": 3.24816, "loss": 3.24816, "time": 0.8263} +{"mode": "train", "epoch": 112, "iter": 2300, "lr": 0.01531, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40938, "top5_acc": 0.66828, "loss_cls": 3.31031, "loss": 3.31031, "time": 0.81392} +{"mode": "train", "epoch": 112, "iter": 2400, "lr": 0.01529, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40047, "top5_acc": 0.66547, "loss_cls": 3.32337, "loss": 3.32337, "time": 0.81249} +{"mode": "train", "epoch": 112, "iter": 2500, "lr": 0.01527, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41312, "top5_acc": 0.66562, "loss_cls": 3.33592, "loss": 3.33592, "time": 0.81419} +{"mode": "train", "epoch": 112, "iter": 2600, "lr": 0.01525, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41438, "top5_acc": 0.67766, "loss_cls": 3.29314, "loss": 3.29314, "time": 0.81242} +{"mode": "train", "epoch": 112, "iter": 2700, "lr": 0.01523, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41, "top5_acc": 0.66469, "loss_cls": 3.34341, "loss": 3.34341, "time": 0.81771} +{"mode": "train", "epoch": 112, "iter": 2800, "lr": 0.01521, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40172, "top5_acc": 0.65969, "loss_cls": 3.36503, "loss": 3.36503, "time": 0.80891} +{"mode": "train", "epoch": 112, "iter": 2900, "lr": 0.01519, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41031, "top5_acc": 0.66641, "loss_cls": 3.33073, "loss": 3.33073, "time": 0.81482} +{"mode": "train", "epoch": 112, "iter": 3000, "lr": 0.01517, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.40609, "top5_acc": 0.66516, "loss_cls": 3.33604, "loss": 3.33604, "time": 0.8156} +{"mode": "train", "epoch": 112, "iter": 3100, "lr": 0.01515, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41328, "top5_acc": 0.66906, "loss_cls": 3.30787, "loss": 3.30787, "time": 0.81352} +{"mode": "train", "epoch": 112, "iter": 3200, "lr": 0.01513, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40109, "top5_acc": 0.66781, "loss_cls": 3.31406, "loss": 3.31406, "time": 0.81862} +{"mode": "train", "epoch": 112, "iter": 3300, "lr": 0.01511, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41766, "top5_acc": 0.67766, "loss_cls": 3.28895, "loss": 3.28895, "time": 0.81621} +{"mode": "train", "epoch": 112, "iter": 3400, "lr": 0.01509, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41734, "top5_acc": 0.67391, "loss_cls": 3.27539, "loss": 3.27539, "time": 0.81389} +{"mode": "train", "epoch": 112, "iter": 3500, "lr": 0.01507, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41344, "top5_acc": 0.67016, "loss_cls": 3.29899, "loss": 3.29899, "time": 0.81474} +{"mode": "train", "epoch": 112, "iter": 3600, "lr": 0.01505, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42016, "top5_acc": 0.66969, "loss_cls": 3.29203, "loss": 3.29203, "time": 0.81894} +{"mode": "train", "epoch": 112, "iter": 3700, "lr": 0.01503, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41219, "top5_acc": 0.66688, "loss_cls": 3.32857, "loss": 3.32857, "time": 0.81705} +{"mode": "val", "epoch": 112, "iter": 309, "lr": 0.01502, "top1_acc": 0.34564, "top5_acc": 0.59905, "mean_class_accuracy": 0.3455} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.015, "memory": 15990, "data_time": 1.3113, "top1_acc": 0.42578, "top5_acc": 0.67734, "loss_cls": 3.24006, "loss": 3.24006, "time": 2.31875} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.01498, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42188, "top5_acc": 0.68766, "loss_cls": 3.20788, "loss": 3.20788, "time": 0.81418} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.01496, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43109, "top5_acc": 0.68938, "loss_cls": 3.18053, "loss": 3.18053, "time": 0.81703} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.01494, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42297, "top5_acc": 0.67734, "loss_cls": 3.26185, "loss": 3.26185, "time": 0.81723} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.01492, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4325, "top5_acc": 0.68516, "loss_cls": 3.19639, "loss": 3.19639, "time": 0.81818} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.0149, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41234, "top5_acc": 0.67781, "loss_cls": 3.27393, "loss": 3.27393, "time": 0.82344} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.01488, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41828, "top5_acc": 0.68016, "loss_cls": 3.24553, "loss": 3.24553, "time": 0.81599} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.01486, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4175, "top5_acc": 0.67672, "loss_cls": 3.2678, "loss": 3.2678, "time": 0.81282} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.01484, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41578, "top5_acc": 0.68094, "loss_cls": 3.26628, "loss": 3.26628, "time": 0.81692} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.01482, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41031, "top5_acc": 0.67016, "loss_cls": 3.3161, "loss": 3.3161, "time": 0.82327} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41906, "top5_acc": 0.68172, "loss_cls": 3.25716, "loss": 3.25716, "time": 0.81549} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.01478, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40844, "top5_acc": 0.67219, "loss_cls": 3.3137, "loss": 3.3137, "time": 0.81259} +{"mode": "train", "epoch": 113, "iter": 1300, "lr": 0.01476, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41141, "top5_acc": 0.67578, "loss_cls": 3.29518, "loss": 3.29518, "time": 0.81467} +{"mode": "train", "epoch": 113, "iter": 1400, "lr": 0.01474, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42031, "top5_acc": 0.67797, "loss_cls": 3.27087, "loss": 3.27087, "time": 0.81365} +{"mode": "train", "epoch": 113, "iter": 1500, "lr": 0.01472, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42688, "top5_acc": 0.68578, "loss_cls": 3.21608, "loss": 3.21608, "time": 0.81195} +{"mode": "train", "epoch": 113, "iter": 1600, "lr": 0.0147, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42125, "top5_acc": 0.67312, "loss_cls": 3.24917, "loss": 3.24917, "time": 0.81244} +{"mode": "train", "epoch": 113, "iter": 1700, "lr": 0.01468, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42359, "top5_acc": 0.68016, "loss_cls": 3.25352, "loss": 3.25352, "time": 0.81518} +{"mode": "train", "epoch": 113, "iter": 1800, "lr": 0.01466, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41531, "top5_acc": 0.67609, "loss_cls": 3.27094, "loss": 3.27094, "time": 0.81727} +{"mode": "train", "epoch": 113, "iter": 1900, "lr": 0.01464, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42078, "top5_acc": 0.67219, "loss_cls": 3.26061, "loss": 3.26061, "time": 0.81778} +{"mode": "train", "epoch": 113, "iter": 2000, "lr": 0.01462, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42312, "top5_acc": 0.68531, "loss_cls": 3.24925, "loss": 3.24925, "time": 0.81763} +{"mode": "train", "epoch": 113, "iter": 2100, "lr": 0.0146, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41141, "top5_acc": 0.67172, "loss_cls": 3.28438, "loss": 3.28438, "time": 0.82857} +{"mode": "train", "epoch": 113, "iter": 2200, "lr": 0.01458, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41469, "top5_acc": 0.66719, "loss_cls": 3.31342, "loss": 3.31342, "time": 0.8193} +{"mode": "train", "epoch": 113, "iter": 2300, "lr": 0.01456, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4125, "top5_acc": 0.675, "loss_cls": 3.30071, "loss": 3.30071, "time": 0.83352} +{"mode": "train", "epoch": 113, "iter": 2400, "lr": 0.01454, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41094, "top5_acc": 0.67266, "loss_cls": 3.28721, "loss": 3.28721, "time": 0.8195} +{"mode": "train", "epoch": 113, "iter": 2500, "lr": 0.01452, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42203, "top5_acc": 0.67484, "loss_cls": 3.26593, "loss": 3.26593, "time": 0.81557} +{"mode": "train", "epoch": 113, "iter": 2600, "lr": 0.0145, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41203, "top5_acc": 0.65703, "loss_cls": 3.35714, "loss": 3.35714, "time": 0.81679} +{"mode": "train", "epoch": 113, "iter": 2700, "lr": 0.01448, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41531, "top5_acc": 0.67844, "loss_cls": 3.26279, "loss": 3.26279, "time": 0.81503} +{"mode": "train", "epoch": 113, "iter": 2800, "lr": 0.01446, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42281, "top5_acc": 0.67812, "loss_cls": 3.25806, "loss": 3.25806, "time": 0.80935} +{"mode": "train", "epoch": 113, "iter": 2900, "lr": 0.01444, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42, "top5_acc": 0.67109, "loss_cls": 3.29003, "loss": 3.29003, "time": 0.81132} +{"mode": "train", "epoch": 113, "iter": 3000, "lr": 0.01442, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.40781, "top5_acc": 0.67141, "loss_cls": 3.29781, "loss": 3.29781, "time": 0.8124} +{"mode": "train", "epoch": 113, "iter": 3100, "lr": 0.0144, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41359, "top5_acc": 0.67359, "loss_cls": 3.30535, "loss": 3.30535, "time": 0.81441} +{"mode": "train", "epoch": 113, "iter": 3200, "lr": 0.01438, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41859, "top5_acc": 0.67297, "loss_cls": 3.28848, "loss": 3.28848, "time": 0.81339} +{"mode": "train", "epoch": 113, "iter": 3300, "lr": 0.01436, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41109, "top5_acc": 0.67703, "loss_cls": 3.28971, "loss": 3.28971, "time": 0.81118} +{"mode": "train", "epoch": 113, "iter": 3400, "lr": 0.01434, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41266, "top5_acc": 0.67062, "loss_cls": 3.30638, "loss": 3.30638, "time": 0.81545} +{"mode": "train", "epoch": 113, "iter": 3500, "lr": 0.01432, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41281, "top5_acc": 0.66969, "loss_cls": 3.29312, "loss": 3.29312, "time": 0.81816} +{"mode": "train", "epoch": 113, "iter": 3600, "lr": 0.01431, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40969, "top5_acc": 0.66766, "loss_cls": 3.33499, "loss": 3.33499, "time": 0.81155} +{"mode": "train", "epoch": 113, "iter": 3700, "lr": 0.01429, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39922, "top5_acc": 0.65781, "loss_cls": 3.33547, "loss": 3.33547, "time": 0.82418} +{"mode": "val", "epoch": 113, "iter": 309, "lr": 0.01428, "top1_acc": 0.36139, "top5_acc": 0.61161, "mean_class_accuracy": 0.36111} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.01426, "memory": 15990, "data_time": 1.32268, "top1_acc": 0.43156, "top5_acc": 0.69984, "loss_cls": 3.16535, "loss": 3.16535, "time": 2.33978} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.01424, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42094, "top5_acc": 0.67781, "loss_cls": 3.24895, "loss": 3.24895, "time": 0.81529} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.01422, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43125, "top5_acc": 0.68859, "loss_cls": 3.19059, "loss": 3.19059, "time": 0.81416} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.0142, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43391, "top5_acc": 0.69359, "loss_cls": 3.17469, "loss": 3.17469, "time": 0.81728} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.01418, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43016, "top5_acc": 0.68453, "loss_cls": 3.21233, "loss": 3.21233, "time": 0.81716} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.01416, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42469, "top5_acc": 0.67953, "loss_cls": 3.23539, "loss": 3.23539, "time": 0.81865} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.01414, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42953, "top5_acc": 0.69203, "loss_cls": 3.18284, "loss": 3.18284, "time": 0.81923} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.01412, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42469, "top5_acc": 0.67641, "loss_cls": 3.2294, "loss": 3.2294, "time": 0.81391} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.0141, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43344, "top5_acc": 0.68953, "loss_cls": 3.19148, "loss": 3.19148, "time": 0.81604} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.01408, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41531, "top5_acc": 0.67453, "loss_cls": 3.27669, "loss": 3.27669, "time": 0.8135} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.01406, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4125, "top5_acc": 0.67609, "loss_cls": 3.26932, "loss": 3.26932, "time": 0.81082} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.01404, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41938, "top5_acc": 0.67891, "loss_cls": 3.25932, "loss": 3.25932, "time": 0.81663} +{"mode": "train", "epoch": 114, "iter": 1300, "lr": 0.01402, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41062, "top5_acc": 0.66891, "loss_cls": 3.30764, "loss": 3.30764, "time": 0.81878} +{"mode": "train", "epoch": 114, "iter": 1400, "lr": 0.014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4325, "top5_acc": 0.68594, "loss_cls": 3.2092, "loss": 3.2092, "time": 0.81512} +{"mode": "train", "epoch": 114, "iter": 1500, "lr": 0.01398, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41484, "top5_acc": 0.67766, "loss_cls": 3.2656, "loss": 3.2656, "time": 0.81969} +{"mode": "train", "epoch": 114, "iter": 1600, "lr": 0.01397, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43203, "top5_acc": 0.68703, "loss_cls": 3.21425, "loss": 3.21425, "time": 0.81538} +{"mode": "train", "epoch": 114, "iter": 1700, "lr": 0.01395, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41625, "top5_acc": 0.67188, "loss_cls": 3.28535, "loss": 3.28535, "time": 0.81283} +{"mode": "train", "epoch": 114, "iter": 1800, "lr": 0.01393, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41547, "top5_acc": 0.68672, "loss_cls": 3.23191, "loss": 3.23191, "time": 0.81007} +{"mode": "train", "epoch": 114, "iter": 1900, "lr": 0.01391, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41203, "top5_acc": 0.68078, "loss_cls": 3.24546, "loss": 3.24546, "time": 0.8136} +{"mode": "train", "epoch": 114, "iter": 2000, "lr": 0.01389, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42453, "top5_acc": 0.67328, "loss_cls": 3.25522, "loss": 3.25522, "time": 0.81906} +{"mode": "train", "epoch": 114, "iter": 2100, "lr": 0.01387, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42141, "top5_acc": 0.675, "loss_cls": 3.27229, "loss": 3.27229, "time": 0.82052} +{"mode": "train", "epoch": 114, "iter": 2200, "lr": 0.01385, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41078, "top5_acc": 0.67344, "loss_cls": 3.30037, "loss": 3.30037, "time": 0.8202} +{"mode": "train", "epoch": 114, "iter": 2300, "lr": 0.01383, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41969, "top5_acc": 0.68578, "loss_cls": 3.2202, "loss": 3.2202, "time": 0.81906} +{"mode": "train", "epoch": 114, "iter": 2400, "lr": 0.01381, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41812, "top5_acc": 0.67859, "loss_cls": 3.26585, "loss": 3.26585, "time": 0.82112} +{"mode": "train", "epoch": 114, "iter": 2500, "lr": 0.01379, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41406, "top5_acc": 0.67906, "loss_cls": 3.28409, "loss": 3.28409, "time": 0.81067} +{"mode": "train", "epoch": 114, "iter": 2600, "lr": 0.01377, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41781, "top5_acc": 0.67484, "loss_cls": 3.26469, "loss": 3.26469, "time": 0.82033} +{"mode": "train", "epoch": 114, "iter": 2700, "lr": 0.01375, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42516, "top5_acc": 0.67047, "loss_cls": 3.27953, "loss": 3.27953, "time": 0.81406} +{"mode": "train", "epoch": 114, "iter": 2800, "lr": 0.01373, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40969, "top5_acc": 0.66203, "loss_cls": 3.34323, "loss": 3.34323, "time": 0.81526} +{"mode": "train", "epoch": 114, "iter": 2900, "lr": 0.01371, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41641, "top5_acc": 0.6775, "loss_cls": 3.29546, "loss": 3.29546, "time": 0.81534} +{"mode": "train", "epoch": 114, "iter": 3000, "lr": 0.01369, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41422, "top5_acc": 0.67828, "loss_cls": 3.25972, "loss": 3.25972, "time": 0.81471} +{"mode": "train", "epoch": 114, "iter": 3100, "lr": 0.01368, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40453, "top5_acc": 0.66609, "loss_cls": 3.32929, "loss": 3.32929, "time": 0.81355} +{"mode": "train", "epoch": 114, "iter": 3200, "lr": 0.01366, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42734, "top5_acc": 0.67609, "loss_cls": 3.24499, "loss": 3.24499, "time": 0.82244} +{"mode": "train", "epoch": 114, "iter": 3300, "lr": 0.01364, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40969, "top5_acc": 0.66719, "loss_cls": 3.28849, "loss": 3.28849, "time": 0.81224} +{"mode": "train", "epoch": 114, "iter": 3400, "lr": 0.01362, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41094, "top5_acc": 0.66781, "loss_cls": 3.30951, "loss": 3.30951, "time": 0.81416} +{"mode": "train", "epoch": 114, "iter": 3500, "lr": 0.0136, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41922, "top5_acc": 0.67516, "loss_cls": 3.26827, "loss": 3.26827, "time": 0.81765} +{"mode": "train", "epoch": 114, "iter": 3600, "lr": 0.01358, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42062, "top5_acc": 0.67844, "loss_cls": 3.2305, "loss": 3.2305, "time": 0.8139} +{"mode": "train", "epoch": 114, "iter": 3700, "lr": 0.01356, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42672, "top5_acc": 0.68688, "loss_cls": 3.24041, "loss": 3.24041, "time": 0.81527} +{"mode": "val", "epoch": 114, "iter": 309, "lr": 0.01355, "top1_acc": 0.35451, "top5_acc": 0.61237, "mean_class_accuracy": 0.35406} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.01353, "memory": 15990, "data_time": 1.33132, "top1_acc": 0.44156, "top5_acc": 0.69641, "loss_cls": 3.12579, "loss": 3.12579, "time": 2.30725} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.01351, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43203, "top5_acc": 0.69406, "loss_cls": 3.18552, "loss": 3.18552, "time": 0.81394} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.01349, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42984, "top5_acc": 0.69094, "loss_cls": 3.18657, "loss": 3.18657, "time": 0.816} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.01348, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42891, "top5_acc": 0.68453, "loss_cls": 3.20252, "loss": 3.20252, "time": 0.8156} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.01346, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42219, "top5_acc": 0.68266, "loss_cls": 3.24377, "loss": 3.24377, "time": 0.81945} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.01344, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43047, "top5_acc": 0.68359, "loss_cls": 3.19815, "loss": 3.19815, "time": 0.81439} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.01342, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43703, "top5_acc": 0.69703, "loss_cls": 3.16434, "loss": 3.16434, "time": 0.81723} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.0134, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41906, "top5_acc": 0.68469, "loss_cls": 3.21312, "loss": 3.21312, "time": 0.81191} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.01338, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41797, "top5_acc": 0.68422, "loss_cls": 3.24731, "loss": 3.24731, "time": 0.81125} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.01336, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42797, "top5_acc": 0.67984, "loss_cls": 3.21924, "loss": 3.21924, "time": 0.81319} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.01334, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42734, "top5_acc": 0.68594, "loss_cls": 3.20878, "loss": 3.20878, "time": 0.81353} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.01332, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42016, "top5_acc": 0.67453, "loss_cls": 3.27953, "loss": 3.27953, "time": 0.81237} +{"mode": "train", "epoch": 115, "iter": 1300, "lr": 0.0133, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.41047, "top5_acc": 0.67562, "loss_cls": 3.28069, "loss": 3.28069, "time": 0.80979} +{"mode": "train", "epoch": 115, "iter": 1400, "lr": 0.01328, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43281, "top5_acc": 0.68688, "loss_cls": 3.18746, "loss": 3.18746, "time": 0.81182} +{"mode": "train", "epoch": 115, "iter": 1500, "lr": 0.01327, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42188, "top5_acc": 0.68234, "loss_cls": 3.21878, "loss": 3.21878, "time": 0.8136} +{"mode": "train", "epoch": 115, "iter": 1600, "lr": 0.01325, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42625, "top5_acc": 0.67938, "loss_cls": 3.21422, "loss": 3.21422, "time": 0.81828} +{"mode": "train", "epoch": 115, "iter": 1700, "lr": 0.01323, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4225, "top5_acc": 0.68406, "loss_cls": 3.22702, "loss": 3.22702, "time": 0.8169} +{"mode": "train", "epoch": 115, "iter": 1800, "lr": 0.01321, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42438, "top5_acc": 0.67953, "loss_cls": 3.24464, "loss": 3.24464, "time": 0.81641} +{"mode": "train", "epoch": 115, "iter": 1900, "lr": 0.01319, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42172, "top5_acc": 0.68, "loss_cls": 3.25887, "loss": 3.25887, "time": 0.82038} +{"mode": "train", "epoch": 115, "iter": 2000, "lr": 0.01317, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41859, "top5_acc": 0.68203, "loss_cls": 3.25513, "loss": 3.25513, "time": 0.81837} +{"mode": "train", "epoch": 115, "iter": 2100, "lr": 0.01315, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42312, "top5_acc": 0.68266, "loss_cls": 3.2373, "loss": 3.2373, "time": 0.81822} +{"mode": "train", "epoch": 115, "iter": 2200, "lr": 0.01313, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42656, "top5_acc": 0.68516, "loss_cls": 3.21945, "loss": 3.21945, "time": 0.82846} +{"mode": "train", "epoch": 115, "iter": 2300, "lr": 0.01311, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43078, "top5_acc": 0.6875, "loss_cls": 3.22184, "loss": 3.22184, "time": 0.81792} +{"mode": "train", "epoch": 115, "iter": 2400, "lr": 0.0131, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41938, "top5_acc": 0.68781, "loss_cls": 3.22297, "loss": 3.22297, "time": 0.81979} +{"mode": "train", "epoch": 115, "iter": 2500, "lr": 0.01308, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41438, "top5_acc": 0.66984, "loss_cls": 3.28244, "loss": 3.28244, "time": 0.81528} +{"mode": "train", "epoch": 115, "iter": 2600, "lr": 0.01306, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42188, "top5_acc": 0.68062, "loss_cls": 3.23846, "loss": 3.23846, "time": 0.81347} +{"mode": "train", "epoch": 115, "iter": 2700, "lr": 0.01304, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42594, "top5_acc": 0.67812, "loss_cls": 3.22645, "loss": 3.22645, "time": 0.8119} +{"mode": "train", "epoch": 115, "iter": 2800, "lr": 0.01302, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41812, "top5_acc": 0.6725, "loss_cls": 3.29008, "loss": 3.29008, "time": 0.81049} +{"mode": "train", "epoch": 115, "iter": 2900, "lr": 0.013, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41844, "top5_acc": 0.68812, "loss_cls": 3.21345, "loss": 3.21345, "time": 0.81334} +{"mode": "train", "epoch": 115, "iter": 3000, "lr": 0.01298, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42172, "top5_acc": 0.68141, "loss_cls": 3.24176, "loss": 3.24176, "time": 0.8127} +{"mode": "train", "epoch": 115, "iter": 3100, "lr": 0.01296, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42547, "top5_acc": 0.68219, "loss_cls": 3.21121, "loss": 3.21121, "time": 0.81235} +{"mode": "train", "epoch": 115, "iter": 3200, "lr": 0.01295, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42344, "top5_acc": 0.67297, "loss_cls": 3.28635, "loss": 3.28635, "time": 0.81613} +{"mode": "train", "epoch": 115, "iter": 3300, "lr": 0.01293, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41797, "top5_acc": 0.67297, "loss_cls": 3.28166, "loss": 3.28166, "time": 0.81423} +{"mode": "train", "epoch": 115, "iter": 3400, "lr": 0.01291, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41984, "top5_acc": 0.68016, "loss_cls": 3.24604, "loss": 3.24604, "time": 0.81109} +{"mode": "train", "epoch": 115, "iter": 3500, "lr": 0.01289, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41297, "top5_acc": 0.68297, "loss_cls": 3.24893, "loss": 3.24893, "time": 0.81439} +{"mode": "train", "epoch": 115, "iter": 3600, "lr": 0.01287, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42484, "top5_acc": 0.67938, "loss_cls": 3.23239, "loss": 3.23239, "time": 0.81655} +{"mode": "train", "epoch": 115, "iter": 3700, "lr": 0.01285, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42828, "top5_acc": 0.67766, "loss_cls": 3.24862, "loss": 3.24862, "time": 0.81427} +{"mode": "val", "epoch": 115, "iter": 309, "lr": 0.01284, "top1_acc": 0.35947, "top5_acc": 0.61875, "mean_class_accuracy": 0.35922} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.01282, "memory": 15990, "data_time": 1.42653, "top1_acc": 0.44109, "top5_acc": 0.69359, "loss_cls": 3.12583, "loss": 3.12583, "time": 2.42137} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.01281, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44109, "top5_acc": 0.69531, "loss_cls": 3.12104, "loss": 3.12104, "time": 0.82638} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.01279, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43422, "top5_acc": 0.68547, "loss_cls": 3.1876, "loss": 3.1876, "time": 0.8244} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.01277, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43391, "top5_acc": 0.69391, "loss_cls": 3.16714, "loss": 3.16714, "time": 0.82226} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.01275, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42516, "top5_acc": 0.68609, "loss_cls": 3.16793, "loss": 3.16793, "time": 0.82242} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.01273, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42531, "top5_acc": 0.68266, "loss_cls": 3.22429, "loss": 3.22429, "time": 0.81543} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.01271, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43109, "top5_acc": 0.69188, "loss_cls": 3.19122, "loss": 3.19122, "time": 0.82503} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.01269, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43609, "top5_acc": 0.695, "loss_cls": 3.16072, "loss": 3.16072, "time": 0.81943} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.01268, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42734, "top5_acc": 0.695, "loss_cls": 3.18204, "loss": 3.18204, "time": 0.81787} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.01266, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43391, "top5_acc": 0.68812, "loss_cls": 3.20125, "loss": 3.20125, "time": 0.81436} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.01264, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41797, "top5_acc": 0.67672, "loss_cls": 3.2633, "loss": 3.2633, "time": 0.81383} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.01262, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42031, "top5_acc": 0.68703, "loss_cls": 3.22151, "loss": 3.22151, "time": 0.81641} +{"mode": "train", "epoch": 116, "iter": 1300, "lr": 0.0126, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43156, "top5_acc": 0.68281, "loss_cls": 3.22726, "loss": 3.22726, "time": 0.81993} +{"mode": "train", "epoch": 116, "iter": 1400, "lr": 0.01258, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43578, "top5_acc": 0.69422, "loss_cls": 3.142, "loss": 3.142, "time": 0.8134} +{"mode": "train", "epoch": 116, "iter": 1500, "lr": 0.01256, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43406, "top5_acc": 0.69578, "loss_cls": 3.17651, "loss": 3.17651, "time": 0.81559} +{"mode": "train", "epoch": 116, "iter": 1600, "lr": 0.01255, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42906, "top5_acc": 0.68781, "loss_cls": 3.19978, "loss": 3.19978, "time": 0.81157} +{"mode": "train", "epoch": 116, "iter": 1700, "lr": 0.01253, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43094, "top5_acc": 0.68812, "loss_cls": 3.213, "loss": 3.213, "time": 0.81109} +{"mode": "train", "epoch": 116, "iter": 1800, "lr": 0.01251, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43172, "top5_acc": 0.68828, "loss_cls": 3.17609, "loss": 3.17609, "time": 0.81533} +{"mode": "train", "epoch": 116, "iter": 1900, "lr": 0.01249, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42, "top5_acc": 0.67828, "loss_cls": 3.25172, "loss": 3.25172, "time": 0.81434} +{"mode": "train", "epoch": 116, "iter": 2000, "lr": 0.01247, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43234, "top5_acc": 0.69375, "loss_cls": 3.18171, "loss": 3.18171, "time": 0.82176} +{"mode": "train", "epoch": 116, "iter": 2100, "lr": 0.01245, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43438, "top5_acc": 0.68578, "loss_cls": 3.21762, "loss": 3.21762, "time": 0.81798} +{"mode": "train", "epoch": 116, "iter": 2200, "lr": 0.01243, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43688, "top5_acc": 0.68422, "loss_cls": 3.17719, "loss": 3.17719, "time": 0.82512} +{"mode": "train", "epoch": 116, "iter": 2300, "lr": 0.01242, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43125, "top5_acc": 0.68406, "loss_cls": 3.23355, "loss": 3.23355, "time": 0.81832} +{"mode": "train", "epoch": 116, "iter": 2400, "lr": 0.0124, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41156, "top5_acc": 0.67438, "loss_cls": 3.26466, "loss": 3.26466, "time": 0.82018} +{"mode": "train", "epoch": 116, "iter": 2500, "lr": 0.01238, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42281, "top5_acc": 0.67969, "loss_cls": 3.22263, "loss": 3.22263, "time": 0.81833} +{"mode": "train", "epoch": 116, "iter": 2600, "lr": 0.01236, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42516, "top5_acc": 0.68672, "loss_cls": 3.21366, "loss": 3.21366, "time": 0.8166} +{"mode": "train", "epoch": 116, "iter": 2700, "lr": 0.01234, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41703, "top5_acc": 0.67578, "loss_cls": 3.26224, "loss": 3.26224, "time": 0.81962} +{"mode": "train", "epoch": 116, "iter": 2800, "lr": 0.01232, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42125, "top5_acc": 0.67812, "loss_cls": 3.24492, "loss": 3.24492, "time": 0.81251} +{"mode": "train", "epoch": 116, "iter": 2900, "lr": 0.01231, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42234, "top5_acc": 0.69062, "loss_cls": 3.19246, "loss": 3.19246, "time": 0.81322} +{"mode": "train", "epoch": 116, "iter": 3000, "lr": 0.01229, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42453, "top5_acc": 0.6675, "loss_cls": 3.27486, "loss": 3.27486, "time": 0.81863} +{"mode": "train", "epoch": 116, "iter": 3100, "lr": 0.01227, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42547, "top5_acc": 0.68344, "loss_cls": 3.21363, "loss": 3.21363, "time": 0.81768} +{"mode": "train", "epoch": 116, "iter": 3200, "lr": 0.01225, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.435, "top5_acc": 0.69094, "loss_cls": 3.18954, "loss": 3.18954, "time": 0.81093} +{"mode": "train", "epoch": 116, "iter": 3300, "lr": 0.01223, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42438, "top5_acc": 0.67562, "loss_cls": 3.2527, "loss": 3.2527, "time": 0.81544} +{"mode": "train", "epoch": 116, "iter": 3400, "lr": 0.01221, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42125, "top5_acc": 0.67938, "loss_cls": 3.22756, "loss": 3.22756, "time": 0.81451} +{"mode": "train", "epoch": 116, "iter": 3500, "lr": 0.0122, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42141, "top5_acc": 0.68234, "loss_cls": 3.23189, "loss": 3.23189, "time": 0.81305} +{"mode": "train", "epoch": 116, "iter": 3600, "lr": 0.01218, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42406, "top5_acc": 0.685, "loss_cls": 3.22447, "loss": 3.22447, "time": 0.81736} +{"mode": "train", "epoch": 116, "iter": 3700, "lr": 0.01216, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43844, "top5_acc": 0.68969, "loss_cls": 3.18726, "loss": 3.18726, "time": 0.82211} +{"mode": "val", "epoch": 116, "iter": 309, "lr": 0.01215, "top1_acc": 0.3616, "top5_acc": 0.61282, "mean_class_accuracy": 0.36118} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.01213, "memory": 15990, "data_time": 1.3506, "top1_acc": 0.43953, "top5_acc": 0.70703, "loss_cls": 3.12771, "loss": 3.12771, "time": 2.34313} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.01211, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43703, "top5_acc": 0.70078, "loss_cls": 3.14698, "loss": 3.14698, "time": 0.82708} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.0121, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43406, "top5_acc": 0.69203, "loss_cls": 3.16315, "loss": 3.16315, "time": 0.81707} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.01208, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44234, "top5_acc": 0.69438, "loss_cls": 3.12713, "loss": 3.12713, "time": 0.81721} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.01206, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43109, "top5_acc": 0.69188, "loss_cls": 3.16256, "loss": 3.16256, "time": 0.82244} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.01204, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42906, "top5_acc": 0.69266, "loss_cls": 3.18951, "loss": 3.18951, "time": 0.81765} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.01202, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43328, "top5_acc": 0.69406, "loss_cls": 3.17108, "loss": 3.17108, "time": 0.81699} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43531, "top5_acc": 0.68938, "loss_cls": 3.17241, "loss": 3.17241, "time": 0.81367} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.01199, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43703, "top5_acc": 0.70234, "loss_cls": 3.13706, "loss": 3.13706, "time": 0.81408} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.01197, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41969, "top5_acc": 0.69078, "loss_cls": 3.2156, "loss": 3.2156, "time": 0.81004} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.01195, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43219, "top5_acc": 0.69672, "loss_cls": 3.1738, "loss": 3.1738, "time": 0.81036} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.01193, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43672, "top5_acc": 0.69422, "loss_cls": 3.14034, "loss": 3.14034, "time": 0.81285} +{"mode": "train", "epoch": 117, "iter": 1300, "lr": 0.01191, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42438, "top5_acc": 0.68938, "loss_cls": 3.21066, "loss": 3.21066, "time": 0.81574} +{"mode": "train", "epoch": 117, "iter": 1400, "lr": 0.0119, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42594, "top5_acc": 0.67688, "loss_cls": 3.22585, "loss": 3.22585, "time": 0.81925} +{"mode": "train", "epoch": 117, "iter": 1500, "lr": 0.01188, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42766, "top5_acc": 0.68766, "loss_cls": 3.20076, "loss": 3.20076, "time": 0.81425} +{"mode": "train", "epoch": 117, "iter": 1600, "lr": 0.01186, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43453, "top5_acc": 0.68891, "loss_cls": 3.15677, "loss": 3.15677, "time": 0.82078} +{"mode": "train", "epoch": 117, "iter": 1700, "lr": 0.01184, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42719, "top5_acc": 0.68938, "loss_cls": 3.21857, "loss": 3.21857, "time": 0.81129} +{"mode": "train", "epoch": 117, "iter": 1800, "lr": 0.01182, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42891, "top5_acc": 0.68391, "loss_cls": 3.19835, "loss": 3.19835, "time": 0.81681} +{"mode": "train", "epoch": 117, "iter": 1900, "lr": 0.01181, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43516, "top5_acc": 0.69641, "loss_cls": 3.1529, "loss": 3.1529, "time": 0.81697} +{"mode": "train", "epoch": 117, "iter": 2000, "lr": 0.01179, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43594, "top5_acc": 0.68844, "loss_cls": 3.17617, "loss": 3.17617, "time": 0.81751} +{"mode": "train", "epoch": 117, "iter": 2100, "lr": 0.01177, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44625, "top5_acc": 0.70125, "loss_cls": 3.12172, "loss": 3.12172, "time": 0.81605} +{"mode": "train", "epoch": 117, "iter": 2200, "lr": 0.01175, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42844, "top5_acc": 0.68938, "loss_cls": 3.1999, "loss": 3.1999, "time": 0.82692} +{"mode": "train", "epoch": 117, "iter": 2300, "lr": 0.01173, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.435, "top5_acc": 0.69, "loss_cls": 3.17147, "loss": 3.17147, "time": 0.81395} +{"mode": "train", "epoch": 117, "iter": 2400, "lr": 0.01172, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44281, "top5_acc": 0.69047, "loss_cls": 3.14453, "loss": 3.14453, "time": 0.81935} +{"mode": "train", "epoch": 117, "iter": 2500, "lr": 0.0117, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43031, "top5_acc": 0.69328, "loss_cls": 3.19379, "loss": 3.19379, "time": 0.82246} +{"mode": "train", "epoch": 117, "iter": 2600, "lr": 0.01168, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43875, "top5_acc": 0.69422, "loss_cls": 3.1508, "loss": 3.1508, "time": 0.81397} +{"mode": "train", "epoch": 117, "iter": 2700, "lr": 0.01166, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42, "top5_acc": 0.67984, "loss_cls": 3.22433, "loss": 3.22433, "time": 0.81469} +{"mode": "train", "epoch": 117, "iter": 2800, "lr": 0.01164, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42047, "top5_acc": 0.69375, "loss_cls": 3.19158, "loss": 3.19158, "time": 0.81737} +{"mode": "train", "epoch": 117, "iter": 2900, "lr": 0.01163, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43562, "top5_acc": 0.69484, "loss_cls": 3.16276, "loss": 3.16276, "time": 0.81519} +{"mode": "train", "epoch": 117, "iter": 3000, "lr": 0.01161, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42891, "top5_acc": 0.68, "loss_cls": 3.21167, "loss": 3.21167, "time": 0.81564} +{"mode": "train", "epoch": 117, "iter": 3100, "lr": 0.01159, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43094, "top5_acc": 0.67562, "loss_cls": 3.22621, "loss": 3.22621, "time": 0.81448} +{"mode": "train", "epoch": 117, "iter": 3200, "lr": 0.01157, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43938, "top5_acc": 0.69391, "loss_cls": 3.16059, "loss": 3.16059, "time": 0.81628} +{"mode": "train", "epoch": 117, "iter": 3300, "lr": 0.01155, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43422, "top5_acc": 0.69281, "loss_cls": 3.16705, "loss": 3.16705, "time": 0.81942} +{"mode": "train", "epoch": 117, "iter": 3400, "lr": 0.01154, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42375, "top5_acc": 0.68656, "loss_cls": 3.19683, "loss": 3.19683, "time": 0.8147} +{"mode": "train", "epoch": 117, "iter": 3500, "lr": 0.01152, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43641, "top5_acc": 0.67625, "loss_cls": 3.19975, "loss": 3.19975, "time": 0.81316} +{"mode": "train", "epoch": 117, "iter": 3600, "lr": 0.0115, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42156, "top5_acc": 0.68141, "loss_cls": 3.23547, "loss": 3.23547, "time": 0.81702} +{"mode": "train", "epoch": 117, "iter": 3700, "lr": 0.01148, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43, "top5_acc": 0.67609, "loss_cls": 3.24696, "loss": 3.24696, "time": 0.814} +{"mode": "val", "epoch": 117, "iter": 309, "lr": 0.01147, "top1_acc": 0.35911, "top5_acc": 0.61931, "mean_class_accuracy": 0.35893} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.01146, "memory": 15990, "data_time": 1.39261, "top1_acc": 0.44516, "top5_acc": 0.70078, "loss_cls": 3.11849, "loss": 3.11849, "time": 2.3716} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.01144, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4375, "top5_acc": 0.70469, "loss_cls": 3.11204, "loss": 3.11204, "time": 0.81963} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.01142, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44203, "top5_acc": 0.69531, "loss_cls": 3.13683, "loss": 3.13683, "time": 0.81703} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.0114, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43438, "top5_acc": 0.69344, "loss_cls": 3.14821, "loss": 3.14821, "time": 0.81582} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.01139, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44125, "top5_acc": 0.69531, "loss_cls": 3.12837, "loss": 3.12837, "time": 0.81937} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.01137, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44047, "top5_acc": 0.70172, "loss_cls": 3.12785, "loss": 3.12785, "time": 0.8125} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.01135, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45422, "top5_acc": 0.71484, "loss_cls": 3.0732, "loss": 3.0732, "time": 0.81943} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.01133, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43688, "top5_acc": 0.69484, "loss_cls": 3.1459, "loss": 3.1459, "time": 0.81177} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.01131, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43797, "top5_acc": 0.69531, "loss_cls": 3.15616, "loss": 3.15616, "time": 0.82218} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.0113, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43656, "top5_acc": 0.69547, "loss_cls": 3.1628, "loss": 3.1628, "time": 0.8152} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.01128, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43359, "top5_acc": 0.68984, "loss_cls": 3.18054, "loss": 3.18054, "time": 0.81732} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.01126, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44297, "top5_acc": 0.70234, "loss_cls": 3.13679, "loss": 3.13679, "time": 0.81419} +{"mode": "train", "epoch": 118, "iter": 1300, "lr": 0.01124, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44047, "top5_acc": 0.70016, "loss_cls": 3.09246, "loss": 3.09246, "time": 0.81564} +{"mode": "train", "epoch": 118, "iter": 1400, "lr": 0.01123, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44094, "top5_acc": 0.69562, "loss_cls": 3.13396, "loss": 3.13396, "time": 0.81596} +{"mode": "train", "epoch": 118, "iter": 1500, "lr": 0.01121, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43766, "top5_acc": 0.69797, "loss_cls": 3.15894, "loss": 3.15894, "time": 0.81932} +{"mode": "train", "epoch": 118, "iter": 1600, "lr": 0.01119, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43312, "top5_acc": 0.68828, "loss_cls": 3.18088, "loss": 3.18088, "time": 0.81497} +{"mode": "train", "epoch": 118, "iter": 1700, "lr": 0.01117, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44062, "top5_acc": 0.69234, "loss_cls": 3.14193, "loss": 3.14193, "time": 0.81791} +{"mode": "train", "epoch": 118, "iter": 1800, "lr": 0.01116, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43781, "top5_acc": 0.69109, "loss_cls": 3.16967, "loss": 3.16967, "time": 0.81666} +{"mode": "train", "epoch": 118, "iter": 1900, "lr": 0.01114, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45047, "top5_acc": 0.69344, "loss_cls": 3.11095, "loss": 3.11095, "time": 0.81673} +{"mode": "train", "epoch": 118, "iter": 2000, "lr": 0.01112, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43234, "top5_acc": 0.68594, "loss_cls": 3.17727, "loss": 3.17727, "time": 0.81769} +{"mode": "train", "epoch": 118, "iter": 2100, "lr": 0.0111, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44141, "top5_acc": 0.69875, "loss_cls": 3.13356, "loss": 3.13356, "time": 0.81701} +{"mode": "train", "epoch": 118, "iter": 2200, "lr": 0.01109, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44328, "top5_acc": 0.70688, "loss_cls": 3.11123, "loss": 3.11123, "time": 0.83775} +{"mode": "train", "epoch": 118, "iter": 2300, "lr": 0.01107, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43141, "top5_acc": 0.69141, "loss_cls": 3.1859, "loss": 3.1859, "time": 0.81882} +{"mode": "train", "epoch": 118, "iter": 2400, "lr": 0.01105, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43953, "top5_acc": 0.69578, "loss_cls": 3.14698, "loss": 3.14698, "time": 0.82586} +{"mode": "train", "epoch": 118, "iter": 2500, "lr": 0.01103, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42078, "top5_acc": 0.68578, "loss_cls": 3.21068, "loss": 3.21068, "time": 0.82087} +{"mode": "train", "epoch": 118, "iter": 2600, "lr": 0.01102, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43672, "top5_acc": 0.69328, "loss_cls": 3.15457, "loss": 3.15457, "time": 0.81527} +{"mode": "train", "epoch": 118, "iter": 2700, "lr": 0.011, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43688, "top5_acc": 0.68969, "loss_cls": 3.15318, "loss": 3.15318, "time": 0.81944} +{"mode": "train", "epoch": 118, "iter": 2800, "lr": 0.01098, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43859, "top5_acc": 0.69719, "loss_cls": 3.1589, "loss": 3.1589, "time": 0.81485} +{"mode": "train", "epoch": 118, "iter": 2900, "lr": 0.01096, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42359, "top5_acc": 0.67609, "loss_cls": 3.25599, "loss": 3.25599, "time": 0.82435} +{"mode": "train", "epoch": 118, "iter": 3000, "lr": 0.01095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43688, "top5_acc": 0.69562, "loss_cls": 3.15995, "loss": 3.15995, "time": 0.81414} +{"mode": "train", "epoch": 118, "iter": 3100, "lr": 0.01093, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43453, "top5_acc": 0.68766, "loss_cls": 3.19917, "loss": 3.19917, "time": 0.82139} +{"mode": "train", "epoch": 118, "iter": 3200, "lr": 0.01091, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43203, "top5_acc": 0.68406, "loss_cls": 3.20632, "loss": 3.20632, "time": 0.81766} +{"mode": "train", "epoch": 118, "iter": 3300, "lr": 0.01089, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42734, "top5_acc": 0.68234, "loss_cls": 3.20458, "loss": 3.20458, "time": 0.81323} +{"mode": "train", "epoch": 118, "iter": 3400, "lr": 0.01088, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44047, "top5_acc": 0.69484, "loss_cls": 3.15177, "loss": 3.15177, "time": 0.81508} +{"mode": "train", "epoch": 118, "iter": 3500, "lr": 0.01086, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43625, "top5_acc": 0.685, "loss_cls": 3.14817, "loss": 3.14817, "time": 0.81488} +{"mode": "train", "epoch": 118, "iter": 3600, "lr": 0.01084, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44109, "top5_acc": 0.69016, "loss_cls": 3.17436, "loss": 3.17436, "time": 0.813} +{"mode": "train", "epoch": 118, "iter": 3700, "lr": 0.01082, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43344, "top5_acc": 0.68906, "loss_cls": 3.19515, "loss": 3.19515, "time": 0.81623} +{"mode": "val", "epoch": 118, "iter": 309, "lr": 0.01082, "top1_acc": 0.37, "top5_acc": 0.62478, "mean_class_accuracy": 0.36949} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.0108, "memory": 15990, "data_time": 1.308, "top1_acc": 0.45688, "top5_acc": 0.71984, "loss_cls": 3.02013, "loss": 3.02013, "time": 2.28305} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.01078, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45594, "top5_acc": 0.70359, "loss_cls": 3.09159, "loss": 3.09159, "time": 0.82138} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.01076, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44391, "top5_acc": 0.70156, "loss_cls": 3.098, "loss": 3.098, "time": 0.81665} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.01075, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44203, "top5_acc": 0.69875, "loss_cls": 3.10864, "loss": 3.10864, "time": 0.81835} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.01073, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45219, "top5_acc": 0.705, "loss_cls": 3.05795, "loss": 3.05795, "time": 0.8201} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.01071, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44219, "top5_acc": 0.70406, "loss_cls": 3.09908, "loss": 3.09908, "time": 0.82027} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.01069, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43672, "top5_acc": 0.70156, "loss_cls": 3.13428, "loss": 3.13428, "time": 0.81765} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.01068, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44219, "top5_acc": 0.69469, "loss_cls": 3.1392, "loss": 3.1392, "time": 0.81752} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.01066, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44234, "top5_acc": 0.69719, "loss_cls": 3.13691, "loss": 3.13691, "time": 0.81676} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.01064, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43984, "top5_acc": 0.71094, "loss_cls": 3.0892, "loss": 3.0892, "time": 0.81386} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.01063, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44, "top5_acc": 0.69891, "loss_cls": 3.13925, "loss": 3.13925, "time": 0.82291} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.01061, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44516, "top5_acc": 0.69766, "loss_cls": 3.11004, "loss": 3.11004, "time": 0.81909} +{"mode": "train", "epoch": 119, "iter": 1300, "lr": 0.01059, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43688, "top5_acc": 0.69828, "loss_cls": 3.11458, "loss": 3.11458, "time": 0.81553} +{"mode": "train", "epoch": 119, "iter": 1400, "lr": 0.01057, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43594, "top5_acc": 0.68766, "loss_cls": 3.16634, "loss": 3.16634, "time": 0.81257} +{"mode": "train", "epoch": 119, "iter": 1500, "lr": 0.01056, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44953, "top5_acc": 0.7025, "loss_cls": 3.11644, "loss": 3.11644, "time": 0.82299} +{"mode": "train", "epoch": 119, "iter": 1600, "lr": 0.01054, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44219, "top5_acc": 0.70125, "loss_cls": 3.09774, "loss": 3.09774, "time": 0.81209} +{"mode": "train", "epoch": 119, "iter": 1700, "lr": 0.01052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43406, "top5_acc": 0.70016, "loss_cls": 3.14508, "loss": 3.14508, "time": 0.8133} +{"mode": "train", "epoch": 119, "iter": 1800, "lr": 0.0105, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43875, "top5_acc": 0.69625, "loss_cls": 3.11819, "loss": 3.11819, "time": 0.81581} +{"mode": "train", "epoch": 119, "iter": 1900, "lr": 0.01049, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44484, "top5_acc": 0.68891, "loss_cls": 3.1503, "loss": 3.1503, "time": 0.81418} +{"mode": "train", "epoch": 119, "iter": 2000, "lr": 0.01047, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43297, "top5_acc": 0.70203, "loss_cls": 3.12184, "loss": 3.12184, "time": 0.81638} +{"mode": "train", "epoch": 119, "iter": 2100, "lr": 0.01045, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44562, "top5_acc": 0.69953, "loss_cls": 3.09338, "loss": 3.09338, "time": 0.81549} +{"mode": "train", "epoch": 119, "iter": 2200, "lr": 0.01044, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.43812, "top5_acc": 0.70531, "loss_cls": 3.10617, "loss": 3.10617, "time": 0.82929} +{"mode": "train", "epoch": 119, "iter": 2300, "lr": 0.01042, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43703, "top5_acc": 0.69562, "loss_cls": 3.14558, "loss": 3.14558, "time": 0.8203} +{"mode": "train", "epoch": 119, "iter": 2400, "lr": 0.0104, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45016, "top5_acc": 0.69672, "loss_cls": 3.11774, "loss": 3.11774, "time": 0.82234} +{"mode": "train", "epoch": 119, "iter": 2500, "lr": 0.01039, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44219, "top5_acc": 0.69203, "loss_cls": 3.1205, "loss": 3.1205, "time": 0.81202} +{"mode": "train", "epoch": 119, "iter": 2600, "lr": 0.01037, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44062, "top5_acc": 0.69469, "loss_cls": 3.17493, "loss": 3.17493, "time": 0.81965} +{"mode": "train", "epoch": 119, "iter": 2700, "lr": 0.01035, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43953, "top5_acc": 0.68812, "loss_cls": 3.174, "loss": 3.174, "time": 0.81898} +{"mode": "train", "epoch": 119, "iter": 2800, "lr": 0.01033, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44969, "top5_acc": 0.6975, "loss_cls": 3.13391, "loss": 3.13391, "time": 0.81201} +{"mode": "train", "epoch": 119, "iter": 2900, "lr": 0.01032, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43469, "top5_acc": 0.69547, "loss_cls": 3.14107, "loss": 3.14107, "time": 0.81515} +{"mode": "train", "epoch": 119, "iter": 3000, "lr": 0.0103, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.43906, "top5_acc": 0.69109, "loss_cls": 3.13116, "loss": 3.13116, "time": 0.81194} +{"mode": "train", "epoch": 119, "iter": 3100, "lr": 0.01028, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43219, "top5_acc": 0.68719, "loss_cls": 3.15941, "loss": 3.15941, "time": 0.81379} +{"mode": "train", "epoch": 119, "iter": 3200, "lr": 0.01027, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43531, "top5_acc": 0.68828, "loss_cls": 3.17567, "loss": 3.17567, "time": 0.81039} +{"mode": "train", "epoch": 119, "iter": 3300, "lr": 0.01025, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43766, "top5_acc": 0.69375, "loss_cls": 3.14598, "loss": 3.14598, "time": 0.81252} +{"mode": "train", "epoch": 119, "iter": 3400, "lr": 0.01023, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4425, "top5_acc": 0.69766, "loss_cls": 3.11717, "loss": 3.11717, "time": 0.81532} +{"mode": "train", "epoch": 119, "iter": 3500, "lr": 0.01022, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43656, "top5_acc": 0.70359, "loss_cls": 3.1322, "loss": 3.1322, "time": 0.8136} +{"mode": "train", "epoch": 119, "iter": 3600, "lr": 0.0102, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44219, "top5_acc": 0.69406, "loss_cls": 3.13982, "loss": 3.13982, "time": 0.81381} +{"mode": "train", "epoch": 119, "iter": 3700, "lr": 0.01018, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43297, "top5_acc": 0.68516, "loss_cls": 3.19899, "loss": 3.19899, "time": 0.8215} +{"mode": "val", "epoch": 119, "iter": 309, "lr": 0.01017, "top1_acc": 0.37943, "top5_acc": 0.63496, "mean_class_accuracy": 0.37916} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.01016, "memory": 15990, "data_time": 1.34939, "top1_acc": 0.46375, "top5_acc": 0.71797, "loss_cls": 3.02922, "loss": 3.02922, "time": 2.34379} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.01014, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44, "top5_acc": 0.70078, "loss_cls": 3.09974, "loss": 3.09974, "time": 0.82864} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.01012, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45844, "top5_acc": 0.71719, "loss_cls": 3.01832, "loss": 3.01832, "time": 0.82644} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.01011, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45062, "top5_acc": 0.71266, "loss_cls": 3.06553, "loss": 3.06553, "time": 0.82605} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.01009, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43906, "top5_acc": 0.69953, "loss_cls": 3.08628, "loss": 3.08628, "time": 0.83083} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.01007, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45, "top5_acc": 0.71172, "loss_cls": 3.06585, "loss": 3.06585, "time": 0.82817} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.01006, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44875, "top5_acc": 0.7025, "loss_cls": 3.08486, "loss": 3.08486, "time": 0.82425} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.01004, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45344, "top5_acc": 0.70922, "loss_cls": 3.09302, "loss": 3.09302, "time": 0.83005} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.01002, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45438, "top5_acc": 0.70297, "loss_cls": 3.07365, "loss": 3.07365, "time": 0.83045} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.01001, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44953, "top5_acc": 0.70312, "loss_cls": 3.09466, "loss": 3.09466, "time": 0.82418} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00999, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44688, "top5_acc": 0.71031, "loss_cls": 3.08853, "loss": 3.08853, "time": 0.83023} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.00997, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45344, "top5_acc": 0.71078, "loss_cls": 3.0683, "loss": 3.0683, "time": 0.82791} +{"mode": "train", "epoch": 120, "iter": 1300, "lr": 0.00996, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44359, "top5_acc": 0.70438, "loss_cls": 3.10356, "loss": 3.10356, "time": 0.82293} +{"mode": "train", "epoch": 120, "iter": 1400, "lr": 0.00994, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44641, "top5_acc": 0.70156, "loss_cls": 3.09066, "loss": 3.09066, "time": 0.83111} +{"mode": "train", "epoch": 120, "iter": 1500, "lr": 0.00992, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44375, "top5_acc": 0.70719, "loss_cls": 3.08032, "loss": 3.08032, "time": 0.82658} +{"mode": "train", "epoch": 120, "iter": 1600, "lr": 0.0099, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45578, "top5_acc": 0.70438, "loss_cls": 3.07178, "loss": 3.07178, "time": 0.83159} +{"mode": "train", "epoch": 120, "iter": 1700, "lr": 0.00989, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44234, "top5_acc": 0.69625, "loss_cls": 3.13822, "loss": 3.13822, "time": 0.82989} +{"mode": "train", "epoch": 120, "iter": 1800, "lr": 0.00987, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45203, "top5_acc": 0.70406, "loss_cls": 3.1011, "loss": 3.1011, "time": 0.82841} +{"mode": "train", "epoch": 120, "iter": 1900, "lr": 0.00985, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43906, "top5_acc": 0.69766, "loss_cls": 3.13648, "loss": 3.13648, "time": 0.82647} +{"mode": "train", "epoch": 120, "iter": 2000, "lr": 0.00984, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44453, "top5_acc": 0.69594, "loss_cls": 3.1379, "loss": 3.1379, "time": 0.83431} +{"mode": "train", "epoch": 120, "iter": 2100, "lr": 0.00982, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44531, "top5_acc": 0.70312, "loss_cls": 3.09504, "loss": 3.09504, "time": 0.83551} +{"mode": "train", "epoch": 120, "iter": 2200, "lr": 0.0098, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44938, "top5_acc": 0.70547, "loss_cls": 3.09526, "loss": 3.09526, "time": 0.83333} +{"mode": "train", "epoch": 120, "iter": 2300, "lr": 0.00979, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.44078, "top5_acc": 0.70047, "loss_cls": 3.11318, "loss": 3.11318, "time": 0.8348} +{"mode": "train", "epoch": 120, "iter": 2400, "lr": 0.00977, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44828, "top5_acc": 0.70641, "loss_cls": 3.09119, "loss": 3.09119, "time": 0.83417} +{"mode": "train", "epoch": 120, "iter": 2500, "lr": 0.00976, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44922, "top5_acc": 0.70516, "loss_cls": 3.09734, "loss": 3.09734, "time": 0.83506} +{"mode": "train", "epoch": 120, "iter": 2600, "lr": 0.00974, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43844, "top5_acc": 0.69406, "loss_cls": 3.13985, "loss": 3.13985, "time": 0.8351} +{"mode": "train", "epoch": 120, "iter": 2700, "lr": 0.00972, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43641, "top5_acc": 0.68844, "loss_cls": 3.16987, "loss": 3.16987, "time": 0.83434} +{"mode": "train", "epoch": 120, "iter": 2800, "lr": 0.00971, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43453, "top5_acc": 0.69875, "loss_cls": 3.13153, "loss": 3.13153, "time": 0.82862} +{"mode": "train", "epoch": 120, "iter": 2900, "lr": 0.00969, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43562, "top5_acc": 0.69188, "loss_cls": 3.16448, "loss": 3.16448, "time": 0.83183} +{"mode": "train", "epoch": 120, "iter": 3000, "lr": 0.00967, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44094, "top5_acc": 0.70172, "loss_cls": 3.10535, "loss": 3.10535, "time": 0.82001} +{"mode": "train", "epoch": 120, "iter": 3100, "lr": 0.00966, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44344, "top5_acc": 0.69938, "loss_cls": 3.12509, "loss": 3.12509, "time": 0.82756} +{"mode": "train", "epoch": 120, "iter": 3200, "lr": 0.00964, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45359, "top5_acc": 0.70844, "loss_cls": 3.05334, "loss": 3.05334, "time": 0.82185} +{"mode": "train", "epoch": 120, "iter": 3300, "lr": 0.00962, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44141, "top5_acc": 0.69469, "loss_cls": 3.1334, "loss": 3.1334, "time": 0.82241} +{"mode": "train", "epoch": 120, "iter": 3400, "lr": 0.00961, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44719, "top5_acc": 0.70062, "loss_cls": 3.12184, "loss": 3.12184, "time": 0.82538} +{"mode": "train", "epoch": 120, "iter": 3500, "lr": 0.00959, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44328, "top5_acc": 0.70188, "loss_cls": 3.12134, "loss": 3.12134, "time": 0.82656} +{"mode": "train", "epoch": 120, "iter": 3600, "lr": 0.00957, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44062, "top5_acc": 0.69547, "loss_cls": 3.12991, "loss": 3.12991, "time": 0.82361} +{"mode": "train", "epoch": 120, "iter": 3700, "lr": 0.00956, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44297, "top5_acc": 0.68719, "loss_cls": 3.15358, "loss": 3.15358, "time": 0.8259} +{"mode": "val", "epoch": 120, "iter": 309, "lr": 0.00955, "top1_acc": 0.37659, "top5_acc": 0.63303, "mean_class_accuracy": 0.37625} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00953, "memory": 15990, "data_time": 1.33632, "top1_acc": 0.45844, "top5_acc": 0.71656, "loss_cls": 3.02515, "loss": 3.02515, "time": 2.33178} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00952, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46719, "top5_acc": 0.71641, "loss_cls": 3.00412, "loss": 3.00412, "time": 0.83106} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.0095, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45922, "top5_acc": 0.71188, "loss_cls": 3.03889, "loss": 3.03889, "time": 0.82373} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00948, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44969, "top5_acc": 0.70562, "loss_cls": 3.07492, "loss": 3.07492, "time": 0.82623} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00947, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45359, "top5_acc": 0.70734, "loss_cls": 3.05644, "loss": 3.05644, "time": 0.83186} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00945, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44906, "top5_acc": 0.71109, "loss_cls": 3.08394, "loss": 3.08394, "time": 0.82845} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.00943, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45016, "top5_acc": 0.71062, "loss_cls": 3.05337, "loss": 3.05337, "time": 0.83431} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00942, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45797, "top5_acc": 0.71281, "loss_cls": 3.04113, "loss": 3.04113, "time": 0.83393} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.0094, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45484, "top5_acc": 0.71062, "loss_cls": 3.05751, "loss": 3.05751, "time": 0.83139} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00939, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44297, "top5_acc": 0.70469, "loss_cls": 3.12577, "loss": 3.12577, "time": 0.83389} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00937, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45203, "top5_acc": 0.70781, "loss_cls": 3.06024, "loss": 3.06024, "time": 0.82949} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00935, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45422, "top5_acc": 0.70484, "loss_cls": 3.04795, "loss": 3.04795, "time": 0.82086} +{"mode": "train", "epoch": 121, "iter": 1300, "lr": 0.00934, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44094, "top5_acc": 0.70281, "loss_cls": 3.08526, "loss": 3.08526, "time": 0.83153} +{"mode": "train", "epoch": 121, "iter": 1400, "lr": 0.00932, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44188, "top5_acc": 0.69766, "loss_cls": 3.10967, "loss": 3.10967, "time": 0.82672} +{"mode": "train", "epoch": 121, "iter": 1500, "lr": 0.0093, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44219, "top5_acc": 0.70891, "loss_cls": 3.07828, "loss": 3.07828, "time": 0.82641} +{"mode": "train", "epoch": 121, "iter": 1600, "lr": 0.00929, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44906, "top5_acc": 0.70781, "loss_cls": 3.07023, "loss": 3.07023, "time": 0.82371} +{"mode": "train", "epoch": 121, "iter": 1700, "lr": 0.00927, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45328, "top5_acc": 0.70547, "loss_cls": 3.07868, "loss": 3.07868, "time": 0.82931} +{"mode": "train", "epoch": 121, "iter": 1800, "lr": 0.00926, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45781, "top5_acc": 0.71453, "loss_cls": 3.01763, "loss": 3.01763, "time": 0.82573} +{"mode": "train", "epoch": 121, "iter": 1900, "lr": 0.00924, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45328, "top5_acc": 0.70719, "loss_cls": 3.06652, "loss": 3.06652, "time": 0.82176} +{"mode": "train", "epoch": 121, "iter": 2000, "lr": 0.00922, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44656, "top5_acc": 0.70594, "loss_cls": 3.10317, "loss": 3.10317, "time": 0.83299} +{"mode": "train", "epoch": 121, "iter": 2100, "lr": 0.00921, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44016, "top5_acc": 0.69969, "loss_cls": 3.12509, "loss": 3.12509, "time": 0.82434} +{"mode": "train", "epoch": 121, "iter": 2200, "lr": 0.00919, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.46016, "top5_acc": 0.70906, "loss_cls": 3.05242, "loss": 3.05242, "time": 0.8391} +{"mode": "train", "epoch": 121, "iter": 2300, "lr": 0.00917, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.43922, "top5_acc": 0.70047, "loss_cls": 3.11824, "loss": 3.11824, "time": 0.83375} +{"mode": "train", "epoch": 121, "iter": 2400, "lr": 0.00916, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.44516, "top5_acc": 0.70844, "loss_cls": 3.08995, "loss": 3.08995, "time": 0.83379} +{"mode": "train", "epoch": 121, "iter": 2500, "lr": 0.00914, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44609, "top5_acc": 0.69281, "loss_cls": 3.11402, "loss": 3.11402, "time": 0.82851} +{"mode": "train", "epoch": 121, "iter": 2600, "lr": 0.00913, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45062, "top5_acc": 0.7125, "loss_cls": 3.07487, "loss": 3.07487, "time": 0.83093} +{"mode": "train", "epoch": 121, "iter": 2700, "lr": 0.00911, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44141, "top5_acc": 0.69859, "loss_cls": 3.11696, "loss": 3.11696, "time": 0.82704} +{"mode": "train", "epoch": 121, "iter": 2800, "lr": 0.00909, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.4475, "top5_acc": 0.70344, "loss_cls": 3.09657, "loss": 3.09657, "time": 0.83144} +{"mode": "train", "epoch": 121, "iter": 2900, "lr": 0.00908, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45953, "top5_acc": 0.71562, "loss_cls": 3.02504, "loss": 3.02504, "time": 0.82529} +{"mode": "train", "epoch": 121, "iter": 3000, "lr": 0.00906, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44531, "top5_acc": 0.70047, "loss_cls": 3.11792, "loss": 3.11792, "time": 0.82651} +{"mode": "train", "epoch": 121, "iter": 3100, "lr": 0.00905, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45156, "top5_acc": 0.70656, "loss_cls": 3.07647, "loss": 3.07647, "time": 0.82407} +{"mode": "train", "epoch": 121, "iter": 3200, "lr": 0.00903, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45172, "top5_acc": 0.70766, "loss_cls": 3.05117, "loss": 3.05117, "time": 0.82815} +{"mode": "train", "epoch": 121, "iter": 3300, "lr": 0.00901, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45109, "top5_acc": 0.71078, "loss_cls": 3.03803, "loss": 3.03803, "time": 0.82769} +{"mode": "train", "epoch": 121, "iter": 3400, "lr": 0.009, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44625, "top5_acc": 0.69969, "loss_cls": 3.09965, "loss": 3.09965, "time": 0.81954} +{"mode": "train", "epoch": 121, "iter": 3500, "lr": 0.00898, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44109, "top5_acc": 0.70125, "loss_cls": 3.12916, "loss": 3.12916, "time": 0.82341} +{"mode": "train", "epoch": 121, "iter": 3600, "lr": 0.00897, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.4575, "top5_acc": 0.71719, "loss_cls": 3.01569, "loss": 3.01569, "time": 0.82447} +{"mode": "train", "epoch": 121, "iter": 3700, "lr": 0.00895, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.45047, "top5_acc": 0.7025, "loss_cls": 3.08683, "loss": 3.08683, "time": 0.82188} +{"mode": "val", "epoch": 121, "iter": 309, "lr": 0.00894, "top1_acc": 0.3894, "top5_acc": 0.64494, "mean_class_accuracy": 0.38922} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00893, "memory": 15990, "data_time": 1.34032, "top1_acc": 0.47641, "top5_acc": 0.72266, "loss_cls": 2.96793, "loss": 2.96793, "time": 2.32633} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00891, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45578, "top5_acc": 0.71594, "loss_cls": 3.00782, "loss": 3.00782, "time": 0.82515} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.00889, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46281, "top5_acc": 0.72062, "loss_cls": 3.00505, "loss": 3.00505, "time": 0.81769} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00888, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45406, "top5_acc": 0.71688, "loss_cls": 3.02078, "loss": 3.02078, "time": 0.81965} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00886, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46484, "top5_acc": 0.72203, "loss_cls": 2.99491, "loss": 2.99491, "time": 0.818} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00885, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46609, "top5_acc": 0.71672, "loss_cls": 3.01833, "loss": 3.01833, "time": 0.82324} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00883, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45781, "top5_acc": 0.72094, "loss_cls": 3.00453, "loss": 3.00453, "time": 0.81754} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00882, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46516, "top5_acc": 0.72, "loss_cls": 2.98645, "loss": 2.98645, "time": 0.82281} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.0088, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46156, "top5_acc": 0.71906, "loss_cls": 3.01773, "loss": 3.01773, "time": 0.81466} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00878, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46125, "top5_acc": 0.71781, "loss_cls": 3.01042, "loss": 3.01042, "time": 0.82012} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00877, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45359, "top5_acc": 0.71375, "loss_cls": 3.03263, "loss": 3.03263, "time": 0.81419} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.00875, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45609, "top5_acc": 0.70531, "loss_cls": 3.05121, "loss": 3.05121, "time": 0.81776} +{"mode": "train", "epoch": 122, "iter": 1300, "lr": 0.00874, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45984, "top5_acc": 0.71078, "loss_cls": 3.04049, "loss": 3.04049, "time": 0.82412} +{"mode": "train", "epoch": 122, "iter": 1400, "lr": 0.00872, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45891, "top5_acc": 0.70672, "loss_cls": 3.05798, "loss": 3.05798, "time": 0.81318} +{"mode": "train", "epoch": 122, "iter": 1500, "lr": 0.0087, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45094, "top5_acc": 0.71, "loss_cls": 3.03845, "loss": 3.03845, "time": 0.81636} +{"mode": "train", "epoch": 122, "iter": 1600, "lr": 0.00869, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44438, "top5_acc": 0.71266, "loss_cls": 3.06739, "loss": 3.06739, "time": 0.81985} +{"mode": "train", "epoch": 122, "iter": 1700, "lr": 0.00867, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44984, "top5_acc": 0.71125, "loss_cls": 3.06026, "loss": 3.06026, "time": 0.81396} +{"mode": "train", "epoch": 122, "iter": 1800, "lr": 0.00866, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45375, "top5_acc": 0.70594, "loss_cls": 3.03701, "loss": 3.03701, "time": 0.8209} +{"mode": "train", "epoch": 122, "iter": 1900, "lr": 0.00864, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.445, "top5_acc": 0.70266, "loss_cls": 3.08012, "loss": 3.08012, "time": 0.81709} +{"mode": "train", "epoch": 122, "iter": 2000, "lr": 0.00863, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.44625, "top5_acc": 0.705, "loss_cls": 3.0803, "loss": 3.0803, "time": 0.82049} +{"mode": "train", "epoch": 122, "iter": 2100, "lr": 0.00861, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44328, "top5_acc": 0.71219, "loss_cls": 3.08455, "loss": 3.08455, "time": 0.82701} +{"mode": "train", "epoch": 122, "iter": 2200, "lr": 0.00859, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44078, "top5_acc": 0.69406, "loss_cls": 3.14398, "loss": 3.14398, "time": 0.82879} +{"mode": "train", "epoch": 122, "iter": 2300, "lr": 0.00858, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45297, "top5_acc": 0.70859, "loss_cls": 3.06761, "loss": 3.06761, "time": 0.82033} +{"mode": "train", "epoch": 122, "iter": 2400, "lr": 0.00856, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45672, "top5_acc": 0.71234, "loss_cls": 3.04896, "loss": 3.04896, "time": 0.81485} +{"mode": "train", "epoch": 122, "iter": 2500, "lr": 0.00855, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45688, "top5_acc": 0.71391, "loss_cls": 3.05861, "loss": 3.05861, "time": 0.81773} +{"mode": "train", "epoch": 122, "iter": 2600, "lr": 0.00853, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45547, "top5_acc": 0.71344, "loss_cls": 3.03348, "loss": 3.03348, "time": 0.81065} +{"mode": "train", "epoch": 122, "iter": 2700, "lr": 0.00852, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45656, "top5_acc": 0.71406, "loss_cls": 3.06242, "loss": 3.06242, "time": 0.8154} +{"mode": "train", "epoch": 122, "iter": 2800, "lr": 0.0085, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45453, "top5_acc": 0.71, "loss_cls": 3.06377, "loss": 3.06377, "time": 0.81881} +{"mode": "train", "epoch": 122, "iter": 2900, "lr": 0.00849, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45547, "top5_acc": 0.70422, "loss_cls": 3.0651, "loss": 3.0651, "time": 0.81862} +{"mode": "train", "epoch": 122, "iter": 3000, "lr": 0.00847, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45797, "top5_acc": 0.71047, "loss_cls": 3.01904, "loss": 3.01904, "time": 0.81534} +{"mode": "train", "epoch": 122, "iter": 3100, "lr": 0.00845, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45359, "top5_acc": 0.70875, "loss_cls": 3.04088, "loss": 3.04088, "time": 0.81423} +{"mode": "train", "epoch": 122, "iter": 3200, "lr": 0.00844, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4425, "top5_acc": 0.69938, "loss_cls": 3.0907, "loss": 3.0907, "time": 0.81085} +{"mode": "train", "epoch": 122, "iter": 3300, "lr": 0.00842, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44922, "top5_acc": 0.71438, "loss_cls": 3.05037, "loss": 3.05037, "time": 0.81552} +{"mode": "train", "epoch": 122, "iter": 3400, "lr": 0.00841, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45641, "top5_acc": 0.70453, "loss_cls": 3.06901, "loss": 3.06901, "time": 0.81438} +{"mode": "train", "epoch": 122, "iter": 3500, "lr": 0.00839, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45547, "top5_acc": 0.7125, "loss_cls": 3.03184, "loss": 3.03184, "time": 0.81128} +{"mode": "train", "epoch": 122, "iter": 3600, "lr": 0.00838, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46094, "top5_acc": 0.71453, "loss_cls": 3.04097, "loss": 3.04097, "time": 0.82507} +{"mode": "train", "epoch": 122, "iter": 3700, "lr": 0.00836, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45812, "top5_acc": 0.70562, "loss_cls": 3.04715, "loss": 3.04715, "time": 0.81715} +{"mode": "val", "epoch": 122, "iter": 309, "lr": 0.00835, "top1_acc": 0.3812, "top5_acc": 0.6344, "mean_class_accuracy": 0.38107} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00834, "memory": 15990, "data_time": 1.3185, "top1_acc": 0.47375, "top5_acc": 0.72016, "loss_cls": 2.96581, "loss": 2.96581, "time": 2.32264} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00832, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47703, "top5_acc": 0.72672, "loss_cls": 2.91546, "loss": 2.91546, "time": 0.81848} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00831, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47766, "top5_acc": 0.7275, "loss_cls": 2.93427, "loss": 2.93427, "time": 0.8225} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00829, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47109, "top5_acc": 0.72625, "loss_cls": 2.9476, "loss": 2.9476, "time": 0.82042} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00828, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45984, "top5_acc": 0.72078, "loss_cls": 2.9783, "loss": 2.9783, "time": 0.81649} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00826, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47453, "top5_acc": 0.72938, "loss_cls": 2.93257, "loss": 2.93257, "time": 0.82023} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00825, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.475, "top5_acc": 0.72422, "loss_cls": 2.9738, "loss": 2.9738, "time": 0.8158} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.00823, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45828, "top5_acc": 0.71656, "loss_cls": 2.99561, "loss": 2.99561, "time": 0.81535} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00822, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45969, "top5_acc": 0.71953, "loss_cls": 2.98435, "loss": 2.98435, "time": 0.81495} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.0082, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45375, "top5_acc": 0.71734, "loss_cls": 3.0181, "loss": 3.0181, "time": 0.81309} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00818, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45922, "top5_acc": 0.72047, "loss_cls": 2.99822, "loss": 2.99822, "time": 0.81548} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00817, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45906, "top5_acc": 0.72359, "loss_cls": 2.97902, "loss": 2.97902, "time": 0.81972} +{"mode": "train", "epoch": 123, "iter": 1300, "lr": 0.00815, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46594, "top5_acc": 0.72016, "loss_cls": 2.98838, "loss": 2.98838, "time": 0.81709} +{"mode": "train", "epoch": 123, "iter": 1400, "lr": 0.00814, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45734, "top5_acc": 0.71031, "loss_cls": 3.03936, "loss": 3.03936, "time": 0.81305} +{"mode": "train", "epoch": 123, "iter": 1500, "lr": 0.00812, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46062, "top5_acc": 0.71109, "loss_cls": 3.03448, "loss": 3.03448, "time": 0.81922} +{"mode": "train", "epoch": 123, "iter": 1600, "lr": 0.00811, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44812, "top5_acc": 0.70312, "loss_cls": 3.06082, "loss": 3.06082, "time": 0.81724} +{"mode": "train", "epoch": 123, "iter": 1700, "lr": 0.00809, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45859, "top5_acc": 0.71047, "loss_cls": 3.02672, "loss": 3.02672, "time": 0.82066} +{"mode": "train", "epoch": 123, "iter": 1800, "lr": 0.00808, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4525, "top5_acc": 0.70578, "loss_cls": 3.06609, "loss": 3.06609, "time": 0.80997} +{"mode": "train", "epoch": 123, "iter": 1900, "lr": 0.00806, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45609, "top5_acc": 0.70922, "loss_cls": 3.03122, "loss": 3.03122, "time": 0.81256} +{"mode": "train", "epoch": 123, "iter": 2000, "lr": 0.00805, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44062, "top5_acc": 0.69797, "loss_cls": 3.11553, "loss": 3.11553, "time": 0.81933} +{"mode": "train", "epoch": 123, "iter": 2100, "lr": 0.00803, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44203, "top5_acc": 0.70656, "loss_cls": 3.08779, "loss": 3.08779, "time": 0.81219} +{"mode": "train", "epoch": 123, "iter": 2200, "lr": 0.00802, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45797, "top5_acc": 0.71875, "loss_cls": 3.00831, "loss": 3.00831, "time": 0.82786} +{"mode": "train", "epoch": 123, "iter": 2300, "lr": 0.008, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.455, "top5_acc": 0.70781, "loss_cls": 3.0353, "loss": 3.0353, "time": 0.81755} +{"mode": "train", "epoch": 123, "iter": 2400, "lr": 0.00799, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46, "top5_acc": 0.71453, "loss_cls": 3.03116, "loss": 3.03116, "time": 0.82035} +{"mode": "train", "epoch": 123, "iter": 2500, "lr": 0.00797, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45797, "top5_acc": 0.71516, "loss_cls": 3.02268, "loss": 3.02268, "time": 0.8166} +{"mode": "train", "epoch": 123, "iter": 2600, "lr": 0.00796, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44922, "top5_acc": 0.70469, "loss_cls": 3.08482, "loss": 3.08482, "time": 0.81457} +{"mode": "train", "epoch": 123, "iter": 2700, "lr": 0.00794, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46, "top5_acc": 0.71547, "loss_cls": 3.014, "loss": 3.014, "time": 0.81339} +{"mode": "train", "epoch": 123, "iter": 2800, "lr": 0.00793, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46547, "top5_acc": 0.71484, "loss_cls": 3.03209, "loss": 3.03209, "time": 0.81207} +{"mode": "train", "epoch": 123, "iter": 2900, "lr": 0.00791, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45719, "top5_acc": 0.71484, "loss_cls": 3.04846, "loss": 3.04846, "time": 0.8123} +{"mode": "train", "epoch": 123, "iter": 3000, "lr": 0.0079, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46094, "top5_acc": 0.71609, "loss_cls": 3.01293, "loss": 3.01293, "time": 0.80892} +{"mode": "train", "epoch": 123, "iter": 3100, "lr": 0.00788, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46078, "top5_acc": 0.71828, "loss_cls": 3.00907, "loss": 3.00907, "time": 0.81914} +{"mode": "train", "epoch": 123, "iter": 3200, "lr": 0.00787, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45797, "top5_acc": 0.71016, "loss_cls": 3.04877, "loss": 3.04877, "time": 0.81301} +{"mode": "train", "epoch": 123, "iter": 3300, "lr": 0.00785, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45344, "top5_acc": 0.70906, "loss_cls": 3.05213, "loss": 3.05213, "time": 0.81875} +{"mode": "train", "epoch": 123, "iter": 3400, "lr": 0.00784, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47172, "top5_acc": 0.71484, "loss_cls": 3.01443, "loss": 3.01443, "time": 0.81104} +{"mode": "train", "epoch": 123, "iter": 3500, "lr": 0.00782, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45391, "top5_acc": 0.70562, "loss_cls": 3.07724, "loss": 3.07724, "time": 0.81452} +{"mode": "train", "epoch": 123, "iter": 3600, "lr": 0.00781, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45156, "top5_acc": 0.71031, "loss_cls": 3.03942, "loss": 3.03942, "time": 0.81954} +{"mode": "train", "epoch": 123, "iter": 3700, "lr": 0.00779, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45125, "top5_acc": 0.71, "loss_cls": 3.06241, "loss": 3.06241, "time": 0.8148} +{"mode": "val", "epoch": 123, "iter": 309, "lr": 0.00778, "top1_acc": 0.38631, "top5_acc": 0.6382, "mean_class_accuracy": 0.38601} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00777, "memory": 15990, "data_time": 1.30672, "top1_acc": 0.47078, "top5_acc": 0.73062, "loss_cls": 2.92619, "loss": 2.92619, "time": 2.28628} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00775, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48375, "top5_acc": 0.7425, "loss_cls": 2.87234, "loss": 2.87234, "time": 0.82038} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00774, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46859, "top5_acc": 0.72062, "loss_cls": 2.96565, "loss": 2.96565, "time": 0.82037} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.00772, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46625, "top5_acc": 0.73047, "loss_cls": 2.9879, "loss": 2.9879, "time": 0.81941} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00771, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48609, "top5_acc": 0.73453, "loss_cls": 2.89242, "loss": 2.89242, "time": 0.8193} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00769, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46828, "top5_acc": 0.72281, "loss_cls": 2.95399, "loss": 2.95399, "time": 0.81616} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00768, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47547, "top5_acc": 0.72562, "loss_cls": 2.92898, "loss": 2.92898, "time": 0.81436} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00766, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47359, "top5_acc": 0.72969, "loss_cls": 2.94147, "loss": 2.94147, "time": 0.81189} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00765, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46547, "top5_acc": 0.71766, "loss_cls": 2.9755, "loss": 2.9755, "time": 0.81227} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00763, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46359, "top5_acc": 0.72438, "loss_cls": 2.99596, "loss": 2.99596, "time": 0.81377} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00762, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46359, "top5_acc": 0.71922, "loss_cls": 3.01068, "loss": 3.01068, "time": 0.81398} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.0076, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46312, "top5_acc": 0.72734, "loss_cls": 2.95884, "loss": 2.95884, "time": 0.82234} +{"mode": "train", "epoch": 124, "iter": 1300, "lr": 0.00759, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46031, "top5_acc": 0.71406, "loss_cls": 3.01239, "loss": 3.01239, "time": 0.81605} +{"mode": "train", "epoch": 124, "iter": 1400, "lr": 0.00758, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45859, "top5_acc": 0.72359, "loss_cls": 2.99572, "loss": 2.99572, "time": 0.82285} +{"mode": "train", "epoch": 124, "iter": 1500, "lr": 0.00756, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46, "top5_acc": 0.71734, "loss_cls": 3.00565, "loss": 3.00565, "time": 0.82086} +{"mode": "train", "epoch": 124, "iter": 1600, "lr": 0.00755, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46125, "top5_acc": 0.71484, "loss_cls": 3.03837, "loss": 3.03837, "time": 0.81264} +{"mode": "train", "epoch": 124, "iter": 1700, "lr": 0.00753, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45906, "top5_acc": 0.72188, "loss_cls": 2.9799, "loss": 2.9799, "time": 0.81514} +{"mode": "train", "epoch": 124, "iter": 1800, "lr": 0.00752, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47172, "top5_acc": 0.725, "loss_cls": 2.94614, "loss": 2.94614, "time": 0.81422} +{"mode": "train", "epoch": 124, "iter": 1900, "lr": 0.0075, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46016, "top5_acc": 0.71781, "loss_cls": 3.01107, "loss": 3.01107, "time": 0.81391} +{"mode": "train", "epoch": 124, "iter": 2000, "lr": 0.00749, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46766, "top5_acc": 0.72047, "loss_cls": 2.95816, "loss": 2.95816, "time": 0.8196} +{"mode": "train", "epoch": 124, "iter": 2100, "lr": 0.00747, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46562, "top5_acc": 0.71281, "loss_cls": 2.9943, "loss": 2.9943, "time": 0.821} +{"mode": "train", "epoch": 124, "iter": 2200, "lr": 0.00746, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45422, "top5_acc": 0.71609, "loss_cls": 2.99448, "loss": 2.99448, "time": 0.83135} +{"mode": "train", "epoch": 124, "iter": 2300, "lr": 0.00744, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47031, "top5_acc": 0.72234, "loss_cls": 2.96814, "loss": 2.96814, "time": 0.81335} +{"mode": "train", "epoch": 124, "iter": 2400, "lr": 0.00743, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46328, "top5_acc": 0.70641, "loss_cls": 3.03825, "loss": 3.03825, "time": 0.81659} +{"mode": "train", "epoch": 124, "iter": 2500, "lr": 0.00741, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45719, "top5_acc": 0.71078, "loss_cls": 3.03706, "loss": 3.03706, "time": 0.81528} +{"mode": "train", "epoch": 124, "iter": 2600, "lr": 0.0074, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45891, "top5_acc": 0.71516, "loss_cls": 2.99954, "loss": 2.99954, "time": 0.8109} +{"mode": "train", "epoch": 124, "iter": 2700, "lr": 0.00738, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46984, "top5_acc": 0.72375, "loss_cls": 2.95639, "loss": 2.95639, "time": 0.81125} +{"mode": "train", "epoch": 124, "iter": 2800, "lr": 0.00737, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46062, "top5_acc": 0.71609, "loss_cls": 2.99404, "loss": 2.99404, "time": 0.81631} +{"mode": "train", "epoch": 124, "iter": 2900, "lr": 0.00735, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47016, "top5_acc": 0.71594, "loss_cls": 2.97636, "loss": 2.97636, "time": 0.81922} +{"mode": "train", "epoch": 124, "iter": 3000, "lr": 0.00734, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45906, "top5_acc": 0.70938, "loss_cls": 3.02951, "loss": 3.02951, "time": 0.81625} +{"mode": "train", "epoch": 124, "iter": 3100, "lr": 0.00733, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45734, "top5_acc": 0.71344, "loss_cls": 3.03587, "loss": 3.03587, "time": 0.81117} +{"mode": "train", "epoch": 124, "iter": 3200, "lr": 0.00731, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46266, "top5_acc": 0.71375, "loss_cls": 3.0377, "loss": 3.0377, "time": 0.81069} +{"mode": "train", "epoch": 124, "iter": 3300, "lr": 0.0073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46922, "top5_acc": 0.72, "loss_cls": 2.99457, "loss": 2.99457, "time": 0.81543} +{"mode": "train", "epoch": 124, "iter": 3400, "lr": 0.00728, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46672, "top5_acc": 0.71594, "loss_cls": 2.99175, "loss": 2.99175, "time": 0.81459} +{"mode": "train", "epoch": 124, "iter": 3500, "lr": 0.00727, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45719, "top5_acc": 0.71, "loss_cls": 3.02688, "loss": 3.02688, "time": 0.81568} +{"mode": "train", "epoch": 124, "iter": 3600, "lr": 0.00725, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45672, "top5_acc": 0.71203, "loss_cls": 3.02731, "loss": 3.02731, "time": 0.81842} +{"mode": "train", "epoch": 124, "iter": 3700, "lr": 0.00724, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45625, "top5_acc": 0.71, "loss_cls": 3.02507, "loss": 3.02507, "time": 0.81788} +{"mode": "val", "epoch": 124, "iter": 309, "lr": 0.00723, "top1_acc": 0.38844, "top5_acc": 0.64696, "mean_class_accuracy": 0.38805} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.00722, "memory": 15990, "data_time": 1.32259, "top1_acc": 0.4925, "top5_acc": 0.74156, "loss_cls": 2.8626, "loss": 2.8626, "time": 2.30293} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.0072, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48391, "top5_acc": 0.73312, "loss_cls": 2.90499, "loss": 2.90499, "time": 0.81892} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00719, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48531, "top5_acc": 0.73406, "loss_cls": 2.87747, "loss": 2.87747, "time": 0.81468} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00717, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47625, "top5_acc": 0.72969, "loss_cls": 2.90963, "loss": 2.90963, "time": 0.82626} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00716, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48594, "top5_acc": 0.73516, "loss_cls": 2.88662, "loss": 2.88662, "time": 0.81724} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00715, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47562, "top5_acc": 0.74125, "loss_cls": 2.87761, "loss": 2.87761, "time": 0.82211} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00713, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46969, "top5_acc": 0.72547, "loss_cls": 2.95339, "loss": 2.95339, "time": 0.81653} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00712, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47547, "top5_acc": 0.72906, "loss_cls": 2.92628, "loss": 2.92628, "time": 0.81338} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.0071, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47297, "top5_acc": 0.72406, "loss_cls": 2.96002, "loss": 2.96002, "time": 0.81719} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.00709, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47906, "top5_acc": 0.73094, "loss_cls": 2.93268, "loss": 2.93268, "time": 0.81562} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00707, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47062, "top5_acc": 0.72797, "loss_cls": 2.94488, "loss": 2.94488, "time": 0.81375} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00706, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46953, "top5_acc": 0.71656, "loss_cls": 2.99489, "loss": 2.99489, "time": 0.81307} +{"mode": "train", "epoch": 125, "iter": 1300, "lr": 0.00704, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47766, "top5_acc": 0.72375, "loss_cls": 2.93603, "loss": 2.93603, "time": 0.81756} +{"mode": "train", "epoch": 125, "iter": 1400, "lr": 0.00703, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46656, "top5_acc": 0.72906, "loss_cls": 2.95749, "loss": 2.95749, "time": 0.81844} +{"mode": "train", "epoch": 125, "iter": 1500, "lr": 0.00702, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46969, "top5_acc": 0.73031, "loss_cls": 2.94645, "loss": 2.94645, "time": 0.81188} +{"mode": "train", "epoch": 125, "iter": 1600, "lr": 0.007, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47203, "top5_acc": 0.72094, "loss_cls": 2.9672, "loss": 2.9672, "time": 0.81414} +{"mode": "train", "epoch": 125, "iter": 1700, "lr": 0.00699, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47672, "top5_acc": 0.72391, "loss_cls": 2.94688, "loss": 2.94688, "time": 0.81596} +{"mode": "train", "epoch": 125, "iter": 1800, "lr": 0.00697, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46422, "top5_acc": 0.71375, "loss_cls": 2.97084, "loss": 2.97084, "time": 0.81838} +{"mode": "train", "epoch": 125, "iter": 1900, "lr": 0.00696, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46266, "top5_acc": 0.72078, "loss_cls": 2.99706, "loss": 2.99706, "time": 0.82097} +{"mode": "train", "epoch": 125, "iter": 2000, "lr": 0.00694, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47047, "top5_acc": 0.72812, "loss_cls": 2.94392, "loss": 2.94392, "time": 0.81574} +{"mode": "train", "epoch": 125, "iter": 2100, "lr": 0.00693, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46031, "top5_acc": 0.72406, "loss_cls": 2.98149, "loss": 2.98149, "time": 0.8129} +{"mode": "train", "epoch": 125, "iter": 2200, "lr": 0.00692, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47719, "top5_acc": 0.72578, "loss_cls": 2.92387, "loss": 2.92387, "time": 0.82398} +{"mode": "train", "epoch": 125, "iter": 2300, "lr": 0.0069, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46953, "top5_acc": 0.73141, "loss_cls": 2.95257, "loss": 2.95257, "time": 0.82396} +{"mode": "train", "epoch": 125, "iter": 2400, "lr": 0.00689, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46297, "top5_acc": 0.72156, "loss_cls": 2.95786, "loss": 2.95786, "time": 0.81563} +{"mode": "train", "epoch": 125, "iter": 2500, "lr": 0.00687, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46484, "top5_acc": 0.71609, "loss_cls": 2.94905, "loss": 2.94905, "time": 0.81855} +{"mode": "train", "epoch": 125, "iter": 2600, "lr": 0.00686, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46, "top5_acc": 0.72031, "loss_cls": 2.99973, "loss": 2.99973, "time": 0.81284} +{"mode": "train", "epoch": 125, "iter": 2700, "lr": 0.00685, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46422, "top5_acc": 0.72172, "loss_cls": 2.97407, "loss": 2.97407, "time": 0.81121} +{"mode": "train", "epoch": 125, "iter": 2800, "lr": 0.00683, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46812, "top5_acc": 0.72438, "loss_cls": 2.97103, "loss": 2.97103, "time": 0.8155} +{"mode": "train", "epoch": 125, "iter": 2900, "lr": 0.00682, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47125, "top5_acc": 0.73141, "loss_cls": 2.92324, "loss": 2.92324, "time": 0.80986} +{"mode": "train", "epoch": 125, "iter": 3000, "lr": 0.0068, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47062, "top5_acc": 0.72062, "loss_cls": 2.95853, "loss": 2.95853, "time": 0.81522} +{"mode": "train", "epoch": 125, "iter": 3100, "lr": 0.00679, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46766, "top5_acc": 0.72172, "loss_cls": 2.96737, "loss": 2.96737, "time": 0.81891} +{"mode": "train", "epoch": 125, "iter": 3200, "lr": 0.00678, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.4675, "top5_acc": 0.71812, "loss_cls": 2.97221, "loss": 2.97221, "time": 0.81036} +{"mode": "train", "epoch": 125, "iter": 3300, "lr": 0.00676, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46844, "top5_acc": 0.72656, "loss_cls": 2.95, "loss": 2.95, "time": 0.81852} +{"mode": "train", "epoch": 125, "iter": 3400, "lr": 0.00675, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4775, "top5_acc": 0.72188, "loss_cls": 2.93962, "loss": 2.93962, "time": 0.81256} +{"mode": "train", "epoch": 125, "iter": 3500, "lr": 0.00673, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46906, "top5_acc": 0.71906, "loss_cls": 3.00093, "loss": 3.00093, "time": 0.81514} +{"mode": "train", "epoch": 125, "iter": 3600, "lr": 0.00672, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45797, "top5_acc": 0.71688, "loss_cls": 3.03204, "loss": 3.03204, "time": 0.81742} +{"mode": "train", "epoch": 125, "iter": 3700, "lr": 0.00671, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45812, "top5_acc": 0.71312, "loss_cls": 3.02389, "loss": 3.02389, "time": 0.81296} +{"mode": "val", "epoch": 125, "iter": 309, "lr": 0.0067, "top1_acc": 0.40207, "top5_acc": 0.65284, "mean_class_accuracy": 0.40192} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00668, "memory": 15990, "data_time": 1.30569, "top1_acc": 0.49312, "top5_acc": 0.74297, "loss_cls": 2.8293, "loss": 2.8293, "time": 2.28828} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00667, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.495, "top5_acc": 0.73656, "loss_cls": 2.85742, "loss": 2.85742, "time": 0.82249} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00666, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47844, "top5_acc": 0.72531, "loss_cls": 2.90961, "loss": 2.90961, "time": 0.81696} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00664, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49266, "top5_acc": 0.74203, "loss_cls": 2.85899, "loss": 2.85899, "time": 0.81559} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00663, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49344, "top5_acc": 0.75031, "loss_cls": 2.81313, "loss": 2.81313, "time": 0.82004} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00662, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47734, "top5_acc": 0.73578, "loss_cls": 2.872, "loss": 2.872, "time": 0.82458} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0066, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47344, "top5_acc": 0.73547, "loss_cls": 2.90745, "loss": 2.90745, "time": 0.81879} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00659, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47562, "top5_acc": 0.73672, "loss_cls": 2.89988, "loss": 2.89988, "time": 0.82134} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00657, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48578, "top5_acc": 0.73922, "loss_cls": 2.86475, "loss": 2.86475, "time": 0.81416} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00656, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47062, "top5_acc": 0.72781, "loss_cls": 2.97601, "loss": 2.97601, "time": 0.81202} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00655, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48078, "top5_acc": 0.73328, "loss_cls": 2.91667, "loss": 2.91667, "time": 0.81492} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00653, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47062, "top5_acc": 0.72719, "loss_cls": 2.92432, "loss": 2.92432, "time": 0.81635} +{"mode": "train", "epoch": 126, "iter": 1300, "lr": 0.00652, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47234, "top5_acc": 0.73016, "loss_cls": 2.9212, "loss": 2.9212, "time": 0.81564} +{"mode": "train", "epoch": 126, "iter": 1400, "lr": 0.0065, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47516, "top5_acc": 0.73234, "loss_cls": 2.91875, "loss": 2.91875, "time": 0.81387} +{"mode": "train", "epoch": 126, "iter": 1500, "lr": 0.00649, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46828, "top5_acc": 0.72484, "loss_cls": 2.94634, "loss": 2.94634, "time": 0.81236} +{"mode": "train", "epoch": 126, "iter": 1600, "lr": 0.00648, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47734, "top5_acc": 0.73203, "loss_cls": 2.92418, "loss": 2.92418, "time": 0.81469} +{"mode": "train", "epoch": 126, "iter": 1700, "lr": 0.00646, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47516, "top5_acc": 0.73141, "loss_cls": 2.94439, "loss": 2.94439, "time": 0.81421} +{"mode": "train", "epoch": 126, "iter": 1800, "lr": 0.00645, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47062, "top5_acc": 0.72391, "loss_cls": 2.97156, "loss": 2.97156, "time": 0.81096} +{"mode": "train", "epoch": 126, "iter": 1900, "lr": 0.00644, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46797, "top5_acc": 0.72531, "loss_cls": 2.94386, "loss": 2.94386, "time": 0.81548} +{"mode": "train", "epoch": 126, "iter": 2000, "lr": 0.00642, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47891, "top5_acc": 0.72297, "loss_cls": 2.93161, "loss": 2.93161, "time": 0.81929} +{"mode": "train", "epoch": 126, "iter": 2100, "lr": 0.00641, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46812, "top5_acc": 0.71812, "loss_cls": 2.93978, "loss": 2.93978, "time": 0.82224} +{"mode": "train", "epoch": 126, "iter": 2200, "lr": 0.00639, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47297, "top5_acc": 0.72594, "loss_cls": 2.92309, "loss": 2.92309, "time": 0.82856} +{"mode": "train", "epoch": 126, "iter": 2300, "lr": 0.00638, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48328, "top5_acc": 0.73438, "loss_cls": 2.90621, "loss": 2.90621, "time": 0.82708} +{"mode": "train", "epoch": 126, "iter": 2400, "lr": 0.00637, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47031, "top5_acc": 0.72031, "loss_cls": 2.97915, "loss": 2.97915, "time": 0.82796} +{"mode": "train", "epoch": 126, "iter": 2500, "lr": 0.00635, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46875, "top5_acc": 0.73125, "loss_cls": 2.94517, "loss": 2.94517, "time": 0.81793} +{"mode": "train", "epoch": 126, "iter": 2600, "lr": 0.00634, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46656, "top5_acc": 0.72, "loss_cls": 2.9543, "loss": 2.9543, "time": 0.8172} +{"mode": "train", "epoch": 126, "iter": 2700, "lr": 0.00633, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46219, "top5_acc": 0.72203, "loss_cls": 2.96218, "loss": 2.96218, "time": 0.81351} +{"mode": "train", "epoch": 126, "iter": 2800, "lr": 0.00631, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47047, "top5_acc": 0.71828, "loss_cls": 2.9601, "loss": 2.9601, "time": 0.81623} +{"mode": "train", "epoch": 126, "iter": 2900, "lr": 0.0063, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45938, "top5_acc": 0.72031, "loss_cls": 2.9704, "loss": 2.9704, "time": 0.81658} +{"mode": "train", "epoch": 126, "iter": 3000, "lr": 0.00629, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47375, "top5_acc": 0.72281, "loss_cls": 2.94272, "loss": 2.94272, "time": 0.81091} +{"mode": "train", "epoch": 126, "iter": 3100, "lr": 0.00627, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47938, "top5_acc": 0.72734, "loss_cls": 2.9338, "loss": 2.9338, "time": 0.81503} +{"mode": "train", "epoch": 126, "iter": 3200, "lr": 0.00626, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47688, "top5_acc": 0.72922, "loss_cls": 2.92709, "loss": 2.92709, "time": 0.81927} +{"mode": "train", "epoch": 126, "iter": 3300, "lr": 0.00625, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47281, "top5_acc": 0.72406, "loss_cls": 2.92448, "loss": 2.92448, "time": 0.81376} +{"mode": "train", "epoch": 126, "iter": 3400, "lr": 0.00623, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47156, "top5_acc": 0.72188, "loss_cls": 2.94743, "loss": 2.94743, "time": 0.81522} +{"mode": "train", "epoch": 126, "iter": 3500, "lr": 0.00622, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47422, "top5_acc": 0.71734, "loss_cls": 2.94166, "loss": 2.94166, "time": 0.81259} +{"mode": "train", "epoch": 126, "iter": 3600, "lr": 0.0062, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49406, "top5_acc": 0.74469, "loss_cls": 2.8368, "loss": 2.8368, "time": 0.81868} +{"mode": "train", "epoch": 126, "iter": 3700, "lr": 0.00619, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46672, "top5_acc": 0.71906, "loss_cls": 2.98456, "loss": 2.98456, "time": 0.81178} +{"mode": "val", "epoch": 126, "iter": 309, "lr": 0.00618, "top1_acc": 0.39513, "top5_acc": 0.65522, "mean_class_accuracy": 0.39487} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00617, "memory": 15990, "data_time": 1.34641, "top1_acc": 0.50109, "top5_acc": 0.74438, "loss_cls": 2.81275, "loss": 2.81275, "time": 2.33707} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00616, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.50484, "top5_acc": 0.75703, "loss_cls": 2.78767, "loss": 2.78767, "time": 0.83137} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00614, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49547, "top5_acc": 0.74781, "loss_cls": 2.80901, "loss": 2.80901, "time": 0.82567} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00613, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48594, "top5_acc": 0.74141, "loss_cls": 2.82699, "loss": 2.82699, "time": 0.84036} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.00612, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49469, "top5_acc": 0.73734, "loss_cls": 2.84517, "loss": 2.84517, "time": 0.83} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.0061, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47656, "top5_acc": 0.73125, "loss_cls": 2.88725, "loss": 2.88725, "time": 0.82656} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00609, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49188, "top5_acc": 0.74109, "loss_cls": 2.83341, "loss": 2.83341, "time": 0.83674} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00608, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49469, "top5_acc": 0.74031, "loss_cls": 2.84035, "loss": 2.84035, "time": 0.83536} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00606, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48828, "top5_acc": 0.7375, "loss_cls": 2.8434, "loss": 2.8434, "time": 0.83679} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00605, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49531, "top5_acc": 0.74656, "loss_cls": 2.8147, "loss": 2.8147, "time": 0.83027} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00604, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47797, "top5_acc": 0.73078, "loss_cls": 2.92552, "loss": 2.92552, "time": 0.82781} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00602, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49016, "top5_acc": 0.74156, "loss_cls": 2.87367, "loss": 2.87367, "time": 0.82612} +{"mode": "train", "epoch": 127, "iter": 1300, "lr": 0.00601, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48344, "top5_acc": 0.73484, "loss_cls": 2.89946, "loss": 2.89946, "time": 0.82934} +{"mode": "train", "epoch": 127, "iter": 1400, "lr": 0.006, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49078, "top5_acc": 0.73766, "loss_cls": 2.88167, "loss": 2.88167, "time": 0.82808} +{"mode": "train", "epoch": 127, "iter": 1500, "lr": 0.00598, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47297, "top5_acc": 0.73703, "loss_cls": 2.8935, "loss": 2.8935, "time": 0.82634} +{"mode": "train", "epoch": 127, "iter": 1600, "lr": 0.00597, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49, "top5_acc": 0.73719, "loss_cls": 2.87016, "loss": 2.87016, "time": 0.81554} +{"mode": "train", "epoch": 127, "iter": 1700, "lr": 0.00596, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48078, "top5_acc": 0.73562, "loss_cls": 2.88448, "loss": 2.88448, "time": 0.81651} +{"mode": "train", "epoch": 127, "iter": 1800, "lr": 0.00594, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47172, "top5_acc": 0.7275, "loss_cls": 2.93559, "loss": 2.93559, "time": 0.81603} +{"mode": "train", "epoch": 127, "iter": 1900, "lr": 0.00593, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47422, "top5_acc": 0.73047, "loss_cls": 2.92112, "loss": 2.92112, "time": 0.81435} +{"mode": "train", "epoch": 127, "iter": 2000, "lr": 0.00592, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48484, "top5_acc": 0.74016, "loss_cls": 2.86441, "loss": 2.86441, "time": 0.81685} +{"mode": "train", "epoch": 127, "iter": 2100, "lr": 0.00591, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47594, "top5_acc": 0.72766, "loss_cls": 2.91291, "loss": 2.91291, "time": 0.82457} +{"mode": "train", "epoch": 127, "iter": 2200, "lr": 0.00589, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.47688, "top5_acc": 0.73, "loss_cls": 2.91093, "loss": 2.91093, "time": 0.82916} +{"mode": "train", "epoch": 127, "iter": 2300, "lr": 0.00588, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48141, "top5_acc": 0.73562, "loss_cls": 2.87838, "loss": 2.87838, "time": 0.82251} +{"mode": "train", "epoch": 127, "iter": 2400, "lr": 0.00587, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48812, "top5_acc": 0.72828, "loss_cls": 2.91293, "loss": 2.91293, "time": 0.82566} +{"mode": "train", "epoch": 127, "iter": 2500, "lr": 0.00585, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47328, "top5_acc": 0.72203, "loss_cls": 2.94747, "loss": 2.94747, "time": 0.81705} +{"mode": "train", "epoch": 127, "iter": 2600, "lr": 0.00584, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46578, "top5_acc": 0.72312, "loss_cls": 2.95363, "loss": 2.95363, "time": 0.81369} +{"mode": "train", "epoch": 127, "iter": 2700, "lr": 0.00583, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48062, "top5_acc": 0.73266, "loss_cls": 2.9251, "loss": 2.9251, "time": 0.81937} +{"mode": "train", "epoch": 127, "iter": 2800, "lr": 0.00581, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48406, "top5_acc": 0.73922, "loss_cls": 2.87142, "loss": 2.87142, "time": 0.81814} +{"mode": "train", "epoch": 127, "iter": 2900, "lr": 0.0058, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48766, "top5_acc": 0.73594, "loss_cls": 2.86339, "loss": 2.86339, "time": 0.81493} +{"mode": "train", "epoch": 127, "iter": 3000, "lr": 0.00579, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47109, "top5_acc": 0.72609, "loss_cls": 2.94803, "loss": 2.94803, "time": 0.81551} +{"mode": "train", "epoch": 127, "iter": 3100, "lr": 0.00577, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46703, "top5_acc": 0.72188, "loss_cls": 2.96796, "loss": 2.96796, "time": 0.81583} +{"mode": "train", "epoch": 127, "iter": 3200, "lr": 0.00576, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48484, "top5_acc": 0.73906, "loss_cls": 2.88343, "loss": 2.88343, "time": 0.81708} +{"mode": "train", "epoch": 127, "iter": 3300, "lr": 0.00575, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47438, "top5_acc": 0.72891, "loss_cls": 2.93294, "loss": 2.93294, "time": 0.81752} +{"mode": "train", "epoch": 127, "iter": 3400, "lr": 0.00573, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47, "top5_acc": 0.72922, "loss_cls": 2.93552, "loss": 2.93552, "time": 0.81505} +{"mode": "train", "epoch": 127, "iter": 3500, "lr": 0.00572, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47312, "top5_acc": 0.72656, "loss_cls": 2.9478, "loss": 2.9478, "time": 0.8207} +{"mode": "train", "epoch": 127, "iter": 3600, "lr": 0.00571, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47328, "top5_acc": 0.73094, "loss_cls": 2.93236, "loss": 2.93236, "time": 0.81949} +{"mode": "train", "epoch": 127, "iter": 3700, "lr": 0.0057, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47156, "top5_acc": 0.72906, "loss_cls": 2.9224, "loss": 2.9224, "time": 0.81466} +{"mode": "val", "epoch": 127, "iter": 309, "lr": 0.00569, "top1_acc": 0.4046, "top5_acc": 0.65902, "mean_class_accuracy": 0.40436} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00568, "memory": 15990, "data_time": 1.36826, "top1_acc": 0.51656, "top5_acc": 0.76453, "loss_cls": 2.71008, "loss": 2.71008, "time": 2.36093} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.00566, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49312, "top5_acc": 0.74469, "loss_cls": 2.80801, "loss": 2.80801, "time": 0.83674} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00565, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49281, "top5_acc": 0.74656, "loss_cls": 2.79645, "loss": 2.79645, "time": 0.83313} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00564, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.49594, "top5_acc": 0.75312, "loss_cls": 2.79942, "loss": 2.79942, "time": 0.83495} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00563, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.49391, "top5_acc": 0.74406, "loss_cls": 2.82772, "loss": 2.82772, "time": 0.82509} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00561, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49141, "top5_acc": 0.74906, "loss_cls": 2.80513, "loss": 2.80513, "time": 0.81986} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.0056, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48844, "top5_acc": 0.74328, "loss_cls": 2.8423, "loss": 2.8423, "time": 0.82184} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00559, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49922, "top5_acc": 0.75188, "loss_cls": 2.76536, "loss": 2.76536, "time": 0.81269} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00557, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48547, "top5_acc": 0.74078, "loss_cls": 2.85428, "loss": 2.85428, "time": 0.81464} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00556, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48984, "top5_acc": 0.73719, "loss_cls": 2.84493, "loss": 2.84493, "time": 0.81552} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00555, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48797, "top5_acc": 0.74, "loss_cls": 2.83838, "loss": 2.83838, "time": 0.81977} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00554, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49125, "top5_acc": 0.73594, "loss_cls": 2.84682, "loss": 2.84682, "time": 0.81516} +{"mode": "train", "epoch": 128, "iter": 1300, "lr": 0.00552, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48703, "top5_acc": 0.73531, "loss_cls": 2.87952, "loss": 2.87952, "time": 0.81394} +{"mode": "train", "epoch": 128, "iter": 1400, "lr": 0.00551, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48203, "top5_acc": 0.74172, "loss_cls": 2.86173, "loss": 2.86173, "time": 0.81033} +{"mode": "train", "epoch": 128, "iter": 1500, "lr": 0.0055, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48031, "top5_acc": 0.73438, "loss_cls": 2.88087, "loss": 2.88087, "time": 0.81522} +{"mode": "train", "epoch": 128, "iter": 1600, "lr": 0.00548, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47781, "top5_acc": 0.73422, "loss_cls": 2.91386, "loss": 2.91386, "time": 0.81254} +{"mode": "train", "epoch": 128, "iter": 1700, "lr": 0.00547, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48031, "top5_acc": 0.7325, "loss_cls": 2.86508, "loss": 2.86508, "time": 0.81237} +{"mode": "train", "epoch": 128, "iter": 1800, "lr": 0.00546, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49219, "top5_acc": 0.73953, "loss_cls": 2.84212, "loss": 2.84212, "time": 0.81653} +{"mode": "train", "epoch": 128, "iter": 1900, "lr": 0.00545, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48484, "top5_acc": 0.73172, "loss_cls": 2.89348, "loss": 2.89348, "time": 0.81566} +{"mode": "train", "epoch": 128, "iter": 2000, "lr": 0.00543, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49328, "top5_acc": 0.73781, "loss_cls": 2.84566, "loss": 2.84566, "time": 0.81376} +{"mode": "train", "epoch": 128, "iter": 2100, "lr": 0.00542, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48094, "top5_acc": 0.73953, "loss_cls": 2.85725, "loss": 2.85725, "time": 0.81377} +{"mode": "train", "epoch": 128, "iter": 2200, "lr": 0.00541, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48172, "top5_acc": 0.73344, "loss_cls": 2.88223, "loss": 2.88223, "time": 0.82955} +{"mode": "train", "epoch": 128, "iter": 2300, "lr": 0.0054, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48078, "top5_acc": 0.73344, "loss_cls": 2.86777, "loss": 2.86777, "time": 0.82256} +{"mode": "train", "epoch": 128, "iter": 2400, "lr": 0.00538, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48422, "top5_acc": 0.73328, "loss_cls": 2.88159, "loss": 2.88159, "time": 0.81867} +{"mode": "train", "epoch": 128, "iter": 2500, "lr": 0.00537, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48266, "top5_acc": 0.73594, "loss_cls": 2.87805, "loss": 2.87805, "time": 0.81649} +{"mode": "train", "epoch": 128, "iter": 2600, "lr": 0.00536, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49688, "top5_acc": 0.74422, "loss_cls": 2.81633, "loss": 2.81633, "time": 0.81548} +{"mode": "train", "epoch": 128, "iter": 2700, "lr": 0.00535, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4725, "top5_acc": 0.73438, "loss_cls": 2.90741, "loss": 2.90741, "time": 0.81607} +{"mode": "train", "epoch": 128, "iter": 2800, "lr": 0.00533, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48656, "top5_acc": 0.74641, "loss_cls": 2.82155, "loss": 2.82155, "time": 0.81248} +{"mode": "train", "epoch": 128, "iter": 2900, "lr": 0.00532, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48078, "top5_acc": 0.73719, "loss_cls": 2.90838, "loss": 2.90838, "time": 0.81563} +{"mode": "train", "epoch": 128, "iter": 3000, "lr": 0.00531, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49156, "top5_acc": 0.73672, "loss_cls": 2.87532, "loss": 2.87532, "time": 0.81516} +{"mode": "train", "epoch": 128, "iter": 3100, "lr": 0.0053, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.485, "top5_acc": 0.73188, "loss_cls": 2.90748, "loss": 2.90748, "time": 0.81744} +{"mode": "train", "epoch": 128, "iter": 3200, "lr": 0.00528, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48453, "top5_acc": 0.72375, "loss_cls": 2.90805, "loss": 2.90805, "time": 0.8199} +{"mode": "train", "epoch": 128, "iter": 3300, "lr": 0.00527, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48188, "top5_acc": 0.73719, "loss_cls": 2.88652, "loss": 2.88652, "time": 0.81526} +{"mode": "train", "epoch": 128, "iter": 3400, "lr": 0.00526, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47953, "top5_acc": 0.735, "loss_cls": 2.89001, "loss": 2.89001, "time": 0.8136} +{"mode": "train", "epoch": 128, "iter": 3500, "lr": 0.00525, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47188, "top5_acc": 0.73344, "loss_cls": 2.92154, "loss": 2.92154, "time": 0.81315} +{"mode": "train", "epoch": 128, "iter": 3600, "lr": 0.00523, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.47766, "top5_acc": 0.73109, "loss_cls": 2.91249, "loss": 2.91249, "time": 0.81629} +{"mode": "train", "epoch": 128, "iter": 3700, "lr": 0.00522, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49125, "top5_acc": 0.74312, "loss_cls": 2.82408, "loss": 2.82408, "time": 0.81238} +{"mode": "val", "epoch": 128, "iter": 309, "lr": 0.00521, "top1_acc": 0.40617, "top5_acc": 0.66039, "mean_class_accuracy": 0.40586} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.0052, "memory": 15990, "data_time": 1.32156, "top1_acc": 0.50484, "top5_acc": 0.76266, "loss_cls": 2.72944, "loss": 2.72944, "time": 2.29676} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00519, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49844, "top5_acc": 0.76172, "loss_cls": 2.7618, "loss": 2.7618, "time": 0.8223} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00518, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49953, "top5_acc": 0.74672, "loss_cls": 2.79144, "loss": 2.79144, "time": 0.82412} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00516, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.50109, "top5_acc": 0.76109, "loss_cls": 2.75173, "loss": 2.75173, "time": 0.81432} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00515, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49641, "top5_acc": 0.75672, "loss_cls": 2.7474, "loss": 2.7474, "time": 0.82224} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00514, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.50891, "top5_acc": 0.74891, "loss_cls": 2.76529, "loss": 2.76529, "time": 0.82232} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00513, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49531, "top5_acc": 0.74625, "loss_cls": 2.82393, "loss": 2.82393, "time": 0.81692} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00512, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49938, "top5_acc": 0.75141, "loss_cls": 2.77631, "loss": 2.77631, "time": 0.81565} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.0051, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49641, "top5_acc": 0.74656, "loss_cls": 2.78633, "loss": 2.78633, "time": 0.81509} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00509, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48828, "top5_acc": 0.73422, "loss_cls": 2.85339, "loss": 2.85339, "time": 0.81295} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00508, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49859, "top5_acc": 0.74062, "loss_cls": 2.82512, "loss": 2.82512, "time": 0.81294} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.00507, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49031, "top5_acc": 0.74578, "loss_cls": 2.82126, "loss": 2.82126, "time": 0.81967} +{"mode": "train", "epoch": 129, "iter": 1300, "lr": 0.00505, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50531, "top5_acc": 0.74844, "loss_cls": 2.77817, "loss": 2.77817, "time": 0.81722} +{"mode": "train", "epoch": 129, "iter": 1400, "lr": 0.00504, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50953, "top5_acc": 0.75562, "loss_cls": 2.74307, "loss": 2.74307, "time": 0.81992} +{"mode": "train", "epoch": 129, "iter": 1500, "lr": 0.00503, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49172, "top5_acc": 0.74047, "loss_cls": 2.83064, "loss": 2.83064, "time": 0.811} +{"mode": "train", "epoch": 129, "iter": 1600, "lr": 0.00502, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48453, "top5_acc": 0.73266, "loss_cls": 2.85613, "loss": 2.85613, "time": 0.81447} +{"mode": "train", "epoch": 129, "iter": 1700, "lr": 0.00501, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49453, "top5_acc": 0.74266, "loss_cls": 2.8376, "loss": 2.8376, "time": 0.81346} +{"mode": "train", "epoch": 129, "iter": 1800, "lr": 0.00499, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49828, "top5_acc": 0.73969, "loss_cls": 2.80875, "loss": 2.80875, "time": 0.81688} +{"mode": "train", "epoch": 129, "iter": 1900, "lr": 0.00498, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49578, "top5_acc": 0.74984, "loss_cls": 2.80261, "loss": 2.80261, "time": 0.81269} +{"mode": "train", "epoch": 129, "iter": 2000, "lr": 0.00497, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49047, "top5_acc": 0.73797, "loss_cls": 2.84169, "loss": 2.84169, "time": 0.81536} +{"mode": "train", "epoch": 129, "iter": 2100, "lr": 0.00496, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49391, "top5_acc": 0.74297, "loss_cls": 2.86203, "loss": 2.86203, "time": 0.82145} +{"mode": "train", "epoch": 129, "iter": 2200, "lr": 0.00494, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48625, "top5_acc": 0.74109, "loss_cls": 2.84856, "loss": 2.84856, "time": 0.82735} +{"mode": "train", "epoch": 129, "iter": 2300, "lr": 0.00493, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47641, "top5_acc": 0.73188, "loss_cls": 2.908, "loss": 2.908, "time": 0.82232} +{"mode": "train", "epoch": 129, "iter": 2400, "lr": 0.00492, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50266, "top5_acc": 0.75344, "loss_cls": 2.79082, "loss": 2.79082, "time": 0.81663} +{"mode": "train", "epoch": 129, "iter": 2500, "lr": 0.00491, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48594, "top5_acc": 0.74141, "loss_cls": 2.82953, "loss": 2.82953, "time": 0.81617} +{"mode": "train", "epoch": 129, "iter": 2600, "lr": 0.0049, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49531, "top5_acc": 0.74906, "loss_cls": 2.78366, "loss": 2.78366, "time": 0.81711} +{"mode": "train", "epoch": 129, "iter": 2700, "lr": 0.00488, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50094, "top5_acc": 0.74531, "loss_cls": 2.80071, "loss": 2.80071, "time": 0.8139} +{"mode": "train", "epoch": 129, "iter": 2800, "lr": 0.00487, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48922, "top5_acc": 0.73984, "loss_cls": 2.85542, "loss": 2.85542, "time": 0.82201} +{"mode": "train", "epoch": 129, "iter": 2900, "lr": 0.00486, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48625, "top5_acc": 0.73859, "loss_cls": 2.86141, "loss": 2.86141, "time": 0.81424} +{"mode": "train", "epoch": 129, "iter": 3000, "lr": 0.00485, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49156, "top5_acc": 0.74984, "loss_cls": 2.81474, "loss": 2.81474, "time": 0.82115} +{"mode": "train", "epoch": 129, "iter": 3100, "lr": 0.00484, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50703, "top5_acc": 0.74656, "loss_cls": 2.79273, "loss": 2.79273, "time": 0.81279} +{"mode": "train", "epoch": 129, "iter": 3200, "lr": 0.00482, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48547, "top5_acc": 0.73719, "loss_cls": 2.85157, "loss": 2.85157, "time": 0.81986} +{"mode": "train", "epoch": 129, "iter": 3300, "lr": 0.00481, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47266, "top5_acc": 0.72797, "loss_cls": 2.9026, "loss": 2.9026, "time": 0.81349} +{"mode": "train", "epoch": 129, "iter": 3400, "lr": 0.0048, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48719, "top5_acc": 0.74875, "loss_cls": 2.82494, "loss": 2.82494, "time": 0.81752} +{"mode": "train", "epoch": 129, "iter": 3500, "lr": 0.00479, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47344, "top5_acc": 0.73562, "loss_cls": 2.88608, "loss": 2.88608, "time": 0.81654} +{"mode": "train", "epoch": 129, "iter": 3600, "lr": 0.00478, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48266, "top5_acc": 0.73953, "loss_cls": 2.88491, "loss": 2.88491, "time": 0.82197} +{"mode": "train", "epoch": 129, "iter": 3700, "lr": 0.00476, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49531, "top5_acc": 0.74266, "loss_cls": 2.84225, "loss": 2.84225, "time": 0.81398} +{"mode": "val", "epoch": 129, "iter": 309, "lr": 0.00476, "top1_acc": 0.41012, "top5_acc": 0.66454, "mean_class_accuracy": 0.40985} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00475, "memory": 15990, "data_time": 1.31255, "top1_acc": 0.51969, "top5_acc": 0.7625, "loss_cls": 2.69963, "loss": 2.69963, "time": 2.29344} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00473, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50281, "top5_acc": 0.75359, "loss_cls": 2.74035, "loss": 2.74035, "time": 0.81536} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00472, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49906, "top5_acc": 0.75844, "loss_cls": 2.73049, "loss": 2.73049, "time": 0.8197} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00471, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51125, "top5_acc": 0.75328, "loss_cls": 2.72546, "loss": 2.72546, "time": 0.81778} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.0047, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51234, "top5_acc": 0.75859, "loss_cls": 2.72916, "loss": 2.72916, "time": 0.82331} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00469, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49688, "top5_acc": 0.75062, "loss_cls": 2.78641, "loss": 2.78641, "time": 0.81471} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00468, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49547, "top5_acc": 0.74812, "loss_cls": 2.79584, "loss": 2.79584, "time": 0.81253} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00466, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49875, "top5_acc": 0.75359, "loss_cls": 2.75187, "loss": 2.75187, "time": 0.81419} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00465, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49906, "top5_acc": 0.74375, "loss_cls": 2.8105, "loss": 2.8105, "time": 0.8174} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.00464, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5025, "top5_acc": 0.75141, "loss_cls": 2.77805, "loss": 2.77805, "time": 0.81559} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.00463, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49984, "top5_acc": 0.74859, "loss_cls": 2.76932, "loss": 2.76932, "time": 0.81537} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00462, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50859, "top5_acc": 0.75078, "loss_cls": 2.76123, "loss": 2.76123, "time": 0.81109} +{"mode": "train", "epoch": 130, "iter": 1300, "lr": 0.00461, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48453, "top5_acc": 0.74312, "loss_cls": 2.82007, "loss": 2.82007, "time": 0.81246} +{"mode": "train", "epoch": 130, "iter": 1400, "lr": 0.00459, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50344, "top5_acc": 0.75094, "loss_cls": 2.77024, "loss": 2.77024, "time": 0.817} +{"mode": "train", "epoch": 130, "iter": 1500, "lr": 0.00458, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49859, "top5_acc": 0.75688, "loss_cls": 2.77494, "loss": 2.77494, "time": 0.81696} +{"mode": "train", "epoch": 130, "iter": 1600, "lr": 0.00457, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49047, "top5_acc": 0.74453, "loss_cls": 2.81558, "loss": 2.81558, "time": 0.81469} +{"mode": "train", "epoch": 130, "iter": 1700, "lr": 0.00456, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49719, "top5_acc": 0.74047, "loss_cls": 2.83099, "loss": 2.83099, "time": 0.81342} +{"mode": "train", "epoch": 130, "iter": 1800, "lr": 0.00455, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49312, "top5_acc": 0.74781, "loss_cls": 2.8114, "loss": 2.8114, "time": 0.8181} +{"mode": "train", "epoch": 130, "iter": 1900, "lr": 0.00454, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49766, "top5_acc": 0.74766, "loss_cls": 2.77432, "loss": 2.77432, "time": 0.81276} +{"mode": "train", "epoch": 130, "iter": 2000, "lr": 0.00452, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50078, "top5_acc": 0.75625, "loss_cls": 2.76684, "loss": 2.76684, "time": 0.81558} +{"mode": "train", "epoch": 130, "iter": 2100, "lr": 0.00451, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49922, "top5_acc": 0.74719, "loss_cls": 2.79847, "loss": 2.79847, "time": 0.81395} +{"mode": "train", "epoch": 130, "iter": 2200, "lr": 0.0045, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50078, "top5_acc": 0.75266, "loss_cls": 2.7837, "loss": 2.7837, "time": 0.81559} +{"mode": "train", "epoch": 130, "iter": 2300, "lr": 0.00449, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49438, "top5_acc": 0.74828, "loss_cls": 2.79988, "loss": 2.79988, "time": 0.83165} +{"mode": "train", "epoch": 130, "iter": 2400, "lr": 0.00448, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50187, "top5_acc": 0.74859, "loss_cls": 2.78874, "loss": 2.78874, "time": 0.82062} +{"mode": "train", "epoch": 130, "iter": 2500, "lr": 0.00447, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50562, "top5_acc": 0.75703, "loss_cls": 2.75586, "loss": 2.75586, "time": 0.81778} +{"mode": "train", "epoch": 130, "iter": 2600, "lr": 0.00445, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49641, "top5_acc": 0.75547, "loss_cls": 2.78449, "loss": 2.78449, "time": 0.81475} +{"mode": "train", "epoch": 130, "iter": 2700, "lr": 0.00444, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49328, "top5_acc": 0.74953, "loss_cls": 2.80453, "loss": 2.80453, "time": 0.81653} +{"mode": "train", "epoch": 130, "iter": 2800, "lr": 0.00443, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4975, "top5_acc": 0.74719, "loss_cls": 2.79302, "loss": 2.79302, "time": 0.81738} +{"mode": "train", "epoch": 130, "iter": 2900, "lr": 0.00442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49172, "top5_acc": 0.74266, "loss_cls": 2.82746, "loss": 2.82746, "time": 0.81474} +{"mode": "train", "epoch": 130, "iter": 3000, "lr": 0.00441, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49203, "top5_acc": 0.74141, "loss_cls": 2.82232, "loss": 2.82232, "time": 0.81714} +{"mode": "train", "epoch": 130, "iter": 3100, "lr": 0.0044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49953, "top5_acc": 0.75016, "loss_cls": 2.79045, "loss": 2.79045, "time": 0.81471} +{"mode": "train", "epoch": 130, "iter": 3200, "lr": 0.00439, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49531, "top5_acc": 0.74797, "loss_cls": 2.80718, "loss": 2.80718, "time": 0.81363} +{"mode": "train", "epoch": 130, "iter": 3300, "lr": 0.00437, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48922, "top5_acc": 0.74391, "loss_cls": 2.84149, "loss": 2.84149, "time": 0.81023} +{"mode": "train", "epoch": 130, "iter": 3400, "lr": 0.00436, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50859, "top5_acc": 0.76094, "loss_cls": 2.73685, "loss": 2.73685, "time": 0.81603} +{"mode": "train", "epoch": 130, "iter": 3500, "lr": 0.00435, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50516, "top5_acc": 0.74984, "loss_cls": 2.75922, "loss": 2.75922, "time": 0.81348} +{"mode": "train", "epoch": 130, "iter": 3600, "lr": 0.00434, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48797, "top5_acc": 0.74047, "loss_cls": 2.84688, "loss": 2.84688, "time": 0.81962} +{"mode": "train", "epoch": 130, "iter": 3700, "lr": 0.00433, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5, "top5_acc": 0.75484, "loss_cls": 2.76546, "loss": 2.76546, "time": 0.80896} +{"mode": "val", "epoch": 130, "iter": 309, "lr": 0.00432, "top1_acc": 0.40698, "top5_acc": 0.66337, "mean_class_accuracy": 0.40676} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00431, "memory": 15990, "data_time": 1.29556, "top1_acc": 0.51688, "top5_acc": 0.76094, "loss_cls": 2.70096, "loss": 2.70096, "time": 2.27312} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.0043, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52578, "top5_acc": 0.76453, "loss_cls": 2.66624, "loss": 2.66624, "time": 0.81222} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00429, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52109, "top5_acc": 0.76953, "loss_cls": 2.64854, "loss": 2.64854, "time": 0.81647} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00428, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51531, "top5_acc": 0.765, "loss_cls": 2.69725, "loss": 2.69725, "time": 0.82456} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00427, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50187, "top5_acc": 0.75484, "loss_cls": 2.75813, "loss": 2.75813, "time": 0.81393} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00425, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51141, "top5_acc": 0.75156, "loss_cls": 2.75484, "loss": 2.75484, "time": 0.81766} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00424, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50359, "top5_acc": 0.76484, "loss_cls": 2.73245, "loss": 2.73245, "time": 0.81639} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00423, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51922, "top5_acc": 0.77328, "loss_cls": 2.66317, "loss": 2.66317, "time": 0.81443} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00422, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.51062, "top5_acc": 0.75281, "loss_cls": 2.73247, "loss": 2.73247, "time": 0.81472} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.00421, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.50922, "top5_acc": 0.75672, "loss_cls": 2.73403, "loss": 2.73403, "time": 0.81251} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.0042, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.515, "top5_acc": 0.76859, "loss_cls": 2.68039, "loss": 2.68039, "time": 0.81538} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00419, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51047, "top5_acc": 0.765, "loss_cls": 2.70671, "loss": 2.70671, "time": 0.8147} +{"mode": "train", "epoch": 131, "iter": 1300, "lr": 0.00418, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51594, "top5_acc": 0.76562, "loss_cls": 2.70614, "loss": 2.70614, "time": 0.81446} +{"mode": "train", "epoch": 131, "iter": 1400, "lr": 0.00417, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.50703, "top5_acc": 0.74906, "loss_cls": 2.77626, "loss": 2.77626, "time": 0.8116} +{"mode": "train", "epoch": 131, "iter": 1500, "lr": 0.00415, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50469, "top5_acc": 0.75891, "loss_cls": 2.72816, "loss": 2.72816, "time": 0.81857} +{"mode": "train", "epoch": 131, "iter": 1600, "lr": 0.00414, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49203, "top5_acc": 0.74594, "loss_cls": 2.80062, "loss": 2.80062, "time": 0.81403} +{"mode": "train", "epoch": 131, "iter": 1700, "lr": 0.00413, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51297, "top5_acc": 0.75844, "loss_cls": 2.6844, "loss": 2.6844, "time": 0.81621} +{"mode": "train", "epoch": 131, "iter": 1800, "lr": 0.00412, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51406, "top5_acc": 0.75375, "loss_cls": 2.74396, "loss": 2.74396, "time": 0.81719} +{"mode": "train", "epoch": 131, "iter": 1900, "lr": 0.00411, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.50813, "top5_acc": 0.76266, "loss_cls": 2.72157, "loss": 2.72157, "time": 0.81128} +{"mode": "train", "epoch": 131, "iter": 2000, "lr": 0.0041, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49906, "top5_acc": 0.74406, "loss_cls": 2.80183, "loss": 2.80183, "time": 0.81053} +{"mode": "train", "epoch": 131, "iter": 2100, "lr": 0.00409, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.49656, "top5_acc": 0.74672, "loss_cls": 2.79352, "loss": 2.79352, "time": 0.81337} +{"mode": "train", "epoch": 131, "iter": 2200, "lr": 0.00408, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.50453, "top5_acc": 0.76094, "loss_cls": 2.77018, "loss": 2.77018, "time": 0.82} +{"mode": "train", "epoch": 131, "iter": 2300, "lr": 0.00407, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50187, "top5_acc": 0.75328, "loss_cls": 2.78599, "loss": 2.78599, "time": 0.82322} +{"mode": "train", "epoch": 131, "iter": 2400, "lr": 0.00405, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51297, "top5_acc": 0.76125, "loss_cls": 2.72623, "loss": 2.72623, "time": 0.82256} +{"mode": "train", "epoch": 131, "iter": 2500, "lr": 0.00404, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51172, "top5_acc": 0.75438, "loss_cls": 2.75111, "loss": 2.75111, "time": 0.82088} +{"mode": "train", "epoch": 131, "iter": 2600, "lr": 0.00403, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50141, "top5_acc": 0.74891, "loss_cls": 2.79088, "loss": 2.79088, "time": 0.81739} +{"mode": "train", "epoch": 131, "iter": 2700, "lr": 0.00402, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50422, "top5_acc": 0.75016, "loss_cls": 2.76748, "loss": 2.76748, "time": 0.81538} +{"mode": "train", "epoch": 131, "iter": 2800, "lr": 0.00401, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49547, "top5_acc": 0.74219, "loss_cls": 2.80278, "loss": 2.80278, "time": 0.81116} +{"mode": "train", "epoch": 131, "iter": 2900, "lr": 0.004, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.505, "top5_acc": 0.76219, "loss_cls": 2.73303, "loss": 2.73303, "time": 0.81488} +{"mode": "train", "epoch": 131, "iter": 3000, "lr": 0.00399, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50688, "top5_acc": 0.76047, "loss_cls": 2.72886, "loss": 2.72886, "time": 0.81702} +{"mode": "train", "epoch": 131, "iter": 3100, "lr": 0.00398, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49766, "top5_acc": 0.74469, "loss_cls": 2.79755, "loss": 2.79755, "time": 0.81945} +{"mode": "train", "epoch": 131, "iter": 3200, "lr": 0.00397, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49672, "top5_acc": 0.74219, "loss_cls": 2.83028, "loss": 2.83028, "time": 0.81132} +{"mode": "train", "epoch": 131, "iter": 3300, "lr": 0.00396, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51109, "top5_acc": 0.76125, "loss_cls": 2.70719, "loss": 2.70719, "time": 0.8134} +{"mode": "train", "epoch": 131, "iter": 3400, "lr": 0.00394, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49734, "top5_acc": 0.74656, "loss_cls": 2.79033, "loss": 2.79033, "time": 0.81561} +{"mode": "train", "epoch": 131, "iter": 3500, "lr": 0.00393, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51125, "top5_acc": 0.75938, "loss_cls": 2.72464, "loss": 2.72464, "time": 0.81016} +{"mode": "train", "epoch": 131, "iter": 3600, "lr": 0.00392, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50297, "top5_acc": 0.74969, "loss_cls": 2.73273, "loss": 2.73273, "time": 0.81915} +{"mode": "train", "epoch": 131, "iter": 3700, "lr": 0.00391, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50641, "top5_acc": 0.76031, "loss_cls": 2.73343, "loss": 2.73343, "time": 0.81658} +{"mode": "val", "epoch": 131, "iter": 309, "lr": 0.00391, "top1_acc": 0.41914, "top5_acc": 0.67158, "mean_class_accuracy": 0.41894} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.0039, "memory": 15990, "data_time": 1.32239, "top1_acc": 0.52344, "top5_acc": 0.76766, "loss_cls": 2.64549, "loss": 2.64549, "time": 2.30054} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00389, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52234, "top5_acc": 0.77188, "loss_cls": 2.67158, "loss": 2.67158, "time": 0.8192} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00387, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52562, "top5_acc": 0.77047, "loss_cls": 2.62233, "loss": 2.62233, "time": 0.81992} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00386, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51391, "top5_acc": 0.76578, "loss_cls": 2.68588, "loss": 2.68588, "time": 0.8189} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00385, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.525, "top5_acc": 0.77016, "loss_cls": 2.64204, "loss": 2.64204, "time": 0.81881} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00384, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52109, "top5_acc": 0.76734, "loss_cls": 2.65931, "loss": 2.65931, "time": 0.81544} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00383, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51984, "top5_acc": 0.76516, "loss_cls": 2.68845, "loss": 2.68845, "time": 0.81229} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00382, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52312, "top5_acc": 0.76625, "loss_cls": 2.66781, "loss": 2.66781, "time": 0.81658} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00381, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51328, "top5_acc": 0.75891, "loss_cls": 2.73317, "loss": 2.73317, "time": 0.82151} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0038, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51734, "top5_acc": 0.77375, "loss_cls": 2.67682, "loss": 2.67682, "time": 0.82043} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00379, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51219, "top5_acc": 0.75422, "loss_cls": 2.72002, "loss": 2.72002, "time": 0.81641} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00378, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52203, "top5_acc": 0.76047, "loss_cls": 2.71303, "loss": 2.71303, "time": 0.81377} +{"mode": "train", "epoch": 132, "iter": 1300, "lr": 0.00377, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50328, "top5_acc": 0.7575, "loss_cls": 2.74179, "loss": 2.74179, "time": 0.81592} +{"mode": "train", "epoch": 132, "iter": 1400, "lr": 0.00376, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52281, "top5_acc": 0.77, "loss_cls": 2.65215, "loss": 2.65215, "time": 0.81594} +{"mode": "train", "epoch": 132, "iter": 1500, "lr": 0.00375, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51, "top5_acc": 0.77188, "loss_cls": 2.68605, "loss": 2.68605, "time": 0.818} +{"mode": "train", "epoch": 132, "iter": 1600, "lr": 0.00374, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50813, "top5_acc": 0.75812, "loss_cls": 2.72476, "loss": 2.72476, "time": 0.81341} +{"mode": "train", "epoch": 132, "iter": 1700, "lr": 0.00372, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51031, "top5_acc": 0.76609, "loss_cls": 2.71018, "loss": 2.71018, "time": 0.81293} +{"mode": "train", "epoch": 132, "iter": 1800, "lr": 0.00371, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51844, "top5_acc": 0.76438, "loss_cls": 2.69305, "loss": 2.69305, "time": 0.81577} +{"mode": "train", "epoch": 132, "iter": 1900, "lr": 0.0037, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51328, "top5_acc": 0.76156, "loss_cls": 2.70817, "loss": 2.70817, "time": 0.8138} +{"mode": "train", "epoch": 132, "iter": 2000, "lr": 0.00369, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52078, "top5_acc": 0.76016, "loss_cls": 2.69599, "loss": 2.69599, "time": 0.81146} +{"mode": "train", "epoch": 132, "iter": 2100, "lr": 0.00368, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51891, "top5_acc": 0.76109, "loss_cls": 2.7013, "loss": 2.7013, "time": 0.81986} +{"mode": "train", "epoch": 132, "iter": 2200, "lr": 0.00367, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.53359, "top5_acc": 0.76672, "loss_cls": 2.63493, "loss": 2.63493, "time": 0.82743} +{"mode": "train", "epoch": 132, "iter": 2300, "lr": 0.00366, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49406, "top5_acc": 0.75141, "loss_cls": 2.77413, "loss": 2.77413, "time": 0.82198} +{"mode": "train", "epoch": 132, "iter": 2400, "lr": 0.00365, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50109, "top5_acc": 0.75359, "loss_cls": 2.74438, "loss": 2.74438, "time": 0.8323} +{"mode": "train", "epoch": 132, "iter": 2500, "lr": 0.00364, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52203, "top5_acc": 0.76453, "loss_cls": 2.68549, "loss": 2.68549, "time": 0.82039} +{"mode": "train", "epoch": 132, "iter": 2600, "lr": 0.00363, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.5175, "top5_acc": 0.76188, "loss_cls": 2.69565, "loss": 2.69565, "time": 0.82189} +{"mode": "train", "epoch": 132, "iter": 2700, "lr": 0.00362, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50688, "top5_acc": 0.75922, "loss_cls": 2.72902, "loss": 2.72902, "time": 0.82107} +{"mode": "train", "epoch": 132, "iter": 2800, "lr": 0.00361, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50047, "top5_acc": 0.75094, "loss_cls": 2.74194, "loss": 2.74194, "time": 0.81538} +{"mode": "train", "epoch": 132, "iter": 2900, "lr": 0.0036, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52141, "top5_acc": 0.76125, "loss_cls": 2.70476, "loss": 2.70476, "time": 0.81231} +{"mode": "train", "epoch": 132, "iter": 3000, "lr": 0.00359, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51938, "top5_acc": 0.75516, "loss_cls": 2.74421, "loss": 2.74421, "time": 0.81881} +{"mode": "train", "epoch": 132, "iter": 3100, "lr": 0.00358, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51641, "top5_acc": 0.76422, "loss_cls": 2.70588, "loss": 2.70588, "time": 0.81693} +{"mode": "train", "epoch": 132, "iter": 3200, "lr": 0.00357, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51094, "top5_acc": 0.75656, "loss_cls": 2.71677, "loss": 2.71677, "time": 0.81232} +{"mode": "train", "epoch": 132, "iter": 3300, "lr": 0.00356, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50391, "top5_acc": 0.74953, "loss_cls": 2.763, "loss": 2.763, "time": 0.81333} +{"mode": "train", "epoch": 132, "iter": 3400, "lr": 0.00355, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51031, "top5_acc": 0.76547, "loss_cls": 2.69841, "loss": 2.69841, "time": 0.80925} +{"mode": "train", "epoch": 132, "iter": 3500, "lr": 0.00354, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51625, "top5_acc": 0.75531, "loss_cls": 2.713, "loss": 2.713, "time": 0.81596} +{"mode": "train", "epoch": 132, "iter": 3600, "lr": 0.00353, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50656, "top5_acc": 0.76078, "loss_cls": 2.71196, "loss": 2.71196, "time": 0.8166} +{"mode": "train", "epoch": 132, "iter": 3700, "lr": 0.00352, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50953, "top5_acc": 0.75422, "loss_cls": 2.73734, "loss": 2.73734, "time": 0.81548} +{"mode": "val", "epoch": 132, "iter": 309, "lr": 0.00351, "top1_acc": 0.4162, "top5_acc": 0.67117, "mean_class_accuracy": 0.41602} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.0035, "memory": 15990, "data_time": 1.28997, "top1_acc": 0.54344, "top5_acc": 0.77906, "loss_cls": 2.58046, "loss": 2.58046, "time": 2.26301} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00349, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54312, "top5_acc": 0.78641, "loss_cls": 2.56102, "loss": 2.56102, "time": 0.81322} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00348, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.5325, "top5_acc": 0.78016, "loss_cls": 2.5978, "loss": 2.5978, "time": 0.81227} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00347, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.52844, "top5_acc": 0.77375, "loss_cls": 2.62381, "loss": 2.62381, "time": 0.82779} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00346, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53391, "top5_acc": 0.77422, "loss_cls": 2.58873, "loss": 2.58873, "time": 0.81455} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00345, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.52297, "top5_acc": 0.77281, "loss_cls": 2.63426, "loss": 2.63426, "time": 0.81692} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00344, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52547, "top5_acc": 0.76312, "loss_cls": 2.65174, "loss": 2.65174, "time": 0.81649} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00343, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52219, "top5_acc": 0.76484, "loss_cls": 2.65294, "loss": 2.65294, "time": 0.81125} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00342, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51609, "top5_acc": 0.76641, "loss_cls": 2.67041, "loss": 2.67041, "time": 0.81377} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.00341, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52188, "top5_acc": 0.77328, "loss_cls": 2.63841, "loss": 2.63841, "time": 0.81682} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0034, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52766, "top5_acc": 0.76875, "loss_cls": 2.61814, "loss": 2.61814, "time": 0.8181} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00339, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52562, "top5_acc": 0.77422, "loss_cls": 2.64304, "loss": 2.64304, "time": 0.81291} +{"mode": "train", "epoch": 133, "iter": 1300, "lr": 0.00338, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51859, "top5_acc": 0.76062, "loss_cls": 2.67536, "loss": 2.67536, "time": 0.81817} +{"mode": "train", "epoch": 133, "iter": 1400, "lr": 0.00337, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52078, "top5_acc": 0.7725, "loss_cls": 2.65002, "loss": 2.65002, "time": 0.81197} +{"mode": "train", "epoch": 133, "iter": 1500, "lr": 0.00336, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.52359, "top5_acc": 0.76781, "loss_cls": 2.6456, "loss": 2.6456, "time": 0.8111} +{"mode": "train", "epoch": 133, "iter": 1600, "lr": 0.00335, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51453, "top5_acc": 0.76172, "loss_cls": 2.70064, "loss": 2.70064, "time": 0.81563} +{"mode": "train", "epoch": 133, "iter": 1700, "lr": 0.00334, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51172, "top5_acc": 0.76625, "loss_cls": 2.69116, "loss": 2.69116, "time": 0.81218} +{"mode": "train", "epoch": 133, "iter": 1800, "lr": 0.00333, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50922, "top5_acc": 0.76406, "loss_cls": 2.72747, "loss": 2.72747, "time": 0.81297} +{"mode": "train", "epoch": 133, "iter": 1900, "lr": 0.00332, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51859, "top5_acc": 0.76531, "loss_cls": 2.67029, "loss": 2.67029, "time": 0.81472} +{"mode": "train", "epoch": 133, "iter": 2000, "lr": 0.00331, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52391, "top5_acc": 0.77375, "loss_cls": 2.62521, "loss": 2.62521, "time": 0.8093} +{"mode": "train", "epoch": 133, "iter": 2100, "lr": 0.0033, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52141, "top5_acc": 0.77953, "loss_cls": 2.62608, "loss": 2.62608, "time": 0.81436} +{"mode": "train", "epoch": 133, "iter": 2200, "lr": 0.00329, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.51219, "top5_acc": 0.76125, "loss_cls": 2.69768, "loss": 2.69768, "time": 0.82008} +{"mode": "train", "epoch": 133, "iter": 2300, "lr": 0.00328, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53219, "top5_acc": 0.76469, "loss_cls": 2.63691, "loss": 2.63691, "time": 0.81727} +{"mode": "train", "epoch": 133, "iter": 2400, "lr": 0.00327, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.52391, "top5_acc": 0.77281, "loss_cls": 2.65177, "loss": 2.65177, "time": 0.82711} +{"mode": "train", "epoch": 133, "iter": 2500, "lr": 0.00326, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52234, "top5_acc": 0.77141, "loss_cls": 2.66486, "loss": 2.66486, "time": 0.81623} +{"mode": "train", "epoch": 133, "iter": 2600, "lr": 0.00325, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52094, "top5_acc": 0.76672, "loss_cls": 2.66367, "loss": 2.66367, "time": 0.82179} +{"mode": "train", "epoch": 133, "iter": 2700, "lr": 0.00324, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50562, "top5_acc": 0.75547, "loss_cls": 2.73819, "loss": 2.73819, "time": 0.81335} +{"mode": "train", "epoch": 133, "iter": 2800, "lr": 0.00323, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50453, "top5_acc": 0.76125, "loss_cls": 2.72237, "loss": 2.72237, "time": 0.82035} +{"mode": "train", "epoch": 133, "iter": 2900, "lr": 0.00322, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51531, "top5_acc": 0.7625, "loss_cls": 2.67712, "loss": 2.67712, "time": 0.81274} +{"mode": "train", "epoch": 133, "iter": 3000, "lr": 0.00321, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51484, "top5_acc": 0.77281, "loss_cls": 2.66835, "loss": 2.66835, "time": 0.81479} +{"mode": "train", "epoch": 133, "iter": 3100, "lr": 0.0032, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51469, "top5_acc": 0.77047, "loss_cls": 2.66517, "loss": 2.66517, "time": 0.81176} +{"mode": "train", "epoch": 133, "iter": 3200, "lr": 0.00319, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.515, "top5_acc": 0.76016, "loss_cls": 2.70614, "loss": 2.70614, "time": 0.81807} +{"mode": "train", "epoch": 133, "iter": 3300, "lr": 0.00318, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51297, "top5_acc": 0.76062, "loss_cls": 2.70252, "loss": 2.70252, "time": 0.81498} +{"mode": "train", "epoch": 133, "iter": 3400, "lr": 0.00317, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.5075, "top5_acc": 0.75859, "loss_cls": 2.71877, "loss": 2.71877, "time": 0.80961} +{"mode": "train", "epoch": 133, "iter": 3500, "lr": 0.00316, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51875, "top5_acc": 0.76875, "loss_cls": 2.67642, "loss": 2.67642, "time": 0.8167} +{"mode": "train", "epoch": 133, "iter": 3600, "lr": 0.00315, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.52609, "top5_acc": 0.76938, "loss_cls": 2.66764, "loss": 2.66764, "time": 0.81925} +{"mode": "train", "epoch": 133, "iter": 3700, "lr": 0.00314, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52797, "top5_acc": 0.76125, "loss_cls": 2.66649, "loss": 2.66649, "time": 0.81261} +{"mode": "val", "epoch": 133, "iter": 309, "lr": 0.00314, "top1_acc": 0.42324, "top5_acc": 0.67492, "mean_class_accuracy": 0.42299} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00313, "memory": 15990, "data_time": 1.33523, "top1_acc": 0.54109, "top5_acc": 0.78938, "loss_cls": 2.52739, "loss": 2.52739, "time": 2.31462} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00312, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54891, "top5_acc": 0.78203, "loss_cls": 2.52901, "loss": 2.52901, "time": 0.8149} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00311, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53531, "top5_acc": 0.78188, "loss_cls": 2.57862, "loss": 2.57862, "time": 0.81575} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.0031, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52672, "top5_acc": 0.77734, "loss_cls": 2.60596, "loss": 2.60596, "time": 0.81964} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00309, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53969, "top5_acc": 0.78938, "loss_cls": 2.55162, "loss": 2.55162, "time": 0.8186} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00308, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53219, "top5_acc": 0.77719, "loss_cls": 2.59803, "loss": 2.59803, "time": 0.81743} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00307, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53594, "top5_acc": 0.78047, "loss_cls": 2.58626, "loss": 2.58626, "time": 0.81342} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00306, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52703, "top5_acc": 0.77719, "loss_cls": 2.59059, "loss": 2.59059, "time": 0.81113} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00305, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52281, "top5_acc": 0.76766, "loss_cls": 2.63046, "loss": 2.63046, "time": 0.81569} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00304, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54031, "top5_acc": 0.78234, "loss_cls": 2.55669, "loss": 2.55669, "time": 0.81337} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00303, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53188, "top5_acc": 0.77438, "loss_cls": 2.61583, "loss": 2.61583, "time": 0.80843} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.00302, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52328, "top5_acc": 0.77734, "loss_cls": 2.60953, "loss": 2.60953, "time": 0.81703} +{"mode": "train", "epoch": 134, "iter": 1300, "lr": 0.00301, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53125, "top5_acc": 0.77328, "loss_cls": 2.62414, "loss": 2.62414, "time": 0.81407} +{"mode": "train", "epoch": 134, "iter": 1400, "lr": 0.003, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52203, "top5_acc": 0.77594, "loss_cls": 2.63652, "loss": 2.63652, "time": 0.81632} +{"mode": "train", "epoch": 134, "iter": 1500, "lr": 0.00299, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52266, "top5_acc": 0.77375, "loss_cls": 2.63197, "loss": 2.63197, "time": 0.8151} +{"mode": "train", "epoch": 134, "iter": 1600, "lr": 0.00298, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52969, "top5_acc": 0.77469, "loss_cls": 2.61359, "loss": 2.61359, "time": 0.81647} +{"mode": "train", "epoch": 134, "iter": 1700, "lr": 0.00297, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.525, "top5_acc": 0.77438, "loss_cls": 2.6156, "loss": 2.6156, "time": 0.81571} +{"mode": "train", "epoch": 134, "iter": 1800, "lr": 0.00296, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52641, "top5_acc": 0.76891, "loss_cls": 2.67009, "loss": 2.67009, "time": 0.81715} +{"mode": "train", "epoch": 134, "iter": 1900, "lr": 0.00295, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53016, "top5_acc": 0.77312, "loss_cls": 2.62444, "loss": 2.62444, "time": 0.81404} +{"mode": "train", "epoch": 134, "iter": 2000, "lr": 0.00294, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53375, "top5_acc": 0.77312, "loss_cls": 2.59382, "loss": 2.59382, "time": 0.81484} +{"mode": "train", "epoch": 134, "iter": 2100, "lr": 0.00293, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52344, "top5_acc": 0.76734, "loss_cls": 2.64801, "loss": 2.64801, "time": 0.81846} +{"mode": "train", "epoch": 134, "iter": 2200, "lr": 0.00293, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52219, "top5_acc": 0.77016, "loss_cls": 2.64625, "loss": 2.64625, "time": 0.82451} +{"mode": "train", "epoch": 134, "iter": 2300, "lr": 0.00292, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52688, "top5_acc": 0.77469, "loss_cls": 2.59859, "loss": 2.59859, "time": 0.81585} +{"mode": "train", "epoch": 134, "iter": 2400, "lr": 0.00291, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53469, "top5_acc": 0.77828, "loss_cls": 2.59972, "loss": 2.59972, "time": 0.8219} +{"mode": "train", "epoch": 134, "iter": 2500, "lr": 0.0029, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53531, "top5_acc": 0.77391, "loss_cls": 2.60178, "loss": 2.60178, "time": 0.826} +{"mode": "train", "epoch": 134, "iter": 2600, "lr": 0.00289, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53109, "top5_acc": 0.7775, "loss_cls": 2.59397, "loss": 2.59397, "time": 0.82199} +{"mode": "train", "epoch": 134, "iter": 2700, "lr": 0.00288, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52078, "top5_acc": 0.76266, "loss_cls": 2.67744, "loss": 2.67744, "time": 0.81255} +{"mode": "train", "epoch": 134, "iter": 2800, "lr": 0.00287, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52312, "top5_acc": 0.765, "loss_cls": 2.67536, "loss": 2.67536, "time": 0.8106} +{"mode": "train", "epoch": 134, "iter": 2900, "lr": 0.00286, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51938, "top5_acc": 0.76578, "loss_cls": 2.67892, "loss": 2.67892, "time": 0.81619} +{"mode": "train", "epoch": 134, "iter": 3000, "lr": 0.00285, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53016, "top5_acc": 0.77031, "loss_cls": 2.64999, "loss": 2.64999, "time": 0.82258} +{"mode": "train", "epoch": 134, "iter": 3100, "lr": 0.00284, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51469, "top5_acc": 0.75828, "loss_cls": 2.68641, "loss": 2.68641, "time": 0.82027} +{"mode": "train", "epoch": 134, "iter": 3200, "lr": 0.00283, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52484, "top5_acc": 0.77219, "loss_cls": 2.61828, "loss": 2.61828, "time": 0.81611} +{"mode": "train", "epoch": 134, "iter": 3300, "lr": 0.00282, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52672, "top5_acc": 0.77656, "loss_cls": 2.62274, "loss": 2.62274, "time": 0.81333} +{"mode": "train", "epoch": 134, "iter": 3400, "lr": 0.00281, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52234, "top5_acc": 0.77078, "loss_cls": 2.64749, "loss": 2.64749, "time": 0.81594} +{"mode": "train", "epoch": 134, "iter": 3500, "lr": 0.0028, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54047, "top5_acc": 0.77766, "loss_cls": 2.58756, "loss": 2.58756, "time": 0.81381} +{"mode": "train", "epoch": 134, "iter": 3600, "lr": 0.00279, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52375, "top5_acc": 0.76172, "loss_cls": 2.64436, "loss": 2.64436, "time": 0.82245} +{"mode": "train", "epoch": 134, "iter": 3700, "lr": 0.00279, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53047, "top5_acc": 0.77203, "loss_cls": 2.64247, "loss": 2.64247, "time": 0.81094} +{"mode": "val", "epoch": 134, "iter": 309, "lr": 0.00278, "top1_acc": 0.42972, "top5_acc": 0.67756, "mean_class_accuracy": 0.4294} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00277, "memory": 15990, "data_time": 1.32341, "top1_acc": 0.55109, "top5_acc": 0.79047, "loss_cls": 2.53319, "loss": 2.53319, "time": 2.29635} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00276, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55437, "top5_acc": 0.79266, "loss_cls": 2.50564, "loss": 2.50564, "time": 0.81877} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00275, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54547, "top5_acc": 0.79, "loss_cls": 2.50728, "loss": 2.50728, "time": 0.81935} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00274, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55391, "top5_acc": 0.79656, "loss_cls": 2.47908, "loss": 2.47908, "time": 0.81329} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00274, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.53812, "top5_acc": 0.78062, "loss_cls": 2.58209, "loss": 2.58209, "time": 0.81777} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00273, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.53578, "top5_acc": 0.78312, "loss_cls": 2.54067, "loss": 2.54067, "time": 0.8216} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00272, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53328, "top5_acc": 0.78953, "loss_cls": 2.55215, "loss": 2.55215, "time": 0.81178} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00271, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.535, "top5_acc": 0.78234, "loss_cls": 2.55579, "loss": 2.55579, "time": 0.81364} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.0027, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.5375, "top5_acc": 0.78531, "loss_cls": 2.53041, "loss": 2.53041, "time": 0.81758} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00269, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53625, "top5_acc": 0.77703, "loss_cls": 2.58684, "loss": 2.58684, "time": 0.81083} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00268, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54766, "top5_acc": 0.78672, "loss_cls": 2.55418, "loss": 2.55418, "time": 0.81625} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00267, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54109, "top5_acc": 0.78109, "loss_cls": 2.5494, "loss": 2.5494, "time": 0.81566} +{"mode": "train", "epoch": 135, "iter": 1300, "lr": 0.00266, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53609, "top5_acc": 0.77844, "loss_cls": 2.59735, "loss": 2.59735, "time": 0.81307} +{"mode": "train", "epoch": 135, "iter": 1400, "lr": 0.00265, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54141, "top5_acc": 0.77891, "loss_cls": 2.55866, "loss": 2.55866, "time": 0.81154} +{"mode": "train", "epoch": 135, "iter": 1500, "lr": 0.00265, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53969, "top5_acc": 0.77969, "loss_cls": 2.57658, "loss": 2.57658, "time": 0.81298} +{"mode": "train", "epoch": 135, "iter": 1600, "lr": 0.00264, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52906, "top5_acc": 0.77828, "loss_cls": 2.59683, "loss": 2.59683, "time": 0.81649} +{"mode": "train", "epoch": 135, "iter": 1700, "lr": 0.00263, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52703, "top5_acc": 0.78, "loss_cls": 2.61745, "loss": 2.61745, "time": 0.81887} +{"mode": "train", "epoch": 135, "iter": 1800, "lr": 0.00262, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53953, "top5_acc": 0.78328, "loss_cls": 2.55541, "loss": 2.55541, "time": 0.81935} +{"mode": "train", "epoch": 135, "iter": 1900, "lr": 0.00261, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.53688, "top5_acc": 0.78375, "loss_cls": 2.54841, "loss": 2.54841, "time": 0.81357} +{"mode": "train", "epoch": 135, "iter": 2000, "lr": 0.0026, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53938, "top5_acc": 0.78219, "loss_cls": 2.54403, "loss": 2.54403, "time": 0.80978} +{"mode": "train", "epoch": 135, "iter": 2100, "lr": 0.00259, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54875, "top5_acc": 0.78812, "loss_cls": 2.51648, "loss": 2.51648, "time": 0.81326} +{"mode": "train", "epoch": 135, "iter": 2200, "lr": 0.00258, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.53203, "top5_acc": 0.78016, "loss_cls": 2.5871, "loss": 2.5871, "time": 0.82173} +{"mode": "train", "epoch": 135, "iter": 2300, "lr": 0.00257, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.54578, "top5_acc": 0.77906, "loss_cls": 2.57245, "loss": 2.57245, "time": 0.81923} +{"mode": "train", "epoch": 135, "iter": 2400, "lr": 0.00256, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.53953, "top5_acc": 0.77859, "loss_cls": 2.56902, "loss": 2.56902, "time": 0.81842} +{"mode": "train", "epoch": 135, "iter": 2500, "lr": 0.00256, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52922, "top5_acc": 0.77859, "loss_cls": 2.5872, "loss": 2.5872, "time": 0.82817} +{"mode": "train", "epoch": 135, "iter": 2600, "lr": 0.00255, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54109, "top5_acc": 0.77734, "loss_cls": 2.56986, "loss": 2.56986, "time": 0.82033} +{"mode": "train", "epoch": 135, "iter": 2700, "lr": 0.00254, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54453, "top5_acc": 0.77875, "loss_cls": 2.55149, "loss": 2.55149, "time": 0.82047} +{"mode": "train", "epoch": 135, "iter": 2800, "lr": 0.00253, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5225, "top5_acc": 0.77219, "loss_cls": 2.64302, "loss": 2.64302, "time": 0.81381} +{"mode": "train", "epoch": 135, "iter": 2900, "lr": 0.00252, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52984, "top5_acc": 0.77766, "loss_cls": 2.59356, "loss": 2.59356, "time": 0.81642} +{"mode": "train", "epoch": 135, "iter": 3000, "lr": 0.00251, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53594, "top5_acc": 0.77547, "loss_cls": 2.62237, "loss": 2.62237, "time": 0.81467} +{"mode": "train", "epoch": 135, "iter": 3100, "lr": 0.0025, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53406, "top5_acc": 0.775, "loss_cls": 2.59821, "loss": 2.59821, "time": 0.81692} +{"mode": "train", "epoch": 135, "iter": 3200, "lr": 0.00249, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.535, "top5_acc": 0.77125, "loss_cls": 2.61022, "loss": 2.61022, "time": 0.81778} +{"mode": "train", "epoch": 135, "iter": 3300, "lr": 0.00249, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52844, "top5_acc": 0.77891, "loss_cls": 2.57993, "loss": 2.57993, "time": 0.81664} +{"mode": "train", "epoch": 135, "iter": 3400, "lr": 0.00248, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53016, "top5_acc": 0.77781, "loss_cls": 2.61494, "loss": 2.61494, "time": 0.81454} +{"mode": "train", "epoch": 135, "iter": 3500, "lr": 0.00247, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53156, "top5_acc": 0.78031, "loss_cls": 2.5763, "loss": 2.5763, "time": 0.81358} +{"mode": "train", "epoch": 135, "iter": 3600, "lr": 0.00246, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.52141, "top5_acc": 0.77203, "loss_cls": 2.64474, "loss": 2.64474, "time": 0.81401} +{"mode": "train", "epoch": 135, "iter": 3700, "lr": 0.00245, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52938, "top5_acc": 0.77391, "loss_cls": 2.58583, "loss": 2.58583, "time": 0.81609} +{"mode": "val", "epoch": 135, "iter": 309, "lr": 0.00245, "top1_acc": 0.42866, "top5_acc": 0.6846, "mean_class_accuracy": 0.4284} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00244, "memory": 15990, "data_time": 1.34505, "top1_acc": 0.56453, "top5_acc": 0.80469, "loss_cls": 2.41802, "loss": 2.41802, "time": 2.31868} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.00243, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54828, "top5_acc": 0.7875, "loss_cls": 2.51088, "loss": 2.51088, "time": 0.81205} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00242, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54688, "top5_acc": 0.795, "loss_cls": 2.46458, "loss": 2.46458, "time": 0.81831} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00241, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55609, "top5_acc": 0.79641, "loss_cls": 2.45592, "loss": 2.45592, "time": 0.82169} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.0024, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55422, "top5_acc": 0.79297, "loss_cls": 2.46428, "loss": 2.46428, "time": 0.8168} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.0024, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.55437, "top5_acc": 0.795, "loss_cls": 2.47988, "loss": 2.47988, "time": 0.82467} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00239, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55156, "top5_acc": 0.78188, "loss_cls": 2.51153, "loss": 2.51153, "time": 0.81616} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00238, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55937, "top5_acc": 0.795, "loss_cls": 2.46292, "loss": 2.46292, "time": 0.81725} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00237, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56, "top5_acc": 0.79719, "loss_cls": 2.47517, "loss": 2.47517, "time": 0.81588} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00236, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54562, "top5_acc": 0.78453, "loss_cls": 2.52714, "loss": 2.52714, "time": 0.82654} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00235, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55047, "top5_acc": 0.79031, "loss_cls": 2.52311, "loss": 2.52311, "time": 0.81447} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00234, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54359, "top5_acc": 0.78406, "loss_cls": 2.52061, "loss": 2.52061, "time": 0.81926} +{"mode": "train", "epoch": 136, "iter": 1300, "lr": 0.00234, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53938, "top5_acc": 0.78797, "loss_cls": 2.5273, "loss": 2.5273, "time": 0.8172} +{"mode": "train", "epoch": 136, "iter": 1400, "lr": 0.00233, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54875, "top5_acc": 0.79688, "loss_cls": 2.47611, "loss": 2.47611, "time": 0.81195} +{"mode": "train", "epoch": 136, "iter": 1500, "lr": 0.00232, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56188, "top5_acc": 0.79609, "loss_cls": 2.46124, "loss": 2.46124, "time": 0.81638} +{"mode": "train", "epoch": 136, "iter": 1600, "lr": 0.00231, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54484, "top5_acc": 0.78422, "loss_cls": 2.55398, "loss": 2.55398, "time": 0.81022} +{"mode": "train", "epoch": 136, "iter": 1700, "lr": 0.0023, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52859, "top5_acc": 0.77203, "loss_cls": 2.59346, "loss": 2.59346, "time": 0.81376} +{"mode": "train", "epoch": 136, "iter": 1800, "lr": 0.00229, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54969, "top5_acc": 0.79, "loss_cls": 2.52653, "loss": 2.52653, "time": 0.81287} +{"mode": "train", "epoch": 136, "iter": 1900, "lr": 0.00229, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54312, "top5_acc": 0.78531, "loss_cls": 2.52308, "loss": 2.52308, "time": 0.81602} +{"mode": "train", "epoch": 136, "iter": 2000, "lr": 0.00228, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54047, "top5_acc": 0.78719, "loss_cls": 2.53234, "loss": 2.53234, "time": 0.81363} +{"mode": "train", "epoch": 136, "iter": 2100, "lr": 0.00227, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53828, "top5_acc": 0.78141, "loss_cls": 2.55128, "loss": 2.55128, "time": 0.81291} +{"mode": "train", "epoch": 136, "iter": 2200, "lr": 0.00226, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.54156, "top5_acc": 0.78266, "loss_cls": 2.56655, "loss": 2.56655, "time": 0.81828} +{"mode": "train", "epoch": 136, "iter": 2300, "lr": 0.00225, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.54062, "top5_acc": 0.78797, "loss_cls": 2.53556, "loss": 2.53556, "time": 0.81732} +{"mode": "train", "epoch": 136, "iter": 2400, "lr": 0.00224, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55141, "top5_acc": 0.79219, "loss_cls": 2.4982, "loss": 2.4982, "time": 0.82028} +{"mode": "train", "epoch": 136, "iter": 2500, "lr": 0.00224, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55437, "top5_acc": 0.79484, "loss_cls": 2.4826, "loss": 2.4826, "time": 0.82088} +{"mode": "train", "epoch": 136, "iter": 2600, "lr": 0.00223, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54141, "top5_acc": 0.77312, "loss_cls": 2.59047, "loss": 2.59047, "time": 0.81968} +{"mode": "train", "epoch": 136, "iter": 2700, "lr": 0.00222, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54469, "top5_acc": 0.78453, "loss_cls": 2.52611, "loss": 2.52611, "time": 0.81406} +{"mode": "train", "epoch": 136, "iter": 2800, "lr": 0.00221, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53672, "top5_acc": 0.78531, "loss_cls": 2.54518, "loss": 2.54518, "time": 0.8185} +{"mode": "train", "epoch": 136, "iter": 2900, "lr": 0.0022, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53422, "top5_acc": 0.78094, "loss_cls": 2.57409, "loss": 2.57409, "time": 0.81328} +{"mode": "train", "epoch": 136, "iter": 3000, "lr": 0.00219, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54703, "top5_acc": 0.78875, "loss_cls": 2.5259, "loss": 2.5259, "time": 0.8121} +{"mode": "train", "epoch": 136, "iter": 3100, "lr": 0.00219, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53891, "top5_acc": 0.78609, "loss_cls": 2.53633, "loss": 2.53633, "time": 0.81484} +{"mode": "train", "epoch": 136, "iter": 3200, "lr": 0.00218, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53812, "top5_acc": 0.78, "loss_cls": 2.5737, "loss": 2.5737, "time": 0.81045} +{"mode": "train", "epoch": 136, "iter": 3300, "lr": 0.00217, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54172, "top5_acc": 0.78453, "loss_cls": 2.55156, "loss": 2.55156, "time": 0.81602} +{"mode": "train", "epoch": 136, "iter": 3400, "lr": 0.00216, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53422, "top5_acc": 0.77625, "loss_cls": 2.59617, "loss": 2.59617, "time": 0.81943} +{"mode": "train", "epoch": 136, "iter": 3500, "lr": 0.00215, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54, "top5_acc": 0.7875, "loss_cls": 2.53166, "loss": 2.53166, "time": 0.81452} +{"mode": "train", "epoch": 136, "iter": 3600, "lr": 0.00215, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.54609, "top5_acc": 0.77672, "loss_cls": 2.54408, "loss": 2.54408, "time": 0.81401} +{"mode": "train", "epoch": 136, "iter": 3700, "lr": 0.00214, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53797, "top5_acc": 0.78078, "loss_cls": 2.54769, "loss": 2.54769, "time": 0.82246} +{"mode": "val", "epoch": 136, "iter": 309, "lr": 0.00213, "top1_acc": 0.4317, "top5_acc": 0.68683, "mean_class_accuracy": 0.43141} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00213, "memory": 15990, "data_time": 1.34335, "top1_acc": 0.57141, "top5_acc": 0.8025, "loss_cls": 2.39905, "loss": 2.39905, "time": 2.32463} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00212, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55, "top5_acc": 0.78781, "loss_cls": 2.52173, "loss": 2.52173, "time": 0.83335} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00211, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56672, "top5_acc": 0.80062, "loss_cls": 2.42654, "loss": 2.42654, "time": 0.82874} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.0021, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55266, "top5_acc": 0.79438, "loss_cls": 2.47999, "loss": 2.47999, "time": 0.82992} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.00209, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.56766, "top5_acc": 0.8025, "loss_cls": 2.40769, "loss": 2.40769, "time": 0.8209} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.00209, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.56484, "top5_acc": 0.79734, "loss_cls": 2.43206, "loss": 2.43206, "time": 0.83077} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00208, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.56766, "top5_acc": 0.79578, "loss_cls": 2.44236, "loss": 2.44236, "time": 0.83632} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00207, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55641, "top5_acc": 0.79734, "loss_cls": 2.4878, "loss": 2.4878, "time": 0.8354} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00206, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55391, "top5_acc": 0.795, "loss_cls": 2.46378, "loss": 2.46378, "time": 0.82824} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00205, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56156, "top5_acc": 0.79859, "loss_cls": 2.43238, "loss": 2.43238, "time": 0.83042} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00205, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55984, "top5_acc": 0.79156, "loss_cls": 2.46214, "loss": 2.46214, "time": 0.82558} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00204, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55562, "top5_acc": 0.79172, "loss_cls": 2.46598, "loss": 2.46598, "time": 0.83232} +{"mode": "train", "epoch": 137, "iter": 1300, "lr": 0.00203, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54937, "top5_acc": 0.79875, "loss_cls": 2.48139, "loss": 2.48139, "time": 0.82497} +{"mode": "train", "epoch": 137, "iter": 1400, "lr": 0.00202, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55281, "top5_acc": 0.78844, "loss_cls": 2.4944, "loss": 2.4944, "time": 0.82431} +{"mode": "train", "epoch": 137, "iter": 1500, "lr": 0.00201, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54484, "top5_acc": 0.78938, "loss_cls": 2.49705, "loss": 2.49705, "time": 0.82696} +{"mode": "train", "epoch": 137, "iter": 1600, "lr": 0.00201, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.56031, "top5_acc": 0.79656, "loss_cls": 2.45155, "loss": 2.45155, "time": 0.82806} +{"mode": "train", "epoch": 137, "iter": 1700, "lr": 0.002, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55797, "top5_acc": 0.79984, "loss_cls": 2.46172, "loss": 2.46172, "time": 0.82429} +{"mode": "train", "epoch": 137, "iter": 1800, "lr": 0.00199, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55734, "top5_acc": 0.79797, "loss_cls": 2.47701, "loss": 2.47701, "time": 0.82391} +{"mode": "train", "epoch": 137, "iter": 1900, "lr": 0.00198, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55437, "top5_acc": 0.79312, "loss_cls": 2.46472, "loss": 2.46472, "time": 0.83555} +{"mode": "train", "epoch": 137, "iter": 2000, "lr": 0.00198, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55922, "top5_acc": 0.79703, "loss_cls": 2.45969, "loss": 2.45969, "time": 0.81925} +{"mode": "train", "epoch": 137, "iter": 2100, "lr": 0.00197, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54578, "top5_acc": 0.78609, "loss_cls": 2.48713, "loss": 2.48713, "time": 0.81608} +{"mode": "train", "epoch": 137, "iter": 2200, "lr": 0.00196, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54516, "top5_acc": 0.79062, "loss_cls": 2.49318, "loss": 2.49318, "time": 0.82062} +{"mode": "train", "epoch": 137, "iter": 2300, "lr": 0.00195, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55344, "top5_acc": 0.79, "loss_cls": 2.50467, "loss": 2.50467, "time": 0.8194} +{"mode": "train", "epoch": 137, "iter": 2400, "lr": 0.00194, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55016, "top5_acc": 0.79281, "loss_cls": 2.50788, "loss": 2.50788, "time": 0.81696} +{"mode": "train", "epoch": 137, "iter": 2500, "lr": 0.00194, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55141, "top5_acc": 0.78781, "loss_cls": 2.49697, "loss": 2.49697, "time": 0.82559} +{"mode": "train", "epoch": 137, "iter": 2600, "lr": 0.00193, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55078, "top5_acc": 0.79984, "loss_cls": 2.47643, "loss": 2.47643, "time": 0.82053} +{"mode": "train", "epoch": 137, "iter": 2700, "lr": 0.00192, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55016, "top5_acc": 0.78531, "loss_cls": 2.50469, "loss": 2.50469, "time": 0.81761} +{"mode": "train", "epoch": 137, "iter": 2800, "lr": 0.00191, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55688, "top5_acc": 0.79484, "loss_cls": 2.46517, "loss": 2.46517, "time": 0.81533} +{"mode": "train", "epoch": 137, "iter": 2900, "lr": 0.00191, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54641, "top5_acc": 0.78797, "loss_cls": 2.49485, "loss": 2.49485, "time": 0.81615} +{"mode": "train", "epoch": 137, "iter": 3000, "lr": 0.0019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55234, "top5_acc": 0.77984, "loss_cls": 2.50029, "loss": 2.50029, "time": 0.82432} +{"mode": "train", "epoch": 137, "iter": 3100, "lr": 0.00189, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55062, "top5_acc": 0.79438, "loss_cls": 2.47985, "loss": 2.47985, "time": 0.82105} +{"mode": "train", "epoch": 137, "iter": 3200, "lr": 0.00188, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55516, "top5_acc": 0.79828, "loss_cls": 2.47278, "loss": 2.47278, "time": 0.81281} +{"mode": "train", "epoch": 137, "iter": 3300, "lr": 0.00188, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55437, "top5_acc": 0.79297, "loss_cls": 2.47678, "loss": 2.47678, "time": 0.81702} +{"mode": "train", "epoch": 137, "iter": 3400, "lr": 0.00187, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54641, "top5_acc": 0.79203, "loss_cls": 2.4876, "loss": 2.4876, "time": 0.81369} +{"mode": "train", "epoch": 137, "iter": 3500, "lr": 0.00186, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54859, "top5_acc": 0.78938, "loss_cls": 2.4893, "loss": 2.4893, "time": 0.81608} +{"mode": "train", "epoch": 137, "iter": 3600, "lr": 0.00185, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54812, "top5_acc": 0.79734, "loss_cls": 2.47224, "loss": 2.47224, "time": 0.81399} +{"mode": "train", "epoch": 137, "iter": 3700, "lr": 0.00185, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54062, "top5_acc": 0.78844, "loss_cls": 2.53518, "loss": 2.53518, "time": 0.81702} +{"mode": "val", "epoch": 137, "iter": 309, "lr": 0.00184, "top1_acc": 0.43985, "top5_acc": 0.68855, "mean_class_accuracy": 0.43953} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00183, "memory": 15990, "data_time": 1.3242, "top1_acc": 0.565, "top5_acc": 0.80578, "loss_cls": 2.40569, "loss": 2.40569, "time": 2.29858} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00183, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57016, "top5_acc": 0.80812, "loss_cls": 2.37084, "loss": 2.37084, "time": 0.81364} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00182, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58516, "top5_acc": 0.81344, "loss_cls": 2.3273, "loss": 2.3273, "time": 0.8186} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00181, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57453, "top5_acc": 0.80438, "loss_cls": 2.37595, "loss": 2.37595, "time": 0.81769} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.0018, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.57047, "top5_acc": 0.80766, "loss_cls": 2.37385, "loss": 2.37385, "time": 0.82263} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.0018, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56922, "top5_acc": 0.81078, "loss_cls": 2.37755, "loss": 2.37755, "time": 0.81698} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00179, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57078, "top5_acc": 0.8075, "loss_cls": 2.38242, "loss": 2.38242, "time": 0.81276} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00178, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56781, "top5_acc": 0.8075, "loss_cls": 2.4037, "loss": 2.4037, "time": 0.81077} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00177, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57016, "top5_acc": 0.80844, "loss_cls": 2.37363, "loss": 2.37363, "time": 0.81583} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00177, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56656, "top5_acc": 0.79844, "loss_cls": 2.40789, "loss": 2.40789, "time": 0.81549} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.00176, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56, "top5_acc": 0.80125, "loss_cls": 2.43972, "loss": 2.43972, "time": 0.81404} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.00175, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55578, "top5_acc": 0.80156, "loss_cls": 2.42965, "loss": 2.42965, "time": 0.81686} +{"mode": "train", "epoch": 138, "iter": 1300, "lr": 0.00175, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56859, "top5_acc": 0.80672, "loss_cls": 2.40027, "loss": 2.40027, "time": 0.81273} +{"mode": "train", "epoch": 138, "iter": 1400, "lr": 0.00174, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.56109, "top5_acc": 0.79609, "loss_cls": 2.46428, "loss": 2.46428, "time": 0.8112} +{"mode": "train", "epoch": 138, "iter": 1500, "lr": 0.00173, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57312, "top5_acc": 0.80656, "loss_cls": 2.38308, "loss": 2.38308, "time": 0.82073} +{"mode": "train", "epoch": 138, "iter": 1600, "lr": 0.00172, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56031, "top5_acc": 0.80438, "loss_cls": 2.43363, "loss": 2.43363, "time": 0.81202} +{"mode": "train", "epoch": 138, "iter": 1700, "lr": 0.00172, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55125, "top5_acc": 0.79766, "loss_cls": 2.45148, "loss": 2.45148, "time": 0.81486} +{"mode": "train", "epoch": 138, "iter": 1800, "lr": 0.00171, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56734, "top5_acc": 0.80453, "loss_cls": 2.40541, "loss": 2.40541, "time": 0.81744} +{"mode": "train", "epoch": 138, "iter": 1900, "lr": 0.0017, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56641, "top5_acc": 0.79578, "loss_cls": 2.42741, "loss": 2.42741, "time": 0.81709} +{"mode": "train", "epoch": 138, "iter": 2000, "lr": 0.00169, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.56406, "top5_acc": 0.80359, "loss_cls": 2.41316, "loss": 2.41316, "time": 0.81235} +{"mode": "train", "epoch": 138, "iter": 2100, "lr": 0.00169, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56672, "top5_acc": 0.79953, "loss_cls": 2.42495, "loss": 2.42495, "time": 0.81734} +{"mode": "train", "epoch": 138, "iter": 2200, "lr": 0.00168, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56938, "top5_acc": 0.80062, "loss_cls": 2.42293, "loss": 2.42293, "time": 0.81711} +{"mode": "train", "epoch": 138, "iter": 2300, "lr": 0.00167, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55688, "top5_acc": 0.80188, "loss_cls": 2.4442, "loss": 2.4442, "time": 0.8262} +{"mode": "train", "epoch": 138, "iter": 2400, "lr": 0.00167, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56844, "top5_acc": 0.80094, "loss_cls": 2.40949, "loss": 2.40949, "time": 0.81743} +{"mode": "train", "epoch": 138, "iter": 2500, "lr": 0.00166, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56375, "top5_acc": 0.79828, "loss_cls": 2.44263, "loss": 2.44263, "time": 0.81981} +{"mode": "train", "epoch": 138, "iter": 2600, "lr": 0.00165, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55359, "top5_acc": 0.79938, "loss_cls": 2.45125, "loss": 2.45125, "time": 0.81873} +{"mode": "train", "epoch": 138, "iter": 2700, "lr": 0.00164, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55984, "top5_acc": 0.79469, "loss_cls": 2.44804, "loss": 2.44804, "time": 0.81877} +{"mode": "train", "epoch": 138, "iter": 2800, "lr": 0.00164, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55953, "top5_acc": 0.79453, "loss_cls": 2.45961, "loss": 2.45961, "time": 0.8216} +{"mode": "train", "epoch": 138, "iter": 2900, "lr": 0.00163, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55906, "top5_acc": 0.80078, "loss_cls": 2.42156, "loss": 2.42156, "time": 0.81338} +{"mode": "train", "epoch": 138, "iter": 3000, "lr": 0.00162, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55359, "top5_acc": 0.79312, "loss_cls": 2.4818, "loss": 2.4818, "time": 0.81767} +{"mode": "train", "epoch": 138, "iter": 3100, "lr": 0.00162, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56672, "top5_acc": 0.79672, "loss_cls": 2.44513, "loss": 2.44513, "time": 0.81472} +{"mode": "train", "epoch": 138, "iter": 3200, "lr": 0.00161, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55906, "top5_acc": 0.80203, "loss_cls": 2.44647, "loss": 2.44647, "time": 0.81353} +{"mode": "train", "epoch": 138, "iter": 3300, "lr": 0.0016, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56469, "top5_acc": 0.79781, "loss_cls": 2.4343, "loss": 2.4343, "time": 0.81109} +{"mode": "train", "epoch": 138, "iter": 3400, "lr": 0.0016, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55125, "top5_acc": 0.79672, "loss_cls": 2.46943, "loss": 2.46943, "time": 0.81618} +{"mode": "train", "epoch": 138, "iter": 3500, "lr": 0.00159, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56859, "top5_acc": 0.79672, "loss_cls": 2.44731, "loss": 2.44731, "time": 0.81414} +{"mode": "train", "epoch": 138, "iter": 3600, "lr": 0.00158, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56031, "top5_acc": 0.79656, "loss_cls": 2.44507, "loss": 2.44507, "time": 0.81137} +{"mode": "train", "epoch": 138, "iter": 3700, "lr": 0.00157, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55609, "top5_acc": 0.79609, "loss_cls": 2.45891, "loss": 2.45891, "time": 0.81359} +{"mode": "val", "epoch": 138, "iter": 309, "lr": 0.00157, "top1_acc": 0.44137, "top5_acc": 0.69017, "mean_class_accuracy": 0.44106} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00156, "memory": 15990, "data_time": 1.28754, "top1_acc": 0.59453, "top5_acc": 0.82, "loss_cls": 2.29858, "loss": 2.29858, "time": 2.25723} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00156, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58922, "top5_acc": 0.82172, "loss_cls": 2.30395, "loss": 2.30395, "time": 0.81733} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00155, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58125, "top5_acc": 0.81297, "loss_cls": 2.32194, "loss": 2.32194, "time": 0.82061} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00154, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58031, "top5_acc": 0.81938, "loss_cls": 2.31077, "loss": 2.31077, "time": 0.81778} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00154, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56906, "top5_acc": 0.805, "loss_cls": 2.39555, "loss": 2.39555, "time": 0.81755} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00153, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58141, "top5_acc": 0.82281, "loss_cls": 2.31375, "loss": 2.31375, "time": 0.81638} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00152, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58125, "top5_acc": 0.81094, "loss_cls": 2.33245, "loss": 2.33245, "time": 0.81191} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00152, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58219, "top5_acc": 0.81047, "loss_cls": 2.3498, "loss": 2.3498, "time": 0.81115} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00151, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57703, "top5_acc": 0.81359, "loss_cls": 2.35872, "loss": 2.35872, "time": 0.81733} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.0015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57719, "top5_acc": 0.80891, "loss_cls": 2.3662, "loss": 2.3662, "time": 0.80998} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.0015, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58484, "top5_acc": 0.81625, "loss_cls": 2.32749, "loss": 2.32749, "time": 0.81364} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00149, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56422, "top5_acc": 0.81062, "loss_cls": 2.38929, "loss": 2.38929, "time": 0.814} +{"mode": "train", "epoch": 139, "iter": 1300, "lr": 0.00148, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57266, "top5_acc": 0.79953, "loss_cls": 2.37997, "loss": 2.37997, "time": 0.81756} +{"mode": "train", "epoch": 139, "iter": 1400, "lr": 0.00148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57344, "top5_acc": 0.81156, "loss_cls": 2.36357, "loss": 2.36357, "time": 0.81696} +{"mode": "train", "epoch": 139, "iter": 1500, "lr": 0.00147, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.57953, "top5_acc": 0.81438, "loss_cls": 2.34223, "loss": 2.34223, "time": 0.81641} +{"mode": "train", "epoch": 139, "iter": 1600, "lr": 0.00146, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58141, "top5_acc": 0.81578, "loss_cls": 2.32569, "loss": 2.32569, "time": 0.81576} +{"mode": "train", "epoch": 139, "iter": 1700, "lr": 0.00145, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57359, "top5_acc": 0.81172, "loss_cls": 2.36267, "loss": 2.36267, "time": 0.8139} +{"mode": "train", "epoch": 139, "iter": 1800, "lr": 0.00145, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57109, "top5_acc": 0.80359, "loss_cls": 2.38802, "loss": 2.38802, "time": 0.81666} +{"mode": "train", "epoch": 139, "iter": 1900, "lr": 0.00144, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56953, "top5_acc": 0.81188, "loss_cls": 2.37747, "loss": 2.37747, "time": 0.81432} +{"mode": "train", "epoch": 139, "iter": 2000, "lr": 0.00143, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.5725, "top5_acc": 0.81062, "loss_cls": 2.35977, "loss": 2.35977, "time": 0.80947} +{"mode": "train", "epoch": 139, "iter": 2100, "lr": 0.00143, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56875, "top5_acc": 0.80188, "loss_cls": 2.40596, "loss": 2.40596, "time": 0.81494} +{"mode": "train", "epoch": 139, "iter": 2200, "lr": 0.00142, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57141, "top5_acc": 0.80109, "loss_cls": 2.4137, "loss": 2.4137, "time": 0.82142} +{"mode": "train", "epoch": 139, "iter": 2300, "lr": 0.00142, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57922, "top5_acc": 0.815, "loss_cls": 2.3212, "loss": 2.3212, "time": 0.81521} +{"mode": "train", "epoch": 139, "iter": 2400, "lr": 0.00141, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57688, "top5_acc": 0.80984, "loss_cls": 2.37279, "loss": 2.37279, "time": 0.82084} +{"mode": "train", "epoch": 139, "iter": 2500, "lr": 0.0014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.565, "top5_acc": 0.80562, "loss_cls": 2.39925, "loss": 2.39925, "time": 0.81622} +{"mode": "train", "epoch": 139, "iter": 2600, "lr": 0.0014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56969, "top5_acc": 0.80547, "loss_cls": 2.39297, "loss": 2.39297, "time": 0.82432} +{"mode": "train", "epoch": 139, "iter": 2700, "lr": 0.00139, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57141, "top5_acc": 0.81219, "loss_cls": 2.39195, "loss": 2.39195, "time": 0.81737} +{"mode": "train", "epoch": 139, "iter": 2800, "lr": 0.00138, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57047, "top5_acc": 0.80344, "loss_cls": 2.41606, "loss": 2.41606, "time": 0.82159} +{"mode": "train", "epoch": 139, "iter": 2900, "lr": 0.00138, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56703, "top5_acc": 0.80297, "loss_cls": 2.38899, "loss": 2.38899, "time": 0.81993} +{"mode": "train", "epoch": 139, "iter": 3000, "lr": 0.00137, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56719, "top5_acc": 0.80922, "loss_cls": 2.38318, "loss": 2.38318, "time": 0.81685} +{"mode": "train", "epoch": 139, "iter": 3100, "lr": 0.00136, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57125, "top5_acc": 0.80125, "loss_cls": 2.40977, "loss": 2.40977, "time": 0.8102} +{"mode": "train", "epoch": 139, "iter": 3200, "lr": 0.00136, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57016, "top5_acc": 0.805, "loss_cls": 2.39023, "loss": 2.39023, "time": 0.81503} +{"mode": "train", "epoch": 139, "iter": 3300, "lr": 0.00135, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56359, "top5_acc": 0.79875, "loss_cls": 2.4152, "loss": 2.4152, "time": 0.8176} +{"mode": "train", "epoch": 139, "iter": 3400, "lr": 0.00134, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56125, "top5_acc": 0.79938, "loss_cls": 2.44564, "loss": 2.44564, "time": 0.81216} +{"mode": "train", "epoch": 139, "iter": 3500, "lr": 0.00134, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56594, "top5_acc": 0.79875, "loss_cls": 2.39598, "loss": 2.39598, "time": 0.82004} +{"mode": "train", "epoch": 139, "iter": 3600, "lr": 0.00133, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55688, "top5_acc": 0.7975, "loss_cls": 2.44632, "loss": 2.44632, "time": 0.81811} +{"mode": "train", "epoch": 139, "iter": 3700, "lr": 0.00132, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57266, "top5_acc": 0.80984, "loss_cls": 2.36331, "loss": 2.36331, "time": 0.81727} +{"mode": "val", "epoch": 139, "iter": 309, "lr": 0.00132, "top1_acc": 0.44796, "top5_acc": 0.6924, "mean_class_accuracy": 0.44772} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00131, "memory": 15990, "data_time": 1.28773, "top1_acc": 0.60641, "top5_acc": 0.83391, "loss_cls": 2.21394, "loss": 2.21394, "time": 2.26321} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00131, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.6025, "top5_acc": 0.82516, "loss_cls": 2.24447, "loss": 2.24447, "time": 0.81717} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.0013, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.58422, "top5_acc": 0.81797, "loss_cls": 2.30318, "loss": 2.30318, "time": 0.81817} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.0013, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58703, "top5_acc": 0.81578, "loss_cls": 2.32749, "loss": 2.32749, "time": 0.8209} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00129, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58781, "top5_acc": 0.8125, "loss_cls": 2.34301, "loss": 2.34301, "time": 0.82129} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.00128, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59875, "top5_acc": 0.82453, "loss_cls": 2.26026, "loss": 2.26026, "time": 0.81938} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.00128, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59016, "top5_acc": 0.81953, "loss_cls": 2.29353, "loss": 2.29353, "time": 0.82047} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00127, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.58938, "top5_acc": 0.81844, "loss_cls": 2.28576, "loss": 2.28576, "time": 0.81582} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00126, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58, "top5_acc": 0.81594, "loss_cls": 2.30935, "loss": 2.30935, "time": 0.81926} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00126, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58156, "top5_acc": 0.81938, "loss_cls": 2.32845, "loss": 2.32845, "time": 0.81461} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00125, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59078, "top5_acc": 0.82344, "loss_cls": 2.29106, "loss": 2.29106, "time": 0.81691} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00125, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58953, "top5_acc": 0.82312, "loss_cls": 2.29461, "loss": 2.29461, "time": 0.81159} +{"mode": "train", "epoch": 140, "iter": 1300, "lr": 0.00124, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57828, "top5_acc": 0.815, "loss_cls": 2.33666, "loss": 2.33666, "time": 0.81428} +{"mode": "train", "epoch": 140, "iter": 1400, "lr": 0.00123, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57922, "top5_acc": 0.81484, "loss_cls": 2.32688, "loss": 2.32688, "time": 0.81737} +{"mode": "train", "epoch": 140, "iter": 1500, "lr": 0.00123, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59547, "top5_acc": 0.82141, "loss_cls": 2.26642, "loss": 2.26642, "time": 0.81331} +{"mode": "train", "epoch": 140, "iter": 1600, "lr": 0.00122, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58562, "top5_acc": 0.81672, "loss_cls": 2.30596, "loss": 2.30596, "time": 0.8148} +{"mode": "train", "epoch": 140, "iter": 1700, "lr": 0.00121, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59125, "top5_acc": 0.81859, "loss_cls": 2.27329, "loss": 2.27329, "time": 0.82099} +{"mode": "train", "epoch": 140, "iter": 1800, "lr": 0.00121, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57328, "top5_acc": 0.82078, "loss_cls": 2.3261, "loss": 2.3261, "time": 0.81546} +{"mode": "train", "epoch": 140, "iter": 1900, "lr": 0.0012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59016, "top5_acc": 0.82516, "loss_cls": 2.26206, "loss": 2.26206, "time": 0.81897} +{"mode": "train", "epoch": 140, "iter": 2000, "lr": 0.0012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58375, "top5_acc": 0.81672, "loss_cls": 2.32973, "loss": 2.32973, "time": 0.81001} +{"mode": "train", "epoch": 140, "iter": 2100, "lr": 0.00119, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58547, "top5_acc": 0.81812, "loss_cls": 2.28573, "loss": 2.28573, "time": 0.81633} +{"mode": "train", "epoch": 140, "iter": 2200, "lr": 0.00118, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.57891, "top5_acc": 0.81469, "loss_cls": 2.31811, "loss": 2.31811, "time": 0.81935} +{"mode": "train", "epoch": 140, "iter": 2300, "lr": 0.00118, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57359, "top5_acc": 0.81156, "loss_cls": 2.36137, "loss": 2.36137, "time": 0.8254} +{"mode": "train", "epoch": 140, "iter": 2400, "lr": 0.00117, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.58984, "top5_acc": 0.81391, "loss_cls": 2.29408, "loss": 2.29408, "time": 0.81527} +{"mode": "train", "epoch": 140, "iter": 2500, "lr": 0.00117, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57641, "top5_acc": 0.80594, "loss_cls": 2.36066, "loss": 2.36066, "time": 0.82061} +{"mode": "train", "epoch": 140, "iter": 2600, "lr": 0.00116, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57141, "top5_acc": 0.80281, "loss_cls": 2.38808, "loss": 2.38808, "time": 0.82084} +{"mode": "train", "epoch": 140, "iter": 2700, "lr": 0.00115, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58234, "top5_acc": 0.81609, "loss_cls": 2.30455, "loss": 2.30455, "time": 0.82395} +{"mode": "train", "epoch": 140, "iter": 2800, "lr": 0.00115, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.56844, "top5_acc": 0.80281, "loss_cls": 2.39731, "loss": 2.39731, "time": 0.8174} +{"mode": "train", "epoch": 140, "iter": 2900, "lr": 0.00114, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58375, "top5_acc": 0.81453, "loss_cls": 2.30574, "loss": 2.30574, "time": 0.81792} +{"mode": "train", "epoch": 140, "iter": 3000, "lr": 0.00114, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57094, "top5_acc": 0.81016, "loss_cls": 2.35605, "loss": 2.35605, "time": 0.81403} +{"mode": "train", "epoch": 140, "iter": 3100, "lr": 0.00113, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58031, "top5_acc": 0.81516, "loss_cls": 2.35188, "loss": 2.35188, "time": 0.81188} +{"mode": "train", "epoch": 140, "iter": 3200, "lr": 0.00112, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58375, "top5_acc": 0.81859, "loss_cls": 2.30751, "loss": 2.30751, "time": 0.81605} +{"mode": "train", "epoch": 140, "iter": 3300, "lr": 0.00112, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58219, "top5_acc": 0.81703, "loss_cls": 2.32285, "loss": 2.32285, "time": 0.81711} +{"mode": "train", "epoch": 140, "iter": 3400, "lr": 0.00111, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57016, "top5_acc": 0.80734, "loss_cls": 2.3504, "loss": 2.3504, "time": 0.81729} +{"mode": "train", "epoch": 140, "iter": 3500, "lr": 0.00111, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57875, "top5_acc": 0.81266, "loss_cls": 2.33248, "loss": 2.33248, "time": 0.81401} +{"mode": "train", "epoch": 140, "iter": 3600, "lr": 0.0011, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58891, "top5_acc": 0.81719, "loss_cls": 2.31609, "loss": 2.31609, "time": 0.82417} +{"mode": "train", "epoch": 140, "iter": 3700, "lr": 0.0011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58312, "top5_acc": 0.80812, "loss_cls": 2.34964, "loss": 2.34964, "time": 0.81702} +{"mode": "val", "epoch": 140, "iter": 309, "lr": 0.00109, "top1_acc": 0.44649, "top5_acc": 0.6928, "mean_class_accuracy": 0.44626} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00109, "memory": 15990, "data_time": 1.28235, "top1_acc": 0.60312, "top5_acc": 0.83406, "loss_cls": 2.20957, "loss": 2.20957, "time": 2.25731} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00108, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.6025, "top5_acc": 0.82547, "loss_cls": 2.23362, "loss": 2.23362, "time": 0.81248} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00108, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60641, "top5_acc": 0.82547, "loss_cls": 2.20485, "loss": 2.20485, "time": 0.82063} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00107, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59531, "top5_acc": 0.82656, "loss_cls": 2.26562, "loss": 2.26562, "time": 0.81877} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00106, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.59656, "top5_acc": 0.82938, "loss_cls": 2.23999, "loss": 2.23999, "time": 0.82352} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00106, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59656, "top5_acc": 0.83016, "loss_cls": 2.22866, "loss": 2.22866, "time": 0.8171} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00105, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60875, "top5_acc": 0.83031, "loss_cls": 2.21438, "loss": 2.21438, "time": 0.81287} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00105, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.59172, "top5_acc": 0.81812, "loss_cls": 2.27437, "loss": 2.27437, "time": 0.8103} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00104, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59859, "top5_acc": 0.8225, "loss_cls": 2.24322, "loss": 2.24322, "time": 0.80978} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00104, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59344, "top5_acc": 0.82422, "loss_cls": 2.27573, "loss": 2.27573, "time": 0.8158} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00103, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58719, "top5_acc": 0.81859, "loss_cls": 2.27583, "loss": 2.27583, "time": 0.81381} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00102, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.595, "top5_acc": 0.82016, "loss_cls": 2.2599, "loss": 2.2599, "time": 0.81376} +{"mode": "train", "epoch": 141, "iter": 1300, "lr": 0.00102, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59078, "top5_acc": 0.82531, "loss_cls": 2.2816, "loss": 2.2816, "time": 0.81232} +{"mode": "train", "epoch": 141, "iter": 1400, "lr": 0.00101, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59938, "top5_acc": 0.82531, "loss_cls": 2.24211, "loss": 2.24211, "time": 0.81287} +{"mode": "train", "epoch": 141, "iter": 1500, "lr": 0.00101, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59469, "top5_acc": 0.82875, "loss_cls": 2.24182, "loss": 2.24182, "time": 0.81379} +{"mode": "train", "epoch": 141, "iter": 1600, "lr": 0.001, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.59062, "top5_acc": 0.82125, "loss_cls": 2.2638, "loss": 2.2638, "time": 0.81023} +{"mode": "train", "epoch": 141, "iter": 1700, "lr": 0.001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.595, "top5_acc": 0.82641, "loss_cls": 2.24823, "loss": 2.24823, "time": 0.81818} +{"mode": "train", "epoch": 141, "iter": 1800, "lr": 0.00099, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58938, "top5_acc": 0.82172, "loss_cls": 2.26437, "loss": 2.26437, "time": 0.8191} +{"mode": "train", "epoch": 141, "iter": 1900, "lr": 0.00099, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59594, "top5_acc": 0.82953, "loss_cls": 2.24154, "loss": 2.24154, "time": 0.81482} +{"mode": "train", "epoch": 141, "iter": 2000, "lr": 0.00098, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59984, "top5_acc": 0.83062, "loss_cls": 2.22969, "loss": 2.22969, "time": 0.81515} +{"mode": "train", "epoch": 141, "iter": 2100, "lr": 0.00097, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59734, "top5_acc": 0.82281, "loss_cls": 2.2738, "loss": 2.2738, "time": 0.82407} +{"mode": "train", "epoch": 141, "iter": 2200, "lr": 0.00097, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59047, "top5_acc": 0.82031, "loss_cls": 2.26536, "loss": 2.26536, "time": 0.82214} +{"mode": "train", "epoch": 141, "iter": 2300, "lr": 0.00096, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59078, "top5_acc": 0.82359, "loss_cls": 2.25066, "loss": 2.25066, "time": 0.82194} +{"mode": "train", "epoch": 141, "iter": 2400, "lr": 0.00096, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59625, "top5_acc": 0.81609, "loss_cls": 2.27791, "loss": 2.27791, "time": 0.81923} +{"mode": "train", "epoch": 141, "iter": 2500, "lr": 0.00095, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58891, "top5_acc": 0.81562, "loss_cls": 2.29277, "loss": 2.29277, "time": 0.81905} +{"mode": "train", "epoch": 141, "iter": 2600, "lr": 0.00095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58297, "top5_acc": 0.82453, "loss_cls": 2.29206, "loss": 2.29206, "time": 0.81123} +{"mode": "train", "epoch": 141, "iter": 2700, "lr": 0.00094, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59078, "top5_acc": 0.81906, "loss_cls": 2.28248, "loss": 2.28248, "time": 0.82592} +{"mode": "train", "epoch": 141, "iter": 2800, "lr": 0.00094, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5925, "top5_acc": 0.82484, "loss_cls": 2.26885, "loss": 2.26885, "time": 0.8129} +{"mode": "train", "epoch": 141, "iter": 2900, "lr": 0.00093, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59578, "top5_acc": 0.82609, "loss_cls": 2.24951, "loss": 2.24951, "time": 0.8193} +{"mode": "train", "epoch": 141, "iter": 3000, "lr": 0.00093, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60141, "top5_acc": 0.82109, "loss_cls": 2.24937, "loss": 2.24937, "time": 0.81477} +{"mode": "train", "epoch": 141, "iter": 3100, "lr": 0.00092, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58578, "top5_acc": 0.81672, "loss_cls": 2.2819, "loss": 2.2819, "time": 0.81388} +{"mode": "train", "epoch": 141, "iter": 3200, "lr": 0.00091, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58734, "top5_acc": 0.82469, "loss_cls": 2.26246, "loss": 2.26246, "time": 0.81511} +{"mode": "train", "epoch": 141, "iter": 3300, "lr": 0.00091, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59, "top5_acc": 0.81953, "loss_cls": 2.25758, "loss": 2.25758, "time": 0.81188} +{"mode": "train", "epoch": 141, "iter": 3400, "lr": 0.0009, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59844, "top5_acc": 0.82516, "loss_cls": 2.26239, "loss": 2.26239, "time": 0.81887} +{"mode": "train", "epoch": 141, "iter": 3500, "lr": 0.0009, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58844, "top5_acc": 0.81453, "loss_cls": 2.3094, "loss": 2.3094, "time": 0.81904} +{"mode": "train", "epoch": 141, "iter": 3600, "lr": 0.00089, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59031, "top5_acc": 0.82172, "loss_cls": 2.27947, "loss": 2.27947, "time": 0.81406} +{"mode": "train", "epoch": 141, "iter": 3700, "lr": 0.00089, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.59391, "top5_acc": 0.82062, "loss_cls": 2.25494, "loss": 2.25494, "time": 0.81047} +{"mode": "val", "epoch": 141, "iter": 309, "lr": 0.00089, "top1_acc": 0.4477, "top5_acc": 0.7003, "mean_class_accuracy": 0.44739} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00088, "memory": 15990, "data_time": 1.30708, "top1_acc": 0.61141, "top5_acc": 0.84141, "loss_cls": 2.14605, "loss": 2.14605, "time": 2.28534} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00088, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.61625, "top5_acc": 0.83641, "loss_cls": 2.15221, "loss": 2.15221, "time": 0.81646} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00087, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.61469, "top5_acc": 0.84047, "loss_cls": 2.15457, "loss": 2.15457, "time": 0.82097} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00086, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.61844, "top5_acc": 0.83688, "loss_cls": 2.15097, "loss": 2.15097, "time": 0.81825} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.00086, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.60984, "top5_acc": 0.83234, "loss_cls": 2.18343, "loss": 2.18343, "time": 0.8208} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.00085, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60984, "top5_acc": 0.83359, "loss_cls": 2.18014, "loss": 2.18014, "time": 0.81993} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.00085, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.6225, "top5_acc": 0.83969, "loss_cls": 2.15825, "loss": 2.15825, "time": 0.81618} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00084, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61297, "top5_acc": 0.83969, "loss_cls": 2.1471, "loss": 2.1471, "time": 0.8165} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00084, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60531, "top5_acc": 0.83797, "loss_cls": 2.20062, "loss": 2.20062, "time": 0.81377} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00083, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61, "top5_acc": 0.83562, "loss_cls": 2.181, "loss": 2.181, "time": 0.81418} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00083, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61438, "top5_acc": 0.83703, "loss_cls": 2.17322, "loss": 2.17322, "time": 0.81065} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00082, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60578, "top5_acc": 0.83219, "loss_cls": 2.20078, "loss": 2.20078, "time": 0.82086} +{"mode": "train", "epoch": 142, "iter": 1300, "lr": 0.00082, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59375, "top5_acc": 0.82609, "loss_cls": 2.24619, "loss": 2.24619, "time": 0.81294} +{"mode": "train", "epoch": 142, "iter": 1400, "lr": 0.00081, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60906, "top5_acc": 0.83641, "loss_cls": 2.18823, "loss": 2.18823, "time": 0.81416} +{"mode": "train", "epoch": 142, "iter": 1500, "lr": 0.00081, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59391, "top5_acc": 0.82469, "loss_cls": 2.24827, "loss": 2.24827, "time": 0.81478} +{"mode": "train", "epoch": 142, "iter": 1600, "lr": 0.0008, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60672, "top5_acc": 0.82734, "loss_cls": 2.21442, "loss": 2.21442, "time": 0.81565} +{"mode": "train", "epoch": 142, "iter": 1700, "lr": 0.0008, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62328, "top5_acc": 0.83562, "loss_cls": 2.13748, "loss": 2.13748, "time": 0.81537} +{"mode": "train", "epoch": 142, "iter": 1800, "lr": 0.00079, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59953, "top5_acc": 0.82453, "loss_cls": 2.24595, "loss": 2.24595, "time": 0.81631} +{"mode": "train", "epoch": 142, "iter": 1900, "lr": 0.00079, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60484, "top5_acc": 0.82953, "loss_cls": 2.20435, "loss": 2.20435, "time": 0.81299} +{"mode": "train", "epoch": 142, "iter": 2000, "lr": 0.00078, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60234, "top5_acc": 0.82625, "loss_cls": 2.23335, "loss": 2.23335, "time": 0.82038} +{"mode": "train", "epoch": 142, "iter": 2100, "lr": 0.00078, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60406, "top5_acc": 0.83297, "loss_cls": 2.20136, "loss": 2.20136, "time": 0.81531} +{"mode": "train", "epoch": 142, "iter": 2200, "lr": 0.00077, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.60969, "top5_acc": 0.83906, "loss_cls": 2.18096, "loss": 2.18096, "time": 0.8174} +{"mode": "train", "epoch": 142, "iter": 2300, "lr": 0.00077, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.60562, "top5_acc": 0.82719, "loss_cls": 2.21668, "loss": 2.21668, "time": 0.81411} +{"mode": "train", "epoch": 142, "iter": 2400, "lr": 0.00076, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59875, "top5_acc": 0.82391, "loss_cls": 2.225, "loss": 2.225, "time": 0.82055} +{"mode": "train", "epoch": 142, "iter": 2500, "lr": 0.00076, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59734, "top5_acc": 0.82656, "loss_cls": 2.24391, "loss": 2.24391, "time": 0.81486} +{"mode": "train", "epoch": 142, "iter": 2600, "lr": 0.00075, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.60359, "top5_acc": 0.83094, "loss_cls": 2.20938, "loss": 2.20938, "time": 0.81219} +{"mode": "train", "epoch": 142, "iter": 2700, "lr": 0.00075, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.60688, "top5_acc": 0.82516, "loss_cls": 2.23602, "loss": 2.23602, "time": 0.82949} +{"mode": "train", "epoch": 142, "iter": 2800, "lr": 0.00075, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59531, "top5_acc": 0.82141, "loss_cls": 2.24071, "loss": 2.24071, "time": 0.81817} +{"mode": "train", "epoch": 142, "iter": 2900, "lr": 0.00074, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60109, "top5_acc": 0.82766, "loss_cls": 2.21005, "loss": 2.21005, "time": 0.81751} +{"mode": "train", "epoch": 142, "iter": 3000, "lr": 0.00074, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61594, "top5_acc": 0.83672, "loss_cls": 2.16729, "loss": 2.16729, "time": 0.81701} +{"mode": "train", "epoch": 142, "iter": 3100, "lr": 0.00073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60203, "top5_acc": 0.82781, "loss_cls": 2.22398, "loss": 2.22398, "time": 0.81511} +{"mode": "train", "epoch": 142, "iter": 3200, "lr": 0.00073, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.59531, "top5_acc": 0.82531, "loss_cls": 2.23296, "loss": 2.23296, "time": 0.80961} +{"mode": "train", "epoch": 142, "iter": 3300, "lr": 0.00072, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60203, "top5_acc": 0.82953, "loss_cls": 2.2009, "loss": 2.2009, "time": 0.81013} +{"mode": "train", "epoch": 142, "iter": 3400, "lr": 0.00072, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61172, "top5_acc": 0.83406, "loss_cls": 2.1648, "loss": 2.1648, "time": 0.81438} +{"mode": "train", "epoch": 142, "iter": 3500, "lr": 0.00071, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60547, "top5_acc": 0.82516, "loss_cls": 2.22661, "loss": 2.22661, "time": 0.81339} +{"mode": "train", "epoch": 142, "iter": 3600, "lr": 0.00071, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59203, "top5_acc": 0.82297, "loss_cls": 2.23338, "loss": 2.23338, "time": 0.82172} +{"mode": "train", "epoch": 142, "iter": 3700, "lr": 0.0007, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59344, "top5_acc": 0.82156, "loss_cls": 2.2574, "loss": 2.2574, "time": 0.81468} +{"mode": "val", "epoch": 142, "iter": 309, "lr": 0.0007, "top1_acc": 0.45358, "top5_acc": 0.69858, "mean_class_accuracy": 0.45331} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.0007, "memory": 15990, "data_time": 1.33518, "top1_acc": 0.63141, "top5_acc": 0.84891, "loss_cls": 2.06936, "loss": 2.06936, "time": 2.31185} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00069, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62875, "top5_acc": 0.84516, "loss_cls": 2.09212, "loss": 2.09212, "time": 0.8158} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00069, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61859, "top5_acc": 0.84219, "loss_cls": 2.12155, "loss": 2.12155, "time": 0.81563} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00068, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62281, "top5_acc": 0.84625, "loss_cls": 2.10248, "loss": 2.10248, "time": 0.8203} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00068, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.61375, "top5_acc": 0.83625, "loss_cls": 2.16928, "loss": 2.16928, "time": 0.81553} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00067, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61828, "top5_acc": 0.8325, "loss_cls": 2.15272, "loss": 2.15272, "time": 0.817} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00067, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62609, "top5_acc": 0.845, "loss_cls": 2.12179, "loss": 2.12179, "time": 0.81499} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00066, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61828, "top5_acc": 0.84859, "loss_cls": 2.11558, "loss": 2.11558, "time": 0.81564} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00066, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61375, "top5_acc": 0.83641, "loss_cls": 2.16212, "loss": 2.16212, "time": 0.8164} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60781, "top5_acc": 0.83609, "loss_cls": 2.17914, "loss": 2.17914, "time": 0.81391} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61766, "top5_acc": 0.83812, "loss_cls": 2.1448, "loss": 2.1448, "time": 0.81399} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00065, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60672, "top5_acc": 0.83781, "loss_cls": 2.16936, "loss": 2.16936, "time": 0.82712} +{"mode": "train", "epoch": 143, "iter": 1300, "lr": 0.00064, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61438, "top5_acc": 0.83953, "loss_cls": 2.12335, "loss": 2.12335, "time": 0.81712} +{"mode": "train", "epoch": 143, "iter": 1400, "lr": 0.00064, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62328, "top5_acc": 0.84766, "loss_cls": 2.12165, "loss": 2.12165, "time": 0.80993} +{"mode": "train", "epoch": 143, "iter": 1500, "lr": 0.00063, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62891, "top5_acc": 0.85016, "loss_cls": 2.08362, "loss": 2.08362, "time": 0.81515} +{"mode": "train", "epoch": 143, "iter": 1600, "lr": 0.00063, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60984, "top5_acc": 0.83484, "loss_cls": 2.17716, "loss": 2.17716, "time": 0.81262} +{"mode": "train", "epoch": 143, "iter": 1700, "lr": 0.00062, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61, "top5_acc": 0.84141, "loss_cls": 2.16077, "loss": 2.16077, "time": 0.8151} +{"mode": "train", "epoch": 143, "iter": 1800, "lr": 0.00062, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.60562, "top5_acc": 0.83562, "loss_cls": 2.17864, "loss": 2.17864, "time": 0.81494} +{"mode": "train", "epoch": 143, "iter": 1900, "lr": 0.00061, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61656, "top5_acc": 0.83266, "loss_cls": 2.14745, "loss": 2.14745, "time": 0.81634} +{"mode": "train", "epoch": 143, "iter": 2000, "lr": 0.00061, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61812, "top5_acc": 0.8425, "loss_cls": 2.13281, "loss": 2.13281, "time": 0.81153} +{"mode": "train", "epoch": 143, "iter": 2100, "lr": 0.00061, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62531, "top5_acc": 0.83469, "loss_cls": 2.14846, "loss": 2.14846, "time": 0.81677} +{"mode": "train", "epoch": 143, "iter": 2200, "lr": 0.0006, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.60594, "top5_acc": 0.82797, "loss_cls": 2.1996, "loss": 2.1996, "time": 0.82019} +{"mode": "train", "epoch": 143, "iter": 2300, "lr": 0.0006, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61594, "top5_acc": 0.84688, "loss_cls": 2.12531, "loss": 2.12531, "time": 0.81413} +{"mode": "train", "epoch": 143, "iter": 2400, "lr": 0.00059, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.60891, "top5_acc": 0.83047, "loss_cls": 2.15648, "loss": 2.15648, "time": 0.8218} +{"mode": "train", "epoch": 143, "iter": 2500, "lr": 0.00059, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61922, "top5_acc": 0.84391, "loss_cls": 2.14298, "loss": 2.14298, "time": 0.81518} +{"mode": "train", "epoch": 143, "iter": 2600, "lr": 0.00058, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62062, "top5_acc": 0.83766, "loss_cls": 2.14926, "loss": 2.14926, "time": 0.8112} +{"mode": "train", "epoch": 143, "iter": 2700, "lr": 0.00058, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61172, "top5_acc": 0.83312, "loss_cls": 2.18204, "loss": 2.18204, "time": 0.81962} +{"mode": "train", "epoch": 143, "iter": 2800, "lr": 0.00058, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61406, "top5_acc": 0.83344, "loss_cls": 2.17678, "loss": 2.17678, "time": 0.82169} +{"mode": "train", "epoch": 143, "iter": 2900, "lr": 0.00057, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.61969, "top5_acc": 0.84375, "loss_cls": 2.12041, "loss": 2.12041, "time": 0.81252} +{"mode": "train", "epoch": 143, "iter": 3000, "lr": 0.00057, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62938, "top5_acc": 0.84812, "loss_cls": 2.08724, "loss": 2.08724, "time": 0.81255} +{"mode": "train", "epoch": 143, "iter": 3100, "lr": 0.00056, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61031, "top5_acc": 0.83891, "loss_cls": 2.16793, "loss": 2.16793, "time": 0.81317} +{"mode": "train", "epoch": 143, "iter": 3200, "lr": 0.00056, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60484, "top5_acc": 0.83531, "loss_cls": 2.16955, "loss": 2.16955, "time": 0.81714} +{"mode": "train", "epoch": 143, "iter": 3300, "lr": 0.00055, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60328, "top5_acc": 0.8275, "loss_cls": 2.22873, "loss": 2.22873, "time": 0.81206} +{"mode": "train", "epoch": 143, "iter": 3400, "lr": 0.00055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61578, "top5_acc": 0.83969, "loss_cls": 2.1216, "loss": 2.1216, "time": 0.82013} +{"mode": "train", "epoch": 143, "iter": 3500, "lr": 0.00055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61781, "top5_acc": 0.84734, "loss_cls": 2.11741, "loss": 2.11741, "time": 0.81628} +{"mode": "train", "epoch": 143, "iter": 3600, "lr": 0.00054, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.61359, "top5_acc": 0.83672, "loss_cls": 2.17365, "loss": 2.17365, "time": 0.81208} +{"mode": "train", "epoch": 143, "iter": 3700, "lr": 0.00054, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61625, "top5_acc": 0.83453, "loss_cls": 2.14746, "loss": 2.14746, "time": 0.82051} +{"mode": "val", "epoch": 143, "iter": 309, "lr": 0.00054, "top1_acc": 0.45525, "top5_acc": 0.69944, "mean_class_accuracy": 0.45502} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00053, "memory": 15990, "data_time": 1.32967, "top1_acc": 0.62406, "top5_acc": 0.8525, "loss_cls": 2.07207, "loss": 2.07207, "time": 2.31019} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00053, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63047, "top5_acc": 0.85047, "loss_cls": 2.07993, "loss": 2.07993, "time": 0.81759} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00052, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.63406, "top5_acc": 0.84875, "loss_cls": 2.08445, "loss": 2.08445, "time": 0.81925} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00052, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63094, "top5_acc": 0.84312, "loss_cls": 2.09601, "loss": 2.09601, "time": 0.81512} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00052, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62922, "top5_acc": 0.84812, "loss_cls": 2.09511, "loss": 2.09511, "time": 0.81769} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00051, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.62203, "top5_acc": 0.8475, "loss_cls": 2.10512, "loss": 2.10512, "time": 0.81535} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00051, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62406, "top5_acc": 0.84266, "loss_cls": 2.10281, "loss": 2.10281, "time": 0.81694} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.0005, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62203, "top5_acc": 0.84266, "loss_cls": 2.11263, "loss": 2.11263, "time": 0.81129} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.0005, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.63406, "top5_acc": 0.85047, "loss_cls": 2.06766, "loss": 2.06766, "time": 0.81095} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.0005, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63266, "top5_acc": 0.85094, "loss_cls": 2.05967, "loss": 2.05967, "time": 0.81568} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.00049, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63609, "top5_acc": 0.84672, "loss_cls": 2.05761, "loss": 2.05761, "time": 0.81704} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.00049, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.63484, "top5_acc": 0.85344, "loss_cls": 2.04067, "loss": 2.04067, "time": 0.81198} +{"mode": "train", "epoch": 144, "iter": 1300, "lr": 0.00048, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62313, "top5_acc": 0.83938, "loss_cls": 2.11548, "loss": 2.11548, "time": 0.81597} +{"mode": "train", "epoch": 144, "iter": 1400, "lr": 0.00048, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63031, "top5_acc": 0.8375, "loss_cls": 2.1033, "loss": 2.1033, "time": 0.81738} +{"mode": "train", "epoch": 144, "iter": 1500, "lr": 0.00048, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62703, "top5_acc": 0.84781, "loss_cls": 2.0929, "loss": 2.0929, "time": 0.81364} +{"mode": "train", "epoch": 144, "iter": 1600, "lr": 0.00047, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63141, "top5_acc": 0.84547, "loss_cls": 2.05908, "loss": 2.05908, "time": 0.81265} +{"mode": "train", "epoch": 144, "iter": 1700, "lr": 0.00047, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63016, "top5_acc": 0.84594, "loss_cls": 2.09043, "loss": 2.09043, "time": 0.8137} +{"mode": "train", "epoch": 144, "iter": 1800, "lr": 0.00047, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.6275, "top5_acc": 0.84641, "loss_cls": 2.10853, "loss": 2.10853, "time": 0.815} +{"mode": "train", "epoch": 144, "iter": 1900, "lr": 0.00046, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63438, "top5_acc": 0.85, "loss_cls": 2.0545, "loss": 2.0545, "time": 0.81623} +{"mode": "train", "epoch": 144, "iter": 2000, "lr": 0.00046, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62844, "top5_acc": 0.84625, "loss_cls": 2.07319, "loss": 2.07319, "time": 0.82361} +{"mode": "train", "epoch": 144, "iter": 2100, "lr": 0.00045, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62906, "top5_acc": 0.84719, "loss_cls": 2.08656, "loss": 2.08656, "time": 0.81679} +{"mode": "train", "epoch": 144, "iter": 2200, "lr": 0.00045, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.62453, "top5_acc": 0.84016, "loss_cls": 2.12668, "loss": 2.12668, "time": 0.82063} +{"mode": "train", "epoch": 144, "iter": 2300, "lr": 0.00045, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62313, "top5_acc": 0.84562, "loss_cls": 2.10125, "loss": 2.10125, "time": 0.82323} +{"mode": "train", "epoch": 144, "iter": 2400, "lr": 0.00044, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63203, "top5_acc": 0.84016, "loss_cls": 2.095, "loss": 2.095, "time": 0.81649} +{"mode": "train", "epoch": 144, "iter": 2500, "lr": 0.00044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63, "top5_acc": 0.84609, "loss_cls": 2.09042, "loss": 2.09042, "time": 0.82199} +{"mode": "train", "epoch": 144, "iter": 2600, "lr": 0.00044, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62609, "top5_acc": 0.84875, "loss_cls": 2.06458, "loss": 2.06458, "time": 0.81409} +{"mode": "train", "epoch": 144, "iter": 2700, "lr": 0.00043, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62687, "top5_acc": 0.85156, "loss_cls": 2.07577, "loss": 2.07577, "time": 0.81354} +{"mode": "train", "epoch": 144, "iter": 2800, "lr": 0.00043, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.62406, "top5_acc": 0.84781, "loss_cls": 2.07616, "loss": 2.07616, "time": 0.82902} +{"mode": "train", "epoch": 144, "iter": 2900, "lr": 0.00042, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.61953, "top5_acc": 0.84094, "loss_cls": 2.13516, "loss": 2.13516, "time": 0.81571} +{"mode": "train", "epoch": 144, "iter": 3000, "lr": 0.00042, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62453, "top5_acc": 0.84578, "loss_cls": 2.10509, "loss": 2.10509, "time": 0.82003} +{"mode": "train", "epoch": 144, "iter": 3100, "lr": 0.00042, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62031, "top5_acc": 0.84734, "loss_cls": 2.12047, "loss": 2.12047, "time": 0.81603} +{"mode": "train", "epoch": 144, "iter": 3200, "lr": 0.00041, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61047, "top5_acc": 0.84062, "loss_cls": 2.14774, "loss": 2.14774, "time": 0.81397} +{"mode": "train", "epoch": 144, "iter": 3300, "lr": 0.00041, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62109, "top5_acc": 0.83531, "loss_cls": 2.11011, "loss": 2.11011, "time": 0.8176} +{"mode": "train", "epoch": 144, "iter": 3400, "lr": 0.00041, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61078, "top5_acc": 0.84188, "loss_cls": 2.14252, "loss": 2.14252, "time": 0.81378} +{"mode": "train", "epoch": 144, "iter": 3500, "lr": 0.0004, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63078, "top5_acc": 0.84203, "loss_cls": 2.10739, "loss": 2.10739, "time": 0.8116} +{"mode": "train", "epoch": 144, "iter": 3600, "lr": 0.0004, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61984, "top5_acc": 0.83906, "loss_cls": 2.11344, "loss": 2.11344, "time": 0.82102} +{"mode": "train", "epoch": 144, "iter": 3700, "lr": 0.0004, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.63062, "top5_acc": 0.84469, "loss_cls": 2.0679, "loss": 2.0679, "time": 0.81614} +{"mode": "val", "epoch": 144, "iter": 309, "lr": 0.00039, "top1_acc": 0.45753, "top5_acc": 0.69853, "mean_class_accuracy": 0.45721} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.00039, "memory": 15990, "data_time": 1.32852, "top1_acc": 0.64797, "top5_acc": 0.86031, "loss_cls": 1.98885, "loss": 1.98885, "time": 2.30598} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 0.00039, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64719, "top5_acc": 0.86344, "loss_cls": 1.99537, "loss": 1.99537, "time": 0.81722} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 0.00038, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63281, "top5_acc": 0.85094, "loss_cls": 2.05294, "loss": 2.05294, "time": 0.82188} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 0.00038, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64547, "top5_acc": 0.85766, "loss_cls": 2.00913, "loss": 2.00913, "time": 0.82371} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 0.00038, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64062, "top5_acc": 0.85609, "loss_cls": 2.03699, "loss": 2.03699, "time": 0.81716} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 0.00037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64688, "top5_acc": 0.85594, "loss_cls": 2.00249, "loss": 2.00249, "time": 0.81892} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 0.00037, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63875, "top5_acc": 0.85047, "loss_cls": 2.04312, "loss": 2.04312, "time": 0.82662} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 0.00037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.645, "top5_acc": 0.85547, "loss_cls": 1.99445, "loss": 1.99445, "time": 0.81718} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 0.00036, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63469, "top5_acc": 0.85203, "loss_cls": 2.01962, "loss": 2.01962, "time": 0.81222} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 0.00036, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63516, "top5_acc": 0.85359, "loss_cls": 2.04293, "loss": 2.04293, "time": 0.81142} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 0.00036, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64078, "top5_acc": 0.85391, "loss_cls": 2.04281, "loss": 2.04281, "time": 0.81248} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 0.00035, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63844, "top5_acc": 0.85719, "loss_cls": 2.01485, "loss": 2.01485, "time": 0.81607} +{"mode": "train", "epoch": 145, "iter": 1300, "lr": 0.00035, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63391, "top5_acc": 0.85656, "loss_cls": 2.02426, "loss": 2.02426, "time": 0.8153} +{"mode": "train", "epoch": 145, "iter": 1400, "lr": 0.00035, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63844, "top5_acc": 0.85406, "loss_cls": 2.04235, "loss": 2.04235, "time": 0.81828} +{"mode": "train", "epoch": 145, "iter": 1500, "lr": 0.00034, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64281, "top5_acc": 0.85328, "loss_cls": 2.00909, "loss": 2.00909, "time": 0.81455} +{"mode": "train", "epoch": 145, "iter": 1600, "lr": 0.00034, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63359, "top5_acc": 0.84562, "loss_cls": 2.07382, "loss": 2.07382, "time": 0.81373} +{"mode": "train", "epoch": 145, "iter": 1700, "lr": 0.00034, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62969, "top5_acc": 0.84766, "loss_cls": 2.07483, "loss": 2.07483, "time": 0.8134} +{"mode": "train", "epoch": 145, "iter": 1800, "lr": 0.00033, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63094, "top5_acc": 0.84312, "loss_cls": 2.07304, "loss": 2.07304, "time": 0.81304} +{"mode": "train", "epoch": 145, "iter": 1900, "lr": 0.00033, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.63828, "top5_acc": 0.84625, "loss_cls": 2.06075, "loss": 2.06075, "time": 0.81533} +{"mode": "train", "epoch": 145, "iter": 2000, "lr": 0.00033, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63625, "top5_acc": 0.85359, "loss_cls": 2.03824, "loss": 2.03824, "time": 0.81597} +{"mode": "train", "epoch": 145, "iter": 2100, "lr": 0.00032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63781, "top5_acc": 0.85781, "loss_cls": 2.03391, "loss": 2.03391, "time": 0.81829} +{"mode": "train", "epoch": 145, "iter": 2200, "lr": 0.00032, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63625, "top5_acc": 0.85094, "loss_cls": 2.03649, "loss": 2.03649, "time": 0.81858} +{"mode": "train", "epoch": 145, "iter": 2300, "lr": 0.00032, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.63578, "top5_acc": 0.85, "loss_cls": 2.05169, "loss": 2.05169, "time": 0.81756} +{"mode": "train", "epoch": 145, "iter": 2400, "lr": 0.00031, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63188, "top5_acc": 0.85328, "loss_cls": 2.04501, "loss": 2.04501, "time": 0.81539} +{"mode": "train", "epoch": 145, "iter": 2500, "lr": 0.00031, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63562, "top5_acc": 0.85406, "loss_cls": 2.03075, "loss": 2.03075, "time": 0.82276} +{"mode": "train", "epoch": 145, "iter": 2600, "lr": 0.00031, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64172, "top5_acc": 0.85078, "loss_cls": 2.01643, "loss": 2.01643, "time": 0.81578} +{"mode": "train", "epoch": 145, "iter": 2700, "lr": 0.00031, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64, "top5_acc": 0.85203, "loss_cls": 2.04642, "loss": 2.04642, "time": 0.81506} +{"mode": "train", "epoch": 145, "iter": 2800, "lr": 0.0003, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.63656, "top5_acc": 0.85781, "loss_cls": 2.01552, "loss": 2.01552, "time": 0.8148} +{"mode": "train", "epoch": 145, "iter": 2900, "lr": 0.0003, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.635, "top5_acc": 0.84969, "loss_cls": 2.05802, "loss": 2.05802, "time": 0.82339} +{"mode": "train", "epoch": 145, "iter": 3000, "lr": 0.0003, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.6425, "top5_acc": 0.85375, "loss_cls": 2.03577, "loss": 2.03577, "time": 0.81842} +{"mode": "train", "epoch": 145, "iter": 3100, "lr": 0.00029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63172, "top5_acc": 0.84406, "loss_cls": 2.07752, "loss": 2.07752, "time": 0.81646} +{"mode": "train", "epoch": 145, "iter": 3200, "lr": 0.00029, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.63641, "top5_acc": 0.85406, "loss_cls": 2.04592, "loss": 2.04592, "time": 0.81121} +{"mode": "train", "epoch": 145, "iter": 3300, "lr": 0.00029, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63219, "top5_acc": 0.8425, "loss_cls": 2.11068, "loss": 2.11068, "time": 0.81474} +{"mode": "train", "epoch": 145, "iter": 3400, "lr": 0.00028, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63484, "top5_acc": 0.84609, "loss_cls": 2.05449, "loss": 2.05449, "time": 0.81217} +{"mode": "train", "epoch": 145, "iter": 3500, "lr": 0.00028, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63609, "top5_acc": 0.85609, "loss_cls": 2.04355, "loss": 2.04355, "time": 0.81212} +{"mode": "train", "epoch": 145, "iter": 3600, "lr": 0.00028, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63969, "top5_acc": 0.85422, "loss_cls": 2.02742, "loss": 2.02742, "time": 0.81397} +{"mode": "train", "epoch": 145, "iter": 3700, "lr": 0.00028, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62297, "top5_acc": 0.84578, "loss_cls": 2.09586, "loss": 2.09586, "time": 0.8121} +{"mode": "val", "epoch": 145, "iter": 309, "lr": 0.00027, "top1_acc": 0.45469, "top5_acc": 0.70091, "mean_class_accuracy": 0.45443} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 0.00027, "memory": 15990, "data_time": 1.31418, "top1_acc": 0.64109, "top5_acc": 0.86047, "loss_cls": 2.01653, "loss": 2.01653, "time": 2.29369} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 0.00027, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65, "top5_acc": 0.85953, "loss_cls": 1.97007, "loss": 1.97007, "time": 0.81736} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 0.00027, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65375, "top5_acc": 0.85547, "loss_cls": 1.98653, "loss": 1.98653, "time": 0.82104} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 0.00026, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65125, "top5_acc": 0.86328, "loss_cls": 1.97224, "loss": 1.97224, "time": 0.81352} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 0.00026, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.65188, "top5_acc": 0.86641, "loss_cls": 1.96867, "loss": 1.96867, "time": 0.8207} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 0.00026, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.655, "top5_acc": 0.85828, "loss_cls": 1.991, "loss": 1.991, "time": 0.81903} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 0.00025, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64672, "top5_acc": 0.85609, "loss_cls": 1.99541, "loss": 1.99541, "time": 0.8102} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 0.00025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65328, "top5_acc": 0.85562, "loss_cls": 1.99264, "loss": 1.99264, "time": 0.81942} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 0.00025, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63688, "top5_acc": 0.85891, "loss_cls": 2.03468, "loss": 2.03468, "time": 0.81109} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 0.00025, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63391, "top5_acc": 0.85359, "loss_cls": 2.01309, "loss": 2.01309, "time": 0.81492} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 0.00024, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63266, "top5_acc": 0.84891, "loss_cls": 2.0364, "loss": 2.0364, "time": 0.8177} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 0.00024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65469, "top5_acc": 0.86359, "loss_cls": 1.96002, "loss": 1.96002, "time": 0.81479} +{"mode": "train", "epoch": 146, "iter": 1300, "lr": 0.00024, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63703, "top5_acc": 0.85016, "loss_cls": 2.05049, "loss": 2.05049, "time": 0.81379} +{"mode": "train", "epoch": 146, "iter": 1400, "lr": 0.00023, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65375, "top5_acc": 0.86125, "loss_cls": 1.97227, "loss": 1.97227, "time": 0.81403} +{"mode": "train", "epoch": 146, "iter": 1500, "lr": 0.00023, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64375, "top5_acc": 0.85516, "loss_cls": 2.01873, "loss": 2.01873, "time": 0.81602} +{"mode": "train", "epoch": 146, "iter": 1600, "lr": 0.00023, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64734, "top5_acc": 0.86109, "loss_cls": 1.99788, "loss": 1.99788, "time": 0.8186} +{"mode": "train", "epoch": 146, "iter": 1700, "lr": 0.00023, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65297, "top5_acc": 0.86047, "loss_cls": 1.98717, "loss": 1.98717, "time": 0.81259} +{"mode": "train", "epoch": 146, "iter": 1800, "lr": 0.00022, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64938, "top5_acc": 0.85797, "loss_cls": 2.00255, "loss": 2.00255, "time": 0.81633} +{"mode": "train", "epoch": 146, "iter": 1900, "lr": 0.00022, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64938, "top5_acc": 0.86094, "loss_cls": 2.00794, "loss": 2.00794, "time": 0.81594} +{"mode": "train", "epoch": 146, "iter": 2000, "lr": 0.00022, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64609, "top5_acc": 0.86203, "loss_cls": 1.99954, "loss": 1.99954, "time": 0.81269} +{"mode": "train", "epoch": 146, "iter": 2100, "lr": 0.00022, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.6425, "top5_acc": 0.85656, "loss_cls": 2.0205, "loss": 2.0205, "time": 0.8137} +{"mode": "train", "epoch": 146, "iter": 2200, "lr": 0.00021, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.65016, "top5_acc": 0.86469, "loss_cls": 1.94879, "loss": 1.94879, "time": 0.81986} +{"mode": "train", "epoch": 146, "iter": 2300, "lr": 0.00021, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65062, "top5_acc": 0.86141, "loss_cls": 1.96684, "loss": 1.96684, "time": 0.81729} +{"mode": "train", "epoch": 146, "iter": 2400, "lr": 0.00021, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.64688, "top5_acc": 0.85812, "loss_cls": 2.00203, "loss": 2.00203, "time": 0.81811} +{"mode": "train", "epoch": 146, "iter": 2500, "lr": 0.00021, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.64234, "top5_acc": 0.85359, "loss_cls": 2.0191, "loss": 2.0191, "time": 0.82035} +{"mode": "train", "epoch": 146, "iter": 2600, "lr": 0.0002, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64344, "top5_acc": 0.865, "loss_cls": 1.9855, "loss": 1.9855, "time": 0.81727} +{"mode": "train", "epoch": 146, "iter": 2700, "lr": 0.0002, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64484, "top5_acc": 0.85031, "loss_cls": 2.03065, "loss": 2.03065, "time": 0.81712} +{"mode": "train", "epoch": 146, "iter": 2800, "lr": 0.0002, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64609, "top5_acc": 0.85281, "loss_cls": 1.99506, "loss": 1.99506, "time": 0.81977} +{"mode": "train", "epoch": 146, "iter": 2900, "lr": 0.0002, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.65094, "top5_acc": 0.86438, "loss_cls": 1.98537, "loss": 1.98537, "time": 0.83119} +{"mode": "train", "epoch": 146, "iter": 3000, "lr": 0.00019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65312, "top5_acc": 0.86328, "loss_cls": 1.95093, "loss": 1.95093, "time": 0.8187} +{"mode": "train", "epoch": 146, "iter": 3100, "lr": 0.00019, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.64609, "top5_acc": 0.86109, "loss_cls": 2.00122, "loss": 2.00122, "time": 0.82914} +{"mode": "train", "epoch": 146, "iter": 3200, "lr": 0.00019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66156, "top5_acc": 0.865, "loss_cls": 1.94037, "loss": 1.94037, "time": 0.82045} +{"mode": "train", "epoch": 146, "iter": 3300, "lr": 0.00019, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65391, "top5_acc": 0.85484, "loss_cls": 1.99066, "loss": 1.99066, "time": 0.81464} +{"mode": "train", "epoch": 146, "iter": 3400, "lr": 0.00018, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.6575, "top5_acc": 0.86156, "loss_cls": 1.95991, "loss": 1.95991, "time": 0.81483} +{"mode": "train", "epoch": 146, "iter": 3500, "lr": 0.00018, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.64312, "top5_acc": 0.85844, "loss_cls": 2.00487, "loss": 2.00487, "time": 0.81891} +{"mode": "train", "epoch": 146, "iter": 3600, "lr": 0.00018, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.64797, "top5_acc": 0.85828, "loss_cls": 1.98815, "loss": 1.98815, "time": 0.81946} +{"mode": "train", "epoch": 146, "iter": 3700, "lr": 0.00018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65172, "top5_acc": 0.86406, "loss_cls": 1.97737, "loss": 1.97737, "time": 0.81659} +{"mode": "val", "epoch": 146, "iter": 309, "lr": 0.00018, "top1_acc": 0.45596, "top5_acc": 0.69994, "mean_class_accuracy": 0.45572} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 0.00017, "memory": 15990, "data_time": 1.33294, "top1_acc": 0.65859, "top5_acc": 0.86234, "loss_cls": 1.94893, "loss": 1.94893, "time": 2.32168} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 0.00017, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66, "top5_acc": 0.86688, "loss_cls": 1.93301, "loss": 1.93301, "time": 0.81678} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 0.00017, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.65672, "top5_acc": 0.85766, "loss_cls": 1.95725, "loss": 1.95725, "time": 0.81858} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 0.00017, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65312, "top5_acc": 0.86688, "loss_cls": 1.93051, "loss": 1.93051, "time": 0.81543} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 0.00016, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65062, "top5_acc": 0.86328, "loss_cls": 1.9746, "loss": 1.9746, "time": 0.81811} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 0.00016, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66109, "top5_acc": 0.86828, "loss_cls": 1.93295, "loss": 1.93295, "time": 0.81534} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 0.00016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65, "top5_acc": 0.85438, "loss_cls": 1.99523, "loss": 1.99523, "time": 0.81473} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 0.00016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66453, "top5_acc": 0.86922, "loss_cls": 1.91512, "loss": 1.91512, "time": 0.816} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 0.00015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66219, "top5_acc": 0.86016, "loss_cls": 1.97041, "loss": 1.97041, "time": 0.81842} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 0.00015, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64656, "top5_acc": 0.85609, "loss_cls": 2.00206, "loss": 2.00206, "time": 0.81457} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 0.00015, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65953, "top5_acc": 0.86734, "loss_cls": 1.95847, "loss": 1.95847, "time": 0.81424} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 0.00015, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66844, "top5_acc": 0.87062, "loss_cls": 1.90706, "loss": 1.90706, "time": 0.81588} +{"mode": "train", "epoch": 147, "iter": 1300, "lr": 0.00015, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65703, "top5_acc": 0.86656, "loss_cls": 1.92075, "loss": 1.92075, "time": 0.81516} +{"mode": "train", "epoch": 147, "iter": 1400, "lr": 0.00014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65938, "top5_acc": 0.86422, "loss_cls": 1.96416, "loss": 1.96416, "time": 0.81358} +{"mode": "train", "epoch": 147, "iter": 1500, "lr": 0.00014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64828, "top5_acc": 0.86047, "loss_cls": 1.99131, "loss": 1.99131, "time": 0.82019} +{"mode": "train", "epoch": 147, "iter": 1600, "lr": 0.00014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64797, "top5_acc": 0.86203, "loss_cls": 1.98726, "loss": 1.98726, "time": 0.81699} +{"mode": "train", "epoch": 147, "iter": 1700, "lr": 0.00014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65734, "top5_acc": 0.86156, "loss_cls": 1.95968, "loss": 1.95968, "time": 0.80969} +{"mode": "train", "epoch": 147, "iter": 1800, "lr": 0.00014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66047, "top5_acc": 0.86422, "loss_cls": 1.94465, "loss": 1.94465, "time": 0.81781} +{"mode": "train", "epoch": 147, "iter": 1900, "lr": 0.00013, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65828, "top5_acc": 0.86391, "loss_cls": 1.95411, "loss": 1.95411, "time": 0.8167} +{"mode": "train", "epoch": 147, "iter": 2000, "lr": 0.00013, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65969, "top5_acc": 0.86578, "loss_cls": 1.94434, "loss": 1.94434, "time": 0.81576} +{"mode": "train", "epoch": 147, "iter": 2100, "lr": 0.00013, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.6575, "top5_acc": 0.86047, "loss_cls": 1.96044, "loss": 1.96044, "time": 0.81477} +{"mode": "train", "epoch": 147, "iter": 2200, "lr": 0.00013, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.65281, "top5_acc": 0.86453, "loss_cls": 1.94659, "loss": 1.94659, "time": 0.81908} +{"mode": "train", "epoch": 147, "iter": 2300, "lr": 0.00013, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65984, "top5_acc": 0.86469, "loss_cls": 1.93567, "loss": 1.93567, "time": 0.81384} +{"mode": "train", "epoch": 147, "iter": 2400, "lr": 0.00012, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65609, "top5_acc": 0.86094, "loss_cls": 1.94332, "loss": 1.94332, "time": 0.82344} +{"mode": "train", "epoch": 147, "iter": 2500, "lr": 0.00012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65594, "top5_acc": 0.86094, "loss_cls": 1.95918, "loss": 1.95918, "time": 0.81345} +{"mode": "train", "epoch": 147, "iter": 2600, "lr": 0.00012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64641, "top5_acc": 0.85906, "loss_cls": 1.97742, "loss": 1.97742, "time": 0.81427} +{"mode": "train", "epoch": 147, "iter": 2700, "lr": 0.00012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65, "top5_acc": 0.85844, "loss_cls": 1.97881, "loss": 1.97881, "time": 0.81166} +{"mode": "train", "epoch": 147, "iter": 2800, "lr": 0.00012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64906, "top5_acc": 0.86469, "loss_cls": 1.9781, "loss": 1.9781, "time": 0.81262} +{"mode": "train", "epoch": 147, "iter": 2900, "lr": 0.00011, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.65922, "top5_acc": 0.86766, "loss_cls": 1.91793, "loss": 1.91793, "time": 0.83136} +{"mode": "train", "epoch": 147, "iter": 3000, "lr": 0.00011, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64734, "top5_acc": 0.85906, "loss_cls": 1.98186, "loss": 1.98186, "time": 0.81569} +{"mode": "train", "epoch": 147, "iter": 3100, "lr": 0.00011, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.65531, "top5_acc": 0.85938, "loss_cls": 1.96422, "loss": 1.96422, "time": 0.8238} +{"mode": "train", "epoch": 147, "iter": 3200, "lr": 0.00011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65531, "top5_acc": 0.86281, "loss_cls": 1.955, "loss": 1.955, "time": 0.81719} +{"mode": "train", "epoch": 147, "iter": 3300, "lr": 0.00011, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.65312, "top5_acc": 0.86766, "loss_cls": 1.94691, "loss": 1.94691, "time": 0.81873} +{"mode": "train", "epoch": 147, "iter": 3400, "lr": 0.0001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66188, "top5_acc": 0.87, "loss_cls": 1.93434, "loss": 1.93434, "time": 0.81381} +{"mode": "train", "epoch": 147, "iter": 3500, "lr": 0.0001, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66062, "top5_acc": 0.87203, "loss_cls": 1.92949, "loss": 1.92949, "time": 0.81642} +{"mode": "train", "epoch": 147, "iter": 3600, "lr": 0.0001, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.65375, "top5_acc": 0.85984, "loss_cls": 1.97796, "loss": 1.97796, "time": 0.81726} +{"mode": "train", "epoch": 147, "iter": 3700, "lr": 0.0001, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.65641, "top5_acc": 0.87219, "loss_cls": 1.91941, "loss": 1.91941, "time": 0.81307} +{"mode": "val", "epoch": 147, "iter": 309, "lr": 0.0001, "top1_acc": 0.45788, "top5_acc": 0.70303, "mean_class_accuracy": 0.45762} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 0.0001, "memory": 15990, "data_time": 1.29073, "top1_acc": 0.65484, "top5_acc": 0.87141, "loss_cls": 1.93212, "loss": 1.93212, "time": 2.28078} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 0.0001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66906, "top5_acc": 0.86953, "loss_cls": 1.91718, "loss": 1.91718, "time": 0.82048} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 9e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66422, "top5_acc": 0.86781, "loss_cls": 1.9156, "loss": 1.9156, "time": 0.8195} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 9e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65891, "top5_acc": 0.85797, "loss_cls": 1.96332, "loss": 1.96332, "time": 0.816} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 9e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66062, "top5_acc": 0.86016, "loss_cls": 1.94204, "loss": 1.94204, "time": 0.81702} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 9e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.6575, "top5_acc": 0.86781, "loss_cls": 1.92825, "loss": 1.92825, "time": 0.81982} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 9e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66016, "top5_acc": 0.87094, "loss_cls": 1.92236, "loss": 1.92236, "time": 0.81944} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 9e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66188, "top5_acc": 0.86922, "loss_cls": 1.9141, "loss": 1.9141, "time": 0.81779} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 8e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65859, "top5_acc": 0.86812, "loss_cls": 1.92628, "loss": 1.92628, "time": 0.81601} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 8e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66234, "top5_acc": 0.87141, "loss_cls": 1.93274, "loss": 1.93274, "time": 0.81375} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 8e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66031, "top5_acc": 0.86906, "loss_cls": 1.91214, "loss": 1.91214, "time": 0.81081} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 8e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65688, "top5_acc": 0.86641, "loss_cls": 1.9469, "loss": 1.9469, "time": 0.81971} +{"mode": "train", "epoch": 148, "iter": 1300, "lr": 8e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65719, "top5_acc": 0.86859, "loss_cls": 1.93847, "loss": 1.93847, "time": 0.8117} +{"mode": "train", "epoch": 148, "iter": 1400, "lr": 8e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65859, "top5_acc": 0.86422, "loss_cls": 1.95179, "loss": 1.95179, "time": 0.81256} +{"mode": "train", "epoch": 148, "iter": 1500, "lr": 7e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66438, "top5_acc": 0.86078, "loss_cls": 1.951, "loss": 1.951, "time": 0.81226} +{"mode": "train", "epoch": 148, "iter": 1600, "lr": 7e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66141, "top5_acc": 0.86922, "loss_cls": 1.93801, "loss": 1.93801, "time": 0.8232} +{"mode": "train", "epoch": 148, "iter": 1700, "lr": 7e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65812, "top5_acc": 0.86875, "loss_cls": 1.92768, "loss": 1.92768, "time": 0.81275} +{"mode": "train", "epoch": 148, "iter": 1800, "lr": 7e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66969, "top5_acc": 0.88078, "loss_cls": 1.87454, "loss": 1.87454, "time": 0.818} +{"mode": "train", "epoch": 148, "iter": 1900, "lr": 7e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65828, "top5_acc": 0.8675, "loss_cls": 1.93468, "loss": 1.93468, "time": 0.81532} +{"mode": "train", "epoch": 148, "iter": 2000, "lr": 7e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66609, "top5_acc": 0.86484, "loss_cls": 1.90428, "loss": 1.90428, "time": 0.8169} +{"mode": "train", "epoch": 148, "iter": 2100, "lr": 7e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65844, "top5_acc": 0.86391, "loss_cls": 1.93474, "loss": 1.93474, "time": 0.82126} +{"mode": "train", "epoch": 148, "iter": 2200, "lr": 6e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65328, "top5_acc": 0.86781, "loss_cls": 1.93461, "loss": 1.93461, "time": 0.81871} +{"mode": "train", "epoch": 148, "iter": 2300, "lr": 6e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66094, "top5_acc": 0.86641, "loss_cls": 1.91542, "loss": 1.91542, "time": 0.82141} +{"mode": "train", "epoch": 148, "iter": 2400, "lr": 6e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.65906, "top5_acc": 0.86547, "loss_cls": 1.93318, "loss": 1.93318, "time": 0.82069} +{"mode": "train", "epoch": 148, "iter": 2500, "lr": 6e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65188, "top5_acc": 0.86625, "loss_cls": 1.95041, "loss": 1.95041, "time": 0.81843} +{"mode": "train", "epoch": 148, "iter": 2600, "lr": 6e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66766, "top5_acc": 0.86406, "loss_cls": 1.93997, "loss": 1.93997, "time": 0.81388} +{"mode": "train", "epoch": 148, "iter": 2700, "lr": 6e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66, "top5_acc": 0.86516, "loss_cls": 1.94173, "loss": 1.94173, "time": 0.82028} +{"mode": "train", "epoch": 148, "iter": 2800, "lr": 6e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66812, "top5_acc": 0.86422, "loss_cls": 1.90938, "loss": 1.90938, "time": 0.81428} +{"mode": "train", "epoch": 148, "iter": 2900, "lr": 5e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67266, "top5_acc": 0.87344, "loss_cls": 1.87118, "loss": 1.87118, "time": 0.8208} +{"mode": "train", "epoch": 148, "iter": 3000, "lr": 5e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.67969, "top5_acc": 0.8775, "loss_cls": 1.85701, "loss": 1.85701, "time": 0.81984} +{"mode": "train", "epoch": 148, "iter": 3100, "lr": 5e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66562, "top5_acc": 0.87125, "loss_cls": 1.89325, "loss": 1.89325, "time": 0.82217} +{"mode": "train", "epoch": 148, "iter": 3200, "lr": 5e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66016, "top5_acc": 0.86641, "loss_cls": 1.93041, "loss": 1.93041, "time": 0.81373} +{"mode": "train", "epoch": 148, "iter": 3300, "lr": 5e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65922, "top5_acc": 0.86766, "loss_cls": 1.94463, "loss": 1.94463, "time": 0.81025} +{"mode": "train", "epoch": 148, "iter": 3400, "lr": 5e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66141, "top5_acc": 0.86812, "loss_cls": 1.93298, "loss": 1.93298, "time": 0.82314} +{"mode": "train", "epoch": 148, "iter": 3500, "lr": 5e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66469, "top5_acc": 0.86938, "loss_cls": 1.91502, "loss": 1.91502, "time": 0.81389} +{"mode": "train", "epoch": 148, "iter": 3600, "lr": 5e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65656, "top5_acc": 0.86375, "loss_cls": 1.9532, "loss": 1.9532, "time": 0.81827} +{"mode": "train", "epoch": 148, "iter": 3700, "lr": 4e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.6675, "top5_acc": 0.87156, "loss_cls": 1.91198, "loss": 1.91198, "time": 0.81947} +{"mode": "val", "epoch": 148, "iter": 309, "lr": 4e-05, "top1_acc": 0.45738, "top5_acc": 0.70222, "mean_class_accuracy": 0.45711} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 4e-05, "memory": 15990, "data_time": 1.28472, "top1_acc": 0.66328, "top5_acc": 0.87078, "loss_cls": 1.92076, "loss": 1.92076, "time": 2.26887} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 4e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66594, "top5_acc": 0.87594, "loss_cls": 1.90537, "loss": 1.90537, "time": 0.81366} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 4e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.66875, "top5_acc": 0.87094, "loss_cls": 1.89443, "loss": 1.89443, "time": 0.82186} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 4e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65, "top5_acc": 0.86203, "loss_cls": 1.98483, "loss": 1.98483, "time": 0.82302} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 4e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.67188, "top5_acc": 0.8725, "loss_cls": 1.89042, "loss": 1.89042, "time": 0.82022} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 4e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66469, "top5_acc": 0.86781, "loss_cls": 1.90886, "loss": 1.90886, "time": 0.81648} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 4e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66625, "top5_acc": 0.8675, "loss_cls": 1.90643, "loss": 1.90643, "time": 0.81604} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 4e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66219, "top5_acc": 0.86656, "loss_cls": 1.90493, "loss": 1.90493, "time": 0.8232} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 3e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67062, "top5_acc": 0.87625, "loss_cls": 1.8869, "loss": 1.8869, "time": 0.81038} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 3e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66328, "top5_acc": 0.87391, "loss_cls": 1.89751, "loss": 1.89751, "time": 0.81832} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 3e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67031, "top5_acc": 0.86938, "loss_cls": 1.88298, "loss": 1.88298, "time": 0.81967} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 3e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66922, "top5_acc": 0.87156, "loss_cls": 1.90152, "loss": 1.90152, "time": 0.81761} +{"mode": "train", "epoch": 149, "iter": 1300, "lr": 3e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66766, "top5_acc": 0.87219, "loss_cls": 1.9007, "loss": 1.9007, "time": 0.81794} +{"mode": "train", "epoch": 149, "iter": 1400, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67344, "top5_acc": 0.87141, "loss_cls": 1.88257, "loss": 1.88257, "time": 0.81657} +{"mode": "train", "epoch": 149, "iter": 1500, "lr": 3e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66672, "top5_acc": 0.87438, "loss_cls": 1.87929, "loss": 1.87929, "time": 0.81236} +{"mode": "train", "epoch": 149, "iter": 1600, "lr": 3e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65891, "top5_acc": 0.86891, "loss_cls": 1.92394, "loss": 1.92394, "time": 0.81643} +{"mode": "train", "epoch": 149, "iter": 1700, "lr": 3e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65797, "top5_acc": 0.8725, "loss_cls": 1.91533, "loss": 1.91533, "time": 0.81378} +{"mode": "train", "epoch": 149, "iter": 1800, "lr": 3e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.665, "top5_acc": 0.86688, "loss_cls": 1.92724, "loss": 1.92724, "time": 0.81504} +{"mode": "train", "epoch": 149, "iter": 1900, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66672, "top5_acc": 0.87141, "loss_cls": 1.91379, "loss": 1.91379, "time": 0.81583} +{"mode": "train", "epoch": 149, "iter": 2000, "lr": 2e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.675, "top5_acc": 0.87438, "loss_cls": 1.88003, "loss": 1.88003, "time": 0.81597} +{"mode": "train", "epoch": 149, "iter": 2100, "lr": 2e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66219, "top5_acc": 0.87266, "loss_cls": 1.91119, "loss": 1.91119, "time": 0.81584} +{"mode": "train", "epoch": 149, "iter": 2200, "lr": 2e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67062, "top5_acc": 0.87078, "loss_cls": 1.92316, "loss": 1.92316, "time": 0.81612} +{"mode": "train", "epoch": 149, "iter": 2300, "lr": 2e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66984, "top5_acc": 0.86781, "loss_cls": 1.91767, "loss": 1.91767, "time": 0.81648} +{"mode": "train", "epoch": 149, "iter": 2400, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67078, "top5_acc": 0.8725, "loss_cls": 1.86804, "loss": 1.86804, "time": 0.82339} +{"mode": "train", "epoch": 149, "iter": 2500, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66656, "top5_acc": 0.86922, "loss_cls": 1.91493, "loss": 1.91493, "time": 0.8148} +{"mode": "train", "epoch": 149, "iter": 2600, "lr": 2e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.6625, "top5_acc": 0.86734, "loss_cls": 1.91471, "loss": 1.91471, "time": 0.81736} +{"mode": "train", "epoch": 149, "iter": 2700, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65859, "top5_acc": 0.86141, "loss_cls": 1.94681, "loss": 1.94681, "time": 0.8132} +{"mode": "train", "epoch": 149, "iter": 2800, "lr": 2e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67578, "top5_acc": 0.87812, "loss_cls": 1.86952, "loss": 1.86952, "time": 0.81699} +{"mode": "train", "epoch": 149, "iter": 2900, "lr": 2e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66859, "top5_acc": 0.86891, "loss_cls": 1.91372, "loss": 1.91372, "time": 0.81461} +{"mode": "train", "epoch": 149, "iter": 3000, "lr": 2e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66859, "top5_acc": 0.86703, "loss_cls": 1.91647, "loss": 1.91647, "time": 0.82722} +{"mode": "train", "epoch": 149, "iter": 3100, "lr": 2e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67375, "top5_acc": 0.86922, "loss_cls": 1.89936, "loss": 1.89936, "time": 0.81586} +{"mode": "train", "epoch": 149, "iter": 3200, "lr": 1e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66359, "top5_acc": 0.86688, "loss_cls": 1.92216, "loss": 1.92216, "time": 0.82305} +{"mode": "train", "epoch": 149, "iter": 3300, "lr": 1e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67391, "top5_acc": 0.88297, "loss_cls": 1.86672, "loss": 1.86672, "time": 0.82029} +{"mode": "train", "epoch": 149, "iter": 3400, "lr": 1e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66766, "top5_acc": 0.86641, "loss_cls": 1.93774, "loss": 1.93774, "time": 0.80991} +{"mode": "train", "epoch": 149, "iter": 3500, "lr": 1e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66734, "top5_acc": 0.87109, "loss_cls": 1.91299, "loss": 1.91299, "time": 0.81426} +{"mode": "train", "epoch": 149, "iter": 3600, "lr": 1e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.66125, "top5_acc": 0.87, "loss_cls": 1.91765, "loss": 1.91765, "time": 0.81332} +{"mode": "train", "epoch": 149, "iter": 3700, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66766, "top5_acc": 0.86891, "loss_cls": 1.90341, "loss": 1.90341, "time": 0.81143} +{"mode": "val", "epoch": 149, "iter": 309, "lr": 1e-05, "top1_acc": 0.45636, "top5_acc": 0.70162, "mean_class_accuracy": 0.45606} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 1e-05, "memory": 15990, "data_time": 1.27799, "top1_acc": 0.67516, "top5_acc": 0.87375, "loss_cls": 1.87804, "loss": 1.87804, "time": 2.25454} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67344, "top5_acc": 0.87156, "loss_cls": 1.86881, "loss": 1.86881, "time": 0.81454} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67969, "top5_acc": 0.87391, "loss_cls": 1.87567, "loss": 1.87567, "time": 0.82071} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67578, "top5_acc": 0.87688, "loss_cls": 1.87631, "loss": 1.87631, "time": 0.81556} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 1e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66188, "top5_acc": 0.86328, "loss_cls": 1.93343, "loss": 1.93343, "time": 0.82469} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67172, "top5_acc": 0.87844, "loss_cls": 1.87046, "loss": 1.87046, "time": 0.81625} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 1e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66234, "top5_acc": 0.86891, "loss_cls": 1.90348, "loss": 1.90348, "time": 0.81977} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 1e-05, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.67734, "top5_acc": 0.87672, "loss_cls": 1.86266, "loss": 1.86266, "time": 0.81306} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 1e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65672, "top5_acc": 0.86875, "loss_cls": 1.93479, "loss": 1.93479, "time": 0.81198} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65844, "top5_acc": 0.86422, "loss_cls": 1.92742, "loss": 1.92742, "time": 0.81426} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 1e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.68156, "top5_acc": 0.88016, "loss_cls": 1.85093, "loss": 1.85093, "time": 0.81349} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66734, "top5_acc": 0.87219, "loss_cls": 1.90262, "loss": 1.90262, "time": 0.8133} +{"mode": "train", "epoch": 150, "iter": 1300, "lr": 0.0, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66328, "top5_acc": 0.86594, "loss_cls": 1.91986, "loss": 1.91986, "time": 0.81387} +{"mode": "train", "epoch": 150, "iter": 1400, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67219, "top5_acc": 0.87078, "loss_cls": 1.91093, "loss": 1.91093, "time": 0.81335} +{"mode": "train", "epoch": 150, "iter": 1500, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66703, "top5_acc": 0.8675, "loss_cls": 1.89213, "loss": 1.89213, "time": 0.81499} +{"mode": "train", "epoch": 150, "iter": 1600, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67062, "top5_acc": 0.87844, "loss_cls": 1.89461, "loss": 1.89461, "time": 0.80809} +{"mode": "train", "epoch": 150, "iter": 1700, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66953, "top5_acc": 0.8725, "loss_cls": 1.88064, "loss": 1.88064, "time": 0.81526} +{"mode": "train", "epoch": 150, "iter": 1800, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66156, "top5_acc": 0.86375, "loss_cls": 1.92605, "loss": 1.92605, "time": 0.82131} +{"mode": "train", "epoch": 150, "iter": 1900, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66938, "top5_acc": 0.87125, "loss_cls": 1.87816, "loss": 1.87816, "time": 0.81307} +{"mode": "train", "epoch": 150, "iter": 2000, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65812, "top5_acc": 0.86484, "loss_cls": 1.9557, "loss": 1.9557, "time": 0.81303} +{"mode": "train", "epoch": 150, "iter": 2100, "lr": 0.0, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66688, "top5_acc": 0.86969, "loss_cls": 1.91371, "loss": 1.91371, "time": 0.81414} +{"mode": "train", "epoch": 150, "iter": 2200, "lr": 0.0, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66656, "top5_acc": 0.87328, "loss_cls": 1.89542, "loss": 1.89542, "time": 0.81781} +{"mode": "train", "epoch": 150, "iter": 2300, "lr": 0.0, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66531, "top5_acc": 0.87234, "loss_cls": 1.91753, "loss": 1.91753, "time": 0.81776} +{"mode": "train", "epoch": 150, "iter": 2400, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65938, "top5_acc": 0.87047, "loss_cls": 1.89964, "loss": 1.89964, "time": 0.81642} +{"mode": "train", "epoch": 150, "iter": 2500, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67328, "top5_acc": 0.87578, "loss_cls": 1.86925, "loss": 1.86925, "time": 0.81643} +{"mode": "train", "epoch": 150, "iter": 2600, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67422, "top5_acc": 0.87797, "loss_cls": 1.8614, "loss": 1.8614, "time": 0.8153} +{"mode": "train", "epoch": 150, "iter": 2700, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67359, "top5_acc": 0.86859, "loss_cls": 1.89346, "loss": 1.89346, "time": 0.81298} +{"mode": "train", "epoch": 150, "iter": 2800, "lr": 0.0, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66516, "top5_acc": 0.86609, "loss_cls": 1.91536, "loss": 1.91536, "time": 0.81118} +{"mode": "train", "epoch": 150, "iter": 2900, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66984, "top5_acc": 0.87125, "loss_cls": 1.8708, "loss": 1.8708, "time": 0.81447} +{"mode": "train", "epoch": 150, "iter": 3000, "lr": 0.0, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.66234, "top5_acc": 0.86797, "loss_cls": 1.91662, "loss": 1.91662, "time": 0.81968} +{"mode": "train", "epoch": 150, "iter": 3100, "lr": 0.0, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.67375, "top5_acc": 0.87656, "loss_cls": 1.88565, "loss": 1.88565, "time": 0.82121} +{"mode": "train", "epoch": 150, "iter": 3200, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66578, "top5_acc": 0.87328, "loss_cls": 1.91448, "loss": 1.91448, "time": 0.81835} +{"mode": "train", "epoch": 150, "iter": 3300, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66109, "top5_acc": 0.86812, "loss_cls": 1.92512, "loss": 1.92512, "time": 0.81832} +{"mode": "train", "epoch": 150, "iter": 3400, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66828, "top5_acc": 0.87328, "loss_cls": 1.89727, "loss": 1.89727, "time": 0.81567} +{"mode": "train", "epoch": 150, "iter": 3500, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66406, "top5_acc": 0.875, "loss_cls": 1.89106, "loss": 1.89106, "time": 0.81932} +{"mode": "train", "epoch": 150, "iter": 3600, "lr": 0.0, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66453, "top5_acc": 0.86719, "loss_cls": 1.89817, "loss": 1.89817, "time": 0.81463} +{"mode": "train", "epoch": 150, "iter": 3700, "lr": 0.0, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.66875, "top5_acc": 0.87422, "loss_cls": 1.90739, "loss": 1.90739, "time": 0.82156} +{"mode": "val", "epoch": 150, "iter": 309, "lr": 0.0, "top1_acc": 0.45707, "top5_acc": 0.70258, "mean_class_accuracy": 0.45682} diff --git a/k400/b_2/b_2.py b/k400/b_2/b_2.py new file mode 100644 index 0000000000000000000000000000000000000000..a4bde1b80845610fe9d9cb4e960378ba3b7f0e4d --- /dev/null +++ b/k400/b_2/b_2.py @@ -0,0 +1,133 @@ +modality = 'b' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/b_2' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/k400/b_2/best_pred.pkl b/k400/b_2/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..4cc6b2b3c464b7c501aa508471e78d0c9342ca3f --- /dev/null +++ b/k400/b_2/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c9bb9215bd7bf5a94f597b5d8b9628810d254d6ecb2902241c9444628c383b6e +size 44883444 diff --git a/k400/b_2/best_top1_acc_epoch_147.pth b/k400/b_2/best_top1_acc_epoch_147.pth new file mode 100644 index 0000000000000000000000000000000000000000..bdbb11094c50d5cbadedb84ace1b6e0e1eb08e4c --- /dev/null +++ b/k400/b_2/best_top1_acc_epoch_147.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7ddd6bf7f2a3385154fcc25765c93d0eaccd08f3c959d58e0d7e2549cf2eddb6 +size 32926705 diff --git a/k400/b_3/20240716_062611.log b/k400/b_3/20240716_062611.log new file mode 100644 index 0000000000000000000000000000000000000000..3ea499d80945529733c81412786ade735889be5c --- /dev/null +++ b/k400/b_3/20240716_062611.log @@ -0,0 +1,7313 @@ +2024-07-16 06:26:11,753 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2024-07-16 06:26:12,047 - pyskl - INFO - Config: modality = 'b' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/b_3' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2024-07-16 06:26:12,047 - pyskl - INFO - Set random seed to 1307045849, deterministic: False +2024-07-16 06:26:24,070 - pyskl - INFO - 239737 videos remain after valid thresholding +2024-07-16 06:26:41,876 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-07-16 06:26:41,877 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3 +2024-07-16 06:26:41,882 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2024-07-16 06:26:41,900 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2024-07-16 06:26:41,903 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3 by HardDiskBackend. +2024-07-16 06:29:43,954 - pyskl - INFO - Epoch [1][100/3746] lr: 1.000e-01, eta: 11 days, 20:05:34, time: 1.820, data_time: 1.124, memory: 15990, top1_acc: 0.0070, top5_acc: 0.0323, loss_cls: 6.4072, loss: 6.4072 +2024-07-16 06:30:53,094 - pyskl - INFO - Epoch [1][200/3746] lr: 1.000e-01, eta: 8 days, 3:57:31, time: 0.691, data_time: 0.000, memory: 15990, top1_acc: 0.0128, top5_acc: 0.0566, loss_cls: 6.3071, loss: 6.3071 +2024-07-16 06:32:02,227 - pyskl - INFO - Epoch [1][300/3746] lr: 1.000e-01, eta: 6 days, 22:33:52, time: 0.691, data_time: 0.000, memory: 15990, top1_acc: 0.0220, top5_acc: 0.0817, loss_cls: 6.0894, loss: 6.0894 +2024-07-16 06:33:11,311 - pyskl - INFO - Epoch [1][400/3746] lr: 1.000e-01, eta: 6 days, 7:50:21, time: 0.691, data_time: 0.000, memory: 15990, top1_acc: 0.0236, top5_acc: 0.0862, loss_cls: 6.0244, loss: 6.0244 +2024-07-16 06:34:20,419 - pyskl - INFO - Epoch [1][500/3746] lr: 1.000e-01, eta: 5 days, 23:00:11, time: 0.691, data_time: 0.000, memory: 15990, top1_acc: 0.0264, top5_acc: 0.0994, loss_cls: 5.9194, loss: 5.9194 +2024-07-16 06:35:29,520 - pyskl - INFO - Epoch [1][600/3746] lr: 1.000e-01, eta: 5 days, 17:06:16, time: 0.691, data_time: 0.000, memory: 15990, top1_acc: 0.0308, top5_acc: 0.1142, loss_cls: 5.8241, loss: 5.8241 +2024-07-16 06:36:38,609 - pyskl - INFO - Epoch [1][700/3746] lr: 1.000e-01, eta: 5 days, 12:52:59, time: 0.691, data_time: 0.000, memory: 15990, top1_acc: 0.0359, top5_acc: 0.1256, loss_cls: 5.7915, loss: 5.7915 +2024-07-16 06:37:47,539 - pyskl - INFO - Epoch [1][800/3746] lr: 1.000e-01, eta: 5 days, 9:40:53, time: 0.689, data_time: 0.000, memory: 15990, top1_acc: 0.0352, top5_acc: 0.1323, loss_cls: 5.7651, loss: 5.7651 +2024-07-16 06:38:56,446 - pyskl - INFO - Epoch [1][900/3746] lr: 1.000e-01, eta: 5 days, 7:10:58, time: 0.689, data_time: 0.000, memory: 15990, top1_acc: 0.0442, top5_acc: 0.1520, loss_cls: 5.6845, loss: 5.6845 +2024-07-16 06:40:05,401 - pyskl - INFO - Epoch [1][1000/3746] lr: 1.000e-01, eta: 5 days, 5:11:15, time: 0.690, data_time: 0.000, memory: 15990, top1_acc: 0.0439, top5_acc: 0.1567, loss_cls: 5.6446, loss: 5.6446 +2024-07-16 06:41:14,421 - pyskl - INFO - Epoch [1][1100/3746] lr: 1.000e-01, eta: 5 days, 3:33:39, time: 0.690, data_time: 0.000, memory: 15990, top1_acc: 0.0478, top5_acc: 0.1648, loss_cls: 5.6179, loss: 5.6179 +2024-07-16 06:42:23,425 - pyskl - INFO - Epoch [1][1200/3746] lr: 1.000e-01, eta: 5 days, 2:12:00, time: 0.690, data_time: 0.000, memory: 15990, top1_acc: 0.0456, top5_acc: 0.1552, loss_cls: 5.6083, loss: 5.6083 +2024-07-16 06:43:32,414 - pyskl - INFO - Epoch [1][1300/3746] lr: 1.000e-01, eta: 5 days, 1:02:38, time: 0.690, data_time: 0.000, memory: 15990, top1_acc: 0.0525, top5_acc: 0.1691, loss_cls: 5.5607, loss: 5.5607 +2024-07-16 06:44:41,444 - pyskl - INFO - Epoch [1][1400/3746] lr: 1.000e-01, eta: 5 days, 0:03:16, time: 0.690, data_time: 0.000, memory: 15990, top1_acc: 0.0534, top5_acc: 0.1755, loss_cls: 5.5225, loss: 5.5225 +2024-07-16 06:45:50,393 - pyskl - INFO - Epoch [1][1500/3746] lr: 1.000e-01, eta: 4 days, 23:11:10, time: 0.689, data_time: 0.000, memory: 15990, top1_acc: 0.0608, top5_acc: 0.1886, loss_cls: 5.4953, loss: 5.4953 +2024-07-16 06:46:59,344 - pyskl - INFO - Epoch [1][1600/3746] lr: 1.000e-01, eta: 4 days, 22:25:27, time: 0.690, data_time: 0.000, memory: 15990, top1_acc: 0.0631, top5_acc: 0.1942, loss_cls: 5.4820, loss: 5.4820 +2024-07-16 06:48:09,507 - pyskl - INFO - Epoch [1][1700/3746] lr: 1.000e-01, eta: 4 days, 21:51:38, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0658, top5_acc: 0.1930, loss_cls: 5.4484, loss: 5.4484 +2024-07-16 06:49:20,282 - pyskl - INFO - Epoch [1][1800/3746] lr: 1.000e-01, eta: 4 days, 21:24:37, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.0656, top5_acc: 0.1969, loss_cls: 5.4586, loss: 5.4586 +2024-07-16 06:50:31,001 - pyskl - INFO - Epoch [1][1900/3746] lr: 1.000e-01, eta: 4 days, 21:00:02, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.0706, top5_acc: 0.2106, loss_cls: 5.4101, loss: 5.4101 +2024-07-16 06:51:41,411 - pyskl - INFO - Epoch [1][2000/3746] lr: 1.000e-01, eta: 4 days, 20:36:22, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0663, top5_acc: 0.2048, loss_cls: 5.3923, loss: 5.3923 +2024-07-16 06:52:51,557 - pyskl - INFO - Epoch [1][2100/3746] lr: 1.000e-01, eta: 4 days, 20:13:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0680, top5_acc: 0.2145, loss_cls: 5.3793, loss: 5.3793 +2024-07-16 06:54:01,804 - pyskl - INFO - Epoch [1][2200/3746] lr: 1.000e-01, eta: 4 days, 19:53:20, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0762, top5_acc: 0.2244, loss_cls: 5.3369, loss: 5.3369 +2024-07-16 06:55:12,513 - pyskl - INFO - Epoch [1][2300/3746] lr: 1.000e-01, eta: 4 days, 19:36:33, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.0733, top5_acc: 0.2195, loss_cls: 5.3625, loss: 5.3625 +2024-07-16 06:56:23,001 - pyskl - INFO - Epoch [1][2400/3746] lr: 1.000e-01, eta: 4 days, 19:20:13, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0833, top5_acc: 0.2441, loss_cls: 5.2674, loss: 5.2674 +2024-07-16 06:57:33,166 - pyskl - INFO - Epoch [1][2500/3746] lr: 1.000e-01, eta: 4 days, 19:03:53, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0830, top5_acc: 0.2436, loss_cls: 5.2453, loss: 5.2453 +2024-07-16 06:58:43,594 - pyskl - INFO - Epoch [1][2600/3746] lr: 9.999e-02, eta: 4 days, 18:49:40, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0786, top5_acc: 0.2386, loss_cls: 5.2893, loss: 5.2893 +2024-07-16 06:59:53,814 - pyskl - INFO - Epoch [1][2700/3746] lr: 9.999e-02, eta: 4 days, 18:35:42, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0864, top5_acc: 0.2539, loss_cls: 5.2588, loss: 5.2588 +2024-07-16 07:01:04,178 - pyskl - INFO - Epoch [1][2800/3746] lr: 9.999e-02, eta: 4 days, 18:23:07, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0920, top5_acc: 0.2545, loss_cls: 5.2238, loss: 5.2238 +2024-07-16 07:02:14,370 - pyskl - INFO - Epoch [1][2900/3746] lr: 9.999e-02, eta: 4 days, 18:10:46, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0928, top5_acc: 0.2542, loss_cls: 5.2155, loss: 5.2155 +2024-07-16 07:03:24,290 - pyskl - INFO - Epoch [1][3000/3746] lr: 9.999e-02, eta: 4 days, 17:58:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0928, top5_acc: 0.2542, loss_cls: 5.1923, loss: 5.1923 +2024-07-16 07:04:34,127 - pyskl - INFO - Epoch [1][3100/3746] lr: 9.999e-02, eta: 4 days, 17:46:21, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1044, top5_acc: 0.2695, loss_cls: 5.1670, loss: 5.1670 +2024-07-16 07:05:44,090 - pyskl - INFO - Epoch [1][3200/3746] lr: 9.999e-02, eta: 4 days, 17:35:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1138, top5_acc: 0.2823, loss_cls: 5.0907, loss: 5.0907 +2024-07-16 07:06:54,173 - pyskl - INFO - Epoch [1][3300/3746] lr: 9.999e-02, eta: 4 days, 17:25:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1134, top5_acc: 0.2838, loss_cls: 5.0910, loss: 5.0910 +2024-07-16 07:08:04,226 - pyskl - INFO - Epoch [1][3400/3746] lr: 9.999e-02, eta: 4 days, 17:15:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1055, top5_acc: 0.2716, loss_cls: 5.1215, loss: 5.1215 +2024-07-16 07:09:14,548 - pyskl - INFO - Epoch [1][3500/3746] lr: 9.999e-02, eta: 4 days, 17:07:31, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1089, top5_acc: 0.2944, loss_cls: 5.0549, loss: 5.0549 +2024-07-16 07:10:24,462 - pyskl - INFO - Epoch [1][3600/3746] lr: 9.999e-02, eta: 4 days, 16:58:30, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1067, top5_acc: 0.2859, loss_cls: 5.0751, loss: 5.0751 +2024-07-16 07:11:34,926 - pyskl - INFO - Epoch [1][3700/3746] lr: 9.999e-02, eta: 4 days, 16:51:17, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1183, top5_acc: 0.3000, loss_cls: 5.0545, loss: 5.0545 +2024-07-16 07:12:09,509 - pyskl - INFO - Saving checkpoint at 1 epochs +2024-07-16 07:13:58,851 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 07:13:59,505 - pyskl - INFO - +top1_acc 0.0629 +top5_acc 0.1998 +2024-07-16 07:13:59,505 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 07:13:59,543 - pyskl - INFO - +mean_acc 0.0630 +2024-07-16 07:13:59,778 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2024-07-16 07:13:59,778 - pyskl - INFO - Best top1_acc is 0.0629 at 1 epoch. +2024-07-16 07:13:59,788 - pyskl - INFO - Epoch(val) [1][309] top1_acc: 0.0629, top5_acc: 0.1998, mean_class_accuracy: 0.0630 +2024-07-16 07:17:13,306 - pyskl - INFO - Epoch [2][100/3746] lr: 9.999e-02, eta: 4 days, 20:20:31, time: 1.935, data_time: 1.231, memory: 15990, top1_acc: 0.1189, top5_acc: 0.3025, loss_cls: 4.9914, loss: 4.9914 +2024-07-16 07:18:23,119 - pyskl - INFO - Epoch [2][200/3746] lr: 9.999e-02, eta: 4 days, 20:06:55, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1175, top5_acc: 0.3034, loss_cls: 5.0184, loss: 5.0184 +2024-07-16 07:19:33,202 - pyskl - INFO - Epoch [2][300/3746] lr: 9.999e-02, eta: 4 days, 19:54:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1214, top5_acc: 0.3102, loss_cls: 4.9761, loss: 4.9761 +2024-07-16 07:20:43,493 - pyskl - INFO - Epoch [2][400/3746] lr: 9.999e-02, eta: 4 days, 19:43:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1272, top5_acc: 0.3214, loss_cls: 4.9150, loss: 4.9150 +2024-07-16 07:21:53,567 - pyskl - INFO - Epoch [2][500/3746] lr: 9.999e-02, eta: 4 days, 19:31:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1261, top5_acc: 0.3284, loss_cls: 4.9205, loss: 4.9205 +2024-07-16 07:23:03,654 - pyskl - INFO - Epoch [2][600/3746] lr: 9.999e-02, eta: 4 days, 19:20:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1263, top5_acc: 0.3150, loss_cls: 4.9348, loss: 4.9348 +2024-07-16 07:24:13,868 - pyskl - INFO - Epoch [2][700/3746] lr: 9.998e-02, eta: 4 days, 19:10:49, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1225, top5_acc: 0.3139, loss_cls: 4.9687, loss: 4.9687 +2024-07-16 07:25:24,129 - pyskl - INFO - Epoch [2][800/3746] lr: 9.998e-02, eta: 4 days, 19:01:10, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1325, top5_acc: 0.3328, loss_cls: 4.9109, loss: 4.9109 +2024-07-16 07:26:34,242 - pyskl - INFO - Epoch [2][900/3746] lr: 9.998e-02, eta: 4 days, 18:51:34, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1355, top5_acc: 0.3277, loss_cls: 4.8957, loss: 4.8957 +2024-07-16 07:27:44,369 - pyskl - INFO - Epoch [2][1000/3746] lr: 9.998e-02, eta: 4 days, 18:42:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1364, top5_acc: 0.3308, loss_cls: 4.8991, loss: 4.8991 +2024-07-16 07:28:54,558 - pyskl - INFO - Epoch [2][1100/3746] lr: 9.998e-02, eta: 4 days, 18:33:35, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1333, top5_acc: 0.3330, loss_cls: 4.8839, loss: 4.8839 +2024-07-16 07:30:04,602 - pyskl - INFO - Epoch [2][1200/3746] lr: 9.998e-02, eta: 4 days, 18:24:52, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1397, top5_acc: 0.3455, loss_cls: 4.8327, loss: 4.8327 +2024-07-16 07:31:14,483 - pyskl - INFO - Epoch [2][1300/3746] lr: 9.998e-02, eta: 4 days, 18:16:08, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1442, top5_acc: 0.3447, loss_cls: 4.8473, loss: 4.8473 +2024-07-16 07:32:24,248 - pyskl - INFO - Epoch [2][1400/3746] lr: 9.998e-02, eta: 4 days, 18:07:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1369, top5_acc: 0.3494, loss_cls: 4.8352, loss: 4.8352 +2024-07-16 07:33:34,715 - pyskl - INFO - Epoch [2][1500/3746] lr: 9.998e-02, eta: 4 days, 18:00:23, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1498, top5_acc: 0.3591, loss_cls: 4.7870, loss: 4.7870 +2024-07-16 07:34:45,362 - pyskl - INFO - Epoch [2][1600/3746] lr: 9.998e-02, eta: 4 days, 17:53:48, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1466, top5_acc: 0.3577, loss_cls: 4.7827, loss: 4.7827 +2024-07-16 07:35:56,855 - pyskl - INFO - Epoch [2][1700/3746] lr: 9.998e-02, eta: 4 days, 17:48:52, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1487, top5_acc: 0.3539, loss_cls: 4.7825, loss: 4.7825 +2024-07-16 07:37:08,016 - pyskl - INFO - Epoch [2][1800/3746] lr: 9.998e-02, eta: 4 days, 17:43:30, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1406, top5_acc: 0.3552, loss_cls: 4.8127, loss: 4.8127 +2024-07-16 07:38:19,049 - pyskl - INFO - Epoch [2][1900/3746] lr: 9.998e-02, eta: 4 days, 17:38:04, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1569, top5_acc: 0.3664, loss_cls: 4.7582, loss: 4.7582 +2024-07-16 07:39:29,776 - pyskl - INFO - Epoch [2][2000/3746] lr: 9.997e-02, eta: 4 days, 17:32:18, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1506, top5_acc: 0.3725, loss_cls: 4.7219, loss: 4.7219 +2024-07-16 07:40:40,425 - pyskl - INFO - Epoch [2][2100/3746] lr: 9.997e-02, eta: 4 days, 17:26:34, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1578, top5_acc: 0.3767, loss_cls: 4.7012, loss: 4.7012 +2024-07-16 07:41:50,648 - pyskl - INFO - Epoch [2][2200/3746] lr: 9.997e-02, eta: 4 days, 17:20:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1648, top5_acc: 0.3802, loss_cls: 4.6899, loss: 4.6899 +2024-07-16 07:43:00,965 - pyskl - INFO - Epoch [2][2300/3746] lr: 9.997e-02, eta: 4 days, 17:14:23, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1588, top5_acc: 0.3745, loss_cls: 4.7140, loss: 4.7140 +2024-07-16 07:44:11,182 - pyskl - INFO - Epoch [2][2400/3746] lr: 9.997e-02, eta: 4 days, 17:08:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1620, top5_acc: 0.3703, loss_cls: 4.7017, loss: 4.7017 +2024-07-16 07:45:21,410 - pyskl - INFO - Epoch [2][2500/3746] lr: 9.997e-02, eta: 4 days, 17:02:42, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1631, top5_acc: 0.3803, loss_cls: 4.6932, loss: 4.6932 +2024-07-16 07:46:31,857 - pyskl - INFO - Epoch [2][2600/3746] lr: 9.997e-02, eta: 4 days, 16:57:24, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1669, top5_acc: 0.3861, loss_cls: 4.6800, loss: 4.6800 +2024-07-16 07:47:42,037 - pyskl - INFO - Epoch [2][2700/3746] lr: 9.997e-02, eta: 4 days, 16:51:51, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1667, top5_acc: 0.3855, loss_cls: 4.6508, loss: 4.6508 +2024-07-16 07:48:52,498 - pyskl - INFO - Epoch [2][2800/3746] lr: 9.997e-02, eta: 4 days, 16:46:49, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1711, top5_acc: 0.3945, loss_cls: 4.6295, loss: 4.6295 +2024-07-16 07:50:02,976 - pyskl - INFO - Epoch [2][2900/3746] lr: 9.997e-02, eta: 4 days, 16:41:57, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1692, top5_acc: 0.3905, loss_cls: 4.6461, loss: 4.6461 +2024-07-16 07:51:13,094 - pyskl - INFO - Epoch [2][3000/3746] lr: 9.996e-02, eta: 4 days, 16:36:41, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1600, top5_acc: 0.3802, loss_cls: 4.6670, loss: 4.6670 +2024-07-16 07:52:23,291 - pyskl - INFO - Epoch [2][3100/3746] lr: 9.996e-02, eta: 4 days, 16:31:38, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1659, top5_acc: 0.3877, loss_cls: 4.6202, loss: 4.6202 +2024-07-16 07:53:33,311 - pyskl - INFO - Epoch [2][3200/3746] lr: 9.996e-02, eta: 4 days, 16:26:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1728, top5_acc: 0.3952, loss_cls: 4.6374, loss: 4.6374 +2024-07-16 07:54:43,250 - pyskl - INFO - Epoch [2][3300/3746] lr: 9.996e-02, eta: 4 days, 16:21:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1611, top5_acc: 0.3812, loss_cls: 4.6638, loss: 4.6638 +2024-07-16 07:55:53,587 - pyskl - INFO - Epoch [2][3400/3746] lr: 9.996e-02, eta: 4 days, 16:16:47, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1769, top5_acc: 0.4078, loss_cls: 4.5653, loss: 4.5653 +2024-07-16 07:57:03,451 - pyskl - INFO - Epoch [2][3500/3746] lr: 9.996e-02, eta: 4 days, 16:11:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1741, top5_acc: 0.3969, loss_cls: 4.5956, loss: 4.5956 +2024-07-16 07:58:13,287 - pyskl - INFO - Epoch [2][3600/3746] lr: 9.996e-02, eta: 4 days, 16:06:47, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1811, top5_acc: 0.4058, loss_cls: 4.5881, loss: 4.5881 +2024-07-16 07:59:23,292 - pyskl - INFO - Epoch [2][3700/3746] lr: 9.996e-02, eta: 4 days, 16:02:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1805, top5_acc: 0.4012, loss_cls: 4.5935, loss: 4.5935 +2024-07-16 07:59:57,931 - pyskl - INFO - Saving checkpoint at 2 epochs +2024-07-16 08:01:46,644 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 08:01:47,298 - pyskl - INFO - +top1_acc 0.1296 +top5_acc 0.3221 +2024-07-16 08:01:47,299 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 08:01:47,338 - pyskl - INFO - +mean_acc 0.1294 +2024-07-16 08:01:47,342 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_1.pth was removed +2024-07-16 08:01:47,580 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2024-07-16 08:01:47,581 - pyskl - INFO - Best top1_acc is 0.1296 at 2 epoch. +2024-07-16 08:01:47,591 - pyskl - INFO - Epoch(val) [2][309] top1_acc: 0.1296, top5_acc: 0.3221, mean_class_accuracy: 0.1294 +2024-07-16 08:04:59,855 - pyskl - INFO - Epoch [3][100/3746] lr: 9.995e-02, eta: 4 days, 17:45:04, time: 1.923, data_time: 1.221, memory: 15990, top1_acc: 0.1789, top5_acc: 0.4133, loss_cls: 4.5602, loss: 4.5602 +2024-07-16 08:06:09,778 - pyskl - INFO - Epoch [3][200/3746] lr: 9.995e-02, eta: 4 days, 17:39:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1806, top5_acc: 0.4037, loss_cls: 4.5150, loss: 4.5150 +2024-07-16 08:07:20,049 - pyskl - INFO - Epoch [3][300/3746] lr: 9.995e-02, eta: 4 days, 17:33:38, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1817, top5_acc: 0.4097, loss_cls: 4.5480, loss: 4.5480 +2024-07-16 08:08:30,101 - pyskl - INFO - Epoch [3][400/3746] lr: 9.995e-02, eta: 4 days, 17:28:03, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1756, top5_acc: 0.4064, loss_cls: 4.5601, loss: 4.5601 +2024-07-16 08:09:40,267 - pyskl - INFO - Epoch [3][500/3746] lr: 9.995e-02, eta: 4 days, 17:22:42, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1894, top5_acc: 0.4109, loss_cls: 4.5312, loss: 4.5312 +2024-07-16 08:10:50,527 - pyskl - INFO - Epoch [3][600/3746] lr: 9.995e-02, eta: 4 days, 17:17:33, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1836, top5_acc: 0.4173, loss_cls: 4.5242, loss: 4.5242 +2024-07-16 08:12:00,385 - pyskl - INFO - Epoch [3][700/3746] lr: 9.995e-02, eta: 4 days, 17:12:04, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1827, top5_acc: 0.4025, loss_cls: 4.5676, loss: 4.5676 +2024-07-16 08:13:10,356 - pyskl - INFO - Epoch [3][800/3746] lr: 9.995e-02, eta: 4 days, 17:06:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1888, top5_acc: 0.4130, loss_cls: 4.5367, loss: 4.5367 +2024-07-16 08:14:20,426 - pyskl - INFO - Epoch [3][900/3746] lr: 9.994e-02, eta: 4 days, 17:01:44, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1900, top5_acc: 0.4153, loss_cls: 4.5155, loss: 4.5155 +2024-07-16 08:15:30,325 - pyskl - INFO - Epoch [3][1000/3746] lr: 9.994e-02, eta: 4 days, 16:56:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1992, top5_acc: 0.4322, loss_cls: 4.4803, loss: 4.4803 +2024-07-16 08:16:40,651 - pyskl - INFO - Epoch [3][1100/3746] lr: 9.994e-02, eta: 4 days, 16:51:59, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1856, top5_acc: 0.4109, loss_cls: 4.4922, loss: 4.4922 +2024-07-16 08:17:51,039 - pyskl - INFO - Epoch [3][1200/3746] lr: 9.994e-02, eta: 4 days, 16:47:32, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1953, top5_acc: 0.4195, loss_cls: 4.5034, loss: 4.5034 +2024-07-16 08:19:01,663 - pyskl - INFO - Epoch [3][1300/3746] lr: 9.994e-02, eta: 4 days, 16:43:24, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1870, top5_acc: 0.4244, loss_cls: 4.4773, loss: 4.4773 +2024-07-16 08:20:12,193 - pyskl - INFO - Epoch [3][1400/3746] lr: 9.994e-02, eta: 4 days, 16:39:14, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1847, top5_acc: 0.4175, loss_cls: 4.5013, loss: 4.5013 +2024-07-16 08:21:23,242 - pyskl - INFO - Epoch [3][1500/3746] lr: 9.994e-02, eta: 4 days, 16:35:40, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1889, top5_acc: 0.4245, loss_cls: 4.4736, loss: 4.4736 +2024-07-16 08:22:33,612 - pyskl - INFO - Epoch [3][1600/3746] lr: 9.994e-02, eta: 4 days, 16:31:27, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1828, top5_acc: 0.4192, loss_cls: 4.5194, loss: 4.5194 +2024-07-16 08:23:44,912 - pyskl - INFO - Epoch [3][1700/3746] lr: 9.993e-02, eta: 4 days, 16:28:15, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2014, top5_acc: 0.4233, loss_cls: 4.4635, loss: 4.4635 +2024-07-16 08:24:55,779 - pyskl - INFO - Epoch [3][1800/3746] lr: 9.993e-02, eta: 4 days, 16:24:40, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1942, top5_acc: 0.4291, loss_cls: 4.4840, loss: 4.4840 +2024-07-16 08:26:06,951 - pyskl - INFO - Epoch [3][1900/3746] lr: 9.993e-02, eta: 4 days, 16:21:25, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1913, top5_acc: 0.4277, loss_cls: 4.4530, loss: 4.4530 +2024-07-16 08:27:17,887 - pyskl - INFO - Epoch [3][2000/3746] lr: 9.993e-02, eta: 4 days, 16:18:00, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4356, loss_cls: 4.4241, loss: 4.4241 +2024-07-16 08:28:28,334 - pyskl - INFO - Epoch [3][2100/3746] lr: 9.993e-02, eta: 4 days, 16:14:09, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1928, top5_acc: 0.4283, loss_cls: 4.4639, loss: 4.4639 +2024-07-16 08:29:38,546 - pyskl - INFO - Epoch [3][2200/3746] lr: 9.993e-02, eta: 4 days, 16:10:08, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1900, top5_acc: 0.4300, loss_cls: 4.4724, loss: 4.4724 +2024-07-16 08:30:48,911 - pyskl - INFO - Epoch [3][2300/3746] lr: 9.993e-02, eta: 4 days, 16:06:19, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1923, top5_acc: 0.4292, loss_cls: 4.4564, loss: 4.4564 +2024-07-16 08:31:58,928 - pyskl - INFO - Epoch [3][2400/3746] lr: 9.992e-02, eta: 4 days, 16:02:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1998, top5_acc: 0.4303, loss_cls: 4.4388, loss: 4.4388 +2024-07-16 08:33:08,923 - pyskl - INFO - Epoch [3][2500/3746] lr: 9.992e-02, eta: 4 days, 15:58:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4295, loss_cls: 4.4266, loss: 4.4266 +2024-07-16 08:34:18,975 - pyskl - INFO - Epoch [3][2600/3746] lr: 9.992e-02, eta: 4 days, 15:54:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4369, loss_cls: 4.3858, loss: 4.3858 +2024-07-16 08:35:29,331 - pyskl - INFO - Epoch [3][2700/3746] lr: 9.992e-02, eta: 4 days, 15:50:38, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4406, loss_cls: 4.4276, loss: 4.4276 +2024-07-16 08:36:39,482 - pyskl - INFO - Epoch [3][2800/3746] lr: 9.992e-02, eta: 4 days, 15:46:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4386, loss_cls: 4.4138, loss: 4.4138 +2024-07-16 08:37:49,670 - pyskl - INFO - Epoch [3][2900/3746] lr: 9.992e-02, eta: 4 days, 15:43:14, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4439, loss_cls: 4.3914, loss: 4.3914 +2024-07-16 08:38:59,875 - pyskl - INFO - Epoch [3][3000/3746] lr: 9.991e-02, eta: 4 days, 15:39:38, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4403, loss_cls: 4.4036, loss: 4.4036 +2024-07-16 08:40:09,920 - pyskl - INFO - Epoch [3][3100/3746] lr: 9.991e-02, eta: 4 days, 15:35:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4353, loss_cls: 4.3873, loss: 4.3873 +2024-07-16 08:41:20,111 - pyskl - INFO - Epoch [3][3200/3746] lr: 9.991e-02, eta: 4 days, 15:32:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1952, top5_acc: 0.4359, loss_cls: 4.4135, loss: 4.4135 +2024-07-16 08:42:30,229 - pyskl - INFO - Epoch [3][3300/3746] lr: 9.991e-02, eta: 4 days, 15:28:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4313, loss_cls: 4.4698, loss: 4.4698 +2024-07-16 08:43:40,364 - pyskl - INFO - Epoch [3][3400/3746] lr: 9.991e-02, eta: 4 days, 15:25:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4445, loss_cls: 4.4022, loss: 4.4022 +2024-07-16 08:44:50,238 - pyskl - INFO - Epoch [3][3500/3746] lr: 9.991e-02, eta: 4 days, 15:21:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2069, top5_acc: 0.4450, loss_cls: 4.3748, loss: 4.3748 +2024-07-16 08:46:00,024 - pyskl - INFO - Epoch [3][3600/3746] lr: 9.990e-02, eta: 4 days, 15:18:04, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4412, loss_cls: 4.3970, loss: 4.3970 +2024-07-16 08:47:10,098 - pyskl - INFO - Epoch [3][3700/3746] lr: 9.990e-02, eta: 4 days, 15:14:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2028, top5_acc: 0.4445, loss_cls: 4.3878, loss: 4.3878 +2024-07-16 08:47:44,704 - pyskl - INFO - Saving checkpoint at 3 epochs +2024-07-16 08:49:34,547 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 08:49:35,193 - pyskl - INFO - +top1_acc 0.1293 +top5_acc 0.3240 +2024-07-16 08:49:35,193 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 08:49:35,230 - pyskl - INFO - +mean_acc 0.1293 +2024-07-16 08:49:35,239 - pyskl - INFO - Epoch(val) [3][309] top1_acc: 0.1293, top5_acc: 0.3240, mean_class_accuracy: 0.1293 +2024-07-16 08:52:48,590 - pyskl - INFO - Epoch [4][100/3746] lr: 9.990e-02, eta: 4 days, 16:23:26, time: 1.933, data_time: 1.232, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4469, loss_cls: 4.3256, loss: 4.3256 +2024-07-16 08:53:58,955 - pyskl - INFO - Epoch [4][200/3746] lr: 9.990e-02, eta: 4 days, 16:19:42, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2059, top5_acc: 0.4406, loss_cls: 4.3689, loss: 4.3689 +2024-07-16 08:55:09,315 - pyskl - INFO - Epoch [4][300/3746] lr: 9.990e-02, eta: 4 days, 16:16:01, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4431, loss_cls: 4.3682, loss: 4.3682 +2024-07-16 08:56:19,368 - pyskl - INFO - Epoch [4][400/3746] lr: 9.989e-02, eta: 4 days, 16:12:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4558, loss_cls: 4.3575, loss: 4.3575 +2024-07-16 08:57:29,370 - pyskl - INFO - Epoch [4][500/3746] lr: 9.989e-02, eta: 4 days, 16:08:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4456, loss_cls: 4.3689, loss: 4.3689 +2024-07-16 08:58:39,723 - pyskl - INFO - Epoch [4][600/3746] lr: 9.989e-02, eta: 4 days, 16:04:40, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4434, loss_cls: 4.3913, loss: 4.3913 +2024-07-16 08:59:49,677 - pyskl - INFO - Epoch [4][700/3746] lr: 9.989e-02, eta: 4 days, 16:00:50, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4492, loss_cls: 4.3618, loss: 4.3618 +2024-07-16 09:01:00,018 - pyskl - INFO - Epoch [4][800/3746] lr: 9.989e-02, eta: 4 days, 15:57:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4491, loss_cls: 4.3553, loss: 4.3553 +2024-07-16 09:02:10,487 - pyskl - INFO - Epoch [4][900/3746] lr: 9.988e-02, eta: 4 days, 15:53:59, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4573, loss_cls: 4.3359, loss: 4.3359 +2024-07-16 09:03:20,693 - pyskl - INFO - Epoch [4][1000/3746] lr: 9.988e-02, eta: 4 days, 15:50:28, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4553, loss_cls: 4.3490, loss: 4.3490 +2024-07-16 09:04:30,736 - pyskl - INFO - Epoch [4][1100/3746] lr: 9.988e-02, eta: 4 days, 15:46:52, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4516, loss_cls: 4.3460, loss: 4.3460 +2024-07-16 09:05:40,955 - pyskl - INFO - Epoch [4][1200/3746] lr: 9.988e-02, eta: 4 days, 15:43:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4517, loss_cls: 4.3520, loss: 4.3520 +2024-07-16 09:06:51,138 - pyskl - INFO - Epoch [4][1300/3746] lr: 9.988e-02, eta: 4 days, 15:40:01, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4489, loss_cls: 4.3843, loss: 4.3843 +2024-07-16 09:08:01,486 - pyskl - INFO - Epoch [4][1400/3746] lr: 9.988e-02, eta: 4 days, 15:36:44, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4486, loss_cls: 4.3548, loss: 4.3548 +2024-07-16 09:09:12,020 - pyskl - INFO - Epoch [4][1500/3746] lr: 9.987e-02, eta: 4 days, 15:33:38, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4481, loss_cls: 4.3609, loss: 4.3609 +2024-07-16 09:10:22,809 - pyskl - INFO - Epoch [4][1600/3746] lr: 9.987e-02, eta: 4 days, 15:30:45, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4534, loss_cls: 4.3658, loss: 4.3658 +2024-07-16 09:11:34,324 - pyskl - INFO - Epoch [4][1700/3746] lr: 9.987e-02, eta: 4 days, 15:28:24, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4464, loss_cls: 4.3557, loss: 4.3557 +2024-07-16 09:12:45,961 - pyskl - INFO - Epoch [4][1800/3746] lr: 9.987e-02, eta: 4 days, 15:26:09, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4484, loss_cls: 4.3387, loss: 4.3387 +2024-07-16 09:13:57,034 - pyskl - INFO - Epoch [4][1900/3746] lr: 9.987e-02, eta: 4 days, 15:23:32, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4495, loss_cls: 4.3644, loss: 4.3644 +2024-07-16 09:15:08,554 - pyskl - INFO - Epoch [4][2000/3746] lr: 9.986e-02, eta: 4 days, 15:21:14, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4614, loss_cls: 4.3372, loss: 4.3372 +2024-07-16 09:16:19,050 - pyskl - INFO - Epoch [4][2100/3746] lr: 9.986e-02, eta: 4 days, 15:18:15, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4595, loss_cls: 4.2919, loss: 4.2919 +2024-07-16 09:17:29,811 - pyskl - INFO - Epoch [4][2200/3746] lr: 9.986e-02, eta: 4 days, 15:15:29, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4592, loss_cls: 4.2950, loss: 4.2950 +2024-07-16 09:18:40,111 - pyskl - INFO - Epoch [4][2300/3746] lr: 9.986e-02, eta: 4 days, 15:12:25, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4561, loss_cls: 4.3158, loss: 4.3158 +2024-07-16 09:19:50,582 - pyskl - INFO - Epoch [4][2400/3746] lr: 9.985e-02, eta: 4 days, 15:09:30, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4567, loss_cls: 4.3340, loss: 4.3340 +2024-07-16 09:21:00,848 - pyskl - INFO - Epoch [4][2500/3746] lr: 9.985e-02, eta: 4 days, 15:06:29, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4584, loss_cls: 4.3544, loss: 4.3544 +2024-07-16 09:22:10,962 - pyskl - INFO - Epoch [4][2600/3746] lr: 9.985e-02, eta: 4 days, 15:03:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4470, loss_cls: 4.3504, loss: 4.3504 +2024-07-16 09:23:21,027 - pyskl - INFO - Epoch [4][2700/3746] lr: 9.985e-02, eta: 4 days, 15:00:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4612, loss_cls: 4.3106, loss: 4.3106 +2024-07-16 09:24:30,965 - pyskl - INFO - Epoch [4][2800/3746] lr: 9.985e-02, eta: 4 days, 14:57:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4530, loss_cls: 4.3387, loss: 4.3387 +2024-07-16 09:25:41,016 - pyskl - INFO - Epoch [4][2900/3746] lr: 9.984e-02, eta: 4 days, 14:54:03, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4531, loss_cls: 4.3284, loss: 4.3284 +2024-07-16 09:26:51,099 - pyskl - INFO - Epoch [4][3000/3746] lr: 9.984e-02, eta: 4 days, 14:51:02, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4594, loss_cls: 4.3374, loss: 4.3374 +2024-07-16 09:28:01,229 - pyskl - INFO - Epoch [4][3100/3746] lr: 9.984e-02, eta: 4 days, 14:48:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4561, loss_cls: 4.3008, loss: 4.3008 +2024-07-16 09:29:11,160 - pyskl - INFO - Epoch [4][3200/3746] lr: 9.984e-02, eta: 4 days, 14:45:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4623, loss_cls: 4.2913, loss: 4.2913 +2024-07-16 09:30:21,278 - pyskl - INFO - Epoch [4][3300/3746] lr: 9.983e-02, eta: 4 days, 14:42:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4637, loss_cls: 4.3130, loss: 4.3130 +2024-07-16 09:31:31,482 - pyskl - INFO - Epoch [4][3400/3746] lr: 9.983e-02, eta: 4 days, 14:39:16, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4625, loss_cls: 4.2769, loss: 4.2769 +2024-07-16 09:32:41,240 - pyskl - INFO - Epoch [4][3500/3746] lr: 9.983e-02, eta: 4 days, 14:36:11, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4631, loss_cls: 4.2813, loss: 4.2813 +2024-07-16 09:33:51,543 - pyskl - INFO - Epoch [4][3600/3746] lr: 9.983e-02, eta: 4 days, 14:33:27, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4516, loss_cls: 4.3442, loss: 4.3442 +2024-07-16 09:35:01,488 - pyskl - INFO - Epoch [4][3700/3746] lr: 9.983e-02, eta: 4 days, 14:30:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4616, loss_cls: 4.3107, loss: 4.3107 +2024-07-16 09:35:36,074 - pyskl - INFO - Saving checkpoint at 4 epochs +2024-07-16 09:37:26,282 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 09:37:26,944 - pyskl - INFO - +top1_acc 0.1470 +top5_acc 0.3499 +2024-07-16 09:37:26,944 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 09:37:26,985 - pyskl - INFO - +mean_acc 0.1468 +2024-07-16 09:37:26,990 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_2.pth was removed +2024-07-16 09:37:27,230 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2024-07-16 09:37:27,231 - pyskl - INFO - Best top1_acc is 0.1470 at 4 epoch. +2024-07-16 09:37:27,241 - pyskl - INFO - Epoch(val) [4][309] top1_acc: 0.1470, top5_acc: 0.3499, mean_class_accuracy: 0.1468 +2024-07-16 09:40:44,892 - pyskl - INFO - Epoch [5][100/3746] lr: 9.982e-02, eta: 4 days, 15:24:01, time: 1.976, data_time: 1.268, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4552, loss_cls: 4.2943, loss: 4.2943 +2024-07-16 09:41:55,422 - pyskl - INFO - Epoch [5][200/3746] lr: 9.982e-02, eta: 4 days, 15:21:06, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4692, loss_cls: 4.2568, loss: 4.2568 +2024-07-16 09:43:06,001 - pyskl - INFO - Epoch [5][300/3746] lr: 9.982e-02, eta: 4 days, 15:18:15, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4658, loss_cls: 4.2838, loss: 4.2838 +2024-07-16 09:44:15,883 - pyskl - INFO - Epoch [5][400/3746] lr: 9.982e-02, eta: 4 days, 15:15:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4658, loss_cls: 4.2986, loss: 4.2986 +2024-07-16 09:45:26,008 - pyskl - INFO - Epoch [5][500/3746] lr: 9.981e-02, eta: 4 days, 15:11:55, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4650, loss_cls: 4.2820, loss: 4.2820 +2024-07-16 09:46:36,055 - pyskl - INFO - Epoch [5][600/3746] lr: 9.981e-02, eta: 4 days, 15:08:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4659, loss_cls: 4.2663, loss: 4.2663 +2024-07-16 09:47:46,105 - pyskl - INFO - Epoch [5][700/3746] lr: 9.981e-02, eta: 4 days, 15:05:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4675, loss_cls: 4.2876, loss: 4.2876 +2024-07-16 09:48:56,341 - pyskl - INFO - Epoch [5][800/3746] lr: 9.981e-02, eta: 4 days, 15:02:48, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4662, loss_cls: 4.2797, loss: 4.2797 +2024-07-16 09:50:06,485 - pyskl - INFO - Epoch [5][900/3746] lr: 9.980e-02, eta: 4 days, 14:59:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4703, loss_cls: 4.2766, loss: 4.2766 +2024-07-16 09:51:16,775 - pyskl - INFO - Epoch [5][1000/3746] lr: 9.980e-02, eta: 4 days, 14:56:58, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4622, loss_cls: 4.3032, loss: 4.3032 +2024-07-16 09:52:27,279 - pyskl - INFO - Epoch [5][1100/3746] lr: 9.980e-02, eta: 4 days, 14:54:14, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4611, loss_cls: 4.3388, loss: 4.3388 +2024-07-16 09:53:37,547 - pyskl - INFO - Epoch [5][1200/3746] lr: 9.980e-02, eta: 4 days, 14:51:24, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4711, loss_cls: 4.2794, loss: 4.2794 +2024-07-16 09:54:47,807 - pyskl - INFO - Epoch [5][1300/3746] lr: 9.979e-02, eta: 4 days, 14:48:34, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4694, loss_cls: 4.2909, loss: 4.2909 +2024-07-16 09:55:57,756 - pyskl - INFO - Epoch [5][1400/3746] lr: 9.979e-02, eta: 4 days, 14:45:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4677, loss_cls: 4.3146, loss: 4.3146 +2024-07-16 09:57:08,627 - pyskl - INFO - Epoch [5][1500/3746] lr: 9.979e-02, eta: 4 days, 14:43:09, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4500, loss_cls: 4.3391, loss: 4.3391 +2024-07-16 09:58:19,504 - pyskl - INFO - Epoch [5][1600/3746] lr: 9.979e-02, eta: 4 days, 14:40:44, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4681, loss_cls: 4.2750, loss: 4.2750 +2024-07-16 09:59:30,803 - pyskl - INFO - Epoch [5][1700/3746] lr: 9.978e-02, eta: 4 days, 14:38:33, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4669, loss_cls: 4.2771, loss: 4.2771 +2024-07-16 10:00:42,235 - pyskl - INFO - Epoch [5][1800/3746] lr: 9.978e-02, eta: 4 days, 14:36:27, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2194, top5_acc: 0.4575, loss_cls: 4.3114, loss: 4.3114 +2024-07-16 10:01:53,171 - pyskl - INFO - Epoch [5][1900/3746] lr: 9.978e-02, eta: 4 days, 14:34:06, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4586, loss_cls: 4.3204, loss: 4.3204 +2024-07-16 10:03:03,899 - pyskl - INFO - Epoch [5][2000/3746] lr: 9.977e-02, eta: 4 days, 14:31:39, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4792, loss_cls: 4.2261, loss: 4.2261 +2024-07-16 10:04:14,608 - pyskl - INFO - Epoch [5][2100/3746] lr: 9.977e-02, eta: 4 days, 14:29:12, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4689, loss_cls: 4.2584, loss: 4.2584 +2024-07-16 10:05:24,612 - pyskl - INFO - Epoch [5][2200/3746] lr: 9.977e-02, eta: 4 days, 14:26:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4720, loss_cls: 4.2474, loss: 4.2474 +2024-07-16 10:06:34,968 - pyskl - INFO - Epoch [5][2300/3746] lr: 9.977e-02, eta: 4 days, 14:23:48, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4781, loss_cls: 4.2222, loss: 4.2222 +2024-07-16 10:07:44,902 - pyskl - INFO - Epoch [5][2400/3746] lr: 9.976e-02, eta: 4 days, 14:21:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4697, loss_cls: 4.2686, loss: 4.2686 +2024-07-16 10:08:54,904 - pyskl - INFO - Epoch [5][2500/3746] lr: 9.976e-02, eta: 4 days, 14:18:15, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4688, loss_cls: 4.2951, loss: 4.2951 +2024-07-16 10:10:04,791 - pyskl - INFO - Epoch [5][2600/3746] lr: 9.976e-02, eta: 4 days, 14:15:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4728, loss_cls: 4.2659, loss: 4.2659 +2024-07-16 10:11:14,776 - pyskl - INFO - Epoch [5][2700/3746] lr: 9.976e-02, eta: 4 days, 14:12:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4641, loss_cls: 4.2944, loss: 4.2944 +2024-07-16 10:12:24,906 - pyskl - INFO - Epoch [5][2800/3746] lr: 9.975e-02, eta: 4 days, 14:10:06, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4681, loss_cls: 4.2700, loss: 4.2700 +2024-07-16 10:13:35,203 - pyskl - INFO - Epoch [5][2900/3746] lr: 9.975e-02, eta: 4 days, 14:07:34, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4791, loss_cls: 4.2169, loss: 4.2169 +2024-07-16 10:14:45,341 - pyskl - INFO - Epoch [5][3000/3746] lr: 9.975e-02, eta: 4 days, 14:04:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4667, loss_cls: 4.2554, loss: 4.2554 +2024-07-16 10:15:55,137 - pyskl - INFO - Epoch [5][3100/3746] lr: 9.974e-02, eta: 4 days, 14:02:13, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4748, loss_cls: 4.2348, loss: 4.2348 +2024-07-16 10:17:05,115 - pyskl - INFO - Epoch [5][3200/3746] lr: 9.974e-02, eta: 4 days, 13:59:35, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4684, loss_cls: 4.2376, loss: 4.2376 +2024-07-16 10:18:14,995 - pyskl - INFO - Epoch [5][3300/3746] lr: 9.974e-02, eta: 4 days, 13:56:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4689, loss_cls: 4.2620, loss: 4.2620 +2024-07-16 10:19:24,802 - pyskl - INFO - Epoch [5][3400/3746] lr: 9.974e-02, eta: 4 days, 13:54:13, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4797, loss_cls: 4.2016, loss: 4.2016 +2024-07-16 10:20:35,025 - pyskl - INFO - Epoch [5][3500/3746] lr: 9.973e-02, eta: 4 days, 13:51:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4773, loss_cls: 4.2235, loss: 4.2235 +2024-07-16 10:21:45,293 - pyskl - INFO - Epoch [5][3600/3746] lr: 9.973e-02, eta: 4 days, 13:49:18, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4755, loss_cls: 4.2428, loss: 4.2428 +2024-07-16 10:22:55,607 - pyskl - INFO - Epoch [5][3700/3746] lr: 9.973e-02, eta: 4 days, 13:46:54, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4630, loss_cls: 4.2688, loss: 4.2688 +2024-07-16 10:23:29,972 - pyskl - INFO - Saving checkpoint at 5 epochs +2024-07-16 10:25:19,850 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 10:25:20,499 - pyskl - INFO - +top1_acc 0.1655 +top5_acc 0.3809 +2024-07-16 10:25:20,499 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 10:25:20,536 - pyskl - INFO - +mean_acc 0.1655 +2024-07-16 10:25:20,540 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_4.pth was removed +2024-07-16 10:25:20,769 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2024-07-16 10:25:20,770 - pyskl - INFO - Best top1_acc is 0.1655 at 5 epoch. +2024-07-16 10:25:20,780 - pyskl - INFO - Epoch(val) [5][309] top1_acc: 0.1655, top5_acc: 0.3809, mean_class_accuracy: 0.1655 +2024-07-16 10:28:35,470 - pyskl - INFO - Epoch [6][100/3746] lr: 9.972e-02, eta: 4 days, 14:27:39, time: 1.947, data_time: 1.244, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4788, loss_cls: 4.2397, loss: 4.2397 +2024-07-16 10:29:45,658 - pyskl - INFO - Epoch [6][200/3746] lr: 9.972e-02, eta: 4 days, 14:24:59, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4700, loss_cls: 4.1994, loss: 4.1994 +2024-07-16 10:30:55,657 - pyskl - INFO - Epoch [6][300/3746] lr: 9.972e-02, eta: 4 days, 14:22:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4727, loss_cls: 4.2274, loss: 4.2274 +2024-07-16 10:32:05,874 - pyskl - INFO - Epoch [6][400/3746] lr: 9.971e-02, eta: 4 days, 14:19:36, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4825, loss_cls: 4.2048, loss: 4.2048 +2024-07-16 10:33:16,257 - pyskl - INFO - Epoch [6][500/3746] lr: 9.971e-02, eta: 4 days, 14:17:04, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4764, loss_cls: 4.1904, loss: 4.1904 +2024-07-16 10:34:26,361 - pyskl - INFO - Epoch [6][600/3746] lr: 9.971e-02, eta: 4 days, 14:14:25, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4794, loss_cls: 4.2399, loss: 4.2399 +2024-07-16 10:35:36,246 - pyskl - INFO - Epoch [6][700/3746] lr: 9.971e-02, eta: 4 days, 14:11:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4780, loss_cls: 4.2153, loss: 4.2153 +2024-07-16 10:36:46,159 - pyskl - INFO - Epoch [6][800/3746] lr: 9.970e-02, eta: 4 days, 14:08:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4750, loss_cls: 4.1888, loss: 4.1888 +2024-07-16 10:37:56,064 - pyskl - INFO - Epoch [6][900/3746] lr: 9.970e-02, eta: 4 days, 14:06:17, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4783, loss_cls: 4.1947, loss: 4.1947 +2024-07-16 10:39:06,005 - pyskl - INFO - Epoch [6][1000/3746] lr: 9.970e-02, eta: 4 days, 14:03:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4822, loss_cls: 4.2126, loss: 4.2126 +2024-07-16 10:40:16,354 - pyskl - INFO - Epoch [6][1100/3746] lr: 9.969e-02, eta: 4 days, 14:01:09, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4730, loss_cls: 4.2319, loss: 4.2319 +2024-07-16 10:41:27,035 - pyskl - INFO - Epoch [6][1200/3746] lr: 9.969e-02, eta: 4 days, 13:58:51, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4764, loss_cls: 4.2162, loss: 4.2162 +2024-07-16 10:42:37,466 - pyskl - INFO - Epoch [6][1300/3746] lr: 9.969e-02, eta: 4 days, 13:56:27, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4725, loss_cls: 4.2540, loss: 4.2540 +2024-07-16 10:43:47,314 - pyskl - INFO - Epoch [6][1400/3746] lr: 9.968e-02, eta: 4 days, 13:53:48, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4766, loss_cls: 4.1991, loss: 4.1991 +2024-07-16 10:44:57,271 - pyskl - INFO - Epoch [6][1500/3746] lr: 9.968e-02, eta: 4 days, 13:51:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4781, loss_cls: 4.2175, loss: 4.2175 +2024-07-16 10:46:07,898 - pyskl - INFO - Epoch [6][1600/3746] lr: 9.968e-02, eta: 4 days, 13:48:57, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4761, loss_cls: 4.2231, loss: 4.2231 +2024-07-16 10:47:18,813 - pyskl - INFO - Epoch [6][1700/3746] lr: 9.967e-02, eta: 4 days, 13:46:48, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4731, loss_cls: 4.2086, loss: 4.2086 +2024-07-16 10:48:30,123 - pyskl - INFO - Epoch [6][1800/3746] lr: 9.967e-02, eta: 4 days, 13:44:51, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4659, loss_cls: 4.2538, loss: 4.2538 +2024-07-16 10:49:40,785 - pyskl - INFO - Epoch [6][1900/3746] lr: 9.967e-02, eta: 4 days, 13:42:37, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4745, loss_cls: 4.2210, loss: 4.2210 +2024-07-16 10:50:51,362 - pyskl - INFO - Epoch [6][2000/3746] lr: 9.966e-02, eta: 4 days, 13:40:22, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4705, loss_cls: 4.2291, loss: 4.2291 +2024-07-16 10:52:01,816 - pyskl - INFO - Epoch [6][2100/3746] lr: 9.966e-02, eta: 4 days, 13:38:04, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4777, loss_cls: 4.2334, loss: 4.2334 +2024-07-16 10:53:11,948 - pyskl - INFO - Epoch [6][2200/3746] lr: 9.966e-02, eta: 4 days, 13:35:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4822, loss_cls: 4.2001, loss: 4.2001 +2024-07-16 10:54:22,043 - pyskl - INFO - Epoch [6][2300/3746] lr: 9.965e-02, eta: 4 days, 13:33:12, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4856, loss_cls: 4.1966, loss: 4.1966 +2024-07-16 10:55:31,963 - pyskl - INFO - Epoch [6][2400/3746] lr: 9.965e-02, eta: 4 days, 13:30:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4713, loss_cls: 4.2385, loss: 4.2385 +2024-07-16 10:56:41,614 - pyskl - INFO - Epoch [6][2500/3746] lr: 9.965e-02, eta: 4 days, 13:28:07, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4833, loss_cls: 4.1617, loss: 4.1617 +2024-07-16 10:57:51,583 - pyskl - INFO - Epoch [6][2600/3746] lr: 9.964e-02, eta: 4 days, 13:25:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4736, loss_cls: 4.2535, loss: 4.2535 +2024-07-16 10:59:01,632 - pyskl - INFO - Epoch [6][2700/3746] lr: 9.964e-02, eta: 4 days, 13:23:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4817, loss_cls: 4.2093, loss: 4.2093 +2024-07-16 11:00:11,452 - pyskl - INFO - Epoch [6][2800/3746] lr: 9.964e-02, eta: 4 days, 13:20:46, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4811, loss_cls: 4.1934, loss: 4.1934 +2024-07-16 11:01:21,473 - pyskl - INFO - Epoch [6][2900/3746] lr: 9.963e-02, eta: 4 days, 13:18:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4817, loss_cls: 4.2129, loss: 4.2129 +2024-07-16 11:02:31,390 - pyskl - INFO - Epoch [6][3000/3746] lr: 9.963e-02, eta: 4 days, 13:15:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4827, loss_cls: 4.2316, loss: 4.2316 +2024-07-16 11:03:41,182 - pyskl - INFO - Epoch [6][3100/3746] lr: 9.963e-02, eta: 4 days, 13:13:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4777, loss_cls: 4.2243, loss: 4.2243 +2024-07-16 11:04:51,141 - pyskl - INFO - Epoch [6][3200/3746] lr: 9.962e-02, eta: 4 days, 13:11:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4833, loss_cls: 4.2023, loss: 4.2023 +2024-07-16 11:06:00,985 - pyskl - INFO - Epoch [6][3300/3746] lr: 9.962e-02, eta: 4 days, 13:08:42, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4827, loss_cls: 4.1920, loss: 4.1920 +2024-07-16 11:07:10,843 - pyskl - INFO - Epoch [6][3400/3746] lr: 9.962e-02, eta: 4 days, 13:06:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4766, loss_cls: 4.2074, loss: 4.2074 +2024-07-16 11:08:20,557 - pyskl - INFO - Epoch [6][3500/3746] lr: 9.961e-02, eta: 4 days, 13:03:51, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4936, loss_cls: 4.1747, loss: 4.1747 +2024-07-16 11:09:30,719 - pyskl - INFO - Epoch [6][3600/3746] lr: 9.961e-02, eta: 4 days, 13:01:35, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4763, loss_cls: 4.2123, loss: 4.2123 +2024-07-16 11:10:40,826 - pyskl - INFO - Epoch [6][3700/3746] lr: 9.961e-02, eta: 4 days, 12:59:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4828, loss_cls: 4.1944, loss: 4.1944 +2024-07-16 11:11:15,207 - pyskl - INFO - Saving checkpoint at 6 epochs +2024-07-16 11:13:04,572 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 11:13:05,228 - pyskl - INFO - +top1_acc 0.1438 +top5_acc 0.3494 +2024-07-16 11:13:05,228 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 11:13:05,272 - pyskl - INFO - +mean_acc 0.1434 +2024-07-16 11:13:05,284 - pyskl - INFO - Epoch(val) [6][309] top1_acc: 0.1438, top5_acc: 0.3494, mean_class_accuracy: 0.1434 +2024-07-16 11:16:18,552 - pyskl - INFO - Epoch [7][100/3746] lr: 9.960e-02, eta: 4 days, 13:32:13, time: 1.933, data_time: 1.231, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4817, loss_cls: 4.1721, loss: 4.1721 +2024-07-16 11:17:28,455 - pyskl - INFO - Epoch [7][200/3746] lr: 9.960e-02, eta: 4 days, 13:29:44, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4902, loss_cls: 4.1409, loss: 4.1409 +2024-07-16 11:18:38,438 - pyskl - INFO - Epoch [7][300/3746] lr: 9.960e-02, eta: 4 days, 13:27:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4870, loss_cls: 4.1800, loss: 4.1800 +2024-07-16 11:19:48,454 - pyskl - INFO - Epoch [7][400/3746] lr: 9.959e-02, eta: 4 days, 13:24:51, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4881, loss_cls: 4.1458, loss: 4.1458 +2024-07-16 11:20:58,587 - pyskl - INFO - Epoch [7][500/3746] lr: 9.959e-02, eta: 4 days, 13:22:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4848, loss_cls: 4.1598, loss: 4.1598 +2024-07-16 11:22:09,063 - pyskl - INFO - Epoch [7][600/3746] lr: 9.958e-02, eta: 4 days, 13:20:16, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4817, loss_cls: 4.1876, loss: 4.1876 +2024-07-16 11:23:18,794 - pyskl - INFO - Epoch [7][700/3746] lr: 9.958e-02, eta: 4 days, 13:17:45, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4953, loss_cls: 4.1625, loss: 4.1625 +2024-07-16 11:24:28,625 - pyskl - INFO - Epoch [7][800/3746] lr: 9.958e-02, eta: 4 days, 13:15:18, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4778, loss_cls: 4.2262, loss: 4.2262 +2024-07-16 11:25:38,632 - pyskl - INFO - Epoch [7][900/3746] lr: 9.957e-02, eta: 4 days, 13:12:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4895, loss_cls: 4.1528, loss: 4.1528 +2024-07-16 11:26:48,722 - pyskl - INFO - Epoch [7][1000/3746] lr: 9.957e-02, eta: 4 days, 13:10:36, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4811, loss_cls: 4.1962, loss: 4.1962 +2024-07-16 11:27:59,011 - pyskl - INFO - Epoch [7][1100/3746] lr: 9.957e-02, eta: 4 days, 13:08:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4875, loss_cls: 4.1745, loss: 4.1745 +2024-07-16 11:29:09,109 - pyskl - INFO - Epoch [7][1200/3746] lr: 9.956e-02, eta: 4 days, 13:06:02, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4733, loss_cls: 4.2090, loss: 4.2090 +2024-07-16 11:30:19,170 - pyskl - INFO - Epoch [7][1300/3746] lr: 9.956e-02, eta: 4 days, 13:03:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4838, loss_cls: 4.2350, loss: 4.2350 +2024-07-16 11:31:28,939 - pyskl - INFO - Epoch [7][1400/3746] lr: 9.956e-02, eta: 4 days, 13:01:18, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4923, loss_cls: 4.1441, loss: 4.1441 +2024-07-16 11:32:38,982 - pyskl - INFO - Epoch [7][1500/3746] lr: 9.955e-02, eta: 4 days, 12:59:00, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4858, loss_cls: 4.2009, loss: 4.2009 +2024-07-16 11:33:50,059 - pyskl - INFO - Epoch [7][1600/3746] lr: 9.955e-02, eta: 4 days, 12:57:06, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4784, loss_cls: 4.2206, loss: 4.2206 +2024-07-16 11:35:01,173 - pyskl - INFO - Epoch [7][1700/3746] lr: 9.954e-02, eta: 4 days, 12:55:12, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4880, loss_cls: 4.1499, loss: 4.1499 +2024-07-16 11:36:12,777 - pyskl - INFO - Epoch [7][1800/3746] lr: 9.954e-02, eta: 4 days, 12:53:30, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4764, loss_cls: 4.2176, loss: 4.2176 +2024-07-16 11:37:23,346 - pyskl - INFO - Epoch [7][1900/3746] lr: 9.954e-02, eta: 4 days, 12:51:26, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4845, loss_cls: 4.1784, loss: 4.1784 +2024-07-16 11:38:33,926 - pyskl - INFO - Epoch [7][2000/3746] lr: 9.953e-02, eta: 4 days, 12:49:22, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4966, loss_cls: 4.1538, loss: 4.1538 +2024-07-16 11:39:44,391 - pyskl - INFO - Epoch [7][2100/3746] lr: 9.953e-02, eta: 4 days, 12:47:15, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4844, loss_cls: 4.1712, loss: 4.1712 +2024-07-16 11:40:54,435 - pyskl - INFO - Epoch [7][2200/3746] lr: 9.952e-02, eta: 4 days, 12:45:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4867, loss_cls: 4.1850, loss: 4.1850 +2024-07-16 11:42:04,274 - pyskl - INFO - Epoch [7][2300/3746] lr: 9.952e-02, eta: 4 days, 12:42:42, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4756, loss_cls: 4.2157, loss: 4.2157 +2024-07-16 11:43:14,125 - pyskl - INFO - Epoch [7][2400/3746] lr: 9.952e-02, eta: 4 days, 12:40:24, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4855, loss_cls: 4.2122, loss: 4.2122 +2024-07-16 11:44:23,662 - pyskl - INFO - Epoch [7][2500/3746] lr: 9.951e-02, eta: 4 days, 12:38:00, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4873, loss_cls: 4.1588, loss: 4.1588 +2024-07-16 11:45:33,732 - pyskl - INFO - Epoch [7][2600/3746] lr: 9.951e-02, eta: 4 days, 12:35:48, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4942, loss_cls: 4.1639, loss: 4.1639 +2024-07-16 11:46:43,768 - pyskl - INFO - Epoch [7][2700/3746] lr: 9.951e-02, eta: 4 days, 12:33:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4916, loss_cls: 4.1626, loss: 4.1626 +2024-07-16 11:47:53,845 - pyskl - INFO - Epoch [7][2800/3746] lr: 9.950e-02, eta: 4 days, 12:31:25, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4909, loss_cls: 4.1896, loss: 4.1896 +2024-07-16 11:49:04,114 - pyskl - INFO - Epoch [7][2900/3746] lr: 9.950e-02, eta: 4 days, 12:29:18, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4723, loss_cls: 4.2314, loss: 4.2314 +2024-07-16 11:50:13,985 - pyskl - INFO - Epoch [7][3000/3746] lr: 9.949e-02, eta: 4 days, 12:27:04, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4811, loss_cls: 4.1942, loss: 4.1942 +2024-07-16 11:51:23,972 - pyskl - INFO - Epoch [7][3100/3746] lr: 9.949e-02, eta: 4 days, 12:24:52, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4895, loss_cls: 4.1547, loss: 4.1547 +2024-07-16 11:52:33,934 - pyskl - INFO - Epoch [7][3200/3746] lr: 9.949e-02, eta: 4 days, 12:22:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4750, loss_cls: 4.1876, loss: 4.1876 +2024-07-16 11:53:44,148 - pyskl - INFO - Epoch [7][3300/3746] lr: 9.948e-02, eta: 4 days, 12:20:35, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4828, loss_cls: 4.2071, loss: 4.2071 +2024-07-16 11:54:53,864 - pyskl - INFO - Epoch [7][3400/3746] lr: 9.948e-02, eta: 4 days, 12:18:20, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4914, loss_cls: 4.1612, loss: 4.1612 +2024-07-16 11:56:03,818 - pyskl - INFO - Epoch [7][3500/3746] lr: 9.947e-02, eta: 4 days, 12:16:09, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4844, loss_cls: 4.2250, loss: 4.2250 +2024-07-16 11:57:13,714 - pyskl - INFO - Epoch [7][3600/3746] lr: 9.947e-02, eta: 4 days, 12:13:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4870, loss_cls: 4.1597, loss: 4.1597 +2024-07-16 11:58:23,502 - pyskl - INFO - Epoch [7][3700/3746] lr: 9.947e-02, eta: 4 days, 12:11:46, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4895, loss_cls: 4.1543, loss: 4.1543 +2024-07-16 11:58:58,205 - pyskl - INFO - Saving checkpoint at 7 epochs +2024-07-16 12:00:48,121 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 12:00:48,780 - pyskl - INFO - +top1_acc 0.1291 +top5_acc 0.3142 +2024-07-16 12:00:48,780 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 12:00:48,818 - pyskl - INFO - +mean_acc 0.1289 +2024-07-16 12:00:48,827 - pyskl - INFO - Epoch(val) [7][309] top1_acc: 0.1291, top5_acc: 0.3142, mean_class_accuracy: 0.1289 +2024-07-16 12:04:04,220 - pyskl - INFO - Epoch [8][100/3746] lr: 9.946e-02, eta: 4 days, 12:40:15, time: 1.954, data_time: 1.254, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5031, loss_cls: 4.0913, loss: 4.0913 +2024-07-16 12:05:13,969 - pyskl - INFO - Epoch [8][200/3746] lr: 9.946e-02, eta: 4 days, 12:37:55, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4756, loss_cls: 4.1758, loss: 4.1758 +2024-07-16 12:06:23,775 - pyskl - INFO - Epoch [8][300/3746] lr: 9.945e-02, eta: 4 days, 12:35:37, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4894, loss_cls: 4.1519, loss: 4.1519 +2024-07-16 12:07:33,777 - pyskl - INFO - Epoch [8][400/3746] lr: 9.945e-02, eta: 4 days, 12:33:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4789, loss_cls: 4.1702, loss: 4.1702 +2024-07-16 12:08:43,661 - pyskl - INFO - Epoch [8][500/3746] lr: 9.944e-02, eta: 4 days, 12:31:08, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4795, loss_cls: 4.1950, loss: 4.1950 +2024-07-16 12:09:53,436 - pyskl - INFO - Epoch [8][600/3746] lr: 9.944e-02, eta: 4 days, 12:28:50, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4947, loss_cls: 4.1562, loss: 4.1562 +2024-07-16 12:11:03,452 - pyskl - INFO - Epoch [8][700/3746] lr: 9.943e-02, eta: 4 days, 12:26:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4841, loss_cls: 4.1741, loss: 4.1741 +2024-07-16 12:12:13,244 - pyskl - INFO - Epoch [8][800/3746] lr: 9.943e-02, eta: 4 days, 12:24:22, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4922, loss_cls: 4.1580, loss: 4.1580 +2024-07-16 12:13:23,287 - pyskl - INFO - Epoch [8][900/3746] lr: 9.943e-02, eta: 4 days, 12:22:12, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4945, loss_cls: 4.1180, loss: 4.1180 +2024-07-16 12:14:33,289 - pyskl - INFO - Epoch [8][1000/3746] lr: 9.942e-02, eta: 4 days, 12:20:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4866, loss_cls: 4.1667, loss: 4.1667 +2024-07-16 12:15:43,191 - pyskl - INFO - Epoch [8][1100/3746] lr: 9.942e-02, eta: 4 days, 12:17:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4869, loss_cls: 4.1462, loss: 4.1462 +2024-07-16 12:16:52,915 - pyskl - INFO - Epoch [8][1200/3746] lr: 9.941e-02, eta: 4 days, 12:15:33, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4856, loss_cls: 4.1672, loss: 4.1672 +2024-07-16 12:18:02,822 - pyskl - INFO - Epoch [8][1300/3746] lr: 9.941e-02, eta: 4 days, 12:13:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4791, loss_cls: 4.1988, loss: 4.1988 +2024-07-16 12:19:12,609 - pyskl - INFO - Epoch [8][1400/3746] lr: 9.940e-02, eta: 4 days, 12:11:08, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4858, loss_cls: 4.1684, loss: 4.1684 +2024-07-16 12:20:22,779 - pyskl - INFO - Epoch [8][1500/3746] lr: 9.940e-02, eta: 4 days, 12:09:03, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4769, loss_cls: 4.2029, loss: 4.2029 +2024-07-16 12:21:33,790 - pyskl - INFO - Epoch [8][1600/3746] lr: 9.940e-02, eta: 4 days, 12:07:14, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4942, loss_cls: 4.1571, loss: 4.1571 +2024-07-16 12:22:44,570 - pyskl - INFO - Epoch [8][1700/3746] lr: 9.939e-02, eta: 4 days, 12:05:21, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4863, loss_cls: 4.1717, loss: 4.1717 +2024-07-16 12:23:56,309 - pyskl - INFO - Epoch [8][1800/3746] lr: 9.939e-02, eta: 4 days, 12:03:46, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4755, loss_cls: 4.2263, loss: 4.2263 +2024-07-16 12:25:06,972 - pyskl - INFO - Epoch [8][1900/3746] lr: 9.938e-02, eta: 4 days, 12:01:52, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4772, loss_cls: 4.2395, loss: 4.2395 +2024-07-16 12:26:18,141 - pyskl - INFO - Epoch [8][2000/3746] lr: 9.938e-02, eta: 4 days, 12:00:07, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4870, loss_cls: 4.1892, loss: 4.1892 +2024-07-16 12:27:29,011 - pyskl - INFO - Epoch [8][2100/3746] lr: 9.937e-02, eta: 4 days, 11:58:17, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4981, loss_cls: 4.1428, loss: 4.1428 +2024-07-16 12:28:39,140 - pyskl - INFO - Epoch [8][2200/3746] lr: 9.937e-02, eta: 4 days, 11:56:13, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4948, loss_cls: 4.1230, loss: 4.1230 +2024-07-16 12:29:49,090 - pyskl - INFO - Epoch [8][2300/3746] lr: 9.937e-02, eta: 4 days, 11:54:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4895, loss_cls: 4.1633, loss: 4.1633 +2024-07-16 12:30:59,232 - pyskl - INFO - Epoch [8][2400/3746] lr: 9.936e-02, eta: 4 days, 11:52:03, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4830, loss_cls: 4.1737, loss: 4.1737 +2024-07-16 12:32:09,274 - pyskl - INFO - Epoch [8][2500/3746] lr: 9.936e-02, eta: 4 days, 11:49:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4961, loss_cls: 4.1484, loss: 4.1484 +2024-07-16 12:33:19,429 - pyskl - INFO - Epoch [8][2600/3746] lr: 9.935e-02, eta: 4 days, 11:47:57, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4820, loss_cls: 4.1759, loss: 4.1759 +2024-07-16 12:34:29,483 - pyskl - INFO - Epoch [8][2700/3746] lr: 9.935e-02, eta: 4 days, 11:45:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4913, loss_cls: 4.1526, loss: 4.1526 +2024-07-16 12:35:39,475 - pyskl - INFO - Epoch [8][2800/3746] lr: 9.934e-02, eta: 4 days, 11:43:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4938, loss_cls: 4.1621, loss: 4.1621 +2024-07-16 12:36:49,454 - pyskl - INFO - Epoch [8][2900/3746] lr: 9.934e-02, eta: 4 days, 11:41:45, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4903, loss_cls: 4.1908, loss: 4.1908 +2024-07-16 12:37:59,429 - pyskl - INFO - Epoch [8][3000/3746] lr: 9.933e-02, eta: 4 days, 11:39:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4741, loss_cls: 4.2085, loss: 4.2085 +2024-07-16 12:39:09,469 - pyskl - INFO - Epoch [8][3100/3746] lr: 9.933e-02, eta: 4 days, 11:37:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4889, loss_cls: 4.1446, loss: 4.1446 +2024-07-16 12:40:19,545 - pyskl - INFO - Epoch [8][3200/3746] lr: 9.933e-02, eta: 4 days, 11:35:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4948, loss_cls: 4.1409, loss: 4.1409 +2024-07-16 12:41:29,557 - pyskl - INFO - Epoch [8][3300/3746] lr: 9.932e-02, eta: 4 days, 11:33:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4831, loss_cls: 4.2096, loss: 4.2096 +2024-07-16 12:42:39,655 - pyskl - INFO - Epoch [8][3400/3746] lr: 9.932e-02, eta: 4 days, 11:31:36, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4959, loss_cls: 4.1404, loss: 4.1404 +2024-07-16 12:43:49,397 - pyskl - INFO - Epoch [8][3500/3746] lr: 9.931e-02, eta: 4 days, 11:29:30, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4914, loss_cls: 4.1745, loss: 4.1745 +2024-07-16 12:44:59,219 - pyskl - INFO - Epoch [8][3600/3746] lr: 9.931e-02, eta: 4 days, 11:27:25, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.4952, loss_cls: 4.1221, loss: 4.1221 +2024-07-16 12:46:08,977 - pyskl - INFO - Epoch [8][3700/3746] lr: 9.930e-02, eta: 4 days, 11:25:20, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4839, loss_cls: 4.1716, loss: 4.1716 +2024-07-16 12:46:43,228 - pyskl - INFO - Saving checkpoint at 8 epochs +2024-07-16 12:48:33,860 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 12:48:34,533 - pyskl - INFO - +top1_acc 0.1771 +top5_acc 0.4028 +2024-07-16 12:48:34,533 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 12:48:34,572 - pyskl - INFO - +mean_acc 0.1767 +2024-07-16 12:48:34,577 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_5.pth was removed +2024-07-16 12:48:34,818 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_8.pth. +2024-07-16 12:48:34,819 - pyskl - INFO - Best top1_acc is 0.1771 at 8 epoch. +2024-07-16 12:48:34,829 - pyskl - INFO - Epoch(val) [8][309] top1_acc: 0.1771, top5_acc: 0.4028, mean_class_accuracy: 0.1767 +2024-07-16 12:51:52,890 - pyskl - INFO - Epoch [9][100/3746] lr: 9.930e-02, eta: 4 days, 11:50:40, time: 1.981, data_time: 1.280, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4936, loss_cls: 4.1499, loss: 4.1499 +2024-07-16 12:53:02,754 - pyskl - INFO - Epoch [9][200/3746] lr: 9.929e-02, eta: 4 days, 11:48:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5044, loss_cls: 4.1023, loss: 4.1023 +2024-07-16 12:54:12,500 - pyskl - INFO - Epoch [9][300/3746] lr: 9.929e-02, eta: 4 days, 11:46:22, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4867, loss_cls: 4.1589, loss: 4.1589 +2024-07-16 12:55:22,256 - pyskl - INFO - Epoch [9][400/3746] lr: 9.928e-02, eta: 4 days, 11:44:12, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4888, loss_cls: 4.1397, loss: 4.1397 +2024-07-16 12:56:31,980 - pyskl - INFO - Epoch [9][500/3746] lr: 9.928e-02, eta: 4 days, 11:42:03, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4998, loss_cls: 4.1441, loss: 4.1441 +2024-07-16 12:57:41,785 - pyskl - INFO - Epoch [9][600/3746] lr: 9.927e-02, eta: 4 days, 11:39:55, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4886, loss_cls: 4.1785, loss: 4.1785 +2024-07-16 12:58:51,631 - pyskl - INFO - Epoch [9][700/3746] lr: 9.927e-02, eta: 4 days, 11:37:48, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5038, loss_cls: 4.1095, loss: 4.1095 +2024-07-16 13:00:01,439 - pyskl - INFO - Epoch [9][800/3746] lr: 9.926e-02, eta: 4 days, 11:35:41, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4848, loss_cls: 4.1804, loss: 4.1804 +2024-07-16 13:01:11,258 - pyskl - INFO - Epoch [9][900/3746] lr: 9.926e-02, eta: 4 days, 11:33:35, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4894, loss_cls: 4.1264, loss: 4.1264 +2024-07-16 13:02:21,265 - pyskl - INFO - Epoch [9][1000/3746] lr: 9.925e-02, eta: 4 days, 11:31:32, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4844, loss_cls: 4.1872, loss: 4.1872 +2024-07-16 13:03:31,050 - pyskl - INFO - Epoch [9][1100/3746] lr: 9.925e-02, eta: 4 days, 11:29:26, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4911, loss_cls: 4.1656, loss: 4.1656 +2024-07-16 13:04:41,079 - pyskl - INFO - Epoch [9][1200/3746] lr: 9.924e-02, eta: 4 days, 11:27:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.4961, loss_cls: 4.1094, loss: 4.1094 +2024-07-16 13:05:51,188 - pyskl - INFO - Epoch [9][1300/3746] lr: 9.924e-02, eta: 4 days, 11:25:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4950, loss_cls: 4.1517, loss: 4.1517 +2024-07-16 13:07:01,154 - pyskl - INFO - Epoch [9][1400/3746] lr: 9.923e-02, eta: 4 days, 11:23:22, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4864, loss_cls: 4.1540, loss: 4.1540 +2024-07-16 13:08:11,469 - pyskl - INFO - Epoch [9][1500/3746] lr: 9.923e-02, eta: 4 days, 11:21:26, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4847, loss_cls: 4.1958, loss: 4.1958 +2024-07-16 13:09:22,402 - pyskl - INFO - Epoch [9][1600/3746] lr: 9.922e-02, eta: 4 days, 11:19:40, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4958, loss_cls: 4.1501, loss: 4.1501 +2024-07-16 13:10:33,336 - pyskl - INFO - Epoch [9][1700/3746] lr: 9.922e-02, eta: 4 days, 11:17:55, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4823, loss_cls: 4.1895, loss: 4.1895 +2024-07-16 13:11:44,978 - pyskl - INFO - Epoch [9][1800/3746] lr: 9.921e-02, eta: 4 days, 11:16:22, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4877, loss_cls: 4.1544, loss: 4.1544 +2024-07-16 13:12:55,440 - pyskl - INFO - Epoch [9][1900/3746] lr: 9.921e-02, eta: 4 days, 11:14:30, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4805, loss_cls: 4.1721, loss: 4.1721 +2024-07-16 13:14:05,507 - pyskl - INFO - Epoch [9][2000/3746] lr: 9.920e-02, eta: 4 days, 11:12:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4956, loss_cls: 4.1401, loss: 4.1401 +2024-07-16 13:15:15,580 - pyskl - INFO - Epoch [9][2100/3746] lr: 9.920e-02, eta: 4 days, 11:10:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4944, loss_cls: 4.1281, loss: 4.1281 +2024-07-16 13:16:26,136 - pyskl - INFO - Epoch [9][2200/3746] lr: 9.919e-02, eta: 4 days, 11:08:43, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4967, loss_cls: 4.1183, loss: 4.1183 +2024-07-16 13:17:36,447 - pyskl - INFO - Epoch [9][2300/3746] lr: 9.919e-02, eta: 4 days, 11:06:49, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4964, loss_cls: 4.1281, loss: 4.1281 +2024-07-16 13:18:46,538 - pyskl - INFO - Epoch [9][2400/3746] lr: 9.918e-02, eta: 4 days, 11:04:51, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4889, loss_cls: 4.1463, loss: 4.1463 +2024-07-16 13:19:56,930 - pyskl - INFO - Epoch [9][2500/3746] lr: 9.918e-02, eta: 4 days, 11:02:59, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.4994, loss_cls: 4.0913, loss: 4.0913 +2024-07-16 13:21:07,629 - pyskl - INFO - Epoch [9][2600/3746] lr: 9.917e-02, eta: 4 days, 11:01:12, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4867, loss_cls: 4.1655, loss: 4.1655 +2024-07-16 13:22:17,793 - pyskl - INFO - Epoch [9][2700/3746] lr: 9.917e-02, eta: 4 days, 10:59:17, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4959, loss_cls: 4.1519, loss: 4.1519 +2024-07-16 13:23:27,857 - pyskl - INFO - Epoch [9][2800/3746] lr: 9.916e-02, eta: 4 days, 10:57:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4780, loss_cls: 4.1985, loss: 4.1985 +2024-07-16 13:24:38,043 - pyskl - INFO - Epoch [9][2900/3746] lr: 9.916e-02, eta: 4 days, 10:55:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5011, loss_cls: 4.1167, loss: 4.1167 +2024-07-16 13:25:48,317 - pyskl - INFO - Epoch [9][3000/3746] lr: 9.915e-02, eta: 4 days, 10:53:34, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4989, loss_cls: 4.1136, loss: 4.1136 +2024-07-16 13:26:58,364 - pyskl - INFO - Epoch [9][3100/3746] lr: 9.915e-02, eta: 4 days, 10:51:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4867, loss_cls: 4.1417, loss: 4.1417 +2024-07-16 13:28:08,247 - pyskl - INFO - Epoch [9][3200/3746] lr: 9.914e-02, eta: 4 days, 10:49:39, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4900, loss_cls: 4.1486, loss: 4.1486 +2024-07-16 13:29:18,240 - pyskl - INFO - Epoch [9][3300/3746] lr: 9.914e-02, eta: 4 days, 10:47:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4905, loss_cls: 4.1363, loss: 4.1363 +2024-07-16 13:30:28,318 - pyskl - INFO - Epoch [9][3400/3746] lr: 9.913e-02, eta: 4 days, 10:45:48, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4889, loss_cls: 4.1483, loss: 4.1483 +2024-07-16 13:31:38,403 - pyskl - INFO - Epoch [9][3500/3746] lr: 9.913e-02, eta: 4 days, 10:43:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4911, loss_cls: 4.1398, loss: 4.1398 +2024-07-16 13:32:48,450 - pyskl - INFO - Epoch [9][3600/3746] lr: 9.912e-02, eta: 4 days, 10:41:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4897, loss_cls: 4.1475, loss: 4.1475 +2024-07-16 13:33:58,755 - pyskl - INFO - Epoch [9][3700/3746] lr: 9.912e-02, eta: 4 days, 10:40:09, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4905, loss_cls: 4.1264, loss: 4.1264 +2024-07-16 13:34:32,990 - pyskl - INFO - Saving checkpoint at 9 epochs +2024-07-16 13:36:23,530 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 13:36:24,191 - pyskl - INFO - +top1_acc 0.1497 +top5_acc 0.3592 +2024-07-16 13:36:24,191 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 13:36:24,229 - pyskl - INFO - +mean_acc 0.1494 +2024-07-16 13:36:24,238 - pyskl - INFO - Epoch(val) [9][309] top1_acc: 0.1497, top5_acc: 0.3592, mean_class_accuracy: 0.1494 +2024-07-16 13:39:38,672 - pyskl - INFO - Epoch [10][100/3746] lr: 9.911e-02, eta: 4 days, 11:01:21, time: 1.944, data_time: 1.241, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4930, loss_cls: 4.1028, loss: 4.1028 +2024-07-16 13:40:48,680 - pyskl - INFO - Epoch [10][200/3746] lr: 9.910e-02, eta: 4 days, 10:59:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4963, loss_cls: 4.1195, loss: 4.1195 +2024-07-16 13:41:58,816 - pyskl - INFO - Epoch [10][300/3746] lr: 9.910e-02, eta: 4 days, 10:57:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4930, loss_cls: 4.1501, loss: 4.1501 +2024-07-16 13:43:08,843 - pyskl - INFO - Epoch [10][400/3746] lr: 9.909e-02, eta: 4 days, 10:55:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4947, loss_cls: 4.0871, loss: 4.0871 +2024-07-16 13:44:18,955 - pyskl - INFO - Epoch [10][500/3746] lr: 9.909e-02, eta: 4 days, 10:53:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5098, loss_cls: 4.0774, loss: 4.0774 +2024-07-16 13:45:28,770 - pyskl - INFO - Epoch [10][600/3746] lr: 9.908e-02, eta: 4 days, 10:51:31, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4838, loss_cls: 4.1900, loss: 4.1900 +2024-07-16 13:46:38,795 - pyskl - INFO - Epoch [10][700/3746] lr: 9.908e-02, eta: 4 days, 10:49:33, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4931, loss_cls: 4.1295, loss: 4.1295 +2024-07-16 13:47:48,687 - pyskl - INFO - Epoch [10][800/3746] lr: 9.907e-02, eta: 4 days, 10:47:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5038, loss_cls: 4.0943, loss: 4.0943 +2024-07-16 13:48:58,593 - pyskl - INFO - Epoch [10][900/3746] lr: 9.907e-02, eta: 4 days, 10:45:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.4902, loss_cls: 4.0924, loss: 4.0924 +2024-07-16 13:50:08,451 - pyskl - INFO - Epoch [10][1000/3746] lr: 9.906e-02, eta: 4 days, 10:43:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4973, loss_cls: 4.1001, loss: 4.1001 +2024-07-16 13:51:18,666 - pyskl - INFO - Epoch [10][1100/3746] lr: 9.906e-02, eta: 4 days, 10:41:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5016, loss_cls: 4.1155, loss: 4.1155 +2024-07-16 13:52:28,865 - pyskl - INFO - Epoch [10][1200/3746] lr: 9.905e-02, eta: 4 days, 10:39:51, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.5006, loss_cls: 4.1140, loss: 4.1140 +2024-07-16 13:53:38,991 - pyskl - INFO - Epoch [10][1300/3746] lr: 9.905e-02, eta: 4 days, 10:37:56, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4769, loss_cls: 4.1837, loss: 4.1837 +2024-07-16 13:54:48,834 - pyskl - INFO - Epoch [10][1400/3746] lr: 9.904e-02, eta: 4 days, 10:35:58, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.4963, loss_cls: 4.0983, loss: 4.0983 +2024-07-16 13:55:59,237 - pyskl - INFO - Epoch [10][1500/3746] lr: 9.903e-02, eta: 4 days, 10:34:09, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4944, loss_cls: 4.1118, loss: 4.1118 +2024-07-16 13:57:10,276 - pyskl - INFO - Epoch [10][1600/3746] lr: 9.903e-02, eta: 4 days, 10:32:29, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4961, loss_cls: 4.1192, loss: 4.1192 +2024-07-16 13:58:20,908 - pyskl - INFO - Epoch [10][1700/3746] lr: 9.902e-02, eta: 4 days, 10:30:44, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4916, loss_cls: 4.1582, loss: 4.1582 +2024-07-16 13:59:32,293 - pyskl - INFO - Epoch [10][1800/3746] lr: 9.902e-02, eta: 4 days, 10:29:09, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4964, loss_cls: 4.1506, loss: 4.1506 +2024-07-16 14:00:42,948 - pyskl - INFO - Epoch [10][1900/3746] lr: 9.901e-02, eta: 4 days, 10:27:24, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4950, loss_cls: 4.1053, loss: 4.1053 +2024-07-16 14:01:53,459 - pyskl - INFO - Epoch [10][2000/3746] lr: 9.901e-02, eta: 4 days, 10:25:37, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4913, loss_cls: 4.1670, loss: 4.1670 +2024-07-16 14:03:04,375 - pyskl - INFO - Epoch [10][2100/3746] lr: 9.900e-02, eta: 4 days, 10:23:57, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4856, loss_cls: 4.1516, loss: 4.1516 +2024-07-16 14:04:14,580 - pyskl - INFO - Epoch [10][2200/3746] lr: 9.900e-02, eta: 4 days, 10:22:06, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5078, loss_cls: 4.0869, loss: 4.0869 +2024-07-16 14:05:24,917 - pyskl - INFO - Epoch [10][2300/3746] lr: 9.899e-02, eta: 4 days, 10:20:17, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4878, loss_cls: 4.1552, loss: 4.1552 +2024-07-16 14:06:35,051 - pyskl - INFO - Epoch [10][2400/3746] lr: 9.898e-02, eta: 4 days, 10:18:25, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4863, loss_cls: 4.1607, loss: 4.1607 +2024-07-16 14:07:44,889 - pyskl - INFO - Epoch [10][2500/3746] lr: 9.898e-02, eta: 4 days, 10:16:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4983, loss_cls: 4.0999, loss: 4.0999 +2024-07-16 14:08:55,079 - pyskl - INFO - Epoch [10][2600/3746] lr: 9.897e-02, eta: 4 days, 10:14:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4969, loss_cls: 4.1052, loss: 4.1052 +2024-07-16 14:10:05,083 - pyskl - INFO - Epoch [10][2700/3746] lr: 9.897e-02, eta: 4 days, 10:12:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4953, loss_cls: 4.1407, loss: 4.1407 +2024-07-16 14:11:14,699 - pyskl - INFO - Epoch [10][2800/3746] lr: 9.896e-02, eta: 4 days, 10:10:49, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4958, loss_cls: 4.1442, loss: 4.1442 +2024-07-16 14:12:24,577 - pyskl - INFO - Epoch [10][2900/3746] lr: 9.896e-02, eta: 4 days, 10:08:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.4883, loss_cls: 4.1516, loss: 4.1516 +2024-07-16 14:13:34,653 - pyskl - INFO - Epoch [10][3000/3746] lr: 9.895e-02, eta: 4 days, 10:07:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4919, loss_cls: 4.1271, loss: 4.1271 +2024-07-16 14:14:44,399 - pyskl - INFO - Epoch [10][3100/3746] lr: 9.894e-02, eta: 4 days, 10:05:08, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5027, loss_cls: 4.1083, loss: 4.1083 +2024-07-16 14:15:54,351 - pyskl - INFO - Epoch [10][3200/3746] lr: 9.894e-02, eta: 4 days, 10:03:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5080, loss_cls: 4.0922, loss: 4.0922 +2024-07-16 14:17:04,189 - pyskl - INFO - Epoch [10][3300/3746] lr: 9.893e-02, eta: 4 days, 10:01:22, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4866, loss_cls: 4.1691, loss: 4.1691 +2024-07-16 14:18:14,175 - pyskl - INFO - Epoch [10][3400/3746] lr: 9.893e-02, eta: 4 days, 9:59:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4911, loss_cls: 4.1921, loss: 4.1921 +2024-07-16 14:19:23,936 - pyskl - INFO - Epoch [10][3500/3746] lr: 9.892e-02, eta: 4 days, 9:57:37, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4916, loss_cls: 4.1215, loss: 4.1215 +2024-07-16 14:20:33,631 - pyskl - INFO - Epoch [10][3600/3746] lr: 9.892e-02, eta: 4 days, 9:55:42, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4963, loss_cls: 4.1478, loss: 4.1478 +2024-07-16 14:21:43,708 - pyskl - INFO - Epoch [10][3700/3746] lr: 9.891e-02, eta: 4 days, 9:53:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4889, loss_cls: 4.1699, loss: 4.1699 +2024-07-16 14:22:18,353 - pyskl - INFO - Saving checkpoint at 10 epochs +2024-07-16 14:24:08,020 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 14:24:08,725 - pyskl - INFO - +top1_acc 0.1592 +top5_acc 0.3620 +2024-07-16 14:24:08,726 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 14:24:08,766 - pyskl - INFO - +mean_acc 0.1591 +2024-07-16 14:24:08,775 - pyskl - INFO - Epoch(val) [10][309] top1_acc: 0.1592, top5_acc: 0.3620, mean_class_accuracy: 0.1591 +2024-07-16 14:27:24,168 - pyskl - INFO - Epoch [11][100/3746] lr: 9.890e-02, eta: 4 days, 10:12:52, time: 1.954, data_time: 1.247, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4970, loss_cls: 4.0977, loss: 4.0977 +2024-07-16 14:28:34,085 - pyskl - INFO - Epoch [11][200/3746] lr: 9.890e-02, eta: 4 days, 10:10:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4989, loss_cls: 4.1015, loss: 4.1015 +2024-07-16 14:29:43,908 - pyskl - INFO - Epoch [11][300/3746] lr: 9.889e-02, eta: 4 days, 10:09:02, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4938, loss_cls: 4.0976, loss: 4.0976 +2024-07-16 14:30:53,616 - pyskl - INFO - Epoch [11][400/3746] lr: 9.888e-02, eta: 4 days, 10:07:04, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5012, loss_cls: 4.0678, loss: 4.0678 +2024-07-16 14:32:03,788 - pyskl - INFO - Epoch [11][500/3746] lr: 9.888e-02, eta: 4 days, 10:05:14, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4944, loss_cls: 4.1097, loss: 4.1097 +2024-07-16 14:33:13,664 - pyskl - INFO - Epoch [11][600/3746] lr: 9.887e-02, eta: 4 days, 10:03:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4922, loss_cls: 4.1365, loss: 4.1365 +2024-07-16 14:34:23,647 - pyskl - INFO - Epoch [11][700/3746] lr: 9.887e-02, eta: 4 days, 10:01:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4930, loss_cls: 4.1178, loss: 4.1178 +2024-07-16 14:35:33,655 - pyskl - INFO - Epoch [11][800/3746] lr: 9.886e-02, eta: 4 days, 9:59:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4917, loss_cls: 4.1260, loss: 4.1260 +2024-07-16 14:36:43,808 - pyskl - INFO - Epoch [11][900/3746] lr: 9.885e-02, eta: 4 days, 9:57:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4897, loss_cls: 4.1677, loss: 4.1677 +2024-07-16 14:37:53,683 - pyskl - INFO - Epoch [11][1000/3746] lr: 9.885e-02, eta: 4 days, 9:55:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4981, loss_cls: 4.1284, loss: 4.1284 +2024-07-16 14:39:03,685 - pyskl - INFO - Epoch [11][1100/3746] lr: 9.884e-02, eta: 4 days, 9:53:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4881, loss_cls: 4.1592, loss: 4.1592 +2024-07-16 14:40:13,733 - pyskl - INFO - Epoch [11][1200/3746] lr: 9.884e-02, eta: 4 days, 9:52:09, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.4916, loss_cls: 4.1186, loss: 4.1186 +2024-07-16 14:41:23,762 - pyskl - INFO - Epoch [11][1300/3746] lr: 9.883e-02, eta: 4 days, 9:50:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4988, loss_cls: 4.0974, loss: 4.0974 +2024-07-16 14:42:33,401 - pyskl - INFO - Epoch [11][1400/3746] lr: 9.882e-02, eta: 4 days, 9:48:22, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4945, loss_cls: 4.1327, loss: 4.1327 +2024-07-16 14:43:44,092 - pyskl - INFO - Epoch [11][1500/3746] lr: 9.882e-02, eta: 4 days, 9:46:41, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5014, loss_cls: 4.0933, loss: 4.0933 +2024-07-16 14:44:54,945 - pyskl - INFO - Epoch [11][1600/3746] lr: 9.881e-02, eta: 4 days, 9:45:01, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4998, loss_cls: 4.1195, loss: 4.1195 +2024-07-16 14:46:05,459 - pyskl - INFO - Epoch [11][1700/3746] lr: 9.881e-02, eta: 4 days, 9:43:18, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.4994, loss_cls: 4.0995, loss: 4.0995 +2024-07-16 14:47:16,661 - pyskl - INFO - Epoch [11][1800/3746] lr: 9.880e-02, eta: 4 days, 9:41:44, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5028, loss_cls: 4.1225, loss: 4.1225 +2024-07-16 14:48:27,530 - pyskl - INFO - Epoch [11][1900/3746] lr: 9.879e-02, eta: 4 days, 9:40:05, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5006, loss_cls: 4.0752, loss: 4.0752 +2024-07-16 14:49:37,942 - pyskl - INFO - Epoch [11][2000/3746] lr: 9.879e-02, eta: 4 days, 9:38:21, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5031, loss_cls: 4.0942, loss: 4.0942 +2024-07-16 14:50:48,058 - pyskl - INFO - Epoch [11][2100/3746] lr: 9.878e-02, eta: 4 days, 9:36:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4942, loss_cls: 4.1444, loss: 4.1444 +2024-07-16 14:51:58,159 - pyskl - INFO - Epoch [11][2200/3746] lr: 9.878e-02, eta: 4 days, 9:34:44, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5003, loss_cls: 4.0909, loss: 4.0909 +2024-07-16 14:53:08,072 - pyskl - INFO - Epoch [11][2300/3746] lr: 9.877e-02, eta: 4 days, 9:32:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5053, loss_cls: 4.0872, loss: 4.0872 +2024-07-16 14:54:17,998 - pyskl - INFO - Epoch [11][2400/3746] lr: 9.876e-02, eta: 4 days, 9:31:04, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4875, loss_cls: 4.1430, loss: 4.1430 +2024-07-16 14:55:27,885 - pyskl - INFO - Epoch [11][2500/3746] lr: 9.876e-02, eta: 4 days, 9:29:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4881, loss_cls: 4.1716, loss: 4.1716 +2024-07-16 14:56:37,706 - pyskl - INFO - Epoch [11][2600/3746] lr: 9.875e-02, eta: 4 days, 9:27:23, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4992, loss_cls: 4.1292, loss: 4.1292 +2024-07-16 14:57:47,556 - pyskl - INFO - Epoch [11][2700/3746] lr: 9.874e-02, eta: 4 days, 9:25:32, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4819, loss_cls: 4.1861, loss: 4.1861 +2024-07-16 14:58:57,654 - pyskl - INFO - Epoch [11][2800/3746] lr: 9.874e-02, eta: 4 days, 9:23:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5064, loss_cls: 4.0981, loss: 4.0981 +2024-07-16 15:00:07,561 - pyskl - INFO - Epoch [11][2900/3746] lr: 9.873e-02, eta: 4 days, 9:21:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.4998, loss_cls: 4.1223, loss: 4.1223 +2024-07-16 15:01:17,617 - pyskl - INFO - Epoch [11][3000/3746] lr: 9.873e-02, eta: 4 days, 9:20:09, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4961, loss_cls: 4.1224, loss: 4.1224 +2024-07-16 15:02:27,566 - pyskl - INFO - Epoch [11][3100/3746] lr: 9.872e-02, eta: 4 days, 9:18:20, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.4975, loss_cls: 4.1041, loss: 4.1041 +2024-07-16 15:03:37,438 - pyskl - INFO - Epoch [11][3200/3746] lr: 9.871e-02, eta: 4 days, 9:16:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5044, loss_cls: 4.1064, loss: 4.1064 +2024-07-16 15:04:47,159 - pyskl - INFO - Epoch [11][3300/3746] lr: 9.871e-02, eta: 4 days, 9:14:40, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5000, loss_cls: 4.1130, loss: 4.1130 +2024-07-16 15:05:57,339 - pyskl - INFO - Epoch [11][3400/3746] lr: 9.870e-02, eta: 4 days, 9:12:55, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5027, loss_cls: 4.0879, loss: 4.0879 +2024-07-16 15:07:07,253 - pyskl - INFO - Epoch [11][3500/3746] lr: 9.869e-02, eta: 4 days, 9:11:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4988, loss_cls: 4.1357, loss: 4.1357 +2024-07-16 15:08:17,039 - pyskl - INFO - Epoch [11][3600/3746] lr: 9.869e-02, eta: 4 days, 9:09:17, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5066, loss_cls: 4.0420, loss: 4.0420 +2024-07-16 15:09:27,018 - pyskl - INFO - Epoch [11][3700/3746] lr: 9.868e-02, eta: 4 days, 9:07:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4941, loss_cls: 4.1438, loss: 4.1438 +2024-07-16 15:10:01,411 - pyskl - INFO - Saving checkpoint at 11 epochs +2024-07-16 15:11:51,860 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 15:11:52,529 - pyskl - INFO - +top1_acc 0.1754 +top5_acc 0.3945 +2024-07-16 15:11:52,529 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 15:11:52,569 - pyskl - INFO - +mean_acc 0.1750 +2024-07-16 15:11:52,583 - pyskl - INFO - Epoch(val) [11][309] top1_acc: 0.1754, top5_acc: 0.3945, mean_class_accuracy: 0.1750 +2024-07-16 15:15:05,500 - pyskl - INFO - Epoch [12][100/3746] lr: 9.867e-02, eta: 4 days, 9:23:59, time: 1.929, data_time: 1.230, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5133, loss_cls: 4.0319, loss: 4.0319 +2024-07-16 15:16:15,344 - pyskl - INFO - Epoch [12][200/3746] lr: 9.867e-02, eta: 4 days, 9:22:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5039, loss_cls: 4.1020, loss: 4.1020 +2024-07-16 15:17:25,445 - pyskl - INFO - Epoch [12][300/3746] lr: 9.866e-02, eta: 4 days, 9:20:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.5033, loss_cls: 4.0991, loss: 4.0991 +2024-07-16 15:18:35,415 - pyskl - INFO - Epoch [12][400/3746] lr: 9.865e-02, eta: 4 days, 9:18:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.4966, loss_cls: 4.1355, loss: 4.1355 +2024-07-16 15:19:45,113 - pyskl - INFO - Epoch [12][500/3746] lr: 9.865e-02, eta: 4 days, 9:16:38, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4909, loss_cls: 4.1412, loss: 4.1412 +2024-07-16 15:20:54,964 - pyskl - INFO - Epoch [12][600/3746] lr: 9.864e-02, eta: 4 days, 9:14:48, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5012, loss_cls: 4.0759, loss: 4.0759 +2024-07-16 15:22:04,714 - pyskl - INFO - Epoch [12][700/3746] lr: 9.863e-02, eta: 4 days, 9:12:57, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4966, loss_cls: 4.1071, loss: 4.1071 +2024-07-16 15:23:14,638 - pyskl - INFO - Epoch [12][800/3746] lr: 9.863e-02, eta: 4 days, 9:11:08, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4992, loss_cls: 4.0965, loss: 4.0965 +2024-07-16 15:24:24,287 - pyskl - INFO - Epoch [12][900/3746] lr: 9.862e-02, eta: 4 days, 9:09:15, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5086, loss_cls: 4.0868, loss: 4.0868 +2024-07-16 15:25:34,330 - pyskl - INFO - Epoch [12][1000/3746] lr: 9.861e-02, eta: 4 days, 9:07:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4989, loss_cls: 4.1142, loss: 4.1142 +2024-07-16 15:26:44,224 - pyskl - INFO - Epoch [12][1100/3746] lr: 9.861e-02, eta: 4 days, 9:05:39, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4930, loss_cls: 4.1363, loss: 4.1363 +2024-07-16 15:27:54,306 - pyskl - INFO - Epoch [12][1200/3746] lr: 9.860e-02, eta: 4 days, 9:03:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5128, loss_cls: 4.0767, loss: 4.0767 +2024-07-16 15:29:04,259 - pyskl - INFO - Epoch [12][1300/3746] lr: 9.859e-02, eta: 4 days, 9:02:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4925, loss_cls: 4.1322, loss: 4.1322 +2024-07-16 15:30:14,142 - pyskl - INFO - Epoch [12][1400/3746] lr: 9.859e-02, eta: 4 days, 9:00:17, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5061, loss_cls: 4.0889, loss: 4.0889 +2024-07-16 15:31:24,311 - pyskl - INFO - Epoch [12][1500/3746] lr: 9.858e-02, eta: 4 days, 8:58:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5091, loss_cls: 4.0515, loss: 4.0515 +2024-07-16 15:32:34,919 - pyskl - INFO - Epoch [12][1600/3746] lr: 9.857e-02, eta: 4 days, 8:56:53, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5025, loss_cls: 4.1062, loss: 4.1062 +2024-07-16 15:33:45,386 - pyskl - INFO - Epoch [12][1700/3746] lr: 9.857e-02, eta: 4 days, 8:55:12, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5022, loss_cls: 4.0921, loss: 4.0921 +2024-07-16 15:34:56,863 - pyskl - INFO - Epoch [12][1800/3746] lr: 9.856e-02, eta: 4 days, 8:53:44, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4991, loss_cls: 4.0863, loss: 4.0863 +2024-07-16 15:36:07,523 - pyskl - INFO - Epoch [12][1900/3746] lr: 9.855e-02, eta: 4 days, 8:52:06, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5083, loss_cls: 4.0785, loss: 4.0785 +2024-07-16 15:37:18,088 - pyskl - INFO - Epoch [12][2000/3746] lr: 9.855e-02, eta: 4 days, 8:50:26, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5012, loss_cls: 4.0984, loss: 4.0984 +2024-07-16 15:38:28,559 - pyskl - INFO - Epoch [12][2100/3746] lr: 9.854e-02, eta: 4 days, 8:48:46, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5016, loss_cls: 4.1179, loss: 4.1179 +2024-07-16 15:39:38,861 - pyskl - INFO - Epoch [12][2200/3746] lr: 9.853e-02, eta: 4 days, 8:47:04, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4861, loss_cls: 4.1446, loss: 4.1446 +2024-07-16 15:40:48,920 - pyskl - INFO - Epoch [12][2300/3746] lr: 9.853e-02, eta: 4 days, 8:45:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5094, loss_cls: 4.0887, loss: 4.0887 +2024-07-16 15:41:58,902 - pyskl - INFO - Epoch [12][2400/3746] lr: 9.852e-02, eta: 4 days, 8:43:33, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5042, loss_cls: 4.1004, loss: 4.1004 +2024-07-16 15:43:08,501 - pyskl - INFO - Epoch [12][2500/3746] lr: 9.851e-02, eta: 4 days, 8:41:43, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4892, loss_cls: 4.1557, loss: 4.1557 +2024-07-16 15:44:18,826 - pyskl - INFO - Epoch [12][2600/3746] lr: 9.851e-02, eta: 4 days, 8:40:02, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4842, loss_cls: 4.1924, loss: 4.1924 +2024-07-16 15:45:28,991 - pyskl - INFO - Epoch [12][2700/3746] lr: 9.850e-02, eta: 4 days, 8:38:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4859, loss_cls: 4.1666, loss: 4.1666 +2024-07-16 15:46:38,922 - pyskl - INFO - Epoch [12][2800/3746] lr: 9.849e-02, eta: 4 days, 8:36:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.4956, loss_cls: 4.1127, loss: 4.1127 +2024-07-16 15:47:48,779 - pyskl - INFO - Epoch [12][2900/3746] lr: 9.849e-02, eta: 4 days, 8:34:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5061, loss_cls: 4.1091, loss: 4.1091 +2024-07-16 15:48:58,560 - pyskl - INFO - Epoch [12][3000/3746] lr: 9.848e-02, eta: 4 days, 8:33:00, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4964, loss_cls: 4.1454, loss: 4.1454 +2024-07-16 15:50:08,274 - pyskl - INFO - Epoch [12][3100/3746] lr: 9.847e-02, eta: 4 days, 8:31:13, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4938, loss_cls: 4.1205, loss: 4.1205 +2024-07-16 15:51:18,321 - pyskl - INFO - Epoch [12][3200/3746] lr: 9.847e-02, eta: 4 days, 8:29:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5023, loss_cls: 4.0825, loss: 4.0825 +2024-07-16 15:52:28,659 - pyskl - INFO - Epoch [12][3300/3746] lr: 9.846e-02, eta: 4 days, 8:27:49, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4953, loss_cls: 4.1159, loss: 4.1159 +2024-07-16 15:53:38,598 - pyskl - INFO - Epoch [12][3400/3746] lr: 9.845e-02, eta: 4 days, 8:26:04, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4956, loss_cls: 4.1123, loss: 4.1123 +2024-07-16 15:54:49,068 - pyskl - INFO - Epoch [12][3500/3746] lr: 9.845e-02, eta: 4 days, 8:24:26, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4830, loss_cls: 4.1280, loss: 4.1280 +2024-07-16 15:55:58,954 - pyskl - INFO - Epoch [12][3600/3746] lr: 9.844e-02, eta: 4 days, 8:22:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4988, loss_cls: 4.1121, loss: 4.1121 +2024-07-16 15:57:08,876 - pyskl - INFO - Epoch [12][3700/3746] lr: 9.843e-02, eta: 4 days, 8:20:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5064, loss_cls: 4.0738, loss: 4.0738 +2024-07-16 15:57:43,078 - pyskl - INFO - Saving checkpoint at 12 epochs +2024-07-16 15:59:32,756 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 15:59:33,409 - pyskl - INFO - +top1_acc 0.1762 +top5_acc 0.3895 +2024-07-16 15:59:33,409 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 15:59:33,445 - pyskl - INFO - +mean_acc 0.1758 +2024-07-16 15:59:33,455 - pyskl - INFO - Epoch(val) [12][309] top1_acc: 0.1762, top5_acc: 0.3895, mean_class_accuracy: 0.1758 +2024-07-16 16:02:49,645 - pyskl - INFO - Epoch [13][100/3746] lr: 9.842e-02, eta: 4 days, 8:36:25, time: 1.962, data_time: 1.258, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5162, loss_cls: 4.0371, loss: 4.0371 +2024-07-16 16:03:59,674 - pyskl - INFO - Epoch [13][200/3746] lr: 9.842e-02, eta: 4 days, 8:34:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.4947, loss_cls: 4.0763, loss: 4.0763 +2024-07-16 16:05:09,654 - pyskl - INFO - Epoch [13][300/3746] lr: 9.841e-02, eta: 4 days, 8:32:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5088, loss_cls: 4.0670, loss: 4.0670 +2024-07-16 16:06:19,548 - pyskl - INFO - Epoch [13][400/3746] lr: 9.840e-02, eta: 4 days, 8:31:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5005, loss_cls: 4.0642, loss: 4.0642 +2024-07-16 16:07:29,302 - pyskl - INFO - Epoch [13][500/3746] lr: 9.839e-02, eta: 4 days, 8:29:19, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4981, loss_cls: 4.1024, loss: 4.1024 +2024-07-16 16:08:39,110 - pyskl - INFO - Epoch [13][600/3746] lr: 9.839e-02, eta: 4 days, 8:27:32, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4986, loss_cls: 4.1045, loss: 4.1045 +2024-07-16 16:09:48,935 - pyskl - INFO - Epoch [13][700/3746] lr: 9.838e-02, eta: 4 days, 8:25:45, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5061, loss_cls: 4.0860, loss: 4.0860 +2024-07-16 16:10:58,678 - pyskl - INFO - Epoch [13][800/3746] lr: 9.837e-02, eta: 4 days, 8:23:58, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5028, loss_cls: 4.1020, loss: 4.1020 +2024-07-16 16:12:08,480 - pyskl - INFO - Epoch [13][900/3746] lr: 9.837e-02, eta: 4 days, 8:22:11, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4959, loss_cls: 4.1070, loss: 4.1070 +2024-07-16 16:13:18,353 - pyskl - INFO - Epoch [13][1000/3746] lr: 9.836e-02, eta: 4 days, 8:20:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4983, loss_cls: 4.1187, loss: 4.1187 +2024-07-16 16:14:28,169 - pyskl - INFO - Epoch [13][1100/3746] lr: 9.835e-02, eta: 4 days, 8:18:39, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5000, loss_cls: 4.0783, loss: 4.0783 +2024-07-16 16:15:38,045 - pyskl - INFO - Epoch [13][1200/3746] lr: 9.834e-02, eta: 4 days, 8:16:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5033, loss_cls: 4.0883, loss: 4.0883 +2024-07-16 16:16:47,965 - pyskl - INFO - Epoch [13][1300/3746] lr: 9.834e-02, eta: 4 days, 8:15:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4917, loss_cls: 4.1230, loss: 4.1230 +2024-07-16 16:17:57,980 - pyskl - INFO - Epoch [13][1400/3746] lr: 9.833e-02, eta: 4 days, 8:13:25, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4966, loss_cls: 4.0949, loss: 4.0949 +2024-07-16 16:19:08,130 - pyskl - INFO - Epoch [13][1500/3746] lr: 9.832e-02, eta: 4 days, 8:11:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4947, loss_cls: 4.1385, loss: 4.1385 +2024-07-16 16:20:18,510 - pyskl - INFO - Epoch [13][1600/3746] lr: 9.832e-02, eta: 4 days, 8:10:04, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5072, loss_cls: 4.0584, loss: 4.0584 +2024-07-16 16:21:28,927 - pyskl - INFO - Epoch [13][1700/3746] lr: 9.831e-02, eta: 4 days, 8:08:25, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5025, loss_cls: 4.0785, loss: 4.0785 +2024-07-16 16:22:40,568 - pyskl - INFO - Epoch [13][1800/3746] lr: 9.830e-02, eta: 4 days, 8:07:00, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5033, loss_cls: 4.1160, loss: 4.1160 +2024-07-16 16:23:51,081 - pyskl - INFO - Epoch [13][1900/3746] lr: 9.829e-02, eta: 4 days, 8:05:22, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4961, loss_cls: 4.1222, loss: 4.1222 +2024-07-16 16:25:01,236 - pyskl - INFO - Epoch [13][2000/3746] lr: 9.829e-02, eta: 4 days, 8:03:41, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4933, loss_cls: 4.1239, loss: 4.1239 +2024-07-16 16:26:11,660 - pyskl - INFO - Epoch [13][2100/3746] lr: 9.828e-02, eta: 4 days, 8:02:03, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5025, loss_cls: 4.0983, loss: 4.0983 +2024-07-16 16:27:21,542 - pyskl - INFO - Epoch [13][2200/3746] lr: 9.827e-02, eta: 4 days, 8:00:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5052, loss_cls: 4.0931, loss: 4.0931 +2024-07-16 16:28:31,479 - pyskl - INFO - Epoch [13][2300/3746] lr: 9.827e-02, eta: 4 days, 7:58:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4909, loss_cls: 4.1234, loss: 4.1234 +2024-07-16 16:29:41,405 - pyskl - INFO - Epoch [13][2400/3746] lr: 9.826e-02, eta: 4 days, 7:56:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.4970, loss_cls: 4.1107, loss: 4.1107 +2024-07-16 16:30:51,257 - pyskl - INFO - Epoch [13][2500/3746] lr: 9.825e-02, eta: 4 days, 7:55:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.5000, loss_cls: 4.0907, loss: 4.0907 +2024-07-16 16:32:00,886 - pyskl - INFO - Epoch [13][2600/3746] lr: 9.824e-02, eta: 4 days, 7:53:22, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5002, loss_cls: 4.0991, loss: 4.0991 +2024-07-16 16:33:10,619 - pyskl - INFO - Epoch [13][2700/3746] lr: 9.824e-02, eta: 4 days, 7:51:37, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4975, loss_cls: 4.0998, loss: 4.0998 +2024-07-16 16:34:20,395 - pyskl - INFO - Epoch [13][2800/3746] lr: 9.823e-02, eta: 4 days, 7:49:53, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5009, loss_cls: 4.1218, loss: 4.1218 +2024-07-16 16:35:30,208 - pyskl - INFO - Epoch [13][2900/3746] lr: 9.822e-02, eta: 4 days, 7:48:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5030, loss_cls: 4.0967, loss: 4.0967 +2024-07-16 16:36:40,225 - pyskl - INFO - Epoch [13][3000/3746] lr: 9.821e-02, eta: 4 days, 7:46:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5131, loss_cls: 4.0498, loss: 4.0498 +2024-07-16 16:37:50,303 - pyskl - INFO - Epoch [13][3100/3746] lr: 9.821e-02, eta: 4 days, 7:44:47, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4834, loss_cls: 4.1595, loss: 4.1595 +2024-07-16 16:39:00,425 - pyskl - INFO - Epoch [13][3200/3746] lr: 9.820e-02, eta: 4 days, 7:43:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4980, loss_cls: 4.0991, loss: 4.0991 +2024-07-16 16:40:10,390 - pyskl - INFO - Epoch [13][3300/3746] lr: 9.819e-02, eta: 4 days, 7:41:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5009, loss_cls: 4.1046, loss: 4.1046 +2024-07-16 16:41:20,661 - pyskl - INFO - Epoch [13][3400/3746] lr: 9.818e-02, eta: 4 days, 7:39:48, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.4959, loss_cls: 4.0710, loss: 4.0710 +2024-07-16 16:42:30,834 - pyskl - INFO - Epoch [13][3500/3746] lr: 9.818e-02, eta: 4 days, 7:38:08, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4911, loss_cls: 4.1380, loss: 4.1380 +2024-07-16 16:43:40,682 - pyskl - INFO - Epoch [13][3600/3746] lr: 9.817e-02, eta: 4 days, 7:36:26, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.4998, loss_cls: 4.1064, loss: 4.1064 +2024-07-16 16:44:50,566 - pyskl - INFO - Epoch [13][3700/3746] lr: 9.816e-02, eta: 4 days, 7:34:44, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4925, loss_cls: 4.1118, loss: 4.1118 +2024-07-16 16:45:24,847 - pyskl - INFO - Saving checkpoint at 13 epochs +2024-07-16 16:47:15,514 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 16:47:16,177 - pyskl - INFO - +top1_acc 0.1782 +top5_acc 0.3926 +2024-07-16 16:47:16,177 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 16:47:16,216 - pyskl - INFO - +mean_acc 0.1780 +2024-07-16 16:47:16,220 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_8.pth was removed +2024-07-16 16:47:16,462 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_13.pth. +2024-07-16 16:47:16,462 - pyskl - INFO - Best top1_acc is 0.1782 at 13 epoch. +2024-07-16 16:47:16,473 - pyskl - INFO - Epoch(val) [13][309] top1_acc: 0.1782, top5_acc: 0.3926, mean_class_accuracy: 0.1780 +2024-07-16 16:50:34,273 - pyskl - INFO - Epoch [14][100/3746] lr: 9.815e-02, eta: 4 days, 7:49:02, time: 1.978, data_time: 1.275, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5077, loss_cls: 4.0665, loss: 4.0665 +2024-07-16 16:51:44,424 - pyskl - INFO - Epoch [14][200/3746] lr: 9.814e-02, eta: 4 days, 7:47:21, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5030, loss_cls: 4.0901, loss: 4.0901 +2024-07-16 16:52:54,471 - pyskl - INFO - Epoch [14][300/3746] lr: 9.814e-02, eta: 4 days, 7:45:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5141, loss_cls: 4.0438, loss: 4.0438 +2024-07-16 16:54:04,764 - pyskl - INFO - Epoch [14][400/3746] lr: 9.813e-02, eta: 4 days, 7:44:00, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4997, loss_cls: 4.1155, loss: 4.1155 +2024-07-16 16:55:14,536 - pyskl - INFO - Epoch [14][500/3746] lr: 9.812e-02, eta: 4 days, 7:42:15, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5031, loss_cls: 4.0974, loss: 4.0974 +2024-07-16 16:56:24,756 - pyskl - INFO - Epoch [14][600/3746] lr: 9.811e-02, eta: 4 days, 7:40:36, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4906, loss_cls: 4.1412, loss: 4.1412 +2024-07-16 16:57:34,709 - pyskl - INFO - Epoch [14][700/3746] lr: 9.811e-02, eta: 4 days, 7:38:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5028, loss_cls: 4.0946, loss: 4.0946 +2024-07-16 16:58:44,671 - pyskl - INFO - Epoch [14][800/3746] lr: 9.810e-02, eta: 4 days, 7:37:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5083, loss_cls: 4.0554, loss: 4.0554 +2024-07-16 16:59:54,762 - pyskl - INFO - Epoch [14][900/3746] lr: 9.809e-02, eta: 4 days, 7:35:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4920, loss_cls: 4.1174, loss: 4.1174 +2024-07-16 17:01:04,887 - pyskl - INFO - Epoch [14][1000/3746] lr: 9.808e-02, eta: 4 days, 7:33:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5017, loss_cls: 4.1032, loss: 4.1032 +2024-07-16 17:02:14,972 - pyskl - INFO - Epoch [14][1100/3746] lr: 9.807e-02, eta: 4 days, 7:32:09, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.5011, loss_cls: 4.0843, loss: 4.0843 +2024-07-16 17:03:24,697 - pyskl - INFO - Epoch [14][1200/3746] lr: 9.807e-02, eta: 4 days, 7:30:25, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4877, loss_cls: 4.1530, loss: 4.1530 +2024-07-16 17:04:34,823 - pyskl - INFO - Epoch [14][1300/3746] lr: 9.806e-02, eta: 4 days, 7:28:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.4986, loss_cls: 4.1132, loss: 4.1132 +2024-07-16 17:05:45,291 - pyskl - INFO - Epoch [14][1400/3746] lr: 9.805e-02, eta: 4 days, 7:27:09, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5067, loss_cls: 4.0750, loss: 4.0750 +2024-07-16 17:06:55,499 - pyskl - INFO - Epoch [14][1500/3746] lr: 9.804e-02, eta: 4 days, 7:25:30, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5109, loss_cls: 4.0445, loss: 4.0445 +2024-07-16 17:08:06,526 - pyskl - INFO - Epoch [14][1600/3746] lr: 9.804e-02, eta: 4 days, 7:23:59, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4963, loss_cls: 4.1193, loss: 4.1193 +2024-07-16 17:09:16,754 - pyskl - INFO - Epoch [14][1700/3746] lr: 9.803e-02, eta: 4 days, 7:22:21, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4972, loss_cls: 4.0896, loss: 4.0896 +2024-07-16 17:10:27,560 - pyskl - INFO - Epoch [14][1800/3746] lr: 9.802e-02, eta: 4 days, 7:20:48, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4964, loss_cls: 4.1144, loss: 4.1144 +2024-07-16 17:11:38,220 - pyskl - INFO - Epoch [14][1900/3746] lr: 9.801e-02, eta: 4 days, 7:19:15, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5069, loss_cls: 4.0622, loss: 4.0622 +2024-07-16 17:12:48,522 - pyskl - INFO - Epoch [14][2000/3746] lr: 9.800e-02, eta: 4 days, 7:17:37, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.4975, loss_cls: 4.1175, loss: 4.1175 +2024-07-16 17:13:59,107 - pyskl - INFO - Epoch [14][2100/3746] lr: 9.800e-02, eta: 4 days, 7:16:03, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5094, loss_cls: 4.0809, loss: 4.0809 +2024-07-16 17:15:09,720 - pyskl - INFO - Epoch [14][2200/3746] lr: 9.799e-02, eta: 4 days, 7:14:29, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4997, loss_cls: 4.0973, loss: 4.0973 +2024-07-16 17:16:19,400 - pyskl - INFO - Epoch [14][2300/3746] lr: 9.798e-02, eta: 4 days, 7:12:45, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5098, loss_cls: 4.0977, loss: 4.0977 +2024-07-16 17:17:29,818 - pyskl - INFO - Epoch [14][2400/3746] lr: 9.797e-02, eta: 4 days, 7:11:09, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.4986, loss_cls: 4.1009, loss: 4.1009 +2024-07-16 17:18:39,744 - pyskl - INFO - Epoch [14][2500/3746] lr: 9.797e-02, eta: 4 days, 7:09:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4905, loss_cls: 4.1064, loss: 4.1064 +2024-07-16 17:19:49,552 - pyskl - INFO - Epoch [14][2600/3746] lr: 9.796e-02, eta: 4 days, 7:07:47, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4936, loss_cls: 4.1227, loss: 4.1227 +2024-07-16 17:20:59,542 - pyskl - INFO - Epoch [14][2700/3746] lr: 9.795e-02, eta: 4 days, 7:06:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5062, loss_cls: 4.0663, loss: 4.0663 +2024-07-16 17:22:09,431 - pyskl - INFO - Epoch [14][2800/3746] lr: 9.794e-02, eta: 4 days, 7:04:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5073, loss_cls: 4.0957, loss: 4.0957 +2024-07-16 17:23:19,420 - pyskl - INFO - Epoch [14][2900/3746] lr: 9.793e-02, eta: 4 days, 7:02:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5066, loss_cls: 4.0828, loss: 4.0828 +2024-07-16 17:24:29,468 - pyskl - INFO - Epoch [14][3000/3746] lr: 9.793e-02, eta: 4 days, 7:01:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5142, loss_cls: 4.0475, loss: 4.0475 +2024-07-16 17:25:39,726 - pyskl - INFO - Epoch [14][3100/3746] lr: 9.792e-02, eta: 4 days, 6:59:32, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5012, loss_cls: 4.0960, loss: 4.0960 +2024-07-16 17:26:49,524 - pyskl - INFO - Epoch [14][3200/3746] lr: 9.791e-02, eta: 4 days, 6:57:51, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.4998, loss_cls: 4.1039, loss: 4.1039 +2024-07-16 17:27:59,070 - pyskl - INFO - Epoch [14][3300/3746] lr: 9.790e-02, eta: 4 days, 6:56:07, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5030, loss_cls: 4.1007, loss: 4.1007 +2024-07-16 17:29:08,966 - pyskl - INFO - Epoch [14][3400/3746] lr: 9.789e-02, eta: 4 days, 6:54:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5016, loss_cls: 4.1003, loss: 4.1003 +2024-07-16 17:30:18,908 - pyskl - INFO - Epoch [14][3500/3746] lr: 9.789e-02, eta: 4 days, 6:52:48, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4975, loss_cls: 4.0976, loss: 4.0976 +2024-07-16 17:31:28,897 - pyskl - INFO - Epoch [14][3600/3746] lr: 9.788e-02, eta: 4 days, 6:51:09, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5056, loss_cls: 4.0613, loss: 4.0613 +2024-07-16 17:32:38,758 - pyskl - INFO - Epoch [14][3700/3746] lr: 9.787e-02, eta: 4 days, 6:49:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4970, loss_cls: 4.0981, loss: 4.0981 +2024-07-16 17:33:13,200 - pyskl - INFO - Saving checkpoint at 14 epochs +2024-07-16 17:35:02,893 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 17:35:03,552 - pyskl - INFO - +top1_acc 0.1547 +top5_acc 0.3752 +2024-07-16 17:35:03,552 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 17:35:03,590 - pyskl - INFO - +mean_acc 0.1545 +2024-07-16 17:35:03,599 - pyskl - INFO - Epoch(val) [14][309] top1_acc: 0.1547, top5_acc: 0.3752, mean_class_accuracy: 0.1545 +2024-07-16 17:38:19,782 - pyskl - INFO - Epoch [15][100/3746] lr: 9.786e-02, eta: 4 days, 7:02:17, time: 1.962, data_time: 1.260, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5005, loss_cls: 4.0614, loss: 4.0614 +2024-07-16 17:39:29,821 - pyskl - INFO - Epoch [15][200/3746] lr: 9.785e-02, eta: 4 days, 7:00:37, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5297, loss_cls: 3.9747, loss: 3.9747 +2024-07-16 17:40:39,730 - pyskl - INFO - Epoch [15][300/3746] lr: 9.784e-02, eta: 4 days, 6:58:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5103, loss_cls: 4.0644, loss: 4.0644 +2024-07-16 17:41:49,489 - pyskl - INFO - Epoch [15][400/3746] lr: 9.783e-02, eta: 4 days, 6:57:14, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5088, loss_cls: 4.0602, loss: 4.0602 +2024-07-16 17:42:59,451 - pyskl - INFO - Epoch [15][500/3746] lr: 9.783e-02, eta: 4 days, 6:55:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5044, loss_cls: 4.0941, loss: 4.0941 +2024-07-16 17:44:09,517 - pyskl - INFO - Epoch [15][600/3746] lr: 9.782e-02, eta: 4 days, 6:53:55, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5020, loss_cls: 4.0912, loss: 4.0912 +2024-07-16 17:45:19,440 - pyskl - INFO - Epoch [15][700/3746] lr: 9.781e-02, eta: 4 days, 6:52:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5066, loss_cls: 4.0683, loss: 4.0683 +2024-07-16 17:46:30,063 - pyskl - INFO - Epoch [15][800/3746] lr: 9.780e-02, eta: 4 days, 6:50:41, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4977, loss_cls: 4.1079, loss: 4.1079 +2024-07-16 17:47:40,220 - pyskl - INFO - Epoch [15][900/3746] lr: 9.779e-02, eta: 4 days, 6:49:03, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5012, loss_cls: 4.0661, loss: 4.0661 +2024-07-16 17:48:50,528 - pyskl - INFO - Epoch [15][1000/3746] lr: 9.778e-02, eta: 4 days, 6:47:27, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5064, loss_cls: 4.0768, loss: 4.0768 +2024-07-16 17:50:00,756 - pyskl - INFO - Epoch [15][1100/3746] lr: 9.778e-02, eta: 4 days, 6:45:50, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5053, loss_cls: 4.0704, loss: 4.0704 +2024-07-16 17:51:10,592 - pyskl - INFO - Epoch [15][1200/3746] lr: 9.777e-02, eta: 4 days, 6:44:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5020, loss_cls: 4.0927, loss: 4.0927 +2024-07-16 17:52:20,325 - pyskl - INFO - Epoch [15][1300/3746] lr: 9.776e-02, eta: 4 days, 6:42:27, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.4970, loss_cls: 4.1021, loss: 4.1021 +2024-07-16 17:53:30,235 - pyskl - INFO - Epoch [15][1400/3746] lr: 9.775e-02, eta: 4 days, 6:40:48, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5041, loss_cls: 4.0968, loss: 4.0968 +2024-07-16 17:54:40,748 - pyskl - INFO - Epoch [15][1500/3746] lr: 9.774e-02, eta: 4 days, 6:39:14, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5062, loss_cls: 4.0752, loss: 4.0752 +2024-07-16 17:55:51,528 - pyskl - INFO - Epoch [15][1600/3746] lr: 9.773e-02, eta: 4 days, 6:37:43, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5048, loss_cls: 4.0967, loss: 4.0967 +2024-07-16 17:57:01,592 - pyskl - INFO - Epoch [15][1700/3746] lr: 9.773e-02, eta: 4 days, 6:36:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4927, loss_cls: 4.1136, loss: 4.1136 +2024-07-16 17:58:12,970 - pyskl - INFO - Epoch [15][1800/3746] lr: 9.772e-02, eta: 4 days, 6:34:39, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4897, loss_cls: 4.1341, loss: 4.1341 +2024-07-16 17:59:23,579 - pyskl - INFO - Epoch [15][1900/3746] lr: 9.771e-02, eta: 4 days, 6:33:06, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.4944, loss_cls: 4.1058, loss: 4.1058 +2024-07-16 18:00:34,227 - pyskl - INFO - Epoch [15][2000/3746] lr: 9.770e-02, eta: 4 days, 6:31:34, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5038, loss_cls: 4.0863, loss: 4.0863 +2024-07-16 18:01:45,099 - pyskl - INFO - Epoch [15][2100/3746] lr: 9.769e-02, eta: 4 days, 6:30:04, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4969, loss_cls: 4.1125, loss: 4.1125 +2024-07-16 18:02:55,301 - pyskl - INFO - Epoch [15][2200/3746] lr: 9.768e-02, eta: 4 days, 6:28:28, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.4939, loss_cls: 4.1197, loss: 4.1197 +2024-07-16 18:04:05,650 - pyskl - INFO - Epoch [15][2300/3746] lr: 9.768e-02, eta: 4 days, 6:26:53, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5069, loss_cls: 4.0964, loss: 4.0964 +2024-07-16 18:05:15,707 - pyskl - INFO - Epoch [15][2400/3746] lr: 9.767e-02, eta: 4 days, 6:25:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5066, loss_cls: 4.1083, loss: 4.1083 +2024-07-16 18:06:26,130 - pyskl - INFO - Epoch [15][2500/3746] lr: 9.766e-02, eta: 4 days, 6:23:42, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5033, loss_cls: 4.1101, loss: 4.1101 +2024-07-16 18:07:36,009 - pyskl - INFO - Epoch [15][2600/3746] lr: 9.765e-02, eta: 4 days, 6:22:03, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4964, loss_cls: 4.1056, loss: 4.1056 +2024-07-16 18:08:45,938 - pyskl - INFO - Epoch [15][2700/3746] lr: 9.764e-02, eta: 4 days, 6:20:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.4983, loss_cls: 4.0779, loss: 4.0779 +2024-07-16 18:09:55,745 - pyskl - INFO - Epoch [15][2800/3746] lr: 9.763e-02, eta: 4 days, 6:18:46, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.4961, loss_cls: 4.0859, loss: 4.0859 +2024-07-16 18:11:05,705 - pyskl - INFO - Epoch [15][2900/3746] lr: 9.763e-02, eta: 4 days, 6:17:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5114, loss_cls: 4.0506, loss: 4.0506 +2024-07-16 18:12:15,548 - pyskl - INFO - Epoch [15][3000/3746] lr: 9.762e-02, eta: 4 days, 6:15:29, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5095, loss_cls: 4.0626, loss: 4.0626 +2024-07-16 18:13:25,343 - pyskl - INFO - Epoch [15][3100/3746] lr: 9.761e-02, eta: 4 days, 6:13:50, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5080, loss_cls: 4.0444, loss: 4.0444 +2024-07-16 18:14:35,198 - pyskl - INFO - Epoch [15][3200/3746] lr: 9.760e-02, eta: 4 days, 6:12:12, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5077, loss_cls: 4.1010, loss: 4.1010 +2024-07-16 18:15:45,106 - pyskl - INFO - Epoch [15][3300/3746] lr: 9.759e-02, eta: 4 days, 6:10:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5003, loss_cls: 4.1184, loss: 4.1184 +2024-07-16 18:16:55,205 - pyskl - INFO - Epoch [15][3400/3746] lr: 9.758e-02, eta: 4 days, 6:08:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4967, loss_cls: 4.1260, loss: 4.1260 +2024-07-16 18:18:05,155 - pyskl - INFO - Epoch [15][3500/3746] lr: 9.757e-02, eta: 4 days, 6:07:21, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4916, loss_cls: 4.1399, loss: 4.1399 +2024-07-16 18:19:15,305 - pyskl - INFO - Epoch [15][3600/3746] lr: 9.757e-02, eta: 4 days, 6:05:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5080, loss_cls: 4.0942, loss: 4.0942 +2024-07-16 18:20:25,575 - pyskl - INFO - Epoch [15][3700/3746] lr: 9.756e-02, eta: 4 days, 6:04:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5073, loss_cls: 4.0762, loss: 4.0762 +2024-07-16 18:20:59,815 - pyskl - INFO - Saving checkpoint at 15 epochs +2024-07-16 18:22:48,251 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 18:22:48,920 - pyskl - INFO - +top1_acc 0.1798 +top5_acc 0.4115 +2024-07-16 18:22:48,920 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 18:22:48,957 - pyskl - INFO - +mean_acc 0.1796 +2024-07-16 18:22:48,961 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_13.pth was removed +2024-07-16 18:22:49,203 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_15.pth. +2024-07-16 18:22:49,204 - pyskl - INFO - Best top1_acc is 0.1798 at 15 epoch. +2024-07-16 18:22:49,214 - pyskl - INFO - Epoch(val) [15][309] top1_acc: 0.1798, top5_acc: 0.4115, mean_class_accuracy: 0.1796 +2024-07-16 18:26:03,074 - pyskl - INFO - Epoch [16][100/3746] lr: 9.754e-02, eta: 4 days, 6:15:33, time: 1.939, data_time: 1.236, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5109, loss_cls: 4.0774, loss: 4.0774 +2024-07-16 18:27:13,237 - pyskl - INFO - Epoch [16][200/3746] lr: 9.754e-02, eta: 4 days, 6:13:57, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5133, loss_cls: 4.0401, loss: 4.0401 +2024-07-16 18:28:23,331 - pyskl - INFO - Epoch [16][300/3746] lr: 9.753e-02, eta: 4 days, 6:12:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5038, loss_cls: 4.1052, loss: 4.1052 +2024-07-16 18:29:33,330 - pyskl - INFO - Epoch [16][400/3746] lr: 9.752e-02, eta: 4 days, 6:10:42, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4995, loss_cls: 4.1181, loss: 4.1181 +2024-07-16 18:30:43,165 - pyskl - INFO - Epoch [16][500/3746] lr: 9.751e-02, eta: 4 days, 6:09:03, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5097, loss_cls: 4.0451, loss: 4.0451 +2024-07-16 18:31:53,287 - pyskl - INFO - Epoch [16][600/3746] lr: 9.750e-02, eta: 4 days, 6:07:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5067, loss_cls: 4.0583, loss: 4.0583 +2024-07-16 18:33:03,183 - pyskl - INFO - Epoch [16][700/3746] lr: 9.749e-02, eta: 4 days, 6:05:48, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5109, loss_cls: 4.0429, loss: 4.0429 +2024-07-16 18:34:12,910 - pyskl - INFO - Epoch [16][800/3746] lr: 9.748e-02, eta: 4 days, 6:04:08, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5155, loss_cls: 4.0573, loss: 4.0573 +2024-07-16 18:35:22,817 - pyskl - INFO - Epoch [16][900/3746] lr: 9.747e-02, eta: 4 days, 6:02:30, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.4973, loss_cls: 4.0991, loss: 4.0991 +2024-07-16 18:36:32,721 - pyskl - INFO - Epoch [16][1000/3746] lr: 9.747e-02, eta: 4 days, 6:00:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5084, loss_cls: 4.0540, loss: 4.0540 +2024-07-16 18:37:42,724 - pyskl - INFO - Epoch [16][1100/3746] lr: 9.746e-02, eta: 4 days, 5:59:15, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4994, loss_cls: 4.0986, loss: 4.0986 +2024-07-16 18:38:52,639 - pyskl - INFO - Epoch [16][1200/3746] lr: 9.745e-02, eta: 4 days, 5:57:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5036, loss_cls: 4.0797, loss: 4.0797 +2024-07-16 18:40:02,760 - pyskl - INFO - Epoch [16][1300/3746] lr: 9.744e-02, eta: 4 days, 5:56:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4938, loss_cls: 4.1385, loss: 4.1385 +2024-07-16 18:41:12,659 - pyskl - INFO - Epoch [16][1400/3746] lr: 9.743e-02, eta: 4 days, 5:54:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5056, loss_cls: 4.0755, loss: 4.0755 +2024-07-16 18:42:23,141 - pyskl - INFO - Epoch [16][1500/3746] lr: 9.742e-02, eta: 4 days, 5:52:51, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5133, loss_cls: 4.0581, loss: 4.0581 +2024-07-16 18:43:33,824 - pyskl - INFO - Epoch [16][1600/3746] lr: 9.741e-02, eta: 4 days, 5:51:20, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5020, loss_cls: 4.0961, loss: 4.0961 +2024-07-16 18:44:44,480 - pyskl - INFO - Epoch [16][1700/3746] lr: 9.740e-02, eta: 4 days, 5:49:49, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5044, loss_cls: 4.0783, loss: 4.0783 +2024-07-16 18:45:55,658 - pyskl - INFO - Epoch [16][1800/3746] lr: 9.740e-02, eta: 4 days, 5:48:23, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5012, loss_cls: 4.1072, loss: 4.1072 +2024-07-16 18:47:06,758 - pyskl - INFO - Epoch [16][1900/3746] lr: 9.739e-02, eta: 4 days, 5:46:56, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5097, loss_cls: 4.0573, loss: 4.0573 +2024-07-16 18:48:17,348 - pyskl - INFO - Epoch [16][2000/3746] lr: 9.738e-02, eta: 4 days, 5:45:25, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5034, loss_cls: 4.0737, loss: 4.0737 +2024-07-16 18:49:27,931 - pyskl - INFO - Epoch [16][2100/3746] lr: 9.737e-02, eta: 4 days, 5:43:53, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5017, loss_cls: 4.0958, loss: 4.0958 +2024-07-16 18:50:38,161 - pyskl - INFO - Epoch [16][2200/3746] lr: 9.736e-02, eta: 4 days, 5:42:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5102, loss_cls: 4.0825, loss: 4.0825 +2024-07-16 18:51:47,999 - pyskl - INFO - Epoch [16][2300/3746] lr: 9.735e-02, eta: 4 days, 5:40:42, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5066, loss_cls: 4.0797, loss: 4.0797 +2024-07-16 18:52:57,856 - pyskl - INFO - Epoch [16][2400/3746] lr: 9.734e-02, eta: 4 days, 5:39:04, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4984, loss_cls: 4.0847, loss: 4.0847 +2024-07-16 18:54:08,221 - pyskl - INFO - Epoch [16][2500/3746] lr: 9.733e-02, eta: 4 days, 5:37:32, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5064, loss_cls: 4.0462, loss: 4.0462 +2024-07-16 18:55:18,288 - pyskl - INFO - Epoch [16][2600/3746] lr: 9.732e-02, eta: 4 days, 5:35:56, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5159, loss_cls: 4.0057, loss: 4.0057 +2024-07-16 18:56:28,275 - pyskl - INFO - Epoch [16][2700/3746] lr: 9.731e-02, eta: 4 days, 5:34:21, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.4972, loss_cls: 4.0820, loss: 4.0820 +2024-07-16 18:57:38,390 - pyskl - INFO - Epoch [16][2800/3746] lr: 9.731e-02, eta: 4 days, 5:32:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5003, loss_cls: 4.0793, loss: 4.0793 +2024-07-16 18:58:48,189 - pyskl - INFO - Epoch [16][2900/3746] lr: 9.730e-02, eta: 4 days, 5:31:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5016, loss_cls: 4.1062, loss: 4.1062 +2024-07-16 18:59:58,435 - pyskl - INFO - Epoch [16][3000/3746] lr: 9.729e-02, eta: 4 days, 5:29:35, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5034, loss_cls: 4.0923, loss: 4.0923 +2024-07-16 19:01:08,279 - pyskl - INFO - Epoch [16][3100/3746] lr: 9.728e-02, eta: 4 days, 5:27:58, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4947, loss_cls: 4.1211, loss: 4.1211 +2024-07-16 19:02:18,314 - pyskl - INFO - Epoch [16][3200/3746] lr: 9.727e-02, eta: 4 days, 5:26:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.5038, loss_cls: 4.0658, loss: 4.0658 +2024-07-16 19:03:28,191 - pyskl - INFO - Epoch [16][3300/3746] lr: 9.726e-02, eta: 4 days, 5:24:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5056, loss_cls: 4.0804, loss: 4.0804 +2024-07-16 19:04:38,420 - pyskl - INFO - Epoch [16][3400/3746] lr: 9.725e-02, eta: 4 days, 5:23:14, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5005, loss_cls: 4.0827, loss: 4.0827 +2024-07-16 19:05:48,385 - pyskl - INFO - Epoch [16][3500/3746] lr: 9.724e-02, eta: 4 days, 5:21:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5111, loss_cls: 4.0883, loss: 4.0883 +2024-07-16 19:06:58,258 - pyskl - INFO - Epoch [16][3600/3746] lr: 9.723e-02, eta: 4 days, 5:20:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5027, loss_cls: 4.0881, loss: 4.0881 +2024-07-16 19:08:08,223 - pyskl - INFO - Epoch [16][3700/3746] lr: 9.722e-02, eta: 4 days, 5:18:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4952, loss_cls: 4.1170, loss: 4.1170 +2024-07-16 19:08:42,393 - pyskl - INFO - Saving checkpoint at 16 epochs +2024-07-16 19:10:31,138 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 19:10:31,796 - pyskl - INFO - +top1_acc 0.1820 +top5_acc 0.4029 +2024-07-16 19:10:31,797 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 19:10:31,834 - pyskl - INFO - +mean_acc 0.1819 +2024-07-16 19:10:31,838 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_15.pth was removed +2024-07-16 19:10:32,074 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_16.pth. +2024-07-16 19:10:32,074 - pyskl - INFO - Best top1_acc is 0.1820 at 16 epoch. +2024-07-16 19:10:32,084 - pyskl - INFO - Epoch(val) [16][309] top1_acc: 0.1820, top5_acc: 0.4029, mean_class_accuracy: 0.1819 +2024-07-16 19:13:47,178 - pyskl - INFO - Epoch [17][100/3746] lr: 9.721e-02, eta: 4 days, 5:29:05, time: 1.951, data_time: 1.248, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5186, loss_cls: 4.0321, loss: 4.0321 +2024-07-16 19:14:57,414 - pyskl - INFO - Epoch [17][200/3746] lr: 9.720e-02, eta: 4 days, 5:27:31, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5120, loss_cls: 4.0219, loss: 4.0219 +2024-07-16 19:16:07,525 - pyskl - INFO - Epoch [17][300/3746] lr: 9.719e-02, eta: 4 days, 5:25:56, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5097, loss_cls: 4.0431, loss: 4.0431 +2024-07-16 19:17:17,480 - pyskl - INFO - Epoch [17][400/3746] lr: 9.718e-02, eta: 4 days, 5:24:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5128, loss_cls: 4.0198, loss: 4.0198 +2024-07-16 19:18:27,414 - pyskl - INFO - Epoch [17][500/3746] lr: 9.717e-02, eta: 4 days, 5:22:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5005, loss_cls: 4.1054, loss: 4.1054 +2024-07-16 19:19:37,271 - pyskl - INFO - Epoch [17][600/3746] lr: 9.716e-02, eta: 4 days, 5:21:06, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5041, loss_cls: 4.0954, loss: 4.0954 +2024-07-16 19:20:47,022 - pyskl - INFO - Epoch [17][700/3746] lr: 9.715e-02, eta: 4 days, 5:19:28, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5086, loss_cls: 4.0544, loss: 4.0544 +2024-07-16 19:21:56,972 - pyskl - INFO - Epoch [17][800/3746] lr: 9.714e-02, eta: 4 days, 5:17:52, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5150, loss_cls: 4.0306, loss: 4.0306 +2024-07-16 19:23:06,956 - pyskl - INFO - Epoch [17][900/3746] lr: 9.714e-02, eta: 4 days, 5:16:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4994, loss_cls: 4.0888, loss: 4.0888 +2024-07-16 19:24:17,027 - pyskl - INFO - Epoch [17][1000/3746] lr: 9.713e-02, eta: 4 days, 5:14:42, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5117, loss_cls: 4.0398, loss: 4.0398 +2024-07-16 19:25:27,255 - pyskl - INFO - Epoch [17][1100/3746] lr: 9.712e-02, eta: 4 days, 5:13:08, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.4997, loss_cls: 4.0927, loss: 4.0927 +2024-07-16 19:26:37,390 - pyskl - INFO - Epoch [17][1200/3746] lr: 9.711e-02, eta: 4 days, 5:11:34, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5019, loss_cls: 4.0969, loss: 4.0969 +2024-07-16 19:27:47,605 - pyskl - INFO - Epoch [17][1300/3746] lr: 9.710e-02, eta: 4 days, 5:10:01, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4992, loss_cls: 4.0958, loss: 4.0958 +2024-07-16 19:28:57,367 - pyskl - INFO - Epoch [17][1400/3746] lr: 9.709e-02, eta: 4 days, 5:08:24, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5125, loss_cls: 4.0839, loss: 4.0839 +2024-07-16 19:30:07,929 - pyskl - INFO - Epoch [17][1500/3746] lr: 9.708e-02, eta: 4 days, 5:06:53, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5092, loss_cls: 4.0657, loss: 4.0657 +2024-07-16 19:31:18,334 - pyskl - INFO - Epoch [17][1600/3746] lr: 9.707e-02, eta: 4 days, 5:05:21, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5106, loss_cls: 4.0485, loss: 4.0485 +2024-07-16 19:32:28,688 - pyskl - INFO - Epoch [17][1700/3746] lr: 9.706e-02, eta: 4 days, 5:03:49, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5033, loss_cls: 4.0547, loss: 4.0547 +2024-07-16 19:33:39,496 - pyskl - INFO - Epoch [17][1800/3746] lr: 9.705e-02, eta: 4 days, 5:02:21, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4998, loss_cls: 4.0982, loss: 4.0982 +2024-07-16 19:34:50,288 - pyskl - INFO - Epoch [17][1900/3746] lr: 9.704e-02, eta: 4 days, 5:00:53, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4989, loss_cls: 4.0965, loss: 4.0965 +2024-07-16 19:36:00,849 - pyskl - INFO - Epoch [17][2000/3746] lr: 9.703e-02, eta: 4 days, 4:59:23, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5025, loss_cls: 4.0609, loss: 4.0609 +2024-07-16 19:37:11,734 - pyskl - INFO - Epoch [17][2100/3746] lr: 9.702e-02, eta: 4 days, 4:57:55, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5150, loss_cls: 4.0594, loss: 4.0594 +2024-07-16 19:38:21,881 - pyskl - INFO - Epoch [17][2200/3746] lr: 9.701e-02, eta: 4 days, 4:56:22, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5108, loss_cls: 4.0884, loss: 4.0884 +2024-07-16 19:39:32,208 - pyskl - INFO - Epoch [17][2300/3746] lr: 9.700e-02, eta: 4 days, 4:54:50, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5075, loss_cls: 4.0797, loss: 4.0797 +2024-07-16 19:40:42,426 - pyskl - INFO - Epoch [17][2400/3746] lr: 9.699e-02, eta: 4 days, 4:53:17, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5075, loss_cls: 4.0959, loss: 4.0959 +2024-07-16 19:41:52,485 - pyskl - INFO - Epoch [17][2500/3746] lr: 9.698e-02, eta: 4 days, 4:51:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5095, loss_cls: 4.0373, loss: 4.0373 +2024-07-16 19:43:02,817 - pyskl - INFO - Epoch [17][2600/3746] lr: 9.697e-02, eta: 4 days, 4:50:12, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5100, loss_cls: 4.0691, loss: 4.0691 +2024-07-16 19:44:12,721 - pyskl - INFO - Epoch [17][2700/3746] lr: 9.697e-02, eta: 4 days, 4:48:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5100, loss_cls: 4.0594, loss: 4.0594 +2024-07-16 19:45:22,896 - pyskl - INFO - Epoch [17][2800/3746] lr: 9.696e-02, eta: 4 days, 4:47:04, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.4978, loss_cls: 4.0882, loss: 4.0882 +2024-07-16 19:46:32,902 - pyskl - INFO - Epoch [17][2900/3746] lr: 9.695e-02, eta: 4 days, 4:45:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5023, loss_cls: 4.0848, loss: 4.0848 +2024-07-16 19:47:42,594 - pyskl - INFO - Epoch [17][3000/3746] lr: 9.694e-02, eta: 4 days, 4:43:54, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5189, loss_cls: 4.0290, loss: 4.0290 +2024-07-16 19:48:52,464 - pyskl - INFO - Epoch [17][3100/3746] lr: 9.693e-02, eta: 4 days, 4:42:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.4972, loss_cls: 4.1015, loss: 4.1015 +2024-07-16 19:50:02,281 - pyskl - INFO - Epoch [17][3200/3746] lr: 9.692e-02, eta: 4 days, 4:40:44, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5059, loss_cls: 4.0528, loss: 4.0528 +2024-07-16 19:51:12,260 - pyskl - INFO - Epoch [17][3300/3746] lr: 9.691e-02, eta: 4 days, 4:39:10, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4916, loss_cls: 4.1372, loss: 4.1372 +2024-07-16 19:52:22,176 - pyskl - INFO - Epoch [17][3400/3746] lr: 9.690e-02, eta: 4 days, 4:37:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5120, loss_cls: 4.0675, loss: 4.0675 +2024-07-16 19:53:32,064 - pyskl - INFO - Epoch [17][3500/3746] lr: 9.689e-02, eta: 4 days, 4:36:01, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4977, loss_cls: 4.1187, loss: 4.1187 +2024-07-16 19:54:41,901 - pyskl - INFO - Epoch [17][3600/3746] lr: 9.688e-02, eta: 4 days, 4:34:26, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4959, loss_cls: 4.1115, loss: 4.1115 +2024-07-16 19:55:51,653 - pyskl - INFO - Epoch [17][3700/3746] lr: 9.687e-02, eta: 4 days, 4:32:51, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5133, loss_cls: 4.0543, loss: 4.0543 +2024-07-16 19:56:26,354 - pyskl - INFO - Saving checkpoint at 17 epochs +2024-07-16 19:58:15,565 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 19:58:16,228 - pyskl - INFO - +top1_acc 0.1698 +top5_acc 0.3697 +2024-07-16 19:58:16,228 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 19:58:16,265 - pyskl - INFO - +mean_acc 0.1696 +2024-07-16 19:58:16,275 - pyskl - INFO - Epoch(val) [17][309] top1_acc: 0.1698, top5_acc: 0.3697, mean_class_accuracy: 0.1696 +2024-07-16 20:01:33,047 - pyskl - INFO - Epoch [18][100/3746] lr: 9.685e-02, eta: 4 days, 4:42:53, time: 1.968, data_time: 1.268, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5095, loss_cls: 4.0024, loss: 4.0024 +2024-07-16 20:02:43,262 - pyskl - INFO - Epoch [18][200/3746] lr: 9.684e-02, eta: 4 days, 4:41:20, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5211, loss_cls: 4.0004, loss: 4.0004 +2024-07-16 20:03:52,858 - pyskl - INFO - Epoch [18][300/3746] lr: 9.683e-02, eta: 4 days, 4:39:43, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5050, loss_cls: 4.0663, loss: 4.0663 +2024-07-16 20:05:02,565 - pyskl - INFO - Epoch [18][400/3746] lr: 9.683e-02, eta: 4 days, 4:38:06, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4977, loss_cls: 4.1107, loss: 4.1107 +2024-07-16 20:06:12,432 - pyskl - INFO - Epoch [18][500/3746] lr: 9.682e-02, eta: 4 days, 4:36:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5119, loss_cls: 4.0505, loss: 4.0505 +2024-07-16 20:07:22,578 - pyskl - INFO - Epoch [18][600/3746] lr: 9.681e-02, eta: 4 days, 4:34:57, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5083, loss_cls: 4.0623, loss: 4.0623 +2024-07-16 20:08:32,319 - pyskl - INFO - Epoch [18][700/3746] lr: 9.680e-02, eta: 4 days, 4:33:21, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5127, loss_cls: 4.0562, loss: 4.0562 +2024-07-16 20:09:42,223 - pyskl - INFO - Epoch [18][800/3746] lr: 9.679e-02, eta: 4 days, 4:31:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5011, loss_cls: 4.0760, loss: 4.0760 +2024-07-16 20:10:52,236 - pyskl - INFO - Epoch [18][900/3746] lr: 9.678e-02, eta: 4 days, 4:30:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4991, loss_cls: 4.0870, loss: 4.0870 +2024-07-16 20:12:02,119 - pyskl - INFO - Epoch [18][1000/3746] lr: 9.677e-02, eta: 4 days, 4:28:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5020, loss_cls: 4.0777, loss: 4.0777 +2024-07-16 20:13:11,980 - pyskl - INFO - Epoch [18][1100/3746] lr: 9.676e-02, eta: 4 days, 4:27:03, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5139, loss_cls: 4.0588, loss: 4.0588 +2024-07-16 20:14:22,032 - pyskl - INFO - Epoch [18][1200/3746] lr: 9.675e-02, eta: 4 days, 4:25:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5123, loss_cls: 4.0573, loss: 4.0573 +2024-07-16 20:15:32,327 - pyskl - INFO - Epoch [18][1300/3746] lr: 9.674e-02, eta: 4 days, 4:23:58, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.4945, loss_cls: 4.1123, loss: 4.1123 +2024-07-16 20:16:42,266 - pyskl - INFO - Epoch [18][1400/3746] lr: 9.673e-02, eta: 4 days, 4:22:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5058, loss_cls: 4.0677, loss: 4.0677 +2024-07-16 20:17:52,601 - pyskl - INFO - Epoch [18][1500/3746] lr: 9.672e-02, eta: 4 days, 4:20:53, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5106, loss_cls: 4.0518, loss: 4.0518 +2024-07-16 20:19:03,261 - pyskl - INFO - Epoch [18][1600/3746] lr: 9.671e-02, eta: 4 days, 4:19:25, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5059, loss_cls: 4.0677, loss: 4.0677 +2024-07-16 20:20:13,224 - pyskl - INFO - Epoch [18][1700/3746] lr: 9.670e-02, eta: 4 days, 4:17:51, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5008, loss_cls: 4.1148, loss: 4.1148 +2024-07-16 20:21:23,614 - pyskl - INFO - Epoch [18][1800/3746] lr: 9.669e-02, eta: 4 days, 4:16:20, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5084, loss_cls: 4.0684, loss: 4.0684 +2024-07-16 20:22:34,474 - pyskl - INFO - Epoch [18][1900/3746] lr: 9.668e-02, eta: 4 days, 4:14:54, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5139, loss_cls: 4.0358, loss: 4.0358 +2024-07-16 20:23:44,766 - pyskl - INFO - Epoch [18][2000/3746] lr: 9.667e-02, eta: 4 days, 4:13:23, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5125, loss_cls: 4.0648, loss: 4.0648 +2024-07-16 20:24:54,977 - pyskl - INFO - Epoch [18][2100/3746] lr: 9.666e-02, eta: 4 days, 4:11:51, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5117, loss_cls: 4.0470, loss: 4.0470 +2024-07-16 20:26:05,667 - pyskl - INFO - Epoch [18][2200/3746] lr: 9.665e-02, eta: 4 days, 4:10:23, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4908, loss_cls: 4.1145, loss: 4.1145 +2024-07-16 20:27:16,021 - pyskl - INFO - Epoch [18][2300/3746] lr: 9.664e-02, eta: 4 days, 4:08:53, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5083, loss_cls: 4.0648, loss: 4.0648 +2024-07-16 20:28:25,906 - pyskl - INFO - Epoch [18][2400/3746] lr: 9.663e-02, eta: 4 days, 4:07:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5012, loss_cls: 4.0832, loss: 4.0832 +2024-07-16 20:29:35,630 - pyskl - INFO - Epoch [18][2500/3746] lr: 9.662e-02, eta: 4 days, 4:05:44, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5150, loss_cls: 4.0052, loss: 4.0052 +2024-07-16 20:30:45,467 - pyskl - INFO - Epoch [18][2600/3746] lr: 9.661e-02, eta: 4 days, 4:04:10, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5086, loss_cls: 4.0385, loss: 4.0385 +2024-07-16 20:31:55,315 - pyskl - INFO - Epoch [18][2700/3746] lr: 9.660e-02, eta: 4 days, 4:02:36, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5077, loss_cls: 4.0669, loss: 4.0669 +2024-07-16 20:33:05,265 - pyskl - INFO - Epoch [18][2800/3746] lr: 9.659e-02, eta: 4 days, 4:01:03, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.4992, loss_cls: 4.0987, loss: 4.0987 +2024-07-16 20:34:15,400 - pyskl - INFO - Epoch [18][2900/3746] lr: 9.658e-02, eta: 4 days, 3:59:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5142, loss_cls: 4.0729, loss: 4.0729 +2024-07-16 20:35:25,324 - pyskl - INFO - Epoch [18][3000/3746] lr: 9.657e-02, eta: 4 days, 3:57:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5059, loss_cls: 4.0881, loss: 4.0881 +2024-07-16 20:36:35,387 - pyskl - INFO - Epoch [18][3100/3746] lr: 9.656e-02, eta: 4 days, 3:56:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5064, loss_cls: 4.0524, loss: 4.0524 +2024-07-16 20:37:45,421 - pyskl - INFO - Epoch [18][3200/3746] lr: 9.654e-02, eta: 4 days, 3:54:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5097, loss_cls: 4.0643, loss: 4.0643 +2024-07-16 20:38:55,355 - pyskl - INFO - Epoch [18][3300/3746] lr: 9.653e-02, eta: 4 days, 3:53:21, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.5011, loss_cls: 4.1247, loss: 4.1247 +2024-07-16 20:40:05,293 - pyskl - INFO - Epoch [18][3400/3746] lr: 9.652e-02, eta: 4 days, 3:51:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4888, loss_cls: 4.1178, loss: 4.1178 +2024-07-16 20:41:15,103 - pyskl - INFO - Epoch [18][3500/3746] lr: 9.651e-02, eta: 4 days, 3:50:15, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5009, loss_cls: 4.0704, loss: 4.0704 +2024-07-16 20:42:25,125 - pyskl - INFO - Epoch [18][3600/3746] lr: 9.650e-02, eta: 4 days, 3:48:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5002, loss_cls: 4.0794, loss: 4.0794 +2024-07-16 20:43:35,011 - pyskl - INFO - Epoch [18][3700/3746] lr: 9.649e-02, eta: 4 days, 3:47:10, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5100, loss_cls: 4.0504, loss: 4.0504 +2024-07-16 20:44:09,341 - pyskl - INFO - Saving checkpoint at 18 epochs +2024-07-16 20:45:58,893 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 20:45:59,554 - pyskl - INFO - +top1_acc 0.1910 +top5_acc 0.4186 +2024-07-16 20:45:59,554 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 20:45:59,594 - pyskl - INFO - +mean_acc 0.1908 +2024-07-16 20:45:59,599 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_16.pth was removed +2024-07-16 20:45:59,832 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_18.pth. +2024-07-16 20:45:59,833 - pyskl - INFO - Best top1_acc is 0.1910 at 18 epoch. +2024-07-16 20:45:59,843 - pyskl - INFO - Epoch(val) [18][309] top1_acc: 0.1910, top5_acc: 0.4186, mean_class_accuracy: 0.1908 +2024-07-16 20:49:15,862 - pyskl - INFO - Epoch [19][100/3746] lr: 9.648e-02, eta: 4 days, 3:56:23, time: 1.960, data_time: 1.254, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5070, loss_cls: 4.0591, loss: 4.0591 +2024-07-16 20:50:26,257 - pyskl - INFO - Epoch [19][200/3746] lr: 9.647e-02, eta: 4 days, 3:54:52, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5081, loss_cls: 4.0499, loss: 4.0499 +2024-07-16 20:51:36,338 - pyskl - INFO - Epoch [19][300/3746] lr: 9.646e-02, eta: 4 days, 3:53:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5155, loss_cls: 4.0345, loss: 4.0345 +2024-07-16 20:52:46,296 - pyskl - INFO - Epoch [19][400/3746] lr: 9.645e-02, eta: 4 days, 3:51:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5039, loss_cls: 4.0764, loss: 4.0764 +2024-07-16 20:53:56,328 - pyskl - INFO - Epoch [19][500/3746] lr: 9.644e-02, eta: 4 days, 3:50:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5002, loss_cls: 4.0959, loss: 4.0959 +2024-07-16 20:55:06,490 - pyskl - INFO - Epoch [19][600/3746] lr: 9.643e-02, eta: 4 days, 3:48:42, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5117, loss_cls: 4.0298, loss: 4.0298 +2024-07-16 20:56:16,549 - pyskl - INFO - Epoch [19][700/3746] lr: 9.642e-02, eta: 4 days, 3:47:10, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5091, loss_cls: 4.0533, loss: 4.0533 +2024-07-16 20:57:26,293 - pyskl - INFO - Epoch [19][800/3746] lr: 9.641e-02, eta: 4 days, 3:45:36, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5152, loss_cls: 4.0339, loss: 4.0339 +2024-07-16 20:58:36,301 - pyskl - INFO - Epoch [19][900/3746] lr: 9.640e-02, eta: 4 days, 3:44:03, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5086, loss_cls: 4.0728, loss: 4.0728 +2024-07-16 20:59:46,459 - pyskl - INFO - Epoch [19][1000/3746] lr: 9.639e-02, eta: 4 days, 3:42:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5047, loss_cls: 4.0579, loss: 4.0579 +2024-07-16 21:00:56,345 - pyskl - INFO - Epoch [19][1100/3746] lr: 9.637e-02, eta: 4 days, 3:40:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5128, loss_cls: 4.0254, loss: 4.0254 +2024-07-16 21:02:06,631 - pyskl - INFO - Epoch [19][1200/3746] lr: 9.636e-02, eta: 4 days, 3:39:28, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5016, loss_cls: 4.0682, loss: 4.0682 +2024-07-16 21:03:16,712 - pyskl - INFO - Epoch [19][1300/3746] lr: 9.635e-02, eta: 4 days, 3:37:56, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5194, loss_cls: 4.0147, loss: 4.0147 +2024-07-16 21:04:26,422 - pyskl - INFO - Epoch [19][1400/3746] lr: 9.634e-02, eta: 4 days, 3:36:22, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5053, loss_cls: 4.0865, loss: 4.0865 +2024-07-16 21:05:36,722 - pyskl - INFO - Epoch [19][1500/3746] lr: 9.633e-02, eta: 4 days, 3:34:52, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5023, loss_cls: 4.0857, loss: 4.0857 +2024-07-16 21:06:47,401 - pyskl - INFO - Epoch [19][1600/3746] lr: 9.632e-02, eta: 4 days, 3:33:25, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5073, loss_cls: 4.0807, loss: 4.0807 +2024-07-16 21:07:57,613 - pyskl - INFO - Epoch [19][1700/3746] lr: 9.631e-02, eta: 4 days, 3:31:54, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5078, loss_cls: 4.0498, loss: 4.0498 +2024-07-16 21:09:07,952 - pyskl - INFO - Epoch [19][1800/3746] lr: 9.630e-02, eta: 4 days, 3:30:24, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5033, loss_cls: 4.1116, loss: 4.1116 +2024-07-16 21:10:18,783 - pyskl - INFO - Epoch [19][1900/3746] lr: 9.629e-02, eta: 4 days, 3:28:58, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5089, loss_cls: 4.0555, loss: 4.0555 +2024-07-16 21:11:29,492 - pyskl - INFO - Epoch [19][2000/3746] lr: 9.628e-02, eta: 4 days, 3:27:31, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5055, loss_cls: 4.1003, loss: 4.1003 +2024-07-16 21:12:39,826 - pyskl - INFO - Epoch [19][2100/3746] lr: 9.627e-02, eta: 4 days, 3:26:02, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5027, loss_cls: 4.0590, loss: 4.0590 +2024-07-16 21:13:50,210 - pyskl - INFO - Epoch [19][2200/3746] lr: 9.626e-02, eta: 4 days, 3:24:33, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5133, loss_cls: 4.0514, loss: 4.0514 +2024-07-16 21:15:00,538 - pyskl - INFO - Epoch [19][2300/3746] lr: 9.625e-02, eta: 4 days, 3:23:03, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5034, loss_cls: 4.0875, loss: 4.0875 +2024-07-16 21:16:10,918 - pyskl - INFO - Epoch [19][2400/3746] lr: 9.624e-02, eta: 4 days, 3:21:34, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5066, loss_cls: 4.0709, loss: 4.0709 +2024-07-16 21:17:20,864 - pyskl - INFO - Epoch [19][2500/3746] lr: 9.623e-02, eta: 4 days, 3:20:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5100, loss_cls: 4.0717, loss: 4.0717 +2024-07-16 21:18:30,817 - pyskl - INFO - Epoch [19][2600/3746] lr: 9.622e-02, eta: 4 days, 3:18:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.4966, loss_cls: 4.1051, loss: 4.1051 +2024-07-16 21:19:40,885 - pyskl - INFO - Epoch [19][2700/3746] lr: 9.621e-02, eta: 4 days, 3:16:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5123, loss_cls: 4.0480, loss: 4.0480 +2024-07-16 21:20:50,634 - pyskl - INFO - Epoch [19][2800/3746] lr: 9.620e-02, eta: 4 days, 3:15:26, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5114, loss_cls: 4.0698, loss: 4.0698 +2024-07-16 21:22:00,858 - pyskl - INFO - Epoch [19][2900/3746] lr: 9.618e-02, eta: 4 days, 3:13:56, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5030, loss_cls: 4.0594, loss: 4.0594 +2024-07-16 21:23:11,202 - pyskl - INFO - Epoch [19][3000/3746] lr: 9.617e-02, eta: 4 days, 3:12:27, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5075, loss_cls: 4.0838, loss: 4.0838 +2024-07-16 21:24:21,097 - pyskl - INFO - Epoch [19][3100/3746] lr: 9.616e-02, eta: 4 days, 3:10:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5006, loss_cls: 4.0688, loss: 4.0688 +2024-07-16 21:25:30,973 - pyskl - INFO - Epoch [19][3200/3746] lr: 9.615e-02, eta: 4 days, 3:09:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5042, loss_cls: 4.0707, loss: 4.0707 +2024-07-16 21:26:40,687 - pyskl - INFO - Epoch [19][3300/3746] lr: 9.614e-02, eta: 4 days, 3:07:50, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5123, loss_cls: 4.0331, loss: 4.0331 +2024-07-16 21:27:50,996 - pyskl - INFO - Epoch [19][3400/3746] lr: 9.613e-02, eta: 4 days, 3:06:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5067, loss_cls: 4.0904, loss: 4.0904 +2024-07-16 21:29:00,851 - pyskl - INFO - Epoch [19][3500/3746] lr: 9.612e-02, eta: 4 days, 3:04:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5125, loss_cls: 4.0430, loss: 4.0430 +2024-07-16 21:30:10,917 - pyskl - INFO - Epoch [19][3600/3746] lr: 9.611e-02, eta: 4 days, 3:03:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5150, loss_cls: 4.0457, loss: 4.0457 +2024-07-16 21:31:20,856 - pyskl - INFO - Epoch [19][3700/3746] lr: 9.610e-02, eta: 4 days, 3:01:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5148, loss_cls: 4.0307, loss: 4.0307 +2024-07-16 21:31:55,272 - pyskl - INFO - Saving checkpoint at 19 epochs +2024-07-16 21:33:45,285 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 21:33:45,944 - pyskl - INFO - +top1_acc 0.1757 +top5_acc 0.3925 +2024-07-16 21:33:45,944 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 21:33:45,982 - pyskl - INFO - +mean_acc 0.1756 +2024-07-16 21:33:45,991 - pyskl - INFO - Epoch(val) [19][309] top1_acc: 0.1757, top5_acc: 0.3925, mean_class_accuracy: 0.1756 +2024-07-16 21:37:01,671 - pyskl - INFO - Epoch [20][100/3746] lr: 9.608e-02, eta: 4 days, 3:10:18, time: 1.957, data_time: 1.255, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5195, loss_cls: 4.0047, loss: 4.0047 +2024-07-16 21:38:11,945 - pyskl - INFO - Epoch [20][200/3746] lr: 9.607e-02, eta: 4 days, 3:08:48, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5177, loss_cls: 4.0229, loss: 4.0229 +2024-07-16 21:39:21,718 - pyskl - INFO - Epoch [20][300/3746] lr: 9.606e-02, eta: 4 days, 3:07:15, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5098, loss_cls: 4.0294, loss: 4.0294 +2024-07-16 21:40:31,732 - pyskl - INFO - Epoch [20][400/3746] lr: 9.605e-02, eta: 4 days, 3:05:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.4998, loss_cls: 4.1125, loss: 4.1125 +2024-07-16 21:41:41,513 - pyskl - INFO - Epoch [20][500/3746] lr: 9.604e-02, eta: 4 days, 3:04:10, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5105, loss_cls: 4.0730, loss: 4.0730 +2024-07-16 21:42:51,388 - pyskl - INFO - Epoch [20][600/3746] lr: 9.603e-02, eta: 4 days, 3:02:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5045, loss_cls: 4.0595, loss: 4.0595 +2024-07-16 21:44:01,096 - pyskl - INFO - Epoch [20][700/3746] lr: 9.602e-02, eta: 4 days, 3:01:04, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5023, loss_cls: 4.0755, loss: 4.0755 +2024-07-16 21:45:10,943 - pyskl - INFO - Epoch [20][800/3746] lr: 9.601e-02, eta: 4 days, 2:59:32, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5214, loss_cls: 4.0161, loss: 4.0161 +2024-07-16 21:46:20,674 - pyskl - INFO - Epoch [20][900/3746] lr: 9.600e-02, eta: 4 days, 2:57:59, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5130, loss_cls: 4.0493, loss: 4.0493 +2024-07-16 21:47:30,500 - pyskl - INFO - Epoch [20][1000/3746] lr: 9.598e-02, eta: 4 days, 2:56:26, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5072, loss_cls: 4.0732, loss: 4.0732 +2024-07-16 21:48:40,397 - pyskl - INFO - Epoch [20][1100/3746] lr: 9.597e-02, eta: 4 days, 2:54:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5089, loss_cls: 4.0569, loss: 4.0569 +2024-07-16 21:49:50,369 - pyskl - INFO - Epoch [20][1200/3746] lr: 9.596e-02, eta: 4 days, 2:53:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5077, loss_cls: 4.0560, loss: 4.0560 +2024-07-16 21:51:00,306 - pyskl - INFO - Epoch [20][1300/3746] lr: 9.595e-02, eta: 4 days, 2:51:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5167, loss_cls: 4.0342, loss: 4.0342 +2024-07-16 21:52:09,994 - pyskl - INFO - Epoch [20][1400/3746] lr: 9.594e-02, eta: 4 days, 2:50:18, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5091, loss_cls: 4.0637, loss: 4.0637 +2024-07-16 21:53:20,449 - pyskl - INFO - Epoch [20][1500/3746] lr: 9.593e-02, eta: 4 days, 2:48:50, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5100, loss_cls: 4.0496, loss: 4.0496 +2024-07-16 21:54:30,760 - pyskl - INFO - Epoch [20][1600/3746] lr: 9.592e-02, eta: 4 days, 2:47:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.4991, loss_cls: 4.0794, loss: 4.0794 +2024-07-16 21:55:40,951 - pyskl - INFO - Epoch [20][1700/3746] lr: 9.591e-02, eta: 4 days, 2:45:52, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5123, loss_cls: 4.0448, loss: 4.0448 +2024-07-16 21:56:51,610 - pyskl - INFO - Epoch [20][1800/3746] lr: 9.590e-02, eta: 4 days, 2:44:25, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5095, loss_cls: 4.0425, loss: 4.0425 +2024-07-16 21:58:02,092 - pyskl - INFO - Epoch [20][1900/3746] lr: 9.588e-02, eta: 4 days, 2:42:58, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5066, loss_cls: 4.0736, loss: 4.0736 +2024-07-16 21:59:12,808 - pyskl - INFO - Epoch [20][2000/3746] lr: 9.587e-02, eta: 4 days, 2:41:32, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5081, loss_cls: 4.0494, loss: 4.0494 +2024-07-16 22:00:23,391 - pyskl - INFO - Epoch [20][2100/3746] lr: 9.586e-02, eta: 4 days, 2:40:05, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4998, loss_cls: 4.1017, loss: 4.1017 +2024-07-16 22:01:33,551 - pyskl - INFO - Epoch [20][2200/3746] lr: 9.585e-02, eta: 4 days, 2:38:35, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5072, loss_cls: 4.0481, loss: 4.0481 +2024-07-16 22:02:43,659 - pyskl - INFO - Epoch [20][2300/3746] lr: 9.584e-02, eta: 4 days, 2:37:06, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.4991, loss_cls: 4.0549, loss: 4.0549 +2024-07-16 22:03:53,828 - pyskl - INFO - Epoch [20][2400/3746] lr: 9.583e-02, eta: 4 days, 2:35:36, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5188, loss_cls: 4.0350, loss: 4.0350 +2024-07-16 22:05:04,046 - pyskl - INFO - Epoch [20][2500/3746] lr: 9.582e-02, eta: 4 days, 2:34:07, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5052, loss_cls: 4.0704, loss: 4.0704 +2024-07-16 22:06:13,951 - pyskl - INFO - Epoch [20][2600/3746] lr: 9.581e-02, eta: 4 days, 2:32:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5072, loss_cls: 4.0466, loss: 4.0466 +2024-07-16 22:07:24,188 - pyskl - INFO - Epoch [20][2700/3746] lr: 9.580e-02, eta: 4 days, 2:31:07, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5091, loss_cls: 4.0673, loss: 4.0673 +2024-07-16 22:08:34,017 - pyskl - INFO - Epoch [20][2800/3746] lr: 9.578e-02, eta: 4 days, 2:29:36, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5102, loss_cls: 4.0258, loss: 4.0258 +2024-07-16 22:09:44,437 - pyskl - INFO - Epoch [20][2900/3746] lr: 9.577e-02, eta: 4 days, 2:28:08, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5000, loss_cls: 4.0835, loss: 4.0835 +2024-07-16 22:10:54,591 - pyskl - INFO - Epoch [20][3000/3746] lr: 9.576e-02, eta: 4 days, 2:26:39, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5008, loss_cls: 4.0962, loss: 4.0962 +2024-07-16 22:12:04,538 - pyskl - INFO - Epoch [20][3100/3746] lr: 9.575e-02, eta: 4 days, 2:25:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5095, loss_cls: 4.0682, loss: 4.0682 +2024-07-16 22:13:14,468 - pyskl - INFO - Epoch [20][3200/3746] lr: 9.574e-02, eta: 4 days, 2:23:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5211, loss_cls: 4.0290, loss: 4.0290 +2024-07-16 22:14:24,469 - pyskl - INFO - Epoch [20][3300/3746] lr: 9.573e-02, eta: 4 days, 2:22:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5103, loss_cls: 4.0512, loss: 4.0512 +2024-07-16 22:15:34,316 - pyskl - INFO - Epoch [20][3400/3746] lr: 9.572e-02, eta: 4 days, 2:20:37, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5084, loss_cls: 4.0709, loss: 4.0709 +2024-07-16 22:16:44,212 - pyskl - INFO - Epoch [20][3500/3746] lr: 9.571e-02, eta: 4 days, 2:19:06, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.4966, loss_cls: 4.1007, loss: 4.1007 +2024-07-16 22:17:54,068 - pyskl - INFO - Epoch [20][3600/3746] lr: 9.569e-02, eta: 4 days, 2:17:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5019, loss_cls: 4.0828, loss: 4.0828 +2024-07-16 22:19:04,004 - pyskl - INFO - Epoch [20][3700/3746] lr: 9.568e-02, eta: 4 days, 2:16:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5088, loss_cls: 4.0649, loss: 4.0649 +2024-07-16 22:19:38,089 - pyskl - INFO - Saving checkpoint at 20 epochs +2024-07-16 22:21:27,609 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 22:21:28,270 - pyskl - INFO - +top1_acc 0.1897 +top5_acc 0.3956 +2024-07-16 22:21:28,270 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 22:21:28,307 - pyskl - INFO - +mean_acc 0.1896 +2024-07-16 22:21:28,316 - pyskl - INFO - Epoch(val) [20][309] top1_acc: 0.1897, top5_acc: 0.3956, mean_class_accuracy: 0.1896 +2024-07-16 22:24:45,404 - pyskl - INFO - Epoch [21][100/3746] lr: 9.567e-02, eta: 4 days, 2:24:10, time: 1.971, data_time: 1.266, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5123, loss_cls: 4.0377, loss: 4.0377 +2024-07-16 22:25:55,379 - pyskl - INFO - Epoch [21][200/3746] lr: 9.565e-02, eta: 4 days, 2:22:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5120, loss_cls: 4.0149, loss: 4.0149 +2024-07-16 22:27:05,210 - pyskl - INFO - Epoch [21][300/3746] lr: 9.564e-02, eta: 4 days, 2:21:08, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5145, loss_cls: 4.0298, loss: 4.0298 +2024-07-16 22:28:15,128 - pyskl - INFO - Epoch [21][400/3746] lr: 9.563e-02, eta: 4 days, 2:19:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5138, loss_cls: 4.0302, loss: 4.0302 +2024-07-16 22:29:24,835 - pyskl - INFO - Epoch [21][500/3746] lr: 9.562e-02, eta: 4 days, 2:18:04, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5012, loss_cls: 4.0933, loss: 4.0933 +2024-07-16 22:30:34,672 - pyskl - INFO - Epoch [21][600/3746] lr: 9.561e-02, eta: 4 days, 2:16:33, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5141, loss_cls: 4.0396, loss: 4.0396 +2024-07-16 22:31:44,349 - pyskl - INFO - Epoch [21][700/3746] lr: 9.560e-02, eta: 4 days, 2:15:00, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5055, loss_cls: 4.0742, loss: 4.0742 +2024-07-16 22:32:54,072 - pyskl - INFO - Epoch [21][800/3746] lr: 9.559e-02, eta: 4 days, 2:13:28, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5172, loss_cls: 4.0282, loss: 4.0282 +2024-07-16 22:34:03,901 - pyskl - INFO - Epoch [21][900/3746] lr: 9.557e-02, eta: 4 days, 2:11:57, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5112, loss_cls: 4.0530, loss: 4.0530 +2024-07-16 22:35:13,683 - pyskl - INFO - Epoch [21][1000/3746] lr: 9.556e-02, eta: 4 days, 2:10:25, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5119, loss_cls: 4.0366, loss: 4.0366 +2024-07-16 22:36:23,580 - pyskl - INFO - Epoch [21][1100/3746] lr: 9.555e-02, eta: 4 days, 2:08:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5000, loss_cls: 4.0937, loss: 4.0937 +2024-07-16 22:37:33,497 - pyskl - INFO - Epoch [21][1200/3746] lr: 9.554e-02, eta: 4 days, 2:07:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5138, loss_cls: 4.0364, loss: 4.0364 +2024-07-16 22:38:43,275 - pyskl - INFO - Epoch [21][1300/3746] lr: 9.553e-02, eta: 4 days, 2:05:52, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5114, loss_cls: 4.0638, loss: 4.0638 +2024-07-16 22:39:53,111 - pyskl - INFO - Epoch [21][1400/3746] lr: 9.552e-02, eta: 4 days, 2:04:21, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5122, loss_cls: 4.0619, loss: 4.0619 +2024-07-16 22:41:03,444 - pyskl - INFO - Epoch [21][1500/3746] lr: 9.551e-02, eta: 4 days, 2:02:54, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5123, loss_cls: 4.0779, loss: 4.0779 +2024-07-16 22:42:13,882 - pyskl - INFO - Epoch [21][1600/3746] lr: 9.549e-02, eta: 4 days, 2:01:26, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5072, loss_cls: 4.0614, loss: 4.0614 +2024-07-16 22:43:24,396 - pyskl - INFO - Epoch [21][1700/3746] lr: 9.548e-02, eta: 4 days, 2:00:00, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5097, loss_cls: 4.0602, loss: 4.0602 +2024-07-16 22:44:34,535 - pyskl - INFO - Epoch [21][1800/3746] lr: 9.547e-02, eta: 4 days, 1:58:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5123, loss_cls: 4.0293, loss: 4.0293 +2024-07-16 22:45:45,178 - pyskl - INFO - Epoch [21][1900/3746] lr: 9.546e-02, eta: 4 days, 1:57:05, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5217, loss_cls: 3.9939, loss: 3.9939 +2024-07-16 22:46:56,332 - pyskl - INFO - Epoch [21][2000/3746] lr: 9.545e-02, eta: 4 days, 1:55:43, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5188, loss_cls: 4.0241, loss: 4.0241 +2024-07-16 22:48:06,874 - pyskl - INFO - Epoch [21][2100/3746] lr: 9.544e-02, eta: 4 days, 1:54:17, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5098, loss_cls: 4.0716, loss: 4.0716 +2024-07-16 22:49:17,296 - pyskl - INFO - Epoch [21][2200/3746] lr: 9.542e-02, eta: 4 days, 1:52:50, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5059, loss_cls: 4.0562, loss: 4.0562 +2024-07-16 22:50:27,626 - pyskl - INFO - Epoch [21][2300/3746] lr: 9.541e-02, eta: 4 days, 1:51:22, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5070, loss_cls: 4.0755, loss: 4.0755 +2024-07-16 22:51:37,818 - pyskl - INFO - Epoch [21][2400/3746] lr: 9.540e-02, eta: 4 days, 1:49:54, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4959, loss_cls: 4.0969, loss: 4.0969 +2024-07-16 22:52:47,560 - pyskl - INFO - Epoch [21][2500/3746] lr: 9.539e-02, eta: 4 days, 1:48:23, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5194, loss_cls: 4.0192, loss: 4.0192 +2024-07-16 22:53:57,618 - pyskl - INFO - Epoch [21][2600/3746] lr: 9.538e-02, eta: 4 days, 1:46:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5094, loss_cls: 4.0488, loss: 4.0488 +2024-07-16 22:55:07,438 - pyskl - INFO - Epoch [21][2700/3746] lr: 9.537e-02, eta: 4 days, 1:45:23, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5136, loss_cls: 4.0430, loss: 4.0430 +2024-07-16 22:56:17,515 - pyskl - INFO - Epoch [21][2800/3746] lr: 9.535e-02, eta: 4 days, 1:43:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5233, loss_cls: 3.9940, loss: 3.9940 +2024-07-16 22:57:27,378 - pyskl - INFO - Epoch [21][2900/3746] lr: 9.534e-02, eta: 4 days, 1:42:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5136, loss_cls: 4.0375, loss: 4.0375 +2024-07-16 22:58:37,588 - pyskl - INFO - Epoch [21][3000/3746] lr: 9.533e-02, eta: 4 days, 1:40:56, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5031, loss_cls: 4.0905, loss: 4.0905 +2024-07-16 22:59:47,656 - pyskl - INFO - Epoch [21][3100/3746] lr: 9.532e-02, eta: 4 days, 1:39:28, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5077, loss_cls: 4.0556, loss: 4.0556 +2024-07-16 23:00:57,519 - pyskl - INFO - Epoch [21][3200/3746] lr: 9.531e-02, eta: 4 days, 1:37:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5164, loss_cls: 4.0329, loss: 4.0329 +2024-07-16 23:02:07,586 - pyskl - INFO - Epoch [21][3300/3746] lr: 9.529e-02, eta: 4 days, 1:36:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5141, loss_cls: 4.0509, loss: 4.0509 +2024-07-16 23:03:17,430 - pyskl - INFO - Epoch [21][3400/3746] lr: 9.528e-02, eta: 4 days, 1:34:59, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5103, loss_cls: 4.0324, loss: 4.0324 +2024-07-16 23:04:27,287 - pyskl - INFO - Epoch [21][3500/3746] lr: 9.527e-02, eta: 4 days, 1:33:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5083, loss_cls: 4.0321, loss: 4.0321 +2024-07-16 23:05:37,102 - pyskl - INFO - Epoch [21][3600/3746] lr: 9.526e-02, eta: 4 days, 1:31:59, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.4995, loss_cls: 4.0862, loss: 4.0862 +2024-07-16 23:06:47,208 - pyskl - INFO - Epoch [21][3700/3746] lr: 9.525e-02, eta: 4 days, 1:30:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5081, loss_cls: 4.0590, loss: 4.0590 +2024-07-16 23:07:21,611 - pyskl - INFO - Saving checkpoint at 21 epochs +2024-07-16 23:09:10,393 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 23:09:11,054 - pyskl - INFO - +top1_acc 0.1697 +top5_acc 0.3806 +2024-07-16 23:09:11,055 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 23:09:11,092 - pyskl - INFO - +mean_acc 0.1695 +2024-07-16 23:09:11,102 - pyskl - INFO - Epoch(val) [21][309] top1_acc: 0.1697, top5_acc: 0.3806, mean_class_accuracy: 0.1695 +2024-07-16 23:12:24,497 - pyskl - INFO - Epoch [22][100/3746] lr: 9.523e-02, eta: 4 days, 1:37:41, time: 1.934, data_time: 1.235, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5277, loss_cls: 3.9745, loss: 3.9745 +2024-07-16 23:13:34,741 - pyskl - INFO - Epoch [22][200/3746] lr: 9.522e-02, eta: 4 days, 1:36:13, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5162, loss_cls: 4.0232, loss: 4.0232 +2024-07-16 23:14:45,071 - pyskl - INFO - Epoch [22][300/3746] lr: 9.521e-02, eta: 4 days, 1:34:45, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5142, loss_cls: 4.0597, loss: 4.0597 +2024-07-16 23:15:55,197 - pyskl - INFO - Epoch [22][400/3746] lr: 9.519e-02, eta: 4 days, 1:33:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5186, loss_cls: 4.0248, loss: 4.0248 +2024-07-16 23:17:05,132 - pyskl - INFO - Epoch [22][500/3746] lr: 9.518e-02, eta: 4 days, 1:31:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5055, loss_cls: 4.0425, loss: 4.0425 +2024-07-16 23:18:15,170 - pyskl - INFO - Epoch [22][600/3746] lr: 9.517e-02, eta: 4 days, 1:30:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5238, loss_cls: 4.0091, loss: 4.0091 +2024-07-16 23:19:25,150 - pyskl - INFO - Epoch [22][700/3746] lr: 9.516e-02, eta: 4 days, 1:28:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5144, loss_cls: 4.0526, loss: 4.0526 +2024-07-16 23:20:35,048 - pyskl - INFO - Epoch [22][800/3746] lr: 9.515e-02, eta: 4 days, 1:27:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5112, loss_cls: 4.0385, loss: 4.0385 +2024-07-16 23:21:44,969 - pyskl - INFO - Epoch [22][900/3746] lr: 9.513e-02, eta: 4 days, 1:25:48, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5052, loss_cls: 4.0736, loss: 4.0736 +2024-07-16 23:22:54,725 - pyskl - INFO - Epoch [22][1000/3746] lr: 9.512e-02, eta: 4 days, 1:24:18, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5219, loss_cls: 3.9943, loss: 3.9943 +2024-07-16 23:24:04,531 - pyskl - INFO - Epoch [22][1100/3746] lr: 9.511e-02, eta: 4 days, 1:22:47, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5100, loss_cls: 4.0832, loss: 4.0832 +2024-07-16 23:25:14,543 - pyskl - INFO - Epoch [22][1200/3746] lr: 9.510e-02, eta: 4 days, 1:21:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5141, loss_cls: 4.0302, loss: 4.0302 +2024-07-16 23:26:24,347 - pyskl - INFO - Epoch [22][1300/3746] lr: 9.509e-02, eta: 4 days, 1:19:48, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5081, loss_cls: 4.0566, loss: 4.0566 +2024-07-16 23:27:34,385 - pyskl - INFO - Epoch [22][1400/3746] lr: 9.507e-02, eta: 4 days, 1:18:19, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5000, loss_cls: 4.0762, loss: 4.0762 +2024-07-16 23:28:44,667 - pyskl - INFO - Epoch [22][1500/3746] lr: 9.506e-02, eta: 4 days, 1:16:52, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5122, loss_cls: 4.0240, loss: 4.0240 +2024-07-16 23:29:55,316 - pyskl - INFO - Epoch [22][1600/3746] lr: 9.505e-02, eta: 4 days, 1:15:27, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5106, loss_cls: 4.0295, loss: 4.0295 +2024-07-16 23:31:05,543 - pyskl - INFO - Epoch [22][1700/3746] lr: 9.504e-02, eta: 4 days, 1:14:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5119, loss_cls: 4.0578, loss: 4.0578 +2024-07-16 23:32:16,242 - pyskl - INFO - Epoch [22][1800/3746] lr: 9.502e-02, eta: 4 days, 1:12:35, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5178, loss_cls: 4.0166, loss: 4.0166 +2024-07-16 23:33:26,704 - pyskl - INFO - Epoch [22][1900/3746] lr: 9.501e-02, eta: 4 days, 1:11:09, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5131, loss_cls: 4.0527, loss: 4.0527 +2024-07-16 23:34:37,483 - pyskl - INFO - Epoch [22][2000/3746] lr: 9.500e-02, eta: 4 days, 1:09:45, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5069, loss_cls: 4.0679, loss: 4.0679 +2024-07-16 23:35:47,813 - pyskl - INFO - Epoch [22][2100/3746] lr: 9.499e-02, eta: 4 days, 1:08:18, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5212, loss_cls: 4.0117, loss: 4.0117 +2024-07-16 23:36:58,342 - pyskl - INFO - Epoch [22][2200/3746] lr: 9.498e-02, eta: 4 days, 1:06:53, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5086, loss_cls: 4.0741, loss: 4.0741 +2024-07-16 23:38:09,552 - pyskl - INFO - Epoch [22][2300/3746] lr: 9.496e-02, eta: 4 days, 1:05:31, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5097, loss_cls: 4.0622, loss: 4.0622 +2024-07-16 23:39:19,654 - pyskl - INFO - Epoch [22][2400/3746] lr: 9.495e-02, eta: 4 days, 1:04:03, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5133, loss_cls: 4.0447, loss: 4.0447 +2024-07-16 23:40:29,666 - pyskl - INFO - Epoch [22][2500/3746] lr: 9.494e-02, eta: 4 days, 1:02:35, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5131, loss_cls: 4.0427, loss: 4.0427 +2024-07-16 23:41:39,359 - pyskl - INFO - Epoch [22][2600/3746] lr: 9.493e-02, eta: 4 days, 1:01:05, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5006, loss_cls: 4.0914, loss: 4.0914 +2024-07-16 23:42:49,552 - pyskl - INFO - Epoch [22][2700/3746] lr: 9.491e-02, eta: 4 days, 0:59:37, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.4992, loss_cls: 4.0834, loss: 4.0834 +2024-07-16 23:43:59,593 - pyskl - INFO - Epoch [22][2800/3746] lr: 9.490e-02, eta: 4 days, 0:58:09, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5138, loss_cls: 4.0511, loss: 4.0511 +2024-07-16 23:45:09,356 - pyskl - INFO - Epoch [22][2900/3746] lr: 9.489e-02, eta: 4 days, 0:56:39, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4989, loss_cls: 4.0987, loss: 4.0987 +2024-07-16 23:46:19,583 - pyskl - INFO - Epoch [22][3000/3746] lr: 9.488e-02, eta: 4 days, 0:55:13, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5020, loss_cls: 4.0578, loss: 4.0578 +2024-07-16 23:47:29,446 - pyskl - INFO - Epoch [22][3100/3746] lr: 9.487e-02, eta: 4 days, 0:53:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5042, loss_cls: 4.0575, loss: 4.0575 +2024-07-16 23:48:39,267 - pyskl - INFO - Epoch [22][3200/3746] lr: 9.485e-02, eta: 4 days, 0:52:14, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4992, loss_cls: 4.1016, loss: 4.1016 +2024-07-16 23:49:49,106 - pyskl - INFO - Epoch [22][3300/3746] lr: 9.484e-02, eta: 4 days, 0:50:45, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5116, loss_cls: 4.0671, loss: 4.0671 +2024-07-16 23:50:59,454 - pyskl - INFO - Epoch [22][3400/3746] lr: 9.483e-02, eta: 4 days, 0:49:19, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5125, loss_cls: 4.0159, loss: 4.0159 +2024-07-16 23:52:09,414 - pyskl - INFO - Epoch [22][3500/3746] lr: 9.482e-02, eta: 4 days, 0:47:51, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5102, loss_cls: 4.0681, loss: 4.0681 +2024-07-16 23:53:19,296 - pyskl - INFO - Epoch [22][3600/3746] lr: 9.480e-02, eta: 4 days, 0:46:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5172, loss_cls: 4.0123, loss: 4.0123 +2024-07-16 23:54:29,270 - pyskl - INFO - Epoch [22][3700/3746] lr: 9.479e-02, eta: 4 days, 0:44:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5072, loss_cls: 4.0688, loss: 4.0688 +2024-07-16 23:55:03,324 - pyskl - INFO - Saving checkpoint at 22 epochs +2024-07-16 23:56:52,644 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 23:56:53,320 - pyskl - INFO - +top1_acc 0.1960 +top5_acc 0.4217 +2024-07-16 23:56:53,320 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 23:56:53,360 - pyskl - INFO - +mean_acc 0.1960 +2024-07-16 23:56:53,364 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_18.pth was removed +2024-07-16 23:56:53,601 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_22.pth. +2024-07-16 23:56:53,601 - pyskl - INFO - Best top1_acc is 0.1960 at 22 epoch. +2024-07-16 23:56:53,614 - pyskl - INFO - Epoch(val) [22][309] top1_acc: 0.1960, top5_acc: 0.4217, mean_class_accuracy: 0.1960 +2024-07-17 00:00:09,221 - pyskl - INFO - Epoch [23][100/3746] lr: 9.477e-02, eta: 4 days, 0:51:48, time: 1.956, data_time: 1.254, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5230, loss_cls: 3.9735, loss: 3.9735 +2024-07-17 00:01:19,561 - pyskl - INFO - Epoch [23][200/3746] lr: 9.476e-02, eta: 4 days, 0:50:22, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5153, loss_cls: 3.9939, loss: 3.9939 +2024-07-17 00:02:29,546 - pyskl - INFO - Epoch [23][300/3746] lr: 9.475e-02, eta: 4 days, 0:48:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5097, loss_cls: 4.0352, loss: 4.0352 +2024-07-17 00:03:39,430 - pyskl - INFO - Epoch [23][400/3746] lr: 9.474e-02, eta: 4 days, 0:47:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5114, loss_cls: 4.0281, loss: 4.0281 +2024-07-17 00:04:49,523 - pyskl - INFO - Epoch [23][500/3746] lr: 9.472e-02, eta: 4 days, 0:45:56, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5161, loss_cls: 4.0281, loss: 4.0281 +2024-07-17 00:05:59,498 - pyskl - INFO - Epoch [23][600/3746] lr: 9.471e-02, eta: 4 days, 0:44:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5206, loss_cls: 4.0170, loss: 4.0170 +2024-07-17 00:07:09,526 - pyskl - INFO - Epoch [23][700/3746] lr: 9.470e-02, eta: 4 days, 0:42:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5048, loss_cls: 4.0926, loss: 4.0926 +2024-07-17 00:08:19,717 - pyskl - INFO - Epoch [23][800/3746] lr: 9.469e-02, eta: 4 days, 0:41:31, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4995, loss_cls: 4.0954, loss: 4.0954 +2024-07-17 00:09:29,396 - pyskl - INFO - Epoch [23][900/3746] lr: 9.467e-02, eta: 4 days, 0:40:01, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5052, loss_cls: 4.0812, loss: 4.0812 +2024-07-17 00:10:39,210 - pyskl - INFO - Epoch [23][1000/3746] lr: 9.466e-02, eta: 4 days, 0:38:32, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5194, loss_cls: 3.9921, loss: 3.9921 +2024-07-17 00:11:48,928 - pyskl - INFO - Epoch [23][1100/3746] lr: 9.465e-02, eta: 4 days, 0:37:02, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5136, loss_cls: 4.0254, loss: 4.0254 +2024-07-17 00:12:58,852 - pyskl - INFO - Epoch [23][1200/3746] lr: 9.464e-02, eta: 4 days, 0:35:33, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5116, loss_cls: 4.0693, loss: 4.0693 +2024-07-17 00:14:08,673 - pyskl - INFO - Epoch [23][1300/3746] lr: 9.462e-02, eta: 4 days, 0:34:04, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5197, loss_cls: 4.0272, loss: 4.0272 +2024-07-17 00:15:18,542 - pyskl - INFO - Epoch [23][1400/3746] lr: 9.461e-02, eta: 4 days, 0:32:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5138, loss_cls: 4.0196, loss: 4.0196 +2024-07-17 00:16:28,912 - pyskl - INFO - Epoch [23][1500/3746] lr: 9.460e-02, eta: 4 days, 0:31:09, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5122, loss_cls: 4.0493, loss: 4.0493 +2024-07-17 00:17:39,316 - pyskl - INFO - Epoch [23][1600/3746] lr: 9.459e-02, eta: 4 days, 0:29:44, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.4981, loss_cls: 4.0612, loss: 4.0612 +2024-07-17 00:18:49,257 - pyskl - INFO - Epoch [23][1700/3746] lr: 9.457e-02, eta: 4 days, 0:28:15, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5070, loss_cls: 4.0731, loss: 4.0731 +2024-07-17 00:19:59,934 - pyskl - INFO - Epoch [23][1800/3746] lr: 9.456e-02, eta: 4 days, 0:26:51, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5206, loss_cls: 4.0025, loss: 4.0025 +2024-07-17 00:21:10,182 - pyskl - INFO - Epoch [23][1900/3746] lr: 9.455e-02, eta: 4 days, 0:25:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4967, loss_cls: 4.1056, loss: 4.1056 +2024-07-17 00:22:20,797 - pyskl - INFO - Epoch [23][2000/3746] lr: 9.453e-02, eta: 4 days, 0:24:00, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5150, loss_cls: 4.0359, loss: 4.0359 +2024-07-17 00:23:31,576 - pyskl - INFO - Epoch [23][2100/3746] lr: 9.452e-02, eta: 4 days, 0:22:37, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5136, loss_cls: 4.0205, loss: 4.0205 +2024-07-17 00:24:42,280 - pyskl - INFO - Epoch [23][2200/3746] lr: 9.451e-02, eta: 4 days, 0:21:13, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5114, loss_cls: 4.0269, loss: 4.0269 +2024-07-17 00:25:53,032 - pyskl - INFO - Epoch [23][2300/3746] lr: 9.450e-02, eta: 4 days, 0:19:50, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5141, loss_cls: 4.0249, loss: 4.0249 +2024-07-17 00:27:03,432 - pyskl - INFO - Epoch [23][2400/3746] lr: 9.448e-02, eta: 4 days, 0:18:24, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5017, loss_cls: 4.1049, loss: 4.1049 +2024-07-17 00:28:13,640 - pyskl - INFO - Epoch [23][2500/3746] lr: 9.447e-02, eta: 4 days, 0:16:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5156, loss_cls: 4.0211, loss: 4.0211 +2024-07-17 00:29:23,781 - pyskl - INFO - Epoch [23][2600/3746] lr: 9.446e-02, eta: 4 days, 0:15:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5122, loss_cls: 4.0583, loss: 4.0583 +2024-07-17 00:30:33,637 - pyskl - INFO - Epoch [23][2700/3746] lr: 9.445e-02, eta: 4 days, 0:14:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5134, loss_cls: 4.0601, loss: 4.0601 +2024-07-17 00:31:43,700 - pyskl - INFO - Epoch [23][2800/3746] lr: 9.443e-02, eta: 4 days, 0:12:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5130, loss_cls: 4.0204, loss: 4.0204 +2024-07-17 00:32:53,453 - pyskl - INFO - Epoch [23][2900/3746] lr: 9.442e-02, eta: 4 days, 0:11:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5078, loss_cls: 4.0775, loss: 4.0775 +2024-07-17 00:34:03,498 - pyskl - INFO - Epoch [23][3000/3746] lr: 9.441e-02, eta: 4 days, 0:09:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5236, loss_cls: 3.9971, loss: 3.9971 +2024-07-17 00:35:13,291 - pyskl - INFO - Epoch [23][3100/3746] lr: 9.439e-02, eta: 4 days, 0:08:10, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5081, loss_cls: 4.0602, loss: 4.0602 +2024-07-17 00:36:23,219 - pyskl - INFO - Epoch [23][3200/3746] lr: 9.438e-02, eta: 4 days, 0:06:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5166, loss_cls: 4.0536, loss: 4.0536 +2024-07-17 00:37:33,492 - pyskl - INFO - Epoch [23][3300/3746] lr: 9.437e-02, eta: 4 days, 0:05:17, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5058, loss_cls: 4.0522, loss: 4.0522 +2024-07-17 00:38:43,731 - pyskl - INFO - Epoch [23][3400/3746] lr: 9.436e-02, eta: 4 days, 0:03:51, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5106, loss_cls: 4.0517, loss: 4.0517 +2024-07-17 00:39:53,607 - pyskl - INFO - Epoch [23][3500/3746] lr: 9.434e-02, eta: 4 days, 0:02:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5042, loss_cls: 4.0520, loss: 4.0520 +2024-07-17 00:41:03,400 - pyskl - INFO - Epoch [23][3600/3746] lr: 9.433e-02, eta: 4 days, 0:00:55, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5038, loss_cls: 4.1091, loss: 4.1091 +2024-07-17 00:42:12,950 - pyskl - INFO - Epoch [23][3700/3746] lr: 9.432e-02, eta: 3 days, 23:59:25, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5136, loss_cls: 4.0211, loss: 4.0211 +2024-07-17 00:42:47,228 - pyskl - INFO - Saving checkpoint at 23 epochs +2024-07-17 00:44:37,965 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 00:44:38,635 - pyskl - INFO - +top1_acc 0.1974 +top5_acc 0.4199 +2024-07-17 00:44:38,635 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 00:44:38,672 - pyskl - INFO - +mean_acc 0.1971 +2024-07-17 00:44:38,676 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_22.pth was removed +2024-07-17 00:44:38,918 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_23.pth. +2024-07-17 00:44:38,919 - pyskl - INFO - Best top1_acc is 0.1974 at 23 epoch. +2024-07-17 00:44:38,931 - pyskl - INFO - Epoch(val) [23][309] top1_acc: 0.1974, top5_acc: 0.4199, mean_class_accuracy: 0.1971 +2024-07-17 00:47:56,314 - pyskl - INFO - Epoch [24][100/3746] lr: 9.430e-02, eta: 4 days, 0:06:02, time: 1.974, data_time: 1.272, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5150, loss_cls: 4.0232, loss: 4.0232 +2024-07-17 00:49:06,402 - pyskl - INFO - Epoch [24][200/3746] lr: 9.428e-02, eta: 4 days, 0:04:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5095, loss_cls: 4.0721, loss: 4.0721 +2024-07-17 00:50:16,268 - pyskl - INFO - Epoch [24][300/3746] lr: 9.427e-02, eta: 4 days, 0:03:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5178, loss_cls: 4.0425, loss: 4.0425 +2024-07-17 00:51:26,437 - pyskl - INFO - Epoch [24][400/3746] lr: 9.426e-02, eta: 4 days, 0:01:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5191, loss_cls: 3.9795, loss: 3.9795 +2024-07-17 00:52:36,366 - pyskl - INFO - Epoch [24][500/3746] lr: 9.425e-02, eta: 4 days, 0:00:12, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5134, loss_cls: 4.0451, loss: 4.0451 +2024-07-17 00:53:46,350 - pyskl - INFO - Epoch [24][600/3746] lr: 9.423e-02, eta: 3 days, 23:58:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5138, loss_cls: 4.0311, loss: 4.0311 +2024-07-17 00:54:56,190 - pyskl - INFO - Epoch [24][700/3746] lr: 9.422e-02, eta: 3 days, 23:57:16, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5116, loss_cls: 4.0584, loss: 4.0584 +2024-07-17 00:56:06,197 - pyskl - INFO - Epoch [24][800/3746] lr: 9.421e-02, eta: 3 days, 23:55:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5053, loss_cls: 4.0732, loss: 4.0732 +2024-07-17 00:57:15,897 - pyskl - INFO - Epoch [24][900/3746] lr: 9.419e-02, eta: 3 days, 23:54:19, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5186, loss_cls: 4.0345, loss: 4.0345 +2024-07-17 00:58:25,570 - pyskl - INFO - Epoch [24][1000/3746] lr: 9.418e-02, eta: 3 days, 23:52:50, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5138, loss_cls: 4.0420, loss: 4.0420 +2024-07-17 00:59:35,353 - pyskl - INFO - Epoch [24][1100/3746] lr: 9.417e-02, eta: 3 days, 23:51:21, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5058, loss_cls: 4.0527, loss: 4.0527 +2024-07-17 01:00:45,462 - pyskl - INFO - Epoch [24][1200/3746] lr: 9.415e-02, eta: 3 days, 23:49:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5145, loss_cls: 4.0430, loss: 4.0430 +2024-07-17 01:01:55,473 - pyskl - INFO - Epoch [24][1300/3746] lr: 9.414e-02, eta: 3 days, 23:48:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5142, loss_cls: 4.0413, loss: 4.0413 +2024-07-17 01:03:05,572 - pyskl - INFO - Epoch [24][1400/3746] lr: 9.413e-02, eta: 3 days, 23:47:00, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5142, loss_cls: 4.0171, loss: 4.0171 +2024-07-17 01:04:16,241 - pyskl - INFO - Epoch [24][1500/3746] lr: 9.411e-02, eta: 3 days, 23:45:36, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5095, loss_cls: 4.0489, loss: 4.0489 +2024-07-17 01:05:26,516 - pyskl - INFO - Epoch [24][1600/3746] lr: 9.410e-02, eta: 3 days, 23:44:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5136, loss_cls: 4.0251, loss: 4.0251 +2024-07-17 01:06:37,073 - pyskl - INFO - Epoch [24][1700/3746] lr: 9.409e-02, eta: 3 days, 23:42:47, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5092, loss_cls: 4.0607, loss: 4.0607 +2024-07-17 01:07:47,419 - pyskl - INFO - Epoch [24][1800/3746] lr: 9.407e-02, eta: 3 days, 23:41:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5139, loss_cls: 4.0248, loss: 4.0248 +2024-07-17 01:08:57,733 - pyskl - INFO - Epoch [24][1900/3746] lr: 9.406e-02, eta: 3 days, 23:39:56, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5009, loss_cls: 4.1072, loss: 4.1072 +2024-07-17 01:10:08,116 - pyskl - INFO - Epoch [24][2000/3746] lr: 9.405e-02, eta: 3 days, 23:38:31, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5116, loss_cls: 4.0361, loss: 4.0361 +2024-07-17 01:11:19,018 - pyskl - INFO - Epoch [24][2100/3746] lr: 9.404e-02, eta: 3 days, 23:37:09, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5123, loss_cls: 4.0541, loss: 4.0541 +2024-07-17 01:12:29,354 - pyskl - INFO - Epoch [24][2200/3746] lr: 9.402e-02, eta: 3 days, 23:35:43, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5078, loss_cls: 4.0475, loss: 4.0475 +2024-07-17 01:13:40,004 - pyskl - INFO - Epoch [24][2300/3746] lr: 9.401e-02, eta: 3 days, 23:34:20, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5198, loss_cls: 4.0321, loss: 4.0321 +2024-07-17 01:14:50,314 - pyskl - INFO - Epoch [24][2400/3746] lr: 9.400e-02, eta: 3 days, 23:32:54, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5123, loss_cls: 4.0829, loss: 4.0829 +2024-07-17 01:16:00,537 - pyskl - INFO - Epoch [24][2500/3746] lr: 9.398e-02, eta: 3 days, 23:31:29, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5086, loss_cls: 4.0459, loss: 4.0459 +2024-07-17 01:17:10,715 - pyskl - INFO - Epoch [24][2600/3746] lr: 9.397e-02, eta: 3 days, 23:30:03, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5214, loss_cls: 3.9858, loss: 3.9858 +2024-07-17 01:18:20,782 - pyskl - INFO - Epoch [24][2700/3746] lr: 9.396e-02, eta: 3 days, 23:28:36, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5102, loss_cls: 4.0092, loss: 4.0092 +2024-07-17 01:19:30,712 - pyskl - INFO - Epoch [24][2800/3746] lr: 9.394e-02, eta: 3 days, 23:27:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5288, loss_cls: 3.9935, loss: 3.9935 +2024-07-17 01:20:40,446 - pyskl - INFO - Epoch [24][2900/3746] lr: 9.393e-02, eta: 3 days, 23:25:41, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5059, loss_cls: 4.0720, loss: 4.0720 +2024-07-17 01:21:50,661 - pyskl - INFO - Epoch [24][3000/3746] lr: 9.392e-02, eta: 3 days, 23:24:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5083, loss_cls: 4.0454, loss: 4.0454 +2024-07-17 01:23:00,747 - pyskl - INFO - Epoch [24][3100/3746] lr: 9.390e-02, eta: 3 days, 23:22:49, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5066, loss_cls: 4.0608, loss: 4.0608 +2024-07-17 01:24:10,865 - pyskl - INFO - Epoch [24][3200/3746] lr: 9.389e-02, eta: 3 days, 23:21:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5120, loss_cls: 4.0426, loss: 4.0426 +2024-07-17 01:25:20,891 - pyskl - INFO - Epoch [24][3300/3746] lr: 9.388e-02, eta: 3 days, 23:19:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5095, loss_cls: 4.0468, loss: 4.0468 +2024-07-17 01:26:30,694 - pyskl - INFO - Epoch [24][3400/3746] lr: 9.386e-02, eta: 3 days, 23:18:29, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5200, loss_cls: 4.0165, loss: 4.0165 +2024-07-17 01:27:40,880 - pyskl - INFO - Epoch [24][3500/3746] lr: 9.385e-02, eta: 3 days, 23:17:03, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5212, loss_cls: 3.9980, loss: 3.9980 +2024-07-17 01:28:50,950 - pyskl - INFO - Epoch [24][3600/3746] lr: 9.384e-02, eta: 3 days, 23:15:37, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5044, loss_cls: 4.0656, loss: 4.0656 +2024-07-17 01:30:00,897 - pyskl - INFO - Epoch [24][3700/3746] lr: 9.382e-02, eta: 3 days, 23:14:10, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5166, loss_cls: 4.0248, loss: 4.0248 +2024-07-17 01:30:34,974 - pyskl - INFO - Saving checkpoint at 24 epochs +2024-07-17 01:32:25,539 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 01:32:26,213 - pyskl - INFO - +top1_acc 0.1776 +top5_acc 0.3950 +2024-07-17 01:32:26,213 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 01:32:26,251 - pyskl - INFO - +mean_acc 0.1774 +2024-07-17 01:32:26,263 - pyskl - INFO - Epoch(val) [24][309] top1_acc: 0.1776, top5_acc: 0.3950, mean_class_accuracy: 0.1774 +2024-07-17 01:35:42,619 - pyskl - INFO - Epoch [25][100/3746] lr: 9.380e-02, eta: 3 days, 23:20:18, time: 1.963, data_time: 1.262, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5155, loss_cls: 3.9773, loss: 3.9773 +2024-07-17 01:36:52,870 - pyskl - INFO - Epoch [25][200/3746] lr: 9.379e-02, eta: 3 days, 23:18:52, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5127, loss_cls: 4.0154, loss: 4.0154 +2024-07-17 01:38:02,728 - pyskl - INFO - Epoch [25][300/3746] lr: 9.378e-02, eta: 3 days, 23:17:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5136, loss_cls: 4.0345, loss: 4.0345 +2024-07-17 01:39:12,777 - pyskl - INFO - Epoch [25][400/3746] lr: 9.376e-02, eta: 3 days, 23:15:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5211, loss_cls: 3.9863, loss: 3.9863 +2024-07-17 01:40:22,672 - pyskl - INFO - Epoch [25][500/3746] lr: 9.375e-02, eta: 3 days, 23:14:30, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5088, loss_cls: 4.0383, loss: 4.0383 +2024-07-17 01:41:32,605 - pyskl - INFO - Epoch [25][600/3746] lr: 9.373e-02, eta: 3 days, 23:13:03, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5053, loss_cls: 4.0578, loss: 4.0578 +2024-07-17 01:42:42,628 - pyskl - INFO - Epoch [25][700/3746] lr: 9.372e-02, eta: 3 days, 23:11:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5230, loss_cls: 4.0027, loss: 4.0027 +2024-07-17 01:43:52,556 - pyskl - INFO - Epoch [25][800/3746] lr: 9.371e-02, eta: 3 days, 23:10:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5206, loss_cls: 3.9838, loss: 3.9838 +2024-07-17 01:45:02,398 - pyskl - INFO - Epoch [25][900/3746] lr: 9.369e-02, eta: 3 days, 23:08:41, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5164, loss_cls: 4.0184, loss: 4.0184 +2024-07-17 01:46:12,195 - pyskl - INFO - Epoch [25][1000/3746] lr: 9.368e-02, eta: 3 days, 23:07:14, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5153, loss_cls: 4.0408, loss: 4.0408 +2024-07-17 01:47:21,879 - pyskl - INFO - Epoch [25][1100/3746] lr: 9.367e-02, eta: 3 days, 23:05:45, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5106, loss_cls: 4.0650, loss: 4.0650 +2024-07-17 01:48:31,629 - pyskl - INFO - Epoch [25][1200/3746] lr: 9.365e-02, eta: 3 days, 23:04:17, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5141, loss_cls: 4.0502, loss: 4.0502 +2024-07-17 01:49:41,333 - pyskl - INFO - Epoch [25][1300/3746] lr: 9.364e-02, eta: 3 days, 23:02:49, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5177, loss_cls: 4.0134, loss: 4.0134 +2024-07-17 01:50:51,399 - pyskl - INFO - Epoch [25][1400/3746] lr: 9.363e-02, eta: 3 days, 23:01:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5169, loss_cls: 4.0202, loss: 4.0202 +2024-07-17 01:52:02,358 - pyskl - INFO - Epoch [25][1500/3746] lr: 9.361e-02, eta: 3 days, 23:00:01, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5042, loss_cls: 4.0920, loss: 4.0920 +2024-07-17 01:53:13,218 - pyskl - INFO - Epoch [25][1600/3746] lr: 9.360e-02, eta: 3 days, 22:58:39, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5003, loss_cls: 4.0841, loss: 4.0841 +2024-07-17 01:54:23,256 - pyskl - INFO - Epoch [25][1700/3746] lr: 9.358e-02, eta: 3 days, 22:57:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5131, loss_cls: 4.0273, loss: 4.0273 +2024-07-17 01:55:33,617 - pyskl - INFO - Epoch [25][1800/3746] lr: 9.357e-02, eta: 3 days, 22:55:48, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5064, loss_cls: 4.0516, loss: 4.0516 +2024-07-17 01:56:44,120 - pyskl - INFO - Epoch [25][1900/3746] lr: 9.356e-02, eta: 3 days, 22:54:25, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5169, loss_cls: 4.0284, loss: 4.0284 +2024-07-17 01:57:54,656 - pyskl - INFO - Epoch [25][2000/3746] lr: 9.354e-02, eta: 3 days, 22:53:01, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5184, loss_cls: 4.0094, loss: 4.0094 +2024-07-17 01:59:05,177 - pyskl - INFO - Epoch [25][2100/3746] lr: 9.353e-02, eta: 3 days, 22:51:37, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5155, loss_cls: 4.0480, loss: 4.0480 +2024-07-17 02:00:15,882 - pyskl - INFO - Epoch [25][2200/3746] lr: 9.352e-02, eta: 3 days, 22:50:14, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5164, loss_cls: 4.0312, loss: 4.0312 +2024-07-17 02:01:26,664 - pyskl - INFO - Epoch [25][2300/3746] lr: 9.350e-02, eta: 3 days, 22:48:52, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5170, loss_cls: 4.0300, loss: 4.0300 +2024-07-17 02:02:36,666 - pyskl - INFO - Epoch [25][2400/3746] lr: 9.349e-02, eta: 3 days, 22:47:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5086, loss_cls: 4.0443, loss: 4.0443 +2024-07-17 02:03:46,342 - pyskl - INFO - Epoch [25][2500/3746] lr: 9.347e-02, eta: 3 days, 22:45:58, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5011, loss_cls: 4.0563, loss: 4.0563 +2024-07-17 02:04:56,333 - pyskl - INFO - Epoch [25][2600/3746] lr: 9.346e-02, eta: 3 days, 22:44:32, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5070, loss_cls: 4.0725, loss: 4.0725 +2024-07-17 02:06:06,278 - pyskl - INFO - Epoch [25][2700/3746] lr: 9.345e-02, eta: 3 days, 22:43:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5088, loss_cls: 4.0580, loss: 4.0580 +2024-07-17 02:07:16,292 - pyskl - INFO - Epoch [25][2800/3746] lr: 9.343e-02, eta: 3 days, 22:41:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5069, loss_cls: 4.0668, loss: 4.0668 +2024-07-17 02:08:26,002 - pyskl - INFO - Epoch [25][2900/3746] lr: 9.342e-02, eta: 3 days, 22:40:12, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5031, loss_cls: 4.0467, loss: 4.0467 +2024-07-17 02:09:35,869 - pyskl - INFO - Epoch [25][3000/3746] lr: 9.341e-02, eta: 3 days, 22:38:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5117, loss_cls: 4.0250, loss: 4.0250 +2024-07-17 02:10:45,792 - pyskl - INFO - Epoch [25][3100/3746] lr: 9.339e-02, eta: 3 days, 22:37:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5094, loss_cls: 4.0489, loss: 4.0489 +2024-07-17 02:11:55,804 - pyskl - INFO - Epoch [25][3200/3746] lr: 9.338e-02, eta: 3 days, 22:35:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5069, loss_cls: 4.0633, loss: 4.0633 +2024-07-17 02:13:05,776 - pyskl - INFO - Epoch [25][3300/3746] lr: 9.336e-02, eta: 3 days, 22:34:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5055, loss_cls: 4.0685, loss: 4.0685 +2024-07-17 02:14:15,820 - pyskl - INFO - Epoch [25][3400/3746] lr: 9.335e-02, eta: 3 days, 22:33:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5016, loss_cls: 4.0732, loss: 4.0732 +2024-07-17 02:15:25,867 - pyskl - INFO - Epoch [25][3500/3746] lr: 9.334e-02, eta: 3 days, 22:31:35, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5208, loss_cls: 3.9802, loss: 3.9802 +2024-07-17 02:16:35,760 - pyskl - INFO - Epoch [25][3600/3746] lr: 9.332e-02, eta: 3 days, 22:30:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5117, loss_cls: 4.0341, loss: 4.0341 +2024-07-17 02:17:45,757 - pyskl - INFO - Epoch [25][3700/3746] lr: 9.331e-02, eta: 3 days, 22:28:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5205, loss_cls: 4.0044, loss: 4.0044 +2024-07-17 02:18:19,957 - pyskl - INFO - Saving checkpoint at 25 epochs +2024-07-17 02:20:12,179 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 02:20:12,850 - pyskl - INFO - +top1_acc 0.2067 +top5_acc 0.4377 +2024-07-17 02:20:12,850 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 02:20:12,893 - pyskl - INFO - +mean_acc 0.2065 +2024-07-17 02:20:12,897 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_23.pth was removed +2024-07-17 02:20:13,149 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_25.pth. +2024-07-17 02:20:13,149 - pyskl - INFO - Best top1_acc is 0.2067 at 25 epoch. +2024-07-17 02:20:13,164 - pyskl - INFO - Epoch(val) [25][309] top1_acc: 0.2067, top5_acc: 0.4377, mean_class_accuracy: 0.2065 +2024-07-17 02:23:33,192 - pyskl - INFO - Epoch [26][100/3746] lr: 9.329e-02, eta: 3 days, 22:34:46, time: 2.000, data_time: 1.297, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5230, loss_cls: 3.9783, loss: 3.9783 +2024-07-17 02:24:43,740 - pyskl - INFO - Epoch [26][200/3746] lr: 9.327e-02, eta: 3 days, 22:33:23, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5241, loss_cls: 3.9672, loss: 3.9672 +2024-07-17 02:25:53,950 - pyskl - INFO - Epoch [26][300/3746] lr: 9.326e-02, eta: 3 days, 22:31:57, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5255, loss_cls: 3.9415, loss: 3.9415 +2024-07-17 02:27:04,283 - pyskl - INFO - Epoch [26][400/3746] lr: 9.325e-02, eta: 3 days, 22:30:33, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5078, loss_cls: 4.0612, loss: 4.0612 +2024-07-17 02:28:14,760 - pyskl - INFO - Epoch [26][500/3746] lr: 9.323e-02, eta: 3 days, 22:29:09, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5181, loss_cls: 4.0158, loss: 4.0158 +2024-07-17 02:29:24,862 - pyskl - INFO - Epoch [26][600/3746] lr: 9.322e-02, eta: 3 days, 22:27:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5108, loss_cls: 4.0679, loss: 4.0679 +2024-07-17 02:30:35,026 - pyskl - INFO - Epoch [26][700/3746] lr: 9.320e-02, eta: 3 days, 22:26:18, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5278, loss_cls: 3.9772, loss: 3.9772 +2024-07-17 02:31:45,254 - pyskl - INFO - Epoch [26][800/3746] lr: 9.319e-02, eta: 3 days, 22:24:53, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5081, loss_cls: 4.0553, loss: 4.0553 +2024-07-17 02:32:55,139 - pyskl - INFO - Epoch [26][900/3746] lr: 9.318e-02, eta: 3 days, 22:23:26, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5138, loss_cls: 4.0417, loss: 4.0417 +2024-07-17 02:34:05,146 - pyskl - INFO - Epoch [26][1000/3746] lr: 9.316e-02, eta: 3 days, 22:22:00, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5078, loss_cls: 4.0632, loss: 4.0632 +2024-07-17 02:35:15,385 - pyskl - INFO - Epoch [26][1100/3746] lr: 9.315e-02, eta: 3 days, 22:20:35, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5180, loss_cls: 3.9933, loss: 3.9933 +2024-07-17 02:36:25,571 - pyskl - INFO - Epoch [26][1200/3746] lr: 9.313e-02, eta: 3 days, 22:19:10, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5166, loss_cls: 4.0476, loss: 4.0476 +2024-07-17 02:37:35,711 - pyskl - INFO - Epoch [26][1300/3746] lr: 9.312e-02, eta: 3 days, 22:17:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5050, loss_cls: 4.0359, loss: 4.0359 +2024-07-17 02:38:45,566 - pyskl - INFO - Epoch [26][1400/3746] lr: 9.310e-02, eta: 3 days, 22:16:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5142, loss_cls: 4.0163, loss: 4.0163 +2024-07-17 02:39:56,391 - pyskl - INFO - Epoch [26][1500/3746] lr: 9.309e-02, eta: 3 days, 22:14:56, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5153, loss_cls: 4.0399, loss: 4.0399 +2024-07-17 02:41:07,040 - pyskl - INFO - Epoch [26][1600/3746] lr: 9.308e-02, eta: 3 days, 22:13:34, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5209, loss_cls: 4.0229, loss: 4.0229 +2024-07-17 02:42:17,356 - pyskl - INFO - Epoch [26][1700/3746] lr: 9.306e-02, eta: 3 days, 22:12:09, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5103, loss_cls: 4.0648, loss: 4.0648 +2024-07-17 02:43:28,086 - pyskl - INFO - Epoch [26][1800/3746] lr: 9.305e-02, eta: 3 days, 22:10:47, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5248, loss_cls: 3.9661, loss: 3.9661 +2024-07-17 02:44:38,949 - pyskl - INFO - Epoch [26][1900/3746] lr: 9.303e-02, eta: 3 days, 22:09:25, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5081, loss_cls: 4.0809, loss: 4.0809 +2024-07-17 02:45:49,802 - pyskl - INFO - Epoch [26][2000/3746] lr: 9.302e-02, eta: 3 days, 22:08:04, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5247, loss_cls: 4.0125, loss: 4.0125 +2024-07-17 02:47:00,565 - pyskl - INFO - Epoch [26][2100/3746] lr: 9.300e-02, eta: 3 days, 22:06:42, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5108, loss_cls: 4.0342, loss: 4.0342 +2024-07-17 02:48:11,424 - pyskl - INFO - Epoch [26][2200/3746] lr: 9.299e-02, eta: 3 days, 22:05:20, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5111, loss_cls: 4.0347, loss: 4.0347 +2024-07-17 02:49:22,052 - pyskl - INFO - Epoch [26][2300/3746] lr: 9.298e-02, eta: 3 days, 22:03:58, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5070, loss_cls: 4.0397, loss: 4.0397 +2024-07-17 02:50:32,862 - pyskl - INFO - Epoch [26][2400/3746] lr: 9.296e-02, eta: 3 days, 22:02:36, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5145, loss_cls: 4.0342, loss: 4.0342 +2024-07-17 02:51:43,416 - pyskl - INFO - Epoch [26][2500/3746] lr: 9.295e-02, eta: 3 days, 22:01:13, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5119, loss_cls: 4.0147, loss: 4.0147 +2024-07-17 02:52:53,655 - pyskl - INFO - Epoch [26][2600/3746] lr: 9.293e-02, eta: 3 days, 21:59:48, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5075, loss_cls: 4.0932, loss: 4.0932 +2024-07-17 02:54:03,666 - pyskl - INFO - Epoch [26][2700/3746] lr: 9.292e-02, eta: 3 days, 21:58:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5108, loss_cls: 4.0561, loss: 4.0561 +2024-07-17 02:55:13,796 - pyskl - INFO - Epoch [26][2800/3746] lr: 9.290e-02, eta: 3 days, 21:56:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5141, loss_cls: 4.0425, loss: 4.0425 +2024-07-17 02:56:23,732 - pyskl - INFO - Epoch [26][2900/3746] lr: 9.289e-02, eta: 3 days, 21:55:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5048, loss_cls: 4.0701, loss: 4.0701 +2024-07-17 02:57:33,572 - pyskl - INFO - Epoch [26][3000/3746] lr: 9.288e-02, eta: 3 days, 21:54:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5003, loss_cls: 4.0769, loss: 4.0769 +2024-07-17 02:58:43,537 - pyskl - INFO - Epoch [26][3100/3746] lr: 9.286e-02, eta: 3 days, 21:52:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5073, loss_cls: 4.0625, loss: 4.0625 +2024-07-17 02:59:53,591 - pyskl - INFO - Epoch [26][3200/3746] lr: 9.285e-02, eta: 3 days, 21:51:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5200, loss_cls: 4.0059, loss: 4.0059 +2024-07-17 03:01:03,581 - pyskl - INFO - Epoch [26][3300/3746] lr: 9.283e-02, eta: 3 days, 21:49:50, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5231, loss_cls: 4.0295, loss: 4.0295 +2024-07-17 03:02:13,516 - pyskl - INFO - Epoch [26][3400/3746] lr: 9.282e-02, eta: 3 days, 21:48:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5253, loss_cls: 4.0045, loss: 4.0045 +2024-07-17 03:03:23,730 - pyskl - INFO - Epoch [26][3500/3746] lr: 9.280e-02, eta: 3 days, 21:47:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5159, loss_cls: 4.0364, loss: 4.0364 +2024-07-17 03:04:33,845 - pyskl - INFO - Epoch [26][3600/3746] lr: 9.279e-02, eta: 3 days, 21:45:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5061, loss_cls: 4.0717, loss: 4.0717 +2024-07-17 03:05:43,937 - pyskl - INFO - Epoch [26][3700/3746] lr: 9.278e-02, eta: 3 days, 21:44:10, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5192, loss_cls: 3.9753, loss: 3.9753 +2024-07-17 03:06:18,121 - pyskl - INFO - Saving checkpoint at 26 epochs +2024-07-17 03:08:10,223 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 03:08:10,895 - pyskl - INFO - +top1_acc 0.1775 +top5_acc 0.3984 +2024-07-17 03:08:10,895 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 03:08:10,935 - pyskl - INFO - +mean_acc 0.1773 +2024-07-17 03:08:10,946 - pyskl - INFO - Epoch(val) [26][309] top1_acc: 0.1775, top5_acc: 0.3984, mean_class_accuracy: 0.1773 +2024-07-17 03:11:30,575 - pyskl - INFO - Epoch [27][100/3746] lr: 9.275e-02, eta: 3 days, 21:49:50, time: 1.996, data_time: 1.292, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5333, loss_cls: 3.9301, loss: 3.9301 +2024-07-17 03:12:40,978 - pyskl - INFO - Epoch [27][200/3746] lr: 9.274e-02, eta: 3 days, 21:48:26, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5150, loss_cls: 4.0166, loss: 4.0166 +2024-07-17 03:13:51,041 - pyskl - INFO - Epoch [27][300/3746] lr: 9.272e-02, eta: 3 days, 21:47:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5158, loss_cls: 4.0350, loss: 4.0350 +2024-07-17 03:15:01,394 - pyskl - INFO - Epoch [27][400/3746] lr: 9.271e-02, eta: 3 days, 21:45:36, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5148, loss_cls: 4.0155, loss: 4.0155 +2024-07-17 03:16:11,598 - pyskl - INFO - Epoch [27][500/3746] lr: 9.270e-02, eta: 3 days, 21:44:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5320, loss_cls: 3.9603, loss: 3.9603 +2024-07-17 03:17:21,359 - pyskl - INFO - Epoch [27][600/3746] lr: 9.268e-02, eta: 3 days, 21:42:45, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5164, loss_cls: 4.0076, loss: 4.0076 +2024-07-17 03:18:31,268 - pyskl - INFO - Epoch [27][700/3746] lr: 9.267e-02, eta: 3 days, 21:41:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5264, loss_cls: 3.9616, loss: 3.9616 +2024-07-17 03:19:41,446 - pyskl - INFO - Epoch [27][800/3746] lr: 9.265e-02, eta: 3 days, 21:39:54, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5233, loss_cls: 4.0336, loss: 4.0336 +2024-07-17 03:20:51,558 - pyskl - INFO - Epoch [27][900/3746] lr: 9.264e-02, eta: 3 days, 21:38:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5205, loss_cls: 4.0188, loss: 4.0188 +2024-07-17 03:22:01,688 - pyskl - INFO - Epoch [27][1000/3746] lr: 9.262e-02, eta: 3 days, 21:37:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5080, loss_cls: 4.0456, loss: 4.0456 +2024-07-17 03:23:11,538 - pyskl - INFO - Epoch [27][1100/3746] lr: 9.261e-02, eta: 3 days, 21:35:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5100, loss_cls: 4.0384, loss: 4.0384 +2024-07-17 03:24:21,396 - pyskl - INFO - Epoch [27][1200/3746] lr: 9.259e-02, eta: 3 days, 21:34:12, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5175, loss_cls: 4.0409, loss: 4.0409 +2024-07-17 03:25:31,375 - pyskl - INFO - Epoch [27][1300/3746] lr: 9.258e-02, eta: 3 days, 21:32:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5125, loss_cls: 4.0364, loss: 4.0364 +2024-07-17 03:26:41,460 - pyskl - INFO - Epoch [27][1400/3746] lr: 9.256e-02, eta: 3 days, 21:31:22, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5073, loss_cls: 4.0353, loss: 4.0353 +2024-07-17 03:27:52,144 - pyskl - INFO - Epoch [27][1500/3746] lr: 9.255e-02, eta: 3 days, 21:29:59, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5075, loss_cls: 4.0303, loss: 4.0303 +2024-07-17 03:29:03,116 - pyskl - INFO - Epoch [27][1600/3746] lr: 9.253e-02, eta: 3 days, 21:28:39, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5356, loss_cls: 3.9659, loss: 3.9659 +2024-07-17 03:30:13,615 - pyskl - INFO - Epoch [27][1700/3746] lr: 9.252e-02, eta: 3 days, 21:27:16, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5097, loss_cls: 4.0490, loss: 4.0490 +2024-07-17 03:31:24,101 - pyskl - INFO - Epoch [27][1800/3746] lr: 9.251e-02, eta: 3 days, 21:25:53, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5067, loss_cls: 4.0401, loss: 4.0401 +2024-07-17 03:32:34,596 - pyskl - INFO - Epoch [27][1900/3746] lr: 9.249e-02, eta: 3 days, 21:24:30, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5064, loss_cls: 4.0554, loss: 4.0554 +2024-07-17 03:33:44,951 - pyskl - INFO - Epoch [27][2000/3746] lr: 9.248e-02, eta: 3 days, 21:23:06, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5162, loss_cls: 4.0122, loss: 4.0122 +2024-07-17 03:34:56,149 - pyskl - INFO - Epoch [27][2100/3746] lr: 9.246e-02, eta: 3 days, 21:21:47, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5092, loss_cls: 4.0503, loss: 4.0503 +2024-07-17 03:36:06,524 - pyskl - INFO - Epoch [27][2200/3746] lr: 9.245e-02, eta: 3 days, 21:20:23, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5114, loss_cls: 4.0214, loss: 4.0214 +2024-07-17 03:37:17,637 - pyskl - INFO - Epoch [27][2300/3746] lr: 9.243e-02, eta: 3 days, 21:19:03, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5048, loss_cls: 4.0517, loss: 4.0517 +2024-07-17 03:38:28,000 - pyskl - INFO - Epoch [27][2400/3746] lr: 9.242e-02, eta: 3 days, 21:17:40, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5170, loss_cls: 4.0325, loss: 4.0325 +2024-07-17 03:39:38,176 - pyskl - INFO - Epoch [27][2500/3746] lr: 9.240e-02, eta: 3 days, 21:16:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5089, loss_cls: 4.0250, loss: 4.0250 +2024-07-17 03:40:48,107 - pyskl - INFO - Epoch [27][2600/3746] lr: 9.239e-02, eta: 3 days, 21:14:50, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5184, loss_cls: 4.0037, loss: 4.0037 +2024-07-17 03:41:58,199 - pyskl - INFO - Epoch [27][2700/3746] lr: 9.237e-02, eta: 3 days, 21:13:25, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5139, loss_cls: 4.0419, loss: 4.0419 +2024-07-17 03:43:08,232 - pyskl - INFO - Epoch [27][2800/3746] lr: 9.236e-02, eta: 3 days, 21:12:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5230, loss_cls: 3.9978, loss: 3.9978 +2024-07-17 03:44:18,514 - pyskl - INFO - Epoch [27][2900/3746] lr: 9.234e-02, eta: 3 days, 21:10:37, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5194, loss_cls: 4.0145, loss: 4.0145 +2024-07-17 03:45:28,787 - pyskl - INFO - Epoch [27][3000/3746] lr: 9.233e-02, eta: 3 days, 21:09:13, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5175, loss_cls: 4.0227, loss: 4.0227 +2024-07-17 03:46:38,831 - pyskl - INFO - Epoch [27][3100/3746] lr: 9.231e-02, eta: 3 days, 21:07:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5117, loss_cls: 4.0497, loss: 4.0497 +2024-07-17 03:47:48,964 - pyskl - INFO - Epoch [27][3200/3746] lr: 9.230e-02, eta: 3 days, 21:06:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5234, loss_cls: 3.9858, loss: 3.9858 +2024-07-17 03:48:58,953 - pyskl - INFO - Epoch [27][3300/3746] lr: 9.228e-02, eta: 3 days, 21:04:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5239, loss_cls: 3.9998, loss: 3.9998 +2024-07-17 03:50:08,872 - pyskl - INFO - Epoch [27][3400/3746] lr: 9.227e-02, eta: 3 days, 21:03:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5083, loss_cls: 4.0666, loss: 4.0666 +2024-07-17 03:51:19,016 - pyskl - INFO - Epoch [27][3500/3746] lr: 9.225e-02, eta: 3 days, 21:02:10, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5092, loss_cls: 4.0614, loss: 4.0614 +2024-07-17 03:52:29,052 - pyskl - INFO - Epoch [27][3600/3746] lr: 9.224e-02, eta: 3 days, 21:00:45, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5181, loss_cls: 4.0418, loss: 4.0418 +2024-07-17 03:53:39,335 - pyskl - INFO - Epoch [27][3700/3746] lr: 9.222e-02, eta: 3 days, 20:59:22, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5083, loss_cls: 4.0489, loss: 4.0489 +2024-07-17 03:54:13,509 - pyskl - INFO - Saving checkpoint at 27 epochs +2024-07-17 03:56:03,722 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 03:56:04,391 - pyskl - INFO - +top1_acc 0.1860 +top5_acc 0.4172 +2024-07-17 03:56:04,392 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 03:56:04,431 - pyskl - INFO - +mean_acc 0.1857 +2024-07-17 03:56:04,442 - pyskl - INFO - Epoch(val) [27][309] top1_acc: 0.1860, top5_acc: 0.4172, mean_class_accuracy: 0.1857 +2024-07-17 03:59:21,564 - pyskl - INFO - Epoch [28][100/3746] lr: 9.220e-02, eta: 3 days, 21:04:30, time: 1.971, data_time: 1.268, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5272, loss_cls: 3.9688, loss: 3.9688 +2024-07-17 04:00:31,438 - pyskl - INFO - Epoch [28][200/3746] lr: 9.219e-02, eta: 3 days, 21:03:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5156, loss_cls: 4.0539, loss: 4.0539 +2024-07-17 04:01:41,387 - pyskl - INFO - Epoch [28][300/3746] lr: 9.217e-02, eta: 3 days, 21:01:39, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5128, loss_cls: 4.0152, loss: 4.0152 +2024-07-17 04:02:51,373 - pyskl - INFO - Epoch [28][400/3746] lr: 9.216e-02, eta: 3 days, 21:00:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5131, loss_cls: 4.0187, loss: 4.0187 +2024-07-17 04:04:01,231 - pyskl - INFO - Epoch [28][500/3746] lr: 9.214e-02, eta: 3 days, 20:58:48, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5183, loss_cls: 4.0126, loss: 4.0126 +2024-07-17 04:05:11,360 - pyskl - INFO - Epoch [28][600/3746] lr: 9.213e-02, eta: 3 days, 20:57:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5227, loss_cls: 3.9576, loss: 3.9576 +2024-07-17 04:06:21,207 - pyskl - INFO - Epoch [28][700/3746] lr: 9.211e-02, eta: 3 days, 20:55:58, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5169, loss_cls: 4.0082, loss: 4.0082 +2024-07-17 04:07:31,092 - pyskl - INFO - Epoch [28][800/3746] lr: 9.210e-02, eta: 3 days, 20:54:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5083, loss_cls: 4.0495, loss: 4.0495 +2024-07-17 04:08:40,775 - pyskl - INFO - Epoch [28][900/3746] lr: 9.208e-02, eta: 3 days, 20:53:06, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5202, loss_cls: 4.0037, loss: 4.0037 +2024-07-17 04:09:50,901 - pyskl - INFO - Epoch [28][1000/3746] lr: 9.207e-02, eta: 3 days, 20:51:42, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5228, loss_cls: 3.9846, loss: 3.9846 +2024-07-17 04:11:00,983 - pyskl - INFO - Epoch [28][1100/3746] lr: 9.205e-02, eta: 3 days, 20:50:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5222, loss_cls: 4.0111, loss: 4.0111 +2024-07-17 04:12:11,133 - pyskl - INFO - Epoch [28][1200/3746] lr: 9.204e-02, eta: 3 days, 20:48:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5103, loss_cls: 4.0054, loss: 4.0054 +2024-07-17 04:13:20,921 - pyskl - INFO - Epoch [28][1300/3746] lr: 9.202e-02, eta: 3 days, 20:47:27, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5167, loss_cls: 4.0035, loss: 4.0035 +2024-07-17 04:14:30,679 - pyskl - INFO - Epoch [28][1400/3746] lr: 9.201e-02, eta: 3 days, 20:46:01, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5172, loss_cls: 4.0282, loss: 4.0282 +2024-07-17 04:15:40,989 - pyskl - INFO - Epoch [28][1500/3746] lr: 9.199e-02, eta: 3 days, 20:44:38, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5194, loss_cls: 4.0067, loss: 4.0067 +2024-07-17 04:16:51,328 - pyskl - INFO - Epoch [28][1600/3746] lr: 9.198e-02, eta: 3 days, 20:43:15, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5262, loss_cls: 3.9866, loss: 3.9866 +2024-07-17 04:18:01,738 - pyskl - INFO - Epoch [28][1700/3746] lr: 9.196e-02, eta: 3 days, 20:41:52, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5191, loss_cls: 4.0121, loss: 4.0121 +2024-07-17 04:19:12,371 - pyskl - INFO - Epoch [28][1800/3746] lr: 9.194e-02, eta: 3 days, 20:40:30, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5095, loss_cls: 4.0204, loss: 4.0204 +2024-07-17 04:20:22,853 - pyskl - INFO - Epoch [28][1900/3746] lr: 9.193e-02, eta: 3 days, 20:39:07, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5153, loss_cls: 4.0288, loss: 4.0288 +2024-07-17 04:21:33,101 - pyskl - INFO - Epoch [28][2000/3746] lr: 9.191e-02, eta: 3 days, 20:37:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5220, loss_cls: 4.0096, loss: 4.0096 +2024-07-17 04:22:44,026 - pyskl - INFO - Epoch [28][2100/3746] lr: 9.190e-02, eta: 3 days, 20:36:23, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5206, loss_cls: 4.0435, loss: 4.0435 +2024-07-17 04:23:54,669 - pyskl - INFO - Epoch [28][2200/3746] lr: 9.188e-02, eta: 3 days, 20:35:01, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5155, loss_cls: 4.0214, loss: 4.0214 +2024-07-17 04:25:05,108 - pyskl - INFO - Epoch [28][2300/3746] lr: 9.187e-02, eta: 3 days, 20:33:39, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5133, loss_cls: 4.0293, loss: 4.0293 +2024-07-17 04:26:15,312 - pyskl - INFO - Epoch [28][2400/3746] lr: 9.185e-02, eta: 3 days, 20:32:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5142, loss_cls: 4.0166, loss: 4.0166 +2024-07-17 04:27:25,169 - pyskl - INFO - Epoch [28][2500/3746] lr: 9.184e-02, eta: 3 days, 20:30:50, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5131, loss_cls: 4.0471, loss: 4.0471 +2024-07-17 04:28:35,037 - pyskl - INFO - Epoch [28][2600/3746] lr: 9.182e-02, eta: 3 days, 20:29:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5183, loss_cls: 4.0074, loss: 4.0074 +2024-07-17 04:29:44,969 - pyskl - INFO - Epoch [28][2700/3746] lr: 9.181e-02, eta: 3 days, 20:28:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5197, loss_cls: 4.0207, loss: 4.0207 +2024-07-17 04:30:54,746 - pyskl - INFO - Epoch [28][2800/3746] lr: 9.179e-02, eta: 3 days, 20:26:35, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5114, loss_cls: 4.0457, loss: 4.0457 +2024-07-17 04:32:04,713 - pyskl - INFO - Epoch [28][2900/3746] lr: 9.178e-02, eta: 3 days, 20:25:10, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5192, loss_cls: 3.9940, loss: 3.9940 +2024-07-17 04:33:14,391 - pyskl - INFO - Epoch [28][3000/3746] lr: 9.176e-02, eta: 3 days, 20:23:44, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5034, loss_cls: 4.0346, loss: 4.0346 +2024-07-17 04:34:24,168 - pyskl - INFO - Epoch [28][3100/3746] lr: 9.175e-02, eta: 3 days, 20:22:19, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5231, loss_cls: 4.0050, loss: 4.0050 +2024-07-17 04:35:33,961 - pyskl - INFO - Epoch [28][3200/3746] lr: 9.173e-02, eta: 3 days, 20:20:54, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5242, loss_cls: 3.9768, loss: 3.9768 +2024-07-17 04:36:44,182 - pyskl - INFO - Epoch [28][3300/3746] lr: 9.172e-02, eta: 3 days, 20:19:30, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5153, loss_cls: 4.0014, loss: 4.0014 +2024-07-17 04:37:54,113 - pyskl - INFO - Epoch [28][3400/3746] lr: 9.170e-02, eta: 3 days, 20:18:06, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5080, loss_cls: 4.0054, loss: 4.0054 +2024-07-17 04:39:03,983 - pyskl - INFO - Epoch [28][3500/3746] lr: 9.168e-02, eta: 3 days, 20:16:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5108, loss_cls: 4.0565, loss: 4.0565 +2024-07-17 04:40:14,289 - pyskl - INFO - Epoch [28][3600/3746] lr: 9.167e-02, eta: 3 days, 20:15:18, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5172, loss_cls: 4.0321, loss: 4.0321 +2024-07-17 04:41:24,116 - pyskl - INFO - Epoch [28][3700/3746] lr: 9.165e-02, eta: 3 days, 20:13:53, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5089, loss_cls: 4.0522, loss: 4.0522 +2024-07-17 04:41:58,311 - pyskl - INFO - Saving checkpoint at 28 epochs +2024-07-17 04:43:47,912 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 04:43:48,591 - pyskl - INFO - +top1_acc 0.1819 +top5_acc 0.4063 +2024-07-17 04:43:48,592 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 04:43:48,631 - pyskl - INFO - +mean_acc 0.1816 +2024-07-17 04:43:48,641 - pyskl - INFO - Epoch(val) [28][309] top1_acc: 0.1819, top5_acc: 0.4063, mean_class_accuracy: 0.1816 +2024-07-17 04:47:06,079 - pyskl - INFO - Epoch [29][100/3746] lr: 9.163e-02, eta: 3 days, 20:18:45, time: 1.974, data_time: 1.275, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5136, loss_cls: 4.0240, loss: 4.0240 +2024-07-17 04:48:16,613 - pyskl - INFO - Epoch [29][200/3746] lr: 9.162e-02, eta: 3 days, 20:17:22, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5228, loss_cls: 3.9824, loss: 3.9824 +2024-07-17 04:49:26,617 - pyskl - INFO - Epoch [29][300/3746] lr: 9.160e-02, eta: 3 days, 20:15:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5067, loss_cls: 4.0182, loss: 4.0182 +2024-07-17 04:50:36,340 - pyskl - INFO - Epoch [29][400/3746] lr: 9.158e-02, eta: 3 days, 20:14:32, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5253, loss_cls: 4.0045, loss: 4.0045 +2024-07-17 04:51:46,040 - pyskl - INFO - Epoch [29][500/3746] lr: 9.157e-02, eta: 3 days, 20:13:06, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5183, loss_cls: 3.9919, loss: 3.9919 +2024-07-17 04:52:55,860 - pyskl - INFO - Epoch [29][600/3746] lr: 9.155e-02, eta: 3 days, 20:11:41, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5155, loss_cls: 4.0248, loss: 4.0248 +2024-07-17 04:54:05,659 - pyskl - INFO - Epoch [29][700/3746] lr: 9.154e-02, eta: 3 days, 20:10:16, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5194, loss_cls: 4.0201, loss: 4.0201 +2024-07-17 04:55:15,869 - pyskl - INFO - Epoch [29][800/3746] lr: 9.152e-02, eta: 3 days, 20:08:52, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5083, loss_cls: 4.0517, loss: 4.0517 +2024-07-17 04:56:25,574 - pyskl - INFO - Epoch [29][900/3746] lr: 9.151e-02, eta: 3 days, 20:07:26, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5180, loss_cls: 3.9967, loss: 3.9967 +2024-07-17 04:57:35,513 - pyskl - INFO - Epoch [29][1000/3746] lr: 9.149e-02, eta: 3 days, 20:06:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5167, loss_cls: 4.0241, loss: 4.0241 +2024-07-17 04:58:45,352 - pyskl - INFO - Epoch [29][1100/3746] lr: 9.148e-02, eta: 3 days, 20:04:37, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5184, loss_cls: 3.9837, loss: 3.9837 +2024-07-17 04:59:55,216 - pyskl - INFO - Epoch [29][1200/3746] lr: 9.146e-02, eta: 3 days, 20:03:12, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5173, loss_cls: 4.0215, loss: 4.0215 +2024-07-17 05:01:05,285 - pyskl - INFO - Epoch [29][1300/3746] lr: 9.144e-02, eta: 3 days, 20:01:48, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5177, loss_cls: 3.9957, loss: 3.9957 +2024-07-17 05:02:15,345 - pyskl - INFO - Epoch [29][1400/3746] lr: 9.143e-02, eta: 3 days, 20:00:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5138, loss_cls: 4.0369, loss: 4.0369 +2024-07-17 05:03:25,792 - pyskl - INFO - Epoch [29][1500/3746] lr: 9.141e-02, eta: 3 days, 19:59:01, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5236, loss_cls: 3.9942, loss: 3.9942 +2024-07-17 05:04:36,267 - pyskl - INFO - Epoch [29][1600/3746] lr: 9.140e-02, eta: 3 days, 19:57:39, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5253, loss_cls: 3.9920, loss: 3.9920 +2024-07-17 05:05:46,457 - pyskl - INFO - Epoch [29][1700/3746] lr: 9.138e-02, eta: 3 days, 19:56:16, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5133, loss_cls: 4.0143, loss: 4.0143 +2024-07-17 05:06:56,528 - pyskl - INFO - Epoch [29][1800/3746] lr: 9.137e-02, eta: 3 days, 19:54:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5158, loss_cls: 4.0267, loss: 4.0267 +2024-07-17 05:08:07,172 - pyskl - INFO - Epoch [29][1900/3746] lr: 9.135e-02, eta: 3 days, 19:53:30, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5158, loss_cls: 4.0040, loss: 4.0040 +2024-07-17 05:09:17,500 - pyskl - INFO - Epoch [29][2000/3746] lr: 9.133e-02, eta: 3 days, 19:52:08, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5148, loss_cls: 4.0100, loss: 4.0100 +2024-07-17 05:10:28,458 - pyskl - INFO - Epoch [29][2100/3746] lr: 9.132e-02, eta: 3 days, 19:50:48, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5080, loss_cls: 4.0349, loss: 4.0349 +2024-07-17 05:11:38,977 - pyskl - INFO - Epoch [29][2200/3746] lr: 9.130e-02, eta: 3 days, 19:49:26, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5209, loss_cls: 4.0084, loss: 4.0084 +2024-07-17 05:12:50,065 - pyskl - INFO - Epoch [29][2300/3746] lr: 9.129e-02, eta: 3 days, 19:48:06, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5200, loss_cls: 4.0255, loss: 4.0255 +2024-07-17 05:14:00,390 - pyskl - INFO - Epoch [29][2400/3746] lr: 9.127e-02, eta: 3 days, 19:46:44, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5266, loss_cls: 3.9615, loss: 3.9615 +2024-07-17 05:15:10,246 - pyskl - INFO - Epoch [29][2500/3746] lr: 9.126e-02, eta: 3 days, 19:45:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5116, loss_cls: 4.0551, loss: 4.0551 +2024-07-17 05:16:20,169 - pyskl - INFO - Epoch [29][2600/3746] lr: 9.124e-02, eta: 3 days, 19:43:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5225, loss_cls: 4.0061, loss: 4.0061 +2024-07-17 05:17:29,996 - pyskl - INFO - Epoch [29][2700/3746] lr: 9.122e-02, eta: 3 days, 19:42:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5109, loss_cls: 4.0263, loss: 4.0263 +2024-07-17 05:18:39,840 - pyskl - INFO - Epoch [29][2800/3746] lr: 9.121e-02, eta: 3 days, 19:41:05, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5252, loss_cls: 3.9987, loss: 3.9987 +2024-07-17 05:19:49,963 - pyskl - INFO - Epoch [29][2900/3746] lr: 9.119e-02, eta: 3 days, 19:39:42, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5169, loss_cls: 4.0195, loss: 4.0195 +2024-07-17 05:20:59,823 - pyskl - INFO - Epoch [29][3000/3746] lr: 9.118e-02, eta: 3 days, 19:38:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5142, loss_cls: 4.0470, loss: 4.0470 +2024-07-17 05:22:09,880 - pyskl - INFO - Epoch [29][3100/3746] lr: 9.116e-02, eta: 3 days, 19:36:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5203, loss_cls: 4.0160, loss: 4.0160 +2024-07-17 05:23:19,745 - pyskl - INFO - Epoch [29][3200/3746] lr: 9.114e-02, eta: 3 days, 19:35:30, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5102, loss_cls: 4.0173, loss: 4.0173 +2024-07-17 05:24:29,877 - pyskl - INFO - Epoch [29][3300/3746] lr: 9.113e-02, eta: 3 days, 19:34:06, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5166, loss_cls: 4.0163, loss: 4.0163 +2024-07-17 05:25:39,435 - pyskl - INFO - Epoch [29][3400/3746] lr: 9.111e-02, eta: 3 days, 19:32:41, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5194, loss_cls: 4.0009, loss: 4.0009 +2024-07-17 05:26:49,321 - pyskl - INFO - Epoch [29][3500/3746] lr: 9.110e-02, eta: 3 days, 19:31:16, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5150, loss_cls: 4.0372, loss: 4.0372 +2024-07-17 05:27:59,045 - pyskl - INFO - Epoch [29][3600/3746] lr: 9.108e-02, eta: 3 days, 19:29:52, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5138, loss_cls: 4.0466, loss: 4.0466 +2024-07-17 05:29:08,946 - pyskl - INFO - Epoch [29][3700/3746] lr: 9.106e-02, eta: 3 days, 19:28:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5109, loss_cls: 4.0191, loss: 4.0191 +2024-07-17 05:29:42,908 - pyskl - INFO - Saving checkpoint at 29 epochs +2024-07-17 05:31:32,200 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 05:31:32,923 - pyskl - INFO - +top1_acc 0.1885 +top5_acc 0.4157 +2024-07-17 05:31:32,924 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 05:31:32,961 - pyskl - INFO - +mean_acc 0.1881 +2024-07-17 05:31:32,970 - pyskl - INFO - Epoch(val) [29][309] top1_acc: 0.1885, top5_acc: 0.4157, mean_class_accuracy: 0.1881 +2024-07-17 05:34:58,673 - pyskl - INFO - Epoch [30][100/3746] lr: 9.104e-02, eta: 3 days, 19:33:37, time: 2.057, data_time: 1.250, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5159, loss_cls: 4.0153, loss: 4.0153 +2024-07-17 05:36:19,618 - pyskl - INFO - Epoch [30][200/3746] lr: 9.103e-02, eta: 3 days, 19:32:58, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5095, loss_cls: 4.0222, loss: 4.0222 +2024-07-17 05:37:39,879 - pyskl - INFO - Epoch [30][300/3746] lr: 9.101e-02, eta: 3 days, 19:32:17, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5158, loss_cls: 3.9971, loss: 3.9971 +2024-07-17 05:39:00,188 - pyskl - INFO - Epoch [30][400/3746] lr: 9.099e-02, eta: 3 days, 19:31:35, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5253, loss_cls: 3.9720, loss: 3.9720 +2024-07-17 05:40:20,269 - pyskl - INFO - Epoch [30][500/3746] lr: 9.098e-02, eta: 3 days, 19:30:53, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5234, loss_cls: 3.9729, loss: 3.9729 +2024-07-17 05:41:41,277 - pyskl - INFO - Epoch [30][600/3746] lr: 9.096e-02, eta: 3 days, 19:30:14, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5247, loss_cls: 3.9886, loss: 3.9886 +2024-07-17 05:43:01,565 - pyskl - INFO - Epoch [30][700/3746] lr: 9.095e-02, eta: 3 days, 19:29:33, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5109, loss_cls: 4.0395, loss: 4.0395 +2024-07-17 05:44:21,540 - pyskl - INFO - Epoch [30][800/3746] lr: 9.093e-02, eta: 3 days, 19:28:49, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5275, loss_cls: 3.9649, loss: 3.9649 +2024-07-17 05:45:42,215 - pyskl - INFO - Epoch [30][900/3746] lr: 9.091e-02, eta: 3 days, 19:28:09, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5191, loss_cls: 4.0365, loss: 4.0365 +2024-07-17 05:47:02,295 - pyskl - INFO - Epoch [30][1000/3746] lr: 9.090e-02, eta: 3 days, 19:27:27, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5192, loss_cls: 3.9969, loss: 3.9969 +2024-07-17 05:48:22,366 - pyskl - INFO - Epoch [30][1100/3746] lr: 9.088e-02, eta: 3 days, 19:26:44, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5188, loss_cls: 4.0199, loss: 4.0199 +2024-07-17 05:49:42,164 - pyskl - INFO - Epoch [30][1200/3746] lr: 9.087e-02, eta: 3 days, 19:26:00, time: 0.798, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5164, loss_cls: 4.0140, loss: 4.0140 +2024-07-17 05:51:02,505 - pyskl - INFO - Epoch [30][1300/3746] lr: 9.085e-02, eta: 3 days, 19:25:18, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5217, loss_cls: 4.0266, loss: 4.0266 +2024-07-17 05:52:23,084 - pyskl - INFO - Epoch [30][1400/3746] lr: 9.083e-02, eta: 3 days, 19:24:37, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5112, loss_cls: 4.0011, loss: 4.0011 +2024-07-17 05:53:43,300 - pyskl - INFO - Epoch [30][1500/3746] lr: 9.082e-02, eta: 3 days, 19:23:54, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5239, loss_cls: 3.9863, loss: 3.9863 +2024-07-17 05:55:04,099 - pyskl - INFO - Epoch [30][1600/3746] lr: 9.080e-02, eta: 3 days, 19:23:14, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5139, loss_cls: 4.0484, loss: 4.0484 +2024-07-17 05:56:24,292 - pyskl - INFO - Epoch [30][1700/3746] lr: 9.078e-02, eta: 3 days, 19:22:31, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5264, loss_cls: 3.9742, loss: 3.9742 +2024-07-17 05:57:45,786 - pyskl - INFO - Epoch [30][1800/3746] lr: 9.077e-02, eta: 3 days, 19:21:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5056, loss_cls: 4.0620, loss: 4.0620 +2024-07-17 05:59:07,050 - pyskl - INFO - Epoch [30][1900/3746] lr: 9.075e-02, eta: 3 days, 19:21:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5255, loss_cls: 3.9988, loss: 3.9988 +2024-07-17 06:00:28,653 - pyskl - INFO - Epoch [30][2000/3746] lr: 9.074e-02, eta: 3 days, 19:20:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5217, loss_cls: 4.0004, loss: 4.0004 +2024-07-17 06:01:49,666 - pyskl - INFO - Epoch [30][2100/3746] lr: 9.072e-02, eta: 3 days, 19:19:58, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5098, loss_cls: 4.0214, loss: 4.0214 +2024-07-17 06:03:09,935 - pyskl - INFO - Epoch [30][2200/3746] lr: 9.070e-02, eta: 3 days, 19:19:16, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5183, loss_cls: 4.0241, loss: 4.0241 +2024-07-17 06:04:30,334 - pyskl - INFO - Epoch [30][2300/3746] lr: 9.069e-02, eta: 3 days, 19:18:33, time: 0.804, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5184, loss_cls: 4.0385, loss: 4.0385 +2024-07-17 06:05:50,377 - pyskl - INFO - Epoch [30][2400/3746] lr: 9.067e-02, eta: 3 days, 19:17:49, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5038, loss_cls: 4.0911, loss: 4.0911 +2024-07-17 06:07:10,299 - pyskl - INFO - Epoch [30][2500/3746] lr: 9.065e-02, eta: 3 days, 19:17:05, time: 0.799, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5147, loss_cls: 4.0205, loss: 4.0205 +2024-07-17 06:08:30,420 - pyskl - INFO - Epoch [30][2600/3746] lr: 9.064e-02, eta: 3 days, 19:16:21, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5236, loss_cls: 3.9704, loss: 3.9704 +2024-07-17 06:09:50,927 - pyskl - INFO - Epoch [30][2700/3746] lr: 9.062e-02, eta: 3 days, 19:15:39, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5150, loss_cls: 4.0131, loss: 4.0131 +2024-07-17 06:11:11,215 - pyskl - INFO - Epoch [30][2800/3746] lr: 9.061e-02, eta: 3 days, 19:14:56, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5166, loss_cls: 4.0178, loss: 4.0178 +2024-07-17 06:12:31,384 - pyskl - INFO - Epoch [30][2900/3746] lr: 9.059e-02, eta: 3 days, 19:14:12, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5133, loss_cls: 4.0270, loss: 4.0270 +2024-07-17 06:13:52,404 - pyskl - INFO - Epoch [30][3000/3746] lr: 9.057e-02, eta: 3 days, 19:13:32, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5184, loss_cls: 4.0189, loss: 4.0189 +2024-07-17 06:15:12,429 - pyskl - INFO - Epoch [30][3100/3746] lr: 9.056e-02, eta: 3 days, 19:12:48, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5127, loss_cls: 4.0435, loss: 4.0435 +2024-07-17 06:16:32,476 - pyskl - INFO - Epoch [30][3200/3746] lr: 9.054e-02, eta: 3 days, 19:12:03, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5173, loss_cls: 4.0383, loss: 4.0383 +2024-07-17 06:17:52,252 - pyskl - INFO - Epoch [30][3300/3746] lr: 9.052e-02, eta: 3 days, 19:11:18, time: 0.798, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5158, loss_cls: 4.0280, loss: 4.0280 +2024-07-17 06:19:12,078 - pyskl - INFO - Epoch [30][3400/3746] lr: 9.051e-02, eta: 3 days, 19:10:32, time: 0.798, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5112, loss_cls: 4.0195, loss: 4.0195 +2024-07-17 06:20:31,918 - pyskl - INFO - Epoch [30][3500/3746] lr: 9.049e-02, eta: 3 days, 19:09:47, time: 0.798, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5120, loss_cls: 4.0331, loss: 4.0331 +2024-07-17 06:21:52,234 - pyskl - INFO - Epoch [30][3600/3746] lr: 9.047e-02, eta: 3 days, 19:09:04, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5105, loss_cls: 4.0353, loss: 4.0353 +2024-07-17 06:23:12,593 - pyskl - INFO - Epoch [30][3700/3746] lr: 9.046e-02, eta: 3 days, 19:08:20, time: 0.804, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5264, loss_cls: 3.9450, loss: 3.9450 +2024-07-17 06:23:51,745 - pyskl - INFO - Saving checkpoint at 30 epochs +2024-07-17 06:25:41,865 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 06:25:42,523 - pyskl - INFO - +top1_acc 0.1983 +top5_acc 0.4225 +2024-07-17 06:25:42,523 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 06:25:42,560 - pyskl - INFO - +mean_acc 0.1980 +2024-07-17 06:25:42,569 - pyskl - INFO - Epoch(val) [30][309] top1_acc: 0.1983, top5_acc: 0.4225, mean_class_accuracy: 0.1980 +2024-07-17 06:29:25,920 - pyskl - INFO - Epoch [31][100/3746] lr: 9.043e-02, eta: 3 days, 19:14:20, time: 2.233, data_time: 1.258, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5167, loss_cls: 4.2593, loss: 4.2593 +2024-07-17 06:30:47,608 - pyskl - INFO - Epoch [31][200/3746] lr: 9.042e-02, eta: 3 days, 19:13:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5211, loss_cls: 4.1988, loss: 4.1988 +2024-07-17 06:32:09,262 - pyskl - INFO - Epoch [31][300/3746] lr: 9.040e-02, eta: 3 days, 19:13:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5306, loss_cls: 4.1769, loss: 4.1769 +2024-07-17 06:33:30,529 - pyskl - INFO - Epoch [31][400/3746] lr: 9.039e-02, eta: 3 days, 19:12:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5177, loss_cls: 4.2082, loss: 4.2082 +2024-07-17 06:34:51,883 - pyskl - INFO - Epoch [31][500/3746] lr: 9.037e-02, eta: 3 days, 19:11:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5294, loss_cls: 4.1768, loss: 4.1768 +2024-07-17 06:36:13,361 - pyskl - INFO - Epoch [31][600/3746] lr: 9.035e-02, eta: 3 days, 19:11:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5216, loss_cls: 4.2242, loss: 4.2242 +2024-07-17 06:37:35,129 - pyskl - INFO - Epoch [31][700/3746] lr: 9.034e-02, eta: 3 days, 19:10:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5205, loss_cls: 4.2229, loss: 4.2229 +2024-07-17 06:38:57,121 - pyskl - INFO - Epoch [31][800/3746] lr: 9.032e-02, eta: 3 days, 19:09:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5159, loss_cls: 4.2103, loss: 4.2103 +2024-07-17 06:40:18,937 - pyskl - INFO - Epoch [31][900/3746] lr: 9.030e-02, eta: 3 days, 19:09:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5228, loss_cls: 4.2022, loss: 4.2022 +2024-07-17 06:41:40,404 - pyskl - INFO - Epoch [31][1000/3746] lr: 9.029e-02, eta: 3 days, 19:08:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5209, loss_cls: 4.2295, loss: 4.2295 +2024-07-17 06:43:02,262 - pyskl - INFO - Epoch [31][1100/3746] lr: 9.027e-02, eta: 3 days, 19:07:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5131, loss_cls: 4.2321, loss: 4.2321 +2024-07-17 06:44:24,135 - pyskl - INFO - Epoch [31][1200/3746] lr: 9.025e-02, eta: 3 days, 19:07:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5242, loss_cls: 4.2022, loss: 4.2022 +2024-07-17 06:45:46,130 - pyskl - INFO - Epoch [31][1300/3746] lr: 9.024e-02, eta: 3 days, 19:06:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5227, loss_cls: 4.2095, loss: 4.2095 +2024-07-17 06:47:08,243 - pyskl - INFO - Epoch [31][1400/3746] lr: 9.022e-02, eta: 3 days, 19:05:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5116, loss_cls: 4.2466, loss: 4.2466 +2024-07-17 06:48:30,274 - pyskl - INFO - Epoch [31][1500/3746] lr: 9.020e-02, eta: 3 days, 19:05:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5256, loss_cls: 4.2054, loss: 4.2054 +2024-07-17 06:49:52,219 - pyskl - INFO - Epoch [31][1600/3746] lr: 9.019e-02, eta: 3 days, 19:04:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5189, loss_cls: 4.2358, loss: 4.2358 +2024-07-17 06:51:13,954 - pyskl - INFO - Epoch [31][1700/3746] lr: 9.017e-02, eta: 3 days, 19:03:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5253, loss_cls: 4.2098, loss: 4.2098 +2024-07-17 06:52:36,345 - pyskl - INFO - Epoch [31][1800/3746] lr: 9.015e-02, eta: 3 days, 19:03:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5197, loss_cls: 4.2019, loss: 4.2019 +2024-07-17 06:53:58,920 - pyskl - INFO - Epoch [31][1900/3746] lr: 9.014e-02, eta: 3 days, 19:02:41, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5144, loss_cls: 4.2456, loss: 4.2456 +2024-07-17 06:55:21,501 - pyskl - INFO - Epoch [31][2000/3746] lr: 9.012e-02, eta: 3 days, 19:02:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5123, loss_cls: 4.2313, loss: 4.2313 +2024-07-17 06:56:43,427 - pyskl - INFO - Epoch [31][2100/3746] lr: 9.010e-02, eta: 3 days, 19:01:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5122, loss_cls: 4.2195, loss: 4.2195 +2024-07-17 06:58:05,481 - pyskl - INFO - Epoch [31][2200/3746] lr: 9.009e-02, eta: 3 days, 19:00:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5234, loss_cls: 4.1815, loss: 4.1815 +2024-07-17 06:59:26,988 - pyskl - INFO - Epoch [31][2300/3746] lr: 9.007e-02, eta: 3 days, 19:00:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5127, loss_cls: 4.2652, loss: 4.2652 +2024-07-17 07:00:48,463 - pyskl - INFO - Epoch [31][2400/3746] lr: 9.005e-02, eta: 3 days, 18:59:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5200, loss_cls: 4.2104, loss: 4.2104 +2024-07-17 07:02:09,999 - pyskl - INFO - Epoch [31][2500/3746] lr: 9.004e-02, eta: 3 days, 18:58:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5178, loss_cls: 4.2017, loss: 4.2017 +2024-07-17 07:03:31,235 - pyskl - INFO - Epoch [31][2600/3746] lr: 9.002e-02, eta: 3 days, 18:58:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5092, loss_cls: 4.2478, loss: 4.2478 +2024-07-17 07:04:53,048 - pyskl - INFO - Epoch [31][2700/3746] lr: 9.000e-02, eta: 3 days, 18:57:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5203, loss_cls: 4.2281, loss: 4.2281 +2024-07-17 07:06:14,479 - pyskl - INFO - Epoch [31][2800/3746] lr: 8.999e-02, eta: 3 days, 18:56:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5188, loss_cls: 4.2291, loss: 4.2291 +2024-07-17 07:07:35,949 - pyskl - INFO - Epoch [31][2900/3746] lr: 8.997e-02, eta: 3 days, 18:55:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5155, loss_cls: 4.2209, loss: 4.2209 +2024-07-17 07:08:57,640 - pyskl - INFO - Epoch [31][3000/3746] lr: 8.995e-02, eta: 3 days, 18:55:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5131, loss_cls: 4.2822, loss: 4.2822 +2024-07-17 07:10:18,828 - pyskl - INFO - Epoch [31][3100/3746] lr: 8.994e-02, eta: 3 days, 18:54:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5181, loss_cls: 4.2328, loss: 4.2328 +2024-07-17 07:11:40,406 - pyskl - INFO - Epoch [31][3200/3746] lr: 8.992e-02, eta: 3 days, 18:53:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5092, loss_cls: 4.2673, loss: 4.2673 +2024-07-17 07:13:02,062 - pyskl - INFO - Epoch [31][3300/3746] lr: 8.990e-02, eta: 3 days, 18:53:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5153, loss_cls: 4.2668, loss: 4.2668 +2024-07-17 07:14:23,398 - pyskl - INFO - Epoch [31][3400/3746] lr: 8.989e-02, eta: 3 days, 18:52:29, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5164, loss_cls: 4.1908, loss: 4.1908 +2024-07-17 07:15:44,953 - pyskl - INFO - Epoch [31][3500/3746] lr: 8.987e-02, eta: 3 days, 18:51:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5131, loss_cls: 4.2488, loss: 4.2488 +2024-07-17 07:17:06,408 - pyskl - INFO - Epoch [31][3600/3746] lr: 8.985e-02, eta: 3 days, 18:51:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5128, loss_cls: 4.2656, loss: 4.2656 +2024-07-17 07:18:28,645 - pyskl - INFO - Epoch [31][3700/3746] lr: 8.983e-02, eta: 3 days, 18:50:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5155, loss_cls: 4.2479, loss: 4.2479 +2024-07-17 07:19:08,107 - pyskl - INFO - Saving checkpoint at 31 epochs +2024-07-17 07:20:57,652 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 07:20:58,310 - pyskl - INFO - +top1_acc 0.1889 +top5_acc 0.4130 +2024-07-17 07:20:58,311 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 07:20:58,348 - pyskl - INFO - +mean_acc 0.1887 +2024-07-17 07:20:58,357 - pyskl - INFO - Epoch(val) [31][309] top1_acc: 0.1889, top5_acc: 0.4130, mean_class_accuracy: 0.1887 +2024-07-17 07:24:42,464 - pyskl - INFO - Epoch [32][100/3746] lr: 8.981e-02, eta: 3 days, 18:56:08, time: 2.241, data_time: 1.247, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5258, loss_cls: 4.1951, loss: 4.1951 +2024-07-17 07:26:04,489 - pyskl - INFO - Epoch [32][200/3746] lr: 8.979e-02, eta: 3 days, 18:55:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5295, loss_cls: 4.1670, loss: 4.1670 +2024-07-17 07:27:25,771 - pyskl - INFO - Epoch [32][300/3746] lr: 8.978e-02, eta: 3 days, 18:54:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5216, loss_cls: 4.1945, loss: 4.1945 +2024-07-17 07:28:47,288 - pyskl - INFO - Epoch [32][400/3746] lr: 8.976e-02, eta: 3 days, 18:54:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5177, loss_cls: 4.1997, loss: 4.1997 +2024-07-17 07:30:08,575 - pyskl - INFO - Epoch [32][500/3746] lr: 8.974e-02, eta: 3 days, 18:53:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5303, loss_cls: 4.1607, loss: 4.1607 +2024-07-17 07:31:30,278 - pyskl - INFO - Epoch [32][600/3746] lr: 8.973e-02, eta: 3 days, 18:52:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5234, loss_cls: 4.2107, loss: 4.2107 +2024-07-17 07:32:51,970 - pyskl - INFO - Epoch [32][700/3746] lr: 8.971e-02, eta: 3 days, 18:51:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5234, loss_cls: 4.2163, loss: 4.2163 +2024-07-17 07:34:13,231 - pyskl - INFO - Epoch [32][800/3746] lr: 8.969e-02, eta: 3 days, 18:51:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5192, loss_cls: 4.2379, loss: 4.2379 +2024-07-17 07:35:34,946 - pyskl - INFO - Epoch [32][900/3746] lr: 8.967e-02, eta: 3 days, 18:50:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5264, loss_cls: 4.2042, loss: 4.2042 +2024-07-17 07:36:56,568 - pyskl - INFO - Epoch [32][1000/3746] lr: 8.966e-02, eta: 3 days, 18:49:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5159, loss_cls: 4.2485, loss: 4.2485 +2024-07-17 07:38:18,004 - pyskl - INFO - Epoch [32][1100/3746] lr: 8.964e-02, eta: 3 days, 18:49:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5020, loss_cls: 4.2602, loss: 4.2602 +2024-07-17 07:39:40,280 - pyskl - INFO - Epoch [32][1200/3746] lr: 8.962e-02, eta: 3 days, 18:48:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5125, loss_cls: 4.2158, loss: 4.2158 +2024-07-17 07:41:01,799 - pyskl - INFO - Epoch [32][1300/3746] lr: 8.961e-02, eta: 3 days, 18:47:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5116, loss_cls: 4.2427, loss: 4.2427 +2024-07-17 07:42:24,170 - pyskl - INFO - Epoch [32][1400/3746] lr: 8.959e-02, eta: 3 days, 18:46:56, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5188, loss_cls: 4.2269, loss: 4.2269 +2024-07-17 07:43:45,559 - pyskl - INFO - Epoch [32][1500/3746] lr: 8.957e-02, eta: 3 days, 18:46:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5172, loss_cls: 4.2328, loss: 4.2328 +2024-07-17 07:45:07,440 - pyskl - INFO - Epoch [32][1600/3746] lr: 8.955e-02, eta: 3 days, 18:45:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5278, loss_cls: 4.1896, loss: 4.1896 +2024-07-17 07:46:29,547 - pyskl - INFO - Epoch [32][1700/3746] lr: 8.954e-02, eta: 3 days, 18:44:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5114, loss_cls: 4.2592, loss: 4.2592 +2024-07-17 07:47:52,455 - pyskl - INFO - Epoch [32][1800/3746] lr: 8.952e-02, eta: 3 days, 18:44:11, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5147, loss_cls: 4.2425, loss: 4.2425 +2024-07-17 07:49:14,973 - pyskl - INFO - Epoch [32][1900/3746] lr: 8.950e-02, eta: 3 days, 18:43:31, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5173, loss_cls: 4.2129, loss: 4.2129 +2024-07-17 07:50:37,284 - pyskl - INFO - Epoch [32][2000/3746] lr: 8.949e-02, eta: 3 days, 18:42:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5214, loss_cls: 4.2370, loss: 4.2370 +2024-07-17 07:52:00,060 - pyskl - INFO - Epoch [32][2100/3746] lr: 8.947e-02, eta: 3 days, 18:42:11, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5209, loss_cls: 4.2059, loss: 4.2059 +2024-07-17 07:53:21,909 - pyskl - INFO - Epoch [32][2200/3746] lr: 8.945e-02, eta: 3 days, 18:41:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5162, loss_cls: 4.2148, loss: 4.2148 +2024-07-17 07:54:44,298 - pyskl - INFO - Epoch [32][2300/3746] lr: 8.943e-02, eta: 3 days, 18:40:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5169, loss_cls: 4.2046, loss: 4.2046 +2024-07-17 07:56:06,246 - pyskl - INFO - Epoch [32][2400/3746] lr: 8.942e-02, eta: 3 days, 18:40:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5159, loss_cls: 4.2139, loss: 4.2139 +2024-07-17 07:57:28,035 - pyskl - INFO - Epoch [32][2500/3746] lr: 8.940e-02, eta: 3 days, 18:39:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5231, loss_cls: 4.2046, loss: 4.2046 +2024-07-17 07:58:49,552 - pyskl - INFO - Epoch [32][2600/3746] lr: 8.938e-02, eta: 3 days, 18:38:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5173, loss_cls: 4.2081, loss: 4.2081 +2024-07-17 08:00:10,883 - pyskl - INFO - Epoch [32][2700/3746] lr: 8.937e-02, eta: 3 days, 18:37:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5056, loss_cls: 4.2697, loss: 4.2697 +2024-07-17 08:01:32,241 - pyskl - INFO - Epoch [32][2800/3746] lr: 8.935e-02, eta: 3 days, 18:37:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5264, loss_cls: 4.2108, loss: 4.2108 +2024-07-17 08:02:53,627 - pyskl - INFO - Epoch [32][2900/3746] lr: 8.933e-02, eta: 3 days, 18:36:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5280, loss_cls: 4.2010, loss: 4.2010 +2024-07-17 08:04:14,773 - pyskl - INFO - Epoch [32][3000/3746] lr: 8.931e-02, eta: 3 days, 18:35:37, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5195, loss_cls: 4.2174, loss: 4.2174 +2024-07-17 08:05:36,339 - pyskl - INFO - Epoch [32][3100/3746] lr: 8.930e-02, eta: 3 days, 18:34:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5180, loss_cls: 4.2286, loss: 4.2286 +2024-07-17 08:06:58,171 - pyskl - INFO - Epoch [32][3200/3746] lr: 8.928e-02, eta: 3 days, 18:34:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5086, loss_cls: 4.2682, loss: 4.2682 +2024-07-17 08:08:19,605 - pyskl - INFO - Epoch [32][3300/3746] lr: 8.926e-02, eta: 3 days, 18:33:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5195, loss_cls: 4.2201, loss: 4.2201 +2024-07-17 08:09:41,155 - pyskl - INFO - Epoch [32][3400/3746] lr: 8.924e-02, eta: 3 days, 18:32:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5228, loss_cls: 4.1734, loss: 4.1734 +2024-07-17 08:11:02,662 - pyskl - INFO - Epoch [32][3500/3746] lr: 8.923e-02, eta: 3 days, 18:31:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5012, loss_cls: 4.2651, loss: 4.2651 +2024-07-17 08:12:24,464 - pyskl - INFO - Epoch [32][3600/3746] lr: 8.921e-02, eta: 3 days, 18:31:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5178, loss_cls: 4.2432, loss: 4.2432 +2024-07-17 08:13:45,737 - pyskl - INFO - Epoch [32][3700/3746] lr: 8.919e-02, eta: 3 days, 18:30:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5175, loss_cls: 4.2293, loss: 4.2293 +2024-07-17 08:14:24,978 - pyskl - INFO - Saving checkpoint at 32 epochs +2024-07-17 08:16:15,177 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 08:16:15,865 - pyskl - INFO - +top1_acc 0.1928 +top5_acc 0.4269 +2024-07-17 08:16:15,865 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 08:16:15,911 - pyskl - INFO - +mean_acc 0.1924 +2024-07-17 08:16:15,930 - pyskl - INFO - Epoch(val) [32][309] top1_acc: 0.1928, top5_acc: 0.4269, mean_class_accuracy: 0.1924 +2024-07-17 08:20:03,113 - pyskl - INFO - Epoch [33][100/3746] lr: 8.917e-02, eta: 3 days, 18:35:59, time: 2.272, data_time: 1.296, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5242, loss_cls: 4.1736, loss: 4.1736 +2024-07-17 08:21:24,483 - pyskl - INFO - Epoch [33][200/3746] lr: 8.915e-02, eta: 3 days, 18:35:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5258, loss_cls: 4.1649, loss: 4.1649 +2024-07-17 08:22:45,986 - pyskl - INFO - Epoch [33][300/3746] lr: 8.913e-02, eta: 3 days, 18:34:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5300, loss_cls: 4.2039, loss: 4.2039 +2024-07-17 08:24:07,679 - pyskl - INFO - Epoch [33][400/3746] lr: 8.912e-02, eta: 3 days, 18:33:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5206, loss_cls: 4.1973, loss: 4.1973 +2024-07-17 08:25:29,813 - pyskl - INFO - Epoch [33][500/3746] lr: 8.910e-02, eta: 3 days, 18:32:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5141, loss_cls: 4.2241, loss: 4.2241 +2024-07-17 08:26:51,239 - pyskl - INFO - Epoch [33][600/3746] lr: 8.908e-02, eta: 3 days, 18:32:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5191, loss_cls: 4.2113, loss: 4.2113 +2024-07-17 08:28:12,709 - pyskl - INFO - Epoch [33][700/3746] lr: 8.906e-02, eta: 3 days, 18:31:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5236, loss_cls: 4.1927, loss: 4.1927 +2024-07-17 08:29:34,166 - pyskl - INFO - Epoch [33][800/3746] lr: 8.905e-02, eta: 3 days, 18:30:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5180, loss_cls: 4.2066, loss: 4.2066 +2024-07-17 08:30:55,956 - pyskl - INFO - Epoch [33][900/3746] lr: 8.903e-02, eta: 3 days, 18:29:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5130, loss_cls: 4.2585, loss: 4.2585 +2024-07-17 08:32:17,251 - pyskl - INFO - Epoch [33][1000/3746] lr: 8.901e-02, eta: 3 days, 18:29:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5241, loss_cls: 4.2467, loss: 4.2467 +2024-07-17 08:33:38,757 - pyskl - INFO - Epoch [33][1100/3746] lr: 8.899e-02, eta: 3 days, 18:28:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5148, loss_cls: 4.2548, loss: 4.2548 +2024-07-17 08:35:00,799 - pyskl - INFO - Epoch [33][1200/3746] lr: 8.898e-02, eta: 3 days, 18:27:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5281, loss_cls: 4.1667, loss: 4.1667 +2024-07-17 08:36:22,452 - pyskl - INFO - Epoch [33][1300/3746] lr: 8.896e-02, eta: 3 days, 18:26:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5120, loss_cls: 4.2438, loss: 4.2438 +2024-07-17 08:37:44,712 - pyskl - INFO - Epoch [33][1400/3746] lr: 8.894e-02, eta: 3 days, 18:26:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5252, loss_cls: 4.1976, loss: 4.1976 +2024-07-17 08:39:06,238 - pyskl - INFO - Epoch [33][1500/3746] lr: 8.892e-02, eta: 3 days, 18:25:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5311, loss_cls: 4.1681, loss: 4.1681 +2024-07-17 08:40:27,868 - pyskl - INFO - Epoch [33][1600/3746] lr: 8.891e-02, eta: 3 days, 18:24:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5256, loss_cls: 4.2090, loss: 4.2090 +2024-07-17 08:41:49,572 - pyskl - INFO - Epoch [33][1700/3746] lr: 8.889e-02, eta: 3 days, 18:23:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5241, loss_cls: 4.2021, loss: 4.2021 +2024-07-17 08:43:11,788 - pyskl - INFO - Epoch [33][1800/3746] lr: 8.887e-02, eta: 3 days, 18:23:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5166, loss_cls: 4.2555, loss: 4.2555 +2024-07-17 08:44:34,316 - pyskl - INFO - Epoch [33][1900/3746] lr: 8.885e-02, eta: 3 days, 18:22:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5228, loss_cls: 4.2170, loss: 4.2170 +2024-07-17 08:45:55,977 - pyskl - INFO - Epoch [33][2000/3746] lr: 8.884e-02, eta: 3 days, 18:21:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5180, loss_cls: 4.2389, loss: 4.2389 +2024-07-17 08:47:18,332 - pyskl - INFO - Epoch [33][2100/3746] lr: 8.882e-02, eta: 3 days, 18:20:56, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5156, loss_cls: 4.2563, loss: 4.2563 +2024-07-17 08:48:40,237 - pyskl - INFO - Epoch [33][2200/3746] lr: 8.880e-02, eta: 3 days, 18:20:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5092, loss_cls: 4.2711, loss: 4.2711 +2024-07-17 08:50:01,820 - pyskl - INFO - Epoch [33][2300/3746] lr: 8.878e-02, eta: 3 days, 18:19:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5142, loss_cls: 4.2672, loss: 4.2672 +2024-07-17 08:51:23,064 - pyskl - INFO - Epoch [33][2400/3746] lr: 8.876e-02, eta: 3 days, 18:18:37, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5234, loss_cls: 4.2228, loss: 4.2228 +2024-07-17 08:52:45,127 - pyskl - INFO - Epoch [33][2500/3746] lr: 8.875e-02, eta: 3 days, 18:17:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5212, loss_cls: 4.2232, loss: 4.2232 +2024-07-17 08:54:06,487 - pyskl - INFO - Epoch [33][2600/3746] lr: 8.873e-02, eta: 3 days, 18:17:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5278, loss_cls: 4.2071, loss: 4.2071 +2024-07-17 08:55:27,992 - pyskl - INFO - Epoch [33][2700/3746] lr: 8.871e-02, eta: 3 days, 18:16:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5245, loss_cls: 4.1824, loss: 4.1824 +2024-07-17 08:56:49,567 - pyskl - INFO - Epoch [33][2800/3746] lr: 8.869e-02, eta: 3 days, 18:15:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5205, loss_cls: 4.1860, loss: 4.1860 +2024-07-17 08:58:11,131 - pyskl - INFO - Epoch [33][2900/3746] lr: 8.868e-02, eta: 3 days, 18:14:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5156, loss_cls: 4.2060, loss: 4.2060 +2024-07-17 08:59:33,430 - pyskl - INFO - Epoch [33][3000/3746] lr: 8.866e-02, eta: 3 days, 18:14:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5225, loss_cls: 4.2027, loss: 4.2027 +2024-07-17 09:00:54,735 - pyskl - INFO - Epoch [33][3100/3746] lr: 8.864e-02, eta: 3 days, 18:13:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5167, loss_cls: 4.2153, loss: 4.2153 +2024-07-17 09:02:15,888 - pyskl - INFO - Epoch [33][3200/3746] lr: 8.862e-02, eta: 3 days, 18:12:24, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5106, loss_cls: 4.2474, loss: 4.2474 +2024-07-17 09:03:37,345 - pyskl - INFO - Epoch [33][3300/3746] lr: 8.861e-02, eta: 3 days, 18:11:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5134, loss_cls: 4.2477, loss: 4.2477 +2024-07-17 09:04:58,723 - pyskl - INFO - Epoch [33][3400/3746] lr: 8.859e-02, eta: 3 days, 18:10:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5266, loss_cls: 4.1691, loss: 4.1691 +2024-07-17 09:06:20,277 - pyskl - INFO - Epoch [33][3500/3746] lr: 8.857e-02, eta: 3 days, 18:10:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5202, loss_cls: 4.2095, loss: 4.2095 +2024-07-17 09:07:41,845 - pyskl - INFO - Epoch [33][3600/3746] lr: 8.855e-02, eta: 3 days, 18:09:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5361, loss_cls: 4.1742, loss: 4.1742 +2024-07-17 09:09:03,520 - pyskl - INFO - Epoch [33][3700/3746] lr: 8.853e-02, eta: 3 days, 18:08:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5153, loss_cls: 4.2475, loss: 4.2475 +2024-07-17 09:09:42,789 - pyskl - INFO - Saving checkpoint at 33 epochs +2024-07-17 09:11:31,244 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 09:11:31,910 - pyskl - INFO - +top1_acc 0.1978 +top5_acc 0.4177 +2024-07-17 09:11:31,910 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 09:11:31,950 - pyskl - INFO - +mean_acc 0.1974 +2024-07-17 09:11:31,960 - pyskl - INFO - Epoch(val) [33][309] top1_acc: 0.1978, top5_acc: 0.4177, mean_class_accuracy: 0.1974 +2024-07-17 09:15:13,571 - pyskl - INFO - Epoch [34][100/3746] lr: 8.851e-02, eta: 3 days, 18:13:21, time: 2.216, data_time: 1.243, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5389, loss_cls: 4.1202, loss: 4.1202 +2024-07-17 09:16:36,343 - pyskl - INFO - Epoch [34][200/3746] lr: 8.849e-02, eta: 3 days, 18:12:38, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5230, loss_cls: 4.1680, loss: 4.1680 +2024-07-17 09:17:58,263 - pyskl - INFO - Epoch [34][300/3746] lr: 8.847e-02, eta: 3 days, 18:11:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5269, loss_cls: 4.1899, loss: 4.1899 +2024-07-17 09:19:19,701 - pyskl - INFO - Epoch [34][400/3746] lr: 8.845e-02, eta: 3 days, 18:11:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5298, loss_cls: 4.1709, loss: 4.1709 +2024-07-17 09:20:41,303 - pyskl - INFO - Epoch [34][500/3746] lr: 8.844e-02, eta: 3 days, 18:10:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5158, loss_cls: 4.1985, loss: 4.1985 +2024-07-17 09:22:03,022 - pyskl - INFO - Epoch [34][600/3746] lr: 8.842e-02, eta: 3 days, 18:09:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5212, loss_cls: 4.2156, loss: 4.2156 +2024-07-17 09:23:24,678 - pyskl - INFO - Epoch [34][700/3746] lr: 8.840e-02, eta: 3 days, 18:08:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5158, loss_cls: 4.2210, loss: 4.2210 +2024-07-17 09:24:46,196 - pyskl - INFO - Epoch [34][800/3746] lr: 8.838e-02, eta: 3 days, 18:07:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5394, loss_cls: 4.1630, loss: 4.1630 +2024-07-17 09:26:07,972 - pyskl - INFO - Epoch [34][900/3746] lr: 8.836e-02, eta: 3 days, 18:07:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5258, loss_cls: 4.2011, loss: 4.2011 +2024-07-17 09:27:29,653 - pyskl - INFO - Epoch [34][1000/3746] lr: 8.835e-02, eta: 3 days, 18:06:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5184, loss_cls: 4.2643, loss: 4.2643 +2024-07-17 09:28:51,083 - pyskl - INFO - Epoch [34][1100/3746] lr: 8.833e-02, eta: 3 days, 18:05:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5220, loss_cls: 4.2054, loss: 4.2054 +2024-07-17 09:30:14,079 - pyskl - INFO - Epoch [34][1200/3746] lr: 8.831e-02, eta: 3 days, 18:04:44, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5206, loss_cls: 4.2142, loss: 4.2142 +2024-07-17 09:31:35,864 - pyskl - INFO - Epoch [34][1300/3746] lr: 8.829e-02, eta: 3 days, 18:03:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5191, loss_cls: 4.2253, loss: 4.2253 +2024-07-17 09:32:58,192 - pyskl - INFO - Epoch [34][1400/3746] lr: 8.828e-02, eta: 3 days, 18:03:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5275, loss_cls: 4.1968, loss: 4.1968 +2024-07-17 09:34:19,828 - pyskl - INFO - Epoch [34][1500/3746] lr: 8.826e-02, eta: 3 days, 18:02:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5191, loss_cls: 4.2001, loss: 4.2001 +2024-07-17 09:35:41,917 - pyskl - INFO - Epoch [34][1600/3746] lr: 8.824e-02, eta: 3 days, 18:01:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5309, loss_cls: 4.1855, loss: 4.1855 +2024-07-17 09:37:03,795 - pyskl - INFO - Epoch [34][1700/3746] lr: 8.822e-02, eta: 3 days, 18:00:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5275, loss_cls: 4.1825, loss: 4.1825 +2024-07-17 09:38:25,582 - pyskl - INFO - Epoch [34][1800/3746] lr: 8.820e-02, eta: 3 days, 18:00:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5167, loss_cls: 4.2571, loss: 4.2571 +2024-07-17 09:39:48,571 - pyskl - INFO - Epoch [34][1900/3746] lr: 8.819e-02, eta: 3 days, 17:59:16, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5089, loss_cls: 4.2625, loss: 4.2625 +2024-07-17 09:41:10,817 - pyskl - INFO - Epoch [34][2000/3746] lr: 8.817e-02, eta: 3 days, 17:58:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5256, loss_cls: 4.2000, loss: 4.2000 +2024-07-17 09:42:32,636 - pyskl - INFO - Epoch [34][2100/3746] lr: 8.815e-02, eta: 3 days, 17:57:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5036, loss_cls: 4.2804, loss: 4.2804 +2024-07-17 09:43:54,451 - pyskl - INFO - Epoch [34][2200/3746] lr: 8.813e-02, eta: 3 days, 17:56:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5227, loss_cls: 4.2010, loss: 4.2010 +2024-07-17 09:45:16,292 - pyskl - INFO - Epoch [34][2300/3746] lr: 8.811e-02, eta: 3 days, 17:56:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5125, loss_cls: 4.2565, loss: 4.2565 +2024-07-17 09:46:37,961 - pyskl - INFO - Epoch [34][2400/3746] lr: 8.809e-02, eta: 3 days, 17:55:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5206, loss_cls: 4.2203, loss: 4.2203 +2024-07-17 09:47:59,413 - pyskl - INFO - Epoch [34][2500/3746] lr: 8.808e-02, eta: 3 days, 17:54:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5256, loss_cls: 4.2126, loss: 4.2126 +2024-07-17 09:49:20,533 - pyskl - INFO - Epoch [34][2600/3746] lr: 8.806e-02, eta: 3 days, 17:53:37, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5330, loss_cls: 4.1963, loss: 4.1963 +2024-07-17 09:50:42,272 - pyskl - INFO - Epoch [34][2700/3746] lr: 8.804e-02, eta: 3 days, 17:52:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5125, loss_cls: 4.2180, loss: 4.2180 +2024-07-17 09:52:03,757 - pyskl - INFO - Epoch [34][2800/3746] lr: 8.802e-02, eta: 3 days, 17:51:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5278, loss_cls: 4.1767, loss: 4.1767 +2024-07-17 09:53:25,058 - pyskl - INFO - Epoch [34][2900/3746] lr: 8.800e-02, eta: 3 days, 17:51:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5202, loss_cls: 4.2062, loss: 4.2062 +2024-07-17 09:54:46,229 - pyskl - INFO - Epoch [34][3000/3746] lr: 8.799e-02, eta: 3 days, 17:50:18, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5298, loss_cls: 4.1843, loss: 4.1843 +2024-07-17 09:56:08,193 - pyskl - INFO - Epoch [34][3100/3746] lr: 8.797e-02, eta: 3 days, 17:49:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5181, loss_cls: 4.2279, loss: 4.2279 +2024-07-17 09:57:29,903 - pyskl - INFO - Epoch [34][3200/3746] lr: 8.795e-02, eta: 3 days, 17:48:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5194, loss_cls: 4.2139, loss: 4.2139 +2024-07-17 09:58:51,741 - pyskl - INFO - Epoch [34][3300/3746] lr: 8.793e-02, eta: 3 days, 17:47:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5245, loss_cls: 4.1911, loss: 4.1911 +2024-07-17 10:00:13,163 - pyskl - INFO - Epoch [34][3400/3746] lr: 8.791e-02, eta: 3 days, 17:47:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5212, loss_cls: 4.2275, loss: 4.2275 +2024-07-17 10:01:35,489 - pyskl - INFO - Epoch [34][3500/3746] lr: 8.789e-02, eta: 3 days, 17:46:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5342, loss_cls: 4.1581, loss: 4.1581 +2024-07-17 10:02:57,054 - pyskl - INFO - Epoch [34][3600/3746] lr: 8.788e-02, eta: 3 days, 17:45:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5141, loss_cls: 4.2586, loss: 4.2586 +2024-07-17 10:04:18,314 - pyskl - INFO - Epoch [34][3700/3746] lr: 8.786e-02, eta: 3 days, 17:44:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5194, loss_cls: 4.2191, loss: 4.2191 +2024-07-17 10:04:57,618 - pyskl - INFO - Saving checkpoint at 34 epochs +2024-07-17 10:06:46,399 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 10:06:47,061 - pyskl - INFO - +top1_acc 0.1945 +top5_acc 0.4252 +2024-07-17 10:06:47,061 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 10:06:47,099 - pyskl - INFO - +mean_acc 0.1943 +2024-07-17 10:06:47,109 - pyskl - INFO - Epoch(val) [34][309] top1_acc: 0.1945, top5_acc: 0.4252, mean_class_accuracy: 0.1943 +2024-07-17 10:10:32,135 - pyskl - INFO - Epoch [35][100/3746] lr: 8.783e-02, eta: 3 days, 17:49:24, time: 2.250, data_time: 1.271, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5286, loss_cls: 4.1798, loss: 4.1798 +2024-07-17 10:11:53,367 - pyskl - INFO - Epoch [35][200/3746] lr: 8.781e-02, eta: 3 days, 17:48:33, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5339, loss_cls: 4.1312, loss: 4.1312 +2024-07-17 10:13:14,649 - pyskl - INFO - Epoch [35][300/3746] lr: 8.780e-02, eta: 3 days, 17:47:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5316, loss_cls: 4.1547, loss: 4.1547 +2024-07-17 10:14:35,933 - pyskl - INFO - Epoch [35][400/3746] lr: 8.778e-02, eta: 3 days, 17:46:50, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5272, loss_cls: 4.1962, loss: 4.1962 +2024-07-17 10:15:57,441 - pyskl - INFO - Epoch [35][500/3746] lr: 8.776e-02, eta: 3 days, 17:46:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5233, loss_cls: 4.2027, loss: 4.2027 +2024-07-17 10:17:19,171 - pyskl - INFO - Epoch [35][600/3746] lr: 8.774e-02, eta: 3 days, 17:45:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5355, loss_cls: 4.1418, loss: 4.1418 +2024-07-17 10:18:40,717 - pyskl - INFO - Epoch [35][700/3746] lr: 8.772e-02, eta: 3 days, 17:44:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5270, loss_cls: 4.1942, loss: 4.1942 +2024-07-17 10:20:02,449 - pyskl - INFO - Epoch [35][800/3746] lr: 8.770e-02, eta: 3 days, 17:43:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5284, loss_cls: 4.1830, loss: 4.1830 +2024-07-17 10:21:24,233 - pyskl - INFO - Epoch [35][900/3746] lr: 8.769e-02, eta: 3 days, 17:42:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5166, loss_cls: 4.2269, loss: 4.2269 +2024-07-17 10:22:45,961 - pyskl - INFO - Epoch [35][1000/3746] lr: 8.767e-02, eta: 3 days, 17:41:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5317, loss_cls: 4.2061, loss: 4.2061 +2024-07-17 10:24:07,885 - pyskl - INFO - Epoch [35][1100/3746] lr: 8.765e-02, eta: 3 days, 17:41:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5145, loss_cls: 4.2640, loss: 4.2640 +2024-07-17 10:25:29,933 - pyskl - INFO - Epoch [35][1200/3746] lr: 8.763e-02, eta: 3 days, 17:40:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5223, loss_cls: 4.2000, loss: 4.2000 +2024-07-17 10:26:51,875 - pyskl - INFO - Epoch [35][1300/3746] lr: 8.761e-02, eta: 3 days, 17:39:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5212, loss_cls: 4.1933, loss: 4.1933 +2024-07-17 10:28:14,391 - pyskl - INFO - Epoch [35][1400/3746] lr: 8.759e-02, eta: 3 days, 17:38:34, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5183, loss_cls: 4.2070, loss: 4.2070 +2024-07-17 10:29:36,298 - pyskl - INFO - Epoch [35][1500/3746] lr: 8.757e-02, eta: 3 days, 17:37:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5238, loss_cls: 4.2254, loss: 4.2254 +2024-07-17 10:30:57,970 - pyskl - INFO - Epoch [35][1600/3746] lr: 8.756e-02, eta: 3 days, 17:36:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5189, loss_cls: 4.2121, loss: 4.2121 +2024-07-17 10:32:20,346 - pyskl - INFO - Epoch [35][1700/3746] lr: 8.754e-02, eta: 3 days, 17:36:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5294, loss_cls: 4.2038, loss: 4.2038 +2024-07-17 10:33:42,843 - pyskl - INFO - Epoch [35][1800/3746] lr: 8.752e-02, eta: 3 days, 17:35:18, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5248, loss_cls: 4.1557, loss: 4.1557 +2024-07-17 10:35:04,949 - pyskl - INFO - Epoch [35][1900/3746] lr: 8.750e-02, eta: 3 days, 17:34:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5208, loss_cls: 4.2312, loss: 4.2312 +2024-07-17 10:36:26,941 - pyskl - INFO - Epoch [35][2000/3746] lr: 8.748e-02, eta: 3 days, 17:33:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5286, loss_cls: 4.2076, loss: 4.2076 +2024-07-17 10:37:48,737 - pyskl - INFO - Epoch [35][2100/3746] lr: 8.746e-02, eta: 3 days, 17:32:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5108, loss_cls: 4.2566, loss: 4.2566 +2024-07-17 10:39:10,234 - pyskl - INFO - Epoch [35][2200/3746] lr: 8.745e-02, eta: 3 days, 17:31:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5164, loss_cls: 4.2071, loss: 4.2071 +2024-07-17 10:40:32,147 - pyskl - INFO - Epoch [35][2300/3746] lr: 8.743e-02, eta: 3 days, 17:31:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5330, loss_cls: 4.1428, loss: 4.1428 +2024-07-17 10:41:53,471 - pyskl - INFO - Epoch [35][2400/3746] lr: 8.741e-02, eta: 3 days, 17:30:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5184, loss_cls: 4.2435, loss: 4.2435 +2024-07-17 10:43:15,266 - pyskl - INFO - Epoch [35][2500/3746] lr: 8.739e-02, eta: 3 days, 17:29:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5161, loss_cls: 4.2269, loss: 4.2269 +2024-07-17 10:44:37,057 - pyskl - INFO - Epoch [35][2600/3746] lr: 8.737e-02, eta: 3 days, 17:28:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5288, loss_cls: 4.1627, loss: 4.1627 +2024-07-17 10:45:58,448 - pyskl - INFO - Epoch [35][2700/3746] lr: 8.735e-02, eta: 3 days, 17:27:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5244, loss_cls: 4.2212, loss: 4.2212 +2024-07-17 10:47:19,829 - pyskl - INFO - Epoch [35][2800/3746] lr: 8.733e-02, eta: 3 days, 17:26:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5328, loss_cls: 4.1650, loss: 4.1650 +2024-07-17 10:48:41,318 - pyskl - INFO - Epoch [35][2900/3746] lr: 8.732e-02, eta: 3 days, 17:25:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5133, loss_cls: 4.2224, loss: 4.2224 +2024-07-17 10:50:02,934 - pyskl - INFO - Epoch [35][3000/3746] lr: 8.730e-02, eta: 3 days, 17:25:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5128, loss_cls: 4.2522, loss: 4.2522 +2024-07-17 10:51:23,961 - pyskl - INFO - Epoch [35][3100/3746] lr: 8.728e-02, eta: 3 days, 17:24:14, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5189, loss_cls: 4.1958, loss: 4.1958 +2024-07-17 10:52:46,075 - pyskl - INFO - Epoch [35][3200/3746] lr: 8.726e-02, eta: 3 days, 17:23:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5167, loss_cls: 4.2147, loss: 4.2147 +2024-07-17 10:54:07,495 - pyskl - INFO - Epoch [35][3300/3746] lr: 8.724e-02, eta: 3 days, 17:22:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5256, loss_cls: 4.2221, loss: 4.2221 +2024-07-17 10:55:29,139 - pyskl - INFO - Epoch [35][3400/3746] lr: 8.722e-02, eta: 3 days, 17:21:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5295, loss_cls: 4.1671, loss: 4.1671 +2024-07-17 10:56:50,705 - pyskl - INFO - Epoch [35][3500/3746] lr: 8.720e-02, eta: 3 days, 17:20:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5108, loss_cls: 4.2324, loss: 4.2324 +2024-07-17 10:58:12,189 - pyskl - INFO - Epoch [35][3600/3746] lr: 8.718e-02, eta: 3 days, 17:19:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5300, loss_cls: 4.2095, loss: 4.2095 +2024-07-17 10:59:34,169 - pyskl - INFO - Epoch [35][3700/3746] lr: 8.717e-02, eta: 3 days, 17:19:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5248, loss_cls: 4.2081, loss: 4.2081 +2024-07-17 11:00:13,860 - pyskl - INFO - Saving checkpoint at 35 epochs +2024-07-17 11:02:04,867 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 11:02:05,523 - pyskl - INFO - +top1_acc 0.1741 +top5_acc 0.3873 +2024-07-17 11:02:05,523 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 11:02:05,560 - pyskl - INFO - +mean_acc 0.1738 +2024-07-17 11:02:05,571 - pyskl - INFO - Epoch(val) [35][309] top1_acc: 0.1741, top5_acc: 0.3873, mean_class_accuracy: 0.1738 +2024-07-17 11:05:50,001 - pyskl - INFO - Epoch [36][100/3746] lr: 8.714e-02, eta: 3 days, 17:23:37, time: 2.244, data_time: 1.272, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5280, loss_cls: 4.1740, loss: 4.1740 +2024-07-17 11:07:11,341 - pyskl - INFO - Epoch [36][200/3746] lr: 8.712e-02, eta: 3 days, 17:22:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5323, loss_cls: 4.1507, loss: 4.1507 +2024-07-17 11:08:33,219 - pyskl - INFO - Epoch [36][300/3746] lr: 8.710e-02, eta: 3 days, 17:21:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5222, loss_cls: 4.2195, loss: 4.2195 +2024-07-17 11:09:54,709 - pyskl - INFO - Epoch [36][400/3746] lr: 8.708e-02, eta: 3 days, 17:21:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5227, loss_cls: 4.2202, loss: 4.2202 +2024-07-17 11:11:16,415 - pyskl - INFO - Epoch [36][500/3746] lr: 8.706e-02, eta: 3 days, 17:20:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5261, loss_cls: 4.1958, loss: 4.1958 +2024-07-17 11:12:38,135 - pyskl - INFO - Epoch [36][600/3746] lr: 8.704e-02, eta: 3 days, 17:19:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5288, loss_cls: 4.1737, loss: 4.1737 +2024-07-17 11:13:59,442 - pyskl - INFO - Epoch [36][700/3746] lr: 8.703e-02, eta: 3 days, 17:18:23, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5164, loss_cls: 4.2254, loss: 4.2254 +2024-07-17 11:15:20,936 - pyskl - INFO - Epoch [36][800/3746] lr: 8.701e-02, eta: 3 days, 17:17:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5247, loss_cls: 4.2219, loss: 4.2219 +2024-07-17 11:16:42,562 - pyskl - INFO - Epoch [36][900/3746] lr: 8.699e-02, eta: 3 days, 17:16:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5281, loss_cls: 4.1796, loss: 4.1796 +2024-07-17 11:18:04,509 - pyskl - INFO - Epoch [36][1000/3746] lr: 8.697e-02, eta: 3 days, 17:15:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5380, loss_cls: 4.1591, loss: 4.1591 +2024-07-17 11:19:26,234 - pyskl - INFO - Epoch [36][1100/3746] lr: 8.695e-02, eta: 3 days, 17:14:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5206, loss_cls: 4.2341, loss: 4.2341 +2024-07-17 11:20:48,104 - pyskl - INFO - Epoch [36][1200/3746] lr: 8.693e-02, eta: 3 days, 17:14:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5180, loss_cls: 4.2026, loss: 4.2026 +2024-07-17 11:22:10,208 - pyskl - INFO - Epoch [36][1300/3746] lr: 8.691e-02, eta: 3 days, 17:13:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5189, loss_cls: 4.2172, loss: 4.2172 +2024-07-17 11:23:32,192 - pyskl - INFO - Epoch [36][1400/3746] lr: 8.689e-02, eta: 3 days, 17:12:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5180, loss_cls: 4.2305, loss: 4.2305 +2024-07-17 11:24:53,886 - pyskl - INFO - Epoch [36][1500/3746] lr: 8.688e-02, eta: 3 days, 17:11:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5181, loss_cls: 4.2206, loss: 4.2206 +2024-07-17 11:26:15,719 - pyskl - INFO - Epoch [36][1600/3746] lr: 8.686e-02, eta: 3 days, 17:10:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5177, loss_cls: 4.2086, loss: 4.2086 +2024-07-17 11:27:37,951 - pyskl - INFO - Epoch [36][1700/3746] lr: 8.684e-02, eta: 3 days, 17:09:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5298, loss_cls: 4.2010, loss: 4.2010 +2024-07-17 11:28:59,574 - pyskl - INFO - Epoch [36][1800/3746] lr: 8.682e-02, eta: 3 days, 17:08:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5270, loss_cls: 4.2076, loss: 4.2076 +2024-07-17 11:30:22,264 - pyskl - INFO - Epoch [36][1900/3746] lr: 8.680e-02, eta: 3 days, 17:08:02, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5203, loss_cls: 4.2171, loss: 4.2171 +2024-07-17 11:31:44,102 - pyskl - INFO - Epoch [36][2000/3746] lr: 8.678e-02, eta: 3 days, 17:07:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5180, loss_cls: 4.2200, loss: 4.2200 +2024-07-17 11:33:06,522 - pyskl - INFO - Epoch [36][2100/3746] lr: 8.676e-02, eta: 3 days, 17:06:20, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5342, loss_cls: 4.1220, loss: 4.1220 +2024-07-17 11:34:28,321 - pyskl - INFO - Epoch [36][2200/3746] lr: 8.674e-02, eta: 3 days, 17:05:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5152, loss_cls: 4.2304, loss: 4.2304 +2024-07-17 11:35:49,953 - pyskl - INFO - Epoch [36][2300/3746] lr: 8.672e-02, eta: 3 days, 17:04:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5367, loss_cls: 4.1359, loss: 4.1359 +2024-07-17 11:37:11,424 - pyskl - INFO - Epoch [36][2400/3746] lr: 8.671e-02, eta: 3 days, 17:03:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5209, loss_cls: 4.2215, loss: 4.2215 +2024-07-17 11:38:33,488 - pyskl - INFO - Epoch [36][2500/3746] lr: 8.669e-02, eta: 3 days, 17:02:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5172, loss_cls: 4.2655, loss: 4.2655 +2024-07-17 11:39:55,030 - pyskl - INFO - Epoch [36][2600/3746] lr: 8.667e-02, eta: 3 days, 17:01:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5270, loss_cls: 4.1866, loss: 4.1866 +2024-07-17 11:41:16,382 - pyskl - INFO - Epoch [36][2700/3746] lr: 8.665e-02, eta: 3 days, 17:01:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5297, loss_cls: 4.1847, loss: 4.1847 +2024-07-17 11:42:37,828 - pyskl - INFO - Epoch [36][2800/3746] lr: 8.663e-02, eta: 3 days, 17:00:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5167, loss_cls: 4.2452, loss: 4.2452 +2024-07-17 11:43:58,898 - pyskl - INFO - Epoch [36][2900/3746] lr: 8.661e-02, eta: 3 days, 16:59:13, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5317, loss_cls: 4.2017, loss: 4.2017 +2024-07-17 11:45:20,304 - pyskl - INFO - Epoch [36][3000/3746] lr: 8.659e-02, eta: 3 days, 16:58:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5238, loss_cls: 4.1771, loss: 4.1771 +2024-07-17 11:46:41,578 - pyskl - INFO - Epoch [36][3100/3746] lr: 8.657e-02, eta: 3 days, 16:57:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5145, loss_cls: 4.2026, loss: 4.2026 +2024-07-17 11:48:03,419 - pyskl - INFO - Epoch [36][3200/3746] lr: 8.655e-02, eta: 3 days, 16:56:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5159, loss_cls: 4.2493, loss: 4.2493 +2024-07-17 11:49:24,521 - pyskl - INFO - Epoch [36][3300/3746] lr: 8.653e-02, eta: 3 days, 16:55:36, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5259, loss_cls: 4.1718, loss: 4.1718 +2024-07-17 11:50:45,863 - pyskl - INFO - Epoch [36][3400/3746] lr: 8.651e-02, eta: 3 days, 16:54:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5147, loss_cls: 4.2167, loss: 4.2167 +2024-07-17 11:52:07,328 - pyskl - INFO - Epoch [36][3500/3746] lr: 8.650e-02, eta: 3 days, 16:53:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5183, loss_cls: 4.2052, loss: 4.2052 +2024-07-17 11:53:29,173 - pyskl - INFO - Epoch [36][3600/3746] lr: 8.648e-02, eta: 3 days, 16:52:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5166, loss_cls: 4.2351, loss: 4.2351 +2024-07-17 11:54:50,522 - pyskl - INFO - Epoch [36][3700/3746] lr: 8.646e-02, eta: 3 days, 16:52:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5209, loss_cls: 4.2139, loss: 4.2139 +2024-07-17 11:55:29,917 - pyskl - INFO - Saving checkpoint at 36 epochs +2024-07-17 11:57:19,151 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 11:57:19,802 - pyskl - INFO - +top1_acc 0.1990 +top5_acc 0.4326 +2024-07-17 11:57:19,802 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 11:57:19,841 - pyskl - INFO - +mean_acc 0.1987 +2024-07-17 11:57:19,850 - pyskl - INFO - Epoch(val) [36][309] top1_acc: 0.1990, top5_acc: 0.4326, mean_class_accuracy: 0.1987 +2024-07-17 12:01:03,946 - pyskl - INFO - Epoch [37][100/3746] lr: 8.643e-02, eta: 3 days, 16:56:14, time: 2.241, data_time: 1.262, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5398, loss_cls: 4.1578, loss: 4.1578 +2024-07-17 12:02:25,756 - pyskl - INFO - Epoch [37][200/3746] lr: 8.641e-02, eta: 3 days, 16:55:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5134, loss_cls: 4.2447, loss: 4.2447 +2024-07-17 12:03:48,133 - pyskl - INFO - Epoch [37][300/3746] lr: 8.639e-02, eta: 3 days, 16:54:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5392, loss_cls: 4.1300, loss: 4.1300 +2024-07-17 12:05:09,393 - pyskl - INFO - Epoch [37][400/3746] lr: 8.637e-02, eta: 3 days, 16:53:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5186, loss_cls: 4.1924, loss: 4.1924 +2024-07-17 12:06:31,390 - pyskl - INFO - Epoch [37][500/3746] lr: 8.635e-02, eta: 3 days, 16:52:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5295, loss_cls: 4.1790, loss: 4.1790 +2024-07-17 12:07:53,394 - pyskl - INFO - Epoch [37][600/3746] lr: 8.633e-02, eta: 3 days, 16:51:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5337, loss_cls: 4.1636, loss: 4.1636 +2024-07-17 12:09:14,907 - pyskl - INFO - Epoch [37][700/3746] lr: 8.631e-02, eta: 3 days, 16:50:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5284, loss_cls: 4.1486, loss: 4.1486 +2024-07-17 12:10:36,626 - pyskl - INFO - Epoch [37][800/3746] lr: 8.630e-02, eta: 3 days, 16:49:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5353, loss_cls: 4.1614, loss: 4.1614 +2024-07-17 12:11:58,658 - pyskl - INFO - Epoch [37][900/3746] lr: 8.628e-02, eta: 3 days, 16:49:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5219, loss_cls: 4.1855, loss: 4.1855 +2024-07-17 12:13:20,386 - pyskl - INFO - Epoch [37][1000/3746] lr: 8.626e-02, eta: 3 days, 16:48:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5222, loss_cls: 4.2374, loss: 4.2374 +2024-07-17 12:14:42,154 - pyskl - INFO - Epoch [37][1100/3746] lr: 8.624e-02, eta: 3 days, 16:47:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5256, loss_cls: 4.1700, loss: 4.1700 +2024-07-17 12:16:04,641 - pyskl - INFO - Epoch [37][1200/3746] lr: 8.622e-02, eta: 3 days, 16:46:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5297, loss_cls: 4.1936, loss: 4.1936 +2024-07-17 12:17:27,385 - pyskl - INFO - Epoch [37][1300/3746] lr: 8.620e-02, eta: 3 days, 16:45:35, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5202, loss_cls: 4.2202, loss: 4.2202 +2024-07-17 12:18:49,739 - pyskl - INFO - Epoch [37][1400/3746] lr: 8.618e-02, eta: 3 days, 16:44:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5262, loss_cls: 4.1830, loss: 4.1830 +2024-07-17 12:20:11,186 - pyskl - INFO - Epoch [37][1500/3746] lr: 8.616e-02, eta: 3 days, 16:43:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5244, loss_cls: 4.1916, loss: 4.1916 +2024-07-17 12:21:33,038 - pyskl - INFO - Epoch [37][1600/3746] lr: 8.614e-02, eta: 3 days, 16:42:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5083, loss_cls: 4.2362, loss: 4.2362 +2024-07-17 12:22:55,711 - pyskl - INFO - Epoch [37][1700/3746] lr: 8.612e-02, eta: 3 days, 16:42:03, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5231, loss_cls: 4.1905, loss: 4.1905 +2024-07-17 12:24:17,166 - pyskl - INFO - Epoch [37][1800/3746] lr: 8.610e-02, eta: 3 days, 16:41:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5255, loss_cls: 4.2099, loss: 4.2099 +2024-07-17 12:25:39,622 - pyskl - INFO - Epoch [37][1900/3746] lr: 8.608e-02, eta: 3 days, 16:40:15, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5225, loss_cls: 4.2358, loss: 4.2358 +2024-07-17 12:27:01,319 - pyskl - INFO - Epoch [37][2000/3746] lr: 8.606e-02, eta: 3 days, 16:39:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5298, loss_cls: 4.1604, loss: 4.1604 +2024-07-17 12:28:24,062 - pyskl - INFO - Epoch [37][2100/3746] lr: 8.604e-02, eta: 3 days, 16:38:29, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5316, loss_cls: 4.1867, loss: 4.1867 +2024-07-17 12:29:46,185 - pyskl - INFO - Epoch [37][2200/3746] lr: 8.602e-02, eta: 3 days, 16:37:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5184, loss_cls: 4.1893, loss: 4.1893 +2024-07-17 12:31:07,530 - pyskl - INFO - Epoch [37][2300/3746] lr: 8.601e-02, eta: 3 days, 16:36:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5169, loss_cls: 4.2134, loss: 4.2134 +2024-07-17 12:32:28,944 - pyskl - INFO - Epoch [37][2400/3746] lr: 8.599e-02, eta: 3 days, 16:35:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5233, loss_cls: 4.2292, loss: 4.2292 +2024-07-17 12:33:50,596 - pyskl - INFO - Epoch [37][2500/3746] lr: 8.597e-02, eta: 3 days, 16:34:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5216, loss_cls: 4.2131, loss: 4.2131 +2024-07-17 12:35:11,907 - pyskl - INFO - Epoch [37][2600/3746] lr: 8.595e-02, eta: 3 days, 16:33:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5152, loss_cls: 4.2522, loss: 4.2522 +2024-07-17 12:36:33,732 - pyskl - INFO - Epoch [37][2700/3746] lr: 8.593e-02, eta: 3 days, 16:33:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5152, loss_cls: 4.2506, loss: 4.2506 +2024-07-17 12:37:55,529 - pyskl - INFO - Epoch [37][2800/3746] lr: 8.591e-02, eta: 3 days, 16:32:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5314, loss_cls: 4.1463, loss: 4.1463 +2024-07-17 12:39:17,253 - pyskl - INFO - Epoch [37][2900/3746] lr: 8.589e-02, eta: 3 days, 16:31:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5189, loss_cls: 4.2051, loss: 4.2051 +2024-07-17 12:40:38,765 - pyskl - INFO - Epoch [37][3000/3746] lr: 8.587e-02, eta: 3 days, 16:30:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5212, loss_cls: 4.2042, loss: 4.2042 +2024-07-17 12:42:00,392 - pyskl - INFO - Epoch [37][3100/3746] lr: 8.585e-02, eta: 3 days, 16:29:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5280, loss_cls: 4.1900, loss: 4.1900 +2024-07-17 12:43:22,104 - pyskl - INFO - Epoch [37][3200/3746] lr: 8.583e-02, eta: 3 days, 16:28:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5227, loss_cls: 4.2088, loss: 4.2088 +2024-07-17 12:44:43,765 - pyskl - INFO - Epoch [37][3300/3746] lr: 8.581e-02, eta: 3 days, 16:27:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5216, loss_cls: 4.2095, loss: 4.2095 +2024-07-17 12:46:05,214 - pyskl - INFO - Epoch [37][3400/3746] lr: 8.579e-02, eta: 3 days, 16:26:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5258, loss_cls: 4.1931, loss: 4.1931 +2024-07-17 12:47:26,791 - pyskl - INFO - Epoch [37][3500/3746] lr: 8.577e-02, eta: 3 days, 16:25:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5233, loss_cls: 4.2070, loss: 4.2070 +2024-07-17 12:48:48,691 - pyskl - INFO - Epoch [37][3600/3746] lr: 8.575e-02, eta: 3 days, 16:24:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5316, loss_cls: 4.1385, loss: 4.1385 +2024-07-17 12:50:10,346 - pyskl - INFO - Epoch [37][3700/3746] lr: 8.573e-02, eta: 3 days, 16:23:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5281, loss_cls: 4.1728, loss: 4.1728 +2024-07-17 12:50:49,784 - pyskl - INFO - Saving checkpoint at 37 epochs +2024-07-17 12:52:40,732 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 12:52:41,392 - pyskl - INFO - +top1_acc 0.2093 +top5_acc 0.4381 +2024-07-17 12:52:41,393 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 12:52:41,431 - pyskl - INFO - +mean_acc 0.2091 +2024-07-17 12:52:41,435 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_25.pth was removed +2024-07-17 12:52:41,747 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_37.pth. +2024-07-17 12:52:41,748 - pyskl - INFO - Best top1_acc is 0.2093 at 37 epoch. +2024-07-17 12:52:41,758 - pyskl - INFO - Epoch(val) [37][309] top1_acc: 0.2093, top5_acc: 0.4381, mean_class_accuracy: 0.2091 +2024-07-17 12:56:27,954 - pyskl - INFO - Epoch [38][100/3746] lr: 8.570e-02, eta: 3 days, 16:27:54, time: 2.262, data_time: 1.265, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5306, loss_cls: 4.1670, loss: 4.1670 +2024-07-17 12:57:49,889 - pyskl - INFO - Epoch [38][200/3746] lr: 8.568e-02, eta: 3 days, 16:26:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5428, loss_cls: 4.0969, loss: 4.0969 +2024-07-17 12:59:11,940 - pyskl - INFO - Epoch [38][300/3746] lr: 8.567e-02, eta: 3 days, 16:26:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5448, loss_cls: 4.0995, loss: 4.0995 +2024-07-17 13:00:33,832 - pyskl - INFO - Epoch [38][400/3746] lr: 8.565e-02, eta: 3 days, 16:25:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5284, loss_cls: 4.1607, loss: 4.1607 +2024-07-17 13:01:55,845 - pyskl - INFO - Epoch [38][500/3746] lr: 8.563e-02, eta: 3 days, 16:24:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5397, loss_cls: 4.1333, loss: 4.1333 +2024-07-17 13:03:17,577 - pyskl - INFO - Epoch [38][600/3746] lr: 8.561e-02, eta: 3 days, 16:23:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5255, loss_cls: 4.1453, loss: 4.1453 +2024-07-17 13:04:39,186 - pyskl - INFO - Epoch [38][700/3746] lr: 8.559e-02, eta: 3 days, 16:22:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5294, loss_cls: 4.1519, loss: 4.1519 +2024-07-17 13:06:00,752 - pyskl - INFO - Epoch [38][800/3746] lr: 8.557e-02, eta: 3 days, 16:21:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5261, loss_cls: 4.1671, loss: 4.1671 +2024-07-17 13:07:23,552 - pyskl - INFO - Epoch [38][900/3746] lr: 8.555e-02, eta: 3 days, 16:20:34, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5200, loss_cls: 4.2201, loss: 4.2201 +2024-07-17 13:08:45,511 - pyskl - INFO - Epoch [38][1000/3746] lr: 8.553e-02, eta: 3 days, 16:19:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5309, loss_cls: 4.1780, loss: 4.1780 +2024-07-17 13:10:07,318 - pyskl - INFO - Epoch [38][1100/3746] lr: 8.551e-02, eta: 3 days, 16:18:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5352, loss_cls: 4.1557, loss: 4.1557 +2024-07-17 13:11:29,816 - pyskl - INFO - Epoch [38][1200/3746] lr: 8.549e-02, eta: 3 days, 16:17:49, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5230, loss_cls: 4.1933, loss: 4.1933 +2024-07-17 13:12:51,720 - pyskl - INFO - Epoch [38][1300/3746] lr: 8.547e-02, eta: 3 days, 16:16:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5195, loss_cls: 4.2629, loss: 4.2629 +2024-07-17 13:14:13,795 - pyskl - INFO - Epoch [38][1400/3746] lr: 8.545e-02, eta: 3 days, 16:15:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5219, loss_cls: 4.1884, loss: 4.1884 +2024-07-17 13:15:35,552 - pyskl - INFO - Epoch [38][1500/3746] lr: 8.543e-02, eta: 3 days, 16:15:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5352, loss_cls: 4.1404, loss: 4.1404 +2024-07-17 13:16:58,064 - pyskl - INFO - Epoch [38][1600/3746] lr: 8.541e-02, eta: 3 days, 16:14:09, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5225, loss_cls: 4.2046, loss: 4.2046 +2024-07-17 13:18:19,532 - pyskl - INFO - Epoch [38][1700/3746] lr: 8.539e-02, eta: 3 days, 16:13:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5325, loss_cls: 4.1712, loss: 4.1712 +2024-07-17 13:19:41,940 - pyskl - INFO - Epoch [38][1800/3746] lr: 8.537e-02, eta: 3 days, 16:12:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5312, loss_cls: 4.1885, loss: 4.1885 +2024-07-17 13:21:04,432 - pyskl - INFO - Epoch [38][1900/3746] lr: 8.535e-02, eta: 3 days, 16:11:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5242, loss_cls: 4.1927, loss: 4.1927 +2024-07-17 13:22:26,286 - pyskl - INFO - Epoch [38][2000/3746] lr: 8.533e-02, eta: 3 days, 16:10:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5156, loss_cls: 4.2669, loss: 4.2669 +2024-07-17 13:23:48,697 - pyskl - INFO - Epoch [38][2100/3746] lr: 8.531e-02, eta: 3 days, 16:09:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5198, loss_cls: 4.2169, loss: 4.2169 +2024-07-17 13:25:10,591 - pyskl - INFO - Epoch [38][2200/3746] lr: 8.529e-02, eta: 3 days, 16:08:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5258, loss_cls: 4.1826, loss: 4.1826 +2024-07-17 13:26:32,855 - pyskl - INFO - Epoch [38][2300/3746] lr: 8.527e-02, eta: 3 days, 16:07:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5314, loss_cls: 4.1618, loss: 4.1618 +2024-07-17 13:27:54,483 - pyskl - INFO - Epoch [38][2400/3746] lr: 8.525e-02, eta: 3 days, 16:06:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5198, loss_cls: 4.1955, loss: 4.1955 +2024-07-17 13:29:15,997 - pyskl - INFO - Epoch [38][2500/3746] lr: 8.523e-02, eta: 3 days, 16:05:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5277, loss_cls: 4.1901, loss: 4.1901 +2024-07-17 13:30:37,828 - pyskl - INFO - Epoch [38][2600/3746] lr: 8.521e-02, eta: 3 days, 16:04:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5258, loss_cls: 4.1717, loss: 4.1717 +2024-07-17 13:31:59,222 - pyskl - INFO - Epoch [38][2700/3746] lr: 8.519e-02, eta: 3 days, 16:03:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5173, loss_cls: 4.2205, loss: 4.2205 +2024-07-17 13:33:20,711 - pyskl - INFO - Epoch [38][2800/3746] lr: 8.517e-02, eta: 3 days, 16:03:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5181, loss_cls: 4.2288, loss: 4.2288 +2024-07-17 13:34:42,380 - pyskl - INFO - Epoch [38][2900/3746] lr: 8.515e-02, eta: 3 days, 16:02:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5184, loss_cls: 4.2404, loss: 4.2404 +2024-07-17 13:36:03,896 - pyskl - INFO - Epoch [38][3000/3746] lr: 8.513e-02, eta: 3 days, 16:01:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5239, loss_cls: 4.1613, loss: 4.1613 +2024-07-17 13:37:25,638 - pyskl - INFO - Epoch [38][3100/3746] lr: 8.511e-02, eta: 3 days, 16:00:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5309, loss_cls: 4.2038, loss: 4.2038 +2024-07-17 13:38:47,076 - pyskl - INFO - Epoch [38][3200/3746] lr: 8.509e-02, eta: 3 days, 15:59:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5144, loss_cls: 4.2349, loss: 4.2349 +2024-07-17 13:40:08,546 - pyskl - INFO - Epoch [38][3300/3746] lr: 8.507e-02, eta: 3 days, 15:58:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5255, loss_cls: 4.1763, loss: 4.1763 +2024-07-17 13:41:30,141 - pyskl - INFO - Epoch [38][3400/3746] lr: 8.505e-02, eta: 3 days, 15:57:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5344, loss_cls: 4.1804, loss: 4.1804 +2024-07-17 13:42:51,981 - pyskl - INFO - Epoch [38][3500/3746] lr: 8.503e-02, eta: 3 days, 15:56:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5291, loss_cls: 4.1534, loss: 4.1534 +2024-07-17 13:44:13,579 - pyskl - INFO - Epoch [38][3600/3746] lr: 8.501e-02, eta: 3 days, 15:55:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5138, loss_cls: 4.2291, loss: 4.2291 +2024-07-17 13:45:35,855 - pyskl - INFO - Epoch [38][3700/3746] lr: 8.499e-02, eta: 3 days, 15:54:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5427, loss_cls: 4.1070, loss: 4.1070 +2024-07-17 13:46:15,474 - pyskl - INFO - Saving checkpoint at 38 epochs +2024-07-17 13:48:06,604 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 13:48:07,269 - pyskl - INFO - +top1_acc 0.1935 +top5_acc 0.4201 +2024-07-17 13:48:07,269 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 13:48:07,312 - pyskl - INFO - +mean_acc 0.1933 +2024-07-17 13:48:07,323 - pyskl - INFO - Epoch(val) [38][309] top1_acc: 0.1935, top5_acc: 0.4201, mean_class_accuracy: 0.1933 +2024-07-17 13:51:56,600 - pyskl - INFO - Epoch [39][100/3746] lr: 8.496e-02, eta: 3 days, 15:58:29, time: 2.293, data_time: 1.301, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5397, loss_cls: 4.1091, loss: 4.1091 +2024-07-17 13:53:18,167 - pyskl - INFO - Epoch [39][200/3746] lr: 8.494e-02, eta: 3 days, 15:57:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5258, loss_cls: 4.1509, loss: 4.1509 +2024-07-17 13:54:40,174 - pyskl - INFO - Epoch [39][300/3746] lr: 8.492e-02, eta: 3 days, 15:56:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5278, loss_cls: 4.2205, loss: 4.2205 +2024-07-17 13:56:01,722 - pyskl - INFO - Epoch [39][400/3746] lr: 8.490e-02, eta: 3 days, 15:55:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5353, loss_cls: 4.1372, loss: 4.1372 +2024-07-17 13:57:23,860 - pyskl - INFO - Epoch [39][500/3746] lr: 8.488e-02, eta: 3 days, 15:54:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5211, loss_cls: 4.1607, loss: 4.1607 +2024-07-17 13:58:45,278 - pyskl - INFO - Epoch [39][600/3746] lr: 8.486e-02, eta: 3 days, 15:53:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5303, loss_cls: 4.1753, loss: 4.1753 +2024-07-17 14:00:06,569 - pyskl - INFO - Epoch [39][700/3746] lr: 8.484e-02, eta: 3 days, 15:52:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5178, loss_cls: 4.2666, loss: 4.2666 +2024-07-17 14:01:28,832 - pyskl - INFO - Epoch [39][800/3746] lr: 8.482e-02, eta: 3 days, 15:51:48, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5377, loss_cls: 4.1630, loss: 4.1630 +2024-07-17 14:02:50,635 - pyskl - INFO - Epoch [39][900/3746] lr: 8.480e-02, eta: 3 days, 15:50:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5300, loss_cls: 4.1834, loss: 4.1834 +2024-07-17 14:04:12,045 - pyskl - INFO - Epoch [39][1000/3746] lr: 8.478e-02, eta: 3 days, 15:49:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5300, loss_cls: 4.1518, loss: 4.1518 +2024-07-17 14:05:34,010 - pyskl - INFO - Epoch [39][1100/3746] lr: 8.476e-02, eta: 3 days, 15:48:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5372, loss_cls: 4.1725, loss: 4.1725 +2024-07-17 14:06:55,825 - pyskl - INFO - Epoch [39][1200/3746] lr: 8.474e-02, eta: 3 days, 15:47:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5306, loss_cls: 4.1683, loss: 4.1683 +2024-07-17 14:08:17,457 - pyskl - INFO - Epoch [39][1300/3746] lr: 8.472e-02, eta: 3 days, 15:47:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5222, loss_cls: 4.1741, loss: 4.1741 +2024-07-17 14:09:39,035 - pyskl - INFO - Epoch [39][1400/3746] lr: 8.470e-02, eta: 3 days, 15:46:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5286, loss_cls: 4.1629, loss: 4.1629 +2024-07-17 14:11:00,820 - pyskl - INFO - Epoch [39][1500/3746] lr: 8.468e-02, eta: 3 days, 15:45:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5239, loss_cls: 4.1648, loss: 4.1648 +2024-07-17 14:12:22,832 - pyskl - INFO - Epoch [39][1600/3746] lr: 8.466e-02, eta: 3 days, 15:44:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5278, loss_cls: 4.1853, loss: 4.1853 +2024-07-17 14:13:44,424 - pyskl - INFO - Epoch [39][1700/3746] lr: 8.464e-02, eta: 3 days, 15:43:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5238, loss_cls: 4.1906, loss: 4.1906 +2024-07-17 14:15:06,865 - pyskl - INFO - Epoch [39][1800/3746] lr: 8.462e-02, eta: 3 days, 15:42:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5180, loss_cls: 4.2139, loss: 4.2139 +2024-07-17 14:16:28,928 - pyskl - INFO - Epoch [39][1900/3746] lr: 8.460e-02, eta: 3 days, 15:41:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5256, loss_cls: 4.1723, loss: 4.1723 +2024-07-17 14:17:50,658 - pyskl - INFO - Epoch [39][2000/3746] lr: 8.458e-02, eta: 3 days, 15:40:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5245, loss_cls: 4.1805, loss: 4.1805 +2024-07-17 14:19:13,888 - pyskl - INFO - Epoch [39][2100/3746] lr: 8.456e-02, eta: 3 days, 15:39:26, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5327, loss_cls: 4.1389, loss: 4.1389 +2024-07-17 14:20:35,922 - pyskl - INFO - Epoch [39][2200/3746] lr: 8.454e-02, eta: 3 days, 15:38:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5386, loss_cls: 4.1427, loss: 4.1427 +2024-07-17 14:21:58,130 - pyskl - INFO - Epoch [39][2300/3746] lr: 8.452e-02, eta: 3 days, 15:37:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5270, loss_cls: 4.1874, loss: 4.1874 +2024-07-17 14:23:20,135 - pyskl - INFO - Epoch [39][2400/3746] lr: 8.450e-02, eta: 3 days, 15:36:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5169, loss_cls: 4.2150, loss: 4.2150 +2024-07-17 14:24:41,468 - pyskl - INFO - Epoch [39][2500/3746] lr: 8.448e-02, eta: 3 days, 15:35:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5342, loss_cls: 4.1282, loss: 4.1282 +2024-07-17 14:26:03,127 - pyskl - INFO - Epoch [39][2600/3746] lr: 8.446e-02, eta: 3 days, 15:34:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5222, loss_cls: 4.2068, loss: 4.2068 +2024-07-17 14:27:25,151 - pyskl - INFO - Epoch [39][2700/3746] lr: 8.444e-02, eta: 3 days, 15:33:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5222, loss_cls: 4.1835, loss: 4.1835 +2024-07-17 14:28:46,700 - pyskl - INFO - Epoch [39][2800/3746] lr: 8.442e-02, eta: 3 days, 15:32:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5202, loss_cls: 4.1841, loss: 4.1841 +2024-07-17 14:30:08,309 - pyskl - INFO - Epoch [39][2900/3746] lr: 8.440e-02, eta: 3 days, 15:31:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5208, loss_cls: 4.2015, loss: 4.2015 +2024-07-17 14:31:29,741 - pyskl - INFO - Epoch [39][3000/3746] lr: 8.438e-02, eta: 3 days, 15:30:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5180, loss_cls: 4.2372, loss: 4.2372 +2024-07-17 14:32:52,301 - pyskl - INFO - Epoch [39][3100/3746] lr: 8.436e-02, eta: 3 days, 15:29:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5358, loss_cls: 4.1475, loss: 4.1475 +2024-07-17 14:34:13,833 - pyskl - INFO - Epoch [39][3200/3746] lr: 8.434e-02, eta: 3 days, 15:28:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5156, loss_cls: 4.2168, loss: 4.2168 +2024-07-17 14:35:35,851 - pyskl - INFO - Epoch [39][3300/3746] lr: 8.432e-02, eta: 3 days, 15:27:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5227, loss_cls: 4.1915, loss: 4.1915 +2024-07-17 14:36:57,898 - pyskl - INFO - Epoch [39][3400/3746] lr: 8.430e-02, eta: 3 days, 15:26:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5228, loss_cls: 4.1861, loss: 4.1861 +2024-07-17 14:38:20,436 - pyskl - INFO - Epoch [39][3500/3746] lr: 8.428e-02, eta: 3 days, 15:26:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5255, loss_cls: 4.1826, loss: 4.1826 +2024-07-17 14:39:42,255 - pyskl - INFO - Epoch [39][3600/3746] lr: 8.426e-02, eta: 3 days, 15:25:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5198, loss_cls: 4.1901, loss: 4.1901 +2024-07-17 14:41:04,168 - pyskl - INFO - Epoch [39][3700/3746] lr: 8.424e-02, eta: 3 days, 15:24:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5219, loss_cls: 4.2133, loss: 4.2133 +2024-07-17 14:41:43,615 - pyskl - INFO - Saving checkpoint at 39 epochs +2024-07-17 14:43:33,731 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 14:43:34,389 - pyskl - INFO - +top1_acc 0.2264 +top5_acc 0.4661 +2024-07-17 14:43:34,389 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 14:43:34,426 - pyskl - INFO - +mean_acc 0.2261 +2024-07-17 14:43:34,431 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_37.pth was removed +2024-07-17 14:43:34,688 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_39.pth. +2024-07-17 14:43:34,689 - pyskl - INFO - Best top1_acc is 0.2264 at 39 epoch. +2024-07-17 14:43:34,699 - pyskl - INFO - Epoch(val) [39][309] top1_acc: 0.2264, top5_acc: 0.4661, mean_class_accuracy: 0.2261 +2024-07-17 14:47:17,739 - pyskl - INFO - Epoch [40][100/3746] lr: 8.421e-02, eta: 3 days, 15:27:33, time: 2.230, data_time: 1.259, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5334, loss_cls: 4.1173, loss: 4.1173 +2024-07-17 14:48:39,972 - pyskl - INFO - Epoch [40][200/3746] lr: 8.419e-02, eta: 3 days, 15:26:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5395, loss_cls: 4.1379, loss: 4.1379 +2024-07-17 14:50:01,944 - pyskl - INFO - Epoch [40][300/3746] lr: 8.417e-02, eta: 3 days, 15:25:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5378, loss_cls: 4.1292, loss: 4.1292 +2024-07-17 14:51:23,180 - pyskl - INFO - Epoch [40][400/3746] lr: 8.415e-02, eta: 3 days, 15:24:38, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5367, loss_cls: 4.1434, loss: 4.1434 +2024-07-17 14:52:44,593 - pyskl - INFO - Epoch [40][500/3746] lr: 8.413e-02, eta: 3 days, 15:23:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5231, loss_cls: 4.2051, loss: 4.2051 +2024-07-17 14:54:06,213 - pyskl - INFO - Epoch [40][600/3746] lr: 8.411e-02, eta: 3 days, 15:22:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5364, loss_cls: 4.1437, loss: 4.1437 +2024-07-17 14:55:27,852 - pyskl - INFO - Epoch [40][700/3746] lr: 8.408e-02, eta: 3 days, 15:21:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5266, loss_cls: 4.2014, loss: 4.2014 +2024-07-17 14:56:50,076 - pyskl - INFO - Epoch [40][800/3746] lr: 8.406e-02, eta: 3 days, 15:20:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5284, loss_cls: 4.1879, loss: 4.1879 +2024-07-17 14:58:11,758 - pyskl - INFO - Epoch [40][900/3746] lr: 8.404e-02, eta: 3 days, 15:19:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5112, loss_cls: 4.2520, loss: 4.2520 +2024-07-17 14:59:33,017 - pyskl - INFO - Epoch [40][1000/3746] lr: 8.402e-02, eta: 3 days, 15:18:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5375, loss_cls: 4.1407, loss: 4.1407 +2024-07-17 15:00:55,431 - pyskl - INFO - Epoch [40][1100/3746] lr: 8.400e-02, eta: 3 days, 15:17:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5300, loss_cls: 4.1620, loss: 4.1620 +2024-07-17 15:02:17,484 - pyskl - INFO - Epoch [40][1200/3746] lr: 8.398e-02, eta: 3 days, 15:16:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5250, loss_cls: 4.1998, loss: 4.1998 +2024-07-17 15:03:39,778 - pyskl - INFO - Epoch [40][1300/3746] lr: 8.396e-02, eta: 3 days, 15:15:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5253, loss_cls: 4.1822, loss: 4.1822 +2024-07-17 15:05:01,304 - pyskl - INFO - Epoch [40][1400/3746] lr: 8.394e-02, eta: 3 days, 15:14:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5309, loss_cls: 4.2042, loss: 4.2042 +2024-07-17 15:06:23,787 - pyskl - INFO - Epoch [40][1500/3746] lr: 8.392e-02, eta: 3 days, 15:13:54, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5172, loss_cls: 4.2271, loss: 4.2271 +2024-07-17 15:07:45,804 - pyskl - INFO - Epoch [40][1600/3746] lr: 8.390e-02, eta: 3 days, 15:12:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5355, loss_cls: 4.1593, loss: 4.1593 +2024-07-17 15:09:07,660 - pyskl - INFO - Epoch [40][1700/3746] lr: 8.388e-02, eta: 3 days, 15:11:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5219, loss_cls: 4.2178, loss: 4.2178 +2024-07-17 15:10:29,395 - pyskl - INFO - Epoch [40][1800/3746] lr: 8.386e-02, eta: 3 days, 15:10:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5363, loss_cls: 4.1384, loss: 4.1384 +2024-07-17 15:11:51,612 - pyskl - INFO - Epoch [40][1900/3746] lr: 8.384e-02, eta: 3 days, 15:09:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5269, loss_cls: 4.1752, loss: 4.1752 +2024-07-17 15:13:13,703 - pyskl - INFO - Epoch [40][2000/3746] lr: 8.382e-02, eta: 3 days, 15:09:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5214, loss_cls: 4.2136, loss: 4.2136 +2024-07-17 15:14:36,338 - pyskl - INFO - Epoch [40][2100/3746] lr: 8.380e-02, eta: 3 days, 15:08:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5162, loss_cls: 4.2130, loss: 4.2130 +2024-07-17 15:15:58,835 - pyskl - INFO - Epoch [40][2200/3746] lr: 8.378e-02, eta: 3 days, 15:07:07, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5389, loss_cls: 4.1470, loss: 4.1470 +2024-07-17 15:17:20,460 - pyskl - INFO - Epoch [40][2300/3746] lr: 8.376e-02, eta: 3 days, 15:06:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5334, loss_cls: 4.1605, loss: 4.1605 +2024-07-17 15:18:42,006 - pyskl - INFO - Epoch [40][2400/3746] lr: 8.374e-02, eta: 3 days, 15:05:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5308, loss_cls: 4.1668, loss: 4.1668 +2024-07-17 15:20:04,001 - pyskl - INFO - Epoch [40][2500/3746] lr: 8.371e-02, eta: 3 days, 15:04:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5306, loss_cls: 4.1757, loss: 4.1757 +2024-07-17 15:21:25,502 - pyskl - INFO - Epoch [40][2600/3746] lr: 8.369e-02, eta: 3 days, 15:03:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5266, loss_cls: 4.1870, loss: 4.1870 +2024-07-17 15:22:47,393 - pyskl - INFO - Epoch [40][2700/3746] lr: 8.367e-02, eta: 3 days, 15:02:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5241, loss_cls: 4.1895, loss: 4.1895 +2024-07-17 15:24:08,756 - pyskl - INFO - Epoch [40][2800/3746] lr: 8.365e-02, eta: 3 days, 15:01:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5337, loss_cls: 4.1858, loss: 4.1858 +2024-07-17 15:25:30,548 - pyskl - INFO - Epoch [40][2900/3746] lr: 8.363e-02, eta: 3 days, 15:00:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5244, loss_cls: 4.2014, loss: 4.2014 +2024-07-17 15:26:52,056 - pyskl - INFO - Epoch [40][3000/3746] lr: 8.361e-02, eta: 3 days, 14:59:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5252, loss_cls: 4.1816, loss: 4.1816 +2024-07-17 15:28:13,691 - pyskl - INFO - Epoch [40][3100/3746] lr: 8.359e-02, eta: 3 days, 14:58:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5297, loss_cls: 4.1764, loss: 4.1764 +2024-07-17 15:29:35,407 - pyskl - INFO - Epoch [40][3200/3746] lr: 8.357e-02, eta: 3 days, 14:57:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5241, loss_cls: 4.1672, loss: 4.1672 +2024-07-17 15:30:56,773 - pyskl - INFO - Epoch [40][3300/3746] lr: 8.355e-02, eta: 3 days, 14:56:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5288, loss_cls: 4.1685, loss: 4.1685 +2024-07-17 15:32:19,369 - pyskl - INFO - Epoch [40][3400/3746] lr: 8.353e-02, eta: 3 days, 14:55:12, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5262, loss_cls: 4.1627, loss: 4.1627 +2024-07-17 15:33:41,068 - pyskl - INFO - Epoch [40][3500/3746] lr: 8.351e-02, eta: 3 days, 14:54:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5344, loss_cls: 4.1487, loss: 4.1487 +2024-07-17 15:35:02,575 - pyskl - INFO - Epoch [40][3600/3746] lr: 8.349e-02, eta: 3 days, 14:53:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5152, loss_cls: 4.2153, loss: 4.2153 +2024-07-17 15:36:23,977 - pyskl - INFO - Epoch [40][3700/3746] lr: 8.347e-02, eta: 3 days, 14:52:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5183, loss_cls: 4.2033, loss: 4.2033 +2024-07-17 15:37:03,561 - pyskl - INFO - Saving checkpoint at 40 epochs +2024-07-17 15:38:53,689 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 15:38:54,349 - pyskl - INFO - +top1_acc 0.2084 +top5_acc 0.4446 +2024-07-17 15:38:54,349 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 15:38:54,386 - pyskl - INFO - +mean_acc 0.2082 +2024-07-17 15:38:54,396 - pyskl - INFO - Epoch(val) [40][309] top1_acc: 0.2084, top5_acc: 0.4446, mean_class_accuracy: 0.2082 +2024-07-17 15:42:40,183 - pyskl - INFO - Epoch [41][100/3746] lr: 8.344e-02, eta: 3 days, 14:55:36, time: 2.258, data_time: 1.284, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5380, loss_cls: 4.1337, loss: 4.1337 +2024-07-17 15:44:02,158 - pyskl - INFO - Epoch [41][200/3746] lr: 8.342e-02, eta: 3 days, 14:54:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5325, loss_cls: 4.1456, loss: 4.1456 +2024-07-17 15:45:23,858 - pyskl - INFO - Epoch [41][300/3746] lr: 8.339e-02, eta: 3 days, 14:53:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5344, loss_cls: 4.1222, loss: 4.1222 +2024-07-17 15:46:45,293 - pyskl - INFO - Epoch [41][400/3746] lr: 8.337e-02, eta: 3 days, 14:52:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5363, loss_cls: 4.1266, loss: 4.1266 +2024-07-17 15:48:06,960 - pyskl - INFO - Epoch [41][500/3746] lr: 8.335e-02, eta: 3 days, 14:51:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5131, loss_cls: 4.2255, loss: 4.2255 +2024-07-17 15:49:29,093 - pyskl - INFO - Epoch [41][600/3746] lr: 8.333e-02, eta: 3 days, 14:50:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5320, loss_cls: 4.1616, loss: 4.1616 +2024-07-17 15:50:50,593 - pyskl - INFO - Epoch [41][700/3746] lr: 8.331e-02, eta: 3 days, 14:49:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5344, loss_cls: 4.1445, loss: 4.1445 +2024-07-17 15:52:11,829 - pyskl - INFO - Epoch [41][800/3746] lr: 8.329e-02, eta: 3 days, 14:48:33, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5203, loss_cls: 4.2019, loss: 4.2019 +2024-07-17 15:53:33,624 - pyskl - INFO - Epoch [41][900/3746] lr: 8.327e-02, eta: 3 days, 14:47:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5231, loss_cls: 4.1810, loss: 4.1810 +2024-07-17 15:54:55,707 - pyskl - INFO - Epoch [41][1000/3746] lr: 8.325e-02, eta: 3 days, 14:46:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5231, loss_cls: 4.1317, loss: 4.1317 +2024-07-17 15:56:17,645 - pyskl - INFO - Epoch [41][1100/3746] lr: 8.323e-02, eta: 3 days, 14:45:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5311, loss_cls: 4.1312, loss: 4.1312 +2024-07-17 15:57:40,106 - pyskl - INFO - Epoch [41][1200/3746] lr: 8.321e-02, eta: 3 days, 14:44:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5180, loss_cls: 4.2030, loss: 4.2030 +2024-07-17 15:59:03,213 - pyskl - INFO - Epoch [41][1300/3746] lr: 8.319e-02, eta: 3 days, 14:43:39, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5234, loss_cls: 4.1471, loss: 4.1471 +2024-07-17 16:00:24,944 - pyskl - INFO - Epoch [41][1400/3746] lr: 8.316e-02, eta: 3 days, 14:42:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5314, loss_cls: 4.1679, loss: 4.1679 +2024-07-17 16:01:47,575 - pyskl - INFO - Epoch [41][1500/3746] lr: 8.314e-02, eta: 3 days, 14:41:40, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5330, loss_cls: 4.1280, loss: 4.1280 +2024-07-17 16:03:09,348 - pyskl - INFO - Epoch [41][1600/3746] lr: 8.312e-02, eta: 3 days, 14:40:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5325, loss_cls: 4.1781, loss: 4.1781 +2024-07-17 16:04:31,922 - pyskl - INFO - Epoch [41][1700/3746] lr: 8.310e-02, eta: 3 days, 14:39:42, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5269, loss_cls: 4.2027, loss: 4.2027 +2024-07-17 16:05:53,851 - pyskl - INFO - Epoch [41][1800/3746] lr: 8.308e-02, eta: 3 days, 14:38:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5336, loss_cls: 4.1531, loss: 4.1531 +2024-07-17 16:07:16,042 - pyskl - INFO - Epoch [41][1900/3746] lr: 8.306e-02, eta: 3 days, 14:37:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5402, loss_cls: 4.1073, loss: 4.1073 +2024-07-17 16:08:37,816 - pyskl - INFO - Epoch [41][2000/3746] lr: 8.304e-02, eta: 3 days, 14:36:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5170, loss_cls: 4.2281, loss: 4.2281 +2024-07-17 16:09:59,843 - pyskl - INFO - Epoch [41][2100/3746] lr: 8.302e-02, eta: 3 days, 14:35:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5239, loss_cls: 4.1757, loss: 4.1757 +2024-07-17 16:11:22,281 - pyskl - INFO - Epoch [41][2200/3746] lr: 8.300e-02, eta: 3 days, 14:34:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5366, loss_cls: 4.1643, loss: 4.1643 +2024-07-17 16:12:43,930 - pyskl - INFO - Epoch [41][2300/3746] lr: 8.298e-02, eta: 3 days, 14:33:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5184, loss_cls: 4.1961, loss: 4.1961 +2024-07-17 16:14:05,712 - pyskl - INFO - Epoch [41][2400/3746] lr: 8.296e-02, eta: 3 days, 14:32:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5236, loss_cls: 4.1870, loss: 4.1870 +2024-07-17 16:15:27,872 - pyskl - INFO - Epoch [41][2500/3746] lr: 8.293e-02, eta: 3 days, 14:31:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5392, loss_cls: 4.1456, loss: 4.1456 +2024-07-17 16:16:49,930 - pyskl - INFO - Epoch [41][2600/3746] lr: 8.291e-02, eta: 3 days, 14:30:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5145, loss_cls: 4.2036, loss: 4.2036 +2024-07-17 16:18:11,755 - pyskl - INFO - Epoch [41][2700/3746] lr: 8.289e-02, eta: 3 days, 14:29:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5206, loss_cls: 4.2062, loss: 4.2062 +2024-07-17 16:19:33,721 - pyskl - INFO - Epoch [41][2800/3746] lr: 8.287e-02, eta: 3 days, 14:28:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5314, loss_cls: 4.1501, loss: 4.1501 +2024-07-17 16:20:55,527 - pyskl - INFO - Epoch [41][2900/3746] lr: 8.285e-02, eta: 3 days, 14:27:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5197, loss_cls: 4.2293, loss: 4.2293 +2024-07-17 16:22:17,369 - pyskl - INFO - Epoch [41][3000/3746] lr: 8.283e-02, eta: 3 days, 14:26:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5209, loss_cls: 4.2118, loss: 4.2118 +2024-07-17 16:23:38,811 - pyskl - INFO - Epoch [41][3100/3746] lr: 8.281e-02, eta: 3 days, 14:25:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5311, loss_cls: 4.1462, loss: 4.1462 +2024-07-17 16:25:01,021 - pyskl - INFO - Epoch [41][3200/3746] lr: 8.279e-02, eta: 3 days, 14:24:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5356, loss_cls: 4.1419, loss: 4.1419 +2024-07-17 16:26:22,541 - pyskl - INFO - Epoch [41][3300/3746] lr: 8.277e-02, eta: 3 days, 14:23:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5333, loss_cls: 4.1671, loss: 4.1671 +2024-07-17 16:27:43,754 - pyskl - INFO - Epoch [41][3400/3746] lr: 8.274e-02, eta: 3 days, 14:22:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5363, loss_cls: 4.1335, loss: 4.1335 +2024-07-17 16:29:04,991 - pyskl - INFO - Epoch [41][3500/3746] lr: 8.272e-02, eta: 3 days, 14:21:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5333, loss_cls: 4.1584, loss: 4.1584 +2024-07-17 16:30:26,810 - pyskl - INFO - Epoch [41][3600/3746] lr: 8.270e-02, eta: 3 days, 14:20:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5311, loss_cls: 4.1510, loss: 4.1510 +2024-07-17 16:31:48,731 - pyskl - INFO - Epoch [41][3700/3746] lr: 8.268e-02, eta: 3 days, 14:19:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5327, loss_cls: 4.1659, loss: 4.1659 +2024-07-17 16:32:28,397 - pyskl - INFO - Saving checkpoint at 41 epochs +2024-07-17 16:34:19,293 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 16:34:19,952 - pyskl - INFO - +top1_acc 0.1987 +top5_acc 0.4282 +2024-07-17 16:34:19,952 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 16:34:19,990 - pyskl - INFO - +mean_acc 0.1985 +2024-07-17 16:34:20,000 - pyskl - INFO - Epoch(val) [41][309] top1_acc: 0.1987, top5_acc: 0.4282, mean_class_accuracy: 0.1985 +2024-07-17 16:38:11,627 - pyskl - INFO - Epoch [42][100/3746] lr: 8.265e-02, eta: 3 days, 14:22:59, time: 2.316, data_time: 1.328, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5325, loss_cls: 4.1244, loss: 4.1244 +2024-07-17 16:39:34,065 - pyskl - INFO - Epoch [42][200/3746] lr: 8.263e-02, eta: 3 days, 14:22:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5400, loss_cls: 4.1004, loss: 4.1004 +2024-07-17 16:40:56,016 - pyskl - INFO - Epoch [42][300/3746] lr: 8.261e-02, eta: 3 days, 14:20:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5339, loss_cls: 4.1426, loss: 4.1426 +2024-07-17 16:42:17,624 - pyskl - INFO - Epoch [42][400/3746] lr: 8.259e-02, eta: 3 days, 14:19:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5261, loss_cls: 4.1817, loss: 4.1817 +2024-07-17 16:43:39,473 - pyskl - INFO - Epoch [42][500/3746] lr: 8.257e-02, eta: 3 days, 14:18:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5333, loss_cls: 4.1355, loss: 4.1355 +2024-07-17 16:45:01,100 - pyskl - INFO - Epoch [42][600/3746] lr: 8.254e-02, eta: 3 days, 14:17:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5325, loss_cls: 4.1368, loss: 4.1368 +2024-07-17 16:46:22,988 - pyskl - INFO - Epoch [42][700/3746] lr: 8.252e-02, eta: 3 days, 14:16:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5283, loss_cls: 4.2008, loss: 4.2008 +2024-07-17 16:47:44,960 - pyskl - INFO - Epoch [42][800/3746] lr: 8.250e-02, eta: 3 days, 14:15:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5272, loss_cls: 4.1501, loss: 4.1501 +2024-07-17 16:49:06,480 - pyskl - INFO - Epoch [42][900/3746] lr: 8.248e-02, eta: 3 days, 14:14:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5420, loss_cls: 4.0979, loss: 4.0979 +2024-07-17 16:50:28,652 - pyskl - INFO - Epoch [42][1000/3746] lr: 8.246e-02, eta: 3 days, 14:13:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5245, loss_cls: 4.1657, loss: 4.1657 +2024-07-17 16:51:50,444 - pyskl - INFO - Epoch [42][1100/3746] lr: 8.244e-02, eta: 3 days, 14:12:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5323, loss_cls: 4.1632, loss: 4.1632 +2024-07-17 16:53:12,519 - pyskl - INFO - Epoch [42][1200/3746] lr: 8.242e-02, eta: 3 days, 14:11:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5259, loss_cls: 4.1865, loss: 4.1865 +2024-07-17 16:54:34,568 - pyskl - INFO - Epoch [42][1300/3746] lr: 8.240e-02, eta: 3 days, 14:10:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5311, loss_cls: 4.1505, loss: 4.1505 +2024-07-17 16:55:56,032 - pyskl - INFO - Epoch [42][1400/3746] lr: 8.237e-02, eta: 3 days, 14:09:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5378, loss_cls: 4.1330, loss: 4.1330 +2024-07-17 16:57:18,235 - pyskl - INFO - Epoch [42][1500/3746] lr: 8.235e-02, eta: 3 days, 14:08:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5183, loss_cls: 4.2226, loss: 4.2226 +2024-07-17 16:58:39,840 - pyskl - INFO - Epoch [42][1600/3746] lr: 8.233e-02, eta: 3 days, 14:07:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5448, loss_cls: 4.0934, loss: 4.0934 +2024-07-17 17:00:01,728 - pyskl - INFO - Epoch [42][1700/3746] lr: 8.231e-02, eta: 3 days, 14:06:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5316, loss_cls: 4.1779, loss: 4.1779 +2024-07-17 17:01:23,665 - pyskl - INFO - Epoch [42][1800/3746] lr: 8.229e-02, eta: 3 days, 14:05:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5359, loss_cls: 4.1745, loss: 4.1745 +2024-07-17 17:02:45,917 - pyskl - INFO - Epoch [42][1900/3746] lr: 8.227e-02, eta: 3 days, 14:04:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5245, loss_cls: 4.1936, loss: 4.1936 +2024-07-17 17:04:08,130 - pyskl - INFO - Epoch [42][2000/3746] lr: 8.225e-02, eta: 3 days, 14:03:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5433, loss_cls: 4.1145, loss: 4.1145 +2024-07-17 17:05:29,692 - pyskl - INFO - Epoch [42][2100/3746] lr: 8.222e-02, eta: 3 days, 14:02:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5294, loss_cls: 4.1485, loss: 4.1485 +2024-07-17 17:06:52,452 - pyskl - INFO - Epoch [42][2200/3746] lr: 8.220e-02, eta: 3 days, 14:01:35, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5323, loss_cls: 4.1462, loss: 4.1462 +2024-07-17 17:08:14,374 - pyskl - INFO - Epoch [42][2300/3746] lr: 8.218e-02, eta: 3 days, 14:00:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5242, loss_cls: 4.1502, loss: 4.1502 +2024-07-17 17:09:36,279 - pyskl - INFO - Epoch [42][2400/3746] lr: 8.216e-02, eta: 3 days, 13:59:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5219, loss_cls: 4.1744, loss: 4.1744 +2024-07-17 17:10:58,049 - pyskl - INFO - Epoch [42][2500/3746] lr: 8.214e-02, eta: 3 days, 13:58:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5191, loss_cls: 4.1830, loss: 4.1830 +2024-07-17 17:12:19,920 - pyskl - INFO - Epoch [42][2600/3746] lr: 8.212e-02, eta: 3 days, 13:57:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5298, loss_cls: 4.1500, loss: 4.1500 +2024-07-17 17:13:41,309 - pyskl - INFO - Epoch [42][2700/3746] lr: 8.210e-02, eta: 3 days, 13:56:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5316, loss_cls: 4.1443, loss: 4.1443 +2024-07-17 17:15:02,777 - pyskl - INFO - Epoch [42][2800/3746] lr: 8.207e-02, eta: 3 days, 13:55:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5327, loss_cls: 4.1264, loss: 4.1264 +2024-07-17 17:16:24,197 - pyskl - INFO - Epoch [42][2900/3746] lr: 8.205e-02, eta: 3 days, 13:54:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5314, loss_cls: 4.1850, loss: 4.1850 +2024-07-17 17:17:45,886 - pyskl - INFO - Epoch [42][3000/3746] lr: 8.203e-02, eta: 3 days, 13:53:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5375, loss_cls: 4.1217, loss: 4.1217 +2024-07-17 17:19:07,383 - pyskl - INFO - Epoch [42][3100/3746] lr: 8.201e-02, eta: 3 days, 13:52:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5288, loss_cls: 4.1546, loss: 4.1546 +2024-07-17 17:20:29,363 - pyskl - INFO - Epoch [42][3200/3746] lr: 8.199e-02, eta: 3 days, 13:51:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5202, loss_cls: 4.1756, loss: 4.1756 +2024-07-17 17:21:51,174 - pyskl - INFO - Epoch [42][3300/3746] lr: 8.197e-02, eta: 3 days, 13:50:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5250, loss_cls: 4.2030, loss: 4.2030 +2024-07-17 17:23:12,765 - pyskl - INFO - Epoch [42][3400/3746] lr: 8.195e-02, eta: 3 days, 13:49:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5320, loss_cls: 4.1953, loss: 4.1953 +2024-07-17 17:24:33,948 - pyskl - INFO - Epoch [42][3500/3746] lr: 8.192e-02, eta: 3 days, 13:48:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5316, loss_cls: 4.1523, loss: 4.1523 +2024-07-17 17:25:55,524 - pyskl - INFO - Epoch [42][3600/3746] lr: 8.190e-02, eta: 3 days, 13:47:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5270, loss_cls: 4.1782, loss: 4.1782 +2024-07-17 17:27:16,881 - pyskl - INFO - Epoch [42][3700/3746] lr: 8.188e-02, eta: 3 days, 13:45:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5286, loss_cls: 4.2028, loss: 4.2028 +2024-07-17 17:27:56,268 - pyskl - INFO - Saving checkpoint at 42 epochs +2024-07-17 17:29:46,658 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 17:29:47,322 - pyskl - INFO - +top1_acc 0.2102 +top5_acc 0.4357 +2024-07-17 17:29:47,322 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 17:29:47,362 - pyskl - INFO - +mean_acc 0.2101 +2024-07-17 17:29:47,374 - pyskl - INFO - Epoch(val) [42][309] top1_acc: 0.2102, top5_acc: 0.4357, mean_class_accuracy: 0.2101 +2024-07-17 17:33:36,507 - pyskl - INFO - Epoch [43][100/3746] lr: 8.185e-02, eta: 3 days, 13:49:09, time: 2.291, data_time: 1.313, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5302, loss_cls: 4.1509, loss: 4.1509 +2024-07-17 17:34:58,993 - pyskl - INFO - Epoch [43][200/3746] lr: 8.183e-02, eta: 3 days, 13:48:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5353, loss_cls: 4.1321, loss: 4.1321 +2024-07-17 17:36:20,738 - pyskl - INFO - Epoch [43][300/3746] lr: 8.181e-02, eta: 3 days, 13:47:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5469, loss_cls: 4.0861, loss: 4.0861 +2024-07-17 17:37:42,452 - pyskl - INFO - Epoch [43][400/3746] lr: 8.179e-02, eta: 3 days, 13:46:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5286, loss_cls: 4.1502, loss: 4.1502 +2024-07-17 17:39:04,061 - pyskl - INFO - Epoch [43][500/3746] lr: 8.176e-02, eta: 3 days, 13:45:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5291, loss_cls: 4.1212, loss: 4.1212 +2024-07-17 17:40:25,499 - pyskl - INFO - Epoch [43][600/3746] lr: 8.174e-02, eta: 3 days, 13:43:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5405, loss_cls: 4.0976, loss: 4.0976 +2024-07-17 17:41:47,107 - pyskl - INFO - Epoch [43][700/3746] lr: 8.172e-02, eta: 3 days, 13:42:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5409, loss_cls: 4.1080, loss: 4.1080 +2024-07-17 17:43:09,019 - pyskl - INFO - Epoch [43][800/3746] lr: 8.170e-02, eta: 3 days, 13:41:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5295, loss_cls: 4.1466, loss: 4.1466 +2024-07-17 17:44:31,465 - pyskl - INFO - Epoch [43][900/3746] lr: 8.168e-02, eta: 3 days, 13:40:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5386, loss_cls: 4.1117, loss: 4.1117 +2024-07-17 17:45:53,297 - pyskl - INFO - Epoch [43][1000/3746] lr: 8.166e-02, eta: 3 days, 13:39:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5314, loss_cls: 4.1185, loss: 4.1185 +2024-07-17 17:47:15,014 - pyskl - INFO - Epoch [43][1100/3746] lr: 8.163e-02, eta: 3 days, 13:38:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5231, loss_cls: 4.1839, loss: 4.1839 +2024-07-17 17:48:36,931 - pyskl - INFO - Epoch [43][1200/3746] lr: 8.161e-02, eta: 3 days, 13:37:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5309, loss_cls: 4.1726, loss: 4.1726 +2024-07-17 17:49:58,940 - pyskl - INFO - Epoch [43][1300/3746] lr: 8.159e-02, eta: 3 days, 13:36:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5358, loss_cls: 4.1469, loss: 4.1469 +2024-07-17 17:51:20,705 - pyskl - INFO - Epoch [43][1400/3746] lr: 8.157e-02, eta: 3 days, 13:35:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5344, loss_cls: 4.1421, loss: 4.1421 +2024-07-17 17:52:42,302 - pyskl - INFO - Epoch [43][1500/3746] lr: 8.155e-02, eta: 3 days, 13:34:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5306, loss_cls: 4.1838, loss: 4.1838 +2024-07-17 17:54:04,219 - pyskl - INFO - Epoch [43][1600/3746] lr: 8.153e-02, eta: 3 days, 13:33:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5288, loss_cls: 4.1780, loss: 4.1780 +2024-07-17 17:55:26,356 - pyskl - INFO - Epoch [43][1700/3746] lr: 8.150e-02, eta: 3 days, 13:32:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5277, loss_cls: 4.1803, loss: 4.1803 +2024-07-17 17:56:47,739 - pyskl - INFO - Epoch [43][1800/3746] lr: 8.148e-02, eta: 3 days, 13:31:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5373, loss_cls: 4.1025, loss: 4.1025 +2024-07-17 17:58:10,837 - pyskl - INFO - Epoch [43][1900/3746] lr: 8.146e-02, eta: 3 days, 13:30:26, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5352, loss_cls: 4.1437, loss: 4.1437 +2024-07-17 17:59:32,424 - pyskl - INFO - Epoch [43][2000/3746] lr: 8.144e-02, eta: 3 days, 13:29:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5219, loss_cls: 4.1699, loss: 4.1699 +2024-07-17 18:00:54,796 - pyskl - INFO - Epoch [43][2100/3746] lr: 8.142e-02, eta: 3 days, 13:28:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5241, loss_cls: 4.1833, loss: 4.1833 +2024-07-17 18:02:17,159 - pyskl - INFO - Epoch [43][2200/3746] lr: 8.140e-02, eta: 3 days, 13:27:19, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5342, loss_cls: 4.1469, loss: 4.1469 +2024-07-17 18:03:38,889 - pyskl - INFO - Epoch [43][2300/3746] lr: 8.137e-02, eta: 3 days, 13:26:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5266, loss_cls: 4.1746, loss: 4.1746 +2024-07-17 18:05:01,062 - pyskl - INFO - Epoch [43][2400/3746] lr: 8.135e-02, eta: 3 days, 13:25:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5337, loss_cls: 4.1758, loss: 4.1758 +2024-07-17 18:06:23,331 - pyskl - INFO - Epoch [43][2500/3746] lr: 8.133e-02, eta: 3 days, 13:24:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5258, loss_cls: 4.1437, loss: 4.1437 +2024-07-17 18:07:44,927 - pyskl - INFO - Epoch [43][2600/3746] lr: 8.131e-02, eta: 3 days, 13:23:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5230, loss_cls: 4.1988, loss: 4.1988 +2024-07-17 18:09:06,782 - pyskl - INFO - Epoch [43][2700/3746] lr: 8.129e-02, eta: 3 days, 13:22:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5256, loss_cls: 4.1700, loss: 4.1700 +2024-07-17 18:10:28,181 - pyskl - INFO - Epoch [43][2800/3746] lr: 8.126e-02, eta: 3 days, 13:21:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5280, loss_cls: 4.1821, loss: 4.1821 +2024-07-17 18:11:49,814 - pyskl - INFO - Epoch [43][2900/3746] lr: 8.124e-02, eta: 3 days, 13:19:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5223, loss_cls: 4.1753, loss: 4.1753 +2024-07-17 18:13:11,747 - pyskl - INFO - Epoch [43][3000/3746] lr: 8.122e-02, eta: 3 days, 13:18:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5398, loss_cls: 4.1126, loss: 4.1126 +2024-07-17 18:14:33,173 - pyskl - INFO - Epoch [43][3100/3746] lr: 8.120e-02, eta: 3 days, 13:17:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5202, loss_cls: 4.2061, loss: 4.2061 +2024-07-17 18:15:54,228 - pyskl - INFO - Epoch [43][3200/3746] lr: 8.118e-02, eta: 3 days, 13:16:46, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5355, loss_cls: 4.1605, loss: 4.1605 +2024-07-17 18:17:16,090 - pyskl - INFO - Epoch [43][3300/3746] lr: 8.116e-02, eta: 3 days, 13:15:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5327, loss_cls: 4.1395, loss: 4.1395 +2024-07-17 18:18:37,664 - pyskl - INFO - Epoch [43][3400/3746] lr: 8.113e-02, eta: 3 days, 13:14:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5420, loss_cls: 4.1357, loss: 4.1357 +2024-07-17 18:19:59,323 - pyskl - INFO - Epoch [43][3500/3746] lr: 8.111e-02, eta: 3 days, 13:13:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5289, loss_cls: 4.1867, loss: 4.1867 +2024-07-17 18:21:20,830 - pyskl - INFO - Epoch [43][3600/3746] lr: 8.109e-02, eta: 3 days, 13:12:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5372, loss_cls: 4.1398, loss: 4.1398 +2024-07-17 18:22:42,163 - pyskl - INFO - Epoch [43][3700/3746] lr: 8.107e-02, eta: 3 days, 13:11:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5327, loss_cls: 4.1420, loss: 4.1420 +2024-07-17 18:23:21,550 - pyskl - INFO - Saving checkpoint at 43 epochs +2024-07-17 18:25:11,698 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 18:25:12,355 - pyskl - INFO - +top1_acc 0.2129 +top5_acc 0.4474 +2024-07-17 18:25:12,355 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 18:25:12,393 - pyskl - INFO - +mean_acc 0.2126 +2024-07-17 18:25:12,403 - pyskl - INFO - Epoch(val) [43][309] top1_acc: 0.2129, top5_acc: 0.4474, mean_class_accuracy: 0.2126 +2024-07-17 18:28:59,220 - pyskl - INFO - Epoch [44][100/3746] lr: 8.104e-02, eta: 3 days, 13:14:21, time: 2.268, data_time: 1.281, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5337, loss_cls: 4.1415, loss: 4.1415 +2024-07-17 18:30:21,054 - pyskl - INFO - Epoch [44][200/3746] lr: 8.101e-02, eta: 3 days, 13:13:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5437, loss_cls: 4.0970, loss: 4.0970 +2024-07-17 18:31:42,772 - pyskl - INFO - Epoch [44][300/3746] lr: 8.099e-02, eta: 3 days, 13:12:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5384, loss_cls: 4.1210, loss: 4.1210 +2024-07-17 18:33:04,925 - pyskl - INFO - Epoch [44][400/3746] lr: 8.097e-02, eta: 3 days, 13:11:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5298, loss_cls: 4.1395, loss: 4.1395 +2024-07-17 18:34:27,004 - pyskl - INFO - Epoch [44][500/3746] lr: 8.095e-02, eta: 3 days, 13:10:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5353, loss_cls: 4.1591, loss: 4.1591 +2024-07-17 18:35:48,646 - pyskl - INFO - Epoch [44][600/3746] lr: 8.093e-02, eta: 3 days, 13:09:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5420, loss_cls: 4.1182, loss: 4.1182 +2024-07-17 18:37:10,029 - pyskl - INFO - Epoch [44][700/3746] lr: 8.090e-02, eta: 3 days, 13:07:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5381, loss_cls: 4.1132, loss: 4.1132 +2024-07-17 18:38:31,499 - pyskl - INFO - Epoch [44][800/3746] lr: 8.088e-02, eta: 3 days, 13:06:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5431, loss_cls: 4.1057, loss: 4.1057 +2024-07-17 18:39:52,648 - pyskl - INFO - Epoch [44][900/3746] lr: 8.086e-02, eta: 3 days, 13:05:49, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5312, loss_cls: 4.1602, loss: 4.1602 +2024-07-17 18:41:14,077 - pyskl - INFO - Epoch [44][1000/3746] lr: 8.084e-02, eta: 3 days, 13:04:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5284, loss_cls: 4.1375, loss: 4.1375 +2024-07-17 18:42:35,902 - pyskl - INFO - Epoch [44][1100/3746] lr: 8.082e-02, eta: 3 days, 13:03:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5370, loss_cls: 4.1396, loss: 4.1396 +2024-07-17 18:43:57,584 - pyskl - INFO - Epoch [44][1200/3746] lr: 8.079e-02, eta: 3 days, 13:02:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5403, loss_cls: 4.1012, loss: 4.1012 +2024-07-17 18:45:19,511 - pyskl - INFO - Epoch [44][1300/3746] lr: 8.077e-02, eta: 3 days, 13:01:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5300, loss_cls: 4.1749, loss: 4.1749 +2024-07-17 18:46:41,211 - pyskl - INFO - Epoch [44][1400/3746] lr: 8.075e-02, eta: 3 days, 13:00:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5328, loss_cls: 4.1414, loss: 4.1414 +2024-07-17 18:48:03,202 - pyskl - INFO - Epoch [44][1500/3746] lr: 8.073e-02, eta: 3 days, 12:59:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5336, loss_cls: 4.1546, loss: 4.1546 +2024-07-17 18:49:25,061 - pyskl - INFO - Epoch [44][1600/3746] lr: 8.071e-02, eta: 3 days, 12:58:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5342, loss_cls: 4.1656, loss: 4.1656 +2024-07-17 18:50:47,109 - pyskl - INFO - Epoch [44][1700/3746] lr: 8.068e-02, eta: 3 days, 12:57:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5322, loss_cls: 4.1517, loss: 4.1517 +2024-07-17 18:52:08,940 - pyskl - INFO - Epoch [44][1800/3746] lr: 8.066e-02, eta: 3 days, 12:56:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5411, loss_cls: 4.1375, loss: 4.1375 +2024-07-17 18:53:30,302 - pyskl - INFO - Epoch [44][1900/3746] lr: 8.064e-02, eta: 3 days, 12:55:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5384, loss_cls: 4.1477, loss: 4.1477 +2024-07-17 18:54:52,113 - pyskl - INFO - Epoch [44][2000/3746] lr: 8.062e-02, eta: 3 days, 12:54:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5288, loss_cls: 4.1819, loss: 4.1819 +2024-07-17 18:56:13,613 - pyskl - INFO - Epoch [44][2100/3746] lr: 8.060e-02, eta: 3 days, 12:53:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5316, loss_cls: 4.1620, loss: 4.1620 +2024-07-17 18:57:35,460 - pyskl - INFO - Epoch [44][2200/3746] lr: 8.057e-02, eta: 3 days, 12:51:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5386, loss_cls: 4.1250, loss: 4.1250 +2024-07-17 18:58:57,235 - pyskl - INFO - Epoch [44][2300/3746] lr: 8.055e-02, eta: 3 days, 12:50:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5344, loss_cls: 4.1655, loss: 4.1655 +2024-07-17 19:00:19,791 - pyskl - INFO - Epoch [44][2400/3746] lr: 8.053e-02, eta: 3 days, 12:49:51, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5314, loss_cls: 4.1515, loss: 4.1515 +2024-07-17 19:01:41,485 - pyskl - INFO - Epoch [44][2500/3746] lr: 8.051e-02, eta: 3 days, 12:48:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5286, loss_cls: 4.1573, loss: 4.1573 +2024-07-17 19:03:03,296 - pyskl - INFO - Epoch [44][2600/3746] lr: 8.048e-02, eta: 3 days, 12:47:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5302, loss_cls: 4.1495, loss: 4.1495 +2024-07-17 19:04:24,975 - pyskl - INFO - Epoch [44][2700/3746] lr: 8.046e-02, eta: 3 days, 12:46:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5267, loss_cls: 4.1686, loss: 4.1686 +2024-07-17 19:05:47,337 - pyskl - INFO - Epoch [44][2800/3746] lr: 8.044e-02, eta: 3 days, 12:45:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5320, loss_cls: 4.1534, loss: 4.1534 +2024-07-17 19:07:09,016 - pyskl - INFO - Epoch [44][2900/3746] lr: 8.042e-02, eta: 3 days, 12:44:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5286, loss_cls: 4.1815, loss: 4.1815 +2024-07-17 19:08:30,709 - pyskl - INFO - Epoch [44][3000/3746] lr: 8.040e-02, eta: 3 days, 12:43:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5409, loss_cls: 4.1149, loss: 4.1149 +2024-07-17 19:09:52,361 - pyskl - INFO - Epoch [44][3100/3746] lr: 8.037e-02, eta: 3 days, 12:42:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5312, loss_cls: 4.1665, loss: 4.1665 +2024-07-17 19:11:13,817 - pyskl - INFO - Epoch [44][3200/3746] lr: 8.035e-02, eta: 3 days, 12:41:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5236, loss_cls: 4.1598, loss: 4.1598 +2024-07-17 19:12:35,394 - pyskl - INFO - Epoch [44][3300/3746] lr: 8.033e-02, eta: 3 days, 12:40:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5364, loss_cls: 4.1562, loss: 4.1562 +2024-07-17 19:13:57,088 - pyskl - INFO - Epoch [44][3400/3746] lr: 8.031e-02, eta: 3 days, 12:39:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5377, loss_cls: 4.1308, loss: 4.1308 +2024-07-17 19:15:19,049 - pyskl - INFO - Epoch [44][3500/3746] lr: 8.028e-02, eta: 3 days, 12:38:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5228, loss_cls: 4.2102, loss: 4.2102 +2024-07-17 19:16:40,838 - pyskl - INFO - Epoch [44][3600/3746] lr: 8.026e-02, eta: 3 days, 12:36:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5244, loss_cls: 4.1678, loss: 4.1678 +2024-07-17 19:18:02,396 - pyskl - INFO - Epoch [44][3700/3746] lr: 8.024e-02, eta: 3 days, 12:35:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5367, loss_cls: 4.1295, loss: 4.1295 +2024-07-17 19:18:41,534 - pyskl - INFO - Saving checkpoint at 44 epochs +2024-07-17 19:20:31,517 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 19:20:32,182 - pyskl - INFO - +top1_acc 0.2041 +top5_acc 0.4333 +2024-07-17 19:20:32,182 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 19:20:32,222 - pyskl - INFO - +mean_acc 0.2040 +2024-07-17 19:20:32,234 - pyskl - INFO - Epoch(val) [44][309] top1_acc: 0.2041, top5_acc: 0.4333, mean_class_accuracy: 0.2040 +2024-07-17 19:24:21,129 - pyskl - INFO - Epoch [45][100/3746] lr: 8.021e-02, eta: 3 days, 12:38:43, time: 2.289, data_time: 1.306, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5525, loss_cls: 4.0688, loss: 4.0688 +2024-07-17 19:25:43,002 - pyskl - INFO - Epoch [45][200/3746] lr: 8.019e-02, eta: 3 days, 12:37:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5505, loss_cls: 4.0560, loss: 4.0560 +2024-07-17 19:27:04,371 - pyskl - INFO - Epoch [45][300/3746] lr: 8.016e-02, eta: 3 days, 12:36:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5331, loss_cls: 4.1402, loss: 4.1402 +2024-07-17 19:28:25,778 - pyskl - INFO - Epoch [45][400/3746] lr: 8.014e-02, eta: 3 days, 12:35:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5358, loss_cls: 4.1225, loss: 4.1225 +2024-07-17 19:29:47,411 - pyskl - INFO - Epoch [45][500/3746] lr: 8.012e-02, eta: 3 days, 12:34:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5364, loss_cls: 4.1080, loss: 4.1080 +2024-07-17 19:31:09,364 - pyskl - INFO - Epoch [45][600/3746] lr: 8.010e-02, eta: 3 days, 12:33:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5348, loss_cls: 4.1370, loss: 4.1370 +2024-07-17 19:32:30,963 - pyskl - INFO - Epoch [45][700/3746] lr: 8.007e-02, eta: 3 days, 12:32:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5270, loss_cls: 4.1826, loss: 4.1826 +2024-07-17 19:33:52,711 - pyskl - INFO - Epoch [45][800/3746] lr: 8.005e-02, eta: 3 days, 12:31:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5395, loss_cls: 4.1261, loss: 4.1261 +2024-07-17 19:35:14,256 - pyskl - INFO - Epoch [45][900/3746] lr: 8.003e-02, eta: 3 days, 12:30:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5439, loss_cls: 4.0848, loss: 4.0848 +2024-07-17 19:36:35,974 - pyskl - INFO - Epoch [45][1000/3746] lr: 8.001e-02, eta: 3 days, 12:28:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5225, loss_cls: 4.1614, loss: 4.1614 +2024-07-17 19:37:57,802 - pyskl - INFO - Epoch [45][1100/3746] lr: 7.998e-02, eta: 3 days, 12:27:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5302, loss_cls: 4.1590, loss: 4.1590 +2024-07-17 19:39:19,680 - pyskl - INFO - Epoch [45][1200/3746] lr: 7.996e-02, eta: 3 days, 12:26:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5392, loss_cls: 4.0972, loss: 4.0972 +2024-07-17 19:40:42,332 - pyskl - INFO - Epoch [45][1300/3746] lr: 7.994e-02, eta: 3 days, 12:25:45, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5320, loss_cls: 4.1231, loss: 4.1231 +2024-07-17 19:42:03,932 - pyskl - INFO - Epoch [45][1400/3746] lr: 7.992e-02, eta: 3 days, 12:24:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5280, loss_cls: 4.1477, loss: 4.1477 +2024-07-17 19:43:25,828 - pyskl - INFO - Epoch [45][1500/3746] lr: 7.990e-02, eta: 3 days, 12:23:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5222, loss_cls: 4.1549, loss: 4.1549 +2024-07-17 19:44:47,800 - pyskl - INFO - Epoch [45][1600/3746] lr: 7.987e-02, eta: 3 days, 12:22:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5298, loss_cls: 4.1401, loss: 4.1401 +2024-07-17 19:46:09,616 - pyskl - INFO - Epoch [45][1700/3746] lr: 7.985e-02, eta: 3 days, 12:21:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5269, loss_cls: 4.1843, loss: 4.1843 +2024-07-17 19:47:31,153 - pyskl - INFO - Epoch [45][1800/3746] lr: 7.983e-02, eta: 3 days, 12:20:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5416, loss_cls: 4.1046, loss: 4.1046 +2024-07-17 19:48:53,160 - pyskl - INFO - Epoch [45][1900/3746] lr: 7.981e-02, eta: 3 days, 12:19:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5331, loss_cls: 4.1218, loss: 4.1218 +2024-07-17 19:50:14,919 - pyskl - INFO - Epoch [45][2000/3746] lr: 7.978e-02, eta: 3 days, 12:18:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5262, loss_cls: 4.1755, loss: 4.1755 +2024-07-17 19:51:37,080 - pyskl - INFO - Epoch [45][2100/3746] lr: 7.976e-02, eta: 3 days, 12:17:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5334, loss_cls: 4.1416, loss: 4.1416 +2024-07-17 19:52:58,963 - pyskl - INFO - Epoch [45][2200/3746] lr: 7.974e-02, eta: 3 days, 12:16:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5464, loss_cls: 4.0715, loss: 4.0715 +2024-07-17 19:54:20,898 - pyskl - INFO - Epoch [45][2300/3746] lr: 7.972e-02, eta: 3 days, 12:14:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5184, loss_cls: 4.2219, loss: 4.2219 +2024-07-17 19:55:43,041 - pyskl - INFO - Epoch [45][2400/3746] lr: 7.969e-02, eta: 3 days, 12:13:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5439, loss_cls: 4.1034, loss: 4.1034 +2024-07-17 19:57:04,998 - pyskl - INFO - Epoch [45][2500/3746] lr: 7.967e-02, eta: 3 days, 12:12:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5373, loss_cls: 4.1129, loss: 4.1129 +2024-07-17 19:58:26,745 - pyskl - INFO - Epoch [45][2600/3746] lr: 7.965e-02, eta: 3 days, 12:11:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5306, loss_cls: 4.1617, loss: 4.1617 +2024-07-17 19:59:48,294 - pyskl - INFO - Epoch [45][2700/3746] lr: 7.963e-02, eta: 3 days, 12:10:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5231, loss_cls: 4.1687, loss: 4.1687 +2024-07-17 20:01:09,603 - pyskl - INFO - Epoch [45][2800/3746] lr: 7.960e-02, eta: 3 days, 12:09:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5384, loss_cls: 4.1059, loss: 4.1059 +2024-07-17 20:02:30,943 - pyskl - INFO - Epoch [45][2900/3746] lr: 7.958e-02, eta: 3 days, 12:08:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5252, loss_cls: 4.1763, loss: 4.1763 +2024-07-17 20:03:52,615 - pyskl - INFO - Epoch [45][3000/3746] lr: 7.956e-02, eta: 3 days, 12:07:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5277, loss_cls: 4.1492, loss: 4.1492 +2024-07-17 20:05:14,554 - pyskl - INFO - Epoch [45][3100/3746] lr: 7.954e-02, eta: 3 days, 12:06:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5369, loss_cls: 4.1025, loss: 4.1025 +2024-07-17 20:06:36,342 - pyskl - INFO - Epoch [45][3200/3746] lr: 7.951e-02, eta: 3 days, 12:05:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5202, loss_cls: 4.1825, loss: 4.1825 +2024-07-17 20:07:58,106 - pyskl - INFO - Epoch [45][3300/3746] lr: 7.949e-02, eta: 3 days, 12:04:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5303, loss_cls: 4.1890, loss: 4.1890 +2024-07-17 20:09:19,745 - pyskl - INFO - Epoch [45][3400/3746] lr: 7.947e-02, eta: 3 days, 12:02:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5259, loss_cls: 4.1601, loss: 4.1601 +2024-07-17 20:10:41,840 - pyskl - INFO - Epoch [45][3500/3746] lr: 7.945e-02, eta: 3 days, 12:01:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5262, loss_cls: 4.1663, loss: 4.1663 +2024-07-17 20:12:03,341 - pyskl - INFO - Epoch [45][3600/3746] lr: 7.942e-02, eta: 3 days, 12:00:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5400, loss_cls: 4.1536, loss: 4.1536 +2024-07-17 20:13:24,757 - pyskl - INFO - Epoch [45][3700/3746] lr: 7.940e-02, eta: 3 days, 11:59:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5289, loss_cls: 4.1586, loss: 4.1586 +2024-07-17 20:14:04,051 - pyskl - INFO - Saving checkpoint at 45 epochs +2024-07-17 20:15:53,727 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 20:15:54,390 - pyskl - INFO - +top1_acc 0.2069 +top5_acc 0.4446 +2024-07-17 20:15:54,391 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 20:15:54,429 - pyskl - INFO - +mean_acc 0.2067 +2024-07-17 20:15:54,439 - pyskl - INFO - Epoch(val) [45][309] top1_acc: 0.2069, top5_acc: 0.4446, mean_class_accuracy: 0.2067 +2024-07-17 20:19:42,821 - pyskl - INFO - Epoch [46][100/3746] lr: 7.937e-02, eta: 3 days, 12:02:19, time: 2.284, data_time: 1.299, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5467, loss_cls: 4.0732, loss: 4.0732 +2024-07-17 20:21:04,705 - pyskl - INFO - Epoch [46][200/3746] lr: 7.934e-02, eta: 3 days, 12:01:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5406, loss_cls: 4.1388, loss: 4.1388 +2024-07-17 20:22:26,938 - pyskl - INFO - Epoch [46][300/3746] lr: 7.932e-02, eta: 3 days, 12:00:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5548, loss_cls: 4.0736, loss: 4.0736 +2024-07-17 20:23:48,878 - pyskl - INFO - Epoch [46][400/3746] lr: 7.930e-02, eta: 3 days, 11:59:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5408, loss_cls: 4.1117, loss: 4.1117 +2024-07-17 20:25:10,866 - pyskl - INFO - Epoch [46][500/3746] lr: 7.928e-02, eta: 3 days, 11:57:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5320, loss_cls: 4.1369, loss: 4.1369 +2024-07-17 20:26:32,620 - pyskl - INFO - Epoch [46][600/3746] lr: 7.925e-02, eta: 3 days, 11:56:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5244, loss_cls: 4.1700, loss: 4.1700 +2024-07-17 20:27:54,472 - pyskl - INFO - Epoch [46][700/3746] lr: 7.923e-02, eta: 3 days, 11:55:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5359, loss_cls: 4.0947, loss: 4.0947 +2024-07-17 20:29:16,003 - pyskl - INFO - Epoch [46][800/3746] lr: 7.921e-02, eta: 3 days, 11:54:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5323, loss_cls: 4.1475, loss: 4.1475 +2024-07-17 20:30:38,005 - pyskl - INFO - Epoch [46][900/3746] lr: 7.919e-02, eta: 3 days, 11:53:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5314, loss_cls: 4.1669, loss: 4.1669 +2024-07-17 20:32:00,126 - pyskl - INFO - Epoch [46][1000/3746] lr: 7.916e-02, eta: 3 days, 11:52:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5384, loss_cls: 4.1101, loss: 4.1101 +2024-07-17 20:33:21,634 - pyskl - INFO - Epoch [46][1100/3746] lr: 7.914e-02, eta: 3 days, 11:51:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5300, loss_cls: 4.1394, loss: 4.1394 +2024-07-17 20:34:44,515 - pyskl - INFO - Epoch [46][1200/3746] lr: 7.912e-02, eta: 3 days, 11:50:20, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5373, loss_cls: 4.1439, loss: 4.1439 +2024-07-17 20:36:07,160 - pyskl - INFO - Epoch [46][1300/3746] lr: 7.909e-02, eta: 3 days, 11:49:17, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5244, loss_cls: 4.1486, loss: 4.1486 +2024-07-17 20:37:29,189 - pyskl - INFO - Epoch [46][1400/3746] lr: 7.907e-02, eta: 3 days, 11:48:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5269, loss_cls: 4.1717, loss: 4.1717 +2024-07-17 20:38:51,297 - pyskl - INFO - Epoch [46][1500/3746] lr: 7.905e-02, eta: 3 days, 11:47:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5389, loss_cls: 4.1259, loss: 4.1259 +2024-07-17 20:40:13,594 - pyskl - INFO - Epoch [46][1600/3746] lr: 7.903e-02, eta: 3 days, 11:46:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5295, loss_cls: 4.1390, loss: 4.1390 +2024-07-17 20:41:34,986 - pyskl - INFO - Epoch [46][1700/3746] lr: 7.900e-02, eta: 3 days, 11:44:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5273, loss_cls: 4.1896, loss: 4.1896 +2024-07-17 20:42:56,319 - pyskl - INFO - Epoch [46][1800/3746] lr: 7.898e-02, eta: 3 days, 11:43:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5387, loss_cls: 4.1184, loss: 4.1184 +2024-07-17 20:44:17,759 - pyskl - INFO - Epoch [46][1900/3746] lr: 7.896e-02, eta: 3 days, 11:42:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5377, loss_cls: 4.1202, loss: 4.1202 +2024-07-17 20:45:39,939 - pyskl - INFO - Epoch [46][2000/3746] lr: 7.894e-02, eta: 3 days, 11:41:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5286, loss_cls: 4.1684, loss: 4.1684 +2024-07-17 20:47:02,143 - pyskl - INFO - Epoch [46][2100/3746] lr: 7.891e-02, eta: 3 days, 11:40:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5505, loss_cls: 4.0608, loss: 4.0608 +2024-07-17 20:48:24,713 - pyskl - INFO - Epoch [46][2200/3746] lr: 7.889e-02, eta: 3 days, 11:39:26, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5305, loss_cls: 4.1513, loss: 4.1513 +2024-07-17 20:49:46,487 - pyskl - INFO - Epoch [46][2300/3746] lr: 7.887e-02, eta: 3 days, 11:38:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5495, loss_cls: 4.0676, loss: 4.0676 +2024-07-17 20:51:08,441 - pyskl - INFO - Epoch [46][2400/3746] lr: 7.884e-02, eta: 3 days, 11:37:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5284, loss_cls: 4.1834, loss: 4.1834 +2024-07-17 20:52:29,852 - pyskl - INFO - Epoch [46][2500/3746] lr: 7.882e-02, eta: 3 days, 11:36:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5373, loss_cls: 4.1407, loss: 4.1407 +2024-07-17 20:53:51,487 - pyskl - INFO - Epoch [46][2600/3746] lr: 7.880e-02, eta: 3 days, 11:35:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5339, loss_cls: 4.1481, loss: 4.1481 +2024-07-17 20:55:13,363 - pyskl - INFO - Epoch [46][2700/3746] lr: 7.878e-02, eta: 3 days, 11:33:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5375, loss_cls: 4.1107, loss: 4.1107 +2024-07-17 20:56:35,134 - pyskl - INFO - Epoch [46][2800/3746] lr: 7.875e-02, eta: 3 days, 11:32:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5405, loss_cls: 4.1069, loss: 4.1069 +2024-07-17 20:57:56,752 - pyskl - INFO - Epoch [46][2900/3746] lr: 7.873e-02, eta: 3 days, 11:31:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5341, loss_cls: 4.1317, loss: 4.1317 +2024-07-17 20:59:18,745 - pyskl - INFO - Epoch [46][3000/3746] lr: 7.871e-02, eta: 3 days, 11:30:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5239, loss_cls: 4.1886, loss: 4.1886 +2024-07-17 21:00:40,677 - pyskl - INFO - Epoch [46][3100/3746] lr: 7.868e-02, eta: 3 days, 11:29:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5361, loss_cls: 4.1562, loss: 4.1562 +2024-07-17 21:02:02,726 - pyskl - INFO - Epoch [46][3200/3746] lr: 7.866e-02, eta: 3 days, 11:28:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5377, loss_cls: 4.1139, loss: 4.1139 +2024-07-17 21:03:24,425 - pyskl - INFO - Epoch [46][3300/3746] lr: 7.864e-02, eta: 3 days, 11:27:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5275, loss_cls: 4.1807, loss: 4.1807 +2024-07-17 21:04:45,949 - pyskl - INFO - Epoch [46][3400/3746] lr: 7.862e-02, eta: 3 days, 11:26:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5372, loss_cls: 4.1354, loss: 4.1354 +2024-07-17 21:06:07,780 - pyskl - INFO - Epoch [46][3500/3746] lr: 7.859e-02, eta: 3 days, 11:25:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5255, loss_cls: 4.1880, loss: 4.1880 +2024-07-17 21:07:29,548 - pyskl - INFO - Epoch [46][3600/3746] lr: 7.857e-02, eta: 3 days, 11:24:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5291, loss_cls: 4.1692, loss: 4.1692 +2024-07-17 21:08:50,888 - pyskl - INFO - Epoch [46][3700/3746] lr: 7.855e-02, eta: 3 days, 11:22:52, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5422, loss_cls: 4.0901, loss: 4.0901 +2024-07-17 21:09:30,098 - pyskl - INFO - Saving checkpoint at 46 epochs +2024-07-17 21:11:21,414 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 21:11:22,105 - pyskl - INFO - +top1_acc 0.2272 +top5_acc 0.4624 +2024-07-17 21:11:22,106 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 21:11:22,154 - pyskl - INFO - +mean_acc 0.2269 +2024-07-17 21:11:22,159 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_39.pth was removed +2024-07-17 21:11:22,423 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_46.pth. +2024-07-17 21:11:22,424 - pyskl - INFO - Best top1_acc is 0.2272 at 46 epoch. +2024-07-17 21:11:22,440 - pyskl - INFO - Epoch(val) [46][309] top1_acc: 0.2272, top5_acc: 0.4624, mean_class_accuracy: 0.2269 +2024-07-17 21:15:09,701 - pyskl - INFO - Epoch [47][100/3746] lr: 7.851e-02, eta: 3 days, 11:25:19, time: 2.273, data_time: 1.285, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5530, loss_cls: 4.0623, loss: 4.0623 +2024-07-17 21:16:32,462 - pyskl - INFO - Epoch [47][200/3746] lr: 7.849e-02, eta: 3 days, 11:24:15, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5406, loss_cls: 4.0897, loss: 4.0897 +2024-07-17 21:17:54,366 - pyskl - INFO - Epoch [47][300/3746] lr: 7.847e-02, eta: 3 days, 11:23:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5280, loss_cls: 4.1641, loss: 4.1641 +2024-07-17 21:19:15,936 - pyskl - INFO - Epoch [47][400/3746] lr: 7.844e-02, eta: 3 days, 11:22:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5352, loss_cls: 4.1224, loss: 4.1224 +2024-07-17 21:20:37,594 - pyskl - INFO - Epoch [47][500/3746] lr: 7.842e-02, eta: 3 days, 11:20:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5375, loss_cls: 4.1073, loss: 4.1073 +2024-07-17 21:21:59,141 - pyskl - INFO - Epoch [47][600/3746] lr: 7.840e-02, eta: 3 days, 11:19:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5339, loss_cls: 4.1172, loss: 4.1172 +2024-07-17 21:23:20,634 - pyskl - INFO - Epoch [47][700/3746] lr: 7.838e-02, eta: 3 days, 11:18:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5369, loss_cls: 4.1519, loss: 4.1519 +2024-07-17 21:24:41,872 - pyskl - INFO - Epoch [47][800/3746] lr: 7.835e-02, eta: 3 days, 11:17:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5444, loss_cls: 4.0642, loss: 4.0642 +2024-07-17 21:26:04,286 - pyskl - INFO - Epoch [47][900/3746] lr: 7.833e-02, eta: 3 days, 11:16:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5400, loss_cls: 4.1207, loss: 4.1207 +2024-07-17 21:27:26,339 - pyskl - INFO - Epoch [47][1000/3746] lr: 7.831e-02, eta: 3 days, 11:15:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5309, loss_cls: 4.1505, loss: 4.1505 +2024-07-17 21:28:48,470 - pyskl - INFO - Epoch [47][1100/3746] lr: 7.828e-02, eta: 3 days, 11:14:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5427, loss_cls: 4.0986, loss: 4.0986 +2024-07-17 21:30:10,621 - pyskl - INFO - Epoch [47][1200/3746] lr: 7.826e-02, eta: 3 days, 11:13:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5389, loss_cls: 4.1511, loss: 4.1511 +2024-07-17 21:31:32,704 - pyskl - INFO - Epoch [47][1300/3746] lr: 7.824e-02, eta: 3 days, 11:12:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5334, loss_cls: 4.1408, loss: 4.1408 +2024-07-17 21:32:54,542 - pyskl - INFO - Epoch [47][1400/3746] lr: 7.821e-02, eta: 3 days, 11:10:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5311, loss_cls: 4.1501, loss: 4.1501 +2024-07-17 21:34:16,344 - pyskl - INFO - Epoch [47][1500/3746] lr: 7.819e-02, eta: 3 days, 11:09:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5409, loss_cls: 4.1305, loss: 4.1305 +2024-07-17 21:35:38,720 - pyskl - INFO - Epoch [47][1600/3746] lr: 7.817e-02, eta: 3 days, 11:08:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5344, loss_cls: 4.1644, loss: 4.1644 +2024-07-17 21:37:00,339 - pyskl - INFO - Epoch [47][1700/3746] lr: 7.814e-02, eta: 3 days, 11:07:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5258, loss_cls: 4.1604, loss: 4.1604 +2024-07-17 21:38:21,886 - pyskl - INFO - Epoch [47][1800/3746] lr: 7.812e-02, eta: 3 days, 11:06:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5358, loss_cls: 4.1292, loss: 4.1292 +2024-07-17 21:39:43,724 - pyskl - INFO - Epoch [47][1900/3746] lr: 7.810e-02, eta: 3 days, 11:05:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5423, loss_cls: 4.1081, loss: 4.1081 +2024-07-17 21:41:06,145 - pyskl - INFO - Epoch [47][2000/3746] lr: 7.808e-02, eta: 3 days, 11:04:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5411, loss_cls: 4.0933, loss: 4.0933 +2024-07-17 21:42:27,991 - pyskl - INFO - Epoch [47][2100/3746] lr: 7.805e-02, eta: 3 days, 11:03:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5348, loss_cls: 4.1238, loss: 4.1238 +2024-07-17 21:43:50,530 - pyskl - INFO - Epoch [47][2200/3746] lr: 7.803e-02, eta: 3 days, 11:02:05, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5292, loss_cls: 4.1735, loss: 4.1735 +2024-07-17 21:45:12,857 - pyskl - INFO - Epoch [47][2300/3746] lr: 7.801e-02, eta: 3 days, 11:01:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5400, loss_cls: 4.0675, loss: 4.0675 +2024-07-17 21:46:34,700 - pyskl - INFO - Epoch [47][2400/3746] lr: 7.798e-02, eta: 3 days, 10:59:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5358, loss_cls: 4.1707, loss: 4.1707 +2024-07-17 21:47:56,462 - pyskl - INFO - Epoch [47][2500/3746] lr: 7.796e-02, eta: 3 days, 10:58:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5397, loss_cls: 4.1231, loss: 4.1231 +2024-07-17 21:49:17,697 - pyskl - INFO - Epoch [47][2600/3746] lr: 7.794e-02, eta: 3 days, 10:57:38, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5273, loss_cls: 4.1440, loss: 4.1440 +2024-07-17 21:50:39,769 - pyskl - INFO - Epoch [47][2700/3746] lr: 7.791e-02, eta: 3 days, 10:56:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5442, loss_cls: 4.1172, loss: 4.1172 +2024-07-17 21:52:01,155 - pyskl - INFO - Epoch [47][2800/3746] lr: 7.789e-02, eta: 3 days, 10:55:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5295, loss_cls: 4.1533, loss: 4.1533 +2024-07-17 21:53:23,002 - pyskl - INFO - Epoch [47][2900/3746] lr: 7.787e-02, eta: 3 days, 10:54:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5389, loss_cls: 4.0819, loss: 4.0819 +2024-07-17 21:54:44,909 - pyskl - INFO - Epoch [47][3000/3746] lr: 7.784e-02, eta: 3 days, 10:53:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5288, loss_cls: 4.1706, loss: 4.1706 +2024-07-17 21:56:06,764 - pyskl - INFO - Epoch [47][3100/3746] lr: 7.782e-02, eta: 3 days, 10:52:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5384, loss_cls: 4.1086, loss: 4.1086 +2024-07-17 21:57:28,097 - pyskl - INFO - Epoch [47][3200/3746] lr: 7.780e-02, eta: 3 days, 10:50:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5372, loss_cls: 4.1298, loss: 4.1298 +2024-07-17 21:58:49,742 - pyskl - INFO - Epoch [47][3300/3746] lr: 7.777e-02, eta: 3 days, 10:49:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5298, loss_cls: 4.1350, loss: 4.1350 +2024-07-17 22:00:11,300 - pyskl - INFO - Epoch [47][3400/3746] lr: 7.775e-02, eta: 3 days, 10:48:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5300, loss_cls: 4.1527, loss: 4.1527 +2024-07-17 22:01:33,102 - pyskl - INFO - Epoch [47][3500/3746] lr: 7.773e-02, eta: 3 days, 10:47:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5348, loss_cls: 4.1238, loss: 4.1238 +2024-07-17 22:02:54,532 - pyskl - INFO - Epoch [47][3600/3746] lr: 7.770e-02, eta: 3 days, 10:46:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5361, loss_cls: 4.1179, loss: 4.1179 +2024-07-17 22:04:15,861 - pyskl - INFO - Epoch [47][3700/3746] lr: 7.768e-02, eta: 3 days, 10:45:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5334, loss_cls: 4.1253, loss: 4.1253 +2024-07-17 22:04:55,314 - pyskl - INFO - Saving checkpoint at 47 epochs +2024-07-17 22:06:47,143 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 22:06:47,806 - pyskl - INFO - +top1_acc 0.2126 +top5_acc 0.4357 +2024-07-17 22:06:47,806 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 22:06:47,845 - pyskl - INFO - +mean_acc 0.2126 +2024-07-17 22:06:47,856 - pyskl - INFO - Epoch(val) [47][309] top1_acc: 0.2126, top5_acc: 0.4357, mean_class_accuracy: 0.2126 +2024-07-17 22:10:33,654 - pyskl - INFO - Epoch [48][100/3746] lr: 7.765e-02, eta: 3 days, 10:47:31, time: 2.258, data_time: 1.278, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5475, loss_cls: 4.0546, loss: 4.0546 +2024-07-17 22:11:55,053 - pyskl - INFO - Epoch [48][200/3746] lr: 7.762e-02, eta: 3 days, 10:46:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5473, loss_cls: 4.0605, loss: 4.0605 +2024-07-17 22:13:16,289 - pyskl - INFO - Epoch [48][300/3746] lr: 7.760e-02, eta: 3 days, 10:45:15, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5364, loss_cls: 4.1062, loss: 4.1062 +2024-07-17 22:14:38,054 - pyskl - INFO - Epoch [48][400/3746] lr: 7.758e-02, eta: 3 days, 10:44:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5369, loss_cls: 4.1338, loss: 4.1338 +2024-07-17 22:15:59,846 - pyskl - INFO - Epoch [48][500/3746] lr: 7.755e-02, eta: 3 days, 10:43:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5402, loss_cls: 4.0859, loss: 4.0859 +2024-07-17 22:17:21,698 - pyskl - INFO - Epoch [48][600/3746] lr: 7.753e-02, eta: 3 days, 10:41:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5505, loss_cls: 4.0526, loss: 4.0526 +2024-07-17 22:18:42,909 - pyskl - INFO - Epoch [48][700/3746] lr: 7.751e-02, eta: 3 days, 10:40:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5430, loss_cls: 4.1017, loss: 4.1017 +2024-07-17 22:20:04,497 - pyskl - INFO - Epoch [48][800/3746] lr: 7.748e-02, eta: 3 days, 10:39:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5356, loss_cls: 4.1623, loss: 4.1623 +2024-07-17 22:21:26,015 - pyskl - INFO - Epoch [48][900/3746] lr: 7.746e-02, eta: 3 days, 10:38:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5306, loss_cls: 4.1215, loss: 4.1215 +2024-07-17 22:22:48,166 - pyskl - INFO - Epoch [48][1000/3746] lr: 7.744e-02, eta: 3 days, 10:37:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5389, loss_cls: 4.0976, loss: 4.0976 +2024-07-17 22:24:09,916 - pyskl - INFO - Epoch [48][1100/3746] lr: 7.741e-02, eta: 3 days, 10:36:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5439, loss_cls: 4.0675, loss: 4.0675 +2024-07-17 22:25:32,610 - pyskl - INFO - Epoch [48][1200/3746] lr: 7.739e-02, eta: 3 days, 10:35:08, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5364, loss_cls: 4.1473, loss: 4.1473 +2024-07-17 22:26:54,653 - pyskl - INFO - Epoch [48][1300/3746] lr: 7.737e-02, eta: 3 days, 10:34:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5430, loss_cls: 4.1095, loss: 4.1095 +2024-07-17 22:28:16,631 - pyskl - INFO - Epoch [48][1400/3746] lr: 7.734e-02, eta: 3 days, 10:32:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5336, loss_cls: 4.1279, loss: 4.1279 +2024-07-17 22:29:38,416 - pyskl - INFO - Epoch [48][1500/3746] lr: 7.732e-02, eta: 3 days, 10:31:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5420, loss_cls: 4.1048, loss: 4.1048 +2024-07-17 22:31:00,817 - pyskl - INFO - Epoch [48][1600/3746] lr: 7.730e-02, eta: 3 days, 10:30:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5392, loss_cls: 4.1317, loss: 4.1317 +2024-07-17 22:32:22,948 - pyskl - INFO - Epoch [48][1700/3746] lr: 7.727e-02, eta: 3 days, 10:29:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5327, loss_cls: 4.1535, loss: 4.1535 +2024-07-17 22:33:44,484 - pyskl - INFO - Epoch [48][1800/3746] lr: 7.725e-02, eta: 3 days, 10:28:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5409, loss_cls: 4.1070, loss: 4.1070 +2024-07-17 22:35:06,573 - pyskl - INFO - Epoch [48][1900/3746] lr: 7.723e-02, eta: 3 days, 10:27:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5297, loss_cls: 4.1504, loss: 4.1504 +2024-07-17 22:36:28,656 - pyskl - INFO - Epoch [48][2000/3746] lr: 7.720e-02, eta: 3 days, 10:26:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5359, loss_cls: 4.1366, loss: 4.1366 +2024-07-17 22:37:50,667 - pyskl - INFO - Epoch [48][2100/3746] lr: 7.718e-02, eta: 3 days, 10:25:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5344, loss_cls: 4.1350, loss: 4.1350 +2024-07-17 22:39:12,294 - pyskl - INFO - Epoch [48][2200/3746] lr: 7.716e-02, eta: 3 days, 10:23:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5291, loss_cls: 4.1640, loss: 4.1640 +2024-07-17 22:40:35,350 - pyskl - INFO - Epoch [48][2300/3746] lr: 7.713e-02, eta: 3 days, 10:22:50, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5355, loss_cls: 4.1119, loss: 4.1119 +2024-07-17 22:41:57,395 - pyskl - INFO - Epoch [48][2400/3746] lr: 7.711e-02, eta: 3 days, 10:21:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5428, loss_cls: 4.0569, loss: 4.0569 +2024-07-17 22:43:19,205 - pyskl - INFO - Epoch [48][2500/3746] lr: 7.709e-02, eta: 3 days, 10:20:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5238, loss_cls: 4.1668, loss: 4.1668 +2024-07-17 22:44:41,466 - pyskl - INFO - Epoch [48][2600/3746] lr: 7.706e-02, eta: 3 days, 10:19:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5464, loss_cls: 4.0826, loss: 4.0826 +2024-07-17 22:46:03,285 - pyskl - INFO - Epoch [48][2700/3746] lr: 7.704e-02, eta: 3 days, 10:18:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5408, loss_cls: 4.0870, loss: 4.0870 +2024-07-17 22:47:25,322 - pyskl - INFO - Epoch [48][2800/3746] lr: 7.701e-02, eta: 3 days, 10:17:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5437, loss_cls: 4.0892, loss: 4.0892 +2024-07-17 22:48:47,056 - pyskl - INFO - Epoch [48][2900/3746] lr: 7.699e-02, eta: 3 days, 10:16:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5341, loss_cls: 4.1446, loss: 4.1446 +2024-07-17 22:50:08,689 - pyskl - INFO - Epoch [48][3000/3746] lr: 7.697e-02, eta: 3 days, 10:14:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5292, loss_cls: 4.1454, loss: 4.1454 +2024-07-17 22:51:30,401 - pyskl - INFO - Epoch [48][3100/3746] lr: 7.694e-02, eta: 3 days, 10:13:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5350, loss_cls: 4.1102, loss: 4.1102 +2024-07-17 22:52:51,957 - pyskl - INFO - Epoch [48][3200/3746] lr: 7.692e-02, eta: 3 days, 10:12:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5397, loss_cls: 4.1245, loss: 4.1245 +2024-07-17 22:54:13,879 - pyskl - INFO - Epoch [48][3300/3746] lr: 7.690e-02, eta: 3 days, 10:11:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5305, loss_cls: 4.1407, loss: 4.1407 +2024-07-17 22:55:35,601 - pyskl - INFO - Epoch [48][3400/3746] lr: 7.687e-02, eta: 3 days, 10:10:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5355, loss_cls: 4.1434, loss: 4.1434 +2024-07-17 22:56:57,109 - pyskl - INFO - Epoch [48][3500/3746] lr: 7.685e-02, eta: 3 days, 10:09:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5262, loss_cls: 4.1747, loss: 4.1747 +2024-07-17 22:58:19,022 - pyskl - INFO - Epoch [48][3600/3746] lr: 7.683e-02, eta: 3 days, 10:08:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5355, loss_cls: 4.1816, loss: 4.1816 +2024-07-17 22:59:40,443 - pyskl - INFO - Epoch [48][3700/3746] lr: 7.680e-02, eta: 3 days, 10:07:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5361, loss_cls: 4.1442, loss: 4.1442 +2024-07-17 23:00:20,240 - pyskl - INFO - Saving checkpoint at 48 epochs +2024-07-17 23:02:10,898 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 23:02:11,554 - pyskl - INFO - +top1_acc 0.2261 +top5_acc 0.4554 +2024-07-17 23:02:11,554 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 23:02:11,592 - pyskl - INFO - +mean_acc 0.2257 +2024-07-17 23:02:11,602 - pyskl - INFO - Epoch(val) [48][309] top1_acc: 0.2261, top5_acc: 0.4554, mean_class_accuracy: 0.2257 +2024-07-17 23:05:59,260 - pyskl - INFO - Epoch [49][100/3746] lr: 7.677e-02, eta: 3 days, 10:09:11, time: 2.276, data_time: 1.293, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5459, loss_cls: 4.0223, loss: 4.0223 +2024-07-17 23:07:21,888 - pyskl - INFO - Epoch [49][200/3746] lr: 7.674e-02, eta: 3 days, 10:08:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5453, loss_cls: 4.0733, loss: 4.0733 +2024-07-17 23:08:44,266 - pyskl - INFO - Epoch [49][300/3746] lr: 7.672e-02, eta: 3 days, 10:06:58, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5495, loss_cls: 4.0399, loss: 4.0399 +2024-07-17 23:10:06,279 - pyskl - INFO - Epoch [49][400/3746] lr: 7.670e-02, eta: 3 days, 10:05:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5394, loss_cls: 4.1006, loss: 4.1006 +2024-07-17 23:11:27,901 - pyskl - INFO - Epoch [49][500/3746] lr: 7.667e-02, eta: 3 days, 10:04:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5373, loss_cls: 4.1305, loss: 4.1305 +2024-07-17 23:12:49,369 - pyskl - INFO - Epoch [49][600/3746] lr: 7.665e-02, eta: 3 days, 10:03:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5475, loss_cls: 4.0813, loss: 4.0813 +2024-07-17 23:14:10,790 - pyskl - INFO - Epoch [49][700/3746] lr: 7.663e-02, eta: 3 days, 10:02:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5359, loss_cls: 4.1471, loss: 4.1471 +2024-07-17 23:15:32,996 - pyskl - INFO - Epoch [49][800/3746] lr: 7.660e-02, eta: 3 days, 10:01:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5492, loss_cls: 4.0752, loss: 4.0752 +2024-07-17 23:16:54,494 - pyskl - INFO - Epoch [49][900/3746] lr: 7.658e-02, eta: 3 days, 10:00:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5437, loss_cls: 4.0835, loss: 4.0835 +2024-07-17 23:18:16,876 - pyskl - INFO - Epoch [49][1000/3746] lr: 7.656e-02, eta: 3 days, 9:59:01, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5309, loss_cls: 4.1455, loss: 4.1455 +2024-07-17 23:19:38,575 - pyskl - INFO - Epoch [49][1100/3746] lr: 7.653e-02, eta: 3 days, 9:57:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5383, loss_cls: 4.1000, loss: 4.1000 +2024-07-17 23:21:01,262 - pyskl - INFO - Epoch [49][1200/3746] lr: 7.651e-02, eta: 3 days, 9:56:46, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5450, loss_cls: 4.0915, loss: 4.0915 +2024-07-17 23:22:23,344 - pyskl - INFO - Epoch [49][1300/3746] lr: 7.648e-02, eta: 3 days, 9:55:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5314, loss_cls: 4.1472, loss: 4.1472 +2024-07-17 23:23:46,015 - pyskl - INFO - Epoch [49][1400/3746] lr: 7.646e-02, eta: 3 days, 9:54:31, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5484, loss_cls: 4.1005, loss: 4.1005 +2024-07-17 23:25:08,271 - pyskl - INFO - Epoch [49][1500/3746] lr: 7.644e-02, eta: 3 days, 9:53:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5455, loss_cls: 4.0780, loss: 4.0780 +2024-07-17 23:26:30,345 - pyskl - INFO - Epoch [49][1600/3746] lr: 7.641e-02, eta: 3 days, 9:52:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5408, loss_cls: 4.1227, loss: 4.1227 +2024-07-17 23:27:51,653 - pyskl - INFO - Epoch [49][1700/3746] lr: 7.639e-02, eta: 3 days, 9:51:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5492, loss_cls: 4.0864, loss: 4.0864 +2024-07-17 23:29:13,657 - pyskl - INFO - Epoch [49][1800/3746] lr: 7.637e-02, eta: 3 days, 9:49:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5312, loss_cls: 4.1342, loss: 4.1342 +2024-07-17 23:30:35,562 - pyskl - INFO - Epoch [49][1900/3746] lr: 7.634e-02, eta: 3 days, 9:48:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5336, loss_cls: 4.1289, loss: 4.1289 +2024-07-17 23:31:57,619 - pyskl - INFO - Epoch [49][2000/3746] lr: 7.632e-02, eta: 3 days, 9:47:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5406, loss_cls: 4.1273, loss: 4.1273 +2024-07-17 23:33:19,129 - pyskl - INFO - Epoch [49][2100/3746] lr: 7.629e-02, eta: 3 days, 9:46:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5381, loss_cls: 4.1167, loss: 4.1167 +2024-07-17 23:34:41,425 - pyskl - INFO - Epoch [49][2200/3746] lr: 7.627e-02, eta: 3 days, 9:45:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5323, loss_cls: 4.1709, loss: 4.1709 +2024-07-17 23:36:03,901 - pyskl - INFO - Epoch [49][2300/3746] lr: 7.625e-02, eta: 3 days, 9:44:19, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5363, loss_cls: 4.1200, loss: 4.1200 +2024-07-17 23:37:25,720 - pyskl - INFO - Epoch [49][2400/3746] lr: 7.622e-02, eta: 3 days, 9:43:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5359, loss_cls: 4.1160, loss: 4.1160 +2024-07-17 23:38:47,665 - pyskl - INFO - Epoch [49][2500/3746] lr: 7.620e-02, eta: 3 days, 9:42:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5309, loss_cls: 4.1348, loss: 4.1348 +2024-07-17 23:40:09,195 - pyskl - INFO - Epoch [49][2600/3746] lr: 7.618e-02, eta: 3 days, 9:40:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5353, loss_cls: 4.1050, loss: 4.1050 +2024-07-17 23:41:30,845 - pyskl - INFO - Epoch [49][2700/3746] lr: 7.615e-02, eta: 3 days, 9:39:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5356, loss_cls: 4.1645, loss: 4.1645 +2024-07-17 23:42:52,657 - pyskl - INFO - Epoch [49][2800/3746] lr: 7.613e-02, eta: 3 days, 9:38:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5319, loss_cls: 4.1522, loss: 4.1522 +2024-07-17 23:44:14,389 - pyskl - INFO - Epoch [49][2900/3746] lr: 7.610e-02, eta: 3 days, 9:37:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5402, loss_cls: 4.1161, loss: 4.1161 +2024-07-17 23:45:35,846 - pyskl - INFO - Epoch [49][3000/3746] lr: 7.608e-02, eta: 3 days, 9:36:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5389, loss_cls: 4.0802, loss: 4.0802 +2024-07-17 23:46:57,374 - pyskl - INFO - Epoch [49][3100/3746] lr: 7.606e-02, eta: 3 days, 9:35:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5413, loss_cls: 4.0940, loss: 4.0940 +2024-07-17 23:48:19,261 - pyskl - INFO - Epoch [49][3200/3746] lr: 7.603e-02, eta: 3 days, 9:34:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5391, loss_cls: 4.1085, loss: 4.1085 +2024-07-17 23:49:41,265 - pyskl - INFO - Epoch [49][3300/3746] lr: 7.601e-02, eta: 3 days, 9:32:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5302, loss_cls: 4.1515, loss: 4.1515 +2024-07-17 23:51:02,994 - pyskl - INFO - Epoch [49][3400/3746] lr: 7.598e-02, eta: 3 days, 9:31:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5380, loss_cls: 4.1296, loss: 4.1296 +2024-07-17 23:52:24,786 - pyskl - INFO - Epoch [49][3500/3746] lr: 7.596e-02, eta: 3 days, 9:30:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5359, loss_cls: 4.1265, loss: 4.1265 +2024-07-17 23:53:46,359 - pyskl - INFO - Epoch [49][3600/3746] lr: 7.594e-02, eta: 3 days, 9:29:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5378, loss_cls: 4.1283, loss: 4.1283 +2024-07-17 23:55:08,309 - pyskl - INFO - Epoch [49][3700/3746] lr: 7.591e-02, eta: 3 days, 9:28:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5337, loss_cls: 4.1439, loss: 4.1439 +2024-07-17 23:55:47,440 - pyskl - INFO - Saving checkpoint at 49 epochs +2024-07-17 23:57:38,241 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 23:57:38,905 - pyskl - INFO - +top1_acc 0.2230 +top5_acc 0.4688 +2024-07-17 23:57:38,905 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 23:57:38,943 - pyskl - INFO - +mean_acc 0.2226 +2024-07-17 23:57:38,955 - pyskl - INFO - Epoch(val) [49][309] top1_acc: 0.2230, top5_acc: 0.4688, mean_class_accuracy: 0.2226 +2024-07-18 00:01:27,388 - pyskl - INFO - Epoch [50][100/3746] lr: 7.588e-02, eta: 3 days, 9:30:22, time: 2.284, data_time: 1.300, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5369, loss_cls: 4.1108, loss: 4.1108 +2024-07-18 00:02:49,161 - pyskl - INFO - Epoch [50][200/3746] lr: 7.585e-02, eta: 3 days, 9:29:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5514, loss_cls: 4.0672, loss: 4.0672 +2024-07-18 00:04:11,894 - pyskl - INFO - Epoch [50][300/3746] lr: 7.583e-02, eta: 3 days, 9:28:06, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5389, loss_cls: 4.0959, loss: 4.0959 +2024-07-18 00:05:34,045 - pyskl - INFO - Epoch [50][400/3746] lr: 7.581e-02, eta: 3 days, 9:26:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5497, loss_cls: 4.0453, loss: 4.0453 +2024-07-18 00:06:55,477 - pyskl - INFO - Epoch [50][500/3746] lr: 7.578e-02, eta: 3 days, 9:25:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5423, loss_cls: 4.1122, loss: 4.1122 +2024-07-18 00:08:17,441 - pyskl - INFO - Epoch [50][600/3746] lr: 7.576e-02, eta: 3 days, 9:24:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5472, loss_cls: 4.0852, loss: 4.0852 +2024-07-18 00:09:39,369 - pyskl - INFO - Epoch [50][700/3746] lr: 7.573e-02, eta: 3 days, 9:23:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5500, loss_cls: 4.0512, loss: 4.0512 +2024-07-18 00:11:01,167 - pyskl - INFO - Epoch [50][800/3746] lr: 7.571e-02, eta: 3 days, 9:22:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5398, loss_cls: 4.0945, loss: 4.0945 +2024-07-18 00:12:23,653 - pyskl - INFO - Epoch [50][900/3746] lr: 7.569e-02, eta: 3 days, 9:21:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5337, loss_cls: 4.1496, loss: 4.1496 +2024-07-18 00:13:45,795 - pyskl - INFO - Epoch [50][1000/3746] lr: 7.566e-02, eta: 3 days, 9:20:06, time: 0.821, data_time: 0.001, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5447, loss_cls: 4.0644, loss: 4.0644 +2024-07-18 00:15:07,177 - pyskl - INFO - Epoch [50][1100/3746] lr: 7.564e-02, eta: 3 days, 9:18:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5314, loss_cls: 4.1452, loss: 4.1452 +2024-07-18 00:16:29,204 - pyskl - INFO - Epoch [50][1200/3746] lr: 7.561e-02, eta: 3 days, 9:17:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5430, loss_cls: 4.0836, loss: 4.0836 +2024-07-18 00:17:51,299 - pyskl - INFO - Epoch [50][1300/3746] lr: 7.559e-02, eta: 3 days, 9:16:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5431, loss_cls: 4.0927, loss: 4.0927 +2024-07-18 00:19:13,145 - pyskl - INFO - Epoch [50][1400/3746] lr: 7.557e-02, eta: 3 days, 9:15:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5405, loss_cls: 4.1111, loss: 4.1111 +2024-07-18 00:20:35,160 - pyskl - INFO - Epoch [50][1500/3746] lr: 7.554e-02, eta: 3 days, 9:14:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5417, loss_cls: 4.0703, loss: 4.0703 +2024-07-18 00:21:56,298 - pyskl - INFO - Epoch [50][1600/3746] lr: 7.552e-02, eta: 3 days, 9:13:11, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5353, loss_cls: 4.1209, loss: 4.1209 +2024-07-18 00:23:17,918 - pyskl - INFO - Epoch [50][1700/3746] lr: 7.549e-02, eta: 3 days, 9:12:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5458, loss_cls: 4.0709, loss: 4.0709 +2024-07-18 00:24:39,863 - pyskl - INFO - Epoch [50][1800/3746] lr: 7.547e-02, eta: 3 days, 9:10:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5305, loss_cls: 4.1450, loss: 4.1450 +2024-07-18 00:26:01,507 - pyskl - INFO - Epoch [50][1900/3746] lr: 7.545e-02, eta: 3 days, 9:09:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5422, loss_cls: 4.0745, loss: 4.0745 +2024-07-18 00:27:23,663 - pyskl - INFO - Epoch [50][2000/3746] lr: 7.542e-02, eta: 3 days, 9:08:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5397, loss_cls: 4.1044, loss: 4.1044 +2024-07-18 00:28:45,502 - pyskl - INFO - Epoch [50][2100/3746] lr: 7.540e-02, eta: 3 days, 9:07:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5325, loss_cls: 4.1526, loss: 4.1526 +2024-07-18 00:30:07,775 - pyskl - INFO - Epoch [50][2200/3746] lr: 7.537e-02, eta: 3 days, 9:06:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5392, loss_cls: 4.1074, loss: 4.1074 +2024-07-18 00:31:30,271 - pyskl - INFO - Epoch [50][2300/3746] lr: 7.535e-02, eta: 3 days, 9:05:10, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5498, loss_cls: 4.0476, loss: 4.0476 +2024-07-18 00:32:51,955 - pyskl - INFO - Epoch [50][2400/3746] lr: 7.533e-02, eta: 3 days, 9:04:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5394, loss_cls: 4.1101, loss: 4.1101 +2024-07-18 00:34:13,777 - pyskl - INFO - Epoch [50][2500/3746] lr: 7.530e-02, eta: 3 days, 9:02:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5309, loss_cls: 4.1713, loss: 4.1713 +2024-07-18 00:35:35,996 - pyskl - INFO - Epoch [50][2600/3746] lr: 7.528e-02, eta: 3 days, 9:01:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5380, loss_cls: 4.1416, loss: 4.1416 +2024-07-18 00:36:57,972 - pyskl - INFO - Epoch [50][2700/3746] lr: 7.525e-02, eta: 3 days, 9:00:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5372, loss_cls: 4.1414, loss: 4.1414 +2024-07-18 00:38:19,625 - pyskl - INFO - Epoch [50][2800/3746] lr: 7.523e-02, eta: 3 days, 8:59:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5395, loss_cls: 4.0882, loss: 4.0882 +2024-07-18 00:39:41,223 - pyskl - INFO - Epoch [50][2900/3746] lr: 7.520e-02, eta: 3 days, 8:58:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5394, loss_cls: 4.0954, loss: 4.0954 +2024-07-18 00:41:03,008 - pyskl - INFO - Epoch [50][3000/3746] lr: 7.518e-02, eta: 3 days, 8:57:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5441, loss_cls: 4.1134, loss: 4.1134 +2024-07-18 00:42:24,766 - pyskl - INFO - Epoch [50][3100/3746] lr: 7.516e-02, eta: 3 days, 8:55:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5369, loss_cls: 4.1066, loss: 4.1066 +2024-07-18 00:43:45,962 - pyskl - INFO - Epoch [50][3200/3746] lr: 7.513e-02, eta: 3 days, 8:54:46, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5428, loss_cls: 4.0969, loss: 4.0969 +2024-07-18 00:45:07,151 - pyskl - INFO - Epoch [50][3300/3746] lr: 7.511e-02, eta: 3 days, 8:53:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5350, loss_cls: 4.1485, loss: 4.1485 +2024-07-18 00:46:29,065 - pyskl - INFO - Epoch [50][3400/3746] lr: 7.508e-02, eta: 3 days, 8:52:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5381, loss_cls: 4.1022, loss: 4.1022 +2024-07-18 00:47:50,969 - pyskl - INFO - Epoch [50][3500/3746] lr: 7.506e-02, eta: 3 days, 8:51:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5370, loss_cls: 4.0938, loss: 4.0938 +2024-07-18 00:49:12,440 - pyskl - INFO - Epoch [50][3600/3746] lr: 7.504e-02, eta: 3 days, 8:50:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5333, loss_cls: 4.1413, loss: 4.1413 +2024-07-18 00:50:33,669 - pyskl - INFO - Epoch [50][3700/3746] lr: 7.501e-02, eta: 3 days, 8:48:57, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5470, loss_cls: 4.0770, loss: 4.0770 +2024-07-18 00:51:13,783 - pyskl - INFO - Saving checkpoint at 50 epochs +2024-07-18 00:53:04,643 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 00:53:05,321 - pyskl - INFO - +top1_acc 0.2167 +top5_acc 0.4526 +2024-07-18 00:53:05,322 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 00:53:05,366 - pyskl - INFO - +mean_acc 0.2165 +2024-07-18 00:53:05,377 - pyskl - INFO - Epoch(val) [50][309] top1_acc: 0.2167, top5_acc: 0.4526, mean_class_accuracy: 0.2165 +2024-07-18 00:56:53,666 - pyskl - INFO - Epoch [51][100/3746] lr: 7.498e-02, eta: 3 days, 8:50:53, time: 2.283, data_time: 1.300, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5572, loss_cls: 4.0289, loss: 4.0289 +2024-07-18 00:58:15,895 - pyskl - INFO - Epoch [51][200/3746] lr: 7.495e-02, eta: 3 days, 8:49:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5544, loss_cls: 3.9857, loss: 3.9857 +2024-07-18 00:59:37,845 - pyskl - INFO - Epoch [51][300/3746] lr: 7.493e-02, eta: 3 days, 8:48:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5527, loss_cls: 4.0571, loss: 4.0571 +2024-07-18 01:00:59,241 - pyskl - INFO - Epoch [51][400/3746] lr: 7.490e-02, eta: 3 days, 8:47:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5425, loss_cls: 4.0897, loss: 4.0897 +2024-07-18 01:02:20,824 - pyskl - INFO - Epoch [51][500/3746] lr: 7.488e-02, eta: 3 days, 8:46:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5491, loss_cls: 4.0606, loss: 4.0606 +2024-07-18 01:03:42,441 - pyskl - INFO - Epoch [51][600/3746] lr: 7.485e-02, eta: 3 days, 8:45:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5444, loss_cls: 4.0890, loss: 4.0890 +2024-07-18 01:05:04,312 - pyskl - INFO - Epoch [51][700/3746] lr: 7.483e-02, eta: 3 days, 8:43:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5530, loss_cls: 4.0186, loss: 4.0186 +2024-07-18 01:06:25,997 - pyskl - INFO - Epoch [51][800/3746] lr: 7.481e-02, eta: 3 days, 8:42:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5303, loss_cls: 4.1447, loss: 4.1447 +2024-07-18 01:07:47,823 - pyskl - INFO - Epoch [51][900/3746] lr: 7.478e-02, eta: 3 days, 8:41:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5273, loss_cls: 4.1988, loss: 4.1988 +2024-07-18 01:09:10,081 - pyskl - INFO - Epoch [51][1000/3746] lr: 7.476e-02, eta: 3 days, 8:40:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5527, loss_cls: 4.0301, loss: 4.0301 +2024-07-18 01:10:33,023 - pyskl - INFO - Epoch [51][1100/3746] lr: 7.473e-02, eta: 3 days, 8:39:19, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5414, loss_cls: 4.1042, loss: 4.1042 +2024-07-18 01:11:55,407 - pyskl - INFO - Epoch [51][1200/3746] lr: 7.471e-02, eta: 3 days, 8:38:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5483, loss_cls: 4.0722, loss: 4.0722 +2024-07-18 01:13:17,815 - pyskl - INFO - Epoch [51][1300/3746] lr: 7.468e-02, eta: 3 days, 8:37:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5448, loss_cls: 4.1001, loss: 4.1001 +2024-07-18 01:14:40,403 - pyskl - INFO - Epoch [51][1400/3746] lr: 7.466e-02, eta: 3 days, 8:35:54, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5352, loss_cls: 4.1188, loss: 4.1188 +2024-07-18 01:16:03,370 - pyskl - INFO - Epoch [51][1500/3746] lr: 7.464e-02, eta: 3 days, 8:34:47, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5341, loss_cls: 4.1061, loss: 4.1061 +2024-07-18 01:17:24,998 - pyskl - INFO - Epoch [51][1600/3746] lr: 7.461e-02, eta: 3 days, 8:33:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5405, loss_cls: 4.0864, loss: 4.0864 +2024-07-18 01:18:46,630 - pyskl - INFO - Epoch [51][1700/3746] lr: 7.459e-02, eta: 3 days, 8:32:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5267, loss_cls: 4.1202, loss: 4.1202 +2024-07-18 01:20:08,732 - pyskl - INFO - Epoch [51][1800/3746] lr: 7.456e-02, eta: 3 days, 8:31:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5358, loss_cls: 4.1330, loss: 4.1330 +2024-07-18 01:21:30,640 - pyskl - INFO - Epoch [51][1900/3746] lr: 7.454e-02, eta: 3 days, 8:30:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5347, loss_cls: 4.1074, loss: 4.1074 +2024-07-18 01:22:52,231 - pyskl - INFO - Epoch [51][2000/3746] lr: 7.451e-02, eta: 3 days, 8:28:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5344, loss_cls: 4.1055, loss: 4.1055 +2024-07-18 01:24:14,373 - pyskl - INFO - Epoch [51][2100/3746] lr: 7.449e-02, eta: 3 days, 8:27:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5373, loss_cls: 4.1025, loss: 4.1025 +2024-07-18 01:25:37,194 - pyskl - INFO - Epoch [51][2200/3746] lr: 7.447e-02, eta: 3 days, 8:26:40, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5508, loss_cls: 4.0531, loss: 4.0531 +2024-07-18 01:26:59,667 - pyskl - INFO - Epoch [51][2300/3746] lr: 7.444e-02, eta: 3 days, 8:25:32, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5408, loss_cls: 4.0987, loss: 4.0987 +2024-07-18 01:28:21,784 - pyskl - INFO - Epoch [51][2400/3746] lr: 7.442e-02, eta: 3 days, 8:24:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5381, loss_cls: 4.0989, loss: 4.0989 +2024-07-18 01:29:44,219 - pyskl - INFO - Epoch [51][2500/3746] lr: 7.439e-02, eta: 3 days, 8:23:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5334, loss_cls: 4.1246, loss: 4.1246 +2024-07-18 01:31:06,796 - pyskl - INFO - Epoch [51][2600/3746] lr: 7.437e-02, eta: 3 days, 8:22:06, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5486, loss_cls: 4.0747, loss: 4.0747 +2024-07-18 01:32:28,548 - pyskl - INFO - Epoch [51][2700/3746] lr: 7.434e-02, eta: 3 days, 8:20:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5464, loss_cls: 4.0831, loss: 4.0831 +2024-07-18 01:33:49,681 - pyskl - INFO - Epoch [51][2800/3746] lr: 7.432e-02, eta: 3 days, 8:19:44, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5428, loss_cls: 4.1143, loss: 4.1143 +2024-07-18 01:35:10,969 - pyskl - INFO - Epoch [51][2900/3746] lr: 7.429e-02, eta: 3 days, 8:18:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5355, loss_cls: 4.1574, loss: 4.1574 +2024-07-18 01:36:32,588 - pyskl - INFO - Epoch [51][3000/3746] lr: 7.427e-02, eta: 3 days, 8:17:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5380, loss_cls: 4.0984, loss: 4.0984 +2024-07-18 01:37:54,181 - pyskl - INFO - Epoch [51][3100/3746] lr: 7.425e-02, eta: 3 days, 8:16:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5312, loss_cls: 4.1347, loss: 4.1347 +2024-07-18 01:39:15,881 - pyskl - INFO - Epoch [51][3200/3746] lr: 7.422e-02, eta: 3 days, 8:15:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5413, loss_cls: 4.1279, loss: 4.1279 +2024-07-18 01:40:37,290 - pyskl - INFO - Epoch [51][3300/3746] lr: 7.420e-02, eta: 3 days, 8:13:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5433, loss_cls: 4.0945, loss: 4.0945 +2024-07-18 01:41:58,700 - pyskl - INFO - Epoch [51][3400/3746] lr: 7.417e-02, eta: 3 days, 8:12:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5472, loss_cls: 4.0783, loss: 4.0783 +2024-07-18 01:43:20,405 - pyskl - INFO - Epoch [51][3500/3746] lr: 7.415e-02, eta: 3 days, 8:11:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5472, loss_cls: 4.0530, loss: 4.0530 +2024-07-18 01:44:41,888 - pyskl - INFO - Epoch [51][3600/3746] lr: 7.412e-02, eta: 3 days, 8:10:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5256, loss_cls: 4.1637, loss: 4.1637 +2024-07-18 01:46:03,236 - pyskl - INFO - Epoch [51][3700/3746] lr: 7.410e-02, eta: 3 days, 8:09:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5302, loss_cls: 4.1322, loss: 4.1322 +2024-07-18 01:46:43,016 - pyskl - INFO - Saving checkpoint at 51 epochs +2024-07-18 01:48:33,835 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 01:48:34,502 - pyskl - INFO - +top1_acc 0.2209 +top5_acc 0.4530 +2024-07-18 01:48:34,502 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 01:48:34,543 - pyskl - INFO - +mean_acc 0.2207 +2024-07-18 01:48:34,555 - pyskl - INFO - Epoch(val) [51][309] top1_acc: 0.2209, top5_acc: 0.4530, mean_class_accuracy: 0.2207 +2024-07-18 01:52:22,228 - pyskl - INFO - Epoch [52][100/3746] lr: 7.406e-02, eta: 3 days, 8:10:57, time: 2.277, data_time: 1.292, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5531, loss_cls: 4.0364, loss: 4.0364 +2024-07-18 01:53:44,864 - pyskl - INFO - Epoch [52][200/3746] lr: 7.404e-02, eta: 3 days, 8:09:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5487, loss_cls: 4.0720, loss: 4.0720 +2024-07-18 01:55:06,417 - pyskl - INFO - Epoch [52][300/3746] lr: 7.401e-02, eta: 3 days, 8:08:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5536, loss_cls: 4.0591, loss: 4.0591 +2024-07-18 01:56:28,183 - pyskl - INFO - Epoch [52][400/3746] lr: 7.399e-02, eta: 3 days, 8:07:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5331, loss_cls: 4.1289, loss: 4.1289 +2024-07-18 01:57:49,698 - pyskl - INFO - Epoch [52][500/3746] lr: 7.397e-02, eta: 3 days, 8:06:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5298, loss_cls: 4.1221, loss: 4.1221 +2024-07-18 01:59:11,215 - pyskl - INFO - Epoch [52][600/3746] lr: 7.394e-02, eta: 3 days, 8:05:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5391, loss_cls: 4.0717, loss: 4.0717 +2024-07-18 02:00:32,753 - pyskl - INFO - Epoch [52][700/3746] lr: 7.392e-02, eta: 3 days, 8:03:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5433, loss_cls: 4.1286, loss: 4.1286 +2024-07-18 02:01:53,787 - pyskl - INFO - Epoch [52][800/3746] lr: 7.389e-02, eta: 3 days, 8:02:43, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5548, loss_cls: 4.0241, loss: 4.0241 +2024-07-18 02:03:15,938 - pyskl - INFO - Epoch [52][900/3746] lr: 7.387e-02, eta: 3 days, 8:01:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5502, loss_cls: 4.0552, loss: 4.0552 +2024-07-18 02:04:37,699 - pyskl - INFO - Epoch [52][1000/3746] lr: 7.384e-02, eta: 3 days, 8:00:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5514, loss_cls: 4.0670, loss: 4.0670 +2024-07-18 02:05:59,219 - pyskl - INFO - Epoch [52][1100/3746] lr: 7.382e-02, eta: 3 days, 7:59:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5437, loss_cls: 4.0930, loss: 4.0930 +2024-07-18 02:07:21,147 - pyskl - INFO - Epoch [52][1200/3746] lr: 7.379e-02, eta: 3 days, 7:58:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5391, loss_cls: 4.1186, loss: 4.1186 +2024-07-18 02:08:42,827 - pyskl - INFO - Epoch [52][1300/3746] lr: 7.377e-02, eta: 3 days, 7:56:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5430, loss_cls: 4.1136, loss: 4.1136 +2024-07-18 02:10:04,757 - pyskl - INFO - Epoch [52][1400/3746] lr: 7.374e-02, eta: 3 days, 7:55:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5420, loss_cls: 4.0868, loss: 4.0868 +2024-07-18 02:11:26,643 - pyskl - INFO - Epoch [52][1500/3746] lr: 7.372e-02, eta: 3 days, 7:54:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5431, loss_cls: 4.1020, loss: 4.1020 +2024-07-18 02:12:48,473 - pyskl - INFO - Epoch [52][1600/3746] lr: 7.370e-02, eta: 3 days, 7:53:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5430, loss_cls: 4.0763, loss: 4.0763 +2024-07-18 02:14:10,295 - pyskl - INFO - Epoch [52][1700/3746] lr: 7.367e-02, eta: 3 days, 7:52:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5430, loss_cls: 4.0879, loss: 4.0879 +2024-07-18 02:15:31,926 - pyskl - INFO - Epoch [52][1800/3746] lr: 7.365e-02, eta: 3 days, 7:51:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5609, loss_cls: 4.0233, loss: 4.0233 +2024-07-18 02:16:53,424 - pyskl - INFO - Epoch [52][1900/3746] lr: 7.362e-02, eta: 3 days, 7:49:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5480, loss_cls: 4.0804, loss: 4.0804 +2024-07-18 02:18:15,066 - pyskl - INFO - Epoch [52][2000/3746] lr: 7.360e-02, eta: 3 days, 7:48:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5430, loss_cls: 4.0690, loss: 4.0690 +2024-07-18 02:19:37,303 - pyskl - INFO - Epoch [52][2100/3746] lr: 7.357e-02, eta: 3 days, 7:47:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5383, loss_cls: 4.1131, loss: 4.1131 +2024-07-18 02:20:59,686 - pyskl - INFO - Epoch [52][2200/3746] lr: 7.355e-02, eta: 3 days, 7:46:19, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5517, loss_cls: 4.0723, loss: 4.0723 +2024-07-18 02:22:21,728 - pyskl - INFO - Epoch [52][2300/3746] lr: 7.352e-02, eta: 3 days, 7:45:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5434, loss_cls: 4.0840, loss: 4.0840 +2024-07-18 02:23:43,926 - pyskl - INFO - Epoch [52][2400/3746] lr: 7.350e-02, eta: 3 days, 7:44:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5417, loss_cls: 4.1082, loss: 4.1082 +2024-07-18 02:25:05,681 - pyskl - INFO - Epoch [52][2500/3746] lr: 7.347e-02, eta: 3 days, 7:42:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5336, loss_cls: 4.1456, loss: 4.1456 +2024-07-18 02:26:27,771 - pyskl - INFO - Epoch [52][2600/3746] lr: 7.345e-02, eta: 3 days, 7:41:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5383, loss_cls: 4.1075, loss: 4.1075 +2024-07-18 02:27:49,959 - pyskl - INFO - Epoch [52][2700/3746] lr: 7.342e-02, eta: 3 days, 7:40:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5391, loss_cls: 4.0874, loss: 4.0874 +2024-07-18 02:29:11,437 - pyskl - INFO - Epoch [52][2800/3746] lr: 7.340e-02, eta: 3 days, 7:39:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5380, loss_cls: 4.0916, loss: 4.0916 +2024-07-18 02:30:32,637 - pyskl - INFO - Epoch [52][2900/3746] lr: 7.337e-02, eta: 3 days, 7:38:07, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5342, loss_cls: 4.1079, loss: 4.1079 +2024-07-18 02:31:54,110 - pyskl - INFO - Epoch [52][3000/3746] lr: 7.335e-02, eta: 3 days, 7:36:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5416, loss_cls: 4.1193, loss: 4.1193 +2024-07-18 02:33:15,523 - pyskl - INFO - Epoch [52][3100/3746] lr: 7.332e-02, eta: 3 days, 7:35:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5442, loss_cls: 4.1086, loss: 4.1086 +2024-07-18 02:34:37,131 - pyskl - INFO - Epoch [52][3200/3746] lr: 7.330e-02, eta: 3 days, 7:34:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5536, loss_cls: 4.0713, loss: 4.0713 +2024-07-18 02:35:58,805 - pyskl - INFO - Epoch [52][3300/3746] lr: 7.328e-02, eta: 3 days, 7:33:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5441, loss_cls: 4.1018, loss: 4.1018 +2024-07-18 02:37:20,717 - pyskl - INFO - Epoch [52][3400/3746] lr: 7.325e-02, eta: 3 days, 7:32:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5427, loss_cls: 4.0853, loss: 4.0853 +2024-07-18 02:38:42,614 - pyskl - INFO - Epoch [52][3500/3746] lr: 7.323e-02, eta: 3 days, 7:31:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5387, loss_cls: 4.1082, loss: 4.1082 +2024-07-18 02:40:04,261 - pyskl - INFO - Epoch [52][3600/3746] lr: 7.320e-02, eta: 3 days, 7:29:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5366, loss_cls: 4.1194, loss: 4.1194 +2024-07-18 02:41:25,663 - pyskl - INFO - Epoch [52][3700/3746] lr: 7.318e-02, eta: 3 days, 7:28:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5413, loss_cls: 4.0946, loss: 4.0946 +2024-07-18 02:42:04,851 - pyskl - INFO - Saving checkpoint at 52 epochs +2024-07-18 02:43:55,720 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 02:43:56,380 - pyskl - INFO - +top1_acc 0.2120 +top5_acc 0.4510 +2024-07-18 02:43:56,380 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 02:43:56,419 - pyskl - INFO - +mean_acc 0.2116 +2024-07-18 02:43:56,430 - pyskl - INFO - Epoch(val) [52][309] top1_acc: 0.2120, top5_acc: 0.4510, mean_class_accuracy: 0.2116 +2024-07-18 02:47:46,870 - pyskl - INFO - Epoch [53][100/3746] lr: 7.314e-02, eta: 3 days, 7:30:25, time: 2.304, data_time: 1.317, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5587, loss_cls: 4.0339, loss: 4.0339 +2024-07-18 02:49:08,710 - pyskl - INFO - Epoch [53][200/3746] lr: 7.312e-02, eta: 3 days, 7:29:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5586, loss_cls: 4.0195, loss: 4.0195 +2024-07-18 02:50:30,233 - pyskl - INFO - Epoch [53][300/3746] lr: 7.309e-02, eta: 3 days, 7:28:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5467, loss_cls: 4.0557, loss: 4.0557 +2024-07-18 02:51:51,773 - pyskl - INFO - Epoch [53][400/3746] lr: 7.307e-02, eta: 3 days, 7:26:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5411, loss_cls: 4.0664, loss: 4.0664 +2024-07-18 02:53:13,608 - pyskl - INFO - Epoch [53][500/3746] lr: 7.304e-02, eta: 3 days, 7:25:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5456, loss_cls: 4.0571, loss: 4.0571 +2024-07-18 02:54:35,222 - pyskl - INFO - Epoch [53][600/3746] lr: 7.302e-02, eta: 3 days, 7:24:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5519, loss_cls: 4.0452, loss: 4.0452 +2024-07-18 02:55:57,297 - pyskl - INFO - Epoch [53][700/3746] lr: 7.299e-02, eta: 3 days, 7:23:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5436, loss_cls: 4.0523, loss: 4.0523 +2024-07-18 02:57:18,353 - pyskl - INFO - Epoch [53][800/3746] lr: 7.297e-02, eta: 3 days, 7:22:07, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5480, loss_cls: 4.0885, loss: 4.0885 +2024-07-18 02:58:40,241 - pyskl - INFO - Epoch [53][900/3746] lr: 7.294e-02, eta: 3 days, 7:20:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5463, loss_cls: 4.0939, loss: 4.0939 +2024-07-18 03:00:01,457 - pyskl - INFO - Epoch [53][1000/3746] lr: 7.292e-02, eta: 3 days, 7:19:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5513, loss_cls: 4.0547, loss: 4.0547 +2024-07-18 03:01:23,713 - pyskl - INFO - Epoch [53][1100/3746] lr: 7.289e-02, eta: 3 days, 7:18:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5520, loss_cls: 4.0531, loss: 4.0531 +2024-07-18 03:02:46,120 - pyskl - INFO - Epoch [53][1200/3746] lr: 7.287e-02, eta: 3 days, 7:17:25, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5450, loss_cls: 4.0571, loss: 4.0571 +2024-07-18 03:04:08,319 - pyskl - INFO - Epoch [53][1300/3746] lr: 7.284e-02, eta: 3 days, 7:16:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5416, loss_cls: 4.0999, loss: 4.0999 +2024-07-18 03:05:30,492 - pyskl - INFO - Epoch [53][1400/3746] lr: 7.282e-02, eta: 3 days, 7:15:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5414, loss_cls: 4.1017, loss: 4.1017 +2024-07-18 03:06:52,250 - pyskl - INFO - Epoch [53][1500/3746] lr: 7.279e-02, eta: 3 days, 7:13:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5417, loss_cls: 4.1207, loss: 4.1207 +2024-07-18 03:08:13,795 - pyskl - INFO - Epoch [53][1600/3746] lr: 7.277e-02, eta: 3 days, 7:12:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5350, loss_cls: 4.1277, loss: 4.1277 +2024-07-18 03:09:35,467 - pyskl - INFO - Epoch [53][1700/3746] lr: 7.274e-02, eta: 3 days, 7:11:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5544, loss_cls: 4.0452, loss: 4.0452 +2024-07-18 03:10:57,347 - pyskl - INFO - Epoch [53][1800/3746] lr: 7.272e-02, eta: 3 days, 7:10:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5456, loss_cls: 4.0726, loss: 4.0726 +2024-07-18 03:12:19,207 - pyskl - INFO - Epoch [53][1900/3746] lr: 7.269e-02, eta: 3 days, 7:09:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5450, loss_cls: 4.0652, loss: 4.0652 +2024-07-18 03:13:40,986 - pyskl - INFO - Epoch [53][2000/3746] lr: 7.267e-02, eta: 3 days, 7:07:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5480, loss_cls: 4.0851, loss: 4.0851 +2024-07-18 03:15:03,801 - pyskl - INFO - Epoch [53][2100/3746] lr: 7.264e-02, eta: 3 days, 7:06:48, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5514, loss_cls: 4.0607, loss: 4.0607 +2024-07-18 03:16:25,457 - pyskl - INFO - Epoch [53][2200/3746] lr: 7.262e-02, eta: 3 days, 7:05:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5366, loss_cls: 4.1087, loss: 4.1087 +2024-07-18 03:17:47,415 - pyskl - INFO - Epoch [53][2300/3746] lr: 7.259e-02, eta: 3 days, 7:04:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5459, loss_cls: 4.0795, loss: 4.0795 +2024-07-18 03:19:10,045 - pyskl - INFO - Epoch [53][2400/3746] lr: 7.257e-02, eta: 3 days, 7:03:17, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5573, loss_cls: 4.0470, loss: 4.0470 +2024-07-18 03:20:31,789 - pyskl - INFO - Epoch [53][2500/3746] lr: 7.254e-02, eta: 3 days, 7:02:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5461, loss_cls: 4.0869, loss: 4.0869 +2024-07-18 03:21:53,927 - pyskl - INFO - Epoch [53][2600/3746] lr: 7.252e-02, eta: 3 days, 7:00:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5406, loss_cls: 4.0848, loss: 4.0848 +2024-07-18 03:23:15,762 - pyskl - INFO - Epoch [53][2700/3746] lr: 7.249e-02, eta: 3 days, 6:59:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5406, loss_cls: 4.1063, loss: 4.1063 +2024-07-18 03:24:37,529 - pyskl - INFO - Epoch [53][2800/3746] lr: 7.247e-02, eta: 3 days, 6:58:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5337, loss_cls: 4.1374, loss: 4.1374 +2024-07-18 03:25:59,011 - pyskl - INFO - Epoch [53][2900/3746] lr: 7.244e-02, eta: 3 days, 6:57:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5372, loss_cls: 4.0805, loss: 4.0805 +2024-07-18 03:27:20,588 - pyskl - INFO - Epoch [53][3000/3746] lr: 7.242e-02, eta: 3 days, 6:56:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5511, loss_cls: 4.0396, loss: 4.0396 +2024-07-18 03:28:42,114 - pyskl - INFO - Epoch [53][3100/3746] lr: 7.239e-02, eta: 3 days, 6:54:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5303, loss_cls: 4.1603, loss: 4.1603 +2024-07-18 03:30:03,825 - pyskl - INFO - Epoch [53][3200/3746] lr: 7.237e-02, eta: 3 days, 6:53:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5373, loss_cls: 4.0888, loss: 4.0888 +2024-07-18 03:31:25,512 - pyskl - INFO - Epoch [53][3300/3746] lr: 7.234e-02, eta: 3 days, 6:52:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5377, loss_cls: 4.0656, loss: 4.0656 +2024-07-18 03:32:47,075 - pyskl - INFO - Epoch [53][3400/3746] lr: 7.232e-02, eta: 3 days, 6:51:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5372, loss_cls: 4.0847, loss: 4.0847 +2024-07-18 03:34:08,485 - pyskl - INFO - Epoch [53][3500/3746] lr: 7.229e-02, eta: 3 days, 6:50:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5353, loss_cls: 4.1181, loss: 4.1181 +2024-07-18 03:35:30,056 - pyskl - INFO - Epoch [53][3600/3746] lr: 7.227e-02, eta: 3 days, 6:49:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5470, loss_cls: 4.0928, loss: 4.0928 +2024-07-18 03:36:51,788 - pyskl - INFO - Epoch [53][3700/3746] lr: 7.224e-02, eta: 3 days, 6:47:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5483, loss_cls: 4.0929, loss: 4.0929 +2024-07-18 03:37:31,377 - pyskl - INFO - Saving checkpoint at 53 epochs +2024-07-18 03:39:21,921 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 03:39:22,581 - pyskl - INFO - +top1_acc 0.2129 +top5_acc 0.4464 +2024-07-18 03:39:22,581 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 03:39:22,620 - pyskl - INFO - +mean_acc 0.2128 +2024-07-18 03:39:22,631 - pyskl - INFO - Epoch(val) [53][309] top1_acc: 0.2129, top5_acc: 0.4464, mean_class_accuracy: 0.2128 +2024-07-18 03:43:10,936 - pyskl - INFO - Epoch [54][100/3746] lr: 7.221e-02, eta: 3 days, 6:49:24, time: 2.283, data_time: 1.300, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5575, loss_cls: 4.0021, loss: 4.0021 +2024-07-18 03:44:32,911 - pyskl - INFO - Epoch [54][200/3746] lr: 7.218e-02, eta: 3 days, 6:48:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5522, loss_cls: 4.0607, loss: 4.0607 +2024-07-18 03:45:54,937 - pyskl - INFO - Epoch [54][300/3746] lr: 7.216e-02, eta: 3 days, 6:47:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5456, loss_cls: 4.0716, loss: 4.0716 +2024-07-18 03:47:16,886 - pyskl - INFO - Epoch [54][400/3746] lr: 7.213e-02, eta: 3 days, 6:45:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5516, loss_cls: 4.0545, loss: 4.0545 +2024-07-18 03:48:38,416 - pyskl - INFO - Epoch [54][500/3746] lr: 7.211e-02, eta: 3 days, 6:44:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5475, loss_cls: 4.0441, loss: 4.0441 +2024-07-18 03:50:00,236 - pyskl - INFO - Epoch [54][600/3746] lr: 7.208e-02, eta: 3 days, 6:43:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5472, loss_cls: 4.0453, loss: 4.0453 +2024-07-18 03:51:21,928 - pyskl - INFO - Epoch [54][700/3746] lr: 7.206e-02, eta: 3 days, 6:42:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5491, loss_cls: 4.0620, loss: 4.0620 +2024-07-18 03:52:43,544 - pyskl - INFO - Epoch [54][800/3746] lr: 7.203e-02, eta: 3 days, 6:41:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5475, loss_cls: 4.0643, loss: 4.0643 +2024-07-18 03:54:05,381 - pyskl - INFO - Epoch [54][900/3746] lr: 7.201e-02, eta: 3 days, 6:39:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5472, loss_cls: 4.0946, loss: 4.0946 +2024-07-18 03:55:26,979 - pyskl - INFO - Epoch [54][1000/3746] lr: 7.198e-02, eta: 3 days, 6:38:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5489, loss_cls: 4.0493, loss: 4.0493 +2024-07-18 03:56:49,524 - pyskl - INFO - Epoch [54][1100/3746] lr: 7.196e-02, eta: 3 days, 6:37:31, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5383, loss_cls: 4.1098, loss: 4.1098 +2024-07-18 03:58:11,006 - pyskl - INFO - Epoch [54][1200/3746] lr: 7.193e-02, eta: 3 days, 6:36:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5455, loss_cls: 4.0221, loss: 4.0221 +2024-07-18 03:59:32,406 - pyskl - INFO - Epoch [54][1300/3746] lr: 7.191e-02, eta: 3 days, 6:35:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5458, loss_cls: 4.0895, loss: 4.0895 +2024-07-18 04:00:54,569 - pyskl - INFO - Epoch [54][1400/3746] lr: 7.188e-02, eta: 3 days, 6:33:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5433, loss_cls: 4.0806, loss: 4.0806 +2024-07-18 04:02:16,602 - pyskl - INFO - Epoch [54][1500/3746] lr: 7.186e-02, eta: 3 days, 6:32:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5370, loss_cls: 4.1226, loss: 4.1226 +2024-07-18 04:03:37,931 - pyskl - INFO - Epoch [54][1600/3746] lr: 7.183e-02, eta: 3 days, 6:31:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5484, loss_cls: 4.0901, loss: 4.0901 +2024-07-18 04:05:00,024 - pyskl - INFO - Epoch [54][1700/3746] lr: 7.181e-02, eta: 3 days, 6:30:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5445, loss_cls: 4.0784, loss: 4.0784 +2024-07-18 04:06:21,687 - pyskl - INFO - Epoch [54][1800/3746] lr: 7.178e-02, eta: 3 days, 6:29:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5414, loss_cls: 4.0879, loss: 4.0879 +2024-07-18 04:07:42,938 - pyskl - INFO - Epoch [54][1900/3746] lr: 7.176e-02, eta: 3 days, 6:27:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5531, loss_cls: 4.0569, loss: 4.0569 +2024-07-18 04:09:04,234 - pyskl - INFO - Epoch [54][2000/3746] lr: 7.173e-02, eta: 3 days, 6:26:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5383, loss_cls: 4.1021, loss: 4.1021 +2024-07-18 04:10:26,328 - pyskl - INFO - Epoch [54][2100/3746] lr: 7.170e-02, eta: 3 days, 6:25:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5445, loss_cls: 4.0851, loss: 4.0851 +2024-07-18 04:11:47,909 - pyskl - INFO - Epoch [54][2200/3746] lr: 7.168e-02, eta: 3 days, 6:24:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5459, loss_cls: 4.0915, loss: 4.0915 +2024-07-18 04:13:09,766 - pyskl - INFO - Epoch [54][2300/3746] lr: 7.165e-02, eta: 3 days, 6:23:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5427, loss_cls: 4.0940, loss: 4.0940 +2024-07-18 04:14:32,383 - pyskl - INFO - Epoch [54][2400/3746] lr: 7.163e-02, eta: 3 days, 6:22:00, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5441, loss_cls: 4.0653, loss: 4.0653 +2024-07-18 04:15:53,797 - pyskl - INFO - Epoch [54][2500/3746] lr: 7.160e-02, eta: 3 days, 6:20:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5456, loss_cls: 4.0804, loss: 4.0804 +2024-07-18 04:17:16,276 - pyskl - INFO - Epoch [54][2600/3746] lr: 7.158e-02, eta: 3 days, 6:19:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5486, loss_cls: 4.0307, loss: 4.0307 +2024-07-18 04:18:37,936 - pyskl - INFO - Epoch [54][2700/3746] lr: 7.155e-02, eta: 3 days, 6:18:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5395, loss_cls: 4.1131, loss: 4.1131 +2024-07-18 04:19:59,612 - pyskl - INFO - Epoch [54][2800/3746] lr: 7.153e-02, eta: 3 days, 6:17:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5409, loss_cls: 4.0894, loss: 4.0894 +2024-07-18 04:21:21,174 - pyskl - INFO - Epoch [54][2900/3746] lr: 7.150e-02, eta: 3 days, 6:16:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5408, loss_cls: 4.0838, loss: 4.0838 +2024-07-18 04:22:42,886 - pyskl - INFO - Epoch [54][3000/3746] lr: 7.148e-02, eta: 3 days, 6:14:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5453, loss_cls: 4.0384, loss: 4.0384 +2024-07-18 04:24:04,360 - pyskl - INFO - Epoch [54][3100/3746] lr: 7.145e-02, eta: 3 days, 6:13:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5436, loss_cls: 4.0901, loss: 4.0901 +2024-07-18 04:25:25,900 - pyskl - INFO - Epoch [54][3200/3746] lr: 7.143e-02, eta: 3 days, 6:12:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5498, loss_cls: 4.0493, loss: 4.0493 +2024-07-18 04:26:47,559 - pyskl - INFO - Epoch [54][3300/3746] lr: 7.140e-02, eta: 3 days, 6:11:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5394, loss_cls: 4.1017, loss: 4.1017 +2024-07-18 04:28:09,423 - pyskl - INFO - Epoch [54][3400/3746] lr: 7.138e-02, eta: 3 days, 6:10:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5484, loss_cls: 4.0862, loss: 4.0862 +2024-07-18 04:29:30,884 - pyskl - INFO - Epoch [54][3500/3746] lr: 7.135e-02, eta: 3 days, 6:08:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5339, loss_cls: 4.1126, loss: 4.1126 +2024-07-18 04:30:53,142 - pyskl - INFO - Epoch [54][3600/3746] lr: 7.133e-02, eta: 3 days, 6:07:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5380, loss_cls: 4.1036, loss: 4.1036 +2024-07-18 04:32:14,653 - pyskl - INFO - Epoch [54][3700/3746] lr: 7.130e-02, eta: 3 days, 6:06:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5442, loss_cls: 4.0923, loss: 4.0923 +2024-07-18 04:32:54,125 - pyskl - INFO - Saving checkpoint at 54 epochs +2024-07-18 04:34:44,978 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 04:34:45,637 - pyskl - INFO - +top1_acc 0.2234 +top5_acc 0.4690 +2024-07-18 04:34:45,637 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 04:34:45,676 - pyskl - INFO - +mean_acc 0.2230 +2024-07-18 04:34:45,688 - pyskl - INFO - Epoch(val) [54][309] top1_acc: 0.2234, top5_acc: 0.4690, mean_class_accuracy: 0.2230 +2024-07-18 04:38:34,953 - pyskl - INFO - Epoch [55][100/3746] lr: 7.126e-02, eta: 3 days, 6:07:56, time: 2.293, data_time: 1.304, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5627, loss_cls: 3.9885, loss: 3.9885 +2024-07-18 04:39:57,239 - pyskl - INFO - Epoch [55][200/3746] lr: 7.124e-02, eta: 3 days, 6:06:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5414, loss_cls: 4.0752, loss: 4.0752 +2024-07-18 04:41:18,893 - pyskl - INFO - Epoch [55][300/3746] lr: 7.121e-02, eta: 3 days, 6:05:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5592, loss_cls: 4.0118, loss: 4.0118 +2024-07-18 04:42:40,956 - pyskl - INFO - Epoch [55][400/3746] lr: 7.119e-02, eta: 3 days, 6:04:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5448, loss_cls: 4.0648, loss: 4.0648 +2024-07-18 04:44:02,700 - pyskl - INFO - Epoch [55][500/3746] lr: 7.116e-02, eta: 3 days, 6:03:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5423, loss_cls: 4.0678, loss: 4.0678 +2024-07-18 04:45:24,725 - pyskl - INFO - Epoch [55][600/3746] lr: 7.114e-02, eta: 3 days, 6:01:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5508, loss_cls: 4.0790, loss: 4.0790 +2024-07-18 04:46:46,058 - pyskl - INFO - Epoch [55][700/3746] lr: 7.111e-02, eta: 3 days, 6:00:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5445, loss_cls: 4.0575, loss: 4.0575 +2024-07-18 04:48:08,468 - pyskl - INFO - Epoch [55][800/3746] lr: 7.109e-02, eta: 3 days, 5:59:34, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5389, loss_cls: 4.0735, loss: 4.0735 +2024-07-18 04:49:30,940 - pyskl - INFO - Epoch [55][900/3746] lr: 7.106e-02, eta: 3 days, 5:58:23, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5509, loss_cls: 4.0267, loss: 4.0267 +2024-07-18 04:50:52,826 - pyskl - INFO - Epoch [55][1000/3746] lr: 7.104e-02, eta: 3 days, 5:57:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5484, loss_cls: 4.0673, loss: 4.0673 +2024-07-18 04:52:15,285 - pyskl - INFO - Epoch [55][1100/3746] lr: 7.101e-02, eta: 3 days, 5:56:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5478, loss_cls: 4.0696, loss: 4.0696 +2024-07-18 04:53:37,033 - pyskl - INFO - Epoch [55][1200/3746] lr: 7.099e-02, eta: 3 days, 5:54:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5622, loss_cls: 3.9955, loss: 3.9955 +2024-07-18 04:54:59,024 - pyskl - INFO - Epoch [55][1300/3746] lr: 7.096e-02, eta: 3 days, 5:53:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5436, loss_cls: 4.0848, loss: 4.0848 +2024-07-18 04:56:21,712 - pyskl - INFO - Epoch [55][1400/3746] lr: 7.093e-02, eta: 3 days, 5:52:26, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5389, loss_cls: 4.0632, loss: 4.0632 +2024-07-18 04:57:43,466 - pyskl - INFO - Epoch [55][1500/3746] lr: 7.091e-02, eta: 3 days, 5:51:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5437, loss_cls: 4.0426, loss: 4.0426 +2024-07-18 04:59:05,409 - pyskl - INFO - Epoch [55][1600/3746] lr: 7.088e-02, eta: 3 days, 5:50:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5498, loss_cls: 4.0457, loss: 4.0457 +2024-07-18 05:00:27,013 - pyskl - INFO - Epoch [55][1700/3746] lr: 7.086e-02, eta: 3 days, 5:48:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5453, loss_cls: 4.0817, loss: 4.0817 +2024-07-18 05:01:48,460 - pyskl - INFO - Epoch [55][1800/3746] lr: 7.083e-02, eta: 3 days, 5:47:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5369, loss_cls: 4.1231, loss: 4.1231 +2024-07-18 05:03:10,428 - pyskl - INFO - Epoch [55][1900/3746] lr: 7.081e-02, eta: 3 days, 5:46:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5478, loss_cls: 4.0759, loss: 4.0759 +2024-07-18 05:04:32,448 - pyskl - INFO - Epoch [55][2000/3746] lr: 7.078e-02, eta: 3 days, 5:45:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5486, loss_cls: 4.1002, loss: 4.1002 +2024-07-18 05:05:54,466 - pyskl - INFO - Epoch [55][2100/3746] lr: 7.076e-02, eta: 3 days, 5:44:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5561, loss_cls: 4.0526, loss: 4.0526 +2024-07-18 05:07:16,186 - pyskl - INFO - Epoch [55][2200/3746] lr: 7.073e-02, eta: 3 days, 5:42:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5323, loss_cls: 4.1491, loss: 4.1491 +2024-07-18 05:08:37,653 - pyskl - INFO - Epoch [55][2300/3746] lr: 7.071e-02, eta: 3 days, 5:41:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5586, loss_cls: 4.0216, loss: 4.0216 +2024-07-18 05:09:59,316 - pyskl - INFO - Epoch [55][2400/3746] lr: 7.068e-02, eta: 3 days, 5:40:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5541, loss_cls: 4.0451, loss: 4.0451 +2024-07-18 05:11:22,314 - pyskl - INFO - Epoch [55][2500/3746] lr: 7.065e-02, eta: 3 days, 5:39:15, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5375, loss_cls: 4.1085, loss: 4.1085 +2024-07-18 05:12:44,151 - pyskl - INFO - Epoch [55][2600/3746] lr: 7.063e-02, eta: 3 days, 5:38:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5422, loss_cls: 4.0604, loss: 4.0604 +2024-07-18 05:14:05,850 - pyskl - INFO - Epoch [55][2700/3746] lr: 7.060e-02, eta: 3 days, 5:36:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5470, loss_cls: 4.0211, loss: 4.0211 +2024-07-18 05:15:27,191 - pyskl - INFO - Epoch [55][2800/3746] lr: 7.058e-02, eta: 3 days, 5:35:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5455, loss_cls: 4.0782, loss: 4.0782 +2024-07-18 05:16:48,648 - pyskl - INFO - Epoch [55][2900/3746] lr: 7.055e-02, eta: 3 days, 5:34:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5502, loss_cls: 4.0608, loss: 4.0608 +2024-07-18 05:18:10,235 - pyskl - INFO - Epoch [55][3000/3746] lr: 7.053e-02, eta: 3 days, 5:33:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5436, loss_cls: 4.0860, loss: 4.0860 +2024-07-18 05:19:31,796 - pyskl - INFO - Epoch [55][3100/3746] lr: 7.050e-02, eta: 3 days, 5:31:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5450, loss_cls: 4.0953, loss: 4.0953 +2024-07-18 05:20:53,527 - pyskl - INFO - Epoch [55][3200/3746] lr: 7.048e-02, eta: 3 days, 5:30:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5448, loss_cls: 4.0761, loss: 4.0761 +2024-07-18 05:22:15,078 - pyskl - INFO - Epoch [55][3300/3746] lr: 7.045e-02, eta: 3 days, 5:29:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5519, loss_cls: 4.0225, loss: 4.0225 +2024-07-18 05:23:36,671 - pyskl - INFO - Epoch [55][3400/3746] lr: 7.043e-02, eta: 3 days, 5:28:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5480, loss_cls: 4.0687, loss: 4.0687 +2024-07-18 05:24:58,687 - pyskl - INFO - Epoch [55][3500/3746] lr: 7.040e-02, eta: 3 days, 5:27:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5498, loss_cls: 4.0460, loss: 4.0460 +2024-07-18 05:26:20,400 - pyskl - INFO - Epoch [55][3600/3746] lr: 7.037e-02, eta: 3 days, 5:25:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5459, loss_cls: 4.0950, loss: 4.0950 +2024-07-18 05:27:41,802 - pyskl - INFO - Epoch [55][3700/3746] lr: 7.035e-02, eta: 3 days, 5:24:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5555, loss_cls: 4.0382, loss: 4.0382 +2024-07-18 05:28:21,283 - pyskl - INFO - Saving checkpoint at 55 epochs +2024-07-18 05:30:11,481 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 05:30:12,151 - pyskl - INFO - +top1_acc 0.2307 +top5_acc 0.4683 +2024-07-18 05:30:12,152 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 05:30:12,190 - pyskl - INFO - +mean_acc 0.2307 +2024-07-18 05:30:12,196 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_46.pth was removed +2024-07-18 05:30:12,457 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_55.pth. +2024-07-18 05:30:12,457 - pyskl - INFO - Best top1_acc is 0.2307 at 55 epoch. +2024-07-18 05:30:12,469 - pyskl - INFO - Epoch(val) [55][309] top1_acc: 0.2307, top5_acc: 0.4683, mean_class_accuracy: 0.2307 +2024-07-18 05:34:00,521 - pyskl - INFO - Epoch [56][100/3746] lr: 7.031e-02, eta: 3 days, 5:26:07, time: 2.280, data_time: 1.294, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5444, loss_cls: 4.0797, loss: 4.0797 +2024-07-18 05:35:22,406 - pyskl - INFO - Epoch [56][200/3746] lr: 7.029e-02, eta: 3 days, 5:24:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5606, loss_cls: 4.0292, loss: 4.0292 +2024-07-18 05:36:44,340 - pyskl - INFO - Epoch [56][300/3746] lr: 7.026e-02, eta: 3 days, 5:23:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5508, loss_cls: 4.0305, loss: 4.0305 +2024-07-18 05:38:05,898 - pyskl - INFO - Epoch [56][400/3746] lr: 7.023e-02, eta: 3 days, 5:22:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5558, loss_cls: 4.0504, loss: 4.0504 +2024-07-18 05:39:27,136 - pyskl - INFO - Epoch [56][500/3746] lr: 7.021e-02, eta: 3 days, 5:21:16, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5523, loss_cls: 4.0445, loss: 4.0445 +2024-07-18 05:40:48,460 - pyskl - INFO - Epoch [56][600/3746] lr: 7.018e-02, eta: 3 days, 5:20:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5456, loss_cls: 4.0519, loss: 4.0519 +2024-07-18 05:42:10,422 - pyskl - INFO - Epoch [56][700/3746] lr: 7.016e-02, eta: 3 days, 5:18:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5511, loss_cls: 4.0349, loss: 4.0349 +2024-07-18 05:43:32,029 - pyskl - INFO - Epoch [56][800/3746] lr: 7.013e-02, eta: 3 days, 5:17:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5572, loss_cls: 4.0128, loss: 4.0128 +2024-07-18 05:44:53,908 - pyskl - INFO - Epoch [56][900/3746] lr: 7.011e-02, eta: 3 days, 5:16:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5472, loss_cls: 4.0524, loss: 4.0524 +2024-07-18 05:46:15,594 - pyskl - INFO - Epoch [56][1000/3746] lr: 7.008e-02, eta: 3 days, 5:15:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5548, loss_cls: 4.0312, loss: 4.0312 +2024-07-18 05:47:37,876 - pyskl - INFO - Epoch [56][1100/3746] lr: 7.006e-02, eta: 3 days, 5:14:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5442, loss_cls: 4.0760, loss: 4.0760 +2024-07-18 05:48:59,667 - pyskl - INFO - Epoch [56][1200/3746] lr: 7.003e-02, eta: 3 days, 5:12:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5573, loss_cls: 4.0498, loss: 4.0498 +2024-07-18 05:50:22,380 - pyskl - INFO - Epoch [56][1300/3746] lr: 7.000e-02, eta: 3 days, 5:11:37, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5559, loss_cls: 4.0003, loss: 4.0003 +2024-07-18 05:51:44,612 - pyskl - INFO - Epoch [56][1400/3746] lr: 6.998e-02, eta: 3 days, 5:10:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5497, loss_cls: 4.0621, loss: 4.0621 +2024-07-18 05:53:07,209 - pyskl - INFO - Epoch [56][1500/3746] lr: 6.995e-02, eta: 3 days, 5:09:15, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5639, loss_cls: 3.9902, loss: 3.9902 +2024-07-18 05:54:28,850 - pyskl - INFO - Epoch [56][1600/3746] lr: 6.993e-02, eta: 3 days, 5:08:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5398, loss_cls: 4.0840, loss: 4.0840 +2024-07-18 05:55:49,808 - pyskl - INFO - Epoch [56][1700/3746] lr: 6.990e-02, eta: 3 days, 5:06:48, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5491, loss_cls: 4.0720, loss: 4.0720 +2024-07-18 05:57:11,854 - pyskl - INFO - Epoch [56][1800/3746] lr: 6.988e-02, eta: 3 days, 5:05:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5475, loss_cls: 4.0648, loss: 4.0648 +2024-07-18 05:58:32,876 - pyskl - INFO - Epoch [56][1900/3746] lr: 6.985e-02, eta: 3 days, 5:04:22, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5534, loss_cls: 4.0459, loss: 4.0459 +2024-07-18 05:59:55,120 - pyskl - INFO - Epoch [56][2000/3746] lr: 6.983e-02, eta: 3 days, 5:03:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5517, loss_cls: 4.0387, loss: 4.0387 +2024-07-18 06:01:16,654 - pyskl - INFO - Epoch [56][2100/3746] lr: 6.980e-02, eta: 3 days, 5:01:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5506, loss_cls: 4.0867, loss: 4.0867 +2024-07-18 06:02:38,511 - pyskl - INFO - Epoch [56][2200/3746] lr: 6.977e-02, eta: 3 days, 5:00:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5572, loss_cls: 4.0105, loss: 4.0105 +2024-07-18 06:04:00,274 - pyskl - INFO - Epoch [56][2300/3746] lr: 6.975e-02, eta: 3 days, 4:59:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5569, loss_cls: 4.0306, loss: 4.0306 +2024-07-18 06:05:22,731 - pyskl - INFO - Epoch [56][2400/3746] lr: 6.972e-02, eta: 3 days, 4:58:20, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5406, loss_cls: 4.1068, loss: 4.1068 +2024-07-18 06:06:44,537 - pyskl - INFO - Epoch [56][2500/3746] lr: 6.970e-02, eta: 3 days, 4:57:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5413, loss_cls: 4.0911, loss: 4.0911 +2024-07-18 06:08:06,791 - pyskl - INFO - Epoch [56][2600/3746] lr: 6.967e-02, eta: 3 days, 4:55:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5480, loss_cls: 4.0512, loss: 4.0512 +2024-07-18 06:09:29,032 - pyskl - INFO - Epoch [56][2700/3746] lr: 6.965e-02, eta: 3 days, 4:54:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5531, loss_cls: 4.0609, loss: 4.0609 +2024-07-18 06:10:50,978 - pyskl - INFO - Epoch [56][2800/3746] lr: 6.962e-02, eta: 3 days, 4:53:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5606, loss_cls: 3.9889, loss: 3.9889 +2024-07-18 06:12:12,599 - pyskl - INFO - Epoch [56][2900/3746] lr: 6.959e-02, eta: 3 days, 4:52:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5402, loss_cls: 4.0776, loss: 4.0776 +2024-07-18 06:13:34,023 - pyskl - INFO - Epoch [56][3000/3746] lr: 6.957e-02, eta: 3 days, 4:51:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5569, loss_cls: 4.0251, loss: 4.0251 +2024-07-18 06:14:55,524 - pyskl - INFO - Epoch [56][3100/3746] lr: 6.954e-02, eta: 3 days, 4:49:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5398, loss_cls: 4.1184, loss: 4.1184 +2024-07-18 06:16:17,414 - pyskl - INFO - Epoch [56][3200/3746] lr: 6.952e-02, eta: 3 days, 4:48:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5367, loss_cls: 4.1207, loss: 4.1207 +2024-07-18 06:17:38,960 - pyskl - INFO - Epoch [56][3300/3746] lr: 6.949e-02, eta: 3 days, 4:47:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5531, loss_cls: 4.0430, loss: 4.0430 +2024-07-18 06:18:59,843 - pyskl - INFO - Epoch [56][3400/3746] lr: 6.947e-02, eta: 3 days, 4:46:12, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5491, loss_cls: 4.0570, loss: 4.0570 +2024-07-18 06:20:21,404 - pyskl - INFO - Epoch [56][3500/3746] lr: 6.944e-02, eta: 3 days, 4:44:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5473, loss_cls: 4.0840, loss: 4.0840 +2024-07-18 06:21:42,739 - pyskl - INFO - Epoch [56][3600/3746] lr: 6.941e-02, eta: 3 days, 4:43:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5481, loss_cls: 4.0598, loss: 4.0598 +2024-07-18 06:23:03,991 - pyskl - INFO - Epoch [56][3700/3746] lr: 6.939e-02, eta: 3 days, 4:42:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5433, loss_cls: 4.0963, loss: 4.0963 +2024-07-18 06:23:43,554 - pyskl - INFO - Saving checkpoint at 56 epochs +2024-07-18 06:25:35,140 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 06:25:35,801 - pyskl - INFO - +top1_acc 0.2342 +top5_acc 0.4707 +2024-07-18 06:25:35,801 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 06:25:35,840 - pyskl - INFO - +mean_acc 0.2339 +2024-07-18 06:25:35,844 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_55.pth was removed +2024-07-18 06:25:36,092 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_56.pth. +2024-07-18 06:25:36,093 - pyskl - INFO - Best top1_acc is 0.2342 at 56 epoch. +2024-07-18 06:25:36,104 - pyskl - INFO - Epoch(val) [56][309] top1_acc: 0.2342, top5_acc: 0.4707, mean_class_accuracy: 0.2339 +2024-07-18 06:29:26,549 - pyskl - INFO - Epoch [57][100/3746] lr: 6.935e-02, eta: 3 days, 4:43:52, time: 2.304, data_time: 1.307, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5609, loss_cls: 4.0057, loss: 4.0057 +2024-07-18 06:30:48,560 - pyskl - INFO - Epoch [57][200/3746] lr: 6.932e-02, eta: 3 days, 4:42:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5603, loss_cls: 3.9682, loss: 3.9682 +2024-07-18 06:32:10,529 - pyskl - INFO - Epoch [57][300/3746] lr: 6.930e-02, eta: 3 days, 4:41:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5592, loss_cls: 4.0056, loss: 4.0056 +2024-07-18 06:33:32,144 - pyskl - INFO - Epoch [57][400/3746] lr: 6.927e-02, eta: 3 days, 4:40:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5528, loss_cls: 4.0260, loss: 4.0260 +2024-07-18 06:34:53,783 - pyskl - INFO - Epoch [57][500/3746] lr: 6.925e-02, eta: 3 days, 4:39:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5547, loss_cls: 4.0293, loss: 4.0293 +2024-07-18 06:36:16,581 - pyskl - INFO - Epoch [57][600/3746] lr: 6.922e-02, eta: 3 days, 4:37:49, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5392, loss_cls: 4.0847, loss: 4.0847 +2024-07-18 06:37:38,201 - pyskl - INFO - Epoch [57][700/3746] lr: 6.920e-02, eta: 3 days, 4:36:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5486, loss_cls: 4.0548, loss: 4.0548 +2024-07-18 06:39:00,032 - pyskl - INFO - Epoch [57][800/3746] lr: 6.917e-02, eta: 3 days, 4:35:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5442, loss_cls: 4.0650, loss: 4.0650 +2024-07-18 06:40:22,632 - pyskl - INFO - Epoch [57][900/3746] lr: 6.914e-02, eta: 3 days, 4:34:11, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5444, loss_cls: 4.0779, loss: 4.0779 +2024-07-18 06:41:44,753 - pyskl - INFO - Epoch [57][1000/3746] lr: 6.912e-02, eta: 3 days, 4:32:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5397, loss_cls: 4.0901, loss: 4.0901 +2024-07-18 06:43:06,804 - pyskl - INFO - Epoch [57][1100/3746] lr: 6.909e-02, eta: 3 days, 4:31:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5463, loss_cls: 4.0876, loss: 4.0876 +2024-07-18 06:44:28,423 - pyskl - INFO - Epoch [57][1200/3746] lr: 6.907e-02, eta: 3 days, 4:30:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5508, loss_cls: 4.0729, loss: 4.0729 +2024-07-18 06:45:51,236 - pyskl - INFO - Epoch [57][1300/3746] lr: 6.904e-02, eta: 3 days, 4:29:22, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5541, loss_cls: 4.0196, loss: 4.0196 +2024-07-18 06:47:12,820 - pyskl - INFO - Epoch [57][1400/3746] lr: 6.901e-02, eta: 3 days, 4:28:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5531, loss_cls: 4.0308, loss: 4.0308 +2024-07-18 06:48:34,712 - pyskl - INFO - Epoch [57][1500/3746] lr: 6.899e-02, eta: 3 days, 4:26:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5467, loss_cls: 4.0841, loss: 4.0841 +2024-07-18 06:49:56,488 - pyskl - INFO - Epoch [57][1600/3746] lr: 6.896e-02, eta: 3 days, 4:25:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5505, loss_cls: 4.0266, loss: 4.0266 +2024-07-18 06:51:18,108 - pyskl - INFO - Epoch [57][1700/3746] lr: 6.894e-02, eta: 3 days, 4:24:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5566, loss_cls: 4.0081, loss: 4.0081 +2024-07-18 06:52:40,240 - pyskl - INFO - Epoch [57][1800/3746] lr: 6.891e-02, eta: 3 days, 4:23:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5498, loss_cls: 4.0766, loss: 4.0766 +2024-07-18 06:54:01,940 - pyskl - INFO - Epoch [57][1900/3746] lr: 6.889e-02, eta: 3 days, 4:22:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5566, loss_cls: 4.0106, loss: 4.0106 +2024-07-18 06:55:23,425 - pyskl - INFO - Epoch [57][2000/3746] lr: 6.886e-02, eta: 3 days, 4:20:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5505, loss_cls: 4.0464, loss: 4.0464 +2024-07-18 06:56:45,112 - pyskl - INFO - Epoch [57][2100/3746] lr: 6.883e-02, eta: 3 days, 4:19:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5573, loss_cls: 4.0401, loss: 4.0401 +2024-07-18 06:58:07,034 - pyskl - INFO - Epoch [57][2200/3746] lr: 6.881e-02, eta: 3 days, 4:18:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5478, loss_cls: 4.0694, loss: 4.0694 +2024-07-18 06:59:29,177 - pyskl - INFO - Epoch [57][2300/3746] lr: 6.878e-02, eta: 3 days, 4:17:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5566, loss_cls: 4.0136, loss: 4.0136 +2024-07-18 07:00:51,613 - pyskl - INFO - Epoch [57][2400/3746] lr: 6.876e-02, eta: 3 days, 4:15:59, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5500, loss_cls: 4.0368, loss: 4.0368 +2024-07-18 07:02:13,591 - pyskl - INFO - Epoch [57][2500/3746] lr: 6.873e-02, eta: 3 days, 4:14:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5475, loss_cls: 4.0584, loss: 4.0584 +2024-07-18 07:03:35,553 - pyskl - INFO - Epoch [57][2600/3746] lr: 6.870e-02, eta: 3 days, 4:13:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5520, loss_cls: 4.0330, loss: 4.0330 +2024-07-18 07:04:57,085 - pyskl - INFO - Epoch [57][2700/3746] lr: 6.868e-02, eta: 3 days, 4:12:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5541, loss_cls: 4.0322, loss: 4.0322 +2024-07-18 07:06:19,097 - pyskl - INFO - Epoch [57][2800/3746] lr: 6.865e-02, eta: 3 days, 4:11:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5536, loss_cls: 4.0659, loss: 4.0659 +2024-07-18 07:07:40,753 - pyskl - INFO - Epoch [57][2900/3746] lr: 6.863e-02, eta: 3 days, 4:09:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5414, loss_cls: 4.1030, loss: 4.1030 +2024-07-18 07:09:02,234 - pyskl - INFO - Epoch [57][3000/3746] lr: 6.860e-02, eta: 3 days, 4:08:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5461, loss_cls: 4.0707, loss: 4.0707 +2024-07-18 07:10:23,594 - pyskl - INFO - Epoch [57][3100/3746] lr: 6.857e-02, eta: 3 days, 4:07:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5500, loss_cls: 4.0792, loss: 4.0792 +2024-07-18 07:11:45,204 - pyskl - INFO - Epoch [57][3200/3746] lr: 6.855e-02, eta: 3 days, 4:06:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5552, loss_cls: 4.0202, loss: 4.0202 +2024-07-18 07:13:06,746 - pyskl - INFO - Epoch [57][3300/3746] lr: 6.852e-02, eta: 3 days, 4:04:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5455, loss_cls: 4.0503, loss: 4.0503 +2024-07-18 07:14:28,908 - pyskl - INFO - Epoch [57][3400/3746] lr: 6.850e-02, eta: 3 days, 4:03:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5595, loss_cls: 4.0259, loss: 4.0259 +2024-07-18 07:15:51,135 - pyskl - INFO - Epoch [57][3500/3746] lr: 6.847e-02, eta: 3 days, 4:02:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5473, loss_cls: 4.0695, loss: 4.0695 +2024-07-18 07:17:12,672 - pyskl - INFO - Epoch [57][3600/3746] lr: 6.844e-02, eta: 3 days, 4:01:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5452, loss_cls: 4.0817, loss: 4.0817 +2024-07-18 07:18:34,300 - pyskl - INFO - Epoch [57][3700/3746] lr: 6.842e-02, eta: 3 days, 4:00:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5516, loss_cls: 4.0420, loss: 4.0420 +2024-07-18 07:19:13,557 - pyskl - INFO - Saving checkpoint at 57 epochs +2024-07-18 07:21:03,738 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 07:21:04,519 - pyskl - INFO - +top1_acc 0.2357 +top5_acc 0.4808 +2024-07-18 07:21:04,519 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 07:21:04,572 - pyskl - INFO - +mean_acc 0.2353 +2024-07-18 07:21:04,576 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_56.pth was removed +2024-07-18 07:21:04,879 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_57.pth. +2024-07-18 07:21:04,879 - pyskl - INFO - Best top1_acc is 0.2357 at 57 epoch. +2024-07-18 07:21:04,895 - pyskl - INFO - Epoch(val) [57][309] top1_acc: 0.2357, top5_acc: 0.4808, mean_class_accuracy: 0.2353 +2024-07-18 07:24:51,292 - pyskl - INFO - Epoch [58][100/3746] lr: 6.838e-02, eta: 3 days, 4:01:15, time: 2.264, data_time: 1.284, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5481, loss_cls: 4.0207, loss: 4.0207 +2024-07-18 07:26:12,761 - pyskl - INFO - Epoch [58][200/3746] lr: 6.835e-02, eta: 3 days, 4:00:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5616, loss_cls: 3.9940, loss: 3.9940 +2024-07-18 07:27:34,713 - pyskl - INFO - Epoch [58][300/3746] lr: 6.833e-02, eta: 3 days, 3:58:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5589, loss_cls: 3.9987, loss: 3.9987 +2024-07-18 07:28:56,436 - pyskl - INFO - Epoch [58][400/3746] lr: 6.830e-02, eta: 3 days, 3:57:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5528, loss_cls: 4.0470, loss: 4.0470 +2024-07-18 07:30:18,031 - pyskl - INFO - Epoch [58][500/3746] lr: 6.828e-02, eta: 3 days, 3:56:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5597, loss_cls: 3.9772, loss: 3.9772 +2024-07-18 07:31:39,277 - pyskl - INFO - Epoch [58][600/3746] lr: 6.825e-02, eta: 3 days, 3:55:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5619, loss_cls: 3.9755, loss: 3.9755 +2024-07-18 07:33:00,750 - pyskl - INFO - Epoch [58][700/3746] lr: 6.822e-02, eta: 3 days, 3:53:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5600, loss_cls: 3.9878, loss: 3.9878 +2024-07-18 07:34:22,436 - pyskl - INFO - Epoch [58][800/3746] lr: 6.820e-02, eta: 3 days, 3:52:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5577, loss_cls: 3.9968, loss: 3.9968 +2024-07-18 07:35:44,494 - pyskl - INFO - Epoch [58][900/3746] lr: 6.817e-02, eta: 3 days, 3:51:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5505, loss_cls: 4.0423, loss: 4.0423 +2024-07-18 07:37:06,881 - pyskl - INFO - Epoch [58][1000/3746] lr: 6.815e-02, eta: 3 days, 3:50:13, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5617, loss_cls: 3.9938, loss: 3.9938 +2024-07-18 07:38:29,122 - pyskl - INFO - Epoch [58][1100/3746] lr: 6.812e-02, eta: 3 days, 3:49:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5548, loss_cls: 4.0494, loss: 4.0494 +2024-07-18 07:39:50,999 - pyskl - INFO - Epoch [58][1200/3746] lr: 6.809e-02, eta: 3 days, 3:47:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5464, loss_cls: 4.0984, loss: 4.0984 +2024-07-18 07:41:13,624 - pyskl - INFO - Epoch [58][1300/3746] lr: 6.807e-02, eta: 3 days, 3:46:35, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5486, loss_cls: 4.0488, loss: 4.0488 +2024-07-18 07:42:35,723 - pyskl - INFO - Epoch [58][1400/3746] lr: 6.804e-02, eta: 3 days, 3:45:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5525, loss_cls: 4.0265, loss: 4.0265 +2024-07-18 07:43:57,619 - pyskl - INFO - Epoch [58][1500/3746] lr: 6.802e-02, eta: 3 days, 3:44:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5555, loss_cls: 4.0340, loss: 4.0340 +2024-07-18 07:45:19,060 - pyskl - INFO - Epoch [58][1600/3746] lr: 6.799e-02, eta: 3 days, 3:42:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5469, loss_cls: 4.0337, loss: 4.0337 +2024-07-18 07:46:40,518 - pyskl - INFO - Epoch [58][1700/3746] lr: 6.796e-02, eta: 3 days, 3:41:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5542, loss_cls: 3.9971, loss: 3.9971 +2024-07-18 07:48:01,963 - pyskl - INFO - Epoch [58][1800/3746] lr: 6.794e-02, eta: 3 days, 3:40:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5536, loss_cls: 4.0087, loss: 4.0087 +2024-07-18 07:49:23,675 - pyskl - INFO - Epoch [58][1900/3746] lr: 6.791e-02, eta: 3 days, 3:39:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5397, loss_cls: 4.0509, loss: 4.0509 +2024-07-18 07:50:45,625 - pyskl - INFO - Epoch [58][2000/3746] lr: 6.789e-02, eta: 3 days, 3:38:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5467, loss_cls: 4.0491, loss: 4.0491 +2024-07-18 07:52:07,373 - pyskl - INFO - Epoch [58][2100/3746] lr: 6.786e-02, eta: 3 days, 3:36:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5530, loss_cls: 4.0461, loss: 4.0461 +2024-07-18 07:53:29,901 - pyskl - INFO - Epoch [58][2200/3746] lr: 6.783e-02, eta: 3 days, 3:35:34, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5600, loss_cls: 3.9720, loss: 3.9720 +2024-07-18 07:54:51,352 - pyskl - INFO - Epoch [58][2300/3746] lr: 6.781e-02, eta: 3 days, 3:34:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5523, loss_cls: 4.0199, loss: 4.0199 +2024-07-18 07:56:13,577 - pyskl - INFO - Epoch [58][2400/3746] lr: 6.778e-02, eta: 3 days, 3:33:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5573, loss_cls: 4.0253, loss: 4.0253 +2024-07-18 07:57:35,987 - pyskl - INFO - Epoch [58][2500/3746] lr: 6.775e-02, eta: 3 days, 3:31:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5509, loss_cls: 4.0643, loss: 4.0643 +2024-07-18 07:58:57,451 - pyskl - INFO - Epoch [58][2600/3746] lr: 6.773e-02, eta: 3 days, 3:30:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5503, loss_cls: 4.0797, loss: 4.0797 +2024-07-18 08:00:18,724 - pyskl - INFO - Epoch [58][2700/3746] lr: 6.770e-02, eta: 3 days, 3:29:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5480, loss_cls: 4.0700, loss: 4.0700 +2024-07-18 08:01:40,134 - pyskl - INFO - Epoch [58][2800/3746] lr: 6.768e-02, eta: 3 days, 3:28:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5516, loss_cls: 4.0491, loss: 4.0491 +2024-07-18 08:03:01,887 - pyskl - INFO - Epoch [58][2900/3746] lr: 6.765e-02, eta: 3 days, 3:26:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5386, loss_cls: 4.1075, loss: 4.1075 +2024-07-18 08:04:23,530 - pyskl - INFO - Epoch [58][3000/3746] lr: 6.762e-02, eta: 3 days, 3:25:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5553, loss_cls: 4.0206, loss: 4.0206 +2024-07-18 08:05:44,841 - pyskl - INFO - Epoch [58][3100/3746] lr: 6.760e-02, eta: 3 days, 3:24:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5439, loss_cls: 4.0588, loss: 4.0588 +2024-07-18 08:07:07,103 - pyskl - INFO - Epoch [58][3200/3746] lr: 6.757e-02, eta: 3 days, 3:23:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5337, loss_cls: 4.0996, loss: 4.0996 +2024-07-18 08:08:28,884 - pyskl - INFO - Epoch [58][3300/3746] lr: 6.755e-02, eta: 3 days, 3:22:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5413, loss_cls: 4.1012, loss: 4.1012 +2024-07-18 08:09:50,401 - pyskl - INFO - Epoch [58][3400/3746] lr: 6.752e-02, eta: 3 days, 3:20:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5425, loss_cls: 4.0569, loss: 4.0569 +2024-07-18 08:11:11,985 - pyskl - INFO - Epoch [58][3500/3746] lr: 6.749e-02, eta: 3 days, 3:19:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5403, loss_cls: 4.1132, loss: 4.1132 +2024-07-18 08:12:33,698 - pyskl - INFO - Epoch [58][3600/3746] lr: 6.747e-02, eta: 3 days, 3:18:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5547, loss_cls: 4.0464, loss: 4.0464 +2024-07-18 08:13:55,940 - pyskl - INFO - Epoch [58][3700/3746] lr: 6.744e-02, eta: 3 days, 3:17:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5466, loss_cls: 4.1009, loss: 4.1009 +2024-07-18 08:14:35,796 - pyskl - INFO - Saving checkpoint at 58 epochs +2024-07-18 08:16:27,362 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 08:16:28,026 - pyskl - INFO - +top1_acc 0.2334 +top5_acc 0.4697 +2024-07-18 08:16:28,026 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 08:16:28,065 - pyskl - INFO - +mean_acc 0.2330 +2024-07-18 08:16:28,077 - pyskl - INFO - Epoch(val) [58][309] top1_acc: 0.2334, top5_acc: 0.4697, mean_class_accuracy: 0.2330 +2024-07-18 08:20:17,043 - pyskl - INFO - Epoch [59][100/3746] lr: 6.740e-02, eta: 3 days, 3:18:16, time: 2.290, data_time: 1.309, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5550, loss_cls: 4.0043, loss: 4.0043 +2024-07-18 08:21:39,318 - pyskl - INFO - Epoch [59][200/3746] lr: 6.738e-02, eta: 3 days, 3:17:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5569, loss_cls: 4.0163, loss: 4.0163 +2024-07-18 08:23:01,132 - pyskl - INFO - Epoch [59][300/3746] lr: 6.735e-02, eta: 3 days, 3:15:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5513, loss_cls: 4.0512, loss: 4.0512 +2024-07-18 08:24:23,727 - pyskl - INFO - Epoch [59][400/3746] lr: 6.732e-02, eta: 3 days, 3:14:36, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5583, loss_cls: 3.9725, loss: 3.9725 +2024-07-18 08:25:45,651 - pyskl - INFO - Epoch [59][500/3746] lr: 6.730e-02, eta: 3 days, 3:13:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5569, loss_cls: 3.9921, loss: 3.9921 +2024-07-18 08:27:07,532 - pyskl - INFO - Epoch [59][600/3746] lr: 6.727e-02, eta: 3 days, 3:12:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5533, loss_cls: 4.0144, loss: 4.0144 +2024-07-18 08:28:29,056 - pyskl - INFO - Epoch [59][700/3746] lr: 6.725e-02, eta: 3 days, 3:10:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5466, loss_cls: 4.1077, loss: 4.1077 +2024-07-18 08:29:51,567 - pyskl - INFO - Epoch [59][800/3746] lr: 6.722e-02, eta: 3 days, 3:09:42, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5547, loss_cls: 4.0262, loss: 4.0262 +2024-07-18 08:31:13,271 - pyskl - INFO - Epoch [59][900/3746] lr: 6.719e-02, eta: 3 days, 3:08:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5484, loss_cls: 4.0152, loss: 4.0152 +2024-07-18 08:32:35,285 - pyskl - INFO - Epoch [59][1000/3746] lr: 6.717e-02, eta: 3 days, 3:07:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5433, loss_cls: 4.0703, loss: 4.0703 +2024-07-18 08:33:57,626 - pyskl - INFO - Epoch [59][1100/3746] lr: 6.714e-02, eta: 3 days, 3:06:02, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5600, loss_cls: 4.0223, loss: 4.0223 +2024-07-18 08:35:19,280 - pyskl - INFO - Epoch [59][1200/3746] lr: 6.711e-02, eta: 3 days, 3:04:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5614, loss_cls: 3.9983, loss: 3.9983 +2024-07-18 08:36:40,969 - pyskl - INFO - Epoch [59][1300/3746] lr: 6.709e-02, eta: 3 days, 3:03:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5527, loss_cls: 4.0210, loss: 4.0210 +2024-07-18 08:38:03,116 - pyskl - INFO - Epoch [59][1400/3746] lr: 6.706e-02, eta: 3 days, 3:02:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5509, loss_cls: 4.0577, loss: 4.0577 +2024-07-18 08:39:24,841 - pyskl - INFO - Epoch [59][1500/3746] lr: 6.704e-02, eta: 3 days, 3:01:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5602, loss_cls: 4.0117, loss: 4.0117 +2024-07-18 08:40:46,664 - pyskl - INFO - Epoch [59][1600/3746] lr: 6.701e-02, eta: 3 days, 2:59:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5480, loss_cls: 4.0852, loss: 4.0852 +2024-07-18 08:42:07,916 - pyskl - INFO - Epoch [59][1700/3746] lr: 6.698e-02, eta: 3 days, 2:58:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5463, loss_cls: 4.0427, loss: 4.0427 +2024-07-18 08:43:29,740 - pyskl - INFO - Epoch [59][1800/3746] lr: 6.696e-02, eta: 3 days, 2:57:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5580, loss_cls: 4.0143, loss: 4.0143 +2024-07-18 08:44:51,156 - pyskl - INFO - Epoch [59][1900/3746] lr: 6.693e-02, eta: 3 days, 2:56:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5628, loss_cls: 3.9946, loss: 3.9946 +2024-07-18 08:46:12,739 - pyskl - INFO - Epoch [59][2000/3746] lr: 6.690e-02, eta: 3 days, 2:54:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5602, loss_cls: 3.9824, loss: 3.9824 +2024-07-18 08:47:34,229 - pyskl - INFO - Epoch [59][2100/3746] lr: 6.688e-02, eta: 3 days, 2:53:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5569, loss_cls: 4.0266, loss: 4.0266 +2024-07-18 08:48:57,022 - pyskl - INFO - Epoch [59][2200/3746] lr: 6.685e-02, eta: 3 days, 2:52:29, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5394, loss_cls: 4.0917, loss: 4.0917 +2024-07-18 08:50:18,668 - pyskl - INFO - Epoch [59][2300/3746] lr: 6.682e-02, eta: 3 days, 2:51:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5492, loss_cls: 4.0441, loss: 4.0441 +2024-07-18 08:51:40,730 - pyskl - INFO - Epoch [59][2400/3746] lr: 6.680e-02, eta: 3 days, 2:50:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5550, loss_cls: 4.0453, loss: 4.0453 +2024-07-18 08:53:03,187 - pyskl - INFO - Epoch [59][2500/3746] lr: 6.677e-02, eta: 3 days, 2:48:48, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5555, loss_cls: 4.0101, loss: 4.0101 +2024-07-18 08:54:25,233 - pyskl - INFO - Epoch [59][2600/3746] lr: 6.675e-02, eta: 3 days, 2:47:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5567, loss_cls: 4.0305, loss: 4.0305 +2024-07-18 08:55:46,774 - pyskl - INFO - Epoch [59][2700/3746] lr: 6.672e-02, eta: 3 days, 2:46:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5547, loss_cls: 3.9987, loss: 3.9987 +2024-07-18 08:57:08,143 - pyskl - INFO - Epoch [59][2800/3746] lr: 6.669e-02, eta: 3 days, 2:45:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5570, loss_cls: 4.0462, loss: 4.0462 +2024-07-18 08:58:29,812 - pyskl - INFO - Epoch [59][2900/3746] lr: 6.667e-02, eta: 3 days, 2:43:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5389, loss_cls: 4.0860, loss: 4.0860 +2024-07-18 08:59:51,648 - pyskl - INFO - Epoch [59][3000/3746] lr: 6.664e-02, eta: 3 days, 2:42:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5445, loss_cls: 4.0559, loss: 4.0559 +2024-07-18 09:01:12,965 - pyskl - INFO - Epoch [59][3100/3746] lr: 6.661e-02, eta: 3 days, 2:41:23, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5470, loss_cls: 4.0212, loss: 4.0212 +2024-07-18 09:02:35,020 - pyskl - INFO - Epoch [59][3200/3746] lr: 6.659e-02, eta: 3 days, 2:40:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5539, loss_cls: 4.0552, loss: 4.0552 +2024-07-18 09:03:56,768 - pyskl - INFO - Epoch [59][3300/3746] lr: 6.656e-02, eta: 3 days, 2:38:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5531, loss_cls: 4.0083, loss: 4.0083 +2024-07-18 09:05:18,438 - pyskl - INFO - Epoch [59][3400/3746] lr: 6.653e-02, eta: 3 days, 2:37:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5552, loss_cls: 4.0413, loss: 4.0413 +2024-07-18 09:06:40,265 - pyskl - INFO - Epoch [59][3500/3746] lr: 6.651e-02, eta: 3 days, 2:36:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5620, loss_cls: 3.9907, loss: 3.9907 +2024-07-18 09:08:02,012 - pyskl - INFO - Epoch [59][3600/3746] lr: 6.648e-02, eta: 3 days, 2:35:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5459, loss_cls: 4.0642, loss: 4.0642 +2024-07-18 09:09:23,446 - pyskl - INFO - Epoch [59][3700/3746] lr: 6.646e-02, eta: 3 days, 2:33:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5547, loss_cls: 4.0180, loss: 4.0180 +2024-07-18 09:10:02,697 - pyskl - INFO - Saving checkpoint at 59 epochs +2024-07-18 09:11:53,631 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 09:11:54,303 - pyskl - INFO - +top1_acc 0.2249 +top5_acc 0.4689 +2024-07-18 09:11:54,304 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 09:11:54,348 - pyskl - INFO - +mean_acc 0.2246 +2024-07-18 09:11:54,360 - pyskl - INFO - Epoch(val) [59][309] top1_acc: 0.2249, top5_acc: 0.4689, mean_class_accuracy: 0.2246 +2024-07-18 09:15:42,956 - pyskl - INFO - Epoch [60][100/3746] lr: 6.642e-02, eta: 3 days, 2:34:59, time: 2.286, data_time: 1.297, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5695, loss_cls: 3.9550, loss: 3.9550 +2024-07-18 09:17:04,844 - pyskl - INFO - Epoch [60][200/3746] lr: 6.639e-02, eta: 3 days, 2:33:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5631, loss_cls: 3.9657, loss: 3.9657 +2024-07-18 09:18:27,108 - pyskl - INFO - Epoch [60][300/3746] lr: 6.636e-02, eta: 3 days, 2:32:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5622, loss_cls: 4.0046, loss: 4.0046 +2024-07-18 09:19:49,057 - pyskl - INFO - Epoch [60][400/3746] lr: 6.634e-02, eta: 3 days, 2:31:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5733, loss_cls: 3.9406, loss: 3.9406 +2024-07-18 09:21:10,641 - pyskl - INFO - Epoch [60][500/3746] lr: 6.631e-02, eta: 3 days, 2:30:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5578, loss_cls: 3.9817, loss: 3.9817 +2024-07-18 09:22:32,298 - pyskl - INFO - Epoch [60][600/3746] lr: 6.629e-02, eta: 3 days, 2:28:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5606, loss_cls: 4.0345, loss: 4.0345 +2024-07-18 09:23:53,876 - pyskl - INFO - Epoch [60][700/3746] lr: 6.626e-02, eta: 3 days, 2:27:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5645, loss_cls: 3.9741, loss: 3.9741 +2024-07-18 09:25:16,254 - pyskl - INFO - Epoch [60][800/3746] lr: 6.623e-02, eta: 3 days, 2:26:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5539, loss_cls: 4.0143, loss: 4.0143 +2024-07-18 09:26:37,802 - pyskl - INFO - Epoch [60][900/3746] lr: 6.621e-02, eta: 3 days, 2:25:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5517, loss_cls: 4.0460, loss: 4.0460 +2024-07-18 09:27:59,900 - pyskl - INFO - Epoch [60][1000/3746] lr: 6.618e-02, eta: 3 days, 2:23:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5608, loss_cls: 3.9902, loss: 3.9902 +2024-07-18 09:29:21,674 - pyskl - INFO - Epoch [60][1100/3746] lr: 6.615e-02, eta: 3 days, 2:22:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5600, loss_cls: 4.0376, loss: 4.0376 +2024-07-18 09:30:43,423 - pyskl - INFO - Epoch [60][1200/3746] lr: 6.613e-02, eta: 3 days, 2:21:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5470, loss_cls: 4.0357, loss: 4.0357 +2024-07-18 09:32:05,568 - pyskl - INFO - Epoch [60][1300/3746] lr: 6.610e-02, eta: 3 days, 2:20:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5581, loss_cls: 4.0030, loss: 4.0030 +2024-07-18 09:33:27,044 - pyskl - INFO - Epoch [60][1400/3746] lr: 6.607e-02, eta: 3 days, 2:18:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5513, loss_cls: 4.0472, loss: 4.0472 +2024-07-18 09:34:49,089 - pyskl - INFO - Epoch [60][1500/3746] lr: 6.605e-02, eta: 3 days, 2:17:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5547, loss_cls: 4.0345, loss: 4.0345 +2024-07-18 09:36:10,491 - pyskl - INFO - Epoch [60][1600/3746] lr: 6.602e-02, eta: 3 days, 2:16:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5489, loss_cls: 4.0274, loss: 4.0274 +2024-07-18 09:37:32,566 - pyskl - INFO - Epoch [60][1700/3746] lr: 6.599e-02, eta: 3 days, 2:15:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5522, loss_cls: 4.0374, loss: 4.0374 +2024-07-18 09:38:54,031 - pyskl - INFO - Epoch [60][1800/3746] lr: 6.597e-02, eta: 3 days, 2:13:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5473, loss_cls: 4.0481, loss: 4.0481 +2024-07-18 09:40:15,520 - pyskl - INFO - Epoch [60][1900/3746] lr: 6.594e-02, eta: 3 days, 2:12:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5506, loss_cls: 4.0363, loss: 4.0363 +2024-07-18 09:41:37,025 - pyskl - INFO - Epoch [60][2000/3746] lr: 6.591e-02, eta: 3 days, 2:11:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5495, loss_cls: 4.0440, loss: 4.0440 +2024-07-18 09:42:59,092 - pyskl - INFO - Epoch [60][2100/3746] lr: 6.589e-02, eta: 3 days, 2:10:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5620, loss_cls: 4.0230, loss: 4.0230 +2024-07-18 09:44:21,851 - pyskl - INFO - Epoch [60][2200/3746] lr: 6.586e-02, eta: 3 days, 2:09:02, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5473, loss_cls: 4.0429, loss: 4.0429 +2024-07-18 09:45:43,764 - pyskl - INFO - Epoch [60][2300/3746] lr: 6.584e-02, eta: 3 days, 2:07:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5519, loss_cls: 4.0429, loss: 4.0429 +2024-07-18 09:47:05,797 - pyskl - INFO - Epoch [60][2400/3746] lr: 6.581e-02, eta: 3 days, 2:06:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5613, loss_cls: 4.0382, loss: 4.0382 +2024-07-18 09:48:27,619 - pyskl - INFO - Epoch [60][2500/3746] lr: 6.578e-02, eta: 3 days, 2:05:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5530, loss_cls: 4.0322, loss: 4.0322 +2024-07-18 09:49:49,292 - pyskl - INFO - Epoch [60][2600/3746] lr: 6.576e-02, eta: 3 days, 2:04:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5492, loss_cls: 4.0437, loss: 4.0437 +2024-07-18 09:51:11,078 - pyskl - INFO - Epoch [60][2700/3746] lr: 6.573e-02, eta: 3 days, 2:02:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5559, loss_cls: 4.0394, loss: 4.0394 +2024-07-18 09:52:32,844 - pyskl - INFO - Epoch [60][2800/3746] lr: 6.570e-02, eta: 3 days, 2:01:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5494, loss_cls: 4.0823, loss: 4.0823 +2024-07-18 09:53:54,390 - pyskl - INFO - Epoch [60][2900/3746] lr: 6.568e-02, eta: 3 days, 2:00:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5539, loss_cls: 4.0248, loss: 4.0248 +2024-07-18 09:55:15,932 - pyskl - INFO - Epoch [60][3000/3746] lr: 6.565e-02, eta: 3 days, 1:59:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5664, loss_cls: 3.9984, loss: 3.9984 +2024-07-18 09:56:37,599 - pyskl - INFO - Epoch [60][3100/3746] lr: 6.562e-02, eta: 3 days, 1:57:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5547, loss_cls: 4.0142, loss: 4.0142 +2024-07-18 09:57:59,725 - pyskl - INFO - Epoch [60][3200/3746] lr: 6.560e-02, eta: 3 days, 1:56:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5602, loss_cls: 3.9944, loss: 3.9944 +2024-07-18 09:59:20,929 - pyskl - INFO - Epoch [60][3300/3746] lr: 6.557e-02, eta: 3 days, 1:55:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5369, loss_cls: 4.0648, loss: 4.0648 +2024-07-18 10:00:42,686 - pyskl - INFO - Epoch [60][3400/3746] lr: 6.554e-02, eta: 3 days, 1:54:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5478, loss_cls: 4.0463, loss: 4.0463 +2024-07-18 10:02:04,638 - pyskl - INFO - Epoch [60][3500/3746] lr: 6.552e-02, eta: 3 days, 1:52:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5564, loss_cls: 3.9931, loss: 3.9931 +2024-07-18 10:03:26,579 - pyskl - INFO - Epoch [60][3600/3746] lr: 6.549e-02, eta: 3 days, 1:51:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5484, loss_cls: 4.0481, loss: 4.0481 +2024-07-18 10:04:47,991 - pyskl - INFO - Epoch [60][3700/3746] lr: 6.546e-02, eta: 3 days, 1:50:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5453, loss_cls: 4.0645, loss: 4.0645 +2024-07-18 10:05:27,430 - pyskl - INFO - Saving checkpoint at 60 epochs +2024-07-18 10:07:17,664 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 10:07:18,318 - pyskl - INFO - +top1_acc 0.2403 +top5_acc 0.4779 +2024-07-18 10:07:18,318 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 10:07:18,358 - pyskl - INFO - +mean_acc 0.2401 +2024-07-18 10:07:18,362 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_57.pth was removed +2024-07-18 10:07:18,614 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_60.pth. +2024-07-18 10:07:18,615 - pyskl - INFO - Best top1_acc is 0.2403 at 60 epoch. +2024-07-18 10:07:18,626 - pyskl - INFO - Epoch(val) [60][309] top1_acc: 0.2403, top5_acc: 0.4779, mean_class_accuracy: 0.2401 +2024-07-18 10:11:08,657 - pyskl - INFO - Epoch [61][100/3746] lr: 6.542e-02, eta: 3 days, 1:51:22, time: 2.300, data_time: 1.292, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5620, loss_cls: 3.9710, loss: 3.9710 +2024-07-18 10:12:30,421 - pyskl - INFO - Epoch [61][200/3746] lr: 6.540e-02, eta: 3 days, 1:50:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5667, loss_cls: 3.9480, loss: 3.9480 +2024-07-18 10:13:52,060 - pyskl - INFO - Epoch [61][300/3746] lr: 6.537e-02, eta: 3 days, 1:48:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5520, loss_cls: 4.0271, loss: 4.0271 +2024-07-18 10:15:13,771 - pyskl - INFO - Epoch [61][400/3746] lr: 6.534e-02, eta: 3 days, 1:47:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5505, loss_cls: 4.0399, loss: 4.0399 +2024-07-18 10:16:35,459 - pyskl - INFO - Epoch [61][500/3746] lr: 6.532e-02, eta: 3 days, 1:46:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5533, loss_cls: 3.9966, loss: 3.9966 +2024-07-18 10:17:57,496 - pyskl - INFO - Epoch [61][600/3746] lr: 6.529e-02, eta: 3 days, 1:45:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5611, loss_cls: 3.9810, loss: 3.9810 +2024-07-18 10:19:19,269 - pyskl - INFO - Epoch [61][700/3746] lr: 6.526e-02, eta: 3 days, 1:43:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5608, loss_cls: 4.0021, loss: 4.0021 +2024-07-18 10:20:41,122 - pyskl - INFO - Epoch [61][800/3746] lr: 6.524e-02, eta: 3 days, 1:42:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5542, loss_cls: 4.0060, loss: 4.0060 +2024-07-18 10:22:02,493 - pyskl - INFO - Epoch [61][900/3746] lr: 6.521e-02, eta: 3 days, 1:41:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5563, loss_cls: 4.0383, loss: 4.0383 +2024-07-18 10:23:24,552 - pyskl - INFO - Epoch [61][1000/3746] lr: 6.519e-02, eta: 3 days, 1:40:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5572, loss_cls: 3.9923, loss: 3.9923 +2024-07-18 10:24:46,770 - pyskl - INFO - Epoch [61][1100/3746] lr: 6.516e-02, eta: 3 days, 1:38:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5500, loss_cls: 4.0442, loss: 4.0442 +2024-07-18 10:26:09,197 - pyskl - INFO - Epoch [61][1200/3746] lr: 6.513e-02, eta: 3 days, 1:37:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5622, loss_cls: 4.0166, loss: 4.0166 +2024-07-18 10:27:31,452 - pyskl - INFO - Epoch [61][1300/3746] lr: 6.511e-02, eta: 3 days, 1:36:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5613, loss_cls: 3.9766, loss: 3.9766 +2024-07-18 10:28:52,565 - pyskl - INFO - Epoch [61][1400/3746] lr: 6.508e-02, eta: 3 days, 1:35:14, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5550, loss_cls: 3.9749, loss: 3.9749 +2024-07-18 10:30:14,455 - pyskl - INFO - Epoch [61][1500/3746] lr: 6.505e-02, eta: 3 days, 1:33:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5659, loss_cls: 3.9593, loss: 3.9593 +2024-07-18 10:31:36,089 - pyskl - INFO - Epoch [61][1600/3746] lr: 6.503e-02, eta: 3 days, 1:32:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5606, loss_cls: 4.0231, loss: 4.0231 +2024-07-18 10:32:57,594 - pyskl - INFO - Epoch [61][1700/3746] lr: 6.500e-02, eta: 3 days, 1:31:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5437, loss_cls: 4.0640, loss: 4.0640 +2024-07-18 10:34:19,174 - pyskl - INFO - Epoch [61][1800/3746] lr: 6.497e-02, eta: 3 days, 1:30:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5608, loss_cls: 4.0063, loss: 4.0063 +2024-07-18 10:35:40,428 - pyskl - INFO - Epoch [61][1900/3746] lr: 6.495e-02, eta: 3 days, 1:28:59, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5511, loss_cls: 4.0366, loss: 4.0366 +2024-07-18 10:37:01,847 - pyskl - INFO - Epoch [61][2000/3746] lr: 6.492e-02, eta: 3 days, 1:27:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5564, loss_cls: 4.0284, loss: 4.0284 +2024-07-18 10:38:23,084 - pyskl - INFO - Epoch [61][2100/3746] lr: 6.489e-02, eta: 3 days, 1:26:28, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5572, loss_cls: 4.0296, loss: 4.0296 +2024-07-18 10:39:45,193 - pyskl - INFO - Epoch [61][2200/3746] lr: 6.487e-02, eta: 3 days, 1:25:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5477, loss_cls: 4.0422, loss: 4.0422 +2024-07-18 10:41:07,092 - pyskl - INFO - Epoch [61][2300/3746] lr: 6.484e-02, eta: 3 days, 1:24:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5639, loss_cls: 3.9823, loss: 3.9823 +2024-07-18 10:42:28,899 - pyskl - INFO - Epoch [61][2400/3746] lr: 6.481e-02, eta: 3 days, 1:22:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5500, loss_cls: 4.0625, loss: 4.0625 +2024-07-18 10:43:50,998 - pyskl - INFO - Epoch [61][2500/3746] lr: 6.478e-02, eta: 3 days, 1:21:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5480, loss_cls: 4.0195, loss: 4.0195 +2024-07-18 10:45:13,116 - pyskl - INFO - Epoch [61][2600/3746] lr: 6.476e-02, eta: 3 days, 1:20:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5489, loss_cls: 4.0689, loss: 4.0689 +2024-07-18 10:46:35,476 - pyskl - INFO - Epoch [61][2700/3746] lr: 6.473e-02, eta: 3 days, 1:19:03, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5434, loss_cls: 4.0796, loss: 4.0796 +2024-07-18 10:47:57,425 - pyskl - INFO - Epoch [61][2800/3746] lr: 6.470e-02, eta: 3 days, 1:17:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5594, loss_cls: 3.9677, loss: 3.9677 +2024-07-18 10:49:19,097 - pyskl - INFO - Epoch [61][2900/3746] lr: 6.468e-02, eta: 3 days, 1:16:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5511, loss_cls: 4.0332, loss: 4.0332 +2024-07-18 10:50:40,879 - pyskl - INFO - Epoch [61][3000/3746] lr: 6.465e-02, eta: 3 days, 1:15:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5616, loss_cls: 3.9769, loss: 3.9769 +2024-07-18 10:52:02,616 - pyskl - INFO - Epoch [61][3100/3746] lr: 6.462e-02, eta: 3 days, 1:14:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5525, loss_cls: 4.0463, loss: 4.0463 +2024-07-18 10:53:25,028 - pyskl - INFO - Epoch [61][3200/3746] lr: 6.460e-02, eta: 3 days, 1:12:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5622, loss_cls: 4.0008, loss: 4.0008 +2024-07-18 10:54:46,856 - pyskl - INFO - Epoch [61][3300/3746] lr: 6.457e-02, eta: 3 days, 1:11:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5463, loss_cls: 4.0746, loss: 4.0746 +2024-07-18 10:56:08,266 - pyskl - INFO - Epoch [61][3400/3746] lr: 6.454e-02, eta: 3 days, 1:10:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5573, loss_cls: 3.9992, loss: 3.9992 +2024-07-18 10:57:29,793 - pyskl - INFO - Epoch [61][3500/3746] lr: 6.452e-02, eta: 3 days, 1:09:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5653, loss_cls: 3.9843, loss: 3.9843 +2024-07-18 10:58:51,170 - pyskl - INFO - Epoch [61][3600/3746] lr: 6.449e-02, eta: 3 days, 1:07:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5520, loss_cls: 4.0093, loss: 4.0093 +2024-07-18 11:00:12,652 - pyskl - INFO - Epoch [61][3700/3746] lr: 6.446e-02, eta: 3 days, 1:06:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5536, loss_cls: 4.0113, loss: 4.0113 +2024-07-18 11:00:51,764 - pyskl - INFO - Saving checkpoint at 61 epochs +2024-07-18 11:02:41,555 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 11:02:42,239 - pyskl - INFO - +top1_acc 0.2429 +top5_acc 0.4881 +2024-07-18 11:02:42,239 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 11:02:42,279 - pyskl - INFO - +mean_acc 0.2427 +2024-07-18 11:02:42,283 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_60.pth was removed +2024-07-18 11:02:42,548 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_61.pth. +2024-07-18 11:02:42,549 - pyskl - INFO - Best top1_acc is 0.2429 at 61 epoch. +2024-07-18 11:02:42,563 - pyskl - INFO - Epoch(val) [61][309] top1_acc: 0.2429, top5_acc: 0.4881, mean_class_accuracy: 0.2427 +2024-07-18 11:06:27,043 - pyskl - INFO - Epoch [62][100/3746] lr: 6.443e-02, eta: 3 days, 1:07:18, time: 2.245, data_time: 1.258, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5630, loss_cls: 3.9925, loss: 3.9925 +2024-07-18 11:07:49,007 - pyskl - INFO - Epoch [62][200/3746] lr: 6.440e-02, eta: 3 days, 1:06:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5586, loss_cls: 3.9878, loss: 3.9878 +2024-07-18 11:09:10,909 - pyskl - INFO - Epoch [62][300/3746] lr: 6.437e-02, eta: 3 days, 1:04:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5628, loss_cls: 3.9558, loss: 3.9558 +2024-07-18 11:10:32,427 - pyskl - INFO - Epoch [62][400/3746] lr: 6.434e-02, eta: 3 days, 1:03:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5566, loss_cls: 4.0085, loss: 4.0085 +2024-07-18 11:11:54,345 - pyskl - INFO - Epoch [62][500/3746] lr: 6.432e-02, eta: 3 days, 1:02:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5586, loss_cls: 3.9858, loss: 3.9858 +2024-07-18 11:13:15,747 - pyskl - INFO - Epoch [62][600/3746] lr: 6.429e-02, eta: 3 days, 1:01:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5530, loss_cls: 4.0498, loss: 4.0498 +2024-07-18 11:14:37,178 - pyskl - INFO - Epoch [62][700/3746] lr: 6.426e-02, eta: 3 days, 0:59:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5758, loss_cls: 3.9285, loss: 3.9285 +2024-07-18 11:15:58,708 - pyskl - INFO - Epoch [62][800/3746] lr: 6.424e-02, eta: 3 days, 0:58:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5553, loss_cls: 4.0196, loss: 4.0196 +2024-07-18 11:17:20,851 - pyskl - INFO - Epoch [62][900/3746] lr: 6.421e-02, eta: 3 days, 0:57:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5766, loss_cls: 3.9486, loss: 3.9486 +2024-07-18 11:18:42,900 - pyskl - INFO - Epoch [62][1000/3746] lr: 6.418e-02, eta: 3 days, 0:56:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5559, loss_cls: 4.0239, loss: 4.0239 +2024-07-18 11:20:05,224 - pyskl - INFO - Epoch [62][1100/3746] lr: 6.416e-02, eta: 3 days, 0:54:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5559, loss_cls: 3.9969, loss: 3.9969 +2024-07-18 11:21:27,414 - pyskl - INFO - Epoch [62][1200/3746] lr: 6.413e-02, eta: 3 days, 0:53:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5622, loss_cls: 4.0076, loss: 4.0076 +2024-07-18 11:22:49,800 - pyskl - INFO - Epoch [62][1300/3746] lr: 6.410e-02, eta: 3 days, 0:52:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5486, loss_cls: 4.0532, loss: 4.0532 +2024-07-18 11:24:11,655 - pyskl - INFO - Epoch [62][1400/3746] lr: 6.408e-02, eta: 3 days, 0:51:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5553, loss_cls: 4.0301, loss: 4.0301 +2024-07-18 11:25:33,378 - pyskl - INFO - Epoch [62][1500/3746] lr: 6.405e-02, eta: 3 days, 0:49:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5659, loss_cls: 3.9845, loss: 3.9845 +2024-07-18 11:26:55,015 - pyskl - INFO - Epoch [62][1600/3746] lr: 6.402e-02, eta: 3 days, 0:48:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5578, loss_cls: 4.0201, loss: 4.0201 +2024-07-18 11:28:16,485 - pyskl - INFO - Epoch [62][1700/3746] lr: 6.400e-02, eta: 3 days, 0:47:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5534, loss_cls: 4.0139, loss: 4.0139 +2024-07-18 11:29:38,045 - pyskl - INFO - Epoch [62][1800/3746] lr: 6.397e-02, eta: 3 days, 0:46:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5573, loss_cls: 4.0098, loss: 4.0098 +2024-07-18 11:30:59,395 - pyskl - INFO - Epoch [62][1900/3746] lr: 6.394e-02, eta: 3 days, 0:44:50, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5502, loss_cls: 4.0687, loss: 4.0687 +2024-07-18 11:32:20,949 - pyskl - INFO - Epoch [62][2000/3746] lr: 6.392e-02, eta: 3 days, 0:43:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5655, loss_cls: 3.9613, loss: 3.9613 +2024-07-18 11:33:43,077 - pyskl - INFO - Epoch [62][2100/3746] lr: 6.389e-02, eta: 3 days, 0:42:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5522, loss_cls: 4.0074, loss: 4.0074 +2024-07-18 11:35:04,759 - pyskl - INFO - Epoch [62][2200/3746] lr: 6.386e-02, eta: 3 days, 0:41:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5556, loss_cls: 4.0107, loss: 4.0107 +2024-07-18 11:36:27,200 - pyskl - INFO - Epoch [62][2300/3746] lr: 6.384e-02, eta: 3 days, 0:39:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5595, loss_cls: 3.9970, loss: 3.9970 +2024-07-18 11:37:49,281 - pyskl - INFO - Epoch [62][2400/3746] lr: 6.381e-02, eta: 3 days, 0:38:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5439, loss_cls: 4.0480, loss: 4.0480 +2024-07-18 11:39:11,206 - pyskl - INFO - Epoch [62][2500/3746] lr: 6.378e-02, eta: 3 days, 0:37:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5578, loss_cls: 3.9593, loss: 3.9593 +2024-07-18 11:40:33,651 - pyskl - INFO - Epoch [62][2600/3746] lr: 6.375e-02, eta: 3 days, 0:36:07, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5613, loss_cls: 3.9887, loss: 3.9887 +2024-07-18 11:41:56,017 - pyskl - INFO - Epoch [62][2700/3746] lr: 6.373e-02, eta: 3 days, 0:34:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5544, loss_cls: 4.0416, loss: 4.0416 +2024-07-18 11:43:18,156 - pyskl - INFO - Epoch [62][2800/3746] lr: 6.370e-02, eta: 3 days, 0:33:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5670, loss_cls: 3.9825, loss: 3.9825 +2024-07-18 11:44:39,878 - pyskl - INFO - Epoch [62][2900/3746] lr: 6.367e-02, eta: 3 days, 0:32:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5531, loss_cls: 3.9989, loss: 3.9989 +2024-07-18 11:46:01,616 - pyskl - INFO - Epoch [62][3000/3746] lr: 6.365e-02, eta: 3 days, 0:31:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5586, loss_cls: 3.9846, loss: 3.9846 +2024-07-18 11:47:23,548 - pyskl - INFO - Epoch [62][3100/3746] lr: 6.362e-02, eta: 3 days, 0:29:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5648, loss_cls: 3.9721, loss: 3.9721 +2024-07-18 11:48:44,910 - pyskl - INFO - Epoch [62][3200/3746] lr: 6.359e-02, eta: 3 days, 0:28:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5631, loss_cls: 3.9710, loss: 3.9710 +2024-07-18 11:50:06,513 - pyskl - INFO - Epoch [62][3300/3746] lr: 6.357e-02, eta: 3 days, 0:27:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5597, loss_cls: 4.0114, loss: 4.0114 +2024-07-18 11:51:27,817 - pyskl - INFO - Epoch [62][3400/3746] lr: 6.354e-02, eta: 3 days, 0:26:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5575, loss_cls: 3.9931, loss: 3.9931 +2024-07-18 11:52:49,174 - pyskl - INFO - Epoch [62][3500/3746] lr: 6.351e-02, eta: 3 days, 0:24:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5520, loss_cls: 4.0455, loss: 4.0455 +2024-07-18 11:54:10,931 - pyskl - INFO - Epoch [62][3600/3746] lr: 6.349e-02, eta: 3 days, 0:23:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5583, loss_cls: 4.0361, loss: 4.0361 +2024-07-18 11:55:32,950 - pyskl - INFO - Epoch [62][3700/3746] lr: 6.346e-02, eta: 3 days, 0:22:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5567, loss_cls: 4.0121, loss: 4.0121 +2024-07-18 11:56:12,341 - pyskl - INFO - Saving checkpoint at 62 epochs +2024-07-18 11:58:02,895 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 11:58:03,568 - pyskl - INFO - +top1_acc 0.2309 +top5_acc 0.4772 +2024-07-18 11:58:03,568 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 11:58:03,613 - pyskl - INFO - +mean_acc 0.2307 +2024-07-18 11:58:03,630 - pyskl - INFO - Epoch(val) [62][309] top1_acc: 0.2309, top5_acc: 0.4772, mean_class_accuracy: 0.2307 +2024-07-18 12:01:54,149 - pyskl - INFO - Epoch [63][100/3746] lr: 6.342e-02, eta: 3 days, 0:23:08, time: 2.305, data_time: 1.320, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5666, loss_cls: 3.9616, loss: 3.9616 +2024-07-18 12:03:16,519 - pyskl - INFO - Epoch [63][200/3746] lr: 6.339e-02, eta: 3 days, 0:21:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5644, loss_cls: 3.9844, loss: 3.9844 +2024-07-18 12:04:38,301 - pyskl - INFO - Epoch [63][300/3746] lr: 6.337e-02, eta: 3 days, 0:20:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5550, loss_cls: 3.9899, loss: 3.9899 +2024-07-18 12:06:00,028 - pyskl - INFO - Epoch [63][400/3746] lr: 6.334e-02, eta: 3 days, 0:19:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5677, loss_cls: 3.9553, loss: 3.9553 +2024-07-18 12:07:21,942 - pyskl - INFO - Epoch [63][500/3746] lr: 6.331e-02, eta: 3 days, 0:18:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5558, loss_cls: 3.9978, loss: 3.9978 +2024-07-18 12:08:43,535 - pyskl - INFO - Epoch [63][600/3746] lr: 6.328e-02, eta: 3 days, 0:16:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5564, loss_cls: 4.0230, loss: 4.0230 +2024-07-18 12:10:05,273 - pyskl - INFO - Epoch [63][700/3746] lr: 6.326e-02, eta: 3 days, 0:15:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5577, loss_cls: 4.0127, loss: 4.0127 +2024-07-18 12:11:26,840 - pyskl - INFO - Epoch [63][800/3746] lr: 6.323e-02, eta: 3 days, 0:14:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5583, loss_cls: 3.9720, loss: 3.9720 +2024-07-18 12:12:49,325 - pyskl - INFO - Epoch [63][900/3746] lr: 6.320e-02, eta: 3 days, 0:13:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5623, loss_cls: 4.0021, loss: 4.0021 +2024-07-18 12:14:12,221 - pyskl - INFO - Epoch [63][1000/3746] lr: 6.318e-02, eta: 3 days, 0:11:54, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5617, loss_cls: 3.9983, loss: 3.9983 +2024-07-18 12:15:34,253 - pyskl - INFO - Epoch [63][1100/3746] lr: 6.315e-02, eta: 3 days, 0:10:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5641, loss_cls: 4.0038, loss: 4.0038 +2024-07-18 12:16:55,845 - pyskl - INFO - Epoch [63][1200/3746] lr: 6.312e-02, eta: 3 days, 0:09:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5630, loss_cls: 3.9525, loss: 3.9525 +2024-07-18 12:18:16,996 - pyskl - INFO - Epoch [63][1300/3746] lr: 6.310e-02, eta: 3 days, 0:08:07, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5623, loss_cls: 3.9837, loss: 3.9837 +2024-07-18 12:19:38,549 - pyskl - INFO - Epoch [63][1400/3746] lr: 6.307e-02, eta: 3 days, 0:06:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5633, loss_cls: 3.9718, loss: 3.9718 +2024-07-18 12:20:59,735 - pyskl - INFO - Epoch [63][1500/3746] lr: 6.304e-02, eta: 3 days, 0:05:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5503, loss_cls: 4.0335, loss: 4.0335 +2024-07-18 12:22:21,416 - pyskl - INFO - Epoch [63][1600/3746] lr: 6.301e-02, eta: 3 days, 0:04:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5573, loss_cls: 3.9785, loss: 3.9785 +2024-07-18 12:23:43,153 - pyskl - INFO - Epoch [63][1700/3746] lr: 6.299e-02, eta: 3 days, 0:03:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5614, loss_cls: 3.9903, loss: 3.9903 +2024-07-18 12:25:04,788 - pyskl - INFO - Epoch [63][1800/3746] lr: 6.296e-02, eta: 3 days, 0:01:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5520, loss_cls: 4.0448, loss: 4.0448 +2024-07-18 12:26:26,204 - pyskl - INFO - Epoch [63][1900/3746] lr: 6.293e-02, eta: 3 days, 0:00:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5523, loss_cls: 3.9990, loss: 3.9990 +2024-07-18 12:27:47,710 - pyskl - INFO - Epoch [63][2000/3746] lr: 6.291e-02, eta: 2 days, 23:59:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5591, loss_cls: 4.0173, loss: 4.0173 +2024-07-18 12:29:09,239 - pyskl - INFO - Epoch [63][2100/3746] lr: 6.288e-02, eta: 2 days, 23:58:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5555, loss_cls: 4.0274, loss: 4.0274 +2024-07-18 12:30:30,985 - pyskl - INFO - Epoch [63][2200/3746] lr: 6.285e-02, eta: 2 days, 23:56:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5569, loss_cls: 3.9868, loss: 3.9868 +2024-07-18 12:31:53,300 - pyskl - INFO - Epoch [63][2300/3746] lr: 6.283e-02, eta: 2 days, 23:55:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5686, loss_cls: 3.9374, loss: 3.9374 +2024-07-18 12:33:15,169 - pyskl - INFO - Epoch [63][2400/3746] lr: 6.280e-02, eta: 2 days, 23:54:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5598, loss_cls: 3.9985, loss: 3.9985 +2024-07-18 12:34:37,366 - pyskl - INFO - Epoch [63][2500/3746] lr: 6.277e-02, eta: 2 days, 23:53:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5645, loss_cls: 3.9700, loss: 3.9700 +2024-07-18 12:35:59,656 - pyskl - INFO - Epoch [63][2600/3746] lr: 6.274e-02, eta: 2 days, 23:51:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5600, loss_cls: 3.9685, loss: 3.9685 +2024-07-18 12:37:22,244 - pyskl - INFO - Epoch [63][2700/3746] lr: 6.272e-02, eta: 2 days, 23:50:33, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5553, loss_cls: 3.9914, loss: 3.9914 +2024-07-18 12:38:44,213 - pyskl - INFO - Epoch [63][2800/3746] lr: 6.269e-02, eta: 2 days, 23:49:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5670, loss_cls: 3.9601, loss: 3.9601 +2024-07-18 12:40:05,789 - pyskl - INFO - Epoch [63][2900/3746] lr: 6.266e-02, eta: 2 days, 23:48:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5530, loss_cls: 3.9982, loss: 3.9982 +2024-07-18 12:41:27,658 - pyskl - INFO - Epoch [63][3000/3746] lr: 6.264e-02, eta: 2 days, 23:46:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5539, loss_cls: 4.0108, loss: 4.0108 +2024-07-18 12:42:49,101 - pyskl - INFO - Epoch [63][3100/3746] lr: 6.261e-02, eta: 2 days, 23:45:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5453, loss_cls: 4.0298, loss: 4.0298 +2024-07-18 12:44:10,461 - pyskl - INFO - Epoch [63][3200/3746] lr: 6.258e-02, eta: 2 days, 23:44:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5541, loss_cls: 3.9891, loss: 3.9891 +2024-07-18 12:45:31,967 - pyskl - INFO - Epoch [63][3300/3746] lr: 6.256e-02, eta: 2 days, 23:42:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5461, loss_cls: 4.0662, loss: 4.0662 +2024-07-18 12:46:53,308 - pyskl - INFO - Epoch [63][3400/3746] lr: 6.253e-02, eta: 2 days, 23:41:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5627, loss_cls: 3.9645, loss: 3.9645 +2024-07-18 12:48:14,873 - pyskl - INFO - Epoch [63][3500/3746] lr: 6.250e-02, eta: 2 days, 23:40:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5563, loss_cls: 4.0350, loss: 4.0350 +2024-07-18 12:49:36,225 - pyskl - INFO - Epoch [63][3600/3746] lr: 6.247e-02, eta: 2 days, 23:39:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5613, loss_cls: 3.9845, loss: 3.9845 +2024-07-18 12:50:57,861 - pyskl - INFO - Epoch [63][3700/3746] lr: 6.245e-02, eta: 2 days, 23:37:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5513, loss_cls: 4.0045, loss: 4.0045 +2024-07-18 12:51:37,381 - pyskl - INFO - Saving checkpoint at 63 epochs +2024-07-18 12:53:28,382 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 12:53:29,066 - pyskl - INFO - +top1_acc 0.2415 +top5_acc 0.4865 +2024-07-18 12:53:29,067 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 12:53:29,107 - pyskl - INFO - +mean_acc 0.2413 +2024-07-18 12:53:29,118 - pyskl - INFO - Epoch(val) [63][309] top1_acc: 0.2415, top5_acc: 0.4865, mean_class_accuracy: 0.2413 +2024-07-18 12:57:17,151 - pyskl - INFO - Epoch [64][100/3746] lr: 6.241e-02, eta: 2 days, 23:38:36, time: 2.280, data_time: 1.296, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5645, loss_cls: 3.9534, loss: 3.9534 +2024-07-18 12:58:39,257 - pyskl - INFO - Epoch [64][200/3746] lr: 6.238e-02, eta: 2 days, 23:37:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5641, loss_cls: 3.9297, loss: 3.9297 +2024-07-18 13:00:00,751 - pyskl - INFO - Epoch [64][300/3746] lr: 6.235e-02, eta: 2 days, 23:36:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5611, loss_cls: 3.9435, loss: 3.9435 +2024-07-18 13:01:22,754 - pyskl - INFO - Epoch [64][400/3746] lr: 6.233e-02, eta: 2 days, 23:34:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5636, loss_cls: 3.9496, loss: 3.9496 +2024-07-18 13:02:44,640 - pyskl - INFO - Epoch [64][500/3746] lr: 6.230e-02, eta: 2 days, 23:33:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5561, loss_cls: 3.9859, loss: 3.9859 +2024-07-18 13:04:06,072 - pyskl - INFO - Epoch [64][600/3746] lr: 6.227e-02, eta: 2 days, 23:32:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5652, loss_cls: 3.9626, loss: 3.9626 +2024-07-18 13:05:27,690 - pyskl - INFO - Epoch [64][700/3746] lr: 6.225e-02, eta: 2 days, 23:31:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5672, loss_cls: 3.9310, loss: 3.9310 +2024-07-18 13:06:49,567 - pyskl - INFO - Epoch [64][800/3746] lr: 6.222e-02, eta: 2 days, 23:29:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5728, loss_cls: 3.9150, loss: 3.9150 +2024-07-18 13:08:11,231 - pyskl - INFO - Epoch [64][900/3746] lr: 6.219e-02, eta: 2 days, 23:28:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5689, loss_cls: 3.9318, loss: 3.9318 +2024-07-18 13:09:33,682 - pyskl - INFO - Epoch [64][1000/3746] lr: 6.216e-02, eta: 2 days, 23:27:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5542, loss_cls: 4.0093, loss: 4.0093 +2024-07-18 13:10:56,077 - pyskl - INFO - Epoch [64][1100/3746] lr: 6.214e-02, eta: 2 days, 23:26:01, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5631, loss_cls: 3.9960, loss: 3.9960 +2024-07-18 13:12:18,080 - pyskl - INFO - Epoch [64][1200/3746] lr: 6.211e-02, eta: 2 days, 23:24:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5603, loss_cls: 3.9649, loss: 3.9649 +2024-07-18 13:13:40,158 - pyskl - INFO - Epoch [64][1300/3746] lr: 6.208e-02, eta: 2 days, 23:23:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5508, loss_cls: 4.0393, loss: 4.0393 +2024-07-18 13:15:01,985 - pyskl - INFO - Epoch [64][1400/3746] lr: 6.206e-02, eta: 2 days, 23:22:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5506, loss_cls: 4.0506, loss: 4.0506 +2024-07-18 13:16:24,267 - pyskl - INFO - Epoch [64][1500/3746] lr: 6.203e-02, eta: 2 days, 23:21:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5527, loss_cls: 4.0093, loss: 4.0093 +2024-07-18 13:17:45,881 - pyskl - INFO - Epoch [64][1600/3746] lr: 6.200e-02, eta: 2 days, 23:19:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5569, loss_cls: 4.0198, loss: 4.0198 +2024-07-18 13:19:07,754 - pyskl - INFO - Epoch [64][1700/3746] lr: 6.197e-02, eta: 2 days, 23:18:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5578, loss_cls: 4.0199, loss: 4.0199 +2024-07-18 13:20:29,837 - pyskl - INFO - Epoch [64][1800/3746] lr: 6.195e-02, eta: 2 days, 23:17:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5652, loss_cls: 3.9704, loss: 3.9704 +2024-07-18 13:21:52,028 - pyskl - INFO - Epoch [64][1900/3746] lr: 6.192e-02, eta: 2 days, 23:15:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5667, loss_cls: 3.9510, loss: 3.9510 +2024-07-18 13:23:13,794 - pyskl - INFO - Epoch [64][2000/3746] lr: 6.189e-02, eta: 2 days, 23:14:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5605, loss_cls: 4.0057, loss: 4.0057 +2024-07-18 13:24:35,972 - pyskl - INFO - Epoch [64][2100/3746] lr: 6.187e-02, eta: 2 days, 23:13:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5708, loss_cls: 3.9267, loss: 3.9267 +2024-07-18 13:25:58,175 - pyskl - INFO - Epoch [64][2200/3746] lr: 6.184e-02, eta: 2 days, 23:12:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5502, loss_cls: 4.0362, loss: 4.0362 +2024-07-18 13:27:20,997 - pyskl - INFO - Epoch [64][2300/3746] lr: 6.181e-02, eta: 2 days, 23:10:58, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5616, loss_cls: 4.0016, loss: 4.0016 +2024-07-18 13:28:43,072 - pyskl - INFO - Epoch [64][2400/3746] lr: 6.178e-02, eta: 2 days, 23:09:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5697, loss_cls: 4.0067, loss: 4.0067 +2024-07-18 13:30:05,521 - pyskl - INFO - Epoch [64][2500/3746] lr: 6.176e-02, eta: 2 days, 23:08:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5545, loss_cls: 4.0165, loss: 4.0165 +2024-07-18 13:31:28,498 - pyskl - INFO - Epoch [64][2600/3746] lr: 6.173e-02, eta: 2 days, 23:07:14, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5697, loss_cls: 3.9702, loss: 3.9702 +2024-07-18 13:32:50,632 - pyskl - INFO - Epoch [64][2700/3746] lr: 6.170e-02, eta: 2 days, 23:05:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5536, loss_cls: 4.0207, loss: 4.0207 +2024-07-18 13:34:12,288 - pyskl - INFO - Epoch [64][2800/3746] lr: 6.168e-02, eta: 2 days, 23:04:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5577, loss_cls: 4.0006, loss: 4.0006 +2024-07-18 13:35:34,574 - pyskl - INFO - Epoch [64][2900/3746] lr: 6.165e-02, eta: 2 days, 23:03:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5567, loss_cls: 4.0286, loss: 4.0286 +2024-07-18 13:36:56,868 - pyskl - INFO - Epoch [64][3000/3746] lr: 6.162e-02, eta: 2 days, 23:02:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5625, loss_cls: 3.9509, loss: 3.9509 +2024-07-18 13:38:18,669 - pyskl - INFO - Epoch [64][3100/3746] lr: 6.159e-02, eta: 2 days, 23:00:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5573, loss_cls: 4.0237, loss: 4.0237 +2024-07-18 13:39:40,472 - pyskl - INFO - Epoch [64][3200/3746] lr: 6.157e-02, eta: 2 days, 22:59:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5547, loss_cls: 4.0340, loss: 4.0340 +2024-07-18 13:41:02,642 - pyskl - INFO - Epoch [64][3300/3746] lr: 6.154e-02, eta: 2 days, 22:58:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5598, loss_cls: 3.9609, loss: 3.9609 +2024-07-18 13:42:24,661 - pyskl - INFO - Epoch [64][3400/3746] lr: 6.151e-02, eta: 2 days, 22:57:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5645, loss_cls: 3.9769, loss: 3.9769 +2024-07-18 13:43:46,201 - pyskl - INFO - Epoch [64][3500/3746] lr: 6.148e-02, eta: 2 days, 22:55:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5686, loss_cls: 3.9695, loss: 3.9695 +2024-07-18 13:45:07,996 - pyskl - INFO - Epoch [64][3600/3746] lr: 6.146e-02, eta: 2 days, 22:54:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5602, loss_cls: 3.9916, loss: 3.9916 +2024-07-18 13:46:29,934 - pyskl - INFO - Epoch [64][3700/3746] lr: 6.143e-02, eta: 2 days, 22:53:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5619, loss_cls: 3.9993, loss: 3.9993 +2024-07-18 13:47:09,555 - pyskl - INFO - Saving checkpoint at 64 epochs +2024-07-18 13:49:01,159 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 13:49:01,927 - pyskl - INFO - +top1_acc 0.2158 +top5_acc 0.4606 +2024-07-18 13:49:01,928 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 13:49:01,968 - pyskl - INFO - +mean_acc 0.2156 +2024-07-18 13:49:01,979 - pyskl - INFO - Epoch(val) [64][309] top1_acc: 0.2158, top5_acc: 0.4606, mean_class_accuracy: 0.2156 +2024-07-18 13:52:48,695 - pyskl - INFO - Epoch [65][100/3746] lr: 6.139e-02, eta: 2 days, 22:53:57, time: 2.267, data_time: 1.277, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5583, loss_cls: 4.0117, loss: 4.0117 +2024-07-18 13:54:11,502 - pyskl - INFO - Epoch [65][200/3746] lr: 6.136e-02, eta: 2 days, 22:52:43, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5753, loss_cls: 3.9200, loss: 3.9200 +2024-07-18 13:55:33,487 - pyskl - INFO - Epoch [65][300/3746] lr: 6.134e-02, eta: 2 days, 22:51:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5722, loss_cls: 3.9326, loss: 3.9326 +2024-07-18 13:56:55,131 - pyskl - INFO - Epoch [65][400/3746] lr: 6.131e-02, eta: 2 days, 22:50:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5633, loss_cls: 3.9661, loss: 3.9661 +2024-07-18 13:58:16,786 - pyskl - INFO - Epoch [65][500/3746] lr: 6.128e-02, eta: 2 days, 22:48:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5681, loss_cls: 3.9250, loss: 3.9250 +2024-07-18 13:59:38,912 - pyskl - INFO - Epoch [65][600/3746] lr: 6.125e-02, eta: 2 days, 22:47:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5591, loss_cls: 3.9872, loss: 3.9872 +2024-07-18 14:01:00,692 - pyskl - INFO - Epoch [65][700/3746] lr: 6.123e-02, eta: 2 days, 22:46:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5780, loss_cls: 3.9398, loss: 3.9398 +2024-07-18 14:02:22,418 - pyskl - INFO - Epoch [65][800/3746] lr: 6.120e-02, eta: 2 days, 22:45:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5619, loss_cls: 3.9772, loss: 3.9772 +2024-07-18 14:03:44,408 - pyskl - INFO - Epoch [65][900/3746] lr: 6.117e-02, eta: 2 days, 22:43:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5634, loss_cls: 3.9648, loss: 3.9648 +2024-07-18 14:05:07,678 - pyskl - INFO - Epoch [65][1000/3746] lr: 6.115e-02, eta: 2 days, 22:42:38, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5539, loss_cls: 4.0368, loss: 4.0368 +2024-07-18 14:06:29,935 - pyskl - INFO - Epoch [65][1100/3746] lr: 6.112e-02, eta: 2 days, 22:41:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5597, loss_cls: 3.9911, loss: 3.9911 +2024-07-18 14:07:51,756 - pyskl - INFO - Epoch [65][1200/3746] lr: 6.109e-02, eta: 2 days, 22:40:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5644, loss_cls: 3.9327, loss: 3.9327 +2024-07-18 14:09:13,647 - pyskl - INFO - Epoch [65][1300/3746] lr: 6.106e-02, eta: 2 days, 22:38:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5591, loss_cls: 3.9575, loss: 3.9575 +2024-07-18 14:10:35,534 - pyskl - INFO - Epoch [65][1400/3746] lr: 6.104e-02, eta: 2 days, 22:37:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5608, loss_cls: 4.0017, loss: 4.0017 +2024-07-18 14:11:57,218 - pyskl - INFO - Epoch [65][1500/3746] lr: 6.101e-02, eta: 2 days, 22:36:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5553, loss_cls: 4.0290, loss: 4.0290 +2024-07-18 14:13:18,928 - pyskl - INFO - Epoch [65][1600/3746] lr: 6.098e-02, eta: 2 days, 22:35:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5592, loss_cls: 3.9882, loss: 3.9882 +2024-07-18 14:14:40,427 - pyskl - INFO - Epoch [65][1700/3746] lr: 6.095e-02, eta: 2 days, 22:33:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5625, loss_cls: 3.9860, loss: 3.9860 +2024-07-18 14:16:02,423 - pyskl - INFO - Epoch [65][1800/3746] lr: 6.093e-02, eta: 2 days, 22:32:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5617, loss_cls: 3.9574, loss: 3.9574 +2024-07-18 14:17:24,382 - pyskl - INFO - Epoch [65][1900/3746] lr: 6.090e-02, eta: 2 days, 22:31:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5694, loss_cls: 3.9514, loss: 3.9514 +2024-07-18 14:18:45,993 - pyskl - INFO - Epoch [65][2000/3746] lr: 6.087e-02, eta: 2 days, 22:30:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5616, loss_cls: 3.9850, loss: 3.9850 +2024-07-18 14:20:07,281 - pyskl - INFO - Epoch [65][2100/3746] lr: 6.085e-02, eta: 2 days, 22:28:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5428, loss_cls: 4.0500, loss: 4.0500 +2024-07-18 14:21:28,811 - pyskl - INFO - Epoch [65][2200/3746] lr: 6.082e-02, eta: 2 days, 22:27:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5630, loss_cls: 3.9924, loss: 3.9924 +2024-07-18 14:22:50,797 - pyskl - INFO - Epoch [65][2300/3746] lr: 6.079e-02, eta: 2 days, 22:26:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5614, loss_cls: 3.9974, loss: 3.9974 +2024-07-18 14:24:12,655 - pyskl - INFO - Epoch [65][2400/3746] lr: 6.076e-02, eta: 2 days, 22:24:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5711, loss_cls: 3.9411, loss: 3.9411 +2024-07-18 14:25:34,791 - pyskl - INFO - Epoch [65][2500/3746] lr: 6.074e-02, eta: 2 days, 22:23:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5608, loss_cls: 3.9653, loss: 3.9653 +2024-07-18 14:26:57,367 - pyskl - INFO - Epoch [65][2600/3746] lr: 6.071e-02, eta: 2 days, 22:22:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5706, loss_cls: 3.9414, loss: 3.9414 +2024-07-18 14:28:19,318 - pyskl - INFO - Epoch [65][2700/3746] lr: 6.068e-02, eta: 2 days, 22:21:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5436, loss_cls: 4.0615, loss: 4.0615 +2024-07-18 14:29:41,647 - pyskl - INFO - Epoch [65][2800/3746] lr: 6.065e-02, eta: 2 days, 22:19:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5573, loss_cls: 4.0164, loss: 4.0164 +2024-07-18 14:31:03,088 - pyskl - INFO - Epoch [65][2900/3746] lr: 6.063e-02, eta: 2 days, 22:18:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5711, loss_cls: 3.9566, loss: 3.9566 +2024-07-18 14:32:24,614 - pyskl - INFO - Epoch [65][3000/3746] lr: 6.060e-02, eta: 2 days, 22:17:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5605, loss_cls: 3.9743, loss: 3.9743 +2024-07-18 14:33:46,000 - pyskl - INFO - Epoch [65][3100/3746] lr: 6.057e-02, eta: 2 days, 22:16:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5473, loss_cls: 4.0604, loss: 4.0604 +2024-07-18 14:35:07,421 - pyskl - INFO - Epoch [65][3200/3746] lr: 6.055e-02, eta: 2 days, 22:14:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5719, loss_cls: 3.9413, loss: 3.9413 +2024-07-18 14:36:28,775 - pyskl - INFO - Epoch [65][3300/3746] lr: 6.052e-02, eta: 2 days, 22:13:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5705, loss_cls: 3.9414, loss: 3.9414 +2024-07-18 14:37:49,992 - pyskl - INFO - Epoch [65][3400/3746] lr: 6.049e-02, eta: 2 days, 22:12:15, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5603, loss_cls: 4.0020, loss: 4.0020 +2024-07-18 14:39:11,807 - pyskl - INFO - Epoch [65][3500/3746] lr: 6.046e-02, eta: 2 days, 22:10:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5589, loss_cls: 4.0100, loss: 4.0100 +2024-07-18 14:40:33,361 - pyskl - INFO - Epoch [65][3600/3746] lr: 6.044e-02, eta: 2 days, 22:09:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5544, loss_cls: 3.9892, loss: 3.9892 +2024-07-18 14:41:54,908 - pyskl - INFO - Epoch [65][3700/3746] lr: 6.041e-02, eta: 2 days, 22:08:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5647, loss_cls: 3.9788, loss: 3.9788 +2024-07-18 14:42:34,833 - pyskl - INFO - Saving checkpoint at 65 epochs +2024-07-18 14:44:26,825 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 14:44:27,475 - pyskl - INFO - +top1_acc 0.2364 +top5_acc 0.4749 +2024-07-18 14:44:27,475 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 14:44:27,514 - pyskl - INFO - +mean_acc 0.2361 +2024-07-18 14:44:27,525 - pyskl - INFO - Epoch(val) [65][309] top1_acc: 0.2364, top5_acc: 0.4749, mean_class_accuracy: 0.2361 +2024-07-18 14:48:10,568 - pyskl - INFO - Epoch [66][100/3746] lr: 6.037e-02, eta: 2 days, 22:08:51, time: 2.230, data_time: 1.247, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5625, loss_cls: 3.9836, loss: 3.9836 +2024-07-18 14:49:32,615 - pyskl - INFO - Epoch [66][200/3746] lr: 6.034e-02, eta: 2 days, 22:07:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5744, loss_cls: 3.9105, loss: 3.9105 +2024-07-18 14:50:54,087 - pyskl - INFO - Epoch [66][300/3746] lr: 6.031e-02, eta: 2 days, 22:06:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5752, loss_cls: 3.9273, loss: 3.9273 +2024-07-18 14:52:15,771 - pyskl - INFO - Epoch [66][400/3746] lr: 6.029e-02, eta: 2 days, 22:05:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5723, loss_cls: 3.9443, loss: 3.9443 +2024-07-18 14:53:37,293 - pyskl - INFO - Epoch [66][500/3746] lr: 6.026e-02, eta: 2 days, 22:03:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5639, loss_cls: 3.9599, loss: 3.9599 +2024-07-18 14:54:59,097 - pyskl - INFO - Epoch [66][600/3746] lr: 6.023e-02, eta: 2 days, 22:02:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5689, loss_cls: 3.9402, loss: 3.9402 +2024-07-18 14:56:20,950 - pyskl - INFO - Epoch [66][700/3746] lr: 6.020e-02, eta: 2 days, 22:01:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5569, loss_cls: 4.0135, loss: 4.0135 +2024-07-18 14:57:43,246 - pyskl - INFO - Epoch [66][800/3746] lr: 6.018e-02, eta: 2 days, 21:59:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5598, loss_cls: 4.0107, loss: 4.0107 +2024-07-18 14:59:04,794 - pyskl - INFO - Epoch [66][900/3746] lr: 6.015e-02, eta: 2 days, 21:58:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5595, loss_cls: 4.0015, loss: 4.0015 +2024-07-18 15:00:26,949 - pyskl - INFO - Epoch [66][1000/3746] lr: 6.012e-02, eta: 2 days, 21:57:26, time: 0.822, data_time: 0.001, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5670, loss_cls: 3.9528, loss: 3.9528 +2024-07-18 15:01:49,437 - pyskl - INFO - Epoch [66][1100/3746] lr: 6.009e-02, eta: 2 days, 21:56:10, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5631, loss_cls: 3.9582, loss: 3.9582 +2024-07-18 15:03:11,366 - pyskl - INFO - Epoch [66][1200/3746] lr: 6.007e-02, eta: 2 days, 21:54:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5672, loss_cls: 3.9214, loss: 3.9214 +2024-07-18 15:04:32,720 - pyskl - INFO - Epoch [66][1300/3746] lr: 6.004e-02, eta: 2 days, 21:53:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5645, loss_cls: 4.0072, loss: 4.0072 +2024-07-18 15:05:53,895 - pyskl - INFO - Epoch [66][1400/3746] lr: 6.001e-02, eta: 2 days, 21:52:21, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5825, loss_cls: 3.8908, loss: 3.8908 +2024-07-18 15:07:15,817 - pyskl - INFO - Epoch [66][1500/3746] lr: 5.999e-02, eta: 2 days, 21:51:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5564, loss_cls: 3.9992, loss: 3.9992 +2024-07-18 15:08:37,468 - pyskl - INFO - Epoch [66][1600/3746] lr: 5.996e-02, eta: 2 days, 21:49:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5511, loss_cls: 4.0221, loss: 4.0221 +2024-07-18 15:09:59,104 - pyskl - INFO - Epoch [66][1700/3746] lr: 5.993e-02, eta: 2 days, 21:48:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5661, loss_cls: 3.9645, loss: 3.9645 +2024-07-18 15:11:20,903 - pyskl - INFO - Epoch [66][1800/3746] lr: 5.990e-02, eta: 2 days, 21:47:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5652, loss_cls: 3.9324, loss: 3.9324 +2024-07-18 15:12:42,183 - pyskl - INFO - Epoch [66][1900/3746] lr: 5.988e-02, eta: 2 days, 21:45:59, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5500, loss_cls: 4.0296, loss: 4.0296 +2024-07-18 15:14:03,584 - pyskl - INFO - Epoch [66][2000/3746] lr: 5.985e-02, eta: 2 days, 21:44:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5669, loss_cls: 3.9889, loss: 3.9889 +2024-07-18 15:15:25,201 - pyskl - INFO - Epoch [66][2100/3746] lr: 5.982e-02, eta: 2 days, 21:43:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5680, loss_cls: 3.9622, loss: 3.9622 +2024-07-18 15:16:46,713 - pyskl - INFO - Epoch [66][2200/3746] lr: 5.979e-02, eta: 2 days, 21:42:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5734, loss_cls: 3.9404, loss: 3.9404 +2024-07-18 15:18:07,981 - pyskl - INFO - Epoch [66][2300/3746] lr: 5.977e-02, eta: 2 days, 21:40:52, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5586, loss_cls: 4.0052, loss: 4.0052 +2024-07-18 15:19:29,678 - pyskl - INFO - Epoch [66][2400/3746] lr: 5.974e-02, eta: 2 days, 21:39:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5659, loss_cls: 3.9594, loss: 3.9594 +2024-07-18 15:20:50,805 - pyskl - INFO - Epoch [66][2500/3746] lr: 5.971e-02, eta: 2 days, 21:38:19, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5520, loss_cls: 4.0203, loss: 4.0203 +2024-07-18 15:22:13,242 - pyskl - INFO - Epoch [66][2600/3746] lr: 5.968e-02, eta: 2 days, 21:37:03, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5670, loss_cls: 3.9482, loss: 3.9482 +2024-07-18 15:23:35,253 - pyskl - INFO - Epoch [66][2700/3746] lr: 5.966e-02, eta: 2 days, 21:35:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5614, loss_cls: 3.9637, loss: 3.9637 +2024-07-18 15:24:57,147 - pyskl - INFO - Epoch [66][2800/3746] lr: 5.963e-02, eta: 2 days, 21:34:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5733, loss_cls: 3.9233, loss: 3.9233 +2024-07-18 15:26:18,906 - pyskl - INFO - Epoch [66][2900/3746] lr: 5.960e-02, eta: 2 days, 21:33:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5658, loss_cls: 3.9774, loss: 3.9774 +2024-07-18 15:27:40,384 - pyskl - INFO - Epoch [66][3000/3746] lr: 5.957e-02, eta: 2 days, 21:31:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5636, loss_cls: 3.9565, loss: 3.9565 +2024-07-18 15:29:02,184 - pyskl - INFO - Epoch [66][3100/3746] lr: 5.955e-02, eta: 2 days, 21:30:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5587, loss_cls: 3.9866, loss: 3.9866 +2024-07-18 15:30:23,929 - pyskl - INFO - Epoch [66][3200/3746] lr: 5.952e-02, eta: 2 days, 21:29:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5561, loss_cls: 3.9988, loss: 3.9988 +2024-07-18 15:31:45,704 - pyskl - INFO - Epoch [66][3300/3746] lr: 5.949e-02, eta: 2 days, 21:28:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5639, loss_cls: 3.9597, loss: 3.9597 +2024-07-18 15:33:07,422 - pyskl - INFO - Epoch [66][3400/3746] lr: 5.946e-02, eta: 2 days, 21:26:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5608, loss_cls: 3.9754, loss: 3.9754 +2024-07-18 15:34:28,784 - pyskl - INFO - Epoch [66][3500/3746] lr: 5.944e-02, eta: 2 days, 21:25:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5559, loss_cls: 3.9912, loss: 3.9912 +2024-07-18 15:35:50,546 - pyskl - INFO - Epoch [66][3600/3746] lr: 5.941e-02, eta: 2 days, 21:24:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5605, loss_cls: 3.9907, loss: 3.9907 +2024-07-18 15:37:12,725 - pyskl - INFO - Epoch [66][3700/3746] lr: 5.938e-02, eta: 2 days, 21:23:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5616, loss_cls: 3.9413, loss: 3.9413 +2024-07-18 15:37:52,539 - pyskl - INFO - Saving checkpoint at 66 epochs +2024-07-18 15:39:43,394 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 15:39:44,053 - pyskl - INFO - +top1_acc 0.2483 +top5_acc 0.4962 +2024-07-18 15:39:44,053 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 15:39:44,092 - pyskl - INFO - +mean_acc 0.2481 +2024-07-18 15:39:44,097 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_61.pth was removed +2024-07-18 15:39:44,354 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_66.pth. +2024-07-18 15:39:44,355 - pyskl - INFO - Best top1_acc is 0.2483 at 66 epoch. +2024-07-18 15:39:44,366 - pyskl - INFO - Epoch(val) [66][309] top1_acc: 0.2483, top5_acc: 0.4962, mean_class_accuracy: 0.2481 +2024-07-18 15:43:33,763 - pyskl - INFO - Epoch [67][100/3746] lr: 5.934e-02, eta: 2 days, 21:23:33, time: 2.294, data_time: 1.309, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5783, loss_cls: 3.8668, loss: 3.8668 +2024-07-18 15:44:55,832 - pyskl - INFO - Epoch [67][200/3746] lr: 5.931e-02, eta: 2 days, 21:22:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5819, loss_cls: 3.8652, loss: 3.8652 +2024-07-18 15:46:17,744 - pyskl - INFO - Epoch [67][300/3746] lr: 5.929e-02, eta: 2 days, 21:21:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5694, loss_cls: 3.9590, loss: 3.9590 +2024-07-18 15:47:39,618 - pyskl - INFO - Epoch [67][400/3746] lr: 5.926e-02, eta: 2 days, 21:19:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5728, loss_cls: 3.9067, loss: 3.9067 +2024-07-18 15:49:01,169 - pyskl - INFO - Epoch [67][500/3746] lr: 5.923e-02, eta: 2 days, 21:18:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5669, loss_cls: 3.9504, loss: 3.9504 +2024-07-18 15:50:22,799 - pyskl - INFO - Epoch [67][600/3746] lr: 5.920e-02, eta: 2 days, 21:17:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5669, loss_cls: 3.9334, loss: 3.9334 +2024-07-18 15:51:44,413 - pyskl - INFO - Epoch [67][700/3746] lr: 5.918e-02, eta: 2 days, 21:15:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5664, loss_cls: 3.9687, loss: 3.9687 +2024-07-18 15:53:06,914 - pyskl - INFO - Epoch [67][800/3746] lr: 5.915e-02, eta: 2 days, 21:14:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5628, loss_cls: 3.9803, loss: 3.9803 +2024-07-18 15:54:28,654 - pyskl - INFO - Epoch [67][900/3746] lr: 5.912e-02, eta: 2 days, 21:13:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5594, loss_cls: 4.0008, loss: 4.0008 +2024-07-18 15:55:51,175 - pyskl - INFO - Epoch [67][1000/3746] lr: 5.909e-02, eta: 2 days, 21:12:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5575, loss_cls: 3.9529, loss: 3.9529 +2024-07-18 15:57:13,659 - pyskl - INFO - Epoch [67][1100/3746] lr: 5.907e-02, eta: 2 days, 21:10:51, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5683, loss_cls: 3.9164, loss: 3.9164 +2024-07-18 15:58:35,694 - pyskl - INFO - Epoch [67][1200/3746] lr: 5.904e-02, eta: 2 days, 21:09:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5677, loss_cls: 3.9533, loss: 3.9533 +2024-07-18 15:59:57,301 - pyskl - INFO - Epoch [67][1300/3746] lr: 5.901e-02, eta: 2 days, 21:08:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5753, loss_cls: 3.9524, loss: 3.9524 +2024-07-18 16:01:19,004 - pyskl - INFO - Epoch [67][1400/3746] lr: 5.898e-02, eta: 2 days, 21:07:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5656, loss_cls: 3.9594, loss: 3.9594 +2024-07-18 16:02:40,571 - pyskl - INFO - Epoch [67][1500/3746] lr: 5.896e-02, eta: 2 days, 21:05:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5619, loss_cls: 3.9582, loss: 3.9582 +2024-07-18 16:04:02,332 - pyskl - INFO - Epoch [67][1600/3746] lr: 5.893e-02, eta: 2 days, 21:04:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5684, loss_cls: 3.9686, loss: 3.9686 +2024-07-18 16:05:24,147 - pyskl - INFO - Epoch [67][1700/3746] lr: 5.890e-02, eta: 2 days, 21:03:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5558, loss_cls: 3.9954, loss: 3.9954 +2024-07-18 16:06:45,855 - pyskl - INFO - Epoch [67][1800/3746] lr: 5.887e-02, eta: 2 days, 21:01:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5545, loss_cls: 3.9672, loss: 3.9672 +2024-07-18 16:08:07,335 - pyskl - INFO - Epoch [67][1900/3746] lr: 5.885e-02, eta: 2 days, 21:00:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5616, loss_cls: 3.9749, loss: 3.9749 +2024-07-18 16:09:29,381 - pyskl - INFO - Epoch [67][2000/3746] lr: 5.882e-02, eta: 2 days, 20:59:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5727, loss_cls: 3.9554, loss: 3.9554 +2024-07-18 16:10:51,177 - pyskl - INFO - Epoch [67][2100/3746] lr: 5.879e-02, eta: 2 days, 20:58:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5761, loss_cls: 3.9310, loss: 3.9310 +2024-07-18 16:12:12,537 - pyskl - INFO - Epoch [67][2200/3746] lr: 5.876e-02, eta: 2 days, 20:56:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5675, loss_cls: 3.9695, loss: 3.9695 +2024-07-18 16:13:34,262 - pyskl - INFO - Epoch [67][2300/3746] lr: 5.874e-02, eta: 2 days, 20:55:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5619, loss_cls: 3.9937, loss: 3.9937 +2024-07-18 16:14:55,429 - pyskl - INFO - Epoch [67][2400/3746] lr: 5.871e-02, eta: 2 days, 20:54:15, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5609, loss_cls: 3.9520, loss: 3.9520 +2024-07-18 16:16:17,172 - pyskl - INFO - Epoch [67][2500/3746] lr: 5.868e-02, eta: 2 days, 20:52:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5702, loss_cls: 3.9319, loss: 3.9319 +2024-07-18 16:17:39,321 - pyskl - INFO - Epoch [67][2600/3746] lr: 5.865e-02, eta: 2 days, 20:51:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5622, loss_cls: 3.9834, loss: 3.9834 +2024-07-18 16:19:01,218 - pyskl - INFO - Epoch [67][2700/3746] lr: 5.863e-02, eta: 2 days, 20:50:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5675, loss_cls: 3.9414, loss: 3.9414 +2024-07-18 16:20:22,998 - pyskl - INFO - Epoch [67][2800/3746] lr: 5.860e-02, eta: 2 days, 20:49:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5566, loss_cls: 4.0100, loss: 4.0100 +2024-07-18 16:21:44,748 - pyskl - INFO - Epoch [67][2900/3746] lr: 5.857e-02, eta: 2 days, 20:47:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5623, loss_cls: 3.9593, loss: 3.9593 +2024-07-18 16:23:06,149 - pyskl - INFO - Epoch [67][3000/3746] lr: 5.854e-02, eta: 2 days, 20:46:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5608, loss_cls: 3.9799, loss: 3.9799 +2024-07-18 16:24:27,837 - pyskl - INFO - Epoch [67][3100/3746] lr: 5.852e-02, eta: 2 days, 20:45:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5597, loss_cls: 3.9758, loss: 3.9758 +2024-07-18 16:25:49,541 - pyskl - INFO - Epoch [67][3200/3746] lr: 5.849e-02, eta: 2 days, 20:44:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5608, loss_cls: 4.0212, loss: 4.0212 +2024-07-18 16:27:11,328 - pyskl - INFO - Epoch [67][3300/3746] lr: 5.846e-02, eta: 2 days, 20:42:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5709, loss_cls: 3.9493, loss: 3.9493 +2024-07-18 16:28:33,457 - pyskl - INFO - Epoch [67][3400/3746] lr: 5.843e-02, eta: 2 days, 20:41:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5666, loss_cls: 3.9421, loss: 3.9421 +2024-07-18 16:29:54,960 - pyskl - INFO - Epoch [67][3500/3746] lr: 5.841e-02, eta: 2 days, 20:40:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5663, loss_cls: 3.9375, loss: 3.9375 +2024-07-18 16:31:17,030 - pyskl - INFO - Epoch [67][3600/3746] lr: 5.838e-02, eta: 2 days, 20:38:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5711, loss_cls: 3.9522, loss: 3.9522 +2024-07-18 16:32:39,408 - pyskl - INFO - Epoch [67][3700/3746] lr: 5.835e-02, eta: 2 days, 20:37:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5578, loss_cls: 3.9901, loss: 3.9901 +2024-07-18 16:33:19,069 - pyskl - INFO - Saving checkpoint at 67 epochs +2024-07-18 16:35:10,142 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 16:35:10,806 - pyskl - INFO - +top1_acc 0.2524 +top5_acc 0.4975 +2024-07-18 16:35:10,807 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 16:35:10,848 - pyskl - INFO - +mean_acc 0.2523 +2024-07-18 16:35:10,853 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_66.pth was removed +2024-07-18 16:35:11,109 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_67.pth. +2024-07-18 16:35:11,110 - pyskl - INFO - Best top1_acc is 0.2524 at 67 epoch. +2024-07-18 16:35:11,122 - pyskl - INFO - Epoch(val) [67][309] top1_acc: 0.2524, top5_acc: 0.4975, mean_class_accuracy: 0.2523 +2024-07-18 16:38:57,160 - pyskl - INFO - Epoch [68][100/3746] lr: 5.831e-02, eta: 2 days, 20:38:01, time: 2.260, data_time: 1.276, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5705, loss_cls: 3.9168, loss: 3.9168 +2024-07-18 16:40:18,942 - pyskl - INFO - Epoch [68][200/3746] lr: 5.828e-02, eta: 2 days, 20:36:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5716, loss_cls: 3.9464, loss: 3.9464 +2024-07-18 16:41:40,667 - pyskl - INFO - Epoch [68][300/3746] lr: 5.826e-02, eta: 2 days, 20:35:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5755, loss_cls: 3.9345, loss: 3.9345 +2024-07-18 16:43:02,276 - pyskl - INFO - Epoch [68][400/3746] lr: 5.823e-02, eta: 2 days, 20:34:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5708, loss_cls: 3.9161, loss: 3.9161 +2024-07-18 16:44:23,936 - pyskl - INFO - Epoch [68][500/3746] lr: 5.820e-02, eta: 2 days, 20:32:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5733, loss_cls: 3.9316, loss: 3.9316 +2024-07-18 16:45:45,483 - pyskl - INFO - Epoch [68][600/3746] lr: 5.817e-02, eta: 2 days, 20:31:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5597, loss_cls: 3.9729, loss: 3.9729 +2024-07-18 16:47:07,089 - pyskl - INFO - Epoch [68][700/3746] lr: 5.815e-02, eta: 2 days, 20:30:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5709, loss_cls: 3.9369, loss: 3.9369 +2024-07-18 16:48:29,510 - pyskl - INFO - Epoch [68][800/3746] lr: 5.812e-02, eta: 2 days, 20:29:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5673, loss_cls: 3.9488, loss: 3.9488 +2024-07-18 16:49:52,171 - pyskl - INFO - Epoch [68][900/3746] lr: 5.809e-02, eta: 2 days, 20:27:49, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5714, loss_cls: 3.9289, loss: 3.9289 +2024-07-18 16:51:14,980 - pyskl - INFO - Epoch [68][1000/3746] lr: 5.806e-02, eta: 2 days, 20:26:33, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5686, loss_cls: 3.9689, loss: 3.9689 +2024-07-18 16:52:36,886 - pyskl - INFO - Epoch [68][1100/3746] lr: 5.804e-02, eta: 2 days, 20:25:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5659, loss_cls: 3.9357, loss: 3.9357 +2024-07-18 16:53:58,998 - pyskl - INFO - Epoch [68][1200/3746] lr: 5.801e-02, eta: 2 days, 20:24:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5781, loss_cls: 3.8938, loss: 3.8938 +2024-07-18 16:55:20,947 - pyskl - INFO - Epoch [68][1300/3746] lr: 5.798e-02, eta: 2 days, 20:22:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5591, loss_cls: 3.9828, loss: 3.9828 +2024-07-18 16:56:42,651 - pyskl - INFO - Epoch [68][1400/3746] lr: 5.795e-02, eta: 2 days, 20:21:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5737, loss_cls: 3.9568, loss: 3.9568 +2024-07-18 16:58:04,485 - pyskl - INFO - Epoch [68][1500/3746] lr: 5.792e-02, eta: 2 days, 20:20:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5713, loss_cls: 3.9435, loss: 3.9435 +2024-07-18 16:59:25,956 - pyskl - INFO - Epoch [68][1600/3746] lr: 5.790e-02, eta: 2 days, 20:18:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5717, loss_cls: 3.9494, loss: 3.9494 +2024-07-18 17:00:46,977 - pyskl - INFO - Epoch [68][1700/3746] lr: 5.787e-02, eta: 2 days, 20:17:36, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5761, loss_cls: 3.8928, loss: 3.8928 +2024-07-18 17:02:08,835 - pyskl - INFO - Epoch [68][1800/3746] lr: 5.784e-02, eta: 2 days, 20:16:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5661, loss_cls: 4.0093, loss: 4.0093 +2024-07-18 17:03:30,383 - pyskl - INFO - Epoch [68][1900/3746] lr: 5.781e-02, eta: 2 days, 20:15:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5656, loss_cls: 3.9402, loss: 3.9402 +2024-07-18 17:04:52,262 - pyskl - INFO - Epoch [68][2000/3746] lr: 5.779e-02, eta: 2 days, 20:13:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5556, loss_cls: 3.9833, loss: 3.9833 +2024-07-18 17:06:13,733 - pyskl - INFO - Epoch [68][2100/3746] lr: 5.776e-02, eta: 2 days, 20:12:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5672, loss_cls: 3.9418, loss: 3.9418 +2024-07-18 17:07:35,690 - pyskl - INFO - Epoch [68][2200/3746] lr: 5.773e-02, eta: 2 days, 20:11:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5609, loss_cls: 3.9819, loss: 3.9819 +2024-07-18 17:08:56,941 - pyskl - INFO - Epoch [68][2300/3746] lr: 5.770e-02, eta: 2 days, 20:09:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5680, loss_cls: 3.9471, loss: 3.9471 +2024-07-18 17:10:18,249 - pyskl - INFO - Epoch [68][2400/3746] lr: 5.768e-02, eta: 2 days, 20:08:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5717, loss_cls: 3.9314, loss: 3.9314 +2024-07-18 17:11:40,838 - pyskl - INFO - Epoch [68][2500/3746] lr: 5.765e-02, eta: 2 days, 20:07:21, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5619, loss_cls: 3.9881, loss: 3.9881 +2024-07-18 17:13:02,648 - pyskl - INFO - Epoch [68][2600/3746] lr: 5.762e-02, eta: 2 days, 20:06:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5705, loss_cls: 3.9274, loss: 3.9274 +2024-07-18 17:14:24,688 - pyskl - INFO - Epoch [68][2700/3746] lr: 5.759e-02, eta: 2 days, 20:04:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5636, loss_cls: 3.9690, loss: 3.9690 +2024-07-18 17:15:46,248 - pyskl - INFO - Epoch [68][2800/3746] lr: 5.757e-02, eta: 2 days, 20:03:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5572, loss_cls: 3.9907, loss: 3.9907 +2024-07-18 17:17:08,488 - pyskl - INFO - Epoch [68][2900/3746] lr: 5.754e-02, eta: 2 days, 20:02:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5647, loss_cls: 3.9949, loss: 3.9949 +2024-07-18 17:18:30,169 - pyskl - INFO - Epoch [68][3000/3746] lr: 5.751e-02, eta: 2 days, 20:00:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5633, loss_cls: 3.9940, loss: 3.9940 +2024-07-18 17:19:51,890 - pyskl - INFO - Epoch [68][3100/3746] lr: 5.748e-02, eta: 2 days, 19:59:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5770, loss_cls: 3.9191, loss: 3.9191 +2024-07-18 17:21:13,461 - pyskl - INFO - Epoch [68][3200/3746] lr: 5.746e-02, eta: 2 days, 19:58:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5572, loss_cls: 3.9769, loss: 3.9769 +2024-07-18 17:22:34,965 - pyskl - INFO - Epoch [68][3300/3746] lr: 5.743e-02, eta: 2 days, 19:57:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5720, loss_cls: 3.9085, loss: 3.9085 +2024-07-18 17:23:57,044 - pyskl - INFO - Epoch [68][3400/3746] lr: 5.740e-02, eta: 2 days, 19:55:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5614, loss_cls: 3.9460, loss: 3.9460 +2024-07-18 17:25:18,537 - pyskl - INFO - Epoch [68][3500/3746] lr: 5.737e-02, eta: 2 days, 19:54:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5800, loss_cls: 3.9128, loss: 3.9128 +2024-07-18 17:26:39,902 - pyskl - INFO - Epoch [68][3600/3746] lr: 5.734e-02, eta: 2 days, 19:53:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5780, loss_cls: 3.9110, loss: 3.9110 +2024-07-18 17:28:01,838 - pyskl - INFO - Epoch [68][3700/3746] lr: 5.732e-02, eta: 2 days, 19:51:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5652, loss_cls: 3.9685, loss: 3.9685 +2024-07-18 17:28:41,596 - pyskl - INFO - Saving checkpoint at 68 epochs +2024-07-18 17:30:32,692 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 17:30:33,458 - pyskl - INFO - +top1_acc 0.2509 +top5_acc 0.4966 +2024-07-18 17:30:33,458 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 17:30:33,497 - pyskl - INFO - +mean_acc 0.2506 +2024-07-18 17:30:33,509 - pyskl - INFO - Epoch(val) [68][309] top1_acc: 0.2509, top5_acc: 0.4966, mean_class_accuracy: 0.2506 +2024-07-18 17:34:23,675 - pyskl - INFO - Epoch [69][100/3746] lr: 5.728e-02, eta: 2 days, 19:52:21, time: 2.302, data_time: 1.299, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5845, loss_cls: 3.8469, loss: 3.8469 +2024-07-18 17:35:45,158 - pyskl - INFO - Epoch [69][200/3746] lr: 5.725e-02, eta: 2 days, 19:51:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5769, loss_cls: 3.9054, loss: 3.9054 +2024-07-18 17:37:06,877 - pyskl - INFO - Epoch [69][300/3746] lr: 5.722e-02, eta: 2 days, 19:49:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5720, loss_cls: 3.8906, loss: 3.8906 +2024-07-18 17:38:28,672 - pyskl - INFO - Epoch [69][400/3746] lr: 5.719e-02, eta: 2 days, 19:48:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5830, loss_cls: 3.8506, loss: 3.8506 +2024-07-18 17:39:50,315 - pyskl - INFO - Epoch [69][500/3746] lr: 5.717e-02, eta: 2 days, 19:47:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5706, loss_cls: 3.9324, loss: 3.9324 +2024-07-18 17:41:11,606 - pyskl - INFO - Epoch [69][600/3746] lr: 5.714e-02, eta: 2 days, 19:45:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5744, loss_cls: 3.9136, loss: 3.9136 +2024-07-18 17:42:33,090 - pyskl - INFO - Epoch [69][700/3746] lr: 5.711e-02, eta: 2 days, 19:44:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5728, loss_cls: 3.9498, loss: 3.9498 +2024-07-18 17:43:55,811 - pyskl - INFO - Epoch [69][800/3746] lr: 5.708e-02, eta: 2 days, 19:43:22, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5694, loss_cls: 3.9259, loss: 3.9259 +2024-07-18 17:45:17,930 - pyskl - INFO - Epoch [69][900/3746] lr: 5.706e-02, eta: 2 days, 19:42:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5769, loss_cls: 3.9029, loss: 3.9029 +2024-07-18 17:46:40,302 - pyskl - INFO - Epoch [69][1000/3746] lr: 5.703e-02, eta: 2 days, 19:40:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5709, loss_cls: 3.9477, loss: 3.9477 +2024-07-18 17:48:02,700 - pyskl - INFO - Epoch [69][1100/3746] lr: 5.700e-02, eta: 2 days, 19:39:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5619, loss_cls: 3.9583, loss: 3.9583 +2024-07-18 17:49:24,887 - pyskl - INFO - Epoch [69][1200/3746] lr: 5.697e-02, eta: 2 days, 19:38:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5648, loss_cls: 3.9595, loss: 3.9595 +2024-07-18 17:50:46,772 - pyskl - INFO - Epoch [69][1300/3746] lr: 5.694e-02, eta: 2 days, 19:37:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5681, loss_cls: 3.9288, loss: 3.9288 +2024-07-18 17:52:08,799 - pyskl - INFO - Epoch [69][1400/3746] lr: 5.692e-02, eta: 2 days, 19:35:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5783, loss_cls: 3.9345, loss: 3.9345 +2024-07-18 17:53:30,516 - pyskl - INFO - Epoch [69][1500/3746] lr: 5.689e-02, eta: 2 days, 19:34:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5713, loss_cls: 3.9122, loss: 3.9122 +2024-07-18 17:54:52,877 - pyskl - INFO - Epoch [69][1600/3746] lr: 5.686e-02, eta: 2 days, 19:33:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5791, loss_cls: 3.8894, loss: 3.8894 +2024-07-18 17:56:14,752 - pyskl - INFO - Epoch [69][1700/3746] lr: 5.683e-02, eta: 2 days, 19:31:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5620, loss_cls: 3.9471, loss: 3.9471 +2024-07-18 17:57:36,493 - pyskl - INFO - Epoch [69][1800/3746] lr: 5.681e-02, eta: 2 days, 19:30:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5691, loss_cls: 3.9423, loss: 3.9423 +2024-07-18 17:58:57,964 - pyskl - INFO - Epoch [69][1900/3746] lr: 5.678e-02, eta: 2 days, 19:29:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5694, loss_cls: 3.9309, loss: 3.9309 +2024-07-18 18:00:19,970 - pyskl - INFO - Epoch [69][2000/3746] lr: 5.675e-02, eta: 2 days, 19:28:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5645, loss_cls: 3.9629, loss: 3.9629 +2024-07-18 18:01:41,621 - pyskl - INFO - Epoch [69][2100/3746] lr: 5.672e-02, eta: 2 days, 19:26:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5741, loss_cls: 3.9278, loss: 3.9278 +2024-07-18 18:03:03,269 - pyskl - INFO - Epoch [69][2200/3746] lr: 5.670e-02, eta: 2 days, 19:25:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5614, loss_cls: 3.9857, loss: 3.9857 +2024-07-18 18:04:25,493 - pyskl - INFO - Epoch [69][2300/3746] lr: 5.667e-02, eta: 2 days, 19:24:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5706, loss_cls: 3.9475, loss: 3.9475 +2024-07-18 18:05:47,155 - pyskl - INFO - Epoch [69][2400/3746] lr: 5.664e-02, eta: 2 days, 19:22:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5705, loss_cls: 3.9361, loss: 3.9361 +2024-07-18 18:07:09,836 - pyskl - INFO - Epoch [69][2500/3746] lr: 5.661e-02, eta: 2 days, 19:21:38, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5584, loss_cls: 3.9796, loss: 3.9796 +2024-07-18 18:08:31,610 - pyskl - INFO - Epoch [69][2600/3746] lr: 5.658e-02, eta: 2 days, 19:20:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5711, loss_cls: 3.9341, loss: 3.9341 +2024-07-18 18:09:54,003 - pyskl - INFO - Epoch [69][2700/3746] lr: 5.656e-02, eta: 2 days, 19:19:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5689, loss_cls: 3.9141, loss: 3.9141 +2024-07-18 18:11:16,431 - pyskl - INFO - Epoch [69][2800/3746] lr: 5.653e-02, eta: 2 days, 19:17:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5561, loss_cls: 3.9675, loss: 3.9675 +2024-07-18 18:12:38,357 - pyskl - INFO - Epoch [69][2900/3746] lr: 5.650e-02, eta: 2 days, 19:16:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5769, loss_cls: 3.9143, loss: 3.9143 +2024-07-18 18:14:00,126 - pyskl - INFO - Epoch [69][3000/3746] lr: 5.647e-02, eta: 2 days, 19:15:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5669, loss_cls: 3.9431, loss: 3.9431 +2024-07-18 18:15:21,755 - pyskl - INFO - Epoch [69][3100/3746] lr: 5.645e-02, eta: 2 days, 19:13:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5625, loss_cls: 3.9640, loss: 3.9640 +2024-07-18 18:16:43,313 - pyskl - INFO - Epoch [69][3200/3746] lr: 5.642e-02, eta: 2 days, 19:12:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5737, loss_cls: 3.9249, loss: 3.9249 +2024-07-18 18:18:04,640 - pyskl - INFO - Epoch [69][3300/3746] lr: 5.639e-02, eta: 2 days, 19:11:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5627, loss_cls: 3.9559, loss: 3.9559 +2024-07-18 18:19:26,090 - pyskl - INFO - Epoch [69][3400/3746] lr: 5.636e-02, eta: 2 days, 19:10:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5664, loss_cls: 3.9655, loss: 3.9655 +2024-07-18 18:20:47,728 - pyskl - INFO - Epoch [69][3500/3746] lr: 5.634e-02, eta: 2 days, 19:08:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5584, loss_cls: 3.9987, loss: 3.9987 +2024-07-18 18:22:09,311 - pyskl - INFO - Epoch [69][3600/3746] lr: 5.631e-02, eta: 2 days, 19:07:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5578, loss_cls: 3.9716, loss: 3.9716 +2024-07-18 18:23:30,580 - pyskl - INFO - Epoch [69][3700/3746] lr: 5.628e-02, eta: 2 days, 19:06:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5720, loss_cls: 3.9225, loss: 3.9225 +2024-07-18 18:24:09,907 - pyskl - INFO - Saving checkpoint at 69 epochs +2024-07-18 18:26:00,764 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 18:26:01,426 - pyskl - INFO - +top1_acc 0.2437 +top5_acc 0.4882 +2024-07-18 18:26:01,426 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 18:26:01,468 - pyskl - INFO - +mean_acc 0.2434 +2024-07-18 18:26:01,480 - pyskl - INFO - Epoch(val) [69][309] top1_acc: 0.2437, top5_acc: 0.4882, mean_class_accuracy: 0.2434 +2024-07-18 18:29:49,970 - pyskl - INFO - Epoch [70][100/3746] lr: 5.624e-02, eta: 2 days, 19:06:28, time: 2.285, data_time: 1.297, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5716, loss_cls: 3.9196, loss: 3.9196 +2024-07-18 18:31:12,403 - pyskl - INFO - Epoch [70][200/3746] lr: 5.621e-02, eta: 2 days, 19:05:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5808, loss_cls: 3.8636, loss: 3.8636 +2024-07-18 18:32:34,040 - pyskl - INFO - Epoch [70][300/3746] lr: 5.618e-02, eta: 2 days, 19:03:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5764, loss_cls: 3.8595, loss: 3.8595 +2024-07-18 18:33:55,558 - pyskl - INFO - Epoch [70][400/3746] lr: 5.616e-02, eta: 2 days, 19:02:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5828, loss_cls: 3.8316, loss: 3.8316 +2024-07-18 18:35:17,222 - pyskl - INFO - Epoch [70][500/3746] lr: 5.613e-02, eta: 2 days, 19:01:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5627, loss_cls: 3.9800, loss: 3.9800 +2024-07-18 18:36:38,823 - pyskl - INFO - Epoch [70][600/3746] lr: 5.610e-02, eta: 2 days, 19:00:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5594, loss_cls: 3.9794, loss: 3.9794 +2024-07-18 18:38:00,858 - pyskl - INFO - Epoch [70][700/3746] lr: 5.607e-02, eta: 2 days, 18:58:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5761, loss_cls: 3.9199, loss: 3.9199 +2024-07-18 18:39:22,819 - pyskl - INFO - Epoch [70][800/3746] lr: 5.605e-02, eta: 2 days, 18:57:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5741, loss_cls: 3.9179, loss: 3.9179 +2024-07-18 18:40:45,109 - pyskl - INFO - Epoch [70][900/3746] lr: 5.602e-02, eta: 2 days, 18:56:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5739, loss_cls: 3.9227, loss: 3.9227 +2024-07-18 18:42:07,047 - pyskl - INFO - Epoch [70][1000/3746] lr: 5.599e-02, eta: 2 days, 18:54:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5642, loss_cls: 3.9117, loss: 3.9117 +2024-07-18 18:43:28,974 - pyskl - INFO - Epoch [70][1100/3746] lr: 5.596e-02, eta: 2 days, 18:53:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5731, loss_cls: 3.9019, loss: 3.9019 +2024-07-18 18:44:50,663 - pyskl - INFO - Epoch [70][1200/3746] lr: 5.593e-02, eta: 2 days, 18:52:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5686, loss_cls: 3.9165, loss: 3.9165 +2024-07-18 18:46:12,929 - pyskl - INFO - Epoch [70][1300/3746] lr: 5.591e-02, eta: 2 days, 18:51:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5694, loss_cls: 3.9276, loss: 3.9276 +2024-07-18 18:47:35,192 - pyskl - INFO - Epoch [70][1400/3746] lr: 5.588e-02, eta: 2 days, 18:49:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5745, loss_cls: 3.9308, loss: 3.9308 +2024-07-18 18:48:56,895 - pyskl - INFO - Epoch [70][1500/3746] lr: 5.585e-02, eta: 2 days, 18:48:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5680, loss_cls: 3.9459, loss: 3.9459 +2024-07-18 18:50:18,535 - pyskl - INFO - Epoch [70][1600/3746] lr: 5.582e-02, eta: 2 days, 18:47:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5691, loss_cls: 3.9154, loss: 3.9154 +2024-07-18 18:51:40,276 - pyskl - INFO - Epoch [70][1700/3746] lr: 5.580e-02, eta: 2 days, 18:45:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5702, loss_cls: 3.9386, loss: 3.9386 +2024-07-18 18:53:01,868 - pyskl - INFO - Epoch [70][1800/3746] lr: 5.577e-02, eta: 2 days, 18:44:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5687, loss_cls: 3.9396, loss: 3.9396 +2024-07-18 18:54:23,895 - pyskl - INFO - Epoch [70][1900/3746] lr: 5.574e-02, eta: 2 days, 18:43:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5564, loss_cls: 3.9769, loss: 3.9769 +2024-07-18 18:55:45,490 - pyskl - INFO - Epoch [70][2000/3746] lr: 5.571e-02, eta: 2 days, 18:42:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5689, loss_cls: 3.9525, loss: 3.9525 +2024-07-18 18:57:07,385 - pyskl - INFO - Epoch [70][2100/3746] lr: 5.568e-02, eta: 2 days, 18:40:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5695, loss_cls: 3.9120, loss: 3.9120 +2024-07-18 18:58:29,041 - pyskl - INFO - Epoch [70][2200/3746] lr: 5.566e-02, eta: 2 days, 18:39:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5731, loss_cls: 3.9219, loss: 3.9219 +2024-07-18 18:59:50,538 - pyskl - INFO - Epoch [70][2300/3746] lr: 5.563e-02, eta: 2 days, 18:38:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5655, loss_cls: 3.9488, loss: 3.9488 +2024-07-18 19:01:12,497 - pyskl - INFO - Epoch [70][2400/3746] lr: 5.560e-02, eta: 2 days, 18:36:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5683, loss_cls: 3.9303, loss: 3.9303 +2024-07-18 19:02:34,468 - pyskl - INFO - Epoch [70][2500/3746] lr: 5.557e-02, eta: 2 days, 18:35:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5737, loss_cls: 3.8995, loss: 3.8995 +2024-07-18 19:03:56,436 - pyskl - INFO - Epoch [70][2600/3746] lr: 5.555e-02, eta: 2 days, 18:34:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5683, loss_cls: 3.9595, loss: 3.9595 +2024-07-18 19:05:18,855 - pyskl - INFO - Epoch [70][2700/3746] lr: 5.552e-02, eta: 2 days, 18:33:04, time: 0.824, data_time: 0.001, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5720, loss_cls: 3.8981, loss: 3.8981 +2024-07-18 19:06:40,936 - pyskl - INFO - Epoch [70][2800/3746] lr: 5.549e-02, eta: 2 days, 18:31:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5656, loss_cls: 3.9402, loss: 3.9402 +2024-07-18 19:08:03,348 - pyskl - INFO - Epoch [70][2900/3746] lr: 5.546e-02, eta: 2 days, 18:30:31, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5687, loss_cls: 3.9632, loss: 3.9632 +2024-07-18 19:09:24,951 - pyskl - INFO - Epoch [70][3000/3746] lr: 5.543e-02, eta: 2 days, 18:29:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5777, loss_cls: 3.8814, loss: 3.8814 +2024-07-18 19:10:47,522 - pyskl - INFO - Epoch [70][3100/3746] lr: 5.541e-02, eta: 2 days, 18:27:57, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5698, loss_cls: 3.9314, loss: 3.9314 +2024-07-18 19:12:09,291 - pyskl - INFO - Epoch [70][3200/3746] lr: 5.538e-02, eta: 2 days, 18:26:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5648, loss_cls: 3.9671, loss: 3.9671 +2024-07-18 19:13:30,703 - pyskl - INFO - Epoch [70][3300/3746] lr: 5.535e-02, eta: 2 days, 18:25:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5787, loss_cls: 3.8778, loss: 3.8778 +2024-07-18 19:14:52,591 - pyskl - INFO - Epoch [70][3400/3746] lr: 5.532e-02, eta: 2 days, 18:24:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5655, loss_cls: 3.9464, loss: 3.9464 +2024-07-18 19:16:14,313 - pyskl - INFO - Epoch [70][3500/3746] lr: 5.530e-02, eta: 2 days, 18:22:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5598, loss_cls: 3.9683, loss: 3.9683 +2024-07-18 19:17:35,966 - pyskl - INFO - Epoch [70][3600/3746] lr: 5.527e-02, eta: 2 days, 18:21:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5747, loss_cls: 3.9156, loss: 3.9156 +2024-07-18 19:18:57,611 - pyskl - INFO - Epoch [70][3700/3746] lr: 5.524e-02, eta: 2 days, 18:20:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5700, loss_cls: 3.9257, loss: 3.9257 +2024-07-18 19:19:37,090 - pyskl - INFO - Saving checkpoint at 70 epochs +2024-07-18 19:21:27,227 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 19:21:27,889 - pyskl - INFO - +top1_acc 0.2429 +top5_acc 0.4868 +2024-07-18 19:21:27,889 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 19:21:27,929 - pyskl - INFO - +mean_acc 0.2427 +2024-07-18 19:21:27,941 - pyskl - INFO - Epoch(val) [70][309] top1_acc: 0.2429, top5_acc: 0.4868, mean_class_accuracy: 0.2427 +2024-07-18 19:25:16,600 - pyskl - INFO - Epoch [71][100/3746] lr: 5.520e-02, eta: 2 days, 18:20:25, time: 2.287, data_time: 1.299, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5800, loss_cls: 3.8568, loss: 3.8568 +2024-07-18 19:26:38,520 - pyskl - INFO - Epoch [71][200/3746] lr: 5.517e-02, eta: 2 days, 18:19:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5789, loss_cls: 3.8383, loss: 3.8383 +2024-07-18 19:28:00,422 - pyskl - INFO - Epoch [71][300/3746] lr: 5.514e-02, eta: 2 days, 18:17:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5637, loss_cls: 3.9296, loss: 3.9296 +2024-07-18 19:29:22,576 - pyskl - INFO - Epoch [71][400/3746] lr: 5.512e-02, eta: 2 days, 18:16:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5752, loss_cls: 3.8762, loss: 3.8762 +2024-07-18 19:30:44,128 - pyskl - INFO - Epoch [71][500/3746] lr: 5.509e-02, eta: 2 days, 18:15:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5677, loss_cls: 3.9512, loss: 3.9512 +2024-07-18 19:32:05,895 - pyskl - INFO - Epoch [71][600/3746] lr: 5.506e-02, eta: 2 days, 18:13:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5641, loss_cls: 3.9754, loss: 3.9754 +2024-07-18 19:33:28,050 - pyskl - INFO - Epoch [71][700/3746] lr: 5.503e-02, eta: 2 days, 18:12:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5691, loss_cls: 3.9396, loss: 3.9396 +2024-07-18 19:34:50,373 - pyskl - INFO - Epoch [71][800/3746] lr: 5.500e-02, eta: 2 days, 18:11:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5695, loss_cls: 3.9173, loss: 3.9173 +2024-07-18 19:36:12,963 - pyskl - INFO - Epoch [71][900/3746] lr: 5.498e-02, eta: 2 days, 18:10:08, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5742, loss_cls: 3.9439, loss: 3.9439 +2024-07-18 19:37:35,234 - pyskl - INFO - Epoch [71][1000/3746] lr: 5.495e-02, eta: 2 days, 18:08:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5858, loss_cls: 3.8884, loss: 3.8884 +2024-07-18 19:38:56,714 - pyskl - INFO - Epoch [71][1100/3746] lr: 5.492e-02, eta: 2 days, 18:07:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5787, loss_cls: 3.8753, loss: 3.8753 +2024-07-18 19:40:18,943 - pyskl - INFO - Epoch [71][1200/3746] lr: 5.489e-02, eta: 2 days, 18:06:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5752, loss_cls: 3.8927, loss: 3.8927 +2024-07-18 19:41:41,159 - pyskl - INFO - Epoch [71][1300/3746] lr: 5.487e-02, eta: 2 days, 18:05:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5730, loss_cls: 3.9031, loss: 3.9031 +2024-07-18 19:43:02,648 - pyskl - INFO - Epoch [71][1400/3746] lr: 5.484e-02, eta: 2 days, 18:03:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5737, loss_cls: 3.9329, loss: 3.9329 +2024-07-18 19:44:24,069 - pyskl - INFO - Epoch [71][1500/3746] lr: 5.481e-02, eta: 2 days, 18:02:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5730, loss_cls: 3.9135, loss: 3.9135 +2024-07-18 19:45:45,624 - pyskl - INFO - Epoch [71][1600/3746] lr: 5.478e-02, eta: 2 days, 18:01:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5844, loss_cls: 3.8761, loss: 3.8761 +2024-07-18 19:47:06,981 - pyskl - INFO - Epoch [71][1700/3746] lr: 5.475e-02, eta: 2 days, 17:59:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5797, loss_cls: 3.8801, loss: 3.8801 +2024-07-18 19:48:29,367 - pyskl - INFO - Epoch [71][1800/3746] lr: 5.473e-02, eta: 2 days, 17:58:32, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5737, loss_cls: 3.9151, loss: 3.9151 +2024-07-18 19:49:51,417 - pyskl - INFO - Epoch [71][1900/3746] lr: 5.470e-02, eta: 2 days, 17:57:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5741, loss_cls: 3.8829, loss: 3.8829 +2024-07-18 19:51:13,203 - pyskl - INFO - Epoch [71][2000/3746] lr: 5.467e-02, eta: 2 days, 17:55:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5652, loss_cls: 3.9441, loss: 3.9441 +2024-07-18 19:52:34,761 - pyskl - INFO - Epoch [71][2100/3746] lr: 5.464e-02, eta: 2 days, 17:54:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5719, loss_cls: 3.9354, loss: 3.9354 +2024-07-18 19:53:56,752 - pyskl - INFO - Epoch [71][2200/3746] lr: 5.461e-02, eta: 2 days, 17:53:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5613, loss_cls: 3.9663, loss: 3.9663 +2024-07-18 19:55:18,760 - pyskl - INFO - Epoch [71][2300/3746] lr: 5.459e-02, eta: 2 days, 17:52:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5631, loss_cls: 3.9795, loss: 3.9795 +2024-07-18 19:56:40,209 - pyskl - INFO - Epoch [71][2400/3746] lr: 5.456e-02, eta: 2 days, 17:50:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5680, loss_cls: 3.9439, loss: 3.9439 +2024-07-18 19:58:02,258 - pyskl - INFO - Epoch [71][2500/3746] lr: 5.453e-02, eta: 2 days, 17:49:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5769, loss_cls: 3.9141, loss: 3.9141 +2024-07-18 19:59:24,502 - pyskl - INFO - Epoch [71][2600/3746] lr: 5.450e-02, eta: 2 days, 17:48:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5830, loss_cls: 3.8645, loss: 3.8645 +2024-07-18 20:00:47,751 - pyskl - INFO - Epoch [71][2700/3746] lr: 5.448e-02, eta: 2 days, 17:46:57, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5787, loss_cls: 3.8748, loss: 3.8748 +2024-07-18 20:02:10,493 - pyskl - INFO - Epoch [71][2800/3746] lr: 5.445e-02, eta: 2 days, 17:45:41, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5767, loss_cls: 3.8932, loss: 3.8932 +2024-07-18 20:03:32,808 - pyskl - INFO - Epoch [71][2900/3746] lr: 5.442e-02, eta: 2 days, 17:44:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5680, loss_cls: 3.9616, loss: 3.9616 +2024-07-18 20:04:54,341 - pyskl - INFO - Epoch [71][3000/3746] lr: 5.439e-02, eta: 2 days, 17:43:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5677, loss_cls: 3.9135, loss: 3.9135 +2024-07-18 20:06:16,335 - pyskl - INFO - Epoch [71][3100/3746] lr: 5.436e-02, eta: 2 days, 17:41:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5730, loss_cls: 3.9262, loss: 3.9262 +2024-07-18 20:07:37,748 - pyskl - INFO - Epoch [71][3200/3746] lr: 5.434e-02, eta: 2 days, 17:40:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5745, loss_cls: 3.9075, loss: 3.9075 +2024-07-18 20:08:59,550 - pyskl - INFO - Epoch [71][3300/3746] lr: 5.431e-02, eta: 2 days, 17:39:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5781, loss_cls: 3.8868, loss: 3.8868 +2024-07-18 20:10:21,297 - pyskl - INFO - Epoch [71][3400/3746] lr: 5.428e-02, eta: 2 days, 17:37:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5634, loss_cls: 3.9364, loss: 3.9364 +2024-07-18 20:11:42,947 - pyskl - INFO - Epoch [71][3500/3746] lr: 5.425e-02, eta: 2 days, 17:36:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5719, loss_cls: 3.9226, loss: 3.9226 +2024-07-18 20:13:04,157 - pyskl - INFO - Epoch [71][3600/3746] lr: 5.422e-02, eta: 2 days, 17:35:21, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5758, loss_cls: 3.8801, loss: 3.8801 +2024-07-18 20:14:25,551 - pyskl - INFO - Epoch [71][3700/3746] lr: 5.420e-02, eta: 2 days, 17:34:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5736, loss_cls: 3.9673, loss: 3.9673 +2024-07-18 20:15:04,975 - pyskl - INFO - Saving checkpoint at 71 epochs +2024-07-18 20:16:54,723 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 20:16:55,376 - pyskl - INFO - +top1_acc 0.2471 +top5_acc 0.4985 +2024-07-18 20:16:55,376 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 20:16:55,416 - pyskl - INFO - +mean_acc 0.2470 +2024-07-18 20:16:55,428 - pyskl - INFO - Epoch(val) [71][309] top1_acc: 0.2471, top5_acc: 0.4985, mean_class_accuracy: 0.2470 +2024-07-18 20:20:39,769 - pyskl - INFO - Epoch [72][100/3746] lr: 5.416e-02, eta: 2 days, 17:34:06, time: 2.243, data_time: 1.262, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5853, loss_cls: 3.8655, loss: 3.8655 +2024-07-18 20:22:02,066 - pyskl - INFO - Epoch [72][200/3746] lr: 5.413e-02, eta: 2 days, 17:32:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5842, loss_cls: 3.8381, loss: 3.8381 +2024-07-18 20:23:23,741 - pyskl - INFO - Epoch [72][300/3746] lr: 5.410e-02, eta: 2 days, 17:31:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5753, loss_cls: 3.8848, loss: 3.8848 +2024-07-18 20:24:45,320 - pyskl - INFO - Epoch [72][400/3746] lr: 5.407e-02, eta: 2 days, 17:30:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5887, loss_cls: 3.8259, loss: 3.8259 +2024-07-18 20:26:06,864 - pyskl - INFO - Epoch [72][500/3746] lr: 5.404e-02, eta: 2 days, 17:28:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5786, loss_cls: 3.8719, loss: 3.8719 +2024-07-18 20:27:28,344 - pyskl - INFO - Epoch [72][600/3746] lr: 5.402e-02, eta: 2 days, 17:27:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5723, loss_cls: 3.8910, loss: 3.8910 +2024-07-18 20:28:50,659 - pyskl - INFO - Epoch [72][700/3746] lr: 5.399e-02, eta: 2 days, 17:26:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5681, loss_cls: 3.9288, loss: 3.9288 +2024-07-18 20:30:12,416 - pyskl - INFO - Epoch [72][800/3746] lr: 5.396e-02, eta: 2 days, 17:25:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5920, loss_cls: 3.8344, loss: 3.8344 +2024-07-18 20:31:34,541 - pyskl - INFO - Epoch [72][900/3746] lr: 5.393e-02, eta: 2 days, 17:23:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5702, loss_cls: 3.9379, loss: 3.9379 +2024-07-18 20:32:56,338 - pyskl - INFO - Epoch [72][1000/3746] lr: 5.391e-02, eta: 2 days, 17:22:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5680, loss_cls: 3.9366, loss: 3.9366 +2024-07-18 20:34:18,425 - pyskl - INFO - Epoch [72][1100/3746] lr: 5.388e-02, eta: 2 days, 17:21:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5783, loss_cls: 3.8871, loss: 3.8871 +2024-07-18 20:35:40,312 - pyskl - INFO - Epoch [72][1200/3746] lr: 5.385e-02, eta: 2 days, 17:19:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5820, loss_cls: 3.8868, loss: 3.8868 +2024-07-18 20:37:02,822 - pyskl - INFO - Epoch [72][1300/3746] lr: 5.382e-02, eta: 2 days, 17:18:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5780, loss_cls: 3.8686, loss: 3.8686 +2024-07-18 20:38:24,959 - pyskl - INFO - Epoch [72][1400/3746] lr: 5.379e-02, eta: 2 days, 17:17:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5728, loss_cls: 3.9027, loss: 3.9027 +2024-07-18 20:39:47,222 - pyskl - INFO - Epoch [72][1500/3746] lr: 5.377e-02, eta: 2 days, 17:16:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5777, loss_cls: 3.8948, loss: 3.8948 +2024-07-18 20:41:08,889 - pyskl - INFO - Epoch [72][1600/3746] lr: 5.374e-02, eta: 2 days, 17:14:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5703, loss_cls: 3.9157, loss: 3.9157 +2024-07-18 20:42:31,218 - pyskl - INFO - Epoch [72][1700/3746] lr: 5.371e-02, eta: 2 days, 17:13:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5677, loss_cls: 3.9613, loss: 3.9613 +2024-07-18 20:43:52,625 - pyskl - INFO - Epoch [72][1800/3746] lr: 5.368e-02, eta: 2 days, 17:12:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5750, loss_cls: 3.9278, loss: 3.9278 +2024-07-18 20:45:14,501 - pyskl - INFO - Epoch [72][1900/3746] lr: 5.365e-02, eta: 2 days, 17:10:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5736, loss_cls: 3.8990, loss: 3.8990 +2024-07-18 20:46:35,926 - pyskl - INFO - Epoch [72][2000/3746] lr: 5.363e-02, eta: 2 days, 17:09:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5752, loss_cls: 3.9048, loss: 3.9048 +2024-07-18 20:47:57,475 - pyskl - INFO - Epoch [72][2100/3746] lr: 5.360e-02, eta: 2 days, 17:08:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5733, loss_cls: 3.9223, loss: 3.9223 +2024-07-18 20:49:19,176 - pyskl - INFO - Epoch [72][2200/3746] lr: 5.357e-02, eta: 2 days, 17:06:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5736, loss_cls: 3.9452, loss: 3.9452 +2024-07-18 20:50:41,185 - pyskl - INFO - Epoch [72][2300/3746] lr: 5.354e-02, eta: 2 days, 17:05:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5723, loss_cls: 3.9052, loss: 3.9052 +2024-07-18 20:52:02,642 - pyskl - INFO - Epoch [72][2400/3746] lr: 5.352e-02, eta: 2 days, 17:04:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5695, loss_cls: 3.9263, loss: 3.9263 +2024-07-18 20:53:24,327 - pyskl - INFO - Epoch [72][2500/3746] lr: 5.349e-02, eta: 2 days, 17:03:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5759, loss_cls: 3.8935, loss: 3.8935 +2024-07-18 20:54:45,712 - pyskl - INFO - Epoch [72][2600/3746] lr: 5.346e-02, eta: 2 days, 17:01:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5716, loss_cls: 3.9447, loss: 3.9447 +2024-07-18 20:56:08,074 - pyskl - INFO - Epoch [72][2700/3746] lr: 5.343e-02, eta: 2 days, 17:00:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5672, loss_cls: 3.9222, loss: 3.9222 +2024-07-18 20:57:29,510 - pyskl - INFO - Epoch [72][2800/3746] lr: 5.340e-02, eta: 2 days, 16:59:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5717, loss_cls: 3.9396, loss: 3.9396 +2024-07-18 20:58:51,451 - pyskl - INFO - Epoch [72][2900/3746] lr: 5.338e-02, eta: 2 days, 16:57:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5705, loss_cls: 3.9277, loss: 3.9277 +2024-07-18 21:00:13,483 - pyskl - INFO - Epoch [72][3000/3746] lr: 5.335e-02, eta: 2 days, 16:56:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5798, loss_cls: 3.8801, loss: 3.8801 +2024-07-18 21:01:35,139 - pyskl - INFO - Epoch [72][3100/3746] lr: 5.332e-02, eta: 2 days, 16:55:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5714, loss_cls: 3.9102, loss: 3.9102 +2024-07-18 21:02:57,052 - pyskl - INFO - Epoch [72][3200/3746] lr: 5.329e-02, eta: 2 days, 16:54:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5648, loss_cls: 3.9575, loss: 3.9575 +2024-07-18 21:04:18,749 - pyskl - INFO - Epoch [72][3300/3746] lr: 5.326e-02, eta: 2 days, 16:52:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5777, loss_cls: 3.8906, loss: 3.8906 +2024-07-18 21:05:40,483 - pyskl - INFO - Epoch [72][3400/3746] lr: 5.324e-02, eta: 2 days, 16:51:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5703, loss_cls: 3.9658, loss: 3.9658 +2024-07-18 21:07:02,223 - pyskl - INFO - Epoch [72][3500/3746] lr: 5.321e-02, eta: 2 days, 16:50:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5759, loss_cls: 3.8837, loss: 3.8837 +2024-07-18 21:08:23,794 - pyskl - INFO - Epoch [72][3600/3746] lr: 5.318e-02, eta: 2 days, 16:48:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5761, loss_cls: 3.9342, loss: 3.9342 +2024-07-18 21:09:45,881 - pyskl - INFO - Epoch [72][3700/3746] lr: 5.315e-02, eta: 2 days, 16:47:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5656, loss_cls: 3.9479, loss: 3.9479 +2024-07-18 21:10:25,675 - pyskl - INFO - Saving checkpoint at 72 epochs +2024-07-18 21:12:17,448 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 21:12:18,104 - pyskl - INFO - +top1_acc 0.2498 +top5_acc 0.4951 +2024-07-18 21:12:18,104 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 21:12:18,143 - pyskl - INFO - +mean_acc 0.2496 +2024-07-18 21:12:18,154 - pyskl - INFO - Epoch(val) [72][309] top1_acc: 0.2498, top5_acc: 0.4951, mean_class_accuracy: 0.2496 +2024-07-18 21:16:02,513 - pyskl - INFO - Epoch [73][100/3746] lr: 5.311e-02, eta: 2 days, 16:47:34, time: 2.243, data_time: 1.263, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5850, loss_cls: 3.8421, loss: 3.8421 +2024-07-18 21:17:24,215 - pyskl - INFO - Epoch [73][200/3746] lr: 5.308e-02, eta: 2 days, 16:46:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5869, loss_cls: 3.8693, loss: 3.8693 +2024-07-18 21:18:45,574 - pyskl - INFO - Epoch [73][300/3746] lr: 5.306e-02, eta: 2 days, 16:44:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5820, loss_cls: 3.8482, loss: 3.8482 +2024-07-18 21:20:07,071 - pyskl - INFO - Epoch [73][400/3746] lr: 5.303e-02, eta: 2 days, 16:43:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5820, loss_cls: 3.8681, loss: 3.8681 +2024-07-18 21:21:28,701 - pyskl - INFO - Epoch [73][500/3746] lr: 5.300e-02, eta: 2 days, 16:42:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5720, loss_cls: 3.9251, loss: 3.9251 +2024-07-18 21:22:50,180 - pyskl - INFO - Epoch [73][600/3746] lr: 5.297e-02, eta: 2 days, 16:41:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5847, loss_cls: 3.8405, loss: 3.8405 +2024-07-18 21:24:12,298 - pyskl - INFO - Epoch [73][700/3746] lr: 5.294e-02, eta: 2 days, 16:39:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5761, loss_cls: 3.8861, loss: 3.8861 +2024-07-18 21:25:33,839 - pyskl - INFO - Epoch [73][800/3746] lr: 5.292e-02, eta: 2 days, 16:38:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5611, loss_cls: 4.0090, loss: 4.0090 +2024-07-18 21:26:56,351 - pyskl - INFO - Epoch [73][900/3746] lr: 5.289e-02, eta: 2 days, 16:37:11, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5780, loss_cls: 3.9001, loss: 3.9001 +2024-07-18 21:28:18,176 - pyskl - INFO - Epoch [73][1000/3746] lr: 5.286e-02, eta: 2 days, 16:35:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5772, loss_cls: 3.9244, loss: 3.9244 +2024-07-18 21:29:40,125 - pyskl - INFO - Epoch [73][1100/3746] lr: 5.283e-02, eta: 2 days, 16:34:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5783, loss_cls: 3.9219, loss: 3.9219 +2024-07-18 21:31:02,205 - pyskl - INFO - Epoch [73][1200/3746] lr: 5.280e-02, eta: 2 days, 16:33:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5819, loss_cls: 3.8700, loss: 3.8700 +2024-07-18 21:32:24,400 - pyskl - INFO - Epoch [73][1300/3746] lr: 5.278e-02, eta: 2 days, 16:32:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5763, loss_cls: 3.9122, loss: 3.9122 +2024-07-18 21:33:45,920 - pyskl - INFO - Epoch [73][1400/3746] lr: 5.275e-02, eta: 2 days, 16:30:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5842, loss_cls: 3.8669, loss: 3.8669 +2024-07-18 21:35:07,651 - pyskl - INFO - Epoch [73][1500/3746] lr: 5.272e-02, eta: 2 days, 16:29:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5684, loss_cls: 3.9334, loss: 3.9334 +2024-07-18 21:36:29,167 - pyskl - INFO - Epoch [73][1600/3746] lr: 5.269e-02, eta: 2 days, 16:28:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5766, loss_cls: 3.8912, loss: 3.8912 +2024-07-18 21:37:50,848 - pyskl - INFO - Epoch [73][1700/3746] lr: 5.267e-02, eta: 2 days, 16:26:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5648, loss_cls: 3.9383, loss: 3.9383 +2024-07-18 21:39:12,456 - pyskl - INFO - Epoch [73][1800/3746] lr: 5.264e-02, eta: 2 days, 16:25:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5808, loss_cls: 3.8723, loss: 3.8723 +2024-07-18 21:40:34,293 - pyskl - INFO - Epoch [73][1900/3746] lr: 5.261e-02, eta: 2 days, 16:24:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5756, loss_cls: 3.8938, loss: 3.8938 +2024-07-18 21:41:56,096 - pyskl - INFO - Epoch [73][2000/3746] lr: 5.258e-02, eta: 2 days, 16:22:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5803, loss_cls: 3.9077, loss: 3.9077 +2024-07-18 21:43:17,450 - pyskl - INFO - Epoch [73][2100/3746] lr: 5.255e-02, eta: 2 days, 16:21:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5841, loss_cls: 3.8829, loss: 3.8829 +2024-07-18 21:44:39,384 - pyskl - INFO - Epoch [73][2200/3746] lr: 5.253e-02, eta: 2 days, 16:20:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5692, loss_cls: 3.9522, loss: 3.9522 +2024-07-18 21:46:01,451 - pyskl - INFO - Epoch [73][2300/3746] lr: 5.250e-02, eta: 2 days, 16:19:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5772, loss_cls: 3.9101, loss: 3.9101 +2024-07-18 21:47:22,904 - pyskl - INFO - Epoch [73][2400/3746] lr: 5.247e-02, eta: 2 days, 16:17:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5772, loss_cls: 3.8796, loss: 3.8796 +2024-07-18 21:48:44,794 - pyskl - INFO - Epoch [73][2500/3746] lr: 5.244e-02, eta: 2 days, 16:16:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5786, loss_cls: 3.8591, loss: 3.8591 +2024-07-18 21:50:06,133 - pyskl - INFO - Epoch [73][2600/3746] lr: 5.241e-02, eta: 2 days, 16:15:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5855, loss_cls: 3.8736, loss: 3.8736 +2024-07-18 21:51:27,779 - pyskl - INFO - Epoch [73][2700/3746] lr: 5.239e-02, eta: 2 days, 16:13:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5764, loss_cls: 3.8957, loss: 3.8957 +2024-07-18 21:52:50,534 - pyskl - INFO - Epoch [73][2800/3746] lr: 5.236e-02, eta: 2 days, 16:12:33, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5784, loss_cls: 3.8720, loss: 3.8720 +2024-07-18 21:54:13,492 - pyskl - INFO - Epoch [73][2900/3746] lr: 5.233e-02, eta: 2 days, 16:11:16, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5719, loss_cls: 3.9176, loss: 3.9176 +2024-07-18 21:55:35,803 - pyskl - INFO - Epoch [73][3000/3746] lr: 5.230e-02, eta: 2 days, 16:09:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5664, loss_cls: 3.9075, loss: 3.9075 +2024-07-18 21:56:57,968 - pyskl - INFO - Epoch [73][3100/3746] lr: 5.227e-02, eta: 2 days, 16:08:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5831, loss_cls: 3.8798, loss: 3.8798 +2024-07-18 21:58:19,823 - pyskl - INFO - Epoch [73][3200/3746] lr: 5.225e-02, eta: 2 days, 16:07:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5742, loss_cls: 3.9202, loss: 3.9202 +2024-07-18 21:59:41,833 - pyskl - INFO - Epoch [73][3300/3746] lr: 5.222e-02, eta: 2 days, 16:06:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5716, loss_cls: 3.9166, loss: 3.9166 +2024-07-18 22:01:03,841 - pyskl - INFO - Epoch [73][3400/3746] lr: 5.219e-02, eta: 2 days, 16:04:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5777, loss_cls: 3.8904, loss: 3.8904 +2024-07-18 22:02:25,134 - pyskl - INFO - Epoch [73][3500/3746] lr: 5.216e-02, eta: 2 days, 16:03:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5844, loss_cls: 3.8256, loss: 3.8256 +2024-07-18 22:03:46,895 - pyskl - INFO - Epoch [73][3600/3746] lr: 5.213e-02, eta: 2 days, 16:02:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5764, loss_cls: 3.8826, loss: 3.8826 +2024-07-18 22:05:08,014 - pyskl - INFO - Epoch [73][3700/3746] lr: 5.211e-02, eta: 2 days, 16:00:53, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5695, loss_cls: 3.9451, loss: 3.9451 +2024-07-18 22:05:47,457 - pyskl - INFO - Saving checkpoint at 73 epochs +2024-07-18 22:07:38,290 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 22:07:38,951 - pyskl - INFO - +top1_acc 0.2592 +top5_acc 0.5114 +2024-07-18 22:07:38,951 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 22:07:38,991 - pyskl - INFO - +mean_acc 0.2592 +2024-07-18 22:07:38,996 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_67.pth was removed +2024-07-18 22:07:39,253 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_73.pth. +2024-07-18 22:07:39,254 - pyskl - INFO - Best top1_acc is 0.2592 at 73 epoch. +2024-07-18 22:07:39,265 - pyskl - INFO - Epoch(val) [73][309] top1_acc: 0.2592, top5_acc: 0.5114, mean_class_accuracy: 0.2592 +2024-07-18 22:11:29,237 - pyskl - INFO - Epoch [74][100/3746] lr: 5.207e-02, eta: 2 days, 16:00:56, time: 2.300, data_time: 1.314, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5878, loss_cls: 3.8679, loss: 3.8679 +2024-07-18 22:12:51,532 - pyskl - INFO - Epoch [74][200/3746] lr: 5.204e-02, eta: 2 days, 15:59:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5941, loss_cls: 3.7866, loss: 3.7866 +2024-07-18 22:14:14,345 - pyskl - INFO - Epoch [74][300/3746] lr: 5.201e-02, eta: 2 days, 15:58:22, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5830, loss_cls: 3.8532, loss: 3.8532 +2024-07-18 22:15:36,454 - pyskl - INFO - Epoch [74][400/3746] lr: 5.198e-02, eta: 2 days, 15:57:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5887, loss_cls: 3.8414, loss: 3.8414 +2024-07-18 22:16:58,046 - pyskl - INFO - Epoch [74][500/3746] lr: 5.195e-02, eta: 2 days, 15:55:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5842, loss_cls: 3.8663, loss: 3.8663 +2024-07-18 22:18:19,626 - pyskl - INFO - Epoch [74][600/3746] lr: 5.193e-02, eta: 2 days, 15:54:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5802, loss_cls: 3.9134, loss: 3.9134 +2024-07-18 22:19:41,764 - pyskl - INFO - Epoch [74][700/3746] lr: 5.190e-02, eta: 2 days, 15:53:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5814, loss_cls: 3.9059, loss: 3.9059 +2024-07-18 22:21:03,856 - pyskl - INFO - Epoch [74][800/3746] lr: 5.187e-02, eta: 2 days, 15:51:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5828, loss_cls: 3.8465, loss: 3.8465 +2024-07-18 22:22:26,349 - pyskl - INFO - Epoch [74][900/3746] lr: 5.184e-02, eta: 2 days, 15:50:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5863, loss_cls: 3.8701, loss: 3.8701 +2024-07-18 22:23:47,997 - pyskl - INFO - Epoch [74][1000/3746] lr: 5.181e-02, eta: 2 days, 15:49:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5803, loss_cls: 3.8827, loss: 3.8827 +2024-07-18 22:25:10,230 - pyskl - INFO - Epoch [74][1100/3746] lr: 5.179e-02, eta: 2 days, 15:48:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5730, loss_cls: 3.9297, loss: 3.9297 +2024-07-18 22:26:32,226 - pyskl - INFO - Epoch [74][1200/3746] lr: 5.176e-02, eta: 2 days, 15:46:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5773, loss_cls: 3.9048, loss: 3.9048 +2024-07-18 22:27:54,292 - pyskl - INFO - Epoch [74][1300/3746] lr: 5.173e-02, eta: 2 days, 15:45:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5822, loss_cls: 3.8861, loss: 3.8861 +2024-07-18 22:29:16,424 - pyskl - INFO - Epoch [74][1400/3746] lr: 5.170e-02, eta: 2 days, 15:44:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5816, loss_cls: 3.8620, loss: 3.8620 +2024-07-18 22:30:38,483 - pyskl - INFO - Epoch [74][1500/3746] lr: 5.168e-02, eta: 2 days, 15:42:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5739, loss_cls: 3.9093, loss: 3.9093 +2024-07-18 22:32:00,624 - pyskl - INFO - Epoch [74][1600/3746] lr: 5.165e-02, eta: 2 days, 15:41:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5656, loss_cls: 3.9318, loss: 3.9318 +2024-07-18 22:33:22,758 - pyskl - INFO - Epoch [74][1700/3746] lr: 5.162e-02, eta: 2 days, 15:40:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5805, loss_cls: 3.8810, loss: 3.8810 +2024-07-18 22:34:44,609 - pyskl - INFO - Epoch [74][1800/3746] lr: 5.159e-02, eta: 2 days, 15:38:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5927, loss_cls: 3.8123, loss: 3.8123 +2024-07-18 22:36:06,546 - pyskl - INFO - Epoch [74][1900/3746] lr: 5.156e-02, eta: 2 days, 15:37:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5772, loss_cls: 3.8623, loss: 3.8623 +2024-07-18 22:37:28,500 - pyskl - INFO - Epoch [74][2000/3746] lr: 5.154e-02, eta: 2 days, 15:36:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5794, loss_cls: 3.8689, loss: 3.8689 +2024-07-18 22:38:50,116 - pyskl - INFO - Epoch [74][2100/3746] lr: 5.151e-02, eta: 2 days, 15:35:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5722, loss_cls: 3.9276, loss: 3.9276 +2024-07-18 22:40:11,572 - pyskl - INFO - Epoch [74][2200/3746] lr: 5.148e-02, eta: 2 days, 15:33:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5631, loss_cls: 3.9628, loss: 3.9628 +2024-07-18 22:41:33,118 - pyskl - INFO - Epoch [74][2300/3746] lr: 5.145e-02, eta: 2 days, 15:32:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5858, loss_cls: 3.8342, loss: 3.8342 +2024-07-18 22:42:54,503 - pyskl - INFO - Epoch [74][2400/3746] lr: 5.142e-02, eta: 2 days, 15:31:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5755, loss_cls: 3.9275, loss: 3.9275 +2024-07-18 22:44:16,272 - pyskl - INFO - Epoch [74][2500/3746] lr: 5.140e-02, eta: 2 days, 15:29:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5789, loss_cls: 3.8781, loss: 3.8781 +2024-07-18 22:45:37,603 - pyskl - INFO - Epoch [74][2600/3746] lr: 5.137e-02, eta: 2 days, 15:28:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5673, loss_cls: 3.8836, loss: 3.8836 +2024-07-18 22:46:59,374 - pyskl - INFO - Epoch [74][2700/3746] lr: 5.134e-02, eta: 2 days, 15:27:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5736, loss_cls: 3.9153, loss: 3.9153 +2024-07-18 22:48:21,615 - pyskl - INFO - Epoch [74][2800/3746] lr: 5.131e-02, eta: 2 days, 15:25:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5763, loss_cls: 3.8997, loss: 3.8997 +2024-07-18 22:49:43,719 - pyskl - INFO - Epoch [74][2900/3746] lr: 5.128e-02, eta: 2 days, 15:24:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5719, loss_cls: 3.9145, loss: 3.9145 +2024-07-18 22:51:06,128 - pyskl - INFO - Epoch [74][3000/3746] lr: 5.126e-02, eta: 2 days, 15:23:19, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5753, loss_cls: 3.8943, loss: 3.8943 +2024-07-18 22:52:27,689 - pyskl - INFO - Epoch [74][3100/3746] lr: 5.123e-02, eta: 2 days, 15:22:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5839, loss_cls: 3.8566, loss: 3.8566 +2024-07-18 22:53:49,229 - pyskl - INFO - Epoch [74][3200/3746] lr: 5.120e-02, eta: 2 days, 15:20:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5764, loss_cls: 3.8757, loss: 3.8757 +2024-07-18 22:55:10,412 - pyskl - INFO - Epoch [74][3300/3746] lr: 5.117e-02, eta: 2 days, 15:19:24, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5763, loss_cls: 3.8783, loss: 3.8783 +2024-07-18 22:56:31,801 - pyskl - INFO - Epoch [74][3400/3746] lr: 5.114e-02, eta: 2 days, 15:18:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5787, loss_cls: 3.8995, loss: 3.8995 +2024-07-18 22:57:53,603 - pyskl - INFO - Epoch [74][3500/3746] lr: 5.112e-02, eta: 2 days, 15:16:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5736, loss_cls: 3.8633, loss: 3.8633 +2024-07-18 22:59:15,124 - pyskl - INFO - Epoch [74][3600/3746] lr: 5.109e-02, eta: 2 days, 15:15:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5791, loss_cls: 3.8941, loss: 3.8941 +2024-07-18 23:00:36,991 - pyskl - INFO - Epoch [74][3700/3746] lr: 5.106e-02, eta: 2 days, 15:14:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5697, loss_cls: 3.9070, loss: 3.9070 +2024-07-18 23:01:16,523 - pyskl - INFO - Saving checkpoint at 74 epochs +2024-07-18 23:03:06,348 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 23:03:07,004 - pyskl - INFO - +top1_acc 0.2641 +top5_acc 0.5081 +2024-07-18 23:03:07,004 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 23:03:07,043 - pyskl - INFO - +mean_acc 0.2640 +2024-07-18 23:03:07,047 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_73.pth was removed +2024-07-18 23:03:07,301 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_74.pth. +2024-07-18 23:03:07,301 - pyskl - INFO - Best top1_acc is 0.2641 at 74 epoch. +2024-07-18 23:03:07,313 - pyskl - INFO - Epoch(val) [74][309] top1_acc: 0.2641, top5_acc: 0.5081, mean_class_accuracy: 0.2640 +2024-07-18 23:06:52,269 - pyskl - INFO - Epoch [75][100/3746] lr: 5.102e-02, eta: 2 days, 15:14:06, time: 2.249, data_time: 1.270, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5667, loss_cls: 3.9414, loss: 3.9414 +2024-07-18 23:08:14,237 - pyskl - INFO - Epoch [75][200/3746] lr: 5.099e-02, eta: 2 days, 15:12:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5889, loss_cls: 3.8280, loss: 3.8280 +2024-07-18 23:09:36,038 - pyskl - INFO - Epoch [75][300/3746] lr: 5.096e-02, eta: 2 days, 15:11:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5869, loss_cls: 3.8498, loss: 3.8498 +2024-07-18 23:10:57,750 - pyskl - INFO - Epoch [75][400/3746] lr: 5.094e-02, eta: 2 days, 15:10:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5819, loss_cls: 3.8602, loss: 3.8602 +2024-07-18 23:12:19,320 - pyskl - INFO - Epoch [75][500/3746] lr: 5.091e-02, eta: 2 days, 15:08:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5992, loss_cls: 3.8202, loss: 3.8202 +2024-07-18 23:13:40,894 - pyskl - INFO - Epoch [75][600/3746] lr: 5.088e-02, eta: 2 days, 15:07:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5853, loss_cls: 3.8347, loss: 3.8347 +2024-07-18 23:15:02,674 - pyskl - INFO - Epoch [75][700/3746] lr: 5.085e-02, eta: 2 days, 15:06:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5816, loss_cls: 3.8579, loss: 3.8579 +2024-07-18 23:16:24,492 - pyskl - INFO - Epoch [75][800/3746] lr: 5.082e-02, eta: 2 days, 15:04:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5739, loss_cls: 3.8997, loss: 3.8997 +2024-07-18 23:17:46,864 - pyskl - INFO - Epoch [75][900/3746] lr: 5.080e-02, eta: 2 days, 15:03:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5813, loss_cls: 3.8512, loss: 3.8512 +2024-07-18 23:19:08,522 - pyskl - INFO - Epoch [75][1000/3746] lr: 5.077e-02, eta: 2 days, 15:02:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5759, loss_cls: 3.8928, loss: 3.8928 +2024-07-18 23:20:30,697 - pyskl - INFO - Epoch [75][1100/3746] lr: 5.074e-02, eta: 2 days, 15:01:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5775, loss_cls: 3.9063, loss: 3.9063 +2024-07-18 23:21:53,205 - pyskl - INFO - Epoch [75][1200/3746] lr: 5.071e-02, eta: 2 days, 14:59:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5828, loss_cls: 3.8799, loss: 3.8799 +2024-07-18 23:23:14,813 - pyskl - INFO - Epoch [75][1300/3746] lr: 5.068e-02, eta: 2 days, 14:58:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5775, loss_cls: 3.8970, loss: 3.8970 +2024-07-18 23:24:37,268 - pyskl - INFO - Epoch [75][1400/3746] lr: 5.066e-02, eta: 2 days, 14:57:11, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5725, loss_cls: 3.9159, loss: 3.9159 +2024-07-18 23:25:58,744 - pyskl - INFO - Epoch [75][1500/3746] lr: 5.063e-02, eta: 2 days, 14:55:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5837, loss_cls: 3.8379, loss: 3.8379 +2024-07-18 23:27:20,712 - pyskl - INFO - Epoch [75][1600/3746] lr: 5.060e-02, eta: 2 days, 14:54:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5780, loss_cls: 3.8720, loss: 3.8720 +2024-07-18 23:28:42,407 - pyskl - INFO - Epoch [75][1700/3746] lr: 5.057e-02, eta: 2 days, 14:53:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5677, loss_cls: 3.9332, loss: 3.9332 +2024-07-18 23:30:04,231 - pyskl - INFO - Epoch [75][1800/3746] lr: 5.054e-02, eta: 2 days, 14:51:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5723, loss_cls: 3.9002, loss: 3.9002 +2024-07-18 23:31:26,175 - pyskl - INFO - Epoch [75][1900/3746] lr: 5.052e-02, eta: 2 days, 14:50:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5847, loss_cls: 3.8912, loss: 3.8912 +2024-07-18 23:32:48,328 - pyskl - INFO - Epoch [75][2000/3746] lr: 5.049e-02, eta: 2 days, 14:49:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5750, loss_cls: 3.8767, loss: 3.8767 +2024-07-18 23:34:09,668 - pyskl - INFO - Epoch [75][2100/3746] lr: 5.046e-02, eta: 2 days, 14:48:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5781, loss_cls: 3.8489, loss: 3.8489 +2024-07-18 23:35:31,555 - pyskl - INFO - Epoch [75][2200/3746] lr: 5.043e-02, eta: 2 days, 14:46:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5723, loss_cls: 3.8805, loss: 3.8805 +2024-07-18 23:36:53,299 - pyskl - INFO - Epoch [75][2300/3746] lr: 5.040e-02, eta: 2 days, 14:45:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5866, loss_cls: 3.8348, loss: 3.8348 +2024-07-18 23:38:14,877 - pyskl - INFO - Epoch [75][2400/3746] lr: 5.038e-02, eta: 2 days, 14:44:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5742, loss_cls: 3.9147, loss: 3.9147 +2024-07-18 23:39:36,478 - pyskl - INFO - Epoch [75][2500/3746] lr: 5.035e-02, eta: 2 days, 14:42:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5797, loss_cls: 3.8451, loss: 3.8451 +2024-07-18 23:40:57,915 - pyskl - INFO - Epoch [75][2600/3746] lr: 5.032e-02, eta: 2 days, 14:41:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5753, loss_cls: 3.8851, loss: 3.8851 +2024-07-18 23:42:19,651 - pyskl - INFO - Epoch [75][2700/3746] lr: 5.029e-02, eta: 2 days, 14:40:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5828, loss_cls: 3.8728, loss: 3.8728 +2024-07-18 23:43:41,317 - pyskl - INFO - Epoch [75][2800/3746] lr: 5.026e-02, eta: 2 days, 14:38:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5759, loss_cls: 3.8700, loss: 3.8700 +2024-07-18 23:45:03,773 - pyskl - INFO - Epoch [75][2900/3746] lr: 5.024e-02, eta: 2 days, 14:37:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5734, loss_cls: 3.8995, loss: 3.8995 +2024-07-18 23:46:25,445 - pyskl - INFO - Epoch [75][3000/3746] lr: 5.021e-02, eta: 2 days, 14:36:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5767, loss_cls: 3.8608, loss: 3.8608 +2024-07-18 23:47:47,517 - pyskl - INFO - Epoch [75][3100/3746] lr: 5.018e-02, eta: 2 days, 14:35:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5828, loss_cls: 3.8730, loss: 3.8730 +2024-07-18 23:49:09,652 - pyskl - INFO - Epoch [75][3200/3746] lr: 5.015e-02, eta: 2 days, 14:33:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5786, loss_cls: 3.9001, loss: 3.9001 +2024-07-18 23:50:31,276 - pyskl - INFO - Epoch [75][3300/3746] lr: 5.012e-02, eta: 2 days, 14:32:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5792, loss_cls: 3.8681, loss: 3.8681 +2024-07-18 23:51:53,118 - pyskl - INFO - Epoch [75][3400/3746] lr: 5.010e-02, eta: 2 days, 14:31:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5855, loss_cls: 3.8459, loss: 3.8459 +2024-07-18 23:53:14,817 - pyskl - INFO - Epoch [75][3500/3746] lr: 5.007e-02, eta: 2 days, 14:29:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5827, loss_cls: 3.8729, loss: 3.8729 +2024-07-18 23:54:36,122 - pyskl - INFO - Epoch [75][3600/3746] lr: 5.004e-02, eta: 2 days, 14:28:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5784, loss_cls: 3.8596, loss: 3.8596 +2024-07-18 23:55:58,090 - pyskl - INFO - Epoch [75][3700/3746] lr: 5.001e-02, eta: 2 days, 14:27:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5722, loss_cls: 3.9261, loss: 3.9261 +2024-07-18 23:56:37,486 - pyskl - INFO - Saving checkpoint at 75 epochs +2024-07-18 23:58:26,904 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 23:58:27,558 - pyskl - INFO - +top1_acc 0.2613 +top5_acc 0.5101 +2024-07-18 23:58:27,558 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 23:58:27,599 - pyskl - INFO - +mean_acc 0.2610 +2024-07-18 23:58:27,611 - pyskl - INFO - Epoch(val) [75][309] top1_acc: 0.2613, top5_acc: 0.5101, mean_class_accuracy: 0.2610 +2024-07-19 00:02:10,945 - pyskl - INFO - Epoch [76][100/3746] lr: 4.997e-02, eta: 2 days, 14:27:02, time: 2.233, data_time: 1.253, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5789, loss_cls: 3.8774, loss: 3.8774 +2024-07-19 00:03:32,428 - pyskl - INFO - Epoch [76][200/3746] lr: 4.994e-02, eta: 2 days, 14:25:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5872, loss_cls: 3.8038, loss: 3.8038 +2024-07-19 00:04:54,129 - pyskl - INFO - Epoch [76][300/3746] lr: 4.992e-02, eta: 2 days, 14:24:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5867, loss_cls: 3.8197, loss: 3.8197 +2024-07-19 00:06:15,555 - pyskl - INFO - Epoch [76][400/3746] lr: 4.989e-02, eta: 2 days, 14:23:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5737, loss_cls: 3.8830, loss: 3.8830 +2024-07-19 00:07:37,389 - pyskl - INFO - Epoch [76][500/3746] lr: 4.986e-02, eta: 2 days, 14:21:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5877, loss_cls: 3.8270, loss: 3.8270 +2024-07-19 00:08:59,856 - pyskl - INFO - Epoch [76][600/3746] lr: 4.983e-02, eta: 2 days, 14:20:30, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5856, loss_cls: 3.8284, loss: 3.8284 +2024-07-19 00:10:21,197 - pyskl - INFO - Epoch [76][700/3746] lr: 4.980e-02, eta: 2 days, 14:19:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5891, loss_cls: 3.8178, loss: 3.8178 +2024-07-19 00:11:42,930 - pyskl - INFO - Epoch [76][800/3746] lr: 4.978e-02, eta: 2 days, 14:17:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5919, loss_cls: 3.8122, loss: 3.8122 +2024-07-19 00:13:04,899 - pyskl - INFO - Epoch [76][900/3746] lr: 4.975e-02, eta: 2 days, 14:16:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5759, loss_cls: 3.8670, loss: 3.8670 +2024-07-19 00:14:27,038 - pyskl - INFO - Epoch [76][1000/3746] lr: 4.972e-02, eta: 2 days, 14:15:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5880, loss_cls: 3.8694, loss: 3.8694 +2024-07-19 00:15:48,112 - pyskl - INFO - Epoch [76][1100/3746] lr: 4.969e-02, eta: 2 days, 14:13:58, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5809, loss_cls: 3.8555, loss: 3.8555 +2024-07-19 00:17:10,860 - pyskl - INFO - Epoch [76][1200/3746] lr: 4.966e-02, eta: 2 days, 14:12:40, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5945, loss_cls: 3.8310, loss: 3.8310 +2024-07-19 00:18:32,900 - pyskl - INFO - Epoch [76][1300/3746] lr: 4.964e-02, eta: 2 days, 14:11:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5827, loss_cls: 3.8652, loss: 3.8652 +2024-07-19 00:19:54,901 - pyskl - INFO - Epoch [76][1400/3746] lr: 4.961e-02, eta: 2 days, 14:10:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5753, loss_cls: 3.8676, loss: 3.8676 +2024-07-19 00:21:16,422 - pyskl - INFO - Epoch [76][1500/3746] lr: 4.958e-02, eta: 2 days, 14:08:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5898, loss_cls: 3.8419, loss: 3.8419 +2024-07-19 00:22:37,784 - pyskl - INFO - Epoch [76][1600/3746] lr: 4.955e-02, eta: 2 days, 14:07:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5811, loss_cls: 3.8559, loss: 3.8559 +2024-07-19 00:23:58,821 - pyskl - INFO - Epoch [76][1700/3746] lr: 4.953e-02, eta: 2 days, 14:06:08, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5780, loss_cls: 3.9023, loss: 3.9023 +2024-07-19 00:25:20,444 - pyskl - INFO - Epoch [76][1800/3746] lr: 4.950e-02, eta: 2 days, 14:04:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5789, loss_cls: 3.8854, loss: 3.8854 +2024-07-19 00:26:41,842 - pyskl - INFO - Epoch [76][1900/3746] lr: 4.947e-02, eta: 2 days, 14:03:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5881, loss_cls: 3.8069, loss: 3.8069 +2024-07-19 00:28:03,431 - pyskl - INFO - Epoch [76][2000/3746] lr: 4.944e-02, eta: 2 days, 14:02:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5905, loss_cls: 3.8454, loss: 3.8454 +2024-07-19 00:29:25,180 - pyskl - INFO - Epoch [76][2100/3746] lr: 4.941e-02, eta: 2 days, 14:00:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5748, loss_cls: 3.9158, loss: 3.9158 +2024-07-19 00:30:47,152 - pyskl - INFO - Epoch [76][2200/3746] lr: 4.939e-02, eta: 2 days, 13:59:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5822, loss_cls: 3.8548, loss: 3.8548 +2024-07-19 00:32:09,218 - pyskl - INFO - Epoch [76][2300/3746] lr: 4.936e-02, eta: 2 days, 13:58:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5794, loss_cls: 3.8867, loss: 3.8867 +2024-07-19 00:33:30,667 - pyskl - INFO - Epoch [76][2400/3746] lr: 4.933e-02, eta: 2 days, 13:56:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5772, loss_cls: 3.9073, loss: 3.9073 +2024-07-19 00:34:52,265 - pyskl - INFO - Epoch [76][2500/3746] lr: 4.930e-02, eta: 2 days, 13:55:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5819, loss_cls: 3.8896, loss: 3.8896 +2024-07-19 00:36:13,736 - pyskl - INFO - Epoch [76][2600/3746] lr: 4.927e-02, eta: 2 days, 13:54:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5650, loss_cls: 3.9342, loss: 3.9342 +2024-07-19 00:37:35,738 - pyskl - INFO - Epoch [76][2700/3746] lr: 4.925e-02, eta: 2 days, 13:53:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5783, loss_cls: 3.8708, loss: 3.8708 +2024-07-19 00:38:57,999 - pyskl - INFO - Epoch [76][2800/3746] lr: 4.922e-02, eta: 2 days, 13:51:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5759, loss_cls: 3.8812, loss: 3.8812 +2024-07-19 00:40:19,933 - pyskl - INFO - Epoch [76][2900/3746] lr: 4.919e-02, eta: 2 days, 13:50:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5823, loss_cls: 3.8620, loss: 3.8620 +2024-07-19 00:41:42,898 - pyskl - INFO - Epoch [76][3000/3746] lr: 4.916e-02, eta: 2 days, 13:49:10, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5870, loss_cls: 3.8231, loss: 3.8231 +2024-07-19 00:43:04,201 - pyskl - INFO - Epoch [76][3100/3746] lr: 4.913e-02, eta: 2 days, 13:47:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5855, loss_cls: 3.8476, loss: 3.8476 +2024-07-19 00:44:25,953 - pyskl - INFO - Epoch [76][3200/3746] lr: 4.911e-02, eta: 2 days, 13:46:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5839, loss_cls: 3.8634, loss: 3.8634 +2024-07-19 00:45:47,934 - pyskl - INFO - Epoch [76][3300/3746] lr: 4.908e-02, eta: 2 days, 13:45:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5747, loss_cls: 3.8700, loss: 3.8700 +2024-07-19 00:47:09,408 - pyskl - INFO - Epoch [76][3400/3746] lr: 4.905e-02, eta: 2 days, 13:43:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5823, loss_cls: 3.8496, loss: 3.8496 +2024-07-19 00:48:31,177 - pyskl - INFO - Epoch [76][3500/3746] lr: 4.902e-02, eta: 2 days, 13:42:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5808, loss_cls: 3.9077, loss: 3.9077 +2024-07-19 00:49:53,023 - pyskl - INFO - Epoch [76][3600/3746] lr: 4.899e-02, eta: 2 days, 13:41:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5733, loss_cls: 3.9364, loss: 3.9364 +2024-07-19 00:51:14,484 - pyskl - INFO - Epoch [76][3700/3746] lr: 4.897e-02, eta: 2 days, 13:40:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5852, loss_cls: 3.8285, loss: 3.8285 +2024-07-19 00:51:54,353 - pyskl - INFO - Saving checkpoint at 76 epochs +2024-07-19 00:53:43,917 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 00:53:44,579 - pyskl - INFO - +top1_acc 0.2579 +top5_acc 0.5041 +2024-07-19 00:53:44,579 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 00:53:44,619 - pyskl - INFO - +mean_acc 0.2578 +2024-07-19 00:53:44,631 - pyskl - INFO - Epoch(val) [76][309] top1_acc: 0.2579, top5_acc: 0.5041, mean_class_accuracy: 0.2578 +2024-07-19 00:57:35,645 - pyskl - INFO - Epoch [77][100/3746] lr: 4.893e-02, eta: 2 days, 13:39:54, time: 2.310, data_time: 1.318, memory: 15990, top1_acc: 0.3397, top5_acc: 0.5961, loss_cls: 3.7757, loss: 3.7757 +2024-07-19 00:58:57,567 - pyskl - INFO - Epoch [77][200/3746] lr: 4.890e-02, eta: 2 days, 13:38:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5919, loss_cls: 3.8235, loss: 3.8235 +2024-07-19 01:00:18,926 - pyskl - INFO - Epoch [77][300/3746] lr: 4.887e-02, eta: 2 days, 13:37:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5952, loss_cls: 3.8158, loss: 3.8158 +2024-07-19 01:01:41,066 - pyskl - INFO - Epoch [77][400/3746] lr: 4.884e-02, eta: 2 days, 13:35:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5798, loss_cls: 3.8546, loss: 3.8546 +2024-07-19 01:03:02,954 - pyskl - INFO - Epoch [77][500/3746] lr: 4.881e-02, eta: 2 days, 13:34:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5908, loss_cls: 3.8327, loss: 3.8327 +2024-07-19 01:04:25,108 - pyskl - INFO - Epoch [77][600/3746] lr: 4.879e-02, eta: 2 days, 13:33:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5773, loss_cls: 3.8671, loss: 3.8671 +2024-07-19 01:05:46,825 - pyskl - INFO - Epoch [77][700/3746] lr: 4.876e-02, eta: 2 days, 13:32:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5922, loss_cls: 3.8063, loss: 3.8063 +2024-07-19 01:07:09,236 - pyskl - INFO - Epoch [77][800/3746] lr: 4.873e-02, eta: 2 days, 13:30:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5866, loss_cls: 3.8474, loss: 3.8474 +2024-07-19 01:08:30,915 - pyskl - INFO - Epoch [77][900/3746] lr: 4.870e-02, eta: 2 days, 13:29:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5691, loss_cls: 3.9032, loss: 3.9032 +2024-07-19 01:09:52,639 - pyskl - INFO - Epoch [77][1000/3746] lr: 4.867e-02, eta: 2 days, 13:28:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5920, loss_cls: 3.8145, loss: 3.8145 +2024-07-19 01:11:14,373 - pyskl - INFO - Epoch [77][1100/3746] lr: 4.865e-02, eta: 2 days, 13:26:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5886, loss_cls: 3.8394, loss: 3.8394 +2024-07-19 01:12:36,787 - pyskl - INFO - Epoch [77][1200/3746] lr: 4.862e-02, eta: 2 days, 13:25:32, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5981, loss_cls: 3.8115, loss: 3.8115 +2024-07-19 01:13:58,264 - pyskl - INFO - Epoch [77][1300/3746] lr: 4.859e-02, eta: 2 days, 13:24:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5872, loss_cls: 3.8431, loss: 3.8431 +2024-07-19 01:15:20,158 - pyskl - INFO - Epoch [77][1400/3746] lr: 4.856e-02, eta: 2 days, 13:22:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5892, loss_cls: 3.8401, loss: 3.8401 +2024-07-19 01:16:42,188 - pyskl - INFO - Epoch [77][1500/3746] lr: 4.853e-02, eta: 2 days, 13:21:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5842, loss_cls: 3.8221, loss: 3.8221 +2024-07-19 01:18:03,930 - pyskl - INFO - Epoch [77][1600/3746] lr: 4.851e-02, eta: 2 days, 13:20:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5944, loss_cls: 3.8275, loss: 3.8275 +2024-07-19 01:19:25,426 - pyskl - INFO - Epoch [77][1700/3746] lr: 4.848e-02, eta: 2 days, 13:18:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5739, loss_cls: 3.8816, loss: 3.8816 +2024-07-19 01:20:46,795 - pyskl - INFO - Epoch [77][1800/3746] lr: 4.845e-02, eta: 2 days, 13:17:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5686, loss_cls: 3.9031, loss: 3.9031 +2024-07-19 01:22:08,088 - pyskl - INFO - Epoch [77][1900/3746] lr: 4.842e-02, eta: 2 days, 13:16:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5778, loss_cls: 3.8775, loss: 3.8775 +2024-07-19 01:23:29,980 - pyskl - INFO - Epoch [77][2000/3746] lr: 4.839e-02, eta: 2 days, 13:15:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5880, loss_cls: 3.8055, loss: 3.8055 +2024-07-19 01:24:51,554 - pyskl - INFO - Epoch [77][2100/3746] lr: 4.837e-02, eta: 2 days, 13:13:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5817, loss_cls: 3.8414, loss: 3.8414 +2024-07-19 01:26:13,084 - pyskl - INFO - Epoch [77][2200/3746] lr: 4.834e-02, eta: 2 days, 13:12:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5778, loss_cls: 3.8725, loss: 3.8725 +2024-07-19 01:27:35,320 - pyskl - INFO - Epoch [77][2300/3746] lr: 4.831e-02, eta: 2 days, 13:11:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5809, loss_cls: 3.8603, loss: 3.8603 +2024-07-19 01:28:56,886 - pyskl - INFO - Epoch [77][2400/3746] lr: 4.828e-02, eta: 2 days, 13:09:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5844, loss_cls: 3.8507, loss: 3.8507 +2024-07-19 01:30:18,984 - pyskl - INFO - Epoch [77][2500/3746] lr: 4.825e-02, eta: 2 days, 13:08:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5927, loss_cls: 3.8190, loss: 3.8190 +2024-07-19 01:31:40,485 - pyskl - INFO - Epoch [77][2600/3746] lr: 4.823e-02, eta: 2 days, 13:07:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5716, loss_cls: 3.8928, loss: 3.8928 +2024-07-19 01:33:02,486 - pyskl - INFO - Epoch [77][2700/3746] lr: 4.820e-02, eta: 2 days, 13:05:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5678, loss_cls: 3.9127, loss: 3.9127 +2024-07-19 01:34:24,817 - pyskl - INFO - Epoch [77][2800/3746] lr: 4.817e-02, eta: 2 days, 13:04:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5908, loss_cls: 3.8257, loss: 3.8257 +2024-07-19 01:35:47,304 - pyskl - INFO - Epoch [77][2900/3746] lr: 4.814e-02, eta: 2 days, 13:03:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5786, loss_cls: 3.8760, loss: 3.8760 +2024-07-19 01:37:10,393 - pyskl - INFO - Epoch [77][3000/3746] lr: 4.811e-02, eta: 2 days, 13:02:00, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5789, loss_cls: 3.8560, loss: 3.8560 +2024-07-19 01:38:31,921 - pyskl - INFO - Epoch [77][3100/3746] lr: 4.809e-02, eta: 2 days, 13:00:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5800, loss_cls: 3.9078, loss: 3.9078 +2024-07-19 01:39:54,221 - pyskl - INFO - Epoch [77][3200/3746] lr: 4.806e-02, eta: 2 days, 12:59:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5891, loss_cls: 3.8269, loss: 3.8269 +2024-07-19 01:41:15,757 - pyskl - INFO - Epoch [77][3300/3746] lr: 4.803e-02, eta: 2 days, 12:58:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5833, loss_cls: 3.8531, loss: 3.8531 +2024-07-19 01:42:38,065 - pyskl - INFO - Epoch [77][3400/3746] lr: 4.800e-02, eta: 2 days, 12:56:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5897, loss_cls: 3.8337, loss: 3.8337 +2024-07-19 01:43:59,612 - pyskl - INFO - Epoch [77][3500/3746] lr: 4.798e-02, eta: 2 days, 12:55:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5881, loss_cls: 3.8599, loss: 3.8599 +2024-07-19 01:45:21,161 - pyskl - INFO - Epoch [77][3600/3746] lr: 4.795e-02, eta: 2 days, 12:54:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5828, loss_cls: 3.8641, loss: 3.8641 +2024-07-19 01:46:42,777 - pyskl - INFO - Epoch [77][3700/3746] lr: 4.792e-02, eta: 2 days, 12:52:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5842, loss_cls: 3.8368, loss: 3.8368 +2024-07-19 01:47:22,313 - pyskl - INFO - Saving checkpoint at 77 epochs +2024-07-19 01:49:13,345 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 01:49:14,159 - pyskl - INFO - +top1_acc 0.2538 +top5_acc 0.5087 +2024-07-19 01:49:14,159 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 01:49:14,209 - pyskl - INFO - +mean_acc 0.2535 +2024-07-19 01:49:14,222 - pyskl - INFO - Epoch(val) [77][309] top1_acc: 0.2538, top5_acc: 0.5087, mean_class_accuracy: 0.2535 +2024-07-19 01:53:03,712 - pyskl - INFO - Epoch [78][100/3746] lr: 4.788e-02, eta: 2 days, 12:52:40, time: 2.295, data_time: 1.304, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5791, loss_cls: 3.8648, loss: 3.8648 +2024-07-19 01:54:26,720 - pyskl - INFO - Epoch [78][200/3746] lr: 4.785e-02, eta: 2 days, 12:51:22, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6106, loss_cls: 3.7373, loss: 3.7373 +2024-07-19 01:55:49,296 - pyskl - INFO - Epoch [78][300/3746] lr: 4.782e-02, eta: 2 days, 12:50:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5934, loss_cls: 3.7998, loss: 3.7998 +2024-07-19 01:57:11,416 - pyskl - INFO - Epoch [78][400/3746] lr: 4.779e-02, eta: 2 days, 12:48:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5844, loss_cls: 3.8277, loss: 3.8277 +2024-07-19 01:58:33,055 - pyskl - INFO - Epoch [78][500/3746] lr: 4.777e-02, eta: 2 days, 12:47:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5844, loss_cls: 3.8237, loss: 3.8237 +2024-07-19 01:59:55,390 - pyskl - INFO - Epoch [78][600/3746] lr: 4.774e-02, eta: 2 days, 12:46:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5936, loss_cls: 3.8052, loss: 3.8052 +2024-07-19 02:01:16,997 - pyskl - INFO - Epoch [78][700/3746] lr: 4.771e-02, eta: 2 days, 12:44:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5852, loss_cls: 3.8541, loss: 3.8541 +2024-07-19 02:02:39,274 - pyskl - INFO - Epoch [78][800/3746] lr: 4.768e-02, eta: 2 days, 12:43:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5819, loss_cls: 3.8492, loss: 3.8492 +2024-07-19 02:04:01,456 - pyskl - INFO - Epoch [78][900/3746] lr: 4.766e-02, eta: 2 days, 12:42:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5914, loss_cls: 3.7838, loss: 3.7838 +2024-07-19 02:05:23,308 - pyskl - INFO - Epoch [78][1000/3746] lr: 4.763e-02, eta: 2 days, 12:40:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5758, loss_cls: 3.8659, loss: 3.8659 +2024-07-19 02:06:45,165 - pyskl - INFO - Epoch [78][1100/3746] lr: 4.760e-02, eta: 2 days, 12:39:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5917, loss_cls: 3.8163, loss: 3.8163 +2024-07-19 02:08:06,797 - pyskl - INFO - Epoch [78][1200/3746] lr: 4.757e-02, eta: 2 days, 12:38:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5809, loss_cls: 3.8254, loss: 3.8254 +2024-07-19 02:09:28,910 - pyskl - INFO - Epoch [78][1300/3746] lr: 4.754e-02, eta: 2 days, 12:36:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5786, loss_cls: 3.8625, loss: 3.8625 +2024-07-19 02:10:50,343 - pyskl - INFO - Epoch [78][1400/3746] lr: 4.752e-02, eta: 2 days, 12:35:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5905, loss_cls: 3.8437, loss: 3.8437 +2024-07-19 02:12:12,234 - pyskl - INFO - Epoch [78][1500/3746] lr: 4.749e-02, eta: 2 days, 12:34:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5936, loss_cls: 3.8045, loss: 3.8045 +2024-07-19 02:13:34,110 - pyskl - INFO - Epoch [78][1600/3746] lr: 4.746e-02, eta: 2 days, 12:33:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5828, loss_cls: 3.8887, loss: 3.8887 +2024-07-19 02:14:55,541 - pyskl - INFO - Epoch [78][1700/3746] lr: 4.743e-02, eta: 2 days, 12:31:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5950, loss_cls: 3.7873, loss: 3.7873 +2024-07-19 02:16:16,984 - pyskl - INFO - Epoch [78][1800/3746] lr: 4.740e-02, eta: 2 days, 12:30:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5820, loss_cls: 3.8485, loss: 3.8485 +2024-07-19 02:17:38,624 - pyskl - INFO - Epoch [78][1900/3746] lr: 4.738e-02, eta: 2 days, 12:29:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5931, loss_cls: 3.8171, loss: 3.8171 +2024-07-19 02:19:00,201 - pyskl - INFO - Epoch [78][2000/3746] lr: 4.735e-02, eta: 2 days, 12:27:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5858, loss_cls: 3.8458, loss: 3.8458 +2024-07-19 02:20:22,100 - pyskl - INFO - Epoch [78][2100/3746] lr: 4.732e-02, eta: 2 days, 12:26:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5875, loss_cls: 3.8500, loss: 3.8500 +2024-07-19 02:21:44,210 - pyskl - INFO - Epoch [78][2200/3746] lr: 4.729e-02, eta: 2 days, 12:25:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5848, loss_cls: 3.8329, loss: 3.8329 +2024-07-19 02:23:06,266 - pyskl - INFO - Epoch [78][2300/3746] lr: 4.726e-02, eta: 2 days, 12:23:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5794, loss_cls: 3.8653, loss: 3.8653 +2024-07-19 02:24:27,963 - pyskl - INFO - Epoch [78][2400/3746] lr: 4.724e-02, eta: 2 days, 12:22:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5839, loss_cls: 3.8632, loss: 3.8632 +2024-07-19 02:25:49,846 - pyskl - INFO - Epoch [78][2500/3746] lr: 4.721e-02, eta: 2 days, 12:21:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5877, loss_cls: 3.8185, loss: 3.8185 +2024-07-19 02:27:11,258 - pyskl - INFO - Epoch [78][2600/3746] lr: 4.718e-02, eta: 2 days, 12:19:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5823, loss_cls: 3.8449, loss: 3.8449 +2024-07-19 02:28:33,090 - pyskl - INFO - Epoch [78][2700/3746] lr: 4.715e-02, eta: 2 days, 12:18:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5733, loss_cls: 3.8982, loss: 3.8982 +2024-07-19 02:29:55,557 - pyskl - INFO - Epoch [78][2800/3746] lr: 4.712e-02, eta: 2 days, 12:17:19, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5723, loss_cls: 3.8727, loss: 3.8727 +2024-07-19 02:31:17,384 - pyskl - INFO - Epoch [78][2900/3746] lr: 4.710e-02, eta: 2 days, 12:16:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5891, loss_cls: 3.8347, loss: 3.8347 +2024-07-19 02:32:39,484 - pyskl - INFO - Epoch [78][3000/3746] lr: 4.707e-02, eta: 2 days, 12:14:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5919, loss_cls: 3.8196, loss: 3.8196 +2024-07-19 02:34:01,136 - pyskl - INFO - Epoch [78][3100/3746] lr: 4.704e-02, eta: 2 days, 12:13:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5891, loss_cls: 3.8323, loss: 3.8323 +2024-07-19 02:35:22,632 - pyskl - INFO - Epoch [78][3200/3746] lr: 4.701e-02, eta: 2 days, 12:12:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5869, loss_cls: 3.8470, loss: 3.8470 +2024-07-19 02:36:44,596 - pyskl - INFO - Epoch [78][3300/3746] lr: 4.699e-02, eta: 2 days, 12:10:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5859, loss_cls: 3.8631, loss: 3.8631 +2024-07-19 02:38:06,100 - pyskl - INFO - Epoch [78][3400/3746] lr: 4.696e-02, eta: 2 days, 12:09:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5841, loss_cls: 3.8452, loss: 3.8452 +2024-07-19 02:39:28,159 - pyskl - INFO - Epoch [78][3500/3746] lr: 4.693e-02, eta: 2 days, 12:08:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5833, loss_cls: 3.8519, loss: 3.8519 +2024-07-19 02:40:50,078 - pyskl - INFO - Epoch [78][3600/3746] lr: 4.690e-02, eta: 2 days, 12:06:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5830, loss_cls: 3.8290, loss: 3.8290 +2024-07-19 02:42:11,935 - pyskl - INFO - Epoch [78][3700/3746] lr: 4.687e-02, eta: 2 days, 12:05:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5845, loss_cls: 3.8562, loss: 3.8562 +2024-07-19 02:42:51,484 - pyskl - INFO - Saving checkpoint at 78 epochs +2024-07-19 02:44:42,671 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 02:44:43,333 - pyskl - INFO - +top1_acc 0.2642 +top5_acc 0.5130 +2024-07-19 02:44:43,333 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 02:44:43,373 - pyskl - INFO - +mean_acc 0.2641 +2024-07-19 02:44:43,377 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_74.pth was removed +2024-07-19 02:44:43,635 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_78.pth. +2024-07-19 02:44:43,635 - pyskl - INFO - Best top1_acc is 0.2642 at 78 epoch. +2024-07-19 02:44:43,647 - pyskl - INFO - Epoch(val) [78][309] top1_acc: 0.2642, top5_acc: 0.5130, mean_class_accuracy: 0.2641 +2024-07-19 02:48:34,300 - pyskl - INFO - Epoch [79][100/3746] lr: 4.683e-02, eta: 2 days, 12:05:18, time: 2.306, data_time: 1.323, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5919, loss_cls: 3.7784, loss: 3.7784 +2024-07-19 02:49:56,916 - pyskl - INFO - Epoch [79][200/3746] lr: 4.680e-02, eta: 2 days, 12:04:00, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6006, loss_cls: 3.7601, loss: 3.7601 +2024-07-19 02:51:19,266 - pyskl - INFO - Epoch [79][300/3746] lr: 4.678e-02, eta: 2 days, 12:02:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.5944, loss_cls: 3.7800, loss: 3.7800 +2024-07-19 02:52:40,883 - pyskl - INFO - Epoch [79][400/3746] lr: 4.675e-02, eta: 2 days, 12:01:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5952, loss_cls: 3.8146, loss: 3.8146 +2024-07-19 02:54:02,632 - pyskl - INFO - Epoch [79][500/3746] lr: 4.672e-02, eta: 2 days, 12:00:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5827, loss_cls: 3.8519, loss: 3.8519 +2024-07-19 02:55:25,028 - pyskl - INFO - Epoch [79][600/3746] lr: 4.669e-02, eta: 2 days, 11:58:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5906, loss_cls: 3.8036, loss: 3.8036 +2024-07-19 02:56:46,749 - pyskl - INFO - Epoch [79][700/3746] lr: 4.667e-02, eta: 2 days, 11:57:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5864, loss_cls: 3.8042, loss: 3.8042 +2024-07-19 02:58:08,800 - pyskl - INFO - Epoch [79][800/3746] lr: 4.664e-02, eta: 2 days, 11:56:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5905, loss_cls: 3.7804, loss: 3.7804 +2024-07-19 02:59:30,392 - pyskl - INFO - Epoch [79][900/3746] lr: 4.661e-02, eta: 2 days, 11:54:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5806, loss_cls: 3.8510, loss: 3.8510 +2024-07-19 03:00:52,328 - pyskl - INFO - Epoch [79][1000/3746] lr: 4.658e-02, eta: 2 days, 11:53:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5909, loss_cls: 3.8041, loss: 3.8041 +2024-07-19 03:02:13,727 - pyskl - INFO - Epoch [79][1100/3746] lr: 4.655e-02, eta: 2 days, 11:52:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.5903, loss_cls: 3.7823, loss: 3.7823 +2024-07-19 03:03:35,799 - pyskl - INFO - Epoch [79][1200/3746] lr: 4.653e-02, eta: 2 days, 11:50:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5800, loss_cls: 3.8796, loss: 3.8796 +2024-07-19 03:04:57,560 - pyskl - INFO - Epoch [79][1300/3746] lr: 4.650e-02, eta: 2 days, 11:49:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5806, loss_cls: 3.8327, loss: 3.8327 +2024-07-19 03:06:19,251 - pyskl - INFO - Epoch [79][1400/3746] lr: 4.647e-02, eta: 2 days, 11:48:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5833, loss_cls: 3.8177, loss: 3.8177 +2024-07-19 03:07:41,171 - pyskl - INFO - Epoch [79][1500/3746] lr: 4.644e-02, eta: 2 days, 11:46:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5878, loss_cls: 3.8412, loss: 3.8412 +2024-07-19 03:09:03,499 - pyskl - INFO - Epoch [79][1600/3746] lr: 4.641e-02, eta: 2 days, 11:45:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5808, loss_cls: 3.8777, loss: 3.8777 +2024-07-19 03:10:25,371 - pyskl - INFO - Epoch [79][1700/3746] lr: 4.639e-02, eta: 2 days, 11:44:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5870, loss_cls: 3.8311, loss: 3.8311 +2024-07-19 03:11:47,585 - pyskl - INFO - Epoch [79][1800/3746] lr: 4.636e-02, eta: 2 days, 11:43:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5945, loss_cls: 3.8119, loss: 3.8119 +2024-07-19 03:13:09,080 - pyskl - INFO - Epoch [79][1900/3746] lr: 4.633e-02, eta: 2 days, 11:41:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5872, loss_cls: 3.8104, loss: 3.8104 +2024-07-19 03:14:30,786 - pyskl - INFO - Epoch [79][2000/3746] lr: 4.630e-02, eta: 2 days, 11:40:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5820, loss_cls: 3.8381, loss: 3.8381 +2024-07-19 03:15:52,091 - pyskl - INFO - Epoch [79][2100/3746] lr: 4.628e-02, eta: 2 days, 11:39:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5802, loss_cls: 3.8482, loss: 3.8482 +2024-07-19 03:17:13,833 - pyskl - INFO - Epoch [79][2200/3746] lr: 4.625e-02, eta: 2 days, 11:37:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5936, loss_cls: 3.7767, loss: 3.7767 +2024-07-19 03:18:35,897 - pyskl - INFO - Epoch [79][2300/3746] lr: 4.622e-02, eta: 2 days, 11:36:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5969, loss_cls: 3.7945, loss: 3.7945 +2024-07-19 03:19:57,811 - pyskl - INFO - Epoch [79][2400/3746] lr: 4.619e-02, eta: 2 days, 11:35:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5667, loss_cls: 3.8904, loss: 3.8904 +2024-07-19 03:21:20,171 - pyskl - INFO - Epoch [79][2500/3746] lr: 4.616e-02, eta: 2 days, 11:33:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.6030, loss_cls: 3.7625, loss: 3.7625 +2024-07-19 03:22:42,151 - pyskl - INFO - Epoch [79][2600/3746] lr: 4.614e-02, eta: 2 days, 11:32:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5822, loss_cls: 3.8625, loss: 3.8625 +2024-07-19 03:24:03,315 - pyskl - INFO - Epoch [79][2700/3746] lr: 4.611e-02, eta: 2 days, 11:31:11, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5944, loss_cls: 3.7837, loss: 3.7837 +2024-07-19 03:25:25,939 - pyskl - INFO - Epoch [79][2800/3746] lr: 4.608e-02, eta: 2 days, 11:29:53, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5845, loss_cls: 3.8616, loss: 3.8616 +2024-07-19 03:26:47,453 - pyskl - INFO - Epoch [79][2900/3746] lr: 4.605e-02, eta: 2 days, 11:28:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5898, loss_cls: 3.8109, loss: 3.8109 +2024-07-19 03:28:09,430 - pyskl - INFO - Epoch [79][3000/3746] lr: 4.602e-02, eta: 2 days, 11:27:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5805, loss_cls: 3.8628, loss: 3.8628 +2024-07-19 03:29:32,612 - pyskl - INFO - Epoch [79][3100/3746] lr: 4.600e-02, eta: 2 days, 11:25:57, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5798, loss_cls: 3.8632, loss: 3.8632 +2024-07-19 03:30:54,875 - pyskl - INFO - Epoch [79][3200/3746] lr: 4.597e-02, eta: 2 days, 11:24:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5864, loss_cls: 3.8346, loss: 3.8346 +2024-07-19 03:32:16,434 - pyskl - INFO - Epoch [79][3300/3746] lr: 4.594e-02, eta: 2 days, 11:23:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5780, loss_cls: 3.8982, loss: 3.8982 +2024-07-19 03:33:38,377 - pyskl - INFO - Epoch [79][3400/3746] lr: 4.591e-02, eta: 2 days, 11:22:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5786, loss_cls: 3.8693, loss: 3.8693 +2024-07-19 03:35:00,064 - pyskl - INFO - Epoch [79][3500/3746] lr: 4.588e-02, eta: 2 days, 11:20:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5900, loss_cls: 3.8278, loss: 3.8278 +2024-07-19 03:36:21,382 - pyskl - INFO - Epoch [79][3600/3746] lr: 4.586e-02, eta: 2 days, 11:19:23, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5803, loss_cls: 3.8685, loss: 3.8685 +2024-07-19 03:37:43,801 - pyskl - INFO - Epoch [79][3700/3746] lr: 4.583e-02, eta: 2 days, 11:18:05, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5883, loss_cls: 3.8197, loss: 3.8197 +2024-07-19 03:38:23,320 - pyskl - INFO - Saving checkpoint at 79 epochs +2024-07-19 03:40:13,621 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 03:40:14,286 - pyskl - INFO - +top1_acc 0.2789 +top5_acc 0.5327 +2024-07-19 03:40:14,286 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 03:40:14,326 - pyskl - INFO - +mean_acc 0.2788 +2024-07-19 03:40:14,330 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_78.pth was removed +2024-07-19 03:40:14,587 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_79.pth. +2024-07-19 03:40:14,588 - pyskl - INFO - Best top1_acc is 0.2789 at 79 epoch. +2024-07-19 03:40:14,600 - pyskl - INFO - Epoch(val) [79][309] top1_acc: 0.2789, top5_acc: 0.5327, mean_class_accuracy: 0.2788 +2024-07-19 03:44:04,309 - pyskl - INFO - Epoch [80][100/3746] lr: 4.579e-02, eta: 2 days, 11:17:49, time: 2.297, data_time: 1.310, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6000, loss_cls: 3.7644, loss: 3.7644 +2024-07-19 03:45:26,273 - pyskl - INFO - Epoch [80][200/3746] lr: 4.576e-02, eta: 2 days, 11:16:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.6041, loss_cls: 3.7546, loss: 3.7546 +2024-07-19 03:46:48,098 - pyskl - INFO - Epoch [80][300/3746] lr: 4.573e-02, eta: 2 days, 11:15:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5944, loss_cls: 3.7944, loss: 3.7944 +2024-07-19 03:48:09,860 - pyskl - INFO - Epoch [80][400/3746] lr: 4.570e-02, eta: 2 days, 11:13:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5900, loss_cls: 3.7988, loss: 3.7988 +2024-07-19 03:49:31,779 - pyskl - INFO - Epoch [80][500/3746] lr: 4.568e-02, eta: 2 days, 11:12:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5995, loss_cls: 3.7446, loss: 3.7446 +2024-07-19 03:50:53,762 - pyskl - INFO - Epoch [80][600/3746] lr: 4.565e-02, eta: 2 days, 11:11:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5953, loss_cls: 3.7747, loss: 3.7747 +2024-07-19 03:52:16,170 - pyskl - INFO - Epoch [80][700/3746] lr: 4.562e-02, eta: 2 days, 11:09:56, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5894, loss_cls: 3.7970, loss: 3.7970 +2024-07-19 03:53:38,383 - pyskl - INFO - Epoch [80][800/3746] lr: 4.559e-02, eta: 2 days, 11:08:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5886, loss_cls: 3.8413, loss: 3.8413 +2024-07-19 03:55:00,009 - pyskl - INFO - Epoch [80][900/3746] lr: 4.557e-02, eta: 2 days, 11:07:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.5952, loss_cls: 3.8035, loss: 3.8035 +2024-07-19 03:56:21,595 - pyskl - INFO - Epoch [80][1000/3746] lr: 4.554e-02, eta: 2 days, 11:05:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.6014, loss_cls: 3.7739, loss: 3.7739 +2024-07-19 03:57:43,287 - pyskl - INFO - Epoch [80][1100/3746] lr: 4.551e-02, eta: 2 days, 11:04:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5998, loss_cls: 3.7823, loss: 3.7823 +2024-07-19 03:59:05,256 - pyskl - INFO - Epoch [80][1200/3746] lr: 4.548e-02, eta: 2 days, 11:03:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5847, loss_cls: 3.8610, loss: 3.8610 +2024-07-19 04:00:27,124 - pyskl - INFO - Epoch [80][1300/3746] lr: 4.545e-02, eta: 2 days, 11:02:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5927, loss_cls: 3.7940, loss: 3.7940 +2024-07-19 04:01:49,202 - pyskl - INFO - Epoch [80][1400/3746] lr: 4.543e-02, eta: 2 days, 11:00:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5925, loss_cls: 3.8031, loss: 3.8031 +2024-07-19 04:03:11,182 - pyskl - INFO - Epoch [80][1500/3746] lr: 4.540e-02, eta: 2 days, 10:59:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5816, loss_cls: 3.8557, loss: 3.8557 +2024-07-19 04:04:32,481 - pyskl - INFO - Epoch [80][1600/3746] lr: 4.537e-02, eta: 2 days, 10:58:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5833, loss_cls: 3.8414, loss: 3.8414 +2024-07-19 04:05:53,761 - pyskl - INFO - Epoch [80][1700/3746] lr: 4.534e-02, eta: 2 days, 10:56:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5892, loss_cls: 3.8282, loss: 3.8282 +2024-07-19 04:07:15,072 - pyskl - INFO - Epoch [80][1800/3746] lr: 4.532e-02, eta: 2 days, 10:55:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5747, loss_cls: 3.8372, loss: 3.8372 +2024-07-19 04:08:36,213 - pyskl - INFO - Epoch [80][1900/3746] lr: 4.529e-02, eta: 2 days, 10:54:07, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5961, loss_cls: 3.8059, loss: 3.8059 +2024-07-19 04:09:58,022 - pyskl - INFO - Epoch [80][2000/3746] lr: 4.526e-02, eta: 2 days, 10:52:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5889, loss_cls: 3.7928, loss: 3.7928 +2024-07-19 04:11:19,665 - pyskl - INFO - Epoch [80][2100/3746] lr: 4.523e-02, eta: 2 days, 10:51:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5808, loss_cls: 3.8581, loss: 3.8581 +2024-07-19 04:12:41,283 - pyskl - INFO - Epoch [80][2200/3746] lr: 4.520e-02, eta: 2 days, 10:50:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5909, loss_cls: 3.7877, loss: 3.7877 +2024-07-19 04:14:03,370 - pyskl - INFO - Epoch [80][2300/3746] lr: 4.518e-02, eta: 2 days, 10:48:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5861, loss_cls: 3.8351, loss: 3.8351 +2024-07-19 04:15:24,984 - pyskl - INFO - Epoch [80][2400/3746] lr: 4.515e-02, eta: 2 days, 10:47:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5892, loss_cls: 3.8228, loss: 3.8228 +2024-07-19 04:16:46,592 - pyskl - INFO - Epoch [80][2500/3746] lr: 4.512e-02, eta: 2 days, 10:46:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5850, loss_cls: 3.8289, loss: 3.8289 +2024-07-19 04:18:07,993 - pyskl - INFO - Epoch [80][2600/3746] lr: 4.509e-02, eta: 2 days, 10:44:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5936, loss_cls: 3.7914, loss: 3.7914 +2024-07-19 04:19:29,381 - pyskl - INFO - Epoch [80][2700/3746] lr: 4.506e-02, eta: 2 days, 10:43:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5809, loss_cls: 3.8345, loss: 3.8345 +2024-07-19 04:20:51,277 - pyskl - INFO - Epoch [80][2800/3746] lr: 4.504e-02, eta: 2 days, 10:42:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.5897, loss_cls: 3.7884, loss: 3.7884 +2024-07-19 04:22:12,773 - pyskl - INFO - Epoch [80][2900/3746] lr: 4.501e-02, eta: 2 days, 10:40:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5844, loss_cls: 3.8076, loss: 3.8076 +2024-07-19 04:23:34,919 - pyskl - INFO - Epoch [80][3000/3746] lr: 4.498e-02, eta: 2 days, 10:39:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5839, loss_cls: 3.8294, loss: 3.8294 +2024-07-19 04:24:57,208 - pyskl - INFO - Epoch [80][3100/3746] lr: 4.495e-02, eta: 2 days, 10:38:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5872, loss_cls: 3.8043, loss: 3.8043 +2024-07-19 04:26:19,832 - pyskl - INFO - Epoch [80][3200/3746] lr: 4.493e-02, eta: 2 days, 10:37:00, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5947, loss_cls: 3.7791, loss: 3.7791 +2024-07-19 04:27:41,550 - pyskl - INFO - Epoch [80][3300/3746] lr: 4.490e-02, eta: 2 days, 10:35:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5864, loss_cls: 3.8319, loss: 3.8319 +2024-07-19 04:29:03,289 - pyskl - INFO - Epoch [80][3400/3746] lr: 4.487e-02, eta: 2 days, 10:34:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5792, loss_cls: 3.8557, loss: 3.8557 +2024-07-19 04:30:24,951 - pyskl - INFO - Epoch [80][3500/3746] lr: 4.484e-02, eta: 2 days, 10:33:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5744, loss_cls: 3.8669, loss: 3.8669 +2024-07-19 04:31:46,655 - pyskl - INFO - Epoch [80][3600/3746] lr: 4.481e-02, eta: 2 days, 10:31:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5813, loss_cls: 3.8710, loss: 3.8710 +2024-07-19 04:33:08,343 - pyskl - INFO - Epoch [80][3700/3746] lr: 4.479e-02, eta: 2 days, 10:30:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5928, loss_cls: 3.8136, loss: 3.8136 +2024-07-19 04:33:47,796 - pyskl - INFO - Saving checkpoint at 80 epochs +2024-07-19 04:35:38,596 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 04:35:39,258 - pyskl - INFO - +top1_acc 0.2783 +top5_acc 0.5241 +2024-07-19 04:35:39,259 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 04:35:39,300 - pyskl - INFO - +mean_acc 0.2777 +2024-07-19 04:35:39,312 - pyskl - INFO - Epoch(val) [80][309] top1_acc: 0.2783, top5_acc: 0.5241, mean_class_accuracy: 0.2777 +2024-07-19 04:39:30,654 - pyskl - INFO - Epoch [81][100/3746] lr: 4.475e-02, eta: 2 days, 10:30:07, time: 2.313, data_time: 1.328, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6122, loss_cls: 3.6855, loss: 3.6855 +2024-07-19 04:40:53,002 - pyskl - INFO - Epoch [81][200/3746] lr: 4.472e-02, eta: 2 days, 10:28:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6009, loss_cls: 3.7384, loss: 3.7384 +2024-07-19 04:42:15,047 - pyskl - INFO - Epoch [81][300/3746] lr: 4.469e-02, eta: 2 days, 10:27:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5887, loss_cls: 3.8059, loss: 3.8059 +2024-07-19 04:43:36,784 - pyskl - INFO - Epoch [81][400/3746] lr: 4.466e-02, eta: 2 days, 10:26:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.5973, loss_cls: 3.7660, loss: 3.7660 +2024-07-19 04:44:58,810 - pyskl - INFO - Epoch [81][500/3746] lr: 4.463e-02, eta: 2 days, 10:24:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.6006, loss_cls: 3.7833, loss: 3.7833 +2024-07-19 04:46:20,853 - pyskl - INFO - Epoch [81][600/3746] lr: 4.461e-02, eta: 2 days, 10:23:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5966, loss_cls: 3.7496, loss: 3.7496 +2024-07-19 04:47:43,253 - pyskl - INFO - Epoch [81][700/3746] lr: 4.458e-02, eta: 2 days, 10:22:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5800, loss_cls: 3.8465, loss: 3.8465 +2024-07-19 04:49:05,410 - pyskl - INFO - Epoch [81][800/3746] lr: 4.455e-02, eta: 2 days, 10:20:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5906, loss_cls: 3.8031, loss: 3.8031 +2024-07-19 04:50:27,025 - pyskl - INFO - Epoch [81][900/3746] lr: 4.452e-02, eta: 2 days, 10:19:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.6025, loss_cls: 3.7776, loss: 3.7776 +2024-07-19 04:51:48,448 - pyskl - INFO - Epoch [81][1000/3746] lr: 4.450e-02, eta: 2 days, 10:18:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5847, loss_cls: 3.8302, loss: 3.8302 +2024-07-19 04:53:09,775 - pyskl - INFO - Epoch [81][1100/3746] lr: 4.447e-02, eta: 2 days, 10:16:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5962, loss_cls: 3.8040, loss: 3.8040 +2024-07-19 04:54:31,804 - pyskl - INFO - Epoch [81][1200/3746] lr: 4.444e-02, eta: 2 days, 10:15:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5878, loss_cls: 3.8295, loss: 3.8295 +2024-07-19 04:55:53,358 - pyskl - INFO - Epoch [81][1300/3746] lr: 4.441e-02, eta: 2 days, 10:14:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.6064, loss_cls: 3.7482, loss: 3.7482 +2024-07-19 04:57:15,419 - pyskl - INFO - Epoch [81][1400/3746] lr: 4.438e-02, eta: 2 days, 10:13:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5892, loss_cls: 3.8200, loss: 3.8200 +2024-07-19 04:58:37,456 - pyskl - INFO - Epoch [81][1500/3746] lr: 4.436e-02, eta: 2 days, 10:11:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5863, loss_cls: 3.8522, loss: 3.8522 +2024-07-19 04:59:59,397 - pyskl - INFO - Epoch [81][1600/3746] lr: 4.433e-02, eta: 2 days, 10:10:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5995, loss_cls: 3.7645, loss: 3.7645 +2024-07-19 05:01:21,057 - pyskl - INFO - Epoch [81][1700/3746] lr: 4.430e-02, eta: 2 days, 10:09:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5953, loss_cls: 3.8022, loss: 3.8022 +2024-07-19 05:02:42,588 - pyskl - INFO - Epoch [81][1800/3746] lr: 4.427e-02, eta: 2 days, 10:07:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5950, loss_cls: 3.8076, loss: 3.8076 +2024-07-19 05:04:04,297 - pyskl - INFO - Epoch [81][1900/3746] lr: 4.425e-02, eta: 2 days, 10:06:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5947, loss_cls: 3.7928, loss: 3.7928 +2024-07-19 05:05:25,780 - pyskl - INFO - Epoch [81][2000/3746] lr: 4.422e-02, eta: 2 days, 10:05:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.5967, loss_cls: 3.7727, loss: 3.7727 +2024-07-19 05:06:47,634 - pyskl - INFO - Epoch [81][2100/3746] lr: 4.419e-02, eta: 2 days, 10:03:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5886, loss_cls: 3.8011, loss: 3.8011 +2024-07-19 05:08:09,418 - pyskl - INFO - Epoch [81][2200/3746] lr: 4.416e-02, eta: 2 days, 10:02:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5913, loss_cls: 3.8078, loss: 3.8078 +2024-07-19 05:09:31,056 - pyskl - INFO - Epoch [81][2300/3746] lr: 4.413e-02, eta: 2 days, 10:01:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.5950, loss_cls: 3.7720, loss: 3.7720 +2024-07-19 05:10:53,043 - pyskl - INFO - Epoch [81][2400/3746] lr: 4.411e-02, eta: 2 days, 9:59:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5991, loss_cls: 3.7712, loss: 3.7712 +2024-07-19 05:12:14,675 - pyskl - INFO - Epoch [81][2500/3746] lr: 4.408e-02, eta: 2 days, 9:58:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5763, loss_cls: 3.8257, loss: 3.8257 +2024-07-19 05:13:36,774 - pyskl - INFO - Epoch [81][2600/3746] lr: 4.405e-02, eta: 2 days, 9:57:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5850, loss_cls: 3.8463, loss: 3.8463 +2024-07-19 05:14:58,810 - pyskl - INFO - Epoch [81][2700/3746] lr: 4.402e-02, eta: 2 days, 9:55:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5867, loss_cls: 3.8328, loss: 3.8328 +2024-07-19 05:16:20,746 - pyskl - INFO - Epoch [81][2800/3746] lr: 4.400e-02, eta: 2 days, 9:54:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.5939, loss_cls: 3.7987, loss: 3.7987 +2024-07-19 05:17:42,603 - pyskl - INFO - Epoch [81][2900/3746] lr: 4.397e-02, eta: 2 days, 9:53:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5831, loss_cls: 3.8316, loss: 3.8316 +2024-07-19 05:19:04,412 - pyskl - INFO - Epoch [81][3000/3746] lr: 4.394e-02, eta: 2 days, 9:51:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5889, loss_cls: 3.8389, loss: 3.8389 +2024-07-19 05:20:27,446 - pyskl - INFO - Epoch [81][3100/3746] lr: 4.391e-02, eta: 2 days, 9:50:37, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5848, loss_cls: 3.8795, loss: 3.8795 +2024-07-19 05:21:49,336 - pyskl - INFO - Epoch [81][3200/3746] lr: 4.389e-02, eta: 2 days, 9:49:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5861, loss_cls: 3.8501, loss: 3.8501 +2024-07-19 05:23:10,833 - pyskl - INFO - Epoch [81][3300/3746] lr: 4.386e-02, eta: 2 days, 9:47:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5911, loss_cls: 3.8052, loss: 3.8052 +2024-07-19 05:24:32,480 - pyskl - INFO - Epoch [81][3400/3746] lr: 4.383e-02, eta: 2 days, 9:46:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5773, loss_cls: 3.8804, loss: 3.8804 +2024-07-19 05:25:53,974 - pyskl - INFO - Epoch [81][3500/3746] lr: 4.380e-02, eta: 2 days, 9:45:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5930, loss_cls: 3.8175, loss: 3.8175 +2024-07-19 05:27:15,775 - pyskl - INFO - Epoch [81][3600/3746] lr: 4.377e-02, eta: 2 days, 9:44:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.5914, loss_cls: 3.7867, loss: 3.7867 +2024-07-19 05:28:37,427 - pyskl - INFO - Epoch [81][3700/3746] lr: 4.375e-02, eta: 2 days, 9:42:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5930, loss_cls: 3.7762, loss: 3.7762 +2024-07-19 05:29:16,806 - pyskl - INFO - Saving checkpoint at 81 epochs +2024-07-19 05:31:07,965 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 05:31:08,628 - pyskl - INFO - +top1_acc 0.2575 +top5_acc 0.5026 +2024-07-19 05:31:08,628 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 05:31:08,668 - pyskl - INFO - +mean_acc 0.2571 +2024-07-19 05:31:08,680 - pyskl - INFO - Epoch(val) [81][309] top1_acc: 0.2575, top5_acc: 0.5026, mean_class_accuracy: 0.2571 +2024-07-19 05:34:57,330 - pyskl - INFO - Epoch [82][100/3746] lr: 4.371e-02, eta: 2 days, 9:42:18, time: 2.286, data_time: 1.301, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5913, loss_cls: 3.7512, loss: 3.7512 +2024-07-19 05:36:19,388 - pyskl - INFO - Epoch [82][200/3746] lr: 4.368e-02, eta: 2 days, 9:40:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6116, loss_cls: 3.7301, loss: 3.7301 +2024-07-19 05:37:41,440 - pyskl - INFO - Epoch [82][300/3746] lr: 4.365e-02, eta: 2 days, 9:39:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5906, loss_cls: 3.8081, loss: 3.8081 +2024-07-19 05:39:03,086 - pyskl - INFO - Epoch [82][400/3746] lr: 4.362e-02, eta: 2 days, 9:38:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.5981, loss_cls: 3.7250, loss: 3.7250 +2024-07-19 05:40:25,567 - pyskl - INFO - Epoch [82][500/3746] lr: 4.359e-02, eta: 2 days, 9:37:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6084, loss_cls: 3.6949, loss: 3.6949 +2024-07-19 05:41:47,505 - pyskl - INFO - Epoch [82][600/3746] lr: 4.357e-02, eta: 2 days, 9:35:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5906, loss_cls: 3.7963, loss: 3.7963 +2024-07-19 05:43:09,655 - pyskl - INFO - Epoch [82][700/3746] lr: 4.354e-02, eta: 2 days, 9:34:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5845, loss_cls: 3.8326, loss: 3.8326 +2024-07-19 05:44:31,641 - pyskl - INFO - Epoch [82][800/3746] lr: 4.351e-02, eta: 2 days, 9:33:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.5986, loss_cls: 3.7537, loss: 3.7537 +2024-07-19 05:45:53,755 - pyskl - INFO - Epoch [82][900/3746] lr: 4.348e-02, eta: 2 days, 9:31:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5952, loss_cls: 3.7823, loss: 3.7823 +2024-07-19 05:47:15,370 - pyskl - INFO - Epoch [82][1000/3746] lr: 4.346e-02, eta: 2 days, 9:30:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5817, loss_cls: 3.8373, loss: 3.8373 +2024-07-19 05:48:37,190 - pyskl - INFO - Epoch [82][1100/3746] lr: 4.343e-02, eta: 2 days, 9:29:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5909, loss_cls: 3.7872, loss: 3.7872 +2024-07-19 05:50:00,039 - pyskl - INFO - Epoch [82][1200/3746] lr: 4.340e-02, eta: 2 days, 9:27:49, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5856, loss_cls: 3.8430, loss: 3.8430 +2024-07-19 05:51:21,796 - pyskl - INFO - Epoch [82][1300/3746] lr: 4.337e-02, eta: 2 days, 9:26:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5844, loss_cls: 3.8077, loss: 3.8077 +2024-07-19 05:52:44,188 - pyskl - INFO - Epoch [82][1400/3746] lr: 4.335e-02, eta: 2 days, 9:25:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.5978, loss_cls: 3.7629, loss: 3.7629 +2024-07-19 05:54:06,502 - pyskl - INFO - Epoch [82][1500/3746] lr: 4.332e-02, eta: 2 days, 9:23:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5988, loss_cls: 3.7761, loss: 3.7761 +2024-07-19 05:55:28,366 - pyskl - INFO - Epoch [82][1600/3746] lr: 4.329e-02, eta: 2 days, 9:22:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.6002, loss_cls: 3.7701, loss: 3.7701 +2024-07-19 05:56:50,092 - pyskl - INFO - Epoch [82][1700/3746] lr: 4.326e-02, eta: 2 days, 9:21:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5984, loss_cls: 3.7729, loss: 3.7729 +2024-07-19 05:58:11,797 - pyskl - INFO - Epoch [82][1800/3746] lr: 4.323e-02, eta: 2 days, 9:19:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5972, loss_cls: 3.8101, loss: 3.8101 +2024-07-19 05:59:34,016 - pyskl - INFO - Epoch [82][1900/3746] lr: 4.321e-02, eta: 2 days, 9:18:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.5881, loss_cls: 3.7731, loss: 3.7731 +2024-07-19 06:00:55,466 - pyskl - INFO - Epoch [82][2000/3746] lr: 4.318e-02, eta: 2 days, 9:17:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5938, loss_cls: 3.7846, loss: 3.7846 +2024-07-19 06:02:17,918 - pyskl - INFO - Epoch [82][2100/3746] lr: 4.315e-02, eta: 2 days, 9:15:58, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5855, loss_cls: 3.8435, loss: 3.8435 +2024-07-19 06:03:39,186 - pyskl - INFO - Epoch [82][2200/3746] lr: 4.312e-02, eta: 2 days, 9:14:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5956, loss_cls: 3.8095, loss: 3.8095 +2024-07-19 06:05:00,772 - pyskl - INFO - Epoch [82][2300/3746] lr: 4.310e-02, eta: 2 days, 9:13:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5861, loss_cls: 3.8133, loss: 3.8133 +2024-07-19 06:06:22,410 - pyskl - INFO - Epoch [82][2400/3746] lr: 4.307e-02, eta: 2 days, 9:11:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5952, loss_cls: 3.7826, loss: 3.7826 +2024-07-19 06:07:44,171 - pyskl - INFO - Epoch [82][2500/3746] lr: 4.304e-02, eta: 2 days, 9:10:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5845, loss_cls: 3.8397, loss: 3.8397 +2024-07-19 06:09:06,324 - pyskl - INFO - Epoch [82][2600/3746] lr: 4.301e-02, eta: 2 days, 9:09:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5930, loss_cls: 3.8086, loss: 3.8086 +2024-07-19 06:10:28,145 - pyskl - INFO - Epoch [82][2700/3746] lr: 4.299e-02, eta: 2 days, 9:08:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5811, loss_cls: 3.8411, loss: 3.8411 +2024-07-19 06:11:49,521 - pyskl - INFO - Epoch [82][2800/3746] lr: 4.296e-02, eta: 2 days, 9:06:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5813, loss_cls: 3.8320, loss: 3.8320 +2024-07-19 06:13:11,638 - pyskl - INFO - Epoch [82][2900/3746] lr: 4.293e-02, eta: 2 days, 9:05:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5944, loss_cls: 3.7656, loss: 3.7656 +2024-07-19 06:14:33,858 - pyskl - INFO - Epoch [82][3000/3746] lr: 4.290e-02, eta: 2 days, 9:04:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5902, loss_cls: 3.8041, loss: 3.8041 +2024-07-19 06:15:56,002 - pyskl - INFO - Epoch [82][3100/3746] lr: 4.287e-02, eta: 2 days, 9:02:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6012, loss_cls: 3.7325, loss: 3.7325 +2024-07-19 06:17:18,402 - pyskl - INFO - Epoch [82][3200/3746] lr: 4.285e-02, eta: 2 days, 9:01:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5956, loss_cls: 3.7994, loss: 3.7994 +2024-07-19 06:18:40,459 - pyskl - INFO - Epoch [82][3300/3746] lr: 4.282e-02, eta: 2 days, 9:00:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5952, loss_cls: 3.7949, loss: 3.7949 +2024-07-19 06:20:02,414 - pyskl - INFO - Epoch [82][3400/3746] lr: 4.279e-02, eta: 2 days, 8:58:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5936, loss_cls: 3.7731, loss: 3.7731 +2024-07-19 06:21:24,051 - pyskl - INFO - Epoch [82][3500/3746] lr: 4.276e-02, eta: 2 days, 8:57:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5827, loss_cls: 3.8179, loss: 3.8179 +2024-07-19 06:22:45,571 - pyskl - INFO - Epoch [82][3600/3746] lr: 4.274e-02, eta: 2 days, 8:56:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.6000, loss_cls: 3.7646, loss: 3.7646 +2024-07-19 06:24:07,154 - pyskl - INFO - Epoch [82][3700/3746] lr: 4.271e-02, eta: 2 days, 8:54:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5916, loss_cls: 3.8103, loss: 3.8103 +2024-07-19 06:24:47,025 - pyskl - INFO - Saving checkpoint at 82 epochs +2024-07-19 06:26:38,358 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 06:26:39,040 - pyskl - INFO - +top1_acc 0.2734 +top5_acc 0.5303 +2024-07-19 06:26:39,040 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 06:26:39,085 - pyskl - INFO - +mean_acc 0.2731 +2024-07-19 06:26:39,098 - pyskl - INFO - Epoch(val) [82][309] top1_acc: 0.2734, top5_acc: 0.5303, mean_class_accuracy: 0.2731 +2024-07-19 06:30:29,409 - pyskl - INFO - Epoch [83][100/3746] lr: 4.267e-02, eta: 2 days, 8:54:26, time: 2.303, data_time: 1.319, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5994, loss_cls: 3.7581, loss: 3.7581 +2024-07-19 06:31:51,902 - pyskl - INFO - Epoch [83][200/3746] lr: 4.264e-02, eta: 2 days, 8:53:07, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5975, loss_cls: 3.7549, loss: 3.7549 +2024-07-19 06:33:13,720 - pyskl - INFO - Epoch [83][300/3746] lr: 4.261e-02, eta: 2 days, 8:51:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.5972, loss_cls: 3.7428, loss: 3.7428 +2024-07-19 06:34:35,331 - pyskl - INFO - Epoch [83][400/3746] lr: 4.259e-02, eta: 2 days, 8:50:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.5975, loss_cls: 3.7205, loss: 3.7205 +2024-07-19 06:35:57,752 - pyskl - INFO - Epoch [83][500/3746] lr: 4.256e-02, eta: 2 days, 8:49:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.5958, loss_cls: 3.7451, loss: 3.7451 +2024-07-19 06:37:19,112 - pyskl - INFO - Epoch [83][600/3746] lr: 4.253e-02, eta: 2 days, 8:47:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5958, loss_cls: 3.7563, loss: 3.7563 +2024-07-19 06:38:41,438 - pyskl - INFO - Epoch [83][700/3746] lr: 4.250e-02, eta: 2 days, 8:46:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5925, loss_cls: 3.7925, loss: 3.7925 +2024-07-19 06:40:03,453 - pyskl - INFO - Epoch [83][800/3746] lr: 4.247e-02, eta: 2 days, 8:45:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.6033, loss_cls: 3.7415, loss: 3.7415 +2024-07-19 06:41:25,145 - pyskl - INFO - Epoch [83][900/3746] lr: 4.245e-02, eta: 2 days, 8:43:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5991, loss_cls: 3.7654, loss: 3.7654 +2024-07-19 06:42:46,805 - pyskl - INFO - Epoch [83][1000/3746] lr: 4.242e-02, eta: 2 days, 8:42:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.5983, loss_cls: 3.7632, loss: 3.7632 +2024-07-19 06:44:08,631 - pyskl - INFO - Epoch [83][1100/3746] lr: 4.239e-02, eta: 2 days, 8:41:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6045, loss_cls: 3.7536, loss: 3.7536 +2024-07-19 06:45:31,008 - pyskl - INFO - Epoch [83][1200/3746] lr: 4.236e-02, eta: 2 days, 8:39:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5863, loss_cls: 3.8166, loss: 3.8166 +2024-07-19 06:46:52,927 - pyskl - INFO - Epoch [83][1300/3746] lr: 4.234e-02, eta: 2 days, 8:38:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5961, loss_cls: 3.7587, loss: 3.7587 +2024-07-19 06:48:14,504 - pyskl - INFO - Epoch [83][1400/3746] lr: 4.231e-02, eta: 2 days, 8:37:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5939, loss_cls: 3.7683, loss: 3.7683 +2024-07-19 06:49:36,282 - pyskl - INFO - Epoch [83][1500/3746] lr: 4.228e-02, eta: 2 days, 8:35:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.5864, loss_cls: 3.7854, loss: 3.7854 +2024-07-19 06:50:57,827 - pyskl - INFO - Epoch [83][1600/3746] lr: 4.225e-02, eta: 2 days, 8:34:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5902, loss_cls: 3.8460, loss: 3.8460 +2024-07-19 06:52:19,659 - pyskl - INFO - Epoch [83][1700/3746] lr: 4.223e-02, eta: 2 days, 8:33:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5991, loss_cls: 3.7876, loss: 3.7876 +2024-07-19 06:53:40,956 - pyskl - INFO - Epoch [83][1800/3746] lr: 4.220e-02, eta: 2 days, 8:31:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5955, loss_cls: 3.7646, loss: 3.7646 +2024-07-19 06:55:02,943 - pyskl - INFO - Epoch [83][1900/3746] lr: 4.217e-02, eta: 2 days, 8:30:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.6011, loss_cls: 3.7625, loss: 3.7625 +2024-07-19 06:56:24,686 - pyskl - INFO - Epoch [83][2000/3746] lr: 4.214e-02, eta: 2 days, 8:29:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.5961, loss_cls: 3.7979, loss: 3.7979 +2024-07-19 06:57:46,166 - pyskl - INFO - Epoch [83][2100/3746] lr: 4.212e-02, eta: 2 days, 8:27:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5959, loss_cls: 3.7753, loss: 3.7753 +2024-07-19 06:59:07,327 - pyskl - INFO - Epoch [83][2200/3746] lr: 4.209e-02, eta: 2 days, 8:26:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5902, loss_cls: 3.7968, loss: 3.7968 +2024-07-19 07:00:29,244 - pyskl - INFO - Epoch [83][2300/3746] lr: 4.206e-02, eta: 2 days, 8:25:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5998, loss_cls: 3.7802, loss: 3.7802 +2024-07-19 07:01:51,159 - pyskl - INFO - Epoch [83][2400/3746] lr: 4.203e-02, eta: 2 days, 8:24:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5913, loss_cls: 3.8113, loss: 3.8113 +2024-07-19 07:03:13,046 - pyskl - INFO - Epoch [83][2500/3746] lr: 4.201e-02, eta: 2 days, 8:22:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.6006, loss_cls: 3.7724, loss: 3.7724 +2024-07-19 07:04:34,797 - pyskl - INFO - Epoch [83][2600/3746] lr: 4.198e-02, eta: 2 days, 8:21:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5877, loss_cls: 3.8046, loss: 3.8046 +2024-07-19 07:05:56,727 - pyskl - INFO - Epoch [83][2700/3746] lr: 4.195e-02, eta: 2 days, 8:20:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5836, loss_cls: 3.7931, loss: 3.7931 +2024-07-19 07:07:18,146 - pyskl - INFO - Epoch [83][2800/3746] lr: 4.192e-02, eta: 2 days, 8:18:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.5972, loss_cls: 3.7421, loss: 3.7421 +2024-07-19 07:08:40,654 - pyskl - INFO - Epoch [83][2900/3746] lr: 4.190e-02, eta: 2 days, 8:17:24, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.5945, loss_cls: 3.7457, loss: 3.7457 +2024-07-19 07:10:02,484 - pyskl - INFO - Epoch [83][3000/3746] lr: 4.187e-02, eta: 2 days, 8:16:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6102, loss_cls: 3.7052, loss: 3.7052 +2024-07-19 07:11:25,099 - pyskl - INFO - Epoch [83][3100/3746] lr: 4.184e-02, eta: 2 days, 8:14:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5908, loss_cls: 3.7957, loss: 3.7957 +2024-07-19 07:12:47,320 - pyskl - INFO - Epoch [83][3200/3746] lr: 4.181e-02, eta: 2 days, 8:13:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.5975, loss_cls: 3.7882, loss: 3.7882 +2024-07-19 07:14:09,676 - pyskl - INFO - Epoch [83][3300/3746] lr: 4.178e-02, eta: 2 days, 8:12:08, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6012, loss_cls: 3.7525, loss: 3.7525 +2024-07-19 07:15:31,218 - pyskl - INFO - Epoch [83][3400/3746] lr: 4.176e-02, eta: 2 days, 8:10:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5933, loss_cls: 3.8427, loss: 3.8427 +2024-07-19 07:16:53,227 - pyskl - INFO - Epoch [83][3500/3746] lr: 4.173e-02, eta: 2 days, 8:09:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.6012, loss_cls: 3.8027, loss: 3.8027 +2024-07-19 07:18:14,769 - pyskl - INFO - Epoch [83][3600/3746] lr: 4.170e-02, eta: 2 days, 8:08:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5864, loss_cls: 3.8433, loss: 3.8433 +2024-07-19 07:19:36,556 - pyskl - INFO - Epoch [83][3700/3746] lr: 4.167e-02, eta: 2 days, 8:06:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5934, loss_cls: 3.7841, loss: 3.7841 +2024-07-19 07:20:16,079 - pyskl - INFO - Saving checkpoint at 83 epochs +2024-07-19 07:22:06,659 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 07:22:07,326 - pyskl - INFO - +top1_acc 0.2820 +top5_acc 0.5274 +2024-07-19 07:22:07,326 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 07:22:07,367 - pyskl - INFO - +mean_acc 0.2817 +2024-07-19 07:22:07,372 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_79.pth was removed +2024-07-19 07:22:07,633 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_83.pth. +2024-07-19 07:22:07,633 - pyskl - INFO - Best top1_acc is 0.2820 at 83 epoch. +2024-07-19 07:22:07,646 - pyskl - INFO - Epoch(val) [83][309] top1_acc: 0.2820, top5_acc: 0.5274, mean_class_accuracy: 0.2817 +2024-07-19 07:25:58,113 - pyskl - INFO - Epoch [84][100/3746] lr: 4.163e-02, eta: 2 days, 8:06:24, time: 2.305, data_time: 1.319, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6125, loss_cls: 3.7216, loss: 3.7216 +2024-07-19 07:27:20,416 - pyskl - INFO - Epoch [84][200/3746] lr: 4.161e-02, eta: 2 days, 8:05:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.5967, loss_cls: 3.7496, loss: 3.7496 +2024-07-19 07:28:42,305 - pyskl - INFO - Epoch [84][300/3746] lr: 4.158e-02, eta: 2 days, 8:03:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5961, loss_cls: 3.7526, loss: 3.7526 +2024-07-19 07:30:04,127 - pyskl - INFO - Epoch [84][400/3746] lr: 4.155e-02, eta: 2 days, 8:02:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.5947, loss_cls: 3.7614, loss: 3.7614 +2024-07-19 07:31:26,990 - pyskl - INFO - Epoch [84][500/3746] lr: 4.152e-02, eta: 2 days, 8:01:08, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.6059, loss_cls: 3.7748, loss: 3.7748 +2024-07-19 07:32:48,853 - pyskl - INFO - Epoch [84][600/3746] lr: 4.150e-02, eta: 2 days, 7:59:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.6017, loss_cls: 3.7824, loss: 3.7824 +2024-07-19 07:34:11,040 - pyskl - INFO - Epoch [84][700/3746] lr: 4.147e-02, eta: 2 days, 7:58:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6067, loss_cls: 3.7345, loss: 3.7345 +2024-07-19 07:35:32,822 - pyskl - INFO - Epoch [84][800/3746] lr: 4.144e-02, eta: 2 days, 7:57:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5902, loss_cls: 3.7874, loss: 3.7874 +2024-07-19 07:36:54,794 - pyskl - INFO - Epoch [84][900/3746] lr: 4.141e-02, eta: 2 days, 7:55:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5955, loss_cls: 3.7907, loss: 3.7907 +2024-07-19 07:38:16,467 - pyskl - INFO - Epoch [84][1000/3746] lr: 4.139e-02, eta: 2 days, 7:54:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5986, loss_cls: 3.7651, loss: 3.7651 +2024-07-19 07:39:38,079 - pyskl - INFO - Epoch [84][1100/3746] lr: 4.136e-02, eta: 2 days, 7:53:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6020, loss_cls: 3.7374, loss: 3.7374 +2024-07-19 07:40:59,900 - pyskl - INFO - Epoch [84][1200/3746] lr: 4.133e-02, eta: 2 days, 7:51:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.5908, loss_cls: 3.7821, loss: 3.7821 +2024-07-19 07:42:21,899 - pyskl - INFO - Epoch [84][1300/3746] lr: 4.130e-02, eta: 2 days, 7:50:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5866, loss_cls: 3.7998, loss: 3.7998 +2024-07-19 07:43:44,266 - pyskl - INFO - Epoch [84][1400/3746] lr: 4.128e-02, eta: 2 days, 7:49:13, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.6055, loss_cls: 3.7557, loss: 3.7557 +2024-07-19 07:45:06,770 - pyskl - INFO - Epoch [84][1500/3746] lr: 4.125e-02, eta: 2 days, 7:47:54, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6002, loss_cls: 3.7422, loss: 3.7422 +2024-07-19 07:46:28,205 - pyskl - INFO - Epoch [84][1600/3746] lr: 4.122e-02, eta: 2 days, 7:46:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5939, loss_cls: 3.7719, loss: 3.7719 +2024-07-19 07:47:50,137 - pyskl - INFO - Epoch [84][1700/3746] lr: 4.119e-02, eta: 2 days, 7:45:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.6020, loss_cls: 3.7411, loss: 3.7411 +2024-07-19 07:49:12,067 - pyskl - INFO - Epoch [84][1800/3746] lr: 4.117e-02, eta: 2 days, 7:43:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.6022, loss_cls: 3.7649, loss: 3.7649 +2024-07-19 07:50:33,534 - pyskl - INFO - Epoch [84][1900/3746] lr: 4.114e-02, eta: 2 days, 7:42:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.5977, loss_cls: 3.7651, loss: 3.7651 +2024-07-19 07:51:55,372 - pyskl - INFO - Epoch [84][2000/3746] lr: 4.111e-02, eta: 2 days, 7:41:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5994, loss_cls: 3.7560, loss: 3.7560 +2024-07-19 07:53:16,839 - pyskl - INFO - Epoch [84][2100/3746] lr: 4.108e-02, eta: 2 days, 7:39:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.6036, loss_cls: 3.7550, loss: 3.7550 +2024-07-19 07:54:38,593 - pyskl - INFO - Epoch [84][2200/3746] lr: 4.106e-02, eta: 2 days, 7:38:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6072, loss_cls: 3.7110, loss: 3.7110 +2024-07-19 07:56:00,340 - pyskl - INFO - Epoch [84][2300/3746] lr: 4.103e-02, eta: 2 days, 7:37:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6022, loss_cls: 3.7429, loss: 3.7429 +2024-07-19 07:57:22,013 - pyskl - INFO - Epoch [84][2400/3746] lr: 4.100e-02, eta: 2 days, 7:35:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6020, loss_cls: 3.7530, loss: 3.7530 +2024-07-19 07:58:43,930 - pyskl - INFO - Epoch [84][2500/3746] lr: 4.097e-02, eta: 2 days, 7:34:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5911, loss_cls: 3.8146, loss: 3.8146 +2024-07-19 08:00:05,522 - pyskl - INFO - Epoch [84][2600/3746] lr: 4.095e-02, eta: 2 days, 7:33:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5917, loss_cls: 3.8180, loss: 3.8180 +2024-07-19 08:01:27,079 - pyskl - INFO - Epoch [84][2700/3746] lr: 4.092e-02, eta: 2 days, 7:31:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5923, loss_cls: 3.7779, loss: 3.7779 +2024-07-19 08:02:49,069 - pyskl - INFO - Epoch [84][2800/3746] lr: 4.089e-02, eta: 2 days, 7:30:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.6019, loss_cls: 3.7640, loss: 3.7640 +2024-07-19 08:04:11,175 - pyskl - INFO - Epoch [84][2900/3746] lr: 4.086e-02, eta: 2 days, 7:29:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5945, loss_cls: 3.7913, loss: 3.7913 +2024-07-19 08:05:33,073 - pyskl - INFO - Epoch [84][3000/3746] lr: 4.084e-02, eta: 2 days, 7:28:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.5967, loss_cls: 3.7607, loss: 3.7607 +2024-07-19 08:06:56,321 - pyskl - INFO - Epoch [84][3100/3746] lr: 4.081e-02, eta: 2 days, 7:26:43, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5956, loss_cls: 3.7614, loss: 3.7614 +2024-07-19 08:08:18,182 - pyskl - INFO - Epoch [84][3200/3746] lr: 4.078e-02, eta: 2 days, 7:25:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5955, loss_cls: 3.8054, loss: 3.8054 +2024-07-19 08:09:40,313 - pyskl - INFO - Epoch [84][3300/3746] lr: 4.075e-02, eta: 2 days, 7:24:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.5986, loss_cls: 3.7799, loss: 3.7799 +2024-07-19 08:11:02,241 - pyskl - INFO - Epoch [84][3400/3746] lr: 4.073e-02, eta: 2 days, 7:22:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6039, loss_cls: 3.7392, loss: 3.7392 +2024-07-19 08:12:23,630 - pyskl - INFO - Epoch [84][3500/3746] lr: 4.070e-02, eta: 2 days, 7:21:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5737, loss_cls: 3.8633, loss: 3.8633 +2024-07-19 08:13:45,049 - pyskl - INFO - Epoch [84][3600/3746] lr: 4.067e-02, eta: 2 days, 7:20:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6117, loss_cls: 3.7228, loss: 3.7228 +2024-07-19 08:15:06,426 - pyskl - INFO - Epoch [84][3700/3746] lr: 4.064e-02, eta: 2 days, 7:18:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5894, loss_cls: 3.8080, loss: 3.8080 +2024-07-19 08:15:45,801 - pyskl - INFO - Saving checkpoint at 84 epochs +2024-07-19 08:17:37,025 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 08:17:37,688 - pyskl - INFO - +top1_acc 0.2712 +top5_acc 0.5268 +2024-07-19 08:17:37,688 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 08:17:37,728 - pyskl - INFO - +mean_acc 0.2710 +2024-07-19 08:17:37,740 - pyskl - INFO - Epoch(val) [84][309] top1_acc: 0.2712, top5_acc: 0.5268, mean_class_accuracy: 0.2710 +2024-07-19 08:21:24,076 - pyskl - INFO - Epoch [85][100/3746] lr: 4.060e-02, eta: 2 days, 7:18:13, time: 2.263, data_time: 1.282, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6167, loss_cls: 3.6441, loss: 3.6441 +2024-07-19 08:22:45,734 - pyskl - INFO - Epoch [85][200/3746] lr: 4.058e-02, eta: 2 days, 7:16:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.6017, loss_cls: 3.7366, loss: 3.7366 +2024-07-19 08:24:07,653 - pyskl - INFO - Epoch [85][300/3746] lr: 4.055e-02, eta: 2 days, 7:15:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6000, loss_cls: 3.7240, loss: 3.7240 +2024-07-19 08:25:29,245 - pyskl - INFO - Epoch [85][400/3746] lr: 4.052e-02, eta: 2 days, 7:14:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.6009, loss_cls: 3.7109, loss: 3.7109 +2024-07-19 08:26:50,780 - pyskl - INFO - Epoch [85][500/3746] lr: 4.049e-02, eta: 2 days, 7:12:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.6058, loss_cls: 3.7471, loss: 3.7471 +2024-07-19 08:28:12,648 - pyskl - INFO - Epoch [85][600/3746] lr: 4.047e-02, eta: 2 days, 7:11:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6034, loss_cls: 3.7033, loss: 3.7033 +2024-07-19 08:29:34,703 - pyskl - INFO - Epoch [85][700/3746] lr: 4.044e-02, eta: 2 days, 7:10:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6045, loss_cls: 3.7497, loss: 3.7497 +2024-07-19 08:30:56,423 - pyskl - INFO - Epoch [85][800/3746] lr: 4.041e-02, eta: 2 days, 7:08:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6106, loss_cls: 3.6796, loss: 3.6796 +2024-07-19 08:32:18,588 - pyskl - INFO - Epoch [85][900/3746] lr: 4.038e-02, eta: 2 days, 7:07:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.6089, loss_cls: 3.7219, loss: 3.7219 +2024-07-19 08:33:40,478 - pyskl - INFO - Epoch [85][1000/3746] lr: 4.036e-02, eta: 2 days, 7:06:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6130, loss_cls: 3.6774, loss: 3.6774 +2024-07-19 08:35:02,523 - pyskl - INFO - Epoch [85][1100/3746] lr: 4.033e-02, eta: 2 days, 7:04:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5930, loss_cls: 3.7990, loss: 3.7990 +2024-07-19 08:36:24,015 - pyskl - INFO - Epoch [85][1200/3746] lr: 4.030e-02, eta: 2 days, 7:03:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6092, loss_cls: 3.7056, loss: 3.7056 +2024-07-19 08:37:45,908 - pyskl - INFO - Epoch [85][1300/3746] lr: 4.027e-02, eta: 2 days, 7:02:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.6084, loss_cls: 3.7239, loss: 3.7239 +2024-07-19 08:39:07,893 - pyskl - INFO - Epoch [85][1400/3746] lr: 4.025e-02, eta: 2 days, 7:00:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.5983, loss_cls: 3.7434, loss: 3.7434 +2024-07-19 08:40:29,863 - pyskl - INFO - Epoch [85][1500/3746] lr: 4.022e-02, eta: 2 days, 6:59:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5969, loss_cls: 3.7705, loss: 3.7705 +2024-07-19 08:41:51,540 - pyskl - INFO - Epoch [85][1600/3746] lr: 4.019e-02, eta: 2 days, 6:58:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.6067, loss_cls: 3.7404, loss: 3.7404 +2024-07-19 08:43:13,565 - pyskl - INFO - Epoch [85][1700/3746] lr: 4.016e-02, eta: 2 days, 6:57:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.6002, loss_cls: 3.7748, loss: 3.7748 +2024-07-19 08:44:35,588 - pyskl - INFO - Epoch [85][1800/3746] lr: 4.014e-02, eta: 2 days, 6:55:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5988, loss_cls: 3.7732, loss: 3.7732 +2024-07-19 08:45:57,409 - pyskl - INFO - Epoch [85][1900/3746] lr: 4.011e-02, eta: 2 days, 6:54:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.6062, loss_cls: 3.7562, loss: 3.7562 +2024-07-19 08:47:19,035 - pyskl - INFO - Epoch [85][2000/3746] lr: 4.008e-02, eta: 2 days, 6:53:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5919, loss_cls: 3.7808, loss: 3.7808 +2024-07-19 08:48:40,457 - pyskl - INFO - Epoch [85][2100/3746] lr: 4.006e-02, eta: 2 days, 6:51:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.5988, loss_cls: 3.7756, loss: 3.7756 +2024-07-19 08:50:01,848 - pyskl - INFO - Epoch [85][2200/3746] lr: 4.003e-02, eta: 2 days, 6:50:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5869, loss_cls: 3.8304, loss: 3.8304 +2024-07-19 08:51:23,528 - pyskl - INFO - Epoch [85][2300/3746] lr: 4.000e-02, eta: 2 days, 6:49:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5980, loss_cls: 3.7931, loss: 3.7931 +2024-07-19 08:52:45,255 - pyskl - INFO - Epoch [85][2400/3746] lr: 3.997e-02, eta: 2 days, 6:47:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6044, loss_cls: 3.7103, loss: 3.7103 +2024-07-19 08:54:06,641 - pyskl - INFO - Epoch [85][2500/3746] lr: 3.995e-02, eta: 2 days, 6:46:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.5909, loss_cls: 3.7846, loss: 3.7846 +2024-07-19 08:55:28,315 - pyskl - INFO - Epoch [85][2600/3746] lr: 3.992e-02, eta: 2 days, 6:45:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.5964, loss_cls: 3.7915, loss: 3.7915 +2024-07-19 08:56:49,524 - pyskl - INFO - Epoch [85][2700/3746] lr: 3.989e-02, eta: 2 days, 6:43:42, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5973, loss_cls: 3.7721, loss: 3.7721 +2024-07-19 08:58:10,605 - pyskl - INFO - Epoch [85][2800/3746] lr: 3.986e-02, eta: 2 days, 6:42:22, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5923, loss_cls: 3.7670, loss: 3.7670 +2024-07-19 08:59:33,527 - pyskl - INFO - Epoch [85][2900/3746] lr: 3.984e-02, eta: 2 days, 6:41:03, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.5998, loss_cls: 3.7433, loss: 3.7433 +2024-07-19 09:00:55,419 - pyskl - INFO - Epoch [85][3000/3746] lr: 3.981e-02, eta: 2 days, 6:39:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6056, loss_cls: 3.7035, loss: 3.7035 +2024-07-19 09:02:18,025 - pyskl - INFO - Epoch [85][3100/3746] lr: 3.978e-02, eta: 2 days, 6:38:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.5983, loss_cls: 3.7490, loss: 3.7490 +2024-07-19 09:03:40,200 - pyskl - INFO - Epoch [85][3200/3746] lr: 3.975e-02, eta: 2 days, 6:37:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.6062, loss_cls: 3.7556, loss: 3.7556 +2024-07-19 09:05:01,882 - pyskl - INFO - Epoch [85][3300/3746] lr: 3.973e-02, eta: 2 days, 6:35:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5984, loss_cls: 3.7545, loss: 3.7545 +2024-07-19 09:06:23,493 - pyskl - INFO - Epoch [85][3400/3746] lr: 3.970e-02, eta: 2 days, 6:34:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.5983, loss_cls: 3.7546, loss: 3.7546 +2024-07-19 09:07:44,955 - pyskl - INFO - Epoch [85][3500/3746] lr: 3.967e-02, eta: 2 days, 6:33:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.5930, loss_cls: 3.7635, loss: 3.7635 +2024-07-19 09:09:06,542 - pyskl - INFO - Epoch [85][3600/3746] lr: 3.964e-02, eta: 2 days, 6:31:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6012, loss_cls: 3.7820, loss: 3.7820 +2024-07-19 09:10:27,917 - pyskl - INFO - Epoch [85][3700/3746] lr: 3.962e-02, eta: 2 days, 6:30:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5900, loss_cls: 3.7797, loss: 3.7797 +2024-07-19 09:11:07,545 - pyskl - INFO - Saving checkpoint at 85 epochs +2024-07-19 09:12:59,940 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 09:13:00,615 - pyskl - INFO - +top1_acc 0.2852 +top5_acc 0.5306 +2024-07-19 09:13:00,615 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 09:13:00,658 - pyskl - INFO - +mean_acc 0.2848 +2024-07-19 09:13:00,663 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_83.pth was removed +2024-07-19 09:13:00,922 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_85.pth. +2024-07-19 09:13:00,923 - pyskl - INFO - Best top1_acc is 0.2852 at 85 epoch. +2024-07-19 09:13:00,940 - pyskl - INFO - Epoch(val) [85][309] top1_acc: 0.2852, top5_acc: 0.5306, mean_class_accuracy: 0.2848 +2024-07-19 09:16:49,885 - pyskl - INFO - Epoch [86][100/3746] lr: 3.958e-02, eta: 2 days, 6:29:53, time: 2.289, data_time: 1.305, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6158, loss_cls: 3.6702, loss: 3.6702 +2024-07-19 09:18:12,585 - pyskl - INFO - Epoch [86][200/3746] lr: 3.955e-02, eta: 2 days, 6:28:34, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6166, loss_cls: 3.6673, loss: 3.6673 +2024-07-19 09:19:34,704 - pyskl - INFO - Epoch [86][300/3746] lr: 3.952e-02, eta: 2 days, 6:27:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5928, loss_cls: 3.7987, loss: 3.7987 +2024-07-19 09:20:57,185 - pyskl - INFO - Epoch [86][400/3746] lr: 3.950e-02, eta: 2 days, 6:25:56, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6075, loss_cls: 3.7038, loss: 3.7038 +2024-07-19 09:22:19,123 - pyskl - INFO - Epoch [86][500/3746] lr: 3.947e-02, eta: 2 days, 6:24:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6034, loss_cls: 3.7180, loss: 3.7180 +2024-07-19 09:23:41,316 - pyskl - INFO - Epoch [86][600/3746] lr: 3.944e-02, eta: 2 days, 6:23:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.6025, loss_cls: 3.7374, loss: 3.7374 +2024-07-19 09:25:03,647 - pyskl - INFO - Epoch [86][700/3746] lr: 3.941e-02, eta: 2 days, 6:21:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6133, loss_cls: 3.6648, loss: 3.6648 +2024-07-19 09:26:25,336 - pyskl - INFO - Epoch [86][800/3746] lr: 3.939e-02, eta: 2 days, 6:20:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5948, loss_cls: 3.7470, loss: 3.7470 +2024-07-19 09:27:47,703 - pyskl - INFO - Epoch [86][900/3746] lr: 3.936e-02, eta: 2 days, 6:19:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6016, loss_cls: 3.7348, loss: 3.7348 +2024-07-19 09:29:09,303 - pyskl - INFO - Epoch [86][1000/3746] lr: 3.933e-02, eta: 2 days, 6:17:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6059, loss_cls: 3.7294, loss: 3.7294 +2024-07-19 09:30:31,495 - pyskl - INFO - Epoch [86][1100/3746] lr: 3.930e-02, eta: 2 days, 6:16:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6150, loss_cls: 3.6797, loss: 3.6797 +2024-07-19 09:31:53,222 - pyskl - INFO - Epoch [86][1200/3746] lr: 3.928e-02, eta: 2 days, 6:15:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.6009, loss_cls: 3.7712, loss: 3.7712 +2024-07-19 09:33:15,316 - pyskl - INFO - Epoch [86][1300/3746] lr: 3.925e-02, eta: 2 days, 6:14:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6030, loss_cls: 3.7383, loss: 3.7383 +2024-07-19 09:34:37,087 - pyskl - INFO - Epoch [86][1400/3746] lr: 3.922e-02, eta: 2 days, 6:12:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.5972, loss_cls: 3.7589, loss: 3.7589 +2024-07-19 09:35:58,877 - pyskl - INFO - Epoch [86][1500/3746] lr: 3.919e-02, eta: 2 days, 6:11:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6014, loss_cls: 3.7470, loss: 3.7470 +2024-07-19 09:37:20,630 - pyskl - INFO - Epoch [86][1600/3746] lr: 3.917e-02, eta: 2 days, 6:10:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6070, loss_cls: 3.7295, loss: 3.7295 +2024-07-19 09:38:42,848 - pyskl - INFO - Epoch [86][1700/3746] lr: 3.914e-02, eta: 2 days, 6:08:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.6000, loss_cls: 3.7411, loss: 3.7411 +2024-07-19 09:40:04,328 - pyskl - INFO - Epoch [86][1800/3746] lr: 3.911e-02, eta: 2 days, 6:07:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6028, loss_cls: 3.7223, loss: 3.7223 +2024-07-19 09:41:26,076 - pyskl - INFO - Epoch [86][1900/3746] lr: 3.909e-02, eta: 2 days, 6:06:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.5964, loss_cls: 3.7666, loss: 3.7666 +2024-07-19 09:42:47,506 - pyskl - INFO - Epoch [86][2000/3746] lr: 3.906e-02, eta: 2 days, 6:04:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6050, loss_cls: 3.7269, loss: 3.7269 +2024-07-19 09:44:09,395 - pyskl - INFO - Epoch [86][2100/3746] lr: 3.903e-02, eta: 2 days, 6:03:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5916, loss_cls: 3.7773, loss: 3.7773 +2024-07-19 09:45:31,041 - pyskl - INFO - Epoch [86][2200/3746] lr: 3.900e-02, eta: 2 days, 6:02:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5911, loss_cls: 3.7852, loss: 3.7852 +2024-07-19 09:46:52,943 - pyskl - INFO - Epoch [86][2300/3746] lr: 3.898e-02, eta: 2 days, 6:00:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5969, loss_cls: 3.7695, loss: 3.7695 +2024-07-19 09:48:14,480 - pyskl - INFO - Epoch [86][2400/3746] lr: 3.895e-02, eta: 2 days, 5:59:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.6006, loss_cls: 3.7466, loss: 3.7466 +2024-07-19 09:49:36,041 - pyskl - INFO - Epoch [86][2500/3746] lr: 3.892e-02, eta: 2 days, 5:58:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.6055, loss_cls: 3.7055, loss: 3.7055 +2024-07-19 09:50:57,815 - pyskl - INFO - Epoch [86][2600/3746] lr: 3.889e-02, eta: 2 days, 5:56:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6116, loss_cls: 3.6684, loss: 3.6684 +2024-07-19 09:52:19,198 - pyskl - INFO - Epoch [86][2700/3746] lr: 3.887e-02, eta: 2 days, 5:55:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6131, loss_cls: 3.7024, loss: 3.7024 +2024-07-19 09:53:41,733 - pyskl - INFO - Epoch [86][2800/3746] lr: 3.884e-02, eta: 2 days, 5:54:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5948, loss_cls: 3.7902, loss: 3.7902 +2024-07-19 09:55:03,291 - pyskl - INFO - Epoch [86][2900/3746] lr: 3.881e-02, eta: 2 days, 5:52:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6033, loss_cls: 3.7309, loss: 3.7309 +2024-07-19 09:56:25,333 - pyskl - INFO - Epoch [86][3000/3746] lr: 3.879e-02, eta: 2 days, 5:51:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5992, loss_cls: 3.7661, loss: 3.7661 +2024-07-19 09:57:47,265 - pyskl - INFO - Epoch [86][3100/3746] lr: 3.876e-02, eta: 2 days, 5:50:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6092, loss_cls: 3.7181, loss: 3.7181 +2024-07-19 09:59:09,374 - pyskl - INFO - Epoch [86][3200/3746] lr: 3.873e-02, eta: 2 days, 5:48:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6009, loss_cls: 3.7488, loss: 3.7488 +2024-07-19 10:00:31,683 - pyskl - INFO - Epoch [86][3300/3746] lr: 3.870e-02, eta: 2 days, 5:47:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.6016, loss_cls: 3.7459, loss: 3.7459 +2024-07-19 10:01:53,666 - pyskl - INFO - Epoch [86][3400/3746] lr: 3.868e-02, eta: 2 days, 5:46:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.6014, loss_cls: 3.7679, loss: 3.7679 +2024-07-19 10:03:15,462 - pyskl - INFO - Epoch [86][3500/3746] lr: 3.865e-02, eta: 2 days, 5:44:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6089, loss_cls: 3.7068, loss: 3.7068 +2024-07-19 10:04:37,038 - pyskl - INFO - Epoch [86][3600/3746] lr: 3.862e-02, eta: 2 days, 5:43:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5920, loss_cls: 3.7989, loss: 3.7989 +2024-07-19 10:05:58,872 - pyskl - INFO - Epoch [86][3700/3746] lr: 3.860e-02, eta: 2 days, 5:42:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5933, loss_cls: 3.8224, loss: 3.8224 +2024-07-19 10:06:38,522 - pyskl - INFO - Saving checkpoint at 86 epochs +2024-07-19 10:08:30,749 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 10:08:31,417 - pyskl - INFO - +top1_acc 0.2887 +top5_acc 0.5346 +2024-07-19 10:08:31,417 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 10:08:31,468 - pyskl - INFO - +mean_acc 0.2885 +2024-07-19 10:08:31,476 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_85.pth was removed +2024-07-19 10:08:31,732 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_86.pth. +2024-07-19 10:08:31,733 - pyskl - INFO - Best top1_acc is 0.2887 at 86 epoch. +2024-07-19 10:08:31,746 - pyskl - INFO - Epoch(val) [86][309] top1_acc: 0.2887, top5_acc: 0.5346, mean_class_accuracy: 0.2885 +2024-07-19 10:12:19,747 - pyskl - INFO - Epoch [87][100/3746] lr: 3.856e-02, eta: 2 days, 5:41:31, time: 2.280, data_time: 1.297, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6250, loss_cls: 3.6064, loss: 3.6064 +2024-07-19 10:13:42,376 - pyskl - INFO - Epoch [87][200/3746] lr: 3.853e-02, eta: 2 days, 5:40:12, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6009, loss_cls: 3.7507, loss: 3.7507 +2024-07-19 10:15:04,919 - pyskl - INFO - Epoch [87][300/3746] lr: 3.850e-02, eta: 2 days, 5:38:52, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6141, loss_cls: 3.6926, loss: 3.6926 +2024-07-19 10:16:27,413 - pyskl - INFO - Epoch [87][400/3746] lr: 3.847e-02, eta: 2 days, 5:37:33, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6002, loss_cls: 3.7492, loss: 3.7492 +2024-07-19 10:17:49,426 - pyskl - INFO - Epoch [87][500/3746] lr: 3.845e-02, eta: 2 days, 5:36:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6112, loss_cls: 3.7009, loss: 3.7009 +2024-07-19 10:19:11,551 - pyskl - INFO - Epoch [87][600/3746] lr: 3.842e-02, eta: 2 days, 5:34:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6159, loss_cls: 3.6856, loss: 3.6856 +2024-07-19 10:20:33,466 - pyskl - INFO - Epoch [87][700/3746] lr: 3.839e-02, eta: 2 days, 5:33:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6112, loss_cls: 3.7134, loss: 3.7134 +2024-07-19 10:21:55,313 - pyskl - INFO - Epoch [87][800/3746] lr: 3.837e-02, eta: 2 days, 5:32:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6073, loss_cls: 3.6828, loss: 3.6828 +2024-07-19 10:23:16,543 - pyskl - INFO - Epoch [87][900/3746] lr: 3.834e-02, eta: 2 days, 5:30:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6122, loss_cls: 3.6693, loss: 3.6693 +2024-07-19 10:24:38,532 - pyskl - INFO - Epoch [87][1000/3746] lr: 3.831e-02, eta: 2 days, 5:29:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6153, loss_cls: 3.6996, loss: 3.6996 +2024-07-19 10:26:00,141 - pyskl - INFO - Epoch [87][1100/3746] lr: 3.828e-02, eta: 2 days, 5:28:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.6012, loss_cls: 3.7482, loss: 3.7482 +2024-07-19 10:27:21,361 - pyskl - INFO - Epoch [87][1200/3746] lr: 3.826e-02, eta: 2 days, 5:26:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6017, loss_cls: 3.7239, loss: 3.7239 +2024-07-19 10:28:43,721 - pyskl - INFO - Epoch [87][1300/3746] lr: 3.823e-02, eta: 2 days, 5:25:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6047, loss_cls: 3.7185, loss: 3.7185 +2024-07-19 10:30:05,214 - pyskl - INFO - Epoch [87][1400/3746] lr: 3.820e-02, eta: 2 days, 5:24:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.5991, loss_cls: 3.7656, loss: 3.7656 +2024-07-19 10:31:27,196 - pyskl - INFO - Epoch [87][1500/3746] lr: 3.817e-02, eta: 2 days, 5:22:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.5953, loss_cls: 3.7504, loss: 3.7504 +2024-07-19 10:32:49,398 - pyskl - INFO - Epoch [87][1600/3746] lr: 3.815e-02, eta: 2 days, 5:21:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6052, loss_cls: 3.7032, loss: 3.7032 +2024-07-19 10:34:11,127 - pyskl - INFO - Epoch [87][1700/3746] lr: 3.812e-02, eta: 2 days, 5:20:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6070, loss_cls: 3.7378, loss: 3.7378 +2024-07-19 10:35:32,831 - pyskl - INFO - Epoch [87][1800/3746] lr: 3.809e-02, eta: 2 days, 5:18:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6072, loss_cls: 3.7238, loss: 3.7238 +2024-07-19 10:36:54,380 - pyskl - INFO - Epoch [87][1900/3746] lr: 3.807e-02, eta: 2 days, 5:17:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6084, loss_cls: 3.7013, loss: 3.7013 +2024-07-19 10:38:16,653 - pyskl - INFO - Epoch [87][2000/3746] lr: 3.804e-02, eta: 2 days, 5:16:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.5969, loss_cls: 3.7351, loss: 3.7351 +2024-07-19 10:39:38,916 - pyskl - INFO - Epoch [87][2100/3746] lr: 3.801e-02, eta: 2 days, 5:14:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.5995, loss_cls: 3.7405, loss: 3.7405 +2024-07-19 10:41:00,309 - pyskl - INFO - Epoch [87][2200/3746] lr: 3.798e-02, eta: 2 days, 5:13:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5905, loss_cls: 3.7918, loss: 3.7918 +2024-07-19 10:42:22,067 - pyskl - INFO - Epoch [87][2300/3746] lr: 3.796e-02, eta: 2 days, 5:12:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6055, loss_cls: 3.7304, loss: 3.7304 +2024-07-19 10:43:43,264 - pyskl - INFO - Epoch [87][2400/3746] lr: 3.793e-02, eta: 2 days, 5:10:56, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.5992, loss_cls: 3.7215, loss: 3.7215 +2024-07-19 10:45:05,029 - pyskl - INFO - Epoch [87][2500/3746] lr: 3.790e-02, eta: 2 days, 5:09:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6036, loss_cls: 3.7296, loss: 3.7296 +2024-07-19 10:46:26,619 - pyskl - INFO - Epoch [87][2600/3746] lr: 3.788e-02, eta: 2 days, 5:08:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6003, loss_cls: 3.7069, loss: 3.7069 +2024-07-19 10:47:48,692 - pyskl - INFO - Epoch [87][2700/3746] lr: 3.785e-02, eta: 2 days, 5:06:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5975, loss_cls: 3.7757, loss: 3.7757 +2024-07-19 10:49:10,269 - pyskl - INFO - Epoch [87][2800/3746] lr: 3.782e-02, eta: 2 days, 5:05:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6058, loss_cls: 3.7159, loss: 3.7159 +2024-07-19 10:50:31,869 - pyskl - INFO - Epoch [87][2900/3746] lr: 3.779e-02, eta: 2 days, 5:04:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3452, top5_acc: 0.5991, loss_cls: 3.7371, loss: 3.7371 +2024-07-19 10:51:54,171 - pyskl - INFO - Epoch [87][3000/3746] lr: 3.777e-02, eta: 2 days, 5:02:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.6067, loss_cls: 3.7329, loss: 3.7329 +2024-07-19 10:53:16,288 - pyskl - INFO - Epoch [87][3100/3746] lr: 3.774e-02, eta: 2 days, 5:01:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6189, loss_cls: 3.6760, loss: 3.6760 +2024-07-19 10:54:38,859 - pyskl - INFO - Epoch [87][3200/3746] lr: 3.771e-02, eta: 2 days, 5:00:19, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.6008, loss_cls: 3.7585, loss: 3.7585 +2024-07-19 10:56:01,207 - pyskl - INFO - Epoch [87][3300/3746] lr: 3.769e-02, eta: 2 days, 4:58:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.6059, loss_cls: 3.7414, loss: 3.7414 +2024-07-19 10:57:23,429 - pyskl - INFO - Epoch [87][3400/3746] lr: 3.766e-02, eta: 2 days, 4:57:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3425, top5_acc: 0.6003, loss_cls: 3.7717, loss: 3.7717 +2024-07-19 10:58:45,292 - pyskl - INFO - Epoch [87][3500/3746] lr: 3.763e-02, eta: 2 days, 4:56:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5894, loss_cls: 3.7762, loss: 3.7762 +2024-07-19 11:00:07,143 - pyskl - INFO - Epoch [87][3600/3746] lr: 3.761e-02, eta: 2 days, 4:55:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6114, loss_cls: 3.6877, loss: 3.6877 +2024-07-19 11:01:28,765 - pyskl - INFO - Epoch [87][3700/3746] lr: 3.758e-02, eta: 2 days, 4:53:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.6039, loss_cls: 3.7302, loss: 3.7302 +2024-07-19 11:02:08,108 - pyskl - INFO - Saving checkpoint at 87 epochs +2024-07-19 11:04:00,423 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 11:04:01,086 - pyskl - INFO - +top1_acc 0.2859 +top5_acc 0.5410 +2024-07-19 11:04:01,086 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 11:04:01,127 - pyskl - INFO - +mean_acc 0.2856 +2024-07-19 11:04:01,139 - pyskl - INFO - Epoch(val) [87][309] top1_acc: 0.2859, top5_acc: 0.5410, mean_class_accuracy: 0.2856 +2024-07-19 11:07:50,549 - pyskl - INFO - Epoch [88][100/3746] lr: 3.754e-02, eta: 2 days, 4:53:03, time: 2.294, data_time: 1.298, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6173, loss_cls: 3.6666, loss: 3.6666 +2024-07-19 11:09:13,003 - pyskl - INFO - Epoch [88][200/3746] lr: 3.751e-02, eta: 2 days, 4:51:43, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6102, loss_cls: 3.7124, loss: 3.7124 +2024-07-19 11:10:35,255 - pyskl - INFO - Epoch [88][300/3746] lr: 3.748e-02, eta: 2 days, 4:50:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6180, loss_cls: 3.6777, loss: 3.6777 +2024-07-19 11:11:57,851 - pyskl - INFO - Epoch [88][400/3746] lr: 3.746e-02, eta: 2 days, 4:49:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6147, loss_cls: 3.6589, loss: 3.6589 +2024-07-19 11:13:20,187 - pyskl - INFO - Epoch [88][500/3746] lr: 3.743e-02, eta: 2 days, 4:47:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6119, loss_cls: 3.6683, loss: 3.6683 +2024-07-19 11:14:42,642 - pyskl - INFO - Epoch [88][600/3746] lr: 3.740e-02, eta: 2 days, 4:46:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6159, loss_cls: 3.6685, loss: 3.6685 +2024-07-19 11:16:04,262 - pyskl - INFO - Epoch [88][700/3746] lr: 3.738e-02, eta: 2 days, 4:45:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6044, loss_cls: 3.7158, loss: 3.7158 +2024-07-19 11:17:26,100 - pyskl - INFO - Epoch [88][800/3746] lr: 3.735e-02, eta: 2 days, 4:43:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6025, loss_cls: 3.7469, loss: 3.7469 +2024-07-19 11:18:47,864 - pyskl - INFO - Epoch [88][900/3746] lr: 3.732e-02, eta: 2 days, 4:42:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6300, loss_cls: 3.6274, loss: 3.6274 +2024-07-19 11:20:10,106 - pyskl - INFO - Epoch [88][1000/3746] lr: 3.730e-02, eta: 2 days, 4:41:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6077, loss_cls: 3.7135, loss: 3.7135 +2024-07-19 11:21:31,906 - pyskl - INFO - Epoch [88][1100/3746] lr: 3.727e-02, eta: 2 days, 4:39:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6134, loss_cls: 3.6795, loss: 3.6795 +2024-07-19 11:22:53,870 - pyskl - INFO - Epoch [88][1200/3746] lr: 3.724e-02, eta: 2 days, 4:38:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6002, loss_cls: 3.7509, loss: 3.7509 +2024-07-19 11:24:15,448 - pyskl - INFO - Epoch [88][1300/3746] lr: 3.721e-02, eta: 2 days, 4:37:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6027, loss_cls: 3.7304, loss: 3.7304 +2024-07-19 11:25:37,932 - pyskl - INFO - Epoch [88][1400/3746] lr: 3.719e-02, eta: 2 days, 4:35:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6095, loss_cls: 3.6775, loss: 3.6775 +2024-07-19 11:26:59,980 - pyskl - INFO - Epoch [88][1500/3746] lr: 3.716e-02, eta: 2 days, 4:34:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6142, loss_cls: 3.6830, loss: 3.6830 +2024-07-19 11:28:21,752 - pyskl - INFO - Epoch [88][1600/3746] lr: 3.713e-02, eta: 2 days, 4:33:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6045, loss_cls: 3.7143, loss: 3.7143 +2024-07-19 11:29:44,079 - pyskl - INFO - Epoch [88][1700/3746] lr: 3.711e-02, eta: 2 days, 4:31:48, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6002, loss_cls: 3.7418, loss: 3.7418 +2024-07-19 11:31:05,852 - pyskl - INFO - Epoch [88][1800/3746] lr: 3.708e-02, eta: 2 days, 4:30:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.6070, loss_cls: 3.7269, loss: 3.7269 +2024-07-19 11:32:27,376 - pyskl - INFO - Epoch [88][1900/3746] lr: 3.705e-02, eta: 2 days, 4:29:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6025, loss_cls: 3.7214, loss: 3.7214 +2024-07-19 11:33:48,554 - pyskl - INFO - Epoch [88][2000/3746] lr: 3.703e-02, eta: 2 days, 4:27:47, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.6012, loss_cls: 3.7575, loss: 3.7575 +2024-07-19 11:35:10,419 - pyskl - INFO - Epoch [88][2100/3746] lr: 3.700e-02, eta: 2 days, 4:26:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6086, loss_cls: 3.6908, loss: 3.6908 +2024-07-19 11:36:31,956 - pyskl - INFO - Epoch [88][2200/3746] lr: 3.697e-02, eta: 2 days, 4:25:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6170, loss_cls: 3.6603, loss: 3.6603 +2024-07-19 11:37:53,741 - pyskl - INFO - Epoch [88][2300/3746] lr: 3.694e-02, eta: 2 days, 4:23:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.6067, loss_cls: 3.7035, loss: 3.7035 +2024-07-19 11:39:15,650 - pyskl - INFO - Epoch [88][2400/3746] lr: 3.692e-02, eta: 2 days, 4:22:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5947, loss_cls: 3.7607, loss: 3.7607 +2024-07-19 11:40:36,972 - pyskl - INFO - Epoch [88][2500/3746] lr: 3.689e-02, eta: 2 days, 4:21:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6025, loss_cls: 3.7195, loss: 3.7195 +2024-07-19 11:41:58,525 - pyskl - INFO - Epoch [88][2600/3746] lr: 3.686e-02, eta: 2 days, 4:19:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6044, loss_cls: 3.7237, loss: 3.7237 +2024-07-19 11:43:19,793 - pyskl - INFO - Epoch [88][2700/3746] lr: 3.684e-02, eta: 2 days, 4:18:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6083, loss_cls: 3.7172, loss: 3.7172 +2024-07-19 11:44:41,679 - pyskl - INFO - Epoch [88][2800/3746] lr: 3.681e-02, eta: 2 days, 4:17:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6078, loss_cls: 3.7177, loss: 3.7177 +2024-07-19 11:46:03,911 - pyskl - INFO - Epoch [88][2900/3746] lr: 3.678e-02, eta: 2 days, 4:15:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.6023, loss_cls: 3.7259, loss: 3.7259 +2024-07-19 11:47:25,871 - pyskl - INFO - Epoch [88][3000/3746] lr: 3.676e-02, eta: 2 days, 4:14:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.6042, loss_cls: 3.7189, loss: 3.7189 +2024-07-19 11:48:47,500 - pyskl - INFO - Epoch [88][3100/3746] lr: 3.673e-02, eta: 2 days, 4:13:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6036, loss_cls: 3.7147, loss: 3.7147 +2024-07-19 11:50:09,802 - pyskl - INFO - Epoch [88][3200/3746] lr: 3.670e-02, eta: 2 days, 4:11:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6075, loss_cls: 3.7056, loss: 3.7056 +2024-07-19 11:51:31,604 - pyskl - INFO - Epoch [88][3300/3746] lr: 3.667e-02, eta: 2 days, 4:10:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6034, loss_cls: 3.7138, loss: 3.7138 +2024-07-19 11:52:53,128 - pyskl - INFO - Epoch [88][3400/3746] lr: 3.665e-02, eta: 2 days, 4:09:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.6034, loss_cls: 3.7336, loss: 3.7336 +2024-07-19 11:54:14,384 - pyskl - INFO - Epoch [88][3500/3746] lr: 3.662e-02, eta: 2 days, 4:07:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6017, loss_cls: 3.7270, loss: 3.7270 +2024-07-19 11:55:35,521 - pyskl - INFO - Epoch [88][3600/3746] lr: 3.659e-02, eta: 2 days, 4:06:27, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6058, loss_cls: 3.7458, loss: 3.7458 +2024-07-19 11:56:57,019 - pyskl - INFO - Epoch [88][3700/3746] lr: 3.657e-02, eta: 2 days, 4:05:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.6058, loss_cls: 3.7337, loss: 3.7337 +2024-07-19 11:57:36,757 - pyskl - INFO - Saving checkpoint at 88 epochs +2024-07-19 11:59:29,266 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 11:59:29,925 - pyskl - INFO - +top1_acc 0.2943 +top5_acc 0.5430 +2024-07-19 11:59:29,926 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 11:59:29,966 - pyskl - INFO - +mean_acc 0.2940 +2024-07-19 11:59:29,970 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_86.pth was removed +2024-07-19 11:59:30,226 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_88.pth. +2024-07-19 11:59:30,226 - pyskl - INFO - Best top1_acc is 0.2943 at 88 epoch. +2024-07-19 11:59:30,238 - pyskl - INFO - Epoch(val) [88][309] top1_acc: 0.2943, top5_acc: 0.5430, mean_class_accuracy: 0.2940 +2024-07-19 12:03:17,636 - pyskl - INFO - Epoch [89][100/3746] lr: 3.653e-02, eta: 2 days, 4:04:26, time: 2.274, data_time: 1.292, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6106, loss_cls: 3.6554, loss: 3.6554 +2024-07-19 12:04:39,988 - pyskl - INFO - Epoch [89][200/3746] lr: 3.650e-02, eta: 2 days, 4:03:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6230, loss_cls: 3.6597, loss: 3.6597 +2024-07-19 12:06:01,995 - pyskl - INFO - Epoch [89][300/3746] lr: 3.647e-02, eta: 2 days, 4:01:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6173, loss_cls: 3.6454, loss: 3.6454 +2024-07-19 12:07:24,417 - pyskl - INFO - Epoch [89][400/3746] lr: 3.645e-02, eta: 2 days, 4:00:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.6092, loss_cls: 3.7045, loss: 3.7045 +2024-07-19 12:08:46,474 - pyskl - INFO - Epoch [89][500/3746] lr: 3.642e-02, eta: 2 days, 3:59:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6067, loss_cls: 3.6773, loss: 3.6773 +2024-07-19 12:10:08,616 - pyskl - INFO - Epoch [89][600/3746] lr: 3.639e-02, eta: 2 days, 3:57:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6259, loss_cls: 3.6293, loss: 3.6293 +2024-07-19 12:11:30,415 - pyskl - INFO - Epoch [89][700/3746] lr: 3.637e-02, eta: 2 days, 3:56:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6250, loss_cls: 3.6238, loss: 3.6238 +2024-07-19 12:12:52,310 - pyskl - INFO - Epoch [89][800/3746] lr: 3.634e-02, eta: 2 days, 3:55:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6111, loss_cls: 3.6636, loss: 3.6636 +2024-07-19 12:14:13,736 - pyskl - INFO - Epoch [89][900/3746] lr: 3.631e-02, eta: 2 days, 3:53:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6048, loss_cls: 3.7113, loss: 3.7113 +2024-07-19 12:15:35,532 - pyskl - INFO - Epoch [89][1000/3746] lr: 3.629e-02, eta: 2 days, 3:52:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6169, loss_cls: 3.6686, loss: 3.6686 +2024-07-19 12:16:57,275 - pyskl - INFO - Epoch [89][1100/3746] lr: 3.626e-02, eta: 2 days, 3:51:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6211, loss_cls: 3.6219, loss: 3.6219 +2024-07-19 12:18:19,029 - pyskl - INFO - Epoch [89][1200/3746] lr: 3.623e-02, eta: 2 days, 3:49:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6081, loss_cls: 3.6853, loss: 3.6853 +2024-07-19 12:19:41,081 - pyskl - INFO - Epoch [89][1300/3746] lr: 3.620e-02, eta: 2 days, 3:48:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6069, loss_cls: 3.7231, loss: 3.7231 +2024-07-19 12:21:02,774 - pyskl - INFO - Epoch [89][1400/3746] lr: 3.618e-02, eta: 2 days, 3:47:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6158, loss_cls: 3.6840, loss: 3.6840 +2024-07-19 12:22:24,327 - pyskl - INFO - Epoch [89][1500/3746] lr: 3.615e-02, eta: 2 days, 3:45:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6078, loss_cls: 3.6907, loss: 3.6907 +2024-07-19 12:23:46,528 - pyskl - INFO - Epoch [89][1600/3746] lr: 3.612e-02, eta: 2 days, 3:44:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6055, loss_cls: 3.6907, loss: 3.6907 +2024-07-19 12:25:07,760 - pyskl - INFO - Epoch [89][1700/3746] lr: 3.610e-02, eta: 2 days, 3:43:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.6130, loss_cls: 3.7164, loss: 3.7164 +2024-07-19 12:26:29,366 - pyskl - INFO - Epoch [89][1800/3746] lr: 3.607e-02, eta: 2 days, 3:41:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6073, loss_cls: 3.6836, loss: 3.6836 +2024-07-19 12:27:51,147 - pyskl - INFO - Epoch [89][1900/3746] lr: 3.604e-02, eta: 2 days, 3:40:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.5905, loss_cls: 3.7631, loss: 3.7631 +2024-07-19 12:29:13,230 - pyskl - INFO - Epoch [89][2000/3746] lr: 3.602e-02, eta: 2 days, 3:39:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6052, loss_cls: 3.7123, loss: 3.7123 +2024-07-19 12:30:34,879 - pyskl - INFO - Epoch [89][2100/3746] lr: 3.599e-02, eta: 2 days, 3:37:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6017, loss_cls: 3.7288, loss: 3.7288 +2024-07-19 12:31:56,192 - pyskl - INFO - Epoch [89][2200/3746] lr: 3.596e-02, eta: 2 days, 3:36:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6023, loss_cls: 3.7231, loss: 3.7231 +2024-07-19 12:33:18,191 - pyskl - INFO - Epoch [89][2300/3746] lr: 3.594e-02, eta: 2 days, 3:35:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.5992, loss_cls: 3.8033, loss: 3.8033 +2024-07-19 12:34:40,220 - pyskl - INFO - Epoch [89][2400/3746] lr: 3.591e-02, eta: 2 days, 3:33:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5941, loss_cls: 3.7780, loss: 3.7780 +2024-07-19 12:36:02,178 - pyskl - INFO - Epoch [89][2500/3746] lr: 3.588e-02, eta: 2 days, 3:32:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6100, loss_cls: 3.6851, loss: 3.6851 +2024-07-19 12:37:23,832 - pyskl - INFO - Epoch [89][2600/3746] lr: 3.586e-02, eta: 2 days, 3:31:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6220, loss_cls: 3.6107, loss: 3.6107 +2024-07-19 12:38:46,106 - pyskl - INFO - Epoch [89][2700/3746] lr: 3.583e-02, eta: 2 days, 3:29:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.5964, loss_cls: 3.7403, loss: 3.7403 +2024-07-19 12:40:08,897 - pyskl - INFO - Epoch [89][2800/3746] lr: 3.580e-02, eta: 2 days, 3:28:27, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6122, loss_cls: 3.6792, loss: 3.6792 +2024-07-19 12:41:30,637 - pyskl - INFO - Epoch [89][2900/3746] lr: 3.578e-02, eta: 2 days, 3:27:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.6058, loss_cls: 3.7188, loss: 3.7188 +2024-07-19 12:42:52,367 - pyskl - INFO - Epoch [89][3000/3746] lr: 3.575e-02, eta: 2 days, 3:25:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6117, loss_cls: 3.6894, loss: 3.6894 +2024-07-19 12:44:14,158 - pyskl - INFO - Epoch [89][3100/3746] lr: 3.572e-02, eta: 2 days, 3:24:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3541, top5_acc: 0.6056, loss_cls: 3.6987, loss: 3.6987 +2024-07-19 12:45:36,320 - pyskl - INFO - Epoch [89][3200/3746] lr: 3.569e-02, eta: 2 days, 3:23:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6133, loss_cls: 3.6808, loss: 3.6808 +2024-07-19 12:46:57,738 - pyskl - INFO - Epoch [89][3300/3746] lr: 3.567e-02, eta: 2 days, 3:21:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6022, loss_cls: 3.7007, loss: 3.7007 +2024-07-19 12:48:19,518 - pyskl - INFO - Epoch [89][3400/3746] lr: 3.564e-02, eta: 2 days, 3:20:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3541, top5_acc: 0.6077, loss_cls: 3.6902, loss: 3.6902 +2024-07-19 12:49:41,286 - pyskl - INFO - Epoch [89][3500/3746] lr: 3.561e-02, eta: 2 days, 3:19:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3541, top5_acc: 0.6095, loss_cls: 3.7089, loss: 3.7089 +2024-07-19 12:51:03,169 - pyskl - INFO - Epoch [89][3600/3746] lr: 3.559e-02, eta: 2 days, 3:17:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5953, loss_cls: 3.7606, loss: 3.7606 +2024-07-19 12:52:24,320 - pyskl - INFO - Epoch [89][3700/3746] lr: 3.556e-02, eta: 2 days, 3:16:26, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.5977, loss_cls: 3.7478, loss: 3.7478 +2024-07-19 12:53:04,062 - pyskl - INFO - Saving checkpoint at 89 epochs +2024-07-19 12:54:55,364 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 12:54:56,035 - pyskl - INFO - +top1_acc 0.2427 +top5_acc 0.4919 +2024-07-19 12:54:56,035 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 12:54:56,082 - pyskl - INFO - +mean_acc 0.2425 +2024-07-19 12:54:56,095 - pyskl - INFO - Epoch(val) [89][309] top1_acc: 0.2427, top5_acc: 0.4919, mean_class_accuracy: 0.2425 +2024-07-19 12:58:48,880 - pyskl - INFO - Epoch [90][100/3746] lr: 3.552e-02, eta: 2 days, 3:15:47, time: 2.328, data_time: 1.313, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6192, loss_cls: 3.6421, loss: 3.6421 +2024-07-19 13:00:11,374 - pyskl - INFO - Epoch [90][200/3746] lr: 3.550e-02, eta: 2 days, 3:14:27, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6209, loss_cls: 3.6137, loss: 3.6137 +2024-07-19 13:01:33,303 - pyskl - INFO - Epoch [90][300/3746] lr: 3.547e-02, eta: 2 days, 3:13:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6223, loss_cls: 3.6223, loss: 3.6223 +2024-07-19 13:02:55,479 - pyskl - INFO - Epoch [90][400/3746] lr: 3.544e-02, eta: 2 days, 3:11:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6223, loss_cls: 3.6638, loss: 3.6638 +2024-07-19 13:04:17,111 - pyskl - INFO - Epoch [90][500/3746] lr: 3.541e-02, eta: 2 days, 3:10:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6098, loss_cls: 3.6964, loss: 3.6964 +2024-07-19 13:05:39,126 - pyskl - INFO - Epoch [90][600/3746] lr: 3.539e-02, eta: 2 days, 3:09:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6177, loss_cls: 3.6534, loss: 3.6534 +2024-07-19 13:07:01,028 - pyskl - INFO - Epoch [90][700/3746] lr: 3.536e-02, eta: 2 days, 3:07:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6116, loss_cls: 3.6880, loss: 3.6880 +2024-07-19 13:08:22,539 - pyskl - INFO - Epoch [90][800/3746] lr: 3.533e-02, eta: 2 days, 3:06:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6133, loss_cls: 3.7058, loss: 3.7058 +2024-07-19 13:09:44,212 - pyskl - INFO - Epoch [90][900/3746] lr: 3.531e-02, eta: 2 days, 3:05:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6147, loss_cls: 3.6751, loss: 3.6751 +2024-07-19 13:11:05,721 - pyskl - INFO - Epoch [90][1000/3746] lr: 3.528e-02, eta: 2 days, 3:03:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6205, loss_cls: 3.6599, loss: 3.6599 +2024-07-19 13:12:27,209 - pyskl - INFO - Epoch [90][1100/3746] lr: 3.525e-02, eta: 2 days, 3:02:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6239, loss_cls: 3.6534, loss: 3.6534 +2024-07-19 13:13:49,369 - pyskl - INFO - Epoch [90][1200/3746] lr: 3.523e-02, eta: 2 days, 3:01:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6045, loss_cls: 3.6847, loss: 3.6847 +2024-07-19 13:15:11,850 - pyskl - INFO - Epoch [90][1300/3746] lr: 3.520e-02, eta: 2 days, 2:59:46, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6119, loss_cls: 3.7125, loss: 3.7125 +2024-07-19 13:16:33,980 - pyskl - INFO - Epoch [90][1400/3746] lr: 3.517e-02, eta: 2 days, 2:58:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6208, loss_cls: 3.6599, loss: 3.6599 +2024-07-19 13:17:56,680 - pyskl - INFO - Epoch [90][1500/3746] lr: 3.515e-02, eta: 2 days, 2:57:07, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6133, loss_cls: 3.6941, loss: 3.6941 +2024-07-19 13:19:18,749 - pyskl - INFO - Epoch [90][1600/3746] lr: 3.512e-02, eta: 2 days, 2:55:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6056, loss_cls: 3.7021, loss: 3.7021 +2024-07-19 13:20:40,427 - pyskl - INFO - Epoch [90][1700/3746] lr: 3.509e-02, eta: 2 days, 2:54:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6066, loss_cls: 3.7030, loss: 3.7030 +2024-07-19 13:22:02,440 - pyskl - INFO - Epoch [90][1800/3746] lr: 3.507e-02, eta: 2 days, 2:53:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6116, loss_cls: 3.6877, loss: 3.6877 +2024-07-19 13:23:24,097 - pyskl - INFO - Epoch [90][1900/3746] lr: 3.504e-02, eta: 2 days, 2:51:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6025, loss_cls: 3.7347, loss: 3.7347 +2024-07-19 13:24:45,845 - pyskl - INFO - Epoch [90][2000/3746] lr: 3.501e-02, eta: 2 days, 2:50:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6092, loss_cls: 3.6743, loss: 3.6743 +2024-07-19 13:26:07,539 - pyskl - INFO - Epoch [90][2100/3746] lr: 3.499e-02, eta: 2 days, 2:49:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.6019, loss_cls: 3.7163, loss: 3.7163 +2024-07-19 13:27:28,557 - pyskl - INFO - Epoch [90][2200/3746] lr: 3.496e-02, eta: 2 days, 2:47:45, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6055, loss_cls: 3.7043, loss: 3.7043 +2024-07-19 13:28:50,175 - pyskl - INFO - Epoch [90][2300/3746] lr: 3.493e-02, eta: 2 days, 2:46:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6166, loss_cls: 3.6644, loss: 3.6644 +2024-07-19 13:30:11,834 - pyskl - INFO - Epoch [90][2400/3746] lr: 3.491e-02, eta: 2 days, 2:45:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6242, loss_cls: 3.6586, loss: 3.6586 +2024-07-19 13:31:33,714 - pyskl - INFO - Epoch [90][2500/3746] lr: 3.488e-02, eta: 2 days, 2:43:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.5986, loss_cls: 3.7718, loss: 3.7718 +2024-07-19 13:32:55,758 - pyskl - INFO - Epoch [90][2600/3746] lr: 3.485e-02, eta: 2 days, 2:42:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6075, loss_cls: 3.7413, loss: 3.7413 +2024-07-19 13:34:17,339 - pyskl - INFO - Epoch [90][2700/3746] lr: 3.483e-02, eta: 2 days, 2:41:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6030, loss_cls: 3.7207, loss: 3.7207 +2024-07-19 13:35:39,087 - pyskl - INFO - Epoch [90][2800/3746] lr: 3.480e-02, eta: 2 days, 2:39:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6119, loss_cls: 3.6757, loss: 3.6757 +2024-07-19 13:37:00,865 - pyskl - INFO - Epoch [90][2900/3746] lr: 3.477e-02, eta: 2 days, 2:38:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6097, loss_cls: 3.6904, loss: 3.6904 +2024-07-19 13:38:23,586 - pyskl - INFO - Epoch [90][3000/3746] lr: 3.475e-02, eta: 2 days, 2:37:05, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6019, loss_cls: 3.7611, loss: 3.7611 +2024-07-19 13:39:45,277 - pyskl - INFO - Epoch [90][3100/3746] lr: 3.472e-02, eta: 2 days, 2:35:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6102, loss_cls: 3.6832, loss: 3.6832 +2024-07-19 13:41:07,276 - pyskl - INFO - Epoch [90][3200/3746] lr: 3.469e-02, eta: 2 days, 2:34:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6173, loss_cls: 3.6427, loss: 3.6427 +2024-07-19 13:42:29,467 - pyskl - INFO - Epoch [90][3300/3746] lr: 3.467e-02, eta: 2 days, 2:33:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6130, loss_cls: 3.6596, loss: 3.6596 +2024-07-19 13:43:51,443 - pyskl - INFO - Epoch [90][3400/3746] lr: 3.464e-02, eta: 2 days, 2:31:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.5992, loss_cls: 3.7260, loss: 3.7260 +2024-07-19 13:45:12,880 - pyskl - INFO - Epoch [90][3500/3746] lr: 3.461e-02, eta: 2 days, 2:30:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6081, loss_cls: 3.6731, loss: 3.6731 +2024-07-19 13:46:34,216 - pyskl - INFO - Epoch [90][3600/3746] lr: 3.459e-02, eta: 2 days, 2:29:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6112, loss_cls: 3.6965, loss: 3.6965 +2024-07-19 13:47:55,893 - pyskl - INFO - Epoch [90][3700/3746] lr: 3.456e-02, eta: 2 days, 2:27:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6114, loss_cls: 3.6975, loss: 3.6975 +2024-07-19 13:48:35,581 - pyskl - INFO - Saving checkpoint at 90 epochs +2024-07-19 13:50:27,510 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 13:50:28,173 - pyskl - INFO - +top1_acc 0.2757 +top5_acc 0.5351 +2024-07-19 13:50:28,173 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 13:50:28,212 - pyskl - INFO - +mean_acc 0.2756 +2024-07-19 13:50:28,223 - pyskl - INFO - Epoch(val) [90][309] top1_acc: 0.2757, top5_acc: 0.5351, mean_class_accuracy: 0.2756 +2024-07-19 13:54:16,876 - pyskl - INFO - Epoch [91][100/3746] lr: 3.452e-02, eta: 2 days, 2:26:59, time: 2.286, data_time: 1.302, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6203, loss_cls: 3.6051, loss: 3.6051 +2024-07-19 13:55:39,035 - pyskl - INFO - Epoch [91][200/3746] lr: 3.450e-02, eta: 2 days, 2:25:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6086, loss_cls: 3.6756, loss: 3.6756 +2024-07-19 13:57:01,738 - pyskl - INFO - Epoch [91][300/3746] lr: 3.447e-02, eta: 2 days, 2:24:20, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6183, loss_cls: 3.6436, loss: 3.6436 +2024-07-19 13:58:23,665 - pyskl - INFO - Epoch [91][400/3746] lr: 3.444e-02, eta: 2 days, 2:22:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6236, loss_cls: 3.6252, loss: 3.6252 +2024-07-19 13:59:46,461 - pyskl - INFO - Epoch [91][500/3746] lr: 3.442e-02, eta: 2 days, 2:21:40, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6081, loss_cls: 3.7237, loss: 3.7237 +2024-07-19 14:01:08,537 - pyskl - INFO - Epoch [91][600/3746] lr: 3.439e-02, eta: 2 days, 2:20:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6138, loss_cls: 3.6826, loss: 3.6826 +2024-07-19 14:02:30,348 - pyskl - INFO - Epoch [91][700/3746] lr: 3.436e-02, eta: 2 days, 2:19:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6136, loss_cls: 3.6674, loss: 3.6674 +2024-07-19 14:03:52,043 - pyskl - INFO - Epoch [91][800/3746] lr: 3.434e-02, eta: 2 days, 2:17:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6116, loss_cls: 3.6860, loss: 3.6860 +2024-07-19 14:05:14,255 - pyskl - INFO - Epoch [91][900/3746] lr: 3.431e-02, eta: 2 days, 2:16:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6083, loss_cls: 3.6699, loss: 3.6699 +2024-07-19 14:06:36,815 - pyskl - INFO - Epoch [91][1000/3746] lr: 3.428e-02, eta: 2 days, 2:15:00, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6042, loss_cls: 3.6712, loss: 3.6712 +2024-07-19 14:07:58,720 - pyskl - INFO - Epoch [91][1100/3746] lr: 3.426e-02, eta: 2 days, 2:13:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3452, top5_acc: 0.5972, loss_cls: 3.7504, loss: 3.7504 +2024-07-19 14:09:20,927 - pyskl - INFO - Epoch [91][1200/3746] lr: 3.423e-02, eta: 2 days, 2:12:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6116, loss_cls: 3.6770, loss: 3.6770 +2024-07-19 14:10:42,856 - pyskl - INFO - Epoch [91][1300/3746] lr: 3.420e-02, eta: 2 days, 2:10:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6020, loss_cls: 3.7092, loss: 3.7092 +2024-07-19 14:12:04,277 - pyskl - INFO - Epoch [91][1400/3746] lr: 3.418e-02, eta: 2 days, 2:09:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6152, loss_cls: 3.6655, loss: 3.6655 +2024-07-19 14:13:26,565 - pyskl - INFO - Epoch [91][1500/3746] lr: 3.415e-02, eta: 2 days, 2:08:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6208, loss_cls: 3.6282, loss: 3.6282 +2024-07-19 14:14:48,180 - pyskl - INFO - Epoch [91][1600/3746] lr: 3.412e-02, eta: 2 days, 2:06:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6098, loss_cls: 3.6678, loss: 3.6678 +2024-07-19 14:16:09,695 - pyskl - INFO - Epoch [91][1700/3746] lr: 3.410e-02, eta: 2 days, 2:05:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3588, top5_acc: 0.6234, loss_cls: 3.6411, loss: 3.6411 +2024-07-19 14:17:31,377 - pyskl - INFO - Epoch [91][1800/3746] lr: 3.407e-02, eta: 2 days, 2:04:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6166, loss_cls: 3.6492, loss: 3.6492 +2024-07-19 14:18:53,552 - pyskl - INFO - Epoch [91][1900/3746] lr: 3.405e-02, eta: 2 days, 2:02:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6230, loss_cls: 3.6258, loss: 3.6258 +2024-07-19 14:20:14,926 - pyskl - INFO - Epoch [91][2000/3746] lr: 3.402e-02, eta: 2 days, 2:01:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6153, loss_cls: 3.6812, loss: 3.6812 +2024-07-19 14:21:36,594 - pyskl - INFO - Epoch [91][2100/3746] lr: 3.399e-02, eta: 2 days, 2:00:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6206, loss_cls: 3.6411, loss: 3.6411 +2024-07-19 14:22:58,379 - pyskl - INFO - Epoch [91][2200/3746] lr: 3.397e-02, eta: 2 days, 1:58:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6097, loss_cls: 3.6995, loss: 3.6995 +2024-07-19 14:24:19,835 - pyskl - INFO - Epoch [91][2300/3746] lr: 3.394e-02, eta: 2 days, 1:57:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6181, loss_cls: 3.6415, loss: 3.6415 +2024-07-19 14:25:41,701 - pyskl - INFO - Epoch [91][2400/3746] lr: 3.391e-02, eta: 2 days, 1:56:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6191, loss_cls: 3.6361, loss: 3.6361 +2024-07-19 14:27:03,194 - pyskl - INFO - Epoch [91][2500/3746] lr: 3.389e-02, eta: 2 days, 1:54:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6116, loss_cls: 3.6766, loss: 3.6766 +2024-07-19 14:28:25,231 - pyskl - INFO - Epoch [91][2600/3746] lr: 3.386e-02, eta: 2 days, 1:53:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6136, loss_cls: 3.6952, loss: 3.6952 +2024-07-19 14:29:47,209 - pyskl - INFO - Epoch [91][2700/3746] lr: 3.383e-02, eta: 2 days, 1:52:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.6127, loss_cls: 3.7182, loss: 3.7182 +2024-07-19 14:31:08,848 - pyskl - INFO - Epoch [91][2800/3746] lr: 3.381e-02, eta: 2 days, 1:50:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6173, loss_cls: 3.7010, loss: 3.7010 +2024-07-19 14:32:30,312 - pyskl - INFO - Epoch [91][2900/3746] lr: 3.378e-02, eta: 2 days, 1:49:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6112, loss_cls: 3.7048, loss: 3.7048 +2024-07-19 14:33:53,395 - pyskl - INFO - Epoch [91][3000/3746] lr: 3.375e-02, eta: 2 days, 1:48:16, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6153, loss_cls: 3.6634, loss: 3.6634 +2024-07-19 14:35:15,271 - pyskl - INFO - Epoch [91][3100/3746] lr: 3.373e-02, eta: 2 days, 1:46:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6112, loss_cls: 3.7005, loss: 3.7005 +2024-07-19 14:36:37,615 - pyskl - INFO - Epoch [91][3200/3746] lr: 3.370e-02, eta: 2 days, 1:45:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6084, loss_cls: 3.6779, loss: 3.6779 +2024-07-19 14:37:59,813 - pyskl - INFO - Epoch [91][3300/3746] lr: 3.367e-02, eta: 2 days, 1:44:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6098, loss_cls: 3.6940, loss: 3.6940 +2024-07-19 14:39:21,803 - pyskl - INFO - Epoch [91][3400/3746] lr: 3.365e-02, eta: 2 days, 1:42:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6072, loss_cls: 3.6818, loss: 3.6818 +2024-07-19 14:40:44,050 - pyskl - INFO - Epoch [91][3500/3746] lr: 3.362e-02, eta: 2 days, 1:41:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6153, loss_cls: 3.6517, loss: 3.6517 +2024-07-19 14:42:05,425 - pyskl - INFO - Epoch [91][3600/3746] lr: 3.360e-02, eta: 2 days, 1:40:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6128, loss_cls: 3.6920, loss: 3.6920 +2024-07-19 14:43:27,458 - pyskl - INFO - Epoch [91][3700/3746] lr: 3.357e-02, eta: 2 days, 1:38:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6141, loss_cls: 3.6965, loss: 3.6965 +2024-07-19 14:44:07,057 - pyskl - INFO - Saving checkpoint at 91 epochs +2024-07-19 14:45:57,989 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 14:45:58,643 - pyskl - INFO - +top1_acc 0.2901 +top5_acc 0.5483 +2024-07-19 14:45:58,643 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 14:45:58,681 - pyskl - INFO - +mean_acc 0.2899 +2024-07-19 14:45:58,692 - pyskl - INFO - Epoch(val) [91][309] top1_acc: 0.2901, top5_acc: 0.5483, mean_class_accuracy: 0.2899 +2024-07-19 14:49:42,271 - pyskl - INFO - Epoch [92][100/3746] lr: 3.353e-02, eta: 2 days, 1:38:05, time: 2.236, data_time: 1.260, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6258, loss_cls: 3.5866, loss: 3.5866 +2024-07-19 14:51:04,577 - pyskl - INFO - Epoch [92][200/3746] lr: 3.350e-02, eta: 2 days, 1:36:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6220, loss_cls: 3.5827, loss: 3.5827 +2024-07-19 14:52:27,229 - pyskl - INFO - Epoch [92][300/3746] lr: 3.348e-02, eta: 2 days, 1:35:25, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6270, loss_cls: 3.5740, loss: 3.5740 +2024-07-19 14:53:49,504 - pyskl - INFO - Epoch [92][400/3746] lr: 3.345e-02, eta: 2 days, 1:34:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6173, loss_cls: 3.6152, loss: 3.6152 +2024-07-19 14:55:12,096 - pyskl - INFO - Epoch [92][500/3746] lr: 3.342e-02, eta: 2 days, 1:32:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6109, loss_cls: 3.6968, loss: 3.6968 +2024-07-19 14:56:34,000 - pyskl - INFO - Epoch [92][600/3746] lr: 3.340e-02, eta: 2 days, 1:31:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6105, loss_cls: 3.6570, loss: 3.6570 +2024-07-19 14:57:55,451 - pyskl - INFO - Epoch [92][700/3746] lr: 3.337e-02, eta: 2 days, 1:30:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6180, loss_cls: 3.6463, loss: 3.6463 +2024-07-19 14:59:17,038 - pyskl - INFO - Epoch [92][800/3746] lr: 3.335e-02, eta: 2 days, 1:28:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6255, loss_cls: 3.6405, loss: 3.6405 +2024-07-19 15:00:38,766 - pyskl - INFO - Epoch [92][900/3746] lr: 3.332e-02, eta: 2 days, 1:27:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6150, loss_cls: 3.6529, loss: 3.6529 +2024-07-19 15:02:00,335 - pyskl - INFO - Epoch [92][1000/3746] lr: 3.329e-02, eta: 2 days, 1:26:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6239, loss_cls: 3.6456, loss: 3.6456 +2024-07-19 15:03:21,712 - pyskl - INFO - Epoch [92][1100/3746] lr: 3.327e-02, eta: 2 days, 1:24:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6202, loss_cls: 3.6482, loss: 3.6482 +2024-07-19 15:04:43,080 - pyskl - INFO - Epoch [92][1200/3746] lr: 3.324e-02, eta: 2 days, 1:23:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6144, loss_cls: 3.6694, loss: 3.6694 +2024-07-19 15:06:05,341 - pyskl - INFO - Epoch [92][1300/3746] lr: 3.321e-02, eta: 2 days, 1:22:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6178, loss_cls: 3.6298, loss: 3.6298 +2024-07-19 15:07:27,762 - pyskl - INFO - Epoch [92][1400/3746] lr: 3.319e-02, eta: 2 days, 1:20:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6134, loss_cls: 3.6851, loss: 3.6851 +2024-07-19 15:08:49,538 - pyskl - INFO - Epoch [92][1500/3746] lr: 3.316e-02, eta: 2 days, 1:19:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6181, loss_cls: 3.6374, loss: 3.6374 +2024-07-19 15:10:10,938 - pyskl - INFO - Epoch [92][1600/3746] lr: 3.314e-02, eta: 2 days, 1:18:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6175, loss_cls: 3.6586, loss: 3.6586 +2024-07-19 15:11:32,671 - pyskl - INFO - Epoch [92][1700/3746] lr: 3.311e-02, eta: 2 days, 1:16:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6092, loss_cls: 3.6904, loss: 3.6904 +2024-07-19 15:12:54,411 - pyskl - INFO - Epoch [92][1800/3746] lr: 3.308e-02, eta: 2 days, 1:15:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6222, loss_cls: 3.6440, loss: 3.6440 +2024-07-19 15:14:15,825 - pyskl - INFO - Epoch [92][1900/3746] lr: 3.306e-02, eta: 2 days, 1:14:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6194, loss_cls: 3.6321, loss: 3.6321 +2024-07-19 15:15:37,188 - pyskl - INFO - Epoch [92][2000/3746] lr: 3.303e-02, eta: 2 days, 1:12:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6289, loss_cls: 3.5670, loss: 3.5670 +2024-07-19 15:16:58,678 - pyskl - INFO - Epoch [92][2100/3746] lr: 3.300e-02, eta: 2 days, 1:11:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6241, loss_cls: 3.6010, loss: 3.6010 +2024-07-19 15:18:20,174 - pyskl - INFO - Epoch [92][2200/3746] lr: 3.298e-02, eta: 2 days, 1:09:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6123, loss_cls: 3.6923, loss: 3.6923 +2024-07-19 15:19:42,388 - pyskl - INFO - Epoch [92][2300/3746] lr: 3.295e-02, eta: 2 days, 1:08:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6019, loss_cls: 3.7458, loss: 3.7458 +2024-07-19 15:21:03,849 - pyskl - INFO - Epoch [92][2400/3746] lr: 3.292e-02, eta: 2 days, 1:07:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6070, loss_cls: 3.6844, loss: 3.6844 +2024-07-19 15:22:25,419 - pyskl - INFO - Epoch [92][2500/3746] lr: 3.290e-02, eta: 2 days, 1:05:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6211, loss_cls: 3.6564, loss: 3.6564 +2024-07-19 15:23:47,674 - pyskl - INFO - Epoch [92][2600/3746] lr: 3.287e-02, eta: 2 days, 1:04:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6070, loss_cls: 3.7148, loss: 3.7148 +2024-07-19 15:25:09,355 - pyskl - INFO - Epoch [92][2700/3746] lr: 3.285e-02, eta: 2 days, 1:03:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6008, loss_cls: 3.7158, loss: 3.7158 +2024-07-19 15:26:31,108 - pyskl - INFO - Epoch [92][2800/3746] lr: 3.282e-02, eta: 2 days, 1:01:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6230, loss_cls: 3.6433, loss: 3.6433 +2024-07-19 15:27:53,263 - pyskl - INFO - Epoch [92][2900/3746] lr: 3.279e-02, eta: 2 days, 1:00:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6000, loss_cls: 3.7353, loss: 3.7353 +2024-07-19 15:29:15,328 - pyskl - INFO - Epoch [92][3000/3746] lr: 3.277e-02, eta: 2 days, 0:59:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6103, loss_cls: 3.6744, loss: 3.6744 +2024-07-19 15:30:37,042 - pyskl - INFO - Epoch [92][3100/3746] lr: 3.274e-02, eta: 2 days, 0:57:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.6016, loss_cls: 3.7134, loss: 3.7134 +2024-07-19 15:31:59,010 - pyskl - INFO - Epoch [92][3200/3746] lr: 3.271e-02, eta: 2 days, 0:56:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6045, loss_cls: 3.6887, loss: 3.6887 +2024-07-19 15:33:20,871 - pyskl - INFO - Epoch [92][3300/3746] lr: 3.269e-02, eta: 2 days, 0:55:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6258, loss_cls: 3.6117, loss: 3.6117 +2024-07-19 15:34:42,554 - pyskl - INFO - Epoch [92][3400/3746] lr: 3.266e-02, eta: 2 days, 0:53:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6081, loss_cls: 3.6756, loss: 3.6756 +2024-07-19 15:36:04,837 - pyskl - INFO - Epoch [92][3500/3746] lr: 3.264e-02, eta: 2 days, 0:52:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6178, loss_cls: 3.6678, loss: 3.6678 +2024-07-19 15:37:26,468 - pyskl - INFO - Epoch [92][3600/3746] lr: 3.261e-02, eta: 2 days, 0:51:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6162, loss_cls: 3.6437, loss: 3.6437 +2024-07-19 15:38:48,168 - pyskl - INFO - Epoch [92][3700/3746] lr: 3.258e-02, eta: 2 days, 0:49:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6069, loss_cls: 3.6979, loss: 3.6979 +2024-07-19 15:39:27,717 - pyskl - INFO - Saving checkpoint at 92 epochs +2024-07-19 15:41:18,409 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 15:41:19,141 - pyskl - INFO - +top1_acc 0.2910 +top5_acc 0.5439 +2024-07-19 15:41:19,141 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 15:41:19,186 - pyskl - INFO - +mean_acc 0.2907 +2024-07-19 15:41:19,198 - pyskl - INFO - Epoch(val) [92][309] top1_acc: 0.2910, top5_acc: 0.5439, mean_class_accuracy: 0.2907 +2024-07-19 15:45:07,764 - pyskl - INFO - Epoch [93][100/3746] lr: 3.255e-02, eta: 2 days, 0:49:06, time: 2.286, data_time: 1.300, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6300, loss_cls: 3.5622, loss: 3.5622 +2024-07-19 15:46:30,446 - pyskl - INFO - Epoch [93][200/3746] lr: 3.252e-02, eta: 2 days, 0:47:46, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6206, loss_cls: 3.6388, loss: 3.6388 +2024-07-19 15:47:53,236 - pyskl - INFO - Epoch [93][300/3746] lr: 3.249e-02, eta: 2 days, 0:46:27, time: 0.828, data_time: 0.001, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6277, loss_cls: 3.5945, loss: 3.5945 +2024-07-19 15:49:14,625 - pyskl - INFO - Epoch [93][400/3746] lr: 3.247e-02, eta: 2 days, 0:45:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6145, loss_cls: 3.6653, loss: 3.6653 +2024-07-19 15:50:37,441 - pyskl - INFO - Epoch [93][500/3746] lr: 3.244e-02, eta: 2 days, 0:43:46, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6244, loss_cls: 3.6205, loss: 3.6205 +2024-07-19 15:51:59,844 - pyskl - INFO - Epoch [93][600/3746] lr: 3.241e-02, eta: 2 days, 0:42:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6272, loss_cls: 3.5660, loss: 3.5660 +2024-07-19 15:53:21,777 - pyskl - INFO - Epoch [93][700/3746] lr: 3.239e-02, eta: 2 days, 0:41:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6252, loss_cls: 3.6351, loss: 3.6351 +2024-07-19 15:54:43,399 - pyskl - INFO - Epoch [93][800/3746] lr: 3.236e-02, eta: 2 days, 0:39:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6172, loss_cls: 3.6507, loss: 3.6507 +2024-07-19 15:56:05,098 - pyskl - INFO - Epoch [93][900/3746] lr: 3.234e-02, eta: 2 days, 0:38:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6186, loss_cls: 3.6604, loss: 3.6604 +2024-07-19 15:57:26,990 - pyskl - INFO - Epoch [93][1000/3746] lr: 3.231e-02, eta: 2 days, 0:37:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6258, loss_cls: 3.6225, loss: 3.6225 +2024-07-19 15:58:49,267 - pyskl - INFO - Epoch [93][1100/3746] lr: 3.228e-02, eta: 2 days, 0:35:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6052, loss_cls: 3.6877, loss: 3.6877 +2024-07-19 16:00:11,002 - pyskl - INFO - Epoch [93][1200/3746] lr: 3.226e-02, eta: 2 days, 0:34:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3741, top5_acc: 0.6166, loss_cls: 3.5970, loss: 3.5970 +2024-07-19 16:01:32,426 - pyskl - INFO - Epoch [93][1300/3746] lr: 3.223e-02, eta: 2 days, 0:33:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6153, loss_cls: 3.6497, loss: 3.6497 +2024-07-19 16:02:54,755 - pyskl - INFO - Epoch [93][1400/3746] lr: 3.221e-02, eta: 2 days, 0:31:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6250, loss_cls: 3.5990, loss: 3.5990 +2024-07-19 16:04:16,384 - pyskl - INFO - Epoch [93][1500/3746] lr: 3.218e-02, eta: 2 days, 0:30:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6239, loss_cls: 3.6110, loss: 3.6110 +2024-07-19 16:05:39,006 - pyskl - INFO - Epoch [93][1600/3746] lr: 3.215e-02, eta: 2 days, 0:29:03, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6241, loss_cls: 3.5975, loss: 3.5975 +2024-07-19 16:07:00,637 - pyskl - INFO - Epoch [93][1700/3746] lr: 3.213e-02, eta: 2 days, 0:27:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6212, loss_cls: 3.6412, loss: 3.6412 +2024-07-19 16:08:22,616 - pyskl - INFO - Epoch [93][1800/3746] lr: 3.210e-02, eta: 2 days, 0:26:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6200, loss_cls: 3.6326, loss: 3.6326 +2024-07-19 16:09:44,252 - pyskl - INFO - Epoch [93][1900/3746] lr: 3.207e-02, eta: 2 days, 0:25:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6072, loss_cls: 3.6672, loss: 3.6672 +2024-07-19 16:11:05,988 - pyskl - INFO - Epoch [93][2000/3746] lr: 3.205e-02, eta: 2 days, 0:23:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6094, loss_cls: 3.6935, loss: 3.6935 +2024-07-19 16:12:27,640 - pyskl - INFO - Epoch [93][2100/3746] lr: 3.202e-02, eta: 2 days, 0:22:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6247, loss_cls: 3.5932, loss: 3.5932 +2024-07-19 16:13:49,048 - pyskl - INFO - Epoch [93][2200/3746] lr: 3.200e-02, eta: 2 days, 0:21:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6073, loss_cls: 3.6564, loss: 3.6564 +2024-07-19 16:15:10,869 - pyskl - INFO - Epoch [93][2300/3746] lr: 3.197e-02, eta: 2 days, 0:19:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6227, loss_cls: 3.6317, loss: 3.6317 +2024-07-19 16:16:31,994 - pyskl - INFO - Epoch [93][2400/3746] lr: 3.194e-02, eta: 2 days, 0:18:19, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6169, loss_cls: 3.6433, loss: 3.6433 +2024-07-19 16:17:53,746 - pyskl - INFO - Epoch [93][2500/3746] lr: 3.192e-02, eta: 2 days, 0:16:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6131, loss_cls: 3.6314, loss: 3.6314 +2024-07-19 16:19:15,368 - pyskl - INFO - Epoch [93][2600/3746] lr: 3.189e-02, eta: 2 days, 0:15:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6206, loss_cls: 3.6679, loss: 3.6679 +2024-07-19 16:20:37,144 - pyskl - INFO - Epoch [93][2700/3746] lr: 3.187e-02, eta: 2 days, 0:14:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6225, loss_cls: 3.6444, loss: 3.6444 +2024-07-19 16:21:58,563 - pyskl - INFO - Epoch [93][2800/3746] lr: 3.184e-02, eta: 2 days, 0:12:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6128, loss_cls: 3.6745, loss: 3.6745 +2024-07-19 16:23:20,255 - pyskl - INFO - Epoch [93][2900/3746] lr: 3.181e-02, eta: 2 days, 0:11:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6100, loss_cls: 3.6733, loss: 3.6733 +2024-07-19 16:24:41,878 - pyskl - INFO - Epoch [93][3000/3746] lr: 3.179e-02, eta: 2 days, 0:10:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6200, loss_cls: 3.6625, loss: 3.6625 +2024-07-19 16:26:03,546 - pyskl - INFO - Epoch [93][3100/3746] lr: 3.176e-02, eta: 2 days, 0:08:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6158, loss_cls: 3.6209, loss: 3.6209 +2024-07-19 16:27:25,179 - pyskl - INFO - Epoch [93][3200/3746] lr: 3.174e-02, eta: 2 days, 0:07:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6244, loss_cls: 3.6320, loss: 3.6320 +2024-07-19 16:28:47,606 - pyskl - INFO - Epoch [93][3300/3746] lr: 3.171e-02, eta: 2 days, 0:06:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6142, loss_cls: 3.6760, loss: 3.6760 +2024-07-19 16:30:09,505 - pyskl - INFO - Epoch [93][3400/3746] lr: 3.168e-02, eta: 2 days, 0:04:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6238, loss_cls: 3.6459, loss: 3.6459 +2024-07-19 16:31:31,414 - pyskl - INFO - Epoch [93][3500/3746] lr: 3.166e-02, eta: 2 days, 0:03:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6230, loss_cls: 3.6223, loss: 3.6223 +2024-07-19 16:32:53,596 - pyskl - INFO - Epoch [93][3600/3746] lr: 3.163e-02, eta: 2 days, 0:02:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6117, loss_cls: 3.6945, loss: 3.6945 +2024-07-19 16:34:15,400 - pyskl - INFO - Epoch [93][3700/3746] lr: 3.161e-02, eta: 2 days, 0:00:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6198, loss_cls: 3.6465, loss: 3.6465 +2024-07-19 16:34:55,166 - pyskl - INFO - Saving checkpoint at 93 epochs +2024-07-19 16:36:45,724 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 16:36:46,458 - pyskl - INFO - +top1_acc 0.2941 +top5_acc 0.5532 +2024-07-19 16:36:46,458 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 16:36:46,495 - pyskl - INFO - +mean_acc 0.2939 +2024-07-19 16:36:46,506 - pyskl - INFO - Epoch(val) [93][309] top1_acc: 0.2941, top5_acc: 0.5532, mean_class_accuracy: 0.2939 +2024-07-19 16:40:34,775 - pyskl - INFO - Epoch [94][100/3746] lr: 3.157e-02, eta: 2 days, 0:00:03, time: 2.283, data_time: 1.303, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6339, loss_cls: 3.5699, loss: 3.5699 +2024-07-19 16:41:57,431 - pyskl - INFO - Epoch [94][200/3746] lr: 3.154e-02, eta: 1 day, 23:58:43, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6372, loss_cls: 3.5342, loss: 3.5342 +2024-07-19 16:43:19,862 - pyskl - INFO - Epoch [94][300/3746] lr: 3.152e-02, eta: 1 day, 23:57:23, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6323, loss_cls: 3.5699, loss: 3.5699 +2024-07-19 16:44:41,407 - pyskl - INFO - Epoch [94][400/3746] lr: 3.149e-02, eta: 1 day, 23:56:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6248, loss_cls: 3.5626, loss: 3.5626 +2024-07-19 16:46:03,660 - pyskl - INFO - Epoch [94][500/3746] lr: 3.146e-02, eta: 1 day, 23:54:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6247, loss_cls: 3.6149, loss: 3.6149 +2024-07-19 16:47:25,740 - pyskl - INFO - Epoch [94][600/3746] lr: 3.144e-02, eta: 1 day, 23:53:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6253, loss_cls: 3.6260, loss: 3.6260 +2024-07-19 16:48:47,459 - pyskl - INFO - Epoch [94][700/3746] lr: 3.141e-02, eta: 1 day, 23:52:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6236, loss_cls: 3.6040, loss: 3.6040 +2024-07-19 16:50:08,870 - pyskl - INFO - Epoch [94][800/3746] lr: 3.139e-02, eta: 1 day, 23:50:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6183, loss_cls: 3.6311, loss: 3.6311 +2024-07-19 16:51:30,969 - pyskl - INFO - Epoch [94][900/3746] lr: 3.136e-02, eta: 1 day, 23:49:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6223, loss_cls: 3.5976, loss: 3.5976 +2024-07-19 16:52:52,856 - pyskl - INFO - Epoch [94][1000/3746] lr: 3.133e-02, eta: 1 day, 23:48:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6330, loss_cls: 3.5739, loss: 3.5739 +2024-07-19 16:54:14,407 - pyskl - INFO - Epoch [94][1100/3746] lr: 3.131e-02, eta: 1 day, 23:46:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6239, loss_cls: 3.6021, loss: 3.6021 +2024-07-19 16:55:36,304 - pyskl - INFO - Epoch [94][1200/3746] lr: 3.128e-02, eta: 1 day, 23:45:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6211, loss_cls: 3.6559, loss: 3.6559 +2024-07-19 16:56:58,025 - pyskl - INFO - Epoch [94][1300/3746] lr: 3.126e-02, eta: 1 day, 23:43:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6228, loss_cls: 3.6434, loss: 3.6434 +2024-07-19 16:58:20,854 - pyskl - INFO - Epoch [94][1400/3746] lr: 3.123e-02, eta: 1 day, 23:42:39, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6228, loss_cls: 3.6177, loss: 3.6177 +2024-07-19 16:59:42,569 - pyskl - INFO - Epoch [94][1500/3746] lr: 3.120e-02, eta: 1 day, 23:41:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6133, loss_cls: 3.6485, loss: 3.6485 +2024-07-19 17:01:04,129 - pyskl - INFO - Epoch [94][1600/3746] lr: 3.118e-02, eta: 1 day, 23:39:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6214, loss_cls: 3.6462, loss: 3.6462 +2024-07-19 17:02:26,491 - pyskl - INFO - Epoch [94][1700/3746] lr: 3.115e-02, eta: 1 day, 23:38:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6150, loss_cls: 3.6969, loss: 3.6969 +2024-07-19 17:03:48,385 - pyskl - INFO - Epoch [94][1800/3746] lr: 3.113e-02, eta: 1 day, 23:37:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6122, loss_cls: 3.6681, loss: 3.6681 +2024-07-19 17:05:09,910 - pyskl - INFO - Epoch [94][1900/3746] lr: 3.110e-02, eta: 1 day, 23:35:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3594, top5_acc: 0.6255, loss_cls: 3.6009, loss: 3.6009 +2024-07-19 17:06:31,731 - pyskl - INFO - Epoch [94][2000/3746] lr: 3.108e-02, eta: 1 day, 23:34:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6167, loss_cls: 3.6502, loss: 3.6502 +2024-07-19 17:07:53,318 - pyskl - INFO - Epoch [94][2100/3746] lr: 3.105e-02, eta: 1 day, 23:33:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6205, loss_cls: 3.6045, loss: 3.6045 +2024-07-19 17:09:15,086 - pyskl - INFO - Epoch [94][2200/3746] lr: 3.102e-02, eta: 1 day, 23:31:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6070, loss_cls: 3.6662, loss: 3.6662 +2024-07-19 17:10:36,556 - pyskl - INFO - Epoch [94][2300/3746] lr: 3.100e-02, eta: 1 day, 23:30:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6152, loss_cls: 3.6594, loss: 3.6594 +2024-07-19 17:11:58,486 - pyskl - INFO - Epoch [94][2400/3746] lr: 3.097e-02, eta: 1 day, 23:29:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6234, loss_cls: 3.5924, loss: 3.5924 +2024-07-19 17:13:20,747 - pyskl - INFO - Epoch [94][2500/3746] lr: 3.095e-02, eta: 1 day, 23:27:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6198, loss_cls: 3.6379, loss: 3.6379 +2024-07-19 17:14:42,476 - pyskl - INFO - Epoch [94][2600/3746] lr: 3.092e-02, eta: 1 day, 23:26:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6205, loss_cls: 3.6379, loss: 3.6379 +2024-07-19 17:16:04,427 - pyskl - INFO - Epoch [94][2700/3746] lr: 3.089e-02, eta: 1 day, 23:25:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6327, loss_cls: 3.6055, loss: 3.6055 +2024-07-19 17:17:25,681 - pyskl - INFO - Epoch [94][2800/3746] lr: 3.087e-02, eta: 1 day, 23:23:52, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6244, loss_cls: 3.6124, loss: 3.6124 +2024-07-19 17:18:48,013 - pyskl - INFO - Epoch [94][2900/3746] lr: 3.084e-02, eta: 1 day, 23:22:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6250, loss_cls: 3.6169, loss: 3.6169 +2024-07-19 17:20:09,313 - pyskl - INFO - Epoch [94][3000/3746] lr: 3.082e-02, eta: 1 day, 23:21:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6125, loss_cls: 3.6763, loss: 3.6763 +2024-07-19 17:21:30,814 - pyskl - INFO - Epoch [94][3100/3746] lr: 3.079e-02, eta: 1 day, 23:19:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6164, loss_cls: 3.6533, loss: 3.6533 +2024-07-19 17:22:53,157 - pyskl - INFO - Epoch [94][3200/3746] lr: 3.077e-02, eta: 1 day, 23:18:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6166, loss_cls: 3.6344, loss: 3.6344 +2024-07-19 17:24:15,134 - pyskl - INFO - Epoch [94][3300/3746] lr: 3.074e-02, eta: 1 day, 23:17:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6236, loss_cls: 3.6257, loss: 3.6257 +2024-07-19 17:25:37,118 - pyskl - INFO - Epoch [94][3400/3746] lr: 3.071e-02, eta: 1 day, 23:15:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6095, loss_cls: 3.6807, loss: 3.6807 +2024-07-19 17:26:59,682 - pyskl - INFO - Epoch [94][3500/3746] lr: 3.069e-02, eta: 1 day, 23:14:29, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6112, loss_cls: 3.6921, loss: 3.6921 +2024-07-19 17:28:21,275 - pyskl - INFO - Epoch [94][3600/3746] lr: 3.066e-02, eta: 1 day, 23:13:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6314, loss_cls: 3.5910, loss: 3.5910 +2024-07-19 17:29:42,836 - pyskl - INFO - Epoch [94][3700/3746] lr: 3.064e-02, eta: 1 day, 23:11:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6189, loss_cls: 3.6262, loss: 3.6262 +2024-07-19 17:30:22,393 - pyskl - INFO - Saving checkpoint at 94 epochs +2024-07-19 17:32:13,246 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 17:32:13,900 - pyskl - INFO - +top1_acc 0.2981 +top5_acc 0.5581 +2024-07-19 17:32:13,900 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 17:32:13,939 - pyskl - INFO - +mean_acc 0.2978 +2024-07-19 17:32:13,944 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_88.pth was removed +2024-07-19 17:32:14,200 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_94.pth. +2024-07-19 17:32:14,201 - pyskl - INFO - Best top1_acc is 0.2981 at 94 epoch. +2024-07-19 17:32:14,212 - pyskl - INFO - Epoch(val) [94][309] top1_acc: 0.2981, top5_acc: 0.5581, mean_class_accuracy: 0.2978 +2024-07-19 17:35:57,384 - pyskl - INFO - Epoch [95][100/3746] lr: 3.060e-02, eta: 1 day, 23:10:52, time: 2.232, data_time: 1.254, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6273, loss_cls: 3.5469, loss: 3.5469 +2024-07-19 17:37:19,130 - pyskl - INFO - Epoch [95][200/3746] lr: 3.057e-02, eta: 1 day, 23:09:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6228, loss_cls: 3.5757, loss: 3.5757 +2024-07-19 17:38:42,021 - pyskl - INFO - Epoch [95][300/3746] lr: 3.055e-02, eta: 1 day, 23:08:12, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6202, loss_cls: 3.6215, loss: 3.6215 +2024-07-19 17:40:03,880 - pyskl - INFO - Epoch [95][400/3746] lr: 3.052e-02, eta: 1 day, 23:06:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6289, loss_cls: 3.5796, loss: 3.5796 +2024-07-19 17:41:26,424 - pyskl - INFO - Epoch [95][500/3746] lr: 3.050e-02, eta: 1 day, 23:05:31, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6253, loss_cls: 3.5896, loss: 3.5896 +2024-07-19 17:42:48,014 - pyskl - INFO - Epoch [95][600/3746] lr: 3.047e-02, eta: 1 day, 23:04:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6175, loss_cls: 3.6272, loss: 3.6272 +2024-07-19 17:44:09,291 - pyskl - INFO - Epoch [95][700/3746] lr: 3.044e-02, eta: 1 day, 23:02:50, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6317, loss_cls: 3.5771, loss: 3.5771 +2024-07-19 17:45:30,954 - pyskl - INFO - Epoch [95][800/3746] lr: 3.042e-02, eta: 1 day, 23:01:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6252, loss_cls: 3.5937, loss: 3.5937 +2024-07-19 17:46:52,397 - pyskl - INFO - Epoch [95][900/3746] lr: 3.039e-02, eta: 1 day, 23:00:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6264, loss_cls: 3.6077, loss: 3.6077 +2024-07-19 17:48:13,574 - pyskl - INFO - Epoch [95][1000/3746] lr: 3.037e-02, eta: 1 day, 22:58:47, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6289, loss_cls: 3.6041, loss: 3.6041 +2024-07-19 17:49:34,941 - pyskl - INFO - Epoch [95][1100/3746] lr: 3.034e-02, eta: 1 day, 22:57:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6130, loss_cls: 3.6483, loss: 3.6483 +2024-07-19 17:50:56,401 - pyskl - INFO - Epoch [95][1200/3746] lr: 3.032e-02, eta: 1 day, 22:56:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6236, loss_cls: 3.6154, loss: 3.6154 +2024-07-19 17:52:18,188 - pyskl - INFO - Epoch [95][1300/3746] lr: 3.029e-02, eta: 1 day, 22:54:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6300, loss_cls: 3.5510, loss: 3.5510 +2024-07-19 17:53:39,675 - pyskl - INFO - Epoch [95][1400/3746] lr: 3.026e-02, eta: 1 day, 22:53:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6236, loss_cls: 3.6312, loss: 3.6312 +2024-07-19 17:55:01,622 - pyskl - INFO - Epoch [95][1500/3746] lr: 3.024e-02, eta: 1 day, 22:52:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6198, loss_cls: 3.5656, loss: 3.5656 +2024-07-19 17:56:23,232 - pyskl - INFO - Epoch [95][1600/3746] lr: 3.021e-02, eta: 1 day, 22:50:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6177, loss_cls: 3.6318, loss: 3.6318 +2024-07-19 17:57:45,316 - pyskl - INFO - Epoch [95][1700/3746] lr: 3.019e-02, eta: 1 day, 22:49:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6247, loss_cls: 3.6064, loss: 3.6064 +2024-07-19 17:59:07,425 - pyskl - INFO - Epoch [95][1800/3746] lr: 3.016e-02, eta: 1 day, 22:48:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6245, loss_cls: 3.5986, loss: 3.5986 +2024-07-19 18:00:29,262 - pyskl - INFO - Epoch [95][1900/3746] lr: 3.014e-02, eta: 1 day, 22:46:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6216, loss_cls: 3.5771, loss: 3.5771 +2024-07-19 18:01:51,004 - pyskl - INFO - Epoch [95][2000/3746] lr: 3.011e-02, eta: 1 day, 22:45:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6223, loss_cls: 3.6253, loss: 3.6253 +2024-07-19 18:03:12,802 - pyskl - INFO - Epoch [95][2100/3746] lr: 3.008e-02, eta: 1 day, 22:44:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6145, loss_cls: 3.6522, loss: 3.6522 +2024-07-19 18:04:34,254 - pyskl - INFO - Epoch [95][2200/3746] lr: 3.006e-02, eta: 1 day, 22:42:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6170, loss_cls: 3.6084, loss: 3.6084 +2024-07-19 18:05:56,092 - pyskl - INFO - Epoch [95][2300/3746] lr: 3.003e-02, eta: 1 day, 22:41:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6294, loss_cls: 3.5943, loss: 3.5943 +2024-07-19 18:07:18,119 - pyskl - INFO - Epoch [95][2400/3746] lr: 3.001e-02, eta: 1 day, 22:39:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6209, loss_cls: 3.6423, loss: 3.6423 +2024-07-19 18:08:39,820 - pyskl - INFO - Epoch [95][2500/3746] lr: 2.998e-02, eta: 1 day, 22:38:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6286, loss_cls: 3.5879, loss: 3.5879 +2024-07-19 18:10:01,434 - pyskl - INFO - Epoch [95][2600/3746] lr: 2.996e-02, eta: 1 day, 22:37:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6239, loss_cls: 3.6133, loss: 3.6133 +2024-07-19 18:11:23,378 - pyskl - INFO - Epoch [95][2700/3746] lr: 2.993e-02, eta: 1 day, 22:35:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6220, loss_cls: 3.6293, loss: 3.6293 +2024-07-19 18:12:45,349 - pyskl - INFO - Epoch [95][2800/3746] lr: 2.991e-02, eta: 1 day, 22:34:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6116, loss_cls: 3.6837, loss: 3.6837 +2024-07-19 18:14:07,258 - pyskl - INFO - Epoch [95][2900/3746] lr: 2.988e-02, eta: 1 day, 22:33:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6253, loss_cls: 3.6018, loss: 3.6018 +2024-07-19 18:15:28,766 - pyskl - INFO - Epoch [95][3000/3746] lr: 2.985e-02, eta: 1 day, 22:31:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6256, loss_cls: 3.6046, loss: 3.6046 +2024-07-19 18:16:51,327 - pyskl - INFO - Epoch [95][3100/3746] lr: 2.983e-02, eta: 1 day, 22:30:36, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6206, loss_cls: 3.6118, loss: 3.6118 +2024-07-19 18:18:13,410 - pyskl - INFO - Epoch [95][3200/3746] lr: 2.980e-02, eta: 1 day, 22:29:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6266, loss_cls: 3.5981, loss: 3.5981 +2024-07-19 18:19:35,487 - pyskl - INFO - Epoch [95][3300/3746] lr: 2.978e-02, eta: 1 day, 22:27:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3720, top5_acc: 0.6308, loss_cls: 3.5847, loss: 3.5847 +2024-07-19 18:20:57,298 - pyskl - INFO - Epoch [95][3400/3746] lr: 2.975e-02, eta: 1 day, 22:26:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6164, loss_cls: 3.6711, loss: 3.6711 +2024-07-19 18:22:19,325 - pyskl - INFO - Epoch [95][3500/3746] lr: 2.973e-02, eta: 1 day, 22:25:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6159, loss_cls: 3.6013, loss: 3.6013 +2024-07-19 18:23:41,229 - pyskl - INFO - Epoch [95][3600/3746] lr: 2.970e-02, eta: 1 day, 22:23:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3691, top5_acc: 0.6239, loss_cls: 3.5961, loss: 3.5961 +2024-07-19 18:25:02,930 - pyskl - INFO - Epoch [95][3700/3746] lr: 2.968e-02, eta: 1 day, 22:22:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6177, loss_cls: 3.6306, loss: 3.6306 +2024-07-19 18:25:42,770 - pyskl - INFO - Saving checkpoint at 95 epochs +2024-07-19 18:27:33,848 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 18:27:34,539 - pyskl - INFO - +top1_acc 0.2918 +top5_acc 0.5438 +2024-07-19 18:27:34,540 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 18:27:34,578 - pyskl - INFO - +mean_acc 0.2915 +2024-07-19 18:27:34,590 - pyskl - INFO - Epoch(val) [95][309] top1_acc: 0.2918, top5_acc: 0.5438, mean_class_accuracy: 0.2915 +2024-07-19 18:31:23,181 - pyskl - INFO - Epoch [96][100/3746] lr: 2.964e-02, eta: 1 day, 22:21:39, time: 2.286, data_time: 1.300, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6316, loss_cls: 3.5721, loss: 3.5721 +2024-07-19 18:32:44,807 - pyskl - INFO - Epoch [96][200/3746] lr: 2.961e-02, eta: 1 day, 22:20:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6381, loss_cls: 3.5396, loss: 3.5396 +2024-07-19 18:34:06,329 - pyskl - INFO - Epoch [96][300/3746] lr: 2.959e-02, eta: 1 day, 22:18:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3844, top5_acc: 0.6347, loss_cls: 3.5115, loss: 3.5115 +2024-07-19 18:35:27,809 - pyskl - INFO - Epoch [96][400/3746] lr: 2.956e-02, eta: 1 day, 22:17:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6411, loss_cls: 3.5156, loss: 3.5156 +2024-07-19 18:36:49,465 - pyskl - INFO - Epoch [96][500/3746] lr: 2.954e-02, eta: 1 day, 22:16:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6308, loss_cls: 3.5654, loss: 3.5654 +2024-07-19 18:38:11,869 - pyskl - INFO - Epoch [96][600/3746] lr: 2.951e-02, eta: 1 day, 22:14:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6159, loss_cls: 3.6236, loss: 3.6236 +2024-07-19 18:39:33,517 - pyskl - INFO - Epoch [96][700/3746] lr: 2.948e-02, eta: 1 day, 22:13:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6212, loss_cls: 3.6020, loss: 3.6020 +2024-07-19 18:40:55,400 - pyskl - INFO - Epoch [96][800/3746] lr: 2.946e-02, eta: 1 day, 22:12:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6216, loss_cls: 3.5815, loss: 3.5815 +2024-07-19 18:42:16,607 - pyskl - INFO - Epoch [96][900/3746] lr: 2.943e-02, eta: 1 day, 22:10:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6292, loss_cls: 3.5843, loss: 3.5843 +2024-07-19 18:43:38,205 - pyskl - INFO - Epoch [96][1000/3746] lr: 2.941e-02, eta: 1 day, 22:09:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6245, loss_cls: 3.6307, loss: 3.6307 +2024-07-19 18:44:59,600 - pyskl - INFO - Epoch [96][1100/3746] lr: 2.938e-02, eta: 1 day, 22:08:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6364, loss_cls: 3.5395, loss: 3.5395 +2024-07-19 18:46:20,900 - pyskl - INFO - Epoch [96][1200/3746] lr: 2.936e-02, eta: 1 day, 22:06:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6358, loss_cls: 3.5700, loss: 3.5700 +2024-07-19 18:47:42,547 - pyskl - INFO - Epoch [96][1300/3746] lr: 2.933e-02, eta: 1 day, 22:05:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6286, loss_cls: 3.5878, loss: 3.5878 +2024-07-19 18:49:04,772 - pyskl - INFO - Epoch [96][1400/3746] lr: 2.931e-02, eta: 1 day, 22:04:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6298, loss_cls: 3.5779, loss: 3.5779 +2024-07-19 18:50:26,504 - pyskl - INFO - Epoch [96][1500/3746] lr: 2.928e-02, eta: 1 day, 22:02:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6134, loss_cls: 3.6379, loss: 3.6379 +2024-07-19 18:51:48,719 - pyskl - INFO - Epoch [96][1600/3746] lr: 2.926e-02, eta: 1 day, 22:01:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6250, loss_cls: 3.5684, loss: 3.5684 +2024-07-19 18:53:10,121 - pyskl - INFO - Epoch [96][1700/3746] lr: 2.923e-02, eta: 1 day, 22:00:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6353, loss_cls: 3.5357, loss: 3.5357 +2024-07-19 18:54:32,066 - pyskl - INFO - Epoch [96][1800/3746] lr: 2.920e-02, eta: 1 day, 21:58:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6109, loss_cls: 3.6641, loss: 3.6641 +2024-07-19 18:55:53,715 - pyskl - INFO - Epoch [96][1900/3746] lr: 2.918e-02, eta: 1 day, 21:57:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6269, loss_cls: 3.5976, loss: 3.5976 +2024-07-19 18:57:15,039 - pyskl - INFO - Epoch [96][2000/3746] lr: 2.915e-02, eta: 1 day, 21:56:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6283, loss_cls: 3.5838, loss: 3.5838 +2024-07-19 18:58:36,177 - pyskl - INFO - Epoch [96][2100/3746] lr: 2.913e-02, eta: 1 day, 21:54:44, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6170, loss_cls: 3.6353, loss: 3.6353 +2024-07-19 18:59:58,187 - pyskl - INFO - Epoch [96][2200/3746] lr: 2.910e-02, eta: 1 day, 21:53:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6336, loss_cls: 3.5496, loss: 3.5496 +2024-07-19 19:01:19,975 - pyskl - INFO - Epoch [96][2300/3746] lr: 2.908e-02, eta: 1 day, 21:52:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6184, loss_cls: 3.6685, loss: 3.6685 +2024-07-19 19:02:41,791 - pyskl - INFO - Epoch [96][2400/3746] lr: 2.905e-02, eta: 1 day, 21:50:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6342, loss_cls: 3.5908, loss: 3.5908 +2024-07-19 19:04:03,789 - pyskl - INFO - Epoch [96][2500/3746] lr: 2.903e-02, eta: 1 day, 21:49:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3691, top5_acc: 0.6238, loss_cls: 3.6149, loss: 3.6149 +2024-07-19 19:05:25,451 - pyskl - INFO - Epoch [96][2600/3746] lr: 2.900e-02, eta: 1 day, 21:48:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6283, loss_cls: 3.5664, loss: 3.5664 +2024-07-19 19:06:47,713 - pyskl - INFO - Epoch [96][2700/3746] lr: 2.898e-02, eta: 1 day, 21:46:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6223, loss_cls: 3.5993, loss: 3.5993 +2024-07-19 19:08:09,039 - pyskl - INFO - Epoch [96][2800/3746] lr: 2.895e-02, eta: 1 day, 21:45:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6281, loss_cls: 3.5781, loss: 3.5781 +2024-07-19 19:09:30,810 - pyskl - INFO - Epoch [96][2900/3746] lr: 2.893e-02, eta: 1 day, 21:44:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6247, loss_cls: 3.6087, loss: 3.6087 +2024-07-19 19:10:53,377 - pyskl - INFO - Epoch [96][3000/3746] lr: 2.890e-02, eta: 1 day, 21:42:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6192, loss_cls: 3.6255, loss: 3.6255 +2024-07-19 19:12:15,679 - pyskl - INFO - Epoch [96][3100/3746] lr: 2.887e-02, eta: 1 day, 21:41:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6292, loss_cls: 3.5825, loss: 3.5825 +2024-07-19 19:13:37,571 - pyskl - INFO - Epoch [96][3200/3746] lr: 2.885e-02, eta: 1 day, 21:39:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6152, loss_cls: 3.6389, loss: 3.6389 +2024-07-19 19:14:59,643 - pyskl - INFO - Epoch [96][3300/3746] lr: 2.882e-02, eta: 1 day, 21:38:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6223, loss_cls: 3.6150, loss: 3.6150 +2024-07-19 19:16:21,017 - pyskl - INFO - Epoch [96][3400/3746] lr: 2.880e-02, eta: 1 day, 21:37:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6175, loss_cls: 3.6139, loss: 3.6139 +2024-07-19 19:17:43,696 - pyskl - INFO - Epoch [96][3500/3746] lr: 2.877e-02, eta: 1 day, 21:35:57, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3588, top5_acc: 0.6219, loss_cls: 3.6278, loss: 3.6278 +2024-07-19 19:19:06,198 - pyskl - INFO - Epoch [96][3600/3746] lr: 2.875e-02, eta: 1 day, 21:34:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6195, loss_cls: 3.6166, loss: 3.6166 +2024-07-19 19:20:28,290 - pyskl - INFO - Epoch [96][3700/3746] lr: 2.872e-02, eta: 1 day, 21:33:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6125, loss_cls: 3.6597, loss: 3.6597 +2024-07-19 19:21:07,917 - pyskl - INFO - Saving checkpoint at 96 epochs +2024-07-19 19:22:59,750 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 19:23:00,412 - pyskl - INFO - +top1_acc 0.3087 +top5_acc 0.5651 +2024-07-19 19:23:00,413 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 19:23:00,452 - pyskl - INFO - +mean_acc 0.3085 +2024-07-19 19:23:00,456 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_94.pth was removed +2024-07-19 19:23:00,713 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_96.pth. +2024-07-19 19:23:00,714 - pyskl - INFO - Best top1_acc is 0.3087 at 96 epoch. +2024-07-19 19:23:00,725 - pyskl - INFO - Epoch(val) [96][309] top1_acc: 0.3087, top5_acc: 0.5651, mean_class_accuracy: 0.3085 +2024-07-19 19:26:48,065 - pyskl - INFO - Epoch [97][100/3746] lr: 2.869e-02, eta: 1 day, 21:32:19, time: 2.273, data_time: 1.288, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6438, loss_cls: 3.5047, loss: 3.5047 +2024-07-19 19:28:10,210 - pyskl - INFO - Epoch [97][200/3746] lr: 2.866e-02, eta: 1 day, 21:30:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6452, loss_cls: 3.5239, loss: 3.5239 +2024-07-19 19:29:31,795 - pyskl - INFO - Epoch [97][300/3746] lr: 2.864e-02, eta: 1 day, 21:29:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6275, loss_cls: 3.5980, loss: 3.5980 +2024-07-19 19:30:54,159 - pyskl - INFO - Epoch [97][400/3746] lr: 2.861e-02, eta: 1 day, 21:28:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6261, loss_cls: 3.5746, loss: 3.5746 +2024-07-19 19:32:16,017 - pyskl - INFO - Epoch [97][500/3746] lr: 2.858e-02, eta: 1 day, 21:26:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6339, loss_cls: 3.5535, loss: 3.5535 +2024-07-19 19:33:37,380 - pyskl - INFO - Epoch [97][600/3746] lr: 2.856e-02, eta: 1 day, 21:25:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6350, loss_cls: 3.5293, loss: 3.5293 +2024-07-19 19:34:59,318 - pyskl - INFO - Epoch [97][700/3746] lr: 2.853e-02, eta: 1 day, 21:24:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6366, loss_cls: 3.5427, loss: 3.5427 +2024-07-19 19:36:21,440 - pyskl - INFO - Epoch [97][800/3746] lr: 2.851e-02, eta: 1 day, 21:22:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6288, loss_cls: 3.5650, loss: 3.5650 +2024-07-19 19:37:42,737 - pyskl - INFO - Epoch [97][900/3746] lr: 2.848e-02, eta: 1 day, 21:21:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6419, loss_cls: 3.5103, loss: 3.5103 +2024-07-19 19:39:05,109 - pyskl - INFO - Epoch [97][1000/3746] lr: 2.846e-02, eta: 1 day, 21:20:13, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6270, loss_cls: 3.5852, loss: 3.5852 +2024-07-19 19:40:26,856 - pyskl - INFO - Epoch [97][1100/3746] lr: 2.843e-02, eta: 1 day, 21:18:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6192, loss_cls: 3.5837, loss: 3.5837 +2024-07-19 19:41:48,548 - pyskl - INFO - Epoch [97][1200/3746] lr: 2.841e-02, eta: 1 day, 21:17:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6244, loss_cls: 3.6020, loss: 3.6020 +2024-07-19 19:43:10,216 - pyskl - INFO - Epoch [97][1300/3746] lr: 2.838e-02, eta: 1 day, 21:16:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6192, loss_cls: 3.6398, loss: 3.6398 +2024-07-19 19:44:32,897 - pyskl - INFO - Epoch [97][1400/3746] lr: 2.836e-02, eta: 1 day, 21:14:51, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6195, loss_cls: 3.6190, loss: 3.6190 +2024-07-19 19:45:54,488 - pyskl - INFO - Epoch [97][1500/3746] lr: 2.833e-02, eta: 1 day, 21:13:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6245, loss_cls: 3.6074, loss: 3.6074 +2024-07-19 19:47:16,606 - pyskl - INFO - Epoch [97][1600/3746] lr: 2.831e-02, eta: 1 day, 21:12:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6309, loss_cls: 3.5748, loss: 3.5748 +2024-07-19 19:48:38,716 - pyskl - INFO - Epoch [97][1700/3746] lr: 2.828e-02, eta: 1 day, 21:10:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6211, loss_cls: 3.5988, loss: 3.5988 +2024-07-19 19:50:00,515 - pyskl - INFO - Epoch [97][1800/3746] lr: 2.826e-02, eta: 1 day, 21:09:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6328, loss_cls: 3.5840, loss: 3.5840 +2024-07-19 19:51:22,334 - pyskl - INFO - Epoch [97][1900/3746] lr: 2.823e-02, eta: 1 day, 21:08:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6247, loss_cls: 3.6167, loss: 3.6167 +2024-07-19 19:52:43,903 - pyskl - INFO - Epoch [97][2000/3746] lr: 2.821e-02, eta: 1 day, 21:06:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6327, loss_cls: 3.5710, loss: 3.5710 +2024-07-19 19:54:05,441 - pyskl - INFO - Epoch [97][2100/3746] lr: 2.818e-02, eta: 1 day, 21:05:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6297, loss_cls: 3.5568, loss: 3.5568 +2024-07-19 19:55:27,211 - pyskl - INFO - Epoch [97][2200/3746] lr: 2.816e-02, eta: 1 day, 21:04:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6245, loss_cls: 3.5745, loss: 3.5745 +2024-07-19 19:56:49,331 - pyskl - INFO - Epoch [97][2300/3746] lr: 2.813e-02, eta: 1 day, 21:02:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6250, loss_cls: 3.5989, loss: 3.5989 +2024-07-19 19:58:10,924 - pyskl - INFO - Epoch [97][2400/3746] lr: 2.811e-02, eta: 1 day, 21:01:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6228, loss_cls: 3.5999, loss: 3.5999 +2024-07-19 19:59:32,312 - pyskl - INFO - Epoch [97][2500/3746] lr: 2.808e-02, eta: 1 day, 21:00:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6333, loss_cls: 3.5616, loss: 3.5616 +2024-07-19 20:00:53,879 - pyskl - INFO - Epoch [97][2600/3746] lr: 2.806e-02, eta: 1 day, 20:58:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6309, loss_cls: 3.5558, loss: 3.5558 +2024-07-19 20:02:15,368 - pyskl - INFO - Epoch [97][2700/3746] lr: 2.803e-02, eta: 1 day, 20:57:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6298, loss_cls: 3.5659, loss: 3.5659 +2024-07-19 20:03:37,182 - pyskl - INFO - Epoch [97][2800/3746] lr: 2.801e-02, eta: 1 day, 20:56:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6330, loss_cls: 3.5599, loss: 3.5599 +2024-07-19 20:04:59,121 - pyskl - INFO - Epoch [97][2900/3746] lr: 2.798e-02, eta: 1 day, 20:54:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6352, loss_cls: 3.5571, loss: 3.5571 +2024-07-19 20:06:21,576 - pyskl - INFO - Epoch [97][3000/3746] lr: 2.796e-02, eta: 1 day, 20:53:20, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6288, loss_cls: 3.5713, loss: 3.5713 +2024-07-19 20:07:43,548 - pyskl - INFO - Epoch [97][3100/3746] lr: 2.793e-02, eta: 1 day, 20:51:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6295, loss_cls: 3.6001, loss: 3.6001 +2024-07-19 20:09:05,494 - pyskl - INFO - Epoch [97][3200/3746] lr: 2.791e-02, eta: 1 day, 20:50:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6158, loss_cls: 3.6414, loss: 3.6414 +2024-07-19 20:10:27,438 - pyskl - INFO - Epoch [97][3300/3746] lr: 2.788e-02, eta: 1 day, 20:49:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6278, loss_cls: 3.5771, loss: 3.5771 +2024-07-19 20:11:48,886 - pyskl - INFO - Epoch [97][3400/3746] lr: 2.786e-02, eta: 1 day, 20:47:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6222, loss_cls: 3.5728, loss: 3.5728 +2024-07-19 20:13:11,315 - pyskl - INFO - Epoch [97][3500/3746] lr: 2.783e-02, eta: 1 day, 20:46:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6300, loss_cls: 3.5813, loss: 3.5813 +2024-07-19 20:14:33,009 - pyskl - INFO - Epoch [97][3600/3746] lr: 2.781e-02, eta: 1 day, 20:45:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3748, top5_acc: 0.6328, loss_cls: 3.5680, loss: 3.5680 +2024-07-19 20:15:54,961 - pyskl - INFO - Epoch [97][3700/3746] lr: 2.778e-02, eta: 1 day, 20:43:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6216, loss_cls: 3.6033, loss: 3.6033 +2024-07-19 20:16:34,430 - pyskl - INFO - Saving checkpoint at 97 epochs +2024-07-19 20:18:26,213 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 20:18:26,869 - pyskl - INFO - +top1_acc 0.2897 +top5_acc 0.5507 +2024-07-19 20:18:26,869 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 20:18:26,908 - pyskl - INFO - +mean_acc 0.2895 +2024-07-19 20:18:26,919 - pyskl - INFO - Epoch(val) [97][309] top1_acc: 0.2897, top5_acc: 0.5507, mean_class_accuracy: 0.2895 +2024-07-19 20:22:15,879 - pyskl - INFO - Epoch [98][100/3746] lr: 2.774e-02, eta: 1 day, 20:42:57, time: 2.290, data_time: 1.280, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6484, loss_cls: 3.4726, loss: 3.4726 +2024-07-19 20:23:38,196 - pyskl - INFO - Epoch [98][200/3746] lr: 2.772e-02, eta: 1 day, 20:41:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6416, loss_cls: 3.5004, loss: 3.5004 +2024-07-19 20:25:00,305 - pyskl - INFO - Epoch [98][300/3746] lr: 2.769e-02, eta: 1 day, 20:40:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6373, loss_cls: 3.5293, loss: 3.5293 +2024-07-19 20:26:21,827 - pyskl - INFO - Epoch [98][400/3746] lr: 2.767e-02, eta: 1 day, 20:38:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6367, loss_cls: 3.5314, loss: 3.5314 +2024-07-19 20:27:43,812 - pyskl - INFO - Epoch [98][500/3746] lr: 2.764e-02, eta: 1 day, 20:37:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6241, loss_cls: 3.5894, loss: 3.5894 +2024-07-19 20:29:05,590 - pyskl - INFO - Epoch [98][600/3746] lr: 2.762e-02, eta: 1 day, 20:36:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6333, loss_cls: 3.5565, loss: 3.5565 +2024-07-19 20:30:27,424 - pyskl - INFO - Epoch [98][700/3746] lr: 2.759e-02, eta: 1 day, 20:34:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6391, loss_cls: 3.5153, loss: 3.5153 +2024-07-19 20:31:48,890 - pyskl - INFO - Epoch [98][800/3746] lr: 2.757e-02, eta: 1 day, 20:33:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6394, loss_cls: 3.5311, loss: 3.5311 +2024-07-19 20:33:10,331 - pyskl - INFO - Epoch [98][900/3746] lr: 2.754e-02, eta: 1 day, 20:32:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6362, loss_cls: 3.5468, loss: 3.5468 +2024-07-19 20:34:31,831 - pyskl - INFO - Epoch [98][1000/3746] lr: 2.752e-02, eta: 1 day, 20:30:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6369, loss_cls: 3.5280, loss: 3.5280 +2024-07-19 20:35:53,335 - pyskl - INFO - Epoch [98][1100/3746] lr: 2.749e-02, eta: 1 day, 20:29:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6392, loss_cls: 3.5459, loss: 3.5459 +2024-07-19 20:37:15,019 - pyskl - INFO - Epoch [98][1200/3746] lr: 2.747e-02, eta: 1 day, 20:28:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6439, loss_cls: 3.5147, loss: 3.5147 +2024-07-19 20:38:36,345 - pyskl - INFO - Epoch [98][1300/3746] lr: 2.744e-02, eta: 1 day, 20:26:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6341, loss_cls: 3.5590, loss: 3.5590 +2024-07-19 20:39:58,440 - pyskl - INFO - Epoch [98][1400/3746] lr: 2.742e-02, eta: 1 day, 20:25:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6342, loss_cls: 3.5636, loss: 3.5636 +2024-07-19 20:41:20,093 - pyskl - INFO - Epoch [98][1500/3746] lr: 2.739e-02, eta: 1 day, 20:24:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6294, loss_cls: 3.5753, loss: 3.5753 +2024-07-19 20:42:42,476 - pyskl - INFO - Epoch [98][1600/3746] lr: 2.737e-02, eta: 1 day, 20:22:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6355, loss_cls: 3.5511, loss: 3.5511 +2024-07-19 20:44:04,258 - pyskl - INFO - Epoch [98][1700/3746] lr: 2.734e-02, eta: 1 day, 20:21:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6212, loss_cls: 3.5980, loss: 3.5980 +2024-07-19 20:45:26,176 - pyskl - INFO - Epoch [98][1800/3746] lr: 2.732e-02, eta: 1 day, 20:20:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6323, loss_cls: 3.5672, loss: 3.5672 +2024-07-19 20:46:47,774 - pyskl - INFO - Epoch [98][1900/3746] lr: 2.729e-02, eta: 1 day, 20:18:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6284, loss_cls: 3.5937, loss: 3.5937 +2024-07-19 20:48:10,078 - pyskl - INFO - Epoch [98][2000/3746] lr: 2.727e-02, eta: 1 day, 20:17:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6247, loss_cls: 3.5921, loss: 3.5921 +2024-07-19 20:49:31,422 - pyskl - INFO - Epoch [98][2100/3746] lr: 2.724e-02, eta: 1 day, 20:16:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6211, loss_cls: 3.6166, loss: 3.6166 +2024-07-19 20:50:52,797 - pyskl - INFO - Epoch [98][2200/3746] lr: 2.722e-02, eta: 1 day, 20:14:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6358, loss_cls: 3.5474, loss: 3.5474 +2024-07-19 20:52:14,245 - pyskl - INFO - Epoch [98][2300/3746] lr: 2.719e-02, eta: 1 day, 20:13:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6297, loss_cls: 3.5936, loss: 3.5936 +2024-07-19 20:53:36,098 - pyskl - INFO - Epoch [98][2400/3746] lr: 2.717e-02, eta: 1 day, 20:11:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6206, loss_cls: 3.6250, loss: 3.6250 +2024-07-19 20:54:58,071 - pyskl - INFO - Epoch [98][2500/3746] lr: 2.714e-02, eta: 1 day, 20:10:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6180, loss_cls: 3.6258, loss: 3.6258 +2024-07-19 20:56:19,766 - pyskl - INFO - Epoch [98][2600/3746] lr: 2.712e-02, eta: 1 day, 20:09:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6283, loss_cls: 3.5859, loss: 3.5859 +2024-07-19 20:57:41,750 - pyskl - INFO - Epoch [98][2700/3746] lr: 2.709e-02, eta: 1 day, 20:07:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6402, loss_cls: 3.5537, loss: 3.5537 +2024-07-19 20:59:03,064 - pyskl - INFO - Epoch [98][2800/3746] lr: 2.707e-02, eta: 1 day, 20:06:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6241, loss_cls: 3.5613, loss: 3.5613 +2024-07-19 21:00:24,491 - pyskl - INFO - Epoch [98][2900/3746] lr: 2.705e-02, eta: 1 day, 20:05:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6298, loss_cls: 3.5711, loss: 3.5711 +2024-07-19 21:01:46,223 - pyskl - INFO - Epoch [98][3000/3746] lr: 2.702e-02, eta: 1 day, 20:03:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3720, top5_acc: 0.6298, loss_cls: 3.5459, loss: 3.5459 +2024-07-19 21:03:08,357 - pyskl - INFO - Epoch [98][3100/3746] lr: 2.700e-02, eta: 1 day, 20:02:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6277, loss_cls: 3.5911, loss: 3.5911 +2024-07-19 21:04:29,895 - pyskl - INFO - Epoch [98][3200/3746] lr: 2.697e-02, eta: 1 day, 20:01:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6241, loss_cls: 3.5669, loss: 3.5669 +2024-07-19 21:05:52,051 - pyskl - INFO - Epoch [98][3300/3746] lr: 2.695e-02, eta: 1 day, 19:59:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6330, loss_cls: 3.5337, loss: 3.5337 +2024-07-19 21:07:14,017 - pyskl - INFO - Epoch [98][3400/3746] lr: 2.692e-02, eta: 1 day, 19:58:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6408, loss_cls: 3.5544, loss: 3.5544 +2024-07-19 21:08:36,925 - pyskl - INFO - Epoch [98][3500/3746] lr: 2.690e-02, eta: 1 day, 19:57:11, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3541, top5_acc: 0.6116, loss_cls: 3.6558, loss: 3.6558 +2024-07-19 21:09:58,786 - pyskl - INFO - Epoch [98][3600/3746] lr: 2.687e-02, eta: 1 day, 19:55:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6303, loss_cls: 3.5724, loss: 3.5724 +2024-07-19 21:11:21,514 - pyskl - INFO - Epoch [98][3700/3746] lr: 2.685e-02, eta: 1 day, 19:54:30, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6348, loss_cls: 3.5097, loss: 3.5097 +2024-07-19 21:12:01,464 - pyskl - INFO - Saving checkpoint at 98 epochs +2024-07-19 21:13:53,312 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 21:13:53,990 - pyskl - INFO - +top1_acc 0.3085 +top5_acc 0.5680 +2024-07-19 21:13:53,990 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 21:13:54,029 - pyskl - INFO - +mean_acc 0.3082 +2024-07-19 21:13:54,041 - pyskl - INFO - Epoch(val) [98][309] top1_acc: 0.3085, top5_acc: 0.5680, mean_class_accuracy: 0.3082 +2024-07-19 21:17:41,408 - pyskl - INFO - Epoch [99][100/3746] lr: 2.681e-02, eta: 1 day, 19:53:30, time: 2.274, data_time: 1.288, memory: 15990, top1_acc: 0.3739, top5_acc: 0.6405, loss_cls: 3.5134, loss: 3.5134 +2024-07-19 21:19:04,497 - pyskl - INFO - Epoch [99][200/3746] lr: 2.679e-02, eta: 1 day, 19:52:09, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6473, loss_cls: 3.4725, loss: 3.4725 +2024-07-19 21:20:26,237 - pyskl - INFO - Epoch [99][300/3746] lr: 2.676e-02, eta: 1 day, 19:50:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6352, loss_cls: 3.5181, loss: 3.5181 +2024-07-19 21:21:48,625 - pyskl - INFO - Epoch [99][400/3746] lr: 2.674e-02, eta: 1 day, 19:49:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6416, loss_cls: 3.5027, loss: 3.5027 +2024-07-19 21:23:10,817 - pyskl - INFO - Epoch [99][500/3746] lr: 2.671e-02, eta: 1 day, 19:48:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6391, loss_cls: 3.5245, loss: 3.5245 +2024-07-19 21:24:32,845 - pyskl - INFO - Epoch [99][600/3746] lr: 2.669e-02, eta: 1 day, 19:46:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6483, loss_cls: 3.4583, loss: 3.4583 +2024-07-19 21:25:55,252 - pyskl - INFO - Epoch [99][700/3746] lr: 2.666e-02, eta: 1 day, 19:45:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6453, loss_cls: 3.4922, loss: 3.4922 +2024-07-19 21:27:16,905 - pyskl - INFO - Epoch [99][800/3746] lr: 2.664e-02, eta: 1 day, 19:44:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6334, loss_cls: 3.5232, loss: 3.5232 +2024-07-19 21:28:38,488 - pyskl - INFO - Epoch [99][900/3746] lr: 2.661e-02, eta: 1 day, 19:42:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6405, loss_cls: 3.5241, loss: 3.5241 +2024-07-19 21:29:59,934 - pyskl - INFO - Epoch [99][1000/3746] lr: 2.659e-02, eta: 1 day, 19:41:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6477, loss_cls: 3.5164, loss: 3.5164 +2024-07-19 21:31:21,627 - pyskl - INFO - Epoch [99][1100/3746] lr: 2.656e-02, eta: 1 day, 19:40:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6341, loss_cls: 3.5271, loss: 3.5271 +2024-07-19 21:32:43,579 - pyskl - INFO - Epoch [99][1200/3746] lr: 2.654e-02, eta: 1 day, 19:38:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6331, loss_cls: 3.5692, loss: 3.5692 +2024-07-19 21:34:05,801 - pyskl - INFO - Epoch [99][1300/3746] lr: 2.651e-02, eta: 1 day, 19:37:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6272, loss_cls: 3.5838, loss: 3.5838 +2024-07-19 21:35:28,408 - pyskl - INFO - Epoch [99][1400/3746] lr: 2.649e-02, eta: 1 day, 19:36:01, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6291, loss_cls: 3.5598, loss: 3.5598 +2024-07-19 21:36:49,811 - pyskl - INFO - Epoch [99][1500/3746] lr: 2.646e-02, eta: 1 day, 19:34:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6305, loss_cls: 3.5509, loss: 3.5509 +2024-07-19 21:38:12,061 - pyskl - INFO - Epoch [99][1600/3746] lr: 2.644e-02, eta: 1 day, 19:33:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6273, loss_cls: 3.5779, loss: 3.5779 +2024-07-19 21:39:33,553 - pyskl - INFO - Epoch [99][1700/3746] lr: 2.642e-02, eta: 1 day, 19:31:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6367, loss_cls: 3.5258, loss: 3.5258 +2024-07-19 21:40:55,452 - pyskl - INFO - Epoch [99][1800/3746] lr: 2.639e-02, eta: 1 day, 19:30:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3686, top5_acc: 0.6234, loss_cls: 3.5946, loss: 3.5946 +2024-07-19 21:42:16,854 - pyskl - INFO - Epoch [99][1900/3746] lr: 2.637e-02, eta: 1 day, 19:29:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6388, loss_cls: 3.5331, loss: 3.5331 +2024-07-19 21:43:38,365 - pyskl - INFO - Epoch [99][2000/3746] lr: 2.634e-02, eta: 1 day, 19:27:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6331, loss_cls: 3.5452, loss: 3.5452 +2024-07-19 21:45:00,023 - pyskl - INFO - Epoch [99][2100/3746] lr: 2.632e-02, eta: 1 day, 19:26:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6300, loss_cls: 3.5757, loss: 3.5757 +2024-07-19 21:46:21,963 - pyskl - INFO - Epoch [99][2200/3746] lr: 2.629e-02, eta: 1 day, 19:25:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6323, loss_cls: 3.5564, loss: 3.5564 +2024-07-19 21:47:43,568 - pyskl - INFO - Epoch [99][2300/3746] lr: 2.627e-02, eta: 1 day, 19:23:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6328, loss_cls: 3.5532, loss: 3.5532 +2024-07-19 21:49:04,973 - pyskl - INFO - Epoch [99][2400/3746] lr: 2.624e-02, eta: 1 day, 19:22:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6302, loss_cls: 3.5922, loss: 3.5922 +2024-07-19 21:50:26,401 - pyskl - INFO - Epoch [99][2500/3746] lr: 2.622e-02, eta: 1 day, 19:21:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6383, loss_cls: 3.5254, loss: 3.5254 +2024-07-19 21:51:47,774 - pyskl - INFO - Epoch [99][2600/3746] lr: 2.619e-02, eta: 1 day, 19:19:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6228, loss_cls: 3.5955, loss: 3.5955 +2024-07-19 21:53:09,591 - pyskl - INFO - Epoch [99][2700/3746] lr: 2.617e-02, eta: 1 day, 19:18:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6302, loss_cls: 3.5573, loss: 3.5573 +2024-07-19 21:54:31,408 - pyskl - INFO - Epoch [99][2800/3746] lr: 2.614e-02, eta: 1 day, 19:17:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6320, loss_cls: 3.5754, loss: 3.5754 +2024-07-19 21:55:53,584 - pyskl - INFO - Epoch [99][2900/3746] lr: 2.612e-02, eta: 1 day, 19:15:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6342, loss_cls: 3.5474, loss: 3.5474 +2024-07-19 21:57:14,923 - pyskl - INFO - Epoch [99][3000/3746] lr: 2.610e-02, eta: 1 day, 19:14:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6311, loss_cls: 3.5673, loss: 3.5673 +2024-07-19 21:58:36,740 - pyskl - INFO - Epoch [99][3100/3746] lr: 2.607e-02, eta: 1 day, 19:13:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6277, loss_cls: 3.5593, loss: 3.5593 +2024-07-19 21:59:59,038 - pyskl - INFO - Epoch [99][3200/3746] lr: 2.605e-02, eta: 1 day, 19:11:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6238, loss_cls: 3.5846, loss: 3.5846 +2024-07-19 22:01:20,919 - pyskl - INFO - Epoch [99][3300/3746] lr: 2.602e-02, eta: 1 day, 19:10:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6272, loss_cls: 3.5766, loss: 3.5766 +2024-07-19 22:02:42,668 - pyskl - INFO - Epoch [99][3400/3746] lr: 2.600e-02, eta: 1 day, 19:09:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6408, loss_cls: 3.5169, loss: 3.5169 +2024-07-19 22:04:05,008 - pyskl - INFO - Epoch [99][3500/3746] lr: 2.597e-02, eta: 1 day, 19:07:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6292, loss_cls: 3.5732, loss: 3.5732 +2024-07-19 22:05:26,433 - pyskl - INFO - Epoch [99][3600/3746] lr: 2.595e-02, eta: 1 day, 19:06:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6244, loss_cls: 3.5630, loss: 3.5630 +2024-07-19 22:06:48,550 - pyskl - INFO - Epoch [99][3700/3746] lr: 2.592e-02, eta: 1 day, 19:05:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6388, loss_cls: 3.5438, loss: 3.5438 +2024-07-19 22:07:28,250 - pyskl - INFO - Saving checkpoint at 99 epochs +2024-07-19 22:09:20,491 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 22:09:21,184 - pyskl - INFO - +top1_acc 0.3174 +top5_acc 0.5702 +2024-07-19 22:09:21,184 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 22:09:21,223 - pyskl - INFO - +mean_acc 0.3170 +2024-07-19 22:09:21,228 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_96.pth was removed +2024-07-19 22:09:21,482 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_99.pth. +2024-07-19 22:09:21,483 - pyskl - INFO - Best top1_acc is 0.3174 at 99 epoch. +2024-07-19 22:09:21,494 - pyskl - INFO - Epoch(val) [99][309] top1_acc: 0.3174, top5_acc: 0.5702, mean_class_accuracy: 0.3170 +2024-07-19 22:13:11,211 - pyskl - INFO - Epoch [100][100/3746] lr: 2.589e-02, eta: 1 day, 19:04:00, time: 2.297, data_time: 1.312, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6498, loss_cls: 3.4858, loss: 3.4858 +2024-07-19 22:14:34,051 - pyskl - INFO - Epoch [100][200/3746] lr: 2.586e-02, eta: 1 day, 19:02:39, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6353, loss_cls: 3.5435, loss: 3.5435 +2024-07-19 22:15:56,124 - pyskl - INFO - Epoch [100][300/3746] lr: 2.584e-02, eta: 1 day, 19:01:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3708, top5_acc: 0.6473, loss_cls: 3.4974, loss: 3.4974 +2024-07-19 22:17:18,456 - pyskl - INFO - Epoch [100][400/3746] lr: 2.581e-02, eta: 1 day, 18:59:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6416, loss_cls: 3.5064, loss: 3.5064 +2024-07-19 22:18:40,254 - pyskl - INFO - Epoch [100][500/3746] lr: 2.579e-02, eta: 1 day, 18:58:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6484, loss_cls: 3.4697, loss: 3.4697 +2024-07-19 22:20:02,782 - pyskl - INFO - Epoch [100][600/3746] lr: 2.577e-02, eta: 1 day, 18:57:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6439, loss_cls: 3.4900, loss: 3.4900 +2024-07-19 22:21:24,822 - pyskl - INFO - Epoch [100][700/3746] lr: 2.574e-02, eta: 1 day, 18:55:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6411, loss_cls: 3.5020, loss: 3.5020 +2024-07-19 22:22:46,537 - pyskl - INFO - Epoch [100][800/3746] lr: 2.572e-02, eta: 1 day, 18:54:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6436, loss_cls: 3.4935, loss: 3.4935 +2024-07-19 22:24:08,258 - pyskl - INFO - Epoch [100][900/3746] lr: 2.569e-02, eta: 1 day, 18:53:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6281, loss_cls: 3.5453, loss: 3.5453 +2024-07-19 22:25:30,122 - pyskl - INFO - Epoch [100][1000/3746] lr: 2.567e-02, eta: 1 day, 18:51:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6409, loss_cls: 3.5099, loss: 3.5099 +2024-07-19 22:26:52,021 - pyskl - INFO - Epoch [100][1100/3746] lr: 2.564e-02, eta: 1 day, 18:50:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6289, loss_cls: 3.5510, loss: 3.5510 +2024-07-19 22:28:13,480 - pyskl - INFO - Epoch [100][1200/3746] lr: 2.562e-02, eta: 1 day, 18:49:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6425, loss_cls: 3.4992, loss: 3.4992 +2024-07-19 22:29:34,836 - pyskl - INFO - Epoch [100][1300/3746] lr: 2.559e-02, eta: 1 day, 18:47:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6264, loss_cls: 3.5752, loss: 3.5752 +2024-07-19 22:30:56,798 - pyskl - INFO - Epoch [100][1400/3746] lr: 2.557e-02, eta: 1 day, 18:46:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6478, loss_cls: 3.4754, loss: 3.4754 +2024-07-19 22:32:18,124 - pyskl - INFO - Epoch [100][1500/3746] lr: 2.555e-02, eta: 1 day, 18:45:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6397, loss_cls: 3.4846, loss: 3.4846 +2024-07-19 22:33:39,806 - pyskl - INFO - Epoch [100][1600/3746] lr: 2.552e-02, eta: 1 day, 18:43:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6400, loss_cls: 3.5166, loss: 3.5166 +2024-07-19 22:35:01,713 - pyskl - INFO - Epoch [100][1700/3746] lr: 2.550e-02, eta: 1 day, 18:42:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6288, loss_cls: 3.5448, loss: 3.5448 +2024-07-19 22:36:23,873 - pyskl - INFO - Epoch [100][1800/3746] lr: 2.547e-02, eta: 1 day, 18:41:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6211, loss_cls: 3.5884, loss: 3.5884 +2024-07-19 22:37:45,680 - pyskl - INFO - Epoch [100][1900/3746] lr: 2.545e-02, eta: 1 day, 18:39:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6341, loss_cls: 3.5153, loss: 3.5153 +2024-07-19 22:39:07,193 - pyskl - INFO - Epoch [100][2000/3746] lr: 2.542e-02, eta: 1 day, 18:38:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6289, loss_cls: 3.5510, loss: 3.5510 +2024-07-19 22:40:29,239 - pyskl - INFO - Epoch [100][2100/3746] lr: 2.540e-02, eta: 1 day, 18:37:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6338, loss_cls: 3.5354, loss: 3.5354 +2024-07-19 22:41:51,302 - pyskl - INFO - Epoch [100][2200/3746] lr: 2.538e-02, eta: 1 day, 18:35:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6317, loss_cls: 3.5734, loss: 3.5734 +2024-07-19 22:43:13,086 - pyskl - INFO - Epoch [100][2300/3746] lr: 2.535e-02, eta: 1 day, 18:34:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6295, loss_cls: 3.5638, loss: 3.5638 +2024-07-19 22:44:35,447 - pyskl - INFO - Epoch [100][2400/3746] lr: 2.533e-02, eta: 1 day, 18:33:01, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6403, loss_cls: 3.5033, loss: 3.5033 +2024-07-19 22:45:57,389 - pyskl - INFO - Epoch [100][2500/3746] lr: 2.530e-02, eta: 1 day, 18:31:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6312, loss_cls: 3.5452, loss: 3.5452 +2024-07-19 22:47:18,901 - pyskl - INFO - Epoch [100][2600/3746] lr: 2.528e-02, eta: 1 day, 18:30:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6302, loss_cls: 3.5735, loss: 3.5735 +2024-07-19 22:48:40,529 - pyskl - INFO - Epoch [100][2700/3746] lr: 2.525e-02, eta: 1 day, 18:28:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6369, loss_cls: 3.5093, loss: 3.5093 +2024-07-19 22:50:02,131 - pyskl - INFO - Epoch [100][2800/3746] lr: 2.523e-02, eta: 1 day, 18:27:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6334, loss_cls: 3.5505, loss: 3.5505 +2024-07-19 22:51:24,065 - pyskl - INFO - Epoch [100][2900/3746] lr: 2.521e-02, eta: 1 day, 18:26:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6364, loss_cls: 3.5079, loss: 3.5079 +2024-07-19 22:52:45,853 - pyskl - INFO - Epoch [100][3000/3746] lr: 2.518e-02, eta: 1 day, 18:24:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6334, loss_cls: 3.5310, loss: 3.5310 +2024-07-19 22:54:07,894 - pyskl - INFO - Epoch [100][3100/3746] lr: 2.516e-02, eta: 1 day, 18:23:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6298, loss_cls: 3.5486, loss: 3.5486 +2024-07-19 22:55:30,033 - pyskl - INFO - Epoch [100][3200/3746] lr: 2.513e-02, eta: 1 day, 18:22:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6273, loss_cls: 3.5592, loss: 3.5592 +2024-07-19 22:56:52,409 - pyskl - INFO - Epoch [100][3300/3746] lr: 2.511e-02, eta: 1 day, 18:20:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3908, top5_acc: 0.6480, loss_cls: 3.4720, loss: 3.4720 +2024-07-19 22:58:14,115 - pyskl - INFO - Epoch [100][3400/3746] lr: 2.508e-02, eta: 1 day, 18:19:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6400, loss_cls: 3.5293, loss: 3.5293 +2024-07-19 22:59:36,344 - pyskl - INFO - Epoch [100][3500/3746] lr: 2.506e-02, eta: 1 day, 18:18:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6225, loss_cls: 3.6002, loss: 3.6002 +2024-07-19 23:00:57,958 - pyskl - INFO - Epoch [100][3600/3746] lr: 2.504e-02, eta: 1 day, 18:16:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6270, loss_cls: 3.5763, loss: 3.5763 +2024-07-19 23:02:20,056 - pyskl - INFO - Epoch [100][3700/3746] lr: 2.501e-02, eta: 1 day, 18:15:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6414, loss_cls: 3.5362, loss: 3.5362 +2024-07-19 23:02:59,754 - pyskl - INFO - Saving checkpoint at 100 epochs +2024-07-19 23:04:51,610 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 23:04:52,275 - pyskl - INFO - +top1_acc 0.3146 +top5_acc 0.5689 +2024-07-19 23:04:52,275 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 23:04:52,316 - pyskl - INFO - +mean_acc 0.3142 +2024-07-19 23:04:52,327 - pyskl - INFO - Epoch(val) [100][309] top1_acc: 0.3146, top5_acc: 0.5689, mean_class_accuracy: 0.3142 +2024-07-19 23:08:42,100 - pyskl - INFO - Epoch [101][100/3746] lr: 2.498e-02, eta: 1 day, 18:14:27, time: 2.298, data_time: 1.312, memory: 15990, top1_acc: 0.3933, top5_acc: 0.6486, loss_cls: 3.4506, loss: 3.4506 +2024-07-19 23:10:04,446 - pyskl - INFO - Epoch [101][200/3746] lr: 2.495e-02, eta: 1 day, 18:13:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6425, loss_cls: 3.5098, loss: 3.5098 +2024-07-19 23:11:26,468 - pyskl - INFO - Epoch [101][300/3746] lr: 2.493e-02, eta: 1 day, 18:11:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6545, loss_cls: 3.4321, loss: 3.4321 +2024-07-19 23:12:48,453 - pyskl - INFO - Epoch [101][400/3746] lr: 2.490e-02, eta: 1 day, 18:10:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6381, loss_cls: 3.5160, loss: 3.5160 +2024-07-19 23:14:10,748 - pyskl - INFO - Epoch [101][500/3746] lr: 2.488e-02, eta: 1 day, 18:09:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6522, loss_cls: 3.4352, loss: 3.4352 +2024-07-19 23:15:33,172 - pyskl - INFO - Epoch [101][600/3746] lr: 2.486e-02, eta: 1 day, 18:07:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6516, loss_cls: 3.4836, loss: 3.4836 +2024-07-19 23:16:55,115 - pyskl - INFO - Epoch [101][700/3746] lr: 2.483e-02, eta: 1 day, 18:06:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6492, loss_cls: 3.4813, loss: 3.4813 +2024-07-19 23:18:16,977 - pyskl - INFO - Epoch [101][800/3746] lr: 2.481e-02, eta: 1 day, 18:05:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6444, loss_cls: 3.4893, loss: 3.4893 +2024-07-19 23:19:38,690 - pyskl - INFO - Epoch [101][900/3746] lr: 2.478e-02, eta: 1 day, 18:03:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6483, loss_cls: 3.4634, loss: 3.4634 +2024-07-19 23:21:00,129 - pyskl - INFO - Epoch [101][1000/3746] lr: 2.476e-02, eta: 1 day, 18:02:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6361, loss_cls: 3.5054, loss: 3.5054 +2024-07-19 23:22:21,987 - pyskl - INFO - Epoch [101][1100/3746] lr: 2.473e-02, eta: 1 day, 18:00:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6283, loss_cls: 3.5853, loss: 3.5853 +2024-07-19 23:23:43,502 - pyskl - INFO - Epoch [101][1200/3746] lr: 2.471e-02, eta: 1 day, 17:59:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6405, loss_cls: 3.4987, loss: 3.4987 +2024-07-19 23:25:05,036 - pyskl - INFO - Epoch [101][1300/3746] lr: 2.469e-02, eta: 1 day, 17:58:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6352, loss_cls: 3.5055, loss: 3.5055 +2024-07-19 23:26:26,522 - pyskl - INFO - Epoch [101][1400/3746] lr: 2.466e-02, eta: 1 day, 17:56:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6402, loss_cls: 3.4931, loss: 3.4931 +2024-07-19 23:27:48,452 - pyskl - INFO - Epoch [101][1500/3746] lr: 2.464e-02, eta: 1 day, 17:55:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6320, loss_cls: 3.5331, loss: 3.5331 +2024-07-19 23:29:10,306 - pyskl - INFO - Epoch [101][1600/3746] lr: 2.461e-02, eta: 1 day, 17:54:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6422, loss_cls: 3.4828, loss: 3.4828 +2024-07-19 23:30:32,052 - pyskl - INFO - Epoch [101][1700/3746] lr: 2.459e-02, eta: 1 day, 17:52:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6453, loss_cls: 3.4828, loss: 3.4828 +2024-07-19 23:31:53,710 - pyskl - INFO - Epoch [101][1800/3746] lr: 2.457e-02, eta: 1 day, 17:51:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6348, loss_cls: 3.5508, loss: 3.5508 +2024-07-19 23:33:16,207 - pyskl - INFO - Epoch [101][1900/3746] lr: 2.454e-02, eta: 1 day, 17:50:10, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6475, loss_cls: 3.4975, loss: 3.4975 +2024-07-19 23:34:37,879 - pyskl - INFO - Epoch [101][2000/3746] lr: 2.452e-02, eta: 1 day, 17:48:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6378, loss_cls: 3.5200, loss: 3.5200 +2024-07-19 23:35:59,572 - pyskl - INFO - Epoch [101][2100/3746] lr: 2.449e-02, eta: 1 day, 17:47:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6502, loss_cls: 3.4679, loss: 3.4679 +2024-07-19 23:37:20,900 - pyskl - INFO - Epoch [101][2200/3746] lr: 2.447e-02, eta: 1 day, 17:46:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6397, loss_cls: 3.4851, loss: 3.4851 +2024-07-19 23:38:42,515 - pyskl - INFO - Epoch [101][2300/3746] lr: 2.445e-02, eta: 1 day, 17:44:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6339, loss_cls: 3.5819, loss: 3.5819 +2024-07-19 23:40:04,329 - pyskl - INFO - Epoch [101][2400/3746] lr: 2.442e-02, eta: 1 day, 17:43:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6345, loss_cls: 3.5288, loss: 3.5288 +2024-07-19 23:41:25,586 - pyskl - INFO - Epoch [101][2500/3746] lr: 2.440e-02, eta: 1 day, 17:42:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6405, loss_cls: 3.5317, loss: 3.5317 +2024-07-19 23:42:47,312 - pyskl - INFO - Epoch [101][2600/3746] lr: 2.437e-02, eta: 1 day, 17:40:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6459, loss_cls: 3.5010, loss: 3.5010 +2024-07-19 23:44:09,190 - pyskl - INFO - Epoch [101][2700/3746] lr: 2.435e-02, eta: 1 day, 17:39:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6334, loss_cls: 3.5517, loss: 3.5517 +2024-07-19 23:45:31,248 - pyskl - INFO - Epoch [101][2800/3746] lr: 2.433e-02, eta: 1 day, 17:38:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6384, loss_cls: 3.5280, loss: 3.5280 +2024-07-19 23:46:52,714 - pyskl - INFO - Epoch [101][2900/3746] lr: 2.430e-02, eta: 1 day, 17:36:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6342, loss_cls: 3.5490, loss: 3.5490 +2024-07-19 23:48:14,540 - pyskl - INFO - Epoch [101][3000/3746] lr: 2.428e-02, eta: 1 day, 17:35:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6334, loss_cls: 3.5272, loss: 3.5272 +2024-07-19 23:49:36,911 - pyskl - INFO - Epoch [101][3100/3746] lr: 2.425e-02, eta: 1 day, 17:33:58, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6348, loss_cls: 3.5726, loss: 3.5726 +2024-07-19 23:50:58,592 - pyskl - INFO - Epoch [101][3200/3746] lr: 2.423e-02, eta: 1 day, 17:32:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6378, loss_cls: 3.5033, loss: 3.5033 +2024-07-19 23:52:21,128 - pyskl - INFO - Epoch [101][3300/3746] lr: 2.421e-02, eta: 1 day, 17:31:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6425, loss_cls: 3.4909, loss: 3.4909 +2024-07-19 23:53:43,652 - pyskl - INFO - Epoch [101][3400/3746] lr: 2.418e-02, eta: 1 day, 17:29:56, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6427, loss_cls: 3.5447, loss: 3.5447 +2024-07-19 23:55:05,380 - pyskl - INFO - Epoch [101][3500/3746] lr: 2.416e-02, eta: 1 day, 17:28:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6353, loss_cls: 3.5359, loss: 3.5359 +2024-07-19 23:56:27,341 - pyskl - INFO - Epoch [101][3600/3746] lr: 2.413e-02, eta: 1 day, 17:27:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6384, loss_cls: 3.5410, loss: 3.5410 +2024-07-19 23:57:50,008 - pyskl - INFO - Epoch [101][3700/3746] lr: 2.411e-02, eta: 1 day, 17:25:54, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3859, top5_acc: 0.6502, loss_cls: 3.4803, loss: 3.4803 +2024-07-19 23:58:30,198 - pyskl - INFO - Saving checkpoint at 101 epochs +2024-07-20 00:00:22,684 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 00:00:23,521 - pyskl - INFO - +top1_acc 0.3142 +top5_acc 0.5729 +2024-07-20 00:00:23,521 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 00:00:23,566 - pyskl - INFO - +mean_acc 0.3139 +2024-07-20 00:00:23,580 - pyskl - INFO - Epoch(val) [101][309] top1_acc: 0.3142, top5_acc: 0.5729, mean_class_accuracy: 0.3139 +2024-07-20 00:04:13,302 - pyskl - INFO - Epoch [102][100/3746] lr: 2.407e-02, eta: 1 day, 17:24:49, time: 2.297, data_time: 1.308, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6625, loss_cls: 3.3674, loss: 3.3674 +2024-07-20 00:05:35,778 - pyskl - INFO - Epoch [102][200/3746] lr: 2.405e-02, eta: 1 day, 17:23:28, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6641, loss_cls: 3.3941, loss: 3.3941 +2024-07-20 00:06:58,045 - pyskl - INFO - Epoch [102][300/3746] lr: 2.403e-02, eta: 1 day, 17:22:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6434, loss_cls: 3.4639, loss: 3.4639 +2024-07-20 00:08:20,564 - pyskl - INFO - Epoch [102][400/3746] lr: 2.400e-02, eta: 1 day, 17:20:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6381, loss_cls: 3.4792, loss: 3.4792 +2024-07-20 00:09:42,542 - pyskl - INFO - Epoch [102][500/3746] lr: 2.398e-02, eta: 1 day, 17:19:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6492, loss_cls: 3.4604, loss: 3.4604 +2024-07-20 00:11:04,444 - pyskl - INFO - Epoch [102][600/3746] lr: 2.396e-02, eta: 1 day, 17:18:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6316, loss_cls: 3.5353, loss: 3.5353 +2024-07-20 00:12:26,141 - pyskl - INFO - Epoch [102][700/3746] lr: 2.393e-02, eta: 1 day, 17:16:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6422, loss_cls: 3.4955, loss: 3.4955 +2024-07-20 00:13:47,743 - pyskl - INFO - Epoch [102][800/3746] lr: 2.391e-02, eta: 1 day, 17:15:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6414, loss_cls: 3.5021, loss: 3.5021 +2024-07-20 00:15:09,335 - pyskl - INFO - Epoch [102][900/3746] lr: 2.388e-02, eta: 1 day, 17:14:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6381, loss_cls: 3.4879, loss: 3.4879 +2024-07-20 00:16:31,092 - pyskl - INFO - Epoch [102][1000/3746] lr: 2.386e-02, eta: 1 day, 17:12:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6441, loss_cls: 3.5013, loss: 3.5013 +2024-07-20 00:17:53,205 - pyskl - INFO - Epoch [102][1100/3746] lr: 2.384e-02, eta: 1 day, 17:11:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6422, loss_cls: 3.4732, loss: 3.4732 +2024-07-20 00:19:15,035 - pyskl - INFO - Epoch [102][1200/3746] lr: 2.381e-02, eta: 1 day, 17:09:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6514, loss_cls: 3.4492, loss: 3.4492 +2024-07-20 00:20:37,108 - pyskl - INFO - Epoch [102][1300/3746] lr: 2.379e-02, eta: 1 day, 17:08:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6422, loss_cls: 3.5048, loss: 3.5048 +2024-07-20 00:21:58,989 - pyskl - INFO - Epoch [102][1400/3746] lr: 2.376e-02, eta: 1 day, 17:07:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6531, loss_cls: 3.4261, loss: 3.4261 +2024-07-20 00:23:20,792 - pyskl - INFO - Epoch [102][1500/3746] lr: 2.374e-02, eta: 1 day, 17:05:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6427, loss_cls: 3.5108, loss: 3.5108 +2024-07-20 00:24:42,636 - pyskl - INFO - Epoch [102][1600/3746] lr: 2.372e-02, eta: 1 day, 17:04:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6403, loss_cls: 3.5079, loss: 3.5079 +2024-07-20 00:26:04,703 - pyskl - INFO - Epoch [102][1700/3746] lr: 2.369e-02, eta: 1 day, 17:03:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3858, top5_acc: 0.6433, loss_cls: 3.4806, loss: 3.4806 +2024-07-20 00:27:27,210 - pyskl - INFO - Epoch [102][1800/3746] lr: 2.367e-02, eta: 1 day, 17:01:53, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6353, loss_cls: 3.5198, loss: 3.5198 +2024-07-20 00:28:49,416 - pyskl - INFO - Epoch [102][1900/3746] lr: 2.365e-02, eta: 1 day, 17:00:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6442, loss_cls: 3.4903, loss: 3.4903 +2024-07-20 00:30:11,168 - pyskl - INFO - Epoch [102][2000/3746] lr: 2.362e-02, eta: 1 day, 16:59:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6448, loss_cls: 3.4901, loss: 3.4901 +2024-07-20 00:31:32,772 - pyskl - INFO - Epoch [102][2100/3746] lr: 2.360e-02, eta: 1 day, 16:57:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3741, top5_acc: 0.6420, loss_cls: 3.4798, loss: 3.4798 +2024-07-20 00:32:54,242 - pyskl - INFO - Epoch [102][2200/3746] lr: 2.357e-02, eta: 1 day, 16:56:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6392, loss_cls: 3.4943, loss: 3.4943 +2024-07-20 00:34:15,933 - pyskl - INFO - Epoch [102][2300/3746] lr: 2.355e-02, eta: 1 day, 16:55:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6442, loss_cls: 3.5197, loss: 3.5197 +2024-07-20 00:35:37,220 - pyskl - INFO - Epoch [102][2400/3746] lr: 2.353e-02, eta: 1 day, 16:53:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6420, loss_cls: 3.5229, loss: 3.5229 +2024-07-20 00:36:59,004 - pyskl - INFO - Epoch [102][2500/3746] lr: 2.350e-02, eta: 1 day, 16:52:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6338, loss_cls: 3.5289, loss: 3.5289 +2024-07-20 00:38:20,235 - pyskl - INFO - Epoch [102][2600/3746] lr: 2.348e-02, eta: 1 day, 16:51:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6380, loss_cls: 3.5354, loss: 3.5354 +2024-07-20 00:39:41,769 - pyskl - INFO - Epoch [102][2700/3746] lr: 2.346e-02, eta: 1 day, 16:49:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6402, loss_cls: 3.4735, loss: 3.4735 +2024-07-20 00:41:03,843 - pyskl - INFO - Epoch [102][2800/3746] lr: 2.343e-02, eta: 1 day, 16:48:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6517, loss_cls: 3.4626, loss: 3.4626 +2024-07-20 00:42:25,568 - pyskl - INFO - Epoch [102][2900/3746] lr: 2.341e-02, eta: 1 day, 16:47:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6305, loss_cls: 3.5418, loss: 3.5418 +2024-07-20 00:43:47,361 - pyskl - INFO - Epoch [102][3000/3746] lr: 2.339e-02, eta: 1 day, 16:45:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6328, loss_cls: 3.5434, loss: 3.5434 +2024-07-20 00:45:09,116 - pyskl - INFO - Epoch [102][3100/3746] lr: 2.336e-02, eta: 1 day, 16:44:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6392, loss_cls: 3.4887, loss: 3.4887 +2024-07-20 00:46:31,004 - pyskl - INFO - Epoch [102][3200/3746] lr: 2.334e-02, eta: 1 day, 16:42:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6314, loss_cls: 3.5612, loss: 3.5612 +2024-07-20 00:47:52,628 - pyskl - INFO - Epoch [102][3300/3746] lr: 2.331e-02, eta: 1 day, 16:41:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6406, loss_cls: 3.4960, loss: 3.4960 +2024-07-20 00:49:15,029 - pyskl - INFO - Epoch [102][3400/3746] lr: 2.329e-02, eta: 1 day, 16:40:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3872, top5_acc: 0.6416, loss_cls: 3.4829, loss: 3.4829 +2024-07-20 00:50:36,599 - pyskl - INFO - Epoch [102][3500/3746] lr: 2.327e-02, eta: 1 day, 16:38:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6394, loss_cls: 3.5285, loss: 3.5285 +2024-07-20 00:51:58,382 - pyskl - INFO - Epoch [102][3600/3746] lr: 2.324e-02, eta: 1 day, 16:37:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6408, loss_cls: 3.5121, loss: 3.5121 +2024-07-20 00:53:20,299 - pyskl - INFO - Epoch [102][3700/3746] lr: 2.322e-02, eta: 1 day, 16:36:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3831, top5_acc: 0.6495, loss_cls: 3.4675, loss: 3.4675 +2024-07-20 00:53:59,981 - pyskl - INFO - Saving checkpoint at 102 epochs +2024-07-20 00:55:52,034 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 00:55:52,693 - pyskl - INFO - +top1_acc 0.3242 +top5_acc 0.5759 +2024-07-20 00:55:52,694 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 00:55:52,733 - pyskl - INFO - +mean_acc 0.3240 +2024-07-20 00:55:52,738 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_99.pth was removed +2024-07-20 00:55:52,996 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_102.pth. +2024-07-20 00:55:52,997 - pyskl - INFO - Best top1_acc is 0.3242 at 102 epoch. +2024-07-20 00:55:53,008 - pyskl - INFO - Epoch(val) [102][309] top1_acc: 0.3242, top5_acc: 0.5759, mean_class_accuracy: 0.3240 +2024-07-20 00:59:39,621 - pyskl - INFO - Epoch [103][100/3746] lr: 2.319e-02, eta: 1 day, 16:35:05, time: 2.266, data_time: 1.284, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6589, loss_cls: 3.3851, loss: 3.3851 +2024-07-20 01:01:01,641 - pyskl - INFO - Epoch [103][200/3746] lr: 2.316e-02, eta: 1 day, 16:33:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6534, loss_cls: 3.3922, loss: 3.3922 +2024-07-20 01:02:24,028 - pyskl - INFO - Epoch [103][300/3746] lr: 2.314e-02, eta: 1 day, 16:32:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6548, loss_cls: 3.4480, loss: 3.4480 +2024-07-20 01:03:45,935 - pyskl - INFO - Epoch [103][400/3746] lr: 2.311e-02, eta: 1 day, 16:31:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6500, loss_cls: 3.4689, loss: 3.4689 +2024-07-20 01:05:07,933 - pyskl - INFO - Epoch [103][500/3746] lr: 2.309e-02, eta: 1 day, 16:29:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6448, loss_cls: 3.4495, loss: 3.4495 +2024-07-20 01:06:30,497 - pyskl - INFO - Epoch [103][600/3746] lr: 2.307e-02, eta: 1 day, 16:28:21, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6527, loss_cls: 3.4597, loss: 3.4597 +2024-07-20 01:07:51,803 - pyskl - INFO - Epoch [103][700/3746] lr: 2.304e-02, eta: 1 day, 16:27:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6519, loss_cls: 3.4671, loss: 3.4671 +2024-07-20 01:09:13,744 - pyskl - INFO - Epoch [103][800/3746] lr: 2.302e-02, eta: 1 day, 16:25:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6522, loss_cls: 3.4436, loss: 3.4436 +2024-07-20 01:10:35,273 - pyskl - INFO - Epoch [103][900/3746] lr: 2.300e-02, eta: 1 day, 16:24:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6469, loss_cls: 3.4605, loss: 3.4605 +2024-07-20 01:11:56,725 - pyskl - INFO - Epoch [103][1000/3746] lr: 2.297e-02, eta: 1 day, 16:22:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3903, top5_acc: 0.6433, loss_cls: 3.4598, loss: 3.4598 +2024-07-20 01:13:18,205 - pyskl - INFO - Epoch [103][1100/3746] lr: 2.295e-02, eta: 1 day, 16:21:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6394, loss_cls: 3.4867, loss: 3.4867 +2024-07-20 01:14:39,900 - pyskl - INFO - Epoch [103][1200/3746] lr: 2.293e-02, eta: 1 day, 16:20:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6412, loss_cls: 3.4997, loss: 3.4997 +2024-07-20 01:16:01,199 - pyskl - INFO - Epoch [103][1300/3746] lr: 2.290e-02, eta: 1 day, 16:18:52, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6444, loss_cls: 3.4900, loss: 3.4900 +2024-07-20 01:17:22,864 - pyskl - INFO - Epoch [103][1400/3746] lr: 2.288e-02, eta: 1 day, 16:17:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6527, loss_cls: 3.4536, loss: 3.4536 +2024-07-20 01:18:45,234 - pyskl - INFO - Epoch [103][1500/3746] lr: 2.286e-02, eta: 1 day, 16:16:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6403, loss_cls: 3.4743, loss: 3.4743 +2024-07-20 01:20:07,087 - pyskl - INFO - Epoch [103][1600/3746] lr: 2.283e-02, eta: 1 day, 16:14:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6528, loss_cls: 3.4463, loss: 3.4463 +2024-07-20 01:21:29,798 - pyskl - INFO - Epoch [103][1700/3746] lr: 2.281e-02, eta: 1 day, 16:13:29, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3762, top5_acc: 0.6400, loss_cls: 3.5039, loss: 3.5039 +2024-07-20 01:22:51,630 - pyskl - INFO - Epoch [103][1800/3746] lr: 2.279e-02, eta: 1 day, 16:12:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6445, loss_cls: 3.4707, loss: 3.4707 +2024-07-20 01:24:13,600 - pyskl - INFO - Epoch [103][1900/3746] lr: 2.276e-02, eta: 1 day, 16:10:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6514, loss_cls: 3.4475, loss: 3.4475 +2024-07-20 01:25:35,152 - pyskl - INFO - Epoch [103][2000/3746] lr: 2.274e-02, eta: 1 day, 16:09:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6447, loss_cls: 3.4834, loss: 3.4834 +2024-07-20 01:26:56,646 - pyskl - INFO - Epoch [103][2100/3746] lr: 2.272e-02, eta: 1 day, 16:08:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6488, loss_cls: 3.4701, loss: 3.4701 +2024-07-20 01:28:18,402 - pyskl - INFO - Epoch [103][2200/3746] lr: 2.269e-02, eta: 1 day, 16:06:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6353, loss_cls: 3.5289, loss: 3.5289 +2024-07-20 01:29:39,975 - pyskl - INFO - Epoch [103][2300/3746] lr: 2.267e-02, eta: 1 day, 16:05:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6459, loss_cls: 3.4826, loss: 3.4826 +2024-07-20 01:31:01,947 - pyskl - INFO - Epoch [103][2400/3746] lr: 2.264e-02, eta: 1 day, 16:04:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6506, loss_cls: 3.4423, loss: 3.4423 +2024-07-20 01:32:23,248 - pyskl - INFO - Epoch [103][2500/3746] lr: 2.262e-02, eta: 1 day, 16:02:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6417, loss_cls: 3.4822, loss: 3.4822 +2024-07-20 01:33:45,460 - pyskl - INFO - Epoch [103][2600/3746] lr: 2.260e-02, eta: 1 day, 16:01:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6452, loss_cls: 3.4843, loss: 3.4843 +2024-07-20 01:35:07,123 - pyskl - INFO - Epoch [103][2700/3746] lr: 2.257e-02, eta: 1 day, 15:59:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6388, loss_cls: 3.5027, loss: 3.5027 +2024-07-20 01:36:28,712 - pyskl - INFO - Epoch [103][2800/3746] lr: 2.255e-02, eta: 1 day, 15:58:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6434, loss_cls: 3.4907, loss: 3.4907 +2024-07-20 01:37:50,554 - pyskl - INFO - Epoch [103][2900/3746] lr: 2.253e-02, eta: 1 day, 15:57:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6391, loss_cls: 3.4833, loss: 3.4833 +2024-07-20 01:39:12,242 - pyskl - INFO - Epoch [103][3000/3746] lr: 2.250e-02, eta: 1 day, 15:55:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3837, top5_acc: 0.6355, loss_cls: 3.5124, loss: 3.5124 +2024-07-20 01:40:34,617 - pyskl - INFO - Epoch [103][3100/3746] lr: 2.248e-02, eta: 1 day, 15:54:34, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6522, loss_cls: 3.4662, loss: 3.4662 +2024-07-20 01:41:56,460 - pyskl - INFO - Epoch [103][3200/3746] lr: 2.246e-02, eta: 1 day, 15:53:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6528, loss_cls: 3.4432, loss: 3.4432 +2024-07-20 01:43:18,336 - pyskl - INFO - Epoch [103][3300/3746] lr: 2.243e-02, eta: 1 day, 15:51:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6356, loss_cls: 3.4980, loss: 3.4980 +2024-07-20 01:44:40,442 - pyskl - INFO - Epoch [103][3400/3746] lr: 2.241e-02, eta: 1 day, 15:50:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6419, loss_cls: 3.4674, loss: 3.4674 +2024-07-20 01:46:02,136 - pyskl - INFO - Epoch [103][3500/3746] lr: 2.239e-02, eta: 1 day, 15:49:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6394, loss_cls: 3.4944, loss: 3.4944 +2024-07-20 01:47:24,125 - pyskl - INFO - Epoch [103][3600/3746] lr: 2.236e-02, eta: 1 day, 15:47:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6369, loss_cls: 3.5036, loss: 3.5036 +2024-07-20 01:48:46,107 - pyskl - INFO - Epoch [103][3700/3746] lr: 2.234e-02, eta: 1 day, 15:46:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6319, loss_cls: 3.5092, loss: 3.5092 +2024-07-20 01:49:25,776 - pyskl - INFO - Saving checkpoint at 103 epochs +2024-07-20 01:51:16,681 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 01:51:17,343 - pyskl - INFO - +top1_acc 0.3162 +top5_acc 0.5745 +2024-07-20 01:51:17,343 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 01:51:17,382 - pyskl - INFO - +mean_acc 0.3160 +2024-07-20 01:51:17,393 - pyskl - INFO - Epoch(val) [103][309] top1_acc: 0.3162, top5_acc: 0.5745, mean_class_accuracy: 0.3160 +2024-07-20 01:55:05,071 - pyskl - INFO - Epoch [104][100/3746] lr: 2.231e-02, eta: 1 day, 15:45:18, time: 2.277, data_time: 1.274, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6466, loss_cls: 3.4194, loss: 3.4194 +2024-07-20 01:56:26,978 - pyskl - INFO - Epoch [104][200/3746] lr: 2.228e-02, eta: 1 day, 15:43:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6592, loss_cls: 3.3808, loss: 3.3808 +2024-07-20 01:57:49,560 - pyskl - INFO - Epoch [104][300/3746] lr: 2.226e-02, eta: 1 day, 15:42:37, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3986, top5_acc: 0.6592, loss_cls: 3.4025, loss: 3.4025 +2024-07-20 01:59:11,535 - pyskl - INFO - Epoch [104][400/3746] lr: 2.224e-02, eta: 1 day, 15:41:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6464, loss_cls: 3.4356, loss: 3.4356 +2024-07-20 02:00:34,667 - pyskl - INFO - Epoch [104][500/3746] lr: 2.221e-02, eta: 1 day, 15:39:55, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3923, top5_acc: 0.6575, loss_cls: 3.4405, loss: 3.4405 +2024-07-20 02:01:56,282 - pyskl - INFO - Epoch [104][600/3746] lr: 2.219e-02, eta: 1 day, 15:38:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6503, loss_cls: 3.4357, loss: 3.4357 +2024-07-20 02:03:17,921 - pyskl - INFO - Epoch [104][700/3746] lr: 2.217e-02, eta: 1 day, 15:37:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4095, top5_acc: 0.6617, loss_cls: 3.3776, loss: 3.3776 +2024-07-20 02:04:39,753 - pyskl - INFO - Epoch [104][800/3746] lr: 2.214e-02, eta: 1 day, 15:35:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6570, loss_cls: 3.4228, loss: 3.4228 +2024-07-20 02:06:02,383 - pyskl - INFO - Epoch [104][900/3746] lr: 2.212e-02, eta: 1 day, 15:34:31, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6556, loss_cls: 3.4653, loss: 3.4653 +2024-07-20 02:07:24,273 - pyskl - INFO - Epoch [104][1000/3746] lr: 2.210e-02, eta: 1 day, 15:33:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6459, loss_cls: 3.4861, loss: 3.4861 +2024-07-20 02:08:45,725 - pyskl - INFO - Epoch [104][1100/3746] lr: 2.208e-02, eta: 1 day, 15:31:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6517, loss_cls: 3.4177, loss: 3.4177 +2024-07-20 02:10:07,331 - pyskl - INFO - Epoch [104][1200/3746] lr: 2.205e-02, eta: 1 day, 15:30:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6581, loss_cls: 3.4008, loss: 3.4008 +2024-07-20 02:11:28,971 - pyskl - INFO - Epoch [104][1300/3746] lr: 2.203e-02, eta: 1 day, 15:29:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6645, loss_cls: 3.3934, loss: 3.3934 +2024-07-20 02:12:51,093 - pyskl - INFO - Epoch [104][1400/3746] lr: 2.201e-02, eta: 1 day, 15:27:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6489, loss_cls: 3.4442, loss: 3.4442 +2024-07-20 02:14:13,001 - pyskl - INFO - Epoch [104][1500/3746] lr: 2.198e-02, eta: 1 day, 15:26:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3966, top5_acc: 0.6531, loss_cls: 3.4047, loss: 3.4047 +2024-07-20 02:15:34,894 - pyskl - INFO - Epoch [104][1600/3746] lr: 2.196e-02, eta: 1 day, 15:25:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6494, loss_cls: 3.4644, loss: 3.4644 +2024-07-20 02:16:57,603 - pyskl - INFO - Epoch [104][1700/3746] lr: 2.194e-02, eta: 1 day, 15:23:42, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6378, loss_cls: 3.5096, loss: 3.5096 +2024-07-20 02:18:19,510 - pyskl - INFO - Epoch [104][1800/3746] lr: 2.191e-02, eta: 1 day, 15:22:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6492, loss_cls: 3.4493, loss: 3.4493 +2024-07-20 02:19:41,868 - pyskl - INFO - Epoch [104][1900/3746] lr: 2.189e-02, eta: 1 day, 15:21:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3933, top5_acc: 0.6481, loss_cls: 3.4664, loss: 3.4664 +2024-07-20 02:21:03,906 - pyskl - INFO - Epoch [104][2000/3746] lr: 2.187e-02, eta: 1 day, 15:19:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6497, loss_cls: 3.4542, loss: 3.4542 +2024-07-20 02:22:25,765 - pyskl - INFO - Epoch [104][2100/3746] lr: 2.184e-02, eta: 1 day, 15:18:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6477, loss_cls: 3.4766, loss: 3.4766 +2024-07-20 02:23:47,169 - pyskl - INFO - Epoch [104][2200/3746] lr: 2.182e-02, eta: 1 day, 15:16:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6408, loss_cls: 3.5255, loss: 3.5255 +2024-07-20 02:25:08,393 - pyskl - INFO - Epoch [104][2300/3746] lr: 2.180e-02, eta: 1 day, 15:15:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6353, loss_cls: 3.5036, loss: 3.5036 +2024-07-20 02:26:30,162 - pyskl - INFO - Epoch [104][2400/3746] lr: 2.177e-02, eta: 1 day, 15:14:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6455, loss_cls: 3.4947, loss: 3.4947 +2024-07-20 02:27:51,569 - pyskl - INFO - Epoch [104][2500/3746] lr: 2.175e-02, eta: 1 day, 15:12:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6386, loss_cls: 3.5041, loss: 3.5041 +2024-07-20 02:29:12,914 - pyskl - INFO - Epoch [104][2600/3746] lr: 2.173e-02, eta: 1 day, 15:11:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6358, loss_cls: 3.4891, loss: 3.4891 +2024-07-20 02:30:34,483 - pyskl - INFO - Epoch [104][2700/3746] lr: 2.171e-02, eta: 1 day, 15:10:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6397, loss_cls: 3.5116, loss: 3.5116 +2024-07-20 02:31:56,542 - pyskl - INFO - Epoch [104][2800/3746] lr: 2.168e-02, eta: 1 day, 15:08:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6488, loss_cls: 3.4757, loss: 3.4757 +2024-07-20 02:33:18,311 - pyskl - INFO - Epoch [104][2900/3746] lr: 2.166e-02, eta: 1 day, 15:07:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6486, loss_cls: 3.4543, loss: 3.4543 +2024-07-20 02:34:39,873 - pyskl - INFO - Epoch [104][3000/3746] lr: 2.164e-02, eta: 1 day, 15:06:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6397, loss_cls: 3.5147, loss: 3.5147 +2024-07-20 02:36:01,552 - pyskl - INFO - Epoch [104][3100/3746] lr: 2.161e-02, eta: 1 day, 15:04:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6420, loss_cls: 3.4852, loss: 3.4852 +2024-07-20 02:37:23,528 - pyskl - INFO - Epoch [104][3200/3746] lr: 2.159e-02, eta: 1 day, 15:03:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3837, top5_acc: 0.6428, loss_cls: 3.5023, loss: 3.5023 +2024-07-20 02:38:45,149 - pyskl - INFO - Epoch [104][3300/3746] lr: 2.157e-02, eta: 1 day, 15:02:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6466, loss_cls: 3.4765, loss: 3.4765 +2024-07-20 02:40:06,843 - pyskl - INFO - Epoch [104][3400/3746] lr: 2.154e-02, eta: 1 day, 15:00:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6528, loss_cls: 3.4365, loss: 3.4365 +2024-07-20 02:41:28,676 - pyskl - INFO - Epoch [104][3500/3746] lr: 2.152e-02, eta: 1 day, 14:59:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6517, loss_cls: 3.4370, loss: 3.4370 +2024-07-20 02:42:50,577 - pyskl - INFO - Epoch [104][3600/3746] lr: 2.150e-02, eta: 1 day, 14:58:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6523, loss_cls: 3.4352, loss: 3.4352 +2024-07-20 02:44:12,635 - pyskl - INFO - Epoch [104][3700/3746] lr: 2.148e-02, eta: 1 day, 14:56:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6428, loss_cls: 3.4759, loss: 3.4759 +2024-07-20 02:44:52,847 - pyskl - INFO - Saving checkpoint at 104 epochs +2024-07-20 02:46:44,744 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 02:46:45,509 - pyskl - INFO - +top1_acc 0.3319 +top5_acc 0.5912 +2024-07-20 02:46:45,509 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 02:46:45,554 - pyskl - INFO - +mean_acc 0.3316 +2024-07-20 02:46:45,558 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_102.pth was removed +2024-07-20 02:46:45,822 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_104.pth. +2024-07-20 02:46:45,823 - pyskl - INFO - Best top1_acc is 0.3319 at 104 epoch. +2024-07-20 02:46:45,838 - pyskl - INFO - Epoch(val) [104][309] top1_acc: 0.3319, top5_acc: 0.5912, mean_class_accuracy: 0.3316 +2024-07-20 02:50:41,563 - pyskl - INFO - Epoch [105][100/3746] lr: 2.144e-02, eta: 1 day, 14:55:32, time: 2.357, data_time: 1.353, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6628, loss_cls: 3.3506, loss: 3.3506 +2024-07-20 02:52:03,763 - pyskl - INFO - Epoch [105][200/3746] lr: 2.142e-02, eta: 1 day, 14:54:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6531, loss_cls: 3.4342, loss: 3.4342 +2024-07-20 02:53:26,852 - pyskl - INFO - Epoch [105][300/3746] lr: 2.140e-02, eta: 1 day, 14:52:50, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6580, loss_cls: 3.3704, loss: 3.3704 +2024-07-20 02:54:48,429 - pyskl - INFO - Epoch [105][400/3746] lr: 2.137e-02, eta: 1 day, 14:51:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6597, loss_cls: 3.3939, loss: 3.3939 +2024-07-20 02:56:09,809 - pyskl - INFO - Epoch [105][500/3746] lr: 2.135e-02, eta: 1 day, 14:50:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6608, loss_cls: 3.4184, loss: 3.4184 +2024-07-20 02:57:31,621 - pyskl - INFO - Epoch [105][600/3746] lr: 2.133e-02, eta: 1 day, 14:48:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6484, loss_cls: 3.4493, loss: 3.4493 +2024-07-20 02:58:52,916 - pyskl - INFO - Epoch [105][700/3746] lr: 2.130e-02, eta: 1 day, 14:47:25, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3980, top5_acc: 0.6555, loss_cls: 3.4202, loss: 3.4202 +2024-07-20 03:00:14,955 - pyskl - INFO - Epoch [105][800/3746] lr: 2.128e-02, eta: 1 day, 14:46:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6583, loss_cls: 3.4072, loss: 3.4072 +2024-07-20 03:01:36,543 - pyskl - INFO - Epoch [105][900/3746] lr: 2.126e-02, eta: 1 day, 14:44:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6512, loss_cls: 3.4395, loss: 3.4395 +2024-07-20 03:02:58,469 - pyskl - INFO - Epoch [105][1000/3746] lr: 2.124e-02, eta: 1 day, 14:43:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3858, top5_acc: 0.6453, loss_cls: 3.4727, loss: 3.4727 +2024-07-20 03:04:19,913 - pyskl - INFO - Epoch [105][1100/3746] lr: 2.121e-02, eta: 1 day, 14:42:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6573, loss_cls: 3.4124, loss: 3.4124 +2024-07-20 03:05:42,051 - pyskl - INFO - Epoch [105][1200/3746] lr: 2.119e-02, eta: 1 day, 14:40:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3992, top5_acc: 0.6569, loss_cls: 3.4002, loss: 3.4002 +2024-07-20 03:07:03,986 - pyskl - INFO - Epoch [105][1300/3746] lr: 2.117e-02, eta: 1 day, 14:39:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6509, loss_cls: 3.4543, loss: 3.4543 +2024-07-20 03:08:25,936 - pyskl - INFO - Epoch [105][1400/3746] lr: 2.114e-02, eta: 1 day, 14:37:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6605, loss_cls: 3.4147, loss: 3.4147 +2024-07-20 03:09:48,363 - pyskl - INFO - Epoch [105][1500/3746] lr: 2.112e-02, eta: 1 day, 14:36:36, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3923, top5_acc: 0.6530, loss_cls: 3.4291, loss: 3.4291 +2024-07-20 03:11:10,255 - pyskl - INFO - Epoch [105][1600/3746] lr: 2.110e-02, eta: 1 day, 14:35:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6514, loss_cls: 3.4453, loss: 3.4453 +2024-07-20 03:12:32,457 - pyskl - INFO - Epoch [105][1700/3746] lr: 2.108e-02, eta: 1 day, 14:33:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6538, loss_cls: 3.4154, loss: 3.4154 +2024-07-20 03:13:54,847 - pyskl - INFO - Epoch [105][1800/3746] lr: 2.105e-02, eta: 1 day, 14:32:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6427, loss_cls: 3.4799, loss: 3.4799 +2024-07-20 03:15:16,772 - pyskl - INFO - Epoch [105][1900/3746] lr: 2.103e-02, eta: 1 day, 14:31:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6497, loss_cls: 3.4591, loss: 3.4591 +2024-07-20 03:16:38,486 - pyskl - INFO - Epoch [105][2000/3746] lr: 2.101e-02, eta: 1 day, 14:29:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6488, loss_cls: 3.5058, loss: 3.5058 +2024-07-20 03:18:00,065 - pyskl - INFO - Epoch [105][2100/3746] lr: 2.098e-02, eta: 1 day, 14:28:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6575, loss_cls: 3.4071, loss: 3.4071 +2024-07-20 03:19:21,755 - pyskl - INFO - Epoch [105][2200/3746] lr: 2.096e-02, eta: 1 day, 14:27:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3859, top5_acc: 0.6520, loss_cls: 3.4630, loss: 3.4630 +2024-07-20 03:20:43,130 - pyskl - INFO - Epoch [105][2300/3746] lr: 2.094e-02, eta: 1 day, 14:25:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6395, loss_cls: 3.4616, loss: 3.4616 +2024-07-20 03:22:04,893 - pyskl - INFO - Epoch [105][2400/3746] lr: 2.092e-02, eta: 1 day, 14:24:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3980, top5_acc: 0.6547, loss_cls: 3.4221, loss: 3.4221 +2024-07-20 03:23:26,337 - pyskl - INFO - Epoch [105][2500/3746] lr: 2.089e-02, eta: 1 day, 14:23:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6484, loss_cls: 3.4597, loss: 3.4597 +2024-07-20 03:24:47,969 - pyskl - INFO - Epoch [105][2600/3746] lr: 2.087e-02, eta: 1 day, 14:21:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6538, loss_cls: 3.4243, loss: 3.4243 +2024-07-20 03:26:10,086 - pyskl - INFO - Epoch [105][2700/3746] lr: 2.085e-02, eta: 1 day, 14:20:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6527, loss_cls: 3.4113, loss: 3.4113 +2024-07-20 03:27:31,491 - pyskl - INFO - Epoch [105][2800/3746] lr: 2.083e-02, eta: 1 day, 14:19:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6427, loss_cls: 3.4775, loss: 3.4775 +2024-07-20 03:28:52,944 - pyskl - INFO - Epoch [105][2900/3746] lr: 2.080e-02, eta: 1 day, 14:17:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6427, loss_cls: 3.4624, loss: 3.4624 +2024-07-20 03:30:14,913 - pyskl - INFO - Epoch [105][3000/3746] lr: 2.078e-02, eta: 1 day, 14:16:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6462, loss_cls: 3.4737, loss: 3.4737 +2024-07-20 03:31:36,882 - pyskl - INFO - Epoch [105][3100/3746] lr: 2.076e-02, eta: 1 day, 14:14:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6511, loss_cls: 3.4341, loss: 3.4341 +2024-07-20 03:32:58,496 - pyskl - INFO - Epoch [105][3200/3746] lr: 2.073e-02, eta: 1 day, 14:13:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3989, top5_acc: 0.6516, loss_cls: 3.4263, loss: 3.4263 +2024-07-20 03:34:20,197 - pyskl - INFO - Epoch [105][3300/3746] lr: 2.071e-02, eta: 1 day, 14:12:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6434, loss_cls: 3.4376, loss: 3.4376 +2024-07-20 03:35:42,108 - pyskl - INFO - Epoch [105][3400/3746] lr: 2.069e-02, eta: 1 day, 14:10:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6538, loss_cls: 3.4504, loss: 3.4504 +2024-07-20 03:37:03,877 - pyskl - INFO - Epoch [105][3500/3746] lr: 2.067e-02, eta: 1 day, 14:09:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6556, loss_cls: 3.4560, loss: 3.4560 +2024-07-20 03:38:25,894 - pyskl - INFO - Epoch [105][3600/3746] lr: 2.064e-02, eta: 1 day, 14:08:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6433, loss_cls: 3.4455, loss: 3.4455 +2024-07-20 03:39:47,588 - pyskl - INFO - Epoch [105][3700/3746] lr: 2.062e-02, eta: 1 day, 14:06:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6438, loss_cls: 3.4547, loss: 3.4547 +2024-07-20 03:40:27,532 - pyskl - INFO - Saving checkpoint at 105 epochs +2024-07-20 03:42:19,160 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 03:42:19,821 - pyskl - INFO - +top1_acc 0.3316 +top5_acc 0.5849 +2024-07-20 03:42:19,821 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 03:42:19,860 - pyskl - INFO - +mean_acc 0.3314 +2024-07-20 03:42:19,871 - pyskl - INFO - Epoch(val) [105][309] top1_acc: 0.3316, top5_acc: 0.5849, mean_class_accuracy: 0.3314 +2024-07-20 03:46:09,089 - pyskl - INFO - Epoch [106][100/3746] lr: 2.059e-02, eta: 1 day, 14:05:38, time: 2.292, data_time: 1.303, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6561, loss_cls: 3.3945, loss: 3.3945 +2024-07-20 03:47:31,198 - pyskl - INFO - Epoch [106][200/3746] lr: 2.057e-02, eta: 1 day, 14:04:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3992, top5_acc: 0.6680, loss_cls: 3.3488, loss: 3.3488 +2024-07-20 03:48:53,665 - pyskl - INFO - Epoch [106][300/3746] lr: 2.054e-02, eta: 1 day, 14:02:56, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4156, top5_acc: 0.6675, loss_cls: 3.3472, loss: 3.3472 +2024-07-20 03:50:15,757 - pyskl - INFO - Epoch [106][400/3746] lr: 2.052e-02, eta: 1 day, 14:01:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6561, loss_cls: 3.3960, loss: 3.3960 +2024-07-20 03:51:37,419 - pyskl - INFO - Epoch [106][500/3746] lr: 2.050e-02, eta: 1 day, 14:00:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6669, loss_cls: 3.3308, loss: 3.3308 +2024-07-20 03:52:59,161 - pyskl - INFO - Epoch [106][600/3746] lr: 2.048e-02, eta: 1 day, 13:58:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6500, loss_cls: 3.4416, loss: 3.4416 +2024-07-20 03:54:21,038 - pyskl - INFO - Epoch [106][700/3746] lr: 2.045e-02, eta: 1 day, 13:57:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3858, top5_acc: 0.6514, loss_cls: 3.4459, loss: 3.4459 +2024-07-20 03:55:42,789 - pyskl - INFO - Epoch [106][800/3746] lr: 2.043e-02, eta: 1 day, 13:56:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3909, top5_acc: 0.6445, loss_cls: 3.4459, loss: 3.4459 +2024-07-20 03:57:04,472 - pyskl - INFO - Epoch [106][900/3746] lr: 2.041e-02, eta: 1 day, 13:54:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6550, loss_cls: 3.3984, loss: 3.3984 +2024-07-20 03:58:26,334 - pyskl - INFO - Epoch [106][1000/3746] lr: 2.039e-02, eta: 1 day, 13:53:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6533, loss_cls: 3.4330, loss: 3.4330 +2024-07-20 03:59:48,267 - pyskl - INFO - Epoch [106][1100/3746] lr: 2.036e-02, eta: 1 day, 13:52:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6480, loss_cls: 3.4214, loss: 3.4214 +2024-07-20 04:01:10,552 - pyskl - INFO - Epoch [106][1200/3746] lr: 2.034e-02, eta: 1 day, 13:50:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6441, loss_cls: 3.4716, loss: 3.4716 +2024-07-20 04:02:32,517 - pyskl - INFO - Epoch [106][1300/3746] lr: 2.032e-02, eta: 1 day, 13:49:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6541, loss_cls: 3.4563, loss: 3.4563 +2024-07-20 04:03:54,495 - pyskl - INFO - Epoch [106][1400/3746] lr: 2.030e-02, eta: 1 day, 13:48:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6541, loss_cls: 3.4356, loss: 3.4356 +2024-07-20 04:05:16,075 - pyskl - INFO - Epoch [106][1500/3746] lr: 2.027e-02, eta: 1 day, 13:46:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6589, loss_cls: 3.3660, loss: 3.3660 +2024-07-20 04:06:38,022 - pyskl - INFO - Epoch [106][1600/3746] lr: 2.025e-02, eta: 1 day, 13:45:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6611, loss_cls: 3.4014, loss: 3.4014 +2024-07-20 04:07:59,805 - pyskl - INFO - Epoch [106][1700/3746] lr: 2.023e-02, eta: 1 day, 13:43:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6617, loss_cls: 3.3811, loss: 3.3811 +2024-07-20 04:09:21,581 - pyskl - INFO - Epoch [106][1800/3746] lr: 2.021e-02, eta: 1 day, 13:42:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6517, loss_cls: 3.4290, loss: 3.4290 +2024-07-20 04:10:43,352 - pyskl - INFO - Epoch [106][1900/3746] lr: 2.018e-02, eta: 1 day, 13:41:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3898, top5_acc: 0.6439, loss_cls: 3.4415, loss: 3.4415 +2024-07-20 04:12:04,940 - pyskl - INFO - Epoch [106][2000/3746] lr: 2.016e-02, eta: 1 day, 13:39:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6495, loss_cls: 3.4033, loss: 3.4033 +2024-07-20 04:13:26,627 - pyskl - INFO - Epoch [106][2100/3746] lr: 2.014e-02, eta: 1 day, 13:38:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6559, loss_cls: 3.4049, loss: 3.4049 +2024-07-20 04:14:48,334 - pyskl - INFO - Epoch [106][2200/3746] lr: 2.012e-02, eta: 1 day, 13:37:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6517, loss_cls: 3.4123, loss: 3.4123 +2024-07-20 04:16:10,536 - pyskl - INFO - Epoch [106][2300/3746] lr: 2.009e-02, eta: 1 day, 13:35:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6538, loss_cls: 3.4069, loss: 3.4069 +2024-07-20 04:17:31,910 - pyskl - INFO - Epoch [106][2400/3746] lr: 2.007e-02, eta: 1 day, 13:34:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6503, loss_cls: 3.4088, loss: 3.4088 +2024-07-20 04:18:53,152 - pyskl - INFO - Epoch [106][2500/3746] lr: 2.005e-02, eta: 1 day, 13:33:09, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3748, top5_acc: 0.6361, loss_cls: 3.5338, loss: 3.5338 +2024-07-20 04:20:14,475 - pyskl - INFO - Epoch [106][2600/3746] lr: 2.003e-02, eta: 1 day, 13:31:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6552, loss_cls: 3.4421, loss: 3.4421 +2024-07-20 04:21:36,101 - pyskl - INFO - Epoch [106][2700/3746] lr: 2.000e-02, eta: 1 day, 13:30:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3986, top5_acc: 0.6497, loss_cls: 3.4545, loss: 3.4545 +2024-07-20 04:22:57,457 - pyskl - INFO - Epoch [106][2800/3746] lr: 1.998e-02, eta: 1 day, 13:29:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6542, loss_cls: 3.4341, loss: 3.4341 +2024-07-20 04:24:19,134 - pyskl - INFO - Epoch [106][2900/3746] lr: 1.996e-02, eta: 1 day, 13:27:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6447, loss_cls: 3.4642, loss: 3.4642 +2024-07-20 04:25:40,341 - pyskl - INFO - Epoch [106][3000/3746] lr: 1.994e-02, eta: 1 day, 13:26:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6547, loss_cls: 3.3970, loss: 3.3970 +2024-07-20 04:27:01,633 - pyskl - INFO - Epoch [106][3100/3746] lr: 1.991e-02, eta: 1 day, 13:25:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6617, loss_cls: 3.3973, loss: 3.3973 +2024-07-20 04:28:24,109 - pyskl - INFO - Epoch [106][3200/3746] lr: 1.989e-02, eta: 1 day, 13:23:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6573, loss_cls: 3.4118, loss: 3.4118 +2024-07-20 04:29:45,480 - pyskl - INFO - Epoch [106][3300/3746] lr: 1.987e-02, eta: 1 day, 13:22:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3858, top5_acc: 0.6512, loss_cls: 3.4399, loss: 3.4399 +2024-07-20 04:31:07,954 - pyskl - INFO - Epoch [106][3400/3746] lr: 1.985e-02, eta: 1 day, 13:20:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3989, top5_acc: 0.6517, loss_cls: 3.4256, loss: 3.4256 +2024-07-20 04:32:29,755 - pyskl - INFO - Epoch [106][3500/3746] lr: 1.983e-02, eta: 1 day, 13:19:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3947, top5_acc: 0.6534, loss_cls: 3.4272, loss: 3.4272 +2024-07-20 04:33:51,617 - pyskl - INFO - Epoch [106][3600/3746] lr: 1.980e-02, eta: 1 day, 13:18:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6572, loss_cls: 3.4143, loss: 3.4143 +2024-07-20 04:35:13,316 - pyskl - INFO - Epoch [106][3700/3746] lr: 1.978e-02, eta: 1 day, 13:16:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3927, top5_acc: 0.6531, loss_cls: 3.4395, loss: 3.4395 +2024-07-20 04:35:53,221 - pyskl - INFO - Saving checkpoint at 106 epochs +2024-07-20 04:37:44,642 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 04:37:45,375 - pyskl - INFO - +top1_acc 0.3341 +top5_acc 0.5936 +2024-07-20 04:37:45,376 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 04:37:45,422 - pyskl - INFO - +mean_acc 0.3339 +2024-07-20 04:37:45,427 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_104.pth was removed +2024-07-20 04:37:45,694 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_106.pth. +2024-07-20 04:37:45,694 - pyskl - INFO - Best top1_acc is 0.3341 at 106 epoch. +2024-07-20 04:37:45,706 - pyskl - INFO - Epoch(val) [106][309] top1_acc: 0.3341, top5_acc: 0.5936, mean_class_accuracy: 0.3339 +2024-07-20 04:41:41,511 - pyskl - INFO - Epoch [107][100/3746] lr: 1.975e-02, eta: 1 day, 13:15:43, time: 2.358, data_time: 1.356, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6645, loss_cls: 3.3349, loss: 3.3349 +2024-07-20 04:43:03,806 - pyskl - INFO - Epoch [107][200/3746] lr: 1.973e-02, eta: 1 day, 13:14:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6695, loss_cls: 3.3251, loss: 3.3251 +2024-07-20 04:44:26,488 - pyskl - INFO - Epoch [107][300/3746] lr: 1.970e-02, eta: 1 day, 13:13:01, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3947, top5_acc: 0.6587, loss_cls: 3.4299, loss: 3.4299 +2024-07-20 04:45:48,027 - pyskl - INFO - Epoch [107][400/3746] lr: 1.968e-02, eta: 1 day, 13:11:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6570, loss_cls: 3.3704, loss: 3.3704 +2024-07-20 04:47:09,992 - pyskl - INFO - Epoch [107][500/3746] lr: 1.966e-02, eta: 1 day, 13:10:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4156, top5_acc: 0.6636, loss_cls: 3.3329, loss: 3.3329 +2024-07-20 04:48:31,646 - pyskl - INFO - Epoch [107][600/3746] lr: 1.964e-02, eta: 1 day, 13:08:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6587, loss_cls: 3.3818, loss: 3.3818 +2024-07-20 04:49:53,500 - pyskl - INFO - Epoch [107][700/3746] lr: 1.961e-02, eta: 1 day, 13:07:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6644, loss_cls: 3.3807, loss: 3.3807 +2024-07-20 04:51:15,282 - pyskl - INFO - Epoch [107][800/3746] lr: 1.959e-02, eta: 1 day, 13:06:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6492, loss_cls: 3.4224, loss: 3.4224 +2024-07-20 04:52:36,922 - pyskl - INFO - Epoch [107][900/3746] lr: 1.957e-02, eta: 1 day, 13:04:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6564, loss_cls: 3.4102, loss: 3.4102 +2024-07-20 04:53:59,123 - pyskl - INFO - Epoch [107][1000/3746] lr: 1.955e-02, eta: 1 day, 13:03:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6561, loss_cls: 3.3883, loss: 3.3883 +2024-07-20 04:55:20,934 - pyskl - INFO - Epoch [107][1100/3746] lr: 1.953e-02, eta: 1 day, 13:02:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6605, loss_cls: 3.3607, loss: 3.3607 +2024-07-20 04:56:42,335 - pyskl - INFO - Epoch [107][1200/3746] lr: 1.950e-02, eta: 1 day, 13:00:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6614, loss_cls: 3.3976, loss: 3.3976 +2024-07-20 04:58:04,225 - pyskl - INFO - Epoch [107][1300/3746] lr: 1.948e-02, eta: 1 day, 12:59:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6469, loss_cls: 3.4504, loss: 3.4504 +2024-07-20 04:59:26,376 - pyskl - INFO - Epoch [107][1400/3746] lr: 1.946e-02, eta: 1 day, 12:58:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6523, loss_cls: 3.3959, loss: 3.3959 +2024-07-20 05:00:48,112 - pyskl - INFO - Epoch [107][1500/3746] lr: 1.944e-02, eta: 1 day, 12:56:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6525, loss_cls: 3.3888, loss: 3.3888 +2024-07-20 05:02:09,815 - pyskl - INFO - Epoch [107][1600/3746] lr: 1.942e-02, eta: 1 day, 12:55:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6552, loss_cls: 3.3830, loss: 3.3830 +2024-07-20 05:03:32,081 - pyskl - INFO - Epoch [107][1700/3746] lr: 1.939e-02, eta: 1 day, 12:54:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6438, loss_cls: 3.4602, loss: 3.4602 +2024-07-20 05:04:54,320 - pyskl - INFO - Epoch [107][1800/3746] lr: 1.937e-02, eta: 1 day, 12:52:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6623, loss_cls: 3.3901, loss: 3.3901 +2024-07-20 05:06:16,527 - pyskl - INFO - Epoch [107][1900/3746] lr: 1.935e-02, eta: 1 day, 12:51:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3908, top5_acc: 0.6512, loss_cls: 3.4276, loss: 3.4276 +2024-07-20 05:07:39,065 - pyskl - INFO - Epoch [107][2000/3746] lr: 1.933e-02, eta: 1 day, 12:50:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6678, loss_cls: 3.3557, loss: 3.3557 +2024-07-20 05:09:00,624 - pyskl - INFO - Epoch [107][2100/3746] lr: 1.930e-02, eta: 1 day, 12:48:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6509, loss_cls: 3.4104, loss: 3.4104 +2024-07-20 05:10:23,118 - pyskl - INFO - Epoch [107][2200/3746] lr: 1.928e-02, eta: 1 day, 12:47:18, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6489, loss_cls: 3.4286, loss: 3.4286 +2024-07-20 05:11:44,499 - pyskl - INFO - Epoch [107][2300/3746] lr: 1.926e-02, eta: 1 day, 12:45:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6561, loss_cls: 3.3865, loss: 3.3865 +2024-07-20 05:13:06,057 - pyskl - INFO - Epoch [107][2400/3746] lr: 1.924e-02, eta: 1 day, 12:44:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6600, loss_cls: 3.3978, loss: 3.3978 +2024-07-20 05:14:27,959 - pyskl - INFO - Epoch [107][2500/3746] lr: 1.922e-02, eta: 1 day, 12:43:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6566, loss_cls: 3.3877, loss: 3.3877 +2024-07-20 05:15:49,497 - pyskl - INFO - Epoch [107][2600/3746] lr: 1.919e-02, eta: 1 day, 12:41:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3927, top5_acc: 0.6631, loss_cls: 3.3884, loss: 3.3884 +2024-07-20 05:17:10,968 - pyskl - INFO - Epoch [107][2700/3746] lr: 1.917e-02, eta: 1 day, 12:40:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3986, top5_acc: 0.6575, loss_cls: 3.3920, loss: 3.3920 +2024-07-20 05:18:33,033 - pyskl - INFO - Epoch [107][2800/3746] lr: 1.915e-02, eta: 1 day, 12:39:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6669, loss_cls: 3.3654, loss: 3.3654 +2024-07-20 05:19:54,737 - pyskl - INFO - Epoch [107][2900/3746] lr: 1.913e-02, eta: 1 day, 12:37:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6514, loss_cls: 3.4157, loss: 3.4157 +2024-07-20 05:21:16,614 - pyskl - INFO - Epoch [107][3000/3746] lr: 1.911e-02, eta: 1 day, 12:36:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6475, loss_cls: 3.4349, loss: 3.4349 +2024-07-20 05:22:38,019 - pyskl - INFO - Epoch [107][3100/3746] lr: 1.908e-02, eta: 1 day, 12:35:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3945, top5_acc: 0.6500, loss_cls: 3.4023, loss: 3.4023 +2024-07-20 05:24:01,019 - pyskl - INFO - Epoch [107][3200/3746] lr: 1.906e-02, eta: 1 day, 12:33:45, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3972, top5_acc: 0.6572, loss_cls: 3.4231, loss: 3.4231 +2024-07-20 05:25:22,813 - pyskl - INFO - Epoch [107][3300/3746] lr: 1.904e-02, eta: 1 day, 12:32:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6566, loss_cls: 3.4201, loss: 3.4201 +2024-07-20 05:26:44,614 - pyskl - INFO - Epoch [107][3400/3746] lr: 1.902e-02, eta: 1 day, 12:31:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6558, loss_cls: 3.3888, loss: 3.3888 +2024-07-20 05:28:06,261 - pyskl - INFO - Epoch [107][3500/3746] lr: 1.900e-02, eta: 1 day, 12:29:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6578, loss_cls: 3.4007, loss: 3.4007 +2024-07-20 05:29:28,461 - pyskl - INFO - Epoch [107][3600/3746] lr: 1.897e-02, eta: 1 day, 12:28:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6525, loss_cls: 3.4333, loss: 3.4333 +2024-07-20 05:30:50,117 - pyskl - INFO - Epoch [107][3700/3746] lr: 1.895e-02, eta: 1 day, 12:26:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6617, loss_cls: 3.3743, loss: 3.3743 +2024-07-20 05:31:29,985 - pyskl - INFO - Saving checkpoint at 107 epochs +2024-07-20 05:33:21,445 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 05:33:22,120 - pyskl - INFO - +top1_acc 0.3326 +top5_acc 0.5880 +2024-07-20 05:33:22,120 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 05:33:22,168 - pyskl - INFO - +mean_acc 0.3323 +2024-07-20 05:33:22,182 - pyskl - INFO - Epoch(val) [107][309] top1_acc: 0.3326, top5_acc: 0.5880, mean_class_accuracy: 0.3323 +2024-07-20 05:37:18,675 - pyskl - INFO - Epoch [108][100/3746] lr: 1.892e-02, eta: 1 day, 12:25:46, time: 2.365, data_time: 1.362, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6731, loss_cls: 3.2854, loss: 3.2854 +2024-07-20 05:38:41,507 - pyskl - INFO - Epoch [108][200/3746] lr: 1.890e-02, eta: 1 day, 12:24:25, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6722, loss_cls: 3.3463, loss: 3.3463 +2024-07-20 05:40:05,397 - pyskl - INFO - Epoch [108][300/3746] lr: 1.888e-02, eta: 1 day, 12:23:05, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4016, top5_acc: 0.6652, loss_cls: 3.3464, loss: 3.3464 +2024-07-20 05:41:29,042 - pyskl - INFO - Epoch [108][400/3746] lr: 1.886e-02, eta: 1 day, 12:21:44, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6578, loss_cls: 3.3663, loss: 3.3663 +2024-07-20 05:42:52,784 - pyskl - INFO - Epoch [108][500/3746] lr: 1.883e-02, eta: 1 day, 12:20:24, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4119, top5_acc: 0.6683, loss_cls: 3.3225, loss: 3.3225 +2024-07-20 05:44:16,331 - pyskl - INFO - Epoch [108][600/3746] lr: 1.881e-02, eta: 1 day, 12:19:03, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6664, loss_cls: 3.3092, loss: 3.3092 +2024-07-20 05:45:40,015 - pyskl - INFO - Epoch [108][700/3746] lr: 1.879e-02, eta: 1 day, 12:17:43, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6581, loss_cls: 3.3890, loss: 3.3890 +2024-07-20 05:47:03,787 - pyskl - INFO - Epoch [108][800/3746] lr: 1.877e-02, eta: 1 day, 12:16:22, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6658, loss_cls: 3.3754, loss: 3.3754 +2024-07-20 05:48:27,404 - pyskl - INFO - Epoch [108][900/3746] lr: 1.875e-02, eta: 1 day, 12:15:01, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6742, loss_cls: 3.3200, loss: 3.3200 +2024-07-20 05:49:51,346 - pyskl - INFO - Epoch [108][1000/3746] lr: 1.872e-02, eta: 1 day, 12:13:41, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6578, loss_cls: 3.3340, loss: 3.3340 +2024-07-20 05:51:15,107 - pyskl - INFO - Epoch [108][1100/3746] lr: 1.870e-02, eta: 1 day, 12:12:20, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3994, top5_acc: 0.6606, loss_cls: 3.3718, loss: 3.3718 +2024-07-20 05:52:38,858 - pyskl - INFO - Epoch [108][1200/3746] lr: 1.868e-02, eta: 1 day, 12:11:00, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3945, top5_acc: 0.6556, loss_cls: 3.4084, loss: 3.4084 +2024-07-20 05:54:02,642 - pyskl - INFO - Epoch [108][1300/3746] lr: 1.866e-02, eta: 1 day, 12:09:39, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6652, loss_cls: 3.3528, loss: 3.3528 +2024-07-20 05:55:26,145 - pyskl - INFO - Epoch [108][1400/3746] lr: 1.864e-02, eta: 1 day, 12:08:19, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6637, loss_cls: 3.3627, loss: 3.3627 +2024-07-20 05:56:49,888 - pyskl - INFO - Epoch [108][1500/3746] lr: 1.862e-02, eta: 1 day, 12:06:58, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4070, top5_acc: 0.6605, loss_cls: 3.3526, loss: 3.3526 +2024-07-20 05:58:12,524 - pyskl - INFO - Epoch [108][1600/3746] lr: 1.859e-02, eta: 1 day, 12:05:37, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4073, top5_acc: 0.6628, loss_cls: 3.3547, loss: 3.3547 +2024-07-20 05:59:35,305 - pyskl - INFO - Epoch [108][1700/3746] lr: 1.857e-02, eta: 1 day, 12:04:16, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3989, top5_acc: 0.6584, loss_cls: 3.3828, loss: 3.3828 +2024-07-20 06:00:59,316 - pyskl - INFO - Epoch [108][1800/3746] lr: 1.855e-02, eta: 1 day, 12:02:56, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6527, loss_cls: 3.4075, loss: 3.4075 +2024-07-20 06:02:22,910 - pyskl - INFO - Epoch [108][1900/3746] lr: 1.853e-02, eta: 1 day, 12:01:35, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6595, loss_cls: 3.4006, loss: 3.4006 +2024-07-20 06:03:45,844 - pyskl - INFO - Epoch [108][2000/3746] lr: 1.851e-02, eta: 1 day, 12:00:14, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6603, loss_cls: 3.3919, loss: 3.3919 +2024-07-20 06:05:08,779 - pyskl - INFO - Epoch [108][2100/3746] lr: 1.848e-02, eta: 1 day, 11:58:53, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6584, loss_cls: 3.3963, loss: 3.3963 +2024-07-20 06:06:31,778 - pyskl - INFO - Epoch [108][2200/3746] lr: 1.846e-02, eta: 1 day, 11:57:32, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6481, loss_cls: 3.4344, loss: 3.4344 +2024-07-20 06:07:54,494 - pyskl - INFO - Epoch [108][2300/3746] lr: 1.844e-02, eta: 1 day, 11:56:11, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6628, loss_cls: 3.3420, loss: 3.3420 +2024-07-20 06:09:17,124 - pyskl - INFO - Epoch [108][2400/3746] lr: 1.842e-02, eta: 1 day, 11:54:50, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6573, loss_cls: 3.4082, loss: 3.4082 +2024-07-20 06:10:39,535 - pyskl - INFO - Epoch [108][2500/3746] lr: 1.840e-02, eta: 1 day, 11:53:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6556, loss_cls: 3.3517, loss: 3.3517 +2024-07-20 06:12:02,230 - pyskl - INFO - Epoch [108][2600/3746] lr: 1.838e-02, eta: 1 day, 11:52:08, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4016, top5_acc: 0.6584, loss_cls: 3.3906, loss: 3.3906 +2024-07-20 06:13:25,054 - pyskl - INFO - Epoch [108][2700/3746] lr: 1.835e-02, eta: 1 day, 11:50:47, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4025, top5_acc: 0.6583, loss_cls: 3.3841, loss: 3.3841 +2024-07-20 06:14:47,441 - pyskl - INFO - Epoch [108][2800/3746] lr: 1.833e-02, eta: 1 day, 11:49:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6556, loss_cls: 3.4192, loss: 3.4192 +2024-07-20 06:16:09,825 - pyskl - INFO - Epoch [108][2900/3746] lr: 1.831e-02, eta: 1 day, 11:48:05, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6661, loss_cls: 3.3878, loss: 3.3878 +2024-07-20 06:17:32,197 - pyskl - INFO - Epoch [108][3000/3746] lr: 1.829e-02, eta: 1 day, 11:46:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6614, loss_cls: 3.3687, loss: 3.3687 +2024-07-20 06:18:53,842 - pyskl - INFO - Epoch [108][3100/3746] lr: 1.827e-02, eta: 1 day, 11:45:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6534, loss_cls: 3.4093, loss: 3.4093 +2024-07-20 06:20:17,396 - pyskl - INFO - Epoch [108][3200/3746] lr: 1.825e-02, eta: 1 day, 11:44:02, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4016, top5_acc: 0.6661, loss_cls: 3.3734, loss: 3.3734 +2024-07-20 06:21:39,770 - pyskl - INFO - Epoch [108][3300/3746] lr: 1.823e-02, eta: 1 day, 11:42:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6508, loss_cls: 3.4093, loss: 3.4093 +2024-07-20 06:23:01,442 - pyskl - INFO - Epoch [108][3400/3746] lr: 1.820e-02, eta: 1 day, 11:41:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4062, top5_acc: 0.6702, loss_cls: 3.3309, loss: 3.3309 +2024-07-20 06:24:24,130 - pyskl - INFO - Epoch [108][3500/3746] lr: 1.818e-02, eta: 1 day, 11:39:58, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6495, loss_cls: 3.4655, loss: 3.4655 +2024-07-20 06:25:47,939 - pyskl - INFO - Epoch [108][3600/3746] lr: 1.816e-02, eta: 1 day, 11:38:38, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6617, loss_cls: 3.3729, loss: 3.3729 +2024-07-20 06:27:10,275 - pyskl - INFO - Epoch [108][3700/3746] lr: 1.814e-02, eta: 1 day, 11:37:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6552, loss_cls: 3.4424, loss: 3.4424 +2024-07-20 06:27:49,945 - pyskl - INFO - Saving checkpoint at 108 epochs +2024-07-20 06:29:41,706 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 06:29:42,464 - pyskl - INFO - +top1_acc 0.3467 +top5_acc 0.5939 +2024-07-20 06:29:42,464 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 06:29:42,506 - pyskl - INFO - +mean_acc 0.3465 +2024-07-20 06:29:42,511 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_106.pth was removed +2024-07-20 06:29:42,772 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_108.pth. +2024-07-20 06:29:42,773 - pyskl - INFO - Best top1_acc is 0.3467 at 108 epoch. +2024-07-20 06:29:42,786 - pyskl - INFO - Epoch(val) [108][309] top1_acc: 0.3467, top5_acc: 0.5939, mean_class_accuracy: 0.3465 +2024-07-20 06:33:34,384 - pyskl - INFO - Epoch [109][100/3746] lr: 1.811e-02, eta: 1 day, 11:36:01, time: 2.316, data_time: 1.334, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6877, loss_cls: 3.2402, loss: 3.2402 +2024-07-20 06:34:57,599 - pyskl - INFO - Epoch [109][200/3746] lr: 1.809e-02, eta: 1 day, 11:34:40, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4181, top5_acc: 0.6759, loss_cls: 3.2663, loss: 3.2663 +2024-07-20 06:36:20,792 - pyskl - INFO - Epoch [109][300/3746] lr: 1.806e-02, eta: 1 day, 11:33:19, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6573, loss_cls: 3.3400, loss: 3.3400 +2024-07-20 06:37:43,637 - pyskl - INFO - Epoch [109][400/3746] lr: 1.804e-02, eta: 1 day, 11:31:58, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6683, loss_cls: 3.3827, loss: 3.3827 +2024-07-20 06:39:06,890 - pyskl - INFO - Epoch [109][500/3746] lr: 1.802e-02, eta: 1 day, 11:30:38, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6623, loss_cls: 3.3509, loss: 3.3509 +2024-07-20 06:40:29,815 - pyskl - INFO - Epoch [109][600/3746] lr: 1.800e-02, eta: 1 day, 11:29:17, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3989, top5_acc: 0.6564, loss_cls: 3.3756, loss: 3.3756 +2024-07-20 06:41:52,783 - pyskl - INFO - Epoch [109][700/3746] lr: 1.798e-02, eta: 1 day, 11:27:56, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6691, loss_cls: 3.3434, loss: 3.3434 +2024-07-20 06:43:14,737 - pyskl - INFO - Epoch [109][800/3746] lr: 1.796e-02, eta: 1 day, 11:26:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6670, loss_cls: 3.3340, loss: 3.3340 +2024-07-20 06:44:37,236 - pyskl - INFO - Epoch [109][900/3746] lr: 1.794e-02, eta: 1 day, 11:25:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6602, loss_cls: 3.3614, loss: 3.3614 +2024-07-20 06:45:59,904 - pyskl - INFO - Epoch [109][1000/3746] lr: 1.791e-02, eta: 1 day, 11:23:52, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6531, loss_cls: 3.4186, loss: 3.4186 +2024-07-20 06:47:22,489 - pyskl - INFO - Epoch [109][1100/3746] lr: 1.789e-02, eta: 1 day, 11:22:31, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6602, loss_cls: 3.3866, loss: 3.3866 +2024-07-20 06:48:44,660 - pyskl - INFO - Epoch [109][1200/3746] lr: 1.787e-02, eta: 1 day, 11:21:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6661, loss_cls: 3.3412, loss: 3.3412 +2024-07-20 06:50:06,965 - pyskl - INFO - Epoch [109][1300/3746] lr: 1.785e-02, eta: 1 day, 11:19:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4008, top5_acc: 0.6605, loss_cls: 3.3708, loss: 3.3708 +2024-07-20 06:51:29,362 - pyskl - INFO - Epoch [109][1400/3746] lr: 1.783e-02, eta: 1 day, 11:18:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6559, loss_cls: 3.3909, loss: 3.3909 +2024-07-20 06:52:52,431 - pyskl - INFO - Epoch [109][1500/3746] lr: 1.781e-02, eta: 1 day, 11:17:06, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6652, loss_cls: 3.3538, loss: 3.3538 +2024-07-20 06:54:14,207 - pyskl - INFO - Epoch [109][1600/3746] lr: 1.779e-02, eta: 1 day, 11:15:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3945, top5_acc: 0.6481, loss_cls: 3.4321, loss: 3.4321 +2024-07-20 06:55:37,037 - pyskl - INFO - Epoch [109][1700/3746] lr: 1.776e-02, eta: 1 day, 11:14:24, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4078, top5_acc: 0.6766, loss_cls: 3.3027, loss: 3.3027 +2024-07-20 06:57:00,861 - pyskl - INFO - Epoch [109][1800/3746] lr: 1.774e-02, eta: 1 day, 11:13:03, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3972, top5_acc: 0.6583, loss_cls: 3.4004, loss: 3.4004 +2024-07-20 06:58:24,483 - pyskl - INFO - Epoch [109][1900/3746] lr: 1.772e-02, eta: 1 day, 11:11:43, time: 0.836, data_time: 0.001, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6744, loss_cls: 3.3118, loss: 3.3118 +2024-07-20 06:59:47,500 - pyskl - INFO - Epoch [109][2000/3746] lr: 1.770e-02, eta: 1 day, 11:10:22, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4138, top5_acc: 0.6708, loss_cls: 3.3061, loss: 3.3061 +2024-07-20 07:01:09,789 - pyskl - INFO - Epoch [109][2100/3746] lr: 1.768e-02, eta: 1 day, 11:09:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6616, loss_cls: 3.3744, loss: 3.3744 +2024-07-20 07:02:32,184 - pyskl - INFO - Epoch [109][2200/3746] lr: 1.766e-02, eta: 1 day, 11:07:39, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6641, loss_cls: 3.3692, loss: 3.3692 +2024-07-20 07:03:54,727 - pyskl - INFO - Epoch [109][2300/3746] lr: 1.764e-02, eta: 1 day, 11:06:18, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6717, loss_cls: 3.3404, loss: 3.3404 +2024-07-20 07:05:16,996 - pyskl - INFO - Epoch [109][2400/3746] lr: 1.761e-02, eta: 1 day, 11:04:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6616, loss_cls: 3.3998, loss: 3.3998 +2024-07-20 07:06:39,561 - pyskl - INFO - Epoch [109][2500/3746] lr: 1.759e-02, eta: 1 day, 11:03:36, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6700, loss_cls: 3.3400, loss: 3.3400 +2024-07-20 07:08:01,699 - pyskl - INFO - Epoch [109][2600/3746] lr: 1.757e-02, eta: 1 day, 11:02:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6584, loss_cls: 3.3786, loss: 3.3786 +2024-07-20 07:09:23,829 - pyskl - INFO - Epoch [109][2700/3746] lr: 1.755e-02, eta: 1 day, 11:00:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6589, loss_cls: 3.3677, loss: 3.3677 +2024-07-20 07:10:45,724 - pyskl - INFO - Epoch [109][2800/3746] lr: 1.753e-02, eta: 1 day, 10:59:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4033, top5_acc: 0.6584, loss_cls: 3.3962, loss: 3.3962 +2024-07-20 07:12:08,503 - pyskl - INFO - Epoch [109][2900/3746] lr: 1.751e-02, eta: 1 day, 10:58:11, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6619, loss_cls: 3.3886, loss: 3.3886 +2024-07-20 07:13:30,353 - pyskl - INFO - Epoch [109][3000/3746] lr: 1.749e-02, eta: 1 day, 10:56:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6587, loss_cls: 3.3481, loss: 3.3481 +2024-07-20 07:14:52,607 - pyskl - INFO - Epoch [109][3100/3746] lr: 1.747e-02, eta: 1 day, 10:55:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6681, loss_cls: 3.3469, loss: 3.3469 +2024-07-20 07:16:15,229 - pyskl - INFO - Epoch [109][3200/3746] lr: 1.744e-02, eta: 1 day, 10:54:07, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6622, loss_cls: 3.3858, loss: 3.3858 +2024-07-20 07:17:36,965 - pyskl - INFO - Epoch [109][3300/3746] lr: 1.742e-02, eta: 1 day, 10:52:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4163, top5_acc: 0.6648, loss_cls: 3.3483, loss: 3.3483 +2024-07-20 07:18:59,119 - pyskl - INFO - Epoch [109][3400/3746] lr: 1.740e-02, eta: 1 day, 10:51:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6544, loss_cls: 3.3864, loss: 3.3864 +2024-07-20 07:20:22,084 - pyskl - INFO - Epoch [109][3500/3746] lr: 1.738e-02, eta: 1 day, 10:50:03, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6623, loss_cls: 3.3852, loss: 3.3852 +2024-07-20 07:21:45,692 - pyskl - INFO - Epoch [109][3600/3746] lr: 1.736e-02, eta: 1 day, 10:48:43, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6591, loss_cls: 3.3805, loss: 3.3805 +2024-07-20 07:23:07,840 - pyskl - INFO - Epoch [109][3700/3746] lr: 1.734e-02, eta: 1 day, 10:47:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4098, top5_acc: 0.6658, loss_cls: 3.3439, loss: 3.3439 +2024-07-20 07:23:47,695 - pyskl - INFO - Saving checkpoint at 109 epochs +2024-07-20 07:25:38,633 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 07:25:39,366 - pyskl - INFO - +top1_acc 0.3331 +top5_acc 0.5851 +2024-07-20 07:25:39,367 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 07:25:39,409 - pyskl - INFO - +mean_acc 0.3328 +2024-07-20 07:25:39,422 - pyskl - INFO - Epoch(val) [109][309] top1_acc: 0.3331, top5_acc: 0.5851, mean_class_accuracy: 0.3328 +2024-07-20 07:29:30,866 - pyskl - INFO - Epoch [110][100/3746] lr: 1.731e-02, eta: 1 day, 10:46:05, time: 2.314, data_time: 1.335, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6850, loss_cls: 3.2635, loss: 3.2635 +2024-07-20 07:30:54,090 - pyskl - INFO - Epoch [110][200/3746] lr: 1.729e-02, eta: 1 day, 10:44:44, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6837, loss_cls: 3.2484, loss: 3.2484 +2024-07-20 07:32:17,616 - pyskl - INFO - Epoch [110][300/3746] lr: 1.727e-02, eta: 1 day, 10:43:23, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4178, top5_acc: 0.6759, loss_cls: 3.2561, loss: 3.2561 +2024-07-20 07:33:40,637 - pyskl - INFO - Epoch [110][400/3746] lr: 1.724e-02, eta: 1 day, 10:42:02, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4106, top5_acc: 0.6741, loss_cls: 3.3090, loss: 3.3090 +2024-07-20 07:35:03,268 - pyskl - INFO - Epoch [110][500/3746] lr: 1.722e-02, eta: 1 day, 10:40:41, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6664, loss_cls: 3.3370, loss: 3.3370 +2024-07-20 07:36:26,114 - pyskl - INFO - Epoch [110][600/3746] lr: 1.720e-02, eta: 1 day, 10:39:20, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4117, top5_acc: 0.6684, loss_cls: 3.3103, loss: 3.3103 +2024-07-20 07:37:49,099 - pyskl - INFO - Epoch [110][700/3746] lr: 1.718e-02, eta: 1 day, 10:37:59, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6702, loss_cls: 3.3171, loss: 3.3171 +2024-07-20 07:39:12,041 - pyskl - INFO - Epoch [110][800/3746] lr: 1.716e-02, eta: 1 day, 10:36:38, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6605, loss_cls: 3.3707, loss: 3.3707 +2024-07-20 07:40:35,276 - pyskl - INFO - Epoch [110][900/3746] lr: 1.714e-02, eta: 1 day, 10:35:17, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6778, loss_cls: 3.3041, loss: 3.3041 +2024-07-20 07:41:57,294 - pyskl - INFO - Epoch [110][1000/3746] lr: 1.712e-02, eta: 1 day, 10:33:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6650, loss_cls: 3.3474, loss: 3.3474 +2024-07-20 07:43:19,380 - pyskl - INFO - Epoch [110][1100/3746] lr: 1.710e-02, eta: 1 day, 10:32:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3977, top5_acc: 0.6539, loss_cls: 3.3937, loss: 3.3937 +2024-07-20 07:44:41,404 - pyskl - INFO - Epoch [110][1200/3746] lr: 1.708e-02, eta: 1 day, 10:31:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6591, loss_cls: 3.3493, loss: 3.3493 +2024-07-20 07:46:04,365 - pyskl - INFO - Epoch [110][1300/3746] lr: 1.705e-02, eta: 1 day, 10:29:52, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4155, top5_acc: 0.6683, loss_cls: 3.3153, loss: 3.3153 +2024-07-20 07:47:27,341 - pyskl - INFO - Epoch [110][1400/3746] lr: 1.703e-02, eta: 1 day, 10:28:31, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4073, top5_acc: 0.6689, loss_cls: 3.3495, loss: 3.3495 +2024-07-20 07:48:50,375 - pyskl - INFO - Epoch [110][1500/3746] lr: 1.701e-02, eta: 1 day, 10:27:10, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4116, top5_acc: 0.6709, loss_cls: 3.3169, loss: 3.3169 +2024-07-20 07:50:11,992 - pyskl - INFO - Epoch [110][1600/3746] lr: 1.699e-02, eta: 1 day, 10:25:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6606, loss_cls: 3.3816, loss: 3.3816 +2024-07-20 07:51:35,337 - pyskl - INFO - Epoch [110][1700/3746] lr: 1.697e-02, eta: 1 day, 10:24:27, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6663, loss_cls: 3.3126, loss: 3.3126 +2024-07-20 07:52:58,385 - pyskl - INFO - Epoch [110][1800/3746] lr: 1.695e-02, eta: 1 day, 10:23:06, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6727, loss_cls: 3.3314, loss: 3.3314 +2024-07-20 07:54:21,547 - pyskl - INFO - Epoch [110][1900/3746] lr: 1.693e-02, eta: 1 day, 10:21:45, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6633, loss_cls: 3.3591, loss: 3.3591 +2024-07-20 07:55:44,271 - pyskl - INFO - Epoch [110][2000/3746] lr: 1.691e-02, eta: 1 day, 10:20:24, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4138, top5_acc: 0.6763, loss_cls: 3.3296, loss: 3.3296 +2024-07-20 07:57:06,048 - pyskl - INFO - Epoch [110][2100/3746] lr: 1.689e-02, eta: 1 day, 10:19:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6555, loss_cls: 3.3689, loss: 3.3689 +2024-07-20 07:58:28,181 - pyskl - INFO - Epoch [110][2200/3746] lr: 1.687e-02, eta: 1 day, 10:17:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6625, loss_cls: 3.3538, loss: 3.3538 +2024-07-20 07:59:50,364 - pyskl - INFO - Epoch [110][2300/3746] lr: 1.685e-02, eta: 1 day, 10:16:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6631, loss_cls: 3.3567, loss: 3.3567 +2024-07-20 08:01:12,972 - pyskl - INFO - Epoch [110][2400/3746] lr: 1.682e-02, eta: 1 day, 10:14:59, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6552, loss_cls: 3.3747, loss: 3.3747 +2024-07-20 08:02:35,307 - pyskl - INFO - Epoch [110][2500/3746] lr: 1.680e-02, eta: 1 day, 10:13:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6627, loss_cls: 3.3656, loss: 3.3656 +2024-07-20 08:03:58,230 - pyskl - INFO - Epoch [110][2600/3746] lr: 1.678e-02, eta: 1 day, 10:12:16, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4156, top5_acc: 0.6689, loss_cls: 3.3218, loss: 3.3218 +2024-07-20 08:05:20,537 - pyskl - INFO - Epoch [110][2700/3746] lr: 1.676e-02, eta: 1 day, 10:10:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6717, loss_cls: 3.3240, loss: 3.3240 +2024-07-20 08:06:42,469 - pyskl - INFO - Epoch [110][2800/3746] lr: 1.674e-02, eta: 1 day, 10:09:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6683, loss_cls: 3.3512, loss: 3.3512 +2024-07-20 08:08:04,692 - pyskl - INFO - Epoch [110][2900/3746] lr: 1.672e-02, eta: 1 day, 10:08:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6603, loss_cls: 3.3640, loss: 3.3640 +2024-07-20 08:09:26,772 - pyskl - INFO - Epoch [110][3000/3746] lr: 1.670e-02, eta: 1 day, 10:06:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6648, loss_cls: 3.3686, loss: 3.3686 +2024-07-20 08:10:49,590 - pyskl - INFO - Epoch [110][3100/3746] lr: 1.668e-02, eta: 1 day, 10:05:30, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6641, loss_cls: 3.3510, loss: 3.3510 +2024-07-20 08:12:12,441 - pyskl - INFO - Epoch [110][3200/3746] lr: 1.666e-02, eta: 1 day, 10:04:09, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6717, loss_cls: 3.3069, loss: 3.3069 +2024-07-20 08:13:34,101 - pyskl - INFO - Epoch [110][3300/3746] lr: 1.664e-02, eta: 1 day, 10:02:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6505, loss_cls: 3.4021, loss: 3.4021 +2024-07-20 08:14:56,789 - pyskl - INFO - Epoch [110][3400/3746] lr: 1.662e-02, eta: 1 day, 10:01:26, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6592, loss_cls: 3.3710, loss: 3.3710 +2024-07-20 08:16:19,724 - pyskl - INFO - Epoch [110][3500/3746] lr: 1.659e-02, eta: 1 day, 10:00:05, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6634, loss_cls: 3.3501, loss: 3.3501 +2024-07-20 08:17:42,860 - pyskl - INFO - Epoch [110][3600/3746] lr: 1.657e-02, eta: 1 day, 9:58:44, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4117, top5_acc: 0.6731, loss_cls: 3.2985, loss: 3.2985 +2024-07-20 08:19:05,018 - pyskl - INFO - Epoch [110][3700/3746] lr: 1.655e-02, eta: 1 day, 9:57:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6717, loss_cls: 3.3014, loss: 3.3014 +2024-07-20 08:19:44,587 - pyskl - INFO - Saving checkpoint at 110 epochs +2024-07-20 08:21:35,951 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 08:21:36,629 - pyskl - INFO - +top1_acc 0.3509 +top5_acc 0.6050 +2024-07-20 08:21:36,629 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 08:21:36,677 - pyskl - INFO - +mean_acc 0.3508 +2024-07-20 08:21:36,681 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_108.pth was removed +2024-07-20 08:21:36,936 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_110.pth. +2024-07-20 08:21:36,937 - pyskl - INFO - Best top1_acc is 0.3509 at 110 epoch. +2024-07-20 08:21:36,951 - pyskl - INFO - Epoch(val) [110][309] top1_acc: 0.3509, top5_acc: 0.6050, mean_class_accuracy: 0.3508 +2024-07-20 08:25:23,976 - pyskl - INFO - Epoch [111][100/3746] lr: 1.652e-02, eta: 1 day, 9:56:03, time: 2.270, data_time: 1.291, memory: 15990, top1_acc: 0.4147, top5_acc: 0.6761, loss_cls: 3.2836, loss: 3.2836 +2024-07-20 08:26:47,353 - pyskl - INFO - Epoch [111][200/3746] lr: 1.650e-02, eta: 1 day, 9:54:42, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6848, loss_cls: 3.2241, loss: 3.2241 +2024-07-20 08:28:10,490 - pyskl - INFO - Epoch [111][300/3746] lr: 1.648e-02, eta: 1 day, 9:53:21, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6709, loss_cls: 3.3181, loss: 3.3181 +2024-07-20 08:29:32,707 - pyskl - INFO - Epoch [111][400/3746] lr: 1.646e-02, eta: 1 day, 9:52:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6697, loss_cls: 3.3575, loss: 3.3575 +2024-07-20 08:30:55,209 - pyskl - INFO - Epoch [111][500/3746] lr: 1.644e-02, eta: 1 day, 9:50:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6741, loss_cls: 3.2694, loss: 3.2694 +2024-07-20 08:32:17,738 - pyskl - INFO - Epoch [111][600/3746] lr: 1.642e-02, eta: 1 day, 9:49:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4183, top5_acc: 0.6847, loss_cls: 3.2418, loss: 3.2418 +2024-07-20 08:33:40,318 - pyskl - INFO - Epoch [111][700/3746] lr: 1.640e-02, eta: 1 day, 9:47:56, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4134, top5_acc: 0.6841, loss_cls: 3.2625, loss: 3.2625 +2024-07-20 08:35:03,160 - pyskl - INFO - Epoch [111][800/3746] lr: 1.638e-02, eta: 1 day, 9:46:35, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6739, loss_cls: 3.3089, loss: 3.3089 +2024-07-20 08:36:25,375 - pyskl - INFO - Epoch [111][900/3746] lr: 1.636e-02, eta: 1 day, 9:45:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6636, loss_cls: 3.3474, loss: 3.3474 +2024-07-20 08:37:47,571 - pyskl - INFO - Epoch [111][1000/3746] lr: 1.634e-02, eta: 1 day, 9:43:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6639, loss_cls: 3.3294, loss: 3.3294 +2024-07-20 08:39:09,526 - pyskl - INFO - Epoch [111][1100/3746] lr: 1.632e-02, eta: 1 day, 9:42:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4147, top5_acc: 0.6748, loss_cls: 3.2945, loss: 3.2945 +2024-07-20 08:40:31,682 - pyskl - INFO - Epoch [111][1200/3746] lr: 1.630e-02, eta: 1 day, 9:41:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4114, top5_acc: 0.6703, loss_cls: 3.3187, loss: 3.3187 +2024-07-20 08:41:54,055 - pyskl - INFO - Epoch [111][1300/3746] lr: 1.627e-02, eta: 1 day, 9:39:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6713, loss_cls: 3.3176, loss: 3.3176 +2024-07-20 08:43:17,172 - pyskl - INFO - Epoch [111][1400/3746] lr: 1.625e-02, eta: 1 day, 9:38:27, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4156, top5_acc: 0.6725, loss_cls: 3.3093, loss: 3.3093 +2024-07-20 08:44:39,425 - pyskl - INFO - Epoch [111][1500/3746] lr: 1.623e-02, eta: 1 day, 9:37:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6795, loss_cls: 3.2861, loss: 3.2861 +2024-07-20 08:46:01,952 - pyskl - INFO - Epoch [111][1600/3746] lr: 1.621e-02, eta: 1 day, 9:35:44, time: 0.825, data_time: 0.001, memory: 15990, top1_acc: 0.3980, top5_acc: 0.6672, loss_cls: 3.3644, loss: 3.3644 +2024-07-20 08:47:25,599 - pyskl - INFO - Epoch [111][1700/3746] lr: 1.619e-02, eta: 1 day, 9:34:24, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4086, top5_acc: 0.6692, loss_cls: 3.3457, loss: 3.3457 +2024-07-20 08:48:48,421 - pyskl - INFO - Epoch [111][1800/3746] lr: 1.617e-02, eta: 1 day, 9:33:02, time: 0.828, data_time: 0.001, memory: 15990, top1_acc: 0.4181, top5_acc: 0.6761, loss_cls: 3.3102, loss: 3.3102 +2024-07-20 08:50:11,006 - pyskl - INFO - Epoch [111][1900/3746] lr: 1.615e-02, eta: 1 day, 9:31:41, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6659, loss_cls: 3.2953, loss: 3.2953 +2024-07-20 08:51:33,188 - pyskl - INFO - Epoch [111][2000/3746] lr: 1.613e-02, eta: 1 day, 9:30:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4145, top5_acc: 0.6717, loss_cls: 3.3028, loss: 3.3028 +2024-07-20 08:52:55,359 - pyskl - INFO - Epoch [111][2100/3746] lr: 1.611e-02, eta: 1 day, 9:28:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3994, top5_acc: 0.6672, loss_cls: 3.3561, loss: 3.3561 +2024-07-20 08:54:17,436 - pyskl - INFO - Epoch [111][2200/3746] lr: 1.609e-02, eta: 1 day, 9:27:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6678, loss_cls: 3.3440, loss: 3.3440 +2024-07-20 08:55:39,774 - pyskl - INFO - Epoch [111][2300/3746] lr: 1.607e-02, eta: 1 day, 9:26:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4044, top5_acc: 0.6748, loss_cls: 3.3157, loss: 3.3157 +2024-07-20 08:57:01,863 - pyskl - INFO - Epoch [111][2400/3746] lr: 1.605e-02, eta: 1 day, 9:24:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4025, top5_acc: 0.6664, loss_cls: 3.3689, loss: 3.3689 +2024-07-20 08:58:24,059 - pyskl - INFO - Epoch [111][2500/3746] lr: 1.603e-02, eta: 1 day, 9:23:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6753, loss_cls: 3.2950, loss: 3.2950 +2024-07-20 08:59:46,394 - pyskl - INFO - Epoch [111][2600/3746] lr: 1.601e-02, eta: 1 day, 9:22:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6677, loss_cls: 3.3203, loss: 3.3203 +2024-07-20 09:01:07,959 - pyskl - INFO - Epoch [111][2700/3746] lr: 1.599e-02, eta: 1 day, 9:20:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6717, loss_cls: 3.3033, loss: 3.3033 +2024-07-20 09:02:29,887 - pyskl - INFO - Epoch [111][2800/3746] lr: 1.597e-02, eta: 1 day, 9:19:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4066, top5_acc: 0.6680, loss_cls: 3.3518, loss: 3.3518 +2024-07-20 09:03:51,978 - pyskl - INFO - Epoch [111][2900/3746] lr: 1.595e-02, eta: 1 day, 9:18:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6734, loss_cls: 3.3304, loss: 3.3304 +2024-07-20 09:05:14,024 - pyskl - INFO - Epoch [111][3000/3746] lr: 1.593e-02, eta: 1 day, 9:16:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4086, top5_acc: 0.6719, loss_cls: 3.3252, loss: 3.3252 +2024-07-20 09:06:35,995 - pyskl - INFO - Epoch [111][3100/3746] lr: 1.590e-02, eta: 1 day, 9:15:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6700, loss_cls: 3.3138, loss: 3.3138 +2024-07-20 09:07:59,168 - pyskl - INFO - Epoch [111][3200/3746] lr: 1.588e-02, eta: 1 day, 9:14:03, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4116, top5_acc: 0.6711, loss_cls: 3.3142, loss: 3.3142 +2024-07-20 09:09:21,461 - pyskl - INFO - Epoch [111][3300/3746] lr: 1.586e-02, eta: 1 day, 9:12:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6573, loss_cls: 3.3855, loss: 3.3855 +2024-07-20 09:10:43,638 - pyskl - INFO - Epoch [111][3400/3746] lr: 1.584e-02, eta: 1 day, 9:11:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6625, loss_cls: 3.3518, loss: 3.3518 +2024-07-20 09:12:06,600 - pyskl - INFO - Epoch [111][3500/3746] lr: 1.582e-02, eta: 1 day, 9:09:59, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4153, top5_acc: 0.6742, loss_cls: 3.2993, loss: 3.2993 +2024-07-20 09:13:30,181 - pyskl - INFO - Epoch [111][3600/3746] lr: 1.580e-02, eta: 1 day, 9:08:38, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6570, loss_cls: 3.3667, loss: 3.3667 +2024-07-20 09:14:52,850 - pyskl - INFO - Epoch [111][3700/3746] lr: 1.578e-02, eta: 1 day, 9:07:17, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6570, loss_cls: 3.3729, loss: 3.3729 +2024-07-20 09:15:32,244 - pyskl - INFO - Saving checkpoint at 111 epochs +2024-07-20 09:17:22,679 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 09:17:23,347 - pyskl - INFO - +top1_acc 0.3451 +top5_acc 0.6026 +2024-07-20 09:17:23,347 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 09:17:23,390 - pyskl - INFO - +mean_acc 0.3447 +2024-07-20 09:17:23,403 - pyskl - INFO - Epoch(val) [111][309] top1_acc: 0.3451, top5_acc: 0.6026, mean_class_accuracy: 0.3447 +2024-07-20 09:21:10,184 - pyskl - INFO - Epoch [112][100/3746] lr: 1.575e-02, eta: 1 day, 9:05:56, time: 2.268, data_time: 1.275, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6922, loss_cls: 3.2309, loss: 3.2309 +2024-07-20 09:22:33,578 - pyskl - INFO - Epoch [112][200/3746] lr: 1.573e-02, eta: 1 day, 9:04:35, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4161, top5_acc: 0.6811, loss_cls: 3.2556, loss: 3.2556 +2024-07-20 09:23:56,519 - pyskl - INFO - Epoch [112][300/3746] lr: 1.571e-02, eta: 1 day, 9:03:14, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4308, top5_acc: 0.6817, loss_cls: 3.2370, loss: 3.2370 +2024-07-20 09:25:19,262 - pyskl - INFO - Epoch [112][400/3746] lr: 1.569e-02, eta: 1 day, 9:01:52, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6866, loss_cls: 3.2442, loss: 3.2442 +2024-07-20 09:26:42,033 - pyskl - INFO - Epoch [112][500/3746] lr: 1.567e-02, eta: 1 day, 9:00:31, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4197, top5_acc: 0.6764, loss_cls: 3.2822, loss: 3.2822 +2024-07-20 09:28:04,224 - pyskl - INFO - Epoch [112][600/3746] lr: 1.565e-02, eta: 1 day, 8:59:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6780, loss_cls: 3.2679, loss: 3.2679 +2024-07-20 09:29:26,616 - pyskl - INFO - Epoch [112][700/3746] lr: 1.563e-02, eta: 1 day, 8:57:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4114, top5_acc: 0.6694, loss_cls: 3.2988, loss: 3.2988 +2024-07-20 09:30:49,028 - pyskl - INFO - Epoch [112][800/3746] lr: 1.561e-02, eta: 1 day, 8:56:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6797, loss_cls: 3.2648, loss: 3.2648 +2024-07-20 09:32:12,096 - pyskl - INFO - Epoch [112][900/3746] lr: 1.559e-02, eta: 1 day, 8:55:06, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4202, top5_acc: 0.6759, loss_cls: 3.2713, loss: 3.2713 +2024-07-20 09:33:34,842 - pyskl - INFO - Epoch [112][1000/3746] lr: 1.557e-02, eta: 1 day, 8:53:45, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6728, loss_cls: 3.2894, loss: 3.2894 +2024-07-20 09:34:56,833 - pyskl - INFO - Epoch [112][1100/3746] lr: 1.555e-02, eta: 1 day, 8:52:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6802, loss_cls: 3.2772, loss: 3.2772 +2024-07-20 09:36:19,275 - pyskl - INFO - Epoch [112][1200/3746] lr: 1.553e-02, eta: 1 day, 8:51:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6808, loss_cls: 3.2645, loss: 3.2645 +2024-07-20 09:37:41,746 - pyskl - INFO - Epoch [112][1300/3746] lr: 1.551e-02, eta: 1 day, 8:49:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6677, loss_cls: 3.3397, loss: 3.3397 +2024-07-20 09:39:04,733 - pyskl - INFO - Epoch [112][1400/3746] lr: 1.549e-02, eta: 1 day, 8:48:20, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4188, top5_acc: 0.6802, loss_cls: 3.2556, loss: 3.2556 +2024-07-20 09:40:26,432 - pyskl - INFO - Epoch [112][1500/3746] lr: 1.547e-02, eta: 1 day, 8:46:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4177, top5_acc: 0.6692, loss_cls: 3.3039, loss: 3.3039 +2024-07-20 09:41:49,788 - pyskl - INFO - Epoch [112][1600/3746] lr: 1.545e-02, eta: 1 day, 8:45:37, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4189, top5_acc: 0.6778, loss_cls: 3.2711, loss: 3.2711 +2024-07-20 09:43:13,174 - pyskl - INFO - Epoch [112][1700/3746] lr: 1.543e-02, eta: 1 day, 8:44:16, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6639, loss_cls: 3.3598, loss: 3.3598 +2024-07-20 09:44:36,391 - pyskl - INFO - Epoch [112][1800/3746] lr: 1.541e-02, eta: 1 day, 8:42:55, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4113, top5_acc: 0.6728, loss_cls: 3.3178, loss: 3.3178 +2024-07-20 09:45:59,866 - pyskl - INFO - Epoch [112][1900/3746] lr: 1.539e-02, eta: 1 day, 8:41:34, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4078, top5_acc: 0.6656, loss_cls: 3.3359, loss: 3.3359 +2024-07-20 09:47:22,736 - pyskl - INFO - Epoch [112][2000/3746] lr: 1.537e-02, eta: 1 day, 8:40:13, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6677, loss_cls: 3.3419, loss: 3.3419 +2024-07-20 09:48:46,288 - pyskl - INFO - Epoch [112][2100/3746] lr: 1.535e-02, eta: 1 day, 8:38:52, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6713, loss_cls: 3.3123, loss: 3.3123 +2024-07-20 09:50:09,283 - pyskl - INFO - Epoch [112][2200/3746] lr: 1.533e-02, eta: 1 day, 8:37:31, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4166, top5_acc: 0.6775, loss_cls: 3.2783, loss: 3.2783 +2024-07-20 09:51:32,955 - pyskl - INFO - Epoch [112][2300/3746] lr: 1.531e-02, eta: 1 day, 8:36:10, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6714, loss_cls: 3.3254, loss: 3.3254 +2024-07-20 09:52:56,269 - pyskl - INFO - Epoch [112][2400/3746] lr: 1.529e-02, eta: 1 day, 8:34:49, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6708, loss_cls: 3.2918, loss: 3.2918 +2024-07-20 09:54:19,772 - pyskl - INFO - Epoch [112][2500/3746] lr: 1.527e-02, eta: 1 day, 8:33:28, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6739, loss_cls: 3.2920, loss: 3.2920 +2024-07-20 09:55:43,082 - pyskl - INFO - Epoch [112][2600/3746] lr: 1.525e-02, eta: 1 day, 8:32:07, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4134, top5_acc: 0.6719, loss_cls: 3.2859, loss: 3.2859 +2024-07-20 09:57:06,319 - pyskl - INFO - Epoch [112][2700/3746] lr: 1.523e-02, eta: 1 day, 8:30:45, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6747, loss_cls: 3.3024, loss: 3.3024 +2024-07-20 09:58:29,803 - pyskl - INFO - Epoch [112][2800/3746] lr: 1.521e-02, eta: 1 day, 8:29:24, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6702, loss_cls: 3.3233, loss: 3.3233 +2024-07-20 09:59:53,150 - pyskl - INFO - Epoch [112][2900/3746] lr: 1.519e-02, eta: 1 day, 8:28:03, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6766, loss_cls: 3.2669, loss: 3.2669 +2024-07-20 10:01:16,650 - pyskl - INFO - Epoch [112][3000/3746] lr: 1.517e-02, eta: 1 day, 8:26:42, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6811, loss_cls: 3.2799, loss: 3.2799 +2024-07-20 10:02:40,326 - pyskl - INFO - Epoch [112][3100/3746] lr: 1.515e-02, eta: 1 day, 8:25:22, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6637, loss_cls: 3.3477, loss: 3.3477 +2024-07-20 10:04:02,790 - pyskl - INFO - Epoch [112][3200/3746] lr: 1.513e-02, eta: 1 day, 8:24:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6798, loss_cls: 3.2912, loss: 3.2912 +2024-07-20 10:05:25,307 - pyskl - INFO - Epoch [112][3300/3746] lr: 1.511e-02, eta: 1 day, 8:22:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6698, loss_cls: 3.2951, loss: 3.2951 +2024-07-20 10:06:48,565 - pyskl - INFO - Epoch [112][3400/3746] lr: 1.509e-02, eta: 1 day, 8:21:18, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6667, loss_cls: 3.3428, loss: 3.3428 +2024-07-20 10:08:12,417 - pyskl - INFO - Epoch [112][3500/3746] lr: 1.507e-02, eta: 1 day, 8:19:57, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4098, top5_acc: 0.6730, loss_cls: 3.3354, loss: 3.3354 +2024-07-20 10:09:35,710 - pyskl - INFO - Epoch [112][3600/3746] lr: 1.505e-02, eta: 1 day, 8:18:36, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4155, top5_acc: 0.6703, loss_cls: 3.3206, loss: 3.3206 +2024-07-20 10:10:58,215 - pyskl - INFO - Epoch [112][3700/3746] lr: 1.503e-02, eta: 1 day, 8:17:15, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4166, top5_acc: 0.6731, loss_cls: 3.2996, loss: 3.2996 +2024-07-20 10:11:38,368 - pyskl - INFO - Saving checkpoint at 112 epochs +2024-07-20 10:13:31,106 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 10:13:31,778 - pyskl - INFO - +top1_acc 0.3520 +top5_acc 0.6104 +2024-07-20 10:13:31,779 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 10:13:31,820 - pyskl - INFO - +mean_acc 0.3517 +2024-07-20 10:13:31,825 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_110.pth was removed +2024-07-20 10:13:32,079 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_112.pth. +2024-07-20 10:13:32,080 - pyskl - INFO - Best top1_acc is 0.3520 at 112 epoch. +2024-07-20 10:13:32,092 - pyskl - INFO - Epoch(val) [112][309] top1_acc: 0.3520, top5_acc: 0.6104, mean_class_accuracy: 0.3517 +2024-07-20 10:17:18,809 - pyskl - INFO - Epoch [113][100/3746] lr: 1.500e-02, eta: 1 day, 8:15:52, time: 2.267, data_time: 1.274, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6955, loss_cls: 3.1827, loss: 3.1827 +2024-07-20 10:18:41,916 - pyskl - INFO - Epoch [113][200/3746] lr: 1.498e-02, eta: 1 day, 8:14:31, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6945, loss_cls: 3.1626, loss: 3.1626 +2024-07-20 10:20:05,570 - pyskl - INFO - Epoch [113][300/3746] lr: 1.496e-02, eta: 1 day, 8:13:10, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4295, top5_acc: 0.6839, loss_cls: 3.2146, loss: 3.2146 +2024-07-20 10:21:28,694 - pyskl - INFO - Epoch [113][400/3746] lr: 1.494e-02, eta: 1 day, 8:11:49, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4245, top5_acc: 0.6805, loss_cls: 3.2524, loss: 3.2524 +2024-07-20 10:22:52,195 - pyskl - INFO - Epoch [113][500/3746] lr: 1.492e-02, eta: 1 day, 8:10:27, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6842, loss_cls: 3.2290, loss: 3.2290 +2024-07-20 10:24:15,791 - pyskl - INFO - Epoch [113][600/3746] lr: 1.490e-02, eta: 1 day, 8:09:06, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6913, loss_cls: 3.1952, loss: 3.1952 +2024-07-20 10:25:39,368 - pyskl - INFO - Epoch [113][700/3746] lr: 1.488e-02, eta: 1 day, 8:07:45, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4247, top5_acc: 0.6809, loss_cls: 3.2355, loss: 3.2355 +2024-07-20 10:27:02,316 - pyskl - INFO - Epoch [113][800/3746] lr: 1.486e-02, eta: 1 day, 8:06:24, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6834, loss_cls: 3.2726, loss: 3.2726 +2024-07-20 10:28:25,653 - pyskl - INFO - Epoch [113][900/3746] lr: 1.484e-02, eta: 1 day, 8:05:03, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6742, loss_cls: 3.2761, loss: 3.2761 +2024-07-20 10:29:48,729 - pyskl - INFO - Epoch [113][1000/3746] lr: 1.482e-02, eta: 1 day, 8:03:42, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4170, top5_acc: 0.6708, loss_cls: 3.2922, loss: 3.2922 +2024-07-20 10:31:12,180 - pyskl - INFO - Epoch [113][1100/3746] lr: 1.480e-02, eta: 1 day, 8:02:21, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6769, loss_cls: 3.2638, loss: 3.2638 +2024-07-20 10:32:35,770 - pyskl - INFO - Epoch [113][1200/3746] lr: 1.478e-02, eta: 1 day, 8:01:00, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6756, loss_cls: 3.2718, loss: 3.2718 +2024-07-20 10:33:59,375 - pyskl - INFO - Epoch [113][1300/3746] lr: 1.476e-02, eta: 1 day, 7:59:39, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4091, top5_acc: 0.6677, loss_cls: 3.3415, loss: 3.3415 +2024-07-20 10:35:22,575 - pyskl - INFO - Epoch [113][1400/3746] lr: 1.474e-02, eta: 1 day, 7:58:18, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4178, top5_acc: 0.6759, loss_cls: 3.2762, loss: 3.2762 +2024-07-20 10:36:45,170 - pyskl - INFO - Epoch [113][1500/3746] lr: 1.472e-02, eta: 1 day, 7:56:56, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6783, loss_cls: 3.2743, loss: 3.2743 +2024-07-20 10:38:07,961 - pyskl - INFO - Epoch [113][1600/3746] lr: 1.470e-02, eta: 1 day, 7:55:35, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6766, loss_cls: 3.2812, loss: 3.2812 +2024-07-20 10:39:31,372 - pyskl - INFO - Epoch [113][1700/3746] lr: 1.468e-02, eta: 1 day, 7:54:14, time: 0.834, data_time: 0.001, memory: 15990, top1_acc: 0.4259, top5_acc: 0.6728, loss_cls: 3.2520, loss: 3.2520 +2024-07-20 10:40:55,136 - pyskl - INFO - Epoch [113][1800/3746] lr: 1.466e-02, eta: 1 day, 7:52:53, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6797, loss_cls: 3.2464, loss: 3.2464 +2024-07-20 10:42:18,491 - pyskl - INFO - Epoch [113][1900/3746] lr: 1.464e-02, eta: 1 day, 7:51:32, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4202, top5_acc: 0.6764, loss_cls: 3.2912, loss: 3.2912 +2024-07-20 10:43:42,418 - pyskl - INFO - Epoch [113][2000/3746] lr: 1.462e-02, eta: 1 day, 7:50:11, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4164, top5_acc: 0.6764, loss_cls: 3.2797, loss: 3.2797 +2024-07-20 10:45:05,983 - pyskl - INFO - Epoch [113][2100/3746] lr: 1.460e-02, eta: 1 day, 7:48:50, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6700, loss_cls: 3.3239, loss: 3.3239 +2024-07-20 10:46:29,548 - pyskl - INFO - Epoch [113][2200/3746] lr: 1.458e-02, eta: 1 day, 7:47:29, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6719, loss_cls: 3.3314, loss: 3.3314 +2024-07-20 10:47:53,179 - pyskl - INFO - Epoch [113][2300/3746] lr: 1.456e-02, eta: 1 day, 7:46:08, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6681, loss_cls: 3.2952, loss: 3.2952 +2024-07-20 10:49:16,698 - pyskl - INFO - Epoch [113][2400/3746] lr: 1.454e-02, eta: 1 day, 7:44:47, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4220, top5_acc: 0.6742, loss_cls: 3.2662, loss: 3.2662 +2024-07-20 10:50:40,049 - pyskl - INFO - Epoch [113][2500/3746] lr: 1.452e-02, eta: 1 day, 7:43:26, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4088, top5_acc: 0.6631, loss_cls: 3.3219, loss: 3.3219 +2024-07-20 10:52:03,319 - pyskl - INFO - Epoch [113][2600/3746] lr: 1.450e-02, eta: 1 day, 7:42:05, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4147, top5_acc: 0.6705, loss_cls: 3.3187, loss: 3.3187 +2024-07-20 10:53:27,142 - pyskl - INFO - Epoch [113][2700/3746] lr: 1.448e-02, eta: 1 day, 7:40:44, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6736, loss_cls: 3.3019, loss: 3.3019 +2024-07-20 10:54:50,776 - pyskl - INFO - Epoch [113][2800/3746] lr: 1.446e-02, eta: 1 day, 7:39:23, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6747, loss_cls: 3.2858, loss: 3.2858 +2024-07-20 10:56:14,457 - pyskl - INFO - Epoch [113][2900/3746] lr: 1.444e-02, eta: 1 day, 7:38:02, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4214, top5_acc: 0.6731, loss_cls: 3.2947, loss: 3.2947 +2024-07-20 10:57:37,921 - pyskl - INFO - Epoch [113][3000/3746] lr: 1.442e-02, eta: 1 day, 7:36:41, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6847, loss_cls: 3.2404, loss: 3.2404 +2024-07-20 10:59:01,619 - pyskl - INFO - Epoch [113][3100/3746] lr: 1.440e-02, eta: 1 day, 7:35:20, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6675, loss_cls: 3.3295, loss: 3.3295 +2024-07-20 11:00:24,295 - pyskl - INFO - Epoch [113][3200/3746] lr: 1.438e-02, eta: 1 day, 7:33:58, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4164, top5_acc: 0.6706, loss_cls: 3.3156, loss: 3.3156 +2024-07-20 11:01:47,512 - pyskl - INFO - Epoch [113][3300/3746] lr: 1.436e-02, eta: 1 day, 7:32:37, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4141, top5_acc: 0.6734, loss_cls: 3.3176, loss: 3.3176 +2024-07-20 11:03:10,469 - pyskl - INFO - Epoch [113][3400/3746] lr: 1.434e-02, eta: 1 day, 7:31:16, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4163, top5_acc: 0.6772, loss_cls: 3.2591, loss: 3.2591 +2024-07-20 11:04:34,121 - pyskl - INFO - Epoch [113][3500/3746] lr: 1.432e-02, eta: 1 day, 7:29:55, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6758, loss_cls: 3.3072, loss: 3.3072 +2024-07-20 11:05:56,708 - pyskl - INFO - Epoch [113][3600/3746] lr: 1.431e-02, eta: 1 day, 7:28:34, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6828, loss_cls: 3.2482, loss: 3.2482 +2024-07-20 11:07:19,489 - pyskl - INFO - Epoch [113][3700/3746] lr: 1.429e-02, eta: 1 day, 7:27:12, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4377, top5_acc: 0.6963, loss_cls: 3.1847, loss: 3.1847 +2024-07-20 11:07:59,984 - pyskl - INFO - Saving checkpoint at 113 epochs +2024-07-20 11:09:51,765 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 11:09:52,435 - pyskl - INFO - +top1_acc 0.3603 +top5_acc 0.6152 +2024-07-20 11:09:52,436 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 11:09:52,477 - pyskl - INFO - +mean_acc 0.3601 +2024-07-20 11:09:52,482 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_112.pth was removed +2024-07-20 11:09:52,729 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_113.pth. +2024-07-20 11:09:52,730 - pyskl - INFO - Best top1_acc is 0.3603 at 113 epoch. +2024-07-20 11:09:52,742 - pyskl - INFO - Epoch(val) [113][309] top1_acc: 0.3603, top5_acc: 0.6152, mean_class_accuracy: 0.3601 +2024-07-20 11:13:42,423 - pyskl - INFO - Epoch [114][100/3746] lr: 1.426e-02, eta: 1 day, 7:25:49, time: 2.297, data_time: 1.303, memory: 15990, top1_acc: 0.4358, top5_acc: 0.6872, loss_cls: 3.1991, loss: 3.1991 +2024-07-20 11:15:05,674 - pyskl - INFO - Epoch [114][200/3746] lr: 1.424e-02, eta: 1 day, 7:24:28, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6931, loss_cls: 3.1976, loss: 3.1976 +2024-07-20 11:16:28,706 - pyskl - INFO - Epoch [114][300/3746] lr: 1.422e-02, eta: 1 day, 7:23:07, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6903, loss_cls: 3.2136, loss: 3.2136 +2024-07-20 11:17:52,288 - pyskl - INFO - Epoch [114][400/3746] lr: 1.420e-02, eta: 1 day, 7:21:46, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6770, loss_cls: 3.2455, loss: 3.2455 +2024-07-20 11:19:15,628 - pyskl - INFO - Epoch [114][500/3746] lr: 1.418e-02, eta: 1 day, 7:20:24, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4247, top5_acc: 0.6811, loss_cls: 3.2481, loss: 3.2481 +2024-07-20 11:20:38,977 - pyskl - INFO - Epoch [114][600/3746] lr: 1.416e-02, eta: 1 day, 7:19:03, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6853, loss_cls: 3.2021, loss: 3.2021 +2024-07-20 11:22:02,231 - pyskl - INFO - Epoch [114][700/3746] lr: 1.414e-02, eta: 1 day, 7:17:42, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6850, loss_cls: 3.2614, loss: 3.2614 +2024-07-20 11:23:24,880 - pyskl - INFO - Epoch [114][800/3746] lr: 1.412e-02, eta: 1 day, 7:16:21, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4283, top5_acc: 0.6853, loss_cls: 3.2378, loss: 3.2378 +2024-07-20 11:24:47,623 - pyskl - INFO - Epoch [114][900/3746] lr: 1.410e-02, eta: 1 day, 7:14:59, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4302, top5_acc: 0.6859, loss_cls: 3.2145, loss: 3.2145 +2024-07-20 11:26:11,012 - pyskl - INFO - Epoch [114][1000/3746] lr: 1.408e-02, eta: 1 day, 7:13:38, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6886, loss_cls: 3.2289, loss: 3.2289 +2024-07-20 11:27:34,512 - pyskl - INFO - Epoch [114][1100/3746] lr: 1.406e-02, eta: 1 day, 7:12:17, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6758, loss_cls: 3.2655, loss: 3.2655 +2024-07-20 11:28:58,087 - pyskl - INFO - Epoch [114][1200/3746] lr: 1.404e-02, eta: 1 day, 7:10:56, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4227, top5_acc: 0.6844, loss_cls: 3.2102, loss: 3.2102 +2024-07-20 11:30:20,900 - pyskl - INFO - Epoch [114][1300/3746] lr: 1.402e-02, eta: 1 day, 7:09:35, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6848, loss_cls: 3.2043, loss: 3.2043 +2024-07-20 11:31:44,572 - pyskl - INFO - Epoch [114][1400/3746] lr: 1.400e-02, eta: 1 day, 7:08:14, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6856, loss_cls: 3.2135, loss: 3.2135 +2024-07-20 11:33:07,664 - pyskl - INFO - Epoch [114][1500/3746] lr: 1.398e-02, eta: 1 day, 7:06:52, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6809, loss_cls: 3.2818, loss: 3.2818 +2024-07-20 11:34:30,202 - pyskl - INFO - Epoch [114][1600/3746] lr: 1.397e-02, eta: 1 day, 7:05:31, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4211, top5_acc: 0.6842, loss_cls: 3.2527, loss: 3.2527 +2024-07-20 11:35:53,381 - pyskl - INFO - Epoch [114][1700/3746] lr: 1.395e-02, eta: 1 day, 7:04:10, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6780, loss_cls: 3.2537, loss: 3.2537 +2024-07-20 11:37:17,334 - pyskl - INFO - Epoch [114][1800/3746] lr: 1.393e-02, eta: 1 day, 7:02:49, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.4308, top5_acc: 0.6866, loss_cls: 3.1813, loss: 3.1813 +2024-07-20 11:38:41,173 - pyskl - INFO - Epoch [114][1900/3746] lr: 1.391e-02, eta: 1 day, 7:01:28, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6719, loss_cls: 3.2741, loss: 3.2741 +2024-07-20 11:40:04,870 - pyskl - INFO - Epoch [114][2000/3746] lr: 1.389e-02, eta: 1 day, 7:00:07, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4169, top5_acc: 0.6819, loss_cls: 3.2362, loss: 3.2362 +2024-07-20 11:41:28,263 - pyskl - INFO - Epoch [114][2100/3746] lr: 1.387e-02, eta: 1 day, 6:58:45, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6819, loss_cls: 3.2469, loss: 3.2469 +2024-07-20 11:42:51,452 - pyskl - INFO - Epoch [114][2200/3746] lr: 1.385e-02, eta: 1 day, 6:57:24, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4163, top5_acc: 0.6670, loss_cls: 3.2852, loss: 3.2852 +2024-07-20 11:44:15,012 - pyskl - INFO - Epoch [114][2300/3746] lr: 1.383e-02, eta: 1 day, 6:56:03, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4145, top5_acc: 0.6720, loss_cls: 3.2842, loss: 3.2842 +2024-07-20 11:45:38,485 - pyskl - INFO - Epoch [114][2400/3746] lr: 1.381e-02, eta: 1 day, 6:54:42, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4238, top5_acc: 0.6870, loss_cls: 3.2304, loss: 3.2304 +2024-07-20 11:47:02,097 - pyskl - INFO - Epoch [114][2500/3746] lr: 1.379e-02, eta: 1 day, 6:53:21, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6737, loss_cls: 3.2876, loss: 3.2876 +2024-07-20 11:48:25,802 - pyskl - INFO - Epoch [114][2600/3746] lr: 1.377e-02, eta: 1 day, 6:52:00, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6708, loss_cls: 3.3126, loss: 3.3126 +2024-07-20 11:49:49,354 - pyskl - INFO - Epoch [114][2700/3746] lr: 1.375e-02, eta: 1 day, 6:50:39, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6716, loss_cls: 3.2804, loss: 3.2804 +2024-07-20 11:51:12,572 - pyskl - INFO - Epoch [114][2800/3746] lr: 1.373e-02, eta: 1 day, 6:49:17, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6819, loss_cls: 3.2412, loss: 3.2412 +2024-07-20 11:52:36,093 - pyskl - INFO - Epoch [114][2900/3746] lr: 1.371e-02, eta: 1 day, 6:47:56, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6758, loss_cls: 3.2624, loss: 3.2624 +2024-07-20 11:54:00,420 - pyskl - INFO - Epoch [114][3000/3746] lr: 1.369e-02, eta: 1 day, 6:46:35, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4138, top5_acc: 0.6673, loss_cls: 3.2983, loss: 3.2983 +2024-07-20 11:55:23,624 - pyskl - INFO - Epoch [114][3100/3746] lr: 1.368e-02, eta: 1 day, 6:45:14, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6711, loss_cls: 3.2822, loss: 3.2822 +2024-07-20 11:56:45,919 - pyskl - INFO - Epoch [114][3200/3746] lr: 1.366e-02, eta: 1 day, 6:43:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6659, loss_cls: 3.3218, loss: 3.3218 +2024-07-20 11:58:09,242 - pyskl - INFO - Epoch [114][3300/3746] lr: 1.364e-02, eta: 1 day, 6:42:32, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6808, loss_cls: 3.2546, loss: 3.2546 +2024-07-20 11:59:32,696 - pyskl - INFO - Epoch [114][3400/3746] lr: 1.362e-02, eta: 1 day, 6:41:10, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4309, top5_acc: 0.6922, loss_cls: 3.2161, loss: 3.2161 +2024-07-20 12:00:56,042 - pyskl - INFO - Epoch [114][3500/3746] lr: 1.360e-02, eta: 1 day, 6:39:49, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4152, top5_acc: 0.6777, loss_cls: 3.2595, loss: 3.2595 +2024-07-20 12:02:19,230 - pyskl - INFO - Epoch [114][3600/3746] lr: 1.358e-02, eta: 1 day, 6:38:28, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4241, top5_acc: 0.6734, loss_cls: 3.2802, loss: 3.2802 +2024-07-20 12:03:42,482 - pyskl - INFO - Epoch [114][3700/3746] lr: 1.356e-02, eta: 1 day, 6:37:07, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4303, top5_acc: 0.6825, loss_cls: 3.2376, loss: 3.2376 +2024-07-20 12:04:22,101 - pyskl - INFO - Saving checkpoint at 114 epochs +2024-07-20 12:06:14,207 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 12:06:14,900 - pyskl - INFO - +top1_acc 0.3722 +top5_acc 0.6285 +2024-07-20 12:06:14,900 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 12:06:14,942 - pyskl - INFO - +mean_acc 0.3717 +2024-07-20 12:06:14,947 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_113.pth was removed +2024-07-20 12:06:15,191 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_114.pth. +2024-07-20 12:06:15,192 - pyskl - INFO - Best top1_acc is 0.3722 at 114 epoch. +2024-07-20 12:06:15,204 - pyskl - INFO - Epoch(val) [114][309] top1_acc: 0.3722, top5_acc: 0.6285, mean_class_accuracy: 0.3717 +2024-07-20 12:10:04,037 - pyskl - INFO - Epoch [115][100/3746] lr: 1.353e-02, eta: 1 day, 6:35:42, time: 2.288, data_time: 1.300, memory: 15990, top1_acc: 0.4480, top5_acc: 0.7091, loss_cls: 3.0957, loss: 3.0957 +2024-07-20 12:11:27,577 - pyskl - INFO - Epoch [115][200/3746] lr: 1.351e-02, eta: 1 day, 6:34:21, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.6889, loss_cls: 3.1474, loss: 3.1474 +2024-07-20 12:12:50,321 - pyskl - INFO - Epoch [115][300/3746] lr: 1.349e-02, eta: 1 day, 6:32:59, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4372, top5_acc: 0.7016, loss_cls: 3.1688, loss: 3.1688 +2024-07-20 12:14:13,161 - pyskl - INFO - Epoch [115][400/3746] lr: 1.348e-02, eta: 1 day, 6:31:38, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4311, top5_acc: 0.6927, loss_cls: 3.1814, loss: 3.1814 +2024-07-20 12:15:36,041 - pyskl - INFO - Epoch [115][500/3746] lr: 1.346e-02, eta: 1 day, 6:30:17, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4272, top5_acc: 0.6878, loss_cls: 3.1965, loss: 3.1965 +2024-07-20 12:16:59,182 - pyskl - INFO - Epoch [115][600/3746] lr: 1.344e-02, eta: 1 day, 6:28:55, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6900, loss_cls: 3.2114, loss: 3.2114 +2024-07-20 12:18:21,842 - pyskl - INFO - Epoch [115][700/3746] lr: 1.342e-02, eta: 1 day, 6:27:34, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4311, top5_acc: 0.6948, loss_cls: 3.1736, loss: 3.1736 +2024-07-20 12:19:44,295 - pyskl - INFO - Epoch [115][800/3746] lr: 1.340e-02, eta: 1 day, 6:26:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6889, loss_cls: 3.2174, loss: 3.2174 +2024-07-20 12:21:07,231 - pyskl - INFO - Epoch [115][900/3746] lr: 1.338e-02, eta: 1 day, 6:24:51, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4355, top5_acc: 0.6958, loss_cls: 3.1751, loss: 3.1751 +2024-07-20 12:22:30,580 - pyskl - INFO - Epoch [115][1000/3746] lr: 1.336e-02, eta: 1 day, 6:23:30, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4266, top5_acc: 0.6845, loss_cls: 3.2107, loss: 3.2107 +2024-07-20 12:23:53,559 - pyskl - INFO - Epoch [115][1100/3746] lr: 1.334e-02, eta: 1 day, 6:22:08, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4189, top5_acc: 0.6709, loss_cls: 3.2723, loss: 3.2723 +2024-07-20 12:25:16,589 - pyskl - INFO - Epoch [115][1200/3746] lr: 1.332e-02, eta: 1 day, 6:20:47, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6813, loss_cls: 3.2593, loss: 3.2593 +2024-07-20 12:26:39,577 - pyskl - INFO - Epoch [115][1300/3746] lr: 1.330e-02, eta: 1 day, 6:19:26, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4164, top5_acc: 0.6831, loss_cls: 3.2550, loss: 3.2550 +2024-07-20 12:28:02,688 - pyskl - INFO - Epoch [115][1400/3746] lr: 1.328e-02, eta: 1 day, 6:18:04, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6833, loss_cls: 3.2299, loss: 3.2299 +2024-07-20 12:29:24,897 - pyskl - INFO - Epoch [115][1500/3746] lr: 1.327e-02, eta: 1 day, 6:16:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6802, loss_cls: 3.2533, loss: 3.2533 +2024-07-20 12:30:47,706 - pyskl - INFO - Epoch [115][1600/3746] lr: 1.325e-02, eta: 1 day, 6:15:21, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4355, top5_acc: 0.6873, loss_cls: 3.1813, loss: 3.1813 +2024-07-20 12:32:11,158 - pyskl - INFO - Epoch [115][1700/3746] lr: 1.323e-02, eta: 1 day, 6:14:00, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6777, loss_cls: 3.2830, loss: 3.2830 +2024-07-20 12:33:34,580 - pyskl - INFO - Epoch [115][1800/3746] lr: 1.321e-02, eta: 1 day, 6:12:39, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6834, loss_cls: 3.2364, loss: 3.2364 +2024-07-20 12:34:58,085 - pyskl - INFO - Epoch [115][1900/3746] lr: 1.319e-02, eta: 1 day, 6:11:18, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6711, loss_cls: 3.2825, loss: 3.2825 +2024-07-20 12:36:21,420 - pyskl - INFO - Epoch [115][2000/3746] lr: 1.317e-02, eta: 1 day, 6:09:57, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4241, top5_acc: 0.6837, loss_cls: 3.2271, loss: 3.2271 +2024-07-20 12:37:44,717 - pyskl - INFO - Epoch [115][2100/3746] lr: 1.315e-02, eta: 1 day, 6:08:35, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6916, loss_cls: 3.2053, loss: 3.2053 +2024-07-20 12:39:08,353 - pyskl - INFO - Epoch [115][2200/3746] lr: 1.313e-02, eta: 1 day, 6:07:14, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4177, top5_acc: 0.6856, loss_cls: 3.2279, loss: 3.2279 +2024-07-20 12:40:32,064 - pyskl - INFO - Epoch [115][2300/3746] lr: 1.311e-02, eta: 1 day, 6:05:53, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4316, top5_acc: 0.6845, loss_cls: 3.2382, loss: 3.2382 +2024-07-20 12:41:55,147 - pyskl - INFO - Epoch [115][2400/3746] lr: 1.310e-02, eta: 1 day, 6:04:32, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6777, loss_cls: 3.2365, loss: 3.2365 +2024-07-20 12:43:18,861 - pyskl - INFO - Epoch [115][2500/3746] lr: 1.308e-02, eta: 1 day, 6:03:11, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4270, top5_acc: 0.6895, loss_cls: 3.2235, loss: 3.2235 +2024-07-20 12:44:42,214 - pyskl - INFO - Epoch [115][2600/3746] lr: 1.306e-02, eta: 1 day, 6:01:49, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6830, loss_cls: 3.2332, loss: 3.2332 +2024-07-20 12:46:05,684 - pyskl - INFO - Epoch [115][2700/3746] lr: 1.304e-02, eta: 1 day, 6:00:28, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4259, top5_acc: 0.6911, loss_cls: 3.1879, loss: 3.1879 +2024-07-20 12:47:29,310 - pyskl - INFO - Epoch [115][2800/3746] lr: 1.302e-02, eta: 1 day, 5:59:07, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4219, top5_acc: 0.6823, loss_cls: 3.2346, loss: 3.2346 +2024-07-20 12:48:53,037 - pyskl - INFO - Epoch [115][2900/3746] lr: 1.300e-02, eta: 1 day, 5:57:46, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6753, loss_cls: 3.2222, loss: 3.2222 +2024-07-20 12:50:16,454 - pyskl - INFO - Epoch [115][3000/3746] lr: 1.298e-02, eta: 1 day, 5:56:25, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4181, top5_acc: 0.6839, loss_cls: 3.2717, loss: 3.2717 +2024-07-20 12:51:39,111 - pyskl - INFO - Epoch [115][3100/3746] lr: 1.296e-02, eta: 1 day, 5:55:03, time: 0.827, data_time: 0.001, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6844, loss_cls: 3.2279, loss: 3.2279 +2024-07-20 12:53:02,746 - pyskl - INFO - Epoch [115][3200/3746] lr: 1.295e-02, eta: 1 day, 5:53:42, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4238, top5_acc: 0.6759, loss_cls: 3.2656, loss: 3.2656 +2024-07-20 12:54:26,526 - pyskl - INFO - Epoch [115][3300/3746] lr: 1.293e-02, eta: 1 day, 5:52:21, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6784, loss_cls: 3.2475, loss: 3.2475 +2024-07-20 12:55:50,356 - pyskl - INFO - Epoch [115][3400/3746] lr: 1.291e-02, eta: 1 day, 5:51:00, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6781, loss_cls: 3.2724, loss: 3.2724 +2024-07-20 12:57:13,471 - pyskl - INFO - Epoch [115][3500/3746] lr: 1.289e-02, eta: 1 day, 5:49:38, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4113, top5_acc: 0.6739, loss_cls: 3.3124, loss: 3.3124 +2024-07-20 12:58:36,667 - pyskl - INFO - Epoch [115][3600/3746] lr: 1.287e-02, eta: 1 day, 5:48:17, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4247, top5_acc: 0.6797, loss_cls: 3.2569, loss: 3.2569 +2024-07-20 12:59:59,898 - pyskl - INFO - Epoch [115][3700/3746] lr: 1.285e-02, eta: 1 day, 5:46:56, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4155, top5_acc: 0.6758, loss_cls: 3.2683, loss: 3.2683 +2024-07-20 13:00:39,693 - pyskl - INFO - Saving checkpoint at 115 epochs +2024-07-20 13:02:31,126 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 13:02:31,800 - pyskl - INFO - +top1_acc 0.3602 +top5_acc 0.6163 +2024-07-20 13:02:31,800 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 13:02:31,843 - pyskl - INFO - +mean_acc 0.3600 +2024-07-20 13:02:31,856 - pyskl - INFO - Epoch(val) [115][309] top1_acc: 0.3602, top5_acc: 0.6163, mean_class_accuracy: 0.3600 +2024-07-20 13:06:24,877 - pyskl - INFO - Epoch [116][100/3746] lr: 1.282e-02, eta: 1 day, 5:45:31, time: 2.330, data_time: 1.336, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6953, loss_cls: 3.1575, loss: 3.1575 +2024-07-20 13:07:47,234 - pyskl - INFO - Epoch [116][200/3746] lr: 1.281e-02, eta: 1 day, 5:44:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.6975, loss_cls: 3.1447, loss: 3.1447 +2024-07-20 13:09:09,120 - pyskl - INFO - Epoch [116][300/3746] lr: 1.279e-02, eta: 1 day, 5:42:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4323, top5_acc: 0.6864, loss_cls: 3.2148, loss: 3.2148 +2024-07-20 13:10:31,224 - pyskl - INFO - Epoch [116][400/3746] lr: 1.277e-02, eta: 1 day, 5:41:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4314, top5_acc: 0.6892, loss_cls: 3.1905, loss: 3.1905 +2024-07-20 13:11:53,049 - pyskl - INFO - Epoch [116][500/3746] lr: 1.275e-02, eta: 1 day, 5:40:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4320, top5_acc: 0.6905, loss_cls: 3.1757, loss: 3.1757 +2024-07-20 13:13:14,691 - pyskl - INFO - Epoch [116][600/3746] lr: 1.273e-02, eta: 1 day, 5:38:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6867, loss_cls: 3.1802, loss: 3.1802 +2024-07-20 13:14:36,347 - pyskl - INFO - Epoch [116][700/3746] lr: 1.271e-02, eta: 1 day, 5:37:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4459, top5_acc: 0.7086, loss_cls: 3.1224, loss: 3.1224 +2024-07-20 13:15:57,892 - pyskl - INFO - Epoch [116][800/3746] lr: 1.269e-02, eta: 1 day, 5:35:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.6905, loss_cls: 3.1846, loss: 3.1846 +2024-07-20 13:17:19,510 - pyskl - INFO - Epoch [116][900/3746] lr: 1.268e-02, eta: 1 day, 5:34:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6892, loss_cls: 3.1821, loss: 3.1821 +2024-07-20 13:18:41,093 - pyskl - INFO - Epoch [116][1000/3746] lr: 1.266e-02, eta: 1 day, 5:33:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4373, top5_acc: 0.6972, loss_cls: 3.1576, loss: 3.1576 +2024-07-20 13:20:02,812 - pyskl - INFO - Epoch [116][1100/3746] lr: 1.264e-02, eta: 1 day, 5:31:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6869, loss_cls: 3.1887, loss: 3.1887 +2024-07-20 13:21:25,078 - pyskl - INFO - Epoch [116][1200/3746] lr: 1.262e-02, eta: 1 day, 5:30:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6783, loss_cls: 3.2584, loss: 3.2584 +2024-07-20 13:22:46,690 - pyskl - INFO - Epoch [116][1300/3746] lr: 1.260e-02, eta: 1 day, 5:29:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4213, top5_acc: 0.6884, loss_cls: 3.2278, loss: 3.2278 +2024-07-20 13:24:08,857 - pyskl - INFO - Epoch [116][1400/3746] lr: 1.258e-02, eta: 1 day, 5:27:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6823, loss_cls: 3.2440, loss: 3.2440 +2024-07-20 13:25:30,765 - pyskl - INFO - Epoch [116][1500/3746] lr: 1.256e-02, eta: 1 day, 5:26:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6891, loss_cls: 3.2345, loss: 3.2345 +2024-07-20 13:26:52,691 - pyskl - INFO - Epoch [116][1600/3746] lr: 1.255e-02, eta: 1 day, 5:25:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4280, top5_acc: 0.6919, loss_cls: 3.1946, loss: 3.1946 +2024-07-20 13:28:14,664 - pyskl - INFO - Epoch [116][1700/3746] lr: 1.253e-02, eta: 1 day, 5:23:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6781, loss_cls: 3.2630, loss: 3.2630 +2024-07-20 13:29:36,906 - pyskl - INFO - Epoch [116][1800/3746] lr: 1.251e-02, eta: 1 day, 5:22:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4283, top5_acc: 0.6870, loss_cls: 3.2107, loss: 3.2107 +2024-07-20 13:30:58,654 - pyskl - INFO - Epoch [116][1900/3746] lr: 1.249e-02, eta: 1 day, 5:21:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4316, top5_acc: 0.6913, loss_cls: 3.2157, loss: 3.2157 +2024-07-20 13:32:20,605 - pyskl - INFO - Epoch [116][2000/3746] lr: 1.247e-02, eta: 1 day, 5:19:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6769, loss_cls: 3.2086, loss: 3.2086 +2024-07-20 13:33:42,954 - pyskl - INFO - Epoch [116][2100/3746] lr: 1.245e-02, eta: 1 day, 5:18:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.7009, loss_cls: 3.1642, loss: 3.1642 +2024-07-20 13:35:05,022 - pyskl - INFO - Epoch [116][2200/3746] lr: 1.243e-02, eta: 1 day, 5:16:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4342, top5_acc: 0.6906, loss_cls: 3.1823, loss: 3.1823 +2024-07-20 13:36:26,457 - pyskl - INFO - Epoch [116][2300/3746] lr: 1.242e-02, eta: 1 day, 5:15:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4227, top5_acc: 0.6833, loss_cls: 3.2211, loss: 3.2211 +2024-07-20 13:37:47,765 - pyskl - INFO - Epoch [116][2400/3746] lr: 1.240e-02, eta: 1 day, 5:14:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4355, top5_acc: 0.7006, loss_cls: 3.1484, loss: 3.1484 +2024-07-20 13:39:09,365 - pyskl - INFO - Epoch [116][2500/3746] lr: 1.238e-02, eta: 1 day, 5:12:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.6848, loss_cls: 3.2083, loss: 3.2083 +2024-07-20 13:40:30,432 - pyskl - INFO - Epoch [116][2600/3746] lr: 1.236e-02, eta: 1 day, 5:11:27, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6777, loss_cls: 3.2293, loss: 3.2293 +2024-07-20 13:41:51,758 - pyskl - INFO - Epoch [116][2700/3746] lr: 1.234e-02, eta: 1 day, 5:10:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4219, top5_acc: 0.6811, loss_cls: 3.2594, loss: 3.2594 +2024-07-20 13:43:13,103 - pyskl - INFO - Epoch [116][2800/3746] lr: 1.232e-02, eta: 1 day, 5:08:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6878, loss_cls: 3.2051, loss: 3.2051 +2024-07-20 13:44:35,218 - pyskl - INFO - Epoch [116][2900/3746] lr: 1.231e-02, eta: 1 day, 5:07:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4277, top5_acc: 0.6878, loss_cls: 3.2394, loss: 3.2394 +2024-07-20 13:45:56,971 - pyskl - INFO - Epoch [116][3000/3746] lr: 1.229e-02, eta: 1 day, 5:06:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6858, loss_cls: 3.2303, loss: 3.2303 +2024-07-20 13:47:19,281 - pyskl - INFO - Epoch [116][3100/3746] lr: 1.227e-02, eta: 1 day, 5:04:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6853, loss_cls: 3.2358, loss: 3.2358 +2024-07-20 13:48:41,226 - pyskl - INFO - Epoch [116][3200/3746] lr: 1.225e-02, eta: 1 day, 5:03:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.6986, loss_cls: 3.1656, loss: 3.1656 +2024-07-20 13:50:03,379 - pyskl - INFO - Epoch [116][3300/3746] lr: 1.223e-02, eta: 1 day, 5:01:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6780, loss_cls: 3.2313, loss: 3.2313 +2024-07-20 13:51:25,093 - pyskl - INFO - Epoch [116][3400/3746] lr: 1.221e-02, eta: 1 day, 5:00:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6819, loss_cls: 3.2369, loss: 3.2369 +2024-07-20 13:52:48,054 - pyskl - INFO - Epoch [116][3500/3746] lr: 1.220e-02, eta: 1 day, 4:59:12, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.6950, loss_cls: 3.1529, loss: 3.1529 +2024-07-20 13:54:10,032 - pyskl - INFO - Epoch [116][3600/3746] lr: 1.218e-02, eta: 1 day, 4:57:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4147, top5_acc: 0.6817, loss_cls: 3.2355, loss: 3.2355 +2024-07-20 13:55:31,867 - pyskl - INFO - Epoch [116][3700/3746] lr: 1.216e-02, eta: 1 day, 4:56:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6827, loss_cls: 3.2076, loss: 3.2076 +2024-07-20 13:56:11,107 - pyskl - INFO - Saving checkpoint at 116 epochs +2024-07-20 13:58:03,628 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 13:58:04,315 - pyskl - INFO - +top1_acc 0.3645 +top5_acc 0.6198 +2024-07-20 13:58:04,315 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 13:58:04,357 - pyskl - INFO - +mean_acc 0.3642 +2024-07-20 13:58:04,368 - pyskl - INFO - Epoch(val) [116][309] top1_acc: 0.3645, top5_acc: 0.6198, mean_class_accuracy: 0.3642 +2024-07-20 14:01:49,775 - pyskl - INFO - Epoch [117][100/3746] lr: 1.213e-02, eta: 1 day, 4:55:00, time: 2.254, data_time: 1.270, memory: 15990, top1_acc: 0.4481, top5_acc: 0.7117, loss_cls: 3.0796, loss: 3.0796 +2024-07-20 14:03:11,491 - pyskl - INFO - Epoch [117][200/3746] lr: 1.211e-02, eta: 1 day, 4:53:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4480, top5_acc: 0.6944, loss_cls: 3.1284, loss: 3.1284 +2024-07-20 14:04:33,401 - pyskl - INFO - Epoch [117][300/3746] lr: 1.210e-02, eta: 1 day, 4:52:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4539, top5_acc: 0.7066, loss_cls: 3.0654, loss: 3.0654 +2024-07-20 14:05:55,231 - pyskl - INFO - Epoch [117][400/3746] lr: 1.208e-02, eta: 1 day, 4:50:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4372, top5_acc: 0.6948, loss_cls: 3.1579, loss: 3.1579 +2024-07-20 14:07:16,615 - pyskl - INFO - Epoch [117][500/3746] lr: 1.206e-02, eta: 1 day, 4:49:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4320, top5_acc: 0.6955, loss_cls: 3.1918, loss: 3.1918 +2024-07-20 14:08:38,313 - pyskl - INFO - Epoch [117][600/3746] lr: 1.204e-02, eta: 1 day, 4:48:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4378, top5_acc: 0.7002, loss_cls: 3.1477, loss: 3.1477 +2024-07-20 14:09:59,760 - pyskl - INFO - Epoch [117][700/3746] lr: 1.202e-02, eta: 1 day, 4:46:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4495, top5_acc: 0.6980, loss_cls: 3.1457, loss: 3.1457 +2024-07-20 14:11:21,438 - pyskl - INFO - Epoch [117][800/3746] lr: 1.200e-02, eta: 1 day, 4:45:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4323, top5_acc: 0.6908, loss_cls: 3.1794, loss: 3.1794 +2024-07-20 14:12:42,973 - pyskl - INFO - Epoch [117][900/3746] lr: 1.199e-02, eta: 1 day, 4:44:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4359, top5_acc: 0.6987, loss_cls: 3.1685, loss: 3.1685 +2024-07-20 14:14:04,417 - pyskl - INFO - Epoch [117][1000/3746] lr: 1.197e-02, eta: 1 day, 4:42:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4311, top5_acc: 0.6900, loss_cls: 3.2028, loss: 3.2028 +2024-07-20 14:15:25,827 - pyskl - INFO - Epoch [117][1100/3746] lr: 1.195e-02, eta: 1 day, 4:41:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4417, top5_acc: 0.6923, loss_cls: 3.1421, loss: 3.1421 +2024-07-20 14:16:47,784 - pyskl - INFO - Epoch [117][1200/3746] lr: 1.193e-02, eta: 1 day, 4:39:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4219, top5_acc: 0.6867, loss_cls: 3.2112, loss: 3.2112 +2024-07-20 14:18:09,740 - pyskl - INFO - Epoch [117][1300/3746] lr: 1.191e-02, eta: 1 day, 4:38:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.6850, loss_cls: 3.2275, loss: 3.2275 +2024-07-20 14:19:31,702 - pyskl - INFO - Epoch [117][1400/3746] lr: 1.190e-02, eta: 1 day, 4:37:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.6967, loss_cls: 3.1717, loss: 3.1717 +2024-07-20 14:20:53,656 - pyskl - INFO - Epoch [117][1500/3746] lr: 1.188e-02, eta: 1 day, 4:35:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4297, top5_acc: 0.6952, loss_cls: 3.1535, loss: 3.1535 +2024-07-20 14:22:15,528 - pyskl - INFO - Epoch [117][1600/3746] lr: 1.186e-02, eta: 1 day, 4:34:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6877, loss_cls: 3.1854, loss: 3.1854 +2024-07-20 14:23:37,208 - pyskl - INFO - Epoch [117][1700/3746] lr: 1.184e-02, eta: 1 day, 4:33:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4384, top5_acc: 0.7086, loss_cls: 3.1518, loss: 3.1518 +2024-07-20 14:24:58,672 - pyskl - INFO - Epoch [117][1800/3746] lr: 1.182e-02, eta: 1 day, 4:31:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4339, top5_acc: 0.6931, loss_cls: 3.1901, loss: 3.1901 +2024-07-20 14:26:20,196 - pyskl - INFO - Epoch [117][1900/3746] lr: 1.181e-02, eta: 1 day, 4:30:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4289, top5_acc: 0.6958, loss_cls: 3.1576, loss: 3.1576 +2024-07-20 14:27:41,465 - pyskl - INFO - Epoch [117][2000/3746] lr: 1.179e-02, eta: 1 day, 4:29:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4387, top5_acc: 0.7005, loss_cls: 3.1543, loss: 3.1543 +2024-07-20 14:29:03,169 - pyskl - INFO - Epoch [117][2100/3746] lr: 1.177e-02, eta: 1 day, 4:27:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6869, loss_cls: 3.1894, loss: 3.1894 +2024-07-20 14:30:24,789 - pyskl - INFO - Epoch [117][2200/3746] lr: 1.175e-02, eta: 1 day, 4:26:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4317, top5_acc: 0.6877, loss_cls: 3.1869, loss: 3.1869 +2024-07-20 14:31:46,610 - pyskl - INFO - Epoch [117][2300/3746] lr: 1.173e-02, eta: 1 day, 4:24:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4316, top5_acc: 0.6922, loss_cls: 3.1705, loss: 3.1705 +2024-07-20 14:33:08,064 - pyskl - INFO - Epoch [117][2400/3746] lr: 1.172e-02, eta: 1 day, 4:23:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6787, loss_cls: 3.2367, loss: 3.2367 +2024-07-20 14:34:29,737 - pyskl - INFO - Epoch [117][2500/3746] lr: 1.170e-02, eta: 1 day, 4:22:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.6922, loss_cls: 3.1583, loss: 3.1583 +2024-07-20 14:35:51,258 - pyskl - INFO - Epoch [117][2600/3746] lr: 1.168e-02, eta: 1 day, 4:20:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4291, top5_acc: 0.6881, loss_cls: 3.1971, loss: 3.1971 +2024-07-20 14:37:13,072 - pyskl - INFO - Epoch [117][2700/3746] lr: 1.166e-02, eta: 1 day, 4:19:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4348, top5_acc: 0.6911, loss_cls: 3.1788, loss: 3.1788 +2024-07-20 14:38:34,700 - pyskl - INFO - Epoch [117][2800/3746] lr: 1.164e-02, eta: 1 day, 4:18:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4320, top5_acc: 0.6914, loss_cls: 3.1803, loss: 3.1803 +2024-07-20 14:39:56,404 - pyskl - INFO - Epoch [117][2900/3746] lr: 1.163e-02, eta: 1 day, 4:16:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4113, top5_acc: 0.6763, loss_cls: 3.2695, loss: 3.2695 +2024-07-20 14:41:17,629 - pyskl - INFO - Epoch [117][3000/3746] lr: 1.161e-02, eta: 1 day, 4:15:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4342, top5_acc: 0.6933, loss_cls: 3.1888, loss: 3.1888 +2024-07-20 14:42:39,370 - pyskl - INFO - Epoch [117][3100/3746] lr: 1.159e-02, eta: 1 day, 4:14:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6831, loss_cls: 3.2160, loss: 3.2160 +2024-07-20 14:44:01,227 - pyskl - INFO - Epoch [117][3200/3746] lr: 1.157e-02, eta: 1 day, 4:12:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6866, loss_cls: 3.1863, loss: 3.1863 +2024-07-20 14:45:22,956 - pyskl - INFO - Epoch [117][3300/3746] lr: 1.155e-02, eta: 1 day, 4:11:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4316, top5_acc: 0.6906, loss_cls: 3.1895, loss: 3.1895 +2024-07-20 14:46:45,466 - pyskl - INFO - Epoch [117][3400/3746] lr: 1.154e-02, eta: 1 day, 4:09:59, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.6884, loss_cls: 3.1876, loss: 3.1876 +2024-07-20 14:48:07,788 - pyskl - INFO - Epoch [117][3500/3746] lr: 1.152e-02, eta: 1 day, 4:08:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6800, loss_cls: 3.2138, loss: 3.2138 +2024-07-20 14:49:29,912 - pyskl - INFO - Epoch [117][3600/3746] lr: 1.150e-02, eta: 1 day, 4:07:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4266, top5_acc: 0.6841, loss_cls: 3.2206, loss: 3.2206 +2024-07-20 14:50:52,078 - pyskl - INFO - Epoch [117][3700/3746] lr: 1.148e-02, eta: 1 day, 4:05:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4297, top5_acc: 0.6950, loss_cls: 3.1883, loss: 3.1883 +2024-07-20 14:51:31,590 - pyskl - INFO - Saving checkpoint at 117 epochs +2024-07-20 14:53:24,229 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 14:53:24,888 - pyskl - INFO - +top1_acc 0.3717 +top5_acc 0.6264 +2024-07-20 14:53:24,888 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 14:53:24,929 - pyskl - INFO - +mean_acc 0.3714 +2024-07-20 14:53:24,940 - pyskl - INFO - Epoch(val) [117][309] top1_acc: 0.3717, top5_acc: 0.6264, mean_class_accuracy: 0.3714 +2024-07-20 14:57:14,829 - pyskl - INFO - Epoch [118][100/3746] lr: 1.146e-02, eta: 1 day, 4:04:26, time: 2.299, data_time: 1.313, memory: 15990, top1_acc: 0.4419, top5_acc: 0.7089, loss_cls: 3.0903, loss: 3.0903 +2024-07-20 14:58:37,245 - pyskl - INFO - Epoch [118][200/3746] lr: 1.144e-02, eta: 1 day, 4:03:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4514, top5_acc: 0.6992, loss_cls: 3.1056, loss: 3.1056 +2024-07-20 14:59:59,067 - pyskl - INFO - Epoch [118][300/3746] lr: 1.142e-02, eta: 1 day, 4:01:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4462, top5_acc: 0.7052, loss_cls: 3.1034, loss: 3.1034 +2024-07-20 15:01:20,673 - pyskl - INFO - Epoch [118][400/3746] lr: 1.140e-02, eta: 1 day, 4:00:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4486, top5_acc: 0.7070, loss_cls: 3.0756, loss: 3.0756 +2024-07-20 15:02:42,161 - pyskl - INFO - Epoch [118][500/3746] lr: 1.139e-02, eta: 1 day, 3:58:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4417, top5_acc: 0.7067, loss_cls: 3.0954, loss: 3.0954 +2024-07-20 15:04:04,028 - pyskl - INFO - Epoch [118][600/3746] lr: 1.137e-02, eta: 1 day, 3:57:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4509, top5_acc: 0.7059, loss_cls: 3.0818, loss: 3.0818 +2024-07-20 15:05:25,817 - pyskl - INFO - Epoch [118][700/3746] lr: 1.135e-02, eta: 1 day, 3:56:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4334, top5_acc: 0.6922, loss_cls: 3.1787, loss: 3.1787 +2024-07-20 15:06:47,597 - pyskl - INFO - Epoch [118][800/3746] lr: 1.133e-02, eta: 1 day, 3:54:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6891, loss_cls: 3.1628, loss: 3.1628 +2024-07-20 15:08:09,316 - pyskl - INFO - Epoch [118][900/3746] lr: 1.131e-02, eta: 1 day, 3:53:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.6919, loss_cls: 3.1635, loss: 3.1635 +2024-07-20 15:09:30,944 - pyskl - INFO - Epoch [118][1000/3746] lr: 1.130e-02, eta: 1 day, 3:52:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4555, top5_acc: 0.7042, loss_cls: 3.0675, loss: 3.0675 +2024-07-20 15:10:52,666 - pyskl - INFO - Epoch [118][1100/3746] lr: 1.128e-02, eta: 1 day, 3:50:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4366, top5_acc: 0.6950, loss_cls: 3.1622, loss: 3.1622 +2024-07-20 15:12:14,643 - pyskl - INFO - Epoch [118][1200/3746] lr: 1.126e-02, eta: 1 day, 3:49:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4469, top5_acc: 0.6966, loss_cls: 3.1340, loss: 3.1340 +2024-07-20 15:13:36,876 - pyskl - INFO - Epoch [118][1300/3746] lr: 1.124e-02, eta: 1 day, 3:48:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4356, top5_acc: 0.6930, loss_cls: 3.1465, loss: 3.1465 +2024-07-20 15:14:58,868 - pyskl - INFO - Epoch [118][1400/3746] lr: 1.123e-02, eta: 1 day, 3:46:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4444, top5_acc: 0.6981, loss_cls: 3.1219, loss: 3.1219 +2024-07-20 15:16:20,664 - pyskl - INFO - Epoch [118][1500/3746] lr: 1.121e-02, eta: 1 day, 3:45:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4313, top5_acc: 0.6864, loss_cls: 3.1862, loss: 3.1862 +2024-07-20 15:17:41,953 - pyskl - INFO - Epoch [118][1600/3746] lr: 1.119e-02, eta: 1 day, 3:43:59, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4428, top5_acc: 0.6980, loss_cls: 3.1348, loss: 3.1348 +2024-07-20 15:19:03,411 - pyskl - INFO - Epoch [118][1700/3746] lr: 1.117e-02, eta: 1 day, 3:42:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4380, top5_acc: 0.7000, loss_cls: 3.1379, loss: 3.1379 +2024-07-20 15:20:25,637 - pyskl - INFO - Epoch [118][1800/3746] lr: 1.116e-02, eta: 1 day, 3:41:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4311, top5_acc: 0.6872, loss_cls: 3.1729, loss: 3.1729 +2024-07-20 15:21:47,495 - pyskl - INFO - Epoch [118][1900/3746] lr: 1.114e-02, eta: 1 day, 3:39:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4470, top5_acc: 0.6998, loss_cls: 3.1278, loss: 3.1278 +2024-07-20 15:23:09,021 - pyskl - INFO - Epoch [118][2000/3746] lr: 1.112e-02, eta: 1 day, 3:38:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.6963, loss_cls: 3.1409, loss: 3.1409 +2024-07-20 15:24:30,927 - pyskl - INFO - Epoch [118][2100/3746] lr: 1.110e-02, eta: 1 day, 3:37:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4400, top5_acc: 0.7052, loss_cls: 3.1168, loss: 3.1168 +2024-07-20 15:25:53,215 - pyskl - INFO - Epoch [118][2200/3746] lr: 1.109e-02, eta: 1 day, 3:35:48, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4373, top5_acc: 0.6995, loss_cls: 3.1304, loss: 3.1304 +2024-07-20 15:27:15,176 - pyskl - INFO - Epoch [118][2300/3746] lr: 1.107e-02, eta: 1 day, 3:34:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4297, top5_acc: 0.6923, loss_cls: 3.1783, loss: 3.1783 +2024-07-20 15:28:37,242 - pyskl - INFO - Epoch [118][2400/3746] lr: 1.105e-02, eta: 1 day, 3:33:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4361, top5_acc: 0.6963, loss_cls: 3.1800, loss: 3.1800 +2024-07-20 15:29:58,815 - pyskl - INFO - Epoch [118][2500/3746] lr: 1.103e-02, eta: 1 day, 3:31:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4345, top5_acc: 0.6905, loss_cls: 3.1734, loss: 3.1734 +2024-07-20 15:31:20,759 - pyskl - INFO - Epoch [118][2600/3746] lr: 1.102e-02, eta: 1 day, 3:30:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4377, top5_acc: 0.6931, loss_cls: 3.1848, loss: 3.1848 +2024-07-20 15:32:41,839 - pyskl - INFO - Epoch [118][2700/3746] lr: 1.100e-02, eta: 1 day, 3:28:58, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4338, top5_acc: 0.6858, loss_cls: 3.1766, loss: 3.1766 +2024-07-20 15:34:03,245 - pyskl - INFO - Epoch [118][2800/3746] lr: 1.098e-02, eta: 1 day, 3:27:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4238, top5_acc: 0.6916, loss_cls: 3.2177, loss: 3.2177 +2024-07-20 15:35:25,066 - pyskl - INFO - Epoch [118][2900/3746] lr: 1.096e-02, eta: 1 day, 3:26:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4356, top5_acc: 0.6823, loss_cls: 3.1964, loss: 3.1964 +2024-07-20 15:36:46,503 - pyskl - INFO - Epoch [118][3000/3746] lr: 1.095e-02, eta: 1 day, 3:24:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.6964, loss_cls: 3.1388, loss: 3.1388 +2024-07-20 15:38:08,396 - pyskl - INFO - Epoch [118][3100/3746] lr: 1.093e-02, eta: 1 day, 3:23:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4328, top5_acc: 0.6827, loss_cls: 3.2161, loss: 3.2161 +2024-07-20 15:39:30,756 - pyskl - INFO - Epoch [118][3200/3746] lr: 1.091e-02, eta: 1 day, 3:22:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4386, top5_acc: 0.6930, loss_cls: 3.1346, loss: 3.1346 +2024-07-20 15:40:52,506 - pyskl - INFO - Epoch [118][3300/3746] lr: 1.089e-02, eta: 1 day, 3:20:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.6898, loss_cls: 3.1823, loss: 3.1823 +2024-07-20 15:42:14,760 - pyskl - INFO - Epoch [118][3400/3746] lr: 1.088e-02, eta: 1 day, 3:19:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4389, top5_acc: 0.7008, loss_cls: 3.1411, loss: 3.1411 +2024-07-20 15:43:36,636 - pyskl - INFO - Epoch [118][3500/3746] lr: 1.086e-02, eta: 1 day, 3:18:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4378, top5_acc: 0.6916, loss_cls: 3.1867, loss: 3.1867 +2024-07-20 15:44:58,775 - pyskl - INFO - Epoch [118][3600/3746] lr: 1.084e-02, eta: 1 day, 3:16:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4347, top5_acc: 0.6870, loss_cls: 3.2023, loss: 3.2023 +2024-07-20 15:46:21,017 - pyskl - INFO - Epoch [118][3700/3746] lr: 1.082e-02, eta: 1 day, 3:15:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.6892, loss_cls: 3.1992, loss: 3.1992 +2024-07-20 15:47:00,708 - pyskl - INFO - Saving checkpoint at 118 epochs +2024-07-20 15:48:52,859 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 15:48:53,519 - pyskl - INFO - +top1_acc 0.3794 +top5_acc 0.6335 +2024-07-20 15:48:53,519 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 15:48:53,559 - pyskl - INFO - +mean_acc 0.3792 +2024-07-20 15:48:53,564 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_114.pth was removed +2024-07-20 15:48:53,822 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2024-07-20 15:48:53,823 - pyskl - INFO - Best top1_acc is 0.3794 at 118 epoch. +2024-07-20 15:48:53,834 - pyskl - INFO - Epoch(val) [118][309] top1_acc: 0.3794, top5_acc: 0.6335, mean_class_accuracy: 0.3792 +2024-07-20 15:52:41,377 - pyskl - INFO - Epoch [119][100/3746] lr: 1.080e-02, eta: 1 day, 3:13:50, time: 2.275, data_time: 1.284, memory: 15990, top1_acc: 0.4606, top5_acc: 0.7133, loss_cls: 3.0246, loss: 3.0246 +2024-07-20 15:54:03,438 - pyskl - INFO - Epoch [119][200/3746] lr: 1.078e-02, eta: 1 day, 3:12:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.7027, loss_cls: 3.1121, loss: 3.1121 +2024-07-20 15:55:25,530 - pyskl - INFO - Epoch [119][300/3746] lr: 1.076e-02, eta: 1 day, 3:11:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7089, loss_cls: 3.0359, loss: 3.0359 +2024-07-20 15:56:47,332 - pyskl - INFO - Epoch [119][400/3746] lr: 1.075e-02, eta: 1 day, 3:09:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4453, top5_acc: 0.7023, loss_cls: 3.1146, loss: 3.1146 +2024-07-20 15:58:09,325 - pyskl - INFO - Epoch [119][500/3746] lr: 1.073e-02, eta: 1 day, 3:08:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4559, top5_acc: 0.7067, loss_cls: 3.0574, loss: 3.0574 +2024-07-20 15:59:30,940 - pyskl - INFO - Epoch [119][600/3746] lr: 1.071e-02, eta: 1 day, 3:07:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4473, top5_acc: 0.7059, loss_cls: 3.0903, loss: 3.0903 +2024-07-20 16:00:52,631 - pyskl - INFO - Epoch [119][700/3746] lr: 1.069e-02, eta: 1 day, 3:05:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4519, top5_acc: 0.7117, loss_cls: 3.0632, loss: 3.0632 +2024-07-20 16:02:14,378 - pyskl - INFO - Epoch [119][800/3746] lr: 1.068e-02, eta: 1 day, 3:04:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.7053, loss_cls: 3.0832, loss: 3.0832 +2024-07-20 16:03:36,024 - pyskl - INFO - Epoch [119][900/3746] lr: 1.066e-02, eta: 1 day, 3:02:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4436, top5_acc: 0.6984, loss_cls: 3.1249, loss: 3.1249 +2024-07-20 16:04:57,291 - pyskl - INFO - Epoch [119][1000/3746] lr: 1.064e-02, eta: 1 day, 3:01:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4530, top5_acc: 0.7106, loss_cls: 3.0877, loss: 3.0877 +2024-07-20 16:06:18,989 - pyskl - INFO - Epoch [119][1100/3746] lr: 1.063e-02, eta: 1 day, 3:00:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.6955, loss_cls: 3.1486, loss: 3.1486 +2024-07-20 16:07:41,532 - pyskl - INFO - Epoch [119][1200/3746] lr: 1.061e-02, eta: 1 day, 2:58:50, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4422, top5_acc: 0.6989, loss_cls: 3.1261, loss: 3.1261 +2024-07-20 16:09:03,685 - pyskl - INFO - Epoch [119][1300/3746] lr: 1.059e-02, eta: 1 day, 2:57:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6936, loss_cls: 3.1664, loss: 3.1664 +2024-07-20 16:10:25,553 - pyskl - INFO - Epoch [119][1400/3746] lr: 1.057e-02, eta: 1 day, 2:56:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4434, top5_acc: 0.6952, loss_cls: 3.1458, loss: 3.1458 +2024-07-20 16:11:47,705 - pyskl - INFO - Epoch [119][1500/3746] lr: 1.056e-02, eta: 1 day, 2:54:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4427, top5_acc: 0.6978, loss_cls: 3.1059, loss: 3.1059 +2024-07-20 16:13:09,260 - pyskl - INFO - Epoch [119][1600/3746] lr: 1.054e-02, eta: 1 day, 2:53:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4314, top5_acc: 0.6906, loss_cls: 3.1874, loss: 3.1874 +2024-07-20 16:14:30,402 - pyskl - INFO - Epoch [119][1700/3746] lr: 1.052e-02, eta: 1 day, 2:52:01, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4428, top5_acc: 0.6898, loss_cls: 3.1409, loss: 3.1409 +2024-07-20 16:15:51,864 - pyskl - INFO - Epoch [119][1800/3746] lr: 1.050e-02, eta: 1 day, 2:50:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4431, top5_acc: 0.6972, loss_cls: 3.1181, loss: 3.1181 +2024-07-20 16:17:13,627 - pyskl - INFO - Epoch [119][1900/3746] lr: 1.049e-02, eta: 1 day, 2:49:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4419, top5_acc: 0.7013, loss_cls: 3.1078, loss: 3.1078 +2024-07-20 16:18:34,643 - pyskl - INFO - Epoch [119][2000/3746] lr: 1.047e-02, eta: 1 day, 2:47:55, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6992, loss_cls: 3.1408, loss: 3.1408 +2024-07-20 16:19:56,653 - pyskl - INFO - Epoch [119][2100/3746] lr: 1.045e-02, eta: 1 day, 2:46:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.6966, loss_cls: 3.1508, loss: 3.1508 +2024-07-20 16:21:18,069 - pyskl - INFO - Epoch [119][2200/3746] lr: 1.044e-02, eta: 1 day, 2:45:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4302, top5_acc: 0.6972, loss_cls: 3.1566, loss: 3.1566 +2024-07-20 16:22:39,594 - pyskl - INFO - Epoch [119][2300/3746] lr: 1.042e-02, eta: 1 day, 2:43:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4403, top5_acc: 0.6906, loss_cls: 3.1826, loss: 3.1826 +2024-07-20 16:24:00,872 - pyskl - INFO - Epoch [119][2400/3746] lr: 1.040e-02, eta: 1 day, 2:42:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4431, top5_acc: 0.6902, loss_cls: 3.1462, loss: 3.1462 +2024-07-20 16:25:22,143 - pyskl - INFO - Epoch [119][2500/3746] lr: 1.039e-02, eta: 1 day, 2:41:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4270, top5_acc: 0.6881, loss_cls: 3.1798, loss: 3.1798 +2024-07-20 16:26:43,759 - pyskl - INFO - Epoch [119][2600/3746] lr: 1.037e-02, eta: 1 day, 2:39:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4427, top5_acc: 0.7002, loss_cls: 3.1214, loss: 3.1214 +2024-07-20 16:28:05,066 - pyskl - INFO - Epoch [119][2700/3746] lr: 1.035e-02, eta: 1 day, 2:38:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4386, top5_acc: 0.6950, loss_cls: 3.1388, loss: 3.1388 +2024-07-20 16:29:26,698 - pyskl - INFO - Epoch [119][2800/3746] lr: 1.033e-02, eta: 1 day, 2:36:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4398, top5_acc: 0.6970, loss_cls: 3.1277, loss: 3.1277 +2024-07-20 16:30:48,005 - pyskl - INFO - Epoch [119][2900/3746] lr: 1.032e-02, eta: 1 day, 2:35:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4384, top5_acc: 0.6911, loss_cls: 3.1526, loss: 3.1526 +2024-07-20 16:32:09,916 - pyskl - INFO - Epoch [119][3000/3746] lr: 1.030e-02, eta: 1 day, 2:34:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4375, top5_acc: 0.6920, loss_cls: 3.1672, loss: 3.1672 +2024-07-20 16:33:32,093 - pyskl - INFO - Epoch [119][3100/3746] lr: 1.028e-02, eta: 1 day, 2:32:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4356, top5_acc: 0.6917, loss_cls: 3.1614, loss: 3.1614 +2024-07-20 16:34:54,243 - pyskl - INFO - Epoch [119][3200/3746] lr: 1.027e-02, eta: 1 day, 2:31:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4456, top5_acc: 0.7122, loss_cls: 3.0734, loss: 3.0734 +2024-07-20 16:36:16,009 - pyskl - INFO - Epoch [119][3300/3746] lr: 1.025e-02, eta: 1 day, 2:30:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4334, top5_acc: 0.6969, loss_cls: 3.1606, loss: 3.1606 +2024-07-20 16:37:38,156 - pyskl - INFO - Epoch [119][3400/3746] lr: 1.023e-02, eta: 1 day, 2:28:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4477, top5_acc: 0.6948, loss_cls: 3.1330, loss: 3.1330 +2024-07-20 16:38:59,990 - pyskl - INFO - Epoch [119][3500/3746] lr: 1.022e-02, eta: 1 day, 2:27:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6989, loss_cls: 3.1593, loss: 3.1593 +2024-07-20 16:40:22,305 - pyskl - INFO - Epoch [119][3600/3746] lr: 1.020e-02, eta: 1 day, 2:26:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4302, top5_acc: 0.6833, loss_cls: 3.2057, loss: 3.2057 +2024-07-20 16:41:43,981 - pyskl - INFO - Epoch [119][3700/3746] lr: 1.018e-02, eta: 1 day, 2:24:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4478, top5_acc: 0.6967, loss_cls: 3.1196, loss: 3.1196 +2024-07-20 16:42:23,182 - pyskl - INFO - Saving checkpoint at 119 epochs +2024-07-20 16:44:15,779 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 16:44:16,440 - pyskl - INFO - +top1_acc 0.3763 +top5_acc 0.6341 +2024-07-20 16:44:16,440 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 16:44:16,482 - pyskl - INFO - +mean_acc 0.3760 +2024-07-20 16:44:16,495 - pyskl - INFO - Epoch(val) [119][309] top1_acc: 0.3763, top5_acc: 0.6341, mean_class_accuracy: 0.3760 +2024-07-20 16:48:01,084 - pyskl - INFO - Epoch [120][100/3746] lr: 1.016e-02, eta: 1 day, 2:23:11, time: 2.246, data_time: 1.263, memory: 15990, top1_acc: 0.4641, top5_acc: 0.7172, loss_cls: 3.0355, loss: 3.0355 +2024-07-20 16:49:23,320 - pyskl - INFO - Epoch [120][200/3746] lr: 1.014e-02, eta: 1 day, 2:21:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4548, top5_acc: 0.7111, loss_cls: 3.0545, loss: 3.0545 +2024-07-20 16:50:45,077 - pyskl - INFO - Epoch [120][300/3746] lr: 1.012e-02, eta: 1 day, 2:20:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4542, top5_acc: 0.7086, loss_cls: 3.0722, loss: 3.0722 +2024-07-20 16:52:06,660 - pyskl - INFO - Epoch [120][400/3746] lr: 1.011e-02, eta: 1 day, 2:19:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4405, top5_acc: 0.7056, loss_cls: 3.0814, loss: 3.0814 +2024-07-20 16:53:28,897 - pyskl - INFO - Epoch [120][500/3746] lr: 1.009e-02, eta: 1 day, 2:17:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.7005, loss_cls: 3.0896, loss: 3.0896 +2024-07-20 16:54:50,411 - pyskl - INFO - Epoch [120][600/3746] lr: 1.007e-02, eta: 1 day, 2:16:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4641, top5_acc: 0.7130, loss_cls: 3.0359, loss: 3.0359 +2024-07-20 16:56:11,427 - pyskl - INFO - Epoch [120][700/3746] lr: 1.006e-02, eta: 1 day, 2:14:59, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4377, top5_acc: 0.7053, loss_cls: 3.1064, loss: 3.1064 +2024-07-20 16:57:33,135 - pyskl - INFO - Epoch [120][800/3746] lr: 1.004e-02, eta: 1 day, 2:13:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4520, top5_acc: 0.7070, loss_cls: 3.0664, loss: 3.0664 +2024-07-20 16:58:54,503 - pyskl - INFO - Epoch [120][900/3746] lr: 1.002e-02, eta: 1 day, 2:12:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.7006, loss_cls: 3.0840, loss: 3.0840 +2024-07-20 17:00:15,845 - pyskl - INFO - Epoch [120][1000/3746] lr: 1.001e-02, eta: 1 day, 2:10:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4439, top5_acc: 0.7080, loss_cls: 3.0977, loss: 3.0977 +2024-07-20 17:01:37,447 - pyskl - INFO - Epoch [120][1100/3746] lr: 9.989e-03, eta: 1 day, 2:09:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4598, top5_acc: 0.7128, loss_cls: 3.0767, loss: 3.0767 +2024-07-20 17:02:59,201 - pyskl - INFO - Epoch [120][1200/3746] lr: 9.972e-03, eta: 1 day, 2:08:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.7066, loss_cls: 3.0946, loss: 3.0946 +2024-07-20 17:04:21,495 - pyskl - INFO - Epoch [120][1300/3746] lr: 9.955e-03, eta: 1 day, 2:06:48, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4508, top5_acc: 0.7111, loss_cls: 3.0677, loss: 3.0677 +2024-07-20 17:05:43,563 - pyskl - INFO - Epoch [120][1400/3746] lr: 9.938e-03, eta: 1 day, 2:05:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4455, top5_acc: 0.6992, loss_cls: 3.0948, loss: 3.0948 +2024-07-20 17:07:05,570 - pyskl - INFO - Epoch [120][1500/3746] lr: 9.922e-03, eta: 1 day, 2:04:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4434, top5_acc: 0.7047, loss_cls: 3.1169, loss: 3.1169 +2024-07-20 17:08:27,100 - pyskl - INFO - Epoch [120][1600/3746] lr: 9.905e-03, eta: 1 day, 2:02:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7094, loss_cls: 3.0649, loss: 3.0649 +2024-07-20 17:09:48,685 - pyskl - INFO - Epoch [120][1700/3746] lr: 9.888e-03, eta: 1 day, 2:01:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4452, top5_acc: 0.7097, loss_cls: 3.0854, loss: 3.0854 +2024-07-20 17:11:10,146 - pyskl - INFO - Epoch [120][1800/3746] lr: 9.871e-03, eta: 1 day, 1:59:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4481, top5_acc: 0.7102, loss_cls: 3.0753, loss: 3.0753 +2024-07-20 17:12:31,634 - pyskl - INFO - Epoch [120][1900/3746] lr: 9.855e-03, eta: 1 day, 1:58:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4402, top5_acc: 0.6991, loss_cls: 3.1255, loss: 3.1255 +2024-07-20 17:13:53,180 - pyskl - INFO - Epoch [120][2000/3746] lr: 9.838e-03, eta: 1 day, 1:57:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.7008, loss_cls: 3.1362, loss: 3.1362 +2024-07-20 17:15:14,676 - pyskl - INFO - Epoch [120][2100/3746] lr: 9.821e-03, eta: 1 day, 1:55:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4478, top5_acc: 0.7111, loss_cls: 3.0696, loss: 3.0696 +2024-07-20 17:16:36,826 - pyskl - INFO - Epoch [120][2200/3746] lr: 9.805e-03, eta: 1 day, 1:54:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4462, top5_acc: 0.6992, loss_cls: 3.1161, loss: 3.1161 +2024-07-20 17:17:58,306 - pyskl - INFO - Epoch [120][2300/3746] lr: 9.788e-03, eta: 1 day, 1:53:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4445, top5_acc: 0.7053, loss_cls: 3.0927, loss: 3.0927 +2024-07-20 17:19:19,665 - pyskl - INFO - Epoch [120][2400/3746] lr: 9.772e-03, eta: 1 day, 1:51:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4394, top5_acc: 0.6894, loss_cls: 3.1705, loss: 3.1705 +2024-07-20 17:20:41,066 - pyskl - INFO - Epoch [120][2500/3746] lr: 9.755e-03, eta: 1 day, 1:50:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4466, top5_acc: 0.7027, loss_cls: 3.1107, loss: 3.1107 +2024-07-20 17:22:02,293 - pyskl - INFO - Epoch [120][2600/3746] lr: 9.738e-03, eta: 1 day, 1:49:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4408, top5_acc: 0.7020, loss_cls: 3.1291, loss: 3.1291 +2024-07-20 17:23:23,720 - pyskl - INFO - Epoch [120][2700/3746] lr: 9.722e-03, eta: 1 day, 1:47:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4363, top5_acc: 0.6958, loss_cls: 3.1261, loss: 3.1261 +2024-07-20 17:24:45,199 - pyskl - INFO - Epoch [120][2800/3746] lr: 9.705e-03, eta: 1 day, 1:46:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4473, top5_acc: 0.6994, loss_cls: 3.0960, loss: 3.0960 +2024-07-20 17:26:06,938 - pyskl - INFO - Epoch [120][2900/3746] lr: 9.689e-03, eta: 1 day, 1:44:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4583, top5_acc: 0.7152, loss_cls: 3.0607, loss: 3.0607 +2024-07-20 17:27:28,946 - pyskl - INFO - Epoch [120][3000/3746] lr: 9.672e-03, eta: 1 day, 1:43:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4464, top5_acc: 0.7005, loss_cls: 3.0947, loss: 3.0947 +2024-07-20 17:28:50,904 - pyskl - INFO - Epoch [120][3100/3746] lr: 9.656e-03, eta: 1 day, 1:42:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4452, top5_acc: 0.6966, loss_cls: 3.1241, loss: 3.1241 +2024-07-20 17:30:12,639 - pyskl - INFO - Epoch [120][3200/3746] lr: 9.639e-03, eta: 1 day, 1:40:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4447, top5_acc: 0.7028, loss_cls: 3.1180, loss: 3.1180 +2024-07-20 17:31:34,553 - pyskl - INFO - Epoch [120][3300/3746] lr: 9.623e-03, eta: 1 day, 1:39:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4484, top5_acc: 0.7013, loss_cls: 3.0921, loss: 3.0921 +2024-07-20 17:32:57,252 - pyskl - INFO - Epoch [120][3400/3746] lr: 9.606e-03, eta: 1 day, 1:38:07, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4467, top5_acc: 0.6995, loss_cls: 3.0971, loss: 3.0971 +2024-07-20 17:34:19,712 - pyskl - INFO - Epoch [120][3500/3746] lr: 9.590e-03, eta: 1 day, 1:36:46, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4358, top5_acc: 0.6970, loss_cls: 3.1348, loss: 3.1348 +2024-07-20 17:35:41,896 - pyskl - INFO - Epoch [120][3600/3746] lr: 9.573e-03, eta: 1 day, 1:35:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4494, top5_acc: 0.7056, loss_cls: 3.1210, loss: 3.1210 +2024-07-20 17:37:03,626 - pyskl - INFO - Epoch [120][3700/3746] lr: 9.557e-03, eta: 1 day, 1:34:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4344, top5_acc: 0.6859, loss_cls: 3.1851, loss: 3.1851 +2024-07-20 17:37:43,413 - pyskl - INFO - Saving checkpoint at 120 epochs +2024-07-20 17:39:35,083 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 17:39:35,741 - pyskl - INFO - +top1_acc 0.3748 +top5_acc 0.6326 +2024-07-20 17:39:35,741 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 17:39:35,780 - pyskl - INFO - +mean_acc 0.3745 +2024-07-20 17:39:35,791 - pyskl - INFO - Epoch(val) [120][309] top1_acc: 0.3748, top5_acc: 0.6326, mean_class_accuracy: 0.3745 +2024-07-20 17:43:21,803 - pyskl - INFO - Epoch [121][100/3746] lr: 9.533e-03, eta: 1 day, 1:32:29, time: 2.260, data_time: 1.273, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7147, loss_cls: 3.0392, loss: 3.0392 +2024-07-20 17:44:44,340 - pyskl - INFO - Epoch [121][200/3746] lr: 9.516e-03, eta: 1 day, 1:31:07, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4558, top5_acc: 0.7116, loss_cls: 3.0304, loss: 3.0304 +2024-07-20 17:46:07,209 - pyskl - INFO - Epoch [121][300/3746] lr: 9.500e-03, eta: 1 day, 1:29:46, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4570, top5_acc: 0.7205, loss_cls: 3.0145, loss: 3.0145 +2024-07-20 17:47:28,958 - pyskl - INFO - Epoch [121][400/3746] lr: 9.484e-03, eta: 1 day, 1:28:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4594, top5_acc: 0.7148, loss_cls: 3.0401, loss: 3.0401 +2024-07-20 17:48:50,300 - pyskl - INFO - Epoch [121][500/3746] lr: 9.467e-03, eta: 1 day, 1:27:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7142, loss_cls: 3.0051, loss: 3.0051 +2024-07-20 17:50:12,113 - pyskl - INFO - Epoch [121][600/3746] lr: 9.451e-03, eta: 1 day, 1:25:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.7077, loss_cls: 3.0669, loss: 3.0669 +2024-07-20 17:51:33,956 - pyskl - INFO - Epoch [121][700/3746] lr: 9.435e-03, eta: 1 day, 1:24:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4569, top5_acc: 0.7156, loss_cls: 3.0244, loss: 3.0244 +2024-07-20 17:52:55,705 - pyskl - INFO - Epoch [121][800/3746] lr: 9.418e-03, eta: 1 day, 1:22:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4619, top5_acc: 0.7145, loss_cls: 3.0269, loss: 3.0269 +2024-07-20 17:54:17,429 - pyskl - INFO - Epoch [121][900/3746] lr: 9.402e-03, eta: 1 day, 1:21:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4578, top5_acc: 0.7083, loss_cls: 3.0661, loss: 3.0661 +2024-07-20 17:55:38,936 - pyskl - INFO - Epoch [121][1000/3746] lr: 9.386e-03, eta: 1 day, 1:20:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4530, top5_acc: 0.7105, loss_cls: 3.0605, loss: 3.0605 +2024-07-20 17:57:01,387 - pyskl - INFO - Epoch [121][1100/3746] lr: 9.369e-03, eta: 1 day, 1:18:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4616, top5_acc: 0.7098, loss_cls: 3.0311, loss: 3.0311 +2024-07-20 17:58:23,092 - pyskl - INFO - Epoch [121][1200/3746] lr: 9.353e-03, eta: 1 day, 1:17:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4555, top5_acc: 0.7017, loss_cls: 3.0771, loss: 3.0771 +2024-07-20 17:59:45,502 - pyskl - INFO - Epoch [121][1300/3746] lr: 9.337e-03, eta: 1 day, 1:16:07, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.7106, loss_cls: 3.0661, loss: 3.0661 +2024-07-20 18:01:07,429 - pyskl - INFO - Epoch [121][1400/3746] lr: 9.321e-03, eta: 1 day, 1:14:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4608, top5_acc: 0.7227, loss_cls: 2.9871, loss: 2.9871 +2024-07-20 18:02:29,178 - pyskl - INFO - Epoch [121][1500/3746] lr: 9.304e-03, eta: 1 day, 1:13:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4545, top5_acc: 0.7063, loss_cls: 3.0728, loss: 3.0728 +2024-07-20 18:03:51,106 - pyskl - INFO - Epoch [121][1600/3746] lr: 9.288e-03, eta: 1 day, 1:12:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4525, top5_acc: 0.7091, loss_cls: 3.0605, loss: 3.0605 +2024-07-20 18:05:13,033 - pyskl - INFO - Epoch [121][1700/3746] lr: 9.272e-03, eta: 1 day, 1:10:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4556, top5_acc: 0.7069, loss_cls: 3.0651, loss: 3.0651 +2024-07-20 18:06:34,541 - pyskl - INFO - Epoch [121][1800/3746] lr: 9.256e-03, eta: 1 day, 1:09:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4662, top5_acc: 0.7056, loss_cls: 3.0387, loss: 3.0387 +2024-07-20 18:07:55,721 - pyskl - INFO - Epoch [121][1900/3746] lr: 9.239e-03, eta: 1 day, 1:07:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7097, loss_cls: 3.0801, loss: 3.0801 +2024-07-20 18:09:17,500 - pyskl - INFO - Epoch [121][2000/3746] lr: 9.223e-03, eta: 1 day, 1:06:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7014, loss_cls: 3.0854, loss: 3.0854 +2024-07-20 18:10:39,043 - pyskl - INFO - Epoch [121][2100/3746] lr: 9.207e-03, eta: 1 day, 1:05:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4358, top5_acc: 0.6902, loss_cls: 3.1441, loss: 3.1441 +2024-07-20 18:12:01,004 - pyskl - INFO - Epoch [121][2200/3746] lr: 9.191e-03, eta: 1 day, 1:03:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4575, top5_acc: 0.7084, loss_cls: 3.0692, loss: 3.0692 +2024-07-20 18:13:22,437 - pyskl - INFO - Epoch [121][2300/3746] lr: 9.175e-03, eta: 1 day, 1:02:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4330, top5_acc: 0.7019, loss_cls: 3.1363, loss: 3.1363 +2024-07-20 18:14:44,286 - pyskl - INFO - Epoch [121][2400/3746] lr: 9.159e-03, eta: 1 day, 1:01:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.6942, loss_cls: 3.1445, loss: 3.1445 +2024-07-20 18:16:06,111 - pyskl - INFO - Epoch [121][2500/3746] lr: 9.142e-03, eta: 1 day, 0:59:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4450, top5_acc: 0.6964, loss_cls: 3.1209, loss: 3.1209 +2024-07-20 18:17:27,526 - pyskl - INFO - Epoch [121][2600/3746] lr: 9.126e-03, eta: 1 day, 0:58:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7036, loss_cls: 3.0927, loss: 3.0927 +2024-07-20 18:18:49,406 - pyskl - INFO - Epoch [121][2700/3746] lr: 9.110e-03, eta: 1 day, 0:56:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4467, top5_acc: 0.7045, loss_cls: 3.0807, loss: 3.0807 +2024-07-20 18:20:10,950 - pyskl - INFO - Epoch [121][2800/3746] lr: 9.094e-03, eta: 1 day, 0:55:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.7028, loss_cls: 3.1218, loss: 3.1218 +2024-07-20 18:21:32,168 - pyskl - INFO - Epoch [121][2900/3746] lr: 9.078e-03, eta: 1 day, 0:54:15, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4483, top5_acc: 0.7103, loss_cls: 3.0590, loss: 3.0590 +2024-07-20 18:22:54,450 - pyskl - INFO - Epoch [121][3000/3746] lr: 9.062e-03, eta: 1 day, 0:52:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4456, top5_acc: 0.7034, loss_cls: 3.0740, loss: 3.0740 +2024-07-20 18:24:17,078 - pyskl - INFO - Epoch [121][3100/3746] lr: 9.046e-03, eta: 1 day, 0:51:32, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6923, loss_cls: 3.1427, loss: 3.1427 +2024-07-20 18:25:38,980 - pyskl - INFO - Epoch [121][3200/3746] lr: 9.030e-03, eta: 1 day, 0:50:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4520, top5_acc: 0.7089, loss_cls: 3.1115, loss: 3.1115 +2024-07-20 18:27:01,170 - pyskl - INFO - Epoch [121][3300/3746] lr: 9.014e-03, eta: 1 day, 0:48:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4461, top5_acc: 0.7063, loss_cls: 3.0776, loss: 3.0776 +2024-07-20 18:28:23,602 - pyskl - INFO - Epoch [121][3400/3746] lr: 8.998e-03, eta: 1 day, 0:47:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4441, top5_acc: 0.7100, loss_cls: 3.0915, loss: 3.0915 +2024-07-20 18:29:45,603 - pyskl - INFO - Epoch [121][3500/3746] lr: 8.982e-03, eta: 1 day, 0:46:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4441, top5_acc: 0.6994, loss_cls: 3.1056, loss: 3.1056 +2024-07-20 18:31:07,962 - pyskl - INFO - Epoch [121][3600/3746] lr: 8.966e-03, eta: 1 day, 0:44:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4562, top5_acc: 0.7053, loss_cls: 3.0647, loss: 3.0647 +2024-07-20 18:32:29,954 - pyskl - INFO - Epoch [121][3700/3746] lr: 8.950e-03, eta: 1 day, 0:43:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4394, top5_acc: 0.6978, loss_cls: 3.1291, loss: 3.1291 +2024-07-20 18:33:09,787 - pyskl - INFO - Saving checkpoint at 121 epochs +2024-07-20 18:35:01,460 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 18:35:02,112 - pyskl - INFO - +top1_acc 0.3861 +top5_acc 0.6434 +2024-07-20 18:35:02,112 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 18:35:02,150 - pyskl - INFO - +mean_acc 0.3859 +2024-07-20 18:35:02,155 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_118.pth was removed +2024-07-20 18:35:02,413 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_121.pth. +2024-07-20 18:35:02,413 - pyskl - INFO - Best top1_acc is 0.3861 at 121 epoch. +2024-07-20 18:35:02,424 - pyskl - INFO - Epoch(val) [121][309] top1_acc: 0.3861, top5_acc: 0.6434, mean_class_accuracy: 0.3859 +2024-07-20 18:38:45,632 - pyskl - INFO - Epoch [122][100/3746] lr: 8.927e-03, eta: 1 day, 0:41:46, time: 2.232, data_time: 1.249, memory: 15990, top1_acc: 0.4870, top5_acc: 0.7277, loss_cls: 2.9239, loss: 2.9239 +2024-07-20 18:40:07,235 - pyskl - INFO - Epoch [122][200/3746] lr: 8.911e-03, eta: 1 day, 0:40:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4655, top5_acc: 0.7141, loss_cls: 2.9907, loss: 2.9907 +2024-07-20 18:41:28,935 - pyskl - INFO - Epoch [122][300/3746] lr: 8.895e-03, eta: 1 day, 0:39:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4670, top5_acc: 0.7284, loss_cls: 2.9764, loss: 2.9764 +2024-07-20 18:42:50,405 - pyskl - INFO - Epoch [122][400/3746] lr: 8.879e-03, eta: 1 day, 0:37:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4631, top5_acc: 0.7191, loss_cls: 2.9938, loss: 2.9938 +2024-07-20 18:44:11,798 - pyskl - INFO - Epoch [122][500/3746] lr: 8.863e-03, eta: 1 day, 0:36:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4533, top5_acc: 0.7100, loss_cls: 3.0493, loss: 3.0493 +2024-07-20 18:45:33,624 - pyskl - INFO - Epoch [122][600/3746] lr: 8.847e-03, eta: 1 day, 0:34:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4702, top5_acc: 0.7177, loss_cls: 2.9769, loss: 2.9769 +2024-07-20 18:46:55,559 - pyskl - INFO - Epoch [122][700/3746] lr: 8.831e-03, eta: 1 day, 0:33:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4619, top5_acc: 0.7134, loss_cls: 3.0268, loss: 3.0268 +2024-07-20 18:48:16,760 - pyskl - INFO - Epoch [122][800/3746] lr: 8.815e-03, eta: 1 day, 0:32:12, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4548, top5_acc: 0.7094, loss_cls: 3.0599, loss: 3.0599 +2024-07-20 18:49:38,142 - pyskl - INFO - Epoch [122][900/3746] lr: 8.800e-03, eta: 1 day, 0:30:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7198, loss_cls: 3.0248, loss: 3.0248 +2024-07-20 18:50:59,508 - pyskl - INFO - Epoch [122][1000/3746] lr: 8.784e-03, eta: 1 day, 0:29:28, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4528, top5_acc: 0.7145, loss_cls: 3.0522, loss: 3.0522 +2024-07-20 18:52:21,609 - pyskl - INFO - Epoch [122][1100/3746] lr: 8.768e-03, eta: 1 day, 0:28:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4628, top5_acc: 0.7205, loss_cls: 3.0118, loss: 3.0118 +2024-07-20 18:53:43,482 - pyskl - INFO - Epoch [122][1200/3746] lr: 8.752e-03, eta: 1 day, 0:26:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7109, loss_cls: 3.0409, loss: 3.0409 +2024-07-20 18:55:05,296 - pyskl - INFO - Epoch [122][1300/3746] lr: 8.736e-03, eta: 1 day, 0:25:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4648, top5_acc: 0.7111, loss_cls: 3.0106, loss: 3.0106 +2024-07-20 18:56:27,485 - pyskl - INFO - Epoch [122][1400/3746] lr: 8.721e-03, eta: 1 day, 0:24:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4502, top5_acc: 0.7045, loss_cls: 3.0633, loss: 3.0633 +2024-07-20 18:57:49,048 - pyskl - INFO - Epoch [122][1500/3746] lr: 8.705e-03, eta: 1 day, 0:22:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4575, top5_acc: 0.7059, loss_cls: 3.0651, loss: 3.0651 +2024-07-20 18:59:09,920 - pyskl - INFO - Epoch [122][1600/3746] lr: 8.689e-03, eta: 1 day, 0:21:16, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7100, loss_cls: 3.0495, loss: 3.0495 +2024-07-20 19:00:30,897 - pyskl - INFO - Epoch [122][1700/3746] lr: 8.673e-03, eta: 1 day, 0:19:54, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4541, top5_acc: 0.6998, loss_cls: 3.0799, loss: 3.0799 +2024-07-20 19:01:52,364 - pyskl - INFO - Epoch [122][1800/3746] lr: 8.658e-03, eta: 1 day, 0:18:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4583, top5_acc: 0.7195, loss_cls: 3.0141, loss: 3.0141 +2024-07-20 19:03:14,037 - pyskl - INFO - Epoch [122][1900/3746] lr: 8.642e-03, eta: 1 day, 0:17:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7044, loss_cls: 3.0731, loss: 3.0731 +2024-07-20 19:04:35,707 - pyskl - INFO - Epoch [122][2000/3746] lr: 8.626e-03, eta: 1 day, 0:15:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4559, top5_acc: 0.7123, loss_cls: 3.0214, loss: 3.0214 +2024-07-20 19:05:57,160 - pyskl - INFO - Epoch [122][2100/3746] lr: 8.610e-03, eta: 1 day, 0:14:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4484, top5_acc: 0.7036, loss_cls: 3.0963, loss: 3.0963 +2024-07-20 19:07:18,543 - pyskl - INFO - Epoch [122][2200/3746] lr: 8.595e-03, eta: 1 day, 0:13:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.7131, loss_cls: 3.0506, loss: 3.0506 +2024-07-20 19:08:40,376 - pyskl - INFO - Epoch [122][2300/3746] lr: 8.579e-03, eta: 1 day, 0:11:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4445, top5_acc: 0.7084, loss_cls: 3.0613, loss: 3.0613 +2024-07-20 19:10:02,076 - pyskl - INFO - Epoch [122][2400/3746] lr: 8.563e-03, eta: 1 day, 0:10:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4489, top5_acc: 0.7013, loss_cls: 3.0970, loss: 3.0970 +2024-07-20 19:11:23,808 - pyskl - INFO - Epoch [122][2500/3746] lr: 8.548e-03, eta: 1 day, 0:08:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4519, top5_acc: 0.7170, loss_cls: 3.0410, loss: 3.0410 +2024-07-20 19:12:45,179 - pyskl - INFO - Epoch [122][2600/3746] lr: 8.532e-03, eta: 1 day, 0:07:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4459, top5_acc: 0.7045, loss_cls: 3.0750, loss: 3.0750 +2024-07-20 19:14:07,076 - pyskl - INFO - Epoch [122][2700/3746] lr: 8.517e-03, eta: 1 day, 0:06:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4561, top5_acc: 0.7131, loss_cls: 3.0688, loss: 3.0688 +2024-07-20 19:15:28,462 - pyskl - INFO - Epoch [122][2800/3746] lr: 8.501e-03, eta: 1 day, 0:04:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.7014, loss_cls: 3.0969, loss: 3.0969 +2024-07-20 19:16:50,252 - pyskl - INFO - Epoch [122][2900/3746] lr: 8.485e-03, eta: 1 day, 0:03:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.7061, loss_cls: 3.0877, loss: 3.0877 +2024-07-20 19:18:12,246 - pyskl - INFO - Epoch [122][3000/3746] lr: 8.470e-03, eta: 1 day, 0:02:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4528, top5_acc: 0.7106, loss_cls: 3.0463, loss: 3.0463 +2024-07-20 19:19:34,056 - pyskl - INFO - Epoch [122][3100/3746] lr: 8.454e-03, eta: 1 day, 0:00:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4505, top5_acc: 0.7006, loss_cls: 3.0936, loss: 3.0936 +2024-07-20 19:20:56,149 - pyskl - INFO - Epoch [122][3200/3746] lr: 8.439e-03, eta: 23:59:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4467, top5_acc: 0.7100, loss_cls: 3.0836, loss: 3.0836 +2024-07-20 19:22:18,653 - pyskl - INFO - Epoch [122][3300/3746] lr: 8.423e-03, eta: 23:58:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4500, top5_acc: 0.7083, loss_cls: 3.0715, loss: 3.0715 +2024-07-20 19:23:40,677 - pyskl - INFO - Epoch [122][3400/3746] lr: 8.408e-03, eta: 23:56:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4641, top5_acc: 0.7116, loss_cls: 3.0317, loss: 3.0317 +2024-07-20 19:25:04,290 - pyskl - INFO - Epoch [122][3500/3746] lr: 8.392e-03, eta: 23:55:20, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4486, top5_acc: 0.7044, loss_cls: 3.0687, loss: 3.0687 +2024-07-20 19:26:26,162 - pyskl - INFO - Epoch [122][3600/3746] lr: 8.377e-03, eta: 23:53:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4575, top5_acc: 0.7014, loss_cls: 3.0988, loss: 3.0988 +2024-07-20 19:27:49,135 - pyskl - INFO - Epoch [122][3700/3746] lr: 8.361e-03, eta: 23:52:36, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4450, top5_acc: 0.7094, loss_cls: 3.0602, loss: 3.0602 +2024-07-20 19:28:28,599 - pyskl - INFO - Saving checkpoint at 122 epochs +2024-07-20 19:30:20,116 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 19:30:20,782 - pyskl - INFO - +top1_acc 0.3873 +top5_acc 0.6396 +2024-07-20 19:30:20,783 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 19:30:20,823 - pyskl - INFO - +mean_acc 0.3870 +2024-07-20 19:30:20,828 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_121.pth was removed +2024-07-20 19:30:21,281 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_122.pth. +2024-07-20 19:30:21,282 - pyskl - INFO - Best top1_acc is 0.3873 at 122 epoch. +2024-07-20 19:30:21,294 - pyskl - INFO - Epoch(val) [122][309] top1_acc: 0.3873, top5_acc: 0.6396, mean_class_accuracy: 0.3870 +2024-07-20 19:34:09,201 - pyskl - INFO - Epoch [123][100/3746] lr: 8.339e-03, eta: 23:51:01, time: 2.279, data_time: 1.291, memory: 15990, top1_acc: 0.4633, top5_acc: 0.7186, loss_cls: 2.9960, loss: 2.9960 +2024-07-20 19:35:31,459 - pyskl - INFO - Epoch [123][200/3746] lr: 8.323e-03, eta: 23:49:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4828, top5_acc: 0.7417, loss_cls: 2.9037, loss: 2.9037 +2024-07-20 19:36:53,102 - pyskl - INFO - Epoch [123][300/3746] lr: 8.308e-03, eta: 23:48:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4698, top5_acc: 0.7305, loss_cls: 2.9538, loss: 2.9538 +2024-07-20 19:38:14,363 - pyskl - INFO - Epoch [123][400/3746] lr: 8.292e-03, eta: 23:46:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4658, top5_acc: 0.7253, loss_cls: 2.9495, loss: 2.9495 +2024-07-20 19:39:35,975 - pyskl - INFO - Epoch [123][500/3746] lr: 8.277e-03, eta: 23:45:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4692, top5_acc: 0.7113, loss_cls: 3.0096, loss: 3.0096 +2024-07-20 19:40:57,513 - pyskl - INFO - Epoch [123][600/3746] lr: 8.262e-03, eta: 23:44:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4659, top5_acc: 0.7269, loss_cls: 2.9523, loss: 2.9523 +2024-07-20 19:42:19,301 - pyskl - INFO - Epoch [123][700/3746] lr: 8.246e-03, eta: 23:42:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4648, top5_acc: 0.7180, loss_cls: 2.9726, loss: 2.9726 +2024-07-20 19:43:41,284 - pyskl - INFO - Epoch [123][800/3746] lr: 8.231e-03, eta: 23:41:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4492, top5_acc: 0.7172, loss_cls: 3.0278, loss: 3.0278 +2024-07-20 19:45:02,754 - pyskl - INFO - Epoch [123][900/3746] lr: 8.215e-03, eta: 23:40:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4605, top5_acc: 0.7086, loss_cls: 3.0285, loss: 3.0285 +2024-07-20 19:46:24,587 - pyskl - INFO - Epoch [123][1000/3746] lr: 8.200e-03, eta: 23:38:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4641, top5_acc: 0.7064, loss_cls: 3.0358, loss: 3.0358 +2024-07-20 19:47:46,183 - pyskl - INFO - Epoch [123][1100/3746] lr: 8.185e-03, eta: 23:37:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4514, top5_acc: 0.7239, loss_cls: 3.0358, loss: 3.0358 +2024-07-20 19:49:07,625 - pyskl - INFO - Epoch [123][1200/3746] lr: 8.169e-03, eta: 23:35:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4784, top5_acc: 0.7230, loss_cls: 2.9474, loss: 2.9474 +2024-07-20 19:50:30,369 - pyskl - INFO - Epoch [123][1300/3746] lr: 8.154e-03, eta: 23:34:37, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4627, top5_acc: 0.7177, loss_cls: 3.0351, loss: 3.0351 +2024-07-20 19:51:52,087 - pyskl - INFO - Epoch [123][1400/3746] lr: 8.139e-03, eta: 23:33:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4606, top5_acc: 0.7137, loss_cls: 3.0299, loss: 3.0299 +2024-07-20 19:53:13,164 - pyskl - INFO - Epoch [123][1500/3746] lr: 8.124e-03, eta: 23:31:53, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4567, top5_acc: 0.7109, loss_cls: 3.0561, loss: 3.0561 +2024-07-20 19:54:34,360 - pyskl - INFO - Epoch [123][1600/3746] lr: 8.108e-03, eta: 23:30:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7225, loss_cls: 2.9634, loss: 2.9634 +2024-07-20 19:55:55,989 - pyskl - INFO - Epoch [123][1700/3746] lr: 8.093e-03, eta: 23:29:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4620, top5_acc: 0.7253, loss_cls: 2.9629, loss: 2.9629 +2024-07-20 19:57:18,061 - pyskl - INFO - Epoch [123][1800/3746] lr: 8.078e-03, eta: 23:27:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4589, top5_acc: 0.7133, loss_cls: 3.0068, loss: 3.0068 +2024-07-20 19:58:39,906 - pyskl - INFO - Epoch [123][1900/3746] lr: 8.063e-03, eta: 23:26:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7163, loss_cls: 3.0117, loss: 3.0117 +2024-07-20 20:00:01,901 - pyskl - INFO - Epoch [123][2000/3746] lr: 8.047e-03, eta: 23:25:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4669, top5_acc: 0.7155, loss_cls: 3.0157, loss: 3.0157 +2024-07-20 20:01:23,646 - pyskl - INFO - Epoch [123][2100/3746] lr: 8.032e-03, eta: 23:23:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4622, top5_acc: 0.7052, loss_cls: 3.0501, loss: 3.0501 +2024-07-20 20:02:44,804 - pyskl - INFO - Epoch [123][2200/3746] lr: 8.017e-03, eta: 23:22:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4483, top5_acc: 0.7025, loss_cls: 3.0959, loss: 3.0959 +2024-07-20 20:04:07,182 - pyskl - INFO - Epoch [123][2300/3746] lr: 8.002e-03, eta: 23:20:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4570, top5_acc: 0.7231, loss_cls: 3.0253, loss: 3.0253 +2024-07-20 20:05:29,557 - pyskl - INFO - Epoch [123][2400/3746] lr: 7.987e-03, eta: 23:19:36, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4548, top5_acc: 0.7209, loss_cls: 3.0325, loss: 3.0325 +2024-07-20 20:06:51,170 - pyskl - INFO - Epoch [123][2500/3746] lr: 7.971e-03, eta: 23:18:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4636, top5_acc: 0.7144, loss_cls: 3.0134, loss: 3.0134 +2024-07-20 20:08:12,713 - pyskl - INFO - Epoch [123][2600/3746] lr: 7.956e-03, eta: 23:16:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4659, top5_acc: 0.7217, loss_cls: 3.0211, loss: 3.0211 +2024-07-20 20:09:34,805 - pyskl - INFO - Epoch [123][2700/3746] lr: 7.941e-03, eta: 23:15:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4491, top5_acc: 0.7086, loss_cls: 3.0638, loss: 3.0638 +2024-07-20 20:10:56,135 - pyskl - INFO - Epoch [123][2800/3746] lr: 7.926e-03, eta: 23:14:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4441, top5_acc: 0.7061, loss_cls: 3.0840, loss: 3.0840 +2024-07-20 20:12:17,954 - pyskl - INFO - Epoch [123][2900/3746] lr: 7.911e-03, eta: 23:12:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7145, loss_cls: 3.0304, loss: 3.0304 +2024-07-20 20:13:40,182 - pyskl - INFO - Epoch [123][3000/3746] lr: 7.896e-03, eta: 23:11:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4606, top5_acc: 0.7136, loss_cls: 3.0399, loss: 3.0399 +2024-07-20 20:15:02,062 - pyskl - INFO - Epoch [123][3100/3746] lr: 7.881e-03, eta: 23:10:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7158, loss_cls: 2.9769, loss: 2.9769 +2024-07-20 20:16:23,872 - pyskl - INFO - Epoch [123][3200/3746] lr: 7.866e-03, eta: 23:08:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4622, top5_acc: 0.7161, loss_cls: 3.0249, loss: 3.0249 +2024-07-20 20:17:46,207 - pyskl - INFO - Epoch [123][3300/3746] lr: 7.851e-03, eta: 23:07:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4614, top5_acc: 0.7178, loss_cls: 3.0109, loss: 3.0109 +2024-07-20 20:19:08,152 - pyskl - INFO - Epoch [123][3400/3746] lr: 7.836e-03, eta: 23:05:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7067, loss_cls: 3.0478, loss: 3.0478 +2024-07-20 20:20:29,900 - pyskl - INFO - Epoch [123][3500/3746] lr: 7.821e-03, eta: 23:04:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4553, top5_acc: 0.7122, loss_cls: 3.0540, loss: 3.0540 +2024-07-20 20:21:52,102 - pyskl - INFO - Epoch [123][3600/3746] lr: 7.806e-03, eta: 23:03:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4533, top5_acc: 0.7059, loss_cls: 3.0581, loss: 3.0581 +2024-07-20 20:23:14,595 - pyskl - INFO - Epoch [123][3700/3746] lr: 7.791e-03, eta: 23:01:50, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7102, loss_cls: 3.0675, loss: 3.0675 +2024-07-20 20:23:54,924 - pyskl - INFO - Saving checkpoint at 123 epochs +2024-07-20 20:25:47,456 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 20:25:48,113 - pyskl - INFO - +top1_acc 0.3920 +top5_acc 0.6493 +2024-07-20 20:25:48,113 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 20:25:48,152 - pyskl - INFO - +mean_acc 0.3917 +2024-07-20 20:25:48,156 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_122.pth was removed +2024-07-20 20:25:48,410 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_123.pth. +2024-07-20 20:25:48,411 - pyskl - INFO - Best top1_acc is 0.3920 at 123 epoch. +2024-07-20 20:25:48,422 - pyskl - INFO - Epoch(val) [123][309] top1_acc: 0.3920, top5_acc: 0.6493, mean_class_accuracy: 0.3917 +2024-07-20 20:29:32,360 - pyskl - INFO - Epoch [124][100/3746] lr: 7.769e-03, eta: 23:00:14, time: 2.239, data_time: 1.256, memory: 15990, top1_acc: 0.4777, top5_acc: 0.7397, loss_cls: 2.8992, loss: 2.8992 +2024-07-20 20:30:54,007 - pyskl - INFO - Epoch [124][200/3746] lr: 7.754e-03, eta: 22:58:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4750, top5_acc: 0.7259, loss_cls: 2.9291, loss: 2.9291 +2024-07-20 20:32:16,229 - pyskl - INFO - Epoch [124][300/3746] lr: 7.739e-03, eta: 22:57:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4728, top5_acc: 0.7284, loss_cls: 2.9245, loss: 2.9245 +2024-07-20 20:33:38,132 - pyskl - INFO - Epoch [124][400/3746] lr: 7.724e-03, eta: 22:56:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4697, top5_acc: 0.7311, loss_cls: 2.9319, loss: 2.9319 +2024-07-20 20:34:59,999 - pyskl - INFO - Epoch [124][500/3746] lr: 7.709e-03, eta: 22:54:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4647, top5_acc: 0.7217, loss_cls: 2.9951, loss: 2.9951 +2024-07-20 20:36:22,197 - pyskl - INFO - Epoch [124][600/3746] lr: 7.694e-03, eta: 22:53:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7272, loss_cls: 2.9494, loss: 2.9494 +2024-07-20 20:37:44,160 - pyskl - INFO - Epoch [124][700/3746] lr: 7.679e-03, eta: 22:52:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4717, top5_acc: 0.7261, loss_cls: 2.9486, loss: 2.9486 +2024-07-20 20:39:05,775 - pyskl - INFO - Epoch [124][800/3746] lr: 7.664e-03, eta: 22:50:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4733, top5_acc: 0.7172, loss_cls: 2.9731, loss: 2.9731 +2024-07-20 20:40:27,297 - pyskl - INFO - Epoch [124][900/3746] lr: 7.649e-03, eta: 22:49:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4645, top5_acc: 0.7312, loss_cls: 2.9715, loss: 2.9715 +2024-07-20 20:41:49,002 - pyskl - INFO - Epoch [124][1000/3746] lr: 7.635e-03, eta: 22:47:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7220, loss_cls: 2.9627, loss: 2.9627 +2024-07-20 20:43:11,205 - pyskl - INFO - Epoch [124][1100/3746] lr: 7.620e-03, eta: 22:46:34, time: 0.822, data_time: 0.001, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7167, loss_cls: 3.0226, loss: 3.0226 +2024-07-20 20:44:32,555 - pyskl - INFO - Epoch [124][1200/3746] lr: 7.605e-03, eta: 22:45:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4612, top5_acc: 0.7186, loss_cls: 3.0071, loss: 3.0071 +2024-07-20 20:45:54,651 - pyskl - INFO - Epoch [124][1300/3746] lr: 7.590e-03, eta: 22:43:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4763, top5_acc: 0.7225, loss_cls: 2.9713, loss: 2.9713 +2024-07-20 20:47:16,861 - pyskl - INFO - Epoch [124][1400/3746] lr: 7.575e-03, eta: 22:42:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4719, top5_acc: 0.7256, loss_cls: 2.9738, loss: 2.9738 +2024-07-20 20:48:38,467 - pyskl - INFO - Epoch [124][1500/3746] lr: 7.561e-03, eta: 22:41:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4606, top5_acc: 0.7175, loss_cls: 3.0133, loss: 3.0133 +2024-07-20 20:50:00,050 - pyskl - INFO - Epoch [124][1600/3746] lr: 7.546e-03, eta: 22:39:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4694, top5_acc: 0.7264, loss_cls: 2.9852, loss: 2.9852 +2024-07-20 20:51:22,089 - pyskl - INFO - Epoch [124][1700/3746] lr: 7.531e-03, eta: 22:38:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4623, top5_acc: 0.7233, loss_cls: 2.9853, loss: 2.9853 +2024-07-20 20:52:43,423 - pyskl - INFO - Epoch [124][1800/3746] lr: 7.516e-03, eta: 22:37:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4695, top5_acc: 0.7231, loss_cls: 2.9806, loss: 2.9806 +2024-07-20 20:54:05,564 - pyskl - INFO - Epoch [124][1900/3746] lr: 7.502e-03, eta: 22:35:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7156, loss_cls: 3.0254, loss: 3.0254 +2024-07-20 20:55:27,113 - pyskl - INFO - Epoch [124][2000/3746] lr: 7.487e-03, eta: 22:34:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7053, loss_cls: 3.0757, loss: 3.0757 +2024-07-20 20:56:48,950 - pyskl - INFO - Epoch [124][2100/3746] lr: 7.472e-03, eta: 22:32:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4727, top5_acc: 0.7247, loss_cls: 2.9585, loss: 2.9585 +2024-07-20 20:58:10,171 - pyskl - INFO - Epoch [124][2200/3746] lr: 7.457e-03, eta: 22:31:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4691, top5_acc: 0.7230, loss_cls: 2.9772, loss: 2.9772 +2024-07-20 20:59:32,507 - pyskl - INFO - Epoch [124][2300/3746] lr: 7.443e-03, eta: 22:30:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4781, top5_acc: 0.7298, loss_cls: 2.9325, loss: 2.9325 +2024-07-20 21:00:55,190 - pyskl - INFO - Epoch [124][2400/3746] lr: 7.428e-03, eta: 22:28:48, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4598, top5_acc: 0.7161, loss_cls: 3.0205, loss: 3.0205 +2024-07-20 21:02:17,680 - pyskl - INFO - Epoch [124][2500/3746] lr: 7.413e-03, eta: 22:27:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4619, top5_acc: 0.7134, loss_cls: 3.0252, loss: 3.0252 +2024-07-20 21:03:39,608 - pyskl - INFO - Epoch [124][2600/3746] lr: 7.399e-03, eta: 22:26:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4673, top5_acc: 0.7217, loss_cls: 2.9589, loss: 2.9589 +2024-07-20 21:05:01,217 - pyskl - INFO - Epoch [124][2700/3746] lr: 7.384e-03, eta: 22:24:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4713, top5_acc: 0.7261, loss_cls: 2.9395, loss: 2.9395 +2024-07-20 21:06:23,345 - pyskl - INFO - Epoch [124][2800/3746] lr: 7.370e-03, eta: 22:23:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4619, top5_acc: 0.7156, loss_cls: 2.9982, loss: 2.9982 +2024-07-20 21:07:44,635 - pyskl - INFO - Epoch [124][2900/3746] lr: 7.355e-03, eta: 22:21:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4562, top5_acc: 0.7108, loss_cls: 3.0519, loss: 3.0519 +2024-07-20 21:09:06,235 - pyskl - INFO - Epoch [124][3000/3746] lr: 7.340e-03, eta: 22:20:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4550, top5_acc: 0.7177, loss_cls: 3.0116, loss: 3.0116 +2024-07-20 21:10:28,893 - pyskl - INFO - Epoch [124][3100/3746] lr: 7.326e-03, eta: 22:19:14, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4603, top5_acc: 0.7084, loss_cls: 3.0518, loss: 3.0518 +2024-07-20 21:11:51,081 - pyskl - INFO - Epoch [124][3200/3746] lr: 7.311e-03, eta: 22:17:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4555, top5_acc: 0.7134, loss_cls: 3.0328, loss: 3.0328 +2024-07-20 21:13:13,179 - pyskl - INFO - Epoch [124][3300/3746] lr: 7.297e-03, eta: 22:16:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4584, top5_acc: 0.7155, loss_cls: 3.0240, loss: 3.0240 +2024-07-20 21:14:34,935 - pyskl - INFO - Epoch [124][3400/3746] lr: 7.282e-03, eta: 22:15:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4683, top5_acc: 0.7142, loss_cls: 3.0109, loss: 3.0109 +2024-07-20 21:15:56,921 - pyskl - INFO - Epoch [124][3500/3746] lr: 7.268e-03, eta: 22:13:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4634, top5_acc: 0.7208, loss_cls: 3.0097, loss: 3.0097 +2024-07-20 21:17:19,519 - pyskl - INFO - Epoch [124][3600/3746] lr: 7.253e-03, eta: 22:12:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4552, top5_acc: 0.7230, loss_cls: 2.9948, loss: 2.9948 +2024-07-20 21:18:41,532 - pyskl - INFO - Epoch [124][3700/3746] lr: 7.239e-03, eta: 22:11:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4552, top5_acc: 0.7148, loss_cls: 3.0264, loss: 3.0264 +2024-07-20 21:19:21,645 - pyskl - INFO - Saving checkpoint at 124 epochs +2024-07-20 21:21:12,240 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 21:21:12,893 - pyskl - INFO - +top1_acc 0.3959 +top5_acc 0.6527 +2024-07-20 21:21:12,893 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 21:21:12,932 - pyskl - INFO - +mean_acc 0.3957 +2024-07-20 21:21:12,936 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_123.pth was removed +2024-07-20 21:21:13,191 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_124.pth. +2024-07-20 21:21:13,192 - pyskl - INFO - Best top1_acc is 0.3959 at 124 epoch. +2024-07-20 21:21:13,203 - pyskl - INFO - Epoch(val) [124][309] top1_acc: 0.3959, top5_acc: 0.6527, mean_class_accuracy: 0.3957 +2024-07-20 21:24:58,531 - pyskl - INFO - Epoch [125][100/3746] lr: 7.217e-03, eta: 22:09:25, time: 2.253, data_time: 1.275, memory: 15990, top1_acc: 0.4800, top5_acc: 0.7330, loss_cls: 2.9120, loss: 2.9120 +2024-07-20 21:26:20,315 - pyskl - INFO - Epoch [125][200/3746] lr: 7.203e-03, eta: 22:08:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4794, top5_acc: 0.7383, loss_cls: 2.8908, loss: 2.8908 +2024-07-20 21:27:42,044 - pyskl - INFO - Epoch [125][300/3746] lr: 7.189e-03, eta: 22:06:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4755, top5_acc: 0.7356, loss_cls: 2.9199, loss: 2.9199 +2024-07-20 21:29:03,653 - pyskl - INFO - Epoch [125][400/3746] lr: 7.174e-03, eta: 22:05:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4813, top5_acc: 0.7350, loss_cls: 2.9231, loss: 2.9231 +2024-07-20 21:30:25,357 - pyskl - INFO - Epoch [125][500/3746] lr: 7.160e-03, eta: 22:03:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4836, top5_acc: 0.7330, loss_cls: 2.9003, loss: 2.9003 +2024-07-20 21:31:47,003 - pyskl - INFO - Epoch [125][600/3746] lr: 7.145e-03, eta: 22:02:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4758, top5_acc: 0.7319, loss_cls: 2.9226, loss: 2.9226 +2024-07-20 21:33:09,160 - pyskl - INFO - Epoch [125][700/3746] lr: 7.131e-03, eta: 22:01:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4869, top5_acc: 0.7266, loss_cls: 2.9241, loss: 2.9241 +2024-07-20 21:34:30,920 - pyskl - INFO - Epoch [125][800/3746] lr: 7.117e-03, eta: 21:59:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4648, top5_acc: 0.7252, loss_cls: 2.9588, loss: 2.9588 +2024-07-20 21:35:52,135 - pyskl - INFO - Epoch [125][900/3746] lr: 7.102e-03, eta: 21:58:29, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4822, top5_acc: 0.7323, loss_cls: 2.8836, loss: 2.8836 +2024-07-20 21:37:14,140 - pyskl - INFO - Epoch [125][1000/3746] lr: 7.088e-03, eta: 21:57:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4709, top5_acc: 0.7264, loss_cls: 2.9483, loss: 2.9483 +2024-07-20 21:38:36,065 - pyskl - INFO - Epoch [125][1100/3746] lr: 7.073e-03, eta: 21:55:45, time: 0.819, data_time: 0.001, memory: 15990, top1_acc: 0.4709, top5_acc: 0.7291, loss_cls: 2.9307, loss: 2.9307 +2024-07-20 21:39:58,259 - pyskl - INFO - Epoch [125][1200/3746] lr: 7.059e-03, eta: 21:54:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4653, top5_acc: 0.7188, loss_cls: 3.0017, loss: 3.0017 +2024-07-20 21:41:19,629 - pyskl - INFO - Epoch [125][1300/3746] lr: 7.045e-03, eta: 21:53:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4750, top5_acc: 0.7267, loss_cls: 2.9428, loss: 2.9428 +2024-07-20 21:42:41,685 - pyskl - INFO - Epoch [125][1400/3746] lr: 7.031e-03, eta: 21:51:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4894, top5_acc: 0.7297, loss_cls: 2.9229, loss: 2.9229 +2024-07-20 21:44:03,073 - pyskl - INFO - Epoch [125][1500/3746] lr: 7.016e-03, eta: 21:50:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4711, top5_acc: 0.7320, loss_cls: 2.9333, loss: 2.9333 +2024-07-20 21:45:25,016 - pyskl - INFO - Epoch [125][1600/3746] lr: 7.002e-03, eta: 21:48:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4747, top5_acc: 0.7258, loss_cls: 2.9767, loss: 2.9767 +2024-07-20 21:46:46,555 - pyskl - INFO - Epoch [125][1700/3746] lr: 6.988e-03, eta: 21:47:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4781, top5_acc: 0.7395, loss_cls: 2.9006, loss: 2.9006 +2024-07-20 21:48:08,437 - pyskl - INFO - Epoch [125][1800/3746] lr: 6.973e-03, eta: 21:46:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4616, top5_acc: 0.7252, loss_cls: 3.0169, loss: 3.0169 +2024-07-20 21:49:29,818 - pyskl - INFO - Epoch [125][1900/3746] lr: 6.959e-03, eta: 21:44:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7228, loss_cls: 2.9563, loss: 2.9563 +2024-07-20 21:50:51,079 - pyskl - INFO - Epoch [125][2000/3746] lr: 6.945e-03, eta: 21:43:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4675, top5_acc: 0.7211, loss_cls: 2.9674, loss: 2.9674 +2024-07-20 21:52:12,995 - pyskl - INFO - Epoch [125][2100/3746] lr: 6.931e-03, eta: 21:42:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4745, top5_acc: 0.7227, loss_cls: 2.9416, loss: 2.9416 +2024-07-20 21:53:34,932 - pyskl - INFO - Epoch [125][2200/3746] lr: 6.917e-03, eta: 21:40:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4742, top5_acc: 0.7256, loss_cls: 2.9293, loss: 2.9293 +2024-07-20 21:54:57,176 - pyskl - INFO - Epoch [125][2300/3746] lr: 6.902e-03, eta: 21:39:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4673, top5_acc: 0.7222, loss_cls: 2.9955, loss: 2.9955 +2024-07-20 21:56:18,883 - pyskl - INFO - Epoch [125][2400/3746] lr: 6.888e-03, eta: 21:37:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4603, top5_acc: 0.7109, loss_cls: 3.0162, loss: 3.0162 +2024-07-20 21:57:40,655 - pyskl - INFO - Epoch [125][2500/3746] lr: 6.874e-03, eta: 21:36:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7247, loss_cls: 2.9649, loss: 2.9649 +2024-07-20 21:59:02,281 - pyskl - INFO - Epoch [125][2600/3746] lr: 6.860e-03, eta: 21:35:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4658, top5_acc: 0.7242, loss_cls: 2.9728, loss: 2.9728 +2024-07-20 22:00:23,797 - pyskl - INFO - Epoch [125][2700/3746] lr: 6.846e-03, eta: 21:33:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4719, top5_acc: 0.7258, loss_cls: 2.9418, loss: 2.9418 +2024-07-20 22:01:45,187 - pyskl - INFO - Epoch [125][2800/3746] lr: 6.832e-03, eta: 21:32:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4587, top5_acc: 0.7188, loss_cls: 3.0039, loss: 3.0039 +2024-07-20 22:03:07,039 - pyskl - INFO - Epoch [125][2900/3746] lr: 6.818e-03, eta: 21:31:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4684, top5_acc: 0.7161, loss_cls: 2.9819, loss: 2.9819 +2024-07-20 22:04:28,975 - pyskl - INFO - Epoch [125][3000/3746] lr: 6.804e-03, eta: 21:29:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4738, top5_acc: 0.7227, loss_cls: 2.9745, loss: 2.9745 +2024-07-20 22:05:51,200 - pyskl - INFO - Epoch [125][3100/3746] lr: 6.789e-03, eta: 21:28:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4708, top5_acc: 0.7220, loss_cls: 2.9665, loss: 2.9665 +2024-07-20 22:07:13,608 - pyskl - INFO - Epoch [125][3200/3746] lr: 6.775e-03, eta: 21:27:03, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4653, top5_acc: 0.7137, loss_cls: 2.9662, loss: 2.9662 +2024-07-20 22:08:35,758 - pyskl - INFO - Epoch [125][3300/3746] lr: 6.761e-03, eta: 21:25:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4627, top5_acc: 0.7216, loss_cls: 2.9796, loss: 2.9796 +2024-07-20 22:09:57,763 - pyskl - INFO - Epoch [125][3400/3746] lr: 6.747e-03, eta: 21:24:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4569, top5_acc: 0.7223, loss_cls: 3.0251, loss: 3.0251 +2024-07-20 22:11:20,023 - pyskl - INFO - Epoch [125][3500/3746] lr: 6.733e-03, eta: 21:22:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7134, loss_cls: 2.9649, loss: 2.9649 +2024-07-20 22:12:42,091 - pyskl - INFO - Epoch [125][3600/3746] lr: 6.719e-03, eta: 21:21:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4619, top5_acc: 0.7238, loss_cls: 3.0019, loss: 3.0019 +2024-07-20 22:14:05,592 - pyskl - INFO - Epoch [125][3700/3746] lr: 6.705e-03, eta: 21:20:13, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4622, top5_acc: 0.7155, loss_cls: 3.0197, loss: 3.0197 +2024-07-20 22:14:46,090 - pyskl - INFO - Saving checkpoint at 125 epochs +2024-07-20 22:16:38,512 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 22:16:39,167 - pyskl - INFO - +top1_acc 0.4036 +top5_acc 0.6572 +2024-07-20 22:16:39,167 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 22:16:39,205 - pyskl - INFO - +mean_acc 0.4034 +2024-07-20 22:16:39,209 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_124.pth was removed +2024-07-20 22:16:39,469 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_125.pth. +2024-07-20 22:16:39,470 - pyskl - INFO - Best top1_acc is 0.4036 at 125 epoch. +2024-07-20 22:16:39,482 - pyskl - INFO - Epoch(val) [125][309] top1_acc: 0.4036, top5_acc: 0.6572, mean_class_accuracy: 0.4034 +2024-07-20 22:20:27,747 - pyskl - INFO - Epoch [126][100/3746] lr: 6.685e-03, eta: 21:18:35, time: 2.283, data_time: 1.301, memory: 15990, top1_acc: 0.4988, top5_acc: 0.7470, loss_cls: 2.8335, loss: 2.8335 +2024-07-20 22:21:50,882 - pyskl - INFO - Epoch [126][200/3746] lr: 6.671e-03, eta: 21:17:13, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4800, top5_acc: 0.7439, loss_cls: 2.8630, loss: 2.8630 +2024-07-20 22:23:12,357 - pyskl - INFO - Epoch [126][300/3746] lr: 6.657e-03, eta: 21:15:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4828, top5_acc: 0.7319, loss_cls: 2.8790, loss: 2.8790 +2024-07-20 22:24:33,696 - pyskl - INFO - Epoch [126][400/3746] lr: 6.643e-03, eta: 21:14:29, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4820, top5_acc: 0.7366, loss_cls: 2.8524, loss: 2.8524 +2024-07-20 22:25:54,990 - pyskl - INFO - Epoch [126][500/3746] lr: 6.629e-03, eta: 21:13:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4831, top5_acc: 0.7408, loss_cls: 2.8546, loss: 2.8546 +2024-07-20 22:27:16,584 - pyskl - INFO - Epoch [126][600/3746] lr: 6.615e-03, eta: 21:11:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4744, top5_acc: 0.7306, loss_cls: 2.9418, loss: 2.9418 +2024-07-20 22:28:38,710 - pyskl - INFO - Epoch [126][700/3746] lr: 6.601e-03, eta: 21:10:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4755, top5_acc: 0.7316, loss_cls: 2.9392, loss: 2.9392 +2024-07-20 22:30:00,428 - pyskl - INFO - Epoch [126][800/3746] lr: 6.587e-03, eta: 21:09:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4847, top5_acc: 0.7264, loss_cls: 2.9086, loss: 2.9086 +2024-07-20 22:31:22,640 - pyskl - INFO - Epoch [126][900/3746] lr: 6.574e-03, eta: 21:07:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4747, top5_acc: 0.7261, loss_cls: 2.9355, loss: 2.9355 +2024-07-20 22:32:45,081 - pyskl - INFO - Epoch [126][1000/3746] lr: 6.560e-03, eta: 21:06:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4697, top5_acc: 0.7205, loss_cls: 2.9649, loss: 2.9649 +2024-07-20 22:34:07,092 - pyskl - INFO - Epoch [126][1100/3746] lr: 6.546e-03, eta: 21:04:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4730, top5_acc: 0.7322, loss_cls: 2.9327, loss: 2.9327 +2024-07-20 22:35:29,298 - pyskl - INFO - Epoch [126][1200/3746] lr: 6.532e-03, eta: 21:03:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4748, top5_acc: 0.7383, loss_cls: 2.8869, loss: 2.8869 +2024-07-20 22:36:51,100 - pyskl - INFO - Epoch [126][1300/3746] lr: 6.518e-03, eta: 21:02:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4797, top5_acc: 0.7302, loss_cls: 2.9255, loss: 2.9255 +2024-07-20 22:38:13,598 - pyskl - INFO - Epoch [126][1400/3746] lr: 6.505e-03, eta: 21:00:49, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4688, top5_acc: 0.7248, loss_cls: 2.9522, loss: 2.9522 +2024-07-20 22:39:35,031 - pyskl - INFO - Epoch [126][1500/3746] lr: 6.491e-03, eta: 20:59:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4794, top5_acc: 0.7320, loss_cls: 2.9002, loss: 2.9002 +2024-07-20 22:40:57,010 - pyskl - INFO - Epoch [126][1600/3746] lr: 6.477e-03, eta: 20:58:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4866, top5_acc: 0.7255, loss_cls: 2.9092, loss: 2.9092 +2024-07-20 22:42:18,671 - pyskl - INFO - Epoch [126][1700/3746] lr: 6.463e-03, eta: 20:56:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4705, top5_acc: 0.7222, loss_cls: 2.9394, loss: 2.9394 +2024-07-20 22:43:39,875 - pyskl - INFO - Epoch [126][1800/3746] lr: 6.449e-03, eta: 20:55:21, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4838, top5_acc: 0.7397, loss_cls: 2.9124, loss: 2.9124 +2024-07-20 22:45:01,932 - pyskl - INFO - Epoch [126][1900/3746] lr: 6.436e-03, eta: 20:53:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4789, top5_acc: 0.7305, loss_cls: 2.9239, loss: 2.9239 +2024-07-20 22:46:23,719 - pyskl - INFO - Epoch [126][2000/3746] lr: 6.422e-03, eta: 20:52:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4773, top5_acc: 0.7292, loss_cls: 2.9434, loss: 2.9434 +2024-07-20 22:47:45,381 - pyskl - INFO - Epoch [126][2100/3746] lr: 6.408e-03, eta: 20:51:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4703, top5_acc: 0.7256, loss_cls: 2.9478, loss: 2.9478 +2024-07-20 22:49:06,912 - pyskl - INFO - Epoch [126][2200/3746] lr: 6.395e-03, eta: 20:49:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4842, top5_acc: 0.7355, loss_cls: 2.8846, loss: 2.8846 +2024-07-20 22:50:28,389 - pyskl - INFO - Epoch [126][2300/3746] lr: 6.381e-03, eta: 20:48:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4684, top5_acc: 0.7259, loss_cls: 2.9743, loss: 2.9743 +2024-07-20 22:51:50,103 - pyskl - INFO - Epoch [126][2400/3746] lr: 6.367e-03, eta: 20:47:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4794, top5_acc: 0.7316, loss_cls: 2.9220, loss: 2.9220 +2024-07-20 22:53:11,841 - pyskl - INFO - Epoch [126][2500/3746] lr: 6.354e-03, eta: 20:45:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4750, top5_acc: 0.7286, loss_cls: 2.9189, loss: 2.9189 +2024-07-20 22:54:33,229 - pyskl - INFO - Epoch [126][2600/3746] lr: 6.340e-03, eta: 20:44:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4795, top5_acc: 0.7364, loss_cls: 2.8873, loss: 2.8873 +2024-07-20 22:55:54,778 - pyskl - INFO - Epoch [126][2700/3746] lr: 6.326e-03, eta: 20:43:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4778, top5_acc: 0.7298, loss_cls: 2.8950, loss: 2.8950 +2024-07-20 22:57:16,751 - pyskl - INFO - Epoch [126][2800/3746] lr: 6.313e-03, eta: 20:41:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4752, top5_acc: 0.7330, loss_cls: 2.9287, loss: 2.9287 +2024-07-20 22:58:38,218 - pyskl - INFO - Epoch [126][2900/3746] lr: 6.299e-03, eta: 20:40:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4637, top5_acc: 0.7209, loss_cls: 2.9866, loss: 2.9866 +2024-07-20 22:59:59,771 - pyskl - INFO - Epoch [126][3000/3746] lr: 6.286e-03, eta: 20:38:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4825, top5_acc: 0.7236, loss_cls: 2.9169, loss: 2.9169 +2024-07-20 23:01:21,730 - pyskl - INFO - Epoch [126][3100/3746] lr: 6.272e-03, eta: 20:37:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4548, top5_acc: 0.7181, loss_cls: 3.0562, loss: 3.0562 +2024-07-20 23:02:43,459 - pyskl - INFO - Epoch [126][3200/3746] lr: 6.259e-03, eta: 20:36:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4688, top5_acc: 0.7186, loss_cls: 2.9672, loss: 2.9672 +2024-07-20 23:04:06,370 - pyskl - INFO - Epoch [126][3300/3746] lr: 6.245e-03, eta: 20:34:50, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4673, top5_acc: 0.7275, loss_cls: 2.9663, loss: 2.9663 +2024-07-20 23:05:28,242 - pyskl - INFO - Epoch [126][3400/3746] lr: 6.231e-03, eta: 20:33:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7305, loss_cls: 2.9314, loss: 2.9314 +2024-07-20 23:06:50,480 - pyskl - INFO - Epoch [126][3500/3746] lr: 6.218e-03, eta: 20:32:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4717, top5_acc: 0.7253, loss_cls: 2.9449, loss: 2.9449 +2024-07-20 23:08:12,151 - pyskl - INFO - Epoch [126][3600/3746] lr: 6.204e-03, eta: 20:30:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4683, top5_acc: 0.7316, loss_cls: 2.9627, loss: 2.9627 +2024-07-20 23:09:34,544 - pyskl - INFO - Epoch [126][3700/3746] lr: 6.191e-03, eta: 20:29:22, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4725, top5_acc: 0.7356, loss_cls: 2.9143, loss: 2.9143 +2024-07-20 23:10:14,369 - pyskl - INFO - Saving checkpoint at 126 epochs +2024-07-20 23:12:05,587 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 23:12:06,263 - pyskl - INFO - +top1_acc 0.4046 +top5_acc 0.6613 +2024-07-20 23:12:06,263 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 23:12:06,306 - pyskl - INFO - +mean_acc 0.4044 +2024-07-20 23:12:06,310 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_125.pth was removed +2024-07-20 23:12:06,580 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_126.pth. +2024-07-20 23:12:06,581 - pyskl - INFO - Best top1_acc is 0.4046 at 126 epoch. +2024-07-20 23:12:06,592 - pyskl - INFO - Epoch(val) [126][309] top1_acc: 0.4046, top5_acc: 0.6613, mean_class_accuracy: 0.4044 +2024-07-20 23:15:54,885 - pyskl - INFO - Epoch [127][100/3746] lr: 6.171e-03, eta: 20:27:43, time: 2.283, data_time: 1.293, memory: 15990, top1_acc: 0.4925, top5_acc: 0.7419, loss_cls: 2.8281, loss: 2.8281 +2024-07-20 23:17:17,514 - pyskl - INFO - Epoch [127][200/3746] lr: 6.158e-03, eta: 20:26:21, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4888, top5_acc: 0.7428, loss_cls: 2.8520, loss: 2.8520 +2024-07-20 23:18:39,799 - pyskl - INFO - Epoch [127][300/3746] lr: 6.144e-03, eta: 20:24:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4839, top5_acc: 0.7436, loss_cls: 2.8614, loss: 2.8614 +2024-07-20 23:20:01,916 - pyskl - INFO - Epoch [127][400/3746] lr: 6.131e-03, eta: 20:23:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4772, top5_acc: 0.7352, loss_cls: 2.8989, loss: 2.8989 +2024-07-20 23:21:23,843 - pyskl - INFO - Epoch [127][500/3746] lr: 6.118e-03, eta: 20:22:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4864, top5_acc: 0.7391, loss_cls: 2.8552, loss: 2.8552 +2024-07-20 23:22:45,541 - pyskl - INFO - Epoch [127][600/3746] lr: 6.104e-03, eta: 20:20:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7386, loss_cls: 2.8562, loss: 2.8562 +2024-07-20 23:24:07,142 - pyskl - INFO - Epoch [127][700/3746] lr: 6.091e-03, eta: 20:19:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4847, top5_acc: 0.7294, loss_cls: 2.8910, loss: 2.8910 +2024-07-20 23:25:29,126 - pyskl - INFO - Epoch [127][800/3746] lr: 6.077e-03, eta: 20:18:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4880, top5_acc: 0.7358, loss_cls: 2.8565, loss: 2.8565 +2024-07-20 23:26:50,830 - pyskl - INFO - Epoch [127][900/3746] lr: 6.064e-03, eta: 20:16:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4827, top5_acc: 0.7323, loss_cls: 2.9262, loss: 2.9262 +2024-07-20 23:28:13,367 - pyskl - INFO - Epoch [127][1000/3746] lr: 6.051e-03, eta: 20:15:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4994, top5_acc: 0.7458, loss_cls: 2.8132, loss: 2.8132 +2024-07-20 23:29:35,056 - pyskl - INFO - Epoch [127][1100/3746] lr: 6.037e-03, eta: 20:14:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4930, top5_acc: 0.7431, loss_cls: 2.8465, loss: 2.8465 +2024-07-20 23:30:57,093 - pyskl - INFO - Epoch [127][1200/3746] lr: 6.024e-03, eta: 20:12:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7358, loss_cls: 2.8722, loss: 2.8722 +2024-07-20 23:32:18,756 - pyskl - INFO - Epoch [127][1300/3746] lr: 6.011e-03, eta: 20:11:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4831, top5_acc: 0.7359, loss_cls: 2.8879, loss: 2.8879 +2024-07-20 23:33:41,062 - pyskl - INFO - Epoch [127][1400/3746] lr: 5.998e-03, eta: 20:09:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7378, loss_cls: 2.8763, loss: 2.8763 +2024-07-20 23:35:02,343 - pyskl - INFO - Epoch [127][1500/3746] lr: 5.984e-03, eta: 20:08:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4872, top5_acc: 0.7339, loss_cls: 2.8807, loss: 2.8807 +2024-07-20 23:36:24,192 - pyskl - INFO - Epoch [127][1600/3746] lr: 5.971e-03, eta: 20:07:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4813, top5_acc: 0.7369, loss_cls: 2.8700, loss: 2.8700 +2024-07-20 23:37:46,077 - pyskl - INFO - Epoch [127][1700/3746] lr: 5.958e-03, eta: 20:05:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4803, top5_acc: 0.7369, loss_cls: 2.8886, loss: 2.8886 +2024-07-20 23:39:08,564 - pyskl - INFO - Epoch [127][1800/3746] lr: 5.945e-03, eta: 20:04:28, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4745, top5_acc: 0.7355, loss_cls: 2.9063, loss: 2.9063 +2024-07-20 23:40:30,456 - pyskl - INFO - Epoch [127][1900/3746] lr: 5.931e-03, eta: 20:03:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4869, top5_acc: 0.7381, loss_cls: 2.8741, loss: 2.8741 +2024-07-20 23:41:51,848 - pyskl - INFO - Epoch [127][2000/3746] lr: 5.918e-03, eta: 20:01:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4756, top5_acc: 0.7303, loss_cls: 2.9328, loss: 2.9328 +2024-07-20 23:43:13,784 - pyskl - INFO - Epoch [127][2100/3746] lr: 5.905e-03, eta: 20:00:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4706, top5_acc: 0.7367, loss_cls: 2.9210, loss: 2.9210 +2024-07-20 23:44:34,995 - pyskl - INFO - Epoch [127][2200/3746] lr: 5.892e-03, eta: 19:59:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4736, top5_acc: 0.7361, loss_cls: 2.9262, loss: 2.9262 +2024-07-20 23:45:56,480 - pyskl - INFO - Epoch [127][2300/3746] lr: 5.879e-03, eta: 19:57:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4803, top5_acc: 0.7338, loss_cls: 2.8866, loss: 2.8866 +2024-07-20 23:47:18,128 - pyskl - INFO - Epoch [127][2400/3746] lr: 5.866e-03, eta: 19:56:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4788, top5_acc: 0.7308, loss_cls: 2.9044, loss: 2.9044 +2024-07-20 23:48:39,936 - pyskl - INFO - Epoch [127][2500/3746] lr: 5.852e-03, eta: 19:54:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7436, loss_cls: 2.8489, loss: 2.8489 +2024-07-20 23:50:01,714 - pyskl - INFO - Epoch [127][2600/3746] lr: 5.839e-03, eta: 19:53:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4739, top5_acc: 0.7334, loss_cls: 2.9143, loss: 2.9143 +2024-07-20 23:51:23,406 - pyskl - INFO - Epoch [127][2700/3746] lr: 5.826e-03, eta: 19:52:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4800, top5_acc: 0.7302, loss_cls: 2.9374, loss: 2.9374 +2024-07-20 23:52:45,778 - pyskl - INFO - Epoch [127][2800/3746] lr: 5.813e-03, eta: 19:50:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4636, top5_acc: 0.7230, loss_cls: 2.9693, loss: 2.9693 +2024-07-20 23:54:07,456 - pyskl - INFO - Epoch [127][2900/3746] lr: 5.800e-03, eta: 19:49:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4800, top5_acc: 0.7338, loss_cls: 2.9149, loss: 2.9149 +2024-07-20 23:55:29,167 - pyskl - INFO - Epoch [127][3000/3746] lr: 5.787e-03, eta: 19:48:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4789, top5_acc: 0.7328, loss_cls: 2.9098, loss: 2.9098 +2024-07-20 23:56:51,428 - pyskl - INFO - Epoch [127][3100/3746] lr: 5.774e-03, eta: 19:46:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4827, top5_acc: 0.7277, loss_cls: 2.9119, loss: 2.9119 +2024-07-20 23:58:13,230 - pyskl - INFO - Epoch [127][3200/3746] lr: 5.761e-03, eta: 19:45:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4747, top5_acc: 0.7320, loss_cls: 2.8994, loss: 2.8994 +2024-07-20 23:59:35,846 - pyskl - INFO - Epoch [127][3300/3746] lr: 5.748e-03, eta: 19:43:57, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4662, top5_acc: 0.7264, loss_cls: 2.9503, loss: 2.9503 +2024-07-21 00:00:57,300 - pyskl - INFO - Epoch [127][3400/3746] lr: 5.735e-03, eta: 19:42:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4858, top5_acc: 0.7300, loss_cls: 2.9066, loss: 2.9066 +2024-07-21 00:02:19,490 - pyskl - INFO - Epoch [127][3500/3746] lr: 5.722e-03, eta: 19:41:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4798, top5_acc: 0.7278, loss_cls: 2.9126, loss: 2.9126 +2024-07-21 00:03:41,532 - pyskl - INFO - Epoch [127][3600/3746] lr: 5.709e-03, eta: 19:39:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4736, top5_acc: 0.7292, loss_cls: 2.9165, loss: 2.9165 +2024-07-21 00:05:03,648 - pyskl - INFO - Epoch [127][3700/3746] lr: 5.696e-03, eta: 19:38:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7344, loss_cls: 2.8972, loss: 2.8972 +2024-07-21 00:05:43,777 - pyskl - INFO - Saving checkpoint at 127 epochs +2024-07-21 00:07:34,729 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 00:07:35,385 - pyskl - INFO - +top1_acc 0.4079 +top5_acc 0.6596 +2024-07-21 00:07:35,385 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 00:07:35,423 - pyskl - INFO - +mean_acc 0.4077 +2024-07-21 00:07:35,427 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_126.pth was removed +2024-07-21 00:07:35,674 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_127.pth. +2024-07-21 00:07:35,675 - pyskl - INFO - Best top1_acc is 0.4079 at 127 epoch. +2024-07-21 00:07:35,686 - pyskl - INFO - Epoch(val) [127][309] top1_acc: 0.4079, top5_acc: 0.6596, mean_class_accuracy: 0.4077 +2024-07-21 00:11:28,604 - pyskl - INFO - Epoch [128][100/3746] lr: 5.677e-03, eta: 19:36:50, time: 2.329, data_time: 1.347, memory: 15990, top1_acc: 0.5080, top5_acc: 0.7567, loss_cls: 2.7538, loss: 2.7538 +2024-07-21 00:12:51,072 - pyskl - INFO - Epoch [128][200/3746] lr: 5.664e-03, eta: 19:35:28, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4933, top5_acc: 0.7480, loss_cls: 2.8150, loss: 2.8150 +2024-07-21 00:14:13,160 - pyskl - INFO - Epoch [128][300/3746] lr: 5.651e-03, eta: 19:34:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4983, top5_acc: 0.7530, loss_cls: 2.7864, loss: 2.7864 +2024-07-21 00:15:34,865 - pyskl - INFO - Epoch [128][400/3746] lr: 5.638e-03, eta: 19:32:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4933, top5_acc: 0.7483, loss_cls: 2.8311, loss: 2.8311 +2024-07-21 00:16:57,161 - pyskl - INFO - Epoch [128][500/3746] lr: 5.625e-03, eta: 19:31:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4819, top5_acc: 0.7450, loss_cls: 2.8696, loss: 2.8696 +2024-07-21 00:18:19,091 - pyskl - INFO - Epoch [128][600/3746] lr: 5.612e-03, eta: 19:29:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4919, top5_acc: 0.7436, loss_cls: 2.8254, loss: 2.8254 +2024-07-21 00:19:40,850 - pyskl - INFO - Epoch [128][700/3746] lr: 5.600e-03, eta: 19:28:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4884, top5_acc: 0.7431, loss_cls: 2.8527, loss: 2.8527 +2024-07-21 00:21:02,328 - pyskl - INFO - Epoch [128][800/3746] lr: 5.587e-03, eta: 19:27:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4914, top5_acc: 0.7406, loss_cls: 2.8430, loss: 2.8430 +2024-07-21 00:22:24,105 - pyskl - INFO - Epoch [128][900/3746] lr: 5.574e-03, eta: 19:25:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4931, top5_acc: 0.7408, loss_cls: 2.8301, loss: 2.8301 +2024-07-21 00:23:46,251 - pyskl - INFO - Epoch [128][1000/3746] lr: 5.561e-03, eta: 19:24:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4881, top5_acc: 0.7377, loss_cls: 2.8601, loss: 2.8601 +2024-07-21 00:25:08,289 - pyskl - INFO - Epoch [128][1100/3746] lr: 5.548e-03, eta: 19:23:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4913, top5_acc: 0.7459, loss_cls: 2.8315, loss: 2.8315 +2024-07-21 00:26:30,216 - pyskl - INFO - Epoch [128][1200/3746] lr: 5.536e-03, eta: 19:21:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4836, top5_acc: 0.7384, loss_cls: 2.8863, loss: 2.8863 +2024-07-21 00:27:52,263 - pyskl - INFO - Epoch [128][1300/3746] lr: 5.523e-03, eta: 19:20:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4827, top5_acc: 0.7380, loss_cls: 2.8944, loss: 2.8944 +2024-07-21 00:29:14,248 - pyskl - INFO - Epoch [128][1400/3746] lr: 5.510e-03, eta: 19:19:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4888, top5_acc: 0.7367, loss_cls: 2.8644, loss: 2.8644 +2024-07-21 00:30:35,515 - pyskl - INFO - Epoch [128][1500/3746] lr: 5.497e-03, eta: 19:17:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4923, top5_acc: 0.7394, loss_cls: 2.8457, loss: 2.8457 +2024-07-21 00:31:57,178 - pyskl - INFO - Epoch [128][1600/3746] lr: 5.485e-03, eta: 19:16:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4895, top5_acc: 0.7422, loss_cls: 2.8575, loss: 2.8575 +2024-07-21 00:33:18,601 - pyskl - INFO - Epoch [128][1700/3746] lr: 5.472e-03, eta: 19:14:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7438, loss_cls: 2.8474, loss: 2.8474 +2024-07-21 00:34:40,271 - pyskl - INFO - Epoch [128][1800/3746] lr: 5.459e-03, eta: 19:13:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5002, top5_acc: 0.7470, loss_cls: 2.8400, loss: 2.8400 +2024-07-21 00:36:02,184 - pyskl - INFO - Epoch [128][1900/3746] lr: 5.446e-03, eta: 19:12:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4788, top5_acc: 0.7309, loss_cls: 2.9062, loss: 2.9062 +2024-07-21 00:37:23,650 - pyskl - INFO - Epoch [128][2000/3746] lr: 5.434e-03, eta: 19:10:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4873, top5_acc: 0.7447, loss_cls: 2.8315, loss: 2.8315 +2024-07-21 00:38:44,975 - pyskl - INFO - Epoch [128][2100/3746] lr: 5.421e-03, eta: 19:09:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4955, top5_acc: 0.7434, loss_cls: 2.8030, loss: 2.8030 +2024-07-21 00:40:06,876 - pyskl - INFO - Epoch [128][2200/3746] lr: 5.408e-03, eta: 19:08:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4834, top5_acc: 0.7345, loss_cls: 2.8753, loss: 2.8753 +2024-07-21 00:41:28,488 - pyskl - INFO - Epoch [128][2300/3746] lr: 5.396e-03, eta: 19:06:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4859, top5_acc: 0.7411, loss_cls: 2.8571, loss: 2.8571 +2024-07-21 00:42:50,457 - pyskl - INFO - Epoch [128][2400/3746] lr: 5.383e-03, eta: 19:05:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4873, top5_acc: 0.7416, loss_cls: 2.8599, loss: 2.8599 +2024-07-21 00:44:12,606 - pyskl - INFO - Epoch [128][2500/3746] lr: 5.370e-03, eta: 19:04:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4880, top5_acc: 0.7412, loss_cls: 2.8435, loss: 2.8435 +2024-07-21 00:45:34,378 - pyskl - INFO - Epoch [128][2600/3746] lr: 5.358e-03, eta: 19:02:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4920, top5_acc: 0.7436, loss_cls: 2.8401, loss: 2.8401 +2024-07-21 00:46:55,784 - pyskl - INFO - Epoch [128][2700/3746] lr: 5.345e-03, eta: 19:01:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4845, top5_acc: 0.7325, loss_cls: 2.8914, loss: 2.8914 +2024-07-21 00:48:17,637 - pyskl - INFO - Epoch [128][2800/3746] lr: 5.333e-03, eta: 18:59:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4828, top5_acc: 0.7447, loss_cls: 2.8666, loss: 2.8666 +2024-07-21 00:49:39,632 - pyskl - INFO - Epoch [128][2900/3746] lr: 5.320e-03, eta: 18:58:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4900, top5_acc: 0.7342, loss_cls: 2.8620, loss: 2.8620 +2024-07-21 00:51:01,561 - pyskl - INFO - Epoch [128][3000/3746] lr: 5.308e-03, eta: 18:57:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4831, top5_acc: 0.7359, loss_cls: 2.8741, loss: 2.8741 +2024-07-21 00:52:23,970 - pyskl - INFO - Epoch [128][3100/3746] lr: 5.295e-03, eta: 18:55:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4898, top5_acc: 0.7423, loss_cls: 2.8372, loss: 2.8372 +2024-07-21 00:53:45,551 - pyskl - INFO - Epoch [128][3200/3746] lr: 5.283e-03, eta: 18:54:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7400, loss_cls: 2.8629, loss: 2.8629 +2024-07-21 00:55:07,877 - pyskl - INFO - Epoch [128][3300/3746] lr: 5.270e-03, eta: 18:53:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4789, top5_acc: 0.7355, loss_cls: 2.9009, loss: 2.9009 +2024-07-21 00:56:30,106 - pyskl - INFO - Epoch [128][3400/3746] lr: 5.258e-03, eta: 18:51:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4866, top5_acc: 0.7373, loss_cls: 2.8691, loss: 2.8691 +2024-07-21 00:57:51,869 - pyskl - INFO - Epoch [128][3500/3746] lr: 5.245e-03, eta: 18:50:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4717, top5_acc: 0.7325, loss_cls: 2.9182, loss: 2.9182 +2024-07-21 00:59:14,534 - pyskl - INFO - Epoch [128][3600/3746] lr: 5.233e-03, eta: 18:48:57, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7273, loss_cls: 2.9293, loss: 2.9293 +2024-07-21 01:00:36,168 - pyskl - INFO - Epoch [128][3700/3746] lr: 5.220e-03, eta: 18:47:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4795, top5_acc: 0.7312, loss_cls: 2.9039, loss: 2.9039 +2024-07-21 01:01:15,865 - pyskl - INFO - Saving checkpoint at 128 epochs +2024-07-21 01:03:07,028 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 01:03:07,699 - pyskl - INFO - +top1_acc 0.4056 +top5_acc 0.6598 +2024-07-21 01:03:07,700 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 01:03:07,742 - pyskl - INFO - +mean_acc 0.4053 +2024-07-21 01:03:07,756 - pyskl - INFO - Epoch(val) [128][309] top1_acc: 0.4056, top5_acc: 0.6598, mean_class_accuracy: 0.4053 +2024-07-21 01:06:59,873 - pyskl - INFO - Epoch [129][100/3746] lr: 5.202e-03, eta: 18:45:54, time: 2.321, data_time: 1.337, memory: 15990, top1_acc: 0.5108, top5_acc: 0.7506, loss_cls: 2.7698, loss: 2.7698 +2024-07-21 01:08:21,923 - pyskl - INFO - Epoch [129][200/3746] lr: 5.190e-03, eta: 18:44:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5058, top5_acc: 0.7506, loss_cls: 2.7527, loss: 2.7527 +2024-07-21 01:09:43,337 - pyskl - INFO - Epoch [129][300/3746] lr: 5.177e-03, eta: 18:43:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4983, top5_acc: 0.7552, loss_cls: 2.7751, loss: 2.7751 +2024-07-21 01:11:05,040 - pyskl - INFO - Epoch [129][400/3746] lr: 5.165e-03, eta: 18:41:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4953, top5_acc: 0.7528, loss_cls: 2.7953, loss: 2.7953 +2024-07-21 01:12:26,864 - pyskl - INFO - Epoch [129][500/3746] lr: 5.153e-03, eta: 18:40:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7572, loss_cls: 2.7673, loss: 2.7673 +2024-07-21 01:13:48,441 - pyskl - INFO - Epoch [129][600/3746] lr: 5.140e-03, eta: 18:39:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4919, top5_acc: 0.7589, loss_cls: 2.7805, loss: 2.7805 +2024-07-21 01:15:09,790 - pyskl - INFO - Epoch [129][700/3746] lr: 5.128e-03, eta: 18:37:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5003, top5_acc: 0.7531, loss_cls: 2.7972, loss: 2.7972 +2024-07-21 01:16:31,244 - pyskl - INFO - Epoch [129][800/3746] lr: 5.116e-03, eta: 18:36:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4941, top5_acc: 0.7483, loss_cls: 2.8021, loss: 2.8021 +2024-07-21 01:17:53,769 - pyskl - INFO - Epoch [129][900/3746] lr: 5.103e-03, eta: 18:34:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4852, top5_acc: 0.7389, loss_cls: 2.8482, loss: 2.8482 +2024-07-21 01:19:15,355 - pyskl - INFO - Epoch [129][1000/3746] lr: 5.091e-03, eta: 18:33:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5045, top5_acc: 0.7555, loss_cls: 2.7595, loss: 2.7595 +2024-07-21 01:20:37,637 - pyskl - INFO - Epoch [129][1100/3746] lr: 5.079e-03, eta: 18:32:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4838, top5_acc: 0.7447, loss_cls: 2.8338, loss: 2.8338 +2024-07-21 01:21:59,307 - pyskl - INFO - Epoch [129][1200/3746] lr: 5.066e-03, eta: 18:30:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4998, top5_acc: 0.7467, loss_cls: 2.7758, loss: 2.7758 +2024-07-21 01:23:20,591 - pyskl - INFO - Epoch [129][1300/3746] lr: 5.054e-03, eta: 18:29:29, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4855, top5_acc: 0.7378, loss_cls: 2.8499, loss: 2.8499 +2024-07-21 01:24:42,396 - pyskl - INFO - Epoch [129][1400/3746] lr: 5.042e-03, eta: 18:28:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5028, top5_acc: 0.7433, loss_cls: 2.8098, loss: 2.8098 +2024-07-21 01:26:04,052 - pyskl - INFO - Epoch [129][1500/3746] lr: 5.030e-03, eta: 18:26:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4823, top5_acc: 0.7416, loss_cls: 2.8556, loss: 2.8556 +2024-07-21 01:27:26,019 - pyskl - INFO - Epoch [129][1600/3746] lr: 5.017e-03, eta: 18:25:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4828, top5_acc: 0.7425, loss_cls: 2.8505, loss: 2.8505 +2024-07-21 01:28:47,558 - pyskl - INFO - Epoch [129][1700/3746] lr: 5.005e-03, eta: 18:24:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4933, top5_acc: 0.7500, loss_cls: 2.8155, loss: 2.8155 +2024-07-21 01:30:09,117 - pyskl - INFO - Epoch [129][1800/3746] lr: 4.993e-03, eta: 18:22:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4955, top5_acc: 0.7459, loss_cls: 2.8088, loss: 2.8088 +2024-07-21 01:31:30,732 - pyskl - INFO - Epoch [129][1900/3746] lr: 4.981e-03, eta: 18:21:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4859, top5_acc: 0.7359, loss_cls: 2.8543, loss: 2.8543 +2024-07-21 01:32:52,198 - pyskl - INFO - Epoch [129][2000/3746] lr: 4.969e-03, eta: 18:19:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4981, top5_acc: 0.7514, loss_cls: 2.8055, loss: 2.8055 +2024-07-21 01:34:13,808 - pyskl - INFO - Epoch [129][2100/3746] lr: 4.957e-03, eta: 18:18:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4953, top5_acc: 0.7408, loss_cls: 2.8390, loss: 2.8390 +2024-07-21 01:35:35,309 - pyskl - INFO - Epoch [129][2200/3746] lr: 4.944e-03, eta: 18:17:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4909, top5_acc: 0.7391, loss_cls: 2.8368, loss: 2.8368 +2024-07-21 01:36:57,310 - pyskl - INFO - Epoch [129][2300/3746] lr: 4.932e-03, eta: 18:15:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4897, top5_acc: 0.7341, loss_cls: 2.8695, loss: 2.8695 +2024-07-21 01:38:19,038 - pyskl - INFO - Epoch [129][2400/3746] lr: 4.920e-03, eta: 18:14:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4927, top5_acc: 0.7447, loss_cls: 2.8303, loss: 2.8303 +2024-07-21 01:39:41,278 - pyskl - INFO - Epoch [129][2500/3746] lr: 4.908e-03, eta: 18:13:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4955, top5_acc: 0.7453, loss_cls: 2.7986, loss: 2.7986 +2024-07-21 01:41:03,065 - pyskl - INFO - Epoch [129][2600/3746] lr: 4.896e-03, eta: 18:11:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5003, top5_acc: 0.7472, loss_cls: 2.8190, loss: 2.8190 +2024-07-21 01:42:24,659 - pyskl - INFO - Epoch [129][2700/3746] lr: 4.884e-03, eta: 18:10:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4997, top5_acc: 0.7497, loss_cls: 2.8009, loss: 2.8009 +2024-07-21 01:43:46,456 - pyskl - INFO - Epoch [129][2800/3746] lr: 4.872e-03, eta: 18:08:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4883, top5_acc: 0.7439, loss_cls: 2.8546, loss: 2.8546 +2024-07-21 01:45:08,094 - pyskl - INFO - Epoch [129][2900/3746] lr: 4.860e-03, eta: 18:07:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4931, top5_acc: 0.7352, loss_cls: 2.8928, loss: 2.8928 +2024-07-21 01:46:29,376 - pyskl - INFO - Epoch [129][3000/3746] lr: 4.848e-03, eta: 18:06:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7400, loss_cls: 2.8378, loss: 2.8378 +2024-07-21 01:47:51,611 - pyskl - INFO - Epoch [129][3100/3746] lr: 4.836e-03, eta: 18:04:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4863, top5_acc: 0.7453, loss_cls: 2.8032, loss: 2.8032 +2024-07-21 01:49:13,546 - pyskl - INFO - Epoch [129][3200/3746] lr: 4.824e-03, eta: 18:03:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4845, top5_acc: 0.7394, loss_cls: 2.8632, loss: 2.8632 +2024-07-21 01:50:35,180 - pyskl - INFO - Epoch [129][3300/3746] lr: 4.812e-03, eta: 18:02:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4833, top5_acc: 0.7372, loss_cls: 2.8737, loss: 2.8737 +2024-07-21 01:51:57,236 - pyskl - INFO - Epoch [129][3400/3746] lr: 4.800e-03, eta: 18:00:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4820, top5_acc: 0.7445, loss_cls: 2.8606, loss: 2.8606 +2024-07-21 01:53:18,796 - pyskl - INFO - Epoch [129][3500/3746] lr: 4.788e-03, eta: 17:59:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4978, top5_acc: 0.7438, loss_cls: 2.8275, loss: 2.8275 +2024-07-21 01:54:41,154 - pyskl - INFO - Epoch [129][3600/3746] lr: 4.776e-03, eta: 17:58:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4895, top5_acc: 0.7398, loss_cls: 2.8493, loss: 2.8493 +2024-07-21 01:56:03,264 - pyskl - INFO - Epoch [129][3700/3746] lr: 4.764e-03, eta: 17:56:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4967, top5_acc: 0.7383, loss_cls: 2.8374, loss: 2.8374 +2024-07-21 01:56:43,644 - pyskl - INFO - Saving checkpoint at 129 epochs +2024-07-21 01:58:37,001 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 01:58:37,662 - pyskl - INFO - +top1_acc 0.4171 +top5_acc 0.6661 +2024-07-21 01:58:37,662 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 01:58:37,701 - pyskl - INFO - +mean_acc 0.4169 +2024-07-21 01:58:37,705 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_127.pth was removed +2024-07-21 01:58:37,953 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_129.pth. +2024-07-21 01:58:37,953 - pyskl - INFO - Best top1_acc is 0.4171 at 129 epoch. +2024-07-21 01:58:37,966 - pyskl - INFO - Epoch(val) [129][309] top1_acc: 0.4171, top5_acc: 0.6661, mean_class_accuracy: 0.4169 +2024-07-21 02:02:30,317 - pyskl - INFO - Epoch [130][100/3746] lr: 4.747e-03, eta: 17:54:56, time: 2.323, data_time: 1.338, memory: 15990, top1_acc: 0.5009, top5_acc: 0.7573, loss_cls: 2.7475, loss: 2.7475 +2024-07-21 02:03:53,099 - pyskl - INFO - Epoch [130][200/3746] lr: 4.735e-03, eta: 17:53:34, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5100, top5_acc: 0.7544, loss_cls: 2.7489, loss: 2.7489 +2024-07-21 02:05:15,425 - pyskl - INFO - Epoch [130][300/3746] lr: 4.723e-03, eta: 17:52:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5095, top5_acc: 0.7569, loss_cls: 2.7296, loss: 2.7296 +2024-07-21 02:06:37,399 - pyskl - INFO - Epoch [130][400/3746] lr: 4.711e-03, eta: 17:50:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5144, top5_acc: 0.7619, loss_cls: 2.7166, loss: 2.7166 +2024-07-21 02:07:59,072 - pyskl - INFO - Epoch [130][500/3746] lr: 4.699e-03, eta: 17:49:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5086, top5_acc: 0.7619, loss_cls: 2.7194, loss: 2.7194 +2024-07-21 02:09:20,980 - pyskl - INFO - Epoch [130][600/3746] lr: 4.688e-03, eta: 17:48:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5078, top5_acc: 0.7527, loss_cls: 2.7623, loss: 2.7623 +2024-07-21 02:10:42,988 - pyskl - INFO - Epoch [130][700/3746] lr: 4.676e-03, eta: 17:46:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5097, top5_acc: 0.7605, loss_cls: 2.7372, loss: 2.7372 +2024-07-21 02:12:04,929 - pyskl - INFO - Epoch [130][800/3746] lr: 4.664e-03, eta: 17:45:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5106, top5_acc: 0.7511, loss_cls: 2.7581, loss: 2.7581 +2024-07-21 02:13:27,075 - pyskl - INFO - Epoch [130][900/3746] lr: 4.652e-03, eta: 17:44:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5009, top5_acc: 0.7609, loss_cls: 2.7455, loss: 2.7455 +2024-07-21 02:14:49,149 - pyskl - INFO - Epoch [130][1000/3746] lr: 4.640e-03, eta: 17:42:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5045, top5_acc: 0.7484, loss_cls: 2.7836, loss: 2.7836 +2024-07-21 02:16:11,668 - pyskl - INFO - Epoch [130][1100/3746] lr: 4.629e-03, eta: 17:41:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5022, top5_acc: 0.7497, loss_cls: 2.7754, loss: 2.7754 +2024-07-21 02:17:33,629 - pyskl - INFO - Epoch [130][1200/3746] lr: 4.617e-03, eta: 17:39:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4966, top5_acc: 0.7492, loss_cls: 2.7707, loss: 2.7707 +2024-07-21 02:18:55,301 - pyskl - INFO - Epoch [130][1300/3746] lr: 4.605e-03, eta: 17:38:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5006, top5_acc: 0.7494, loss_cls: 2.7692, loss: 2.7692 +2024-07-21 02:20:16,815 - pyskl - INFO - Epoch [130][1400/3746] lr: 4.594e-03, eta: 17:37:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5030, top5_acc: 0.7562, loss_cls: 2.7605, loss: 2.7605 +2024-07-21 02:21:38,460 - pyskl - INFO - Epoch [130][1500/3746] lr: 4.582e-03, eta: 17:35:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4917, top5_acc: 0.7406, loss_cls: 2.8428, loss: 2.8428 +2024-07-21 02:23:00,356 - pyskl - INFO - Epoch [130][1600/3746] lr: 4.570e-03, eta: 17:34:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4956, top5_acc: 0.7522, loss_cls: 2.7761, loss: 2.7761 +2024-07-21 02:24:22,050 - pyskl - INFO - Epoch [130][1700/3746] lr: 4.558e-03, eta: 17:33:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4953, top5_acc: 0.7452, loss_cls: 2.8141, loss: 2.8141 +2024-07-21 02:25:43,547 - pyskl - INFO - Epoch [130][1800/3746] lr: 4.547e-03, eta: 17:31:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5053, top5_acc: 0.7527, loss_cls: 2.7707, loss: 2.7707 +2024-07-21 02:27:05,612 - pyskl - INFO - Epoch [130][1900/3746] lr: 4.535e-03, eta: 17:30:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5033, top5_acc: 0.7447, loss_cls: 2.7940, loss: 2.7940 +2024-07-21 02:28:27,382 - pyskl - INFO - Epoch [130][2000/3746] lr: 4.524e-03, eta: 17:28:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5009, top5_acc: 0.7488, loss_cls: 2.7835, loss: 2.7835 +2024-07-21 02:29:48,784 - pyskl - INFO - Epoch [130][2100/3746] lr: 4.512e-03, eta: 17:27:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4889, top5_acc: 0.7430, loss_cls: 2.8462, loss: 2.8462 +2024-07-21 02:31:10,327 - pyskl - INFO - Epoch [130][2200/3746] lr: 4.500e-03, eta: 17:26:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7495, loss_cls: 2.7891, loss: 2.7891 +2024-07-21 02:32:31,715 - pyskl - INFO - Epoch [130][2300/3746] lr: 4.489e-03, eta: 17:24:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4919, top5_acc: 0.7519, loss_cls: 2.8168, loss: 2.8168 +2024-07-21 02:33:53,528 - pyskl - INFO - Epoch [130][2400/3746] lr: 4.477e-03, eta: 17:23:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4938, top5_acc: 0.7520, loss_cls: 2.7962, loss: 2.7962 +2024-07-21 02:35:15,204 - pyskl - INFO - Epoch [130][2500/3746] lr: 4.466e-03, eta: 17:22:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4934, top5_acc: 0.7472, loss_cls: 2.8072, loss: 2.8072 +2024-07-21 02:36:36,765 - pyskl - INFO - Epoch [130][2600/3746] lr: 4.454e-03, eta: 17:20:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4914, top5_acc: 0.7427, loss_cls: 2.8297, loss: 2.8297 +2024-07-21 02:37:58,398 - pyskl - INFO - Epoch [130][2700/3746] lr: 4.443e-03, eta: 17:19:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4972, top5_acc: 0.7455, loss_cls: 2.7944, loss: 2.7944 +2024-07-21 02:39:20,066 - pyskl - INFO - Epoch [130][2800/3746] lr: 4.431e-03, eta: 17:17:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7406, loss_cls: 2.8416, loss: 2.8416 +2024-07-21 02:40:41,876 - pyskl - INFO - Epoch [130][2900/3746] lr: 4.420e-03, eta: 17:16:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5016, top5_acc: 0.7517, loss_cls: 2.7930, loss: 2.7930 +2024-07-21 02:42:03,678 - pyskl - INFO - Epoch [130][3000/3746] lr: 4.408e-03, eta: 17:15:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5072, top5_acc: 0.7559, loss_cls: 2.7816, loss: 2.7816 +2024-07-21 02:43:25,831 - pyskl - INFO - Epoch [130][3100/3746] lr: 4.397e-03, eta: 17:13:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5017, top5_acc: 0.7533, loss_cls: 2.7588, loss: 2.7588 +2024-07-21 02:44:47,408 - pyskl - INFO - Epoch [130][3200/3746] lr: 4.385e-03, eta: 17:12:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4917, top5_acc: 0.7425, loss_cls: 2.8068, loss: 2.8068 +2024-07-21 02:46:09,598 - pyskl - INFO - Epoch [130][3300/3746] lr: 4.374e-03, eta: 17:11:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4983, top5_acc: 0.7477, loss_cls: 2.7968, loss: 2.7968 +2024-07-21 02:47:31,386 - pyskl - INFO - Epoch [130][3400/3746] lr: 4.362e-03, eta: 17:09:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4952, top5_acc: 0.7400, loss_cls: 2.8401, loss: 2.8401 +2024-07-21 02:48:53,252 - pyskl - INFO - Epoch [130][3500/3746] lr: 4.351e-03, eta: 17:08:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7503, loss_cls: 2.8047, loss: 2.8047 +2024-07-21 02:50:15,329 - pyskl - INFO - Epoch [130][3600/3746] lr: 4.339e-03, eta: 17:07:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4992, top5_acc: 0.7519, loss_cls: 2.7655, loss: 2.7655 +2024-07-21 02:51:36,684 - pyskl - INFO - Epoch [130][3700/3746] lr: 4.328e-03, eta: 17:05:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4977, top5_acc: 0.7411, loss_cls: 2.8273, loss: 2.8273 +2024-07-21 02:52:16,168 - pyskl - INFO - Saving checkpoint at 130 epochs +2024-07-21 02:54:07,975 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 02:54:08,651 - pyskl - INFO - +top1_acc 0.4107 +top5_acc 0.6653 +2024-07-21 02:54:08,651 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 02:54:08,697 - pyskl - INFO - +mean_acc 0.4105 +2024-07-21 02:54:08,709 - pyskl - INFO - Epoch(val) [130][309] top1_acc: 0.4107, top5_acc: 0.6653, mean_class_accuracy: 0.4105 +2024-07-21 02:58:01,906 - pyskl - INFO - Epoch [131][100/3746] lr: 4.311e-03, eta: 17:03:57, time: 2.332, data_time: 1.345, memory: 15990, top1_acc: 0.5220, top5_acc: 0.7700, loss_cls: 2.6821, loss: 2.6821 +2024-07-21 02:59:24,324 - pyskl - INFO - Epoch [131][200/3746] lr: 4.300e-03, eta: 17:02:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5183, top5_acc: 0.7739, loss_cls: 2.6603, loss: 2.6603 +2024-07-21 03:00:46,575 - pyskl - INFO - Epoch [131][300/3746] lr: 4.289e-03, eta: 17:01:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5242, top5_acc: 0.7717, loss_cls: 2.6818, loss: 2.6818 +2024-07-21 03:02:08,517 - pyskl - INFO - Epoch [131][400/3746] lr: 4.277e-03, eta: 16:59:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5180, top5_acc: 0.7623, loss_cls: 2.7008, loss: 2.7008 +2024-07-21 03:03:30,019 - pyskl - INFO - Epoch [131][500/3746] lr: 4.266e-03, eta: 16:58:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5086, top5_acc: 0.7602, loss_cls: 2.7275, loss: 2.7275 +2024-07-21 03:04:52,520 - pyskl - INFO - Epoch [131][600/3746] lr: 4.255e-03, eta: 16:57:07, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5127, top5_acc: 0.7655, loss_cls: 2.6978, loss: 2.6978 +2024-07-21 03:06:14,172 - pyskl - INFO - Epoch [131][700/3746] lr: 4.244e-03, eta: 16:55:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5128, top5_acc: 0.7580, loss_cls: 2.7413, loss: 2.7413 +2024-07-21 03:07:36,173 - pyskl - INFO - Epoch [131][800/3746] lr: 4.232e-03, eta: 16:54:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5117, top5_acc: 0.7586, loss_cls: 2.7374, loss: 2.7374 +2024-07-21 03:08:57,796 - pyskl - INFO - Epoch [131][900/3746] lr: 4.221e-03, eta: 16:53:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5005, top5_acc: 0.7533, loss_cls: 2.7548, loss: 2.7548 +2024-07-21 03:10:19,976 - pyskl - INFO - Epoch [131][1000/3746] lr: 4.210e-03, eta: 16:51:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5148, top5_acc: 0.7730, loss_cls: 2.6999, loss: 2.6999 +2024-07-21 03:11:41,873 - pyskl - INFO - Epoch [131][1100/3746] lr: 4.199e-03, eta: 16:50:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5056, top5_acc: 0.7550, loss_cls: 2.7607, loss: 2.7607 +2024-07-21 03:13:03,797 - pyskl - INFO - Epoch [131][1200/3746] lr: 4.187e-03, eta: 16:48:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5072, top5_acc: 0.7592, loss_cls: 2.7317, loss: 2.7317 +2024-07-21 03:14:25,489 - pyskl - INFO - Epoch [131][1300/3746] lr: 4.176e-03, eta: 16:47:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5009, top5_acc: 0.7542, loss_cls: 2.7653, loss: 2.7653 +2024-07-21 03:15:46,863 - pyskl - INFO - Epoch [131][1400/3746] lr: 4.165e-03, eta: 16:46:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4950, top5_acc: 0.7506, loss_cls: 2.7686, loss: 2.7686 +2024-07-21 03:17:08,735 - pyskl - INFO - Epoch [131][1500/3746] lr: 4.154e-03, eta: 16:44:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5103, top5_acc: 0.7523, loss_cls: 2.7438, loss: 2.7438 +2024-07-21 03:18:30,632 - pyskl - INFO - Epoch [131][1600/3746] lr: 4.143e-03, eta: 16:43:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5084, top5_acc: 0.7545, loss_cls: 2.7323, loss: 2.7323 +2024-07-21 03:19:52,326 - pyskl - INFO - Epoch [131][1700/3746] lr: 4.132e-03, eta: 16:42:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4981, top5_acc: 0.7559, loss_cls: 2.7476, loss: 2.7476 +2024-07-21 03:21:14,182 - pyskl - INFO - Epoch [131][1800/3746] lr: 4.120e-03, eta: 16:40:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5117, top5_acc: 0.7552, loss_cls: 2.7492, loss: 2.7492 +2024-07-21 03:22:35,671 - pyskl - INFO - Epoch [131][1900/3746] lr: 4.109e-03, eta: 16:39:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5112, top5_acc: 0.7584, loss_cls: 2.7572, loss: 2.7572 +2024-07-21 03:23:57,280 - pyskl - INFO - Epoch [131][2000/3746] lr: 4.098e-03, eta: 16:37:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4836, top5_acc: 0.7362, loss_cls: 2.8323, loss: 2.8323 +2024-07-21 03:25:18,584 - pyskl - INFO - Epoch [131][2100/3746] lr: 4.087e-03, eta: 16:36:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5005, top5_acc: 0.7531, loss_cls: 2.7727, loss: 2.7727 +2024-07-21 03:26:40,203 - pyskl - INFO - Epoch [131][2200/3746] lr: 4.076e-03, eta: 16:35:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5081, top5_acc: 0.7503, loss_cls: 2.7641, loss: 2.7641 +2024-07-21 03:28:01,928 - pyskl - INFO - Epoch [131][2300/3746] lr: 4.065e-03, eta: 16:33:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4880, top5_acc: 0.7430, loss_cls: 2.8324, loss: 2.8324 +2024-07-21 03:29:23,892 - pyskl - INFO - Epoch [131][2400/3746] lr: 4.054e-03, eta: 16:32:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5067, top5_acc: 0.7452, loss_cls: 2.7833, loss: 2.7833 +2024-07-21 03:30:45,659 - pyskl - INFO - Epoch [131][2500/3746] lr: 4.043e-03, eta: 16:31:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4961, top5_acc: 0.7511, loss_cls: 2.7897, loss: 2.7897 +2024-07-21 03:32:07,461 - pyskl - INFO - Epoch [131][2600/3746] lr: 4.032e-03, eta: 16:29:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5020, top5_acc: 0.7584, loss_cls: 2.7328, loss: 2.7328 +2024-07-21 03:33:29,012 - pyskl - INFO - Epoch [131][2700/3746] lr: 4.021e-03, eta: 16:28:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5120, top5_acc: 0.7562, loss_cls: 2.7338, loss: 2.7338 +2024-07-21 03:34:51,126 - pyskl - INFO - Epoch [131][2800/3746] lr: 4.010e-03, eta: 16:26:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5095, top5_acc: 0.7569, loss_cls: 2.7501, loss: 2.7501 +2024-07-21 03:36:13,141 - pyskl - INFO - Epoch [131][2900/3746] lr: 3.999e-03, eta: 16:25:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5089, top5_acc: 0.7456, loss_cls: 2.7658, loss: 2.7658 +2024-07-21 03:37:34,865 - pyskl - INFO - Epoch [131][3000/3746] lr: 3.988e-03, eta: 16:24:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5041, top5_acc: 0.7545, loss_cls: 2.7748, loss: 2.7748 +2024-07-21 03:38:57,524 - pyskl - INFO - Epoch [131][3100/3746] lr: 3.977e-03, eta: 16:22:53, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5073, top5_acc: 0.7498, loss_cls: 2.7459, loss: 2.7459 +2024-07-21 03:40:20,717 - pyskl - INFO - Epoch [131][3200/3746] lr: 3.966e-03, eta: 16:21:31, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5006, top5_acc: 0.7503, loss_cls: 2.7937, loss: 2.7937 +2024-07-21 03:41:42,878 - pyskl - INFO - Epoch [131][3300/3746] lr: 3.955e-03, eta: 16:20:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5058, top5_acc: 0.7545, loss_cls: 2.7302, loss: 2.7302 +2024-07-21 03:43:04,688 - pyskl - INFO - Epoch [131][3400/3746] lr: 3.945e-03, eta: 16:18:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4972, top5_acc: 0.7500, loss_cls: 2.7905, loss: 2.7905 +2024-07-21 03:44:26,207 - pyskl - INFO - Epoch [131][3500/3746] lr: 3.934e-03, eta: 16:17:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5081, top5_acc: 0.7597, loss_cls: 2.7448, loss: 2.7448 +2024-07-21 03:45:47,763 - pyskl - INFO - Epoch [131][3600/3746] lr: 3.923e-03, eta: 16:16:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5081, top5_acc: 0.7569, loss_cls: 2.7531, loss: 2.7531 +2024-07-21 03:47:09,733 - pyskl - INFO - Epoch [131][3700/3746] lr: 3.912e-03, eta: 16:14:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4973, top5_acc: 0.7491, loss_cls: 2.7663, loss: 2.7663 +2024-07-21 03:47:49,151 - pyskl - INFO - Saving checkpoint at 131 epochs +2024-07-21 03:49:40,379 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 03:49:41,061 - pyskl - INFO - +top1_acc 0.4171 +top5_acc 0.6747 +2024-07-21 03:49:41,062 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 03:49:41,100 - pyskl - INFO - +mean_acc 0.4167 +2024-07-21 03:49:41,113 - pyskl - INFO - Epoch(val) [131][309] top1_acc: 0.4171, top5_acc: 0.6747, mean_class_accuracy: 0.4167 +2024-07-21 03:53:33,906 - pyskl - INFO - Epoch [132][100/3746] lr: 3.896e-03, eta: 16:12:56, time: 2.328, data_time: 1.325, memory: 15990, top1_acc: 0.5247, top5_acc: 0.7684, loss_cls: 2.6734, loss: 2.6734 +2024-07-21 03:54:56,047 - pyskl - INFO - Epoch [132][200/3746] lr: 3.885e-03, eta: 16:11:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5281, top5_acc: 0.7780, loss_cls: 2.6095, loss: 2.6095 +2024-07-21 03:56:17,814 - pyskl - INFO - Epoch [132][300/3746] lr: 3.875e-03, eta: 16:10:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5220, top5_acc: 0.7692, loss_cls: 2.6525, loss: 2.6525 +2024-07-21 03:57:39,695 - pyskl - INFO - Epoch [132][400/3746] lr: 3.864e-03, eta: 16:08:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5291, top5_acc: 0.7714, loss_cls: 2.6596, loss: 2.6596 +2024-07-21 03:59:01,610 - pyskl - INFO - Epoch [132][500/3746] lr: 3.853e-03, eta: 16:07:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5203, top5_acc: 0.7650, loss_cls: 2.6678, loss: 2.6678 +2024-07-21 04:00:23,491 - pyskl - INFO - Epoch [132][600/3746] lr: 3.842e-03, eta: 16:06:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5162, top5_acc: 0.7633, loss_cls: 2.6764, loss: 2.6764 +2024-07-21 04:01:45,294 - pyskl - INFO - Epoch [132][700/3746] lr: 3.831e-03, eta: 16:04:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5261, top5_acc: 0.7675, loss_cls: 2.6605, loss: 2.6605 +2024-07-21 04:03:07,739 - pyskl - INFO - Epoch [132][800/3746] lr: 3.821e-03, eta: 16:03:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5194, top5_acc: 0.7666, loss_cls: 2.6644, loss: 2.6644 +2024-07-21 04:04:29,966 - pyskl - INFO - Epoch [132][900/3746] lr: 3.810e-03, eta: 16:01:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5097, top5_acc: 0.7616, loss_cls: 2.7141, loss: 2.7141 +2024-07-21 04:05:51,334 - pyskl - INFO - Epoch [132][1000/3746] lr: 3.799e-03, eta: 16:00:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5197, top5_acc: 0.7720, loss_cls: 2.6606, loss: 2.6606 +2024-07-21 04:07:13,465 - pyskl - INFO - Epoch [132][1100/3746] lr: 3.789e-03, eta: 15:59:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5056, top5_acc: 0.7548, loss_cls: 2.7342, loss: 2.7342 +2024-07-21 04:08:35,423 - pyskl - INFO - Epoch [132][1200/3746] lr: 3.778e-03, eta: 15:57:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5128, top5_acc: 0.7547, loss_cls: 2.7390, loss: 2.7390 +2024-07-21 04:09:56,759 - pyskl - INFO - Epoch [132][1300/3746] lr: 3.767e-03, eta: 15:56:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5114, top5_acc: 0.7612, loss_cls: 2.6912, loss: 2.6912 +2024-07-21 04:11:18,490 - pyskl - INFO - Epoch [132][1400/3746] lr: 3.757e-03, eta: 15:55:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5230, top5_acc: 0.7719, loss_cls: 2.6286, loss: 2.6286 +2024-07-21 04:12:40,352 - pyskl - INFO - Epoch [132][1500/3746] lr: 3.746e-03, eta: 15:53:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5109, top5_acc: 0.7583, loss_cls: 2.7066, loss: 2.7066 +2024-07-21 04:14:01,962 - pyskl - INFO - Epoch [132][1600/3746] lr: 3.735e-03, eta: 15:52:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5059, top5_acc: 0.7552, loss_cls: 2.7532, loss: 2.7532 +2024-07-21 04:15:23,750 - pyskl - INFO - Epoch [132][1700/3746] lr: 3.725e-03, eta: 15:51:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5156, top5_acc: 0.7533, loss_cls: 2.7166, loss: 2.7166 +2024-07-21 04:16:45,162 - pyskl - INFO - Epoch [132][1800/3746] lr: 3.714e-03, eta: 15:49:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5017, top5_acc: 0.7512, loss_cls: 2.7825, loss: 2.7825 +2024-07-21 04:18:06,506 - pyskl - INFO - Epoch [132][1900/3746] lr: 3.704e-03, eta: 15:48:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5084, top5_acc: 0.7558, loss_cls: 2.7416, loss: 2.7416 +2024-07-21 04:19:27,946 - pyskl - INFO - Epoch [132][2000/3746] lr: 3.693e-03, eta: 15:46:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5048, top5_acc: 0.7605, loss_cls: 2.7512, loss: 2.7512 +2024-07-21 04:20:49,951 - pyskl - INFO - Epoch [132][2100/3746] lr: 3.683e-03, eta: 15:45:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5162, top5_acc: 0.7598, loss_cls: 2.7113, loss: 2.7113 +2024-07-21 04:22:11,399 - pyskl - INFO - Epoch [132][2200/3746] lr: 3.672e-03, eta: 15:44:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5125, top5_acc: 0.7595, loss_cls: 2.6916, loss: 2.6916 +2024-07-21 04:23:33,088 - pyskl - INFO - Epoch [132][2300/3746] lr: 3.662e-03, eta: 15:42:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5061, top5_acc: 0.7502, loss_cls: 2.7739, loss: 2.7739 +2024-07-21 04:24:54,357 - pyskl - INFO - Epoch [132][2400/3746] lr: 3.651e-03, eta: 15:41:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5134, top5_acc: 0.7638, loss_cls: 2.7014, loss: 2.7014 +2024-07-21 04:26:16,940 - pyskl - INFO - Epoch [132][2500/3746] lr: 3.641e-03, eta: 15:40:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5088, top5_acc: 0.7639, loss_cls: 2.7222, loss: 2.7222 +2024-07-21 04:27:38,205 - pyskl - INFO - Epoch [132][2600/3746] lr: 3.630e-03, eta: 15:38:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5161, top5_acc: 0.7650, loss_cls: 2.6777, loss: 2.6777 +2024-07-21 04:28:59,652 - pyskl - INFO - Epoch [132][2700/3746] lr: 3.620e-03, eta: 15:37:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5200, top5_acc: 0.7775, loss_cls: 2.6599, loss: 2.6599 +2024-07-21 04:30:20,973 - pyskl - INFO - Epoch [132][2800/3746] lr: 3.609e-03, eta: 15:35:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5089, top5_acc: 0.7545, loss_cls: 2.7512, loss: 2.7512 +2024-07-21 04:31:42,207 - pyskl - INFO - Epoch [132][2900/3746] lr: 3.599e-03, eta: 15:34:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5098, top5_acc: 0.7580, loss_cls: 2.7466, loss: 2.7466 +2024-07-21 04:33:03,929 - pyskl - INFO - Epoch [132][3000/3746] lr: 3.588e-03, eta: 15:33:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5153, top5_acc: 0.7553, loss_cls: 2.7008, loss: 2.7008 +2024-07-21 04:34:26,218 - pyskl - INFO - Epoch [132][3100/3746] lr: 3.578e-03, eta: 15:31:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5086, top5_acc: 0.7591, loss_cls: 2.7369, loss: 2.7369 +2024-07-21 04:35:48,212 - pyskl - INFO - Epoch [132][3200/3746] lr: 3.568e-03, eta: 15:30:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5011, top5_acc: 0.7616, loss_cls: 2.7559, loss: 2.7559 +2024-07-21 04:37:10,641 - pyskl - INFO - Epoch [132][3300/3746] lr: 3.557e-03, eta: 15:29:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5025, top5_acc: 0.7567, loss_cls: 2.7290, loss: 2.7290 +2024-07-21 04:38:32,529 - pyskl - INFO - Epoch [132][3400/3746] lr: 3.547e-03, eta: 15:27:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5167, top5_acc: 0.7644, loss_cls: 2.6960, loss: 2.6960 +2024-07-21 04:39:54,270 - pyskl - INFO - Epoch [132][3500/3746] lr: 3.537e-03, eta: 15:26:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5066, top5_acc: 0.7472, loss_cls: 2.7588, loss: 2.7588 +2024-07-21 04:41:15,705 - pyskl - INFO - Epoch [132][3600/3746] lr: 3.526e-03, eta: 15:25:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5159, top5_acc: 0.7602, loss_cls: 2.7282, loss: 2.7282 +2024-07-21 04:42:36,957 - pyskl - INFO - Epoch [132][3700/3746] lr: 3.516e-03, eta: 15:23:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5142, top5_acc: 0.7627, loss_cls: 2.7258, loss: 2.7258 +2024-07-21 04:43:16,415 - pyskl - INFO - Saving checkpoint at 132 epochs +2024-07-21 04:45:09,406 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 04:45:10,071 - pyskl - INFO - +top1_acc 0.4228 +top5_acc 0.6733 +2024-07-21 04:45:10,071 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 04:45:10,111 - pyskl - INFO - +mean_acc 0.4226 +2024-07-21 04:45:10,116 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_129.pth was removed +2024-07-21 04:45:10,373 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_132.pth. +2024-07-21 04:45:10,374 - pyskl - INFO - Best top1_acc is 0.4228 at 132 epoch. +2024-07-21 04:45:10,386 - pyskl - INFO - Epoch(val) [132][309] top1_acc: 0.4228, top5_acc: 0.6733, mean_class_accuracy: 0.4226 +2024-07-21 04:49:05,401 - pyskl - INFO - Epoch [133][100/3746] lr: 3.501e-03, eta: 15:21:53, time: 2.350, data_time: 1.342, memory: 15990, top1_acc: 0.5322, top5_acc: 0.7820, loss_cls: 2.6011, loss: 2.6011 +2024-07-21 04:50:27,656 - pyskl - INFO - Epoch [133][200/3746] lr: 3.491e-03, eta: 15:20:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5389, top5_acc: 0.7837, loss_cls: 2.5687, loss: 2.5687 +2024-07-21 04:51:49,371 - pyskl - INFO - Epoch [133][300/3746] lr: 3.480e-03, eta: 15:19:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5217, top5_acc: 0.7805, loss_cls: 2.6279, loss: 2.6279 +2024-07-21 04:53:11,618 - pyskl - INFO - Epoch [133][400/3746] lr: 3.470e-03, eta: 15:17:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5433, top5_acc: 0.7692, loss_cls: 2.5925, loss: 2.5925 +2024-07-21 04:54:33,494 - pyskl - INFO - Epoch [133][500/3746] lr: 3.460e-03, eta: 15:16:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5203, top5_acc: 0.7708, loss_cls: 2.6599, loss: 2.6599 +2024-07-21 04:55:55,401 - pyskl - INFO - Epoch [133][600/3746] lr: 3.450e-03, eta: 15:15:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5164, top5_acc: 0.7744, loss_cls: 2.6701, loss: 2.6701 +2024-07-21 04:57:16,847 - pyskl - INFO - Epoch [133][700/3746] lr: 3.440e-03, eta: 15:13:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5167, top5_acc: 0.7731, loss_cls: 2.6652, loss: 2.6652 +2024-07-21 04:58:39,687 - pyskl - INFO - Epoch [133][800/3746] lr: 3.429e-03, eta: 15:12:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5239, top5_acc: 0.7667, loss_cls: 2.6599, loss: 2.6599 +2024-07-21 05:00:02,001 - pyskl - INFO - Epoch [133][900/3746] lr: 3.419e-03, eta: 15:10:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5248, top5_acc: 0.7688, loss_cls: 2.6841, loss: 2.6841 +2024-07-21 05:01:23,845 - pyskl - INFO - Epoch [133][1000/3746] lr: 3.409e-03, eta: 15:09:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5109, top5_acc: 0.7634, loss_cls: 2.6968, loss: 2.6968 +2024-07-21 05:02:45,799 - pyskl - INFO - Epoch [133][1100/3746] lr: 3.399e-03, eta: 15:08:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5164, top5_acc: 0.7606, loss_cls: 2.6864, loss: 2.6864 +2024-07-21 05:04:07,690 - pyskl - INFO - Epoch [133][1200/3746] lr: 3.389e-03, eta: 15:06:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5266, top5_acc: 0.7767, loss_cls: 2.6333, loss: 2.6333 +2024-07-21 05:05:29,390 - pyskl - INFO - Epoch [133][1300/3746] lr: 3.379e-03, eta: 15:05:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5131, top5_acc: 0.7681, loss_cls: 2.6739, loss: 2.6739 +2024-07-21 05:06:51,013 - pyskl - INFO - Epoch [133][1400/3746] lr: 3.369e-03, eta: 15:04:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5258, top5_acc: 0.7666, loss_cls: 2.6449, loss: 2.6449 +2024-07-21 05:08:12,604 - pyskl - INFO - Epoch [133][1500/3746] lr: 3.359e-03, eta: 15:02:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5303, top5_acc: 0.7703, loss_cls: 2.6439, loss: 2.6439 +2024-07-21 05:09:34,173 - pyskl - INFO - Epoch [133][1600/3746] lr: 3.348e-03, eta: 15:01:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5188, top5_acc: 0.7675, loss_cls: 2.6236, loss: 2.6236 +2024-07-21 05:10:56,070 - pyskl - INFO - Epoch [133][1700/3746] lr: 3.338e-03, eta: 14:59:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5120, top5_acc: 0.7616, loss_cls: 2.6977, loss: 2.6977 +2024-07-21 05:12:17,539 - pyskl - INFO - Epoch [133][1800/3746] lr: 3.328e-03, eta: 14:58:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5142, top5_acc: 0.7647, loss_cls: 2.6814, loss: 2.6814 +2024-07-21 05:13:39,753 - pyskl - INFO - Epoch [133][1900/3746] lr: 3.318e-03, eta: 14:57:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5244, top5_acc: 0.7747, loss_cls: 2.6369, loss: 2.6369 +2024-07-21 05:15:01,667 - pyskl - INFO - Epoch [133][2000/3746] lr: 3.308e-03, eta: 14:55:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5164, top5_acc: 0.7673, loss_cls: 2.6771, loss: 2.6771 +2024-07-21 05:16:23,176 - pyskl - INFO - Epoch [133][2100/3746] lr: 3.298e-03, eta: 14:54:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5141, top5_acc: 0.7612, loss_cls: 2.7125, loss: 2.7125 +2024-07-21 05:17:45,016 - pyskl - INFO - Epoch [133][2200/3746] lr: 3.288e-03, eta: 14:53:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5238, top5_acc: 0.7689, loss_cls: 2.6499, loss: 2.6499 +2024-07-21 05:19:06,551 - pyskl - INFO - Epoch [133][2300/3746] lr: 3.278e-03, eta: 14:51:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5053, top5_acc: 0.7592, loss_cls: 2.7102, loss: 2.7102 +2024-07-21 05:20:27,951 - pyskl - INFO - Epoch [133][2400/3746] lr: 3.268e-03, eta: 14:50:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5150, top5_acc: 0.7662, loss_cls: 2.6873, loss: 2.6873 +2024-07-21 05:21:50,062 - pyskl - INFO - Epoch [133][2500/3746] lr: 3.259e-03, eta: 14:49:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5219, top5_acc: 0.7706, loss_cls: 2.6572, loss: 2.6572 +2024-07-21 05:23:12,457 - pyskl - INFO - Epoch [133][2600/3746] lr: 3.249e-03, eta: 14:47:39, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5245, top5_acc: 0.7703, loss_cls: 2.6507, loss: 2.6507 +2024-07-21 05:24:34,001 - pyskl - INFO - Epoch [133][2700/3746] lr: 3.239e-03, eta: 14:46:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7606, loss_cls: 2.6829, loss: 2.6829 +2024-07-21 05:25:55,432 - pyskl - INFO - Epoch [133][2800/3746] lr: 3.229e-03, eta: 14:44:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5053, top5_acc: 0.7538, loss_cls: 2.7380, loss: 2.7380 +2024-07-21 05:27:16,611 - pyskl - INFO - Epoch [133][2900/3746] lr: 3.219e-03, eta: 14:43:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5142, top5_acc: 0.7623, loss_cls: 2.6909, loss: 2.6909 +2024-07-21 05:28:38,356 - pyskl - INFO - Epoch [133][3000/3746] lr: 3.209e-03, eta: 14:42:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5292, top5_acc: 0.7656, loss_cls: 2.6528, loss: 2.6528 +2024-07-21 05:30:00,604 - pyskl - INFO - Epoch [133][3100/3746] lr: 3.199e-03, eta: 14:40:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5156, top5_acc: 0.7630, loss_cls: 2.6975, loss: 2.6975 +2024-07-21 05:31:22,442 - pyskl - INFO - Epoch [133][3200/3746] lr: 3.189e-03, eta: 14:39:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5209, top5_acc: 0.7677, loss_cls: 2.6394, loss: 2.6394 +2024-07-21 05:32:44,753 - pyskl - INFO - Epoch [133][3300/3746] lr: 3.180e-03, eta: 14:38:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5195, top5_acc: 0.7670, loss_cls: 2.6848, loss: 2.6848 +2024-07-21 05:34:06,672 - pyskl - INFO - Epoch [133][3400/3746] lr: 3.170e-03, eta: 14:36:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5089, top5_acc: 0.7575, loss_cls: 2.7312, loss: 2.7312 +2024-07-21 05:35:28,959 - pyskl - INFO - Epoch [133][3500/3746] lr: 3.160e-03, eta: 14:35:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5145, top5_acc: 0.7664, loss_cls: 2.6992, loss: 2.6992 +2024-07-21 05:36:50,517 - pyskl - INFO - Epoch [133][3600/3746] lr: 3.150e-03, eta: 14:33:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5153, top5_acc: 0.7595, loss_cls: 2.7036, loss: 2.7036 +2024-07-21 05:38:12,390 - pyskl - INFO - Epoch [133][3700/3746] lr: 3.140e-03, eta: 14:32:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5169, top5_acc: 0.7583, loss_cls: 2.7024, loss: 2.7024 +2024-07-21 05:38:52,203 - pyskl - INFO - Saving checkpoint at 133 epochs +2024-07-21 05:40:44,113 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 05:40:44,775 - pyskl - INFO - +top1_acc 0.4275 +top5_acc 0.6779 +2024-07-21 05:40:44,775 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 05:40:44,816 - pyskl - INFO - +mean_acc 0.4273 +2024-07-21 05:40:44,820 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_132.pth was removed +2024-07-21 05:40:45,082 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_133.pth. +2024-07-21 05:40:45,082 - pyskl - INFO - Best top1_acc is 0.4275 at 133 epoch. +2024-07-21 05:40:45,094 - pyskl - INFO - Epoch(val) [133][309] top1_acc: 0.4275, top5_acc: 0.6779, mean_class_accuracy: 0.4273 +2024-07-21 05:44:34,321 - pyskl - INFO - Epoch [134][100/3746] lr: 3.126e-03, eta: 14:30:48, time: 2.292, data_time: 1.308, memory: 15990, top1_acc: 0.5453, top5_acc: 0.7855, loss_cls: 2.5550, loss: 2.5550 +2024-07-21 05:45:56,559 - pyskl - INFO - Epoch [134][200/3746] lr: 3.117e-03, eta: 14:29:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5483, top5_acc: 0.7811, loss_cls: 2.5749, loss: 2.5749 +2024-07-21 05:47:18,456 - pyskl - INFO - Epoch [134][300/3746] lr: 3.107e-03, eta: 14:28:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5413, top5_acc: 0.7823, loss_cls: 2.5875, loss: 2.5875 +2024-07-21 05:48:40,459 - pyskl - INFO - Epoch [134][400/3746] lr: 3.097e-03, eta: 14:26:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5331, top5_acc: 0.7850, loss_cls: 2.5705, loss: 2.5705 +2024-07-21 05:50:02,342 - pyskl - INFO - Epoch [134][500/3746] lr: 3.087e-03, eta: 14:25:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5380, top5_acc: 0.7823, loss_cls: 2.5617, loss: 2.5617 +2024-07-21 05:51:24,059 - pyskl - INFO - Epoch [134][600/3746] lr: 3.078e-03, eta: 14:23:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5284, top5_acc: 0.7789, loss_cls: 2.5937, loss: 2.5937 +2024-07-21 05:52:45,871 - pyskl - INFO - Epoch [134][700/3746] lr: 3.068e-03, eta: 14:22:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5402, top5_acc: 0.7836, loss_cls: 2.5804, loss: 2.5804 +2024-07-21 05:54:08,862 - pyskl - INFO - Epoch [134][800/3746] lr: 3.059e-03, eta: 14:21:13, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5405, top5_acc: 0.7795, loss_cls: 2.5671, loss: 2.5671 +2024-07-21 05:55:30,558 - pyskl - INFO - Epoch [134][900/3746] lr: 3.049e-03, eta: 14:19:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5266, top5_acc: 0.7767, loss_cls: 2.6080, loss: 2.6080 +2024-07-21 05:56:53,231 - pyskl - INFO - Epoch [134][1000/3746] lr: 3.039e-03, eta: 14:18:29, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5312, top5_acc: 0.7802, loss_cls: 2.6028, loss: 2.6028 +2024-07-21 05:58:15,169 - pyskl - INFO - Epoch [134][1100/3746] lr: 3.030e-03, eta: 14:17:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5166, top5_acc: 0.7717, loss_cls: 2.6730, loss: 2.6730 +2024-07-21 05:59:37,086 - pyskl - INFO - Epoch [134][1200/3746] lr: 3.020e-03, eta: 14:15:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5283, top5_acc: 0.7716, loss_cls: 2.6449, loss: 2.6449 +2024-07-21 06:00:58,561 - pyskl - INFO - Epoch [134][1300/3746] lr: 3.011e-03, eta: 14:14:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5291, top5_acc: 0.7764, loss_cls: 2.6078, loss: 2.6078 +2024-07-21 06:02:20,545 - pyskl - INFO - Epoch [134][1400/3746] lr: 3.001e-03, eta: 14:13:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5361, top5_acc: 0.7789, loss_cls: 2.5851, loss: 2.5851 +2024-07-21 06:03:42,815 - pyskl - INFO - Epoch [134][1500/3746] lr: 2.991e-03, eta: 14:11:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5194, top5_acc: 0.7658, loss_cls: 2.6692, loss: 2.6692 +2024-07-21 06:05:04,271 - pyskl - INFO - Epoch [134][1600/3746] lr: 2.982e-03, eta: 14:10:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5217, top5_acc: 0.7669, loss_cls: 2.6630, loss: 2.6630 +2024-07-21 06:06:25,959 - pyskl - INFO - Epoch [134][1700/3746] lr: 2.972e-03, eta: 14:08:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5302, top5_acc: 0.7753, loss_cls: 2.6110, loss: 2.6110 +2024-07-21 06:07:47,845 - pyskl - INFO - Epoch [134][1800/3746] lr: 2.963e-03, eta: 14:07:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5203, top5_acc: 0.7659, loss_cls: 2.6666, loss: 2.6666 +2024-07-21 06:09:09,398 - pyskl - INFO - Epoch [134][1900/3746] lr: 2.953e-03, eta: 14:06:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5231, top5_acc: 0.7775, loss_cls: 2.6182, loss: 2.6182 +2024-07-21 06:10:31,844 - pyskl - INFO - Epoch [134][2000/3746] lr: 2.944e-03, eta: 14:04:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5166, top5_acc: 0.7652, loss_cls: 2.6915, loss: 2.6915 +2024-07-21 06:11:53,566 - pyskl - INFO - Epoch [134][2100/3746] lr: 2.935e-03, eta: 14:03:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5283, top5_acc: 0.7752, loss_cls: 2.6119, loss: 2.6119 +2024-07-21 06:13:14,881 - pyskl - INFO - Epoch [134][2200/3746] lr: 2.925e-03, eta: 14:02:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5266, top5_acc: 0.7736, loss_cls: 2.6252, loss: 2.6252 +2024-07-21 06:14:36,227 - pyskl - INFO - Epoch [134][2300/3746] lr: 2.916e-03, eta: 14:00:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5272, top5_acc: 0.7719, loss_cls: 2.6380, loss: 2.6380 +2024-07-21 06:15:58,586 - pyskl - INFO - Epoch [134][2400/3746] lr: 2.906e-03, eta: 13:59:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5253, top5_acc: 0.7694, loss_cls: 2.6351, loss: 2.6351 +2024-07-21 06:17:20,339 - pyskl - INFO - Epoch [134][2500/3746] lr: 2.897e-03, eta: 13:57:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5319, top5_acc: 0.7712, loss_cls: 2.6499, loss: 2.6499 +2024-07-21 06:18:42,577 - pyskl - INFO - Epoch [134][2600/3746] lr: 2.888e-03, eta: 13:56:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5308, top5_acc: 0.7681, loss_cls: 2.6242, loss: 2.6242 +2024-07-21 06:20:03,792 - pyskl - INFO - Epoch [134][2700/3746] lr: 2.878e-03, eta: 13:55:11, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5202, top5_acc: 0.7672, loss_cls: 2.6601, loss: 2.6601 +2024-07-21 06:21:25,583 - pyskl - INFO - Epoch [134][2800/3746] lr: 2.869e-03, eta: 13:53:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5227, top5_acc: 0.7650, loss_cls: 2.6435, loss: 2.6435 +2024-07-21 06:22:47,245 - pyskl - INFO - Epoch [134][2900/3746] lr: 2.860e-03, eta: 13:52:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5252, top5_acc: 0.7723, loss_cls: 2.6456, loss: 2.6456 +2024-07-21 06:24:09,827 - pyskl - INFO - Epoch [134][3000/3746] lr: 2.850e-03, eta: 13:51:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7714, loss_cls: 2.6523, loss: 2.6523 +2024-07-21 06:25:31,416 - pyskl - INFO - Epoch [134][3100/3746] lr: 2.841e-03, eta: 13:49:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5314, top5_acc: 0.7722, loss_cls: 2.6087, loss: 2.6087 +2024-07-21 06:26:53,877 - pyskl - INFO - Epoch [134][3200/3746] lr: 2.832e-03, eta: 13:48:20, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5331, top5_acc: 0.7825, loss_cls: 2.6000, loss: 2.6000 +2024-07-21 06:28:16,905 - pyskl - INFO - Epoch [134][3300/3746] lr: 2.822e-03, eta: 13:46:58, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5269, top5_acc: 0.7766, loss_cls: 2.6181, loss: 2.6181 +2024-07-21 06:29:38,652 - pyskl - INFO - Epoch [134][3400/3746] lr: 2.813e-03, eta: 13:45:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5180, top5_acc: 0.7655, loss_cls: 2.6584, loss: 2.6584 +2024-07-21 06:31:01,224 - pyskl - INFO - Epoch [134][3500/3746] lr: 2.804e-03, eta: 13:44:14, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5127, top5_acc: 0.7600, loss_cls: 2.7125, loss: 2.7125 +2024-07-21 06:32:23,161 - pyskl - INFO - Epoch [134][3600/3746] lr: 2.795e-03, eta: 13:42:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7720, loss_cls: 2.6233, loss: 2.6233 +2024-07-21 06:33:44,838 - pyskl - INFO - Epoch [134][3700/3746] lr: 2.786e-03, eta: 13:41:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5211, top5_acc: 0.7767, loss_cls: 2.6315, loss: 2.6315 +2024-07-21 06:34:24,538 - pyskl - INFO - Saving checkpoint at 134 epochs +2024-07-21 06:36:16,291 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 06:36:16,971 - pyskl - INFO - +top1_acc 0.4271 +top5_acc 0.6759 +2024-07-21 06:36:16,971 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 06:36:17,014 - pyskl - INFO - +mean_acc 0.4268 +2024-07-21 06:36:17,026 - pyskl - INFO - Epoch(val) [134][309] top1_acc: 0.4271, top5_acc: 0.6759, mean_class_accuracy: 0.4268 +2024-07-21 06:40:05,597 - pyskl - INFO - Epoch [135][100/3746] lr: 2.772e-03, eta: 13:39:42, time: 2.286, data_time: 1.301, memory: 15990, top1_acc: 0.5431, top5_acc: 0.7898, loss_cls: 2.5442, loss: 2.5442 +2024-07-21 06:41:28,709 - pyskl - INFO - Epoch [135][200/3746] lr: 2.763e-03, eta: 13:38:20, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5464, top5_acc: 0.7863, loss_cls: 2.5142, loss: 2.5142 +2024-07-21 06:42:51,764 - pyskl - INFO - Epoch [135][300/3746] lr: 2.754e-03, eta: 13:36:58, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5509, top5_acc: 0.7989, loss_cls: 2.4707, loss: 2.4707 +2024-07-21 06:44:14,975 - pyskl - INFO - Epoch [135][400/3746] lr: 2.745e-03, eta: 13:35:36, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5405, top5_acc: 0.7809, loss_cls: 2.5523, loss: 2.5523 +2024-07-21 06:45:38,624 - pyskl - INFO - Epoch [135][500/3746] lr: 2.735e-03, eta: 13:34:14, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5491, top5_acc: 0.7841, loss_cls: 2.5336, loss: 2.5336 +2024-07-21 06:47:02,273 - pyskl - INFO - Epoch [135][600/3746] lr: 2.726e-03, eta: 13:32:52, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5395, top5_acc: 0.7841, loss_cls: 2.5420, loss: 2.5420 +2024-07-21 06:48:25,845 - pyskl - INFO - Epoch [135][700/3746] lr: 2.717e-03, eta: 13:31:30, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5487, top5_acc: 0.7927, loss_cls: 2.5223, loss: 2.5223 +2024-07-21 06:49:49,611 - pyskl - INFO - Epoch [135][800/3746] lr: 2.708e-03, eta: 13:30:08, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5433, top5_acc: 0.7848, loss_cls: 2.5499, loss: 2.5499 +2024-07-21 06:51:12,746 - pyskl - INFO - Epoch [135][900/3746] lr: 2.699e-03, eta: 13:28:46, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5447, top5_acc: 0.7817, loss_cls: 2.5619, loss: 2.5619 +2024-07-21 06:52:36,929 - pyskl - INFO - Epoch [135][1000/3746] lr: 2.690e-03, eta: 13:27:24, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5395, top5_acc: 0.7891, loss_cls: 2.5464, loss: 2.5464 +2024-07-21 06:54:00,641 - pyskl - INFO - Epoch [135][1100/3746] lr: 2.681e-03, eta: 13:26:02, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5336, top5_acc: 0.7780, loss_cls: 2.5778, loss: 2.5778 +2024-07-21 06:55:24,431 - pyskl - INFO - Epoch [135][1200/3746] lr: 2.672e-03, eta: 13:24:40, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5433, top5_acc: 0.7930, loss_cls: 2.5359, loss: 2.5359 +2024-07-21 06:56:48,156 - pyskl - INFO - Epoch [135][1300/3746] lr: 2.663e-03, eta: 13:23:18, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5373, top5_acc: 0.7866, loss_cls: 2.5506, loss: 2.5506 +2024-07-21 06:58:11,803 - pyskl - INFO - Epoch [135][1400/3746] lr: 2.654e-03, eta: 13:21:56, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5322, top5_acc: 0.7791, loss_cls: 2.5985, loss: 2.5985 +2024-07-21 06:59:35,115 - pyskl - INFO - Epoch [135][1500/3746] lr: 2.645e-03, eta: 13:20:34, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5367, top5_acc: 0.7839, loss_cls: 2.5650, loss: 2.5650 +2024-07-21 07:00:57,332 - pyskl - INFO - Epoch [135][1600/3746] lr: 2.636e-03, eta: 13:19:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5317, top5_acc: 0.7764, loss_cls: 2.6202, loss: 2.6202 +2024-07-21 07:02:20,147 - pyskl - INFO - Epoch [135][1700/3746] lr: 2.627e-03, eta: 13:17:49, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5358, top5_acc: 0.7802, loss_cls: 2.5737, loss: 2.5737 +2024-07-21 07:03:43,855 - pyskl - INFO - Epoch [135][1800/3746] lr: 2.618e-03, eta: 13:16:27, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5386, top5_acc: 0.7795, loss_cls: 2.5773, loss: 2.5773 +2024-07-21 07:05:07,573 - pyskl - INFO - Epoch [135][1900/3746] lr: 2.609e-03, eta: 13:15:05, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5373, top5_acc: 0.7795, loss_cls: 2.5603, loss: 2.5603 +2024-07-21 07:06:31,389 - pyskl - INFO - Epoch [135][2000/3746] lr: 2.600e-03, eta: 13:13:43, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5309, top5_acc: 0.7777, loss_cls: 2.5953, loss: 2.5953 +2024-07-21 07:07:54,502 - pyskl - INFO - Epoch [135][2100/3746] lr: 2.591e-03, eta: 13:12:21, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5395, top5_acc: 0.7816, loss_cls: 2.5831, loss: 2.5831 +2024-07-21 07:09:17,987 - pyskl - INFO - Epoch [135][2200/3746] lr: 2.583e-03, eta: 13:10:59, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5288, top5_acc: 0.7769, loss_cls: 2.6037, loss: 2.6037 +2024-07-21 07:10:41,557 - pyskl - INFO - Epoch [135][2300/3746] lr: 2.574e-03, eta: 13:09:37, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5305, top5_acc: 0.7766, loss_cls: 2.5953, loss: 2.5953 +2024-07-21 07:12:05,666 - pyskl - INFO - Epoch [135][2400/3746] lr: 2.565e-03, eta: 13:08:15, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.5241, top5_acc: 0.7712, loss_cls: 2.6116, loss: 2.6116 +2024-07-21 07:13:29,811 - pyskl - INFO - Epoch [135][2500/3746] lr: 2.556e-03, eta: 13:06:53, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.5331, top5_acc: 0.7789, loss_cls: 2.6109, loss: 2.6109 +2024-07-21 07:14:52,940 - pyskl - INFO - Epoch [135][2600/3746] lr: 2.547e-03, eta: 13:05:31, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5353, top5_acc: 0.7858, loss_cls: 2.5543, loss: 2.5543 +2024-07-21 07:16:16,968 - pyskl - INFO - Epoch [135][2700/3746] lr: 2.538e-03, eta: 13:04:09, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5350, top5_acc: 0.7731, loss_cls: 2.6012, loss: 2.6012 +2024-07-21 07:17:40,144 - pyskl - INFO - Epoch [135][2800/3746] lr: 2.530e-03, eta: 13:02:47, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5289, top5_acc: 0.7708, loss_cls: 2.6013, loss: 2.6013 +2024-07-21 07:19:03,661 - pyskl - INFO - Epoch [135][2900/3746] lr: 2.521e-03, eta: 13:01:25, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5337, top5_acc: 0.7811, loss_cls: 2.5801, loss: 2.5801 +2024-07-21 07:20:27,475 - pyskl - INFO - Epoch [135][3000/3746] lr: 2.512e-03, eta: 13:00:03, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5417, top5_acc: 0.7848, loss_cls: 2.5468, loss: 2.5468 +2024-07-21 07:21:51,357 - pyskl - INFO - Epoch [135][3100/3746] lr: 2.503e-03, eta: 12:58:41, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5252, top5_acc: 0.7698, loss_cls: 2.6402, loss: 2.6402 +2024-07-21 07:23:15,156 - pyskl - INFO - Epoch [135][3200/3746] lr: 2.495e-03, eta: 12:57:19, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5262, top5_acc: 0.7716, loss_cls: 2.6255, loss: 2.6255 +2024-07-21 07:24:38,571 - pyskl - INFO - Epoch [135][3300/3746] lr: 2.486e-03, eta: 12:55:57, time: 0.834, data_time: 0.001, memory: 15990, top1_acc: 0.5145, top5_acc: 0.7681, loss_cls: 2.6653, loss: 2.6653 +2024-07-21 07:26:01,864 - pyskl - INFO - Epoch [135][3400/3746] lr: 2.477e-03, eta: 12:54:35, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5248, top5_acc: 0.7711, loss_cls: 2.6192, loss: 2.6192 +2024-07-21 07:27:25,351 - pyskl - INFO - Epoch [135][3500/3746] lr: 2.469e-03, eta: 12:53:12, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5277, top5_acc: 0.7734, loss_cls: 2.6373, loss: 2.6373 +2024-07-21 07:28:49,054 - pyskl - INFO - Epoch [135][3600/3746] lr: 2.460e-03, eta: 12:51:50, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5250, top5_acc: 0.7659, loss_cls: 2.6461, loss: 2.6461 +2024-07-21 07:30:12,193 - pyskl - INFO - Epoch [135][3700/3746] lr: 2.451e-03, eta: 12:50:28, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5413, top5_acc: 0.7700, loss_cls: 2.6017, loss: 2.6017 +2024-07-21 07:30:52,296 - pyskl - INFO - Saving checkpoint at 135 epochs +2024-07-21 07:32:45,007 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 07:32:45,706 - pyskl - INFO - +top1_acc 0.4312 +top5_acc 0.6802 +2024-07-21 07:32:45,707 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 07:32:45,751 - pyskl - INFO - +mean_acc 0.4310 +2024-07-21 07:32:45,756 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_133.pth was removed +2024-07-21 07:32:46,014 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_135.pth. +2024-07-21 07:32:46,015 - pyskl - INFO - Best top1_acc is 0.4312 at 135 epoch. +2024-07-21 07:32:46,028 - pyskl - INFO - Epoch(val) [135][309] top1_acc: 0.4312, top5_acc: 0.6802, mean_class_accuracy: 0.4310 +2024-07-21 07:36:43,392 - pyskl - INFO - Epoch [136][100/3746] lr: 2.439e-03, eta: 12:48:41, time: 2.374, data_time: 1.378, memory: 15990, top1_acc: 0.5684, top5_acc: 0.7995, loss_cls: 2.4428, loss: 2.4428 +2024-07-21 07:38:06,923 - pyskl - INFO - Epoch [136][200/3746] lr: 2.430e-03, eta: 12:47:19, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5572, top5_acc: 0.7975, loss_cls: 2.4703, loss: 2.4703 +2024-07-21 07:39:30,574 - pyskl - INFO - Epoch [136][300/3746] lr: 2.421e-03, eta: 12:45:57, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5606, top5_acc: 0.7916, loss_cls: 2.4906, loss: 2.4906 +2024-07-21 07:40:54,245 - pyskl - INFO - Epoch [136][400/3746] lr: 2.413e-03, eta: 12:44:35, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5556, top5_acc: 0.7970, loss_cls: 2.4660, loss: 2.4660 +2024-07-21 07:42:17,707 - pyskl - INFO - Epoch [136][500/3746] lr: 2.404e-03, eta: 12:43:13, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5527, top5_acc: 0.7955, loss_cls: 2.4842, loss: 2.4842 +2024-07-21 07:43:40,677 - pyskl - INFO - Epoch [136][600/3746] lr: 2.396e-03, eta: 12:41:51, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5506, top5_acc: 0.7905, loss_cls: 2.5082, loss: 2.5082 +2024-07-21 07:45:04,355 - pyskl - INFO - Epoch [136][700/3746] lr: 2.387e-03, eta: 12:40:29, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5520, top5_acc: 0.7847, loss_cls: 2.5058, loss: 2.5058 +2024-07-21 07:46:27,207 - pyskl - INFO - Epoch [136][800/3746] lr: 2.379e-03, eta: 12:39:07, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5539, top5_acc: 0.7900, loss_cls: 2.5094, loss: 2.5094 +2024-07-21 07:47:50,678 - pyskl - INFO - Epoch [136][900/3746] lr: 2.370e-03, eta: 12:37:45, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5495, top5_acc: 0.7947, loss_cls: 2.5091, loss: 2.5091 +2024-07-21 07:49:14,447 - pyskl - INFO - Epoch [136][1000/3746] lr: 2.362e-03, eta: 12:36:22, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5587, top5_acc: 0.7994, loss_cls: 2.4631, loss: 2.4631 +2024-07-21 07:50:38,217 - pyskl - INFO - Epoch [136][1100/3746] lr: 2.353e-03, eta: 12:35:00, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5445, top5_acc: 0.7789, loss_cls: 2.5324, loss: 2.5324 +2024-07-21 07:52:01,928 - pyskl - INFO - Epoch [136][1200/3746] lr: 2.345e-03, eta: 12:33:38, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5472, top5_acc: 0.7898, loss_cls: 2.5103, loss: 2.5103 +2024-07-21 07:53:25,041 - pyskl - INFO - Epoch [136][1300/3746] lr: 2.336e-03, eta: 12:32:16, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5391, top5_acc: 0.7919, loss_cls: 2.5366, loss: 2.5366 +2024-07-21 07:54:48,158 - pyskl - INFO - Epoch [136][1400/3746] lr: 2.328e-03, eta: 12:30:54, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5433, top5_acc: 0.7892, loss_cls: 2.5400, loss: 2.5400 +2024-07-21 07:56:10,693 - pyskl - INFO - Epoch [136][1500/3746] lr: 2.319e-03, eta: 12:29:32, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5470, top5_acc: 0.7811, loss_cls: 2.5382, loss: 2.5382 +2024-07-21 07:57:33,901 - pyskl - INFO - Epoch [136][1600/3746] lr: 2.311e-03, eta: 12:28:10, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5455, top5_acc: 0.7795, loss_cls: 2.5493, loss: 2.5493 +2024-07-21 07:58:56,570 - pyskl - INFO - Epoch [136][1700/3746] lr: 2.303e-03, eta: 12:26:48, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5447, top5_acc: 0.7880, loss_cls: 2.5204, loss: 2.5204 +2024-07-21 08:00:20,187 - pyskl - INFO - Epoch [136][1800/3746] lr: 2.294e-03, eta: 12:25:25, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5330, top5_acc: 0.7758, loss_cls: 2.5780, loss: 2.5780 +2024-07-21 08:01:43,790 - pyskl - INFO - Epoch [136][1900/3746] lr: 2.286e-03, eta: 12:24:03, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5398, top5_acc: 0.7863, loss_cls: 2.5449, loss: 2.5449 +2024-07-21 08:03:07,310 - pyskl - INFO - Epoch [136][2000/3746] lr: 2.277e-03, eta: 12:22:41, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5473, top5_acc: 0.7878, loss_cls: 2.5151, loss: 2.5151 +2024-07-21 08:04:30,655 - pyskl - INFO - Epoch [136][2100/3746] lr: 2.269e-03, eta: 12:21:19, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5411, top5_acc: 0.7866, loss_cls: 2.5428, loss: 2.5428 +2024-07-21 08:05:54,119 - pyskl - INFO - Epoch [136][2200/3746] lr: 2.261e-03, eta: 12:19:57, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5467, top5_acc: 0.7881, loss_cls: 2.5393, loss: 2.5393 +2024-07-21 08:07:17,516 - pyskl - INFO - Epoch [136][2300/3746] lr: 2.253e-03, eta: 12:18:35, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5366, top5_acc: 0.7817, loss_cls: 2.5596, loss: 2.5596 +2024-07-21 08:08:41,355 - pyskl - INFO - Epoch [136][2400/3746] lr: 2.244e-03, eta: 12:17:13, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5405, top5_acc: 0.7864, loss_cls: 2.5478, loss: 2.5478 +2024-07-21 08:10:04,939 - pyskl - INFO - Epoch [136][2500/3746] lr: 2.236e-03, eta: 12:15:51, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5384, top5_acc: 0.7853, loss_cls: 2.5683, loss: 2.5683 +2024-07-21 08:11:28,757 - pyskl - INFO - Epoch [136][2600/3746] lr: 2.228e-03, eta: 12:14:29, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5302, top5_acc: 0.7822, loss_cls: 2.5728, loss: 2.5728 +2024-07-21 08:12:52,364 - pyskl - INFO - Epoch [136][2700/3746] lr: 2.219e-03, eta: 12:13:07, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5355, top5_acc: 0.7784, loss_cls: 2.5697, loss: 2.5697 +2024-07-21 08:14:15,883 - pyskl - INFO - Epoch [136][2800/3746] lr: 2.211e-03, eta: 12:11:45, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5372, top5_acc: 0.7797, loss_cls: 2.5649, loss: 2.5649 +2024-07-21 08:15:39,656 - pyskl - INFO - Epoch [136][2900/3746] lr: 2.203e-03, eta: 12:10:22, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5431, top5_acc: 0.7828, loss_cls: 2.5578, loss: 2.5578 +2024-07-21 08:17:03,470 - pyskl - INFO - Epoch [136][3000/3746] lr: 2.195e-03, eta: 12:09:00, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5372, top5_acc: 0.7875, loss_cls: 2.5461, loss: 2.5461 +2024-07-21 08:18:26,976 - pyskl - INFO - Epoch [136][3100/3746] lr: 2.187e-03, eta: 12:07:38, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5439, top5_acc: 0.7877, loss_cls: 2.5578, loss: 2.5578 +2024-07-21 08:19:49,851 - pyskl - INFO - Epoch [136][3200/3746] lr: 2.178e-03, eta: 12:06:16, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5364, top5_acc: 0.7834, loss_cls: 2.5766, loss: 2.5766 +2024-07-21 08:21:12,671 - pyskl - INFO - Epoch [136][3300/3746] lr: 2.170e-03, eta: 12:04:54, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5413, top5_acc: 0.7839, loss_cls: 2.5638, loss: 2.5638 +2024-07-21 08:22:36,549 - pyskl - INFO - Epoch [136][3400/3746] lr: 2.162e-03, eta: 12:03:32, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5423, top5_acc: 0.7802, loss_cls: 2.5238, loss: 2.5238 +2024-07-21 08:24:00,925 - pyskl - INFO - Epoch [136][3500/3746] lr: 2.154e-03, eta: 12:02:10, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5466, top5_acc: 0.7872, loss_cls: 2.5220, loss: 2.5220 +2024-07-21 08:25:24,582 - pyskl - INFO - Epoch [136][3600/3746] lr: 2.146e-03, eta: 12:00:48, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5303, top5_acc: 0.7770, loss_cls: 2.6001, loss: 2.6001 +2024-07-21 08:26:47,671 - pyskl - INFO - Epoch [136][3700/3746] lr: 2.138e-03, eta: 11:59:26, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5395, top5_acc: 0.7861, loss_cls: 2.5588, loss: 2.5588 +2024-07-21 08:27:28,312 - pyskl - INFO - Saving checkpoint at 136 epochs +2024-07-21 08:29:21,960 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 08:29:22,654 - pyskl - INFO - +top1_acc 0.4347 +top5_acc 0.6847 +2024-07-21 08:29:22,655 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 08:29:22,696 - pyskl - INFO - +mean_acc 0.4344 +2024-07-21 08:29:22,701 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_135.pth was removed +2024-07-21 08:29:22,960 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_136.pth. +2024-07-21 08:29:22,961 - pyskl - INFO - Best top1_acc is 0.4347 at 136 epoch. +2024-07-21 08:29:22,973 - pyskl - INFO - Epoch(val) [136][309] top1_acc: 0.4347, top5_acc: 0.6847, mean_class_accuracy: 0.4344 +2024-07-21 08:33:15,281 - pyskl - INFO - Epoch [137][100/3746] lr: 2.126e-03, eta: 11:57:37, time: 2.323, data_time: 1.339, memory: 15990, top1_acc: 0.5733, top5_acc: 0.8008, loss_cls: 2.4051, loss: 2.4051 +2024-07-21 08:34:37,003 - pyskl - INFO - Epoch [137][200/3746] lr: 2.118e-03, eta: 11:56:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5686, top5_acc: 0.8041, loss_cls: 2.4313, loss: 2.4313 +2024-07-21 08:35:58,565 - pyskl - INFO - Epoch [137][300/3746] lr: 2.110e-03, eta: 11:54:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5619, top5_acc: 0.8037, loss_cls: 2.4274, loss: 2.4274 +2024-07-21 08:37:19,837 - pyskl - INFO - Epoch [137][400/3746] lr: 2.102e-03, eta: 11:53:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5642, top5_acc: 0.8008, loss_cls: 2.4332, loss: 2.4332 +2024-07-21 08:38:41,904 - pyskl - INFO - Epoch [137][500/3746] lr: 2.094e-03, eta: 11:52:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5606, top5_acc: 0.8028, loss_cls: 2.4085, loss: 2.4085 +2024-07-21 08:40:04,175 - pyskl - INFO - Epoch [137][600/3746] lr: 2.086e-03, eta: 11:50:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5534, top5_acc: 0.7950, loss_cls: 2.4624, loss: 2.4624 +2024-07-21 08:41:26,026 - pyskl - INFO - Epoch [137][700/3746] lr: 2.078e-03, eta: 11:49:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5567, top5_acc: 0.8005, loss_cls: 2.4603, loss: 2.4603 +2024-07-21 08:42:48,092 - pyskl - INFO - Epoch [137][800/3746] lr: 2.070e-03, eta: 11:48:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5487, top5_acc: 0.7917, loss_cls: 2.4925, loss: 2.4925 +2024-07-21 08:44:10,326 - pyskl - INFO - Epoch [137][900/3746] lr: 2.062e-03, eta: 11:46:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5525, top5_acc: 0.7881, loss_cls: 2.5089, loss: 2.5089 +2024-07-21 08:45:32,445 - pyskl - INFO - Epoch [137][1000/3746] lr: 2.054e-03, eta: 11:45:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5523, top5_acc: 0.7936, loss_cls: 2.4685, loss: 2.4685 +2024-07-21 08:46:53,874 - pyskl - INFO - Epoch [137][1100/3746] lr: 2.046e-03, eta: 11:43:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5663, top5_acc: 0.8002, loss_cls: 2.4319, loss: 2.4319 +2024-07-21 08:48:15,368 - pyskl - INFO - Epoch [137][1200/3746] lr: 2.038e-03, eta: 11:42:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5459, top5_acc: 0.7959, loss_cls: 2.4802, loss: 2.4802 +2024-07-21 08:49:37,716 - pyskl - INFO - Epoch [137][1300/3746] lr: 2.030e-03, eta: 11:41:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5581, top5_acc: 0.7928, loss_cls: 2.4847, loss: 2.4847 +2024-07-21 08:50:59,882 - pyskl - INFO - Epoch [137][1400/3746] lr: 2.022e-03, eta: 11:39:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5684, top5_acc: 0.7997, loss_cls: 2.4448, loss: 2.4448 +2024-07-21 08:52:22,270 - pyskl - INFO - Epoch [137][1500/3746] lr: 2.015e-03, eta: 11:38:25, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5569, top5_acc: 0.7947, loss_cls: 2.4802, loss: 2.4802 +2024-07-21 08:53:43,519 - pyskl - INFO - Epoch [137][1600/3746] lr: 2.007e-03, eta: 11:37:03, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5434, top5_acc: 0.7830, loss_cls: 2.5201, loss: 2.5201 +2024-07-21 08:55:05,514 - pyskl - INFO - Epoch [137][1700/3746] lr: 1.999e-03, eta: 11:35:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5405, top5_acc: 0.7898, loss_cls: 2.5302, loss: 2.5302 +2024-07-21 08:56:26,795 - pyskl - INFO - Epoch [137][1800/3746] lr: 1.991e-03, eta: 11:34:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5556, top5_acc: 0.7967, loss_cls: 2.4747, loss: 2.4747 +2024-07-21 08:57:48,572 - pyskl - INFO - Epoch [137][1900/3746] lr: 1.983e-03, eta: 11:32:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5469, top5_acc: 0.7923, loss_cls: 2.4917, loss: 2.4917 +2024-07-21 08:59:10,380 - pyskl - INFO - Epoch [137][2000/3746] lr: 1.976e-03, eta: 11:31:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5452, top5_acc: 0.7967, loss_cls: 2.4978, loss: 2.4978 +2024-07-21 09:00:31,927 - pyskl - INFO - Epoch [137][2100/3746] lr: 1.968e-03, eta: 11:30:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5478, top5_acc: 0.7944, loss_cls: 2.4978, loss: 2.4978 +2024-07-21 09:01:54,103 - pyskl - INFO - Epoch [137][2200/3746] lr: 1.960e-03, eta: 11:28:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5511, top5_acc: 0.7917, loss_cls: 2.4871, loss: 2.4871 +2024-07-21 09:03:16,473 - pyskl - INFO - Epoch [137][2300/3746] lr: 1.952e-03, eta: 11:27:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5522, top5_acc: 0.7883, loss_cls: 2.5010, loss: 2.5010 +2024-07-21 09:04:38,227 - pyskl - INFO - Epoch [137][2400/3746] lr: 1.944e-03, eta: 11:26:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5575, top5_acc: 0.7909, loss_cls: 2.4616, loss: 2.4616 +2024-07-21 09:06:00,331 - pyskl - INFO - Epoch [137][2500/3746] lr: 1.937e-03, eta: 11:24:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5473, top5_acc: 0.7892, loss_cls: 2.5111, loss: 2.5111 +2024-07-21 09:07:21,946 - pyskl - INFO - Epoch [137][2600/3746] lr: 1.929e-03, eta: 11:23:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5525, top5_acc: 0.7967, loss_cls: 2.4792, loss: 2.4792 +2024-07-21 09:08:43,390 - pyskl - INFO - Epoch [137][2700/3746] lr: 1.921e-03, eta: 11:21:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5584, top5_acc: 0.7869, loss_cls: 2.5079, loss: 2.5079 +2024-07-21 09:10:04,877 - pyskl - INFO - Epoch [137][2800/3746] lr: 1.914e-03, eta: 11:20:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5491, top5_acc: 0.7922, loss_cls: 2.4826, loss: 2.4826 +2024-07-21 09:11:27,219 - pyskl - INFO - Epoch [137][2900/3746] lr: 1.906e-03, eta: 11:19:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5452, top5_acc: 0.7923, loss_cls: 2.5092, loss: 2.5092 +2024-07-21 09:12:49,459 - pyskl - INFO - Epoch [137][3000/3746] lr: 1.898e-03, eta: 11:17:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5520, top5_acc: 0.7966, loss_cls: 2.4793, loss: 2.4793 +2024-07-21 09:14:11,547 - pyskl - INFO - Epoch [137][3100/3746] lr: 1.891e-03, eta: 11:16:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5475, top5_acc: 0.7850, loss_cls: 2.5199, loss: 2.5199 +2024-07-21 09:15:33,940 - pyskl - INFO - Epoch [137][3200/3746] lr: 1.883e-03, eta: 11:15:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5475, top5_acc: 0.7844, loss_cls: 2.5462, loss: 2.5462 +2024-07-21 09:16:55,985 - pyskl - INFO - Epoch [137][3300/3746] lr: 1.876e-03, eta: 11:13:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5370, top5_acc: 0.7795, loss_cls: 2.5618, loss: 2.5618 +2024-07-21 09:18:18,203 - pyskl - INFO - Epoch [137][3400/3746] lr: 1.868e-03, eta: 11:12:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5464, top5_acc: 0.7897, loss_cls: 2.4992, loss: 2.4992 +2024-07-21 09:19:39,915 - pyskl - INFO - Epoch [137][3500/3746] lr: 1.860e-03, eta: 11:10:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5550, top5_acc: 0.7933, loss_cls: 2.4844, loss: 2.4844 +2024-07-21 09:21:01,998 - pyskl - INFO - Epoch [137][3600/3746] lr: 1.853e-03, eta: 11:09:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5556, top5_acc: 0.8023, loss_cls: 2.4358, loss: 2.4358 +2024-07-21 09:22:23,073 - pyskl - INFO - Epoch [137][3700/3746] lr: 1.845e-03, eta: 11:08:15, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5486, top5_acc: 0.7878, loss_cls: 2.5133, loss: 2.5133 +2024-07-21 09:23:02,696 - pyskl - INFO - Saving checkpoint at 137 epochs +2024-07-21 09:24:53,971 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 09:24:54,626 - pyskl - INFO - +top1_acc 0.4396 +top5_acc 0.6845 +2024-07-21 09:24:54,626 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 09:24:54,665 - pyskl - INFO - +mean_acc 0.4393 +2024-07-21 09:24:54,670 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_136.pth was removed +2024-07-21 09:24:54,927 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_137.pth. +2024-07-21 09:24:54,928 - pyskl - INFO - Best top1_acc is 0.4396 at 137 epoch. +2024-07-21 09:24:54,939 - pyskl - INFO - Epoch(val) [137][309] top1_acc: 0.4396, top5_acc: 0.6845, mean_class_accuracy: 0.4393 +2024-07-21 09:28:41,358 - pyskl - INFO - Epoch [138][100/3746] lr: 1.834e-03, eta: 11:06:24, time: 2.264, data_time: 1.279, memory: 15990, top1_acc: 0.5809, top5_acc: 0.8064, loss_cls: 2.3913, loss: 2.3913 +2024-07-21 09:30:03,172 - pyskl - INFO - Epoch [138][200/3746] lr: 1.827e-03, eta: 11:05:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5659, top5_acc: 0.8105, loss_cls: 2.3955, loss: 2.3955 +2024-07-21 09:31:25,426 - pyskl - INFO - Epoch [138][300/3746] lr: 1.819e-03, eta: 11:03:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5650, top5_acc: 0.8031, loss_cls: 2.4120, loss: 2.4120 +2024-07-21 09:32:47,017 - pyskl - INFO - Epoch [138][400/3746] lr: 1.812e-03, eta: 11:02:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5720, top5_acc: 0.8103, loss_cls: 2.3692, loss: 2.3692 +2024-07-21 09:34:09,339 - pyskl - INFO - Epoch [138][500/3746] lr: 1.805e-03, eta: 11:00:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5713, top5_acc: 0.8011, loss_cls: 2.4220, loss: 2.4220 +2024-07-21 09:35:30,849 - pyskl - INFO - Epoch [138][600/3746] lr: 1.797e-03, eta: 10:59:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5717, top5_acc: 0.8053, loss_cls: 2.4020, loss: 2.4020 +2024-07-21 09:36:52,866 - pyskl - INFO - Epoch [138][700/3746] lr: 1.790e-03, eta: 10:58:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5637, top5_acc: 0.8094, loss_cls: 2.3975, loss: 2.3975 +2024-07-21 09:38:14,407 - pyskl - INFO - Epoch [138][800/3746] lr: 1.782e-03, eta: 10:56:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5750, top5_acc: 0.8053, loss_cls: 2.3760, loss: 2.3760 +2024-07-21 09:39:36,147 - pyskl - INFO - Epoch [138][900/3746] lr: 1.775e-03, eta: 10:55:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5653, top5_acc: 0.8042, loss_cls: 2.4317, loss: 2.4317 +2024-07-21 09:40:58,184 - pyskl - INFO - Epoch [138][1000/3746] lr: 1.768e-03, eta: 10:54:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5683, top5_acc: 0.8102, loss_cls: 2.3976, loss: 2.3976 +2024-07-21 09:42:20,115 - pyskl - INFO - Epoch [138][1100/3746] lr: 1.760e-03, eta: 10:52:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5589, top5_acc: 0.7869, loss_cls: 2.4772, loss: 2.4772 +2024-07-21 09:43:42,325 - pyskl - INFO - Epoch [138][1200/3746] lr: 1.753e-03, eta: 10:51:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5661, top5_acc: 0.8047, loss_cls: 2.4259, loss: 2.4259 +2024-07-21 09:45:04,592 - pyskl - INFO - Epoch [138][1300/3746] lr: 1.745e-03, eta: 10:49:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5722, top5_acc: 0.8108, loss_cls: 2.3911, loss: 2.3911 +2024-07-21 09:46:26,450 - pyskl - INFO - Epoch [138][1400/3746] lr: 1.738e-03, eta: 10:48:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5631, top5_acc: 0.8053, loss_cls: 2.4091, loss: 2.4091 +2024-07-21 09:47:48,423 - pyskl - INFO - Epoch [138][1500/3746] lr: 1.731e-03, eta: 10:47:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5755, top5_acc: 0.8025, loss_cls: 2.4171, loss: 2.4171 +2024-07-21 09:49:10,440 - pyskl - INFO - Epoch [138][1600/3746] lr: 1.724e-03, eta: 10:45:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5595, top5_acc: 0.7992, loss_cls: 2.4305, loss: 2.4305 +2024-07-21 09:50:32,083 - pyskl - INFO - Epoch [138][1700/3746] lr: 1.716e-03, eta: 10:44:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5695, top5_acc: 0.8030, loss_cls: 2.3864, loss: 2.3864 +2024-07-21 09:51:54,786 - pyskl - INFO - Epoch [138][1800/3746] lr: 1.709e-03, eta: 10:43:05, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5625, top5_acc: 0.7966, loss_cls: 2.4625, loss: 2.4625 +2024-07-21 09:53:16,451 - pyskl - INFO - Epoch [138][1900/3746] lr: 1.702e-03, eta: 10:41:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5661, top5_acc: 0.8087, loss_cls: 2.4139, loss: 2.4139 +2024-07-21 09:54:38,255 - pyskl - INFO - Epoch [138][2000/3746] lr: 1.695e-03, eta: 10:40:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5589, top5_acc: 0.7994, loss_cls: 2.4555, loss: 2.4555 +2024-07-21 09:55:59,621 - pyskl - INFO - Epoch [138][2100/3746] lr: 1.687e-03, eta: 10:38:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5570, top5_acc: 0.7956, loss_cls: 2.4745, loss: 2.4745 +2024-07-21 09:57:21,696 - pyskl - INFO - Epoch [138][2200/3746] lr: 1.680e-03, eta: 10:37:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5442, top5_acc: 0.7903, loss_cls: 2.5131, loss: 2.5131 +2024-07-21 09:58:43,700 - pyskl - INFO - Epoch [138][2300/3746] lr: 1.673e-03, eta: 10:36:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5648, top5_acc: 0.8070, loss_cls: 2.3995, loss: 2.3995 +2024-07-21 10:00:05,210 - pyskl - INFO - Epoch [138][2400/3746] lr: 1.666e-03, eta: 10:34:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5659, top5_acc: 0.8037, loss_cls: 2.4192, loss: 2.4192 +2024-07-21 10:01:27,081 - pyskl - INFO - Epoch [138][2500/3746] lr: 1.659e-03, eta: 10:33:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5494, top5_acc: 0.7898, loss_cls: 2.4830, loss: 2.4830 +2024-07-21 10:02:49,103 - pyskl - INFO - Epoch [138][2600/3746] lr: 1.652e-03, eta: 10:32:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5678, top5_acc: 0.7986, loss_cls: 2.4315, loss: 2.4315 +2024-07-21 10:04:11,795 - pyskl - INFO - Epoch [138][2700/3746] lr: 1.644e-03, eta: 10:30:44, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5513, top5_acc: 0.8053, loss_cls: 2.4385, loss: 2.4385 +2024-07-21 10:05:33,807 - pyskl - INFO - Epoch [138][2800/3746] lr: 1.637e-03, eta: 10:29:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5602, top5_acc: 0.7895, loss_cls: 2.4524, loss: 2.4524 +2024-07-21 10:06:56,019 - pyskl - INFO - Epoch [138][2900/3746] lr: 1.630e-03, eta: 10:28:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5519, top5_acc: 0.7922, loss_cls: 2.4778, loss: 2.4778 +2024-07-21 10:08:18,256 - pyskl - INFO - Epoch [138][3000/3746] lr: 1.623e-03, eta: 10:26:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5523, top5_acc: 0.8014, loss_cls: 2.4623, loss: 2.4623 +2024-07-21 10:09:40,322 - pyskl - INFO - Epoch [138][3100/3746] lr: 1.616e-03, eta: 10:25:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5556, top5_acc: 0.7937, loss_cls: 2.4629, loss: 2.4629 +2024-07-21 10:11:02,263 - pyskl - INFO - Epoch [138][3200/3746] lr: 1.609e-03, eta: 10:23:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5567, top5_acc: 0.7969, loss_cls: 2.4799, loss: 2.4799 +2024-07-21 10:12:24,655 - pyskl - INFO - Epoch [138][3300/3746] lr: 1.602e-03, eta: 10:22:31, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5584, top5_acc: 0.7963, loss_cls: 2.4514, loss: 2.4514 +2024-07-21 10:13:46,299 - pyskl - INFO - Epoch [138][3400/3746] lr: 1.595e-03, eta: 10:21:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5666, top5_acc: 0.7953, loss_cls: 2.4204, loss: 2.4204 +2024-07-21 10:15:07,987 - pyskl - INFO - Epoch [138][3500/3746] lr: 1.588e-03, eta: 10:19:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5497, top5_acc: 0.7934, loss_cls: 2.4760, loss: 2.4760 +2024-07-21 10:16:30,284 - pyskl - INFO - Epoch [138][3600/3746] lr: 1.581e-03, eta: 10:18:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5622, top5_acc: 0.7948, loss_cls: 2.4328, loss: 2.4328 +2024-07-21 10:17:51,858 - pyskl - INFO - Epoch [138][3700/3746] lr: 1.574e-03, eta: 10:17:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5619, top5_acc: 0.8013, loss_cls: 2.4106, loss: 2.4106 +2024-07-21 10:18:31,779 - pyskl - INFO - Saving checkpoint at 138 epochs +2024-07-21 10:20:23,314 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 10:20:23,974 - pyskl - INFO - +top1_acc 0.4411 +top5_acc 0.6909 +2024-07-21 10:20:23,975 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 10:20:24,014 - pyskl - INFO - +mean_acc 0.4409 +2024-07-21 10:20:24,019 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_137.pth was removed +2024-07-21 10:20:24,276 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_138.pth. +2024-07-21 10:20:24,277 - pyskl - INFO - Best top1_acc is 0.4411 at 138 epoch. +2024-07-21 10:20:24,288 - pyskl - INFO - Epoch(val) [138][309] top1_acc: 0.4411, top5_acc: 0.6909, mean_class_accuracy: 0.4409 +2024-07-21 10:24:11,801 - pyskl - INFO - Epoch [139][100/3746] lr: 1.564e-03, eta: 10:15:11, time: 2.275, data_time: 1.300, memory: 15990, top1_acc: 0.5894, top5_acc: 0.8187, loss_cls: 2.3111, loss: 2.3111 +2024-07-21 10:25:34,541 - pyskl - INFO - Epoch [139][200/3746] lr: 1.557e-03, eta: 10:13:48, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5755, top5_acc: 0.8125, loss_cls: 2.3471, loss: 2.3471 +2024-07-21 10:26:56,139 - pyskl - INFO - Epoch [139][300/3746] lr: 1.550e-03, eta: 10:12:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5853, top5_acc: 0.8213, loss_cls: 2.3209, loss: 2.3209 +2024-07-21 10:28:17,889 - pyskl - INFO - Epoch [139][400/3746] lr: 1.543e-03, eta: 10:11:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5717, top5_acc: 0.8059, loss_cls: 2.3894, loss: 2.3894 +2024-07-21 10:29:39,653 - pyskl - INFO - Epoch [139][500/3746] lr: 1.536e-03, eta: 10:09:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5844, top5_acc: 0.8159, loss_cls: 2.3184, loss: 2.3184 +2024-07-21 10:31:02,007 - pyskl - INFO - Epoch [139][600/3746] lr: 1.529e-03, eta: 10:08:19, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5789, top5_acc: 0.8100, loss_cls: 2.3612, loss: 2.3612 +2024-07-21 10:32:23,826 - pyskl - INFO - Epoch [139][700/3746] lr: 1.523e-03, eta: 10:06:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5909, top5_acc: 0.8147, loss_cls: 2.3285, loss: 2.3285 +2024-07-21 10:33:45,701 - pyskl - INFO - Epoch [139][800/3746] lr: 1.516e-03, eta: 10:05:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5630, top5_acc: 0.8027, loss_cls: 2.3877, loss: 2.3877 +2024-07-21 10:35:08,217 - pyskl - INFO - Epoch [139][900/3746] lr: 1.509e-03, eta: 10:04:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5798, top5_acc: 0.8108, loss_cls: 2.3770, loss: 2.3770 +2024-07-21 10:36:29,863 - pyskl - INFO - Epoch [139][1000/3746] lr: 1.502e-03, eta: 10:02:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5608, top5_acc: 0.8069, loss_cls: 2.4047, loss: 2.4047 +2024-07-21 10:37:51,819 - pyskl - INFO - Epoch [139][1100/3746] lr: 1.495e-03, eta: 10:01:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5780, top5_acc: 0.8183, loss_cls: 2.3421, loss: 2.3421 +2024-07-21 10:39:13,408 - pyskl - INFO - Epoch [139][1200/3746] lr: 1.489e-03, eta: 10:00:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5684, top5_acc: 0.8069, loss_cls: 2.3770, loss: 2.3770 +2024-07-21 10:40:34,838 - pyskl - INFO - Epoch [139][1300/3746] lr: 1.482e-03, eta: 9:58:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5698, top5_acc: 0.8159, loss_cls: 2.3879, loss: 2.3879 +2024-07-21 10:41:56,270 - pyskl - INFO - Epoch [139][1400/3746] lr: 1.475e-03, eta: 9:57:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5714, top5_acc: 0.8086, loss_cls: 2.3916, loss: 2.3916 +2024-07-21 10:43:17,895 - pyskl - INFO - Epoch [139][1500/3746] lr: 1.468e-03, eta: 9:55:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5708, top5_acc: 0.8128, loss_cls: 2.3686, loss: 2.3686 +2024-07-21 10:44:39,524 - pyskl - INFO - Epoch [139][1600/3746] lr: 1.462e-03, eta: 9:54:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5702, top5_acc: 0.8128, loss_cls: 2.3599, loss: 2.3599 +2024-07-21 10:46:01,314 - pyskl - INFO - Epoch [139][1700/3746] lr: 1.455e-03, eta: 9:53:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5761, top5_acc: 0.8094, loss_cls: 2.3641, loss: 2.3641 +2024-07-21 10:47:24,041 - pyskl - INFO - Epoch [139][1800/3746] lr: 1.448e-03, eta: 9:51:51, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5795, top5_acc: 0.8164, loss_cls: 2.3218, loss: 2.3218 +2024-07-21 10:48:45,224 - pyskl - INFO - Epoch [139][1900/3746] lr: 1.442e-03, eta: 9:50:29, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5720, top5_acc: 0.8069, loss_cls: 2.3843, loss: 2.3843 +2024-07-21 10:50:07,240 - pyskl - INFO - Epoch [139][2000/3746] lr: 1.435e-03, eta: 9:49:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5777, top5_acc: 0.8169, loss_cls: 2.3549, loss: 2.3549 +2024-07-21 10:51:28,675 - pyskl - INFO - Epoch [139][2100/3746] lr: 1.428e-03, eta: 9:47:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5752, top5_acc: 0.8056, loss_cls: 2.3890, loss: 2.3890 +2024-07-21 10:52:50,574 - pyskl - INFO - Epoch [139][2200/3746] lr: 1.422e-03, eta: 9:46:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5550, top5_acc: 0.8020, loss_cls: 2.4008, loss: 2.4008 +2024-07-21 10:54:13,096 - pyskl - INFO - Epoch [139][2300/3746] lr: 1.415e-03, eta: 9:44:59, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5733, top5_acc: 0.8066, loss_cls: 2.3789, loss: 2.3789 +2024-07-21 10:55:34,561 - pyskl - INFO - Epoch [139][2400/3746] lr: 1.408e-03, eta: 9:43:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5745, top5_acc: 0.8058, loss_cls: 2.3854, loss: 2.3854 +2024-07-21 10:56:56,118 - pyskl - INFO - Epoch [139][2500/3746] lr: 1.402e-03, eta: 9:42:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5589, top5_acc: 0.8025, loss_cls: 2.4206, loss: 2.4206 +2024-07-21 10:58:18,055 - pyskl - INFO - Epoch [139][2600/3746] lr: 1.395e-03, eta: 9:40:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5664, top5_acc: 0.8134, loss_cls: 2.3726, loss: 2.3726 +2024-07-21 10:59:40,030 - pyskl - INFO - Epoch [139][2700/3746] lr: 1.389e-03, eta: 9:39:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5598, top5_acc: 0.8100, loss_cls: 2.3947, loss: 2.3947 +2024-07-21 11:01:01,992 - pyskl - INFO - Epoch [139][2800/3746] lr: 1.382e-03, eta: 9:38:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5766, top5_acc: 0.8077, loss_cls: 2.3727, loss: 2.3727 +2024-07-21 11:02:23,690 - pyskl - INFO - Epoch [139][2900/3746] lr: 1.376e-03, eta: 9:36:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5784, top5_acc: 0.8087, loss_cls: 2.3567, loss: 2.3567 +2024-07-21 11:03:46,121 - pyskl - INFO - Epoch [139][3000/3746] lr: 1.369e-03, eta: 9:35:23, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5520, top5_acc: 0.7970, loss_cls: 2.4693, loss: 2.4693 +2024-07-21 11:05:08,864 - pyskl - INFO - Epoch [139][3100/3746] lr: 1.363e-03, eta: 9:34:01, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5581, top5_acc: 0.8011, loss_cls: 2.4332, loss: 2.4332 +2024-07-21 11:06:30,814 - pyskl - INFO - Epoch [139][3200/3746] lr: 1.356e-03, eta: 9:32:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5652, top5_acc: 0.7941, loss_cls: 2.4211, loss: 2.4211 +2024-07-21 11:07:52,979 - pyskl - INFO - Epoch [139][3300/3746] lr: 1.350e-03, eta: 9:31:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5659, top5_acc: 0.7987, loss_cls: 2.4161, loss: 2.4161 +2024-07-21 11:09:14,868 - pyskl - INFO - Epoch [139][3400/3746] lr: 1.343e-03, eta: 9:29:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5709, top5_acc: 0.8102, loss_cls: 2.3733, loss: 2.3733 +2024-07-21 11:10:36,508 - pyskl - INFO - Epoch [139][3500/3746] lr: 1.337e-03, eta: 9:28:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5717, top5_acc: 0.8050, loss_cls: 2.4082, loss: 2.4082 +2024-07-21 11:11:58,776 - pyskl - INFO - Epoch [139][3600/3746] lr: 1.330e-03, eta: 9:27:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5598, top5_acc: 0.7995, loss_cls: 2.4227, loss: 2.4227 +2024-07-21 11:13:20,782 - pyskl - INFO - Epoch [139][3700/3746] lr: 1.324e-03, eta: 9:25:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5664, top5_acc: 0.8063, loss_cls: 2.4092, loss: 2.4092 +2024-07-21 11:14:00,438 - pyskl - INFO - Saving checkpoint at 139 epochs +2024-07-21 11:15:51,348 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 11:15:52,011 - pyskl - INFO - +top1_acc 0.4491 +top5_acc 0.6907 +2024-07-21 11:15:52,011 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 11:15:52,050 - pyskl - INFO - +mean_acc 0.4488 +2024-07-21 11:15:52,055 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_138.pth was removed +2024-07-21 11:15:52,312 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_139.pth. +2024-07-21 11:15:52,313 - pyskl - INFO - Best top1_acc is 0.4491 at 139 epoch. +2024-07-21 11:15:52,324 - pyskl - INFO - Epoch(val) [139][309] top1_acc: 0.4491, top5_acc: 0.6907, mean_class_accuracy: 0.4488 +2024-07-21 11:19:43,407 - pyskl - INFO - Epoch [140][100/3746] lr: 1.315e-03, eta: 9:23:56, time: 2.311, data_time: 1.317, memory: 15990, top1_acc: 0.6017, top5_acc: 0.8275, loss_cls: 2.2331, loss: 2.2331 +2024-07-21 11:21:05,770 - pyskl - INFO - Epoch [140][200/3746] lr: 1.308e-03, eta: 9:22:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5941, top5_acc: 0.8303, loss_cls: 2.2588, loss: 2.2588 +2024-07-21 11:22:28,120 - pyskl - INFO - Epoch [140][300/3746] lr: 1.302e-03, eta: 9:21:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5908, top5_acc: 0.8136, loss_cls: 2.3387, loss: 2.3387 +2024-07-21 11:23:51,148 - pyskl - INFO - Epoch [140][400/3746] lr: 1.296e-03, eta: 9:19:49, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5936, top5_acc: 0.8222, loss_cls: 2.2762, loss: 2.2762 +2024-07-21 11:25:15,349 - pyskl - INFO - Epoch [140][500/3746] lr: 1.289e-03, eta: 9:18:27, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6014, top5_acc: 0.8275, loss_cls: 2.2641, loss: 2.2641 +2024-07-21 11:26:37,741 - pyskl - INFO - Epoch [140][600/3746] lr: 1.283e-03, eta: 9:17:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5898, top5_acc: 0.8194, loss_cls: 2.2855, loss: 2.2855 +2024-07-21 11:28:00,014 - pyskl - INFO - Epoch [140][700/3746] lr: 1.277e-03, eta: 9:15:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5972, top5_acc: 0.8181, loss_cls: 2.3061, loss: 2.3061 +2024-07-21 11:29:21,538 - pyskl - INFO - Epoch [140][800/3746] lr: 1.271e-03, eta: 9:14:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5920, top5_acc: 0.8247, loss_cls: 2.2764, loss: 2.2764 +2024-07-21 11:30:43,498 - pyskl - INFO - Epoch [140][900/3746] lr: 1.264e-03, eta: 9:12:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5814, top5_acc: 0.8178, loss_cls: 2.3206, loss: 2.3206 +2024-07-21 11:32:05,311 - pyskl - INFO - Epoch [140][1000/3746] lr: 1.258e-03, eta: 9:11:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5861, top5_acc: 0.8217, loss_cls: 2.2980, loss: 2.2980 +2024-07-21 11:33:26,939 - pyskl - INFO - Epoch [140][1100/3746] lr: 1.252e-03, eta: 9:10:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5787, top5_acc: 0.8084, loss_cls: 2.3450, loss: 2.3450 +2024-07-21 11:34:48,956 - pyskl - INFO - Epoch [140][1200/3746] lr: 1.246e-03, eta: 9:08:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5764, top5_acc: 0.8166, loss_cls: 2.3347, loss: 2.3347 +2024-07-21 11:36:10,539 - pyskl - INFO - Epoch [140][1300/3746] lr: 1.239e-03, eta: 9:07:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5750, top5_acc: 0.8125, loss_cls: 2.3492, loss: 2.3492 +2024-07-21 11:37:32,167 - pyskl - INFO - Epoch [140][1400/3746] lr: 1.233e-03, eta: 9:06:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5961, top5_acc: 0.8230, loss_cls: 2.2923, loss: 2.2923 +2024-07-21 11:38:53,667 - pyskl - INFO - Epoch [140][1500/3746] lr: 1.227e-03, eta: 9:04:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5833, top5_acc: 0.8178, loss_cls: 2.2960, loss: 2.2960 +2024-07-21 11:40:15,539 - pyskl - INFO - Epoch [140][1600/3746] lr: 1.221e-03, eta: 9:03:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5820, top5_acc: 0.8114, loss_cls: 2.3454, loss: 2.3454 +2024-07-21 11:41:37,704 - pyskl - INFO - Epoch [140][1700/3746] lr: 1.215e-03, eta: 9:01:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5877, top5_acc: 0.8169, loss_cls: 2.3125, loss: 2.3125 +2024-07-21 11:42:59,803 - pyskl - INFO - Epoch [140][1800/3746] lr: 1.209e-03, eta: 9:00:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5858, top5_acc: 0.8153, loss_cls: 2.3130, loss: 2.3130 +2024-07-21 11:44:21,428 - pyskl - INFO - Epoch [140][1900/3746] lr: 1.203e-03, eta: 8:59:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5880, top5_acc: 0.8144, loss_cls: 2.3052, loss: 2.3052 +2024-07-21 11:45:42,892 - pyskl - INFO - Epoch [140][2000/3746] lr: 1.196e-03, eta: 8:57:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5841, top5_acc: 0.8175, loss_cls: 2.3121, loss: 2.3121 +2024-07-21 11:47:04,803 - pyskl - INFO - Epoch [140][2100/3746] lr: 1.190e-03, eta: 8:56:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5792, top5_acc: 0.8117, loss_cls: 2.3430, loss: 2.3430 +2024-07-21 11:48:26,732 - pyskl - INFO - Epoch [140][2200/3746] lr: 1.184e-03, eta: 8:55:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5759, top5_acc: 0.8131, loss_cls: 2.3284, loss: 2.3284 +2024-07-21 11:49:48,203 - pyskl - INFO - Epoch [140][2300/3746] lr: 1.178e-03, eta: 8:53:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5780, top5_acc: 0.8095, loss_cls: 2.3540, loss: 2.3540 +2024-07-21 11:51:09,645 - pyskl - INFO - Epoch [140][2400/3746] lr: 1.172e-03, eta: 8:52:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5786, top5_acc: 0.8089, loss_cls: 2.3607, loss: 2.3607 +2024-07-21 11:52:31,668 - pyskl - INFO - Epoch [140][2500/3746] lr: 1.166e-03, eta: 8:51:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5666, top5_acc: 0.8061, loss_cls: 2.3804, loss: 2.3804 +2024-07-21 11:53:53,357 - pyskl - INFO - Epoch [140][2600/3746] lr: 1.160e-03, eta: 8:49:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5694, top5_acc: 0.7983, loss_cls: 2.4166, loss: 2.4166 +2024-07-21 11:55:15,615 - pyskl - INFO - Epoch [140][2700/3746] lr: 1.154e-03, eta: 8:48:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5859, top5_acc: 0.8144, loss_cls: 2.3223, loss: 2.3223 +2024-07-21 11:56:37,018 - pyskl - INFO - Epoch [140][2800/3746] lr: 1.148e-03, eta: 8:46:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5789, top5_acc: 0.8184, loss_cls: 2.3295, loss: 2.3295 +2024-07-21 11:57:58,751 - pyskl - INFO - Epoch [140][2900/3746] lr: 1.142e-03, eta: 8:45:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5911, top5_acc: 0.8217, loss_cls: 2.2821, loss: 2.2821 +2024-07-21 11:59:20,645 - pyskl - INFO - Epoch [140][3000/3746] lr: 1.136e-03, eta: 8:44:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5803, top5_acc: 0.8177, loss_cls: 2.3145, loss: 2.3145 +2024-07-21 12:00:43,081 - pyskl - INFO - Epoch [140][3100/3746] lr: 1.131e-03, eta: 8:42:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5856, top5_acc: 0.8183, loss_cls: 2.3096, loss: 2.3096 +2024-07-21 12:02:04,893 - pyskl - INFO - Epoch [140][3200/3746] lr: 1.125e-03, eta: 8:41:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5683, top5_acc: 0.8102, loss_cls: 2.3820, loss: 2.3820 +2024-07-21 12:03:27,635 - pyskl - INFO - Epoch [140][3300/3746] lr: 1.119e-03, eta: 8:40:01, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5725, top5_acc: 0.8150, loss_cls: 2.3450, loss: 2.3450 +2024-07-21 12:04:49,626 - pyskl - INFO - Epoch [140][3400/3746] lr: 1.113e-03, eta: 8:38:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5800, top5_acc: 0.8019, loss_cls: 2.3731, loss: 2.3731 +2024-07-21 12:06:11,934 - pyskl - INFO - Epoch [140][3500/3746] lr: 1.107e-03, eta: 8:37:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5816, top5_acc: 0.8130, loss_cls: 2.3288, loss: 2.3288 +2024-07-21 12:07:33,995 - pyskl - INFO - Epoch [140][3600/3746] lr: 1.101e-03, eta: 8:35:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5802, top5_acc: 0.8163, loss_cls: 2.3025, loss: 2.3025 +2024-07-21 12:08:55,666 - pyskl - INFO - Epoch [140][3700/3746] lr: 1.095e-03, eta: 8:34:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5719, top5_acc: 0.8091, loss_cls: 2.3726, loss: 2.3726 +2024-07-21 12:09:35,472 - pyskl - INFO - Saving checkpoint at 140 epochs +2024-07-21 12:11:26,190 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 12:11:26,854 - pyskl - INFO - +top1_acc 0.4468 +top5_acc 0.6946 +2024-07-21 12:11:26,854 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 12:11:26,893 - pyskl - INFO - +mean_acc 0.4466 +2024-07-21 12:11:26,905 - pyskl - INFO - Epoch(val) [140][309] top1_acc: 0.4468, top5_acc: 0.6946, mean_class_accuracy: 0.4466 +2024-07-21 12:15:16,779 - pyskl - INFO - Epoch [141][100/3746] lr: 1.087e-03, eta: 8:32:39, time: 2.299, data_time: 1.302, memory: 15990, top1_acc: 0.6091, top5_acc: 0.8278, loss_cls: 2.2190, loss: 2.2190 +2024-07-21 12:16:39,190 - pyskl - INFO - Epoch [141][200/3746] lr: 1.081e-03, eta: 8:31:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6094, top5_acc: 0.8297, loss_cls: 2.2081, loss: 2.2081 +2024-07-21 12:18:01,154 - pyskl - INFO - Epoch [141][300/3746] lr: 1.075e-03, eta: 8:29:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6020, top5_acc: 0.8342, loss_cls: 2.2231, loss: 2.2231 +2024-07-21 12:19:23,050 - pyskl - INFO - Epoch [141][400/3746] lr: 1.070e-03, eta: 8:28:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5969, top5_acc: 0.8359, loss_cls: 2.2123, loss: 2.2123 +2024-07-21 12:20:44,814 - pyskl - INFO - Epoch [141][500/3746] lr: 1.064e-03, eta: 8:27:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5998, top5_acc: 0.8305, loss_cls: 2.2288, loss: 2.2288 +2024-07-21 12:22:07,167 - pyskl - INFO - Epoch [141][600/3746] lr: 1.058e-03, eta: 8:25:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6005, top5_acc: 0.8328, loss_cls: 2.2259, loss: 2.2259 +2024-07-21 12:23:28,825 - pyskl - INFO - Epoch [141][700/3746] lr: 1.052e-03, eta: 8:24:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6072, top5_acc: 0.8272, loss_cls: 2.2215, loss: 2.2215 +2024-07-21 12:24:50,466 - pyskl - INFO - Epoch [141][800/3746] lr: 1.047e-03, eta: 8:23:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5941, top5_acc: 0.8155, loss_cls: 2.2694, loss: 2.2694 +2024-07-21 12:26:12,001 - pyskl - INFO - Epoch [141][900/3746] lr: 1.041e-03, eta: 8:21:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6022, top5_acc: 0.8291, loss_cls: 2.2212, loss: 2.2212 +2024-07-21 12:27:33,718 - pyskl - INFO - Epoch [141][1000/3746] lr: 1.035e-03, eta: 8:20:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5900, top5_acc: 0.8236, loss_cls: 2.2782, loss: 2.2782 +2024-07-21 12:28:55,656 - pyskl - INFO - Epoch [141][1100/3746] lr: 1.030e-03, eta: 8:18:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5917, top5_acc: 0.8216, loss_cls: 2.2522, loss: 2.2522 +2024-07-21 12:30:17,275 - pyskl - INFO - Epoch [141][1200/3746] lr: 1.024e-03, eta: 8:17:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6036, top5_acc: 0.8278, loss_cls: 2.2298, loss: 2.2298 +2024-07-21 12:31:38,756 - pyskl - INFO - Epoch [141][1300/3746] lr: 1.018e-03, eta: 8:16:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5877, top5_acc: 0.8231, loss_cls: 2.2625, loss: 2.2625 +2024-07-21 12:33:01,058 - pyskl - INFO - Epoch [141][1400/3746] lr: 1.013e-03, eta: 8:14:48, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5911, top5_acc: 0.8244, loss_cls: 2.2357, loss: 2.2357 +2024-07-21 12:34:22,472 - pyskl - INFO - Epoch [141][1500/3746] lr: 1.007e-03, eta: 8:13:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5908, top5_acc: 0.8145, loss_cls: 2.3029, loss: 2.3029 +2024-07-21 12:35:44,096 - pyskl - INFO - Epoch [141][1600/3746] lr: 1.002e-03, eta: 8:12:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5837, top5_acc: 0.8181, loss_cls: 2.2906, loss: 2.2906 +2024-07-21 12:37:06,425 - pyskl - INFO - Epoch [141][1700/3746] lr: 9.961e-04, eta: 8:10:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5944, top5_acc: 0.8208, loss_cls: 2.2801, loss: 2.2801 +2024-07-21 12:38:28,868 - pyskl - INFO - Epoch [141][1800/3746] lr: 9.905e-04, eta: 8:09:19, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6016, top5_acc: 0.8294, loss_cls: 2.2373, loss: 2.2373 +2024-07-21 12:39:50,797 - pyskl - INFO - Epoch [141][1900/3746] lr: 9.850e-04, eta: 8:07:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5945, top5_acc: 0.8236, loss_cls: 2.2675, loss: 2.2675 +2024-07-21 12:41:12,620 - pyskl - INFO - Epoch [141][2000/3746] lr: 9.795e-04, eta: 8:06:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5938, top5_acc: 0.8180, loss_cls: 2.2859, loss: 2.2859 +2024-07-21 12:42:34,714 - pyskl - INFO - Epoch [141][2100/3746] lr: 9.740e-04, eta: 8:05:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6012, top5_acc: 0.8244, loss_cls: 2.2432, loss: 2.2432 +2024-07-21 12:43:56,340 - pyskl - INFO - Epoch [141][2200/3746] lr: 9.685e-04, eta: 8:03:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5889, top5_acc: 0.8155, loss_cls: 2.2933, loss: 2.2933 +2024-07-21 12:45:18,166 - pyskl - INFO - Epoch [141][2300/3746] lr: 9.630e-04, eta: 8:02:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5880, top5_acc: 0.8194, loss_cls: 2.2871, loss: 2.2871 +2024-07-21 12:46:40,431 - pyskl - INFO - Epoch [141][2400/3746] lr: 9.576e-04, eta: 8:01:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6069, top5_acc: 0.8297, loss_cls: 2.2252, loss: 2.2252 +2024-07-21 12:48:02,626 - pyskl - INFO - Epoch [141][2500/3746] lr: 9.522e-04, eta: 7:59:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5870, top5_acc: 0.8236, loss_cls: 2.2798, loss: 2.2798 +2024-07-21 12:49:24,425 - pyskl - INFO - Epoch [141][2600/3746] lr: 9.467e-04, eta: 7:58:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5938, top5_acc: 0.8286, loss_cls: 2.2452, loss: 2.2452 +2024-07-21 12:50:46,384 - pyskl - INFO - Epoch [141][2700/3746] lr: 9.413e-04, eta: 7:56:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5855, top5_acc: 0.8167, loss_cls: 2.3058, loss: 2.3058 +2024-07-21 12:52:08,217 - pyskl - INFO - Epoch [141][2800/3746] lr: 9.359e-04, eta: 7:55:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5855, top5_acc: 0.8164, loss_cls: 2.2860, loss: 2.2860 +2024-07-21 12:53:29,689 - pyskl - INFO - Epoch [141][2900/3746] lr: 9.306e-04, eta: 7:54:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5934, top5_acc: 0.8280, loss_cls: 2.2434, loss: 2.2434 +2024-07-21 12:54:51,108 - pyskl - INFO - Epoch [141][3000/3746] lr: 9.252e-04, eta: 7:52:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5811, top5_acc: 0.8158, loss_cls: 2.3185, loss: 2.3185 +2024-07-21 12:56:12,998 - pyskl - INFO - Epoch [141][3100/3746] lr: 9.199e-04, eta: 7:51:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5902, top5_acc: 0.8164, loss_cls: 2.3147, loss: 2.3147 +2024-07-21 12:57:34,445 - pyskl - INFO - Epoch [141][3200/3746] lr: 9.145e-04, eta: 7:50:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5855, top5_acc: 0.8247, loss_cls: 2.2847, loss: 2.2847 +2024-07-21 12:58:57,517 - pyskl - INFO - Epoch [141][3300/3746] lr: 9.092e-04, eta: 7:48:44, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5831, top5_acc: 0.8150, loss_cls: 2.2958, loss: 2.2958 +2024-07-21 13:00:18,964 - pyskl - INFO - Epoch [141][3400/3746] lr: 9.039e-04, eta: 7:47:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5881, top5_acc: 0.8142, loss_cls: 2.3189, loss: 2.3189 +2024-07-21 13:01:41,203 - pyskl - INFO - Epoch [141][3500/3746] lr: 8.986e-04, eta: 7:45:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5864, top5_acc: 0.8147, loss_cls: 2.3088, loss: 2.3088 +2024-07-21 13:03:02,781 - pyskl - INFO - Epoch [141][3600/3746] lr: 8.934e-04, eta: 7:44:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5958, top5_acc: 0.8239, loss_cls: 2.2527, loss: 2.2527 +2024-07-21 13:04:24,328 - pyskl - INFO - Epoch [141][3700/3746] lr: 8.881e-04, eta: 7:43:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5798, top5_acc: 0.8131, loss_cls: 2.3355, loss: 2.3355 +2024-07-21 13:05:03,587 - pyskl - INFO - Saving checkpoint at 141 epochs +2024-07-21 13:06:54,752 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 13:06:55,529 - pyskl - INFO - +top1_acc 0.4490 +top5_acc 0.6957 +2024-07-21 13:06:55,529 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 13:06:55,569 - pyskl - INFO - +mean_acc 0.4487 +2024-07-21 13:06:55,581 - pyskl - INFO - Epoch(val) [141][309] top1_acc: 0.4490, top5_acc: 0.6957, mean_class_accuracy: 0.4487 +2024-07-21 13:10:48,842 - pyskl - INFO - Epoch [142][100/3746] lr: 8.805e-04, eta: 7:41:21, time: 2.333, data_time: 1.347, memory: 15990, top1_acc: 0.5978, top5_acc: 0.8270, loss_cls: 2.2049, loss: 2.2049 +2024-07-21 13:12:10,897 - pyskl - INFO - Epoch [142][200/3746] lr: 8.752e-04, eta: 7:39:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6028, top5_acc: 0.8289, loss_cls: 2.2163, loss: 2.2163 +2024-07-21 13:13:33,069 - pyskl - INFO - Epoch [142][300/3746] lr: 8.700e-04, eta: 7:38:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6170, top5_acc: 0.8441, loss_cls: 2.1581, loss: 2.1581 +2024-07-21 13:14:55,670 - pyskl - INFO - Epoch [142][400/3746] lr: 8.649e-04, eta: 7:37:14, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6105, top5_acc: 0.8348, loss_cls: 2.1902, loss: 2.1902 +2024-07-21 13:16:17,313 - pyskl - INFO - Epoch [142][500/3746] lr: 8.597e-04, eta: 7:35:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6044, top5_acc: 0.8305, loss_cls: 2.2070, loss: 2.2070 +2024-07-21 13:17:39,187 - pyskl - INFO - Epoch [142][600/3746] lr: 8.545e-04, eta: 7:34:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6144, top5_acc: 0.8442, loss_cls: 2.1414, loss: 2.1414 +2024-07-21 13:19:00,597 - pyskl - INFO - Epoch [142][700/3746] lr: 8.494e-04, eta: 7:33:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6180, top5_acc: 0.8386, loss_cls: 2.1654, loss: 2.1654 +2024-07-21 13:20:22,657 - pyskl - INFO - Epoch [142][800/3746] lr: 8.443e-04, eta: 7:31:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6173, top5_acc: 0.8387, loss_cls: 2.1414, loss: 2.1414 +2024-07-21 13:21:44,156 - pyskl - INFO - Epoch [142][900/3746] lr: 8.392e-04, eta: 7:30:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6178, top5_acc: 0.8337, loss_cls: 2.1636, loss: 2.1636 +2024-07-21 13:23:05,743 - pyskl - INFO - Epoch [142][1000/3746] lr: 8.341e-04, eta: 7:29:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6042, top5_acc: 0.8330, loss_cls: 2.1980, loss: 2.1980 +2024-07-21 13:24:27,262 - pyskl - INFO - Epoch [142][1100/3746] lr: 8.290e-04, eta: 7:27:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6055, top5_acc: 0.8355, loss_cls: 2.1760, loss: 2.1760 +2024-07-21 13:25:48,815 - pyskl - INFO - Epoch [142][1200/3746] lr: 8.239e-04, eta: 7:26:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6139, top5_acc: 0.8378, loss_cls: 2.1758, loss: 2.1758 +2024-07-21 13:27:10,607 - pyskl - INFO - Epoch [142][1300/3746] lr: 8.189e-04, eta: 7:24:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6034, top5_acc: 0.8252, loss_cls: 2.2404, loss: 2.2404 +2024-07-21 13:28:32,326 - pyskl - INFO - Epoch [142][1400/3746] lr: 8.139e-04, eta: 7:23:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6098, top5_acc: 0.8344, loss_cls: 2.1707, loss: 2.1707 +2024-07-21 13:29:53,945 - pyskl - INFO - Epoch [142][1500/3746] lr: 8.088e-04, eta: 7:22:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5997, top5_acc: 0.8245, loss_cls: 2.2410, loss: 2.2410 +2024-07-21 13:31:15,944 - pyskl - INFO - Epoch [142][1600/3746] lr: 8.038e-04, eta: 7:20:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6058, top5_acc: 0.8319, loss_cls: 2.2048, loss: 2.2048 +2024-07-21 13:32:37,490 - pyskl - INFO - Epoch [142][1700/3746] lr: 7.989e-04, eta: 7:19:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6064, top5_acc: 0.8397, loss_cls: 2.1877, loss: 2.1877 +2024-07-21 13:33:59,715 - pyskl - INFO - Epoch [142][1800/3746] lr: 7.939e-04, eta: 7:18:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5953, top5_acc: 0.8283, loss_cls: 2.2217, loss: 2.2217 +2024-07-21 13:35:21,562 - pyskl - INFO - Epoch [142][1900/3746] lr: 7.889e-04, eta: 7:16:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5847, top5_acc: 0.8233, loss_cls: 2.2715, loss: 2.2715 +2024-07-21 13:36:42,877 - pyskl - INFO - Epoch [142][2000/3746] lr: 7.840e-04, eta: 7:15:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5980, top5_acc: 0.8255, loss_cls: 2.2317, loss: 2.2317 +2024-07-21 13:38:04,457 - pyskl - INFO - Epoch [142][2100/3746] lr: 7.791e-04, eta: 7:13:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5997, top5_acc: 0.8320, loss_cls: 2.2236, loss: 2.2236 +2024-07-21 13:39:25,715 - pyskl - INFO - Epoch [142][2200/3746] lr: 7.742e-04, eta: 7:12:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5997, top5_acc: 0.8314, loss_cls: 2.2173, loss: 2.2173 +2024-07-21 13:40:47,136 - pyskl - INFO - Epoch [142][2300/3746] lr: 7.693e-04, eta: 7:11:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5984, top5_acc: 0.8295, loss_cls: 2.2320, loss: 2.2320 +2024-07-21 13:42:08,750 - pyskl - INFO - Epoch [142][2400/3746] lr: 7.644e-04, eta: 7:09:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5886, top5_acc: 0.8253, loss_cls: 2.2567, loss: 2.2567 +2024-07-21 13:43:30,841 - pyskl - INFO - Epoch [142][2500/3746] lr: 7.595e-04, eta: 7:08:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6003, top5_acc: 0.8273, loss_cls: 2.2373, loss: 2.2373 +2024-07-21 13:44:52,332 - pyskl - INFO - Epoch [142][2600/3746] lr: 7.547e-04, eta: 7:07:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6028, top5_acc: 0.8367, loss_cls: 2.2078, loss: 2.2078 +2024-07-21 13:46:13,608 - pyskl - INFO - Epoch [142][2700/3746] lr: 7.499e-04, eta: 7:05:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6009, top5_acc: 0.8295, loss_cls: 2.2288, loss: 2.2288 +2024-07-21 13:47:34,686 - pyskl - INFO - Epoch [142][2800/3746] lr: 7.450e-04, eta: 7:04:17, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5961, top5_acc: 0.8287, loss_cls: 2.2418, loss: 2.2418 +2024-07-21 13:48:56,039 - pyskl - INFO - Epoch [142][2900/3746] lr: 7.402e-04, eta: 7:02:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6042, top5_acc: 0.8297, loss_cls: 2.2002, loss: 2.2002 +2024-07-21 13:50:17,376 - pyskl - INFO - Epoch [142][3000/3746] lr: 7.355e-04, eta: 7:01:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6094, top5_acc: 0.8336, loss_cls: 2.1999, loss: 2.1999 +2024-07-21 13:51:39,156 - pyskl - INFO - Epoch [142][3100/3746] lr: 7.307e-04, eta: 7:00:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6041, top5_acc: 0.8305, loss_cls: 2.2250, loss: 2.2250 +2024-07-21 13:53:00,967 - pyskl - INFO - Epoch [142][3200/3746] lr: 7.259e-04, eta: 6:58:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6030, top5_acc: 0.8345, loss_cls: 2.2004, loss: 2.2004 +2024-07-21 13:54:23,776 - pyskl - INFO - Epoch [142][3300/3746] lr: 7.212e-04, eta: 6:57:25, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6133, top5_acc: 0.8369, loss_cls: 2.1611, loss: 2.1611 +2024-07-21 13:55:46,305 - pyskl - INFO - Epoch [142][3400/3746] lr: 7.165e-04, eta: 6:56:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6083, top5_acc: 0.8261, loss_cls: 2.2193, loss: 2.2193 +2024-07-21 13:57:08,258 - pyskl - INFO - Epoch [142][3500/3746] lr: 7.118e-04, eta: 6:54:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5989, top5_acc: 0.8297, loss_cls: 2.2210, loss: 2.2210 +2024-07-21 13:58:30,282 - pyskl - INFO - Epoch [142][3600/3746] lr: 7.071e-04, eta: 6:53:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5917, top5_acc: 0.8252, loss_cls: 2.2610, loss: 2.2610 +2024-07-21 13:59:52,326 - pyskl - INFO - Epoch [142][3700/3746] lr: 7.024e-04, eta: 6:51:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5970, top5_acc: 0.8267, loss_cls: 2.2540, loss: 2.2540 +2024-07-21 14:00:31,916 - pyskl - INFO - Saving checkpoint at 142 epochs +2024-07-21 14:02:24,515 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 14:02:25,178 - pyskl - INFO - +top1_acc 0.4550 +top5_acc 0.6987 +2024-07-21 14:02:25,178 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 14:02:25,219 - pyskl - INFO - +mean_acc 0.4548 +2024-07-21 14:02:25,223 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_139.pth was removed +2024-07-21 14:02:25,474 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_142.pth. +2024-07-21 14:02:25,474 - pyskl - INFO - Best top1_acc is 0.4550 at 142 epoch. +2024-07-21 14:02:25,486 - pyskl - INFO - Epoch(val) [142][309] top1_acc: 0.4550, top5_acc: 0.6987, mean_class_accuracy: 0.4548 +2024-07-21 14:06:14,910 - pyskl - INFO - Epoch [143][100/3746] lr: 6.956e-04, eta: 6:50:01, time: 2.294, data_time: 1.311, memory: 15990, top1_acc: 0.6138, top5_acc: 0.8398, loss_cls: 2.1636, loss: 2.1636 +2024-07-21 14:07:36,991 - pyskl - INFO - Epoch [143][200/3746] lr: 6.910e-04, eta: 6:48:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6134, top5_acc: 0.8461, loss_cls: 2.1122, loss: 2.1122 +2024-07-21 14:08:59,541 - pyskl - INFO - Epoch [143][300/3746] lr: 6.863e-04, eta: 6:47:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6120, top5_acc: 0.8297, loss_cls: 2.1828, loss: 2.1828 +2024-07-21 14:10:21,861 - pyskl - INFO - Epoch [143][400/3746] lr: 6.817e-04, eta: 6:45:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6172, top5_acc: 0.8366, loss_cls: 2.1471, loss: 2.1471 +2024-07-21 14:11:43,774 - pyskl - INFO - Epoch [143][500/3746] lr: 6.771e-04, eta: 6:44:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6277, top5_acc: 0.8431, loss_cls: 2.1140, loss: 2.1140 +2024-07-21 14:13:05,896 - pyskl - INFO - Epoch [143][600/3746] lr: 6.725e-04, eta: 6:43:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6184, top5_acc: 0.8472, loss_cls: 2.1182, loss: 2.1182 +2024-07-21 14:14:27,904 - pyskl - INFO - Epoch [143][700/3746] lr: 6.680e-04, eta: 6:41:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6155, top5_acc: 0.8384, loss_cls: 2.1566, loss: 2.1566 +2024-07-21 14:15:49,305 - pyskl - INFO - Epoch [143][800/3746] lr: 6.634e-04, eta: 6:40:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6217, top5_acc: 0.8455, loss_cls: 2.1094, loss: 2.1094 +2024-07-21 14:17:10,756 - pyskl - INFO - Epoch [143][900/3746] lr: 6.589e-04, eta: 6:39:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6152, top5_acc: 0.8416, loss_cls: 2.1461, loss: 2.1461 +2024-07-21 14:18:32,481 - pyskl - INFO - Epoch [143][1000/3746] lr: 6.544e-04, eta: 6:37:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6200, top5_acc: 0.8447, loss_cls: 2.1394, loss: 2.1394 +2024-07-21 14:19:54,349 - pyskl - INFO - Epoch [143][1100/3746] lr: 6.499e-04, eta: 6:36:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6198, top5_acc: 0.8377, loss_cls: 2.1402, loss: 2.1402 +2024-07-21 14:21:16,283 - pyskl - INFO - Epoch [143][1200/3746] lr: 6.454e-04, eta: 6:34:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6106, top5_acc: 0.8484, loss_cls: 2.1442, loss: 2.1442 +2024-07-21 14:22:38,440 - pyskl - INFO - Epoch [143][1300/3746] lr: 6.409e-04, eta: 6:33:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6152, top5_acc: 0.8356, loss_cls: 2.1442, loss: 2.1442 +2024-07-21 14:24:00,430 - pyskl - INFO - Epoch [143][1400/3746] lr: 6.365e-04, eta: 6:32:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6159, top5_acc: 0.8444, loss_cls: 2.1379, loss: 2.1379 +2024-07-21 14:25:22,367 - pyskl - INFO - Epoch [143][1500/3746] lr: 6.320e-04, eta: 6:30:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6100, top5_acc: 0.8425, loss_cls: 2.1629, loss: 2.1629 +2024-07-21 14:26:44,220 - pyskl - INFO - Epoch [143][1600/3746] lr: 6.276e-04, eta: 6:29:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6133, top5_acc: 0.8369, loss_cls: 2.1500, loss: 2.1500 +2024-07-21 14:28:05,996 - pyskl - INFO - Epoch [143][1700/3746] lr: 6.232e-04, eta: 6:28:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6197, top5_acc: 0.8405, loss_cls: 2.1324, loss: 2.1324 +2024-07-21 14:29:27,695 - pyskl - INFO - Epoch [143][1800/3746] lr: 6.188e-04, eta: 6:26:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6223, top5_acc: 0.8405, loss_cls: 2.1536, loss: 2.1536 +2024-07-21 14:30:48,827 - pyskl - INFO - Epoch [143][1900/3746] lr: 6.144e-04, eta: 6:25:18, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6094, top5_acc: 0.8442, loss_cls: 2.1543, loss: 2.1543 +2024-07-21 14:32:10,898 - pyskl - INFO - Epoch [143][2000/3746] lr: 6.101e-04, eta: 6:23:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6161, top5_acc: 0.8381, loss_cls: 2.1697, loss: 2.1697 +2024-07-21 14:33:32,399 - pyskl - INFO - Epoch [143][2100/3746] lr: 6.057e-04, eta: 6:22:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6134, top5_acc: 0.8466, loss_cls: 2.1529, loss: 2.1529 +2024-07-21 14:34:54,173 - pyskl - INFO - Epoch [143][2200/3746] lr: 6.014e-04, eta: 6:21:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6183, top5_acc: 0.8381, loss_cls: 2.1515, loss: 2.1515 +2024-07-21 14:36:16,109 - pyskl - INFO - Epoch [143][2300/3746] lr: 5.971e-04, eta: 6:19:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6111, top5_acc: 0.8413, loss_cls: 2.1691, loss: 2.1691 +2024-07-21 14:37:38,084 - pyskl - INFO - Epoch [143][2400/3746] lr: 5.928e-04, eta: 6:18:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5981, top5_acc: 0.8277, loss_cls: 2.2127, loss: 2.2127 +2024-07-21 14:38:59,731 - pyskl - INFO - Epoch [143][2500/3746] lr: 5.885e-04, eta: 6:17:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6150, top5_acc: 0.8387, loss_cls: 2.1513, loss: 2.1513 +2024-07-21 14:40:21,267 - pyskl - INFO - Epoch [143][2600/3746] lr: 5.842e-04, eta: 6:15:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6130, top5_acc: 0.8409, loss_cls: 2.1558, loss: 2.1558 +2024-07-21 14:41:42,852 - pyskl - INFO - Epoch [143][2700/3746] lr: 5.800e-04, eta: 6:14:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6002, top5_acc: 0.8342, loss_cls: 2.1885, loss: 2.1885 +2024-07-21 14:43:04,508 - pyskl - INFO - Epoch [143][2800/3746] lr: 5.757e-04, eta: 6:12:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6245, top5_acc: 0.8411, loss_cls: 2.1356, loss: 2.1356 +2024-07-21 14:44:26,009 - pyskl - INFO - Epoch [143][2900/3746] lr: 5.715e-04, eta: 6:11:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6094, top5_acc: 0.8313, loss_cls: 2.1865, loss: 2.1865 +2024-07-21 14:45:47,446 - pyskl - INFO - Epoch [143][3000/3746] lr: 5.673e-04, eta: 6:10:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6123, top5_acc: 0.8325, loss_cls: 2.1789, loss: 2.1789 +2024-07-21 14:47:09,354 - pyskl - INFO - Epoch [143][3100/3746] lr: 5.631e-04, eta: 6:08:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6117, top5_acc: 0.8392, loss_cls: 2.1607, loss: 2.1607 +2024-07-21 14:48:30,978 - pyskl - INFO - Epoch [143][3200/3746] lr: 5.590e-04, eta: 6:07:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6214, top5_acc: 0.8384, loss_cls: 2.1623, loss: 2.1623 +2024-07-21 14:49:53,784 - pyskl - INFO - Epoch [143][3300/3746] lr: 5.548e-04, eta: 6:06:05, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6262, top5_acc: 0.8431, loss_cls: 2.1092, loss: 2.1092 +2024-07-21 14:51:15,780 - pyskl - INFO - Epoch [143][3400/3746] lr: 5.506e-04, eta: 6:04:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6152, top5_acc: 0.8383, loss_cls: 2.1657, loss: 2.1657 +2024-07-21 14:52:37,526 - pyskl - INFO - Epoch [143][3500/3746] lr: 5.465e-04, eta: 6:03:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6198, top5_acc: 0.8348, loss_cls: 2.1345, loss: 2.1345 +2024-07-21 14:53:59,483 - pyskl - INFO - Epoch [143][3600/3746] lr: 5.424e-04, eta: 6:01:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6216, top5_acc: 0.8419, loss_cls: 2.1243, loss: 2.1243 +2024-07-21 14:55:20,812 - pyskl - INFO - Epoch [143][3700/3746] lr: 5.383e-04, eta: 6:00:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6109, top5_acc: 0.8350, loss_cls: 2.1816, loss: 2.1816 +2024-07-21 14:56:00,516 - pyskl - INFO - Saving checkpoint at 143 epochs +2024-07-21 14:57:53,649 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 14:57:54,308 - pyskl - INFO - +top1_acc 0.4528 +top5_acc 0.7021 +2024-07-21 14:57:54,308 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 14:57:54,348 - pyskl - INFO - +mean_acc 0.4526 +2024-07-21 14:57:54,360 - pyskl - INFO - Epoch(val) [143][309] top1_acc: 0.4528, top5_acc: 0.7021, mean_class_accuracy: 0.4526 +2024-07-21 15:01:38,675 - pyskl - INFO - Epoch [144][100/3746] lr: 5.323e-04, eta: 5:58:40, time: 2.243, data_time: 1.261, memory: 15990, top1_acc: 0.6338, top5_acc: 0.8461, loss_cls: 2.0567, loss: 2.0567 +2024-07-21 15:03:00,704 - pyskl - INFO - Epoch [144][200/3746] lr: 5.283e-04, eta: 5:57:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6370, top5_acc: 0.8558, loss_cls: 2.0438, loss: 2.0438 +2024-07-21 15:04:22,951 - pyskl - INFO - Epoch [144][300/3746] lr: 5.242e-04, eta: 5:55:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6434, top5_acc: 0.8522, loss_cls: 2.0297, loss: 2.0297 +2024-07-21 15:05:45,317 - pyskl - INFO - Epoch [144][400/3746] lr: 5.202e-04, eta: 5:54:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6292, top5_acc: 0.8445, loss_cls: 2.0949, loss: 2.0949 +2024-07-21 15:07:07,187 - pyskl - INFO - Epoch [144][500/3746] lr: 5.162e-04, eta: 5:53:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6219, top5_acc: 0.8458, loss_cls: 2.1251, loss: 2.1251 +2024-07-21 15:08:28,888 - pyskl - INFO - Epoch [144][600/3746] lr: 5.122e-04, eta: 5:51:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6358, top5_acc: 0.8442, loss_cls: 2.0661, loss: 2.0661 +2024-07-21 15:09:50,601 - pyskl - INFO - Epoch [144][700/3746] lr: 5.082e-04, eta: 5:50:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6339, top5_acc: 0.8527, loss_cls: 2.0535, loss: 2.0535 +2024-07-21 15:11:12,757 - pyskl - INFO - Epoch [144][800/3746] lr: 5.042e-04, eta: 5:49:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6203, top5_acc: 0.8464, loss_cls: 2.1044, loss: 2.1044 +2024-07-21 15:12:34,377 - pyskl - INFO - Epoch [144][900/3746] lr: 5.003e-04, eta: 5:47:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6328, top5_acc: 0.8509, loss_cls: 2.0654, loss: 2.0654 +2024-07-21 15:13:56,239 - pyskl - INFO - Epoch [144][1000/3746] lr: 4.964e-04, eta: 5:46:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6266, top5_acc: 0.8427, loss_cls: 2.1128, loss: 2.1128 +2024-07-21 15:15:17,849 - pyskl - INFO - Epoch [144][1100/3746] lr: 4.924e-04, eta: 5:44:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6344, top5_acc: 0.8502, loss_cls: 2.0633, loss: 2.0633 +2024-07-21 15:16:39,145 - pyskl - INFO - Epoch [144][1200/3746] lr: 4.885e-04, eta: 5:43:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6270, top5_acc: 0.8413, loss_cls: 2.1173, loss: 2.1173 +2024-07-21 15:18:01,348 - pyskl - INFO - Epoch [144][1300/3746] lr: 4.846e-04, eta: 5:42:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6184, top5_acc: 0.8458, loss_cls: 2.1272, loss: 2.1272 +2024-07-21 15:19:23,679 - pyskl - INFO - Epoch [144][1400/3746] lr: 4.808e-04, eta: 5:40:48, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6264, top5_acc: 0.8439, loss_cls: 2.1054, loss: 2.1054 +2024-07-21 15:20:45,147 - pyskl - INFO - Epoch [144][1500/3746] lr: 4.769e-04, eta: 5:39:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6206, top5_acc: 0.8428, loss_cls: 2.1248, loss: 2.1248 +2024-07-21 15:22:07,425 - pyskl - INFO - Epoch [144][1600/3746] lr: 4.731e-04, eta: 5:38:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6345, top5_acc: 0.8464, loss_cls: 2.0914, loss: 2.0914 +2024-07-21 15:23:29,396 - pyskl - INFO - Epoch [144][1700/3746] lr: 4.692e-04, eta: 5:36:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6161, top5_acc: 0.8434, loss_cls: 2.1288, loss: 2.1288 +2024-07-21 15:24:51,502 - pyskl - INFO - Epoch [144][1800/3746] lr: 4.654e-04, eta: 5:35:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6284, top5_acc: 0.8564, loss_cls: 2.0619, loss: 2.0619 +2024-07-21 15:26:13,434 - pyskl - INFO - Epoch [144][1900/3746] lr: 4.616e-04, eta: 5:33:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6316, top5_acc: 0.8512, loss_cls: 2.0610, loss: 2.0610 +2024-07-21 15:27:35,510 - pyskl - INFO - Epoch [144][2000/3746] lr: 4.578e-04, eta: 5:32:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6236, top5_acc: 0.8470, loss_cls: 2.1060, loss: 2.1060 +2024-07-21 15:28:57,634 - pyskl - INFO - Epoch [144][2100/3746] lr: 4.541e-04, eta: 5:31:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6270, top5_acc: 0.8439, loss_cls: 2.0935, loss: 2.0935 +2024-07-21 15:30:19,497 - pyskl - INFO - Epoch [144][2200/3746] lr: 4.503e-04, eta: 5:29:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6298, top5_acc: 0.8545, loss_cls: 2.0680, loss: 2.0680 +2024-07-21 15:31:41,005 - pyskl - INFO - Epoch [144][2300/3746] lr: 4.466e-04, eta: 5:28:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6338, top5_acc: 0.8514, loss_cls: 2.0767, loss: 2.0767 +2024-07-21 15:33:02,237 - pyskl - INFO - Epoch [144][2400/3746] lr: 4.429e-04, eta: 5:27:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6178, top5_acc: 0.8402, loss_cls: 2.1535, loss: 2.1535 +2024-07-21 15:34:24,791 - pyskl - INFO - Epoch [144][2500/3746] lr: 4.392e-04, eta: 5:25:42, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6225, top5_acc: 0.8434, loss_cls: 2.0916, loss: 2.0916 +2024-07-21 15:35:46,865 - pyskl - INFO - Epoch [144][2600/3746] lr: 4.355e-04, eta: 5:24:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6275, top5_acc: 0.8450, loss_cls: 2.0717, loss: 2.0717 +2024-07-21 15:37:08,302 - pyskl - INFO - Epoch [144][2700/3746] lr: 4.318e-04, eta: 5:22:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6233, top5_acc: 0.8508, loss_cls: 2.0885, loss: 2.0885 +2024-07-21 15:38:30,495 - pyskl - INFO - Epoch [144][2800/3746] lr: 4.281e-04, eta: 5:21:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6250, top5_acc: 0.8464, loss_cls: 2.0937, loss: 2.0937 +2024-07-21 15:39:52,679 - pyskl - INFO - Epoch [144][2900/3746] lr: 4.245e-04, eta: 5:20:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6222, top5_acc: 0.8422, loss_cls: 2.1259, loss: 2.1259 +2024-07-21 15:41:14,646 - pyskl - INFO - Epoch [144][3000/3746] lr: 4.209e-04, eta: 5:18:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6198, top5_acc: 0.8402, loss_cls: 2.1254, loss: 2.1254 +2024-07-21 15:42:37,277 - pyskl - INFO - Epoch [144][3100/3746] lr: 4.173e-04, eta: 5:17:28, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6288, top5_acc: 0.8441, loss_cls: 2.1135, loss: 2.1135 +2024-07-21 15:43:59,264 - pyskl - INFO - Epoch [144][3200/3746] lr: 4.137e-04, eta: 5:16:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6198, top5_acc: 0.8478, loss_cls: 2.1167, loss: 2.1167 +2024-07-21 15:45:21,341 - pyskl - INFO - Epoch [144][3300/3746] lr: 4.101e-04, eta: 5:14:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6202, top5_acc: 0.8506, loss_cls: 2.1024, loss: 2.1024 +2024-07-21 15:46:44,095 - pyskl - INFO - Epoch [144][3400/3746] lr: 4.065e-04, eta: 5:13:21, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6209, top5_acc: 0.8488, loss_cls: 2.1147, loss: 2.1147 +2024-07-21 15:48:05,877 - pyskl - INFO - Epoch [144][3500/3746] lr: 4.030e-04, eta: 5:11:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6206, top5_acc: 0.8450, loss_cls: 2.1074, loss: 2.1074 +2024-07-21 15:49:28,140 - pyskl - INFO - Epoch [144][3600/3746] lr: 3.994e-04, eta: 5:10:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6267, top5_acc: 0.8436, loss_cls: 2.1115, loss: 2.1115 +2024-07-21 15:50:49,670 - pyskl - INFO - Epoch [144][3700/3746] lr: 3.959e-04, eta: 5:09:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6288, top5_acc: 0.8430, loss_cls: 2.1048, loss: 2.1048 +2024-07-21 15:51:29,208 - pyskl - INFO - Saving checkpoint at 144 epochs +2024-07-21 15:53:21,064 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 15:53:21,718 - pyskl - INFO - +top1_acc 0.4546 +top5_acc 0.7003 +2024-07-21 15:53:21,719 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 15:53:21,758 - pyskl - INFO - +mean_acc 0.4544 +2024-07-21 15:53:21,770 - pyskl - INFO - Epoch(val) [144][309] top1_acc: 0.4546, top5_acc: 0.7003, mean_class_accuracy: 0.4544 +2024-07-21 15:57:06,316 - pyskl - INFO - Epoch [145][100/3746] lr: 3.908e-04, eta: 5:07:17, time: 2.245, data_time: 1.265, memory: 15990, top1_acc: 0.6334, top5_acc: 0.8509, loss_cls: 2.0718, loss: 2.0718 +2024-07-21 15:58:27,977 - pyskl - INFO - Epoch [145][200/3746] lr: 3.873e-04, eta: 5:05:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6389, top5_acc: 0.8603, loss_cls: 2.0305, loss: 2.0305 +2024-07-21 15:59:50,530 - pyskl - INFO - Epoch [145][300/3746] lr: 3.839e-04, eta: 5:04:33, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6320, top5_acc: 0.8491, loss_cls: 2.0712, loss: 2.0712 +2024-07-21 16:01:12,964 - pyskl - INFO - Epoch [145][400/3746] lr: 3.804e-04, eta: 5:03:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6412, top5_acc: 0.8591, loss_cls: 2.0108, loss: 2.0108 +2024-07-21 16:02:34,837 - pyskl - INFO - Epoch [145][500/3746] lr: 3.770e-04, eta: 5:01:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6439, top5_acc: 0.8580, loss_cls: 2.0087, loss: 2.0087 +2024-07-21 16:03:56,998 - pyskl - INFO - Epoch [145][600/3746] lr: 3.736e-04, eta: 5:00:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6458, top5_acc: 0.8472, loss_cls: 2.0471, loss: 2.0471 +2024-07-21 16:05:18,450 - pyskl - INFO - Epoch [145][700/3746] lr: 3.702e-04, eta: 4:59:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6456, top5_acc: 0.8534, loss_cls: 2.0220, loss: 2.0220 +2024-07-21 16:06:40,180 - pyskl - INFO - Epoch [145][800/3746] lr: 3.668e-04, eta: 4:57:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6367, top5_acc: 0.8573, loss_cls: 2.0317, loss: 2.0317 +2024-07-21 16:08:01,446 - pyskl - INFO - Epoch [145][900/3746] lr: 3.634e-04, eta: 4:56:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6420, top5_acc: 0.8566, loss_cls: 2.0133, loss: 2.0133 +2024-07-21 16:09:23,027 - pyskl - INFO - Epoch [145][1000/3746] lr: 3.600e-04, eta: 4:54:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6442, top5_acc: 0.8539, loss_cls: 2.0209, loss: 2.0209 +2024-07-21 16:10:44,892 - pyskl - INFO - Epoch [145][1100/3746] lr: 3.567e-04, eta: 4:53:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6414, top5_acc: 0.8520, loss_cls: 2.0481, loss: 2.0481 +2024-07-21 16:12:06,175 - pyskl - INFO - Epoch [145][1200/3746] lr: 3.534e-04, eta: 4:52:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6334, top5_acc: 0.8509, loss_cls: 2.0412, loss: 2.0412 +2024-07-21 16:13:27,651 - pyskl - INFO - Epoch [145][1300/3746] lr: 3.501e-04, eta: 4:50:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6430, top5_acc: 0.8530, loss_cls: 2.0386, loss: 2.0386 +2024-07-21 16:14:49,776 - pyskl - INFO - Epoch [145][1400/3746] lr: 3.468e-04, eta: 4:49:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6502, top5_acc: 0.8561, loss_cls: 2.0067, loss: 2.0067 +2024-07-21 16:16:11,225 - pyskl - INFO - Epoch [145][1500/3746] lr: 3.435e-04, eta: 4:48:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6506, top5_acc: 0.8614, loss_cls: 1.9971, loss: 1.9971 +2024-07-21 16:17:33,565 - pyskl - INFO - Epoch [145][1600/3746] lr: 3.402e-04, eta: 4:46:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6348, top5_acc: 0.8495, loss_cls: 2.0658, loss: 2.0658 +2024-07-21 16:18:54,760 - pyskl - INFO - Epoch [145][1700/3746] lr: 3.370e-04, eta: 4:45:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6392, top5_acc: 0.8564, loss_cls: 2.0447, loss: 2.0447 +2024-07-21 16:20:16,142 - pyskl - INFO - Epoch [145][1800/3746] lr: 3.337e-04, eta: 4:43:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6320, top5_acc: 0.8544, loss_cls: 2.0494, loss: 2.0494 +2024-07-21 16:21:37,952 - pyskl - INFO - Epoch [145][1900/3746] lr: 3.305e-04, eta: 4:42:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6386, top5_acc: 0.8531, loss_cls: 2.0485, loss: 2.0485 +2024-07-21 16:22:59,194 - pyskl - INFO - Epoch [145][2000/3746] lr: 3.273e-04, eta: 4:41:11, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6294, top5_acc: 0.8477, loss_cls: 2.0670, loss: 2.0670 +2024-07-21 16:24:20,538 - pyskl - INFO - Epoch [145][2100/3746] lr: 3.241e-04, eta: 4:39:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6344, top5_acc: 0.8548, loss_cls: 2.0546, loss: 2.0546 +2024-07-21 16:25:41,933 - pyskl - INFO - Epoch [145][2200/3746] lr: 3.210e-04, eta: 4:38:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6339, top5_acc: 0.8523, loss_cls: 2.0550, loss: 2.0550 +2024-07-21 16:27:03,669 - pyskl - INFO - Epoch [145][2300/3746] lr: 3.178e-04, eta: 4:37:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6305, top5_acc: 0.8477, loss_cls: 2.0541, loss: 2.0541 +2024-07-21 16:28:25,061 - pyskl - INFO - Epoch [145][2400/3746] lr: 3.147e-04, eta: 4:35:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6367, top5_acc: 0.8547, loss_cls: 2.0113, loss: 2.0113 +2024-07-21 16:29:46,722 - pyskl - INFO - Epoch [145][2500/3746] lr: 3.116e-04, eta: 4:34:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6433, top5_acc: 0.8505, loss_cls: 2.0196, loss: 2.0196 +2024-07-21 16:31:08,394 - pyskl - INFO - Epoch [145][2600/3746] lr: 3.084e-04, eta: 4:32:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6352, top5_acc: 0.8555, loss_cls: 2.0556, loss: 2.0556 +2024-07-21 16:32:30,059 - pyskl - INFO - Epoch [145][2700/3746] lr: 3.054e-04, eta: 4:31:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6422, top5_acc: 0.8536, loss_cls: 2.0290, loss: 2.0290 +2024-07-21 16:33:51,720 - pyskl - INFO - Epoch [145][2800/3746] lr: 3.023e-04, eta: 4:30:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6386, top5_acc: 0.8505, loss_cls: 2.0503, loss: 2.0503 +2024-07-21 16:35:13,552 - pyskl - INFO - Epoch [145][2900/3746] lr: 2.992e-04, eta: 4:28:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6345, top5_acc: 0.8519, loss_cls: 2.0579, loss: 2.0579 +2024-07-21 16:36:35,032 - pyskl - INFO - Epoch [145][3000/3746] lr: 2.962e-04, eta: 4:27:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6388, top5_acc: 0.8583, loss_cls: 2.0394, loss: 2.0394 +2024-07-21 16:37:56,579 - pyskl - INFO - Epoch [145][3100/3746] lr: 2.931e-04, eta: 4:26:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6286, top5_acc: 0.8483, loss_cls: 2.0844, loss: 2.0844 +2024-07-21 16:39:18,990 - pyskl - INFO - Epoch [145][3200/3746] lr: 2.901e-04, eta: 4:24:42, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6331, top5_acc: 0.8530, loss_cls: 2.0412, loss: 2.0412 +2024-07-21 16:40:40,679 - pyskl - INFO - Epoch [145][3300/3746] lr: 2.871e-04, eta: 4:23:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6503, top5_acc: 0.8503, loss_cls: 2.0326, loss: 2.0326 +2024-07-21 16:42:03,025 - pyskl - INFO - Epoch [145][3400/3746] lr: 2.841e-04, eta: 4:21:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6355, top5_acc: 0.8561, loss_cls: 2.0379, loss: 2.0379 +2024-07-21 16:43:24,369 - pyskl - INFO - Epoch [145][3500/3746] lr: 2.812e-04, eta: 4:20:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6320, top5_acc: 0.8431, loss_cls: 2.0752, loss: 2.0752 +2024-07-21 16:44:45,622 - pyskl - INFO - Epoch [145][3600/3746] lr: 2.782e-04, eta: 4:19:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6206, top5_acc: 0.8455, loss_cls: 2.0993, loss: 2.0993 +2024-07-21 16:46:07,231 - pyskl - INFO - Epoch [145][3700/3746] lr: 2.753e-04, eta: 4:17:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6383, top5_acc: 0.8528, loss_cls: 2.0626, loss: 2.0626 +2024-07-21 16:46:46,586 - pyskl - INFO - Saving checkpoint at 145 epochs +2024-07-21 16:48:38,100 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 16:48:38,757 - pyskl - INFO - +top1_acc 0.4549 +top5_acc 0.7032 +2024-07-21 16:48:38,757 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 16:48:38,796 - pyskl - INFO - +mean_acc 0.4547 +2024-07-21 16:48:38,807 - pyskl - INFO - Epoch(val) [145][309] top1_acc: 0.4549, top5_acc: 0.7032, mean_class_accuracy: 0.4547 +2024-07-21 16:52:23,971 - pyskl - INFO - Epoch [146][100/3746] lr: 2.710e-04, eta: 4:15:53, time: 2.252, data_time: 1.273, memory: 15990, top1_acc: 0.6620, top5_acc: 0.8673, loss_cls: 1.9440, loss: 1.9440 +2024-07-21 16:53:46,071 - pyskl - INFO - Epoch [146][200/3746] lr: 2.681e-04, eta: 4:14:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6539, top5_acc: 0.8600, loss_cls: 1.9620, loss: 1.9620 +2024-07-21 16:55:08,354 - pyskl - INFO - Epoch [146][300/3746] lr: 2.652e-04, eta: 4:13:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6533, top5_acc: 0.8695, loss_cls: 1.9534, loss: 1.9534 +2024-07-21 16:56:30,589 - pyskl - INFO - Epoch [146][400/3746] lr: 2.624e-04, eta: 4:11:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6534, top5_acc: 0.8634, loss_cls: 1.9776, loss: 1.9776 +2024-07-21 16:57:52,753 - pyskl - INFO - Epoch [146][500/3746] lr: 2.595e-04, eta: 4:10:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6548, top5_acc: 0.8681, loss_cls: 1.9533, loss: 1.9533 +2024-07-21 16:59:14,420 - pyskl - INFO - Epoch [146][600/3746] lr: 2.567e-04, eta: 4:09:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6427, top5_acc: 0.8561, loss_cls: 2.0103, loss: 2.0103 +2024-07-21 17:00:35,873 - pyskl - INFO - Epoch [146][700/3746] lr: 2.539e-04, eta: 4:07:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6461, top5_acc: 0.8578, loss_cls: 1.9990, loss: 1.9990 +2024-07-21 17:01:57,947 - pyskl - INFO - Epoch [146][800/3746] lr: 2.511e-04, eta: 4:06:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6528, top5_acc: 0.8611, loss_cls: 1.9634, loss: 1.9634 +2024-07-21 17:03:19,504 - pyskl - INFO - Epoch [146][900/3746] lr: 2.483e-04, eta: 4:04:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6480, top5_acc: 0.8570, loss_cls: 1.9968, loss: 1.9968 +2024-07-21 17:04:41,009 - pyskl - INFO - Epoch [146][1000/3746] lr: 2.455e-04, eta: 4:03:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6548, top5_acc: 0.8603, loss_cls: 1.9755, loss: 1.9755 +2024-07-21 17:06:02,843 - pyskl - INFO - Epoch [146][1100/3746] lr: 2.427e-04, eta: 4:02:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6555, top5_acc: 0.8639, loss_cls: 1.9859, loss: 1.9859 +2024-07-21 17:07:24,201 - pyskl - INFO - Epoch [146][1200/3746] lr: 2.400e-04, eta: 4:00:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6500, top5_acc: 0.8578, loss_cls: 2.0095, loss: 2.0095 +2024-07-21 17:08:45,644 - pyskl - INFO - Epoch [146][1300/3746] lr: 2.373e-04, eta: 3:59:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6527, top5_acc: 0.8628, loss_cls: 1.9945, loss: 1.9945 +2024-07-21 17:10:07,488 - pyskl - INFO - Epoch [146][1400/3746] lr: 2.345e-04, eta: 3:58:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6452, top5_acc: 0.8605, loss_cls: 2.0110, loss: 2.0110 +2024-07-21 17:11:29,103 - pyskl - INFO - Epoch [146][1500/3746] lr: 2.318e-04, eta: 3:56:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6430, top5_acc: 0.8588, loss_cls: 1.9997, loss: 1.9997 +2024-07-21 17:12:50,776 - pyskl - INFO - Epoch [146][1600/3746] lr: 2.292e-04, eta: 3:55:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6397, top5_acc: 0.8527, loss_cls: 2.0212, loss: 2.0212 +2024-07-21 17:14:12,583 - pyskl - INFO - Epoch [146][1700/3746] lr: 2.265e-04, eta: 3:53:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6386, top5_acc: 0.8548, loss_cls: 2.0342, loss: 2.0342 +2024-07-21 17:15:34,515 - pyskl - INFO - Epoch [146][1800/3746] lr: 2.239e-04, eta: 3:52:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6419, top5_acc: 0.8531, loss_cls: 2.0276, loss: 2.0276 +2024-07-21 17:16:55,971 - pyskl - INFO - Epoch [146][1900/3746] lr: 2.212e-04, eta: 3:51:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6517, top5_acc: 0.8645, loss_cls: 1.9899, loss: 1.9899 +2024-07-21 17:18:17,342 - pyskl - INFO - Epoch [146][2000/3746] lr: 2.186e-04, eta: 3:49:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6483, top5_acc: 0.8580, loss_cls: 2.0029, loss: 2.0029 +2024-07-21 17:19:38,629 - pyskl - INFO - Epoch [146][2100/3746] lr: 2.160e-04, eta: 3:48:25, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6544, top5_acc: 0.8652, loss_cls: 1.9673, loss: 1.9673 +2024-07-21 17:21:00,005 - pyskl - INFO - Epoch [146][2200/3746] lr: 2.134e-04, eta: 3:47:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6336, top5_acc: 0.8572, loss_cls: 2.0290, loss: 2.0290 +2024-07-21 17:22:21,500 - pyskl - INFO - Epoch [146][2300/3746] lr: 2.108e-04, eta: 3:45:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6430, top5_acc: 0.8558, loss_cls: 1.9978, loss: 1.9978 +2024-07-21 17:23:42,998 - pyskl - INFO - Epoch [146][2400/3746] lr: 2.083e-04, eta: 3:44:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6483, top5_acc: 0.8594, loss_cls: 1.9895, loss: 1.9895 +2024-07-21 17:25:05,117 - pyskl - INFO - Epoch [146][2500/3746] lr: 2.057e-04, eta: 3:42:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6412, top5_acc: 0.8580, loss_cls: 2.0249, loss: 2.0249 +2024-07-21 17:26:26,338 - pyskl - INFO - Epoch [146][2600/3746] lr: 2.032e-04, eta: 3:41:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6512, top5_acc: 0.8625, loss_cls: 1.9737, loss: 1.9737 +2024-07-21 17:27:48,332 - pyskl - INFO - Epoch [146][2700/3746] lr: 2.007e-04, eta: 3:40:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6352, top5_acc: 0.8522, loss_cls: 2.0295, loss: 2.0295 +2024-07-21 17:29:09,384 - pyskl - INFO - Epoch [146][2800/3746] lr: 1.982e-04, eta: 3:38:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6427, top5_acc: 0.8558, loss_cls: 2.0343, loss: 2.0343 +2024-07-21 17:30:30,704 - pyskl - INFO - Epoch [146][2900/3746] lr: 1.957e-04, eta: 3:37:25, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6581, top5_acc: 0.8578, loss_cls: 1.9788, loss: 1.9788 +2024-07-21 17:31:52,173 - pyskl - INFO - Epoch [146][3000/3746] lr: 1.933e-04, eta: 3:36:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6444, top5_acc: 0.8588, loss_cls: 1.9998, loss: 1.9998 +2024-07-21 17:33:13,915 - pyskl - INFO - Epoch [146][3100/3746] lr: 1.908e-04, eta: 3:34:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6350, top5_acc: 0.8534, loss_cls: 2.0395, loss: 2.0395 +2024-07-21 17:34:35,952 - pyskl - INFO - Epoch [146][3200/3746] lr: 1.884e-04, eta: 3:33:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6489, top5_acc: 0.8583, loss_cls: 1.9714, loss: 1.9714 +2024-07-21 17:35:58,176 - pyskl - INFO - Epoch [146][3300/3746] lr: 1.860e-04, eta: 3:31:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6475, top5_acc: 0.8567, loss_cls: 1.9964, loss: 1.9964 +2024-07-21 17:37:19,524 - pyskl - INFO - Epoch [146][3400/3746] lr: 1.836e-04, eta: 3:30:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6378, top5_acc: 0.8588, loss_cls: 2.0154, loss: 2.0154 +2024-07-21 17:38:41,650 - pyskl - INFO - Epoch [146][3500/3746] lr: 1.812e-04, eta: 3:29:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6430, top5_acc: 0.8552, loss_cls: 2.0163, loss: 2.0163 +2024-07-21 17:40:03,500 - pyskl - INFO - Epoch [146][3600/3746] lr: 1.788e-04, eta: 3:27:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6556, top5_acc: 0.8559, loss_cls: 1.9879, loss: 1.9879 +2024-07-21 17:41:25,578 - pyskl - INFO - Epoch [146][3700/3746] lr: 1.765e-04, eta: 3:26:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6439, top5_acc: 0.8562, loss_cls: 2.0254, loss: 2.0254 +2024-07-21 17:42:05,529 - pyskl - INFO - Saving checkpoint at 146 epochs +2024-07-21 17:43:58,785 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 17:43:59,451 - pyskl - INFO - +top1_acc 0.4584 +top5_acc 0.7040 +2024-07-21 17:43:59,451 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 17:43:59,491 - pyskl - INFO - +mean_acc 0.4582 +2024-07-21 17:43:59,496 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_142.pth was removed +2024-07-21 17:43:59,752 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_146.pth. +2024-07-21 17:43:59,753 - pyskl - INFO - Best top1_acc is 0.4584 at 146 epoch. +2024-07-21 17:43:59,764 - pyskl - INFO - Epoch(val) [146][309] top1_acc: 0.4584, top5_acc: 0.7040, mean_class_accuracy: 0.4582 +2024-07-21 17:47:45,001 - pyskl - INFO - Epoch [147][100/3746] lr: 1.730e-04, eta: 3:24:28, time: 2.252, data_time: 1.277, memory: 15990, top1_acc: 0.6636, top5_acc: 0.8689, loss_cls: 1.9108, loss: 1.9108 +2024-07-21 17:49:07,023 - pyskl - INFO - Epoch [147][200/3746] lr: 1.707e-04, eta: 3:23:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6533, top5_acc: 0.8666, loss_cls: 1.9693, loss: 1.9693 +2024-07-21 17:50:30,174 - pyskl - INFO - Epoch [147][300/3746] lr: 1.684e-04, eta: 3:21:43, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6578, top5_acc: 0.8702, loss_cls: 1.9434, loss: 1.9434 +2024-07-21 17:51:53,287 - pyskl - INFO - Epoch [147][400/3746] lr: 1.661e-04, eta: 3:20:21, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6495, top5_acc: 0.8688, loss_cls: 1.9732, loss: 1.9732 +2024-07-21 17:53:15,874 - pyskl - INFO - Epoch [147][500/3746] lr: 1.639e-04, eta: 3:18:59, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6652, top5_acc: 0.8688, loss_cls: 1.9281, loss: 1.9281 +2024-07-21 17:54:37,805 - pyskl - INFO - Epoch [147][600/3746] lr: 1.616e-04, eta: 3:17:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6597, top5_acc: 0.8653, loss_cls: 1.9507, loss: 1.9507 +2024-07-21 17:55:59,060 - pyskl - INFO - Epoch [147][700/3746] lr: 1.594e-04, eta: 3:16:14, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6567, top5_acc: 0.8650, loss_cls: 1.9500, loss: 1.9500 +2024-07-21 17:57:21,388 - pyskl - INFO - Epoch [147][800/3746] lr: 1.572e-04, eta: 3:14:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6586, top5_acc: 0.8645, loss_cls: 1.9571, loss: 1.9571 +2024-07-21 17:58:42,849 - pyskl - INFO - Epoch [147][900/3746] lr: 1.550e-04, eta: 3:13:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6580, top5_acc: 0.8636, loss_cls: 1.9490, loss: 1.9490 +2024-07-21 18:00:04,647 - pyskl - INFO - Epoch [147][1000/3746] lr: 1.528e-04, eta: 3:12:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6536, top5_acc: 0.8634, loss_cls: 1.9786, loss: 1.9786 +2024-07-21 18:01:26,034 - pyskl - INFO - Epoch [147][1100/3746] lr: 1.506e-04, eta: 3:10:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6666, top5_acc: 0.8669, loss_cls: 1.9418, loss: 1.9418 +2024-07-21 18:02:47,687 - pyskl - INFO - Epoch [147][1200/3746] lr: 1.484e-04, eta: 3:09:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6614, top5_acc: 0.8659, loss_cls: 1.9338, loss: 1.9338 +2024-07-21 18:04:08,797 - pyskl - INFO - Epoch [147][1300/3746] lr: 1.463e-04, eta: 3:07:59, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6530, top5_acc: 0.8661, loss_cls: 1.9560, loss: 1.9560 +2024-07-21 18:05:30,936 - pyskl - INFO - Epoch [147][1400/3746] lr: 1.442e-04, eta: 3:06:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6561, top5_acc: 0.8623, loss_cls: 1.9606, loss: 1.9606 +2024-07-21 18:06:52,691 - pyskl - INFO - Epoch [147][1500/3746] lr: 1.420e-04, eta: 3:05:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6656, top5_acc: 0.8711, loss_cls: 1.9359, loss: 1.9359 +2024-07-21 18:08:14,213 - pyskl - INFO - Epoch [147][1600/3746] lr: 1.399e-04, eta: 3:03:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6608, top5_acc: 0.8672, loss_cls: 1.9257, loss: 1.9257 +2024-07-21 18:09:35,863 - pyskl - INFO - Epoch [147][1700/3746] lr: 1.379e-04, eta: 3:02:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6445, top5_acc: 0.8561, loss_cls: 2.0178, loss: 2.0178 +2024-07-21 18:10:57,864 - pyskl - INFO - Epoch [147][1800/3746] lr: 1.358e-04, eta: 3:01:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6552, top5_acc: 0.8564, loss_cls: 1.9759, loss: 1.9759 +2024-07-21 18:12:19,875 - pyskl - INFO - Epoch [147][1900/3746] lr: 1.337e-04, eta: 2:59:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6603, top5_acc: 0.8608, loss_cls: 1.9691, loss: 1.9691 +2024-07-21 18:13:41,313 - pyskl - INFO - Epoch [147][2000/3746] lr: 1.317e-04, eta: 2:58:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6558, top5_acc: 0.8667, loss_cls: 1.9574, loss: 1.9574 +2024-07-21 18:15:02,515 - pyskl - INFO - Epoch [147][2100/3746] lr: 1.297e-04, eta: 2:56:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6547, top5_acc: 0.8581, loss_cls: 1.9816, loss: 1.9816 +2024-07-21 18:16:24,079 - pyskl - INFO - Epoch [147][2200/3746] lr: 1.277e-04, eta: 2:55:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6498, top5_acc: 0.8653, loss_cls: 1.9779, loss: 1.9779 +2024-07-21 18:17:45,187 - pyskl - INFO - Epoch [147][2300/3746] lr: 1.257e-04, eta: 2:54:15, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6531, top5_acc: 0.8641, loss_cls: 1.9691, loss: 1.9691 +2024-07-21 18:19:06,995 - pyskl - INFO - Epoch [147][2400/3746] lr: 1.237e-04, eta: 2:52:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6503, top5_acc: 0.8597, loss_cls: 1.9794, loss: 1.9794 +2024-07-21 18:20:28,417 - pyskl - INFO - Epoch [147][2500/3746] lr: 1.218e-04, eta: 2:51:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6505, top5_acc: 0.8633, loss_cls: 1.9748, loss: 1.9748 +2024-07-21 18:21:49,819 - pyskl - INFO - Epoch [147][2600/3746] lr: 1.198e-04, eta: 2:50:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6573, top5_acc: 0.8689, loss_cls: 1.9338, loss: 1.9338 +2024-07-21 18:23:11,822 - pyskl - INFO - Epoch [147][2700/3746] lr: 1.179e-04, eta: 2:48:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6483, top5_acc: 0.8605, loss_cls: 1.9880, loss: 1.9880 +2024-07-21 18:24:32,925 - pyskl - INFO - Epoch [147][2800/3746] lr: 1.160e-04, eta: 2:47:22, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6616, top5_acc: 0.8622, loss_cls: 1.9529, loss: 1.9529 +2024-07-21 18:25:54,513 - pyskl - INFO - Epoch [147][2900/3746] lr: 1.141e-04, eta: 2:46:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6613, top5_acc: 0.8666, loss_cls: 1.9274, loss: 1.9274 +2024-07-21 18:27:16,261 - pyskl - INFO - Epoch [147][3000/3746] lr: 1.122e-04, eta: 2:44:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6656, top5_acc: 0.8669, loss_cls: 1.9275, loss: 1.9275 +2024-07-21 18:28:37,900 - pyskl - INFO - Epoch [147][3100/3746] lr: 1.103e-04, eta: 2:43:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6533, top5_acc: 0.8648, loss_cls: 1.9572, loss: 1.9572 +2024-07-21 18:29:59,309 - pyskl - INFO - Epoch [147][3200/3746] lr: 1.085e-04, eta: 2:41:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6536, top5_acc: 0.8636, loss_cls: 1.9661, loss: 1.9661 +2024-07-21 18:31:21,679 - pyskl - INFO - Epoch [147][3300/3746] lr: 1.067e-04, eta: 2:40:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6455, top5_acc: 0.8592, loss_cls: 1.9874, loss: 1.9874 +2024-07-21 18:32:43,148 - pyskl - INFO - Epoch [147][3400/3746] lr: 1.048e-04, eta: 2:39:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6545, top5_acc: 0.8611, loss_cls: 1.9685, loss: 1.9685 +2024-07-21 18:34:05,244 - pyskl - INFO - Epoch [147][3500/3746] lr: 1.030e-04, eta: 2:37:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6564, top5_acc: 0.8664, loss_cls: 1.9576, loss: 1.9576 +2024-07-21 18:35:26,798 - pyskl - INFO - Epoch [147][3600/3746] lr: 1.013e-04, eta: 2:36:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6555, top5_acc: 0.8628, loss_cls: 1.9639, loss: 1.9639 +2024-07-21 18:36:49,180 - pyskl - INFO - Epoch [147][3700/3746] lr: 9.949e-05, eta: 2:35:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6567, top5_acc: 0.8666, loss_cls: 1.9555, loss: 1.9555 +2024-07-21 18:37:29,127 - pyskl - INFO - Saving checkpoint at 147 epochs +2024-07-21 18:39:20,734 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 18:39:21,397 - pyskl - INFO - +top1_acc 0.4586 +top5_acc 0.7025 +2024-07-21 18:39:21,397 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 18:39:21,436 - pyskl - INFO - +mean_acc 0.4584 +2024-07-21 18:39:21,441 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_146.pth was removed +2024-07-21 18:39:21,701 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_147.pth. +2024-07-21 18:39:21,702 - pyskl - INFO - Best top1_acc is 0.4586 at 147 epoch. +2024-07-21 18:39:21,713 - pyskl - INFO - Epoch(val) [147][309] top1_acc: 0.4586, top5_acc: 0.7025, mean_class_accuracy: 0.4584 +2024-07-21 18:43:11,721 - pyskl - INFO - Epoch [148][100/3746] lr: 9.693e-05, eta: 2:33:02, time: 2.300, data_time: 1.316, memory: 15990, top1_acc: 0.6705, top5_acc: 0.8756, loss_cls: 1.8913, loss: 1.8913 +2024-07-21 18:44:34,297 - pyskl - INFO - Epoch [148][200/3746] lr: 9.520e-05, eta: 2:31:40, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6562, top5_acc: 0.8611, loss_cls: 1.9594, loss: 1.9594 +2024-07-21 18:45:56,946 - pyskl - INFO - Epoch [148][300/3746] lr: 9.348e-05, eta: 2:30:17, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6717, top5_acc: 0.8694, loss_cls: 1.9133, loss: 1.9133 +2024-07-21 18:47:19,912 - pyskl - INFO - Epoch [148][400/3746] lr: 9.178e-05, eta: 2:28:55, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.6700, top5_acc: 0.8762, loss_cls: 1.8858, loss: 1.8858 +2024-07-21 18:48:42,479 - pyskl - INFO - Epoch [148][500/3746] lr: 9.010e-05, eta: 2:27:32, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6586, top5_acc: 0.8683, loss_cls: 1.9240, loss: 1.9240 +2024-07-21 18:50:04,649 - pyskl - INFO - Epoch [148][600/3746] lr: 8.843e-05, eta: 2:26:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6700, top5_acc: 0.8727, loss_cls: 1.8968, loss: 1.8968 +2024-07-21 18:51:26,196 - pyskl - INFO - Epoch [148][700/3746] lr: 8.678e-05, eta: 2:24:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6608, top5_acc: 0.8659, loss_cls: 1.9339, loss: 1.9339 +2024-07-21 18:52:48,094 - pyskl - INFO - Epoch [148][800/3746] lr: 8.514e-05, eta: 2:23:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6705, top5_acc: 0.8706, loss_cls: 1.9054, loss: 1.9054 +2024-07-21 18:54:10,680 - pyskl - INFO - Epoch [148][900/3746] lr: 8.351e-05, eta: 2:22:03, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6706, top5_acc: 0.8777, loss_cls: 1.8782, loss: 1.8782 +2024-07-21 18:55:32,381 - pyskl - INFO - Epoch [148][1000/3746] lr: 8.191e-05, eta: 2:20:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6652, top5_acc: 0.8691, loss_cls: 1.9159, loss: 1.9159 +2024-07-21 18:56:54,466 - pyskl - INFO - Epoch [148][1100/3746] lr: 8.031e-05, eta: 2:19:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6598, top5_acc: 0.8686, loss_cls: 1.9415, loss: 1.9415 +2024-07-21 18:58:16,264 - pyskl - INFO - Epoch [148][1200/3746] lr: 7.874e-05, eta: 2:17:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6583, top5_acc: 0.8667, loss_cls: 1.9366, loss: 1.9366 +2024-07-21 18:59:37,677 - pyskl - INFO - Epoch [148][1300/3746] lr: 7.718e-05, eta: 2:16:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6641, top5_acc: 0.8627, loss_cls: 1.9423, loss: 1.9423 +2024-07-21 19:00:59,667 - pyskl - INFO - Epoch [148][1400/3746] lr: 7.563e-05, eta: 2:15:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6592, top5_acc: 0.8647, loss_cls: 1.9467, loss: 1.9467 +2024-07-21 19:02:21,165 - pyskl - INFO - Epoch [148][1500/3746] lr: 7.410e-05, eta: 2:13:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6683, top5_acc: 0.8694, loss_cls: 1.9055, loss: 1.9055 +2024-07-21 19:03:42,944 - pyskl - INFO - Epoch [148][1600/3746] lr: 7.259e-05, eta: 2:12:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6613, top5_acc: 0.8606, loss_cls: 1.9501, loss: 1.9501 +2024-07-21 19:05:04,883 - pyskl - INFO - Epoch [148][1700/3746] lr: 7.109e-05, eta: 2:11:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6616, top5_acc: 0.8672, loss_cls: 1.9272, loss: 1.9272 +2024-07-21 19:06:26,691 - pyskl - INFO - Epoch [148][1800/3746] lr: 6.961e-05, eta: 2:09:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6664, top5_acc: 0.8727, loss_cls: 1.9137, loss: 1.9137 +2024-07-21 19:07:48,498 - pyskl - INFO - Epoch [148][1900/3746] lr: 6.814e-05, eta: 2:08:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6647, top5_acc: 0.8678, loss_cls: 1.9374, loss: 1.9374 +2024-07-21 19:09:10,508 - pyskl - INFO - Epoch [148][2000/3746] lr: 6.669e-05, eta: 2:06:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6577, top5_acc: 0.8681, loss_cls: 1.9351, loss: 1.9351 +2024-07-21 19:10:32,425 - pyskl - INFO - Epoch [148][2100/3746] lr: 6.526e-05, eta: 2:05:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6589, top5_acc: 0.8678, loss_cls: 1.9142, loss: 1.9142 +2024-07-21 19:11:54,595 - pyskl - INFO - Epoch [148][2200/3746] lr: 6.384e-05, eta: 2:04:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6661, top5_acc: 0.8667, loss_cls: 1.9267, loss: 1.9267 +2024-07-21 19:13:16,080 - pyskl - INFO - Epoch [148][2300/3746] lr: 6.243e-05, eta: 2:02:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6627, top5_acc: 0.8677, loss_cls: 1.9125, loss: 1.9125 +2024-07-21 19:14:37,485 - pyskl - INFO - Epoch [148][2400/3746] lr: 6.104e-05, eta: 2:01:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6697, top5_acc: 0.8759, loss_cls: 1.8987, loss: 1.8987 +2024-07-21 19:15:58,814 - pyskl - INFO - Epoch [148][2500/3746] lr: 5.967e-05, eta: 2:00:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6678, top5_acc: 0.8692, loss_cls: 1.9196, loss: 1.9196 +2024-07-21 19:17:20,483 - pyskl - INFO - Epoch [148][2600/3746] lr: 5.831e-05, eta: 1:58:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6597, top5_acc: 0.8669, loss_cls: 1.9163, loss: 1.9163 +2024-07-21 19:18:42,246 - pyskl - INFO - Epoch [148][2700/3746] lr: 5.697e-05, eta: 1:57:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6633, top5_acc: 0.8706, loss_cls: 1.9185, loss: 1.9185 +2024-07-21 19:20:04,096 - pyskl - INFO - Epoch [148][2800/3746] lr: 5.564e-05, eta: 1:55:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6545, top5_acc: 0.8627, loss_cls: 1.9602, loss: 1.9602 +2024-07-21 19:21:25,584 - pyskl - INFO - Epoch [148][2900/3746] lr: 5.433e-05, eta: 1:54:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6719, top5_acc: 0.8620, loss_cls: 1.9161, loss: 1.9161 +2024-07-21 19:22:47,601 - pyskl - INFO - Epoch [148][3000/3746] lr: 5.304e-05, eta: 1:53:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6572, top5_acc: 0.8608, loss_cls: 1.9618, loss: 1.9618 +2024-07-21 19:24:09,215 - pyskl - INFO - Epoch [148][3100/3746] lr: 5.176e-05, eta: 1:51:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6641, top5_acc: 0.8739, loss_cls: 1.8970, loss: 1.8970 +2024-07-21 19:25:31,342 - pyskl - INFO - Epoch [148][3200/3746] lr: 5.050e-05, eta: 1:50:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6578, top5_acc: 0.8622, loss_cls: 1.9477, loss: 1.9477 +2024-07-21 19:26:53,725 - pyskl - INFO - Epoch [148][3300/3746] lr: 4.925e-05, eta: 1:49:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6606, top5_acc: 0.8681, loss_cls: 1.9257, loss: 1.9257 +2024-07-21 19:28:15,320 - pyskl - INFO - Epoch [148][3400/3746] lr: 4.801e-05, eta: 1:47:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6552, top5_acc: 0.8575, loss_cls: 1.9819, loss: 1.9819 +2024-07-21 19:29:37,444 - pyskl - INFO - Epoch [148][3500/3746] lr: 4.680e-05, eta: 1:46:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6522, top5_acc: 0.8672, loss_cls: 1.9500, loss: 1.9500 +2024-07-21 19:30:59,344 - pyskl - INFO - Epoch [148][3600/3746] lr: 4.560e-05, eta: 1:44:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6564, top5_acc: 0.8723, loss_cls: 1.9146, loss: 1.9146 +2024-07-21 19:32:21,428 - pyskl - INFO - Epoch [148][3700/3746] lr: 4.441e-05, eta: 1:43:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6648, top5_acc: 0.8692, loss_cls: 1.9260, loss: 1.9260 +2024-07-21 19:33:01,119 - pyskl - INFO - Saving checkpoint at 148 epochs +2024-07-21 19:34:53,199 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 19:34:53,855 - pyskl - INFO - +top1_acc 0.4584 +top5_acc 0.7039 +2024-07-21 19:34:53,855 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 19:34:53,894 - pyskl - INFO - +mean_acc 0.4582 +2024-07-21 19:34:53,906 - pyskl - INFO - Epoch(val) [148][309] top1_acc: 0.4584, top5_acc: 0.7039, mean_class_accuracy: 0.4582 +2024-07-21 19:38:39,611 - pyskl - INFO - Epoch [149][100/3746] lr: 4.271e-05, eta: 1:41:35, time: 2.257, data_time: 1.276, memory: 15990, top1_acc: 0.6531, top5_acc: 0.8692, loss_cls: 1.9267, loss: 1.9267 +2024-07-21 19:40:01,832 - pyskl - INFO - Epoch [149][200/3746] lr: 4.156e-05, eta: 1:40:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6737, top5_acc: 0.8716, loss_cls: 1.8914, loss: 1.8914 +2024-07-21 19:41:23,906 - pyskl - INFO - Epoch [149][300/3746] lr: 4.043e-05, eta: 1:38:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6655, top5_acc: 0.8756, loss_cls: 1.8971, loss: 1.8971 +2024-07-21 19:42:46,789 - pyskl - INFO - Epoch [149][400/3746] lr: 3.931e-05, eta: 1:37:28, time: 0.829, data_time: 0.001, memory: 15990, top1_acc: 0.6569, top5_acc: 0.8612, loss_cls: 1.9407, loss: 1.9407 +2024-07-21 19:44:08,721 - pyskl - INFO - Epoch [149][500/3746] lr: 3.821e-05, eta: 1:36:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6680, top5_acc: 0.8711, loss_cls: 1.8831, loss: 1.8831 +2024-07-21 19:45:30,726 - pyskl - INFO - Epoch [149][600/3746] lr: 3.713e-05, eta: 1:34:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6764, top5_acc: 0.8773, loss_cls: 1.8592, loss: 1.8592 +2024-07-21 19:46:52,847 - pyskl - INFO - Epoch [149][700/3746] lr: 3.606e-05, eta: 1:33:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6692, top5_acc: 0.8772, loss_cls: 1.8753, loss: 1.8753 +2024-07-21 19:48:14,622 - pyskl - INFO - Epoch [149][800/3746] lr: 3.500e-05, eta: 1:31:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6592, top5_acc: 0.8647, loss_cls: 1.9478, loss: 1.9478 +2024-07-21 19:49:36,524 - pyskl - INFO - Epoch [149][900/3746] lr: 3.397e-05, eta: 1:30:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6691, top5_acc: 0.8738, loss_cls: 1.8731, loss: 1.8731 +2024-07-21 19:50:58,773 - pyskl - INFO - Epoch [149][1000/3746] lr: 3.294e-05, eta: 1:29:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6611, top5_acc: 0.8709, loss_cls: 1.9237, loss: 1.9237 +2024-07-21 19:52:20,421 - pyskl - INFO - Epoch [149][1100/3746] lr: 3.194e-05, eta: 1:27:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6755, top5_acc: 0.8762, loss_cls: 1.8792, loss: 1.8792 +2024-07-21 19:53:42,771 - pyskl - INFO - Epoch [149][1200/3746] lr: 3.095e-05, eta: 1:26:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6655, top5_acc: 0.8686, loss_cls: 1.9312, loss: 1.9312 +2024-07-21 19:55:04,418 - pyskl - INFO - Epoch [149][1300/3746] lr: 2.997e-05, eta: 1:25:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6594, top5_acc: 0.8647, loss_cls: 1.9212, loss: 1.9212 +2024-07-21 19:56:26,775 - pyskl - INFO - Epoch [149][1400/3746] lr: 2.901e-05, eta: 1:23:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6609, top5_acc: 0.8672, loss_cls: 1.9232, loss: 1.9232 +2024-07-21 19:57:48,821 - pyskl - INFO - Epoch [149][1500/3746] lr: 2.807e-05, eta: 1:22:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6617, top5_acc: 0.8648, loss_cls: 1.9281, loss: 1.9281 +2024-07-21 19:59:10,269 - pyskl - INFO - Epoch [149][1600/3746] lr: 2.714e-05, eta: 1:20:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6637, top5_acc: 0.8697, loss_cls: 1.9100, loss: 1.9100 +2024-07-21 20:00:32,034 - pyskl - INFO - Epoch [149][1700/3746] lr: 2.622e-05, eta: 1:19:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6633, top5_acc: 0.8672, loss_cls: 1.9082, loss: 1.9082 +2024-07-21 20:01:54,072 - pyskl - INFO - Epoch [149][1800/3746] lr: 2.533e-05, eta: 1:18:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6677, top5_acc: 0.8708, loss_cls: 1.8837, loss: 1.8837 +2024-07-21 20:03:16,114 - pyskl - INFO - Epoch [149][1900/3746] lr: 2.444e-05, eta: 1:16:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6658, top5_acc: 0.8789, loss_cls: 1.8800, loss: 1.8800 +2024-07-21 20:04:37,968 - pyskl - INFO - Epoch [149][2000/3746] lr: 2.358e-05, eta: 1:15:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6584, top5_acc: 0.8614, loss_cls: 1.9537, loss: 1.9537 +2024-07-21 20:05:59,972 - pyskl - INFO - Epoch [149][2100/3746] lr: 2.273e-05, eta: 1:14:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6658, top5_acc: 0.8659, loss_cls: 1.9326, loss: 1.9326 +2024-07-21 20:07:22,085 - pyskl - INFO - Epoch [149][2200/3746] lr: 2.189e-05, eta: 1:12:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6631, top5_acc: 0.8708, loss_cls: 1.9106, loss: 1.9106 +2024-07-21 20:08:43,794 - pyskl - INFO - Epoch [149][2300/3746] lr: 2.107e-05, eta: 1:11:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6616, top5_acc: 0.8664, loss_cls: 1.9324, loss: 1.9324 +2024-07-21 20:10:05,676 - pyskl - INFO - Epoch [149][2400/3746] lr: 2.027e-05, eta: 1:09:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6609, top5_acc: 0.8641, loss_cls: 1.9361, loss: 1.9361 +2024-07-21 20:11:27,641 - pyskl - INFO - Epoch [149][2500/3746] lr: 1.948e-05, eta: 1:08:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6737, top5_acc: 0.8723, loss_cls: 1.8745, loss: 1.8745 +2024-07-21 20:12:49,411 - pyskl - INFO - Epoch [149][2600/3746] lr: 1.871e-05, eta: 1:07:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6523, top5_acc: 0.8598, loss_cls: 1.9455, loss: 1.9455 +2024-07-21 20:14:11,406 - pyskl - INFO - Epoch [149][2700/3746] lr: 1.795e-05, eta: 1:05:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6675, top5_acc: 0.8706, loss_cls: 1.9113, loss: 1.9113 +2024-07-21 20:15:33,119 - pyskl - INFO - Epoch [149][2800/3746] lr: 1.721e-05, eta: 1:04:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6633, top5_acc: 0.8694, loss_cls: 1.9158, loss: 1.9158 +2024-07-21 20:16:54,516 - pyskl - INFO - Epoch [149][2900/3746] lr: 1.649e-05, eta: 1:03:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6739, top5_acc: 0.8673, loss_cls: 1.8873, loss: 1.8873 +2024-07-21 20:18:16,984 - pyskl - INFO - Epoch [149][3000/3746] lr: 1.578e-05, eta: 1:01:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6591, top5_acc: 0.8680, loss_cls: 1.9391, loss: 1.9391 +2024-07-21 20:19:38,569 - pyskl - INFO - Epoch [149][3100/3746] lr: 1.508e-05, eta: 1:00:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6711, top5_acc: 0.8734, loss_cls: 1.9044, loss: 1.9044 +2024-07-21 20:21:01,077 - pyskl - INFO - Epoch [149][3200/3746] lr: 1.440e-05, eta: 0:58:59, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6652, top5_acc: 0.8712, loss_cls: 1.9151, loss: 1.9151 +2024-07-21 20:22:22,701 - pyskl - INFO - Epoch [149][3300/3746] lr: 1.374e-05, eta: 0:57:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6683, top5_acc: 0.8684, loss_cls: 1.9192, loss: 1.9192 +2024-07-21 20:23:44,945 - pyskl - INFO - Epoch [149][3400/3746] lr: 1.309e-05, eta: 0:56:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6730, top5_acc: 0.8719, loss_cls: 1.8898, loss: 1.8898 +2024-07-21 20:25:07,275 - pyskl - INFO - Epoch [149][3500/3746] lr: 1.246e-05, eta: 0:54:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6670, top5_acc: 0.8683, loss_cls: 1.9203, loss: 1.9203 +2024-07-21 20:26:29,844 - pyskl - INFO - Epoch [149][3600/3746] lr: 1.184e-05, eta: 0:53:29, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6713, top5_acc: 0.8762, loss_cls: 1.8584, loss: 1.8584 +2024-07-21 20:27:52,042 - pyskl - INFO - Epoch [149][3700/3746] lr: 1.124e-05, eta: 0:52:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6620, top5_acc: 0.8681, loss_cls: 1.9165, loss: 1.9165 +2024-07-21 20:28:31,720 - pyskl - INFO - Saving checkpoint at 149 epochs +2024-07-21 20:30:23,732 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 20:30:24,389 - pyskl - INFO - +top1_acc 0.4593 +top5_acc 0.7044 +2024-07-21 20:30:24,389 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 20:30:24,430 - pyskl - INFO - +mean_acc 0.4590 +2024-07-21 20:30:24,436 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_147.pth was removed +2024-07-21 20:30:24,688 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_149.pth. +2024-07-21 20:30:24,688 - pyskl - INFO - Best top1_acc is 0.4593 at 149 epoch. +2024-07-21 20:30:24,699 - pyskl - INFO - Epoch(val) [149][309] top1_acc: 0.4593, top5_acc: 0.7044, mean_class_accuracy: 0.4590 +2024-07-21 20:34:12,018 - pyskl - INFO - Epoch [150][100/3746] lr: 1.039e-05, eta: 0:50:07, time: 2.273, data_time: 1.276, memory: 15990, top1_acc: 0.6769, top5_acc: 0.8758, loss_cls: 1.8827, loss: 1.8827 +2024-07-21 20:35:34,768 - pyskl - INFO - Epoch [150][200/3746] lr: 9.832e-06, eta: 0:48:44, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6766, top5_acc: 0.8747, loss_cls: 1.8611, loss: 1.8611 +2024-07-21 20:36:56,298 - pyskl - INFO - Epoch [150][300/3746] lr: 9.285e-06, eta: 0:47:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6753, top5_acc: 0.8750, loss_cls: 1.8899, loss: 1.8899 +2024-07-21 20:38:18,822 - pyskl - INFO - Epoch [150][400/3746] lr: 8.754e-06, eta: 0:45:59, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6783, top5_acc: 0.8683, loss_cls: 1.8767, loss: 1.8767 +2024-07-21 20:39:40,875 - pyskl - INFO - Epoch [150][500/3746] lr: 8.239e-06, eta: 0:44:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6722, top5_acc: 0.8784, loss_cls: 1.8728, loss: 1.8728 +2024-07-21 20:41:02,859 - pyskl - INFO - Epoch [150][600/3746] lr: 7.739e-06, eta: 0:43:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6631, top5_acc: 0.8709, loss_cls: 1.9184, loss: 1.9184 +2024-07-21 20:42:24,656 - pyskl - INFO - Epoch [150][700/3746] lr: 7.255e-06, eta: 0:41:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6681, top5_acc: 0.8709, loss_cls: 1.8837, loss: 1.8837 +2024-07-21 20:43:47,005 - pyskl - INFO - Epoch [150][800/3746] lr: 6.787e-06, eta: 0:40:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6678, top5_acc: 0.8714, loss_cls: 1.9000, loss: 1.9000 +2024-07-21 20:45:08,411 - pyskl - INFO - Epoch [150][900/3746] lr: 6.334e-06, eta: 0:39:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6637, top5_acc: 0.8673, loss_cls: 1.9132, loss: 1.9132 +2024-07-21 20:46:30,067 - pyskl - INFO - Epoch [150][1000/3746] lr: 5.897e-06, eta: 0:37:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6600, top5_acc: 0.8719, loss_cls: 1.9170, loss: 1.9170 +2024-07-21 20:47:52,063 - pyskl - INFO - Epoch [150][1100/3746] lr: 5.475e-06, eta: 0:36:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6628, top5_acc: 0.8717, loss_cls: 1.9202, loss: 1.9202 +2024-07-21 20:49:13,796 - pyskl - INFO - Epoch [150][1200/3746] lr: 5.070e-06, eta: 0:34:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6737, top5_acc: 0.8733, loss_cls: 1.8817, loss: 1.8817 +2024-07-21 20:50:35,447 - pyskl - INFO - Epoch [150][1300/3746] lr: 4.679e-06, eta: 0:33:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6677, top5_acc: 0.8658, loss_cls: 1.9280, loss: 1.9280 +2024-07-21 20:51:57,038 - pyskl - INFO - Epoch [150][1400/3746] lr: 4.305e-06, eta: 0:32:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6661, top5_acc: 0.8750, loss_cls: 1.8814, loss: 1.8814 +2024-07-21 20:53:19,099 - pyskl - INFO - Epoch [150][1500/3746] lr: 3.946e-06, eta: 0:30:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6700, top5_acc: 0.8700, loss_cls: 1.9119, loss: 1.9119 +2024-07-21 20:54:40,770 - pyskl - INFO - Epoch [150][1600/3746] lr: 3.602e-06, eta: 0:29:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6805, top5_acc: 0.8802, loss_cls: 1.8290, loss: 1.8290 +2024-07-21 20:56:02,446 - pyskl - INFO - Epoch [150][1700/3746] lr: 3.275e-06, eta: 0:28:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6672, top5_acc: 0.8714, loss_cls: 1.8974, loss: 1.8974 +2024-07-21 20:57:24,496 - pyskl - INFO - Epoch [150][1800/3746] lr: 2.962e-06, eta: 0:26:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6702, top5_acc: 0.8706, loss_cls: 1.8849, loss: 1.8849 +2024-07-21 20:58:46,428 - pyskl - INFO - Epoch [150][1900/3746] lr: 2.666e-06, eta: 0:25:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6661, top5_acc: 0.8709, loss_cls: 1.8987, loss: 1.8987 +2024-07-21 21:00:08,182 - pyskl - INFO - Epoch [150][2000/3746] lr: 2.385e-06, eta: 0:24:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6559, top5_acc: 0.8712, loss_cls: 1.9323, loss: 1.9323 +2024-07-21 21:01:30,119 - pyskl - INFO - Epoch [150][2100/3746] lr: 2.120e-06, eta: 0:22:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6709, top5_acc: 0.8758, loss_cls: 1.8811, loss: 1.8811 +2024-07-21 21:02:51,792 - pyskl - INFO - Epoch [150][2200/3746] lr: 1.870e-06, eta: 0:21:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6680, top5_acc: 0.8639, loss_cls: 1.9260, loss: 1.9260 +2024-07-21 21:04:13,227 - pyskl - INFO - Epoch [150][2300/3746] lr: 1.636e-06, eta: 0:19:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6713, top5_acc: 0.8758, loss_cls: 1.8736, loss: 1.8736 +2024-07-21 21:05:35,194 - pyskl - INFO - Epoch [150][2400/3746] lr: 1.418e-06, eta: 0:18:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6617, top5_acc: 0.8672, loss_cls: 1.9185, loss: 1.9185 +2024-07-21 21:06:57,112 - pyskl - INFO - Epoch [150][2500/3746] lr: 1.215e-06, eta: 0:17:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6681, top5_acc: 0.8716, loss_cls: 1.9092, loss: 1.9092 +2024-07-21 21:08:18,652 - pyskl - INFO - Epoch [150][2600/3746] lr: 1.028e-06, eta: 0:15:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6645, top5_acc: 0.8675, loss_cls: 1.9425, loss: 1.9425 +2024-07-21 21:09:40,256 - pyskl - INFO - Epoch [150][2700/3746] lr: 8.567e-07, eta: 0:14:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6639, top5_acc: 0.8703, loss_cls: 1.9146, loss: 1.9146 +2024-07-21 21:11:01,957 - pyskl - INFO - Epoch [150][2800/3746] lr: 7.008e-07, eta: 0:13:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6736, top5_acc: 0.8652, loss_cls: 1.9039, loss: 1.9039 +2024-07-21 21:12:23,365 - pyskl - INFO - Epoch [150][2900/3746] lr: 5.606e-07, eta: 0:11:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6637, top5_acc: 0.8620, loss_cls: 1.9373, loss: 1.9373 +2024-07-21 21:13:44,735 - pyskl - INFO - Epoch [150][3000/3746] lr: 4.361e-07, eta: 0:10:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6684, top5_acc: 0.8689, loss_cls: 1.9105, loss: 1.9105 +2024-07-21 21:15:06,370 - pyskl - INFO - Epoch [150][3100/3746] lr: 3.271e-07, eta: 0:08:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6666, top5_acc: 0.8645, loss_cls: 1.9249, loss: 1.9249 +2024-07-21 21:16:28,896 - pyskl - INFO - Epoch [150][3200/3746] lr: 2.338e-07, eta: 0:07:30, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6661, top5_acc: 0.8739, loss_cls: 1.8983, loss: 1.8983 +2024-07-21 21:17:51,135 - pyskl - INFO - Epoch [150][3300/3746] lr: 1.561e-07, eta: 0:06:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6637, top5_acc: 0.8753, loss_cls: 1.8977, loss: 1.8977 +2024-07-21 21:19:12,839 - pyskl - INFO - Epoch [150][3400/3746] lr: 9.410e-08, eta: 0:04:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6713, top5_acc: 0.8688, loss_cls: 1.9002, loss: 1.9002 +2024-07-21 21:20:35,166 - pyskl - INFO - Epoch [150][3500/3746] lr: 4.768e-08, eta: 0:03:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6758, top5_acc: 0.8738, loss_cls: 1.8598, loss: 1.8598 +2024-07-21 21:21:58,346 - pyskl - INFO - Epoch [150][3600/3746] lr: 1.689e-08, eta: 0:02:00, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.6727, top5_acc: 0.8797, loss_cls: 1.8510, loss: 1.8510 +2024-07-21 21:23:20,066 - pyskl - INFO - Epoch [150][3700/3746] lr: 1.726e-09, eta: 0:00:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6717, top5_acc: 0.8727, loss_cls: 1.8571, loss: 1.8571 +2024-07-21 21:23:59,787 - pyskl - INFO - Saving checkpoint at 150 epochs +2024-07-21 21:25:50,541 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 21:25:51,204 - pyskl - INFO - +top1_acc 0.4571 +top5_acc 0.7034 +2024-07-21 21:25:51,204 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 21:25:51,245 - pyskl - INFO - +mean_acc 0.4568 +2024-07-21 21:25:51,257 - pyskl - INFO - Epoch(val) [150][309] top1_acc: 0.4571, top5_acc: 0.7034, mean_class_accuracy: 0.4568 +2024-07-21 21:26:05,466 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-07-21 21:38:11,609 - pyskl - INFO - Testing results of the last checkpoint +2024-07-21 21:38:11,610 - pyskl - INFO - top1_acc: 0.4662 +2024-07-21 21:38:11,610 - pyskl - INFO - top5_acc: 0.7138 +2024-07-21 21:38:11,610 - pyskl - INFO - mean_class_accuracy: 0.4659 +2024-07-21 21:38:11,610 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/k400/b_3/best_top1_acc_epoch_149.pth +2024-07-21 21:50:17,024 - pyskl - INFO - Testing results of the best checkpoint +2024-07-21 21:50:17,024 - pyskl - INFO - top1_acc: 0.4683 +2024-07-21 21:50:17,024 - pyskl - INFO - top5_acc: 0.7139 +2024-07-21 21:50:17,024 - pyskl - INFO - mean_class_accuracy: 0.4680 diff --git a/k400/b_3/20240716_062611.log.json b/k400/b_3/20240716_062611.log.json new file mode 100644 index 0000000000000000000000000000000000000000..39c43216239b9da85616526d7bd85ea2e28381dc --- /dev/null +++ b/k400/b_3/20240716_062611.log.json @@ -0,0 +1,5701 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 1307045849, "config_name": "b_3.py", "work_dir": "b_3", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.1, "memory": 15990, "data_time": 1.12387, "top1_acc": 0.00703, "top5_acc": 0.03234, "loss_cls": 6.40719, "loss": 6.40719, "time": 1.82046} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.1, "memory": 15990, "data_time": 0.00019, "top1_acc": 0.01281, "top5_acc": 0.05656, "loss_cls": 6.30713, "loss": 6.30713, "time": 0.69138} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.1, "memory": 15990, "data_time": 0.00019, "top1_acc": 0.02203, "top5_acc": 0.08172, "loss_cls": 6.08944, "loss": 6.08944, "time": 0.69132} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.1, "memory": 15990, "data_time": 0.00018, "top1_acc": 0.02359, "top5_acc": 0.08625, "loss_cls": 6.02442, "loss": 6.02442, "time": 0.69084} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.1, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.02641, "top5_acc": 0.09938, "loss_cls": 5.91945, "loss": 5.91945, "time": 0.69107} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.1, "memory": 15990, "data_time": 0.00017, "top1_acc": 0.03078, "top5_acc": 0.11422, "loss_cls": 5.82412, "loss": 5.82412, "time": 0.691} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.1, "memory": 15990, "data_time": 0.00017, "top1_acc": 0.03594, "top5_acc": 0.12562, "loss_cls": 5.79145, "loss": 5.79145, "time": 0.69089} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.1, "memory": 15990, "data_time": 0.00016, "top1_acc": 0.03516, "top5_acc": 0.13234, "loss_cls": 5.76515, "loss": 5.76515, "time": 0.6893} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.1, "memory": 15990, "data_time": 0.00016, "top1_acc": 0.04422, "top5_acc": 0.15203, "loss_cls": 5.68451, "loss": 5.68451, "time": 0.68906} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.1, "memory": 15990, "data_time": 0.00016, "top1_acc": 0.04391, "top5_acc": 0.15672, "loss_cls": 5.64462, "loss": 5.64462, "time": 0.68954} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.1, "memory": 15990, "data_time": 0.00016, "top1_acc": 0.04781, "top5_acc": 0.16484, "loss_cls": 5.61788, "loss": 5.61788, "time": 0.6902} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.1, "memory": 15990, "data_time": 0.00016, "top1_acc": 0.04562, "top5_acc": 0.15516, "loss_cls": 5.60829, "loss": 5.60829, "time": 0.69003} +{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.1, "memory": 15990, "data_time": 0.00016, "top1_acc": 0.0525, "top5_acc": 0.16906, "loss_cls": 5.5607, "loss": 5.5607, "time": 0.68989} +{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.1, "memory": 15990, "data_time": 0.00016, "top1_acc": 0.05344, "top5_acc": 0.17547, "loss_cls": 5.52252, "loss": 5.52252, "time": 0.6903} +{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.1, "memory": 15990, "data_time": 0.00016, "top1_acc": 0.06078, "top5_acc": 0.18859, "loss_cls": 5.49533, "loss": 5.49533, "time": 0.68948} +{"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.1, "memory": 15990, "data_time": 0.00018, "top1_acc": 0.06312, "top5_acc": 0.19422, "loss_cls": 5.48203, "loss": 5.48203, "time": 0.6895} +{"mode": "train", "epoch": 1, "iter": 1700, "lr": 0.1, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.06578, "top5_acc": 0.19297, "loss_cls": 5.44844, "loss": 5.44844, "time": 0.70162} +{"mode": "train", "epoch": 1, "iter": 1800, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.06562, "top5_acc": 0.19688, "loss_cls": 5.45863, "loss": 5.45863, "time": 0.70774} +{"mode": "train", "epoch": 1, "iter": 1900, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.07062, "top5_acc": 0.21062, "loss_cls": 5.41007, "loss": 5.41007, "time": 0.70718} +{"mode": "train", "epoch": 1, "iter": 2000, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.06625, "top5_acc": 0.20484, "loss_cls": 5.39228, "loss": 5.39228, "time": 0.70409} +{"mode": "train", "epoch": 1, "iter": 2100, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.06797, "top5_acc": 0.21453, "loss_cls": 5.37929, "loss": 5.37929, "time": 0.70146} +{"mode": "train", "epoch": 1, "iter": 2200, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.07625, "top5_acc": 0.22438, "loss_cls": 5.33688, "loss": 5.33688, "time": 0.70245} +{"mode": "train", "epoch": 1, "iter": 2300, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.07328, "top5_acc": 0.21953, "loss_cls": 5.36254, "loss": 5.36254, "time": 0.70708} +{"mode": "train", "epoch": 1, "iter": 2400, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.08328, "top5_acc": 0.24406, "loss_cls": 5.26741, "loss": 5.26741, "time": 0.70487} +{"mode": "train", "epoch": 1, "iter": 2500, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.08297, "top5_acc": 0.24359, "loss_cls": 5.24531, "loss": 5.24531, "time": 0.70164} +{"mode": "train", "epoch": 1, "iter": 2600, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.07859, "top5_acc": 0.23859, "loss_cls": 5.28931, "loss": 5.28931, "time": 0.70427} +{"mode": "train", "epoch": 1, "iter": 2700, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.08641, "top5_acc": 0.25391, "loss_cls": 5.25879, "loss": 5.25879, "time": 0.7022} +{"mode": "train", "epoch": 1, "iter": 2800, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.09203, "top5_acc": 0.25453, "loss_cls": 5.22384, "loss": 5.22384, "time": 0.70363} +{"mode": "train", "epoch": 1, "iter": 2900, "lr": 0.09999, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.09281, "top5_acc": 0.25422, "loss_cls": 5.21553, "loss": 5.21553, "time": 0.70192} +{"mode": "train", "epoch": 1, "iter": 3000, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.09281, "top5_acc": 0.25422, "loss_cls": 5.19227, "loss": 5.19227, "time": 0.69919} +{"mode": "train", "epoch": 1, "iter": 3100, "lr": 0.09999, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.10438, "top5_acc": 0.26953, "loss_cls": 5.16698, "loss": 5.16698, "time": 0.69836} +{"mode": "train", "epoch": 1, "iter": 3200, "lr": 0.09999, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.11375, "top5_acc": 0.28234, "loss_cls": 5.09072, "loss": 5.09072, "time": 0.69963} +{"mode": "train", "epoch": 1, "iter": 3300, "lr": 0.09999, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.11344, "top5_acc": 0.28375, "loss_cls": 5.09097, "loss": 5.09097, "time": 0.70082} +{"mode": "train", "epoch": 1, "iter": 3400, "lr": 0.09999, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.10547, "top5_acc": 0.27156, "loss_cls": 5.12154, "loss": 5.12154, "time": 0.70052} +{"mode": "train", "epoch": 1, "iter": 3500, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.10891, "top5_acc": 0.29438, "loss_cls": 5.05494, "loss": 5.05494, "time": 0.70321} +{"mode": "train", "epoch": 1, "iter": 3600, "lr": 0.09999, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.10672, "top5_acc": 0.28594, "loss_cls": 5.07514, "loss": 5.07514, "time": 0.69914} +{"mode": "train", "epoch": 1, "iter": 3700, "lr": 0.09999, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.11828, "top5_acc": 0.3, "loss_cls": 5.05446, "loss": 5.05446, "time": 0.70463} +{"mode": "val", "epoch": 1, "iter": 309, "lr": 0.09999, "top1_acc": 0.06286, "top5_acc": 0.19982, "mean_class_accuracy": 0.06299} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.09999, "memory": 15990, "data_time": 1.2314, "top1_acc": 0.11891, "top5_acc": 0.3025, "loss_cls": 4.99145, "loss": 4.99145, "time": 1.93511} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.1175, "top5_acc": 0.30344, "loss_cls": 5.01839, "loss": 5.01839, "time": 0.69812} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.12141, "top5_acc": 0.31016, "loss_cls": 4.97605, "loss": 4.97605, "time": 0.70082} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.12719, "top5_acc": 0.32141, "loss_cls": 4.91498, "loss": 4.91498, "time": 0.7029} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.12609, "top5_acc": 0.32844, "loss_cls": 4.92051, "loss": 4.92051, "time": 0.70073} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.12625, "top5_acc": 0.315, "loss_cls": 4.9348, "loss": 4.9348, "time": 0.70086} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.1225, "top5_acc": 0.31391, "loss_cls": 4.96871, "loss": 4.96871, "time": 0.70213} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.1325, "top5_acc": 0.33281, "loss_cls": 4.91088, "loss": 4.91088, "time": 0.7026} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.13547, "top5_acc": 0.32766, "loss_cls": 4.89565, "loss": 4.89565, "time": 0.70112} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.09998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.13641, "top5_acc": 0.33078, "loss_cls": 4.89908, "loss": 4.89908, "time": 0.70125} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.09998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.13328, "top5_acc": 0.33297, "loss_cls": 4.88393, "loss": 4.88393, "time": 0.70189} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.13969, "top5_acc": 0.34547, "loss_cls": 4.83275, "loss": 4.83275, "time": 0.70043} +{"mode": "train", "epoch": 2, "iter": 1300, "lr": 0.09998, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.14422, "top5_acc": 0.34469, "loss_cls": 4.84733, "loss": 4.84733, "time": 0.69881} +{"mode": "train", "epoch": 2, "iter": 1400, "lr": 0.09998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.13688, "top5_acc": 0.34938, "loss_cls": 4.83523, "loss": 4.83523, "time": 0.69764} +{"mode": "train", "epoch": 2, "iter": 1500, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.14984, "top5_acc": 0.35906, "loss_cls": 4.78698, "loss": 4.78698, "time": 0.70466} +{"mode": "train", "epoch": 2, "iter": 1600, "lr": 0.09998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.14656, "top5_acc": 0.35766, "loss_cls": 4.78273, "loss": 4.78273, "time": 0.70646} +{"mode": "train", "epoch": 2, "iter": 1700, "lr": 0.09998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.14875, "top5_acc": 0.35391, "loss_cls": 4.78247, "loss": 4.78247, "time": 0.71492} +{"mode": "train", "epoch": 2, "iter": 1800, "lr": 0.09998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.14062, "top5_acc": 0.35516, "loss_cls": 4.81268, "loss": 4.81268, "time": 0.7116} +{"mode": "train", "epoch": 2, "iter": 1900, "lr": 0.09998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.15687, "top5_acc": 0.36641, "loss_cls": 4.75822, "loss": 4.75822, "time": 0.71031} +{"mode": "train", "epoch": 2, "iter": 2000, "lr": 0.09997, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.15062, "top5_acc": 0.3725, "loss_cls": 4.72186, "loss": 4.72186, "time": 0.70728} +{"mode": "train", "epoch": 2, "iter": 2100, "lr": 0.09997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15781, "top5_acc": 0.37672, "loss_cls": 4.70118, "loss": 4.70118, "time": 0.70648} +{"mode": "train", "epoch": 2, "iter": 2200, "lr": 0.09997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16484, "top5_acc": 0.38016, "loss_cls": 4.68992, "loss": 4.68992, "time": 0.70222} +{"mode": "train", "epoch": 2, "iter": 2300, "lr": 0.09997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15875, "top5_acc": 0.37453, "loss_cls": 4.71396, "loss": 4.71396, "time": 0.70316} +{"mode": "train", "epoch": 2, "iter": 2400, "lr": 0.09997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16203, "top5_acc": 0.37031, "loss_cls": 4.70165, "loss": 4.70165, "time": 0.70215} +{"mode": "train", "epoch": 2, "iter": 2500, "lr": 0.09997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16312, "top5_acc": 0.38031, "loss_cls": 4.6932, "loss": 4.6932, "time": 0.70227} +{"mode": "train", "epoch": 2, "iter": 2600, "lr": 0.09997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16688, "top5_acc": 0.38609, "loss_cls": 4.68003, "loss": 4.68003, "time": 0.70446} +{"mode": "train", "epoch": 2, "iter": 2700, "lr": 0.09997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16672, "top5_acc": 0.38547, "loss_cls": 4.65078, "loss": 4.65078, "time": 0.70179} +{"mode": "train", "epoch": 2, "iter": 2800, "lr": 0.09997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17109, "top5_acc": 0.39453, "loss_cls": 4.62955, "loss": 4.62955, "time": 0.7046} +{"mode": "train", "epoch": 2, "iter": 2900, "lr": 0.09997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16922, "top5_acc": 0.39047, "loss_cls": 4.64614, "loss": 4.64614, "time": 0.70478} +{"mode": "train", "epoch": 2, "iter": 3000, "lr": 0.09996, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16, "top5_acc": 0.38016, "loss_cls": 4.667, "loss": 4.667, "time": 0.70117} +{"mode": "train", "epoch": 2, "iter": 3100, "lr": 0.09996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16594, "top5_acc": 0.38766, "loss_cls": 4.62018, "loss": 4.62018, "time": 0.70196} +{"mode": "train", "epoch": 2, "iter": 3200, "lr": 0.09996, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.17281, "top5_acc": 0.39516, "loss_cls": 4.63737, "loss": 4.63737, "time": 0.70019} +{"mode": "train", "epoch": 2, "iter": 3300, "lr": 0.09996, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.16109, "top5_acc": 0.38125, "loss_cls": 4.66377, "loss": 4.66377, "time": 0.69938} +{"mode": "train", "epoch": 2, "iter": 3400, "lr": 0.09996, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17688, "top5_acc": 0.40781, "loss_cls": 4.56532, "loss": 4.56532, "time": 0.70336} +{"mode": "train", "epoch": 2, "iter": 3500, "lr": 0.09996, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.17406, "top5_acc": 0.39688, "loss_cls": 4.59556, "loss": 4.59556, "time": 0.69864} +{"mode": "train", "epoch": 2, "iter": 3600, "lr": 0.09996, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.18109, "top5_acc": 0.40578, "loss_cls": 4.58813, "loss": 4.58813, "time": 0.69836} +{"mode": "train", "epoch": 2, "iter": 3700, "lr": 0.09996, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18047, "top5_acc": 0.40125, "loss_cls": 4.59347, "loss": 4.59347, "time": 0.70004} +{"mode": "val", "epoch": 2, "iter": 309, "lr": 0.09996, "top1_acc": 0.12962, "top5_acc": 0.32214, "mean_class_accuracy": 0.12936} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.09995, "memory": 15990, "data_time": 1.22138, "top1_acc": 0.17891, "top5_acc": 0.41328, "loss_cls": 4.56018, "loss": 4.56018, "time": 1.92256} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.09995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18062, "top5_acc": 0.40375, "loss_cls": 4.51499, "loss": 4.51499, "time": 0.69923} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.09995, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18172, "top5_acc": 0.40969, "loss_cls": 4.54801, "loss": 4.54801, "time": 0.7027} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.09995, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17562, "top5_acc": 0.40641, "loss_cls": 4.56012, "loss": 4.56012, "time": 0.70051} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.09995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18938, "top5_acc": 0.41094, "loss_cls": 4.5312, "loss": 4.5312, "time": 0.70165} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.09995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18359, "top5_acc": 0.41734, "loss_cls": 4.52417, "loss": 4.52417, "time": 0.70259} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.09995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18266, "top5_acc": 0.4025, "loss_cls": 4.5676, "loss": 4.5676, "time": 0.69857} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.09995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18875, "top5_acc": 0.41297, "loss_cls": 4.53669, "loss": 4.53669, "time": 0.69971} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.09994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19, "top5_acc": 0.41531, "loss_cls": 4.51547, "loss": 4.51547, "time": 0.70068} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.09994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19922, "top5_acc": 0.43219, "loss_cls": 4.48028, "loss": 4.48028, "time": 0.69899} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.09994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18562, "top5_acc": 0.41094, "loss_cls": 4.49216, "loss": 4.49216, "time": 0.70324} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.09994, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.19531, "top5_acc": 0.41953, "loss_cls": 4.50336, "loss": 4.50336, "time": 0.70387} +{"mode": "train", "epoch": 3, "iter": 1300, "lr": 0.09994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18703, "top5_acc": 0.42438, "loss_cls": 4.47734, "loss": 4.47734, "time": 0.70623} +{"mode": "train", "epoch": 3, "iter": 1400, "lr": 0.09994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18469, "top5_acc": 0.4175, "loss_cls": 4.50134, "loss": 4.50134, "time": 0.7053} +{"mode": "train", "epoch": 3, "iter": 1500, "lr": 0.09994, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.18891, "top5_acc": 0.42453, "loss_cls": 4.47355, "loss": 4.47355, "time": 0.71047} +{"mode": "train", "epoch": 3, "iter": 1600, "lr": 0.09994, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.18281, "top5_acc": 0.41922, "loss_cls": 4.51943, "loss": 4.51943, "time": 0.70369} +{"mode": "train", "epoch": 3, "iter": 1700, "lr": 0.09993, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20141, "top5_acc": 0.42328, "loss_cls": 4.46352, "loss": 4.46352, "time": 0.713} +{"mode": "train", "epoch": 3, "iter": 1800, "lr": 0.09993, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.19422, "top5_acc": 0.42906, "loss_cls": 4.48403, "loss": 4.48403, "time": 0.70866} +{"mode": "train", "epoch": 3, "iter": 1900, "lr": 0.09993, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.19125, "top5_acc": 0.42766, "loss_cls": 4.45301, "loss": 4.45301, "time": 0.71171} +{"mode": "train", "epoch": 3, "iter": 2000, "lr": 0.09993, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20578, "top5_acc": 0.43562, "loss_cls": 4.42412, "loss": 4.42412, "time": 0.70935} +{"mode": "train", "epoch": 3, "iter": 2100, "lr": 0.09993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19281, "top5_acc": 0.42828, "loss_cls": 4.46388, "loss": 4.46388, "time": 0.70446} +{"mode": "train", "epoch": 3, "iter": 2200, "lr": 0.09993, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19, "top5_acc": 0.43, "loss_cls": 4.47239, "loss": 4.47239, "time": 0.70211} +{"mode": "train", "epoch": 3, "iter": 2300, "lr": 0.09993, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19234, "top5_acc": 0.42922, "loss_cls": 4.45643, "loss": 4.45643, "time": 0.70364} +{"mode": "train", "epoch": 3, "iter": 2400, "lr": 0.09992, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19984, "top5_acc": 0.43031, "loss_cls": 4.43876, "loss": 4.43876, "time": 0.70016} +{"mode": "train", "epoch": 3, "iter": 2500, "lr": 0.09992, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20031, "top5_acc": 0.42953, "loss_cls": 4.42664, "loss": 4.42664, "time": 0.69995} +{"mode": "train", "epoch": 3, "iter": 2600, "lr": 0.09992, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21391, "top5_acc": 0.43688, "loss_cls": 4.3858, "loss": 4.3858, "time": 0.70051} +{"mode": "train", "epoch": 3, "iter": 2700, "lr": 0.09992, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20344, "top5_acc": 0.44062, "loss_cls": 4.42763, "loss": 4.42763, "time": 0.70356} +{"mode": "train", "epoch": 3, "iter": 2800, "lr": 0.09992, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21047, "top5_acc": 0.43859, "loss_cls": 4.41382, "loss": 4.41382, "time": 0.7015} +{"mode": "train", "epoch": 3, "iter": 2900, "lr": 0.09992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21047, "top5_acc": 0.44391, "loss_cls": 4.39144, "loss": 4.39144, "time": 0.70187} +{"mode": "train", "epoch": 3, "iter": 3000, "lr": 0.09991, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20734, "top5_acc": 0.44031, "loss_cls": 4.40361, "loss": 4.40361, "time": 0.70204} +{"mode": "train", "epoch": 3, "iter": 3100, "lr": 0.09991, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21328, "top5_acc": 0.43531, "loss_cls": 4.38726, "loss": 4.38726, "time": 0.70044} +{"mode": "train", "epoch": 3, "iter": 3200, "lr": 0.09991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19516, "top5_acc": 0.43594, "loss_cls": 4.41354, "loss": 4.41354, "time": 0.7019} +{"mode": "train", "epoch": 3, "iter": 3300, "lr": 0.09991, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19906, "top5_acc": 0.43125, "loss_cls": 4.46978, "loss": 4.46978, "time": 0.70118} +{"mode": "train", "epoch": 3, "iter": 3400, "lr": 0.09991, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20531, "top5_acc": 0.44453, "loss_cls": 4.40217, "loss": 4.40217, "time": 0.70133} +{"mode": "train", "epoch": 3, "iter": 3500, "lr": 0.09991, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20688, "top5_acc": 0.445, "loss_cls": 4.3748, "loss": 4.3748, "time": 0.69874} +{"mode": "train", "epoch": 3, "iter": 3600, "lr": 0.0999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20578, "top5_acc": 0.44125, "loss_cls": 4.39695, "loss": 4.39695, "time": 0.69785} +{"mode": "train", "epoch": 3, "iter": 3700, "lr": 0.0999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20281, "top5_acc": 0.44453, "loss_cls": 4.38782, "loss": 4.38782, "time": 0.70073} +{"mode": "val", "epoch": 3, "iter": 309, "lr": 0.0999, "top1_acc": 0.12931, "top5_acc": 0.32396, "mean_class_accuracy": 0.12927} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.0999, "memory": 15990, "data_time": 1.23184, "top1_acc": 0.20734, "top5_acc": 0.44688, "loss_cls": 4.32555, "loss": 4.32555, "time": 1.93343} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.0999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20594, "top5_acc": 0.44062, "loss_cls": 4.36895, "loss": 4.36895, "time": 0.70363} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.0999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21125, "top5_acc": 0.44312, "loss_cls": 4.3682, "loss": 4.3682, "time": 0.70359} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.09989, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21297, "top5_acc": 0.45578, "loss_cls": 4.35748, "loss": 4.35748, "time": 0.70053} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.09989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21266, "top5_acc": 0.44562, "loss_cls": 4.36889, "loss": 4.36889, "time": 0.7} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.09989, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20234, "top5_acc": 0.44344, "loss_cls": 4.39132, "loss": 4.39132, "time": 0.70352} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.09989, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21234, "top5_acc": 0.44922, "loss_cls": 4.36177, "loss": 4.36177, "time": 0.69954} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.09989, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21344, "top5_acc": 0.44906, "loss_cls": 4.35532, "loss": 4.35532, "time": 0.7034} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.09988, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.215, "top5_acc": 0.45734, "loss_cls": 4.33591, "loss": 4.33591, "time": 0.70468} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.09988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20938, "top5_acc": 0.45531, "loss_cls": 4.34895, "loss": 4.34895, "time": 0.70205} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.09988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21438, "top5_acc": 0.45156, "loss_cls": 4.34597, "loss": 4.34597, "time": 0.70042} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.09988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21328, "top5_acc": 0.45172, "loss_cls": 4.35201, "loss": 4.35201, "time": 0.70218} +{"mode": "train", "epoch": 4, "iter": 1300, "lr": 0.09988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21359, "top5_acc": 0.44891, "loss_cls": 4.38434, "loss": 4.38434, "time": 0.70182} +{"mode": "train", "epoch": 4, "iter": 1400, "lr": 0.09988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21, "top5_acc": 0.44859, "loss_cls": 4.35484, "loss": 4.35484, "time": 0.70347} +{"mode": "train", "epoch": 4, "iter": 1500, "lr": 0.09987, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21391, "top5_acc": 0.44812, "loss_cls": 4.36086, "loss": 4.36086, "time": 0.70533} +{"mode": "train", "epoch": 4, "iter": 1600, "lr": 0.09987, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21797, "top5_acc": 0.45344, "loss_cls": 4.36579, "loss": 4.36579, "time": 0.70788} +{"mode": "train", "epoch": 4, "iter": 1700, "lr": 0.09987, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21469, "top5_acc": 0.44641, "loss_cls": 4.35573, "loss": 4.35573, "time": 0.71514} +{"mode": "train", "epoch": 4, "iter": 1800, "lr": 0.09987, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21531, "top5_acc": 0.44844, "loss_cls": 4.33868, "loss": 4.33868, "time": 0.71636} +{"mode": "train", "epoch": 4, "iter": 1900, "lr": 0.09987, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22016, "top5_acc": 0.44953, "loss_cls": 4.36442, "loss": 4.36442, "time": 0.71073} +{"mode": "train", "epoch": 4, "iter": 2000, "lr": 0.09986, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21578, "top5_acc": 0.46141, "loss_cls": 4.33715, "loss": 4.33715, "time": 0.71519} +{"mode": "train", "epoch": 4, "iter": 2100, "lr": 0.09986, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22375, "top5_acc": 0.45953, "loss_cls": 4.29189, "loss": 4.29189, "time": 0.70495} +{"mode": "train", "epoch": 4, "iter": 2200, "lr": 0.09986, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22188, "top5_acc": 0.45922, "loss_cls": 4.295, "loss": 4.295, "time": 0.7076} +{"mode": "train", "epoch": 4, "iter": 2300, "lr": 0.09986, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22156, "top5_acc": 0.45609, "loss_cls": 4.31583, "loss": 4.31583, "time": 0.70298} +{"mode": "train", "epoch": 4, "iter": 2400, "lr": 0.09985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21109, "top5_acc": 0.45672, "loss_cls": 4.33396, "loss": 4.33396, "time": 0.7047} +{"mode": "train", "epoch": 4, "iter": 2500, "lr": 0.09985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21656, "top5_acc": 0.45844, "loss_cls": 4.35444, "loss": 4.35444, "time": 0.70265} +{"mode": "train", "epoch": 4, "iter": 2600, "lr": 0.09985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22125, "top5_acc": 0.44703, "loss_cls": 4.35037, "loss": 4.35037, "time": 0.70114} +{"mode": "train", "epoch": 4, "iter": 2700, "lr": 0.09985, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22078, "top5_acc": 0.46125, "loss_cls": 4.31057, "loss": 4.31057, "time": 0.70064} +{"mode": "train", "epoch": 4, "iter": 2800, "lr": 0.09985, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21672, "top5_acc": 0.45297, "loss_cls": 4.33873, "loss": 4.33873, "time": 0.69937} +{"mode": "train", "epoch": 4, "iter": 2900, "lr": 0.09984, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21516, "top5_acc": 0.45312, "loss_cls": 4.32843, "loss": 4.32843, "time": 0.7005} +{"mode": "train", "epoch": 4, "iter": 3000, "lr": 0.09984, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21391, "top5_acc": 0.45938, "loss_cls": 4.33743, "loss": 4.33743, "time": 0.70082} +{"mode": "train", "epoch": 4, "iter": 3100, "lr": 0.09984, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21859, "top5_acc": 0.45609, "loss_cls": 4.30081, "loss": 4.30081, "time": 0.70129} +{"mode": "train", "epoch": 4, "iter": 3200, "lr": 0.09984, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22031, "top5_acc": 0.46234, "loss_cls": 4.29127, "loss": 4.29127, "time": 0.69931} +{"mode": "train", "epoch": 4, "iter": 3300, "lr": 0.09983, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22406, "top5_acc": 0.46375, "loss_cls": 4.31297, "loss": 4.31297, "time": 0.70118} +{"mode": "train", "epoch": 4, "iter": 3400, "lr": 0.09983, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23562, "top5_acc": 0.4625, "loss_cls": 4.27686, "loss": 4.27686, "time": 0.70203} +{"mode": "train", "epoch": 4, "iter": 3500, "lr": 0.09983, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22281, "top5_acc": 0.46312, "loss_cls": 4.28125, "loss": 4.28125, "time": 0.69757} +{"mode": "train", "epoch": 4, "iter": 3600, "lr": 0.09983, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21531, "top5_acc": 0.45156, "loss_cls": 4.34425, "loss": 4.34425, "time": 0.70303} +{"mode": "train", "epoch": 4, "iter": 3700, "lr": 0.09983, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.225, "top5_acc": 0.46156, "loss_cls": 4.3107, "loss": 4.3107, "time": 0.69944} +{"mode": "val", "epoch": 4, "iter": 309, "lr": 0.09982, "top1_acc": 0.14699, "top5_acc": 0.3499, "mean_class_accuracy": 0.1468} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.09982, "memory": 15990, "data_time": 1.26766, "top1_acc": 0.22156, "top5_acc": 0.45516, "loss_cls": 4.29431, "loss": 4.29431, "time": 1.97643} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.09982, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22172, "top5_acc": 0.46922, "loss_cls": 4.25683, "loss": 4.25683, "time": 0.70529} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.09982, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22828, "top5_acc": 0.46578, "loss_cls": 4.28382, "loss": 4.28382, "time": 0.70578} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.09982, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22125, "top5_acc": 0.46578, "loss_cls": 4.29863, "loss": 4.29863, "time": 0.69881} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.09981, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23125, "top5_acc": 0.465, "loss_cls": 4.282, "loss": 4.282, "time": 0.70123} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.09981, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22812, "top5_acc": 0.46594, "loss_cls": 4.26633, "loss": 4.26633, "time": 0.70046} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.09981, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22625, "top5_acc": 0.4675, "loss_cls": 4.28762, "loss": 4.28762, "time": 0.70049} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.09981, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22922, "top5_acc": 0.46625, "loss_cls": 4.27972, "loss": 4.27972, "time": 0.70235} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.0998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22906, "top5_acc": 0.47031, "loss_cls": 4.27663, "loss": 4.27663, "time": 0.70144} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.0998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21828, "top5_acc": 0.46219, "loss_cls": 4.30316, "loss": 4.30316, "time": 0.70289} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.0998, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21656, "top5_acc": 0.46109, "loss_cls": 4.33883, "loss": 4.33883, "time": 0.70502} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.0998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22016, "top5_acc": 0.47109, "loss_cls": 4.27939, "loss": 4.27939, "time": 0.70267} +{"mode": "train", "epoch": 5, "iter": 1300, "lr": 0.09979, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22547, "top5_acc": 0.46938, "loss_cls": 4.2909, "loss": 4.2909, "time": 0.70259} +{"mode": "train", "epoch": 5, "iter": 1400, "lr": 0.09979, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22125, "top5_acc": 0.46766, "loss_cls": 4.3146, "loss": 4.3146, "time": 0.69949} +{"mode": "train", "epoch": 5, "iter": 1500, "lr": 0.09979, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22094, "top5_acc": 0.45, "loss_cls": 4.33905, "loss": 4.33905, "time": 0.70869} +{"mode": "train", "epoch": 5, "iter": 1600, "lr": 0.09979, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22562, "top5_acc": 0.46812, "loss_cls": 4.27499, "loss": 4.27499, "time": 0.70877} +{"mode": "train", "epoch": 5, "iter": 1700, "lr": 0.09978, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22, "top5_acc": 0.46688, "loss_cls": 4.27707, "loss": 4.27707, "time": 0.71298} +{"mode": "train", "epoch": 5, "iter": 1800, "lr": 0.09978, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21938, "top5_acc": 0.4575, "loss_cls": 4.31138, "loss": 4.31138, "time": 0.71431} +{"mode": "train", "epoch": 5, "iter": 1900, "lr": 0.09978, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22094, "top5_acc": 0.45859, "loss_cls": 4.32037, "loss": 4.32037, "time": 0.70935} +{"mode": "train", "epoch": 5, "iter": 2000, "lr": 0.09977, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23344, "top5_acc": 0.47922, "loss_cls": 4.22608, "loss": 4.22608, "time": 0.70726} +{"mode": "train", "epoch": 5, "iter": 2100, "lr": 0.09977, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23141, "top5_acc": 0.46891, "loss_cls": 4.25842, "loss": 4.25842, "time": 0.70709} +{"mode": "train", "epoch": 5, "iter": 2200, "lr": 0.09977, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23547, "top5_acc": 0.47203, "loss_cls": 4.24736, "loss": 4.24736, "time": 0.70003} +{"mode": "train", "epoch": 5, "iter": 2300, "lr": 0.09977, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23906, "top5_acc": 0.47812, "loss_cls": 4.22218, "loss": 4.22218, "time": 0.70355} +{"mode": "train", "epoch": 5, "iter": 2400, "lr": 0.09976, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22406, "top5_acc": 0.46969, "loss_cls": 4.26858, "loss": 4.26858, "time": 0.69933} +{"mode": "train", "epoch": 5, "iter": 2500, "lr": 0.09976, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21875, "top5_acc": 0.46875, "loss_cls": 4.29511, "loss": 4.29511, "time": 0.70002} +{"mode": "train", "epoch": 5, "iter": 2600, "lr": 0.09976, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22609, "top5_acc": 0.47281, "loss_cls": 4.26595, "loss": 4.26595, "time": 0.69886} +{"mode": "train", "epoch": 5, "iter": 2700, "lr": 0.09976, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22609, "top5_acc": 0.46406, "loss_cls": 4.29437, "loss": 4.29437, "time": 0.69985} +{"mode": "train", "epoch": 5, "iter": 2800, "lr": 0.09975, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22641, "top5_acc": 0.46812, "loss_cls": 4.26998, "loss": 4.26998, "time": 0.70129} +{"mode": "train", "epoch": 5, "iter": 2900, "lr": 0.09975, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22891, "top5_acc": 0.47906, "loss_cls": 4.21693, "loss": 4.21693, "time": 0.70296} +{"mode": "train", "epoch": 5, "iter": 3000, "lr": 0.09975, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23016, "top5_acc": 0.46672, "loss_cls": 4.25543, "loss": 4.25543, "time": 0.70137} +{"mode": "train", "epoch": 5, "iter": 3100, "lr": 0.09974, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23141, "top5_acc": 0.47484, "loss_cls": 4.23478, "loss": 4.23478, "time": 0.69795} +{"mode": "train", "epoch": 5, "iter": 3200, "lr": 0.09974, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.22688, "top5_acc": 0.46844, "loss_cls": 4.23755, "loss": 4.23755, "time": 0.69977} +{"mode": "train", "epoch": 5, "iter": 3300, "lr": 0.09974, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22344, "top5_acc": 0.46891, "loss_cls": 4.26197, "loss": 4.26197, "time": 0.69879} +{"mode": "train", "epoch": 5, "iter": 3400, "lr": 0.09974, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23672, "top5_acc": 0.47969, "loss_cls": 4.20163, "loss": 4.20163, "time": 0.69806} +{"mode": "train", "epoch": 5, "iter": 3500, "lr": 0.09973, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23484, "top5_acc": 0.47734, "loss_cls": 4.22349, "loss": 4.22349, "time": 0.70221} +{"mode": "train", "epoch": 5, "iter": 3600, "lr": 0.09973, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23391, "top5_acc": 0.47547, "loss_cls": 4.24278, "loss": 4.24278, "time": 0.70268} +{"mode": "train", "epoch": 5, "iter": 3700, "lr": 0.09973, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23578, "top5_acc": 0.46297, "loss_cls": 4.26879, "loss": 4.26879, "time": 0.70313} +{"mode": "val", "epoch": 5, "iter": 309, "lr": 0.09973, "top1_acc": 0.16548, "top5_acc": 0.38095, "mean_class_accuracy": 0.16546} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.09972, "memory": 15990, "data_time": 1.24353, "top1_acc": 0.23531, "top5_acc": 0.47875, "loss_cls": 4.23972, "loss": 4.23972, "time": 1.94682} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.09972, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24203, "top5_acc": 0.47, "loss_cls": 4.19937, "loss": 4.19937, "time": 0.70187} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.09972, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23375, "top5_acc": 0.47266, "loss_cls": 4.22742, "loss": 4.22742, "time": 0.69997} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.09971, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24422, "top5_acc": 0.4825, "loss_cls": 4.20482, "loss": 4.20482, "time": 0.70216} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.09971, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24281, "top5_acc": 0.47641, "loss_cls": 4.1904, "loss": 4.1904, "time": 0.70382} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.09971, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23422, "top5_acc": 0.47938, "loss_cls": 4.23994, "loss": 4.23994, "time": 0.70103} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.09971, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23016, "top5_acc": 0.47797, "loss_cls": 4.21528, "loss": 4.21528, "time": 0.69884} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.0997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23984, "top5_acc": 0.475, "loss_cls": 4.18879, "loss": 4.18879, "time": 0.69913} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.0997, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24094, "top5_acc": 0.47828, "loss_cls": 4.19472, "loss": 4.19472, "time": 0.69904} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.0997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24469, "top5_acc": 0.48219, "loss_cls": 4.21256, "loss": 4.21256, "time": 0.6994} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.09969, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23156, "top5_acc": 0.47297, "loss_cls": 4.23189, "loss": 4.23189, "time": 0.70349} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.09969, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23422, "top5_acc": 0.47641, "loss_cls": 4.21616, "loss": 4.21616, "time": 0.7068} +{"mode": "train", "epoch": 6, "iter": 1300, "lr": 0.09969, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23844, "top5_acc": 0.4725, "loss_cls": 4.25396, "loss": 4.25396, "time": 0.7043} +{"mode": "train", "epoch": 6, "iter": 1400, "lr": 0.09968, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23594, "top5_acc": 0.47656, "loss_cls": 4.19909, "loss": 4.19909, "time": 0.69848} +{"mode": "train", "epoch": 6, "iter": 1500, "lr": 0.09968, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23828, "top5_acc": 0.47812, "loss_cls": 4.21754, "loss": 4.21754, "time": 0.69955} +{"mode": "train", "epoch": 6, "iter": 1600, "lr": 0.09968, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22703, "top5_acc": 0.47609, "loss_cls": 4.22309, "loss": 4.22309, "time": 0.70627} +{"mode": "train", "epoch": 6, "iter": 1700, "lr": 0.09967, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23609, "top5_acc": 0.47312, "loss_cls": 4.2086, "loss": 4.2086, "time": 0.70913} +{"mode": "train", "epoch": 6, "iter": 1800, "lr": 0.09967, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22953, "top5_acc": 0.46594, "loss_cls": 4.25376, "loss": 4.25376, "time": 0.71309} +{"mode": "train", "epoch": 6, "iter": 1900, "lr": 0.09967, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23547, "top5_acc": 0.47453, "loss_cls": 4.22096, "loss": 4.22096, "time": 0.7066} +{"mode": "train", "epoch": 6, "iter": 2000, "lr": 0.09966, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23453, "top5_acc": 0.47047, "loss_cls": 4.22914, "loss": 4.22914, "time": 0.70577} +{"mode": "train", "epoch": 6, "iter": 2100, "lr": 0.09966, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23297, "top5_acc": 0.47766, "loss_cls": 4.23341, "loss": 4.23341, "time": 0.70453} +{"mode": "train", "epoch": 6, "iter": 2200, "lr": 0.09966, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23688, "top5_acc": 0.48219, "loss_cls": 4.20014, "loss": 4.20014, "time": 0.70131} +{"mode": "train", "epoch": 6, "iter": 2300, "lr": 0.09965, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23234, "top5_acc": 0.48562, "loss_cls": 4.19657, "loss": 4.19657, "time": 0.70095} +{"mode": "train", "epoch": 6, "iter": 2400, "lr": 0.09965, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22844, "top5_acc": 0.47125, "loss_cls": 4.23852, "loss": 4.23852, "time": 0.69919} +{"mode": "train", "epoch": 6, "iter": 2500, "lr": 0.09965, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25078, "top5_acc": 0.48328, "loss_cls": 4.16173, "loss": 4.16173, "time": 0.6965} +{"mode": "train", "epoch": 6, "iter": 2600, "lr": 0.09964, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22922, "top5_acc": 0.47359, "loss_cls": 4.25346, "loss": 4.25346, "time": 0.69969} +{"mode": "train", "epoch": 6, "iter": 2700, "lr": 0.09964, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24359, "top5_acc": 0.48172, "loss_cls": 4.20928, "loss": 4.20928, "time": 0.70047} +{"mode": "train", "epoch": 6, "iter": 2800, "lr": 0.09964, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23875, "top5_acc": 0.48109, "loss_cls": 4.19336, "loss": 4.19336, "time": 0.6982} +{"mode": "train", "epoch": 6, "iter": 2900, "lr": 0.09963, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22812, "top5_acc": 0.48172, "loss_cls": 4.21289, "loss": 4.21289, "time": 0.7002} +{"mode": "train", "epoch": 6, "iter": 3000, "lr": 0.09963, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23234, "top5_acc": 0.48266, "loss_cls": 4.2316, "loss": 4.2316, "time": 0.69916} +{"mode": "train", "epoch": 6, "iter": 3100, "lr": 0.09963, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23344, "top5_acc": 0.47766, "loss_cls": 4.22433, "loss": 4.22433, "time": 0.69791} +{"mode": "train", "epoch": 6, "iter": 3200, "lr": 0.09962, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23578, "top5_acc": 0.48328, "loss_cls": 4.20234, "loss": 4.20234, "time": 0.69958} +{"mode": "train", "epoch": 6, "iter": 3300, "lr": 0.09962, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.235, "top5_acc": 0.48266, "loss_cls": 4.19195, "loss": 4.19195, "time": 0.69844} +{"mode": "train", "epoch": 6, "iter": 3400, "lr": 0.09962, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23703, "top5_acc": 0.47656, "loss_cls": 4.20737, "loss": 4.20737, "time": 0.69856} +{"mode": "train", "epoch": 6, "iter": 3500, "lr": 0.09961, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.23031, "top5_acc": 0.49359, "loss_cls": 4.17475, "loss": 4.17475, "time": 0.69713} +{"mode": "train", "epoch": 6, "iter": 3600, "lr": 0.09961, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23188, "top5_acc": 0.47625, "loss_cls": 4.2123, "loss": 4.2123, "time": 0.70162} +{"mode": "train", "epoch": 6, "iter": 3700, "lr": 0.09961, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24047, "top5_acc": 0.48281, "loss_cls": 4.19438, "loss": 4.19438, "time": 0.70106} +{"mode": "val", "epoch": 6, "iter": 309, "lr": 0.09961, "top1_acc": 0.1438, "top5_acc": 0.34939, "mean_class_accuracy": 0.14343} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0996, "memory": 15990, "data_time": 1.23093, "top1_acc": 0.23828, "top5_acc": 0.48172, "loss_cls": 4.17211, "loss": 4.17211, "time": 1.93261} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0996, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24219, "top5_acc": 0.49016, "loss_cls": 4.14091, "loss": 4.14091, "time": 0.69901} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.0996, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23812, "top5_acc": 0.48703, "loss_cls": 4.18, "loss": 4.18, "time": 0.69982} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.09959, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.245, "top5_acc": 0.48812, "loss_cls": 4.14576, "loss": 4.14576, "time": 0.70016} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.09959, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23938, "top5_acc": 0.48484, "loss_cls": 4.15983, "loss": 4.15983, "time": 0.70133} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.09958, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23391, "top5_acc": 0.48172, "loss_cls": 4.18758, "loss": 4.18758, "time": 0.70474} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.09958, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24219, "top5_acc": 0.49531, "loss_cls": 4.16247, "loss": 4.16247, "time": 0.6973} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.09958, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23188, "top5_acc": 0.47781, "loss_cls": 4.22618, "loss": 4.22618, "time": 0.6983} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.09957, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24438, "top5_acc": 0.48953, "loss_cls": 4.15279, "loss": 4.15279, "time": 0.70006} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.09957, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24656, "top5_acc": 0.48109, "loss_cls": 4.19618, "loss": 4.19618, "time": 0.70089} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.09957, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24453, "top5_acc": 0.4875, "loss_cls": 4.17447, "loss": 4.17447, "time": 0.70288} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.09956, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23562, "top5_acc": 0.47328, "loss_cls": 4.20904, "loss": 4.20904, "time": 0.70097} +{"mode": "train", "epoch": 7, "iter": 1300, "lr": 0.09956, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23875, "top5_acc": 0.48375, "loss_cls": 4.23498, "loss": 4.23498, "time": 0.7006} +{"mode": "train", "epoch": 7, "iter": 1400, "lr": 0.09956, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24234, "top5_acc": 0.49234, "loss_cls": 4.14414, "loss": 4.14414, "time": 0.69769} +{"mode": "train", "epoch": 7, "iter": 1500, "lr": 0.09955, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23875, "top5_acc": 0.48578, "loss_cls": 4.20088, "loss": 4.20088, "time": 0.70042} +{"mode": "train", "epoch": 7, "iter": 1600, "lr": 0.09955, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23312, "top5_acc": 0.47844, "loss_cls": 4.22064, "loss": 4.22064, "time": 0.71077} +{"mode": "train", "epoch": 7, "iter": 1700, "lr": 0.09954, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24578, "top5_acc": 0.48797, "loss_cls": 4.14995, "loss": 4.14995, "time": 0.71102} +{"mode": "train", "epoch": 7, "iter": 1800, "lr": 0.09954, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24688, "top5_acc": 0.47641, "loss_cls": 4.21764, "loss": 4.21764, "time": 0.71614} +{"mode": "train", "epoch": 7, "iter": 1900, "lr": 0.09954, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.245, "top5_acc": 0.48453, "loss_cls": 4.17841, "loss": 4.17841, "time": 0.70569} +{"mode": "train", "epoch": 7, "iter": 2000, "lr": 0.09953, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24734, "top5_acc": 0.49656, "loss_cls": 4.15377, "loss": 4.15377, "time": 0.70579} +{"mode": "train", "epoch": 7, "iter": 2100, "lr": 0.09953, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24188, "top5_acc": 0.48438, "loss_cls": 4.17124, "loss": 4.17124, "time": 0.70464} +{"mode": "train", "epoch": 7, "iter": 2200, "lr": 0.09952, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23875, "top5_acc": 0.48672, "loss_cls": 4.18502, "loss": 4.18502, "time": 0.70043} +{"mode": "train", "epoch": 7, "iter": 2300, "lr": 0.09952, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23516, "top5_acc": 0.47562, "loss_cls": 4.21571, "loss": 4.21571, "time": 0.69839} +{"mode": "train", "epoch": 7, "iter": 2400, "lr": 0.09952, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2375, "top5_acc": 0.48547, "loss_cls": 4.21218, "loss": 4.21218, "time": 0.6985} +{"mode": "train", "epoch": 7, "iter": 2500, "lr": 0.09951, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.245, "top5_acc": 0.48734, "loss_cls": 4.15884, "loss": 4.15884, "time": 0.69536} +{"mode": "train", "epoch": 7, "iter": 2600, "lr": 0.09951, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24531, "top5_acc": 0.49422, "loss_cls": 4.16393, "loss": 4.16393, "time": 0.70069} +{"mode": "train", "epoch": 7, "iter": 2700, "lr": 0.09951, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24484, "top5_acc": 0.49156, "loss_cls": 4.16262, "loss": 4.16262, "time": 0.70036} +{"mode": "train", "epoch": 7, "iter": 2800, "lr": 0.0995, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.23625, "top5_acc": 0.49094, "loss_cls": 4.18965, "loss": 4.18965, "time": 0.70075} +{"mode": "train", "epoch": 7, "iter": 2900, "lr": 0.0995, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.235, "top5_acc": 0.47234, "loss_cls": 4.23138, "loss": 4.23138, "time": 0.70269} +{"mode": "train", "epoch": 7, "iter": 3000, "lr": 0.09949, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24344, "top5_acc": 0.48109, "loss_cls": 4.19416, "loss": 4.19416, "time": 0.69869} +{"mode": "train", "epoch": 7, "iter": 3100, "lr": 0.09949, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24922, "top5_acc": 0.48953, "loss_cls": 4.1547, "loss": 4.1547, "time": 0.69986} +{"mode": "train", "epoch": 7, "iter": 3200, "lr": 0.09949, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23969, "top5_acc": 0.475, "loss_cls": 4.18761, "loss": 4.18761, "time": 0.69961} +{"mode": "train", "epoch": 7, "iter": 3300, "lr": 0.09948, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24141, "top5_acc": 0.48281, "loss_cls": 4.2071, "loss": 4.2071, "time": 0.70213} +{"mode": "train", "epoch": 7, "iter": 3400, "lr": 0.09948, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.23828, "top5_acc": 0.49141, "loss_cls": 4.16125, "loss": 4.16125, "time": 0.69715} +{"mode": "train", "epoch": 7, "iter": 3500, "lr": 0.09947, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24062, "top5_acc": 0.48438, "loss_cls": 4.22504, "loss": 4.22504, "time": 0.69954} +{"mode": "train", "epoch": 7, "iter": 3600, "lr": 0.09947, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24062, "top5_acc": 0.48703, "loss_cls": 4.15973, "loss": 4.15973, "time": 0.69895} +{"mode": "train", "epoch": 7, "iter": 3700, "lr": 0.09947, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24719, "top5_acc": 0.48953, "loss_cls": 4.15426, "loss": 4.15426, "time": 0.69788} +{"mode": "val", "epoch": 7, "iter": 309, "lr": 0.09946, "top1_acc": 0.12906, "top5_acc": 0.31424, "mean_class_accuracy": 0.12889} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.09946, "memory": 15990, "data_time": 1.25361, "top1_acc": 0.25891, "top5_acc": 0.50313, "loss_cls": 4.09133, "loss": 4.09133, "time": 1.95384} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.09946, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24234, "top5_acc": 0.47562, "loss_cls": 4.17581, "loss": 4.17581, "time": 0.69748} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.09945, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23812, "top5_acc": 0.48938, "loss_cls": 4.15195, "loss": 4.15195, "time": 0.69806} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.09945, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24125, "top5_acc": 0.47891, "loss_cls": 4.17021, "loss": 4.17021, "time": 0.70001} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.09944, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23859, "top5_acc": 0.47953, "loss_cls": 4.19502, "loss": 4.19502, "time": 0.69883} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.09944, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24062, "top5_acc": 0.49469, "loss_cls": 4.15624, "loss": 4.15624, "time": 0.69774} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.09943, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23812, "top5_acc": 0.48406, "loss_cls": 4.17413, "loss": 4.17413, "time": 0.70015} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.09943, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24, "top5_acc": 0.49219, "loss_cls": 4.15803, "loss": 4.15803, "time": 0.69791} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.09943, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24953, "top5_acc": 0.49453, "loss_cls": 4.11802, "loss": 4.11802, "time": 0.70042} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.09942, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24578, "top5_acc": 0.48656, "loss_cls": 4.16673, "loss": 4.16673, "time": 0.70002} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.09942, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25047, "top5_acc": 0.48688, "loss_cls": 4.14624, "loss": 4.14624, "time": 0.69901} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.09941, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.24094, "top5_acc": 0.48562, "loss_cls": 4.16716, "loss": 4.16716, "time": 0.69723} +{"mode": "train", "epoch": 8, "iter": 1300, "lr": 0.09941, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23312, "top5_acc": 0.47906, "loss_cls": 4.19877, "loss": 4.19877, "time": 0.69907} +{"mode": "train", "epoch": 8, "iter": 1400, "lr": 0.0994, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24531, "top5_acc": 0.48578, "loss_cls": 4.16838, "loss": 4.16838, "time": 0.69786} +{"mode": "train", "epoch": 8, "iter": 1500, "lr": 0.0994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23453, "top5_acc": 0.47688, "loss_cls": 4.20295, "loss": 4.20295, "time": 0.7017} +{"mode": "train", "epoch": 8, "iter": 1600, "lr": 0.0994, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24578, "top5_acc": 0.49422, "loss_cls": 4.15712, "loss": 4.15712, "time": 0.7101} +{"mode": "train", "epoch": 8, "iter": 1700, "lr": 0.09939, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24828, "top5_acc": 0.48625, "loss_cls": 4.17169, "loss": 4.17169, "time": 0.70779} +{"mode": "train", "epoch": 8, "iter": 1800, "lr": 0.09939, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23891, "top5_acc": 0.47547, "loss_cls": 4.22628, "loss": 4.22628, "time": 0.71738} +{"mode": "train", "epoch": 8, "iter": 1900, "lr": 0.09938, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24031, "top5_acc": 0.47719, "loss_cls": 4.23952, "loss": 4.23952, "time": 0.70663} +{"mode": "train", "epoch": 8, "iter": 2000, "lr": 0.09938, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23875, "top5_acc": 0.48703, "loss_cls": 4.18919, "loss": 4.18919, "time": 0.71168} +{"mode": "train", "epoch": 8, "iter": 2100, "lr": 0.09937, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24844, "top5_acc": 0.49812, "loss_cls": 4.14282, "loss": 4.14282, "time": 0.7087} +{"mode": "train", "epoch": 8, "iter": 2200, "lr": 0.09937, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24531, "top5_acc": 0.49484, "loss_cls": 4.12301, "loss": 4.12301, "time": 0.70127} +{"mode": "train", "epoch": 8, "iter": 2300, "lr": 0.09937, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.24438, "top5_acc": 0.48953, "loss_cls": 4.16325, "loss": 4.16325, "time": 0.6995} +{"mode": "train", "epoch": 8, "iter": 2400, "lr": 0.09936, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24594, "top5_acc": 0.48297, "loss_cls": 4.17369, "loss": 4.17369, "time": 0.70141} +{"mode": "train", "epoch": 8, "iter": 2500, "lr": 0.09936, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24516, "top5_acc": 0.49609, "loss_cls": 4.14838, "loss": 4.14838, "time": 0.70042} +{"mode": "train", "epoch": 8, "iter": 2600, "lr": 0.09935, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24234, "top5_acc": 0.48203, "loss_cls": 4.17591, "loss": 4.17591, "time": 0.70153} +{"mode": "train", "epoch": 8, "iter": 2700, "lr": 0.09935, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24375, "top5_acc": 0.49125, "loss_cls": 4.15263, "loss": 4.15263, "time": 0.70054} +{"mode": "train", "epoch": 8, "iter": 2800, "lr": 0.09934, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24094, "top5_acc": 0.49375, "loss_cls": 4.16206, "loss": 4.16206, "time": 0.69991} +{"mode": "train", "epoch": 8, "iter": 2900, "lr": 0.09934, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24156, "top5_acc": 0.49031, "loss_cls": 4.19079, "loss": 4.19079, "time": 0.69978} +{"mode": "train", "epoch": 8, "iter": 3000, "lr": 0.09933, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23328, "top5_acc": 0.47406, "loss_cls": 4.20849, "loss": 4.20849, "time": 0.69974} +{"mode": "train", "epoch": 8, "iter": 3100, "lr": 0.09933, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24688, "top5_acc": 0.48891, "loss_cls": 4.14457, "loss": 4.14457, "time": 0.70039} +{"mode": "train", "epoch": 8, "iter": 3200, "lr": 0.09933, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24703, "top5_acc": 0.49484, "loss_cls": 4.1409, "loss": 4.1409, "time": 0.70075} +{"mode": "train", "epoch": 8, "iter": 3300, "lr": 0.09932, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.235, "top5_acc": 0.48312, "loss_cls": 4.20961, "loss": 4.20961, "time": 0.70011} +{"mode": "train", "epoch": 8, "iter": 3400, "lr": 0.09932, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24906, "top5_acc": 0.49594, "loss_cls": 4.14042, "loss": 4.14042, "time": 0.70098} +{"mode": "train", "epoch": 8, "iter": 3500, "lr": 0.09931, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.24641, "top5_acc": 0.49141, "loss_cls": 4.17451, "loss": 4.17451, "time": 0.69742} +{"mode": "train", "epoch": 8, "iter": 3600, "lr": 0.09931, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25484, "top5_acc": 0.49516, "loss_cls": 4.12209, "loss": 4.12209, "time": 0.69821} +{"mode": "train", "epoch": 8, "iter": 3700, "lr": 0.0993, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.23844, "top5_acc": 0.48391, "loss_cls": 4.17163, "loss": 4.17163, "time": 0.69758} +{"mode": "val", "epoch": 8, "iter": 309, "lr": 0.0993, "top1_acc": 0.17708, "top5_acc": 0.40278, "mean_class_accuracy": 0.17668} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.0993, "memory": 15990, "data_time": 1.27969, "top1_acc": 0.23938, "top5_acc": 0.49359, "loss_cls": 4.1499, "loss": 4.1499, "time": 1.98053} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.09929, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25656, "top5_acc": 0.50438, "loss_cls": 4.10234, "loss": 4.10234, "time": 0.69863} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.09929, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24484, "top5_acc": 0.48672, "loss_cls": 4.15888, "loss": 4.15888, "time": 0.69745} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.09928, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24984, "top5_acc": 0.48875, "loss_cls": 4.13967, "loss": 4.13967, "time": 0.69755} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.09928, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.24266, "top5_acc": 0.49984, "loss_cls": 4.14412, "loss": 4.14412, "time": 0.69723} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.09927, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24375, "top5_acc": 0.48859, "loss_cls": 4.17848, "loss": 4.17848, "time": 0.69804} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.09927, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25359, "top5_acc": 0.50375, "loss_cls": 4.10946, "loss": 4.10946, "time": 0.69845} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.09926, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24172, "top5_acc": 0.48484, "loss_cls": 4.18044, "loss": 4.18044, "time": 0.69807} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.09926, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.24484, "top5_acc": 0.48938, "loss_cls": 4.12639, "loss": 4.12639, "time": 0.69818} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.09925, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23781, "top5_acc": 0.48438, "loss_cls": 4.18724, "loss": 4.18724, "time": 0.70005} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.09925, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24812, "top5_acc": 0.49109, "loss_cls": 4.16558, "loss": 4.16558, "time": 0.69785} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.09924, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25453, "top5_acc": 0.49609, "loss_cls": 4.1094, "loss": 4.1094, "time": 0.70028} +{"mode": "train", "epoch": 9, "iter": 1300, "lr": 0.09924, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25031, "top5_acc": 0.495, "loss_cls": 4.15174, "loss": 4.15174, "time": 0.70108} +{"mode": "train", "epoch": 9, "iter": 1400, "lr": 0.09923, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.24797, "top5_acc": 0.48641, "loss_cls": 4.15402, "loss": 4.15402, "time": 0.69966} +{"mode": "train", "epoch": 9, "iter": 1500, "lr": 0.09923, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23797, "top5_acc": 0.48469, "loss_cls": 4.19576, "loss": 4.19576, "time": 0.70314} +{"mode": "train", "epoch": 9, "iter": 1600, "lr": 0.09922, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24484, "top5_acc": 0.49578, "loss_cls": 4.15007, "loss": 4.15007, "time": 0.70932} +{"mode": "train", "epoch": 9, "iter": 1700, "lr": 0.09922, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.235, "top5_acc": 0.48234, "loss_cls": 4.18953, "loss": 4.18953, "time": 0.70933} +{"mode": "train", "epoch": 9, "iter": 1800, "lr": 0.09921, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24219, "top5_acc": 0.48766, "loss_cls": 4.15441, "loss": 4.15441, "time": 0.71641} +{"mode": "train", "epoch": 9, "iter": 1900, "lr": 0.09921, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24, "top5_acc": 0.48047, "loss_cls": 4.1721, "loss": 4.1721, "time": 0.70461} +{"mode": "train", "epoch": 9, "iter": 2000, "lr": 0.0992, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25, "top5_acc": 0.49562, "loss_cls": 4.14006, "loss": 4.14006, "time": 0.70067} +{"mode": "train", "epoch": 9, "iter": 2100, "lr": 0.0992, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24297, "top5_acc": 0.49438, "loss_cls": 4.12806, "loss": 4.12806, "time": 0.70072} +{"mode": "train", "epoch": 9, "iter": 2200, "lr": 0.09919, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.24969, "top5_acc": 0.49672, "loss_cls": 4.11825, "loss": 4.11825, "time": 0.70555} +{"mode": "train", "epoch": 9, "iter": 2300, "lr": 0.09919, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24969, "top5_acc": 0.49641, "loss_cls": 4.1281, "loss": 4.1281, "time": 0.7031} +{"mode": "train", "epoch": 9, "iter": 2400, "lr": 0.09918, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24422, "top5_acc": 0.48891, "loss_cls": 4.1463, "loss": 4.1463, "time": 0.7009} +{"mode": "train", "epoch": 9, "iter": 2500, "lr": 0.09918, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25562, "top5_acc": 0.49938, "loss_cls": 4.09128, "loss": 4.09128, "time": 0.70391} +{"mode": "train", "epoch": 9, "iter": 2600, "lr": 0.09917, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24219, "top5_acc": 0.48672, "loss_cls": 4.16553, "loss": 4.16553, "time": 0.70698} +{"mode": "train", "epoch": 9, "iter": 2700, "lr": 0.09917, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25172, "top5_acc": 0.49594, "loss_cls": 4.15188, "loss": 4.15188, "time": 0.70163} +{"mode": "train", "epoch": 9, "iter": 2800, "lr": 0.09916, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23969, "top5_acc": 0.47797, "loss_cls": 4.19847, "loss": 4.19847, "time": 0.70064} +{"mode": "train", "epoch": 9, "iter": 2900, "lr": 0.09916, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25453, "top5_acc": 0.50109, "loss_cls": 4.11666, "loss": 4.11666, "time": 0.70185} +{"mode": "train", "epoch": 9, "iter": 3000, "lr": 0.09915, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.255, "top5_acc": 0.49891, "loss_cls": 4.11359, "loss": 4.11359, "time": 0.70273} +{"mode": "train", "epoch": 9, "iter": 3100, "lr": 0.09915, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24422, "top5_acc": 0.48672, "loss_cls": 4.1417, "loss": 4.1417, "time": 0.70047} +{"mode": "train", "epoch": 9, "iter": 3200, "lr": 0.09914, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24266, "top5_acc": 0.49, "loss_cls": 4.14858, "loss": 4.14858, "time": 0.69882} +{"mode": "train", "epoch": 9, "iter": 3300, "lr": 0.09914, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24188, "top5_acc": 0.49047, "loss_cls": 4.13626, "loss": 4.13626, "time": 0.69992} +{"mode": "train", "epoch": 9, "iter": 3400, "lr": 0.09913, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24547, "top5_acc": 0.48891, "loss_cls": 4.14827, "loss": 4.14827, "time": 0.70077} +{"mode": "train", "epoch": 9, "iter": 3500, "lr": 0.09913, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24578, "top5_acc": 0.49109, "loss_cls": 4.13981, "loss": 4.13981, "time": 0.70084} +{"mode": "train", "epoch": 9, "iter": 3600, "lr": 0.09912, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24859, "top5_acc": 0.48969, "loss_cls": 4.1475, "loss": 4.1475, "time": 0.70047} +{"mode": "train", "epoch": 9, "iter": 3700, "lr": 0.09912, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24875, "top5_acc": 0.49047, "loss_cls": 4.12642, "loss": 4.12642, "time": 0.70304} +{"mode": "val", "epoch": 9, "iter": 309, "lr": 0.09911, "top1_acc": 0.14967, "top5_acc": 0.35917, "mean_class_accuracy": 0.14941} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.09911, "memory": 15990, "data_time": 1.24102, "top1_acc": 0.25062, "top5_acc": 0.49297, "loss_cls": 4.10282, "loss": 4.10282, "time": 1.94426} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.0991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24766, "top5_acc": 0.49625, "loss_cls": 4.11953, "loss": 4.11953, "time": 0.70007} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.0991, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2475, "top5_acc": 0.49297, "loss_cls": 4.15013, "loss": 4.15013, "time": 0.70135} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.09909, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25406, "top5_acc": 0.49469, "loss_cls": 4.08712, "loss": 4.08712, "time": 0.70026} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.09909, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25328, "top5_acc": 0.50984, "loss_cls": 4.07738, "loss": 4.07738, "time": 0.70111} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.09908, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24938, "top5_acc": 0.48375, "loss_cls": 4.18996, "loss": 4.18996, "time": 0.69814} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.09908, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24781, "top5_acc": 0.49312, "loss_cls": 4.12952, "loss": 4.12952, "time": 0.70024} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.09907, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25141, "top5_acc": 0.50375, "loss_cls": 4.09427, "loss": 4.09427, "time": 0.69892} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.09907, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25703, "top5_acc": 0.49016, "loss_cls": 4.09241, "loss": 4.09241, "time": 0.69904} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.09906, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24797, "top5_acc": 0.49734, "loss_cls": 4.10007, "loss": 4.10007, "time": 0.69857} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.09906, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25, "top5_acc": 0.50156, "loss_cls": 4.11548, "loss": 4.11548, "time": 0.70215} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.09905, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24812, "top5_acc": 0.50062, "loss_cls": 4.11397, "loss": 4.11397, "time": 0.70198} +{"mode": "train", "epoch": 10, "iter": 1300, "lr": 0.09905, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23422, "top5_acc": 0.47688, "loss_cls": 4.18375, "loss": 4.18375, "time": 0.70125} +{"mode": "train", "epoch": 10, "iter": 1400, "lr": 0.09904, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26, "top5_acc": 0.49625, "loss_cls": 4.09828, "loss": 4.09828, "time": 0.69842} +{"mode": "train", "epoch": 10, "iter": 1500, "lr": 0.09903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25125, "top5_acc": 0.49438, "loss_cls": 4.11177, "loss": 4.11177, "time": 0.70402} +{"mode": "train", "epoch": 10, "iter": 1600, "lr": 0.09903, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24625, "top5_acc": 0.49609, "loss_cls": 4.11919, "loss": 4.11919, "time": 0.71039} +{"mode": "train", "epoch": 10, "iter": 1700, "lr": 0.09902, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25125, "top5_acc": 0.49156, "loss_cls": 4.15825, "loss": 4.15825, "time": 0.70631} +{"mode": "train", "epoch": 10, "iter": 1800, "lr": 0.09902, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.24891, "top5_acc": 0.49641, "loss_cls": 4.15059, "loss": 4.15059, "time": 0.71384} +{"mode": "train", "epoch": 10, "iter": 1900, "lr": 0.09901, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25109, "top5_acc": 0.495, "loss_cls": 4.10527, "loss": 4.10527, "time": 0.70653} +{"mode": "train", "epoch": 10, "iter": 2000, "lr": 0.09901, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25094, "top5_acc": 0.49125, "loss_cls": 4.16696, "loss": 4.16696, "time": 0.70511} +{"mode": "train", "epoch": 10, "iter": 2100, "lr": 0.099, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24047, "top5_acc": 0.48562, "loss_cls": 4.15156, "loss": 4.15156, "time": 0.70915} +{"mode": "train", "epoch": 10, "iter": 2200, "lr": 0.099, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25516, "top5_acc": 0.50781, "loss_cls": 4.08695, "loss": 4.08695, "time": 0.70204} +{"mode": "train", "epoch": 10, "iter": 2300, "lr": 0.09899, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24375, "top5_acc": 0.48781, "loss_cls": 4.15517, "loss": 4.15517, "time": 0.70336} +{"mode": "train", "epoch": 10, "iter": 2400, "lr": 0.09898, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24812, "top5_acc": 0.48625, "loss_cls": 4.16072, "loss": 4.16072, "time": 0.70132} +{"mode": "train", "epoch": 10, "iter": 2500, "lr": 0.09898, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.2475, "top5_acc": 0.49828, "loss_cls": 4.09986, "loss": 4.09986, "time": 0.69838} +{"mode": "train", "epoch": 10, "iter": 2600, "lr": 0.09897, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24875, "top5_acc": 0.49688, "loss_cls": 4.10519, "loss": 4.10519, "time": 0.70189} +{"mode": "train", "epoch": 10, "iter": 2700, "lr": 0.09897, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24984, "top5_acc": 0.49531, "loss_cls": 4.1407, "loss": 4.1407, "time": 0.70003} +{"mode": "train", "epoch": 10, "iter": 2800, "lr": 0.09896, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25219, "top5_acc": 0.49578, "loss_cls": 4.14419, "loss": 4.14419, "time": 0.69616} +{"mode": "train", "epoch": 10, "iter": 2900, "lr": 0.09896, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25484, "top5_acc": 0.48828, "loss_cls": 4.15155, "loss": 4.15155, "time": 0.69877} +{"mode": "train", "epoch": 10, "iter": 3000, "lr": 0.09895, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24516, "top5_acc": 0.49188, "loss_cls": 4.12711, "loss": 4.12711, "time": 0.70076} +{"mode": "train", "epoch": 10, "iter": 3100, "lr": 0.09894, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25844, "top5_acc": 0.50266, "loss_cls": 4.10831, "loss": 4.10831, "time": 0.69745} +{"mode": "train", "epoch": 10, "iter": 3200, "lr": 0.09894, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26328, "top5_acc": 0.50797, "loss_cls": 4.09216, "loss": 4.09216, "time": 0.69951} +{"mode": "train", "epoch": 10, "iter": 3300, "lr": 0.09893, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24219, "top5_acc": 0.48656, "loss_cls": 4.16914, "loss": 4.16914, "time": 0.69838} +{"mode": "train", "epoch": 10, "iter": 3400, "lr": 0.09893, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24469, "top5_acc": 0.49109, "loss_cls": 4.19213, "loss": 4.19213, "time": 0.69985} +{"mode": "train", "epoch": 10, "iter": 3500, "lr": 0.09892, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24969, "top5_acc": 0.49156, "loss_cls": 4.12151, "loss": 4.12151, "time": 0.6976} +{"mode": "train", "epoch": 10, "iter": 3600, "lr": 0.09892, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24953, "top5_acc": 0.49625, "loss_cls": 4.14783, "loss": 4.14783, "time": 0.69694} +{"mode": "train", "epoch": 10, "iter": 3700, "lr": 0.09891, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24859, "top5_acc": 0.48891, "loss_cls": 4.16994, "loss": 4.16994, "time": 0.70076} +{"mode": "val", "epoch": 10, "iter": 309, "lr": 0.09891, "top1_acc": 0.15925, "top5_acc": 0.36195, "mean_class_accuracy": 0.1591} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.0989, "memory": 15990, "data_time": 1.24656, "top1_acc": 0.25422, "top5_acc": 0.49703, "loss_cls": 4.09767, "loss": 4.09767, "time": 1.95385} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.0989, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24875, "top5_acc": 0.49891, "loss_cls": 4.10152, "loss": 4.10152, "time": 0.69915} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.09889, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24844, "top5_acc": 0.49375, "loss_cls": 4.09758, "loss": 4.09758, "time": 0.69822} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.09888, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25266, "top5_acc": 0.50125, "loss_cls": 4.06784, "loss": 4.06784, "time": 0.69707} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.09888, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25094, "top5_acc": 0.49438, "loss_cls": 4.10966, "loss": 4.10966, "time": 0.70171} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.09887, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.24922, "top5_acc": 0.49219, "loss_cls": 4.1365, "loss": 4.1365, "time": 0.69876} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.09887, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24656, "top5_acc": 0.49297, "loss_cls": 4.11785, "loss": 4.11785, "time": 0.69982} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.09886, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24594, "top5_acc": 0.49172, "loss_cls": 4.12601, "loss": 4.12601, "time": 0.70007} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.09885, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24734, "top5_acc": 0.48969, "loss_cls": 4.16774, "loss": 4.16774, "time": 0.70152} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.09885, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25062, "top5_acc": 0.49812, "loss_cls": 4.12837, "loss": 4.12837, "time": 0.69874} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.09884, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24359, "top5_acc": 0.48812, "loss_cls": 4.15921, "loss": 4.15921, "time": 0.70001} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.09884, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25641, "top5_acc": 0.49156, "loss_cls": 4.11861, "loss": 4.11861, "time": 0.70047} +{"mode": "train", "epoch": 11, "iter": 1300, "lr": 0.09883, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25125, "top5_acc": 0.49875, "loss_cls": 4.09741, "loss": 4.09741, "time": 0.70029} +{"mode": "train", "epoch": 11, "iter": 1400, "lr": 0.09882, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25297, "top5_acc": 0.49453, "loss_cls": 4.13265, "loss": 4.13265, "time": 0.69638} +{"mode": "train", "epoch": 11, "iter": 1500, "lr": 0.09882, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25875, "top5_acc": 0.50141, "loss_cls": 4.09326, "loss": 4.09326, "time": 0.7069} +{"mode": "train", "epoch": 11, "iter": 1600, "lr": 0.09881, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25297, "top5_acc": 0.49984, "loss_cls": 4.11954, "loss": 4.11954, "time": 0.70853} +{"mode": "train", "epoch": 11, "iter": 1700, "lr": 0.09881, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25594, "top5_acc": 0.49938, "loss_cls": 4.09954, "loss": 4.09954, "time": 0.70512} +{"mode": "train", "epoch": 11, "iter": 1800, "lr": 0.0988, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24969, "top5_acc": 0.50281, "loss_cls": 4.12247, "loss": 4.12247, "time": 0.71201} +{"mode": "train", "epoch": 11, "iter": 1900, "lr": 0.09879, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26078, "top5_acc": 0.50062, "loss_cls": 4.07525, "loss": 4.07525, "time": 0.70869} +{"mode": "train", "epoch": 11, "iter": 2000, "lr": 0.09879, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25703, "top5_acc": 0.50313, "loss_cls": 4.09419, "loss": 4.09419, "time": 0.70411} +{"mode": "train", "epoch": 11, "iter": 2100, "lr": 0.09878, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24672, "top5_acc": 0.49422, "loss_cls": 4.14445, "loss": 4.14445, "time": 0.70116} +{"mode": "train", "epoch": 11, "iter": 2200, "lr": 0.09878, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25281, "top5_acc": 0.50031, "loss_cls": 4.09094, "loss": 4.09094, "time": 0.70099} +{"mode": "train", "epoch": 11, "iter": 2300, "lr": 0.09877, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25875, "top5_acc": 0.50531, "loss_cls": 4.08716, "loss": 4.08716, "time": 0.69912} +{"mode": "train", "epoch": 11, "iter": 2400, "lr": 0.09876, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25141, "top5_acc": 0.4875, "loss_cls": 4.14304, "loss": 4.14304, "time": 0.69926} +{"mode": "train", "epoch": 11, "iter": 2500, "lr": 0.09876, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2475, "top5_acc": 0.48812, "loss_cls": 4.17161, "loss": 4.17161, "time": 0.69886} +{"mode": "train", "epoch": 11, "iter": 2600, "lr": 0.09875, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.24859, "top5_acc": 0.49922, "loss_cls": 4.12917, "loss": 4.12917, "time": 0.69821} +{"mode": "train", "epoch": 11, "iter": 2700, "lr": 0.09874, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23969, "top5_acc": 0.48188, "loss_cls": 4.18609, "loss": 4.18609, "time": 0.69849} +{"mode": "train", "epoch": 11, "iter": 2800, "lr": 0.09874, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24781, "top5_acc": 0.50641, "loss_cls": 4.09806, "loss": 4.09806, "time": 0.70097} +{"mode": "train", "epoch": 11, "iter": 2900, "lr": 0.09873, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25719, "top5_acc": 0.49984, "loss_cls": 4.12232, "loss": 4.12232, "time": 0.69906} +{"mode": "train", "epoch": 11, "iter": 3000, "lr": 0.09873, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25219, "top5_acc": 0.49609, "loss_cls": 4.12243, "loss": 4.12243, "time": 0.70055} +{"mode": "train", "epoch": 11, "iter": 3100, "lr": 0.09872, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25359, "top5_acc": 0.4975, "loss_cls": 4.10414, "loss": 4.10414, "time": 0.69949} +{"mode": "train", "epoch": 11, "iter": 3200, "lr": 0.09871, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25, "top5_acc": 0.50438, "loss_cls": 4.10638, "loss": 4.10638, "time": 0.69871} +{"mode": "train", "epoch": 11, "iter": 3300, "lr": 0.09871, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25125, "top5_acc": 0.5, "loss_cls": 4.11295, "loss": 4.11295, "time": 0.69721} +{"mode": "train", "epoch": 11, "iter": 3400, "lr": 0.0987, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25609, "top5_acc": 0.50266, "loss_cls": 4.08793, "loss": 4.08793, "time": 0.70179} +{"mode": "train", "epoch": 11, "iter": 3500, "lr": 0.09869, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.24781, "top5_acc": 0.49875, "loss_cls": 4.13566, "loss": 4.13566, "time": 0.69914} +{"mode": "train", "epoch": 11, "iter": 3600, "lr": 0.09869, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26219, "top5_acc": 0.50656, "loss_cls": 4.04199, "loss": 4.04199, "time": 0.69785} +{"mode": "train", "epoch": 11, "iter": 3700, "lr": 0.09868, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25062, "top5_acc": 0.49406, "loss_cls": 4.1438, "loss": 4.1438, "time": 0.69978} +{"mode": "val", "epoch": 11, "iter": 309, "lr": 0.09868, "top1_acc": 0.1754, "top5_acc": 0.39452, "mean_class_accuracy": 0.17498} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.09867, "memory": 15990, "data_time": 1.22987, "top1_acc": 0.26016, "top5_acc": 0.51328, "loss_cls": 4.0319, "loss": 4.0319, "time": 1.9291} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.09867, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25578, "top5_acc": 0.50391, "loss_cls": 4.10196, "loss": 4.10196, "time": 0.69842} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.09866, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24938, "top5_acc": 0.50328, "loss_cls": 4.09911, "loss": 4.09911, "time": 0.70101} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.09865, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25281, "top5_acc": 0.49656, "loss_cls": 4.13547, "loss": 4.13547, "time": 0.69969} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.09865, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24453, "top5_acc": 0.49094, "loss_cls": 4.14124, "loss": 4.14124, "time": 0.69698} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.09864, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25469, "top5_acc": 0.50125, "loss_cls": 4.07591, "loss": 4.07591, "time": 0.6985} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.09863, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24906, "top5_acc": 0.49656, "loss_cls": 4.10713, "loss": 4.10713, "time": 0.69749} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.09863, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25516, "top5_acc": 0.49922, "loss_cls": 4.09648, "loss": 4.09648, "time": 0.69922} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.09862, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25438, "top5_acc": 0.50859, "loss_cls": 4.08682, "loss": 4.08682, "time": 0.69649} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.09861, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25266, "top5_acc": 0.49891, "loss_cls": 4.11419, "loss": 4.11419, "time": 0.70042} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.09861, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.24516, "top5_acc": 0.49297, "loss_cls": 4.13633, "loss": 4.13633, "time": 0.69893} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.0986, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24781, "top5_acc": 0.51281, "loss_cls": 4.07667, "loss": 4.07667, "time": 0.70081} +{"mode": "train", "epoch": 12, "iter": 1300, "lr": 0.09859, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25016, "top5_acc": 0.4925, "loss_cls": 4.13219, "loss": 4.13219, "time": 0.69952} +{"mode": "train", "epoch": 12, "iter": 1400, "lr": 0.09859, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2575, "top5_acc": 0.50609, "loss_cls": 4.08888, "loss": 4.08888, "time": 0.69882} +{"mode": "train", "epoch": 12, "iter": 1500, "lr": 0.09858, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25594, "top5_acc": 0.50906, "loss_cls": 4.05153, "loss": 4.05153, "time": 0.70169} +{"mode": "train", "epoch": 12, "iter": 1600, "lr": 0.09857, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25219, "top5_acc": 0.5025, "loss_cls": 4.10622, "loss": 4.10622, "time": 0.70607} +{"mode": "train", "epoch": 12, "iter": 1700, "lr": 0.09857, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2525, "top5_acc": 0.50219, "loss_cls": 4.09211, "loss": 4.09211, "time": 0.70466} +{"mode": "train", "epoch": 12, "iter": 1800, "lr": 0.09856, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25297, "top5_acc": 0.49906, "loss_cls": 4.08631, "loss": 4.08631, "time": 0.71477} +{"mode": "train", "epoch": 12, "iter": 1900, "lr": 0.09855, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.255, "top5_acc": 0.50828, "loss_cls": 4.07845, "loss": 4.07845, "time": 0.70659} +{"mode": "train", "epoch": 12, "iter": 2000, "lr": 0.09855, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25875, "top5_acc": 0.50125, "loss_cls": 4.09844, "loss": 4.09844, "time": 0.70564} +{"mode": "train", "epoch": 12, "iter": 2100, "lr": 0.09854, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25297, "top5_acc": 0.50156, "loss_cls": 4.11793, "loss": 4.11793, "time": 0.7047} +{"mode": "train", "epoch": 12, "iter": 2200, "lr": 0.09853, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24781, "top5_acc": 0.48609, "loss_cls": 4.14459, "loss": 4.14459, "time": 0.70301} +{"mode": "train", "epoch": 12, "iter": 2300, "lr": 0.09853, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25766, "top5_acc": 0.50938, "loss_cls": 4.08873, "loss": 4.08873, "time": 0.70058} +{"mode": "train", "epoch": 12, "iter": 2400, "lr": 0.09852, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25938, "top5_acc": 0.50422, "loss_cls": 4.10044, "loss": 4.10044, "time": 0.69981} +{"mode": "train", "epoch": 12, "iter": 2500, "lr": 0.09851, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23984, "top5_acc": 0.48922, "loss_cls": 4.15569, "loss": 4.15569, "time": 0.69598} +{"mode": "train", "epoch": 12, "iter": 2600, "lr": 0.09851, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2475, "top5_acc": 0.48422, "loss_cls": 4.19243, "loss": 4.19243, "time": 0.70325} +{"mode": "train", "epoch": 12, "iter": 2700, "lr": 0.0985, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24547, "top5_acc": 0.48594, "loss_cls": 4.1666, "loss": 4.1666, "time": 0.70164} +{"mode": "train", "epoch": 12, "iter": 2800, "lr": 0.09849, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25438, "top5_acc": 0.49562, "loss_cls": 4.11269, "loss": 4.11269, "time": 0.6993} +{"mode": "train", "epoch": 12, "iter": 2900, "lr": 0.09849, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25188, "top5_acc": 0.50609, "loss_cls": 4.10913, "loss": 4.10913, "time": 0.69857} +{"mode": "train", "epoch": 12, "iter": 3000, "lr": 0.09848, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24625, "top5_acc": 0.49641, "loss_cls": 4.14541, "loss": 4.14541, "time": 0.69779} +{"mode": "train", "epoch": 12, "iter": 3100, "lr": 0.09847, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.24469, "top5_acc": 0.49375, "loss_cls": 4.12046, "loss": 4.12046, "time": 0.69714} +{"mode": "train", "epoch": 12, "iter": 3200, "lr": 0.09847, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25156, "top5_acc": 0.50234, "loss_cls": 4.08253, "loss": 4.08253, "time": 0.70046} +{"mode": "train", "epoch": 12, "iter": 3300, "lr": 0.09846, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24906, "top5_acc": 0.49531, "loss_cls": 4.11586, "loss": 4.11586, "time": 0.70338} +{"mode": "train", "epoch": 12, "iter": 3400, "lr": 0.09845, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25297, "top5_acc": 0.49562, "loss_cls": 4.11235, "loss": 4.11235, "time": 0.69938} +{"mode": "train", "epoch": 12, "iter": 3500, "lr": 0.09845, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24781, "top5_acc": 0.48297, "loss_cls": 4.12796, "loss": 4.12796, "time": 0.7047} +{"mode": "train", "epoch": 12, "iter": 3600, "lr": 0.09844, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24375, "top5_acc": 0.49875, "loss_cls": 4.1121, "loss": 4.1121, "time": 0.69885} +{"mode": "train", "epoch": 12, "iter": 3700, "lr": 0.09843, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25641, "top5_acc": 0.50641, "loss_cls": 4.07379, "loss": 4.07379, "time": 0.69922} +{"mode": "val", "epoch": 12, "iter": 309, "lr": 0.09843, "top1_acc": 0.17616, "top5_acc": 0.38945, "mean_class_accuracy": 0.17582} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.09842, "memory": 15990, "data_time": 1.2576, "top1_acc": 0.26812, "top5_acc": 0.51625, "loss_cls": 4.03713, "loss": 4.03713, "time": 1.96183} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.09842, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25453, "top5_acc": 0.49469, "loss_cls": 4.0763, "loss": 4.0763, "time": 0.70027} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.09841, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26125, "top5_acc": 0.50875, "loss_cls": 4.06698, "loss": 4.06698, "time": 0.6998} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.0984, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26359, "top5_acc": 0.50047, "loss_cls": 4.0642, "loss": 4.0642, "time": 0.69893} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.09839, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.24891, "top5_acc": 0.49812, "loss_cls": 4.10242, "loss": 4.10242, "time": 0.69754} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.09839, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25266, "top5_acc": 0.49859, "loss_cls": 4.10447, "loss": 4.10447, "time": 0.69807} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.09838, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25922, "top5_acc": 0.50609, "loss_cls": 4.08597, "loss": 4.08597, "time": 0.69824} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.09837, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26297, "top5_acc": 0.50281, "loss_cls": 4.10204, "loss": 4.10204, "time": 0.69742} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.09837, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25203, "top5_acc": 0.49594, "loss_cls": 4.10705, "loss": 4.10705, "time": 0.69801} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.09836, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25422, "top5_acc": 0.49828, "loss_cls": 4.1187, "loss": 4.1187, "time": 0.69872} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.09835, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26078, "top5_acc": 0.5, "loss_cls": 4.07827, "loss": 4.07827, "time": 0.69815} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.09834, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25781, "top5_acc": 0.50328, "loss_cls": 4.08828, "loss": 4.08828, "time": 0.69875} +{"mode": "train", "epoch": 13, "iter": 1300, "lr": 0.09834, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.24844, "top5_acc": 0.49172, "loss_cls": 4.12298, "loss": 4.12298, "time": 0.69919} +{"mode": "train", "epoch": 13, "iter": 1400, "lr": 0.09833, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.24719, "top5_acc": 0.49656, "loss_cls": 4.09489, "loss": 4.09489, "time": 0.70015} +{"mode": "train", "epoch": 13, "iter": 1500, "lr": 0.09832, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24766, "top5_acc": 0.49469, "loss_cls": 4.13852, "loss": 4.13852, "time": 0.70148} +{"mode": "train", "epoch": 13, "iter": 1600, "lr": 0.09832, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27078, "top5_acc": 0.50719, "loss_cls": 4.05844, "loss": 4.05844, "time": 0.7038} +{"mode": "train", "epoch": 13, "iter": 1700, "lr": 0.09831, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25656, "top5_acc": 0.5025, "loss_cls": 4.07851, "loss": 4.07851, "time": 0.70415} +{"mode": "train", "epoch": 13, "iter": 1800, "lr": 0.0983, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25125, "top5_acc": 0.50328, "loss_cls": 4.11599, "loss": 4.11599, "time": 0.7164} +{"mode": "train", "epoch": 13, "iter": 1900, "lr": 0.09829, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25375, "top5_acc": 0.49609, "loss_cls": 4.12221, "loss": 4.12221, "time": 0.70513} +{"mode": "train", "epoch": 13, "iter": 2000, "lr": 0.09829, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24938, "top5_acc": 0.49328, "loss_cls": 4.12391, "loss": 4.12391, "time": 0.70154} +{"mode": "train", "epoch": 13, "iter": 2100, "lr": 0.09828, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25, "top5_acc": 0.5025, "loss_cls": 4.09828, "loss": 4.09828, "time": 0.70423} +{"mode": "train", "epoch": 13, "iter": 2200, "lr": 0.09827, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25547, "top5_acc": 0.50516, "loss_cls": 4.09309, "loss": 4.09309, "time": 0.69882} +{"mode": "train", "epoch": 13, "iter": 2300, "lr": 0.09827, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25141, "top5_acc": 0.49094, "loss_cls": 4.12338, "loss": 4.12338, "time": 0.69936} +{"mode": "train", "epoch": 13, "iter": 2400, "lr": 0.09826, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25281, "top5_acc": 0.49703, "loss_cls": 4.11073, "loss": 4.11073, "time": 0.69926} +{"mode": "train", "epoch": 13, "iter": 2500, "lr": 0.09825, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24906, "top5_acc": 0.5, "loss_cls": 4.09068, "loss": 4.09068, "time": 0.69851} +{"mode": "train", "epoch": 13, "iter": 2600, "lr": 0.09824, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24953, "top5_acc": 0.50016, "loss_cls": 4.09908, "loss": 4.09908, "time": 0.69628} +{"mode": "train", "epoch": 13, "iter": 2700, "lr": 0.09824, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25125, "top5_acc": 0.4975, "loss_cls": 4.09982, "loss": 4.09982, "time": 0.69733} +{"mode": "train", "epoch": 13, "iter": 2800, "lr": 0.09823, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25203, "top5_acc": 0.50094, "loss_cls": 4.12183, "loss": 4.12183, "time": 0.69775} +{"mode": "train", "epoch": 13, "iter": 2900, "lr": 0.09822, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25219, "top5_acc": 0.50297, "loss_cls": 4.09667, "loss": 4.09667, "time": 0.69812} +{"mode": "train", "epoch": 13, "iter": 3000, "lr": 0.09821, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26047, "top5_acc": 0.51313, "loss_cls": 4.04982, "loss": 4.04982, "time": 0.70016} +{"mode": "train", "epoch": 13, "iter": 3100, "lr": 0.09821, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24312, "top5_acc": 0.48344, "loss_cls": 4.15947, "loss": 4.15947, "time": 0.70078} +{"mode": "train", "epoch": 13, "iter": 3200, "lr": 0.0982, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24969, "top5_acc": 0.49797, "loss_cls": 4.09908, "loss": 4.09908, "time": 0.70121} +{"mode": "train", "epoch": 13, "iter": 3300, "lr": 0.09819, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25516, "top5_acc": 0.50094, "loss_cls": 4.1046, "loss": 4.1046, "time": 0.69965} +{"mode": "train", "epoch": 13, "iter": 3400, "lr": 0.09818, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25672, "top5_acc": 0.49594, "loss_cls": 4.07097, "loss": 4.07097, "time": 0.7027} +{"mode": "train", "epoch": 13, "iter": 3500, "lr": 0.09818, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24547, "top5_acc": 0.49109, "loss_cls": 4.13802, "loss": 4.13802, "time": 0.70172} +{"mode": "train", "epoch": 13, "iter": 3600, "lr": 0.09817, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25641, "top5_acc": 0.49984, "loss_cls": 4.10642, "loss": 4.10642, "time": 0.69847} +{"mode": "train", "epoch": 13, "iter": 3700, "lr": 0.09816, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25375, "top5_acc": 0.4925, "loss_cls": 4.11185, "loss": 4.11185, "time": 0.69883} +{"mode": "val", "epoch": 13, "iter": 309, "lr": 0.09816, "top1_acc": 0.17819, "top5_acc": 0.39265, "mean_class_accuracy": 0.17803} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.09815, "memory": 15990, "data_time": 1.27513, "top1_acc": 0.26094, "top5_acc": 0.50766, "loss_cls": 4.06647, "loss": 4.06647, "time": 1.97793} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.09814, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25109, "top5_acc": 0.50297, "loss_cls": 4.0901, "loss": 4.0901, "time": 0.7015} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.09814, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26375, "top5_acc": 0.51406, "loss_cls": 4.04384, "loss": 4.04384, "time": 0.70045} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.09813, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25047, "top5_acc": 0.49969, "loss_cls": 4.1155, "loss": 4.1155, "time": 0.70292} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.09812, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25453, "top5_acc": 0.50313, "loss_cls": 4.09735, "loss": 4.09735, "time": 0.69771} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.09811, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24984, "top5_acc": 0.49062, "loss_cls": 4.14118, "loss": 4.14118, "time": 0.70219} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.09811, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24969, "top5_acc": 0.50281, "loss_cls": 4.09462, "loss": 4.09462, "time": 0.69952} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.0981, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25969, "top5_acc": 0.50828, "loss_cls": 4.05542, "loss": 4.05542, "time": 0.69961} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.09809, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24938, "top5_acc": 0.49203, "loss_cls": 4.11742, "loss": 4.11742, "time": 0.7009} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.09808, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25359, "top5_acc": 0.50172, "loss_cls": 4.10317, "loss": 4.10317, "time": 0.70125} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.09807, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24844, "top5_acc": 0.50109, "loss_cls": 4.08434, "loss": 4.08434, "time": 0.70084} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.09807, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25062, "top5_acc": 0.48766, "loss_cls": 4.15303, "loss": 4.15303, "time": 0.69724} +{"mode": "train", "epoch": 14, "iter": 1300, "lr": 0.09806, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25766, "top5_acc": 0.49859, "loss_cls": 4.11324, "loss": 4.11324, "time": 0.70125} +{"mode": "train", "epoch": 14, "iter": 1400, "lr": 0.09805, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25469, "top5_acc": 0.50672, "loss_cls": 4.07502, "loss": 4.07502, "time": 0.70467} +{"mode": "train", "epoch": 14, "iter": 1500, "lr": 0.09804, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26859, "top5_acc": 0.51094, "loss_cls": 4.04455, "loss": 4.04455, "time": 0.70208} +{"mode": "train", "epoch": 14, "iter": 1600, "lr": 0.09804, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24438, "top5_acc": 0.49625, "loss_cls": 4.11931, "loss": 4.11931, "time": 0.71026} +{"mode": "train", "epoch": 14, "iter": 1700, "lr": 0.09803, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25406, "top5_acc": 0.49719, "loss_cls": 4.08957, "loss": 4.08957, "time": 0.70227} +{"mode": "train", "epoch": 14, "iter": 1800, "lr": 0.09802, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24812, "top5_acc": 0.49641, "loss_cls": 4.11439, "loss": 4.11439, "time": 0.70806} +{"mode": "train", "epoch": 14, "iter": 1900, "lr": 0.09801, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25859, "top5_acc": 0.50688, "loss_cls": 4.06221, "loss": 4.06221, "time": 0.70659} +{"mode": "train", "epoch": 14, "iter": 2000, "lr": 0.098, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25281, "top5_acc": 0.4975, "loss_cls": 4.1175, "loss": 4.1175, "time": 0.70301} +{"mode": "train", "epoch": 14, "iter": 2100, "lr": 0.098, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25438, "top5_acc": 0.50938, "loss_cls": 4.0809, "loss": 4.0809, "time": 0.70584} +{"mode": "train", "epoch": 14, "iter": 2200, "lr": 0.09799, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24641, "top5_acc": 0.49969, "loss_cls": 4.09727, "loss": 4.09727, "time": 0.70613} +{"mode": "train", "epoch": 14, "iter": 2300, "lr": 0.09798, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25078, "top5_acc": 0.50984, "loss_cls": 4.09767, "loss": 4.09767, "time": 0.69679} +{"mode": "train", "epoch": 14, "iter": 2400, "lr": 0.09797, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25828, "top5_acc": 0.49859, "loss_cls": 4.10088, "loss": 4.10088, "time": 0.70418} +{"mode": "train", "epoch": 14, "iter": 2500, "lr": 0.09797, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24703, "top5_acc": 0.49047, "loss_cls": 4.1064, "loss": 4.1064, "time": 0.69925} +{"mode": "train", "epoch": 14, "iter": 2600, "lr": 0.09796, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25031, "top5_acc": 0.49359, "loss_cls": 4.12266, "loss": 4.12266, "time": 0.69807} +{"mode": "train", "epoch": 14, "iter": 2700, "lr": 0.09795, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25656, "top5_acc": 0.50625, "loss_cls": 4.06632, "loss": 4.06632, "time": 0.6999} +{"mode": "train", "epoch": 14, "iter": 2800, "lr": 0.09794, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25688, "top5_acc": 0.50734, "loss_cls": 4.09569, "loss": 4.09569, "time": 0.69888} +{"mode": "train", "epoch": 14, "iter": 2900, "lr": 0.09793, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25094, "top5_acc": 0.50656, "loss_cls": 4.08283, "loss": 4.08283, "time": 0.69988} +{"mode": "train", "epoch": 14, "iter": 3000, "lr": 0.09793, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25719, "top5_acc": 0.51422, "loss_cls": 4.04747, "loss": 4.04747, "time": 0.70047} +{"mode": "train", "epoch": 14, "iter": 3100, "lr": 0.09792, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25734, "top5_acc": 0.50125, "loss_cls": 4.09603, "loss": 4.09603, "time": 0.70257} +{"mode": "train", "epoch": 14, "iter": 3200, "lr": 0.09791, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25828, "top5_acc": 0.49984, "loss_cls": 4.10385, "loss": 4.10385, "time": 0.69798} +{"mode": "train", "epoch": 14, "iter": 3300, "lr": 0.0979, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25531, "top5_acc": 0.50297, "loss_cls": 4.10067, "loss": 4.10067, "time": 0.69545} +{"mode": "train", "epoch": 14, "iter": 3400, "lr": 0.09789, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25297, "top5_acc": 0.50156, "loss_cls": 4.10031, "loss": 4.10031, "time": 0.69896} +{"mode": "train", "epoch": 14, "iter": 3500, "lr": 0.09789, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24781, "top5_acc": 0.4975, "loss_cls": 4.09758, "loss": 4.09758, "time": 0.69941} +{"mode": "train", "epoch": 14, "iter": 3600, "lr": 0.09788, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25812, "top5_acc": 0.50562, "loss_cls": 4.06127, "loss": 4.06127, "time": 0.69987} +{"mode": "train", "epoch": 14, "iter": 3700, "lr": 0.09787, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25203, "top5_acc": 0.49703, "loss_cls": 4.09808, "loss": 4.09808, "time": 0.69861} +{"mode": "val", "epoch": 14, "iter": 309, "lr": 0.09787, "top1_acc": 0.15469, "top5_acc": 0.37522, "mean_class_accuracy": 0.15446} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.09786, "memory": 15990, "data_time": 1.2598, "top1_acc": 0.25922, "top5_acc": 0.50047, "loss_cls": 4.06144, "loss": 4.06144, "time": 1.96174} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.09785, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27203, "top5_acc": 0.52969, "loss_cls": 3.97469, "loss": 3.97469, "time": 0.70038} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.09784, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26125, "top5_acc": 0.51031, "loss_cls": 4.0644, "loss": 4.0644, "time": 0.69908} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.09783, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25891, "top5_acc": 0.50875, "loss_cls": 4.06021, "loss": 4.06021, "time": 0.69758} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.09783, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.2525, "top5_acc": 0.50438, "loss_cls": 4.09414, "loss": 4.09414, "time": 0.69961} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.09782, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25688, "top5_acc": 0.50203, "loss_cls": 4.09123, "loss": 4.09123, "time": 0.70065} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.09781, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26062, "top5_acc": 0.50656, "loss_cls": 4.06831, "loss": 4.06831, "time": 0.69922} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.0978, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25047, "top5_acc": 0.49766, "loss_cls": 4.10791, "loss": 4.10791, "time": 0.70621} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.09779, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25859, "top5_acc": 0.50125, "loss_cls": 4.06611, "loss": 4.06611, "time": 0.70156} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.09778, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25297, "top5_acc": 0.50641, "loss_cls": 4.07678, "loss": 4.07678, "time": 0.70307} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.09778, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26266, "top5_acc": 0.50531, "loss_cls": 4.07044, "loss": 4.07044, "time": 0.70228} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.09777, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25344, "top5_acc": 0.50203, "loss_cls": 4.09271, "loss": 4.09271, "time": 0.69835} +{"mode": "train", "epoch": 15, "iter": 1300, "lr": 0.09776, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25938, "top5_acc": 0.49703, "loss_cls": 4.10215, "loss": 4.10215, "time": 0.69733} +{"mode": "train", "epoch": 15, "iter": 1400, "lr": 0.09775, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25453, "top5_acc": 0.50406, "loss_cls": 4.09684, "loss": 4.09684, "time": 0.69909} +{"mode": "train", "epoch": 15, "iter": 1500, "lr": 0.09774, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25422, "top5_acc": 0.50625, "loss_cls": 4.07524, "loss": 4.07524, "time": 0.70511} +{"mode": "train", "epoch": 15, "iter": 1600, "lr": 0.09773, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25781, "top5_acc": 0.50484, "loss_cls": 4.09669, "loss": 4.09669, "time": 0.7078} +{"mode": "train", "epoch": 15, "iter": 1700, "lr": 0.09773, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25375, "top5_acc": 0.49266, "loss_cls": 4.11363, "loss": 4.11363, "time": 0.70063} +{"mode": "train", "epoch": 15, "iter": 1800, "lr": 0.09772, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24484, "top5_acc": 0.48969, "loss_cls": 4.13413, "loss": 4.13413, "time": 0.71377} +{"mode": "train", "epoch": 15, "iter": 1900, "lr": 0.09771, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25453, "top5_acc": 0.49438, "loss_cls": 4.10581, "loss": 4.10581, "time": 0.70608} +{"mode": "train", "epoch": 15, "iter": 2000, "lr": 0.0977, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25625, "top5_acc": 0.50375, "loss_cls": 4.08628, "loss": 4.08628, "time": 0.70647} +{"mode": "train", "epoch": 15, "iter": 2100, "lr": 0.09769, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25219, "top5_acc": 0.49688, "loss_cls": 4.11254, "loss": 4.11254, "time": 0.70871} +{"mode": "train", "epoch": 15, "iter": 2200, "lr": 0.09768, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26312, "top5_acc": 0.49391, "loss_cls": 4.11968, "loss": 4.11968, "time": 0.70201} +{"mode": "train", "epoch": 15, "iter": 2300, "lr": 0.09768, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25328, "top5_acc": 0.50688, "loss_cls": 4.09643, "loss": 4.09643, "time": 0.70348} +{"mode": "train", "epoch": 15, "iter": 2400, "lr": 0.09767, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25984, "top5_acc": 0.50656, "loss_cls": 4.10828, "loss": 4.10828, "time": 0.70056} +{"mode": "train", "epoch": 15, "iter": 2500, "lr": 0.09766, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25984, "top5_acc": 0.50328, "loss_cls": 4.11015, "loss": 4.11015, "time": 0.70423} +{"mode": "train", "epoch": 15, "iter": 2600, "lr": 0.09765, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.24922, "top5_acc": 0.49641, "loss_cls": 4.1056, "loss": 4.1056, "time": 0.69879} +{"mode": "train", "epoch": 15, "iter": 2700, "lr": 0.09764, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26469, "top5_acc": 0.49828, "loss_cls": 4.07793, "loss": 4.07793, "time": 0.69928} +{"mode": "train", "epoch": 15, "iter": 2800, "lr": 0.09763, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26125, "top5_acc": 0.49609, "loss_cls": 4.0859, "loss": 4.0859, "time": 0.69807} +{"mode": "train", "epoch": 15, "iter": 2900, "lr": 0.09763, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26438, "top5_acc": 0.51141, "loss_cls": 4.05055, "loss": 4.05055, "time": 0.69959} +{"mode": "train", "epoch": 15, "iter": 3000, "lr": 0.09762, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26219, "top5_acc": 0.50953, "loss_cls": 4.06258, "loss": 4.06258, "time": 0.69842} +{"mode": "train", "epoch": 15, "iter": 3100, "lr": 0.09761, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26344, "top5_acc": 0.50797, "loss_cls": 4.04438, "loss": 4.04438, "time": 0.69794} +{"mode": "train", "epoch": 15, "iter": 3200, "lr": 0.0976, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2575, "top5_acc": 0.50766, "loss_cls": 4.10096, "loss": 4.10096, "time": 0.69854} +{"mode": "train", "epoch": 15, "iter": 3300, "lr": 0.09759, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25, "top5_acc": 0.50031, "loss_cls": 4.11837, "loss": 4.11837, "time": 0.69908} +{"mode": "train", "epoch": 15, "iter": 3400, "lr": 0.09758, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25094, "top5_acc": 0.49672, "loss_cls": 4.12602, "loss": 4.12602, "time": 0.70099} +{"mode": "train", "epoch": 15, "iter": 3500, "lr": 0.09757, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25297, "top5_acc": 0.49156, "loss_cls": 4.13994, "loss": 4.13994, "time": 0.69949} +{"mode": "train", "epoch": 15, "iter": 3600, "lr": 0.09757, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26047, "top5_acc": 0.50797, "loss_cls": 4.09424, "loss": 4.09424, "time": 0.70149} +{"mode": "train", "epoch": 15, "iter": 3700, "lr": 0.09756, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25594, "top5_acc": 0.50734, "loss_cls": 4.07618, "loss": 4.07618, "time": 0.70269} +{"mode": "val", "epoch": 15, "iter": 309, "lr": 0.09755, "top1_acc": 0.17976, "top5_acc": 0.41149, "mean_class_accuracy": 0.17956} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.09754, "memory": 15990, "data_time": 1.23635, "top1_acc": 0.25812, "top5_acc": 0.51094, "loss_cls": 4.07736, "loss": 4.07736, "time": 1.93853} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.09754, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26359, "top5_acc": 0.51328, "loss_cls": 4.04005, "loss": 4.04005, "time": 0.70162} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.09753, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25641, "top5_acc": 0.50375, "loss_cls": 4.10525, "loss": 4.10525, "time": 0.70093} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.09752, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24656, "top5_acc": 0.49953, "loss_cls": 4.11813, "loss": 4.11813, "time": 0.69998} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.09751, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26109, "top5_acc": 0.50969, "loss_cls": 4.04507, "loss": 4.04507, "time": 0.69834} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.0975, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25625, "top5_acc": 0.50672, "loss_cls": 4.05831, "loss": 4.05831, "time": 0.70121} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.09749, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26172, "top5_acc": 0.51094, "loss_cls": 4.04289, "loss": 4.04289, "time": 0.69896} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.09748, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25531, "top5_acc": 0.51547, "loss_cls": 4.05733, "loss": 4.05733, "time": 0.69725} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.09747, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26156, "top5_acc": 0.49734, "loss_cls": 4.09912, "loss": 4.09912, "time": 0.69906} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.09747, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26281, "top5_acc": 0.50844, "loss_cls": 4.05396, "loss": 4.05396, "time": 0.69904} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.09746, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25141, "top5_acc": 0.49938, "loss_cls": 4.09864, "loss": 4.09864, "time": 0.70002} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.09745, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.2625, "top5_acc": 0.50359, "loss_cls": 4.07971, "loss": 4.07971, "time": 0.69914} +{"mode": "train", "epoch": 16, "iter": 1300, "lr": 0.09744, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25031, "top5_acc": 0.49375, "loss_cls": 4.13853, "loss": 4.13853, "time": 0.7012} +{"mode": "train", "epoch": 16, "iter": 1400, "lr": 0.09743, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25516, "top5_acc": 0.50562, "loss_cls": 4.07547, "loss": 4.07547, "time": 0.69899} +{"mode": "train", "epoch": 16, "iter": 1500, "lr": 0.09742, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25812, "top5_acc": 0.51328, "loss_cls": 4.05808, "loss": 4.05808, "time": 0.70481} +{"mode": "train", "epoch": 16, "iter": 1600, "lr": 0.09741, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25031, "top5_acc": 0.50203, "loss_cls": 4.09609, "loss": 4.09609, "time": 0.70682} +{"mode": "train", "epoch": 16, "iter": 1700, "lr": 0.0974, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25797, "top5_acc": 0.50438, "loss_cls": 4.07825, "loss": 4.07825, "time": 0.70654} +{"mode": "train", "epoch": 16, "iter": 1800, "lr": 0.0974, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25172, "top5_acc": 0.50125, "loss_cls": 4.1072, "loss": 4.1072, "time": 0.71177} +{"mode": "train", "epoch": 16, "iter": 1900, "lr": 0.09739, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25703, "top5_acc": 0.50969, "loss_cls": 4.05734, "loss": 4.05734, "time": 0.71099} +{"mode": "train", "epoch": 16, "iter": 2000, "lr": 0.09738, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25625, "top5_acc": 0.50344, "loss_cls": 4.0737, "loss": 4.0737, "time": 0.7059} +{"mode": "train", "epoch": 16, "iter": 2100, "lr": 0.09737, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25906, "top5_acc": 0.50172, "loss_cls": 4.09585, "loss": 4.09585, "time": 0.70581} +{"mode": "train", "epoch": 16, "iter": 2200, "lr": 0.09736, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26, "top5_acc": 0.51016, "loss_cls": 4.08248, "loss": 4.08248, "time": 0.7023} +{"mode": "train", "epoch": 16, "iter": 2300, "lr": 0.09735, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25219, "top5_acc": 0.50656, "loss_cls": 4.07974, "loss": 4.07974, "time": 0.69838} +{"mode": "train", "epoch": 16, "iter": 2400, "lr": 0.09734, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25016, "top5_acc": 0.49844, "loss_cls": 4.08473, "loss": 4.08473, "time": 0.69856} +{"mode": "train", "epoch": 16, "iter": 2500, "lr": 0.09733, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26672, "top5_acc": 0.50641, "loss_cls": 4.04622, "loss": 4.04622, "time": 0.70364} +{"mode": "train", "epoch": 16, "iter": 2600, "lr": 0.09732, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27297, "top5_acc": 0.51594, "loss_cls": 4.00571, "loss": 4.00571, "time": 0.70067} +{"mode": "train", "epoch": 16, "iter": 2700, "lr": 0.09731, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26266, "top5_acc": 0.49719, "loss_cls": 4.08205, "loss": 4.08205, "time": 0.69985} +{"mode": "train", "epoch": 16, "iter": 2800, "lr": 0.09731, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26047, "top5_acc": 0.50031, "loss_cls": 4.07926, "loss": 4.07926, "time": 0.70115} +{"mode": "train", "epoch": 16, "iter": 2900, "lr": 0.0973, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25125, "top5_acc": 0.50156, "loss_cls": 4.10625, "loss": 4.10625, "time": 0.69799} +{"mode": "train", "epoch": 16, "iter": 3000, "lr": 0.09729, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25516, "top5_acc": 0.50344, "loss_cls": 4.09229, "loss": 4.09229, "time": 0.70245} +{"mode": "train", "epoch": 16, "iter": 3100, "lr": 0.09728, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25297, "top5_acc": 0.49469, "loss_cls": 4.12105, "loss": 4.12105, "time": 0.69844} +{"mode": "train", "epoch": 16, "iter": 3200, "lr": 0.09727, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24844, "top5_acc": 0.50375, "loss_cls": 4.06577, "loss": 4.06577, "time": 0.70034} +{"mode": "train", "epoch": 16, "iter": 3300, "lr": 0.09726, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.2525, "top5_acc": 0.50562, "loss_cls": 4.08036, "loss": 4.08036, "time": 0.69877} +{"mode": "train", "epoch": 16, "iter": 3400, "lr": 0.09725, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.2625, "top5_acc": 0.50047, "loss_cls": 4.08272, "loss": 4.08272, "time": 0.70228} +{"mode": "train", "epoch": 16, "iter": 3500, "lr": 0.09724, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25234, "top5_acc": 0.51109, "loss_cls": 4.08826, "loss": 4.08826, "time": 0.69964} +{"mode": "train", "epoch": 16, "iter": 3600, "lr": 0.09723, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25469, "top5_acc": 0.50266, "loss_cls": 4.08808, "loss": 4.08808, "time": 0.69872} +{"mode": "train", "epoch": 16, "iter": 3700, "lr": 0.09722, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25031, "top5_acc": 0.49516, "loss_cls": 4.11697, "loss": 4.11697, "time": 0.69963} +{"mode": "val", "epoch": 16, "iter": 309, "lr": 0.09722, "top1_acc": 0.18204, "top5_acc": 0.40288, "mean_class_accuracy": 0.18193} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.09721, "memory": 15990, "data_time": 1.24844, "top1_acc": 0.25969, "top5_acc": 0.51859, "loss_cls": 4.03206, "loss": 4.03206, "time": 1.95086} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.0972, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26453, "top5_acc": 0.51203, "loss_cls": 4.02195, "loss": 4.02195, "time": 0.70236} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.09719, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26109, "top5_acc": 0.50969, "loss_cls": 4.0431, "loss": 4.0431, "time": 0.7011} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.09718, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26078, "top5_acc": 0.51281, "loss_cls": 4.01978, "loss": 4.01978, "time": 0.69954} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.09717, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24953, "top5_acc": 0.50047, "loss_cls": 4.10537, "loss": 4.10537, "time": 0.69933} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.09716, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25203, "top5_acc": 0.50406, "loss_cls": 4.0954, "loss": 4.0954, "time": 0.69856} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.09715, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26547, "top5_acc": 0.50859, "loss_cls": 4.05445, "loss": 4.05445, "time": 0.6975} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.09714, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26906, "top5_acc": 0.515, "loss_cls": 4.0306, "loss": 4.0306, "time": 0.6995} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.09714, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24906, "top5_acc": 0.49938, "loss_cls": 4.08879, "loss": 4.08879, "time": 0.69982} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.09713, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26016, "top5_acc": 0.51172, "loss_cls": 4.03985, "loss": 4.03985, "time": 0.7007} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.09712, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25438, "top5_acc": 0.49969, "loss_cls": 4.09272, "loss": 4.09272, "time": 0.70227} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.09711, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25297, "top5_acc": 0.50187, "loss_cls": 4.09688, "loss": 4.09688, "time": 0.70135} +{"mode": "train", "epoch": 17, "iter": 1300, "lr": 0.0971, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24938, "top5_acc": 0.49922, "loss_cls": 4.09583, "loss": 4.09583, "time": 0.70214} +{"mode": "train", "epoch": 17, "iter": 1400, "lr": 0.09709, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27047, "top5_acc": 0.5125, "loss_cls": 4.08391, "loss": 4.08391, "time": 0.69761} +{"mode": "train", "epoch": 17, "iter": 1500, "lr": 0.09708, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25844, "top5_acc": 0.50922, "loss_cls": 4.0657, "loss": 4.0657, "time": 0.7056} +{"mode": "train", "epoch": 17, "iter": 1600, "lr": 0.09707, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25906, "top5_acc": 0.51062, "loss_cls": 4.04854, "loss": 4.04854, "time": 0.70405} +{"mode": "train", "epoch": 17, "iter": 1700, "lr": 0.09706, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26031, "top5_acc": 0.50328, "loss_cls": 4.05469, "loss": 4.05469, "time": 0.70354} +{"mode": "train", "epoch": 17, "iter": 1800, "lr": 0.09705, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24875, "top5_acc": 0.49984, "loss_cls": 4.09818, "loss": 4.09818, "time": 0.70806} +{"mode": "train", "epoch": 17, "iter": 1900, "lr": 0.09704, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25578, "top5_acc": 0.49891, "loss_cls": 4.09647, "loss": 4.09647, "time": 0.70791} +{"mode": "train", "epoch": 17, "iter": 2000, "lr": 0.09703, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.255, "top5_acc": 0.5025, "loss_cls": 4.06087, "loss": 4.06087, "time": 0.70561} +{"mode": "train", "epoch": 17, "iter": 2100, "lr": 0.09702, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25531, "top5_acc": 0.515, "loss_cls": 4.05943, "loss": 4.05943, "time": 0.70885} +{"mode": "train", "epoch": 17, "iter": 2200, "lr": 0.09701, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25641, "top5_acc": 0.51078, "loss_cls": 4.08836, "loss": 4.08836, "time": 0.70146} +{"mode": "train", "epoch": 17, "iter": 2300, "lr": 0.097, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25906, "top5_acc": 0.5075, "loss_cls": 4.07971, "loss": 4.07971, "time": 0.70326} +{"mode": "train", "epoch": 17, "iter": 2400, "lr": 0.09699, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25906, "top5_acc": 0.5075, "loss_cls": 4.09587, "loss": 4.09587, "time": 0.70217} +{"mode": "train", "epoch": 17, "iter": 2500, "lr": 0.09698, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26859, "top5_acc": 0.50953, "loss_cls": 4.03725, "loss": 4.03725, "time": 0.70059} +{"mode": "train", "epoch": 17, "iter": 2600, "lr": 0.09697, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25547, "top5_acc": 0.51, "loss_cls": 4.06908, "loss": 4.06908, "time": 0.70331} +{"mode": "train", "epoch": 17, "iter": 2700, "lr": 0.09697, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26, "top5_acc": 0.51, "loss_cls": 4.05936, "loss": 4.05936, "time": 0.69903} +{"mode": "train", "epoch": 17, "iter": 2800, "lr": 0.09696, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25562, "top5_acc": 0.49781, "loss_cls": 4.08817, "loss": 4.08817, "time": 0.70173} +{"mode": "train", "epoch": 17, "iter": 2900, "lr": 0.09695, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25703, "top5_acc": 0.50234, "loss_cls": 4.08477, "loss": 4.08477, "time": 0.70006} +{"mode": "train", "epoch": 17, "iter": 3000, "lr": 0.09694, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25719, "top5_acc": 0.51891, "loss_cls": 4.02903, "loss": 4.02903, "time": 0.69691} +{"mode": "train", "epoch": 17, "iter": 3100, "lr": 0.09693, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25641, "top5_acc": 0.49719, "loss_cls": 4.10152, "loss": 4.10152, "time": 0.69869} +{"mode": "train", "epoch": 17, "iter": 3200, "lr": 0.09692, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25766, "top5_acc": 0.50594, "loss_cls": 4.05283, "loss": 4.05283, "time": 0.69816} +{"mode": "train", "epoch": 17, "iter": 3300, "lr": 0.09691, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.2525, "top5_acc": 0.49156, "loss_cls": 4.13725, "loss": 4.13725, "time": 0.69978} +{"mode": "train", "epoch": 17, "iter": 3400, "lr": 0.0969, "memory": 15990, "data_time": 0.00019, "top1_acc": 0.25766, "top5_acc": 0.51203, "loss_cls": 4.06748, "loss": 4.06748, "time": 0.69915} +{"mode": "train", "epoch": 17, "iter": 3500, "lr": 0.09689, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25016, "top5_acc": 0.49766, "loss_cls": 4.11867, "loss": 4.11867, "time": 0.69888} +{"mode": "train", "epoch": 17, "iter": 3600, "lr": 0.09688, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25016, "top5_acc": 0.49594, "loss_cls": 4.11148, "loss": 4.11148, "time": 0.69836} +{"mode": "train", "epoch": 17, "iter": 3700, "lr": 0.09687, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26109, "top5_acc": 0.51328, "loss_cls": 4.0543, "loss": 4.0543, "time": 0.69751} +{"mode": "val", "epoch": 17, "iter": 309, "lr": 0.09686, "top1_acc": 0.16983, "top5_acc": 0.3697, "mean_class_accuracy": 0.16957} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.09685, "memory": 15990, "data_time": 1.2684, "top1_acc": 0.26812, "top5_acc": 0.50953, "loss_cls": 4.00239, "loss": 4.00239, "time": 1.96764} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.09684, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27156, "top5_acc": 0.52109, "loss_cls": 4.00036, "loss": 4.00036, "time": 0.70213} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.09683, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26219, "top5_acc": 0.505, "loss_cls": 4.06632, "loss": 4.06632, "time": 0.69595} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.09683, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25297, "top5_acc": 0.49766, "loss_cls": 4.1107, "loss": 4.1107, "time": 0.69707} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.09682, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26016, "top5_acc": 0.51188, "loss_cls": 4.05054, "loss": 4.05054, "time": 0.69866} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.09681, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26516, "top5_acc": 0.50828, "loss_cls": 4.0623, "loss": 4.0623, "time": 0.70145} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.0968, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26625, "top5_acc": 0.51266, "loss_cls": 4.05623, "loss": 4.05623, "time": 0.6974} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.09679, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.255, "top5_acc": 0.50109, "loss_cls": 4.07597, "loss": 4.07597, "time": 0.69903} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.09678, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24812, "top5_acc": 0.49906, "loss_cls": 4.08701, "loss": 4.08701, "time": 0.70012} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.09677, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25094, "top5_acc": 0.50203, "loss_cls": 4.0777, "loss": 4.0777, "time": 0.69882} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.09676, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25578, "top5_acc": 0.51391, "loss_cls": 4.05876, "loss": 4.05876, "time": 0.69861} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.09675, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25281, "top5_acc": 0.51234, "loss_cls": 4.05733, "loss": 4.05733, "time": 0.70052} +{"mode": "train", "epoch": 18, "iter": 1300, "lr": 0.09674, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25234, "top5_acc": 0.49453, "loss_cls": 4.11229, "loss": 4.11229, "time": 0.70293} +{"mode": "train", "epoch": 18, "iter": 1400, "lr": 0.09673, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26281, "top5_acc": 0.50578, "loss_cls": 4.06769, "loss": 4.06769, "time": 0.69938} +{"mode": "train", "epoch": 18, "iter": 1500, "lr": 0.09672, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26797, "top5_acc": 0.51062, "loss_cls": 4.05178, "loss": 4.05178, "time": 0.70334} +{"mode": "train", "epoch": 18, "iter": 1600, "lr": 0.09671, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25719, "top5_acc": 0.50594, "loss_cls": 4.06765, "loss": 4.06765, "time": 0.70659} +{"mode": "train", "epoch": 18, "iter": 1700, "lr": 0.0967, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26016, "top5_acc": 0.50078, "loss_cls": 4.11485, "loss": 4.11485, "time": 0.69962} +{"mode": "train", "epoch": 18, "iter": 1800, "lr": 0.09669, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25969, "top5_acc": 0.50844, "loss_cls": 4.06838, "loss": 4.06838, "time": 0.7039} +{"mode": "train", "epoch": 18, "iter": 1900, "lr": 0.09668, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25797, "top5_acc": 0.51391, "loss_cls": 4.03583, "loss": 4.03583, "time": 0.70859} +{"mode": "train", "epoch": 18, "iter": 2000, "lr": 0.09667, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26438, "top5_acc": 0.5125, "loss_cls": 4.06481, "loss": 4.06481, "time": 0.70291} +{"mode": "train", "epoch": 18, "iter": 2100, "lr": 0.09666, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2625, "top5_acc": 0.51172, "loss_cls": 4.04696, "loss": 4.04696, "time": 0.7021} +{"mode": "train", "epoch": 18, "iter": 2200, "lr": 0.09665, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25188, "top5_acc": 0.49078, "loss_cls": 4.11452, "loss": 4.11452, "time": 0.70689} +{"mode": "train", "epoch": 18, "iter": 2300, "lr": 0.09664, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25484, "top5_acc": 0.50828, "loss_cls": 4.06477, "loss": 4.06477, "time": 0.70353} +{"mode": "train", "epoch": 18, "iter": 2400, "lr": 0.09663, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26422, "top5_acc": 0.50125, "loss_cls": 4.08323, "loss": 4.08323, "time": 0.69885} +{"mode": "train", "epoch": 18, "iter": 2500, "lr": 0.09662, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.27219, "top5_acc": 0.515, "loss_cls": 4.00522, "loss": 4.00522, "time": 0.69723} +{"mode": "train", "epoch": 18, "iter": 2600, "lr": 0.09661, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26172, "top5_acc": 0.50859, "loss_cls": 4.0385, "loss": 4.0385, "time": 0.69837} +{"mode": "train", "epoch": 18, "iter": 2700, "lr": 0.0966, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25594, "top5_acc": 0.50766, "loss_cls": 4.06693, "loss": 4.06693, "time": 0.69847} +{"mode": "train", "epoch": 18, "iter": 2800, "lr": 0.09659, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25359, "top5_acc": 0.49922, "loss_cls": 4.09872, "loss": 4.09872, "time": 0.69949} +{"mode": "train", "epoch": 18, "iter": 2900, "lr": 0.09658, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25203, "top5_acc": 0.51422, "loss_cls": 4.0729, "loss": 4.0729, "time": 0.70135} +{"mode": "train", "epoch": 18, "iter": 3000, "lr": 0.09657, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26078, "top5_acc": 0.50594, "loss_cls": 4.0881, "loss": 4.0881, "time": 0.69922} +{"mode": "train", "epoch": 18, "iter": 3100, "lr": 0.09656, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26687, "top5_acc": 0.50641, "loss_cls": 4.05241, "loss": 4.05241, "time": 0.70062} +{"mode": "train", "epoch": 18, "iter": 3200, "lr": 0.09654, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25922, "top5_acc": 0.50969, "loss_cls": 4.06435, "loss": 4.06435, "time": 0.70033} +{"mode": "train", "epoch": 18, "iter": 3300, "lr": 0.09653, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24719, "top5_acc": 0.50109, "loss_cls": 4.12468, "loss": 4.12468, "time": 0.69934} +{"mode": "train", "epoch": 18, "iter": 3400, "lr": 0.09652, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25016, "top5_acc": 0.48875, "loss_cls": 4.11782, "loss": 4.11782, "time": 0.69936} +{"mode": "train", "epoch": 18, "iter": 3500, "lr": 0.09651, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25781, "top5_acc": 0.50094, "loss_cls": 4.07043, "loss": 4.07043, "time": 0.69809} +{"mode": "train", "epoch": 18, "iter": 3600, "lr": 0.0965, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26141, "top5_acc": 0.50016, "loss_cls": 4.07939, "loss": 4.07939, "time": 0.70021} +{"mode": "train", "epoch": 18, "iter": 3700, "lr": 0.09649, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25891, "top5_acc": 0.51, "loss_cls": 4.05039, "loss": 4.05039, "time": 0.69886} +{"mode": "val", "epoch": 18, "iter": 309, "lr": 0.09649, "top1_acc": 0.19095, "top5_acc": 0.41863, "mean_class_accuracy": 0.19078} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.09648, "memory": 15990, "data_time": 1.25388, "top1_acc": 0.25406, "top5_acc": 0.50703, "loss_cls": 4.0591, "loss": 4.0591, "time": 1.96012} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.09647, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26328, "top5_acc": 0.50813, "loss_cls": 4.04988, "loss": 4.04988, "time": 0.70395} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.09646, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.265, "top5_acc": 0.51547, "loss_cls": 4.03448, "loss": 4.03448, "time": 0.7008} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.09645, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26016, "top5_acc": 0.50391, "loss_cls": 4.07637, "loss": 4.07637, "time": 0.69957} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.09644, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26094, "top5_acc": 0.50016, "loss_cls": 4.09591, "loss": 4.09591, "time": 0.7003} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.09643, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26844, "top5_acc": 0.51172, "loss_cls": 4.02976, "loss": 4.02976, "time": 0.70162} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.09642, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26219, "top5_acc": 0.50906, "loss_cls": 4.05331, "loss": 4.05331, "time": 0.70058} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.09641, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26078, "top5_acc": 0.51516, "loss_cls": 4.03394, "loss": 4.03394, "time": 0.69743} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.0964, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26109, "top5_acc": 0.50859, "loss_cls": 4.0728, "loss": 4.0728, "time": 0.70007} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.09639, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.255, "top5_acc": 0.50469, "loss_cls": 4.05786, "loss": 4.05786, "time": 0.70157} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.09637, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25984, "top5_acc": 0.51281, "loss_cls": 4.02543, "loss": 4.02543, "time": 0.69885} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.09636, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25672, "top5_acc": 0.50156, "loss_cls": 4.0682, "loss": 4.0682, "time": 0.70286} +{"mode": "train", "epoch": 19, "iter": 1300, "lr": 0.09635, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26375, "top5_acc": 0.51938, "loss_cls": 4.01474, "loss": 4.01474, "time": 0.7008} +{"mode": "train", "epoch": 19, "iter": 1400, "lr": 0.09634, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26703, "top5_acc": 0.50531, "loss_cls": 4.08648, "loss": 4.08648, "time": 0.6971} +{"mode": "train", "epoch": 19, "iter": 1500, "lr": 0.09633, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25906, "top5_acc": 0.50234, "loss_cls": 4.08566, "loss": 4.08566, "time": 0.70299} +{"mode": "train", "epoch": 19, "iter": 1600, "lr": 0.09632, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26609, "top5_acc": 0.50734, "loss_cls": 4.08066, "loss": 4.08066, "time": 0.70678} +{"mode": "train", "epoch": 19, "iter": 1700, "lr": 0.09631, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25875, "top5_acc": 0.50781, "loss_cls": 4.04983, "loss": 4.04983, "time": 0.70211} +{"mode": "train", "epoch": 19, "iter": 1800, "lr": 0.0963, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25703, "top5_acc": 0.50328, "loss_cls": 4.11162, "loss": 4.11162, "time": 0.70338} +{"mode": "train", "epoch": 19, "iter": 1900, "lr": 0.09629, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.265, "top5_acc": 0.50891, "loss_cls": 4.05548, "loss": 4.05548, "time": 0.7083} +{"mode": "train", "epoch": 19, "iter": 2000, "lr": 0.09628, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26281, "top5_acc": 0.50547, "loss_cls": 4.10031, "loss": 4.10031, "time": 0.70708} +{"mode": "train", "epoch": 19, "iter": 2100, "lr": 0.09627, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26297, "top5_acc": 0.50266, "loss_cls": 4.05901, "loss": 4.05901, "time": 0.70332} +{"mode": "train", "epoch": 19, "iter": 2200, "lr": 0.09626, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2575, "top5_acc": 0.51328, "loss_cls": 4.05139, "loss": 4.05139, "time": 0.70383} +{"mode": "train", "epoch": 19, "iter": 2300, "lr": 0.09625, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25594, "top5_acc": 0.50344, "loss_cls": 4.08751, "loss": 4.08751, "time": 0.70327} +{"mode": "train", "epoch": 19, "iter": 2400, "lr": 0.09624, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26125, "top5_acc": 0.50656, "loss_cls": 4.07087, "loss": 4.07087, "time": 0.7038} +{"mode": "train", "epoch": 19, "iter": 2500, "lr": 0.09623, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26062, "top5_acc": 0.51, "loss_cls": 4.07167, "loss": 4.07167, "time": 0.69945} +{"mode": "train", "epoch": 19, "iter": 2600, "lr": 0.09622, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26062, "top5_acc": 0.49656, "loss_cls": 4.10515, "loss": 4.10515, "time": 0.69953} +{"mode": "train", "epoch": 19, "iter": 2700, "lr": 0.09621, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25844, "top5_acc": 0.51234, "loss_cls": 4.04804, "loss": 4.04804, "time": 0.70067} +{"mode": "train", "epoch": 19, "iter": 2800, "lr": 0.0962, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25797, "top5_acc": 0.51141, "loss_cls": 4.06977, "loss": 4.06977, "time": 0.69749} +{"mode": "train", "epoch": 19, "iter": 2900, "lr": 0.09618, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25938, "top5_acc": 0.50297, "loss_cls": 4.05937, "loss": 4.05937, "time": 0.70223} +{"mode": "train", "epoch": 19, "iter": 3000, "lr": 0.09617, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25312, "top5_acc": 0.5075, "loss_cls": 4.0838, "loss": 4.0838, "time": 0.70343} +{"mode": "train", "epoch": 19, "iter": 3100, "lr": 0.09616, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25781, "top5_acc": 0.50062, "loss_cls": 4.06881, "loss": 4.06881, "time": 0.69894} +{"mode": "train", "epoch": 19, "iter": 3200, "lr": 0.09615, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26328, "top5_acc": 0.50422, "loss_cls": 4.07069, "loss": 4.07069, "time": 0.69876} +{"mode": "train", "epoch": 19, "iter": 3300, "lr": 0.09614, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26328, "top5_acc": 0.51234, "loss_cls": 4.03305, "loss": 4.03305, "time": 0.69714} +{"mode": "train", "epoch": 19, "iter": 3400, "lr": 0.09613, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25922, "top5_acc": 0.50672, "loss_cls": 4.09038, "loss": 4.09038, "time": 0.70308} +{"mode": "train", "epoch": 19, "iter": 3500, "lr": 0.09612, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26375, "top5_acc": 0.5125, "loss_cls": 4.04302, "loss": 4.04302, "time": 0.69854} +{"mode": "train", "epoch": 19, "iter": 3600, "lr": 0.09611, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26125, "top5_acc": 0.515, "loss_cls": 4.04572, "loss": 4.04572, "time": 0.70066} +{"mode": "train", "epoch": 19, "iter": 3700, "lr": 0.0961, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26719, "top5_acc": 0.51484, "loss_cls": 4.03074, "loss": 4.03074, "time": 0.69938} +{"mode": "val", "epoch": 19, "iter": 309, "lr": 0.09609, "top1_acc": 0.17566, "top5_acc": 0.39249, "mean_class_accuracy": 0.17563} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.09608, "memory": 15990, "data_time": 1.25452, "top1_acc": 0.27422, "top5_acc": 0.51953, "loss_cls": 4.00474, "loss": 4.00474, "time": 1.95672} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.09607, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25922, "top5_acc": 0.51766, "loss_cls": 4.02286, "loss": 4.02286, "time": 0.70273} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.09606, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27094, "top5_acc": 0.50984, "loss_cls": 4.02936, "loss": 4.02936, "time": 0.69773} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.09605, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25312, "top5_acc": 0.49984, "loss_cls": 4.11245, "loss": 4.11245, "time": 0.70013} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.09604, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25969, "top5_acc": 0.51047, "loss_cls": 4.07303, "loss": 4.07303, "time": 0.6978} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.09603, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26484, "top5_acc": 0.50453, "loss_cls": 4.0595, "loss": 4.0595, "time": 0.69874} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.09602, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25766, "top5_acc": 0.50234, "loss_cls": 4.07545, "loss": 4.07545, "time": 0.69708} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.09601, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26687, "top5_acc": 0.52141, "loss_cls": 4.01611, "loss": 4.01611, "time": 0.69846} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.096, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25875, "top5_acc": 0.51297, "loss_cls": 4.04929, "loss": 4.04929, "time": 0.69729} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.09598, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25672, "top5_acc": 0.50719, "loss_cls": 4.07316, "loss": 4.07316, "time": 0.69826} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.09597, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26609, "top5_acc": 0.50891, "loss_cls": 4.05694, "loss": 4.05694, "time": 0.69896} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.09596, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25875, "top5_acc": 0.50766, "loss_cls": 4.05597, "loss": 4.05597, "time": 0.69971} +{"mode": "train", "epoch": 20, "iter": 1300, "lr": 0.09595, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26531, "top5_acc": 0.51672, "loss_cls": 4.03416, "loss": 4.03416, "time": 0.69935} +{"mode": "train", "epoch": 20, "iter": 1400, "lr": 0.09594, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25844, "top5_acc": 0.50906, "loss_cls": 4.06371, "loss": 4.06371, "time": 0.69688} +{"mode": "train", "epoch": 20, "iter": 1500, "lr": 0.09593, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26266, "top5_acc": 0.51, "loss_cls": 4.04961, "loss": 4.04961, "time": 0.70454} +{"mode": "train", "epoch": 20, "iter": 1600, "lr": 0.09592, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25688, "top5_acc": 0.49906, "loss_cls": 4.07944, "loss": 4.07944, "time": 0.7031} +{"mode": "train", "epoch": 20, "iter": 1700, "lr": 0.09591, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26375, "top5_acc": 0.51234, "loss_cls": 4.04476, "loss": 4.04476, "time": 0.7019} +{"mode": "train", "epoch": 20, "iter": 1800, "lr": 0.0959, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26812, "top5_acc": 0.50953, "loss_cls": 4.04251, "loss": 4.04251, "time": 0.70658} +{"mode": "train", "epoch": 20, "iter": 1900, "lr": 0.09588, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26438, "top5_acc": 0.50656, "loss_cls": 4.07356, "loss": 4.07356, "time": 0.70481} +{"mode": "train", "epoch": 20, "iter": 2000, "lr": 0.09587, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26156, "top5_acc": 0.50813, "loss_cls": 4.04944, "loss": 4.04944, "time": 0.70715} +{"mode": "train", "epoch": 20, "iter": 2100, "lr": 0.09586, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24609, "top5_acc": 0.49984, "loss_cls": 4.10169, "loss": 4.10169, "time": 0.70582} +{"mode": "train", "epoch": 20, "iter": 2200, "lr": 0.09585, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26172, "top5_acc": 0.50719, "loss_cls": 4.04814, "loss": 4.04814, "time": 0.7016} +{"mode": "train", "epoch": 20, "iter": 2300, "lr": 0.09584, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26484, "top5_acc": 0.49906, "loss_cls": 4.05488, "loss": 4.05488, "time": 0.70107} +{"mode": "train", "epoch": 20, "iter": 2400, "lr": 0.09583, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26719, "top5_acc": 0.51875, "loss_cls": 4.03503, "loss": 4.03503, "time": 0.70169} +{"mode": "train", "epoch": 20, "iter": 2500, "lr": 0.09582, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25672, "top5_acc": 0.50516, "loss_cls": 4.07036, "loss": 4.07036, "time": 0.70217} +{"mode": "train", "epoch": 20, "iter": 2600, "lr": 0.09581, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25688, "top5_acc": 0.50719, "loss_cls": 4.04663, "loss": 4.04663, "time": 0.69905} +{"mode": "train", "epoch": 20, "iter": 2700, "lr": 0.0958, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25703, "top5_acc": 0.50906, "loss_cls": 4.06731, "loss": 4.06731, "time": 0.70235} +{"mode": "train", "epoch": 20, "iter": 2800, "lr": 0.09578, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26797, "top5_acc": 0.51016, "loss_cls": 4.02582, "loss": 4.02582, "time": 0.69828} +{"mode": "train", "epoch": 20, "iter": 2900, "lr": 0.09577, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25562, "top5_acc": 0.5, "loss_cls": 4.08349, "loss": 4.08349, "time": 0.70419} +{"mode": "train", "epoch": 20, "iter": 3000, "lr": 0.09576, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25453, "top5_acc": 0.50078, "loss_cls": 4.09619, "loss": 4.09619, "time": 0.70154} +{"mode": "train", "epoch": 20, "iter": 3100, "lr": 0.09575, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26141, "top5_acc": 0.50953, "loss_cls": 4.06817, "loss": 4.06817, "time": 0.69946} +{"mode": "train", "epoch": 20, "iter": 3200, "lr": 0.09574, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26203, "top5_acc": 0.52109, "loss_cls": 4.029, "loss": 4.029, "time": 0.6993} +{"mode": "train", "epoch": 20, "iter": 3300, "lr": 0.09573, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26562, "top5_acc": 0.51031, "loss_cls": 4.05124, "loss": 4.05124, "time": 0.7} +{"mode": "train", "epoch": 20, "iter": 3400, "lr": 0.09572, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25391, "top5_acc": 0.50844, "loss_cls": 4.07093, "loss": 4.07093, "time": 0.69846} +{"mode": "train", "epoch": 20, "iter": 3500, "lr": 0.09571, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25953, "top5_acc": 0.49656, "loss_cls": 4.10071, "loss": 4.10071, "time": 0.69895} +{"mode": "train", "epoch": 20, "iter": 3600, "lr": 0.09569, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25453, "top5_acc": 0.50187, "loss_cls": 4.08276, "loss": 4.08276, "time": 0.69855} +{"mode": "train", "epoch": 20, "iter": 3700, "lr": 0.09568, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26156, "top5_acc": 0.50875, "loss_cls": 4.0649, "loss": 4.0649, "time": 0.69935} +{"mode": "val", "epoch": 20, "iter": 309, "lr": 0.09568, "top1_acc": 0.18969, "top5_acc": 0.39563, "mean_class_accuracy": 0.18957} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.09567, "memory": 15990, "data_time": 1.26592, "top1_acc": 0.26922, "top5_acc": 0.51234, "loss_cls": 4.03772, "loss": 4.03772, "time": 1.97079} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.09565, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26203, "top5_acc": 0.51203, "loss_cls": 4.01495, "loss": 4.01495, "time": 0.69975} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.09564, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26281, "top5_acc": 0.51453, "loss_cls": 4.02976, "loss": 4.02976, "time": 0.6983} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.09563, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26203, "top5_acc": 0.51375, "loss_cls": 4.03022, "loss": 4.03022, "time": 0.69917} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.09562, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25344, "top5_acc": 0.50125, "loss_cls": 4.09326, "loss": 4.09326, "time": 0.69706} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.09561, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26891, "top5_acc": 0.51406, "loss_cls": 4.03956, "loss": 4.03956, "time": 0.69837} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.0956, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26219, "top5_acc": 0.50547, "loss_cls": 4.07425, "loss": 4.07425, "time": 0.69676} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.09559, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26562, "top5_acc": 0.51719, "loss_cls": 4.02818, "loss": 4.02818, "time": 0.69722} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.09557, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26609, "top5_acc": 0.51125, "loss_cls": 4.05303, "loss": 4.05303, "time": 0.69828} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.09556, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25188, "top5_acc": 0.51188, "loss_cls": 4.03662, "loss": 4.03662, "time": 0.69781} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.09555, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25562, "top5_acc": 0.5, "loss_cls": 4.09375, "loss": 4.09375, "time": 0.69895} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.09554, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26141, "top5_acc": 0.51375, "loss_cls": 4.0364, "loss": 4.0364, "time": 0.69916} +{"mode": "train", "epoch": 21, "iter": 1300, "lr": 0.09553, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25406, "top5_acc": 0.51141, "loss_cls": 4.06376, "loss": 4.06376, "time": 0.69777} +{"mode": "train", "epoch": 21, "iter": 1400, "lr": 0.09552, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26188, "top5_acc": 0.51219, "loss_cls": 4.06192, "loss": 4.06192, "time": 0.69836} +{"mode": "train", "epoch": 21, "iter": 1500, "lr": 0.09551, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25594, "top5_acc": 0.51234, "loss_cls": 4.07791, "loss": 4.07791, "time": 0.70332} +{"mode": "train", "epoch": 21, "iter": 1600, "lr": 0.09549, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25734, "top5_acc": 0.50719, "loss_cls": 4.06141, "loss": 4.06141, "time": 0.70437} +{"mode": "train", "epoch": 21, "iter": 1700, "lr": 0.09548, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25688, "top5_acc": 0.50969, "loss_cls": 4.06023, "loss": 4.06023, "time": 0.70513} +{"mode": "train", "epoch": 21, "iter": 1800, "lr": 0.09547, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26828, "top5_acc": 0.51234, "loss_cls": 4.0293, "loss": 4.0293, "time": 0.70139} +{"mode": "train", "epoch": 21, "iter": 1900, "lr": 0.09546, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2725, "top5_acc": 0.52172, "loss_cls": 3.99389, "loss": 3.99389, "time": 0.70643} +{"mode": "train", "epoch": 21, "iter": 2000, "lr": 0.09545, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26687, "top5_acc": 0.51875, "loss_cls": 4.02414, "loss": 4.02414, "time": 0.71153} +{"mode": "train", "epoch": 21, "iter": 2100, "lr": 0.09544, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25984, "top5_acc": 0.50984, "loss_cls": 4.07158, "loss": 4.07158, "time": 0.70541} +{"mode": "train", "epoch": 21, "iter": 2200, "lr": 0.09542, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26344, "top5_acc": 0.50594, "loss_cls": 4.05617, "loss": 4.05617, "time": 0.70421} +{"mode": "train", "epoch": 21, "iter": 2300, "lr": 0.09541, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26234, "top5_acc": 0.50703, "loss_cls": 4.07555, "loss": 4.07555, "time": 0.70329} +{"mode": "train", "epoch": 21, "iter": 2400, "lr": 0.0954, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24812, "top5_acc": 0.49594, "loss_cls": 4.09686, "loss": 4.09686, "time": 0.70192} +{"mode": "train", "epoch": 21, "iter": 2500, "lr": 0.09539, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26828, "top5_acc": 0.51938, "loss_cls": 4.01922, "loss": 4.01922, "time": 0.69741} +{"mode": "train", "epoch": 21, "iter": 2600, "lr": 0.09538, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26062, "top5_acc": 0.50938, "loss_cls": 4.04881, "loss": 4.04881, "time": 0.70057} +{"mode": "train", "epoch": 21, "iter": 2700, "lr": 0.09537, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26672, "top5_acc": 0.51359, "loss_cls": 4.04297, "loss": 4.04297, "time": 0.69819} +{"mode": "train", "epoch": 21, "iter": 2800, "lr": 0.09535, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27469, "top5_acc": 0.52328, "loss_cls": 3.99401, "loss": 3.99401, "time": 0.70077} +{"mode": "train", "epoch": 21, "iter": 2900, "lr": 0.09534, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26672, "top5_acc": 0.51359, "loss_cls": 4.03754, "loss": 4.03754, "time": 0.69862} +{"mode": "train", "epoch": 21, "iter": 3000, "lr": 0.09533, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25516, "top5_acc": 0.50313, "loss_cls": 4.09054, "loss": 4.09054, "time": 0.70209} +{"mode": "train", "epoch": 21, "iter": 3100, "lr": 0.09532, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2575, "top5_acc": 0.50766, "loss_cls": 4.0556, "loss": 4.0556, "time": 0.70067} +{"mode": "train", "epoch": 21, "iter": 3200, "lr": 0.09531, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26547, "top5_acc": 0.51641, "loss_cls": 4.03285, "loss": 4.03285, "time": 0.69863} +{"mode": "train", "epoch": 21, "iter": 3300, "lr": 0.09529, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26297, "top5_acc": 0.51406, "loss_cls": 4.05094, "loss": 4.05094, "time": 0.70066} +{"mode": "train", "epoch": 21, "iter": 3400, "lr": 0.09528, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26141, "top5_acc": 0.51031, "loss_cls": 4.03242, "loss": 4.03242, "time": 0.69843} +{"mode": "train", "epoch": 21, "iter": 3500, "lr": 0.09527, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25984, "top5_acc": 0.50828, "loss_cls": 4.03206, "loss": 4.03206, "time": 0.69856} +{"mode": "train", "epoch": 21, "iter": 3600, "lr": 0.09526, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25625, "top5_acc": 0.49953, "loss_cls": 4.08622, "loss": 4.08622, "time": 0.69815} +{"mode": "train", "epoch": 21, "iter": 3700, "lr": 0.09525, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25547, "top5_acc": 0.50813, "loss_cls": 4.05899, "loss": 4.05899, "time": 0.70106} +{"mode": "val", "epoch": 21, "iter": 309, "lr": 0.09524, "top1_acc": 0.16973, "top5_acc": 0.38064, "mean_class_accuracy": 0.16953} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.09523, "memory": 15990, "data_time": 1.23513, "top1_acc": 0.26719, "top5_acc": 0.52766, "loss_cls": 3.97451, "loss": 3.97451, "time": 1.93387} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.09522, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25906, "top5_acc": 0.51625, "loss_cls": 4.02321, "loss": 4.02321, "time": 0.70243} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.09521, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26266, "top5_acc": 0.51422, "loss_cls": 4.05972, "loss": 4.05972, "time": 0.70329} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.09519, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26875, "top5_acc": 0.51859, "loss_cls": 4.02476, "loss": 4.02476, "time": 0.70125} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.09518, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25438, "top5_acc": 0.50547, "loss_cls": 4.0425, "loss": 4.0425, "time": 0.69933} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.09517, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26672, "top5_acc": 0.52375, "loss_cls": 4.00907, "loss": 4.00907, "time": 0.70037} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.09516, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25891, "top5_acc": 0.51438, "loss_cls": 4.05264, "loss": 4.05264, "time": 0.6998} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.09515, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26234, "top5_acc": 0.51125, "loss_cls": 4.03847, "loss": 4.03847, "time": 0.69897} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.09513, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26188, "top5_acc": 0.50516, "loss_cls": 4.07356, "loss": 4.07356, "time": 0.6992} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.09512, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27234, "top5_acc": 0.52188, "loss_cls": 3.99432, "loss": 3.99432, "time": 0.69754} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.09511, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25609, "top5_acc": 0.51, "loss_cls": 4.08317, "loss": 4.08317, "time": 0.69806} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0951, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26734, "top5_acc": 0.51406, "loss_cls": 4.03017, "loss": 4.03017, "time": 0.70012} +{"mode": "train", "epoch": 22, "iter": 1300, "lr": 0.09509, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25938, "top5_acc": 0.50813, "loss_cls": 4.05664, "loss": 4.05664, "time": 0.69803} +{"mode": "train", "epoch": 22, "iter": 1400, "lr": 0.09507, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26328, "top5_acc": 0.5, "loss_cls": 4.07618, "loss": 4.07618, "time": 0.70037} +{"mode": "train", "epoch": 22, "iter": 1500, "lr": 0.09506, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26469, "top5_acc": 0.51219, "loss_cls": 4.024, "loss": 4.024, "time": 0.70281} +{"mode": "train", "epoch": 22, "iter": 1600, "lr": 0.09505, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26078, "top5_acc": 0.51062, "loss_cls": 4.02954, "loss": 4.02954, "time": 0.70649} +{"mode": "train", "epoch": 22, "iter": 1700, "lr": 0.09504, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26141, "top5_acc": 0.51188, "loss_cls": 4.05782, "loss": 4.05782, "time": 0.70226} +{"mode": "train", "epoch": 22, "iter": 1800, "lr": 0.09502, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26297, "top5_acc": 0.51781, "loss_cls": 4.01663, "loss": 4.01663, "time": 0.70698} +{"mode": "train", "epoch": 22, "iter": 1900, "lr": 0.09501, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27328, "top5_acc": 0.51313, "loss_cls": 4.05267, "loss": 4.05267, "time": 0.70461} +{"mode": "train", "epoch": 22, "iter": 2000, "lr": 0.095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25219, "top5_acc": 0.50688, "loss_cls": 4.06787, "loss": 4.06787, "time": 0.70779} +{"mode": "train", "epoch": 22, "iter": 2100, "lr": 0.09499, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26891, "top5_acc": 0.52125, "loss_cls": 4.01172, "loss": 4.01172, "time": 0.70329} +{"mode": "train", "epoch": 22, "iter": 2200, "lr": 0.09498, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25625, "top5_acc": 0.50859, "loss_cls": 4.07411, "loss": 4.07411, "time": 0.70527} +{"mode": "train", "epoch": 22, "iter": 2300, "lr": 0.09496, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26812, "top5_acc": 0.50969, "loss_cls": 4.06222, "loss": 4.06222, "time": 0.71209} +{"mode": "train", "epoch": 22, "iter": 2400, "lr": 0.09495, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26422, "top5_acc": 0.51328, "loss_cls": 4.04468, "loss": 4.04468, "time": 0.70102} +{"mode": "train", "epoch": 22, "iter": 2500, "lr": 0.09494, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25766, "top5_acc": 0.51313, "loss_cls": 4.0427, "loss": 4.0427, "time": 0.70011} +{"mode": "train", "epoch": 22, "iter": 2600, "lr": 0.09493, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26078, "top5_acc": 0.50062, "loss_cls": 4.09137, "loss": 4.09137, "time": 0.69692} +{"mode": "train", "epoch": 22, "iter": 2700, "lr": 0.09491, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.2575, "top5_acc": 0.49922, "loss_cls": 4.0834, "loss": 4.0834, "time": 0.70192} +{"mode": "train", "epoch": 22, "iter": 2800, "lr": 0.0949, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26531, "top5_acc": 0.51375, "loss_cls": 4.05107, "loss": 4.05107, "time": 0.7004} +{"mode": "train", "epoch": 22, "iter": 2900, "lr": 0.09489, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2525, "top5_acc": 0.49891, "loss_cls": 4.09867, "loss": 4.09867, "time": 0.69763} +{"mode": "train", "epoch": 22, "iter": 3000, "lr": 0.09488, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26109, "top5_acc": 0.50203, "loss_cls": 4.05785, "loss": 4.05785, "time": 0.70226} +{"mode": "train", "epoch": 22, "iter": 3100, "lr": 0.09487, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25859, "top5_acc": 0.50422, "loss_cls": 4.05749, "loss": 4.05749, "time": 0.69862} +{"mode": "train", "epoch": 22, "iter": 3200, "lr": 0.09485, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25094, "top5_acc": 0.49922, "loss_cls": 4.10156, "loss": 4.10156, "time": 0.6982} +{"mode": "train", "epoch": 22, "iter": 3300, "lr": 0.09484, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26531, "top5_acc": 0.51156, "loss_cls": 4.06713, "loss": 4.06713, "time": 0.69839} +{"mode": "train", "epoch": 22, "iter": 3400, "lr": 0.09483, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26469, "top5_acc": 0.5125, "loss_cls": 4.01589, "loss": 4.01589, "time": 0.70347} +{"mode": "train", "epoch": 22, "iter": 3500, "lr": 0.09482, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26125, "top5_acc": 0.51016, "loss_cls": 4.06808, "loss": 4.06808, "time": 0.69959} +{"mode": "train", "epoch": 22, "iter": 3600, "lr": 0.0948, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26734, "top5_acc": 0.51719, "loss_cls": 4.01235, "loss": 4.01235, "time": 0.69881} +{"mode": "train", "epoch": 22, "iter": 3700, "lr": 0.09479, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25781, "top5_acc": 0.50719, "loss_cls": 4.06885, "loss": 4.06885, "time": 0.69974} +{"mode": "val", "epoch": 22, "iter": 309, "lr": 0.09479, "top1_acc": 0.19597, "top5_acc": 0.42172, "mean_class_accuracy": 0.19596} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.09477, "memory": 15990, "data_time": 1.25385, "top1_acc": 0.27297, "top5_acc": 0.52297, "loss_cls": 3.97354, "loss": 3.97354, "time": 1.95599} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.09476, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27094, "top5_acc": 0.51531, "loss_cls": 3.99389, "loss": 3.99389, "time": 0.70339} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.09475, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26125, "top5_acc": 0.50969, "loss_cls": 4.03524, "loss": 4.03524, "time": 0.69984} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.09474, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26047, "top5_acc": 0.51141, "loss_cls": 4.02809, "loss": 4.02809, "time": 0.69882} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.09472, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26312, "top5_acc": 0.51609, "loss_cls": 4.02806, "loss": 4.02806, "time": 0.70093} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.09471, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26922, "top5_acc": 0.52062, "loss_cls": 4.01703, "loss": 4.01703, "time": 0.69974} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.0947, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25016, "top5_acc": 0.50484, "loss_cls": 4.09262, "loss": 4.09262, "time": 0.70027} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.09469, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2475, "top5_acc": 0.49953, "loss_cls": 4.09542, "loss": 4.09542, "time": 0.7019} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.09467, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25875, "top5_acc": 0.50516, "loss_cls": 4.08119, "loss": 4.08119, "time": 0.69678} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.09466, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26656, "top5_acc": 0.51938, "loss_cls": 3.9921, "loss": 3.9921, "time": 0.69813} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.09465, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26531, "top5_acc": 0.51359, "loss_cls": 4.02541, "loss": 4.02541, "time": 0.69717} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.09464, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26141, "top5_acc": 0.51156, "loss_cls": 4.0693, "loss": 4.0693, "time": 0.69923} +{"mode": "train", "epoch": 23, "iter": 1300, "lr": 0.09462, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26828, "top5_acc": 0.51969, "loss_cls": 4.02724, "loss": 4.02724, "time": 0.6982} +{"mode": "train", "epoch": 23, "iter": 1400, "lr": 0.09461, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25859, "top5_acc": 0.51375, "loss_cls": 4.01958, "loss": 4.01958, "time": 0.69868} +{"mode": "train", "epoch": 23, "iter": 1500, "lr": 0.0946, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26016, "top5_acc": 0.51219, "loss_cls": 4.04932, "loss": 4.04932, "time": 0.70369} +{"mode": "train", "epoch": 23, "iter": 1600, "lr": 0.09459, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25953, "top5_acc": 0.49812, "loss_cls": 4.06121, "loss": 4.06121, "time": 0.70403} +{"mode": "train", "epoch": 23, "iter": 1700, "lr": 0.09457, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25578, "top5_acc": 0.50703, "loss_cls": 4.07312, "loss": 4.07312, "time": 0.6994} +{"mode": "train", "epoch": 23, "iter": 1800, "lr": 0.09456, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27047, "top5_acc": 0.52062, "loss_cls": 4.00247, "loss": 4.00247, "time": 0.70677} +{"mode": "train", "epoch": 23, "iter": 1900, "lr": 0.09455, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24703, "top5_acc": 0.49672, "loss_cls": 4.10564, "loss": 4.10564, "time": 0.70246} +{"mode": "train", "epoch": 23, "iter": 2000, "lr": 0.09453, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.25531, "top5_acc": 0.515, "loss_cls": 4.03594, "loss": 4.03594, "time": 0.70614} +{"mode": "train", "epoch": 23, "iter": 2100, "lr": 0.09452, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26453, "top5_acc": 0.51359, "loss_cls": 4.02052, "loss": 4.02052, "time": 0.70778} +{"mode": "train", "epoch": 23, "iter": 2200, "lr": 0.09451, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26562, "top5_acc": 0.51141, "loss_cls": 4.02695, "loss": 4.02695, "time": 0.70703} +{"mode": "train", "epoch": 23, "iter": 2300, "lr": 0.0945, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26734, "top5_acc": 0.51406, "loss_cls": 4.0249, "loss": 4.0249, "time": 0.70752} +{"mode": "train", "epoch": 23, "iter": 2400, "lr": 0.09448, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25328, "top5_acc": 0.50172, "loss_cls": 4.10492, "loss": 4.10492, "time": 0.70399} +{"mode": "train", "epoch": 23, "iter": 2500, "lr": 0.09447, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26719, "top5_acc": 0.51562, "loss_cls": 4.02109, "loss": 4.02109, "time": 0.70208} +{"mode": "train", "epoch": 23, "iter": 2600, "lr": 0.09446, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26062, "top5_acc": 0.51219, "loss_cls": 4.05835, "loss": 4.05835, "time": 0.7014} +{"mode": "train", "epoch": 23, "iter": 2700, "lr": 0.09445, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26, "top5_acc": 0.51344, "loss_cls": 4.06009, "loss": 4.06009, "time": 0.69855} +{"mode": "train", "epoch": 23, "iter": 2800, "lr": 0.09443, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26344, "top5_acc": 0.51297, "loss_cls": 4.02043, "loss": 4.02043, "time": 0.70063} +{"mode": "train", "epoch": 23, "iter": 2900, "lr": 0.09442, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25688, "top5_acc": 0.50781, "loss_cls": 4.07752, "loss": 4.07752, "time": 0.69753} +{"mode": "train", "epoch": 23, "iter": 3000, "lr": 0.09441, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27219, "top5_acc": 0.52359, "loss_cls": 3.99708, "loss": 3.99708, "time": 0.70044} +{"mode": "train", "epoch": 23, "iter": 3100, "lr": 0.09439, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25922, "top5_acc": 0.50813, "loss_cls": 4.06017, "loss": 4.06017, "time": 0.69792} +{"mode": "train", "epoch": 23, "iter": 3200, "lr": 0.09438, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.265, "top5_acc": 0.51656, "loss_cls": 4.05356, "loss": 4.05356, "time": 0.69928} +{"mode": "train", "epoch": 23, "iter": 3300, "lr": 0.09437, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25734, "top5_acc": 0.50578, "loss_cls": 4.05218, "loss": 4.05218, "time": 0.70271} +{"mode": "train", "epoch": 23, "iter": 3400, "lr": 0.09436, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25953, "top5_acc": 0.51062, "loss_cls": 4.05167, "loss": 4.05167, "time": 0.70239} +{"mode": "train", "epoch": 23, "iter": 3500, "lr": 0.09434, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25734, "top5_acc": 0.50422, "loss_cls": 4.052, "loss": 4.052, "time": 0.69875} +{"mode": "train", "epoch": 23, "iter": 3600, "lr": 0.09433, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26016, "top5_acc": 0.50375, "loss_cls": 4.10909, "loss": 4.10909, "time": 0.69792} +{"mode": "train", "epoch": 23, "iter": 3700, "lr": 0.09432, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27, "top5_acc": 0.51359, "loss_cls": 4.02111, "loss": 4.02111, "time": 0.6955} +{"mode": "val", "epoch": 23, "iter": 309, "lr": 0.09431, "top1_acc": 0.19744, "top5_acc": 0.4199, "mean_class_accuracy": 0.19714} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.0943, "memory": 15990, "data_time": 1.2721, "top1_acc": 0.26109, "top5_acc": 0.515, "loss_cls": 4.02325, "loss": 4.02325, "time": 1.97374} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.09428, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25281, "top5_acc": 0.50953, "loss_cls": 4.07206, "loss": 4.07206, "time": 0.70088} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.09427, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26891, "top5_acc": 0.51781, "loss_cls": 4.04252, "loss": 4.04252, "time": 0.69865} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.09426, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27453, "top5_acc": 0.51906, "loss_cls": 3.97946, "loss": 3.97946, "time": 0.70168} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.09425, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26062, "top5_acc": 0.51344, "loss_cls": 4.04508, "loss": 4.04508, "time": 0.69928} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.09423, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26391, "top5_acc": 0.51375, "loss_cls": 4.03115, "loss": 4.03115, "time": 0.69983} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.09422, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25906, "top5_acc": 0.51156, "loss_cls": 4.05837, "loss": 4.05837, "time": 0.6984} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.09421, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25844, "top5_acc": 0.50531, "loss_cls": 4.07325, "loss": 4.07325, "time": 0.70005} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.09419, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26328, "top5_acc": 0.51859, "loss_cls": 4.03451, "loss": 4.03451, "time": 0.697} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.09418, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25641, "top5_acc": 0.51375, "loss_cls": 4.04196, "loss": 4.04196, "time": 0.69672} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.09417, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26438, "top5_acc": 0.50578, "loss_cls": 4.05266, "loss": 4.05266, "time": 0.69782} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.09415, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26625, "top5_acc": 0.51453, "loss_cls": 4.04299, "loss": 4.04299, "time": 0.70108} +{"mode": "train", "epoch": 24, "iter": 1300, "lr": 0.09414, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.265, "top5_acc": 0.51422, "loss_cls": 4.04134, "loss": 4.04134, "time": 0.7001} +{"mode": "train", "epoch": 24, "iter": 1400, "lr": 0.09413, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27203, "top5_acc": 0.51422, "loss_cls": 4.01713, "loss": 4.01713, "time": 0.70099} +{"mode": "train", "epoch": 24, "iter": 1500, "lr": 0.09411, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26125, "top5_acc": 0.50953, "loss_cls": 4.04893, "loss": 4.04893, "time": 0.70668} +{"mode": "train", "epoch": 24, "iter": 1600, "lr": 0.0941, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26391, "top5_acc": 0.51359, "loss_cls": 4.02515, "loss": 4.02515, "time": 0.70275} +{"mode": "train", "epoch": 24, "iter": 1700, "lr": 0.09409, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25734, "top5_acc": 0.50922, "loss_cls": 4.06066, "loss": 4.06066, "time": 0.70556} +{"mode": "train", "epoch": 24, "iter": 1800, "lr": 0.09407, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25828, "top5_acc": 0.51391, "loss_cls": 4.02484, "loss": 4.02484, "time": 0.70345} +{"mode": "train", "epoch": 24, "iter": 1900, "lr": 0.09406, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25812, "top5_acc": 0.50094, "loss_cls": 4.10721, "loss": 4.10721, "time": 0.70313} +{"mode": "train", "epoch": 24, "iter": 2000, "lr": 0.09405, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26453, "top5_acc": 0.51156, "loss_cls": 4.03607, "loss": 4.03607, "time": 0.70382} +{"mode": "train", "epoch": 24, "iter": 2100, "lr": 0.09404, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26328, "top5_acc": 0.51234, "loss_cls": 4.05407, "loss": 4.05407, "time": 0.70902} +{"mode": "train", "epoch": 24, "iter": 2200, "lr": 0.09402, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25906, "top5_acc": 0.50781, "loss_cls": 4.0475, "loss": 4.0475, "time": 0.70335} +{"mode": "train", "epoch": 24, "iter": 2300, "lr": 0.09401, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26094, "top5_acc": 0.51984, "loss_cls": 4.03206, "loss": 4.03206, "time": 0.70649} +{"mode": "train", "epoch": 24, "iter": 2400, "lr": 0.094, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25625, "top5_acc": 0.51234, "loss_cls": 4.08289, "loss": 4.08289, "time": 0.7031} +{"mode": "train", "epoch": 24, "iter": 2500, "lr": 0.09398, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25953, "top5_acc": 0.50859, "loss_cls": 4.04591, "loss": 4.04591, "time": 0.70222} +{"mode": "train", "epoch": 24, "iter": 2600, "lr": 0.09397, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27219, "top5_acc": 0.52141, "loss_cls": 3.98581, "loss": 3.98581, "time": 0.70177} +{"mode": "train", "epoch": 24, "iter": 2700, "lr": 0.09396, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26484, "top5_acc": 0.51016, "loss_cls": 4.00918, "loss": 4.00918, "time": 0.70066} +{"mode": "train", "epoch": 24, "iter": 2800, "lr": 0.09394, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27531, "top5_acc": 0.52875, "loss_cls": 3.99355, "loss": 3.99355, "time": 0.69929} +{"mode": "train", "epoch": 24, "iter": 2900, "lr": 0.09393, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26281, "top5_acc": 0.50594, "loss_cls": 4.07203, "loss": 4.07203, "time": 0.69734} +{"mode": "train", "epoch": 24, "iter": 3000, "lr": 0.09392, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26141, "top5_acc": 0.50828, "loss_cls": 4.04542, "loss": 4.04542, "time": 0.70213} +{"mode": "train", "epoch": 24, "iter": 3100, "lr": 0.0939, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26687, "top5_acc": 0.50656, "loss_cls": 4.06084, "loss": 4.06084, "time": 0.70086} +{"mode": "train", "epoch": 24, "iter": 3200, "lr": 0.09389, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25859, "top5_acc": 0.51203, "loss_cls": 4.04256, "loss": 4.04256, "time": 0.70118} +{"mode": "train", "epoch": 24, "iter": 3300, "lr": 0.09388, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25609, "top5_acc": 0.50953, "loss_cls": 4.04684, "loss": 4.04684, "time": 0.70025} +{"mode": "train", "epoch": 24, "iter": 3400, "lr": 0.09386, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26719, "top5_acc": 0.52, "loss_cls": 4.01645, "loss": 4.01645, "time": 0.69802} +{"mode": "train", "epoch": 24, "iter": 3500, "lr": 0.09385, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27688, "top5_acc": 0.52125, "loss_cls": 3.998, "loss": 3.998, "time": 0.70185} +{"mode": "train", "epoch": 24, "iter": 3600, "lr": 0.09384, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25938, "top5_acc": 0.50438, "loss_cls": 4.06557, "loss": 4.06557, "time": 0.7007} +{"mode": "train", "epoch": 24, "iter": 3700, "lr": 0.09382, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26484, "top5_acc": 0.51656, "loss_cls": 4.0248, "loss": 4.0248, "time": 0.69946} +{"mode": "val", "epoch": 24, "iter": 309, "lr": 0.09382, "top1_acc": 0.17758, "top5_acc": 0.39498, "mean_class_accuracy": 0.17742} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.0938, "memory": 15990, "data_time": 1.26162, "top1_acc": 0.27312, "top5_acc": 0.51547, "loss_cls": 3.97734, "loss": 3.97734, "time": 1.96348} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.09379, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26312, "top5_acc": 0.51266, "loss_cls": 4.01535, "loss": 4.01535, "time": 0.70251} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.09378, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26937, "top5_acc": 0.51359, "loss_cls": 4.03447, "loss": 4.03447, "time": 0.69856} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.09376, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26625, "top5_acc": 0.52109, "loss_cls": 3.98634, "loss": 3.98634, "time": 0.70049} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.09375, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25672, "top5_acc": 0.50875, "loss_cls": 4.03827, "loss": 4.03827, "time": 0.69894} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.09373, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26234, "top5_acc": 0.50531, "loss_cls": 4.05784, "loss": 4.05784, "time": 0.69932} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.09372, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26797, "top5_acc": 0.52297, "loss_cls": 4.00275, "loss": 4.00275, "time": 0.70023} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.09371, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27203, "top5_acc": 0.52062, "loss_cls": 3.98381, "loss": 3.98381, "time": 0.69927} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.09369, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26188, "top5_acc": 0.51641, "loss_cls": 4.01844, "loss": 4.01844, "time": 0.69841} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.09368, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26219, "top5_acc": 0.51531, "loss_cls": 4.04077, "loss": 4.04077, "time": 0.69797} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.09367, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.25391, "top5_acc": 0.51062, "loss_cls": 4.06503, "loss": 4.06503, "time": 0.69683} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.09365, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25578, "top5_acc": 0.51406, "loss_cls": 4.05021, "loss": 4.05021, "time": 0.69749} +{"mode": "train", "epoch": 25, "iter": 1300, "lr": 0.09364, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.27062, "top5_acc": 0.51766, "loss_cls": 4.01336, "loss": 4.01336, "time": 0.69703} +{"mode": "train", "epoch": 25, "iter": 1400, "lr": 0.09363, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26687, "top5_acc": 0.51688, "loss_cls": 4.0202, "loss": 4.0202, "time": 0.70066} +{"mode": "train", "epoch": 25, "iter": 1500, "lr": 0.09361, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2575, "top5_acc": 0.50422, "loss_cls": 4.09199, "loss": 4.09199, "time": 0.70958} +{"mode": "train", "epoch": 25, "iter": 1600, "lr": 0.0936, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25594, "top5_acc": 0.50031, "loss_cls": 4.08412, "loss": 4.08412, "time": 0.70858} +{"mode": "train", "epoch": 25, "iter": 1700, "lr": 0.09358, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26234, "top5_acc": 0.51313, "loss_cls": 4.02728, "loss": 4.02728, "time": 0.70038} +{"mode": "train", "epoch": 25, "iter": 1800, "lr": 0.09357, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25781, "top5_acc": 0.50641, "loss_cls": 4.05159, "loss": 4.05159, "time": 0.7036} +{"mode": "train", "epoch": 25, "iter": 1900, "lr": 0.09356, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26188, "top5_acc": 0.51688, "loss_cls": 4.02839, "loss": 4.02839, "time": 0.70502} +{"mode": "train", "epoch": 25, "iter": 2000, "lr": 0.09354, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27391, "top5_acc": 0.51844, "loss_cls": 4.00939, "loss": 4.00939, "time": 0.70536} +{"mode": "train", "epoch": 25, "iter": 2100, "lr": 0.09353, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25641, "top5_acc": 0.51547, "loss_cls": 4.04795, "loss": 4.04795, "time": 0.7052} +{"mode": "train", "epoch": 25, "iter": 2200, "lr": 0.09352, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26719, "top5_acc": 0.51641, "loss_cls": 4.03124, "loss": 4.03124, "time": 0.70704} +{"mode": "train", "epoch": 25, "iter": 2300, "lr": 0.0935, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25938, "top5_acc": 0.51703, "loss_cls": 4.03003, "loss": 4.03003, "time": 0.70781} +{"mode": "train", "epoch": 25, "iter": 2400, "lr": 0.09349, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25828, "top5_acc": 0.50859, "loss_cls": 4.04432, "loss": 4.04432, "time": 0.70001} +{"mode": "train", "epoch": 25, "iter": 2500, "lr": 0.09347, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26578, "top5_acc": 0.50109, "loss_cls": 4.05626, "loss": 4.05626, "time": 0.69675} +{"mode": "train", "epoch": 25, "iter": 2600, "lr": 0.09346, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25406, "top5_acc": 0.50703, "loss_cls": 4.07245, "loss": 4.07245, "time": 0.6999} +{"mode": "train", "epoch": 25, "iter": 2700, "lr": 0.09345, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25938, "top5_acc": 0.50875, "loss_cls": 4.05805, "loss": 4.05805, "time": 0.69944} +{"mode": "train", "epoch": 25, "iter": 2800, "lr": 0.09343, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26469, "top5_acc": 0.50688, "loss_cls": 4.06682, "loss": 4.06682, "time": 0.70014} +{"mode": "train", "epoch": 25, "iter": 2900, "lr": 0.09342, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26453, "top5_acc": 0.50313, "loss_cls": 4.04669, "loss": 4.04669, "time": 0.69709} +{"mode": "train", "epoch": 25, "iter": 3000, "lr": 0.09341, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27266, "top5_acc": 0.51172, "loss_cls": 4.02498, "loss": 4.02498, "time": 0.69866} +{"mode": "train", "epoch": 25, "iter": 3100, "lr": 0.09339, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25562, "top5_acc": 0.50938, "loss_cls": 4.04889, "loss": 4.04889, "time": 0.69922} +{"mode": "train", "epoch": 25, "iter": 3200, "lr": 0.09338, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26234, "top5_acc": 0.50688, "loss_cls": 4.06327, "loss": 4.06327, "time": 0.70011} +{"mode": "train", "epoch": 25, "iter": 3300, "lr": 0.09336, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25734, "top5_acc": 0.50547, "loss_cls": 4.06847, "loss": 4.06847, "time": 0.69972} +{"mode": "train", "epoch": 25, "iter": 3400, "lr": 0.09335, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25797, "top5_acc": 0.50156, "loss_cls": 4.0732, "loss": 4.0732, "time": 0.70043} +{"mode": "train", "epoch": 25, "iter": 3500, "lr": 0.09334, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27922, "top5_acc": 0.52078, "loss_cls": 3.98017, "loss": 3.98017, "time": 0.70046} +{"mode": "train", "epoch": 25, "iter": 3600, "lr": 0.09332, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26359, "top5_acc": 0.51172, "loss_cls": 4.03415, "loss": 4.03415, "time": 0.69892} +{"mode": "train", "epoch": 25, "iter": 3700, "lr": 0.09331, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26328, "top5_acc": 0.52047, "loss_cls": 4.00439, "loss": 4.00439, "time": 0.69996} +{"mode": "val", "epoch": 25, "iter": 309, "lr": 0.0933, "top1_acc": 0.20671, "top5_acc": 0.43767, "mean_class_accuracy": 0.20649} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.09329, "memory": 15990, "data_time": 1.29671, "top1_acc": 0.27375, "top5_acc": 0.52297, "loss_cls": 3.97825, "loss": 3.97825, "time": 2.0002} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.09327, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27453, "top5_acc": 0.52406, "loss_cls": 3.96724, "loss": 3.96724, "time": 0.70547} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.09326, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27875, "top5_acc": 0.52547, "loss_cls": 3.9415, "loss": 3.9415, "time": 0.70208} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.09325, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25391, "top5_acc": 0.50781, "loss_cls": 4.06121, "loss": 4.06121, "time": 0.70333} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.09323, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26609, "top5_acc": 0.51812, "loss_cls": 4.01582, "loss": 4.01582, "time": 0.70476} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.09322, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25531, "top5_acc": 0.51078, "loss_cls": 4.06788, "loss": 4.06788, "time": 0.70101} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.0932, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27422, "top5_acc": 0.52781, "loss_cls": 3.97719, "loss": 3.97719, "time": 0.70163} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.09319, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26312, "top5_acc": 0.50813, "loss_cls": 4.05526, "loss": 4.05526, "time": 0.70227} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.09318, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26953, "top5_acc": 0.51375, "loss_cls": 4.04166, "loss": 4.04166, "time": 0.69884} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.09316, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26078, "top5_acc": 0.50781, "loss_cls": 4.06325, "loss": 4.06325, "time": 0.70006} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.09315, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26781, "top5_acc": 0.51797, "loss_cls": 3.99326, "loss": 3.99326, "time": 0.70238} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.09313, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26516, "top5_acc": 0.51656, "loss_cls": 4.04765, "loss": 4.04765, "time": 0.70185} +{"mode": "train", "epoch": 26, "iter": 1300, "lr": 0.09312, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26484, "top5_acc": 0.505, "loss_cls": 4.0359, "loss": 4.0359, "time": 0.7014} +{"mode": "train", "epoch": 26, "iter": 1400, "lr": 0.0931, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27109, "top5_acc": 0.51422, "loss_cls": 4.01627, "loss": 4.01627, "time": 0.69854} +{"mode": "train", "epoch": 26, "iter": 1500, "lr": 0.09309, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26734, "top5_acc": 0.51531, "loss_cls": 4.03988, "loss": 4.03988, "time": 0.70823} +{"mode": "train", "epoch": 26, "iter": 1600, "lr": 0.09308, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26562, "top5_acc": 0.52094, "loss_cls": 4.02288, "loss": 4.02288, "time": 0.70649} +{"mode": "train", "epoch": 26, "iter": 1700, "lr": 0.09306, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26547, "top5_acc": 0.51031, "loss_cls": 4.06478, "loss": 4.06478, "time": 0.70315} +{"mode": "train", "epoch": 26, "iter": 1800, "lr": 0.09305, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27469, "top5_acc": 0.52484, "loss_cls": 3.96611, "loss": 3.96611, "time": 0.70729} +{"mode": "train", "epoch": 26, "iter": 1900, "lr": 0.09303, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26, "top5_acc": 0.50813, "loss_cls": 4.08092, "loss": 4.08092, "time": 0.70862} +{"mode": "train", "epoch": 26, "iter": 2000, "lr": 0.09302, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26906, "top5_acc": 0.52469, "loss_cls": 4.01255, "loss": 4.01255, "time": 0.70852} +{"mode": "train", "epoch": 26, "iter": 2100, "lr": 0.093, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2625, "top5_acc": 0.51078, "loss_cls": 4.03416, "loss": 4.03416, "time": 0.70762} +{"mode": "train", "epoch": 26, "iter": 2200, "lr": 0.09299, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26719, "top5_acc": 0.51109, "loss_cls": 4.03472, "loss": 4.03472, "time": 0.70858} +{"mode": "train", "epoch": 26, "iter": 2300, "lr": 0.09298, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25984, "top5_acc": 0.50703, "loss_cls": 4.03971, "loss": 4.03971, "time": 0.70627} +{"mode": "train", "epoch": 26, "iter": 2400, "lr": 0.09296, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26, "top5_acc": 0.51453, "loss_cls": 4.03422, "loss": 4.03422, "time": 0.70809} +{"mode": "train", "epoch": 26, "iter": 2500, "lr": 0.09295, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27797, "top5_acc": 0.51188, "loss_cls": 4.01473, "loss": 4.01473, "time": 0.70553} +{"mode": "train", "epoch": 26, "iter": 2600, "lr": 0.09293, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25766, "top5_acc": 0.5075, "loss_cls": 4.09319, "loss": 4.09319, "time": 0.70238} +{"mode": "train", "epoch": 26, "iter": 2700, "lr": 0.09292, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26375, "top5_acc": 0.51078, "loss_cls": 4.0561, "loss": 4.0561, "time": 0.7001} +{"mode": "train", "epoch": 26, "iter": 2800, "lr": 0.0929, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25828, "top5_acc": 0.51406, "loss_cls": 4.04253, "loss": 4.04253, "time": 0.7013} +{"mode": "train", "epoch": 26, "iter": 2900, "lr": 0.09289, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25969, "top5_acc": 0.50484, "loss_cls": 4.07006, "loss": 4.07006, "time": 0.69935} +{"mode": "train", "epoch": 26, "iter": 3000, "lr": 0.09288, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25281, "top5_acc": 0.50031, "loss_cls": 4.0769, "loss": 4.0769, "time": 0.6984} +{"mode": "train", "epoch": 26, "iter": 3100, "lr": 0.09286, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26141, "top5_acc": 0.50734, "loss_cls": 4.06251, "loss": 4.06251, "time": 0.69963} +{"mode": "train", "epoch": 26, "iter": 3200, "lr": 0.09285, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26766, "top5_acc": 0.52, "loss_cls": 4.00588, "loss": 4.00588, "time": 0.70054} +{"mode": "train", "epoch": 26, "iter": 3300, "lr": 0.09283, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27031, "top5_acc": 0.52312, "loss_cls": 4.02952, "loss": 4.02952, "time": 0.69989} +{"mode": "train", "epoch": 26, "iter": 3400, "lr": 0.09282, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26937, "top5_acc": 0.52531, "loss_cls": 4.00449, "loss": 4.00449, "time": 0.69934} +{"mode": "train", "epoch": 26, "iter": 3500, "lr": 0.0928, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26828, "top5_acc": 0.51594, "loss_cls": 4.03642, "loss": 4.03642, "time": 0.70213} +{"mode": "train", "epoch": 26, "iter": 3600, "lr": 0.09279, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26516, "top5_acc": 0.50609, "loss_cls": 4.07168, "loss": 4.07168, "time": 0.70115} +{"mode": "train", "epoch": 26, "iter": 3700, "lr": 0.09278, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26469, "top5_acc": 0.51922, "loss_cls": 3.9753, "loss": 3.9753, "time": 0.70091} +{"mode": "val", "epoch": 26, "iter": 309, "lr": 0.09277, "top1_acc": 0.17753, "top5_acc": 0.39842, "mean_class_accuracy": 0.17729} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.09275, "memory": 15990, "data_time": 1.29155, "top1_acc": 0.275, "top5_acc": 0.53328, "loss_cls": 3.93015, "loss": 3.93015, "time": 1.9962} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.09274, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26891, "top5_acc": 0.515, "loss_cls": 4.0166, "loss": 4.0166, "time": 0.70402} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.09272, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25953, "top5_acc": 0.51578, "loss_cls": 4.03496, "loss": 4.03496, "time": 0.70062} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.09271, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25641, "top5_acc": 0.51484, "loss_cls": 4.01547, "loss": 4.01547, "time": 0.70352} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.0927, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27375, "top5_acc": 0.53203, "loss_cls": 3.96032, "loss": 3.96032, "time": 0.70204} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.09268, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26453, "top5_acc": 0.51641, "loss_cls": 4.00756, "loss": 4.00756, "time": 0.69759} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.09267, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28422, "top5_acc": 0.52641, "loss_cls": 3.96163, "loss": 3.96163, "time": 0.69909} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.09265, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27094, "top5_acc": 0.52328, "loss_cls": 4.03363, "loss": 4.03363, "time": 0.70177} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.09264, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26594, "top5_acc": 0.52047, "loss_cls": 4.01879, "loss": 4.01879, "time": 0.70111} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.09262, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26422, "top5_acc": 0.50797, "loss_cls": 4.04559, "loss": 4.04559, "time": 0.70129} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.09261, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26156, "top5_acc": 0.51, "loss_cls": 4.03843, "loss": 4.03843, "time": 0.69849} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.09259, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27094, "top5_acc": 0.5175, "loss_cls": 4.0409, "loss": 4.0409, "time": 0.69858} +{"mode": "train", "epoch": 27, "iter": 1300, "lr": 0.09258, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26984, "top5_acc": 0.5125, "loss_cls": 4.03643, "loss": 4.03643, "time": 0.69978} +{"mode": "train", "epoch": 27, "iter": 1400, "lr": 0.09256, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26, "top5_acc": 0.50734, "loss_cls": 4.03527, "loss": 4.03527, "time": 0.70084} +{"mode": "train", "epoch": 27, "iter": 1500, "lr": 0.09255, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26359, "top5_acc": 0.5075, "loss_cls": 4.03034, "loss": 4.03034, "time": 0.70683} +{"mode": "train", "epoch": 27, "iter": 1600, "lr": 0.09253, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.53562, "loss_cls": 3.96585, "loss": 3.96585, "time": 0.70971} +{"mode": "train", "epoch": 27, "iter": 1700, "lr": 0.09252, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27141, "top5_acc": 0.50969, "loss_cls": 4.04904, "loss": 4.04904, "time": 0.70498} +{"mode": "train", "epoch": 27, "iter": 1800, "lr": 0.09251, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26, "top5_acc": 0.50672, "loss_cls": 4.04006, "loss": 4.04006, "time": 0.70485} +{"mode": "train", "epoch": 27, "iter": 1900, "lr": 0.09249, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26141, "top5_acc": 0.50641, "loss_cls": 4.05538, "loss": 4.05538, "time": 0.70494} +{"mode": "train", "epoch": 27, "iter": 2000, "lr": 0.09248, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27, "top5_acc": 0.51625, "loss_cls": 4.01221, "loss": 4.01221, "time": 0.70353} +{"mode": "train", "epoch": 27, "iter": 2100, "lr": 0.09246, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25938, "top5_acc": 0.50922, "loss_cls": 4.0503, "loss": 4.0503, "time": 0.71198} +{"mode": "train", "epoch": 27, "iter": 2200, "lr": 0.09245, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26484, "top5_acc": 0.51141, "loss_cls": 4.02141, "loss": 4.02141, "time": 0.70374} +{"mode": "train", "epoch": 27, "iter": 2300, "lr": 0.09243, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26141, "top5_acc": 0.50484, "loss_cls": 4.05167, "loss": 4.05167, "time": 0.71112} +{"mode": "train", "epoch": 27, "iter": 2400, "lr": 0.09242, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25719, "top5_acc": 0.51703, "loss_cls": 4.03246, "loss": 4.03246, "time": 0.70362} +{"mode": "train", "epoch": 27, "iter": 2500, "lr": 0.0924, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26531, "top5_acc": 0.50891, "loss_cls": 4.02498, "loss": 4.02498, "time": 0.70175} +{"mode": "train", "epoch": 27, "iter": 2600, "lr": 0.09239, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26969, "top5_acc": 0.51844, "loss_cls": 4.00372, "loss": 4.00372, "time": 0.69931} +{"mode": "train", "epoch": 27, "iter": 2700, "lr": 0.09237, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25969, "top5_acc": 0.51391, "loss_cls": 4.04187, "loss": 4.04187, "time": 0.70091} +{"mode": "train", "epoch": 27, "iter": 2800, "lr": 0.09236, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26531, "top5_acc": 0.52297, "loss_cls": 3.99775, "loss": 3.99775, "time": 0.70032} +{"mode": "train", "epoch": 27, "iter": 2900, "lr": 0.09234, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26891, "top5_acc": 0.51938, "loss_cls": 4.0145, "loss": 4.0145, "time": 0.70281} +{"mode": "train", "epoch": 27, "iter": 3000, "lr": 0.09233, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26625, "top5_acc": 0.5175, "loss_cls": 4.02271, "loss": 4.02271, "time": 0.70272} +{"mode": "train", "epoch": 27, "iter": 3100, "lr": 0.09231, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25297, "top5_acc": 0.51172, "loss_cls": 4.04966, "loss": 4.04966, "time": 0.70044} +{"mode": "train", "epoch": 27, "iter": 3200, "lr": 0.0923, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26984, "top5_acc": 0.52344, "loss_cls": 3.98584, "loss": 3.98584, "time": 0.70131} +{"mode": "train", "epoch": 27, "iter": 3300, "lr": 0.09228, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27187, "top5_acc": 0.52391, "loss_cls": 3.99978, "loss": 3.99978, "time": 0.69989} +{"mode": "train", "epoch": 27, "iter": 3400, "lr": 0.09227, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25844, "top5_acc": 0.50828, "loss_cls": 4.06662, "loss": 4.06662, "time": 0.69918} +{"mode": "train", "epoch": 27, "iter": 3500, "lr": 0.09225, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26125, "top5_acc": 0.50922, "loss_cls": 4.06135, "loss": 4.06135, "time": 0.70143} +{"mode": "train", "epoch": 27, "iter": 3600, "lr": 0.09224, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.265, "top5_acc": 0.51812, "loss_cls": 4.04179, "loss": 4.04179, "time": 0.70036} +{"mode": "train", "epoch": 27, "iter": 3700, "lr": 0.09222, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26125, "top5_acc": 0.50828, "loss_cls": 4.04891, "loss": 4.04891, "time": 0.70282} +{"mode": "val", "epoch": 27, "iter": 309, "lr": 0.09222, "top1_acc": 0.18604, "top5_acc": 0.41721, "mean_class_accuracy": 0.18574} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.0922, "memory": 15990, "data_time": 1.26822, "top1_acc": 0.27469, "top5_acc": 0.52719, "loss_cls": 3.96884, "loss": 3.96884, "time": 1.97114} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.09219, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26516, "top5_acc": 0.51562, "loss_cls": 4.05385, "loss": 4.05385, "time": 0.69873} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.09217, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.265, "top5_acc": 0.51281, "loss_cls": 4.01517, "loss": 4.01517, "time": 0.69948} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.09216, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26641, "top5_acc": 0.51313, "loss_cls": 4.01871, "loss": 4.01871, "time": 0.69985} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.09214, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26922, "top5_acc": 0.51828, "loss_cls": 4.01258, "loss": 4.01258, "time": 0.69857} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.09213, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26656, "top5_acc": 0.52266, "loss_cls": 3.95765, "loss": 3.95765, "time": 0.70128} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.09211, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.27187, "top5_acc": 0.51688, "loss_cls": 4.00817, "loss": 4.00817, "time": 0.69846} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.0921, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27, "top5_acc": 0.50828, "loss_cls": 4.04947, "loss": 4.04947, "time": 0.69884} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.09208, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27219, "top5_acc": 0.52016, "loss_cls": 4.00371, "loss": 4.00371, "time": 0.69682} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.09207, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27187, "top5_acc": 0.52281, "loss_cls": 3.98458, "loss": 3.98458, "time": 0.70125} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.09205, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27094, "top5_acc": 0.52219, "loss_cls": 4.01106, "loss": 4.01106, "time": 0.70081} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.09204, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26984, "top5_acc": 0.51031, "loss_cls": 4.00539, "loss": 4.00539, "time": 0.70149} +{"mode": "train", "epoch": 28, "iter": 1300, "lr": 0.09202, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26578, "top5_acc": 0.51672, "loss_cls": 4.00354, "loss": 4.00354, "time": 0.69788} +{"mode": "train", "epoch": 28, "iter": 1400, "lr": 0.09201, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26734, "top5_acc": 0.51719, "loss_cls": 4.02824, "loss": 4.02824, "time": 0.69757} +{"mode": "train", "epoch": 28, "iter": 1500, "lr": 0.09199, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26812, "top5_acc": 0.51938, "loss_cls": 4.0067, "loss": 4.0067, "time": 0.70309} +{"mode": "train", "epoch": 28, "iter": 1600, "lr": 0.09198, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26969, "top5_acc": 0.52625, "loss_cls": 3.98658, "loss": 3.98658, "time": 0.70338} +{"mode": "train", "epoch": 28, "iter": 1700, "lr": 0.09196, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26344, "top5_acc": 0.51906, "loss_cls": 4.0121, "loss": 4.0121, "time": 0.70409} +{"mode": "train", "epoch": 28, "iter": 1800, "lr": 0.09194, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26844, "top5_acc": 0.50953, "loss_cls": 4.02041, "loss": 4.02041, "time": 0.70631} +{"mode": "train", "epoch": 28, "iter": 1900, "lr": 0.09193, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25859, "top5_acc": 0.51531, "loss_cls": 4.02883, "loss": 4.02883, "time": 0.70482} +{"mode": "train", "epoch": 28, "iter": 2000, "lr": 0.09191, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27703, "top5_acc": 0.52203, "loss_cls": 4.00961, "loss": 4.00961, "time": 0.70247} +{"mode": "train", "epoch": 28, "iter": 2100, "lr": 0.0919, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26516, "top5_acc": 0.52062, "loss_cls": 4.0435, "loss": 4.0435, "time": 0.70924} +{"mode": "train", "epoch": 28, "iter": 2200, "lr": 0.09188, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27062, "top5_acc": 0.51547, "loss_cls": 4.02136, "loss": 4.02136, "time": 0.70642} +{"mode": "train", "epoch": 28, "iter": 2300, "lr": 0.09187, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26531, "top5_acc": 0.51328, "loss_cls": 4.02928, "loss": 4.02928, "time": 0.70438} +{"mode": "train", "epoch": 28, "iter": 2400, "lr": 0.09185, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26172, "top5_acc": 0.51422, "loss_cls": 4.01659, "loss": 4.01659, "time": 0.70203} +{"mode": "train", "epoch": 28, "iter": 2500, "lr": 0.09184, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25781, "top5_acc": 0.51313, "loss_cls": 4.04712, "loss": 4.04712, "time": 0.69856} +{"mode": "train", "epoch": 28, "iter": 2600, "lr": 0.09182, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26891, "top5_acc": 0.51828, "loss_cls": 4.00736, "loss": 4.00736, "time": 0.69867} +{"mode": "train", "epoch": 28, "iter": 2700, "lr": 0.09181, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26453, "top5_acc": 0.51969, "loss_cls": 4.02067, "loss": 4.02067, "time": 0.69931} +{"mode": "train", "epoch": 28, "iter": 2800, "lr": 0.09179, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26219, "top5_acc": 0.51141, "loss_cls": 4.04574, "loss": 4.04574, "time": 0.69776} +{"mode": "train", "epoch": 28, "iter": 2900, "lr": 0.09178, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27031, "top5_acc": 0.51922, "loss_cls": 3.99405, "loss": 3.99405, "time": 0.69967} +{"mode": "train", "epoch": 28, "iter": 3000, "lr": 0.09176, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26781, "top5_acc": 0.50344, "loss_cls": 4.03462, "loss": 4.03462, "time": 0.69677} +{"mode": "train", "epoch": 28, "iter": 3100, "lr": 0.09175, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27031, "top5_acc": 0.52312, "loss_cls": 4.00505, "loss": 4.00505, "time": 0.69777} +{"mode": "train", "epoch": 28, "iter": 3200, "lr": 0.09173, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27062, "top5_acc": 0.52422, "loss_cls": 3.97682, "loss": 3.97682, "time": 0.69792} +{"mode": "train", "epoch": 28, "iter": 3300, "lr": 0.09172, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27, "top5_acc": 0.51531, "loss_cls": 4.00143, "loss": 4.00143, "time": 0.7022} +{"mode": "train", "epoch": 28, "iter": 3400, "lr": 0.0917, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.2675, "top5_acc": 0.50797, "loss_cls": 4.00535, "loss": 4.00535, "time": 0.6993} +{"mode": "train", "epoch": 28, "iter": 3500, "lr": 0.09168, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26281, "top5_acc": 0.51078, "loss_cls": 4.05648, "loss": 4.05648, "time": 0.6987} +{"mode": "train", "epoch": 28, "iter": 3600, "lr": 0.09167, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26219, "top5_acc": 0.51719, "loss_cls": 4.03207, "loss": 4.03207, "time": 0.70305} +{"mode": "train", "epoch": 28, "iter": 3700, "lr": 0.09165, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26578, "top5_acc": 0.50891, "loss_cls": 4.05224, "loss": 4.05224, "time": 0.69826} +{"mode": "val", "epoch": 28, "iter": 309, "lr": 0.09165, "top1_acc": 0.18194, "top5_acc": 0.40632, "mean_class_accuracy": 0.18162} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.09163, "memory": 15990, "data_time": 1.27485, "top1_acc": 0.26547, "top5_acc": 0.51359, "loss_cls": 4.024, "loss": 4.024, "time": 1.97429} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.09162, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26828, "top5_acc": 0.52281, "loss_cls": 3.98236, "loss": 3.98236, "time": 0.70533} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.0916, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26594, "top5_acc": 0.50672, "loss_cls": 4.01821, "loss": 4.01821, "time": 0.70003} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.09158, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.26937, "top5_acc": 0.52531, "loss_cls": 4.00453, "loss": 4.00453, "time": 0.69723} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.09157, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.27234, "top5_acc": 0.51828, "loss_cls": 3.99186, "loss": 3.99186, "time": 0.697} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.09155, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26328, "top5_acc": 0.51547, "loss_cls": 4.02478, "loss": 4.02478, "time": 0.69819} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.09154, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26844, "top5_acc": 0.51938, "loss_cls": 4.02006, "loss": 4.02006, "time": 0.69798} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.09152, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26156, "top5_acc": 0.50828, "loss_cls": 4.05167, "loss": 4.05167, "time": 0.70209} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.09151, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26891, "top5_acc": 0.51797, "loss_cls": 3.99671, "loss": 3.99671, "time": 0.69704} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.09149, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26328, "top5_acc": 0.51672, "loss_cls": 4.02412, "loss": 4.02412, "time": 0.69939} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.09148, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27297, "top5_acc": 0.51844, "loss_cls": 3.98372, "loss": 3.98372, "time": 0.69838} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.09146, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26797, "top5_acc": 0.51734, "loss_cls": 4.02152, "loss": 4.02152, "time": 0.69863} +{"mode": "train", "epoch": 29, "iter": 1300, "lr": 0.09144, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27344, "top5_acc": 0.51766, "loss_cls": 3.99575, "loss": 3.99575, "time": 0.70068} +{"mode": "train", "epoch": 29, "iter": 1400, "lr": 0.09143, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26156, "top5_acc": 0.51375, "loss_cls": 4.03687, "loss": 4.03687, "time": 0.70059} +{"mode": "train", "epoch": 29, "iter": 1500, "lr": 0.09141, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27688, "top5_acc": 0.52359, "loss_cls": 3.99425, "loss": 3.99425, "time": 0.70446} +{"mode": "train", "epoch": 29, "iter": 1600, "lr": 0.0914, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26359, "top5_acc": 0.52531, "loss_cls": 3.99201, "loss": 3.99201, "time": 0.70474} +{"mode": "train", "epoch": 29, "iter": 1700, "lr": 0.09138, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26312, "top5_acc": 0.51328, "loss_cls": 4.01427, "loss": 4.01427, "time": 0.7019} +{"mode": "train", "epoch": 29, "iter": 1800, "lr": 0.09137, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27016, "top5_acc": 0.51578, "loss_cls": 4.02668, "loss": 4.02668, "time": 0.7007} +{"mode": "train", "epoch": 29, "iter": 1900, "lr": 0.09135, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26703, "top5_acc": 0.51578, "loss_cls": 4.00403, "loss": 4.00403, "time": 0.70643} +{"mode": "train", "epoch": 29, "iter": 2000, "lr": 0.09133, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26766, "top5_acc": 0.51484, "loss_cls": 4.01002, "loss": 4.01002, "time": 0.70327} +{"mode": "train", "epoch": 29, "iter": 2100, "lr": 0.09132, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25984, "top5_acc": 0.50797, "loss_cls": 4.03492, "loss": 4.03492, "time": 0.70957} +{"mode": "train", "epoch": 29, "iter": 2200, "lr": 0.0913, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27437, "top5_acc": 0.52094, "loss_cls": 4.00842, "loss": 4.00842, "time": 0.70519} +{"mode": "train", "epoch": 29, "iter": 2300, "lr": 0.09129, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26328, "top5_acc": 0.52, "loss_cls": 4.02554, "loss": 4.02554, "time": 0.71086} +{"mode": "train", "epoch": 29, "iter": 2400, "lr": 0.09127, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.275, "top5_acc": 0.52656, "loss_cls": 3.96147, "loss": 3.96147, "time": 0.70325} +{"mode": "train", "epoch": 29, "iter": 2500, "lr": 0.09126, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25688, "top5_acc": 0.51156, "loss_cls": 4.05506, "loss": 4.05506, "time": 0.69855} +{"mode": "train", "epoch": 29, "iter": 2600, "lr": 0.09124, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27234, "top5_acc": 0.5225, "loss_cls": 4.00608, "loss": 4.00608, "time": 0.69923} +{"mode": "train", "epoch": 29, "iter": 2700, "lr": 0.09122, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26531, "top5_acc": 0.51094, "loss_cls": 4.02633, "loss": 4.02633, "time": 0.69825} +{"mode": "train", "epoch": 29, "iter": 2800, "lr": 0.09121, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27016, "top5_acc": 0.52516, "loss_cls": 3.99869, "loss": 3.99869, "time": 0.69844} +{"mode": "train", "epoch": 29, "iter": 2900, "lr": 0.09119, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26578, "top5_acc": 0.51688, "loss_cls": 4.01945, "loss": 4.01945, "time": 0.70122} +{"mode": "train", "epoch": 29, "iter": 3000, "lr": 0.09118, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27312, "top5_acc": 0.51422, "loss_cls": 4.047, "loss": 4.047, "time": 0.69859} +{"mode": "train", "epoch": 29, "iter": 3100, "lr": 0.09116, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26203, "top5_acc": 0.52031, "loss_cls": 4.01597, "loss": 4.01597, "time": 0.70057} +{"mode": "train", "epoch": 29, "iter": 3200, "lr": 0.09114, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26391, "top5_acc": 0.51016, "loss_cls": 4.01732, "loss": 4.01732, "time": 0.69863} +{"mode": "train", "epoch": 29, "iter": 3300, "lr": 0.09113, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26, "top5_acc": 0.51656, "loss_cls": 4.01626, "loss": 4.01626, "time": 0.70131} +{"mode": "train", "epoch": 29, "iter": 3400, "lr": 0.09111, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27141, "top5_acc": 0.51938, "loss_cls": 4.00087, "loss": 4.00087, "time": 0.69557} +{"mode": "train", "epoch": 29, "iter": 3500, "lr": 0.0911, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26844, "top5_acc": 0.515, "loss_cls": 4.0372, "loss": 4.0372, "time": 0.69886} +{"mode": "train", "epoch": 29, "iter": 3600, "lr": 0.09108, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25812, "top5_acc": 0.51375, "loss_cls": 4.0466, "loss": 4.0466, "time": 0.69723} +{"mode": "train", "epoch": 29, "iter": 3700, "lr": 0.09106, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26172, "top5_acc": 0.51094, "loss_cls": 4.01914, "loss": 4.01914, "time": 0.699} +{"mode": "val", "epoch": 29, "iter": 309, "lr": 0.09106, "top1_acc": 0.18847, "top5_acc": 0.41569, "mean_class_accuracy": 0.18807} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.09104, "memory": 15990, "data_time": 1.24997, "top1_acc": 0.27078, "top5_acc": 0.51594, "loss_cls": 4.01526, "loss": 4.01526, "time": 2.05696} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.09103, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26031, "top5_acc": 0.50953, "loss_cls": 4.02221, "loss": 4.02221, "time": 0.80943} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.09101, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26891, "top5_acc": 0.51578, "loss_cls": 3.99711, "loss": 3.99711, "time": 0.8026} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.09099, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27234, "top5_acc": 0.52531, "loss_cls": 3.97196, "loss": 3.97196, "time": 0.80308} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.09098, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27187, "top5_acc": 0.52344, "loss_cls": 3.97287, "loss": 3.97287, "time": 0.8008} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.09096, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26844, "top5_acc": 0.52469, "loss_cls": 3.98856, "loss": 3.98856, "time": 0.81006} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.09095, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25922, "top5_acc": 0.51094, "loss_cls": 4.0395, "loss": 4.0395, "time": 0.80288} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.09093, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28188, "top5_acc": 0.5275, "loss_cls": 3.96494, "loss": 3.96494, "time": 0.79975} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.09091, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26375, "top5_acc": 0.51906, "loss_cls": 4.0365, "loss": 4.0365, "time": 0.80674} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.0909, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27531, "top5_acc": 0.51922, "loss_cls": 3.9969, "loss": 3.9969, "time": 0.80078} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.09088, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26531, "top5_acc": 0.51875, "loss_cls": 4.01987, "loss": 4.01987, "time": 0.80071} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.09087, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26547, "top5_acc": 0.51641, "loss_cls": 4.014, "loss": 4.014, "time": 0.79797} +{"mode": "train", "epoch": 30, "iter": 1300, "lr": 0.09085, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26219, "top5_acc": 0.52172, "loss_cls": 4.02665, "loss": 4.02665, "time": 0.80341} +{"mode": "train", "epoch": 30, "iter": 1400, "lr": 0.09083, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27266, "top5_acc": 0.51125, "loss_cls": 4.00114, "loss": 4.00114, "time": 0.80578} +{"mode": "train", "epoch": 30, "iter": 1500, "lr": 0.09082, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26375, "top5_acc": 0.52391, "loss_cls": 3.98626, "loss": 3.98626, "time": 0.80215} +{"mode": "train", "epoch": 30, "iter": 1600, "lr": 0.0908, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26359, "top5_acc": 0.51391, "loss_cls": 4.04839, "loss": 4.04839, "time": 0.80799} +{"mode": "train", "epoch": 30, "iter": 1700, "lr": 0.09078, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26828, "top5_acc": 0.52641, "loss_cls": 3.97423, "loss": 3.97423, "time": 0.80191} +{"mode": "train", "epoch": 30, "iter": 1800, "lr": 0.09077, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26375, "top5_acc": 0.50562, "loss_cls": 4.06203, "loss": 4.06203, "time": 0.81493} +{"mode": "train", "epoch": 30, "iter": 1900, "lr": 0.09075, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27219, "top5_acc": 0.52547, "loss_cls": 3.99882, "loss": 3.99882, "time": 0.81263} +{"mode": "train", "epoch": 30, "iter": 2000, "lr": 0.09074, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27344, "top5_acc": 0.52172, "loss_cls": 4.00044, "loss": 4.00044, "time": 0.81602} +{"mode": "train", "epoch": 30, "iter": 2100, "lr": 0.09072, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.27219, "top5_acc": 0.50984, "loss_cls": 4.0214, "loss": 4.0214, "time": 0.81012} +{"mode": "train", "epoch": 30, "iter": 2200, "lr": 0.0907, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27062, "top5_acc": 0.51828, "loss_cls": 4.02411, "loss": 4.02411, "time": 0.80269} +{"mode": "train", "epoch": 30, "iter": 2300, "lr": 0.09069, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26766, "top5_acc": 0.51844, "loss_cls": 4.03851, "loss": 4.03851, "time": 0.80398} +{"mode": "train", "epoch": 30, "iter": 2400, "lr": 0.09067, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25703, "top5_acc": 0.50375, "loss_cls": 4.09114, "loss": 4.09114, "time": 0.80042} +{"mode": "train", "epoch": 30, "iter": 2500, "lr": 0.09065, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26516, "top5_acc": 0.51469, "loss_cls": 4.02047, "loss": 4.02047, "time": 0.79921} +{"mode": "train", "epoch": 30, "iter": 2600, "lr": 0.09064, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27359, "top5_acc": 0.52359, "loss_cls": 3.97041, "loss": 3.97041, "time": 0.8012} +{"mode": "train", "epoch": 30, "iter": 2700, "lr": 0.09062, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27016, "top5_acc": 0.515, "loss_cls": 4.01312, "loss": 4.01312, "time": 0.80507} +{"mode": "train", "epoch": 30, "iter": 2800, "lr": 0.09061, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26656, "top5_acc": 0.51656, "loss_cls": 4.01784, "loss": 4.01784, "time": 0.80288} +{"mode": "train", "epoch": 30, "iter": 2900, "lr": 0.09059, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26594, "top5_acc": 0.51328, "loss_cls": 4.02697, "loss": 4.02697, "time": 0.80168} +{"mode": "train", "epoch": 30, "iter": 3000, "lr": 0.09057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26703, "top5_acc": 0.51844, "loss_cls": 4.01893, "loss": 4.01893, "time": 0.81019} +{"mode": "train", "epoch": 30, "iter": 3100, "lr": 0.09056, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27, "top5_acc": 0.51266, "loss_cls": 4.04347, "loss": 4.04347, "time": 0.80025} +{"mode": "train", "epoch": 30, "iter": 3200, "lr": 0.09054, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25891, "top5_acc": 0.51734, "loss_cls": 4.03832, "loss": 4.03832, "time": 0.80046} +{"mode": "train", "epoch": 30, "iter": 3300, "lr": 0.09052, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26438, "top5_acc": 0.51578, "loss_cls": 4.02799, "loss": 4.02799, "time": 0.79775} +{"mode": "train", "epoch": 30, "iter": 3400, "lr": 0.09051, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26281, "top5_acc": 0.51125, "loss_cls": 4.01954, "loss": 4.01954, "time": 0.79825} +{"mode": "train", "epoch": 30, "iter": 3500, "lr": 0.09049, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26844, "top5_acc": 0.51203, "loss_cls": 4.03315, "loss": 4.03315, "time": 0.7984} +{"mode": "train", "epoch": 30, "iter": 3600, "lr": 0.09047, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27047, "top5_acc": 0.51047, "loss_cls": 4.03526, "loss": 4.03526, "time": 0.80315} +{"mode": "train", "epoch": 30, "iter": 3700, "lr": 0.09046, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28328, "top5_acc": 0.52641, "loss_cls": 3.94498, "loss": 3.94498, "time": 0.80358} +{"mode": "val", "epoch": 30, "iter": 309, "lr": 0.09045, "top1_acc": 0.1983, "top5_acc": 0.42253, "mean_class_accuracy": 0.19802} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.09043, "memory": 15990, "data_time": 1.25844, "top1_acc": 0.25922, "top5_acc": 0.51672, "loss_cls": 4.2593, "loss": 4.2593, "time": 2.23343} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.09042, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27531, "top5_acc": 0.52109, "loss_cls": 4.19876, "loss": 4.19876, "time": 0.81687} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.0904, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28312, "top5_acc": 0.53062, "loss_cls": 4.17693, "loss": 4.17693, "time": 0.81653} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.09039, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26922, "top5_acc": 0.51766, "loss_cls": 4.20818, "loss": 4.20818, "time": 0.81267} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.09037, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27766, "top5_acc": 0.52938, "loss_cls": 4.17679, "loss": 4.17679, "time": 0.81353} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.09035, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26047, "top5_acc": 0.52156, "loss_cls": 4.22419, "loss": 4.22419, "time": 0.81477} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.09034, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26828, "top5_acc": 0.52047, "loss_cls": 4.22293, "loss": 4.22293, "time": 0.81766} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.09032, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27141, "top5_acc": 0.51594, "loss_cls": 4.21034, "loss": 4.21034, "time": 0.81991} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.52281, "loss_cls": 4.20221, "loss": 4.20221, "time": 0.81815} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.09029, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27203, "top5_acc": 0.52094, "loss_cls": 4.22955, "loss": 4.22955, "time": 0.81467} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.09027, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27359, "top5_acc": 0.51313, "loss_cls": 4.2321, "loss": 4.2321, "time": 0.81857} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.09025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26453, "top5_acc": 0.52422, "loss_cls": 4.20215, "loss": 4.20215, "time": 0.81872} +{"mode": "train", "epoch": 31, "iter": 1300, "lr": 0.09024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27219, "top5_acc": 0.52266, "loss_cls": 4.20946, "loss": 4.20946, "time": 0.81994} +{"mode": "train", "epoch": 31, "iter": 1400, "lr": 0.09022, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26344, "top5_acc": 0.51156, "loss_cls": 4.24664, "loss": 4.24664, "time": 0.82112} +{"mode": "train", "epoch": 31, "iter": 1500, "lr": 0.0902, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27453, "top5_acc": 0.52562, "loss_cls": 4.20538, "loss": 4.20538, "time": 0.8203} +{"mode": "train", "epoch": 31, "iter": 1600, "lr": 0.09019, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26406, "top5_acc": 0.51891, "loss_cls": 4.23579, "loss": 4.23579, "time": 0.81944} +{"mode": "train", "epoch": 31, "iter": 1700, "lr": 0.09017, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27109, "top5_acc": 0.52531, "loss_cls": 4.20976, "loss": 4.20976, "time": 0.81734} +{"mode": "train", "epoch": 31, "iter": 1800, "lr": 0.09015, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26922, "top5_acc": 0.51969, "loss_cls": 4.20192, "loss": 4.20192, "time": 0.82391} +{"mode": "train", "epoch": 31, "iter": 1900, "lr": 0.09014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26266, "top5_acc": 0.51438, "loss_cls": 4.24565, "loss": 4.24565, "time": 0.82573} +{"mode": "train", "epoch": 31, "iter": 2000, "lr": 0.09012, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2675, "top5_acc": 0.51234, "loss_cls": 4.23126, "loss": 4.23126, "time": 0.8258} +{"mode": "train", "epoch": 31, "iter": 2100, "lr": 0.0901, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26891, "top5_acc": 0.51219, "loss_cls": 4.21951, "loss": 4.21951, "time": 0.81926} +{"mode": "train", "epoch": 31, "iter": 2200, "lr": 0.09009, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27719, "top5_acc": 0.52344, "loss_cls": 4.18149, "loss": 4.18149, "time": 0.82053} +{"mode": "train", "epoch": 31, "iter": 2300, "lr": 0.09007, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27016, "top5_acc": 0.51266, "loss_cls": 4.26516, "loss": 4.26516, "time": 0.81507} +{"mode": "train", "epoch": 31, "iter": 2400, "lr": 0.09005, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2725, "top5_acc": 0.52, "loss_cls": 4.21035, "loss": 4.21035, "time": 0.81474} +{"mode": "train", "epoch": 31, "iter": 2500, "lr": 0.09004, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26859, "top5_acc": 0.51781, "loss_cls": 4.20171, "loss": 4.20171, "time": 0.81536} +{"mode": "train", "epoch": 31, "iter": 2600, "lr": 0.09002, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26953, "top5_acc": 0.50922, "loss_cls": 4.24783, "loss": 4.24783, "time": 0.81235} +{"mode": "train", "epoch": 31, "iter": 2700, "lr": 0.09, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26766, "top5_acc": 0.52031, "loss_cls": 4.2281, "loss": 4.2281, "time": 0.81813} +{"mode": "train", "epoch": 31, "iter": 2800, "lr": 0.08999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27312, "top5_acc": 0.51875, "loss_cls": 4.2291, "loss": 4.2291, "time": 0.81431} +{"mode": "train", "epoch": 31, "iter": 2900, "lr": 0.08997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27609, "top5_acc": 0.51547, "loss_cls": 4.22089, "loss": 4.22089, "time": 0.81469} +{"mode": "train", "epoch": 31, "iter": 3000, "lr": 0.08995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26734, "top5_acc": 0.51313, "loss_cls": 4.28216, "loss": 4.28216, "time": 0.8169} +{"mode": "train", "epoch": 31, "iter": 3100, "lr": 0.08994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27016, "top5_acc": 0.51812, "loss_cls": 4.23282, "loss": 4.23282, "time": 0.81187} +{"mode": "train", "epoch": 31, "iter": 3200, "lr": 0.08992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26375, "top5_acc": 0.50922, "loss_cls": 4.26731, "loss": 4.26731, "time": 0.81577} +{"mode": "train", "epoch": 31, "iter": 3300, "lr": 0.0899, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26656, "top5_acc": 0.51531, "loss_cls": 4.26675, "loss": 4.26675, "time": 0.81656} +{"mode": "train", "epoch": 31, "iter": 3400, "lr": 0.08989, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27359, "top5_acc": 0.51641, "loss_cls": 4.1908, "loss": 4.1908, "time": 0.81335} +{"mode": "train", "epoch": 31, "iter": 3500, "lr": 0.08987, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26297, "top5_acc": 0.51313, "loss_cls": 4.24878, "loss": 4.24878, "time": 0.81554} +{"mode": "train", "epoch": 31, "iter": 3600, "lr": 0.08985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27031, "top5_acc": 0.51281, "loss_cls": 4.26557, "loss": 4.26557, "time": 0.81454} +{"mode": "train", "epoch": 31, "iter": 3700, "lr": 0.08983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26891, "top5_acc": 0.51547, "loss_cls": 4.24785, "loss": 4.24785, "time": 0.82237} +{"mode": "val", "epoch": 31, "iter": 309, "lr": 0.08983, "top1_acc": 0.18888, "top5_acc": 0.41296, "mean_class_accuracy": 0.18874} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.08981, "memory": 15990, "data_time": 1.2469, "top1_acc": 0.27281, "top5_acc": 0.52578, "loss_cls": 4.19515, "loss": 4.19515, "time": 2.24099} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.08979, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28062, "top5_acc": 0.52953, "loss_cls": 4.16695, "loss": 4.16695, "time": 0.82024} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.08978, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27047, "top5_acc": 0.52156, "loss_cls": 4.19447, "loss": 4.19447, "time": 0.8128} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.08976, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26609, "top5_acc": 0.51766, "loss_cls": 4.19967, "loss": 4.19967, "time": 0.81517} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.08974, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28062, "top5_acc": 0.53031, "loss_cls": 4.16071, "loss": 4.16071, "time": 0.81285} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.08973, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26797, "top5_acc": 0.52344, "loss_cls": 4.21073, "loss": 4.21073, "time": 0.81703} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.08971, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27391, "top5_acc": 0.52344, "loss_cls": 4.21634, "loss": 4.21634, "time": 0.81691} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.08969, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26031, "top5_acc": 0.51922, "loss_cls": 4.23788, "loss": 4.23788, "time": 0.81261} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.08967, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26906, "top5_acc": 0.52641, "loss_cls": 4.20424, "loss": 4.20424, "time": 0.81714} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.08966, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26719, "top5_acc": 0.51594, "loss_cls": 4.24845, "loss": 4.24845, "time": 0.81621} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.08964, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.2625, "top5_acc": 0.50203, "loss_cls": 4.26019, "loss": 4.26019, "time": 0.81435} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.08962, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26937, "top5_acc": 0.5125, "loss_cls": 4.2158, "loss": 4.2158, "time": 0.82275} +{"mode": "train", "epoch": 32, "iter": 1300, "lr": 0.08961, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25969, "top5_acc": 0.51156, "loss_cls": 4.2427, "loss": 4.2427, "time": 0.81518} +{"mode": "train", "epoch": 32, "iter": 1400, "lr": 0.08959, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26875, "top5_acc": 0.51875, "loss_cls": 4.22687, "loss": 4.22687, "time": 0.82371} +{"mode": "train", "epoch": 32, "iter": 1500, "lr": 0.08957, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26812, "top5_acc": 0.51719, "loss_cls": 4.23281, "loss": 4.23281, "time": 0.81388} +{"mode": "train", "epoch": 32, "iter": 1600, "lr": 0.08955, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26547, "top5_acc": 0.52781, "loss_cls": 4.18956, "loss": 4.18956, "time": 0.8188} +{"mode": "train", "epoch": 32, "iter": 1700, "lr": 0.08954, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26859, "top5_acc": 0.51141, "loss_cls": 4.25916, "loss": 4.25916, "time": 0.82106} +{"mode": "train", "epoch": 32, "iter": 1800, "lr": 0.08952, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26188, "top5_acc": 0.51469, "loss_cls": 4.24252, "loss": 4.24252, "time": 0.82907} +{"mode": "train", "epoch": 32, "iter": 1900, "lr": 0.0895, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26812, "top5_acc": 0.51734, "loss_cls": 4.2129, "loss": 4.2129, "time": 0.82517} +{"mode": "train", "epoch": 32, "iter": 2000, "lr": 0.08949, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26078, "top5_acc": 0.52141, "loss_cls": 4.23698, "loss": 4.23698, "time": 0.82311} +{"mode": "train", "epoch": 32, "iter": 2100, "lr": 0.08947, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26844, "top5_acc": 0.52094, "loss_cls": 4.20589, "loss": 4.20589, "time": 0.82775} +{"mode": "train", "epoch": 32, "iter": 2200, "lr": 0.08945, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26469, "top5_acc": 0.51625, "loss_cls": 4.21478, "loss": 4.21478, "time": 0.81848} +{"mode": "train", "epoch": 32, "iter": 2300, "lr": 0.08943, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27469, "top5_acc": 0.51688, "loss_cls": 4.20462, "loss": 4.20462, "time": 0.82389} +{"mode": "train", "epoch": 32, "iter": 2400, "lr": 0.08942, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27547, "top5_acc": 0.51594, "loss_cls": 4.21387, "loss": 4.21387, "time": 0.81946} +{"mode": "train", "epoch": 32, "iter": 2500, "lr": 0.0894, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26641, "top5_acc": 0.52312, "loss_cls": 4.20457, "loss": 4.20457, "time": 0.81789} +{"mode": "train", "epoch": 32, "iter": 2600, "lr": 0.08938, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26781, "top5_acc": 0.51734, "loss_cls": 4.20806, "loss": 4.20806, "time": 0.81516} +{"mode": "train", "epoch": 32, "iter": 2700, "lr": 0.08937, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25891, "top5_acc": 0.50562, "loss_cls": 4.26966, "loss": 4.26966, "time": 0.81331} +{"mode": "train", "epoch": 32, "iter": 2800, "lr": 0.08935, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27219, "top5_acc": 0.52641, "loss_cls": 4.21083, "loss": 4.21083, "time": 0.81357} +{"mode": "train", "epoch": 32, "iter": 2900, "lr": 0.08933, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27531, "top5_acc": 0.52797, "loss_cls": 4.20101, "loss": 4.20101, "time": 0.81385} +{"mode": "train", "epoch": 32, "iter": 3000, "lr": 0.08931, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27187, "top5_acc": 0.51953, "loss_cls": 4.21738, "loss": 4.21738, "time": 0.81145} +{"mode": "train", "epoch": 32, "iter": 3100, "lr": 0.0893, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26828, "top5_acc": 0.51797, "loss_cls": 4.22855, "loss": 4.22855, "time": 0.81566} +{"mode": "train", "epoch": 32, "iter": 3200, "lr": 0.08928, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26641, "top5_acc": 0.50859, "loss_cls": 4.26821, "loss": 4.26821, "time": 0.81831} +{"mode": "train", "epoch": 32, "iter": 3300, "lr": 0.08926, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27, "top5_acc": 0.51953, "loss_cls": 4.22013, "loss": 4.22013, "time": 0.81433} +{"mode": "train", "epoch": 32, "iter": 3400, "lr": 0.08924, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2775, "top5_acc": 0.52281, "loss_cls": 4.17341, "loss": 4.17341, "time": 0.81549} +{"mode": "train", "epoch": 32, "iter": 3500, "lr": 0.08923, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26094, "top5_acc": 0.50125, "loss_cls": 4.26508, "loss": 4.26508, "time": 0.81506} +{"mode": "train", "epoch": 32, "iter": 3600, "lr": 0.08921, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26594, "top5_acc": 0.51781, "loss_cls": 4.24317, "loss": 4.24317, "time": 0.81801} +{"mode": "train", "epoch": 32, "iter": 3700, "lr": 0.08919, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26828, "top5_acc": 0.5175, "loss_cls": 4.22927, "loss": 4.22927, "time": 0.81272} +{"mode": "val", "epoch": 32, "iter": 309, "lr": 0.08918, "top1_acc": 0.19278, "top5_acc": 0.42689, "mean_class_accuracy": 0.19236} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.08917, "memory": 15990, "data_time": 1.29599, "top1_acc": 0.27437, "top5_acc": 0.52422, "loss_cls": 4.17361, "loss": 4.17361, "time": 2.27171} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.08915, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26969, "top5_acc": 0.52578, "loss_cls": 4.16491, "loss": 4.16491, "time": 0.81369} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.08913, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27172, "top5_acc": 0.53, "loss_cls": 4.20391, "loss": 4.20391, "time": 0.81502} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.08912, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2725, "top5_acc": 0.52062, "loss_cls": 4.19735, "loss": 4.19735, "time": 0.81693} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.0891, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26109, "top5_acc": 0.51406, "loss_cls": 4.22406, "loss": 4.22406, "time": 0.82132} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.08908, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27359, "top5_acc": 0.51906, "loss_cls": 4.21133, "loss": 4.21133, "time": 0.81426} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.08906, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26656, "top5_acc": 0.52359, "loss_cls": 4.19266, "loss": 4.19266, "time": 0.81469} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.08905, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28016, "top5_acc": 0.51797, "loss_cls": 4.20661, "loss": 4.20661, "time": 0.81456} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.08903, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26719, "top5_acc": 0.51297, "loss_cls": 4.25855, "loss": 4.25855, "time": 0.8179} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.08901, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26875, "top5_acc": 0.52406, "loss_cls": 4.24675, "loss": 4.24675, "time": 0.81293} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.08899, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26359, "top5_acc": 0.51484, "loss_cls": 4.25478, "loss": 4.25478, "time": 0.81506} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.08898, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27328, "top5_acc": 0.52812, "loss_cls": 4.16669, "loss": 4.16669, "time": 0.82041} +{"mode": "train", "epoch": 33, "iter": 1300, "lr": 0.08896, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26359, "top5_acc": 0.51203, "loss_cls": 4.24385, "loss": 4.24385, "time": 0.81652} +{"mode": "train", "epoch": 33, "iter": 1400, "lr": 0.08894, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26828, "top5_acc": 0.52516, "loss_cls": 4.1976, "loss": 4.1976, "time": 0.82259} +{"mode": "train", "epoch": 33, "iter": 1500, "lr": 0.08892, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28188, "top5_acc": 0.53109, "loss_cls": 4.16813, "loss": 4.16813, "time": 0.81524} +{"mode": "train", "epoch": 33, "iter": 1600, "lr": 0.08891, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27859, "top5_acc": 0.52562, "loss_cls": 4.209, "loss": 4.209, "time": 0.81629} +{"mode": "train", "epoch": 33, "iter": 1700, "lr": 0.08889, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26734, "top5_acc": 0.52406, "loss_cls": 4.2021, "loss": 4.2021, "time": 0.81703} +{"mode": "train", "epoch": 33, "iter": 1800, "lr": 0.08887, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27422, "top5_acc": 0.51656, "loss_cls": 4.25547, "loss": 4.25547, "time": 0.82215} +{"mode": "train", "epoch": 33, "iter": 1900, "lr": 0.08885, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27125, "top5_acc": 0.52281, "loss_cls": 4.21698, "loss": 4.21698, "time": 0.82527} +{"mode": "train", "epoch": 33, "iter": 2000, "lr": 0.08884, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27688, "top5_acc": 0.51797, "loss_cls": 4.23895, "loss": 4.23895, "time": 0.8166} +{"mode": "train", "epoch": 33, "iter": 2100, "lr": 0.08882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26531, "top5_acc": 0.51562, "loss_cls": 4.25634, "loss": 4.25634, "time": 0.82354} +{"mode": "train", "epoch": 33, "iter": 2200, "lr": 0.0888, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25547, "top5_acc": 0.50922, "loss_cls": 4.27109, "loss": 4.27109, "time": 0.81905} +{"mode": "train", "epoch": 33, "iter": 2300, "lr": 0.08878, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26844, "top5_acc": 0.51422, "loss_cls": 4.26722, "loss": 4.26722, "time": 0.81581} +{"mode": "train", "epoch": 33, "iter": 2400, "lr": 0.08876, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27391, "top5_acc": 0.52344, "loss_cls": 4.22283, "loss": 4.22283, "time": 0.81244} +{"mode": "train", "epoch": 33, "iter": 2500, "lr": 0.08875, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26984, "top5_acc": 0.52125, "loss_cls": 4.22316, "loss": 4.22316, "time": 0.82062} +{"mode": "train", "epoch": 33, "iter": 2600, "lr": 0.08873, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27109, "top5_acc": 0.52781, "loss_cls": 4.20709, "loss": 4.20709, "time": 0.8136} +{"mode": "train", "epoch": 33, "iter": 2700, "lr": 0.08871, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27406, "top5_acc": 0.52453, "loss_cls": 4.18243, "loss": 4.18243, "time": 0.81503} +{"mode": "train", "epoch": 33, "iter": 2800, "lr": 0.08869, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27156, "top5_acc": 0.52047, "loss_cls": 4.18602, "loss": 4.18602, "time": 0.81575} +{"mode": "train", "epoch": 33, "iter": 2900, "lr": 0.08868, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27266, "top5_acc": 0.51562, "loss_cls": 4.20599, "loss": 4.20599, "time": 0.81564} +{"mode": "train", "epoch": 33, "iter": 3000, "lr": 0.08866, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26719, "top5_acc": 0.5225, "loss_cls": 4.20268, "loss": 4.20268, "time": 0.82297} +{"mode": "train", "epoch": 33, "iter": 3100, "lr": 0.08864, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26609, "top5_acc": 0.51672, "loss_cls": 4.21528, "loss": 4.21528, "time": 0.81305} +{"mode": "train", "epoch": 33, "iter": 3200, "lr": 0.08862, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26266, "top5_acc": 0.51062, "loss_cls": 4.24741, "loss": 4.24741, "time": 0.81152} +{"mode": "train", "epoch": 33, "iter": 3300, "lr": 0.08861, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2625, "top5_acc": 0.51344, "loss_cls": 4.24766, "loss": 4.24766, "time": 0.81457} +{"mode": "train", "epoch": 33, "iter": 3400, "lr": 0.08859, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27781, "top5_acc": 0.52656, "loss_cls": 4.1691, "loss": 4.1691, "time": 0.81377} +{"mode": "train", "epoch": 33, "iter": 3500, "lr": 0.08857, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26984, "top5_acc": 0.52016, "loss_cls": 4.20951, "loss": 4.20951, "time": 0.81553} +{"mode": "train", "epoch": 33, "iter": 3600, "lr": 0.08855, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27812, "top5_acc": 0.53609, "loss_cls": 4.17423, "loss": 4.17423, "time": 0.81567} +{"mode": "train", "epoch": 33, "iter": 3700, "lr": 0.08853, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26844, "top5_acc": 0.51531, "loss_cls": 4.24745, "loss": 4.24745, "time": 0.81674} +{"mode": "val", "epoch": 33, "iter": 309, "lr": 0.08853, "top1_acc": 0.19779, "top5_acc": 0.41772, "mean_class_accuracy": 0.19744} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.08851, "memory": 15990, "data_time": 1.2432, "top1_acc": 0.28781, "top5_acc": 0.53891, "loss_cls": 4.12019, "loss": 4.12019, "time": 2.21603} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.08849, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28109, "top5_acc": 0.52297, "loss_cls": 4.16799, "loss": 4.16799, "time": 0.82771} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.08847, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26844, "top5_acc": 0.52688, "loss_cls": 4.18988, "loss": 4.18988, "time": 0.81918} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.08845, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28344, "top5_acc": 0.52984, "loss_cls": 4.1709, "loss": 4.1709, "time": 0.81438} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.08844, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27641, "top5_acc": 0.51578, "loss_cls": 4.19852, "loss": 4.19852, "time": 0.81601} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.08842, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26906, "top5_acc": 0.52125, "loss_cls": 4.21556, "loss": 4.21556, "time": 0.81717} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.0884, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27031, "top5_acc": 0.51578, "loss_cls": 4.22105, "loss": 4.22105, "time": 0.81655} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.08838, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27812, "top5_acc": 0.53938, "loss_cls": 4.16301, "loss": 4.16301, "time": 0.81518} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.08836, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27891, "top5_acc": 0.52578, "loss_cls": 4.20105, "loss": 4.20105, "time": 0.81775} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.08835, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26156, "top5_acc": 0.51844, "loss_cls": 4.26428, "loss": 4.26428, "time": 0.8168} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.08833, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26609, "top5_acc": 0.52203, "loss_cls": 4.2054, "loss": 4.2054, "time": 0.81429} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.08831, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27187, "top5_acc": 0.52062, "loss_cls": 4.21424, "loss": 4.21424, "time": 0.82996} +{"mode": "train", "epoch": 34, "iter": 1300, "lr": 0.08829, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26719, "top5_acc": 0.51906, "loss_cls": 4.22525, "loss": 4.22525, "time": 0.81783} +{"mode": "train", "epoch": 34, "iter": 1400, "lr": 0.08828, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27625, "top5_acc": 0.5275, "loss_cls": 4.1968, "loss": 4.1968, "time": 0.82328} +{"mode": "train", "epoch": 34, "iter": 1500, "lr": 0.08826, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26859, "top5_acc": 0.51906, "loss_cls": 4.20012, "loss": 4.20012, "time": 0.81635} +{"mode": "train", "epoch": 34, "iter": 1600, "lr": 0.08824, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27609, "top5_acc": 0.53094, "loss_cls": 4.18546, "loss": 4.18546, "time": 0.82087} +{"mode": "train", "epoch": 34, "iter": 1700, "lr": 0.08822, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27266, "top5_acc": 0.5275, "loss_cls": 4.18254, "loss": 4.18254, "time": 0.81878} +{"mode": "train", "epoch": 34, "iter": 1800, "lr": 0.0882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26984, "top5_acc": 0.51672, "loss_cls": 4.25711, "loss": 4.25711, "time": 0.81786} +{"mode": "train", "epoch": 34, "iter": 1900, "lr": 0.08819, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26531, "top5_acc": 0.50891, "loss_cls": 4.26246, "loss": 4.26246, "time": 0.82988} +{"mode": "train", "epoch": 34, "iter": 2000, "lr": 0.08817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27953, "top5_acc": 0.52562, "loss_cls": 4.2, "loss": 4.2, "time": 0.82245} +{"mode": "train", "epoch": 34, "iter": 2100, "lr": 0.08815, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25328, "top5_acc": 0.50359, "loss_cls": 4.28041, "loss": 4.28041, "time": 0.81818} +{"mode": "train", "epoch": 34, "iter": 2200, "lr": 0.08813, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27156, "top5_acc": 0.52266, "loss_cls": 4.20095, "loss": 4.20095, "time": 0.81814} +{"mode": "train", "epoch": 34, "iter": 2300, "lr": 0.08811, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27047, "top5_acc": 0.5125, "loss_cls": 4.25646, "loss": 4.25646, "time": 0.8184} +{"mode": "train", "epoch": 34, "iter": 2400, "lr": 0.08809, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26406, "top5_acc": 0.52062, "loss_cls": 4.22031, "loss": 4.22031, "time": 0.81668} +{"mode": "train", "epoch": 34, "iter": 2500, "lr": 0.08808, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27047, "top5_acc": 0.52562, "loss_cls": 4.21264, "loss": 4.21264, "time": 0.81452} +{"mode": "train", "epoch": 34, "iter": 2600, "lr": 0.08806, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27156, "top5_acc": 0.53297, "loss_cls": 4.19631, "loss": 4.19631, "time": 0.81119} +{"mode": "train", "epoch": 34, "iter": 2700, "lr": 0.08804, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27047, "top5_acc": 0.5125, "loss_cls": 4.21802, "loss": 4.21802, "time": 0.81737} +{"mode": "train", "epoch": 34, "iter": 2800, "lr": 0.08802, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27562, "top5_acc": 0.52781, "loss_cls": 4.17672, "loss": 4.17672, "time": 0.81485} +{"mode": "train", "epoch": 34, "iter": 2900, "lr": 0.088, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28172, "top5_acc": 0.52016, "loss_cls": 4.20616, "loss": 4.20616, "time": 0.813} +{"mode": "train", "epoch": 34, "iter": 3000, "lr": 0.08799, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27422, "top5_acc": 0.52984, "loss_cls": 4.18426, "loss": 4.18426, "time": 0.8117} +{"mode": "train", "epoch": 34, "iter": 3100, "lr": 0.08797, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2725, "top5_acc": 0.51812, "loss_cls": 4.2279, "loss": 4.2279, "time": 0.81964} +{"mode": "train", "epoch": 34, "iter": 3200, "lr": 0.08795, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26969, "top5_acc": 0.51938, "loss_cls": 4.21391, "loss": 4.21391, "time": 0.81709} +{"mode": "train", "epoch": 34, "iter": 3300, "lr": 0.08793, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27312, "top5_acc": 0.52453, "loss_cls": 4.19107, "loss": 4.19107, "time": 0.81837} +{"mode": "train", "epoch": 34, "iter": 3400, "lr": 0.08791, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26703, "top5_acc": 0.52125, "loss_cls": 4.22751, "loss": 4.22751, "time": 0.81422} +{"mode": "train", "epoch": 34, "iter": 3500, "lr": 0.08789, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27938, "top5_acc": 0.53422, "loss_cls": 4.15812, "loss": 4.15812, "time": 0.82325} +{"mode": "train", "epoch": 34, "iter": 3600, "lr": 0.08788, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25641, "top5_acc": 0.51406, "loss_cls": 4.25863, "loss": 4.25863, "time": 0.81564} +{"mode": "train", "epoch": 34, "iter": 3700, "lr": 0.08786, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27281, "top5_acc": 0.51938, "loss_cls": 4.21909, "loss": 4.21909, "time": 0.81259} +{"mode": "val", "epoch": 34, "iter": 309, "lr": 0.08785, "top1_acc": 0.19455, "top5_acc": 0.42516, "mean_class_accuracy": 0.19426} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.08783, "memory": 15990, "data_time": 1.27053, "top1_acc": 0.28141, "top5_acc": 0.52859, "loss_cls": 4.1798, "loss": 4.1798, "time": 2.25018} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.08781, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28094, "top5_acc": 0.53391, "loss_cls": 4.13121, "loss": 4.13121, "time": 0.81231} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.0878, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27859, "top5_acc": 0.53156, "loss_cls": 4.15473, "loss": 4.15473, "time": 0.81282} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.08778, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27953, "top5_acc": 0.52719, "loss_cls": 4.19616, "loss": 4.19616, "time": 0.81283} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.08776, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26766, "top5_acc": 0.52328, "loss_cls": 4.20273, "loss": 4.20273, "time": 0.81507} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.08774, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27469, "top5_acc": 0.53547, "loss_cls": 4.14177, "loss": 4.14177, "time": 0.81729} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.08772, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27156, "top5_acc": 0.52703, "loss_cls": 4.19415, "loss": 4.19415, "time": 0.81545} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.0877, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27453, "top5_acc": 0.52844, "loss_cls": 4.183, "loss": 4.183, "time": 0.81731} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.08769, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26875, "top5_acc": 0.51656, "loss_cls": 4.22691, "loss": 4.22691, "time": 0.81783} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.08767, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27734, "top5_acc": 0.53172, "loss_cls": 4.20611, "loss": 4.20611, "time": 0.81727} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.08765, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25469, "top5_acc": 0.51453, "loss_cls": 4.26402, "loss": 4.26402, "time": 0.81923} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.08763, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27359, "top5_acc": 0.52234, "loss_cls": 4.2, "loss": 4.2, "time": 0.82047} +{"mode": "train", "epoch": 35, "iter": 1300, "lr": 0.08761, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27156, "top5_acc": 0.52125, "loss_cls": 4.19329, "loss": 4.19329, "time": 0.8194} +{"mode": "train", "epoch": 35, "iter": 1400, "lr": 0.08759, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26859, "top5_acc": 0.51828, "loss_cls": 4.20696, "loss": 4.20696, "time": 0.82516} +{"mode": "train", "epoch": 35, "iter": 1500, "lr": 0.08757, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26875, "top5_acc": 0.52375, "loss_cls": 4.22541, "loss": 4.22541, "time": 0.81906} +{"mode": "train", "epoch": 35, "iter": 1600, "lr": 0.08756, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26969, "top5_acc": 0.51891, "loss_cls": 4.21208, "loss": 4.21208, "time": 0.8167} +{"mode": "train", "epoch": 35, "iter": 1700, "lr": 0.08754, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27047, "top5_acc": 0.52938, "loss_cls": 4.20377, "loss": 4.20377, "time": 0.82375} +{"mode": "train", "epoch": 35, "iter": 1800, "lr": 0.08752, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28094, "top5_acc": 0.52484, "loss_cls": 4.15574, "loss": 4.15574, "time": 0.82496} +{"mode": "train", "epoch": 35, "iter": 1900, "lr": 0.0875, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26422, "top5_acc": 0.52078, "loss_cls": 4.23124, "loss": 4.23124, "time": 0.82105} +{"mode": "train", "epoch": 35, "iter": 2000, "lr": 0.08748, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26922, "top5_acc": 0.52859, "loss_cls": 4.20763, "loss": 4.20763, "time": 0.81991} +{"mode": "train", "epoch": 35, "iter": 2100, "lr": 0.08746, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27078, "top5_acc": 0.51078, "loss_cls": 4.25658, "loss": 4.25658, "time": 0.81795} +{"mode": "train", "epoch": 35, "iter": 2200, "lr": 0.08745, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26547, "top5_acc": 0.51641, "loss_cls": 4.20706, "loss": 4.20706, "time": 0.81496} +{"mode": "train", "epoch": 35, "iter": 2300, "lr": 0.08743, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2875, "top5_acc": 0.53297, "loss_cls": 4.14277, "loss": 4.14277, "time": 0.81913} +{"mode": "train", "epoch": 35, "iter": 2400, "lr": 0.08741, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26734, "top5_acc": 0.51844, "loss_cls": 4.24347, "loss": 4.24347, "time": 0.81323} +{"mode": "train", "epoch": 35, "iter": 2500, "lr": 0.08739, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26641, "top5_acc": 0.51609, "loss_cls": 4.22689, "loss": 4.22689, "time": 0.81794} +{"mode": "train", "epoch": 35, "iter": 2600, "lr": 0.08737, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27078, "top5_acc": 0.52875, "loss_cls": 4.16267, "loss": 4.16267, "time": 0.81791} +{"mode": "train", "epoch": 35, "iter": 2700, "lr": 0.08735, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26875, "top5_acc": 0.52438, "loss_cls": 4.22123, "loss": 4.22123, "time": 0.8139} +{"mode": "train", "epoch": 35, "iter": 2800, "lr": 0.08733, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28391, "top5_acc": 0.53281, "loss_cls": 4.16504, "loss": 4.16504, "time": 0.81381} +{"mode": "train", "epoch": 35, "iter": 2900, "lr": 0.08732, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26609, "top5_acc": 0.51328, "loss_cls": 4.22239, "loss": 4.22239, "time": 0.81488} +{"mode": "train", "epoch": 35, "iter": 3000, "lr": 0.0873, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26953, "top5_acc": 0.51281, "loss_cls": 4.25216, "loss": 4.25216, "time": 0.81615} +{"mode": "train", "epoch": 35, "iter": 3100, "lr": 0.08728, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2725, "top5_acc": 0.51891, "loss_cls": 4.19581, "loss": 4.19581, "time": 0.81026} +{"mode": "train", "epoch": 35, "iter": 3200, "lr": 0.08726, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27359, "top5_acc": 0.51672, "loss_cls": 4.2147, "loss": 4.2147, "time": 0.82113} +{"mode": "train", "epoch": 35, "iter": 3300, "lr": 0.08724, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27844, "top5_acc": 0.52562, "loss_cls": 4.22214, "loss": 4.22214, "time": 0.81419} +{"mode": "train", "epoch": 35, "iter": 3400, "lr": 0.08722, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27625, "top5_acc": 0.52953, "loss_cls": 4.16706, "loss": 4.16706, "time": 0.81643} +{"mode": "train", "epoch": 35, "iter": 3500, "lr": 0.0872, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27125, "top5_acc": 0.51078, "loss_cls": 4.23237, "loss": 4.23237, "time": 0.81565} +{"mode": "train", "epoch": 35, "iter": 3600, "lr": 0.08718, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27172, "top5_acc": 0.53, "loss_cls": 4.20947, "loss": 4.20947, "time": 0.81483} +{"mode": "train", "epoch": 35, "iter": 3700, "lr": 0.08717, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27047, "top5_acc": 0.52484, "loss_cls": 4.20809, "loss": 4.20809, "time": 0.81979} +{"mode": "val", "epoch": 35, "iter": 309, "lr": 0.08716, "top1_acc": 0.17409, "top5_acc": 0.38733, "mean_class_accuracy": 0.17377} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.08714, "memory": 15990, "data_time": 1.27239, "top1_acc": 0.28297, "top5_acc": 0.52797, "loss_cls": 4.17402, "loss": 4.17402, "time": 2.24422} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.08712, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27891, "top5_acc": 0.53234, "loss_cls": 4.15067, "loss": 4.15067, "time": 0.81339} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.0871, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27359, "top5_acc": 0.52219, "loss_cls": 4.21951, "loss": 4.21951, "time": 0.81877} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.08708, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27125, "top5_acc": 0.52266, "loss_cls": 4.22016, "loss": 4.22016, "time": 0.81489} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.08706, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27328, "top5_acc": 0.52609, "loss_cls": 4.19579, "loss": 4.19579, "time": 0.81704} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.08704, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27641, "top5_acc": 0.52875, "loss_cls": 4.17366, "loss": 4.17366, "time": 0.81719} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.08703, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27312, "top5_acc": 0.51641, "loss_cls": 4.22541, "loss": 4.22541, "time": 0.81307} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.08701, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26984, "top5_acc": 0.52469, "loss_cls": 4.22192, "loss": 4.22192, "time": 0.81492} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.08699, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28062, "top5_acc": 0.52812, "loss_cls": 4.17957, "loss": 4.17957, "time": 0.81625} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.08697, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28156, "top5_acc": 0.53797, "loss_cls": 4.15911, "loss": 4.15911, "time": 0.81946} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.08695, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26281, "top5_acc": 0.52062, "loss_cls": 4.23409, "loss": 4.23409, "time": 0.81725} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.08693, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27547, "top5_acc": 0.51797, "loss_cls": 4.20264, "loss": 4.20264, "time": 0.81869} +{"mode": "train", "epoch": 36, "iter": 1300, "lr": 0.08691, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26422, "top5_acc": 0.51891, "loss_cls": 4.21717, "loss": 4.21717, "time": 0.82102} +{"mode": "train", "epoch": 36, "iter": 1400, "lr": 0.08689, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27078, "top5_acc": 0.51797, "loss_cls": 4.23052, "loss": 4.23052, "time": 0.81984} +{"mode": "train", "epoch": 36, "iter": 1500, "lr": 0.08688, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27047, "top5_acc": 0.51812, "loss_cls": 4.22062, "loss": 4.22062, "time": 0.81693} +{"mode": "train", "epoch": 36, "iter": 1600, "lr": 0.08686, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.275, "top5_acc": 0.51766, "loss_cls": 4.20856, "loss": 4.20856, "time": 0.81832} +{"mode": "train", "epoch": 36, "iter": 1700, "lr": 0.08684, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27547, "top5_acc": 0.52984, "loss_cls": 4.20098, "loss": 4.20098, "time": 0.82231} +{"mode": "train", "epoch": 36, "iter": 1800, "lr": 0.08682, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27484, "top5_acc": 0.52703, "loss_cls": 4.2076, "loss": 4.2076, "time": 0.81623} +{"mode": "train", "epoch": 36, "iter": 1900, "lr": 0.0868, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27516, "top5_acc": 0.52031, "loss_cls": 4.21712, "loss": 4.21712, "time": 0.82689} +{"mode": "train", "epoch": 36, "iter": 2000, "lr": 0.08678, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26891, "top5_acc": 0.51797, "loss_cls": 4.22, "loss": 4.22, "time": 0.81837} +{"mode": "train", "epoch": 36, "iter": 2100, "lr": 0.08676, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27859, "top5_acc": 0.53422, "loss_cls": 4.12195, "loss": 4.12195, "time": 0.82419} +{"mode": "train", "epoch": 36, "iter": 2200, "lr": 0.08674, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27875, "top5_acc": 0.51516, "loss_cls": 4.23039, "loss": 4.23039, "time": 0.81798} +{"mode": "train", "epoch": 36, "iter": 2300, "lr": 0.08672, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28156, "top5_acc": 0.53672, "loss_cls": 4.13593, "loss": 4.13593, "time": 0.81631} +{"mode": "train", "epoch": 36, "iter": 2400, "lr": 0.08671, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.52094, "loss_cls": 4.22148, "loss": 4.22148, "time": 0.81471} +{"mode": "train", "epoch": 36, "iter": 2500, "lr": 0.08669, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.265, "top5_acc": 0.51719, "loss_cls": 4.26546, "loss": 4.26546, "time": 0.82062} +{"mode": "train", "epoch": 36, "iter": 2600, "lr": 0.08667, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27109, "top5_acc": 0.52703, "loss_cls": 4.18659, "loss": 4.18659, "time": 0.81542} +{"mode": "train", "epoch": 36, "iter": 2700, "lr": 0.08665, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27562, "top5_acc": 0.52969, "loss_cls": 4.18473, "loss": 4.18473, "time": 0.81351} +{"mode": "train", "epoch": 36, "iter": 2800, "lr": 0.08663, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.265, "top5_acc": 0.51672, "loss_cls": 4.24519, "loss": 4.24519, "time": 0.81445} +{"mode": "train", "epoch": 36, "iter": 2900, "lr": 0.08661, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27641, "top5_acc": 0.53172, "loss_cls": 4.20175, "loss": 4.20175, "time": 0.8107} +{"mode": "train", "epoch": 36, "iter": 3000, "lr": 0.08659, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27734, "top5_acc": 0.52375, "loss_cls": 4.17705, "loss": 4.17705, "time": 0.81405} +{"mode": "train", "epoch": 36, "iter": 3100, "lr": 0.08657, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26703, "top5_acc": 0.51453, "loss_cls": 4.20258, "loss": 4.20258, "time": 0.81273} +{"mode": "train", "epoch": 36, "iter": 3200, "lr": 0.08655, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26594, "top5_acc": 0.51594, "loss_cls": 4.24932, "loss": 4.24932, "time": 0.8184} +{"mode": "train", "epoch": 36, "iter": 3300, "lr": 0.08653, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28203, "top5_acc": 0.52594, "loss_cls": 4.17181, "loss": 4.17181, "time": 0.81101} +{"mode": "train", "epoch": 36, "iter": 3400, "lr": 0.08651, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26703, "top5_acc": 0.51469, "loss_cls": 4.21665, "loss": 4.21665, "time": 0.81342} +{"mode": "train", "epoch": 36, "iter": 3500, "lr": 0.0865, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26703, "top5_acc": 0.51828, "loss_cls": 4.20518, "loss": 4.20518, "time": 0.81464} +{"mode": "train", "epoch": 36, "iter": 3600, "lr": 0.08648, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.265, "top5_acc": 0.51656, "loss_cls": 4.2351, "loss": 4.2351, "time": 0.81844} +{"mode": "train", "epoch": 36, "iter": 3700, "lr": 0.08646, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27062, "top5_acc": 0.52094, "loss_cls": 4.21395, "loss": 4.21395, "time": 0.81349} +{"mode": "val", "epoch": 36, "iter": 309, "lr": 0.08645, "top1_acc": 0.19896, "top5_acc": 0.43256, "mean_class_accuracy": 0.19872} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.08643, "memory": 15990, "data_time": 1.262, "top1_acc": 0.28344, "top5_acc": 0.53984, "loss_cls": 4.15782, "loss": 4.15782, "time": 2.24088} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.08641, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25938, "top5_acc": 0.51344, "loss_cls": 4.24471, "loss": 4.24471, "time": 0.81809} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.08639, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28766, "top5_acc": 0.53922, "loss_cls": 4.12995, "loss": 4.12995, "time": 0.82376} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.08637, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2675, "top5_acc": 0.51859, "loss_cls": 4.19242, "loss": 4.19242, "time": 0.8126} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.08635, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27312, "top5_acc": 0.52953, "loss_cls": 4.17903, "loss": 4.17903, "time": 0.81995} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.08633, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28547, "top5_acc": 0.53375, "loss_cls": 4.16356, "loss": 4.16356, "time": 0.82003} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.08631, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28562, "top5_acc": 0.52844, "loss_cls": 4.14859, "loss": 4.14859, "time": 0.81512} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0863, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27187, "top5_acc": 0.53531, "loss_cls": 4.16138, "loss": 4.16138, "time": 0.81718} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.08628, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27219, "top5_acc": 0.52188, "loss_cls": 4.18553, "loss": 4.18553, "time": 0.82032} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.08626, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26812, "top5_acc": 0.52219, "loss_cls": 4.2374, "loss": 4.2374, "time": 0.81727} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.08624, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27906, "top5_acc": 0.52562, "loss_cls": 4.17002, "loss": 4.17002, "time": 0.81767} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.08622, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27062, "top5_acc": 0.52969, "loss_cls": 4.19365, "loss": 4.19365, "time": 0.82486} +{"mode": "train", "epoch": 37, "iter": 1300, "lr": 0.0862, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27422, "top5_acc": 0.52016, "loss_cls": 4.22024, "loss": 4.22024, "time": 0.82743} +{"mode": "train", "epoch": 37, "iter": 1400, "lr": 0.08618, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27547, "top5_acc": 0.52625, "loss_cls": 4.18301, "loss": 4.18301, "time": 0.82353} +{"mode": "train", "epoch": 37, "iter": 1500, "lr": 0.08616, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2725, "top5_acc": 0.52438, "loss_cls": 4.19159, "loss": 4.19159, "time": 0.81446} +{"mode": "train", "epoch": 37, "iter": 1600, "lr": 0.08614, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26891, "top5_acc": 0.50828, "loss_cls": 4.23621, "loss": 4.23621, "time": 0.81851} +{"mode": "train", "epoch": 37, "iter": 1700, "lr": 0.08612, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27672, "top5_acc": 0.52312, "loss_cls": 4.19048, "loss": 4.19048, "time": 0.82672} +{"mode": "train", "epoch": 37, "iter": 1800, "lr": 0.0861, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27234, "top5_acc": 0.52547, "loss_cls": 4.20994, "loss": 4.20994, "time": 0.81454} +{"mode": "train", "epoch": 37, "iter": 1900, "lr": 0.08608, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26562, "top5_acc": 0.5225, "loss_cls": 4.23583, "loss": 4.23583, "time": 0.82454} +{"mode": "train", "epoch": 37, "iter": 2000, "lr": 0.08606, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27984, "top5_acc": 0.52984, "loss_cls": 4.16036, "loss": 4.16036, "time": 0.81696} +{"mode": "train", "epoch": 37, "iter": 2100, "lr": 0.08604, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26797, "top5_acc": 0.53156, "loss_cls": 4.18666, "loss": 4.18666, "time": 0.82742} +{"mode": "train", "epoch": 37, "iter": 2200, "lr": 0.08602, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27953, "top5_acc": 0.51844, "loss_cls": 4.18929, "loss": 4.18929, "time": 0.82123} +{"mode": "train", "epoch": 37, "iter": 2300, "lr": 0.08601, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26562, "top5_acc": 0.51688, "loss_cls": 4.21343, "loss": 4.21343, "time": 0.81344} +{"mode": "train", "epoch": 37, "iter": 2400, "lr": 0.08599, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26547, "top5_acc": 0.52328, "loss_cls": 4.22925, "loss": 4.22925, "time": 0.81413} +{"mode": "train", "epoch": 37, "iter": 2500, "lr": 0.08597, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26547, "top5_acc": 0.52156, "loss_cls": 4.21307, "loss": 4.21307, "time": 0.81652} +{"mode": "train", "epoch": 37, "iter": 2600, "lr": 0.08595, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26312, "top5_acc": 0.51516, "loss_cls": 4.25221, "loss": 4.25221, "time": 0.8131} +{"mode": "train", "epoch": 37, "iter": 2700, "lr": 0.08593, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26, "top5_acc": 0.51516, "loss_cls": 4.25058, "loss": 4.25058, "time": 0.81824} +{"mode": "train", "epoch": 37, "iter": 2800, "lr": 0.08591, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28344, "top5_acc": 0.53141, "loss_cls": 4.14626, "loss": 4.14626, "time": 0.81797} +{"mode": "train", "epoch": 37, "iter": 2900, "lr": 0.08589, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26891, "top5_acc": 0.51891, "loss_cls": 4.20513, "loss": 4.20513, "time": 0.81723} +{"mode": "train", "epoch": 37, "iter": 3000, "lr": 0.08587, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27219, "top5_acc": 0.52125, "loss_cls": 4.20421, "loss": 4.20421, "time": 0.81511} +{"mode": "train", "epoch": 37, "iter": 3100, "lr": 0.08585, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26875, "top5_acc": 0.52797, "loss_cls": 4.19003, "loss": 4.19003, "time": 0.81626} +{"mode": "train", "epoch": 37, "iter": 3200, "lr": 0.08583, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27312, "top5_acc": 0.52266, "loss_cls": 4.20883, "loss": 4.20883, "time": 0.81711} +{"mode": "train", "epoch": 37, "iter": 3300, "lr": 0.08581, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26969, "top5_acc": 0.52156, "loss_cls": 4.20946, "loss": 4.20946, "time": 0.8166} +{"mode": "train", "epoch": 37, "iter": 3400, "lr": 0.08579, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27875, "top5_acc": 0.52578, "loss_cls": 4.19309, "loss": 4.19309, "time": 0.81448} +{"mode": "train", "epoch": 37, "iter": 3500, "lr": 0.08577, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27, "top5_acc": 0.52328, "loss_cls": 4.20701, "loss": 4.20701, "time": 0.81577} +{"mode": "train", "epoch": 37, "iter": 3600, "lr": 0.08575, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26984, "top5_acc": 0.53156, "loss_cls": 4.13848, "loss": 4.13848, "time": 0.81898} +{"mode": "train", "epoch": 37, "iter": 3700, "lr": 0.08573, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27609, "top5_acc": 0.52812, "loss_cls": 4.17281, "loss": 4.17281, "time": 0.81654} +{"mode": "val", "epoch": 37, "iter": 309, "lr": 0.08572, "top1_acc": 0.20934, "top5_acc": 0.43808, "mean_class_accuracy": 0.20913} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.0857, "memory": 15990, "data_time": 1.26464, "top1_acc": 0.28094, "top5_acc": 0.53062, "loss_cls": 4.16698, "loss": 4.16698, "time": 2.26187} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.08568, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28656, "top5_acc": 0.54281, "loss_cls": 4.0969, "loss": 4.0969, "time": 0.81934} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.08567, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28641, "top5_acc": 0.54484, "loss_cls": 4.09955, "loss": 4.09955, "time": 0.8205} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.08565, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27594, "top5_acc": 0.52844, "loss_cls": 4.16074, "loss": 4.16074, "time": 0.81891} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.08563, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28797, "top5_acc": 0.53969, "loss_cls": 4.13333, "loss": 4.13333, "time": 0.82012} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.08561, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27938, "top5_acc": 0.52547, "loss_cls": 4.14532, "loss": 4.14532, "time": 0.81731} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.08559, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28516, "top5_acc": 0.52938, "loss_cls": 4.15193, "loss": 4.15193, "time": 0.81608} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.08557, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28031, "top5_acc": 0.52609, "loss_cls": 4.16705, "loss": 4.16705, "time": 0.81565} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.08555, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27047, "top5_acc": 0.52, "loss_cls": 4.22012, "loss": 4.22012, "time": 0.82799} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.08553, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27953, "top5_acc": 0.53094, "loss_cls": 4.17799, "loss": 4.17799, "time": 0.81958} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.08551, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28656, "top5_acc": 0.53516, "loss_cls": 4.15567, "loss": 4.15567, "time": 0.81806} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.08549, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27609, "top5_acc": 0.52297, "loss_cls": 4.19335, "loss": 4.19335, "time": 0.82497} +{"mode": "train", "epoch": 38, "iter": 1300, "lr": 0.08547, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26766, "top5_acc": 0.51953, "loss_cls": 4.26291, "loss": 4.26291, "time": 0.81903} +{"mode": "train", "epoch": 38, "iter": 1400, "lr": 0.08545, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27234, "top5_acc": 0.52188, "loss_cls": 4.18844, "loss": 4.18844, "time": 0.82074} +{"mode": "train", "epoch": 38, "iter": 1500, "lr": 0.08543, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28562, "top5_acc": 0.53516, "loss_cls": 4.14039, "loss": 4.14039, "time": 0.81755} +{"mode": "train", "epoch": 38, "iter": 1600, "lr": 0.08541, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27734, "top5_acc": 0.5225, "loss_cls": 4.20459, "loss": 4.20459, "time": 0.82511} +{"mode": "train", "epoch": 38, "iter": 1700, "lr": 0.08539, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27328, "top5_acc": 0.5325, "loss_cls": 4.17116, "loss": 4.17116, "time": 0.81467} +{"mode": "train", "epoch": 38, "iter": 1800, "lr": 0.08537, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27437, "top5_acc": 0.53125, "loss_cls": 4.18845, "loss": 4.18845, "time": 0.82407} +{"mode": "train", "epoch": 38, "iter": 1900, "lr": 0.08535, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28062, "top5_acc": 0.52422, "loss_cls": 4.19268, "loss": 4.19268, "time": 0.82491} +{"mode": "train", "epoch": 38, "iter": 2000, "lr": 0.08533, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26203, "top5_acc": 0.51562, "loss_cls": 4.26691, "loss": 4.26691, "time": 0.81854} +{"mode": "train", "epoch": 38, "iter": 2100, "lr": 0.08531, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26969, "top5_acc": 0.51984, "loss_cls": 4.21686, "loss": 4.21686, "time": 0.8241} +{"mode": "train", "epoch": 38, "iter": 2200, "lr": 0.08529, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28609, "top5_acc": 0.52578, "loss_cls": 4.18259, "loss": 4.18259, "time": 0.81893} +{"mode": "train", "epoch": 38, "iter": 2300, "lr": 0.08527, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28828, "top5_acc": 0.53141, "loss_cls": 4.16181, "loss": 4.16181, "time": 0.82263} +{"mode": "train", "epoch": 38, "iter": 2400, "lr": 0.08525, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26891, "top5_acc": 0.51984, "loss_cls": 4.19554, "loss": 4.19554, "time": 0.81627} +{"mode": "train", "epoch": 38, "iter": 2500, "lr": 0.08523, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27203, "top5_acc": 0.52766, "loss_cls": 4.19007, "loss": 4.19007, "time": 0.81513} +{"mode": "train", "epoch": 38, "iter": 2600, "lr": 0.08521, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27766, "top5_acc": 0.52578, "loss_cls": 4.17172, "loss": 4.17172, "time": 0.8183} +{"mode": "train", "epoch": 38, "iter": 2700, "lr": 0.08519, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26953, "top5_acc": 0.51734, "loss_cls": 4.22048, "loss": 4.22048, "time": 0.81393} +{"mode": "train", "epoch": 38, "iter": 2800, "lr": 0.08517, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26438, "top5_acc": 0.51812, "loss_cls": 4.2288, "loss": 4.2288, "time": 0.81489} +{"mode": "train", "epoch": 38, "iter": 2900, "lr": 0.08515, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26562, "top5_acc": 0.51844, "loss_cls": 4.24037, "loss": 4.24037, "time": 0.81668} +{"mode": "train", "epoch": 38, "iter": 3000, "lr": 0.08513, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27875, "top5_acc": 0.52391, "loss_cls": 4.16125, "loss": 4.16125, "time": 0.81514} +{"mode": "train", "epoch": 38, "iter": 3100, "lr": 0.08511, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26812, "top5_acc": 0.53094, "loss_cls": 4.20382, "loss": 4.20382, "time": 0.81742} +{"mode": "train", "epoch": 38, "iter": 3200, "lr": 0.08509, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.275, "top5_acc": 0.51438, "loss_cls": 4.23489, "loss": 4.23489, "time": 0.81437} +{"mode": "train", "epoch": 38, "iter": 3300, "lr": 0.08507, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27703, "top5_acc": 0.52547, "loss_cls": 4.17625, "loss": 4.17625, "time": 0.8147} +{"mode": "train", "epoch": 38, "iter": 3400, "lr": 0.08505, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27203, "top5_acc": 0.53438, "loss_cls": 4.18045, "loss": 4.18045, "time": 0.81594} +{"mode": "train", "epoch": 38, "iter": 3500, "lr": 0.08503, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27531, "top5_acc": 0.52906, "loss_cls": 4.1534, "loss": 4.1534, "time": 0.8184} +{"mode": "train", "epoch": 38, "iter": 3600, "lr": 0.08501, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26172, "top5_acc": 0.51375, "loss_cls": 4.22912, "loss": 4.22912, "time": 0.81597} +{"mode": "train", "epoch": 38, "iter": 3700, "lr": 0.08499, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28562, "top5_acc": 0.54266, "loss_cls": 4.10705, "loss": 4.10705, "time": 0.82275} +{"mode": "val", "epoch": 38, "iter": 309, "lr": 0.08498, "top1_acc": 0.19354, "top5_acc": 0.42015, "mean_class_accuracy": 0.19329} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.08496, "memory": 15990, "data_time": 1.30086, "top1_acc": 0.28797, "top5_acc": 0.53969, "loss_cls": 4.1091, "loss": 4.1091, "time": 2.29268} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.08494, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28031, "top5_acc": 0.52578, "loss_cls": 4.15087, "loss": 4.15087, "time": 0.81567} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.08492, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27281, "top5_acc": 0.52781, "loss_cls": 4.22045, "loss": 4.22045, "time": 0.82002} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.0849, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27828, "top5_acc": 0.53531, "loss_cls": 4.13716, "loss": 4.13716, "time": 0.8155} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.08488, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27875, "top5_acc": 0.52109, "loss_cls": 4.1607, "loss": 4.1607, "time": 0.82138} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.08486, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28016, "top5_acc": 0.53031, "loss_cls": 4.17531, "loss": 4.17531, "time": 0.81416} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.08484, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26719, "top5_acc": 0.51781, "loss_cls": 4.26658, "loss": 4.26658, "time": 0.8129} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.08482, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27797, "top5_acc": 0.53766, "loss_cls": 4.16297, "loss": 4.16297, "time": 0.82263} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.0848, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28328, "top5_acc": 0.53, "loss_cls": 4.18345, "loss": 4.18345, "time": 0.81802} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.08478, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27594, "top5_acc": 0.53, "loss_cls": 4.15177, "loss": 4.15177, "time": 0.81409} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.08476, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27766, "top5_acc": 0.53719, "loss_cls": 4.17254, "loss": 4.17254, "time": 0.81965} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.08474, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27688, "top5_acc": 0.53062, "loss_cls": 4.16831, "loss": 4.16831, "time": 0.81813} +{"mode": "train", "epoch": 39, "iter": 1300, "lr": 0.08472, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27469, "top5_acc": 0.52219, "loss_cls": 4.17406, "loss": 4.17406, "time": 0.81631} +{"mode": "train", "epoch": 39, "iter": 1400, "lr": 0.0847, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27422, "top5_acc": 0.52859, "loss_cls": 4.16291, "loss": 4.16291, "time": 0.81578} +{"mode": "train", "epoch": 39, "iter": 1500, "lr": 0.08468, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28406, "top5_acc": 0.52391, "loss_cls": 4.16482, "loss": 4.16482, "time": 0.81784} +{"mode": "train", "epoch": 39, "iter": 1600, "lr": 0.08466, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27734, "top5_acc": 0.52781, "loss_cls": 4.18529, "loss": 4.18529, "time": 0.82011} +{"mode": "train", "epoch": 39, "iter": 1700, "lr": 0.08464, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27422, "top5_acc": 0.52375, "loss_cls": 4.19056, "loss": 4.19056, "time": 0.81591} +{"mode": "train", "epoch": 39, "iter": 1800, "lr": 0.08462, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27609, "top5_acc": 0.51797, "loss_cls": 4.21393, "loss": 4.21393, "time": 0.8244} +{"mode": "train", "epoch": 39, "iter": 1900, "lr": 0.0846, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27531, "top5_acc": 0.52562, "loss_cls": 4.1723, "loss": 4.1723, "time": 0.82062} +{"mode": "train", "epoch": 39, "iter": 2000, "lr": 0.08458, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.52453, "loss_cls": 4.1805, "loss": 4.1805, "time": 0.81729} +{"mode": "train", "epoch": 39, "iter": 2100, "lr": 0.08456, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28438, "top5_acc": 0.53266, "loss_cls": 4.13891, "loss": 4.13891, "time": 0.83228} +{"mode": "train", "epoch": 39, "iter": 2200, "lr": 0.08454, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28391, "top5_acc": 0.53859, "loss_cls": 4.14268, "loss": 4.14268, "time": 0.82034} +{"mode": "train", "epoch": 39, "iter": 2300, "lr": 0.08452, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28, "top5_acc": 0.52703, "loss_cls": 4.18744, "loss": 4.18744, "time": 0.82207} +{"mode": "train", "epoch": 39, "iter": 2400, "lr": 0.0845, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26922, "top5_acc": 0.51688, "loss_cls": 4.21501, "loss": 4.21501, "time": 0.82004} +{"mode": "train", "epoch": 39, "iter": 2500, "lr": 0.08448, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29359, "top5_acc": 0.53422, "loss_cls": 4.12825, "loss": 4.12825, "time": 0.81333} +{"mode": "train", "epoch": 39, "iter": 2600, "lr": 0.08446, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26969, "top5_acc": 0.52219, "loss_cls": 4.20683, "loss": 4.20683, "time": 0.81658} +{"mode": "train", "epoch": 39, "iter": 2700, "lr": 0.08444, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28594, "top5_acc": 0.52219, "loss_cls": 4.18345, "loss": 4.18345, "time": 0.82023} +{"mode": "train", "epoch": 39, "iter": 2800, "lr": 0.08442, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27781, "top5_acc": 0.52016, "loss_cls": 4.18406, "loss": 4.18406, "time": 0.81548} +{"mode": "train", "epoch": 39, "iter": 2900, "lr": 0.0844, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27234, "top5_acc": 0.52078, "loss_cls": 4.20149, "loss": 4.20149, "time": 0.81608} +{"mode": "train", "epoch": 39, "iter": 3000, "lr": 0.08438, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27297, "top5_acc": 0.51797, "loss_cls": 4.23718, "loss": 4.23718, "time": 0.81431} +{"mode": "train", "epoch": 39, "iter": 3100, "lr": 0.08436, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28094, "top5_acc": 0.53578, "loss_cls": 4.14747, "loss": 4.14747, "time": 0.8256} +{"mode": "train", "epoch": 39, "iter": 3200, "lr": 0.08434, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27156, "top5_acc": 0.51562, "loss_cls": 4.21681, "loss": 4.21681, "time": 0.81531} +{"mode": "train", "epoch": 39, "iter": 3300, "lr": 0.08432, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27219, "top5_acc": 0.52266, "loss_cls": 4.19148, "loss": 4.19148, "time": 0.82017} +{"mode": "train", "epoch": 39, "iter": 3400, "lr": 0.0843, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27078, "top5_acc": 0.52281, "loss_cls": 4.18605, "loss": 4.18605, "time": 0.82046} +{"mode": "train", "epoch": 39, "iter": 3500, "lr": 0.08428, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28031, "top5_acc": 0.52547, "loss_cls": 4.18259, "loss": 4.18259, "time": 0.82537} +{"mode": "train", "epoch": 39, "iter": 3600, "lr": 0.08426, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27906, "top5_acc": 0.51984, "loss_cls": 4.19006, "loss": 4.19006, "time": 0.81819} +{"mode": "train", "epoch": 39, "iter": 3700, "lr": 0.08424, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26734, "top5_acc": 0.52188, "loss_cls": 4.21329, "loss": 4.21329, "time": 0.81912} +{"mode": "val", "epoch": 39, "iter": 309, "lr": 0.08423, "top1_acc": 0.22641, "top5_acc": 0.46609, "mean_class_accuracy": 0.22609} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.08421, "memory": 15990, "data_time": 1.25911, "top1_acc": 0.2775, "top5_acc": 0.53344, "loss_cls": 4.11734, "loss": 4.11734, "time": 2.23032} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.08419, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28109, "top5_acc": 0.53953, "loss_cls": 4.1379, "loss": 4.1379, "time": 0.82232} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.08417, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29141, "top5_acc": 0.53781, "loss_cls": 4.12915, "loss": 4.12915, "time": 0.81971} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.08415, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28156, "top5_acc": 0.53672, "loss_cls": 4.14336, "loss": 4.14336, "time": 0.81235} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.08413, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.52312, "loss_cls": 4.20508, "loss": 4.20508, "time": 0.81413} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.08411, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27688, "top5_acc": 0.53641, "loss_cls": 4.14366, "loss": 4.14366, "time": 0.81618} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.08408, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27953, "top5_acc": 0.52656, "loss_cls": 4.20135, "loss": 4.20135, "time": 0.81638} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.08406, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26937, "top5_acc": 0.52844, "loss_cls": 4.18792, "loss": 4.18792, "time": 0.82224} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.08404, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26156, "top5_acc": 0.51125, "loss_cls": 4.25197, "loss": 4.25197, "time": 0.81681} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.08402, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28422, "top5_acc": 0.5375, "loss_cls": 4.14068, "loss": 4.14068, "time": 0.81258} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.084, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28375, "top5_acc": 0.53, "loss_cls": 4.162, "loss": 4.162, "time": 0.82413} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.08398, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27859, "top5_acc": 0.525, "loss_cls": 4.1998, "loss": 4.1998, "time": 0.82052} +{"mode": "train", "epoch": 40, "iter": 1300, "lr": 0.08396, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27047, "top5_acc": 0.52531, "loss_cls": 4.18217, "loss": 4.18217, "time": 0.82292} +{"mode": "train", "epoch": 40, "iter": 1400, "lr": 0.08394, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27234, "top5_acc": 0.53094, "loss_cls": 4.20415, "loss": 4.20415, "time": 0.81526} +{"mode": "train", "epoch": 40, "iter": 1500, "lr": 0.08392, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26703, "top5_acc": 0.51719, "loss_cls": 4.22714, "loss": 4.22714, "time": 0.82482} +{"mode": "train", "epoch": 40, "iter": 1600, "lr": 0.0839, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27875, "top5_acc": 0.53547, "loss_cls": 4.1593, "loss": 4.1593, "time": 0.82017} +{"mode": "train", "epoch": 40, "iter": 1700, "lr": 0.08388, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26625, "top5_acc": 0.52188, "loss_cls": 4.21783, "loss": 4.21783, "time": 0.81854} +{"mode": "train", "epoch": 40, "iter": 1800, "lr": 0.08386, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28703, "top5_acc": 0.53625, "loss_cls": 4.13836, "loss": 4.13836, "time": 0.81735} +{"mode": "train", "epoch": 40, "iter": 1900, "lr": 0.08384, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28953, "top5_acc": 0.52688, "loss_cls": 4.17518, "loss": 4.17518, "time": 0.82216} +{"mode": "train", "epoch": 40, "iter": 2000, "lr": 0.08382, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27375, "top5_acc": 0.52141, "loss_cls": 4.2136, "loss": 4.2136, "time": 0.8209} +{"mode": "train", "epoch": 40, "iter": 2100, "lr": 0.0838, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27172, "top5_acc": 0.51625, "loss_cls": 4.21302, "loss": 4.21302, "time": 0.82635} +{"mode": "train", "epoch": 40, "iter": 2200, "lr": 0.08378, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28234, "top5_acc": 0.53891, "loss_cls": 4.14697, "loss": 4.14697, "time": 0.82496} +{"mode": "train", "epoch": 40, "iter": 2300, "lr": 0.08376, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28141, "top5_acc": 0.53344, "loss_cls": 4.16046, "loss": 4.16046, "time": 0.81624} +{"mode": "train", "epoch": 40, "iter": 2400, "lr": 0.08374, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27437, "top5_acc": 0.53078, "loss_cls": 4.16681, "loss": 4.16681, "time": 0.81546} +{"mode": "train", "epoch": 40, "iter": 2500, "lr": 0.08371, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27594, "top5_acc": 0.53062, "loss_cls": 4.17567, "loss": 4.17567, "time": 0.81993} +{"mode": "train", "epoch": 40, "iter": 2600, "lr": 0.08369, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2775, "top5_acc": 0.52656, "loss_cls": 4.18696, "loss": 4.18696, "time": 0.815} +{"mode": "train", "epoch": 40, "iter": 2700, "lr": 0.08367, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27562, "top5_acc": 0.52406, "loss_cls": 4.18947, "loss": 4.18947, "time": 0.8189} +{"mode": "train", "epoch": 40, "iter": 2800, "lr": 0.08365, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26875, "top5_acc": 0.53375, "loss_cls": 4.18576, "loss": 4.18576, "time": 0.81363} +{"mode": "train", "epoch": 40, "iter": 2900, "lr": 0.08363, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26781, "top5_acc": 0.52438, "loss_cls": 4.20141, "loss": 4.20141, "time": 0.81791} +{"mode": "train", "epoch": 40, "iter": 3000, "lr": 0.08361, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27609, "top5_acc": 0.52516, "loss_cls": 4.18156, "loss": 4.18156, "time": 0.81507} +{"mode": "train", "epoch": 40, "iter": 3100, "lr": 0.08359, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27906, "top5_acc": 0.52969, "loss_cls": 4.17641, "loss": 4.17641, "time": 0.81634} +{"mode": "train", "epoch": 40, "iter": 3200, "lr": 0.08357, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27234, "top5_acc": 0.52406, "loss_cls": 4.16718, "loss": 4.16718, "time": 0.81715} +{"mode": "train", "epoch": 40, "iter": 3300, "lr": 0.08355, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27984, "top5_acc": 0.52875, "loss_cls": 4.16846, "loss": 4.16846, "time": 0.81365} +{"mode": "train", "epoch": 40, "iter": 3400, "lr": 0.08353, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.52625, "loss_cls": 4.16272, "loss": 4.16272, "time": 0.82595} +{"mode": "train", "epoch": 40, "iter": 3500, "lr": 0.08351, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28344, "top5_acc": 0.53438, "loss_cls": 4.14865, "loss": 4.14865, "time": 0.81698} +{"mode": "train", "epoch": 40, "iter": 3600, "lr": 0.08349, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27234, "top5_acc": 0.51516, "loss_cls": 4.21531, "loss": 4.21531, "time": 0.81506} +{"mode": "train", "epoch": 40, "iter": 3700, "lr": 0.08347, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26156, "top5_acc": 0.51828, "loss_cls": 4.20329, "loss": 4.20329, "time": 0.81401} +{"mode": "val", "epoch": 40, "iter": 309, "lr": 0.08346, "top1_acc": 0.20838, "top5_acc": 0.44461, "mean_class_accuracy": 0.20819} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.08344, "memory": 15990, "data_time": 1.28445, "top1_acc": 0.28328, "top5_acc": 0.53797, "loss_cls": 4.13375, "loss": 4.13375, "time": 2.25779} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.08342, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27469, "top5_acc": 0.5325, "loss_cls": 4.14559, "loss": 4.14559, "time": 0.81974} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.08339, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28609, "top5_acc": 0.53438, "loss_cls": 4.12222, "loss": 4.12222, "time": 0.817} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.08337, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27688, "top5_acc": 0.53625, "loss_cls": 4.1266, "loss": 4.1266, "time": 0.81434} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.08335, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26797, "top5_acc": 0.51313, "loss_cls": 4.22549, "loss": 4.22549, "time": 0.81667} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.08333, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26953, "top5_acc": 0.53203, "loss_cls": 4.16165, "loss": 4.16165, "time": 0.82132} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.08331, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.28703, "top5_acc": 0.53438, "loss_cls": 4.14455, "loss": 4.14455, "time": 0.81499} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.08329, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26641, "top5_acc": 0.52031, "loss_cls": 4.20195, "loss": 4.20195, "time": 0.81235} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.08327, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26953, "top5_acc": 0.52312, "loss_cls": 4.18096, "loss": 4.18096, "time": 0.81794} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.08325, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28234, "top5_acc": 0.52312, "loss_cls": 4.13167, "loss": 4.13167, "time": 0.82082} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.08323, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27641, "top5_acc": 0.53109, "loss_cls": 4.13119, "loss": 4.13119, "time": 0.81937} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.08321, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27312, "top5_acc": 0.51797, "loss_cls": 4.20299, "loss": 4.20299, "time": 0.8246} +{"mode": "train", "epoch": 41, "iter": 1300, "lr": 0.08319, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27562, "top5_acc": 0.52344, "loss_cls": 4.14708, "loss": 4.14708, "time": 0.83106} +{"mode": "train", "epoch": 41, "iter": 1400, "lr": 0.08316, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28031, "top5_acc": 0.53141, "loss_cls": 4.16786, "loss": 4.16786, "time": 0.81731} +{"mode": "train", "epoch": 41, "iter": 1500, "lr": 0.08314, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28359, "top5_acc": 0.53297, "loss_cls": 4.12796, "loss": 4.12796, "time": 0.82629} +{"mode": "train", "epoch": 41, "iter": 1600, "lr": 0.08312, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27547, "top5_acc": 0.5325, "loss_cls": 4.1781, "loss": 4.1781, "time": 0.81772} +{"mode": "train", "epoch": 41, "iter": 1700, "lr": 0.0831, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27797, "top5_acc": 0.52688, "loss_cls": 4.20271, "loss": 4.20271, "time": 0.82574} +{"mode": "train", "epoch": 41, "iter": 1800, "lr": 0.08308, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28266, "top5_acc": 0.53359, "loss_cls": 4.15309, "loss": 4.15309, "time": 0.81928} +{"mode": "train", "epoch": 41, "iter": 1900, "lr": 0.08306, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29047, "top5_acc": 0.54016, "loss_cls": 4.10729, "loss": 4.10729, "time": 0.82189} +{"mode": "train", "epoch": 41, "iter": 2000, "lr": 0.08304, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27391, "top5_acc": 0.51703, "loss_cls": 4.22811, "loss": 4.22811, "time": 0.81774} +{"mode": "train", "epoch": 41, "iter": 2100, "lr": 0.08302, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27953, "top5_acc": 0.52391, "loss_cls": 4.17569, "loss": 4.17569, "time": 0.82026} +{"mode": "train", "epoch": 41, "iter": 2200, "lr": 0.083, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27719, "top5_acc": 0.53656, "loss_cls": 4.16432, "loss": 4.16432, "time": 0.82437} +{"mode": "train", "epoch": 41, "iter": 2300, "lr": 0.08298, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27625, "top5_acc": 0.51844, "loss_cls": 4.19608, "loss": 4.19608, "time": 0.81648} +{"mode": "train", "epoch": 41, "iter": 2400, "lr": 0.08296, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27812, "top5_acc": 0.52359, "loss_cls": 4.18702, "loss": 4.18702, "time": 0.81781} +{"mode": "train", "epoch": 41, "iter": 2500, "lr": 0.08293, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28312, "top5_acc": 0.53922, "loss_cls": 4.14558, "loss": 4.14558, "time": 0.8216} +{"mode": "train", "epoch": 41, "iter": 2600, "lr": 0.08291, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26937, "top5_acc": 0.51453, "loss_cls": 4.20364, "loss": 4.20364, "time": 0.82057} +{"mode": "train", "epoch": 41, "iter": 2700, "lr": 0.08289, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27469, "top5_acc": 0.52062, "loss_cls": 4.20625, "loss": 4.20625, "time": 0.81824} +{"mode": "train", "epoch": 41, "iter": 2800, "lr": 0.08287, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28062, "top5_acc": 0.53141, "loss_cls": 4.15005, "loss": 4.15005, "time": 0.81965} +{"mode": "train", "epoch": 41, "iter": 2900, "lr": 0.08285, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26687, "top5_acc": 0.51969, "loss_cls": 4.22935, "loss": 4.22935, "time": 0.81805} +{"mode": "train", "epoch": 41, "iter": 3000, "lr": 0.08283, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26922, "top5_acc": 0.52094, "loss_cls": 4.21182, "loss": 4.21182, "time": 0.81841} +{"mode": "train", "epoch": 41, "iter": 3100, "lr": 0.08281, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27375, "top5_acc": 0.53109, "loss_cls": 4.14624, "loss": 4.14624, "time": 0.81441} +{"mode": "train", "epoch": 41, "iter": 3200, "lr": 0.08279, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27875, "top5_acc": 0.53562, "loss_cls": 4.14192, "loss": 4.14192, "time": 0.8221} +{"mode": "train", "epoch": 41, "iter": 3300, "lr": 0.08277, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27328, "top5_acc": 0.53328, "loss_cls": 4.16708, "loss": 4.16708, "time": 0.81519} +{"mode": "train", "epoch": 41, "iter": 3400, "lr": 0.08274, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27844, "top5_acc": 0.53625, "loss_cls": 4.13347, "loss": 4.13347, "time": 0.81212} +{"mode": "train", "epoch": 41, "iter": 3500, "lr": 0.08272, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28, "top5_acc": 0.53328, "loss_cls": 4.15839, "loss": 4.15839, "time": 0.81237} +{"mode": "train", "epoch": 41, "iter": 3600, "lr": 0.0827, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28859, "top5_acc": 0.53109, "loss_cls": 4.151, "loss": 4.151, "time": 0.81818} +{"mode": "train", "epoch": 41, "iter": 3700, "lr": 0.08268, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27766, "top5_acc": 0.53266, "loss_cls": 4.16587, "loss": 4.16587, "time": 0.81921} +{"mode": "val", "epoch": 41, "iter": 309, "lr": 0.08267, "top1_acc": 0.1987, "top5_acc": 0.4282, "mean_class_accuracy": 0.19853} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.08265, "memory": 15990, "data_time": 1.32769, "top1_acc": 0.28641, "top5_acc": 0.5325, "loss_cls": 4.12437, "loss": 4.12437, "time": 2.31618} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.08263, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28781, "top5_acc": 0.54, "loss_cls": 4.10044, "loss": 4.10044, "time": 0.82436} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.08261, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27984, "top5_acc": 0.53391, "loss_cls": 4.1426, "loss": 4.1426, "time": 0.81951} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.08259, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27953, "top5_acc": 0.52609, "loss_cls": 4.1817, "loss": 4.1817, "time": 0.81607} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.08257, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28031, "top5_acc": 0.53328, "loss_cls": 4.13551, "loss": 4.13551, "time": 0.81848} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.08254, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28797, "top5_acc": 0.5325, "loss_cls": 4.13684, "loss": 4.13684, "time": 0.81627} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.08252, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27641, "top5_acc": 0.52828, "loss_cls": 4.20081, "loss": 4.20081, "time": 0.81887} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.0825, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28188, "top5_acc": 0.52719, "loss_cls": 4.15013, "loss": 4.15013, "time": 0.8197} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.08248, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28484, "top5_acc": 0.54203, "loss_cls": 4.09792, "loss": 4.09792, "time": 0.81519} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.08246, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2775, "top5_acc": 0.52453, "loss_cls": 4.16574, "loss": 4.16574, "time": 0.82171} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.08244, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2775, "top5_acc": 0.53234, "loss_cls": 4.16316, "loss": 4.16316, "time": 0.81792} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.08242, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27297, "top5_acc": 0.52594, "loss_cls": 4.18652, "loss": 4.18652, "time": 0.82074} +{"mode": "train", "epoch": 42, "iter": 1300, "lr": 0.0824, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27531, "top5_acc": 0.53109, "loss_cls": 4.15054, "loss": 4.15054, "time": 0.82048} +{"mode": "train", "epoch": 42, "iter": 1400, "lr": 0.08237, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28828, "top5_acc": 0.53781, "loss_cls": 4.13303, "loss": 4.13303, "time": 0.81463} +{"mode": "train", "epoch": 42, "iter": 1500, "lr": 0.08235, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26641, "top5_acc": 0.51828, "loss_cls": 4.22261, "loss": 4.22261, "time": 0.82202} +{"mode": "train", "epoch": 42, "iter": 1600, "lr": 0.08233, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29109, "top5_acc": 0.54484, "loss_cls": 4.09336, "loss": 4.09336, "time": 0.81604} +{"mode": "train", "epoch": 42, "iter": 1700, "lr": 0.08231, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27906, "top5_acc": 0.53156, "loss_cls": 4.17792, "loss": 4.17792, "time": 0.81887} +{"mode": "train", "epoch": 42, "iter": 1800, "lr": 0.08229, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27437, "top5_acc": 0.53594, "loss_cls": 4.17448, "loss": 4.17448, "time": 0.81936} +{"mode": "train", "epoch": 42, "iter": 1900, "lr": 0.08227, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27016, "top5_acc": 0.52453, "loss_cls": 4.19361, "loss": 4.19361, "time": 0.82251} +{"mode": "train", "epoch": 42, "iter": 2000, "lr": 0.08225, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28594, "top5_acc": 0.54328, "loss_cls": 4.11454, "loss": 4.11454, "time": 0.82213} +{"mode": "train", "epoch": 42, "iter": 2100, "lr": 0.08222, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2775, "top5_acc": 0.52938, "loss_cls": 4.14849, "loss": 4.14849, "time": 0.81561} +{"mode": "train", "epoch": 42, "iter": 2200, "lr": 0.0822, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27953, "top5_acc": 0.53234, "loss_cls": 4.14617, "loss": 4.14617, "time": 0.82759} +{"mode": "train", "epoch": 42, "iter": 2300, "lr": 0.08218, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28312, "top5_acc": 0.52422, "loss_cls": 4.15015, "loss": 4.15015, "time": 0.81921} +{"mode": "train", "epoch": 42, "iter": 2400, "lr": 0.08216, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27609, "top5_acc": 0.52188, "loss_cls": 4.17444, "loss": 4.17444, "time": 0.81904} +{"mode": "train", "epoch": 42, "iter": 2500, "lr": 0.08214, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28516, "top5_acc": 0.51906, "loss_cls": 4.18302, "loss": 4.18302, "time": 0.81769} +{"mode": "train", "epoch": 42, "iter": 2600, "lr": 0.08212, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27797, "top5_acc": 0.52984, "loss_cls": 4.14998, "loss": 4.14998, "time": 0.8187} +{"mode": "train", "epoch": 42, "iter": 2700, "lr": 0.0821, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28906, "top5_acc": 0.53156, "loss_cls": 4.14433, "loss": 4.14433, "time": 0.81389} +{"mode": "train", "epoch": 42, "iter": 2800, "lr": 0.08207, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28438, "top5_acc": 0.53266, "loss_cls": 4.12641, "loss": 4.12641, "time": 0.81467} +{"mode": "train", "epoch": 42, "iter": 2900, "lr": 0.08205, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27719, "top5_acc": 0.53141, "loss_cls": 4.18496, "loss": 4.18496, "time": 0.81418} +{"mode": "train", "epoch": 42, "iter": 3000, "lr": 0.08203, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28672, "top5_acc": 0.5375, "loss_cls": 4.1217, "loss": 4.1217, "time": 0.81689} +{"mode": "train", "epoch": 42, "iter": 3100, "lr": 0.08201, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28359, "top5_acc": 0.52875, "loss_cls": 4.15464, "loss": 4.15464, "time": 0.81496} +{"mode": "train", "epoch": 42, "iter": 3200, "lr": 0.08199, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27406, "top5_acc": 0.52016, "loss_cls": 4.17558, "loss": 4.17558, "time": 0.81979} +{"mode": "train", "epoch": 42, "iter": 3300, "lr": 0.08197, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26984, "top5_acc": 0.525, "loss_cls": 4.20297, "loss": 4.20297, "time": 0.8181} +{"mode": "train", "epoch": 42, "iter": 3400, "lr": 0.08195, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26141, "top5_acc": 0.53203, "loss_cls": 4.19531, "loss": 4.19531, "time": 0.81591} +{"mode": "train", "epoch": 42, "iter": 3500, "lr": 0.08192, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27625, "top5_acc": 0.53156, "loss_cls": 4.15231, "loss": 4.15231, "time": 0.81182} +{"mode": "train", "epoch": 42, "iter": 3600, "lr": 0.0819, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27125, "top5_acc": 0.52703, "loss_cls": 4.17823, "loss": 4.17823, "time": 0.81575} +{"mode": "train", "epoch": 42, "iter": 3700, "lr": 0.08188, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26812, "top5_acc": 0.52859, "loss_cls": 4.20283, "loss": 4.20283, "time": 0.81356} +{"mode": "val", "epoch": 42, "iter": 309, "lr": 0.08187, "top1_acc": 0.2102, "top5_acc": 0.43575, "mean_class_accuracy": 0.21012} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.08185, "memory": 15990, "data_time": 1.31261, "top1_acc": 0.27984, "top5_acc": 0.53016, "loss_cls": 4.15086, "loss": 4.15086, "time": 2.29123} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.08183, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28203, "top5_acc": 0.53531, "loss_cls": 4.13209, "loss": 4.13209, "time": 0.82485} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.08181, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29141, "top5_acc": 0.54688, "loss_cls": 4.08614, "loss": 4.08614, "time": 0.81744} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.08179, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28141, "top5_acc": 0.52859, "loss_cls": 4.15017, "loss": 4.15017, "time": 0.81714} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.08176, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28375, "top5_acc": 0.52906, "loss_cls": 4.12117, "loss": 4.12117, "time": 0.81608} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.08174, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.28875, "top5_acc": 0.54047, "loss_cls": 4.09763, "loss": 4.09763, "time": 0.81437} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.08172, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28547, "top5_acc": 0.54094, "loss_cls": 4.10801, "loss": 4.10801, "time": 0.81607} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.0817, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27516, "top5_acc": 0.52953, "loss_cls": 4.14657, "loss": 4.14657, "time": 0.81911} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.08168, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.285, "top5_acc": 0.53859, "loss_cls": 4.11174, "loss": 4.11174, "time": 0.82445} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.08166, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28656, "top5_acc": 0.53141, "loss_cls": 4.11854, "loss": 4.11854, "time": 0.81831} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.08163, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27953, "top5_acc": 0.52312, "loss_cls": 4.18394, "loss": 4.18394, "time": 0.81716} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.08161, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27547, "top5_acc": 0.53094, "loss_cls": 4.17259, "loss": 4.17259, "time": 0.81916} +{"mode": "train", "epoch": 43, "iter": 1300, "lr": 0.08159, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27187, "top5_acc": 0.53578, "loss_cls": 4.14692, "loss": 4.14692, "time": 0.82007} +{"mode": "train", "epoch": 43, "iter": 1400, "lr": 0.08157, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27891, "top5_acc": 0.53438, "loss_cls": 4.14211, "loss": 4.14211, "time": 0.81765} +{"mode": "train", "epoch": 43, "iter": 1500, "lr": 0.08155, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27344, "top5_acc": 0.53062, "loss_cls": 4.18381, "loss": 4.18381, "time": 0.81597} +{"mode": "train", "epoch": 43, "iter": 1600, "lr": 0.08153, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27203, "top5_acc": 0.52875, "loss_cls": 4.17799, "loss": 4.17799, "time": 0.81915} +{"mode": "train", "epoch": 43, "iter": 1700, "lr": 0.0815, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27562, "top5_acc": 0.52766, "loss_cls": 4.18033, "loss": 4.18033, "time": 0.82136} +{"mode": "train", "epoch": 43, "iter": 1800, "lr": 0.08148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28391, "top5_acc": 0.53734, "loss_cls": 4.10253, "loss": 4.10253, "time": 0.81382} +{"mode": "train", "epoch": 43, "iter": 1900, "lr": 0.08146, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28016, "top5_acc": 0.53516, "loss_cls": 4.14375, "loss": 4.14375, "time": 0.83097} +{"mode": "train", "epoch": 43, "iter": 2000, "lr": 0.08144, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27672, "top5_acc": 0.52188, "loss_cls": 4.16992, "loss": 4.16992, "time": 0.81586} +{"mode": "train", "epoch": 43, "iter": 2100, "lr": 0.08142, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27672, "top5_acc": 0.52406, "loss_cls": 4.18327, "loss": 4.18327, "time": 0.82371} +{"mode": "train", "epoch": 43, "iter": 2200, "lr": 0.0814, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27812, "top5_acc": 0.53422, "loss_cls": 4.14693, "loss": 4.14693, "time": 0.82362} +{"mode": "train", "epoch": 43, "iter": 2300, "lr": 0.08137, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28609, "top5_acc": 0.52656, "loss_cls": 4.17457, "loss": 4.17457, "time": 0.81729} +{"mode": "train", "epoch": 43, "iter": 2400, "lr": 0.08135, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27719, "top5_acc": 0.53375, "loss_cls": 4.17578, "loss": 4.17578, "time": 0.82172} +{"mode": "train", "epoch": 43, "iter": 2500, "lr": 0.08133, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27703, "top5_acc": 0.52578, "loss_cls": 4.14368, "loss": 4.14368, "time": 0.82269} +{"mode": "train", "epoch": 43, "iter": 2600, "lr": 0.08131, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27234, "top5_acc": 0.52297, "loss_cls": 4.19878, "loss": 4.19878, "time": 0.81595} +{"mode": "train", "epoch": 43, "iter": 2700, "lr": 0.08129, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27656, "top5_acc": 0.52562, "loss_cls": 4.16997, "loss": 4.16997, "time": 0.81854} +{"mode": "train", "epoch": 43, "iter": 2800, "lr": 0.08126, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27391, "top5_acc": 0.52797, "loss_cls": 4.18211, "loss": 4.18211, "time": 0.81399} +{"mode": "train", "epoch": 43, "iter": 2900, "lr": 0.08124, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2775, "top5_acc": 0.52234, "loss_cls": 4.17535, "loss": 4.17535, "time": 0.81632} +{"mode": "train", "epoch": 43, "iter": 3000, "lr": 0.08122, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27938, "top5_acc": 0.53984, "loss_cls": 4.11257, "loss": 4.11257, "time": 0.81933} +{"mode": "train", "epoch": 43, "iter": 3100, "lr": 0.0812, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27016, "top5_acc": 0.52016, "loss_cls": 4.2061, "loss": 4.2061, "time": 0.81425} +{"mode": "train", "epoch": 43, "iter": 3200, "lr": 0.08118, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28125, "top5_acc": 0.53547, "loss_cls": 4.16049, "loss": 4.16049, "time": 0.81053} +{"mode": "train", "epoch": 43, "iter": 3300, "lr": 0.08116, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27422, "top5_acc": 0.53266, "loss_cls": 4.13951, "loss": 4.13951, "time": 0.81863} +{"mode": "train", "epoch": 43, "iter": 3400, "lr": 0.08113, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27672, "top5_acc": 0.54203, "loss_cls": 4.13569, "loss": 4.13569, "time": 0.81573} +{"mode": "train", "epoch": 43, "iter": 3500, "lr": 0.08111, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27859, "top5_acc": 0.52891, "loss_cls": 4.18674, "loss": 4.18674, "time": 0.81658} +{"mode": "train", "epoch": 43, "iter": 3600, "lr": 0.08109, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28219, "top5_acc": 0.53719, "loss_cls": 4.13981, "loss": 4.13981, "time": 0.81506} +{"mode": "train", "epoch": 43, "iter": 3700, "lr": 0.08107, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28047, "top5_acc": 0.53266, "loss_cls": 4.142, "loss": 4.142, "time": 0.81333} +{"mode": "val", "epoch": 43, "iter": 309, "lr": 0.08106, "top1_acc": 0.21289, "top5_acc": 0.4474, "mean_class_accuracy": 0.21264} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.08104, "memory": 15990, "data_time": 1.28112, "top1_acc": 0.27906, "top5_acc": 0.53375, "loss_cls": 4.14153, "loss": 4.14153, "time": 2.26809} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.08101, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28516, "top5_acc": 0.54375, "loss_cls": 4.09703, "loss": 4.09703, "time": 0.81832} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.08099, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28328, "top5_acc": 0.53844, "loss_cls": 4.12101, "loss": 4.12101, "time": 0.81717} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.08097, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28625, "top5_acc": 0.52984, "loss_cls": 4.13951, "loss": 4.13951, "time": 0.82152} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.08095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27437, "top5_acc": 0.53531, "loss_cls": 4.15914, "loss": 4.15914, "time": 0.82078} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.08093, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28766, "top5_acc": 0.54203, "loss_cls": 4.11816, "loss": 4.11816, "time": 0.81642} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.0809, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28484, "top5_acc": 0.53812, "loss_cls": 4.11315, "loss": 4.11315, "time": 0.81382} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.08088, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28391, "top5_acc": 0.54312, "loss_cls": 4.10569, "loss": 4.10569, "time": 0.81469} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.08086, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27906, "top5_acc": 0.53125, "loss_cls": 4.16019, "loss": 4.16019, "time": 0.81148} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.08084, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27781, "top5_acc": 0.52844, "loss_cls": 4.13753, "loss": 4.13753, "time": 0.81428} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.08082, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28422, "top5_acc": 0.53703, "loss_cls": 4.13959, "loss": 4.13959, "time": 0.81824} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.08079, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28625, "top5_acc": 0.54031, "loss_cls": 4.10118, "loss": 4.10118, "time": 0.81681} +{"mode": "train", "epoch": 44, "iter": 1300, "lr": 0.08077, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27375, "top5_acc": 0.53, "loss_cls": 4.17486, "loss": 4.17486, "time": 0.81926} +{"mode": "train", "epoch": 44, "iter": 1400, "lr": 0.08075, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28422, "top5_acc": 0.53281, "loss_cls": 4.14137, "loss": 4.14137, "time": 0.81699} +{"mode": "train", "epoch": 44, "iter": 1500, "lr": 0.08073, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28375, "top5_acc": 0.53359, "loss_cls": 4.15463, "loss": 4.15463, "time": 0.8199} +{"mode": "train", "epoch": 44, "iter": 1600, "lr": 0.08071, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28031, "top5_acc": 0.53422, "loss_cls": 4.16564, "loss": 4.16564, "time": 0.81858} +{"mode": "train", "epoch": 44, "iter": 1700, "lr": 0.08068, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27172, "top5_acc": 0.53219, "loss_cls": 4.15174, "loss": 4.15174, "time": 0.82047} +{"mode": "train", "epoch": 44, "iter": 1800, "lr": 0.08066, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28938, "top5_acc": 0.54109, "loss_cls": 4.13751, "loss": 4.13751, "time": 0.8183} +{"mode": "train", "epoch": 44, "iter": 1900, "lr": 0.08064, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28188, "top5_acc": 0.53844, "loss_cls": 4.14769, "loss": 4.14769, "time": 0.81361} +{"mode": "train", "epoch": 44, "iter": 2000, "lr": 0.08062, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27938, "top5_acc": 0.52875, "loss_cls": 4.18189, "loss": 4.18189, "time": 0.8181} +{"mode": "train", "epoch": 44, "iter": 2100, "lr": 0.0806, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27453, "top5_acc": 0.53156, "loss_cls": 4.16197, "loss": 4.16197, "time": 0.81499} +{"mode": "train", "epoch": 44, "iter": 2200, "lr": 0.08057, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28578, "top5_acc": 0.53859, "loss_cls": 4.12499, "loss": 4.12499, "time": 0.81846} +{"mode": "train", "epoch": 44, "iter": 2300, "lr": 0.08055, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27406, "top5_acc": 0.53438, "loss_cls": 4.16546, "loss": 4.16546, "time": 0.81774} +{"mode": "train", "epoch": 44, "iter": 2400, "lr": 0.08053, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27734, "top5_acc": 0.53141, "loss_cls": 4.15148, "loss": 4.15148, "time": 0.82555} +{"mode": "train", "epoch": 44, "iter": 2500, "lr": 0.08051, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28219, "top5_acc": 0.52859, "loss_cls": 4.15727, "loss": 4.15727, "time": 0.81693} +{"mode": "train", "epoch": 44, "iter": 2600, "lr": 0.08048, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28141, "top5_acc": 0.53016, "loss_cls": 4.14949, "loss": 4.14949, "time": 0.81809} +{"mode": "train", "epoch": 44, "iter": 2700, "lr": 0.08046, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27938, "top5_acc": 0.52672, "loss_cls": 4.16857, "loss": 4.16857, "time": 0.81679} +{"mode": "train", "epoch": 44, "iter": 2800, "lr": 0.08044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.275, "top5_acc": 0.53203, "loss_cls": 4.15344, "loss": 4.15344, "time": 0.82362} +{"mode": "train", "epoch": 44, "iter": 2900, "lr": 0.08042, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28078, "top5_acc": 0.52859, "loss_cls": 4.18149, "loss": 4.18149, "time": 0.81678} +{"mode": "train", "epoch": 44, "iter": 3000, "lr": 0.0804, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28547, "top5_acc": 0.54094, "loss_cls": 4.11494, "loss": 4.11494, "time": 0.81693} +{"mode": "train", "epoch": 44, "iter": 3100, "lr": 0.08037, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28359, "top5_acc": 0.53125, "loss_cls": 4.16647, "loss": 4.16647, "time": 0.81651} +{"mode": "train", "epoch": 44, "iter": 3200, "lr": 0.08035, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27531, "top5_acc": 0.52359, "loss_cls": 4.15983, "loss": 4.15983, "time": 0.81455} +{"mode": "train", "epoch": 44, "iter": 3300, "lr": 0.08033, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28375, "top5_acc": 0.53641, "loss_cls": 4.15618, "loss": 4.15618, "time": 0.81577} +{"mode": "train", "epoch": 44, "iter": 3400, "lr": 0.08031, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28641, "top5_acc": 0.53766, "loss_cls": 4.13076, "loss": 4.13076, "time": 0.81693} +{"mode": "train", "epoch": 44, "iter": 3500, "lr": 0.08028, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27922, "top5_acc": 0.52281, "loss_cls": 4.21022, "loss": 4.21022, "time": 0.8196} +{"mode": "train", "epoch": 44, "iter": 3600, "lr": 0.08026, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28125, "top5_acc": 0.52438, "loss_cls": 4.1678, "loss": 4.1678, "time": 0.81788} +{"mode": "train", "epoch": 44, "iter": 3700, "lr": 0.08024, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28172, "top5_acc": 0.53672, "loss_cls": 4.12954, "loss": 4.12954, "time": 0.81557} +{"mode": "val", "epoch": 44, "iter": 309, "lr": 0.08023, "top1_acc": 0.20412, "top5_acc": 0.43327, "mean_class_accuracy": 0.20398} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.08021, "memory": 15990, "data_time": 1.30564, "top1_acc": 0.29609, "top5_acc": 0.5525, "loss_cls": 4.06884, "loss": 4.06884, "time": 2.28886} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.08019, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28953, "top5_acc": 0.55047, "loss_cls": 4.05601, "loss": 4.05601, "time": 0.81872} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.08016, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27906, "top5_acc": 0.53312, "loss_cls": 4.14018, "loss": 4.14018, "time": 0.81368} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.08014, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29219, "top5_acc": 0.53578, "loss_cls": 4.12255, "loss": 4.12255, "time": 0.81406} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.08012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28156, "top5_acc": 0.53641, "loss_cls": 4.10803, "loss": 4.10803, "time": 0.81632} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.0801, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28688, "top5_acc": 0.53484, "loss_cls": 4.13696, "loss": 4.13696, "time": 0.81952} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.08007, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27484, "top5_acc": 0.52703, "loss_cls": 4.18257, "loss": 4.18257, "time": 0.81598} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.08005, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27859, "top5_acc": 0.53953, "loss_cls": 4.12607, "loss": 4.12607, "time": 0.81747} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.08003, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28453, "top5_acc": 0.54391, "loss_cls": 4.08484, "loss": 4.08484, "time": 0.81544} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.08001, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28016, "top5_acc": 0.5225, "loss_cls": 4.16145, "loss": 4.16145, "time": 0.81717} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.07998, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27469, "top5_acc": 0.53016, "loss_cls": 4.159, "loss": 4.159, "time": 0.81827} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.07996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2875, "top5_acc": 0.53922, "loss_cls": 4.09718, "loss": 4.09718, "time": 0.81876} +{"mode": "train", "epoch": 45, "iter": 1300, "lr": 0.07994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28469, "top5_acc": 0.53203, "loss_cls": 4.12305, "loss": 4.12305, "time": 0.82652} +{"mode": "train", "epoch": 45, "iter": 1400, "lr": 0.07992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28094, "top5_acc": 0.52797, "loss_cls": 4.14775, "loss": 4.14775, "time": 0.81599} +{"mode": "train", "epoch": 45, "iter": 1500, "lr": 0.0799, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28641, "top5_acc": 0.52219, "loss_cls": 4.15487, "loss": 4.15487, "time": 0.81894} +{"mode": "train", "epoch": 45, "iter": 1600, "lr": 0.07987, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27844, "top5_acc": 0.52984, "loss_cls": 4.14014, "loss": 4.14014, "time": 0.81972} +{"mode": "train", "epoch": 45, "iter": 1700, "lr": 0.07985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27844, "top5_acc": 0.52688, "loss_cls": 4.18427, "loss": 4.18427, "time": 0.81816} +{"mode": "train", "epoch": 45, "iter": 1800, "lr": 0.07983, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28656, "top5_acc": 0.54156, "loss_cls": 4.10461, "loss": 4.10461, "time": 0.81535} +{"mode": "train", "epoch": 45, "iter": 1900, "lr": 0.07981, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27938, "top5_acc": 0.53312, "loss_cls": 4.12176, "loss": 4.12176, "time": 0.82007} +{"mode": "train", "epoch": 45, "iter": 2000, "lr": 0.07978, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28016, "top5_acc": 0.52625, "loss_cls": 4.17545, "loss": 4.17545, "time": 0.81758} +{"mode": "train", "epoch": 45, "iter": 2100, "lr": 0.07976, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27844, "top5_acc": 0.53344, "loss_cls": 4.14162, "loss": 4.14162, "time": 0.8216} +{"mode": "train", "epoch": 45, "iter": 2200, "lr": 0.07974, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29938, "top5_acc": 0.54641, "loss_cls": 4.07153, "loss": 4.07153, "time": 0.81883} +{"mode": "train", "epoch": 45, "iter": 2300, "lr": 0.07972, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27172, "top5_acc": 0.51844, "loss_cls": 4.22191, "loss": 4.22191, "time": 0.81934} +{"mode": "train", "epoch": 45, "iter": 2400, "lr": 0.07969, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28656, "top5_acc": 0.54391, "loss_cls": 4.1034, "loss": 4.1034, "time": 0.82142} +{"mode": "train", "epoch": 45, "iter": 2500, "lr": 0.07967, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28813, "top5_acc": 0.53734, "loss_cls": 4.11294, "loss": 4.11294, "time": 0.81956} +{"mode": "train", "epoch": 45, "iter": 2600, "lr": 0.07965, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27734, "top5_acc": 0.53062, "loss_cls": 4.16173, "loss": 4.16173, "time": 0.81746} +{"mode": "train", "epoch": 45, "iter": 2700, "lr": 0.07963, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27766, "top5_acc": 0.52312, "loss_cls": 4.1687, "loss": 4.1687, "time": 0.81548} +{"mode": "train", "epoch": 45, "iter": 2800, "lr": 0.0796, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28438, "top5_acc": 0.53844, "loss_cls": 4.10594, "loss": 4.10594, "time": 0.81308} +{"mode": "train", "epoch": 45, "iter": 2900, "lr": 0.07958, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2775, "top5_acc": 0.52516, "loss_cls": 4.17633, "loss": 4.17633, "time": 0.8134} +{"mode": "train", "epoch": 45, "iter": 3000, "lr": 0.07956, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27797, "top5_acc": 0.52766, "loss_cls": 4.14918, "loss": 4.14918, "time": 0.81671} +{"mode": "train", "epoch": 45, "iter": 3100, "lr": 0.07954, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28594, "top5_acc": 0.53688, "loss_cls": 4.10249, "loss": 4.10249, "time": 0.81937} +{"mode": "train", "epoch": 45, "iter": 3200, "lr": 0.07951, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28094, "top5_acc": 0.52016, "loss_cls": 4.18253, "loss": 4.18253, "time": 0.81788} +{"mode": "train", "epoch": 45, "iter": 3300, "lr": 0.07949, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27141, "top5_acc": 0.53031, "loss_cls": 4.18901, "loss": 4.18901, "time": 0.81763} +{"mode": "train", "epoch": 45, "iter": 3400, "lr": 0.07947, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28719, "top5_acc": 0.52594, "loss_cls": 4.16014, "loss": 4.16014, "time": 0.81637} +{"mode": "train", "epoch": 45, "iter": 3500, "lr": 0.07945, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27625, "top5_acc": 0.52625, "loss_cls": 4.16635, "loss": 4.16635, "time": 0.82094} +{"mode": "train", "epoch": 45, "iter": 3600, "lr": 0.07942, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28484, "top5_acc": 0.54, "loss_cls": 4.15359, "loss": 4.15359, "time": 0.815} +{"mode": "train", "epoch": 45, "iter": 3700, "lr": 0.0794, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27391, "top5_acc": 0.52891, "loss_cls": 4.1586, "loss": 4.1586, "time": 0.81416} +{"mode": "val", "epoch": 45, "iter": 309, "lr": 0.07939, "top1_acc": 0.20691, "top5_acc": 0.44456, "mean_class_accuracy": 0.20669} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.07937, "memory": 15990, "data_time": 1.29863, "top1_acc": 0.28547, "top5_acc": 0.54672, "loss_cls": 4.07322, "loss": 4.07322, "time": 2.28373} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.07934, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28297, "top5_acc": 0.54062, "loss_cls": 4.13881, "loss": 4.13881, "time": 0.81883} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.07932, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29, "top5_acc": 0.55484, "loss_cls": 4.07363, "loss": 4.07363, "time": 0.82233} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.0793, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28531, "top5_acc": 0.54078, "loss_cls": 4.11171, "loss": 4.11171, "time": 0.81939} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.07928, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28469, "top5_acc": 0.53203, "loss_cls": 4.13689, "loss": 4.13689, "time": 0.81987} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.07925, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27719, "top5_acc": 0.52438, "loss_cls": 4.16995, "loss": 4.16995, "time": 0.81753} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.07923, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28016, "top5_acc": 0.53594, "loss_cls": 4.09472, "loss": 4.09472, "time": 0.81851} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.07921, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27719, "top5_acc": 0.53234, "loss_cls": 4.14749, "loss": 4.14749, "time": 0.8153} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.07919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27969, "top5_acc": 0.53141, "loss_cls": 4.16687, "loss": 4.16687, "time": 0.82001} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.07916, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28281, "top5_acc": 0.53844, "loss_cls": 4.11012, "loss": 4.11012, "time": 0.8212} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.07914, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27859, "top5_acc": 0.53, "loss_cls": 4.13936, "loss": 4.13936, "time": 0.81507} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.07912, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27844, "top5_acc": 0.53734, "loss_cls": 4.14389, "loss": 4.14389, "time": 0.8288} +{"mode": "train", "epoch": 46, "iter": 1300, "lr": 0.07909, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27437, "top5_acc": 0.52438, "loss_cls": 4.1486, "loss": 4.1486, "time": 0.82645} +{"mode": "train", "epoch": 46, "iter": 1400, "lr": 0.07907, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28156, "top5_acc": 0.52688, "loss_cls": 4.17169, "loss": 4.17169, "time": 0.82028} +{"mode": "train", "epoch": 46, "iter": 1500, "lr": 0.07905, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28281, "top5_acc": 0.53891, "loss_cls": 4.12588, "loss": 4.12588, "time": 0.82106} +{"mode": "train", "epoch": 46, "iter": 1600, "lr": 0.07903, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27531, "top5_acc": 0.52953, "loss_cls": 4.139, "loss": 4.139, "time": 0.82296} +{"mode": "train", "epoch": 46, "iter": 1700, "lr": 0.079, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27812, "top5_acc": 0.52734, "loss_cls": 4.18956, "loss": 4.18956, "time": 0.8139} +{"mode": "train", "epoch": 46, "iter": 1800, "lr": 0.07898, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28328, "top5_acc": 0.53875, "loss_cls": 4.11837, "loss": 4.11837, "time": 0.81333} +{"mode": "train", "epoch": 46, "iter": 1900, "lr": 0.07896, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28359, "top5_acc": 0.53766, "loss_cls": 4.12018, "loss": 4.12018, "time": 0.81438} +{"mode": "train", "epoch": 46, "iter": 2000, "lr": 0.07894, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27406, "top5_acc": 0.52859, "loss_cls": 4.16838, "loss": 4.16838, "time": 0.82179} +{"mode": "train", "epoch": 46, "iter": 2100, "lr": 0.07891, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29344, "top5_acc": 0.55047, "loss_cls": 4.06078, "loss": 4.06078, "time": 0.82203} +{"mode": "train", "epoch": 46, "iter": 2200, "lr": 0.07889, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27391, "top5_acc": 0.53047, "loss_cls": 4.15135, "loss": 4.15135, "time": 0.82569} +{"mode": "train", "epoch": 46, "iter": 2300, "lr": 0.07887, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28406, "top5_acc": 0.54953, "loss_cls": 4.06759, "loss": 4.06759, "time": 0.81773} +{"mode": "train", "epoch": 46, "iter": 2400, "lr": 0.07884, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28109, "top5_acc": 0.52844, "loss_cls": 4.18336, "loss": 4.18336, "time": 0.81953} +{"mode": "train", "epoch": 46, "iter": 2500, "lr": 0.07882, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28078, "top5_acc": 0.53734, "loss_cls": 4.14072, "loss": 4.14072, "time": 0.8141} +{"mode": "train", "epoch": 46, "iter": 2600, "lr": 0.0788, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27516, "top5_acc": 0.53391, "loss_cls": 4.14808, "loss": 4.14808, "time": 0.81634} +{"mode": "train", "epoch": 46, "iter": 2700, "lr": 0.07878, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28531, "top5_acc": 0.5375, "loss_cls": 4.11069, "loss": 4.11069, "time": 0.81874} +{"mode": "train", "epoch": 46, "iter": 2800, "lr": 0.07875, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29641, "top5_acc": 0.54047, "loss_cls": 4.10687, "loss": 4.10687, "time": 0.81771} +{"mode": "train", "epoch": 46, "iter": 2900, "lr": 0.07873, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28953, "top5_acc": 0.53406, "loss_cls": 4.13172, "loss": 4.13172, "time": 0.81617} +{"mode": "train", "epoch": 46, "iter": 3000, "lr": 0.07871, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28016, "top5_acc": 0.52391, "loss_cls": 4.18864, "loss": 4.18864, "time": 0.81993} +{"mode": "train", "epoch": 46, "iter": 3100, "lr": 0.07868, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27797, "top5_acc": 0.53609, "loss_cls": 4.1562, "loss": 4.1562, "time": 0.81931} +{"mode": "train", "epoch": 46, "iter": 3200, "lr": 0.07866, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28219, "top5_acc": 0.53766, "loss_cls": 4.11385, "loss": 4.11385, "time": 0.82049} +{"mode": "train", "epoch": 46, "iter": 3300, "lr": 0.07864, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27203, "top5_acc": 0.5275, "loss_cls": 4.18066, "loss": 4.18066, "time": 0.81698} +{"mode": "train", "epoch": 46, "iter": 3400, "lr": 0.07862, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2825, "top5_acc": 0.53719, "loss_cls": 4.13539, "loss": 4.13539, "time": 0.81523} +{"mode": "train", "epoch": 46, "iter": 3500, "lr": 0.07859, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27547, "top5_acc": 0.52547, "loss_cls": 4.18801, "loss": 4.18801, "time": 0.8183} +{"mode": "train", "epoch": 46, "iter": 3600, "lr": 0.07857, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28062, "top5_acc": 0.52906, "loss_cls": 4.16923, "loss": 4.16923, "time": 0.81768} +{"mode": "train", "epoch": 46, "iter": 3700, "lr": 0.07855, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29484, "top5_acc": 0.54219, "loss_cls": 4.09006, "loss": 4.09006, "time": 0.81339} +{"mode": "val", "epoch": 46, "iter": 309, "lr": 0.07854, "top1_acc": 0.22717, "top5_acc": 0.46239, "mean_class_accuracy": 0.22687} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.07851, "memory": 15990, "data_time": 1.28493, "top1_acc": 0.295, "top5_acc": 0.55297, "loss_cls": 4.0623, "loss": 4.0623, "time": 2.27252} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.07849, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29438, "top5_acc": 0.54062, "loss_cls": 4.08967, "loss": 4.08967, "time": 0.82758} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.07847, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27797, "top5_acc": 0.52797, "loss_cls": 4.16406, "loss": 4.16406, "time": 0.81905} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.07844, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27984, "top5_acc": 0.53516, "loss_cls": 4.12239, "loss": 4.12239, "time": 0.81569} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.07842, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28312, "top5_acc": 0.5375, "loss_cls": 4.10734, "loss": 4.10734, "time": 0.81657} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.0784, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28516, "top5_acc": 0.53391, "loss_cls": 4.11719, "loss": 4.11719, "time": 0.81546} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.07838, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28203, "top5_acc": 0.53688, "loss_cls": 4.15188, "loss": 4.15188, "time": 0.81492} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.07835, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29359, "top5_acc": 0.54438, "loss_cls": 4.06425, "loss": 4.06425, "time": 0.81237} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.07833, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28109, "top5_acc": 0.54, "loss_cls": 4.12066, "loss": 4.12066, "time": 0.82413} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.07831, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29094, "top5_acc": 0.53094, "loss_cls": 4.15055, "loss": 4.15055, "time": 0.82052} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.07828, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.285, "top5_acc": 0.54266, "loss_cls": 4.09864, "loss": 4.09864, "time": 0.8213} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.07826, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27375, "top5_acc": 0.53891, "loss_cls": 4.15114, "loss": 4.15114, "time": 0.8215} +{"mode": "train", "epoch": 47, "iter": 1300, "lr": 0.07824, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28672, "top5_acc": 0.53344, "loss_cls": 4.14077, "loss": 4.14077, "time": 0.82083} +{"mode": "train", "epoch": 47, "iter": 1400, "lr": 0.07821, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28625, "top5_acc": 0.53109, "loss_cls": 4.15007, "loss": 4.15007, "time": 0.81837} +{"mode": "train", "epoch": 47, "iter": 1500, "lr": 0.07819, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28266, "top5_acc": 0.54094, "loss_cls": 4.1305, "loss": 4.1305, "time": 0.818} +{"mode": "train", "epoch": 47, "iter": 1600, "lr": 0.07817, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27531, "top5_acc": 0.53438, "loss_cls": 4.16435, "loss": 4.16435, "time": 0.82375} +{"mode": "train", "epoch": 47, "iter": 1700, "lr": 0.07814, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28188, "top5_acc": 0.52578, "loss_cls": 4.16037, "loss": 4.16037, "time": 0.81618} +{"mode": "train", "epoch": 47, "iter": 1800, "lr": 0.07812, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28234, "top5_acc": 0.53578, "loss_cls": 4.12918, "loss": 4.12918, "time": 0.81547} +{"mode": "train", "epoch": 47, "iter": 1900, "lr": 0.0781, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28703, "top5_acc": 0.54234, "loss_cls": 4.1081, "loss": 4.1081, "time": 0.81837} +{"mode": "train", "epoch": 47, "iter": 2000, "lr": 0.07808, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28938, "top5_acc": 0.54109, "loss_cls": 4.09332, "loss": 4.09332, "time": 0.8242} +{"mode": "train", "epoch": 47, "iter": 2100, "lr": 0.07805, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27906, "top5_acc": 0.53484, "loss_cls": 4.12382, "loss": 4.12382, "time": 0.81845} +{"mode": "train", "epoch": 47, "iter": 2200, "lr": 0.07803, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27812, "top5_acc": 0.52922, "loss_cls": 4.17349, "loss": 4.17349, "time": 0.82538} +{"mode": "train", "epoch": 47, "iter": 2300, "lr": 0.07801, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29766, "top5_acc": 0.54, "loss_cls": 4.06746, "loss": 4.06746, "time": 0.82326} +{"mode": "train", "epoch": 47, "iter": 2400, "lr": 0.07798, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27609, "top5_acc": 0.53578, "loss_cls": 4.17065, "loss": 4.17065, "time": 0.81842} +{"mode": "train", "epoch": 47, "iter": 2500, "lr": 0.07796, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28109, "top5_acc": 0.53969, "loss_cls": 4.12308, "loss": 4.12308, "time": 0.81761} +{"mode": "train", "epoch": 47, "iter": 2600, "lr": 0.07794, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28047, "top5_acc": 0.52734, "loss_cls": 4.14404, "loss": 4.14404, "time": 0.81234} +{"mode": "train", "epoch": 47, "iter": 2700, "lr": 0.07791, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27875, "top5_acc": 0.54422, "loss_cls": 4.11716, "loss": 4.11716, "time": 0.82071} +{"mode": "train", "epoch": 47, "iter": 2800, "lr": 0.07789, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27609, "top5_acc": 0.52953, "loss_cls": 4.15329, "loss": 4.15329, "time": 0.81386} +{"mode": "train", "epoch": 47, "iter": 2900, "lr": 0.07787, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28984, "top5_acc": 0.53891, "loss_cls": 4.08195, "loss": 4.08195, "time": 0.81846} +{"mode": "train", "epoch": 47, "iter": 3000, "lr": 0.07784, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27609, "top5_acc": 0.52875, "loss_cls": 4.17064, "loss": 4.17064, "time": 0.81906} +{"mode": "train", "epoch": 47, "iter": 3100, "lr": 0.07782, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28359, "top5_acc": 0.53844, "loss_cls": 4.10865, "loss": 4.10865, "time": 0.81854} +{"mode": "train", "epoch": 47, "iter": 3200, "lr": 0.0778, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28406, "top5_acc": 0.53719, "loss_cls": 4.12977, "loss": 4.12977, "time": 0.81333} +{"mode": "train", "epoch": 47, "iter": 3300, "lr": 0.07777, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28672, "top5_acc": 0.52984, "loss_cls": 4.13498, "loss": 4.13498, "time": 0.81644} +{"mode": "train", "epoch": 47, "iter": 3400, "lr": 0.07775, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28484, "top5_acc": 0.53, "loss_cls": 4.15266, "loss": 4.15266, "time": 0.81557} +{"mode": "train", "epoch": 47, "iter": 3500, "lr": 0.07773, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28703, "top5_acc": 0.53484, "loss_cls": 4.12382, "loss": 4.12382, "time": 0.81801} +{"mode": "train", "epoch": 47, "iter": 3600, "lr": 0.0777, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28422, "top5_acc": 0.53609, "loss_cls": 4.11793, "loss": 4.11793, "time": 0.81429} +{"mode": "train", "epoch": 47, "iter": 3700, "lr": 0.07768, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28984, "top5_acc": 0.53344, "loss_cls": 4.12535, "loss": 4.12535, "time": 0.81329} +{"mode": "val", "epoch": 47, "iter": 309, "lr": 0.07767, "top1_acc": 0.21263, "top5_acc": 0.4357, "mean_class_accuracy": 0.21255} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.07765, "memory": 15990, "data_time": 1.27751, "top1_acc": 0.29156, "top5_acc": 0.5475, "loss_cls": 4.0546, "loss": 4.0546, "time": 2.25789} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.07762, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29641, "top5_acc": 0.54734, "loss_cls": 4.0605, "loss": 4.0605, "time": 0.81398} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.0776, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29016, "top5_acc": 0.53641, "loss_cls": 4.1062, "loss": 4.1062, "time": 0.81235} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.07758, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28125, "top5_acc": 0.53688, "loss_cls": 4.1338, "loss": 4.1338, "time": 0.81764} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.07755, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28938, "top5_acc": 0.54016, "loss_cls": 4.08587, "loss": 4.08587, "time": 0.81791} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.07753, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29609, "top5_acc": 0.55047, "loss_cls": 4.05256, "loss": 4.05256, "time": 0.81851} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.07751, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28875, "top5_acc": 0.54297, "loss_cls": 4.10166, "loss": 4.10166, "time": 0.8121} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.07748, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27938, "top5_acc": 0.53562, "loss_cls": 4.16226, "loss": 4.16226, "time": 0.81587} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.07746, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28453, "top5_acc": 0.53062, "loss_cls": 4.12146, "loss": 4.12146, "time": 0.81517} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.07744, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28109, "top5_acc": 0.53891, "loss_cls": 4.09762, "loss": 4.09762, "time": 0.8215} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.07741, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28891, "top5_acc": 0.54391, "loss_cls": 4.06747, "loss": 4.06747, "time": 0.81749} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.07739, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27766, "top5_acc": 0.53641, "loss_cls": 4.1473, "loss": 4.1473, "time": 0.82693} +{"mode": "train", "epoch": 48, "iter": 1300, "lr": 0.07737, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28719, "top5_acc": 0.54297, "loss_cls": 4.10945, "loss": 4.10945, "time": 0.82042} +{"mode": "train", "epoch": 48, "iter": 1400, "lr": 0.07734, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28578, "top5_acc": 0.53359, "loss_cls": 4.12792, "loss": 4.12792, "time": 0.81977} +{"mode": "train", "epoch": 48, "iter": 1500, "lr": 0.07732, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29422, "top5_acc": 0.54203, "loss_cls": 4.10475, "loss": 4.10475, "time": 0.81784} +{"mode": "train", "epoch": 48, "iter": 1600, "lr": 0.0773, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28047, "top5_acc": 0.53922, "loss_cls": 4.13166, "loss": 4.13166, "time": 0.82401} +{"mode": "train", "epoch": 48, "iter": 1700, "lr": 0.07727, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27984, "top5_acc": 0.53266, "loss_cls": 4.15349, "loss": 4.15349, "time": 0.8213} +{"mode": "train", "epoch": 48, "iter": 1800, "lr": 0.07725, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28438, "top5_acc": 0.54094, "loss_cls": 4.10698, "loss": 4.10698, "time": 0.81535} +{"mode": "train", "epoch": 48, "iter": 1900, "lr": 0.07723, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2825, "top5_acc": 0.52969, "loss_cls": 4.1504, "loss": 4.1504, "time": 0.82088} +{"mode": "train", "epoch": 48, "iter": 2000, "lr": 0.0772, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28234, "top5_acc": 0.53594, "loss_cls": 4.13659, "loss": 4.13659, "time": 0.82083} +{"mode": "train", "epoch": 48, "iter": 2100, "lr": 0.07718, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28578, "top5_acc": 0.53438, "loss_cls": 4.13503, "loss": 4.13503, "time": 0.8201} +{"mode": "train", "epoch": 48, "iter": 2200, "lr": 0.07716, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27187, "top5_acc": 0.52906, "loss_cls": 4.16397, "loss": 4.16397, "time": 0.81627} +{"mode": "train", "epoch": 48, "iter": 2300, "lr": 0.07713, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28625, "top5_acc": 0.53547, "loss_cls": 4.11187, "loss": 4.11187, "time": 0.83054} +{"mode": "train", "epoch": 48, "iter": 2400, "lr": 0.07711, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29766, "top5_acc": 0.54281, "loss_cls": 4.05688, "loss": 4.05688, "time": 0.82045} +{"mode": "train", "epoch": 48, "iter": 2500, "lr": 0.07709, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27562, "top5_acc": 0.52375, "loss_cls": 4.16683, "loss": 4.16683, "time": 0.81809} +{"mode": "train", "epoch": 48, "iter": 2600, "lr": 0.07706, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28031, "top5_acc": 0.54641, "loss_cls": 4.08262, "loss": 4.08262, "time": 0.82261} +{"mode": "train", "epoch": 48, "iter": 2700, "lr": 0.07704, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29125, "top5_acc": 0.54078, "loss_cls": 4.08698, "loss": 4.08698, "time": 0.81818} +{"mode": "train", "epoch": 48, "iter": 2800, "lr": 0.07701, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29391, "top5_acc": 0.54375, "loss_cls": 4.08925, "loss": 4.08925, "time": 0.82036} +{"mode": "train", "epoch": 48, "iter": 2900, "lr": 0.07699, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28719, "top5_acc": 0.53406, "loss_cls": 4.1446, "loss": 4.1446, "time": 0.81734} +{"mode": "train", "epoch": 48, "iter": 3000, "lr": 0.07697, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28156, "top5_acc": 0.52922, "loss_cls": 4.1454, "loss": 4.1454, "time": 0.81632} +{"mode": "train", "epoch": 48, "iter": 3100, "lr": 0.07694, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28875, "top5_acc": 0.535, "loss_cls": 4.11015, "loss": 4.11015, "time": 0.81712} +{"mode": "train", "epoch": 48, "iter": 3200, "lr": 0.07692, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27578, "top5_acc": 0.53969, "loss_cls": 4.12454, "loss": 4.12454, "time": 0.81554} +{"mode": "train", "epoch": 48, "iter": 3300, "lr": 0.0769, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27812, "top5_acc": 0.53047, "loss_cls": 4.14075, "loss": 4.14075, "time": 0.81921} +{"mode": "train", "epoch": 48, "iter": 3400, "lr": 0.07687, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28453, "top5_acc": 0.53547, "loss_cls": 4.14344, "loss": 4.14344, "time": 0.81722} +{"mode": "train", "epoch": 48, "iter": 3500, "lr": 0.07685, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28141, "top5_acc": 0.52625, "loss_cls": 4.17475, "loss": 4.17475, "time": 0.81508} +{"mode": "train", "epoch": 48, "iter": 3600, "lr": 0.07683, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27781, "top5_acc": 0.53547, "loss_cls": 4.18159, "loss": 4.18159, "time": 0.81912} +{"mode": "train", "epoch": 48, "iter": 3700, "lr": 0.0768, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27938, "top5_acc": 0.53609, "loss_cls": 4.14417, "loss": 4.14417, "time": 0.81421} +{"mode": "val", "epoch": 48, "iter": 309, "lr": 0.07679, "top1_acc": 0.22605, "top5_acc": 0.4554, "mean_class_accuracy": 0.22574} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.07677, "memory": 15990, "data_time": 1.29298, "top1_acc": 0.29844, "top5_acc": 0.54594, "loss_cls": 4.02228, "loss": 4.02228, "time": 2.27648} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.07674, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29063, "top5_acc": 0.54531, "loss_cls": 4.07327, "loss": 4.07327, "time": 0.82628} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.07672, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28891, "top5_acc": 0.54953, "loss_cls": 4.03993, "loss": 4.03993, "time": 0.82376} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.0767, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28453, "top5_acc": 0.53938, "loss_cls": 4.10062, "loss": 4.10062, "time": 0.82012} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.07667, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28172, "top5_acc": 0.53734, "loss_cls": 4.13047, "loss": 4.13047, "time": 0.81621} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.07665, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.29078, "top5_acc": 0.5475, "loss_cls": 4.08134, "loss": 4.08134, "time": 0.81467} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.07663, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28672, "top5_acc": 0.53594, "loss_cls": 4.14707, "loss": 4.14707, "time": 0.81419} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.0766, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29313, "top5_acc": 0.54922, "loss_cls": 4.07521, "loss": 4.07521, "time": 0.82206} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.07658, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29031, "top5_acc": 0.54375, "loss_cls": 4.08352, "loss": 4.08352, "time": 0.81496} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.07656, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28734, "top5_acc": 0.53094, "loss_cls": 4.14551, "loss": 4.14551, "time": 0.82382} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.07653, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29594, "top5_acc": 0.53828, "loss_cls": 4.10003, "loss": 4.10003, "time": 0.81698} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.07651, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28891, "top5_acc": 0.545, "loss_cls": 4.09146, "loss": 4.09146, "time": 0.82686} +{"mode": "train", "epoch": 49, "iter": 1300, "lr": 0.07648, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28062, "top5_acc": 0.53141, "loss_cls": 4.14717, "loss": 4.14717, "time": 0.82081} +{"mode": "train", "epoch": 49, "iter": 1400, "lr": 0.07646, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28562, "top5_acc": 0.54844, "loss_cls": 4.1005, "loss": 4.1005, "time": 0.82671} +{"mode": "train", "epoch": 49, "iter": 1500, "lr": 0.07644, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28766, "top5_acc": 0.54547, "loss_cls": 4.07804, "loss": 4.07804, "time": 0.82255} +{"mode": "train", "epoch": 49, "iter": 1600, "lr": 0.07641, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28891, "top5_acc": 0.54078, "loss_cls": 4.12265, "loss": 4.12265, "time": 0.82073} +{"mode": "train", "epoch": 49, "iter": 1700, "lr": 0.07639, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2825, "top5_acc": 0.54922, "loss_cls": 4.0864, "loss": 4.0864, "time": 0.81308} +{"mode": "train", "epoch": 49, "iter": 1800, "lr": 0.07637, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28656, "top5_acc": 0.53125, "loss_cls": 4.13422, "loss": 4.13422, "time": 0.82003} +{"mode": "train", "epoch": 49, "iter": 1900, "lr": 0.07634, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28922, "top5_acc": 0.53359, "loss_cls": 4.12891, "loss": 4.12891, "time": 0.81903} +{"mode": "train", "epoch": 49, "iter": 2000, "lr": 0.07632, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28375, "top5_acc": 0.54062, "loss_cls": 4.12729, "loss": 4.12729, "time": 0.82057} +{"mode": "train", "epoch": 49, "iter": 2100, "lr": 0.07629, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28688, "top5_acc": 0.53812, "loss_cls": 4.11673, "loss": 4.11673, "time": 0.81509} +{"mode": "train", "epoch": 49, "iter": 2200, "lr": 0.07627, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27734, "top5_acc": 0.53234, "loss_cls": 4.1709, "loss": 4.1709, "time": 0.82296} +{"mode": "train", "epoch": 49, "iter": 2300, "lr": 0.07625, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29203, "top5_acc": 0.53625, "loss_cls": 4.11999, "loss": 4.11999, "time": 0.82475} +{"mode": "train", "epoch": 49, "iter": 2400, "lr": 0.07622, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28531, "top5_acc": 0.53594, "loss_cls": 4.11604, "loss": 4.11604, "time": 0.81818} +{"mode": "train", "epoch": 49, "iter": 2500, "lr": 0.0762, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27266, "top5_acc": 0.53094, "loss_cls": 4.13484, "loss": 4.13484, "time": 0.81943} +{"mode": "train", "epoch": 49, "iter": 2600, "lr": 0.07618, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28438, "top5_acc": 0.53531, "loss_cls": 4.10499, "loss": 4.10499, "time": 0.81529} +{"mode": "train", "epoch": 49, "iter": 2700, "lr": 0.07615, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28328, "top5_acc": 0.53562, "loss_cls": 4.16455, "loss": 4.16455, "time": 0.8165} +{"mode": "train", "epoch": 49, "iter": 2800, "lr": 0.07613, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27297, "top5_acc": 0.53188, "loss_cls": 4.15216, "loss": 4.15216, "time": 0.81811} +{"mode": "train", "epoch": 49, "iter": 2900, "lr": 0.0761, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28203, "top5_acc": 0.54016, "loss_cls": 4.11614, "loss": 4.11614, "time": 0.81731} +{"mode": "train", "epoch": 49, "iter": 3000, "lr": 0.07608, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29391, "top5_acc": 0.53891, "loss_cls": 4.08018, "loss": 4.08018, "time": 0.81456} +{"mode": "train", "epoch": 49, "iter": 3100, "lr": 0.07606, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28969, "top5_acc": 0.54125, "loss_cls": 4.09403, "loss": 4.09403, "time": 0.81527} +{"mode": "train", "epoch": 49, "iter": 3200, "lr": 0.07603, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28422, "top5_acc": 0.53906, "loss_cls": 4.10851, "loss": 4.10851, "time": 0.81887} +{"mode": "train", "epoch": 49, "iter": 3300, "lr": 0.07601, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27781, "top5_acc": 0.53016, "loss_cls": 4.15154, "loss": 4.15154, "time": 0.82003} +{"mode": "train", "epoch": 49, "iter": 3400, "lr": 0.07598, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28406, "top5_acc": 0.53797, "loss_cls": 4.12957, "loss": 4.12957, "time": 0.81728} +{"mode": "train", "epoch": 49, "iter": 3500, "lr": 0.07596, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27656, "top5_acc": 0.53594, "loss_cls": 4.12654, "loss": 4.12654, "time": 0.81791} +{"mode": "train", "epoch": 49, "iter": 3600, "lr": 0.07594, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.285, "top5_acc": 0.53781, "loss_cls": 4.12827, "loss": 4.12827, "time": 0.81572} +{"mode": "train", "epoch": 49, "iter": 3700, "lr": 0.07591, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28328, "top5_acc": 0.53375, "loss_cls": 4.14392, "loss": 4.14392, "time": 0.81949} +{"mode": "val", "epoch": 49, "iter": 309, "lr": 0.0759, "top1_acc": 0.22297, "top5_acc": 0.46877, "mean_class_accuracy": 0.22265} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.07588, "memory": 15990, "data_time": 1.30012, "top1_acc": 0.28766, "top5_acc": 0.53688, "loss_cls": 4.11076, "loss": 4.11076, "time": 2.28423} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.07585, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29469, "top5_acc": 0.55141, "loss_cls": 4.06721, "loss": 4.06721, "time": 0.81772} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.07583, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28828, "top5_acc": 0.53891, "loss_cls": 4.09588, "loss": 4.09588, "time": 0.82732} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.07581, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29234, "top5_acc": 0.54969, "loss_cls": 4.04527, "loss": 4.04527, "time": 0.82149} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.07578, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29156, "top5_acc": 0.54234, "loss_cls": 4.1122, "loss": 4.1122, "time": 0.81431} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.07576, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29109, "top5_acc": 0.54719, "loss_cls": 4.08519, "loss": 4.08519, "time": 0.81963} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.07573, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29703, "top5_acc": 0.55, "loss_cls": 4.05123, "loss": 4.05123, "time": 0.81927} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.07571, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29391, "top5_acc": 0.53984, "loss_cls": 4.09451, "loss": 4.09451, "time": 0.81797} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.07569, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28109, "top5_acc": 0.53375, "loss_cls": 4.14962, "loss": 4.14962, "time": 0.82485} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.07566, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.29969, "top5_acc": 0.54469, "loss_cls": 4.06437, "loss": 4.06437, "time": 0.82141} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.07564, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28297, "top5_acc": 0.53141, "loss_cls": 4.14522, "loss": 4.14522, "time": 0.81382} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.07561, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29031, "top5_acc": 0.54297, "loss_cls": 4.08361, "loss": 4.08361, "time": 0.82026} +{"mode": "train", "epoch": 50, "iter": 1300, "lr": 0.07559, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28141, "top5_acc": 0.54312, "loss_cls": 4.09268, "loss": 4.09268, "time": 0.82094} +{"mode": "train", "epoch": 50, "iter": 1400, "lr": 0.07557, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27781, "top5_acc": 0.54047, "loss_cls": 4.11113, "loss": 4.11113, "time": 0.81846} +{"mode": "train", "epoch": 50, "iter": 1500, "lr": 0.07554, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29359, "top5_acc": 0.54172, "loss_cls": 4.07028, "loss": 4.07028, "time": 0.82013} +{"mode": "train", "epoch": 50, "iter": 1600, "lr": 0.07552, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28281, "top5_acc": 0.53531, "loss_cls": 4.12091, "loss": 4.12091, "time": 0.81138} +{"mode": "train", "epoch": 50, "iter": 1700, "lr": 0.07549, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28609, "top5_acc": 0.54578, "loss_cls": 4.07088, "loss": 4.07088, "time": 0.81618} +{"mode": "train", "epoch": 50, "iter": 1800, "lr": 0.07547, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28562, "top5_acc": 0.53047, "loss_cls": 4.14498, "loss": 4.14498, "time": 0.81945} +{"mode": "train", "epoch": 50, "iter": 1900, "lr": 0.07545, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3, "top5_acc": 0.54219, "loss_cls": 4.0745, "loss": 4.0745, "time": 0.81642} +{"mode": "train", "epoch": 50, "iter": 2000, "lr": 0.07542, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29156, "top5_acc": 0.53969, "loss_cls": 4.10445, "loss": 4.10445, "time": 0.82155} +{"mode": "train", "epoch": 50, "iter": 2100, "lr": 0.0754, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27953, "top5_acc": 0.5325, "loss_cls": 4.15262, "loss": 4.15262, "time": 0.81838} +{"mode": "train", "epoch": 50, "iter": 2200, "lr": 0.07537, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28578, "top5_acc": 0.53922, "loss_cls": 4.10745, "loss": 4.10745, "time": 0.82272} +{"mode": "train", "epoch": 50, "iter": 2300, "lr": 0.07535, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29563, "top5_acc": 0.54984, "loss_cls": 4.04764, "loss": 4.04764, "time": 0.82495} +{"mode": "train", "epoch": 50, "iter": 2400, "lr": 0.07533, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29094, "top5_acc": 0.53938, "loss_cls": 4.11014, "loss": 4.11014, "time": 0.81683} +{"mode": "train", "epoch": 50, "iter": 2500, "lr": 0.0753, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26984, "top5_acc": 0.53094, "loss_cls": 4.17133, "loss": 4.17133, "time": 0.81822} +{"mode": "train", "epoch": 50, "iter": 2600, "lr": 0.07528, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28656, "top5_acc": 0.53797, "loss_cls": 4.14158, "loss": 4.14158, "time": 0.82218} +{"mode": "train", "epoch": 50, "iter": 2700, "lr": 0.07525, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27562, "top5_acc": 0.53719, "loss_cls": 4.14141, "loss": 4.14141, "time": 0.81976} +{"mode": "train", "epoch": 50, "iter": 2800, "lr": 0.07523, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28547, "top5_acc": 0.53953, "loss_cls": 4.08823, "loss": 4.08823, "time": 0.81652} +{"mode": "train", "epoch": 50, "iter": 2900, "lr": 0.0752, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28688, "top5_acc": 0.53938, "loss_cls": 4.0954, "loss": 4.0954, "time": 0.81597} +{"mode": "train", "epoch": 50, "iter": 3000, "lr": 0.07518, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28844, "top5_acc": 0.54406, "loss_cls": 4.11335, "loss": 4.11335, "time": 0.81784} +{"mode": "train", "epoch": 50, "iter": 3100, "lr": 0.07516, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29047, "top5_acc": 0.53688, "loss_cls": 4.10662, "loss": 4.10662, "time": 0.81757} +{"mode": "train", "epoch": 50, "iter": 3200, "lr": 0.07513, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29375, "top5_acc": 0.54281, "loss_cls": 4.09685, "loss": 4.09685, "time": 0.81195} +{"mode": "train", "epoch": 50, "iter": 3300, "lr": 0.07511, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28906, "top5_acc": 0.535, "loss_cls": 4.14845, "loss": 4.14845, "time": 0.81188} +{"mode": "train", "epoch": 50, "iter": 3400, "lr": 0.07508, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28062, "top5_acc": 0.53812, "loss_cls": 4.10224, "loss": 4.10224, "time": 0.81913} +{"mode": "train", "epoch": 50, "iter": 3500, "lr": 0.07506, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28562, "top5_acc": 0.53703, "loss_cls": 4.09376, "loss": 4.09376, "time": 0.81904} +{"mode": "train", "epoch": 50, "iter": 3600, "lr": 0.07504, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28109, "top5_acc": 0.53328, "loss_cls": 4.14133, "loss": 4.14133, "time": 0.8147} +{"mode": "train", "epoch": 50, "iter": 3700, "lr": 0.07501, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29891, "top5_acc": 0.54703, "loss_cls": 4.07703, "loss": 4.07703, "time": 0.81228} +{"mode": "val", "epoch": 50, "iter": 309, "lr": 0.075, "top1_acc": 0.21668, "top5_acc": 0.45257, "mean_class_accuracy": 0.21648} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.07498, "memory": 15990, "data_time": 1.29998, "top1_acc": 0.29734, "top5_acc": 0.55719, "loss_cls": 4.02891, "loss": 4.02891, "time": 2.28278} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.07495, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30125, "top5_acc": 0.55437, "loss_cls": 3.98568, "loss": 3.98568, "time": 0.82228} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.07493, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28859, "top5_acc": 0.55266, "loss_cls": 4.05708, "loss": 4.05708, "time": 0.81949} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.0749, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28984, "top5_acc": 0.5425, "loss_cls": 4.08966, "loss": 4.08966, "time": 0.81396} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.07488, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29156, "top5_acc": 0.54906, "loss_cls": 4.06064, "loss": 4.06064, "time": 0.81582} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.07485, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28297, "top5_acc": 0.54438, "loss_cls": 4.08904, "loss": 4.08904, "time": 0.81615} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.07483, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29953, "top5_acc": 0.55297, "loss_cls": 4.01864, "loss": 4.01864, "time": 0.81871} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.07481, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27969, "top5_acc": 0.53031, "loss_cls": 4.14468, "loss": 4.14468, "time": 0.81684} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.07478, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27938, "top5_acc": 0.52734, "loss_cls": 4.1988, "loss": 4.1988, "time": 0.81825} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.07476, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30281, "top5_acc": 0.55266, "loss_cls": 4.03013, "loss": 4.03013, "time": 0.82257} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.07473, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29047, "top5_acc": 0.54141, "loss_cls": 4.10418, "loss": 4.10418, "time": 0.82941} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.07471, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28859, "top5_acc": 0.54828, "loss_cls": 4.07219, "loss": 4.07219, "time": 0.82384} +{"mode": "train", "epoch": 51, "iter": 1300, "lr": 0.07468, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29188, "top5_acc": 0.54484, "loss_cls": 4.10012, "loss": 4.10012, "time": 0.82407} +{"mode": "train", "epoch": 51, "iter": 1400, "lr": 0.07466, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27719, "top5_acc": 0.53516, "loss_cls": 4.11879, "loss": 4.11879, "time": 0.82587} +{"mode": "train", "epoch": 51, "iter": 1500, "lr": 0.07464, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29094, "top5_acc": 0.53406, "loss_cls": 4.10615, "loss": 4.10615, "time": 0.82966} +{"mode": "train", "epoch": 51, "iter": 1600, "lr": 0.07461, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28562, "top5_acc": 0.54047, "loss_cls": 4.08643, "loss": 4.08643, "time": 0.81628} +{"mode": "train", "epoch": 51, "iter": 1700, "lr": 0.07459, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28719, "top5_acc": 0.52672, "loss_cls": 4.1202, "loss": 4.1202, "time": 0.81631} +{"mode": "train", "epoch": 51, "iter": 1800, "lr": 0.07456, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28016, "top5_acc": 0.53578, "loss_cls": 4.13302, "loss": 4.13302, "time": 0.82101} +{"mode": "train", "epoch": 51, "iter": 1900, "lr": 0.07454, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28297, "top5_acc": 0.53469, "loss_cls": 4.1074, "loss": 4.1074, "time": 0.81907} +{"mode": "train", "epoch": 51, "iter": 2000, "lr": 0.07451, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2875, "top5_acc": 0.53438, "loss_cls": 4.10555, "loss": 4.10555, "time": 0.8159} +{"mode": "train", "epoch": 51, "iter": 2100, "lr": 0.07449, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28844, "top5_acc": 0.53734, "loss_cls": 4.10245, "loss": 4.10245, "time": 0.82141} +{"mode": "train", "epoch": 51, "iter": 2200, "lr": 0.07447, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29781, "top5_acc": 0.55078, "loss_cls": 4.05312, "loss": 4.05312, "time": 0.8282} +{"mode": "train", "epoch": 51, "iter": 2300, "lr": 0.07444, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28953, "top5_acc": 0.54078, "loss_cls": 4.09871, "loss": 4.09871, "time": 0.82472} +{"mode": "train", "epoch": 51, "iter": 2400, "lr": 0.07442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28828, "top5_acc": 0.53812, "loss_cls": 4.09893, "loss": 4.09893, "time": 0.82116} +{"mode": "train", "epoch": 51, "iter": 2500, "lr": 0.07439, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2875, "top5_acc": 0.53344, "loss_cls": 4.12464, "loss": 4.12464, "time": 0.82435} +{"mode": "train", "epoch": 51, "iter": 2600, "lr": 0.07437, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29344, "top5_acc": 0.54859, "loss_cls": 4.07465, "loss": 4.07465, "time": 0.82576} +{"mode": "train", "epoch": 51, "iter": 2700, "lr": 0.07434, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29328, "top5_acc": 0.54641, "loss_cls": 4.08314, "loss": 4.08314, "time": 0.81751} +{"mode": "train", "epoch": 51, "iter": 2800, "lr": 0.07432, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28453, "top5_acc": 0.54281, "loss_cls": 4.11429, "loss": 4.11429, "time": 0.81132} +{"mode": "train", "epoch": 51, "iter": 2900, "lr": 0.07429, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28562, "top5_acc": 0.53547, "loss_cls": 4.15742, "loss": 4.15742, "time": 0.81288} +{"mode": "train", "epoch": 51, "iter": 3000, "lr": 0.07427, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29391, "top5_acc": 0.53797, "loss_cls": 4.0984, "loss": 4.0984, "time": 0.81618} +{"mode": "train", "epoch": 51, "iter": 3100, "lr": 0.07425, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28609, "top5_acc": 0.53125, "loss_cls": 4.13469, "loss": 4.13469, "time": 0.81592} +{"mode": "train", "epoch": 51, "iter": 3200, "lr": 0.07422, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28406, "top5_acc": 0.54125, "loss_cls": 4.12791, "loss": 4.12791, "time": 0.81699} +{"mode": "train", "epoch": 51, "iter": 3300, "lr": 0.0742, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28656, "top5_acc": 0.54328, "loss_cls": 4.09445, "loss": 4.09445, "time": 0.81408} +{"mode": "train", "epoch": 51, "iter": 3400, "lr": 0.07417, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28641, "top5_acc": 0.54719, "loss_cls": 4.07831, "loss": 4.07831, "time": 0.8141} +{"mode": "train", "epoch": 51, "iter": 3500, "lr": 0.07415, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28828, "top5_acc": 0.54719, "loss_cls": 4.05296, "loss": 4.05296, "time": 0.81704} +{"mode": "train", "epoch": 51, "iter": 3600, "lr": 0.07412, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27578, "top5_acc": 0.52562, "loss_cls": 4.16366, "loss": 4.16366, "time": 0.81483} +{"mode": "train", "epoch": 51, "iter": 3700, "lr": 0.0741, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28891, "top5_acc": 0.53016, "loss_cls": 4.13221, "loss": 4.13221, "time": 0.81347} +{"mode": "val", "epoch": 51, "iter": 309, "lr": 0.07409, "top1_acc": 0.22089, "top5_acc": 0.45297, "mean_class_accuracy": 0.2207} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.07406, "memory": 15990, "data_time": 1.29195, "top1_acc": 0.29906, "top5_acc": 0.55312, "loss_cls": 4.03644, "loss": 4.03644, "time": 2.27665} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.07404, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28578, "top5_acc": 0.54875, "loss_cls": 4.07202, "loss": 4.07202, "time": 0.82635} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.07401, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29359, "top5_acc": 0.55359, "loss_cls": 4.05912, "loss": 4.05912, "time": 0.81552} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.07399, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29078, "top5_acc": 0.53312, "loss_cls": 4.12887, "loss": 4.12887, "time": 0.81766} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.07397, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.52984, "loss_cls": 4.12211, "loss": 4.12211, "time": 0.81514} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.07394, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29422, "top5_acc": 0.53906, "loss_cls": 4.0717, "loss": 4.0717, "time": 0.81516} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.07392, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.54328, "loss_cls": 4.12862, "loss": 4.12862, "time": 0.81537} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.07389, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30469, "top5_acc": 0.55484, "loss_cls": 4.02408, "loss": 4.02408, "time": 0.81033} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.07387, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29063, "top5_acc": 0.55016, "loss_cls": 4.05521, "loss": 4.05521, "time": 0.82151} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.07384, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29219, "top5_acc": 0.55141, "loss_cls": 4.06705, "loss": 4.06705, "time": 0.81759} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.07382, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28594, "top5_acc": 0.54375, "loss_cls": 4.09303, "loss": 4.09303, "time": 0.81519} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.07379, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.285, "top5_acc": 0.53906, "loss_cls": 4.11858, "loss": 4.11858, "time": 0.81927} +{"mode": "train", "epoch": 52, "iter": 1300, "lr": 0.07377, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28516, "top5_acc": 0.54297, "loss_cls": 4.11361, "loss": 4.11361, "time": 0.81679} +{"mode": "train", "epoch": 52, "iter": 1400, "lr": 0.07374, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28469, "top5_acc": 0.54203, "loss_cls": 4.08678, "loss": 4.08678, "time": 0.8193} +{"mode": "train", "epoch": 52, "iter": 1500, "lr": 0.07372, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28406, "top5_acc": 0.54312, "loss_cls": 4.102, "loss": 4.102, "time": 0.81885} +{"mode": "train", "epoch": 52, "iter": 1600, "lr": 0.0737, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29266, "top5_acc": 0.54297, "loss_cls": 4.07632, "loss": 4.07632, "time": 0.81829} +{"mode": "train", "epoch": 52, "iter": 1700, "lr": 0.07367, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28922, "top5_acc": 0.54297, "loss_cls": 4.08794, "loss": 4.08794, "time": 0.81821} +{"mode": "train", "epoch": 52, "iter": 1800, "lr": 0.07365, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30047, "top5_acc": 0.56094, "loss_cls": 4.02329, "loss": 4.02329, "time": 0.8163} +{"mode": "train", "epoch": 52, "iter": 1900, "lr": 0.07362, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28453, "top5_acc": 0.54797, "loss_cls": 4.08042, "loss": 4.08042, "time": 0.81497} +{"mode": "train", "epoch": 52, "iter": 2000, "lr": 0.0736, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29344, "top5_acc": 0.54297, "loss_cls": 4.06903, "loss": 4.06903, "time": 0.81641} +{"mode": "train", "epoch": 52, "iter": 2100, "lr": 0.07357, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.28953, "top5_acc": 0.53828, "loss_cls": 4.11307, "loss": 4.11307, "time": 0.82236} +{"mode": "train", "epoch": 52, "iter": 2200, "lr": 0.07355, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28906, "top5_acc": 0.55172, "loss_cls": 4.07226, "loss": 4.07226, "time": 0.82382} +{"mode": "train", "epoch": 52, "iter": 2300, "lr": 0.07352, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29563, "top5_acc": 0.54344, "loss_cls": 4.08398, "loss": 4.08398, "time": 0.82041} +{"mode": "train", "epoch": 52, "iter": 2400, "lr": 0.0735, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28875, "top5_acc": 0.54172, "loss_cls": 4.10817, "loss": 4.10817, "time": 0.82197} +{"mode": "train", "epoch": 52, "iter": 2500, "lr": 0.07347, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28016, "top5_acc": 0.53359, "loss_cls": 4.14561, "loss": 4.14561, "time": 0.81754} +{"mode": "train", "epoch": 52, "iter": 2600, "lr": 0.07345, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28906, "top5_acc": 0.53828, "loss_cls": 4.10749, "loss": 4.10749, "time": 0.82089} +{"mode": "train", "epoch": 52, "iter": 2700, "lr": 0.07342, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29281, "top5_acc": 0.53906, "loss_cls": 4.08735, "loss": 4.08735, "time": 0.82187} +{"mode": "train", "epoch": 52, "iter": 2800, "lr": 0.0734, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28703, "top5_acc": 0.53797, "loss_cls": 4.09158, "loss": 4.09158, "time": 0.81477} +{"mode": "train", "epoch": 52, "iter": 2900, "lr": 0.07337, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28562, "top5_acc": 0.53422, "loss_cls": 4.10793, "loss": 4.10793, "time": 0.812} +{"mode": "train", "epoch": 52, "iter": 3000, "lr": 0.07335, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28562, "top5_acc": 0.54156, "loss_cls": 4.11935, "loss": 4.11935, "time": 0.81472} +{"mode": "train", "epoch": 52, "iter": 3100, "lr": 0.07332, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28562, "top5_acc": 0.54422, "loss_cls": 4.10857, "loss": 4.10857, "time": 0.81412} +{"mode": "train", "epoch": 52, "iter": 3200, "lr": 0.0733, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29063, "top5_acc": 0.55359, "loss_cls": 4.07127, "loss": 4.07127, "time": 0.81607} +{"mode": "train", "epoch": 52, "iter": 3300, "lr": 0.07328, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29125, "top5_acc": 0.54406, "loss_cls": 4.10182, "loss": 4.10182, "time": 0.81674} +{"mode": "train", "epoch": 52, "iter": 3400, "lr": 0.07325, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28922, "top5_acc": 0.54266, "loss_cls": 4.08527, "loss": 4.08527, "time": 0.81911} +{"mode": "train", "epoch": 52, "iter": 3500, "lr": 0.07323, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28562, "top5_acc": 0.53875, "loss_cls": 4.10821, "loss": 4.10821, "time": 0.81896} +{"mode": "train", "epoch": 52, "iter": 3600, "lr": 0.0732, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28906, "top5_acc": 0.53656, "loss_cls": 4.11938, "loss": 4.11938, "time": 0.81646} +{"mode": "train", "epoch": 52, "iter": 3700, "lr": 0.07318, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.295, "top5_acc": 0.54125, "loss_cls": 4.09461, "loss": 4.09461, "time": 0.81401} +{"mode": "val", "epoch": 52, "iter": 309, "lr": 0.07317, "top1_acc": 0.21202, "top5_acc": 0.45105, "mean_class_accuracy": 0.21161} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.07314, "memory": 15990, "data_time": 1.31724, "top1_acc": 0.29453, "top5_acc": 0.55875, "loss_cls": 4.03393, "loss": 4.03393, "time": 2.3043} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.07312, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29938, "top5_acc": 0.55859, "loss_cls": 4.01955, "loss": 4.01955, "time": 0.81838} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.07309, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29344, "top5_acc": 0.54672, "loss_cls": 4.05566, "loss": 4.05566, "time": 0.81522} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.07307, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28984, "top5_acc": 0.54109, "loss_cls": 4.06638, "loss": 4.06638, "time": 0.8154} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.07304, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29281, "top5_acc": 0.54562, "loss_cls": 4.05705, "loss": 4.05705, "time": 0.81834} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.07302, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3, "top5_acc": 0.55188, "loss_cls": 4.04521, "loss": 4.04521, "time": 0.81613} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.07299, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29719, "top5_acc": 0.54359, "loss_cls": 4.05233, "loss": 4.05233, "time": 0.82074} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.07297, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28484, "top5_acc": 0.54797, "loss_cls": 4.08848, "loss": 4.08848, "time": 0.81055} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.07294, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28953, "top5_acc": 0.54625, "loss_cls": 4.09389, "loss": 4.09389, "time": 0.81887} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.07292, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.295, "top5_acc": 0.55125, "loss_cls": 4.05475, "loss": 4.05475, "time": 0.81215} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.07289, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29109, "top5_acc": 0.55203, "loss_cls": 4.05312, "loss": 4.05312, "time": 0.82255} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.07287, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29313, "top5_acc": 0.545, "loss_cls": 4.05709, "loss": 4.05709, "time": 0.82406} +{"mode": "train", "epoch": 53, "iter": 1300, "lr": 0.07284, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28438, "top5_acc": 0.54156, "loss_cls": 4.0999, "loss": 4.0999, "time": 0.82198} +{"mode": "train", "epoch": 53, "iter": 1400, "lr": 0.07282, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28734, "top5_acc": 0.54141, "loss_cls": 4.10166, "loss": 4.10166, "time": 0.82172} +{"mode": "train", "epoch": 53, "iter": 1500, "lr": 0.07279, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27609, "top5_acc": 0.54172, "loss_cls": 4.12065, "loss": 4.12065, "time": 0.81757} +{"mode": "train", "epoch": 53, "iter": 1600, "lr": 0.07277, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28203, "top5_acc": 0.535, "loss_cls": 4.12774, "loss": 4.12774, "time": 0.81545} +{"mode": "train", "epoch": 53, "iter": 1700, "lr": 0.07274, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29938, "top5_acc": 0.55437, "loss_cls": 4.04517, "loss": 4.04517, "time": 0.8167} +{"mode": "train", "epoch": 53, "iter": 1800, "lr": 0.07272, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28766, "top5_acc": 0.54562, "loss_cls": 4.07258, "loss": 4.07258, "time": 0.8188} +{"mode": "train", "epoch": 53, "iter": 1900, "lr": 0.07269, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29578, "top5_acc": 0.545, "loss_cls": 4.06525, "loss": 4.06525, "time": 0.81859} +{"mode": "train", "epoch": 53, "iter": 2000, "lr": 0.07267, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29063, "top5_acc": 0.54797, "loss_cls": 4.08506, "loss": 4.08506, "time": 0.81778} +{"mode": "train", "epoch": 53, "iter": 2100, "lr": 0.07264, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29812, "top5_acc": 0.55141, "loss_cls": 4.06071, "loss": 4.06071, "time": 0.82814} +{"mode": "train", "epoch": 53, "iter": 2200, "lr": 0.07262, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28719, "top5_acc": 0.53656, "loss_cls": 4.10868, "loss": 4.10868, "time": 0.81656} +{"mode": "train", "epoch": 53, "iter": 2300, "lr": 0.07259, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29359, "top5_acc": 0.54594, "loss_cls": 4.07951, "loss": 4.07951, "time": 0.81956} +{"mode": "train", "epoch": 53, "iter": 2400, "lr": 0.07257, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29563, "top5_acc": 0.55734, "loss_cls": 4.04696, "loss": 4.04696, "time": 0.82629} +{"mode": "train", "epoch": 53, "iter": 2500, "lr": 0.07254, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29391, "top5_acc": 0.54609, "loss_cls": 4.0869, "loss": 4.0869, "time": 0.81743} +{"mode": "train", "epoch": 53, "iter": 2600, "lr": 0.07252, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28891, "top5_acc": 0.54062, "loss_cls": 4.08476, "loss": 4.08476, "time": 0.82138} +{"mode": "train", "epoch": 53, "iter": 2700, "lr": 0.07249, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28297, "top5_acc": 0.54062, "loss_cls": 4.10625, "loss": 4.10625, "time": 0.81834} +{"mode": "train", "epoch": 53, "iter": 2800, "lr": 0.07247, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27609, "top5_acc": 0.53375, "loss_cls": 4.13743, "loss": 4.13743, "time": 0.81766} +{"mode": "train", "epoch": 53, "iter": 2900, "lr": 0.07244, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29672, "top5_acc": 0.53719, "loss_cls": 4.0805, "loss": 4.0805, "time": 0.81481} +{"mode": "train", "epoch": 53, "iter": 3000, "lr": 0.07242, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29281, "top5_acc": 0.55109, "loss_cls": 4.03962, "loss": 4.03962, "time": 0.81576} +{"mode": "train", "epoch": 53, "iter": 3100, "lr": 0.07239, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27641, "top5_acc": 0.53031, "loss_cls": 4.16025, "loss": 4.16025, "time": 0.81525} +{"mode": "train", "epoch": 53, "iter": 3200, "lr": 0.07237, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29047, "top5_acc": 0.53734, "loss_cls": 4.08881, "loss": 4.08881, "time": 0.8171} +{"mode": "train", "epoch": 53, "iter": 3300, "lr": 0.07234, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29859, "top5_acc": 0.53766, "loss_cls": 4.06558, "loss": 4.06558, "time": 0.81687} +{"mode": "train", "epoch": 53, "iter": 3400, "lr": 0.07232, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29125, "top5_acc": 0.53719, "loss_cls": 4.0847, "loss": 4.0847, "time": 0.81562} +{"mode": "train", "epoch": 53, "iter": 3500, "lr": 0.07229, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28391, "top5_acc": 0.53531, "loss_cls": 4.11809, "loss": 4.11809, "time": 0.81409} +{"mode": "train", "epoch": 53, "iter": 3600, "lr": 0.07227, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28297, "top5_acc": 0.54703, "loss_cls": 4.09278, "loss": 4.09278, "time": 0.8157} +{"mode": "train", "epoch": 53, "iter": 3700, "lr": 0.07224, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28219, "top5_acc": 0.54828, "loss_cls": 4.09289, "loss": 4.09289, "time": 0.81731} +{"mode": "val", "epoch": 53, "iter": 309, "lr": 0.07223, "top1_acc": 0.21289, "top5_acc": 0.44639, "mean_class_accuracy": 0.21276} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.07221, "memory": 15990, "data_time": 1.29961, "top1_acc": 0.305, "top5_acc": 0.5575, "loss_cls": 4.00208, "loss": 4.00208, "time": 2.28297} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.07218, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29656, "top5_acc": 0.55219, "loss_cls": 4.06072, "loss": 4.06072, "time": 0.81973} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.07216, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29422, "top5_acc": 0.54562, "loss_cls": 4.07164, "loss": 4.07164, "time": 0.82025} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.07213, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29703, "top5_acc": 0.55156, "loss_cls": 4.05447, "loss": 4.05447, "time": 0.81948} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.07211, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29016, "top5_acc": 0.5475, "loss_cls": 4.04409, "loss": 4.04409, "time": 0.81529} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.07208, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2975, "top5_acc": 0.54719, "loss_cls": 4.04527, "loss": 4.04527, "time": 0.81819} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.07206, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29156, "top5_acc": 0.54906, "loss_cls": 4.06199, "loss": 4.06199, "time": 0.81691} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.07203, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29734, "top5_acc": 0.5475, "loss_cls": 4.06434, "loss": 4.06434, "time": 0.81615} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.07201, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28234, "top5_acc": 0.54719, "loss_cls": 4.09458, "loss": 4.09458, "time": 0.81836} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.07198, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29266, "top5_acc": 0.54891, "loss_cls": 4.04933, "loss": 4.04933, "time": 0.81597} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.07196, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29344, "top5_acc": 0.53828, "loss_cls": 4.10977, "loss": 4.10977, "time": 0.82544} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.07193, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30391, "top5_acc": 0.54547, "loss_cls": 4.02207, "loss": 4.02207, "time": 0.81481} +{"mode": "train", "epoch": 54, "iter": 1300, "lr": 0.07191, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.285, "top5_acc": 0.54578, "loss_cls": 4.08947, "loss": 4.08947, "time": 0.81399} +{"mode": "train", "epoch": 54, "iter": 1400, "lr": 0.07188, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29672, "top5_acc": 0.54328, "loss_cls": 4.08061, "loss": 4.08061, "time": 0.82162} +{"mode": "train", "epoch": 54, "iter": 1500, "lr": 0.07186, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28297, "top5_acc": 0.53703, "loss_cls": 4.12261, "loss": 4.12261, "time": 0.82032} +{"mode": "train", "epoch": 54, "iter": 1600, "lr": 0.07183, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29875, "top5_acc": 0.54844, "loss_cls": 4.0901, "loss": 4.0901, "time": 0.81328} +{"mode": "train", "epoch": 54, "iter": 1700, "lr": 0.07181, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28438, "top5_acc": 0.54453, "loss_cls": 4.07844, "loss": 4.07844, "time": 0.82092} +{"mode": "train", "epoch": 54, "iter": 1800, "lr": 0.07178, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28703, "top5_acc": 0.54141, "loss_cls": 4.08791, "loss": 4.08791, "time": 0.81662} +{"mode": "train", "epoch": 54, "iter": 1900, "lr": 0.07176, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.295, "top5_acc": 0.55312, "loss_cls": 4.05691, "loss": 4.05691, "time": 0.8125} +{"mode": "train", "epoch": 54, "iter": 2000, "lr": 0.07173, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29094, "top5_acc": 0.53828, "loss_cls": 4.10206, "loss": 4.10206, "time": 0.81296} +{"mode": "train", "epoch": 54, "iter": 2100, "lr": 0.0717, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28797, "top5_acc": 0.54453, "loss_cls": 4.08508, "loss": 4.08508, "time": 0.82093} +{"mode": "train", "epoch": 54, "iter": 2200, "lr": 0.07168, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28719, "top5_acc": 0.54594, "loss_cls": 4.09148, "loss": 4.09148, "time": 0.8158} +{"mode": "train", "epoch": 54, "iter": 2300, "lr": 0.07165, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28328, "top5_acc": 0.54266, "loss_cls": 4.09399, "loss": 4.09399, "time": 0.81857} +{"mode": "train", "epoch": 54, "iter": 2400, "lr": 0.07163, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28656, "top5_acc": 0.54406, "loss_cls": 4.06529, "loss": 4.06529, "time": 0.82615} +{"mode": "train", "epoch": 54, "iter": 2500, "lr": 0.0716, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28719, "top5_acc": 0.54562, "loss_cls": 4.08044, "loss": 4.08044, "time": 0.81414} +{"mode": "train", "epoch": 54, "iter": 2600, "lr": 0.07158, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29906, "top5_acc": 0.54859, "loss_cls": 4.0307, "loss": 4.0307, "time": 0.82479} +{"mode": "train", "epoch": 54, "iter": 2700, "lr": 0.07155, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28781, "top5_acc": 0.53953, "loss_cls": 4.11311, "loss": 4.11311, "time": 0.81659} +{"mode": "train", "epoch": 54, "iter": 2800, "lr": 0.07153, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29172, "top5_acc": 0.54094, "loss_cls": 4.08936, "loss": 4.08936, "time": 0.81675} +{"mode": "train", "epoch": 54, "iter": 2900, "lr": 0.0715, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28859, "top5_acc": 0.54078, "loss_cls": 4.08381, "loss": 4.08381, "time": 0.81561} +{"mode": "train", "epoch": 54, "iter": 3000, "lr": 0.07148, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29969, "top5_acc": 0.54531, "loss_cls": 4.03837, "loss": 4.03837, "time": 0.81711} +{"mode": "train", "epoch": 54, "iter": 3100, "lr": 0.07145, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29406, "top5_acc": 0.54359, "loss_cls": 4.09015, "loss": 4.09015, "time": 0.81473} +{"mode": "train", "epoch": 54, "iter": 3200, "lr": 0.07143, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28828, "top5_acc": 0.54984, "loss_cls": 4.04929, "loss": 4.04929, "time": 0.8154} +{"mode": "train", "epoch": 54, "iter": 3300, "lr": 0.0714, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28094, "top5_acc": 0.53938, "loss_cls": 4.10167, "loss": 4.10167, "time": 0.81658} +{"mode": "train", "epoch": 54, "iter": 3400, "lr": 0.07138, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2925, "top5_acc": 0.54844, "loss_cls": 4.08615, "loss": 4.08615, "time": 0.81864} +{"mode": "train", "epoch": 54, "iter": 3500, "lr": 0.07135, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27797, "top5_acc": 0.53391, "loss_cls": 4.11264, "loss": 4.11264, "time": 0.8146} +{"mode": "train", "epoch": 54, "iter": 3600, "lr": 0.07133, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28562, "top5_acc": 0.53797, "loss_cls": 4.10363, "loss": 4.10363, "time": 0.82257} +{"mode": "train", "epoch": 54, "iter": 3700, "lr": 0.0713, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28953, "top5_acc": 0.54422, "loss_cls": 4.09228, "loss": 4.09228, "time": 0.81509} +{"mode": "val", "epoch": 54, "iter": 309, "lr": 0.07129, "top1_acc": 0.22337, "top5_acc": 0.46903, "mean_class_accuracy": 0.22298} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.07126, "memory": 15990, "data_time": 1.30422, "top1_acc": 0.30328, "top5_acc": 0.56266, "loss_cls": 3.9885, "loss": 3.9885, "time": 2.29257} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.07124, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29109, "top5_acc": 0.54141, "loss_cls": 4.07521, "loss": 4.07521, "time": 0.82284} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.07121, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30203, "top5_acc": 0.55922, "loss_cls": 4.01175, "loss": 4.01175, "time": 0.81653} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.07119, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28828, "top5_acc": 0.54484, "loss_cls": 4.06482, "loss": 4.06482, "time": 0.82063} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.07116, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28953, "top5_acc": 0.54234, "loss_cls": 4.06782, "loss": 4.06782, "time": 0.81743} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.07114, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29047, "top5_acc": 0.55078, "loss_cls": 4.07901, "loss": 4.07901, "time": 0.82024} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.07111, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29344, "top5_acc": 0.54453, "loss_cls": 4.05749, "loss": 4.05749, "time": 0.81332} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.07109, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28734, "top5_acc": 0.53891, "loss_cls": 4.07353, "loss": 4.07353, "time": 0.82408} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.07106, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30188, "top5_acc": 0.55094, "loss_cls": 4.02671, "loss": 4.02671, "time": 0.82472} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.07104, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28906, "top5_acc": 0.54844, "loss_cls": 4.06729, "loss": 4.06729, "time": 0.81885} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.07101, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28922, "top5_acc": 0.54781, "loss_cls": 4.06963, "loss": 4.06963, "time": 0.82459} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.07099, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30953, "top5_acc": 0.56219, "loss_cls": 3.99551, "loss": 3.99551, "time": 0.81746} +{"mode": "train", "epoch": 55, "iter": 1300, "lr": 0.07096, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28953, "top5_acc": 0.54359, "loss_cls": 4.08475, "loss": 4.08475, "time": 0.8199} +{"mode": "train", "epoch": 55, "iter": 1400, "lr": 0.07093, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30156, "top5_acc": 0.53891, "loss_cls": 4.06323, "loss": 4.06323, "time": 0.82688} +{"mode": "train", "epoch": 55, "iter": 1500, "lr": 0.07091, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29016, "top5_acc": 0.54375, "loss_cls": 4.04259, "loss": 4.04259, "time": 0.81752} +{"mode": "train", "epoch": 55, "iter": 1600, "lr": 0.07088, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29906, "top5_acc": 0.54984, "loss_cls": 4.04573, "loss": 4.04573, "time": 0.81942} +{"mode": "train", "epoch": 55, "iter": 1700, "lr": 0.07086, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28891, "top5_acc": 0.54531, "loss_cls": 4.08167, "loss": 4.08167, "time": 0.81604} +{"mode": "train", "epoch": 55, "iter": 1800, "lr": 0.07083, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28156, "top5_acc": 0.53688, "loss_cls": 4.12308, "loss": 4.12308, "time": 0.81446} +{"mode": "train", "epoch": 55, "iter": 1900, "lr": 0.07081, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2925, "top5_acc": 0.54781, "loss_cls": 4.07588, "loss": 4.07588, "time": 0.81967} +{"mode": "train", "epoch": 55, "iter": 2000, "lr": 0.07078, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28344, "top5_acc": 0.54859, "loss_cls": 4.1002, "loss": 4.1002, "time": 0.82019} +{"mode": "train", "epoch": 55, "iter": 2100, "lr": 0.07076, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29594, "top5_acc": 0.55609, "loss_cls": 4.05264, "loss": 4.05264, "time": 0.82017} +{"mode": "train", "epoch": 55, "iter": 2200, "lr": 0.07073, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27922, "top5_acc": 0.53234, "loss_cls": 4.14912, "loss": 4.14912, "time": 0.8172} +{"mode": "train", "epoch": 55, "iter": 2300, "lr": 0.07071, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29688, "top5_acc": 0.55859, "loss_cls": 4.0216, "loss": 4.0216, "time": 0.81466} +{"mode": "train", "epoch": 55, "iter": 2400, "lr": 0.07068, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29578, "top5_acc": 0.55406, "loss_cls": 4.04511, "loss": 4.04511, "time": 0.81662} +{"mode": "train", "epoch": 55, "iter": 2500, "lr": 0.07065, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28203, "top5_acc": 0.5375, "loss_cls": 4.10849, "loss": 4.10849, "time": 0.82997} +{"mode": "train", "epoch": 55, "iter": 2600, "lr": 0.07063, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29234, "top5_acc": 0.54219, "loss_cls": 4.0604, "loss": 4.0604, "time": 0.81836} +{"mode": "train", "epoch": 55, "iter": 2700, "lr": 0.0706, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29734, "top5_acc": 0.54703, "loss_cls": 4.02114, "loss": 4.02114, "time": 0.81698} +{"mode": "train", "epoch": 55, "iter": 2800, "lr": 0.07058, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29328, "top5_acc": 0.54547, "loss_cls": 4.07819, "loss": 4.07819, "time": 0.81341} +{"mode": "train", "epoch": 55, "iter": 2900, "lr": 0.07055, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29609, "top5_acc": 0.55016, "loss_cls": 4.06085, "loss": 4.06085, "time": 0.81456} +{"mode": "train", "epoch": 55, "iter": 3000, "lr": 0.07053, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29266, "top5_acc": 0.54359, "loss_cls": 4.08595, "loss": 4.08595, "time": 0.81585} +{"mode": "train", "epoch": 55, "iter": 3100, "lr": 0.0705, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28891, "top5_acc": 0.545, "loss_cls": 4.09532, "loss": 4.09532, "time": 0.81561} +{"mode": "train", "epoch": 55, "iter": 3200, "lr": 0.07048, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29344, "top5_acc": 0.54484, "loss_cls": 4.07607, "loss": 4.07607, "time": 0.81731} +{"mode": "train", "epoch": 55, "iter": 3300, "lr": 0.07045, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29875, "top5_acc": 0.55188, "loss_cls": 4.02248, "loss": 4.02248, "time": 0.8155} +{"mode": "train", "epoch": 55, "iter": 3400, "lr": 0.07043, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29672, "top5_acc": 0.54797, "loss_cls": 4.06872, "loss": 4.06872, "time": 0.81593} +{"mode": "train", "epoch": 55, "iter": 3500, "lr": 0.0704, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29625, "top5_acc": 0.54984, "loss_cls": 4.04595, "loss": 4.04595, "time": 0.82015} +{"mode": "train", "epoch": 55, "iter": 3600, "lr": 0.07037, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28875, "top5_acc": 0.54594, "loss_cls": 4.09502, "loss": 4.09502, "time": 0.81712} +{"mode": "train", "epoch": 55, "iter": 3700, "lr": 0.07035, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.29484, "top5_acc": 0.55547, "loss_cls": 4.03815, "loss": 4.03815, "time": 0.81401} +{"mode": "val", "epoch": 55, "iter": 309, "lr": 0.07034, "top1_acc": 0.23071, "top5_acc": 0.46832, "mean_class_accuracy": 0.23074} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.07031, "memory": 15990, "data_time": 1.29408, "top1_acc": 0.29047, "top5_acc": 0.54438, "loss_cls": 4.07969, "loss": 4.07969, "time": 2.28043} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.07029, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29109, "top5_acc": 0.56063, "loss_cls": 4.02918, "loss": 4.02918, "time": 0.81885} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.07026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29875, "top5_acc": 0.55078, "loss_cls": 4.0305, "loss": 4.0305, "time": 0.81933} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.07023, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29281, "top5_acc": 0.55578, "loss_cls": 4.05039, "loss": 4.05039, "time": 0.81557} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.07021, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29141, "top5_acc": 0.55234, "loss_cls": 4.04451, "loss": 4.04451, "time": 0.81238} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.07018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29984, "top5_acc": 0.54562, "loss_cls": 4.05189, "loss": 4.05189, "time": 0.81323} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.07016, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28719, "top5_acc": 0.55109, "loss_cls": 4.0349, "loss": 4.0349, "time": 0.81961} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.07013, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30156, "top5_acc": 0.55719, "loss_cls": 4.01277, "loss": 4.01277, "time": 0.81606} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.07011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2925, "top5_acc": 0.54719, "loss_cls": 4.05238, "loss": 4.05238, "time": 0.81878} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.07008, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30094, "top5_acc": 0.55484, "loss_cls": 4.03123, "loss": 4.03123, "time": 0.81685} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.07006, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29781, "top5_acc": 0.54422, "loss_cls": 4.07597, "loss": 4.07597, "time": 0.82281} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.07003, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29703, "top5_acc": 0.55734, "loss_cls": 4.04979, "loss": 4.04979, "time": 0.81789} +{"mode": "train", "epoch": 56, "iter": 1300, "lr": 0.07, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30594, "top5_acc": 0.55594, "loss_cls": 4.00033, "loss": 4.00033, "time": 0.82712} +{"mode": "train", "epoch": 56, "iter": 1400, "lr": 0.06998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28656, "top5_acc": 0.54969, "loss_cls": 4.06209, "loss": 4.06209, "time": 0.82232} +{"mode": "train", "epoch": 56, "iter": 1500, "lr": 0.06995, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3, "top5_acc": 0.56391, "loss_cls": 3.99024, "loss": 3.99024, "time": 0.82596} +{"mode": "train", "epoch": 56, "iter": 1600, "lr": 0.06993, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29063, "top5_acc": 0.53984, "loss_cls": 4.08395, "loss": 4.08395, "time": 0.8164} +{"mode": "train", "epoch": 56, "iter": 1700, "lr": 0.0699, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29641, "top5_acc": 0.54906, "loss_cls": 4.07199, "loss": 4.07199, "time": 0.80957} +{"mode": "train", "epoch": 56, "iter": 1800, "lr": 0.06988, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28922, "top5_acc": 0.5475, "loss_cls": 4.06476, "loss": 4.06476, "time": 0.82046} +{"mode": "train", "epoch": 56, "iter": 1900, "lr": 0.06985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29719, "top5_acc": 0.55344, "loss_cls": 4.04589, "loss": 4.04589, "time": 0.81021} +{"mode": "train", "epoch": 56, "iter": 2000, "lr": 0.06983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30969, "top5_acc": 0.55172, "loss_cls": 4.03868, "loss": 4.03868, "time": 0.82243} +{"mode": "train", "epoch": 56, "iter": 2100, "lr": 0.0698, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29906, "top5_acc": 0.55062, "loss_cls": 4.08671, "loss": 4.08671, "time": 0.81534} +{"mode": "train", "epoch": 56, "iter": 2200, "lr": 0.06977, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30328, "top5_acc": 0.55719, "loss_cls": 4.01052, "loss": 4.01052, "time": 0.81856} +{"mode": "train", "epoch": 56, "iter": 2300, "lr": 0.06975, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29734, "top5_acc": 0.55688, "loss_cls": 4.03062, "loss": 4.03062, "time": 0.81762} +{"mode": "train", "epoch": 56, "iter": 2400, "lr": 0.06972, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28672, "top5_acc": 0.54062, "loss_cls": 4.10676, "loss": 4.10676, "time": 0.82456} +{"mode": "train", "epoch": 56, "iter": 2500, "lr": 0.0697, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29281, "top5_acc": 0.54125, "loss_cls": 4.09115, "loss": 4.09115, "time": 0.81805} +{"mode": "train", "epoch": 56, "iter": 2600, "lr": 0.06967, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29984, "top5_acc": 0.54797, "loss_cls": 4.05117, "loss": 4.05117, "time": 0.82253} +{"mode": "train", "epoch": 56, "iter": 2700, "lr": 0.06965, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29344, "top5_acc": 0.55312, "loss_cls": 4.06094, "loss": 4.06094, "time": 0.8224} +{"mode": "train", "epoch": 56, "iter": 2800, "lr": 0.06962, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30531, "top5_acc": 0.56063, "loss_cls": 3.98886, "loss": 3.98886, "time": 0.81946} +{"mode": "train", "epoch": 56, "iter": 2900, "lr": 0.06959, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29016, "top5_acc": 0.54016, "loss_cls": 4.07763, "loss": 4.07763, "time": 0.8162} +{"mode": "train", "epoch": 56, "iter": 3000, "lr": 0.06957, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29547, "top5_acc": 0.55688, "loss_cls": 4.02513, "loss": 4.02513, "time": 0.81423} +{"mode": "train", "epoch": 56, "iter": 3100, "lr": 0.06954, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28125, "top5_acc": 0.53984, "loss_cls": 4.11842, "loss": 4.11842, "time": 0.815} +{"mode": "train", "epoch": 56, "iter": 3200, "lr": 0.06952, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27953, "top5_acc": 0.53672, "loss_cls": 4.1207, "loss": 4.1207, "time": 0.81889} +{"mode": "train", "epoch": 56, "iter": 3300, "lr": 0.06949, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29531, "top5_acc": 0.55312, "loss_cls": 4.04304, "loss": 4.04304, "time": 0.81546} +{"mode": "train", "epoch": 56, "iter": 3400, "lr": 0.06947, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29516, "top5_acc": 0.54906, "loss_cls": 4.05701, "loss": 4.05701, "time": 0.80882} +{"mode": "train", "epoch": 56, "iter": 3500, "lr": 0.06944, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28359, "top5_acc": 0.54734, "loss_cls": 4.08402, "loss": 4.08402, "time": 0.8156} +{"mode": "train", "epoch": 56, "iter": 3600, "lr": 0.06941, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29547, "top5_acc": 0.54812, "loss_cls": 4.05979, "loss": 4.05979, "time": 0.81335} +{"mode": "train", "epoch": 56, "iter": 3700, "lr": 0.06939, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28469, "top5_acc": 0.54328, "loss_cls": 4.09629, "loss": 4.09629, "time": 0.81251} +{"mode": "val", "epoch": 56, "iter": 309, "lr": 0.06938, "top1_acc": 0.23421, "top5_acc": 0.4707, "mean_class_accuracy": 0.2339} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.06935, "memory": 15990, "data_time": 1.30681, "top1_acc": 0.30484, "top5_acc": 0.56094, "loss_cls": 4.00567, "loss": 4.00567, "time": 2.30436} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.06932, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31062, "top5_acc": 0.56031, "loss_cls": 3.9682, "loss": 3.9682, "time": 0.8201} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.0693, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30797, "top5_acc": 0.55922, "loss_cls": 4.0056, "loss": 4.0056, "time": 0.81968} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.06927, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3, "top5_acc": 0.55281, "loss_cls": 4.02599, "loss": 4.02599, "time": 0.81614} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.06925, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30016, "top5_acc": 0.55469, "loss_cls": 4.02932, "loss": 4.02932, "time": 0.81638} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.06922, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.295, "top5_acc": 0.53922, "loss_cls": 4.08467, "loss": 4.08467, "time": 0.82797} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.0692, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29469, "top5_acc": 0.54859, "loss_cls": 4.05481, "loss": 4.05481, "time": 0.81619} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.06917, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29266, "top5_acc": 0.54422, "loss_cls": 4.06503, "loss": 4.06503, "time": 0.8183} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.06914, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28719, "top5_acc": 0.54438, "loss_cls": 4.07792, "loss": 4.07792, "time": 0.82599} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.06912, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29047, "top5_acc": 0.53969, "loss_cls": 4.09009, "loss": 4.09009, "time": 0.8212} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.06909, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.285, "top5_acc": 0.54625, "loss_cls": 4.08757, "loss": 4.08757, "time": 0.8205} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.06907, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29266, "top5_acc": 0.55078, "loss_cls": 4.07295, "loss": 4.07295, "time": 0.81619} +{"mode": "train", "epoch": 57, "iter": 1300, "lr": 0.06904, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29766, "top5_acc": 0.55406, "loss_cls": 4.01956, "loss": 4.01956, "time": 0.82812} +{"mode": "train", "epoch": 57, "iter": 1400, "lr": 0.06901, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3025, "top5_acc": 0.55312, "loss_cls": 4.03084, "loss": 4.03084, "time": 0.81583} +{"mode": "train", "epoch": 57, "iter": 1500, "lr": 0.06899, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28781, "top5_acc": 0.54672, "loss_cls": 4.08415, "loss": 4.08415, "time": 0.8189} +{"mode": "train", "epoch": 57, "iter": 1600, "lr": 0.06896, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29641, "top5_acc": 0.55047, "loss_cls": 4.02658, "loss": 4.02658, "time": 0.81777} +{"mode": "train", "epoch": 57, "iter": 1700, "lr": 0.06894, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30406, "top5_acc": 0.55656, "loss_cls": 4.00813, "loss": 4.00813, "time": 0.81619} +{"mode": "train", "epoch": 57, "iter": 1800, "lr": 0.06891, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28719, "top5_acc": 0.54984, "loss_cls": 4.07661, "loss": 4.07661, "time": 0.82131} +{"mode": "train", "epoch": 57, "iter": 1900, "lr": 0.06889, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30094, "top5_acc": 0.55656, "loss_cls": 4.01058, "loss": 4.01058, "time": 0.81699} +{"mode": "train", "epoch": 57, "iter": 2000, "lr": 0.06886, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2925, "top5_acc": 0.55047, "loss_cls": 4.04641, "loss": 4.04641, "time": 0.81484} +{"mode": "train", "epoch": 57, "iter": 2100, "lr": 0.06883, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29188, "top5_acc": 0.55734, "loss_cls": 4.04008, "loss": 4.04008, "time": 0.81686} +{"mode": "train", "epoch": 57, "iter": 2200, "lr": 0.06881, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28844, "top5_acc": 0.54781, "loss_cls": 4.06945, "loss": 4.06945, "time": 0.81922} +{"mode": "train", "epoch": 57, "iter": 2300, "lr": 0.06878, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30156, "top5_acc": 0.55656, "loss_cls": 4.01356, "loss": 4.01356, "time": 0.82142} +{"mode": "train", "epoch": 57, "iter": 2400, "lr": 0.06876, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29531, "top5_acc": 0.55, "loss_cls": 4.03685, "loss": 4.03685, "time": 0.82434} +{"mode": "train", "epoch": 57, "iter": 2500, "lr": 0.06873, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29906, "top5_acc": 0.5475, "loss_cls": 4.05841, "loss": 4.05841, "time": 0.81977} +{"mode": "train", "epoch": 57, "iter": 2600, "lr": 0.0687, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29734, "top5_acc": 0.55203, "loss_cls": 4.03305, "loss": 4.03305, "time": 0.81961} +{"mode": "train", "epoch": 57, "iter": 2700, "lr": 0.06868, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30109, "top5_acc": 0.55406, "loss_cls": 4.03217, "loss": 4.03217, "time": 0.81531} +{"mode": "train", "epoch": 57, "iter": 2800, "lr": 0.06865, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29016, "top5_acc": 0.55359, "loss_cls": 4.06585, "loss": 4.06585, "time": 0.82011} +{"mode": "train", "epoch": 57, "iter": 2900, "lr": 0.06863, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29234, "top5_acc": 0.54141, "loss_cls": 4.10297, "loss": 4.10297, "time": 0.81656} +{"mode": "train", "epoch": 57, "iter": 3000, "lr": 0.0686, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29047, "top5_acc": 0.54609, "loss_cls": 4.07068, "loss": 4.07068, "time": 0.81479} +{"mode": "train", "epoch": 57, "iter": 3100, "lr": 0.06857, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29609, "top5_acc": 0.55, "loss_cls": 4.07922, "loss": 4.07922, "time": 0.8136} +{"mode": "train", "epoch": 57, "iter": 3200, "lr": 0.06855, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30125, "top5_acc": 0.55516, "loss_cls": 4.02019, "loss": 4.02019, "time": 0.81609} +{"mode": "train", "epoch": 57, "iter": 3300, "lr": 0.06852, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29688, "top5_acc": 0.54547, "loss_cls": 4.05025, "loss": 4.05025, "time": 0.81541} +{"mode": "train", "epoch": 57, "iter": 3400, "lr": 0.0685, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29422, "top5_acc": 0.55953, "loss_cls": 4.02593, "loss": 4.02593, "time": 0.82161} +{"mode": "train", "epoch": 57, "iter": 3500, "lr": 0.06847, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29203, "top5_acc": 0.54734, "loss_cls": 4.06954, "loss": 4.06954, "time": 0.82227} +{"mode": "train", "epoch": 57, "iter": 3600, "lr": 0.06844, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29422, "top5_acc": 0.54516, "loss_cls": 4.08175, "loss": 4.08175, "time": 0.81536} +{"mode": "train", "epoch": 57, "iter": 3700, "lr": 0.06842, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29313, "top5_acc": 0.55156, "loss_cls": 4.04197, "loss": 4.04197, "time": 0.81628} +{"mode": "val", "epoch": 57, "iter": 309, "lr": 0.06841, "top1_acc": 0.23573, "top5_acc": 0.48083, "mean_class_accuracy": 0.23528} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.06838, "memory": 15990, "data_time": 1.28396, "top1_acc": 0.30297, "top5_acc": 0.54812, "loss_cls": 4.02068, "loss": 4.02068, "time": 2.26387} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.06835, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30406, "top5_acc": 0.56156, "loss_cls": 3.99399, "loss": 3.99399, "time": 0.81468} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.06833, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3025, "top5_acc": 0.55891, "loss_cls": 3.99866, "loss": 3.99866, "time": 0.81951} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.0683, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29906, "top5_acc": 0.55281, "loss_cls": 4.047, "loss": 4.047, "time": 0.81722} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.06828, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30875, "top5_acc": 0.55969, "loss_cls": 3.97718, "loss": 3.97718, "time": 0.81595} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.06825, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30656, "top5_acc": 0.56188, "loss_cls": 3.97554, "loss": 3.97554, "time": 0.81245} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.06822, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30297, "top5_acc": 0.56, "loss_cls": 3.98779, "loss": 3.98779, "time": 0.81472} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.0682, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29641, "top5_acc": 0.55766, "loss_cls": 3.99678, "loss": 3.99678, "time": 0.81685} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.06817, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30156, "top5_acc": 0.55047, "loss_cls": 4.04229, "loss": 4.04229, "time": 0.82057} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.06815, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30047, "top5_acc": 0.56172, "loss_cls": 3.99377, "loss": 3.99377, "time": 0.82386} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.06812, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29859, "top5_acc": 0.55484, "loss_cls": 4.04939, "loss": 4.04939, "time": 0.8224} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.06809, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29016, "top5_acc": 0.54641, "loss_cls": 4.09837, "loss": 4.09837, "time": 0.81876} +{"mode": "train", "epoch": 58, "iter": 1300, "lr": 0.06807, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30047, "top5_acc": 0.54859, "loss_cls": 4.04884, "loss": 4.04884, "time": 0.82624} +{"mode": "train", "epoch": 58, "iter": 1400, "lr": 0.06804, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29266, "top5_acc": 0.5525, "loss_cls": 4.02649, "loss": 4.02649, "time": 0.82098} +{"mode": "train", "epoch": 58, "iter": 1500, "lr": 0.06802, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29563, "top5_acc": 0.55547, "loss_cls": 4.03399, "loss": 4.03399, "time": 0.81896} +{"mode": "train", "epoch": 58, "iter": 1600, "lr": 0.06799, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29953, "top5_acc": 0.54688, "loss_cls": 4.03374, "loss": 4.03374, "time": 0.8144} +{"mode": "train", "epoch": 58, "iter": 1700, "lr": 0.06796, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30172, "top5_acc": 0.55422, "loss_cls": 3.99709, "loss": 3.99709, "time": 0.81457} +{"mode": "train", "epoch": 58, "iter": 1800, "lr": 0.06794, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29703, "top5_acc": 0.55359, "loss_cls": 4.00872, "loss": 4.00872, "time": 0.81444} +{"mode": "train", "epoch": 58, "iter": 1900, "lr": 0.06791, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29313, "top5_acc": 0.53969, "loss_cls": 4.05089, "loss": 4.05089, "time": 0.81711} +{"mode": "train", "epoch": 58, "iter": 2000, "lr": 0.06789, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29766, "top5_acc": 0.54672, "loss_cls": 4.04905, "loss": 4.04905, "time": 0.81949} +{"mode": "train", "epoch": 58, "iter": 2100, "lr": 0.06786, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29672, "top5_acc": 0.55297, "loss_cls": 4.04613, "loss": 4.04613, "time": 0.81747} +{"mode": "train", "epoch": 58, "iter": 2200, "lr": 0.06783, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3075, "top5_acc": 0.56, "loss_cls": 3.97197, "loss": 3.97197, "time": 0.82527} +{"mode": "train", "epoch": 58, "iter": 2300, "lr": 0.06781, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30219, "top5_acc": 0.55234, "loss_cls": 4.01991, "loss": 4.01991, "time": 0.81451} +{"mode": "train", "epoch": 58, "iter": 2400, "lr": 0.06778, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29469, "top5_acc": 0.55734, "loss_cls": 4.02534, "loss": 4.02534, "time": 0.82224} +{"mode": "train", "epoch": 58, "iter": 2500, "lr": 0.06775, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29406, "top5_acc": 0.55094, "loss_cls": 4.0643, "loss": 4.0643, "time": 0.82409} +{"mode": "train", "epoch": 58, "iter": 2600, "lr": 0.06773, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29531, "top5_acc": 0.55031, "loss_cls": 4.07967, "loss": 4.07967, "time": 0.81463} +{"mode": "train", "epoch": 58, "iter": 2700, "lr": 0.0677, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28938, "top5_acc": 0.54797, "loss_cls": 4.07004, "loss": 4.07004, "time": 0.81272} +{"mode": "train", "epoch": 58, "iter": 2800, "lr": 0.06768, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29109, "top5_acc": 0.55156, "loss_cls": 4.04914, "loss": 4.04914, "time": 0.8141} +{"mode": "train", "epoch": 58, "iter": 2900, "lr": 0.06765, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29938, "top5_acc": 0.53859, "loss_cls": 4.10752, "loss": 4.10752, "time": 0.81752} +{"mode": "train", "epoch": 58, "iter": 3000, "lr": 0.06762, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30141, "top5_acc": 0.55531, "loss_cls": 4.02057, "loss": 4.02057, "time": 0.81642} +{"mode": "train", "epoch": 58, "iter": 3100, "lr": 0.0676, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28984, "top5_acc": 0.54391, "loss_cls": 4.05878, "loss": 4.05878, "time": 0.8131} +{"mode": "train", "epoch": 58, "iter": 3200, "lr": 0.06757, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28891, "top5_acc": 0.53375, "loss_cls": 4.09956, "loss": 4.09956, "time": 0.82261} +{"mode": "train", "epoch": 58, "iter": 3300, "lr": 0.06755, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29594, "top5_acc": 0.54125, "loss_cls": 4.1012, "loss": 4.1012, "time": 0.8178} +{"mode": "train", "epoch": 58, "iter": 3400, "lr": 0.06752, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28531, "top5_acc": 0.5425, "loss_cls": 4.05687, "loss": 4.05687, "time": 0.81517} +{"mode": "train", "epoch": 58, "iter": 3500, "lr": 0.06749, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28641, "top5_acc": 0.54031, "loss_cls": 4.11318, "loss": 4.11318, "time": 0.81583} +{"mode": "train", "epoch": 58, "iter": 3600, "lr": 0.06747, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29125, "top5_acc": 0.55469, "loss_cls": 4.04642, "loss": 4.04642, "time": 0.81713} +{"mode": "train", "epoch": 58, "iter": 3700, "lr": 0.06744, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28781, "top5_acc": 0.54656, "loss_cls": 4.10086, "loss": 4.10086, "time": 0.82241} +{"mode": "val", "epoch": 58, "iter": 309, "lr": 0.06743, "top1_acc": 0.23345, "top5_acc": 0.46969, "mean_class_accuracy": 0.23302} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.0674, "memory": 15990, "data_time": 1.3088, "top1_acc": 0.30438, "top5_acc": 0.555, "loss_cls": 4.00426, "loss": 4.00426, "time": 2.28957} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.06738, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29891, "top5_acc": 0.55688, "loss_cls": 4.01629, "loss": 4.01629, "time": 0.82274} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.06735, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29328, "top5_acc": 0.55125, "loss_cls": 4.05117, "loss": 4.05117, "time": 0.81813} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.06732, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30281, "top5_acc": 0.55828, "loss_cls": 3.97246, "loss": 3.97246, "time": 0.82593} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.0673, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30141, "top5_acc": 0.55688, "loss_cls": 3.99207, "loss": 3.99207, "time": 0.81923} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.06727, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29969, "top5_acc": 0.55328, "loss_cls": 4.0144, "loss": 4.0144, "time": 0.8188} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.06725, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28125, "top5_acc": 0.54656, "loss_cls": 4.10774, "loss": 4.10774, "time": 0.81523} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.06722, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29906, "top5_acc": 0.55469, "loss_cls": 4.02622, "loss": 4.02622, "time": 0.8251} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.06719, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30359, "top5_acc": 0.54844, "loss_cls": 4.0152, "loss": 4.0152, "time": 0.81703} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.06717, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.295, "top5_acc": 0.54328, "loss_cls": 4.07033, "loss": 4.07033, "time": 0.82013} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.06714, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29984, "top5_acc": 0.56, "loss_cls": 4.02227, "loss": 4.02227, "time": 0.8234} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.06711, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30453, "top5_acc": 0.56141, "loss_cls": 3.99832, "loss": 3.99832, "time": 0.81654} +{"mode": "train", "epoch": 59, "iter": 1300, "lr": 0.06709, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29531, "top5_acc": 0.55266, "loss_cls": 4.02102, "loss": 4.02102, "time": 0.81688} +{"mode": "train", "epoch": 59, "iter": 1400, "lr": 0.06706, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29375, "top5_acc": 0.55094, "loss_cls": 4.05769, "loss": 4.05769, "time": 0.82146} +{"mode": "train", "epoch": 59, "iter": 1500, "lr": 0.06704, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30594, "top5_acc": 0.56016, "loss_cls": 4.01166, "loss": 4.01166, "time": 0.81724} +{"mode": "train", "epoch": 59, "iter": 1600, "lr": 0.06701, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28625, "top5_acc": 0.54797, "loss_cls": 4.08517, "loss": 4.08517, "time": 0.81822} +{"mode": "train", "epoch": 59, "iter": 1700, "lr": 0.06698, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29766, "top5_acc": 0.54625, "loss_cls": 4.0427, "loss": 4.0427, "time": 0.81252} +{"mode": "train", "epoch": 59, "iter": 1800, "lr": 0.06696, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30359, "top5_acc": 0.55797, "loss_cls": 4.0143, "loss": 4.0143, "time": 0.81822} +{"mode": "train", "epoch": 59, "iter": 1900, "lr": 0.06693, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30766, "top5_acc": 0.56281, "loss_cls": 3.99457, "loss": 3.99457, "time": 0.81416} +{"mode": "train", "epoch": 59, "iter": 2000, "lr": 0.0669, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30219, "top5_acc": 0.56016, "loss_cls": 3.98243, "loss": 3.98243, "time": 0.81582} +{"mode": "train", "epoch": 59, "iter": 2100, "lr": 0.06688, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29266, "top5_acc": 0.55688, "loss_cls": 4.02663, "loss": 4.02663, "time": 0.81489} +{"mode": "train", "epoch": 59, "iter": 2200, "lr": 0.06685, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29219, "top5_acc": 0.53938, "loss_cls": 4.09169, "loss": 4.09169, "time": 0.82792} +{"mode": "train", "epoch": 59, "iter": 2300, "lr": 0.06682, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29984, "top5_acc": 0.54922, "loss_cls": 4.04412, "loss": 4.04412, "time": 0.81645} +{"mode": "train", "epoch": 59, "iter": 2400, "lr": 0.0668, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30094, "top5_acc": 0.555, "loss_cls": 4.04529, "loss": 4.04529, "time": 0.82061} +{"mode": "train", "epoch": 59, "iter": 2500, "lr": 0.06677, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29625, "top5_acc": 0.55547, "loss_cls": 4.01014, "loss": 4.01014, "time": 0.82457} +{"mode": "train", "epoch": 59, "iter": 2600, "lr": 0.06675, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28875, "top5_acc": 0.55672, "loss_cls": 4.03053, "loss": 4.03053, "time": 0.82045} +{"mode": "train", "epoch": 59, "iter": 2700, "lr": 0.06672, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30016, "top5_acc": 0.55469, "loss_cls": 3.99866, "loss": 3.99866, "time": 0.8154} +{"mode": "train", "epoch": 59, "iter": 2800, "lr": 0.06669, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29656, "top5_acc": 0.55703, "loss_cls": 4.04623, "loss": 4.04623, "time": 0.81369} +{"mode": "train", "epoch": 59, "iter": 2900, "lr": 0.06667, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28813, "top5_acc": 0.53891, "loss_cls": 4.08605, "loss": 4.08605, "time": 0.81668} +{"mode": "train", "epoch": 59, "iter": 3000, "lr": 0.06664, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29563, "top5_acc": 0.54453, "loss_cls": 4.0559, "loss": 4.0559, "time": 0.81835} +{"mode": "train", "epoch": 59, "iter": 3100, "lr": 0.06661, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.295, "top5_acc": 0.54703, "loss_cls": 4.02121, "loss": 4.02121, "time": 0.81316} +{"mode": "train", "epoch": 59, "iter": 3200, "lr": 0.06659, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29563, "top5_acc": 0.55391, "loss_cls": 4.05524, "loss": 4.05524, "time": 0.82055} +{"mode": "train", "epoch": 59, "iter": 3300, "lr": 0.06656, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29922, "top5_acc": 0.55312, "loss_cls": 4.00835, "loss": 4.00835, "time": 0.81746} +{"mode": "train", "epoch": 59, "iter": 3400, "lr": 0.06653, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30516, "top5_acc": 0.55516, "loss_cls": 4.04127, "loss": 4.04127, "time": 0.8167} +{"mode": "train", "epoch": 59, "iter": 3500, "lr": 0.06651, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30203, "top5_acc": 0.56203, "loss_cls": 3.99073, "loss": 3.99073, "time": 0.81826} +{"mode": "train", "epoch": 59, "iter": 3600, "lr": 0.06648, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28531, "top5_acc": 0.54594, "loss_cls": 4.06415, "loss": 4.06415, "time": 0.81745} +{"mode": "train", "epoch": 59, "iter": 3700, "lr": 0.06646, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30016, "top5_acc": 0.55469, "loss_cls": 4.01797, "loss": 4.01797, "time": 0.81434} +{"mode": "val", "epoch": 59, "iter": 309, "lr": 0.06644, "top1_acc": 0.22489, "top5_acc": 0.46893, "mean_class_accuracy": 0.22462} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.06642, "memory": 15990, "data_time": 1.2971, "top1_acc": 0.30641, "top5_acc": 0.56953, "loss_cls": 3.95503, "loss": 3.95503, "time": 2.28587} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.06639, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30578, "top5_acc": 0.56312, "loss_cls": 3.96567, "loss": 3.96567, "time": 0.81886} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.06636, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3, "top5_acc": 0.56219, "loss_cls": 4.0046, "loss": 4.0046, "time": 0.82263} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.06634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31375, "top5_acc": 0.57328, "loss_cls": 3.9406, "loss": 3.9406, "time": 0.81949} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.06631, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30188, "top5_acc": 0.55781, "loss_cls": 3.98174, "loss": 3.98174, "time": 0.81582} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.06629, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29422, "top5_acc": 0.56063, "loss_cls": 4.03446, "loss": 4.03446, "time": 0.81656} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.06626, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.3075, "top5_acc": 0.56453, "loss_cls": 3.97411, "loss": 3.97411, "time": 0.81578} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.06623, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30031, "top5_acc": 0.55391, "loss_cls": 4.01433, "loss": 4.01433, "time": 0.82376} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.06621, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29141, "top5_acc": 0.55172, "loss_cls": 4.046, "loss": 4.046, "time": 0.81547} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.06618, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30375, "top5_acc": 0.56078, "loss_cls": 3.99017, "loss": 3.99017, "time": 0.82097} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.06615, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29906, "top5_acc": 0.56, "loss_cls": 4.03764, "loss": 4.03764, "time": 0.81773} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.06613, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30188, "top5_acc": 0.54703, "loss_cls": 4.03566, "loss": 4.03566, "time": 0.81748} +{"mode": "train", "epoch": 60, "iter": 1300, "lr": 0.0661, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30734, "top5_acc": 0.55812, "loss_cls": 4.00303, "loss": 4.00303, "time": 0.82144} +{"mode": "train", "epoch": 60, "iter": 1400, "lr": 0.06607, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29578, "top5_acc": 0.55125, "loss_cls": 4.04725, "loss": 4.04725, "time": 0.81475} +{"mode": "train", "epoch": 60, "iter": 1500, "lr": 0.06605, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30531, "top5_acc": 0.55469, "loss_cls": 4.03454, "loss": 4.03454, "time": 0.82044} +{"mode": "train", "epoch": 60, "iter": 1600, "lr": 0.06602, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30359, "top5_acc": 0.54891, "loss_cls": 4.02741, "loss": 4.02741, "time": 0.81401} +{"mode": "train", "epoch": 60, "iter": 1700, "lr": 0.06599, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29203, "top5_acc": 0.55219, "loss_cls": 4.03736, "loss": 4.03736, "time": 0.82074} +{"mode": "train", "epoch": 60, "iter": 1800, "lr": 0.06597, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28938, "top5_acc": 0.54734, "loss_cls": 4.04813, "loss": 4.04813, "time": 0.81464} +{"mode": "train", "epoch": 60, "iter": 1900, "lr": 0.06594, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29531, "top5_acc": 0.55062, "loss_cls": 4.03628, "loss": 4.03628, "time": 0.81488} +{"mode": "train", "epoch": 60, "iter": 2000, "lr": 0.06591, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30062, "top5_acc": 0.54953, "loss_cls": 4.04403, "loss": 4.04403, "time": 0.81505} +{"mode": "train", "epoch": 60, "iter": 2100, "lr": 0.06589, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29516, "top5_acc": 0.56203, "loss_cls": 4.02296, "loss": 4.02296, "time": 0.82065} +{"mode": "train", "epoch": 60, "iter": 2200, "lr": 0.06586, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29953, "top5_acc": 0.54734, "loss_cls": 4.04294, "loss": 4.04294, "time": 0.82759} +{"mode": "train", "epoch": 60, "iter": 2300, "lr": 0.06584, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29047, "top5_acc": 0.55188, "loss_cls": 4.04291, "loss": 4.04291, "time": 0.81911} +{"mode": "train", "epoch": 60, "iter": 2400, "lr": 0.06581, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29922, "top5_acc": 0.56125, "loss_cls": 4.03816, "loss": 4.03816, "time": 0.82033} +{"mode": "train", "epoch": 60, "iter": 2500, "lr": 0.06578, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.295, "top5_acc": 0.55297, "loss_cls": 4.03222, "loss": 4.03222, "time": 0.81821} +{"mode": "train", "epoch": 60, "iter": 2600, "lr": 0.06576, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30078, "top5_acc": 0.54922, "loss_cls": 4.04375, "loss": 4.04375, "time": 0.81672} +{"mode": "train", "epoch": 60, "iter": 2700, "lr": 0.06573, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29625, "top5_acc": 0.55594, "loss_cls": 4.03937, "loss": 4.03937, "time": 0.81785} +{"mode": "train", "epoch": 60, "iter": 2800, "lr": 0.0657, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28906, "top5_acc": 0.54937, "loss_cls": 4.0823, "loss": 4.0823, "time": 0.81765} +{"mode": "train", "epoch": 60, "iter": 2900, "lr": 0.06568, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29313, "top5_acc": 0.55391, "loss_cls": 4.02475, "loss": 4.02475, "time": 0.81545} +{"mode": "train", "epoch": 60, "iter": 3000, "lr": 0.06565, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30281, "top5_acc": 0.56641, "loss_cls": 3.99844, "loss": 3.99844, "time": 0.81541} +{"mode": "train", "epoch": 60, "iter": 3100, "lr": 0.06562, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29766, "top5_acc": 0.55469, "loss_cls": 4.01423, "loss": 4.01423, "time": 0.81666} +{"mode": "train", "epoch": 60, "iter": 3200, "lr": 0.0656, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30125, "top5_acc": 0.56016, "loss_cls": 3.99443, "loss": 3.99443, "time": 0.82126} +{"mode": "train", "epoch": 60, "iter": 3300, "lr": 0.06557, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28969, "top5_acc": 0.53688, "loss_cls": 4.06482, "loss": 4.06482, "time": 0.81204} +{"mode": "train", "epoch": 60, "iter": 3400, "lr": 0.06554, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29906, "top5_acc": 0.54781, "loss_cls": 4.04633, "loss": 4.04633, "time": 0.81755} +{"mode": "train", "epoch": 60, "iter": 3500, "lr": 0.06552, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30859, "top5_acc": 0.55641, "loss_cls": 3.99309, "loss": 3.99309, "time": 0.81951} +{"mode": "train", "epoch": 60, "iter": 3600, "lr": 0.06549, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30828, "top5_acc": 0.54844, "loss_cls": 4.04814, "loss": 4.04814, "time": 0.8194} +{"mode": "train", "epoch": 60, "iter": 3700, "lr": 0.06546, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29188, "top5_acc": 0.54531, "loss_cls": 4.06454, "loss": 4.06454, "time": 0.81411} +{"mode": "val", "epoch": 60, "iter": 309, "lr": 0.06545, "top1_acc": 0.24034, "top5_acc": 0.47794, "mean_class_accuracy": 0.24014} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.06542, "memory": 15990, "data_time": 1.29194, "top1_acc": 0.31062, "top5_acc": 0.56203, "loss_cls": 3.97098, "loss": 3.97098, "time": 2.30022} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.0654, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30219, "top5_acc": 0.56672, "loss_cls": 3.94799, "loss": 3.94799, "time": 0.81763} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.06537, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29672, "top5_acc": 0.55203, "loss_cls": 4.02707, "loss": 4.02707, "time": 0.81639} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.06534, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29812, "top5_acc": 0.55047, "loss_cls": 4.03994, "loss": 4.03994, "time": 0.81708} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.06532, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30344, "top5_acc": 0.55328, "loss_cls": 3.9966, "loss": 3.9966, "time": 0.81688} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.06529, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31016, "top5_acc": 0.56109, "loss_cls": 3.981, "loss": 3.981, "time": 0.82036} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.06526, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30141, "top5_acc": 0.56078, "loss_cls": 4.00211, "loss": 4.00211, "time": 0.81772} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.06524, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29953, "top5_acc": 0.55422, "loss_cls": 4.00603, "loss": 4.00603, "time": 0.81853} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.06521, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29859, "top5_acc": 0.55625, "loss_cls": 4.03832, "loss": 4.03832, "time": 0.8137} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.06519, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29781, "top5_acc": 0.55719, "loss_cls": 3.99229, "loss": 3.99229, "time": 0.82057} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.06516, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2975, "top5_acc": 0.55, "loss_cls": 4.04416, "loss": 4.04416, "time": 0.82218} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.06513, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30031, "top5_acc": 0.56219, "loss_cls": 4.01663, "loss": 4.01663, "time": 0.82426} +{"mode": "train", "epoch": 61, "iter": 1300, "lr": 0.06511, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30406, "top5_acc": 0.56125, "loss_cls": 3.97659, "loss": 3.97659, "time": 0.82254} +{"mode": "train", "epoch": 61, "iter": 1400, "lr": 0.06508, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31016, "top5_acc": 0.555, "loss_cls": 3.97487, "loss": 3.97487, "time": 0.81112} +{"mode": "train", "epoch": 61, "iter": 1500, "lr": 0.06505, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31125, "top5_acc": 0.56594, "loss_cls": 3.95927, "loss": 3.95927, "time": 0.8189} +{"mode": "train", "epoch": 61, "iter": 1600, "lr": 0.06503, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30094, "top5_acc": 0.56063, "loss_cls": 4.02308, "loss": 4.02308, "time": 0.81632} +{"mode": "train", "epoch": 61, "iter": 1700, "lr": 0.065, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29047, "top5_acc": 0.54375, "loss_cls": 4.06405, "loss": 4.06405, "time": 0.81505} +{"mode": "train", "epoch": 61, "iter": 1800, "lr": 0.06497, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30422, "top5_acc": 0.56078, "loss_cls": 4.00635, "loss": 4.00635, "time": 0.81579} +{"mode": "train", "epoch": 61, "iter": 1900, "lr": 0.06495, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30266, "top5_acc": 0.55109, "loss_cls": 4.03657, "loss": 4.03657, "time": 0.81253} +{"mode": "train", "epoch": 61, "iter": 2000, "lr": 0.06492, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30266, "top5_acc": 0.55641, "loss_cls": 4.02836, "loss": 4.02836, "time": 0.81418} +{"mode": "train", "epoch": 61, "iter": 2100, "lr": 0.06489, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29766, "top5_acc": 0.55719, "loss_cls": 4.02955, "loss": 4.02955, "time": 0.81235} +{"mode": "train", "epoch": 61, "iter": 2200, "lr": 0.06487, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30375, "top5_acc": 0.54766, "loss_cls": 4.04215, "loss": 4.04215, "time": 0.82108} +{"mode": "train", "epoch": 61, "iter": 2300, "lr": 0.06484, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30594, "top5_acc": 0.56391, "loss_cls": 3.98231, "loss": 3.98231, "time": 0.81899} +{"mode": "train", "epoch": 61, "iter": 2400, "lr": 0.06481, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30297, "top5_acc": 0.55, "loss_cls": 4.06246, "loss": 4.06246, "time": 0.81806} +{"mode": "train", "epoch": 61, "iter": 2500, "lr": 0.06478, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29578, "top5_acc": 0.54797, "loss_cls": 4.0195, "loss": 4.0195, "time": 0.82098} +{"mode": "train", "epoch": 61, "iter": 2600, "lr": 0.06476, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29641, "top5_acc": 0.54891, "loss_cls": 4.0689, "loss": 4.0689, "time": 0.82117} +{"mode": "train", "epoch": 61, "iter": 2700, "lr": 0.06473, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29438, "top5_acc": 0.54344, "loss_cls": 4.07962, "loss": 4.07962, "time": 0.82359} +{"mode": "train", "epoch": 61, "iter": 2800, "lr": 0.0647, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29719, "top5_acc": 0.55937, "loss_cls": 3.96767, "loss": 3.96767, "time": 0.81949} +{"mode": "train", "epoch": 61, "iter": 2900, "lr": 0.06468, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30062, "top5_acc": 0.55109, "loss_cls": 4.03322, "loss": 4.03322, "time": 0.81671} +{"mode": "train", "epoch": 61, "iter": 3000, "lr": 0.06465, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30688, "top5_acc": 0.56156, "loss_cls": 3.97687, "loss": 3.97687, "time": 0.81781} +{"mode": "train", "epoch": 61, "iter": 3100, "lr": 0.06462, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29656, "top5_acc": 0.5525, "loss_cls": 4.04629, "loss": 4.04629, "time": 0.81737} +{"mode": "train", "epoch": 61, "iter": 3200, "lr": 0.0646, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29922, "top5_acc": 0.56219, "loss_cls": 4.00082, "loss": 4.00082, "time": 0.82411} +{"mode": "train", "epoch": 61, "iter": 3300, "lr": 0.06457, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28625, "top5_acc": 0.54625, "loss_cls": 4.07462, "loss": 4.07462, "time": 0.81827} +{"mode": "train", "epoch": 61, "iter": 3400, "lr": 0.06454, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29563, "top5_acc": 0.55734, "loss_cls": 3.99919, "loss": 3.99919, "time": 0.81409} +{"mode": "train", "epoch": 61, "iter": 3500, "lr": 0.06452, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30078, "top5_acc": 0.56531, "loss_cls": 3.98431, "loss": 3.98431, "time": 0.81526} +{"mode": "train", "epoch": 61, "iter": 3600, "lr": 0.06449, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29969, "top5_acc": 0.55203, "loss_cls": 4.00933, "loss": 4.00933, "time": 0.81377} +{"mode": "train", "epoch": 61, "iter": 3700, "lr": 0.06446, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29578, "top5_acc": 0.55359, "loss_cls": 4.01134, "loss": 4.01134, "time": 0.81481} +{"mode": "val", "epoch": 61, "iter": 309, "lr": 0.06445, "top1_acc": 0.24292, "top5_acc": 0.48807, "mean_class_accuracy": 0.24274} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.06443, "memory": 15990, "data_time": 1.25764, "top1_acc": 0.305, "top5_acc": 0.56297, "loss_cls": 3.99251, "loss": 3.99251, "time": 2.24472} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.0644, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30547, "top5_acc": 0.55859, "loss_cls": 3.98781, "loss": 3.98781, "time": 0.81963} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.06437, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30812, "top5_acc": 0.56281, "loss_cls": 3.95581, "loss": 3.95581, "time": 0.81901} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.06434, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3, "top5_acc": 0.55656, "loss_cls": 4.00852, "loss": 4.00852, "time": 0.81517} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.06432, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30828, "top5_acc": 0.55859, "loss_cls": 3.98582, "loss": 3.98582, "time": 0.81916} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.06429, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28859, "top5_acc": 0.55297, "loss_cls": 4.04975, "loss": 4.04975, "time": 0.81402} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.06426, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31047, "top5_acc": 0.57578, "loss_cls": 3.92851, "loss": 3.92851, "time": 0.8143} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.06424, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30062, "top5_acc": 0.55531, "loss_cls": 4.01959, "loss": 4.01959, "time": 0.81529} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.06421, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30781, "top5_acc": 0.57656, "loss_cls": 3.94859, "loss": 3.94859, "time": 0.82142} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.06418, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2975, "top5_acc": 0.55594, "loss_cls": 4.02392, "loss": 4.02392, "time": 0.82048} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.06416, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30594, "top5_acc": 0.55594, "loss_cls": 3.9969, "loss": 3.9969, "time": 0.82314} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.06413, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30156, "top5_acc": 0.56219, "loss_cls": 4.00755, "loss": 4.00755, "time": 0.82199} +{"mode": "train", "epoch": 62, "iter": 1300, "lr": 0.0641, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29203, "top5_acc": 0.54859, "loss_cls": 4.0532, "loss": 4.0532, "time": 0.82384} +{"mode": "train", "epoch": 62, "iter": 1400, "lr": 0.06408, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29609, "top5_acc": 0.55531, "loss_cls": 4.03009, "loss": 4.03009, "time": 0.81855} +{"mode": "train", "epoch": 62, "iter": 1500, "lr": 0.06405, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30547, "top5_acc": 0.56594, "loss_cls": 3.9845, "loss": 3.9845, "time": 0.81722} +{"mode": "train", "epoch": 62, "iter": 1600, "lr": 0.06402, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30547, "top5_acc": 0.55781, "loss_cls": 4.02009, "loss": 4.02009, "time": 0.81637} +{"mode": "train", "epoch": 62, "iter": 1700, "lr": 0.064, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30203, "top5_acc": 0.55344, "loss_cls": 4.0139, "loss": 4.0139, "time": 0.81469} +{"mode": "train", "epoch": 62, "iter": 1800, "lr": 0.06397, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30188, "top5_acc": 0.55734, "loss_cls": 4.0098, "loss": 4.0098, "time": 0.81558} +{"mode": "train", "epoch": 62, "iter": 1900, "lr": 0.06394, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29328, "top5_acc": 0.55016, "loss_cls": 4.06869, "loss": 4.06869, "time": 0.81349} +{"mode": "train", "epoch": 62, "iter": 2000, "lr": 0.06392, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30438, "top5_acc": 0.56547, "loss_cls": 3.96127, "loss": 3.96127, "time": 0.81553} +{"mode": "train", "epoch": 62, "iter": 2100, "lr": 0.06389, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29641, "top5_acc": 0.55219, "loss_cls": 4.00741, "loss": 4.00741, "time": 0.82127} +{"mode": "train", "epoch": 62, "iter": 2200, "lr": 0.06386, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30141, "top5_acc": 0.55562, "loss_cls": 4.01065, "loss": 4.01065, "time": 0.81681} +{"mode": "train", "epoch": 62, "iter": 2300, "lr": 0.06384, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.30594, "top5_acc": 0.55953, "loss_cls": 3.997, "loss": 3.997, "time": 0.82441} +{"mode": "train", "epoch": 62, "iter": 2400, "lr": 0.06381, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29828, "top5_acc": 0.54391, "loss_cls": 4.04799, "loss": 4.04799, "time": 0.8208} +{"mode": "train", "epoch": 62, "iter": 2500, "lr": 0.06378, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31422, "top5_acc": 0.55781, "loss_cls": 3.95925, "loss": 3.95925, "time": 0.81924} +{"mode": "train", "epoch": 62, "iter": 2600, "lr": 0.06375, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30766, "top5_acc": 0.56125, "loss_cls": 3.98872, "loss": 3.98872, "time": 0.82444} +{"mode": "train", "epoch": 62, "iter": 2700, "lr": 0.06373, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29469, "top5_acc": 0.55437, "loss_cls": 4.04164, "loss": 4.04164, "time": 0.82365} +{"mode": "train", "epoch": 62, "iter": 2800, "lr": 0.0637, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30312, "top5_acc": 0.56703, "loss_cls": 3.98247, "loss": 3.98247, "time": 0.82137} +{"mode": "train", "epoch": 62, "iter": 2900, "lr": 0.06367, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31312, "top5_acc": 0.55312, "loss_cls": 3.99889, "loss": 3.99889, "time": 0.81721} +{"mode": "train", "epoch": 62, "iter": 3000, "lr": 0.06365, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30797, "top5_acc": 0.55859, "loss_cls": 3.98455, "loss": 3.98455, "time": 0.81738} +{"mode": "train", "epoch": 62, "iter": 3100, "lr": 0.06362, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30609, "top5_acc": 0.56484, "loss_cls": 3.97208, "loss": 3.97208, "time": 0.81931} +{"mode": "train", "epoch": 62, "iter": 3200, "lr": 0.06359, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30531, "top5_acc": 0.56312, "loss_cls": 3.97102, "loss": 3.97102, "time": 0.81361} +{"mode": "train", "epoch": 62, "iter": 3300, "lr": 0.06357, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30281, "top5_acc": 0.55969, "loss_cls": 4.0114, "loss": 4.0114, "time": 0.81603} +{"mode": "train", "epoch": 62, "iter": 3400, "lr": 0.06354, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30094, "top5_acc": 0.5575, "loss_cls": 3.99309, "loss": 3.99309, "time": 0.81303} +{"mode": "train", "epoch": 62, "iter": 3500, "lr": 0.06351, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28672, "top5_acc": 0.55203, "loss_cls": 4.04547, "loss": 4.04547, "time": 0.81356} +{"mode": "train", "epoch": 62, "iter": 3600, "lr": 0.06349, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29094, "top5_acc": 0.55828, "loss_cls": 4.03612, "loss": 4.03612, "time": 0.81755} +{"mode": "train", "epoch": 62, "iter": 3700, "lr": 0.06346, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30172, "top5_acc": 0.55672, "loss_cls": 4.01208, "loss": 4.01208, "time": 0.82018} +{"mode": "val", "epoch": 62, "iter": 309, "lr": 0.06345, "top1_acc": 0.23087, "top5_acc": 0.47723, "mean_class_accuracy": 0.23071} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.06342, "memory": 15990, "data_time": 1.32044, "top1_acc": 0.31062, "top5_acc": 0.56656, "loss_cls": 3.96156, "loss": 3.96156, "time": 2.30506} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.06339, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30078, "top5_acc": 0.56437, "loss_cls": 3.9844, "loss": 3.9844, "time": 0.82369} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.06337, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30328, "top5_acc": 0.555, "loss_cls": 3.9899, "loss": 3.9899, "time": 0.81781} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.06334, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31688, "top5_acc": 0.56766, "loss_cls": 3.95526, "loss": 3.95526, "time": 0.81726} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.06331, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30703, "top5_acc": 0.55578, "loss_cls": 3.99776, "loss": 3.99776, "time": 0.81913} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.06328, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29906, "top5_acc": 0.55641, "loss_cls": 4.023, "loss": 4.023, "time": 0.81591} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.06326, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30422, "top5_acc": 0.55766, "loss_cls": 4.01269, "loss": 4.01269, "time": 0.81738} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.06323, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30234, "top5_acc": 0.55828, "loss_cls": 3.97197, "loss": 3.97197, "time": 0.81566} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.0632, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.305, "top5_acc": 0.56234, "loss_cls": 4.00215, "loss": 4.00215, "time": 0.82484} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.06318, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30984, "top5_acc": 0.56172, "loss_cls": 3.99827, "loss": 3.99827, "time": 0.82895} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.06315, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29797, "top5_acc": 0.56406, "loss_cls": 4.00379, "loss": 4.00379, "time": 0.82031} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.06312, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30766, "top5_acc": 0.56297, "loss_cls": 3.95254, "loss": 3.95254, "time": 0.81591} +{"mode": "train", "epoch": 63, "iter": 1300, "lr": 0.0631, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29984, "top5_acc": 0.56234, "loss_cls": 3.98369, "loss": 3.98369, "time": 0.8115} +{"mode": "train", "epoch": 63, "iter": 1400, "lr": 0.06307, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30438, "top5_acc": 0.56328, "loss_cls": 3.97181, "loss": 3.97181, "time": 0.81553} +{"mode": "train", "epoch": 63, "iter": 1500, "lr": 0.06304, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29812, "top5_acc": 0.55031, "loss_cls": 4.03352, "loss": 4.03352, "time": 0.81184} +{"mode": "train", "epoch": 63, "iter": 1600, "lr": 0.06301, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30188, "top5_acc": 0.55734, "loss_cls": 3.97847, "loss": 3.97847, "time": 0.81681} +{"mode": "train", "epoch": 63, "iter": 1700, "lr": 0.06299, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3025, "top5_acc": 0.56141, "loss_cls": 3.99031, "loss": 3.99031, "time": 0.81736} +{"mode": "train", "epoch": 63, "iter": 1800, "lr": 0.06296, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28953, "top5_acc": 0.55203, "loss_cls": 4.04485, "loss": 4.04485, "time": 0.81634} +{"mode": "train", "epoch": 63, "iter": 1900, "lr": 0.06293, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29859, "top5_acc": 0.55234, "loss_cls": 3.99904, "loss": 3.99904, "time": 0.81415} +{"mode": "train", "epoch": 63, "iter": 2000, "lr": 0.06291, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30016, "top5_acc": 0.55906, "loss_cls": 4.0173, "loss": 4.0173, "time": 0.81504} +{"mode": "train", "epoch": 63, "iter": 2100, "lr": 0.06288, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30266, "top5_acc": 0.55547, "loss_cls": 4.02744, "loss": 4.02744, "time": 0.81529} +{"mode": "train", "epoch": 63, "iter": 2200, "lr": 0.06285, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30391, "top5_acc": 0.55688, "loss_cls": 3.98676, "loss": 3.98676, "time": 0.81746} +{"mode": "train", "epoch": 63, "iter": 2300, "lr": 0.06283, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30656, "top5_acc": 0.56859, "loss_cls": 3.93743, "loss": 3.93743, "time": 0.82314} +{"mode": "train", "epoch": 63, "iter": 2400, "lr": 0.0628, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30469, "top5_acc": 0.55984, "loss_cls": 3.99851, "loss": 3.99851, "time": 0.81868} +{"mode": "train", "epoch": 63, "iter": 2500, "lr": 0.06277, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30797, "top5_acc": 0.56453, "loss_cls": 3.97003, "loss": 3.97003, "time": 0.82196} +{"mode": "train", "epoch": 63, "iter": 2600, "lr": 0.06274, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31391, "top5_acc": 0.56, "loss_cls": 3.96852, "loss": 3.96852, "time": 0.8229} +{"mode": "train", "epoch": 63, "iter": 2700, "lr": 0.06272, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30062, "top5_acc": 0.55531, "loss_cls": 3.99144, "loss": 3.99144, "time": 0.82587} +{"mode": "train", "epoch": 63, "iter": 2800, "lr": 0.06269, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31328, "top5_acc": 0.56703, "loss_cls": 3.96007, "loss": 3.96007, "time": 0.81968} +{"mode": "train", "epoch": 63, "iter": 2900, "lr": 0.06266, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.305, "top5_acc": 0.55297, "loss_cls": 3.99817, "loss": 3.99817, "time": 0.81575} +{"mode": "train", "epoch": 63, "iter": 3000, "lr": 0.06264, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30328, "top5_acc": 0.55391, "loss_cls": 4.01085, "loss": 4.01085, "time": 0.81868} +{"mode": "train", "epoch": 63, "iter": 3100, "lr": 0.06261, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29656, "top5_acc": 0.54531, "loss_cls": 4.02977, "loss": 4.02977, "time": 0.81443} +{"mode": "train", "epoch": 63, "iter": 3200, "lr": 0.06258, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30266, "top5_acc": 0.55406, "loss_cls": 3.98907, "loss": 3.98907, "time": 0.81358} +{"mode": "train", "epoch": 63, "iter": 3300, "lr": 0.06256, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28766, "top5_acc": 0.54609, "loss_cls": 4.06621, "loss": 4.06621, "time": 0.81506} +{"mode": "train", "epoch": 63, "iter": 3400, "lr": 0.06253, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30875, "top5_acc": 0.56266, "loss_cls": 3.9645, "loss": 3.9645, "time": 0.81339} +{"mode": "train", "epoch": 63, "iter": 3500, "lr": 0.0625, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29812, "top5_acc": 0.55625, "loss_cls": 4.03497, "loss": 4.03497, "time": 0.81564} +{"mode": "train", "epoch": 63, "iter": 3600, "lr": 0.06247, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30438, "top5_acc": 0.56125, "loss_cls": 3.98452, "loss": 3.98452, "time": 0.81351} +{"mode": "train", "epoch": 63, "iter": 3700, "lr": 0.06245, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30047, "top5_acc": 0.55125, "loss_cls": 4.00448, "loss": 4.00448, "time": 0.81636} +{"mode": "val", "epoch": 63, "iter": 309, "lr": 0.06243, "top1_acc": 0.2415, "top5_acc": 0.4865, "mean_class_accuracy": 0.24129} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.06241, "memory": 15990, "data_time": 1.29623, "top1_acc": 0.31109, "top5_acc": 0.56453, "loss_cls": 3.95341, "loss": 3.95341, "time": 2.28025} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.06238, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31609, "top5_acc": 0.56406, "loss_cls": 3.92975, "loss": 3.92975, "time": 0.82104} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.06235, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31703, "top5_acc": 0.56109, "loss_cls": 3.94348, "loss": 3.94348, "time": 0.81494} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.06233, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31719, "top5_acc": 0.56359, "loss_cls": 3.94964, "loss": 3.94964, "time": 0.82002} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.0623, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30359, "top5_acc": 0.55609, "loss_cls": 3.98589, "loss": 3.98589, "time": 0.81885} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.06227, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30859, "top5_acc": 0.56516, "loss_cls": 3.96261, "loss": 3.96261, "time": 0.81431} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.06225, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31406, "top5_acc": 0.56719, "loss_cls": 3.931, "loss": 3.931, "time": 0.81617} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.06222, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31984, "top5_acc": 0.57281, "loss_cls": 3.91495, "loss": 3.91495, "time": 0.81875} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.06219, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32312, "top5_acc": 0.56891, "loss_cls": 3.93181, "loss": 3.93181, "time": 0.81664} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.06216, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30016, "top5_acc": 0.55422, "loss_cls": 4.00935, "loss": 4.00935, "time": 0.8245} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.06214, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30438, "top5_acc": 0.56312, "loss_cls": 3.99599, "loss": 3.99599, "time": 0.82394} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.06211, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31094, "top5_acc": 0.56031, "loss_cls": 3.96487, "loss": 3.96487, "time": 0.82002} +{"mode": "train", "epoch": 64, "iter": 1300, "lr": 0.06208, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30312, "top5_acc": 0.55078, "loss_cls": 4.03925, "loss": 4.03925, "time": 0.82077} +{"mode": "train", "epoch": 64, "iter": 1400, "lr": 0.06206, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29875, "top5_acc": 0.55062, "loss_cls": 4.05062, "loss": 4.05062, "time": 0.81826} +{"mode": "train", "epoch": 64, "iter": 1500, "lr": 0.06203, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29953, "top5_acc": 0.55266, "loss_cls": 4.00933, "loss": 4.00933, "time": 0.82281} +{"mode": "train", "epoch": 64, "iter": 1600, "lr": 0.062, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29844, "top5_acc": 0.55688, "loss_cls": 4.01984, "loss": 4.01984, "time": 0.81614} +{"mode": "train", "epoch": 64, "iter": 1700, "lr": 0.06197, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30031, "top5_acc": 0.55781, "loss_cls": 4.01994, "loss": 4.01994, "time": 0.81872} +{"mode": "train", "epoch": 64, "iter": 1800, "lr": 0.06195, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30641, "top5_acc": 0.56516, "loss_cls": 3.97041, "loss": 3.97041, "time": 0.82083} +{"mode": "train", "epoch": 64, "iter": 1900, "lr": 0.06192, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30828, "top5_acc": 0.56672, "loss_cls": 3.951, "loss": 3.951, "time": 0.82189} +{"mode": "train", "epoch": 64, "iter": 2000, "lr": 0.06189, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29969, "top5_acc": 0.56047, "loss_cls": 4.00569, "loss": 4.00569, "time": 0.81765} +{"mode": "train", "epoch": 64, "iter": 2100, "lr": 0.06187, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31953, "top5_acc": 0.57078, "loss_cls": 3.92669, "loss": 3.92669, "time": 0.82176} +{"mode": "train", "epoch": 64, "iter": 2200, "lr": 0.06184, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.295, "top5_acc": 0.55016, "loss_cls": 4.03617, "loss": 4.03617, "time": 0.82203} +{"mode": "train", "epoch": 64, "iter": 2300, "lr": 0.06181, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29344, "top5_acc": 0.56156, "loss_cls": 4.00157, "loss": 4.00157, "time": 0.82821} +{"mode": "train", "epoch": 64, "iter": 2400, "lr": 0.06178, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29906, "top5_acc": 0.56969, "loss_cls": 4.00672, "loss": 4.00672, "time": 0.82074} +{"mode": "train", "epoch": 64, "iter": 2500, "lr": 0.06176, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30438, "top5_acc": 0.55453, "loss_cls": 4.01655, "loss": 4.01655, "time": 0.82448} +{"mode": "train", "epoch": 64, "iter": 2600, "lr": 0.06173, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.305, "top5_acc": 0.56969, "loss_cls": 3.97019, "loss": 3.97019, "time": 0.82976} +{"mode": "train", "epoch": 64, "iter": 2700, "lr": 0.0617, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29078, "top5_acc": 0.55359, "loss_cls": 4.02065, "loss": 4.02065, "time": 0.82133} +{"mode": "train", "epoch": 64, "iter": 2800, "lr": 0.06168, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31219, "top5_acc": 0.55766, "loss_cls": 4.00062, "loss": 4.00062, "time": 0.81656} +{"mode": "train", "epoch": 64, "iter": 2900, "lr": 0.06165, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29766, "top5_acc": 0.55672, "loss_cls": 4.02864, "loss": 4.02864, "time": 0.82285} +{"mode": "train", "epoch": 64, "iter": 3000, "lr": 0.06162, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29844, "top5_acc": 0.5625, "loss_cls": 3.95085, "loss": 3.95085, "time": 0.82293} +{"mode": "train", "epoch": 64, "iter": 3100, "lr": 0.06159, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2975, "top5_acc": 0.55734, "loss_cls": 4.02373, "loss": 4.02373, "time": 0.818} +{"mode": "train", "epoch": 64, "iter": 3200, "lr": 0.06157, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30047, "top5_acc": 0.55469, "loss_cls": 4.03404, "loss": 4.03404, "time": 0.81803} +{"mode": "train", "epoch": 64, "iter": 3300, "lr": 0.06154, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30906, "top5_acc": 0.55984, "loss_cls": 3.96091, "loss": 3.96091, "time": 0.82169} +{"mode": "train", "epoch": 64, "iter": 3400, "lr": 0.06151, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30578, "top5_acc": 0.56453, "loss_cls": 3.97691, "loss": 3.97691, "time": 0.82018} +{"mode": "train", "epoch": 64, "iter": 3500, "lr": 0.06148, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30922, "top5_acc": 0.56859, "loss_cls": 3.96946, "loss": 3.96946, "time": 0.81539} +{"mode": "train", "epoch": 64, "iter": 3600, "lr": 0.06146, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29969, "top5_acc": 0.56016, "loss_cls": 3.99161, "loss": 3.99161, "time": 0.81794} +{"mode": "train", "epoch": 64, "iter": 3700, "lr": 0.06143, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30812, "top5_acc": 0.56188, "loss_cls": 3.99934, "loss": 3.99934, "time": 0.81937} +{"mode": "val", "epoch": 64, "iter": 309, "lr": 0.06142, "top1_acc": 0.21582, "top5_acc": 0.46057, "mean_class_accuracy": 0.2156} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.06139, "memory": 15990, "data_time": 1.27702, "top1_acc": 0.29812, "top5_acc": 0.55828, "loss_cls": 4.01167, "loss": 4.01167, "time": 2.26707} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.06136, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31203, "top5_acc": 0.57531, "loss_cls": 3.92001, "loss": 3.92001, "time": 0.82806} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.06134, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31734, "top5_acc": 0.57219, "loss_cls": 3.93258, "loss": 3.93258, "time": 0.81984} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.06131, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31234, "top5_acc": 0.56328, "loss_cls": 3.96609, "loss": 3.96609, "time": 0.81644} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.06128, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31438, "top5_acc": 0.56812, "loss_cls": 3.92499, "loss": 3.92499, "time": 0.81654} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.06125, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30078, "top5_acc": 0.55906, "loss_cls": 3.98717, "loss": 3.98717, "time": 0.82125} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.06123, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30922, "top5_acc": 0.57797, "loss_cls": 3.93979, "loss": 3.93979, "time": 0.81779} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0612, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30641, "top5_acc": 0.56188, "loss_cls": 3.97718, "loss": 3.97718, "time": 0.81726} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.06117, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30531, "top5_acc": 0.56344, "loss_cls": 3.96482, "loss": 3.96482, "time": 0.81989} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.06115, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30969, "top5_acc": 0.55391, "loss_cls": 4.03679, "loss": 4.03679, "time": 0.83269} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.06112, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30188, "top5_acc": 0.55969, "loss_cls": 3.99109, "loss": 3.99109, "time": 0.82257} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.06109, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31281, "top5_acc": 0.56437, "loss_cls": 3.93273, "loss": 3.93273, "time": 0.81819} +{"mode": "train", "epoch": 65, "iter": 1300, "lr": 0.06106, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31047, "top5_acc": 0.55906, "loss_cls": 3.95747, "loss": 3.95747, "time": 0.8189} +{"mode": "train", "epoch": 65, "iter": 1400, "lr": 0.06104, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30172, "top5_acc": 0.56078, "loss_cls": 4.00166, "loss": 4.00166, "time": 0.81886} +{"mode": "train", "epoch": 65, "iter": 1500, "lr": 0.06101, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30422, "top5_acc": 0.55531, "loss_cls": 4.02895, "loss": 4.02895, "time": 0.81683} +{"mode": "train", "epoch": 65, "iter": 1600, "lr": 0.06098, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30719, "top5_acc": 0.55922, "loss_cls": 3.9882, "loss": 3.9882, "time": 0.81709} +{"mode": "train", "epoch": 65, "iter": 1700, "lr": 0.06095, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.3025, "top5_acc": 0.5625, "loss_cls": 3.98601, "loss": 3.98601, "time": 0.81498} +{"mode": "train", "epoch": 65, "iter": 1800, "lr": 0.06093, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30594, "top5_acc": 0.56172, "loss_cls": 3.95735, "loss": 3.95735, "time": 0.81995} +{"mode": "train", "epoch": 65, "iter": 1900, "lr": 0.0609, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31406, "top5_acc": 0.56938, "loss_cls": 3.95137, "loss": 3.95137, "time": 0.81958} +{"mode": "train", "epoch": 65, "iter": 2000, "lr": 0.06087, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31125, "top5_acc": 0.56156, "loss_cls": 3.98499, "loss": 3.98499, "time": 0.8161} +{"mode": "train", "epoch": 65, "iter": 2100, "lr": 0.06085, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29094, "top5_acc": 0.54281, "loss_cls": 4.04998, "loss": 4.04998, "time": 0.81287} +{"mode": "train", "epoch": 65, "iter": 2200, "lr": 0.06082, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30953, "top5_acc": 0.56297, "loss_cls": 3.99243, "loss": 3.99243, "time": 0.8153} +{"mode": "train", "epoch": 65, "iter": 2300, "lr": 0.06079, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30375, "top5_acc": 0.56141, "loss_cls": 3.99737, "loss": 3.99737, "time": 0.81985} +{"mode": "train", "epoch": 65, "iter": 2400, "lr": 0.06076, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30828, "top5_acc": 0.57109, "loss_cls": 3.94109, "loss": 3.94109, "time": 0.81856} +{"mode": "train", "epoch": 65, "iter": 2500, "lr": 0.06074, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31094, "top5_acc": 0.56078, "loss_cls": 3.96532, "loss": 3.96532, "time": 0.82136} +{"mode": "train", "epoch": 65, "iter": 2600, "lr": 0.06071, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31328, "top5_acc": 0.57063, "loss_cls": 3.94139, "loss": 3.94139, "time": 0.82575} +{"mode": "train", "epoch": 65, "iter": 2700, "lr": 0.06068, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29719, "top5_acc": 0.54359, "loss_cls": 4.06155, "loss": 4.06155, "time": 0.81951} +{"mode": "train", "epoch": 65, "iter": 2800, "lr": 0.06065, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29703, "top5_acc": 0.55734, "loss_cls": 4.01638, "loss": 4.01638, "time": 0.82328} +{"mode": "train", "epoch": 65, "iter": 2900, "lr": 0.06063, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30938, "top5_acc": 0.57109, "loss_cls": 3.9566, "loss": 3.9566, "time": 0.8144} +{"mode": "train", "epoch": 65, "iter": 3000, "lr": 0.0606, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30906, "top5_acc": 0.56047, "loss_cls": 3.97427, "loss": 3.97427, "time": 0.81526} +{"mode": "train", "epoch": 65, "iter": 3100, "lr": 0.06057, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28641, "top5_acc": 0.54734, "loss_cls": 4.06038, "loss": 4.06038, "time": 0.81385} +{"mode": "train", "epoch": 65, "iter": 3200, "lr": 0.06055, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31234, "top5_acc": 0.57188, "loss_cls": 3.94132, "loss": 3.94132, "time": 0.81421} +{"mode": "train", "epoch": 65, "iter": 3300, "lr": 0.06052, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31109, "top5_acc": 0.57047, "loss_cls": 3.94145, "loss": 3.94145, "time": 0.81353} +{"mode": "train", "epoch": 65, "iter": 3400, "lr": 0.06049, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30484, "top5_acc": 0.56031, "loss_cls": 4.00199, "loss": 4.00199, "time": 0.81216} +{"mode": "train", "epoch": 65, "iter": 3500, "lr": 0.06046, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30031, "top5_acc": 0.55891, "loss_cls": 4.00997, "loss": 4.00997, "time": 0.81815} +{"mode": "train", "epoch": 65, "iter": 3600, "lr": 0.06044, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30031, "top5_acc": 0.55437, "loss_cls": 3.98925, "loss": 3.98925, "time": 0.81553} +{"mode": "train", "epoch": 65, "iter": 3700, "lr": 0.06041, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30719, "top5_acc": 0.56469, "loss_cls": 3.97882, "loss": 3.97882, "time": 0.81546} +{"mode": "val", "epoch": 65, "iter": 309, "lr": 0.0604, "top1_acc": 0.23639, "top5_acc": 0.47485, "mean_class_accuracy": 0.23608} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.06037, "memory": 15990, "data_time": 1.24735, "top1_acc": 0.30438, "top5_acc": 0.5625, "loss_cls": 3.98356, "loss": 3.98356, "time": 2.23034} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.06034, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31984, "top5_acc": 0.57438, "loss_cls": 3.9105, "loss": 3.9105, "time": 0.82047} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.06031, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30828, "top5_acc": 0.57516, "loss_cls": 3.92735, "loss": 3.92735, "time": 0.81471} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.06029, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30406, "top5_acc": 0.57234, "loss_cls": 3.94434, "loss": 3.94434, "time": 0.81683} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.06026, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30484, "top5_acc": 0.56391, "loss_cls": 3.95992, "loss": 3.95992, "time": 0.81521} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.06023, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31031, "top5_acc": 0.56891, "loss_cls": 3.9402, "loss": 3.9402, "time": 0.81803} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.0602, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30031, "top5_acc": 0.55688, "loss_cls": 4.01347, "loss": 4.01347, "time": 0.81853} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.06018, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30406, "top5_acc": 0.55984, "loss_cls": 4.01074, "loss": 4.01074, "time": 0.82295} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.06015, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29953, "top5_acc": 0.55953, "loss_cls": 4.00148, "loss": 4.00148, "time": 0.81546} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.06012, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.31438, "top5_acc": 0.56703, "loss_cls": 3.95284, "loss": 3.95284, "time": 0.82154} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.06009, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31234, "top5_acc": 0.56312, "loss_cls": 3.95821, "loss": 3.95821, "time": 0.82487} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.06007, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31516, "top5_acc": 0.56719, "loss_cls": 3.92137, "loss": 3.92137, "time": 0.81929} +{"mode": "train", "epoch": 66, "iter": 1300, "lr": 0.06004, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30484, "top5_acc": 0.56453, "loss_cls": 4.00721, "loss": 4.00721, "time": 0.81353} +{"mode": "train", "epoch": 66, "iter": 1400, "lr": 0.06001, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30875, "top5_acc": 0.5825, "loss_cls": 3.89078, "loss": 3.89078, "time": 0.81174} +{"mode": "train", "epoch": 66, "iter": 1500, "lr": 0.05999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29563, "top5_acc": 0.55641, "loss_cls": 3.99916, "loss": 3.99916, "time": 0.81921} +{"mode": "train", "epoch": 66, "iter": 1600, "lr": 0.05996, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30047, "top5_acc": 0.55109, "loss_cls": 4.02209, "loss": 4.02209, "time": 0.8165} +{"mode": "train", "epoch": 66, "iter": 1700, "lr": 0.05993, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30812, "top5_acc": 0.56609, "loss_cls": 3.96449, "loss": 3.96449, "time": 0.81635} +{"mode": "train", "epoch": 66, "iter": 1800, "lr": 0.0599, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30734, "top5_acc": 0.56516, "loss_cls": 3.93242, "loss": 3.93242, "time": 0.81798} +{"mode": "train", "epoch": 66, "iter": 1900, "lr": 0.05988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29594, "top5_acc": 0.55, "loss_cls": 4.02961, "loss": 4.02961, "time": 0.81279} +{"mode": "train", "epoch": 66, "iter": 2000, "lr": 0.05985, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29953, "top5_acc": 0.56688, "loss_cls": 3.98886, "loss": 3.98886, "time": 0.814} +{"mode": "train", "epoch": 66, "iter": 2100, "lr": 0.05982, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31031, "top5_acc": 0.56797, "loss_cls": 3.96224, "loss": 3.96224, "time": 0.81616} +{"mode": "train", "epoch": 66, "iter": 2200, "lr": 0.05979, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31328, "top5_acc": 0.57344, "loss_cls": 3.94036, "loss": 3.94036, "time": 0.81511} +{"mode": "train", "epoch": 66, "iter": 2300, "lr": 0.05977, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29812, "top5_acc": 0.55859, "loss_cls": 4.00524, "loss": 4.00524, "time": 0.81268} +{"mode": "train", "epoch": 66, "iter": 2400, "lr": 0.05974, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30531, "top5_acc": 0.56594, "loss_cls": 3.95937, "loss": 3.95937, "time": 0.81696} +{"mode": "train", "epoch": 66, "iter": 2500, "lr": 0.05971, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30328, "top5_acc": 0.55203, "loss_cls": 4.02029, "loss": 4.02029, "time": 0.81126} +{"mode": "train", "epoch": 66, "iter": 2600, "lr": 0.05968, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31641, "top5_acc": 0.56703, "loss_cls": 3.94819, "loss": 3.94819, "time": 0.82436} +{"mode": "train", "epoch": 66, "iter": 2700, "lr": 0.05966, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31516, "top5_acc": 0.56141, "loss_cls": 3.96366, "loss": 3.96366, "time": 0.8201} +{"mode": "train", "epoch": 66, "iter": 2800, "lr": 0.05963, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31734, "top5_acc": 0.57328, "loss_cls": 3.92327, "loss": 3.92327, "time": 0.81893} +{"mode": "train", "epoch": 66, "iter": 2900, "lr": 0.0596, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30078, "top5_acc": 0.56578, "loss_cls": 3.97735, "loss": 3.97735, "time": 0.81758} +{"mode": "train", "epoch": 66, "iter": 3000, "lr": 0.05957, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30953, "top5_acc": 0.56359, "loss_cls": 3.95649, "loss": 3.95649, "time": 0.81477} +{"mode": "train", "epoch": 66, "iter": 3100, "lr": 0.05955, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30734, "top5_acc": 0.55875, "loss_cls": 3.98661, "loss": 3.98661, "time": 0.818} +{"mode": "train", "epoch": 66, "iter": 3200, "lr": 0.05952, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30516, "top5_acc": 0.55609, "loss_cls": 3.99876, "loss": 3.99876, "time": 0.81744} +{"mode": "train", "epoch": 66, "iter": 3300, "lr": 0.05949, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30453, "top5_acc": 0.56391, "loss_cls": 3.95972, "loss": 3.95972, "time": 0.81774} +{"mode": "train", "epoch": 66, "iter": 3400, "lr": 0.05946, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30125, "top5_acc": 0.56078, "loss_cls": 3.97539, "loss": 3.97539, "time": 0.81718} +{"mode": "train", "epoch": 66, "iter": 3500, "lr": 0.05944, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29797, "top5_acc": 0.55594, "loss_cls": 3.99122, "loss": 3.99122, "time": 0.81361} +{"mode": "train", "epoch": 66, "iter": 3600, "lr": 0.05941, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2975, "top5_acc": 0.56047, "loss_cls": 3.99073, "loss": 3.99073, "time": 0.81761} +{"mode": "train", "epoch": 66, "iter": 3700, "lr": 0.05938, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31719, "top5_acc": 0.56156, "loss_cls": 3.94126, "loss": 3.94126, "time": 0.82178} +{"mode": "val", "epoch": 66, "iter": 309, "lr": 0.05937, "top1_acc": 0.24834, "top5_acc": 0.49618, "mean_class_accuracy": 0.24815} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.05934, "memory": 15990, "data_time": 1.3086, "top1_acc": 0.32641, "top5_acc": 0.57828, "loss_cls": 3.86677, "loss": 3.86677, "time": 2.29388} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.05931, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31984, "top5_acc": 0.58188, "loss_cls": 3.86524, "loss": 3.86524, "time": 0.82068} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.05929, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30625, "top5_acc": 0.56938, "loss_cls": 3.95898, "loss": 3.95898, "time": 0.81911} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.05926, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31297, "top5_acc": 0.57281, "loss_cls": 3.90669, "loss": 3.90669, "time": 0.81873} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.05923, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.31781, "top5_acc": 0.56688, "loss_cls": 3.95036, "loss": 3.95036, "time": 0.8155} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.0592, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31062, "top5_acc": 0.56688, "loss_cls": 3.93336, "loss": 3.93336, "time": 0.81629} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.05918, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30656, "top5_acc": 0.56641, "loss_cls": 3.9687, "loss": 3.9687, "time": 0.81612} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.05915, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30719, "top5_acc": 0.56281, "loss_cls": 3.98028, "loss": 3.98028, "time": 0.82501} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.05912, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30031, "top5_acc": 0.55937, "loss_cls": 4.00078, "loss": 4.00078, "time": 0.81739} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.05909, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30797, "top5_acc": 0.5575, "loss_cls": 3.9529, "loss": 3.9529, "time": 0.82519} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.05907, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31984, "top5_acc": 0.56828, "loss_cls": 3.91637, "loss": 3.91637, "time": 0.82484} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.05904, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31219, "top5_acc": 0.56766, "loss_cls": 3.95332, "loss": 3.95332, "time": 0.82034} +{"mode": "train", "epoch": 67, "iter": 1300, "lr": 0.05901, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.305, "top5_acc": 0.57531, "loss_cls": 3.9524, "loss": 3.9524, "time": 0.81606} +{"mode": "train", "epoch": 67, "iter": 1400, "lr": 0.05898, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30781, "top5_acc": 0.56563, "loss_cls": 3.95936, "loss": 3.95936, "time": 0.81702} +{"mode": "train", "epoch": 67, "iter": 1500, "lr": 0.05896, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30922, "top5_acc": 0.56188, "loss_cls": 3.95821, "loss": 3.95821, "time": 0.81566} +{"mode": "train", "epoch": 67, "iter": 1600, "lr": 0.05893, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31156, "top5_acc": 0.56844, "loss_cls": 3.96858, "loss": 3.96858, "time": 0.8176} +{"mode": "train", "epoch": 67, "iter": 1700, "lr": 0.0589, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30859, "top5_acc": 0.55578, "loss_cls": 3.99538, "loss": 3.99538, "time": 0.81814} +{"mode": "train", "epoch": 67, "iter": 1800, "lr": 0.05887, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30766, "top5_acc": 0.55453, "loss_cls": 3.96725, "loss": 3.96725, "time": 0.81707} +{"mode": "train", "epoch": 67, "iter": 1900, "lr": 0.05885, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30969, "top5_acc": 0.56156, "loss_cls": 3.97488, "loss": 3.97488, "time": 0.81479} +{"mode": "train", "epoch": 67, "iter": 2000, "lr": 0.05882, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30891, "top5_acc": 0.57266, "loss_cls": 3.95539, "loss": 3.95539, "time": 0.82045} +{"mode": "train", "epoch": 67, "iter": 2100, "lr": 0.05879, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31109, "top5_acc": 0.57609, "loss_cls": 3.93097, "loss": 3.93097, "time": 0.81794} +{"mode": "train", "epoch": 67, "iter": 2200, "lr": 0.05876, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30547, "top5_acc": 0.5675, "loss_cls": 3.96954, "loss": 3.96954, "time": 0.8136} +{"mode": "train", "epoch": 67, "iter": 2300, "lr": 0.05874, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29984, "top5_acc": 0.56188, "loss_cls": 3.99366, "loss": 3.99366, "time": 0.81724} +{"mode": "train", "epoch": 67, "iter": 2400, "lr": 0.05871, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.305, "top5_acc": 0.56094, "loss_cls": 3.95198, "loss": 3.95198, "time": 0.81166} +{"mode": "train", "epoch": 67, "iter": 2500, "lr": 0.05868, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31281, "top5_acc": 0.57016, "loss_cls": 3.93191, "loss": 3.93191, "time": 0.81743} +{"mode": "train", "epoch": 67, "iter": 2600, "lr": 0.05865, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30562, "top5_acc": 0.56219, "loss_cls": 3.98337, "loss": 3.98337, "time": 0.82148} +{"mode": "train", "epoch": 67, "iter": 2700, "lr": 0.05863, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30766, "top5_acc": 0.5675, "loss_cls": 3.94143, "loss": 3.94143, "time": 0.81896} +{"mode": "train", "epoch": 67, "iter": 2800, "lr": 0.0586, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30359, "top5_acc": 0.55656, "loss_cls": 4.01002, "loss": 4.01002, "time": 0.81779} +{"mode": "train", "epoch": 67, "iter": 2900, "lr": 0.05857, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31047, "top5_acc": 0.56234, "loss_cls": 3.95927, "loss": 3.95927, "time": 0.81749} +{"mode": "train", "epoch": 67, "iter": 3000, "lr": 0.05854, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30609, "top5_acc": 0.56078, "loss_cls": 3.97995, "loss": 3.97995, "time": 0.814} +{"mode": "train", "epoch": 67, "iter": 3100, "lr": 0.05852, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31016, "top5_acc": 0.55969, "loss_cls": 3.97581, "loss": 3.97581, "time": 0.81687} +{"mode": "train", "epoch": 67, "iter": 3200, "lr": 0.05849, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29719, "top5_acc": 0.56078, "loss_cls": 4.02123, "loss": 4.02123, "time": 0.81704} +{"mode": "train", "epoch": 67, "iter": 3300, "lr": 0.05846, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31656, "top5_acc": 0.57094, "loss_cls": 3.94932, "loss": 3.94932, "time": 0.81786} +{"mode": "train", "epoch": 67, "iter": 3400, "lr": 0.05843, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30984, "top5_acc": 0.56656, "loss_cls": 3.9421, "loss": 3.9421, "time": 0.82128} +{"mode": "train", "epoch": 67, "iter": 3500, "lr": 0.05841, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31688, "top5_acc": 0.56625, "loss_cls": 3.93752, "loss": 3.93752, "time": 0.81502} +{"mode": "train", "epoch": 67, "iter": 3600, "lr": 0.05838, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30969, "top5_acc": 0.57109, "loss_cls": 3.95224, "loss": 3.95224, "time": 0.82069} +{"mode": "train", "epoch": 67, "iter": 3700, "lr": 0.05835, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.305, "top5_acc": 0.55781, "loss_cls": 3.99013, "loss": 3.99013, "time": 0.82377} +{"mode": "val", "epoch": 67, "iter": 309, "lr": 0.05834, "top1_acc": 0.25239, "top5_acc": 0.49749, "mean_class_accuracy": 0.25228} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.05831, "memory": 15990, "data_time": 1.27595, "top1_acc": 0.30828, "top5_acc": 0.57047, "loss_cls": 3.91683, "loss": 3.91683, "time": 2.26028} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.05828, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31719, "top5_acc": 0.57156, "loss_cls": 3.9464, "loss": 3.9464, "time": 0.81781} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.05826, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31406, "top5_acc": 0.57547, "loss_cls": 3.93447, "loss": 3.93447, "time": 0.81724} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.05823, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30812, "top5_acc": 0.57078, "loss_cls": 3.91614, "loss": 3.91614, "time": 0.81609} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.0582, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31906, "top5_acc": 0.57328, "loss_cls": 3.93158, "loss": 3.93158, "time": 0.81659} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.05817, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30656, "top5_acc": 0.55969, "loss_cls": 3.97293, "loss": 3.97293, "time": 0.81545} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.05815, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30875, "top5_acc": 0.57094, "loss_cls": 3.93686, "loss": 3.93686, "time": 0.81605} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.05812, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30359, "top5_acc": 0.56734, "loss_cls": 3.94876, "loss": 3.94876, "time": 0.8242} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.05809, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30891, "top5_acc": 0.57141, "loss_cls": 3.92885, "loss": 3.92885, "time": 0.8266} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.05806, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31172, "top5_acc": 0.56859, "loss_cls": 3.96886, "loss": 3.96886, "time": 0.82808} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.05804, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31734, "top5_acc": 0.56594, "loss_cls": 3.93569, "loss": 3.93569, "time": 0.81905} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.05801, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31797, "top5_acc": 0.57812, "loss_cls": 3.89378, "loss": 3.89378, "time": 0.82111} +{"mode": "train", "epoch": 68, "iter": 1300, "lr": 0.05798, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31094, "top5_acc": 0.55906, "loss_cls": 3.98282, "loss": 3.98282, "time": 0.81948} +{"mode": "train", "epoch": 68, "iter": 1400, "lr": 0.05795, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31078, "top5_acc": 0.57375, "loss_cls": 3.95679, "loss": 3.95679, "time": 0.81703} +{"mode": "train", "epoch": 68, "iter": 1500, "lr": 0.05792, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31359, "top5_acc": 0.57125, "loss_cls": 3.9435, "loss": 3.9435, "time": 0.81833} +{"mode": "train", "epoch": 68, "iter": 1600, "lr": 0.0579, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30516, "top5_acc": 0.57172, "loss_cls": 3.9494, "loss": 3.9494, "time": 0.81471} +{"mode": "train", "epoch": 68, "iter": 1700, "lr": 0.05787, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.31547, "top5_acc": 0.57609, "loss_cls": 3.89279, "loss": 3.89279, "time": 0.8102} +{"mode": "train", "epoch": 68, "iter": 1800, "lr": 0.05784, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30406, "top5_acc": 0.56609, "loss_cls": 4.00928, "loss": 4.00928, "time": 0.81857} +{"mode": "train", "epoch": 68, "iter": 1900, "lr": 0.05781, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31156, "top5_acc": 0.56563, "loss_cls": 3.94023, "loss": 3.94023, "time": 0.81548} +{"mode": "train", "epoch": 68, "iter": 2000, "lr": 0.05779, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30375, "top5_acc": 0.55562, "loss_cls": 3.98329, "loss": 3.98329, "time": 0.81878} +{"mode": "train", "epoch": 68, "iter": 2100, "lr": 0.05776, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30906, "top5_acc": 0.56719, "loss_cls": 3.94182, "loss": 3.94182, "time": 0.8147} +{"mode": "train", "epoch": 68, "iter": 2200, "lr": 0.05773, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31141, "top5_acc": 0.56094, "loss_cls": 3.98187, "loss": 3.98187, "time": 0.81956} +{"mode": "train", "epoch": 68, "iter": 2300, "lr": 0.0577, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30203, "top5_acc": 0.56797, "loss_cls": 3.9471, "loss": 3.9471, "time": 0.81251} +{"mode": "train", "epoch": 68, "iter": 2400, "lr": 0.05768, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3125, "top5_acc": 0.57172, "loss_cls": 3.93137, "loss": 3.93137, "time": 0.81306} +{"mode": "train", "epoch": 68, "iter": 2500, "lr": 0.05765, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30344, "top5_acc": 0.56188, "loss_cls": 3.98806, "loss": 3.98806, "time": 0.82588} +{"mode": "train", "epoch": 68, "iter": 2600, "lr": 0.05762, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32453, "top5_acc": 0.57047, "loss_cls": 3.92739, "loss": 3.92739, "time": 0.81809} +{"mode": "train", "epoch": 68, "iter": 2700, "lr": 0.05759, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30234, "top5_acc": 0.56359, "loss_cls": 3.96905, "loss": 3.96905, "time": 0.8204} +{"mode": "train", "epoch": 68, "iter": 2800, "lr": 0.05757, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30406, "top5_acc": 0.55719, "loss_cls": 3.9907, "loss": 3.9907, "time": 0.81559} +{"mode": "train", "epoch": 68, "iter": 2900, "lr": 0.05754, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31172, "top5_acc": 0.56469, "loss_cls": 3.99485, "loss": 3.99485, "time": 0.82239} +{"mode": "train", "epoch": 68, "iter": 3000, "lr": 0.05751, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29969, "top5_acc": 0.56328, "loss_cls": 3.99397, "loss": 3.99397, "time": 0.8168} +{"mode": "train", "epoch": 68, "iter": 3100, "lr": 0.05748, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31766, "top5_acc": 0.57703, "loss_cls": 3.91913, "loss": 3.91913, "time": 0.8172} +{"mode": "train", "epoch": 68, "iter": 3200, "lr": 0.05746, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30797, "top5_acc": 0.55719, "loss_cls": 3.97691, "loss": 3.97691, "time": 0.8157} +{"mode": "train", "epoch": 68, "iter": 3300, "lr": 0.05743, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31625, "top5_acc": 0.57203, "loss_cls": 3.90847, "loss": 3.90847, "time": 0.81503} +{"mode": "train", "epoch": 68, "iter": 3400, "lr": 0.0574, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30703, "top5_acc": 0.56141, "loss_cls": 3.94601, "loss": 3.94601, "time": 0.82078} +{"mode": "train", "epoch": 68, "iter": 3500, "lr": 0.05737, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31859, "top5_acc": 0.58, "loss_cls": 3.91281, "loss": 3.91281, "time": 0.81492} +{"mode": "train", "epoch": 68, "iter": 3600, "lr": 0.05734, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30875, "top5_acc": 0.57797, "loss_cls": 3.91101, "loss": 3.91101, "time": 0.81365} +{"mode": "train", "epoch": 68, "iter": 3700, "lr": 0.05732, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30734, "top5_acc": 0.56516, "loss_cls": 3.9685, "loss": 3.9685, "time": 0.81935} +{"mode": "val", "epoch": 68, "iter": 309, "lr": 0.0573, "top1_acc": 0.25092, "top5_acc": 0.49658, "mean_class_accuracy": 0.25065} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.05728, "memory": 15990, "data_time": 1.29872, "top1_acc": 0.32125, "top5_acc": 0.58453, "loss_cls": 3.84685, "loss": 3.84685, "time": 2.30156} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.05725, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31891, "top5_acc": 0.57688, "loss_cls": 3.90538, "loss": 3.90538, "time": 0.81483} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.05722, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31703, "top5_acc": 0.57203, "loss_cls": 3.89058, "loss": 3.89058, "time": 0.81717} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.05719, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32203, "top5_acc": 0.58297, "loss_cls": 3.8506, "loss": 3.8506, "time": 0.81795} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.05717, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30797, "top5_acc": 0.57063, "loss_cls": 3.93237, "loss": 3.93237, "time": 0.81641} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.05714, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31422, "top5_acc": 0.57438, "loss_cls": 3.91363, "loss": 3.91363, "time": 0.8129} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.05711, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30062, "top5_acc": 0.57281, "loss_cls": 3.94982, "loss": 3.94982, "time": 0.81483} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.05708, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.315, "top5_acc": 0.56938, "loss_cls": 3.92585, "loss": 3.92585, "time": 0.82721} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.05706, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31203, "top5_acc": 0.57688, "loss_cls": 3.90287, "loss": 3.90287, "time": 0.82118} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.05703, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32156, "top5_acc": 0.57094, "loss_cls": 3.94767, "loss": 3.94767, "time": 0.82371} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.057, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31172, "top5_acc": 0.56188, "loss_cls": 3.95834, "loss": 3.95834, "time": 0.82397} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.05697, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30531, "top5_acc": 0.56484, "loss_cls": 3.95945, "loss": 3.95945, "time": 0.82186} +{"mode": "train", "epoch": 69, "iter": 1300, "lr": 0.05694, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31719, "top5_acc": 0.56812, "loss_cls": 3.92876, "loss": 3.92876, "time": 0.81884} +{"mode": "train", "epoch": 69, "iter": 1400, "lr": 0.05692, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32016, "top5_acc": 0.57828, "loss_cls": 3.93453, "loss": 3.93453, "time": 0.82026} +{"mode": "train", "epoch": 69, "iter": 1500, "lr": 0.05689, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32031, "top5_acc": 0.57125, "loss_cls": 3.91219, "loss": 3.91219, "time": 0.81717} +{"mode": "train", "epoch": 69, "iter": 1600, "lr": 0.05686, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32812, "top5_acc": 0.57906, "loss_cls": 3.88941, "loss": 3.88941, "time": 0.8236} +{"mode": "train", "epoch": 69, "iter": 1700, "lr": 0.05683, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30906, "top5_acc": 0.56203, "loss_cls": 3.94708, "loss": 3.94708, "time": 0.81874} +{"mode": "train", "epoch": 69, "iter": 1800, "lr": 0.05681, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31562, "top5_acc": 0.56906, "loss_cls": 3.94234, "loss": 3.94234, "time": 0.81741} +{"mode": "train", "epoch": 69, "iter": 1900, "lr": 0.05678, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30703, "top5_acc": 0.56938, "loss_cls": 3.93088, "loss": 3.93088, "time": 0.8147} +{"mode": "train", "epoch": 69, "iter": 2000, "lr": 0.05675, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30984, "top5_acc": 0.56453, "loss_cls": 3.96288, "loss": 3.96288, "time": 0.82005} +{"mode": "train", "epoch": 69, "iter": 2100, "lr": 0.05672, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31922, "top5_acc": 0.57406, "loss_cls": 3.92782, "loss": 3.92782, "time": 0.81651} +{"mode": "train", "epoch": 69, "iter": 2200, "lr": 0.0567, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30438, "top5_acc": 0.56141, "loss_cls": 3.98567, "loss": 3.98567, "time": 0.81647} +{"mode": "train", "epoch": 69, "iter": 2300, "lr": 0.05667, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31125, "top5_acc": 0.57063, "loss_cls": 3.94748, "loss": 3.94748, "time": 0.82224} +{"mode": "train", "epoch": 69, "iter": 2400, "lr": 0.05664, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.315, "top5_acc": 0.57047, "loss_cls": 3.93612, "loss": 3.93612, "time": 0.81661} +{"mode": "train", "epoch": 69, "iter": 2500, "lr": 0.05661, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30938, "top5_acc": 0.55844, "loss_cls": 3.97963, "loss": 3.97963, "time": 0.8268} +{"mode": "train", "epoch": 69, "iter": 2600, "lr": 0.05658, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32234, "top5_acc": 0.57109, "loss_cls": 3.93409, "loss": 3.93409, "time": 0.81774} +{"mode": "train", "epoch": 69, "iter": 2700, "lr": 0.05656, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31625, "top5_acc": 0.56891, "loss_cls": 3.91413, "loss": 3.91413, "time": 0.82392} +{"mode": "train", "epoch": 69, "iter": 2800, "lr": 0.05653, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30172, "top5_acc": 0.55609, "loss_cls": 3.96753, "loss": 3.96753, "time": 0.82427} +{"mode": "train", "epoch": 69, "iter": 2900, "lr": 0.0565, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31391, "top5_acc": 0.57688, "loss_cls": 3.91432, "loss": 3.91432, "time": 0.81925} +{"mode": "train", "epoch": 69, "iter": 3000, "lr": 0.05647, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.315, "top5_acc": 0.56688, "loss_cls": 3.94313, "loss": 3.94313, "time": 0.81768} +{"mode": "train", "epoch": 69, "iter": 3100, "lr": 0.05645, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30562, "top5_acc": 0.5625, "loss_cls": 3.96404, "loss": 3.96404, "time": 0.81628} +{"mode": "train", "epoch": 69, "iter": 3200, "lr": 0.05642, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31828, "top5_acc": 0.57375, "loss_cls": 3.92489, "loss": 3.92489, "time": 0.81557} +{"mode": "train", "epoch": 69, "iter": 3300, "lr": 0.05639, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30938, "top5_acc": 0.56266, "loss_cls": 3.95589, "loss": 3.95589, "time": 0.81327} +{"mode": "train", "epoch": 69, "iter": 3400, "lr": 0.05636, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30625, "top5_acc": 0.56641, "loss_cls": 3.96553, "loss": 3.96553, "time": 0.81449} +{"mode": "train", "epoch": 69, "iter": 3500, "lr": 0.05634, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30516, "top5_acc": 0.55844, "loss_cls": 3.99872, "loss": 3.99872, "time": 0.81637} +{"mode": "train", "epoch": 69, "iter": 3600, "lr": 0.05631, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29984, "top5_acc": 0.55781, "loss_cls": 3.97161, "loss": 3.97161, "time": 0.81582} +{"mode": "train", "epoch": 69, "iter": 3700, "lr": 0.05628, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31312, "top5_acc": 0.57203, "loss_cls": 3.92252, "loss": 3.92252, "time": 0.81269} +{"mode": "val", "epoch": 69, "iter": 309, "lr": 0.05627, "top1_acc": 0.24373, "top5_acc": 0.48817, "mean_class_accuracy": 0.2434} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.05624, "memory": 15990, "data_time": 1.29727, "top1_acc": 0.31703, "top5_acc": 0.57156, "loss_cls": 3.91957, "loss": 3.91957, "time": 2.28481} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.05621, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32922, "top5_acc": 0.58078, "loss_cls": 3.86364, "loss": 3.86364, "time": 0.82431} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.05618, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31891, "top5_acc": 0.57641, "loss_cls": 3.85953, "loss": 3.85953, "time": 0.81636} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.05616, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32203, "top5_acc": 0.58281, "loss_cls": 3.83157, "loss": 3.83157, "time": 0.81518} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.05613, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30656, "top5_acc": 0.56266, "loss_cls": 3.97997, "loss": 3.97997, "time": 0.81662} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.0561, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3025, "top5_acc": 0.55937, "loss_cls": 3.97944, "loss": 3.97944, "time": 0.816} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.05607, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30625, "top5_acc": 0.57609, "loss_cls": 3.91991, "loss": 3.91991, "time": 0.82034} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.05605, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31922, "top5_acc": 0.57406, "loss_cls": 3.91786, "loss": 3.91786, "time": 0.8196} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.05602, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31734, "top5_acc": 0.57391, "loss_cls": 3.92265, "loss": 3.92265, "time": 0.82289} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.05599, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3225, "top5_acc": 0.56422, "loss_cls": 3.91174, "loss": 3.91174, "time": 0.81937} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.05596, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31875, "top5_acc": 0.57312, "loss_cls": 3.90189, "loss": 3.90189, "time": 0.81927} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.05593, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31547, "top5_acc": 0.56859, "loss_cls": 3.91655, "loss": 3.91655, "time": 0.81688} +{"mode": "train", "epoch": 70, "iter": 1300, "lr": 0.05591, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31219, "top5_acc": 0.56938, "loss_cls": 3.92756, "loss": 3.92756, "time": 0.82265} +{"mode": "train", "epoch": 70, "iter": 1400, "lr": 0.05588, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31453, "top5_acc": 0.57453, "loss_cls": 3.9308, "loss": 3.9308, "time": 0.82262} +{"mode": "train", "epoch": 70, "iter": 1500, "lr": 0.05585, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31359, "top5_acc": 0.56797, "loss_cls": 3.94595, "loss": 3.94595, "time": 0.81702} +{"mode": "train", "epoch": 70, "iter": 1600, "lr": 0.05582, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31562, "top5_acc": 0.56906, "loss_cls": 3.91536, "loss": 3.91536, "time": 0.81639} +{"mode": "train", "epoch": 70, "iter": 1700, "lr": 0.0558, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31516, "top5_acc": 0.57016, "loss_cls": 3.93864, "loss": 3.93864, "time": 0.81741} +{"mode": "train", "epoch": 70, "iter": 1800, "lr": 0.05577, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31547, "top5_acc": 0.56875, "loss_cls": 3.93957, "loss": 3.93957, "time": 0.81591} +{"mode": "train", "epoch": 70, "iter": 1900, "lr": 0.05574, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31031, "top5_acc": 0.55641, "loss_cls": 3.97694, "loss": 3.97694, "time": 0.82026} +{"mode": "train", "epoch": 70, "iter": 2000, "lr": 0.05571, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30516, "top5_acc": 0.56891, "loss_cls": 3.95251, "loss": 3.95251, "time": 0.81595} +{"mode": "train", "epoch": 70, "iter": 2100, "lr": 0.05568, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3125, "top5_acc": 0.56953, "loss_cls": 3.912, "loss": 3.912, "time": 0.81894} +{"mode": "train", "epoch": 70, "iter": 2200, "lr": 0.05566, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31688, "top5_acc": 0.57312, "loss_cls": 3.92191, "loss": 3.92191, "time": 0.81656} +{"mode": "train", "epoch": 70, "iter": 2300, "lr": 0.05563, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30953, "top5_acc": 0.56547, "loss_cls": 3.94885, "loss": 3.94885, "time": 0.81497} +{"mode": "train", "epoch": 70, "iter": 2400, "lr": 0.0556, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31172, "top5_acc": 0.56828, "loss_cls": 3.9303, "loss": 3.9303, "time": 0.81957} +{"mode": "train", "epoch": 70, "iter": 2500, "lr": 0.05557, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32953, "top5_acc": 0.57375, "loss_cls": 3.89952, "loss": 3.89952, "time": 0.8197} +{"mode": "train", "epoch": 70, "iter": 2600, "lr": 0.05555, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31281, "top5_acc": 0.56828, "loss_cls": 3.95949, "loss": 3.95949, "time": 0.81967} +{"mode": "train", "epoch": 70, "iter": 2700, "lr": 0.05552, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.31859, "top5_acc": 0.57203, "loss_cls": 3.89809, "loss": 3.89809, "time": 0.82419} +{"mode": "train", "epoch": 70, "iter": 2800, "lr": 0.05549, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31328, "top5_acc": 0.56563, "loss_cls": 3.9402, "loss": 3.9402, "time": 0.8208} +{"mode": "train", "epoch": 70, "iter": 2900, "lr": 0.05546, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30953, "top5_acc": 0.56875, "loss_cls": 3.96323, "loss": 3.96323, "time": 0.8241} +{"mode": "train", "epoch": 70, "iter": 3000, "lr": 0.05543, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32531, "top5_acc": 0.57766, "loss_cls": 3.88142, "loss": 3.88142, "time": 0.81603} +{"mode": "train", "epoch": 70, "iter": 3100, "lr": 0.05541, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3075, "top5_acc": 0.56984, "loss_cls": 3.93143, "loss": 3.93143, "time": 0.82571} +{"mode": "train", "epoch": 70, "iter": 3200, "lr": 0.05538, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30047, "top5_acc": 0.56484, "loss_cls": 3.96712, "loss": 3.96712, "time": 0.81768} +{"mode": "train", "epoch": 70, "iter": 3300, "lr": 0.05535, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32234, "top5_acc": 0.57875, "loss_cls": 3.87779, "loss": 3.87779, "time": 0.81412} +{"mode": "train", "epoch": 70, "iter": 3400, "lr": 0.05532, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31, "top5_acc": 0.56547, "loss_cls": 3.94639, "loss": 3.94639, "time": 0.81887} +{"mode": "train", "epoch": 70, "iter": 3500, "lr": 0.0553, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30859, "top5_acc": 0.55984, "loss_cls": 3.96834, "loss": 3.96834, "time": 0.81721} +{"mode": "train", "epoch": 70, "iter": 3600, "lr": 0.05527, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31875, "top5_acc": 0.57469, "loss_cls": 3.91557, "loss": 3.91557, "time": 0.81652} +{"mode": "train", "epoch": 70, "iter": 3700, "lr": 0.05524, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31859, "top5_acc": 0.57, "loss_cls": 3.92566, "loss": 3.92566, "time": 0.81644} +{"mode": "val", "epoch": 70, "iter": 309, "lr": 0.05523, "top1_acc": 0.24292, "top5_acc": 0.48675, "mean_class_accuracy": 0.24275} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.0552, "memory": 15990, "data_time": 1.29944, "top1_acc": 0.32344, "top5_acc": 0.58, "loss_cls": 3.85676, "loss": 3.85676, "time": 2.2865} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.05517, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32359, "top5_acc": 0.57891, "loss_cls": 3.83831, "loss": 3.83831, "time": 0.8192} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.05514, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31281, "top5_acc": 0.56375, "loss_cls": 3.92961, "loss": 3.92961, "time": 0.819} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.05512, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32219, "top5_acc": 0.57516, "loss_cls": 3.87621, "loss": 3.87621, "time": 0.82153} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.05509, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31422, "top5_acc": 0.56766, "loss_cls": 3.95118, "loss": 3.95118, "time": 0.81551} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.05506, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30609, "top5_acc": 0.56406, "loss_cls": 3.97541, "loss": 3.97541, "time": 0.81767} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.05503, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31953, "top5_acc": 0.56906, "loss_cls": 3.93958, "loss": 3.93958, "time": 0.82153} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31453, "top5_acc": 0.56953, "loss_cls": 3.9173, "loss": 3.9173, "time": 0.82323} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.05498, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.315, "top5_acc": 0.57422, "loss_cls": 3.94386, "loss": 3.94386, "time": 0.82589} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.05495, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31578, "top5_acc": 0.58578, "loss_cls": 3.88837, "loss": 3.88837, "time": 0.8227} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.05492, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32656, "top5_acc": 0.57875, "loss_cls": 3.8753, "loss": 3.8753, "time": 0.81479} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.05489, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32797, "top5_acc": 0.57516, "loss_cls": 3.89267, "loss": 3.89267, "time": 0.82228} +{"mode": "train", "epoch": 71, "iter": 1300, "lr": 0.05487, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31453, "top5_acc": 0.57297, "loss_cls": 3.90307, "loss": 3.90307, "time": 0.82215} +{"mode": "train", "epoch": 71, "iter": 1400, "lr": 0.05484, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30797, "top5_acc": 0.57375, "loss_cls": 3.9329, "loss": 3.9329, "time": 0.81488} +{"mode": "train", "epoch": 71, "iter": 1500, "lr": 0.05481, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31578, "top5_acc": 0.57297, "loss_cls": 3.91349, "loss": 3.91349, "time": 0.81421} +{"mode": "train", "epoch": 71, "iter": 1600, "lr": 0.05478, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3175, "top5_acc": 0.58438, "loss_cls": 3.87611, "loss": 3.87611, "time": 0.81553} +{"mode": "train", "epoch": 71, "iter": 1700, "lr": 0.05475, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32031, "top5_acc": 0.57969, "loss_cls": 3.88007, "loss": 3.88007, "time": 0.81357} +{"mode": "train", "epoch": 71, "iter": 1800, "lr": 0.05473, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31422, "top5_acc": 0.57375, "loss_cls": 3.91509, "loss": 3.91509, "time": 0.82385} +{"mode": "train", "epoch": 71, "iter": 1900, "lr": 0.0547, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31938, "top5_acc": 0.57406, "loss_cls": 3.8829, "loss": 3.8829, "time": 0.82049} +{"mode": "train", "epoch": 71, "iter": 2000, "lr": 0.05467, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31734, "top5_acc": 0.56516, "loss_cls": 3.94415, "loss": 3.94415, "time": 0.81784} +{"mode": "train", "epoch": 71, "iter": 2100, "lr": 0.05464, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31312, "top5_acc": 0.57188, "loss_cls": 3.93537, "loss": 3.93537, "time": 0.81558} +{"mode": "train", "epoch": 71, "iter": 2200, "lr": 0.05461, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31109, "top5_acc": 0.56125, "loss_cls": 3.9663, "loss": 3.9663, "time": 0.8199} +{"mode": "train", "epoch": 71, "iter": 2300, "lr": 0.05459, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30453, "top5_acc": 0.56312, "loss_cls": 3.97949, "loss": 3.97949, "time": 0.82008} +{"mode": "train", "epoch": 71, "iter": 2400, "lr": 0.05456, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31438, "top5_acc": 0.56797, "loss_cls": 3.94389, "loss": 3.94389, "time": 0.81448} +{"mode": "train", "epoch": 71, "iter": 2500, "lr": 0.05453, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31328, "top5_acc": 0.57688, "loss_cls": 3.91413, "loss": 3.91413, "time": 0.82048} +{"mode": "train", "epoch": 71, "iter": 2600, "lr": 0.0545, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31484, "top5_acc": 0.58297, "loss_cls": 3.86455, "loss": 3.86455, "time": 0.82243} +{"mode": "train", "epoch": 71, "iter": 2700, "lr": 0.05448, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33156, "top5_acc": 0.57875, "loss_cls": 3.87476, "loss": 3.87476, "time": 0.83248} +{"mode": "train", "epoch": 71, "iter": 2800, "lr": 0.05445, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32719, "top5_acc": 0.57672, "loss_cls": 3.89319, "loss": 3.89319, "time": 0.82741} +{"mode": "train", "epoch": 71, "iter": 2900, "lr": 0.05442, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30516, "top5_acc": 0.56797, "loss_cls": 3.96156, "loss": 3.96156, "time": 0.82313} +{"mode": "train", "epoch": 71, "iter": 3000, "lr": 0.05439, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32, "top5_acc": 0.56766, "loss_cls": 3.91349, "loss": 3.91349, "time": 0.81533} +{"mode": "train", "epoch": 71, "iter": 3100, "lr": 0.05436, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31609, "top5_acc": 0.57297, "loss_cls": 3.92617, "loss": 3.92617, "time": 0.81993} +{"mode": "train", "epoch": 71, "iter": 3200, "lr": 0.05434, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31188, "top5_acc": 0.57453, "loss_cls": 3.90753, "loss": 3.90753, "time": 0.81413} +{"mode": "train", "epoch": 71, "iter": 3300, "lr": 0.05431, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31828, "top5_acc": 0.57812, "loss_cls": 3.88679, "loss": 3.88679, "time": 0.81801} +{"mode": "train", "epoch": 71, "iter": 3400, "lr": 0.05428, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31672, "top5_acc": 0.56344, "loss_cls": 3.93635, "loss": 3.93635, "time": 0.81746} +{"mode": "train", "epoch": 71, "iter": 3500, "lr": 0.05425, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30797, "top5_acc": 0.57188, "loss_cls": 3.92256, "loss": 3.92256, "time": 0.8165} +{"mode": "train", "epoch": 71, "iter": 3600, "lr": 0.05422, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32016, "top5_acc": 0.57578, "loss_cls": 3.88013, "loss": 3.88013, "time": 0.81209} +{"mode": "train", "epoch": 71, "iter": 3700, "lr": 0.0542, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31609, "top5_acc": 0.57359, "loss_cls": 3.96729, "loss": 3.96729, "time": 0.81393} +{"mode": "val", "epoch": 71, "iter": 309, "lr": 0.05418, "top1_acc": 0.24707, "top5_acc": 0.49846, "mean_class_accuracy": 0.24697} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.05416, "memory": 15990, "data_time": 1.26172, "top1_acc": 0.32109, "top5_acc": 0.58531, "loss_cls": 3.86552, "loss": 3.86552, "time": 2.24333} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.05413, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32516, "top5_acc": 0.58422, "loss_cls": 3.83806, "loss": 3.83806, "time": 0.82296} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.0541, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32141, "top5_acc": 0.57531, "loss_cls": 3.88476, "loss": 3.88476, "time": 0.81675} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.05407, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32969, "top5_acc": 0.58875, "loss_cls": 3.82592, "loss": 3.82592, "time": 0.81577} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.05404, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32031, "top5_acc": 0.57859, "loss_cls": 3.87187, "loss": 3.87187, "time": 0.81543} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.05402, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32594, "top5_acc": 0.57234, "loss_cls": 3.891, "loss": 3.891, "time": 0.8148} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.05399, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30719, "top5_acc": 0.56812, "loss_cls": 3.92875, "loss": 3.92875, "time": 0.82313} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.05396, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33281, "top5_acc": 0.59203, "loss_cls": 3.83441, "loss": 3.83441, "time": 0.81757} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.05393, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31594, "top5_acc": 0.57016, "loss_cls": 3.93785, "loss": 3.93785, "time": 0.82124} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.05391, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31266, "top5_acc": 0.56797, "loss_cls": 3.93659, "loss": 3.93659, "time": 0.81796} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.05388, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31312, "top5_acc": 0.57828, "loss_cls": 3.88713, "loss": 3.88713, "time": 0.82086} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.05385, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32234, "top5_acc": 0.58203, "loss_cls": 3.88684, "loss": 3.88684, "time": 0.81886} +{"mode": "train", "epoch": 72, "iter": 1300, "lr": 0.05382, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32453, "top5_acc": 0.57797, "loss_cls": 3.86858, "loss": 3.86858, "time": 0.82509} +{"mode": "train", "epoch": 72, "iter": 1400, "lr": 0.05379, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31906, "top5_acc": 0.57281, "loss_cls": 3.90272, "loss": 3.90272, "time": 0.82136} +{"mode": "train", "epoch": 72, "iter": 1500, "lr": 0.05377, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31266, "top5_acc": 0.57766, "loss_cls": 3.89478, "loss": 3.89478, "time": 0.82263} +{"mode": "train", "epoch": 72, "iter": 1600, "lr": 0.05374, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3175, "top5_acc": 0.57031, "loss_cls": 3.91569, "loss": 3.91569, "time": 0.81666} +{"mode": "train", "epoch": 72, "iter": 1700, "lr": 0.05371, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30766, "top5_acc": 0.56766, "loss_cls": 3.96135, "loss": 3.96135, "time": 0.82328} +{"mode": "train", "epoch": 72, "iter": 1800, "lr": 0.05368, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31016, "top5_acc": 0.575, "loss_cls": 3.9278, "loss": 3.9278, "time": 0.81407} +{"mode": "train", "epoch": 72, "iter": 1900, "lr": 0.05365, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33078, "top5_acc": 0.57359, "loss_cls": 3.89895, "loss": 3.89895, "time": 0.81874} +{"mode": "train", "epoch": 72, "iter": 2000, "lr": 0.05363, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31672, "top5_acc": 0.57516, "loss_cls": 3.90484, "loss": 3.90484, "time": 0.81424} +{"mode": "train", "epoch": 72, "iter": 2100, "lr": 0.0536, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31641, "top5_acc": 0.57328, "loss_cls": 3.92234, "loss": 3.92234, "time": 0.81548} +{"mode": "train", "epoch": 72, "iter": 2200, "lr": 0.05357, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30969, "top5_acc": 0.57359, "loss_cls": 3.94524, "loss": 3.94524, "time": 0.817} +{"mode": "train", "epoch": 72, "iter": 2300, "lr": 0.05354, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31609, "top5_acc": 0.57234, "loss_cls": 3.90516, "loss": 3.90516, "time": 0.82008} +{"mode": "train", "epoch": 72, "iter": 2400, "lr": 0.05352, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31266, "top5_acc": 0.56953, "loss_cls": 3.92626, "loss": 3.92626, "time": 0.81455} +{"mode": "train", "epoch": 72, "iter": 2500, "lr": 0.05349, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32922, "top5_acc": 0.57594, "loss_cls": 3.89353, "loss": 3.89353, "time": 0.81685} +{"mode": "train", "epoch": 72, "iter": 2600, "lr": 0.05346, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31188, "top5_acc": 0.57156, "loss_cls": 3.94472, "loss": 3.94472, "time": 0.81384} +{"mode": "train", "epoch": 72, "iter": 2700, "lr": 0.05343, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31094, "top5_acc": 0.56719, "loss_cls": 3.92217, "loss": 3.92217, "time": 0.82361} +{"mode": "train", "epoch": 72, "iter": 2800, "lr": 0.0534, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31375, "top5_acc": 0.57172, "loss_cls": 3.93959, "loss": 3.93959, "time": 0.81435} +{"mode": "train", "epoch": 72, "iter": 2900, "lr": 0.05338, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31469, "top5_acc": 0.57047, "loss_cls": 3.92766, "loss": 3.92766, "time": 0.8194} +{"mode": "train", "epoch": 72, "iter": 3000, "lr": 0.05335, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31062, "top5_acc": 0.57984, "loss_cls": 3.88006, "loss": 3.88006, "time": 0.82031} +{"mode": "train", "epoch": 72, "iter": 3100, "lr": 0.05332, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31562, "top5_acc": 0.57141, "loss_cls": 3.91024, "loss": 3.91024, "time": 0.81656} +{"mode": "train", "epoch": 72, "iter": 3200, "lr": 0.05329, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30359, "top5_acc": 0.56484, "loss_cls": 3.95751, "loss": 3.95751, "time": 0.81912} +{"mode": "train", "epoch": 72, "iter": 3300, "lr": 0.05326, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32547, "top5_acc": 0.57766, "loss_cls": 3.89063, "loss": 3.89063, "time": 0.81697} +{"mode": "train", "epoch": 72, "iter": 3400, "lr": 0.05324, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30719, "top5_acc": 0.57031, "loss_cls": 3.96583, "loss": 3.96583, "time": 0.81733} +{"mode": "train", "epoch": 72, "iter": 3500, "lr": 0.05321, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31469, "top5_acc": 0.57594, "loss_cls": 3.88372, "loss": 3.88372, "time": 0.81739} +{"mode": "train", "epoch": 72, "iter": 3600, "lr": 0.05318, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30562, "top5_acc": 0.57609, "loss_cls": 3.93418, "loss": 3.93418, "time": 0.8157} +{"mode": "train", "epoch": 72, "iter": 3700, "lr": 0.05315, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30391, "top5_acc": 0.56563, "loss_cls": 3.94794, "loss": 3.94794, "time": 0.82086} +{"mode": "val", "epoch": 72, "iter": 309, "lr": 0.05314, "top1_acc": 0.24981, "top5_acc": 0.49506, "mean_class_accuracy": 0.2496} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.05311, "memory": 15990, "data_time": 1.26264, "top1_acc": 0.32484, "top5_acc": 0.585, "loss_cls": 3.84212, "loss": 3.84212, "time": 2.2435} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.05308, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32656, "top5_acc": 0.58688, "loss_cls": 3.86927, "loss": 3.86927, "time": 0.81701} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.05306, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32438, "top5_acc": 0.58203, "loss_cls": 3.84822, "loss": 3.84822, "time": 0.81358} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.05303, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31844, "top5_acc": 0.58203, "loss_cls": 3.86811, "loss": 3.86811, "time": 0.81496} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.053, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30891, "top5_acc": 0.57203, "loss_cls": 3.92512, "loss": 3.92512, "time": 0.8163} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.05297, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31859, "top5_acc": 0.58469, "loss_cls": 3.84053, "loss": 3.84053, "time": 0.81478} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.05294, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32141, "top5_acc": 0.57609, "loss_cls": 3.88612, "loss": 3.88612, "time": 0.82116} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.05292, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29547, "top5_acc": 0.56109, "loss_cls": 4.00904, "loss": 4.00904, "time": 0.81541} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.05289, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32, "top5_acc": 0.57797, "loss_cls": 3.90008, "loss": 3.90008, "time": 0.8251} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.05286, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31453, "top5_acc": 0.57719, "loss_cls": 3.92437, "loss": 3.92437, "time": 0.81824} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.05283, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31891, "top5_acc": 0.57828, "loss_cls": 3.92195, "loss": 3.92195, "time": 0.81948} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.0528, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32531, "top5_acc": 0.58188, "loss_cls": 3.86997, "loss": 3.86997, "time": 0.82079} +{"mode": "train", "epoch": 73, "iter": 1300, "lr": 0.05278, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31703, "top5_acc": 0.57625, "loss_cls": 3.91218, "loss": 3.91218, "time": 0.82194} +{"mode": "train", "epoch": 73, "iter": 1400, "lr": 0.05275, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31859, "top5_acc": 0.58422, "loss_cls": 3.86693, "loss": 3.86693, "time": 0.81519} +{"mode": "train", "epoch": 73, "iter": 1500, "lr": 0.05272, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31172, "top5_acc": 0.56844, "loss_cls": 3.93338, "loss": 3.93338, "time": 0.8173} +{"mode": "train", "epoch": 73, "iter": 1600, "lr": 0.05269, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32141, "top5_acc": 0.57656, "loss_cls": 3.89118, "loss": 3.89118, "time": 0.81516} +{"mode": "train", "epoch": 73, "iter": 1700, "lr": 0.05267, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31047, "top5_acc": 0.56484, "loss_cls": 3.9383, "loss": 3.9383, "time": 0.8168} +{"mode": "train", "epoch": 73, "iter": 1800, "lr": 0.05264, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32641, "top5_acc": 0.58078, "loss_cls": 3.8723, "loss": 3.8723, "time": 0.81608} +{"mode": "train", "epoch": 73, "iter": 1900, "lr": 0.05261, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32, "top5_acc": 0.57563, "loss_cls": 3.89382, "loss": 3.89382, "time": 0.81836} +{"mode": "train", "epoch": 73, "iter": 2000, "lr": 0.05258, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31344, "top5_acc": 0.58031, "loss_cls": 3.90771, "loss": 3.90771, "time": 0.81802} +{"mode": "train", "epoch": 73, "iter": 2100, "lr": 0.05255, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31969, "top5_acc": 0.58406, "loss_cls": 3.88292, "loss": 3.88292, "time": 0.81353} +{"mode": "train", "epoch": 73, "iter": 2200, "lr": 0.05253, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.315, "top5_acc": 0.56922, "loss_cls": 3.95219, "loss": 3.95219, "time": 0.81933} +{"mode": "train", "epoch": 73, "iter": 2300, "lr": 0.0525, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31172, "top5_acc": 0.57719, "loss_cls": 3.91008, "loss": 3.91008, "time": 0.82067} +{"mode": "train", "epoch": 73, "iter": 2400, "lr": 0.05247, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33359, "top5_acc": 0.57719, "loss_cls": 3.87957, "loss": 3.87957, "time": 0.81452} +{"mode": "train", "epoch": 73, "iter": 2500, "lr": 0.05244, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33125, "top5_acc": 0.57859, "loss_cls": 3.85908, "loss": 3.85908, "time": 0.81889} +{"mode": "train", "epoch": 73, "iter": 2600, "lr": 0.05241, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31766, "top5_acc": 0.58547, "loss_cls": 3.87357, "loss": 3.87357, "time": 0.81339} +{"mode": "train", "epoch": 73, "iter": 2700, "lr": 0.05239, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32406, "top5_acc": 0.57641, "loss_cls": 3.89573, "loss": 3.89573, "time": 0.81645} +{"mode": "train", "epoch": 73, "iter": 2800, "lr": 0.05236, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32062, "top5_acc": 0.57844, "loss_cls": 3.87201, "loss": 3.87201, "time": 0.82754} +{"mode": "train", "epoch": 73, "iter": 2900, "lr": 0.05233, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30844, "top5_acc": 0.57188, "loss_cls": 3.91758, "loss": 3.91758, "time": 0.82957} +{"mode": "train", "epoch": 73, "iter": 3000, "lr": 0.0523, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32109, "top5_acc": 0.56641, "loss_cls": 3.9075, "loss": 3.9075, "time": 0.8231} +{"mode": "train", "epoch": 73, "iter": 3100, "lr": 0.05227, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32141, "top5_acc": 0.58312, "loss_cls": 3.87982, "loss": 3.87982, "time": 0.82164} +{"mode": "train", "epoch": 73, "iter": 3200, "lr": 0.05225, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31703, "top5_acc": 0.57422, "loss_cls": 3.92025, "loss": 3.92025, "time": 0.81854} +{"mode": "train", "epoch": 73, "iter": 3300, "lr": 0.05222, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32109, "top5_acc": 0.57156, "loss_cls": 3.91662, "loss": 3.91662, "time": 0.82009} +{"mode": "train", "epoch": 73, "iter": 3400, "lr": 0.05219, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32125, "top5_acc": 0.57766, "loss_cls": 3.89042, "loss": 3.89042, "time": 0.82008} +{"mode": "train", "epoch": 73, "iter": 3500, "lr": 0.05216, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32344, "top5_acc": 0.58438, "loss_cls": 3.82557, "loss": 3.82557, "time": 0.81292} +{"mode": "train", "epoch": 73, "iter": 3600, "lr": 0.05213, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32656, "top5_acc": 0.57641, "loss_cls": 3.88261, "loss": 3.88261, "time": 0.8176} +{"mode": "train", "epoch": 73, "iter": 3700, "lr": 0.05211, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31562, "top5_acc": 0.56953, "loss_cls": 3.94509, "loss": 3.94509, "time": 0.81119} +{"mode": "val", "epoch": 73, "iter": 309, "lr": 0.05209, "top1_acc": 0.25923, "top5_acc": 0.51142, "mean_class_accuracy": 0.25921} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.05207, "memory": 15990, "data_time": 1.31423, "top1_acc": 0.32328, "top5_acc": 0.58781, "loss_cls": 3.8679, "loss": 3.8679, "time": 2.29963} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.05204, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33031, "top5_acc": 0.59406, "loss_cls": 3.78661, "loss": 3.78661, "time": 0.82294} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.05201, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32672, "top5_acc": 0.58297, "loss_cls": 3.85325, "loss": 3.85325, "time": 0.82812} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.05198, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33047, "top5_acc": 0.58875, "loss_cls": 3.84137, "loss": 3.84137, "time": 0.82108} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.05195, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32344, "top5_acc": 0.58422, "loss_cls": 3.86633, "loss": 3.86633, "time": 0.81591} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.05193, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31484, "top5_acc": 0.58016, "loss_cls": 3.91337, "loss": 3.91337, "time": 0.81578} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.0519, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31656, "top5_acc": 0.58141, "loss_cls": 3.9059, "loss": 3.9059, "time": 0.82138} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.05187, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33359, "top5_acc": 0.58281, "loss_cls": 3.84649, "loss": 3.84649, "time": 0.82091} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.05184, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32562, "top5_acc": 0.58625, "loss_cls": 3.87014, "loss": 3.87014, "time": 0.82492} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.05181, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32391, "top5_acc": 0.58031, "loss_cls": 3.88272, "loss": 3.88272, "time": 0.81647} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.05179, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31156, "top5_acc": 0.57297, "loss_cls": 3.92969, "loss": 3.92969, "time": 0.82232} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.05176, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31328, "top5_acc": 0.57734, "loss_cls": 3.90479, "loss": 3.90479, "time": 0.81994} +{"mode": "train", "epoch": 74, "iter": 1300, "lr": 0.05173, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31609, "top5_acc": 0.58219, "loss_cls": 3.88611, "loss": 3.88611, "time": 0.82066} +{"mode": "train", "epoch": 74, "iter": 1400, "lr": 0.0517, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32281, "top5_acc": 0.58156, "loss_cls": 3.86198, "loss": 3.86198, "time": 0.82131} +{"mode": "train", "epoch": 74, "iter": 1500, "lr": 0.05168, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31281, "top5_acc": 0.57391, "loss_cls": 3.9093, "loss": 3.9093, "time": 0.82058} +{"mode": "train", "epoch": 74, "iter": 1600, "lr": 0.05165, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30984, "top5_acc": 0.56563, "loss_cls": 3.93185, "loss": 3.93185, "time": 0.8214} +{"mode": "train", "epoch": 74, "iter": 1700, "lr": 0.05162, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31719, "top5_acc": 0.58047, "loss_cls": 3.88101, "loss": 3.88101, "time": 0.82133} +{"mode": "train", "epoch": 74, "iter": 1800, "lr": 0.05159, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33188, "top5_acc": 0.59266, "loss_cls": 3.81234, "loss": 3.81234, "time": 0.8185} +{"mode": "train", "epoch": 74, "iter": 1900, "lr": 0.05156, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31969, "top5_acc": 0.57719, "loss_cls": 3.86228, "loss": 3.86228, "time": 0.81936} +{"mode": "train", "epoch": 74, "iter": 2000, "lr": 0.05154, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32766, "top5_acc": 0.57938, "loss_cls": 3.86893, "loss": 3.86893, "time": 0.81954} +{"mode": "train", "epoch": 74, "iter": 2100, "lr": 0.05151, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31281, "top5_acc": 0.57219, "loss_cls": 3.9276, "loss": 3.9276, "time": 0.81615} +{"mode": "train", "epoch": 74, "iter": 2200, "lr": 0.05148, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.30547, "top5_acc": 0.56312, "loss_cls": 3.96281, "loss": 3.96281, "time": 0.81456} +{"mode": "train", "epoch": 74, "iter": 2300, "lr": 0.05145, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33266, "top5_acc": 0.58578, "loss_cls": 3.83423, "loss": 3.83423, "time": 0.81545} +{"mode": "train", "epoch": 74, "iter": 2400, "lr": 0.05142, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32016, "top5_acc": 0.57547, "loss_cls": 3.92751, "loss": 3.92751, "time": 0.81385} +{"mode": "train", "epoch": 74, "iter": 2500, "lr": 0.0514, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32422, "top5_acc": 0.57891, "loss_cls": 3.87806, "loss": 3.87806, "time": 0.81767} +{"mode": "train", "epoch": 74, "iter": 2600, "lr": 0.05137, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31562, "top5_acc": 0.56734, "loss_cls": 3.88356, "loss": 3.88356, "time": 0.81331} +{"mode": "train", "epoch": 74, "iter": 2700, "lr": 0.05134, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30781, "top5_acc": 0.57359, "loss_cls": 3.91528, "loss": 3.91528, "time": 0.8177} +{"mode": "train", "epoch": 74, "iter": 2800, "lr": 0.05131, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31141, "top5_acc": 0.57625, "loss_cls": 3.8997, "loss": 3.8997, "time": 0.8224} +{"mode": "train", "epoch": 74, "iter": 2900, "lr": 0.05128, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31734, "top5_acc": 0.57188, "loss_cls": 3.91452, "loss": 3.91452, "time": 0.82103} +{"mode": "train", "epoch": 74, "iter": 3000, "lr": 0.05126, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31578, "top5_acc": 0.57531, "loss_cls": 3.89433, "loss": 3.89433, "time": 0.82408} +{"mode": "train", "epoch": 74, "iter": 3100, "lr": 0.05123, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32203, "top5_acc": 0.58391, "loss_cls": 3.85661, "loss": 3.85661, "time": 0.81561} +{"mode": "train", "epoch": 74, "iter": 3200, "lr": 0.0512, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32234, "top5_acc": 0.57641, "loss_cls": 3.87572, "loss": 3.87572, "time": 0.81539} +{"mode": "train", "epoch": 74, "iter": 3300, "lr": 0.05117, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31516, "top5_acc": 0.57625, "loss_cls": 3.87831, "loss": 3.87831, "time": 0.81183} +{"mode": "train", "epoch": 74, "iter": 3400, "lr": 0.05114, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31594, "top5_acc": 0.57875, "loss_cls": 3.89946, "loss": 3.89946, "time": 0.81387} +{"mode": "train", "epoch": 74, "iter": 3500, "lr": 0.05112, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32219, "top5_acc": 0.57359, "loss_cls": 3.86327, "loss": 3.86327, "time": 0.81802} +{"mode": "train", "epoch": 74, "iter": 3600, "lr": 0.05109, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32516, "top5_acc": 0.57906, "loss_cls": 3.89408, "loss": 3.89408, "time": 0.8152} +{"mode": "train", "epoch": 74, "iter": 3700, "lr": 0.05106, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31641, "top5_acc": 0.56969, "loss_cls": 3.90702, "loss": 3.90702, "time": 0.81867} +{"mode": "val", "epoch": 74, "iter": 309, "lr": 0.05105, "top1_acc": 0.26409, "top5_acc": 0.50813, "mean_class_accuracy": 0.26395} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.05102, "memory": 15990, "data_time": 1.27017, "top1_acc": 0.31047, "top5_acc": 0.56672, "loss_cls": 3.94138, "loss": 3.94138, "time": 2.24947} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.05099, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33047, "top5_acc": 0.58891, "loss_cls": 3.82802, "loss": 3.82802, "time": 0.81967} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.05096, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32094, "top5_acc": 0.58688, "loss_cls": 3.84982, "loss": 3.84982, "time": 0.81801} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.05094, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32438, "top5_acc": 0.58188, "loss_cls": 3.86017, "loss": 3.86017, "time": 0.81711} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.05091, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32906, "top5_acc": 0.59922, "loss_cls": 3.8202, "loss": 3.8202, "time": 0.81569} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.05088, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32656, "top5_acc": 0.58531, "loss_cls": 3.83469, "loss": 3.83469, "time": 0.81573} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.05085, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3275, "top5_acc": 0.58156, "loss_cls": 3.85794, "loss": 3.85794, "time": 0.81779} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.05082, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32562, "top5_acc": 0.57391, "loss_cls": 3.89966, "loss": 3.89966, "time": 0.81816} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.0508, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33141, "top5_acc": 0.58125, "loss_cls": 3.85116, "loss": 3.85116, "time": 0.82372} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.05077, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31734, "top5_acc": 0.57594, "loss_cls": 3.89277, "loss": 3.89277, "time": 0.81657} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.05074, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31734, "top5_acc": 0.5775, "loss_cls": 3.90631, "loss": 3.90631, "time": 0.82174} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.05071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32141, "top5_acc": 0.58281, "loss_cls": 3.87987, "loss": 3.87987, "time": 0.82507} +{"mode": "train", "epoch": 75, "iter": 1300, "lr": 0.05068, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32609, "top5_acc": 0.5775, "loss_cls": 3.89695, "loss": 3.89695, "time": 0.81606} +{"mode": "train", "epoch": 75, "iter": 1400, "lr": 0.05066, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30969, "top5_acc": 0.5725, "loss_cls": 3.91587, "loss": 3.91587, "time": 0.82455} +{"mode": "train", "epoch": 75, "iter": 1500, "lr": 0.05063, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32156, "top5_acc": 0.58375, "loss_cls": 3.83793, "loss": 3.83793, "time": 0.81475} +{"mode": "train", "epoch": 75, "iter": 1600, "lr": 0.0506, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32844, "top5_acc": 0.57797, "loss_cls": 3.87199, "loss": 3.87199, "time": 0.81967} +{"mode": "train", "epoch": 75, "iter": 1700, "lr": 0.05057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31703, "top5_acc": 0.56766, "loss_cls": 3.93315, "loss": 3.93315, "time": 0.81694} +{"mode": "train", "epoch": 75, "iter": 1800, "lr": 0.05054, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31922, "top5_acc": 0.57234, "loss_cls": 3.90019, "loss": 3.90019, "time": 0.81824} +{"mode": "train", "epoch": 75, "iter": 1900, "lr": 0.05052, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31844, "top5_acc": 0.58469, "loss_cls": 3.89118, "loss": 3.89118, "time": 0.81942} +{"mode": "train", "epoch": 75, "iter": 2000, "lr": 0.05049, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32078, "top5_acc": 0.575, "loss_cls": 3.87671, "loss": 3.87671, "time": 0.82152} +{"mode": "train", "epoch": 75, "iter": 2100, "lr": 0.05046, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32922, "top5_acc": 0.57812, "loss_cls": 3.8489, "loss": 3.8489, "time": 0.81339} +{"mode": "train", "epoch": 75, "iter": 2200, "lr": 0.05043, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32234, "top5_acc": 0.57234, "loss_cls": 3.8805, "loss": 3.8805, "time": 0.81887} +{"mode": "train", "epoch": 75, "iter": 2300, "lr": 0.0504, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33219, "top5_acc": 0.58656, "loss_cls": 3.83475, "loss": 3.83475, "time": 0.81743} +{"mode": "train", "epoch": 75, "iter": 2400, "lr": 0.05038, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.31906, "top5_acc": 0.57422, "loss_cls": 3.91475, "loss": 3.91475, "time": 0.81578} +{"mode": "train", "epoch": 75, "iter": 2500, "lr": 0.05035, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32297, "top5_acc": 0.57969, "loss_cls": 3.84513, "loss": 3.84513, "time": 0.816} +{"mode": "train", "epoch": 75, "iter": 2600, "lr": 0.05032, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32453, "top5_acc": 0.57531, "loss_cls": 3.88512, "loss": 3.88512, "time": 0.81436} +{"mode": "train", "epoch": 75, "iter": 2700, "lr": 0.05029, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32734, "top5_acc": 0.58281, "loss_cls": 3.87282, "loss": 3.87282, "time": 0.81735} +{"mode": "train", "epoch": 75, "iter": 2800, "lr": 0.05026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32375, "top5_acc": 0.57594, "loss_cls": 3.87002, "loss": 3.87002, "time": 0.81666} +{"mode": "train", "epoch": 75, "iter": 2900, "lr": 0.05024, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.3225, "top5_acc": 0.57344, "loss_cls": 3.89945, "loss": 3.89945, "time": 0.82455} +{"mode": "train", "epoch": 75, "iter": 3000, "lr": 0.05021, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32719, "top5_acc": 0.57672, "loss_cls": 3.86084, "loss": 3.86084, "time": 0.8167} +{"mode": "train", "epoch": 75, "iter": 3100, "lr": 0.05018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32406, "top5_acc": 0.58281, "loss_cls": 3.87303, "loss": 3.87303, "time": 0.82071} +{"mode": "train", "epoch": 75, "iter": 3200, "lr": 0.05015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32594, "top5_acc": 0.57859, "loss_cls": 3.90014, "loss": 3.90014, "time": 0.82135} +{"mode": "train", "epoch": 75, "iter": 3300, "lr": 0.05012, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32484, "top5_acc": 0.57922, "loss_cls": 3.86809, "loss": 3.86809, "time": 0.81623} +{"mode": "train", "epoch": 75, "iter": 3400, "lr": 0.0501, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33141, "top5_acc": 0.58547, "loss_cls": 3.84586, "loss": 3.84586, "time": 0.81841} +{"mode": "train", "epoch": 75, "iter": 3500, "lr": 0.05007, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32828, "top5_acc": 0.58266, "loss_cls": 3.87288, "loss": 3.87288, "time": 0.81698} +{"mode": "train", "epoch": 75, "iter": 3600, "lr": 0.05004, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3225, "top5_acc": 0.57844, "loss_cls": 3.8596, "loss": 3.8596, "time": 0.81304} +{"mode": "train", "epoch": 75, "iter": 3700, "lr": 0.05001, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31016, "top5_acc": 0.57219, "loss_cls": 3.92608, "loss": 3.92608, "time": 0.81968} +{"mode": "val", "epoch": 75, "iter": 309, "lr": 0.05, "top1_acc": 0.26131, "top5_acc": 0.51005, "mean_class_accuracy": 0.26096} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.04997, "memory": 15990, "data_time": 1.25257, "top1_acc": 0.32141, "top5_acc": 0.57891, "loss_cls": 3.87738, "loss": 3.87738, "time": 2.23325} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.04994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32859, "top5_acc": 0.58719, "loss_cls": 3.80385, "loss": 3.80385, "time": 0.81482} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.04992, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33047, "top5_acc": 0.58672, "loss_cls": 3.81965, "loss": 3.81965, "time": 0.817} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.04989, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32406, "top5_acc": 0.57375, "loss_cls": 3.88299, "loss": 3.88299, "time": 0.81426} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.04986, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33031, "top5_acc": 0.58766, "loss_cls": 3.82696, "loss": 3.82696, "time": 0.81833} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.04983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32891, "top5_acc": 0.58562, "loss_cls": 3.82841, "loss": 3.82841, "time": 0.82465} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.0498, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32359, "top5_acc": 0.58906, "loss_cls": 3.81779, "loss": 3.81779, "time": 0.8134} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.04978, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32547, "top5_acc": 0.59188, "loss_cls": 3.81222, "loss": 3.81222, "time": 0.81732} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.04975, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32344, "top5_acc": 0.57594, "loss_cls": 3.86699, "loss": 3.86699, "time": 0.81968} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.04972, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32547, "top5_acc": 0.58797, "loss_cls": 3.86936, "loss": 3.86936, "time": 0.82138} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.04969, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32516, "top5_acc": 0.58094, "loss_cls": 3.85554, "loss": 3.85554, "time": 0.81073} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.04966, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33594, "top5_acc": 0.59453, "loss_cls": 3.83102, "loss": 3.83102, "time": 0.82747} +{"mode": "train", "epoch": 76, "iter": 1300, "lr": 0.04964, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32391, "top5_acc": 0.58266, "loss_cls": 3.86519, "loss": 3.86519, "time": 0.8204} +{"mode": "train", "epoch": 76, "iter": 1400, "lr": 0.04961, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31875, "top5_acc": 0.57531, "loss_cls": 3.86761, "loss": 3.86761, "time": 0.82} +{"mode": "train", "epoch": 76, "iter": 1500, "lr": 0.04958, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32625, "top5_acc": 0.58984, "loss_cls": 3.84195, "loss": 3.84195, "time": 0.8152} +{"mode": "train", "epoch": 76, "iter": 1600, "lr": 0.04955, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32297, "top5_acc": 0.58109, "loss_cls": 3.85593, "loss": 3.85593, "time": 0.81361} +{"mode": "train", "epoch": 76, "iter": 1700, "lr": 0.04953, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31969, "top5_acc": 0.57797, "loss_cls": 3.90234, "loss": 3.90234, "time": 0.81037} +{"mode": "train", "epoch": 76, "iter": 1800, "lr": 0.0495, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32266, "top5_acc": 0.57891, "loss_cls": 3.88543, "loss": 3.88543, "time": 0.81621} +{"mode": "train", "epoch": 76, "iter": 1900, "lr": 0.04947, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32547, "top5_acc": 0.58812, "loss_cls": 3.80695, "loss": 3.80695, "time": 0.81398} +{"mode": "train", "epoch": 76, "iter": 2000, "lr": 0.04944, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32844, "top5_acc": 0.59047, "loss_cls": 3.84543, "loss": 3.84543, "time": 0.81588} +{"mode": "train", "epoch": 76, "iter": 2100, "lr": 0.04941, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30969, "top5_acc": 0.57484, "loss_cls": 3.91576, "loss": 3.91576, "time": 0.81748} +{"mode": "train", "epoch": 76, "iter": 2200, "lr": 0.04939, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33078, "top5_acc": 0.58219, "loss_cls": 3.85484, "loss": 3.85484, "time": 0.81971} +{"mode": "train", "epoch": 76, "iter": 2300, "lr": 0.04936, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31844, "top5_acc": 0.57938, "loss_cls": 3.88672, "loss": 3.88672, "time": 0.82066} +{"mode": "train", "epoch": 76, "iter": 2400, "lr": 0.04933, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31812, "top5_acc": 0.57719, "loss_cls": 3.90727, "loss": 3.90727, "time": 0.81447} +{"mode": "train", "epoch": 76, "iter": 2500, "lr": 0.0493, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32703, "top5_acc": 0.58188, "loss_cls": 3.88958, "loss": 3.88958, "time": 0.81598} +{"mode": "train", "epoch": 76, "iter": 2600, "lr": 0.04927, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31859, "top5_acc": 0.565, "loss_cls": 3.93416, "loss": 3.93416, "time": 0.8147} +{"mode": "train", "epoch": 76, "iter": 2700, "lr": 0.04925, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33062, "top5_acc": 0.57828, "loss_cls": 3.8708, "loss": 3.8708, "time": 0.82001} +{"mode": "train", "epoch": 76, "iter": 2800, "lr": 0.04922, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31969, "top5_acc": 0.57594, "loss_cls": 3.88123, "loss": 3.88123, "time": 0.8226} +{"mode": "train", "epoch": 76, "iter": 2900, "lr": 0.04919, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32172, "top5_acc": 0.58234, "loss_cls": 3.86197, "loss": 3.86197, "time": 0.81933} +{"mode": "train", "epoch": 76, "iter": 3000, "lr": 0.04916, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33891, "top5_acc": 0.58703, "loss_cls": 3.82308, "loss": 3.82308, "time": 0.82964} +{"mode": "train", "epoch": 76, "iter": 3100, "lr": 0.04913, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31984, "top5_acc": 0.58547, "loss_cls": 3.84759, "loss": 3.84759, "time": 0.81303} +{"mode": "train", "epoch": 76, "iter": 3200, "lr": 0.04911, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32172, "top5_acc": 0.58391, "loss_cls": 3.86341, "loss": 3.86341, "time": 0.81751} +{"mode": "train", "epoch": 76, "iter": 3300, "lr": 0.04908, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31688, "top5_acc": 0.57469, "loss_cls": 3.86999, "loss": 3.86999, "time": 0.81981} +{"mode": "train", "epoch": 76, "iter": 3400, "lr": 0.04905, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3275, "top5_acc": 0.58234, "loss_cls": 3.84963, "loss": 3.84963, "time": 0.81473} +{"mode": "train", "epoch": 76, "iter": 3500, "lr": 0.04902, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32359, "top5_acc": 0.58078, "loss_cls": 3.90767, "loss": 3.90767, "time": 0.81768} +{"mode": "train", "epoch": 76, "iter": 3600, "lr": 0.04899, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31422, "top5_acc": 0.57328, "loss_cls": 3.93638, "loss": 3.93638, "time": 0.81846} +{"mode": "train", "epoch": 76, "iter": 3700, "lr": 0.04897, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32797, "top5_acc": 0.58516, "loss_cls": 3.82845, "loss": 3.82845, "time": 0.8146} +{"mode": "val", "epoch": 76, "iter": 309, "lr": 0.04895, "top1_acc": 0.25791, "top5_acc": 0.50413, "mean_class_accuracy": 0.2578} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.04893, "memory": 15990, "data_time": 1.31798, "top1_acc": 0.33969, "top5_acc": 0.59609, "loss_cls": 3.7757, "loss": 3.7757, "time": 2.31005} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0489, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32891, "top5_acc": 0.59188, "loss_cls": 3.82352, "loss": 3.82352, "time": 0.81921} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.04887, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32016, "top5_acc": 0.59516, "loss_cls": 3.81579, "loss": 3.81579, "time": 0.81358} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.04884, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33406, "top5_acc": 0.57984, "loss_cls": 3.85456, "loss": 3.85456, "time": 0.82138} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.04881, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32672, "top5_acc": 0.59078, "loss_cls": 3.83268, "loss": 3.83268, "time": 0.81888} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.04879, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32656, "top5_acc": 0.57734, "loss_cls": 3.86714, "loss": 3.86714, "time": 0.82152} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.04876, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33203, "top5_acc": 0.59219, "loss_cls": 3.8063, "loss": 3.8063, "time": 0.81716} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.04873, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32203, "top5_acc": 0.58656, "loss_cls": 3.84736, "loss": 3.84736, "time": 0.8241} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.0487, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32641, "top5_acc": 0.56906, "loss_cls": 3.90319, "loss": 3.90319, "time": 0.81678} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.04867, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33031, "top5_acc": 0.59203, "loss_cls": 3.81448, "loss": 3.81448, "time": 0.81723} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.04865, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32719, "top5_acc": 0.58859, "loss_cls": 3.83941, "loss": 3.83941, "time": 0.81733} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.04862, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33438, "top5_acc": 0.59812, "loss_cls": 3.81153, "loss": 3.81153, "time": 0.82413} +{"mode": "train", "epoch": 77, "iter": 1300, "lr": 0.04859, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33328, "top5_acc": 0.58719, "loss_cls": 3.8431, "loss": 3.8431, "time": 0.81477} +{"mode": "train", "epoch": 77, "iter": 1400, "lr": 0.04856, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33266, "top5_acc": 0.58922, "loss_cls": 3.84006, "loss": 3.84006, "time": 0.81893} +{"mode": "train", "epoch": 77, "iter": 1500, "lr": 0.04853, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33016, "top5_acc": 0.58422, "loss_cls": 3.82214, "loss": 3.82214, "time": 0.82029} +{"mode": "train", "epoch": 77, "iter": 1600, "lr": 0.04851, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33031, "top5_acc": 0.59438, "loss_cls": 3.82747, "loss": 3.82747, "time": 0.81742} +{"mode": "train", "epoch": 77, "iter": 1700, "lr": 0.04848, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31906, "top5_acc": 0.57391, "loss_cls": 3.88159, "loss": 3.88159, "time": 0.81495} +{"mode": "train", "epoch": 77, "iter": 1800, "lr": 0.04845, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31859, "top5_acc": 0.56859, "loss_cls": 3.90307, "loss": 3.90307, "time": 0.81368} +{"mode": "train", "epoch": 77, "iter": 1900, "lr": 0.04842, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32562, "top5_acc": 0.57781, "loss_cls": 3.87747, "loss": 3.87747, "time": 0.81293} +{"mode": "train", "epoch": 77, "iter": 2000, "lr": 0.04839, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32812, "top5_acc": 0.58797, "loss_cls": 3.80546, "loss": 3.80546, "time": 0.81891} +{"mode": "train", "epoch": 77, "iter": 2100, "lr": 0.04837, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33266, "top5_acc": 0.58172, "loss_cls": 3.84143, "loss": 3.84143, "time": 0.81572} +{"mode": "train", "epoch": 77, "iter": 2200, "lr": 0.04834, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31766, "top5_acc": 0.57781, "loss_cls": 3.87248, "loss": 3.87248, "time": 0.81529} +{"mode": "train", "epoch": 77, "iter": 2300, "lr": 0.04831, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32828, "top5_acc": 0.58094, "loss_cls": 3.86025, "loss": 3.86025, "time": 0.82235} +{"mode": "train", "epoch": 77, "iter": 2400, "lr": 0.04828, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32547, "top5_acc": 0.58438, "loss_cls": 3.8507, "loss": 3.8507, "time": 0.81566} +{"mode": "train", "epoch": 77, "iter": 2500, "lr": 0.04825, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32328, "top5_acc": 0.59266, "loss_cls": 3.81902, "loss": 3.81902, "time": 0.82097} +{"mode": "train", "epoch": 77, "iter": 2600, "lr": 0.04823, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31891, "top5_acc": 0.57156, "loss_cls": 3.89282, "loss": 3.89282, "time": 0.815} +{"mode": "train", "epoch": 77, "iter": 2700, "lr": 0.0482, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31484, "top5_acc": 0.56781, "loss_cls": 3.9127, "loss": 3.9127, "time": 0.82} +{"mode": "train", "epoch": 77, "iter": 2800, "lr": 0.04817, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33672, "top5_acc": 0.59078, "loss_cls": 3.82568, "loss": 3.82568, "time": 0.8233} +{"mode": "train", "epoch": 77, "iter": 2900, "lr": 0.04814, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32234, "top5_acc": 0.57859, "loss_cls": 3.87599, "loss": 3.87599, "time": 0.82486} +{"mode": "train", "epoch": 77, "iter": 3000, "lr": 0.04811, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32453, "top5_acc": 0.57891, "loss_cls": 3.85602, "loss": 3.85602, "time": 0.83088} +{"mode": "train", "epoch": 77, "iter": 3100, "lr": 0.04809, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31578, "top5_acc": 0.58, "loss_cls": 3.90778, "loss": 3.90778, "time": 0.81527} +{"mode": "train", "epoch": 77, "iter": 3200, "lr": 0.04806, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32734, "top5_acc": 0.58906, "loss_cls": 3.8269, "loss": 3.8269, "time": 0.82299} +{"mode": "train", "epoch": 77, "iter": 3300, "lr": 0.04803, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31969, "top5_acc": 0.58328, "loss_cls": 3.85308, "loss": 3.85308, "time": 0.81536} +{"mode": "train", "epoch": 77, "iter": 3400, "lr": 0.048, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33125, "top5_acc": 0.58969, "loss_cls": 3.83368, "loss": 3.83368, "time": 0.82306} +{"mode": "train", "epoch": 77, "iter": 3500, "lr": 0.04798, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32531, "top5_acc": 0.58812, "loss_cls": 3.8599, "loss": 3.8599, "time": 0.81547} +{"mode": "train", "epoch": 77, "iter": 3600, "lr": 0.04795, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31922, "top5_acc": 0.58281, "loss_cls": 3.86406, "loss": 3.86406, "time": 0.81548} +{"mode": "train", "epoch": 77, "iter": 3700, "lr": 0.04792, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32625, "top5_acc": 0.58422, "loss_cls": 3.83678, "loss": 3.83678, "time": 0.81616} +{"mode": "val", "epoch": 77, "iter": 309, "lr": 0.04791, "top1_acc": 0.25376, "top5_acc": 0.50869, "mean_class_accuracy": 0.25351} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.04788, "memory": 15990, "data_time": 1.30362, "top1_acc": 0.32203, "top5_acc": 0.57906, "loss_cls": 3.86483, "loss": 3.86483, "time": 2.29481} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.04785, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34312, "top5_acc": 0.61062, "loss_cls": 3.73731, "loss": 3.73731, "time": 0.83007} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.04782, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33641, "top5_acc": 0.59344, "loss_cls": 3.79977, "loss": 3.79977, "time": 0.82574} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.04779, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33141, "top5_acc": 0.58438, "loss_cls": 3.82769, "loss": 3.82769, "time": 0.82119} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.04777, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32922, "top5_acc": 0.58438, "loss_cls": 3.82368, "loss": 3.82368, "time": 0.81639} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.04774, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32953, "top5_acc": 0.59359, "loss_cls": 3.80523, "loss": 3.80523, "time": 0.82334} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.04771, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31906, "top5_acc": 0.58516, "loss_cls": 3.85414, "loss": 3.85414, "time": 0.81606} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.04768, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32406, "top5_acc": 0.58188, "loss_cls": 3.84917, "loss": 3.84917, "time": 0.82277} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.04766, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33109, "top5_acc": 0.59141, "loss_cls": 3.78377, "loss": 3.78377, "time": 0.82181} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.04763, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32172, "top5_acc": 0.57578, "loss_cls": 3.86591, "loss": 3.86591, "time": 0.81851} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.0476, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32984, "top5_acc": 0.59172, "loss_cls": 3.81629, "loss": 3.81629, "time": 0.81856} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.04757, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32734, "top5_acc": 0.58094, "loss_cls": 3.82544, "loss": 3.82544, "time": 0.81631} +{"mode": "train", "epoch": 78, "iter": 1300, "lr": 0.04754, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.335, "top5_acc": 0.57859, "loss_cls": 3.86252, "loss": 3.86252, "time": 0.82112} +{"mode": "train", "epoch": 78, "iter": 1400, "lr": 0.04752, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32766, "top5_acc": 0.59047, "loss_cls": 3.84367, "loss": 3.84367, "time": 0.81432} +{"mode": "train", "epoch": 78, "iter": 1500, "lr": 0.04749, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33609, "top5_acc": 0.59359, "loss_cls": 3.80454, "loss": 3.80454, "time": 0.81891} +{"mode": "train", "epoch": 78, "iter": 1600, "lr": 0.04746, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32047, "top5_acc": 0.58281, "loss_cls": 3.88869, "loss": 3.88869, "time": 0.81875} +{"mode": "train", "epoch": 78, "iter": 1700, "lr": 0.04743, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33516, "top5_acc": 0.595, "loss_cls": 3.78734, "loss": 3.78734, "time": 0.8143} +{"mode": "train", "epoch": 78, "iter": 1800, "lr": 0.0474, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32828, "top5_acc": 0.58203, "loss_cls": 3.84846, "loss": 3.84846, "time": 0.81442} +{"mode": "train", "epoch": 78, "iter": 1900, "lr": 0.04738, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32719, "top5_acc": 0.59312, "loss_cls": 3.81711, "loss": 3.81711, "time": 0.81638} +{"mode": "train", "epoch": 78, "iter": 2000, "lr": 0.04735, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32734, "top5_acc": 0.58578, "loss_cls": 3.84582, "loss": 3.84582, "time": 0.81576} +{"mode": "train", "epoch": 78, "iter": 2100, "lr": 0.04732, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32062, "top5_acc": 0.5875, "loss_cls": 3.85005, "loss": 3.85005, "time": 0.81898} +{"mode": "train", "epoch": 78, "iter": 2200, "lr": 0.04729, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33094, "top5_acc": 0.58484, "loss_cls": 3.83286, "loss": 3.83286, "time": 0.82109} +{"mode": "train", "epoch": 78, "iter": 2300, "lr": 0.04726, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31359, "top5_acc": 0.57938, "loss_cls": 3.86529, "loss": 3.86529, "time": 0.82055} +{"mode": "train", "epoch": 78, "iter": 2400, "lr": 0.04724, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32, "top5_acc": 0.58391, "loss_cls": 3.86321, "loss": 3.86321, "time": 0.81696} +{"mode": "train", "epoch": 78, "iter": 2500, "lr": 0.04721, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33484, "top5_acc": 0.58766, "loss_cls": 3.81849, "loss": 3.81849, "time": 0.81882} +{"mode": "train", "epoch": 78, "iter": 2600, "lr": 0.04718, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32734, "top5_acc": 0.58234, "loss_cls": 3.84488, "loss": 3.84488, "time": 0.81411} +{"mode": "train", "epoch": 78, "iter": 2700, "lr": 0.04715, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31234, "top5_acc": 0.57328, "loss_cls": 3.8982, "loss": 3.8982, "time": 0.81831} +{"mode": "train", "epoch": 78, "iter": 2800, "lr": 0.04712, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.3225, "top5_acc": 0.57234, "loss_cls": 3.87267, "loss": 3.87267, "time": 0.82467} +{"mode": "train", "epoch": 78, "iter": 2900, "lr": 0.0471, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32812, "top5_acc": 0.58906, "loss_cls": 3.83469, "loss": 3.83469, "time": 0.81826} +{"mode": "train", "epoch": 78, "iter": 3000, "lr": 0.04707, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33094, "top5_acc": 0.59188, "loss_cls": 3.81957, "loss": 3.81957, "time": 0.82099} +{"mode": "train", "epoch": 78, "iter": 3100, "lr": 0.04704, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3275, "top5_acc": 0.58906, "loss_cls": 3.83234, "loss": 3.83234, "time": 0.81651} +{"mode": "train", "epoch": 78, "iter": 3200, "lr": 0.04701, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32891, "top5_acc": 0.58688, "loss_cls": 3.84702, "loss": 3.84702, "time": 0.81495} +{"mode": "train", "epoch": 78, "iter": 3300, "lr": 0.04699, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32219, "top5_acc": 0.58594, "loss_cls": 3.86309, "loss": 3.86309, "time": 0.81963} +{"mode": "train", "epoch": 78, "iter": 3400, "lr": 0.04696, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32297, "top5_acc": 0.58406, "loss_cls": 3.84525, "loss": 3.84525, "time": 0.81504} +{"mode": "train", "epoch": 78, "iter": 3500, "lr": 0.04693, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32625, "top5_acc": 0.58328, "loss_cls": 3.85185, "loss": 3.85185, "time": 0.82057} +{"mode": "train", "epoch": 78, "iter": 3600, "lr": 0.0469, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33047, "top5_acc": 0.58297, "loss_cls": 3.82896, "loss": 3.82896, "time": 0.81918} +{"mode": "train", "epoch": 78, "iter": 3700, "lr": 0.04687, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32172, "top5_acc": 0.58453, "loss_cls": 3.85621, "loss": 3.85621, "time": 0.81857} +{"mode": "val", "epoch": 78, "iter": 309, "lr": 0.04686, "top1_acc": 0.26425, "top5_acc": 0.51304, "mean_class_accuracy": 0.26409} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.04683, "memory": 15990, "data_time": 1.32263, "top1_acc": 0.33578, "top5_acc": 0.59188, "loss_cls": 3.77837, "loss": 3.77837, "time": 2.30644} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.0468, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34297, "top5_acc": 0.60062, "loss_cls": 3.7601, "loss": 3.7601, "time": 0.82614} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.04678, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33656, "top5_acc": 0.59438, "loss_cls": 3.77997, "loss": 3.77997, "time": 0.82349} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.04675, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32672, "top5_acc": 0.59516, "loss_cls": 3.81463, "loss": 3.81463, "time": 0.81617} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.04672, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32109, "top5_acc": 0.58266, "loss_cls": 3.85195, "loss": 3.85195, "time": 0.81748} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.04669, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33312, "top5_acc": 0.59062, "loss_cls": 3.80361, "loss": 3.80361, "time": 0.82394} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.04667, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33297, "top5_acc": 0.58641, "loss_cls": 3.80418, "loss": 3.80418, "time": 0.8172} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.04664, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33484, "top5_acc": 0.59047, "loss_cls": 3.78039, "loss": 3.78039, "time": 0.82051} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.04661, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32391, "top5_acc": 0.58062, "loss_cls": 3.85103, "loss": 3.85103, "time": 0.81591} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.04658, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33672, "top5_acc": 0.59094, "loss_cls": 3.80414, "loss": 3.80414, "time": 0.81935} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.04655, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33766, "top5_acc": 0.59031, "loss_cls": 3.78226, "loss": 3.78226, "time": 0.81398} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.04653, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32406, "top5_acc": 0.58, "loss_cls": 3.87962, "loss": 3.87962, "time": 0.82071} +{"mode": "train", "epoch": 79, "iter": 1300, "lr": 0.0465, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32469, "top5_acc": 0.58062, "loss_cls": 3.83274, "loss": 3.83274, "time": 0.8176} +{"mode": "train", "epoch": 79, "iter": 1400, "lr": 0.04647, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33047, "top5_acc": 0.58328, "loss_cls": 3.81766, "loss": 3.81766, "time": 0.8169} +{"mode": "train", "epoch": 79, "iter": 1500, "lr": 0.04644, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32828, "top5_acc": 0.58781, "loss_cls": 3.8412, "loss": 3.8412, "time": 0.8192} +{"mode": "train", "epoch": 79, "iter": 1600, "lr": 0.04641, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.325, "top5_acc": 0.58078, "loss_cls": 3.87768, "loss": 3.87768, "time": 0.82327} +{"mode": "train", "epoch": 79, "iter": 1700, "lr": 0.04639, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.335, "top5_acc": 0.58703, "loss_cls": 3.83108, "loss": 3.83108, "time": 0.81871} +{"mode": "train", "epoch": 79, "iter": 1800, "lr": 0.04636, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33125, "top5_acc": 0.59453, "loss_cls": 3.81189, "loss": 3.81189, "time": 0.82214} +{"mode": "train", "epoch": 79, "iter": 1900, "lr": 0.04633, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33531, "top5_acc": 0.58719, "loss_cls": 3.81037, "loss": 3.81037, "time": 0.81494} +{"mode": "train", "epoch": 79, "iter": 2000, "lr": 0.0463, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31781, "top5_acc": 0.58203, "loss_cls": 3.83814, "loss": 3.83814, "time": 0.81705} +{"mode": "train", "epoch": 79, "iter": 2100, "lr": 0.04628, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32109, "top5_acc": 0.58016, "loss_cls": 3.84823, "loss": 3.84823, "time": 0.81304} +{"mode": "train", "epoch": 79, "iter": 2200, "lr": 0.04625, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33641, "top5_acc": 0.59359, "loss_cls": 3.77668, "loss": 3.77668, "time": 0.81742} +{"mode": "train", "epoch": 79, "iter": 2300, "lr": 0.04622, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33016, "top5_acc": 0.59688, "loss_cls": 3.79453, "loss": 3.79453, "time": 0.82063} +{"mode": "train", "epoch": 79, "iter": 2400, "lr": 0.04619, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31781, "top5_acc": 0.56672, "loss_cls": 3.89044, "loss": 3.89044, "time": 0.81913} +{"mode": "train", "epoch": 79, "iter": 2500, "lr": 0.04616, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34062, "top5_acc": 0.60297, "loss_cls": 3.76245, "loss": 3.76245, "time": 0.82359} +{"mode": "train", "epoch": 79, "iter": 2600, "lr": 0.04614, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32281, "top5_acc": 0.58219, "loss_cls": 3.86246, "loss": 3.86246, "time": 0.81979} +{"mode": "train", "epoch": 79, "iter": 2700, "lr": 0.04611, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3375, "top5_acc": 0.59438, "loss_cls": 3.78375, "loss": 3.78375, "time": 0.81164} +{"mode": "train", "epoch": 79, "iter": 2800, "lr": 0.04608, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32734, "top5_acc": 0.58453, "loss_cls": 3.86156, "loss": 3.86156, "time": 0.82622} +{"mode": "train", "epoch": 79, "iter": 2900, "lr": 0.04605, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32875, "top5_acc": 0.58984, "loss_cls": 3.81094, "loss": 3.81094, "time": 0.81513} +{"mode": "train", "epoch": 79, "iter": 3000, "lr": 0.04602, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32578, "top5_acc": 0.58047, "loss_cls": 3.86282, "loss": 3.86282, "time": 0.81976} +{"mode": "train", "epoch": 79, "iter": 3100, "lr": 0.046, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32219, "top5_acc": 0.57984, "loss_cls": 3.86323, "loss": 3.86323, "time": 0.83182} +{"mode": "train", "epoch": 79, "iter": 3200, "lr": 0.04597, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33312, "top5_acc": 0.58641, "loss_cls": 3.83458, "loss": 3.83458, "time": 0.82262} +{"mode": "train", "epoch": 79, "iter": 3300, "lr": 0.04594, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31688, "top5_acc": 0.57797, "loss_cls": 3.89825, "loss": 3.89825, "time": 0.81558} +{"mode": "train", "epoch": 79, "iter": 3400, "lr": 0.04591, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32219, "top5_acc": 0.57859, "loss_cls": 3.86929, "loss": 3.86929, "time": 0.81942} +{"mode": "train", "epoch": 79, "iter": 3500, "lr": 0.04588, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32547, "top5_acc": 0.59, "loss_cls": 3.82782, "loss": 3.82782, "time": 0.81686} +{"mode": "train", "epoch": 79, "iter": 3600, "lr": 0.04586, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32359, "top5_acc": 0.58031, "loss_cls": 3.86852, "loss": 3.86852, "time": 0.81318} +{"mode": "train", "epoch": 79, "iter": 3700, "lr": 0.04583, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33078, "top5_acc": 0.58828, "loss_cls": 3.81972, "loss": 3.81972, "time": 0.82418} +{"mode": "val", "epoch": 79, "iter": 309, "lr": 0.04582, "top1_acc": 0.27893, "top5_acc": 0.5327, "mean_class_accuracy": 0.2788} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.04579, "memory": 15990, "data_time": 1.3097, "top1_acc": 0.33891, "top5_acc": 0.6, "loss_cls": 3.76439, "loss": 3.76439, "time": 2.297} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.04576, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34062, "top5_acc": 0.60406, "loss_cls": 3.75464, "loss": 3.75464, "time": 0.81963} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.04573, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33812, "top5_acc": 0.59438, "loss_cls": 3.79438, "loss": 3.79438, "time": 0.81824} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.0457, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32938, "top5_acc": 0.59, "loss_cls": 3.79884, "loss": 3.79884, "time": 0.81761} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.04568, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33703, "top5_acc": 0.59953, "loss_cls": 3.74463, "loss": 3.74463, "time": 0.81918} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.04565, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33141, "top5_acc": 0.59531, "loss_cls": 3.77466, "loss": 3.77466, "time": 0.81983} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.04562, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33516, "top5_acc": 0.58938, "loss_cls": 3.79698, "loss": 3.79698, "time": 0.82407} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.04559, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32641, "top5_acc": 0.58859, "loss_cls": 3.8413, "loss": 3.8413, "time": 0.82212} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.04557, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33953, "top5_acc": 0.59516, "loss_cls": 3.80349, "loss": 3.80349, "time": 0.81625} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.04554, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33781, "top5_acc": 0.60141, "loss_cls": 3.77391, "loss": 3.77391, "time": 0.81586} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.04551, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33609, "top5_acc": 0.59984, "loss_cls": 3.78233, "loss": 3.78233, "time": 0.81691} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.04548, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32531, "top5_acc": 0.58469, "loss_cls": 3.86097, "loss": 3.86097, "time": 0.81968} +{"mode": "train", "epoch": 80, "iter": 1300, "lr": 0.04545, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33125, "top5_acc": 0.59266, "loss_cls": 3.79403, "loss": 3.79403, "time": 0.81866} +{"mode": "train", "epoch": 80, "iter": 1400, "lr": 0.04543, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32797, "top5_acc": 0.5925, "loss_cls": 3.80312, "loss": 3.80312, "time": 0.82078} +{"mode": "train", "epoch": 80, "iter": 1500, "lr": 0.0454, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32625, "top5_acc": 0.58156, "loss_cls": 3.85568, "loss": 3.85568, "time": 0.81979} +{"mode": "train", "epoch": 80, "iter": 1600, "lr": 0.04537, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32734, "top5_acc": 0.58328, "loss_cls": 3.84145, "loss": 3.84145, "time": 0.81299} +{"mode": "train", "epoch": 80, "iter": 1700, "lr": 0.04534, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32922, "top5_acc": 0.58922, "loss_cls": 3.82821, "loss": 3.82821, "time": 0.81279} +{"mode": "train", "epoch": 80, "iter": 1800, "lr": 0.04532, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31922, "top5_acc": 0.57469, "loss_cls": 3.83723, "loss": 3.83723, "time": 0.8131} +{"mode": "train", "epoch": 80, "iter": 1900, "lr": 0.04529, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32609, "top5_acc": 0.59609, "loss_cls": 3.80588, "loss": 3.80588, "time": 0.8114} +{"mode": "train", "epoch": 80, "iter": 2000, "lr": 0.04526, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32953, "top5_acc": 0.58891, "loss_cls": 3.79283, "loss": 3.79283, "time": 0.81808} +{"mode": "train", "epoch": 80, "iter": 2100, "lr": 0.04523, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32922, "top5_acc": 0.58078, "loss_cls": 3.85807, "loss": 3.85807, "time": 0.81641} +{"mode": "train", "epoch": 80, "iter": 2200, "lr": 0.0452, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33359, "top5_acc": 0.59094, "loss_cls": 3.78773, "loss": 3.78773, "time": 0.81618} +{"mode": "train", "epoch": 80, "iter": 2300, "lr": 0.04518, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32641, "top5_acc": 0.58609, "loss_cls": 3.83508, "loss": 3.83508, "time": 0.82086} +{"mode": "train", "epoch": 80, "iter": 2400, "lr": 0.04515, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33062, "top5_acc": 0.58922, "loss_cls": 3.82278, "loss": 3.82278, "time": 0.81613} +{"mode": "train", "epoch": 80, "iter": 2500, "lr": 0.04512, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33312, "top5_acc": 0.585, "loss_cls": 3.8289, "loss": 3.8289, "time": 0.81608} +{"mode": "train", "epoch": 80, "iter": 2600, "lr": 0.04509, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33141, "top5_acc": 0.59359, "loss_cls": 3.79142, "loss": 3.79142, "time": 0.81399} +{"mode": "train", "epoch": 80, "iter": 2700, "lr": 0.04506, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32844, "top5_acc": 0.58094, "loss_cls": 3.83453, "loss": 3.83453, "time": 0.81387} +{"mode": "train", "epoch": 80, "iter": 2800, "lr": 0.04504, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33547, "top5_acc": 0.58969, "loss_cls": 3.7884, "loss": 3.7884, "time": 0.81896} +{"mode": "train", "epoch": 80, "iter": 2900, "lr": 0.04501, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.3325, "top5_acc": 0.58438, "loss_cls": 3.80762, "loss": 3.80762, "time": 0.81494} +{"mode": "train", "epoch": 80, "iter": 3000, "lr": 0.04498, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32906, "top5_acc": 0.58391, "loss_cls": 3.82938, "loss": 3.82938, "time": 0.82145} +{"mode": "train", "epoch": 80, "iter": 3100, "lr": 0.04495, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32344, "top5_acc": 0.58719, "loss_cls": 3.80428, "loss": 3.80428, "time": 0.82288} +{"mode": "train", "epoch": 80, "iter": 3200, "lr": 0.04493, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33406, "top5_acc": 0.59469, "loss_cls": 3.77909, "loss": 3.77909, "time": 0.82623} +{"mode": "train", "epoch": 80, "iter": 3300, "lr": 0.0449, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32656, "top5_acc": 0.58641, "loss_cls": 3.8319, "loss": 3.8319, "time": 0.81717} +{"mode": "train", "epoch": 80, "iter": 3400, "lr": 0.04487, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32594, "top5_acc": 0.57922, "loss_cls": 3.85567, "loss": 3.85567, "time": 0.81738} +{"mode": "train", "epoch": 80, "iter": 3500, "lr": 0.04484, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32672, "top5_acc": 0.57438, "loss_cls": 3.8669, "loss": 3.8669, "time": 0.81661} +{"mode": "train", "epoch": 80, "iter": 3600, "lr": 0.04481, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.3225, "top5_acc": 0.58125, "loss_cls": 3.87097, "loss": 3.87097, "time": 0.81704} +{"mode": "train", "epoch": 80, "iter": 3700, "lr": 0.04479, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32922, "top5_acc": 0.59281, "loss_cls": 3.81356, "loss": 3.81356, "time": 0.81688} +{"mode": "val", "epoch": 80, "iter": 309, "lr": 0.04477, "top1_acc": 0.27828, "top5_acc": 0.52408, "mean_class_accuracy": 0.27773} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.04475, "memory": 15990, "data_time": 1.32841, "top1_acc": 0.34312, "top5_acc": 0.61219, "loss_cls": 3.68553, "loss": 3.68553, "time": 2.31332} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.04472, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.345, "top5_acc": 0.60094, "loss_cls": 3.73838, "loss": 3.73838, "time": 0.82347} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.04469, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33312, "top5_acc": 0.58875, "loss_cls": 3.80593, "loss": 3.80593, "time": 0.82045} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.04466, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34594, "top5_acc": 0.59734, "loss_cls": 3.76595, "loss": 3.76595, "time": 0.81736} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.04463, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33484, "top5_acc": 0.60062, "loss_cls": 3.78334, "loss": 3.78334, "time": 0.82025} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.04461, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33203, "top5_acc": 0.59656, "loss_cls": 3.7496, "loss": 3.7496, "time": 0.82042} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.04458, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31609, "top5_acc": 0.58, "loss_cls": 3.84647, "loss": 3.84647, "time": 0.82399} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.04455, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32828, "top5_acc": 0.59062, "loss_cls": 3.80307, "loss": 3.80307, "time": 0.82156} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.04452, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33719, "top5_acc": 0.6025, "loss_cls": 3.77759, "loss": 3.77759, "time": 0.81614} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.0445, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32875, "top5_acc": 0.58469, "loss_cls": 3.83023, "loss": 3.83023, "time": 0.81422} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.04447, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33141, "top5_acc": 0.59625, "loss_cls": 3.80399, "loss": 3.80399, "time": 0.81325} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.04444, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32875, "top5_acc": 0.58781, "loss_cls": 3.82948, "loss": 3.82948, "time": 0.82028} +{"mode": "train", "epoch": 81, "iter": 1300, "lr": 0.04441, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33859, "top5_acc": 0.60641, "loss_cls": 3.74818, "loss": 3.74818, "time": 0.81553} +{"mode": "train", "epoch": 81, "iter": 1400, "lr": 0.04438, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32844, "top5_acc": 0.58922, "loss_cls": 3.82001, "loss": 3.82001, "time": 0.82061} +{"mode": "train", "epoch": 81, "iter": 1500, "lr": 0.04436, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32797, "top5_acc": 0.58625, "loss_cls": 3.85221, "loss": 3.85221, "time": 0.82036} +{"mode": "train", "epoch": 81, "iter": 1600, "lr": 0.04433, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33906, "top5_acc": 0.59953, "loss_cls": 3.7645, "loss": 3.7645, "time": 0.8194} +{"mode": "train", "epoch": 81, "iter": 1700, "lr": 0.0443, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32922, "top5_acc": 0.59531, "loss_cls": 3.80225, "loss": 3.80225, "time": 0.81659} +{"mode": "train", "epoch": 81, "iter": 1800, "lr": 0.04427, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33438, "top5_acc": 0.595, "loss_cls": 3.80763, "loss": 3.80763, "time": 0.8153} +{"mode": "train", "epoch": 81, "iter": 1900, "lr": 0.04425, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3325, "top5_acc": 0.59469, "loss_cls": 3.79279, "loss": 3.79279, "time": 0.81708} +{"mode": "train", "epoch": 81, "iter": 2000, "lr": 0.04422, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34, "top5_acc": 0.59672, "loss_cls": 3.77274, "loss": 3.77274, "time": 0.81482} +{"mode": "train", "epoch": 81, "iter": 2100, "lr": 0.04419, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33453, "top5_acc": 0.58859, "loss_cls": 3.80107, "loss": 3.80107, "time": 0.81854} +{"mode": "train", "epoch": 81, "iter": 2200, "lr": 0.04416, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32797, "top5_acc": 0.59125, "loss_cls": 3.80779, "loss": 3.80779, "time": 0.81783} +{"mode": "train", "epoch": 81, "iter": 2300, "lr": 0.04413, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33922, "top5_acc": 0.595, "loss_cls": 3.77202, "loss": 3.77202, "time": 0.81638} +{"mode": "train", "epoch": 81, "iter": 2400, "lr": 0.04411, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33906, "top5_acc": 0.59906, "loss_cls": 3.77118, "loss": 3.77118, "time": 0.81986} +{"mode": "train", "epoch": 81, "iter": 2500, "lr": 0.04408, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33281, "top5_acc": 0.57625, "loss_cls": 3.82572, "loss": 3.82572, "time": 0.81632} +{"mode": "train", "epoch": 81, "iter": 2600, "lr": 0.04405, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32359, "top5_acc": 0.585, "loss_cls": 3.84628, "loss": 3.84628, "time": 0.82097} +{"mode": "train", "epoch": 81, "iter": 2700, "lr": 0.04402, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32797, "top5_acc": 0.58672, "loss_cls": 3.83275, "loss": 3.83275, "time": 0.82035} +{"mode": "train", "epoch": 81, "iter": 2800, "lr": 0.044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33781, "top5_acc": 0.59391, "loss_cls": 3.79865, "loss": 3.79865, "time": 0.81935} +{"mode": "train", "epoch": 81, "iter": 2900, "lr": 0.04397, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31594, "top5_acc": 0.58312, "loss_cls": 3.83162, "loss": 3.83162, "time": 0.81857} +{"mode": "train", "epoch": 81, "iter": 3000, "lr": 0.04394, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33, "top5_acc": 0.58891, "loss_cls": 3.83888, "loss": 3.83888, "time": 0.81808} +{"mode": "train", "epoch": 81, "iter": 3100, "lr": 0.04391, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31844, "top5_acc": 0.58484, "loss_cls": 3.87949, "loss": 3.87949, "time": 0.83031} +{"mode": "train", "epoch": 81, "iter": 3200, "lr": 0.04389, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32, "top5_acc": 0.58609, "loss_cls": 3.85011, "loss": 3.85011, "time": 0.81889} +{"mode": "train", "epoch": 81, "iter": 3300, "lr": 0.04386, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3325, "top5_acc": 0.59109, "loss_cls": 3.80516, "loss": 3.80516, "time": 0.81497} +{"mode": "train", "epoch": 81, "iter": 3400, "lr": 0.04383, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32375, "top5_acc": 0.57734, "loss_cls": 3.88045, "loss": 3.88045, "time": 0.81646} +{"mode": "train", "epoch": 81, "iter": 3500, "lr": 0.0438, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32562, "top5_acc": 0.59297, "loss_cls": 3.81745, "loss": 3.81745, "time": 0.81494} +{"mode": "train", "epoch": 81, "iter": 3600, "lr": 0.04377, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33859, "top5_acc": 0.59141, "loss_cls": 3.78675, "loss": 3.78675, "time": 0.81799} +{"mode": "train", "epoch": 81, "iter": 3700, "lr": 0.04375, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33516, "top5_acc": 0.59297, "loss_cls": 3.77618, "loss": 3.77618, "time": 0.81652} +{"mode": "val", "epoch": 81, "iter": 309, "lr": 0.04373, "top1_acc": 0.25746, "top5_acc": 0.50261, "mean_class_accuracy": 0.25707} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.04371, "memory": 15990, "data_time": 1.30138, "top1_acc": 0.33438, "top5_acc": 0.59125, "loss_cls": 3.75121, "loss": 3.75121, "time": 2.28641} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.04368, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34594, "top5_acc": 0.61156, "loss_cls": 3.73013, "loss": 3.73013, "time": 0.82057} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.04365, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32375, "top5_acc": 0.59062, "loss_cls": 3.80814, "loss": 3.80814, "time": 0.82051} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.04362, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33984, "top5_acc": 0.59812, "loss_cls": 3.72501, "loss": 3.72501, "time": 0.81645} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.04359, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35234, "top5_acc": 0.60844, "loss_cls": 3.69489, "loss": 3.69489, "time": 0.8248} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.04357, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32453, "top5_acc": 0.59062, "loss_cls": 3.79625, "loss": 3.79625, "time": 0.81937} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.04354, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32969, "top5_acc": 0.58453, "loss_cls": 3.83264, "loss": 3.83264, "time": 0.82148} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.04351, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34406, "top5_acc": 0.59859, "loss_cls": 3.75374, "loss": 3.75374, "time": 0.81986} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.04348, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33141, "top5_acc": 0.59516, "loss_cls": 3.78234, "loss": 3.78234, "time": 0.82113} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.04346, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32641, "top5_acc": 0.58172, "loss_cls": 3.83728, "loss": 3.83728, "time": 0.81615} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.04343, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33125, "top5_acc": 0.59094, "loss_cls": 3.78719, "loss": 3.78719, "time": 0.81819} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.0434, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32156, "top5_acc": 0.58562, "loss_cls": 3.843, "loss": 3.843, "time": 0.82848} +{"mode": "train", "epoch": 82, "iter": 1300, "lr": 0.04337, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33344, "top5_acc": 0.58438, "loss_cls": 3.80771, "loss": 3.80771, "time": 0.81755} +{"mode": "train", "epoch": 82, "iter": 1400, "lr": 0.04335, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34125, "top5_acc": 0.59781, "loss_cls": 3.76293, "loss": 3.76293, "time": 0.82392} +{"mode": "train", "epoch": 82, "iter": 1500, "lr": 0.04332, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33094, "top5_acc": 0.59875, "loss_cls": 3.77606, "loss": 3.77606, "time": 0.82313} +{"mode": "train", "epoch": 82, "iter": 1600, "lr": 0.04329, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33672, "top5_acc": 0.60016, "loss_cls": 3.7701, "loss": 3.7701, "time": 0.81863} +{"mode": "train", "epoch": 82, "iter": 1700, "lr": 0.04326, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34031, "top5_acc": 0.59844, "loss_cls": 3.77294, "loss": 3.77294, "time": 0.81726} +{"mode": "train", "epoch": 82, "iter": 1800, "lr": 0.04323, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33312, "top5_acc": 0.59719, "loss_cls": 3.81013, "loss": 3.81013, "time": 0.81704} +{"mode": "train", "epoch": 82, "iter": 1900, "lr": 0.04321, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34219, "top5_acc": 0.58812, "loss_cls": 3.77309, "loss": 3.77309, "time": 0.82218} +{"mode": "train", "epoch": 82, "iter": 2000, "lr": 0.04318, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3375, "top5_acc": 0.59375, "loss_cls": 3.78463, "loss": 3.78463, "time": 0.8145} +{"mode": "train", "epoch": 82, "iter": 2100, "lr": 0.04315, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32906, "top5_acc": 0.58547, "loss_cls": 3.84352, "loss": 3.84352, "time": 0.8245} +{"mode": "train", "epoch": 82, "iter": 2200, "lr": 0.04312, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33375, "top5_acc": 0.59562, "loss_cls": 3.80946, "loss": 3.80946, "time": 0.81268} +{"mode": "train", "epoch": 82, "iter": 2300, "lr": 0.0431, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33156, "top5_acc": 0.58609, "loss_cls": 3.8133, "loss": 3.8133, "time": 0.81585} +{"mode": "train", "epoch": 82, "iter": 2400, "lr": 0.04307, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33141, "top5_acc": 0.59516, "loss_cls": 3.78258, "loss": 3.78258, "time": 0.81637} +{"mode": "train", "epoch": 82, "iter": 2500, "lr": 0.04304, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3225, "top5_acc": 0.58453, "loss_cls": 3.8397, "loss": 3.8397, "time": 0.8176} +{"mode": "train", "epoch": 82, "iter": 2600, "lr": 0.04301, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33328, "top5_acc": 0.59297, "loss_cls": 3.80861, "loss": 3.80861, "time": 0.82152} +{"mode": "train", "epoch": 82, "iter": 2700, "lr": 0.04299, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32672, "top5_acc": 0.58109, "loss_cls": 3.84107, "loss": 3.84107, "time": 0.8182} +{"mode": "train", "epoch": 82, "iter": 2800, "lr": 0.04296, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32219, "top5_acc": 0.58125, "loss_cls": 3.83202, "loss": 3.83202, "time": 0.81375} +{"mode": "train", "epoch": 82, "iter": 2900, "lr": 0.04293, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34047, "top5_acc": 0.59438, "loss_cls": 3.76557, "loss": 3.76557, "time": 0.82116} +{"mode": "train", "epoch": 82, "iter": 3000, "lr": 0.0429, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33203, "top5_acc": 0.59016, "loss_cls": 3.80411, "loss": 3.80411, "time": 0.82219} +{"mode": "train", "epoch": 82, "iter": 3100, "lr": 0.04287, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34844, "top5_acc": 0.60125, "loss_cls": 3.7325, "loss": 3.7325, "time": 0.82143} +{"mode": "train", "epoch": 82, "iter": 3200, "lr": 0.04285, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34047, "top5_acc": 0.59562, "loss_cls": 3.79939, "loss": 3.79939, "time": 0.82399} +{"mode": "train", "epoch": 82, "iter": 3300, "lr": 0.04282, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33484, "top5_acc": 0.59516, "loss_cls": 3.79487, "loss": 3.79487, "time": 0.82057} +{"mode": "train", "epoch": 82, "iter": 3400, "lr": 0.04279, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33438, "top5_acc": 0.59359, "loss_cls": 3.77306, "loss": 3.77306, "time": 0.81953} +{"mode": "train", "epoch": 82, "iter": 3500, "lr": 0.04276, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32672, "top5_acc": 0.58266, "loss_cls": 3.81793, "loss": 3.81793, "time": 0.81637} +{"mode": "train", "epoch": 82, "iter": 3600, "lr": 0.04274, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33531, "top5_acc": 0.6, "loss_cls": 3.76462, "loss": 3.76462, "time": 0.81519} +{"mode": "train", "epoch": 82, "iter": 3700, "lr": 0.04271, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33156, "top5_acc": 0.59156, "loss_cls": 3.81029, "loss": 3.81029, "time": 0.81582} +{"mode": "val", "epoch": 82, "iter": 309, "lr": 0.0427, "top1_acc": 0.27336, "top5_acc": 0.53026, "mean_class_accuracy": 0.27312} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.04267, "memory": 15990, "data_time": 1.3189, "top1_acc": 0.34188, "top5_acc": 0.59938, "loss_cls": 3.75814, "loss": 3.75814, "time": 2.30298} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.04264, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33688, "top5_acc": 0.5975, "loss_cls": 3.7549, "loss": 3.7549, "time": 0.82492} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.04261, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34578, "top5_acc": 0.59719, "loss_cls": 3.74283, "loss": 3.74283, "time": 0.81817} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.04259, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34906, "top5_acc": 0.5975, "loss_cls": 3.72053, "loss": 3.72053, "time": 0.81609} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.04256, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34656, "top5_acc": 0.59578, "loss_cls": 3.74508, "loss": 3.74508, "time": 0.82421} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.04253, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33531, "top5_acc": 0.59578, "loss_cls": 3.75627, "loss": 3.75627, "time": 0.81359} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.0425, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33156, "top5_acc": 0.5925, "loss_cls": 3.79245, "loss": 3.79245, "time": 0.82325} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.04247, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33812, "top5_acc": 0.60328, "loss_cls": 3.74149, "loss": 3.74149, "time": 0.82015} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.04245, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33812, "top5_acc": 0.59906, "loss_cls": 3.7654, "loss": 3.7654, "time": 0.81691} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.04242, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34203, "top5_acc": 0.59828, "loss_cls": 3.76318, "loss": 3.76318, "time": 0.81659} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.04239, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35156, "top5_acc": 0.60453, "loss_cls": 3.75362, "loss": 3.75362, "time": 0.81825} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.04236, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33297, "top5_acc": 0.58625, "loss_cls": 3.81656, "loss": 3.81656, "time": 0.82376} +{"mode": "train", "epoch": 83, "iter": 1300, "lr": 0.04234, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3375, "top5_acc": 0.59609, "loss_cls": 3.75872, "loss": 3.75872, "time": 0.81918} +{"mode": "train", "epoch": 83, "iter": 1400, "lr": 0.04231, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33438, "top5_acc": 0.59391, "loss_cls": 3.76828, "loss": 3.76828, "time": 0.81577} +{"mode": "train", "epoch": 83, "iter": 1500, "lr": 0.04228, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33547, "top5_acc": 0.58641, "loss_cls": 3.7854, "loss": 3.7854, "time": 0.81777} +{"mode": "train", "epoch": 83, "iter": 1600, "lr": 0.04225, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32656, "top5_acc": 0.59016, "loss_cls": 3.84605, "loss": 3.84605, "time": 0.81544} +{"mode": "train", "epoch": 83, "iter": 1700, "lr": 0.04223, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33438, "top5_acc": 0.59906, "loss_cls": 3.78759, "loss": 3.78759, "time": 0.81831} +{"mode": "train", "epoch": 83, "iter": 1800, "lr": 0.0422, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33453, "top5_acc": 0.59547, "loss_cls": 3.76455, "loss": 3.76455, "time": 0.81296} +{"mode": "train", "epoch": 83, "iter": 1900, "lr": 0.04217, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34156, "top5_acc": 0.60109, "loss_cls": 3.76246, "loss": 3.76246, "time": 0.81986} +{"mode": "train", "epoch": 83, "iter": 2000, "lr": 0.04214, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33844, "top5_acc": 0.59609, "loss_cls": 3.79791, "loss": 3.79791, "time": 0.81742} +{"mode": "train", "epoch": 83, "iter": 2100, "lr": 0.04212, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33531, "top5_acc": 0.59594, "loss_cls": 3.77529, "loss": 3.77529, "time": 0.81479} +{"mode": "train", "epoch": 83, "iter": 2200, "lr": 0.04209, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33328, "top5_acc": 0.59016, "loss_cls": 3.7968, "loss": 3.7968, "time": 0.81161} +{"mode": "train", "epoch": 83, "iter": 2300, "lr": 0.04206, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33891, "top5_acc": 0.59984, "loss_cls": 3.78024, "loss": 3.78024, "time": 0.81915} +{"mode": "train", "epoch": 83, "iter": 2400, "lr": 0.04203, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33031, "top5_acc": 0.59125, "loss_cls": 3.81128, "loss": 3.81128, "time": 0.81915} +{"mode": "train", "epoch": 83, "iter": 2500, "lr": 0.04201, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33938, "top5_acc": 0.60062, "loss_cls": 3.77243, "loss": 3.77243, "time": 0.81886} +{"mode": "train", "epoch": 83, "iter": 2600, "lr": 0.04198, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33672, "top5_acc": 0.58766, "loss_cls": 3.80464, "loss": 3.80464, "time": 0.8175} +{"mode": "train", "epoch": 83, "iter": 2700, "lr": 0.04195, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32672, "top5_acc": 0.58359, "loss_cls": 3.79312, "loss": 3.79312, "time": 0.81929} +{"mode": "train", "epoch": 83, "iter": 2800, "lr": 0.04192, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34297, "top5_acc": 0.59719, "loss_cls": 3.74214, "loss": 3.74214, "time": 0.81417} +{"mode": "train", "epoch": 83, "iter": 2900, "lr": 0.0419, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34641, "top5_acc": 0.59453, "loss_cls": 3.74565, "loss": 3.74565, "time": 0.82508} +{"mode": "train", "epoch": 83, "iter": 3000, "lr": 0.04187, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34578, "top5_acc": 0.61016, "loss_cls": 3.70515, "loss": 3.70515, "time": 0.81829} +{"mode": "train", "epoch": 83, "iter": 3100, "lr": 0.04184, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33625, "top5_acc": 0.59078, "loss_cls": 3.79575, "loss": 3.79575, "time": 0.82615} +{"mode": "train", "epoch": 83, "iter": 3200, "lr": 0.04181, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34078, "top5_acc": 0.5975, "loss_cls": 3.78818, "loss": 3.78818, "time": 0.8222} +{"mode": "train", "epoch": 83, "iter": 3300, "lr": 0.04178, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34641, "top5_acc": 0.60125, "loss_cls": 3.75251, "loss": 3.75251, "time": 0.82355} +{"mode": "train", "epoch": 83, "iter": 3400, "lr": 0.04176, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33484, "top5_acc": 0.59328, "loss_cls": 3.84275, "loss": 3.84275, "time": 0.81541} +{"mode": "train", "epoch": 83, "iter": 3500, "lr": 0.04173, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32875, "top5_acc": 0.60125, "loss_cls": 3.80271, "loss": 3.80271, "time": 0.82008} +{"mode": "train", "epoch": 83, "iter": 3600, "lr": 0.0417, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33141, "top5_acc": 0.58641, "loss_cls": 3.84327, "loss": 3.84327, "time": 0.81541} +{"mode": "train", "epoch": 83, "iter": 3700, "lr": 0.04167, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33719, "top5_acc": 0.59344, "loss_cls": 3.78412, "loss": 3.78412, "time": 0.81786} +{"mode": "val", "epoch": 83, "iter": 309, "lr": 0.04166, "top1_acc": 0.28202, "top5_acc": 0.52738, "mean_class_accuracy": 0.28173} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.04163, "memory": 15990, "data_time": 1.31947, "top1_acc": 0.35328, "top5_acc": 0.6125, "loss_cls": 3.72163, "loss": 3.72163, "time": 2.30458} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.04161, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34266, "top5_acc": 0.59672, "loss_cls": 3.74965, "loss": 3.74965, "time": 0.82303} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.04158, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33531, "top5_acc": 0.59609, "loss_cls": 3.75256, "loss": 3.75256, "time": 0.81888} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.04155, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33656, "top5_acc": 0.59469, "loss_cls": 3.76136, "loss": 3.76136, "time": 0.81821} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.04152, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33234, "top5_acc": 0.60594, "loss_cls": 3.77483, "loss": 3.77483, "time": 0.82862} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.0415, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33609, "top5_acc": 0.60172, "loss_cls": 3.78241, "loss": 3.78241, "time": 0.81862} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.04147, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34188, "top5_acc": 0.60672, "loss_cls": 3.73448, "loss": 3.73448, "time": 0.82186} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.04144, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33312, "top5_acc": 0.59016, "loss_cls": 3.78738, "loss": 3.78738, "time": 0.81781} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.04141, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33359, "top5_acc": 0.59547, "loss_cls": 3.79068, "loss": 3.79068, "time": 0.81971} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.04139, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33578, "top5_acc": 0.59859, "loss_cls": 3.76514, "loss": 3.76514, "time": 0.81672} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.04136, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35328, "top5_acc": 0.60203, "loss_cls": 3.73737, "loss": 3.73737, "time": 0.81611} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.04133, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33859, "top5_acc": 0.59078, "loss_cls": 3.78205, "loss": 3.78205, "time": 0.8182} +{"mode": "train", "epoch": 84, "iter": 1300, "lr": 0.0413, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33078, "top5_acc": 0.58656, "loss_cls": 3.79982, "loss": 3.79982, "time": 0.81998} +{"mode": "train", "epoch": 84, "iter": 1400, "lr": 0.04128, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.33766, "top5_acc": 0.60547, "loss_cls": 3.75573, "loss": 3.75573, "time": 0.82366} +{"mode": "train", "epoch": 84, "iter": 1500, "lr": 0.04125, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34344, "top5_acc": 0.60016, "loss_cls": 3.7422, "loss": 3.7422, "time": 0.82504} +{"mode": "train", "epoch": 84, "iter": 1600, "lr": 0.04122, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33828, "top5_acc": 0.59391, "loss_cls": 3.7719, "loss": 3.7719, "time": 0.81434} +{"mode": "train", "epoch": 84, "iter": 1700, "lr": 0.04119, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34141, "top5_acc": 0.60203, "loss_cls": 3.74113, "loss": 3.74113, "time": 0.81931} +{"mode": "train", "epoch": 84, "iter": 1800, "lr": 0.04117, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33844, "top5_acc": 0.60219, "loss_cls": 3.76491, "loss": 3.76491, "time": 0.81929} +{"mode": "train", "epoch": 84, "iter": 1900, "lr": 0.04114, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34344, "top5_acc": 0.59766, "loss_cls": 3.76512, "loss": 3.76512, "time": 0.81466} +{"mode": "train", "epoch": 84, "iter": 2000, "lr": 0.04111, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34141, "top5_acc": 0.59938, "loss_cls": 3.75598, "loss": 3.75598, "time": 0.81837} +{"mode": "train", "epoch": 84, "iter": 2100, "lr": 0.04108, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34219, "top5_acc": 0.60359, "loss_cls": 3.755, "loss": 3.755, "time": 0.81467} +{"mode": "train", "epoch": 84, "iter": 2200, "lr": 0.04106, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34531, "top5_acc": 0.60719, "loss_cls": 3.71103, "loss": 3.71103, "time": 0.81753} +{"mode": "train", "epoch": 84, "iter": 2300, "lr": 0.04103, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34328, "top5_acc": 0.60219, "loss_cls": 3.74287, "loss": 3.74287, "time": 0.81746} +{"mode": "train", "epoch": 84, "iter": 2400, "lr": 0.041, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34016, "top5_acc": 0.60203, "loss_cls": 3.75298, "loss": 3.75298, "time": 0.81673} +{"mode": "train", "epoch": 84, "iter": 2500, "lr": 0.04097, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32875, "top5_acc": 0.59109, "loss_cls": 3.81462, "loss": 3.81462, "time": 0.81916} +{"mode": "train", "epoch": 84, "iter": 2600, "lr": 0.04095, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.3375, "top5_acc": 0.59172, "loss_cls": 3.81796, "loss": 3.81796, "time": 0.81591} +{"mode": "train", "epoch": 84, "iter": 2700, "lr": 0.04092, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.335, "top5_acc": 0.59234, "loss_cls": 3.77787, "loss": 3.77787, "time": 0.81556} +{"mode": "train", "epoch": 84, "iter": 2800, "lr": 0.04089, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34031, "top5_acc": 0.60188, "loss_cls": 3.764, "loss": 3.764, "time": 0.81989} +{"mode": "train", "epoch": 84, "iter": 2900, "lr": 0.04086, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33641, "top5_acc": 0.59453, "loss_cls": 3.7913, "loss": 3.7913, "time": 0.82105} +{"mode": "train", "epoch": 84, "iter": 3000, "lr": 0.04084, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34125, "top5_acc": 0.59672, "loss_cls": 3.76066, "loss": 3.76066, "time": 0.81897} +{"mode": "train", "epoch": 84, "iter": 3100, "lr": 0.04081, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33641, "top5_acc": 0.59562, "loss_cls": 3.76136, "loss": 3.76136, "time": 0.83247} +{"mode": "train", "epoch": 84, "iter": 3200, "lr": 0.04078, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32844, "top5_acc": 0.59547, "loss_cls": 3.80544, "loss": 3.80544, "time": 0.8186} +{"mode": "train", "epoch": 84, "iter": 3300, "lr": 0.04075, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33922, "top5_acc": 0.59859, "loss_cls": 3.7799, "loss": 3.7799, "time": 0.8213} +{"mode": "train", "epoch": 84, "iter": 3400, "lr": 0.04073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34594, "top5_acc": 0.60391, "loss_cls": 3.73924, "loss": 3.73924, "time": 0.81926} +{"mode": "train", "epoch": 84, "iter": 3500, "lr": 0.0407, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32375, "top5_acc": 0.57375, "loss_cls": 3.86334, "loss": 3.86334, "time": 0.81389} +{"mode": "train", "epoch": 84, "iter": 3600, "lr": 0.04067, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34438, "top5_acc": 0.61172, "loss_cls": 3.72284, "loss": 3.72284, "time": 0.81418} +{"mode": "train", "epoch": 84, "iter": 3700, "lr": 0.04064, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33, "top5_acc": 0.58938, "loss_cls": 3.80801, "loss": 3.80801, "time": 0.81376} +{"mode": "val", "epoch": 84, "iter": 309, "lr": 0.04063, "top1_acc": 0.27118, "top5_acc": 0.52682, "mean_class_accuracy": 0.27096} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.0406, "memory": 15990, "data_time": 1.28212, "top1_acc": 0.35375, "top5_acc": 0.61672, "loss_cls": 3.64412, "loss": 3.64412, "time": 2.26328} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.04058, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33938, "top5_acc": 0.60172, "loss_cls": 3.73661, "loss": 3.73661, "time": 0.81657} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.04055, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34891, "top5_acc": 0.6, "loss_cls": 3.724, "loss": 3.724, "time": 0.81917} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.04052, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3375, "top5_acc": 0.60094, "loss_cls": 3.7109, "loss": 3.7109, "time": 0.81591} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.04049, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33531, "top5_acc": 0.60578, "loss_cls": 3.74714, "loss": 3.74714, "time": 0.81533} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.04047, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35391, "top5_acc": 0.60344, "loss_cls": 3.7033, "loss": 3.7033, "time": 0.81867} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.04044, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35047, "top5_acc": 0.60453, "loss_cls": 3.74967, "loss": 3.74967, "time": 0.82053} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.04041, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35062, "top5_acc": 0.61062, "loss_cls": 3.67961, "loss": 3.67961, "time": 0.81719} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.04038, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33922, "top5_acc": 0.60891, "loss_cls": 3.72192, "loss": 3.72192, "time": 0.82164} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.04036, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35109, "top5_acc": 0.61297, "loss_cls": 3.67738, "loss": 3.67738, "time": 0.81889} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.04033, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33391, "top5_acc": 0.59297, "loss_cls": 3.79902, "loss": 3.79902, "time": 0.82044} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.0403, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34969, "top5_acc": 0.60922, "loss_cls": 3.70557, "loss": 3.70557, "time": 0.81491} +{"mode": "train", "epoch": 85, "iter": 1300, "lr": 0.04027, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33719, "top5_acc": 0.60844, "loss_cls": 3.72394, "loss": 3.72394, "time": 0.81892} +{"mode": "train", "epoch": 85, "iter": 1400, "lr": 0.04025, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33984, "top5_acc": 0.59828, "loss_cls": 3.7434, "loss": 3.7434, "time": 0.81984} +{"mode": "train", "epoch": 85, "iter": 1500, "lr": 0.04022, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33297, "top5_acc": 0.59688, "loss_cls": 3.77054, "loss": 3.77054, "time": 0.81968} +{"mode": "train", "epoch": 85, "iter": 1600, "lr": 0.04019, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33609, "top5_acc": 0.60672, "loss_cls": 3.74043, "loss": 3.74043, "time": 0.81676} +{"mode": "train", "epoch": 85, "iter": 1700, "lr": 0.04016, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33484, "top5_acc": 0.60016, "loss_cls": 3.77485, "loss": 3.77485, "time": 0.82025} +{"mode": "train", "epoch": 85, "iter": 1800, "lr": 0.04014, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33266, "top5_acc": 0.59875, "loss_cls": 3.77315, "loss": 3.77315, "time": 0.82022} +{"mode": "train", "epoch": 85, "iter": 1900, "lr": 0.04011, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34062, "top5_acc": 0.60625, "loss_cls": 3.75617, "loss": 3.75617, "time": 0.8182} +{"mode": "train", "epoch": 85, "iter": 2000, "lr": 0.04008, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.335, "top5_acc": 0.59188, "loss_cls": 3.78082, "loss": 3.78082, "time": 0.81625} +{"mode": "train", "epoch": 85, "iter": 2100, "lr": 0.04006, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34469, "top5_acc": 0.59875, "loss_cls": 3.77556, "loss": 3.77556, "time": 0.81422} +{"mode": "train", "epoch": 85, "iter": 2200, "lr": 0.04003, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33594, "top5_acc": 0.58688, "loss_cls": 3.83044, "loss": 3.83044, "time": 0.8139} +{"mode": "train", "epoch": 85, "iter": 2300, "lr": 0.04, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33406, "top5_acc": 0.59797, "loss_cls": 3.79308, "loss": 3.79308, "time": 0.81679} +{"mode": "train", "epoch": 85, "iter": 2400, "lr": 0.03997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34562, "top5_acc": 0.60438, "loss_cls": 3.71027, "loss": 3.71027, "time": 0.81726} +{"mode": "train", "epoch": 85, "iter": 2500, "lr": 0.03995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33953, "top5_acc": 0.59094, "loss_cls": 3.78465, "loss": 3.78465, "time": 0.81385} +{"mode": "train", "epoch": 85, "iter": 2600, "lr": 0.03992, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34016, "top5_acc": 0.59641, "loss_cls": 3.79147, "loss": 3.79147, "time": 0.81673} +{"mode": "train", "epoch": 85, "iter": 2700, "lr": 0.03989, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33625, "top5_acc": 0.59734, "loss_cls": 3.77214, "loss": 3.77214, "time": 0.81208} +{"mode": "train", "epoch": 85, "iter": 2800, "lr": 0.03986, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33891, "top5_acc": 0.59234, "loss_cls": 3.76696, "loss": 3.76696, "time": 0.8108} +{"mode": "train", "epoch": 85, "iter": 2900, "lr": 0.03984, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34344, "top5_acc": 0.59984, "loss_cls": 3.74326, "loss": 3.74326, "time": 0.82921} +{"mode": "train", "epoch": 85, "iter": 3000, "lr": 0.03981, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35266, "top5_acc": 0.60562, "loss_cls": 3.70346, "loss": 3.70346, "time": 0.81891} +{"mode": "train", "epoch": 85, "iter": 3100, "lr": 0.03978, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34125, "top5_acc": 0.59828, "loss_cls": 3.74897, "loss": 3.74897, "time": 0.82605} +{"mode": "train", "epoch": 85, "iter": 3200, "lr": 0.03975, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33938, "top5_acc": 0.60625, "loss_cls": 3.7556, "loss": 3.7556, "time": 0.82174} +{"mode": "train", "epoch": 85, "iter": 3300, "lr": 0.03973, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33438, "top5_acc": 0.59844, "loss_cls": 3.75453, "loss": 3.75453, "time": 0.81681} +{"mode": "train", "epoch": 85, "iter": 3400, "lr": 0.0397, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33984, "top5_acc": 0.59828, "loss_cls": 3.75464, "loss": 3.75464, "time": 0.8161} +{"mode": "train", "epoch": 85, "iter": 3500, "lr": 0.03967, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34797, "top5_acc": 0.59297, "loss_cls": 3.76354, "loss": 3.76354, "time": 0.81461} +{"mode": "train", "epoch": 85, "iter": 3600, "lr": 0.03964, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34188, "top5_acc": 0.60125, "loss_cls": 3.78199, "loss": 3.78199, "time": 0.81587} +{"mode": "train", "epoch": 85, "iter": 3700, "lr": 0.03962, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32953, "top5_acc": 0.59, "loss_cls": 3.77974, "loss": 3.77974, "time": 0.81374} +{"mode": "val", "epoch": 85, "iter": 309, "lr": 0.0396, "top1_acc": 0.28522, "top5_acc": 0.53062, "mean_class_accuracy": 0.28484} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.03958, "memory": 15990, "data_time": 1.30468, "top1_acc": 0.35047, "top5_acc": 0.61578, "loss_cls": 3.67023, "loss": 3.67023, "time": 2.28936} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.03955, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34625, "top5_acc": 0.61656, "loss_cls": 3.66734, "loss": 3.66734, "time": 0.82699} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.03952, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33031, "top5_acc": 0.59281, "loss_cls": 3.79871, "loss": 3.79871, "time": 0.82118} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.0395, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34781, "top5_acc": 0.6075, "loss_cls": 3.70381, "loss": 3.70381, "time": 0.8248} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.03947, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35172, "top5_acc": 0.60344, "loss_cls": 3.71797, "loss": 3.71797, "time": 0.81938} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.03944, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.34219, "top5_acc": 0.6025, "loss_cls": 3.7374, "loss": 3.7374, "time": 0.82192} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.03941, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35078, "top5_acc": 0.61328, "loss_cls": 3.66484, "loss": 3.66484, "time": 0.8233} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.03939, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3375, "top5_acc": 0.59484, "loss_cls": 3.74697, "loss": 3.74697, "time": 0.81688} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.03936, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3475, "top5_acc": 0.60156, "loss_cls": 3.73481, "loss": 3.73481, "time": 0.82366} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.03933, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34812, "top5_acc": 0.60594, "loss_cls": 3.72939, "loss": 3.72939, "time": 0.81599} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.0393, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35109, "top5_acc": 0.615, "loss_cls": 3.67967, "loss": 3.67967, "time": 0.82192} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.03928, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32922, "top5_acc": 0.60094, "loss_cls": 3.77125, "loss": 3.77125, "time": 0.81726} +{"mode": "train", "epoch": 86, "iter": 1300, "lr": 0.03925, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34016, "top5_acc": 0.60297, "loss_cls": 3.73833, "loss": 3.73833, "time": 0.82092} +{"mode": "train", "epoch": 86, "iter": 1400, "lr": 0.03922, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34203, "top5_acc": 0.59719, "loss_cls": 3.75893, "loss": 3.75893, "time": 0.81771} +{"mode": "train", "epoch": 86, "iter": 1500, "lr": 0.03919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34047, "top5_acc": 0.60141, "loss_cls": 3.74705, "loss": 3.74705, "time": 0.81788} +{"mode": "train", "epoch": 86, "iter": 1600, "lr": 0.03917, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34469, "top5_acc": 0.60703, "loss_cls": 3.72946, "loss": 3.72946, "time": 0.81753} +{"mode": "train", "epoch": 86, "iter": 1700, "lr": 0.03914, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33422, "top5_acc": 0.6, "loss_cls": 3.74108, "loss": 3.74108, "time": 0.82217} +{"mode": "train", "epoch": 86, "iter": 1800, "lr": 0.03911, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34438, "top5_acc": 0.60281, "loss_cls": 3.72233, "loss": 3.72233, "time": 0.8148} +{"mode": "train", "epoch": 86, "iter": 1900, "lr": 0.03909, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34, "top5_acc": 0.59641, "loss_cls": 3.76659, "loss": 3.76659, "time": 0.81746} +{"mode": "train", "epoch": 86, "iter": 2000, "lr": 0.03906, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34109, "top5_acc": 0.605, "loss_cls": 3.72692, "loss": 3.72692, "time": 0.8143} +{"mode": "train", "epoch": 86, "iter": 2100, "lr": 0.03903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33672, "top5_acc": 0.59156, "loss_cls": 3.77733, "loss": 3.77733, "time": 0.81888} +{"mode": "train", "epoch": 86, "iter": 2200, "lr": 0.039, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33828, "top5_acc": 0.59109, "loss_cls": 3.78517, "loss": 3.78517, "time": 0.81645} +{"mode": "train", "epoch": 86, "iter": 2300, "lr": 0.03898, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33891, "top5_acc": 0.59688, "loss_cls": 3.76949, "loss": 3.76949, "time": 0.81901} +{"mode": "train", "epoch": 86, "iter": 2400, "lr": 0.03895, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33922, "top5_acc": 0.60062, "loss_cls": 3.74663, "loss": 3.74663, "time": 0.81536} +{"mode": "train", "epoch": 86, "iter": 2500, "lr": 0.03892, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34141, "top5_acc": 0.60547, "loss_cls": 3.70548, "loss": 3.70548, "time": 0.81561} +{"mode": "train", "epoch": 86, "iter": 2600, "lr": 0.03889, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34531, "top5_acc": 0.61156, "loss_cls": 3.66844, "loss": 3.66844, "time": 0.81773} +{"mode": "train", "epoch": 86, "iter": 2700, "lr": 0.03887, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35234, "top5_acc": 0.61312, "loss_cls": 3.70236, "loss": 3.70236, "time": 0.81382} +{"mode": "train", "epoch": 86, "iter": 2800, "lr": 0.03884, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33719, "top5_acc": 0.59484, "loss_cls": 3.79022, "loss": 3.79022, "time": 0.82534} +{"mode": "train", "epoch": 86, "iter": 2900, "lr": 0.03881, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34688, "top5_acc": 0.60328, "loss_cls": 3.73089, "loss": 3.73089, "time": 0.81558} +{"mode": "train", "epoch": 86, "iter": 3000, "lr": 0.03879, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34109, "top5_acc": 0.59922, "loss_cls": 3.76605, "loss": 3.76605, "time": 0.82041} +{"mode": "train", "epoch": 86, "iter": 3100, "lr": 0.03876, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34281, "top5_acc": 0.60922, "loss_cls": 3.71811, "loss": 3.71811, "time": 0.81931} +{"mode": "train", "epoch": 86, "iter": 3200, "lr": 0.03873, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34812, "top5_acc": 0.60094, "loss_cls": 3.74882, "loss": 3.74882, "time": 0.82108} +{"mode": "train", "epoch": 86, "iter": 3300, "lr": 0.0387, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33625, "top5_acc": 0.60156, "loss_cls": 3.74587, "loss": 3.74587, "time": 0.82308} +{"mode": "train", "epoch": 86, "iter": 3400, "lr": 0.03868, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33484, "top5_acc": 0.60141, "loss_cls": 3.76786, "loss": 3.76786, "time": 0.81982} +{"mode": "train", "epoch": 86, "iter": 3500, "lr": 0.03865, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.355, "top5_acc": 0.60891, "loss_cls": 3.70679, "loss": 3.70679, "time": 0.81795} +{"mode": "train", "epoch": 86, "iter": 3600, "lr": 0.03862, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.3375, "top5_acc": 0.59203, "loss_cls": 3.79892, "loss": 3.79892, "time": 0.81576} +{"mode": "train", "epoch": 86, "iter": 3700, "lr": 0.0386, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32922, "top5_acc": 0.59328, "loss_cls": 3.82237, "loss": 3.82237, "time": 0.81833} +{"mode": "val", "epoch": 86, "iter": 309, "lr": 0.03858, "top1_acc": 0.28866, "top5_acc": 0.53457, "mean_class_accuracy": 0.28846} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.03856, "memory": 15990, "data_time": 1.29696, "top1_acc": 0.36625, "top5_acc": 0.625, "loss_cls": 3.60635, "loss": 3.60635, "time": 2.27988} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.03853, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34203, "top5_acc": 0.60094, "loss_cls": 3.75065, "loss": 3.75065, "time": 0.82632} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.0385, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35016, "top5_acc": 0.61406, "loss_cls": 3.69263, "loss": 3.69263, "time": 0.82542} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.03847, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34609, "top5_acc": 0.60016, "loss_cls": 3.74922, "loss": 3.74922, "time": 0.82493} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.03845, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34641, "top5_acc": 0.61125, "loss_cls": 3.70089, "loss": 3.70089, "time": 0.82012} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.03842, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35094, "top5_acc": 0.61594, "loss_cls": 3.68562, "loss": 3.68562, "time": 0.82124} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.03839, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.345, "top5_acc": 0.61125, "loss_cls": 3.71338, "loss": 3.71338, "time": 0.81914} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.03837, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35391, "top5_acc": 0.60734, "loss_cls": 3.68282, "loss": 3.68282, "time": 0.81846} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.03834, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35219, "top5_acc": 0.61219, "loss_cls": 3.66926, "loss": 3.66926, "time": 0.81229} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.03831, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34906, "top5_acc": 0.61531, "loss_cls": 3.69956, "loss": 3.69956, "time": 0.81988} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.03828, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34141, "top5_acc": 0.60125, "loss_cls": 3.74816, "loss": 3.74816, "time": 0.81608} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.03826, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34438, "top5_acc": 0.60172, "loss_cls": 3.7239, "loss": 3.7239, "time": 0.81219} +{"mode": "train", "epoch": 87, "iter": 1300, "lr": 0.03823, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34453, "top5_acc": 0.60469, "loss_cls": 3.71847, "loss": 3.71847, "time": 0.82359} +{"mode": "train", "epoch": 87, "iter": 1400, "lr": 0.0382, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33969, "top5_acc": 0.59906, "loss_cls": 3.76561, "loss": 3.76561, "time": 0.81492} +{"mode": "train", "epoch": 87, "iter": 1500, "lr": 0.03817, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34359, "top5_acc": 0.59531, "loss_cls": 3.75044, "loss": 3.75044, "time": 0.81982} +{"mode": "train", "epoch": 87, "iter": 1600, "lr": 0.03815, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34562, "top5_acc": 0.60516, "loss_cls": 3.7032, "loss": 3.7032, "time": 0.822} +{"mode": "train", "epoch": 87, "iter": 1700, "lr": 0.03812, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34297, "top5_acc": 0.60703, "loss_cls": 3.73775, "loss": 3.73775, "time": 0.81728} +{"mode": "train", "epoch": 87, "iter": 1800, "lr": 0.03809, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34562, "top5_acc": 0.60719, "loss_cls": 3.72376, "loss": 3.72376, "time": 0.81703} +{"mode": "train", "epoch": 87, "iter": 1900, "lr": 0.03807, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34672, "top5_acc": 0.60844, "loss_cls": 3.70133, "loss": 3.70133, "time": 0.81548} +{"mode": "train", "epoch": 87, "iter": 2000, "lr": 0.03804, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34844, "top5_acc": 0.59688, "loss_cls": 3.73513, "loss": 3.73513, "time": 0.82272} +{"mode": "train", "epoch": 87, "iter": 2100, "lr": 0.03801, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34453, "top5_acc": 0.59953, "loss_cls": 3.74053, "loss": 3.74053, "time": 0.82262} +{"mode": "train", "epoch": 87, "iter": 2200, "lr": 0.03798, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33875, "top5_acc": 0.59047, "loss_cls": 3.79182, "loss": 3.79182, "time": 0.81393} +{"mode": "train", "epoch": 87, "iter": 2300, "lr": 0.03796, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34375, "top5_acc": 0.60547, "loss_cls": 3.73039, "loss": 3.73039, "time": 0.81757} +{"mode": "train", "epoch": 87, "iter": 2400, "lr": 0.03793, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35047, "top5_acc": 0.59922, "loss_cls": 3.72152, "loss": 3.72152, "time": 0.81197} +{"mode": "train", "epoch": 87, "iter": 2500, "lr": 0.0379, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34172, "top5_acc": 0.60359, "loss_cls": 3.72963, "loss": 3.72963, "time": 0.81764} +{"mode": "train", "epoch": 87, "iter": 2600, "lr": 0.03788, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34438, "top5_acc": 0.60031, "loss_cls": 3.70688, "loss": 3.70688, "time": 0.81589} +{"mode": "train", "epoch": 87, "iter": 2700, "lr": 0.03785, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34109, "top5_acc": 0.5975, "loss_cls": 3.77566, "loss": 3.77566, "time": 0.82071} +{"mode": "train", "epoch": 87, "iter": 2800, "lr": 0.03782, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34797, "top5_acc": 0.60578, "loss_cls": 3.71588, "loss": 3.71588, "time": 0.81577} +{"mode": "train", "epoch": 87, "iter": 2900, "lr": 0.03779, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34516, "top5_acc": 0.59906, "loss_cls": 3.73711, "loss": 3.73711, "time": 0.81599} +{"mode": "train", "epoch": 87, "iter": 3000, "lr": 0.03777, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34141, "top5_acc": 0.60672, "loss_cls": 3.7329, "loss": 3.7329, "time": 0.82302} +{"mode": "train", "epoch": 87, "iter": 3100, "lr": 0.03774, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35016, "top5_acc": 0.61891, "loss_cls": 3.67598, "loss": 3.67598, "time": 0.82115} +{"mode": "train", "epoch": 87, "iter": 3200, "lr": 0.03771, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33969, "top5_acc": 0.60078, "loss_cls": 3.75847, "loss": 3.75847, "time": 0.8257} +{"mode": "train", "epoch": 87, "iter": 3300, "lr": 0.03769, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34031, "top5_acc": 0.60594, "loss_cls": 3.7414, "loss": 3.7414, "time": 0.82347} +{"mode": "train", "epoch": 87, "iter": 3400, "lr": 0.03766, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3425, "top5_acc": 0.60031, "loss_cls": 3.77171, "loss": 3.77171, "time": 0.82221} +{"mode": "train", "epoch": 87, "iter": 3500, "lr": 0.03763, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33469, "top5_acc": 0.58938, "loss_cls": 3.77617, "loss": 3.77617, "time": 0.81863} +{"mode": "train", "epoch": 87, "iter": 3600, "lr": 0.03761, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35344, "top5_acc": 0.61141, "loss_cls": 3.68774, "loss": 3.68774, "time": 0.8185} +{"mode": "train", "epoch": 87, "iter": 3700, "lr": 0.03758, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33672, "top5_acc": 0.60391, "loss_cls": 3.73017, "loss": 3.73017, "time": 0.81621} +{"mode": "val", "epoch": 87, "iter": 309, "lr": 0.03757, "top1_acc": 0.28592, "top5_acc": 0.541, "mean_class_accuracy": 0.28561} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.03754, "memory": 15990, "data_time": 1.29825, "top1_acc": 0.3525, "top5_acc": 0.61734, "loss_cls": 3.66663, "loss": 3.66663, "time": 2.29401} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.03751, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34812, "top5_acc": 0.61016, "loss_cls": 3.71241, "loss": 3.71241, "time": 0.82452} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.03748, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35438, "top5_acc": 0.61797, "loss_cls": 3.67769, "loss": 3.67769, "time": 0.8225} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.03746, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36312, "top5_acc": 0.61469, "loss_cls": 3.65892, "loss": 3.65892, "time": 0.82595} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.03743, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36281, "top5_acc": 0.61187, "loss_cls": 3.66831, "loss": 3.66831, "time": 0.82334} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.0374, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35859, "top5_acc": 0.61594, "loss_cls": 3.66847, "loss": 3.66847, "time": 0.82454} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.03738, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35422, "top5_acc": 0.60438, "loss_cls": 3.7158, "loss": 3.7158, "time": 0.8162} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.03735, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34438, "top5_acc": 0.6025, "loss_cls": 3.74688, "loss": 3.74688, "time": 0.81837} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.03732, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36094, "top5_acc": 0.63, "loss_cls": 3.62736, "loss": 3.62736, "time": 0.81763} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.0373, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35188, "top5_acc": 0.60766, "loss_cls": 3.71355, "loss": 3.71355, "time": 0.82241} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.03727, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34656, "top5_acc": 0.61344, "loss_cls": 3.67946, "loss": 3.67946, "time": 0.818} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.03724, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34172, "top5_acc": 0.60016, "loss_cls": 3.75091, "loss": 3.75091, "time": 0.81962} +{"mode": "train", "epoch": 88, "iter": 1300, "lr": 0.03721, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35188, "top5_acc": 0.60266, "loss_cls": 3.73043, "loss": 3.73043, "time": 0.81577} +{"mode": "train", "epoch": 88, "iter": 1400, "lr": 0.03719, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35125, "top5_acc": 0.60953, "loss_cls": 3.67754, "loss": 3.67754, "time": 0.82483} +{"mode": "train", "epoch": 88, "iter": 1500, "lr": 0.03716, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35672, "top5_acc": 0.61422, "loss_cls": 3.683, "loss": 3.683, "time": 0.82047} +{"mode": "train", "epoch": 88, "iter": 1600, "lr": 0.03713, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34281, "top5_acc": 0.60453, "loss_cls": 3.71429, "loss": 3.71429, "time": 0.81772} +{"mode": "train", "epoch": 88, "iter": 1700, "lr": 0.03711, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34188, "top5_acc": 0.60016, "loss_cls": 3.74183, "loss": 3.74183, "time": 0.82326} +{"mode": "train", "epoch": 88, "iter": 1800, "lr": 0.03708, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34094, "top5_acc": 0.60703, "loss_cls": 3.72691, "loss": 3.72691, "time": 0.81772} +{"mode": "train", "epoch": 88, "iter": 1900, "lr": 0.03705, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34, "top5_acc": 0.6025, "loss_cls": 3.72138, "loss": 3.72138, "time": 0.81522} +{"mode": "train", "epoch": 88, "iter": 2000, "lr": 0.03703, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33672, "top5_acc": 0.60125, "loss_cls": 3.75753, "loss": 3.75753, "time": 0.81177} +{"mode": "train", "epoch": 88, "iter": 2100, "lr": 0.037, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35016, "top5_acc": 0.60859, "loss_cls": 3.69076, "loss": 3.69076, "time": 0.81864} +{"mode": "train", "epoch": 88, "iter": 2200, "lr": 0.03697, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35094, "top5_acc": 0.61703, "loss_cls": 3.66028, "loss": 3.66028, "time": 0.81537} +{"mode": "train", "epoch": 88, "iter": 2300, "lr": 0.03694, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33984, "top5_acc": 0.60672, "loss_cls": 3.7035, "loss": 3.7035, "time": 0.81784} +{"mode": "train", "epoch": 88, "iter": 2400, "lr": 0.03692, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33266, "top5_acc": 0.59469, "loss_cls": 3.76067, "loss": 3.76067, "time": 0.81907} +{"mode": "train", "epoch": 88, "iter": 2500, "lr": 0.03689, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34562, "top5_acc": 0.6025, "loss_cls": 3.71946, "loss": 3.71946, "time": 0.81322} +{"mode": "train", "epoch": 88, "iter": 2600, "lr": 0.03686, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34391, "top5_acc": 0.60438, "loss_cls": 3.72369, "loss": 3.72369, "time": 0.81552} +{"mode": "train", "epoch": 88, "iter": 2700, "lr": 0.03684, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34891, "top5_acc": 0.60828, "loss_cls": 3.71717, "loss": 3.71717, "time": 0.81267} +{"mode": "train", "epoch": 88, "iter": 2800, "lr": 0.03681, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34969, "top5_acc": 0.60781, "loss_cls": 3.71772, "loss": 3.71772, "time": 0.81885} +{"mode": "train", "epoch": 88, "iter": 2900, "lr": 0.03678, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33828, "top5_acc": 0.60234, "loss_cls": 3.72592, "loss": 3.72592, "time": 0.82231} +{"mode": "train", "epoch": 88, "iter": 3000, "lr": 0.03676, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33703, "top5_acc": 0.60422, "loss_cls": 3.71893, "loss": 3.71893, "time": 0.81959} +{"mode": "train", "epoch": 88, "iter": 3100, "lr": 0.03673, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34781, "top5_acc": 0.60359, "loss_cls": 3.71472, "loss": 3.71472, "time": 0.81628} +{"mode": "train", "epoch": 88, "iter": 3200, "lr": 0.0367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34812, "top5_acc": 0.6075, "loss_cls": 3.70564, "loss": 3.70564, "time": 0.82302} +{"mode": "train", "epoch": 88, "iter": 3300, "lr": 0.03667, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34766, "top5_acc": 0.60344, "loss_cls": 3.71381, "loss": 3.71381, "time": 0.81801} +{"mode": "train", "epoch": 88, "iter": 3400, "lr": 0.03665, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33922, "top5_acc": 0.60344, "loss_cls": 3.73364, "loss": 3.73364, "time": 0.81523} +{"mode": "train", "epoch": 88, "iter": 3500, "lr": 0.03662, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34188, "top5_acc": 0.60172, "loss_cls": 3.72699, "loss": 3.72699, "time": 0.81255} +{"mode": "train", "epoch": 88, "iter": 3600, "lr": 0.03659, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34266, "top5_acc": 0.60578, "loss_cls": 3.74582, "loss": 3.74582, "time": 0.81136} +{"mode": "train", "epoch": 88, "iter": 3700, "lr": 0.03657, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34062, "top5_acc": 0.60578, "loss_cls": 3.7337, "loss": 3.7337, "time": 0.81497} +{"mode": "val", "epoch": 88, "iter": 309, "lr": 0.03655, "top1_acc": 0.29428, "top5_acc": 0.54298, "mean_class_accuracy": 0.29402} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.03653, "memory": 15990, "data_time": 1.29156, "top1_acc": 0.35344, "top5_acc": 0.61062, "loss_cls": 3.65542, "loss": 3.65542, "time": 2.2739} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0365, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35969, "top5_acc": 0.62297, "loss_cls": 3.65967, "loss": 3.65967, "time": 0.82351} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.03647, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36031, "top5_acc": 0.61734, "loss_cls": 3.64535, "loss": 3.64535, "time": 0.82006} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.03645, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34125, "top5_acc": 0.60922, "loss_cls": 3.70446, "loss": 3.70446, "time": 0.82421} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.03642, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35125, "top5_acc": 0.60672, "loss_cls": 3.67734, "loss": 3.67734, "time": 0.82055} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.03639, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35516, "top5_acc": 0.62594, "loss_cls": 3.62928, "loss": 3.62928, "time": 0.82142} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.03637, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36281, "top5_acc": 0.625, "loss_cls": 3.62378, "loss": 3.62378, "time": 0.81798} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.03634, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35766, "top5_acc": 0.61109, "loss_cls": 3.66356, "loss": 3.66356, "time": 0.81895} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.03631, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.34734, "top5_acc": 0.60484, "loss_cls": 3.71126, "loss": 3.71126, "time": 0.81425} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.03629, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35469, "top5_acc": 0.61687, "loss_cls": 3.66862, "loss": 3.66862, "time": 0.81795} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.03626, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35859, "top5_acc": 0.62109, "loss_cls": 3.62192, "loss": 3.62192, "time": 0.81742} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.03623, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35266, "top5_acc": 0.60812, "loss_cls": 3.68527, "loss": 3.68527, "time": 0.81753} +{"mode": "train", "epoch": 89, "iter": 1300, "lr": 0.0362, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34391, "top5_acc": 0.60688, "loss_cls": 3.72312, "loss": 3.72312, "time": 0.82051} +{"mode": "train", "epoch": 89, "iter": 1400, "lr": 0.03618, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35391, "top5_acc": 0.61578, "loss_cls": 3.68401, "loss": 3.68401, "time": 0.81692} +{"mode": "train", "epoch": 89, "iter": 1500, "lr": 0.03615, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34766, "top5_acc": 0.60781, "loss_cls": 3.69067, "loss": 3.69067, "time": 0.81552} +{"mode": "train", "epoch": 89, "iter": 1600, "lr": 0.03612, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34406, "top5_acc": 0.60547, "loss_cls": 3.69074, "loss": 3.69074, "time": 0.82201} +{"mode": "train", "epoch": 89, "iter": 1700, "lr": 0.0361, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33953, "top5_acc": 0.61297, "loss_cls": 3.7164, "loss": 3.7164, "time": 0.8123} +{"mode": "train", "epoch": 89, "iter": 1800, "lr": 0.03607, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34953, "top5_acc": 0.60734, "loss_cls": 3.68357, "loss": 3.68357, "time": 0.81606} +{"mode": "train", "epoch": 89, "iter": 1900, "lr": 0.03604, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34453, "top5_acc": 0.59047, "loss_cls": 3.76315, "loss": 3.76315, "time": 0.8178} +{"mode": "train", "epoch": 89, "iter": 2000, "lr": 0.03602, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34969, "top5_acc": 0.60516, "loss_cls": 3.71228, "loss": 3.71228, "time": 0.82083} +{"mode": "train", "epoch": 89, "iter": 2100, "lr": 0.03599, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35328, "top5_acc": 0.60172, "loss_cls": 3.72877, "loss": 3.72877, "time": 0.81648} +{"mode": "train", "epoch": 89, "iter": 2200, "lr": 0.03596, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3525, "top5_acc": 0.60234, "loss_cls": 3.72312, "loss": 3.72312, "time": 0.81312} +{"mode": "train", "epoch": 89, "iter": 2300, "lr": 0.03594, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33766, "top5_acc": 0.59922, "loss_cls": 3.80326, "loss": 3.80326, "time": 0.81999} +{"mode": "train", "epoch": 89, "iter": 2400, "lr": 0.03591, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33312, "top5_acc": 0.59406, "loss_cls": 3.77796, "loss": 3.77796, "time": 0.82027} +{"mode": "train", "epoch": 89, "iter": 2500, "lr": 0.03588, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34734, "top5_acc": 0.61, "loss_cls": 3.68514, "loss": 3.68514, "time": 0.81958} +{"mode": "train", "epoch": 89, "iter": 2600, "lr": 0.03586, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36031, "top5_acc": 0.62203, "loss_cls": 3.61069, "loss": 3.61069, "time": 0.81653} +{"mode": "train", "epoch": 89, "iter": 2700, "lr": 0.03583, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34266, "top5_acc": 0.59641, "loss_cls": 3.74031, "loss": 3.74031, "time": 0.82273} +{"mode": "train", "epoch": 89, "iter": 2800, "lr": 0.0358, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35453, "top5_acc": 0.61219, "loss_cls": 3.67915, "loss": 3.67915, "time": 0.8279} +{"mode": "train", "epoch": 89, "iter": 2900, "lr": 0.03578, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.33422, "top5_acc": 0.60578, "loss_cls": 3.71884, "loss": 3.71884, "time": 0.81739} +{"mode": "train", "epoch": 89, "iter": 3000, "lr": 0.03575, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34797, "top5_acc": 0.61172, "loss_cls": 3.68937, "loss": 3.68937, "time": 0.81728} +{"mode": "train", "epoch": 89, "iter": 3100, "lr": 0.03572, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35406, "top5_acc": 0.60562, "loss_cls": 3.69875, "loss": 3.69875, "time": 0.81792} +{"mode": "train", "epoch": 89, "iter": 3200, "lr": 0.03569, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.35125, "top5_acc": 0.61328, "loss_cls": 3.68084, "loss": 3.68084, "time": 0.82161} +{"mode": "train", "epoch": 89, "iter": 3300, "lr": 0.03567, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34938, "top5_acc": 0.60219, "loss_cls": 3.70066, "loss": 3.70066, "time": 0.81417} +{"mode": "train", "epoch": 89, "iter": 3400, "lr": 0.03564, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35406, "top5_acc": 0.60766, "loss_cls": 3.69023, "loss": 3.69023, "time": 0.81778} +{"mode": "train", "epoch": 89, "iter": 3500, "lr": 0.03561, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35406, "top5_acc": 0.60953, "loss_cls": 3.70895, "loss": 3.70895, "time": 0.81768} +{"mode": "train", "epoch": 89, "iter": 3600, "lr": 0.03559, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.3325, "top5_acc": 0.59531, "loss_cls": 3.76064, "loss": 3.76064, "time": 0.81882} +{"mode": "train", "epoch": 89, "iter": 3700, "lr": 0.03556, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34203, "top5_acc": 0.59766, "loss_cls": 3.74776, "loss": 3.74776, "time": 0.8115} +{"mode": "val", "epoch": 89, "iter": 309, "lr": 0.03555, "top1_acc": 0.24267, "top5_acc": 0.49192, "mean_class_accuracy": 0.24246} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.03552, "memory": 15990, "data_time": 1.31285, "top1_acc": 0.35438, "top5_acc": 0.61922, "loss_cls": 3.6421, "loss": 3.6421, "time": 2.32776} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.0355, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35625, "top5_acc": 0.62094, "loss_cls": 3.61367, "loss": 3.61367, "time": 0.82493} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.03547, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35547, "top5_acc": 0.62234, "loss_cls": 3.62226, "loss": 3.62226, "time": 0.81927} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.03544, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35609, "top5_acc": 0.62234, "loss_cls": 3.66383, "loss": 3.66383, "time": 0.82175} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.03541, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34562, "top5_acc": 0.60984, "loss_cls": 3.6964, "loss": 3.6964, "time": 0.8163} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.03539, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35625, "top5_acc": 0.61766, "loss_cls": 3.65336, "loss": 3.65336, "time": 0.82014} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.03536, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3525, "top5_acc": 0.61156, "loss_cls": 3.68798, "loss": 3.68798, "time": 0.81901} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.03533, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35219, "top5_acc": 0.61328, "loss_cls": 3.70576, "loss": 3.70576, "time": 0.81511} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.03531, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35172, "top5_acc": 0.61469, "loss_cls": 3.67506, "loss": 3.67506, "time": 0.81671} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.03528, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35984, "top5_acc": 0.62047, "loss_cls": 3.65991, "loss": 3.65991, "time": 0.81508} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.03525, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35469, "top5_acc": 0.62391, "loss_cls": 3.65337, "loss": 3.65337, "time": 0.81487} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.03523, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3525, "top5_acc": 0.60453, "loss_cls": 3.68472, "loss": 3.68472, "time": 0.82158} +{"mode": "train", "epoch": 90, "iter": 1300, "lr": 0.0352, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34781, "top5_acc": 0.61187, "loss_cls": 3.71245, "loss": 3.71245, "time": 0.82481} +{"mode": "train", "epoch": 90, "iter": 1400, "lr": 0.03517, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35766, "top5_acc": 0.62078, "loss_cls": 3.65992, "loss": 3.65992, "time": 0.82129} +{"mode": "train", "epoch": 90, "iter": 1500, "lr": 0.03515, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34656, "top5_acc": 0.61328, "loss_cls": 3.6941, "loss": 3.6941, "time": 0.82699} +{"mode": "train", "epoch": 90, "iter": 1600, "lr": 0.03512, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34906, "top5_acc": 0.60562, "loss_cls": 3.70209, "loss": 3.70209, "time": 0.82068} +{"mode": "train", "epoch": 90, "iter": 1700, "lr": 0.03509, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35031, "top5_acc": 0.60656, "loss_cls": 3.70297, "loss": 3.70297, "time": 0.81677} +{"mode": "train", "epoch": 90, "iter": 1800, "lr": 0.03507, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34984, "top5_acc": 0.61156, "loss_cls": 3.68771, "loss": 3.68771, "time": 0.82012} +{"mode": "train", "epoch": 90, "iter": 1900, "lr": 0.03504, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3475, "top5_acc": 0.6025, "loss_cls": 3.73467, "loss": 3.73467, "time": 0.81657} +{"mode": "train", "epoch": 90, "iter": 2000, "lr": 0.03501, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35422, "top5_acc": 0.60922, "loss_cls": 3.67426, "loss": 3.67426, "time": 0.81747} +{"mode": "train", "epoch": 90, "iter": 2100, "lr": 0.03499, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34156, "top5_acc": 0.60188, "loss_cls": 3.71632, "loss": 3.71632, "time": 0.81693} +{"mode": "train", "epoch": 90, "iter": 2200, "lr": 0.03496, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35516, "top5_acc": 0.60547, "loss_cls": 3.70431, "loss": 3.70431, "time": 0.81017} +{"mode": "train", "epoch": 90, "iter": 2300, "lr": 0.03493, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35109, "top5_acc": 0.61656, "loss_cls": 3.66444, "loss": 3.66444, "time": 0.81617} +{"mode": "train", "epoch": 90, "iter": 2400, "lr": 0.03491, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34781, "top5_acc": 0.62422, "loss_cls": 3.65857, "loss": 3.65857, "time": 0.81658} +{"mode": "train", "epoch": 90, "iter": 2500, "lr": 0.03488, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34, "top5_acc": 0.59859, "loss_cls": 3.77182, "loss": 3.77182, "time": 0.8188} +{"mode": "train", "epoch": 90, "iter": 2600, "lr": 0.03485, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34016, "top5_acc": 0.6075, "loss_cls": 3.74125, "loss": 3.74125, "time": 0.82043} +{"mode": "train", "epoch": 90, "iter": 2700, "lr": 0.03483, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34859, "top5_acc": 0.60297, "loss_cls": 3.72071, "loss": 3.72071, "time": 0.81581} +{"mode": "train", "epoch": 90, "iter": 2800, "lr": 0.0348, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35328, "top5_acc": 0.61187, "loss_cls": 3.67569, "loss": 3.67569, "time": 0.81747} +{"mode": "train", "epoch": 90, "iter": 2900, "lr": 0.03477, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34828, "top5_acc": 0.60969, "loss_cls": 3.69039, "loss": 3.69039, "time": 0.81777} +{"mode": "train", "epoch": 90, "iter": 3000, "lr": 0.03475, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34484, "top5_acc": 0.60188, "loss_cls": 3.76113, "loss": 3.76113, "time": 0.8272} +{"mode": "train", "epoch": 90, "iter": 3100, "lr": 0.03472, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35156, "top5_acc": 0.61016, "loss_cls": 3.68318, "loss": 3.68318, "time": 0.8169} +{"mode": "train", "epoch": 90, "iter": 3200, "lr": 0.03469, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36203, "top5_acc": 0.61734, "loss_cls": 3.64267, "loss": 3.64267, "time": 0.81999} +{"mode": "train", "epoch": 90, "iter": 3300, "lr": 0.03467, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35609, "top5_acc": 0.61297, "loss_cls": 3.65958, "loss": 3.65958, "time": 0.8219} +{"mode": "train", "epoch": 90, "iter": 3400, "lr": 0.03464, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34781, "top5_acc": 0.59922, "loss_cls": 3.72604, "loss": 3.72604, "time": 0.81974} +{"mode": "train", "epoch": 90, "iter": 3500, "lr": 0.03461, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35531, "top5_acc": 0.60812, "loss_cls": 3.67308, "loss": 3.67308, "time": 0.81437} +{"mode": "train", "epoch": 90, "iter": 3600, "lr": 0.03459, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35047, "top5_acc": 0.61125, "loss_cls": 3.69647, "loss": 3.69647, "time": 0.81335} +{"mode": "train", "epoch": 90, "iter": 3700, "lr": 0.03456, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35297, "top5_acc": 0.61141, "loss_cls": 3.69751, "loss": 3.69751, "time": 0.81676} +{"mode": "val", "epoch": 90, "iter": 309, "lr": 0.03455, "top1_acc": 0.27574, "top5_acc": 0.53513, "mean_class_accuracy": 0.27557} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.03452, "memory": 15990, "data_time": 1.30227, "top1_acc": 0.35234, "top5_acc": 0.62031, "loss_cls": 3.60512, "loss": 3.60512, "time": 2.28642} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0345, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36141, "top5_acc": 0.60859, "loss_cls": 3.67562, "loss": 3.67562, "time": 0.82157} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.03447, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35422, "top5_acc": 0.61828, "loss_cls": 3.6436, "loss": 3.6436, "time": 0.82703} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.03444, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35672, "top5_acc": 0.62359, "loss_cls": 3.62518, "loss": 3.62518, "time": 0.81926} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.03442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34281, "top5_acc": 0.60812, "loss_cls": 3.72365, "loss": 3.72365, "time": 0.82795} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.03439, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35734, "top5_acc": 0.61375, "loss_cls": 3.68259, "loss": 3.68259, "time": 0.82075} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.03436, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35859, "top5_acc": 0.61359, "loss_cls": 3.66741, "loss": 3.66741, "time": 0.81811} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.03434, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34641, "top5_acc": 0.61156, "loss_cls": 3.686, "loss": 3.686, "time": 0.81694} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.03431, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35047, "top5_acc": 0.60828, "loss_cls": 3.66993, "loss": 3.66993, "time": 0.82211} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.03428, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35672, "top5_acc": 0.60422, "loss_cls": 3.67123, "loss": 3.67123, "time": 0.82559} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.03426, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34516, "top5_acc": 0.59719, "loss_cls": 3.75041, "loss": 3.75041, "time": 0.81904} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.03423, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35578, "top5_acc": 0.61156, "loss_cls": 3.67696, "loss": 3.67696, "time": 0.82206} +{"mode": "train", "epoch": 91, "iter": 1300, "lr": 0.0342, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34359, "top5_acc": 0.60203, "loss_cls": 3.70923, "loss": 3.70923, "time": 0.81928} +{"mode": "train", "epoch": 91, "iter": 1400, "lr": 0.03418, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35859, "top5_acc": 0.61516, "loss_cls": 3.66549, "loss": 3.66549, "time": 0.8142} +{"mode": "train", "epoch": 91, "iter": 1500, "lr": 0.03415, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35984, "top5_acc": 0.62078, "loss_cls": 3.62816, "loss": 3.62816, "time": 0.82287} +{"mode": "train", "epoch": 91, "iter": 1600, "lr": 0.03412, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34734, "top5_acc": 0.60984, "loss_cls": 3.66781, "loss": 3.66781, "time": 0.81614} +{"mode": "train", "epoch": 91, "iter": 1700, "lr": 0.0341, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35875, "top5_acc": 0.62344, "loss_cls": 3.64106, "loss": 3.64106, "time": 0.81515} +{"mode": "train", "epoch": 91, "iter": 1800, "lr": 0.03407, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35734, "top5_acc": 0.61656, "loss_cls": 3.64922, "loss": 3.64922, "time": 0.81681} +{"mode": "train", "epoch": 91, "iter": 1900, "lr": 0.03405, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36578, "top5_acc": 0.62297, "loss_cls": 3.62579, "loss": 3.62579, "time": 0.82174} +{"mode": "train", "epoch": 91, "iter": 2000, "lr": 0.03402, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35578, "top5_acc": 0.61531, "loss_cls": 3.68123, "loss": 3.68123, "time": 0.81374} +{"mode": "train", "epoch": 91, "iter": 2100, "lr": 0.03399, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35297, "top5_acc": 0.62062, "loss_cls": 3.64111, "loss": 3.64111, "time": 0.81666} +{"mode": "train", "epoch": 91, "iter": 2200, "lr": 0.03397, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34344, "top5_acc": 0.60969, "loss_cls": 3.69951, "loss": 3.69951, "time": 0.81785} +{"mode": "train", "epoch": 91, "iter": 2300, "lr": 0.03394, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35562, "top5_acc": 0.61812, "loss_cls": 3.64147, "loss": 3.64147, "time": 0.81455} +{"mode": "train", "epoch": 91, "iter": 2400, "lr": 0.03391, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36031, "top5_acc": 0.61906, "loss_cls": 3.63615, "loss": 3.63615, "time": 0.81865} +{"mode": "train", "epoch": 91, "iter": 2500, "lr": 0.03389, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35156, "top5_acc": 0.61156, "loss_cls": 3.67663, "loss": 3.67663, "time": 0.81492} +{"mode": "train", "epoch": 91, "iter": 2600, "lr": 0.03386, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34812, "top5_acc": 0.61359, "loss_cls": 3.69523, "loss": 3.69523, "time": 0.82035} +{"mode": "train", "epoch": 91, "iter": 2700, "lr": 0.03383, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34156, "top5_acc": 0.61266, "loss_cls": 3.71825, "loss": 3.71825, "time": 0.81977} +{"mode": "train", "epoch": 91, "iter": 2800, "lr": 0.03381, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34438, "top5_acc": 0.61734, "loss_cls": 3.70099, "loss": 3.70099, "time": 0.81638} +{"mode": "train", "epoch": 91, "iter": 2900, "lr": 0.03378, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35062, "top5_acc": 0.61125, "loss_cls": 3.70485, "loss": 3.70485, "time": 0.81464} +{"mode": "train", "epoch": 91, "iter": 3000, "lr": 0.03375, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35703, "top5_acc": 0.61531, "loss_cls": 3.66338, "loss": 3.66338, "time": 0.83081} +{"mode": "train", "epoch": 91, "iter": 3100, "lr": 0.03373, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35547, "top5_acc": 0.61125, "loss_cls": 3.70047, "loss": 3.70047, "time": 0.81875} +{"mode": "train", "epoch": 91, "iter": 3200, "lr": 0.0337, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35484, "top5_acc": 0.60844, "loss_cls": 3.67786, "loss": 3.67786, "time": 0.82343} +{"mode": "train", "epoch": 91, "iter": 3300, "lr": 0.03367, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35516, "top5_acc": 0.60984, "loss_cls": 3.69396, "loss": 3.69396, "time": 0.82198} +{"mode": "train", "epoch": 91, "iter": 3400, "lr": 0.03365, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35312, "top5_acc": 0.60719, "loss_cls": 3.68178, "loss": 3.68178, "time": 0.81989} +{"mode": "train", "epoch": 91, "iter": 3500, "lr": 0.03362, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35609, "top5_acc": 0.61531, "loss_cls": 3.65167, "loss": 3.65167, "time": 0.82247} +{"mode": "train", "epoch": 91, "iter": 3600, "lr": 0.0336, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.34656, "top5_acc": 0.61281, "loss_cls": 3.69196, "loss": 3.69196, "time": 0.81374} +{"mode": "train", "epoch": 91, "iter": 3700, "lr": 0.03357, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34953, "top5_acc": 0.61406, "loss_cls": 3.69655, "loss": 3.69655, "time": 0.82032} +{"mode": "val", "epoch": 91, "iter": 309, "lr": 0.03356, "top1_acc": 0.29008, "top5_acc": 0.54835, "mean_class_accuracy": 0.28994} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.03353, "memory": 15990, "data_time": 1.26048, "top1_acc": 0.36594, "top5_acc": 0.62578, "loss_cls": 3.58662, "loss": 3.58662, "time": 2.2357} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.0335, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36734, "top5_acc": 0.62203, "loss_cls": 3.58268, "loss": 3.58268, "time": 0.82305} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.03348, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36891, "top5_acc": 0.62703, "loss_cls": 3.57396, "loss": 3.57396, "time": 0.8265} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.03345, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36406, "top5_acc": 0.61734, "loss_cls": 3.61515, "loss": 3.61515, "time": 0.82274} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.03342, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35625, "top5_acc": 0.61094, "loss_cls": 3.69681, "loss": 3.69681, "time": 0.82591} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.0334, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35359, "top5_acc": 0.61047, "loss_cls": 3.65699, "loss": 3.65699, "time": 0.81904} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.03337, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36703, "top5_acc": 0.61797, "loss_cls": 3.64635, "loss": 3.64635, "time": 0.8145} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.03335, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.35844, "top5_acc": 0.62547, "loss_cls": 3.64049, "loss": 3.64049, "time": 0.81586} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.03332, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36359, "top5_acc": 0.615, "loss_cls": 3.65288, "loss": 3.65288, "time": 0.81727} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.03329, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35812, "top5_acc": 0.62391, "loss_cls": 3.6456, "loss": 3.6456, "time": 0.81568} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.03327, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.36141, "top5_acc": 0.62016, "loss_cls": 3.64815, "loss": 3.64815, "time": 0.81376} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.03324, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35203, "top5_acc": 0.61438, "loss_cls": 3.66939, "loss": 3.66939, "time": 0.81368} +{"mode": "train", "epoch": 92, "iter": 1300, "lr": 0.03321, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36344, "top5_acc": 0.61781, "loss_cls": 3.62977, "loss": 3.62977, "time": 0.8226} +{"mode": "train", "epoch": 92, "iter": 1400, "lr": 0.03319, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34812, "top5_acc": 0.61344, "loss_cls": 3.68506, "loss": 3.68506, "time": 0.8242} +{"mode": "train", "epoch": 92, "iter": 1500, "lr": 0.03316, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34828, "top5_acc": 0.61812, "loss_cls": 3.63742, "loss": 3.63742, "time": 0.81775} +{"mode": "train", "epoch": 92, "iter": 1600, "lr": 0.03314, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35625, "top5_acc": 0.6175, "loss_cls": 3.65859, "loss": 3.65859, "time": 0.814} +{"mode": "train", "epoch": 92, "iter": 1700, "lr": 0.03311, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35672, "top5_acc": 0.60922, "loss_cls": 3.69042, "loss": 3.69042, "time": 0.81732} +{"mode": "train", "epoch": 92, "iter": 1800, "lr": 0.03308, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36094, "top5_acc": 0.62219, "loss_cls": 3.64395, "loss": 3.64395, "time": 0.81739} +{"mode": "train", "epoch": 92, "iter": 1900, "lr": 0.03306, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35969, "top5_acc": 0.61938, "loss_cls": 3.63206, "loss": 3.63206, "time": 0.81414} +{"mode": "train", "epoch": 92, "iter": 2000, "lr": 0.03303, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36719, "top5_acc": 0.62891, "loss_cls": 3.56696, "loss": 3.56696, "time": 0.81362} +{"mode": "train", "epoch": 92, "iter": 2100, "lr": 0.033, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36391, "top5_acc": 0.62406, "loss_cls": 3.60098, "loss": 3.60098, "time": 0.81488} +{"mode": "train", "epoch": 92, "iter": 2200, "lr": 0.03298, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34109, "top5_acc": 0.61234, "loss_cls": 3.69229, "loss": 3.69229, "time": 0.81496} +{"mode": "train", "epoch": 92, "iter": 2300, "lr": 0.03295, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34047, "top5_acc": 0.60188, "loss_cls": 3.74582, "loss": 3.74582, "time": 0.82214} +{"mode": "train", "epoch": 92, "iter": 2400, "lr": 0.03292, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.34875, "top5_acc": 0.60703, "loss_cls": 3.68444, "loss": 3.68444, "time": 0.8146} +{"mode": "train", "epoch": 92, "iter": 2500, "lr": 0.0329, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34578, "top5_acc": 0.62109, "loss_cls": 3.65642, "loss": 3.65642, "time": 0.81569} +{"mode": "train", "epoch": 92, "iter": 2600, "lr": 0.03287, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3475, "top5_acc": 0.60703, "loss_cls": 3.71485, "loss": 3.71485, "time": 0.82255} +{"mode": "train", "epoch": 92, "iter": 2700, "lr": 0.03285, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35469, "top5_acc": 0.60078, "loss_cls": 3.71581, "loss": 3.71581, "time": 0.8168} +{"mode": "train", "epoch": 92, "iter": 2800, "lr": 0.03282, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35797, "top5_acc": 0.62297, "loss_cls": 3.64333, "loss": 3.64333, "time": 0.81752} +{"mode": "train", "epoch": 92, "iter": 2900, "lr": 0.03279, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34375, "top5_acc": 0.6, "loss_cls": 3.73535, "loss": 3.73535, "time": 0.82154} +{"mode": "train", "epoch": 92, "iter": 3000, "lr": 0.03277, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35062, "top5_acc": 0.61031, "loss_cls": 3.67436, "loss": 3.67436, "time": 0.82064} +{"mode": "train", "epoch": 92, "iter": 3100, "lr": 0.03274, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33828, "top5_acc": 0.60156, "loss_cls": 3.71342, "loss": 3.71342, "time": 0.81712} +{"mode": "train", "epoch": 92, "iter": 3200, "lr": 0.03271, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34875, "top5_acc": 0.60453, "loss_cls": 3.68865, "loss": 3.68865, "time": 0.81968} +{"mode": "train", "epoch": 92, "iter": 3300, "lr": 0.03269, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36375, "top5_acc": 0.62578, "loss_cls": 3.61168, "loss": 3.61168, "time": 0.8186} +{"mode": "train", "epoch": 92, "iter": 3400, "lr": 0.03266, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34922, "top5_acc": 0.60812, "loss_cls": 3.67557, "loss": 3.67557, "time": 0.81682} +{"mode": "train", "epoch": 92, "iter": 3500, "lr": 0.03264, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35812, "top5_acc": 0.61781, "loss_cls": 3.66777, "loss": 3.66777, "time": 0.82283} +{"mode": "train", "epoch": 92, "iter": 3600, "lr": 0.03261, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35906, "top5_acc": 0.61625, "loss_cls": 3.64366, "loss": 3.64366, "time": 0.81629} +{"mode": "train", "epoch": 92, "iter": 3700, "lr": 0.03258, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35859, "top5_acc": 0.60688, "loss_cls": 3.69793, "loss": 3.69793, "time": 0.81699} +{"mode": "val", "epoch": 92, "iter": 309, "lr": 0.03257, "top1_acc": 0.29099, "top5_acc": 0.54389, "mean_class_accuracy": 0.29074} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.03255, "memory": 15990, "data_time": 1.30047, "top1_acc": 0.37953, "top5_acc": 0.63, "loss_cls": 3.56222, "loss": 3.56222, "time": 2.28558} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.03252, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36031, "top5_acc": 0.62062, "loss_cls": 3.63877, "loss": 3.63877, "time": 0.8268} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.03249, "memory": 15990, "data_time": 0.00073, "top1_acc": 0.36188, "top5_acc": 0.62766, "loss_cls": 3.59448, "loss": 3.59448, "time": 0.82789} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.03247, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34797, "top5_acc": 0.61453, "loss_cls": 3.66532, "loss": 3.66532, "time": 0.81388} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.03244, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36438, "top5_acc": 0.62438, "loss_cls": 3.62054, "loss": 3.62054, "time": 0.82814} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.03241, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36312, "top5_acc": 0.62719, "loss_cls": 3.56601, "loss": 3.56601, "time": 0.82401} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.03239, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35656, "top5_acc": 0.62516, "loss_cls": 3.63511, "loss": 3.63511, "time": 0.81933} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.03236, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35859, "top5_acc": 0.61719, "loss_cls": 3.65072, "loss": 3.65072, "time": 0.81621} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.03234, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36125, "top5_acc": 0.61859, "loss_cls": 3.66045, "loss": 3.66045, "time": 0.81699} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.03231, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36641, "top5_acc": 0.62578, "loss_cls": 3.6225, "loss": 3.6225, "time": 0.81891} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.03228, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34703, "top5_acc": 0.60516, "loss_cls": 3.68766, "loss": 3.68766, "time": 0.82275} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.03226, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37406, "top5_acc": 0.61656, "loss_cls": 3.59698, "loss": 3.59698, "time": 0.81735} +{"mode": "train", "epoch": 93, "iter": 1300, "lr": 0.03223, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35734, "top5_acc": 0.61531, "loss_cls": 3.64973, "loss": 3.64973, "time": 0.81423} +{"mode": "train", "epoch": 93, "iter": 1400, "lr": 0.03221, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36516, "top5_acc": 0.625, "loss_cls": 3.59901, "loss": 3.59901, "time": 0.82328} +{"mode": "train", "epoch": 93, "iter": 1500, "lr": 0.03218, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36812, "top5_acc": 0.62391, "loss_cls": 3.61103, "loss": 3.61103, "time": 0.81628} +{"mode": "train", "epoch": 93, "iter": 1600, "lr": 0.03215, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37062, "top5_acc": 0.62406, "loss_cls": 3.59751, "loss": 3.59751, "time": 0.82621} +{"mode": "train", "epoch": 93, "iter": 1700, "lr": 0.03213, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36, "top5_acc": 0.62125, "loss_cls": 3.64116, "loss": 3.64116, "time": 0.8163} +{"mode": "train", "epoch": 93, "iter": 1800, "lr": 0.0321, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36422, "top5_acc": 0.62, "loss_cls": 3.63262, "loss": 3.63262, "time": 0.81978} +{"mode": "train", "epoch": 93, "iter": 1900, "lr": 0.03207, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35438, "top5_acc": 0.60719, "loss_cls": 3.66718, "loss": 3.66718, "time": 0.81635} +{"mode": "train", "epoch": 93, "iter": 2000, "lr": 0.03205, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34703, "top5_acc": 0.60938, "loss_cls": 3.69347, "loss": 3.69347, "time": 0.81736} +{"mode": "train", "epoch": 93, "iter": 2100, "lr": 0.03202, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36328, "top5_acc": 0.62469, "loss_cls": 3.59319, "loss": 3.59319, "time": 0.81651} +{"mode": "train", "epoch": 93, "iter": 2200, "lr": 0.032, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35438, "top5_acc": 0.60734, "loss_cls": 3.65636, "loss": 3.65636, "time": 0.81407} +{"mode": "train", "epoch": 93, "iter": 2300, "lr": 0.03197, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36672, "top5_acc": 0.62266, "loss_cls": 3.63173, "loss": 3.63173, "time": 0.8182} +{"mode": "train", "epoch": 93, "iter": 2400, "lr": 0.03194, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35484, "top5_acc": 0.61687, "loss_cls": 3.6433, "loss": 3.6433, "time": 0.81125} +{"mode": "train", "epoch": 93, "iter": 2500, "lr": 0.03192, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36719, "top5_acc": 0.61312, "loss_cls": 3.63144, "loss": 3.63144, "time": 0.81751} +{"mode": "train", "epoch": 93, "iter": 2600, "lr": 0.03189, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35531, "top5_acc": 0.62062, "loss_cls": 3.66795, "loss": 3.66795, "time": 0.81621} +{"mode": "train", "epoch": 93, "iter": 2700, "lr": 0.03187, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35375, "top5_acc": 0.6225, "loss_cls": 3.64437, "loss": 3.64437, "time": 0.81775} +{"mode": "train", "epoch": 93, "iter": 2800, "lr": 0.03184, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.355, "top5_acc": 0.61281, "loss_cls": 3.6745, "loss": 3.6745, "time": 0.81419} +{"mode": "train", "epoch": 93, "iter": 2900, "lr": 0.03181, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35719, "top5_acc": 0.61, "loss_cls": 3.67328, "loss": 3.67328, "time": 0.81691} +{"mode": "train", "epoch": 93, "iter": 3000, "lr": 0.03179, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36125, "top5_acc": 0.62, "loss_cls": 3.66254, "loss": 3.66254, "time": 0.81622} +{"mode": "train", "epoch": 93, "iter": 3100, "lr": 0.03176, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36141, "top5_acc": 0.61578, "loss_cls": 3.62091, "loss": 3.62091, "time": 0.81667} +{"mode": "train", "epoch": 93, "iter": 3200, "lr": 0.03174, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35859, "top5_acc": 0.62438, "loss_cls": 3.63202, "loss": 3.63202, "time": 0.81632} +{"mode": "train", "epoch": 93, "iter": 3300, "lr": 0.03171, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34562, "top5_acc": 0.61422, "loss_cls": 3.67605, "loss": 3.67605, "time": 0.82426} +{"mode": "train", "epoch": 93, "iter": 3400, "lr": 0.03168, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35594, "top5_acc": 0.62375, "loss_cls": 3.64588, "loss": 3.64588, "time": 0.81898} +{"mode": "train", "epoch": 93, "iter": 3500, "lr": 0.03166, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37016, "top5_acc": 0.62297, "loss_cls": 3.62229, "loss": 3.62229, "time": 0.81908} +{"mode": "train", "epoch": 93, "iter": 3600, "lr": 0.03163, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35, "top5_acc": 0.61172, "loss_cls": 3.69446, "loss": 3.69446, "time": 0.82182} +{"mode": "train", "epoch": 93, "iter": 3700, "lr": 0.03161, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36125, "top5_acc": 0.61984, "loss_cls": 3.64654, "loss": 3.64654, "time": 0.81803} +{"mode": "val", "epoch": 93, "iter": 309, "lr": 0.03159, "top1_acc": 0.29413, "top5_acc": 0.55316, "mean_class_accuracy": 0.29395} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.03157, "memory": 15990, "data_time": 1.303, "top1_acc": 0.36469, "top5_acc": 0.63391, "loss_cls": 3.56988, "loss": 3.56988, "time": 2.2826} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.03154, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37547, "top5_acc": 0.63719, "loss_cls": 3.53421, "loss": 3.53421, "time": 0.82655} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.03152, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36625, "top5_acc": 0.63234, "loss_cls": 3.56989, "loss": 3.56989, "time": 0.8243} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.03149, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37, "top5_acc": 0.62484, "loss_cls": 3.56262, "loss": 3.56262, "time": 0.81544} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.03146, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36094, "top5_acc": 0.62469, "loss_cls": 3.6149, "loss": 3.6149, "time": 0.82252} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.03144, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36016, "top5_acc": 0.62531, "loss_cls": 3.62602, "loss": 3.62602, "time": 0.82079} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.03141, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.3675, "top5_acc": 0.62359, "loss_cls": 3.60403, "loss": 3.60403, "time": 0.81718} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.03139, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36109, "top5_acc": 0.61828, "loss_cls": 3.63112, "loss": 3.63112, "time": 0.81409} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.03136, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36672, "top5_acc": 0.62234, "loss_cls": 3.59761, "loss": 3.59761, "time": 0.82099} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.03133, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37172, "top5_acc": 0.63297, "loss_cls": 3.5739, "loss": 3.5739, "time": 0.81885} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.03131, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36328, "top5_acc": 0.62391, "loss_cls": 3.60207, "loss": 3.60207, "time": 0.8155} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.03128, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35906, "top5_acc": 0.62109, "loss_cls": 3.65591, "loss": 3.65591, "time": 0.81896} +{"mode": "train", "epoch": 94, "iter": 1300, "lr": 0.03126, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35625, "top5_acc": 0.62281, "loss_cls": 3.64338, "loss": 3.64338, "time": 0.81721} +{"mode": "train", "epoch": 94, "iter": 1400, "lr": 0.03123, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35703, "top5_acc": 0.62281, "loss_cls": 3.61765, "loss": 3.61765, "time": 0.82828} +{"mode": "train", "epoch": 94, "iter": 1500, "lr": 0.0312, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35844, "top5_acc": 0.61328, "loss_cls": 3.64853, "loss": 3.64853, "time": 0.81714} +{"mode": "train", "epoch": 94, "iter": 1600, "lr": 0.03118, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36141, "top5_acc": 0.62141, "loss_cls": 3.64625, "loss": 3.64625, "time": 0.81559} +{"mode": "train", "epoch": 94, "iter": 1700, "lr": 0.03115, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35281, "top5_acc": 0.615, "loss_cls": 3.69687, "loss": 3.69687, "time": 0.82362} +{"mode": "train", "epoch": 94, "iter": 1800, "lr": 0.03113, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35516, "top5_acc": 0.61219, "loss_cls": 3.66813, "loss": 3.66813, "time": 0.81893} +{"mode": "train", "epoch": 94, "iter": 1900, "lr": 0.0311, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35938, "top5_acc": 0.62547, "loss_cls": 3.60091, "loss": 3.60091, "time": 0.81524} +{"mode": "train", "epoch": 94, "iter": 2000, "lr": 0.03108, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35109, "top5_acc": 0.61672, "loss_cls": 3.65018, "loss": 3.65018, "time": 0.81821} +{"mode": "train", "epoch": 94, "iter": 2100, "lr": 0.03105, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36266, "top5_acc": 0.62047, "loss_cls": 3.60452, "loss": 3.60452, "time": 0.81586} +{"mode": "train", "epoch": 94, "iter": 2200, "lr": 0.03102, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35281, "top5_acc": 0.60703, "loss_cls": 3.66617, "loss": 3.66617, "time": 0.81767} +{"mode": "train", "epoch": 94, "iter": 2300, "lr": 0.031, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35906, "top5_acc": 0.61516, "loss_cls": 3.65936, "loss": 3.65936, "time": 0.81469} +{"mode": "train", "epoch": 94, "iter": 2400, "lr": 0.03097, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36359, "top5_acc": 0.62344, "loss_cls": 3.59244, "loss": 3.59244, "time": 0.81929} +{"mode": "train", "epoch": 94, "iter": 2500, "lr": 0.03095, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3575, "top5_acc": 0.61984, "loss_cls": 3.63785, "loss": 3.63785, "time": 0.82261} +{"mode": "train", "epoch": 94, "iter": 2600, "lr": 0.03092, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35641, "top5_acc": 0.62047, "loss_cls": 3.63789, "loss": 3.63789, "time": 0.81728} +{"mode": "train", "epoch": 94, "iter": 2700, "lr": 0.03089, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36484, "top5_acc": 0.63266, "loss_cls": 3.60554, "loss": 3.60554, "time": 0.8195} +{"mode": "train", "epoch": 94, "iter": 2800, "lr": 0.03087, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35828, "top5_acc": 0.62438, "loss_cls": 3.61236, "loss": 3.61236, "time": 0.81253} +{"mode": "train", "epoch": 94, "iter": 2900, "lr": 0.03084, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36453, "top5_acc": 0.625, "loss_cls": 3.61688, "loss": 3.61688, "time": 0.8233} +{"mode": "train", "epoch": 94, "iter": 3000, "lr": 0.03082, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34578, "top5_acc": 0.6125, "loss_cls": 3.67634, "loss": 3.67634, "time": 0.81299} +{"mode": "train", "epoch": 94, "iter": 3100, "lr": 0.03079, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35531, "top5_acc": 0.61641, "loss_cls": 3.65331, "loss": 3.65331, "time": 0.815} +{"mode": "train", "epoch": 94, "iter": 3200, "lr": 0.03077, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35984, "top5_acc": 0.61656, "loss_cls": 3.63438, "loss": 3.63438, "time": 0.82342} +{"mode": "train", "epoch": 94, "iter": 3300, "lr": 0.03074, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35812, "top5_acc": 0.62359, "loss_cls": 3.62572, "loss": 3.62572, "time": 0.81977} +{"mode": "train", "epoch": 94, "iter": 3400, "lr": 0.03071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35031, "top5_acc": 0.60953, "loss_cls": 3.68072, "loss": 3.68072, "time": 0.81983} +{"mode": "train", "epoch": 94, "iter": 3500, "lr": 0.03069, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34781, "top5_acc": 0.61125, "loss_cls": 3.69207, "loss": 3.69207, "time": 0.82563} +{"mode": "train", "epoch": 94, "iter": 3600, "lr": 0.03066, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36875, "top5_acc": 0.63141, "loss_cls": 3.59098, "loss": 3.59098, "time": 0.81592} +{"mode": "train", "epoch": 94, "iter": 3700, "lr": 0.03064, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36203, "top5_acc": 0.61891, "loss_cls": 3.62615, "loss": 3.62615, "time": 0.81561} +{"mode": "val", "epoch": 94, "iter": 309, "lr": 0.03062, "top1_acc": 0.29808, "top5_acc": 0.55812, "mean_class_accuracy": 0.29782} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.0306, "memory": 15990, "data_time": 1.25436, "top1_acc": 0.37797, "top5_acc": 0.62734, "loss_cls": 3.54693, "loss": 3.54693, "time": 2.23163} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.03057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36719, "top5_acc": 0.62281, "loss_cls": 3.57567, "loss": 3.57567, "time": 0.81745} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.03055, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35438, "top5_acc": 0.62016, "loss_cls": 3.62153, "loss": 3.62153, "time": 0.8289} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.03052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36281, "top5_acc": 0.62891, "loss_cls": 3.57961, "loss": 3.57961, "time": 0.81857} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.0305, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36484, "top5_acc": 0.62531, "loss_cls": 3.58965, "loss": 3.58965, "time": 0.82543} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.03047, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35828, "top5_acc": 0.6175, "loss_cls": 3.62725, "loss": 3.62725, "time": 0.81589} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.03044, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37359, "top5_acc": 0.63172, "loss_cls": 3.57706, "loss": 3.57706, "time": 0.81276} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.03042, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36828, "top5_acc": 0.62516, "loss_cls": 3.59371, "loss": 3.59371, "time": 0.81663} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.03039, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35984, "top5_acc": 0.62641, "loss_cls": 3.6077, "loss": 3.6077, "time": 0.81441} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.03037, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35891, "top5_acc": 0.62891, "loss_cls": 3.60414, "loss": 3.60414, "time": 0.81176} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.03034, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35188, "top5_acc": 0.61297, "loss_cls": 3.6483, "loss": 3.6483, "time": 0.81366} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.03032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35625, "top5_acc": 0.62359, "loss_cls": 3.61537, "loss": 3.61537, "time": 0.81459} +{"mode": "train", "epoch": 95, "iter": 1300, "lr": 0.03029, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37594, "top5_acc": 0.63, "loss_cls": 3.55104, "loss": 3.55104, "time": 0.81786} +{"mode": "train", "epoch": 95, "iter": 1400, "lr": 0.03026, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36062, "top5_acc": 0.62359, "loss_cls": 3.63115, "loss": 3.63115, "time": 0.81486} +{"mode": "train", "epoch": 95, "iter": 1500, "lr": 0.03024, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37672, "top5_acc": 0.61984, "loss_cls": 3.56562, "loss": 3.56562, "time": 0.81945} +{"mode": "train", "epoch": 95, "iter": 1600, "lr": 0.03021, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35672, "top5_acc": 0.61766, "loss_cls": 3.63182, "loss": 3.63182, "time": 0.81609} +{"mode": "train", "epoch": 95, "iter": 1700, "lr": 0.03019, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.365, "top5_acc": 0.62469, "loss_cls": 3.60636, "loss": 3.60636, "time": 0.82083} +{"mode": "train", "epoch": 95, "iter": 1800, "lr": 0.03016, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35859, "top5_acc": 0.62453, "loss_cls": 3.59856, "loss": 3.59856, "time": 0.82108} +{"mode": "train", "epoch": 95, "iter": 1900, "lr": 0.03014, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37125, "top5_acc": 0.62156, "loss_cls": 3.57705, "loss": 3.57705, "time": 0.81836} +{"mode": "train", "epoch": 95, "iter": 2000, "lr": 0.03011, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35969, "top5_acc": 0.62234, "loss_cls": 3.62532, "loss": 3.62532, "time": 0.81741} +{"mode": "train", "epoch": 95, "iter": 2100, "lr": 0.03008, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35, "top5_acc": 0.61453, "loss_cls": 3.65222, "loss": 3.65222, "time": 0.81797} +{"mode": "train", "epoch": 95, "iter": 2200, "lr": 0.03006, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37016, "top5_acc": 0.61703, "loss_cls": 3.60837, "loss": 3.60837, "time": 0.81451} +{"mode": "train", "epoch": 95, "iter": 2300, "lr": 0.03003, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36312, "top5_acc": 0.62938, "loss_cls": 3.59428, "loss": 3.59428, "time": 0.81837} +{"mode": "train", "epoch": 95, "iter": 2400, "lr": 0.03001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36016, "top5_acc": 0.62094, "loss_cls": 3.64228, "loss": 3.64228, "time": 0.82027} +{"mode": "train", "epoch": 95, "iter": 2500, "lr": 0.02998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36312, "top5_acc": 0.62859, "loss_cls": 3.58794, "loss": 3.58794, "time": 0.817} +{"mode": "train", "epoch": 95, "iter": 2600, "lr": 0.02996, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35969, "top5_acc": 0.62391, "loss_cls": 3.61334, "loss": 3.61334, "time": 0.81613} +{"mode": "train", "epoch": 95, "iter": 2700, "lr": 0.02993, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36391, "top5_acc": 0.62203, "loss_cls": 3.62929, "loss": 3.62929, "time": 0.81943} +{"mode": "train", "epoch": 95, "iter": 2800, "lr": 0.02991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35359, "top5_acc": 0.61156, "loss_cls": 3.68373, "loss": 3.68373, "time": 0.8197} +{"mode": "train", "epoch": 95, "iter": 2900, "lr": 0.02988, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36297, "top5_acc": 0.62531, "loss_cls": 3.60178, "loss": 3.60178, "time": 0.81908} +{"mode": "train", "epoch": 95, "iter": 3000, "lr": 0.02985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36016, "top5_acc": 0.62562, "loss_cls": 3.60457, "loss": 3.60457, "time": 0.81507} +{"mode": "train", "epoch": 95, "iter": 3100, "lr": 0.02983, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35969, "top5_acc": 0.62062, "loss_cls": 3.61177, "loss": 3.61177, "time": 0.8256} +{"mode": "train", "epoch": 95, "iter": 3200, "lr": 0.0298, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36484, "top5_acc": 0.62656, "loss_cls": 3.59809, "loss": 3.59809, "time": 0.82082} +{"mode": "train", "epoch": 95, "iter": 3300, "lr": 0.02978, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37203, "top5_acc": 0.63078, "loss_cls": 3.58473, "loss": 3.58473, "time": 0.82076} +{"mode": "train", "epoch": 95, "iter": 3400, "lr": 0.02975, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35328, "top5_acc": 0.61641, "loss_cls": 3.67115, "loss": 3.67115, "time": 0.8181} +{"mode": "train", "epoch": 95, "iter": 3500, "lr": 0.02973, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36844, "top5_acc": 0.61594, "loss_cls": 3.60135, "loss": 3.60135, "time": 0.82026} +{"mode": "train", "epoch": 95, "iter": 3600, "lr": 0.0297, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36906, "top5_acc": 0.62391, "loss_cls": 3.59615, "loss": 3.59615, "time": 0.81903} +{"mode": "train", "epoch": 95, "iter": 3700, "lr": 0.02968, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36312, "top5_acc": 0.61766, "loss_cls": 3.63065, "loss": 3.63065, "time": 0.817} +{"mode": "val", "epoch": 95, "iter": 309, "lr": 0.02966, "top1_acc": 0.2918, "top5_acc": 0.54379, "mean_class_accuracy": 0.29153} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.02964, "memory": 15990, "data_time": 1.29953, "top1_acc": 0.36797, "top5_acc": 0.63156, "loss_cls": 3.57214, "loss": 3.57214, "time": 2.28582} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.02961, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3775, "top5_acc": 0.63813, "loss_cls": 3.53963, "loss": 3.53963, "time": 0.81625} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.02959, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38438, "top5_acc": 0.63469, "loss_cls": 3.5115, "loss": 3.5115, "time": 0.81521} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.02956, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37453, "top5_acc": 0.64109, "loss_cls": 3.5156, "loss": 3.5156, "time": 0.81479} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.02954, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36797, "top5_acc": 0.63078, "loss_cls": 3.56544, "loss": 3.56544, "time": 0.81655} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.02951, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35984, "top5_acc": 0.61594, "loss_cls": 3.6236, "loss": 3.6236, "time": 0.82403} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.02948, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37266, "top5_acc": 0.62125, "loss_cls": 3.60196, "loss": 3.60196, "time": 0.81647} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.02946, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37, "top5_acc": 0.62156, "loss_cls": 3.58154, "loss": 3.58154, "time": 0.81882} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.02943, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36297, "top5_acc": 0.62922, "loss_cls": 3.58434, "loss": 3.58434, "time": 0.81206} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.02941, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35438, "top5_acc": 0.62453, "loss_cls": 3.63068, "loss": 3.63068, "time": 0.81597} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.02938, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3775, "top5_acc": 0.63641, "loss_cls": 3.53951, "loss": 3.53951, "time": 0.81394} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.02936, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37453, "top5_acc": 0.63578, "loss_cls": 3.57001, "loss": 3.57001, "time": 0.81299} +{"mode": "train", "epoch": 96, "iter": 1300, "lr": 0.02933, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36812, "top5_acc": 0.62859, "loss_cls": 3.58784, "loss": 3.58784, "time": 0.81645} +{"mode": "train", "epoch": 96, "iter": 1400, "lr": 0.02931, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36312, "top5_acc": 0.62984, "loss_cls": 3.57794, "loss": 3.57794, "time": 0.82225} +{"mode": "train", "epoch": 96, "iter": 1500, "lr": 0.02928, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34906, "top5_acc": 0.61344, "loss_cls": 3.63788, "loss": 3.63788, "time": 0.81731} +{"mode": "train", "epoch": 96, "iter": 1600, "lr": 0.02926, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37531, "top5_acc": 0.625, "loss_cls": 3.56841, "loss": 3.56841, "time": 0.82214} +{"mode": "train", "epoch": 96, "iter": 1700, "lr": 0.02923, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37109, "top5_acc": 0.63531, "loss_cls": 3.53572, "loss": 3.53572, "time": 0.81402} +{"mode": "train", "epoch": 96, "iter": 1800, "lr": 0.0292, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35375, "top5_acc": 0.61094, "loss_cls": 3.66413, "loss": 3.66413, "time": 0.81944} +{"mode": "train", "epoch": 96, "iter": 1900, "lr": 0.02918, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36578, "top5_acc": 0.62687, "loss_cls": 3.59761, "loss": 3.59761, "time": 0.81648} +{"mode": "train", "epoch": 96, "iter": 2000, "lr": 0.02915, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35609, "top5_acc": 0.62828, "loss_cls": 3.58385, "loss": 3.58385, "time": 0.81323} +{"mode": "train", "epoch": 96, "iter": 2100, "lr": 0.02913, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35859, "top5_acc": 0.61703, "loss_cls": 3.63529, "loss": 3.63529, "time": 0.81137} +{"mode": "train", "epoch": 96, "iter": 2200, "lr": 0.0291, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37531, "top5_acc": 0.63359, "loss_cls": 3.54963, "loss": 3.54963, "time": 0.82009} +{"mode": "train", "epoch": 96, "iter": 2300, "lr": 0.02908, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36375, "top5_acc": 0.61844, "loss_cls": 3.66849, "loss": 3.66849, "time": 0.81788} +{"mode": "train", "epoch": 96, "iter": 2400, "lr": 0.02905, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36766, "top5_acc": 0.63422, "loss_cls": 3.59083, "loss": 3.59083, "time": 0.81815} +{"mode": "train", "epoch": 96, "iter": 2500, "lr": 0.02903, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36906, "top5_acc": 0.62375, "loss_cls": 3.6149, "loss": 3.6149, "time": 0.81997} +{"mode": "train", "epoch": 96, "iter": 2600, "lr": 0.029, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36688, "top5_acc": 0.62828, "loss_cls": 3.56638, "loss": 3.56638, "time": 0.81661} +{"mode": "train", "epoch": 96, "iter": 2700, "lr": 0.02898, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36875, "top5_acc": 0.62234, "loss_cls": 3.5993, "loss": 3.5993, "time": 0.82261} +{"mode": "train", "epoch": 96, "iter": 2800, "lr": 0.02895, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37719, "top5_acc": 0.62813, "loss_cls": 3.57806, "loss": 3.57806, "time": 0.81326} +{"mode": "train", "epoch": 96, "iter": 2900, "lr": 0.02893, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35859, "top5_acc": 0.62469, "loss_cls": 3.60874, "loss": 3.60874, "time": 0.8177} +{"mode": "train", "epoch": 96, "iter": 3000, "lr": 0.0289, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36109, "top5_acc": 0.61922, "loss_cls": 3.62553, "loss": 3.62553, "time": 0.82566} +{"mode": "train", "epoch": 96, "iter": 3100, "lr": 0.02887, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.365, "top5_acc": 0.62922, "loss_cls": 3.58253, "loss": 3.58253, "time": 0.82301} +{"mode": "train", "epoch": 96, "iter": 3200, "lr": 0.02885, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35906, "top5_acc": 0.61516, "loss_cls": 3.63889, "loss": 3.63889, "time": 0.81891} +{"mode": "train", "epoch": 96, "iter": 3300, "lr": 0.02882, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36359, "top5_acc": 0.62234, "loss_cls": 3.615, "loss": 3.615, "time": 0.82071} +{"mode": "train", "epoch": 96, "iter": 3400, "lr": 0.0288, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35719, "top5_acc": 0.6175, "loss_cls": 3.61388, "loss": 3.61388, "time": 0.81374} +{"mode": "train", "epoch": 96, "iter": 3500, "lr": 0.02877, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35875, "top5_acc": 0.62187, "loss_cls": 3.62777, "loss": 3.62777, "time": 0.82678} +{"mode": "train", "epoch": 96, "iter": 3600, "lr": 0.02875, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36953, "top5_acc": 0.61953, "loss_cls": 3.61664, "loss": 3.61664, "time": 0.82501} +{"mode": "train", "epoch": 96, "iter": 3700, "lr": 0.02872, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35609, "top5_acc": 0.6125, "loss_cls": 3.65975, "loss": 3.65975, "time": 0.82091} +{"mode": "val", "epoch": 96, "iter": 309, "lr": 0.02871, "top1_acc": 0.30867, "top5_acc": 0.56511, "mean_class_accuracy": 0.30846} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.02869, "memory": 15990, "data_time": 1.28812, "top1_acc": 0.37141, "top5_acc": 0.64375, "loss_cls": 3.50469, "loss": 3.50469, "time": 2.2733} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.02866, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37188, "top5_acc": 0.64516, "loss_cls": 3.52389, "loss": 3.52389, "time": 0.82145} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.02864, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36422, "top5_acc": 0.6275, "loss_cls": 3.59799, "loss": 3.59799, "time": 0.81583} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.02861, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36578, "top5_acc": 0.62609, "loss_cls": 3.57456, "loss": 3.57456, "time": 0.82363} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.02858, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37328, "top5_acc": 0.63391, "loss_cls": 3.55349, "loss": 3.55349, "time": 0.81858} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.02856, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37578, "top5_acc": 0.635, "loss_cls": 3.52931, "loss": 3.52931, "time": 0.81362} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.02853, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3775, "top5_acc": 0.63656, "loss_cls": 3.54267, "loss": 3.54267, "time": 0.81936} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.02851, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37281, "top5_acc": 0.62875, "loss_cls": 3.56501, "loss": 3.56501, "time": 0.82122} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.02848, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.375, "top5_acc": 0.64188, "loss_cls": 3.51033, "loss": 3.51033, "time": 0.81295} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.02846, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36578, "top5_acc": 0.62703, "loss_cls": 3.5852, "loss": 3.5852, "time": 0.82371} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.02843, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36984, "top5_acc": 0.61922, "loss_cls": 3.58375, "loss": 3.58375, "time": 0.81746} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.02841, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36312, "top5_acc": 0.62438, "loss_cls": 3.60199, "loss": 3.60199, "time": 0.81691} +{"mode": "train", "epoch": 97, "iter": 1300, "lr": 0.02838, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35891, "top5_acc": 0.61922, "loss_cls": 3.63983, "loss": 3.63983, "time": 0.81666} +{"mode": "train", "epoch": 97, "iter": 1400, "lr": 0.02836, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36281, "top5_acc": 0.61953, "loss_cls": 3.61903, "loss": 3.61903, "time": 0.82681} +{"mode": "train", "epoch": 97, "iter": 1500, "lr": 0.02833, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36688, "top5_acc": 0.62453, "loss_cls": 3.6074, "loss": 3.6074, "time": 0.81591} +{"mode": "train", "epoch": 97, "iter": 1600, "lr": 0.02831, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36578, "top5_acc": 0.63094, "loss_cls": 3.57479, "loss": 3.57479, "time": 0.82117} +{"mode": "train", "epoch": 97, "iter": 1700, "lr": 0.02828, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36828, "top5_acc": 0.62109, "loss_cls": 3.59879, "loss": 3.59879, "time": 0.82108} +{"mode": "train", "epoch": 97, "iter": 1800, "lr": 0.02826, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36281, "top5_acc": 0.63281, "loss_cls": 3.58399, "loss": 3.58399, "time": 0.81799} +{"mode": "train", "epoch": 97, "iter": 1900, "lr": 0.02823, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36078, "top5_acc": 0.62469, "loss_cls": 3.61673, "loss": 3.61673, "time": 0.81817} +{"mode": "train", "epoch": 97, "iter": 2000, "lr": 0.02821, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37109, "top5_acc": 0.63266, "loss_cls": 3.57096, "loss": 3.57096, "time": 0.81569} +{"mode": "train", "epoch": 97, "iter": 2100, "lr": 0.02818, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36984, "top5_acc": 0.62969, "loss_cls": 3.5568, "loss": 3.5568, "time": 0.81538} +{"mode": "train", "epoch": 97, "iter": 2200, "lr": 0.02816, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.375, "top5_acc": 0.62453, "loss_cls": 3.57448, "loss": 3.57448, "time": 0.81769} +{"mode": "train", "epoch": 97, "iter": 2300, "lr": 0.02813, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36594, "top5_acc": 0.625, "loss_cls": 3.59895, "loss": 3.59895, "time": 0.82119} +{"mode": "train", "epoch": 97, "iter": 2400, "lr": 0.02811, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36922, "top5_acc": 0.62281, "loss_cls": 3.5999, "loss": 3.5999, "time": 0.81592} +{"mode": "train", "epoch": 97, "iter": 2500, "lr": 0.02808, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37047, "top5_acc": 0.63328, "loss_cls": 3.56161, "loss": 3.56161, "time": 0.81388} +{"mode": "train", "epoch": 97, "iter": 2600, "lr": 0.02806, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36766, "top5_acc": 0.63094, "loss_cls": 3.55585, "loss": 3.55585, "time": 0.81565} +{"mode": "train", "epoch": 97, "iter": 2700, "lr": 0.02803, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37422, "top5_acc": 0.62984, "loss_cls": 3.56593, "loss": 3.56593, "time": 0.81489} +{"mode": "train", "epoch": 97, "iter": 2800, "lr": 0.02801, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36891, "top5_acc": 0.63297, "loss_cls": 3.55991, "loss": 3.55991, "time": 0.81813} +{"mode": "train", "epoch": 97, "iter": 2900, "lr": 0.02798, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37797, "top5_acc": 0.63516, "loss_cls": 3.55711, "loss": 3.55711, "time": 0.81938} +{"mode": "train", "epoch": 97, "iter": 3000, "lr": 0.02796, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37516, "top5_acc": 0.62875, "loss_cls": 3.57134, "loss": 3.57134, "time": 0.82454} +{"mode": "train", "epoch": 97, "iter": 3100, "lr": 0.02793, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36453, "top5_acc": 0.62953, "loss_cls": 3.60005, "loss": 3.60005, "time": 0.81971} +{"mode": "train", "epoch": 97, "iter": 3200, "lr": 0.02791, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36609, "top5_acc": 0.61578, "loss_cls": 3.64143, "loss": 3.64143, "time": 0.81945} +{"mode": "train", "epoch": 97, "iter": 3300, "lr": 0.02788, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36266, "top5_acc": 0.62781, "loss_cls": 3.57705, "loss": 3.57705, "time": 0.81944} +{"mode": "train", "epoch": 97, "iter": 3400, "lr": 0.02786, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36922, "top5_acc": 0.62219, "loss_cls": 3.57277, "loss": 3.57277, "time": 0.81446} +{"mode": "train", "epoch": 97, "iter": 3500, "lr": 0.02783, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36141, "top5_acc": 0.63, "loss_cls": 3.58125, "loss": 3.58125, "time": 0.82428} +{"mode": "train", "epoch": 97, "iter": 3600, "lr": 0.02781, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37484, "top5_acc": 0.63281, "loss_cls": 3.56804, "loss": 3.56804, "time": 0.81694} +{"mode": "train", "epoch": 97, "iter": 3700, "lr": 0.02778, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37109, "top5_acc": 0.62156, "loss_cls": 3.60334, "loss": 3.60334, "time": 0.81951} +{"mode": "val", "epoch": 97, "iter": 309, "lr": 0.02777, "top1_acc": 0.28972, "top5_acc": 0.55068, "mean_class_accuracy": 0.28954} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.02774, "memory": 15990, "data_time": 1.28031, "top1_acc": 0.38469, "top5_acc": 0.64844, "loss_cls": 3.47265, "loss": 3.47265, "time": 2.28951} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.02772, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37922, "top5_acc": 0.64156, "loss_cls": 3.50039, "loss": 3.50039, "time": 0.82316} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.02769, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37672, "top5_acc": 0.63734, "loss_cls": 3.52933, "loss": 3.52933, "time": 0.82108} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.02767, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37938, "top5_acc": 0.63672, "loss_cls": 3.53139, "loss": 3.53139, "time": 0.8152} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.02764, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37125, "top5_acc": 0.62406, "loss_cls": 3.58939, "loss": 3.58939, "time": 0.81983} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.02762, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36953, "top5_acc": 0.63328, "loss_cls": 3.55653, "loss": 3.55653, "time": 0.81777} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.02759, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37938, "top5_acc": 0.63906, "loss_cls": 3.51529, "loss": 3.51529, "time": 0.81833} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.02757, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37734, "top5_acc": 0.63938, "loss_cls": 3.53115, "loss": 3.53115, "time": 0.81465} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.02754, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.37734, "top5_acc": 0.63625, "loss_cls": 3.54682, "loss": 3.54682, "time": 0.8144} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.02752, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37438, "top5_acc": 0.63688, "loss_cls": 3.52799, "loss": 3.52799, "time": 0.81499} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.02749, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37047, "top5_acc": 0.63922, "loss_cls": 3.54588, "loss": 3.54588, "time": 0.81504} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.02747, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.38422, "top5_acc": 0.64391, "loss_cls": 3.51471, "loss": 3.51471, "time": 0.81683} +{"mode": "train", "epoch": 98, "iter": 1300, "lr": 0.02744, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37297, "top5_acc": 0.63406, "loss_cls": 3.55902, "loss": 3.55902, "time": 0.81325} +{"mode": "train", "epoch": 98, "iter": 1400, "lr": 0.02742, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36406, "top5_acc": 0.63422, "loss_cls": 3.56361, "loss": 3.56361, "time": 0.82095} +{"mode": "train", "epoch": 98, "iter": 1500, "lr": 0.02739, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.365, "top5_acc": 0.62938, "loss_cls": 3.57534, "loss": 3.57534, "time": 0.81652} +{"mode": "train", "epoch": 98, "iter": 1600, "lr": 0.02737, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.3775, "top5_acc": 0.63547, "loss_cls": 3.55105, "loss": 3.55105, "time": 0.82381} +{"mode": "train", "epoch": 98, "iter": 1700, "lr": 0.02734, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36984, "top5_acc": 0.62125, "loss_cls": 3.59804, "loss": 3.59804, "time": 0.81781} +{"mode": "train", "epoch": 98, "iter": 1800, "lr": 0.02732, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36781, "top5_acc": 0.63234, "loss_cls": 3.56715, "loss": 3.56715, "time": 0.81917} +{"mode": "train", "epoch": 98, "iter": 1900, "lr": 0.02729, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37047, "top5_acc": 0.62844, "loss_cls": 3.59367, "loss": 3.59367, "time": 0.81597} +{"mode": "train", "epoch": 98, "iter": 2000, "lr": 0.02727, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36953, "top5_acc": 0.62469, "loss_cls": 3.59212, "loss": 3.59212, "time": 0.82304} +{"mode": "train", "epoch": 98, "iter": 2100, "lr": 0.02724, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36375, "top5_acc": 0.62109, "loss_cls": 3.61659, "loss": 3.61659, "time": 0.81343} +{"mode": "train", "epoch": 98, "iter": 2200, "lr": 0.02722, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37859, "top5_acc": 0.63578, "loss_cls": 3.54744, "loss": 3.54744, "time": 0.81374} +{"mode": "train", "epoch": 98, "iter": 2300, "lr": 0.02719, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36453, "top5_acc": 0.62969, "loss_cls": 3.59357, "loss": 3.59357, "time": 0.81448} +{"mode": "train", "epoch": 98, "iter": 2400, "lr": 0.02717, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.35953, "top5_acc": 0.62062, "loss_cls": 3.62501, "loss": 3.62501, "time": 0.81852} +{"mode": "train", "epoch": 98, "iter": 2500, "lr": 0.02714, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36438, "top5_acc": 0.61797, "loss_cls": 3.62584, "loss": 3.62584, "time": 0.81972} +{"mode": "train", "epoch": 98, "iter": 2600, "lr": 0.02712, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37125, "top5_acc": 0.62828, "loss_cls": 3.58593, "loss": 3.58593, "time": 0.81695} +{"mode": "train", "epoch": 98, "iter": 2700, "lr": 0.02709, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37031, "top5_acc": 0.64016, "loss_cls": 3.55371, "loss": 3.55371, "time": 0.81983} +{"mode": "train", "epoch": 98, "iter": 2800, "lr": 0.02707, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36781, "top5_acc": 0.62406, "loss_cls": 3.56133, "loss": 3.56133, "time": 0.81313} +{"mode": "train", "epoch": 98, "iter": 2900, "lr": 0.02705, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36984, "top5_acc": 0.62984, "loss_cls": 3.57114, "loss": 3.57114, "time": 0.81427} +{"mode": "train", "epoch": 98, "iter": 3000, "lr": 0.02702, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37203, "top5_acc": 0.62984, "loss_cls": 3.54591, "loss": 3.54591, "time": 0.8173} +{"mode": "train", "epoch": 98, "iter": 3100, "lr": 0.027, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36281, "top5_acc": 0.62766, "loss_cls": 3.59114, "loss": 3.59114, "time": 0.82133} +{"mode": "train", "epoch": 98, "iter": 3200, "lr": 0.02697, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36156, "top5_acc": 0.62406, "loss_cls": 3.5669, "loss": 3.5669, "time": 0.81538} +{"mode": "train", "epoch": 98, "iter": 3300, "lr": 0.02695, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37516, "top5_acc": 0.63297, "loss_cls": 3.53372, "loss": 3.53372, "time": 0.82155} +{"mode": "train", "epoch": 98, "iter": 3400, "lr": 0.02692, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37297, "top5_acc": 0.64078, "loss_cls": 3.55443, "loss": 3.55443, "time": 0.81965} +{"mode": "train", "epoch": 98, "iter": 3500, "lr": 0.0269, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35406, "top5_acc": 0.61156, "loss_cls": 3.65579, "loss": 3.65579, "time": 0.82907} +{"mode": "train", "epoch": 98, "iter": 3600, "lr": 0.02687, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36828, "top5_acc": 0.63031, "loss_cls": 3.57243, "loss": 3.57243, "time": 0.81859} +{"mode": "train", "epoch": 98, "iter": 3700, "lr": 0.02685, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37922, "top5_acc": 0.63484, "loss_cls": 3.5097, "loss": 3.5097, "time": 0.82727} +{"mode": "val", "epoch": 98, "iter": 309, "lr": 0.02684, "top1_acc": 0.30851, "top5_acc": 0.56805, "mean_class_accuracy": 0.30815} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.02681, "memory": 15990, "data_time": 1.28849, "top1_acc": 0.37391, "top5_acc": 0.64047, "loss_cls": 3.51335, "loss": 3.51335, "time": 2.27358} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.02679, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38656, "top5_acc": 0.64734, "loss_cls": 3.47254, "loss": 3.47254, "time": 0.83088} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.02676, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38234, "top5_acc": 0.63516, "loss_cls": 3.5181, "loss": 3.5181, "time": 0.81739} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.02674, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38297, "top5_acc": 0.64156, "loss_cls": 3.50265, "loss": 3.50265, "time": 0.82387} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.02671, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37219, "top5_acc": 0.63906, "loss_cls": 3.52446, "loss": 3.52446, "time": 0.82191} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.02669, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38906, "top5_acc": 0.64828, "loss_cls": 3.45828, "loss": 3.45828, "time": 0.82027} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.02666, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38359, "top5_acc": 0.64531, "loss_cls": 3.49224, "loss": 3.49224, "time": 0.82406} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.02664, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37422, "top5_acc": 0.63344, "loss_cls": 3.52321, "loss": 3.52321, "time": 0.81652} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.02661, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36406, "top5_acc": 0.64047, "loss_cls": 3.52411, "loss": 3.52411, "time": 0.81583} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.02659, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.38172, "top5_acc": 0.64766, "loss_cls": 3.51644, "loss": 3.51644, "time": 0.81444} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.02656, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37516, "top5_acc": 0.63406, "loss_cls": 3.52709, "loss": 3.52709, "time": 0.81693} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.02654, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36656, "top5_acc": 0.63313, "loss_cls": 3.56916, "loss": 3.56916, "time": 0.81951} +{"mode": "train", "epoch": 99, "iter": 1300, "lr": 0.02651, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36375, "top5_acc": 0.62719, "loss_cls": 3.58379, "loss": 3.58379, "time": 0.82221} +{"mode": "train", "epoch": 99, "iter": 1400, "lr": 0.02649, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3725, "top5_acc": 0.62906, "loss_cls": 3.5598, "loss": 3.5598, "time": 0.82605} +{"mode": "train", "epoch": 99, "iter": 1500, "lr": 0.02646, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36578, "top5_acc": 0.63047, "loss_cls": 3.55087, "loss": 3.55087, "time": 0.81403} +{"mode": "train", "epoch": 99, "iter": 1600, "lr": 0.02644, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37031, "top5_acc": 0.62734, "loss_cls": 3.57787, "loss": 3.57787, "time": 0.82249} +{"mode": "train", "epoch": 99, "iter": 1700, "lr": 0.02642, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37094, "top5_acc": 0.63672, "loss_cls": 3.52575, "loss": 3.52575, "time": 0.81491} +{"mode": "train", "epoch": 99, "iter": 1800, "lr": 0.02639, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36859, "top5_acc": 0.62344, "loss_cls": 3.59456, "loss": 3.59456, "time": 0.81898} +{"mode": "train", "epoch": 99, "iter": 1900, "lr": 0.02637, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37562, "top5_acc": 0.63875, "loss_cls": 3.53307, "loss": 3.53307, "time": 0.81401} +{"mode": "train", "epoch": 99, "iter": 2000, "lr": 0.02634, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37594, "top5_acc": 0.63313, "loss_cls": 3.54515, "loss": 3.54515, "time": 0.81511} +{"mode": "train", "epoch": 99, "iter": 2100, "lr": 0.02632, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37188, "top5_acc": 0.63, "loss_cls": 3.57574, "loss": 3.57574, "time": 0.81657} +{"mode": "train", "epoch": 99, "iter": 2200, "lr": 0.02629, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37844, "top5_acc": 0.63234, "loss_cls": 3.55643, "loss": 3.55643, "time": 0.8194} +{"mode": "train", "epoch": 99, "iter": 2300, "lr": 0.02627, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37281, "top5_acc": 0.63281, "loss_cls": 3.55322, "loss": 3.55322, "time": 0.81604} +{"mode": "train", "epoch": 99, "iter": 2400, "lr": 0.02624, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36625, "top5_acc": 0.63016, "loss_cls": 3.59224, "loss": 3.59224, "time": 0.81404} +{"mode": "train", "epoch": 99, "iter": 2500, "lr": 0.02622, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38016, "top5_acc": 0.63828, "loss_cls": 3.52538, "loss": 3.52538, "time": 0.81428} +{"mode": "train", "epoch": 99, "iter": 2600, "lr": 0.02619, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36422, "top5_acc": 0.62281, "loss_cls": 3.5955, "loss": 3.5955, "time": 0.81371} +{"mode": "train", "epoch": 99, "iter": 2700, "lr": 0.02617, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37297, "top5_acc": 0.63016, "loss_cls": 3.55727, "loss": 3.55727, "time": 0.81816} +{"mode": "train", "epoch": 99, "iter": 2800, "lr": 0.02614, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36203, "top5_acc": 0.63203, "loss_cls": 3.57537, "loss": 3.57537, "time": 0.81817} +{"mode": "train", "epoch": 99, "iter": 2900, "lr": 0.02612, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37547, "top5_acc": 0.63422, "loss_cls": 3.54737, "loss": 3.54737, "time": 0.82175} +{"mode": "train", "epoch": 99, "iter": 3000, "lr": 0.0261, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36797, "top5_acc": 0.63109, "loss_cls": 3.56726, "loss": 3.56726, "time": 0.81337} +{"mode": "train", "epoch": 99, "iter": 3100, "lr": 0.02607, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3725, "top5_acc": 0.62766, "loss_cls": 3.55933, "loss": 3.55933, "time": 0.81816} +{"mode": "train", "epoch": 99, "iter": 3200, "lr": 0.02605, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37359, "top5_acc": 0.62375, "loss_cls": 3.58461, "loss": 3.58461, "time": 0.82297} +{"mode": "train", "epoch": 99, "iter": 3300, "lr": 0.02602, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36828, "top5_acc": 0.62719, "loss_cls": 3.57665, "loss": 3.57665, "time": 0.8188} +{"mode": "train", "epoch": 99, "iter": 3400, "lr": 0.026, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37094, "top5_acc": 0.64078, "loss_cls": 3.51694, "loss": 3.51694, "time": 0.81749} +{"mode": "train", "epoch": 99, "iter": 3500, "lr": 0.02597, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37172, "top5_acc": 0.62922, "loss_cls": 3.57322, "loss": 3.57322, "time": 0.82339} +{"mode": "train", "epoch": 99, "iter": 3600, "lr": 0.02595, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37281, "top5_acc": 0.62438, "loss_cls": 3.56296, "loss": 3.56296, "time": 0.81424} +{"mode": "train", "epoch": 99, "iter": 3700, "lr": 0.02592, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37047, "top5_acc": 0.63875, "loss_cls": 3.54376, "loss": 3.54376, "time": 0.82116} +{"mode": "val", "epoch": 99, "iter": 309, "lr": 0.02591, "top1_acc": 0.31738, "top5_acc": 0.57023, "mean_class_accuracy": 0.31702} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.02589, "memory": 15990, "data_time": 1.31154, "top1_acc": 0.38078, "top5_acc": 0.64984, "loss_cls": 3.48583, "loss": 3.48583, "time": 2.29708} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.02586, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37438, "top5_acc": 0.63531, "loss_cls": 3.54349, "loss": 3.54349, "time": 0.82839} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.02584, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37078, "top5_acc": 0.64734, "loss_cls": 3.49743, "loss": 3.49743, "time": 0.82072} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.02581, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3825, "top5_acc": 0.64156, "loss_cls": 3.50641, "loss": 3.50641, "time": 0.82331} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.02579, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38672, "top5_acc": 0.64844, "loss_cls": 3.46967, "loss": 3.46967, "time": 0.81797} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.02577, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38953, "top5_acc": 0.64391, "loss_cls": 3.49, "loss": 3.49, "time": 0.82527} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.02574, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37984, "top5_acc": 0.64109, "loss_cls": 3.50196, "loss": 3.50196, "time": 0.82039} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.02572, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38234, "top5_acc": 0.64359, "loss_cls": 3.49348, "loss": 3.49348, "time": 0.81715} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.02569, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37531, "top5_acc": 0.62813, "loss_cls": 3.54534, "loss": 3.54534, "time": 0.8172} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.02567, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38094, "top5_acc": 0.64094, "loss_cls": 3.5099, "loss": 3.5099, "time": 0.81863} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.02564, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37922, "top5_acc": 0.62891, "loss_cls": 3.55102, "loss": 3.55102, "time": 0.81898} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.02562, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38344, "top5_acc": 0.6425, "loss_cls": 3.49923, "loss": 3.49923, "time": 0.81458} +{"mode": "train", "epoch": 100, "iter": 1300, "lr": 0.02559, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36578, "top5_acc": 0.62641, "loss_cls": 3.57524, "loss": 3.57524, "time": 0.81355} +{"mode": "train", "epoch": 100, "iter": 1400, "lr": 0.02557, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38469, "top5_acc": 0.64781, "loss_cls": 3.47543, "loss": 3.47543, "time": 0.81961} +{"mode": "train", "epoch": 100, "iter": 1500, "lr": 0.02555, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38734, "top5_acc": 0.63969, "loss_cls": 3.4846, "loss": 3.4846, "time": 0.81325} +{"mode": "train", "epoch": 100, "iter": 1600, "lr": 0.02552, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36969, "top5_acc": 0.64, "loss_cls": 3.51659, "loss": 3.51659, "time": 0.81681} +{"mode": "train", "epoch": 100, "iter": 1700, "lr": 0.0255, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36656, "top5_acc": 0.62875, "loss_cls": 3.54478, "loss": 3.54478, "time": 0.81907} +{"mode": "train", "epoch": 100, "iter": 1800, "lr": 0.02547, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36688, "top5_acc": 0.62109, "loss_cls": 3.58844, "loss": 3.58844, "time": 0.82159} +{"mode": "train", "epoch": 100, "iter": 1900, "lr": 0.02545, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36953, "top5_acc": 0.63406, "loss_cls": 3.51529, "loss": 3.51529, "time": 0.81806} +{"mode": "train", "epoch": 100, "iter": 2000, "lr": 0.02542, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37688, "top5_acc": 0.62891, "loss_cls": 3.55098, "loss": 3.55098, "time": 0.81512} +{"mode": "train", "epoch": 100, "iter": 2100, "lr": 0.0254, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37953, "top5_acc": 0.63375, "loss_cls": 3.53541, "loss": 3.53541, "time": 0.82045} +{"mode": "train", "epoch": 100, "iter": 2200, "lr": 0.02538, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36625, "top5_acc": 0.63172, "loss_cls": 3.57341, "loss": 3.57341, "time": 0.82063} +{"mode": "train", "epoch": 100, "iter": 2300, "lr": 0.02535, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.3675, "top5_acc": 0.62953, "loss_cls": 3.56379, "loss": 3.56379, "time": 0.81784} +{"mode": "train", "epoch": 100, "iter": 2400, "lr": 0.02533, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37906, "top5_acc": 0.64031, "loss_cls": 3.5033, "loss": 3.5033, "time": 0.82359} +{"mode": "train", "epoch": 100, "iter": 2500, "lr": 0.0253, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37891, "top5_acc": 0.63125, "loss_cls": 3.54516, "loss": 3.54516, "time": 0.81942} +{"mode": "train", "epoch": 100, "iter": 2600, "lr": 0.02528, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36328, "top5_acc": 0.63016, "loss_cls": 3.5735, "loss": 3.5735, "time": 0.81511} +{"mode": "train", "epoch": 100, "iter": 2700, "lr": 0.02525, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38828, "top5_acc": 0.63688, "loss_cls": 3.50934, "loss": 3.50934, "time": 0.81627} +{"mode": "train", "epoch": 100, "iter": 2800, "lr": 0.02523, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37531, "top5_acc": 0.63344, "loss_cls": 3.55054, "loss": 3.55054, "time": 0.81602} +{"mode": "train", "epoch": 100, "iter": 2900, "lr": 0.02521, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38328, "top5_acc": 0.63641, "loss_cls": 3.50793, "loss": 3.50793, "time": 0.81933} +{"mode": "train", "epoch": 100, "iter": 3000, "lr": 0.02518, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37688, "top5_acc": 0.63344, "loss_cls": 3.53097, "loss": 3.53097, "time": 0.81785} +{"mode": "train", "epoch": 100, "iter": 3100, "lr": 0.02516, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37469, "top5_acc": 0.62984, "loss_cls": 3.54857, "loss": 3.54857, "time": 0.82041} +{"mode": "train", "epoch": 100, "iter": 3200, "lr": 0.02513, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37516, "top5_acc": 0.62734, "loss_cls": 3.55917, "loss": 3.55917, "time": 0.82139} +{"mode": "train", "epoch": 100, "iter": 3300, "lr": 0.02511, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39078, "top5_acc": 0.64797, "loss_cls": 3.47201, "loss": 3.47201, "time": 0.82375} +{"mode": "train", "epoch": 100, "iter": 3400, "lr": 0.02508, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36844, "top5_acc": 0.64, "loss_cls": 3.52927, "loss": 3.52927, "time": 0.81705} +{"mode": "train", "epoch": 100, "iter": 3500, "lr": 0.02506, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36953, "top5_acc": 0.6225, "loss_cls": 3.60015, "loss": 3.60015, "time": 0.82229} +{"mode": "train", "epoch": 100, "iter": 3600, "lr": 0.02504, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37031, "top5_acc": 0.62703, "loss_cls": 3.57633, "loss": 3.57633, "time": 0.81613} +{"mode": "train", "epoch": 100, "iter": 3700, "lr": 0.02501, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37562, "top5_acc": 0.64141, "loss_cls": 3.53615, "loss": 3.53615, "time": 0.82097} +{"mode": "val", "epoch": 100, "iter": 309, "lr": 0.025, "top1_acc": 0.31459, "top5_acc": 0.56886, "mean_class_accuracy": 0.31421} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.02498, "memory": 15990, "data_time": 1.31157, "top1_acc": 0.39328, "top5_acc": 0.64859, "loss_cls": 3.45057, "loss": 3.45057, "time": 2.29763} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.02495, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38344, "top5_acc": 0.6425, "loss_cls": 3.50983, "loss": 3.50983, "time": 0.82345} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.02493, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38781, "top5_acc": 0.65453, "loss_cls": 3.43211, "loss": 3.43211, "time": 0.82021} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.0249, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39, "top5_acc": 0.63813, "loss_cls": 3.51603, "loss": 3.51603, "time": 0.81985} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.02488, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38562, "top5_acc": 0.65219, "loss_cls": 3.4352, "loss": 3.4352, "time": 0.82293} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.02486, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37953, "top5_acc": 0.65156, "loss_cls": 3.48361, "loss": 3.48361, "time": 0.82423} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.02483, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38781, "top5_acc": 0.64922, "loss_cls": 3.48131, "loss": 3.48131, "time": 0.81942} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.02481, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38672, "top5_acc": 0.64438, "loss_cls": 3.48932, "loss": 3.48932, "time": 0.81861} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.02478, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37844, "top5_acc": 0.64828, "loss_cls": 3.46339, "loss": 3.46339, "time": 0.81712} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.02476, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38406, "top5_acc": 0.63609, "loss_cls": 3.50539, "loss": 3.50539, "time": 0.81439} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.02473, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36297, "top5_acc": 0.62828, "loss_cls": 3.58528, "loss": 3.58528, "time": 0.81858} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.02471, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37734, "top5_acc": 0.64047, "loss_cls": 3.49872, "loss": 3.49872, "time": 0.81513} +{"mode": "train", "epoch": 101, "iter": 1300, "lr": 0.02469, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38344, "top5_acc": 0.63516, "loss_cls": 3.50547, "loss": 3.50547, "time": 0.81534} +{"mode": "train", "epoch": 101, "iter": 1400, "lr": 0.02466, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38672, "top5_acc": 0.64016, "loss_cls": 3.49312, "loss": 3.49312, "time": 0.81485} +{"mode": "train", "epoch": 101, "iter": 1500, "lr": 0.02464, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37578, "top5_acc": 0.63203, "loss_cls": 3.53309, "loss": 3.53309, "time": 0.81929} +{"mode": "train", "epoch": 101, "iter": 1600, "lr": 0.02461, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39125, "top5_acc": 0.64219, "loss_cls": 3.48278, "loss": 3.48278, "time": 0.81853} +{"mode": "train", "epoch": 101, "iter": 1700, "lr": 0.02459, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37984, "top5_acc": 0.64531, "loss_cls": 3.4828, "loss": 3.4828, "time": 0.81745} +{"mode": "train", "epoch": 101, "iter": 1800, "lr": 0.02457, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36703, "top5_acc": 0.63484, "loss_cls": 3.55082, "loss": 3.55082, "time": 0.81657} +{"mode": "train", "epoch": 101, "iter": 1900, "lr": 0.02454, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38406, "top5_acc": 0.6475, "loss_cls": 3.49746, "loss": 3.49746, "time": 0.82497} +{"mode": "train", "epoch": 101, "iter": 2000, "lr": 0.02452, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38031, "top5_acc": 0.63781, "loss_cls": 3.51997, "loss": 3.51997, "time": 0.81671} +{"mode": "train", "epoch": 101, "iter": 2100, "lr": 0.02449, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.39359, "top5_acc": 0.65016, "loss_cls": 3.46793, "loss": 3.46793, "time": 0.81692} +{"mode": "train", "epoch": 101, "iter": 2200, "lr": 0.02447, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37953, "top5_acc": 0.63969, "loss_cls": 3.48507, "loss": 3.48507, "time": 0.81327} +{"mode": "train", "epoch": 101, "iter": 2300, "lr": 0.02445, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36688, "top5_acc": 0.63391, "loss_cls": 3.58191, "loss": 3.58191, "time": 0.81614} +{"mode": "train", "epoch": 101, "iter": 2400, "lr": 0.02442, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37031, "top5_acc": 0.63453, "loss_cls": 3.52884, "loss": 3.52884, "time": 0.81813} +{"mode": "train", "epoch": 101, "iter": 2500, "lr": 0.0244, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37578, "top5_acc": 0.64047, "loss_cls": 3.53165, "loss": 3.53165, "time": 0.81256} +{"mode": "train", "epoch": 101, "iter": 2600, "lr": 0.02437, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.38141, "top5_acc": 0.64594, "loss_cls": 3.50099, "loss": 3.50099, "time": 0.81726} +{"mode": "train", "epoch": 101, "iter": 2700, "lr": 0.02435, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37422, "top5_acc": 0.63344, "loss_cls": 3.5517, "loss": 3.5517, "time": 0.81876} +{"mode": "train", "epoch": 101, "iter": 2800, "lr": 0.02433, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38406, "top5_acc": 0.63844, "loss_cls": 3.528, "loss": 3.528, "time": 0.82058} +{"mode": "train", "epoch": 101, "iter": 2900, "lr": 0.0243, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37766, "top5_acc": 0.63422, "loss_cls": 3.54899, "loss": 3.54899, "time": 0.81465} +{"mode": "train", "epoch": 101, "iter": 3000, "lr": 0.02428, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37844, "top5_acc": 0.63344, "loss_cls": 3.52719, "loss": 3.52719, "time": 0.81826} +{"mode": "train", "epoch": 101, "iter": 3100, "lr": 0.02425, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37109, "top5_acc": 0.63484, "loss_cls": 3.57264, "loss": 3.57264, "time": 0.8237} +{"mode": "train", "epoch": 101, "iter": 3200, "lr": 0.02423, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37828, "top5_acc": 0.63781, "loss_cls": 3.50333, "loss": 3.50333, "time": 0.8168} +{"mode": "train", "epoch": 101, "iter": 3300, "lr": 0.02421, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37828, "top5_acc": 0.6425, "loss_cls": 3.49092, "loss": 3.49092, "time": 0.82536} +{"mode": "train", "epoch": 101, "iter": 3400, "lr": 0.02418, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37734, "top5_acc": 0.64266, "loss_cls": 3.54474, "loss": 3.54474, "time": 0.82523} +{"mode": "train", "epoch": 101, "iter": 3500, "lr": 0.02416, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3775, "top5_acc": 0.63531, "loss_cls": 3.53591, "loss": 3.53591, "time": 0.81727} +{"mode": "train", "epoch": 101, "iter": 3600, "lr": 0.02413, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37578, "top5_acc": 0.63844, "loss_cls": 3.541, "loss": 3.541, "time": 0.81961} +{"mode": "train", "epoch": 101, "iter": 3700, "lr": 0.02411, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38594, "top5_acc": 0.65016, "loss_cls": 3.48028, "loss": 3.48028, "time": 0.82666} +{"mode": "val", "epoch": 101, "iter": 309, "lr": 0.0241, "top1_acc": 0.31424, "top5_acc": 0.57291, "mean_class_accuracy": 0.31391} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.02407, "memory": 15990, "data_time": 1.30756, "top1_acc": 0.40266, "top5_acc": 0.6625, "loss_cls": 3.3674, "loss": 3.3674, "time": 2.29711} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.02405, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4, "top5_acc": 0.66406, "loss_cls": 3.39408, "loss": 3.39408, "time": 0.82474} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.02403, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38328, "top5_acc": 0.64344, "loss_cls": 3.46391, "loss": 3.46391, "time": 0.82266} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.024, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38062, "top5_acc": 0.63813, "loss_cls": 3.4792, "loss": 3.4792, "time": 0.82518} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.02398, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38344, "top5_acc": 0.64922, "loss_cls": 3.46045, "loss": 3.46045, "time": 0.81977} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.02396, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37922, "top5_acc": 0.63156, "loss_cls": 3.5353, "loss": 3.5353, "time": 0.81901} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.02393, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38531, "top5_acc": 0.64219, "loss_cls": 3.49552, "loss": 3.49552, "time": 0.81697} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.02391, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37906, "top5_acc": 0.64141, "loss_cls": 3.50212, "loss": 3.50212, "time": 0.81601} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.02388, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38688, "top5_acc": 0.63813, "loss_cls": 3.48791, "loss": 3.48791, "time": 0.81591} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.02386, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37328, "top5_acc": 0.64406, "loss_cls": 3.50132, "loss": 3.50132, "time": 0.81755} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.02384, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38781, "top5_acc": 0.64219, "loss_cls": 3.47316, "loss": 3.47316, "time": 0.82112} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.02381, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39375, "top5_acc": 0.65141, "loss_cls": 3.44915, "loss": 3.44915, "time": 0.8183} +{"mode": "train", "epoch": 102, "iter": 1300, "lr": 0.02379, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38016, "top5_acc": 0.64219, "loss_cls": 3.50482, "loss": 3.50482, "time": 0.82071} +{"mode": "train", "epoch": 102, "iter": 1400, "lr": 0.02376, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39375, "top5_acc": 0.65312, "loss_cls": 3.42611, "loss": 3.42611, "time": 0.8188} +{"mode": "train", "epoch": 102, "iter": 1500, "lr": 0.02374, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37828, "top5_acc": 0.64266, "loss_cls": 3.51078, "loss": 3.51078, "time": 0.81802} +{"mode": "train", "epoch": 102, "iter": 1600, "lr": 0.02372, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37438, "top5_acc": 0.64031, "loss_cls": 3.50791, "loss": 3.50791, "time": 0.81843} +{"mode": "train", "epoch": 102, "iter": 1700, "lr": 0.02369, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38578, "top5_acc": 0.64328, "loss_cls": 3.48061, "loss": 3.48061, "time": 0.82067} +{"mode": "train", "epoch": 102, "iter": 1800, "lr": 0.02367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37281, "top5_acc": 0.63531, "loss_cls": 3.51985, "loss": 3.51985, "time": 0.82506} +{"mode": "train", "epoch": 102, "iter": 1900, "lr": 0.02365, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38656, "top5_acc": 0.64422, "loss_cls": 3.49035, "loss": 3.49035, "time": 0.82204} +{"mode": "train", "epoch": 102, "iter": 2000, "lr": 0.02362, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37969, "top5_acc": 0.64484, "loss_cls": 3.49014, "loss": 3.49014, "time": 0.81752} +{"mode": "train", "epoch": 102, "iter": 2100, "lr": 0.0236, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37406, "top5_acc": 0.64203, "loss_cls": 3.4798, "loss": 3.4798, "time": 0.81604} +{"mode": "train", "epoch": 102, "iter": 2200, "lr": 0.02357, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37812, "top5_acc": 0.63922, "loss_cls": 3.49425, "loss": 3.49425, "time": 0.81469} +{"mode": "train", "epoch": 102, "iter": 2300, "lr": 0.02355, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37609, "top5_acc": 0.64422, "loss_cls": 3.51971, "loss": 3.51971, "time": 0.8169} +{"mode": "train", "epoch": 102, "iter": 2400, "lr": 0.02353, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38688, "top5_acc": 0.64203, "loss_cls": 3.52291, "loss": 3.52291, "time": 0.81286} +{"mode": "train", "epoch": 102, "iter": 2500, "lr": 0.0235, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38, "top5_acc": 0.63375, "loss_cls": 3.52895, "loss": 3.52895, "time": 0.81784} +{"mode": "train", "epoch": 102, "iter": 2600, "lr": 0.02348, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.37641, "top5_acc": 0.63797, "loss_cls": 3.53539, "loss": 3.53539, "time": 0.8123} +{"mode": "train", "epoch": 102, "iter": 2700, "lr": 0.02346, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38938, "top5_acc": 0.64016, "loss_cls": 3.47354, "loss": 3.47354, "time": 0.81533} +{"mode": "train", "epoch": 102, "iter": 2800, "lr": 0.02343, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3875, "top5_acc": 0.65172, "loss_cls": 3.46257, "loss": 3.46257, "time": 0.82073} +{"mode": "train", "epoch": 102, "iter": 2900, "lr": 0.02341, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37375, "top5_acc": 0.63047, "loss_cls": 3.5418, "loss": 3.5418, "time": 0.81724} +{"mode": "train", "epoch": 102, "iter": 3000, "lr": 0.02339, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36812, "top5_acc": 0.63281, "loss_cls": 3.54344, "loss": 3.54344, "time": 0.81792} +{"mode": "train", "epoch": 102, "iter": 3100, "lr": 0.02336, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37984, "top5_acc": 0.63922, "loss_cls": 3.48867, "loss": 3.48867, "time": 0.81754} +{"mode": "train", "epoch": 102, "iter": 3200, "lr": 0.02334, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37656, "top5_acc": 0.63141, "loss_cls": 3.56121, "loss": 3.56121, "time": 0.81888} +{"mode": "train", "epoch": 102, "iter": 3300, "lr": 0.02331, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38859, "top5_acc": 0.64062, "loss_cls": 3.49604, "loss": 3.49604, "time": 0.81623} +{"mode": "train", "epoch": 102, "iter": 3400, "lr": 0.02329, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38719, "top5_acc": 0.64156, "loss_cls": 3.48286, "loss": 3.48286, "time": 0.82399} +{"mode": "train", "epoch": 102, "iter": 3500, "lr": 0.02327, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38109, "top5_acc": 0.63938, "loss_cls": 3.52854, "loss": 3.52854, "time": 0.81569} +{"mode": "train", "epoch": 102, "iter": 3600, "lr": 0.02324, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37453, "top5_acc": 0.64078, "loss_cls": 3.51207, "loss": 3.51207, "time": 0.81783} +{"mode": "train", "epoch": 102, "iter": 3700, "lr": 0.02322, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38312, "top5_acc": 0.64953, "loss_cls": 3.46753, "loss": 3.46753, "time": 0.81916} +{"mode": "val", "epoch": 102, "iter": 309, "lr": 0.02321, "top1_acc": 0.32422, "top5_acc": 0.5759, "mean_class_accuracy": 0.32398} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.02319, "memory": 15990, "data_time": 1.28419, "top1_acc": 0.39828, "top5_acc": 0.65891, "loss_cls": 3.38506, "loss": 3.38506, "time": 2.26604} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.02316, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40234, "top5_acc": 0.65344, "loss_cls": 3.39224, "loss": 3.39224, "time": 0.8202} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.02314, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39672, "top5_acc": 0.65484, "loss_cls": 3.44802, "loss": 3.44802, "time": 0.82386} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.02311, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39016, "top5_acc": 0.65, "loss_cls": 3.46893, "loss": 3.46893, "time": 0.81906} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.02309, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37141, "top5_acc": 0.64484, "loss_cls": 3.44951, "loss": 3.44951, "time": 0.81997} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.02307, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38406, "top5_acc": 0.65266, "loss_cls": 3.45969, "loss": 3.45969, "time": 0.82563} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.02304, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38344, "top5_acc": 0.65188, "loss_cls": 3.46707, "loss": 3.46707, "time": 0.81305} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.02302, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38953, "top5_acc": 0.65219, "loss_cls": 3.44361, "loss": 3.44361, "time": 0.8194} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.023, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39375, "top5_acc": 0.64688, "loss_cls": 3.46046, "loss": 3.46046, "time": 0.81528} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.02297, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39031, "top5_acc": 0.64328, "loss_cls": 3.45979, "loss": 3.45979, "time": 0.81451} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.02295, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38453, "top5_acc": 0.63938, "loss_cls": 3.48672, "loss": 3.48672, "time": 0.81479} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.02293, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.3825, "top5_acc": 0.64125, "loss_cls": 3.49967, "loss": 3.49967, "time": 0.81694} +{"mode": "train", "epoch": 103, "iter": 1300, "lr": 0.0229, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38109, "top5_acc": 0.64438, "loss_cls": 3.48998, "loss": 3.48998, "time": 0.81298} +{"mode": "train", "epoch": 103, "iter": 1400, "lr": 0.02288, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38672, "top5_acc": 0.65266, "loss_cls": 3.45356, "loss": 3.45356, "time": 0.81664} +{"mode": "train", "epoch": 103, "iter": 1500, "lr": 0.02286, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38031, "top5_acc": 0.64031, "loss_cls": 3.47431, "loss": 3.47431, "time": 0.82369} +{"mode": "train", "epoch": 103, "iter": 1600, "lr": 0.02283, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39062, "top5_acc": 0.65281, "loss_cls": 3.44632, "loss": 3.44632, "time": 0.81852} +{"mode": "train", "epoch": 103, "iter": 1700, "lr": 0.02281, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37625, "top5_acc": 0.64, "loss_cls": 3.50389, "loss": 3.50389, "time": 0.8271} +{"mode": "train", "epoch": 103, "iter": 1800, "lr": 0.02279, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38047, "top5_acc": 0.64453, "loss_cls": 3.47072, "loss": 3.47072, "time": 0.81831} +{"mode": "train", "epoch": 103, "iter": 1900, "lr": 0.02276, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38094, "top5_acc": 0.65141, "loss_cls": 3.44751, "loss": 3.44751, "time": 0.81969} +{"mode": "train", "epoch": 103, "iter": 2000, "lr": 0.02274, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3925, "top5_acc": 0.64469, "loss_cls": 3.48337, "loss": 3.48337, "time": 0.81551} +{"mode": "train", "epoch": 103, "iter": 2100, "lr": 0.02272, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38109, "top5_acc": 0.64875, "loss_cls": 3.4701, "loss": 3.4701, "time": 0.81493} +{"mode": "train", "epoch": 103, "iter": 2200, "lr": 0.02269, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37766, "top5_acc": 0.63531, "loss_cls": 3.52894, "loss": 3.52894, "time": 0.81754} +{"mode": "train", "epoch": 103, "iter": 2300, "lr": 0.02267, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38406, "top5_acc": 0.64594, "loss_cls": 3.48259, "loss": 3.48259, "time": 0.81573} +{"mode": "train", "epoch": 103, "iter": 2400, "lr": 0.02264, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38828, "top5_acc": 0.65062, "loss_cls": 3.44228, "loss": 3.44228, "time": 0.8197} +{"mode": "train", "epoch": 103, "iter": 2500, "lr": 0.02262, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39047, "top5_acc": 0.64172, "loss_cls": 3.48225, "loss": 3.48225, "time": 0.81301} +{"mode": "train", "epoch": 103, "iter": 2600, "lr": 0.0226, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38812, "top5_acc": 0.64516, "loss_cls": 3.48429, "loss": 3.48429, "time": 0.82211} +{"mode": "train", "epoch": 103, "iter": 2700, "lr": 0.02257, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38031, "top5_acc": 0.63875, "loss_cls": 3.50267, "loss": 3.50267, "time": 0.81662} +{"mode": "train", "epoch": 103, "iter": 2800, "lr": 0.02255, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38297, "top5_acc": 0.64344, "loss_cls": 3.49074, "loss": 3.49074, "time": 0.81589} +{"mode": "train", "epoch": 103, "iter": 2900, "lr": 0.02253, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38094, "top5_acc": 0.63906, "loss_cls": 3.48329, "loss": 3.48329, "time": 0.81839} +{"mode": "train", "epoch": 103, "iter": 3000, "lr": 0.0225, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38375, "top5_acc": 0.63547, "loss_cls": 3.51236, "loss": 3.51236, "time": 0.81689} +{"mode": "train", "epoch": 103, "iter": 3100, "lr": 0.02248, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38953, "top5_acc": 0.65219, "loss_cls": 3.46619, "loss": 3.46619, "time": 0.82374} +{"mode": "train", "epoch": 103, "iter": 3200, "lr": 0.02246, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3875, "top5_acc": 0.65281, "loss_cls": 3.44322, "loss": 3.44322, "time": 0.81842} +{"mode": "train", "epoch": 103, "iter": 3300, "lr": 0.02243, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38797, "top5_acc": 0.63562, "loss_cls": 3.498, "loss": 3.498, "time": 0.81875} +{"mode": "train", "epoch": 103, "iter": 3400, "lr": 0.02241, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38656, "top5_acc": 0.64188, "loss_cls": 3.46739, "loss": 3.46739, "time": 0.82105} +{"mode": "train", "epoch": 103, "iter": 3500, "lr": 0.02239, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38281, "top5_acc": 0.63938, "loss_cls": 3.49444, "loss": 3.49444, "time": 0.81693} +{"mode": "train", "epoch": 103, "iter": 3600, "lr": 0.02236, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38625, "top5_acc": 0.63688, "loss_cls": 3.50358, "loss": 3.50358, "time": 0.81989} +{"mode": "train", "epoch": 103, "iter": 3700, "lr": 0.02234, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38156, "top5_acc": 0.63188, "loss_cls": 3.50921, "loss": 3.50921, "time": 0.81981} +{"mode": "val", "epoch": 103, "iter": 309, "lr": 0.02233, "top1_acc": 0.31621, "top5_acc": 0.57453, "mean_class_accuracy": 0.31602} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.02231, "memory": 15990, "data_time": 1.27411, "top1_acc": 0.39344, "top5_acc": 0.64656, "loss_cls": 3.41942, "loss": 3.41942, "time": 2.2767} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.02228, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40031, "top5_acc": 0.65922, "loss_cls": 3.38085, "loss": 3.38085, "time": 0.81905} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.02226, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39859, "top5_acc": 0.65922, "loss_cls": 3.40248, "loss": 3.40248, "time": 0.82581} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.02224, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38781, "top5_acc": 0.64641, "loss_cls": 3.43562, "loss": 3.43562, "time": 0.81974} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.02221, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39234, "top5_acc": 0.6575, "loss_cls": 3.44047, "loss": 3.44047, "time": 0.83131} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.02219, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39422, "top5_acc": 0.65031, "loss_cls": 3.43569, "loss": 3.43569, "time": 0.81614} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.02217, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40953, "top5_acc": 0.66172, "loss_cls": 3.37762, "loss": 3.37762, "time": 0.81638} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.02214, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39188, "top5_acc": 0.65703, "loss_cls": 3.42276, "loss": 3.42276, "time": 0.81831} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.02212, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38297, "top5_acc": 0.65562, "loss_cls": 3.46526, "loss": 3.46526, "time": 0.8263} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.0221, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38062, "top5_acc": 0.64594, "loss_cls": 3.48609, "loss": 3.48609, "time": 0.81889} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.02208, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39188, "top5_acc": 0.65172, "loss_cls": 3.41768, "loss": 3.41768, "time": 0.81451} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.02205, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.40031, "top5_acc": 0.65812, "loss_cls": 3.40083, "loss": 3.40083, "time": 0.81605} +{"mode": "train", "epoch": 104, "iter": 1300, "lr": 0.02203, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.39516, "top5_acc": 0.66453, "loss_cls": 3.39339, "loss": 3.39339, "time": 0.81639} +{"mode": "train", "epoch": 104, "iter": 1400, "lr": 0.02201, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38922, "top5_acc": 0.64891, "loss_cls": 3.44416, "loss": 3.44416, "time": 0.82121} +{"mode": "train", "epoch": 104, "iter": 1500, "lr": 0.02198, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39656, "top5_acc": 0.65312, "loss_cls": 3.40469, "loss": 3.40469, "time": 0.81907} +{"mode": "train", "epoch": 104, "iter": 1600, "lr": 0.02196, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38781, "top5_acc": 0.64938, "loss_cls": 3.4644, "loss": 3.4644, "time": 0.81893} +{"mode": "train", "epoch": 104, "iter": 1700, "lr": 0.02194, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38469, "top5_acc": 0.63781, "loss_cls": 3.5096, "loss": 3.5096, "time": 0.82708} +{"mode": "train", "epoch": 104, "iter": 1800, "lr": 0.02191, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.385, "top5_acc": 0.64922, "loss_cls": 3.44932, "loss": 3.44932, "time": 0.81906} +{"mode": "train", "epoch": 104, "iter": 1900, "lr": 0.02189, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39328, "top5_acc": 0.64812, "loss_cls": 3.46644, "loss": 3.46644, "time": 0.82356} +{"mode": "train", "epoch": 104, "iter": 2000, "lr": 0.02187, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38328, "top5_acc": 0.64969, "loss_cls": 3.45415, "loss": 3.45415, "time": 0.82038} +{"mode": "train", "epoch": 104, "iter": 2100, "lr": 0.02184, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38516, "top5_acc": 0.64766, "loss_cls": 3.47662, "loss": 3.47662, "time": 0.81858} +{"mode": "train", "epoch": 104, "iter": 2200, "lr": 0.02182, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37422, "top5_acc": 0.64078, "loss_cls": 3.52552, "loss": 3.52552, "time": 0.81403} +{"mode": "train", "epoch": 104, "iter": 2300, "lr": 0.0218, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38109, "top5_acc": 0.63531, "loss_cls": 3.50361, "loss": 3.50361, "time": 0.81224} +{"mode": "train", "epoch": 104, "iter": 2400, "lr": 0.02177, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3725, "top5_acc": 0.64547, "loss_cls": 3.49469, "loss": 3.49469, "time": 0.81768} +{"mode": "train", "epoch": 104, "iter": 2500, "lr": 0.02175, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38469, "top5_acc": 0.63859, "loss_cls": 3.50411, "loss": 3.50411, "time": 0.81406} +{"mode": "train", "epoch": 104, "iter": 2600, "lr": 0.02173, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38078, "top5_acc": 0.63578, "loss_cls": 3.48906, "loss": 3.48906, "time": 0.81344} +{"mode": "train", "epoch": 104, "iter": 2700, "lr": 0.02171, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37641, "top5_acc": 0.63969, "loss_cls": 3.51157, "loss": 3.51157, "time": 0.81568} +{"mode": "train", "epoch": 104, "iter": 2800, "lr": 0.02168, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37891, "top5_acc": 0.64875, "loss_cls": 3.4757, "loss": 3.4757, "time": 0.82059} +{"mode": "train", "epoch": 104, "iter": 2900, "lr": 0.02166, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38859, "top5_acc": 0.64859, "loss_cls": 3.45426, "loss": 3.45426, "time": 0.81768} +{"mode": "train", "epoch": 104, "iter": 3000, "lr": 0.02164, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38156, "top5_acc": 0.63969, "loss_cls": 3.51468, "loss": 3.51468, "time": 0.81561} +{"mode": "train", "epoch": 104, "iter": 3100, "lr": 0.02161, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3875, "top5_acc": 0.64203, "loss_cls": 3.4852, "loss": 3.4852, "time": 0.81678} +{"mode": "train", "epoch": 104, "iter": 3200, "lr": 0.02159, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38375, "top5_acc": 0.64281, "loss_cls": 3.50228, "loss": 3.50228, "time": 0.81975} +{"mode": "train", "epoch": 104, "iter": 3300, "lr": 0.02157, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38812, "top5_acc": 0.64656, "loss_cls": 3.47654, "loss": 3.47654, "time": 0.81621} +{"mode": "train", "epoch": 104, "iter": 3400, "lr": 0.02154, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39062, "top5_acc": 0.65281, "loss_cls": 3.43655, "loss": 3.43655, "time": 0.81693} +{"mode": "train", "epoch": 104, "iter": 3500, "lr": 0.02152, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38562, "top5_acc": 0.65172, "loss_cls": 3.43704, "loss": 3.43704, "time": 0.81832} +{"mode": "train", "epoch": 104, "iter": 3600, "lr": 0.0215, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39359, "top5_acc": 0.65234, "loss_cls": 3.43516, "loss": 3.43516, "time": 0.819} +{"mode": "train", "epoch": 104, "iter": 3700, "lr": 0.02148, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38422, "top5_acc": 0.64281, "loss_cls": 3.47586, "loss": 3.47586, "time": 0.82057} +{"mode": "val", "epoch": 104, "iter": 309, "lr": 0.02146, "top1_acc": 0.33192, "top5_acc": 0.5912, "mean_class_accuracy": 0.33163} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.02144, "memory": 15990, "data_time": 1.35266, "top1_acc": 0.40312, "top5_acc": 0.66281, "loss_cls": 3.35061, "loss": 3.35061, "time": 2.35714} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.02142, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39531, "top5_acc": 0.65312, "loss_cls": 3.43419, "loss": 3.43419, "time": 0.82199} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.0214, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40016, "top5_acc": 0.65797, "loss_cls": 3.37042, "loss": 3.37042, "time": 0.83088} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.02137, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39875, "top5_acc": 0.65969, "loss_cls": 3.39387, "loss": 3.39387, "time": 0.81576} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.02135, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39359, "top5_acc": 0.66078, "loss_cls": 3.41844, "loss": 3.41844, "time": 0.81379} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.02133, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38938, "top5_acc": 0.64844, "loss_cls": 3.44934, "loss": 3.44934, "time": 0.81811} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.0213, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39797, "top5_acc": 0.65547, "loss_cls": 3.42018, "loss": 3.42018, "time": 0.81294} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.02128, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39156, "top5_acc": 0.65828, "loss_cls": 3.40719, "loss": 3.40719, "time": 0.82038} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.02126, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38891, "top5_acc": 0.65125, "loss_cls": 3.43951, "loss": 3.43951, "time": 0.81587} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.02124, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38578, "top5_acc": 0.64531, "loss_cls": 3.47267, "loss": 3.47267, "time": 0.81924} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.02121, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39156, "top5_acc": 0.65734, "loss_cls": 3.41239, "loss": 3.41239, "time": 0.81444} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.02119, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39922, "top5_acc": 0.65688, "loss_cls": 3.40022, "loss": 3.40022, "time": 0.82137} +{"mode": "train", "epoch": 105, "iter": 1300, "lr": 0.02117, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39312, "top5_acc": 0.65094, "loss_cls": 3.45431, "loss": 3.45431, "time": 0.81933} +{"mode": "train", "epoch": 105, "iter": 1400, "lr": 0.02114, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39188, "top5_acc": 0.66047, "loss_cls": 3.41468, "loss": 3.41468, "time": 0.81949} +{"mode": "train", "epoch": 105, "iter": 1500, "lr": 0.02112, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39234, "top5_acc": 0.65297, "loss_cls": 3.42913, "loss": 3.42913, "time": 0.82426} +{"mode": "train", "epoch": 105, "iter": 1600, "lr": 0.0211, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39062, "top5_acc": 0.65141, "loss_cls": 3.44529, "loss": 3.44529, "time": 0.81891} +{"mode": "train", "epoch": 105, "iter": 1700, "lr": 0.02108, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39516, "top5_acc": 0.65375, "loss_cls": 3.41541, "loss": 3.41541, "time": 0.82201} +{"mode": "train", "epoch": 105, "iter": 1800, "lr": 0.02105, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38016, "top5_acc": 0.64266, "loss_cls": 3.4799, "loss": 3.4799, "time": 0.82389} +{"mode": "train", "epoch": 105, "iter": 1900, "lr": 0.02103, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37719, "top5_acc": 0.64969, "loss_cls": 3.4591, "loss": 3.4591, "time": 0.81924} +{"mode": "train", "epoch": 105, "iter": 2000, "lr": 0.02101, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37797, "top5_acc": 0.64875, "loss_cls": 3.50583, "loss": 3.50583, "time": 0.81713} +{"mode": "train", "epoch": 105, "iter": 2100, "lr": 0.02098, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39844, "top5_acc": 0.6575, "loss_cls": 3.40713, "loss": 3.40713, "time": 0.81579} +{"mode": "train", "epoch": 105, "iter": 2200, "lr": 0.02096, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38594, "top5_acc": 0.65203, "loss_cls": 3.46302, "loss": 3.46302, "time": 0.81689} +{"mode": "train", "epoch": 105, "iter": 2300, "lr": 0.02094, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38047, "top5_acc": 0.63953, "loss_cls": 3.46163, "loss": 3.46163, "time": 0.81375} +{"mode": "train", "epoch": 105, "iter": 2400, "lr": 0.02092, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39797, "top5_acc": 0.65469, "loss_cls": 3.42205, "loss": 3.42205, "time": 0.81761} +{"mode": "train", "epoch": 105, "iter": 2500, "lr": 0.02089, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38469, "top5_acc": 0.64844, "loss_cls": 3.45967, "loss": 3.45967, "time": 0.81443} +{"mode": "train", "epoch": 105, "iter": 2600, "lr": 0.02087, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39344, "top5_acc": 0.65375, "loss_cls": 3.42428, "loss": 3.42428, "time": 0.81632} +{"mode": "train", "epoch": 105, "iter": 2700, "lr": 0.02085, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39297, "top5_acc": 0.65266, "loss_cls": 3.41129, "loss": 3.41129, "time": 0.82115} +{"mode": "train", "epoch": 105, "iter": 2800, "lr": 0.02083, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37938, "top5_acc": 0.64266, "loss_cls": 3.47752, "loss": 3.47752, "time": 0.81405} +{"mode": "train", "epoch": 105, "iter": 2900, "lr": 0.0208, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38609, "top5_acc": 0.64266, "loss_cls": 3.46235, "loss": 3.46235, "time": 0.81452} +{"mode": "train", "epoch": 105, "iter": 3000, "lr": 0.02078, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38531, "top5_acc": 0.64625, "loss_cls": 3.4737, "loss": 3.4737, "time": 0.81968} +{"mode": "train", "epoch": 105, "iter": 3100, "lr": 0.02076, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39312, "top5_acc": 0.65109, "loss_cls": 3.43415, "loss": 3.43415, "time": 0.81969} +{"mode": "train", "epoch": 105, "iter": 3200, "lr": 0.02073, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39891, "top5_acc": 0.65156, "loss_cls": 3.42627, "loss": 3.42627, "time": 0.81613} +{"mode": "train", "epoch": 105, "iter": 3300, "lr": 0.02071, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39844, "top5_acc": 0.64344, "loss_cls": 3.43757, "loss": 3.43757, "time": 0.817} +{"mode": "train", "epoch": 105, "iter": 3400, "lr": 0.02069, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39062, "top5_acc": 0.65375, "loss_cls": 3.45043, "loss": 3.45043, "time": 0.8191} +{"mode": "train", "epoch": 105, "iter": 3500, "lr": 0.02067, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38859, "top5_acc": 0.65562, "loss_cls": 3.456, "loss": 3.456, "time": 0.81768} +{"mode": "train", "epoch": 105, "iter": 3600, "lr": 0.02064, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39422, "top5_acc": 0.64328, "loss_cls": 3.44548, "loss": 3.44548, "time": 0.82016} +{"mode": "train", "epoch": 105, "iter": 3700, "lr": 0.02062, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39562, "top5_acc": 0.64375, "loss_cls": 3.45472, "loss": 3.45472, "time": 0.81692} +{"mode": "val", "epoch": 105, "iter": 309, "lr": 0.02061, "top1_acc": 0.33161, "top5_acc": 0.58492, "mean_class_accuracy": 0.33143} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.02059, "memory": 15990, "data_time": 1.30274, "top1_acc": 0.40125, "top5_acc": 0.65609, "loss_cls": 3.39451, "loss": 3.39451, "time": 2.29209} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.02057, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39922, "top5_acc": 0.66797, "loss_cls": 3.34877, "loss": 3.34877, "time": 0.82108} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.02054, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41562, "top5_acc": 0.6675, "loss_cls": 3.34716, "loss": 3.34716, "time": 0.82465} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.02052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40266, "top5_acc": 0.65609, "loss_cls": 3.39595, "loss": 3.39595, "time": 0.82092} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.0205, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4075, "top5_acc": 0.66688, "loss_cls": 3.33078, "loss": 3.33078, "time": 0.81661} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.02048, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38688, "top5_acc": 0.65, "loss_cls": 3.44159, "loss": 3.44159, "time": 0.81741} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.02045, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38578, "top5_acc": 0.65141, "loss_cls": 3.44594, "loss": 3.44594, "time": 0.81875} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.02043, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39094, "top5_acc": 0.64453, "loss_cls": 3.44589, "loss": 3.44589, "time": 0.81751} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.02041, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39828, "top5_acc": 0.655, "loss_cls": 3.39838, "loss": 3.39838, "time": 0.81682} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.02039, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38469, "top5_acc": 0.65328, "loss_cls": 3.43295, "loss": 3.43295, "time": 0.8186} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.02036, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39172, "top5_acc": 0.64797, "loss_cls": 3.42143, "loss": 3.42143, "time": 0.81933} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.02034, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39156, "top5_acc": 0.64406, "loss_cls": 3.47163, "loss": 3.47163, "time": 0.82284} +{"mode": "train", "epoch": 106, "iter": 1300, "lr": 0.02032, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38781, "top5_acc": 0.65406, "loss_cls": 3.45626, "loss": 3.45626, "time": 0.81965} +{"mode": "train", "epoch": 106, "iter": 1400, "lr": 0.0203, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39641, "top5_acc": 0.65406, "loss_cls": 3.43558, "loss": 3.43558, "time": 0.81977} +{"mode": "train", "epoch": 106, "iter": 1500, "lr": 0.02027, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40453, "top5_acc": 0.65891, "loss_cls": 3.36602, "loss": 3.36602, "time": 0.81579} +{"mode": "train", "epoch": 106, "iter": 1600, "lr": 0.02025, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39297, "top5_acc": 0.66109, "loss_cls": 3.40137, "loss": 3.40137, "time": 0.81947} +{"mode": "train", "epoch": 106, "iter": 1700, "lr": 0.02023, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39828, "top5_acc": 0.66172, "loss_cls": 3.38106, "loss": 3.38106, "time": 0.81781} +{"mode": "train", "epoch": 106, "iter": 1800, "lr": 0.02021, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38938, "top5_acc": 0.65172, "loss_cls": 3.42897, "loss": 3.42897, "time": 0.81775} +{"mode": "train", "epoch": 106, "iter": 1900, "lr": 0.02018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38984, "top5_acc": 0.64391, "loss_cls": 3.4415, "loss": 3.4415, "time": 0.8177} +{"mode": "train", "epoch": 106, "iter": 2000, "lr": 0.02016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39781, "top5_acc": 0.64953, "loss_cls": 3.40332, "loss": 3.40332, "time": 0.81588} +{"mode": "train", "epoch": 106, "iter": 2100, "lr": 0.02014, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.40125, "top5_acc": 0.65594, "loss_cls": 3.40491, "loss": 3.40491, "time": 0.81686} +{"mode": "train", "epoch": 106, "iter": 2200, "lr": 0.02012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39609, "top5_acc": 0.65172, "loss_cls": 3.41234, "loss": 3.41234, "time": 0.81706} +{"mode": "train", "epoch": 106, "iter": 2300, "lr": 0.02009, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39812, "top5_acc": 0.65375, "loss_cls": 3.40688, "loss": 3.40688, "time": 0.82201} +{"mode": "train", "epoch": 106, "iter": 2400, "lr": 0.02007, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.39422, "top5_acc": 0.65031, "loss_cls": 3.40876, "loss": 3.40876, "time": 0.81373} +{"mode": "train", "epoch": 106, "iter": 2500, "lr": 0.02005, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37484, "top5_acc": 0.63609, "loss_cls": 3.53383, "loss": 3.53383, "time": 0.81241} +{"mode": "train", "epoch": 106, "iter": 2600, "lr": 0.02003, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.39016, "top5_acc": 0.65516, "loss_cls": 3.44211, "loss": 3.44211, "time": 0.81322} +{"mode": "train", "epoch": 106, "iter": 2700, "lr": 0.02, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.39859, "top5_acc": 0.64969, "loss_cls": 3.4545, "loss": 3.4545, "time": 0.81625} +{"mode": "train", "epoch": 106, "iter": 2800, "lr": 0.01998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39422, "top5_acc": 0.65422, "loss_cls": 3.43405, "loss": 3.43405, "time": 0.81355} +{"mode": "train", "epoch": 106, "iter": 2900, "lr": 0.01996, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37672, "top5_acc": 0.64469, "loss_cls": 3.46418, "loss": 3.46418, "time": 0.81676} +{"mode": "train", "epoch": 106, "iter": 3000, "lr": 0.01994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39047, "top5_acc": 0.65469, "loss_cls": 3.39695, "loss": 3.39695, "time": 0.81206} +{"mode": "train", "epoch": 106, "iter": 3100, "lr": 0.01991, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39609, "top5_acc": 0.66172, "loss_cls": 3.39735, "loss": 3.39735, "time": 0.81291} +{"mode": "train", "epoch": 106, "iter": 3200, "lr": 0.01989, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39359, "top5_acc": 0.65734, "loss_cls": 3.41178, "loss": 3.41178, "time": 0.82476} +{"mode": "train", "epoch": 106, "iter": 3300, "lr": 0.01987, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38578, "top5_acc": 0.65125, "loss_cls": 3.4399, "loss": 3.4399, "time": 0.8137} +{"mode": "train", "epoch": 106, "iter": 3400, "lr": 0.01985, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39891, "top5_acc": 0.65172, "loss_cls": 3.4256, "loss": 3.4256, "time": 0.82473} +{"mode": "train", "epoch": 106, "iter": 3500, "lr": 0.01983, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39469, "top5_acc": 0.65344, "loss_cls": 3.42723, "loss": 3.42723, "time": 0.818} +{"mode": "train", "epoch": 106, "iter": 3600, "lr": 0.0198, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38969, "top5_acc": 0.65719, "loss_cls": 3.41434, "loss": 3.41434, "time": 0.81861} +{"mode": "train", "epoch": 106, "iter": 3700, "lr": 0.01978, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39266, "top5_acc": 0.65312, "loss_cls": 3.43954, "loss": 3.43954, "time": 0.81698} +{"mode": "val", "epoch": 106, "iter": 309, "lr": 0.01977, "top1_acc": 0.33409, "top5_acc": 0.59358, "mean_class_accuracy": 0.3339} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.01975, "memory": 15990, "data_time": 1.35641, "top1_acc": 0.39984, "top5_acc": 0.66453, "loss_cls": 3.33493, "loss": 3.33493, "time": 2.35796} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.01973, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40359, "top5_acc": 0.66953, "loss_cls": 3.32514, "loss": 3.32514, "time": 0.82294} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.0197, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39469, "top5_acc": 0.65875, "loss_cls": 3.42988, "loss": 3.42988, "time": 0.82681} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.01968, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39781, "top5_acc": 0.65703, "loss_cls": 3.37044, "loss": 3.37044, "time": 0.81538} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.01966, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41562, "top5_acc": 0.66359, "loss_cls": 3.33294, "loss": 3.33294, "time": 0.81964} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.01964, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39953, "top5_acc": 0.65875, "loss_cls": 3.38185, "loss": 3.38185, "time": 0.81653} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.01961, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39516, "top5_acc": 0.66438, "loss_cls": 3.38074, "loss": 3.38074, "time": 0.81854} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.01959, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3925, "top5_acc": 0.64922, "loss_cls": 3.42241, "loss": 3.42241, "time": 0.81781} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.01957, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39781, "top5_acc": 0.65641, "loss_cls": 3.41023, "loss": 3.41023, "time": 0.81639} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.01955, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39828, "top5_acc": 0.65609, "loss_cls": 3.38834, "loss": 3.38834, "time": 0.82201} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.01953, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40531, "top5_acc": 0.66047, "loss_cls": 3.36065, "loss": 3.36065, "time": 0.8181} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.0195, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4, "top5_acc": 0.66141, "loss_cls": 3.39764, "loss": 3.39764, "time": 0.814} +{"mode": "train", "epoch": 107, "iter": 1300, "lr": 0.01948, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38516, "top5_acc": 0.64688, "loss_cls": 3.45036, "loss": 3.45036, "time": 0.81888} +{"mode": "train", "epoch": 107, "iter": 1400, "lr": 0.01946, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40188, "top5_acc": 0.65234, "loss_cls": 3.39591, "loss": 3.39591, "time": 0.82151} +{"mode": "train", "epoch": 107, "iter": 1500, "lr": 0.01944, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39953, "top5_acc": 0.6525, "loss_cls": 3.38881, "loss": 3.38881, "time": 0.81735} +{"mode": "train", "epoch": 107, "iter": 1600, "lr": 0.01942, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40578, "top5_acc": 0.65516, "loss_cls": 3.38305, "loss": 3.38305, "time": 0.81702} +{"mode": "train", "epoch": 107, "iter": 1700, "lr": 0.01939, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38703, "top5_acc": 0.64375, "loss_cls": 3.46019, "loss": 3.46019, "time": 0.82265} +{"mode": "train", "epoch": 107, "iter": 1800, "lr": 0.01937, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40641, "top5_acc": 0.66234, "loss_cls": 3.39005, "loss": 3.39005, "time": 0.82239} +{"mode": "train", "epoch": 107, "iter": 1900, "lr": 0.01935, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39078, "top5_acc": 0.65125, "loss_cls": 3.42759, "loss": 3.42759, "time": 0.82206} +{"mode": "train", "epoch": 107, "iter": 2000, "lr": 0.01933, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40516, "top5_acc": 0.66781, "loss_cls": 3.35569, "loss": 3.35569, "time": 0.82537} +{"mode": "train", "epoch": 107, "iter": 2100, "lr": 0.0193, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39594, "top5_acc": 0.65094, "loss_cls": 3.41041, "loss": 3.41041, "time": 0.81558} +{"mode": "train", "epoch": 107, "iter": 2200, "lr": 0.01928, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40469, "top5_acc": 0.64891, "loss_cls": 3.42861, "loss": 3.42861, "time": 0.82494} +{"mode": "train", "epoch": 107, "iter": 2300, "lr": 0.01926, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39516, "top5_acc": 0.65609, "loss_cls": 3.38648, "loss": 3.38648, "time": 0.8138} +{"mode": "train", "epoch": 107, "iter": 2400, "lr": 0.01924, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39484, "top5_acc": 0.66, "loss_cls": 3.39778, "loss": 3.39778, "time": 0.81556} +{"mode": "train", "epoch": 107, "iter": 2500, "lr": 0.01922, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39484, "top5_acc": 0.65656, "loss_cls": 3.38765, "loss": 3.38765, "time": 0.81901} +{"mode": "train", "epoch": 107, "iter": 2600, "lr": 0.01919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39266, "top5_acc": 0.66312, "loss_cls": 3.38844, "loss": 3.38844, "time": 0.81538} +{"mode": "train", "epoch": 107, "iter": 2700, "lr": 0.01917, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39859, "top5_acc": 0.6575, "loss_cls": 3.39198, "loss": 3.39198, "time": 0.8147} +{"mode": "train", "epoch": 107, "iter": 2800, "lr": 0.01915, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39875, "top5_acc": 0.66688, "loss_cls": 3.36538, "loss": 3.36538, "time": 0.82063} +{"mode": "train", "epoch": 107, "iter": 2900, "lr": 0.01913, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39953, "top5_acc": 0.65141, "loss_cls": 3.41565, "loss": 3.41565, "time": 0.81703} +{"mode": "train", "epoch": 107, "iter": 3000, "lr": 0.01911, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39172, "top5_acc": 0.6475, "loss_cls": 3.43487, "loss": 3.43487, "time": 0.81877} +{"mode": "train", "epoch": 107, "iter": 3100, "lr": 0.01908, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39453, "top5_acc": 0.65, "loss_cls": 3.40234, "loss": 3.40234, "time": 0.81404} +{"mode": "train", "epoch": 107, "iter": 3200, "lr": 0.01906, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39719, "top5_acc": 0.65719, "loss_cls": 3.42306, "loss": 3.42306, "time": 0.83} +{"mode": "train", "epoch": 107, "iter": 3300, "lr": 0.01904, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38922, "top5_acc": 0.65656, "loss_cls": 3.42013, "loss": 3.42013, "time": 0.81793} +{"mode": "train", "epoch": 107, "iter": 3400, "lr": 0.01902, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39531, "top5_acc": 0.65578, "loss_cls": 3.38883, "loss": 3.38883, "time": 0.818} +{"mode": "train", "epoch": 107, "iter": 3500, "lr": 0.019, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39391, "top5_acc": 0.65781, "loss_cls": 3.40071, "loss": 3.40071, "time": 0.81646} +{"mode": "train", "epoch": 107, "iter": 3600, "lr": 0.01897, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39172, "top5_acc": 0.6525, "loss_cls": 3.4333, "loss": 3.4333, "time": 0.822} +{"mode": "train", "epoch": 107, "iter": 3700, "lr": 0.01895, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40234, "top5_acc": 0.66172, "loss_cls": 3.37429, "loss": 3.37429, "time": 0.81654} +{"mode": "val", "epoch": 107, "iter": 309, "lr": 0.01894, "top1_acc": 0.33262, "top5_acc": 0.58801, "mean_class_accuracy": 0.33234} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.01892, "memory": 15990, "data_time": 1.36167, "top1_acc": 0.40531, "top5_acc": 0.67312, "loss_cls": 3.28536, "loss": 3.28536, "time": 2.36481} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0189, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40969, "top5_acc": 0.67219, "loss_cls": 3.34626, "loss": 3.34626, "time": 0.82831} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.01888, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40156, "top5_acc": 0.66516, "loss_cls": 3.34645, "loss": 3.34645, "time": 0.83889} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.01886, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40688, "top5_acc": 0.65781, "loss_cls": 3.36635, "loss": 3.36635, "time": 0.83644} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.01883, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41188, "top5_acc": 0.66828, "loss_cls": 3.32247, "loss": 3.32247, "time": 0.8374} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.01881, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41266, "top5_acc": 0.66641, "loss_cls": 3.3092, "loss": 3.3092, "time": 0.83546} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.01879, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40516, "top5_acc": 0.65812, "loss_cls": 3.38896, "loss": 3.38896, "time": 0.83683} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.01877, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40562, "top5_acc": 0.66578, "loss_cls": 3.37537, "loss": 3.37537, "time": 0.83771} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.01875, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41484, "top5_acc": 0.67422, "loss_cls": 3.31998, "loss": 3.31998, "time": 0.83615} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.01872, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40891, "top5_acc": 0.65781, "loss_cls": 3.33404, "loss": 3.33404, "time": 0.83941} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.0187, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39938, "top5_acc": 0.66062, "loss_cls": 3.37177, "loss": 3.37177, "time": 0.8376} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.01868, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39453, "top5_acc": 0.65562, "loss_cls": 3.40842, "loss": 3.40842, "time": 0.83749} +{"mode": "train", "epoch": 108, "iter": 1300, "lr": 0.01866, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40297, "top5_acc": 0.66516, "loss_cls": 3.35283, "loss": 3.35283, "time": 0.83783} +{"mode": "train", "epoch": 108, "iter": 1400, "lr": 0.01864, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40141, "top5_acc": 0.66375, "loss_cls": 3.36267, "loss": 3.36267, "time": 0.83502} +{"mode": "train", "epoch": 108, "iter": 1500, "lr": 0.01862, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40703, "top5_acc": 0.66047, "loss_cls": 3.35265, "loss": 3.35265, "time": 0.83741} +{"mode": "train", "epoch": 108, "iter": 1600, "lr": 0.01859, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40734, "top5_acc": 0.66281, "loss_cls": 3.3547, "loss": 3.3547, "time": 0.82635} +{"mode": "train", "epoch": 108, "iter": 1700, "lr": 0.01857, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39891, "top5_acc": 0.65844, "loss_cls": 3.38285, "loss": 3.38285, "time": 0.8278} +{"mode": "train", "epoch": 108, "iter": 1800, "lr": 0.01855, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.39297, "top5_acc": 0.65266, "loss_cls": 3.40745, "loss": 3.40745, "time": 0.84011} +{"mode": "train", "epoch": 108, "iter": 1900, "lr": 0.01853, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39422, "top5_acc": 0.65953, "loss_cls": 3.40062, "loss": 3.40062, "time": 0.83592} +{"mode": "train", "epoch": 108, "iter": 2000, "lr": 0.01851, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39984, "top5_acc": 0.66031, "loss_cls": 3.3919, "loss": 3.3919, "time": 0.82933} +{"mode": "train", "epoch": 108, "iter": 2100, "lr": 0.01848, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39734, "top5_acc": 0.65844, "loss_cls": 3.39633, "loss": 3.39633, "time": 0.82935} +{"mode": "train", "epoch": 108, "iter": 2200, "lr": 0.01846, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38688, "top5_acc": 0.64812, "loss_cls": 3.43444, "loss": 3.43444, "time": 0.82998} +{"mode": "train", "epoch": 108, "iter": 2300, "lr": 0.01844, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40141, "top5_acc": 0.66281, "loss_cls": 3.342, "loss": 3.342, "time": 0.82715} +{"mode": "train", "epoch": 108, "iter": 2400, "lr": 0.01842, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39047, "top5_acc": 0.65734, "loss_cls": 3.40824, "loss": 3.40824, "time": 0.82629} +{"mode": "train", "epoch": 108, "iter": 2500, "lr": 0.0184, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40531, "top5_acc": 0.65562, "loss_cls": 3.35174, "loss": 3.35174, "time": 0.82411} +{"mode": "train", "epoch": 108, "iter": 2600, "lr": 0.01838, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40156, "top5_acc": 0.65844, "loss_cls": 3.39061, "loss": 3.39061, "time": 0.82694} +{"mode": "train", "epoch": 108, "iter": 2700, "lr": 0.01835, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4025, "top5_acc": 0.65828, "loss_cls": 3.38413, "loss": 3.38413, "time": 0.82823} +{"mode": "train", "epoch": 108, "iter": 2800, "lr": 0.01833, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39156, "top5_acc": 0.65562, "loss_cls": 3.41923, "loss": 3.41923, "time": 0.82386} +{"mode": "train", "epoch": 108, "iter": 2900, "lr": 0.01831, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39953, "top5_acc": 0.66609, "loss_cls": 3.38778, "loss": 3.38778, "time": 0.82382} +{"mode": "train", "epoch": 108, "iter": 3000, "lr": 0.01829, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40391, "top5_acc": 0.66141, "loss_cls": 3.36873, "loss": 3.36873, "time": 0.82372} +{"mode": "train", "epoch": 108, "iter": 3100, "lr": 0.01827, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39828, "top5_acc": 0.65344, "loss_cls": 3.40934, "loss": 3.40934, "time": 0.81644} +{"mode": "train", "epoch": 108, "iter": 3200, "lr": 0.01825, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40156, "top5_acc": 0.66609, "loss_cls": 3.3734, "loss": 3.3734, "time": 0.83553} +{"mode": "train", "epoch": 108, "iter": 3300, "lr": 0.01823, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39828, "top5_acc": 0.65078, "loss_cls": 3.40926, "loss": 3.40926, "time": 0.82373} +{"mode": "train", "epoch": 108, "iter": 3400, "lr": 0.0182, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40625, "top5_acc": 0.67016, "loss_cls": 3.33087, "loss": 3.33087, "time": 0.81671} +{"mode": "train", "epoch": 108, "iter": 3500, "lr": 0.01818, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38547, "top5_acc": 0.64953, "loss_cls": 3.46551, "loss": 3.46551, "time": 0.82688} +{"mode": "train", "epoch": 108, "iter": 3600, "lr": 0.01816, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39562, "top5_acc": 0.66172, "loss_cls": 3.37292, "loss": 3.37292, "time": 0.83808} +{"mode": "train", "epoch": 108, "iter": 3700, "lr": 0.01814, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38969, "top5_acc": 0.65516, "loss_cls": 3.44239, "loss": 3.44239, "time": 0.82336} +{"mode": "val", "epoch": 108, "iter": 309, "lr": 0.01813, "top1_acc": 0.34671, "top5_acc": 0.59388, "mean_class_accuracy": 0.34651} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.01811, "memory": 15990, "data_time": 1.33358, "top1_acc": 0.42219, "top5_acc": 0.68766, "loss_cls": 3.24015, "loss": 3.24015, "time": 2.31588} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.01809, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41812, "top5_acc": 0.67594, "loss_cls": 3.26634, "loss": 3.26634, "time": 0.83214} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.01806, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41, "top5_acc": 0.65734, "loss_cls": 3.33997, "loss": 3.33997, "time": 0.83192} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.01804, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39531, "top5_acc": 0.66828, "loss_cls": 3.38265, "loss": 3.38265, "time": 0.82844} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.01802, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40812, "top5_acc": 0.66234, "loss_cls": 3.35089, "loss": 3.35089, "time": 0.83252} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.018, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39891, "top5_acc": 0.65641, "loss_cls": 3.37561, "loss": 3.37561, "time": 0.82923} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.01798, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40641, "top5_acc": 0.66906, "loss_cls": 3.34339, "loss": 3.34339, "time": 0.82968} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.01796, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39984, "top5_acc": 0.66703, "loss_cls": 3.33398, "loss": 3.33398, "time": 0.81953} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.01794, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40281, "top5_acc": 0.66016, "loss_cls": 3.36138, "loss": 3.36138, "time": 0.82498} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.01791, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40125, "top5_acc": 0.65312, "loss_cls": 3.41861, "loss": 3.41861, "time": 0.82667} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.01789, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40688, "top5_acc": 0.66016, "loss_cls": 3.38655, "loss": 3.38655, "time": 0.82584} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.01787, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39875, "top5_acc": 0.66609, "loss_cls": 3.34117, "loss": 3.34117, "time": 0.8217} +{"mode": "train", "epoch": 109, "iter": 1300, "lr": 0.01785, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40078, "top5_acc": 0.66047, "loss_cls": 3.3708, "loss": 3.3708, "time": 0.82304} +{"mode": "train", "epoch": 109, "iter": 1400, "lr": 0.01783, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39688, "top5_acc": 0.65594, "loss_cls": 3.39092, "loss": 3.39092, "time": 0.82396} +{"mode": "train", "epoch": 109, "iter": 1500, "lr": 0.01781, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.405, "top5_acc": 0.66516, "loss_cls": 3.35384, "loss": 3.35384, "time": 0.83068} +{"mode": "train", "epoch": 109, "iter": 1600, "lr": 0.01779, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39453, "top5_acc": 0.64812, "loss_cls": 3.43206, "loss": 3.43206, "time": 0.81775} +{"mode": "train", "epoch": 109, "iter": 1700, "lr": 0.01776, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40781, "top5_acc": 0.67656, "loss_cls": 3.30274, "loss": 3.30274, "time": 0.82829} +{"mode": "train", "epoch": 109, "iter": 1800, "lr": 0.01774, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39719, "top5_acc": 0.65828, "loss_cls": 3.40043, "loss": 3.40043, "time": 0.83823} +{"mode": "train", "epoch": 109, "iter": 1900, "lr": 0.01772, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.40562, "top5_acc": 0.67438, "loss_cls": 3.31182, "loss": 3.31182, "time": 0.83621} +{"mode": "train", "epoch": 109, "iter": 2000, "lr": 0.0177, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41375, "top5_acc": 0.67078, "loss_cls": 3.30606, "loss": 3.30606, "time": 0.83016} +{"mode": "train", "epoch": 109, "iter": 2100, "lr": 0.01768, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40469, "top5_acc": 0.66156, "loss_cls": 3.37442, "loss": 3.37442, "time": 0.82288} +{"mode": "train", "epoch": 109, "iter": 2200, "lr": 0.01766, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40219, "top5_acc": 0.66406, "loss_cls": 3.36919, "loss": 3.36919, "time": 0.82394} +{"mode": "train", "epoch": 109, "iter": 2300, "lr": 0.01764, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4075, "top5_acc": 0.67172, "loss_cls": 3.34042, "loss": 3.34042, "time": 0.82542} +{"mode": "train", "epoch": 109, "iter": 2400, "lr": 0.01761, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39578, "top5_acc": 0.66156, "loss_cls": 3.39981, "loss": 3.39981, "time": 0.82268} +{"mode": "train", "epoch": 109, "iter": 2500, "lr": 0.01759, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40641, "top5_acc": 0.67, "loss_cls": 3.33996, "loss": 3.33996, "time": 0.82564} +{"mode": "train", "epoch": 109, "iter": 2600, "lr": 0.01757, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40828, "top5_acc": 0.65844, "loss_cls": 3.37862, "loss": 3.37862, "time": 0.82137} +{"mode": "train", "epoch": 109, "iter": 2700, "lr": 0.01755, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40359, "top5_acc": 0.65891, "loss_cls": 3.36765, "loss": 3.36765, "time": 0.82129} +{"mode": "train", "epoch": 109, "iter": 2800, "lr": 0.01753, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40328, "top5_acc": 0.65844, "loss_cls": 3.39625, "loss": 3.39625, "time": 0.81894} +{"mode": "train", "epoch": 109, "iter": 2900, "lr": 0.01751, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40109, "top5_acc": 0.66188, "loss_cls": 3.38858, "loss": 3.38858, "time": 0.82779} +{"mode": "train", "epoch": 109, "iter": 3000, "lr": 0.01749, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4075, "top5_acc": 0.65875, "loss_cls": 3.34809, "loss": 3.34809, "time": 0.81849} +{"mode": "train", "epoch": 109, "iter": 3100, "lr": 0.01747, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40641, "top5_acc": 0.66812, "loss_cls": 3.34687, "loss": 3.34687, "time": 0.82253} +{"mode": "train", "epoch": 109, "iter": 3200, "lr": 0.01744, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39625, "top5_acc": 0.66219, "loss_cls": 3.38577, "loss": 3.38577, "time": 0.82621} +{"mode": "train", "epoch": 109, "iter": 3300, "lr": 0.01742, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41625, "top5_acc": 0.66484, "loss_cls": 3.34835, "loss": 3.34835, "time": 0.81735} +{"mode": "train", "epoch": 109, "iter": 3400, "lr": 0.0174, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39578, "top5_acc": 0.65438, "loss_cls": 3.38636, "loss": 3.38636, "time": 0.82153} +{"mode": "train", "epoch": 109, "iter": 3500, "lr": 0.01738, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39688, "top5_acc": 0.66234, "loss_cls": 3.38518, "loss": 3.38518, "time": 0.82964} +{"mode": "train", "epoch": 109, "iter": 3600, "lr": 0.01736, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.40672, "top5_acc": 0.65906, "loss_cls": 3.38052, "loss": 3.38052, "time": 0.83607} +{"mode": "train", "epoch": 109, "iter": 3700, "lr": 0.01734, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40984, "top5_acc": 0.66578, "loss_cls": 3.34387, "loss": 3.34387, "time": 0.82148} +{"mode": "val", "epoch": 109, "iter": 309, "lr": 0.01733, "top1_acc": 0.33313, "top5_acc": 0.58512, "mean_class_accuracy": 0.33278} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.01731, "memory": 15990, "data_time": 1.3351, "top1_acc": 0.42156, "top5_acc": 0.685, "loss_cls": 3.26354, "loss": 3.26354, "time": 2.31433} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.01729, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42531, "top5_acc": 0.68375, "loss_cls": 3.24845, "loss": 3.24845, "time": 0.83222} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.01727, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41781, "top5_acc": 0.67594, "loss_cls": 3.25609, "loss": 3.25609, "time": 0.83525} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.01724, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41062, "top5_acc": 0.67406, "loss_cls": 3.30899, "loss": 3.30899, "time": 0.8302} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.01722, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40359, "top5_acc": 0.66641, "loss_cls": 3.337, "loss": 3.337, "time": 0.8263} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.0172, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41172, "top5_acc": 0.66844, "loss_cls": 3.31032, "loss": 3.31032, "time": 0.82845} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.01718, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40938, "top5_acc": 0.67016, "loss_cls": 3.31706, "loss": 3.31706, "time": 0.82984} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.01716, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39875, "top5_acc": 0.66047, "loss_cls": 3.37071, "loss": 3.37071, "time": 0.82941} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.01714, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41047, "top5_acc": 0.67781, "loss_cls": 3.3041, "loss": 3.3041, "time": 0.83234} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.01712, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40234, "top5_acc": 0.665, "loss_cls": 3.34744, "loss": 3.34744, "time": 0.82017} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.0171, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39766, "top5_acc": 0.65391, "loss_cls": 3.39372, "loss": 3.39372, "time": 0.82084} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.01708, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40406, "top5_acc": 0.65906, "loss_cls": 3.34935, "loss": 3.34935, "time": 0.82024} +{"mode": "train", "epoch": 110, "iter": 1300, "lr": 0.01705, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41547, "top5_acc": 0.66828, "loss_cls": 3.3153, "loss": 3.3153, "time": 0.8296} +{"mode": "train", "epoch": 110, "iter": 1400, "lr": 0.01703, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40734, "top5_acc": 0.66891, "loss_cls": 3.34946, "loss": 3.34946, "time": 0.82975} +{"mode": "train", "epoch": 110, "iter": 1500, "lr": 0.01701, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41156, "top5_acc": 0.67094, "loss_cls": 3.31695, "loss": 3.31695, "time": 0.83033} +{"mode": "train", "epoch": 110, "iter": 1600, "lr": 0.01699, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40359, "top5_acc": 0.66062, "loss_cls": 3.38161, "loss": 3.38161, "time": 0.81616} +{"mode": "train", "epoch": 110, "iter": 1700, "lr": 0.01697, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41203, "top5_acc": 0.66625, "loss_cls": 3.3126, "loss": 3.3126, "time": 0.83344} +{"mode": "train", "epoch": 110, "iter": 1800, "lr": 0.01695, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40891, "top5_acc": 0.67266, "loss_cls": 3.33136, "loss": 3.33136, "time": 0.83047} +{"mode": "train", "epoch": 110, "iter": 1900, "lr": 0.01693, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39906, "top5_acc": 0.66328, "loss_cls": 3.35912, "loss": 3.35912, "time": 0.83161} +{"mode": "train", "epoch": 110, "iter": 2000, "lr": 0.01691, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41375, "top5_acc": 0.67625, "loss_cls": 3.32958, "loss": 3.32958, "time": 0.82724} +{"mode": "train", "epoch": 110, "iter": 2100, "lr": 0.01689, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40266, "top5_acc": 0.65547, "loss_cls": 3.36893, "loss": 3.36893, "time": 0.81777} +{"mode": "train", "epoch": 110, "iter": 2200, "lr": 0.01687, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40641, "top5_acc": 0.6625, "loss_cls": 3.35377, "loss": 3.35377, "time": 0.82132} +{"mode": "train", "epoch": 110, "iter": 2300, "lr": 0.01685, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40562, "top5_acc": 0.66312, "loss_cls": 3.3567, "loss": 3.3567, "time": 0.82182} +{"mode": "train", "epoch": 110, "iter": 2400, "lr": 0.01682, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40609, "top5_acc": 0.65516, "loss_cls": 3.37467, "loss": 3.37467, "time": 0.82608} +{"mode": "train", "epoch": 110, "iter": 2500, "lr": 0.0168, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.405, "top5_acc": 0.66266, "loss_cls": 3.36556, "loss": 3.36556, "time": 0.82333} +{"mode": "train", "epoch": 110, "iter": 2600, "lr": 0.01678, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41562, "top5_acc": 0.66891, "loss_cls": 3.32184, "loss": 3.32184, "time": 0.82923} +{"mode": "train", "epoch": 110, "iter": 2700, "lr": 0.01676, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40578, "top5_acc": 0.67172, "loss_cls": 3.32399, "loss": 3.32399, "time": 0.82305} +{"mode": "train", "epoch": 110, "iter": 2800, "lr": 0.01674, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40688, "top5_acc": 0.66828, "loss_cls": 3.3512, "loss": 3.3512, "time": 0.81931} +{"mode": "train", "epoch": 110, "iter": 2900, "lr": 0.01672, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40516, "top5_acc": 0.66031, "loss_cls": 3.36399, "loss": 3.36399, "time": 0.82223} +{"mode": "train", "epoch": 110, "iter": 3000, "lr": 0.0167, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39953, "top5_acc": 0.66484, "loss_cls": 3.3686, "loss": 3.3686, "time": 0.82079} +{"mode": "train", "epoch": 110, "iter": 3100, "lr": 0.01668, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.40406, "top5_acc": 0.66406, "loss_cls": 3.35103, "loss": 3.35103, "time": 0.82817} +{"mode": "train", "epoch": 110, "iter": 3200, "lr": 0.01666, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41219, "top5_acc": 0.67172, "loss_cls": 3.30694, "loss": 3.30694, "time": 0.8285} +{"mode": "train", "epoch": 110, "iter": 3300, "lr": 0.01664, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39625, "top5_acc": 0.65047, "loss_cls": 3.4021, "loss": 3.4021, "time": 0.8166} +{"mode": "train", "epoch": 110, "iter": 3400, "lr": 0.01662, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40234, "top5_acc": 0.65922, "loss_cls": 3.37099, "loss": 3.37099, "time": 0.82687} +{"mode": "train", "epoch": 110, "iter": 3500, "lr": 0.01659, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.40797, "top5_acc": 0.66344, "loss_cls": 3.35011, "loss": 3.35011, "time": 0.82935} +{"mode": "train", "epoch": 110, "iter": 3600, "lr": 0.01657, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41172, "top5_acc": 0.67312, "loss_cls": 3.29851, "loss": 3.29851, "time": 0.83135} +{"mode": "train", "epoch": 110, "iter": 3700, "lr": 0.01655, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41938, "top5_acc": 0.67172, "loss_cls": 3.30136, "loss": 3.30136, "time": 0.82157} +{"mode": "val", "epoch": 110, "iter": 309, "lr": 0.01654, "top1_acc": 0.35091, "top5_acc": 0.60497, "mean_class_accuracy": 0.35077} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.01652, "memory": 15990, "data_time": 1.29071, "top1_acc": 0.41469, "top5_acc": 0.67609, "loss_cls": 3.28365, "loss": 3.28365, "time": 2.27016} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.0165, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42078, "top5_acc": 0.68484, "loss_cls": 3.22412, "loss": 3.22412, "time": 0.83375} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.01648, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40672, "top5_acc": 0.67094, "loss_cls": 3.31815, "loss": 3.31815, "time": 0.83137} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.01646, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39875, "top5_acc": 0.66969, "loss_cls": 3.35748, "loss": 3.35748, "time": 0.82215} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.01644, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42094, "top5_acc": 0.67406, "loss_cls": 3.26937, "loss": 3.26937, "time": 0.82501} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.01642, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41828, "top5_acc": 0.68469, "loss_cls": 3.24178, "loss": 3.24178, "time": 0.82528} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.0164, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41344, "top5_acc": 0.68406, "loss_cls": 3.26254, "loss": 3.26254, "time": 0.8258} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.01638, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40922, "top5_acc": 0.67391, "loss_cls": 3.3089, "loss": 3.3089, "time": 0.82841} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.01636, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40609, "top5_acc": 0.66359, "loss_cls": 3.34738, "loss": 3.34738, "time": 0.82213} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.01634, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40844, "top5_acc": 0.66391, "loss_cls": 3.32939, "loss": 3.32939, "time": 0.82195} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.01632, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41469, "top5_acc": 0.67484, "loss_cls": 3.2945, "loss": 3.2945, "time": 0.81954} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.0163, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41141, "top5_acc": 0.67031, "loss_cls": 3.31867, "loss": 3.31867, "time": 0.82154} +{"mode": "train", "epoch": 111, "iter": 1300, "lr": 0.01627, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41281, "top5_acc": 0.67125, "loss_cls": 3.31762, "loss": 3.31762, "time": 0.82373} +{"mode": "train", "epoch": 111, "iter": 1400, "lr": 0.01625, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41562, "top5_acc": 0.6725, "loss_cls": 3.30927, "loss": 3.30927, "time": 0.83116} +{"mode": "train", "epoch": 111, "iter": 1500, "lr": 0.01623, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41234, "top5_acc": 0.67953, "loss_cls": 3.28605, "loss": 3.28605, "time": 0.82251} +{"mode": "train", "epoch": 111, "iter": 1600, "lr": 0.01621, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.39797, "top5_acc": 0.66719, "loss_cls": 3.36439, "loss": 3.36439, "time": 0.82527} +{"mode": "train", "epoch": 111, "iter": 1700, "lr": 0.01619, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40859, "top5_acc": 0.66922, "loss_cls": 3.34571, "loss": 3.34571, "time": 0.83645} +{"mode": "train", "epoch": 111, "iter": 1800, "lr": 0.01617, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.41812, "top5_acc": 0.67609, "loss_cls": 3.31025, "loss": 3.31025, "time": 0.82822} +{"mode": "train", "epoch": 111, "iter": 1900, "lr": 0.01615, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41203, "top5_acc": 0.66594, "loss_cls": 3.29529, "loss": 3.29529, "time": 0.82584} +{"mode": "train", "epoch": 111, "iter": 2000, "lr": 0.01613, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41453, "top5_acc": 0.67172, "loss_cls": 3.30276, "loss": 3.30276, "time": 0.82182} +{"mode": "train", "epoch": 111, "iter": 2100, "lr": 0.01611, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39938, "top5_acc": 0.66719, "loss_cls": 3.35606, "loss": 3.35606, "time": 0.8217} +{"mode": "train", "epoch": 111, "iter": 2200, "lr": 0.01609, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41281, "top5_acc": 0.66781, "loss_cls": 3.34402, "loss": 3.34402, "time": 0.82076} +{"mode": "train", "epoch": 111, "iter": 2300, "lr": 0.01607, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40438, "top5_acc": 0.67484, "loss_cls": 3.31567, "loss": 3.31567, "time": 0.82336} +{"mode": "train", "epoch": 111, "iter": 2400, "lr": 0.01605, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4025, "top5_acc": 0.66641, "loss_cls": 3.36892, "loss": 3.36892, "time": 0.82088} +{"mode": "train", "epoch": 111, "iter": 2500, "lr": 0.01603, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42234, "top5_acc": 0.67531, "loss_cls": 3.29504, "loss": 3.29504, "time": 0.82194} +{"mode": "train", "epoch": 111, "iter": 2600, "lr": 0.01601, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40766, "top5_acc": 0.66766, "loss_cls": 3.32026, "loss": 3.32026, "time": 0.82334} +{"mode": "train", "epoch": 111, "iter": 2700, "lr": 0.01599, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42, "top5_acc": 0.67172, "loss_cls": 3.30332, "loss": 3.30332, "time": 0.81564} +{"mode": "train", "epoch": 111, "iter": 2800, "lr": 0.01597, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40656, "top5_acc": 0.66797, "loss_cls": 3.35182, "loss": 3.35182, "time": 0.81927} +{"mode": "train", "epoch": 111, "iter": 2900, "lr": 0.01595, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40453, "top5_acc": 0.67344, "loss_cls": 3.33039, "loss": 3.33039, "time": 0.8209} +{"mode": "train", "epoch": 111, "iter": 3000, "lr": 0.01593, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40859, "top5_acc": 0.67188, "loss_cls": 3.32516, "loss": 3.32516, "time": 0.82045} +{"mode": "train", "epoch": 111, "iter": 3100, "lr": 0.0159, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41938, "top5_acc": 0.67, "loss_cls": 3.31383, "loss": 3.31383, "time": 0.81969} +{"mode": "train", "epoch": 111, "iter": 3200, "lr": 0.01588, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.41156, "top5_acc": 0.67109, "loss_cls": 3.3142, "loss": 3.3142, "time": 0.83173} +{"mode": "train", "epoch": 111, "iter": 3300, "lr": 0.01586, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40031, "top5_acc": 0.65734, "loss_cls": 3.38548, "loss": 3.38548, "time": 0.82292} +{"mode": "train", "epoch": 111, "iter": 3400, "lr": 0.01584, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40109, "top5_acc": 0.6625, "loss_cls": 3.3518, "loss": 3.3518, "time": 0.82176} +{"mode": "train", "epoch": 111, "iter": 3500, "lr": 0.01582, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41531, "top5_acc": 0.67422, "loss_cls": 3.29929, "loss": 3.29929, "time": 0.82961} +{"mode": "train", "epoch": 111, "iter": 3600, "lr": 0.0158, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40641, "top5_acc": 0.65703, "loss_cls": 3.36669, "loss": 3.36669, "time": 0.83581} +{"mode": "train", "epoch": 111, "iter": 3700, "lr": 0.01578, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40969, "top5_acc": 0.65703, "loss_cls": 3.37286, "loss": 3.37286, "time": 0.82667} +{"mode": "val", "epoch": 111, "iter": 309, "lr": 0.01577, "top1_acc": 0.34508, "top5_acc": 0.60259, "mean_class_accuracy": 0.34469} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.01575, "memory": 15990, "data_time": 1.27531, "top1_acc": 0.42094, "top5_acc": 0.69219, "loss_cls": 3.23086, "loss": 3.23086, "time": 2.2677} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.01573, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41609, "top5_acc": 0.68109, "loss_cls": 3.25562, "loss": 3.25562, "time": 0.83393} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.01571, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43078, "top5_acc": 0.68172, "loss_cls": 3.23704, "loss": 3.23704, "time": 0.8294} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.01569, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41922, "top5_acc": 0.68656, "loss_cls": 3.24419, "loss": 3.24419, "time": 0.82742} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.01567, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41969, "top5_acc": 0.67641, "loss_cls": 3.28219, "loss": 3.28219, "time": 0.82769} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.01565, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41938, "top5_acc": 0.67797, "loss_cls": 3.2679, "loss": 3.2679, "time": 0.8219} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.01563, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41141, "top5_acc": 0.66938, "loss_cls": 3.29879, "loss": 3.29879, "time": 0.82391} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.01561, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42328, "top5_acc": 0.67969, "loss_cls": 3.26484, "loss": 3.26484, "time": 0.82411} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.01559, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42016, "top5_acc": 0.67594, "loss_cls": 3.27127, "loss": 3.27127, "time": 0.83066} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.01557, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41797, "top5_acc": 0.67281, "loss_cls": 3.28937, "loss": 3.28937, "time": 0.82745} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.01555, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41906, "top5_acc": 0.68016, "loss_cls": 3.27722, "loss": 3.27722, "time": 0.81991} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.01553, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41438, "top5_acc": 0.68078, "loss_cls": 3.26448, "loss": 3.26448, "time": 0.82441} +{"mode": "train", "epoch": 112, "iter": 1300, "lr": 0.01551, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.405, "top5_acc": 0.66766, "loss_cls": 3.33972, "loss": 3.33972, "time": 0.82469} +{"mode": "train", "epoch": 112, "iter": 1400, "lr": 0.01549, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41875, "top5_acc": 0.68016, "loss_cls": 3.25555, "loss": 3.25555, "time": 0.82986} +{"mode": "train", "epoch": 112, "iter": 1500, "lr": 0.01547, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41766, "top5_acc": 0.66922, "loss_cls": 3.30386, "loss": 3.30386, "time": 0.81698} +{"mode": "train", "epoch": 112, "iter": 1600, "lr": 0.01545, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41891, "top5_acc": 0.67781, "loss_cls": 3.27108, "loss": 3.27108, "time": 0.83355} +{"mode": "train", "epoch": 112, "iter": 1700, "lr": 0.01543, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40188, "top5_acc": 0.66391, "loss_cls": 3.35978, "loss": 3.35978, "time": 0.83386} +{"mode": "train", "epoch": 112, "iter": 1800, "lr": 0.01541, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41125, "top5_acc": 0.67281, "loss_cls": 3.31783, "loss": 3.31783, "time": 0.83216} +{"mode": "train", "epoch": 112, "iter": 1900, "lr": 0.01539, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40781, "top5_acc": 0.66562, "loss_cls": 3.33589, "loss": 3.33589, "time": 0.83474} +{"mode": "train", "epoch": 112, "iter": 2000, "lr": 0.01537, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40031, "top5_acc": 0.66766, "loss_cls": 3.34191, "loss": 3.34191, "time": 0.82869} +{"mode": "train", "epoch": 112, "iter": 2100, "lr": 0.01535, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40484, "top5_acc": 0.67125, "loss_cls": 3.31234, "loss": 3.31234, "time": 0.83551} +{"mode": "train", "epoch": 112, "iter": 2200, "lr": 0.01533, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41656, "top5_acc": 0.6775, "loss_cls": 3.27834, "loss": 3.27834, "time": 0.82994} +{"mode": "train", "epoch": 112, "iter": 2300, "lr": 0.01531, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40922, "top5_acc": 0.67141, "loss_cls": 3.32536, "loss": 3.32536, "time": 0.83671} +{"mode": "train", "epoch": 112, "iter": 2400, "lr": 0.01529, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41234, "top5_acc": 0.67078, "loss_cls": 3.29178, "loss": 3.29178, "time": 0.83313} +{"mode": "train", "epoch": 112, "iter": 2500, "lr": 0.01527, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41219, "top5_acc": 0.67391, "loss_cls": 3.29204, "loss": 3.29204, "time": 0.83502} +{"mode": "train", "epoch": 112, "iter": 2600, "lr": 0.01525, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41344, "top5_acc": 0.67188, "loss_cls": 3.28592, "loss": 3.28592, "time": 0.83309} +{"mode": "train", "epoch": 112, "iter": 2700, "lr": 0.01523, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40562, "top5_acc": 0.67469, "loss_cls": 3.30244, "loss": 3.30244, "time": 0.83236} +{"mode": "train", "epoch": 112, "iter": 2800, "lr": 0.01521, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40938, "top5_acc": 0.67016, "loss_cls": 3.32333, "loss": 3.32333, "time": 0.83484} +{"mode": "train", "epoch": 112, "iter": 2900, "lr": 0.01519, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41844, "top5_acc": 0.67656, "loss_cls": 3.26689, "loss": 3.26689, "time": 0.83346} +{"mode": "train", "epoch": 112, "iter": 3000, "lr": 0.01517, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42078, "top5_acc": 0.68109, "loss_cls": 3.27988, "loss": 3.27988, "time": 0.83499} +{"mode": "train", "epoch": 112, "iter": 3100, "lr": 0.01515, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40562, "top5_acc": 0.66375, "loss_cls": 3.34768, "loss": 3.34768, "time": 0.83675} +{"mode": "train", "epoch": 112, "iter": 3200, "lr": 0.01513, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40766, "top5_acc": 0.67984, "loss_cls": 3.29117, "loss": 3.29117, "time": 0.82463} +{"mode": "train", "epoch": 112, "iter": 3300, "lr": 0.01511, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41906, "top5_acc": 0.66984, "loss_cls": 3.29509, "loss": 3.29509, "time": 0.82516} +{"mode": "train", "epoch": 112, "iter": 3400, "lr": 0.01509, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40891, "top5_acc": 0.66672, "loss_cls": 3.34285, "loss": 3.34285, "time": 0.83257} +{"mode": "train", "epoch": 112, "iter": 3500, "lr": 0.01507, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40984, "top5_acc": 0.67297, "loss_cls": 3.33536, "loss": 3.33536, "time": 0.83851} +{"mode": "train", "epoch": 112, "iter": 3600, "lr": 0.01505, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41547, "top5_acc": 0.67031, "loss_cls": 3.32058, "loss": 3.32058, "time": 0.83292} +{"mode": "train", "epoch": 112, "iter": 3700, "lr": 0.01503, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41656, "top5_acc": 0.67312, "loss_cls": 3.29959, "loss": 3.29959, "time": 0.82504} +{"mode": "val", "epoch": 112, "iter": 309, "lr": 0.01502, "top1_acc": 0.35202, "top5_acc": 0.61039, "mean_class_accuracy": 0.3517} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.015, "memory": 15990, "data_time": 1.27416, "top1_acc": 0.43328, "top5_acc": 0.69547, "loss_cls": 3.18272, "loss": 3.18272, "time": 2.26708} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.01498, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.4325, "top5_acc": 0.69453, "loss_cls": 3.16256, "loss": 3.16256, "time": 0.83106} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.01496, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42953, "top5_acc": 0.68391, "loss_cls": 3.21464, "loss": 3.21464, "time": 0.83652} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.01494, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42453, "top5_acc": 0.68047, "loss_cls": 3.2524, "loss": 3.2524, "time": 0.83123} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.01492, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.42875, "top5_acc": 0.68422, "loss_cls": 3.22896, "loss": 3.22896, "time": 0.83499} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.0149, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42781, "top5_acc": 0.69125, "loss_cls": 3.19521, "loss": 3.19521, "time": 0.83595} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.01488, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42469, "top5_acc": 0.68094, "loss_cls": 3.23546, "loss": 3.23546, "time": 0.83577} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.01486, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41906, "top5_acc": 0.68344, "loss_cls": 3.2726, "loss": 3.2726, "time": 0.82947} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.01484, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41844, "top5_acc": 0.67422, "loss_cls": 3.27608, "loss": 3.27608, "time": 0.83335} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.01482, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41703, "top5_acc": 0.67078, "loss_cls": 3.2922, "loss": 3.2922, "time": 0.83075} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0148, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42297, "top5_acc": 0.67688, "loss_cls": 3.26375, "loss": 3.26375, "time": 0.83451} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.01478, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41859, "top5_acc": 0.67562, "loss_cls": 3.2718, "loss": 3.2718, "time": 0.83589} +{"mode": "train", "epoch": 113, "iter": 1300, "lr": 0.01476, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40906, "top5_acc": 0.66766, "loss_cls": 3.3415, "loss": 3.3415, "time": 0.83603} +{"mode": "train", "epoch": 113, "iter": 1400, "lr": 0.01474, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41781, "top5_acc": 0.67594, "loss_cls": 3.27617, "loss": 3.27617, "time": 0.83199} +{"mode": "train", "epoch": 113, "iter": 1500, "lr": 0.01472, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42328, "top5_acc": 0.67828, "loss_cls": 3.2743, "loss": 3.2743, "time": 0.82595} +{"mode": "train", "epoch": 113, "iter": 1600, "lr": 0.0147, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41219, "top5_acc": 0.67656, "loss_cls": 3.28117, "loss": 3.28117, "time": 0.82789} +{"mode": "train", "epoch": 113, "iter": 1700, "lr": 0.01468, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.42594, "top5_acc": 0.67281, "loss_cls": 3.25204, "loss": 3.25204, "time": 0.8341} +{"mode": "train", "epoch": 113, "iter": 1800, "lr": 0.01466, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42328, "top5_acc": 0.67969, "loss_cls": 3.24642, "loss": 3.24642, "time": 0.83763} +{"mode": "train", "epoch": 113, "iter": 1900, "lr": 0.01464, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42016, "top5_acc": 0.67641, "loss_cls": 3.29116, "loss": 3.29116, "time": 0.83354} +{"mode": "train", "epoch": 113, "iter": 2000, "lr": 0.01462, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41641, "top5_acc": 0.67641, "loss_cls": 3.2797, "loss": 3.2797, "time": 0.83926} +{"mode": "train", "epoch": 113, "iter": 2100, "lr": 0.0146, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41922, "top5_acc": 0.67, "loss_cls": 3.32386, "loss": 3.32386, "time": 0.83564} +{"mode": "train", "epoch": 113, "iter": 2200, "lr": 0.01458, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41328, "top5_acc": 0.67188, "loss_cls": 3.33144, "loss": 3.33144, "time": 0.83564} +{"mode": "train", "epoch": 113, "iter": 2300, "lr": 0.01456, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41719, "top5_acc": 0.66812, "loss_cls": 3.29517, "loss": 3.29517, "time": 0.8363} +{"mode": "train", "epoch": 113, "iter": 2400, "lr": 0.01454, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42203, "top5_acc": 0.67422, "loss_cls": 3.26621, "loss": 3.26621, "time": 0.83518} +{"mode": "train", "epoch": 113, "iter": 2500, "lr": 0.01452, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40875, "top5_acc": 0.66312, "loss_cls": 3.32187, "loss": 3.32187, "time": 0.8335} +{"mode": "train", "epoch": 113, "iter": 2600, "lr": 0.0145, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41469, "top5_acc": 0.67047, "loss_cls": 3.31866, "loss": 3.31866, "time": 0.83269} +{"mode": "train", "epoch": 113, "iter": 2700, "lr": 0.01448, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41594, "top5_acc": 0.67359, "loss_cls": 3.30187, "loss": 3.30187, "time": 0.83822} +{"mode": "train", "epoch": 113, "iter": 2800, "lr": 0.01446, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41312, "top5_acc": 0.67469, "loss_cls": 3.28584, "loss": 3.28584, "time": 0.83633} +{"mode": "train", "epoch": 113, "iter": 2900, "lr": 0.01444, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42141, "top5_acc": 0.67312, "loss_cls": 3.29473, "loss": 3.29473, "time": 0.83681} +{"mode": "train", "epoch": 113, "iter": 3000, "lr": 0.01442, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.42547, "top5_acc": 0.68469, "loss_cls": 3.24036, "loss": 3.24036, "time": 0.83463} +{"mode": "train", "epoch": 113, "iter": 3100, "lr": 0.0144, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40266, "top5_acc": 0.6675, "loss_cls": 3.32949, "loss": 3.32949, "time": 0.83697} +{"mode": "train", "epoch": 113, "iter": 3200, "lr": 0.01438, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41641, "top5_acc": 0.67062, "loss_cls": 3.3156, "loss": 3.3156, "time": 0.82676} +{"mode": "train", "epoch": 113, "iter": 3300, "lr": 0.01436, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41406, "top5_acc": 0.67344, "loss_cls": 3.31762, "loss": 3.31762, "time": 0.83215} +{"mode": "train", "epoch": 113, "iter": 3400, "lr": 0.01434, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41625, "top5_acc": 0.67719, "loss_cls": 3.25912, "loss": 3.25912, "time": 0.82956} +{"mode": "train", "epoch": 113, "iter": 3500, "lr": 0.01432, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40844, "top5_acc": 0.67578, "loss_cls": 3.30715, "loss": 3.30715, "time": 0.83651} +{"mode": "train", "epoch": 113, "iter": 3600, "lr": 0.01431, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42234, "top5_acc": 0.68281, "loss_cls": 3.24821, "loss": 3.24821, "time": 0.82586} +{"mode": "train", "epoch": 113, "iter": 3700, "lr": 0.01429, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43766, "top5_acc": 0.69625, "loss_cls": 3.18473, "loss": 3.18473, "time": 0.8278} +{"mode": "val", "epoch": 113, "iter": 309, "lr": 0.01428, "top1_acc": 0.36028, "top5_acc": 0.61515, "mean_class_accuracy": 0.36007} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.01426, "memory": 15990, "data_time": 1.30257, "top1_acc": 0.43578, "top5_acc": 0.68719, "loss_cls": 3.19914, "loss": 3.19914, "time": 2.29671} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.01424, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43516, "top5_acc": 0.69312, "loss_cls": 3.19761, "loss": 3.19761, "time": 0.8325} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.01422, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42781, "top5_acc": 0.69031, "loss_cls": 3.21361, "loss": 3.21361, "time": 0.83031} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.0142, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42, "top5_acc": 0.67703, "loss_cls": 3.24552, "loss": 3.24552, "time": 0.8358} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.01418, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42469, "top5_acc": 0.68109, "loss_cls": 3.24808, "loss": 3.24808, "time": 0.83339} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.01416, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43516, "top5_acc": 0.68531, "loss_cls": 3.20209, "loss": 3.20209, "time": 0.83348} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.01414, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42234, "top5_acc": 0.685, "loss_cls": 3.26137, "loss": 3.26137, "time": 0.83253} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.01412, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42828, "top5_acc": 0.68531, "loss_cls": 3.2378, "loss": 3.2378, "time": 0.82648} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.0141, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43016, "top5_acc": 0.68594, "loss_cls": 3.2145, "loss": 3.2145, "time": 0.82741} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.01408, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.4225, "top5_acc": 0.68859, "loss_cls": 3.22895, "loss": 3.22895, "time": 0.83388} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.01406, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42391, "top5_acc": 0.67578, "loss_cls": 3.26551, "loss": 3.26551, "time": 0.835} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.01404, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42266, "top5_acc": 0.68438, "loss_cls": 3.21021, "loss": 3.21021, "time": 0.83572} +{"mode": "train", "epoch": 114, "iter": 1300, "lr": 0.01402, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.42234, "top5_acc": 0.68484, "loss_cls": 3.20426, "loss": 3.20426, "time": 0.82813} +{"mode": "train", "epoch": 114, "iter": 1400, "lr": 0.014, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42844, "top5_acc": 0.68562, "loss_cls": 3.21354, "loss": 3.21354, "time": 0.8367} +{"mode": "train", "epoch": 114, "iter": 1500, "lr": 0.01398, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.41594, "top5_acc": 0.68094, "loss_cls": 3.28175, "loss": 3.28175, "time": 0.83092} +{"mode": "train", "epoch": 114, "iter": 1600, "lr": 0.01397, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42109, "top5_acc": 0.68422, "loss_cls": 3.25271, "loss": 3.25271, "time": 0.82526} +{"mode": "train", "epoch": 114, "iter": 1700, "lr": 0.01395, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.42156, "top5_acc": 0.67797, "loss_cls": 3.25366, "loss": 3.25366, "time": 0.83189} +{"mode": "train", "epoch": 114, "iter": 1800, "lr": 0.01393, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43078, "top5_acc": 0.68656, "loss_cls": 3.18131, "loss": 3.18131, "time": 0.83952} +{"mode": "train", "epoch": 114, "iter": 1900, "lr": 0.01391, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42031, "top5_acc": 0.67188, "loss_cls": 3.27408, "loss": 3.27408, "time": 0.83838} +{"mode": "train", "epoch": 114, "iter": 2000, "lr": 0.01389, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41688, "top5_acc": 0.68188, "loss_cls": 3.23621, "loss": 3.23621, "time": 0.83696} +{"mode": "train", "epoch": 114, "iter": 2100, "lr": 0.01387, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42234, "top5_acc": 0.68188, "loss_cls": 3.24693, "loss": 3.24693, "time": 0.83392} +{"mode": "train", "epoch": 114, "iter": 2200, "lr": 0.01385, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41625, "top5_acc": 0.66703, "loss_cls": 3.28519, "loss": 3.28519, "time": 0.83188} +{"mode": "train", "epoch": 114, "iter": 2300, "lr": 0.01383, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41453, "top5_acc": 0.67203, "loss_cls": 3.28419, "loss": 3.28419, "time": 0.83559} +{"mode": "train", "epoch": 114, "iter": 2400, "lr": 0.01381, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42375, "top5_acc": 0.68703, "loss_cls": 3.23043, "loss": 3.23043, "time": 0.83472} +{"mode": "train", "epoch": 114, "iter": 2500, "lr": 0.01379, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42, "top5_acc": 0.67375, "loss_cls": 3.28761, "loss": 3.28761, "time": 0.83611} +{"mode": "train", "epoch": 114, "iter": 2600, "lr": 0.01377, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4, "top5_acc": 0.67078, "loss_cls": 3.3126, "loss": 3.3126, "time": 0.83704} +{"mode": "train", "epoch": 114, "iter": 2700, "lr": 0.01375, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41906, "top5_acc": 0.67156, "loss_cls": 3.28036, "loss": 3.28036, "time": 0.83551} +{"mode": "train", "epoch": 114, "iter": 2800, "lr": 0.01373, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.425, "top5_acc": 0.68188, "loss_cls": 3.24122, "loss": 3.24122, "time": 0.83217} +{"mode": "train", "epoch": 114, "iter": 2900, "lr": 0.01371, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42531, "top5_acc": 0.67578, "loss_cls": 3.26243, "loss": 3.26243, "time": 0.8352} +{"mode": "train", "epoch": 114, "iter": 3000, "lr": 0.01369, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.41375, "top5_acc": 0.66734, "loss_cls": 3.2983, "loss": 3.2983, "time": 0.84326} +{"mode": "train", "epoch": 114, "iter": 3100, "lr": 0.01368, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.42078, "top5_acc": 0.67109, "loss_cls": 3.2822, "loss": 3.2822, "time": 0.83204} +{"mode": "train", "epoch": 114, "iter": 3200, "lr": 0.01366, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41219, "top5_acc": 0.66594, "loss_cls": 3.32185, "loss": 3.32185, "time": 0.82294} +{"mode": "train", "epoch": 114, "iter": 3300, "lr": 0.01364, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41938, "top5_acc": 0.68078, "loss_cls": 3.25463, "loss": 3.25463, "time": 0.83322} +{"mode": "train", "epoch": 114, "iter": 3400, "lr": 0.01362, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.43094, "top5_acc": 0.69219, "loss_cls": 3.21613, "loss": 3.21613, "time": 0.83453} +{"mode": "train", "epoch": 114, "iter": 3500, "lr": 0.0136, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41516, "top5_acc": 0.67766, "loss_cls": 3.25952, "loss": 3.25952, "time": 0.83345} +{"mode": "train", "epoch": 114, "iter": 3600, "lr": 0.01358, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42406, "top5_acc": 0.67344, "loss_cls": 3.28019, "loss": 3.28019, "time": 0.83186} +{"mode": "train", "epoch": 114, "iter": 3700, "lr": 0.01356, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43031, "top5_acc": 0.6825, "loss_cls": 3.2376, "loss": 3.2376, "time": 0.83252} +{"mode": "val", "epoch": 114, "iter": 309, "lr": 0.01355, "top1_acc": 0.37223, "top5_acc": 0.62848, "mean_class_accuracy": 0.37175} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.01353, "memory": 15990, "data_time": 1.30035, "top1_acc": 0.44797, "top5_acc": 0.70906, "loss_cls": 3.09573, "loss": 3.09573, "time": 2.28824} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.01351, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43969, "top5_acc": 0.68891, "loss_cls": 3.14744, "loss": 3.14744, "time": 0.83539} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.01349, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43719, "top5_acc": 0.70156, "loss_cls": 3.16881, "loss": 3.16881, "time": 0.82743} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.01348, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43109, "top5_acc": 0.69266, "loss_cls": 3.18143, "loss": 3.18143, "time": 0.82839} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.01346, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42719, "top5_acc": 0.68781, "loss_cls": 3.19647, "loss": 3.19647, "time": 0.82878} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.01344, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42391, "top5_acc": 0.69, "loss_cls": 3.21142, "loss": 3.21142, "time": 0.8314} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.01342, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43109, "top5_acc": 0.69484, "loss_cls": 3.1736, "loss": 3.1736, "time": 0.82659} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.0134, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42844, "top5_acc": 0.68891, "loss_cls": 3.21737, "loss": 3.21737, "time": 0.82452} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.01338, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43547, "top5_acc": 0.69578, "loss_cls": 3.17514, "loss": 3.17514, "time": 0.82935} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.01336, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42656, "top5_acc": 0.68453, "loss_cls": 3.21073, "loss": 3.21073, "time": 0.83348} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.01334, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41891, "top5_acc": 0.67094, "loss_cls": 3.27226, "loss": 3.27226, "time": 0.82977} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.01332, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41719, "top5_acc": 0.68125, "loss_cls": 3.25926, "loss": 3.25926, "time": 0.83029} +{"mode": "train", "epoch": 115, "iter": 1300, "lr": 0.0133, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41641, "top5_acc": 0.68312, "loss_cls": 3.255, "loss": 3.255, "time": 0.82987} +{"mode": "train", "epoch": 115, "iter": 1400, "lr": 0.01328, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41938, "top5_acc": 0.68328, "loss_cls": 3.22993, "loss": 3.22993, "time": 0.8311} +{"mode": "train", "epoch": 115, "iter": 1500, "lr": 0.01327, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42547, "top5_acc": 0.68016, "loss_cls": 3.25326, "loss": 3.25326, "time": 0.82208} +{"mode": "train", "epoch": 115, "iter": 1600, "lr": 0.01325, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43547, "top5_acc": 0.68734, "loss_cls": 3.1813, "loss": 3.1813, "time": 0.82808} +{"mode": "train", "epoch": 115, "iter": 1700, "lr": 0.01323, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42094, "top5_acc": 0.67766, "loss_cls": 3.28301, "loss": 3.28301, "time": 0.83451} +{"mode": "train", "epoch": 115, "iter": 1800, "lr": 0.01321, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42531, "top5_acc": 0.68344, "loss_cls": 3.23644, "loss": 3.23644, "time": 0.83421} +{"mode": "train", "epoch": 115, "iter": 1900, "lr": 0.01319, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41797, "top5_acc": 0.67109, "loss_cls": 3.2825, "loss": 3.2825, "time": 0.83505} +{"mode": "train", "epoch": 115, "iter": 2000, "lr": 0.01317, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42406, "top5_acc": 0.68375, "loss_cls": 3.22709, "loss": 3.22709, "time": 0.83334} +{"mode": "train", "epoch": 115, "iter": 2100, "lr": 0.01315, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42875, "top5_acc": 0.69156, "loss_cls": 3.20534, "loss": 3.20534, "time": 0.83296} +{"mode": "train", "epoch": 115, "iter": 2200, "lr": 0.01313, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41766, "top5_acc": 0.68562, "loss_cls": 3.22795, "loss": 3.22795, "time": 0.83634} +{"mode": "train", "epoch": 115, "iter": 2300, "lr": 0.01311, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43156, "top5_acc": 0.68453, "loss_cls": 3.23821, "loss": 3.23821, "time": 0.83711} +{"mode": "train", "epoch": 115, "iter": 2400, "lr": 0.0131, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.425, "top5_acc": 0.67766, "loss_cls": 3.2365, "loss": 3.2365, "time": 0.83081} +{"mode": "train", "epoch": 115, "iter": 2500, "lr": 0.01308, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42703, "top5_acc": 0.68953, "loss_cls": 3.22352, "loss": 3.22352, "time": 0.83714} +{"mode": "train", "epoch": 115, "iter": 2600, "lr": 0.01306, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.425, "top5_acc": 0.68297, "loss_cls": 3.2332, "loss": 3.2332, "time": 0.83352} +{"mode": "train", "epoch": 115, "iter": 2700, "lr": 0.01304, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42594, "top5_acc": 0.69109, "loss_cls": 3.18795, "loss": 3.18795, "time": 0.83468} +{"mode": "train", "epoch": 115, "iter": 2800, "lr": 0.01302, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42188, "top5_acc": 0.68234, "loss_cls": 3.2346, "loss": 3.2346, "time": 0.83626} +{"mode": "train", "epoch": 115, "iter": 2900, "lr": 0.013, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41859, "top5_acc": 0.67531, "loss_cls": 3.2222, "loss": 3.2222, "time": 0.83726} +{"mode": "train", "epoch": 115, "iter": 3000, "lr": 0.01298, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41812, "top5_acc": 0.68391, "loss_cls": 3.27172, "loss": 3.27172, "time": 0.83415} +{"mode": "train", "epoch": 115, "iter": 3100, "lr": 0.01296, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.425, "top5_acc": 0.68438, "loss_cls": 3.22794, "loss": 3.22794, "time": 0.82657} +{"mode": "train", "epoch": 115, "iter": 3200, "lr": 0.01295, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42375, "top5_acc": 0.67594, "loss_cls": 3.26556, "loss": 3.26556, "time": 0.83634} +{"mode": "train", "epoch": 115, "iter": 3300, "lr": 0.01293, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42422, "top5_acc": 0.67844, "loss_cls": 3.24755, "loss": 3.24755, "time": 0.83779} +{"mode": "train", "epoch": 115, "iter": 3400, "lr": 0.01291, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.41938, "top5_acc": 0.67812, "loss_cls": 3.27245, "loss": 3.27245, "time": 0.83829} +{"mode": "train", "epoch": 115, "iter": 3500, "lr": 0.01289, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41125, "top5_acc": 0.67391, "loss_cls": 3.31239, "loss": 3.31239, "time": 0.83114} +{"mode": "train", "epoch": 115, "iter": 3600, "lr": 0.01287, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42469, "top5_acc": 0.67969, "loss_cls": 3.25686, "loss": 3.25686, "time": 0.83195} +{"mode": "train", "epoch": 115, "iter": 3700, "lr": 0.01285, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41547, "top5_acc": 0.67578, "loss_cls": 3.2683, "loss": 3.2683, "time": 0.8323} +{"mode": "val", "epoch": 115, "iter": 309, "lr": 0.01284, "top1_acc": 0.36023, "top5_acc": 0.61627, "mean_class_accuracy": 0.36} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.01282, "memory": 15990, "data_time": 1.33643, "top1_acc": 0.42609, "top5_acc": 0.69531, "loss_cls": 3.1575, "loss": 3.1575, "time": 2.33012} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.01281, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44203, "top5_acc": 0.6975, "loss_cls": 3.14473, "loss": 3.14473, "time": 0.82355} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.01279, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43234, "top5_acc": 0.68641, "loss_cls": 3.21475, "loss": 3.21475, "time": 0.81884} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.01277, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43141, "top5_acc": 0.68922, "loss_cls": 3.19046, "loss": 3.19046, "time": 0.82102} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.01275, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43203, "top5_acc": 0.69047, "loss_cls": 3.17568, "loss": 3.17568, "time": 0.81825} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.01273, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43516, "top5_acc": 0.68672, "loss_cls": 3.18021, "loss": 3.18021, "time": 0.81641} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.01271, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44594, "top5_acc": 0.70859, "loss_cls": 3.12243, "loss": 3.12243, "time": 0.81655} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.01269, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43047, "top5_acc": 0.69047, "loss_cls": 3.18458, "loss": 3.18458, "time": 0.81545} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.01268, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43359, "top5_acc": 0.68922, "loss_cls": 3.18213, "loss": 3.18213, "time": 0.81617} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.01266, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43734, "top5_acc": 0.69719, "loss_cls": 3.15763, "loss": 3.15763, "time": 0.81582} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.01264, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42422, "top5_acc": 0.68688, "loss_cls": 3.1887, "loss": 3.1887, "time": 0.81718} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.01262, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42359, "top5_acc": 0.67828, "loss_cls": 3.25836, "loss": 3.25836, "time": 0.82265} +{"mode": "train", "epoch": 116, "iter": 1300, "lr": 0.0126, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42125, "top5_acc": 0.68844, "loss_cls": 3.22778, "loss": 3.22778, "time": 0.81611} +{"mode": "train", "epoch": 116, "iter": 1400, "lr": 0.01258, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42938, "top5_acc": 0.68234, "loss_cls": 3.24404, "loss": 3.24404, "time": 0.82166} +{"mode": "train", "epoch": 116, "iter": 1500, "lr": 0.01256, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42219, "top5_acc": 0.68906, "loss_cls": 3.23452, "loss": 3.23452, "time": 0.81907} +{"mode": "train", "epoch": 116, "iter": 1600, "lr": 0.01255, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42797, "top5_acc": 0.69188, "loss_cls": 3.19456, "loss": 3.19456, "time": 0.81925} +{"mode": "train", "epoch": 116, "iter": 1700, "lr": 0.01253, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41844, "top5_acc": 0.67812, "loss_cls": 3.26302, "loss": 3.26302, "time": 0.81972} +{"mode": "train", "epoch": 116, "iter": 1800, "lr": 0.01251, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42828, "top5_acc": 0.68703, "loss_cls": 3.21068, "loss": 3.21068, "time": 0.82242} +{"mode": "train", "epoch": 116, "iter": 1900, "lr": 0.01249, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43156, "top5_acc": 0.69125, "loss_cls": 3.2157, "loss": 3.2157, "time": 0.81747} +{"mode": "train", "epoch": 116, "iter": 2000, "lr": 0.01247, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42328, "top5_acc": 0.67688, "loss_cls": 3.20865, "loss": 3.20865, "time": 0.8195} +{"mode": "train", "epoch": 116, "iter": 2100, "lr": 0.01245, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43328, "top5_acc": 0.70094, "loss_cls": 3.16421, "loss": 3.16421, "time": 0.82348} +{"mode": "train", "epoch": 116, "iter": 2200, "lr": 0.01243, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43422, "top5_acc": 0.69062, "loss_cls": 3.18229, "loss": 3.18229, "time": 0.82068} +{"mode": "train", "epoch": 116, "iter": 2300, "lr": 0.01242, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.42266, "top5_acc": 0.68328, "loss_cls": 3.22109, "loss": 3.22109, "time": 0.81434} +{"mode": "train", "epoch": 116, "iter": 2400, "lr": 0.0124, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43547, "top5_acc": 0.70062, "loss_cls": 3.1484, "loss": 3.1484, "time": 0.81307} +{"mode": "train", "epoch": 116, "iter": 2500, "lr": 0.01238, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43047, "top5_acc": 0.68484, "loss_cls": 3.20828, "loss": 3.20828, "time": 0.816} +{"mode": "train", "epoch": 116, "iter": 2600, "lr": 0.01236, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.4175, "top5_acc": 0.67766, "loss_cls": 3.22933, "loss": 3.22933, "time": 0.81066} +{"mode": "train", "epoch": 116, "iter": 2700, "lr": 0.01234, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42188, "top5_acc": 0.68109, "loss_cls": 3.25942, "loss": 3.25942, "time": 0.81325} +{"mode": "train", "epoch": 116, "iter": 2800, "lr": 0.01232, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.4225, "top5_acc": 0.68781, "loss_cls": 3.20513, "loss": 3.20513, "time": 0.81344} +{"mode": "train", "epoch": 116, "iter": 2900, "lr": 0.01231, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42766, "top5_acc": 0.68781, "loss_cls": 3.23941, "loss": 3.23941, "time": 0.82114} +{"mode": "train", "epoch": 116, "iter": 3000, "lr": 0.01229, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43359, "top5_acc": 0.68578, "loss_cls": 3.23033, "loss": 3.23033, "time": 0.81752} +{"mode": "train", "epoch": 116, "iter": 3100, "lr": 0.01227, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42844, "top5_acc": 0.68531, "loss_cls": 3.23583, "loss": 3.23583, "time": 0.82309} +{"mode": "train", "epoch": 116, "iter": 3200, "lr": 0.01225, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43672, "top5_acc": 0.69859, "loss_cls": 3.16564, "loss": 3.16564, "time": 0.81944} +{"mode": "train", "epoch": 116, "iter": 3300, "lr": 0.01223, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41797, "top5_acc": 0.67797, "loss_cls": 3.23128, "loss": 3.23128, "time": 0.82153} +{"mode": "train", "epoch": 116, "iter": 3400, "lr": 0.01221, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42938, "top5_acc": 0.68188, "loss_cls": 3.23688, "loss": 3.23688, "time": 0.81713} +{"mode": "train", "epoch": 116, "iter": 3500, "lr": 0.0122, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.43672, "top5_acc": 0.695, "loss_cls": 3.15288, "loss": 3.15288, "time": 0.8296} +{"mode": "train", "epoch": 116, "iter": 3600, "lr": 0.01218, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41469, "top5_acc": 0.68172, "loss_cls": 3.2355, "loss": 3.2355, "time": 0.81977} +{"mode": "train", "epoch": 116, "iter": 3700, "lr": 0.01216, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42938, "top5_acc": 0.68266, "loss_cls": 3.20763, "loss": 3.20763, "time": 0.81834} +{"mode": "val", "epoch": 116, "iter": 309, "lr": 0.01215, "top1_acc": 0.36453, "top5_acc": 0.61976, "mean_class_accuracy": 0.3642} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.01213, "memory": 15990, "data_time": 1.27036, "top1_acc": 0.44812, "top5_acc": 0.71172, "loss_cls": 3.0796, "loss": 3.0796, "time": 2.25398} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.01211, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44797, "top5_acc": 0.69438, "loss_cls": 3.12839, "loss": 3.12839, "time": 0.81716} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.0121, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45391, "top5_acc": 0.70656, "loss_cls": 3.06541, "loss": 3.06541, "time": 0.81908} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.01208, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43719, "top5_acc": 0.69484, "loss_cls": 3.15792, "loss": 3.15792, "time": 0.8183} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.01206, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.43203, "top5_acc": 0.69547, "loss_cls": 3.19179, "loss": 3.19179, "time": 0.81383} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.01204, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.43781, "top5_acc": 0.70016, "loss_cls": 3.14767, "loss": 3.14767, "time": 0.81696} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.01202, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44953, "top5_acc": 0.69797, "loss_cls": 3.14569, "loss": 3.14569, "time": 0.81447} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.012, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43234, "top5_acc": 0.69078, "loss_cls": 3.17942, "loss": 3.17942, "time": 0.81677} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.01199, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43594, "top5_acc": 0.69875, "loss_cls": 3.16848, "loss": 3.16848, "time": 0.81534} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.01197, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43109, "top5_acc": 0.69, "loss_cls": 3.20283, "loss": 3.20283, "time": 0.81443} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.01195, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44172, "top5_acc": 0.69234, "loss_cls": 3.14213, "loss": 3.14213, "time": 0.81408} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.01193, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42188, "top5_acc": 0.68672, "loss_cls": 3.21116, "loss": 3.21116, "time": 0.81957} +{"mode": "train", "epoch": 117, "iter": 1300, "lr": 0.01191, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43531, "top5_acc": 0.685, "loss_cls": 3.22747, "loss": 3.22747, "time": 0.81955} +{"mode": "train", "epoch": 117, "iter": 1400, "lr": 0.0119, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43531, "top5_acc": 0.69672, "loss_cls": 3.17166, "loss": 3.17166, "time": 0.81961} +{"mode": "train", "epoch": 117, "iter": 1500, "lr": 0.01188, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42969, "top5_acc": 0.69516, "loss_cls": 3.15354, "loss": 3.15354, "time": 0.81953} +{"mode": "train", "epoch": 117, "iter": 1600, "lr": 0.01186, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43188, "top5_acc": 0.68766, "loss_cls": 3.18542, "loss": 3.18542, "time": 0.81872} +{"mode": "train", "epoch": 117, "iter": 1700, "lr": 0.01184, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43844, "top5_acc": 0.70859, "loss_cls": 3.15183, "loss": 3.15183, "time": 0.81679} +{"mode": "train", "epoch": 117, "iter": 1800, "lr": 0.01182, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43391, "top5_acc": 0.69312, "loss_cls": 3.19008, "loss": 3.19008, "time": 0.81462} +{"mode": "train", "epoch": 117, "iter": 1900, "lr": 0.01181, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42891, "top5_acc": 0.69578, "loss_cls": 3.15761, "loss": 3.15761, "time": 0.81524} +{"mode": "train", "epoch": 117, "iter": 2000, "lr": 0.01179, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43875, "top5_acc": 0.70047, "loss_cls": 3.15433, "loss": 3.15433, "time": 0.81267} +{"mode": "train", "epoch": 117, "iter": 2100, "lr": 0.01177, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4325, "top5_acc": 0.68688, "loss_cls": 3.18935, "loss": 3.18935, "time": 0.81704} +{"mode": "train", "epoch": 117, "iter": 2200, "lr": 0.01175, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43172, "top5_acc": 0.68766, "loss_cls": 3.18691, "loss": 3.18691, "time": 0.81619} +{"mode": "train", "epoch": 117, "iter": 2300, "lr": 0.01173, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43156, "top5_acc": 0.69219, "loss_cls": 3.1705, "loss": 3.1705, "time": 0.8182} +{"mode": "train", "epoch": 117, "iter": 2400, "lr": 0.01172, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42609, "top5_acc": 0.67875, "loss_cls": 3.23667, "loss": 3.23667, "time": 0.81453} +{"mode": "train", "epoch": 117, "iter": 2500, "lr": 0.0117, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43531, "top5_acc": 0.69219, "loss_cls": 3.15833, "loss": 3.15833, "time": 0.81672} +{"mode": "train", "epoch": 117, "iter": 2600, "lr": 0.01168, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42906, "top5_acc": 0.68812, "loss_cls": 3.19708, "loss": 3.19708, "time": 0.8152} +{"mode": "train", "epoch": 117, "iter": 2700, "lr": 0.01166, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43484, "top5_acc": 0.69109, "loss_cls": 3.17885, "loss": 3.17885, "time": 0.81813} +{"mode": "train", "epoch": 117, "iter": 2800, "lr": 0.01164, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43203, "top5_acc": 0.69141, "loss_cls": 3.18026, "loss": 3.18026, "time": 0.81626} +{"mode": "train", "epoch": 117, "iter": 2900, "lr": 0.01163, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41125, "top5_acc": 0.67625, "loss_cls": 3.26947, "loss": 3.26947, "time": 0.81704} +{"mode": "train", "epoch": 117, "iter": 3000, "lr": 0.01161, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43422, "top5_acc": 0.69328, "loss_cls": 3.18877, "loss": 3.18877, "time": 0.81224} +{"mode": "train", "epoch": 117, "iter": 3100, "lr": 0.01159, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42781, "top5_acc": 0.68312, "loss_cls": 3.21596, "loss": 3.21596, "time": 0.8174} +{"mode": "train", "epoch": 117, "iter": 3200, "lr": 0.01157, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43359, "top5_acc": 0.68656, "loss_cls": 3.18633, "loss": 3.18633, "time": 0.81857} +{"mode": "train", "epoch": 117, "iter": 3300, "lr": 0.01155, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43156, "top5_acc": 0.69062, "loss_cls": 3.18948, "loss": 3.18948, "time": 0.81728} +{"mode": "train", "epoch": 117, "iter": 3400, "lr": 0.01154, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43641, "top5_acc": 0.68844, "loss_cls": 3.18765, "loss": 3.18765, "time": 0.82509} +{"mode": "train", "epoch": 117, "iter": 3500, "lr": 0.01152, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42234, "top5_acc": 0.68, "loss_cls": 3.21382, "loss": 3.21382, "time": 0.82321} +{"mode": "train", "epoch": 117, "iter": 3600, "lr": 0.0115, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42656, "top5_acc": 0.68406, "loss_cls": 3.22063, "loss": 3.22063, "time": 0.82123} +{"mode": "train", "epoch": 117, "iter": 3700, "lr": 0.01148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42969, "top5_acc": 0.695, "loss_cls": 3.18833, "loss": 3.18833, "time": 0.82165} +{"mode": "val", "epoch": 117, "iter": 309, "lr": 0.01147, "top1_acc": 0.37168, "top5_acc": 0.6264, "mean_class_accuracy": 0.37138} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.01146, "memory": 15990, "data_time": 1.31305, "top1_acc": 0.44188, "top5_acc": 0.70891, "loss_cls": 3.09026, "loss": 3.09026, "time": 2.2988} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.01144, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45141, "top5_acc": 0.69922, "loss_cls": 3.10557, "loss": 3.10557, "time": 0.82415} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.01142, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44625, "top5_acc": 0.70516, "loss_cls": 3.10337, "loss": 3.10337, "time": 0.8182} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.0114, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44859, "top5_acc": 0.70703, "loss_cls": 3.07564, "loss": 3.07564, "time": 0.81605} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.01139, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44172, "top5_acc": 0.70672, "loss_cls": 3.09545, "loss": 3.09545, "time": 0.81487} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.01137, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45094, "top5_acc": 0.70594, "loss_cls": 3.08178, "loss": 3.08178, "time": 0.81867} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.01135, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43344, "top5_acc": 0.69219, "loss_cls": 3.17872, "loss": 3.17872, "time": 0.81787} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.01133, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42875, "top5_acc": 0.68906, "loss_cls": 3.16281, "loss": 3.16281, "time": 0.81779} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.01131, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.43906, "top5_acc": 0.69188, "loss_cls": 3.16353, "loss": 3.16353, "time": 0.81719} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.0113, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45547, "top5_acc": 0.70422, "loss_cls": 3.06752, "loss": 3.06752, "time": 0.81626} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.01128, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43656, "top5_acc": 0.695, "loss_cls": 3.16222, "loss": 3.16222, "time": 0.81721} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.01126, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44688, "top5_acc": 0.69656, "loss_cls": 3.13403, "loss": 3.13403, "time": 0.81976} +{"mode": "train", "epoch": 118, "iter": 1300, "lr": 0.01124, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43562, "top5_acc": 0.69297, "loss_cls": 3.14653, "loss": 3.14653, "time": 0.82233} +{"mode": "train", "epoch": 118, "iter": 1400, "lr": 0.01123, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44438, "top5_acc": 0.69812, "loss_cls": 3.12194, "loss": 3.12194, "time": 0.8199} +{"mode": "train", "epoch": 118, "iter": 1500, "lr": 0.01121, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43125, "top5_acc": 0.68641, "loss_cls": 3.18616, "loss": 3.18616, "time": 0.81796} +{"mode": "train", "epoch": 118, "iter": 1600, "lr": 0.01119, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44281, "top5_acc": 0.69797, "loss_cls": 3.13483, "loss": 3.13483, "time": 0.81289} +{"mode": "train", "epoch": 118, "iter": 1700, "lr": 0.01117, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43797, "top5_acc": 0.7, "loss_cls": 3.13793, "loss": 3.13793, "time": 0.81457} +{"mode": "train", "epoch": 118, "iter": 1800, "lr": 0.01116, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43109, "top5_acc": 0.68719, "loss_cls": 3.17285, "loss": 3.17285, "time": 0.82225} +{"mode": "train", "epoch": 118, "iter": 1900, "lr": 0.01114, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44703, "top5_acc": 0.69984, "loss_cls": 3.12777, "loss": 3.12777, "time": 0.81857} +{"mode": "train", "epoch": 118, "iter": 2000, "lr": 0.01112, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43906, "top5_acc": 0.69625, "loss_cls": 3.1409, "loss": 3.1409, "time": 0.81525} +{"mode": "train", "epoch": 118, "iter": 2100, "lr": 0.0111, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44, "top5_acc": 0.70516, "loss_cls": 3.11677, "loss": 3.11677, "time": 0.81905} +{"mode": "train", "epoch": 118, "iter": 2200, "lr": 0.01109, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43734, "top5_acc": 0.69953, "loss_cls": 3.1304, "loss": 3.1304, "time": 0.82287} +{"mode": "train", "epoch": 118, "iter": 2300, "lr": 0.01107, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42969, "top5_acc": 0.69234, "loss_cls": 3.1783, "loss": 3.1783, "time": 0.8196} +{"mode": "train", "epoch": 118, "iter": 2400, "lr": 0.01105, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43609, "top5_acc": 0.69625, "loss_cls": 3.17995, "loss": 3.17995, "time": 0.82065} +{"mode": "train", "epoch": 118, "iter": 2500, "lr": 0.01103, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43453, "top5_acc": 0.69047, "loss_cls": 3.17336, "loss": 3.17336, "time": 0.81572} +{"mode": "train", "epoch": 118, "iter": 2600, "lr": 0.01102, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43766, "top5_acc": 0.69312, "loss_cls": 3.18485, "loss": 3.18485, "time": 0.81943} +{"mode": "train", "epoch": 118, "iter": 2700, "lr": 0.011, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43375, "top5_acc": 0.68578, "loss_cls": 3.17658, "loss": 3.17658, "time": 0.81079} +{"mode": "train", "epoch": 118, "iter": 2800, "lr": 0.01098, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42375, "top5_acc": 0.69156, "loss_cls": 3.21766, "loss": 3.21766, "time": 0.81405} +{"mode": "train", "epoch": 118, "iter": 2900, "lr": 0.01096, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43562, "top5_acc": 0.68234, "loss_cls": 3.1964, "loss": 3.1964, "time": 0.81821} +{"mode": "train", "epoch": 118, "iter": 3000, "lr": 0.01095, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44109, "top5_acc": 0.69641, "loss_cls": 3.13881, "loss": 3.13881, "time": 0.81436} +{"mode": "train", "epoch": 118, "iter": 3100, "lr": 0.01093, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43281, "top5_acc": 0.68266, "loss_cls": 3.21605, "loss": 3.21605, "time": 0.81892} +{"mode": "train", "epoch": 118, "iter": 3200, "lr": 0.01091, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43859, "top5_acc": 0.69297, "loss_cls": 3.13465, "loss": 3.13465, "time": 0.82359} +{"mode": "train", "epoch": 118, "iter": 3300, "lr": 0.01089, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43953, "top5_acc": 0.68984, "loss_cls": 3.18232, "loss": 3.18232, "time": 0.81749} +{"mode": "train", "epoch": 118, "iter": 3400, "lr": 0.01088, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43891, "top5_acc": 0.70078, "loss_cls": 3.14114, "loss": 3.14114, "time": 0.82253} +{"mode": "train", "epoch": 118, "iter": 3500, "lr": 0.01086, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43781, "top5_acc": 0.69156, "loss_cls": 3.1867, "loss": 3.1867, "time": 0.81875} +{"mode": "train", "epoch": 118, "iter": 3600, "lr": 0.01084, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43469, "top5_acc": 0.68703, "loss_cls": 3.20233, "loss": 3.20233, "time": 0.82138} +{"mode": "train", "epoch": 118, "iter": 3700, "lr": 0.01082, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43672, "top5_acc": 0.68922, "loss_cls": 3.19919, "loss": 3.19919, "time": 0.82241} +{"mode": "val", "epoch": 118, "iter": 309, "lr": 0.01082, "top1_acc": 0.37937, "top5_acc": 0.63354, "mean_class_accuracy": 0.3792} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.0108, "memory": 15990, "data_time": 1.28352, "top1_acc": 0.46062, "top5_acc": 0.71328, "loss_cls": 3.02457, "loss": 3.02457, "time": 2.27534} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.01078, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44234, "top5_acc": 0.70266, "loss_cls": 3.11208, "loss": 3.11208, "time": 0.8206} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.01076, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45312, "top5_acc": 0.70891, "loss_cls": 3.03592, "loss": 3.03592, "time": 0.8209} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.01075, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44531, "top5_acc": 0.70234, "loss_cls": 3.11461, "loss": 3.11461, "time": 0.81801} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.01073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45594, "top5_acc": 0.70672, "loss_cls": 3.05736, "loss": 3.05736, "time": 0.81991} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.01071, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44734, "top5_acc": 0.70594, "loss_cls": 3.09034, "loss": 3.09034, "time": 0.81614} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.01069, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45188, "top5_acc": 0.71172, "loss_cls": 3.06315, "loss": 3.06315, "time": 0.8169} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.01068, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44969, "top5_acc": 0.70531, "loss_cls": 3.08318, "loss": 3.08318, "time": 0.81747} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.01066, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44359, "top5_acc": 0.69844, "loss_cls": 3.12492, "loss": 3.12492, "time": 0.81645} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.01064, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45297, "top5_acc": 0.71062, "loss_cls": 3.0877, "loss": 3.0877, "time": 0.81267} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.01063, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.43906, "top5_acc": 0.69547, "loss_cls": 3.14859, "loss": 3.14859, "time": 0.81697} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.01061, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44219, "top5_acc": 0.69891, "loss_cls": 3.12605, "loss": 3.12605, "time": 0.82542} +{"mode": "train", "epoch": 119, "iter": 1300, "lr": 0.01059, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43516, "top5_acc": 0.69359, "loss_cls": 3.16639, "loss": 3.16639, "time": 0.82152} +{"mode": "train", "epoch": 119, "iter": 1400, "lr": 0.01057, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44344, "top5_acc": 0.69516, "loss_cls": 3.14584, "loss": 3.14584, "time": 0.81867} +{"mode": "train", "epoch": 119, "iter": 1500, "lr": 0.01056, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44266, "top5_acc": 0.69781, "loss_cls": 3.10592, "loss": 3.10592, "time": 0.82151} +{"mode": "train", "epoch": 119, "iter": 1600, "lr": 0.01054, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43141, "top5_acc": 0.69062, "loss_cls": 3.18741, "loss": 3.18741, "time": 0.81554} +{"mode": "train", "epoch": 119, "iter": 1700, "lr": 0.01052, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44281, "top5_acc": 0.68984, "loss_cls": 3.14088, "loss": 3.14088, "time": 0.81142} +{"mode": "train", "epoch": 119, "iter": 1800, "lr": 0.0105, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44312, "top5_acc": 0.69719, "loss_cls": 3.11811, "loss": 3.11811, "time": 0.81461} +{"mode": "train", "epoch": 119, "iter": 1900, "lr": 0.01049, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44188, "top5_acc": 0.70125, "loss_cls": 3.10778, "loss": 3.10778, "time": 0.81761} +{"mode": "train", "epoch": 119, "iter": 2000, "lr": 0.01047, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43328, "top5_acc": 0.69922, "loss_cls": 3.14083, "loss": 3.14083, "time": 0.81016} +{"mode": "train", "epoch": 119, "iter": 2100, "lr": 0.01045, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43641, "top5_acc": 0.69656, "loss_cls": 3.15081, "loss": 3.15081, "time": 0.82009} +{"mode": "train", "epoch": 119, "iter": 2200, "lr": 0.01044, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43016, "top5_acc": 0.69719, "loss_cls": 3.15663, "loss": 3.15663, "time": 0.81415} +{"mode": "train", "epoch": 119, "iter": 2300, "lr": 0.01042, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44031, "top5_acc": 0.69062, "loss_cls": 3.18258, "loss": 3.18258, "time": 0.81525} +{"mode": "train", "epoch": 119, "iter": 2400, "lr": 0.0104, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44312, "top5_acc": 0.69016, "loss_cls": 3.14615, "loss": 3.14615, "time": 0.81277} +{"mode": "train", "epoch": 119, "iter": 2500, "lr": 0.01039, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42703, "top5_acc": 0.68812, "loss_cls": 3.17985, "loss": 3.17985, "time": 0.8127} +{"mode": "train", "epoch": 119, "iter": 2600, "lr": 0.01037, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44266, "top5_acc": 0.70016, "loss_cls": 3.12136, "loss": 3.12136, "time": 0.81615} +{"mode": "train", "epoch": 119, "iter": 2700, "lr": 0.01035, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.43859, "top5_acc": 0.695, "loss_cls": 3.13877, "loss": 3.13877, "time": 0.81307} +{"mode": "train", "epoch": 119, "iter": 2800, "lr": 0.01033, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43984, "top5_acc": 0.69703, "loss_cls": 3.1277, "loss": 3.1277, "time": 0.81631} +{"mode": "train", "epoch": 119, "iter": 2900, "lr": 0.01032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43844, "top5_acc": 0.69109, "loss_cls": 3.15257, "loss": 3.15257, "time": 0.81306} +{"mode": "train", "epoch": 119, "iter": 3000, "lr": 0.0103, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4375, "top5_acc": 0.69203, "loss_cls": 3.16724, "loss": 3.16724, "time": 0.81911} +{"mode": "train", "epoch": 119, "iter": 3100, "lr": 0.01028, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43562, "top5_acc": 0.69172, "loss_cls": 3.16142, "loss": 3.16142, "time": 0.82176} +{"mode": "train", "epoch": 119, "iter": 3200, "lr": 0.01027, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44562, "top5_acc": 0.71219, "loss_cls": 3.07336, "loss": 3.07336, "time": 0.82148} +{"mode": "train", "epoch": 119, "iter": 3300, "lr": 0.01025, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43344, "top5_acc": 0.69688, "loss_cls": 3.16059, "loss": 3.16059, "time": 0.81766} +{"mode": "train", "epoch": 119, "iter": 3400, "lr": 0.01023, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44766, "top5_acc": 0.69484, "loss_cls": 3.13303, "loss": 3.13303, "time": 0.82146} +{"mode": "train", "epoch": 119, "iter": 3500, "lr": 0.01022, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43516, "top5_acc": 0.69891, "loss_cls": 3.15935, "loss": 3.15935, "time": 0.81833} +{"mode": "train", "epoch": 119, "iter": 3600, "lr": 0.0102, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43016, "top5_acc": 0.68328, "loss_cls": 3.2057, "loss": 3.2057, "time": 0.82314} +{"mode": "train", "epoch": 119, "iter": 3700, "lr": 0.01018, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44781, "top5_acc": 0.69672, "loss_cls": 3.11963, "loss": 3.11963, "time": 0.81675} +{"mode": "val", "epoch": 119, "iter": 309, "lr": 0.01017, "top1_acc": 0.37634, "top5_acc": 0.63415, "mean_class_accuracy": 0.37602} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.01016, "memory": 15990, "data_time": 1.2635, "top1_acc": 0.46406, "top5_acc": 0.71719, "loss_cls": 3.0355, "loss": 3.0355, "time": 2.2458} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.01014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45484, "top5_acc": 0.71109, "loss_cls": 3.0545, "loss": 3.0545, "time": 0.82235} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.01012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45422, "top5_acc": 0.70859, "loss_cls": 3.07223, "loss": 3.07223, "time": 0.81756} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.01011, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44047, "top5_acc": 0.70562, "loss_cls": 3.08138, "loss": 3.08138, "time": 0.81582} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.01009, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44203, "top5_acc": 0.70047, "loss_cls": 3.08964, "loss": 3.08964, "time": 0.82236} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.01007, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46406, "top5_acc": 0.71297, "loss_cls": 3.03588, "loss": 3.03588, "time": 0.81514} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.01006, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43766, "top5_acc": 0.70531, "loss_cls": 3.10643, "loss": 3.10643, "time": 0.81015} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.01004, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45203, "top5_acc": 0.70703, "loss_cls": 3.06644, "loss": 3.06644, "time": 0.81707} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.01002, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44969, "top5_acc": 0.70062, "loss_cls": 3.08396, "loss": 3.08396, "time": 0.81367} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.01001, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44391, "top5_acc": 0.70797, "loss_cls": 3.09768, "loss": 3.09768, "time": 0.81342} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45984, "top5_acc": 0.71281, "loss_cls": 3.07674, "loss": 3.07674, "time": 0.81601} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.00997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44203, "top5_acc": 0.70656, "loss_cls": 3.09458, "loss": 3.09458, "time": 0.81753} +{"mode": "train", "epoch": 120, "iter": 1300, "lr": 0.00996, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45078, "top5_acc": 0.71109, "loss_cls": 3.06774, "loss": 3.06774, "time": 0.82293} +{"mode": "train", "epoch": 120, "iter": 1400, "lr": 0.00994, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44547, "top5_acc": 0.69922, "loss_cls": 3.09478, "loss": 3.09478, "time": 0.82067} +{"mode": "train", "epoch": 120, "iter": 1500, "lr": 0.00992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44344, "top5_acc": 0.70469, "loss_cls": 3.11691, "loss": 3.11691, "time": 0.82006} +{"mode": "train", "epoch": 120, "iter": 1600, "lr": 0.0099, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45219, "top5_acc": 0.70938, "loss_cls": 3.06493, "loss": 3.06493, "time": 0.8153} +{"mode": "train", "epoch": 120, "iter": 1700, "lr": 0.00989, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44516, "top5_acc": 0.70969, "loss_cls": 3.08537, "loss": 3.08537, "time": 0.81584} +{"mode": "train", "epoch": 120, "iter": 1800, "lr": 0.00987, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44812, "top5_acc": 0.71016, "loss_cls": 3.0753, "loss": 3.0753, "time": 0.81461} +{"mode": "train", "epoch": 120, "iter": 1900, "lr": 0.00985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44016, "top5_acc": 0.69906, "loss_cls": 3.12546, "loss": 3.12546, "time": 0.81487} +{"mode": "train", "epoch": 120, "iter": 2000, "lr": 0.00984, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44094, "top5_acc": 0.70078, "loss_cls": 3.13616, "loss": 3.13616, "time": 0.81545} +{"mode": "train", "epoch": 120, "iter": 2100, "lr": 0.00982, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44781, "top5_acc": 0.71109, "loss_cls": 3.06965, "loss": 3.06965, "time": 0.81495} +{"mode": "train", "epoch": 120, "iter": 2200, "lr": 0.0098, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44625, "top5_acc": 0.69922, "loss_cls": 3.11607, "loss": 3.11607, "time": 0.82149} +{"mode": "train", "epoch": 120, "iter": 2300, "lr": 0.00979, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44453, "top5_acc": 0.70531, "loss_cls": 3.0927, "loss": 3.0927, "time": 0.81479} +{"mode": "train", "epoch": 120, "iter": 2400, "lr": 0.00977, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.43938, "top5_acc": 0.68938, "loss_cls": 3.17052, "loss": 3.17052, "time": 0.81358} +{"mode": "train", "epoch": 120, "iter": 2500, "lr": 0.00976, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44656, "top5_acc": 0.70266, "loss_cls": 3.11073, "loss": 3.11073, "time": 0.814} +{"mode": "train", "epoch": 120, "iter": 2600, "lr": 0.00974, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44078, "top5_acc": 0.70203, "loss_cls": 3.12905, "loss": 3.12905, "time": 0.81227} +{"mode": "train", "epoch": 120, "iter": 2700, "lr": 0.00972, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43625, "top5_acc": 0.69578, "loss_cls": 3.12608, "loss": 3.12608, "time": 0.81425} +{"mode": "train", "epoch": 120, "iter": 2800, "lr": 0.00971, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44734, "top5_acc": 0.69938, "loss_cls": 3.09598, "loss": 3.09598, "time": 0.81478} +{"mode": "train", "epoch": 120, "iter": 2900, "lr": 0.00969, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45828, "top5_acc": 0.71516, "loss_cls": 3.06068, "loss": 3.06068, "time": 0.81738} +{"mode": "train", "epoch": 120, "iter": 3000, "lr": 0.00967, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44641, "top5_acc": 0.70047, "loss_cls": 3.09466, "loss": 3.09466, "time": 0.82007} +{"mode": "train", "epoch": 120, "iter": 3100, "lr": 0.00966, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44516, "top5_acc": 0.69656, "loss_cls": 3.12413, "loss": 3.12413, "time": 0.81958} +{"mode": "train", "epoch": 120, "iter": 3200, "lr": 0.00964, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44469, "top5_acc": 0.70281, "loss_cls": 3.11798, "loss": 3.11798, "time": 0.81734} +{"mode": "train", "epoch": 120, "iter": 3300, "lr": 0.00962, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44844, "top5_acc": 0.70125, "loss_cls": 3.09214, "loss": 3.09214, "time": 0.81913} +{"mode": "train", "epoch": 120, "iter": 3400, "lr": 0.00961, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44672, "top5_acc": 0.69953, "loss_cls": 3.09706, "loss": 3.09706, "time": 0.82699} +{"mode": "train", "epoch": 120, "iter": 3500, "lr": 0.00959, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43578, "top5_acc": 0.69703, "loss_cls": 3.13479, "loss": 3.13479, "time": 0.82459} +{"mode": "train", "epoch": 120, "iter": 3600, "lr": 0.00957, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44938, "top5_acc": 0.70562, "loss_cls": 3.12099, "loss": 3.12099, "time": 0.82183} +{"mode": "train", "epoch": 120, "iter": 3700, "lr": 0.00956, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43438, "top5_acc": 0.68594, "loss_cls": 3.18505, "loss": 3.18505, "time": 0.8173} +{"mode": "val", "epoch": 120, "iter": 309, "lr": 0.00955, "top1_acc": 0.37477, "top5_acc": 0.63258, "mean_class_accuracy": 0.37454} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00953, "memory": 15990, "data_time": 1.27278, "top1_acc": 0.45812, "top5_acc": 0.71469, "loss_cls": 3.03918, "loss": 3.03918, "time": 2.26003} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00952, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45578, "top5_acc": 0.71156, "loss_cls": 3.0304, "loss": 3.0304, "time": 0.82537} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.0095, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45703, "top5_acc": 0.72047, "loss_cls": 3.01447, "loss": 3.01447, "time": 0.82867} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00948, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45938, "top5_acc": 0.71484, "loss_cls": 3.04007, "loss": 3.04007, "time": 0.81749} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00947, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46672, "top5_acc": 0.71422, "loss_cls": 3.0051, "loss": 3.0051, "time": 0.81341} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00945, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44875, "top5_acc": 0.70766, "loss_cls": 3.0669, "loss": 3.0669, "time": 0.81811} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.00943, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45688, "top5_acc": 0.71562, "loss_cls": 3.02437, "loss": 3.02437, "time": 0.81842} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00942, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46188, "top5_acc": 0.71453, "loss_cls": 3.02689, "loss": 3.02689, "time": 0.81748} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.0094, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45781, "top5_acc": 0.70828, "loss_cls": 3.06613, "loss": 3.06613, "time": 0.81723} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00939, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45297, "top5_acc": 0.71047, "loss_cls": 3.06055, "loss": 3.06055, "time": 0.81506} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00937, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46156, "top5_acc": 0.70984, "loss_cls": 3.03107, "loss": 3.03107, "time": 0.8245} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00935, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45547, "top5_acc": 0.70172, "loss_cls": 3.07707, "loss": 3.07707, "time": 0.81704} +{"mode": "train", "epoch": 121, "iter": 1300, "lr": 0.00934, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44984, "top5_acc": 0.71062, "loss_cls": 3.06609, "loss": 3.06609, "time": 0.82409} +{"mode": "train", "epoch": 121, "iter": 1400, "lr": 0.00932, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46078, "top5_acc": 0.72266, "loss_cls": 2.98714, "loss": 2.98714, "time": 0.81926} +{"mode": "train", "epoch": 121, "iter": 1500, "lr": 0.0093, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45453, "top5_acc": 0.70625, "loss_cls": 3.07279, "loss": 3.07279, "time": 0.81748} +{"mode": "train", "epoch": 121, "iter": 1600, "lr": 0.00929, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4525, "top5_acc": 0.70906, "loss_cls": 3.0605, "loss": 3.0605, "time": 0.81927} +{"mode": "train", "epoch": 121, "iter": 1700, "lr": 0.00927, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45562, "top5_acc": 0.70688, "loss_cls": 3.06507, "loss": 3.06507, "time": 0.81926} +{"mode": "train", "epoch": 121, "iter": 1800, "lr": 0.00926, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46625, "top5_acc": 0.70562, "loss_cls": 3.0387, "loss": 3.0387, "time": 0.81508} +{"mode": "train", "epoch": 121, "iter": 1900, "lr": 0.00924, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45219, "top5_acc": 0.70969, "loss_cls": 3.08013, "loss": 3.08013, "time": 0.8118} +{"mode": "train", "epoch": 121, "iter": 2000, "lr": 0.00922, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45219, "top5_acc": 0.70141, "loss_cls": 3.08541, "loss": 3.08541, "time": 0.81779} +{"mode": "train", "epoch": 121, "iter": 2100, "lr": 0.00921, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43578, "top5_acc": 0.69016, "loss_cls": 3.14405, "loss": 3.14405, "time": 0.81541} +{"mode": "train", "epoch": 121, "iter": 2200, "lr": 0.00919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4575, "top5_acc": 0.70844, "loss_cls": 3.06923, "loss": 3.06923, "time": 0.81961} +{"mode": "train", "epoch": 121, "iter": 2300, "lr": 0.00917, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43297, "top5_acc": 0.70188, "loss_cls": 3.13629, "loss": 3.13629, "time": 0.81432} +{"mode": "train", "epoch": 121, "iter": 2400, "lr": 0.00916, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44203, "top5_acc": 0.69422, "loss_cls": 3.14447, "loss": 3.14447, "time": 0.81848} +{"mode": "train", "epoch": 121, "iter": 2500, "lr": 0.00914, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.445, "top5_acc": 0.69641, "loss_cls": 3.12094, "loss": 3.12094, "time": 0.81824} +{"mode": "train", "epoch": 121, "iter": 2600, "lr": 0.00913, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45219, "top5_acc": 0.70359, "loss_cls": 3.09274, "loss": 3.09274, "time": 0.81415} +{"mode": "train", "epoch": 121, "iter": 2700, "lr": 0.00911, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44672, "top5_acc": 0.70453, "loss_cls": 3.0807, "loss": 3.0807, "time": 0.81879} +{"mode": "train", "epoch": 121, "iter": 2800, "lr": 0.00909, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43969, "top5_acc": 0.70281, "loss_cls": 3.12184, "loss": 3.12184, "time": 0.81542} +{"mode": "train", "epoch": 121, "iter": 2900, "lr": 0.00908, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44828, "top5_acc": 0.71031, "loss_cls": 3.05898, "loss": 3.05898, "time": 0.81218} +{"mode": "train", "epoch": 121, "iter": 3000, "lr": 0.00906, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44562, "top5_acc": 0.70344, "loss_cls": 3.07396, "loss": 3.07396, "time": 0.82281} +{"mode": "train", "epoch": 121, "iter": 3100, "lr": 0.00905, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43188, "top5_acc": 0.69234, "loss_cls": 3.14271, "loss": 3.14271, "time": 0.82627} +{"mode": "train", "epoch": 121, "iter": 3200, "lr": 0.00903, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45203, "top5_acc": 0.70891, "loss_cls": 3.11153, "loss": 3.11153, "time": 0.81902} +{"mode": "train", "epoch": 121, "iter": 3300, "lr": 0.00901, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44609, "top5_acc": 0.70625, "loss_cls": 3.07758, "loss": 3.07758, "time": 0.82189} +{"mode": "train", "epoch": 121, "iter": 3400, "lr": 0.009, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44406, "top5_acc": 0.71, "loss_cls": 3.09149, "loss": 3.09149, "time": 0.82431} +{"mode": "train", "epoch": 121, "iter": 3500, "lr": 0.00898, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44406, "top5_acc": 0.69938, "loss_cls": 3.10565, "loss": 3.10565, "time": 0.82001} +{"mode": "train", "epoch": 121, "iter": 3600, "lr": 0.00897, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45625, "top5_acc": 0.70531, "loss_cls": 3.06466, "loss": 3.06466, "time": 0.82358} +{"mode": "train", "epoch": 121, "iter": 3700, "lr": 0.00895, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43938, "top5_acc": 0.69781, "loss_cls": 3.12913, "loss": 3.12913, "time": 0.81991} +{"mode": "val", "epoch": 121, "iter": 309, "lr": 0.00894, "top1_acc": 0.38606, "top5_acc": 0.64342, "mean_class_accuracy": 0.38587} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00893, "memory": 15990, "data_time": 1.24866, "top1_acc": 0.48703, "top5_acc": 0.72766, "loss_cls": 2.92391, "loss": 2.92391, "time": 2.23199} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00891, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46547, "top5_acc": 0.71406, "loss_cls": 2.99075, "loss": 2.99075, "time": 0.81602} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.00889, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46703, "top5_acc": 0.72844, "loss_cls": 2.97635, "loss": 2.97635, "time": 0.81699} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00888, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46312, "top5_acc": 0.71906, "loss_cls": 2.99383, "loss": 2.99383, "time": 0.8147} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00886, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45328, "top5_acc": 0.71, "loss_cls": 3.0493, "loss": 3.0493, "time": 0.81392} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00885, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.47016, "top5_acc": 0.71766, "loss_cls": 2.97694, "loss": 2.97694, "time": 0.81825} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00883, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46188, "top5_acc": 0.71344, "loss_cls": 3.02678, "loss": 3.02678, "time": 0.81934} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00882, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45484, "top5_acc": 0.70938, "loss_cls": 3.05994, "loss": 3.05994, "time": 0.812} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.0088, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45656, "top5_acc": 0.71984, "loss_cls": 3.02482, "loss": 3.02482, "time": 0.81381} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00878, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45281, "top5_acc": 0.71453, "loss_cls": 3.05223, "loss": 3.05223, "time": 0.81366} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00877, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46281, "top5_acc": 0.72047, "loss_cls": 3.01175, "loss": 3.01175, "time": 0.82099} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.00875, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45812, "top5_acc": 0.71094, "loss_cls": 3.04085, "loss": 3.04085, "time": 0.81872} +{"mode": "train", "epoch": 122, "iter": 1300, "lr": 0.00874, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46484, "top5_acc": 0.71109, "loss_cls": 3.01065, "loss": 3.01065, "time": 0.81814} +{"mode": "train", "epoch": 122, "iter": 1400, "lr": 0.00872, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45016, "top5_acc": 0.70453, "loss_cls": 3.06325, "loss": 3.06325, "time": 0.82188} +{"mode": "train", "epoch": 122, "iter": 1500, "lr": 0.0087, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4575, "top5_acc": 0.70594, "loss_cls": 3.06511, "loss": 3.06511, "time": 0.81562} +{"mode": "train", "epoch": 122, "iter": 1600, "lr": 0.00869, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45812, "top5_acc": 0.71, "loss_cls": 3.04955, "loss": 3.04955, "time": 0.80871} +{"mode": "train", "epoch": 122, "iter": 1700, "lr": 0.00867, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45406, "top5_acc": 0.69984, "loss_cls": 3.07989, "loss": 3.07989, "time": 0.80975} +{"mode": "train", "epoch": 122, "iter": 1800, "lr": 0.00866, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45828, "top5_acc": 0.71953, "loss_cls": 3.01413, "loss": 3.01413, "time": 0.81467} +{"mode": "train", "epoch": 122, "iter": 1900, "lr": 0.00864, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45656, "top5_acc": 0.70438, "loss_cls": 3.07312, "loss": 3.07312, "time": 0.81672} +{"mode": "train", "epoch": 122, "iter": 2000, "lr": 0.00863, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45594, "top5_acc": 0.71234, "loss_cls": 3.02141, "loss": 3.02141, "time": 0.81669} +{"mode": "train", "epoch": 122, "iter": 2100, "lr": 0.00861, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44844, "top5_acc": 0.70359, "loss_cls": 3.09635, "loss": 3.09635, "time": 0.81453} +{"mode": "train", "epoch": 122, "iter": 2200, "lr": 0.00859, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44719, "top5_acc": 0.71312, "loss_cls": 3.05061, "loss": 3.05061, "time": 0.81382} +{"mode": "train", "epoch": 122, "iter": 2300, "lr": 0.00858, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44453, "top5_acc": 0.70844, "loss_cls": 3.06133, "loss": 3.06133, "time": 0.81832} +{"mode": "train", "epoch": 122, "iter": 2400, "lr": 0.00856, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44891, "top5_acc": 0.70125, "loss_cls": 3.09696, "loss": 3.09696, "time": 0.817} +{"mode": "train", "epoch": 122, "iter": 2500, "lr": 0.00855, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45188, "top5_acc": 0.71703, "loss_cls": 3.04096, "loss": 3.04096, "time": 0.81731} +{"mode": "train", "epoch": 122, "iter": 2600, "lr": 0.00853, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44594, "top5_acc": 0.70453, "loss_cls": 3.07501, "loss": 3.07501, "time": 0.8137} +{"mode": "train", "epoch": 122, "iter": 2700, "lr": 0.00852, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45609, "top5_acc": 0.71312, "loss_cls": 3.06883, "loss": 3.06883, "time": 0.81896} +{"mode": "train", "epoch": 122, "iter": 2800, "lr": 0.0085, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44875, "top5_acc": 0.70141, "loss_cls": 3.09693, "loss": 3.09693, "time": 0.81385} +{"mode": "train", "epoch": 122, "iter": 2900, "lr": 0.00849, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44203, "top5_acc": 0.70609, "loss_cls": 3.08766, "loss": 3.08766, "time": 0.8179} +{"mode": "train", "epoch": 122, "iter": 3000, "lr": 0.00847, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45281, "top5_acc": 0.71062, "loss_cls": 3.04628, "loss": 3.04628, "time": 0.81993} +{"mode": "train", "epoch": 122, "iter": 3100, "lr": 0.00845, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45047, "top5_acc": 0.70062, "loss_cls": 3.09359, "loss": 3.09359, "time": 0.8181} +{"mode": "train", "epoch": 122, "iter": 3200, "lr": 0.00844, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44672, "top5_acc": 0.71, "loss_cls": 3.08365, "loss": 3.08365, "time": 0.82091} +{"mode": "train", "epoch": 122, "iter": 3300, "lr": 0.00842, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45, "top5_acc": 0.70828, "loss_cls": 3.07145, "loss": 3.07145, "time": 0.82504} +{"mode": "train", "epoch": 122, "iter": 3400, "lr": 0.00841, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46406, "top5_acc": 0.71156, "loss_cls": 3.03165, "loss": 3.03165, "time": 0.82023} +{"mode": "train", "epoch": 122, "iter": 3500, "lr": 0.00839, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44859, "top5_acc": 0.70438, "loss_cls": 3.06869, "loss": 3.06869, "time": 0.83612} +{"mode": "train", "epoch": 122, "iter": 3600, "lr": 0.00838, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4575, "top5_acc": 0.70141, "loss_cls": 3.09877, "loss": 3.09877, "time": 0.81871} +{"mode": "train", "epoch": 122, "iter": 3700, "lr": 0.00836, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.445, "top5_acc": 0.70938, "loss_cls": 3.06023, "loss": 3.06023, "time": 0.82972} +{"mode": "val", "epoch": 122, "iter": 309, "lr": 0.00835, "top1_acc": 0.38728, "top5_acc": 0.63957, "mean_class_accuracy": 0.38696} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00834, "memory": 15990, "data_time": 1.2909, "top1_acc": 0.46328, "top5_acc": 0.71859, "loss_cls": 2.99598, "loss": 2.99598, "time": 2.27898} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00832, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48281, "top5_acc": 0.74172, "loss_cls": 2.90367, "loss": 2.90367, "time": 0.82257} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00831, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46984, "top5_acc": 0.73047, "loss_cls": 2.9538, "loss": 2.9538, "time": 0.81642} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00829, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46578, "top5_acc": 0.72531, "loss_cls": 2.94953, "loss": 2.94953, "time": 0.8126} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00828, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46922, "top5_acc": 0.71125, "loss_cls": 3.00958, "loss": 3.00958, "time": 0.81611} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00826, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46594, "top5_acc": 0.72688, "loss_cls": 2.95232, "loss": 2.95232, "time": 0.81537} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00825, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46484, "top5_acc": 0.71797, "loss_cls": 2.97265, "loss": 2.97265, "time": 0.81787} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.00823, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44922, "top5_acc": 0.71719, "loss_cls": 3.02783, "loss": 3.02783, "time": 0.81982} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00822, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46047, "top5_acc": 0.70859, "loss_cls": 3.02851, "loss": 3.02851, "time": 0.8147} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.0082, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46406, "top5_acc": 0.70641, "loss_cls": 3.03575, "loss": 3.03575, "time": 0.81832} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00818, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.45141, "top5_acc": 0.72391, "loss_cls": 3.0358, "loss": 3.0358, "time": 0.81595} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00817, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47844, "top5_acc": 0.72297, "loss_cls": 2.94744, "loss": 2.94744, "time": 0.81441} +{"mode": "train", "epoch": 123, "iter": 1300, "lr": 0.00815, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46266, "top5_acc": 0.71766, "loss_cls": 3.03513, "loss": 3.03513, "time": 0.82743} +{"mode": "train", "epoch": 123, "iter": 1400, "lr": 0.00814, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46062, "top5_acc": 0.71375, "loss_cls": 3.02995, "loss": 3.02995, "time": 0.81717} +{"mode": "train", "epoch": 123, "iter": 1500, "lr": 0.00812, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45672, "top5_acc": 0.71094, "loss_cls": 3.05606, "loss": 3.05606, "time": 0.81076} +{"mode": "train", "epoch": 123, "iter": 1600, "lr": 0.00811, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46172, "top5_acc": 0.7225, "loss_cls": 2.9634, "loss": 2.9634, "time": 0.81195} +{"mode": "train", "epoch": 123, "iter": 1700, "lr": 0.00809, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46203, "top5_acc": 0.72531, "loss_cls": 2.96295, "loss": 2.96295, "time": 0.81629} +{"mode": "train", "epoch": 123, "iter": 1800, "lr": 0.00808, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45891, "top5_acc": 0.71328, "loss_cls": 3.00684, "loss": 3.00684, "time": 0.82071} +{"mode": "train", "epoch": 123, "iter": 1900, "lr": 0.00806, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46109, "top5_acc": 0.71625, "loss_cls": 3.01171, "loss": 3.01171, "time": 0.81844} +{"mode": "train", "epoch": 123, "iter": 2000, "lr": 0.00805, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46688, "top5_acc": 0.71547, "loss_cls": 3.01571, "loss": 3.01571, "time": 0.81994} +{"mode": "train", "epoch": 123, "iter": 2100, "lr": 0.00803, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46219, "top5_acc": 0.70516, "loss_cls": 3.05012, "loss": 3.05012, "time": 0.81744} +{"mode": "train", "epoch": 123, "iter": 2200, "lr": 0.00802, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44828, "top5_acc": 0.7025, "loss_cls": 3.09593, "loss": 3.09593, "time": 0.81158} +{"mode": "train", "epoch": 123, "iter": 2300, "lr": 0.008, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45703, "top5_acc": 0.72312, "loss_cls": 3.02531, "loss": 3.02531, "time": 0.82377} +{"mode": "train", "epoch": 123, "iter": 2400, "lr": 0.00799, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45484, "top5_acc": 0.72094, "loss_cls": 3.03247, "loss": 3.03247, "time": 0.82374} +{"mode": "train", "epoch": 123, "iter": 2500, "lr": 0.00797, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46359, "top5_acc": 0.71438, "loss_cls": 3.01338, "loss": 3.01338, "time": 0.81611} +{"mode": "train", "epoch": 123, "iter": 2600, "lr": 0.00796, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46594, "top5_acc": 0.72172, "loss_cls": 3.02106, "loss": 3.02106, "time": 0.81542} +{"mode": "train", "epoch": 123, "iter": 2700, "lr": 0.00794, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44906, "top5_acc": 0.70859, "loss_cls": 3.0638, "loss": 3.0638, "time": 0.82092} +{"mode": "train", "epoch": 123, "iter": 2800, "lr": 0.00793, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44406, "top5_acc": 0.70609, "loss_cls": 3.08397, "loss": 3.08397, "time": 0.8133} +{"mode": "train", "epoch": 123, "iter": 2900, "lr": 0.00791, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45312, "top5_acc": 0.71453, "loss_cls": 3.03043, "loss": 3.03043, "time": 0.81818} +{"mode": "train", "epoch": 123, "iter": 3000, "lr": 0.0079, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46062, "top5_acc": 0.71359, "loss_cls": 3.03987, "loss": 3.03987, "time": 0.82227} +{"mode": "train", "epoch": 123, "iter": 3100, "lr": 0.00788, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46172, "top5_acc": 0.71578, "loss_cls": 2.9769, "loss": 2.9769, "time": 0.81879} +{"mode": "train", "epoch": 123, "iter": 3200, "lr": 0.00787, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46219, "top5_acc": 0.71609, "loss_cls": 3.02491, "loss": 3.02491, "time": 0.81809} +{"mode": "train", "epoch": 123, "iter": 3300, "lr": 0.00785, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46141, "top5_acc": 0.71781, "loss_cls": 3.01087, "loss": 3.01087, "time": 0.82334} +{"mode": "train", "epoch": 123, "iter": 3400, "lr": 0.00784, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45812, "top5_acc": 0.70672, "loss_cls": 3.0478, "loss": 3.0478, "time": 0.81945} +{"mode": "train", "epoch": 123, "iter": 3500, "lr": 0.00782, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45531, "top5_acc": 0.71219, "loss_cls": 3.05399, "loss": 3.05399, "time": 0.81747} +{"mode": "train", "epoch": 123, "iter": 3600, "lr": 0.00781, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45328, "top5_acc": 0.70594, "loss_cls": 3.0581, "loss": 3.0581, "time": 0.82201} +{"mode": "train", "epoch": 123, "iter": 3700, "lr": 0.00779, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45219, "top5_acc": 0.71016, "loss_cls": 3.06748, "loss": 3.06748, "time": 0.82493} +{"mode": "val", "epoch": 123, "iter": 309, "lr": 0.00778, "top1_acc": 0.39204, "top5_acc": 0.64929, "mean_class_accuracy": 0.3917} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00777, "memory": 15990, "data_time": 1.25611, "top1_acc": 0.47766, "top5_acc": 0.73969, "loss_cls": 2.89922, "loss": 2.89922, "time": 2.23929} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00775, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.475, "top5_acc": 0.72594, "loss_cls": 2.92913, "loss": 2.92913, "time": 0.81647} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00774, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47281, "top5_acc": 0.72844, "loss_cls": 2.92449, "loss": 2.92449, "time": 0.82221} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.00772, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46969, "top5_acc": 0.73109, "loss_cls": 2.93189, "loss": 2.93189, "time": 0.81901} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00771, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46469, "top5_acc": 0.72172, "loss_cls": 2.99507, "loss": 2.99507, "time": 0.81866} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00769, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46672, "top5_acc": 0.72719, "loss_cls": 2.9494, "loss": 2.9494, "time": 0.82197} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00768, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47172, "top5_acc": 0.72609, "loss_cls": 2.9486, "loss": 2.9486, "time": 0.81962} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00766, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47328, "top5_acc": 0.71719, "loss_cls": 2.97312, "loss": 2.97312, "time": 0.81614} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00765, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46453, "top5_acc": 0.73125, "loss_cls": 2.97153, "loss": 2.97153, "time": 0.81521} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00763, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.47, "top5_acc": 0.72203, "loss_cls": 2.96271, "loss": 2.96271, "time": 0.81705} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00762, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.46172, "top5_acc": 0.71672, "loss_cls": 3.02264, "loss": 3.02264, "time": 0.82202} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.0076, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46125, "top5_acc": 0.71859, "loss_cls": 3.00712, "loss": 3.00712, "time": 0.81349} +{"mode": "train", "epoch": 124, "iter": 1300, "lr": 0.00759, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47625, "top5_acc": 0.7225, "loss_cls": 2.97134, "loss": 2.97134, "time": 0.82095} +{"mode": "train", "epoch": 124, "iter": 1400, "lr": 0.00758, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47188, "top5_acc": 0.72562, "loss_cls": 2.9738, "loss": 2.9738, "time": 0.82209} +{"mode": "train", "epoch": 124, "iter": 1500, "lr": 0.00756, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46062, "top5_acc": 0.7175, "loss_cls": 3.01326, "loss": 3.01326, "time": 0.81605} +{"mode": "train", "epoch": 124, "iter": 1600, "lr": 0.00755, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46938, "top5_acc": 0.72641, "loss_cls": 2.98521, "loss": 2.98521, "time": 0.81582} +{"mode": "train", "epoch": 124, "iter": 1700, "lr": 0.00753, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46234, "top5_acc": 0.72328, "loss_cls": 2.98533, "loss": 2.98533, "time": 0.82039} +{"mode": "train", "epoch": 124, "iter": 1800, "lr": 0.00752, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.46953, "top5_acc": 0.72312, "loss_cls": 2.98061, "loss": 2.98061, "time": 0.81333} +{"mode": "train", "epoch": 124, "iter": 1900, "lr": 0.0075, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46422, "top5_acc": 0.71562, "loss_cls": 3.0254, "loss": 3.0254, "time": 0.8214} +{"mode": "train", "epoch": 124, "iter": 2000, "lr": 0.00749, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45312, "top5_acc": 0.70531, "loss_cls": 3.07568, "loss": 3.07568, "time": 0.81548} +{"mode": "train", "epoch": 124, "iter": 2100, "lr": 0.00747, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47266, "top5_acc": 0.72469, "loss_cls": 2.95852, "loss": 2.95852, "time": 0.81836} +{"mode": "train", "epoch": 124, "iter": 2200, "lr": 0.00746, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.46906, "top5_acc": 0.72297, "loss_cls": 2.97718, "loss": 2.97718, "time": 0.8122} +{"mode": "train", "epoch": 124, "iter": 2300, "lr": 0.00744, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.47812, "top5_acc": 0.72984, "loss_cls": 2.93252, "loss": 2.93252, "time": 0.82335} +{"mode": "train", "epoch": 124, "iter": 2400, "lr": 0.00743, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45984, "top5_acc": 0.71609, "loss_cls": 3.02047, "loss": 3.02047, "time": 0.82682} +{"mode": "train", "epoch": 124, "iter": 2500, "lr": 0.00741, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46188, "top5_acc": 0.71344, "loss_cls": 3.02517, "loss": 3.02517, "time": 0.82489} +{"mode": "train", "epoch": 124, "iter": 2600, "lr": 0.0074, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46734, "top5_acc": 0.72172, "loss_cls": 2.95891, "loss": 2.95891, "time": 0.81927} +{"mode": "train", "epoch": 124, "iter": 2700, "lr": 0.00738, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.47125, "top5_acc": 0.72609, "loss_cls": 2.93949, "loss": 2.93949, "time": 0.81608} +{"mode": "train", "epoch": 124, "iter": 2800, "lr": 0.00737, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46188, "top5_acc": 0.71562, "loss_cls": 2.99824, "loss": 2.99824, "time": 0.82127} +{"mode": "train", "epoch": 124, "iter": 2900, "lr": 0.00735, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45625, "top5_acc": 0.71078, "loss_cls": 3.0519, "loss": 3.0519, "time": 0.81289} +{"mode": "train", "epoch": 124, "iter": 3000, "lr": 0.00734, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.455, "top5_acc": 0.71766, "loss_cls": 3.01155, "loss": 3.01155, "time": 0.81599} +{"mode": "train", "epoch": 124, "iter": 3100, "lr": 0.00733, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46031, "top5_acc": 0.70844, "loss_cls": 3.05179, "loss": 3.05179, "time": 0.82658} +{"mode": "train", "epoch": 124, "iter": 3200, "lr": 0.00731, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45547, "top5_acc": 0.71344, "loss_cls": 3.03278, "loss": 3.03278, "time": 0.82187} +{"mode": "train", "epoch": 124, "iter": 3300, "lr": 0.0073, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45844, "top5_acc": 0.71547, "loss_cls": 3.02397, "loss": 3.02397, "time": 0.82098} +{"mode": "train", "epoch": 124, "iter": 3400, "lr": 0.00728, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46828, "top5_acc": 0.71422, "loss_cls": 3.01089, "loss": 3.01089, "time": 0.81755} +{"mode": "train", "epoch": 124, "iter": 3500, "lr": 0.00727, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46344, "top5_acc": 0.72078, "loss_cls": 3.00974, "loss": 3.00974, "time": 0.81985} +{"mode": "train", "epoch": 124, "iter": 3600, "lr": 0.00725, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45516, "top5_acc": 0.72297, "loss_cls": 2.99478, "loss": 2.99478, "time": 0.82597} +{"mode": "train", "epoch": 124, "iter": 3700, "lr": 0.00724, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45516, "top5_acc": 0.71484, "loss_cls": 3.0264, "loss": 3.0264, "time": 0.82013} +{"mode": "val", "epoch": 124, "iter": 309, "lr": 0.00723, "top1_acc": 0.39594, "top5_acc": 0.65269, "mean_class_accuracy": 0.3957} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.00722, "memory": 15990, "data_time": 1.27484, "top1_acc": 0.48, "top5_acc": 0.73297, "loss_cls": 2.91204, "loss": 2.91204, "time": 2.2532} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.0072, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47938, "top5_acc": 0.73828, "loss_cls": 2.89078, "loss": 2.89078, "time": 0.81782} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00719, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47547, "top5_acc": 0.73562, "loss_cls": 2.91993, "loss": 2.91993, "time": 0.81728} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00717, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48125, "top5_acc": 0.735, "loss_cls": 2.92309, "loss": 2.92309, "time": 0.81609} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00716, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48359, "top5_acc": 0.73297, "loss_cls": 2.90029, "loss": 2.90029, "time": 0.81703} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00715, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47578, "top5_acc": 0.73188, "loss_cls": 2.92255, "loss": 2.92255, "time": 0.81644} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00713, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48688, "top5_acc": 0.72656, "loss_cls": 2.92411, "loss": 2.92411, "time": 0.82157} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00712, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46484, "top5_acc": 0.72516, "loss_cls": 2.95883, "loss": 2.95883, "time": 0.81759} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.0071, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48219, "top5_acc": 0.73234, "loss_cls": 2.88357, "loss": 2.88357, "time": 0.81214} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.00709, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47094, "top5_acc": 0.72641, "loss_cls": 2.94834, "loss": 2.94834, "time": 0.82004} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00707, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.47094, "top5_acc": 0.72906, "loss_cls": 2.93071, "loss": 2.93071, "time": 0.81924} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00706, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46531, "top5_acc": 0.71875, "loss_cls": 3.00167, "loss": 3.00167, "time": 0.82193} +{"mode": "train", "epoch": 125, "iter": 1300, "lr": 0.00704, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.475, "top5_acc": 0.72672, "loss_cls": 2.9428, "loss": 2.9428, "time": 0.8137} +{"mode": "train", "epoch": 125, "iter": 1400, "lr": 0.00703, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48938, "top5_acc": 0.72969, "loss_cls": 2.92289, "loss": 2.92289, "time": 0.82055} +{"mode": "train", "epoch": 125, "iter": 1500, "lr": 0.00702, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47109, "top5_acc": 0.73203, "loss_cls": 2.93331, "loss": 2.93331, "time": 0.81387} +{"mode": "train", "epoch": 125, "iter": 1600, "lr": 0.007, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47469, "top5_acc": 0.72578, "loss_cls": 2.97665, "loss": 2.97665, "time": 0.81942} +{"mode": "train", "epoch": 125, "iter": 1700, "lr": 0.00699, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47812, "top5_acc": 0.73953, "loss_cls": 2.90059, "loss": 2.90059, "time": 0.81538} +{"mode": "train", "epoch": 125, "iter": 1800, "lr": 0.00697, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46156, "top5_acc": 0.72516, "loss_cls": 3.01688, "loss": 3.01688, "time": 0.81881} +{"mode": "train", "epoch": 125, "iter": 1900, "lr": 0.00696, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.47, "top5_acc": 0.72281, "loss_cls": 2.95633, "loss": 2.95633, "time": 0.8138} +{"mode": "train", "epoch": 125, "iter": 2000, "lr": 0.00694, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4675, "top5_acc": 0.72109, "loss_cls": 2.96739, "loss": 2.96739, "time": 0.81261} +{"mode": "train", "epoch": 125, "iter": 2100, "lr": 0.00693, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47453, "top5_acc": 0.72266, "loss_cls": 2.94158, "loss": 2.94158, "time": 0.81915} +{"mode": "train", "epoch": 125, "iter": 2200, "lr": 0.00692, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47422, "top5_acc": 0.72562, "loss_cls": 2.92927, "loss": 2.92927, "time": 0.81936} +{"mode": "train", "epoch": 125, "iter": 2300, "lr": 0.0069, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46734, "top5_acc": 0.72219, "loss_cls": 2.99554, "loss": 2.99554, "time": 0.82244} +{"mode": "train", "epoch": 125, "iter": 2400, "lr": 0.00689, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46031, "top5_acc": 0.71094, "loss_cls": 3.01616, "loss": 3.01616, "time": 0.81706} +{"mode": "train", "epoch": 125, "iter": 2500, "lr": 0.00687, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47219, "top5_acc": 0.72469, "loss_cls": 2.96493, "loss": 2.96493, "time": 0.81772} +{"mode": "train", "epoch": 125, "iter": 2600, "lr": 0.00686, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46578, "top5_acc": 0.72422, "loss_cls": 2.97275, "loss": 2.97275, "time": 0.81625} +{"mode": "train", "epoch": 125, "iter": 2700, "lr": 0.00685, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.47188, "top5_acc": 0.72578, "loss_cls": 2.94182, "loss": 2.94182, "time": 0.81515} +{"mode": "train", "epoch": 125, "iter": 2800, "lr": 0.00683, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.45875, "top5_acc": 0.71875, "loss_cls": 3.00389, "loss": 3.00389, "time": 0.81389} +{"mode": "train", "epoch": 125, "iter": 2900, "lr": 0.00682, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46844, "top5_acc": 0.71609, "loss_cls": 2.98185, "loss": 2.98185, "time": 0.81851} +{"mode": "train", "epoch": 125, "iter": 3000, "lr": 0.0068, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47375, "top5_acc": 0.72266, "loss_cls": 2.97445, "loss": 2.97445, "time": 0.81935} +{"mode": "train", "epoch": 125, "iter": 3100, "lr": 0.00679, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47078, "top5_acc": 0.72203, "loss_cls": 2.96653, "loss": 2.96653, "time": 0.82224} +{"mode": "train", "epoch": 125, "iter": 3200, "lr": 0.00678, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46531, "top5_acc": 0.71375, "loss_cls": 2.96618, "loss": 2.96618, "time": 0.82408} +{"mode": "train", "epoch": 125, "iter": 3300, "lr": 0.00676, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46266, "top5_acc": 0.72156, "loss_cls": 2.9796, "loss": 2.9796, "time": 0.82149} +{"mode": "train", "epoch": 125, "iter": 3400, "lr": 0.00675, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45688, "top5_acc": 0.72234, "loss_cls": 3.02513, "loss": 3.02513, "time": 0.82004} +{"mode": "train", "epoch": 125, "iter": 3500, "lr": 0.00673, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46422, "top5_acc": 0.71344, "loss_cls": 2.96488, "loss": 2.96488, "time": 0.8226} +{"mode": "train", "epoch": 125, "iter": 3600, "lr": 0.00672, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46188, "top5_acc": 0.72375, "loss_cls": 3.00188, "loss": 3.00188, "time": 0.82067} +{"mode": "train", "epoch": 125, "iter": 3700, "lr": 0.00671, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46219, "top5_acc": 0.71547, "loss_cls": 3.01971, "loss": 3.01971, "time": 0.835} +{"mode": "val", "epoch": 125, "iter": 309, "lr": 0.0067, "top1_acc": 0.40364, "top5_acc": 0.65719, "mean_class_accuracy": 0.40335} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00668, "memory": 15990, "data_time": 1.30088, "top1_acc": 0.49875, "top5_acc": 0.74703, "loss_cls": 2.83352, "loss": 2.83352, "time": 2.28256} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00667, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48, "top5_acc": 0.74391, "loss_cls": 2.86301, "loss": 2.86301, "time": 0.83134} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00666, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.48281, "top5_acc": 0.73188, "loss_cls": 2.87905, "loss": 2.87905, "time": 0.81474} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00664, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48203, "top5_acc": 0.73656, "loss_cls": 2.85236, "loss": 2.85236, "time": 0.81338} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00663, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.48312, "top5_acc": 0.74078, "loss_cls": 2.85463, "loss": 2.85463, "time": 0.81293} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00662, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47438, "top5_acc": 0.73062, "loss_cls": 2.94175, "loss": 2.94175, "time": 0.81594} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0066, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47547, "top5_acc": 0.73156, "loss_cls": 2.93921, "loss": 2.93921, "time": 0.82125} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00659, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.48469, "top5_acc": 0.72641, "loss_cls": 2.90855, "loss": 2.90855, "time": 0.81717} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00657, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47469, "top5_acc": 0.72609, "loss_cls": 2.93553, "loss": 2.93553, "time": 0.82211} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00656, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46969, "top5_acc": 0.72047, "loss_cls": 2.96493, "loss": 2.96493, "time": 0.8244} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00655, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47297, "top5_acc": 0.73219, "loss_cls": 2.93272, "loss": 2.93272, "time": 0.8201} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00653, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47484, "top5_acc": 0.73828, "loss_cls": 2.88695, "loss": 2.88695, "time": 0.82205} +{"mode": "train", "epoch": 126, "iter": 1300, "lr": 0.00652, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47969, "top5_acc": 0.73016, "loss_cls": 2.92549, "loss": 2.92549, "time": 0.81801} +{"mode": "train", "epoch": 126, "iter": 1400, "lr": 0.0065, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46875, "top5_acc": 0.72484, "loss_cls": 2.95221, "loss": 2.95221, "time": 0.82497} +{"mode": "train", "epoch": 126, "iter": 1500, "lr": 0.00649, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47938, "top5_acc": 0.73203, "loss_cls": 2.90021, "loss": 2.90021, "time": 0.81433} +{"mode": "train", "epoch": 126, "iter": 1600, "lr": 0.00648, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48656, "top5_acc": 0.72547, "loss_cls": 2.90921, "loss": 2.90921, "time": 0.81977} +{"mode": "train", "epoch": 126, "iter": 1700, "lr": 0.00646, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47047, "top5_acc": 0.72219, "loss_cls": 2.93941, "loss": 2.93941, "time": 0.8166} +{"mode": "train", "epoch": 126, "iter": 1800, "lr": 0.00645, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.48375, "top5_acc": 0.73969, "loss_cls": 2.91243, "loss": 2.91243, "time": 0.81204} +{"mode": "train", "epoch": 126, "iter": 1900, "lr": 0.00644, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47891, "top5_acc": 0.73047, "loss_cls": 2.92386, "loss": 2.92386, "time": 0.82056} +{"mode": "train", "epoch": 126, "iter": 2000, "lr": 0.00642, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47734, "top5_acc": 0.72922, "loss_cls": 2.94339, "loss": 2.94339, "time": 0.81786} +{"mode": "train", "epoch": 126, "iter": 2100, "lr": 0.00641, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47031, "top5_acc": 0.72562, "loss_cls": 2.94775, "loss": 2.94775, "time": 0.81661} +{"mode": "train", "epoch": 126, "iter": 2200, "lr": 0.00639, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48422, "top5_acc": 0.73547, "loss_cls": 2.88459, "loss": 2.88459, "time": 0.8153} +{"mode": "train", "epoch": 126, "iter": 2300, "lr": 0.00638, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46844, "top5_acc": 0.72594, "loss_cls": 2.97428, "loss": 2.97428, "time": 0.81476} +{"mode": "train", "epoch": 126, "iter": 2400, "lr": 0.00637, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47938, "top5_acc": 0.73156, "loss_cls": 2.92205, "loss": 2.92205, "time": 0.81713} +{"mode": "train", "epoch": 126, "iter": 2500, "lr": 0.00635, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.475, "top5_acc": 0.72859, "loss_cls": 2.91893, "loss": 2.91893, "time": 0.81737} +{"mode": "train", "epoch": 126, "iter": 2600, "lr": 0.00634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47953, "top5_acc": 0.73641, "loss_cls": 2.8873, "loss": 2.8873, "time": 0.81387} +{"mode": "train", "epoch": 126, "iter": 2700, "lr": 0.00633, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47781, "top5_acc": 0.72984, "loss_cls": 2.89501, "loss": 2.89501, "time": 0.81548} +{"mode": "train", "epoch": 126, "iter": 2800, "lr": 0.00631, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47516, "top5_acc": 0.73297, "loss_cls": 2.92873, "loss": 2.92873, "time": 0.81972} +{"mode": "train", "epoch": 126, "iter": 2900, "lr": 0.0063, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46375, "top5_acc": 0.72094, "loss_cls": 2.9866, "loss": 2.9866, "time": 0.81466} +{"mode": "train", "epoch": 126, "iter": 3000, "lr": 0.00629, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4825, "top5_acc": 0.72359, "loss_cls": 2.91688, "loss": 2.91688, "time": 0.81552} +{"mode": "train", "epoch": 126, "iter": 3100, "lr": 0.00627, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45484, "top5_acc": 0.71812, "loss_cls": 3.05622, "loss": 3.05622, "time": 0.81958} +{"mode": "train", "epoch": 126, "iter": 3200, "lr": 0.00626, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46875, "top5_acc": 0.71859, "loss_cls": 2.96723, "loss": 2.96723, "time": 0.81728} +{"mode": "train", "epoch": 126, "iter": 3300, "lr": 0.00625, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46734, "top5_acc": 0.7275, "loss_cls": 2.96634, "loss": 2.96634, "time": 0.82911} +{"mode": "train", "epoch": 126, "iter": 3400, "lr": 0.00623, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47219, "top5_acc": 0.73047, "loss_cls": 2.93143, "loss": 2.93143, "time": 0.81871} +{"mode": "train", "epoch": 126, "iter": 3500, "lr": 0.00622, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47172, "top5_acc": 0.72531, "loss_cls": 2.94487, "loss": 2.94487, "time": 0.82237} +{"mode": "train", "epoch": 126, "iter": 3600, "lr": 0.0062, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.46828, "top5_acc": 0.73156, "loss_cls": 2.96274, "loss": 2.96274, "time": 0.81671} +{"mode": "train", "epoch": 126, "iter": 3700, "lr": 0.00619, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4725, "top5_acc": 0.73562, "loss_cls": 2.91428, "loss": 2.91428, "time": 0.82392} +{"mode": "val", "epoch": 126, "iter": 309, "lr": 0.00618, "top1_acc": 0.40465, "top5_acc": 0.6613, "mean_class_accuracy": 0.40443} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00617, "memory": 15990, "data_time": 1.29345, "top1_acc": 0.4925, "top5_acc": 0.74188, "loss_cls": 2.8281, "loss": 2.8281, "time": 2.28283} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00616, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.48875, "top5_acc": 0.74281, "loss_cls": 2.85203, "loss": 2.85203, "time": 0.82627} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00614, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48391, "top5_acc": 0.74359, "loss_cls": 2.86137, "loss": 2.86137, "time": 0.82284} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00613, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47719, "top5_acc": 0.73516, "loss_cls": 2.89891, "loss": 2.89891, "time": 0.82115} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.00612, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48641, "top5_acc": 0.73906, "loss_cls": 2.8552, "loss": 2.8552, "time": 0.81927} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.0061, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48078, "top5_acc": 0.73859, "loss_cls": 2.85618, "loss": 2.85618, "time": 0.81698} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00609, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48469, "top5_acc": 0.72938, "loss_cls": 2.89101, "loss": 2.89101, "time": 0.816} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00608, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48797, "top5_acc": 0.73578, "loss_cls": 2.85653, "loss": 2.85653, "time": 0.81983} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00606, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48266, "top5_acc": 0.73234, "loss_cls": 2.92615, "loss": 2.92615, "time": 0.81703} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00605, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49938, "top5_acc": 0.74578, "loss_cls": 2.81316, "loss": 2.81316, "time": 0.82537} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00604, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49297, "top5_acc": 0.74312, "loss_cls": 2.84653, "loss": 2.84653, "time": 0.81688} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00602, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49109, "top5_acc": 0.73578, "loss_cls": 2.87221, "loss": 2.87221, "time": 0.82036} +{"mode": "train", "epoch": 127, "iter": 1300, "lr": 0.00601, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48312, "top5_acc": 0.73594, "loss_cls": 2.88788, "loss": 2.88788, "time": 0.81661} +{"mode": "train", "epoch": 127, "iter": 1400, "lr": 0.006, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49109, "top5_acc": 0.73781, "loss_cls": 2.87631, "loss": 2.87631, "time": 0.82306} +{"mode": "train", "epoch": 127, "iter": 1500, "lr": 0.00598, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48719, "top5_acc": 0.73391, "loss_cls": 2.88071, "loss": 2.88071, "time": 0.8128} +{"mode": "train", "epoch": 127, "iter": 1600, "lr": 0.00597, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48125, "top5_acc": 0.73688, "loss_cls": 2.87001, "loss": 2.87001, "time": 0.81848} +{"mode": "train", "epoch": 127, "iter": 1700, "lr": 0.00596, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48031, "top5_acc": 0.73688, "loss_cls": 2.88861, "loss": 2.88861, "time": 0.81884} +{"mode": "train", "epoch": 127, "iter": 1800, "lr": 0.00594, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47453, "top5_acc": 0.73547, "loss_cls": 2.90626, "loss": 2.90626, "time": 0.82486} +{"mode": "train", "epoch": 127, "iter": 1900, "lr": 0.00593, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48688, "top5_acc": 0.73812, "loss_cls": 2.87409, "loss": 2.87409, "time": 0.81892} +{"mode": "train", "epoch": 127, "iter": 2000, "lr": 0.00592, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47562, "top5_acc": 0.73031, "loss_cls": 2.93279, "loss": 2.93279, "time": 0.81391} +{"mode": "train", "epoch": 127, "iter": 2100, "lr": 0.00591, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47062, "top5_acc": 0.73672, "loss_cls": 2.92096, "loss": 2.92096, "time": 0.81934} +{"mode": "train", "epoch": 127, "iter": 2200, "lr": 0.00589, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47359, "top5_acc": 0.73609, "loss_cls": 2.92622, "loss": 2.92622, "time": 0.8121} +{"mode": "train", "epoch": 127, "iter": 2300, "lr": 0.00588, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48031, "top5_acc": 0.73375, "loss_cls": 2.88664, "loss": 2.88664, "time": 0.81484} +{"mode": "train", "epoch": 127, "iter": 2400, "lr": 0.00587, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47875, "top5_acc": 0.73078, "loss_cls": 2.90441, "loss": 2.90441, "time": 0.81647} +{"mode": "train", "epoch": 127, "iter": 2500, "lr": 0.00585, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49109, "top5_acc": 0.74359, "loss_cls": 2.84892, "loss": 2.84892, "time": 0.81807} +{"mode": "train", "epoch": 127, "iter": 2600, "lr": 0.00584, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47391, "top5_acc": 0.73344, "loss_cls": 2.91429, "loss": 2.91429, "time": 0.81778} +{"mode": "train", "epoch": 127, "iter": 2700, "lr": 0.00583, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48, "top5_acc": 0.73016, "loss_cls": 2.93742, "loss": 2.93742, "time": 0.81691} +{"mode": "train", "epoch": 127, "iter": 2800, "lr": 0.00581, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46359, "top5_acc": 0.72297, "loss_cls": 2.96927, "loss": 2.96927, "time": 0.82371} +{"mode": "train", "epoch": 127, "iter": 2900, "lr": 0.0058, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48, "top5_acc": 0.73375, "loss_cls": 2.91487, "loss": 2.91487, "time": 0.81677} +{"mode": "train", "epoch": 127, "iter": 3000, "lr": 0.00579, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47891, "top5_acc": 0.73281, "loss_cls": 2.90978, "loss": 2.90978, "time": 0.81711} +{"mode": "train", "epoch": 127, "iter": 3100, "lr": 0.00577, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48266, "top5_acc": 0.72766, "loss_cls": 2.91194, "loss": 2.91194, "time": 0.8226} +{"mode": "train", "epoch": 127, "iter": 3200, "lr": 0.00576, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47469, "top5_acc": 0.73203, "loss_cls": 2.89935, "loss": 2.89935, "time": 0.81801} +{"mode": "train", "epoch": 127, "iter": 3300, "lr": 0.00575, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46625, "top5_acc": 0.72641, "loss_cls": 2.95027, "loss": 2.95027, "time": 0.82615} +{"mode": "train", "epoch": 127, "iter": 3400, "lr": 0.00573, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48578, "top5_acc": 0.73, "loss_cls": 2.90657, "loss": 2.90657, "time": 0.81453} +{"mode": "train", "epoch": 127, "iter": 3500, "lr": 0.00572, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47984, "top5_acc": 0.72781, "loss_cls": 2.91256, "loss": 2.91256, "time": 0.82189} +{"mode": "train", "epoch": 127, "iter": 3600, "lr": 0.00571, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47359, "top5_acc": 0.72922, "loss_cls": 2.9165, "loss": 2.9165, "time": 0.82042} +{"mode": "train", "epoch": 127, "iter": 3700, "lr": 0.0057, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48484, "top5_acc": 0.73438, "loss_cls": 2.89716, "loss": 2.89716, "time": 0.82115} +{"mode": "val", "epoch": 127, "iter": 309, "lr": 0.00569, "top1_acc": 0.40789, "top5_acc": 0.65958, "mean_class_accuracy": 0.40767} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00568, "memory": 15990, "data_time": 1.34659, "top1_acc": 0.50797, "top5_acc": 0.75672, "loss_cls": 2.7538, "loss": 2.7538, "time": 2.32909} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.00566, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49328, "top5_acc": 0.74797, "loss_cls": 2.81496, "loss": 2.81496, "time": 0.82466} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00565, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49828, "top5_acc": 0.75297, "loss_cls": 2.78642, "loss": 2.78642, "time": 0.82088} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00564, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49328, "top5_acc": 0.74828, "loss_cls": 2.83113, "loss": 2.83113, "time": 0.81705} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00563, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48188, "top5_acc": 0.745, "loss_cls": 2.86959, "loss": 2.86959, "time": 0.82294} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00561, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49188, "top5_acc": 0.74359, "loss_cls": 2.82538, "loss": 2.82538, "time": 0.81929} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.0056, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48844, "top5_acc": 0.74312, "loss_cls": 2.85266, "loss": 2.85266, "time": 0.81759} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00559, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49141, "top5_acc": 0.74062, "loss_cls": 2.84301, "loss": 2.84301, "time": 0.81476} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00557, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.49312, "top5_acc": 0.74078, "loss_cls": 2.83013, "loss": 2.83013, "time": 0.81776} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00556, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48812, "top5_acc": 0.73766, "loss_cls": 2.86008, "loss": 2.86008, "time": 0.82145} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00555, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49125, "top5_acc": 0.74594, "loss_cls": 2.8315, "loss": 2.8315, "time": 0.82037} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00554, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48359, "top5_acc": 0.73844, "loss_cls": 2.88625, "loss": 2.88625, "time": 0.81926} +{"mode": "train", "epoch": 128, "iter": 1300, "lr": 0.00552, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48266, "top5_acc": 0.73797, "loss_cls": 2.89437, "loss": 2.89437, "time": 0.82046} +{"mode": "train", "epoch": 128, "iter": 1400, "lr": 0.00551, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48875, "top5_acc": 0.73672, "loss_cls": 2.86438, "loss": 2.86438, "time": 0.81984} +{"mode": "train", "epoch": 128, "iter": 1500, "lr": 0.0055, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49234, "top5_acc": 0.73938, "loss_cls": 2.84567, "loss": 2.84567, "time": 0.81265} +{"mode": "train", "epoch": 128, "iter": 1600, "lr": 0.00548, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48953, "top5_acc": 0.74219, "loss_cls": 2.85754, "loss": 2.85754, "time": 0.81663} +{"mode": "train", "epoch": 128, "iter": 1700, "lr": 0.00547, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49109, "top5_acc": 0.74375, "loss_cls": 2.84736, "loss": 2.84736, "time": 0.81422} +{"mode": "train", "epoch": 128, "iter": 1800, "lr": 0.00546, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50016, "top5_acc": 0.74703, "loss_cls": 2.84003, "loss": 2.84003, "time": 0.81669} +{"mode": "train", "epoch": 128, "iter": 1900, "lr": 0.00545, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47875, "top5_acc": 0.73094, "loss_cls": 2.90616, "loss": 2.90616, "time": 0.81911} +{"mode": "train", "epoch": 128, "iter": 2000, "lr": 0.00543, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48734, "top5_acc": 0.74469, "loss_cls": 2.83154, "loss": 2.83154, "time": 0.81466} +{"mode": "train", "epoch": 128, "iter": 2100, "lr": 0.00542, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49547, "top5_acc": 0.74344, "loss_cls": 2.80305, "loss": 2.80305, "time": 0.81324} +{"mode": "train", "epoch": 128, "iter": 2200, "lr": 0.00541, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48344, "top5_acc": 0.73453, "loss_cls": 2.87534, "loss": 2.87534, "time": 0.819} +{"mode": "train", "epoch": 128, "iter": 2300, "lr": 0.0054, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48594, "top5_acc": 0.74109, "loss_cls": 2.85708, "loss": 2.85708, "time": 0.81612} +{"mode": "train", "epoch": 128, "iter": 2400, "lr": 0.00538, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48734, "top5_acc": 0.74156, "loss_cls": 2.85991, "loss": 2.85991, "time": 0.81968} +{"mode": "train", "epoch": 128, "iter": 2500, "lr": 0.00537, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48797, "top5_acc": 0.74125, "loss_cls": 2.84353, "loss": 2.84353, "time": 0.82148} +{"mode": "train", "epoch": 128, "iter": 2600, "lr": 0.00536, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49203, "top5_acc": 0.74359, "loss_cls": 2.8401, "loss": 2.8401, "time": 0.81771} +{"mode": "train", "epoch": 128, "iter": 2700, "lr": 0.00535, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48453, "top5_acc": 0.7325, "loss_cls": 2.89135, "loss": 2.89135, "time": 0.81406} +{"mode": "train", "epoch": 128, "iter": 2800, "lr": 0.00533, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48281, "top5_acc": 0.74469, "loss_cls": 2.86657, "loss": 2.86657, "time": 0.81852} +{"mode": "train", "epoch": 128, "iter": 2900, "lr": 0.00532, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.49, "top5_acc": 0.73422, "loss_cls": 2.86198, "loss": 2.86198, "time": 0.81994} +{"mode": "train", "epoch": 128, "iter": 3000, "lr": 0.00531, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48312, "top5_acc": 0.73594, "loss_cls": 2.87412, "loss": 2.87412, "time": 0.81927} +{"mode": "train", "epoch": 128, "iter": 3100, "lr": 0.0053, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48984, "top5_acc": 0.74234, "loss_cls": 2.83715, "loss": 2.83715, "time": 0.82408} +{"mode": "train", "epoch": 128, "iter": 3200, "lr": 0.00528, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48562, "top5_acc": 0.74, "loss_cls": 2.8629, "loss": 2.8629, "time": 0.8158} +{"mode": "train", "epoch": 128, "iter": 3300, "lr": 0.00527, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47891, "top5_acc": 0.73547, "loss_cls": 2.9009, "loss": 2.9009, "time": 0.82326} +{"mode": "train", "epoch": 128, "iter": 3400, "lr": 0.00526, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48656, "top5_acc": 0.73734, "loss_cls": 2.86906, "loss": 2.86906, "time": 0.82228} +{"mode": "train", "epoch": 128, "iter": 3500, "lr": 0.00525, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47172, "top5_acc": 0.7325, "loss_cls": 2.91824, "loss": 2.91824, "time": 0.81762} +{"mode": "train", "epoch": 128, "iter": 3600, "lr": 0.00523, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47594, "top5_acc": 0.72734, "loss_cls": 2.92928, "loss": 2.92928, "time": 0.82664} +{"mode": "train", "epoch": 128, "iter": 3700, "lr": 0.00522, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47953, "top5_acc": 0.73125, "loss_cls": 2.90388, "loss": 2.90388, "time": 0.81633} +{"mode": "val", "epoch": 128, "iter": 309, "lr": 0.00521, "top1_acc": 0.40556, "top5_acc": 0.65978, "mean_class_accuracy": 0.40528} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.0052, "memory": 15990, "data_time": 1.33665, "top1_acc": 0.51078, "top5_acc": 0.75062, "loss_cls": 2.76976, "loss": 2.76976, "time": 2.32108} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00519, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50578, "top5_acc": 0.75062, "loss_cls": 2.75268, "loss": 2.75268, "time": 0.8205} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00518, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.49828, "top5_acc": 0.75516, "loss_cls": 2.77514, "loss": 2.77514, "time": 0.81413} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00516, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49531, "top5_acc": 0.75281, "loss_cls": 2.79526, "loss": 2.79526, "time": 0.81703} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00515, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49688, "top5_acc": 0.75719, "loss_cls": 2.76729, "loss": 2.76729, "time": 0.81822} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00514, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49188, "top5_acc": 0.75891, "loss_cls": 2.78048, "loss": 2.78048, "time": 0.81576} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00513, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.50031, "top5_acc": 0.75312, "loss_cls": 2.79719, "loss": 2.79719, "time": 0.81348} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00512, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.49406, "top5_acc": 0.74828, "loss_cls": 2.80209, "loss": 2.80209, "time": 0.81453} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.0051, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48516, "top5_acc": 0.73891, "loss_cls": 2.84823, "loss": 2.84823, "time": 0.82524} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00509, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50453, "top5_acc": 0.75547, "loss_cls": 2.75947, "loss": 2.75947, "time": 0.81585} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00508, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48375, "top5_acc": 0.74469, "loss_cls": 2.83379, "loss": 2.83379, "time": 0.82281} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.00507, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49984, "top5_acc": 0.74672, "loss_cls": 2.77581, "loss": 2.77581, "time": 0.8167} +{"mode": "train", "epoch": 129, "iter": 1300, "lr": 0.00505, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.48547, "top5_acc": 0.73781, "loss_cls": 2.8499, "loss": 2.8499, "time": 0.81283} +{"mode": "train", "epoch": 129, "iter": 1400, "lr": 0.00504, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50281, "top5_acc": 0.74328, "loss_cls": 2.80976, "loss": 2.80976, "time": 0.81804} +{"mode": "train", "epoch": 129, "iter": 1500, "lr": 0.00503, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48234, "top5_acc": 0.74156, "loss_cls": 2.85559, "loss": 2.85559, "time": 0.81655} +{"mode": "train", "epoch": 129, "iter": 1600, "lr": 0.00502, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48281, "top5_acc": 0.7425, "loss_cls": 2.85046, "loss": 2.85046, "time": 0.81966} +{"mode": "train", "epoch": 129, "iter": 1700, "lr": 0.00501, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49328, "top5_acc": 0.75, "loss_cls": 2.81545, "loss": 2.81545, "time": 0.81538} +{"mode": "train", "epoch": 129, "iter": 1800, "lr": 0.00499, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49547, "top5_acc": 0.74594, "loss_cls": 2.80883, "loss": 2.80883, "time": 0.81558} +{"mode": "train", "epoch": 129, "iter": 1900, "lr": 0.00498, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.48594, "top5_acc": 0.73594, "loss_cls": 2.85425, "loss": 2.85425, "time": 0.81615} +{"mode": "train", "epoch": 129, "iter": 2000, "lr": 0.00497, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49812, "top5_acc": 0.75141, "loss_cls": 2.80552, "loss": 2.80552, "time": 0.81465} +{"mode": "train", "epoch": 129, "iter": 2100, "lr": 0.00496, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49531, "top5_acc": 0.74078, "loss_cls": 2.83899, "loss": 2.83899, "time": 0.81609} +{"mode": "train", "epoch": 129, "iter": 2200, "lr": 0.00494, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49094, "top5_acc": 0.73906, "loss_cls": 2.83681, "loss": 2.83681, "time": 0.81499} +{"mode": "train", "epoch": 129, "iter": 2300, "lr": 0.00493, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48969, "top5_acc": 0.73406, "loss_cls": 2.86951, "loss": 2.86951, "time": 0.82001} +{"mode": "train", "epoch": 129, "iter": 2400, "lr": 0.00492, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49266, "top5_acc": 0.74469, "loss_cls": 2.8303, "loss": 2.8303, "time": 0.81727} +{"mode": "train", "epoch": 129, "iter": 2500, "lr": 0.00491, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49547, "top5_acc": 0.74531, "loss_cls": 2.79861, "loss": 2.79861, "time": 0.82239} +{"mode": "train", "epoch": 129, "iter": 2600, "lr": 0.0049, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50031, "top5_acc": 0.74719, "loss_cls": 2.81896, "loss": 2.81896, "time": 0.81786} +{"mode": "train", "epoch": 129, "iter": 2700, "lr": 0.00488, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49969, "top5_acc": 0.74969, "loss_cls": 2.80095, "loss": 2.80095, "time": 0.81594} +{"mode": "train", "epoch": 129, "iter": 2800, "lr": 0.00487, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48828, "top5_acc": 0.74391, "loss_cls": 2.85464, "loss": 2.85464, "time": 0.81796} +{"mode": "train", "epoch": 129, "iter": 2900, "lr": 0.00486, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.49312, "top5_acc": 0.73516, "loss_cls": 2.89275, "loss": 2.89275, "time": 0.81636} +{"mode": "train", "epoch": 129, "iter": 3000, "lr": 0.00485, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.48484, "top5_acc": 0.74, "loss_cls": 2.83776, "loss": 2.83776, "time": 0.81281} +{"mode": "train", "epoch": 129, "iter": 3100, "lr": 0.00484, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48625, "top5_acc": 0.74531, "loss_cls": 2.80322, "loss": 2.80322, "time": 0.82234} +{"mode": "train", "epoch": 129, "iter": 3200, "lr": 0.00482, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48453, "top5_acc": 0.73938, "loss_cls": 2.8632, "loss": 2.8632, "time": 0.81934} +{"mode": "train", "epoch": 129, "iter": 3300, "lr": 0.00481, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48328, "top5_acc": 0.73719, "loss_cls": 2.87368, "loss": 2.87368, "time": 0.81633} +{"mode": "train", "epoch": 129, "iter": 3400, "lr": 0.0048, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48203, "top5_acc": 0.74453, "loss_cls": 2.86061, "loss": 2.86061, "time": 0.82055} +{"mode": "train", "epoch": 129, "iter": 3500, "lr": 0.00479, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49781, "top5_acc": 0.74375, "loss_cls": 2.82754, "loss": 2.82754, "time": 0.81559} +{"mode": "train", "epoch": 129, "iter": 3600, "lr": 0.00478, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48953, "top5_acc": 0.73984, "loss_cls": 2.84934, "loss": 2.84934, "time": 0.82358} +{"mode": "train", "epoch": 129, "iter": 3700, "lr": 0.00476, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49672, "top5_acc": 0.73828, "loss_cls": 2.83737, "loss": 2.83737, "time": 0.82108} +{"mode": "val", "epoch": 129, "iter": 309, "lr": 0.00476, "top1_acc": 0.41711, "top5_acc": 0.66606, "mean_class_accuracy": 0.41688} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00475, "memory": 15990, "data_time": 1.33824, "top1_acc": 0.50094, "top5_acc": 0.75734, "loss_cls": 2.74747, "loss": 2.74747, "time": 2.32342} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00473, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51, "top5_acc": 0.75438, "loss_cls": 2.74894, "loss": 2.74894, "time": 0.8278} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00472, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50953, "top5_acc": 0.75688, "loss_cls": 2.72956, "loss": 2.72956, "time": 0.82325} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00471, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.51438, "top5_acc": 0.76188, "loss_cls": 2.71655, "loss": 2.71655, "time": 0.81973} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.0047, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.50859, "top5_acc": 0.76188, "loss_cls": 2.7194, "loss": 2.7194, "time": 0.81671} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00469, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50781, "top5_acc": 0.75266, "loss_cls": 2.76228, "loss": 2.76228, "time": 0.81908} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00468, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50969, "top5_acc": 0.76047, "loss_cls": 2.73724, "loss": 2.73724, "time": 0.82006} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00466, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51062, "top5_acc": 0.75109, "loss_cls": 2.75811, "loss": 2.75811, "time": 0.81941} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00465, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50094, "top5_acc": 0.76094, "loss_cls": 2.74549, "loss": 2.74549, "time": 0.82145} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.00464, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50453, "top5_acc": 0.74844, "loss_cls": 2.78357, "loss": 2.78357, "time": 0.82073} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.00463, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50219, "top5_acc": 0.74969, "loss_cls": 2.77542, "loss": 2.77542, "time": 0.82518} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00462, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49656, "top5_acc": 0.74922, "loss_cls": 2.77074, "loss": 2.77074, "time": 0.8196} +{"mode": "train", "epoch": 130, "iter": 1300, "lr": 0.00461, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50062, "top5_acc": 0.74938, "loss_cls": 2.76924, "loss": 2.76924, "time": 0.81671} +{"mode": "train", "epoch": 130, "iter": 1400, "lr": 0.00459, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50297, "top5_acc": 0.75625, "loss_cls": 2.76049, "loss": 2.76049, "time": 0.81513} +{"mode": "train", "epoch": 130, "iter": 1500, "lr": 0.00458, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49172, "top5_acc": 0.74062, "loss_cls": 2.84275, "loss": 2.84275, "time": 0.81644} +{"mode": "train", "epoch": 130, "iter": 1600, "lr": 0.00457, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49562, "top5_acc": 0.75219, "loss_cls": 2.77614, "loss": 2.77614, "time": 0.81895} +{"mode": "train", "epoch": 130, "iter": 1700, "lr": 0.00456, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49531, "top5_acc": 0.74516, "loss_cls": 2.81413, "loss": 2.81413, "time": 0.81693} +{"mode": "train", "epoch": 130, "iter": 1800, "lr": 0.00455, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50531, "top5_acc": 0.75266, "loss_cls": 2.77067, "loss": 2.77067, "time": 0.81496} +{"mode": "train", "epoch": 130, "iter": 1900, "lr": 0.00454, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.50328, "top5_acc": 0.74469, "loss_cls": 2.79398, "loss": 2.79398, "time": 0.82064} +{"mode": "train", "epoch": 130, "iter": 2000, "lr": 0.00452, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50094, "top5_acc": 0.74875, "loss_cls": 2.78349, "loss": 2.78349, "time": 0.81769} +{"mode": "train", "epoch": 130, "iter": 2100, "lr": 0.00451, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48891, "top5_acc": 0.74297, "loss_cls": 2.84619, "loss": 2.84619, "time": 0.81401} +{"mode": "train", "epoch": 130, "iter": 2200, "lr": 0.0045, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.49578, "top5_acc": 0.74953, "loss_cls": 2.78908, "loss": 2.78908, "time": 0.81542} +{"mode": "train", "epoch": 130, "iter": 2300, "lr": 0.00449, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.49188, "top5_acc": 0.75188, "loss_cls": 2.81681, "loss": 2.81681, "time": 0.81387} +{"mode": "train", "epoch": 130, "iter": 2400, "lr": 0.00448, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.49375, "top5_acc": 0.75203, "loss_cls": 2.79619, "loss": 2.79619, "time": 0.81812} +{"mode": "train", "epoch": 130, "iter": 2500, "lr": 0.00447, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49344, "top5_acc": 0.74719, "loss_cls": 2.80723, "loss": 2.80723, "time": 0.81675} +{"mode": "train", "epoch": 130, "iter": 2600, "lr": 0.00445, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49141, "top5_acc": 0.74266, "loss_cls": 2.82973, "loss": 2.82973, "time": 0.8156} +{"mode": "train", "epoch": 130, "iter": 2700, "lr": 0.00444, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49719, "top5_acc": 0.74547, "loss_cls": 2.79437, "loss": 2.79437, "time": 0.81632} +{"mode": "train", "epoch": 130, "iter": 2800, "lr": 0.00443, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.49109, "top5_acc": 0.74062, "loss_cls": 2.84159, "loss": 2.84159, "time": 0.81667} +{"mode": "train", "epoch": 130, "iter": 2900, "lr": 0.00442, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50156, "top5_acc": 0.75172, "loss_cls": 2.79295, "loss": 2.79295, "time": 0.81809} +{"mode": "train", "epoch": 130, "iter": 3000, "lr": 0.00441, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50719, "top5_acc": 0.75594, "loss_cls": 2.78164, "loss": 2.78164, "time": 0.81802} +{"mode": "train", "epoch": 130, "iter": 3100, "lr": 0.0044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50172, "top5_acc": 0.75328, "loss_cls": 2.75885, "loss": 2.75885, "time": 0.82152} +{"mode": "train", "epoch": 130, "iter": 3200, "lr": 0.00439, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49172, "top5_acc": 0.7425, "loss_cls": 2.80677, "loss": 2.80677, "time": 0.81577} +{"mode": "train", "epoch": 130, "iter": 3300, "lr": 0.00437, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49828, "top5_acc": 0.74766, "loss_cls": 2.79678, "loss": 2.79678, "time": 0.82189} +{"mode": "train", "epoch": 130, "iter": 3400, "lr": 0.00436, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49516, "top5_acc": 0.74, "loss_cls": 2.84015, "loss": 2.84015, "time": 0.81787} +{"mode": "train", "epoch": 130, "iter": 3500, "lr": 0.00435, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49688, "top5_acc": 0.75031, "loss_cls": 2.80471, "loss": 2.80471, "time": 0.81865} +{"mode": "train", "epoch": 130, "iter": 3600, "lr": 0.00434, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49922, "top5_acc": 0.75188, "loss_cls": 2.76551, "loss": 2.76551, "time": 0.82076} +{"mode": "train", "epoch": 130, "iter": 3700, "lr": 0.00433, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49766, "top5_acc": 0.74109, "loss_cls": 2.82728, "loss": 2.82728, "time": 0.81355} +{"mode": "val", "epoch": 130, "iter": 309, "lr": 0.00432, "top1_acc": 0.41073, "top5_acc": 0.66535, "mean_class_accuracy": 0.41054} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00431, "memory": 15990, "data_time": 1.34472, "top1_acc": 0.52203, "top5_acc": 0.77, "loss_cls": 2.6821, "loss": 2.6821, "time": 2.33188} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.0043, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51828, "top5_acc": 0.77391, "loss_cls": 2.6603, "loss": 2.6603, "time": 0.82416} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00429, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52422, "top5_acc": 0.77172, "loss_cls": 2.6818, "loss": 2.6818, "time": 0.8225} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00428, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51797, "top5_acc": 0.76234, "loss_cls": 2.7008, "loss": 2.7008, "time": 0.81942} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00427, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50859, "top5_acc": 0.76016, "loss_cls": 2.72747, "loss": 2.72747, "time": 0.81501} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00425, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51266, "top5_acc": 0.76547, "loss_cls": 2.69784, "loss": 2.69784, "time": 0.825} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00424, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51281, "top5_acc": 0.75797, "loss_cls": 2.74127, "loss": 2.74127, "time": 0.81651} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00423, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51172, "top5_acc": 0.75859, "loss_cls": 2.73745, "loss": 2.73745, "time": 0.82} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00422, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50047, "top5_acc": 0.75328, "loss_cls": 2.75475, "loss": 2.75475, "time": 0.81622} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.00421, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51484, "top5_acc": 0.77297, "loss_cls": 2.69987, "loss": 2.69987, "time": 0.82179} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.0042, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50562, "top5_acc": 0.755, "loss_cls": 2.76066, "loss": 2.76066, "time": 0.81896} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00419, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50719, "top5_acc": 0.75922, "loss_cls": 2.73174, "loss": 2.73174, "time": 0.81923} +{"mode": "train", "epoch": 131, "iter": 1300, "lr": 0.00418, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50094, "top5_acc": 0.75422, "loss_cls": 2.76533, "loss": 2.76533, "time": 0.81691} +{"mode": "train", "epoch": 131, "iter": 1400, "lr": 0.00417, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.495, "top5_acc": 0.75062, "loss_cls": 2.76862, "loss": 2.76862, "time": 0.81374} +{"mode": "train", "epoch": 131, "iter": 1500, "lr": 0.00415, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51031, "top5_acc": 0.75234, "loss_cls": 2.74383, "loss": 2.74383, "time": 0.8187} +{"mode": "train", "epoch": 131, "iter": 1600, "lr": 0.00414, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50844, "top5_acc": 0.75453, "loss_cls": 2.73232, "loss": 2.73232, "time": 0.81896} +{"mode": "train", "epoch": 131, "iter": 1700, "lr": 0.00413, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49812, "top5_acc": 0.75594, "loss_cls": 2.74763, "loss": 2.74763, "time": 0.81694} +{"mode": "train", "epoch": 131, "iter": 1800, "lr": 0.00412, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51172, "top5_acc": 0.75516, "loss_cls": 2.74921, "loss": 2.74921, "time": 0.81856} +{"mode": "train", "epoch": 131, "iter": 1900, "lr": 0.00411, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.51125, "top5_acc": 0.75844, "loss_cls": 2.75718, "loss": 2.75718, "time": 0.81487} +{"mode": "train", "epoch": 131, "iter": 2000, "lr": 0.0041, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48359, "top5_acc": 0.73625, "loss_cls": 2.83229, "loss": 2.83229, "time": 0.81608} +{"mode": "train", "epoch": 131, "iter": 2100, "lr": 0.00409, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50047, "top5_acc": 0.75312, "loss_cls": 2.77272, "loss": 2.77272, "time": 0.81303} +{"mode": "train", "epoch": 131, "iter": 2200, "lr": 0.00408, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50813, "top5_acc": 0.75031, "loss_cls": 2.76409, "loss": 2.76409, "time": 0.81618} +{"mode": "train", "epoch": 131, "iter": 2300, "lr": 0.00407, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48797, "top5_acc": 0.74297, "loss_cls": 2.83238, "loss": 2.83238, "time": 0.81724} +{"mode": "train", "epoch": 131, "iter": 2400, "lr": 0.00405, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50672, "top5_acc": 0.74516, "loss_cls": 2.78326, "loss": 2.78326, "time": 0.81963} +{"mode": "train", "epoch": 131, "iter": 2500, "lr": 0.00404, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49609, "top5_acc": 0.75109, "loss_cls": 2.78972, "loss": 2.78972, "time": 0.81766} +{"mode": "train", "epoch": 131, "iter": 2600, "lr": 0.00403, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50203, "top5_acc": 0.75844, "loss_cls": 2.73281, "loss": 2.73281, "time": 0.81802} +{"mode": "train", "epoch": 131, "iter": 2700, "lr": 0.00402, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51203, "top5_acc": 0.75625, "loss_cls": 2.73383, "loss": 2.73383, "time": 0.8155} +{"mode": "train", "epoch": 131, "iter": 2800, "lr": 0.00401, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50953, "top5_acc": 0.75688, "loss_cls": 2.75006, "loss": 2.75006, "time": 0.82113} +{"mode": "train", "epoch": 131, "iter": 2900, "lr": 0.004, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50891, "top5_acc": 0.74562, "loss_cls": 2.7658, "loss": 2.7658, "time": 0.82014} +{"mode": "train", "epoch": 131, "iter": 3000, "lr": 0.00399, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50406, "top5_acc": 0.75453, "loss_cls": 2.77477, "loss": 2.77477, "time": 0.81723} +{"mode": "train", "epoch": 131, "iter": 3100, "lr": 0.00398, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.50734, "top5_acc": 0.74984, "loss_cls": 2.74588, "loss": 2.74588, "time": 0.82658} +{"mode": "train", "epoch": 131, "iter": 3200, "lr": 0.00397, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50062, "top5_acc": 0.75031, "loss_cls": 2.79366, "loss": 2.79366, "time": 0.83192} +{"mode": "train", "epoch": 131, "iter": 3300, "lr": 0.00396, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50578, "top5_acc": 0.75453, "loss_cls": 2.7302, "loss": 2.7302, "time": 0.8216} +{"mode": "train", "epoch": 131, "iter": 3400, "lr": 0.00394, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.49719, "top5_acc": 0.75, "loss_cls": 2.79054, "loss": 2.79054, "time": 0.81809} +{"mode": "train", "epoch": 131, "iter": 3500, "lr": 0.00393, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50813, "top5_acc": 0.75969, "loss_cls": 2.7448, "loss": 2.7448, "time": 0.81518} +{"mode": "train", "epoch": 131, "iter": 3600, "lr": 0.00392, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50813, "top5_acc": 0.75688, "loss_cls": 2.75315, "loss": 2.75315, "time": 0.81555} +{"mode": "train", "epoch": 131, "iter": 3700, "lr": 0.00391, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49734, "top5_acc": 0.74906, "loss_cls": 2.76633, "loss": 2.76633, "time": 0.81969} +{"mode": "val", "epoch": 131, "iter": 309, "lr": 0.00391, "top1_acc": 0.41706, "top5_acc": 0.67467, "mean_class_accuracy": 0.41669} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.0039, "memory": 15990, "data_time": 1.32483, "top1_acc": 0.52469, "top5_acc": 0.76844, "loss_cls": 2.67342, "loss": 2.67342, "time": 2.32784} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00389, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52812, "top5_acc": 0.77797, "loss_cls": 2.60955, "loss": 2.60955, "time": 0.8214} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00387, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52203, "top5_acc": 0.76922, "loss_cls": 2.65255, "loss": 2.65255, "time": 0.81765} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00386, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52906, "top5_acc": 0.77141, "loss_cls": 2.65965, "loss": 2.65965, "time": 0.81881} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00385, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52031, "top5_acc": 0.765, "loss_cls": 2.66776, "loss": 2.66776, "time": 0.81913} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00384, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51625, "top5_acc": 0.76328, "loss_cls": 2.67638, "loss": 2.67638, "time": 0.8188} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00383, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52609, "top5_acc": 0.7675, "loss_cls": 2.66053, "loss": 2.66053, "time": 0.81803} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00382, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51938, "top5_acc": 0.76656, "loss_cls": 2.66445, "loss": 2.66445, "time": 0.82443} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00381, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.50969, "top5_acc": 0.76156, "loss_cls": 2.71406, "loss": 2.71406, "time": 0.82227} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0038, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51969, "top5_acc": 0.77203, "loss_cls": 2.66058, "loss": 2.66058, "time": 0.81366} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00379, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.50562, "top5_acc": 0.75484, "loss_cls": 2.73423, "loss": 2.73423, "time": 0.82131} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00378, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51281, "top5_acc": 0.75469, "loss_cls": 2.73902, "loss": 2.73902, "time": 0.81957} +{"mode": "train", "epoch": 132, "iter": 1300, "lr": 0.00377, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.51141, "top5_acc": 0.76125, "loss_cls": 2.6912, "loss": 2.6912, "time": 0.81335} +{"mode": "train", "epoch": 132, "iter": 1400, "lr": 0.00376, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52297, "top5_acc": 0.77188, "loss_cls": 2.62858, "loss": 2.62858, "time": 0.8173} +{"mode": "train", "epoch": 132, "iter": 1500, "lr": 0.00375, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51094, "top5_acc": 0.75828, "loss_cls": 2.70664, "loss": 2.70664, "time": 0.81861} +{"mode": "train", "epoch": 132, "iter": 1600, "lr": 0.00374, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50594, "top5_acc": 0.75516, "loss_cls": 2.75319, "loss": 2.75319, "time": 0.81609} +{"mode": "train", "epoch": 132, "iter": 1700, "lr": 0.00372, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51562, "top5_acc": 0.75328, "loss_cls": 2.71656, "loss": 2.71656, "time": 0.81787} +{"mode": "train", "epoch": 132, "iter": 1800, "lr": 0.00371, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.50172, "top5_acc": 0.75125, "loss_cls": 2.78252, "loss": 2.78252, "time": 0.81411} +{"mode": "train", "epoch": 132, "iter": 1900, "lr": 0.0037, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50844, "top5_acc": 0.75578, "loss_cls": 2.74157, "loss": 2.74157, "time": 0.81343} +{"mode": "train", "epoch": 132, "iter": 2000, "lr": 0.00369, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50484, "top5_acc": 0.76047, "loss_cls": 2.75119, "loss": 2.75119, "time": 0.81439} +{"mode": "train", "epoch": 132, "iter": 2100, "lr": 0.00368, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51625, "top5_acc": 0.75984, "loss_cls": 2.71128, "loss": 2.71128, "time": 0.82004} +{"mode": "train", "epoch": 132, "iter": 2200, "lr": 0.00367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5125, "top5_acc": 0.75953, "loss_cls": 2.69158, "loss": 2.69158, "time": 0.81447} +{"mode": "train", "epoch": 132, "iter": 2300, "lr": 0.00366, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50609, "top5_acc": 0.75016, "loss_cls": 2.77394, "loss": 2.77394, "time": 0.81688} +{"mode": "train", "epoch": 132, "iter": 2400, "lr": 0.00365, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51344, "top5_acc": 0.76375, "loss_cls": 2.70135, "loss": 2.70135, "time": 0.81268} +{"mode": "train", "epoch": 132, "iter": 2500, "lr": 0.00364, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50875, "top5_acc": 0.76391, "loss_cls": 2.72219, "loss": 2.72219, "time": 0.82582} +{"mode": "train", "epoch": 132, "iter": 2600, "lr": 0.00363, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51609, "top5_acc": 0.765, "loss_cls": 2.67767, "loss": 2.67767, "time": 0.81264} +{"mode": "train", "epoch": 132, "iter": 2700, "lr": 0.00362, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52, "top5_acc": 0.7775, "loss_cls": 2.65991, "loss": 2.65991, "time": 0.81446} +{"mode": "train", "epoch": 132, "iter": 2800, "lr": 0.00361, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50891, "top5_acc": 0.75453, "loss_cls": 2.75119, "loss": 2.75119, "time": 0.8132} +{"mode": "train", "epoch": 132, "iter": 2900, "lr": 0.0036, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.50984, "top5_acc": 0.75797, "loss_cls": 2.74663, "loss": 2.74663, "time": 0.81233} +{"mode": "train", "epoch": 132, "iter": 3000, "lr": 0.00359, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51531, "top5_acc": 0.75531, "loss_cls": 2.70082, "loss": 2.70082, "time": 0.8172} +{"mode": "train", "epoch": 132, "iter": 3100, "lr": 0.00358, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50859, "top5_acc": 0.75906, "loss_cls": 2.73694, "loss": 2.73694, "time": 0.82288} +{"mode": "train", "epoch": 132, "iter": 3200, "lr": 0.00357, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50109, "top5_acc": 0.76156, "loss_cls": 2.75591, "loss": 2.75591, "time": 0.81994} +{"mode": "train", "epoch": 132, "iter": 3300, "lr": 0.00356, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.5025, "top5_acc": 0.75672, "loss_cls": 2.72898, "loss": 2.72898, "time": 0.82428} +{"mode": "train", "epoch": 132, "iter": 3400, "lr": 0.00355, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51672, "top5_acc": 0.76438, "loss_cls": 2.69603, "loss": 2.69603, "time": 0.81887} +{"mode": "train", "epoch": 132, "iter": 3500, "lr": 0.00354, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50656, "top5_acc": 0.74719, "loss_cls": 2.7588, "loss": 2.7588, "time": 0.81741} +{"mode": "train", "epoch": 132, "iter": 3600, "lr": 0.00353, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51594, "top5_acc": 0.76016, "loss_cls": 2.72817, "loss": 2.72817, "time": 0.81434} +{"mode": "train", "epoch": 132, "iter": 3700, "lr": 0.00352, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51422, "top5_acc": 0.76266, "loss_cls": 2.72579, "loss": 2.72579, "time": 0.81251} +{"mode": "val", "epoch": 132, "iter": 309, "lr": 0.00351, "top1_acc": 0.42283, "top5_acc": 0.67325, "mean_class_accuracy": 0.42258} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.0035, "memory": 15990, "data_time": 1.34197, "top1_acc": 0.53219, "top5_acc": 0.78203, "loss_cls": 2.60108, "loss": 2.60108, "time": 2.35007} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00349, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53891, "top5_acc": 0.78375, "loss_cls": 2.56872, "loss": 2.56872, "time": 0.82253} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00348, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52172, "top5_acc": 0.78047, "loss_cls": 2.62791, "loss": 2.62791, "time": 0.81714} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00347, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54328, "top5_acc": 0.76922, "loss_cls": 2.5925, "loss": 2.5925, "time": 0.82246} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00346, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52031, "top5_acc": 0.77078, "loss_cls": 2.65994, "loss": 2.65994, "time": 0.81875} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00345, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.51641, "top5_acc": 0.77438, "loss_cls": 2.67014, "loss": 2.67014, "time": 0.81906} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00344, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51672, "top5_acc": 0.77312, "loss_cls": 2.66517, "loss": 2.66517, "time": 0.81445} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00343, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52391, "top5_acc": 0.76672, "loss_cls": 2.65992, "loss": 2.65992, "time": 0.8284} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00342, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52484, "top5_acc": 0.76875, "loss_cls": 2.68413, "loss": 2.68413, "time": 0.82312} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.00341, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51094, "top5_acc": 0.76344, "loss_cls": 2.69676, "loss": 2.69676, "time": 0.81843} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0034, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.51641, "top5_acc": 0.76062, "loss_cls": 2.68635, "loss": 2.68635, "time": 0.81954} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00339, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52656, "top5_acc": 0.77672, "loss_cls": 2.63327, "loss": 2.63327, "time": 0.81891} +{"mode": "train", "epoch": 133, "iter": 1300, "lr": 0.00338, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.51313, "top5_acc": 0.76812, "loss_cls": 2.67388, "loss": 2.67388, "time": 0.81699} +{"mode": "train", "epoch": 133, "iter": 1400, "lr": 0.00337, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52578, "top5_acc": 0.76656, "loss_cls": 2.64492, "loss": 2.64492, "time": 0.81622} +{"mode": "train", "epoch": 133, "iter": 1500, "lr": 0.00336, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53031, "top5_acc": 0.77031, "loss_cls": 2.64391, "loss": 2.64391, "time": 0.8159} +{"mode": "train", "epoch": 133, "iter": 1600, "lr": 0.00335, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51875, "top5_acc": 0.7675, "loss_cls": 2.62361, "loss": 2.62361, "time": 0.81568} +{"mode": "train", "epoch": 133, "iter": 1700, "lr": 0.00334, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.51203, "top5_acc": 0.76156, "loss_cls": 2.69774, "loss": 2.69774, "time": 0.81896} +{"mode": "train", "epoch": 133, "iter": 1800, "lr": 0.00333, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51422, "top5_acc": 0.76469, "loss_cls": 2.6814, "loss": 2.6814, "time": 0.81468} +{"mode": "train", "epoch": 133, "iter": 1900, "lr": 0.00332, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52438, "top5_acc": 0.77469, "loss_cls": 2.63695, "loss": 2.63695, "time": 0.82213} +{"mode": "train", "epoch": 133, "iter": 2000, "lr": 0.00331, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51641, "top5_acc": 0.76734, "loss_cls": 2.67707, "loss": 2.67707, "time": 0.81913} +{"mode": "train", "epoch": 133, "iter": 2100, "lr": 0.0033, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.51406, "top5_acc": 0.76125, "loss_cls": 2.71254, "loss": 2.71254, "time": 0.81508} +{"mode": "train", "epoch": 133, "iter": 2200, "lr": 0.00329, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52375, "top5_acc": 0.76891, "loss_cls": 2.64995, "loss": 2.64995, "time": 0.8184} +{"mode": "train", "epoch": 133, "iter": 2300, "lr": 0.00328, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50531, "top5_acc": 0.75922, "loss_cls": 2.71018, "loss": 2.71018, "time": 0.81533} +{"mode": "train", "epoch": 133, "iter": 2400, "lr": 0.00327, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.515, "top5_acc": 0.76625, "loss_cls": 2.6873, "loss": 2.6873, "time": 0.814} +{"mode": "train", "epoch": 133, "iter": 2500, "lr": 0.00326, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52188, "top5_acc": 0.77062, "loss_cls": 2.65715, "loss": 2.65715, "time": 0.8211} +{"mode": "train", "epoch": 133, "iter": 2600, "lr": 0.00325, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.52453, "top5_acc": 0.77031, "loss_cls": 2.65065, "loss": 2.65065, "time": 0.82394} +{"mode": "train", "epoch": 133, "iter": 2700, "lr": 0.00324, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52281, "top5_acc": 0.76062, "loss_cls": 2.68288, "loss": 2.68288, "time": 0.81543} +{"mode": "train", "epoch": 133, "iter": 2800, "lr": 0.00323, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50531, "top5_acc": 0.75375, "loss_cls": 2.73799, "loss": 2.73799, "time": 0.81431} +{"mode": "train", "epoch": 133, "iter": 2900, "lr": 0.00322, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.51422, "top5_acc": 0.76234, "loss_cls": 2.69085, "loss": 2.69085, "time": 0.81178} +{"mode": "train", "epoch": 133, "iter": 3000, "lr": 0.00321, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52922, "top5_acc": 0.76562, "loss_cls": 2.65275, "loss": 2.65275, "time": 0.81744} +{"mode": "train", "epoch": 133, "iter": 3100, "lr": 0.0032, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51562, "top5_acc": 0.76297, "loss_cls": 2.69752, "loss": 2.69752, "time": 0.82247} +{"mode": "train", "epoch": 133, "iter": 3200, "lr": 0.00319, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52094, "top5_acc": 0.76766, "loss_cls": 2.63935, "loss": 2.63935, "time": 0.81837} +{"mode": "train", "epoch": 133, "iter": 3300, "lr": 0.00318, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51953, "top5_acc": 0.76703, "loss_cls": 2.68482, "loss": 2.68482, "time": 0.8231} +{"mode": "train", "epoch": 133, "iter": 3400, "lr": 0.00317, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50891, "top5_acc": 0.7575, "loss_cls": 2.73121, "loss": 2.73121, "time": 0.81919} +{"mode": "train", "epoch": 133, "iter": 3500, "lr": 0.00316, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51453, "top5_acc": 0.76641, "loss_cls": 2.6992, "loss": 2.6992, "time": 0.82286} +{"mode": "train", "epoch": 133, "iter": 3600, "lr": 0.00315, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51531, "top5_acc": 0.75953, "loss_cls": 2.70359, "loss": 2.70359, "time": 0.81557} +{"mode": "train", "epoch": 133, "iter": 3700, "lr": 0.00314, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51688, "top5_acc": 0.75828, "loss_cls": 2.7024, "loss": 2.7024, "time": 0.81872} +{"mode": "val", "epoch": 133, "iter": 309, "lr": 0.00314, "top1_acc": 0.42749, "top5_acc": 0.67791, "mean_class_accuracy": 0.4273} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00313, "memory": 15990, "data_time": 1.30817, "top1_acc": 0.54531, "top5_acc": 0.78547, "loss_cls": 2.55499, "loss": 2.55499, "time": 2.29218} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00312, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54828, "top5_acc": 0.78109, "loss_cls": 2.57492, "loss": 2.57492, "time": 0.82237} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00311, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54125, "top5_acc": 0.78234, "loss_cls": 2.58754, "loss": 2.58754, "time": 0.81896} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.0031, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53312, "top5_acc": 0.785, "loss_cls": 2.57045, "loss": 2.57045, "time": 0.82001} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00309, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53797, "top5_acc": 0.78234, "loss_cls": 2.56174, "loss": 2.56174, "time": 0.81882} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00308, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52844, "top5_acc": 0.77891, "loss_cls": 2.59373, "loss": 2.59373, "time": 0.81716} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00307, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54016, "top5_acc": 0.78359, "loss_cls": 2.58044, "loss": 2.58044, "time": 0.81812} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00306, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54047, "top5_acc": 0.77953, "loss_cls": 2.5671, "loss": 2.5671, "time": 0.82989} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00305, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52656, "top5_acc": 0.77672, "loss_cls": 2.60802, "loss": 2.60802, "time": 0.81695} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00304, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53125, "top5_acc": 0.78016, "loss_cls": 2.60283, "loss": 2.60283, "time": 0.82672} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00303, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.51656, "top5_acc": 0.77172, "loss_cls": 2.67302, "loss": 2.67302, "time": 0.81938} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.00302, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52828, "top5_acc": 0.77156, "loss_cls": 2.64493, "loss": 2.64493, "time": 0.81916} +{"mode": "train", "epoch": 134, "iter": 1300, "lr": 0.00301, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52906, "top5_acc": 0.77641, "loss_cls": 2.60777, "loss": 2.60777, "time": 0.81475} +{"mode": "train", "epoch": 134, "iter": 1400, "lr": 0.003, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53609, "top5_acc": 0.77891, "loss_cls": 2.58508, "loss": 2.58508, "time": 0.81982} +{"mode": "train", "epoch": 134, "iter": 1500, "lr": 0.00299, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51938, "top5_acc": 0.76578, "loss_cls": 2.66921, "loss": 2.66921, "time": 0.82269} +{"mode": "train", "epoch": 134, "iter": 1600, "lr": 0.00298, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52172, "top5_acc": 0.76688, "loss_cls": 2.66303, "loss": 2.66303, "time": 0.81456} +{"mode": "train", "epoch": 134, "iter": 1700, "lr": 0.00297, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53016, "top5_acc": 0.77531, "loss_cls": 2.611, "loss": 2.611, "time": 0.81686} +{"mode": "train", "epoch": 134, "iter": 1800, "lr": 0.00296, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52031, "top5_acc": 0.76594, "loss_cls": 2.66663, "loss": 2.66663, "time": 0.81885} +{"mode": "train", "epoch": 134, "iter": 1900, "lr": 0.00295, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52312, "top5_acc": 0.7775, "loss_cls": 2.61818, "loss": 2.61818, "time": 0.81552} +{"mode": "train", "epoch": 134, "iter": 2000, "lr": 0.00294, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51656, "top5_acc": 0.76516, "loss_cls": 2.69151, "loss": 2.69151, "time": 0.82445} +{"mode": "train", "epoch": 134, "iter": 2100, "lr": 0.00293, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52828, "top5_acc": 0.77516, "loss_cls": 2.61187, "loss": 2.61187, "time": 0.81721} +{"mode": "train", "epoch": 134, "iter": 2200, "lr": 0.00293, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52656, "top5_acc": 0.77359, "loss_cls": 2.62519, "loss": 2.62519, "time": 0.81315} +{"mode": "train", "epoch": 134, "iter": 2300, "lr": 0.00292, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52719, "top5_acc": 0.77188, "loss_cls": 2.63797, "loss": 2.63797, "time": 0.81345} +{"mode": "train", "epoch": 134, "iter": 2400, "lr": 0.00291, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52531, "top5_acc": 0.76938, "loss_cls": 2.63512, "loss": 2.63512, "time": 0.82358} +{"mode": "train", "epoch": 134, "iter": 2500, "lr": 0.0029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53188, "top5_acc": 0.77125, "loss_cls": 2.64993, "loss": 2.64993, "time": 0.81752} +{"mode": "train", "epoch": 134, "iter": 2600, "lr": 0.00289, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53078, "top5_acc": 0.76812, "loss_cls": 2.62415, "loss": 2.62415, "time": 0.82237} +{"mode": "train", "epoch": 134, "iter": 2700, "lr": 0.00288, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52016, "top5_acc": 0.76719, "loss_cls": 2.66012, "loss": 2.66012, "time": 0.81215} +{"mode": "train", "epoch": 134, "iter": 2800, "lr": 0.00287, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.52266, "top5_acc": 0.765, "loss_cls": 2.64345, "loss": 2.64345, "time": 0.8179} +{"mode": "train", "epoch": 134, "iter": 2900, "lr": 0.00286, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52516, "top5_acc": 0.77234, "loss_cls": 2.6456, "loss": 2.6456, "time": 0.81662} +{"mode": "train", "epoch": 134, "iter": 3000, "lr": 0.00285, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52281, "top5_acc": 0.77141, "loss_cls": 2.6523, "loss": 2.6523, "time": 0.82581} +{"mode": "train", "epoch": 134, "iter": 3100, "lr": 0.00284, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.53141, "top5_acc": 0.77219, "loss_cls": 2.60867, "loss": 2.60867, "time": 0.81589} +{"mode": "train", "epoch": 134, "iter": 3200, "lr": 0.00283, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53312, "top5_acc": 0.7825, "loss_cls": 2.59997, "loss": 2.59997, "time": 0.82459} +{"mode": "train", "epoch": 134, "iter": 3300, "lr": 0.00282, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52688, "top5_acc": 0.77656, "loss_cls": 2.61806, "loss": 2.61806, "time": 0.83027} +{"mode": "train", "epoch": 134, "iter": 3400, "lr": 0.00281, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51797, "top5_acc": 0.76547, "loss_cls": 2.65836, "loss": 2.65836, "time": 0.81747} +{"mode": "train", "epoch": 134, "iter": 3500, "lr": 0.0028, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.51266, "top5_acc": 0.76, "loss_cls": 2.71248, "loss": 2.71248, "time": 0.82571} +{"mode": "train", "epoch": 134, "iter": 3600, "lr": 0.00279, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52281, "top5_acc": 0.77203, "loss_cls": 2.62333, "loss": 2.62333, "time": 0.81936} +{"mode": "train", "epoch": 134, "iter": 3700, "lr": 0.00279, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52109, "top5_acc": 0.77672, "loss_cls": 2.63146, "loss": 2.63146, "time": 0.81676} +{"mode": "val", "epoch": 134, "iter": 309, "lr": 0.00278, "top1_acc": 0.42709, "top5_acc": 0.67594, "mean_class_accuracy": 0.42685} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00277, "memory": 15990, "data_time": 1.30133, "top1_acc": 0.54312, "top5_acc": 0.78984, "loss_cls": 2.54424, "loss": 2.54424, "time": 2.28561} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00276, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54641, "top5_acc": 0.78625, "loss_cls": 2.5142, "loss": 2.5142, "time": 0.83111} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00275, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55094, "top5_acc": 0.79891, "loss_cls": 2.4707, "loss": 2.4707, "time": 0.83053} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00274, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54047, "top5_acc": 0.78094, "loss_cls": 2.55233, "loss": 2.55233, "time": 0.8321} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00274, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54906, "top5_acc": 0.78406, "loss_cls": 2.53355, "loss": 2.53355, "time": 0.83648} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00273, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53953, "top5_acc": 0.78406, "loss_cls": 2.54203, "loss": 2.54203, "time": 0.83648} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00272, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54875, "top5_acc": 0.79266, "loss_cls": 2.52228, "loss": 2.52228, "time": 0.8357} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00271, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54328, "top5_acc": 0.78484, "loss_cls": 2.5499, "loss": 2.5499, "time": 0.83766} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.0027, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54469, "top5_acc": 0.78172, "loss_cls": 2.56186, "loss": 2.56186, "time": 0.83133} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00269, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.53953, "top5_acc": 0.78906, "loss_cls": 2.54636, "loss": 2.54636, "time": 0.84183} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00268, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53359, "top5_acc": 0.77797, "loss_cls": 2.57781, "loss": 2.57781, "time": 0.83711} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00267, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54328, "top5_acc": 0.79297, "loss_cls": 2.53593, "loss": 2.53593, "time": 0.83789} +{"mode": "train", "epoch": 135, "iter": 1300, "lr": 0.00266, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.53734, "top5_acc": 0.78656, "loss_cls": 2.55063, "loss": 2.55063, "time": 0.83723} +{"mode": "train", "epoch": 135, "iter": 1400, "lr": 0.00265, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.53219, "top5_acc": 0.77906, "loss_cls": 2.59852, "loss": 2.59852, "time": 0.83647} +{"mode": "train", "epoch": 135, "iter": 1500, "lr": 0.00265, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.53672, "top5_acc": 0.78391, "loss_cls": 2.56503, "loss": 2.56503, "time": 0.8331} +{"mode": "train", "epoch": 135, "iter": 1600, "lr": 0.00264, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53172, "top5_acc": 0.77641, "loss_cls": 2.62017, "loss": 2.62017, "time": 0.82216} +{"mode": "train", "epoch": 135, "iter": 1700, "lr": 0.00263, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.53578, "top5_acc": 0.78016, "loss_cls": 2.57366, "loss": 2.57366, "time": 0.82814} +{"mode": "train", "epoch": 135, "iter": 1800, "lr": 0.00262, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53859, "top5_acc": 0.77953, "loss_cls": 2.57727, "loss": 2.57727, "time": 0.83706} +{"mode": "train", "epoch": 135, "iter": 1900, "lr": 0.00261, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53734, "top5_acc": 0.77953, "loss_cls": 2.56028, "loss": 2.56028, "time": 0.83717} +{"mode": "train", "epoch": 135, "iter": 2000, "lr": 0.0026, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53094, "top5_acc": 0.77766, "loss_cls": 2.5953, "loss": 2.5953, "time": 0.83815} +{"mode": "train", "epoch": 135, "iter": 2100, "lr": 0.00259, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53953, "top5_acc": 0.78156, "loss_cls": 2.58312, "loss": 2.58312, "time": 0.83112} +{"mode": "train", "epoch": 135, "iter": 2200, "lr": 0.00258, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52875, "top5_acc": 0.77688, "loss_cls": 2.6037, "loss": 2.6037, "time": 0.83484} +{"mode": "train", "epoch": 135, "iter": 2300, "lr": 0.00257, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53047, "top5_acc": 0.77656, "loss_cls": 2.59534, "loss": 2.59534, "time": 0.83569} +{"mode": "train", "epoch": 135, "iter": 2400, "lr": 0.00256, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.52406, "top5_acc": 0.77125, "loss_cls": 2.61164, "loss": 2.61164, "time": 0.84108} +{"mode": "train", "epoch": 135, "iter": 2500, "lr": 0.00256, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.53312, "top5_acc": 0.77891, "loss_cls": 2.61095, "loss": 2.61095, "time": 0.84143} +{"mode": "train", "epoch": 135, "iter": 2600, "lr": 0.00255, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53531, "top5_acc": 0.78578, "loss_cls": 2.55429, "loss": 2.55429, "time": 0.83128} +{"mode": "train", "epoch": 135, "iter": 2700, "lr": 0.00254, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.535, "top5_acc": 0.77312, "loss_cls": 2.60119, "loss": 2.60119, "time": 0.84025} +{"mode": "train", "epoch": 135, "iter": 2800, "lr": 0.00253, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.52891, "top5_acc": 0.77078, "loss_cls": 2.60128, "loss": 2.60128, "time": 0.83176} +{"mode": "train", "epoch": 135, "iter": 2900, "lr": 0.00252, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53375, "top5_acc": 0.78109, "loss_cls": 2.5801, "loss": 2.5801, "time": 0.83516} +{"mode": "train", "epoch": 135, "iter": 3000, "lr": 0.00251, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54172, "top5_acc": 0.78484, "loss_cls": 2.54681, "loss": 2.54681, "time": 0.83812} +{"mode": "train", "epoch": 135, "iter": 3100, "lr": 0.0025, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.52516, "top5_acc": 0.76984, "loss_cls": 2.64019, "loss": 2.64019, "time": 0.83881} +{"mode": "train", "epoch": 135, "iter": 3200, "lr": 0.00249, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.52625, "top5_acc": 0.77156, "loss_cls": 2.62554, "loss": 2.62554, "time": 0.83797} +{"mode": "train", "epoch": 135, "iter": 3300, "lr": 0.00249, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.51453, "top5_acc": 0.76812, "loss_cls": 2.66526, "loss": 2.66526, "time": 0.83414} +{"mode": "train", "epoch": 135, "iter": 3400, "lr": 0.00248, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.52484, "top5_acc": 0.77109, "loss_cls": 2.61915, "loss": 2.61915, "time": 0.83292} +{"mode": "train", "epoch": 135, "iter": 3500, "lr": 0.00247, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.52766, "top5_acc": 0.77344, "loss_cls": 2.63732, "loss": 2.63732, "time": 0.83485} +{"mode": "train", "epoch": 135, "iter": 3600, "lr": 0.00246, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.525, "top5_acc": 0.76594, "loss_cls": 2.64607, "loss": 2.64607, "time": 0.83704} +{"mode": "train", "epoch": 135, "iter": 3700, "lr": 0.00245, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54125, "top5_acc": 0.77, "loss_cls": 2.6017, "loss": 2.6017, "time": 0.83137} +{"mode": "val", "epoch": 135, "iter": 309, "lr": 0.00245, "top1_acc": 0.43124, "top5_acc": 0.68024, "mean_class_accuracy": 0.431} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00244, "memory": 15990, "data_time": 1.37778, "top1_acc": 0.56844, "top5_acc": 0.79953, "loss_cls": 2.44283, "loss": 2.44283, "time": 2.37353} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.00243, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55719, "top5_acc": 0.7975, "loss_cls": 2.47035, "loss": 2.47035, "time": 0.83529} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00242, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.56063, "top5_acc": 0.79156, "loss_cls": 2.49059, "loss": 2.49059, "time": 0.8365} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00241, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.55562, "top5_acc": 0.79703, "loss_cls": 2.46601, "loss": 2.46601, "time": 0.8367} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.0024, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55266, "top5_acc": 0.79547, "loss_cls": 2.48422, "loss": 2.48422, "time": 0.83461} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.0024, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55062, "top5_acc": 0.79047, "loss_cls": 2.50822, "loss": 2.50822, "time": 0.82968} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00239, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55203, "top5_acc": 0.78469, "loss_cls": 2.50584, "loss": 2.50584, "time": 0.83678} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00238, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55391, "top5_acc": 0.79, "loss_cls": 2.50945, "loss": 2.50945, "time": 0.82851} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00237, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.54953, "top5_acc": 0.79469, "loss_cls": 2.50906, "loss": 2.50906, "time": 0.8347} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00236, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55875, "top5_acc": 0.79938, "loss_cls": 2.46309, "loss": 2.46309, "time": 0.83768} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00235, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.54453, "top5_acc": 0.77891, "loss_cls": 2.53236, "loss": 2.53236, "time": 0.83769} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00234, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54719, "top5_acc": 0.78984, "loss_cls": 2.51026, "loss": 2.51026, "time": 0.83709} +{"mode": "train", "epoch": 136, "iter": 1300, "lr": 0.00234, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53906, "top5_acc": 0.79188, "loss_cls": 2.5366, "loss": 2.5366, "time": 0.83111} +{"mode": "train", "epoch": 136, "iter": 1400, "lr": 0.00233, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54328, "top5_acc": 0.78922, "loss_cls": 2.54004, "loss": 2.54004, "time": 0.83116} +{"mode": "train", "epoch": 136, "iter": 1500, "lr": 0.00232, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54703, "top5_acc": 0.78109, "loss_cls": 2.53816, "loss": 2.53816, "time": 0.82534} +{"mode": "train", "epoch": 136, "iter": 1600, "lr": 0.00231, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54547, "top5_acc": 0.77953, "loss_cls": 2.54934, "loss": 2.54934, "time": 0.83207} +{"mode": "train", "epoch": 136, "iter": 1700, "lr": 0.0023, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54469, "top5_acc": 0.78797, "loss_cls": 2.52042, "loss": 2.52042, "time": 0.82668} +{"mode": "train", "epoch": 136, "iter": 1800, "lr": 0.00229, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53297, "top5_acc": 0.77578, "loss_cls": 2.57796, "loss": 2.57796, "time": 0.83615} +{"mode": "train", "epoch": 136, "iter": 1900, "lr": 0.00229, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53984, "top5_acc": 0.78625, "loss_cls": 2.54491, "loss": 2.54491, "time": 0.83602} +{"mode": "train", "epoch": 136, "iter": 2000, "lr": 0.00228, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54734, "top5_acc": 0.78781, "loss_cls": 2.51514, "loss": 2.51514, "time": 0.83519} +{"mode": "train", "epoch": 136, "iter": 2100, "lr": 0.00227, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54109, "top5_acc": 0.78656, "loss_cls": 2.54278, "loss": 2.54278, "time": 0.83343} +{"mode": "train", "epoch": 136, "iter": 2200, "lr": 0.00226, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54672, "top5_acc": 0.78812, "loss_cls": 2.53931, "loss": 2.53931, "time": 0.83464} +{"mode": "train", "epoch": 136, "iter": 2300, "lr": 0.00225, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53656, "top5_acc": 0.78172, "loss_cls": 2.55962, "loss": 2.55962, "time": 0.83396} +{"mode": "train", "epoch": 136, "iter": 2400, "lr": 0.00224, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54047, "top5_acc": 0.78641, "loss_cls": 2.54782, "loss": 2.54782, "time": 0.83838} +{"mode": "train", "epoch": 136, "iter": 2500, "lr": 0.00224, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53844, "top5_acc": 0.78531, "loss_cls": 2.56835, "loss": 2.56835, "time": 0.83583} +{"mode": "train", "epoch": 136, "iter": 2600, "lr": 0.00223, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53016, "top5_acc": 0.78219, "loss_cls": 2.57279, "loss": 2.57279, "time": 0.83817} +{"mode": "train", "epoch": 136, "iter": 2700, "lr": 0.00222, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53547, "top5_acc": 0.77844, "loss_cls": 2.56972, "loss": 2.56972, "time": 0.83606} +{"mode": "train", "epoch": 136, "iter": 2800, "lr": 0.00221, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53719, "top5_acc": 0.77969, "loss_cls": 2.56488, "loss": 2.56488, "time": 0.83518} +{"mode": "train", "epoch": 136, "iter": 2900, "lr": 0.0022, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54312, "top5_acc": 0.78281, "loss_cls": 2.5578, "loss": 2.5578, "time": 0.83772} +{"mode": "train", "epoch": 136, "iter": 3000, "lr": 0.00219, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.53719, "top5_acc": 0.7875, "loss_cls": 2.5461, "loss": 2.5461, "time": 0.83813} +{"mode": "train", "epoch": 136, "iter": 3100, "lr": 0.00219, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54391, "top5_acc": 0.78766, "loss_cls": 2.55785, "loss": 2.55785, "time": 0.83504} +{"mode": "train", "epoch": 136, "iter": 3200, "lr": 0.00218, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.53641, "top5_acc": 0.78344, "loss_cls": 2.57657, "loss": 2.57657, "time": 0.82875} +{"mode": "train", "epoch": 136, "iter": 3300, "lr": 0.00217, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.54125, "top5_acc": 0.78391, "loss_cls": 2.56376, "loss": 2.56376, "time": 0.82819} +{"mode": "train", "epoch": 136, "iter": 3400, "lr": 0.00216, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.54234, "top5_acc": 0.78016, "loss_cls": 2.52382, "loss": 2.52382, "time": 0.83876} +{"mode": "train", "epoch": 136, "iter": 3500, "lr": 0.00215, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54656, "top5_acc": 0.78719, "loss_cls": 2.52205, "loss": 2.52205, "time": 0.84375} +{"mode": "train", "epoch": 136, "iter": 3600, "lr": 0.00215, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53031, "top5_acc": 0.77703, "loss_cls": 2.60011, "loss": 2.60011, "time": 0.83656} +{"mode": "train", "epoch": 136, "iter": 3700, "lr": 0.00214, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53953, "top5_acc": 0.78609, "loss_cls": 2.55876, "loss": 2.55876, "time": 0.83088} +{"mode": "val", "epoch": 136, "iter": 309, "lr": 0.00213, "top1_acc": 0.43469, "top5_acc": 0.68475, "mean_class_accuracy": 0.43444} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00213, "memory": 15990, "data_time": 1.33871, "top1_acc": 0.57328, "top5_acc": 0.80078, "loss_cls": 2.40506, "loss": 2.40506, "time": 2.32298} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00212, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56859, "top5_acc": 0.80406, "loss_cls": 2.43133, "loss": 2.43133, "time": 0.81722} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00211, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.56188, "top5_acc": 0.80375, "loss_cls": 2.42741, "loss": 2.42741, "time": 0.8156} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.0021, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56422, "top5_acc": 0.80078, "loss_cls": 2.43317, "loss": 2.43317, "time": 0.81271} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.00209, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56063, "top5_acc": 0.80281, "loss_cls": 2.40846, "loss": 2.40846, "time": 0.82066} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.00209, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55344, "top5_acc": 0.795, "loss_cls": 2.46239, "loss": 2.46239, "time": 0.8227} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00208, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55672, "top5_acc": 0.80047, "loss_cls": 2.46033, "loss": 2.46033, "time": 0.81849} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00207, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54875, "top5_acc": 0.79172, "loss_cls": 2.49251, "loss": 2.49251, "time": 0.82066} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00206, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.5525, "top5_acc": 0.78812, "loss_cls": 2.50887, "loss": 2.50887, "time": 0.82233} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00205, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55234, "top5_acc": 0.79359, "loss_cls": 2.46855, "loss": 2.46855, "time": 0.82118} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00205, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56625, "top5_acc": 0.80016, "loss_cls": 2.43191, "loss": 2.43191, "time": 0.81429} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00204, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54594, "top5_acc": 0.79594, "loss_cls": 2.48016, "loss": 2.48016, "time": 0.81492} +{"mode": "train", "epoch": 137, "iter": 1300, "lr": 0.00203, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55812, "top5_acc": 0.79281, "loss_cls": 2.48468, "loss": 2.48468, "time": 0.82348} +{"mode": "train", "epoch": 137, "iter": 1400, "lr": 0.00202, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56844, "top5_acc": 0.79969, "loss_cls": 2.44477, "loss": 2.44477, "time": 0.82165} +{"mode": "train", "epoch": 137, "iter": 1500, "lr": 0.00201, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.55688, "top5_acc": 0.79469, "loss_cls": 2.48017, "loss": 2.48017, "time": 0.82386} +{"mode": "train", "epoch": 137, "iter": 1600, "lr": 0.00201, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54344, "top5_acc": 0.78297, "loss_cls": 2.52008, "loss": 2.52008, "time": 0.81249} +{"mode": "train", "epoch": 137, "iter": 1700, "lr": 0.002, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54047, "top5_acc": 0.78984, "loss_cls": 2.53022, "loss": 2.53022, "time": 0.81993} +{"mode": "train", "epoch": 137, "iter": 1800, "lr": 0.00199, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55562, "top5_acc": 0.79672, "loss_cls": 2.47469, "loss": 2.47469, "time": 0.8128} +{"mode": "train", "epoch": 137, "iter": 1900, "lr": 0.00198, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.54688, "top5_acc": 0.79234, "loss_cls": 2.49174, "loss": 2.49174, "time": 0.81776} +{"mode": "train", "epoch": 137, "iter": 2000, "lr": 0.00198, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54516, "top5_acc": 0.79672, "loss_cls": 2.49784, "loss": 2.49784, "time": 0.81807} +{"mode": "train", "epoch": 137, "iter": 2100, "lr": 0.00197, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.54781, "top5_acc": 0.79438, "loss_cls": 2.4978, "loss": 2.4978, "time": 0.81546} +{"mode": "train", "epoch": 137, "iter": 2200, "lr": 0.00196, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55109, "top5_acc": 0.79172, "loss_cls": 2.48707, "loss": 2.48707, "time": 0.82175} +{"mode": "train", "epoch": 137, "iter": 2300, "lr": 0.00195, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55219, "top5_acc": 0.78828, "loss_cls": 2.50104, "loss": 2.50104, "time": 0.8237} +{"mode": "train", "epoch": 137, "iter": 2400, "lr": 0.00194, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5575, "top5_acc": 0.79094, "loss_cls": 2.46161, "loss": 2.46161, "time": 0.81753} +{"mode": "train", "epoch": 137, "iter": 2500, "lr": 0.00194, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54734, "top5_acc": 0.78922, "loss_cls": 2.51112, "loss": 2.51112, "time": 0.82103} +{"mode": "train", "epoch": 137, "iter": 2600, "lr": 0.00193, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.5525, "top5_acc": 0.79672, "loss_cls": 2.47918, "loss": 2.47918, "time": 0.81614} +{"mode": "train", "epoch": 137, "iter": 2700, "lr": 0.00192, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55844, "top5_acc": 0.78688, "loss_cls": 2.50792, "loss": 2.50792, "time": 0.81444} +{"mode": "train", "epoch": 137, "iter": 2800, "lr": 0.00191, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54906, "top5_acc": 0.79219, "loss_cls": 2.48263, "loss": 2.48263, "time": 0.81487} +{"mode": "train", "epoch": 137, "iter": 2900, "lr": 0.00191, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54516, "top5_acc": 0.79234, "loss_cls": 2.5092, "loss": 2.5092, "time": 0.82341} +{"mode": "train", "epoch": 137, "iter": 3000, "lr": 0.0019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55203, "top5_acc": 0.79656, "loss_cls": 2.47928, "loss": 2.47928, "time": 0.82239} +{"mode": "train", "epoch": 137, "iter": 3100, "lr": 0.00189, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5475, "top5_acc": 0.785, "loss_cls": 2.51992, "loss": 2.51992, "time": 0.82087} +{"mode": "train", "epoch": 137, "iter": 3200, "lr": 0.00188, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.5475, "top5_acc": 0.78438, "loss_cls": 2.54619, "loss": 2.54619, "time": 0.82392} +{"mode": "train", "epoch": 137, "iter": 3300, "lr": 0.00188, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53703, "top5_acc": 0.77953, "loss_cls": 2.56183, "loss": 2.56183, "time": 0.82044} +{"mode": "train", "epoch": 137, "iter": 3400, "lr": 0.00187, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54641, "top5_acc": 0.78969, "loss_cls": 2.49918, "loss": 2.49918, "time": 0.82217} +{"mode": "train", "epoch": 137, "iter": 3500, "lr": 0.00186, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.555, "top5_acc": 0.79328, "loss_cls": 2.48436, "loss": 2.48436, "time": 0.81712} +{"mode": "train", "epoch": 137, "iter": 3600, "lr": 0.00185, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55562, "top5_acc": 0.80234, "loss_cls": 2.43582, "loss": 2.43582, "time": 0.82081} +{"mode": "train", "epoch": 137, "iter": 3700, "lr": 0.00185, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54859, "top5_acc": 0.78781, "loss_cls": 2.51332, "loss": 2.51332, "time": 0.81075} +{"mode": "val", "epoch": 137, "iter": 309, "lr": 0.00184, "top1_acc": 0.4396, "top5_acc": 0.68455, "mean_class_accuracy": 0.43932} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00183, "memory": 15990, "data_time": 1.27916, "top1_acc": 0.58094, "top5_acc": 0.80641, "loss_cls": 2.3913, "loss": 2.3913, "time": 2.26411} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00183, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56594, "top5_acc": 0.81047, "loss_cls": 2.3955, "loss": 2.3955, "time": 0.81813} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00182, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.565, "top5_acc": 0.80312, "loss_cls": 2.41204, "loss": 2.41204, "time": 0.82253} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00181, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57203, "top5_acc": 0.81031, "loss_cls": 2.36924, "loss": 2.36924, "time": 0.8159} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.0018, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57125, "top5_acc": 0.80109, "loss_cls": 2.42199, "loss": 2.42199, "time": 0.82321} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.0018, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57172, "top5_acc": 0.80531, "loss_cls": 2.40197, "loss": 2.40197, "time": 0.8151} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00179, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56375, "top5_acc": 0.80938, "loss_cls": 2.39752, "loss": 2.39752, "time": 0.82016} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00178, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.575, "top5_acc": 0.80531, "loss_cls": 2.376, "loss": 2.376, "time": 0.8154} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00177, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.56531, "top5_acc": 0.80422, "loss_cls": 2.43166, "loss": 2.43166, "time": 0.81739} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00177, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56828, "top5_acc": 0.81016, "loss_cls": 2.39764, "loss": 2.39764, "time": 0.82037} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.00176, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55891, "top5_acc": 0.78688, "loss_cls": 2.47725, "loss": 2.47725, "time": 0.8193} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.00175, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56609, "top5_acc": 0.80469, "loss_cls": 2.42588, "loss": 2.42588, "time": 0.82209} +{"mode": "train", "epoch": 138, "iter": 1300, "lr": 0.00175, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57219, "top5_acc": 0.81078, "loss_cls": 2.3911, "loss": 2.3911, "time": 0.82267} +{"mode": "train", "epoch": 138, "iter": 1400, "lr": 0.00174, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56312, "top5_acc": 0.80531, "loss_cls": 2.40906, "loss": 2.40906, "time": 0.81857} +{"mode": "train", "epoch": 138, "iter": 1500, "lr": 0.00173, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57547, "top5_acc": 0.8025, "loss_cls": 2.4171, "loss": 2.4171, "time": 0.81971} +{"mode": "train", "epoch": 138, "iter": 1600, "lr": 0.00172, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55953, "top5_acc": 0.79922, "loss_cls": 2.43047, "loss": 2.43047, "time": 0.82016} +{"mode": "train", "epoch": 138, "iter": 1700, "lr": 0.00172, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.56953, "top5_acc": 0.80297, "loss_cls": 2.38643, "loss": 2.38643, "time": 0.81642} +{"mode": "train", "epoch": 138, "iter": 1800, "lr": 0.00171, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5625, "top5_acc": 0.79656, "loss_cls": 2.46255, "loss": 2.46255, "time": 0.82702} +{"mode": "train", "epoch": 138, "iter": 1900, "lr": 0.0017, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56609, "top5_acc": 0.80875, "loss_cls": 2.41393, "loss": 2.41393, "time": 0.81664} +{"mode": "train", "epoch": 138, "iter": 2000, "lr": 0.00169, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55891, "top5_acc": 0.79938, "loss_cls": 2.45545, "loss": 2.45545, "time": 0.81803} +{"mode": "train", "epoch": 138, "iter": 2100, "lr": 0.00169, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55703, "top5_acc": 0.79562, "loss_cls": 2.47445, "loss": 2.47445, "time": 0.81365} +{"mode": "train", "epoch": 138, "iter": 2200, "lr": 0.00168, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.54422, "top5_acc": 0.79031, "loss_cls": 2.51314, "loss": 2.51314, "time": 0.82074} +{"mode": "train", "epoch": 138, "iter": 2300, "lr": 0.00167, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56484, "top5_acc": 0.80703, "loss_cls": 2.39948, "loss": 2.39948, "time": 0.82003} +{"mode": "train", "epoch": 138, "iter": 2400, "lr": 0.00167, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56594, "top5_acc": 0.80375, "loss_cls": 2.41916, "loss": 2.41916, "time": 0.81509} +{"mode": "train", "epoch": 138, "iter": 2500, "lr": 0.00166, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54937, "top5_acc": 0.78984, "loss_cls": 2.48297, "loss": 2.48297, "time": 0.8187} +{"mode": "train", "epoch": 138, "iter": 2600, "lr": 0.00165, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56781, "top5_acc": 0.79859, "loss_cls": 2.43146, "loss": 2.43146, "time": 0.82022} +{"mode": "train", "epoch": 138, "iter": 2700, "lr": 0.00164, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55125, "top5_acc": 0.80531, "loss_cls": 2.43851, "loss": 2.43851, "time": 0.82691} +{"mode": "train", "epoch": 138, "iter": 2800, "lr": 0.00164, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56016, "top5_acc": 0.78953, "loss_cls": 2.45238, "loss": 2.45238, "time": 0.82012} +{"mode": "train", "epoch": 138, "iter": 2900, "lr": 0.00163, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55188, "top5_acc": 0.79219, "loss_cls": 2.47783, "loss": 2.47783, "time": 0.82211} +{"mode": "train", "epoch": 138, "iter": 3000, "lr": 0.00162, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55234, "top5_acc": 0.80141, "loss_cls": 2.46235, "loss": 2.46235, "time": 0.82236} +{"mode": "train", "epoch": 138, "iter": 3100, "lr": 0.00162, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55562, "top5_acc": 0.79375, "loss_cls": 2.46293, "loss": 2.46293, "time": 0.82065} +{"mode": "train", "epoch": 138, "iter": 3200, "lr": 0.00161, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55672, "top5_acc": 0.79688, "loss_cls": 2.47993, "loss": 2.47993, "time": 0.8194} +{"mode": "train", "epoch": 138, "iter": 3300, "lr": 0.0016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55844, "top5_acc": 0.79625, "loss_cls": 2.45145, "loss": 2.45145, "time": 0.82392} +{"mode": "train", "epoch": 138, "iter": 3400, "lr": 0.0016, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56656, "top5_acc": 0.79531, "loss_cls": 2.42036, "loss": 2.42036, "time": 0.81643} +{"mode": "train", "epoch": 138, "iter": 3500, "lr": 0.00159, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54969, "top5_acc": 0.79344, "loss_cls": 2.47597, "loss": 2.47597, "time": 0.81687} +{"mode": "train", "epoch": 138, "iter": 3600, "lr": 0.00158, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56219, "top5_acc": 0.79484, "loss_cls": 2.43275, "loss": 2.43275, "time": 0.82297} +{"mode": "train", "epoch": 138, "iter": 3700, "lr": 0.00157, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.56188, "top5_acc": 0.80125, "loss_cls": 2.41058, "loss": 2.41058, "time": 0.81573} +{"mode": "val", "epoch": 138, "iter": 309, "lr": 0.00157, "top1_acc": 0.44112, "top5_acc": 0.69093, "mean_class_accuracy": 0.44089} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00156, "memory": 15990, "data_time": 1.29965, "top1_acc": 0.58938, "top5_acc": 0.81875, "loss_cls": 2.31113, "loss": 2.31113, "time": 2.27506} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00156, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57547, "top5_acc": 0.8125, "loss_cls": 2.34706, "loss": 2.34706, "time": 0.82738} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00155, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58531, "top5_acc": 0.82125, "loss_cls": 2.32095, "loss": 2.32095, "time": 0.81598} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00154, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57172, "top5_acc": 0.80594, "loss_cls": 2.38936, "loss": 2.38936, "time": 0.81749} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00154, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58438, "top5_acc": 0.81594, "loss_cls": 2.31842, "loss": 2.31842, "time": 0.81763} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00153, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57891, "top5_acc": 0.81, "loss_cls": 2.36125, "loss": 2.36125, "time": 0.82352} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00152, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59094, "top5_acc": 0.81469, "loss_cls": 2.32846, "loss": 2.32846, "time": 0.81818} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00152, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56297, "top5_acc": 0.80266, "loss_cls": 2.38773, "loss": 2.38773, "time": 0.81874} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00151, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57984, "top5_acc": 0.81078, "loss_cls": 2.37701, "loss": 2.37701, "time": 0.82515} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.0015, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56078, "top5_acc": 0.80688, "loss_cls": 2.40471, "loss": 2.40471, "time": 0.81646} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.0015, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57797, "top5_acc": 0.81828, "loss_cls": 2.34213, "loss": 2.34213, "time": 0.81955} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00149, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56844, "top5_acc": 0.80688, "loss_cls": 2.37699, "loss": 2.37699, "time": 0.81588} +{"mode": "train", "epoch": 139, "iter": 1300, "lr": 0.00148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56984, "top5_acc": 0.81594, "loss_cls": 2.38787, "loss": 2.38787, "time": 0.81429} +{"mode": "train", "epoch": 139, "iter": 1400, "lr": 0.00148, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57141, "top5_acc": 0.80859, "loss_cls": 2.39156, "loss": 2.39156, "time": 0.81432} +{"mode": "train", "epoch": 139, "iter": 1500, "lr": 0.00147, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57078, "top5_acc": 0.81281, "loss_cls": 2.36857, "loss": 2.36857, "time": 0.81624} +{"mode": "train", "epoch": 139, "iter": 1600, "lr": 0.00146, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57016, "top5_acc": 0.81281, "loss_cls": 2.35994, "loss": 2.35994, "time": 0.81628} +{"mode": "train", "epoch": 139, "iter": 1700, "lr": 0.00145, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57609, "top5_acc": 0.80938, "loss_cls": 2.36409, "loss": 2.36409, "time": 0.8179} +{"mode": "train", "epoch": 139, "iter": 1800, "lr": 0.00145, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57953, "top5_acc": 0.81641, "loss_cls": 2.32179, "loss": 2.32179, "time": 0.82726} +{"mode": "train", "epoch": 139, "iter": 1900, "lr": 0.00144, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.57203, "top5_acc": 0.80688, "loss_cls": 2.38435, "loss": 2.38435, "time": 0.81183} +{"mode": "train", "epoch": 139, "iter": 2000, "lr": 0.00143, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.57766, "top5_acc": 0.81688, "loss_cls": 2.35495, "loss": 2.35495, "time": 0.82015} +{"mode": "train", "epoch": 139, "iter": 2100, "lr": 0.00143, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57516, "top5_acc": 0.80562, "loss_cls": 2.38902, "loss": 2.38902, "time": 0.81435} +{"mode": "train", "epoch": 139, "iter": 2200, "lr": 0.00142, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.555, "top5_acc": 0.80203, "loss_cls": 2.40075, "loss": 2.40075, "time": 0.81898} +{"mode": "train", "epoch": 139, "iter": 2300, "lr": 0.00142, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57328, "top5_acc": 0.80656, "loss_cls": 2.37887, "loss": 2.37887, "time": 0.8252} +{"mode": "train", "epoch": 139, "iter": 2400, "lr": 0.00141, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.57453, "top5_acc": 0.80578, "loss_cls": 2.38536, "loss": 2.38536, "time": 0.81464} +{"mode": "train", "epoch": 139, "iter": 2500, "lr": 0.0014, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55891, "top5_acc": 0.8025, "loss_cls": 2.42057, "loss": 2.42057, "time": 0.81557} +{"mode": "train", "epoch": 139, "iter": 2600, "lr": 0.0014, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56641, "top5_acc": 0.81344, "loss_cls": 2.37261, "loss": 2.37261, "time": 0.81936} +{"mode": "train", "epoch": 139, "iter": 2700, "lr": 0.00139, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55984, "top5_acc": 0.81, "loss_cls": 2.39468, "loss": 2.39468, "time": 0.81974} +{"mode": "train", "epoch": 139, "iter": 2800, "lr": 0.00138, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57656, "top5_acc": 0.80766, "loss_cls": 2.37268, "loss": 2.37268, "time": 0.81962} +{"mode": "train", "epoch": 139, "iter": 2900, "lr": 0.00138, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57844, "top5_acc": 0.80875, "loss_cls": 2.35673, "loss": 2.35673, "time": 0.81697} +{"mode": "train", "epoch": 139, "iter": 3000, "lr": 0.00137, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55203, "top5_acc": 0.79703, "loss_cls": 2.46932, "loss": 2.46932, "time": 0.8243} +{"mode": "train", "epoch": 139, "iter": 3100, "lr": 0.00136, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.55812, "top5_acc": 0.80109, "loss_cls": 2.43316, "loss": 2.43316, "time": 0.82743} +{"mode": "train", "epoch": 139, "iter": 3200, "lr": 0.00136, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56516, "top5_acc": 0.79406, "loss_cls": 2.42113, "loss": 2.42113, "time": 0.81948} +{"mode": "train", "epoch": 139, "iter": 3300, "lr": 0.00135, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56594, "top5_acc": 0.79875, "loss_cls": 2.41614, "loss": 2.41614, "time": 0.82165} +{"mode": "train", "epoch": 139, "iter": 3400, "lr": 0.00134, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57094, "top5_acc": 0.81016, "loss_cls": 2.37334, "loss": 2.37334, "time": 0.81888} +{"mode": "train", "epoch": 139, "iter": 3500, "lr": 0.00134, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57172, "top5_acc": 0.805, "loss_cls": 2.40822, "loss": 2.40822, "time": 0.81639} +{"mode": "train", "epoch": 139, "iter": 3600, "lr": 0.00133, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55984, "top5_acc": 0.79953, "loss_cls": 2.42267, "loss": 2.42267, "time": 0.82267} +{"mode": "train", "epoch": 139, "iter": 3700, "lr": 0.00132, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56641, "top5_acc": 0.80625, "loss_cls": 2.40923, "loss": 2.40923, "time": 0.82006} +{"mode": "val", "epoch": 139, "iter": 309, "lr": 0.00132, "top1_acc": 0.44907, "top5_acc": 0.69068, "mean_class_accuracy": 0.44884} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00131, "memory": 15990, "data_time": 1.31745, "top1_acc": 0.60172, "top5_acc": 0.8275, "loss_cls": 2.23308, "loss": 2.23308, "time": 2.31074} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00131, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59406, "top5_acc": 0.83031, "loss_cls": 2.25882, "loss": 2.25882, "time": 0.82362} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.0013, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59078, "top5_acc": 0.81359, "loss_cls": 2.33866, "loss": 2.33866, "time": 0.82349} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.0013, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.59359, "top5_acc": 0.82219, "loss_cls": 2.27621, "loss": 2.27621, "time": 0.83027} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00129, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.60141, "top5_acc": 0.8275, "loss_cls": 2.26412, "loss": 2.26412, "time": 0.842} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.00128, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58984, "top5_acc": 0.81938, "loss_cls": 2.28549, "loss": 2.28549, "time": 0.82391} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.00128, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59719, "top5_acc": 0.81812, "loss_cls": 2.30612, "loss": 2.30612, "time": 0.82273} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00127, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59203, "top5_acc": 0.82469, "loss_cls": 2.27641, "loss": 2.27641, "time": 0.81523} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00126, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58141, "top5_acc": 0.81781, "loss_cls": 2.32065, "loss": 2.32065, "time": 0.81958} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00126, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58609, "top5_acc": 0.82172, "loss_cls": 2.298, "loss": 2.298, "time": 0.81812} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00125, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57875, "top5_acc": 0.80844, "loss_cls": 2.34498, "loss": 2.34498, "time": 0.81628} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00125, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57641, "top5_acc": 0.81656, "loss_cls": 2.33475, "loss": 2.33475, "time": 0.82016} +{"mode": "train", "epoch": 140, "iter": 1300, "lr": 0.00124, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.575, "top5_acc": 0.8125, "loss_cls": 2.34916, "loss": 2.34916, "time": 0.81582} +{"mode": "train", "epoch": 140, "iter": 1400, "lr": 0.00123, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59609, "top5_acc": 0.82297, "loss_cls": 2.29231, "loss": 2.29231, "time": 0.81627} +{"mode": "train", "epoch": 140, "iter": 1500, "lr": 0.00123, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58328, "top5_acc": 0.81781, "loss_cls": 2.29603, "loss": 2.29603, "time": 0.81499} +{"mode": "train", "epoch": 140, "iter": 1600, "lr": 0.00122, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58203, "top5_acc": 0.81141, "loss_cls": 2.34542, "loss": 2.34542, "time": 0.81871} +{"mode": "train", "epoch": 140, "iter": 1700, "lr": 0.00121, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58766, "top5_acc": 0.81688, "loss_cls": 2.31252, "loss": 2.31252, "time": 0.82163} +{"mode": "train", "epoch": 140, "iter": 1800, "lr": 0.00121, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58578, "top5_acc": 0.81531, "loss_cls": 2.31301, "loss": 2.31301, "time": 0.821} +{"mode": "train", "epoch": 140, "iter": 1900, "lr": 0.0012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58797, "top5_acc": 0.81438, "loss_cls": 2.30517, "loss": 2.30517, "time": 0.81624} +{"mode": "train", "epoch": 140, "iter": 2000, "lr": 0.0012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58406, "top5_acc": 0.8175, "loss_cls": 2.3121, "loss": 2.3121, "time": 0.81463} +{"mode": "train", "epoch": 140, "iter": 2100, "lr": 0.00119, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57922, "top5_acc": 0.81172, "loss_cls": 2.343, "loss": 2.343, "time": 0.8191} +{"mode": "train", "epoch": 140, "iter": 2200, "lr": 0.00118, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57594, "top5_acc": 0.81312, "loss_cls": 2.32839, "loss": 2.32839, "time": 0.81928} +{"mode": "train", "epoch": 140, "iter": 2300, "lr": 0.00118, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57797, "top5_acc": 0.80953, "loss_cls": 2.35396, "loss": 2.35396, "time": 0.8147} +{"mode": "train", "epoch": 140, "iter": 2400, "lr": 0.00117, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57859, "top5_acc": 0.80891, "loss_cls": 2.36067, "loss": 2.36067, "time": 0.8144} +{"mode": "train", "epoch": 140, "iter": 2500, "lr": 0.00117, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56656, "top5_acc": 0.80609, "loss_cls": 2.38036, "loss": 2.38036, "time": 0.82022} +{"mode": "train", "epoch": 140, "iter": 2600, "lr": 0.00116, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56938, "top5_acc": 0.79828, "loss_cls": 2.41664, "loss": 2.41664, "time": 0.81688} +{"mode": "train", "epoch": 140, "iter": 2700, "lr": 0.00115, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58594, "top5_acc": 0.81438, "loss_cls": 2.32225, "loss": 2.32225, "time": 0.82258} +{"mode": "train", "epoch": 140, "iter": 2800, "lr": 0.00115, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57891, "top5_acc": 0.81844, "loss_cls": 2.32953, "loss": 2.32953, "time": 0.81402} +{"mode": "train", "epoch": 140, "iter": 2900, "lr": 0.00114, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59109, "top5_acc": 0.82172, "loss_cls": 2.28214, "loss": 2.28214, "time": 0.81731} +{"mode": "train", "epoch": 140, "iter": 3000, "lr": 0.00114, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58031, "top5_acc": 0.81766, "loss_cls": 2.3145, "loss": 2.3145, "time": 0.81894} +{"mode": "train", "epoch": 140, "iter": 3100, "lr": 0.00113, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.58562, "top5_acc": 0.81828, "loss_cls": 2.30962, "loss": 2.30962, "time": 0.82435} +{"mode": "train", "epoch": 140, "iter": 3200, "lr": 0.00112, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56828, "top5_acc": 0.81016, "loss_cls": 2.38198, "loss": 2.38198, "time": 0.81812} +{"mode": "train", "epoch": 140, "iter": 3300, "lr": 0.00112, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.5725, "top5_acc": 0.815, "loss_cls": 2.34503, "loss": 2.34503, "time": 0.8274} +{"mode": "train", "epoch": 140, "iter": 3400, "lr": 0.00111, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58, "top5_acc": 0.80188, "loss_cls": 2.37315, "loss": 2.37315, "time": 0.81991} +{"mode": "train", "epoch": 140, "iter": 3500, "lr": 0.00111, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58156, "top5_acc": 0.81297, "loss_cls": 2.32877, "loss": 2.32877, "time": 0.82306} +{"mode": "train", "epoch": 140, "iter": 3600, "lr": 0.0011, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58016, "top5_acc": 0.81625, "loss_cls": 2.30249, "loss": 2.30249, "time": 0.82061} +{"mode": "train", "epoch": 140, "iter": 3700, "lr": 0.0011, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57188, "top5_acc": 0.80906, "loss_cls": 2.37259, "loss": 2.37259, "time": 0.8167} +{"mode": "val", "epoch": 140, "iter": 309, "lr": 0.00109, "top1_acc": 0.44684, "top5_acc": 0.69463, "mean_class_accuracy": 0.44658} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00109, "memory": 15990, "data_time": 1.30245, "top1_acc": 0.60906, "top5_acc": 0.82781, "loss_cls": 2.21896, "loss": 2.21896, "time": 2.29865} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00108, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60938, "top5_acc": 0.82969, "loss_cls": 2.20809, "loss": 2.20809, "time": 0.8241} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00108, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60203, "top5_acc": 0.83422, "loss_cls": 2.22307, "loss": 2.22307, "time": 0.81963} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00107, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59688, "top5_acc": 0.83594, "loss_cls": 2.21225, "loss": 2.21225, "time": 0.81895} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00106, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59984, "top5_acc": 0.83047, "loss_cls": 2.2288, "loss": 2.2288, "time": 0.81762} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00106, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.60047, "top5_acc": 0.83281, "loss_cls": 2.22595, "loss": 2.22595, "time": 0.82352} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00105, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60719, "top5_acc": 0.82719, "loss_cls": 2.22146, "loss": 2.22146, "time": 0.81657} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00105, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59406, "top5_acc": 0.81547, "loss_cls": 2.26944, "loss": 2.26944, "time": 0.81641} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00104, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60219, "top5_acc": 0.82906, "loss_cls": 2.22121, "loss": 2.22121, "time": 0.81533} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00104, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.59, "top5_acc": 0.82359, "loss_cls": 2.27816, "loss": 2.27816, "time": 0.81716} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00103, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59172, "top5_acc": 0.82156, "loss_cls": 2.25218, "loss": 2.25218, "time": 0.81937} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00102, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60359, "top5_acc": 0.82781, "loss_cls": 2.22982, "loss": 2.22982, "time": 0.81617} +{"mode": "train", "epoch": 141, "iter": 1300, "lr": 0.00102, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58766, "top5_acc": 0.82312, "loss_cls": 2.26253, "loss": 2.26253, "time": 0.81481} +{"mode": "train", "epoch": 141, "iter": 1400, "lr": 0.00101, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59109, "top5_acc": 0.82438, "loss_cls": 2.23571, "loss": 2.23571, "time": 0.82301} +{"mode": "train", "epoch": 141, "iter": 1500, "lr": 0.00101, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59078, "top5_acc": 0.81453, "loss_cls": 2.3029, "loss": 2.3029, "time": 0.81414} +{"mode": "train", "epoch": 141, "iter": 1600, "lr": 0.001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58375, "top5_acc": 0.81812, "loss_cls": 2.29056, "loss": 2.29056, "time": 0.81623} +{"mode": "train", "epoch": 141, "iter": 1700, "lr": 0.001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59438, "top5_acc": 0.82078, "loss_cls": 2.2801, "loss": 2.2801, "time": 0.82328} +{"mode": "train", "epoch": 141, "iter": 1800, "lr": 0.00099, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60156, "top5_acc": 0.82938, "loss_cls": 2.23732, "loss": 2.23732, "time": 0.82443} +{"mode": "train", "epoch": 141, "iter": 1900, "lr": 0.00099, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59453, "top5_acc": 0.82359, "loss_cls": 2.26753, "loss": 2.26753, "time": 0.81928} +{"mode": "train", "epoch": 141, "iter": 2000, "lr": 0.00098, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59375, "top5_acc": 0.81797, "loss_cls": 2.28586, "loss": 2.28586, "time": 0.81823} +{"mode": "train", "epoch": 141, "iter": 2100, "lr": 0.00097, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60125, "top5_acc": 0.82438, "loss_cls": 2.24323, "loss": 2.24323, "time": 0.82093} +{"mode": "train", "epoch": 141, "iter": 2200, "lr": 0.00097, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58891, "top5_acc": 0.81547, "loss_cls": 2.29327, "loss": 2.29327, "time": 0.81624} +{"mode": "train", "epoch": 141, "iter": 2300, "lr": 0.00096, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58797, "top5_acc": 0.81938, "loss_cls": 2.2871, "loss": 2.2871, "time": 0.81826} +{"mode": "train", "epoch": 141, "iter": 2400, "lr": 0.00096, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.60688, "top5_acc": 0.82969, "loss_cls": 2.22524, "loss": 2.22524, "time": 0.82264} +{"mode": "train", "epoch": 141, "iter": 2500, "lr": 0.00095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58703, "top5_acc": 0.82359, "loss_cls": 2.27983, "loss": 2.27983, "time": 0.82194} +{"mode": "train", "epoch": 141, "iter": 2600, "lr": 0.00095, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59375, "top5_acc": 0.82859, "loss_cls": 2.24525, "loss": 2.24525, "time": 0.81798} +{"mode": "train", "epoch": 141, "iter": 2700, "lr": 0.00094, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58547, "top5_acc": 0.81672, "loss_cls": 2.30576, "loss": 2.30576, "time": 0.81958} +{"mode": "train", "epoch": 141, "iter": 2800, "lr": 0.00094, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58547, "top5_acc": 0.81641, "loss_cls": 2.28602, "loss": 2.28602, "time": 0.81833} +{"mode": "train", "epoch": 141, "iter": 2900, "lr": 0.00093, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59344, "top5_acc": 0.82797, "loss_cls": 2.24339, "loss": 2.24339, "time": 0.8147} +{"mode": "train", "epoch": 141, "iter": 3000, "lr": 0.00093, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58109, "top5_acc": 0.81578, "loss_cls": 2.31855, "loss": 2.31855, "time": 0.81419} +{"mode": "train", "epoch": 141, "iter": 3100, "lr": 0.00092, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59016, "top5_acc": 0.81641, "loss_cls": 2.31475, "loss": 2.31475, "time": 0.81889} +{"mode": "train", "epoch": 141, "iter": 3200, "lr": 0.00091, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58547, "top5_acc": 0.82469, "loss_cls": 2.28472, "loss": 2.28472, "time": 0.81446} +{"mode": "train", "epoch": 141, "iter": 3300, "lr": 0.00091, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58312, "top5_acc": 0.815, "loss_cls": 2.29584, "loss": 2.29584, "time": 0.83071} +{"mode": "train", "epoch": 141, "iter": 3400, "lr": 0.0009, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58812, "top5_acc": 0.81422, "loss_cls": 2.31887, "loss": 2.31887, "time": 0.81447} +{"mode": "train", "epoch": 141, "iter": 3500, "lr": 0.0009, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58641, "top5_acc": 0.81469, "loss_cls": 2.30876, "loss": 2.30876, "time": 0.82238} +{"mode": "train", "epoch": 141, "iter": 3600, "lr": 0.00089, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59578, "top5_acc": 0.82391, "loss_cls": 2.25265, "loss": 2.25265, "time": 0.81577} +{"mode": "train", "epoch": 141, "iter": 3700, "lr": 0.00089, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57984, "top5_acc": 0.81312, "loss_cls": 2.33551, "loss": 2.33551, "time": 0.81547} +{"mode": "val", "epoch": 141, "iter": 309, "lr": 0.00089, "top1_acc": 0.44902, "top5_acc": 0.69574, "mean_class_accuracy": 0.44872} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00088, "memory": 15990, "data_time": 1.34725, "top1_acc": 0.59781, "top5_acc": 0.82703, "loss_cls": 2.20488, "loss": 2.20488, "time": 2.33252} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00088, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.60281, "top5_acc": 0.82891, "loss_cls": 2.21629, "loss": 2.21629, "time": 0.82053} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00087, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.61703, "top5_acc": 0.84406, "loss_cls": 2.15811, "loss": 2.15811, "time": 0.82171} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00086, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61047, "top5_acc": 0.83484, "loss_cls": 2.19016, "loss": 2.19016, "time": 0.826} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.00086, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60438, "top5_acc": 0.83047, "loss_cls": 2.20698, "loss": 2.20698, "time": 0.81642} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.00085, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.61438, "top5_acc": 0.84422, "loss_cls": 2.14139, "loss": 2.14139, "time": 0.81873} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.00085, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61797, "top5_acc": 0.83859, "loss_cls": 2.16539, "loss": 2.16539, "time": 0.81408} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00084, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61734, "top5_acc": 0.83875, "loss_cls": 2.14144, "loss": 2.14144, "time": 0.8206} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00084, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.61781, "top5_acc": 0.83375, "loss_cls": 2.16363, "loss": 2.16363, "time": 0.81498} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00083, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60422, "top5_acc": 0.83297, "loss_cls": 2.19798, "loss": 2.19798, "time": 0.81586} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00083, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60547, "top5_acc": 0.83547, "loss_cls": 2.17604, "loss": 2.17604, "time": 0.81518} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00082, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61391, "top5_acc": 0.83781, "loss_cls": 2.17584, "loss": 2.17584, "time": 0.81552} +{"mode": "train", "epoch": 142, "iter": 1300, "lr": 0.00082, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60344, "top5_acc": 0.82516, "loss_cls": 2.24039, "loss": 2.24039, "time": 0.81791} +{"mode": "train", "epoch": 142, "iter": 1400, "lr": 0.00081, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60984, "top5_acc": 0.83438, "loss_cls": 2.17074, "loss": 2.17074, "time": 0.81718} +{"mode": "train", "epoch": 142, "iter": 1500, "lr": 0.00081, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.59969, "top5_acc": 0.82453, "loss_cls": 2.24095, "loss": 2.24095, "time": 0.81618} +{"mode": "train", "epoch": 142, "iter": 1600, "lr": 0.0008, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60578, "top5_acc": 0.83188, "loss_cls": 2.20484, "loss": 2.20484, "time": 0.81999} +{"mode": "train", "epoch": 142, "iter": 1700, "lr": 0.0008, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60641, "top5_acc": 0.83969, "loss_cls": 2.18771, "loss": 2.18771, "time": 0.81544} +{"mode": "train", "epoch": 142, "iter": 1800, "lr": 0.00079, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59531, "top5_acc": 0.82828, "loss_cls": 2.22173, "loss": 2.22173, "time": 0.82225} +{"mode": "train", "epoch": 142, "iter": 1900, "lr": 0.00079, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58469, "top5_acc": 0.82328, "loss_cls": 2.27146, "loss": 2.27146, "time": 0.81846} +{"mode": "train", "epoch": 142, "iter": 2000, "lr": 0.00078, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.59797, "top5_acc": 0.82547, "loss_cls": 2.23174, "loss": 2.23174, "time": 0.81314} +{"mode": "train", "epoch": 142, "iter": 2100, "lr": 0.00078, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59969, "top5_acc": 0.83203, "loss_cls": 2.22365, "loss": 2.22365, "time": 0.8158} +{"mode": "train", "epoch": 142, "iter": 2200, "lr": 0.00077, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59969, "top5_acc": 0.83141, "loss_cls": 2.21732, "loss": 2.21732, "time": 0.81257} +{"mode": "train", "epoch": 142, "iter": 2300, "lr": 0.00077, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59844, "top5_acc": 0.82953, "loss_cls": 2.232, "loss": 2.232, "time": 0.8142} +{"mode": "train", "epoch": 142, "iter": 2400, "lr": 0.00076, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58859, "top5_acc": 0.82531, "loss_cls": 2.25666, "loss": 2.25666, "time": 0.81613} +{"mode": "train", "epoch": 142, "iter": 2500, "lr": 0.00076, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60031, "top5_acc": 0.82734, "loss_cls": 2.2373, "loss": 2.2373, "time": 0.8209} +{"mode": "train", "epoch": 142, "iter": 2600, "lr": 0.00075, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60281, "top5_acc": 0.83672, "loss_cls": 2.20775, "loss": 2.20775, "time": 0.81491} +{"mode": "train", "epoch": 142, "iter": 2700, "lr": 0.00075, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60094, "top5_acc": 0.82953, "loss_cls": 2.22883, "loss": 2.22883, "time": 0.81275} +{"mode": "train", "epoch": 142, "iter": 2800, "lr": 0.00075, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.59609, "top5_acc": 0.82875, "loss_cls": 2.24176, "loss": 2.24176, "time": 0.81077} +{"mode": "train", "epoch": 142, "iter": 2900, "lr": 0.00074, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.60422, "top5_acc": 0.82969, "loss_cls": 2.2002, "loss": 2.2002, "time": 0.81353} +{"mode": "train", "epoch": 142, "iter": 3000, "lr": 0.00074, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60938, "top5_acc": 0.83359, "loss_cls": 2.19988, "loss": 2.19988, "time": 0.81335} +{"mode": "train", "epoch": 142, "iter": 3100, "lr": 0.00073, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60406, "top5_acc": 0.83047, "loss_cls": 2.22502, "loss": 2.22502, "time": 0.81779} +{"mode": "train", "epoch": 142, "iter": 3200, "lr": 0.00073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60297, "top5_acc": 0.83453, "loss_cls": 2.20035, "loss": 2.20035, "time": 0.81811} +{"mode": "train", "epoch": 142, "iter": 3300, "lr": 0.00072, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.61328, "top5_acc": 0.83688, "loss_cls": 2.16109, "loss": 2.16109, "time": 0.82808} +{"mode": "train", "epoch": 142, "iter": 3400, "lr": 0.00072, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60828, "top5_acc": 0.82609, "loss_cls": 2.21925, "loss": 2.21925, "time": 0.82528} +{"mode": "train", "epoch": 142, "iter": 3500, "lr": 0.00071, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59891, "top5_acc": 0.82969, "loss_cls": 2.22105, "loss": 2.22105, "time": 0.81952} +{"mode": "train", "epoch": 142, "iter": 3600, "lr": 0.00071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59172, "top5_acc": 0.82516, "loss_cls": 2.26095, "loss": 2.26095, "time": 0.82024} +{"mode": "train", "epoch": 142, "iter": 3700, "lr": 0.0007, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59703, "top5_acc": 0.82672, "loss_cls": 2.25397, "loss": 2.25397, "time": 0.82043} +{"mode": "val", "epoch": 142, "iter": 309, "lr": 0.0007, "top1_acc": 0.45505, "top5_acc": 0.69868, "mean_class_accuracy": 0.45482} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.0007, "memory": 15990, "data_time": 1.31062, "top1_acc": 0.61375, "top5_acc": 0.83984, "loss_cls": 2.16358, "loss": 2.16358, "time": 2.29415} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00069, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.61344, "top5_acc": 0.84609, "loss_cls": 2.11217, "loss": 2.11217, "time": 0.82079} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00069, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.61203, "top5_acc": 0.82969, "loss_cls": 2.18284, "loss": 2.18284, "time": 0.82549} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00068, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.61719, "top5_acc": 0.83656, "loss_cls": 2.14711, "loss": 2.14711, "time": 0.82319} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00068, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.62766, "top5_acc": 0.84312, "loss_cls": 2.11402, "loss": 2.11402, "time": 0.81912} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00067, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61844, "top5_acc": 0.84719, "loss_cls": 2.11816, "loss": 2.11816, "time": 0.82121} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00067, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61547, "top5_acc": 0.83844, "loss_cls": 2.15661, "loss": 2.15661, "time": 0.82007} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00066, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62172, "top5_acc": 0.84547, "loss_cls": 2.10936, "loss": 2.10936, "time": 0.81401} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00066, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61516, "top5_acc": 0.84156, "loss_cls": 2.14614, "loss": 2.14614, "time": 0.8145} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00065, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.62, "top5_acc": 0.84469, "loss_cls": 2.13944, "loss": 2.13944, "time": 0.81724} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61984, "top5_acc": 0.83766, "loss_cls": 2.14021, "loss": 2.14021, "time": 0.81867} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00065, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61062, "top5_acc": 0.84844, "loss_cls": 2.1442, "loss": 2.1442, "time": 0.81933} +{"mode": "train", "epoch": 143, "iter": 1300, "lr": 0.00064, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61516, "top5_acc": 0.83562, "loss_cls": 2.14417, "loss": 2.14417, "time": 0.82156} +{"mode": "train", "epoch": 143, "iter": 1400, "lr": 0.00064, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61594, "top5_acc": 0.84438, "loss_cls": 2.13789, "loss": 2.13789, "time": 0.8199} +{"mode": "train", "epoch": 143, "iter": 1500, "lr": 0.00063, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61, "top5_acc": 0.8425, "loss_cls": 2.16289, "loss": 2.16289, "time": 0.81935} +{"mode": "train", "epoch": 143, "iter": 1600, "lr": 0.00063, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61328, "top5_acc": 0.83688, "loss_cls": 2.15, "loss": 2.15, "time": 0.81852} +{"mode": "train", "epoch": 143, "iter": 1700, "lr": 0.00062, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61969, "top5_acc": 0.84047, "loss_cls": 2.13243, "loss": 2.13243, "time": 0.81775} +{"mode": "train", "epoch": 143, "iter": 1800, "lr": 0.00062, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62234, "top5_acc": 0.84047, "loss_cls": 2.15365, "loss": 2.15365, "time": 0.81699} +{"mode": "train", "epoch": 143, "iter": 1900, "lr": 0.00061, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.60938, "top5_acc": 0.84422, "loss_cls": 2.15432, "loss": 2.15432, "time": 0.81131} +{"mode": "train", "epoch": 143, "iter": 2000, "lr": 0.00061, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61609, "top5_acc": 0.83812, "loss_cls": 2.16972, "loss": 2.16972, "time": 0.82069} +{"mode": "train", "epoch": 143, "iter": 2100, "lr": 0.00061, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61344, "top5_acc": 0.84656, "loss_cls": 2.15287, "loss": 2.15287, "time": 0.815} +{"mode": "train", "epoch": 143, "iter": 2200, "lr": 0.0006, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61828, "top5_acc": 0.83812, "loss_cls": 2.15145, "loss": 2.15145, "time": 0.81773} +{"mode": "train", "epoch": 143, "iter": 2300, "lr": 0.0006, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61109, "top5_acc": 0.84125, "loss_cls": 2.16907, "loss": 2.16907, "time": 0.81935} +{"mode": "train", "epoch": 143, "iter": 2400, "lr": 0.00059, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59812, "top5_acc": 0.82766, "loss_cls": 2.21274, "loss": 2.21274, "time": 0.81974} +{"mode": "train", "epoch": 143, "iter": 2500, "lr": 0.00059, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.615, "top5_acc": 0.83875, "loss_cls": 2.1513, "loss": 2.1513, "time": 0.81646} +{"mode": "train", "epoch": 143, "iter": 2600, "lr": 0.00058, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61297, "top5_acc": 0.84094, "loss_cls": 2.15581, "loss": 2.15581, "time": 0.81535} +{"mode": "train", "epoch": 143, "iter": 2700, "lr": 0.00058, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60016, "top5_acc": 0.83422, "loss_cls": 2.18849, "loss": 2.18849, "time": 0.81585} +{"mode": "train", "epoch": 143, "iter": 2800, "lr": 0.00058, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62453, "top5_acc": 0.84109, "loss_cls": 2.13557, "loss": 2.13557, "time": 0.81654} +{"mode": "train", "epoch": 143, "iter": 2900, "lr": 0.00057, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60938, "top5_acc": 0.83125, "loss_cls": 2.18648, "loss": 2.18648, "time": 0.81501} +{"mode": "train", "epoch": 143, "iter": 3000, "lr": 0.00057, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.61234, "top5_acc": 0.8325, "loss_cls": 2.17892, "loss": 2.17892, "time": 0.81436} +{"mode": "train", "epoch": 143, "iter": 3100, "lr": 0.00056, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61172, "top5_acc": 0.83922, "loss_cls": 2.16074, "loss": 2.16074, "time": 0.81907} +{"mode": "train", "epoch": 143, "iter": 3200, "lr": 0.00056, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62141, "top5_acc": 0.83844, "loss_cls": 2.16228, "loss": 2.16228, "time": 0.81623} +{"mode": "train", "epoch": 143, "iter": 3300, "lr": 0.00055, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.62625, "top5_acc": 0.84312, "loss_cls": 2.10918, "loss": 2.10918, "time": 0.82806} +{"mode": "train", "epoch": 143, "iter": 3400, "lr": 0.00055, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61516, "top5_acc": 0.83828, "loss_cls": 2.16575, "loss": 2.16575, "time": 0.81995} +{"mode": "train", "epoch": 143, "iter": 3500, "lr": 0.00055, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61984, "top5_acc": 0.83484, "loss_cls": 2.13449, "loss": 2.13449, "time": 0.81745} +{"mode": "train", "epoch": 143, "iter": 3600, "lr": 0.00054, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62156, "top5_acc": 0.84188, "loss_cls": 2.12434, "loss": 2.12434, "time": 0.81957} +{"mode": "train", "epoch": 143, "iter": 3700, "lr": 0.00054, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.61094, "top5_acc": 0.835, "loss_cls": 2.18156, "loss": 2.18156, "time": 0.81328} +{"mode": "val", "epoch": 143, "iter": 309, "lr": 0.00054, "top1_acc": 0.45282, "top5_acc": 0.70207, "mean_class_accuracy": 0.45259} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00053, "memory": 15990, "data_time": 1.26104, "top1_acc": 0.63375, "top5_acc": 0.84609, "loss_cls": 2.05668, "loss": 2.05668, "time": 2.24306} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00053, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.63703, "top5_acc": 0.85578, "loss_cls": 2.04382, "loss": 2.04382, "time": 0.82028} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64344, "top5_acc": 0.85219, "loss_cls": 2.02968, "loss": 2.02968, "time": 0.82246} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62922, "top5_acc": 0.84453, "loss_cls": 2.09495, "loss": 2.09495, "time": 0.82365} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00052, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.62187, "top5_acc": 0.84578, "loss_cls": 2.12514, "loss": 2.12514, "time": 0.81869} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00051, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63578, "top5_acc": 0.84422, "loss_cls": 2.06605, "loss": 2.06605, "time": 0.817} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00051, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63391, "top5_acc": 0.85266, "loss_cls": 2.05348, "loss": 2.05348, "time": 0.81712} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.0005, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62031, "top5_acc": 0.84641, "loss_cls": 2.10438, "loss": 2.10438, "time": 0.82155} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.0005, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63281, "top5_acc": 0.85094, "loss_cls": 2.06543, "loss": 2.06543, "time": 0.8162} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.0005, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62656, "top5_acc": 0.84266, "loss_cls": 2.11283, "loss": 2.11283, "time": 0.81861} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.00049, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63438, "top5_acc": 0.85016, "loss_cls": 2.06333, "loss": 2.06333, "time": 0.81609} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.00049, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.62703, "top5_acc": 0.84125, "loss_cls": 2.11731, "loss": 2.11731, "time": 0.81295} +{"mode": "train", "epoch": 144, "iter": 1300, "lr": 0.00048, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.61844, "top5_acc": 0.84578, "loss_cls": 2.1272, "loss": 2.1272, "time": 0.82202} +{"mode": "train", "epoch": 144, "iter": 1400, "lr": 0.00048, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62641, "top5_acc": 0.84391, "loss_cls": 2.10544, "loss": 2.10544, "time": 0.8233} +{"mode": "train", "epoch": 144, "iter": 1500, "lr": 0.00048, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62062, "top5_acc": 0.84281, "loss_cls": 2.12478, "loss": 2.12478, "time": 0.81467} +{"mode": "train", "epoch": 144, "iter": 1600, "lr": 0.00047, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63453, "top5_acc": 0.84641, "loss_cls": 2.09136, "loss": 2.09136, "time": 0.82277} +{"mode": "train", "epoch": 144, "iter": 1700, "lr": 0.00047, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61609, "top5_acc": 0.84344, "loss_cls": 2.12878, "loss": 2.12878, "time": 0.8197} +{"mode": "train", "epoch": 144, "iter": 1800, "lr": 0.00047, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62844, "top5_acc": 0.85641, "loss_cls": 2.06193, "loss": 2.06193, "time": 0.82105} +{"mode": "train", "epoch": 144, "iter": 1900, "lr": 0.00046, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63156, "top5_acc": 0.85125, "loss_cls": 2.06099, "loss": 2.06099, "time": 0.81931} +{"mode": "train", "epoch": 144, "iter": 2000, "lr": 0.00046, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62359, "top5_acc": 0.84703, "loss_cls": 2.10599, "loss": 2.10599, "time": 0.82076} +{"mode": "train", "epoch": 144, "iter": 2100, "lr": 0.00045, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62703, "top5_acc": 0.84391, "loss_cls": 2.09349, "loss": 2.09349, "time": 0.82122} +{"mode": "train", "epoch": 144, "iter": 2200, "lr": 0.00045, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.62984, "top5_acc": 0.85453, "loss_cls": 2.06796, "loss": 2.06796, "time": 0.81862} +{"mode": "train", "epoch": 144, "iter": 2300, "lr": 0.00045, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63375, "top5_acc": 0.85141, "loss_cls": 2.07671, "loss": 2.07671, "time": 0.81508} +{"mode": "train", "epoch": 144, "iter": 2400, "lr": 0.00044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61781, "top5_acc": 0.84016, "loss_cls": 2.15353, "loss": 2.15353, "time": 0.81231} +{"mode": "train", "epoch": 144, "iter": 2500, "lr": 0.00044, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.6225, "top5_acc": 0.84344, "loss_cls": 2.09164, "loss": 2.09164, "time": 0.82553} +{"mode": "train", "epoch": 144, "iter": 2600, "lr": 0.00044, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.6275, "top5_acc": 0.845, "loss_cls": 2.07174, "loss": 2.07174, "time": 0.82074} +{"mode": "train", "epoch": 144, "iter": 2700, "lr": 0.00043, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62328, "top5_acc": 0.85078, "loss_cls": 2.08853, "loss": 2.08853, "time": 0.81436} +{"mode": "train", "epoch": 144, "iter": 2800, "lr": 0.00043, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.625, "top5_acc": 0.84641, "loss_cls": 2.09373, "loss": 2.09373, "time": 0.82193} +{"mode": "train", "epoch": 144, "iter": 2900, "lr": 0.00042, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62219, "top5_acc": 0.84219, "loss_cls": 2.12589, "loss": 2.12589, "time": 0.82184} +{"mode": "train", "epoch": 144, "iter": 3000, "lr": 0.00042, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61984, "top5_acc": 0.84016, "loss_cls": 2.12543, "loss": 2.12543, "time": 0.81965} +{"mode": "train", "epoch": 144, "iter": 3100, "lr": 0.00042, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62875, "top5_acc": 0.84406, "loss_cls": 2.1135, "loss": 2.1135, "time": 0.8263} +{"mode": "train", "epoch": 144, "iter": 3200, "lr": 0.00041, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61984, "top5_acc": 0.84781, "loss_cls": 2.11668, "loss": 2.11668, "time": 0.81987} +{"mode": "train", "epoch": 144, "iter": 3300, "lr": 0.00041, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62016, "top5_acc": 0.85062, "loss_cls": 2.10239, "loss": 2.10239, "time": 0.82076} +{"mode": "train", "epoch": 144, "iter": 3400, "lr": 0.00041, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62094, "top5_acc": 0.84875, "loss_cls": 2.11474, "loss": 2.11474, "time": 0.82753} +{"mode": "train", "epoch": 144, "iter": 3500, "lr": 0.0004, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62062, "top5_acc": 0.845, "loss_cls": 2.10744, "loss": 2.10744, "time": 0.81781} +{"mode": "train", "epoch": 144, "iter": 3600, "lr": 0.0004, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62672, "top5_acc": 0.84359, "loss_cls": 2.11152, "loss": 2.11152, "time": 0.82262} +{"mode": "train", "epoch": 144, "iter": 3700, "lr": 0.0004, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62875, "top5_acc": 0.84297, "loss_cls": 2.10479, "loss": 2.10479, "time": 0.8153} +{"mode": "val", "epoch": 144, "iter": 309, "lr": 0.00039, "top1_acc": 0.45464, "top5_acc": 0.7003, "mean_class_accuracy": 0.4544} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.00039, "memory": 15990, "data_time": 1.2649, "top1_acc": 0.63344, "top5_acc": 0.85094, "loss_cls": 2.07185, "loss": 2.07185, "time": 2.24536} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 0.00039, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63891, "top5_acc": 0.86031, "loss_cls": 2.03047, "loss": 2.03047, "time": 0.8166} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 0.00038, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.63203, "top5_acc": 0.84906, "loss_cls": 2.07119, "loss": 2.07119, "time": 0.82553} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 0.00038, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64125, "top5_acc": 0.85906, "loss_cls": 2.0108, "loss": 2.0108, "time": 0.82433} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 0.00038, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64391, "top5_acc": 0.85797, "loss_cls": 2.00868, "loss": 2.00868, "time": 0.81872} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 0.00037, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64578, "top5_acc": 0.84719, "loss_cls": 2.04707, "loss": 2.04707, "time": 0.8216} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 0.00037, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.64562, "top5_acc": 0.85344, "loss_cls": 2.02196, "loss": 2.02196, "time": 0.81452} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 0.00037, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.63672, "top5_acc": 0.85734, "loss_cls": 2.03172, "loss": 2.03172, "time": 0.81728} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 0.00036, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64203, "top5_acc": 0.85656, "loss_cls": 2.01333, "loss": 2.01333, "time": 0.81266} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 0.00036, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64422, "top5_acc": 0.85391, "loss_cls": 2.02093, "loss": 2.02093, "time": 0.8158} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 0.00036, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64141, "top5_acc": 0.85203, "loss_cls": 2.0481, "loss": 2.0481, "time": 0.81865} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 0.00035, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63344, "top5_acc": 0.85094, "loss_cls": 2.04122, "loss": 2.04122, "time": 0.81282} +{"mode": "train", "epoch": 145, "iter": 1300, "lr": 0.00035, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64297, "top5_acc": 0.85297, "loss_cls": 2.03858, "loss": 2.03858, "time": 0.81475} +{"mode": "train", "epoch": 145, "iter": 1400, "lr": 0.00035, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65016, "top5_acc": 0.85609, "loss_cls": 2.00667, "loss": 2.00667, "time": 0.82124} +{"mode": "train", "epoch": 145, "iter": 1500, "lr": 0.00034, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65062, "top5_acc": 0.86141, "loss_cls": 1.99709, "loss": 1.99709, "time": 0.81448} +{"mode": "train", "epoch": 145, "iter": 1600, "lr": 0.00034, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63484, "top5_acc": 0.84953, "loss_cls": 2.06578, "loss": 2.06578, "time": 0.8234} +{"mode": "train", "epoch": 145, "iter": 1700, "lr": 0.00034, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.63922, "top5_acc": 0.85641, "loss_cls": 2.04472, "loss": 2.04472, "time": 0.81194} +{"mode": "train", "epoch": 145, "iter": 1800, "lr": 0.00033, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63203, "top5_acc": 0.85438, "loss_cls": 2.04942, "loss": 2.04942, "time": 0.81381} +{"mode": "train", "epoch": 145, "iter": 1900, "lr": 0.00033, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.63859, "top5_acc": 0.85312, "loss_cls": 2.04854, "loss": 2.04854, "time": 0.8181} +{"mode": "train", "epoch": 145, "iter": 2000, "lr": 0.00033, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.62938, "top5_acc": 0.84766, "loss_cls": 2.06695, "loss": 2.06695, "time": 0.81241} +{"mode": "train", "epoch": 145, "iter": 2100, "lr": 0.00032, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63438, "top5_acc": 0.85484, "loss_cls": 2.05458, "loss": 2.05458, "time": 0.81343} +{"mode": "train", "epoch": 145, "iter": 2200, "lr": 0.00032, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63391, "top5_acc": 0.85234, "loss_cls": 2.05497, "loss": 2.05497, "time": 0.81394} +{"mode": "train", "epoch": 145, "iter": 2300, "lr": 0.00032, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.63047, "top5_acc": 0.84766, "loss_cls": 2.05405, "loss": 2.05405, "time": 0.81735} +{"mode": "train", "epoch": 145, "iter": 2400, "lr": 0.00031, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63672, "top5_acc": 0.85469, "loss_cls": 2.01132, "loss": 2.01132, "time": 0.81391} +{"mode": "train", "epoch": 145, "iter": 2500, "lr": 0.00031, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.64328, "top5_acc": 0.85047, "loss_cls": 2.01964, "loss": 2.01964, "time": 0.8166} +{"mode": "train", "epoch": 145, "iter": 2600, "lr": 0.00031, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63516, "top5_acc": 0.85547, "loss_cls": 2.05561, "loss": 2.05561, "time": 0.81671} +{"mode": "train", "epoch": 145, "iter": 2700, "lr": 0.00031, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64219, "top5_acc": 0.85359, "loss_cls": 2.029, "loss": 2.029, "time": 0.81665} +{"mode": "train", "epoch": 145, "iter": 2800, "lr": 0.0003, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63859, "top5_acc": 0.85047, "loss_cls": 2.05025, "loss": 2.05025, "time": 0.8166} +{"mode": "train", "epoch": 145, "iter": 2900, "lr": 0.0003, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63453, "top5_acc": 0.85188, "loss_cls": 2.05793, "loss": 2.05793, "time": 0.81831} +{"mode": "train", "epoch": 145, "iter": 3000, "lr": 0.0003, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63875, "top5_acc": 0.85828, "loss_cls": 2.03939, "loss": 2.03939, "time": 0.81479} +{"mode": "train", "epoch": 145, "iter": 3100, "lr": 0.00029, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62859, "top5_acc": 0.84828, "loss_cls": 2.08438, "loss": 2.08438, "time": 0.81546} +{"mode": "train", "epoch": 145, "iter": 3200, "lr": 0.00029, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63313, "top5_acc": 0.85297, "loss_cls": 2.04121, "loss": 2.04121, "time": 0.82411} +{"mode": "train", "epoch": 145, "iter": 3300, "lr": 0.00029, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65031, "top5_acc": 0.85031, "loss_cls": 2.03257, "loss": 2.03257, "time": 0.81688} +{"mode": "train", "epoch": 145, "iter": 3400, "lr": 0.00028, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63547, "top5_acc": 0.85609, "loss_cls": 2.03787, "loss": 2.03787, "time": 0.82345} +{"mode": "train", "epoch": 145, "iter": 3500, "lr": 0.00028, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63203, "top5_acc": 0.84312, "loss_cls": 2.07521, "loss": 2.07521, "time": 0.81343} +{"mode": "train", "epoch": 145, "iter": 3600, "lr": 0.00028, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62062, "top5_acc": 0.84547, "loss_cls": 2.09925, "loss": 2.09925, "time": 0.81252} +{"mode": "train", "epoch": 145, "iter": 3700, "lr": 0.00028, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63828, "top5_acc": 0.85281, "loss_cls": 2.06263, "loss": 2.06263, "time": 0.81608} +{"mode": "val", "epoch": 145, "iter": 309, "lr": 0.00027, "top1_acc": 0.45495, "top5_acc": 0.70324, "mean_class_accuracy": 0.45473} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 0.00027, "memory": 15990, "data_time": 1.27285, "top1_acc": 0.66203, "top5_acc": 0.86734, "loss_cls": 1.94396, "loss": 1.94396, "time": 2.25155} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 0.00027, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65391, "top5_acc": 0.86, "loss_cls": 1.96198, "loss": 1.96198, "time": 0.82099} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 0.00027, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65328, "top5_acc": 0.86953, "loss_cls": 1.95339, "loss": 1.95339, "time": 0.82282} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 0.00026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65344, "top5_acc": 0.86344, "loss_cls": 1.97762, "loss": 1.97762, "time": 0.82234} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 0.00026, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.65484, "top5_acc": 0.86812, "loss_cls": 1.95334, "loss": 1.95334, "time": 0.82163} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 0.00026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64266, "top5_acc": 0.85609, "loss_cls": 2.01027, "loss": 2.01027, "time": 0.81667} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 0.00025, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.64609, "top5_acc": 0.85781, "loss_cls": 1.99902, "loss": 1.99902, "time": 0.81452} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 0.00025, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65281, "top5_acc": 0.86109, "loss_cls": 1.96336, "loss": 1.96336, "time": 0.82073} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 0.00025, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64797, "top5_acc": 0.85703, "loss_cls": 1.99683, "loss": 1.99683, "time": 0.81556} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 0.00025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65484, "top5_acc": 0.86031, "loss_cls": 1.97548, "loss": 1.97548, "time": 0.81504} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 0.00024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65547, "top5_acc": 0.86391, "loss_cls": 1.98593, "loss": 1.98593, "time": 0.81833} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 0.00024, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.65, "top5_acc": 0.85781, "loss_cls": 2.00952, "loss": 2.00952, "time": 0.81357} +{"mode": "train", "epoch": 146, "iter": 1300, "lr": 0.00024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65266, "top5_acc": 0.86281, "loss_cls": 1.9945, "loss": 1.9945, "time": 0.81442} +{"mode": "train", "epoch": 146, "iter": 1400, "lr": 0.00023, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64516, "top5_acc": 0.86047, "loss_cls": 2.01102, "loss": 2.01102, "time": 0.81843} +{"mode": "train", "epoch": 146, "iter": 1500, "lr": 0.00023, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64297, "top5_acc": 0.85875, "loss_cls": 1.9997, "loss": 1.9997, "time": 0.81614} +{"mode": "train", "epoch": 146, "iter": 1600, "lr": 0.00023, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63969, "top5_acc": 0.85266, "loss_cls": 2.02124, "loss": 2.02124, "time": 0.81672} +{"mode": "train", "epoch": 146, "iter": 1700, "lr": 0.00023, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63859, "top5_acc": 0.85484, "loss_cls": 2.03416, "loss": 2.03416, "time": 0.81806} +{"mode": "train", "epoch": 146, "iter": 1800, "lr": 0.00022, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64188, "top5_acc": 0.85312, "loss_cls": 2.02764, "loss": 2.02764, "time": 0.81931} +{"mode": "train", "epoch": 146, "iter": 1900, "lr": 0.00022, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.65172, "top5_acc": 0.86453, "loss_cls": 1.98992, "loss": 1.98992, "time": 0.81455} +{"mode": "train", "epoch": 146, "iter": 2000, "lr": 0.00022, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.64828, "top5_acc": 0.85797, "loss_cls": 2.00287, "loss": 2.00287, "time": 0.8137} +{"mode": "train", "epoch": 146, "iter": 2100, "lr": 0.00022, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.65438, "top5_acc": 0.86516, "loss_cls": 1.96732, "loss": 1.96732, "time": 0.81287} +{"mode": "train", "epoch": 146, "iter": 2200, "lr": 0.00021, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.63359, "top5_acc": 0.85719, "loss_cls": 2.02896, "loss": 2.02896, "time": 0.81375} +{"mode": "train", "epoch": 146, "iter": 2300, "lr": 0.00021, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64297, "top5_acc": 0.85578, "loss_cls": 1.99781, "loss": 1.99781, "time": 0.81494} +{"mode": "train", "epoch": 146, "iter": 2400, "lr": 0.00021, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.64828, "top5_acc": 0.85938, "loss_cls": 1.9895, "loss": 1.9895, "time": 0.81498} +{"mode": "train", "epoch": 146, "iter": 2500, "lr": 0.00021, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.64125, "top5_acc": 0.85797, "loss_cls": 2.0249, "loss": 2.0249, "time": 0.82118} +{"mode": "train", "epoch": 146, "iter": 2600, "lr": 0.0002, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.65125, "top5_acc": 0.8625, "loss_cls": 1.97369, "loss": 1.97369, "time": 0.8122} +{"mode": "train", "epoch": 146, "iter": 2700, "lr": 0.0002, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63516, "top5_acc": 0.85219, "loss_cls": 2.02949, "loss": 2.02949, "time": 0.81992} +{"mode": "train", "epoch": 146, "iter": 2800, "lr": 0.0002, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.64266, "top5_acc": 0.85578, "loss_cls": 2.03431, "loss": 2.03431, "time": 0.81051} +{"mode": "train", "epoch": 146, "iter": 2900, "lr": 0.0002, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.65812, "top5_acc": 0.85781, "loss_cls": 1.97875, "loss": 1.97875, "time": 0.81319} +{"mode": "train", "epoch": 146, "iter": 3000, "lr": 0.00019, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64438, "top5_acc": 0.85875, "loss_cls": 1.99976, "loss": 1.99976, "time": 0.81468} +{"mode": "train", "epoch": 146, "iter": 3100, "lr": 0.00019, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.635, "top5_acc": 0.85344, "loss_cls": 2.03948, "loss": 2.03948, "time": 0.81741} +{"mode": "train", "epoch": 146, "iter": 3200, "lr": 0.00019, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64891, "top5_acc": 0.85828, "loss_cls": 1.97139, "loss": 1.97139, "time": 0.82036} +{"mode": "train", "epoch": 146, "iter": 3300, "lr": 0.00019, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.6475, "top5_acc": 0.85672, "loss_cls": 1.99641, "loss": 1.99641, "time": 0.82224} +{"mode": "train", "epoch": 146, "iter": 3400, "lr": 0.00018, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63781, "top5_acc": 0.85875, "loss_cls": 2.01537, "loss": 2.01537, "time": 0.81347} +{"mode": "train", "epoch": 146, "iter": 3500, "lr": 0.00018, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64297, "top5_acc": 0.85516, "loss_cls": 2.01627, "loss": 2.01627, "time": 0.82124} +{"mode": "train", "epoch": 146, "iter": 3600, "lr": 0.00018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65562, "top5_acc": 0.85594, "loss_cls": 1.98789, "loss": 1.98789, "time": 0.8185} +{"mode": "train", "epoch": 146, "iter": 3700, "lr": 0.00018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64391, "top5_acc": 0.85625, "loss_cls": 2.02538, "loss": 2.02538, "time": 0.82077} +{"mode": "val", "epoch": 146, "iter": 309, "lr": 0.00018, "top1_acc": 0.45844, "top5_acc": 0.70405, "mean_class_accuracy": 0.45821} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 0.00017, "memory": 15990, "data_time": 1.27699, "top1_acc": 0.66359, "top5_acc": 0.86891, "loss_cls": 1.91084, "loss": 1.91084, "time": 2.25229} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 0.00017, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65328, "top5_acc": 0.86656, "loss_cls": 1.96932, "loss": 1.96932, "time": 0.82021} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 0.00017, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.65781, "top5_acc": 0.87016, "loss_cls": 1.94335, "loss": 1.94335, "time": 0.8315} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 0.00017, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64953, "top5_acc": 0.86875, "loss_cls": 1.97325, "loss": 1.97325, "time": 0.83111} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 0.00016, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66516, "top5_acc": 0.86875, "loss_cls": 1.92807, "loss": 1.92807, "time": 0.82587} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 0.00016, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.65969, "top5_acc": 0.86531, "loss_cls": 1.95065, "loss": 1.95065, "time": 0.8193} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 0.00016, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65672, "top5_acc": 0.865, "loss_cls": 1.95004, "loss": 1.95004, "time": 0.81253} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 0.00016, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65859, "top5_acc": 0.86453, "loss_cls": 1.95713, "loss": 1.95713, "time": 0.82328} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 0.00015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65797, "top5_acc": 0.86359, "loss_cls": 1.94898, "loss": 1.94898, "time": 0.8146} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 0.00015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65359, "top5_acc": 0.86344, "loss_cls": 1.97857, "loss": 1.97857, "time": 0.81796} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 0.00015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66656, "top5_acc": 0.86688, "loss_cls": 1.9418, "loss": 1.9418, "time": 0.81387} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 0.00015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66141, "top5_acc": 0.86594, "loss_cls": 1.93384, "loss": 1.93384, "time": 0.81652} +{"mode": "train", "epoch": 147, "iter": 1300, "lr": 0.00015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65297, "top5_acc": 0.86609, "loss_cls": 1.95604, "loss": 1.95604, "time": 0.81109} +{"mode": "train", "epoch": 147, "iter": 1400, "lr": 0.00014, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.65609, "top5_acc": 0.86234, "loss_cls": 1.96061, "loss": 1.96061, "time": 0.82139} +{"mode": "train", "epoch": 147, "iter": 1500, "lr": 0.00014, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66562, "top5_acc": 0.87109, "loss_cls": 1.93595, "loss": 1.93595, "time": 0.81754} +{"mode": "train", "epoch": 147, "iter": 1600, "lr": 0.00014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66078, "top5_acc": 0.86719, "loss_cls": 1.92573, "loss": 1.92573, "time": 0.81522} +{"mode": "train", "epoch": 147, "iter": 1700, "lr": 0.00014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64453, "top5_acc": 0.85609, "loss_cls": 2.0178, "loss": 2.0178, "time": 0.81649} +{"mode": "train", "epoch": 147, "iter": 1800, "lr": 0.00014, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65516, "top5_acc": 0.85641, "loss_cls": 1.97588, "loss": 1.97588, "time": 0.82} +{"mode": "train", "epoch": 147, "iter": 1900, "lr": 0.00013, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.66031, "top5_acc": 0.86078, "loss_cls": 1.96906, "loss": 1.96906, "time": 0.8201} +{"mode": "train", "epoch": 147, "iter": 2000, "lr": 0.00013, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65578, "top5_acc": 0.86672, "loss_cls": 1.95742, "loss": 1.95742, "time": 0.81437} +{"mode": "train", "epoch": 147, "iter": 2100, "lr": 0.00013, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.65469, "top5_acc": 0.85812, "loss_cls": 1.98161, "loss": 1.98161, "time": 0.81202} +{"mode": "train", "epoch": 147, "iter": 2200, "lr": 0.00013, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.64984, "top5_acc": 0.86531, "loss_cls": 1.97794, "loss": 1.97794, "time": 0.81563} +{"mode": "train", "epoch": 147, "iter": 2300, "lr": 0.00013, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65312, "top5_acc": 0.86406, "loss_cls": 1.96909, "loss": 1.96909, "time": 0.81106} +{"mode": "train", "epoch": 147, "iter": 2400, "lr": 0.00012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65031, "top5_acc": 0.85969, "loss_cls": 1.9794, "loss": 1.9794, "time": 0.81807} +{"mode": "train", "epoch": 147, "iter": 2500, "lr": 0.00012, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.65047, "top5_acc": 0.86328, "loss_cls": 1.97476, "loss": 1.97476, "time": 0.81421} +{"mode": "train", "epoch": 147, "iter": 2600, "lr": 0.00012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65734, "top5_acc": 0.86891, "loss_cls": 1.93376, "loss": 1.93376, "time": 0.81402} +{"mode": "train", "epoch": 147, "iter": 2700, "lr": 0.00012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64828, "top5_acc": 0.86047, "loss_cls": 1.98796, "loss": 1.98796, "time": 0.82002} +{"mode": "train", "epoch": 147, "iter": 2800, "lr": 0.00012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66156, "top5_acc": 0.86219, "loss_cls": 1.95285, "loss": 1.95285, "time": 0.81102} +{"mode": "train", "epoch": 147, "iter": 2900, "lr": 0.00011, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66125, "top5_acc": 0.86656, "loss_cls": 1.92739, "loss": 1.92739, "time": 0.81588} +{"mode": "train", "epoch": 147, "iter": 3000, "lr": 0.00011, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66562, "top5_acc": 0.86688, "loss_cls": 1.92746, "loss": 1.92746, "time": 0.81746} +{"mode": "train", "epoch": 147, "iter": 3100, "lr": 0.00011, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.65328, "top5_acc": 0.86484, "loss_cls": 1.95724, "loss": 1.95724, "time": 0.81639} +{"mode": "train", "epoch": 147, "iter": 3200, "lr": 0.00011, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65359, "top5_acc": 0.86359, "loss_cls": 1.96609, "loss": 1.96609, "time": 0.81408} +{"mode": "train", "epoch": 147, "iter": 3300, "lr": 0.00011, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.64547, "top5_acc": 0.85922, "loss_cls": 1.98737, "loss": 1.98737, "time": 0.82369} +{"mode": "train", "epoch": 147, "iter": 3400, "lr": 0.0001, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.65453, "top5_acc": 0.86109, "loss_cls": 1.96848, "loss": 1.96848, "time": 0.81468} +{"mode": "train", "epoch": 147, "iter": 3500, "lr": 0.0001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65641, "top5_acc": 0.86641, "loss_cls": 1.95758, "loss": 1.95758, "time": 0.82096} +{"mode": "train", "epoch": 147, "iter": 3600, "lr": 0.0001, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65547, "top5_acc": 0.86281, "loss_cls": 1.96393, "loss": 1.96393, "time": 0.81553} +{"mode": "train", "epoch": 147, "iter": 3700, "lr": 0.0001, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.65672, "top5_acc": 0.86656, "loss_cls": 1.95547, "loss": 1.95547, "time": 0.82381} +{"mode": "val", "epoch": 147, "iter": 309, "lr": 0.0001, "top1_acc": 0.45859, "top5_acc": 0.70248, "mean_class_accuracy": 0.45837} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 0.0001, "memory": 15990, "data_time": 1.31638, "top1_acc": 0.67047, "top5_acc": 0.87562, "loss_cls": 1.89132, "loss": 1.89132, "time": 2.29998} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 0.0001, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65625, "top5_acc": 0.86109, "loss_cls": 1.95935, "loss": 1.95935, "time": 0.82575} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 9e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67172, "top5_acc": 0.86938, "loss_cls": 1.91332, "loss": 1.91332, "time": 0.82648} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 9e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67, "top5_acc": 0.87625, "loss_cls": 1.88576, "loss": 1.88576, "time": 0.82965} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 9e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.65859, "top5_acc": 0.86828, "loss_cls": 1.924, "loss": 1.924, "time": 0.82566} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 9e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67, "top5_acc": 0.87266, "loss_cls": 1.89684, "loss": 1.89684, "time": 0.82169} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 9e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66078, "top5_acc": 0.86594, "loss_cls": 1.93391, "loss": 1.93391, "time": 0.81546} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 9e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67047, "top5_acc": 0.87062, "loss_cls": 1.90538, "loss": 1.90538, "time": 0.81897} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 8e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67062, "top5_acc": 0.87766, "loss_cls": 1.87815, "loss": 1.87815, "time": 0.82585} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 8e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66516, "top5_acc": 0.86906, "loss_cls": 1.91587, "loss": 1.91587, "time": 0.81701} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 8e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65984, "top5_acc": 0.86859, "loss_cls": 1.94154, "loss": 1.94154, "time": 0.82083} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 8e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65828, "top5_acc": 0.86672, "loss_cls": 1.93657, "loss": 1.93657, "time": 0.81798} +{"mode": "train", "epoch": 148, "iter": 1300, "lr": 8e-05, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.66406, "top5_acc": 0.86266, "loss_cls": 1.94229, "loss": 1.94229, "time": 0.81412} +{"mode": "train", "epoch": 148, "iter": 1400, "lr": 8e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65922, "top5_acc": 0.86469, "loss_cls": 1.94675, "loss": 1.94675, "time": 0.81989} +{"mode": "train", "epoch": 148, "iter": 1500, "lr": 7e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66828, "top5_acc": 0.86938, "loss_cls": 1.90546, "loss": 1.90546, "time": 0.81497} +{"mode": "train", "epoch": 148, "iter": 1600, "lr": 7e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66125, "top5_acc": 0.86062, "loss_cls": 1.95011, "loss": 1.95011, "time": 0.81778} +{"mode": "train", "epoch": 148, "iter": 1700, "lr": 7e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66156, "top5_acc": 0.86719, "loss_cls": 1.92717, "loss": 1.92717, "time": 0.81937} +{"mode": "train", "epoch": 148, "iter": 1800, "lr": 7e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66641, "top5_acc": 0.87266, "loss_cls": 1.9137, "loss": 1.9137, "time": 0.81808} +{"mode": "train", "epoch": 148, "iter": 1900, "lr": 7e-05, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.66469, "top5_acc": 0.86781, "loss_cls": 1.93741, "loss": 1.93741, "time": 0.81805} +{"mode": "train", "epoch": 148, "iter": 2000, "lr": 7e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65766, "top5_acc": 0.86812, "loss_cls": 1.93515, "loss": 1.93515, "time": 0.8201} +{"mode": "train", "epoch": 148, "iter": 2100, "lr": 7e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.65891, "top5_acc": 0.86781, "loss_cls": 1.91416, "loss": 1.91416, "time": 0.81916} +{"mode": "train", "epoch": 148, "iter": 2200, "lr": 6e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66609, "top5_acc": 0.86672, "loss_cls": 1.92674, "loss": 1.92674, "time": 0.8217} +{"mode": "train", "epoch": 148, "iter": 2300, "lr": 6e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66266, "top5_acc": 0.86766, "loss_cls": 1.91245, "loss": 1.91245, "time": 0.81484} +{"mode": "train", "epoch": 148, "iter": 2400, "lr": 6e-05, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.66969, "top5_acc": 0.87594, "loss_cls": 1.89873, "loss": 1.89873, "time": 0.81404} +{"mode": "train", "epoch": 148, "iter": 2500, "lr": 6e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66781, "top5_acc": 0.86922, "loss_cls": 1.91956, "loss": 1.91956, "time": 0.81328} +{"mode": "train", "epoch": 148, "iter": 2600, "lr": 6e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65969, "top5_acc": 0.86688, "loss_cls": 1.91628, "loss": 1.91628, "time": 0.81668} +{"mode": "train", "epoch": 148, "iter": 2700, "lr": 6e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66328, "top5_acc": 0.87062, "loss_cls": 1.91855, "loss": 1.91855, "time": 0.81762} +{"mode": "train", "epoch": 148, "iter": 2800, "lr": 6e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65453, "top5_acc": 0.86266, "loss_cls": 1.96016, "loss": 1.96016, "time": 0.81849} +{"mode": "train", "epoch": 148, "iter": 2900, "lr": 5e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67188, "top5_acc": 0.86203, "loss_cls": 1.91612, "loss": 1.91612, "time": 0.81487} +{"mode": "train", "epoch": 148, "iter": 3000, "lr": 5e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65719, "top5_acc": 0.86078, "loss_cls": 1.96182, "loss": 1.96182, "time": 0.82016} +{"mode": "train", "epoch": 148, "iter": 3100, "lr": 5e-05, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.66406, "top5_acc": 0.87391, "loss_cls": 1.897, "loss": 1.897, "time": 0.81614} +{"mode": "train", "epoch": 148, "iter": 3200, "lr": 5e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65781, "top5_acc": 0.86219, "loss_cls": 1.94767, "loss": 1.94767, "time": 0.82125} +{"mode": "train", "epoch": 148, "iter": 3300, "lr": 5e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66062, "top5_acc": 0.86812, "loss_cls": 1.92571, "loss": 1.92571, "time": 0.82383} +{"mode": "train", "epoch": 148, "iter": 3400, "lr": 5e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65516, "top5_acc": 0.8575, "loss_cls": 1.98188, "loss": 1.98188, "time": 0.81594} +{"mode": "train", "epoch": 148, "iter": 3500, "lr": 5e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65219, "top5_acc": 0.86719, "loss_cls": 1.95001, "loss": 1.95001, "time": 0.82124} +{"mode": "train", "epoch": 148, "iter": 3600, "lr": 5e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65641, "top5_acc": 0.87234, "loss_cls": 1.91461, "loss": 1.91461, "time": 0.81899} +{"mode": "train", "epoch": 148, "iter": 3700, "lr": 4e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66484, "top5_acc": 0.86922, "loss_cls": 1.92603, "loss": 1.92603, "time": 0.82083} +{"mode": "val", "epoch": 148, "iter": 309, "lr": 4e-05, "top1_acc": 0.45839, "top5_acc": 0.7039, "mean_class_accuracy": 0.45817} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 4e-05, "memory": 15990, "data_time": 1.276, "top1_acc": 0.65312, "top5_acc": 0.86922, "loss_cls": 1.92666, "loss": 1.92666, "time": 2.25697} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 4e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67375, "top5_acc": 0.87156, "loss_cls": 1.89136, "loss": 1.89136, "time": 0.8222} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 4e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66547, "top5_acc": 0.87562, "loss_cls": 1.8971, "loss": 1.8971, "time": 0.82072} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 4e-05, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.65688, "top5_acc": 0.86125, "loss_cls": 1.94071, "loss": 1.94071, "time": 0.82882} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 4e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66797, "top5_acc": 0.87109, "loss_cls": 1.88312, "loss": 1.88312, "time": 0.81931} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 4e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67641, "top5_acc": 0.87734, "loss_cls": 1.85922, "loss": 1.85922, "time": 0.82004} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 4e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.66922, "top5_acc": 0.87719, "loss_cls": 1.87528, "loss": 1.87528, "time": 0.82121} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 4e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65922, "top5_acc": 0.86469, "loss_cls": 1.94777, "loss": 1.94777, "time": 0.81773} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 3e-05, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.66906, "top5_acc": 0.87375, "loss_cls": 1.87305, "loss": 1.87305, "time": 0.81901} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66109, "top5_acc": 0.87094, "loss_cls": 1.92374, "loss": 1.92374, "time": 0.82248} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67547, "top5_acc": 0.87625, "loss_cls": 1.87921, "loss": 1.87921, "time": 0.81647} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66547, "top5_acc": 0.86859, "loss_cls": 1.93123, "loss": 1.93123, "time": 0.82349} +{"mode": "train", "epoch": 149, "iter": 1300, "lr": 3e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65938, "top5_acc": 0.86469, "loss_cls": 1.92118, "loss": 1.92118, "time": 0.81646} +{"mode": "train", "epoch": 149, "iter": 1400, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66094, "top5_acc": 0.86719, "loss_cls": 1.92319, "loss": 1.92319, "time": 0.82357} +{"mode": "train", "epoch": 149, "iter": 1500, "lr": 3e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.66172, "top5_acc": 0.86484, "loss_cls": 1.92807, "loss": 1.92807, "time": 0.82045} +{"mode": "train", "epoch": 149, "iter": 1600, "lr": 3e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66375, "top5_acc": 0.86969, "loss_cls": 1.91002, "loss": 1.91002, "time": 0.81447} +{"mode": "train", "epoch": 149, "iter": 1700, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66328, "top5_acc": 0.86719, "loss_cls": 1.90824, "loss": 1.90824, "time": 0.81764} +{"mode": "train", "epoch": 149, "iter": 1800, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66766, "top5_acc": 0.87078, "loss_cls": 1.88372, "loss": 1.88372, "time": 0.82036} +{"mode": "train", "epoch": 149, "iter": 1900, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66578, "top5_acc": 0.87891, "loss_cls": 1.88005, "loss": 1.88005, "time": 0.8204} +{"mode": "train", "epoch": 149, "iter": 2000, "lr": 2e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65844, "top5_acc": 0.86141, "loss_cls": 1.95368, "loss": 1.95368, "time": 0.81855} +{"mode": "train", "epoch": 149, "iter": 2100, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66578, "top5_acc": 0.86594, "loss_cls": 1.93261, "loss": 1.93261, "time": 0.82002} +{"mode": "train", "epoch": 149, "iter": 2200, "lr": 2e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66312, "top5_acc": 0.87078, "loss_cls": 1.91064, "loss": 1.91064, "time": 0.82113} +{"mode": "train", "epoch": 149, "iter": 2300, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66156, "top5_acc": 0.86641, "loss_cls": 1.93236, "loss": 1.93236, "time": 0.81709} +{"mode": "train", "epoch": 149, "iter": 2400, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66094, "top5_acc": 0.86406, "loss_cls": 1.93606, "loss": 1.93606, "time": 0.81881} +{"mode": "train", "epoch": 149, "iter": 2500, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67375, "top5_acc": 0.87234, "loss_cls": 1.87447, "loss": 1.87447, "time": 0.81965} +{"mode": "train", "epoch": 149, "iter": 2600, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65234, "top5_acc": 0.85984, "loss_cls": 1.94553, "loss": 1.94553, "time": 0.81769} +{"mode": "train", "epoch": 149, "iter": 2700, "lr": 2e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.6675, "top5_acc": 0.87062, "loss_cls": 1.9113, "loss": 1.9113, "time": 0.81994} +{"mode": "train", "epoch": 149, "iter": 2800, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66328, "top5_acc": 0.86938, "loss_cls": 1.91576, "loss": 1.91576, "time": 0.81712} +{"mode": "train", "epoch": 149, "iter": 2900, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67391, "top5_acc": 0.86734, "loss_cls": 1.88733, "loss": 1.88733, "time": 0.81397} +{"mode": "train", "epoch": 149, "iter": 3000, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65906, "top5_acc": 0.86797, "loss_cls": 1.9391, "loss": 1.9391, "time": 0.82467} +{"mode": "train", "epoch": 149, "iter": 3100, "lr": 2e-05, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.67109, "top5_acc": 0.87344, "loss_cls": 1.90435, "loss": 1.90435, "time": 0.81584} +{"mode": "train", "epoch": 149, "iter": 3200, "lr": 1e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.66516, "top5_acc": 0.87125, "loss_cls": 1.91509, "loss": 1.91509, "time": 0.82508} +{"mode": "train", "epoch": 149, "iter": 3300, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66828, "top5_acc": 0.86844, "loss_cls": 1.91923, "loss": 1.91923, "time": 0.81623} +{"mode": "train", "epoch": 149, "iter": 3400, "lr": 1e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67297, "top5_acc": 0.87188, "loss_cls": 1.88975, "loss": 1.88975, "time": 0.82243} +{"mode": "train", "epoch": 149, "iter": 3500, "lr": 1e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66703, "top5_acc": 0.86828, "loss_cls": 1.92032, "loss": 1.92032, "time": 0.8233} +{"mode": "train", "epoch": 149, "iter": 3600, "lr": 1e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.67125, "top5_acc": 0.87625, "loss_cls": 1.85837, "loss": 1.85837, "time": 0.82568} +{"mode": "train", "epoch": 149, "iter": 3700, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66203, "top5_acc": 0.86812, "loss_cls": 1.91646, "loss": 1.91646, "time": 0.82197} +{"mode": "val", "epoch": 149, "iter": 309, "lr": 1e-05, "top1_acc": 0.4593, "top5_acc": 0.7044, "mean_class_accuracy": 0.45903} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 1e-05, "memory": 15990, "data_time": 1.27584, "top1_acc": 0.67688, "top5_acc": 0.87578, "loss_cls": 1.88266, "loss": 1.88266, "time": 2.2731} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67656, "top5_acc": 0.87469, "loss_cls": 1.86115, "loss": 1.86115, "time": 0.8275} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 1e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67531, "top5_acc": 0.875, "loss_cls": 1.88989, "loss": 1.88989, "time": 0.81529} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 1e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67828, "top5_acc": 0.86828, "loss_cls": 1.87666, "loss": 1.87666, "time": 0.82522} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67219, "top5_acc": 0.87844, "loss_cls": 1.87278, "loss": 1.87278, "time": 0.82052} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66312, "top5_acc": 0.87094, "loss_cls": 1.9184, "loss": 1.9184, "time": 0.81983} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66812, "top5_acc": 0.87094, "loss_cls": 1.88374, "loss": 1.88374, "time": 0.81797} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66781, "top5_acc": 0.87141, "loss_cls": 1.89996, "loss": 1.89996, "time": 0.82348} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 1e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66375, "top5_acc": 0.86734, "loss_cls": 1.91323, "loss": 1.91323, "time": 0.81404} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 1e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66, "top5_acc": 0.87188, "loss_cls": 1.91701, "loss": 1.91701, "time": 0.81655} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 1e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66281, "top5_acc": 0.87172, "loss_cls": 1.92024, "loss": 1.92024, "time": 0.81996} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 1e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67375, "top5_acc": 0.87328, "loss_cls": 1.88172, "loss": 1.88172, "time": 0.81732} +{"mode": "train", "epoch": 150, "iter": 1300, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66766, "top5_acc": 0.86578, "loss_cls": 1.92799, "loss": 1.92799, "time": 0.8165} +{"mode": "train", "epoch": 150, "iter": 1400, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66609, "top5_acc": 0.875, "loss_cls": 1.88142, "loss": 1.88142, "time": 0.8159} +{"mode": "train", "epoch": 150, "iter": 1500, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67, "top5_acc": 0.87, "loss_cls": 1.91191, "loss": 1.91191, "time": 0.8206} +{"mode": "train", "epoch": 150, "iter": 1600, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68047, "top5_acc": 0.88016, "loss_cls": 1.82905, "loss": 1.82905, "time": 0.81671} +{"mode": "train", "epoch": 150, "iter": 1700, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66719, "top5_acc": 0.87141, "loss_cls": 1.89741, "loss": 1.89741, "time": 0.81674} +{"mode": "train", "epoch": 150, "iter": 1800, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67016, "top5_acc": 0.87062, "loss_cls": 1.88495, "loss": 1.88495, "time": 0.82049} +{"mode": "train", "epoch": 150, "iter": 1900, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66609, "top5_acc": 0.87094, "loss_cls": 1.89866, "loss": 1.89866, "time": 0.81932} +{"mode": "train", "epoch": 150, "iter": 2000, "lr": 0.0, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.65594, "top5_acc": 0.87125, "loss_cls": 1.93233, "loss": 1.93233, "time": 0.81752} +{"mode": "train", "epoch": 150, "iter": 2100, "lr": 0.0, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.67094, "top5_acc": 0.87578, "loss_cls": 1.88112, "loss": 1.88112, "time": 0.81937} +{"mode": "train", "epoch": 150, "iter": 2200, "lr": 0.0, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.66797, "top5_acc": 0.86391, "loss_cls": 1.92604, "loss": 1.92604, "time": 0.81671} +{"mode": "train", "epoch": 150, "iter": 2300, "lr": 0.0, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.67125, "top5_acc": 0.87578, "loss_cls": 1.87359, "loss": 1.87359, "time": 0.81434} +{"mode": "train", "epoch": 150, "iter": 2400, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66172, "top5_acc": 0.86719, "loss_cls": 1.91851, "loss": 1.91851, "time": 0.81966} +{"mode": "train", "epoch": 150, "iter": 2500, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66812, "top5_acc": 0.87156, "loss_cls": 1.90917, "loss": 1.90917, "time": 0.81917} +{"mode": "train", "epoch": 150, "iter": 2600, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66453, "top5_acc": 0.8675, "loss_cls": 1.94254, "loss": 1.94254, "time": 0.8154} +{"mode": "train", "epoch": 150, "iter": 2700, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66391, "top5_acc": 0.87031, "loss_cls": 1.91456, "loss": 1.91456, "time": 0.81603} +{"mode": "train", "epoch": 150, "iter": 2800, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67359, "top5_acc": 0.86516, "loss_cls": 1.90386, "loss": 1.90386, "time": 0.817} +{"mode": "train", "epoch": 150, "iter": 2900, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66375, "top5_acc": 0.86203, "loss_cls": 1.93728, "loss": 1.93728, "time": 0.81407} +{"mode": "train", "epoch": 150, "iter": 3000, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66844, "top5_acc": 0.86891, "loss_cls": 1.9105, "loss": 1.9105, "time": 0.8137} +{"mode": "train", "epoch": 150, "iter": 3100, "lr": 0.0, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.66656, "top5_acc": 0.86453, "loss_cls": 1.92493, "loss": 1.92493, "time": 0.81634} +{"mode": "train", "epoch": 150, "iter": 3200, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66609, "top5_acc": 0.87391, "loss_cls": 1.89826, "loss": 1.89826, "time": 0.82525} +{"mode": "train", "epoch": 150, "iter": 3300, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66375, "top5_acc": 0.87531, "loss_cls": 1.89766, "loss": 1.89766, "time": 0.82238} +{"mode": "train", "epoch": 150, "iter": 3400, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67125, "top5_acc": 0.86875, "loss_cls": 1.90023, "loss": 1.90023, "time": 0.81703} +{"mode": "train", "epoch": 150, "iter": 3500, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67578, "top5_acc": 0.87375, "loss_cls": 1.8598, "loss": 1.8598, "time": 0.82326} +{"mode": "train", "epoch": 150, "iter": 3600, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67266, "top5_acc": 0.87969, "loss_cls": 1.85097, "loss": 1.85097, "time": 0.83179} +{"mode": "train", "epoch": 150, "iter": 3700, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67172, "top5_acc": 0.87266, "loss_cls": 1.85713, "loss": 1.85713, "time": 0.8172} +{"mode": "val", "epoch": 150, "iter": 309, "lr": 0.0, "top1_acc": 0.45707, "top5_acc": 0.70344, "mean_class_accuracy": 0.4568} diff --git a/k400/b_3/b_3.py b/k400/b_3/b_3.py new file mode 100644 index 0000000000000000000000000000000000000000..4618fe3947b6f8ebfb9af60bc664d5afaadf3585 --- /dev/null +++ b/k400/b_3/b_3.py @@ -0,0 +1,133 @@ +modality = 'b' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/b_3' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['b']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/k400/b_3/best_pred.pkl b/k400/b_3/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..8a2ada97c5e58c1789755a040138f979b360206e --- /dev/null +++ b/k400/b_3/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eabad2666b95c92f1d571698ff8b4593ee27f700aba3fb844905fbe0617cab57 +size 44884165 diff --git a/k400/b_3/best_top1_acc_epoch_149.pth b/k400/b_3/best_top1_acc_epoch_149.pth new file mode 100644 index 0000000000000000000000000000000000000000..777583d5700761de26cf2c8e76d95928e057494e --- /dev/null +++ b/k400/b_3/best_top1_acc_epoch_149.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bab3ee9e563e9e30a1bde10c6c6a0f85bd0742080d7030aa1a6307bd7049ca10 +size 32926705 diff --git a/k400/bm/20241226_014612.log b/k400/bm/20241226_014612.log new file mode 100644 index 0000000000000000000000000000000000000000..f7b28ba56c38dfa94d8e410bd37373ae56213c13 --- /dev/null +++ b/k400/bm/20241226_014612.log @@ -0,0 +1,7325 @@ +2024-12-26 01:46:12,799 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2024-12-26 01:46:13,185 - pyskl - INFO - Config: modality = 'bm' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/bm' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2024-12-26 01:46:13,185 - pyskl - INFO - Set random seed to 1828476928, deterministic: False +2024-12-26 01:46:23,451 - pyskl - INFO - 239737 videos remain after valid thresholding +2024-12-26 01:46:37,412 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-12-26 01:46:37,414 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm +2024-12-26 01:46:37,421 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2024-12-26 01:46:37,444 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2024-12-26 01:46:37,448 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm by HardDiskBackend. +2024-12-26 01:49:52,263 - pyskl - INFO - Epoch [1][100/3746] lr: 1.000e-01, eta: 12 days, 16:00:30, time: 1.948, data_time: 1.243, memory: 15990, top1_acc: 0.0052, top5_acc: 0.0239, loss_cls: 6.4785, loss: 6.4785 +2024-12-26 01:51:02,453 - pyskl - INFO - Epoch [1][200/3746] lr: 1.000e-01, eta: 8 days, 14:44:03, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0097, top5_acc: 0.0436, loss_cls: 6.4414, loss: 6.4414 +2024-12-26 01:52:13,509 - pyskl - INFO - Epoch [1][300/3746] lr: 1.000e-01, eta: 7 days, 6:44:48, time: 0.711, data_time: 0.001, memory: 15990, top1_acc: 0.0150, top5_acc: 0.0581, loss_cls: 6.2688, loss: 6.2688 +2024-12-26 01:53:24,883 - pyskl - INFO - Epoch [1][400/3746] lr: 1.000e-01, eta: 6 days, 14:52:02, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0170, top5_acc: 0.0698, loss_cls: 6.1636, loss: 6.1636 +2024-12-26 01:54:36,269 - pyskl - INFO - Epoch [1][500/3746] lr: 1.000e-01, eta: 6 days, 5:20:07, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0231, top5_acc: 0.0825, loss_cls: 6.1142, loss: 6.1142 +2024-12-26 01:55:47,633 - pyskl - INFO - Epoch [1][600/3746] lr: 1.000e-01, eta: 5 days, 22:58:06, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0234, top5_acc: 0.0897, loss_cls: 6.0307, loss: 6.0307 +2024-12-26 01:56:58,739 - pyskl - INFO - Epoch [1][700/3746] lr: 1.000e-01, eta: 5 days, 18:21:26, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.0256, top5_acc: 0.1000, loss_cls: 5.9525, loss: 5.9525 +2024-12-26 01:58:09,804 - pyskl - INFO - Epoch [1][800/3746] lr: 1.000e-01, eta: 5 days, 14:53:11, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.0278, top5_acc: 0.1062, loss_cls: 5.9337, loss: 5.9337 +2024-12-26 01:59:21,125 - pyskl - INFO - Epoch [1][900/3746] lr: 1.000e-01, eta: 5 days, 12:13:35, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.0253, top5_acc: 0.1114, loss_cls: 5.8775, loss: 5.8775 +2024-12-26 02:00:32,468 - pyskl - INFO - Epoch [1][1000/3746] lr: 1.000e-01, eta: 5 days, 10:05:53, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.0333, top5_acc: 0.1236, loss_cls: 5.8272, loss: 5.8272 +2024-12-26 02:01:43,877 - pyskl - INFO - Epoch [1][1100/3746] lr: 1.000e-01, eta: 5 days, 8:21:44, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0372, top5_acc: 0.1255, loss_cls: 5.8295, loss: 5.8295 +2024-12-26 02:02:54,974 - pyskl - INFO - Epoch [1][1200/3746] lr: 1.000e-01, eta: 5 days, 6:52:20, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.0373, top5_acc: 0.1256, loss_cls: 5.7605, loss: 5.7605 +2024-12-26 02:04:06,491 - pyskl - INFO - Epoch [1][1300/3746] lr: 1.000e-01, eta: 5 days, 5:39:31, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0383, top5_acc: 0.1372, loss_cls: 5.7597, loss: 5.7597 +2024-12-26 02:05:17,980 - pyskl - INFO - Epoch [1][1400/3746] lr: 1.000e-01, eta: 5 days, 4:36:44, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0342, top5_acc: 0.1297, loss_cls: 5.7581, loss: 5.7581 +2024-12-26 02:06:29,202 - pyskl - INFO - Epoch [1][1500/3746] lr: 1.000e-01, eta: 5 days, 3:40:30, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.0419, top5_acc: 0.1420, loss_cls: 5.7300, loss: 5.7300 +2024-12-26 02:07:40,643 - pyskl - INFO - Epoch [1][1600/3746] lr: 1.000e-01, eta: 5 days, 2:52:26, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0473, top5_acc: 0.1530, loss_cls: 5.6835, loss: 5.6835 +2024-12-26 02:08:52,263 - pyskl - INFO - Epoch [1][1700/3746] lr: 1.000e-01, eta: 5 days, 2:10:52, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.0380, top5_acc: 0.1470, loss_cls: 5.6831, loss: 5.6831 +2024-12-26 02:10:04,477 - pyskl - INFO - Epoch [1][1800/3746] lr: 1.000e-01, eta: 5 days, 1:36:51, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.0481, top5_acc: 0.1522, loss_cls: 5.6504, loss: 5.6504 +2024-12-26 02:11:16,087 - pyskl - INFO - Epoch [1][1900/3746] lr: 1.000e-01, eta: 5 days, 1:03:21, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.0477, top5_acc: 0.1516, loss_cls: 5.6660, loss: 5.6660 +2024-12-26 02:12:27,938 - pyskl - INFO - Epoch [1][2000/3746] lr: 1.000e-01, eta: 5 days, 0:34:11, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.0481, top5_acc: 0.1530, loss_cls: 5.6640, loss: 5.6640 +2024-12-26 02:13:39,990 - pyskl - INFO - Epoch [1][2100/3746] lr: 1.000e-01, eta: 5 days, 0:08:34, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.0516, top5_acc: 0.1625, loss_cls: 5.6090, loss: 5.6090 +2024-12-26 02:14:51,908 - pyskl - INFO - Epoch [1][2200/3746] lr: 1.000e-01, eta: 4 days, 23:44:37, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.0498, top5_acc: 0.1642, loss_cls: 5.5966, loss: 5.5966 +2024-12-26 02:16:03,659 - pyskl - INFO - Epoch [1][2300/3746] lr: 1.000e-01, eta: 4 days, 23:21:58, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.0533, top5_acc: 0.1608, loss_cls: 5.6188, loss: 5.6188 +2024-12-26 02:17:15,867 - pyskl - INFO - Epoch [1][2400/3746] lr: 1.000e-01, eta: 4 days, 23:02:53, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.0567, top5_acc: 0.1719, loss_cls: 5.5845, loss: 5.5845 +2024-12-26 02:18:27,895 - pyskl - INFO - Epoch [1][2500/3746] lr: 1.000e-01, eta: 4 days, 22:44:33, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.0563, top5_acc: 0.1816, loss_cls: 5.5664, loss: 5.5664 +2024-12-26 02:19:39,650 - pyskl - INFO - Epoch [1][2600/3746] lr: 9.999e-02, eta: 4 days, 22:26:33, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.0597, top5_acc: 0.1770, loss_cls: 5.5623, loss: 5.5623 +2024-12-26 02:20:51,430 - pyskl - INFO - Epoch [1][2700/3746] lr: 9.999e-02, eta: 4 days, 22:09:54, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.0570, top5_acc: 0.1839, loss_cls: 5.5344, loss: 5.5344 +2024-12-26 02:22:03,221 - pyskl - INFO - Epoch [1][2800/3746] lr: 9.999e-02, eta: 4 days, 21:54:23, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.0597, top5_acc: 0.1855, loss_cls: 5.5180, loss: 5.5180 +2024-12-26 02:23:15,318 - pyskl - INFO - Epoch [1][2900/3746] lr: 9.999e-02, eta: 4 days, 21:40:50, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.0581, top5_acc: 0.1830, loss_cls: 5.5166, loss: 5.5166 +2024-12-26 02:24:27,080 - pyskl - INFO - Epoch [1][3000/3746] lr: 9.999e-02, eta: 4 days, 21:27:04, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.0628, top5_acc: 0.1913, loss_cls: 5.5040, loss: 5.5040 +2024-12-26 02:25:39,229 - pyskl - INFO - Epoch [1][3100/3746] lr: 9.999e-02, eta: 4 days, 21:15:16, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.0684, top5_acc: 0.1948, loss_cls: 5.4759, loss: 5.4759 +2024-12-26 02:26:50,793 - pyskl - INFO - Epoch [1][3200/3746] lr: 9.999e-02, eta: 4 days, 21:02:26, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.0603, top5_acc: 0.1920, loss_cls: 5.4538, loss: 5.4538 +2024-12-26 02:28:02,926 - pyskl - INFO - Epoch [1][3300/3746] lr: 9.999e-02, eta: 4 days, 20:51:55, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.0658, top5_acc: 0.2008, loss_cls: 5.4535, loss: 5.4535 +2024-12-26 02:29:14,435 - pyskl - INFO - Epoch [1][3400/3746] lr: 9.999e-02, eta: 4 days, 20:40:14, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0691, top5_acc: 0.2108, loss_cls: 5.4374, loss: 5.4374 +2024-12-26 02:30:26,342 - pyskl - INFO - Epoch [1][3500/3746] lr: 9.999e-02, eta: 4 days, 20:30:13, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.0722, top5_acc: 0.2100, loss_cls: 5.4533, loss: 5.4533 +2024-12-26 02:31:38,270 - pyskl - INFO - Epoch [1][3600/3746] lr: 9.999e-02, eta: 4 days, 20:20:44, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.0731, top5_acc: 0.2144, loss_cls: 5.4225, loss: 5.4225 +2024-12-26 02:32:50,178 - pyskl - INFO - Epoch [1][3700/3746] lr: 9.999e-02, eta: 4 days, 20:11:39, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.0706, top5_acc: 0.2173, loss_cls: 5.3835, loss: 5.3835 +2024-12-26 02:33:25,733 - pyskl - INFO - Saving checkpoint at 1 epochs +2024-12-26 02:35:22,614 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 02:35:23,396 - pyskl - INFO - +top1_acc 0.0462 +top5_acc 0.1450 +2024-12-26 02:35:23,397 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 02:35:23,434 - pyskl - INFO - +mean_acc 0.0463 +2024-12-26 02:35:23,690 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2024-12-26 02:35:23,691 - pyskl - INFO - Best top1_acc is 0.0462 at 1 epoch. +2024-12-26 02:35:23,700 - pyskl - INFO - Epoch(val) [1][309] top1_acc: 0.0462, top5_acc: 0.1450, mean_class_accuracy: 0.0463 +2024-12-26 02:38:58,428 - pyskl - INFO - Epoch [2][100/3746] lr: 9.999e-02, eta: 5 days, 0:24:30, time: 2.147, data_time: 1.431, memory: 15990, top1_acc: 0.0716, top5_acc: 0.2238, loss_cls: 5.3279, loss: 5.3279 +2024-12-26 02:40:10,100 - pyskl - INFO - Epoch [2][200/3746] lr: 9.999e-02, eta: 5 days, 0:09:04, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.0798, top5_acc: 0.2338, loss_cls: 5.3496, loss: 5.3496 +2024-12-26 02:41:21,497 - pyskl - INFO - Epoch [2][300/3746] lr: 9.999e-02, eta: 4 days, 23:53:41, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0803, top5_acc: 0.2309, loss_cls: 5.3252, loss: 5.3252 +2024-12-26 02:42:32,837 - pyskl - INFO - Epoch [2][400/3746] lr: 9.999e-02, eta: 4 days, 23:38:52, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.0769, top5_acc: 0.2316, loss_cls: 5.3303, loss: 5.3303 +2024-12-26 02:43:43,763 - pyskl - INFO - Epoch [2][500/3746] lr: 9.999e-02, eta: 4 days, 23:23:47, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.0833, top5_acc: 0.2358, loss_cls: 5.3009, loss: 5.3009 +2024-12-26 02:44:54,556 - pyskl - INFO - Epoch [2][600/3746] lr: 9.999e-02, eta: 4 days, 23:09:04, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.0867, top5_acc: 0.2423, loss_cls: 5.2743, loss: 5.2743 +2024-12-26 02:46:05,451 - pyskl - INFO - Epoch [2][700/3746] lr: 9.998e-02, eta: 4 days, 22:55:10, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.0825, top5_acc: 0.2345, loss_cls: 5.3021, loss: 5.3021 +2024-12-26 02:47:16,834 - pyskl - INFO - Epoch [2][800/3746] lr: 9.998e-02, eta: 4 days, 22:42:49, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0875, top5_acc: 0.2430, loss_cls: 5.2899, loss: 5.2899 +2024-12-26 02:48:27,726 - pyskl - INFO - Epoch [2][900/3746] lr: 9.998e-02, eta: 4 days, 22:29:58, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.0934, top5_acc: 0.2503, loss_cls: 5.2469, loss: 5.2469 +2024-12-26 02:49:38,831 - pyskl - INFO - Epoch [2][1000/3746] lr: 9.998e-02, eta: 4 days, 22:18:02, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.0880, top5_acc: 0.2533, loss_cls: 5.2480, loss: 5.2480 +2024-12-26 02:50:50,129 - pyskl - INFO - Epoch [2][1100/3746] lr: 9.998e-02, eta: 4 days, 22:06:54, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.0911, top5_acc: 0.2573, loss_cls: 5.2193, loss: 5.2193 +2024-12-26 02:52:01,565 - pyskl - INFO - Epoch [2][1200/3746] lr: 9.998e-02, eta: 4 days, 21:56:26, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0948, top5_acc: 0.2630, loss_cls: 5.1841, loss: 5.1841 +2024-12-26 02:53:13,078 - pyskl - INFO - Epoch [2][1300/3746] lr: 9.998e-02, eta: 4 days, 21:46:29, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0920, top5_acc: 0.2678, loss_cls: 5.1643, loss: 5.1643 +2024-12-26 02:54:24,886 - pyskl - INFO - Epoch [2][1400/3746] lr: 9.998e-02, eta: 4 days, 21:37:24, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.0945, top5_acc: 0.2614, loss_cls: 5.2214, loss: 5.2214 +2024-12-26 02:55:36,774 - pyskl - INFO - Epoch [2][1500/3746] lr: 9.998e-02, eta: 4 days, 21:28:46, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1058, top5_acc: 0.2761, loss_cls: 5.1531, loss: 5.1531 +2024-12-26 02:56:48,277 - pyskl - INFO - Epoch [2][1600/3746] lr: 9.998e-02, eta: 4 days, 21:19:44, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0964, top5_acc: 0.2637, loss_cls: 5.1772, loss: 5.1772 +2024-12-26 02:58:00,131 - pyskl - INFO - Epoch [2][1700/3746] lr: 9.998e-02, eta: 4 days, 21:11:35, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.0986, top5_acc: 0.2759, loss_cls: 5.1722, loss: 5.1722 +2024-12-26 02:59:12,109 - pyskl - INFO - Epoch [2][1800/3746] lr: 9.998e-02, eta: 4 days, 21:03:54, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1031, top5_acc: 0.2706, loss_cls: 5.1805, loss: 5.1805 +2024-12-26 03:00:24,069 - pyskl - INFO - Epoch [2][1900/3746] lr: 9.998e-02, eta: 4 days, 20:56:25, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1123, top5_acc: 0.2773, loss_cls: 5.1030, loss: 5.1030 +2024-12-26 03:01:35,804 - pyskl - INFO - Epoch [2][2000/3746] lr: 9.997e-02, eta: 4 days, 20:48:47, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1053, top5_acc: 0.2770, loss_cls: 5.1226, loss: 5.1226 +2024-12-26 03:02:47,703 - pyskl - INFO - Epoch [2][2100/3746] lr: 9.997e-02, eta: 4 days, 20:41:38, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1000, top5_acc: 0.2809, loss_cls: 5.1391, loss: 5.1391 +2024-12-26 03:03:59,440 - pyskl - INFO - Epoch [2][2200/3746] lr: 9.997e-02, eta: 4 days, 20:34:25, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1087, top5_acc: 0.2795, loss_cls: 5.1102, loss: 5.1102 +2024-12-26 03:05:11,377 - pyskl - INFO - Epoch [2][2300/3746] lr: 9.997e-02, eta: 4 days, 20:27:44, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1089, top5_acc: 0.2859, loss_cls: 5.0964, loss: 5.0964 +2024-12-26 03:06:23,150 - pyskl - INFO - Epoch [2][2400/3746] lr: 9.997e-02, eta: 4 days, 20:20:58, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1109, top5_acc: 0.2880, loss_cls: 5.0810, loss: 5.0810 +2024-12-26 03:07:35,061 - pyskl - INFO - Epoch [2][2500/3746] lr: 9.997e-02, eta: 4 days, 20:14:35, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1094, top5_acc: 0.2917, loss_cls: 5.0937, loss: 5.0937 +2024-12-26 03:08:46,810 - pyskl - INFO - Epoch [2][2600/3746] lr: 9.997e-02, eta: 4 days, 20:08:07, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1075, top5_acc: 0.2892, loss_cls: 5.0652, loss: 5.0652 +2024-12-26 03:09:58,749 - pyskl - INFO - Epoch [2][2700/3746] lr: 9.997e-02, eta: 4 days, 20:02:06, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1109, top5_acc: 0.3002, loss_cls: 5.0926, loss: 5.0926 +2024-12-26 03:11:10,802 - pyskl - INFO - Epoch [2][2800/3746] lr: 9.997e-02, eta: 4 days, 19:56:23, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1042, top5_acc: 0.2817, loss_cls: 5.1067, loss: 5.1067 +2024-12-26 03:12:22,615 - pyskl - INFO - Epoch [2][2900/3746] lr: 9.997e-02, eta: 4 days, 19:50:29, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1105, top5_acc: 0.2970, loss_cls: 5.0551, loss: 5.0551 +2024-12-26 03:13:34,387 - pyskl - INFO - Epoch [2][3000/3746] lr: 9.996e-02, eta: 4 days, 19:44:39, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1005, top5_acc: 0.2873, loss_cls: 5.0593, loss: 5.0593 +2024-12-26 03:14:46,258 - pyskl - INFO - Epoch [2][3100/3746] lr: 9.996e-02, eta: 4 days, 19:39:06, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1177, top5_acc: 0.3061, loss_cls: 5.0341, loss: 5.0341 +2024-12-26 03:15:58,081 - pyskl - INFO - Epoch [2][3200/3746] lr: 9.996e-02, eta: 4 days, 19:33:36, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1170, top5_acc: 0.3098, loss_cls: 5.0057, loss: 5.0057 +2024-12-26 03:17:09,785 - pyskl - INFO - Epoch [2][3300/3746] lr: 9.996e-02, eta: 4 days, 19:28:04, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1209, top5_acc: 0.3120, loss_cls: 4.9935, loss: 4.9935 +2024-12-26 03:18:21,459 - pyskl - INFO - Epoch [2][3400/3746] lr: 9.996e-02, eta: 4 days, 19:22:37, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1136, top5_acc: 0.3019, loss_cls: 5.0450, loss: 5.0450 +2024-12-26 03:19:33,201 - pyskl - INFO - Epoch [2][3500/3746] lr: 9.996e-02, eta: 4 days, 19:17:23, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1184, top5_acc: 0.3014, loss_cls: 5.0227, loss: 5.0227 +2024-12-26 03:20:45,332 - pyskl - INFO - Epoch [2][3600/3746] lr: 9.996e-02, eta: 4 days, 19:12:44, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1239, top5_acc: 0.3092, loss_cls: 4.9953, loss: 4.9953 +2024-12-26 03:21:57,225 - pyskl - INFO - Epoch [2][3700/3746] lr: 9.996e-02, eta: 4 days, 19:07:53, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1178, top5_acc: 0.3127, loss_cls: 5.0175, loss: 5.0175 +2024-12-26 03:22:32,937 - pyskl - INFO - Saving checkpoint at 2 epochs +2024-12-26 03:24:31,124 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 03:24:32,078 - pyskl - INFO - +top1_acc 0.0822 +top5_acc 0.2384 +2024-12-26 03:24:32,078 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 03:24:32,115 - pyskl - INFO - +mean_acc 0.0820 +2024-12-26 03:24:32,120 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_1.pth was removed +2024-12-26 03:24:32,377 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2024-12-26 03:24:32,377 - pyskl - INFO - Best top1_acc is 0.0822 at 2 epoch. +2024-12-26 03:24:32,387 - pyskl - INFO - Epoch(val) [2][309] top1_acc: 0.0822, top5_acc: 0.2384, mean_class_accuracy: 0.0820 +2024-12-26 03:28:06,826 - pyskl - INFO - Epoch [3][100/3746] lr: 9.995e-02, eta: 4 days, 21:14:12, time: 2.144, data_time: 1.427, memory: 15990, top1_acc: 0.1237, top5_acc: 0.3109, loss_cls: 5.0033, loss: 5.0033 +2024-12-26 03:29:18,532 - pyskl - INFO - Epoch [3][200/3746] lr: 9.995e-02, eta: 4 days, 21:07:36, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1228, top5_acc: 0.3113, loss_cls: 4.9857, loss: 4.9857 +2024-12-26 03:30:29,846 - pyskl - INFO - Epoch [3][300/3746] lr: 9.995e-02, eta: 4 days, 21:00:41, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1278, top5_acc: 0.3173, loss_cls: 4.9498, loss: 4.9498 +2024-12-26 03:31:41,054 - pyskl - INFO - Epoch [3][400/3746] lr: 9.995e-02, eta: 4 days, 20:53:47, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1197, top5_acc: 0.3119, loss_cls: 4.9904, loss: 4.9904 +2024-12-26 03:32:51,987 - pyskl - INFO - Epoch [3][500/3746] lr: 9.995e-02, eta: 4 days, 20:46:42, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1269, top5_acc: 0.3250, loss_cls: 4.9444, loss: 4.9444 +2024-12-26 03:34:02,769 - pyskl - INFO - Epoch [3][600/3746] lr: 9.995e-02, eta: 4 days, 20:39:36, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1266, top5_acc: 0.3166, loss_cls: 4.9680, loss: 4.9680 +2024-12-26 03:35:13,627 - pyskl - INFO - Epoch [3][700/3746] lr: 9.995e-02, eta: 4 days, 20:32:44, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1286, top5_acc: 0.3220, loss_cls: 4.9422, loss: 4.9422 +2024-12-26 03:36:24,441 - pyskl - INFO - Epoch [3][800/3746] lr: 9.995e-02, eta: 4 days, 20:25:57, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1216, top5_acc: 0.3181, loss_cls: 4.9642, loss: 4.9642 +2024-12-26 03:37:35,466 - pyskl - INFO - Epoch [3][900/3746] lr: 9.994e-02, eta: 4 days, 20:19:32, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1217, top5_acc: 0.3116, loss_cls: 4.9748, loss: 4.9748 +2024-12-26 03:38:46,886 - pyskl - INFO - Epoch [3][1000/3746] lr: 9.994e-02, eta: 4 days, 20:13:40, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1356, top5_acc: 0.3280, loss_cls: 4.9089, loss: 4.9089 +2024-12-26 03:39:57,880 - pyskl - INFO - Epoch [3][1100/3746] lr: 9.994e-02, eta: 4 days, 20:07:27, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1375, top5_acc: 0.3319, loss_cls: 4.9108, loss: 4.9108 +2024-12-26 03:41:09,056 - pyskl - INFO - Epoch [3][1200/3746] lr: 9.994e-02, eta: 4 days, 20:01:33, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1306, top5_acc: 0.3205, loss_cls: 4.9381, loss: 4.9381 +2024-12-26 03:42:19,971 - pyskl - INFO - Epoch [3][1300/3746] lr: 9.994e-02, eta: 4 days, 19:55:29, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1291, top5_acc: 0.3298, loss_cls: 4.9208, loss: 4.9208 +2024-12-26 03:43:31,268 - pyskl - INFO - Epoch [3][1400/3746] lr: 9.994e-02, eta: 4 days, 19:49:55, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1359, top5_acc: 0.3325, loss_cls: 4.8891, loss: 4.8891 +2024-12-26 03:44:42,704 - pyskl - INFO - Epoch [3][1500/3746] lr: 9.994e-02, eta: 4 days, 19:44:35, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1261, top5_acc: 0.3211, loss_cls: 4.9437, loss: 4.9437 +2024-12-26 03:45:54,042 - pyskl - INFO - Epoch [3][1600/3746] lr: 9.994e-02, eta: 4 days, 19:39:15, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1338, top5_acc: 0.3314, loss_cls: 4.8727, loss: 4.8727 +2024-12-26 03:47:05,462 - pyskl - INFO - Epoch [3][1700/3746] lr: 9.993e-02, eta: 4 days, 19:34:05, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1345, top5_acc: 0.3297, loss_cls: 4.9278, loss: 4.9278 +2024-12-26 03:48:16,854 - pyskl - INFO - Epoch [3][1800/3746] lr: 9.993e-02, eta: 4 days, 19:28:59, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1366, top5_acc: 0.3278, loss_cls: 4.8907, loss: 4.8907 +2024-12-26 03:49:28,644 - pyskl - INFO - Epoch [3][1900/3746] lr: 9.993e-02, eta: 4 days, 19:24:21, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1313, top5_acc: 0.3319, loss_cls: 4.9014, loss: 4.9014 +2024-12-26 03:50:40,458 - pyskl - INFO - Epoch [3][2000/3746] lr: 9.993e-02, eta: 4 days, 19:19:49, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1425, top5_acc: 0.3366, loss_cls: 4.8680, loss: 4.8680 +2024-12-26 03:51:52,244 - pyskl - INFO - Epoch [3][2100/3746] lr: 9.993e-02, eta: 4 days, 19:15:20, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1400, top5_acc: 0.3336, loss_cls: 4.8722, loss: 4.8722 +2024-12-26 03:53:04,039 - pyskl - INFO - Epoch [3][2200/3746] lr: 9.993e-02, eta: 4 days, 19:10:55, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1328, top5_acc: 0.3311, loss_cls: 4.8864, loss: 4.8864 +2024-12-26 03:54:16,022 - pyskl - INFO - Epoch [3][2300/3746] lr: 9.993e-02, eta: 4 days, 19:06:45, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1406, top5_acc: 0.3392, loss_cls: 4.8623, loss: 4.8623 +2024-12-26 03:55:27,845 - pyskl - INFO - Epoch [3][2400/3746] lr: 9.992e-02, eta: 4 days, 19:02:29, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1348, top5_acc: 0.3248, loss_cls: 4.9284, loss: 4.9284 +2024-12-26 03:56:39,508 - pyskl - INFO - Epoch [3][2500/3746] lr: 9.992e-02, eta: 4 days, 18:58:08, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1322, top5_acc: 0.3292, loss_cls: 4.8705, loss: 4.8705 +2024-12-26 03:57:51,351 - pyskl - INFO - Epoch [3][2600/3746] lr: 9.992e-02, eta: 4 days, 18:54:01, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1428, top5_acc: 0.3478, loss_cls: 4.8265, loss: 4.8265 +2024-12-26 03:59:03,482 - pyskl - INFO - Epoch [3][2700/3746] lr: 9.992e-02, eta: 4 days, 18:50:13, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1367, top5_acc: 0.3373, loss_cls: 4.9027, loss: 4.9027 +2024-12-26 04:00:15,119 - pyskl - INFO - Epoch [3][2800/3746] lr: 9.992e-02, eta: 4 days, 18:46:01, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1414, top5_acc: 0.3400, loss_cls: 4.8688, loss: 4.8688 +2024-12-26 04:01:27,232 - pyskl - INFO - Epoch [3][2900/3746] lr: 9.992e-02, eta: 4 days, 18:42:18, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1314, top5_acc: 0.3311, loss_cls: 4.8887, loss: 4.8887 +2024-12-26 04:02:38,878 - pyskl - INFO - Epoch [3][3000/3746] lr: 9.991e-02, eta: 4 days, 18:38:13, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1356, top5_acc: 0.3409, loss_cls: 4.8477, loss: 4.8477 +2024-12-26 04:03:50,662 - pyskl - INFO - Epoch [3][3100/3746] lr: 9.991e-02, eta: 4 days, 18:34:19, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1398, top5_acc: 0.3447, loss_cls: 4.8370, loss: 4.8370 +2024-12-26 04:05:02,724 - pyskl - INFO - Epoch [3][3200/3746] lr: 9.991e-02, eta: 4 days, 18:30:42, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1439, top5_acc: 0.3398, loss_cls: 4.8466, loss: 4.8466 +2024-12-26 04:06:14,554 - pyskl - INFO - Epoch [3][3300/3746] lr: 9.991e-02, eta: 4 days, 18:26:56, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1402, top5_acc: 0.3459, loss_cls: 4.8533, loss: 4.8533 +2024-12-26 04:07:26,396 - pyskl - INFO - Epoch [3][3400/3746] lr: 9.991e-02, eta: 4 days, 18:23:14, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1441, top5_acc: 0.3503, loss_cls: 4.8096, loss: 4.8096 +2024-12-26 04:08:38,068 - pyskl - INFO - Epoch [3][3500/3746] lr: 9.991e-02, eta: 4 days, 18:19:26, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1405, top5_acc: 0.3419, loss_cls: 4.8584, loss: 4.8584 +2024-12-26 04:09:49,864 - pyskl - INFO - Epoch [3][3600/3746] lr: 9.990e-02, eta: 4 days, 18:15:46, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1480, top5_acc: 0.3559, loss_cls: 4.8067, loss: 4.8067 +2024-12-26 04:11:01,964 - pyskl - INFO - Epoch [3][3700/3746] lr: 9.990e-02, eta: 4 days, 18:12:24, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1481, top5_acc: 0.3473, loss_cls: 4.8368, loss: 4.8368 +2024-12-26 04:11:37,536 - pyskl - INFO - Saving checkpoint at 3 epochs +2024-12-26 04:13:35,684 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 04:13:36,424 - pyskl - INFO - +top1_acc 0.0767 +top5_acc 0.2122 +2024-12-26 04:13:36,424 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 04:13:36,474 - pyskl - INFO - +mean_acc 0.0765 +2024-12-26 04:13:36,489 - pyskl - INFO - Epoch(val) [3][309] top1_acc: 0.0767, top5_acc: 0.2122, mean_class_accuracy: 0.0765 +2024-12-26 04:17:10,944 - pyskl - INFO - Epoch [4][100/3746] lr: 9.990e-02, eta: 4 days, 19:35:56, time: 2.144, data_time: 1.427, memory: 15990, top1_acc: 0.1325, top5_acc: 0.3380, loss_cls: 4.8570, loss: 4.8570 +2024-12-26 04:18:22,922 - pyskl - INFO - Epoch [4][200/3746] lr: 9.990e-02, eta: 4 days, 19:31:46, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1550, top5_acc: 0.3597, loss_cls: 4.8021, loss: 4.8021 +2024-12-26 04:19:34,377 - pyskl - INFO - Epoch [4][300/3746] lr: 9.990e-02, eta: 4 days, 19:27:15, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1417, top5_acc: 0.3472, loss_cls: 4.8058, loss: 4.8058 +2024-12-26 04:20:45,839 - pyskl - INFO - Epoch [4][400/3746] lr: 9.989e-02, eta: 4 days, 19:22:48, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1497, top5_acc: 0.3475, loss_cls: 4.7958, loss: 4.7958 +2024-12-26 04:21:57,252 - pyskl - INFO - Epoch [4][500/3746] lr: 9.989e-02, eta: 4 days, 19:18:21, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1437, top5_acc: 0.3477, loss_cls: 4.8157, loss: 4.8157 +2024-12-26 04:23:08,037 - pyskl - INFO - Epoch [4][600/3746] lr: 9.989e-02, eta: 4 days, 19:13:29, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1519, top5_acc: 0.3538, loss_cls: 4.7996, loss: 4.7996 +2024-12-26 04:24:19,442 - pyskl - INFO - Epoch [4][700/3746] lr: 9.989e-02, eta: 4 days, 19:09:09, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1491, top5_acc: 0.3567, loss_cls: 4.8061, loss: 4.8061 +2024-12-26 04:25:30,430 - pyskl - INFO - Epoch [4][800/3746] lr: 9.989e-02, eta: 4 days, 19:04:33, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1469, top5_acc: 0.3486, loss_cls: 4.8121, loss: 4.8121 +2024-12-26 04:26:41,591 - pyskl - INFO - Epoch [4][900/3746] lr: 9.988e-02, eta: 4 days, 19:00:08, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1525, top5_acc: 0.3502, loss_cls: 4.8069, loss: 4.8069 +2024-12-26 04:27:52,572 - pyskl - INFO - Epoch [4][1000/3746] lr: 9.988e-02, eta: 4 days, 18:55:39, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1470, top5_acc: 0.3614, loss_cls: 4.7820, loss: 4.7820 +2024-12-26 04:29:03,649 - pyskl - INFO - Epoch [4][1100/3746] lr: 9.988e-02, eta: 4 days, 18:51:16, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1473, top5_acc: 0.3444, loss_cls: 4.7882, loss: 4.7882 +2024-12-26 04:30:14,703 - pyskl - INFO - Epoch [4][1200/3746] lr: 9.988e-02, eta: 4 days, 18:46:56, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1552, top5_acc: 0.3542, loss_cls: 4.7882, loss: 4.7882 +2024-12-26 04:31:25,658 - pyskl - INFO - Epoch [4][1300/3746] lr: 9.988e-02, eta: 4 days, 18:42:35, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1506, top5_acc: 0.3627, loss_cls: 4.7570, loss: 4.7570 +2024-12-26 04:32:36,503 - pyskl - INFO - Epoch [4][1400/3746] lr: 9.988e-02, eta: 4 days, 18:38:12, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1519, top5_acc: 0.3517, loss_cls: 4.8078, loss: 4.8078 +2024-12-26 04:33:47,699 - pyskl - INFO - Epoch [4][1500/3746] lr: 9.987e-02, eta: 4 days, 18:34:07, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1606, top5_acc: 0.3711, loss_cls: 4.7180, loss: 4.7180 +2024-12-26 04:34:58,849 - pyskl - INFO - Epoch [4][1600/3746] lr: 9.987e-02, eta: 4 days, 18:30:02, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1528, top5_acc: 0.3667, loss_cls: 4.7930, loss: 4.7930 +2024-12-26 04:36:09,877 - pyskl - INFO - Epoch [4][1700/3746] lr: 9.987e-02, eta: 4 days, 18:25:56, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1458, top5_acc: 0.3606, loss_cls: 4.7658, loss: 4.7658 +2024-12-26 04:37:21,308 - pyskl - INFO - Epoch [4][1800/3746] lr: 9.987e-02, eta: 4 days, 18:22:08, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1547, top5_acc: 0.3570, loss_cls: 4.7869, loss: 4.7869 +2024-12-26 04:38:32,217 - pyskl - INFO - Epoch [4][1900/3746] lr: 9.987e-02, eta: 4 days, 18:18:02, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1470, top5_acc: 0.3558, loss_cls: 4.7810, loss: 4.7810 +2024-12-26 04:39:43,412 - pyskl - INFO - Epoch [4][2000/3746] lr: 9.986e-02, eta: 4 days, 18:14:10, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1584, top5_acc: 0.3664, loss_cls: 4.7552, loss: 4.7552 +2024-12-26 04:40:54,735 - pyskl - INFO - Epoch [4][2100/3746] lr: 9.986e-02, eta: 4 days, 18:10:25, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1566, top5_acc: 0.3706, loss_cls: 4.7696, loss: 4.7696 +2024-12-26 04:42:06,103 - pyskl - INFO - Epoch [4][2200/3746] lr: 9.986e-02, eta: 4 days, 18:06:45, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1594, top5_acc: 0.3641, loss_cls: 4.7485, loss: 4.7485 +2024-12-26 04:43:17,354 - pyskl - INFO - Epoch [4][2300/3746] lr: 9.986e-02, eta: 4 days, 18:03:02, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1505, top5_acc: 0.3589, loss_cls: 4.7858, loss: 4.7858 +2024-12-26 04:44:28,646 - pyskl - INFO - Epoch [4][2400/3746] lr: 9.985e-02, eta: 4 days, 17:59:23, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1475, top5_acc: 0.3555, loss_cls: 4.7904, loss: 4.7904 +2024-12-26 04:45:40,131 - pyskl - INFO - Epoch [4][2500/3746] lr: 9.985e-02, eta: 4 days, 17:55:54, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1558, top5_acc: 0.3681, loss_cls: 4.7774, loss: 4.7774 +2024-12-26 04:46:52,284 - pyskl - INFO - Epoch [4][2600/3746] lr: 9.985e-02, eta: 4 days, 17:52:54, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1503, top5_acc: 0.3505, loss_cls: 4.7983, loss: 4.7983 +2024-12-26 04:48:04,015 - pyskl - INFO - Epoch [4][2700/3746] lr: 9.985e-02, eta: 4 days, 17:49:38, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1622, top5_acc: 0.3705, loss_cls: 4.7100, loss: 4.7100 +2024-12-26 04:49:15,718 - pyskl - INFO - Epoch [4][2800/3746] lr: 9.985e-02, eta: 4 days, 17:46:23, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1623, top5_acc: 0.3689, loss_cls: 4.7105, loss: 4.7105 +2024-12-26 04:50:27,459 - pyskl - INFO - Epoch [4][2900/3746] lr: 9.984e-02, eta: 4 days, 17:43:11, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1600, top5_acc: 0.3681, loss_cls: 4.7252, loss: 4.7252 +2024-12-26 04:51:39,381 - pyskl - INFO - Epoch [4][3000/3746] lr: 9.984e-02, eta: 4 days, 17:40:08, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1605, top5_acc: 0.3738, loss_cls: 4.7308, loss: 4.7308 +2024-12-26 04:52:51,187 - pyskl - INFO - Epoch [4][3100/3746] lr: 9.984e-02, eta: 4 days, 17:37:02, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1522, top5_acc: 0.3613, loss_cls: 4.7582, loss: 4.7582 +2024-12-26 04:54:02,936 - pyskl - INFO - Epoch [4][3200/3746] lr: 9.984e-02, eta: 4 days, 17:33:55, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1584, top5_acc: 0.3639, loss_cls: 4.7166, loss: 4.7166 +2024-12-26 04:55:15,036 - pyskl - INFO - Epoch [4][3300/3746] lr: 9.983e-02, eta: 4 days, 17:31:04, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1539, top5_acc: 0.3539, loss_cls: 4.7734, loss: 4.7734 +2024-12-26 04:56:26,800 - pyskl - INFO - Epoch [4][3400/3746] lr: 9.983e-02, eta: 4 days, 17:28:01, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1605, top5_acc: 0.3734, loss_cls: 4.7467, loss: 4.7467 +2024-12-26 04:57:38,852 - pyskl - INFO - Epoch [4][3500/3746] lr: 9.983e-02, eta: 4 days, 17:25:10, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1642, top5_acc: 0.3730, loss_cls: 4.7135, loss: 4.7135 +2024-12-26 04:58:50,615 - pyskl - INFO - Epoch [4][3600/3746] lr: 9.983e-02, eta: 4 days, 17:22:10, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1622, top5_acc: 0.3734, loss_cls: 4.7388, loss: 4.7388 +2024-12-26 05:00:02,384 - pyskl - INFO - Epoch [4][3700/3746] lr: 9.983e-02, eta: 4 days, 17:19:11, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1708, top5_acc: 0.3805, loss_cls: 4.7028, loss: 4.7028 +2024-12-26 05:00:38,009 - pyskl - INFO - Saving checkpoint at 4 epochs +2024-12-26 05:02:36,116 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 05:02:37,151 - pyskl - INFO - +top1_acc 0.0964 +top5_acc 0.2659 +2024-12-26 05:02:37,151 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 05:02:37,206 - pyskl - INFO - +mean_acc 0.0963 +2024-12-26 05:02:37,214 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_2.pth was removed +2024-12-26 05:02:37,725 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2024-12-26 05:02:37,726 - pyskl - INFO - Best top1_acc is 0.0964 at 4 epoch. +2024-12-26 05:02:37,739 - pyskl - INFO - Epoch(val) [4][309] top1_acc: 0.0964, top5_acc: 0.2659, mean_class_accuracy: 0.0963 +2024-12-26 05:06:15,780 - pyskl - INFO - Epoch [5][100/3746] lr: 9.982e-02, eta: 4 days, 18:23:19, time: 2.180, data_time: 1.460, memory: 15990, top1_acc: 0.1606, top5_acc: 0.3772, loss_cls: 4.6992, loss: 4.6992 +2024-12-26 05:07:27,805 - pyskl - INFO - Epoch [5][200/3746] lr: 9.982e-02, eta: 4 days, 18:20:05, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1631, top5_acc: 0.3777, loss_cls: 4.6963, loss: 4.6963 +2024-12-26 05:08:39,828 - pyskl - INFO - Epoch [5][300/3746] lr: 9.982e-02, eta: 4 days, 18:16:53, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1566, top5_acc: 0.3777, loss_cls: 4.7023, loss: 4.7023 +2024-12-26 05:09:51,620 - pyskl - INFO - Epoch [5][400/3746] lr: 9.982e-02, eta: 4 days, 18:13:34, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1703, top5_acc: 0.3789, loss_cls: 4.6838, loss: 4.6838 +2024-12-26 05:11:03,304 - pyskl - INFO - Epoch [5][500/3746] lr: 9.981e-02, eta: 4 days, 18:10:14, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1625, top5_acc: 0.3688, loss_cls: 4.7228, loss: 4.7228 +2024-12-26 05:12:14,487 - pyskl - INFO - Epoch [5][600/3746] lr: 9.981e-02, eta: 4 days, 18:06:37, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1689, top5_acc: 0.3797, loss_cls: 4.6779, loss: 4.6779 +2024-12-26 05:13:25,916 - pyskl - INFO - Epoch [5][700/3746] lr: 9.981e-02, eta: 4 days, 18:03:10, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1545, top5_acc: 0.3733, loss_cls: 4.7190, loss: 4.7190 +2024-12-26 05:14:37,301 - pyskl - INFO - Epoch [5][800/3746] lr: 9.981e-02, eta: 4 days, 17:59:44, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1555, top5_acc: 0.3727, loss_cls: 4.7302, loss: 4.7302 +2024-12-26 05:15:49,061 - pyskl - INFO - Epoch [5][900/3746] lr: 9.980e-02, eta: 4 days, 17:56:33, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1645, top5_acc: 0.3755, loss_cls: 4.6883, loss: 4.6883 +2024-12-26 05:17:00,600 - pyskl - INFO - Epoch [5][1000/3746] lr: 9.980e-02, eta: 4 days, 17:53:15, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1681, top5_acc: 0.3859, loss_cls: 4.6924, loss: 4.6924 +2024-12-26 05:18:11,967 - pyskl - INFO - Epoch [5][1100/3746] lr: 9.980e-02, eta: 4 days, 17:49:53, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1539, top5_acc: 0.3680, loss_cls: 4.7522, loss: 4.7522 +2024-12-26 05:19:23,316 - pyskl - INFO - Epoch [5][1200/3746] lr: 9.980e-02, eta: 4 days, 17:46:32, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1652, top5_acc: 0.3827, loss_cls: 4.6855, loss: 4.6855 +2024-12-26 05:20:34,461 - pyskl - INFO - Epoch [5][1300/3746] lr: 9.979e-02, eta: 4 days, 17:43:06, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1642, top5_acc: 0.3758, loss_cls: 4.7251, loss: 4.7251 +2024-12-26 05:21:45,627 - pyskl - INFO - Epoch [5][1400/3746] lr: 9.979e-02, eta: 4 days, 17:39:42, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1675, top5_acc: 0.3825, loss_cls: 4.6953, loss: 4.6953 +2024-12-26 05:22:57,011 - pyskl - INFO - Epoch [5][1500/3746] lr: 9.979e-02, eta: 4 days, 17:36:27, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1631, top5_acc: 0.3762, loss_cls: 4.7155, loss: 4.7155 +2024-12-26 05:24:08,343 - pyskl - INFO - Epoch [5][1600/3746] lr: 9.979e-02, eta: 4 days, 17:33:12, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1666, top5_acc: 0.3791, loss_cls: 4.7029, loss: 4.7029 +2024-12-26 05:25:20,164 - pyskl - INFO - Epoch [5][1700/3746] lr: 9.978e-02, eta: 4 days, 17:30:14, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1608, top5_acc: 0.3663, loss_cls: 4.7188, loss: 4.7188 +2024-12-26 05:26:31,249 - pyskl - INFO - Epoch [5][1800/3746] lr: 9.978e-02, eta: 4 days, 17:26:54, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1545, top5_acc: 0.3702, loss_cls: 4.7212, loss: 4.7212 +2024-12-26 05:27:42,844 - pyskl - INFO - Epoch [5][1900/3746] lr: 9.978e-02, eta: 4 days, 17:23:52, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1648, top5_acc: 0.3767, loss_cls: 4.6935, loss: 4.6935 +2024-12-26 05:28:54,109 - pyskl - INFO - Epoch [5][2000/3746] lr: 9.977e-02, eta: 4 days, 17:20:40, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1620, top5_acc: 0.3764, loss_cls: 4.6756, loss: 4.6756 +2024-12-26 05:30:05,587 - pyskl - INFO - Epoch [5][2100/3746] lr: 9.977e-02, eta: 4 days, 17:17:37, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1595, top5_acc: 0.3752, loss_cls: 4.6967, loss: 4.6967 +2024-12-26 05:31:16,599 - pyskl - INFO - Epoch [5][2200/3746] lr: 9.977e-02, eta: 4 days, 17:14:20, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1736, top5_acc: 0.3850, loss_cls: 4.6664, loss: 4.6664 +2024-12-26 05:32:28,218 - pyskl - INFO - Epoch [5][2300/3746] lr: 9.977e-02, eta: 4 days, 17:11:23, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1694, top5_acc: 0.3847, loss_cls: 4.6616, loss: 4.6616 +2024-12-26 05:33:39,769 - pyskl - INFO - Epoch [5][2400/3746] lr: 9.976e-02, eta: 4 days, 17:08:26, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1703, top5_acc: 0.3866, loss_cls: 4.6375, loss: 4.6375 +2024-12-26 05:34:51,575 - pyskl - INFO - Epoch [5][2500/3746] lr: 9.976e-02, eta: 4 days, 17:05:38, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1586, top5_acc: 0.3700, loss_cls: 4.7173, loss: 4.7173 +2024-12-26 05:36:02,960 - pyskl - INFO - Epoch [5][2600/3746] lr: 9.976e-02, eta: 4 days, 17:02:38, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1566, top5_acc: 0.3828, loss_cls: 4.6860, loss: 4.6860 +2024-12-26 05:37:14,236 - pyskl - INFO - Epoch [5][2700/3746] lr: 9.976e-02, eta: 4 days, 16:59:36, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1708, top5_acc: 0.3856, loss_cls: 4.6608, loss: 4.6608 +2024-12-26 05:38:26,289 - pyskl - INFO - Epoch [5][2800/3746] lr: 9.975e-02, eta: 4 days, 16:56:59, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1658, top5_acc: 0.3837, loss_cls: 4.6802, loss: 4.6802 +2024-12-26 05:39:38,121 - pyskl - INFO - Epoch [5][2900/3746] lr: 9.975e-02, eta: 4 days, 16:54:16, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1731, top5_acc: 0.3919, loss_cls: 4.6756, loss: 4.6756 +2024-12-26 05:40:50,078 - pyskl - INFO - Epoch [5][3000/3746] lr: 9.975e-02, eta: 4 days, 16:51:38, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1709, top5_acc: 0.3709, loss_cls: 4.6674, loss: 4.6674 +2024-12-26 05:42:02,140 - pyskl - INFO - Epoch [5][3100/3746] lr: 9.974e-02, eta: 4 days, 16:49:04, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1700, top5_acc: 0.3839, loss_cls: 4.6821, loss: 4.6821 +2024-12-26 05:43:14,098 - pyskl - INFO - Epoch [5][3200/3746] lr: 9.974e-02, eta: 4 days, 16:46:27, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1638, top5_acc: 0.3819, loss_cls: 4.6895, loss: 4.6895 +2024-12-26 05:44:25,913 - pyskl - INFO - Epoch [5][3300/3746] lr: 9.974e-02, eta: 4 days, 16:43:48, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1680, top5_acc: 0.3791, loss_cls: 4.6967, loss: 4.6967 +2024-12-26 05:45:37,774 - pyskl - INFO - Epoch [5][3400/3746] lr: 9.974e-02, eta: 4 days, 16:41:10, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1648, top5_acc: 0.3906, loss_cls: 4.6673, loss: 4.6673 +2024-12-26 05:46:49,590 - pyskl - INFO - Epoch [5][3500/3746] lr: 9.973e-02, eta: 4 days, 16:38:33, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1673, top5_acc: 0.3828, loss_cls: 4.6695, loss: 4.6695 +2024-12-26 05:48:01,393 - pyskl - INFO - Epoch [5][3600/3746] lr: 9.973e-02, eta: 4 days, 16:35:56, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1664, top5_acc: 0.3820, loss_cls: 4.6825, loss: 4.6825 +2024-12-26 05:49:13,444 - pyskl - INFO - Epoch [5][3700/3746] lr: 9.973e-02, eta: 4 days, 16:33:27, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1748, top5_acc: 0.3923, loss_cls: 4.6552, loss: 4.6552 +2024-12-26 05:49:48,776 - pyskl - INFO - Saving checkpoint at 5 epochs +2024-12-26 05:51:45,693 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 05:51:46,660 - pyskl - INFO - +top1_acc 0.0990 +top5_acc 0.2651 +2024-12-26 05:51:46,660 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 05:51:46,699 - pyskl - INFO - +mean_acc 0.0988 +2024-12-26 05:51:46,704 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_4.pth was removed +2024-12-26 05:51:47,012 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2024-12-26 05:51:47,013 - pyskl - INFO - Best top1_acc is 0.0990 at 5 epoch. +2024-12-26 05:51:47,025 - pyskl - INFO - Epoch(val) [5][309] top1_acc: 0.0990, top5_acc: 0.2651, mean_class_accuracy: 0.0988 +2024-12-26 05:55:22,962 - pyskl - INFO - Epoch [6][100/3746] lr: 9.972e-02, eta: 4 days, 17:23:04, time: 2.159, data_time: 1.442, memory: 15990, top1_acc: 0.1775, top5_acc: 0.4077, loss_cls: 4.5947, loss: 4.5947 +2024-12-26 05:56:34,847 - pyskl - INFO - Epoch [6][200/3746] lr: 9.972e-02, eta: 4 days, 17:20:15, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1753, top5_acc: 0.3903, loss_cls: 4.6116, loss: 4.6116 +2024-12-26 05:57:46,566 - pyskl - INFO - Epoch [6][300/3746] lr: 9.972e-02, eta: 4 days, 17:17:22, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1653, top5_acc: 0.3809, loss_cls: 4.6397, loss: 4.6397 +2024-12-26 05:58:58,174 - pyskl - INFO - Epoch [6][400/3746] lr: 9.971e-02, eta: 4 days, 17:14:27, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1709, top5_acc: 0.3789, loss_cls: 4.6810, loss: 4.6810 +2024-12-26 06:00:09,594 - pyskl - INFO - Epoch [6][500/3746] lr: 9.971e-02, eta: 4 days, 17:11:28, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1723, top5_acc: 0.3894, loss_cls: 4.6671, loss: 4.6671 +2024-12-26 06:01:21,204 - pyskl - INFO - Epoch [6][600/3746] lr: 9.971e-02, eta: 4 days, 17:08:35, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1716, top5_acc: 0.3889, loss_cls: 4.6372, loss: 4.6372 +2024-12-26 06:02:32,826 - pyskl - INFO - Epoch [6][700/3746] lr: 9.971e-02, eta: 4 days, 17:05:44, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1777, top5_acc: 0.3902, loss_cls: 4.6405, loss: 4.6405 +2024-12-26 06:03:44,345 - pyskl - INFO - Epoch [6][800/3746] lr: 9.970e-02, eta: 4 days, 17:02:50, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1744, top5_acc: 0.4000, loss_cls: 4.6449, loss: 4.6449 +2024-12-26 06:04:55,675 - pyskl - INFO - Epoch [6][900/3746] lr: 9.970e-02, eta: 4 days, 16:59:53, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1673, top5_acc: 0.3859, loss_cls: 4.6711, loss: 4.6711 +2024-12-26 06:06:06,883 - pyskl - INFO - Epoch [6][1000/3746] lr: 9.970e-02, eta: 4 days, 16:56:53, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1692, top5_acc: 0.3858, loss_cls: 4.6737, loss: 4.6737 +2024-12-26 06:07:18,688 - pyskl - INFO - Epoch [6][1100/3746] lr: 9.969e-02, eta: 4 days, 16:54:11, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1589, top5_acc: 0.3744, loss_cls: 4.6942, loss: 4.6942 +2024-12-26 06:08:29,898 - pyskl - INFO - Epoch [6][1200/3746] lr: 9.969e-02, eta: 4 days, 16:51:13, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1812, top5_acc: 0.3969, loss_cls: 4.6256, loss: 4.6256 +2024-12-26 06:09:41,307 - pyskl - INFO - Epoch [6][1300/3746] lr: 9.969e-02, eta: 4 days, 16:48:22, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1708, top5_acc: 0.3883, loss_cls: 4.6676, loss: 4.6676 +2024-12-26 06:10:52,976 - pyskl - INFO - Epoch [6][1400/3746] lr: 9.968e-02, eta: 4 days, 16:45:39, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1655, top5_acc: 0.3773, loss_cls: 4.6948, loss: 4.6948 +2024-12-26 06:12:04,893 - pyskl - INFO - Epoch [6][1500/3746] lr: 9.968e-02, eta: 4 days, 16:43:04, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1773, top5_acc: 0.3877, loss_cls: 4.6097, loss: 4.6097 +2024-12-26 06:13:16,572 - pyskl - INFO - Epoch [6][1600/3746] lr: 9.968e-02, eta: 4 days, 16:40:22, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1803, top5_acc: 0.3955, loss_cls: 4.6333, loss: 4.6333 +2024-12-26 06:14:28,195 - pyskl - INFO - Epoch [6][1700/3746] lr: 9.967e-02, eta: 4 days, 16:37:41, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1733, top5_acc: 0.3837, loss_cls: 4.6608, loss: 4.6608 +2024-12-26 06:15:39,774 - pyskl - INFO - Epoch [6][1800/3746] lr: 9.967e-02, eta: 4 days, 16:34:59, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1748, top5_acc: 0.3886, loss_cls: 4.6254, loss: 4.6254 +2024-12-26 06:16:51,348 - pyskl - INFO - Epoch [6][1900/3746] lr: 9.967e-02, eta: 4 days, 16:32:18, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1761, top5_acc: 0.3934, loss_cls: 4.6144, loss: 4.6144 +2024-12-26 06:18:02,986 - pyskl - INFO - Epoch [6][2000/3746] lr: 9.966e-02, eta: 4 days, 16:29:39, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1688, top5_acc: 0.3923, loss_cls: 4.6377, loss: 4.6377 +2024-12-26 06:19:14,674 - pyskl - INFO - Epoch [6][2100/3746] lr: 9.966e-02, eta: 4 days, 16:27:02, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1702, top5_acc: 0.3909, loss_cls: 4.6419, loss: 4.6419 +2024-12-26 06:20:26,726 - pyskl - INFO - Epoch [6][2200/3746] lr: 9.966e-02, eta: 4 days, 16:24:36, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1717, top5_acc: 0.3917, loss_cls: 4.6320, loss: 4.6320 +2024-12-26 06:21:38,425 - pyskl - INFO - Epoch [6][2300/3746] lr: 9.965e-02, eta: 4 days, 16:22:01, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1734, top5_acc: 0.3902, loss_cls: 4.6426, loss: 4.6426 +2024-12-26 06:22:50,760 - pyskl - INFO - Epoch [6][2400/3746] lr: 9.965e-02, eta: 4 days, 16:19:43, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1691, top5_acc: 0.3889, loss_cls: 4.6690, loss: 4.6690 +2024-12-26 06:24:02,833 - pyskl - INFO - Epoch [6][2500/3746] lr: 9.965e-02, eta: 4 days, 16:17:20, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1720, top5_acc: 0.3900, loss_cls: 4.6488, loss: 4.6488 +2024-12-26 06:25:14,886 - pyskl - INFO - Epoch [6][2600/3746] lr: 9.964e-02, eta: 4 days, 16:14:56, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1627, top5_acc: 0.3783, loss_cls: 4.6911, loss: 4.6911 +2024-12-26 06:26:26,621 - pyskl - INFO - Epoch [6][2700/3746] lr: 9.964e-02, eta: 4 days, 16:12:25, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1702, top5_acc: 0.3914, loss_cls: 4.6502, loss: 4.6502 +2024-12-26 06:27:38,651 - pyskl - INFO - Epoch [6][2800/3746] lr: 9.964e-02, eta: 4 days, 16:10:02, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1719, top5_acc: 0.3912, loss_cls: 4.6436, loss: 4.6436 +2024-12-26 06:28:50,458 - pyskl - INFO - Epoch [6][2900/3746] lr: 9.963e-02, eta: 4 days, 16:07:35, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1750, top5_acc: 0.3945, loss_cls: 4.6299, loss: 4.6299 +2024-12-26 06:30:02,375 - pyskl - INFO - Epoch [6][3000/3746] lr: 9.963e-02, eta: 4 days, 16:05:10, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1775, top5_acc: 0.3908, loss_cls: 4.6487, loss: 4.6487 +2024-12-26 06:31:14,792 - pyskl - INFO - Epoch [6][3100/3746] lr: 9.963e-02, eta: 4 days, 16:02:59, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.1722, top5_acc: 0.3917, loss_cls: 4.6110, loss: 4.6110 +2024-12-26 06:32:26,734 - pyskl - INFO - Epoch [6][3200/3746] lr: 9.962e-02, eta: 4 days, 16:00:37, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1750, top5_acc: 0.3939, loss_cls: 4.6396, loss: 4.6396 +2024-12-26 06:33:38,791 - pyskl - INFO - Epoch [6][3300/3746] lr: 9.962e-02, eta: 4 days, 15:58:18, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1723, top5_acc: 0.3920, loss_cls: 4.6088, loss: 4.6088 +2024-12-26 06:34:50,663 - pyskl - INFO - Epoch [6][3400/3746] lr: 9.962e-02, eta: 4 days, 15:55:55, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1789, top5_acc: 0.3906, loss_cls: 4.6235, loss: 4.6235 +2024-12-26 06:36:02,910 - pyskl - INFO - Epoch [6][3500/3746] lr: 9.961e-02, eta: 4 days, 15:53:42, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1741, top5_acc: 0.3948, loss_cls: 4.6059, loss: 4.6059 +2024-12-26 06:37:15,312 - pyskl - INFO - Epoch [6][3600/3746] lr: 9.961e-02, eta: 4 days, 15:51:33, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.1748, top5_acc: 0.3881, loss_cls: 4.6163, loss: 4.6163 +2024-12-26 06:38:27,550 - pyskl - INFO - Epoch [6][3700/3746] lr: 9.961e-02, eta: 4 days, 15:49:21, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1736, top5_acc: 0.3917, loss_cls: 4.6265, loss: 4.6265 +2024-12-26 06:39:03,277 - pyskl - INFO - Saving checkpoint at 6 epochs +2024-12-26 06:41:02,571 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 06:41:03,675 - pyskl - INFO - +top1_acc 0.0858 +top5_acc 0.2386 +2024-12-26 06:41:03,675 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 06:41:03,713 - pyskl - INFO - +mean_acc 0.0857 +2024-12-26 06:41:03,731 - pyskl - INFO - Epoch(val) [6][309] top1_acc: 0.0858, top5_acc: 0.2386, mean_class_accuracy: 0.0857 +2024-12-26 06:44:41,821 - pyskl - INFO - Epoch [7][100/3746] lr: 9.960e-02, eta: 4 days, 16:30:59, time: 2.181, data_time: 1.459, memory: 15990, top1_acc: 0.1809, top5_acc: 0.4116, loss_cls: 4.5591, loss: 4.5591 +2024-12-26 06:45:53,568 - pyskl - INFO - Epoch [7][200/3746] lr: 9.960e-02, eta: 4 days, 16:28:24, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1850, top5_acc: 0.4069, loss_cls: 4.5799, loss: 4.5799 +2024-12-26 06:47:05,364 - pyskl - INFO - Epoch [7][300/3746] lr: 9.960e-02, eta: 4 days, 16:25:51, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1775, top5_acc: 0.4016, loss_cls: 4.5844, loss: 4.5844 +2024-12-26 06:48:17,051 - pyskl - INFO - Epoch [7][400/3746] lr: 9.959e-02, eta: 4 days, 16:23:16, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1864, top5_acc: 0.4061, loss_cls: 4.5763, loss: 4.5763 +2024-12-26 06:49:28,719 - pyskl - INFO - Epoch [7][500/3746] lr: 9.959e-02, eta: 4 days, 16:20:41, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1717, top5_acc: 0.4005, loss_cls: 4.6028, loss: 4.6028 +2024-12-26 06:50:39,873 - pyskl - INFO - Epoch [7][600/3746] lr: 9.958e-02, eta: 4 days, 16:17:55, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1725, top5_acc: 0.3867, loss_cls: 4.6461, loss: 4.6461 +2024-12-26 06:51:51,247 - pyskl - INFO - Epoch [7][700/3746] lr: 9.958e-02, eta: 4 days, 16:15:15, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1778, top5_acc: 0.4102, loss_cls: 4.5706, loss: 4.5706 +2024-12-26 06:53:02,615 - pyskl - INFO - Epoch [7][800/3746] lr: 9.958e-02, eta: 4 days, 16:12:36, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1744, top5_acc: 0.3844, loss_cls: 4.6491, loss: 4.6491 +2024-12-26 06:54:13,684 - pyskl - INFO - Epoch [7][900/3746] lr: 9.957e-02, eta: 4 days, 16:09:50, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1808, top5_acc: 0.4052, loss_cls: 4.5842, loss: 4.5842 +2024-12-26 06:55:25,171 - pyskl - INFO - Epoch [7][1000/3746] lr: 9.957e-02, eta: 4 days, 16:07:15, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1769, top5_acc: 0.3989, loss_cls: 4.6254, loss: 4.6254 +2024-12-26 06:56:36,433 - pyskl - INFO - Epoch [7][1100/3746] lr: 9.957e-02, eta: 4 days, 16:04:36, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1761, top5_acc: 0.3902, loss_cls: 4.6360, loss: 4.6360 +2024-12-26 06:57:47,771 - pyskl - INFO - Epoch [7][1200/3746] lr: 9.956e-02, eta: 4 days, 16:01:59, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1816, top5_acc: 0.3991, loss_cls: 4.6009, loss: 4.6009 +2024-12-26 06:58:58,925 - pyskl - INFO - Epoch [7][1300/3746] lr: 9.956e-02, eta: 4 days, 15:59:18, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1764, top5_acc: 0.3908, loss_cls: 4.6300, loss: 4.6300 +2024-12-26 07:00:10,373 - pyskl - INFO - Epoch [7][1400/3746] lr: 9.956e-02, eta: 4 days, 15:56:45, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1828, top5_acc: 0.3967, loss_cls: 4.6043, loss: 4.6043 +2024-12-26 07:01:22,129 - pyskl - INFO - Epoch [7][1500/3746] lr: 9.955e-02, eta: 4 days, 15:54:19, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1817, top5_acc: 0.4061, loss_cls: 4.6104, loss: 4.6104 +2024-12-26 07:02:33,673 - pyskl - INFO - Epoch [7][1600/3746] lr: 9.955e-02, eta: 4 days, 15:51:50, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1753, top5_acc: 0.4000, loss_cls: 4.6258, loss: 4.6258 +2024-12-26 07:03:45,242 - pyskl - INFO - Epoch [7][1700/3746] lr: 9.954e-02, eta: 4 days, 15:49:21, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1725, top5_acc: 0.4059, loss_cls: 4.5853, loss: 4.5853 +2024-12-26 07:04:57,308 - pyskl - INFO - Epoch [7][1800/3746] lr: 9.954e-02, eta: 4 days, 15:47:04, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1775, top5_acc: 0.3936, loss_cls: 4.6067, loss: 4.6067 +2024-12-26 07:06:09,108 - pyskl - INFO - Epoch [7][1900/3746] lr: 9.954e-02, eta: 4 days, 15:44:42, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1812, top5_acc: 0.3905, loss_cls: 4.6261, loss: 4.6261 +2024-12-26 07:07:21,195 - pyskl - INFO - Epoch [7][2000/3746] lr: 9.953e-02, eta: 4 days, 15:42:27, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1823, top5_acc: 0.4016, loss_cls: 4.6007, loss: 4.6007 +2024-12-26 07:08:32,693 - pyskl - INFO - Epoch [7][2100/3746] lr: 9.953e-02, eta: 4 days, 15:39:59, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1869, top5_acc: 0.4119, loss_cls: 4.5508, loss: 4.5508 +2024-12-26 07:09:44,476 - pyskl - INFO - Epoch [7][2200/3746] lr: 9.952e-02, eta: 4 days, 15:37:38, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1734, top5_acc: 0.3903, loss_cls: 4.6238, loss: 4.6238 +2024-12-26 07:10:56,006 - pyskl - INFO - Epoch [7][2300/3746] lr: 9.952e-02, eta: 4 days, 15:35:12, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1817, top5_acc: 0.3952, loss_cls: 4.6200, loss: 4.6200 +2024-12-26 07:12:08,035 - pyskl - INFO - Epoch [7][2400/3746] lr: 9.952e-02, eta: 4 days, 15:32:58, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1795, top5_acc: 0.4016, loss_cls: 4.5765, loss: 4.5765 +2024-12-26 07:13:19,657 - pyskl - INFO - Epoch [7][2500/3746] lr: 9.951e-02, eta: 4 days, 15:30:35, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1800, top5_acc: 0.3978, loss_cls: 4.5863, loss: 4.5863 +2024-12-26 07:14:31,625 - pyskl - INFO - Epoch [7][2600/3746] lr: 9.951e-02, eta: 4 days, 15:28:21, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1830, top5_acc: 0.4016, loss_cls: 4.5867, loss: 4.5867 +2024-12-26 07:15:43,189 - pyskl - INFO - Epoch [7][2700/3746] lr: 9.951e-02, eta: 4 days, 15:25:58, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1842, top5_acc: 0.3944, loss_cls: 4.6073, loss: 4.6073 +2024-12-26 07:16:55,088 - pyskl - INFO - Epoch [7][2800/3746] lr: 9.950e-02, eta: 4 days, 15:23:43, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1702, top5_acc: 0.3908, loss_cls: 4.6317, loss: 4.6317 +2024-12-26 07:18:06,616 - pyskl - INFO - Epoch [7][2900/3746] lr: 9.950e-02, eta: 4 days, 15:21:20, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1897, top5_acc: 0.4050, loss_cls: 4.5807, loss: 4.5807 +2024-12-26 07:19:18,451 - pyskl - INFO - Epoch [7][3000/3746] lr: 9.949e-02, eta: 4 days, 15:19:05, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1855, top5_acc: 0.4062, loss_cls: 4.5492, loss: 4.5492 +2024-12-26 07:20:30,608 - pyskl - INFO - Epoch [7][3100/3746] lr: 9.949e-02, eta: 4 days, 15:16:57, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1892, top5_acc: 0.4031, loss_cls: 4.5727, loss: 4.5727 +2024-12-26 07:21:42,725 - pyskl - INFO - Epoch [7][3200/3746] lr: 9.949e-02, eta: 4 days, 15:14:48, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1759, top5_acc: 0.4012, loss_cls: 4.5873, loss: 4.5873 +2024-12-26 07:22:55,112 - pyskl - INFO - Epoch [7][3300/3746] lr: 9.948e-02, eta: 4 days, 15:12:45, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.1859, top5_acc: 0.4056, loss_cls: 4.5945, loss: 4.5945 +2024-12-26 07:24:06,998 - pyskl - INFO - Epoch [7][3400/3746] lr: 9.948e-02, eta: 4 days, 15:10:33, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1808, top5_acc: 0.3953, loss_cls: 4.5995, loss: 4.5995 +2024-12-26 07:25:19,473 - pyskl - INFO - Epoch [7][3500/3746] lr: 9.947e-02, eta: 4 days, 15:08:33, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.1786, top5_acc: 0.4008, loss_cls: 4.5802, loss: 4.5802 +2024-12-26 07:26:31,157 - pyskl - INFO - Epoch [7][3600/3746] lr: 9.947e-02, eta: 4 days, 15:06:17, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1767, top5_acc: 0.4050, loss_cls: 4.5871, loss: 4.5871 +2024-12-26 07:27:42,935 - pyskl - INFO - Epoch [7][3700/3746] lr: 9.947e-02, eta: 4 days, 15:04:04, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1748, top5_acc: 0.3906, loss_cls: 4.6524, loss: 4.6524 +2024-12-26 07:28:18,382 - pyskl - INFO - Saving checkpoint at 7 epochs +2024-12-26 07:30:16,794 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 07:30:17,709 - pyskl - INFO - +top1_acc 0.1233 +top5_acc 0.3097 +2024-12-26 07:30:17,709 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 07:30:17,751 - pyskl - INFO - +mean_acc 0.1232 +2024-12-26 07:30:17,756 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_5.pth was removed +2024-12-26 07:30:18,050 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2024-12-26 07:30:18,050 - pyskl - INFO - Best top1_acc is 0.1233 at 7 epoch. +2024-12-26 07:30:18,062 - pyskl - INFO - Epoch(val) [7][309] top1_acc: 0.1233, top5_acc: 0.3097, mean_class_accuracy: 0.1232 +2024-12-26 07:33:56,560 - pyskl - INFO - Epoch [8][100/3746] lr: 9.946e-02, eta: 4 days, 15:39:23, time: 2.185, data_time: 1.470, memory: 15990, top1_acc: 0.1795, top5_acc: 0.4047, loss_cls: 4.5550, loss: 4.5550 +2024-12-26 07:35:08,404 - pyskl - INFO - Epoch [8][200/3746] lr: 9.946e-02, eta: 4 days, 15:37:03, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1908, top5_acc: 0.4161, loss_cls: 4.5170, loss: 4.5170 +2024-12-26 07:36:20,170 - pyskl - INFO - Epoch [8][300/3746] lr: 9.945e-02, eta: 4 days, 15:34:42, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1794, top5_acc: 0.4128, loss_cls: 4.5404, loss: 4.5404 +2024-12-26 07:37:32,169 - pyskl - INFO - Epoch [8][400/3746] lr: 9.945e-02, eta: 4 days, 15:32:26, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1859, top5_acc: 0.4097, loss_cls: 4.5809, loss: 4.5809 +2024-12-26 07:38:43,976 - pyskl - INFO - Epoch [8][500/3746] lr: 9.944e-02, eta: 4 days, 15:30:07, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1773, top5_acc: 0.4052, loss_cls: 4.5534, loss: 4.5534 +2024-12-26 07:39:55,111 - pyskl - INFO - Epoch [8][600/3746] lr: 9.944e-02, eta: 4 days, 15:27:34, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1900, top5_acc: 0.4163, loss_cls: 4.5077, loss: 4.5077 +2024-12-26 07:41:06,610 - pyskl - INFO - Epoch [8][700/3746] lr: 9.943e-02, eta: 4 days, 15:25:10, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1772, top5_acc: 0.3998, loss_cls: 4.5835, loss: 4.5835 +2024-12-26 07:42:18,013 - pyskl - INFO - Epoch [8][800/3746] lr: 9.943e-02, eta: 4 days, 15:22:44, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1778, top5_acc: 0.4034, loss_cls: 4.5692, loss: 4.5692 +2024-12-26 07:43:29,087 - pyskl - INFO - Epoch [8][900/3746] lr: 9.943e-02, eta: 4 days, 15:20:13, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1747, top5_acc: 0.3969, loss_cls: 4.5914, loss: 4.5914 +2024-12-26 07:44:40,529 - pyskl - INFO - Epoch [8][1000/3746] lr: 9.942e-02, eta: 4 days, 15:17:49, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1817, top5_acc: 0.3958, loss_cls: 4.6078, loss: 4.6078 +2024-12-26 07:45:51,768 - pyskl - INFO - Epoch [8][1100/3746] lr: 9.942e-02, eta: 4 days, 15:15:22, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1963, top5_acc: 0.4172, loss_cls: 4.5254, loss: 4.5254 +2024-12-26 07:47:03,151 - pyskl - INFO - Epoch [8][1200/3746] lr: 9.941e-02, eta: 4 days, 15:12:58, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1869, top5_acc: 0.4066, loss_cls: 4.5797, loss: 4.5797 +2024-12-26 07:48:14,242 - pyskl - INFO - Epoch [8][1300/3746] lr: 9.941e-02, eta: 4 days, 15:10:28, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1797, top5_acc: 0.3986, loss_cls: 4.5935, loss: 4.5935 +2024-12-26 07:49:25,754 - pyskl - INFO - Epoch [8][1400/3746] lr: 9.940e-02, eta: 4 days, 15:08:08, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1880, top5_acc: 0.4034, loss_cls: 4.5914, loss: 4.5914 +2024-12-26 07:50:36,925 - pyskl - INFO - Epoch [8][1500/3746] lr: 9.940e-02, eta: 4 days, 15:05:42, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1839, top5_acc: 0.4164, loss_cls: 4.5061, loss: 4.5061 +2024-12-26 07:51:48,352 - pyskl - INFO - Epoch [8][1600/3746] lr: 9.940e-02, eta: 4 days, 15:03:21, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1856, top5_acc: 0.4092, loss_cls: 4.5648, loss: 4.5648 +2024-12-26 07:52:59,879 - pyskl - INFO - Epoch [8][1700/3746] lr: 9.939e-02, eta: 4 days, 15:01:02, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1902, top5_acc: 0.4098, loss_cls: 4.5389, loss: 4.5389 +2024-12-26 07:54:12,023 - pyskl - INFO - Epoch [8][1800/3746] lr: 9.939e-02, eta: 4 days, 14:58:56, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1928, top5_acc: 0.4152, loss_cls: 4.5205, loss: 4.5205 +2024-12-26 07:55:23,730 - pyskl - INFO - Epoch [8][1900/3746] lr: 9.938e-02, eta: 4 days, 14:56:41, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1897, top5_acc: 0.4067, loss_cls: 4.5438, loss: 4.5438 +2024-12-26 07:56:35,487 - pyskl - INFO - Epoch [8][2000/3746] lr: 9.938e-02, eta: 4 days, 14:54:29, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1822, top5_acc: 0.4041, loss_cls: 4.5975, loss: 4.5975 +2024-12-26 07:57:47,413 - pyskl - INFO - Epoch [8][2100/3746] lr: 9.937e-02, eta: 4 days, 14:52:19, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1792, top5_acc: 0.4039, loss_cls: 4.5726, loss: 4.5726 +2024-12-26 07:58:59,069 - pyskl - INFO - Epoch [8][2200/3746] lr: 9.937e-02, eta: 4 days, 14:50:05, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1836, top5_acc: 0.4022, loss_cls: 4.5909, loss: 4.5909 +2024-12-26 08:00:10,906 - pyskl - INFO - Epoch [8][2300/3746] lr: 9.937e-02, eta: 4 days, 14:47:55, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1903, top5_acc: 0.4127, loss_cls: 4.5562, loss: 4.5562 +2024-12-26 08:01:22,794 - pyskl - INFO - Epoch [8][2400/3746] lr: 9.936e-02, eta: 4 days, 14:45:47, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1787, top5_acc: 0.4005, loss_cls: 4.5999, loss: 4.5999 +2024-12-26 08:02:34,840 - pyskl - INFO - Epoch [8][2500/3746] lr: 9.936e-02, eta: 4 days, 14:43:41, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1800, top5_acc: 0.4033, loss_cls: 4.5694, loss: 4.5694 +2024-12-26 08:03:46,675 - pyskl - INFO - Epoch [8][2600/3746] lr: 9.935e-02, eta: 4 days, 14:41:32, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.1777, top5_acc: 0.4052, loss_cls: 4.5661, loss: 4.5661 +2024-12-26 08:04:58,772 - pyskl - INFO - Epoch [8][2700/3746] lr: 9.935e-02, eta: 4 days, 14:39:28, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1750, top5_acc: 0.3995, loss_cls: 4.5680, loss: 4.5680 +2024-12-26 08:06:10,431 - pyskl - INFO - Epoch [8][2800/3746] lr: 9.934e-02, eta: 4 days, 14:37:17, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1781, top5_acc: 0.3975, loss_cls: 4.5873, loss: 4.5873 +2024-12-26 08:07:22,323 - pyskl - INFO - Epoch [8][2900/3746] lr: 9.934e-02, eta: 4 days, 14:35:10, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1906, top5_acc: 0.4161, loss_cls: 4.5196, loss: 4.5196 +2024-12-26 08:08:34,008 - pyskl - INFO - Epoch [8][3000/3746] lr: 9.933e-02, eta: 4 days, 14:33:00, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1859, top5_acc: 0.4045, loss_cls: 4.5782, loss: 4.5782 +2024-12-26 08:09:45,770 - pyskl - INFO - Epoch [8][3100/3746] lr: 9.933e-02, eta: 4 days, 14:30:52, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1894, top5_acc: 0.4081, loss_cls: 4.5540, loss: 4.5540 +2024-12-26 08:10:57,517 - pyskl - INFO - Epoch [8][3200/3746] lr: 9.933e-02, eta: 4 days, 14:28:44, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1883, top5_acc: 0.4039, loss_cls: 4.5925, loss: 4.5925 +2024-12-26 08:12:09,591 - pyskl - INFO - Epoch [8][3300/3746] lr: 9.932e-02, eta: 4 days, 14:26:42, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1844, top5_acc: 0.4025, loss_cls: 4.5850, loss: 4.5850 +2024-12-26 08:13:21,351 - pyskl - INFO - Epoch [8][3400/3746] lr: 9.932e-02, eta: 4 days, 14:24:34, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1883, top5_acc: 0.4141, loss_cls: 4.5236, loss: 4.5236 +2024-12-26 08:14:32,988 - pyskl - INFO - Epoch [8][3500/3746] lr: 9.931e-02, eta: 4 days, 14:22:25, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1902, top5_acc: 0.4119, loss_cls: 4.5379, loss: 4.5379 +2024-12-26 08:15:44,804 - pyskl - INFO - Epoch [8][3600/3746] lr: 9.931e-02, eta: 4 days, 14:20:20, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1864, top5_acc: 0.4058, loss_cls: 4.5583, loss: 4.5583 +2024-12-26 08:16:56,807 - pyskl - INFO - Epoch [8][3700/3746] lr: 9.930e-02, eta: 4 days, 14:18:18, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1723, top5_acc: 0.4012, loss_cls: 4.5932, loss: 4.5932 +2024-12-26 08:17:32,460 - pyskl - INFO - Saving checkpoint at 8 epochs +2024-12-26 08:19:31,179 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 08:19:32,035 - pyskl - INFO - +top1_acc 0.1203 +top5_acc 0.3104 +2024-12-26 08:19:32,036 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 08:19:32,083 - pyskl - INFO - +mean_acc 0.1201 +2024-12-26 08:19:32,099 - pyskl - INFO - Epoch(val) [8][309] top1_acc: 0.1203, top5_acc: 0.3104, mean_class_accuracy: 0.1201 +2024-12-26 08:23:08,354 - pyskl - INFO - Epoch [9][100/3746] lr: 9.930e-02, eta: 4 days, 14:48:06, time: 2.162, data_time: 1.451, memory: 15990, top1_acc: 0.1925, top5_acc: 0.4192, loss_cls: 4.4953, loss: 4.4953 +2024-12-26 08:24:20,377 - pyskl - INFO - Epoch [9][200/3746] lr: 9.929e-02, eta: 4 days, 14:45:59, time: 0.720, data_time: 0.001, memory: 15990, top1_acc: 0.1942, top5_acc: 0.4217, loss_cls: 4.4964, loss: 4.4964 +2024-12-26 08:25:32,113 - pyskl - INFO - Epoch [9][300/3746] lr: 9.929e-02, eta: 4 days, 14:43:46, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1947, top5_acc: 0.4134, loss_cls: 4.5061, loss: 4.5061 +2024-12-26 08:26:43,809 - pyskl - INFO - Epoch [9][400/3746] lr: 9.928e-02, eta: 4 days, 14:41:34, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1811, top5_acc: 0.4123, loss_cls: 4.5618, loss: 4.5618 +2024-12-26 08:27:55,285 - pyskl - INFO - Epoch [9][500/3746] lr: 9.928e-02, eta: 4 days, 14:39:18, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1914, top5_acc: 0.4242, loss_cls: 4.4973, loss: 4.4973 +2024-12-26 08:29:06,883 - pyskl - INFO - Epoch [9][600/3746] lr: 9.927e-02, eta: 4 days, 14:37:05, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1917, top5_acc: 0.4155, loss_cls: 4.5197, loss: 4.5197 +2024-12-26 08:30:18,400 - pyskl - INFO - Epoch [9][700/3746] lr: 9.927e-02, eta: 4 days, 14:34:50, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1881, top5_acc: 0.4191, loss_cls: 4.5202, loss: 4.5202 +2024-12-26 08:31:29,614 - pyskl - INFO - Epoch [9][800/3746] lr: 9.926e-02, eta: 4 days, 14:32:31, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1928, top5_acc: 0.4088, loss_cls: 4.5538, loss: 4.5538 +2024-12-26 08:32:40,999 - pyskl - INFO - Epoch [9][900/3746] lr: 9.926e-02, eta: 4 days, 14:30:15, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1952, top5_acc: 0.4139, loss_cls: 4.4997, loss: 4.4997 +2024-12-26 08:33:51,865 - pyskl - INFO - Epoch [9][1000/3746] lr: 9.925e-02, eta: 4 days, 14:27:51, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1847, top5_acc: 0.4062, loss_cls: 4.5779, loss: 4.5779 +2024-12-26 08:35:03,304 - pyskl - INFO - Epoch [9][1100/3746] lr: 9.925e-02, eta: 4 days, 14:25:37, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1886, top5_acc: 0.4091, loss_cls: 4.5386, loss: 4.5386 +2024-12-26 08:36:14,555 - pyskl - INFO - Epoch [9][1200/3746] lr: 9.924e-02, eta: 4 days, 14:23:20, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1791, top5_acc: 0.4058, loss_cls: 4.5614, loss: 4.5614 +2024-12-26 08:37:26,143 - pyskl - INFO - Epoch [9][1300/3746] lr: 9.924e-02, eta: 4 days, 14:21:09, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1877, top5_acc: 0.4145, loss_cls: 4.5343, loss: 4.5343 +2024-12-26 08:38:37,367 - pyskl - INFO - Epoch [9][1400/3746] lr: 9.923e-02, eta: 4 days, 14:18:53, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1902, top5_acc: 0.4095, loss_cls: 4.5293, loss: 4.5293 +2024-12-26 08:39:48,582 - pyskl - INFO - Epoch [9][1500/3746] lr: 9.923e-02, eta: 4 days, 14:16:36, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1845, top5_acc: 0.4047, loss_cls: 4.5882, loss: 4.5882 +2024-12-26 08:40:59,968 - pyskl - INFO - Epoch [9][1600/3746] lr: 9.922e-02, eta: 4 days, 14:14:23, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1812, top5_acc: 0.4109, loss_cls: 4.5397, loss: 4.5397 +2024-12-26 08:42:11,425 - pyskl - INFO - Epoch [9][1700/3746] lr: 9.922e-02, eta: 4 days, 14:12:12, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1920, top5_acc: 0.4164, loss_cls: 4.5406, loss: 4.5406 +2024-12-26 08:43:22,763 - pyskl - INFO - Epoch [9][1800/3746] lr: 9.921e-02, eta: 4 days, 14:09:59, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1889, top5_acc: 0.4077, loss_cls: 4.5406, loss: 4.5406 +2024-12-26 08:44:33,734 - pyskl - INFO - Epoch [9][1900/3746] lr: 9.921e-02, eta: 4 days, 14:07:40, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1900, top5_acc: 0.4184, loss_cls: 4.5101, loss: 4.5101 +2024-12-26 08:45:44,943 - pyskl - INFO - Epoch [9][2000/3746] lr: 9.920e-02, eta: 4 days, 14:05:26, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1866, top5_acc: 0.4025, loss_cls: 4.5937, loss: 4.5937 +2024-12-26 08:46:56,970 - pyskl - INFO - Epoch [9][2100/3746] lr: 9.920e-02, eta: 4 days, 14:03:26, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1894, top5_acc: 0.4198, loss_cls: 4.5241, loss: 4.5241 +2024-12-26 08:48:09,170 - pyskl - INFO - Epoch [9][2200/3746] lr: 9.919e-02, eta: 4 days, 14:01:28, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1852, top5_acc: 0.4095, loss_cls: 4.5622, loss: 4.5622 +2024-12-26 08:49:20,812 - pyskl - INFO - Epoch [9][2300/3746] lr: 9.919e-02, eta: 4 days, 13:59:22, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1964, top5_acc: 0.4106, loss_cls: 4.5154, loss: 4.5154 +2024-12-26 08:50:32,544 - pyskl - INFO - Epoch [9][2400/3746] lr: 9.918e-02, eta: 4 days, 13:57:18, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1925, top5_acc: 0.4184, loss_cls: 4.5293, loss: 4.5293 +2024-12-26 08:51:44,282 - pyskl - INFO - Epoch [9][2500/3746] lr: 9.918e-02, eta: 4 days, 13:55:14, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1955, top5_acc: 0.4030, loss_cls: 4.5585, loss: 4.5585 +2024-12-26 08:52:56,092 - pyskl - INFO - Epoch [9][2600/3746] lr: 9.917e-02, eta: 4 days, 13:53:11, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1883, top5_acc: 0.4016, loss_cls: 4.5558, loss: 4.5558 +2024-12-26 08:54:07,952 - pyskl - INFO - Epoch [9][2700/3746] lr: 9.917e-02, eta: 4 days, 13:51:10, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1930, top5_acc: 0.4134, loss_cls: 4.5000, loss: 4.5000 +2024-12-26 08:55:19,764 - pyskl - INFO - Epoch [9][2800/3746] lr: 9.916e-02, eta: 4 days, 13:49:08, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1916, top5_acc: 0.4033, loss_cls: 4.5430, loss: 4.5430 +2024-12-26 08:56:31,729 - pyskl - INFO - Epoch [9][2900/3746] lr: 9.916e-02, eta: 4 days, 13:47:09, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1837, top5_acc: 0.4023, loss_cls: 4.5467, loss: 4.5467 +2024-12-26 08:57:43,458 - pyskl - INFO - Epoch [9][3000/3746] lr: 9.915e-02, eta: 4 days, 13:45:07, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1953, top5_acc: 0.4147, loss_cls: 4.4785, loss: 4.4785 +2024-12-26 08:58:55,148 - pyskl - INFO - Epoch [9][3100/3746] lr: 9.915e-02, eta: 4 days, 13:43:04, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1809, top5_acc: 0.4014, loss_cls: 4.6060, loss: 4.6060 +2024-12-26 09:00:07,175 - pyskl - INFO - Epoch [9][3200/3746] lr: 9.914e-02, eta: 4 days, 13:41:07, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1903, top5_acc: 0.4080, loss_cls: 4.5414, loss: 4.5414 +2024-12-26 09:01:19,185 - pyskl - INFO - Epoch [9][3300/3746] lr: 9.914e-02, eta: 4 days, 13:39:10, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1913, top5_acc: 0.4053, loss_cls: 4.5502, loss: 4.5502 +2024-12-26 09:02:31,285 - pyskl - INFO - Epoch [9][3400/3746] lr: 9.913e-02, eta: 4 days, 13:37:14, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1977, top5_acc: 0.4252, loss_cls: 4.5100, loss: 4.5100 +2024-12-26 09:03:43,320 - pyskl - INFO - Epoch [9][3500/3746] lr: 9.913e-02, eta: 4 days, 13:35:18, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1870, top5_acc: 0.4113, loss_cls: 4.5270, loss: 4.5270 +2024-12-26 09:04:55,053 - pyskl - INFO - Epoch [9][3600/3746] lr: 9.912e-02, eta: 4 days, 13:33:17, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1841, top5_acc: 0.4081, loss_cls: 4.5722, loss: 4.5722 +2024-12-26 09:06:07,030 - pyskl - INFO - Epoch [9][3700/3746] lr: 9.912e-02, eta: 4 days, 13:31:21, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1856, top5_acc: 0.4094, loss_cls: 4.5387, loss: 4.5387 +2024-12-26 09:06:42,193 - pyskl - INFO - Saving checkpoint at 9 epochs +2024-12-26 09:08:39,537 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 09:08:40,599 - pyskl - INFO - +top1_acc 0.1258 +top5_acc 0.3110 +2024-12-26 09:08:40,599 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 09:08:40,635 - pyskl - INFO - +mean_acc 0.1256 +2024-12-26 09:08:40,640 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_7.pth was removed +2024-12-26 09:08:40,913 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_9.pth. +2024-12-26 09:08:40,913 - pyskl - INFO - Best top1_acc is 0.1258 at 9 epoch. +2024-12-26 09:08:40,923 - pyskl - INFO - Epoch(val) [9][309] top1_acc: 0.1258, top5_acc: 0.3110, mean_class_accuracy: 0.1256 +2024-12-26 09:12:19,706 - pyskl - INFO - Epoch [10][100/3746] lr: 9.911e-02, eta: 4 days, 13:58:06, time: 2.188, data_time: 1.468, memory: 15990, top1_acc: 0.2028, top5_acc: 0.4305, loss_cls: 4.4565, loss: 4.4565 +2024-12-26 09:13:31,823 - pyskl - INFO - Epoch [10][200/3746] lr: 9.910e-02, eta: 4 days, 13:56:07, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4258, loss_cls: 4.4795, loss: 4.4795 +2024-12-26 09:14:43,459 - pyskl - INFO - Epoch [10][300/3746] lr: 9.910e-02, eta: 4 days, 13:54:01, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1853, top5_acc: 0.4044, loss_cls: 4.5474, loss: 4.5474 +2024-12-26 09:15:54,940 - pyskl - INFO - Epoch [10][400/3746] lr: 9.909e-02, eta: 4 days, 13:51:52, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1819, top5_acc: 0.4089, loss_cls: 4.5451, loss: 4.5451 +2024-12-26 09:17:06,385 - pyskl - INFO - Epoch [10][500/3746] lr: 9.909e-02, eta: 4 days, 13:49:43, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1894, top5_acc: 0.4214, loss_cls: 4.4929, loss: 4.4929 +2024-12-26 09:18:17,595 - pyskl - INFO - Epoch [10][600/3746] lr: 9.908e-02, eta: 4 days, 13:47:31, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1920, top5_acc: 0.4206, loss_cls: 4.5066, loss: 4.5066 +2024-12-26 09:19:28,633 - pyskl - INFO - Epoch [10][700/3746] lr: 9.908e-02, eta: 4 days, 13:45:17, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1916, top5_acc: 0.4181, loss_cls: 4.5013, loss: 4.5013 +2024-12-26 09:20:39,934 - pyskl - INFO - Epoch [10][800/3746] lr: 9.907e-02, eta: 4 days, 13:43:07, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1888, top5_acc: 0.4037, loss_cls: 4.5514, loss: 4.5514 +2024-12-26 09:21:51,137 - pyskl - INFO - Epoch [10][900/3746] lr: 9.907e-02, eta: 4 days, 13:40:55, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1909, top5_acc: 0.4172, loss_cls: 4.5109, loss: 4.5109 +2024-12-26 09:23:02,341 - pyskl - INFO - Epoch [10][1000/3746] lr: 9.906e-02, eta: 4 days, 13:38:45, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1880, top5_acc: 0.4233, loss_cls: 4.4939, loss: 4.4939 +2024-12-26 09:24:13,774 - pyskl - INFO - Epoch [10][1100/3746] lr: 9.906e-02, eta: 4 days, 13:36:38, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1902, top5_acc: 0.4150, loss_cls: 4.5178, loss: 4.5178 +2024-12-26 09:25:25,030 - pyskl - INFO - Epoch [10][1200/3746] lr: 9.905e-02, eta: 4 days, 13:34:28, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1847, top5_acc: 0.4091, loss_cls: 4.5744, loss: 4.5744 +2024-12-26 09:26:36,415 - pyskl - INFO - Epoch [10][1300/3746] lr: 9.905e-02, eta: 4 days, 13:32:21, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1869, top5_acc: 0.4134, loss_cls: 4.5330, loss: 4.5330 +2024-12-26 09:27:47,764 - pyskl - INFO - Epoch [10][1400/3746] lr: 9.904e-02, eta: 4 days, 13:30:14, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1839, top5_acc: 0.4097, loss_cls: 4.5604, loss: 4.5604 +2024-12-26 09:28:59,059 - pyskl - INFO - Epoch [10][1500/3746] lr: 9.903e-02, eta: 4 days, 13:28:06, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1925, top5_acc: 0.4256, loss_cls: 4.4647, loss: 4.4647 +2024-12-26 09:30:10,245 - pyskl - INFO - Epoch [10][1600/3746] lr: 9.903e-02, eta: 4 days, 13:25:57, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1870, top5_acc: 0.4191, loss_cls: 4.4775, loss: 4.4775 +2024-12-26 09:31:22,073 - pyskl - INFO - Epoch [10][1700/3746] lr: 9.902e-02, eta: 4 days, 13:23:58, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1900, top5_acc: 0.4158, loss_cls: 4.5183, loss: 4.5183 +2024-12-26 09:32:33,287 - pyskl - INFO - Epoch [10][1800/3746] lr: 9.902e-02, eta: 4 days, 13:21:50, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1827, top5_acc: 0.4080, loss_cls: 4.5532, loss: 4.5532 +2024-12-26 09:33:44,835 - pyskl - INFO - Epoch [10][1900/3746] lr: 9.901e-02, eta: 4 days, 13:19:47, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1925, top5_acc: 0.4192, loss_cls: 4.5262, loss: 4.5262 +2024-12-26 09:34:55,965 - pyskl - INFO - Epoch [10][2000/3746] lr: 9.901e-02, eta: 4 days, 13:17:38, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1872, top5_acc: 0.4034, loss_cls: 4.5533, loss: 4.5533 +2024-12-26 09:36:07,458 - pyskl - INFO - Epoch [10][2100/3746] lr: 9.900e-02, eta: 4 days, 13:15:35, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1963, top5_acc: 0.4250, loss_cls: 4.4840, loss: 4.4840 +2024-12-26 09:37:19,134 - pyskl - INFO - Epoch [10][2200/3746] lr: 9.900e-02, eta: 4 days, 13:13:35, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1894, top5_acc: 0.4125, loss_cls: 4.5551, loss: 4.5551 +2024-12-26 09:38:31,041 - pyskl - INFO - Epoch [10][2300/3746] lr: 9.899e-02, eta: 4 days, 13:11:39, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1867, top5_acc: 0.4103, loss_cls: 4.5450, loss: 4.5450 +2024-12-26 09:39:42,741 - pyskl - INFO - Epoch [10][2400/3746] lr: 9.898e-02, eta: 4 days, 13:09:40, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1939, top5_acc: 0.4155, loss_cls: 4.5177, loss: 4.5177 +2024-12-26 09:40:54,530 - pyskl - INFO - Epoch [10][2500/3746] lr: 9.898e-02, eta: 4 days, 13:07:42, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1848, top5_acc: 0.4083, loss_cls: 4.5482, loss: 4.5482 +2024-12-26 09:42:06,416 - pyskl - INFO - Epoch [10][2600/3746] lr: 9.897e-02, eta: 4 days, 13:05:46, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1941, top5_acc: 0.4152, loss_cls: 4.5408, loss: 4.5408 +2024-12-26 09:43:18,508 - pyskl - INFO - Epoch [10][2700/3746] lr: 9.897e-02, eta: 4 days, 13:03:53, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1953, top5_acc: 0.4272, loss_cls: 4.4601, loss: 4.4601 +2024-12-26 09:44:30,371 - pyskl - INFO - Epoch [10][2800/3746] lr: 9.896e-02, eta: 4 days, 13:01:58, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4242, loss_cls: 4.4692, loss: 4.4692 +2024-12-26 09:45:42,267 - pyskl - INFO - Epoch [10][2900/3746] lr: 9.896e-02, eta: 4 days, 13:00:02, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.1966, top5_acc: 0.4131, loss_cls: 4.5252, loss: 4.5252 +2024-12-26 09:46:54,199 - pyskl - INFO - Epoch [10][3000/3746] lr: 9.895e-02, eta: 4 days, 12:58:08, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1916, top5_acc: 0.4128, loss_cls: 4.5270, loss: 4.5270 +2024-12-26 09:48:06,102 - pyskl - INFO - Epoch [10][3100/3746] lr: 9.894e-02, eta: 4 days, 12:56:14, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1913, top5_acc: 0.4144, loss_cls: 4.5062, loss: 4.5062 +2024-12-26 09:49:17,972 - pyskl - INFO - Epoch [10][3200/3746] lr: 9.894e-02, eta: 4 days, 12:54:19, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1942, top5_acc: 0.4195, loss_cls: 4.5075, loss: 4.5075 +2024-12-26 09:50:29,947 - pyskl - INFO - Epoch [10][3300/3746] lr: 9.893e-02, eta: 4 days, 12:52:26, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1866, top5_acc: 0.4141, loss_cls: 4.5631, loss: 4.5631 +2024-12-26 09:51:41,990 - pyskl - INFO - Epoch [10][3400/3746] lr: 9.893e-02, eta: 4 days, 12:50:34, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1950, top5_acc: 0.4225, loss_cls: 4.4889, loss: 4.4889 +2024-12-26 09:52:53,690 - pyskl - INFO - Epoch [10][3500/3746] lr: 9.892e-02, eta: 4 days, 12:48:37, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1944, top5_acc: 0.4248, loss_cls: 4.4840, loss: 4.4840 +2024-12-26 09:54:05,648 - pyskl - INFO - Epoch [10][3600/3746] lr: 9.892e-02, eta: 4 days, 12:46:45, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1972, top5_acc: 0.4189, loss_cls: 4.5187, loss: 4.5187 +2024-12-26 09:55:17,472 - pyskl - INFO - Epoch [10][3700/3746] lr: 9.891e-02, eta: 4 days, 12:44:50, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1973, top5_acc: 0.4166, loss_cls: 4.5230, loss: 4.5230 +2024-12-26 09:55:53,130 - pyskl - INFO - Saving checkpoint at 10 epochs +2024-12-26 09:57:50,517 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 09:57:51,522 - pyskl - INFO - +top1_acc 0.1123 +top5_acc 0.2919 +2024-12-26 09:57:51,523 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 09:57:51,564 - pyskl - INFO - +mean_acc 0.1121 +2024-12-26 09:57:51,579 - pyskl - INFO - Epoch(val) [10][309] top1_acc: 0.1123, top5_acc: 0.2919, mean_class_accuracy: 0.1121 +2024-12-26 10:01:25,903 - pyskl - INFO - Epoch [11][100/3746] lr: 9.890e-02, eta: 4 days, 13:07:32, time: 2.143, data_time: 1.427, memory: 15990, top1_acc: 0.1875, top5_acc: 0.4195, loss_cls: 4.5069, loss: 4.5069 +2024-12-26 10:02:37,362 - pyskl - INFO - Epoch [11][200/3746] lr: 9.890e-02, eta: 4 days, 13:05:29, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1916, top5_acc: 0.4163, loss_cls: 4.4945, loss: 4.4945 +2024-12-26 10:03:49,095 - pyskl - INFO - Epoch [11][300/3746] lr: 9.889e-02, eta: 4 days, 13:03:30, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4245, loss_cls: 4.4925, loss: 4.4925 +2024-12-26 10:05:00,822 - pyskl - INFO - Epoch [11][400/3746] lr: 9.888e-02, eta: 4 days, 13:01:31, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1897, top5_acc: 0.4145, loss_cls: 4.5266, loss: 4.5266 +2024-12-26 10:06:12,413 - pyskl - INFO - Epoch [11][500/3746] lr: 9.888e-02, eta: 4 days, 12:59:30, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.1900, top5_acc: 0.4230, loss_cls: 4.4968, loss: 4.4968 +2024-12-26 10:07:23,801 - pyskl - INFO - Epoch [11][600/3746] lr: 9.887e-02, eta: 4 days, 12:57:27, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1917, top5_acc: 0.4214, loss_cls: 4.5199, loss: 4.5199 +2024-12-26 10:08:35,263 - pyskl - INFO - Epoch [11][700/3746] lr: 9.887e-02, eta: 4 days, 12:55:25, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1931, top5_acc: 0.4036, loss_cls: 4.5386, loss: 4.5386 +2024-12-26 10:09:46,681 - pyskl - INFO - Epoch [11][800/3746] lr: 9.886e-02, eta: 4 days, 12:53:23, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1964, top5_acc: 0.4206, loss_cls: 4.5110, loss: 4.5110 +2024-12-26 10:10:58,103 - pyskl - INFO - Epoch [11][900/3746] lr: 9.885e-02, eta: 4 days, 12:51:21, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1886, top5_acc: 0.4142, loss_cls: 4.5134, loss: 4.5134 +2024-12-26 10:12:10,164 - pyskl - INFO - Epoch [11][1000/3746] lr: 9.885e-02, eta: 4 days, 12:49:29, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1864, top5_acc: 0.4100, loss_cls: 4.5171, loss: 4.5171 +2024-12-26 10:13:21,774 - pyskl - INFO - Epoch [11][1100/3746] lr: 9.884e-02, eta: 4 days, 12:47:30, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1850, top5_acc: 0.4141, loss_cls: 4.5441, loss: 4.5441 +2024-12-26 10:14:33,858 - pyskl - INFO - Epoch [11][1200/3746] lr: 9.884e-02, eta: 4 days, 12:45:38, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1875, top5_acc: 0.4228, loss_cls: 4.4982, loss: 4.4982 +2024-12-26 10:15:45,912 - pyskl - INFO - Epoch [11][1300/3746] lr: 9.883e-02, eta: 4 days, 12:43:45, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1834, top5_acc: 0.4066, loss_cls: 4.5241, loss: 4.5241 +2024-12-26 10:16:58,118 - pyskl - INFO - Epoch [11][1400/3746] lr: 9.882e-02, eta: 4 days, 12:41:55, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1798, top5_acc: 0.4120, loss_cls: 4.5441, loss: 4.5441 +2024-12-26 10:18:10,267 - pyskl - INFO - Epoch [11][1500/3746] lr: 9.882e-02, eta: 4 days, 12:40:05, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1920, top5_acc: 0.4189, loss_cls: 4.5153, loss: 4.5153 +2024-12-26 10:19:22,593 - pyskl - INFO - Epoch [11][1600/3746] lr: 9.881e-02, eta: 4 days, 12:38:17, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1844, top5_acc: 0.4097, loss_cls: 4.5418, loss: 4.5418 +2024-12-26 10:20:34,647 - pyskl - INFO - Epoch [11][1700/3746] lr: 9.881e-02, eta: 4 days, 12:36:25, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4278, loss_cls: 4.4462, loss: 4.4462 +2024-12-26 10:21:46,732 - pyskl - INFO - Epoch [11][1800/3746] lr: 9.880e-02, eta: 4 days, 12:34:34, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2025, top5_acc: 0.4372, loss_cls: 4.4667, loss: 4.4667 +2024-12-26 10:22:58,539 - pyskl - INFO - Epoch [11][1900/3746] lr: 9.879e-02, eta: 4 days, 12:32:40, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1922, top5_acc: 0.4138, loss_cls: 4.4987, loss: 4.4987 +2024-12-26 10:24:10,565 - pyskl - INFO - Epoch [11][2000/3746] lr: 9.879e-02, eta: 4 days, 12:30:49, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2009, top5_acc: 0.4203, loss_cls: 4.4734, loss: 4.4734 +2024-12-26 10:25:22,598 - pyskl - INFO - Epoch [11][2100/3746] lr: 9.878e-02, eta: 4 days, 12:28:58, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1995, top5_acc: 0.4230, loss_cls: 4.4928, loss: 4.4928 +2024-12-26 10:26:34,842 - pyskl - INFO - Epoch [11][2200/3746] lr: 9.878e-02, eta: 4 days, 12:27:10, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1983, top5_acc: 0.4222, loss_cls: 4.5022, loss: 4.5022 +2024-12-26 10:27:46,931 - pyskl - INFO - Epoch [11][2300/3746] lr: 9.877e-02, eta: 4 days, 12:25:20, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1908, top5_acc: 0.4109, loss_cls: 4.5373, loss: 4.5373 +2024-12-26 10:28:59,004 - pyskl - INFO - Epoch [11][2400/3746] lr: 9.876e-02, eta: 4 days, 12:23:30, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1917, top5_acc: 0.4148, loss_cls: 4.5235, loss: 4.5235 +2024-12-26 10:30:10,971 - pyskl - INFO - Epoch [11][2500/3746] lr: 9.876e-02, eta: 4 days, 12:21:39, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2002, top5_acc: 0.4277, loss_cls: 4.4718, loss: 4.4718 +2024-12-26 10:31:22,996 - pyskl - INFO - Epoch [11][2600/3746] lr: 9.875e-02, eta: 4 days, 12:19:49, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1998, top5_acc: 0.4217, loss_cls: 4.4777, loss: 4.4777 +2024-12-26 10:32:35,260 - pyskl - INFO - Epoch [11][2700/3746] lr: 9.874e-02, eta: 4 days, 12:18:02, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4261, loss_cls: 4.4929, loss: 4.4929 +2024-12-26 10:33:47,186 - pyskl - INFO - Epoch [11][2800/3746] lr: 9.874e-02, eta: 4 days, 12:16:11, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1902, top5_acc: 0.4222, loss_cls: 4.4912, loss: 4.4912 +2024-12-26 10:34:59,435 - pyskl - INFO - Epoch [11][2900/3746] lr: 9.873e-02, eta: 4 days, 12:14:24, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1902, top5_acc: 0.4166, loss_cls: 4.5022, loss: 4.5022 +2024-12-26 10:36:11,576 - pyskl - INFO - Epoch [11][3000/3746] lr: 9.873e-02, eta: 4 days, 12:12:36, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4264, loss_cls: 4.4764, loss: 4.4764 +2024-12-26 10:37:23,579 - pyskl - INFO - Epoch [11][3100/3746] lr: 9.872e-02, eta: 4 days, 12:10:47, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1934, top5_acc: 0.4136, loss_cls: 4.5092, loss: 4.5092 +2024-12-26 10:38:35,907 - pyskl - INFO - Epoch [11][3200/3746] lr: 9.871e-02, eta: 4 days, 12:09:02, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4234, loss_cls: 4.4894, loss: 4.4894 +2024-12-26 10:39:47,889 - pyskl - INFO - Epoch [11][3300/3746] lr: 9.871e-02, eta: 4 days, 12:07:12, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4289, loss_cls: 4.4550, loss: 4.4550 +2024-12-26 10:40:59,907 - pyskl - INFO - Epoch [11][3400/3746] lr: 9.870e-02, eta: 4 days, 12:05:23, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1928, top5_acc: 0.4166, loss_cls: 4.5242, loss: 4.5242 +2024-12-26 10:42:11,795 - pyskl - INFO - Epoch [11][3500/3746] lr: 9.869e-02, eta: 4 days, 12:03:33, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1948, top5_acc: 0.4153, loss_cls: 4.4938, loss: 4.4938 +2024-12-26 10:43:24,493 - pyskl - INFO - Epoch [11][3600/3746] lr: 9.869e-02, eta: 4 days, 12:01:53, time: 0.727, data_time: 0.000, memory: 15990, top1_acc: 0.1875, top5_acc: 0.4155, loss_cls: 4.5219, loss: 4.5219 +2024-12-26 10:44:36,435 - pyskl - INFO - Epoch [11][3700/3746] lr: 9.868e-02, eta: 4 days, 12:00:04, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1931, top5_acc: 0.4167, loss_cls: 4.5009, loss: 4.5009 +2024-12-26 10:45:11,943 - pyskl - INFO - Saving checkpoint at 11 epochs +2024-12-26 10:47:11,665 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 10:47:12,558 - pyskl - INFO - +top1_acc 0.1217 +top5_acc 0.3060 +2024-12-26 10:47:12,558 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 10:47:12,614 - pyskl - INFO - +mean_acc 0.1215 +2024-12-26 10:47:12,632 - pyskl - INFO - Epoch(val) [11][309] top1_acc: 0.1217, top5_acc: 0.3060, mean_class_accuracy: 0.1215 +2024-12-26 10:50:51,854 - pyskl - INFO - Epoch [12][100/3746] lr: 9.867e-02, eta: 4 days, 12:21:24, time: 2.192, data_time: 1.475, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4406, loss_cls: 4.3968, loss: 4.3968 +2024-12-26 10:52:03,903 - pyskl - INFO - Epoch [12][200/3746] lr: 9.867e-02, eta: 4 days, 12:19:33, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1958, top5_acc: 0.4248, loss_cls: 4.4503, loss: 4.4503 +2024-12-26 10:53:15,519 - pyskl - INFO - Epoch [12][300/3746] lr: 9.866e-02, eta: 4 days, 12:17:36, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1931, top5_acc: 0.4233, loss_cls: 4.4590, loss: 4.4590 +2024-12-26 10:54:26,879 - pyskl - INFO - Epoch [12][400/3746] lr: 9.865e-02, eta: 4 days, 12:15:37, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4186, loss_cls: 4.4753, loss: 4.4753 +2024-12-26 10:55:38,293 - pyskl - INFO - Epoch [12][500/3746] lr: 9.865e-02, eta: 4 days, 12:13:39, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1973, top5_acc: 0.4219, loss_cls: 4.4736, loss: 4.4736 +2024-12-26 10:56:49,572 - pyskl - INFO - Epoch [12][600/3746] lr: 9.864e-02, eta: 4 days, 12:11:39, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1938, top5_acc: 0.4209, loss_cls: 4.4777, loss: 4.4777 +2024-12-26 10:58:01,241 - pyskl - INFO - Epoch [12][700/3746] lr: 9.863e-02, eta: 4 days, 12:09:44, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2033, top5_acc: 0.4241, loss_cls: 4.4628, loss: 4.4628 +2024-12-26 10:59:12,543 - pyskl - INFO - Epoch [12][800/3746] lr: 9.863e-02, eta: 4 days, 12:07:44, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1927, top5_acc: 0.4272, loss_cls: 4.4835, loss: 4.4835 +2024-12-26 11:00:24,087 - pyskl - INFO - Epoch [12][900/3746] lr: 9.862e-02, eta: 4 days, 12:05:48, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1984, top5_acc: 0.4292, loss_cls: 4.4648, loss: 4.4648 +2024-12-26 11:01:35,509 - pyskl - INFO - Epoch [12][1000/3746] lr: 9.861e-02, eta: 4 days, 12:03:51, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1939, top5_acc: 0.4142, loss_cls: 4.4936, loss: 4.4936 +2024-12-26 11:02:46,690 - pyskl - INFO - Epoch [12][1100/3746] lr: 9.861e-02, eta: 4 days, 12:01:51, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4186, loss_cls: 4.4980, loss: 4.4980 +2024-12-26 11:03:58,751 - pyskl - INFO - Epoch [12][1200/3746] lr: 9.860e-02, eta: 4 days, 12:00:02, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2002, top5_acc: 0.4222, loss_cls: 4.4633, loss: 4.4633 +2024-12-26 11:05:10,568 - pyskl - INFO - Epoch [12][1300/3746] lr: 9.859e-02, eta: 4 days, 11:58:10, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1936, top5_acc: 0.4214, loss_cls: 4.4859, loss: 4.4859 +2024-12-26 11:06:22,663 - pyskl - INFO - Epoch [12][1400/3746] lr: 9.859e-02, eta: 4 days, 11:56:22, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4277, loss_cls: 4.4386, loss: 4.4386 +2024-12-26 11:07:34,438 - pyskl - INFO - Epoch [12][1500/3746] lr: 9.858e-02, eta: 4 days, 11:54:30, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4200, loss_cls: 4.5072, loss: 4.5072 +2024-12-26 11:08:46,504 - pyskl - INFO - Epoch [12][1600/3746] lr: 9.857e-02, eta: 4 days, 11:52:42, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1908, top5_acc: 0.4097, loss_cls: 4.5385, loss: 4.5385 +2024-12-26 11:09:58,743 - pyskl - INFO - Epoch [12][1700/3746] lr: 9.857e-02, eta: 4 days, 11:50:56, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1970, top5_acc: 0.4131, loss_cls: 4.5316, loss: 4.5316 +2024-12-26 11:11:10,482 - pyskl - INFO - Epoch [12][1800/3746] lr: 9.856e-02, eta: 4 days, 11:49:04, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2011, top5_acc: 0.4153, loss_cls: 4.5104, loss: 4.5104 +2024-12-26 11:12:22,318 - pyskl - INFO - Epoch [12][1900/3746] lr: 9.855e-02, eta: 4 days, 11:47:13, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4216, loss_cls: 4.4748, loss: 4.4748 +2024-12-26 11:13:34,126 - pyskl - INFO - Epoch [12][2000/3746] lr: 9.855e-02, eta: 4 days, 11:45:23, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1894, top5_acc: 0.4211, loss_cls: 4.5085, loss: 4.5085 +2024-12-26 11:14:46,075 - pyskl - INFO - Epoch [12][2100/3746] lr: 9.854e-02, eta: 4 days, 11:43:34, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1911, top5_acc: 0.4248, loss_cls: 4.4866, loss: 4.4866 +2024-12-26 11:15:57,962 - pyskl - INFO - Epoch [12][2200/3746] lr: 9.853e-02, eta: 4 days, 11:41:45, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1917, top5_acc: 0.4234, loss_cls: 4.4983, loss: 4.4983 +2024-12-26 11:17:09,754 - pyskl - INFO - Epoch [12][2300/3746] lr: 9.853e-02, eta: 4 days, 11:39:54, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1945, top5_acc: 0.4208, loss_cls: 4.4970, loss: 4.4970 +2024-12-26 11:18:21,913 - pyskl - INFO - Epoch [12][2400/3746] lr: 9.852e-02, eta: 4 days, 11:38:08, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1992, top5_acc: 0.4172, loss_cls: 4.4751, loss: 4.4751 +2024-12-26 11:19:34,227 - pyskl - INFO - Epoch [12][2500/3746] lr: 9.851e-02, eta: 4 days, 11:36:25, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1956, top5_acc: 0.4198, loss_cls: 4.5020, loss: 4.5020 +2024-12-26 11:20:46,185 - pyskl - INFO - Epoch [12][2600/3746] lr: 9.851e-02, eta: 4 days, 11:34:37, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1923, top5_acc: 0.4159, loss_cls: 4.4992, loss: 4.4992 +2024-12-26 11:21:58,311 - pyskl - INFO - Epoch [12][2700/3746] lr: 9.850e-02, eta: 4 days, 11:32:51, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4364, loss_cls: 4.4491, loss: 4.4491 +2024-12-26 11:23:10,212 - pyskl - INFO - Epoch [12][2800/3746] lr: 9.849e-02, eta: 4 days, 11:31:03, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1902, top5_acc: 0.4166, loss_cls: 4.5065, loss: 4.5065 +2024-12-26 11:24:22,265 - pyskl - INFO - Epoch [12][2900/3746] lr: 9.849e-02, eta: 4 days, 11:29:17, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1989, top5_acc: 0.4150, loss_cls: 4.5137, loss: 4.5137 +2024-12-26 11:25:34,405 - pyskl - INFO - Epoch [12][3000/3746] lr: 9.848e-02, eta: 4 days, 11:27:31, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1841, top5_acc: 0.4122, loss_cls: 4.5331, loss: 4.5331 +2024-12-26 11:26:46,262 - pyskl - INFO - Epoch [12][3100/3746] lr: 9.847e-02, eta: 4 days, 11:25:43, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4280, loss_cls: 4.4801, loss: 4.4801 +2024-12-26 11:27:58,354 - pyskl - INFO - Epoch [12][3200/3746] lr: 9.847e-02, eta: 4 days, 11:23:58, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1989, top5_acc: 0.4247, loss_cls: 4.4623, loss: 4.4623 +2024-12-26 11:29:10,258 - pyskl - INFO - Epoch [12][3300/3746] lr: 9.846e-02, eta: 4 days, 11:22:10, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2009, top5_acc: 0.4205, loss_cls: 4.5178, loss: 4.5178 +2024-12-26 11:30:22,033 - pyskl - INFO - Epoch [12][3400/3746] lr: 9.845e-02, eta: 4 days, 11:20:22, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1944, top5_acc: 0.4238, loss_cls: 4.4954, loss: 4.4954 +2024-12-26 11:31:34,149 - pyskl - INFO - Epoch [12][3500/3746] lr: 9.845e-02, eta: 4 days, 11:18:37, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1927, top5_acc: 0.4222, loss_cls: 4.4913, loss: 4.4913 +2024-12-26 11:32:46,208 - pyskl - INFO - Epoch [12][3600/3746] lr: 9.844e-02, eta: 4 days, 11:16:52, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1911, top5_acc: 0.4197, loss_cls: 4.4952, loss: 4.4952 +2024-12-26 11:33:58,234 - pyskl - INFO - Epoch [12][3700/3746] lr: 9.843e-02, eta: 4 days, 11:15:07, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1898, top5_acc: 0.4178, loss_cls: 4.5191, loss: 4.5191 +2024-12-26 11:34:33,901 - pyskl - INFO - Saving checkpoint at 12 epochs +2024-12-26 11:36:32,898 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 11:36:33,702 - pyskl - INFO - +top1_acc 0.1101 +top5_acc 0.2819 +2024-12-26 11:36:33,702 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 11:36:33,751 - pyskl - INFO - +mean_acc 0.1098 +2024-12-26 11:36:33,765 - pyskl - INFO - Epoch(val) [12][309] top1_acc: 0.1101, top5_acc: 0.2819, mean_class_accuracy: 0.1098 +2024-12-26 11:40:08,210 - pyskl - INFO - Epoch [13][100/3746] lr: 9.842e-02, eta: 4 days, 11:33:27, time: 2.144, data_time: 1.430, memory: 15990, top1_acc: 0.1877, top5_acc: 0.4163, loss_cls: 4.5016, loss: 4.5016 +2024-12-26 11:41:19,676 - pyskl - INFO - Epoch [13][200/3746] lr: 9.842e-02, eta: 4 days, 11:31:32, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4350, loss_cls: 4.4476, loss: 4.4476 +2024-12-26 11:42:31,357 - pyskl - INFO - Epoch [13][300/3746] lr: 9.841e-02, eta: 4 days, 11:29:40, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4294, loss_cls: 4.4563, loss: 4.4563 +2024-12-26 11:43:43,026 - pyskl - INFO - Epoch [13][400/3746] lr: 9.840e-02, eta: 4 days, 11:27:49, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4341, loss_cls: 4.4170, loss: 4.4170 +2024-12-26 11:44:54,511 - pyskl - INFO - Epoch [13][500/3746] lr: 9.839e-02, eta: 4 days, 11:25:55, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1975, top5_acc: 0.4222, loss_cls: 4.4631, loss: 4.4631 +2024-12-26 11:46:05,984 - pyskl - INFO - Epoch [13][600/3746] lr: 9.839e-02, eta: 4 days, 11:24:01, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4242, loss_cls: 4.4520, loss: 4.4520 +2024-12-26 11:47:17,446 - pyskl - INFO - Epoch [13][700/3746] lr: 9.838e-02, eta: 4 days, 11:22:08, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4300, loss_cls: 4.4584, loss: 4.4584 +2024-12-26 11:48:28,972 - pyskl - INFO - Epoch [13][800/3746] lr: 9.837e-02, eta: 4 days, 11:20:15, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4236, loss_cls: 4.4886, loss: 4.4886 +2024-12-26 11:49:40,289 - pyskl - INFO - Epoch [13][900/3746] lr: 9.837e-02, eta: 4 days, 11:18:20, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1975, top5_acc: 0.4206, loss_cls: 4.4849, loss: 4.4849 +2024-12-26 11:50:51,520 - pyskl - INFO - Epoch [13][1000/3746] lr: 9.836e-02, eta: 4 days, 11:16:25, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1978, top5_acc: 0.4264, loss_cls: 4.4747, loss: 4.4747 +2024-12-26 11:52:02,925 - pyskl - INFO - Epoch [13][1100/3746] lr: 9.835e-02, eta: 4 days, 11:14:31, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1881, top5_acc: 0.4148, loss_cls: 4.5298, loss: 4.5298 +2024-12-26 11:53:14,520 - pyskl - INFO - Epoch [13][1200/3746] lr: 9.834e-02, eta: 4 days, 11:12:40, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1948, top5_acc: 0.4139, loss_cls: 4.5022, loss: 4.5022 +2024-12-26 11:54:26,531 - pyskl - INFO - Epoch [13][1300/3746] lr: 9.834e-02, eta: 4 days, 11:10:54, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1956, top5_acc: 0.4138, loss_cls: 4.5143, loss: 4.5143 +2024-12-26 11:55:38,400 - pyskl - INFO - Epoch [13][1400/3746] lr: 9.833e-02, eta: 4 days, 11:09:06, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4302, loss_cls: 4.4328, loss: 4.4328 +2024-12-26 11:56:50,851 - pyskl - INFO - Epoch [13][1500/3746] lr: 9.832e-02, eta: 4 days, 11:07:25, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.1970, top5_acc: 0.4242, loss_cls: 4.4613, loss: 4.4613 +2024-12-26 11:58:02,772 - pyskl - INFO - Epoch [13][1600/3746] lr: 9.832e-02, eta: 4 days, 11:05:38, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4108, loss_cls: 4.4874, loss: 4.4874 +2024-12-26 11:59:14,705 - pyskl - INFO - Epoch [13][1700/3746] lr: 9.831e-02, eta: 4 days, 11:03:51, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2005, top5_acc: 0.4353, loss_cls: 4.4279, loss: 4.4279 +2024-12-26 12:00:26,657 - pyskl - INFO - Epoch [13][1800/3746] lr: 9.830e-02, eta: 4 days, 11:02:05, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4333, loss_cls: 4.4534, loss: 4.4534 +2024-12-26 12:01:38,559 - pyskl - INFO - Epoch [13][1900/3746] lr: 9.829e-02, eta: 4 days, 11:00:19, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4314, loss_cls: 4.4543, loss: 4.4543 +2024-12-26 12:02:50,628 - pyskl - INFO - Epoch [13][2000/3746] lr: 9.829e-02, eta: 4 days, 10:58:34, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4342, loss_cls: 4.4366, loss: 4.4366 +2024-12-26 12:04:02,961 - pyskl - INFO - Epoch [13][2100/3746] lr: 9.828e-02, eta: 4 days, 10:56:52, time: 0.723, data_time: 0.001, memory: 15990, top1_acc: 0.1883, top5_acc: 0.4194, loss_cls: 4.4949, loss: 4.4949 +2024-12-26 12:05:15,126 - pyskl - INFO - Epoch [13][2200/3746] lr: 9.827e-02, eta: 4 days, 10:55:09, time: 0.722, data_time: 0.001, memory: 15990, top1_acc: 0.1972, top5_acc: 0.4200, loss_cls: 4.4752, loss: 4.4752 +2024-12-26 12:06:27,147 - pyskl - INFO - Epoch [13][2300/3746] lr: 9.827e-02, eta: 4 days, 10:53:24, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2014, top5_acc: 0.4202, loss_cls: 4.4728, loss: 4.4728 +2024-12-26 12:07:39,291 - pyskl - INFO - Epoch [13][2400/3746] lr: 9.826e-02, eta: 4 days, 10:51:41, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1959, top5_acc: 0.4222, loss_cls: 4.4991, loss: 4.4991 +2024-12-26 12:08:51,106 - pyskl - INFO - Epoch [13][2500/3746] lr: 9.825e-02, eta: 4 days, 10:49:54, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.1955, top5_acc: 0.4294, loss_cls: 4.4858, loss: 4.4858 +2024-12-26 12:10:03,314 - pyskl - INFO - Epoch [13][2600/3746] lr: 9.824e-02, eta: 4 days, 10:48:12, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1944, top5_acc: 0.4242, loss_cls: 4.4730, loss: 4.4730 +2024-12-26 12:11:15,548 - pyskl - INFO - Epoch [13][2700/3746] lr: 9.824e-02, eta: 4 days, 10:46:30, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1942, top5_acc: 0.4191, loss_cls: 4.4971, loss: 4.4971 +2024-12-26 12:12:27,450 - pyskl - INFO - Epoch [13][2800/3746] lr: 9.823e-02, eta: 4 days, 10:44:44, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4191, loss_cls: 4.5032, loss: 4.5032 +2024-12-26 12:13:39,725 - pyskl - INFO - Epoch [13][2900/3746] lr: 9.822e-02, eta: 4 days, 10:43:03, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1973, top5_acc: 0.4200, loss_cls: 4.5051, loss: 4.5051 +2024-12-26 12:14:51,709 - pyskl - INFO - Epoch [13][3000/3746] lr: 9.821e-02, eta: 4 days, 10:41:19, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1992, top5_acc: 0.4219, loss_cls: 4.4881, loss: 4.4881 +2024-12-26 12:16:03,914 - pyskl - INFO - Epoch [13][3100/3746] lr: 9.821e-02, eta: 4 days, 10:39:37, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1950, top5_acc: 0.4141, loss_cls: 4.5069, loss: 4.5069 +2024-12-26 12:17:15,675 - pyskl - INFO - Epoch [13][3200/3746] lr: 9.820e-02, eta: 4 days, 10:37:51, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.1892, top5_acc: 0.4219, loss_cls: 4.4827, loss: 4.4827 +2024-12-26 12:18:27,851 - pyskl - INFO - Epoch [13][3300/3746] lr: 9.819e-02, eta: 4 days, 10:36:09, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2011, top5_acc: 0.4278, loss_cls: 4.4653, loss: 4.4653 +2024-12-26 12:19:39,619 - pyskl - INFO - Epoch [13][3400/3746] lr: 9.818e-02, eta: 4 days, 10:34:23, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1931, top5_acc: 0.4127, loss_cls: 4.5015, loss: 4.5015 +2024-12-26 12:20:51,663 - pyskl - INFO - Epoch [13][3500/3746] lr: 9.818e-02, eta: 4 days, 10:32:40, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2036, top5_acc: 0.4277, loss_cls: 4.4391, loss: 4.4391 +2024-12-26 12:22:03,441 - pyskl - INFO - Epoch [13][3600/3746] lr: 9.817e-02, eta: 4 days, 10:30:54, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1963, top5_acc: 0.4147, loss_cls: 4.5244, loss: 4.5244 +2024-12-26 12:23:15,543 - pyskl - INFO - Epoch [13][3700/3746] lr: 9.816e-02, eta: 4 days, 10:29:12, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1963, top5_acc: 0.4256, loss_cls: 4.4832, loss: 4.4832 +2024-12-26 12:23:51,353 - pyskl - INFO - Saving checkpoint at 13 epochs +2024-12-26 12:25:50,292 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 12:25:51,107 - pyskl - INFO - +top1_acc 0.1433 +top5_acc 0.3419 +2024-12-26 12:25:51,107 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 12:25:51,149 - pyskl - INFO - +mean_acc 0.1430 +2024-12-26 12:25:51,154 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_9.pth was removed +2024-12-26 12:25:51,417 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_13.pth. +2024-12-26 12:25:51,418 - pyskl - INFO - Best top1_acc is 0.1433 at 13 epoch. +2024-12-26 12:25:51,427 - pyskl - INFO - Epoch(val) [13][309] top1_acc: 0.1433, top5_acc: 0.3419, mean_class_accuracy: 0.1430 +2024-12-26 12:29:25,847 - pyskl - INFO - Epoch [14][100/3746] lr: 9.815e-02, eta: 4 days, 10:45:51, time: 2.144, data_time: 1.428, memory: 15990, top1_acc: 0.2036, top5_acc: 0.4317, loss_cls: 4.4325, loss: 4.4325 +2024-12-26 12:30:37,241 - pyskl - INFO - Epoch [14][200/3746] lr: 9.814e-02, eta: 4 days, 10:43:59, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4308, loss_cls: 4.4505, loss: 4.4505 +2024-12-26 12:31:48,955 - pyskl - INFO - Epoch [14][300/3746] lr: 9.814e-02, eta: 4 days, 10:42:11, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4325, loss_cls: 4.4477, loss: 4.4477 +2024-12-26 12:33:00,378 - pyskl - INFO - Epoch [14][400/3746] lr: 9.813e-02, eta: 4 days, 10:40:20, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4297, loss_cls: 4.4532, loss: 4.4532 +2024-12-26 12:34:11,647 - pyskl - INFO - Epoch [14][500/3746] lr: 9.812e-02, eta: 4 days, 10:38:27, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2027, top5_acc: 0.4297, loss_cls: 4.4640, loss: 4.4640 +2024-12-26 12:35:23,290 - pyskl - INFO - Epoch [14][600/3746] lr: 9.811e-02, eta: 4 days, 10:36:38, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4273, loss_cls: 4.4528, loss: 4.4528 +2024-12-26 12:36:34,609 - pyskl - INFO - Epoch [14][700/3746] lr: 9.811e-02, eta: 4 days, 10:34:47, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4216, loss_cls: 4.4493, loss: 4.4493 +2024-12-26 12:37:46,567 - pyskl - INFO - Epoch [14][800/3746] lr: 9.810e-02, eta: 4 days, 10:33:02, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1958, top5_acc: 0.4239, loss_cls: 4.4851, loss: 4.4851 +2024-12-26 12:38:58,095 - pyskl - INFO - Epoch [14][900/3746] lr: 9.809e-02, eta: 4 days, 10:31:12, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1955, top5_acc: 0.4264, loss_cls: 4.4873, loss: 4.4873 +2024-12-26 12:40:09,438 - pyskl - INFO - Epoch [14][1000/3746] lr: 9.808e-02, eta: 4 days, 10:29:21, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1939, top5_acc: 0.4264, loss_cls: 4.4827, loss: 4.4827 +2024-12-26 12:41:21,229 - pyskl - INFO - Epoch [14][1100/3746] lr: 9.807e-02, eta: 4 days, 10:27:35, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2028, top5_acc: 0.4245, loss_cls: 4.4739, loss: 4.4739 +2024-12-26 12:42:33,337 - pyskl - INFO - Epoch [14][1200/3746] lr: 9.807e-02, eta: 4 days, 10:25:52, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1992, top5_acc: 0.4241, loss_cls: 4.4559, loss: 4.4559 +2024-12-26 12:43:45,637 - pyskl - INFO - Epoch [14][1300/3746] lr: 9.806e-02, eta: 4 days, 10:24:11, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2011, top5_acc: 0.4289, loss_cls: 4.4547, loss: 4.4547 +2024-12-26 12:44:58,045 - pyskl - INFO - Epoch [14][1400/3746] lr: 9.805e-02, eta: 4 days, 10:22:32, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4228, loss_cls: 4.4665, loss: 4.4665 +2024-12-26 12:46:10,016 - pyskl - INFO - Epoch [14][1500/3746] lr: 9.804e-02, eta: 4 days, 10:20:48, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1983, top5_acc: 0.4234, loss_cls: 4.4690, loss: 4.4690 +2024-12-26 12:47:21,885 - pyskl - INFO - Epoch [14][1600/3746] lr: 9.804e-02, eta: 4 days, 10:19:03, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1973, top5_acc: 0.4255, loss_cls: 4.4601, loss: 4.4601 +2024-12-26 12:48:34,257 - pyskl - INFO - Epoch [14][1700/3746] lr: 9.803e-02, eta: 4 days, 10:17:24, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4247, loss_cls: 4.4738, loss: 4.4738 +2024-12-26 12:49:46,201 - pyskl - INFO - Epoch [14][1800/3746] lr: 9.802e-02, eta: 4 days, 10:15:40, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4281, loss_cls: 4.4498, loss: 4.4498 +2024-12-26 12:50:57,874 - pyskl - INFO - Epoch [14][1900/3746] lr: 9.801e-02, eta: 4 days, 10:13:53, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1967, top5_acc: 0.4183, loss_cls: 4.4831, loss: 4.4831 +2024-12-26 12:52:10,055 - pyskl - INFO - Epoch [14][2000/3746] lr: 9.800e-02, eta: 4 days, 10:12:12, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1931, top5_acc: 0.4313, loss_cls: 4.4795, loss: 4.4795 +2024-12-26 12:53:21,947 - pyskl - INFO - Epoch [14][2100/3746] lr: 9.800e-02, eta: 4 days, 10:10:28, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1984, top5_acc: 0.4227, loss_cls: 4.4875, loss: 4.4875 +2024-12-26 12:54:33,867 - pyskl - INFO - Epoch [14][2200/3746] lr: 9.799e-02, eta: 4 days, 10:08:45, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1931, top5_acc: 0.4319, loss_cls: 4.4829, loss: 4.4829 +2024-12-26 12:55:45,438 - pyskl - INFO - Epoch [14][2300/3746] lr: 9.798e-02, eta: 4 days, 10:06:58, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1891, top5_acc: 0.4234, loss_cls: 4.4742, loss: 4.4742 +2024-12-26 12:56:57,329 - pyskl - INFO - Epoch [14][2400/3746] lr: 9.797e-02, eta: 4 days, 10:05:14, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1983, top5_acc: 0.4283, loss_cls: 4.4524, loss: 4.4524 +2024-12-26 12:58:09,179 - pyskl - INFO - Epoch [14][2500/3746] lr: 9.797e-02, eta: 4 days, 10:03:30, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4245, loss_cls: 4.4838, loss: 4.4838 +2024-12-26 12:59:21,057 - pyskl - INFO - Epoch [14][2600/3746] lr: 9.796e-02, eta: 4 days, 10:01:47, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1966, top5_acc: 0.4213, loss_cls: 4.4726, loss: 4.4726 +2024-12-26 13:00:32,844 - pyskl - INFO - Epoch [14][2700/3746] lr: 9.795e-02, eta: 4 days, 10:00:02, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1955, top5_acc: 0.4238, loss_cls: 4.4761, loss: 4.4761 +2024-12-26 13:01:44,763 - pyskl - INFO - Epoch [14][2800/3746] lr: 9.794e-02, eta: 4 days, 9:58:20, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2028, top5_acc: 0.4263, loss_cls: 4.4544, loss: 4.4544 +2024-12-26 13:02:57,156 - pyskl - INFO - Epoch [14][2900/3746] lr: 9.793e-02, eta: 4 days, 9:56:42, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.1997, top5_acc: 0.4278, loss_cls: 4.4768, loss: 4.4768 +2024-12-26 13:04:09,162 - pyskl - INFO - Epoch [14][3000/3746] lr: 9.793e-02, eta: 4 days, 9:55:00, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1983, top5_acc: 0.4233, loss_cls: 4.4940, loss: 4.4940 +2024-12-26 13:05:20,900 - pyskl - INFO - Epoch [14][3100/3746] lr: 9.792e-02, eta: 4 days, 9:53:16, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4363, loss_cls: 4.4412, loss: 4.4412 +2024-12-26 13:06:32,671 - pyskl - INFO - Epoch [14][3200/3746] lr: 9.791e-02, eta: 4 days, 9:51:32, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4305, loss_cls: 4.4337, loss: 4.4337 +2024-12-26 13:07:44,442 - pyskl - INFO - Epoch [14][3300/3746] lr: 9.790e-02, eta: 4 days, 9:49:48, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4330, loss_cls: 4.4628, loss: 4.4628 +2024-12-26 13:08:56,549 - pyskl - INFO - Epoch [14][3400/3746] lr: 9.789e-02, eta: 4 days, 9:48:08, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1956, top5_acc: 0.4197, loss_cls: 4.4876, loss: 4.4876 +2024-12-26 13:10:08,482 - pyskl - INFO - Epoch [14][3500/3746] lr: 9.789e-02, eta: 4 days, 9:46:26, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2025, top5_acc: 0.4367, loss_cls: 4.4326, loss: 4.4326 +2024-12-26 13:11:20,523 - pyskl - INFO - Epoch [14][3600/3746] lr: 9.788e-02, eta: 4 days, 9:44:45, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1952, top5_acc: 0.4233, loss_cls: 4.4821, loss: 4.4821 +2024-12-26 13:12:32,628 - pyskl - INFO - Epoch [14][3700/3746] lr: 9.787e-02, eta: 4 days, 9:43:05, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1894, top5_acc: 0.4194, loss_cls: 4.5132, loss: 4.5132 +2024-12-26 13:13:08,211 - pyskl - INFO - Saving checkpoint at 14 epochs +2024-12-26 13:15:06,135 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 13:15:07,008 - pyskl - INFO - +top1_acc 0.1266 +top5_acc 0.3128 +2024-12-26 13:15:07,008 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 13:15:07,055 - pyskl - INFO - +mean_acc 0.1265 +2024-12-26 13:15:07,066 - pyskl - INFO - Epoch(val) [14][309] top1_acc: 0.1266, top5_acc: 0.3128, mean_class_accuracy: 0.1265 +2024-12-26 13:18:42,898 - pyskl - INFO - Epoch [15][100/3746] lr: 9.786e-02, eta: 4 days, 9:58:31, time: 2.158, data_time: 1.443, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4414, loss_cls: 4.4290, loss: 4.4290 +2024-12-26 13:19:54,246 - pyskl - INFO - Epoch [15][200/3746] lr: 9.785e-02, eta: 4 days, 9:56:42, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4356, loss_cls: 4.4437, loss: 4.4437 +2024-12-26 13:21:05,751 - pyskl - INFO - Epoch [15][300/3746] lr: 9.784e-02, eta: 4 days, 9:54:54, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2011, top5_acc: 0.4372, loss_cls: 4.4024, loss: 4.4024 +2024-12-26 13:22:16,996 - pyskl - INFO - Epoch [15][400/3746] lr: 9.783e-02, eta: 4 days, 9:53:04, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1997, top5_acc: 0.4338, loss_cls: 4.4534, loss: 4.4534 +2024-12-26 13:23:28,679 - pyskl - INFO - Epoch [15][500/3746] lr: 9.783e-02, eta: 4 days, 9:51:19, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1967, top5_acc: 0.4330, loss_cls: 4.4517, loss: 4.4517 +2024-12-26 13:24:40,221 - pyskl - INFO - Epoch [15][600/3746] lr: 9.782e-02, eta: 4 days, 9:49:32, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4236, loss_cls: 4.4657, loss: 4.4657 +2024-12-26 13:25:51,412 - pyskl - INFO - Epoch [15][700/3746] lr: 9.781e-02, eta: 4 days, 9:47:42, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1978, top5_acc: 0.4245, loss_cls: 4.4445, loss: 4.4445 +2024-12-26 13:27:02,901 - pyskl - INFO - Epoch [15][800/3746] lr: 9.780e-02, eta: 4 days, 9:45:55, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1984, top5_acc: 0.4308, loss_cls: 4.4559, loss: 4.4559 +2024-12-26 13:28:14,168 - pyskl - INFO - Epoch [15][900/3746] lr: 9.779e-02, eta: 4 days, 9:44:05, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4314, loss_cls: 4.4211, loss: 4.4211 +2024-12-26 13:29:25,664 - pyskl - INFO - Epoch [15][1000/3746] lr: 9.778e-02, eta: 4 days, 9:42:19, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4345, loss_cls: 4.3915, loss: 4.3915 +2024-12-26 13:30:37,684 - pyskl - INFO - Epoch [15][1100/3746] lr: 9.778e-02, eta: 4 days, 9:40:37, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4278, loss_cls: 4.4298, loss: 4.4298 +2024-12-26 13:31:49,550 - pyskl - INFO - Epoch [15][1200/3746] lr: 9.777e-02, eta: 4 days, 9:38:54, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2033, top5_acc: 0.4319, loss_cls: 4.4451, loss: 4.4451 +2024-12-26 13:33:00,736 - pyskl - INFO - Epoch [15][1300/3746] lr: 9.776e-02, eta: 4 days, 9:37:05, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1973, top5_acc: 0.4200, loss_cls: 4.4760, loss: 4.4760 +2024-12-26 13:34:12,577 - pyskl - INFO - Epoch [15][1400/3746] lr: 9.775e-02, eta: 4 days, 9:35:22, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.1823, top5_acc: 0.4166, loss_cls: 4.4937, loss: 4.4937 +2024-12-26 13:35:24,642 - pyskl - INFO - Epoch [15][1500/3746] lr: 9.774e-02, eta: 4 days, 9:33:41, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1997, top5_acc: 0.4255, loss_cls: 4.4631, loss: 4.4631 +2024-12-26 13:36:36,279 - pyskl - INFO - Epoch [15][1600/3746] lr: 9.773e-02, eta: 4 days, 9:31:56, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2009, top5_acc: 0.4288, loss_cls: 4.4473, loss: 4.4473 +2024-12-26 13:37:48,045 - pyskl - INFO - Epoch [15][1700/3746] lr: 9.773e-02, eta: 4 days, 9:30:13, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4394, loss_cls: 4.4202, loss: 4.4202 +2024-12-26 13:39:00,108 - pyskl - INFO - Epoch [15][1800/3746] lr: 9.772e-02, eta: 4 days, 9:28:33, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1975, top5_acc: 0.4263, loss_cls: 4.4865, loss: 4.4865 +2024-12-26 13:40:12,143 - pyskl - INFO - Epoch [15][1900/3746] lr: 9.771e-02, eta: 4 days, 9:26:52, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4306, loss_cls: 4.4488, loss: 4.4488 +2024-12-26 13:41:24,092 - pyskl - INFO - Epoch [15][2000/3746] lr: 9.770e-02, eta: 4 days, 9:25:11, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1931, top5_acc: 0.4244, loss_cls: 4.4716, loss: 4.4716 +2024-12-26 13:42:36,171 - pyskl - INFO - Epoch [15][2100/3746] lr: 9.769e-02, eta: 4 days, 9:23:31, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1938, top5_acc: 0.4191, loss_cls: 4.5158, loss: 4.5158 +2024-12-26 13:43:48,380 - pyskl - INFO - Epoch [15][2200/3746] lr: 9.768e-02, eta: 4 days, 9:21:52, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1975, top5_acc: 0.4250, loss_cls: 4.4601, loss: 4.4601 +2024-12-26 13:45:00,494 - pyskl - INFO - Epoch [15][2300/3746] lr: 9.768e-02, eta: 4 days, 9:20:12, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4269, loss_cls: 4.4544, loss: 4.4544 +2024-12-26 13:46:12,571 - pyskl - INFO - Epoch [15][2400/3746] lr: 9.767e-02, eta: 4 days, 9:18:33, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1961, top5_acc: 0.4088, loss_cls: 4.5124, loss: 4.5124 +2024-12-26 13:47:24,631 - pyskl - INFO - Epoch [15][2500/3746] lr: 9.766e-02, eta: 4 days, 9:16:53, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1977, top5_acc: 0.4297, loss_cls: 4.4698, loss: 4.4698 +2024-12-26 13:48:36,690 - pyskl - INFO - Epoch [15][2600/3746] lr: 9.765e-02, eta: 4 days, 9:15:13, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2019, top5_acc: 0.4273, loss_cls: 4.4539, loss: 4.4539 +2024-12-26 13:49:48,569 - pyskl - INFO - Epoch [15][2700/3746] lr: 9.764e-02, eta: 4 days, 9:13:32, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2047, top5_acc: 0.4398, loss_cls: 4.4322, loss: 4.4322 +2024-12-26 13:51:00,561 - pyskl - INFO - Epoch [15][2800/3746] lr: 9.763e-02, eta: 4 days, 9:11:52, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4402, loss_cls: 4.4234, loss: 4.4234 +2024-12-26 13:52:12,279 - pyskl - INFO - Epoch [15][2900/3746] lr: 9.763e-02, eta: 4 days, 9:10:10, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1953, top5_acc: 0.4264, loss_cls: 4.5102, loss: 4.5102 +2024-12-26 13:53:24,543 - pyskl - INFO - Epoch [15][3000/3746] lr: 9.762e-02, eta: 4 days, 9:08:32, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4200, loss_cls: 4.4857, loss: 4.4857 +2024-12-26 13:54:36,580 - pyskl - INFO - Epoch [15][3100/3746] lr: 9.761e-02, eta: 4 days, 9:06:53, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4306, loss_cls: 4.4093, loss: 4.4093 +2024-12-26 13:55:48,664 - pyskl - INFO - Epoch [15][3200/3746] lr: 9.760e-02, eta: 4 days, 9:05:14, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1923, top5_acc: 0.4261, loss_cls: 4.4584, loss: 4.4584 +2024-12-26 13:57:00,574 - pyskl - INFO - Epoch [15][3300/3746] lr: 9.759e-02, eta: 4 days, 9:03:34, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1902, top5_acc: 0.4200, loss_cls: 4.4744, loss: 4.4744 +2024-12-26 13:58:12,418 - pyskl - INFO - Epoch [15][3400/3746] lr: 9.758e-02, eta: 4 days, 9:01:53, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4302, loss_cls: 4.4589, loss: 4.4589 +2024-12-26 13:59:24,473 - pyskl - INFO - Epoch [15][3500/3746] lr: 9.757e-02, eta: 4 days, 9:00:14, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1984, top5_acc: 0.4275, loss_cls: 4.4835, loss: 4.4835 +2024-12-26 14:00:36,723 - pyskl - INFO - Epoch [15][3600/3746] lr: 9.757e-02, eta: 4 days, 8:58:37, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1995, top5_acc: 0.4280, loss_cls: 4.4658, loss: 4.4658 +2024-12-26 14:01:48,785 - pyskl - INFO - Epoch [15][3700/3746] lr: 9.756e-02, eta: 4 days, 8:56:58, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4330, loss_cls: 4.4404, loss: 4.4404 +2024-12-26 14:02:24,306 - pyskl - INFO - Saving checkpoint at 15 epochs +2024-12-26 14:04:23,526 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 14:04:24,469 - pyskl - INFO - +top1_acc 0.1356 +top5_acc 0.3332 +2024-12-26 14:04:24,469 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 14:04:24,514 - pyskl - INFO - +mean_acc 0.1356 +2024-12-26 14:04:24,529 - pyskl - INFO - Epoch(val) [15][309] top1_acc: 0.1356, top5_acc: 0.3332, mean_class_accuracy: 0.1356 +2024-12-26 14:08:03,562 - pyskl - INFO - Epoch [16][100/3746] lr: 9.754e-02, eta: 4 days, 9:11:37, time: 2.190, data_time: 1.472, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4356, loss_cls: 4.4070, loss: 4.4070 +2024-12-26 14:09:15,262 - pyskl - INFO - Epoch [16][200/3746] lr: 9.754e-02, eta: 4 days, 9:09:53, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2025, top5_acc: 0.4298, loss_cls: 4.4886, loss: 4.4886 +2024-12-26 14:10:26,925 - pyskl - INFO - Epoch [16][300/3746] lr: 9.753e-02, eta: 4 days, 9:08:10, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4297, loss_cls: 4.4337, loss: 4.4337 +2024-12-26 14:11:37,990 - pyskl - INFO - Epoch [16][400/3746] lr: 9.752e-02, eta: 4 days, 9:06:21, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4314, loss_cls: 4.4694, loss: 4.4694 +2024-12-26 14:12:49,398 - pyskl - INFO - Epoch [16][500/3746] lr: 9.751e-02, eta: 4 days, 9:04:35, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4277, loss_cls: 4.4463, loss: 4.4463 +2024-12-26 14:14:00,660 - pyskl - INFO - Epoch [16][600/3746] lr: 9.750e-02, eta: 4 days, 9:02:48, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1975, top5_acc: 0.4189, loss_cls: 4.4671, loss: 4.4671 +2024-12-26 14:15:11,821 - pyskl - INFO - Epoch [16][700/3746] lr: 9.749e-02, eta: 4 days, 9:01:00, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4266, loss_cls: 4.4583, loss: 4.4583 +2024-12-26 14:16:22,910 - pyskl - INFO - Epoch [16][800/3746] lr: 9.748e-02, eta: 4 days, 8:59:12, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1963, top5_acc: 0.4258, loss_cls: 4.4586, loss: 4.4586 +2024-12-26 14:17:34,504 - pyskl - INFO - Epoch [16][900/3746] lr: 9.747e-02, eta: 4 days, 8:57:28, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4314, loss_cls: 4.4404, loss: 4.4404 +2024-12-26 14:18:45,777 - pyskl - INFO - Epoch [16][1000/3746] lr: 9.747e-02, eta: 4 days, 8:55:42, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1983, top5_acc: 0.4342, loss_cls: 4.4563, loss: 4.4563 +2024-12-26 14:19:57,187 - pyskl - INFO - Epoch [16][1100/3746] lr: 9.746e-02, eta: 4 days, 8:53:56, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1945, top5_acc: 0.4230, loss_cls: 4.4721, loss: 4.4721 +2024-12-26 14:21:08,697 - pyskl - INFO - Epoch [16][1200/3746] lr: 9.745e-02, eta: 4 days, 8:52:12, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4336, loss_cls: 4.4099, loss: 4.4099 +2024-12-26 14:22:20,999 - pyskl - INFO - Epoch [16][1300/3746] lr: 9.744e-02, eta: 4 days, 8:50:35, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1959, top5_acc: 0.4286, loss_cls: 4.4475, loss: 4.4475 +2024-12-26 14:23:32,817 - pyskl - INFO - Epoch [16][1400/3746] lr: 9.743e-02, eta: 4 days, 8:48:54, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4253, loss_cls: 4.4708, loss: 4.4708 +2024-12-26 14:24:44,721 - pyskl - INFO - Epoch [16][1500/3746] lr: 9.742e-02, eta: 4 days, 8:47:14, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4292, loss_cls: 4.4387, loss: 4.4387 +2024-12-26 14:25:56,642 - pyskl - INFO - Epoch [16][1600/3746] lr: 9.741e-02, eta: 4 days, 8:45:34, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4370, loss_cls: 4.4310, loss: 4.4310 +2024-12-26 14:27:09,051 - pyskl - INFO - Epoch [16][1700/3746] lr: 9.740e-02, eta: 4 days, 8:43:58, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2078, top5_acc: 0.4295, loss_cls: 4.4398, loss: 4.4398 +2024-12-26 14:28:21,071 - pyskl - INFO - Epoch [16][1800/3746] lr: 9.740e-02, eta: 4 days, 8:42:19, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4197, loss_cls: 4.4783, loss: 4.4783 +2024-12-26 14:29:33,236 - pyskl - INFO - Epoch [16][1900/3746] lr: 9.739e-02, eta: 4 days, 8:40:41, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4194, loss_cls: 4.4581, loss: 4.4581 +2024-12-26 14:30:45,253 - pyskl - INFO - Epoch [16][2000/3746] lr: 9.738e-02, eta: 4 days, 8:39:02, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2033, top5_acc: 0.4334, loss_cls: 4.4440, loss: 4.4440 +2024-12-26 14:31:57,522 - pyskl - INFO - Epoch [16][2100/3746] lr: 9.737e-02, eta: 4 days, 8:37:26, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2011, top5_acc: 0.4220, loss_cls: 4.4483, loss: 4.4483 +2024-12-26 14:33:09,105 - pyskl - INFO - Epoch [16][2200/3746] lr: 9.736e-02, eta: 4 days, 8:35:43, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2019, top5_acc: 0.4323, loss_cls: 4.4259, loss: 4.4259 +2024-12-26 14:34:21,428 - pyskl - INFO - Epoch [16][2300/3746] lr: 9.735e-02, eta: 4 days, 8:34:07, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1858, top5_acc: 0.4316, loss_cls: 4.4782, loss: 4.4782 +2024-12-26 14:35:33,227 - pyskl - INFO - Epoch [16][2400/3746] lr: 9.734e-02, eta: 4 days, 8:32:27, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4486, loss_cls: 4.4133, loss: 4.4133 +2024-12-26 14:36:45,086 - pyskl - INFO - Epoch [16][2500/3746] lr: 9.733e-02, eta: 4 days, 8:30:47, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2000, top5_acc: 0.4275, loss_cls: 4.4717, loss: 4.4717 +2024-12-26 14:37:56,780 - pyskl - INFO - Epoch [16][2600/3746] lr: 9.732e-02, eta: 4 days, 8:29:06, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4416, loss_cls: 4.4236, loss: 4.4236 +2024-12-26 14:39:08,724 - pyskl - INFO - Epoch [16][2700/3746] lr: 9.731e-02, eta: 4 days, 8:27:27, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4339, loss_cls: 4.4480, loss: 4.4480 +2024-12-26 14:40:21,017 - pyskl - INFO - Epoch [16][2800/3746] lr: 9.731e-02, eta: 4 days, 8:25:51, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4380, loss_cls: 4.4352, loss: 4.4352 +2024-12-26 14:41:32,672 - pyskl - INFO - Epoch [16][2900/3746] lr: 9.730e-02, eta: 4 days, 8:24:10, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2030, top5_acc: 0.4345, loss_cls: 4.4331, loss: 4.4331 +2024-12-26 14:42:44,885 - pyskl - INFO - Epoch [16][3000/3746] lr: 9.729e-02, eta: 4 days, 8:22:34, time: 0.722, data_time: 0.001, memory: 15990, top1_acc: 0.2011, top5_acc: 0.4200, loss_cls: 4.4818, loss: 4.4818 +2024-12-26 14:43:57,097 - pyskl - INFO - Epoch [16][3100/3746] lr: 9.728e-02, eta: 4 days, 8:20:58, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4256, loss_cls: 4.4584, loss: 4.4584 +2024-12-26 14:45:09,363 - pyskl - INFO - Epoch [16][3200/3746] lr: 9.727e-02, eta: 4 days, 8:19:22, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4344, loss_cls: 4.4319, loss: 4.4319 +2024-12-26 14:46:21,279 - pyskl - INFO - Epoch [16][3300/3746] lr: 9.726e-02, eta: 4 days, 8:17:43, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4359, loss_cls: 4.4221, loss: 4.4221 +2024-12-26 14:47:33,319 - pyskl - INFO - Epoch [16][3400/3746] lr: 9.725e-02, eta: 4 days, 8:16:06, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2014, top5_acc: 0.4230, loss_cls: 4.4668, loss: 4.4668 +2024-12-26 14:48:45,173 - pyskl - INFO - Epoch [16][3500/3746] lr: 9.724e-02, eta: 4 days, 8:14:27, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4317, loss_cls: 4.4641, loss: 4.4641 +2024-12-26 14:49:56,959 - pyskl - INFO - Epoch [16][3600/3746] lr: 9.723e-02, eta: 4 days, 8:12:48, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4266, loss_cls: 4.4796, loss: 4.4796 +2024-12-26 14:51:09,111 - pyskl - INFO - Epoch [16][3700/3746] lr: 9.722e-02, eta: 4 days, 8:11:11, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2027, top5_acc: 0.4345, loss_cls: 4.4547, loss: 4.4547 +2024-12-26 14:51:45,049 - pyskl - INFO - Saving checkpoint at 16 epochs +2024-12-26 14:53:43,884 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 14:53:44,908 - pyskl - INFO - +top1_acc 0.1431 +top5_acc 0.3427 +2024-12-26 14:53:44,908 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 14:53:44,957 - pyskl - INFO - +mean_acc 0.1431 +2024-12-26 14:53:44,969 - pyskl - INFO - Epoch(val) [16][309] top1_acc: 0.1431, top5_acc: 0.3427, mean_class_accuracy: 0.1431 +2024-12-26 14:57:19,487 - pyskl - INFO - Epoch [17][100/3746] lr: 9.721e-02, eta: 4 days, 8:24:04, time: 2.145, data_time: 1.427, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4444, loss_cls: 4.4201, loss: 4.4201 +2024-12-26 14:58:30,629 - pyskl - INFO - Epoch [17][200/3746] lr: 9.720e-02, eta: 4 days, 8:22:17, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1933, top5_acc: 0.4258, loss_cls: 4.4649, loss: 4.4649 +2024-12-26 14:59:42,158 - pyskl - INFO - Epoch [17][300/3746] lr: 9.719e-02, eta: 4 days, 8:20:35, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1989, top5_acc: 0.4319, loss_cls: 4.4317, loss: 4.4317 +2024-12-26 15:00:53,349 - pyskl - INFO - Epoch [17][400/3746] lr: 9.718e-02, eta: 4 days, 8:18:49, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1963, top5_acc: 0.4269, loss_cls: 4.4372, loss: 4.4372 +2024-12-26 15:02:04,828 - pyskl - INFO - Epoch [17][500/3746] lr: 9.717e-02, eta: 4 days, 8:17:06, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4269, loss_cls: 4.4679, loss: 4.4679 +2024-12-26 15:03:16,442 - pyskl - INFO - Epoch [17][600/3746] lr: 9.716e-02, eta: 4 days, 8:15:24, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4219, loss_cls: 4.4684, loss: 4.4684 +2024-12-26 15:04:27,705 - pyskl - INFO - Epoch [17][700/3746] lr: 9.715e-02, eta: 4 days, 8:13:40, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4322, loss_cls: 4.4445, loss: 4.4445 +2024-12-26 15:05:39,030 - pyskl - INFO - Epoch [17][800/3746] lr: 9.714e-02, eta: 4 days, 8:11:56, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4366, loss_cls: 4.3964, loss: 4.3964 +2024-12-26 15:06:50,519 - pyskl - INFO - Epoch [17][900/3746] lr: 9.714e-02, eta: 4 days, 8:10:13, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4369, loss_cls: 4.4048, loss: 4.4048 +2024-12-26 15:08:01,874 - pyskl - INFO - Epoch [17][1000/3746] lr: 9.713e-02, eta: 4 days, 8:08:30, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2036, top5_acc: 0.4288, loss_cls: 4.4730, loss: 4.4730 +2024-12-26 15:09:13,213 - pyskl - INFO - Epoch [17][1100/3746] lr: 9.712e-02, eta: 4 days, 8:06:46, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2002, top5_acc: 0.4234, loss_cls: 4.4599, loss: 4.4599 +2024-12-26 15:10:24,495 - pyskl - INFO - Epoch [17][1200/3746] lr: 9.711e-02, eta: 4 days, 8:05:02, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4272, loss_cls: 4.4460, loss: 4.4460 +2024-12-26 15:11:36,096 - pyskl - INFO - Epoch [17][1300/3746] lr: 9.710e-02, eta: 4 days, 8:03:21, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4361, loss_cls: 4.4373, loss: 4.4373 +2024-12-26 15:12:48,073 - pyskl - INFO - Epoch [17][1400/3746] lr: 9.709e-02, eta: 4 days, 8:01:43, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4300, loss_cls: 4.4386, loss: 4.4386 +2024-12-26 15:14:00,100 - pyskl - INFO - Epoch [17][1500/3746] lr: 9.708e-02, eta: 4 days, 8:00:05, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1970, top5_acc: 0.4230, loss_cls: 4.4718, loss: 4.4718 +2024-12-26 15:15:11,967 - pyskl - INFO - Epoch [17][1600/3746] lr: 9.707e-02, eta: 4 days, 7:58:26, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2019, top5_acc: 0.4308, loss_cls: 4.4611, loss: 4.4611 +2024-12-26 15:16:24,286 - pyskl - INFO - Epoch [17][1700/3746] lr: 9.706e-02, eta: 4 days, 7:56:51, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4259, loss_cls: 4.4498, loss: 4.4498 +2024-12-26 15:17:35,961 - pyskl - INFO - Epoch [17][1800/3746] lr: 9.705e-02, eta: 4 days, 7:55:11, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4363, loss_cls: 4.4215, loss: 4.4215 +2024-12-26 15:18:48,047 - pyskl - INFO - Epoch [17][1900/3746] lr: 9.704e-02, eta: 4 days, 7:53:35, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4391, loss_cls: 4.4190, loss: 4.4190 +2024-12-26 15:19:59,947 - pyskl - INFO - Epoch [17][2000/3746] lr: 9.703e-02, eta: 4 days, 7:51:56, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4289, loss_cls: 4.4528, loss: 4.4528 +2024-12-26 15:21:12,329 - pyskl - INFO - Epoch [17][2100/3746] lr: 9.702e-02, eta: 4 days, 7:50:22, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.1916, top5_acc: 0.4320, loss_cls: 4.4780, loss: 4.4780 +2024-12-26 15:22:24,418 - pyskl - INFO - Epoch [17][2200/3746] lr: 9.701e-02, eta: 4 days, 7:48:46, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4425, loss_cls: 4.4132, loss: 4.4132 +2024-12-26 15:23:36,347 - pyskl - INFO - Epoch [17][2300/3746] lr: 9.700e-02, eta: 4 days, 7:47:08, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4238, loss_cls: 4.4315, loss: 4.4315 +2024-12-26 15:24:48,743 - pyskl - INFO - Epoch [17][2400/3746] lr: 9.699e-02, eta: 4 days, 7:45:34, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4330, loss_cls: 4.4238, loss: 4.4238 +2024-12-26 15:26:00,782 - pyskl - INFO - Epoch [17][2500/3746] lr: 9.698e-02, eta: 4 days, 7:43:58, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1967, top5_acc: 0.4295, loss_cls: 4.4530, loss: 4.4530 +2024-12-26 15:27:13,091 - pyskl - INFO - Epoch [17][2600/3746] lr: 9.697e-02, eta: 4 days, 7:42:23, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4367, loss_cls: 4.4423, loss: 4.4423 +2024-12-26 15:28:25,190 - pyskl - INFO - Epoch [17][2700/3746] lr: 9.697e-02, eta: 4 days, 7:40:47, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4298, loss_cls: 4.3998, loss: 4.3998 +2024-12-26 15:29:37,023 - pyskl - INFO - Epoch [17][2800/3746] lr: 9.696e-02, eta: 4 days, 7:39:09, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2045, top5_acc: 0.4359, loss_cls: 4.4221, loss: 4.4221 +2024-12-26 15:30:48,645 - pyskl - INFO - Epoch [17][2900/3746] lr: 9.695e-02, eta: 4 days, 7:37:29, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2002, top5_acc: 0.4292, loss_cls: 4.4737, loss: 4.4737 +2024-12-26 15:32:00,662 - pyskl - INFO - Epoch [17][3000/3746] lr: 9.694e-02, eta: 4 days, 7:35:53, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4448, loss_cls: 4.4262, loss: 4.4262 +2024-12-26 15:33:12,481 - pyskl - INFO - Epoch [17][3100/3746] lr: 9.693e-02, eta: 4 days, 7:34:15, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2069, top5_acc: 0.4333, loss_cls: 4.4022, loss: 4.4022 +2024-12-26 15:34:24,241 - pyskl - INFO - Epoch [17][3200/3746] lr: 9.692e-02, eta: 4 days, 7:32:37, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4223, loss_cls: 4.4799, loss: 4.4799 +2024-12-26 15:35:36,269 - pyskl - INFO - Epoch [17][3300/3746] lr: 9.691e-02, eta: 4 days, 7:31:01, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4333, loss_cls: 4.4250, loss: 4.4250 +2024-12-26 15:36:48,232 - pyskl - INFO - Epoch [17][3400/3746] lr: 9.690e-02, eta: 4 days, 7:29:24, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4284, loss_cls: 4.4599, loss: 4.4599 +2024-12-26 15:38:00,111 - pyskl - INFO - Epoch [17][3500/3746] lr: 9.689e-02, eta: 4 days, 7:27:47, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4397, loss_cls: 4.4581, loss: 4.4581 +2024-12-26 15:39:12,217 - pyskl - INFO - Epoch [17][3600/3746] lr: 9.688e-02, eta: 4 days, 7:26:12, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4437, loss_cls: 4.4294, loss: 4.4294 +2024-12-26 15:40:24,371 - pyskl - INFO - Epoch [17][3700/3746] lr: 9.687e-02, eta: 4 days, 7:24:37, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4370, loss_cls: 4.4180, loss: 4.4180 +2024-12-26 15:40:59,690 - pyskl - INFO - Saving checkpoint at 17 epochs +2024-12-26 15:42:58,365 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 15:42:59,400 - pyskl - INFO - +top1_acc 0.1323 +top5_acc 0.3129 +2024-12-26 15:42:59,400 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 15:42:59,461 - pyskl - INFO - +mean_acc 0.1322 +2024-12-26 15:42:59,472 - pyskl - INFO - Epoch(val) [17][309] top1_acc: 0.1323, top5_acc: 0.3129, mean_class_accuracy: 0.1322 +2024-12-26 15:46:36,876 - pyskl - INFO - Epoch [18][100/3746] lr: 9.685e-02, eta: 4 days, 7:36:54, time: 2.174, data_time: 1.459, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4456, loss_cls: 4.3925, loss: 4.3925 +2024-12-26 15:47:48,315 - pyskl - INFO - Epoch [18][200/3746] lr: 9.684e-02, eta: 4 days, 7:35:12, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4327, loss_cls: 4.4548, loss: 4.4548 +2024-12-26 15:49:00,009 - pyskl - INFO - Epoch [18][300/3746] lr: 9.683e-02, eta: 4 days, 7:33:32, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1905, top5_acc: 0.4258, loss_cls: 4.4700, loss: 4.4700 +2024-12-26 15:50:11,741 - pyskl - INFO - Epoch [18][400/3746] lr: 9.683e-02, eta: 4 days, 7:31:53, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4359, loss_cls: 4.4171, loss: 4.4171 +2024-12-26 15:51:23,282 - pyskl - INFO - Epoch [18][500/3746] lr: 9.682e-02, eta: 4 days, 7:30:12, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4241, loss_cls: 4.4578, loss: 4.4578 +2024-12-26 15:52:34,899 - pyskl - INFO - Epoch [18][600/3746] lr: 9.681e-02, eta: 4 days, 7:28:32, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4330, loss_cls: 4.4303, loss: 4.4303 +2024-12-26 15:53:46,279 - pyskl - INFO - Epoch [18][700/3746] lr: 9.680e-02, eta: 4 days, 7:26:51, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2000, top5_acc: 0.4281, loss_cls: 4.4465, loss: 4.4465 +2024-12-26 15:54:57,518 - pyskl - INFO - Epoch [18][800/3746] lr: 9.679e-02, eta: 4 days, 7:25:08, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4394, loss_cls: 4.4012, loss: 4.4012 +2024-12-26 15:56:08,760 - pyskl - INFO - Epoch [18][900/3746] lr: 9.678e-02, eta: 4 days, 7:23:25, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2025, top5_acc: 0.4409, loss_cls: 4.4154, loss: 4.4154 +2024-12-26 15:57:20,378 - pyskl - INFO - Epoch [18][1000/3746] lr: 9.677e-02, eta: 4 days, 7:21:45, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4319, loss_cls: 4.4136, loss: 4.4136 +2024-12-26 15:58:32,088 - pyskl - INFO - Epoch [18][1100/3746] lr: 9.676e-02, eta: 4 days, 7:20:07, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1984, top5_acc: 0.4273, loss_cls: 4.4442, loss: 4.4442 +2024-12-26 15:59:43,959 - pyskl - INFO - Epoch [18][1200/3746] lr: 9.675e-02, eta: 4 days, 7:18:29, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4447, loss_cls: 4.4071, loss: 4.4071 +2024-12-26 16:00:55,414 - pyskl - INFO - Epoch [18][1300/3746] lr: 9.674e-02, eta: 4 days, 7:16:48, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2002, top5_acc: 0.4305, loss_cls: 4.4166, loss: 4.4166 +2024-12-26 16:02:06,873 - pyskl - INFO - Epoch [18][1400/3746] lr: 9.673e-02, eta: 4 days, 7:15:08, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4302, loss_cls: 4.4355, loss: 4.4355 +2024-12-26 16:03:19,264 - pyskl - INFO - Epoch [18][1500/3746] lr: 9.672e-02, eta: 4 days, 7:13:35, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2037, top5_acc: 0.4255, loss_cls: 4.4282, loss: 4.4282 +2024-12-26 16:04:31,508 - pyskl - INFO - Epoch [18][1600/3746] lr: 9.671e-02, eta: 4 days, 7:12:00, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1966, top5_acc: 0.4289, loss_cls: 4.4614, loss: 4.4614 +2024-12-26 16:05:43,291 - pyskl - INFO - Epoch [18][1700/3746] lr: 9.670e-02, eta: 4 days, 7:10:22, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4378, loss_cls: 4.4317, loss: 4.4317 +2024-12-26 16:06:55,494 - pyskl - INFO - Epoch [18][1800/3746] lr: 9.669e-02, eta: 4 days, 7:08:48, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1948, top5_acc: 0.4245, loss_cls: 4.4631, loss: 4.4631 +2024-12-26 16:08:07,490 - pyskl - INFO - Epoch [18][1900/3746] lr: 9.668e-02, eta: 4 days, 7:07:12, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4420, loss_cls: 4.3952, loss: 4.3952 +2024-12-26 16:09:19,655 - pyskl - INFO - Epoch [18][2000/3746] lr: 9.667e-02, eta: 4 days, 7:05:37, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1978, top5_acc: 0.4281, loss_cls: 4.4531, loss: 4.4531 +2024-12-26 16:10:31,788 - pyskl - INFO - Epoch [18][2100/3746] lr: 9.666e-02, eta: 4 days, 7:04:02, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4341, loss_cls: 4.4224, loss: 4.4224 +2024-12-26 16:11:43,716 - pyskl - INFO - Epoch [18][2200/3746] lr: 9.665e-02, eta: 4 days, 7:02:26, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4306, loss_cls: 4.4362, loss: 4.4362 +2024-12-26 16:12:55,841 - pyskl - INFO - Epoch [18][2300/3746] lr: 9.664e-02, eta: 4 days, 7:00:51, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4322, loss_cls: 4.4132, loss: 4.4132 +2024-12-26 16:14:07,637 - pyskl - INFO - Epoch [18][2400/3746] lr: 9.663e-02, eta: 4 days, 6:59:14, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4325, loss_cls: 4.4178, loss: 4.4178 +2024-12-26 16:15:19,343 - pyskl - INFO - Epoch [18][2500/3746] lr: 9.662e-02, eta: 4 days, 6:57:36, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4409, loss_cls: 4.4213, loss: 4.4213 +2024-12-26 16:16:30,842 - pyskl - INFO - Epoch [18][2600/3746] lr: 9.661e-02, eta: 4 days, 6:55:57, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4297, loss_cls: 4.4282, loss: 4.4282 +2024-12-26 16:17:42,568 - pyskl - INFO - Epoch [18][2700/3746] lr: 9.660e-02, eta: 4 days, 6:54:19, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4350, loss_cls: 4.4115, loss: 4.4115 +2024-12-26 16:18:54,459 - pyskl - INFO - Epoch [18][2800/3746] lr: 9.659e-02, eta: 4 days, 6:52:43, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4411, loss_cls: 4.4163, loss: 4.4163 +2024-12-26 16:20:06,674 - pyskl - INFO - Epoch [18][2900/3746] lr: 9.658e-02, eta: 4 days, 6:51:10, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4258, loss_cls: 4.4445, loss: 4.4445 +2024-12-26 16:21:18,917 - pyskl - INFO - Epoch [18][3000/3746] lr: 9.657e-02, eta: 4 days, 6:49:36, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1963, top5_acc: 0.4316, loss_cls: 4.4542, loss: 4.4542 +2024-12-26 16:22:30,581 - pyskl - INFO - Epoch [18][3100/3746] lr: 9.656e-02, eta: 4 days, 6:47:59, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4303, loss_cls: 4.4375, loss: 4.4375 +2024-12-26 16:23:42,541 - pyskl - INFO - Epoch [18][3200/3746] lr: 9.654e-02, eta: 4 days, 6:46:23, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2037, top5_acc: 0.4369, loss_cls: 4.4164, loss: 4.4164 +2024-12-26 16:24:54,707 - pyskl - INFO - Epoch [18][3300/3746] lr: 9.653e-02, eta: 4 days, 6:44:49, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2025, top5_acc: 0.4278, loss_cls: 4.4530, loss: 4.4530 +2024-12-26 16:26:07,146 - pyskl - INFO - Epoch [18][3400/3746] lr: 9.652e-02, eta: 4 days, 6:43:18, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4313, loss_cls: 4.4555, loss: 4.4555 +2024-12-26 16:27:19,211 - pyskl - INFO - Epoch [18][3500/3746] lr: 9.651e-02, eta: 4 days, 6:41:43, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4317, loss_cls: 4.4259, loss: 4.4259 +2024-12-26 16:28:31,068 - pyskl - INFO - Epoch [18][3600/3746] lr: 9.650e-02, eta: 4 days, 6:40:07, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4403, loss_cls: 4.3989, loss: 4.3989 +2024-12-26 16:29:42,934 - pyskl - INFO - Epoch [18][3700/3746] lr: 9.649e-02, eta: 4 days, 6:38:32, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4519, loss_cls: 4.3822, loss: 4.3822 +2024-12-26 16:30:18,622 - pyskl - INFO - Saving checkpoint at 18 epochs +2024-12-26 16:32:18,599 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 16:32:19,444 - pyskl - INFO - +top1_acc 0.1324 +top5_acc 0.3145 +2024-12-26 16:32:19,444 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 16:32:19,491 - pyskl - INFO - +mean_acc 0.1321 +2024-12-26 16:32:19,506 - pyskl - INFO - Epoch(val) [18][309] top1_acc: 0.1324, top5_acc: 0.3145, mean_class_accuracy: 0.1321 +2024-12-26 16:35:51,177 - pyskl - INFO - Epoch [19][100/3746] lr: 9.648e-02, eta: 4 days, 6:49:14, time: 2.117, data_time: 1.402, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4364, loss_cls: 4.4274, loss: 4.4274 +2024-12-26 16:37:02,522 - pyskl - INFO - Epoch [19][200/3746] lr: 9.647e-02, eta: 4 days, 6:47:33, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2030, top5_acc: 0.4387, loss_cls: 4.4131, loss: 4.4131 +2024-12-26 16:38:14,325 - pyskl - INFO - Epoch [19][300/3746] lr: 9.646e-02, eta: 4 days, 6:45:56, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4395, loss_cls: 4.3849, loss: 4.3849 +2024-12-26 16:39:25,630 - pyskl - INFO - Epoch [19][400/3746] lr: 9.645e-02, eta: 4 days, 6:44:15, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4331, loss_cls: 4.4356, loss: 4.4356 +2024-12-26 16:40:37,237 - pyskl - INFO - Epoch [19][500/3746] lr: 9.644e-02, eta: 4 days, 6:42:36, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4342, loss_cls: 4.4355, loss: 4.4355 +2024-12-26 16:41:48,750 - pyskl - INFO - Epoch [19][600/3746] lr: 9.643e-02, eta: 4 days, 6:40:57, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4294, loss_cls: 4.4545, loss: 4.4545 +2024-12-26 16:43:00,313 - pyskl - INFO - Epoch [19][700/3746] lr: 9.642e-02, eta: 4 days, 6:39:19, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4422, loss_cls: 4.4112, loss: 4.4112 +2024-12-26 16:44:11,252 - pyskl - INFO - Epoch [19][800/3746] lr: 9.641e-02, eta: 4 days, 6:37:35, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2027, top5_acc: 0.4392, loss_cls: 4.4103, loss: 4.4103 +2024-12-26 16:45:22,888 - pyskl - INFO - Epoch [19][900/3746] lr: 9.640e-02, eta: 4 days, 6:35:57, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4384, loss_cls: 4.4096, loss: 4.4096 +2024-12-26 16:46:34,300 - pyskl - INFO - Epoch [19][1000/3746] lr: 9.639e-02, eta: 4 days, 6:34:18, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4367, loss_cls: 4.4342, loss: 4.4342 +2024-12-26 16:47:45,968 - pyskl - INFO - Epoch [19][1100/3746] lr: 9.637e-02, eta: 4 days, 6:32:40, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4266, loss_cls: 4.4537, loss: 4.4537 +2024-12-26 16:48:57,638 - pyskl - INFO - Epoch [19][1200/3746] lr: 9.636e-02, eta: 4 days, 6:31:03, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4442, loss_cls: 4.3877, loss: 4.3877 +2024-12-26 16:50:09,001 - pyskl - INFO - Epoch [19][1300/3746] lr: 9.635e-02, eta: 4 days, 6:29:23, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4359, loss_cls: 4.4293, loss: 4.4293 +2024-12-26 16:51:20,463 - pyskl - INFO - Epoch [19][1400/3746] lr: 9.634e-02, eta: 4 days, 6:27:44, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4311, loss_cls: 4.4285, loss: 4.4285 +2024-12-26 16:52:32,091 - pyskl - INFO - Epoch [19][1500/3746] lr: 9.633e-02, eta: 4 days, 6:26:07, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4233, loss_cls: 4.4529, loss: 4.4529 +2024-12-26 16:53:43,843 - pyskl - INFO - Epoch [19][1600/3746] lr: 9.632e-02, eta: 4 days, 6:24:30, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4342, loss_cls: 4.4473, loss: 4.4473 +2024-12-26 16:54:56,149 - pyskl - INFO - Epoch [19][1700/3746] lr: 9.631e-02, eta: 4 days, 6:22:58, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4319, loss_cls: 4.4419, loss: 4.4419 +2024-12-26 16:56:08,262 - pyskl - INFO - Epoch [19][1800/3746] lr: 9.630e-02, eta: 4 days, 6:21:24, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4366, loss_cls: 4.4123, loss: 4.4123 +2024-12-26 16:57:19,897 - pyskl - INFO - Epoch [19][1900/3746] lr: 9.629e-02, eta: 4 days, 6:19:46, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2047, top5_acc: 0.4297, loss_cls: 4.4095, loss: 4.4095 +2024-12-26 16:58:31,816 - pyskl - INFO - Epoch [19][2000/3746] lr: 9.628e-02, eta: 4 days, 6:18:11, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4352, loss_cls: 4.4278, loss: 4.4278 +2024-12-26 16:59:43,383 - pyskl - INFO - Epoch [19][2100/3746] lr: 9.627e-02, eta: 4 days, 6:16:34, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4344, loss_cls: 4.4208, loss: 4.4208 +2024-12-26 17:00:55,381 - pyskl - INFO - Epoch [19][2200/3746] lr: 9.626e-02, eta: 4 days, 6:14:59, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2006, top5_acc: 0.4319, loss_cls: 4.4271, loss: 4.4271 +2024-12-26 17:02:07,943 - pyskl - INFO - Epoch [19][2300/3746] lr: 9.625e-02, eta: 4 days, 6:13:29, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2002, top5_acc: 0.4328, loss_cls: 4.4241, loss: 4.4241 +2024-12-26 17:03:19,964 - pyskl - INFO - Epoch [19][2400/3746] lr: 9.624e-02, eta: 4 days, 6:11:55, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1892, top5_acc: 0.4267, loss_cls: 4.4623, loss: 4.4623 +2024-12-26 17:04:31,839 - pyskl - INFO - Epoch [19][2500/3746] lr: 9.623e-02, eta: 4 days, 6:10:20, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4375, loss_cls: 4.4073, loss: 4.4073 +2024-12-26 17:05:43,684 - pyskl - INFO - Epoch [19][2600/3746] lr: 9.622e-02, eta: 4 days, 6:08:44, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1952, top5_acc: 0.4303, loss_cls: 4.4133, loss: 4.4133 +2024-12-26 17:06:55,395 - pyskl - INFO - Epoch [19][2700/3746] lr: 9.621e-02, eta: 4 days, 6:07:08, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4361, loss_cls: 4.4129, loss: 4.4129 +2024-12-26 17:08:07,380 - pyskl - INFO - Epoch [19][2800/3746] lr: 9.620e-02, eta: 4 days, 6:05:34, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4381, loss_cls: 4.4147, loss: 4.4147 +2024-12-26 17:09:19,540 - pyskl - INFO - Epoch [19][2900/3746] lr: 9.618e-02, eta: 4 days, 6:04:01, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4319, loss_cls: 4.4655, loss: 4.4655 +2024-12-26 17:10:31,427 - pyskl - INFO - Epoch [19][3000/3746] lr: 9.617e-02, eta: 4 days, 6:02:26, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4363, loss_cls: 4.4096, loss: 4.4096 +2024-12-26 17:11:43,451 - pyskl - INFO - Epoch [19][3100/3746] lr: 9.616e-02, eta: 4 days, 6:00:53, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4394, loss_cls: 4.3967, loss: 4.3967 +2024-12-26 17:12:55,301 - pyskl - INFO - Epoch [19][3200/3746] lr: 9.615e-02, eta: 4 days, 5:59:18, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2036, top5_acc: 0.4317, loss_cls: 4.4332, loss: 4.4332 +2024-12-26 17:14:07,470 - pyskl - INFO - Epoch [19][3300/3746] lr: 9.614e-02, eta: 4 days, 5:57:45, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4425, loss_cls: 4.3736, loss: 4.3736 +2024-12-26 17:15:19,380 - pyskl - INFO - Epoch [19][3400/3746] lr: 9.613e-02, eta: 4 days, 5:56:11, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4384, loss_cls: 4.4150, loss: 4.4150 +2024-12-26 17:16:31,380 - pyskl - INFO - Epoch [19][3500/3746] lr: 9.612e-02, eta: 4 days, 5:54:37, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2056, top5_acc: 0.4272, loss_cls: 4.4125, loss: 4.4125 +2024-12-26 17:17:43,023 - pyskl - INFO - Epoch [19][3600/3746] lr: 9.611e-02, eta: 4 days, 5:53:01, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4422, loss_cls: 4.3937, loss: 4.3937 +2024-12-26 17:18:54,922 - pyskl - INFO - Epoch [19][3700/3746] lr: 9.610e-02, eta: 4 days, 5:51:27, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4328, loss_cls: 4.4286, loss: 4.4286 +2024-12-26 17:19:30,528 - pyskl - INFO - Saving checkpoint at 19 epochs +2024-12-26 17:21:29,963 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 17:21:30,834 - pyskl - INFO - +top1_acc 0.1467 +top5_acc 0.3481 +2024-12-26 17:21:30,835 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 17:21:30,881 - pyskl - INFO - +mean_acc 0.1467 +2024-12-26 17:21:30,890 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_13.pth was removed +2024-12-26 17:21:31,229 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_19.pth. +2024-12-26 17:21:31,230 - pyskl - INFO - Best top1_acc is 0.1467 at 19 epoch. +2024-12-26 17:21:31,250 - pyskl - INFO - Epoch(val) [19][309] top1_acc: 0.1467, top5_acc: 0.3481, mean_class_accuracy: 0.1467 +2024-12-26 17:25:07,802 - pyskl - INFO - Epoch [20][100/3746] lr: 9.608e-02, eta: 4 days, 6:01:58, time: 2.165, data_time: 1.449, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4384, loss_cls: 4.3645, loss: 4.3645 +2024-12-26 17:26:19,447 - pyskl - INFO - Epoch [20][200/3746] lr: 9.607e-02, eta: 4 days, 6:00:21, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4330, loss_cls: 4.4112, loss: 4.4112 +2024-12-26 17:27:30,881 - pyskl - INFO - Epoch [20][300/3746] lr: 9.606e-02, eta: 4 days, 5:58:42, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4377, loss_cls: 4.4010, loss: 4.4010 +2024-12-26 17:28:42,438 - pyskl - INFO - Epoch [20][400/3746] lr: 9.605e-02, eta: 4 days, 5:57:05, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2000, top5_acc: 0.4370, loss_cls: 4.4363, loss: 4.4363 +2024-12-26 17:29:53,902 - pyskl - INFO - Epoch [20][500/3746] lr: 9.604e-02, eta: 4 days, 5:55:27, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4436, loss_cls: 4.3599, loss: 4.3599 +2024-12-26 17:31:05,744 - pyskl - INFO - Epoch [20][600/3746] lr: 9.603e-02, eta: 4 days, 5:53:51, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4406, loss_cls: 4.3926, loss: 4.3926 +2024-12-26 17:32:17,032 - pyskl - INFO - Epoch [20][700/3746] lr: 9.602e-02, eta: 4 days, 5:52:12, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1973, top5_acc: 0.4289, loss_cls: 4.4395, loss: 4.4395 +2024-12-26 17:33:28,516 - pyskl - INFO - Epoch [20][800/3746] lr: 9.601e-02, eta: 4 days, 5:50:34, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4348, loss_cls: 4.4362, loss: 4.4362 +2024-12-26 17:34:39,950 - pyskl - INFO - Epoch [20][900/3746] lr: 9.600e-02, eta: 4 days, 5:48:57, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4411, loss_cls: 4.3760, loss: 4.3760 +2024-12-26 17:35:51,443 - pyskl - INFO - Epoch [20][1000/3746] lr: 9.598e-02, eta: 4 days, 5:47:19, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4411, loss_cls: 4.3694, loss: 4.3694 +2024-12-26 17:37:02,739 - pyskl - INFO - Epoch [20][1100/3746] lr: 9.597e-02, eta: 4 days, 5:45:40, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4358, loss_cls: 4.4241, loss: 4.4241 +2024-12-26 17:38:14,178 - pyskl - INFO - Epoch [20][1200/3746] lr: 9.596e-02, eta: 4 days, 5:44:03, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1992, top5_acc: 0.4286, loss_cls: 4.4264, loss: 4.4264 +2024-12-26 17:39:25,711 - pyskl - INFO - Epoch [20][1300/3746] lr: 9.595e-02, eta: 4 days, 5:42:26, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4461, loss_cls: 4.3993, loss: 4.3993 +2024-12-26 17:40:37,087 - pyskl - INFO - Epoch [20][1400/3746] lr: 9.594e-02, eta: 4 days, 5:40:48, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4389, loss_cls: 4.4062, loss: 4.4062 +2024-12-26 17:41:48,653 - pyskl - INFO - Epoch [20][1500/3746] lr: 9.593e-02, eta: 4 days, 5:39:11, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4275, loss_cls: 4.4327, loss: 4.4327 +2024-12-26 17:43:00,316 - pyskl - INFO - Epoch [20][1600/3746] lr: 9.592e-02, eta: 4 days, 5:37:35, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4269, loss_cls: 4.4575, loss: 4.4575 +2024-12-26 17:44:12,100 - pyskl - INFO - Epoch [20][1700/3746] lr: 9.591e-02, eta: 4 days, 5:36:00, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4325, loss_cls: 4.4282, loss: 4.4282 +2024-12-26 17:45:24,474 - pyskl - INFO - Epoch [20][1800/3746] lr: 9.590e-02, eta: 4 days, 5:34:29, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4391, loss_cls: 4.4243, loss: 4.4243 +2024-12-26 17:46:36,314 - pyskl - INFO - Epoch [20][1900/3746] lr: 9.588e-02, eta: 4 days, 5:32:55, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4369, loss_cls: 4.3988, loss: 4.3988 +2024-12-26 17:47:48,180 - pyskl - INFO - Epoch [20][2000/3746] lr: 9.587e-02, eta: 4 days, 5:31:20, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4389, loss_cls: 4.4130, loss: 4.4130 +2024-12-26 17:48:59,954 - pyskl - INFO - Epoch [20][2100/3746] lr: 9.586e-02, eta: 4 days, 5:29:45, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4320, loss_cls: 4.4273, loss: 4.4273 +2024-12-26 17:50:11,926 - pyskl - INFO - Epoch [20][2200/3746] lr: 9.585e-02, eta: 4 days, 5:28:12, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4309, loss_cls: 4.4412, loss: 4.4412 +2024-12-26 17:51:24,020 - pyskl - INFO - Epoch [20][2300/3746] lr: 9.584e-02, eta: 4 days, 5:26:39, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2037, top5_acc: 0.4352, loss_cls: 4.4264, loss: 4.4264 +2024-12-26 17:52:36,479 - pyskl - INFO - Epoch [20][2400/3746] lr: 9.583e-02, eta: 4 days, 5:25:09, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4347, loss_cls: 4.3976, loss: 4.3976 +2024-12-26 17:53:48,437 - pyskl - INFO - Epoch [20][2500/3746] lr: 9.582e-02, eta: 4 days, 5:23:36, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4381, loss_cls: 4.4312, loss: 4.4312 +2024-12-26 17:55:00,458 - pyskl - INFO - Epoch [20][2600/3746] lr: 9.581e-02, eta: 4 days, 5:22:03, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1995, top5_acc: 0.4247, loss_cls: 4.4380, loss: 4.4380 +2024-12-26 17:56:12,563 - pyskl - INFO - Epoch [20][2700/3746] lr: 9.580e-02, eta: 4 days, 5:20:31, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4298, loss_cls: 4.4437, loss: 4.4437 +2024-12-26 17:57:24,555 - pyskl - INFO - Epoch [20][2800/3746] lr: 9.578e-02, eta: 4 days, 5:18:58, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2056, top5_acc: 0.4322, loss_cls: 4.4166, loss: 4.4166 +2024-12-26 17:58:36,303 - pyskl - INFO - Epoch [20][2900/3746] lr: 9.577e-02, eta: 4 days, 5:17:23, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4373, loss_cls: 4.4144, loss: 4.4144 +2024-12-26 17:59:48,364 - pyskl - INFO - Epoch [20][3000/3746] lr: 9.576e-02, eta: 4 days, 5:15:51, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4408, loss_cls: 4.4072, loss: 4.4072 +2024-12-26 18:01:00,396 - pyskl - INFO - Epoch [20][3100/3746] lr: 9.575e-02, eta: 4 days, 5:14:18, time: 0.720, data_time: 0.001, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4405, loss_cls: 4.4053, loss: 4.4053 +2024-12-26 18:02:12,429 - pyskl - INFO - Epoch [20][3200/3746] lr: 9.574e-02, eta: 4 days, 5:12:45, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4348, loss_cls: 4.4419, loss: 4.4419 +2024-12-26 18:03:24,734 - pyskl - INFO - Epoch [20][3300/3746] lr: 9.573e-02, eta: 4 days, 5:11:15, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4412, loss_cls: 4.4052, loss: 4.4052 +2024-12-26 18:04:36,311 - pyskl - INFO - Epoch [20][3400/3746] lr: 9.572e-02, eta: 4 days, 5:09:39, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4347, loss_cls: 4.4161, loss: 4.4161 +2024-12-26 18:05:48,111 - pyskl - INFO - Epoch [20][3500/3746] lr: 9.571e-02, eta: 4 days, 5:08:06, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4305, loss_cls: 4.3899, loss: 4.3899 +2024-12-26 18:07:00,215 - pyskl - INFO - Epoch [20][3600/3746] lr: 9.569e-02, eta: 4 days, 5:06:34, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4375, loss_cls: 4.4149, loss: 4.4149 +2024-12-26 18:08:12,053 - pyskl - INFO - Epoch [20][3700/3746] lr: 9.568e-02, eta: 4 days, 5:05:00, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2047, top5_acc: 0.4428, loss_cls: 4.3967, loss: 4.3967 +2024-12-26 18:08:47,676 - pyskl - INFO - Saving checkpoint at 20 epochs +2024-12-26 18:10:47,717 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 18:10:48,663 - pyskl - INFO - +top1_acc 0.1517 +top5_acc 0.3443 +2024-12-26 18:10:48,664 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 18:10:48,701 - pyskl - INFO - +mean_acc 0.1516 +2024-12-26 18:10:48,705 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_19.pth was removed +2024-12-26 18:10:48,960 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_20.pth. +2024-12-26 18:10:48,961 - pyskl - INFO - Best top1_acc is 0.1517 at 20 epoch. +2024-12-26 18:10:48,973 - pyskl - INFO - Epoch(val) [20][309] top1_acc: 0.1517, top5_acc: 0.3443, mean_class_accuracy: 0.1516 +2024-12-26 18:14:23,356 - pyskl - INFO - Epoch [21][100/3746] lr: 9.567e-02, eta: 4 days, 5:14:34, time: 2.144, data_time: 1.426, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4414, loss_cls: 4.3675, loss: 4.3675 +2024-12-26 18:15:34,794 - pyskl - INFO - Epoch [21][200/3746] lr: 9.565e-02, eta: 4 days, 5:12:57, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4367, loss_cls: 4.4103, loss: 4.4103 +2024-12-26 18:16:46,099 - pyskl - INFO - Epoch [21][300/3746] lr: 9.564e-02, eta: 4 days, 5:11:20, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4394, loss_cls: 4.4024, loss: 4.4024 +2024-12-26 18:17:57,289 - pyskl - INFO - Epoch [21][400/3746] lr: 9.563e-02, eta: 4 days, 5:09:41, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4280, loss_cls: 4.4356, loss: 4.4356 +2024-12-26 18:19:08,561 - pyskl - INFO - Epoch [21][500/3746] lr: 9.562e-02, eta: 4 days, 5:08:03, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1983, top5_acc: 0.4298, loss_cls: 4.4168, loss: 4.4168 +2024-12-26 18:20:20,633 - pyskl - INFO - Epoch [21][600/3746] lr: 9.561e-02, eta: 4 days, 5:06:31, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4263, loss_cls: 4.4399, loss: 4.4399 +2024-12-26 18:21:32,259 - pyskl - INFO - Epoch [21][700/3746] lr: 9.560e-02, eta: 4 days, 5:04:55, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2045, top5_acc: 0.4327, loss_cls: 4.4291, loss: 4.4291 +2024-12-26 18:22:43,738 - pyskl - INFO - Epoch [21][800/3746] lr: 9.559e-02, eta: 4 days, 5:03:19, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4387, loss_cls: 4.3721, loss: 4.3721 +2024-12-26 18:23:55,044 - pyskl - INFO - Epoch [21][900/3746] lr: 9.557e-02, eta: 4 days, 5:01:41, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4319, loss_cls: 4.4447, loss: 4.4447 +2024-12-26 18:25:06,473 - pyskl - INFO - Epoch [21][1000/3746] lr: 9.556e-02, eta: 4 days, 5:00:05, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4406, loss_cls: 4.4081, loss: 4.4081 +2024-12-26 18:26:17,858 - pyskl - INFO - Epoch [21][1100/3746] lr: 9.555e-02, eta: 4 days, 4:58:28, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4344, loss_cls: 4.4404, loss: 4.4404 +2024-12-26 18:27:29,003 - pyskl - INFO - Epoch [21][1200/3746] lr: 9.554e-02, eta: 4 days, 4:56:50, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4342, loss_cls: 4.4387, loss: 4.4387 +2024-12-26 18:28:40,086 - pyskl - INFO - Epoch [21][1300/3746] lr: 9.553e-02, eta: 4 days, 4:55:11, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4456, loss_cls: 4.3435, loss: 4.3435 +2024-12-26 18:29:52,251 - pyskl - INFO - Epoch [21][1400/3746] lr: 9.552e-02, eta: 4 days, 4:53:40, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2069, top5_acc: 0.4347, loss_cls: 4.4310, loss: 4.4310 +2024-12-26 18:31:04,356 - pyskl - INFO - Epoch [21][1500/3746] lr: 9.551e-02, eta: 4 days, 4:52:08, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4475, loss_cls: 4.3637, loss: 4.3637 +2024-12-26 18:32:16,645 - pyskl - INFO - Epoch [21][1600/3746] lr: 9.549e-02, eta: 4 days, 4:50:37, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4391, loss_cls: 4.3879, loss: 4.3879 +2024-12-26 18:33:28,597 - pyskl - INFO - Epoch [21][1700/3746] lr: 9.548e-02, eta: 4 days, 4:49:04, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4431, loss_cls: 4.4154, loss: 4.4154 +2024-12-26 18:34:40,814 - pyskl - INFO - Epoch [21][1800/3746] lr: 9.547e-02, eta: 4 days, 4:47:33, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2008, top5_acc: 0.4311, loss_cls: 4.4469, loss: 4.4469 +2024-12-26 18:35:52,829 - pyskl - INFO - Epoch [21][1900/3746] lr: 9.546e-02, eta: 4 days, 4:46:01, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4436, loss_cls: 4.3897, loss: 4.3897 +2024-12-26 18:37:05,274 - pyskl - INFO - Epoch [21][2000/3746] lr: 9.545e-02, eta: 4 days, 4:44:31, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4378, loss_cls: 4.4029, loss: 4.4029 +2024-12-26 18:38:17,450 - pyskl - INFO - Epoch [21][2100/3746] lr: 9.544e-02, eta: 4 days, 4:43:00, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1959, top5_acc: 0.4294, loss_cls: 4.4389, loss: 4.4389 +2024-12-26 18:39:29,508 - pyskl - INFO - Epoch [21][2200/3746] lr: 9.542e-02, eta: 4 days, 4:41:28, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2155, top5_acc: 0.4423, loss_cls: 4.3833, loss: 4.3833 +2024-12-26 18:40:41,677 - pyskl - INFO - Epoch [21][2300/3746] lr: 9.541e-02, eta: 4 days, 4:39:57, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4380, loss_cls: 4.4249, loss: 4.4249 +2024-12-26 18:41:53,446 - pyskl - INFO - Epoch [21][2400/3746] lr: 9.540e-02, eta: 4 days, 4:38:23, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2028, top5_acc: 0.4361, loss_cls: 4.4410, loss: 4.4410 +2024-12-26 18:43:05,114 - pyskl - INFO - Epoch [21][2500/3746] lr: 9.539e-02, eta: 4 days, 4:36:49, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2005, top5_acc: 0.4473, loss_cls: 4.3835, loss: 4.3835 +2024-12-26 18:44:17,014 - pyskl - INFO - Epoch [21][2600/3746] lr: 9.538e-02, eta: 4 days, 4:35:17, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2000, top5_acc: 0.4230, loss_cls: 4.4639, loss: 4.4639 +2024-12-26 18:45:28,816 - pyskl - INFO - Epoch [21][2700/3746] lr: 9.537e-02, eta: 4 days, 4:33:43, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4408, loss_cls: 4.4280, loss: 4.4280 +2024-12-26 18:46:40,611 - pyskl - INFO - Epoch [21][2800/3746] lr: 9.535e-02, eta: 4 days, 4:32:10, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4372, loss_cls: 4.4176, loss: 4.4176 +2024-12-26 18:47:53,012 - pyskl - INFO - Epoch [21][2900/3746] lr: 9.534e-02, eta: 4 days, 4:30:41, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4331, loss_cls: 4.4181, loss: 4.4181 +2024-12-26 18:49:05,019 - pyskl - INFO - Epoch [21][3000/3746] lr: 9.533e-02, eta: 4 days, 4:29:09, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4556, loss_cls: 4.3494, loss: 4.3494 +2024-12-26 18:50:17,150 - pyskl - INFO - Epoch [21][3100/3746] lr: 9.532e-02, eta: 4 days, 4:27:38, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4289, loss_cls: 4.4286, loss: 4.4286 +2024-12-26 18:51:29,025 - pyskl - INFO - Epoch [21][3200/3746] lr: 9.531e-02, eta: 4 days, 4:26:05, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2036, top5_acc: 0.4316, loss_cls: 4.4467, loss: 4.4467 +2024-12-26 18:52:40,944 - pyskl - INFO - Epoch [21][3300/3746] lr: 9.529e-02, eta: 4 days, 4:24:33, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4330, loss_cls: 4.4341, loss: 4.4341 +2024-12-26 18:53:52,911 - pyskl - INFO - Epoch [21][3400/3746] lr: 9.528e-02, eta: 4 days, 4:23:01, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4411, loss_cls: 4.3902, loss: 4.3902 +2024-12-26 18:55:05,037 - pyskl - INFO - Epoch [21][3500/3746] lr: 9.527e-02, eta: 4 days, 4:21:31, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4327, loss_cls: 4.4305, loss: 4.4305 +2024-12-26 18:56:16,926 - pyskl - INFO - Epoch [21][3600/3746] lr: 9.526e-02, eta: 4 days, 4:19:58, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4331, loss_cls: 4.3889, loss: 4.3889 +2024-12-26 18:57:28,849 - pyskl - INFO - Epoch [21][3700/3746] lr: 9.525e-02, eta: 4 days, 4:18:26, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4278, loss_cls: 4.4617, loss: 4.4617 +2024-12-26 18:58:04,398 - pyskl - INFO - Saving checkpoint at 21 epochs +2024-12-26 19:00:03,141 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 19:00:04,072 - pyskl - INFO - +top1_acc 0.1541 +top5_acc 0.3584 +2024-12-26 19:00:04,072 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 19:00:04,119 - pyskl - INFO - +mean_acc 0.1541 +2024-12-26 19:00:04,124 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_20.pth was removed +2024-12-26 19:00:04,416 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_21.pth. +2024-12-26 19:00:04,416 - pyskl - INFO - Best top1_acc is 0.1541 at 21 epoch. +2024-12-26 19:00:04,426 - pyskl - INFO - Epoch(val) [21][309] top1_acc: 0.1541, top5_acc: 0.3584, mean_class_accuracy: 0.1541 +2024-12-26 19:03:38,899 - pyskl - INFO - Epoch [22][100/3746] lr: 9.523e-02, eta: 4 days, 4:27:24, time: 2.145, data_time: 1.429, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4439, loss_cls: 4.3970, loss: 4.3970 +2024-12-26 19:04:50,027 - pyskl - INFO - Epoch [22][200/3746] lr: 9.522e-02, eta: 4 days, 4:25:46, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2108, top5_acc: 0.4397, loss_cls: 4.3742, loss: 4.3742 +2024-12-26 19:06:01,464 - pyskl - INFO - Epoch [22][300/3746] lr: 9.521e-02, eta: 4 days, 4:24:10, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4297, loss_cls: 4.4386, loss: 4.4386 +2024-12-26 19:07:12,909 - pyskl - INFO - Epoch [22][400/3746] lr: 9.519e-02, eta: 4 days, 4:22:35, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4505, loss_cls: 4.3345, loss: 4.3345 +2024-12-26 19:08:24,324 - pyskl - INFO - Epoch [22][500/3746] lr: 9.518e-02, eta: 4 days, 4:20:59, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4436, loss_cls: 4.3935, loss: 4.3935 +2024-12-26 19:09:36,209 - pyskl - INFO - Epoch [22][600/3746] lr: 9.517e-02, eta: 4 days, 4:19:26, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4403, loss_cls: 4.3925, loss: 4.3925 +2024-12-26 19:10:47,901 - pyskl - INFO - Epoch [22][700/3746] lr: 9.516e-02, eta: 4 days, 4:17:52, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2078, top5_acc: 0.4436, loss_cls: 4.3995, loss: 4.3995 +2024-12-26 19:11:59,518 - pyskl - INFO - Epoch [22][800/3746] lr: 9.515e-02, eta: 4 days, 4:16:18, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4231, loss_cls: 4.4527, loss: 4.4527 +2024-12-26 19:13:11,028 - pyskl - INFO - Epoch [22][900/3746] lr: 9.513e-02, eta: 4 days, 4:14:43, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4455, loss_cls: 4.3741, loss: 4.3741 +2024-12-26 19:14:22,652 - pyskl - INFO - Epoch [22][1000/3746] lr: 9.512e-02, eta: 4 days, 4:13:09, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4459, loss_cls: 4.3674, loss: 4.3674 +2024-12-26 19:15:33,932 - pyskl - INFO - Epoch [22][1100/3746] lr: 9.511e-02, eta: 4 days, 4:11:33, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4342, loss_cls: 4.4098, loss: 4.4098 +2024-12-26 19:16:45,403 - pyskl - INFO - Epoch [22][1200/3746] lr: 9.510e-02, eta: 4 days, 4:09:58, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4412, loss_cls: 4.3999, loss: 4.3999 +2024-12-26 19:17:57,051 - pyskl - INFO - Epoch [22][1300/3746] lr: 9.509e-02, eta: 4 days, 4:08:24, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4514, loss_cls: 4.3427, loss: 4.3427 +2024-12-26 19:19:08,705 - pyskl - INFO - Epoch [22][1400/3746] lr: 9.507e-02, eta: 4 days, 4:06:50, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4313, loss_cls: 4.4411, loss: 4.4411 +2024-12-26 19:20:20,552 - pyskl - INFO - Epoch [22][1500/3746] lr: 9.506e-02, eta: 4 days, 4:05:17, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4363, loss_cls: 4.4054, loss: 4.4054 +2024-12-26 19:21:32,899 - pyskl - INFO - Epoch [22][1600/3746] lr: 9.505e-02, eta: 4 days, 4:03:48, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4456, loss_cls: 4.3868, loss: 4.3868 +2024-12-26 19:22:45,035 - pyskl - INFO - Epoch [22][1700/3746] lr: 9.504e-02, eta: 4 days, 4:02:17, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4400, loss_cls: 4.3810, loss: 4.3810 +2024-12-26 19:23:57,189 - pyskl - INFO - Epoch [22][1800/3746] lr: 9.502e-02, eta: 4 days, 4:00:47, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4363, loss_cls: 4.4684, loss: 4.4684 +2024-12-26 19:25:09,271 - pyskl - INFO - Epoch [22][1900/3746] lr: 9.501e-02, eta: 4 days, 3:59:16, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1963, top5_acc: 0.4319, loss_cls: 4.4411, loss: 4.4411 +2024-12-26 19:26:21,492 - pyskl - INFO - Epoch [22][2000/3746] lr: 9.500e-02, eta: 4 days, 3:57:46, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4292, loss_cls: 4.4314, loss: 4.4314 +2024-12-26 19:27:33,058 - pyskl - INFO - Epoch [22][2100/3746] lr: 9.499e-02, eta: 4 days, 3:56:12, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4327, loss_cls: 4.4157, loss: 4.4157 +2024-12-26 19:28:44,745 - pyskl - INFO - Epoch [22][2200/3746] lr: 9.498e-02, eta: 4 days, 3:54:39, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4420, loss_cls: 4.4038, loss: 4.4038 +2024-12-26 19:29:57,073 - pyskl - INFO - Epoch [22][2300/3746] lr: 9.496e-02, eta: 4 days, 3:53:09, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2037, top5_acc: 0.4395, loss_cls: 4.4014, loss: 4.4014 +2024-12-26 19:31:09,691 - pyskl - INFO - Epoch [22][2400/3746] lr: 9.495e-02, eta: 4 days, 3:51:42, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2036, top5_acc: 0.4384, loss_cls: 4.4184, loss: 4.4184 +2024-12-26 19:32:21,763 - pyskl - INFO - Epoch [22][2500/3746] lr: 9.494e-02, eta: 4 days, 3:50:11, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4286, loss_cls: 4.4365, loss: 4.4365 +2024-12-26 19:33:34,218 - pyskl - INFO - Epoch [22][2600/3746] lr: 9.493e-02, eta: 4 days, 3:48:43, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4467, loss_cls: 4.3961, loss: 4.3961 +2024-12-26 19:34:46,145 - pyskl - INFO - Epoch [22][2700/3746] lr: 9.491e-02, eta: 4 days, 3:47:11, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4541, loss_cls: 4.3659, loss: 4.3659 +2024-12-26 19:35:58,428 - pyskl - INFO - Epoch [22][2800/3746] lr: 9.490e-02, eta: 4 days, 3:45:42, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4412, loss_cls: 4.4191, loss: 4.4191 +2024-12-26 19:37:10,761 - pyskl - INFO - Epoch [22][2900/3746] lr: 9.489e-02, eta: 4 days, 3:44:13, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4528, loss_cls: 4.3973, loss: 4.3973 +2024-12-26 19:38:22,260 - pyskl - INFO - Epoch [22][3000/3746] lr: 9.488e-02, eta: 4 days, 3:42:39, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2000, top5_acc: 0.4330, loss_cls: 4.4361, loss: 4.4361 +2024-12-26 19:39:34,175 - pyskl - INFO - Epoch [22][3100/3746] lr: 9.487e-02, eta: 4 days, 3:41:07, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4273, loss_cls: 4.4667, loss: 4.4667 +2024-12-26 19:40:46,108 - pyskl - INFO - Epoch [22][3200/3746] lr: 9.485e-02, eta: 4 days, 3:39:36, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4319, loss_cls: 4.3991, loss: 4.3991 +2024-12-26 19:41:58,735 - pyskl - INFO - Epoch [22][3300/3746] lr: 9.484e-02, eta: 4 days, 3:38:09, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4317, loss_cls: 4.4332, loss: 4.4332 +2024-12-26 19:43:10,854 - pyskl - INFO - Epoch [22][3400/3746] lr: 9.483e-02, eta: 4 days, 3:36:39, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4334, loss_cls: 4.4268, loss: 4.4268 +2024-12-26 19:44:23,055 - pyskl - INFO - Epoch [22][3500/3746] lr: 9.482e-02, eta: 4 days, 3:35:09, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4441, loss_cls: 4.3868, loss: 4.3868 +2024-12-26 19:45:34,878 - pyskl - INFO - Epoch [22][3600/3746] lr: 9.480e-02, eta: 4 days, 3:33:38, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4369, loss_cls: 4.4398, loss: 4.4398 +2024-12-26 19:46:47,003 - pyskl - INFO - Epoch [22][3700/3746] lr: 9.479e-02, eta: 4 days, 3:32:08, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4364, loss_cls: 4.3888, loss: 4.3888 +2024-12-26 19:47:22,645 - pyskl - INFO - Saving checkpoint at 22 epochs +2024-12-26 19:49:23,348 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 19:49:24,284 - pyskl - INFO - +top1_acc 0.1497 +top5_acc 0.3443 +2024-12-26 19:49:24,285 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 19:49:24,332 - pyskl - INFO - +mean_acc 0.1494 +2024-12-26 19:49:24,344 - pyskl - INFO - Epoch(val) [22][309] top1_acc: 0.1497, top5_acc: 0.3443, mean_class_accuracy: 0.1494 +2024-12-26 19:53:02,099 - pyskl - INFO - Epoch [23][100/3746] lr: 9.477e-02, eta: 4 days, 3:40:50, time: 2.177, data_time: 1.461, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4472, loss_cls: 4.3657, loss: 4.3657 +2024-12-26 19:54:13,361 - pyskl - INFO - Epoch [23][200/3746] lr: 9.476e-02, eta: 4 days, 3:39:14, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4545, loss_cls: 4.3469, loss: 4.3469 +2024-12-26 19:55:24,654 - pyskl - INFO - Epoch [23][300/3746] lr: 9.475e-02, eta: 4 days, 3:37:39, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4439, loss_cls: 4.3877, loss: 4.3877 +2024-12-26 19:56:36,269 - pyskl - INFO - Epoch [23][400/3746] lr: 9.474e-02, eta: 4 days, 3:36:05, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4459, loss_cls: 4.4010, loss: 4.4010 +2024-12-26 19:57:47,498 - pyskl - INFO - Epoch [23][500/3746] lr: 9.472e-02, eta: 4 days, 3:34:30, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4375, loss_cls: 4.4099, loss: 4.4099 +2024-12-26 19:58:59,124 - pyskl - INFO - Epoch [23][600/3746] lr: 9.471e-02, eta: 4 days, 3:32:56, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2056, top5_acc: 0.4412, loss_cls: 4.4084, loss: 4.4084 +2024-12-26 20:00:10,440 - pyskl - INFO - Epoch [23][700/3746] lr: 9.470e-02, eta: 4 days, 3:31:21, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4408, loss_cls: 4.4009, loss: 4.4009 +2024-12-26 20:01:21,751 - pyskl - INFO - Epoch [23][800/3746] lr: 9.469e-02, eta: 4 days, 3:29:46, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4319, loss_cls: 4.4143, loss: 4.4143 +2024-12-26 20:02:33,380 - pyskl - INFO - Epoch [23][900/3746] lr: 9.467e-02, eta: 4 days, 3:28:13, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4361, loss_cls: 4.3966, loss: 4.3966 +2024-12-26 20:03:44,607 - pyskl - INFO - Epoch [23][1000/3746] lr: 9.466e-02, eta: 4 days, 3:26:37, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4431, loss_cls: 4.3786, loss: 4.3786 +2024-12-26 20:04:55,944 - pyskl - INFO - Epoch [23][1100/3746] lr: 9.465e-02, eta: 4 days, 3:25:03, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4434, loss_cls: 4.4048, loss: 4.4048 +2024-12-26 20:06:07,278 - pyskl - INFO - Epoch [23][1200/3746] lr: 9.464e-02, eta: 4 days, 3:23:28, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4503, loss_cls: 4.3559, loss: 4.3559 +2024-12-26 20:07:18,460 - pyskl - INFO - Epoch [23][1300/3746] lr: 9.462e-02, eta: 4 days, 3:21:52, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2078, top5_acc: 0.4328, loss_cls: 4.4141, loss: 4.4141 +2024-12-26 20:08:30,260 - pyskl - INFO - Epoch [23][1400/3746] lr: 9.461e-02, eta: 4 days, 3:20:20, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4539, loss_cls: 4.3634, loss: 4.3634 +2024-12-26 20:09:42,136 - pyskl - INFO - Epoch [23][1500/3746] lr: 9.460e-02, eta: 4 days, 3:18:49, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4544, loss_cls: 4.3667, loss: 4.3667 +2024-12-26 20:10:54,013 - pyskl - INFO - Epoch [23][1600/3746] lr: 9.459e-02, eta: 4 days, 3:17:17, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4466, loss_cls: 4.3838, loss: 4.3838 +2024-12-26 20:12:06,349 - pyskl - INFO - Epoch [23][1700/3746] lr: 9.457e-02, eta: 4 days, 3:15:49, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2025, top5_acc: 0.4278, loss_cls: 4.4519, loss: 4.4519 +2024-12-26 20:13:18,220 - pyskl - INFO - Epoch [23][1800/3746] lr: 9.456e-02, eta: 4 days, 3:14:17, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4294, loss_cls: 4.4056, loss: 4.4056 +2024-12-26 20:14:30,357 - pyskl - INFO - Epoch [23][1900/3746] lr: 9.455e-02, eta: 4 days, 3:12:47, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4392, loss_cls: 4.4280, loss: 4.4280 +2024-12-26 20:15:42,766 - pyskl - INFO - Epoch [23][2000/3746] lr: 9.453e-02, eta: 4 days, 3:11:19, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.1992, top5_acc: 0.4400, loss_cls: 4.4131, loss: 4.4131 +2024-12-26 20:16:54,703 - pyskl - INFO - Epoch [23][2100/3746] lr: 9.452e-02, eta: 4 days, 3:09:48, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4397, loss_cls: 4.4028, loss: 4.4028 +2024-12-26 20:18:06,259 - pyskl - INFO - Epoch [23][2200/3746] lr: 9.451e-02, eta: 4 days, 3:08:15, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4508, loss_cls: 4.3385, loss: 4.3385 +2024-12-26 20:19:18,225 - pyskl - INFO - Epoch [23][2300/3746] lr: 9.450e-02, eta: 4 days, 3:06:45, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4411, loss_cls: 4.4029, loss: 4.4029 +2024-12-26 20:20:30,011 - pyskl - INFO - Epoch [23][2400/3746] lr: 9.448e-02, eta: 4 days, 3:05:13, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4434, loss_cls: 4.3902, loss: 4.3902 +2024-12-26 20:21:41,985 - pyskl - INFO - Epoch [23][2500/3746] lr: 9.447e-02, eta: 4 days, 3:03:43, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2078, top5_acc: 0.4372, loss_cls: 4.3722, loss: 4.3722 +2024-12-26 20:22:53,986 - pyskl - INFO - Epoch [23][2600/3746] lr: 9.446e-02, eta: 4 days, 3:02:12, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4411, loss_cls: 4.3929, loss: 4.3929 +2024-12-26 20:24:06,181 - pyskl - INFO - Epoch [23][2700/3746] lr: 9.445e-02, eta: 4 days, 3:00:43, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1970, top5_acc: 0.4384, loss_cls: 4.4218, loss: 4.4218 +2024-12-26 20:25:18,252 - pyskl - INFO - Epoch [23][2800/3746] lr: 9.443e-02, eta: 4 days, 2:59:13, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4411, loss_cls: 4.3669, loss: 4.3669 +2024-12-26 20:26:30,229 - pyskl - INFO - Epoch [23][2900/3746] lr: 9.442e-02, eta: 4 days, 2:57:43, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4411, loss_cls: 4.4014, loss: 4.4014 +2024-12-26 20:27:42,535 - pyskl - INFO - Epoch [23][3000/3746] lr: 9.441e-02, eta: 4 days, 2:56:15, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4369, loss_cls: 4.4230, loss: 4.4230 +2024-12-26 20:28:54,450 - pyskl - INFO - Epoch [23][3100/3746] lr: 9.439e-02, eta: 4 days, 2:54:44, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2047, top5_acc: 0.4369, loss_cls: 4.4150, loss: 4.4150 +2024-12-26 20:30:06,554 - pyskl - INFO - Epoch [23][3200/3746] lr: 9.438e-02, eta: 4 days, 2:53:15, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4331, loss_cls: 4.4077, loss: 4.4077 +2024-12-26 20:31:18,523 - pyskl - INFO - Epoch [23][3300/3746] lr: 9.437e-02, eta: 4 days, 2:51:45, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2019, top5_acc: 0.4387, loss_cls: 4.4266, loss: 4.4266 +2024-12-26 20:32:30,681 - pyskl - INFO - Epoch [23][3400/3746] lr: 9.436e-02, eta: 4 days, 2:50:15, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2028, top5_acc: 0.4322, loss_cls: 4.4182, loss: 4.4182 +2024-12-26 20:33:42,422 - pyskl - INFO - Epoch [23][3500/3746] lr: 9.434e-02, eta: 4 days, 2:48:44, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2078, top5_acc: 0.4336, loss_cls: 4.4212, loss: 4.4212 +2024-12-26 20:34:54,339 - pyskl - INFO - Epoch [23][3600/3746] lr: 9.433e-02, eta: 4 days, 2:47:14, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1959, top5_acc: 0.4297, loss_cls: 4.4585, loss: 4.4585 +2024-12-26 20:36:06,198 - pyskl - INFO - Epoch [23][3700/3746] lr: 9.432e-02, eta: 4 days, 2:45:43, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4425, loss_cls: 4.3640, loss: 4.3640 +2024-12-26 20:36:41,608 - pyskl - INFO - Saving checkpoint at 23 epochs +2024-12-26 20:38:41,549 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 20:38:42,494 - pyskl - INFO - +top1_acc 0.1538 +top5_acc 0.3611 +2024-12-26 20:38:42,494 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 20:38:42,537 - pyskl - INFO - +mean_acc 0.1538 +2024-12-26 20:38:42,560 - pyskl - INFO - Epoch(val) [23][309] top1_acc: 0.1538, top5_acc: 0.3611, mean_class_accuracy: 0.1538 +2024-12-26 20:42:18,192 - pyskl - INFO - Epoch [24][100/3746] lr: 9.430e-02, eta: 4 days, 2:53:41, time: 2.156, data_time: 1.442, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4370, loss_cls: 4.3864, loss: 4.3864 +2024-12-26 20:43:29,775 - pyskl - INFO - Epoch [24][200/3746] lr: 9.428e-02, eta: 4 days, 2:52:09, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4506, loss_cls: 4.3684, loss: 4.3684 +2024-12-26 20:44:41,551 - pyskl - INFO - Epoch [24][300/3746] lr: 9.427e-02, eta: 4 days, 2:50:37, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4487, loss_cls: 4.3212, loss: 4.3212 +2024-12-26 20:45:53,630 - pyskl - INFO - Epoch [24][400/3746] lr: 9.426e-02, eta: 4 days, 2:49:07, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4402, loss_cls: 4.4107, loss: 4.4107 +2024-12-26 20:47:05,277 - pyskl - INFO - Epoch [24][500/3746] lr: 9.425e-02, eta: 4 days, 2:47:34, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4363, loss_cls: 4.4073, loss: 4.4073 +2024-12-26 20:48:16,751 - pyskl - INFO - Epoch [24][600/3746] lr: 9.423e-02, eta: 4 days, 2:46:01, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2033, top5_acc: 0.4420, loss_cls: 4.4138, loss: 4.4138 +2024-12-26 20:49:28,463 - pyskl - INFO - Epoch [24][700/3746] lr: 9.422e-02, eta: 4 days, 2:44:29, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4395, loss_cls: 4.3746, loss: 4.3746 +2024-12-26 20:50:39,798 - pyskl - INFO - Epoch [24][800/3746] lr: 9.421e-02, eta: 4 days, 2:42:55, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4378, loss_cls: 4.3959, loss: 4.3959 +2024-12-26 20:51:51,020 - pyskl - INFO - Epoch [24][900/3746] lr: 9.419e-02, eta: 4 days, 2:41:21, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4430, loss_cls: 4.3869, loss: 4.3869 +2024-12-26 20:53:02,427 - pyskl - INFO - Epoch [24][1000/3746] lr: 9.418e-02, eta: 4 days, 2:39:48, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4363, loss_cls: 4.4030, loss: 4.4030 +2024-12-26 20:54:13,637 - pyskl - INFO - Epoch [24][1100/3746] lr: 9.417e-02, eta: 4 days, 2:38:13, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4520, loss_cls: 4.3531, loss: 4.3531 +2024-12-26 20:55:24,796 - pyskl - INFO - Epoch [24][1200/3746] lr: 9.415e-02, eta: 4 days, 2:36:38, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4419, loss_cls: 4.3945, loss: 4.3945 +2024-12-26 20:56:36,287 - pyskl - INFO - Epoch [24][1300/3746] lr: 9.414e-02, eta: 4 days, 2:35:06, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4359, loss_cls: 4.3925, loss: 4.3925 +2024-12-26 20:57:47,768 - pyskl - INFO - Epoch [24][1400/3746] lr: 9.413e-02, eta: 4 days, 2:33:33, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4289, loss_cls: 4.4143, loss: 4.4143 +2024-12-26 20:58:58,955 - pyskl - INFO - Epoch [24][1500/3746] lr: 9.411e-02, eta: 4 days, 2:31:58, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4356, loss_cls: 4.4250, loss: 4.4250 +2024-12-26 21:00:10,264 - pyskl - INFO - Epoch [24][1600/3746] lr: 9.410e-02, eta: 4 days, 2:30:25, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4389, loss_cls: 4.3817, loss: 4.3817 +2024-12-26 21:01:21,930 - pyskl - INFO - Epoch [24][1700/3746] lr: 9.409e-02, eta: 4 days, 2:28:53, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4458, loss_cls: 4.3581, loss: 4.3581 +2024-12-26 21:02:33,981 - pyskl - INFO - Epoch [24][1800/3746] lr: 9.407e-02, eta: 4 days, 2:27:24, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4347, loss_cls: 4.3958, loss: 4.3958 +2024-12-26 21:03:45,731 - pyskl - INFO - Epoch [24][1900/3746] lr: 9.406e-02, eta: 4 days, 2:25:52, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4398, loss_cls: 4.4119, loss: 4.4119 +2024-12-26 21:04:57,388 - pyskl - INFO - Epoch [24][2000/3746] lr: 9.405e-02, eta: 4 days, 2:24:21, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2045, top5_acc: 0.4389, loss_cls: 4.3879, loss: 4.3879 +2024-12-26 21:06:09,399 - pyskl - INFO - Epoch [24][2100/3746] lr: 9.404e-02, eta: 4 days, 2:22:51, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4345, loss_cls: 4.4225, loss: 4.4225 +2024-12-26 21:07:21,486 - pyskl - INFO - Epoch [24][2200/3746] lr: 9.402e-02, eta: 4 days, 2:21:22, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2056, top5_acc: 0.4370, loss_cls: 4.3866, loss: 4.3866 +2024-12-26 21:08:33,488 - pyskl - INFO - Epoch [24][2300/3746] lr: 9.401e-02, eta: 4 days, 2:19:52, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4473, loss_cls: 4.3748, loss: 4.3748 +2024-12-26 21:09:45,306 - pyskl - INFO - Epoch [24][2400/3746] lr: 9.400e-02, eta: 4 days, 2:18:22, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4342, loss_cls: 4.4287, loss: 4.4287 +2024-12-26 21:10:57,038 - pyskl - INFO - Epoch [24][2500/3746] lr: 9.398e-02, eta: 4 days, 2:16:51, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2092, top5_acc: 0.4359, loss_cls: 4.4287, loss: 4.4287 +2024-12-26 21:12:08,979 - pyskl - INFO - Epoch [24][2600/3746] lr: 9.397e-02, eta: 4 days, 2:15:21, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4361, loss_cls: 4.4116, loss: 4.4116 +2024-12-26 21:13:21,059 - pyskl - INFO - Epoch [24][2700/3746] lr: 9.396e-02, eta: 4 days, 2:13:52, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4467, loss_cls: 4.3847, loss: 4.3847 +2024-12-26 21:14:33,070 - pyskl - INFO - Epoch [24][2800/3746] lr: 9.394e-02, eta: 4 days, 2:12:23, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4350, loss_cls: 4.4068, loss: 4.4068 +2024-12-26 21:15:44,957 - pyskl - INFO - Epoch [24][2900/3746] lr: 9.393e-02, eta: 4 days, 2:10:53, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4319, loss_cls: 4.4324, loss: 4.4324 +2024-12-26 21:16:56,968 - pyskl - INFO - Epoch [24][3000/3746] lr: 9.392e-02, eta: 4 days, 2:09:23, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4325, loss_cls: 4.4214, loss: 4.4214 +2024-12-26 21:18:08,966 - pyskl - INFO - Epoch [24][3100/3746] lr: 9.390e-02, eta: 4 days, 2:07:54, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4373, loss_cls: 4.4105, loss: 4.4105 +2024-12-26 21:19:20,956 - pyskl - INFO - Epoch [24][3200/3746] lr: 9.389e-02, eta: 4 days, 2:06:25, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2114, top5_acc: 0.4531, loss_cls: 4.3436, loss: 4.3436 +2024-12-26 21:20:33,033 - pyskl - INFO - Epoch [24][3300/3746] lr: 9.388e-02, eta: 4 days, 2:04:56, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4377, loss_cls: 4.4181, loss: 4.4181 +2024-12-26 21:21:44,899 - pyskl - INFO - Epoch [24][3400/3746] lr: 9.386e-02, eta: 4 days, 2:03:26, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4425, loss_cls: 4.3875, loss: 4.3875 +2024-12-26 21:22:57,087 - pyskl - INFO - Epoch [24][3500/3746] lr: 9.385e-02, eta: 4 days, 2:01:58, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4339, loss_cls: 4.4188, loss: 4.4188 +2024-12-26 21:24:08,964 - pyskl - INFO - Epoch [24][3600/3746] lr: 9.384e-02, eta: 4 days, 2:00:28, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4447, loss_cls: 4.4087, loss: 4.4087 +2024-12-26 21:25:20,905 - pyskl - INFO - Epoch [24][3700/3746] lr: 9.382e-02, eta: 4 days, 1:58:59, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4427, loss_cls: 4.3974, loss: 4.3974 +2024-12-26 21:25:56,244 - pyskl - INFO - Saving checkpoint at 24 epochs +2024-12-26 21:27:56,303 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 21:27:57,334 - pyskl - INFO - +top1_acc 0.1396 +top5_acc 0.3342 +2024-12-26 21:27:57,334 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 21:27:57,392 - pyskl - INFO - +mean_acc 0.1397 +2024-12-26 21:27:57,405 - pyskl - INFO - Epoch(val) [24][309] top1_acc: 0.1396, top5_acc: 0.3342, mean_class_accuracy: 0.1397 +2024-12-26 21:31:34,207 - pyskl - INFO - Epoch [25][100/3746] lr: 9.380e-02, eta: 4 days, 2:06:34, time: 2.168, data_time: 1.449, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4492, loss_cls: 4.3455, loss: 4.3455 +2024-12-26 21:32:45,484 - pyskl - INFO - Epoch [25][200/3746] lr: 9.379e-02, eta: 4 days, 2:05:01, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4480, loss_cls: 4.3675, loss: 4.3675 +2024-12-26 21:33:56,983 - pyskl - INFO - Epoch [25][300/3746] lr: 9.378e-02, eta: 4 days, 2:03:28, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4398, loss_cls: 4.3844, loss: 4.3844 +2024-12-26 21:35:08,391 - pyskl - INFO - Epoch [25][400/3746] lr: 9.376e-02, eta: 4 days, 2:01:56, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4527, loss_cls: 4.3663, loss: 4.3663 +2024-12-26 21:36:19,970 - pyskl - INFO - Epoch [25][500/3746] lr: 9.375e-02, eta: 4 days, 2:00:24, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4461, loss_cls: 4.3506, loss: 4.3506 +2024-12-26 21:37:31,638 - pyskl - INFO - Epoch [25][600/3746] lr: 9.373e-02, eta: 4 days, 1:58:52, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4414, loss_cls: 4.4062, loss: 4.4062 +2024-12-26 21:38:43,071 - pyskl - INFO - Epoch [25][700/3746] lr: 9.372e-02, eta: 4 days, 1:57:20, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4528, loss_cls: 4.3581, loss: 4.3581 +2024-12-26 21:39:54,423 - pyskl - INFO - Epoch [25][800/3746] lr: 9.371e-02, eta: 4 days, 1:55:47, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4389, loss_cls: 4.3732, loss: 4.3732 +2024-12-26 21:41:05,639 - pyskl - INFO - Epoch [25][900/3746] lr: 9.369e-02, eta: 4 days, 1:54:14, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4516, loss_cls: 4.3460, loss: 4.3460 +2024-12-26 21:42:16,791 - pyskl - INFO - Epoch [25][1000/3746] lr: 9.368e-02, eta: 4 days, 1:52:40, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4355, loss_cls: 4.4241, loss: 4.4241 +2024-12-26 21:43:28,281 - pyskl - INFO - Epoch [25][1100/3746] lr: 9.367e-02, eta: 4 days, 1:51:08, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4427, loss_cls: 4.3879, loss: 4.3879 +2024-12-26 21:44:40,087 - pyskl - INFO - Epoch [25][1200/3746] lr: 9.365e-02, eta: 4 days, 1:49:37, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4389, loss_cls: 4.3818, loss: 4.3818 +2024-12-26 21:45:51,811 - pyskl - INFO - Epoch [25][1300/3746] lr: 9.364e-02, eta: 4 days, 1:48:07, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4506, loss_cls: 4.3767, loss: 4.3767 +2024-12-26 21:47:03,101 - pyskl - INFO - Epoch [25][1400/3746] lr: 9.363e-02, eta: 4 days, 1:46:34, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4486, loss_cls: 4.3469, loss: 4.3469 +2024-12-26 21:48:14,269 - pyskl - INFO - Epoch [25][1500/3746] lr: 9.361e-02, eta: 4 days, 1:45:00, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4319, loss_cls: 4.4184, loss: 4.4184 +2024-12-26 21:49:25,757 - pyskl - INFO - Epoch [25][1600/3746] lr: 9.360e-02, eta: 4 days, 1:43:29, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4398, loss_cls: 4.3987, loss: 4.3987 +2024-12-26 21:50:37,882 - pyskl - INFO - Epoch [25][1700/3746] lr: 9.358e-02, eta: 4 days, 1:42:00, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4378, loss_cls: 4.4069, loss: 4.4069 +2024-12-26 21:51:49,913 - pyskl - INFO - Epoch [25][1800/3746] lr: 9.357e-02, eta: 4 days, 1:40:31, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4377, loss_cls: 4.3610, loss: 4.3610 +2024-12-26 21:53:01,862 - pyskl - INFO - Epoch [25][1900/3746] lr: 9.356e-02, eta: 4 days, 1:39:02, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4386, loss_cls: 4.4075, loss: 4.4075 +2024-12-26 21:54:13,694 - pyskl - INFO - Epoch [25][2000/3746] lr: 9.354e-02, eta: 4 days, 1:37:32, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4453, loss_cls: 4.3918, loss: 4.3918 +2024-12-26 21:55:25,930 - pyskl - INFO - Epoch [25][2100/3746] lr: 9.353e-02, eta: 4 days, 1:36:04, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4439, loss_cls: 4.4024, loss: 4.4024 +2024-12-26 21:56:38,056 - pyskl - INFO - Epoch [25][2200/3746] lr: 9.352e-02, eta: 4 days, 1:34:36, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4409, loss_cls: 4.4047, loss: 4.4047 +2024-12-26 21:57:50,274 - pyskl - INFO - Epoch [25][2300/3746] lr: 9.350e-02, eta: 4 days, 1:33:08, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4462, loss_cls: 4.3786, loss: 4.3786 +2024-12-26 21:59:02,493 - pyskl - INFO - Epoch [25][2400/3746] lr: 9.349e-02, eta: 4 days, 1:31:40, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4383, loss_cls: 4.4308, loss: 4.4308 +2024-12-26 22:00:14,235 - pyskl - INFO - Epoch [25][2500/3746] lr: 9.347e-02, eta: 4 days, 1:30:10, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4355, loss_cls: 4.4189, loss: 4.4189 +2024-12-26 22:01:26,481 - pyskl - INFO - Epoch [25][2600/3746] lr: 9.346e-02, eta: 4 days, 1:28:43, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4469, loss_cls: 4.3973, loss: 4.3973 +2024-12-26 22:02:38,524 - pyskl - INFO - Epoch [25][2700/3746] lr: 9.345e-02, eta: 4 days, 1:27:14, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2030, top5_acc: 0.4303, loss_cls: 4.4272, loss: 4.4272 +2024-12-26 22:03:50,835 - pyskl - INFO - Epoch [25][2800/3746] lr: 9.343e-02, eta: 4 days, 1:25:47, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4472, loss_cls: 4.3764, loss: 4.3764 +2024-12-26 22:05:03,193 - pyskl - INFO - Epoch [25][2900/3746] lr: 9.342e-02, eta: 4 days, 1:24:20, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4458, loss_cls: 4.3718, loss: 4.3718 +2024-12-26 22:06:15,050 - pyskl - INFO - Epoch [25][3000/3746] lr: 9.341e-02, eta: 4 days, 1:22:51, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4444, loss_cls: 4.3655, loss: 4.3655 +2024-12-26 22:07:27,263 - pyskl - INFO - Epoch [25][3100/3746] lr: 9.339e-02, eta: 4 days, 1:21:23, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4394, loss_cls: 4.3839, loss: 4.3839 +2024-12-26 22:08:39,435 - pyskl - INFO - Epoch [25][3200/3746] lr: 9.338e-02, eta: 4 days, 1:19:55, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2028, top5_acc: 0.4403, loss_cls: 4.4052, loss: 4.4052 +2024-12-26 22:09:51,095 - pyskl - INFO - Epoch [25][3300/3746] lr: 9.336e-02, eta: 4 days, 1:18:25, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4473, loss_cls: 4.3619, loss: 4.3619 +2024-12-26 22:11:02,956 - pyskl - INFO - Epoch [25][3400/3746] lr: 9.335e-02, eta: 4 days, 1:16:56, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4316, loss_cls: 4.4513, loss: 4.4513 +2024-12-26 22:12:14,810 - pyskl - INFO - Epoch [25][3500/3746] lr: 9.334e-02, eta: 4 days, 1:15:27, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4489, loss_cls: 4.3748, loss: 4.3748 +2024-12-26 22:13:26,663 - pyskl - INFO - Epoch [25][3600/3746] lr: 9.332e-02, eta: 4 days, 1:13:58, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2114, top5_acc: 0.4423, loss_cls: 4.4021, loss: 4.4021 +2024-12-26 22:14:38,505 - pyskl - INFO - Epoch [25][3700/3746] lr: 9.331e-02, eta: 4 days, 1:12:28, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4389, loss_cls: 4.3736, loss: 4.3736 +2024-12-26 22:15:13,937 - pyskl - INFO - Saving checkpoint at 25 epochs +2024-12-26 22:17:13,131 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 22:17:13,844 - pyskl - INFO - +top1_acc 0.1459 +top5_acc 0.3457 +2024-12-26 22:17:13,844 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 22:17:13,915 - pyskl - INFO - +mean_acc 0.1456 +2024-12-26 22:17:13,943 - pyskl - INFO - Epoch(val) [25][309] top1_acc: 0.1459, top5_acc: 0.3457, mean_class_accuracy: 0.1456 +2024-12-26 22:20:50,370 - pyskl - INFO - Epoch [26][100/3746] lr: 9.329e-02, eta: 4 days, 1:19:35, time: 2.164, data_time: 1.449, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4508, loss_cls: 4.3376, loss: 4.3376 +2024-12-26 22:22:01,571 - pyskl - INFO - Epoch [26][200/3746] lr: 9.327e-02, eta: 4 days, 1:18:02, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4363, loss_cls: 4.3676, loss: 4.3676 +2024-12-26 22:23:12,757 - pyskl - INFO - Epoch [26][300/3746] lr: 9.326e-02, eta: 4 days, 1:16:29, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4611, loss_cls: 4.3329, loss: 4.3329 +2024-12-26 22:24:24,156 - pyskl - INFO - Epoch [26][400/3746] lr: 9.325e-02, eta: 4 days, 1:14:57, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4453, loss_cls: 4.3806, loss: 4.3806 +2024-12-26 22:25:35,664 - pyskl - INFO - Epoch [26][500/3746] lr: 9.323e-02, eta: 4 days, 1:13:26, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4461, loss_cls: 4.3437, loss: 4.3437 +2024-12-26 22:26:47,214 - pyskl - INFO - Epoch [26][600/3746] lr: 9.322e-02, eta: 4 days, 1:11:55, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4450, loss_cls: 4.3759, loss: 4.3759 +2024-12-26 22:27:58,662 - pyskl - INFO - Epoch [26][700/3746] lr: 9.320e-02, eta: 4 days, 1:10:23, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4442, loss_cls: 4.3770, loss: 4.3770 +2024-12-26 22:29:10,035 - pyskl - INFO - Epoch [26][800/3746] lr: 9.319e-02, eta: 4 days, 1:08:51, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4412, loss_cls: 4.3639, loss: 4.3639 +2024-12-26 22:30:21,456 - pyskl - INFO - Epoch [26][900/3746] lr: 9.318e-02, eta: 4 days, 1:07:20, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4492, loss_cls: 4.3692, loss: 4.3692 +2024-12-26 22:31:32,768 - pyskl - INFO - Epoch [26][1000/3746] lr: 9.316e-02, eta: 4 days, 1:05:48, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4427, loss_cls: 4.3708, loss: 4.3708 +2024-12-26 22:32:44,818 - pyskl - INFO - Epoch [26][1100/3746] lr: 9.315e-02, eta: 4 days, 1:04:19, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4417, loss_cls: 4.3881, loss: 4.3881 +2024-12-26 22:33:56,287 - pyskl - INFO - Epoch [26][1200/3746] lr: 9.313e-02, eta: 4 days, 1:02:48, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4467, loss_cls: 4.3578, loss: 4.3578 +2024-12-26 22:35:07,565 - pyskl - INFO - Epoch [26][1300/3746] lr: 9.312e-02, eta: 4 days, 1:01:16, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4358, loss_cls: 4.3992, loss: 4.3992 +2024-12-26 22:36:19,084 - pyskl - INFO - Epoch [26][1400/3746] lr: 9.310e-02, eta: 4 days, 0:59:45, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4483, loss_cls: 4.3632, loss: 4.3632 +2024-12-26 22:37:30,298 - pyskl - INFO - Epoch [26][1500/3746] lr: 9.309e-02, eta: 4 days, 0:58:12, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4495, loss_cls: 4.3603, loss: 4.3603 +2024-12-26 22:38:41,852 - pyskl - INFO - Epoch [26][1600/3746] lr: 9.308e-02, eta: 4 days, 0:56:42, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4369, loss_cls: 4.4053, loss: 4.4053 +2024-12-26 22:39:53,294 - pyskl - INFO - Epoch [26][1700/3746] lr: 9.306e-02, eta: 4 days, 0:55:11, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4605, loss_cls: 4.3244, loss: 4.3244 +2024-12-26 22:41:04,992 - pyskl - INFO - Epoch [26][1800/3746] lr: 9.305e-02, eta: 4 days, 0:53:41, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4486, loss_cls: 4.3572, loss: 4.3572 +2024-12-26 22:42:16,905 - pyskl - INFO - Epoch [26][1900/3746] lr: 9.303e-02, eta: 4 days, 0:52:12, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4439, loss_cls: 4.3725, loss: 4.3725 +2024-12-26 22:43:28,973 - pyskl - INFO - Epoch [26][2000/3746] lr: 9.302e-02, eta: 4 days, 0:50:44, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4366, loss_cls: 4.3936, loss: 4.3936 +2024-12-26 22:44:40,887 - pyskl - INFO - Epoch [26][2100/3746] lr: 9.300e-02, eta: 4 days, 0:49:15, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4320, loss_cls: 4.4236, loss: 4.4236 +2024-12-26 22:45:52,706 - pyskl - INFO - Epoch [26][2200/3746] lr: 9.299e-02, eta: 4 days, 0:47:46, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2155, top5_acc: 0.4409, loss_cls: 4.3577, loss: 4.3577 +2024-12-26 22:47:04,672 - pyskl - INFO - Epoch [26][2300/3746] lr: 9.298e-02, eta: 4 days, 0:46:18, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4348, loss_cls: 4.3913, loss: 4.3913 +2024-12-26 22:48:16,515 - pyskl - INFO - Epoch [26][2400/3746] lr: 9.296e-02, eta: 4 days, 0:44:49, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4245, loss_cls: 4.4334, loss: 4.4334 +2024-12-26 22:49:28,437 - pyskl - INFO - Epoch [26][2500/3746] lr: 9.295e-02, eta: 4 days, 0:43:20, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4506, loss_cls: 4.3899, loss: 4.3899 +2024-12-26 22:50:40,318 - pyskl - INFO - Epoch [26][2600/3746] lr: 9.293e-02, eta: 4 days, 0:41:52, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4416, loss_cls: 4.3850, loss: 4.3850 +2024-12-26 22:51:51,905 - pyskl - INFO - Epoch [26][2700/3746] lr: 9.292e-02, eta: 4 days, 0:40:21, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4461, loss_cls: 4.4103, loss: 4.4103 +2024-12-26 22:53:04,338 - pyskl - INFO - Epoch [26][2800/3746] lr: 9.290e-02, eta: 4 days, 0:38:55, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4458, loss_cls: 4.3729, loss: 4.3729 +2024-12-26 22:54:16,384 - pyskl - INFO - Epoch [26][2900/3746] lr: 9.289e-02, eta: 4 days, 0:37:28, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4416, loss_cls: 4.4095, loss: 4.4095 +2024-12-26 22:55:28,388 - pyskl - INFO - Epoch [26][3000/3746] lr: 9.288e-02, eta: 4 days, 0:36:00, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2028, top5_acc: 0.4348, loss_cls: 4.4083, loss: 4.4083 +2024-12-26 22:56:40,217 - pyskl - INFO - Epoch [26][3100/3746] lr: 9.286e-02, eta: 4 days, 0:34:31, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4483, loss_cls: 4.3952, loss: 4.3952 +2024-12-26 22:57:52,517 - pyskl - INFO - Epoch [26][3200/3746] lr: 9.285e-02, eta: 4 days, 0:33:04, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4386, loss_cls: 4.3959, loss: 4.3959 +2024-12-26 22:59:04,566 - pyskl - INFO - Epoch [26][3300/3746] lr: 9.283e-02, eta: 4 days, 0:31:37, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4528, loss_cls: 4.3446, loss: 4.3446 +2024-12-26 23:00:16,691 - pyskl - INFO - Epoch [26][3400/3746] lr: 9.282e-02, eta: 4 days, 0:30:10, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4384, loss_cls: 4.4304, loss: 4.4304 +2024-12-26 23:01:28,830 - pyskl - INFO - Epoch [26][3500/3746] lr: 9.280e-02, eta: 4 days, 0:28:42, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4309, loss_cls: 4.4079, loss: 4.4079 +2024-12-26 23:02:40,682 - pyskl - INFO - Epoch [26][3600/3746] lr: 9.279e-02, eta: 4 days, 0:27:14, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4464, loss_cls: 4.3720, loss: 4.3720 +2024-12-26 23:03:52,716 - pyskl - INFO - Epoch [26][3700/3746] lr: 9.278e-02, eta: 4 days, 0:25:46, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4433, loss_cls: 4.3956, loss: 4.3956 +2024-12-26 23:04:28,125 - pyskl - INFO - Saving checkpoint at 26 epochs +2024-12-26 23:06:27,169 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 23:06:28,033 - pyskl - INFO - +top1_acc 0.1383 +top5_acc 0.3328 +2024-12-26 23:06:28,034 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 23:06:28,092 - pyskl - INFO - +mean_acc 0.1381 +2024-12-26 23:06:28,114 - pyskl - INFO - Epoch(val) [26][309] top1_acc: 0.1383, top5_acc: 0.3328, mean_class_accuracy: 0.1381 +2024-12-26 23:10:14,489 - pyskl - INFO - Epoch [27][100/3746] lr: 9.275e-02, eta: 4 days, 0:33:16, time: 2.264, data_time: 1.543, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4609, loss_cls: 4.3140, loss: 4.3140 +2024-12-26 23:11:26,635 - pyskl - INFO - Epoch [27][200/3746] lr: 9.274e-02, eta: 4 days, 0:31:48, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4425, loss_cls: 4.3788, loss: 4.3788 +2024-12-26 23:12:38,828 - pyskl - INFO - Epoch [27][300/3746] lr: 9.272e-02, eta: 4 days, 0:30:21, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4527, loss_cls: 4.3664, loss: 4.3664 +2024-12-26 23:13:50,605 - pyskl - INFO - Epoch [27][400/3746] lr: 9.271e-02, eta: 4 days, 0:28:51, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4503, loss_cls: 4.3500, loss: 4.3500 +2024-12-26 23:15:02,219 - pyskl - INFO - Epoch [27][500/3746] lr: 9.270e-02, eta: 4 days, 0:27:21, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4452, loss_cls: 4.3736, loss: 4.3736 +2024-12-26 23:16:14,061 - pyskl - INFO - Epoch [27][600/3746] lr: 9.268e-02, eta: 4 days, 0:25:52, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4444, loss_cls: 4.3661, loss: 4.3661 +2024-12-26 23:17:25,463 - pyskl - INFO - Epoch [27][700/3746] lr: 9.267e-02, eta: 4 days, 0:24:21, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4364, loss_cls: 4.3935, loss: 4.3935 +2024-12-26 23:18:37,408 - pyskl - INFO - Epoch [27][800/3746] lr: 9.265e-02, eta: 4 days, 0:22:52, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4406, loss_cls: 4.3862, loss: 4.3862 +2024-12-26 23:19:49,500 - pyskl - INFO - Epoch [27][900/3746] lr: 9.264e-02, eta: 4 days, 0:21:25, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4420, loss_cls: 4.3862, loss: 4.3862 +2024-12-26 23:21:01,256 - pyskl - INFO - Epoch [27][1000/3746] lr: 9.262e-02, eta: 4 days, 0:19:55, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4378, loss_cls: 4.3962, loss: 4.3962 +2024-12-26 23:22:12,986 - pyskl - INFO - Epoch [27][1100/3746] lr: 9.261e-02, eta: 4 days, 0:18:26, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4355, loss_cls: 4.3972, loss: 4.3972 +2024-12-26 23:23:24,829 - pyskl - INFO - Epoch [27][1200/3746] lr: 9.259e-02, eta: 4 days, 0:16:57, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4439, loss_cls: 4.3913, loss: 4.3913 +2024-12-26 23:24:36,875 - pyskl - INFO - Epoch [27][1300/3746] lr: 9.258e-02, eta: 4 days, 0:15:29, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4450, loss_cls: 4.3683, loss: 4.3683 +2024-12-26 23:25:49,002 - pyskl - INFO - Epoch [27][1400/3746] lr: 9.256e-02, eta: 4 days, 0:14:02, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4542, loss_cls: 4.3637, loss: 4.3637 +2024-12-26 23:27:01,287 - pyskl - INFO - Epoch [27][1500/3746] lr: 9.255e-02, eta: 4 days, 0:12:35, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4403, loss_cls: 4.3774, loss: 4.3774 +2024-12-26 23:28:13,413 - pyskl - INFO - Epoch [27][1600/3746] lr: 9.253e-02, eta: 4 days, 0:11:08, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4448, loss_cls: 4.4062, loss: 4.4062 +2024-12-26 23:29:25,351 - pyskl - INFO - Epoch [27][1700/3746] lr: 9.252e-02, eta: 4 days, 0:09:40, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2078, top5_acc: 0.4400, loss_cls: 4.4059, loss: 4.4059 +2024-12-26 23:30:37,283 - pyskl - INFO - Epoch [27][1800/3746] lr: 9.251e-02, eta: 4 days, 0:08:12, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4494, loss_cls: 4.3149, loss: 4.3149 +2024-12-26 23:31:49,186 - pyskl - INFO - Epoch [27][1900/3746] lr: 9.249e-02, eta: 4 days, 0:06:43, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4436, loss_cls: 4.4046, loss: 4.4046 +2024-12-26 23:33:01,417 - pyskl - INFO - Epoch [27][2000/3746] lr: 9.248e-02, eta: 4 days, 0:05:17, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4545, loss_cls: 4.3379, loss: 4.3379 +2024-12-26 23:34:13,497 - pyskl - INFO - Epoch [27][2100/3746] lr: 9.246e-02, eta: 4 days, 0:03:49, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2078, top5_acc: 0.4367, loss_cls: 4.4077, loss: 4.4077 +2024-12-26 23:35:25,477 - pyskl - INFO - Epoch [27][2200/3746] lr: 9.245e-02, eta: 4 days, 0:02:21, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4433, loss_cls: 4.3755, loss: 4.3755 +2024-12-26 23:36:37,686 - pyskl - INFO - Epoch [27][2300/3746] lr: 9.243e-02, eta: 4 days, 0:00:55, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4430, loss_cls: 4.4024, loss: 4.4024 +2024-12-26 23:37:49,589 - pyskl - INFO - Epoch [27][2400/3746] lr: 9.242e-02, eta: 3 days, 23:59:27, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4350, loss_cls: 4.3986, loss: 4.3986 +2024-12-26 23:39:01,327 - pyskl - INFO - Epoch [27][2500/3746] lr: 9.240e-02, eta: 3 days, 23:57:58, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4408, loss_cls: 4.4091, loss: 4.4091 +2024-12-26 23:40:13,172 - pyskl - INFO - Epoch [27][2600/3746] lr: 9.239e-02, eta: 3 days, 23:56:29, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4436, loss_cls: 4.3817, loss: 4.3817 +2024-12-26 23:41:25,116 - pyskl - INFO - Epoch [27][2700/3746] lr: 9.237e-02, eta: 3 days, 23:55:02, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2194, top5_acc: 0.4564, loss_cls: 4.3260, loss: 4.3260 +2024-12-26 23:42:37,239 - pyskl - INFO - Epoch [27][2800/3746] lr: 9.236e-02, eta: 3 days, 23:53:35, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4447, loss_cls: 4.3844, loss: 4.3844 +2024-12-26 23:43:49,048 - pyskl - INFO - Epoch [27][2900/3746] lr: 9.234e-02, eta: 3 days, 23:52:06, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4455, loss_cls: 4.3635, loss: 4.3635 +2024-12-26 23:45:01,014 - pyskl - INFO - Epoch [27][3000/3746] lr: 9.233e-02, eta: 3 days, 23:50:38, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4439, loss_cls: 4.3959, loss: 4.3959 +2024-12-26 23:46:12,974 - pyskl - INFO - Epoch [27][3100/3746] lr: 9.231e-02, eta: 3 days, 23:49:11, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4464, loss_cls: 4.3549, loss: 4.3549 +2024-12-26 23:47:24,895 - pyskl - INFO - Epoch [27][3200/3746] lr: 9.230e-02, eta: 3 days, 23:47:43, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4400, loss_cls: 4.3897, loss: 4.3897 +2024-12-26 23:48:36,770 - pyskl - INFO - Epoch [27][3300/3746] lr: 9.228e-02, eta: 3 days, 23:46:15, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4575, loss_cls: 4.3635, loss: 4.3635 +2024-12-26 23:49:49,385 - pyskl - INFO - Epoch [27][3400/3746] lr: 9.227e-02, eta: 3 days, 23:44:51, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2078, top5_acc: 0.4341, loss_cls: 4.4002, loss: 4.4002 +2024-12-26 23:51:01,193 - pyskl - INFO - Epoch [27][3500/3746] lr: 9.225e-02, eta: 3 days, 23:43:22, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4402, loss_cls: 4.3745, loss: 4.3745 +2024-12-26 23:52:13,276 - pyskl - INFO - Epoch [27][3600/3746] lr: 9.224e-02, eta: 3 days, 23:41:55, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2033, top5_acc: 0.4306, loss_cls: 4.4278, loss: 4.4278 +2024-12-26 23:53:24,895 - pyskl - INFO - Epoch [27][3700/3746] lr: 9.222e-02, eta: 3 days, 23:40:26, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4414, loss_cls: 4.4005, loss: 4.4005 +2024-12-26 23:54:00,338 - pyskl - INFO - Saving checkpoint at 27 epochs +2024-12-26 23:55:59,708 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 23:56:00,436 - pyskl - INFO - +top1_acc 0.1347 +top5_acc 0.3325 +2024-12-26 23:56:00,437 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 23:56:00,477 - pyskl - INFO - +mean_acc 0.1347 +2024-12-26 23:56:00,489 - pyskl - INFO - Epoch(val) [27][309] top1_acc: 0.1347, top5_acc: 0.3325, mean_class_accuracy: 0.1347 +2024-12-26 23:59:41,803 - pyskl - INFO - Epoch [28][100/3746] lr: 9.220e-02, eta: 3 days, 23:47:08, time: 2.213, data_time: 1.494, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4597, loss_cls: 4.2846, loss: 4.2846 +2024-12-27 00:00:53,091 - pyskl - INFO - Epoch [28][200/3746] lr: 9.219e-02, eta: 3 days, 23:45:37, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4430, loss_cls: 4.3626, loss: 4.3626 +2024-12-27 00:02:04,925 - pyskl - INFO - Epoch [28][300/3746] lr: 9.217e-02, eta: 3 days, 23:44:08, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4480, loss_cls: 4.3491, loss: 4.3491 +2024-12-27 00:03:16,461 - pyskl - INFO - Epoch [28][400/3746] lr: 9.216e-02, eta: 3 days, 23:42:38, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4503, loss_cls: 4.3497, loss: 4.3497 +2024-12-27 00:04:28,012 - pyskl - INFO - Epoch [28][500/3746] lr: 9.214e-02, eta: 3 days, 23:41:08, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4434, loss_cls: 4.4041, loss: 4.4041 +2024-12-27 00:05:39,531 - pyskl - INFO - Epoch [28][600/3746] lr: 9.213e-02, eta: 3 days, 23:39:38, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4430, loss_cls: 4.3754, loss: 4.3754 +2024-12-27 00:06:50,960 - pyskl - INFO - Epoch [28][700/3746] lr: 9.211e-02, eta: 3 days, 23:38:08, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4411, loss_cls: 4.3803, loss: 4.3803 +2024-12-27 00:08:02,450 - pyskl - INFO - Epoch [28][800/3746] lr: 9.210e-02, eta: 3 days, 23:36:38, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4448, loss_cls: 4.3619, loss: 4.3619 +2024-12-27 00:09:13,572 - pyskl - INFO - Epoch [28][900/3746] lr: 9.208e-02, eta: 3 days, 23:35:07, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4475, loss_cls: 4.3584, loss: 4.3584 +2024-12-27 00:10:25,058 - pyskl - INFO - Epoch [28][1000/3746] lr: 9.207e-02, eta: 3 days, 23:33:37, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4348, loss_cls: 4.4298, loss: 4.4298 +2024-12-27 00:11:36,425 - pyskl - INFO - Epoch [28][1100/3746] lr: 9.205e-02, eta: 3 days, 23:32:06, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4420, loss_cls: 4.3626, loss: 4.3626 +2024-12-27 00:12:47,559 - pyskl - INFO - Epoch [28][1200/3746] lr: 9.204e-02, eta: 3 days, 23:30:35, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4517, loss_cls: 4.3244, loss: 4.3244 +2024-12-27 00:13:59,478 - pyskl - INFO - Epoch [28][1300/3746] lr: 9.202e-02, eta: 3 days, 23:29:07, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4400, loss_cls: 4.3743, loss: 4.3743 +2024-12-27 00:15:10,949 - pyskl - INFO - Epoch [28][1400/3746] lr: 9.201e-02, eta: 3 days, 23:27:37, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4459, loss_cls: 4.3900, loss: 4.3900 +2024-12-27 00:16:22,259 - pyskl - INFO - Epoch [28][1500/3746] lr: 9.199e-02, eta: 3 days, 23:26:07, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4439, loss_cls: 4.3920, loss: 4.3920 +2024-12-27 00:17:33,523 - pyskl - INFO - Epoch [28][1600/3746] lr: 9.198e-02, eta: 3 days, 23:24:36, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4523, loss_cls: 4.3565, loss: 4.3565 +2024-12-27 00:18:45,361 - pyskl - INFO - Epoch [28][1700/3746] lr: 9.196e-02, eta: 3 days, 23:23:08, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4494, loss_cls: 4.3528, loss: 4.3528 +2024-12-27 00:19:57,169 - pyskl - INFO - Epoch [28][1800/3746] lr: 9.194e-02, eta: 3 days, 23:21:40, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4487, loss_cls: 4.3451, loss: 4.3451 +2024-12-27 00:21:08,934 - pyskl - INFO - Epoch [28][1900/3746] lr: 9.193e-02, eta: 3 days, 23:20:12, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4534, loss_cls: 4.3397, loss: 4.3397 +2024-12-27 00:22:21,211 - pyskl - INFO - Epoch [28][2000/3746] lr: 9.191e-02, eta: 3 days, 23:18:46, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4472, loss_cls: 4.3565, loss: 4.3565 +2024-12-27 00:23:32,936 - pyskl - INFO - Epoch [28][2100/3746] lr: 9.190e-02, eta: 3 days, 23:17:17, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4450, loss_cls: 4.3869, loss: 4.3869 +2024-12-27 00:24:44,910 - pyskl - INFO - Epoch [28][2200/3746] lr: 9.188e-02, eta: 3 days, 23:15:50, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4430, loss_cls: 4.3921, loss: 4.3921 +2024-12-27 00:25:56,764 - pyskl - INFO - Epoch [28][2300/3746] lr: 9.187e-02, eta: 3 days, 23:14:22, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4437, loss_cls: 4.3990, loss: 4.3990 +2024-12-27 00:27:08,691 - pyskl - INFO - Epoch [28][2400/3746] lr: 9.185e-02, eta: 3 days, 23:12:55, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4483, loss_cls: 4.3669, loss: 4.3669 +2024-12-27 00:28:20,823 - pyskl - INFO - Epoch [28][2500/3746] lr: 9.184e-02, eta: 3 days, 23:11:28, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2108, top5_acc: 0.4481, loss_cls: 4.3618, loss: 4.3618 +2024-12-27 00:29:32,781 - pyskl - INFO - Epoch [28][2600/3746] lr: 9.182e-02, eta: 3 days, 23:10:01, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4281, loss_cls: 4.4231, loss: 4.4231 +2024-12-27 00:30:44,621 - pyskl - INFO - Epoch [28][2700/3746] lr: 9.181e-02, eta: 3 days, 23:08:33, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4439, loss_cls: 4.3640, loss: 4.3640 +2024-12-27 00:31:56,448 - pyskl - INFO - Epoch [28][2800/3746] lr: 9.179e-02, eta: 3 days, 23:07:05, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4339, loss_cls: 4.4032, loss: 4.4032 +2024-12-27 00:33:08,339 - pyskl - INFO - Epoch [28][2900/3746] lr: 9.178e-02, eta: 3 days, 23:05:38, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4361, loss_cls: 4.4016, loss: 4.4016 +2024-12-27 00:34:20,471 - pyskl - INFO - Epoch [28][3000/3746] lr: 9.176e-02, eta: 3 days, 23:04:12, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4417, loss_cls: 4.3738, loss: 4.3738 +2024-12-27 00:35:32,721 - pyskl - INFO - Epoch [28][3100/3746] lr: 9.175e-02, eta: 3 days, 23:02:46, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4466, loss_cls: 4.3635, loss: 4.3635 +2024-12-27 00:36:44,495 - pyskl - INFO - Epoch [28][3200/3746] lr: 9.173e-02, eta: 3 days, 23:01:18, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2014, top5_acc: 0.4400, loss_cls: 4.3948, loss: 4.3948 +2024-12-27 00:37:56,444 - pyskl - INFO - Epoch [28][3300/3746] lr: 9.172e-02, eta: 3 days, 22:59:51, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4567, loss_cls: 4.3372, loss: 4.3372 +2024-12-27 00:39:08,541 - pyskl - INFO - Epoch [28][3400/3746] lr: 9.170e-02, eta: 3 days, 22:58:24, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4417, loss_cls: 4.3968, loss: 4.3968 +2024-12-27 00:40:20,632 - pyskl - INFO - Epoch [28][3500/3746] lr: 9.168e-02, eta: 3 days, 22:56:58, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4570, loss_cls: 4.3345, loss: 4.3345 +2024-12-27 00:41:32,681 - pyskl - INFO - Epoch [28][3600/3746] lr: 9.167e-02, eta: 3 days, 22:55:31, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2131, top5_acc: 0.4498, loss_cls: 4.3671, loss: 4.3671 +2024-12-27 00:42:44,570 - pyskl - INFO - Epoch [28][3700/3746] lr: 9.165e-02, eta: 3 days, 22:54:04, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4417, loss_cls: 4.4105, loss: 4.4105 +2024-12-27 00:43:19,689 - pyskl - INFO - Saving checkpoint at 28 epochs +2024-12-27 00:45:18,730 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 00:45:19,530 - pyskl - INFO - +top1_acc 0.1504 +top5_acc 0.3357 +2024-12-27 00:45:19,530 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 00:45:19,581 - pyskl - INFO - +mean_acc 0.1501 +2024-12-27 00:45:19,612 - pyskl - INFO - Epoch(val) [28][309] top1_acc: 0.1504, top5_acc: 0.3357, mean_class_accuracy: 0.1501 +2024-12-27 00:48:54,492 - pyskl - INFO - Epoch [29][100/3746] lr: 9.163e-02, eta: 3 days, 22:59:55, time: 2.149, data_time: 1.433, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4566, loss_cls: 4.3346, loss: 4.3346 +2024-12-27 00:50:06,150 - pyskl - INFO - Epoch [29][200/3746] lr: 9.162e-02, eta: 3 days, 22:58:26, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4508, loss_cls: 4.3435, loss: 4.3435 +2024-12-27 00:51:18,020 - pyskl - INFO - Epoch [29][300/3746] lr: 9.160e-02, eta: 3 days, 22:56:59, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4448, loss_cls: 4.3891, loss: 4.3891 +2024-12-27 00:52:29,356 - pyskl - INFO - Epoch [29][400/3746] lr: 9.158e-02, eta: 3 days, 22:55:29, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4425, loss_cls: 4.3494, loss: 4.3494 +2024-12-27 00:53:40,804 - pyskl - INFO - Epoch [29][500/3746] lr: 9.157e-02, eta: 3 days, 22:53:59, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4480, loss_cls: 4.3541, loss: 4.3541 +2024-12-27 00:54:52,024 - pyskl - INFO - Epoch [29][600/3746] lr: 9.155e-02, eta: 3 days, 22:52:29, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4450, loss_cls: 4.3778, loss: 4.3778 +2024-12-27 00:56:03,499 - pyskl - INFO - Epoch [29][700/3746] lr: 9.154e-02, eta: 3 days, 22:50:59, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2036, top5_acc: 0.4516, loss_cls: 4.3767, loss: 4.3767 +2024-12-27 00:57:15,673 - pyskl - INFO - Epoch [29][800/3746] lr: 9.152e-02, eta: 3 days, 22:49:33, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2131, top5_acc: 0.4491, loss_cls: 4.3401, loss: 4.3401 +2024-12-27 00:58:27,475 - pyskl - INFO - Epoch [29][900/3746] lr: 9.151e-02, eta: 3 days, 22:48:05, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4372, loss_cls: 4.4018, loss: 4.4018 +2024-12-27 00:59:39,168 - pyskl - INFO - Epoch [29][1000/3746] lr: 9.149e-02, eta: 3 days, 22:46:37, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4525, loss_cls: 4.3259, loss: 4.3259 +2024-12-27 01:00:50,860 - pyskl - INFO - Epoch [29][1100/3746] lr: 9.148e-02, eta: 3 days, 22:45:08, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4520, loss_cls: 4.3514, loss: 4.3514 +2024-12-27 01:02:02,525 - pyskl - INFO - Epoch [29][1200/3746] lr: 9.146e-02, eta: 3 days, 22:43:40, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4442, loss_cls: 4.3804, loss: 4.3804 +2024-12-27 01:03:13,827 - pyskl - INFO - Epoch [29][1300/3746] lr: 9.144e-02, eta: 3 days, 22:42:10, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4523, loss_cls: 4.3409, loss: 4.3409 +2024-12-27 01:04:25,574 - pyskl - INFO - Epoch [29][1400/3746] lr: 9.143e-02, eta: 3 days, 22:40:42, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4342, loss_cls: 4.3638, loss: 4.3638 +2024-12-27 01:05:36,731 - pyskl - INFO - Epoch [29][1500/3746] lr: 9.141e-02, eta: 3 days, 22:39:12, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4408, loss_cls: 4.4080, loss: 4.4080 +2024-12-27 01:06:48,338 - pyskl - INFO - Epoch [29][1600/3746] lr: 9.140e-02, eta: 3 days, 22:37:43, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4484, loss_cls: 4.3567, loss: 4.3567 +2024-12-27 01:08:00,053 - pyskl - INFO - Epoch [29][1700/3746] lr: 9.138e-02, eta: 3 days, 22:36:15, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4341, loss_cls: 4.4311, loss: 4.4311 +2024-12-27 01:09:12,035 - pyskl - INFO - Epoch [29][1800/3746] lr: 9.137e-02, eta: 3 days, 22:34:48, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4567, loss_cls: 4.3105, loss: 4.3105 +2024-12-27 01:10:24,284 - pyskl - INFO - Epoch [29][1900/3746] lr: 9.135e-02, eta: 3 days, 22:33:23, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4555, loss_cls: 4.2959, loss: 4.2959 +2024-12-27 01:11:36,313 - pyskl - INFO - Epoch [29][2000/3746] lr: 9.133e-02, eta: 3 days, 22:31:56, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4592, loss_cls: 4.3117, loss: 4.3117 +2024-12-27 01:12:48,188 - pyskl - INFO - Epoch [29][2100/3746] lr: 9.132e-02, eta: 3 days, 22:30:29, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4417, loss_cls: 4.3694, loss: 4.3694 +2024-12-27 01:13:59,991 - pyskl - INFO - Epoch [29][2200/3746] lr: 9.130e-02, eta: 3 days, 22:29:02, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4461, loss_cls: 4.3704, loss: 4.3704 +2024-12-27 01:15:11,907 - pyskl - INFO - Epoch [29][2300/3746] lr: 9.129e-02, eta: 3 days, 22:27:35, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4392, loss_cls: 4.3854, loss: 4.3854 +2024-12-27 01:16:23,709 - pyskl - INFO - Epoch [29][2400/3746] lr: 9.127e-02, eta: 3 days, 22:26:07, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2114, top5_acc: 0.4411, loss_cls: 4.3792, loss: 4.3792 +2024-12-27 01:17:35,533 - pyskl - INFO - Epoch [29][2500/3746] lr: 9.126e-02, eta: 3 days, 22:24:40, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4550, loss_cls: 4.3502, loss: 4.3502 +2024-12-27 01:18:47,354 - pyskl - INFO - Epoch [29][2600/3746] lr: 9.124e-02, eta: 3 days, 22:23:13, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4406, loss_cls: 4.3767, loss: 4.3767 +2024-12-27 01:19:59,286 - pyskl - INFO - Epoch [29][2700/3746] lr: 9.122e-02, eta: 3 days, 22:21:46, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4417, loss_cls: 4.3816, loss: 4.3816 +2024-12-27 01:21:11,725 - pyskl - INFO - Epoch [29][2800/3746] lr: 9.121e-02, eta: 3 days, 22:20:21, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4384, loss_cls: 4.4016, loss: 4.4016 +2024-12-27 01:22:23,933 - pyskl - INFO - Epoch [29][2900/3746] lr: 9.119e-02, eta: 3 days, 22:18:56, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4464, loss_cls: 4.3814, loss: 4.3814 +2024-12-27 01:23:35,770 - pyskl - INFO - Epoch [29][3000/3746] lr: 9.118e-02, eta: 3 days, 22:17:28, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4456, loss_cls: 4.3655, loss: 4.3655 +2024-12-27 01:24:47,715 - pyskl - INFO - Epoch [29][3100/3746] lr: 9.116e-02, eta: 3 days, 22:16:02, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4420, loss_cls: 4.3498, loss: 4.3498 +2024-12-27 01:25:59,578 - pyskl - INFO - Epoch [29][3200/3746] lr: 9.114e-02, eta: 3 days, 22:14:35, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4381, loss_cls: 4.3837, loss: 4.3837 +2024-12-27 01:27:11,466 - pyskl - INFO - Epoch [29][3300/3746] lr: 9.113e-02, eta: 3 days, 22:13:08, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4512, loss_cls: 4.3755, loss: 4.3755 +2024-12-27 01:28:23,415 - pyskl - INFO - Epoch [29][3400/3746] lr: 9.111e-02, eta: 3 days, 22:11:41, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4505, loss_cls: 4.3621, loss: 4.3621 +2024-12-27 01:29:35,333 - pyskl - INFO - Epoch [29][3500/3746] lr: 9.110e-02, eta: 3 days, 22:10:15, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4531, loss_cls: 4.3432, loss: 4.3432 +2024-12-27 01:30:47,355 - pyskl - INFO - Epoch [29][3600/3746] lr: 9.108e-02, eta: 3 days, 22:08:49, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4461, loss_cls: 4.3680, loss: 4.3680 +2024-12-27 01:31:59,146 - pyskl - INFO - Epoch [29][3700/3746] lr: 9.106e-02, eta: 3 days, 22:07:22, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4428, loss_cls: 4.3633, loss: 4.3633 +2024-12-27 01:32:34,343 - pyskl - INFO - Saving checkpoint at 29 epochs +2024-12-27 01:34:32,236 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 01:34:33,110 - pyskl - INFO - +top1_acc 0.0865 +top5_acc 0.2493 +2024-12-27 01:34:33,110 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 01:34:33,154 - pyskl - INFO - +mean_acc 0.0865 +2024-12-27 01:34:33,165 - pyskl - INFO - Epoch(val) [29][309] top1_acc: 0.0865, top5_acc: 0.2493, mean_class_accuracy: 0.0865 +2024-12-27 01:38:22,951 - pyskl - INFO - Epoch [30][100/3746] lr: 9.104e-02, eta: 3 days, 22:13:55, time: 2.298, data_time: 1.470, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4486, loss_cls: 4.3694, loss: 4.3694 +2024-12-27 01:39:45,487 - pyskl - INFO - Epoch [30][200/3746] lr: 9.103e-02, eta: 3 days, 22:13:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4503, loss_cls: 4.3376, loss: 4.3376 +2024-12-27 01:41:07,975 - pyskl - INFO - Epoch [30][300/3746] lr: 9.101e-02, eta: 3 days, 22:12:29, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4461, loss_cls: 4.3675, loss: 4.3675 +2024-12-27 01:42:30,718 - pyskl - INFO - Epoch [30][400/3746] lr: 9.099e-02, eta: 3 days, 22:11:47, time: 0.827, data_time: 0.001, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4516, loss_cls: 4.3422, loss: 4.3422 +2024-12-27 01:43:52,941 - pyskl - INFO - Epoch [30][500/3746] lr: 9.098e-02, eta: 3 days, 22:11:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4487, loss_cls: 4.3551, loss: 4.3551 +2024-12-27 01:45:16,113 - pyskl - INFO - Epoch [30][600/3746] lr: 9.096e-02, eta: 3 days, 22:10:22, time: 0.832, data_time: 0.001, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4470, loss_cls: 4.3511, loss: 4.3511 +2024-12-27 01:46:38,889 - pyskl - INFO - Epoch [30][700/3746] lr: 9.095e-02, eta: 3 days, 22:09:40, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4491, loss_cls: 4.3714, loss: 4.3714 +2024-12-27 01:48:02,413 - pyskl - INFO - Epoch [30][800/3746] lr: 9.093e-02, eta: 3 days, 22:09:00, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4397, loss_cls: 4.4018, loss: 4.4018 +2024-12-27 01:49:25,722 - pyskl - INFO - Epoch [30][900/3746] lr: 9.091e-02, eta: 3 days, 22:08:20, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4445, loss_cls: 4.3566, loss: 4.3566 +2024-12-27 01:50:49,564 - pyskl - INFO - Epoch [30][1000/3746] lr: 9.090e-02, eta: 3 days, 22:07:42, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4422, loss_cls: 4.3984, loss: 4.3984 +2024-12-27 01:52:13,038 - pyskl - INFO - Epoch [30][1100/3746] lr: 9.088e-02, eta: 3 days, 22:07:02, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4609, loss_cls: 4.3148, loss: 4.3148 +2024-12-27 01:53:36,450 - pyskl - INFO - Epoch [30][1200/3746] lr: 9.087e-02, eta: 3 days, 22:06:22, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4492, loss_cls: 4.3679, loss: 4.3679 +2024-12-27 01:54:59,732 - pyskl - INFO - Epoch [30][1300/3746] lr: 9.085e-02, eta: 3 days, 22:05:42, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4552, loss_cls: 4.3249, loss: 4.3249 +2024-12-27 01:56:22,994 - pyskl - INFO - Epoch [30][1400/3746] lr: 9.083e-02, eta: 3 days, 22:05:01, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4469, loss_cls: 4.3723, loss: 4.3723 +2024-12-27 01:57:46,860 - pyskl - INFO - Epoch [30][1500/3746] lr: 9.082e-02, eta: 3 days, 22:04:22, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4533, loss_cls: 4.3166, loss: 4.3166 +2024-12-27 01:59:10,100 - pyskl - INFO - Epoch [30][1600/3746] lr: 9.080e-02, eta: 3 days, 22:03:41, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4578, loss_cls: 4.3111, loss: 4.3111 +2024-12-27 02:00:33,796 - pyskl - INFO - Epoch [30][1700/3746] lr: 9.078e-02, eta: 3 days, 22:03:02, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4552, loss_cls: 4.3499, loss: 4.3499 +2024-12-27 02:01:57,617 - pyskl - INFO - Epoch [30][1800/3746] lr: 9.077e-02, eta: 3 days, 22:02:23, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4400, loss_cls: 4.3541, loss: 4.3541 +2024-12-27 02:03:21,393 - pyskl - INFO - Epoch [30][1900/3746] lr: 9.075e-02, eta: 3 days, 22:01:44, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4589, loss_cls: 4.3127, loss: 4.3127 +2024-12-27 02:04:45,609 - pyskl - INFO - Epoch [30][2000/3746] lr: 9.074e-02, eta: 3 days, 22:01:07, time: 0.842, data_time: 0.001, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4420, loss_cls: 4.3929, loss: 4.3929 +2024-12-27 02:06:09,711 - pyskl - INFO - Epoch [30][2100/3746] lr: 9.072e-02, eta: 3 days, 22:00:29, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4389, loss_cls: 4.4086, loss: 4.4086 +2024-12-27 02:07:34,302 - pyskl - INFO - Epoch [30][2200/3746] lr: 9.070e-02, eta: 3 days, 21:59:53, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4478, loss_cls: 4.3635, loss: 4.3635 +2024-12-27 02:08:57,963 - pyskl - INFO - Epoch [30][2300/3746] lr: 9.069e-02, eta: 3 days, 21:59:13, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4545, loss_cls: 4.3341, loss: 4.3341 +2024-12-27 02:10:22,095 - pyskl - INFO - Epoch [30][2400/3746] lr: 9.067e-02, eta: 3 days, 21:58:35, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4458, loss_cls: 4.3425, loss: 4.3425 +2024-12-27 02:11:46,425 - pyskl - INFO - Epoch [30][2500/3746] lr: 9.065e-02, eta: 3 days, 21:57:57, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4487, loss_cls: 4.3424, loss: 4.3424 +2024-12-27 02:13:10,042 - pyskl - INFO - Epoch [30][2600/3746] lr: 9.064e-02, eta: 3 days, 21:57:17, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4400, loss_cls: 4.3884, loss: 4.3884 +2024-12-27 02:14:33,414 - pyskl - INFO - Epoch [30][2700/3746] lr: 9.062e-02, eta: 3 days, 21:56:36, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4570, loss_cls: 4.3126, loss: 4.3126 +2024-12-27 02:15:57,416 - pyskl - INFO - Epoch [30][2800/3746] lr: 9.061e-02, eta: 3 days, 21:55:57, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4455, loss_cls: 4.3654, loss: 4.3654 +2024-12-27 02:17:21,231 - pyskl - INFO - Epoch [30][2900/3746] lr: 9.059e-02, eta: 3 days, 21:55:17, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4522, loss_cls: 4.3282, loss: 4.3282 +2024-12-27 02:18:44,942 - pyskl - INFO - Epoch [30][3000/3746] lr: 9.057e-02, eta: 3 days, 21:54:37, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4380, loss_cls: 4.4198, loss: 4.4198 +2024-12-27 02:20:08,575 - pyskl - INFO - Epoch [30][3100/3746] lr: 9.056e-02, eta: 3 days, 21:53:56, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4467, loss_cls: 4.3852, loss: 4.3852 +2024-12-27 02:21:32,683 - pyskl - INFO - Epoch [30][3200/3746] lr: 9.054e-02, eta: 3 days, 21:53:17, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4411, loss_cls: 4.3559, loss: 4.3559 +2024-12-27 02:22:56,542 - pyskl - INFO - Epoch [30][3300/3746] lr: 9.052e-02, eta: 3 days, 21:52:37, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4422, loss_cls: 4.3823, loss: 4.3823 +2024-12-27 02:24:20,509 - pyskl - INFO - Epoch [30][3400/3746] lr: 9.051e-02, eta: 3 days, 21:51:58, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4498, loss_cls: 4.3445, loss: 4.3445 +2024-12-27 02:25:44,626 - pyskl - INFO - Epoch [30][3500/3746] lr: 9.049e-02, eta: 3 days, 21:51:19, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4473, loss_cls: 4.3723, loss: 4.3723 +2024-12-27 02:27:08,520 - pyskl - INFO - Epoch [30][3600/3746] lr: 9.047e-02, eta: 3 days, 21:50:39, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4500, loss_cls: 4.3497, loss: 4.3497 +2024-12-27 02:28:31,400 - pyskl - INFO - Epoch [30][3700/3746] lr: 9.046e-02, eta: 3 days, 21:49:55, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4442, loss_cls: 4.3849, loss: 4.3849 +2024-12-27 02:29:11,404 - pyskl - INFO - Saving checkpoint at 30 epochs +2024-12-27 02:31:10,493 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 02:31:11,198 - pyskl - INFO - +top1_acc 0.1362 +top5_acc 0.3183 +2024-12-27 02:31:11,198 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 02:31:11,234 - pyskl - INFO - +mean_acc 0.1362 +2024-12-27 02:31:11,244 - pyskl - INFO - Epoch(val) [30][309] top1_acc: 0.1362, top5_acc: 0.3183, mean_class_accuracy: 0.1362 +2024-12-27 02:35:19,594 - pyskl - INFO - Epoch [31][100/3746] lr: 9.043e-02, eta: 3 days, 21:57:19, time: 2.483, data_time: 1.454, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4608, loss_cls: 4.5648, loss: 4.5648 +2024-12-27 02:36:45,610 - pyskl - INFO - Epoch [31][200/3746] lr: 9.042e-02, eta: 3 days, 21:56:47, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4695, loss_cls: 4.5218, loss: 4.5218 +2024-12-27 02:38:11,747 - pyskl - INFO - Epoch [31][300/3746] lr: 9.040e-02, eta: 3 days, 21:56:15, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4539, loss_cls: 4.5766, loss: 4.5766 +2024-12-27 02:39:37,166 - pyskl - INFO - Epoch [31][400/3746] lr: 9.039e-02, eta: 3 days, 21:55:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2131, top5_acc: 0.4500, loss_cls: 4.5714, loss: 4.5714 +2024-12-27 02:41:02,172 - pyskl - INFO - Epoch [31][500/3746] lr: 9.037e-02, eta: 3 days, 21:55:03, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4530, loss_cls: 4.5797, loss: 4.5797 +2024-12-27 02:42:27,246 - pyskl - INFO - Epoch [31][600/3746] lr: 9.035e-02, eta: 3 days, 21:54:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4495, loss_cls: 4.5832, loss: 4.5832 +2024-12-27 02:43:52,418 - pyskl - INFO - Epoch [31][700/3746] lr: 9.034e-02, eta: 3 days, 21:53:51, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4473, loss_cls: 4.6209, loss: 4.6209 +2024-12-27 02:45:18,030 - pyskl - INFO - Epoch [31][800/3746] lr: 9.032e-02, eta: 3 days, 21:53:16, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4527, loss_cls: 4.5625, loss: 4.5625 +2024-12-27 02:46:43,192 - pyskl - INFO - Epoch [31][900/3746] lr: 9.030e-02, eta: 3 days, 21:52:40, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4356, loss_cls: 4.6524, loss: 4.6524 +2024-12-27 02:48:08,034 - pyskl - INFO - Epoch [31][1000/3746] lr: 9.029e-02, eta: 3 days, 21:52:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4641, loss_cls: 4.5434, loss: 4.5434 +2024-12-27 02:49:32,847 - pyskl - INFO - Epoch [31][1100/3746] lr: 9.027e-02, eta: 3 days, 21:51:24, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4530, loss_cls: 4.5738, loss: 4.5738 +2024-12-27 02:50:58,416 - pyskl - INFO - Epoch [31][1200/3746] lr: 9.025e-02, eta: 3 days, 21:50:49, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4519, loss_cls: 4.5972, loss: 4.5972 +2024-12-27 02:52:24,394 - pyskl - INFO - Epoch [31][1300/3746] lr: 9.024e-02, eta: 3 days, 21:50:16, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4419, loss_cls: 4.6258, loss: 4.6258 +2024-12-27 02:53:50,090 - pyskl - INFO - Epoch [31][1400/3746] lr: 9.022e-02, eta: 3 days, 21:49:41, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4522, loss_cls: 4.5598, loss: 4.5598 +2024-12-27 02:55:15,683 - pyskl - INFO - Epoch [31][1500/3746] lr: 9.020e-02, eta: 3 days, 21:49:06, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4530, loss_cls: 4.5665, loss: 4.5665 +2024-12-27 02:56:41,978 - pyskl - INFO - Epoch [31][1600/3746] lr: 9.019e-02, eta: 3 days, 21:48:33, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4536, loss_cls: 4.6271, loss: 4.6271 +2024-12-27 02:58:07,825 - pyskl - INFO - Epoch [31][1700/3746] lr: 9.017e-02, eta: 3 days, 21:47:59, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4416, loss_cls: 4.6156, loss: 4.6156 +2024-12-27 02:59:33,378 - pyskl - INFO - Epoch [31][1800/3746] lr: 9.015e-02, eta: 3 days, 21:47:23, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4480, loss_cls: 4.5818, loss: 4.5818 +2024-12-27 03:00:59,274 - pyskl - INFO - Epoch [31][1900/3746] lr: 9.014e-02, eta: 3 days, 21:46:49, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4650, loss_cls: 4.5386, loss: 4.5386 +2024-12-27 03:02:24,673 - pyskl - INFO - Epoch [31][2000/3746] lr: 9.012e-02, eta: 3 days, 21:46:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4528, loss_cls: 4.5474, loss: 4.5474 +2024-12-27 03:03:50,366 - pyskl - INFO - Epoch [31][2100/3746] lr: 9.010e-02, eta: 3 days, 21:45:37, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4528, loss_cls: 4.5750, loss: 4.5750 +2024-12-27 03:05:16,032 - pyskl - INFO - Epoch [31][2200/3746] lr: 9.009e-02, eta: 3 days, 21:45:02, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4462, loss_cls: 4.5933, loss: 4.5933 +2024-12-27 03:06:41,685 - pyskl - INFO - Epoch [31][2300/3746] lr: 9.007e-02, eta: 3 days, 21:44:26, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4494, loss_cls: 4.6021, loss: 4.6021 +2024-12-27 03:08:07,437 - pyskl - INFO - Epoch [31][2400/3746] lr: 9.005e-02, eta: 3 days, 21:43:50, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4405, loss_cls: 4.6256, loss: 4.6256 +2024-12-27 03:09:33,202 - pyskl - INFO - Epoch [31][2500/3746] lr: 9.004e-02, eta: 3 days, 21:43:15, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4461, loss_cls: 4.5906, loss: 4.5906 +2024-12-27 03:10:58,837 - pyskl - INFO - Epoch [31][2600/3746] lr: 9.002e-02, eta: 3 days, 21:42:39, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4533, loss_cls: 4.5878, loss: 4.5878 +2024-12-27 03:12:24,767 - pyskl - INFO - Epoch [31][2700/3746] lr: 9.000e-02, eta: 3 days, 21:42:04, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4514, loss_cls: 4.5790, loss: 4.5790 +2024-12-27 03:13:51,012 - pyskl - INFO - Epoch [31][2800/3746] lr: 8.999e-02, eta: 3 days, 21:41:30, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4423, loss_cls: 4.6338, loss: 4.6338 +2024-12-27 03:15:17,234 - pyskl - INFO - Epoch [31][2900/3746] lr: 8.997e-02, eta: 3 days, 21:40:56, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2037, top5_acc: 0.4323, loss_cls: 4.6682, loss: 4.6682 +2024-12-27 03:16:42,921 - pyskl - INFO - Epoch [31][3000/3746] lr: 8.995e-02, eta: 3 days, 21:40:20, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2092, top5_acc: 0.4448, loss_cls: 4.6185, loss: 4.6185 +2024-12-27 03:18:08,361 - pyskl - INFO - Epoch [31][3100/3746] lr: 8.994e-02, eta: 3 days, 21:39:43, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4562, loss_cls: 4.5647, loss: 4.5647 +2024-12-27 03:19:34,569 - pyskl - INFO - Epoch [31][3200/3746] lr: 8.992e-02, eta: 3 days, 21:39:09, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4512, loss_cls: 4.5911, loss: 4.5911 +2024-12-27 03:21:00,374 - pyskl - INFO - Epoch [31][3300/3746] lr: 8.990e-02, eta: 3 days, 21:38:33, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4566, loss_cls: 4.5510, loss: 4.5510 +2024-12-27 03:22:26,672 - pyskl - INFO - Epoch [31][3400/3746] lr: 8.989e-02, eta: 3 days, 21:37:59, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4434, loss_cls: 4.6358, loss: 4.6358 +2024-12-27 03:23:52,521 - pyskl - INFO - Epoch [31][3500/3746] lr: 8.987e-02, eta: 3 days, 21:37:23, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4481, loss_cls: 4.6354, loss: 4.6354 +2024-12-27 03:25:18,429 - pyskl - INFO - Epoch [31][3600/3746] lr: 8.985e-02, eta: 3 days, 21:36:47, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4561, loss_cls: 4.5748, loss: 4.5748 +2024-12-27 03:26:43,615 - pyskl - INFO - Epoch [31][3700/3746] lr: 8.983e-02, eta: 3 days, 21:36:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4630, loss_cls: 4.5251, loss: 4.5251 +2024-12-27 03:27:25,146 - pyskl - INFO - Saving checkpoint at 31 epochs +2024-12-27 03:29:26,967 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 03:29:27,758 - pyskl - INFO - +top1_acc 0.1638 +top5_acc 0.3642 +2024-12-27 03:29:27,758 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 03:29:27,798 - pyskl - INFO - +mean_acc 0.1636 +2024-12-27 03:29:27,803 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_21.pth was removed +2024-12-27 03:29:28,075 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_31.pth. +2024-12-27 03:29:28,076 - pyskl - INFO - Best top1_acc is 0.1638 at 31 epoch. +2024-12-27 03:29:28,090 - pyskl - INFO - Epoch(val) [31][309] top1_acc: 0.1638, top5_acc: 0.3642, mean_class_accuracy: 0.1636 +2024-12-27 03:33:49,562 - pyskl - INFO - Epoch [32][100/3746] lr: 8.981e-02, eta: 3 days, 21:43:57, time: 2.615, data_time: 1.576, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4534, loss_cls: 4.5752, loss: 4.5752 +2024-12-27 03:35:15,270 - pyskl - INFO - Epoch [32][200/3746] lr: 8.979e-02, eta: 3 days, 21:43:20, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4567, loss_cls: 4.5623, loss: 4.5623 +2024-12-27 03:36:41,457 - pyskl - INFO - Epoch [32][300/3746] lr: 8.978e-02, eta: 3 days, 21:42:44, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4502, loss_cls: 4.5899, loss: 4.5899 +2024-12-27 03:38:07,107 - pyskl - INFO - Epoch [32][400/3746] lr: 8.976e-02, eta: 3 days, 21:42:06, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4506, loss_cls: 4.5900, loss: 4.5900 +2024-12-27 03:39:32,431 - pyskl - INFO - Epoch [32][500/3746] lr: 8.974e-02, eta: 3 days, 21:41:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4511, loss_cls: 4.5944, loss: 4.5944 +2024-12-27 03:40:57,988 - pyskl - INFO - Epoch [32][600/3746] lr: 8.973e-02, eta: 3 days, 21:40:49, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4583, loss_cls: 4.5480, loss: 4.5480 +2024-12-27 03:42:23,756 - pyskl - INFO - Epoch [32][700/3746] lr: 8.971e-02, eta: 3 days, 21:40:11, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4575, loss_cls: 4.5487, loss: 4.5487 +2024-12-27 03:43:49,599 - pyskl - INFO - Epoch [32][800/3746] lr: 8.969e-02, eta: 3 days, 21:39:34, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4437, loss_cls: 4.6253, loss: 4.6253 +2024-12-27 03:45:15,279 - pyskl - INFO - Epoch [32][900/3746] lr: 8.967e-02, eta: 3 days, 21:38:56, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4505, loss_cls: 4.5978, loss: 4.5978 +2024-12-27 03:46:41,079 - pyskl - INFO - Epoch [32][1000/3746] lr: 8.966e-02, eta: 3 days, 21:38:18, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4492, loss_cls: 4.6102, loss: 4.6102 +2024-12-27 03:48:06,700 - pyskl - INFO - Epoch [32][1100/3746] lr: 8.964e-02, eta: 3 days, 21:37:39, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4498, loss_cls: 4.5516, loss: 4.5516 +2024-12-27 03:49:32,363 - pyskl - INFO - Epoch [32][1200/3746] lr: 8.962e-02, eta: 3 days, 21:37:01, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4494, loss_cls: 4.5742, loss: 4.5742 +2024-12-27 03:50:58,768 - pyskl - INFO - Epoch [32][1300/3746] lr: 8.961e-02, eta: 3 days, 21:36:25, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4608, loss_cls: 4.5777, loss: 4.5777 +2024-12-27 03:52:24,727 - pyskl - INFO - Epoch [32][1400/3746] lr: 8.959e-02, eta: 3 days, 21:35:48, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4611, loss_cls: 4.5504, loss: 4.5504 +2024-12-27 03:53:50,639 - pyskl - INFO - Epoch [32][1500/3746] lr: 8.957e-02, eta: 3 days, 21:35:10, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4531, loss_cls: 4.5734, loss: 4.5734 +2024-12-27 03:55:16,966 - pyskl - INFO - Epoch [32][1600/3746] lr: 8.955e-02, eta: 3 days, 21:34:34, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4402, loss_cls: 4.6598, loss: 4.6598 +2024-12-27 03:56:43,207 - pyskl - INFO - Epoch [32][1700/3746] lr: 8.954e-02, eta: 3 days, 21:33:57, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2114, top5_acc: 0.4439, loss_cls: 4.6144, loss: 4.6144 +2024-12-27 03:58:09,179 - pyskl - INFO - Epoch [32][1800/3746] lr: 8.952e-02, eta: 3 days, 21:33:19, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4530, loss_cls: 4.5772, loss: 4.5772 +2024-12-27 03:59:35,447 - pyskl - INFO - Epoch [32][1900/3746] lr: 8.950e-02, eta: 3 days, 21:32:43, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4525, loss_cls: 4.5557, loss: 4.5557 +2024-12-27 04:01:01,406 - pyskl - INFO - Epoch [32][2000/3746] lr: 8.949e-02, eta: 3 days, 21:32:05, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4548, loss_cls: 4.5429, loss: 4.5429 +2024-12-27 04:02:27,756 - pyskl - INFO - Epoch [32][2100/3746] lr: 8.947e-02, eta: 3 days, 21:31:28, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4492, loss_cls: 4.5710, loss: 4.5710 +2024-12-27 04:03:53,567 - pyskl - INFO - Epoch [32][2200/3746] lr: 8.945e-02, eta: 3 days, 21:30:49, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4477, loss_cls: 4.5736, loss: 4.5736 +2024-12-27 04:05:19,949 - pyskl - INFO - Epoch [32][2300/3746] lr: 8.943e-02, eta: 3 days, 21:30:13, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4537, loss_cls: 4.5601, loss: 4.5601 +2024-12-27 04:06:46,218 - pyskl - INFO - Epoch [32][2400/3746] lr: 8.942e-02, eta: 3 days, 21:29:36, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4439, loss_cls: 4.6373, loss: 4.6373 +2024-12-27 04:08:12,427 - pyskl - INFO - Epoch [32][2500/3746] lr: 8.940e-02, eta: 3 days, 21:28:58, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4400, loss_cls: 4.6269, loss: 4.6269 +2024-12-27 04:09:38,881 - pyskl - INFO - Epoch [32][2600/3746] lr: 8.938e-02, eta: 3 days, 21:28:22, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4420, loss_cls: 4.6074, loss: 4.6074 +2024-12-27 04:11:04,559 - pyskl - INFO - Epoch [32][2700/3746] lr: 8.937e-02, eta: 3 days, 21:27:42, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4411, loss_cls: 4.6294, loss: 4.6294 +2024-12-27 04:12:31,166 - pyskl - INFO - Epoch [32][2800/3746] lr: 8.935e-02, eta: 3 days, 21:27:06, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4520, loss_cls: 4.5552, loss: 4.5552 +2024-12-27 04:13:57,090 - pyskl - INFO - Epoch [32][2900/3746] lr: 8.933e-02, eta: 3 days, 21:26:27, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4478, loss_cls: 4.5985, loss: 4.5985 +2024-12-27 04:15:23,501 - pyskl - INFO - Epoch [32][3000/3746] lr: 8.931e-02, eta: 3 days, 21:25:50, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4600, loss_cls: 4.5545, loss: 4.5545 +2024-12-27 04:16:49,731 - pyskl - INFO - Epoch [32][3100/3746] lr: 8.930e-02, eta: 3 days, 21:25:12, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4445, loss_cls: 4.6018, loss: 4.6018 +2024-12-27 04:18:16,089 - pyskl - INFO - Epoch [32][3200/3746] lr: 8.928e-02, eta: 3 days, 21:24:35, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4517, loss_cls: 4.5778, loss: 4.5778 +2024-12-27 04:19:42,430 - pyskl - INFO - Epoch [32][3300/3746] lr: 8.926e-02, eta: 3 days, 21:23:57, time: 0.863, data_time: 0.001, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4533, loss_cls: 4.5736, loss: 4.5736 +2024-12-27 04:21:08,268 - pyskl - INFO - Epoch [32][3400/3746] lr: 8.924e-02, eta: 3 days, 21:23:17, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4500, loss_cls: 4.5944, loss: 4.5944 +2024-12-27 04:22:34,060 - pyskl - INFO - Epoch [32][3500/3746] lr: 8.923e-02, eta: 3 days, 21:22:38, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4573, loss_cls: 4.5283, loss: 4.5283 +2024-12-27 04:23:59,538 - pyskl - INFO - Epoch [32][3600/3746] lr: 8.921e-02, eta: 3 days, 21:21:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4652, loss_cls: 4.5191, loss: 4.5191 +2024-12-27 04:25:24,664 - pyskl - INFO - Epoch [32][3700/3746] lr: 8.919e-02, eta: 3 days, 21:21:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4536, loss_cls: 4.6168, loss: 4.6168 +2024-12-27 04:26:06,091 - pyskl - INFO - Saving checkpoint at 32 epochs +2024-12-27 04:28:06,893 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 04:28:07,647 - pyskl - INFO - +top1_acc 0.1707 +top5_acc 0.3761 +2024-12-27 04:28:07,647 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 04:28:07,699 - pyskl - INFO - +mean_acc 0.1706 +2024-12-27 04:28:07,709 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_31.pth was removed +2024-12-27 04:28:08,032 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_32.pth. +2024-12-27 04:28:08,035 - pyskl - INFO - Best top1_acc is 0.1707 at 32 epoch. +2024-12-27 04:28:08,051 - pyskl - INFO - Epoch(val) [32][309] top1_acc: 0.1707, top5_acc: 0.3761, mean_class_accuracy: 0.1706 +2024-12-27 04:32:33,357 - pyskl - INFO - Epoch [33][100/3746] lr: 8.917e-02, eta: 3 days, 21:28:52, time: 2.653, data_time: 1.608, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4609, loss_cls: 4.5279, loss: 4.5279 +2024-12-27 04:33:59,502 - pyskl - INFO - Epoch [33][200/3746] lr: 8.915e-02, eta: 3 days, 21:28:12, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4664, loss_cls: 4.5350, loss: 4.5350 +2024-12-27 04:35:25,358 - pyskl - INFO - Epoch [33][300/3746] lr: 8.913e-02, eta: 3 days, 21:27:32, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4505, loss_cls: 4.5792, loss: 4.5792 +2024-12-27 04:36:51,228 - pyskl - INFO - Epoch [33][400/3746] lr: 8.912e-02, eta: 3 days, 21:26:51, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4566, loss_cls: 4.5872, loss: 4.5872 +2024-12-27 04:38:16,487 - pyskl - INFO - Epoch [33][500/3746] lr: 8.910e-02, eta: 3 days, 21:26:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4517, loss_cls: 4.5598, loss: 4.5598 +2024-12-27 04:39:41,781 - pyskl - INFO - Epoch [33][600/3746] lr: 8.908e-02, eta: 3 days, 21:25:25, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4612, loss_cls: 4.5388, loss: 4.5388 +2024-12-27 04:41:07,741 - pyskl - INFO - Epoch [33][700/3746] lr: 8.906e-02, eta: 3 days, 21:24:45, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4445, loss_cls: 4.5911, loss: 4.5911 +2024-12-27 04:42:34,144 - pyskl - INFO - Epoch [33][800/3746] lr: 8.905e-02, eta: 3 days, 21:24:06, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4519, loss_cls: 4.5587, loss: 4.5587 +2024-12-27 04:44:01,021 - pyskl - INFO - Epoch [33][900/3746] lr: 8.903e-02, eta: 3 days, 21:23:29, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4567, loss_cls: 4.5686, loss: 4.5686 +2024-12-27 04:45:27,076 - pyskl - INFO - Epoch [33][1000/3746] lr: 8.901e-02, eta: 3 days, 21:22:49, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4455, loss_cls: 4.6055, loss: 4.6055 +2024-12-27 04:46:53,959 - pyskl - INFO - Epoch [33][1100/3746] lr: 8.899e-02, eta: 3 days, 21:22:11, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4573, loss_cls: 4.5512, loss: 4.5512 +2024-12-27 04:48:20,563 - pyskl - INFO - Epoch [33][1200/3746] lr: 8.898e-02, eta: 3 days, 21:21:33, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4544, loss_cls: 4.5640, loss: 4.5640 +2024-12-27 04:49:47,145 - pyskl - INFO - Epoch [33][1300/3746] lr: 8.896e-02, eta: 3 days, 21:20:54, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4456, loss_cls: 4.5758, loss: 4.5758 +2024-12-27 04:51:13,621 - pyskl - INFO - Epoch [33][1400/3746] lr: 8.894e-02, eta: 3 days, 21:20:15, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4522, loss_cls: 4.5745, loss: 4.5745 +2024-12-27 04:52:39,750 - pyskl - INFO - Epoch [33][1500/3746] lr: 8.892e-02, eta: 3 days, 21:19:35, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4572, loss_cls: 4.5538, loss: 4.5538 +2024-12-27 04:54:05,802 - pyskl - INFO - Epoch [33][1600/3746] lr: 8.891e-02, eta: 3 days, 21:18:54, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4519, loss_cls: 4.5738, loss: 4.5738 +2024-12-27 04:55:31,904 - pyskl - INFO - Epoch [33][1700/3746] lr: 8.889e-02, eta: 3 days, 21:18:13, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4473, loss_cls: 4.6051, loss: 4.6051 +2024-12-27 04:56:58,436 - pyskl - INFO - Epoch [33][1800/3746] lr: 8.887e-02, eta: 3 days, 21:17:34, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4573, loss_cls: 4.5541, loss: 4.5541 +2024-12-27 04:58:25,241 - pyskl - INFO - Epoch [33][1900/3746] lr: 8.885e-02, eta: 3 days, 21:16:56, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4452, loss_cls: 4.6262, loss: 4.6262 +2024-12-27 04:59:51,787 - pyskl - INFO - Epoch [33][2000/3746] lr: 8.884e-02, eta: 3 days, 21:16:16, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4570, loss_cls: 4.5701, loss: 4.5701 +2024-12-27 05:01:18,301 - pyskl - INFO - Epoch [33][2100/3746] lr: 8.882e-02, eta: 3 days, 21:15:37, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4503, loss_cls: 4.5787, loss: 4.5787 +2024-12-27 05:02:44,966 - pyskl - INFO - Epoch [33][2200/3746] lr: 8.880e-02, eta: 3 days, 21:14:58, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4502, loss_cls: 4.6090, loss: 4.6090 +2024-12-27 05:04:11,174 - pyskl - INFO - Epoch [33][2300/3746] lr: 8.878e-02, eta: 3 days, 21:14:17, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4564, loss_cls: 4.5739, loss: 4.5739 +2024-12-27 05:05:37,155 - pyskl - INFO - Epoch [33][2400/3746] lr: 8.876e-02, eta: 3 days, 21:13:35, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4591, loss_cls: 4.5666, loss: 4.5666 +2024-12-27 05:07:03,235 - pyskl - INFO - Epoch [33][2500/3746] lr: 8.875e-02, eta: 3 days, 21:12:54, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4466, loss_cls: 4.5848, loss: 4.5848 +2024-12-27 05:08:29,199 - pyskl - INFO - Epoch [33][2600/3746] lr: 8.873e-02, eta: 3 days, 21:12:12, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4486, loss_cls: 4.5961, loss: 4.5961 +2024-12-27 05:09:55,234 - pyskl - INFO - Epoch [33][2700/3746] lr: 8.871e-02, eta: 3 days, 21:11:30, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4478, loss_cls: 4.6256, loss: 4.6256 +2024-12-27 05:11:21,909 - pyskl - INFO - Epoch [33][2800/3746] lr: 8.869e-02, eta: 3 days, 21:10:51, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.2131, top5_acc: 0.4528, loss_cls: 4.5756, loss: 4.5756 +2024-12-27 05:12:48,663 - pyskl - INFO - Epoch [33][2900/3746] lr: 8.868e-02, eta: 3 days, 21:10:12, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4545, loss_cls: 4.5603, loss: 4.5603 +2024-12-27 05:14:14,795 - pyskl - INFO - Epoch [33][3000/3746] lr: 8.866e-02, eta: 3 days, 21:09:30, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4562, loss_cls: 4.5587, loss: 4.5587 +2024-12-27 05:15:40,818 - pyskl - INFO - Epoch [33][3100/3746] lr: 8.864e-02, eta: 3 days, 21:08:48, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4572, loss_cls: 4.5756, loss: 4.5756 +2024-12-27 05:17:07,454 - pyskl - INFO - Epoch [33][3200/3746] lr: 8.862e-02, eta: 3 days, 21:08:08, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4541, loss_cls: 4.5568, loss: 4.5568 +2024-12-27 05:18:33,852 - pyskl - INFO - Epoch [33][3300/3746] lr: 8.861e-02, eta: 3 days, 21:07:27, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4553, loss_cls: 4.5525, loss: 4.5525 +2024-12-27 05:20:00,265 - pyskl - INFO - Epoch [33][3400/3746] lr: 8.859e-02, eta: 3 days, 21:06:47, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4553, loss_cls: 4.5739, loss: 4.5739 +2024-12-27 05:21:25,609 - pyskl - INFO - Epoch [33][3500/3746] lr: 8.857e-02, eta: 3 days, 21:06:02, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4606, loss_cls: 4.5539, loss: 4.5539 +2024-12-27 05:22:50,494 - pyskl - INFO - Epoch [33][3600/3746] lr: 8.855e-02, eta: 3 days, 21:05:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4331, loss_cls: 4.6392, loss: 4.6392 +2024-12-27 05:24:16,170 - pyskl - INFO - Epoch [33][3700/3746] lr: 8.853e-02, eta: 3 days, 21:04:32, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4503, loss_cls: 4.5815, loss: 4.5815 +2024-12-27 05:24:57,577 - pyskl - INFO - Saving checkpoint at 33 epochs +2024-12-27 05:26:57,542 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 05:26:58,299 - pyskl - INFO - +top1_acc 0.1460 +top5_acc 0.3419 +2024-12-27 05:26:58,299 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 05:26:58,353 - pyskl - INFO - +mean_acc 0.1458 +2024-12-27 05:26:58,367 - pyskl - INFO - Epoch(val) [33][309] top1_acc: 0.1460, top5_acc: 0.3419, mean_class_accuracy: 0.1458 +2024-12-27 05:31:21,567 - pyskl - INFO - Epoch [34][100/3746] lr: 8.851e-02, eta: 3 days, 21:11:37, time: 2.632, data_time: 1.593, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4595, loss_cls: 4.5270, loss: 4.5270 +2024-12-27 05:32:47,543 - pyskl - INFO - Epoch [34][200/3746] lr: 8.849e-02, eta: 3 days, 21:10:54, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4455, loss_cls: 4.5584, loss: 4.5584 +2024-12-27 05:34:13,454 - pyskl - INFO - Epoch [34][300/3746] lr: 8.847e-02, eta: 3 days, 21:10:10, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4595, loss_cls: 4.5595, loss: 4.5595 +2024-12-27 05:35:39,186 - pyskl - INFO - Epoch [34][400/3746] lr: 8.845e-02, eta: 3 days, 21:09:26, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4558, loss_cls: 4.5321, loss: 4.5321 +2024-12-27 05:37:04,992 - pyskl - INFO - Epoch [34][500/3746] lr: 8.844e-02, eta: 3 days, 21:08:42, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4542, loss_cls: 4.5496, loss: 4.5496 +2024-12-27 05:38:30,055 - pyskl - INFO - Epoch [34][600/3746] lr: 8.842e-02, eta: 3 days, 21:07:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4517, loss_cls: 4.5530, loss: 4.5530 +2024-12-27 05:39:55,896 - pyskl - INFO - Epoch [34][700/3746] lr: 8.840e-02, eta: 3 days, 21:07:11, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2131, top5_acc: 0.4500, loss_cls: 4.6038, loss: 4.6038 +2024-12-27 05:41:22,010 - pyskl - INFO - Epoch [34][800/3746] lr: 8.838e-02, eta: 3 days, 21:06:28, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4492, loss_cls: 4.5827, loss: 4.5827 +2024-12-27 05:42:47,725 - pyskl - INFO - Epoch [34][900/3746] lr: 8.836e-02, eta: 3 days, 21:05:43, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4577, loss_cls: 4.5401, loss: 4.5401 +2024-12-27 05:44:13,805 - pyskl - INFO - Epoch [34][1000/3746] lr: 8.835e-02, eta: 3 days, 21:05:00, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4434, loss_cls: 4.6083, loss: 4.6083 +2024-12-27 05:45:40,107 - pyskl - INFO - Epoch [34][1100/3746] lr: 8.833e-02, eta: 3 days, 21:04:17, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4509, loss_cls: 4.5891, loss: 4.5891 +2024-12-27 05:47:06,093 - pyskl - INFO - Epoch [34][1200/3746] lr: 8.831e-02, eta: 3 days, 21:03:33, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4498, loss_cls: 4.5757, loss: 4.5757 +2024-12-27 05:48:32,171 - pyskl - INFO - Epoch [34][1300/3746] lr: 8.829e-02, eta: 3 days, 21:02:49, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4633, loss_cls: 4.5376, loss: 4.5376 +2024-12-27 05:49:58,574 - pyskl - INFO - Epoch [34][1400/3746] lr: 8.828e-02, eta: 3 days, 21:02:07, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4508, loss_cls: 4.6146, loss: 4.6146 +2024-12-27 05:51:25,176 - pyskl - INFO - Epoch [34][1500/3746] lr: 8.826e-02, eta: 3 days, 21:01:25, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4580, loss_cls: 4.5649, loss: 4.5649 +2024-12-27 05:52:51,321 - pyskl - INFO - Epoch [34][1600/3746] lr: 8.824e-02, eta: 3 days, 21:00:41, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4492, loss_cls: 4.5882, loss: 4.5882 +2024-12-27 05:54:17,804 - pyskl - INFO - Epoch [34][1700/3746] lr: 8.822e-02, eta: 3 days, 20:59:59, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4583, loss_cls: 4.5541, loss: 4.5541 +2024-12-27 05:55:43,886 - pyskl - INFO - Epoch [34][1800/3746] lr: 8.820e-02, eta: 3 days, 20:59:15, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4597, loss_cls: 4.5384, loss: 4.5384 +2024-12-27 05:57:10,024 - pyskl - INFO - Epoch [34][1900/3746] lr: 8.819e-02, eta: 3 days, 20:58:31, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4495, loss_cls: 4.5826, loss: 4.5826 +2024-12-27 05:58:36,404 - pyskl - INFO - Epoch [34][2000/3746] lr: 8.817e-02, eta: 3 days, 20:57:48, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4542, loss_cls: 4.5800, loss: 4.5800 +2024-12-27 06:00:02,322 - pyskl - INFO - Epoch [34][2100/3746] lr: 8.815e-02, eta: 3 days, 20:57:03, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4494, loss_cls: 4.5864, loss: 4.5864 +2024-12-27 06:01:29,124 - pyskl - INFO - Epoch [34][2200/3746] lr: 8.813e-02, eta: 3 days, 20:56:21, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4434, loss_cls: 4.6276, loss: 4.6276 +2024-12-27 06:02:55,443 - pyskl - INFO - Epoch [34][2300/3746] lr: 8.811e-02, eta: 3 days, 20:55:38, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2194, top5_acc: 0.4458, loss_cls: 4.5926, loss: 4.5926 +2024-12-27 06:04:21,994 - pyskl - INFO - Epoch [34][2400/3746] lr: 8.809e-02, eta: 3 days, 20:54:55, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4491, loss_cls: 4.5532, loss: 4.5532 +2024-12-27 06:05:48,793 - pyskl - INFO - Epoch [34][2500/3746] lr: 8.808e-02, eta: 3 days, 20:54:13, time: 0.868, data_time: 0.001, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4547, loss_cls: 4.5435, loss: 4.5435 +2024-12-27 06:07:15,064 - pyskl - INFO - Epoch [34][2600/3746] lr: 8.806e-02, eta: 3 days, 20:53:29, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4514, loss_cls: 4.5952, loss: 4.5952 +2024-12-27 06:08:41,397 - pyskl - INFO - Epoch [34][2700/3746] lr: 8.804e-02, eta: 3 days, 20:52:45, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4552, loss_cls: 4.5831, loss: 4.5831 +2024-12-27 06:10:07,468 - pyskl - INFO - Epoch [34][2800/3746] lr: 8.802e-02, eta: 3 days, 20:52:01, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4361, loss_cls: 4.6142, loss: 4.6142 +2024-12-27 06:11:33,805 - pyskl - INFO - Epoch [34][2900/3746] lr: 8.800e-02, eta: 3 days, 20:51:17, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4553, loss_cls: 4.5494, loss: 4.5494 +2024-12-27 06:13:00,283 - pyskl - INFO - Epoch [34][3000/3746] lr: 8.799e-02, eta: 3 days, 20:50:33, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4566, loss_cls: 4.5859, loss: 4.5859 +2024-12-27 06:14:26,437 - pyskl - INFO - Epoch [34][3100/3746] lr: 8.797e-02, eta: 3 days, 20:49:49, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4495, loss_cls: 4.5780, loss: 4.5780 +2024-12-27 06:15:52,894 - pyskl - INFO - Epoch [34][3200/3746] lr: 8.795e-02, eta: 3 days, 20:49:05, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4466, loss_cls: 4.6016, loss: 4.6016 +2024-12-27 06:17:19,266 - pyskl - INFO - Epoch [34][3300/3746] lr: 8.793e-02, eta: 3 days, 20:48:21, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4481, loss_cls: 4.5915, loss: 4.5915 +2024-12-27 06:18:44,385 - pyskl - INFO - Epoch [34][3400/3746] lr: 8.791e-02, eta: 3 days, 20:47:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4591, loss_cls: 4.5443, loss: 4.5443 +2024-12-27 06:20:09,330 - pyskl - INFO - Epoch [34][3500/3746] lr: 8.789e-02, eta: 3 days, 20:46:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4662, loss_cls: 4.5457, loss: 4.5457 +2024-12-27 06:21:34,924 - pyskl - INFO - Epoch [34][3600/3746] lr: 8.788e-02, eta: 3 days, 20:45:57, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4509, loss_cls: 4.5909, loss: 4.5909 +2024-12-27 06:23:00,320 - pyskl - INFO - Epoch [34][3700/3746] lr: 8.786e-02, eta: 3 days, 20:45:09, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4562, loss_cls: 4.5589, loss: 4.5589 +2024-12-27 06:23:41,716 - pyskl - INFO - Saving checkpoint at 34 epochs +2024-12-27 06:25:42,105 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 06:25:42,849 - pyskl - INFO - +top1_acc 0.1745 +top5_acc 0.3873 +2024-12-27 06:25:42,850 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 06:25:42,902 - pyskl - INFO - +mean_acc 0.1745 +2024-12-27 06:25:42,910 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_32.pth was removed +2024-12-27 06:25:43,338 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_34.pth. +2024-12-27 06:25:43,340 - pyskl - INFO - Best top1_acc is 0.1745 at 34 epoch. +2024-12-27 06:25:43,366 - pyskl - INFO - Epoch(val) [34][309] top1_acc: 0.1745, top5_acc: 0.3873, mean_class_accuracy: 0.1745 +2024-12-27 06:30:10,451 - pyskl - INFO - Epoch [35][100/3746] lr: 8.783e-02, eta: 3 days, 20:52:05, time: 2.671, data_time: 1.618, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4566, loss_cls: 4.5376, loss: 4.5376 +2024-12-27 06:31:37,142 - pyskl - INFO - Epoch [35][200/3746] lr: 8.781e-02, eta: 3 days, 20:51:21, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4519, loss_cls: 4.5383, loss: 4.5383 +2024-12-27 06:33:03,467 - pyskl - INFO - Epoch [35][300/3746] lr: 8.780e-02, eta: 3 days, 20:50:36, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2131, top5_acc: 0.4472, loss_cls: 4.5744, loss: 4.5744 +2024-12-27 06:34:29,308 - pyskl - INFO - Epoch [35][400/3746] lr: 8.778e-02, eta: 3 days, 20:49:49, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4620, loss_cls: 4.5291, loss: 4.5291 +2024-12-27 06:35:54,616 - pyskl - INFO - Epoch [35][500/3746] lr: 8.776e-02, eta: 3 days, 20:49:00, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4580, loss_cls: 4.5506, loss: 4.5506 +2024-12-27 06:37:19,680 - pyskl - INFO - Epoch [35][600/3746] lr: 8.774e-02, eta: 3 days, 20:48:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4500, loss_cls: 4.5431, loss: 4.5431 +2024-12-27 06:38:45,538 - pyskl - INFO - Epoch [35][700/3746] lr: 8.772e-02, eta: 3 days, 20:47:24, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4536, loss_cls: 4.5674, loss: 4.5674 +2024-12-27 06:40:10,856 - pyskl - INFO - Epoch [35][800/3746] lr: 8.770e-02, eta: 3 days, 20:46:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4531, loss_cls: 4.5808, loss: 4.5808 +2024-12-27 06:41:35,499 - pyskl - INFO - Epoch [35][900/3746] lr: 8.769e-02, eta: 3 days, 20:45:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4506, loss_cls: 4.5680, loss: 4.5680 +2024-12-27 06:43:00,845 - pyskl - INFO - Epoch [35][1000/3746] lr: 8.767e-02, eta: 3 days, 20:44:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4514, loss_cls: 4.5924, loss: 4.5924 +2024-12-27 06:44:26,035 - pyskl - INFO - Epoch [35][1100/3746] lr: 8.765e-02, eta: 3 days, 20:44:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4581, loss_cls: 4.5721, loss: 4.5721 +2024-12-27 06:45:51,061 - pyskl - INFO - Epoch [35][1200/3746] lr: 8.763e-02, eta: 3 days, 20:43:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4541, loss_cls: 4.5818, loss: 4.5818 +2024-12-27 06:47:16,329 - pyskl - INFO - Epoch [35][1300/3746] lr: 8.761e-02, eta: 3 days, 20:42:26, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4580, loss_cls: 4.5610, loss: 4.5610 +2024-12-27 06:48:41,521 - pyskl - INFO - Epoch [35][1400/3746] lr: 8.759e-02, eta: 3 days, 20:41:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4475, loss_cls: 4.5990, loss: 4.5990 +2024-12-27 06:50:06,503 - pyskl - INFO - Epoch [35][1500/3746] lr: 8.757e-02, eta: 3 days, 20:40:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4487, loss_cls: 4.6147, loss: 4.6147 +2024-12-27 06:51:31,820 - pyskl - INFO - Epoch [35][1600/3746] lr: 8.756e-02, eta: 3 days, 20:39:57, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4537, loss_cls: 4.5753, loss: 4.5753 +2024-12-27 06:52:56,624 - pyskl - INFO - Epoch [35][1700/3746] lr: 8.754e-02, eta: 3 days, 20:39:06, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4470, loss_cls: 4.5884, loss: 4.5884 +2024-12-27 06:54:22,172 - pyskl - INFO - Epoch [35][1800/3746] lr: 8.752e-02, eta: 3 days, 20:38:17, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4617, loss_cls: 4.5648, loss: 4.5648 +2024-12-27 06:55:48,097 - pyskl - INFO - Epoch [35][1900/3746] lr: 8.750e-02, eta: 3 days, 20:37:30, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4511, loss_cls: 4.5717, loss: 4.5717 +2024-12-27 06:57:13,946 - pyskl - INFO - Epoch [35][2000/3746] lr: 8.748e-02, eta: 3 days, 20:36:42, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4483, loss_cls: 4.5995, loss: 4.5995 +2024-12-27 06:58:39,533 - pyskl - INFO - Epoch [35][2100/3746] lr: 8.746e-02, eta: 3 days, 20:35:53, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4386, loss_cls: 4.5798, loss: 4.5798 +2024-12-27 07:00:05,906 - pyskl - INFO - Epoch [35][2200/3746] lr: 8.745e-02, eta: 3 days, 20:35:07, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4558, loss_cls: 4.5650, loss: 4.5650 +2024-12-27 07:01:32,244 - pyskl - INFO - Epoch [35][2300/3746] lr: 8.743e-02, eta: 3 days, 20:34:21, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4641, loss_cls: 4.5439, loss: 4.5439 +2024-12-27 07:02:57,872 - pyskl - INFO - Epoch [35][2400/3746] lr: 8.741e-02, eta: 3 days, 20:33:32, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4547, loss_cls: 4.5825, loss: 4.5825 +2024-12-27 07:04:23,554 - pyskl - INFO - Epoch [35][2500/3746] lr: 8.739e-02, eta: 3 days, 20:32:44, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4455, loss_cls: 4.5812, loss: 4.5812 +2024-12-27 07:05:49,266 - pyskl - INFO - Epoch [35][2600/3746] lr: 8.737e-02, eta: 3 days, 20:31:55, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4556, loss_cls: 4.5559, loss: 4.5559 +2024-12-27 07:07:14,642 - pyskl - INFO - Epoch [35][2700/3746] lr: 8.735e-02, eta: 3 days, 20:31:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4555, loss_cls: 4.5338, loss: 4.5338 +2024-12-27 07:08:40,174 - pyskl - INFO - Epoch [35][2800/3746] lr: 8.733e-02, eta: 3 days, 20:30:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4606, loss_cls: 4.5474, loss: 4.5474 +2024-12-27 07:10:06,351 - pyskl - INFO - Epoch [35][2900/3746] lr: 8.732e-02, eta: 3 days, 20:29:29, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4666, loss_cls: 4.5323, loss: 4.5323 +2024-12-27 07:11:32,552 - pyskl - INFO - Epoch [35][3000/3746] lr: 8.730e-02, eta: 3 days, 20:28:42, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4447, loss_cls: 4.5881, loss: 4.5881 +2024-12-27 07:12:58,708 - pyskl - INFO - Epoch [35][3100/3746] lr: 8.728e-02, eta: 3 days, 20:27:54, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4570, loss_cls: 4.5326, loss: 4.5326 +2024-12-27 07:14:24,634 - pyskl - INFO - Epoch [35][3200/3746] lr: 8.726e-02, eta: 3 days, 20:27:06, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4550, loss_cls: 4.5700, loss: 4.5700 +2024-12-27 07:15:50,711 - pyskl - INFO - Epoch [35][3300/3746] lr: 8.724e-02, eta: 3 days, 20:26:19, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4592, loss_cls: 4.5379, loss: 4.5379 +2024-12-27 07:17:15,755 - pyskl - INFO - Epoch [35][3400/3746] lr: 8.722e-02, eta: 3 days, 20:25:27, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4637, loss_cls: 4.5500, loss: 4.5500 +2024-12-27 07:18:40,395 - pyskl - INFO - Epoch [35][3500/3746] lr: 8.720e-02, eta: 3 days, 20:24:35, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4525, loss_cls: 4.5920, loss: 4.5920 +2024-12-27 07:20:06,258 - pyskl - INFO - Epoch [35][3600/3746] lr: 8.718e-02, eta: 3 days, 20:23:46, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4592, loss_cls: 4.5564, loss: 4.5564 +2024-12-27 07:21:31,186 - pyskl - INFO - Epoch [35][3700/3746] lr: 8.717e-02, eta: 3 days, 20:22:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4452, loss_cls: 4.5797, loss: 4.5797 +2024-12-27 07:22:12,226 - pyskl - INFO - Saving checkpoint at 35 epochs +2024-12-27 07:24:11,894 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 07:24:12,590 - pyskl - INFO - +top1_acc 0.1619 +top5_acc 0.3657 +2024-12-27 07:24:12,590 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 07:24:12,627 - pyskl - INFO - +mean_acc 0.1617 +2024-12-27 07:24:12,637 - pyskl - INFO - Epoch(val) [35][309] top1_acc: 0.1619, top5_acc: 0.3657, mean_class_accuracy: 0.1617 +2024-12-27 07:28:30,079 - pyskl - INFO - Epoch [36][100/3746] lr: 8.714e-02, eta: 3 days, 20:28:57, time: 2.574, data_time: 1.531, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4709, loss_cls: 4.5036, loss: 4.5036 +2024-12-27 07:29:55,561 - pyskl - INFO - Epoch [36][200/3746] lr: 8.712e-02, eta: 3 days, 20:28:06, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4648, loss_cls: 4.5031, loss: 4.5031 +2024-12-27 07:31:20,769 - pyskl - INFO - Epoch [36][300/3746] lr: 8.710e-02, eta: 3 days, 20:27:15, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4495, loss_cls: 4.5656, loss: 4.5656 +2024-12-27 07:32:46,254 - pyskl - INFO - Epoch [36][400/3746] lr: 8.708e-02, eta: 3 days, 20:26:24, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.2194, top5_acc: 0.4600, loss_cls: 4.5567, loss: 4.5567 +2024-12-27 07:34:11,332 - pyskl - INFO - Epoch [36][500/3746] lr: 8.706e-02, eta: 3 days, 20:25:32, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4597, loss_cls: 4.5490, loss: 4.5490 +2024-12-27 07:35:37,094 - pyskl - INFO - Epoch [36][600/3746] lr: 8.704e-02, eta: 3 days, 20:24:42, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4578, loss_cls: 4.5586, loss: 4.5586 +2024-12-27 07:37:01,972 - pyskl - INFO - Epoch [36][700/3746] lr: 8.703e-02, eta: 3 days, 20:23:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4586, loss_cls: 4.5702, loss: 4.5702 +2024-12-27 07:38:27,364 - pyskl - INFO - Epoch [36][800/3746] lr: 8.701e-02, eta: 3 days, 20:22:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4580, loss_cls: 4.5367, loss: 4.5367 +2024-12-27 07:39:52,693 - pyskl - INFO - Epoch [36][900/3746] lr: 8.699e-02, eta: 3 days, 20:22:07, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4628, loss_cls: 4.5058, loss: 4.5058 +2024-12-27 07:41:17,679 - pyskl - INFO - Epoch [36][1000/3746] lr: 8.697e-02, eta: 3 days, 20:21:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4573, loss_cls: 4.5674, loss: 4.5674 +2024-12-27 07:42:42,737 - pyskl - INFO - Epoch [36][1100/3746] lr: 8.695e-02, eta: 3 days, 20:20:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4569, loss_cls: 4.5402, loss: 4.5402 +2024-12-27 07:44:08,319 - pyskl - INFO - Epoch [36][1200/3746] lr: 8.693e-02, eta: 3 days, 20:19:32, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4592, loss_cls: 4.5529, loss: 4.5529 +2024-12-27 07:45:33,868 - pyskl - INFO - Epoch [36][1300/3746] lr: 8.691e-02, eta: 3 days, 20:18:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4641, loss_cls: 4.5281, loss: 4.5281 +2024-12-27 07:46:59,088 - pyskl - INFO - Epoch [36][1400/3746] lr: 8.689e-02, eta: 3 days, 20:17:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4527, loss_cls: 4.5568, loss: 4.5568 +2024-12-27 07:48:23,706 - pyskl - INFO - Epoch [36][1500/3746] lr: 8.688e-02, eta: 3 days, 20:16:55, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4555, loss_cls: 4.5489, loss: 4.5489 +2024-12-27 07:49:48,733 - pyskl - INFO - Epoch [36][1600/3746] lr: 8.686e-02, eta: 3 days, 20:16:02, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4483, loss_cls: 4.6137, loss: 4.6137 +2024-12-27 07:51:13,653 - pyskl - INFO - Epoch [36][1700/3746] lr: 8.684e-02, eta: 3 days, 20:15:09, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4481, loss_cls: 4.5888, loss: 4.5888 +2024-12-27 07:52:38,991 - pyskl - INFO - Epoch [36][1800/3746] lr: 8.682e-02, eta: 3 days, 20:14:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4545, loss_cls: 4.5707, loss: 4.5707 +2024-12-27 07:54:04,395 - pyskl - INFO - Epoch [36][1900/3746] lr: 8.680e-02, eta: 3 days, 20:13:26, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4647, loss_cls: 4.5580, loss: 4.5580 +2024-12-27 07:55:30,027 - pyskl - INFO - Epoch [36][2000/3746] lr: 8.678e-02, eta: 3 days, 20:12:35, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4420, loss_cls: 4.6070, loss: 4.6070 +2024-12-27 07:56:56,302 - pyskl - INFO - Epoch [36][2100/3746] lr: 8.676e-02, eta: 3 days, 20:11:46, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4480, loss_cls: 4.5805, loss: 4.5805 +2024-12-27 07:58:22,041 - pyskl - INFO - Epoch [36][2200/3746] lr: 8.674e-02, eta: 3 days, 20:10:55, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4520, loss_cls: 4.5645, loss: 4.5645 +2024-12-27 07:59:47,638 - pyskl - INFO - Epoch [36][2300/3746] lr: 8.672e-02, eta: 3 days, 20:10:04, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4520, loss_cls: 4.5653, loss: 4.5653 +2024-12-27 08:01:13,192 - pyskl - INFO - Epoch [36][2400/3746] lr: 8.671e-02, eta: 3 days, 20:09:13, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4544, loss_cls: 4.5708, loss: 4.5708 +2024-12-27 08:02:39,167 - pyskl - INFO - Epoch [36][2500/3746] lr: 8.669e-02, eta: 3 days, 20:08:23, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4553, loss_cls: 4.5580, loss: 4.5580 +2024-12-27 08:04:05,196 - pyskl - INFO - Epoch [36][2600/3746] lr: 8.667e-02, eta: 3 days, 20:07:33, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4442, loss_cls: 4.6071, loss: 4.6071 +2024-12-27 08:05:31,406 - pyskl - INFO - Epoch [36][2700/3746] lr: 8.665e-02, eta: 3 days, 20:06:43, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4484, loss_cls: 4.5660, loss: 4.5660 +2024-12-27 08:06:57,884 - pyskl - INFO - Epoch [36][2800/3746] lr: 8.663e-02, eta: 3 days, 20:05:55, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4592, loss_cls: 4.5652, loss: 4.5652 +2024-12-27 08:08:24,156 - pyskl - INFO - Epoch [36][2900/3746] lr: 8.661e-02, eta: 3 days, 20:05:05, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4594, loss_cls: 4.5333, loss: 4.5333 +2024-12-27 08:09:50,057 - pyskl - INFO - Epoch [36][3000/3746] lr: 8.659e-02, eta: 3 days, 20:04:15, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4475, loss_cls: 4.6088, loss: 4.6088 +2024-12-27 08:11:15,722 - pyskl - INFO - Epoch [36][3100/3746] lr: 8.657e-02, eta: 3 days, 20:03:23, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4556, loss_cls: 4.5560, loss: 4.5560 +2024-12-27 08:12:41,376 - pyskl - INFO - Epoch [36][3200/3746] lr: 8.655e-02, eta: 3 days, 20:02:32, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4455, loss_cls: 4.6140, loss: 4.6140 +2024-12-27 08:14:06,699 - pyskl - INFO - Epoch [36][3300/3746] lr: 8.653e-02, eta: 3 days, 20:01:39, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4630, loss_cls: 4.5381, loss: 4.5381 +2024-12-27 08:15:31,512 - pyskl - INFO - Epoch [36][3400/3746] lr: 8.651e-02, eta: 3 days, 20:00:45, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4553, loss_cls: 4.5500, loss: 4.5500 +2024-12-27 08:16:56,717 - pyskl - INFO - Epoch [36][3500/3746] lr: 8.650e-02, eta: 3 days, 19:59:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4519, loss_cls: 4.6198, loss: 4.6198 +2024-12-27 08:18:22,146 - pyskl - INFO - Epoch [36][3600/3746] lr: 8.648e-02, eta: 3 days, 19:58:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4619, loss_cls: 4.5407, loss: 4.5407 +2024-12-27 08:19:47,006 - pyskl - INFO - Epoch [36][3700/3746] lr: 8.646e-02, eta: 3 days, 19:58:05, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4583, loss_cls: 4.5289, loss: 4.5289 +2024-12-27 08:20:28,344 - pyskl - INFO - Saving checkpoint at 36 epochs +2024-12-27 08:22:27,749 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 08:22:28,470 - pyskl - INFO - +top1_acc 0.1370 +top5_acc 0.3312 +2024-12-27 08:22:28,470 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 08:22:28,523 - pyskl - INFO - +mean_acc 0.1368 +2024-12-27 08:22:28,539 - pyskl - INFO - Epoch(val) [36][309] top1_acc: 0.1370, top5_acc: 0.3312, mean_class_accuracy: 0.1368 +2024-12-27 08:26:41,132 - pyskl - INFO - Epoch [37][100/3746] lr: 8.643e-02, eta: 3 days, 20:03:33, time: 2.526, data_time: 1.496, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4669, loss_cls: 4.5054, loss: 4.5054 +2024-12-27 08:28:05,763 - pyskl - INFO - Epoch [37][200/3746] lr: 8.641e-02, eta: 3 days, 20:02:37, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4622, loss_cls: 4.5254, loss: 4.5254 +2024-12-27 08:29:30,605 - pyskl - INFO - Epoch [37][300/3746] lr: 8.639e-02, eta: 3 days, 20:01:43, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4537, loss_cls: 4.5558, loss: 4.5558 +2024-12-27 08:30:55,742 - pyskl - INFO - Epoch [37][400/3746] lr: 8.637e-02, eta: 3 days, 20:00:49, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4511, loss_cls: 4.5722, loss: 4.5722 +2024-12-27 08:32:21,246 - pyskl - INFO - Epoch [37][500/3746] lr: 8.635e-02, eta: 3 days, 19:59:56, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4623, loss_cls: 4.5625, loss: 4.5625 +2024-12-27 08:33:45,972 - pyskl - INFO - Epoch [37][600/3746] lr: 8.633e-02, eta: 3 days, 19:59:00, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4672, loss_cls: 4.5164, loss: 4.5164 +2024-12-27 08:35:10,311 - pyskl - INFO - Epoch [37][700/3746] lr: 8.631e-02, eta: 3 days, 19:58:04, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4525, loss_cls: 4.5533, loss: 4.5533 +2024-12-27 08:36:34,810 - pyskl - INFO - Epoch [37][800/3746] lr: 8.630e-02, eta: 3 days, 19:57:08, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4472, loss_cls: 4.6018, loss: 4.6018 +2024-12-27 08:37:59,298 - pyskl - INFO - Epoch [37][900/3746] lr: 8.628e-02, eta: 3 days, 19:56:11, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4512, loss_cls: 4.5972, loss: 4.5972 +2024-12-27 08:39:23,472 - pyskl - INFO - Epoch [37][1000/3746] lr: 8.626e-02, eta: 3 days, 19:55:14, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4630, loss_cls: 4.5216, loss: 4.5216 +2024-12-27 08:40:47,928 - pyskl - INFO - Epoch [37][1100/3746] lr: 8.624e-02, eta: 3 days, 19:54:18, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4625, loss_cls: 4.5344, loss: 4.5344 +2024-12-27 08:42:12,116 - pyskl - INFO - Epoch [37][1200/3746] lr: 8.622e-02, eta: 3 days, 19:53:20, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4520, loss_cls: 4.5804, loss: 4.5804 +2024-12-27 08:43:36,595 - pyskl - INFO - Epoch [37][1300/3746] lr: 8.620e-02, eta: 3 days, 19:52:24, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4602, loss_cls: 4.5364, loss: 4.5364 +2024-12-27 08:45:01,567 - pyskl - INFO - Epoch [37][1400/3746] lr: 8.618e-02, eta: 3 days, 19:51:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4598, loss_cls: 4.5569, loss: 4.5569 +2024-12-27 08:46:26,633 - pyskl - INFO - Epoch [37][1500/3746] lr: 8.616e-02, eta: 3 days, 19:50:34, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4569, loss_cls: 4.5626, loss: 4.5626 +2024-12-27 08:47:51,196 - pyskl - INFO - Epoch [37][1600/3746] lr: 8.614e-02, eta: 3 days, 19:49:38, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4484, loss_cls: 4.5714, loss: 4.5714 +2024-12-27 08:49:15,508 - pyskl - INFO - Epoch [37][1700/3746] lr: 8.612e-02, eta: 3 days, 19:48:41, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4473, loss_cls: 4.5968, loss: 4.5968 +2024-12-27 08:50:39,916 - pyskl - INFO - Epoch [37][1800/3746] lr: 8.610e-02, eta: 3 days, 19:47:44, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4544, loss_cls: 4.5686, loss: 4.5686 +2024-12-27 08:52:04,518 - pyskl - INFO - Epoch [37][1900/3746] lr: 8.608e-02, eta: 3 days, 19:46:48, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4702, loss_cls: 4.5243, loss: 4.5243 +2024-12-27 08:53:29,759 - pyskl - INFO - Epoch [37][2000/3746] lr: 8.606e-02, eta: 3 days, 19:45:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4652, loss_cls: 4.5615, loss: 4.5615 +2024-12-27 08:54:55,626 - pyskl - INFO - Epoch [37][2100/3746] lr: 8.604e-02, eta: 3 days, 19:45:01, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4573, loss_cls: 4.5723, loss: 4.5723 +2024-12-27 08:56:21,287 - pyskl - INFO - Epoch [37][2200/3746] lr: 8.602e-02, eta: 3 days, 19:44:08, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4586, loss_cls: 4.5340, loss: 4.5340 +2024-12-27 08:57:46,532 - pyskl - INFO - Epoch [37][2300/3746] lr: 8.601e-02, eta: 3 days, 19:43:13, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4516, loss_cls: 4.5661, loss: 4.5661 +2024-12-27 08:59:12,101 - pyskl - INFO - Epoch [37][2400/3746] lr: 8.599e-02, eta: 3 days, 19:42:20, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4497, loss_cls: 4.5664, loss: 4.5664 +2024-12-27 09:00:37,781 - pyskl - INFO - Epoch [37][2500/3746] lr: 8.597e-02, eta: 3 days, 19:41:27, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4600, loss_cls: 4.5389, loss: 4.5389 +2024-12-27 09:02:03,034 - pyskl - INFO - Epoch [37][2600/3746] lr: 8.595e-02, eta: 3 days, 19:40:32, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4708, loss_cls: 4.5136, loss: 4.5136 +2024-12-27 09:03:29,330 - pyskl - INFO - Epoch [37][2700/3746] lr: 8.593e-02, eta: 3 days, 19:39:41, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4634, loss_cls: 4.5046, loss: 4.5046 +2024-12-27 09:04:54,948 - pyskl - INFO - Epoch [37][2800/3746] lr: 8.591e-02, eta: 3 days, 19:38:47, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4606, loss_cls: 4.5642, loss: 4.5642 +2024-12-27 09:06:20,676 - pyskl - INFO - Epoch [37][2900/3746] lr: 8.589e-02, eta: 3 days, 19:37:54, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4567, loss_cls: 4.5553, loss: 4.5553 +2024-12-27 09:07:46,716 - pyskl - INFO - Epoch [37][3000/3746] lr: 8.587e-02, eta: 3 days, 19:37:01, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4503, loss_cls: 4.5715, loss: 4.5715 +2024-12-27 09:09:12,221 - pyskl - INFO - Epoch [37][3100/3746] lr: 8.585e-02, eta: 3 days, 19:36:07, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4537, loss_cls: 4.5880, loss: 4.5880 +2024-12-27 09:10:38,433 - pyskl - INFO - Epoch [37][3200/3746] lr: 8.583e-02, eta: 3 days, 19:35:15, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4650, loss_cls: 4.5416, loss: 4.5416 +2024-12-27 09:12:03,587 - pyskl - INFO - Epoch [37][3300/3746] lr: 8.581e-02, eta: 3 days, 19:34:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4611, loss_cls: 4.5073, loss: 4.5073 +2024-12-27 09:13:28,822 - pyskl - INFO - Epoch [37][3400/3746] lr: 8.579e-02, eta: 3 days, 19:33:25, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4409, loss_cls: 4.6355, loss: 4.6355 +2024-12-27 09:14:54,488 - pyskl - INFO - Epoch [37][3500/3746] lr: 8.577e-02, eta: 3 days, 19:32:32, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4516, loss_cls: 4.5943, loss: 4.5943 +2024-12-27 09:16:20,154 - pyskl - INFO - Epoch [37][3600/3746] lr: 8.575e-02, eta: 3 days, 19:31:38, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4592, loss_cls: 4.5424, loss: 4.5424 +2024-12-27 09:17:45,244 - pyskl - INFO - Epoch [37][3700/3746] lr: 8.573e-02, eta: 3 days, 19:30:42, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4658, loss_cls: 4.5408, loss: 4.5408 +2024-12-27 09:18:26,265 - pyskl - INFO - Saving checkpoint at 37 epochs +2024-12-27 09:20:25,644 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 09:20:26,407 - pyskl - INFO - +top1_acc 0.1270 +top5_acc 0.3090 +2024-12-27 09:20:26,407 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 09:20:26,455 - pyskl - INFO - +mean_acc 0.1268 +2024-12-27 09:20:26,469 - pyskl - INFO - Epoch(val) [37][309] top1_acc: 0.1270, top5_acc: 0.3090, mean_class_accuracy: 0.1268 +2024-12-27 09:24:42,468 - pyskl - INFO - Epoch [38][100/3746] lr: 8.570e-02, eta: 3 days, 19:36:03, time: 2.560, data_time: 1.540, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4650, loss_cls: 4.4782, loss: 4.4782 +2024-12-27 09:26:06,768 - pyskl - INFO - Epoch [38][200/3746] lr: 8.568e-02, eta: 3 days, 19:35:04, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4577, loss_cls: 4.5313, loss: 4.5313 +2024-12-27 09:27:32,071 - pyskl - INFO - Epoch [38][300/3746] lr: 8.567e-02, eta: 3 days, 19:34:09, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4755, loss_cls: 4.4784, loss: 4.4784 +2024-12-27 09:28:56,412 - pyskl - INFO - Epoch [38][400/3746] lr: 8.565e-02, eta: 3 days, 19:33:11, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4602, loss_cls: 4.5556, loss: 4.5556 +2024-12-27 09:30:21,117 - pyskl - INFO - Epoch [38][500/3746] lr: 8.563e-02, eta: 3 days, 19:32:13, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4577, loss_cls: 4.5410, loss: 4.5410 +2024-12-27 09:31:45,985 - pyskl - INFO - Epoch [38][600/3746] lr: 8.561e-02, eta: 3 days, 19:31:16, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4645, loss_cls: 4.5285, loss: 4.5285 +2024-12-27 09:33:10,958 - pyskl - INFO - Epoch [38][700/3746] lr: 8.559e-02, eta: 3 days, 19:30:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4625, loss_cls: 4.5116, loss: 4.5116 +2024-12-27 09:34:35,531 - pyskl - INFO - Epoch [38][800/3746] lr: 8.557e-02, eta: 3 days, 19:29:22, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4516, loss_cls: 4.5768, loss: 4.5768 +2024-12-27 09:36:00,497 - pyskl - INFO - Epoch [38][900/3746] lr: 8.555e-02, eta: 3 days, 19:28:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4562, loss_cls: 4.5548, loss: 4.5548 +2024-12-27 09:37:25,283 - pyskl - INFO - Epoch [38][1000/3746] lr: 8.553e-02, eta: 3 days, 19:27:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4597, loss_cls: 4.5207, loss: 4.5207 +2024-12-27 09:38:50,376 - pyskl - INFO - Epoch [38][1100/3746] lr: 8.551e-02, eta: 3 days, 19:26:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4514, loss_cls: 4.5626, loss: 4.5626 +2024-12-27 09:40:15,898 - pyskl - INFO - Epoch [38][1200/3746] lr: 8.549e-02, eta: 3 days, 19:25:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4587, loss_cls: 4.5329, loss: 4.5329 +2024-12-27 09:41:40,955 - pyskl - INFO - Epoch [38][1300/3746] lr: 8.547e-02, eta: 3 days, 19:24:39, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4641, loss_cls: 4.5266, loss: 4.5266 +2024-12-27 09:43:06,121 - pyskl - INFO - Epoch [38][1400/3746] lr: 8.545e-02, eta: 3 days, 19:23:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4681, loss_cls: 4.5274, loss: 4.5274 +2024-12-27 09:44:31,617 - pyskl - INFO - Epoch [38][1500/3746] lr: 8.543e-02, eta: 3 days, 19:22:48, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4534, loss_cls: 4.5639, loss: 4.5639 +2024-12-27 09:45:57,022 - pyskl - INFO - Epoch [38][1600/3746] lr: 8.541e-02, eta: 3 days, 19:21:52, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4722, loss_cls: 4.4955, loss: 4.4955 +2024-12-27 09:47:22,126 - pyskl - INFO - Epoch [38][1700/3746] lr: 8.539e-02, eta: 3 days, 19:20:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4684, loss_cls: 4.5451, loss: 4.5451 +2024-12-27 09:48:48,031 - pyskl - INFO - Epoch [38][1800/3746] lr: 8.537e-02, eta: 3 days, 19:20:01, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4503, loss_cls: 4.5962, loss: 4.5962 +2024-12-27 09:50:13,409 - pyskl - INFO - Epoch [38][1900/3746] lr: 8.535e-02, eta: 3 days, 19:19:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4567, loss_cls: 4.5663, loss: 4.5663 +2024-12-27 09:51:39,352 - pyskl - INFO - Epoch [38][2000/3746] lr: 8.533e-02, eta: 3 days, 19:18:11, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4606, loss_cls: 4.5563, loss: 4.5563 +2024-12-27 09:53:05,091 - pyskl - INFO - Epoch [38][2100/3746] lr: 8.531e-02, eta: 3 days, 19:17:16, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4528, loss_cls: 4.5539, loss: 4.5539 +2024-12-27 09:54:30,696 - pyskl - INFO - Epoch [38][2200/3746] lr: 8.529e-02, eta: 3 days, 19:16:21, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4666, loss_cls: 4.5228, loss: 4.5228 +2024-12-27 09:55:56,362 - pyskl - INFO - Epoch [38][2300/3746] lr: 8.527e-02, eta: 3 days, 19:15:25, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4656, loss_cls: 4.4788, loss: 4.4788 +2024-12-27 09:57:21,868 - pyskl - INFO - Epoch [38][2400/3746] lr: 8.525e-02, eta: 3 days, 19:14:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4678, loss_cls: 4.5081, loss: 4.5081 +2024-12-27 09:58:47,314 - pyskl - INFO - Epoch [38][2500/3746] lr: 8.523e-02, eta: 3 days, 19:13:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4569, loss_cls: 4.5747, loss: 4.5747 +2024-12-27 10:00:12,773 - pyskl - INFO - Epoch [38][2600/3746] lr: 8.521e-02, eta: 3 days, 19:12:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4561, loss_cls: 4.5501, loss: 4.5501 +2024-12-27 10:01:37,805 - pyskl - INFO - Epoch [38][2700/3746] lr: 8.519e-02, eta: 3 days, 19:11:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4566, loss_cls: 4.5898, loss: 4.5898 +2024-12-27 10:03:03,333 - pyskl - INFO - Epoch [38][2800/3746] lr: 8.517e-02, eta: 3 days, 19:10:45, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4527, loss_cls: 4.5922, loss: 4.5922 +2024-12-27 10:04:29,113 - pyskl - INFO - Epoch [38][2900/3746] lr: 8.515e-02, eta: 3 days, 19:09:50, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4536, loss_cls: 4.5794, loss: 4.5794 +2024-12-27 10:05:55,189 - pyskl - INFO - Epoch [38][3000/3746] lr: 8.513e-02, eta: 3 days, 19:08:55, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4581, loss_cls: 4.5319, loss: 4.5319 +2024-12-27 10:07:21,468 - pyskl - INFO - Epoch [38][3100/3746] lr: 8.511e-02, eta: 3 days, 19:08:02, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4611, loss_cls: 4.5379, loss: 4.5379 +2024-12-27 10:08:47,114 - pyskl - INFO - Epoch [38][3200/3746] lr: 8.509e-02, eta: 3 days, 19:07:06, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4611, loss_cls: 4.5174, loss: 4.5174 +2024-12-27 10:10:12,512 - pyskl - INFO - Epoch [38][3300/3746] lr: 8.507e-02, eta: 3 days, 19:06:09, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4539, loss_cls: 4.5494, loss: 4.5494 +2024-12-27 10:11:37,420 - pyskl - INFO - Epoch [38][3400/3746] lr: 8.505e-02, eta: 3 days, 19:05:12, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4567, loss_cls: 4.5521, loss: 4.5521 +2024-12-27 10:13:02,753 - pyskl - INFO - Epoch [38][3500/3746] lr: 8.503e-02, eta: 3 days, 19:04:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4553, loss_cls: 4.5956, loss: 4.5956 +2024-12-27 10:14:27,608 - pyskl - INFO - Epoch [38][3600/3746] lr: 8.501e-02, eta: 3 days, 19:03:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4614, loss_cls: 4.5146, loss: 4.5146 +2024-12-27 10:15:52,010 - pyskl - INFO - Epoch [38][3700/3746] lr: 8.499e-02, eta: 3 days, 19:02:17, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4509, loss_cls: 4.5933, loss: 4.5933 +2024-12-27 10:16:32,793 - pyskl - INFO - Saving checkpoint at 38 epochs +2024-12-27 10:18:32,151 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 10:18:32,897 - pyskl - INFO - +top1_acc 0.1452 +top5_acc 0.3583 +2024-12-27 10:18:32,897 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 10:18:32,939 - pyskl - INFO - +mean_acc 0.1450 +2024-12-27 10:18:32,954 - pyskl - INFO - Epoch(val) [38][309] top1_acc: 0.1452, top5_acc: 0.3583, mean_class_accuracy: 0.1450 +2024-12-27 10:22:57,718 - pyskl - INFO - Epoch [39][100/3746] lr: 8.496e-02, eta: 3 days, 19:07:47, time: 2.648, data_time: 1.603, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4736, loss_cls: 4.4551, loss: 4.4551 +2024-12-27 10:24:24,087 - pyskl - INFO - Epoch [39][200/3746] lr: 8.494e-02, eta: 3 days, 19:06:53, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4545, loss_cls: 4.5408, loss: 4.5408 +2024-12-27 10:25:50,220 - pyskl - INFO - Epoch [39][300/3746] lr: 8.492e-02, eta: 3 days, 19:05:58, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4694, loss_cls: 4.5054, loss: 4.5054 +2024-12-27 10:27:16,153 - pyskl - INFO - Epoch [39][400/3746] lr: 8.490e-02, eta: 3 days, 19:05:02, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4634, loss_cls: 4.5149, loss: 4.5149 +2024-12-27 10:28:41,125 - pyskl - INFO - Epoch [39][500/3746] lr: 8.488e-02, eta: 3 days, 19:04:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4587, loss_cls: 4.5505, loss: 4.5505 +2024-12-27 10:30:06,413 - pyskl - INFO - Epoch [39][600/3746] lr: 8.486e-02, eta: 3 days, 19:03:06, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4622, loss_cls: 4.5288, loss: 4.5288 +2024-12-27 10:31:31,428 - pyskl - INFO - Epoch [39][700/3746] lr: 8.484e-02, eta: 3 days, 19:02:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4580, loss_cls: 4.5475, loss: 4.5475 +2024-12-27 10:32:56,203 - pyskl - INFO - Epoch [39][800/3746] lr: 8.482e-02, eta: 3 days, 19:01:09, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4564, loss_cls: 4.5234, loss: 4.5234 +2024-12-27 10:34:21,410 - pyskl - INFO - Epoch [39][900/3746] lr: 8.480e-02, eta: 3 days, 19:00:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4595, loss_cls: 4.5116, loss: 4.5116 +2024-12-27 10:35:46,034 - pyskl - INFO - Epoch [39][1000/3746] lr: 8.478e-02, eta: 3 days, 18:59:11, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4633, loss_cls: 4.4970, loss: 4.4970 +2024-12-27 10:37:11,341 - pyskl - INFO - Epoch [39][1100/3746] lr: 8.476e-02, eta: 3 days, 18:58:13, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4580, loss_cls: 4.5486, loss: 4.5486 +2024-12-27 10:38:36,816 - pyskl - INFO - Epoch [39][1200/3746] lr: 8.474e-02, eta: 3 days, 18:57:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4520, loss_cls: 4.5637, loss: 4.5637 +2024-12-27 10:40:02,096 - pyskl - INFO - Epoch [39][1300/3746] lr: 8.472e-02, eta: 3 days, 18:56:18, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4619, loss_cls: 4.5328, loss: 4.5328 +2024-12-27 10:41:27,107 - pyskl - INFO - Epoch [39][1400/3746] lr: 8.470e-02, eta: 3 days, 18:55:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4620, loss_cls: 4.5490, loss: 4.5490 +2024-12-27 10:42:52,328 - pyskl - INFO - Epoch [39][1500/3746] lr: 8.468e-02, eta: 3 days, 18:54:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4483, loss_cls: 4.5901, loss: 4.5901 +2024-12-27 10:44:17,878 - pyskl - INFO - Epoch [39][1600/3746] lr: 8.466e-02, eta: 3 days, 18:53:24, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4509, loss_cls: 4.5786, loss: 4.5786 +2024-12-27 10:45:42,647 - pyskl - INFO - Epoch [39][1700/3746] lr: 8.464e-02, eta: 3 days, 18:52:25, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4709, loss_cls: 4.4992, loss: 4.4992 +2024-12-27 10:47:07,409 - pyskl - INFO - Epoch [39][1800/3746] lr: 8.462e-02, eta: 3 days, 18:51:25, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4558, loss_cls: 4.5725, loss: 4.5725 +2024-12-27 10:48:32,325 - pyskl - INFO - Epoch [39][1900/3746] lr: 8.460e-02, eta: 3 days, 18:50:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4664, loss_cls: 4.5029, loss: 4.5029 +2024-12-27 10:49:57,508 - pyskl - INFO - Epoch [39][2000/3746] lr: 8.458e-02, eta: 3 days, 18:49:28, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4647, loss_cls: 4.5342, loss: 4.5342 +2024-12-27 10:51:24,413 - pyskl - INFO - Epoch [39][2100/3746] lr: 8.456e-02, eta: 3 days, 18:48:34, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4705, loss_cls: 4.5027, loss: 4.5027 +2024-12-27 10:52:50,402 - pyskl - INFO - Epoch [39][2200/3746] lr: 8.454e-02, eta: 3 days, 18:47:38, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4425, loss_cls: 4.6152, loss: 4.6152 +2024-12-27 10:54:16,127 - pyskl - INFO - Epoch [39][2300/3746] lr: 8.452e-02, eta: 3 days, 18:46:41, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4581, loss_cls: 4.5502, loss: 4.5502 +2024-12-27 10:55:41,861 - pyskl - INFO - Epoch [39][2400/3746] lr: 8.450e-02, eta: 3 days, 18:45:44, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4595, loss_cls: 4.5518, loss: 4.5518 +2024-12-27 10:57:07,865 - pyskl - INFO - Epoch [39][2500/3746] lr: 8.448e-02, eta: 3 days, 18:44:48, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4620, loss_cls: 4.5339, loss: 4.5339 +2024-12-27 10:58:33,537 - pyskl - INFO - Epoch [39][2600/3746] lr: 8.446e-02, eta: 3 days, 18:43:51, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4683, loss_cls: 4.5059, loss: 4.5059 +2024-12-27 10:59:59,555 - pyskl - INFO - Epoch [39][2700/3746] lr: 8.444e-02, eta: 3 days, 18:42:54, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4609, loss_cls: 4.5343, loss: 4.5343 +2024-12-27 11:01:25,368 - pyskl - INFO - Epoch [39][2800/3746] lr: 8.442e-02, eta: 3 days, 18:41:57, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4680, loss_cls: 4.5148, loss: 4.5148 +2024-12-27 11:02:51,641 - pyskl - INFO - Epoch [39][2900/3746] lr: 8.440e-02, eta: 3 days, 18:41:02, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4688, loss_cls: 4.5050, loss: 4.5050 +2024-12-27 11:04:17,563 - pyskl - INFO - Epoch [39][3000/3746] lr: 8.438e-02, eta: 3 days, 18:40:05, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4612, loss_cls: 4.5000, loss: 4.5000 +2024-12-27 11:05:43,155 - pyskl - INFO - Epoch [39][3100/3746] lr: 8.436e-02, eta: 3 days, 18:39:08, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4639, loss_cls: 4.5193, loss: 4.5193 +2024-12-27 11:07:08,046 - pyskl - INFO - Epoch [39][3200/3746] lr: 8.434e-02, eta: 3 days, 18:38:08, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4577, loss_cls: 4.5531, loss: 4.5531 +2024-12-27 11:08:33,386 - pyskl - INFO - Epoch [39][3300/3746] lr: 8.432e-02, eta: 3 days, 18:37:09, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4506, loss_cls: 4.5632, loss: 4.5632 +2024-12-27 11:09:58,647 - pyskl - INFO - Epoch [39][3400/3746] lr: 8.430e-02, eta: 3 days, 18:36:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4506, loss_cls: 4.5789, loss: 4.5789 +2024-12-27 11:11:24,344 - pyskl - INFO - Epoch [39][3500/3746] lr: 8.428e-02, eta: 3 days, 18:35:13, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4541, loss_cls: 4.5607, loss: 4.5607 +2024-12-27 11:12:49,125 - pyskl - INFO - Epoch [39][3600/3746] lr: 8.426e-02, eta: 3 days, 18:34:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4617, loss_cls: 4.5360, loss: 4.5360 +2024-12-27 11:14:13,770 - pyskl - INFO - Epoch [39][3700/3746] lr: 8.424e-02, eta: 3 days, 18:33:12, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4605, loss_cls: 4.4978, loss: 4.4978 +2024-12-27 11:14:55,058 - pyskl - INFO - Saving checkpoint at 39 epochs +2024-12-27 11:16:54,981 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 11:16:55,726 - pyskl - INFO - +top1_acc 0.1626 +top5_acc 0.3730 +2024-12-27 11:16:55,726 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 11:16:55,769 - pyskl - INFO - +mean_acc 0.1625 +2024-12-27 11:16:55,783 - pyskl - INFO - Epoch(val) [39][309] top1_acc: 0.1626, top5_acc: 0.3730, mean_class_accuracy: 0.1625 +2024-12-27 11:21:09,547 - pyskl - INFO - Epoch [40][100/3746] lr: 8.421e-02, eta: 3 days, 18:37:54, time: 2.538, data_time: 1.504, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4734, loss_cls: 4.4705, loss: 4.4705 +2024-12-27 11:22:34,045 - pyskl - INFO - Epoch [40][200/3746] lr: 8.419e-02, eta: 3 days, 18:36:53, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4641, loss_cls: 4.5288, loss: 4.5288 +2024-12-27 11:23:59,317 - pyskl - INFO - Epoch [40][300/3746] lr: 8.417e-02, eta: 3 days, 18:35:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4556, loss_cls: 4.5446, loss: 4.5446 +2024-12-27 11:25:24,763 - pyskl - INFO - Epoch [40][400/3746] lr: 8.415e-02, eta: 3 days, 18:34:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4659, loss_cls: 4.5176, loss: 4.5176 +2024-12-27 11:26:50,275 - pyskl - INFO - Epoch [40][500/3746] lr: 8.413e-02, eta: 3 days, 18:33:56, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4702, loss_cls: 4.5223, loss: 4.5223 +2024-12-27 11:28:15,750 - pyskl - INFO - Epoch [40][600/3746] lr: 8.411e-02, eta: 3 days, 18:32:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4683, loss_cls: 4.4659, loss: 4.4659 +2024-12-27 11:29:40,890 - pyskl - INFO - Epoch [40][700/3746] lr: 8.408e-02, eta: 3 days, 18:31:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4616, loss_cls: 4.5563, loss: 4.5563 +2024-12-27 11:31:05,996 - pyskl - INFO - Epoch [40][800/3746] lr: 8.406e-02, eta: 3 days, 18:30:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4631, loss_cls: 4.4898, loss: 4.4898 +2024-12-27 11:32:31,090 - pyskl - INFO - Epoch [40][900/3746] lr: 8.404e-02, eta: 3 days, 18:29:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4636, loss_cls: 4.5199, loss: 4.5199 +2024-12-27 11:33:55,864 - pyskl - INFO - Epoch [40][1000/3746] lr: 8.402e-02, eta: 3 days, 18:28:57, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4648, loss_cls: 4.4982, loss: 4.4982 +2024-12-27 11:35:20,739 - pyskl - INFO - Epoch [40][1100/3746] lr: 8.400e-02, eta: 3 days, 18:27:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4517, loss_cls: 4.5618, loss: 4.5618 +2024-12-27 11:36:45,833 - pyskl - INFO - Epoch [40][1200/3746] lr: 8.398e-02, eta: 3 days, 18:26:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4680, loss_cls: 4.5142, loss: 4.5142 +2024-12-27 11:38:10,587 - pyskl - INFO - Epoch [40][1300/3746] lr: 8.396e-02, eta: 3 days, 18:25:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4713, loss_cls: 4.5023, loss: 4.5023 +2024-12-27 11:39:35,756 - pyskl - INFO - Epoch [40][1400/3746] lr: 8.394e-02, eta: 3 days, 18:24:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4434, loss_cls: 4.6042, loss: 4.6042 +2024-12-27 11:41:00,867 - pyskl - INFO - Epoch [40][1500/3746] lr: 8.392e-02, eta: 3 days, 18:23:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4648, loss_cls: 4.5205, loss: 4.5205 +2024-12-27 11:42:25,849 - pyskl - INFO - Epoch [40][1600/3746] lr: 8.390e-02, eta: 3 days, 18:22:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4716, loss_cls: 4.4807, loss: 4.4807 +2024-12-27 11:43:51,177 - pyskl - INFO - Epoch [40][1700/3746] lr: 8.388e-02, eta: 3 days, 18:21:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4603, loss_cls: 4.5497, loss: 4.5497 +2024-12-27 11:45:15,927 - pyskl - INFO - Epoch [40][1800/3746] lr: 8.386e-02, eta: 3 days, 18:20:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4509, loss_cls: 4.5611, loss: 4.5611 +2024-12-27 11:46:41,280 - pyskl - INFO - Epoch [40][1900/3746] lr: 8.384e-02, eta: 3 days, 18:19:54, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4627, loss_cls: 4.5555, loss: 4.5555 +2024-12-27 11:48:06,453 - pyskl - INFO - Epoch [40][2000/3746] lr: 8.382e-02, eta: 3 days, 18:18:54, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4591, loss_cls: 4.5530, loss: 4.5530 +2024-12-27 11:49:31,840 - pyskl - INFO - Epoch [40][2100/3746] lr: 8.380e-02, eta: 3 days, 18:17:54, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4516, loss_cls: 4.5605, loss: 4.5605 +2024-12-27 11:50:56,648 - pyskl - INFO - Epoch [40][2200/3746] lr: 8.378e-02, eta: 3 days, 18:16:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4583, loss_cls: 4.5219, loss: 4.5219 +2024-12-27 11:52:21,896 - pyskl - INFO - Epoch [40][2300/3746] lr: 8.376e-02, eta: 3 days, 18:15:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4562, loss_cls: 4.5507, loss: 4.5507 +2024-12-27 11:53:47,476 - pyskl - INFO - Epoch [40][2400/3746] lr: 8.374e-02, eta: 3 days, 18:14:54, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4608, loss_cls: 4.5256, loss: 4.5256 +2024-12-27 11:55:13,107 - pyskl - INFO - Epoch [40][2500/3746] lr: 8.371e-02, eta: 3 days, 18:13:55, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4534, loss_cls: 4.5576, loss: 4.5576 +2024-12-27 11:56:38,683 - pyskl - INFO - Epoch [40][2600/3746] lr: 8.369e-02, eta: 3 days, 18:12:56, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4634, loss_cls: 4.5435, loss: 4.5435 +2024-12-27 11:58:04,809 - pyskl - INFO - Epoch [40][2700/3746] lr: 8.367e-02, eta: 3 days, 18:11:58, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4556, loss_cls: 4.5557, loss: 4.5557 +2024-12-27 11:59:30,501 - pyskl - INFO - Epoch [40][2800/3746] lr: 8.365e-02, eta: 3 days, 18:10:59, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4711, loss_cls: 4.5027, loss: 4.5027 +2024-12-27 12:00:56,680 - pyskl - INFO - Epoch [40][2900/3746] lr: 8.363e-02, eta: 3 days, 18:10:02, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4688, loss_cls: 4.4851, loss: 4.4851 +2024-12-27 12:02:22,136 - pyskl - INFO - Epoch [40][3000/3746] lr: 8.361e-02, eta: 3 days, 18:09:02, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4731, loss_cls: 4.4704, loss: 4.4704 +2024-12-27 12:03:47,592 - pyskl - INFO - Epoch [40][3100/3746] lr: 8.359e-02, eta: 3 days, 18:08:02, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4573, loss_cls: 4.5267, loss: 4.5267 +2024-12-27 12:05:12,702 - pyskl - INFO - Epoch [40][3200/3746] lr: 8.357e-02, eta: 3 days, 18:07:01, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4661, loss_cls: 4.5154, loss: 4.5154 +2024-12-27 12:06:37,556 - pyskl - INFO - Epoch [40][3300/3746] lr: 8.355e-02, eta: 3 days, 18:06:00, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4655, loss_cls: 4.5105, loss: 4.5105 +2024-12-27 12:08:02,936 - pyskl - INFO - Epoch [40][3400/3746] lr: 8.353e-02, eta: 3 days, 18:05:00, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4561, loss_cls: 4.5733, loss: 4.5733 +2024-12-27 12:09:28,705 - pyskl - INFO - Epoch [40][3500/3746] lr: 8.351e-02, eta: 3 days, 18:04:01, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4636, loss_cls: 4.5101, loss: 4.5101 +2024-12-27 12:10:53,421 - pyskl - INFO - Epoch [40][3600/3746] lr: 8.349e-02, eta: 3 days, 18:02:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4653, loss_cls: 4.5312, loss: 4.5312 +2024-12-27 12:12:18,151 - pyskl - INFO - Epoch [40][3700/3746] lr: 8.347e-02, eta: 3 days, 18:01:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4562, loss_cls: 4.5530, loss: 4.5530 +2024-12-27 12:12:59,373 - pyskl - INFO - Saving checkpoint at 40 epochs +2024-12-27 12:14:59,649 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 12:15:00,468 - pyskl - INFO - +top1_acc 0.1486 +top5_acc 0.3422 +2024-12-27 12:15:00,468 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 12:15:00,520 - pyskl - INFO - +mean_acc 0.1485 +2024-12-27 12:15:00,537 - pyskl - INFO - Epoch(val) [40][309] top1_acc: 0.1486, top5_acc: 0.3422, mean_class_accuracy: 0.1485 +2024-12-27 12:19:11,558 - pyskl - INFO - Epoch [41][100/3746] lr: 8.344e-02, eta: 3 days, 18:06:17, time: 2.510, data_time: 1.487, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4700, loss_cls: 4.4701, loss: 4.4701 +2024-12-27 12:20:35,979 - pyskl - INFO - Epoch [41][200/3746] lr: 8.342e-02, eta: 3 days, 18:05:13, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4697, loss_cls: 4.5074, loss: 4.5074 +2024-12-27 12:21:59,967 - pyskl - INFO - Epoch [41][300/3746] lr: 8.339e-02, eta: 3 days, 18:04:09, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4717, loss_cls: 4.5069, loss: 4.5069 +2024-12-27 12:23:24,616 - pyskl - INFO - Epoch [41][400/3746] lr: 8.337e-02, eta: 3 days, 18:03:06, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4684, loss_cls: 4.4706, loss: 4.4706 +2024-12-27 12:24:49,818 - pyskl - INFO - Epoch [41][500/3746] lr: 8.335e-02, eta: 3 days, 18:02:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4630, loss_cls: 4.5266, loss: 4.5266 +2024-12-27 12:26:15,109 - pyskl - INFO - Epoch [41][600/3746] lr: 8.333e-02, eta: 3 days, 18:01:04, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4527, loss_cls: 4.5436, loss: 4.5436 +2024-12-27 12:27:39,863 - pyskl - INFO - Epoch [41][700/3746] lr: 8.331e-02, eta: 3 days, 18:00:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4603, loss_cls: 4.5187, loss: 4.5187 +2024-12-27 12:29:05,287 - pyskl - INFO - Epoch [41][800/3746] lr: 8.329e-02, eta: 3 days, 17:59:01, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4800, loss_cls: 4.4619, loss: 4.4619 +2024-12-27 12:30:30,244 - pyskl - INFO - Epoch [41][900/3746] lr: 8.327e-02, eta: 3 days, 17:58:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4741, loss_cls: 4.4766, loss: 4.4766 +2024-12-27 12:31:55,533 - pyskl - INFO - Epoch [41][1000/3746] lr: 8.325e-02, eta: 3 days, 17:56:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4620, loss_cls: 4.5257, loss: 4.5257 +2024-12-27 12:33:20,702 - pyskl - INFO - Epoch [41][1100/3746] lr: 8.323e-02, eta: 3 days, 17:55:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4516, loss_cls: 4.5261, loss: 4.5261 +2024-12-27 12:34:45,343 - pyskl - INFO - Epoch [41][1200/3746] lr: 8.321e-02, eta: 3 days, 17:54:54, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4669, loss_cls: 4.5114, loss: 4.5114 +2024-12-27 12:36:10,005 - pyskl - INFO - Epoch [41][1300/3746] lr: 8.319e-02, eta: 3 days, 17:53:52, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4655, loss_cls: 4.5335, loss: 4.5335 +2024-12-27 12:37:34,633 - pyskl - INFO - Epoch [41][1400/3746] lr: 8.316e-02, eta: 3 days, 17:52:49, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4673, loss_cls: 4.5072, loss: 4.5072 +2024-12-27 12:38:59,722 - pyskl - INFO - Epoch [41][1500/3746] lr: 8.314e-02, eta: 3 days, 17:51:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4589, loss_cls: 4.5508, loss: 4.5508 +2024-12-27 12:40:25,146 - pyskl - INFO - Epoch [41][1600/3746] lr: 8.312e-02, eta: 3 days, 17:50:46, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4561, loss_cls: 4.5382, loss: 4.5382 +2024-12-27 12:41:50,423 - pyskl - INFO - Epoch [41][1700/3746] lr: 8.310e-02, eta: 3 days, 17:49:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4650, loss_cls: 4.5188, loss: 4.5188 +2024-12-27 12:43:15,978 - pyskl - INFO - Epoch [41][1800/3746] lr: 8.308e-02, eta: 3 days, 17:48:44, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4617, loss_cls: 4.5257, loss: 4.5257 +2024-12-27 12:44:41,476 - pyskl - INFO - Epoch [41][1900/3746] lr: 8.306e-02, eta: 3 days, 17:47:43, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4616, loss_cls: 4.5288, loss: 4.5288 +2024-12-27 12:46:06,316 - pyskl - INFO - Epoch [41][2000/3746] lr: 8.304e-02, eta: 3 days, 17:46:41, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4569, loss_cls: 4.5555, loss: 4.5555 +2024-12-27 12:47:31,231 - pyskl - INFO - Epoch [41][2100/3746] lr: 8.302e-02, eta: 3 days, 17:45:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4619, loss_cls: 4.5336, loss: 4.5336 +2024-12-27 12:48:56,847 - pyskl - INFO - Epoch [41][2200/3746] lr: 8.300e-02, eta: 3 days, 17:44:38, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4650, loss_cls: 4.5049, loss: 4.5049 +2024-12-27 12:50:21,833 - pyskl - INFO - Epoch [41][2300/3746] lr: 8.298e-02, eta: 3 days, 17:43:36, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4550, loss_cls: 4.5660, loss: 4.5660 +2024-12-27 12:51:47,020 - pyskl - INFO - Epoch [41][2400/3746] lr: 8.296e-02, eta: 3 days, 17:42:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4591, loss_cls: 4.5427, loss: 4.5427 +2024-12-27 12:53:12,236 - pyskl - INFO - Epoch [41][2500/3746] lr: 8.293e-02, eta: 3 days, 17:41:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4656, loss_cls: 4.5300, loss: 4.5300 +2024-12-27 12:54:38,029 - pyskl - INFO - Epoch [41][2600/3746] lr: 8.291e-02, eta: 3 days, 17:40:32, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4627, loss_cls: 4.5112, loss: 4.5112 +2024-12-27 12:56:04,872 - pyskl - INFO - Epoch [41][2700/3746] lr: 8.289e-02, eta: 3 days, 17:39:35, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4577, loss_cls: 4.5046, loss: 4.5046 +2024-12-27 12:57:30,964 - pyskl - INFO - Epoch [41][2800/3746] lr: 8.287e-02, eta: 3 days, 17:38:35, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4536, loss_cls: 4.5049, loss: 4.5049 +2024-12-27 12:58:57,272 - pyskl - INFO - Epoch [41][2900/3746] lr: 8.285e-02, eta: 3 days, 17:37:37, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4656, loss_cls: 4.5202, loss: 4.5202 +2024-12-27 13:00:22,823 - pyskl - INFO - Epoch [41][3000/3746] lr: 8.283e-02, eta: 3 days, 17:36:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4630, loss_cls: 4.5312, loss: 4.5312 +2024-12-27 13:01:48,512 - pyskl - INFO - Epoch [41][3100/3746] lr: 8.281e-02, eta: 3 days, 17:35:35, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4605, loss_cls: 4.5373, loss: 4.5373 +2024-12-27 13:03:13,794 - pyskl - INFO - Epoch [41][3200/3746] lr: 8.279e-02, eta: 3 days, 17:34:33, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4614, loss_cls: 4.5344, loss: 4.5344 +2024-12-27 13:04:38,851 - pyskl - INFO - Epoch [41][3300/3746] lr: 8.277e-02, eta: 3 days, 17:33:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4567, loss_cls: 4.5363, loss: 4.5363 +2024-12-27 13:06:04,386 - pyskl - INFO - Epoch [41][3400/3746] lr: 8.274e-02, eta: 3 days, 17:32:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4577, loss_cls: 4.5325, loss: 4.5325 +2024-12-27 13:07:29,967 - pyskl - INFO - Epoch [41][3500/3746] lr: 8.272e-02, eta: 3 days, 17:31:29, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4784, loss_cls: 4.4636, loss: 4.4636 +2024-12-27 13:08:55,051 - pyskl - INFO - Epoch [41][3600/3746] lr: 8.270e-02, eta: 3 days, 17:30:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4688, loss_cls: 4.4906, loss: 4.4906 +2024-12-27 13:10:19,727 - pyskl - INFO - Epoch [41][3700/3746] lr: 8.268e-02, eta: 3 days, 17:29:23, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4713, loss_cls: 4.5018, loss: 4.5018 +2024-12-27 13:11:01,100 - pyskl - INFO - Saving checkpoint at 41 epochs +2024-12-27 13:13:01,646 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 13:13:02,375 - pyskl - INFO - +top1_acc 0.1584 +top5_acc 0.3668 +2024-12-27 13:13:02,376 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 13:13:02,411 - pyskl - INFO - +mean_acc 0.1584 +2024-12-27 13:13:02,423 - pyskl - INFO - Epoch(val) [41][309] top1_acc: 0.1584, top5_acc: 0.3668, mean_class_accuracy: 0.1584 +2024-12-27 13:17:13,615 - pyskl - INFO - Epoch [42][100/3746] lr: 8.265e-02, eta: 3 days, 17:33:29, time: 2.512, data_time: 1.475, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4745, loss_cls: 4.5067, loss: 4.5067 +2024-12-27 13:18:38,757 - pyskl - INFO - Epoch [42][200/3746] lr: 8.263e-02, eta: 3 days, 17:32:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4622, loss_cls: 4.5061, loss: 4.5061 +2024-12-27 13:20:04,186 - pyskl - INFO - Epoch [42][300/3746] lr: 8.261e-02, eta: 3 days, 17:31:24, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4648, loss_cls: 4.4853, loss: 4.4853 +2024-12-27 13:21:29,224 - pyskl - INFO - Epoch [42][400/3746] lr: 8.259e-02, eta: 3 days, 17:30:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4477, loss_cls: 4.5327, loss: 4.5327 +2024-12-27 13:22:54,264 - pyskl - INFO - Epoch [42][500/3746] lr: 8.257e-02, eta: 3 days, 17:29:18, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4698, loss_cls: 4.4762, loss: 4.4762 +2024-12-27 13:24:19,447 - pyskl - INFO - Epoch [42][600/3746] lr: 8.254e-02, eta: 3 days, 17:28:16, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4614, loss_cls: 4.5154, loss: 4.5154 +2024-12-27 13:25:44,330 - pyskl - INFO - Epoch [42][700/3746] lr: 8.252e-02, eta: 3 days, 17:27:12, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4641, loss_cls: 4.5113, loss: 4.5113 +2024-12-27 13:27:09,082 - pyskl - INFO - Epoch [42][800/3746] lr: 8.250e-02, eta: 3 days, 17:26:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4708, loss_cls: 4.4405, loss: 4.4405 +2024-12-27 13:28:34,252 - pyskl - INFO - Epoch [42][900/3746] lr: 8.248e-02, eta: 3 days, 17:25:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4688, loss_cls: 4.4916, loss: 4.4916 +2024-12-27 13:29:59,838 - pyskl - INFO - Epoch [42][1000/3746] lr: 8.246e-02, eta: 3 days, 17:24:04, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4597, loss_cls: 4.5099, loss: 4.5099 +2024-12-27 13:31:25,300 - pyskl - INFO - Epoch [42][1100/3746] lr: 8.244e-02, eta: 3 days, 17:23:02, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4602, loss_cls: 4.5212, loss: 4.5212 +2024-12-27 13:32:50,342 - pyskl - INFO - Epoch [42][1200/3746] lr: 8.242e-02, eta: 3 days, 17:21:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4689, loss_cls: 4.5051, loss: 4.5051 +2024-12-27 13:34:15,688 - pyskl - INFO - Epoch [42][1300/3746] lr: 8.240e-02, eta: 3 days, 17:20:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4692, loss_cls: 4.5009, loss: 4.5009 +2024-12-27 13:35:41,124 - pyskl - INFO - Epoch [42][1400/3746] lr: 8.237e-02, eta: 3 days, 17:19:54, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4580, loss_cls: 4.5420, loss: 4.5420 +2024-12-27 13:37:06,549 - pyskl - INFO - Epoch [42][1500/3746] lr: 8.235e-02, eta: 3 days, 17:18:52, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4659, loss_cls: 4.5103, loss: 4.5103 +2024-12-27 13:38:31,732 - pyskl - INFO - Epoch [42][1600/3746] lr: 8.233e-02, eta: 3 days, 17:17:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4620, loss_cls: 4.5131, loss: 4.5131 +2024-12-27 13:39:56,864 - pyskl - INFO - Epoch [42][1700/3746] lr: 8.231e-02, eta: 3 days, 17:16:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4695, loss_cls: 4.4935, loss: 4.4935 +2024-12-27 13:41:22,216 - pyskl - INFO - Epoch [42][1800/3746] lr: 8.229e-02, eta: 3 days, 17:15:43, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4655, loss_cls: 4.5095, loss: 4.5095 +2024-12-27 13:42:47,243 - pyskl - INFO - Epoch [42][1900/3746] lr: 8.227e-02, eta: 3 days, 17:14:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4655, loss_cls: 4.5321, loss: 4.5321 +2024-12-27 13:44:12,685 - pyskl - INFO - Epoch [42][2000/3746] lr: 8.225e-02, eta: 3 days, 17:13:38, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4600, loss_cls: 4.5222, loss: 4.5222 +2024-12-27 13:45:37,706 - pyskl - INFO - Epoch [42][2100/3746] lr: 8.222e-02, eta: 3 days, 17:12:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4550, loss_cls: 4.5025, loss: 4.5025 +2024-12-27 13:47:02,911 - pyskl - INFO - Epoch [42][2200/3746] lr: 8.220e-02, eta: 3 days, 17:11:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4766, loss_cls: 4.4687, loss: 4.4687 +2024-12-27 13:48:28,158 - pyskl - INFO - Epoch [42][2300/3746] lr: 8.218e-02, eta: 3 days, 17:10:28, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4673, loss_cls: 4.5030, loss: 4.5030 +2024-12-27 13:49:53,652 - pyskl - INFO - Epoch [42][2400/3746] lr: 8.216e-02, eta: 3 days, 17:09:26, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4689, loss_cls: 4.5047, loss: 4.5047 +2024-12-27 13:51:19,032 - pyskl - INFO - Epoch [42][2500/3746] lr: 8.214e-02, eta: 3 days, 17:08:23, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4688, loss_cls: 4.4636, loss: 4.4636 +2024-12-27 13:52:45,019 - pyskl - INFO - Epoch [42][2600/3746] lr: 8.212e-02, eta: 3 days, 17:07:22, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4608, loss_cls: 4.5267, loss: 4.5267 +2024-12-27 13:54:10,755 - pyskl - INFO - Epoch [42][2700/3746] lr: 8.210e-02, eta: 3 days, 17:06:20, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4495, loss_cls: 4.5542, loss: 4.5542 +2024-12-27 13:55:36,729 - pyskl - INFO - Epoch [42][2800/3746] lr: 8.207e-02, eta: 3 days, 17:05:19, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4703, loss_cls: 4.5002, loss: 4.5002 +2024-12-27 13:57:02,169 - pyskl - INFO - Epoch [42][2900/3746] lr: 8.205e-02, eta: 3 days, 17:04:16, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4637, loss_cls: 4.5211, loss: 4.5211 +2024-12-27 13:58:28,063 - pyskl - INFO - Epoch [42][3000/3746] lr: 8.203e-02, eta: 3 days, 17:03:15, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4739, loss_cls: 4.4828, loss: 4.4828 +2024-12-27 13:59:53,099 - pyskl - INFO - Epoch [42][3100/3746] lr: 8.201e-02, eta: 3 days, 17:02:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4639, loss_cls: 4.5161, loss: 4.5161 +2024-12-27 14:01:18,117 - pyskl - INFO - Epoch [42][3200/3746] lr: 8.199e-02, eta: 3 days, 17:01:07, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4511, loss_cls: 4.6031, loss: 4.6031 +2024-12-27 14:02:43,833 - pyskl - INFO - Epoch [42][3300/3746] lr: 8.197e-02, eta: 3 days, 17:00:05, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4692, loss_cls: 4.5456, loss: 4.5456 +2024-12-27 14:04:09,041 - pyskl - INFO - Epoch [42][3400/3746] lr: 8.195e-02, eta: 3 days, 16:59:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4608, loss_cls: 4.5760, loss: 4.5760 +2024-12-27 14:05:34,274 - pyskl - INFO - Epoch [42][3500/3746] lr: 8.192e-02, eta: 3 days, 16:57:58, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4541, loss_cls: 4.5624, loss: 4.5624 +2024-12-27 14:06:58,836 - pyskl - INFO - Epoch [42][3600/3746] lr: 8.190e-02, eta: 3 days, 16:56:53, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4702, loss_cls: 4.4824, loss: 4.4824 +2024-12-27 14:08:23,766 - pyskl - INFO - Epoch [42][3700/3746] lr: 8.188e-02, eta: 3 days, 16:55:49, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4622, loss_cls: 4.5294, loss: 4.5294 +2024-12-27 14:09:05,007 - pyskl - INFO - Saving checkpoint at 42 epochs +2024-12-27 14:11:04,773 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 14:11:05,538 - pyskl - INFO - +top1_acc 0.1541 +top5_acc 0.3602 +2024-12-27 14:11:05,538 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 14:11:05,589 - pyskl - INFO - +mean_acc 0.1541 +2024-12-27 14:11:05,610 - pyskl - INFO - Epoch(val) [42][309] top1_acc: 0.1541, top5_acc: 0.3602, mean_class_accuracy: 0.1541 +2024-12-27 14:15:26,345 - pyskl - INFO - Epoch [43][100/3746] lr: 8.185e-02, eta: 3 days, 17:00:07, time: 2.607, data_time: 1.576, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4733, loss_cls: 4.4551, loss: 4.4551 +2024-12-27 14:16:51,422 - pyskl - INFO - Epoch [43][200/3746] lr: 8.183e-02, eta: 3 days, 16:59:02, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4808, loss_cls: 4.4217, loss: 4.4217 +2024-12-27 14:18:16,787 - pyskl - INFO - Epoch [43][300/3746] lr: 8.181e-02, eta: 3 days, 16:57:59, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4583, loss_cls: 4.5251, loss: 4.5251 +2024-12-27 14:19:42,278 - pyskl - INFO - Epoch [43][400/3746] lr: 8.179e-02, eta: 3 days, 16:56:56, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4778, loss_cls: 4.4457, loss: 4.4457 +2024-12-27 14:21:07,520 - pyskl - INFO - Epoch [43][500/3746] lr: 8.176e-02, eta: 3 days, 16:55:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4647, loss_cls: 4.5031, loss: 4.5031 +2024-12-27 14:22:32,810 - pyskl - INFO - Epoch [43][600/3746] lr: 8.174e-02, eta: 3 days, 16:54:48, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4597, loss_cls: 4.5280, loss: 4.5280 +2024-12-27 14:23:58,093 - pyskl - INFO - Epoch [43][700/3746] lr: 8.172e-02, eta: 3 days, 16:53:44, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4786, loss_cls: 4.4429, loss: 4.4429 +2024-12-27 14:25:22,932 - pyskl - INFO - Epoch [43][800/3746] lr: 8.170e-02, eta: 3 days, 16:52:39, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4573, loss_cls: 4.5041, loss: 4.5041 +2024-12-27 14:26:47,609 - pyskl - INFO - Epoch [43][900/3746] lr: 8.168e-02, eta: 3 days, 16:51:34, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4617, loss_cls: 4.5033, loss: 4.5033 +2024-12-27 14:28:12,250 - pyskl - INFO - Epoch [43][1000/3746] lr: 8.166e-02, eta: 3 days, 16:50:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4637, loss_cls: 4.5214, loss: 4.5214 +2024-12-27 14:29:36,334 - pyskl - INFO - Epoch [43][1100/3746] lr: 8.163e-02, eta: 3 days, 16:49:22, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4608, loss_cls: 4.5318, loss: 4.5318 +2024-12-27 14:31:00,571 - pyskl - INFO - Epoch [43][1200/3746] lr: 8.161e-02, eta: 3 days, 16:48:15, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4736, loss_cls: 4.4795, loss: 4.4795 +2024-12-27 14:32:24,617 - pyskl - INFO - Epoch [43][1300/3746] lr: 8.159e-02, eta: 3 days, 16:47:08, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4614, loss_cls: 4.5159, loss: 4.5159 +2024-12-27 14:33:49,249 - pyskl - INFO - Epoch [43][1400/3746] lr: 8.157e-02, eta: 3 days, 16:46:02, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4545, loss_cls: 4.5360, loss: 4.5360 +2024-12-27 14:35:14,264 - pyskl - INFO - Epoch [43][1500/3746] lr: 8.155e-02, eta: 3 days, 16:44:58, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4650, loss_cls: 4.5127, loss: 4.5127 +2024-12-27 14:36:39,204 - pyskl - INFO - Epoch [43][1600/3746] lr: 8.153e-02, eta: 3 days, 16:43:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4694, loss_cls: 4.4920, loss: 4.4920 +2024-12-27 14:38:04,735 - pyskl - INFO - Epoch [43][1700/3746] lr: 8.150e-02, eta: 3 days, 16:42:49, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4747, loss_cls: 4.4836, loss: 4.4836 +2024-12-27 14:39:30,614 - pyskl - INFO - Epoch [43][1800/3746] lr: 8.148e-02, eta: 3 days, 16:41:47, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4673, loss_cls: 4.5078, loss: 4.5078 +2024-12-27 14:40:56,085 - pyskl - INFO - Epoch [43][1900/3746] lr: 8.146e-02, eta: 3 days, 16:40:43, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4739, loss_cls: 4.4460, loss: 4.4460 +2024-12-27 14:42:22,150 - pyskl - INFO - Epoch [43][2000/3746] lr: 8.144e-02, eta: 3 days, 16:39:41, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4766, loss_cls: 4.4632, loss: 4.4632 +2024-12-27 14:43:47,214 - pyskl - INFO - Epoch [43][2100/3746] lr: 8.142e-02, eta: 3 days, 16:38:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4691, loss_cls: 4.5201, loss: 4.5201 +2024-12-27 14:45:12,170 - pyskl - INFO - Epoch [43][2200/3746] lr: 8.140e-02, eta: 3 days, 16:37:31, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4642, loss_cls: 4.5067, loss: 4.5067 +2024-12-27 14:46:37,607 - pyskl - INFO - Epoch [43][2300/3746] lr: 8.137e-02, eta: 3 days, 16:36:28, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4739, loss_cls: 4.4944, loss: 4.4944 +2024-12-27 14:48:02,871 - pyskl - INFO - Epoch [43][2400/3746] lr: 8.135e-02, eta: 3 days, 16:35:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4711, loss_cls: 4.5015, loss: 4.5015 +2024-12-27 14:49:28,326 - pyskl - INFO - Epoch [43][2500/3746] lr: 8.133e-02, eta: 3 days, 16:34:19, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4594, loss_cls: 4.5193, loss: 4.5193 +2024-12-27 14:50:53,349 - pyskl - INFO - Epoch [43][2600/3746] lr: 8.131e-02, eta: 3 days, 16:33:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4505, loss_cls: 4.5631, loss: 4.5631 +2024-12-27 14:52:18,321 - pyskl - INFO - Epoch [43][2700/3746] lr: 8.129e-02, eta: 3 days, 16:32:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4709, loss_cls: 4.4987, loss: 4.4987 +2024-12-27 14:53:43,341 - pyskl - INFO - Epoch [43][2800/3746] lr: 8.126e-02, eta: 3 days, 16:31:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4575, loss_cls: 4.5517, loss: 4.5517 +2024-12-27 14:55:08,043 - pyskl - INFO - Epoch [43][2900/3746] lr: 8.124e-02, eta: 3 days, 16:29:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4697, loss_cls: 4.4971, loss: 4.4971 +2024-12-27 14:56:32,628 - pyskl - INFO - Epoch [43][3000/3746] lr: 8.122e-02, eta: 3 days, 16:28:52, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4683, loss_cls: 4.5045, loss: 4.5045 +2024-12-27 14:57:57,173 - pyskl - INFO - Epoch [43][3100/3746] lr: 8.120e-02, eta: 3 days, 16:27:46, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4578, loss_cls: 4.5258, loss: 4.5258 +2024-12-27 14:59:21,360 - pyskl - INFO - Epoch [43][3200/3746] lr: 8.118e-02, eta: 3 days, 16:26:39, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4709, loss_cls: 4.4753, loss: 4.4753 +2024-12-27 15:00:45,473 - pyskl - INFO - Epoch [43][3300/3746] lr: 8.116e-02, eta: 3 days, 16:25:32, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4570, loss_cls: 4.5708, loss: 4.5708 +2024-12-27 15:02:10,674 - pyskl - INFO - Epoch [43][3400/3746] lr: 8.113e-02, eta: 3 days, 16:24:27, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4591, loss_cls: 4.5459, loss: 4.5459 +2024-12-27 15:03:35,789 - pyskl - INFO - Epoch [43][3500/3746] lr: 8.111e-02, eta: 3 days, 16:23:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4700, loss_cls: 4.4922, loss: 4.4922 +2024-12-27 15:05:00,214 - pyskl - INFO - Epoch [43][3600/3746] lr: 8.109e-02, eta: 3 days, 16:22:15, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4630, loss_cls: 4.5253, loss: 4.5253 +2024-12-27 15:06:24,531 - pyskl - INFO - Epoch [43][3700/3746] lr: 8.107e-02, eta: 3 days, 16:21:08, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4606, loss_cls: 4.5206, loss: 4.5206 +2024-12-27 15:07:05,642 - pyskl - INFO - Saving checkpoint at 43 epochs +2024-12-27 15:09:05,714 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 15:09:06,474 - pyskl - INFO - +top1_acc 0.1671 +top5_acc 0.3768 +2024-12-27 15:09:06,475 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 15:09:06,518 - pyskl - INFO - +mean_acc 0.1669 +2024-12-27 15:09:06,530 - pyskl - INFO - Epoch(val) [43][309] top1_acc: 0.1671, top5_acc: 0.3768, mean_class_accuracy: 0.1669 +2024-12-27 15:13:28,393 - pyskl - INFO - Epoch [44][100/3746] lr: 8.104e-02, eta: 3 days, 16:25:16, time: 2.619, data_time: 1.561, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4738, loss_cls: 4.4825, loss: 4.4825 +2024-12-27 15:14:53,921 - pyskl - INFO - Epoch [44][200/3746] lr: 8.101e-02, eta: 3 days, 16:24:11, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4688, loss_cls: 4.4840, loss: 4.4840 +2024-12-27 15:16:20,137 - pyskl - INFO - Epoch [44][300/3746] lr: 8.099e-02, eta: 3 days, 16:23:09, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4598, loss_cls: 4.5027, loss: 4.5027 +2024-12-27 15:17:45,369 - pyskl - INFO - Epoch [44][400/3746] lr: 8.097e-02, eta: 3 days, 16:22:04, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4614, loss_cls: 4.5281, loss: 4.5281 +2024-12-27 15:19:10,923 - pyskl - INFO - Epoch [44][500/3746] lr: 8.095e-02, eta: 3 days, 16:20:59, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4686, loss_cls: 4.5284, loss: 4.5284 +2024-12-27 15:20:36,350 - pyskl - INFO - Epoch [44][600/3746] lr: 8.093e-02, eta: 3 days, 16:19:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4612, loss_cls: 4.5223, loss: 4.5223 +2024-12-27 15:22:02,728 - pyskl - INFO - Epoch [44][700/3746] lr: 8.090e-02, eta: 3 days, 16:18:52, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4659, loss_cls: 4.4819, loss: 4.4819 +2024-12-27 15:23:28,584 - pyskl - INFO - Epoch [44][800/3746] lr: 8.088e-02, eta: 3 days, 16:17:49, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4702, loss_cls: 4.5014, loss: 4.5014 +2024-12-27 15:24:54,967 - pyskl - INFO - Epoch [44][900/3746] lr: 8.086e-02, eta: 3 days, 16:16:46, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4719, loss_cls: 4.4410, loss: 4.4410 +2024-12-27 15:26:20,522 - pyskl - INFO - Epoch [44][1000/3746] lr: 8.084e-02, eta: 3 days, 16:15:42, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4758, loss_cls: 4.4625, loss: 4.4625 +2024-12-27 15:27:46,451 - pyskl - INFO - Epoch [44][1100/3746] lr: 8.082e-02, eta: 3 days, 16:14:38, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4661, loss_cls: 4.5221, loss: 4.5221 +2024-12-27 15:29:12,012 - pyskl - INFO - Epoch [44][1200/3746] lr: 8.079e-02, eta: 3 days, 16:13:34, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4683, loss_cls: 4.4583, loss: 4.4583 +2024-12-27 15:30:37,575 - pyskl - INFO - Epoch [44][1300/3746] lr: 8.077e-02, eta: 3 days, 16:12:29, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4763, loss_cls: 4.5015, loss: 4.5015 +2024-12-27 15:32:03,386 - pyskl - INFO - Epoch [44][1400/3746] lr: 8.075e-02, eta: 3 days, 16:11:25, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4605, loss_cls: 4.5273, loss: 4.5273 +2024-12-27 15:33:29,447 - pyskl - INFO - Epoch [44][1500/3746] lr: 8.073e-02, eta: 3 days, 16:10:22, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4686, loss_cls: 4.5127, loss: 4.5127 +2024-12-27 15:34:55,160 - pyskl - INFO - Epoch [44][1600/3746] lr: 8.071e-02, eta: 3 days, 16:09:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4678, loss_cls: 4.4993, loss: 4.4993 +2024-12-27 15:36:21,594 - pyskl - INFO - Epoch [44][1700/3746] lr: 8.068e-02, eta: 3 days, 16:08:15, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4648, loss_cls: 4.4799, loss: 4.4799 +2024-12-27 15:37:47,293 - pyskl - INFO - Epoch [44][1800/3746] lr: 8.066e-02, eta: 3 days, 16:07:11, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4642, loss_cls: 4.5224, loss: 4.5224 +2024-12-27 15:39:13,537 - pyskl - INFO - Epoch [44][1900/3746] lr: 8.064e-02, eta: 3 days, 16:06:08, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4658, loss_cls: 4.5033, loss: 4.5033 +2024-12-27 15:40:39,761 - pyskl - INFO - Epoch [44][2000/3746] lr: 8.062e-02, eta: 3 days, 16:05:05, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4791, loss_cls: 4.4597, loss: 4.4597 +2024-12-27 15:42:05,842 - pyskl - INFO - Epoch [44][2100/3746] lr: 8.060e-02, eta: 3 days, 16:04:01, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4664, loss_cls: 4.5063, loss: 4.5063 +2024-12-27 15:43:32,246 - pyskl - INFO - Epoch [44][2200/3746] lr: 8.057e-02, eta: 3 days, 16:02:59, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4627, loss_cls: 4.5208, loss: 4.5208 +2024-12-27 15:44:58,373 - pyskl - INFO - Epoch [44][2300/3746] lr: 8.055e-02, eta: 3 days, 16:01:55, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4656, loss_cls: 4.5300, loss: 4.5300 +2024-12-27 15:46:24,475 - pyskl - INFO - Epoch [44][2400/3746] lr: 8.053e-02, eta: 3 days, 16:00:52, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4725, loss_cls: 4.4686, loss: 4.4686 +2024-12-27 15:47:51,075 - pyskl - INFO - Epoch [44][2500/3746] lr: 8.051e-02, eta: 3 days, 15:59:49, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4702, loss_cls: 4.5158, loss: 4.5158 +2024-12-27 15:49:17,031 - pyskl - INFO - Epoch [44][2600/3746] lr: 8.048e-02, eta: 3 days, 15:58:46, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4695, loss_cls: 4.4672, loss: 4.4672 +2024-12-27 15:50:43,644 - pyskl - INFO - Epoch [44][2700/3746] lr: 8.046e-02, eta: 3 days, 15:57:43, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4605, loss_cls: 4.4945, loss: 4.4945 +2024-12-27 15:52:09,947 - pyskl - INFO - Epoch [44][2800/3746] lr: 8.044e-02, eta: 3 days, 15:56:40, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4697, loss_cls: 4.4959, loss: 4.4959 +2024-12-27 15:53:36,225 - pyskl - INFO - Epoch [44][2900/3746] lr: 8.042e-02, eta: 3 days, 15:55:37, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4733, loss_cls: 4.4602, loss: 4.4602 +2024-12-27 15:55:01,196 - pyskl - INFO - Epoch [44][3000/3746] lr: 8.040e-02, eta: 3 days, 15:54:30, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4656, loss_cls: 4.4981, loss: 4.4981 +2024-12-27 15:56:26,435 - pyskl - INFO - Epoch [44][3100/3746] lr: 8.037e-02, eta: 3 days, 15:53:25, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4605, loss_cls: 4.5292, loss: 4.5292 +2024-12-27 15:57:51,671 - pyskl - INFO - Epoch [44][3200/3746] lr: 8.035e-02, eta: 3 days, 15:52:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4677, loss_cls: 4.5231, loss: 4.5231 +2024-12-27 15:59:18,033 - pyskl - INFO - Epoch [44][3300/3746] lr: 8.033e-02, eta: 3 days, 15:51:16, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4703, loss_cls: 4.4920, loss: 4.4920 +2024-12-27 16:00:43,894 - pyskl - INFO - Epoch [44][3400/3746] lr: 8.031e-02, eta: 3 days, 15:50:11, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4697, loss_cls: 4.4640, loss: 4.4640 +2024-12-27 16:02:08,865 - pyskl - INFO - Epoch [44][3500/3746] lr: 8.028e-02, eta: 3 days, 15:49:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4750, loss_cls: 4.4493, loss: 4.4493 +2024-12-27 16:03:34,305 - pyskl - INFO - Epoch [44][3600/3746] lr: 8.026e-02, eta: 3 days, 15:48:00, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4686, loss_cls: 4.5045, loss: 4.5045 +2024-12-27 16:04:59,944 - pyskl - INFO - Epoch [44][3700/3746] lr: 8.024e-02, eta: 3 days, 15:46:55, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4667, loss_cls: 4.5043, loss: 4.5043 +2024-12-27 16:05:41,306 - pyskl - INFO - Saving checkpoint at 44 epochs +2024-12-27 16:07:41,532 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 16:07:42,327 - pyskl - INFO - +top1_acc 0.1582 +top5_acc 0.3643 +2024-12-27 16:07:42,327 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 16:07:42,379 - pyskl - INFO - +mean_acc 0.1580 +2024-12-27 16:07:42,395 - pyskl - INFO - Epoch(val) [44][309] top1_acc: 0.1582, top5_acc: 0.3643, mean_class_accuracy: 0.1580 +2024-12-27 16:12:05,944 - pyskl - INFO - Epoch [45][100/3746] lr: 8.021e-02, eta: 3 days, 15:50:53, time: 2.635, data_time: 1.605, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4728, loss_cls: 4.4504, loss: 4.4504 +2024-12-27 16:13:31,028 - pyskl - INFO - Epoch [45][200/3746] lr: 8.019e-02, eta: 3 days, 15:49:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4733, loss_cls: 4.4632, loss: 4.4632 +2024-12-27 16:14:56,088 - pyskl - INFO - Epoch [45][300/3746] lr: 8.016e-02, eta: 3 days, 15:48:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4764, loss_cls: 4.4870, loss: 4.4870 +2024-12-27 16:16:21,294 - pyskl - INFO - Epoch [45][400/3746] lr: 8.014e-02, eta: 3 days, 15:47:33, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4778, loss_cls: 4.4513, loss: 4.4513 +2024-12-27 16:17:46,422 - pyskl - INFO - Epoch [45][500/3746] lr: 8.012e-02, eta: 3 days, 15:46:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4717, loss_cls: 4.4853, loss: 4.4853 +2024-12-27 16:19:11,264 - pyskl - INFO - Epoch [45][600/3746] lr: 8.010e-02, eta: 3 days, 15:45:19, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4622, loss_cls: 4.5112, loss: 4.5112 +2024-12-27 16:20:36,599 - pyskl - INFO - Epoch [45][700/3746] lr: 8.007e-02, eta: 3 days, 15:44:13, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4798, loss_cls: 4.4550, loss: 4.4550 +2024-12-27 16:22:01,233 - pyskl - INFO - Epoch [45][800/3746] lr: 8.005e-02, eta: 3 days, 15:43:05, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4652, loss_cls: 4.4773, loss: 4.4773 +2024-12-27 16:23:26,516 - pyskl - INFO - Epoch [45][900/3746] lr: 8.003e-02, eta: 3 days, 15:41:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4678, loss_cls: 4.4865, loss: 4.4865 +2024-12-27 16:24:51,132 - pyskl - INFO - Epoch [45][1000/3746] lr: 8.001e-02, eta: 3 days, 15:40:51, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4600, loss_cls: 4.4951, loss: 4.4951 +2024-12-27 16:26:15,624 - pyskl - INFO - Epoch [45][1100/3746] lr: 7.998e-02, eta: 3 days, 15:39:43, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4606, loss_cls: 4.5069, loss: 4.5069 +2024-12-27 16:27:39,788 - pyskl - INFO - Epoch [45][1200/3746] lr: 7.996e-02, eta: 3 days, 15:38:34, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4802, loss_cls: 4.4526, loss: 4.4526 +2024-12-27 16:29:04,551 - pyskl - INFO - Epoch [45][1300/3746] lr: 7.994e-02, eta: 3 days, 15:37:26, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4573, loss_cls: 4.5326, loss: 4.5326 +2024-12-27 16:30:29,820 - pyskl - INFO - Epoch [45][1400/3746] lr: 7.992e-02, eta: 3 days, 15:36:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4773, loss_cls: 4.4674, loss: 4.4674 +2024-12-27 16:31:54,862 - pyskl - INFO - Epoch [45][1500/3746] lr: 7.990e-02, eta: 3 days, 15:35:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4705, loss_cls: 4.4541, loss: 4.4541 +2024-12-27 16:33:19,962 - pyskl - INFO - Epoch [45][1600/3746] lr: 7.987e-02, eta: 3 days, 15:34:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4713, loss_cls: 4.4692, loss: 4.4692 +2024-12-27 16:34:44,790 - pyskl - INFO - Epoch [45][1700/3746] lr: 7.985e-02, eta: 3 days, 15:32:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4725, loss_cls: 4.4565, loss: 4.4565 +2024-12-27 16:36:09,775 - pyskl - INFO - Epoch [45][1800/3746] lr: 7.983e-02, eta: 3 days, 15:31:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4623, loss_cls: 4.5294, loss: 4.5294 +2024-12-27 16:37:34,623 - pyskl - INFO - Epoch [45][1900/3746] lr: 7.981e-02, eta: 3 days, 15:30:43, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4611, loss_cls: 4.5138, loss: 4.5138 +2024-12-27 16:38:59,787 - pyskl - INFO - Epoch [45][2000/3746] lr: 7.978e-02, eta: 3 days, 15:29:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4705, loss_cls: 4.4658, loss: 4.4658 +2024-12-27 16:40:24,695 - pyskl - INFO - Epoch [45][2100/3746] lr: 7.976e-02, eta: 3 days, 15:28:29, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4689, loss_cls: 4.5214, loss: 4.5214 +2024-12-27 16:41:49,597 - pyskl - INFO - Epoch [45][2200/3746] lr: 7.974e-02, eta: 3 days, 15:27:22, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4756, loss_cls: 4.4673, loss: 4.4673 +2024-12-27 16:43:14,275 - pyskl - INFO - Epoch [45][2300/3746] lr: 7.972e-02, eta: 3 days, 15:26:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4622, loss_cls: 4.5183, loss: 4.5183 +2024-12-27 16:44:39,645 - pyskl - INFO - Epoch [45][2400/3746] lr: 7.969e-02, eta: 3 days, 15:25:07, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4706, loss_cls: 4.4760, loss: 4.4760 +2024-12-27 16:46:05,311 - pyskl - INFO - Epoch [45][2500/3746] lr: 7.967e-02, eta: 3 days, 15:24:01, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4684, loss_cls: 4.4736, loss: 4.4736 +2024-12-27 16:47:30,673 - pyskl - INFO - Epoch [45][2600/3746] lr: 7.965e-02, eta: 3 days, 15:22:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4662, loss_cls: 4.4801, loss: 4.4801 +2024-12-27 16:48:55,948 - pyskl - INFO - Epoch [45][2700/3746] lr: 7.963e-02, eta: 3 days, 15:21:48, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4583, loss_cls: 4.5283, loss: 4.5283 +2024-12-27 16:50:20,986 - pyskl - INFO - Epoch [45][2800/3746] lr: 7.960e-02, eta: 3 days, 15:20:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4694, loss_cls: 4.4785, loss: 4.4785 +2024-12-27 16:51:45,817 - pyskl - INFO - Epoch [45][2900/3746] lr: 7.958e-02, eta: 3 days, 15:19:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4847, loss_cls: 4.4600, loss: 4.4600 +2024-12-27 16:53:10,598 - pyskl - INFO - Epoch [45][3000/3746] lr: 7.956e-02, eta: 3 days, 15:18:25, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4545, loss_cls: 4.5624, loss: 4.5624 +2024-12-27 16:54:35,119 - pyskl - INFO - Epoch [45][3100/3746] lr: 7.954e-02, eta: 3 days, 15:17:17, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4752, loss_cls: 4.4668, loss: 4.4668 +2024-12-27 16:55:59,534 - pyskl - INFO - Epoch [45][3200/3746] lr: 7.951e-02, eta: 3 days, 15:16:08, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4691, loss_cls: 4.4879, loss: 4.4879 +2024-12-27 16:57:24,806 - pyskl - INFO - Epoch [45][3300/3746] lr: 7.949e-02, eta: 3 days, 15:15:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4689, loss_cls: 4.5222, loss: 4.5222 +2024-12-27 16:58:49,577 - pyskl - INFO - Epoch [45][3400/3746] lr: 7.947e-02, eta: 3 days, 15:13:53, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4623, loss_cls: 4.4855, loss: 4.4855 +2024-12-27 17:00:14,516 - pyskl - INFO - Epoch [45][3500/3746] lr: 7.945e-02, eta: 3 days, 15:12:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4684, loss_cls: 4.4763, loss: 4.4763 +2024-12-27 17:01:38,513 - pyskl - INFO - Epoch [45][3600/3746] lr: 7.942e-02, eta: 3 days, 15:11:35, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4650, loss_cls: 4.5081, loss: 4.5081 +2024-12-27 17:03:02,761 - pyskl - INFO - Epoch [45][3700/3746] lr: 7.940e-02, eta: 3 days, 15:10:26, time: 0.842, data_time: 0.001, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4642, loss_cls: 4.4971, loss: 4.4971 +2024-12-27 17:03:44,009 - pyskl - INFO - Saving checkpoint at 45 epochs +2024-12-27 17:05:43,640 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 17:05:44,530 - pyskl - INFO - +top1_acc 0.1480 +top5_acc 0.3490 +2024-12-27 17:05:44,530 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 17:05:44,594 - pyskl - INFO - +mean_acc 0.1477 +2024-12-27 17:05:44,620 - pyskl - INFO - Epoch(val) [45][309] top1_acc: 0.1480, top5_acc: 0.3490, mean_class_accuracy: 0.1477 +2024-12-27 17:10:09,708 - pyskl - INFO - Epoch [46][100/3746] lr: 7.937e-02, eta: 3 days, 15:14:16, time: 2.651, data_time: 1.602, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4658, loss_cls: 4.4890, loss: 4.4890 +2024-12-27 17:11:36,050 - pyskl - INFO - Epoch [46][200/3746] lr: 7.934e-02, eta: 3 days, 15:13:11, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4637, loss_cls: 4.4816, loss: 4.4816 +2024-12-27 17:13:02,352 - pyskl - INFO - Epoch [46][300/3746] lr: 7.932e-02, eta: 3 days, 15:12:06, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4755, loss_cls: 4.4722, loss: 4.4722 +2024-12-27 17:14:28,669 - pyskl - INFO - Epoch [46][400/3746] lr: 7.930e-02, eta: 3 days, 15:11:01, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4859, loss_cls: 4.3860, loss: 4.3860 +2024-12-27 17:15:54,429 - pyskl - INFO - Epoch [46][500/3746] lr: 7.928e-02, eta: 3 days, 15:09:55, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4662, loss_cls: 4.4948, loss: 4.4948 +2024-12-27 17:17:19,568 - pyskl - INFO - Epoch [46][600/3746] lr: 7.925e-02, eta: 3 days, 15:08:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4808, loss_cls: 4.4666, loss: 4.4666 +2024-12-27 17:18:45,094 - pyskl - INFO - Epoch [46][700/3746] lr: 7.923e-02, eta: 3 days, 15:07:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4619, loss_cls: 4.4819, loss: 4.4819 +2024-12-27 17:20:11,084 - pyskl - INFO - Epoch [46][800/3746] lr: 7.921e-02, eta: 3 days, 15:06:35, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4717, loss_cls: 4.4878, loss: 4.4878 +2024-12-27 17:21:37,446 - pyskl - INFO - Epoch [46][900/3746] lr: 7.919e-02, eta: 3 days, 15:05:30, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4772, loss_cls: 4.4554, loss: 4.4554 +2024-12-27 17:23:03,695 - pyskl - INFO - Epoch [46][1000/3746] lr: 7.916e-02, eta: 3 days, 15:04:25, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4711, loss_cls: 4.4718, loss: 4.4718 +2024-12-27 17:24:29,943 - pyskl - INFO - Epoch [46][1100/3746] lr: 7.914e-02, eta: 3 days, 15:03:19, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4716, loss_cls: 4.4743, loss: 4.4743 +2024-12-27 17:25:56,068 - pyskl - INFO - Epoch [46][1200/3746] lr: 7.912e-02, eta: 3 days, 15:02:14, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4755, loss_cls: 4.4727, loss: 4.4727 +2024-12-27 17:27:21,657 - pyskl - INFO - Epoch [46][1300/3746] lr: 7.909e-02, eta: 3 days, 15:01:07, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4580, loss_cls: 4.5427, loss: 4.5427 +2024-12-27 17:28:47,066 - pyskl - INFO - Epoch [46][1400/3746] lr: 7.907e-02, eta: 3 days, 15:00:00, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4653, loss_cls: 4.4816, loss: 4.4816 +2024-12-27 17:30:12,877 - pyskl - INFO - Epoch [46][1500/3746] lr: 7.905e-02, eta: 3 days, 14:58:54, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4692, loss_cls: 4.4808, loss: 4.4808 +2024-12-27 17:31:39,009 - pyskl - INFO - Epoch [46][1600/3746] lr: 7.903e-02, eta: 3 days, 14:57:48, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4728, loss_cls: 4.5010, loss: 4.5010 +2024-12-27 17:33:05,128 - pyskl - INFO - Epoch [46][1700/3746] lr: 7.900e-02, eta: 3 days, 14:56:42, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4789, loss_cls: 4.4247, loss: 4.4247 +2024-12-27 17:34:31,080 - pyskl - INFO - Epoch [46][1800/3746] lr: 7.898e-02, eta: 3 days, 14:55:36, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4709, loss_cls: 4.4690, loss: 4.4690 +2024-12-27 17:35:57,024 - pyskl - INFO - Epoch [46][1900/3746] lr: 7.896e-02, eta: 3 days, 14:54:30, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4716, loss_cls: 4.4634, loss: 4.4634 +2024-12-27 17:37:23,275 - pyskl - INFO - Epoch [46][2000/3746] lr: 7.894e-02, eta: 3 days, 14:53:25, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4788, loss_cls: 4.4466, loss: 4.4466 +2024-12-27 17:38:50,262 - pyskl - INFO - Epoch [46][2100/3746] lr: 7.891e-02, eta: 3 days, 14:52:21, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4653, loss_cls: 4.4737, loss: 4.4737 +2024-12-27 17:40:16,837 - pyskl - INFO - Epoch [46][2200/3746] lr: 7.889e-02, eta: 3 days, 14:51:16, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4662, loss_cls: 4.5069, loss: 4.5069 +2024-12-27 17:41:43,014 - pyskl - INFO - Epoch [46][2300/3746] lr: 7.887e-02, eta: 3 days, 14:50:10, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4620, loss_cls: 4.5058, loss: 4.5058 +2024-12-27 17:43:09,241 - pyskl - INFO - Epoch [46][2400/3746] lr: 7.884e-02, eta: 3 days, 14:49:05, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4561, loss_cls: 4.5173, loss: 4.5173 +2024-12-27 17:44:34,946 - pyskl - INFO - Epoch [46][2500/3746] lr: 7.882e-02, eta: 3 days, 14:47:58, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4841, loss_cls: 4.4239, loss: 4.4239 +2024-12-27 17:46:01,535 - pyskl - INFO - Epoch [46][2600/3746] lr: 7.880e-02, eta: 3 days, 14:46:53, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4708, loss_cls: 4.4741, loss: 4.4741 +2024-12-27 17:47:27,533 - pyskl - INFO - Epoch [46][2700/3746] lr: 7.878e-02, eta: 3 days, 14:45:47, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4688, loss_cls: 4.5210, loss: 4.5210 +2024-12-27 17:48:53,346 - pyskl - INFO - Epoch [46][2800/3746] lr: 7.875e-02, eta: 3 days, 14:44:40, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4622, loss_cls: 4.4854, loss: 4.4854 +2024-12-27 17:50:19,360 - pyskl - INFO - Epoch [46][2900/3746] lr: 7.873e-02, eta: 3 days, 14:43:34, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4609, loss_cls: 4.5157, loss: 4.5157 +2024-12-27 17:51:44,670 - pyskl - INFO - Epoch [46][3000/3746] lr: 7.871e-02, eta: 3 days, 14:42:26, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4761, loss_cls: 4.4686, loss: 4.4686 +2024-12-27 17:53:10,007 - pyskl - INFO - Epoch [46][3100/3746] lr: 7.868e-02, eta: 3 days, 14:41:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4680, loss_cls: 4.5026, loss: 4.5026 +2024-12-27 17:54:36,008 - pyskl - INFO - Epoch [46][3200/3746] lr: 7.866e-02, eta: 3 days, 14:40:12, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4586, loss_cls: 4.5143, loss: 4.5143 +2024-12-27 17:56:01,568 - pyskl - INFO - Epoch [46][3300/3746] lr: 7.864e-02, eta: 3 days, 14:39:05, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4644, loss_cls: 4.5054, loss: 4.5054 +2024-12-27 17:57:27,272 - pyskl - INFO - Epoch [46][3400/3746] lr: 7.862e-02, eta: 3 days, 14:37:58, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4736, loss_cls: 4.4776, loss: 4.4776 +2024-12-27 17:58:52,239 - pyskl - INFO - Epoch [46][3500/3746] lr: 7.859e-02, eta: 3 days, 14:36:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4686, loss_cls: 4.4854, loss: 4.4854 +2024-12-27 18:00:17,211 - pyskl - INFO - Epoch [46][3600/3746] lr: 7.857e-02, eta: 3 days, 14:35:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4742, loss_cls: 4.4775, loss: 4.4775 +2024-12-27 18:01:43,183 - pyskl - INFO - Epoch [46][3700/3746] lr: 7.855e-02, eta: 3 days, 14:34:34, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4639, loss_cls: 4.4921, loss: 4.4921 +2024-12-27 18:02:25,229 - pyskl - INFO - Saving checkpoint at 46 epochs +2024-12-27 18:04:26,550 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 18:04:27,430 - pyskl - INFO - +top1_acc 0.1696 +top5_acc 0.3758 +2024-12-27 18:04:27,430 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 18:04:27,477 - pyskl - INFO - +mean_acc 0.1694 +2024-12-27 18:04:27,492 - pyskl - INFO - Epoch(val) [46][309] top1_acc: 0.1696, top5_acc: 0.3758, mean_class_accuracy: 0.1694 +2024-12-27 18:08:47,679 - pyskl - INFO - Epoch [47][100/3746] lr: 7.851e-02, eta: 3 days, 14:38:01, time: 2.602, data_time: 1.558, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4778, loss_cls: 4.4488, loss: 4.4488 +2024-12-27 18:10:12,324 - pyskl - INFO - Epoch [47][200/3746] lr: 7.849e-02, eta: 3 days, 14:36:52, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4777, loss_cls: 4.4500, loss: 4.4500 +2024-12-27 18:11:37,000 - pyskl - INFO - Epoch [47][300/3746] lr: 7.847e-02, eta: 3 days, 14:35:42, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4763, loss_cls: 4.4444, loss: 4.4444 +2024-12-27 18:13:01,947 - pyskl - INFO - Epoch [47][400/3746] lr: 7.844e-02, eta: 3 days, 14:34:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4738, loss_cls: 4.4435, loss: 4.4435 +2024-12-27 18:14:26,557 - pyskl - INFO - Epoch [47][500/3746] lr: 7.842e-02, eta: 3 days, 14:33:23, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4786, loss_cls: 4.4334, loss: 4.4334 +2024-12-27 18:15:51,679 - pyskl - INFO - Epoch [47][600/3746] lr: 7.840e-02, eta: 3 days, 14:32:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4677, loss_cls: 4.4401, loss: 4.4401 +2024-12-27 18:17:16,672 - pyskl - INFO - Epoch [47][700/3746] lr: 7.838e-02, eta: 3 days, 14:31:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4673, loss_cls: 4.4830, loss: 4.4830 +2024-12-27 18:18:41,757 - pyskl - INFO - Epoch [47][800/3746] lr: 7.835e-02, eta: 3 days, 14:29:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4708, loss_cls: 4.4558, loss: 4.4558 +2024-12-27 18:20:06,362 - pyskl - INFO - Epoch [47][900/3746] lr: 7.833e-02, eta: 3 days, 14:28:46, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4686, loss_cls: 4.4752, loss: 4.4752 +2024-12-27 18:21:30,907 - pyskl - INFO - Epoch [47][1000/3746] lr: 7.831e-02, eta: 3 days, 14:27:36, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4683, loss_cls: 4.4973, loss: 4.4973 +2024-12-27 18:22:55,674 - pyskl - INFO - Epoch [47][1100/3746] lr: 7.828e-02, eta: 3 days, 14:26:26, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4673, loss_cls: 4.4977, loss: 4.4977 +2024-12-27 18:24:21,073 - pyskl - INFO - Epoch [47][1200/3746] lr: 7.826e-02, eta: 3 days, 14:25:18, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4761, loss_cls: 4.4484, loss: 4.4484 +2024-12-27 18:25:45,802 - pyskl - INFO - Epoch [47][1300/3746] lr: 7.824e-02, eta: 3 days, 14:24:08, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4767, loss_cls: 4.4464, loss: 4.4464 +2024-12-27 18:27:10,571 - pyskl - INFO - Epoch [47][1400/3746] lr: 7.821e-02, eta: 3 days, 14:22:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4755, loss_cls: 4.4691, loss: 4.4691 +2024-12-27 18:28:35,833 - pyskl - INFO - Epoch [47][1500/3746] lr: 7.819e-02, eta: 3 days, 14:21:50, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4641, loss_cls: 4.5043, loss: 4.5043 +2024-12-27 18:30:00,523 - pyskl - INFO - Epoch [47][1600/3746] lr: 7.817e-02, eta: 3 days, 14:20:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4745, loss_cls: 4.4551, loss: 4.4551 +2024-12-27 18:31:25,206 - pyskl - INFO - Epoch [47][1700/3746] lr: 7.814e-02, eta: 3 days, 14:19:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4748, loss_cls: 4.4725, loss: 4.4725 +2024-12-27 18:32:49,413 - pyskl - INFO - Epoch [47][1800/3746] lr: 7.812e-02, eta: 3 days, 14:18:19, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4797, loss_cls: 4.4405, loss: 4.4405 +2024-12-27 18:34:13,916 - pyskl - INFO - Epoch [47][1900/3746] lr: 7.810e-02, eta: 3 days, 14:17:09, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4739, loss_cls: 4.4504, loss: 4.4504 +2024-12-27 18:35:39,035 - pyskl - INFO - Epoch [47][2000/3746] lr: 7.808e-02, eta: 3 days, 14:16:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4667, loss_cls: 4.5164, loss: 4.5164 +2024-12-27 18:37:03,794 - pyskl - INFO - Epoch [47][2100/3746] lr: 7.805e-02, eta: 3 days, 14:14:50, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4698, loss_cls: 4.4770, loss: 4.4770 +2024-12-27 18:38:28,671 - pyskl - INFO - Epoch [47][2200/3746] lr: 7.803e-02, eta: 3 days, 14:13:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4775, loss_cls: 4.4737, loss: 4.4737 +2024-12-27 18:39:53,853 - pyskl - INFO - Epoch [47][2300/3746] lr: 7.801e-02, eta: 3 days, 14:12:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4623, loss_cls: 4.5281, loss: 4.5281 +2024-12-27 18:41:19,196 - pyskl - INFO - Epoch [47][2400/3746] lr: 7.798e-02, eta: 3 days, 14:11:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4759, loss_cls: 4.4437, loss: 4.4437 +2024-12-27 18:42:44,732 - pyskl - INFO - Epoch [47][2500/3746] lr: 7.796e-02, eta: 3 days, 14:10:15, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4689, loss_cls: 4.4983, loss: 4.4983 +2024-12-27 18:44:09,563 - pyskl - INFO - Epoch [47][2600/3746] lr: 7.794e-02, eta: 3 days, 14:09:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4742, loss_cls: 4.4314, loss: 4.4314 +2024-12-27 18:45:34,432 - pyskl - INFO - Epoch [47][2700/3746] lr: 7.791e-02, eta: 3 days, 14:07:55, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4716, loss_cls: 4.4829, loss: 4.4829 +2024-12-27 18:46:59,972 - pyskl - INFO - Epoch [47][2800/3746] lr: 7.789e-02, eta: 3 days, 14:06:47, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4669, loss_cls: 4.4815, loss: 4.4815 +2024-12-27 18:48:25,347 - pyskl - INFO - Epoch [47][2900/3746] lr: 7.787e-02, eta: 3 days, 14:05:38, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4622, loss_cls: 4.4907, loss: 4.4907 +2024-12-27 18:49:49,850 - pyskl - INFO - Epoch [47][3000/3746] lr: 7.784e-02, eta: 3 days, 14:04:28, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4703, loss_cls: 4.4636, loss: 4.4636 +2024-12-27 18:51:14,379 - pyskl - INFO - Epoch [47][3100/3746] lr: 7.782e-02, eta: 3 days, 14:03:17, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4747, loss_cls: 4.4579, loss: 4.4579 +2024-12-27 18:52:39,791 - pyskl - INFO - Epoch [47][3200/3746] lr: 7.780e-02, eta: 3 days, 14:02:09, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4742, loss_cls: 4.4847, loss: 4.4847 +2024-12-27 18:54:05,382 - pyskl - INFO - Epoch [47][3300/3746] lr: 7.777e-02, eta: 3 days, 14:01:00, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4725, loss_cls: 4.4581, loss: 4.4581 +2024-12-27 18:55:29,879 - pyskl - INFO - Epoch [47][3400/3746] lr: 7.775e-02, eta: 3 days, 13:59:50, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4705, loss_cls: 4.5102, loss: 4.5102 +2024-12-27 18:56:53,997 - pyskl - INFO - Epoch [47][3500/3746] lr: 7.773e-02, eta: 3 days, 13:58:38, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4627, loss_cls: 4.5061, loss: 4.5061 +2024-12-27 18:58:18,651 - pyskl - INFO - Epoch [47][3600/3746] lr: 7.770e-02, eta: 3 days, 13:57:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4783, loss_cls: 4.4256, loss: 4.4256 +2024-12-27 18:59:42,984 - pyskl - INFO - Epoch [47][3700/3746] lr: 7.768e-02, eta: 3 days, 13:56:17, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4698, loss_cls: 4.4714, loss: 4.4714 +2024-12-27 19:00:23,929 - pyskl - INFO - Saving checkpoint at 47 epochs +2024-12-27 19:02:22,359 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 19:02:23,375 - pyskl - INFO - +top1_acc 0.1736 +top5_acc 0.3765 +2024-12-27 19:02:23,376 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 19:02:23,420 - pyskl - INFO - +mean_acc 0.1734 +2024-12-27 19:02:23,435 - pyskl - INFO - Epoch(val) [47][309] top1_acc: 0.1736, top5_acc: 0.3765, mean_class_accuracy: 0.1734 +2024-12-27 19:06:51,104 - pyskl - INFO - Epoch [48][100/3746] lr: 7.765e-02, eta: 3 days, 13:59:50, time: 2.677, data_time: 1.620, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4797, loss_cls: 4.4206, loss: 4.4206 +2024-12-27 19:08:16,748 - pyskl - INFO - Epoch [48][200/3746] lr: 7.762e-02, eta: 3 days, 13:58:41, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4936, loss_cls: 4.3856, loss: 4.3856 +2024-12-27 19:09:42,926 - pyskl - INFO - Epoch [48][300/3746] lr: 7.760e-02, eta: 3 days, 13:57:34, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4767, loss_cls: 4.4514, loss: 4.4514 +2024-12-27 19:11:08,944 - pyskl - INFO - Epoch [48][400/3746] lr: 7.758e-02, eta: 3 days, 13:56:26, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4692, loss_cls: 4.5043, loss: 4.5043 +2024-12-27 19:12:34,016 - pyskl - INFO - Epoch [48][500/3746] lr: 7.755e-02, eta: 3 days, 13:55:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4802, loss_cls: 4.4445, loss: 4.4445 +2024-12-27 19:13:59,420 - pyskl - INFO - Epoch [48][600/3746] lr: 7.753e-02, eta: 3 days, 13:54:07, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4734, loss_cls: 4.4556, loss: 4.4556 +2024-12-27 19:15:24,153 - pyskl - INFO - Epoch [48][700/3746] lr: 7.751e-02, eta: 3 days, 13:52:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4730, loss_cls: 4.4592, loss: 4.4592 +2024-12-27 19:16:49,861 - pyskl - INFO - Epoch [48][800/3746] lr: 7.748e-02, eta: 3 days, 13:51:48, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4727, loss_cls: 4.4664, loss: 4.4664 +2024-12-27 19:18:14,227 - pyskl - INFO - Epoch [48][900/3746] lr: 7.746e-02, eta: 3 days, 13:50:37, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4789, loss_cls: 4.4442, loss: 4.4442 +2024-12-27 19:19:39,307 - pyskl - INFO - Epoch [48][1000/3746] lr: 7.744e-02, eta: 3 days, 13:49:27, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4817, loss_cls: 4.4348, loss: 4.4348 +2024-12-27 19:21:04,368 - pyskl - INFO - Epoch [48][1100/3746] lr: 7.741e-02, eta: 3 days, 13:48:17, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4770, loss_cls: 4.4703, loss: 4.4703 +2024-12-27 19:22:29,456 - pyskl - INFO - Epoch [48][1200/3746] lr: 7.739e-02, eta: 3 days, 13:47:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4642, loss_cls: 4.5032, loss: 4.5032 +2024-12-27 19:23:54,691 - pyskl - INFO - Epoch [48][1300/3746] lr: 7.737e-02, eta: 3 days, 13:45:58, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4714, loss_cls: 4.4668, loss: 4.4668 +2024-12-27 19:25:20,973 - pyskl - INFO - Epoch [48][1400/3746] lr: 7.734e-02, eta: 3 days, 13:44:50, time: 0.863, data_time: 0.001, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4808, loss_cls: 4.4111, loss: 4.4111 +2024-12-27 19:26:46,815 - pyskl - INFO - Epoch [48][1500/3746] lr: 7.732e-02, eta: 3 days, 13:43:42, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4848, loss_cls: 4.4061, loss: 4.4061 +2024-12-27 19:28:12,494 - pyskl - INFO - Epoch [48][1600/3746] lr: 7.730e-02, eta: 3 days, 13:42:33, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4708, loss_cls: 4.4595, loss: 4.4595 +2024-12-27 19:29:37,967 - pyskl - INFO - Epoch [48][1700/3746] lr: 7.727e-02, eta: 3 days, 13:41:24, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4795, loss_cls: 4.4753, loss: 4.4753 +2024-12-27 19:31:03,859 - pyskl - INFO - Epoch [48][1800/3746] lr: 7.725e-02, eta: 3 days, 13:40:16, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4666, loss_cls: 4.4611, loss: 4.4611 +2024-12-27 19:32:29,483 - pyskl - INFO - Epoch [48][1900/3746] lr: 7.723e-02, eta: 3 days, 13:39:07, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4672, loss_cls: 4.4893, loss: 4.4893 +2024-12-27 19:33:55,162 - pyskl - INFO - Epoch [48][2000/3746] lr: 7.720e-02, eta: 3 days, 13:37:58, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4750, loss_cls: 4.4510, loss: 4.4510 +2024-12-27 19:35:20,872 - pyskl - INFO - Epoch [48][2100/3746] lr: 7.718e-02, eta: 3 days, 13:36:50, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4717, loss_cls: 4.4637, loss: 4.4637 +2024-12-27 19:36:46,990 - pyskl - INFO - Epoch [48][2200/3746] lr: 7.716e-02, eta: 3 days, 13:35:42, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4741, loss_cls: 4.4655, loss: 4.4655 +2024-12-27 19:38:12,922 - pyskl - INFO - Epoch [48][2300/3746] lr: 7.713e-02, eta: 3 days, 13:34:34, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4711, loss_cls: 4.4807, loss: 4.4807 +2024-12-27 19:39:38,995 - pyskl - INFO - Epoch [48][2400/3746] lr: 7.711e-02, eta: 3 days, 13:33:26, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4669, loss_cls: 4.4731, loss: 4.4731 +2024-12-27 19:41:05,626 - pyskl - INFO - Epoch [48][2500/3746] lr: 7.709e-02, eta: 3 days, 13:32:19, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4612, loss_cls: 4.5338, loss: 4.5338 +2024-12-27 19:42:31,546 - pyskl - INFO - Epoch [48][2600/3746] lr: 7.706e-02, eta: 3 days, 13:31:10, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4661, loss_cls: 4.4893, loss: 4.4893 +2024-12-27 19:43:57,633 - pyskl - INFO - Epoch [48][2700/3746] lr: 7.704e-02, eta: 3 days, 13:30:02, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4734, loss_cls: 4.4709, loss: 4.4709 +2024-12-27 19:45:23,779 - pyskl - INFO - Epoch [48][2800/3746] lr: 7.701e-02, eta: 3 days, 13:28:54, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4688, loss_cls: 4.4645, loss: 4.4645 +2024-12-27 19:46:49,297 - pyskl - INFO - Epoch [48][2900/3746] lr: 7.699e-02, eta: 3 days, 13:27:45, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4716, loss_cls: 4.4865, loss: 4.4865 +2024-12-27 19:48:15,051 - pyskl - INFO - Epoch [48][3000/3746] lr: 7.697e-02, eta: 3 days, 13:26:36, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4822, loss_cls: 4.4107, loss: 4.4107 +2024-12-27 19:49:40,683 - pyskl - INFO - Epoch [48][3100/3746] lr: 7.694e-02, eta: 3 days, 13:25:27, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4759, loss_cls: 4.4358, loss: 4.4358 +2024-12-27 19:51:06,769 - pyskl - INFO - Epoch [48][3200/3746] lr: 7.692e-02, eta: 3 days, 13:24:19, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4605, loss_cls: 4.5109, loss: 4.5109 +2024-12-27 19:52:32,158 - pyskl - INFO - Epoch [48][3300/3746] lr: 7.690e-02, eta: 3 days, 13:23:10, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4684, loss_cls: 4.4682, loss: 4.4682 +2024-12-27 19:53:57,389 - pyskl - INFO - Epoch [48][3400/3746] lr: 7.687e-02, eta: 3 days, 13:22:00, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4666, loss_cls: 4.5023, loss: 4.5023 +2024-12-27 19:55:22,140 - pyskl - INFO - Epoch [48][3500/3746] lr: 7.685e-02, eta: 3 days, 13:20:49, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4627, loss_cls: 4.5185, loss: 4.5185 +2024-12-27 19:56:47,336 - pyskl - INFO - Epoch [48][3600/3746] lr: 7.683e-02, eta: 3 days, 13:19:39, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4772, loss_cls: 4.4318, loss: 4.4318 +2024-12-27 19:58:13,224 - pyskl - INFO - Epoch [48][3700/3746] lr: 7.680e-02, eta: 3 days, 13:18:30, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4692, loss_cls: 4.4768, loss: 4.4768 +2024-12-27 19:58:55,073 - pyskl - INFO - Saving checkpoint at 48 epochs +2024-12-27 20:00:55,391 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 20:00:56,346 - pyskl - INFO - +top1_acc 0.1752 +top5_acc 0.3917 +2024-12-27 20:00:56,346 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 20:00:56,412 - pyskl - INFO - +mean_acc 0.1750 +2024-12-27 20:00:56,420 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_34.pth was removed +2024-12-27 20:00:56,830 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_48.pth. +2024-12-27 20:00:56,831 - pyskl - INFO - Best top1_acc is 0.1752 at 48 epoch. +2024-12-27 20:00:56,851 - pyskl - INFO - Epoch(val) [48][309] top1_acc: 0.1752, top5_acc: 0.3917, mean_class_accuracy: 0.1750 +2024-12-27 20:05:22,130 - pyskl - INFO - Epoch [49][100/3746] lr: 7.677e-02, eta: 3 days, 13:21:47, time: 2.653, data_time: 1.588, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4816, loss_cls: 4.4372, loss: 4.4372 +2024-12-27 20:06:48,067 - pyskl - INFO - Epoch [49][200/3746] lr: 7.674e-02, eta: 3 days, 13:20:38, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4833, loss_cls: 4.3918, loss: 4.3918 +2024-12-27 20:08:14,548 - pyskl - INFO - Epoch [49][300/3746] lr: 7.672e-02, eta: 3 days, 13:19:30, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4763, loss_cls: 4.4468, loss: 4.4468 +2024-12-27 20:09:40,312 - pyskl - INFO - Epoch [49][400/3746] lr: 7.670e-02, eta: 3 days, 13:18:21, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4692, loss_cls: 4.4671, loss: 4.4671 +2024-12-27 20:11:05,760 - pyskl - INFO - Epoch [49][500/3746] lr: 7.667e-02, eta: 3 days, 13:17:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4777, loss_cls: 4.4427, loss: 4.4427 +2024-12-27 20:12:31,454 - pyskl - INFO - Epoch [49][600/3746] lr: 7.665e-02, eta: 3 days, 13:16:02, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4775, loss_cls: 4.4075, loss: 4.4075 +2024-12-27 20:13:56,783 - pyskl - INFO - Epoch [49][700/3746] lr: 7.663e-02, eta: 3 days, 13:14:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4742, loss_cls: 4.4618, loss: 4.4618 +2024-12-27 20:15:22,102 - pyskl - INFO - Epoch [49][800/3746] lr: 7.660e-02, eta: 3 days, 13:13:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4739, loss_cls: 4.4653, loss: 4.4653 +2024-12-27 20:16:47,500 - pyskl - INFO - Epoch [49][900/3746] lr: 7.658e-02, eta: 3 days, 13:12:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4769, loss_cls: 4.4610, loss: 4.4610 +2024-12-27 20:18:12,816 - pyskl - INFO - Epoch [49][1000/3746] lr: 7.656e-02, eta: 3 days, 13:11:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4714, loss_cls: 4.4676, loss: 4.4676 +2024-12-27 20:19:38,161 - pyskl - INFO - Epoch [49][1100/3746] lr: 7.653e-02, eta: 3 days, 13:10:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4736, loss_cls: 4.4674, loss: 4.4674 +2024-12-27 20:21:03,043 - pyskl - INFO - Epoch [49][1200/3746] lr: 7.651e-02, eta: 3 days, 13:09:00, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4764, loss_cls: 4.4337, loss: 4.4337 +2024-12-27 20:22:28,189 - pyskl - INFO - Epoch [49][1300/3746] lr: 7.648e-02, eta: 3 days, 13:07:49, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4791, loss_cls: 4.4364, loss: 4.4364 +2024-12-27 20:23:53,469 - pyskl - INFO - Epoch [49][1400/3746] lr: 7.646e-02, eta: 3 days, 13:06:39, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4713, loss_cls: 4.4786, loss: 4.4786 +2024-12-27 20:25:19,221 - pyskl - INFO - Epoch [49][1500/3746] lr: 7.644e-02, eta: 3 days, 13:05:29, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4788, loss_cls: 4.4286, loss: 4.4286 +2024-12-27 20:26:45,199 - pyskl - INFO - Epoch [49][1600/3746] lr: 7.641e-02, eta: 3 days, 13:04:20, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4719, loss_cls: 4.4364, loss: 4.4364 +2024-12-27 20:28:10,921 - pyskl - INFO - Epoch [49][1700/3746] lr: 7.639e-02, eta: 3 days, 13:03:11, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4692, loss_cls: 4.4533, loss: 4.4533 +2024-12-27 20:29:36,573 - pyskl - INFO - Epoch [49][1800/3746] lr: 7.637e-02, eta: 3 days, 13:02:01, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4750, loss_cls: 4.4625, loss: 4.4625 +2024-12-27 20:31:02,528 - pyskl - INFO - Epoch [49][1900/3746] lr: 7.634e-02, eta: 3 days, 13:00:52, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4722, loss_cls: 4.4798, loss: 4.4798 +2024-12-27 20:32:28,577 - pyskl - INFO - Epoch [49][2000/3746] lr: 7.632e-02, eta: 3 days, 12:59:43, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4767, loss_cls: 4.4300, loss: 4.4300 +2024-12-27 20:33:54,256 - pyskl - INFO - Epoch [49][2100/3746] lr: 7.629e-02, eta: 3 days, 12:58:33, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4702, loss_cls: 4.4649, loss: 4.4649 +2024-12-27 20:35:20,036 - pyskl - INFO - Epoch [49][2200/3746] lr: 7.627e-02, eta: 3 days, 12:57:24, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4642, loss_cls: 4.5006, loss: 4.5006 +2024-12-27 20:36:45,706 - pyskl - INFO - Epoch [49][2300/3746] lr: 7.625e-02, eta: 3 days, 12:56:14, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4781, loss_cls: 4.4250, loss: 4.4250 +2024-12-27 20:38:11,469 - pyskl - INFO - Epoch [49][2400/3746] lr: 7.622e-02, eta: 3 days, 12:55:04, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4683, loss_cls: 4.4788, loss: 4.4788 +2024-12-27 20:39:37,762 - pyskl - INFO - Epoch [49][2500/3746] lr: 7.620e-02, eta: 3 days, 12:53:56, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4694, loss_cls: 4.4579, loss: 4.4579 +2024-12-27 20:41:03,655 - pyskl - INFO - Epoch [49][2600/3746] lr: 7.618e-02, eta: 3 days, 12:52:47, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4681, loss_cls: 4.4733, loss: 4.4733 +2024-12-27 20:42:29,596 - pyskl - INFO - Epoch [49][2700/3746] lr: 7.615e-02, eta: 3 days, 12:51:37, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4823, loss_cls: 4.4181, loss: 4.4181 +2024-12-27 20:43:54,652 - pyskl - INFO - Epoch [49][2800/3746] lr: 7.613e-02, eta: 3 days, 12:50:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4614, loss_cls: 4.5331, loss: 4.5331 +2024-12-27 20:45:19,588 - pyskl - INFO - Epoch [49][2900/3746] lr: 7.610e-02, eta: 3 days, 12:49:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4672, loss_cls: 4.4711, loss: 4.4711 +2024-12-27 20:46:45,127 - pyskl - INFO - Epoch [49][3000/3746] lr: 7.608e-02, eta: 3 days, 12:48:05, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4731, loss_cls: 4.4803, loss: 4.4803 +2024-12-27 20:48:11,177 - pyskl - INFO - Epoch [49][3100/3746] lr: 7.606e-02, eta: 3 days, 12:46:56, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4733, loss_cls: 4.4376, loss: 4.4376 +2024-12-27 20:49:36,778 - pyskl - INFO - Epoch [49][3200/3746] lr: 7.603e-02, eta: 3 days, 12:45:46, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4772, loss_cls: 4.4834, loss: 4.4834 +2024-12-27 20:51:02,066 - pyskl - INFO - Epoch [49][3300/3746] lr: 7.601e-02, eta: 3 days, 12:44:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4672, loss_cls: 4.4933, loss: 4.4933 +2024-12-27 20:52:27,115 - pyskl - INFO - Epoch [49][3400/3746] lr: 7.598e-02, eta: 3 days, 12:43:24, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4670, loss_cls: 4.4773, loss: 4.4773 +2024-12-27 20:53:51,712 - pyskl - INFO - Epoch [49][3500/3746] lr: 7.596e-02, eta: 3 days, 12:42:11, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4594, loss_cls: 4.4848, loss: 4.4848 +2024-12-27 20:55:16,732 - pyskl - INFO - Epoch [49][3600/3746] lr: 7.594e-02, eta: 3 days, 12:41:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4766, loss_cls: 4.4790, loss: 4.4790 +2024-12-27 20:56:43,024 - pyskl - INFO - Epoch [49][3700/3746] lr: 7.591e-02, eta: 3 days, 12:39:51, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4755, loss_cls: 4.4383, loss: 4.4383 +2024-12-27 20:57:24,366 - pyskl - INFO - Saving checkpoint at 49 epochs +2024-12-27 20:59:24,672 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 20:59:25,687 - pyskl - INFO - +top1_acc 0.1647 +top5_acc 0.3672 +2024-12-27 20:59:25,687 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 20:59:25,737 - pyskl - INFO - +mean_acc 0.1646 +2024-12-27 20:59:25,751 - pyskl - INFO - Epoch(val) [49][309] top1_acc: 0.1647, top5_acc: 0.3672, mean_class_accuracy: 0.1646 +2024-12-27 21:03:47,008 - pyskl - INFO - Epoch [50][100/3746] lr: 7.588e-02, eta: 3 days, 12:42:50, time: 2.612, data_time: 1.586, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4772, loss_cls: 4.4198, loss: 4.4198 +2024-12-27 21:05:11,978 - pyskl - INFO - Epoch [50][200/3746] lr: 7.585e-02, eta: 3 days, 12:41:38, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4934, loss_cls: 4.3873, loss: 4.3873 +2024-12-27 21:06:36,824 - pyskl - INFO - Epoch [50][300/3746] lr: 7.583e-02, eta: 3 days, 12:40:26, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4878, loss_cls: 4.3990, loss: 4.3990 +2024-12-27 21:08:01,865 - pyskl - INFO - Epoch [50][400/3746] lr: 7.581e-02, eta: 3 days, 12:39:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4820, loss_cls: 4.4273, loss: 4.4273 +2024-12-27 21:09:26,383 - pyskl - INFO - Epoch [50][500/3746] lr: 7.578e-02, eta: 3 days, 12:38:02, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4823, loss_cls: 4.4067, loss: 4.4067 +2024-12-27 21:10:51,105 - pyskl - INFO - Epoch [50][600/3746] lr: 7.576e-02, eta: 3 days, 12:36:50, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4778, loss_cls: 4.4213, loss: 4.4213 +2024-12-27 21:12:16,102 - pyskl - INFO - Epoch [50][700/3746] lr: 7.573e-02, eta: 3 days, 12:35:38, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4780, loss_cls: 4.4371, loss: 4.4371 +2024-12-27 21:13:40,754 - pyskl - INFO - Epoch [50][800/3746] lr: 7.571e-02, eta: 3 days, 12:34:25, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4788, loss_cls: 4.4408, loss: 4.4408 +2024-12-27 21:15:05,343 - pyskl - INFO - Epoch [50][900/3746] lr: 7.569e-02, eta: 3 days, 12:33:13, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4752, loss_cls: 4.4455, loss: 4.4455 +2024-12-27 21:16:30,575 - pyskl - INFO - Epoch [50][1000/3746] lr: 7.566e-02, eta: 3 days, 12:32:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4720, loss_cls: 4.4773, loss: 4.4773 +2024-12-27 21:17:55,442 - pyskl - INFO - Epoch [50][1100/3746] lr: 7.564e-02, eta: 3 days, 12:30:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4733, loss_cls: 4.4693, loss: 4.4693 +2024-12-27 21:19:20,803 - pyskl - INFO - Epoch [50][1200/3746] lr: 7.561e-02, eta: 3 days, 12:29:38, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4616, loss_cls: 4.4981, loss: 4.4981 +2024-12-27 21:20:45,445 - pyskl - INFO - Epoch [50][1300/3746] lr: 7.559e-02, eta: 3 days, 12:28:26, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4894, loss_cls: 4.4042, loss: 4.4042 +2024-12-27 21:22:10,889 - pyskl - INFO - Epoch [50][1400/3746] lr: 7.557e-02, eta: 3 days, 12:27:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4752, loss_cls: 4.4312, loss: 4.4312 +2024-12-27 21:23:35,981 - pyskl - INFO - Epoch [50][1500/3746] lr: 7.554e-02, eta: 3 days, 12:26:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4625, loss_cls: 4.5030, loss: 4.5030 +2024-12-27 21:25:00,686 - pyskl - INFO - Epoch [50][1600/3746] lr: 7.552e-02, eta: 3 days, 12:24:51, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4742, loss_cls: 4.4701, loss: 4.4701 +2024-12-27 21:26:25,609 - pyskl - INFO - Epoch [50][1700/3746] lr: 7.549e-02, eta: 3 days, 12:23:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4733, loss_cls: 4.4299, loss: 4.4299 +2024-12-27 21:27:50,669 - pyskl - INFO - Epoch [50][1800/3746] lr: 7.547e-02, eta: 3 days, 12:22:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4828, loss_cls: 4.4236, loss: 4.4236 +2024-12-27 21:29:15,855 - pyskl - INFO - Epoch [50][1900/3746] lr: 7.545e-02, eta: 3 days, 12:21:16, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4784, loss_cls: 4.4531, loss: 4.4531 +2024-12-27 21:30:41,525 - pyskl - INFO - Epoch [50][2000/3746] lr: 7.542e-02, eta: 3 days, 12:20:05, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4764, loss_cls: 4.4472, loss: 4.4472 +2024-12-27 21:32:06,535 - pyskl - INFO - Epoch [50][2100/3746] lr: 7.540e-02, eta: 3 days, 12:18:53, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4691, loss_cls: 4.4518, loss: 4.4518 +2024-12-27 21:33:31,698 - pyskl - INFO - Epoch [50][2200/3746] lr: 7.537e-02, eta: 3 days, 12:17:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4817, loss_cls: 4.4196, loss: 4.4196 +2024-12-27 21:34:56,650 - pyskl - INFO - Epoch [50][2300/3746] lr: 7.535e-02, eta: 3 days, 12:16:30, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4786, loss_cls: 4.4282, loss: 4.4282 +2024-12-27 21:36:21,202 - pyskl - INFO - Epoch [50][2400/3746] lr: 7.533e-02, eta: 3 days, 12:15:17, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4709, loss_cls: 4.4926, loss: 4.4926 +2024-12-27 21:37:46,254 - pyskl - INFO - Epoch [50][2500/3746] lr: 7.530e-02, eta: 3 days, 12:14:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4611, loss_cls: 4.5094, loss: 4.5094 +2024-12-27 21:39:11,901 - pyskl - INFO - Epoch [50][2600/3746] lr: 7.528e-02, eta: 3 days, 12:12:54, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4759, loss_cls: 4.4795, loss: 4.4795 +2024-12-27 21:40:36,825 - pyskl - INFO - Epoch [50][2700/3746] lr: 7.525e-02, eta: 3 days, 12:11:42, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4675, loss_cls: 4.4928, loss: 4.4928 +2024-12-27 21:42:02,423 - pyskl - INFO - Epoch [50][2800/3746] lr: 7.523e-02, eta: 3 days, 12:10:31, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4808, loss_cls: 4.4069, loss: 4.4069 +2024-12-27 21:43:27,467 - pyskl - INFO - Epoch [50][2900/3746] lr: 7.520e-02, eta: 3 days, 12:09:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4716, loss_cls: 4.4650, loss: 4.4650 +2024-12-27 21:44:51,565 - pyskl - INFO - Epoch [50][3000/3746] lr: 7.518e-02, eta: 3 days, 12:08:06, time: 0.841, data_time: 0.001, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4806, loss_cls: 4.4399, loss: 4.4399 +2024-12-27 21:46:16,421 - pyskl - INFO - Epoch [50][3100/3746] lr: 7.516e-02, eta: 3 days, 12:06:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4786, loss_cls: 4.4504, loss: 4.4504 +2024-12-27 21:47:41,134 - pyskl - INFO - Epoch [50][3200/3746] lr: 7.513e-02, eta: 3 days, 12:05:41, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4730, loss_cls: 4.4756, loss: 4.4756 +2024-12-27 21:49:05,560 - pyskl - INFO - Epoch [50][3300/3746] lr: 7.511e-02, eta: 3 days, 12:04:27, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4642, loss_cls: 4.4804, loss: 4.4804 +2024-12-27 21:50:30,102 - pyskl - INFO - Epoch [50][3400/3746] lr: 7.508e-02, eta: 3 days, 12:03:14, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4775, loss_cls: 4.4616, loss: 4.4616 +2024-12-27 21:51:54,353 - pyskl - INFO - Epoch [50][3500/3746] lr: 7.506e-02, eta: 3 days, 12:02:01, time: 0.843, data_time: 0.001, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4728, loss_cls: 4.4669, loss: 4.4669 +2024-12-27 21:53:18,708 - pyskl - INFO - Epoch [50][3600/3746] lr: 7.504e-02, eta: 3 days, 12:00:47, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4816, loss_cls: 4.4368, loss: 4.4368 +2024-12-27 21:54:43,267 - pyskl - INFO - Epoch [50][3700/3746] lr: 7.501e-02, eta: 3 days, 11:59:34, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4672, loss_cls: 4.5268, loss: 4.5268 +2024-12-27 21:55:24,238 - pyskl - INFO - Saving checkpoint at 50 epochs +2024-12-27 21:57:24,339 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 21:57:25,036 - pyskl - INFO - +top1_acc 0.1627 +top5_acc 0.3744 +2024-12-27 21:57:25,036 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 21:57:25,081 - pyskl - INFO - +mean_acc 0.1624 +2024-12-27 21:57:25,096 - pyskl - INFO - Epoch(val) [50][309] top1_acc: 0.1627, top5_acc: 0.3744, mean_class_accuracy: 0.1624 +2024-12-27 22:01:43,465 - pyskl - INFO - Epoch [51][100/3746] lr: 7.498e-02, eta: 3 days, 12:02:17, time: 2.584, data_time: 1.564, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4856, loss_cls: 4.4276, loss: 4.4276 +2024-12-27 22:03:08,523 - pyskl - INFO - Epoch [51][200/3746] lr: 7.495e-02, eta: 3 days, 12:01:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4689, loss_cls: 4.4447, loss: 4.4447 +2024-12-27 22:04:33,568 - pyskl - INFO - Epoch [51][300/3746] lr: 7.493e-02, eta: 3 days, 11:59:53, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4788, loss_cls: 4.4046, loss: 4.4046 +2024-12-27 22:05:59,033 - pyskl - INFO - Epoch [51][400/3746] lr: 7.490e-02, eta: 3 days, 11:58:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4789, loss_cls: 4.4369, loss: 4.4369 +2024-12-27 22:07:23,748 - pyskl - INFO - Epoch [51][500/3746] lr: 7.488e-02, eta: 3 days, 11:57:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4769, loss_cls: 4.4324, loss: 4.4324 +2024-12-27 22:08:48,449 - pyskl - INFO - Epoch [51][600/3746] lr: 7.485e-02, eta: 3 days, 11:56:15, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4811, loss_cls: 4.4265, loss: 4.4265 +2024-12-27 22:10:13,222 - pyskl - INFO - Epoch [51][700/3746] lr: 7.483e-02, eta: 3 days, 11:55:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4759, loss_cls: 4.4334, loss: 4.4334 +2024-12-27 22:11:37,791 - pyskl - INFO - Epoch [51][800/3746] lr: 7.481e-02, eta: 3 days, 11:53:49, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4713, loss_cls: 4.4713, loss: 4.4713 +2024-12-27 22:13:02,427 - pyskl - INFO - Epoch [51][900/3746] lr: 7.478e-02, eta: 3 days, 11:52:36, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4852, loss_cls: 4.4366, loss: 4.4366 +2024-12-27 22:14:26,828 - pyskl - INFO - Epoch [51][1000/3746] lr: 7.476e-02, eta: 3 days, 11:51:22, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4786, loss_cls: 4.4188, loss: 4.4188 +2024-12-27 22:15:50,843 - pyskl - INFO - Epoch [51][1100/3746] lr: 7.473e-02, eta: 3 days, 11:50:08, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4644, loss_cls: 4.4934, loss: 4.4934 +2024-12-27 22:17:15,090 - pyskl - INFO - Epoch [51][1200/3746] lr: 7.471e-02, eta: 3 days, 11:48:54, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4709, loss_cls: 4.4843, loss: 4.4843 +2024-12-27 22:18:39,307 - pyskl - INFO - Epoch [51][1300/3746] lr: 7.468e-02, eta: 3 days, 11:47:40, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4834, loss_cls: 4.4080, loss: 4.4080 +2024-12-27 22:20:03,987 - pyskl - INFO - Epoch [51][1400/3746] lr: 7.466e-02, eta: 3 days, 11:46:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4770, loss_cls: 4.4562, loss: 4.4562 +2024-12-27 22:21:28,561 - pyskl - INFO - Epoch [51][1500/3746] lr: 7.464e-02, eta: 3 days, 11:45:13, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4805, loss_cls: 4.4010, loss: 4.4010 +2024-12-27 22:22:53,393 - pyskl - INFO - Epoch [51][1600/3746] lr: 7.461e-02, eta: 3 days, 11:44:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4816, loss_cls: 4.4473, loss: 4.4473 +2024-12-27 22:24:18,039 - pyskl - INFO - Epoch [51][1700/3746] lr: 7.459e-02, eta: 3 days, 11:42:47, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4900, loss_cls: 4.4141, loss: 4.4141 +2024-12-27 22:25:43,517 - pyskl - INFO - Epoch [51][1800/3746] lr: 7.456e-02, eta: 3 days, 11:41:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4819, loss_cls: 4.4294, loss: 4.4294 +2024-12-27 22:27:08,170 - pyskl - INFO - Epoch [51][1900/3746] lr: 7.454e-02, eta: 3 days, 11:40:22, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4655, loss_cls: 4.5166, loss: 4.5166 +2024-12-27 22:28:32,781 - pyskl - INFO - Epoch [51][2000/3746] lr: 7.451e-02, eta: 3 days, 11:39:09, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4781, loss_cls: 4.4317, loss: 4.4317 +2024-12-27 22:29:58,018 - pyskl - INFO - Epoch [51][2100/3746] lr: 7.449e-02, eta: 3 days, 11:37:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4777, loss_cls: 4.4650, loss: 4.4650 +2024-12-27 22:31:22,925 - pyskl - INFO - Epoch [51][2200/3746] lr: 7.447e-02, eta: 3 days, 11:36:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4813, loss_cls: 4.4267, loss: 4.4267 +2024-12-27 22:32:48,065 - pyskl - INFO - Epoch [51][2300/3746] lr: 7.444e-02, eta: 3 days, 11:35:32, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4752, loss_cls: 4.4519, loss: 4.4519 +2024-12-27 22:34:13,026 - pyskl - INFO - Epoch [51][2400/3746] lr: 7.442e-02, eta: 3 days, 11:34:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4853, loss_cls: 4.3874, loss: 4.3874 +2024-12-27 22:35:37,203 - pyskl - INFO - Epoch [51][2500/3746] lr: 7.439e-02, eta: 3 days, 11:33:05, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4884, loss_cls: 4.4082, loss: 4.4082 +2024-12-27 22:37:01,836 - pyskl - INFO - Epoch [51][2600/3746] lr: 7.437e-02, eta: 3 days, 11:31:51, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4650, loss_cls: 4.4750, loss: 4.4750 +2024-12-27 22:38:25,899 - pyskl - INFO - Epoch [51][2700/3746] lr: 7.434e-02, eta: 3 days, 11:30:37, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4748, loss_cls: 4.4552, loss: 4.4552 +2024-12-27 22:39:50,691 - pyskl - INFO - Epoch [51][2800/3746] lr: 7.432e-02, eta: 3 days, 11:29:24, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4802, loss_cls: 4.4452, loss: 4.4452 +2024-12-27 22:41:15,492 - pyskl - INFO - Epoch [51][2900/3746] lr: 7.429e-02, eta: 3 days, 11:28:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4675, loss_cls: 4.4636, loss: 4.4636 +2024-12-27 22:42:39,492 - pyskl - INFO - Epoch [51][3000/3746] lr: 7.427e-02, eta: 3 days, 11:26:56, time: 0.840, data_time: 0.001, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4855, loss_cls: 4.4005, loss: 4.4005 +2024-12-27 22:44:04,058 - pyskl - INFO - Epoch [51][3100/3746] lr: 7.425e-02, eta: 3 days, 11:25:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4792, loss_cls: 4.4251, loss: 4.4251 +2024-12-27 22:45:28,334 - pyskl - INFO - Epoch [51][3200/3746] lr: 7.422e-02, eta: 3 days, 11:24:28, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4706, loss_cls: 4.4883, loss: 4.4883 +2024-12-27 22:46:52,373 - pyskl - INFO - Epoch [51][3300/3746] lr: 7.420e-02, eta: 3 days, 11:23:13, time: 0.840, data_time: 0.001, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4738, loss_cls: 4.4720, loss: 4.4720 +2024-12-27 22:48:17,191 - pyskl - INFO - Epoch [51][3400/3746] lr: 7.417e-02, eta: 3 days, 11:22:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4761, loss_cls: 4.4640, loss: 4.4640 +2024-12-27 22:49:41,746 - pyskl - INFO - Epoch [51][3500/3746] lr: 7.415e-02, eta: 3 days, 11:20:47, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4786, loss_cls: 4.4669, loss: 4.4669 +2024-12-27 22:51:05,938 - pyskl - INFO - Epoch [51][3600/3746] lr: 7.412e-02, eta: 3 days, 11:19:32, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4797, loss_cls: 4.4474, loss: 4.4474 +2024-12-27 22:52:30,484 - pyskl - INFO - Epoch [51][3700/3746] lr: 7.410e-02, eta: 3 days, 11:18:19, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4872, loss_cls: 4.3808, loss: 4.3808 +2024-12-27 22:53:11,518 - pyskl - INFO - Saving checkpoint at 51 epochs +2024-12-27 22:55:10,356 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 22:55:11,627 - pyskl - INFO - +top1_acc 0.1725 +top5_acc 0.3862 +2024-12-27 22:55:11,627 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 22:55:11,676 - pyskl - INFO - +mean_acc 0.1722 +2024-12-27 22:55:11,691 - pyskl - INFO - Epoch(val) [51][309] top1_acc: 0.1725, top5_acc: 0.3862, mean_class_accuracy: 0.1722 +2024-12-27 22:59:32,487 - pyskl - INFO - Epoch [52][100/3746] lr: 7.406e-02, eta: 3 days, 11:20:57, time: 2.608, data_time: 1.577, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4942, loss_cls: 4.3393, loss: 4.3393 +2024-12-27 23:00:58,220 - pyskl - INFO - Epoch [52][200/3746] lr: 7.404e-02, eta: 3 days, 11:19:46, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4825, loss_cls: 4.4054, loss: 4.4054 +2024-12-27 23:02:23,445 - pyskl - INFO - Epoch [52][300/3746] lr: 7.401e-02, eta: 3 days, 11:18:33, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4728, loss_cls: 4.4337, loss: 4.4337 +2024-12-27 23:03:48,862 - pyskl - INFO - Epoch [52][400/3746] lr: 7.399e-02, eta: 3 days, 11:17:21, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4953, loss_cls: 4.3360, loss: 4.3360 +2024-12-27 23:05:14,332 - pyskl - INFO - Epoch [52][500/3746] lr: 7.397e-02, eta: 3 days, 11:16:09, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4797, loss_cls: 4.4431, loss: 4.4431 +2024-12-27 23:06:39,617 - pyskl - INFO - Epoch [52][600/3746] lr: 7.394e-02, eta: 3 days, 11:14:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4772, loss_cls: 4.4144, loss: 4.4144 +2024-12-27 23:08:04,224 - pyskl - INFO - Epoch [52][700/3746] lr: 7.392e-02, eta: 3 days, 11:13:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4733, loss_cls: 4.4432, loss: 4.4432 +2024-12-27 23:09:28,317 - pyskl - INFO - Epoch [52][800/3746] lr: 7.389e-02, eta: 3 days, 11:12:27, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4823, loss_cls: 4.4114, loss: 4.4114 +2024-12-27 23:10:52,627 - pyskl - INFO - Epoch [52][900/3746] lr: 7.387e-02, eta: 3 days, 11:11:13, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4827, loss_cls: 4.4360, loss: 4.4360 +2024-12-27 23:12:17,378 - pyskl - INFO - Epoch [52][1000/3746] lr: 7.384e-02, eta: 3 days, 11:09:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4823, loss_cls: 4.4016, loss: 4.4016 +2024-12-27 23:13:42,224 - pyskl - INFO - Epoch [52][1100/3746] lr: 7.382e-02, eta: 3 days, 11:08:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4808, loss_cls: 4.4435, loss: 4.4435 +2024-12-27 23:15:06,459 - pyskl - INFO - Epoch [52][1200/3746] lr: 7.379e-02, eta: 3 days, 11:07:31, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4870, loss_cls: 4.4099, loss: 4.4099 +2024-12-27 23:16:30,374 - pyskl - INFO - Epoch [52][1300/3746] lr: 7.377e-02, eta: 3 days, 11:06:16, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4763, loss_cls: 4.4164, loss: 4.4164 +2024-12-27 23:17:54,594 - pyskl - INFO - Epoch [52][1400/3746] lr: 7.374e-02, eta: 3 days, 11:05:01, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4894, loss_cls: 4.3704, loss: 4.3704 +2024-12-27 23:19:19,172 - pyskl - INFO - Epoch [52][1500/3746] lr: 7.372e-02, eta: 3 days, 11:03:47, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4816, loss_cls: 4.4093, loss: 4.4093 +2024-12-27 23:20:43,893 - pyskl - INFO - Epoch [52][1600/3746] lr: 7.370e-02, eta: 3 days, 11:02:33, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4763, loss_cls: 4.4421, loss: 4.4421 +2024-12-27 23:22:08,171 - pyskl - INFO - Epoch [52][1700/3746] lr: 7.367e-02, eta: 3 days, 11:01:19, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4808, loss_cls: 4.3961, loss: 4.3961 +2024-12-27 23:23:33,299 - pyskl - INFO - Epoch [52][1800/3746] lr: 7.365e-02, eta: 3 days, 11:00:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4678, loss_cls: 4.4718, loss: 4.4718 +2024-12-27 23:24:57,915 - pyskl - INFO - Epoch [52][1900/3746] lr: 7.362e-02, eta: 3 days, 10:58:52, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4722, loss_cls: 4.4372, loss: 4.4372 +2024-12-27 23:26:22,393 - pyskl - INFO - Epoch [52][2000/3746] lr: 7.360e-02, eta: 3 days, 10:57:38, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4781, loss_cls: 4.4418, loss: 4.4418 +2024-12-27 23:27:47,331 - pyskl - INFO - Epoch [52][2100/3746] lr: 7.357e-02, eta: 3 days, 10:56:24, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4733, loss_cls: 4.4655, loss: 4.4655 +2024-12-27 23:29:11,829 - pyskl - INFO - Epoch [52][2200/3746] lr: 7.355e-02, eta: 3 days, 10:55:10, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4813, loss_cls: 4.4374, loss: 4.4374 +2024-12-27 23:30:36,758 - pyskl - INFO - Epoch [52][2300/3746] lr: 7.352e-02, eta: 3 days, 10:53:57, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4727, loss_cls: 4.4865, loss: 4.4865 +2024-12-27 23:32:01,507 - pyskl - INFO - Epoch [52][2400/3746] lr: 7.350e-02, eta: 3 days, 10:52:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4700, loss_cls: 4.4767, loss: 4.4767 +2024-12-27 23:33:26,273 - pyskl - INFO - Epoch [52][2500/3746] lr: 7.347e-02, eta: 3 days, 10:51:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4755, loss_cls: 4.4550, loss: 4.4550 +2024-12-27 23:34:51,158 - pyskl - INFO - Epoch [52][2600/3746] lr: 7.345e-02, eta: 3 days, 10:50:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4864, loss_cls: 4.4116, loss: 4.4116 +2024-12-27 23:36:16,328 - pyskl - INFO - Epoch [52][2700/3746] lr: 7.342e-02, eta: 3 days, 10:49:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4747, loss_cls: 4.4654, loss: 4.4654 +2024-12-27 23:37:40,431 - pyskl - INFO - Epoch [52][2800/3746] lr: 7.340e-02, eta: 3 days, 10:47:47, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4658, loss_cls: 4.5157, loss: 4.5157 +2024-12-27 23:39:04,866 - pyskl - INFO - Epoch [52][2900/3746] lr: 7.337e-02, eta: 3 days, 10:46:33, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4841, loss_cls: 4.4100, loss: 4.4100 +2024-12-27 23:40:29,907 - pyskl - INFO - Epoch [52][3000/3746] lr: 7.335e-02, eta: 3 days, 10:45:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4856, loss_cls: 4.4004, loss: 4.4004 +2024-12-27 23:41:54,524 - pyskl - INFO - Epoch [52][3100/3746] lr: 7.332e-02, eta: 3 days, 10:44:05, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4686, loss_cls: 4.4693, loss: 4.4693 +2024-12-27 23:43:18,777 - pyskl - INFO - Epoch [52][3200/3746] lr: 7.330e-02, eta: 3 days, 10:42:51, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4755, loss_cls: 4.4316, loss: 4.4316 +2024-12-27 23:44:43,508 - pyskl - INFO - Epoch [52][3300/3746] lr: 7.328e-02, eta: 3 days, 10:41:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4791, loss_cls: 4.4500, loss: 4.4500 +2024-12-27 23:46:07,722 - pyskl - INFO - Epoch [52][3400/3746] lr: 7.325e-02, eta: 3 days, 10:40:22, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4827, loss_cls: 4.4318, loss: 4.4318 +2024-12-27 23:47:32,055 - pyskl - INFO - Epoch [52][3500/3746] lr: 7.323e-02, eta: 3 days, 10:39:07, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4777, loss_cls: 4.4196, loss: 4.4196 +2024-12-27 23:48:56,361 - pyskl - INFO - Epoch [52][3600/3746] lr: 7.320e-02, eta: 3 days, 10:37:52, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4806, loss_cls: 4.4400, loss: 4.4400 +2024-12-27 23:50:20,642 - pyskl - INFO - Epoch [52][3700/3746] lr: 7.318e-02, eta: 3 days, 10:36:38, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4742, loss_cls: 4.4189, loss: 4.4189 +2024-12-27 23:51:01,384 - pyskl - INFO - Saving checkpoint at 52 epochs +2024-12-27 23:52:59,637 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 23:53:00,402 - pyskl - INFO - +top1_acc 0.1751 +top5_acc 0.3906 +2024-12-27 23:53:00,402 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 23:53:00,447 - pyskl - INFO - +mean_acc 0.1750 +2024-12-27 23:53:00,466 - pyskl - INFO - Epoch(val) [52][309] top1_acc: 0.1751, top5_acc: 0.3906, mean_class_accuracy: 0.1750 +2024-12-27 23:57:16,665 - pyskl - INFO - Epoch [53][100/3746] lr: 7.314e-02, eta: 3 days, 10:38:59, time: 2.562, data_time: 1.538, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4795, loss_cls: 4.4342, loss: 4.4342 +2024-12-27 23:58:41,458 - pyskl - INFO - Epoch [53][200/3746] lr: 7.312e-02, eta: 3 days, 10:37:45, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4900, loss_cls: 4.3919, loss: 4.3919 +2024-12-28 00:00:06,006 - pyskl - INFO - Epoch [53][300/3746] lr: 7.309e-02, eta: 3 days, 10:36:30, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4922, loss_cls: 4.3683, loss: 4.3683 +2024-12-28 00:01:30,977 - pyskl - INFO - Epoch [53][400/3746] lr: 7.307e-02, eta: 3 days, 10:35:17, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4889, loss_cls: 4.3733, loss: 4.3733 +2024-12-28 00:02:55,353 - pyskl - INFO - Epoch [53][500/3746] lr: 7.304e-02, eta: 3 days, 10:34:02, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4770, loss_cls: 4.4386, loss: 4.4386 +2024-12-28 00:04:19,791 - pyskl - INFO - Epoch [53][600/3746] lr: 7.302e-02, eta: 3 days, 10:32:47, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4788, loss_cls: 4.4520, loss: 4.4520 +2024-12-28 00:05:44,919 - pyskl - INFO - Epoch [53][700/3746] lr: 7.299e-02, eta: 3 days, 10:31:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4767, loss_cls: 4.4433, loss: 4.4433 +2024-12-28 00:07:08,992 - pyskl - INFO - Epoch [53][800/3746] lr: 7.297e-02, eta: 3 days, 10:30:18, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4753, loss_cls: 4.4252, loss: 4.4252 +2024-12-28 00:08:34,220 - pyskl - INFO - Epoch [53][900/3746] lr: 7.294e-02, eta: 3 days, 10:29:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4761, loss_cls: 4.4388, loss: 4.4388 +2024-12-28 00:09:59,091 - pyskl - INFO - Epoch [53][1000/3746] lr: 7.292e-02, eta: 3 days, 10:27:51, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4725, loss_cls: 4.4463, loss: 4.4463 +2024-12-28 00:11:23,806 - pyskl - INFO - Epoch [53][1100/3746] lr: 7.289e-02, eta: 3 days, 10:26:36, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4825, loss_cls: 4.3864, loss: 4.3864 +2024-12-28 00:12:48,243 - pyskl - INFO - Epoch [53][1200/3746] lr: 7.287e-02, eta: 3 days, 10:25:21, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4719, loss_cls: 4.4416, loss: 4.4416 +2024-12-28 00:14:13,111 - pyskl - INFO - Epoch [53][1300/3746] lr: 7.284e-02, eta: 3 days, 10:24:07, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4747, loss_cls: 4.4332, loss: 4.4332 +2024-12-28 00:15:38,008 - pyskl - INFO - Epoch [53][1400/3746] lr: 7.282e-02, eta: 3 days, 10:22:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4845, loss_cls: 4.3979, loss: 4.3979 +2024-12-28 00:17:03,280 - pyskl - INFO - Epoch [53][1500/3746] lr: 7.279e-02, eta: 3 days, 10:21:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4791, loss_cls: 4.4032, loss: 4.4032 +2024-12-28 00:18:28,353 - pyskl - INFO - Epoch [53][1600/3746] lr: 7.277e-02, eta: 3 days, 10:20:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4716, loss_cls: 4.4644, loss: 4.4644 +2024-12-28 00:19:53,310 - pyskl - INFO - Epoch [53][1700/3746] lr: 7.274e-02, eta: 3 days, 10:19:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4827, loss_cls: 4.3977, loss: 4.3977 +2024-12-28 00:21:18,805 - pyskl - INFO - Epoch [53][1800/3746] lr: 7.272e-02, eta: 3 days, 10:18:00, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4833, loss_cls: 4.4526, loss: 4.4526 +2024-12-28 00:22:43,235 - pyskl - INFO - Epoch [53][1900/3746] lr: 7.269e-02, eta: 3 days, 10:16:45, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4759, loss_cls: 4.4401, loss: 4.4401 +2024-12-28 00:24:08,376 - pyskl - INFO - Epoch [53][2000/3746] lr: 7.267e-02, eta: 3 days, 10:15:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4850, loss_cls: 4.4223, loss: 4.4223 +2024-12-28 00:25:33,564 - pyskl - INFO - Epoch [53][2100/3746] lr: 7.264e-02, eta: 3 days, 10:14:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4783, loss_cls: 4.4297, loss: 4.4297 +2024-12-28 00:26:58,297 - pyskl - INFO - Epoch [53][2200/3746] lr: 7.262e-02, eta: 3 days, 10:13:03, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4702, loss_cls: 4.4443, loss: 4.4443 +2024-12-28 00:28:23,241 - pyskl - INFO - Epoch [53][2300/3746] lr: 7.259e-02, eta: 3 days, 10:11:49, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4752, loss_cls: 4.4353, loss: 4.4353 +2024-12-28 00:29:48,240 - pyskl - INFO - Epoch [53][2400/3746] lr: 7.257e-02, eta: 3 days, 10:10:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4723, loss_cls: 4.4482, loss: 4.4482 +2024-12-28 00:31:13,671 - pyskl - INFO - Epoch [53][2500/3746] lr: 7.254e-02, eta: 3 days, 10:09:22, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4792, loss_cls: 4.4119, loss: 4.4119 +2024-12-28 00:32:38,841 - pyskl - INFO - Epoch [53][2600/3746] lr: 7.252e-02, eta: 3 days, 10:08:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4853, loss_cls: 4.4132, loss: 4.4132 +2024-12-28 00:34:03,821 - pyskl - INFO - Epoch [53][2700/3746] lr: 7.249e-02, eta: 3 days, 10:06:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4855, loss_cls: 4.4105, loss: 4.4105 +2024-12-28 00:35:28,747 - pyskl - INFO - Epoch [53][2800/3746] lr: 7.247e-02, eta: 3 days, 10:05:40, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4898, loss_cls: 4.4097, loss: 4.4097 +2024-12-28 00:36:54,021 - pyskl - INFO - Epoch [53][2900/3746] lr: 7.244e-02, eta: 3 days, 10:04:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4694, loss_cls: 4.4609, loss: 4.4609 +2024-12-28 00:38:19,031 - pyskl - INFO - Epoch [53][3000/3746] lr: 7.242e-02, eta: 3 days, 10:03:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4845, loss_cls: 4.4072, loss: 4.4072 +2024-12-28 00:39:43,872 - pyskl - INFO - Epoch [53][3100/3746] lr: 7.239e-02, eta: 3 days, 10:01:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4744, loss_cls: 4.4631, loss: 4.4631 +2024-12-28 00:41:08,876 - pyskl - INFO - Epoch [53][3200/3746] lr: 7.237e-02, eta: 3 days, 10:00:44, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4823, loss_cls: 4.4488, loss: 4.4488 +2024-12-28 00:42:33,526 - pyskl - INFO - Epoch [53][3300/3746] lr: 7.234e-02, eta: 3 days, 9:59:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4838, loss_cls: 4.4140, loss: 4.4140 +2024-12-28 00:43:58,372 - pyskl - INFO - Epoch [53][3400/3746] lr: 7.232e-02, eta: 3 days, 9:58:15, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4848, loss_cls: 4.3990, loss: 4.3990 +2024-12-28 00:45:22,385 - pyskl - INFO - Epoch [53][3500/3746] lr: 7.229e-02, eta: 3 days, 9:57:00, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4822, loss_cls: 4.3962, loss: 4.3962 +2024-12-28 00:46:46,880 - pyskl - INFO - Epoch [53][3600/3746] lr: 7.227e-02, eta: 3 days, 9:55:45, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4948, loss_cls: 4.3776, loss: 4.3776 +2024-12-28 00:48:11,295 - pyskl - INFO - Epoch [53][3700/3746] lr: 7.224e-02, eta: 3 days, 9:54:29, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4727, loss_cls: 4.4253, loss: 4.4253 +2024-12-28 00:48:52,275 - pyskl - INFO - Saving checkpoint at 53 epochs +2024-12-28 00:50:53,732 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 00:50:54,583 - pyskl - INFO - +top1_acc 0.1695 +top5_acc 0.3835 +2024-12-28 00:50:54,583 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 00:50:54,639 - pyskl - INFO - +mean_acc 0.1695 +2024-12-28 00:50:54,655 - pyskl - INFO - Epoch(val) [53][309] top1_acc: 0.1695, top5_acc: 0.3835, mean_class_accuracy: 0.1695 +2024-12-28 00:55:10,837 - pyskl - INFO - Epoch [54][100/3746] lr: 7.221e-02, eta: 3 days, 9:56:43, time: 2.562, data_time: 1.540, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4794, loss_cls: 4.4069, loss: 4.4069 +2024-12-28 00:56:35,530 - pyskl - INFO - Epoch [54][200/3746] lr: 7.218e-02, eta: 3 days, 9:55:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4886, loss_cls: 4.3956, loss: 4.3956 +2024-12-28 00:57:59,926 - pyskl - INFO - Epoch [54][300/3746] lr: 7.216e-02, eta: 3 days, 9:54:13, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4914, loss_cls: 4.3718, loss: 4.3718 +2024-12-28 00:59:24,808 - pyskl - INFO - Epoch [54][400/3746] lr: 7.213e-02, eta: 3 days, 9:52:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4802, loss_cls: 4.4174, loss: 4.4174 +2024-12-28 01:00:49,135 - pyskl - INFO - Epoch [54][500/3746] lr: 7.211e-02, eta: 3 days, 9:51:42, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4911, loss_cls: 4.3895, loss: 4.3895 +2024-12-28 01:02:13,441 - pyskl - INFO - Epoch [54][600/3746] lr: 7.208e-02, eta: 3 days, 9:50:27, time: 0.843, data_time: 0.001, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4873, loss_cls: 4.3841, loss: 4.3841 +2024-12-28 01:03:37,953 - pyskl - INFO - Epoch [54][700/3746] lr: 7.206e-02, eta: 3 days, 9:49:12, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4816, loss_cls: 4.4212, loss: 4.4212 +2024-12-28 01:05:02,636 - pyskl - INFO - Epoch [54][800/3746] lr: 7.203e-02, eta: 3 days, 9:47:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4889, loss_cls: 4.4151, loss: 4.4151 +2024-12-28 01:06:27,001 - pyskl - INFO - Epoch [54][900/3746] lr: 7.201e-02, eta: 3 days, 9:46:41, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4756, loss_cls: 4.4275, loss: 4.4275 +2024-12-28 01:07:51,224 - pyskl - INFO - Epoch [54][1000/3746] lr: 7.198e-02, eta: 3 days, 9:45:25, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4705, loss_cls: 4.4743, loss: 4.4743 +2024-12-28 01:09:15,612 - pyskl - INFO - Epoch [54][1100/3746] lr: 7.196e-02, eta: 3 days, 9:44:10, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4888, loss_cls: 4.4190, loss: 4.4190 +2024-12-28 01:10:39,936 - pyskl - INFO - Epoch [54][1200/3746] lr: 7.193e-02, eta: 3 days, 9:42:54, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4817, loss_cls: 4.4082, loss: 4.4082 +2024-12-28 01:12:04,102 - pyskl - INFO - Epoch [54][1300/3746] lr: 7.191e-02, eta: 3 days, 9:41:38, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4816, loss_cls: 4.4259, loss: 4.4259 +2024-12-28 01:13:28,340 - pyskl - INFO - Epoch [54][1400/3746] lr: 7.188e-02, eta: 3 days, 9:40:23, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4805, loss_cls: 4.3701, loss: 4.3701 +2024-12-28 01:14:52,363 - pyskl - INFO - Epoch [54][1500/3746] lr: 7.186e-02, eta: 3 days, 9:39:06, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4777, loss_cls: 4.4338, loss: 4.4338 +2024-12-28 01:16:17,084 - pyskl - INFO - Epoch [54][1600/3746] lr: 7.183e-02, eta: 3 days, 9:37:52, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4700, loss_cls: 4.4561, loss: 4.4561 +2024-12-28 01:17:41,788 - pyskl - INFO - Epoch [54][1700/3746] lr: 7.181e-02, eta: 3 days, 9:36:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4858, loss_cls: 4.4019, loss: 4.4019 +2024-12-28 01:19:05,548 - pyskl - INFO - Epoch [54][1800/3746] lr: 7.178e-02, eta: 3 days, 9:35:20, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4892, loss_cls: 4.4043, loss: 4.4043 +2024-12-28 01:20:30,624 - pyskl - INFO - Epoch [54][1900/3746] lr: 7.176e-02, eta: 3 days, 9:34:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4831, loss_cls: 4.4009, loss: 4.4009 +2024-12-28 01:21:54,644 - pyskl - INFO - Epoch [54][2000/3746] lr: 7.173e-02, eta: 3 days, 9:32:49, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4894, loss_cls: 4.3948, loss: 4.3948 +2024-12-28 01:23:19,099 - pyskl - INFO - Epoch [54][2100/3746] lr: 7.170e-02, eta: 3 days, 9:31:34, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4728, loss_cls: 4.4349, loss: 4.4349 +2024-12-28 01:24:43,847 - pyskl - INFO - Epoch [54][2200/3746] lr: 7.168e-02, eta: 3 days, 9:30:19, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4905, loss_cls: 4.3877, loss: 4.3877 +2024-12-28 01:26:08,888 - pyskl - INFO - Epoch [54][2300/3746] lr: 7.165e-02, eta: 3 days, 9:29:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4814, loss_cls: 4.4172, loss: 4.4172 +2024-12-28 01:27:33,638 - pyskl - INFO - Epoch [54][2400/3746] lr: 7.163e-02, eta: 3 days, 9:27:50, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4677, loss_cls: 4.4799, loss: 4.4799 +2024-12-28 01:28:58,697 - pyskl - INFO - Epoch [54][2500/3746] lr: 7.160e-02, eta: 3 days, 9:26:35, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4748, loss_cls: 4.4638, loss: 4.4638 +2024-12-28 01:30:23,553 - pyskl - INFO - Epoch [54][2600/3746] lr: 7.158e-02, eta: 3 days, 9:25:20, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4906, loss_cls: 4.3699, loss: 4.3699 +2024-12-28 01:31:48,757 - pyskl - INFO - Epoch [54][2700/3746] lr: 7.155e-02, eta: 3 days, 9:24:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4767, loss_cls: 4.4421, loss: 4.4421 +2024-12-28 01:33:13,870 - pyskl - INFO - Epoch [54][2800/3746] lr: 7.153e-02, eta: 3 days, 9:22:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4891, loss_cls: 4.3986, loss: 4.3986 +2024-12-28 01:34:38,683 - pyskl - INFO - Epoch [54][2900/3746] lr: 7.150e-02, eta: 3 days, 9:21:37, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4806, loss_cls: 4.4156, loss: 4.4156 +2024-12-28 01:36:03,744 - pyskl - INFO - Epoch [54][3000/3746] lr: 7.148e-02, eta: 3 days, 9:20:23, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4736, loss_cls: 4.4508, loss: 4.4508 +2024-12-28 01:37:28,828 - pyskl - INFO - Epoch [54][3100/3746] lr: 7.145e-02, eta: 3 days, 9:19:08, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4859, loss_cls: 4.4218, loss: 4.4218 +2024-12-28 01:38:54,117 - pyskl - INFO - Epoch [54][3200/3746] lr: 7.143e-02, eta: 3 days, 9:17:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4820, loss_cls: 4.4295, loss: 4.4295 +2024-12-28 01:40:18,662 - pyskl - INFO - Epoch [54][3300/3746] lr: 7.140e-02, eta: 3 days, 9:16:39, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4934, loss_cls: 4.3371, loss: 4.3371 +2024-12-28 01:41:43,332 - pyskl - INFO - Epoch [54][3400/3746] lr: 7.138e-02, eta: 3 days, 9:15:23, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4845, loss_cls: 4.4096, loss: 4.4096 +2024-12-28 01:43:07,468 - pyskl - INFO - Epoch [54][3500/3746] lr: 7.135e-02, eta: 3 days, 9:14:07, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4877, loss_cls: 4.4031, loss: 4.4031 +2024-12-28 01:44:32,363 - pyskl - INFO - Epoch [54][3600/3746] lr: 7.133e-02, eta: 3 days, 9:12:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4747, loss_cls: 4.4631, loss: 4.4631 +2024-12-28 01:45:57,293 - pyskl - INFO - Epoch [54][3700/3746] lr: 7.130e-02, eta: 3 days, 9:11:38, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4820, loss_cls: 4.4364, loss: 4.4364 +2024-12-28 01:46:38,431 - pyskl - INFO - Saving checkpoint at 54 epochs +2024-12-28 01:48:36,985 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 01:48:37,675 - pyskl - INFO - +top1_acc 0.1760 +top5_acc 0.3914 +2024-12-28 01:48:37,676 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 01:48:37,722 - pyskl - INFO - +mean_acc 0.1758 +2024-12-28 01:48:37,728 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_48.pth was removed +2024-12-28 01:48:37,983 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_54.pth. +2024-12-28 01:48:37,984 - pyskl - INFO - Best top1_acc is 0.1760 at 54 epoch. +2024-12-28 01:48:37,999 - pyskl - INFO - Epoch(val) [54][309] top1_acc: 0.1760, top5_acc: 0.3914, mean_class_accuracy: 0.1758 +2024-12-28 01:53:04,615 - pyskl - INFO - Epoch [55][100/3746] lr: 7.126e-02, eta: 3 days, 9:14:02, time: 2.666, data_time: 1.636, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4939, loss_cls: 4.3510, loss: 4.3510 +2024-12-28 01:54:29,749 - pyskl - INFO - Epoch [55][200/3746] lr: 7.124e-02, eta: 3 days, 9:12:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4797, loss_cls: 4.4327, loss: 4.4327 +2024-12-28 01:55:54,962 - pyskl - INFO - Epoch [55][300/3746] lr: 7.121e-02, eta: 3 days, 9:11:33, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.4997, loss_cls: 4.3462, loss: 4.3462 +2024-12-28 01:57:20,398 - pyskl - INFO - Epoch [55][400/3746] lr: 7.119e-02, eta: 3 days, 9:10:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4698, loss_cls: 4.4544, loss: 4.4544 +2024-12-28 01:58:45,671 - pyskl - INFO - Epoch [55][500/3746] lr: 7.116e-02, eta: 3 days, 9:09:04, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4813, loss_cls: 4.4331, loss: 4.4331 +2024-12-28 02:00:11,953 - pyskl - INFO - Epoch [55][600/3746] lr: 7.114e-02, eta: 3 days, 9:07:52, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4689, loss_cls: 4.4598, loss: 4.4598 +2024-12-28 02:01:37,694 - pyskl - INFO - Epoch [55][700/3746] lr: 7.111e-02, eta: 3 days, 9:06:38, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4763, loss_cls: 4.4071, loss: 4.4071 +2024-12-28 02:03:02,831 - pyskl - INFO - Epoch [55][800/3746] lr: 7.109e-02, eta: 3 days, 9:05:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4819, loss_cls: 4.3825, loss: 4.3825 +2024-12-28 02:04:27,634 - pyskl - INFO - Epoch [55][900/3746] lr: 7.106e-02, eta: 3 days, 9:04:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4778, loss_cls: 4.4412, loss: 4.4412 +2024-12-28 02:05:52,528 - pyskl - INFO - Epoch [55][1000/3746] lr: 7.104e-02, eta: 3 days, 9:02:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4814, loss_cls: 4.4252, loss: 4.4252 +2024-12-28 02:07:17,586 - pyskl - INFO - Epoch [55][1100/3746] lr: 7.101e-02, eta: 3 days, 9:01:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4788, loss_cls: 4.4564, loss: 4.4564 +2024-12-28 02:08:42,449 - pyskl - INFO - Epoch [55][1200/3746] lr: 7.099e-02, eta: 3 days, 9:00:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4855, loss_cls: 4.3856, loss: 4.3856 +2024-12-28 02:10:07,344 - pyskl - INFO - Epoch [55][1300/3746] lr: 7.096e-02, eta: 3 days, 8:59:08, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4831, loss_cls: 4.4064, loss: 4.4064 +2024-12-28 02:11:32,515 - pyskl - INFO - Epoch [55][1400/3746] lr: 7.093e-02, eta: 3 days, 8:57:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4913, loss_cls: 4.3808, loss: 4.3808 +2024-12-28 02:12:57,916 - pyskl - INFO - Epoch [55][1500/3746] lr: 7.091e-02, eta: 3 days, 8:56:39, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4802, loss_cls: 4.4121, loss: 4.4121 +2024-12-28 02:14:22,689 - pyskl - INFO - Epoch [55][1600/3746] lr: 7.088e-02, eta: 3 days, 8:55:23, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4797, loss_cls: 4.3933, loss: 4.3933 +2024-12-28 02:15:47,709 - pyskl - INFO - Epoch [55][1700/3746] lr: 7.086e-02, eta: 3 days, 8:54:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4872, loss_cls: 4.4095, loss: 4.4095 +2024-12-28 02:17:13,218 - pyskl - INFO - Epoch [55][1800/3746] lr: 7.083e-02, eta: 3 days, 8:52:54, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4769, loss_cls: 4.4426, loss: 4.4426 +2024-12-28 02:18:38,948 - pyskl - INFO - Epoch [55][1900/3746] lr: 7.081e-02, eta: 3 days, 8:51:41, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4800, loss_cls: 4.4048, loss: 4.4048 +2024-12-28 02:20:03,446 - pyskl - INFO - Epoch [55][2000/3746] lr: 7.078e-02, eta: 3 days, 8:50:25, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4822, loss_cls: 4.4217, loss: 4.4217 +2024-12-28 02:21:28,517 - pyskl - INFO - Epoch [55][2100/3746] lr: 7.076e-02, eta: 3 days, 8:49:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4777, loss_cls: 4.4489, loss: 4.4489 +2024-12-28 02:22:54,179 - pyskl - INFO - Epoch [55][2200/3746] lr: 7.073e-02, eta: 3 days, 8:47:56, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4713, loss_cls: 4.4661, loss: 4.4661 +2024-12-28 02:24:19,713 - pyskl - INFO - Epoch [55][2300/3746] lr: 7.071e-02, eta: 3 days, 8:46:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4841, loss_cls: 4.3955, loss: 4.3955 +2024-12-28 02:25:45,631 - pyskl - INFO - Epoch [55][2400/3746] lr: 7.068e-02, eta: 3 days, 8:45:28, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4745, loss_cls: 4.4286, loss: 4.4286 +2024-12-28 02:27:10,609 - pyskl - INFO - Epoch [55][2500/3746] lr: 7.065e-02, eta: 3 days, 8:44:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4986, loss_cls: 4.3491, loss: 4.3491 +2024-12-28 02:28:36,472 - pyskl - INFO - Epoch [55][2600/3746] lr: 7.063e-02, eta: 3 days, 8:43:00, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4925, loss_cls: 4.3373, loss: 4.3373 +2024-12-28 02:30:02,357 - pyskl - INFO - Epoch [55][2700/3746] lr: 7.060e-02, eta: 3 days, 8:41:46, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4958, loss_cls: 4.3677, loss: 4.3677 +2024-12-28 02:31:27,284 - pyskl - INFO - Epoch [55][2800/3746] lr: 7.058e-02, eta: 3 days, 8:40:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4830, loss_cls: 4.4238, loss: 4.4238 +2024-12-28 02:32:52,135 - pyskl - INFO - Epoch [55][2900/3746] lr: 7.055e-02, eta: 3 days, 8:39:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4805, loss_cls: 4.4234, loss: 4.4234 +2024-12-28 02:34:16,114 - pyskl - INFO - Epoch [55][3000/3746] lr: 7.053e-02, eta: 3 days, 8:37:58, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4773, loss_cls: 4.4383, loss: 4.4383 +2024-12-28 02:35:40,673 - pyskl - INFO - Epoch [55][3100/3746] lr: 7.050e-02, eta: 3 days, 8:36:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4928, loss_cls: 4.3679, loss: 4.3679 +2024-12-28 02:37:04,956 - pyskl - INFO - Epoch [55][3200/3746] lr: 7.048e-02, eta: 3 days, 8:35:26, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4844, loss_cls: 4.4198, loss: 4.4198 +2024-12-28 02:38:29,473 - pyskl - INFO - Epoch [55][3300/3746] lr: 7.045e-02, eta: 3 days, 8:34:10, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4888, loss_cls: 4.3755, loss: 4.3755 +2024-12-28 02:39:54,461 - pyskl - INFO - Epoch [55][3400/3746] lr: 7.043e-02, eta: 3 days, 8:32:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4745, loss_cls: 4.4283, loss: 4.4283 +2024-12-28 02:41:18,605 - pyskl - INFO - Epoch [55][3500/3746] lr: 7.040e-02, eta: 3 days, 8:31:38, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4781, loss_cls: 4.4282, loss: 4.4282 +2024-12-28 02:42:42,887 - pyskl - INFO - Epoch [55][3600/3746] lr: 7.037e-02, eta: 3 days, 8:30:22, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4861, loss_cls: 4.4012, loss: 4.4012 +2024-12-28 02:44:07,487 - pyskl - INFO - Epoch [55][3700/3746] lr: 7.035e-02, eta: 3 days, 8:29:06, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4783, loss_cls: 4.4336, loss: 4.4336 +2024-12-28 02:44:48,576 - pyskl - INFO - Saving checkpoint at 55 epochs +2024-12-28 02:46:47,442 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 02:46:48,332 - pyskl - INFO - +top1_acc 0.1891 +top5_acc 0.4065 +2024-12-28 02:46:48,332 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 02:46:48,381 - pyskl - INFO - +mean_acc 0.1887 +2024-12-28 02:46:48,388 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_54.pth was removed +2024-12-28 02:46:48,649 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_55.pth. +2024-12-28 02:46:48,650 - pyskl - INFO - Best top1_acc is 0.1891 at 55 epoch. +2024-12-28 02:46:48,668 - pyskl - INFO - Epoch(val) [55][309] top1_acc: 0.1891, top5_acc: 0.4065, mean_class_accuracy: 0.1887 +2024-12-28 02:51:07,579 - pyskl - INFO - Epoch [56][100/3746] lr: 7.031e-02, eta: 3 days, 8:31:09, time: 2.589, data_time: 1.548, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4919, loss_cls: 4.3644, loss: 4.3644 +2024-12-28 02:52:33,499 - pyskl - INFO - Epoch [56][200/3746] lr: 7.029e-02, eta: 3 days, 8:29:55, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4898, loss_cls: 4.3457, loss: 4.3457 +2024-12-28 02:53:59,070 - pyskl - INFO - Epoch [56][300/3746] lr: 7.026e-02, eta: 3 days, 8:28:40, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4811, loss_cls: 4.4138, loss: 4.4138 +2024-12-28 02:55:24,694 - pyskl - INFO - Epoch [56][400/3746] lr: 7.023e-02, eta: 3 days, 8:27:26, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4791, loss_cls: 4.4118, loss: 4.4118 +2024-12-28 02:56:50,849 - pyskl - INFO - Epoch [56][500/3746] lr: 7.021e-02, eta: 3 days, 8:26:13, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4892, loss_cls: 4.3820, loss: 4.3820 +2024-12-28 02:58:17,034 - pyskl - INFO - Epoch [56][600/3746] lr: 7.018e-02, eta: 3 days, 8:24:59, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4772, loss_cls: 4.4335, loss: 4.4335 +2024-12-28 02:59:42,221 - pyskl - INFO - Epoch [56][700/3746] lr: 7.016e-02, eta: 3 days, 8:23:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5027, loss_cls: 4.3194, loss: 4.3194 +2024-12-28 03:01:07,379 - pyskl - INFO - Epoch [56][800/3746] lr: 7.013e-02, eta: 3 days, 8:22:29, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4786, loss_cls: 4.3896, loss: 4.3896 +2024-12-28 03:02:32,206 - pyskl - INFO - Epoch [56][900/3746] lr: 7.011e-02, eta: 3 days, 8:21:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4733, loss_cls: 4.4471, loss: 4.4471 +2024-12-28 03:03:56,928 - pyskl - INFO - Epoch [56][1000/3746] lr: 7.008e-02, eta: 3 days, 8:19:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4795, loss_cls: 4.4322, loss: 4.4322 +2024-12-28 03:05:21,180 - pyskl - INFO - Epoch [56][1100/3746] lr: 7.006e-02, eta: 3 days, 8:18:40, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4998, loss_cls: 4.3380, loss: 4.3380 +2024-12-28 03:06:45,324 - pyskl - INFO - Epoch [56][1200/3746] lr: 7.003e-02, eta: 3 days, 8:17:23, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4869, loss_cls: 4.4109, loss: 4.4109 +2024-12-28 03:08:09,776 - pyskl - INFO - Epoch [56][1300/3746] lr: 7.000e-02, eta: 3 days, 8:16:07, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4802, loss_cls: 4.3929, loss: 4.3929 +2024-12-28 03:09:34,341 - pyskl - INFO - Epoch [56][1400/3746] lr: 6.998e-02, eta: 3 days, 8:14:51, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4805, loss_cls: 4.4136, loss: 4.4136 +2024-12-28 03:10:59,461 - pyskl - INFO - Epoch [56][1500/3746] lr: 6.995e-02, eta: 3 days, 8:13:35, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4788, loss_cls: 4.4205, loss: 4.4205 +2024-12-28 03:12:23,803 - pyskl - INFO - Epoch [56][1600/3746] lr: 6.993e-02, eta: 3 days, 8:12:19, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4902, loss_cls: 4.3886, loss: 4.3886 +2024-12-28 03:13:48,293 - pyskl - INFO - Epoch [56][1700/3746] lr: 6.990e-02, eta: 3 days, 8:11:02, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4869, loss_cls: 4.3658, loss: 4.3658 +2024-12-28 03:15:12,942 - pyskl - INFO - Epoch [56][1800/3746] lr: 6.988e-02, eta: 3 days, 8:09:46, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4866, loss_cls: 4.4103, loss: 4.4103 +2024-12-28 03:16:37,982 - pyskl - INFO - Epoch [56][1900/3746] lr: 6.985e-02, eta: 3 days, 8:08:31, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4797, loss_cls: 4.4637, loss: 4.4637 +2024-12-28 03:18:02,489 - pyskl - INFO - Epoch [56][2000/3746] lr: 6.983e-02, eta: 3 days, 8:07:14, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4978, loss_cls: 4.3621, loss: 4.3621 +2024-12-28 03:19:27,241 - pyskl - INFO - Epoch [56][2100/3746] lr: 6.980e-02, eta: 3 days, 8:05:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4894, loss_cls: 4.4096, loss: 4.4096 +2024-12-28 03:20:51,832 - pyskl - INFO - Epoch [56][2200/3746] lr: 6.977e-02, eta: 3 days, 8:04:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4814, loss_cls: 4.4372, loss: 4.4372 +2024-12-28 03:22:16,719 - pyskl - INFO - Epoch [56][2300/3746] lr: 6.975e-02, eta: 3 days, 8:03:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4692, loss_cls: 4.4534, loss: 4.4534 +2024-12-28 03:23:41,405 - pyskl - INFO - Epoch [56][2400/3746] lr: 6.972e-02, eta: 3 days, 8:02:10, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4816, loss_cls: 4.4234, loss: 4.4234 +2024-12-28 03:25:05,964 - pyskl - INFO - Epoch [56][2500/3746] lr: 6.970e-02, eta: 3 days, 8:00:54, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4817, loss_cls: 4.4132, loss: 4.4132 +2024-12-28 03:26:30,860 - pyskl - INFO - Epoch [56][2600/3746] lr: 6.967e-02, eta: 3 days, 7:59:38, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4777, loss_cls: 4.4483, loss: 4.4483 +2024-12-28 03:27:55,130 - pyskl - INFO - Epoch [56][2700/3746] lr: 6.965e-02, eta: 3 days, 7:58:21, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4848, loss_cls: 4.4354, loss: 4.4354 +2024-12-28 03:29:20,068 - pyskl - INFO - Epoch [56][2800/3746] lr: 6.962e-02, eta: 3 days, 7:57:05, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4998, loss_cls: 4.3262, loss: 4.3262 +2024-12-28 03:30:44,433 - pyskl - INFO - Epoch [56][2900/3746] lr: 6.959e-02, eta: 3 days, 7:55:49, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4819, loss_cls: 4.3865, loss: 4.3865 +2024-12-28 03:32:08,533 - pyskl - INFO - Epoch [56][3000/3746] lr: 6.957e-02, eta: 3 days, 7:54:32, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4675, loss_cls: 4.4481, loss: 4.4481 +2024-12-28 03:33:33,356 - pyskl - INFO - Epoch [56][3100/3746] lr: 6.954e-02, eta: 3 days, 7:53:16, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4764, loss_cls: 4.4214, loss: 4.4214 +2024-12-28 03:34:57,924 - pyskl - INFO - Epoch [56][3200/3746] lr: 6.952e-02, eta: 3 days, 7:51:59, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4883, loss_cls: 4.3757, loss: 4.3757 +2024-12-28 03:36:22,671 - pyskl - INFO - Epoch [56][3300/3746] lr: 6.949e-02, eta: 3 days, 7:50:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4844, loss_cls: 4.4094, loss: 4.4094 +2024-12-28 03:37:47,174 - pyskl - INFO - Epoch [56][3400/3746] lr: 6.947e-02, eta: 3 days, 7:49:27, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4755, loss_cls: 4.4557, loss: 4.4557 +2024-12-28 03:39:11,531 - pyskl - INFO - Epoch [56][3500/3746] lr: 6.944e-02, eta: 3 days, 7:48:10, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4691, loss_cls: 4.4434, loss: 4.4434 +2024-12-28 03:40:35,723 - pyskl - INFO - Epoch [56][3600/3746] lr: 6.941e-02, eta: 3 days, 7:46:53, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4813, loss_cls: 4.4299, loss: 4.4299 +2024-12-28 03:42:00,387 - pyskl - INFO - Epoch [56][3700/3746] lr: 6.939e-02, eta: 3 days, 7:45:36, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4948, loss_cls: 4.3623, loss: 4.3623 +2024-12-28 03:42:41,196 - pyskl - INFO - Saving checkpoint at 56 epochs +2024-12-28 03:44:41,084 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 03:44:42,175 - pyskl - INFO - +top1_acc 0.1932 +top5_acc 0.4132 +2024-12-28 03:44:42,175 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 03:44:42,222 - pyskl - INFO - +mean_acc 0.1930 +2024-12-28 03:44:42,227 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_55.pth was removed +2024-12-28 03:44:42,497 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_56.pth. +2024-12-28 03:44:42,498 - pyskl - INFO - Best top1_acc is 0.1932 at 56 epoch. +2024-12-28 03:44:42,512 - pyskl - INFO - Epoch(val) [56][309] top1_acc: 0.1932, top5_acc: 0.4132, mean_class_accuracy: 0.1930 +2024-12-28 03:49:05,649 - pyskl - INFO - Epoch [57][100/3746] lr: 6.935e-02, eta: 3 days, 7:47:39, time: 2.631, data_time: 1.584, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4864, loss_cls: 4.3876, loss: 4.3876 +2024-12-28 03:50:32,047 - pyskl - INFO - Epoch [57][200/3746] lr: 6.932e-02, eta: 3 days, 7:46:25, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4777, loss_cls: 4.4099, loss: 4.4099 +2024-12-28 03:51:58,875 - pyskl - INFO - Epoch [57][300/3746] lr: 6.930e-02, eta: 3 days, 7:45:13, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4895, loss_cls: 4.3681, loss: 4.3681 +2024-12-28 03:53:25,189 - pyskl - INFO - Epoch [57][400/3746] lr: 6.927e-02, eta: 3 days, 7:43:59, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4817, loss_cls: 4.3784, loss: 4.3784 +2024-12-28 03:54:51,708 - pyskl - INFO - Epoch [57][500/3746] lr: 6.925e-02, eta: 3 days, 7:42:45, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4928, loss_cls: 4.3453, loss: 4.3453 +2024-12-28 03:56:17,999 - pyskl - INFO - Epoch [57][600/3746] lr: 6.922e-02, eta: 3 days, 7:41:32, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5025, loss_cls: 4.3120, loss: 4.3120 +2024-12-28 03:57:44,027 - pyskl - INFO - Epoch [57][700/3746] lr: 6.920e-02, eta: 3 days, 7:40:17, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4903, loss_cls: 4.3976, loss: 4.3976 +2024-12-28 03:59:09,463 - pyskl - INFO - Epoch [57][800/3746] lr: 6.917e-02, eta: 3 days, 7:39:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4886, loss_cls: 4.3715, loss: 4.3715 +2024-12-28 04:00:34,401 - pyskl - INFO - Epoch [57][900/3746] lr: 6.914e-02, eta: 3 days, 7:37:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4866, loss_cls: 4.3935, loss: 4.3935 +2024-12-28 04:01:59,691 - pyskl - INFO - Epoch [57][1000/3746] lr: 6.912e-02, eta: 3 days, 7:36:31, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4800, loss_cls: 4.4208, loss: 4.4208 +2024-12-28 04:03:24,621 - pyskl - INFO - Epoch [57][1100/3746] lr: 6.909e-02, eta: 3 days, 7:35:14, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4836, loss_cls: 4.4062, loss: 4.4062 +2024-12-28 04:04:49,702 - pyskl - INFO - Epoch [57][1200/3746] lr: 6.907e-02, eta: 3 days, 7:33:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4834, loss_cls: 4.3937, loss: 4.3937 +2024-12-28 04:06:15,322 - pyskl - INFO - Epoch [57][1300/3746] lr: 6.904e-02, eta: 3 days, 7:32:44, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4770, loss_cls: 4.4333, loss: 4.4333 +2024-12-28 04:07:40,700 - pyskl - INFO - Epoch [57][1400/3746] lr: 6.901e-02, eta: 3 days, 7:31:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4920, loss_cls: 4.3750, loss: 4.3750 +2024-12-28 04:09:05,886 - pyskl - INFO - Epoch [57][1500/3746] lr: 6.899e-02, eta: 3 days, 7:30:13, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.4906, loss_cls: 4.3799, loss: 4.3799 +2024-12-28 04:10:30,987 - pyskl - INFO - Epoch [57][1600/3746] lr: 6.896e-02, eta: 3 days, 7:28:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4836, loss_cls: 4.4098, loss: 4.4098 +2024-12-28 04:11:56,320 - pyskl - INFO - Epoch [57][1700/3746] lr: 6.894e-02, eta: 3 days, 7:27:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4938, loss_cls: 4.3782, loss: 4.3782 +2024-12-28 04:13:21,683 - pyskl - INFO - Epoch [57][1800/3746] lr: 6.891e-02, eta: 3 days, 7:26:26, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4809, loss_cls: 4.4231, loss: 4.4231 +2024-12-28 04:14:46,955 - pyskl - INFO - Epoch [57][1900/3746] lr: 6.889e-02, eta: 3 days, 7:25:10, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4964, loss_cls: 4.3575, loss: 4.3575 +2024-12-28 04:16:12,452 - pyskl - INFO - Epoch [57][2000/3746] lr: 6.886e-02, eta: 3 days, 7:23:55, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5000, loss_cls: 4.3423, loss: 4.3423 +2024-12-28 04:17:37,264 - pyskl - INFO - Epoch [57][2100/3746] lr: 6.883e-02, eta: 3 days, 7:22:38, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4877, loss_cls: 4.3818, loss: 4.3818 +2024-12-28 04:19:03,109 - pyskl - INFO - Epoch [57][2200/3746] lr: 6.881e-02, eta: 3 days, 7:21:24, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4773, loss_cls: 4.4049, loss: 4.4049 +2024-12-28 04:20:28,169 - pyskl - INFO - Epoch [57][2300/3746] lr: 6.878e-02, eta: 3 days, 7:20:08, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4967, loss_cls: 4.3462, loss: 4.3462 +2024-12-28 04:21:53,384 - pyskl - INFO - Epoch [57][2400/3746] lr: 6.876e-02, eta: 3 days, 7:18:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4773, loss_cls: 4.4167, loss: 4.4167 +2024-12-28 04:23:18,985 - pyskl - INFO - Epoch [57][2500/3746] lr: 6.873e-02, eta: 3 days, 7:17:37, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4681, loss_cls: 4.4412, loss: 4.4412 +2024-12-28 04:24:44,737 - pyskl - INFO - Epoch [57][2600/3746] lr: 6.870e-02, eta: 3 days, 7:16:22, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4889, loss_cls: 4.3755, loss: 4.3755 +2024-12-28 04:26:09,705 - pyskl - INFO - Epoch [57][2700/3746] lr: 6.868e-02, eta: 3 days, 7:15:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4831, loss_cls: 4.4196, loss: 4.4196 +2024-12-28 04:27:34,803 - pyskl - INFO - Epoch [57][2800/3746] lr: 6.865e-02, eta: 3 days, 7:13:50, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4923, loss_cls: 4.3591, loss: 4.3591 +2024-12-28 04:29:00,074 - pyskl - INFO - Epoch [57][2900/3746] lr: 6.863e-02, eta: 3 days, 7:12:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4772, loss_cls: 4.4576, loss: 4.4576 +2024-12-28 04:30:25,884 - pyskl - INFO - Epoch [57][3000/3746] lr: 6.860e-02, eta: 3 days, 7:11:19, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4894, loss_cls: 4.4125, loss: 4.4125 +2024-12-28 04:31:51,826 - pyskl - INFO - Epoch [57][3100/3746] lr: 6.857e-02, eta: 3 days, 7:10:05, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4773, loss_cls: 4.4190, loss: 4.4190 +2024-12-28 04:33:18,038 - pyskl - INFO - Epoch [57][3200/3746] lr: 6.855e-02, eta: 3 days, 7:08:50, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4811, loss_cls: 4.4060, loss: 4.4060 +2024-12-28 04:34:43,862 - pyskl - INFO - Epoch [57][3300/3746] lr: 6.852e-02, eta: 3 days, 7:07:36, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4825, loss_cls: 4.3930, loss: 4.3930 +2024-12-28 04:36:09,025 - pyskl - INFO - Epoch [57][3400/3746] lr: 6.850e-02, eta: 3 days, 7:06:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.4986, loss_cls: 4.3294, loss: 4.3294 +2024-12-28 04:37:33,851 - pyskl - INFO - Epoch [57][3500/3746] lr: 6.847e-02, eta: 3 days, 7:05:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4752, loss_cls: 4.4531, loss: 4.4531 +2024-12-28 04:38:59,547 - pyskl - INFO - Epoch [57][3600/3746] lr: 6.844e-02, eta: 3 days, 7:03:48, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4714, loss_cls: 4.4084, loss: 4.4084 +2024-12-28 04:40:25,640 - pyskl - INFO - Epoch [57][3700/3746] lr: 6.842e-02, eta: 3 days, 7:02:34, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4859, loss_cls: 4.4232, loss: 4.4232 +2024-12-28 04:41:07,472 - pyskl - INFO - Saving checkpoint at 57 epochs +2024-12-28 04:43:08,783 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 04:43:09,564 - pyskl - INFO - +top1_acc 0.1751 +top5_acc 0.3913 +2024-12-28 04:43:09,564 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 04:43:09,622 - pyskl - INFO - +mean_acc 0.1751 +2024-12-28 04:43:09,633 - pyskl - INFO - Epoch(val) [57][309] top1_acc: 0.1751, top5_acc: 0.3913, mean_class_accuracy: 0.1751 +2024-12-28 04:47:28,823 - pyskl - INFO - Epoch [58][100/3746] lr: 6.838e-02, eta: 3 days, 7:04:22, time: 2.592, data_time: 1.563, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4906, loss_cls: 4.3462, loss: 4.3462 +2024-12-28 04:48:53,778 - pyskl - INFO - Epoch [58][200/3746] lr: 6.835e-02, eta: 3 days, 7:03:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4939, loss_cls: 4.3707, loss: 4.3707 +2024-12-28 04:50:18,213 - pyskl - INFO - Epoch [58][300/3746] lr: 6.833e-02, eta: 3 days, 7:01:49, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4963, loss_cls: 4.3649, loss: 4.3649 +2024-12-28 04:51:43,252 - pyskl - INFO - Epoch [58][400/3746] lr: 6.830e-02, eta: 3 days, 7:00:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4900, loss_cls: 4.3677, loss: 4.3677 +2024-12-28 04:53:08,362 - pyskl - INFO - Epoch [58][500/3746] lr: 6.828e-02, eta: 3 days, 6:59:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4783, loss_cls: 4.3886, loss: 4.3886 +2024-12-28 04:54:33,445 - pyskl - INFO - Epoch [58][600/3746] lr: 6.825e-02, eta: 3 days, 6:58:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4814, loss_cls: 4.3873, loss: 4.3873 +2024-12-28 04:55:58,688 - pyskl - INFO - Epoch [58][700/3746] lr: 6.822e-02, eta: 3 days, 6:56:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4870, loss_cls: 4.3755, loss: 4.3755 +2024-12-28 04:57:23,808 - pyskl - INFO - Epoch [58][800/3746] lr: 6.820e-02, eta: 3 days, 6:55:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4858, loss_cls: 4.3880, loss: 4.3880 +2024-12-28 04:58:47,783 - pyskl - INFO - Epoch [58][900/3746] lr: 6.817e-02, eta: 3 days, 6:54:09, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4820, loss_cls: 4.3889, loss: 4.3889 +2024-12-28 05:00:11,783 - pyskl - INFO - Epoch [58][1000/3746] lr: 6.815e-02, eta: 3 days, 6:52:51, time: 0.840, data_time: 0.001, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4788, loss_cls: 4.4127, loss: 4.4127 +2024-12-28 05:01:36,447 - pyskl - INFO - Epoch [58][1100/3746] lr: 6.812e-02, eta: 3 days, 6:51:34, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4978, loss_cls: 4.3485, loss: 4.3485 +2024-12-28 05:03:00,106 - pyskl - INFO - Epoch [58][1200/3746] lr: 6.809e-02, eta: 3 days, 6:50:15, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4925, loss_cls: 4.3744, loss: 4.3744 +2024-12-28 05:04:24,270 - pyskl - INFO - Epoch [58][1300/3746] lr: 6.807e-02, eta: 3 days, 6:48:58, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4863, loss_cls: 4.3735, loss: 4.3735 +2024-12-28 05:05:49,490 - pyskl - INFO - Epoch [58][1400/3746] lr: 6.804e-02, eta: 3 days, 6:47:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4786, loss_cls: 4.4144, loss: 4.4144 +2024-12-28 05:07:14,034 - pyskl - INFO - Epoch [58][1500/3746] lr: 6.802e-02, eta: 3 days, 6:46:24, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4933, loss_cls: 4.3662, loss: 4.3662 +2024-12-28 05:08:38,451 - pyskl - INFO - Epoch [58][1600/3746] lr: 6.799e-02, eta: 3 days, 6:45:07, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4870, loss_cls: 4.4129, loss: 4.4129 +2024-12-28 05:10:02,549 - pyskl - INFO - Epoch [58][1700/3746] lr: 6.796e-02, eta: 3 days, 6:43:49, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4880, loss_cls: 4.3696, loss: 4.3696 +2024-12-28 05:11:26,847 - pyskl - INFO - Epoch [58][1800/3746] lr: 6.794e-02, eta: 3 days, 6:42:31, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4822, loss_cls: 4.3888, loss: 4.3888 +2024-12-28 05:12:51,537 - pyskl - INFO - Epoch [58][1900/3746] lr: 6.791e-02, eta: 3 days, 6:41:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4798, loss_cls: 4.4200, loss: 4.4200 +2024-12-28 05:14:16,656 - pyskl - INFO - Epoch [58][2000/3746] lr: 6.789e-02, eta: 3 days, 6:39:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4920, loss_cls: 4.3662, loss: 4.3662 +2024-12-28 05:15:40,802 - pyskl - INFO - Epoch [58][2100/3746] lr: 6.786e-02, eta: 3 days, 6:38:40, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.4988, loss_cls: 4.3598, loss: 4.3598 +2024-12-28 05:17:04,934 - pyskl - INFO - Epoch [58][2200/3746] lr: 6.783e-02, eta: 3 days, 6:37:22, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4895, loss_cls: 4.3953, loss: 4.3953 +2024-12-28 05:18:29,127 - pyskl - INFO - Epoch [58][2300/3746] lr: 6.781e-02, eta: 3 days, 6:36:04, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.4969, loss_cls: 4.3607, loss: 4.3607 +2024-12-28 05:19:54,210 - pyskl - INFO - Epoch [58][2400/3746] lr: 6.778e-02, eta: 3 days, 6:34:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4928, loss_cls: 4.3530, loss: 4.3530 +2024-12-28 05:21:19,064 - pyskl - INFO - Epoch [58][2500/3746] lr: 6.775e-02, eta: 3 days, 6:33:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4928, loss_cls: 4.3923, loss: 4.3923 +2024-12-28 05:22:44,436 - pyskl - INFO - Epoch [58][2600/3746] lr: 6.773e-02, eta: 3 days, 6:32:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4753, loss_cls: 4.4243, loss: 4.4243 +2024-12-28 05:24:09,247 - pyskl - INFO - Epoch [58][2700/3746] lr: 6.770e-02, eta: 3 days, 6:30:58, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4798, loss_cls: 4.4111, loss: 4.4111 +2024-12-28 05:25:34,529 - pyskl - INFO - Epoch [58][2800/3746] lr: 6.768e-02, eta: 3 days, 6:29:42, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4852, loss_cls: 4.4352, loss: 4.4352 +2024-12-28 05:26:59,669 - pyskl - INFO - Epoch [58][2900/3746] lr: 6.765e-02, eta: 3 days, 6:28:25, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4894, loss_cls: 4.3808, loss: 4.3808 +2024-12-28 05:28:25,187 - pyskl - INFO - Epoch [58][3000/3746] lr: 6.762e-02, eta: 3 days, 6:27:09, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4747, loss_cls: 4.4263, loss: 4.4263 +2024-12-28 05:29:49,916 - pyskl - INFO - Epoch [58][3100/3746] lr: 6.760e-02, eta: 3 days, 6:25:52, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4852, loss_cls: 4.3944, loss: 4.3944 +2024-12-28 05:31:15,056 - pyskl - INFO - Epoch [58][3200/3746] lr: 6.757e-02, eta: 3 days, 6:24:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4795, loss_cls: 4.4109, loss: 4.4109 +2024-12-28 05:32:40,276 - pyskl - INFO - Epoch [58][3300/3746] lr: 6.755e-02, eta: 3 days, 6:23:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4953, loss_cls: 4.3523, loss: 4.3523 +2024-12-28 05:34:05,610 - pyskl - INFO - Epoch [58][3400/3746] lr: 6.752e-02, eta: 3 days, 6:22:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4816, loss_cls: 4.3830, loss: 4.3830 +2024-12-28 05:35:30,199 - pyskl - INFO - Epoch [58][3500/3746] lr: 6.749e-02, eta: 3 days, 6:20:46, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4820, loss_cls: 4.4153, loss: 4.4153 +2024-12-28 05:36:55,000 - pyskl - INFO - Epoch [58][3600/3746] lr: 6.747e-02, eta: 3 days, 6:19:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4991, loss_cls: 4.3558, loss: 4.3558 +2024-12-28 05:38:19,951 - pyskl - INFO - Epoch [58][3700/3746] lr: 6.744e-02, eta: 3 days, 6:18:12, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4805, loss_cls: 4.4221, loss: 4.4221 +2024-12-28 05:39:01,245 - pyskl - INFO - Saving checkpoint at 58 epochs +2024-12-28 05:41:00,026 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 05:41:00,830 - pyskl - INFO - +top1_acc 0.1898 +top5_acc 0.4097 +2024-12-28 05:41:00,830 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 05:41:00,875 - pyskl - INFO - +mean_acc 0.1895 +2024-12-28 05:41:00,887 - pyskl - INFO - Epoch(val) [58][309] top1_acc: 0.1898, top5_acc: 0.4097, mean_class_accuracy: 0.1895 +2024-12-28 05:45:26,927 - pyskl - INFO - Epoch [59][100/3746] lr: 6.740e-02, eta: 3 days, 6:20:05, time: 2.660, data_time: 1.610, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4981, loss_cls: 4.3187, loss: 4.3187 +2024-12-28 05:46:52,483 - pyskl - INFO - Epoch [59][200/3746] lr: 6.738e-02, eta: 3 days, 6:18:49, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4847, loss_cls: 4.4194, loss: 4.4194 +2024-12-28 05:48:17,504 - pyskl - INFO - Epoch [59][300/3746] lr: 6.735e-02, eta: 3 days, 6:17:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4903, loss_cls: 4.3695, loss: 4.3695 +2024-12-28 05:49:43,183 - pyskl - INFO - Epoch [59][400/3746] lr: 6.732e-02, eta: 3 days, 6:16:17, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4897, loss_cls: 4.3673, loss: 4.3673 +2024-12-28 05:51:09,257 - pyskl - INFO - Epoch [59][500/3746] lr: 6.730e-02, eta: 3 days, 6:15:02, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4964, loss_cls: 4.3333, loss: 4.3333 +2024-12-28 05:52:35,102 - pyskl - INFO - Epoch [59][600/3746] lr: 6.727e-02, eta: 3 days, 6:13:46, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.4889, loss_cls: 4.3526, loss: 4.3526 +2024-12-28 05:54:00,722 - pyskl - INFO - Epoch [59][700/3746] lr: 6.725e-02, eta: 3 days, 6:12:30, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4905, loss_cls: 4.3558, loss: 4.3558 +2024-12-28 05:55:26,616 - pyskl - INFO - Epoch [59][800/3746] lr: 6.722e-02, eta: 3 days, 6:11:15, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4936, loss_cls: 4.3617, loss: 4.3617 +2024-12-28 05:56:52,314 - pyskl - INFO - Epoch [59][900/3746] lr: 6.719e-02, eta: 3 days, 6:09:59, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4884, loss_cls: 4.3764, loss: 4.3764 +2024-12-28 05:58:17,431 - pyskl - INFO - Epoch [59][1000/3746] lr: 6.717e-02, eta: 3 days, 6:08:42, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4861, loss_cls: 4.4146, loss: 4.4146 +2024-12-28 05:59:42,728 - pyskl - INFO - Epoch [59][1100/3746] lr: 6.714e-02, eta: 3 days, 6:07:26, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4830, loss_cls: 4.3830, loss: 4.3830 +2024-12-28 06:01:07,660 - pyskl - INFO - Epoch [59][1200/3746] lr: 6.711e-02, eta: 3 days, 6:06:08, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4919, loss_cls: 4.3597, loss: 4.3597 +2024-12-28 06:02:32,572 - pyskl - INFO - Epoch [59][1300/3746] lr: 6.709e-02, eta: 3 days, 6:04:51, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4945, loss_cls: 4.3765, loss: 4.3765 +2024-12-28 06:03:57,082 - pyskl - INFO - Epoch [59][1400/3746] lr: 6.706e-02, eta: 3 days, 6:03:34, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4839, loss_cls: 4.3799, loss: 4.3799 +2024-12-28 06:05:22,324 - pyskl - INFO - Epoch [59][1500/3746] lr: 6.704e-02, eta: 3 days, 6:02:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4952, loss_cls: 4.3635, loss: 4.3635 +2024-12-28 06:06:47,169 - pyskl - INFO - Epoch [59][1600/3746] lr: 6.701e-02, eta: 3 days, 6:01:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4827, loss_cls: 4.4231, loss: 4.4231 +2024-12-28 06:08:12,113 - pyskl - INFO - Epoch [59][1700/3746] lr: 6.698e-02, eta: 3 days, 5:59:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4955, loss_cls: 4.3245, loss: 4.3245 +2024-12-28 06:09:36,802 - pyskl - INFO - Epoch [59][1800/3746] lr: 6.696e-02, eta: 3 days, 5:58:25, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4841, loss_cls: 4.4043, loss: 4.4043 +2024-12-28 06:11:01,580 - pyskl - INFO - Epoch [59][1900/3746] lr: 6.693e-02, eta: 3 days, 5:57:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4869, loss_cls: 4.3741, loss: 4.3741 +2024-12-28 06:12:26,790 - pyskl - INFO - Epoch [59][2000/3746] lr: 6.690e-02, eta: 3 days, 5:55:51, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4891, loss_cls: 4.3813, loss: 4.3813 +2024-12-28 06:13:51,652 - pyskl - INFO - Epoch [59][2100/3746] lr: 6.688e-02, eta: 3 days, 5:54:34, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4906, loss_cls: 4.3866, loss: 4.3866 +2024-12-28 06:15:16,807 - pyskl - INFO - Epoch [59][2200/3746] lr: 6.685e-02, eta: 3 days, 5:53:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4798, loss_cls: 4.3924, loss: 4.3924 +2024-12-28 06:16:41,409 - pyskl - INFO - Epoch [59][2300/3746] lr: 6.682e-02, eta: 3 days, 5:52:00, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4922, loss_cls: 4.3542, loss: 4.3542 +2024-12-28 06:18:06,614 - pyskl - INFO - Epoch [59][2400/3746] lr: 6.680e-02, eta: 3 days, 5:50:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4873, loss_cls: 4.3883, loss: 4.3883 +2024-12-28 06:19:32,017 - pyskl - INFO - Epoch [59][2500/3746] lr: 6.677e-02, eta: 3 days, 5:49:26, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4845, loss_cls: 4.3843, loss: 4.3843 +2024-12-28 06:20:57,341 - pyskl - INFO - Epoch [59][2600/3746] lr: 6.675e-02, eta: 3 days, 5:48:10, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4875, loss_cls: 4.3773, loss: 4.3773 +2024-12-28 06:22:22,471 - pyskl - INFO - Epoch [59][2700/3746] lr: 6.672e-02, eta: 3 days, 5:46:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4986, loss_cls: 4.3366, loss: 4.3366 +2024-12-28 06:23:46,426 - pyskl - INFO - Epoch [59][2800/3746] lr: 6.669e-02, eta: 3 days, 5:45:34, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4853, loss_cls: 4.4083, loss: 4.4083 +2024-12-28 06:25:10,288 - pyskl - INFO - Epoch [59][2900/3746] lr: 6.667e-02, eta: 3 days, 5:44:16, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.4859, loss_cls: 4.3772, loss: 4.3772 +2024-12-28 06:26:34,546 - pyskl - INFO - Epoch [59][3000/3746] lr: 6.664e-02, eta: 3 days, 5:42:57, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4866, loss_cls: 4.3730, loss: 4.3730 +2024-12-28 06:27:58,698 - pyskl - INFO - Epoch [59][3100/3746] lr: 6.661e-02, eta: 3 days, 5:41:39, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4719, loss_cls: 4.4782, loss: 4.4782 +2024-12-28 06:29:23,385 - pyskl - INFO - Epoch [59][3200/3746] lr: 6.659e-02, eta: 3 days, 5:40:21, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.4983, loss_cls: 4.3366, loss: 4.3366 +2024-12-28 06:30:47,887 - pyskl - INFO - Epoch [59][3300/3746] lr: 6.656e-02, eta: 3 days, 5:39:03, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4863, loss_cls: 4.3794, loss: 4.3794 +2024-12-28 06:32:12,754 - pyskl - INFO - Epoch [59][3400/3746] lr: 6.653e-02, eta: 3 days, 5:37:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4888, loss_cls: 4.3902, loss: 4.3902 +2024-12-28 06:33:37,191 - pyskl - INFO - Epoch [59][3500/3746] lr: 6.651e-02, eta: 3 days, 5:36:28, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4934, loss_cls: 4.3676, loss: 4.3676 +2024-12-28 06:35:01,554 - pyskl - INFO - Epoch [59][3600/3746] lr: 6.648e-02, eta: 3 days, 5:35:10, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4888, loss_cls: 4.3680, loss: 4.3680 +2024-12-28 06:36:25,642 - pyskl - INFO - Epoch [59][3700/3746] lr: 6.646e-02, eta: 3 days, 5:33:52, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4830, loss_cls: 4.4233, loss: 4.4233 +2024-12-28 06:37:06,526 - pyskl - INFO - Saving checkpoint at 59 epochs +2024-12-28 06:39:04,864 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 06:39:05,556 - pyskl - INFO - +top1_acc 0.1842 +top5_acc 0.4032 +2024-12-28 06:39:05,557 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 06:39:05,606 - pyskl - INFO - +mean_acc 0.1841 +2024-12-28 06:39:05,620 - pyskl - INFO - Epoch(val) [59][309] top1_acc: 0.1842, top5_acc: 0.4032, mean_class_accuracy: 0.1841 +2024-12-28 06:43:26,046 - pyskl - INFO - Epoch [60][100/3746] lr: 6.642e-02, eta: 3 days, 5:35:29, time: 2.604, data_time: 1.575, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4995, loss_cls: 4.3212, loss: 4.3212 +2024-12-28 06:44:51,969 - pyskl - INFO - Epoch [60][200/3746] lr: 6.639e-02, eta: 3 days, 5:34:13, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5031, loss_cls: 4.3090, loss: 4.3090 +2024-12-28 06:46:17,408 - pyskl - INFO - Epoch [60][300/3746] lr: 6.636e-02, eta: 3 days, 5:32:57, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4928, loss_cls: 4.3770, loss: 4.3770 +2024-12-28 06:47:43,329 - pyskl - INFO - Epoch [60][400/3746] lr: 6.634e-02, eta: 3 days, 5:31:41, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4964, loss_cls: 4.3508, loss: 4.3508 +2024-12-28 06:49:09,457 - pyskl - INFO - Epoch [60][500/3746] lr: 6.631e-02, eta: 3 days, 5:30:25, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4867, loss_cls: 4.3659, loss: 4.3659 +2024-12-28 06:50:35,347 - pyskl - INFO - Epoch [60][600/3746] lr: 6.629e-02, eta: 3 days, 5:29:09, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5020, loss_cls: 4.3264, loss: 4.3264 +2024-12-28 06:52:00,761 - pyskl - INFO - Epoch [60][700/3746] lr: 6.626e-02, eta: 3 days, 5:27:52, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.4920, loss_cls: 4.3491, loss: 4.3491 +2024-12-28 06:53:26,422 - pyskl - INFO - Epoch [60][800/3746] lr: 6.623e-02, eta: 3 days, 5:26:36, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4795, loss_cls: 4.4083, loss: 4.4083 +2024-12-28 06:54:51,239 - pyskl - INFO - Epoch [60][900/3746] lr: 6.621e-02, eta: 3 days, 5:25:18, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4850, loss_cls: 4.3529, loss: 4.3529 +2024-12-28 06:56:16,022 - pyskl - INFO - Epoch [60][1000/3746] lr: 6.618e-02, eta: 3 days, 5:24:01, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4842, loss_cls: 4.4028, loss: 4.4028 +2024-12-28 06:57:40,698 - pyskl - INFO - Epoch [60][1100/3746] lr: 6.615e-02, eta: 3 days, 5:22:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4908, loss_cls: 4.3813, loss: 4.3813 +2024-12-28 06:59:05,802 - pyskl - INFO - Epoch [60][1200/3746] lr: 6.613e-02, eta: 3 days, 5:21:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4922, loss_cls: 4.3726, loss: 4.3726 +2024-12-28 07:00:30,781 - pyskl - INFO - Epoch [60][1300/3746] lr: 6.610e-02, eta: 3 days, 5:20:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.4916, loss_cls: 4.3670, loss: 4.3670 +2024-12-28 07:01:55,480 - pyskl - INFO - Epoch [60][1400/3746] lr: 6.607e-02, eta: 3 days, 5:18:50, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4955, loss_cls: 4.3656, loss: 4.3656 +2024-12-28 07:03:20,074 - pyskl - INFO - Epoch [60][1500/3746] lr: 6.605e-02, eta: 3 days, 5:17:32, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4884, loss_cls: 4.3462, loss: 4.3462 +2024-12-28 07:04:44,944 - pyskl - INFO - Epoch [60][1600/3746] lr: 6.602e-02, eta: 3 days, 5:16:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4931, loss_cls: 4.3603, loss: 4.3603 +2024-12-28 07:06:10,428 - pyskl - INFO - Epoch [60][1700/3746] lr: 6.599e-02, eta: 3 days, 5:14:58, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4978, loss_cls: 4.3517, loss: 4.3517 +2024-12-28 07:07:35,880 - pyskl - INFO - Epoch [60][1800/3746] lr: 6.597e-02, eta: 3 days, 5:13:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4963, loss_cls: 4.3629, loss: 4.3629 +2024-12-28 07:09:01,110 - pyskl - INFO - Epoch [60][1900/3746] lr: 6.594e-02, eta: 3 days, 5:12:24, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4958, loss_cls: 4.3783, loss: 4.3783 +2024-12-28 07:10:25,859 - pyskl - INFO - Epoch [60][2000/3746] lr: 6.591e-02, eta: 3 days, 5:11:07, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4808, loss_cls: 4.3867, loss: 4.3867 +2024-12-28 07:11:50,382 - pyskl - INFO - Epoch [60][2100/3746] lr: 6.589e-02, eta: 3 days, 5:09:48, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.4961, loss_cls: 4.3252, loss: 4.3252 +2024-12-28 07:13:15,236 - pyskl - INFO - Epoch [60][2200/3746] lr: 6.586e-02, eta: 3 days, 5:08:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4906, loss_cls: 4.3508, loss: 4.3508 +2024-12-28 07:14:40,479 - pyskl - INFO - Epoch [60][2300/3746] lr: 6.584e-02, eta: 3 days, 5:07:14, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4922, loss_cls: 4.3569, loss: 4.3569 +2024-12-28 07:16:05,212 - pyskl - INFO - Epoch [60][2400/3746] lr: 6.581e-02, eta: 3 days, 5:05:56, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4892, loss_cls: 4.3987, loss: 4.3987 +2024-12-28 07:17:30,220 - pyskl - INFO - Epoch [60][2500/3746] lr: 6.578e-02, eta: 3 days, 5:04:38, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4889, loss_cls: 4.3961, loss: 4.3961 +2024-12-28 07:18:55,352 - pyskl - INFO - Epoch [60][2600/3746] lr: 6.576e-02, eta: 3 days, 5:03:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4806, loss_cls: 4.3813, loss: 4.3813 +2024-12-28 07:20:20,380 - pyskl - INFO - Epoch [60][2700/3746] lr: 6.573e-02, eta: 3 days, 5:02:04, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4936, loss_cls: 4.3721, loss: 4.3721 +2024-12-28 07:21:45,320 - pyskl - INFO - Epoch [60][2800/3746] lr: 6.570e-02, eta: 3 days, 5:00:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4961, loss_cls: 4.3501, loss: 4.3501 +2024-12-28 07:23:09,638 - pyskl - INFO - Epoch [60][2900/3746] lr: 6.568e-02, eta: 3 days, 4:59:28, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4853, loss_cls: 4.3880, loss: 4.3880 +2024-12-28 07:24:34,904 - pyskl - INFO - Epoch [60][3000/3746] lr: 6.565e-02, eta: 3 days, 4:58:10, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4791, loss_cls: 4.3817, loss: 4.3817 +2024-12-28 07:25:59,747 - pyskl - INFO - Epoch [60][3100/3746] lr: 6.562e-02, eta: 3 days, 4:56:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5053, loss_cls: 4.3224, loss: 4.3224 +2024-12-28 07:27:25,000 - pyskl - INFO - Epoch [60][3200/3746] lr: 6.560e-02, eta: 3 days, 4:55:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4916, loss_cls: 4.3706, loss: 4.3706 +2024-12-28 07:28:49,421 - pyskl - INFO - Epoch [60][3300/3746] lr: 6.557e-02, eta: 3 days, 4:54:17, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4922, loss_cls: 4.3422, loss: 4.3422 +2024-12-28 07:30:14,169 - pyskl - INFO - Epoch [60][3400/3746] lr: 6.554e-02, eta: 3 days, 4:52:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4969, loss_cls: 4.3428, loss: 4.3428 +2024-12-28 07:31:38,990 - pyskl - INFO - Epoch [60][3500/3746] lr: 6.552e-02, eta: 3 days, 4:51:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5025, loss_cls: 4.2963, loss: 4.2963 +2024-12-28 07:33:03,673 - pyskl - INFO - Epoch [60][3600/3746] lr: 6.549e-02, eta: 3 days, 4:50:24, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4806, loss_cls: 4.4013, loss: 4.4013 +2024-12-28 07:34:28,373 - pyskl - INFO - Epoch [60][3700/3746] lr: 6.546e-02, eta: 3 days, 4:49:06, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4855, loss_cls: 4.4016, loss: 4.4016 +2024-12-28 07:35:09,530 - pyskl - INFO - Saving checkpoint at 60 epochs +2024-12-28 07:37:08,720 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 07:37:09,731 - pyskl - INFO - +top1_acc 0.1773 +top5_acc 0.3928 +2024-12-28 07:37:09,731 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 07:37:09,776 - pyskl - INFO - +mean_acc 0.1770 +2024-12-28 07:37:09,789 - pyskl - INFO - Epoch(val) [60][309] top1_acc: 0.1773, top5_acc: 0.3928, mean_class_accuracy: 0.1770 +2024-12-28 07:41:30,790 - pyskl - INFO - Epoch [61][100/3746] lr: 6.542e-02, eta: 3 days, 4:50:38, time: 2.610, data_time: 1.568, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5045, loss_cls: 4.3118, loss: 4.3118 +2024-12-28 07:42:55,891 - pyskl - INFO - Epoch [61][200/3746] lr: 6.540e-02, eta: 3 days, 4:49:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5048, loss_cls: 4.2970, loss: 4.2970 +2024-12-28 07:44:21,270 - pyskl - INFO - Epoch [61][300/3746] lr: 6.537e-02, eta: 3 days, 4:48:03, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4916, loss_cls: 4.3560, loss: 4.3560 +2024-12-28 07:45:46,567 - pyskl - INFO - Epoch [61][400/3746] lr: 6.534e-02, eta: 3 days, 4:46:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5016, loss_cls: 4.2965, loss: 4.2965 +2024-12-28 07:47:12,342 - pyskl - INFO - Epoch [61][500/3746] lr: 6.532e-02, eta: 3 days, 4:45:29, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4830, loss_cls: 4.3564, loss: 4.3564 +2024-12-28 07:48:37,797 - pyskl - INFO - Epoch [61][600/3746] lr: 6.529e-02, eta: 3 days, 4:44:12, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.4939, loss_cls: 4.3453, loss: 4.3453 +2024-12-28 07:50:03,064 - pyskl - INFO - Epoch [61][700/3746] lr: 6.526e-02, eta: 3 days, 4:42:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4967, loss_cls: 4.3367, loss: 4.3367 +2024-12-28 07:51:28,908 - pyskl - INFO - Epoch [61][800/3746] lr: 6.524e-02, eta: 3 days, 4:41:38, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5045, loss_cls: 4.3322, loss: 4.3322 +2024-12-28 07:52:54,265 - pyskl - INFO - Epoch [61][900/3746] lr: 6.521e-02, eta: 3 days, 4:40:21, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4925, loss_cls: 4.3601, loss: 4.3601 +2024-12-28 07:54:19,494 - pyskl - INFO - Epoch [61][1000/3746] lr: 6.519e-02, eta: 3 days, 4:39:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.4911, loss_cls: 4.3892, loss: 4.3892 +2024-12-28 07:55:44,606 - pyskl - INFO - Epoch [61][1100/3746] lr: 6.516e-02, eta: 3 days, 4:37:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4755, loss_cls: 4.4233, loss: 4.4233 +2024-12-28 07:57:09,748 - pyskl - INFO - Epoch [61][1200/3746] lr: 6.513e-02, eta: 3 days, 4:36:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4891, loss_cls: 4.3440, loss: 4.3440 +2024-12-28 07:58:34,857 - pyskl - INFO - Epoch [61][1300/3746] lr: 6.511e-02, eta: 3 days, 4:35:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4994, loss_cls: 4.3453, loss: 4.3453 +2024-12-28 08:00:00,256 - pyskl - INFO - Epoch [61][1400/3746] lr: 6.508e-02, eta: 3 days, 4:33:53, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5005, loss_cls: 4.3039, loss: 4.3039 +2024-12-28 08:01:25,169 - pyskl - INFO - Epoch [61][1500/3746] lr: 6.505e-02, eta: 3 days, 4:32:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5044, loss_cls: 4.3025, loss: 4.3025 +2024-12-28 08:02:50,765 - pyskl - INFO - Epoch [61][1600/3746] lr: 6.503e-02, eta: 3 days, 4:31:19, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4797, loss_cls: 4.4413, loss: 4.4413 +2024-12-28 08:04:16,322 - pyskl - INFO - Epoch [61][1700/3746] lr: 6.500e-02, eta: 3 days, 4:30:02, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4888, loss_cls: 4.3822, loss: 4.3822 +2024-12-28 08:05:41,554 - pyskl - INFO - Epoch [61][1800/3746] lr: 6.497e-02, eta: 3 days, 4:28:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5003, loss_cls: 4.3247, loss: 4.3247 +2024-12-28 08:07:06,571 - pyskl - INFO - Epoch [61][1900/3746] lr: 6.495e-02, eta: 3 days, 4:27:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4877, loss_cls: 4.3749, loss: 4.3749 +2024-12-28 08:08:31,912 - pyskl - INFO - Epoch [61][2000/3746] lr: 6.492e-02, eta: 3 days, 4:26:09, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.4991, loss_cls: 4.3370, loss: 4.3370 +2024-12-28 08:09:57,065 - pyskl - INFO - Epoch [61][2100/3746] lr: 6.489e-02, eta: 3 days, 4:24:51, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4738, loss_cls: 4.4027, loss: 4.4027 +2024-12-28 08:11:21,903 - pyskl - INFO - Epoch [61][2200/3746] lr: 6.487e-02, eta: 3 days, 4:23:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4855, loss_cls: 4.3556, loss: 4.3556 +2024-12-28 08:12:46,943 - pyskl - INFO - Epoch [61][2300/3746] lr: 6.484e-02, eta: 3 days, 4:22:16, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4845, loss_cls: 4.3877, loss: 4.3877 +2024-12-28 08:14:12,153 - pyskl - INFO - Epoch [61][2400/3746] lr: 6.481e-02, eta: 3 days, 4:20:58, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4981, loss_cls: 4.3720, loss: 4.3720 +2024-12-28 08:15:37,506 - pyskl - INFO - Epoch [61][2500/3746] lr: 6.478e-02, eta: 3 days, 4:19:41, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4850, loss_cls: 4.3579, loss: 4.3579 +2024-12-28 08:17:02,540 - pyskl - INFO - Epoch [61][2600/3746] lr: 6.476e-02, eta: 3 days, 4:18:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4964, loss_cls: 4.3837, loss: 4.3837 +2024-12-28 08:18:27,605 - pyskl - INFO - Epoch [61][2700/3746] lr: 6.473e-02, eta: 3 days, 4:17:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4856, loss_cls: 4.3802, loss: 4.3802 +2024-12-28 08:19:52,308 - pyskl - INFO - Epoch [61][2800/3746] lr: 6.470e-02, eta: 3 days, 4:15:47, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4847, loss_cls: 4.3828, loss: 4.3828 +2024-12-28 08:21:16,905 - pyskl - INFO - Epoch [61][2900/3746] lr: 6.468e-02, eta: 3 days, 4:14:28, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4950, loss_cls: 4.3407, loss: 4.3407 +2024-12-28 08:22:43,183 - pyskl - INFO - Epoch [61][3000/3746] lr: 6.465e-02, eta: 3 days, 4:13:12, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4914, loss_cls: 4.3466, loss: 4.3466 +2024-12-28 08:24:08,850 - pyskl - INFO - Epoch [61][3100/3746] lr: 6.462e-02, eta: 3 days, 4:11:55, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4913, loss_cls: 4.3565, loss: 4.3565 +2024-12-28 08:25:35,078 - pyskl - INFO - Epoch [61][3200/3746] lr: 6.460e-02, eta: 3 days, 4:10:39, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4906, loss_cls: 4.3359, loss: 4.3359 +2024-12-28 08:27:01,032 - pyskl - INFO - Epoch [61][3300/3746] lr: 6.457e-02, eta: 3 days, 4:09:23, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4906, loss_cls: 4.3941, loss: 4.3941 +2024-12-28 08:28:26,352 - pyskl - INFO - Epoch [61][3400/3746] lr: 6.454e-02, eta: 3 days, 4:08:05, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.4898, loss_cls: 4.3512, loss: 4.3512 +2024-12-28 08:29:51,737 - pyskl - INFO - Epoch [61][3500/3746] lr: 6.452e-02, eta: 3 days, 4:06:48, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4878, loss_cls: 4.3665, loss: 4.3665 +2024-12-28 08:31:17,499 - pyskl - INFO - Epoch [61][3600/3746] lr: 6.449e-02, eta: 3 days, 4:05:31, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4900, loss_cls: 4.3641, loss: 4.3641 +2024-12-28 08:32:43,258 - pyskl - INFO - Epoch [61][3700/3746] lr: 6.446e-02, eta: 3 days, 4:04:14, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.4991, loss_cls: 4.3381, loss: 4.3381 +2024-12-28 08:33:24,554 - pyskl - INFO - Saving checkpoint at 61 epochs +2024-12-28 08:35:25,726 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 08:35:26,582 - pyskl - INFO - +top1_acc 0.1966 +top5_acc 0.4131 +2024-12-28 08:35:26,583 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 08:35:26,636 - pyskl - INFO - +mean_acc 0.1964 +2024-12-28 08:35:26,641 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_56.pth was removed +2024-12-28 08:35:26,944 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_61.pth. +2024-12-28 08:35:26,945 - pyskl - INFO - Best top1_acc is 0.1966 at 61 epoch. +2024-12-28 08:35:26,959 - pyskl - INFO - Epoch(val) [61][309] top1_acc: 0.1966, top5_acc: 0.4131, mean_class_accuracy: 0.1964 +2024-12-28 08:39:45,703 - pyskl - INFO - Epoch [62][100/3746] lr: 6.443e-02, eta: 3 days, 4:05:37, time: 2.587, data_time: 1.564, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5073, loss_cls: 4.2963, loss: 4.2963 +2024-12-28 08:41:10,447 - pyskl - INFO - Epoch [62][200/3746] lr: 6.440e-02, eta: 3 days, 4:04:18, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4963, loss_cls: 4.3563, loss: 4.3563 +2024-12-28 08:42:34,976 - pyskl - INFO - Epoch [62][300/3746] lr: 6.437e-02, eta: 3 days, 4:03:00, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.4933, loss_cls: 4.3093, loss: 4.3093 +2024-12-28 08:43:59,496 - pyskl - INFO - Epoch [62][400/3746] lr: 6.434e-02, eta: 3 days, 4:01:41, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5066, loss_cls: 4.3022, loss: 4.3022 +2024-12-28 08:45:24,320 - pyskl - INFO - Epoch [62][500/3746] lr: 6.432e-02, eta: 3 days, 4:00:22, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5069, loss_cls: 4.2960, loss: 4.2960 +2024-12-28 08:46:50,274 - pyskl - INFO - Epoch [62][600/3746] lr: 6.429e-02, eta: 3 days, 3:59:06, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4984, loss_cls: 4.3371, loss: 4.3371 +2024-12-28 08:48:15,269 - pyskl - INFO - Epoch [62][700/3746] lr: 6.426e-02, eta: 3 days, 3:57:47, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4950, loss_cls: 4.3103, loss: 4.3103 +2024-12-28 08:49:40,359 - pyskl - INFO - Epoch [62][800/3746] lr: 6.424e-02, eta: 3 days, 3:56:29, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4920, loss_cls: 4.3445, loss: 4.3445 +2024-12-28 08:51:05,649 - pyskl - INFO - Epoch [62][900/3746] lr: 6.421e-02, eta: 3 days, 3:55:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5038, loss_cls: 4.2814, loss: 4.2814 +2024-12-28 08:52:30,437 - pyskl - INFO - Epoch [62][1000/3746] lr: 6.418e-02, eta: 3 days, 3:53:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.4939, loss_cls: 4.3410, loss: 4.3410 +2024-12-28 08:53:55,695 - pyskl - INFO - Epoch [62][1100/3746] lr: 6.416e-02, eta: 3 days, 3:52:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5005, loss_cls: 4.3142, loss: 4.3142 +2024-12-28 08:55:20,380 - pyskl - INFO - Epoch [62][1200/3746] lr: 6.413e-02, eta: 3 days, 3:51:17, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.4994, loss_cls: 4.3441, loss: 4.3441 +2024-12-28 08:56:45,481 - pyskl - INFO - Epoch [62][1300/3746] lr: 6.410e-02, eta: 3 days, 3:49:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4923, loss_cls: 4.3735, loss: 4.3735 +2024-12-28 08:58:09,743 - pyskl - INFO - Epoch [62][1400/3746] lr: 6.408e-02, eta: 3 days, 3:48:40, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4906, loss_cls: 4.3540, loss: 4.3540 +2024-12-28 08:59:33,955 - pyskl - INFO - Epoch [62][1500/3746] lr: 6.405e-02, eta: 3 days, 3:47:20, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.4936, loss_cls: 4.3343, loss: 4.3343 +2024-12-28 09:00:59,136 - pyskl - INFO - Epoch [62][1600/3746] lr: 6.402e-02, eta: 3 days, 3:46:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4884, loss_cls: 4.3349, loss: 4.3349 +2024-12-28 09:02:24,581 - pyskl - INFO - Epoch [62][1700/3746] lr: 6.400e-02, eta: 3 days, 3:44:45, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4975, loss_cls: 4.3496, loss: 4.3496 +2024-12-28 09:03:49,504 - pyskl - INFO - Epoch [62][1800/3746] lr: 6.397e-02, eta: 3 days, 3:43:27, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5028, loss_cls: 4.3138, loss: 4.3138 +2024-12-28 09:05:14,117 - pyskl - INFO - Epoch [62][1900/3746] lr: 6.394e-02, eta: 3 days, 3:42:08, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4977, loss_cls: 4.3467, loss: 4.3467 +2024-12-28 09:06:38,872 - pyskl - INFO - Epoch [62][2000/3746] lr: 6.392e-02, eta: 3 days, 3:40:49, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4942, loss_cls: 4.3516, loss: 4.3516 +2024-12-28 09:08:03,603 - pyskl - INFO - Epoch [62][2100/3746] lr: 6.389e-02, eta: 3 days, 3:39:31, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4959, loss_cls: 4.3571, loss: 4.3571 +2024-12-28 09:09:28,890 - pyskl - INFO - Epoch [62][2200/3746] lr: 6.386e-02, eta: 3 days, 3:38:13, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4873, loss_cls: 4.3676, loss: 4.3676 +2024-12-28 09:10:54,414 - pyskl - INFO - Epoch [62][2300/3746] lr: 6.384e-02, eta: 3 days, 3:36:55, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4853, loss_cls: 4.4057, loss: 4.4057 +2024-12-28 09:12:19,370 - pyskl - INFO - Epoch [62][2400/3746] lr: 6.381e-02, eta: 3 days, 3:35:37, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4983, loss_cls: 4.3456, loss: 4.3456 +2024-12-28 09:13:44,834 - pyskl - INFO - Epoch [62][2500/3746] lr: 6.378e-02, eta: 3 days, 3:34:19, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4947, loss_cls: 4.3579, loss: 4.3579 +2024-12-28 09:15:10,108 - pyskl - INFO - Epoch [62][2600/3746] lr: 6.375e-02, eta: 3 days, 3:33:02, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4903, loss_cls: 4.3505, loss: 4.3505 +2024-12-28 09:16:35,028 - pyskl - INFO - Epoch [62][2700/3746] lr: 6.373e-02, eta: 3 days, 3:31:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4973, loss_cls: 4.3470, loss: 4.3470 +2024-12-28 09:17:59,884 - pyskl - INFO - Epoch [62][2800/3746] lr: 6.370e-02, eta: 3 days, 3:30:25, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5033, loss_cls: 4.3464, loss: 4.3464 +2024-12-28 09:19:24,654 - pyskl - INFO - Epoch [62][2900/3746] lr: 6.367e-02, eta: 3 days, 3:29:06, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4861, loss_cls: 4.3939, loss: 4.3939 +2024-12-28 09:20:49,379 - pyskl - INFO - Epoch [62][3000/3746] lr: 6.365e-02, eta: 3 days, 3:27:48, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.4961, loss_cls: 4.3220, loss: 4.3220 +2024-12-28 09:22:13,952 - pyskl - INFO - Epoch [62][3100/3746] lr: 6.362e-02, eta: 3 days, 3:26:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4952, loss_cls: 4.3446, loss: 4.3446 +2024-12-28 09:23:38,069 - pyskl - INFO - Epoch [62][3200/3746] lr: 6.359e-02, eta: 3 days, 3:25:09, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4870, loss_cls: 4.3347, loss: 4.3347 +2024-12-28 09:25:02,913 - pyskl - INFO - Epoch [62][3300/3746] lr: 6.357e-02, eta: 3 days, 3:23:51, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4858, loss_cls: 4.4090, loss: 4.4090 +2024-12-28 09:26:27,853 - pyskl - INFO - Epoch [62][3400/3746] lr: 6.354e-02, eta: 3 days, 3:22:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4814, loss_cls: 4.3924, loss: 4.3924 +2024-12-28 09:27:52,346 - pyskl - INFO - Epoch [62][3500/3746] lr: 6.351e-02, eta: 3 days, 3:21:13, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4872, loss_cls: 4.3750, loss: 4.3750 +2024-12-28 09:29:16,665 - pyskl - INFO - Epoch [62][3600/3746] lr: 6.349e-02, eta: 3 days, 3:19:54, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4898, loss_cls: 4.3684, loss: 4.3684 +2024-12-28 09:30:41,481 - pyskl - INFO - Epoch [62][3700/3746] lr: 6.346e-02, eta: 3 days, 3:18:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4900, loss_cls: 4.3607, loss: 4.3607 +2024-12-28 09:31:22,258 - pyskl - INFO - Saving checkpoint at 62 epochs +2024-12-28 09:33:21,102 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 09:33:21,815 - pyskl - INFO - +top1_acc 0.1832 +top5_acc 0.4112 +2024-12-28 09:33:21,815 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 09:33:21,870 - pyskl - INFO - +mean_acc 0.1830 +2024-12-28 09:33:21,885 - pyskl - INFO - Epoch(val) [62][309] top1_acc: 0.1832, top5_acc: 0.4112, mean_class_accuracy: 0.1830 +2024-12-28 09:37:38,984 - pyskl - INFO - Epoch [63][100/3746] lr: 6.342e-02, eta: 3 days, 3:19:50, time: 2.571, data_time: 1.537, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5105, loss_cls: 4.2647, loss: 4.2647 +2024-12-28 09:39:04,068 - pyskl - INFO - Epoch [63][200/3746] lr: 6.339e-02, eta: 3 days, 3:18:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5002, loss_cls: 4.2863, loss: 4.2863 +2024-12-28 09:40:28,879 - pyskl - INFO - Epoch [63][300/3746] lr: 6.337e-02, eta: 3 days, 3:17:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4942, loss_cls: 4.3493, loss: 4.3493 +2024-12-28 09:41:53,757 - pyskl - INFO - Epoch [63][400/3746] lr: 6.334e-02, eta: 3 days, 3:15:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5025, loss_cls: 4.3074, loss: 4.3074 +2024-12-28 09:43:18,540 - pyskl - INFO - Epoch [63][500/3746] lr: 6.331e-02, eta: 3 days, 3:14:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5072, loss_cls: 4.3221, loss: 4.3221 +2024-12-28 09:44:43,951 - pyskl - INFO - Epoch [63][600/3746] lr: 6.328e-02, eta: 3 days, 3:13:17, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5002, loss_cls: 4.2916, loss: 4.2916 +2024-12-28 09:46:09,202 - pyskl - INFO - Epoch [63][700/3746] lr: 6.326e-02, eta: 3 days, 3:11:59, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5055, loss_cls: 4.2984, loss: 4.2984 +2024-12-28 09:47:34,588 - pyskl - INFO - Epoch [63][800/3746] lr: 6.323e-02, eta: 3 days, 3:10:41, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4944, loss_cls: 4.3382, loss: 4.3382 +2024-12-28 09:48:59,838 - pyskl - INFO - Epoch [63][900/3746] lr: 6.320e-02, eta: 3 days, 3:09:23, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4853, loss_cls: 4.3872, loss: 4.3872 +2024-12-28 09:50:23,909 - pyskl - INFO - Epoch [63][1000/3746] lr: 6.318e-02, eta: 3 days, 3:08:03, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.4916, loss_cls: 4.3422, loss: 4.3422 +2024-12-28 09:51:48,493 - pyskl - INFO - Epoch [63][1100/3746] lr: 6.315e-02, eta: 3 days, 3:06:44, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.2536, top5_acc: 0.4928, loss_cls: 4.3460, loss: 4.3460 +2024-12-28 09:53:14,007 - pyskl - INFO - Epoch [63][1200/3746] lr: 6.312e-02, eta: 3 days, 3:05:26, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4934, loss_cls: 4.3336, loss: 4.3336 +2024-12-28 09:54:38,840 - pyskl - INFO - Epoch [63][1300/3746] lr: 6.310e-02, eta: 3 days, 3:04:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.4995, loss_cls: 4.3161, loss: 4.3161 +2024-12-28 09:56:03,986 - pyskl - INFO - Epoch [63][1400/3746] lr: 6.307e-02, eta: 3 days, 3:02:49, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.4963, loss_cls: 4.3370, loss: 4.3370 +2024-12-28 09:57:29,192 - pyskl - INFO - Epoch [63][1500/3746] lr: 6.304e-02, eta: 3 days, 3:01:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4902, loss_cls: 4.3711, loss: 4.3711 +2024-12-28 09:58:54,287 - pyskl - INFO - Epoch [63][1600/3746] lr: 6.301e-02, eta: 3 days, 3:00:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.4952, loss_cls: 4.3272, loss: 4.3272 +2024-12-28 10:00:19,367 - pyskl - INFO - Epoch [63][1700/3746] lr: 6.299e-02, eta: 3 days, 2:58:54, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4950, loss_cls: 4.3284, loss: 4.3284 +2024-12-28 10:01:44,538 - pyskl - INFO - Epoch [63][1800/3746] lr: 6.296e-02, eta: 3 days, 2:57:36, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5017, loss_cls: 4.3439, loss: 4.3439 +2024-12-28 10:03:10,129 - pyskl - INFO - Epoch [63][1900/3746] lr: 6.293e-02, eta: 3 days, 2:56:18, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4898, loss_cls: 4.3409, loss: 4.3409 +2024-12-28 10:04:35,279 - pyskl - INFO - Epoch [63][2000/3746] lr: 6.291e-02, eta: 3 days, 2:55:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4864, loss_cls: 4.3668, loss: 4.3668 +2024-12-28 10:06:00,535 - pyskl - INFO - Epoch [63][2100/3746] lr: 6.288e-02, eta: 3 days, 2:53:42, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4950, loss_cls: 4.3564, loss: 4.3564 +2024-12-28 10:07:25,574 - pyskl - INFO - Epoch [63][2200/3746] lr: 6.285e-02, eta: 3 days, 2:52:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4859, loss_cls: 4.3738, loss: 4.3738 +2024-12-28 10:08:50,494 - pyskl - INFO - Epoch [63][2300/3746] lr: 6.283e-02, eta: 3 days, 2:51:05, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.4900, loss_cls: 4.3114, loss: 4.3114 +2024-12-28 10:10:15,228 - pyskl - INFO - Epoch [63][2400/3746] lr: 6.280e-02, eta: 3 days, 2:49:46, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.4997, loss_cls: 4.3118, loss: 4.3118 +2024-12-28 10:11:40,700 - pyskl - INFO - Epoch [63][2500/3746] lr: 6.277e-02, eta: 3 days, 2:48:28, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4917, loss_cls: 4.3161, loss: 4.3161 +2024-12-28 10:13:06,099 - pyskl - INFO - Epoch [63][2600/3746] lr: 6.274e-02, eta: 3 days, 2:47:10, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.5028, loss_cls: 4.3147, loss: 4.3147 +2024-12-28 10:14:30,534 - pyskl - INFO - Epoch [63][2700/3746] lr: 6.272e-02, eta: 3 days, 2:45:50, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4861, loss_cls: 4.4018, loss: 4.4018 +2024-12-28 10:15:54,695 - pyskl - INFO - Epoch [63][2800/3746] lr: 6.269e-02, eta: 3 days, 2:44:30, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4905, loss_cls: 4.3894, loss: 4.3894 +2024-12-28 10:17:19,322 - pyskl - INFO - Epoch [63][2900/3746] lr: 6.266e-02, eta: 3 days, 2:43:11, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4909, loss_cls: 4.3408, loss: 4.3408 +2024-12-28 10:18:43,689 - pyskl - INFO - Epoch [63][3000/3746] lr: 6.264e-02, eta: 3 days, 2:41:52, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4884, loss_cls: 4.4083, loss: 4.4083 +2024-12-28 10:20:08,496 - pyskl - INFO - Epoch [63][3100/3746] lr: 6.261e-02, eta: 3 days, 2:40:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.4923, loss_cls: 4.3702, loss: 4.3702 +2024-12-28 10:21:33,523 - pyskl - INFO - Epoch [63][3200/3746] lr: 6.258e-02, eta: 3 days, 2:39:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.4984, loss_cls: 4.3079, loss: 4.3079 +2024-12-28 10:22:58,026 - pyskl - INFO - Epoch [63][3300/3746] lr: 6.256e-02, eta: 3 days, 2:37:55, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4925, loss_cls: 4.3675, loss: 4.3675 +2024-12-28 10:24:22,461 - pyskl - INFO - Epoch [63][3400/3746] lr: 6.253e-02, eta: 3 days, 2:36:36, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4963, loss_cls: 4.3169, loss: 4.3169 +2024-12-28 10:25:47,045 - pyskl - INFO - Epoch [63][3500/3746] lr: 6.250e-02, eta: 3 days, 2:35:16, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4967, loss_cls: 4.3150, loss: 4.3150 +2024-12-28 10:27:11,713 - pyskl - INFO - Epoch [63][3600/3746] lr: 6.247e-02, eta: 3 days, 2:33:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.4923, loss_cls: 4.3509, loss: 4.3509 +2024-12-28 10:28:36,733 - pyskl - INFO - Epoch [63][3700/3746] lr: 6.245e-02, eta: 3 days, 2:32:39, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4983, loss_cls: 4.3351, loss: 4.3351 +2024-12-28 10:29:17,526 - pyskl - INFO - Saving checkpoint at 63 epochs +2024-12-28 10:31:16,769 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 10:31:17,588 - pyskl - INFO - +top1_acc 0.1970 +top5_acc 0.4188 +2024-12-28 10:31:17,588 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 10:31:17,631 - pyskl - INFO - +mean_acc 0.1969 +2024-12-28 10:31:17,636 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_61.pth was removed +2024-12-28 10:31:17,900 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_63.pth. +2024-12-28 10:31:17,901 - pyskl - INFO - Best top1_acc is 0.1970 at 63 epoch. +2024-12-28 10:31:17,915 - pyskl - INFO - Epoch(val) [63][309] top1_acc: 0.1970, top5_acc: 0.4188, mean_class_accuracy: 0.1969 +2024-12-28 10:35:41,551 - pyskl - INFO - Epoch [64][100/3746] lr: 6.241e-02, eta: 3 days, 2:33:56, time: 2.636, data_time: 1.575, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5112, loss_cls: 4.2496, loss: 4.2496 +2024-12-28 10:37:08,298 - pyskl - INFO - Epoch [64][200/3746] lr: 6.238e-02, eta: 3 days, 2:32:40, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4991, loss_cls: 4.3133, loss: 4.3133 +2024-12-28 10:38:34,319 - pyskl - INFO - Epoch [64][300/3746] lr: 6.235e-02, eta: 3 days, 2:31:23, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.4978, loss_cls: 4.3065, loss: 4.3065 +2024-12-28 10:40:00,687 - pyskl - INFO - Epoch [64][400/3746] lr: 6.233e-02, eta: 3 days, 2:30:06, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5103, loss_cls: 4.2662, loss: 4.2662 +2024-12-28 10:41:27,124 - pyskl - INFO - Epoch [64][500/3746] lr: 6.230e-02, eta: 3 days, 2:28:49, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5062, loss_cls: 4.3305, loss: 4.3305 +2024-12-28 10:42:53,460 - pyskl - INFO - Epoch [64][600/3746] lr: 6.227e-02, eta: 3 days, 2:27:32, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.4981, loss_cls: 4.3074, loss: 4.3074 +2024-12-28 10:44:19,794 - pyskl - INFO - Epoch [64][700/3746] lr: 6.225e-02, eta: 3 days, 2:26:15, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4984, loss_cls: 4.3202, loss: 4.3202 +2024-12-28 10:45:46,175 - pyskl - INFO - Epoch [64][800/3746] lr: 6.222e-02, eta: 3 days, 2:24:58, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4884, loss_cls: 4.3476, loss: 4.3476 +2024-12-28 10:47:11,742 - pyskl - INFO - Epoch [64][900/3746] lr: 6.219e-02, eta: 3 days, 2:23:40, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4933, loss_cls: 4.3837, loss: 4.3837 +2024-12-28 10:48:36,810 - pyskl - INFO - Epoch [64][1000/3746] lr: 6.216e-02, eta: 3 days, 2:22:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5045, loss_cls: 4.3196, loss: 4.3196 +2024-12-28 10:50:02,011 - pyskl - INFO - Epoch [64][1100/3746] lr: 6.214e-02, eta: 3 days, 2:21:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4917, loss_cls: 4.3198, loss: 4.3198 +2024-12-28 10:51:27,034 - pyskl - INFO - Epoch [64][1200/3746] lr: 6.211e-02, eta: 3 days, 2:19:44, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4928, loss_cls: 4.3393, loss: 4.3393 +2024-12-28 10:52:52,074 - pyskl - INFO - Epoch [64][1300/3746] lr: 6.208e-02, eta: 3 days, 2:18:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5023, loss_cls: 4.3183, loss: 4.3183 +2024-12-28 10:54:17,856 - pyskl - INFO - Epoch [64][1400/3746] lr: 6.206e-02, eta: 3 days, 2:17:07, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.4872, loss_cls: 4.3254, loss: 4.3254 +2024-12-28 10:55:42,933 - pyskl - INFO - Epoch [64][1500/3746] lr: 6.203e-02, eta: 3 days, 2:15:48, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4969, loss_cls: 4.2977, loss: 4.2977 +2024-12-28 10:57:08,317 - pyskl - INFO - Epoch [64][1600/3746] lr: 6.200e-02, eta: 3 days, 2:14:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.4986, loss_cls: 4.3280, loss: 4.3280 +2024-12-28 10:58:33,691 - pyskl - INFO - Epoch [64][1700/3746] lr: 6.197e-02, eta: 3 days, 2:13:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5017, loss_cls: 4.3177, loss: 4.3177 +2024-12-28 10:59:58,798 - pyskl - INFO - Epoch [64][1800/3746] lr: 6.195e-02, eta: 3 days, 2:11:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5053, loss_cls: 4.3109, loss: 4.3109 +2024-12-28 11:01:24,512 - pyskl - INFO - Epoch [64][1900/3746] lr: 6.192e-02, eta: 3 days, 2:10:35, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4903, loss_cls: 4.3643, loss: 4.3643 +2024-12-28 11:02:50,196 - pyskl - INFO - Epoch [64][2000/3746] lr: 6.189e-02, eta: 3 days, 2:09:17, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4991, loss_cls: 4.3257, loss: 4.3257 +2024-12-28 11:04:15,379 - pyskl - INFO - Epoch [64][2100/3746] lr: 6.187e-02, eta: 3 days, 2:07:58, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4905, loss_cls: 4.3391, loss: 4.3391 +2024-12-28 11:05:40,520 - pyskl - INFO - Epoch [64][2200/3746] lr: 6.184e-02, eta: 3 days, 2:06:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4917, loss_cls: 4.3464, loss: 4.3464 +2024-12-28 11:07:05,826 - pyskl - INFO - Epoch [64][2300/3746] lr: 6.181e-02, eta: 3 days, 2:05:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5066, loss_cls: 4.3151, loss: 4.3151 +2024-12-28 11:08:31,124 - pyskl - INFO - Epoch [64][2400/3746] lr: 6.178e-02, eta: 3 days, 2:04:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4969, loss_cls: 4.3375, loss: 4.3375 +2024-12-28 11:09:56,829 - pyskl - INFO - Epoch [64][2500/3746] lr: 6.176e-02, eta: 3 days, 2:02:45, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4917, loss_cls: 4.3347, loss: 4.3347 +2024-12-28 11:11:22,605 - pyskl - INFO - Epoch [64][2600/3746] lr: 6.173e-02, eta: 3 days, 2:01:27, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5045, loss_cls: 4.3210, loss: 4.3210 +2024-12-28 11:12:47,305 - pyskl - INFO - Epoch [64][2700/3746] lr: 6.170e-02, eta: 3 days, 2:00:07, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4780, loss_cls: 4.3875, loss: 4.3875 +2024-12-28 11:14:12,658 - pyskl - INFO - Epoch [64][2800/3746] lr: 6.168e-02, eta: 3 days, 1:58:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4945, loss_cls: 4.3619, loss: 4.3619 +2024-12-28 11:15:38,147 - pyskl - INFO - Epoch [64][2900/3746] lr: 6.165e-02, eta: 3 days, 1:57:31, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5009, loss_cls: 4.3370, loss: 4.3370 +2024-12-28 11:17:03,234 - pyskl - INFO - Epoch [64][3000/3746] lr: 6.162e-02, eta: 3 days, 1:56:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4925, loss_cls: 4.3367, loss: 4.3367 +2024-12-28 11:18:29,057 - pyskl - INFO - Epoch [64][3100/3746] lr: 6.159e-02, eta: 3 days, 1:54:54, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5014, loss_cls: 4.3159, loss: 4.3159 +2024-12-28 11:19:53,948 - pyskl - INFO - Epoch [64][3200/3746] lr: 6.157e-02, eta: 3 days, 1:53:35, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4886, loss_cls: 4.3708, loss: 4.3708 +2024-12-28 11:21:18,497 - pyskl - INFO - Epoch [64][3300/3746] lr: 6.154e-02, eta: 3 days, 1:52:15, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5011, loss_cls: 4.3121, loss: 4.3121 +2024-12-28 11:22:43,610 - pyskl - INFO - Epoch [64][3400/3746] lr: 6.151e-02, eta: 3 days, 1:50:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5022, loss_cls: 4.3487, loss: 4.3487 +2024-12-28 11:24:08,975 - pyskl - INFO - Epoch [64][3500/3746] lr: 6.148e-02, eta: 3 days, 1:49:38, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5158, loss_cls: 4.2466, loss: 4.2466 +2024-12-28 11:25:34,236 - pyskl - INFO - Epoch [64][3600/3746] lr: 6.146e-02, eta: 3 days, 1:48:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4945, loss_cls: 4.3173, loss: 4.3173 +2024-12-28 11:27:00,105 - pyskl - INFO - Epoch [64][3700/3746] lr: 6.143e-02, eta: 3 days, 1:47:01, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4898, loss_cls: 4.3868, loss: 4.3868 +2024-12-28 11:27:41,115 - pyskl - INFO - Saving checkpoint at 64 epochs +2024-12-28 11:29:41,738 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 11:29:42,553 - pyskl - INFO - +top1_acc 0.2035 +top5_acc 0.4210 +2024-12-28 11:29:42,553 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 11:29:42,608 - pyskl - INFO - +mean_acc 0.2033 +2024-12-28 11:29:42,615 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_63.pth was removed +2024-12-28 11:29:42,957 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_64.pth. +2024-12-28 11:29:42,958 - pyskl - INFO - Best top1_acc is 0.2035 at 64 epoch. +2024-12-28 11:29:42,976 - pyskl - INFO - Epoch(val) [64][309] top1_acc: 0.2035, top5_acc: 0.4210, mean_class_accuracy: 0.2033 +2024-12-28 11:33:59,312 - pyskl - INFO - Epoch [65][100/3746] lr: 6.139e-02, eta: 3 days, 1:48:04, time: 2.563, data_time: 1.537, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5052, loss_cls: 4.2951, loss: 4.2951 +2024-12-28 11:35:23,893 - pyskl - INFO - Epoch [65][200/3746] lr: 6.136e-02, eta: 3 days, 1:46:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5059, loss_cls: 4.2849, loss: 4.2849 +2024-12-28 11:36:48,775 - pyskl - INFO - Epoch [65][300/3746] lr: 6.134e-02, eta: 3 days, 1:45:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5064, loss_cls: 4.2881, loss: 4.2881 +2024-12-28 11:38:14,144 - pyskl - INFO - Epoch [65][400/3746] lr: 6.131e-02, eta: 3 days, 1:44:06, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4961, loss_cls: 4.3146, loss: 4.3146 +2024-12-28 11:39:39,623 - pyskl - INFO - Epoch [65][500/3746] lr: 6.128e-02, eta: 3 days, 1:42:47, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5012, loss_cls: 4.3029, loss: 4.3029 +2024-12-28 11:41:04,245 - pyskl - INFO - Epoch [65][600/3746] lr: 6.125e-02, eta: 3 days, 1:41:28, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5020, loss_cls: 4.3067, loss: 4.3067 +2024-12-28 11:42:29,117 - pyskl - INFO - Epoch [65][700/3746] lr: 6.123e-02, eta: 3 days, 1:40:08, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.4981, loss_cls: 4.2973, loss: 4.2973 +2024-12-28 11:43:53,819 - pyskl - INFO - Epoch [65][800/3746] lr: 6.120e-02, eta: 3 days, 1:38:49, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4958, loss_cls: 4.3431, loss: 4.3431 +2024-12-28 11:45:18,361 - pyskl - INFO - Epoch [65][900/3746] lr: 6.117e-02, eta: 3 days, 1:37:29, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5062, loss_cls: 4.3110, loss: 4.3110 +2024-12-28 11:46:43,008 - pyskl - INFO - Epoch [65][1000/3746] lr: 6.115e-02, eta: 3 days, 1:36:09, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5072, loss_cls: 4.2993, loss: 4.2993 +2024-12-28 11:48:07,867 - pyskl - INFO - Epoch [65][1100/3746] lr: 6.112e-02, eta: 3 days, 1:34:50, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5011, loss_cls: 4.3429, loss: 4.3429 +2024-12-28 11:49:32,542 - pyskl - INFO - Epoch [65][1200/3746] lr: 6.109e-02, eta: 3 days, 1:33:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4975, loss_cls: 4.3176, loss: 4.3176 +2024-12-28 11:50:57,705 - pyskl - INFO - Epoch [65][1300/3746] lr: 6.106e-02, eta: 3 days, 1:32:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4975, loss_cls: 4.3119, loss: 4.3119 +2024-12-28 11:52:22,798 - pyskl - INFO - Epoch [65][1400/3746] lr: 6.104e-02, eta: 3 days, 1:30:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5112, loss_cls: 4.3070, loss: 4.3070 +2024-12-28 11:53:47,915 - pyskl - INFO - Epoch [65][1500/3746] lr: 6.101e-02, eta: 3 days, 1:29:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4938, loss_cls: 4.3489, loss: 4.3489 +2024-12-28 11:55:13,221 - pyskl - INFO - Epoch [65][1600/3746] lr: 6.098e-02, eta: 3 days, 1:28:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5056, loss_cls: 4.2868, loss: 4.2868 +2024-12-28 11:56:38,648 - pyskl - INFO - Epoch [65][1700/3746] lr: 6.095e-02, eta: 3 days, 1:26:56, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.4936, loss_cls: 4.3418, loss: 4.3418 +2024-12-28 11:58:03,736 - pyskl - INFO - Epoch [65][1800/3746] lr: 6.093e-02, eta: 3 days, 1:25:37, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5022, loss_cls: 4.2770, loss: 4.2770 +2024-12-28 11:59:28,546 - pyskl - INFO - Epoch [65][1900/3746] lr: 6.090e-02, eta: 3 days, 1:24:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4889, loss_cls: 4.3473, loss: 4.3473 +2024-12-28 12:00:53,306 - pyskl - INFO - Epoch [65][2000/3746] lr: 6.087e-02, eta: 3 days, 1:22:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4898, loss_cls: 4.3452, loss: 4.3452 +2024-12-28 12:02:18,578 - pyskl - INFO - Epoch [65][2100/3746] lr: 6.085e-02, eta: 3 days, 1:21:39, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4966, loss_cls: 4.3285, loss: 4.3285 +2024-12-28 12:03:44,006 - pyskl - INFO - Epoch [65][2200/3746] lr: 6.082e-02, eta: 3 days, 1:20:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5086, loss_cls: 4.2603, loss: 4.2603 +2024-12-28 12:05:09,758 - pyskl - INFO - Epoch [65][2300/3746] lr: 6.079e-02, eta: 3 days, 1:19:02, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4945, loss_cls: 4.3379, loss: 4.3379 +2024-12-28 12:06:35,225 - pyskl - INFO - Epoch [65][2400/3746] lr: 6.076e-02, eta: 3 days, 1:17:43, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5022, loss_cls: 4.2890, loss: 4.2890 +2024-12-28 12:08:00,255 - pyskl - INFO - Epoch [65][2500/3746] lr: 6.074e-02, eta: 3 days, 1:16:24, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.4994, loss_cls: 4.3173, loss: 4.3173 +2024-12-28 12:09:25,247 - pyskl - INFO - Epoch [65][2600/3746] lr: 6.071e-02, eta: 3 days, 1:15:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5078, loss_cls: 4.2893, loss: 4.2893 +2024-12-28 12:10:50,132 - pyskl - INFO - Epoch [65][2700/3746] lr: 6.068e-02, eta: 3 days, 1:13:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5033, loss_cls: 4.3014, loss: 4.3014 +2024-12-28 12:12:14,915 - pyskl - INFO - Epoch [65][2800/3746] lr: 6.065e-02, eta: 3 days, 1:12:26, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4902, loss_cls: 4.3505, loss: 4.3505 +2024-12-28 12:13:39,614 - pyskl - INFO - Epoch [65][2900/3746] lr: 6.063e-02, eta: 3 days, 1:11:06, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.4995, loss_cls: 4.2994, loss: 4.2994 +2024-12-28 12:15:04,037 - pyskl - INFO - Epoch [65][3000/3746] lr: 6.060e-02, eta: 3 days, 1:09:46, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5027, loss_cls: 4.3174, loss: 4.3174 +2024-12-28 12:16:27,822 - pyskl - INFO - Epoch [65][3100/3746] lr: 6.057e-02, eta: 3 days, 1:08:25, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5059, loss_cls: 4.2694, loss: 4.2694 +2024-12-28 12:17:52,328 - pyskl - INFO - Epoch [65][3200/3746] lr: 6.055e-02, eta: 3 days, 1:07:05, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5022, loss_cls: 4.3202, loss: 4.3202 +2024-12-28 12:19:17,008 - pyskl - INFO - Epoch [65][3300/3746] lr: 6.052e-02, eta: 3 days, 1:05:46, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4983, loss_cls: 4.3252, loss: 4.3252 +2024-12-28 12:20:41,231 - pyskl - INFO - Epoch [65][3400/3746] lr: 6.049e-02, eta: 3 days, 1:04:25, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4869, loss_cls: 4.3470, loss: 4.3470 +2024-12-28 12:22:05,837 - pyskl - INFO - Epoch [65][3500/3746] lr: 6.046e-02, eta: 3 days, 1:03:06, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4916, loss_cls: 4.3638, loss: 4.3638 +2024-12-28 12:23:30,592 - pyskl - INFO - Epoch [65][3600/3746] lr: 6.044e-02, eta: 3 days, 1:01:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.4952, loss_cls: 4.3445, loss: 4.3445 +2024-12-28 12:24:55,686 - pyskl - INFO - Epoch [65][3700/3746] lr: 6.041e-02, eta: 3 days, 1:00:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.4877, loss_cls: 4.3457, loss: 4.3457 +2024-12-28 12:25:36,897 - pyskl - INFO - Saving checkpoint at 65 epochs +2024-12-28 12:27:34,381 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 12:27:35,177 - pyskl - INFO - +top1_acc 0.1953 +top5_acc 0.4218 +2024-12-28 12:27:35,177 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 12:27:35,225 - pyskl - INFO - +mean_acc 0.1952 +2024-12-28 12:27:35,239 - pyskl - INFO - Epoch(val) [65][309] top1_acc: 0.1953, top5_acc: 0.4218, mean_class_accuracy: 0.1952 +2024-12-28 12:31:59,593 - pyskl - INFO - Epoch [66][100/3746] lr: 6.037e-02, eta: 3 days, 1:01:34, time: 2.643, data_time: 1.595, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5098, loss_cls: 4.2528, loss: 4.2528 +2024-12-28 12:33:25,498 - pyskl - INFO - Epoch [66][200/3746] lr: 6.034e-02, eta: 3 days, 1:00:16, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.4961, loss_cls: 4.2707, loss: 4.2707 +2024-12-28 12:34:50,675 - pyskl - INFO - Epoch [66][300/3746] lr: 6.031e-02, eta: 3 days, 0:58:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5095, loss_cls: 4.2871, loss: 4.2871 +2024-12-28 12:36:16,268 - pyskl - INFO - Epoch [66][400/3746] lr: 6.029e-02, eta: 3 days, 0:57:38, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5103, loss_cls: 4.2967, loss: 4.2967 +2024-12-28 12:37:41,333 - pyskl - INFO - Epoch [66][500/3746] lr: 6.026e-02, eta: 3 days, 0:56:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5028, loss_cls: 4.2631, loss: 4.2631 +2024-12-28 12:39:06,591 - pyskl - INFO - Epoch [66][600/3746] lr: 6.023e-02, eta: 3 days, 0:55:00, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5075, loss_cls: 4.2607, loss: 4.2607 +2024-12-28 12:40:31,951 - pyskl - INFO - Epoch [66][700/3746] lr: 6.020e-02, eta: 3 days, 0:53:41, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5012, loss_cls: 4.2760, loss: 4.2760 +2024-12-28 12:41:56,988 - pyskl - INFO - Epoch [66][800/3746] lr: 6.018e-02, eta: 3 days, 0:52:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.4986, loss_cls: 4.3181, loss: 4.3181 +2024-12-28 12:43:22,114 - pyskl - INFO - Epoch [66][900/3746] lr: 6.015e-02, eta: 3 days, 0:51:02, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5042, loss_cls: 4.3046, loss: 4.3046 +2024-12-28 12:44:47,147 - pyskl - INFO - Epoch [66][1000/3746] lr: 6.012e-02, eta: 3 days, 0:49:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5106, loss_cls: 4.2489, loss: 4.2489 +2024-12-28 12:46:12,539 - pyskl - INFO - Epoch [66][1100/3746] lr: 6.009e-02, eta: 3 days, 0:48:23, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4894, loss_cls: 4.3352, loss: 4.3352 +2024-12-28 12:47:37,474 - pyskl - INFO - Epoch [66][1200/3746] lr: 6.007e-02, eta: 3 days, 0:47:04, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.4992, loss_cls: 4.3043, loss: 4.3043 +2024-12-28 12:49:02,122 - pyskl - INFO - Epoch [66][1300/3746] lr: 6.004e-02, eta: 3 days, 0:45:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5072, loss_cls: 4.2797, loss: 4.2797 +2024-12-28 12:50:26,630 - pyskl - INFO - Epoch [66][1400/3746] lr: 6.001e-02, eta: 3 days, 0:44:24, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.4994, loss_cls: 4.3048, loss: 4.3048 +2024-12-28 12:51:51,372 - pyskl - INFO - Epoch [66][1500/3746] lr: 5.999e-02, eta: 3 days, 0:43:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.4980, loss_cls: 4.2967, loss: 4.2967 +2024-12-28 12:53:15,621 - pyskl - INFO - Epoch [66][1600/3746] lr: 5.996e-02, eta: 3 days, 0:41:43, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5073, loss_cls: 4.2740, loss: 4.2740 +2024-12-28 12:54:40,402 - pyskl - INFO - Epoch [66][1700/3746] lr: 5.993e-02, eta: 3 days, 0:40:24, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5094, loss_cls: 4.2742, loss: 4.2742 +2024-12-28 12:56:05,386 - pyskl - INFO - Epoch [66][1800/3746] lr: 5.990e-02, eta: 3 days, 0:39:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.4892, loss_cls: 4.3512, loss: 4.3512 +2024-12-28 12:57:30,571 - pyskl - INFO - Epoch [66][1900/3746] lr: 5.988e-02, eta: 3 days, 0:37:45, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5058, loss_cls: 4.3051, loss: 4.3051 +2024-12-28 12:58:55,835 - pyskl - INFO - Epoch [66][2000/3746] lr: 5.985e-02, eta: 3 days, 0:36:26, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4956, loss_cls: 4.3205, loss: 4.3205 +2024-12-28 13:00:21,288 - pyskl - INFO - Epoch [66][2100/3746] lr: 5.982e-02, eta: 3 days, 0:35:07, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5088, loss_cls: 4.2714, loss: 4.2714 +2024-12-28 13:01:46,618 - pyskl - INFO - Epoch [66][2200/3746] lr: 5.979e-02, eta: 3 days, 0:33:48, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5008, loss_cls: 4.3328, loss: 4.3328 +2024-12-28 13:03:12,411 - pyskl - INFO - Epoch [66][2300/3746] lr: 5.977e-02, eta: 3 days, 0:32:29, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5077, loss_cls: 4.3299, loss: 4.3299 +2024-12-28 13:04:37,796 - pyskl - INFO - Epoch [66][2400/3746] lr: 5.974e-02, eta: 3 days, 0:31:10, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.4980, loss_cls: 4.3044, loss: 4.3044 +2024-12-28 13:06:03,405 - pyskl - INFO - Epoch [66][2500/3746] lr: 5.971e-02, eta: 3 days, 0:29:51, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4964, loss_cls: 4.3386, loss: 4.3386 +2024-12-28 13:07:29,180 - pyskl - INFO - Epoch [66][2600/3746] lr: 5.968e-02, eta: 3 days, 0:28:33, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5072, loss_cls: 4.2852, loss: 4.2852 +2024-12-28 13:08:54,933 - pyskl - INFO - Epoch [66][2700/3746] lr: 5.966e-02, eta: 3 days, 0:27:14, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4939, loss_cls: 4.3502, loss: 4.3502 +2024-12-28 13:10:19,672 - pyskl - INFO - Epoch [66][2800/3746] lr: 5.963e-02, eta: 3 days, 0:25:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.4991, loss_cls: 4.3691, loss: 4.3691 +2024-12-28 13:11:44,817 - pyskl - INFO - Epoch [66][2900/3746] lr: 5.960e-02, eta: 3 days, 0:24:35, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4992, loss_cls: 4.3277, loss: 4.3277 +2024-12-28 13:13:09,499 - pyskl - INFO - Epoch [66][3000/3746] lr: 5.957e-02, eta: 3 days, 0:23:15, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5009, loss_cls: 4.3343, loss: 4.3343 +2024-12-28 13:14:34,316 - pyskl - INFO - Epoch [66][3100/3746] lr: 5.955e-02, eta: 3 days, 0:21:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5050, loss_cls: 4.3102, loss: 4.3102 +2024-12-28 13:15:59,095 - pyskl - INFO - Epoch [66][3200/3746] lr: 5.952e-02, eta: 3 days, 0:20:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5017, loss_cls: 4.3036, loss: 4.3036 +2024-12-28 13:17:24,229 - pyskl - INFO - Epoch [66][3300/3746] lr: 5.949e-02, eta: 3 days, 0:19:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5050, loss_cls: 4.2667, loss: 4.2667 +2024-12-28 13:18:48,451 - pyskl - INFO - Epoch [66][3400/3746] lr: 5.946e-02, eta: 3 days, 0:17:55, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4983, loss_cls: 4.3394, loss: 4.3394 +2024-12-28 13:20:13,010 - pyskl - INFO - Epoch [66][3500/3746] lr: 5.944e-02, eta: 3 days, 0:16:35, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4909, loss_cls: 4.3544, loss: 4.3544 +2024-12-28 13:21:37,480 - pyskl - INFO - Epoch [66][3600/3746] lr: 5.941e-02, eta: 3 days, 0:15:15, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5012, loss_cls: 4.2781, loss: 4.2781 +2024-12-28 13:23:02,218 - pyskl - INFO - Epoch [66][3700/3746] lr: 5.938e-02, eta: 3 days, 0:13:55, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5011, loss_cls: 4.3206, loss: 4.3206 +2024-12-28 13:23:43,392 - pyskl - INFO - Saving checkpoint at 66 epochs +2024-12-28 13:25:42,949 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 13:25:43,914 - pyskl - INFO - +top1_acc 0.1758 +top5_acc 0.3938 +2024-12-28 13:25:43,914 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 13:25:43,960 - pyskl - INFO - +mean_acc 0.1756 +2024-12-28 13:25:43,977 - pyskl - INFO - Epoch(val) [66][309] top1_acc: 0.1758, top5_acc: 0.3938, mean_class_accuracy: 0.1756 +2024-12-28 13:30:01,469 - pyskl - INFO - Epoch [67][100/3746] lr: 5.934e-02, eta: 3 days, 0:14:48, time: 2.575, data_time: 1.536, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5097, loss_cls: 4.2649, loss: 4.2649 +2024-12-28 13:31:26,603 - pyskl - INFO - Epoch [67][200/3746] lr: 5.931e-02, eta: 3 days, 0:13:29, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5088, loss_cls: 4.2792, loss: 4.2792 +2024-12-28 13:32:52,618 - pyskl - INFO - Epoch [67][300/3746] lr: 5.929e-02, eta: 3 days, 0:12:10, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5147, loss_cls: 4.2613, loss: 4.2613 +2024-12-28 13:34:18,322 - pyskl - INFO - Epoch [67][400/3746] lr: 5.926e-02, eta: 3 days, 0:10:51, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5091, loss_cls: 4.2631, loss: 4.2631 +2024-12-28 13:35:43,459 - pyskl - INFO - Epoch [67][500/3746] lr: 5.923e-02, eta: 3 days, 0:09:32, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5047, loss_cls: 4.2936, loss: 4.2936 +2024-12-28 13:37:09,378 - pyskl - INFO - Epoch [67][600/3746] lr: 5.920e-02, eta: 3 days, 0:08:13, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5077, loss_cls: 4.2694, loss: 4.2694 +2024-12-28 13:38:35,127 - pyskl - INFO - Epoch [67][700/3746] lr: 5.918e-02, eta: 3 days, 0:06:54, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5225, loss_cls: 4.2005, loss: 4.2005 +2024-12-28 13:40:00,866 - pyskl - INFO - Epoch [67][800/3746] lr: 5.915e-02, eta: 3 days, 0:05:35, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5009, loss_cls: 4.3289, loss: 4.3289 +2024-12-28 13:41:26,642 - pyskl - INFO - Epoch [67][900/3746] lr: 5.912e-02, eta: 3 days, 0:04:17, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.5028, loss_cls: 4.3185, loss: 4.3185 +2024-12-28 13:42:51,740 - pyskl - INFO - Epoch [67][1000/3746] lr: 5.909e-02, eta: 3 days, 0:02:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5009, loss_cls: 4.3187, loss: 4.3187 +2024-12-28 13:44:15,800 - pyskl - INFO - Epoch [67][1100/3746] lr: 5.907e-02, eta: 3 days, 0:01:36, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.4934, loss_cls: 4.3218, loss: 4.3218 +2024-12-28 13:45:39,916 - pyskl - INFO - Epoch [67][1200/3746] lr: 5.904e-02, eta: 3 days, 0:00:15, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5153, loss_cls: 4.2466, loss: 4.2466 +2024-12-28 13:47:04,651 - pyskl - INFO - Epoch [67][1300/3746] lr: 5.901e-02, eta: 2 days, 23:58:55, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.4936, loss_cls: 4.3331, loss: 4.3331 +2024-12-28 13:48:28,922 - pyskl - INFO - Epoch [67][1400/3746] lr: 5.898e-02, eta: 2 days, 23:57:34, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4877, loss_cls: 4.3200, loss: 4.3200 +2024-12-28 13:49:53,680 - pyskl - INFO - Epoch [67][1500/3746] lr: 5.896e-02, eta: 2 days, 23:56:14, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5044, loss_cls: 4.2831, loss: 4.2831 +2024-12-28 13:51:18,248 - pyskl - INFO - Epoch [67][1600/3746] lr: 5.893e-02, eta: 2 days, 23:54:54, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5000, loss_cls: 4.2943, loss: 4.2943 +2024-12-28 13:52:43,405 - pyskl - INFO - Epoch [67][1700/3746] lr: 5.890e-02, eta: 2 days, 23:53:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5058, loss_cls: 4.2884, loss: 4.2884 +2024-12-28 13:54:08,096 - pyskl - INFO - Epoch [67][1800/3746] lr: 5.887e-02, eta: 2 days, 23:52:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.4936, loss_cls: 4.2978, loss: 4.2978 +2024-12-28 13:55:33,049 - pyskl - INFO - Epoch [67][1900/3746] lr: 5.885e-02, eta: 2 days, 23:50:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5016, loss_cls: 4.3281, loss: 4.3281 +2024-12-28 13:56:58,341 - pyskl - INFO - Epoch [67][2000/3746] lr: 5.882e-02, eta: 2 days, 23:49:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4931, loss_cls: 4.3550, loss: 4.3550 +2024-12-28 13:58:22,958 - pyskl - INFO - Epoch [67][2100/3746] lr: 5.879e-02, eta: 2 days, 23:48:14, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.5003, loss_cls: 4.3366, loss: 4.3366 +2024-12-28 13:59:47,851 - pyskl - INFO - Epoch [67][2200/3746] lr: 5.876e-02, eta: 2 days, 23:46:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5002, loss_cls: 4.3085, loss: 4.3085 +2024-12-28 14:01:13,200 - pyskl - INFO - Epoch [67][2300/3746] lr: 5.874e-02, eta: 2 days, 23:45:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5008, loss_cls: 4.3096, loss: 4.3096 +2024-12-28 14:02:38,931 - pyskl - INFO - Epoch [67][2400/3746] lr: 5.871e-02, eta: 2 days, 23:44:16, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5048, loss_cls: 4.2723, loss: 4.2723 +2024-12-28 14:04:03,948 - pyskl - INFO - Epoch [67][2500/3746] lr: 5.868e-02, eta: 2 days, 23:42:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5106, loss_cls: 4.2783, loss: 4.2783 +2024-12-28 14:05:28,778 - pyskl - INFO - Epoch [67][2600/3746] lr: 5.865e-02, eta: 2 days, 23:41:36, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2598, top5_acc: 0.4994, loss_cls: 4.2877, loss: 4.2877 +2024-12-28 14:06:53,957 - pyskl - INFO - Epoch [67][2700/3746] lr: 5.863e-02, eta: 2 days, 23:40:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5044, loss_cls: 4.2925, loss: 4.2925 +2024-12-28 14:08:18,620 - pyskl - INFO - Epoch [67][2800/3746] lr: 5.860e-02, eta: 2 days, 23:38:56, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.4959, loss_cls: 4.3011, loss: 4.3011 +2024-12-28 14:09:43,666 - pyskl - INFO - Epoch [67][2900/3746] lr: 5.857e-02, eta: 2 days, 23:37:37, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.4989, loss_cls: 4.2917, loss: 4.2917 +2024-12-28 14:11:08,941 - pyskl - INFO - Epoch [67][3000/3746] lr: 5.854e-02, eta: 2 days, 23:36:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5069, loss_cls: 4.2807, loss: 4.2807 +2024-12-28 14:12:34,032 - pyskl - INFO - Epoch [67][3100/3746] lr: 5.852e-02, eta: 2 days, 23:34:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.4981, loss_cls: 4.3253, loss: 4.3253 +2024-12-28 14:13:58,975 - pyskl - INFO - Epoch [67][3200/3746] lr: 5.849e-02, eta: 2 days, 23:33:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5038, loss_cls: 4.2913, loss: 4.2913 +2024-12-28 14:15:24,033 - pyskl - INFO - Epoch [67][3300/3746] lr: 5.846e-02, eta: 2 days, 23:32:18, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5122, loss_cls: 4.2660, loss: 4.2660 +2024-12-28 14:16:48,814 - pyskl - INFO - Epoch [67][3400/3746] lr: 5.843e-02, eta: 2 days, 23:30:57, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5095, loss_cls: 4.2659, loss: 4.2659 +2024-12-28 14:18:13,091 - pyskl - INFO - Epoch [67][3500/3746] lr: 5.841e-02, eta: 2 days, 23:29:37, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5081, loss_cls: 4.2781, loss: 4.2781 +2024-12-28 14:19:37,162 - pyskl - INFO - Epoch [67][3600/3746] lr: 5.838e-02, eta: 2 days, 23:28:16, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5033, loss_cls: 4.3241, loss: 4.3241 +2024-12-28 14:21:01,844 - pyskl - INFO - Epoch [67][3700/3746] lr: 5.835e-02, eta: 2 days, 23:26:55, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.4988, loss_cls: 4.3227, loss: 4.3227 +2024-12-28 14:21:42,737 - pyskl - INFO - Saving checkpoint at 67 epochs +2024-12-28 14:23:41,752 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 14:23:42,444 - pyskl - INFO - +top1_acc 0.1823 +top5_acc 0.4017 +2024-12-28 14:23:42,444 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 14:23:42,501 - pyskl - INFO - +mean_acc 0.1822 +2024-12-28 14:23:42,520 - pyskl - INFO - Epoch(val) [67][309] top1_acc: 0.1823, top5_acc: 0.4017, mean_class_accuracy: 0.1822 +2024-12-28 14:27:55,167 - pyskl - INFO - Epoch [68][100/3746] lr: 5.831e-02, eta: 2 days, 23:27:38, time: 2.526, data_time: 1.501, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5136, loss_cls: 4.2582, loss: 4.2582 +2024-12-28 14:29:20,212 - pyskl - INFO - Epoch [68][200/3746] lr: 5.828e-02, eta: 2 days, 23:26:18, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5167, loss_cls: 4.2306, loss: 4.2306 +2024-12-28 14:30:46,018 - pyskl - INFO - Epoch [68][300/3746] lr: 5.826e-02, eta: 2 days, 23:24:59, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5070, loss_cls: 4.2772, loss: 4.2772 +2024-12-28 14:32:10,641 - pyskl - INFO - Epoch [68][400/3746] lr: 5.823e-02, eta: 2 days, 23:23:38, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5041, loss_cls: 4.2953, loss: 4.2953 +2024-12-28 14:33:35,414 - pyskl - INFO - Epoch [68][500/3746] lr: 5.820e-02, eta: 2 days, 23:22:18, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5120, loss_cls: 4.2670, loss: 4.2670 +2024-12-28 14:34:59,982 - pyskl - INFO - Epoch [68][600/3746] lr: 5.817e-02, eta: 2 days, 23:20:58, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5075, loss_cls: 4.2736, loss: 4.2736 +2024-12-28 14:36:25,121 - pyskl - INFO - Epoch [68][700/3746] lr: 5.815e-02, eta: 2 days, 23:19:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.4998, loss_cls: 4.3534, loss: 4.3534 +2024-12-28 14:37:49,535 - pyskl - INFO - Epoch [68][800/3746] lr: 5.812e-02, eta: 2 days, 23:18:17, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5102, loss_cls: 4.2964, loss: 4.2964 +2024-12-28 14:39:14,027 - pyskl - INFO - Epoch [68][900/3746] lr: 5.809e-02, eta: 2 days, 23:16:56, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.4922, loss_cls: 4.3255, loss: 4.3255 +2024-12-28 14:40:38,521 - pyskl - INFO - Epoch [68][1000/3746] lr: 5.806e-02, eta: 2 days, 23:15:36, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5123, loss_cls: 4.2600, loss: 4.2600 +2024-12-28 14:42:02,603 - pyskl - INFO - Epoch [68][1100/3746] lr: 5.804e-02, eta: 2 days, 23:14:14, time: 0.841, data_time: 0.001, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5078, loss_cls: 4.2862, loss: 4.2862 +2024-12-28 14:43:26,700 - pyskl - INFO - Epoch [68][1200/3746] lr: 5.801e-02, eta: 2 days, 23:12:53, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4942, loss_cls: 4.3210, loss: 4.3210 +2024-12-28 14:44:51,318 - pyskl - INFO - Epoch [68][1300/3746] lr: 5.798e-02, eta: 2 days, 23:11:33, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5066, loss_cls: 4.2888, loss: 4.2888 +2024-12-28 14:46:15,961 - pyskl - INFO - Epoch [68][1400/3746] lr: 5.795e-02, eta: 2 days, 23:10:12, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5009, loss_cls: 4.2939, loss: 4.2939 +2024-12-28 14:47:40,405 - pyskl - INFO - Epoch [68][1500/3746] lr: 5.792e-02, eta: 2 days, 23:08:52, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5192, loss_cls: 4.2156, loss: 4.2156 +2024-12-28 14:49:05,383 - pyskl - INFO - Epoch [68][1600/3746] lr: 5.790e-02, eta: 2 days, 23:07:31, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5033, loss_cls: 4.2918, loss: 4.2918 +2024-12-28 14:50:30,601 - pyskl - INFO - Epoch [68][1700/3746] lr: 5.787e-02, eta: 2 days, 23:06:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5203, loss_cls: 4.2318, loss: 4.2318 +2024-12-28 14:51:56,088 - pyskl - INFO - Epoch [68][1800/3746] lr: 5.784e-02, eta: 2 days, 23:04:52, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5127, loss_cls: 4.2541, loss: 4.2541 +2024-12-28 14:53:21,007 - pyskl - INFO - Epoch [68][1900/3746] lr: 5.781e-02, eta: 2 days, 23:03:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5016, loss_cls: 4.2803, loss: 4.2803 +2024-12-28 14:54:45,845 - pyskl - INFO - Epoch [68][2000/3746] lr: 5.779e-02, eta: 2 days, 23:02:12, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5150, loss_cls: 4.2517, loss: 4.2517 +2024-12-28 14:56:11,092 - pyskl - INFO - Epoch [68][2100/3746] lr: 5.776e-02, eta: 2 days, 23:00:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.4952, loss_cls: 4.3107, loss: 4.3107 +2024-12-28 14:57:35,973 - pyskl - INFO - Epoch [68][2200/3746] lr: 5.773e-02, eta: 2 days, 22:59:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5080, loss_cls: 4.2576, loss: 4.2576 +2024-12-28 14:59:01,035 - pyskl - INFO - Epoch [68][2300/3746] lr: 5.770e-02, eta: 2 days, 22:58:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5012, loss_cls: 4.2919, loss: 4.2919 +2024-12-28 15:00:26,227 - pyskl - INFO - Epoch [68][2400/3746] lr: 5.768e-02, eta: 2 days, 22:56:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5056, loss_cls: 4.2785, loss: 4.2785 +2024-12-28 15:01:51,123 - pyskl - INFO - Epoch [68][2500/3746] lr: 5.765e-02, eta: 2 days, 22:55:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4983, loss_cls: 4.3084, loss: 4.3084 +2024-12-28 15:03:15,768 - pyskl - INFO - Epoch [68][2600/3746] lr: 5.762e-02, eta: 2 days, 22:54:11, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.4939, loss_cls: 4.2922, loss: 4.2922 +2024-12-28 15:04:40,424 - pyskl - INFO - Epoch [68][2700/3746] lr: 5.759e-02, eta: 2 days, 22:52:51, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5133, loss_cls: 4.2882, loss: 4.2882 +2024-12-28 15:06:05,230 - pyskl - INFO - Epoch [68][2800/3746] lr: 5.757e-02, eta: 2 days, 22:51:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5117, loss_cls: 4.2595, loss: 4.2595 +2024-12-28 15:07:30,030 - pyskl - INFO - Epoch [68][2900/3746] lr: 5.754e-02, eta: 2 days, 22:50:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5075, loss_cls: 4.2642, loss: 4.2642 +2024-12-28 15:08:54,780 - pyskl - INFO - Epoch [68][3000/3746] lr: 5.751e-02, eta: 2 days, 22:48:49, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5041, loss_cls: 4.2688, loss: 4.2688 +2024-12-28 15:10:19,126 - pyskl - INFO - Epoch [68][3100/3746] lr: 5.748e-02, eta: 2 days, 22:47:29, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5089, loss_cls: 4.2706, loss: 4.2706 +2024-12-28 15:11:43,636 - pyskl - INFO - Epoch [68][3200/3746] lr: 5.746e-02, eta: 2 days, 22:46:08, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5136, loss_cls: 4.2734, loss: 4.2734 +2024-12-28 15:13:08,200 - pyskl - INFO - Epoch [68][3300/3746] lr: 5.743e-02, eta: 2 days, 22:44:47, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5036, loss_cls: 4.3001, loss: 4.3001 +2024-12-28 15:14:32,797 - pyskl - INFO - Epoch [68][3400/3746] lr: 5.740e-02, eta: 2 days, 22:43:27, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4984, loss_cls: 4.3367, loss: 4.3367 +2024-12-28 15:15:57,042 - pyskl - INFO - Epoch [68][3500/3746] lr: 5.737e-02, eta: 2 days, 22:42:06, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5078, loss_cls: 4.2776, loss: 4.2776 +2024-12-28 15:17:21,173 - pyskl - INFO - Epoch [68][3600/3746] lr: 5.734e-02, eta: 2 days, 22:40:44, time: 0.841, data_time: 0.001, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5111, loss_cls: 4.2625, loss: 4.2625 +2024-12-28 15:18:45,613 - pyskl - INFO - Epoch [68][3700/3746] lr: 5.732e-02, eta: 2 days, 22:39:24, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5078, loss_cls: 4.2670, loss: 4.2670 +2024-12-28 15:19:26,254 - pyskl - INFO - Saving checkpoint at 68 epochs +2024-12-28 15:21:23,812 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 15:21:24,611 - pyskl - INFO - +top1_acc 0.2037 +top5_acc 0.4237 +2024-12-28 15:21:24,612 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 15:21:24,657 - pyskl - INFO - +mean_acc 0.2035 +2024-12-28 15:21:24,661 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_64.pth was removed +2024-12-28 15:21:25,103 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_68.pth. +2024-12-28 15:21:25,104 - pyskl - INFO - Best top1_acc is 0.2037 at 68 epoch. +2024-12-28 15:21:25,123 - pyskl - INFO - Epoch(val) [68][309] top1_acc: 0.2037, top5_acc: 0.4237, mean_class_accuracy: 0.2035 +2024-12-28 15:25:47,550 - pyskl - INFO - Epoch [69][100/3746] lr: 5.728e-02, eta: 2 days, 22:40:13, time: 2.624, data_time: 1.596, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5191, loss_cls: 4.2210, loss: 4.2210 +2024-12-28 15:27:12,824 - pyskl - INFO - Epoch [69][200/3746] lr: 5.725e-02, eta: 2 days, 22:38:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5052, loss_cls: 4.2884, loss: 4.2884 +2024-12-28 15:28:38,004 - pyskl - INFO - Epoch [69][300/3746] lr: 5.722e-02, eta: 2 days, 22:37:33, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5205, loss_cls: 4.2030, loss: 4.2030 +2024-12-28 15:30:03,652 - pyskl - INFO - Epoch [69][400/3746] lr: 5.719e-02, eta: 2 days, 22:36:14, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5106, loss_cls: 4.2616, loss: 4.2616 +2024-12-28 15:31:28,085 - pyskl - INFO - Epoch [69][500/3746] lr: 5.717e-02, eta: 2 days, 22:34:53, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5061, loss_cls: 4.2664, loss: 4.2664 +2024-12-28 15:32:53,428 - pyskl - INFO - Epoch [69][600/3746] lr: 5.714e-02, eta: 2 days, 22:33:33, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5184, loss_cls: 4.2433, loss: 4.2433 +2024-12-28 15:34:18,322 - pyskl - INFO - Epoch [69][700/3746] lr: 5.711e-02, eta: 2 days, 22:32:12, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5194, loss_cls: 4.2032, loss: 4.2032 +2024-12-28 15:35:43,667 - pyskl - INFO - Epoch [69][800/3746] lr: 5.708e-02, eta: 2 days, 22:30:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5055, loss_cls: 4.2759, loss: 4.2759 +2024-12-28 15:37:09,158 - pyskl - INFO - Epoch [69][900/3746] lr: 5.706e-02, eta: 2 days, 22:29:33, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5020, loss_cls: 4.2646, loss: 4.2646 +2024-12-28 15:38:34,134 - pyskl - INFO - Epoch [69][1000/3746] lr: 5.703e-02, eta: 2 days, 22:28:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5050, loss_cls: 4.2746, loss: 4.2746 +2024-12-28 15:39:58,969 - pyskl - INFO - Epoch [69][1100/3746] lr: 5.700e-02, eta: 2 days, 22:26:52, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5108, loss_cls: 4.2465, loss: 4.2465 +2024-12-28 15:41:24,169 - pyskl - INFO - Epoch [69][1200/3746] lr: 5.697e-02, eta: 2 days, 22:25:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5047, loss_cls: 4.2652, loss: 4.2652 +2024-12-28 15:42:48,264 - pyskl - INFO - Epoch [69][1300/3746] lr: 5.694e-02, eta: 2 days, 22:24:11, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.4928, loss_cls: 4.3091, loss: 4.3091 +2024-12-28 15:44:13,039 - pyskl - INFO - Epoch [69][1400/3746] lr: 5.692e-02, eta: 2 days, 22:22:50, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5061, loss_cls: 4.2901, loss: 4.2901 +2024-12-28 15:45:37,666 - pyskl - INFO - Epoch [69][1500/3746] lr: 5.689e-02, eta: 2 days, 22:21:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5019, loss_cls: 4.2878, loss: 4.2878 +2024-12-28 15:47:01,929 - pyskl - INFO - Epoch [69][1600/3746] lr: 5.686e-02, eta: 2 days, 22:20:08, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5147, loss_cls: 4.2521, loss: 4.2521 +2024-12-28 15:48:25,977 - pyskl - INFO - Epoch [69][1700/3746] lr: 5.683e-02, eta: 2 days, 22:18:47, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5034, loss_cls: 4.2975, loss: 4.2975 +2024-12-28 15:49:50,649 - pyskl - INFO - Epoch [69][1800/3746] lr: 5.681e-02, eta: 2 days, 22:17:26, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5038, loss_cls: 4.3090, loss: 4.3090 +2024-12-28 15:51:15,351 - pyskl - INFO - Epoch [69][1900/3746] lr: 5.678e-02, eta: 2 days, 22:16:05, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5095, loss_cls: 4.2811, loss: 4.2811 +2024-12-28 15:52:39,958 - pyskl - INFO - Epoch [69][2000/3746] lr: 5.675e-02, eta: 2 days, 22:14:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5094, loss_cls: 4.2647, loss: 4.2647 +2024-12-28 15:54:04,875 - pyskl - INFO - Epoch [69][2100/3746] lr: 5.672e-02, eta: 2 days, 22:13:24, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5216, loss_cls: 4.2222, loss: 4.2222 +2024-12-28 15:55:29,815 - pyskl - INFO - Epoch [69][2200/3746] lr: 5.670e-02, eta: 2 days, 22:12:04, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.4959, loss_cls: 4.2949, loss: 4.2949 +2024-12-28 15:56:54,249 - pyskl - INFO - Epoch [69][2300/3746] lr: 5.667e-02, eta: 2 days, 22:10:43, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.4953, loss_cls: 4.3212, loss: 4.3212 +2024-12-28 15:58:19,299 - pyskl - INFO - Epoch [69][2400/3746] lr: 5.664e-02, eta: 2 days, 22:09:22, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.4964, loss_cls: 4.3197, loss: 4.3197 +2024-12-28 15:59:44,501 - pyskl - INFO - Epoch [69][2500/3746] lr: 5.661e-02, eta: 2 days, 22:08:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4928, loss_cls: 4.3285, loss: 4.3285 +2024-12-28 16:01:09,552 - pyskl - INFO - Epoch [69][2600/3746] lr: 5.658e-02, eta: 2 days, 22:06:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5081, loss_cls: 4.2673, loss: 4.2673 +2024-12-28 16:02:34,731 - pyskl - INFO - Epoch [69][2700/3746] lr: 5.656e-02, eta: 2 days, 22:05:22, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5184, loss_cls: 4.2418, loss: 4.2418 +2024-12-28 16:03:59,671 - pyskl - INFO - Epoch [69][2800/3746] lr: 5.653e-02, eta: 2 days, 22:04:01, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5102, loss_cls: 4.2911, loss: 4.2911 +2024-12-28 16:05:24,301 - pyskl - INFO - Epoch [69][2900/3746] lr: 5.650e-02, eta: 2 days, 22:02:41, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4961, loss_cls: 4.2928, loss: 4.2928 +2024-12-28 16:06:48,514 - pyskl - INFO - Epoch [69][3000/3746] lr: 5.647e-02, eta: 2 days, 22:01:19, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.4998, loss_cls: 4.2875, loss: 4.2875 +2024-12-28 16:08:12,398 - pyskl - INFO - Epoch [69][3100/3746] lr: 5.645e-02, eta: 2 days, 21:59:58, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5111, loss_cls: 4.2586, loss: 4.2586 +2024-12-28 16:09:36,418 - pyskl - INFO - Epoch [69][3200/3746] lr: 5.642e-02, eta: 2 days, 21:58:36, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5159, loss_cls: 4.2282, loss: 4.2282 +2024-12-28 16:11:00,425 - pyskl - INFO - Epoch [69][3300/3746] lr: 5.639e-02, eta: 2 days, 21:57:14, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5039, loss_cls: 4.3016, loss: 4.3016 +2024-12-28 16:12:24,905 - pyskl - INFO - Epoch [69][3400/3746] lr: 5.636e-02, eta: 2 days, 21:55:53, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5147, loss_cls: 4.2586, loss: 4.2586 +2024-12-28 16:13:49,427 - pyskl - INFO - Epoch [69][3500/3746] lr: 5.634e-02, eta: 2 days, 21:54:33, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5062, loss_cls: 4.2501, loss: 4.2501 +2024-12-28 16:15:14,253 - pyskl - INFO - Epoch [69][3600/3746] lr: 5.631e-02, eta: 2 days, 21:53:12, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5069, loss_cls: 4.2778, loss: 4.2778 +2024-12-28 16:16:39,450 - pyskl - INFO - Epoch [69][3700/3746] lr: 5.628e-02, eta: 2 days, 21:51:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5048, loss_cls: 4.3043, loss: 4.3043 +2024-12-28 16:17:20,273 - pyskl - INFO - Saving checkpoint at 69 epochs +2024-12-28 16:19:19,121 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 16:19:20,011 - pyskl - INFO - +top1_acc 0.2007 +top5_acc 0.4306 +2024-12-28 16:19:20,011 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 16:19:20,064 - pyskl - INFO - +mean_acc 0.2004 +2024-12-28 16:19:20,080 - pyskl - INFO - Epoch(val) [69][309] top1_acc: 0.2007, top5_acc: 0.4306, mean_class_accuracy: 0.2004 +2024-12-28 16:23:40,026 - pyskl - INFO - Epoch [70][100/3746] lr: 5.624e-02, eta: 2 days, 21:52:34, time: 2.599, data_time: 1.559, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5234, loss_cls: 4.1947, loss: 4.1947 +2024-12-28 16:25:04,725 - pyskl - INFO - Epoch [70][200/3746] lr: 5.621e-02, eta: 2 days, 21:51:13, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5205, loss_cls: 4.2138, loss: 4.2138 +2024-12-28 16:26:28,991 - pyskl - INFO - Epoch [70][300/3746] lr: 5.618e-02, eta: 2 days, 21:49:52, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5178, loss_cls: 4.2120, loss: 4.2120 +2024-12-28 16:27:53,845 - pyskl - INFO - Epoch [70][400/3746] lr: 5.616e-02, eta: 2 days, 21:48:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5244, loss_cls: 4.2131, loss: 4.2131 +2024-12-28 16:29:18,397 - pyskl - INFO - Epoch [70][500/3746] lr: 5.613e-02, eta: 2 days, 21:47:10, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5175, loss_cls: 4.2351, loss: 4.2351 +2024-12-28 16:30:43,928 - pyskl - INFO - Epoch [70][600/3746] lr: 5.610e-02, eta: 2 days, 21:45:50, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5214, loss_cls: 4.2327, loss: 4.2327 +2024-12-28 16:32:08,970 - pyskl - INFO - Epoch [70][700/3746] lr: 5.607e-02, eta: 2 days, 21:44:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5244, loss_cls: 4.2351, loss: 4.2351 +2024-12-28 16:33:34,512 - pyskl - INFO - Epoch [70][800/3746] lr: 5.605e-02, eta: 2 days, 21:43:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5095, loss_cls: 4.2810, loss: 4.2810 +2024-12-28 16:35:00,040 - pyskl - INFO - Epoch [70][900/3746] lr: 5.602e-02, eta: 2 days, 21:41:50, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5095, loss_cls: 4.2444, loss: 4.2444 +2024-12-28 16:36:25,113 - pyskl - INFO - Epoch [70][1000/3746] lr: 5.599e-02, eta: 2 days, 21:40:29, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5117, loss_cls: 4.2898, loss: 4.2898 +2024-12-28 16:37:50,893 - pyskl - INFO - Epoch [70][1100/3746] lr: 5.596e-02, eta: 2 days, 21:39:10, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5019, loss_cls: 4.2990, loss: 4.2990 +2024-12-28 16:39:15,788 - pyskl - INFO - Epoch [70][1200/3746] lr: 5.593e-02, eta: 2 days, 21:37:49, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5056, loss_cls: 4.2660, loss: 4.2660 +2024-12-28 16:40:40,874 - pyskl - INFO - Epoch [70][1300/3746] lr: 5.591e-02, eta: 2 days, 21:36:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5116, loss_cls: 4.2757, loss: 4.2757 +2024-12-28 16:42:05,651 - pyskl - INFO - Epoch [70][1400/3746] lr: 5.588e-02, eta: 2 days, 21:35:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5114, loss_cls: 4.2423, loss: 4.2423 +2024-12-28 16:43:30,098 - pyskl - INFO - Epoch [70][1500/3746] lr: 5.585e-02, eta: 2 days, 21:33:46, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5102, loss_cls: 4.2609, loss: 4.2609 +2024-12-28 16:44:54,606 - pyskl - INFO - Epoch [70][1600/3746] lr: 5.582e-02, eta: 2 days, 21:32:25, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5098, loss_cls: 4.2698, loss: 4.2698 +2024-12-28 16:46:18,600 - pyskl - INFO - Epoch [70][1700/3746] lr: 5.580e-02, eta: 2 days, 21:31:04, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5036, loss_cls: 4.2743, loss: 4.2743 +2024-12-28 16:47:42,659 - pyskl - INFO - Epoch [70][1800/3746] lr: 5.577e-02, eta: 2 days, 21:29:42, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5058, loss_cls: 4.2576, loss: 4.2576 +2024-12-28 16:49:07,512 - pyskl - INFO - Epoch [70][1900/3746] lr: 5.574e-02, eta: 2 days, 21:28:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5042, loss_cls: 4.2602, loss: 4.2602 +2024-12-28 16:50:31,974 - pyskl - INFO - Epoch [70][2000/3746] lr: 5.571e-02, eta: 2 days, 21:27:00, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5167, loss_cls: 4.2543, loss: 4.2543 +2024-12-28 16:51:56,720 - pyskl - INFO - Epoch [70][2100/3746] lr: 5.568e-02, eta: 2 days, 21:25:39, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5188, loss_cls: 4.2258, loss: 4.2258 +2024-12-28 16:53:21,722 - pyskl - INFO - Epoch [70][2200/3746] lr: 5.566e-02, eta: 2 days, 21:24:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5103, loss_cls: 4.2426, loss: 4.2426 +2024-12-28 16:54:46,797 - pyskl - INFO - Epoch [70][2300/3746] lr: 5.563e-02, eta: 2 days, 21:22:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5058, loss_cls: 4.2763, loss: 4.2763 +2024-12-28 16:56:12,003 - pyskl - INFO - Epoch [70][2400/3746] lr: 5.560e-02, eta: 2 days, 21:21:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5145, loss_cls: 4.2348, loss: 4.2348 +2024-12-28 16:57:36,848 - pyskl - INFO - Epoch [70][2500/3746] lr: 5.557e-02, eta: 2 days, 21:20:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.4948, loss_cls: 4.3240, loss: 4.3240 +2024-12-28 16:59:01,366 - pyskl - INFO - Epoch [70][2600/3746] lr: 5.555e-02, eta: 2 days, 21:18:56, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5062, loss_cls: 4.2766, loss: 4.2766 +2024-12-28 17:00:27,103 - pyskl - INFO - Epoch [70][2700/3746] lr: 5.552e-02, eta: 2 days, 21:17:36, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.4997, loss_cls: 4.2888, loss: 4.2888 +2024-12-28 17:01:51,621 - pyskl - INFO - Epoch [70][2800/3746] lr: 5.549e-02, eta: 2 days, 21:16:15, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5038, loss_cls: 4.2470, loss: 4.2470 +2024-12-28 17:03:16,417 - pyskl - INFO - Epoch [70][2900/3746] lr: 5.546e-02, eta: 2 days, 21:14:54, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5033, loss_cls: 4.2903, loss: 4.2903 +2024-12-28 17:04:40,685 - pyskl - INFO - Epoch [70][3000/3746] lr: 5.543e-02, eta: 2 days, 21:13:33, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5027, loss_cls: 4.2990, loss: 4.2990 +2024-12-28 17:06:04,978 - pyskl - INFO - Epoch [70][3100/3746] lr: 5.541e-02, eta: 2 days, 21:12:11, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5078, loss_cls: 4.2495, loss: 4.2495 +2024-12-28 17:07:29,026 - pyskl - INFO - Epoch [70][3200/3746] lr: 5.538e-02, eta: 2 days, 21:10:50, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.4953, loss_cls: 4.3014, loss: 4.3014 +2024-12-28 17:08:53,759 - pyskl - INFO - Epoch [70][3300/3746] lr: 5.535e-02, eta: 2 days, 21:09:29, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5008, loss_cls: 4.3264, loss: 4.3264 +2024-12-28 17:10:18,455 - pyskl - INFO - Epoch [70][3400/3746] lr: 5.532e-02, eta: 2 days, 21:08:08, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5102, loss_cls: 4.2791, loss: 4.2791 +2024-12-28 17:11:43,027 - pyskl - INFO - Epoch [70][3500/3746] lr: 5.530e-02, eta: 2 days, 21:06:47, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5053, loss_cls: 4.2705, loss: 4.2705 +2024-12-28 17:13:06,932 - pyskl - INFO - Epoch [70][3600/3746] lr: 5.527e-02, eta: 2 days, 21:05:25, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5128, loss_cls: 4.2561, loss: 4.2561 +2024-12-28 17:14:31,421 - pyskl - INFO - Epoch [70][3700/3746] lr: 5.524e-02, eta: 2 days, 21:04:03, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5109, loss_cls: 4.2528, loss: 4.2528 +2024-12-28 17:15:12,409 - pyskl - INFO - Saving checkpoint at 70 epochs +2024-12-28 17:17:11,170 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 17:17:11,847 - pyskl - INFO - +top1_acc 0.2080 +top5_acc 0.4311 +2024-12-28 17:17:11,847 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 17:17:11,891 - pyskl - INFO - +mean_acc 0.2078 +2024-12-28 17:17:11,896 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_68.pth was removed +2024-12-28 17:17:12,183 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_70.pth. +2024-12-28 17:17:12,184 - pyskl - INFO - Best top1_acc is 0.2080 at 70 epoch. +2024-12-28 17:17:12,197 - pyskl - INFO - Epoch(val) [70][309] top1_acc: 0.2080, top5_acc: 0.4311, mean_class_accuracy: 0.2078 +2024-12-28 17:21:30,598 - pyskl - INFO - Epoch [71][100/3746] lr: 5.520e-02, eta: 2 days, 21:04:39, time: 2.584, data_time: 1.545, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5212, loss_cls: 4.1954, loss: 4.1954 +2024-12-28 17:22:56,695 - pyskl - INFO - Epoch [71][200/3746] lr: 5.517e-02, eta: 2 days, 21:03:20, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5186, loss_cls: 4.2122, loss: 4.2122 +2024-12-28 17:24:23,191 - pyskl - INFO - Epoch [71][300/3746] lr: 5.514e-02, eta: 2 days, 21:02:01, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5181, loss_cls: 4.1946, loss: 4.1946 +2024-12-28 17:25:49,446 - pyskl - INFO - Epoch [71][400/3746] lr: 5.512e-02, eta: 2 days, 21:00:41, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5206, loss_cls: 4.2148, loss: 4.2148 +2024-12-28 17:27:14,783 - pyskl - INFO - Epoch [71][500/3746] lr: 5.509e-02, eta: 2 days, 20:59:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4913, loss_cls: 4.3047, loss: 4.3047 +2024-12-28 17:28:40,860 - pyskl - INFO - Epoch [71][600/3746] lr: 5.506e-02, eta: 2 days, 20:58:01, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5152, loss_cls: 4.2211, loss: 4.2211 +2024-12-28 17:30:06,238 - pyskl - INFO - Epoch [71][700/3746] lr: 5.503e-02, eta: 2 days, 20:56:41, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5073, loss_cls: 4.2661, loss: 4.2661 +2024-12-28 17:31:31,885 - pyskl - INFO - Epoch [71][800/3746] lr: 5.500e-02, eta: 2 days, 20:55:21, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5116, loss_cls: 4.2652, loss: 4.2652 +2024-12-28 17:32:58,766 - pyskl - INFO - Epoch [71][900/3746] lr: 5.498e-02, eta: 2 days, 20:54:02, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5173, loss_cls: 4.2316, loss: 4.2316 +2024-12-28 17:34:24,652 - pyskl - INFO - Epoch [71][1000/3746] lr: 5.495e-02, eta: 2 days, 20:52:43, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5081, loss_cls: 4.2659, loss: 4.2659 +2024-12-28 17:35:50,256 - pyskl - INFO - Epoch [71][1100/3746] lr: 5.492e-02, eta: 2 days, 20:51:23, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5117, loss_cls: 4.2414, loss: 4.2414 +2024-12-28 17:37:15,669 - pyskl - INFO - Epoch [71][1200/3746] lr: 5.489e-02, eta: 2 days, 20:50:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5048, loss_cls: 4.2908, loss: 4.2908 +2024-12-28 17:38:40,807 - pyskl - INFO - Epoch [71][1300/3746] lr: 5.487e-02, eta: 2 days, 20:48:42, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5169, loss_cls: 4.2484, loss: 4.2484 +2024-12-28 17:40:05,926 - pyskl - INFO - Epoch [71][1400/3746] lr: 5.484e-02, eta: 2 days, 20:47:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5212, loss_cls: 4.2110, loss: 4.2110 +2024-12-28 17:41:31,329 - pyskl - INFO - Epoch [71][1500/3746] lr: 5.481e-02, eta: 2 days, 20:46:01, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5095, loss_cls: 4.2484, loss: 4.2484 +2024-12-28 17:42:56,364 - pyskl - INFO - Epoch [71][1600/3746] lr: 5.478e-02, eta: 2 days, 20:44:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5161, loss_cls: 4.2319, loss: 4.2319 +2024-12-28 17:44:21,584 - pyskl - INFO - Epoch [71][1700/3746] lr: 5.475e-02, eta: 2 days, 20:43:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5148, loss_cls: 4.2355, loss: 4.2355 +2024-12-28 17:45:47,009 - pyskl - INFO - Epoch [71][1800/3746] lr: 5.473e-02, eta: 2 days, 20:41:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5264, loss_cls: 4.2045, loss: 4.2045 +2024-12-28 17:47:12,232 - pyskl - INFO - Epoch [71][1900/3746] lr: 5.470e-02, eta: 2 days, 20:40:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5162, loss_cls: 4.2567, loss: 4.2567 +2024-12-28 17:48:37,923 - pyskl - INFO - Epoch [71][2000/3746] lr: 5.467e-02, eta: 2 days, 20:39:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5109, loss_cls: 4.2692, loss: 4.2692 +2024-12-28 17:50:03,053 - pyskl - INFO - Epoch [71][2100/3746] lr: 5.464e-02, eta: 2 days, 20:37:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5214, loss_cls: 4.1955, loss: 4.1955 +2024-12-28 17:51:28,539 - pyskl - INFO - Epoch [71][2200/3746] lr: 5.461e-02, eta: 2 days, 20:36:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4944, loss_cls: 4.3251, loss: 4.3251 +2024-12-28 17:52:53,860 - pyskl - INFO - Epoch [71][2300/3746] lr: 5.459e-02, eta: 2 days, 20:35:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5067, loss_cls: 4.2581, loss: 4.2581 +2024-12-28 17:54:19,323 - pyskl - INFO - Epoch [71][2400/3746] lr: 5.456e-02, eta: 2 days, 20:33:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5042, loss_cls: 4.2563, loss: 4.2563 +2024-12-28 17:55:44,402 - pyskl - INFO - Epoch [71][2500/3746] lr: 5.453e-02, eta: 2 days, 20:32:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5056, loss_cls: 4.2183, loss: 4.2183 +2024-12-28 17:57:09,885 - pyskl - INFO - Epoch [71][2600/3746] lr: 5.450e-02, eta: 2 days, 20:31:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5117, loss_cls: 4.2776, loss: 4.2776 +2024-12-28 17:58:35,043 - pyskl - INFO - Epoch [71][2700/3746] lr: 5.448e-02, eta: 2 days, 20:29:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5038, loss_cls: 4.2750, loss: 4.2750 +2024-12-28 18:00:00,240 - pyskl - INFO - Epoch [71][2800/3746] lr: 5.445e-02, eta: 2 days, 20:28:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5108, loss_cls: 4.2870, loss: 4.2870 +2024-12-28 18:01:25,307 - pyskl - INFO - Epoch [71][2900/3746] lr: 5.442e-02, eta: 2 days, 20:27:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5033, loss_cls: 4.2983, loss: 4.2983 +2024-12-28 18:02:50,644 - pyskl - INFO - Epoch [71][3000/3746] lr: 5.439e-02, eta: 2 days, 20:25:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5106, loss_cls: 4.2464, loss: 4.2464 +2024-12-28 18:04:15,814 - pyskl - INFO - Epoch [71][3100/3746] lr: 5.436e-02, eta: 2 days, 20:24:33, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5006, loss_cls: 4.2448, loss: 4.2448 +2024-12-28 18:05:40,963 - pyskl - INFO - Epoch [71][3200/3746] lr: 5.434e-02, eta: 2 days, 20:23:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5006, loss_cls: 4.2905, loss: 4.2905 +2024-12-28 18:07:05,790 - pyskl - INFO - Epoch [71][3300/3746] lr: 5.431e-02, eta: 2 days, 20:21:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5095, loss_cls: 4.2455, loss: 4.2455 +2024-12-28 18:08:30,911 - pyskl - INFO - Epoch [71][3400/3746] lr: 5.428e-02, eta: 2 days, 20:20:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5139, loss_cls: 4.2630, loss: 4.2630 +2024-12-28 18:09:55,989 - pyskl - INFO - Epoch [71][3500/3746] lr: 5.425e-02, eta: 2 days, 20:19:09, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5081, loss_cls: 4.2913, loss: 4.2913 +2024-12-28 18:11:21,040 - pyskl - INFO - Epoch [71][3600/3746] lr: 5.422e-02, eta: 2 days, 20:17:49, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5114, loss_cls: 4.2602, loss: 4.2602 +2024-12-28 18:12:46,261 - pyskl - INFO - Epoch [71][3700/3746] lr: 5.420e-02, eta: 2 days, 20:16:28, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5108, loss_cls: 4.2436, loss: 4.2436 +2024-12-28 18:13:27,176 - pyskl - INFO - Saving checkpoint at 71 epochs +2024-12-28 18:15:28,348 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 18:15:29,177 - pyskl - INFO - +top1_acc 0.2065 +top5_acc 0.4396 +2024-12-28 18:15:29,178 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 18:15:29,225 - pyskl - INFO - +mean_acc 0.2062 +2024-12-28 18:15:29,241 - pyskl - INFO - Epoch(val) [71][309] top1_acc: 0.2065, top5_acc: 0.4396, mean_class_accuracy: 0.2062 +2024-12-28 18:19:48,485 - pyskl - INFO - Epoch [72][100/3746] lr: 5.416e-02, eta: 2 days, 20:17:00, time: 2.592, data_time: 1.554, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5273, loss_cls: 4.1649, loss: 4.1649 +2024-12-28 18:21:13,846 - pyskl - INFO - Epoch [72][200/3746] lr: 5.413e-02, eta: 2 days, 20:15:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5234, loss_cls: 4.1859, loss: 4.1859 +2024-12-28 18:22:39,168 - pyskl - INFO - Epoch [72][300/3746] lr: 5.410e-02, eta: 2 days, 20:14:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5172, loss_cls: 4.2097, loss: 4.2097 +2024-12-28 18:24:04,289 - pyskl - INFO - Epoch [72][400/3746] lr: 5.407e-02, eta: 2 days, 20:12:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5144, loss_cls: 4.2291, loss: 4.2291 +2024-12-28 18:25:29,701 - pyskl - INFO - Epoch [72][500/3746] lr: 5.404e-02, eta: 2 days, 20:11:37, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5120, loss_cls: 4.2343, loss: 4.2343 +2024-12-28 18:26:54,956 - pyskl - INFO - Epoch [72][600/3746] lr: 5.402e-02, eta: 2 days, 20:10:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5239, loss_cls: 4.1950, loss: 4.1950 +2024-12-28 18:28:20,576 - pyskl - INFO - Epoch [72][700/3746] lr: 5.399e-02, eta: 2 days, 20:08:56, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5153, loss_cls: 4.2393, loss: 4.2393 +2024-12-28 18:29:45,838 - pyskl - INFO - Epoch [72][800/3746] lr: 5.396e-02, eta: 2 days, 20:07:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5198, loss_cls: 4.2513, loss: 4.2513 +2024-12-28 18:31:11,403 - pyskl - INFO - Epoch [72][900/3746] lr: 5.393e-02, eta: 2 days, 20:06:15, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5116, loss_cls: 4.2737, loss: 4.2737 +2024-12-28 18:32:36,793 - pyskl - INFO - Epoch [72][1000/3746] lr: 5.391e-02, eta: 2 days, 20:04:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5116, loss_cls: 4.2532, loss: 4.2532 +2024-12-28 18:34:02,042 - pyskl - INFO - Epoch [72][1100/3746] lr: 5.388e-02, eta: 2 days, 20:03:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5173, loss_cls: 4.2343, loss: 4.2343 +2024-12-28 18:35:27,169 - pyskl - INFO - Epoch [72][1200/3746] lr: 5.385e-02, eta: 2 days, 20:02:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5177, loss_cls: 4.2077, loss: 4.2077 +2024-12-28 18:36:52,368 - pyskl - INFO - Epoch [72][1300/3746] lr: 5.382e-02, eta: 2 days, 20:00:52, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5036, loss_cls: 4.2798, loss: 4.2798 +2024-12-28 18:38:18,690 - pyskl - INFO - Epoch [72][1400/3746] lr: 5.379e-02, eta: 2 days, 19:59:33, time: 0.863, data_time: 0.001, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5181, loss_cls: 4.2135, loss: 4.2135 +2024-12-28 18:39:44,695 - pyskl - INFO - Epoch [72][1500/3746] lr: 5.377e-02, eta: 2 days, 19:58:13, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5108, loss_cls: 4.2525, loss: 4.2525 +2024-12-28 18:41:10,820 - pyskl - INFO - Epoch [72][1600/3746] lr: 5.374e-02, eta: 2 days, 19:56:53, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5242, loss_cls: 4.1847, loss: 4.1847 +2024-12-28 18:42:36,569 - pyskl - INFO - Epoch [72][1700/3746] lr: 5.371e-02, eta: 2 days, 19:55:33, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5125, loss_cls: 4.2369, loss: 4.2369 +2024-12-28 18:44:02,421 - pyskl - INFO - Epoch [72][1800/3746] lr: 5.368e-02, eta: 2 days, 19:54:13, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5134, loss_cls: 4.2522, loss: 4.2522 +2024-12-28 18:45:28,400 - pyskl - INFO - Epoch [72][1900/3746] lr: 5.365e-02, eta: 2 days, 19:52:53, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5142, loss_cls: 4.2348, loss: 4.2348 +2024-12-28 18:46:54,590 - pyskl - INFO - Epoch [72][2000/3746] lr: 5.363e-02, eta: 2 days, 19:51:33, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5095, loss_cls: 4.2666, loss: 4.2666 +2024-12-28 18:48:20,945 - pyskl - INFO - Epoch [72][2100/3746] lr: 5.360e-02, eta: 2 days, 19:50:14, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5188, loss_cls: 4.2129, loss: 4.2129 +2024-12-28 18:49:46,753 - pyskl - INFO - Epoch [72][2200/3746] lr: 5.357e-02, eta: 2 days, 19:48:53, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5236, loss_cls: 4.2082, loss: 4.2082 +2024-12-28 18:51:12,605 - pyskl - INFO - Epoch [72][2300/3746] lr: 5.354e-02, eta: 2 days, 19:47:33, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5042, loss_cls: 4.2436, loss: 4.2436 +2024-12-28 18:52:38,176 - pyskl - INFO - Epoch [72][2400/3746] lr: 5.352e-02, eta: 2 days, 19:46:13, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5042, loss_cls: 4.2803, loss: 4.2803 +2024-12-28 18:54:03,880 - pyskl - INFO - Epoch [72][2500/3746] lr: 5.349e-02, eta: 2 days, 19:44:53, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5106, loss_cls: 4.2548, loss: 4.2548 +2024-12-28 18:55:29,056 - pyskl - INFO - Epoch [72][2600/3746] lr: 5.346e-02, eta: 2 days, 19:43:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5133, loss_cls: 4.2515, loss: 4.2515 +2024-12-28 18:56:54,024 - pyskl - INFO - Epoch [72][2700/3746] lr: 5.343e-02, eta: 2 days, 19:42:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5180, loss_cls: 4.2149, loss: 4.2149 +2024-12-28 18:58:19,588 - pyskl - INFO - Epoch [72][2800/3746] lr: 5.340e-02, eta: 2 days, 19:40:50, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5205, loss_cls: 4.2137, loss: 4.2137 +2024-12-28 18:59:45,823 - pyskl - INFO - Epoch [72][2900/3746] lr: 5.338e-02, eta: 2 days, 19:39:30, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5164, loss_cls: 4.2318, loss: 4.2318 +2024-12-28 19:01:11,807 - pyskl - INFO - Epoch [72][3000/3746] lr: 5.335e-02, eta: 2 days, 19:38:10, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5166, loss_cls: 4.2124, loss: 4.2124 +2024-12-28 19:02:37,896 - pyskl - INFO - Epoch [72][3100/3746] lr: 5.332e-02, eta: 2 days, 19:36:51, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5100, loss_cls: 4.2566, loss: 4.2566 +2024-12-28 19:04:03,533 - pyskl - INFO - Epoch [72][3200/3746] lr: 5.329e-02, eta: 2 days, 19:35:30, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5181, loss_cls: 4.2024, loss: 4.2024 +2024-12-28 19:05:29,440 - pyskl - INFO - Epoch [72][3300/3746] lr: 5.326e-02, eta: 2 days, 19:34:10, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5041, loss_cls: 4.3000, loss: 4.3000 +2024-12-28 19:06:54,522 - pyskl - INFO - Epoch [72][3400/3746] lr: 5.324e-02, eta: 2 days, 19:32:49, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5105, loss_cls: 4.2273, loss: 4.2273 +2024-12-28 19:08:19,595 - pyskl - INFO - Epoch [72][3500/3746] lr: 5.321e-02, eta: 2 days, 19:31:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5122, loss_cls: 4.2482, loss: 4.2482 +2024-12-28 19:09:44,729 - pyskl - INFO - Epoch [72][3600/3746] lr: 5.318e-02, eta: 2 days, 19:30:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5002, loss_cls: 4.2588, loss: 4.2588 +2024-12-28 19:11:09,711 - pyskl - INFO - Epoch [72][3700/3746] lr: 5.315e-02, eta: 2 days, 19:28:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5005, loss_cls: 4.2909, loss: 4.2909 +2024-12-28 19:11:50,711 - pyskl - INFO - Saving checkpoint at 72 epochs +2024-12-28 19:13:51,882 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 19:13:52,628 - pyskl - INFO - +top1_acc 0.2138 +top5_acc 0.4497 +2024-12-28 19:13:52,628 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 19:13:52,687 - pyskl - INFO - +mean_acc 0.2134 +2024-12-28 19:13:52,691 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_70.pth was removed +2024-12-28 19:13:53,077 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_72.pth. +2024-12-28 19:13:53,078 - pyskl - INFO - Best top1_acc is 0.2138 at 72 epoch. +2024-12-28 19:13:53,099 - pyskl - INFO - Epoch(val) [72][309] top1_acc: 0.2138, top5_acc: 0.4497, mean_class_accuracy: 0.2134 +2024-12-28 19:18:13,459 - pyskl - INFO - Epoch [73][100/3746] lr: 5.311e-02, eta: 2 days, 19:29:15, time: 2.603, data_time: 1.561, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5286, loss_cls: 4.1870, loss: 4.1870 +2024-12-28 19:19:38,699 - pyskl - INFO - Epoch [73][200/3746] lr: 5.308e-02, eta: 2 days, 19:27:54, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5292, loss_cls: 4.1932, loss: 4.1932 +2024-12-28 19:21:04,251 - pyskl - INFO - Epoch [73][300/3746] lr: 5.306e-02, eta: 2 days, 19:26:34, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5175, loss_cls: 4.1986, loss: 4.1986 +2024-12-28 19:22:29,455 - pyskl - INFO - Epoch [73][400/3746] lr: 5.303e-02, eta: 2 days, 19:25:13, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5216, loss_cls: 4.2231, loss: 4.2231 +2024-12-28 19:23:54,447 - pyskl - INFO - Epoch [73][500/3746] lr: 5.300e-02, eta: 2 days, 19:23:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5172, loss_cls: 4.2227, loss: 4.2227 +2024-12-28 19:25:19,807 - pyskl - INFO - Epoch [73][600/3746] lr: 5.297e-02, eta: 2 days, 19:22:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5170, loss_cls: 4.1858, loss: 4.1858 +2024-12-28 19:26:44,998 - pyskl - INFO - Epoch [73][700/3746] lr: 5.294e-02, eta: 2 days, 19:21:09, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5198, loss_cls: 4.2175, loss: 4.2175 +2024-12-28 19:28:10,004 - pyskl - INFO - Epoch [73][800/3746] lr: 5.292e-02, eta: 2 days, 19:19:48, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5166, loss_cls: 4.2196, loss: 4.2196 +2024-12-28 19:29:35,867 - pyskl - INFO - Epoch [73][900/3746] lr: 5.289e-02, eta: 2 days, 19:18:28, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5177, loss_cls: 4.1880, loss: 4.1880 +2024-12-28 19:31:01,227 - pyskl - INFO - Epoch [73][1000/3746] lr: 5.286e-02, eta: 2 days, 19:17:07, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5188, loss_cls: 4.1695, loss: 4.1695 +2024-12-28 19:32:26,858 - pyskl - INFO - Epoch [73][1100/3746] lr: 5.283e-02, eta: 2 days, 19:15:46, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5078, loss_cls: 4.2362, loss: 4.2362 +2024-12-28 19:33:51,853 - pyskl - INFO - Epoch [73][1200/3746] lr: 5.280e-02, eta: 2 days, 19:14:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5116, loss_cls: 4.2432, loss: 4.2432 +2024-12-28 19:35:16,784 - pyskl - INFO - Epoch [73][1300/3746] lr: 5.278e-02, eta: 2 days, 19:13:04, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5053, loss_cls: 4.2849, loss: 4.2849 +2024-12-28 19:36:41,709 - pyskl - INFO - Epoch [73][1400/3746] lr: 5.275e-02, eta: 2 days, 19:11:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5119, loss_cls: 4.2266, loss: 4.2266 +2024-12-28 19:38:06,803 - pyskl - INFO - Epoch [73][1500/3746] lr: 5.272e-02, eta: 2 days, 19:10:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5091, loss_cls: 4.2626, loss: 4.2626 +2024-12-28 19:39:32,250 - pyskl - INFO - Epoch [73][1600/3746] lr: 5.269e-02, eta: 2 days, 19:09:01, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5116, loss_cls: 4.2515, loss: 4.2515 +2024-12-28 19:40:57,756 - pyskl - INFO - Epoch [73][1700/3746] lr: 5.267e-02, eta: 2 days, 19:07:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5220, loss_cls: 4.1809, loss: 4.1809 +2024-12-28 19:42:22,735 - pyskl - INFO - Epoch [73][1800/3746] lr: 5.264e-02, eta: 2 days, 19:06:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5272, loss_cls: 4.1773, loss: 4.1773 +2024-12-28 19:43:48,590 - pyskl - INFO - Epoch [73][1900/3746] lr: 5.261e-02, eta: 2 days, 19:04:58, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5167, loss_cls: 4.2466, loss: 4.2466 +2024-12-28 19:45:13,789 - pyskl - INFO - Epoch [73][2000/3746] lr: 5.258e-02, eta: 2 days, 19:03:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5161, loss_cls: 4.2140, loss: 4.2140 +2024-12-28 19:46:39,229 - pyskl - INFO - Epoch [73][2100/3746] lr: 5.255e-02, eta: 2 days, 19:02:16, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5175, loss_cls: 4.2251, loss: 4.2251 +2024-12-28 19:48:04,715 - pyskl - INFO - Epoch [73][2200/3746] lr: 5.253e-02, eta: 2 days, 19:00:56, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5078, loss_cls: 4.2569, loss: 4.2569 +2024-12-28 19:49:30,039 - pyskl - INFO - Epoch [73][2300/3746] lr: 5.250e-02, eta: 2 days, 18:59:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5080, loss_cls: 4.2375, loss: 4.2375 +2024-12-28 19:50:54,939 - pyskl - INFO - Epoch [73][2400/3746] lr: 5.247e-02, eta: 2 days, 18:58:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5125, loss_cls: 4.2377, loss: 4.2377 +2024-12-28 19:52:20,012 - pyskl - INFO - Epoch [73][2500/3746] lr: 5.244e-02, eta: 2 days, 18:56:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5045, loss_cls: 4.2742, loss: 4.2742 +2024-12-28 19:53:45,055 - pyskl - INFO - Epoch [73][2600/3746] lr: 5.241e-02, eta: 2 days, 18:55:31, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5131, loss_cls: 4.2365, loss: 4.2365 +2024-12-28 19:55:10,529 - pyskl - INFO - Epoch [73][2700/3746] lr: 5.239e-02, eta: 2 days, 18:54:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5170, loss_cls: 4.2283, loss: 4.2283 +2024-12-28 19:56:35,998 - pyskl - INFO - Epoch [73][2800/3746] lr: 5.236e-02, eta: 2 days, 18:52:49, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5161, loss_cls: 4.1990, loss: 4.1990 +2024-12-28 19:58:01,071 - pyskl - INFO - Epoch [73][2900/3746] lr: 5.233e-02, eta: 2 days, 18:51:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5188, loss_cls: 4.2107, loss: 4.2107 +2024-12-28 19:59:26,768 - pyskl - INFO - Epoch [73][3000/3746] lr: 5.230e-02, eta: 2 days, 18:50:08, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5186, loss_cls: 4.2429, loss: 4.2429 +2024-12-28 20:00:52,613 - pyskl - INFO - Epoch [73][3100/3746] lr: 5.227e-02, eta: 2 days, 18:48:47, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5073, loss_cls: 4.2491, loss: 4.2491 +2024-12-28 20:02:18,268 - pyskl - INFO - Epoch [73][3200/3746] lr: 5.225e-02, eta: 2 days, 18:47:27, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5102, loss_cls: 4.2625, loss: 4.2625 +2024-12-28 20:03:43,948 - pyskl - INFO - Epoch [73][3300/3746] lr: 5.222e-02, eta: 2 days, 18:46:06, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5214, loss_cls: 4.1911, loss: 4.1911 +2024-12-28 20:05:09,647 - pyskl - INFO - Epoch [73][3400/3746] lr: 5.219e-02, eta: 2 days, 18:44:46, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5138, loss_cls: 4.2579, loss: 4.2579 +2024-12-28 20:06:34,537 - pyskl - INFO - Epoch [73][3500/3746] lr: 5.216e-02, eta: 2 days, 18:43:24, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5167, loss_cls: 4.2242, loss: 4.2242 +2024-12-28 20:08:00,306 - pyskl - INFO - Epoch [73][3600/3746] lr: 5.213e-02, eta: 2 days, 18:42:04, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5166, loss_cls: 4.2291, loss: 4.2291 +2024-12-28 20:09:25,927 - pyskl - INFO - Epoch [73][3700/3746] lr: 5.211e-02, eta: 2 days, 18:40:43, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5095, loss_cls: 4.2238, loss: 4.2238 +2024-12-28 20:10:06,985 - pyskl - INFO - Saving checkpoint at 73 epochs +2024-12-28 20:12:08,403 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 20:12:09,080 - pyskl - INFO - +top1_acc 0.2059 +top5_acc 0.4403 +2024-12-28 20:12:09,081 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 20:12:09,129 - pyskl - INFO - +mean_acc 0.2058 +2024-12-28 20:12:09,145 - pyskl - INFO - Epoch(val) [73][309] top1_acc: 0.2059, top5_acc: 0.4403, mean_class_accuracy: 0.2058 +2024-12-28 20:16:28,132 - pyskl - INFO - Epoch [74][100/3746] lr: 5.207e-02, eta: 2 days, 18:41:06, time: 2.590, data_time: 1.549, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5431, loss_cls: 4.0968, loss: 4.0968 +2024-12-28 20:17:53,394 - pyskl - INFO - Epoch [74][200/3746] lr: 5.204e-02, eta: 2 days, 18:39:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5306, loss_cls: 4.1726, loss: 4.1726 +2024-12-28 20:19:19,030 - pyskl - INFO - Epoch [74][300/3746] lr: 5.201e-02, eta: 2 days, 18:38:24, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5228, loss_cls: 4.1827, loss: 4.1827 +2024-12-28 20:20:44,241 - pyskl - INFO - Epoch [74][400/3746] lr: 5.198e-02, eta: 2 days, 18:37:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5284, loss_cls: 4.1675, loss: 4.1675 +2024-12-28 20:22:09,565 - pyskl - INFO - Epoch [74][500/3746] lr: 5.195e-02, eta: 2 days, 18:35:42, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5264, loss_cls: 4.1999, loss: 4.1999 +2024-12-28 20:23:35,324 - pyskl - INFO - Epoch [74][600/3746] lr: 5.193e-02, eta: 2 days, 18:34:22, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5280, loss_cls: 4.1768, loss: 4.1768 +2024-12-28 20:25:00,687 - pyskl - INFO - Epoch [74][700/3746] lr: 5.190e-02, eta: 2 days, 18:33:00, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5205, loss_cls: 4.2077, loss: 4.2077 +2024-12-28 20:26:26,272 - pyskl - INFO - Epoch [74][800/3746] lr: 5.187e-02, eta: 2 days, 18:31:40, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5092, loss_cls: 4.2463, loss: 4.2463 +2024-12-28 20:27:51,707 - pyskl - INFO - Epoch [74][900/3746] lr: 5.184e-02, eta: 2 days, 18:30:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5172, loss_cls: 4.2316, loss: 4.2316 +2024-12-28 20:29:16,841 - pyskl - INFO - Epoch [74][1000/3746] lr: 5.181e-02, eta: 2 days, 18:28:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5231, loss_cls: 4.1673, loss: 4.1673 +2024-12-28 20:30:42,373 - pyskl - INFO - Epoch [74][1100/3746] lr: 5.179e-02, eta: 2 days, 18:27:36, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5303, loss_cls: 4.1607, loss: 4.1607 +2024-12-28 20:32:07,706 - pyskl - INFO - Epoch [74][1200/3746] lr: 5.176e-02, eta: 2 days, 18:26:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5156, loss_cls: 4.2237, loss: 4.2237 +2024-12-28 20:33:32,941 - pyskl - INFO - Epoch [74][1300/3746] lr: 5.173e-02, eta: 2 days, 18:24:54, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5217, loss_cls: 4.1999, loss: 4.1999 +2024-12-28 20:34:58,487 - pyskl - INFO - Epoch [74][1400/3746] lr: 5.170e-02, eta: 2 days, 18:23:33, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5112, loss_cls: 4.2471, loss: 4.2471 +2024-12-28 20:36:23,861 - pyskl - INFO - Epoch [74][1500/3746] lr: 5.168e-02, eta: 2 days, 18:22:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5188, loss_cls: 4.2100, loss: 4.2100 +2024-12-28 20:37:48,821 - pyskl - INFO - Epoch [74][1600/3746] lr: 5.165e-02, eta: 2 days, 18:20:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5219, loss_cls: 4.1827, loss: 4.1827 +2024-12-28 20:39:14,716 - pyskl - INFO - Epoch [74][1700/3746] lr: 5.162e-02, eta: 2 days, 18:19:30, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5145, loss_cls: 4.2154, loss: 4.2154 +2024-12-28 20:40:40,502 - pyskl - INFO - Epoch [74][1800/3746] lr: 5.159e-02, eta: 2 days, 18:18:09, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5055, loss_cls: 4.2426, loss: 4.2426 +2024-12-28 20:42:05,411 - pyskl - INFO - Epoch [74][1900/3746] lr: 5.156e-02, eta: 2 days, 18:16:48, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5261, loss_cls: 4.1850, loss: 4.1850 +2024-12-28 20:43:30,417 - pyskl - INFO - Epoch [74][2000/3746] lr: 5.154e-02, eta: 2 days, 18:15:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5166, loss_cls: 4.2194, loss: 4.2194 +2024-12-28 20:44:55,380 - pyskl - INFO - Epoch [74][2100/3746] lr: 5.151e-02, eta: 2 days, 18:14:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5186, loss_cls: 4.2277, loss: 4.2277 +2024-12-28 20:46:20,989 - pyskl - INFO - Epoch [74][2200/3746] lr: 5.148e-02, eta: 2 days, 18:12:44, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5173, loss_cls: 4.2167, loss: 4.2167 +2024-12-28 20:47:46,260 - pyskl - INFO - Epoch [74][2300/3746] lr: 5.145e-02, eta: 2 days, 18:11:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5161, loss_cls: 4.2199, loss: 4.2199 +2024-12-28 20:49:12,131 - pyskl - INFO - Epoch [74][2400/3746] lr: 5.142e-02, eta: 2 days, 18:10:02, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5211, loss_cls: 4.2366, loss: 4.2366 +2024-12-28 20:50:37,624 - pyskl - INFO - Epoch [74][2500/3746] lr: 5.140e-02, eta: 2 days, 18:08:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5119, loss_cls: 4.2395, loss: 4.2395 +2024-12-28 20:52:02,534 - pyskl - INFO - Epoch [74][2600/3746] lr: 5.137e-02, eta: 2 days, 18:07:20, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5123, loss_cls: 4.2621, loss: 4.2621 +2024-12-28 20:53:27,762 - pyskl - INFO - Epoch [74][2700/3746] lr: 5.134e-02, eta: 2 days, 18:05:58, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5269, loss_cls: 4.1633, loss: 4.1633 +2024-12-28 20:54:53,100 - pyskl - INFO - Epoch [74][2800/3746] lr: 5.131e-02, eta: 2 days, 18:04:37, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5192, loss_cls: 4.2093, loss: 4.2093 +2024-12-28 20:56:18,461 - pyskl - INFO - Epoch [74][2900/3746] lr: 5.128e-02, eta: 2 days, 18:03:16, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5142, loss_cls: 4.2485, loss: 4.2485 +2024-12-28 20:57:43,282 - pyskl - INFO - Epoch [74][3000/3746] lr: 5.126e-02, eta: 2 days, 18:01:54, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5156, loss_cls: 4.2178, loss: 4.2178 +2024-12-28 20:59:09,012 - pyskl - INFO - Epoch [74][3100/3746] lr: 5.123e-02, eta: 2 days, 18:00:34, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5231, loss_cls: 4.2049, loss: 4.2049 +2024-12-28 21:00:34,440 - pyskl - INFO - Epoch [74][3200/3746] lr: 5.120e-02, eta: 2 days, 17:59:13, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5062, loss_cls: 4.2450, loss: 4.2450 +2024-12-28 21:01:58,978 - pyskl - INFO - Epoch [74][3300/3746] lr: 5.117e-02, eta: 2 days, 17:57:51, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5169, loss_cls: 4.2146, loss: 4.2146 +2024-12-28 21:03:24,172 - pyskl - INFO - Epoch [74][3400/3746] lr: 5.114e-02, eta: 2 days, 17:56:29, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5073, loss_cls: 4.2482, loss: 4.2482 +2024-12-28 21:04:49,781 - pyskl - INFO - Epoch [74][3500/3746] lr: 5.112e-02, eta: 2 days, 17:55:08, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5175, loss_cls: 4.2578, loss: 4.2578 +2024-12-28 21:06:14,665 - pyskl - INFO - Epoch [74][3600/3746] lr: 5.109e-02, eta: 2 days, 17:53:47, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5109, loss_cls: 4.2250, loss: 4.2250 +2024-12-28 21:07:39,946 - pyskl - INFO - Epoch [74][3700/3746] lr: 5.106e-02, eta: 2 days, 17:52:26, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5175, loss_cls: 4.2392, loss: 4.2392 +2024-12-28 21:08:21,242 - pyskl - INFO - Saving checkpoint at 74 epochs +2024-12-28 21:10:21,959 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 21:10:22,756 - pyskl - INFO - +top1_acc 0.2031 +top5_acc 0.4335 +2024-12-28 21:10:22,756 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 21:10:22,807 - pyskl - INFO - +mean_acc 0.2028 +2024-12-28 21:10:22,820 - pyskl - INFO - Epoch(val) [74][309] top1_acc: 0.2031, top5_acc: 0.4335, mean_class_accuracy: 0.2028 +2024-12-28 21:14:41,839 - pyskl - INFO - Epoch [75][100/3746] lr: 5.102e-02, eta: 2 days, 17:52:45, time: 2.590, data_time: 1.567, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5212, loss_cls: 4.1726, loss: 4.1726 +2024-12-28 21:16:07,479 - pyskl - INFO - Epoch [75][200/3746] lr: 5.099e-02, eta: 2 days, 17:51:24, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5311, loss_cls: 4.1536, loss: 4.1536 +2024-12-28 21:17:32,642 - pyskl - INFO - Epoch [75][300/3746] lr: 5.096e-02, eta: 2 days, 17:50:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5181, loss_cls: 4.2024, loss: 4.2024 +2024-12-28 21:18:57,872 - pyskl - INFO - Epoch [75][400/3746] lr: 5.094e-02, eta: 2 days, 17:48:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5266, loss_cls: 4.1677, loss: 4.1677 +2024-12-28 21:20:23,050 - pyskl - INFO - Epoch [75][500/3746] lr: 5.091e-02, eta: 2 days, 17:47:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5105, loss_cls: 4.2503, loss: 4.2503 +2024-12-28 21:21:47,799 - pyskl - INFO - Epoch [75][600/3746] lr: 5.088e-02, eta: 2 days, 17:45:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5295, loss_cls: 4.1763, loss: 4.1763 +2024-12-28 21:23:13,100 - pyskl - INFO - Epoch [75][700/3746] lr: 5.085e-02, eta: 2 days, 17:44:37, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5231, loss_cls: 4.1725, loss: 4.1725 +2024-12-28 21:24:38,023 - pyskl - INFO - Epoch [75][800/3746] lr: 5.082e-02, eta: 2 days, 17:43:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5169, loss_cls: 4.2279, loss: 4.2279 +2024-12-28 21:26:03,199 - pyskl - INFO - Epoch [75][900/3746] lr: 5.080e-02, eta: 2 days, 17:41:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5283, loss_cls: 4.1751, loss: 4.1751 +2024-12-28 21:27:27,949 - pyskl - INFO - Epoch [75][1000/3746] lr: 5.077e-02, eta: 2 days, 17:40:31, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5239, loss_cls: 4.2150, loss: 4.2150 +2024-12-28 21:28:53,109 - pyskl - INFO - Epoch [75][1100/3746] lr: 5.074e-02, eta: 2 days, 17:39:10, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5241, loss_cls: 4.1722, loss: 4.1722 +2024-12-28 21:30:18,442 - pyskl - INFO - Epoch [75][1200/3746] lr: 5.071e-02, eta: 2 days, 17:37:49, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5205, loss_cls: 4.1905, loss: 4.1905 +2024-12-28 21:31:43,830 - pyskl - INFO - Epoch [75][1300/3746] lr: 5.068e-02, eta: 2 days, 17:36:27, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5078, loss_cls: 4.2679, loss: 4.2679 +2024-12-28 21:33:08,798 - pyskl - INFO - Epoch [75][1400/3746] lr: 5.066e-02, eta: 2 days, 17:35:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5184, loss_cls: 4.1953, loss: 4.1953 +2024-12-28 21:34:33,726 - pyskl - INFO - Epoch [75][1500/3746] lr: 5.063e-02, eta: 2 days, 17:33:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5300, loss_cls: 4.1740, loss: 4.1740 +2024-12-28 21:35:59,154 - pyskl - INFO - Epoch [75][1600/3746] lr: 5.060e-02, eta: 2 days, 17:32:23, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5328, loss_cls: 4.1510, loss: 4.1510 +2024-12-28 21:37:24,488 - pyskl - INFO - Epoch [75][1700/3746] lr: 5.057e-02, eta: 2 days, 17:31:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5175, loss_cls: 4.2255, loss: 4.2255 +2024-12-28 21:38:49,255 - pyskl - INFO - Epoch [75][1800/3746] lr: 5.054e-02, eta: 2 days, 17:29:39, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5203, loss_cls: 4.2237, loss: 4.2237 +2024-12-28 21:40:14,274 - pyskl - INFO - Epoch [75][1900/3746] lr: 5.052e-02, eta: 2 days, 17:28:18, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5173, loss_cls: 4.2116, loss: 4.2116 +2024-12-28 21:41:39,359 - pyskl - INFO - Epoch [75][2000/3746] lr: 5.049e-02, eta: 2 days, 17:26:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5292, loss_cls: 4.1489, loss: 4.1489 +2024-12-28 21:43:05,136 - pyskl - INFO - Epoch [75][2100/3746] lr: 5.046e-02, eta: 2 days, 17:25:35, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5127, loss_cls: 4.2269, loss: 4.2269 +2024-12-28 21:44:30,543 - pyskl - INFO - Epoch [75][2200/3746] lr: 5.043e-02, eta: 2 days, 17:24:14, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5203, loss_cls: 4.2184, loss: 4.2184 +2024-12-28 21:45:55,673 - pyskl - INFO - Epoch [75][2300/3746] lr: 5.040e-02, eta: 2 days, 17:22:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5222, loss_cls: 4.2158, loss: 4.2158 +2024-12-28 21:47:21,274 - pyskl - INFO - Epoch [75][2400/3746] lr: 5.038e-02, eta: 2 days, 17:21:31, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5134, loss_cls: 4.2438, loss: 4.2438 +2024-12-28 21:48:46,723 - pyskl - INFO - Epoch [75][2500/3746] lr: 5.035e-02, eta: 2 days, 17:20:10, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5209, loss_cls: 4.2384, loss: 4.2384 +2024-12-28 21:50:12,167 - pyskl - INFO - Epoch [75][2600/3746] lr: 5.032e-02, eta: 2 days, 17:18:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5148, loss_cls: 4.2119, loss: 4.2119 +2024-12-28 21:51:37,652 - pyskl - INFO - Epoch [75][2700/3746] lr: 5.029e-02, eta: 2 days, 17:17:28, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5222, loss_cls: 4.1702, loss: 4.1702 +2024-12-28 21:53:03,031 - pyskl - INFO - Epoch [75][2800/3746] lr: 5.026e-02, eta: 2 days, 17:16:06, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5189, loss_cls: 4.1859, loss: 4.1859 +2024-12-28 21:54:28,308 - pyskl - INFO - Epoch [75][2900/3746] lr: 5.024e-02, eta: 2 days, 17:14:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5164, loss_cls: 4.2222, loss: 4.2222 +2024-12-28 21:55:53,593 - pyskl - INFO - Epoch [75][3000/3746] lr: 5.021e-02, eta: 2 days, 17:13:24, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5244, loss_cls: 4.1974, loss: 4.1974 +2024-12-28 21:57:19,203 - pyskl - INFO - Epoch [75][3100/3746] lr: 5.018e-02, eta: 2 days, 17:12:03, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5230, loss_cls: 4.1762, loss: 4.1762 +2024-12-28 21:58:44,396 - pyskl - INFO - Epoch [75][3200/3746] lr: 5.015e-02, eta: 2 days, 17:10:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5148, loss_cls: 4.1992, loss: 4.1992 +2024-12-28 22:00:09,475 - pyskl - INFO - Epoch [75][3300/3746] lr: 5.012e-02, eta: 2 days, 17:09:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5248, loss_cls: 4.1843, loss: 4.1843 +2024-12-28 22:01:34,857 - pyskl - INFO - Epoch [75][3400/3746] lr: 5.010e-02, eta: 2 days, 17:07:58, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5123, loss_cls: 4.2218, loss: 4.2218 +2024-12-28 22:02:59,982 - pyskl - INFO - Epoch [75][3500/3746] lr: 5.007e-02, eta: 2 days, 17:06:37, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5188, loss_cls: 4.2064, loss: 4.2064 +2024-12-28 22:04:24,740 - pyskl - INFO - Epoch [75][3600/3746] lr: 5.004e-02, eta: 2 days, 17:05:15, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5186, loss_cls: 4.2169, loss: 4.2169 +2024-12-28 22:05:49,531 - pyskl - INFO - Epoch [75][3700/3746] lr: 5.001e-02, eta: 2 days, 17:03:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5184, loss_cls: 4.2076, loss: 4.2076 +2024-12-28 22:06:30,829 - pyskl - INFO - Saving checkpoint at 75 epochs +2024-12-28 22:08:32,097 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 22:08:32,911 - pyskl - INFO - +top1_acc 0.1987 +top5_acc 0.4170 +2024-12-28 22:08:32,912 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 22:08:32,969 - pyskl - INFO - +mean_acc 0.1983 +2024-12-28 22:08:32,983 - pyskl - INFO - Epoch(val) [75][309] top1_acc: 0.1987, top5_acc: 0.4170, mean_class_accuracy: 0.1983 +2024-12-28 22:12:50,496 - pyskl - INFO - Epoch [76][100/3746] lr: 4.997e-02, eta: 2 days, 17:04:07, time: 2.575, data_time: 1.549, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5312, loss_cls: 4.1366, loss: 4.1366 +2024-12-28 22:14:15,640 - pyskl - INFO - Epoch [76][200/3746] lr: 4.994e-02, eta: 2 days, 17:02:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5406, loss_cls: 4.0962, loss: 4.0962 +2024-12-28 22:15:40,831 - pyskl - INFO - Epoch [76][300/3746] lr: 4.992e-02, eta: 2 days, 17:01:23, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5238, loss_cls: 4.1948, loss: 4.1948 +2024-12-28 22:17:06,763 - pyskl - INFO - Epoch [76][400/3746] lr: 4.989e-02, eta: 2 days, 17:00:02, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5214, loss_cls: 4.2020, loss: 4.2020 +2024-12-28 22:18:31,848 - pyskl - INFO - Epoch [76][500/3746] lr: 4.986e-02, eta: 2 days, 16:58:41, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5211, loss_cls: 4.1835, loss: 4.1835 +2024-12-28 22:19:57,282 - pyskl - INFO - Epoch [76][600/3746] lr: 4.983e-02, eta: 2 days, 16:57:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5288, loss_cls: 4.1508, loss: 4.1508 +2024-12-28 22:21:22,346 - pyskl - INFO - Epoch [76][700/3746] lr: 4.980e-02, eta: 2 days, 16:55:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5291, loss_cls: 4.1292, loss: 4.1292 +2024-12-28 22:22:47,941 - pyskl - INFO - Epoch [76][800/3746] lr: 4.978e-02, eta: 2 days, 16:54:36, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5291, loss_cls: 4.1614, loss: 4.1614 +2024-12-28 22:24:13,526 - pyskl - INFO - Epoch [76][900/3746] lr: 4.975e-02, eta: 2 days, 16:53:15, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5327, loss_cls: 4.1705, loss: 4.1705 +2024-12-28 22:25:38,369 - pyskl - INFO - Epoch [76][1000/3746] lr: 4.972e-02, eta: 2 days, 16:51:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5337, loss_cls: 4.1605, loss: 4.1605 +2024-12-28 22:27:03,757 - pyskl - INFO - Epoch [76][1100/3746] lr: 4.969e-02, eta: 2 days, 16:50:32, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5222, loss_cls: 4.1988, loss: 4.1988 +2024-12-28 22:28:28,609 - pyskl - INFO - Epoch [76][1200/3746] lr: 4.966e-02, eta: 2 days, 16:49:10, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5239, loss_cls: 4.1804, loss: 4.1804 +2024-12-28 22:29:53,892 - pyskl - INFO - Epoch [76][1300/3746] lr: 4.964e-02, eta: 2 days, 16:47:48, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5336, loss_cls: 4.1497, loss: 4.1497 +2024-12-28 22:31:18,951 - pyskl - INFO - Epoch [76][1400/3746] lr: 4.961e-02, eta: 2 days, 16:46:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5289, loss_cls: 4.1390, loss: 4.1390 +2024-12-28 22:32:44,135 - pyskl - INFO - Epoch [76][1500/3746] lr: 4.958e-02, eta: 2 days, 16:45:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5133, loss_cls: 4.2417, loss: 4.2417 +2024-12-28 22:34:09,946 - pyskl - INFO - Epoch [76][1600/3746] lr: 4.955e-02, eta: 2 days, 16:43:44, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5203, loss_cls: 4.2158, loss: 4.2158 +2024-12-28 22:35:35,489 - pyskl - INFO - Epoch [76][1700/3746] lr: 4.953e-02, eta: 2 days, 16:42:22, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5330, loss_cls: 4.1616, loss: 4.1616 +2024-12-28 22:37:01,103 - pyskl - INFO - Epoch [76][1800/3746] lr: 4.950e-02, eta: 2 days, 16:41:01, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5227, loss_cls: 4.2020, loss: 4.2020 +2024-12-28 22:38:26,709 - pyskl - INFO - Epoch [76][1900/3746] lr: 4.947e-02, eta: 2 days, 16:39:40, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5167, loss_cls: 4.2190, loss: 4.2190 +2024-12-28 22:39:52,099 - pyskl - INFO - Epoch [76][2000/3746] lr: 4.944e-02, eta: 2 days, 16:38:18, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5178, loss_cls: 4.2405, loss: 4.2405 +2024-12-28 22:41:17,668 - pyskl - INFO - Epoch [76][2100/3746] lr: 4.941e-02, eta: 2 days, 16:36:57, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5205, loss_cls: 4.2168, loss: 4.2168 +2024-12-28 22:42:42,780 - pyskl - INFO - Epoch [76][2200/3746] lr: 4.939e-02, eta: 2 days, 16:35:35, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5202, loss_cls: 4.2011, loss: 4.2011 +2024-12-28 22:44:08,087 - pyskl - INFO - Epoch [76][2300/3746] lr: 4.936e-02, eta: 2 days, 16:34:14, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5141, loss_cls: 4.2074, loss: 4.2074 +2024-12-28 22:45:33,354 - pyskl - INFO - Epoch [76][2400/3746] lr: 4.933e-02, eta: 2 days, 16:32:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5183, loss_cls: 4.1883, loss: 4.1883 +2024-12-28 22:46:59,213 - pyskl - INFO - Epoch [76][2500/3746] lr: 4.930e-02, eta: 2 days, 16:31:31, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5084, loss_cls: 4.2329, loss: 4.2329 +2024-12-28 22:48:24,420 - pyskl - INFO - Epoch [76][2600/3746] lr: 4.927e-02, eta: 2 days, 16:30:09, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5261, loss_cls: 4.1967, loss: 4.1967 +2024-12-28 22:49:49,912 - pyskl - INFO - Epoch [76][2700/3746] lr: 4.925e-02, eta: 2 days, 16:28:48, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5266, loss_cls: 4.1643, loss: 4.1643 +2024-12-28 22:51:14,978 - pyskl - INFO - Epoch [76][2800/3746] lr: 4.922e-02, eta: 2 days, 16:27:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5136, loss_cls: 4.2336, loss: 4.2336 +2024-12-28 22:52:39,801 - pyskl - INFO - Epoch [76][2900/3746] lr: 4.919e-02, eta: 2 days, 16:26:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5222, loss_cls: 4.2030, loss: 4.2030 +2024-12-28 22:54:04,839 - pyskl - INFO - Epoch [76][3000/3746] lr: 4.916e-02, eta: 2 days, 16:24:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5283, loss_cls: 4.1615, loss: 4.1615 +2024-12-28 22:55:30,305 - pyskl - INFO - Epoch [76][3100/3746] lr: 4.913e-02, eta: 2 days, 16:23:21, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5139, loss_cls: 4.2037, loss: 4.2037 +2024-12-28 22:56:55,311 - pyskl - INFO - Epoch [76][3200/3746] lr: 4.911e-02, eta: 2 days, 16:21:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5236, loss_cls: 4.1895, loss: 4.1895 +2024-12-28 22:58:20,673 - pyskl - INFO - Epoch [76][3300/3746] lr: 4.908e-02, eta: 2 days, 16:20:37, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5306, loss_cls: 4.1633, loss: 4.1633 +2024-12-28 22:59:45,848 - pyskl - INFO - Epoch [76][3400/3746] lr: 4.905e-02, eta: 2 days, 16:19:16, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5220, loss_cls: 4.2285, loss: 4.2285 +2024-12-28 23:01:10,782 - pyskl - INFO - Epoch [76][3500/3746] lr: 4.902e-02, eta: 2 days, 16:17:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5059, loss_cls: 4.2415, loss: 4.2415 +2024-12-28 23:02:36,321 - pyskl - INFO - Epoch [76][3600/3746] lr: 4.899e-02, eta: 2 days, 16:16:32, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5152, loss_cls: 4.2443, loss: 4.2443 +2024-12-28 23:04:01,681 - pyskl - INFO - Epoch [76][3700/3746] lr: 4.897e-02, eta: 2 days, 16:15:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5191, loss_cls: 4.2130, loss: 4.2130 +2024-12-28 23:04:43,280 - pyskl - INFO - Saving checkpoint at 76 epochs +2024-12-28 23:06:44,247 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 23:06:44,926 - pyskl - INFO - +top1_acc 0.2018 +top5_acc 0.4360 +2024-12-28 23:06:44,927 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 23:06:44,975 - pyskl - INFO - +mean_acc 0.2017 +2024-12-28 23:06:44,994 - pyskl - INFO - Epoch(val) [76][309] top1_acc: 0.2018, top5_acc: 0.4360, mean_class_accuracy: 0.2017 +2024-12-28 23:11:04,292 - pyskl - INFO - Epoch [77][100/3746] lr: 4.893e-02, eta: 2 days, 16:15:23, time: 2.593, data_time: 1.554, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5298, loss_cls: 4.1357, loss: 4.1357 +2024-12-28 23:12:29,287 - pyskl - INFO - Epoch [77][200/3746] lr: 4.890e-02, eta: 2 days, 16:14:01, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5289, loss_cls: 4.1633, loss: 4.1633 +2024-12-28 23:13:54,859 - pyskl - INFO - Epoch [77][300/3746] lr: 4.887e-02, eta: 2 days, 16:12:39, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5341, loss_cls: 4.1496, loss: 4.1496 +2024-12-28 23:15:19,973 - pyskl - INFO - Epoch [77][400/3746] lr: 4.884e-02, eta: 2 days, 16:11:17, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5216, loss_cls: 4.1706, loss: 4.1706 +2024-12-28 23:16:45,272 - pyskl - INFO - Epoch [77][500/3746] lr: 4.881e-02, eta: 2 days, 16:09:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5173, loss_cls: 4.1906, loss: 4.1906 +2024-12-28 23:18:09,994 - pyskl - INFO - Epoch [77][600/3746] lr: 4.879e-02, eta: 2 days, 16:08:33, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5333, loss_cls: 4.1460, loss: 4.1460 +2024-12-28 23:19:35,451 - pyskl - INFO - Epoch [77][700/3746] lr: 4.876e-02, eta: 2 days, 16:07:12, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5217, loss_cls: 4.1410, loss: 4.1410 +2024-12-28 23:21:01,437 - pyskl - INFO - Epoch [77][800/3746] lr: 4.873e-02, eta: 2 days, 16:05:51, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5239, loss_cls: 4.2017, loss: 4.2017 +2024-12-28 23:22:27,211 - pyskl - INFO - Epoch [77][900/3746] lr: 4.870e-02, eta: 2 days, 16:04:29, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5275, loss_cls: 4.1652, loss: 4.1652 +2024-12-28 23:23:52,165 - pyskl - INFO - Epoch [77][1000/3746] lr: 4.867e-02, eta: 2 days, 16:03:07, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5278, loss_cls: 4.1782, loss: 4.1782 +2024-12-28 23:25:17,457 - pyskl - INFO - Epoch [77][1100/3746] lr: 4.865e-02, eta: 2 days, 16:01:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5330, loss_cls: 4.1325, loss: 4.1325 +2024-12-28 23:26:42,334 - pyskl - INFO - Epoch [77][1200/3746] lr: 4.862e-02, eta: 2 days, 16:00:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5255, loss_cls: 4.1695, loss: 4.1695 +2024-12-28 23:28:08,311 - pyskl - INFO - Epoch [77][1300/3746] lr: 4.859e-02, eta: 2 days, 15:59:02, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5186, loss_cls: 4.2061, loss: 4.2061 +2024-12-28 23:29:34,004 - pyskl - INFO - Epoch [77][1400/3746] lr: 4.856e-02, eta: 2 days, 15:57:41, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5219, loss_cls: 4.2055, loss: 4.2055 +2024-12-28 23:30:59,587 - pyskl - INFO - Epoch [77][1500/3746] lr: 4.853e-02, eta: 2 days, 15:56:20, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5191, loss_cls: 4.1425, loss: 4.1425 +2024-12-28 23:32:25,532 - pyskl - INFO - Epoch [77][1600/3746] lr: 4.851e-02, eta: 2 days, 15:54:58, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5306, loss_cls: 4.1484, loss: 4.1484 +2024-12-28 23:33:51,162 - pyskl - INFO - Epoch [77][1700/3746] lr: 4.848e-02, eta: 2 days, 15:53:37, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5253, loss_cls: 4.1356, loss: 4.1356 +2024-12-28 23:35:17,088 - pyskl - INFO - Epoch [77][1800/3746] lr: 4.845e-02, eta: 2 days, 15:52:16, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5302, loss_cls: 4.1389, loss: 4.1389 +2024-12-28 23:36:42,934 - pyskl - INFO - Epoch [77][1900/3746] lr: 4.842e-02, eta: 2 days, 15:50:55, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5145, loss_cls: 4.2280, loss: 4.2280 +2024-12-28 23:38:08,836 - pyskl - INFO - Epoch [77][2000/3746] lr: 4.839e-02, eta: 2 days, 15:49:33, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5214, loss_cls: 4.1853, loss: 4.1853 +2024-12-28 23:39:34,248 - pyskl - INFO - Epoch [77][2100/3746] lr: 4.837e-02, eta: 2 days, 15:48:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5236, loss_cls: 4.1959, loss: 4.1959 +2024-12-28 23:40:59,951 - pyskl - INFO - Epoch [77][2200/3746] lr: 4.834e-02, eta: 2 days, 15:46:50, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5169, loss_cls: 4.2148, loss: 4.2148 +2024-12-28 23:42:25,665 - pyskl - INFO - Epoch [77][2300/3746] lr: 4.831e-02, eta: 2 days, 15:45:29, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5327, loss_cls: 4.1455, loss: 4.1455 +2024-12-28 23:43:50,673 - pyskl - INFO - Epoch [77][2400/3746] lr: 4.828e-02, eta: 2 days, 15:44:07, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5277, loss_cls: 4.1574, loss: 4.1574 +2024-12-28 23:45:15,966 - pyskl - INFO - Epoch [77][2500/3746] lr: 4.825e-02, eta: 2 days, 15:42:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5225, loss_cls: 4.1838, loss: 4.1838 +2024-12-28 23:46:41,007 - pyskl - INFO - Epoch [77][2600/3746] lr: 4.823e-02, eta: 2 days, 15:41:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5150, loss_cls: 4.1977, loss: 4.1977 +2024-12-28 23:48:06,114 - pyskl - INFO - Epoch [77][2700/3746] lr: 4.820e-02, eta: 2 days, 15:40:01, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5284, loss_cls: 4.1585, loss: 4.1585 +2024-12-28 23:49:31,613 - pyskl - INFO - Epoch [77][2800/3746] lr: 4.817e-02, eta: 2 days, 15:38:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5245, loss_cls: 4.1860, loss: 4.1860 +2024-12-28 23:50:57,147 - pyskl - INFO - Epoch [77][2900/3746] lr: 4.814e-02, eta: 2 days, 15:37:18, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5264, loss_cls: 4.1708, loss: 4.1708 +2024-12-28 23:52:22,221 - pyskl - INFO - Epoch [77][3000/3746] lr: 4.811e-02, eta: 2 days, 15:35:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5255, loss_cls: 4.1724, loss: 4.1724 +2024-12-28 23:53:47,031 - pyskl - INFO - Epoch [77][3100/3746] lr: 4.809e-02, eta: 2 days, 15:34:34, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5152, loss_cls: 4.1932, loss: 4.1932 +2024-12-28 23:55:12,080 - pyskl - INFO - Epoch [77][3200/3746] lr: 4.806e-02, eta: 2 days, 15:33:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5178, loss_cls: 4.1994, loss: 4.1994 +2024-12-28 23:56:36,677 - pyskl - INFO - Epoch [77][3300/3746] lr: 4.803e-02, eta: 2 days, 15:31:49, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5277, loss_cls: 4.1641, loss: 4.1641 +2024-12-28 23:58:01,542 - pyskl - INFO - Epoch [77][3400/3746] lr: 4.800e-02, eta: 2 days, 15:30:27, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5269, loss_cls: 4.1531, loss: 4.1531 +2024-12-28 23:59:27,072 - pyskl - INFO - Epoch [77][3500/3746] lr: 4.798e-02, eta: 2 days, 15:29:06, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5211, loss_cls: 4.2275, loss: 4.2275 +2024-12-29 00:00:52,317 - pyskl - INFO - Epoch [77][3600/3746] lr: 4.795e-02, eta: 2 days, 15:27:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5169, loss_cls: 4.1934, loss: 4.1934 +2024-12-29 00:02:17,392 - pyskl - INFO - Epoch [77][3700/3746] lr: 4.792e-02, eta: 2 days, 15:26:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5297, loss_cls: 4.1470, loss: 4.1470 +2024-12-29 00:02:58,636 - pyskl - INFO - Saving checkpoint at 77 epochs +2024-12-29 00:04:59,912 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 00:05:00,692 - pyskl - INFO - +top1_acc 0.1802 +top5_acc 0.3951 +2024-12-29 00:05:00,692 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 00:05:00,730 - pyskl - INFO - +mean_acc 0.1801 +2024-12-29 00:05:00,749 - pyskl - INFO - Epoch(val) [77][309] top1_acc: 0.1802, top5_acc: 0.3951, mean_class_accuracy: 0.1801 +2024-12-29 00:09:20,096 - pyskl - INFO - Epoch [78][100/3746] lr: 4.788e-02, eta: 2 days, 15:26:30, time: 2.593, data_time: 1.546, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5319, loss_cls: 4.1670, loss: 4.1670 +2024-12-29 00:10:45,601 - pyskl - INFO - Epoch [78][200/3746] lr: 4.785e-02, eta: 2 days, 15:25:08, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5286, loss_cls: 4.1354, loss: 4.1354 +2024-12-29 00:12:10,843 - pyskl - INFO - Epoch [78][300/3746] lr: 4.782e-02, eta: 2 days, 15:23:46, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5222, loss_cls: 4.1522, loss: 4.1522 +2024-12-29 00:13:36,176 - pyskl - INFO - Epoch [78][400/3746] lr: 4.779e-02, eta: 2 days, 15:22:25, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5283, loss_cls: 4.1771, loss: 4.1771 +2024-12-29 00:15:01,016 - pyskl - INFO - Epoch [78][500/3746] lr: 4.777e-02, eta: 2 days, 15:21:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5302, loss_cls: 4.1849, loss: 4.1849 +2024-12-29 00:16:26,384 - pyskl - INFO - Epoch [78][600/3746] lr: 4.774e-02, eta: 2 days, 15:19:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5367, loss_cls: 4.0998, loss: 4.0998 +2024-12-29 00:17:51,671 - pyskl - INFO - Epoch [78][700/3746] lr: 4.771e-02, eta: 2 days, 15:18:18, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5314, loss_cls: 4.1464, loss: 4.1464 +2024-12-29 00:19:17,127 - pyskl - INFO - Epoch [78][800/3746] lr: 4.768e-02, eta: 2 days, 15:16:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5298, loss_cls: 4.1287, loss: 4.1287 +2024-12-29 00:20:42,338 - pyskl - INFO - Epoch [78][900/3746] lr: 4.766e-02, eta: 2 days, 15:15:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5286, loss_cls: 4.1354, loss: 4.1354 +2024-12-29 00:22:07,193 - pyskl - INFO - Epoch [78][1000/3746] lr: 4.763e-02, eta: 2 days, 15:14:12, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5216, loss_cls: 4.1989, loss: 4.1989 +2024-12-29 00:23:32,700 - pyskl - INFO - Epoch [78][1100/3746] lr: 4.760e-02, eta: 2 days, 15:12:51, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5291, loss_cls: 4.1212, loss: 4.1212 +2024-12-29 00:24:58,012 - pyskl - INFO - Epoch [78][1200/3746] lr: 4.757e-02, eta: 2 days, 15:11:29, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5236, loss_cls: 4.1849, loss: 4.1849 +2024-12-29 00:26:23,039 - pyskl - INFO - Epoch [78][1300/3746] lr: 4.754e-02, eta: 2 days, 15:10:07, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5280, loss_cls: 4.1499, loss: 4.1499 +2024-12-29 00:27:48,164 - pyskl - INFO - Epoch [78][1400/3746] lr: 4.752e-02, eta: 2 days, 15:08:44, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5389, loss_cls: 4.1114, loss: 4.1114 +2024-12-29 00:29:13,074 - pyskl - INFO - Epoch [78][1500/3746] lr: 4.749e-02, eta: 2 days, 15:07:22, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5283, loss_cls: 4.1503, loss: 4.1503 +2024-12-29 00:30:38,628 - pyskl - INFO - Epoch [78][1600/3746] lr: 4.746e-02, eta: 2 days, 15:06:00, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5311, loss_cls: 4.1477, loss: 4.1477 +2024-12-29 00:32:04,424 - pyskl - INFO - Epoch [78][1700/3746] lr: 4.743e-02, eta: 2 days, 15:04:39, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5245, loss_cls: 4.1744, loss: 4.1744 +2024-12-29 00:33:29,716 - pyskl - INFO - Epoch [78][1800/3746] lr: 4.740e-02, eta: 2 days, 15:03:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5261, loss_cls: 4.1628, loss: 4.1628 +2024-12-29 00:34:54,925 - pyskl - INFO - Epoch [78][1900/3746] lr: 4.738e-02, eta: 2 days, 15:01:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5258, loss_cls: 4.1694, loss: 4.1694 +2024-12-29 00:36:20,096 - pyskl - INFO - Epoch [78][2000/3746] lr: 4.735e-02, eta: 2 days, 15:00:33, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5195, loss_cls: 4.1965, loss: 4.1965 +2024-12-29 00:37:45,668 - pyskl - INFO - Epoch [78][2100/3746] lr: 4.732e-02, eta: 2 days, 14:59:11, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5088, loss_cls: 4.2221, loss: 4.2221 +2024-12-29 00:39:11,636 - pyskl - INFO - Epoch [78][2200/3746] lr: 4.729e-02, eta: 2 days, 14:57:50, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5333, loss_cls: 4.1362, loss: 4.1362 +2024-12-29 00:40:36,371 - pyskl - INFO - Epoch [78][2300/3746] lr: 4.726e-02, eta: 2 days, 14:56:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5441, loss_cls: 4.1095, loss: 4.1095 +2024-12-29 00:42:01,397 - pyskl - INFO - Epoch [78][2400/3746] lr: 4.724e-02, eta: 2 days, 14:55:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5197, loss_cls: 4.2119, loss: 4.2119 +2024-12-29 00:43:26,607 - pyskl - INFO - Epoch [78][2500/3746] lr: 4.721e-02, eta: 2 days, 14:53:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5205, loss_cls: 4.2002, loss: 4.2002 +2024-12-29 00:44:51,858 - pyskl - INFO - Epoch [78][2600/3746] lr: 4.718e-02, eta: 2 days, 14:52:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5286, loss_cls: 4.1322, loss: 4.1322 +2024-12-29 00:46:17,219 - pyskl - INFO - Epoch [78][2700/3746] lr: 4.715e-02, eta: 2 days, 14:50:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5105, loss_cls: 4.2355, loss: 4.2355 +2024-12-29 00:47:42,781 - pyskl - INFO - Epoch [78][2800/3746] lr: 4.712e-02, eta: 2 days, 14:49:38, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5192, loss_cls: 4.1957, loss: 4.1957 +2024-12-29 00:49:08,983 - pyskl - INFO - Epoch [78][2900/3746] lr: 4.710e-02, eta: 2 days, 14:48:17, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5200, loss_cls: 4.2056, loss: 4.2056 +2024-12-29 00:50:35,205 - pyskl - INFO - Epoch [78][3000/3746] lr: 4.707e-02, eta: 2 days, 14:46:55, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5298, loss_cls: 4.1881, loss: 4.1881 +2024-12-29 00:52:00,575 - pyskl - INFO - Epoch [78][3100/3746] lr: 4.704e-02, eta: 2 days, 14:45:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5083, loss_cls: 4.2357, loss: 4.2357 +2024-12-29 00:53:25,500 - pyskl - INFO - Epoch [78][3200/3746] lr: 4.701e-02, eta: 2 days, 14:44:11, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5250, loss_cls: 4.1751, loss: 4.1751 +2024-12-29 00:54:50,513 - pyskl - INFO - Epoch [78][3300/3746] lr: 4.699e-02, eta: 2 days, 14:42:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5162, loss_cls: 4.2050, loss: 4.2050 +2024-12-29 00:56:15,099 - pyskl - INFO - Epoch [78][3400/3746] lr: 4.696e-02, eta: 2 days, 14:41:26, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5200, loss_cls: 4.2223, loss: 4.2223 +2024-12-29 00:57:40,656 - pyskl - INFO - Epoch [78][3500/3746] lr: 4.693e-02, eta: 2 days, 14:40:05, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5333, loss_cls: 4.1111, loss: 4.1111 +2024-12-29 00:59:06,737 - pyskl - INFO - Epoch [78][3600/3746] lr: 4.690e-02, eta: 2 days, 14:38:43, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5355, loss_cls: 4.1214, loss: 4.1214 +2024-12-29 01:00:32,249 - pyskl - INFO - Epoch [78][3700/3746] lr: 4.687e-02, eta: 2 days, 14:37:22, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5212, loss_cls: 4.1960, loss: 4.1960 +2024-12-29 01:01:13,508 - pyskl - INFO - Saving checkpoint at 78 epochs +2024-12-29 01:03:14,282 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 01:03:15,173 - pyskl - INFO - +top1_acc 0.2163 +top5_acc 0.4464 +2024-12-29 01:03:15,173 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 01:03:15,232 - pyskl - INFO - +mean_acc 0.2160 +2024-12-29 01:03:15,237 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_72.pth was removed +2024-12-29 01:03:15,582 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_78.pth. +2024-12-29 01:03:15,583 - pyskl - INFO - Best top1_acc is 0.2163 at 78 epoch. +2024-12-29 01:03:15,597 - pyskl - INFO - Epoch(val) [78][309] top1_acc: 0.2163, top5_acc: 0.4464, mean_class_accuracy: 0.2160 +2024-12-29 01:07:41,025 - pyskl - INFO - Epoch [79][100/3746] lr: 4.683e-02, eta: 2 days, 14:37:32, time: 2.654, data_time: 1.596, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5414, loss_cls: 4.0929, loss: 4.0929 +2024-12-29 01:09:06,104 - pyskl - INFO - Epoch [79][200/3746] lr: 4.680e-02, eta: 2 days, 14:36:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5312, loss_cls: 4.1213, loss: 4.1213 +2024-12-29 01:10:30,930 - pyskl - INFO - Epoch [79][300/3746] lr: 4.678e-02, eta: 2 days, 14:34:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5305, loss_cls: 4.1470, loss: 4.1470 +2024-12-29 01:11:56,079 - pyskl - INFO - Epoch [79][400/3746] lr: 4.675e-02, eta: 2 days, 14:33:25, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5298, loss_cls: 4.1356, loss: 4.1356 +2024-12-29 01:13:20,698 - pyskl - INFO - Epoch [79][500/3746] lr: 4.672e-02, eta: 2 days, 14:32:02, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5275, loss_cls: 4.1698, loss: 4.1698 +2024-12-29 01:14:45,895 - pyskl - INFO - Epoch [79][600/3746] lr: 4.669e-02, eta: 2 days, 14:30:40, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5292, loss_cls: 4.1468, loss: 4.1468 +2024-12-29 01:16:11,109 - pyskl - INFO - Epoch [79][700/3746] lr: 4.667e-02, eta: 2 days, 14:29:18, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5366, loss_cls: 4.1099, loss: 4.1099 +2024-12-29 01:17:35,859 - pyskl - INFO - Epoch [79][800/3746] lr: 4.664e-02, eta: 2 days, 14:27:55, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5366, loss_cls: 4.1200, loss: 4.1200 +2024-12-29 01:19:01,455 - pyskl - INFO - Epoch [79][900/3746] lr: 4.661e-02, eta: 2 days, 14:26:33, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5230, loss_cls: 4.1652, loss: 4.1652 +2024-12-29 01:20:26,658 - pyskl - INFO - Epoch [79][1000/3746] lr: 4.658e-02, eta: 2 days, 14:25:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5336, loss_cls: 4.1100, loss: 4.1100 +2024-12-29 01:21:51,973 - pyskl - INFO - Epoch [79][1100/3746] lr: 4.655e-02, eta: 2 days, 14:23:49, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5212, loss_cls: 4.1537, loss: 4.1537 +2024-12-29 01:23:17,216 - pyskl - INFO - Epoch [79][1200/3746] lr: 4.653e-02, eta: 2 days, 14:22:27, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5233, loss_cls: 4.1630, loss: 4.1630 +2024-12-29 01:24:42,247 - pyskl - INFO - Epoch [79][1300/3746] lr: 4.650e-02, eta: 2 days, 14:21:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5375, loss_cls: 4.1226, loss: 4.1226 +2024-12-29 01:26:07,770 - pyskl - INFO - Epoch [79][1400/3746] lr: 4.647e-02, eta: 2 days, 14:19:43, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5373, loss_cls: 4.1401, loss: 4.1401 +2024-12-29 01:27:32,930 - pyskl - INFO - Epoch [79][1500/3746] lr: 4.644e-02, eta: 2 days, 14:18:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5375, loss_cls: 4.1418, loss: 4.1418 +2024-12-29 01:28:58,144 - pyskl - INFO - Epoch [79][1600/3746] lr: 4.641e-02, eta: 2 days, 14:16:58, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5250, loss_cls: 4.1735, loss: 4.1735 +2024-12-29 01:30:23,192 - pyskl - INFO - Epoch [79][1700/3746] lr: 4.639e-02, eta: 2 days, 14:15:36, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5244, loss_cls: 4.1692, loss: 4.1692 +2024-12-29 01:31:48,588 - pyskl - INFO - Epoch [79][1800/3746] lr: 4.636e-02, eta: 2 days, 14:14:14, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5331, loss_cls: 4.1283, loss: 4.1283 +2024-12-29 01:33:13,803 - pyskl - INFO - Epoch [79][1900/3746] lr: 4.633e-02, eta: 2 days, 14:12:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5275, loss_cls: 4.1833, loss: 4.1833 +2024-12-29 01:34:39,005 - pyskl - INFO - Epoch [79][2000/3746] lr: 4.630e-02, eta: 2 days, 14:11:30, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5217, loss_cls: 4.1567, loss: 4.1567 +2024-12-29 01:36:04,450 - pyskl - INFO - Epoch [79][2100/3746] lr: 4.628e-02, eta: 2 days, 14:10:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5283, loss_cls: 4.1651, loss: 4.1651 +2024-12-29 01:37:29,769 - pyskl - INFO - Epoch [79][2200/3746] lr: 4.625e-02, eta: 2 days, 14:08:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5216, loss_cls: 4.1875, loss: 4.1875 +2024-12-29 01:38:54,362 - pyskl - INFO - Epoch [79][2300/3746] lr: 4.622e-02, eta: 2 days, 14:07:23, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5256, loss_cls: 4.2051, loss: 4.2051 +2024-12-29 01:40:19,688 - pyskl - INFO - Epoch [79][2400/3746] lr: 4.619e-02, eta: 2 days, 14:06:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5361, loss_cls: 4.1258, loss: 4.1258 +2024-12-29 01:41:45,208 - pyskl - INFO - Epoch [79][2500/3746] lr: 4.616e-02, eta: 2 days, 14:04:39, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5214, loss_cls: 4.1570, loss: 4.1570 +2024-12-29 01:43:11,248 - pyskl - INFO - Epoch [79][2600/3746] lr: 4.614e-02, eta: 2 days, 14:03:17, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5259, loss_cls: 4.1662, loss: 4.1662 +2024-12-29 01:44:36,674 - pyskl - INFO - Epoch [79][2700/3746] lr: 4.611e-02, eta: 2 days, 14:01:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5363, loss_cls: 4.1572, loss: 4.1572 +2024-12-29 01:46:02,472 - pyskl - INFO - Epoch [79][2800/3746] lr: 4.608e-02, eta: 2 days, 14:00:34, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5288, loss_cls: 4.1854, loss: 4.1854 +2024-12-29 01:47:28,054 - pyskl - INFO - Epoch [79][2900/3746] lr: 4.605e-02, eta: 2 days, 13:59:12, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5348, loss_cls: 4.1482, loss: 4.1482 +2024-12-29 01:48:53,846 - pyskl - INFO - Epoch [79][3000/3746] lr: 4.602e-02, eta: 2 days, 13:57:50, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5127, loss_cls: 4.2203, loss: 4.2203 +2024-12-29 01:50:19,075 - pyskl - INFO - Epoch [79][3100/3746] lr: 4.600e-02, eta: 2 days, 13:56:28, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5292, loss_cls: 4.1279, loss: 4.1279 +2024-12-29 01:51:44,271 - pyskl - INFO - Epoch [79][3200/3746] lr: 4.597e-02, eta: 2 days, 13:55:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5364, loss_cls: 4.1322, loss: 4.1322 +2024-12-29 01:53:08,943 - pyskl - INFO - Epoch [79][3300/3746] lr: 4.594e-02, eta: 2 days, 13:53:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5278, loss_cls: 4.1819, loss: 4.1819 +2024-12-29 01:54:34,035 - pyskl - INFO - Epoch [79][3400/3746] lr: 4.591e-02, eta: 2 days, 13:52:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5255, loss_cls: 4.2069, loss: 4.2069 +2024-12-29 01:55:58,911 - pyskl - INFO - Epoch [79][3500/3746] lr: 4.588e-02, eta: 2 days, 13:50:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5252, loss_cls: 4.1663, loss: 4.1663 +2024-12-29 01:57:24,036 - pyskl - INFO - Epoch [79][3600/3746] lr: 4.586e-02, eta: 2 days, 13:49:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5262, loss_cls: 4.1535, loss: 4.1535 +2024-12-29 01:58:49,291 - pyskl - INFO - Epoch [79][3700/3746] lr: 4.583e-02, eta: 2 days, 13:48:14, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5273, loss_cls: 4.1730, loss: 4.1730 +2024-12-29 01:59:30,393 - pyskl - INFO - Saving checkpoint at 79 epochs +2024-12-29 02:01:31,870 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 02:01:32,587 - pyskl - INFO - +top1_acc 0.2071 +top5_acc 0.4440 +2024-12-29 02:01:32,588 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 02:01:32,637 - pyskl - INFO - +mean_acc 0.2069 +2024-12-29 02:01:32,652 - pyskl - INFO - Epoch(val) [79][309] top1_acc: 0.2071, top5_acc: 0.4440, mean_class_accuracy: 0.2069 +2024-12-29 02:05:51,003 - pyskl - INFO - Epoch [80][100/3746] lr: 4.579e-02, eta: 2 days, 13:48:14, time: 2.583, data_time: 1.559, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5387, loss_cls: 4.1092, loss: 4.1092 +2024-12-29 02:07:15,726 - pyskl - INFO - Epoch [80][200/3746] lr: 4.576e-02, eta: 2 days, 13:46:51, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5466, loss_cls: 4.0724, loss: 4.0724 +2024-12-29 02:08:41,518 - pyskl - INFO - Epoch [80][300/3746] lr: 4.573e-02, eta: 2 days, 13:45:29, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5363, loss_cls: 4.1413, loss: 4.1413 +2024-12-29 02:10:06,513 - pyskl - INFO - Epoch [80][400/3746] lr: 4.570e-02, eta: 2 days, 13:44:07, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5286, loss_cls: 4.1170, loss: 4.1170 +2024-12-29 02:11:31,486 - pyskl - INFO - Epoch [80][500/3746] lr: 4.568e-02, eta: 2 days, 13:42:44, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5384, loss_cls: 4.0830, loss: 4.0830 +2024-12-29 02:12:56,969 - pyskl - INFO - Epoch [80][600/3746] lr: 4.565e-02, eta: 2 days, 13:41:22, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5336, loss_cls: 4.1318, loss: 4.1318 +2024-12-29 02:14:22,321 - pyskl - INFO - Epoch [80][700/3746] lr: 4.562e-02, eta: 2 days, 13:40:00, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5387, loss_cls: 4.1201, loss: 4.1201 +2024-12-29 02:15:47,825 - pyskl - INFO - Epoch [80][800/3746] lr: 4.559e-02, eta: 2 days, 13:38:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5328, loss_cls: 4.1497, loss: 4.1497 +2024-12-29 02:17:13,329 - pyskl - INFO - Epoch [80][900/3746] lr: 4.557e-02, eta: 2 days, 13:37:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5330, loss_cls: 4.1349, loss: 4.1349 +2024-12-29 02:18:38,982 - pyskl - INFO - Epoch [80][1000/3746] lr: 4.554e-02, eta: 2 days, 13:35:54, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5269, loss_cls: 4.1319, loss: 4.1319 +2024-12-29 02:20:03,890 - pyskl - INFO - Epoch [80][1100/3746] lr: 4.551e-02, eta: 2 days, 13:34:31, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5314, loss_cls: 4.1461, loss: 4.1461 +2024-12-29 02:21:28,728 - pyskl - INFO - Epoch [80][1200/3746] lr: 4.548e-02, eta: 2 days, 13:33:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5281, loss_cls: 4.1318, loss: 4.1318 +2024-12-29 02:22:53,691 - pyskl - INFO - Epoch [80][1300/3746] lr: 4.545e-02, eta: 2 days, 13:31:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5312, loss_cls: 4.1244, loss: 4.1244 +2024-12-29 02:24:18,649 - pyskl - INFO - Epoch [80][1400/3746] lr: 4.543e-02, eta: 2 days, 13:30:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5180, loss_cls: 4.1975, loss: 4.1975 +2024-12-29 02:25:43,592 - pyskl - INFO - Epoch [80][1500/3746] lr: 4.540e-02, eta: 2 days, 13:29:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5316, loss_cls: 4.1417, loss: 4.1417 +2024-12-29 02:27:08,987 - pyskl - INFO - Epoch [80][1600/3746] lr: 4.537e-02, eta: 2 days, 13:27:39, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5300, loss_cls: 4.1526, loss: 4.1526 +2024-12-29 02:28:34,602 - pyskl - INFO - Epoch [80][1700/3746] lr: 4.534e-02, eta: 2 days, 13:26:17, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5228, loss_cls: 4.1781, loss: 4.1781 +2024-12-29 02:29:59,886 - pyskl - INFO - Epoch [80][1800/3746] lr: 4.532e-02, eta: 2 days, 13:24:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5306, loss_cls: 4.1296, loss: 4.1296 +2024-12-29 02:31:25,974 - pyskl - INFO - Epoch [80][1900/3746] lr: 4.529e-02, eta: 2 days, 13:23:33, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5322, loss_cls: 4.1051, loss: 4.1051 +2024-12-29 02:32:51,894 - pyskl - INFO - Epoch [80][2000/3746] lr: 4.526e-02, eta: 2 days, 13:22:11, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5314, loss_cls: 4.1328, loss: 4.1328 +2024-12-29 02:34:17,579 - pyskl - INFO - Epoch [80][2100/3746] lr: 4.523e-02, eta: 2 days, 13:20:49, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5291, loss_cls: 4.1452, loss: 4.1452 +2024-12-29 02:35:43,183 - pyskl - INFO - Epoch [80][2200/3746] lr: 4.520e-02, eta: 2 days, 13:19:27, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5292, loss_cls: 4.1503, loss: 4.1503 +2024-12-29 02:37:09,165 - pyskl - INFO - Epoch [80][2300/3746] lr: 4.518e-02, eta: 2 days, 13:18:05, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5342, loss_cls: 4.1353, loss: 4.1353 +2024-12-29 02:38:34,582 - pyskl - INFO - Epoch [80][2400/3746] lr: 4.515e-02, eta: 2 days, 13:16:43, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5322, loss_cls: 4.1067, loss: 4.1067 +2024-12-29 02:39:59,117 - pyskl - INFO - Epoch [80][2500/3746] lr: 4.512e-02, eta: 2 days, 13:15:20, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5219, loss_cls: 4.1573, loss: 4.1573 +2024-12-29 02:41:23,835 - pyskl - INFO - Epoch [80][2600/3746] lr: 4.509e-02, eta: 2 days, 13:13:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5378, loss_cls: 4.1418, loss: 4.1418 +2024-12-29 02:42:48,558 - pyskl - INFO - Epoch [80][2700/3746] lr: 4.506e-02, eta: 2 days, 13:12:35, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5191, loss_cls: 4.2240, loss: 4.2240 +2024-12-29 02:44:13,685 - pyskl - INFO - Epoch [80][2800/3746] lr: 4.504e-02, eta: 2 days, 13:11:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5238, loss_cls: 4.1978, loss: 4.1978 +2024-12-29 02:45:38,483 - pyskl - INFO - Epoch [80][2900/3746] lr: 4.501e-02, eta: 2 days, 13:09:49, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5309, loss_cls: 4.1225, loss: 4.1225 +2024-12-29 02:47:03,084 - pyskl - INFO - Epoch [80][3000/3746] lr: 4.498e-02, eta: 2 days, 13:08:27, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5297, loss_cls: 4.1265, loss: 4.1265 +2024-12-29 02:48:27,634 - pyskl - INFO - Epoch [80][3100/3746] lr: 4.495e-02, eta: 2 days, 13:07:04, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5236, loss_cls: 4.1523, loss: 4.1523 +2024-12-29 02:49:52,589 - pyskl - INFO - Epoch [80][3200/3746] lr: 4.493e-02, eta: 2 days, 13:05:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5295, loss_cls: 4.1522, loss: 4.1522 +2024-12-29 02:51:16,613 - pyskl - INFO - Epoch [80][3300/3746] lr: 4.490e-02, eta: 2 days, 13:04:18, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5248, loss_cls: 4.1441, loss: 4.1441 +2024-12-29 02:52:41,151 - pyskl - INFO - Epoch [80][3400/3746] lr: 4.487e-02, eta: 2 days, 13:02:55, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5383, loss_cls: 4.1332, loss: 4.1332 +2024-12-29 02:54:05,994 - pyskl - INFO - Epoch [80][3500/3746] lr: 4.484e-02, eta: 2 days, 13:01:32, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5275, loss_cls: 4.1463, loss: 4.1463 +2024-12-29 02:55:30,401 - pyskl - INFO - Epoch [80][3600/3746] lr: 4.481e-02, eta: 2 days, 13:00:09, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5277, loss_cls: 4.1547, loss: 4.1547 +2024-12-29 02:56:55,166 - pyskl - INFO - Epoch [80][3700/3746] lr: 4.479e-02, eta: 2 days, 12:58:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5156, loss_cls: 4.1955, loss: 4.1955 +2024-12-29 02:57:36,318 - pyskl - INFO - Saving checkpoint at 80 epochs +2024-12-29 02:59:36,792 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 02:59:37,486 - pyskl - INFO - +top1_acc 0.2117 +top5_acc 0.4505 +2024-12-29 02:59:37,486 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 02:59:37,538 - pyskl - INFO - +mean_acc 0.2115 +2024-12-29 02:59:37,556 - pyskl - INFO - Epoch(val) [80][309] top1_acc: 0.2117, top5_acc: 0.4505, mean_class_accuracy: 0.2115 +2024-12-29 03:03:53,938 - pyskl - INFO - Epoch [81][100/3746] lr: 4.475e-02, eta: 2 days, 12:58:41, time: 2.564, data_time: 1.524, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5359, loss_cls: 4.0921, loss: 4.0921 +2024-12-29 03:05:19,595 - pyskl - INFO - Epoch [81][200/3746] lr: 4.472e-02, eta: 2 days, 12:57:19, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5442, loss_cls: 4.0820, loss: 4.0820 +2024-12-29 03:06:44,731 - pyskl - INFO - Epoch [81][300/3746] lr: 4.469e-02, eta: 2 days, 12:55:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5359, loss_cls: 4.0904, loss: 4.0904 +2024-12-29 03:08:10,011 - pyskl - INFO - Epoch [81][400/3746] lr: 4.466e-02, eta: 2 days, 12:54:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5428, loss_cls: 4.0773, loss: 4.0773 +2024-12-29 03:09:36,098 - pyskl - INFO - Epoch [81][500/3746] lr: 4.463e-02, eta: 2 days, 12:53:12, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5345, loss_cls: 4.1243, loss: 4.1243 +2024-12-29 03:11:02,031 - pyskl - INFO - Epoch [81][600/3746] lr: 4.461e-02, eta: 2 days, 12:51:51, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5319, loss_cls: 4.1298, loss: 4.1298 +2024-12-29 03:12:27,530 - pyskl - INFO - Epoch [81][700/3746] lr: 4.458e-02, eta: 2 days, 12:50:28, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5345, loss_cls: 4.1202, loss: 4.1202 +2024-12-29 03:13:53,387 - pyskl - INFO - Epoch [81][800/3746] lr: 4.455e-02, eta: 2 days, 12:49:06, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5336, loss_cls: 4.1251, loss: 4.1251 +2024-12-29 03:15:18,835 - pyskl - INFO - Epoch [81][900/3746] lr: 4.452e-02, eta: 2 days, 12:47:44, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5333, loss_cls: 4.1199, loss: 4.1199 +2024-12-29 03:16:43,827 - pyskl - INFO - Epoch [81][1000/3746] lr: 4.450e-02, eta: 2 days, 12:46:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5352, loss_cls: 4.1265, loss: 4.1265 +2024-12-29 03:18:08,472 - pyskl - INFO - Epoch [81][1100/3746] lr: 4.447e-02, eta: 2 days, 12:44:58, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5302, loss_cls: 4.1419, loss: 4.1419 +2024-12-29 03:19:32,967 - pyskl - INFO - Epoch [81][1200/3746] lr: 4.444e-02, eta: 2 days, 12:43:35, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5319, loss_cls: 4.1307, loss: 4.1307 +2024-12-29 03:20:56,934 - pyskl - INFO - Epoch [81][1300/3746] lr: 4.441e-02, eta: 2 days, 12:42:12, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5286, loss_cls: 4.1431, loss: 4.1431 +2024-12-29 03:22:21,040 - pyskl - INFO - Epoch [81][1400/3746] lr: 4.438e-02, eta: 2 days, 12:40:48, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5458, loss_cls: 4.0800, loss: 4.0800 +2024-12-29 03:23:45,684 - pyskl - INFO - Epoch [81][1500/3746] lr: 4.436e-02, eta: 2 days, 12:39:25, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5406, loss_cls: 4.0798, loss: 4.0798 +2024-12-29 03:25:10,197 - pyskl - INFO - Epoch [81][1600/3746] lr: 4.433e-02, eta: 2 days, 12:38:02, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5437, loss_cls: 4.0490, loss: 4.0490 +2024-12-29 03:26:34,830 - pyskl - INFO - Epoch [81][1700/3746] lr: 4.430e-02, eta: 2 days, 12:36:39, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5356, loss_cls: 4.1100, loss: 4.1100 +2024-12-29 03:27:59,009 - pyskl - INFO - Epoch [81][1800/3746] lr: 4.427e-02, eta: 2 days, 12:35:16, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5361, loss_cls: 4.1304, loss: 4.1304 +2024-12-29 03:29:23,159 - pyskl - INFO - Epoch [81][1900/3746] lr: 4.425e-02, eta: 2 days, 12:33:52, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5227, loss_cls: 4.1705, loss: 4.1705 +2024-12-29 03:30:47,823 - pyskl - INFO - Epoch [81][2000/3746] lr: 4.422e-02, eta: 2 days, 12:32:29, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5323, loss_cls: 4.1121, loss: 4.1121 +2024-12-29 03:32:12,640 - pyskl - INFO - Epoch [81][2100/3746] lr: 4.419e-02, eta: 2 days, 12:31:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5323, loss_cls: 4.1279, loss: 4.1279 +2024-12-29 03:33:37,808 - pyskl - INFO - Epoch [81][2200/3746] lr: 4.416e-02, eta: 2 days, 12:29:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5289, loss_cls: 4.1215, loss: 4.1215 +2024-12-29 03:35:02,824 - pyskl - INFO - Epoch [81][2300/3746] lr: 4.413e-02, eta: 2 days, 12:28:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5281, loss_cls: 4.1391, loss: 4.1391 +2024-12-29 03:36:27,695 - pyskl - INFO - Epoch [81][2400/3746] lr: 4.411e-02, eta: 2 days, 12:26:59, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5234, loss_cls: 4.1634, loss: 4.1634 +2024-12-29 03:37:53,099 - pyskl - INFO - Epoch [81][2500/3746] lr: 4.408e-02, eta: 2 days, 12:25:36, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5314, loss_cls: 4.1246, loss: 4.1246 +2024-12-29 03:39:18,457 - pyskl - INFO - Epoch [81][2600/3746] lr: 4.405e-02, eta: 2 days, 12:24:14, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5341, loss_cls: 4.0984, loss: 4.0984 +2024-12-29 03:40:43,148 - pyskl - INFO - Epoch [81][2700/3746] lr: 4.402e-02, eta: 2 days, 12:22:51, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5359, loss_cls: 4.1200, loss: 4.1200 +2024-12-29 03:42:08,258 - pyskl - INFO - Epoch [81][2800/3746] lr: 4.400e-02, eta: 2 days, 12:21:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5183, loss_cls: 4.2022, loss: 4.2022 +2024-12-29 03:43:33,157 - pyskl - INFO - Epoch [81][2900/3746] lr: 4.397e-02, eta: 2 days, 12:20:05, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5153, loss_cls: 4.2019, loss: 4.2019 +2024-12-29 03:44:58,029 - pyskl - INFO - Epoch [81][3000/3746] lr: 4.394e-02, eta: 2 days, 12:18:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5337, loss_cls: 4.1485, loss: 4.1485 +2024-12-29 03:46:22,560 - pyskl - INFO - Epoch [81][3100/3746] lr: 4.391e-02, eta: 2 days, 12:17:20, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5392, loss_cls: 4.1239, loss: 4.1239 +2024-12-29 03:47:47,250 - pyskl - INFO - Epoch [81][3200/3746] lr: 4.389e-02, eta: 2 days, 12:15:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5363, loss_cls: 4.1164, loss: 4.1164 +2024-12-29 03:49:11,451 - pyskl - INFO - Epoch [81][3300/3746] lr: 4.386e-02, eta: 2 days, 12:14:33, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5314, loss_cls: 4.1422, loss: 4.1422 +2024-12-29 03:50:35,562 - pyskl - INFO - Epoch [81][3400/3746] lr: 4.383e-02, eta: 2 days, 12:13:10, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5344, loss_cls: 4.1174, loss: 4.1174 +2024-12-29 03:52:00,262 - pyskl - INFO - Epoch [81][3500/3746] lr: 4.380e-02, eta: 2 days, 12:11:47, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5281, loss_cls: 4.1611, loss: 4.1611 +2024-12-29 03:53:24,793 - pyskl - INFO - Epoch [81][3600/3746] lr: 4.377e-02, eta: 2 days, 12:10:24, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5255, loss_cls: 4.1628, loss: 4.1628 +2024-12-29 03:54:49,308 - pyskl - INFO - Epoch [81][3700/3746] lr: 4.375e-02, eta: 2 days, 12:09:01, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5355, loss_cls: 4.0971, loss: 4.0971 +2024-12-29 03:55:29,905 - pyskl - INFO - Saving checkpoint at 81 epochs +2024-12-29 03:57:29,266 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 03:57:29,985 - pyskl - INFO - +top1_acc 0.2225 +top5_acc 0.4557 +2024-12-29 03:57:29,986 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 03:57:30,031 - pyskl - INFO - +mean_acc 0.2223 +2024-12-29 03:57:30,036 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_78.pth was removed +2024-12-29 03:57:30,301 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_81.pth. +2024-12-29 03:57:30,302 - pyskl - INFO - Best top1_acc is 0.2225 at 81 epoch. +2024-12-29 03:57:30,317 - pyskl - INFO - Epoch(val) [81][309] top1_acc: 0.2225, top5_acc: 0.4557, mean_class_accuracy: 0.2223 +2024-12-29 04:01:44,408 - pyskl - INFO - Epoch [82][100/3746] lr: 4.371e-02, eta: 2 days, 12:08:50, time: 2.541, data_time: 1.513, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5448, loss_cls: 4.0521, loss: 4.0521 +2024-12-29 04:03:09,586 - pyskl - INFO - Epoch [82][200/3746] lr: 4.368e-02, eta: 2 days, 12:07:28, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5414, loss_cls: 4.1115, loss: 4.1115 +2024-12-29 04:04:35,016 - pyskl - INFO - Epoch [82][300/3746] lr: 4.365e-02, eta: 2 days, 12:06:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5350, loss_cls: 4.1215, loss: 4.1215 +2024-12-29 04:05:59,991 - pyskl - INFO - Epoch [82][400/3746] lr: 4.362e-02, eta: 2 days, 12:04:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5444, loss_cls: 4.0694, loss: 4.0694 +2024-12-29 04:07:25,100 - pyskl - INFO - Epoch [82][500/3746] lr: 4.359e-02, eta: 2 days, 12:03:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5445, loss_cls: 4.0810, loss: 4.0810 +2024-12-29 04:08:50,292 - pyskl - INFO - Epoch [82][600/3746] lr: 4.357e-02, eta: 2 days, 12:01:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5341, loss_cls: 4.1192, loss: 4.1192 +2024-12-29 04:10:15,614 - pyskl - INFO - Epoch [82][700/3746] lr: 4.354e-02, eta: 2 days, 12:00:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5330, loss_cls: 4.1128, loss: 4.1128 +2024-12-29 04:11:40,991 - pyskl - INFO - Epoch [82][800/3746] lr: 4.351e-02, eta: 2 days, 11:59:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5391, loss_cls: 4.0859, loss: 4.0859 +2024-12-29 04:13:05,999 - pyskl - INFO - Epoch [82][900/3746] lr: 4.348e-02, eta: 2 days, 11:57:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5430, loss_cls: 4.0863, loss: 4.0863 +2024-12-29 04:14:31,412 - pyskl - INFO - Epoch [82][1000/3746] lr: 4.346e-02, eta: 2 days, 11:56:27, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5359, loss_cls: 4.1066, loss: 4.1066 +2024-12-29 04:15:55,911 - pyskl - INFO - Epoch [82][1100/3746] lr: 4.343e-02, eta: 2 days, 11:55:04, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5395, loss_cls: 4.0646, loss: 4.0646 +2024-12-29 04:17:20,655 - pyskl - INFO - Epoch [82][1200/3746] lr: 4.340e-02, eta: 2 days, 11:53:41, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5331, loss_cls: 4.1027, loss: 4.1027 +2024-12-29 04:18:45,640 - pyskl - INFO - Epoch [82][1300/3746] lr: 4.337e-02, eta: 2 days, 11:52:18, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5334, loss_cls: 4.1144, loss: 4.1144 +2024-12-29 04:20:10,093 - pyskl - INFO - Epoch [82][1400/3746] lr: 4.335e-02, eta: 2 days, 11:50:55, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5411, loss_cls: 4.0695, loss: 4.0695 +2024-12-29 04:21:34,957 - pyskl - INFO - Epoch [82][1500/3746] lr: 4.332e-02, eta: 2 days, 11:49:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5286, loss_cls: 4.1060, loss: 4.1060 +2024-12-29 04:22:59,638 - pyskl - INFO - Epoch [82][1600/3746] lr: 4.329e-02, eta: 2 days, 11:48:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5347, loss_cls: 4.1097, loss: 4.1097 +2024-12-29 04:24:24,508 - pyskl - INFO - Epoch [82][1700/3746] lr: 4.326e-02, eta: 2 days, 11:46:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5372, loss_cls: 4.1079, loss: 4.1079 +2024-12-29 04:25:49,054 - pyskl - INFO - Epoch [82][1800/3746] lr: 4.323e-02, eta: 2 days, 11:45:23, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5364, loss_cls: 4.1064, loss: 4.1064 +2024-12-29 04:27:13,933 - pyskl - INFO - Epoch [82][1900/3746] lr: 4.321e-02, eta: 2 days, 11:44:00, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5469, loss_cls: 4.0407, loss: 4.0407 +2024-12-29 04:28:39,271 - pyskl - INFO - Epoch [82][2000/3746] lr: 4.318e-02, eta: 2 days, 11:42:37, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5292, loss_cls: 4.1508, loss: 4.1508 +2024-12-29 04:30:04,961 - pyskl - INFO - Epoch [82][2100/3746] lr: 4.315e-02, eta: 2 days, 11:41:15, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5353, loss_cls: 4.1007, loss: 4.1007 +2024-12-29 04:31:30,439 - pyskl - INFO - Epoch [82][2200/3746] lr: 4.312e-02, eta: 2 days, 11:39:52, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5444, loss_cls: 4.1047, loss: 4.1047 +2024-12-29 04:32:55,928 - pyskl - INFO - Epoch [82][2300/3746] lr: 4.310e-02, eta: 2 days, 11:38:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5270, loss_cls: 4.1290, loss: 4.1290 +2024-12-29 04:34:21,294 - pyskl - INFO - Epoch [82][2400/3746] lr: 4.307e-02, eta: 2 days, 11:37:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5319, loss_cls: 4.1229, loss: 4.1229 +2024-12-29 04:35:46,112 - pyskl - INFO - Epoch [82][2500/3746] lr: 4.304e-02, eta: 2 days, 11:35:45, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5277, loss_cls: 4.1506, loss: 4.1506 +2024-12-29 04:37:10,801 - pyskl - INFO - Epoch [82][2600/3746] lr: 4.301e-02, eta: 2 days, 11:34:21, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5314, loss_cls: 4.1389, loss: 4.1389 +2024-12-29 04:38:36,444 - pyskl - INFO - Epoch [82][2700/3746] lr: 4.299e-02, eta: 2 days, 11:32:59, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5289, loss_cls: 4.1646, loss: 4.1646 +2024-12-29 04:40:01,690 - pyskl - INFO - Epoch [82][2800/3746] lr: 4.296e-02, eta: 2 days, 11:31:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5402, loss_cls: 4.1066, loss: 4.1066 +2024-12-29 04:41:26,667 - pyskl - INFO - Epoch [82][2900/3746] lr: 4.293e-02, eta: 2 days, 11:30:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5284, loss_cls: 4.1211, loss: 4.1211 +2024-12-29 04:42:52,074 - pyskl - INFO - Epoch [82][3000/3746] lr: 4.290e-02, eta: 2 days, 11:28:51, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5220, loss_cls: 4.1683, loss: 4.1683 +2024-12-29 04:44:16,793 - pyskl - INFO - Epoch [82][3100/3746] lr: 4.287e-02, eta: 2 days, 11:27:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5370, loss_cls: 4.1114, loss: 4.1114 +2024-12-29 04:45:41,850 - pyskl - INFO - Epoch [82][3200/3746] lr: 4.285e-02, eta: 2 days, 11:26:05, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5262, loss_cls: 4.1593, loss: 4.1593 +2024-12-29 04:47:06,275 - pyskl - INFO - Epoch [82][3300/3746] lr: 4.282e-02, eta: 2 days, 11:24:42, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5431, loss_cls: 4.0811, loss: 4.0811 +2024-12-29 04:48:31,269 - pyskl - INFO - Epoch [82][3400/3746] lr: 4.279e-02, eta: 2 days, 11:23:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5262, loss_cls: 4.1672, loss: 4.1672 +2024-12-29 04:49:56,551 - pyskl - INFO - Epoch [82][3500/3746] lr: 4.276e-02, eta: 2 days, 11:21:57, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5267, loss_cls: 4.1468, loss: 4.1468 +2024-12-29 04:51:21,606 - pyskl - INFO - Epoch [82][3600/3746] lr: 4.274e-02, eta: 2 days, 11:20:34, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5334, loss_cls: 4.0826, loss: 4.0826 +2024-12-29 04:52:45,978 - pyskl - INFO - Epoch [82][3700/3746] lr: 4.271e-02, eta: 2 days, 11:19:10, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5305, loss_cls: 4.1469, loss: 4.1469 +2024-12-29 04:53:26,649 - pyskl - INFO - Saving checkpoint at 82 epochs +2024-12-29 04:55:26,955 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 04:55:27,888 - pyskl - INFO - +top1_acc 0.2292 +top5_acc 0.4675 +2024-12-29 04:55:27,889 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 04:55:27,960 - pyskl - INFO - +mean_acc 0.2290 +2024-12-29 04:55:27,968 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_81.pth was removed +2024-12-29 04:55:28,252 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_82.pth. +2024-12-29 04:55:28,253 - pyskl - INFO - Best top1_acc is 0.2292 at 82 epoch. +2024-12-29 04:55:28,273 - pyskl - INFO - Epoch(val) [82][309] top1_acc: 0.2292, top5_acc: 0.4675, mean_class_accuracy: 0.2290 +2024-12-29 04:59:46,447 - pyskl - INFO - Epoch [83][100/3746] lr: 4.267e-02, eta: 2 days, 11:19:01, time: 2.582, data_time: 1.545, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5445, loss_cls: 4.0391, loss: 4.0391 +2024-12-29 05:01:12,324 - pyskl - INFO - Epoch [83][200/3746] lr: 4.264e-02, eta: 2 days, 11:17:38, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5395, loss_cls: 4.0444, loss: 4.0444 +2024-12-29 05:02:37,836 - pyskl - INFO - Epoch [83][300/3746] lr: 4.261e-02, eta: 2 days, 11:16:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5489, loss_cls: 4.0412, loss: 4.0412 +2024-12-29 05:04:03,800 - pyskl - INFO - Epoch [83][400/3746] lr: 4.259e-02, eta: 2 days, 11:14:54, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5330, loss_cls: 4.0957, loss: 4.0957 +2024-12-29 05:05:29,850 - pyskl - INFO - Epoch [83][500/3746] lr: 4.256e-02, eta: 2 days, 11:13:32, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5414, loss_cls: 4.1021, loss: 4.1021 +2024-12-29 05:06:55,952 - pyskl - INFO - Epoch [83][600/3746] lr: 4.253e-02, eta: 2 days, 11:12:10, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5466, loss_cls: 4.0722, loss: 4.0722 +2024-12-29 05:08:21,653 - pyskl - INFO - Epoch [83][700/3746] lr: 4.250e-02, eta: 2 days, 11:10:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5327, loss_cls: 4.1077, loss: 4.1077 +2024-12-29 05:09:47,625 - pyskl - INFO - Epoch [83][800/3746] lr: 4.247e-02, eta: 2 days, 11:09:25, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5359, loss_cls: 4.1169, loss: 4.1169 +2024-12-29 05:11:13,901 - pyskl - INFO - Epoch [83][900/3746] lr: 4.245e-02, eta: 2 days, 11:08:03, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5434, loss_cls: 4.0944, loss: 4.0944 +2024-12-29 05:12:39,516 - pyskl - INFO - Epoch [83][1000/3746] lr: 4.242e-02, eta: 2 days, 11:06:41, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5405, loss_cls: 4.0891, loss: 4.0891 +2024-12-29 05:14:05,225 - pyskl - INFO - Epoch [83][1100/3746] lr: 4.239e-02, eta: 2 days, 11:05:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5531, loss_cls: 4.0310, loss: 4.0310 +2024-12-29 05:15:29,652 - pyskl - INFO - Epoch [83][1200/3746] lr: 4.236e-02, eta: 2 days, 11:03:55, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5450, loss_cls: 4.0381, loss: 4.0381 +2024-12-29 05:16:54,509 - pyskl - INFO - Epoch [83][1300/3746] lr: 4.234e-02, eta: 2 days, 11:02:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5291, loss_cls: 4.1301, loss: 4.1301 +2024-12-29 05:18:20,042 - pyskl - INFO - Epoch [83][1400/3746] lr: 4.231e-02, eta: 2 days, 11:01:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5322, loss_cls: 4.1171, loss: 4.1171 +2024-12-29 05:19:45,017 - pyskl - INFO - Epoch [83][1500/3746] lr: 4.228e-02, eta: 2 days, 10:59:47, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5448, loss_cls: 4.0410, loss: 4.0410 +2024-12-29 05:21:09,709 - pyskl - INFO - Epoch [83][1600/3746] lr: 4.225e-02, eta: 2 days, 10:58:23, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5452, loss_cls: 4.0606, loss: 4.0606 +2024-12-29 05:22:34,980 - pyskl - INFO - Epoch [83][1700/3746] lr: 4.223e-02, eta: 2 days, 10:57:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5300, loss_cls: 4.1438, loss: 4.1438 +2024-12-29 05:24:00,140 - pyskl - INFO - Epoch [83][1800/3746] lr: 4.220e-02, eta: 2 days, 10:55:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5312, loss_cls: 4.1489, loss: 4.1489 +2024-12-29 05:25:25,935 - pyskl - INFO - Epoch [83][1900/3746] lr: 4.217e-02, eta: 2 days, 10:54:15, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5345, loss_cls: 4.1184, loss: 4.1184 +2024-12-29 05:26:51,040 - pyskl - INFO - Epoch [83][2000/3746] lr: 4.214e-02, eta: 2 days, 10:52:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5337, loss_cls: 4.1118, loss: 4.1118 +2024-12-29 05:28:16,134 - pyskl - INFO - Epoch [83][2100/3746] lr: 4.212e-02, eta: 2 days, 10:51:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5361, loss_cls: 4.1210, loss: 4.1210 +2024-12-29 05:29:41,204 - pyskl - INFO - Epoch [83][2200/3746] lr: 4.209e-02, eta: 2 days, 10:50:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5367, loss_cls: 4.1068, loss: 4.1068 +2024-12-29 05:31:06,177 - pyskl - INFO - Epoch [83][2300/3746] lr: 4.206e-02, eta: 2 days, 10:48:44, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5252, loss_cls: 4.1570, loss: 4.1570 +2024-12-29 05:32:31,348 - pyskl - INFO - Epoch [83][2400/3746] lr: 4.203e-02, eta: 2 days, 10:47:21, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5420, loss_cls: 4.1103, loss: 4.1103 +2024-12-29 05:33:55,460 - pyskl - INFO - Epoch [83][2500/3746] lr: 4.201e-02, eta: 2 days, 10:45:57, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5280, loss_cls: 4.1284, loss: 4.1284 +2024-12-29 05:35:19,675 - pyskl - INFO - Epoch [83][2600/3746] lr: 4.198e-02, eta: 2 days, 10:44:34, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5381, loss_cls: 4.1000, loss: 4.1000 +2024-12-29 05:36:44,720 - pyskl - INFO - Epoch [83][2700/3746] lr: 4.195e-02, eta: 2 days, 10:43:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5347, loss_cls: 4.0967, loss: 4.0967 +2024-12-29 05:38:09,788 - pyskl - INFO - Epoch [83][2800/3746] lr: 4.192e-02, eta: 2 days, 10:41:48, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5344, loss_cls: 4.1194, loss: 4.1194 +2024-12-29 05:39:34,465 - pyskl - INFO - Epoch [83][2900/3746] lr: 4.190e-02, eta: 2 days, 10:40:25, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5394, loss_cls: 4.1023, loss: 4.1023 +2024-12-29 05:40:59,553 - pyskl - INFO - Epoch [83][3000/3746] lr: 4.187e-02, eta: 2 days, 10:39:02, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5245, loss_cls: 4.1658, loss: 4.1658 +2024-12-29 05:42:23,860 - pyskl - INFO - Epoch [83][3100/3746] lr: 4.184e-02, eta: 2 days, 10:37:38, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5484, loss_cls: 4.0782, loss: 4.0782 +2024-12-29 05:43:48,429 - pyskl - INFO - Epoch [83][3200/3746] lr: 4.181e-02, eta: 2 days, 10:36:15, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5361, loss_cls: 4.1194, loss: 4.1194 +2024-12-29 05:45:12,915 - pyskl - INFO - Epoch [83][3300/3746] lr: 4.178e-02, eta: 2 days, 10:34:52, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5330, loss_cls: 4.1131, loss: 4.1131 +2024-12-29 05:46:37,793 - pyskl - INFO - Epoch [83][3400/3746] lr: 4.176e-02, eta: 2 days, 10:33:29, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5408, loss_cls: 4.1084, loss: 4.1084 +2024-12-29 05:48:02,955 - pyskl - INFO - Epoch [83][3500/3746] lr: 4.173e-02, eta: 2 days, 10:32:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5436, loss_cls: 4.0790, loss: 4.0790 +2024-12-29 05:49:27,728 - pyskl - INFO - Epoch [83][3600/3746] lr: 4.170e-02, eta: 2 days, 10:30:43, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5247, loss_cls: 4.1596, loss: 4.1596 +2024-12-29 05:50:52,415 - pyskl - INFO - Epoch [83][3700/3746] lr: 4.167e-02, eta: 2 days, 10:29:19, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5330, loss_cls: 4.0967, loss: 4.0967 +2024-12-29 05:51:33,417 - pyskl - INFO - Saving checkpoint at 83 epochs +2024-12-29 05:53:32,156 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 05:53:32,910 - pyskl - INFO - +top1_acc 0.2318 +top5_acc 0.4640 +2024-12-29 05:53:32,911 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 05:53:32,969 - pyskl - INFO - +mean_acc 0.2317 +2024-12-29 05:53:32,975 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_82.pth was removed +2024-12-29 05:53:33,258 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_83.pth. +2024-12-29 05:53:33,259 - pyskl - INFO - Best top1_acc is 0.2318 at 83 epoch. +2024-12-29 05:53:33,277 - pyskl - INFO - Epoch(val) [83][309] top1_acc: 0.2318, top5_acc: 0.4640, mean_class_accuracy: 0.2317 +2024-12-29 05:57:47,361 - pyskl - INFO - Epoch [84][100/3746] lr: 4.163e-02, eta: 2 days, 10:29:03, time: 2.541, data_time: 1.504, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5541, loss_cls: 4.0218, loss: 4.0218 +2024-12-29 05:59:12,868 - pyskl - INFO - Epoch [84][200/3746] lr: 4.161e-02, eta: 2 days, 10:27:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5452, loss_cls: 4.0763, loss: 4.0763 +2024-12-29 06:00:37,964 - pyskl - INFO - Epoch [84][300/3746] lr: 4.158e-02, eta: 2 days, 10:26:17, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5570, loss_cls: 4.0282, loss: 4.0282 +2024-12-29 06:02:03,282 - pyskl - INFO - Epoch [84][400/3746] lr: 4.155e-02, eta: 2 days, 10:24:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5425, loss_cls: 4.0372, loss: 4.0372 +2024-12-29 06:03:28,485 - pyskl - INFO - Epoch [84][500/3746] lr: 4.152e-02, eta: 2 days, 10:23:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5552, loss_cls: 4.0320, loss: 4.0320 +2024-12-29 06:04:53,872 - pyskl - INFO - Epoch [84][600/3746] lr: 4.150e-02, eta: 2 days, 10:22:09, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5427, loss_cls: 4.0853, loss: 4.0853 +2024-12-29 06:06:19,359 - pyskl - INFO - Epoch [84][700/3746] lr: 4.147e-02, eta: 2 days, 10:20:46, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5419, loss_cls: 4.0799, loss: 4.0799 +2024-12-29 06:07:45,203 - pyskl - INFO - Epoch [84][800/3746] lr: 4.144e-02, eta: 2 days, 10:19:24, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5459, loss_cls: 4.0570, loss: 4.0570 +2024-12-29 06:09:10,796 - pyskl - INFO - Epoch [84][900/3746] lr: 4.141e-02, eta: 2 days, 10:18:01, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5459, loss_cls: 4.0693, loss: 4.0693 +2024-12-29 06:10:35,434 - pyskl - INFO - Epoch [84][1000/3746] lr: 4.139e-02, eta: 2 days, 10:16:38, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5373, loss_cls: 4.1131, loss: 4.1131 +2024-12-29 06:12:00,135 - pyskl - INFO - Epoch [84][1100/3746] lr: 4.136e-02, eta: 2 days, 10:15:15, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5306, loss_cls: 4.1495, loss: 4.1495 +2024-12-29 06:13:23,909 - pyskl - INFO - Epoch [84][1200/3746] lr: 4.133e-02, eta: 2 days, 10:13:51, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5425, loss_cls: 4.0941, loss: 4.0941 +2024-12-29 06:14:48,051 - pyskl - INFO - Epoch [84][1300/3746] lr: 4.130e-02, eta: 2 days, 10:12:27, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5502, loss_cls: 4.0371, loss: 4.0371 +2024-12-29 06:16:12,091 - pyskl - INFO - Epoch [84][1400/3746] lr: 4.128e-02, eta: 2 days, 10:11:03, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5405, loss_cls: 4.0591, loss: 4.0591 +2024-12-29 06:17:36,022 - pyskl - INFO - Epoch [84][1500/3746] lr: 4.125e-02, eta: 2 days, 10:09:39, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5370, loss_cls: 4.1038, loss: 4.1038 +2024-12-29 06:19:00,321 - pyskl - INFO - Epoch [84][1600/3746] lr: 4.122e-02, eta: 2 days, 10:08:15, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5323, loss_cls: 4.1146, loss: 4.1146 +2024-12-29 06:20:24,903 - pyskl - INFO - Epoch [84][1700/3746] lr: 4.119e-02, eta: 2 days, 10:06:52, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5378, loss_cls: 4.0860, loss: 4.0860 +2024-12-29 06:21:49,421 - pyskl - INFO - Epoch [84][1800/3746] lr: 4.117e-02, eta: 2 days, 10:05:29, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5391, loss_cls: 4.0907, loss: 4.0907 +2024-12-29 06:23:14,692 - pyskl - INFO - Epoch [84][1900/3746] lr: 4.114e-02, eta: 2 days, 10:04:06, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5363, loss_cls: 4.1189, loss: 4.1189 +2024-12-29 06:24:40,097 - pyskl - INFO - Epoch [84][2000/3746] lr: 4.111e-02, eta: 2 days, 10:02:43, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5359, loss_cls: 4.1161, loss: 4.1161 +2024-12-29 06:26:04,901 - pyskl - INFO - Epoch [84][2100/3746] lr: 4.108e-02, eta: 2 days, 10:01:20, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5377, loss_cls: 4.1364, loss: 4.1364 +2024-12-29 06:27:30,440 - pyskl - INFO - Epoch [84][2200/3746] lr: 4.106e-02, eta: 2 days, 9:59:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5386, loss_cls: 4.1233, loss: 4.1233 +2024-12-29 06:28:55,935 - pyskl - INFO - Epoch [84][2300/3746] lr: 4.103e-02, eta: 2 days, 9:58:34, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5505, loss_cls: 4.0357, loss: 4.0357 +2024-12-29 06:30:20,901 - pyskl - INFO - Epoch [84][2400/3746] lr: 4.100e-02, eta: 2 days, 9:57:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5250, loss_cls: 4.1683, loss: 4.1683 +2024-12-29 06:31:46,639 - pyskl - INFO - Epoch [84][2500/3746] lr: 4.097e-02, eta: 2 days, 9:55:49, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5389, loss_cls: 4.0795, loss: 4.0795 +2024-12-29 06:33:11,293 - pyskl - INFO - Epoch [84][2600/3746] lr: 4.095e-02, eta: 2 days, 9:54:25, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5422, loss_cls: 4.0817, loss: 4.0817 +2024-12-29 06:34:36,628 - pyskl - INFO - Epoch [84][2700/3746] lr: 4.092e-02, eta: 2 days, 9:53:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5316, loss_cls: 4.1129, loss: 4.1129 +2024-12-29 06:36:01,533 - pyskl - INFO - Epoch [84][2800/3746] lr: 4.089e-02, eta: 2 days, 9:51:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5411, loss_cls: 4.1075, loss: 4.1075 +2024-12-29 06:37:26,756 - pyskl - INFO - Epoch [84][2900/3746] lr: 4.086e-02, eta: 2 days, 9:50:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5259, loss_cls: 4.1221, loss: 4.1221 +2024-12-29 06:38:52,075 - pyskl - INFO - Epoch [84][3000/3746] lr: 4.084e-02, eta: 2 days, 9:48:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5427, loss_cls: 4.0910, loss: 4.0910 +2024-12-29 06:40:16,994 - pyskl - INFO - Epoch [84][3100/3746] lr: 4.081e-02, eta: 2 days, 9:47:31, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5320, loss_cls: 4.1039, loss: 4.1039 +2024-12-29 06:41:41,792 - pyskl - INFO - Epoch [84][3200/3746] lr: 4.078e-02, eta: 2 days, 9:46:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5558, loss_cls: 4.0370, loss: 4.0370 +2024-12-29 06:43:06,909 - pyskl - INFO - Epoch [84][3300/3746] lr: 4.075e-02, eta: 2 days, 9:44:44, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5358, loss_cls: 4.1035, loss: 4.1035 +2024-12-29 06:44:31,609 - pyskl - INFO - Epoch [84][3400/3746] lr: 4.073e-02, eta: 2 days, 9:43:21, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5511, loss_cls: 4.0495, loss: 4.0495 +2024-12-29 06:45:56,218 - pyskl - INFO - Epoch [84][3500/3746] lr: 4.070e-02, eta: 2 days, 9:41:58, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5328, loss_cls: 4.1335, loss: 4.1335 +2024-12-29 06:47:20,557 - pyskl - INFO - Epoch [84][3600/3746] lr: 4.067e-02, eta: 2 days, 9:40:34, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5358, loss_cls: 4.1303, loss: 4.1303 +2024-12-29 06:48:45,093 - pyskl - INFO - Epoch [84][3700/3746] lr: 4.064e-02, eta: 2 days, 9:39:11, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5339, loss_cls: 4.1136, loss: 4.1136 +2024-12-29 06:49:25,768 - pyskl - INFO - Saving checkpoint at 84 epochs +2024-12-29 06:51:25,163 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 06:51:25,954 - pyskl - INFO - +top1_acc 0.2334 +top5_acc 0.4726 +2024-12-29 06:51:25,955 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 06:51:26,023 - pyskl - INFO - +mean_acc 0.2332 +2024-12-29 06:51:26,029 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_83.pth was removed +2024-12-29 06:51:26,399 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_84.pth. +2024-12-29 06:51:26,400 - pyskl - INFO - Best top1_acc is 0.2334 at 84 epoch. +2024-12-29 06:51:26,430 - pyskl - INFO - Epoch(val) [84][309] top1_acc: 0.2334, top5_acc: 0.4726, mean_class_accuracy: 0.2332 +2024-12-29 06:55:50,419 - pyskl - INFO - Epoch [85][100/3746] lr: 4.060e-02, eta: 2 days, 9:38:59, time: 2.640, data_time: 1.618, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5647, loss_cls: 3.9756, loss: 3.9756 +2024-12-29 06:57:15,277 - pyskl - INFO - Epoch [85][200/3746] lr: 4.058e-02, eta: 2 days, 9:37:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5567, loss_cls: 4.0472, loss: 4.0472 +2024-12-29 06:58:39,789 - pyskl - INFO - Epoch [85][300/3746] lr: 4.055e-02, eta: 2 days, 9:36:12, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5406, loss_cls: 4.0921, loss: 4.0921 +2024-12-29 07:00:04,020 - pyskl - INFO - Epoch [85][400/3746] lr: 4.052e-02, eta: 2 days, 9:34:48, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5527, loss_cls: 4.0118, loss: 4.0118 +2024-12-29 07:01:29,035 - pyskl - INFO - Epoch [85][500/3746] lr: 4.049e-02, eta: 2 days, 9:33:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5452, loss_cls: 4.0544, loss: 4.0544 +2024-12-29 07:02:53,316 - pyskl - INFO - Epoch [85][600/3746] lr: 4.047e-02, eta: 2 days, 9:32:01, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5502, loss_cls: 4.0437, loss: 4.0437 +2024-12-29 07:04:17,773 - pyskl - INFO - Epoch [85][700/3746] lr: 4.044e-02, eta: 2 days, 9:30:38, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5520, loss_cls: 4.0487, loss: 4.0487 +2024-12-29 07:05:41,721 - pyskl - INFO - Epoch [85][800/3746] lr: 4.041e-02, eta: 2 days, 9:29:14, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5414, loss_cls: 4.0810, loss: 4.0810 +2024-12-29 07:07:05,456 - pyskl - INFO - Epoch [85][900/3746] lr: 4.038e-02, eta: 2 days, 9:27:50, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5370, loss_cls: 4.0885, loss: 4.0885 +2024-12-29 07:08:29,761 - pyskl - INFO - Epoch [85][1000/3746] lr: 4.036e-02, eta: 2 days, 9:26:26, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5373, loss_cls: 4.0946, loss: 4.0946 +2024-12-29 07:09:54,900 - pyskl - INFO - Epoch [85][1100/3746] lr: 4.033e-02, eta: 2 days, 9:25:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5463, loss_cls: 4.0792, loss: 4.0792 +2024-12-29 07:11:18,816 - pyskl - INFO - Epoch [85][1200/3746] lr: 4.030e-02, eta: 2 days, 9:23:39, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5402, loss_cls: 4.0784, loss: 4.0784 +2024-12-29 07:12:43,003 - pyskl - INFO - Epoch [85][1300/3746] lr: 4.027e-02, eta: 2 days, 9:22:15, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5464, loss_cls: 4.0411, loss: 4.0411 +2024-12-29 07:14:07,254 - pyskl - INFO - Epoch [85][1400/3746] lr: 4.025e-02, eta: 2 days, 9:20:51, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5502, loss_cls: 4.0215, loss: 4.0215 +2024-12-29 07:15:31,336 - pyskl - INFO - Epoch [85][1500/3746] lr: 4.022e-02, eta: 2 days, 9:19:27, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5323, loss_cls: 4.0941, loss: 4.0941 +2024-12-29 07:16:56,154 - pyskl - INFO - Epoch [85][1600/3746] lr: 4.019e-02, eta: 2 days, 9:18:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5273, loss_cls: 4.1513, loss: 4.1513 +2024-12-29 07:18:20,530 - pyskl - INFO - Epoch [85][1700/3746] lr: 4.016e-02, eta: 2 days, 9:16:40, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5519, loss_cls: 4.0372, loss: 4.0372 +2024-12-29 07:19:45,085 - pyskl - INFO - Epoch [85][1800/3746] lr: 4.014e-02, eta: 2 days, 9:15:17, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5366, loss_cls: 4.1050, loss: 4.1050 +2024-12-29 07:21:09,085 - pyskl - INFO - Epoch [85][1900/3746] lr: 4.011e-02, eta: 2 days, 9:13:53, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5317, loss_cls: 4.1168, loss: 4.1168 +2024-12-29 07:22:33,556 - pyskl - INFO - Epoch [85][2000/3746] lr: 4.008e-02, eta: 2 days, 9:12:29, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5363, loss_cls: 4.0939, loss: 4.0939 +2024-12-29 07:23:58,705 - pyskl - INFO - Epoch [85][2100/3746] lr: 4.006e-02, eta: 2 days, 9:11:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5344, loss_cls: 4.0994, loss: 4.0994 +2024-12-29 07:25:23,818 - pyskl - INFO - Epoch [85][2200/3746] lr: 4.003e-02, eta: 2 days, 9:09:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5413, loss_cls: 4.0837, loss: 4.0837 +2024-12-29 07:26:48,553 - pyskl - INFO - Epoch [85][2300/3746] lr: 4.000e-02, eta: 2 days, 9:08:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5466, loss_cls: 4.0188, loss: 4.0188 +2024-12-29 07:28:12,868 - pyskl - INFO - Epoch [85][2400/3746] lr: 3.997e-02, eta: 2 days, 9:06:56, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5470, loss_cls: 4.0664, loss: 4.0664 +2024-12-29 07:29:38,395 - pyskl - INFO - Epoch [85][2500/3746] lr: 3.995e-02, eta: 2 days, 9:05:33, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5436, loss_cls: 4.0897, loss: 4.0897 +2024-12-29 07:31:03,854 - pyskl - INFO - Epoch [85][2600/3746] lr: 3.992e-02, eta: 2 days, 9:04:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5434, loss_cls: 4.0644, loss: 4.0644 +2024-12-29 07:32:29,134 - pyskl - INFO - Epoch [85][2700/3746] lr: 3.989e-02, eta: 2 days, 9:02:47, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5416, loss_cls: 4.0795, loss: 4.0795 +2024-12-29 07:33:54,041 - pyskl - INFO - Epoch [85][2800/3746] lr: 3.986e-02, eta: 2 days, 9:01:24, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5386, loss_cls: 4.1229, loss: 4.1229 +2024-12-29 07:35:18,991 - pyskl - INFO - Epoch [85][2900/3746] lr: 3.984e-02, eta: 2 days, 9:00:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5452, loss_cls: 4.0737, loss: 4.0737 +2024-12-29 07:36:43,642 - pyskl - INFO - Epoch [85][3000/3746] lr: 3.981e-02, eta: 2 days, 8:58:37, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5452, loss_cls: 4.0569, loss: 4.0569 +2024-12-29 07:38:08,852 - pyskl - INFO - Epoch [85][3100/3746] lr: 3.978e-02, eta: 2 days, 8:57:14, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5325, loss_cls: 4.1202, loss: 4.1202 +2024-12-29 07:39:34,064 - pyskl - INFO - Epoch [85][3200/3746] lr: 3.975e-02, eta: 2 days, 8:55:51, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5303, loss_cls: 4.1209, loss: 4.1209 +2024-12-29 07:40:58,649 - pyskl - INFO - Epoch [85][3300/3746] lr: 3.973e-02, eta: 2 days, 8:54:28, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5375, loss_cls: 4.1065, loss: 4.1065 +2024-12-29 07:42:22,790 - pyskl - INFO - Epoch [85][3400/3746] lr: 3.970e-02, eta: 2 days, 8:53:04, time: 0.841, data_time: 0.001, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5455, loss_cls: 4.0817, loss: 4.0817 +2024-12-29 07:43:47,849 - pyskl - INFO - Epoch [85][3500/3746] lr: 3.967e-02, eta: 2 days, 8:51:41, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5444, loss_cls: 4.0753, loss: 4.0753 +2024-12-29 07:45:12,922 - pyskl - INFO - Epoch [85][3600/3746] lr: 3.964e-02, eta: 2 days, 8:50:18, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5542, loss_cls: 4.0374, loss: 4.0374 +2024-12-29 07:46:37,939 - pyskl - INFO - Epoch [85][3700/3746] lr: 3.962e-02, eta: 2 days, 8:48:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5436, loss_cls: 4.0554, loss: 4.0554 +2024-12-29 07:47:18,482 - pyskl - INFO - Saving checkpoint at 85 epochs +2024-12-29 07:49:16,746 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 07:49:17,462 - pyskl - INFO - +top1_acc 0.2197 +top5_acc 0.4561 +2024-12-29 07:49:17,462 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 07:49:17,518 - pyskl - INFO - +mean_acc 0.2196 +2024-12-29 07:49:17,534 - pyskl - INFO - Epoch(val) [85][309] top1_acc: 0.2197, top5_acc: 0.4561, mean_class_accuracy: 0.2196 +2024-12-29 07:53:28,191 - pyskl - INFO - Epoch [86][100/3746] lr: 3.958e-02, eta: 2 days, 8:48:30, time: 2.506, data_time: 1.480, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5603, loss_cls: 3.9829, loss: 3.9829 +2024-12-29 07:54:53,016 - pyskl - INFO - Epoch [86][200/3746] lr: 3.955e-02, eta: 2 days, 8:47:06, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5558, loss_cls: 4.0096, loss: 4.0096 +2024-12-29 07:56:18,159 - pyskl - INFO - Epoch [86][300/3746] lr: 3.952e-02, eta: 2 days, 8:45:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5508, loss_cls: 4.0420, loss: 4.0420 +2024-12-29 07:57:43,474 - pyskl - INFO - Epoch [86][400/3746] lr: 3.950e-02, eta: 2 days, 8:44:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5447, loss_cls: 4.0358, loss: 4.0358 +2024-12-29 07:59:08,443 - pyskl - INFO - Epoch [86][500/3746] lr: 3.947e-02, eta: 2 days, 8:42:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5552, loss_cls: 3.9991, loss: 3.9991 +2024-12-29 08:00:33,445 - pyskl - INFO - Epoch [86][600/3746] lr: 3.944e-02, eta: 2 days, 8:41:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5514, loss_cls: 4.0153, loss: 4.0153 +2024-12-29 08:01:57,964 - pyskl - INFO - Epoch [86][700/3746] lr: 3.941e-02, eta: 2 days, 8:40:10, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5509, loss_cls: 4.0338, loss: 4.0338 +2024-12-29 08:03:22,897 - pyskl - INFO - Epoch [86][800/3746] lr: 3.939e-02, eta: 2 days, 8:38:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5397, loss_cls: 4.0794, loss: 4.0794 +2024-12-29 08:04:47,396 - pyskl - INFO - Epoch [86][900/3746] lr: 3.936e-02, eta: 2 days, 8:37:23, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5413, loss_cls: 4.0772, loss: 4.0772 +2024-12-29 08:06:12,647 - pyskl - INFO - Epoch [86][1000/3746] lr: 3.933e-02, eta: 2 days, 8:36:00, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5472, loss_cls: 4.0377, loss: 4.0377 +2024-12-29 08:07:37,686 - pyskl - INFO - Epoch [86][1100/3746] lr: 3.930e-02, eta: 2 days, 8:34:36, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5491, loss_cls: 4.0384, loss: 4.0384 +2024-12-29 08:09:02,062 - pyskl - INFO - Epoch [86][1200/3746] lr: 3.928e-02, eta: 2 days, 8:33:13, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5442, loss_cls: 4.0666, loss: 4.0666 +2024-12-29 08:10:26,506 - pyskl - INFO - Epoch [86][1300/3746] lr: 3.925e-02, eta: 2 days, 8:31:49, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5447, loss_cls: 4.0596, loss: 4.0596 +2024-12-29 08:11:50,935 - pyskl - INFO - Epoch [86][1400/3746] lr: 3.922e-02, eta: 2 days, 8:30:25, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5453, loss_cls: 4.0674, loss: 4.0674 +2024-12-29 08:13:15,688 - pyskl - INFO - Epoch [86][1500/3746] lr: 3.919e-02, eta: 2 days, 8:29:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5492, loss_cls: 4.0506, loss: 4.0506 +2024-12-29 08:14:40,312 - pyskl - INFO - Epoch [86][1600/3746] lr: 3.917e-02, eta: 2 days, 8:27:38, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5509, loss_cls: 4.0592, loss: 4.0592 +2024-12-29 08:16:05,209 - pyskl - INFO - Epoch [86][1700/3746] lr: 3.914e-02, eta: 2 days, 8:26:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5487, loss_cls: 4.0737, loss: 4.0737 +2024-12-29 08:17:30,006 - pyskl - INFO - Epoch [86][1800/3746] lr: 3.911e-02, eta: 2 days, 8:24:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5433, loss_cls: 4.0753, loss: 4.0753 +2024-12-29 08:18:54,746 - pyskl - INFO - Epoch [86][1900/3746] lr: 3.909e-02, eta: 2 days, 8:23:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5419, loss_cls: 4.0813, loss: 4.0813 +2024-12-29 08:20:19,575 - pyskl - INFO - Epoch [86][2000/3746] lr: 3.906e-02, eta: 2 days, 8:22:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5433, loss_cls: 4.0639, loss: 4.0639 +2024-12-29 08:21:44,368 - pyskl - INFO - Epoch [86][2100/3746] lr: 3.903e-02, eta: 2 days, 8:20:41, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5403, loss_cls: 4.0562, loss: 4.0562 +2024-12-29 08:23:09,098 - pyskl - INFO - Epoch [86][2200/3746] lr: 3.900e-02, eta: 2 days, 8:19:17, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5473, loss_cls: 4.0431, loss: 4.0431 +2024-12-29 08:24:33,956 - pyskl - INFO - Epoch [86][2300/3746] lr: 3.898e-02, eta: 2 days, 8:17:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5516, loss_cls: 4.0236, loss: 4.0236 +2024-12-29 08:25:59,106 - pyskl - INFO - Epoch [86][2400/3746] lr: 3.895e-02, eta: 2 days, 8:16:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5402, loss_cls: 4.0868, loss: 4.0868 +2024-12-29 08:27:23,876 - pyskl - INFO - Epoch [86][2500/3746] lr: 3.892e-02, eta: 2 days, 8:15:07, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5452, loss_cls: 4.0447, loss: 4.0447 +2024-12-29 08:28:48,246 - pyskl - INFO - Epoch [86][2600/3746] lr: 3.889e-02, eta: 2 days, 8:13:43, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5514, loss_cls: 4.0285, loss: 4.0285 +2024-12-29 08:30:12,072 - pyskl - INFO - Epoch [86][2700/3746] lr: 3.887e-02, eta: 2 days, 8:12:19, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5386, loss_cls: 4.0653, loss: 4.0653 +2024-12-29 08:31:36,508 - pyskl - INFO - Epoch [86][2800/3746] lr: 3.884e-02, eta: 2 days, 8:10:56, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5370, loss_cls: 4.1046, loss: 4.1046 +2024-12-29 08:33:00,659 - pyskl - INFO - Epoch [86][2900/3746] lr: 3.881e-02, eta: 2 days, 8:09:32, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5347, loss_cls: 4.1008, loss: 4.1008 +2024-12-29 08:34:25,146 - pyskl - INFO - Epoch [86][3000/3746] lr: 3.879e-02, eta: 2 days, 8:08:08, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5473, loss_cls: 4.0353, loss: 4.0353 +2024-12-29 08:35:49,511 - pyskl - INFO - Epoch [86][3100/3746] lr: 3.876e-02, eta: 2 days, 8:06:44, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5487, loss_cls: 4.0494, loss: 4.0494 +2024-12-29 08:37:13,943 - pyskl - INFO - Epoch [86][3200/3746] lr: 3.873e-02, eta: 2 days, 8:05:20, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5437, loss_cls: 4.0680, loss: 4.0680 +2024-12-29 08:38:38,056 - pyskl - INFO - Epoch [86][3300/3746] lr: 3.870e-02, eta: 2 days, 8:03:56, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5441, loss_cls: 4.0669, loss: 4.0669 +2024-12-29 08:40:02,433 - pyskl - INFO - Epoch [86][3400/3746] lr: 3.868e-02, eta: 2 days, 8:02:33, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5458, loss_cls: 4.0627, loss: 4.0627 +2024-12-29 08:41:27,042 - pyskl - INFO - Epoch [86][3500/3746] lr: 3.865e-02, eta: 2 days, 8:01:09, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5420, loss_cls: 4.0958, loss: 4.0958 +2024-12-29 08:42:51,959 - pyskl - INFO - Epoch [86][3600/3746] lr: 3.862e-02, eta: 2 days, 7:59:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5395, loss_cls: 4.0973, loss: 4.0973 +2024-12-29 08:44:16,389 - pyskl - INFO - Epoch [86][3700/3746] lr: 3.860e-02, eta: 2 days, 7:58:22, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5377, loss_cls: 4.1297, loss: 4.1297 +2024-12-29 08:44:57,056 - pyskl - INFO - Saving checkpoint at 86 epochs +2024-12-29 08:46:55,413 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 08:46:56,299 - pyskl - INFO - +top1_acc 0.2429 +top5_acc 0.4838 +2024-12-29 08:46:56,299 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 08:46:56,357 - pyskl - INFO - +mean_acc 0.2427 +2024-12-29 08:46:56,362 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_84.pth was removed +2024-12-29 08:46:56,701 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_86.pth. +2024-12-29 08:46:56,702 - pyskl - INFO - Best top1_acc is 0.2429 at 86 epoch. +2024-12-29 08:46:56,717 - pyskl - INFO - Epoch(val) [86][309] top1_acc: 0.2429, top5_acc: 0.4838, mean_class_accuracy: 0.2427 +2024-12-29 08:51:12,203 - pyskl - INFO - Epoch [87][100/3746] lr: 3.856e-02, eta: 2 days, 7:57:58, time: 2.555, data_time: 1.528, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5636, loss_cls: 3.9667, loss: 3.9667 +2024-12-29 08:52:37,228 - pyskl - INFO - Epoch [87][200/3746] lr: 3.853e-02, eta: 2 days, 7:56:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5617, loss_cls: 3.9653, loss: 3.9653 +2024-12-29 08:54:02,217 - pyskl - INFO - Epoch [87][300/3746] lr: 3.850e-02, eta: 2 days, 7:55:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5637, loss_cls: 3.9632, loss: 3.9632 +2024-12-29 08:55:26,911 - pyskl - INFO - Epoch [87][400/3746] lr: 3.847e-02, eta: 2 days, 7:53:48, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5528, loss_cls: 4.0408, loss: 4.0408 +2024-12-29 08:56:51,526 - pyskl - INFO - Epoch [87][500/3746] lr: 3.845e-02, eta: 2 days, 7:52:24, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5514, loss_cls: 4.0278, loss: 4.0278 +2024-12-29 08:58:16,453 - pyskl - INFO - Epoch [87][600/3746] lr: 3.842e-02, eta: 2 days, 7:51:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5533, loss_cls: 4.0014, loss: 4.0014 +2024-12-29 08:59:41,566 - pyskl - INFO - Epoch [87][700/3746] lr: 3.839e-02, eta: 2 days, 7:49:37, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5452, loss_cls: 4.0780, loss: 4.0780 +2024-12-29 09:01:06,536 - pyskl - INFO - Epoch [87][800/3746] lr: 3.837e-02, eta: 2 days, 7:48:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5448, loss_cls: 4.0719, loss: 4.0719 +2024-12-29 09:02:31,248 - pyskl - INFO - Epoch [87][900/3746] lr: 3.834e-02, eta: 2 days, 7:46:50, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5520, loss_cls: 4.0166, loss: 4.0166 +2024-12-29 09:03:56,430 - pyskl - INFO - Epoch [87][1000/3746] lr: 3.831e-02, eta: 2 days, 7:45:27, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5420, loss_cls: 4.0668, loss: 4.0668 +2024-12-29 09:05:21,123 - pyskl - INFO - Epoch [87][1100/3746] lr: 3.828e-02, eta: 2 days, 7:44:03, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5487, loss_cls: 4.0444, loss: 4.0444 +2024-12-29 09:06:46,207 - pyskl - INFO - Epoch [87][1200/3746] lr: 3.826e-02, eta: 2 days, 7:42:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5520, loss_cls: 4.0469, loss: 4.0469 +2024-12-29 09:08:11,000 - pyskl - INFO - Epoch [87][1300/3746] lr: 3.823e-02, eta: 2 days, 7:41:16, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5491, loss_cls: 4.0097, loss: 4.0097 +2024-12-29 09:09:36,589 - pyskl - INFO - Epoch [87][1400/3746] lr: 3.820e-02, eta: 2 days, 7:39:53, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5603, loss_cls: 4.0133, loss: 4.0133 +2024-12-29 09:11:00,844 - pyskl - INFO - Epoch [87][1500/3746] lr: 3.817e-02, eta: 2 days, 7:38:30, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5433, loss_cls: 4.0690, loss: 4.0690 +2024-12-29 09:12:25,281 - pyskl - INFO - Epoch [87][1600/3746] lr: 3.815e-02, eta: 2 days, 7:37:06, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5405, loss_cls: 4.1002, loss: 4.1002 +2024-12-29 09:13:49,906 - pyskl - INFO - Epoch [87][1700/3746] lr: 3.812e-02, eta: 2 days, 7:35:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5356, loss_cls: 4.0663, loss: 4.0663 +2024-12-29 09:15:14,390 - pyskl - INFO - Epoch [87][1800/3746] lr: 3.809e-02, eta: 2 days, 7:34:18, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5436, loss_cls: 4.0489, loss: 4.0489 +2024-12-29 09:16:39,512 - pyskl - INFO - Epoch [87][1900/3746] lr: 3.807e-02, eta: 2 days, 7:32:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5587, loss_cls: 4.0305, loss: 4.0305 +2024-12-29 09:18:04,482 - pyskl - INFO - Epoch [87][2000/3746] lr: 3.804e-02, eta: 2 days, 7:31:31, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5463, loss_cls: 4.0363, loss: 4.0363 +2024-12-29 09:19:29,376 - pyskl - INFO - Epoch [87][2100/3746] lr: 3.801e-02, eta: 2 days, 7:30:08, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5525, loss_cls: 4.0373, loss: 4.0373 +2024-12-29 09:20:53,886 - pyskl - INFO - Epoch [87][2200/3746] lr: 3.798e-02, eta: 2 days, 7:28:44, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5494, loss_cls: 4.0586, loss: 4.0586 +2024-12-29 09:22:18,356 - pyskl - INFO - Epoch [87][2300/3746] lr: 3.796e-02, eta: 2 days, 7:27:20, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5437, loss_cls: 4.0822, loss: 4.0822 +2024-12-29 09:23:43,546 - pyskl - INFO - Epoch [87][2400/3746] lr: 3.793e-02, eta: 2 days, 7:25:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5506, loss_cls: 4.0295, loss: 4.0295 +2024-12-29 09:25:07,570 - pyskl - INFO - Epoch [87][2500/3746] lr: 3.790e-02, eta: 2 days, 7:24:33, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5495, loss_cls: 4.0348, loss: 4.0348 +2024-12-29 09:26:32,584 - pyskl - INFO - Epoch [87][2600/3746] lr: 3.788e-02, eta: 2 days, 7:23:10, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5405, loss_cls: 4.1138, loss: 4.1138 +2024-12-29 09:27:56,920 - pyskl - INFO - Epoch [87][2700/3746] lr: 3.785e-02, eta: 2 days, 7:21:46, time: 0.843, data_time: 0.001, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5444, loss_cls: 4.0269, loss: 4.0269 +2024-12-29 09:29:21,673 - pyskl - INFO - Epoch [87][2800/3746] lr: 3.782e-02, eta: 2 days, 7:20:22, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5425, loss_cls: 4.0603, loss: 4.0603 +2024-12-29 09:30:46,118 - pyskl - INFO - Epoch [87][2900/3746] lr: 3.779e-02, eta: 2 days, 7:18:58, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5502, loss_cls: 4.0442, loss: 4.0442 +2024-12-29 09:32:10,476 - pyskl - INFO - Epoch [87][3000/3746] lr: 3.777e-02, eta: 2 days, 7:17:34, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5483, loss_cls: 4.0420, loss: 4.0420 +2024-12-29 09:33:34,691 - pyskl - INFO - Epoch [87][3100/3746] lr: 3.774e-02, eta: 2 days, 7:16:11, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5470, loss_cls: 4.0420, loss: 4.0420 +2024-12-29 09:34:59,383 - pyskl - INFO - Epoch [87][3200/3746] lr: 3.771e-02, eta: 2 days, 7:14:47, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5398, loss_cls: 4.0901, loss: 4.0901 +2024-12-29 09:36:23,871 - pyskl - INFO - Epoch [87][3300/3746] lr: 3.769e-02, eta: 2 days, 7:13:23, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5423, loss_cls: 4.0584, loss: 4.0584 +2024-12-29 09:37:47,947 - pyskl - INFO - Epoch [87][3400/3746] lr: 3.766e-02, eta: 2 days, 7:11:59, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5383, loss_cls: 4.0809, loss: 4.0809 +2024-12-29 09:39:12,726 - pyskl - INFO - Epoch [87][3500/3746] lr: 3.763e-02, eta: 2 days, 7:10:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5403, loss_cls: 4.0680, loss: 4.0680 +2024-12-29 09:40:37,819 - pyskl - INFO - Epoch [87][3600/3746] lr: 3.761e-02, eta: 2 days, 7:09:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5419, loss_cls: 4.0817, loss: 4.0817 +2024-12-29 09:42:02,797 - pyskl - INFO - Epoch [87][3700/3746] lr: 3.758e-02, eta: 2 days, 7:07:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5553, loss_cls: 4.0493, loss: 4.0493 +2024-12-29 09:42:43,817 - pyskl - INFO - Saving checkpoint at 87 epochs +2024-12-29 09:44:42,958 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 09:44:43,652 - pyskl - INFO - +top1_acc 0.2327 +top5_acc 0.4789 +2024-12-29 09:44:43,653 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 09:44:43,710 - pyskl - INFO - +mean_acc 0.2327 +2024-12-29 09:44:43,727 - pyskl - INFO - Epoch(val) [87][309] top1_acc: 0.2327, top5_acc: 0.4789, mean_class_accuracy: 0.2327 +2024-12-29 09:48:59,753 - pyskl - INFO - Epoch [88][100/3746] lr: 3.754e-02, eta: 2 days, 7:07:22, time: 2.560, data_time: 1.528, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5658, loss_cls: 3.9616, loss: 3.9616 +2024-12-29 09:50:25,336 - pyskl - INFO - Epoch [88][200/3746] lr: 3.751e-02, eta: 2 days, 7:05:59, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5525, loss_cls: 4.0049, loss: 4.0049 +2024-12-29 09:51:50,156 - pyskl - INFO - Epoch [88][300/3746] lr: 3.748e-02, eta: 2 days, 7:04:36, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5541, loss_cls: 4.0258, loss: 4.0258 +2024-12-29 09:53:15,439 - pyskl - INFO - Epoch [88][400/3746] lr: 3.746e-02, eta: 2 days, 7:03:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5500, loss_cls: 4.0115, loss: 4.0115 +2024-12-29 09:54:40,322 - pyskl - INFO - Epoch [88][500/3746] lr: 3.743e-02, eta: 2 days, 7:01:49, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5489, loss_cls: 4.0120, loss: 4.0120 +2024-12-29 09:56:04,843 - pyskl - INFO - Epoch [88][600/3746] lr: 3.740e-02, eta: 2 days, 7:00:25, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5572, loss_cls: 4.0039, loss: 4.0039 +2024-12-29 09:57:29,451 - pyskl - INFO - Epoch [88][700/3746] lr: 3.738e-02, eta: 2 days, 6:59:01, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5575, loss_cls: 3.9760, loss: 3.9760 +2024-12-29 09:58:54,190 - pyskl - INFO - Epoch [88][800/3746] lr: 3.735e-02, eta: 2 days, 6:57:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5555, loss_cls: 3.9938, loss: 3.9938 +2024-12-29 10:00:18,675 - pyskl - INFO - Epoch [88][900/3746] lr: 3.732e-02, eta: 2 days, 6:56:14, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5487, loss_cls: 3.9913, loss: 3.9913 +2024-12-29 10:01:43,719 - pyskl - INFO - Epoch [88][1000/3746] lr: 3.730e-02, eta: 2 days, 6:54:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5564, loss_cls: 4.0322, loss: 4.0322 +2024-12-29 10:03:08,008 - pyskl - INFO - Epoch [88][1100/3746] lr: 3.727e-02, eta: 2 days, 6:53:26, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5520, loss_cls: 4.0298, loss: 4.0298 +2024-12-29 10:04:32,847 - pyskl - INFO - Epoch [88][1200/3746] lr: 3.724e-02, eta: 2 days, 6:52:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5431, loss_cls: 4.0722, loss: 4.0722 +2024-12-29 10:05:57,675 - pyskl - INFO - Epoch [88][1300/3746] lr: 3.721e-02, eta: 2 days, 6:50:39, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5483, loss_cls: 4.0210, loss: 4.0210 +2024-12-29 10:07:22,178 - pyskl - INFO - Epoch [88][1400/3746] lr: 3.719e-02, eta: 2 days, 6:49:15, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5519, loss_cls: 4.0181, loss: 4.0181 +2024-12-29 10:08:46,757 - pyskl - INFO - Epoch [88][1500/3746] lr: 3.716e-02, eta: 2 days, 6:47:51, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5513, loss_cls: 4.0283, loss: 4.0283 +2024-12-29 10:10:11,252 - pyskl - INFO - Epoch [88][1600/3746] lr: 3.713e-02, eta: 2 days, 6:46:27, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5553, loss_cls: 4.0071, loss: 4.0071 +2024-12-29 10:11:35,680 - pyskl - INFO - Epoch [88][1700/3746] lr: 3.711e-02, eta: 2 days, 6:45:03, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5548, loss_cls: 4.0441, loss: 4.0441 +2024-12-29 10:13:00,167 - pyskl - INFO - Epoch [88][1800/3746] lr: 3.708e-02, eta: 2 days, 6:43:40, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5489, loss_cls: 4.0179, loss: 4.0179 +2024-12-29 10:14:24,916 - pyskl - INFO - Epoch [88][1900/3746] lr: 3.705e-02, eta: 2 days, 6:42:16, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5497, loss_cls: 4.0152, loss: 4.0152 +2024-12-29 10:15:49,223 - pyskl - INFO - Epoch [88][2000/3746] lr: 3.703e-02, eta: 2 days, 6:40:52, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5387, loss_cls: 4.0846, loss: 4.0846 +2024-12-29 10:17:13,851 - pyskl - INFO - Epoch [88][2100/3746] lr: 3.700e-02, eta: 2 days, 6:39:28, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5505, loss_cls: 4.0373, loss: 4.0373 +2024-12-29 10:18:38,773 - pyskl - INFO - Epoch [88][2200/3746] lr: 3.697e-02, eta: 2 days, 6:38:05, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5397, loss_cls: 4.0847, loss: 4.0847 +2024-12-29 10:20:03,298 - pyskl - INFO - Epoch [88][2300/3746] lr: 3.694e-02, eta: 2 days, 6:36:41, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5364, loss_cls: 4.0623, loss: 4.0623 +2024-12-29 10:21:28,201 - pyskl - INFO - Epoch [88][2400/3746] lr: 3.692e-02, eta: 2 days, 6:35:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5603, loss_cls: 3.9968, loss: 3.9968 +2024-12-29 10:22:53,038 - pyskl - INFO - Epoch [88][2500/3746] lr: 3.689e-02, eta: 2 days, 6:33:54, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5469, loss_cls: 4.0657, loss: 4.0657 +2024-12-29 10:24:18,118 - pyskl - INFO - Epoch [88][2600/3746] lr: 3.686e-02, eta: 2 days, 6:32:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5495, loss_cls: 4.0383, loss: 4.0383 +2024-12-29 10:25:42,946 - pyskl - INFO - Epoch [88][2700/3746] lr: 3.684e-02, eta: 2 days, 6:31:06, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5400, loss_cls: 4.0602, loss: 4.0602 +2024-12-29 10:27:07,307 - pyskl - INFO - Epoch [88][2800/3746] lr: 3.681e-02, eta: 2 days, 6:29:43, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5594, loss_cls: 4.0031, loss: 4.0031 +2024-12-29 10:28:31,657 - pyskl - INFO - Epoch [88][2900/3746] lr: 3.678e-02, eta: 2 days, 6:28:19, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5581, loss_cls: 4.0200, loss: 4.0200 +2024-12-29 10:29:55,951 - pyskl - INFO - Epoch [88][3000/3746] lr: 3.676e-02, eta: 2 days, 6:26:55, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5442, loss_cls: 4.0838, loss: 4.0838 +2024-12-29 10:31:19,986 - pyskl - INFO - Epoch [88][3100/3746] lr: 3.673e-02, eta: 2 days, 6:25:30, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5372, loss_cls: 4.0987, loss: 4.0987 +2024-12-29 10:32:44,752 - pyskl - INFO - Epoch [88][3200/3746] lr: 3.670e-02, eta: 2 days, 6:24:07, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5506, loss_cls: 4.0431, loss: 4.0431 +2024-12-29 10:34:08,982 - pyskl - INFO - Epoch [88][3300/3746] lr: 3.667e-02, eta: 2 days, 6:22:43, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5516, loss_cls: 3.9999, loss: 3.9999 +2024-12-29 10:35:33,320 - pyskl - INFO - Epoch [88][3400/3746] lr: 3.665e-02, eta: 2 days, 6:21:19, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5461, loss_cls: 4.0642, loss: 4.0642 +2024-12-29 10:36:57,704 - pyskl - INFO - Epoch [88][3500/3746] lr: 3.662e-02, eta: 2 days, 6:19:55, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5458, loss_cls: 4.0829, loss: 4.0829 +2024-12-29 10:38:21,524 - pyskl - INFO - Epoch [88][3600/3746] lr: 3.659e-02, eta: 2 days, 6:18:30, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5463, loss_cls: 3.9950, loss: 3.9950 +2024-12-29 10:39:45,645 - pyskl - INFO - Epoch [88][3700/3746] lr: 3.657e-02, eta: 2 days, 6:17:06, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5516, loss_cls: 4.0560, loss: 4.0560 +2024-12-29 10:40:26,187 - pyskl - INFO - Saving checkpoint at 88 epochs +2024-12-29 10:42:24,587 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 10:42:25,396 - pyskl - INFO - +top1_acc 0.2471 +top5_acc 0.4877 +2024-12-29 10:42:25,396 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 10:42:25,444 - pyskl - INFO - +mean_acc 0.2469 +2024-12-29 10:42:25,448 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_86.pth was removed +2024-12-29 10:42:25,773 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_88.pth. +2024-12-29 10:42:25,774 - pyskl - INFO - Best top1_acc is 0.2471 at 88 epoch. +2024-12-29 10:42:25,789 - pyskl - INFO - Epoch(val) [88][309] top1_acc: 0.2471, top5_acc: 0.4877, mean_class_accuracy: 0.2469 +2024-12-29 10:46:38,329 - pyskl - INFO - Epoch [89][100/3746] lr: 3.653e-02, eta: 2 days, 6:16:35, time: 2.525, data_time: 1.505, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5648, loss_cls: 3.9612, loss: 3.9612 +2024-12-29 10:48:03,657 - pyskl - INFO - Epoch [89][200/3746] lr: 3.650e-02, eta: 2 days, 6:15:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5536, loss_cls: 4.0107, loss: 4.0107 +2024-12-29 10:49:28,336 - pyskl - INFO - Epoch [89][300/3746] lr: 3.647e-02, eta: 2 days, 6:13:48, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5508, loss_cls: 4.0255, loss: 4.0255 +2024-12-29 10:50:53,008 - pyskl - INFO - Epoch [89][400/3746] lr: 3.645e-02, eta: 2 days, 6:12:24, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5519, loss_cls: 4.0252, loss: 4.0252 +2024-12-29 10:52:17,855 - pyskl - INFO - Epoch [89][500/3746] lr: 3.642e-02, eta: 2 days, 6:11:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5548, loss_cls: 4.0184, loss: 4.0184 +2024-12-29 10:53:42,579 - pyskl - INFO - Epoch [89][600/3746] lr: 3.639e-02, eta: 2 days, 6:09:36, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5502, loss_cls: 4.0478, loss: 4.0478 +2024-12-29 10:55:07,849 - pyskl - INFO - Epoch [89][700/3746] lr: 3.637e-02, eta: 2 days, 6:08:13, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5436, loss_cls: 4.0388, loss: 4.0388 +2024-12-29 10:56:32,765 - pyskl - INFO - Epoch [89][800/3746] lr: 3.634e-02, eta: 2 days, 6:06:49, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5539, loss_cls: 3.9594, loss: 3.9594 +2024-12-29 10:57:57,211 - pyskl - INFO - Epoch [89][900/3746] lr: 3.631e-02, eta: 2 days, 6:05:25, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5495, loss_cls: 4.0276, loss: 4.0276 +2024-12-29 10:59:21,592 - pyskl - INFO - Epoch [89][1000/3746] lr: 3.629e-02, eta: 2 days, 6:04:01, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5566, loss_cls: 4.0047, loss: 4.0047 +2024-12-29 11:00:45,707 - pyskl - INFO - Epoch [89][1100/3746] lr: 3.626e-02, eta: 2 days, 6:02:37, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5561, loss_cls: 4.0261, loss: 4.0261 +2024-12-29 11:02:10,508 - pyskl - INFO - Epoch [89][1200/3746] lr: 3.623e-02, eta: 2 days, 6:01:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5519, loss_cls: 4.0302, loss: 4.0302 +2024-12-29 11:03:35,525 - pyskl - INFO - Epoch [89][1300/3746] lr: 3.620e-02, eta: 2 days, 5:59:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5587, loss_cls: 3.9934, loss: 3.9934 +2024-12-29 11:04:59,777 - pyskl - INFO - Epoch [89][1400/3746] lr: 3.618e-02, eta: 2 days, 5:58:26, time: 0.843, data_time: 0.001, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5497, loss_cls: 3.9986, loss: 3.9986 +2024-12-29 11:06:24,211 - pyskl - INFO - Epoch [89][1500/3746] lr: 3.615e-02, eta: 2 days, 5:57:02, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5492, loss_cls: 4.0324, loss: 4.0324 +2024-12-29 11:07:48,225 - pyskl - INFO - Epoch [89][1600/3746] lr: 3.612e-02, eta: 2 days, 5:55:37, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5448, loss_cls: 4.0483, loss: 4.0483 +2024-12-29 11:09:12,397 - pyskl - INFO - Epoch [89][1700/3746] lr: 3.610e-02, eta: 2 days, 5:54:13, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5652, loss_cls: 3.9754, loss: 3.9754 +2024-12-29 11:10:36,686 - pyskl - INFO - Epoch [89][1800/3746] lr: 3.607e-02, eta: 2 days, 5:52:49, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5553, loss_cls: 3.9971, loss: 3.9971 +2024-12-29 11:12:00,661 - pyskl - INFO - Epoch [89][1900/3746] lr: 3.604e-02, eta: 2 days, 5:51:25, time: 0.840, data_time: 0.001, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5545, loss_cls: 4.0132, loss: 4.0132 +2024-12-29 11:13:24,697 - pyskl - INFO - Epoch [89][2000/3746] lr: 3.602e-02, eta: 2 days, 5:50:01, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5463, loss_cls: 4.0287, loss: 4.0287 +2024-12-29 11:14:49,161 - pyskl - INFO - Epoch [89][2100/3746] lr: 3.599e-02, eta: 2 days, 5:48:37, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5544, loss_cls: 4.0467, loss: 4.0467 +2024-12-29 11:16:13,317 - pyskl - INFO - Epoch [89][2200/3746] lr: 3.596e-02, eta: 2 days, 5:47:12, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5481, loss_cls: 4.0282, loss: 4.0282 +2024-12-29 11:17:38,317 - pyskl - INFO - Epoch [89][2300/3746] lr: 3.594e-02, eta: 2 days, 5:45:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5511, loss_cls: 4.0078, loss: 4.0078 +2024-12-29 11:19:02,550 - pyskl - INFO - Epoch [89][2400/3746] lr: 3.591e-02, eta: 2 days, 5:44:25, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5452, loss_cls: 4.0442, loss: 4.0442 +2024-12-29 11:20:26,744 - pyskl - INFO - Epoch [89][2500/3746] lr: 3.588e-02, eta: 2 days, 5:43:00, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5498, loss_cls: 4.0101, loss: 4.0101 +2024-12-29 11:21:51,969 - pyskl - INFO - Epoch [89][2600/3746] lr: 3.586e-02, eta: 2 days, 5:41:37, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5561, loss_cls: 4.0165, loss: 4.0165 +2024-12-29 11:23:16,799 - pyskl - INFO - Epoch [89][2700/3746] lr: 3.583e-02, eta: 2 days, 5:40:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5492, loss_cls: 4.0301, loss: 4.0301 +2024-12-29 11:24:41,119 - pyskl - INFO - Epoch [89][2800/3746] lr: 3.580e-02, eta: 2 days, 5:38:49, time: 0.843, data_time: 0.001, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5489, loss_cls: 4.0205, loss: 4.0205 +2024-12-29 11:26:05,435 - pyskl - INFO - Epoch [89][2900/3746] lr: 3.578e-02, eta: 2 days, 5:37:25, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5509, loss_cls: 4.0626, loss: 4.0626 +2024-12-29 11:27:29,537 - pyskl - INFO - Epoch [89][3000/3746] lr: 3.575e-02, eta: 2 days, 5:36:01, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5409, loss_cls: 4.0464, loss: 4.0464 +2024-12-29 11:28:53,955 - pyskl - INFO - Epoch [89][3100/3746] lr: 3.572e-02, eta: 2 days, 5:34:37, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5573, loss_cls: 4.0227, loss: 4.0227 +2024-12-29 11:30:18,318 - pyskl - INFO - Epoch [89][3200/3746] lr: 3.569e-02, eta: 2 days, 5:33:13, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5614, loss_cls: 3.9873, loss: 3.9873 +2024-12-29 11:31:42,841 - pyskl - INFO - Epoch [89][3300/3746] lr: 3.567e-02, eta: 2 days, 5:31:49, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5467, loss_cls: 4.0482, loss: 4.0482 +2024-12-29 11:33:06,741 - pyskl - INFO - Epoch [89][3400/3746] lr: 3.564e-02, eta: 2 days, 5:30:25, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5541, loss_cls: 4.0259, loss: 4.0259 +2024-12-29 11:34:31,162 - pyskl - INFO - Epoch [89][3500/3746] lr: 3.561e-02, eta: 2 days, 5:29:01, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5558, loss_cls: 4.0308, loss: 4.0308 +2024-12-29 11:35:55,831 - pyskl - INFO - Epoch [89][3600/3746] lr: 3.559e-02, eta: 2 days, 5:27:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5569, loss_cls: 3.9864, loss: 3.9864 +2024-12-29 11:37:20,133 - pyskl - INFO - Epoch [89][3700/3746] lr: 3.556e-02, eta: 2 days, 5:26:13, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5548, loss_cls: 4.0044, loss: 4.0044 +2024-12-29 11:38:00,657 - pyskl - INFO - Saving checkpoint at 89 epochs +2024-12-29 11:39:59,320 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 11:40:00,173 - pyskl - INFO - +top1_acc 0.2447 +top5_acc 0.4814 +2024-12-29 11:40:00,173 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 11:40:00,231 - pyskl - INFO - +mean_acc 0.2446 +2024-12-29 11:40:00,246 - pyskl - INFO - Epoch(val) [89][309] top1_acc: 0.2447, top5_acc: 0.4814, mean_class_accuracy: 0.2446 +2024-12-29 11:44:13,105 - pyskl - INFO - Epoch [90][100/3746] lr: 3.552e-02, eta: 2 days, 5:25:39, time: 2.528, data_time: 1.501, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5644, loss_cls: 3.9405, loss: 3.9405 +2024-12-29 11:45:37,933 - pyskl - INFO - Epoch [90][200/3746] lr: 3.550e-02, eta: 2 days, 5:24:15, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5694, loss_cls: 3.9233, loss: 3.9233 +2024-12-29 11:47:02,791 - pyskl - INFO - Epoch [90][300/3746] lr: 3.547e-02, eta: 2 days, 5:22:51, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5669, loss_cls: 3.9684, loss: 3.9684 +2024-12-29 11:48:27,824 - pyskl - INFO - Epoch [90][400/3746] lr: 3.544e-02, eta: 2 days, 5:21:28, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5533, loss_cls: 4.0059, loss: 4.0059 +2024-12-29 11:49:52,595 - pyskl - INFO - Epoch [90][500/3746] lr: 3.541e-02, eta: 2 days, 5:20:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5541, loss_cls: 4.0032, loss: 4.0032 +2024-12-29 11:51:17,471 - pyskl - INFO - Epoch [90][600/3746] lr: 3.539e-02, eta: 2 days, 5:18:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5541, loss_cls: 4.0033, loss: 4.0033 +2024-12-29 11:52:42,475 - pyskl - INFO - Epoch [90][700/3746] lr: 3.536e-02, eta: 2 days, 5:17:16, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5558, loss_cls: 4.0024, loss: 4.0024 +2024-12-29 11:54:07,168 - pyskl - INFO - Epoch [90][800/3746] lr: 3.533e-02, eta: 2 days, 5:15:53, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5531, loss_cls: 4.0048, loss: 4.0048 +2024-12-29 11:55:32,217 - pyskl - INFO - Epoch [90][900/3746] lr: 3.531e-02, eta: 2 days, 5:14:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5525, loss_cls: 3.9821, loss: 3.9821 +2024-12-29 11:56:56,743 - pyskl - INFO - Epoch [90][1000/3746] lr: 3.528e-02, eta: 2 days, 5:13:05, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5616, loss_cls: 3.9710, loss: 3.9710 +2024-12-29 11:58:21,237 - pyskl - INFO - Epoch [90][1100/3746] lr: 3.525e-02, eta: 2 days, 5:11:41, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5517, loss_cls: 4.0051, loss: 4.0051 +2024-12-29 11:59:46,053 - pyskl - INFO - Epoch [90][1200/3746] lr: 3.523e-02, eta: 2 days, 5:10:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5527, loss_cls: 4.0253, loss: 4.0253 +2024-12-29 12:01:11,080 - pyskl - INFO - Epoch [90][1300/3746] lr: 3.520e-02, eta: 2 days, 5:08:53, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5561, loss_cls: 4.0238, loss: 4.0238 +2024-12-29 12:02:35,587 - pyskl - INFO - Epoch [90][1400/3746] lr: 3.517e-02, eta: 2 days, 5:07:29, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5528, loss_cls: 4.0127, loss: 4.0127 +2024-12-29 12:04:00,515 - pyskl - INFO - Epoch [90][1500/3746] lr: 3.515e-02, eta: 2 days, 5:06:06, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5606, loss_cls: 3.9949, loss: 3.9949 +2024-12-29 12:05:25,598 - pyskl - INFO - Epoch [90][1600/3746] lr: 3.512e-02, eta: 2 days, 5:04:42, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5561, loss_cls: 4.0107, loss: 4.0107 +2024-12-29 12:06:50,999 - pyskl - INFO - Epoch [90][1700/3746] lr: 3.509e-02, eta: 2 days, 5:03:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5617, loss_cls: 3.9576, loss: 3.9576 +2024-12-29 12:08:15,884 - pyskl - INFO - Epoch [90][1800/3746] lr: 3.507e-02, eta: 2 days, 5:01:55, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5406, loss_cls: 4.0697, loss: 4.0697 +2024-12-29 12:09:40,604 - pyskl - INFO - Epoch [90][1900/3746] lr: 3.504e-02, eta: 2 days, 5:00:31, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5539, loss_cls: 3.9844, loss: 3.9844 +2024-12-29 12:11:04,993 - pyskl - INFO - Epoch [90][2000/3746] lr: 3.501e-02, eta: 2 days, 4:59:07, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5608, loss_cls: 3.9952, loss: 3.9952 +2024-12-29 12:12:29,930 - pyskl - INFO - Epoch [90][2100/3746] lr: 3.499e-02, eta: 2 days, 4:57:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5609, loss_cls: 3.9565, loss: 3.9565 +2024-12-29 12:13:54,854 - pyskl - INFO - Epoch [90][2200/3746] lr: 3.496e-02, eta: 2 days, 4:56:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5602, loss_cls: 3.9752, loss: 3.9752 +2024-12-29 12:15:19,935 - pyskl - INFO - Epoch [90][2300/3746] lr: 3.493e-02, eta: 2 days, 4:54:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5539, loss_cls: 4.0306, loss: 4.0306 +2024-12-29 12:16:45,136 - pyskl - INFO - Epoch [90][2400/3746] lr: 3.491e-02, eta: 2 days, 4:53:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5503, loss_cls: 3.9885, loss: 3.9885 +2024-12-29 12:18:10,108 - pyskl - INFO - Epoch [90][2500/3746] lr: 3.488e-02, eta: 2 days, 4:52:09, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5584, loss_cls: 3.9840, loss: 3.9840 +2024-12-29 12:19:34,477 - pyskl - INFO - Epoch [90][2600/3746] lr: 3.485e-02, eta: 2 days, 4:50:44, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5461, loss_cls: 4.0312, loss: 4.0312 +2024-12-29 12:20:58,937 - pyskl - INFO - Epoch [90][2700/3746] lr: 3.483e-02, eta: 2 days, 4:49:20, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5492, loss_cls: 4.0371, loss: 4.0371 +2024-12-29 12:22:23,526 - pyskl - INFO - Epoch [90][2800/3746] lr: 3.480e-02, eta: 2 days, 4:47:56, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5463, loss_cls: 4.0516, loss: 4.0516 +2024-12-29 12:23:48,123 - pyskl - INFO - Epoch [90][2900/3746] lr: 3.477e-02, eta: 2 days, 4:46:32, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5597, loss_cls: 3.9955, loss: 3.9955 +2024-12-29 12:25:12,665 - pyskl - INFO - Epoch [90][3000/3746] lr: 3.475e-02, eta: 2 days, 4:45:08, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5584, loss_cls: 3.9924, loss: 3.9924 +2024-12-29 12:26:37,439 - pyskl - INFO - Epoch [90][3100/3746] lr: 3.472e-02, eta: 2 days, 4:43:45, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5478, loss_cls: 4.0413, loss: 4.0413 +2024-12-29 12:28:02,260 - pyskl - INFO - Epoch [90][3200/3746] lr: 3.469e-02, eta: 2 days, 4:42:21, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5581, loss_cls: 3.9914, loss: 3.9914 +2024-12-29 12:29:27,185 - pyskl - INFO - Epoch [90][3300/3746] lr: 3.467e-02, eta: 2 days, 4:40:57, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5473, loss_cls: 4.0401, loss: 4.0401 +2024-12-29 12:30:51,047 - pyskl - INFO - Epoch [90][3400/3746] lr: 3.464e-02, eta: 2 days, 4:39:33, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5519, loss_cls: 4.0263, loss: 4.0263 +2024-12-29 12:32:15,297 - pyskl - INFO - Epoch [90][3500/3746] lr: 3.461e-02, eta: 2 days, 4:38:08, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5592, loss_cls: 3.9777, loss: 3.9777 +2024-12-29 12:33:39,002 - pyskl - INFO - Epoch [90][3600/3746] lr: 3.459e-02, eta: 2 days, 4:36:44, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5561, loss_cls: 4.0077, loss: 4.0077 +2024-12-29 12:35:03,370 - pyskl - INFO - Epoch [90][3700/3746] lr: 3.456e-02, eta: 2 days, 4:35:20, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5564, loss_cls: 4.0125, loss: 4.0125 +2024-12-29 12:35:43,660 - pyskl - INFO - Saving checkpoint at 90 epochs +2024-12-29 12:37:41,945 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 12:37:42,620 - pyskl - INFO - +top1_acc 0.2233 +top5_acc 0.4518 +2024-12-29 12:37:42,620 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 12:37:42,665 - pyskl - INFO - +mean_acc 0.2231 +2024-12-29 12:37:42,680 - pyskl - INFO - Epoch(val) [90][309] top1_acc: 0.2233, top5_acc: 0.4518, mean_class_accuracy: 0.2231 +2024-12-29 12:41:52,717 - pyskl - INFO - Epoch [91][100/3746] lr: 3.452e-02, eta: 2 days, 4:34:41, time: 2.500, data_time: 1.483, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5703, loss_cls: 3.9369, loss: 3.9369 +2024-12-29 12:43:17,574 - pyskl - INFO - Epoch [91][200/3746] lr: 3.450e-02, eta: 2 days, 4:33:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5580, loss_cls: 3.9523, loss: 3.9523 +2024-12-29 12:44:42,259 - pyskl - INFO - Epoch [91][300/3746] lr: 3.447e-02, eta: 2 days, 4:31:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5602, loss_cls: 3.9770, loss: 3.9770 +2024-12-29 12:46:06,722 - pyskl - INFO - Epoch [91][400/3746] lr: 3.444e-02, eta: 2 days, 4:30:30, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5577, loss_cls: 3.9417, loss: 3.9417 +2024-12-29 12:47:31,886 - pyskl - INFO - Epoch [91][500/3746] lr: 3.442e-02, eta: 2 days, 4:29:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5687, loss_cls: 3.9247, loss: 3.9247 +2024-12-29 12:48:56,109 - pyskl - INFO - Epoch [91][600/3746] lr: 3.439e-02, eta: 2 days, 4:27:42, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5561, loss_cls: 3.9662, loss: 3.9662 +2024-12-29 12:50:20,634 - pyskl - INFO - Epoch [91][700/3746] lr: 3.436e-02, eta: 2 days, 4:26:18, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5514, loss_cls: 4.0140, loss: 4.0140 +2024-12-29 12:51:45,547 - pyskl - INFO - Epoch [91][800/3746] lr: 3.434e-02, eta: 2 days, 4:24:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5587, loss_cls: 3.9683, loss: 3.9683 +2024-12-29 12:53:10,048 - pyskl - INFO - Epoch [91][900/3746] lr: 3.431e-02, eta: 2 days, 4:23:30, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5581, loss_cls: 3.9868, loss: 3.9868 +2024-12-29 12:54:34,578 - pyskl - INFO - Epoch [91][1000/3746] lr: 3.428e-02, eta: 2 days, 4:22:06, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5508, loss_cls: 3.9999, loss: 3.9999 +2024-12-29 12:55:59,115 - pyskl - INFO - Epoch [91][1100/3746] lr: 3.426e-02, eta: 2 days, 4:20:41, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5584, loss_cls: 3.9977, loss: 3.9977 +2024-12-29 12:57:23,094 - pyskl - INFO - Epoch [91][1200/3746] lr: 3.423e-02, eta: 2 days, 4:19:17, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5455, loss_cls: 4.0480, loss: 4.0480 +2024-12-29 12:58:47,437 - pyskl - INFO - Epoch [91][1300/3746] lr: 3.420e-02, eta: 2 days, 4:17:53, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5586, loss_cls: 3.9555, loss: 3.9555 +2024-12-29 13:00:11,810 - pyskl - INFO - Epoch [91][1400/3746] lr: 3.418e-02, eta: 2 days, 4:16:29, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5577, loss_cls: 4.0010, loss: 4.0010 +2024-12-29 13:01:35,979 - pyskl - INFO - Epoch [91][1500/3746] lr: 3.415e-02, eta: 2 days, 4:15:04, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5584, loss_cls: 4.0039, loss: 4.0039 +2024-12-29 13:02:59,994 - pyskl - INFO - Epoch [91][1600/3746] lr: 3.412e-02, eta: 2 days, 4:13:40, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5564, loss_cls: 4.0058, loss: 4.0058 +2024-12-29 13:04:23,963 - pyskl - INFO - Epoch [91][1700/3746] lr: 3.410e-02, eta: 2 days, 4:12:16, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5564, loss_cls: 4.0051, loss: 4.0051 +2024-12-29 13:05:48,527 - pyskl - INFO - Epoch [91][1800/3746] lr: 3.407e-02, eta: 2 days, 4:10:51, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5530, loss_cls: 3.9942, loss: 3.9942 +2024-12-29 13:07:13,161 - pyskl - INFO - Epoch [91][1900/3746] lr: 3.405e-02, eta: 2 days, 4:09:27, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5559, loss_cls: 3.9908, loss: 3.9908 +2024-12-29 13:08:37,552 - pyskl - INFO - Epoch [91][2000/3746] lr: 3.402e-02, eta: 2 days, 4:08:03, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5644, loss_cls: 3.9659, loss: 3.9659 +2024-12-29 13:10:01,886 - pyskl - INFO - Epoch [91][2100/3746] lr: 3.399e-02, eta: 2 days, 4:06:39, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5491, loss_cls: 4.0046, loss: 4.0046 +2024-12-29 13:11:26,081 - pyskl - INFO - Epoch [91][2200/3746] lr: 3.397e-02, eta: 2 days, 4:05:15, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5583, loss_cls: 3.9726, loss: 3.9726 +2024-12-29 13:12:50,779 - pyskl - INFO - Epoch [91][2300/3746] lr: 3.394e-02, eta: 2 days, 4:03:51, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5614, loss_cls: 3.9846, loss: 3.9846 +2024-12-29 13:14:15,739 - pyskl - INFO - Epoch [91][2400/3746] lr: 3.391e-02, eta: 2 days, 4:02:27, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5656, loss_cls: 3.9731, loss: 3.9731 +2024-12-29 13:15:40,417 - pyskl - INFO - Epoch [91][2500/3746] lr: 3.389e-02, eta: 2 days, 4:01:03, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5587, loss_cls: 3.9885, loss: 3.9885 +2024-12-29 13:17:05,422 - pyskl - INFO - Epoch [91][2600/3746] lr: 3.386e-02, eta: 2 days, 3:59:39, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5548, loss_cls: 4.0246, loss: 4.0246 +2024-12-29 13:18:30,412 - pyskl - INFO - Epoch [91][2700/3746] lr: 3.383e-02, eta: 2 days, 3:58:16, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5705, loss_cls: 3.9722, loss: 3.9722 +2024-12-29 13:19:55,751 - pyskl - INFO - Epoch [91][2800/3746] lr: 3.381e-02, eta: 2 days, 3:56:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5589, loss_cls: 4.0124, loss: 4.0124 +2024-12-29 13:21:20,577 - pyskl - INFO - Epoch [91][2900/3746] lr: 3.378e-02, eta: 2 days, 3:55:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5548, loss_cls: 3.9799, loss: 3.9799 +2024-12-29 13:22:45,372 - pyskl - INFO - Epoch [91][3000/3746] lr: 3.375e-02, eta: 2 days, 3:54:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5517, loss_cls: 4.0268, loss: 4.0268 +2024-12-29 13:24:09,996 - pyskl - INFO - Epoch [91][3100/3746] lr: 3.373e-02, eta: 2 days, 3:52:40, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5536, loss_cls: 4.0365, loss: 4.0365 +2024-12-29 13:25:34,898 - pyskl - INFO - Epoch [91][3200/3746] lr: 3.370e-02, eta: 2 days, 3:51:16, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5711, loss_cls: 3.9610, loss: 3.9610 +2024-12-29 13:26:59,798 - pyskl - INFO - Epoch [91][3300/3746] lr: 3.367e-02, eta: 2 days, 3:49:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5570, loss_cls: 3.9894, loss: 3.9894 +2024-12-29 13:28:24,566 - pyskl - INFO - Epoch [91][3400/3746] lr: 3.365e-02, eta: 2 days, 3:48:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5556, loss_cls: 3.9719, loss: 3.9719 +2024-12-29 13:29:49,226 - pyskl - INFO - Epoch [91][3500/3746] lr: 3.362e-02, eta: 2 days, 3:47:05, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5594, loss_cls: 3.9669, loss: 3.9669 +2024-12-29 13:31:14,586 - pyskl - INFO - Epoch [91][3600/3746] lr: 3.360e-02, eta: 2 days, 3:45:41, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5594, loss_cls: 3.9886, loss: 3.9886 +2024-12-29 13:32:39,502 - pyskl - INFO - Epoch [91][3700/3746] lr: 3.357e-02, eta: 2 days, 3:44:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5697, loss_cls: 3.9498, loss: 3.9498 +2024-12-29 13:33:20,393 - pyskl - INFO - Saving checkpoint at 91 epochs +2024-12-29 13:35:20,671 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 13:35:21,459 - pyskl - INFO - +top1_acc 0.2495 +top5_acc 0.4910 +2024-12-29 13:35:21,460 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 13:35:21,510 - pyskl - INFO - +mean_acc 0.2493 +2024-12-29 13:35:21,515 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_88.pth was removed +2024-12-29 13:35:21,788 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_91.pth. +2024-12-29 13:35:21,789 - pyskl - INFO - Best top1_acc is 0.2495 at 91 epoch. +2024-12-29 13:35:21,802 - pyskl - INFO - Epoch(val) [91][309] top1_acc: 0.2495, top5_acc: 0.4910, mean_class_accuracy: 0.2493 +2024-12-29 13:39:44,915 - pyskl - INFO - Epoch [92][100/3746] lr: 3.353e-02, eta: 2 days, 3:43:45, time: 2.631, data_time: 1.594, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5733, loss_cls: 3.9222, loss: 3.9222 +2024-12-29 13:41:11,077 - pyskl - INFO - Epoch [92][200/3746] lr: 3.350e-02, eta: 2 days, 3:42:22, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5634, loss_cls: 3.9699, loss: 3.9699 +2024-12-29 13:42:37,219 - pyskl - INFO - Epoch [92][300/3746] lr: 3.348e-02, eta: 2 days, 3:40:59, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5695, loss_cls: 3.9269, loss: 3.9269 +2024-12-29 13:44:03,702 - pyskl - INFO - Epoch [92][400/3746] lr: 3.345e-02, eta: 2 days, 3:39:36, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5608, loss_cls: 3.9478, loss: 3.9478 +2024-12-29 13:45:30,709 - pyskl - INFO - Epoch [92][500/3746] lr: 3.342e-02, eta: 2 days, 3:38:13, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5595, loss_cls: 3.9775, loss: 3.9775 +2024-12-29 13:46:57,288 - pyskl - INFO - Epoch [92][600/3746] lr: 3.340e-02, eta: 2 days, 3:36:51, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5666, loss_cls: 3.9380, loss: 3.9380 +2024-12-29 13:48:23,817 - pyskl - INFO - Epoch [92][700/3746] lr: 3.337e-02, eta: 2 days, 3:35:28, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5550, loss_cls: 3.9919, loss: 3.9919 +2024-12-29 13:49:49,578 - pyskl - INFO - Epoch [92][800/3746] lr: 3.335e-02, eta: 2 days, 3:34:04, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5544, loss_cls: 3.9731, loss: 3.9731 +2024-12-29 13:51:16,124 - pyskl - INFO - Epoch [92][900/3746] lr: 3.332e-02, eta: 2 days, 3:32:41, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5608, loss_cls: 3.9729, loss: 3.9729 +2024-12-29 13:52:42,599 - pyskl - INFO - Epoch [92][1000/3746] lr: 3.329e-02, eta: 2 days, 3:31:19, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5648, loss_cls: 3.9498, loss: 3.9498 +2024-12-29 13:54:09,460 - pyskl - INFO - Epoch [92][1100/3746] lr: 3.327e-02, eta: 2 days, 3:29:56, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5522, loss_cls: 3.9828, loss: 3.9828 +2024-12-29 13:55:35,173 - pyskl - INFO - Epoch [92][1200/3746] lr: 3.324e-02, eta: 2 days, 3:28:32, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5520, loss_cls: 3.9988, loss: 3.9988 +2024-12-29 13:57:02,642 - pyskl - INFO - Epoch [92][1300/3746] lr: 3.321e-02, eta: 2 days, 3:27:10, time: 0.875, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5647, loss_cls: 3.9580, loss: 3.9580 +2024-12-29 13:58:28,899 - pyskl - INFO - Epoch [92][1400/3746] lr: 3.319e-02, eta: 2 days, 3:25:47, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5578, loss_cls: 3.9969, loss: 3.9969 +2024-12-29 13:59:55,480 - pyskl - INFO - Epoch [92][1500/3746] lr: 3.316e-02, eta: 2 days, 3:24:24, time: 0.866, data_time: 0.001, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5622, loss_cls: 3.9896, loss: 3.9896 +2024-12-29 14:01:21,467 - pyskl - INFO - Epoch [92][1600/3746] lr: 3.314e-02, eta: 2 days, 3:23:01, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5658, loss_cls: 3.9564, loss: 3.9564 +2024-12-29 14:02:46,874 - pyskl - INFO - Epoch [92][1700/3746] lr: 3.311e-02, eta: 2 days, 3:21:37, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5586, loss_cls: 3.9844, loss: 3.9844 +2024-12-29 14:04:13,215 - pyskl - INFO - Epoch [92][1800/3746] lr: 3.308e-02, eta: 2 days, 3:20:14, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5508, loss_cls: 4.0171, loss: 4.0171 +2024-12-29 14:05:39,158 - pyskl - INFO - Epoch [92][1900/3746] lr: 3.306e-02, eta: 2 days, 3:18:51, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5527, loss_cls: 4.0255, loss: 4.0255 +2024-12-29 14:07:04,689 - pyskl - INFO - Epoch [92][2000/3746] lr: 3.303e-02, eta: 2 days, 3:17:28, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5606, loss_cls: 3.9654, loss: 3.9654 +2024-12-29 14:08:30,371 - pyskl - INFO - Epoch [92][2100/3746] lr: 3.300e-02, eta: 2 days, 3:16:04, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5534, loss_cls: 3.9754, loss: 3.9754 +2024-12-29 14:09:55,347 - pyskl - INFO - Epoch [92][2200/3746] lr: 3.298e-02, eta: 2 days, 3:14:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5569, loss_cls: 3.9857, loss: 3.9857 +2024-12-29 14:11:20,775 - pyskl - INFO - Epoch [92][2300/3746] lr: 3.295e-02, eta: 2 days, 3:13:17, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5547, loss_cls: 3.9757, loss: 3.9757 +2024-12-29 14:12:46,285 - pyskl - INFO - Epoch [92][2400/3746] lr: 3.292e-02, eta: 2 days, 3:11:53, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5569, loss_cls: 3.9660, loss: 3.9660 +2024-12-29 14:14:12,169 - pyskl - INFO - Epoch [92][2500/3746] lr: 3.290e-02, eta: 2 days, 3:10:30, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5566, loss_cls: 3.9943, loss: 3.9943 +2024-12-29 14:15:37,748 - pyskl - INFO - Epoch [92][2600/3746] lr: 3.287e-02, eta: 2 days, 3:09:06, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5672, loss_cls: 3.9232, loss: 3.9232 +2024-12-29 14:17:02,902 - pyskl - INFO - Epoch [92][2700/3746] lr: 3.285e-02, eta: 2 days, 3:07:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5617, loss_cls: 3.9879, loss: 3.9879 +2024-12-29 14:18:27,886 - pyskl - INFO - Epoch [92][2800/3746] lr: 3.282e-02, eta: 2 days, 3:06:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5575, loss_cls: 3.9689, loss: 3.9689 +2024-12-29 14:19:53,033 - pyskl - INFO - Epoch [92][2900/3746] lr: 3.279e-02, eta: 2 days, 3:04:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5655, loss_cls: 3.9597, loss: 3.9597 +2024-12-29 14:21:18,060 - pyskl - INFO - Epoch [92][3000/3746] lr: 3.277e-02, eta: 2 days, 3:03:31, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5430, loss_cls: 4.0360, loss: 4.0360 +2024-12-29 14:22:42,817 - pyskl - INFO - Epoch [92][3100/3746] lr: 3.274e-02, eta: 2 days, 3:02:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5598, loss_cls: 3.9896, loss: 3.9896 +2024-12-29 14:24:07,696 - pyskl - INFO - Epoch [92][3200/3746] lr: 3.271e-02, eta: 2 days, 3:00:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5623, loss_cls: 3.9633, loss: 3.9633 +2024-12-29 14:25:32,559 - pyskl - INFO - Epoch [92][3300/3746] lr: 3.269e-02, eta: 2 days, 2:59:19, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5587, loss_cls: 3.9761, loss: 3.9761 +2024-12-29 14:26:57,747 - pyskl - INFO - Epoch [92][3400/3746] lr: 3.266e-02, eta: 2 days, 2:57:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5617, loss_cls: 3.9517, loss: 3.9517 +2024-12-29 14:28:22,655 - pyskl - INFO - Epoch [92][3500/3746] lr: 3.264e-02, eta: 2 days, 2:56:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5611, loss_cls: 3.9679, loss: 3.9679 +2024-12-29 14:29:48,437 - pyskl - INFO - Epoch [92][3600/3746] lr: 3.261e-02, eta: 2 days, 2:55:08, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5541, loss_cls: 3.9983, loss: 3.9983 +2024-12-29 14:31:14,208 - pyskl - INFO - Epoch [92][3700/3746] lr: 3.258e-02, eta: 2 days, 2:53:45, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5644, loss_cls: 3.9879, loss: 3.9879 +2024-12-29 14:31:55,507 - pyskl - INFO - Saving checkpoint at 92 epochs +2024-12-29 14:33:56,190 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 14:33:56,990 - pyskl - INFO - +top1_acc 0.2462 +top5_acc 0.4926 +2024-12-29 14:33:56,990 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 14:33:57,097 - pyskl - INFO - +mean_acc 0.2460 +2024-12-29 14:33:57,124 - pyskl - INFO - Epoch(val) [92][309] top1_acc: 0.2462, top5_acc: 0.4926, mean_class_accuracy: 0.2460 +2024-12-29 14:38:13,969 - pyskl - INFO - Epoch [93][100/3746] lr: 3.255e-02, eta: 2 days, 2:53:06, time: 2.568, data_time: 1.534, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5625, loss_cls: 3.9395, loss: 3.9395 +2024-12-29 14:39:39,543 - pyskl - INFO - Epoch [93][200/3746] lr: 3.252e-02, eta: 2 days, 2:51:42, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5830, loss_cls: 3.8897, loss: 3.8897 +2024-12-29 14:41:04,832 - pyskl - INFO - Epoch [93][300/3746] lr: 3.249e-02, eta: 2 days, 2:50:18, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5698, loss_cls: 3.9567, loss: 3.9567 +2024-12-29 14:42:30,564 - pyskl - INFO - Epoch [93][400/3746] lr: 3.247e-02, eta: 2 days, 2:48:55, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5670, loss_cls: 3.9510, loss: 3.9510 +2024-12-29 14:43:56,431 - pyskl - INFO - Epoch [93][500/3746] lr: 3.244e-02, eta: 2 days, 2:47:32, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5645, loss_cls: 3.9451, loss: 3.9451 +2024-12-29 14:45:21,483 - pyskl - INFO - Epoch [93][600/3746] lr: 3.241e-02, eta: 2 days, 2:46:08, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5658, loss_cls: 3.9486, loss: 3.9486 +2024-12-29 14:46:46,744 - pyskl - INFO - Epoch [93][700/3746] lr: 3.239e-02, eta: 2 days, 2:44:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5731, loss_cls: 3.9288, loss: 3.9288 +2024-12-29 14:48:11,909 - pyskl - INFO - Epoch [93][800/3746] lr: 3.236e-02, eta: 2 days, 2:43:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5653, loss_cls: 3.9127, loss: 3.9127 +2024-12-29 14:49:37,508 - pyskl - INFO - Epoch [93][900/3746] lr: 3.234e-02, eta: 2 days, 2:41:56, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5630, loss_cls: 3.9362, loss: 3.9362 +2024-12-29 14:51:03,508 - pyskl - INFO - Epoch [93][1000/3746] lr: 3.231e-02, eta: 2 days, 2:40:33, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5684, loss_cls: 3.9376, loss: 3.9376 +2024-12-29 14:52:29,011 - pyskl - INFO - Epoch [93][1100/3746] lr: 3.228e-02, eta: 2 days, 2:39:09, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5545, loss_cls: 4.0206, loss: 4.0206 +2024-12-29 14:53:53,804 - pyskl - INFO - Epoch [93][1200/3746] lr: 3.226e-02, eta: 2 days, 2:37:45, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5614, loss_cls: 3.9792, loss: 3.9792 +2024-12-29 14:55:20,021 - pyskl - INFO - Epoch [93][1300/3746] lr: 3.223e-02, eta: 2 days, 2:36:22, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5614, loss_cls: 3.9600, loss: 3.9600 +2024-12-29 14:56:46,001 - pyskl - INFO - Epoch [93][1400/3746] lr: 3.221e-02, eta: 2 days, 2:34:59, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5611, loss_cls: 3.9410, loss: 3.9410 +2024-12-29 14:58:11,814 - pyskl - INFO - Epoch [93][1500/3746] lr: 3.218e-02, eta: 2 days, 2:33:35, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5720, loss_cls: 3.9556, loss: 3.9556 +2024-12-29 14:59:36,736 - pyskl - INFO - Epoch [93][1600/3746] lr: 3.215e-02, eta: 2 days, 2:32:11, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5616, loss_cls: 3.9360, loss: 3.9360 +2024-12-29 15:01:02,009 - pyskl - INFO - Epoch [93][1700/3746] lr: 3.213e-02, eta: 2 days, 2:30:48, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5706, loss_cls: 3.9351, loss: 3.9351 +2024-12-29 15:02:26,507 - pyskl - INFO - Epoch [93][1800/3746] lr: 3.210e-02, eta: 2 days, 2:29:23, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5667, loss_cls: 3.9409, loss: 3.9409 +2024-12-29 15:03:51,774 - pyskl - INFO - Epoch [93][1900/3746] lr: 3.207e-02, eta: 2 days, 2:28:00, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5556, loss_cls: 3.9549, loss: 3.9549 +2024-12-29 15:05:17,496 - pyskl - INFO - Epoch [93][2000/3746] lr: 3.205e-02, eta: 2 days, 2:26:36, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5666, loss_cls: 3.9226, loss: 3.9226 +2024-12-29 15:06:42,677 - pyskl - INFO - Epoch [93][2100/3746] lr: 3.202e-02, eta: 2 days, 2:25:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5567, loss_cls: 4.0339, loss: 4.0339 +2024-12-29 15:08:07,544 - pyskl - INFO - Epoch [93][2200/3746] lr: 3.200e-02, eta: 2 days, 2:23:48, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5625, loss_cls: 3.9207, loss: 3.9207 +2024-12-29 15:09:32,851 - pyskl - INFO - Epoch [93][2300/3746] lr: 3.197e-02, eta: 2 days, 2:22:24, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5527, loss_cls: 4.0109, loss: 4.0109 +2024-12-29 15:10:58,372 - pyskl - INFO - Epoch [93][2400/3746] lr: 3.194e-02, eta: 2 days, 2:21:01, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5697, loss_cls: 3.9405, loss: 3.9405 +2024-12-29 15:12:24,269 - pyskl - INFO - Epoch [93][2500/3746] lr: 3.192e-02, eta: 2 days, 2:19:37, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5594, loss_cls: 3.9530, loss: 3.9530 +2024-12-29 15:13:50,038 - pyskl - INFO - Epoch [93][2600/3746] lr: 3.189e-02, eta: 2 days, 2:18:14, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5548, loss_cls: 3.9795, loss: 3.9795 +2024-12-29 15:15:14,883 - pyskl - INFO - Epoch [93][2700/3746] lr: 3.187e-02, eta: 2 days, 2:16:50, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5663, loss_cls: 3.9533, loss: 3.9533 +2024-12-29 15:16:39,521 - pyskl - INFO - Epoch [93][2800/3746] lr: 3.184e-02, eta: 2 days, 2:15:26, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5616, loss_cls: 3.9980, loss: 3.9980 +2024-12-29 15:18:04,469 - pyskl - INFO - Epoch [93][2900/3746] lr: 3.181e-02, eta: 2 days, 2:14:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5666, loss_cls: 3.9400, loss: 3.9400 +2024-12-29 15:19:29,861 - pyskl - INFO - Epoch [93][3000/3746] lr: 3.179e-02, eta: 2 days, 2:12:38, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5627, loss_cls: 3.9683, loss: 3.9683 +2024-12-29 15:20:55,453 - pyskl - INFO - Epoch [93][3100/3746] lr: 3.176e-02, eta: 2 days, 2:11:14, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5623, loss_cls: 3.9854, loss: 3.9854 +2024-12-29 15:22:21,196 - pyskl - INFO - Epoch [93][3200/3746] lr: 3.174e-02, eta: 2 days, 2:09:51, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5553, loss_cls: 3.9841, loss: 3.9841 +2024-12-29 15:23:46,945 - pyskl - INFO - Epoch [93][3300/3746] lr: 3.171e-02, eta: 2 days, 2:08:27, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5608, loss_cls: 3.9512, loss: 3.9512 +2024-12-29 15:25:12,445 - pyskl - INFO - Epoch [93][3400/3746] lr: 3.168e-02, eta: 2 days, 2:07:04, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5613, loss_cls: 3.9853, loss: 3.9853 +2024-12-29 15:26:38,007 - pyskl - INFO - Epoch [93][3500/3746] lr: 3.166e-02, eta: 2 days, 2:05:40, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5614, loss_cls: 3.9762, loss: 3.9762 +2024-12-29 15:28:02,875 - pyskl - INFO - Epoch [93][3600/3746] lr: 3.163e-02, eta: 2 days, 2:04:16, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5670, loss_cls: 3.9605, loss: 3.9605 +2024-12-29 15:29:28,328 - pyskl - INFO - Epoch [93][3700/3746] lr: 3.161e-02, eta: 2 days, 2:02:52, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5584, loss_cls: 3.9627, loss: 3.9627 +2024-12-29 15:30:08,973 - pyskl - INFO - Saving checkpoint at 93 epochs +2024-12-29 15:32:09,168 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 15:32:09,861 - pyskl - INFO - +top1_acc 0.2495 +top5_acc 0.4968 +2024-12-29 15:32:09,861 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 15:32:09,913 - pyskl - INFO - +mean_acc 0.2494 +2024-12-29 15:32:09,929 - pyskl - INFO - Epoch(val) [93][309] top1_acc: 0.2495, top5_acc: 0.4968, mean_class_accuracy: 0.2494 +2024-12-29 15:36:36,693 - pyskl - INFO - Epoch [94][100/3746] lr: 3.157e-02, eta: 2 days, 2:02:17, time: 2.668, data_time: 1.628, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5802, loss_cls: 3.8685, loss: 3.8685 +2024-12-29 15:38:03,212 - pyskl - INFO - Epoch [94][200/3746] lr: 3.154e-02, eta: 2 days, 2:00:54, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5683, loss_cls: 3.9108, loss: 3.9108 +2024-12-29 15:39:29,382 - pyskl - INFO - Epoch [94][300/3746] lr: 3.152e-02, eta: 2 days, 1:59:31, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5645, loss_cls: 3.9391, loss: 3.9391 +2024-12-29 15:40:55,329 - pyskl - INFO - Epoch [94][400/3746] lr: 3.149e-02, eta: 2 days, 1:58:07, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5570, loss_cls: 3.9902, loss: 3.9902 +2024-12-29 15:42:22,216 - pyskl - INFO - Epoch [94][500/3746] lr: 3.146e-02, eta: 2 days, 1:56:44, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5806, loss_cls: 3.8874, loss: 3.8874 +2024-12-29 15:43:49,059 - pyskl - INFO - Epoch [94][600/3746] lr: 3.144e-02, eta: 2 days, 1:55:21, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5642, loss_cls: 3.9546, loss: 3.9546 +2024-12-29 15:45:15,858 - pyskl - INFO - Epoch [94][700/3746] lr: 3.141e-02, eta: 2 days, 1:53:58, time: 0.868, data_time: 0.001, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5772, loss_cls: 3.8956, loss: 3.8956 +2024-12-29 15:46:41,960 - pyskl - INFO - Epoch [94][800/3746] lr: 3.139e-02, eta: 2 days, 1:52:35, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5663, loss_cls: 3.9499, loss: 3.9499 +2024-12-29 15:48:07,543 - pyskl - INFO - Epoch [94][900/3746] lr: 3.136e-02, eta: 2 days, 1:51:11, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5769, loss_cls: 3.9150, loss: 3.9150 +2024-12-29 15:49:33,625 - pyskl - INFO - Epoch [94][1000/3746] lr: 3.133e-02, eta: 2 days, 1:49:48, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5530, loss_cls: 4.0294, loss: 4.0294 +2024-12-29 15:51:00,522 - pyskl - INFO - Epoch [94][1100/3746] lr: 3.131e-02, eta: 2 days, 1:48:25, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5773, loss_cls: 3.8905, loss: 3.8905 +2024-12-29 15:52:27,125 - pyskl - INFO - Epoch [94][1200/3746] lr: 3.128e-02, eta: 2 days, 1:47:02, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5527, loss_cls: 3.9833, loss: 3.9833 +2024-12-29 15:53:53,582 - pyskl - INFO - Epoch [94][1300/3746] lr: 3.126e-02, eta: 2 days, 1:45:39, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5706, loss_cls: 3.9087, loss: 3.9087 +2024-12-29 15:55:20,310 - pyskl - INFO - Epoch [94][1400/3746] lr: 3.123e-02, eta: 2 days, 1:44:16, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5620, loss_cls: 3.9289, loss: 3.9289 +2024-12-29 15:56:46,348 - pyskl - INFO - Epoch [94][1500/3746] lr: 3.120e-02, eta: 2 days, 1:42:52, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5647, loss_cls: 3.9458, loss: 3.9458 +2024-12-29 15:58:12,462 - pyskl - INFO - Epoch [94][1600/3746] lr: 3.118e-02, eta: 2 days, 1:41:29, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5633, loss_cls: 3.9300, loss: 3.9300 +2024-12-29 15:59:38,040 - pyskl - INFO - Epoch [94][1700/3746] lr: 3.115e-02, eta: 2 days, 1:40:05, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5748, loss_cls: 3.9222, loss: 3.9222 +2024-12-29 16:01:03,029 - pyskl - INFO - Epoch [94][1800/3746] lr: 3.113e-02, eta: 2 days, 1:38:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5652, loss_cls: 3.9356, loss: 3.9356 +2024-12-29 16:02:27,968 - pyskl - INFO - Epoch [94][1900/3746] lr: 3.110e-02, eta: 2 days, 1:37:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5703, loss_cls: 3.9292, loss: 3.9292 +2024-12-29 16:03:53,451 - pyskl - INFO - Epoch [94][2000/3746] lr: 3.108e-02, eta: 2 days, 1:35:53, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5653, loss_cls: 3.9959, loss: 3.9959 +2024-12-29 16:05:19,027 - pyskl - INFO - Epoch [94][2100/3746] lr: 3.105e-02, eta: 2 days, 1:34:29, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5733, loss_cls: 3.9260, loss: 3.9260 +2024-12-29 16:06:44,039 - pyskl - INFO - Epoch [94][2200/3746] lr: 3.102e-02, eta: 2 days, 1:33:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5652, loss_cls: 3.9265, loss: 3.9265 +2024-12-29 16:08:09,646 - pyskl - INFO - Epoch [94][2300/3746] lr: 3.100e-02, eta: 2 days, 1:31:42, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5578, loss_cls: 3.9828, loss: 3.9828 +2024-12-29 16:09:35,115 - pyskl - INFO - Epoch [94][2400/3746] lr: 3.097e-02, eta: 2 days, 1:30:18, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5633, loss_cls: 3.9774, loss: 3.9774 +2024-12-29 16:11:00,648 - pyskl - INFO - Epoch [94][2500/3746] lr: 3.095e-02, eta: 2 days, 1:28:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5625, loss_cls: 3.9943, loss: 3.9943 +2024-12-29 16:12:26,883 - pyskl - INFO - Epoch [94][2600/3746] lr: 3.092e-02, eta: 2 days, 1:27:31, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5617, loss_cls: 3.9475, loss: 3.9475 +2024-12-29 16:13:52,705 - pyskl - INFO - Epoch [94][2700/3746] lr: 3.089e-02, eta: 2 days, 1:26:07, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5581, loss_cls: 3.9982, loss: 3.9982 +2024-12-29 16:15:18,432 - pyskl - INFO - Epoch [94][2800/3746] lr: 3.087e-02, eta: 2 days, 1:24:44, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5798, loss_cls: 3.8951, loss: 3.8951 +2024-12-29 16:16:43,660 - pyskl - INFO - Epoch [94][2900/3746] lr: 3.084e-02, eta: 2 days, 1:23:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5584, loss_cls: 3.9582, loss: 3.9582 +2024-12-29 16:18:08,787 - pyskl - INFO - Epoch [94][3000/3746] lr: 3.082e-02, eta: 2 days, 1:21:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5587, loss_cls: 3.9728, loss: 3.9728 +2024-12-29 16:19:33,920 - pyskl - INFO - Epoch [94][3100/3746] lr: 3.079e-02, eta: 2 days, 1:20:32, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5634, loss_cls: 3.9554, loss: 3.9554 +2024-12-29 16:20:58,882 - pyskl - INFO - Epoch [94][3200/3746] lr: 3.077e-02, eta: 2 days, 1:19:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5672, loss_cls: 3.9194, loss: 3.9194 +2024-12-29 16:22:24,177 - pyskl - INFO - Epoch [94][3300/3746] lr: 3.074e-02, eta: 2 days, 1:17:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5531, loss_cls: 3.9540, loss: 3.9540 +2024-12-29 16:23:49,703 - pyskl - INFO - Epoch [94][3400/3746] lr: 3.071e-02, eta: 2 days, 1:16:20, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5598, loss_cls: 3.9366, loss: 3.9366 +2024-12-29 16:25:15,234 - pyskl - INFO - Epoch [94][3500/3746] lr: 3.069e-02, eta: 2 days, 1:14:56, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5663, loss_cls: 3.9264, loss: 3.9264 +2024-12-29 16:26:39,595 - pyskl - INFO - Epoch [94][3600/3746] lr: 3.066e-02, eta: 2 days, 1:13:32, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5659, loss_cls: 3.9701, loss: 3.9701 +2024-12-29 16:28:04,659 - pyskl - INFO - Epoch [94][3700/3746] lr: 3.064e-02, eta: 2 days, 1:12:08, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5737, loss_cls: 3.9160, loss: 3.9160 +2024-12-29 16:28:45,792 - pyskl - INFO - Saving checkpoint at 94 epochs +2024-12-29 16:30:45,110 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 16:30:45,805 - pyskl - INFO - +top1_acc 0.2356 +top5_acc 0.4718 +2024-12-29 16:30:45,805 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 16:30:45,849 - pyskl - INFO - +mean_acc 0.2353 +2024-12-29 16:30:45,861 - pyskl - INFO - Epoch(val) [94][309] top1_acc: 0.2356, top5_acc: 0.4718, mean_class_accuracy: 0.2353 +2024-12-29 16:35:13,050 - pyskl - INFO - Epoch [95][100/3746] lr: 3.060e-02, eta: 2 days, 1:11:30, time: 2.672, data_time: 1.615, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5803, loss_cls: 3.8670, loss: 3.8670 +2024-12-29 16:36:39,642 - pyskl - INFO - Epoch [95][200/3746] lr: 3.057e-02, eta: 2 days, 1:10:07, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5808, loss_cls: 3.8526, loss: 3.8526 +2024-12-29 16:38:06,978 - pyskl - INFO - Epoch [95][300/3746] lr: 3.055e-02, eta: 2 days, 1:08:44, time: 0.873, data_time: 0.001, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5814, loss_cls: 3.8784, loss: 3.8784 +2024-12-29 16:39:34,710 - pyskl - INFO - Epoch [95][400/3746] lr: 3.052e-02, eta: 2 days, 1:07:22, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5650, loss_cls: 3.9520, loss: 3.9520 +2024-12-29 16:41:01,756 - pyskl - INFO - Epoch [95][500/3746] lr: 3.050e-02, eta: 2 days, 1:05:59, time: 0.870, data_time: 0.001, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5808, loss_cls: 3.8653, loss: 3.8653 +2024-12-29 16:42:28,520 - pyskl - INFO - Epoch [95][600/3746] lr: 3.047e-02, eta: 2 days, 1:04:36, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5764, loss_cls: 3.8905, loss: 3.8905 +2024-12-29 16:43:55,055 - pyskl - INFO - Epoch [95][700/3746] lr: 3.044e-02, eta: 2 days, 1:03:13, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5700, loss_cls: 3.9221, loss: 3.9221 +2024-12-29 16:45:21,539 - pyskl - INFO - Epoch [95][800/3746] lr: 3.042e-02, eta: 2 days, 1:01:49, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5664, loss_cls: 3.9140, loss: 3.9140 +2024-12-29 16:46:48,932 - pyskl - INFO - Epoch [95][900/3746] lr: 3.039e-02, eta: 2 days, 1:00:27, time: 0.874, data_time: 0.001, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5586, loss_cls: 3.9756, loss: 3.9756 +2024-12-29 16:48:15,865 - pyskl - INFO - Epoch [95][1000/3746] lr: 3.037e-02, eta: 2 days, 0:59:03, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5842, loss_cls: 3.8821, loss: 3.8821 +2024-12-29 16:49:42,051 - pyskl - INFO - Epoch [95][1100/3746] lr: 3.034e-02, eta: 2 days, 0:57:40, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5758, loss_cls: 3.8894, loss: 3.8894 +2024-12-29 16:51:08,792 - pyskl - INFO - Epoch [95][1200/3746] lr: 3.032e-02, eta: 2 days, 0:56:17, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5706, loss_cls: 3.9300, loss: 3.9300 +2024-12-29 16:52:34,876 - pyskl - INFO - Epoch [95][1300/3746] lr: 3.029e-02, eta: 2 days, 0:54:53, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5698, loss_cls: 3.9261, loss: 3.9261 +2024-12-29 16:54:01,815 - pyskl - INFO - Epoch [95][1400/3746] lr: 3.026e-02, eta: 2 days, 0:53:30, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5947, loss_cls: 3.8010, loss: 3.8010 +2024-12-29 16:55:28,197 - pyskl - INFO - Epoch [95][1500/3746] lr: 3.024e-02, eta: 2 days, 0:52:07, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5689, loss_cls: 3.9534, loss: 3.9534 +2024-12-29 16:56:54,487 - pyskl - INFO - Epoch [95][1600/3746] lr: 3.021e-02, eta: 2 days, 0:50:44, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5597, loss_cls: 3.9786, loss: 3.9786 +2024-12-29 16:58:20,856 - pyskl - INFO - Epoch [95][1700/3746] lr: 3.019e-02, eta: 2 days, 0:49:20, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5628, loss_cls: 3.9392, loss: 3.9392 +2024-12-29 16:59:46,947 - pyskl - INFO - Epoch [95][1800/3746] lr: 3.016e-02, eta: 2 days, 0:47:57, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5623, loss_cls: 3.9631, loss: 3.9631 +2024-12-29 17:01:12,706 - pyskl - INFO - Epoch [95][1900/3746] lr: 3.014e-02, eta: 2 days, 0:46:33, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5609, loss_cls: 3.9405, loss: 3.9405 +2024-12-29 17:02:39,089 - pyskl - INFO - Epoch [95][2000/3746] lr: 3.011e-02, eta: 2 days, 0:45:10, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5664, loss_cls: 3.9190, loss: 3.9190 +2024-12-29 17:04:05,185 - pyskl - INFO - Epoch [95][2100/3746] lr: 3.008e-02, eta: 2 days, 0:43:46, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5602, loss_cls: 3.9694, loss: 3.9694 +2024-12-29 17:05:31,666 - pyskl - INFO - Epoch [95][2200/3746] lr: 3.006e-02, eta: 2 days, 0:42:23, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5706, loss_cls: 3.9458, loss: 3.9458 +2024-12-29 17:06:57,936 - pyskl - INFO - Epoch [95][2300/3746] lr: 3.003e-02, eta: 2 days, 0:40:59, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5636, loss_cls: 3.9673, loss: 3.9673 +2024-12-29 17:08:24,235 - pyskl - INFO - Epoch [95][2400/3746] lr: 3.001e-02, eta: 2 days, 0:39:36, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5742, loss_cls: 3.8959, loss: 3.8959 +2024-12-29 17:09:49,990 - pyskl - INFO - Epoch [95][2500/3746] lr: 2.998e-02, eta: 2 days, 0:38:12, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5686, loss_cls: 3.9254, loss: 3.9254 +2024-12-29 17:11:15,746 - pyskl - INFO - Epoch [95][2600/3746] lr: 2.996e-02, eta: 2 days, 0:36:49, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5625, loss_cls: 3.9718, loss: 3.9718 +2024-12-29 17:12:40,267 - pyskl - INFO - Epoch [95][2700/3746] lr: 2.993e-02, eta: 2 days, 0:35:24, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5709, loss_cls: 3.9099, loss: 3.9099 +2024-12-29 17:14:04,991 - pyskl - INFO - Epoch [95][2800/3746] lr: 2.991e-02, eta: 2 days, 0:34:00, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5613, loss_cls: 3.9470, loss: 3.9470 +2024-12-29 17:15:29,841 - pyskl - INFO - Epoch [95][2900/3746] lr: 2.988e-02, eta: 2 days, 0:32:36, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5639, loss_cls: 3.9546, loss: 3.9546 +2024-12-29 17:16:54,572 - pyskl - INFO - Epoch [95][3000/3746] lr: 2.985e-02, eta: 2 days, 0:31:11, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5714, loss_cls: 3.8996, loss: 3.8996 +2024-12-29 17:18:19,643 - pyskl - INFO - Epoch [95][3100/3746] lr: 2.983e-02, eta: 2 days, 0:29:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5642, loss_cls: 3.9486, loss: 3.9486 +2024-12-29 17:19:44,703 - pyskl - INFO - Epoch [95][3200/3746] lr: 2.980e-02, eta: 2 days, 0:28:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5667, loss_cls: 3.9531, loss: 3.9531 +2024-12-29 17:21:09,787 - pyskl - INFO - Epoch [95][3300/3746] lr: 2.978e-02, eta: 2 days, 0:26:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5747, loss_cls: 3.9145, loss: 3.9145 +2024-12-29 17:22:34,971 - pyskl - INFO - Epoch [95][3400/3746] lr: 2.975e-02, eta: 2 days, 0:25:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5692, loss_cls: 3.9299, loss: 3.9299 +2024-12-29 17:24:00,594 - pyskl - INFO - Epoch [95][3500/3746] lr: 2.973e-02, eta: 2 days, 0:24:11, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5806, loss_cls: 3.8982, loss: 3.8982 +2024-12-29 17:25:25,188 - pyskl - INFO - Epoch [95][3600/3746] lr: 2.970e-02, eta: 2 days, 0:22:47, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5672, loss_cls: 3.9448, loss: 3.9448 +2024-12-29 17:26:50,778 - pyskl - INFO - Epoch [95][3700/3746] lr: 2.968e-02, eta: 2 days, 0:21:23, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5691, loss_cls: 3.9393, loss: 3.9393 +2024-12-29 17:27:31,900 - pyskl - INFO - Saving checkpoint at 95 epochs +2024-12-29 17:29:31,972 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 17:29:32,901 - pyskl - INFO - +top1_acc 0.2417 +top5_acc 0.4881 +2024-12-29 17:29:32,901 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 17:29:32,954 - pyskl - INFO - +mean_acc 0.2415 +2024-12-29 17:29:32,978 - pyskl - INFO - Epoch(val) [95][309] top1_acc: 0.2417, top5_acc: 0.4881, mean_class_accuracy: 0.2415 +2024-12-29 17:33:55,698 - pyskl - INFO - Epoch [96][100/3746] lr: 2.964e-02, eta: 2 days, 0:20:40, time: 2.627, data_time: 1.566, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5825, loss_cls: 3.8737, loss: 3.8737 +2024-12-29 17:35:22,714 - pyskl - INFO - Epoch [96][200/3746] lr: 2.961e-02, eta: 2 days, 0:19:17, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5802, loss_cls: 3.8386, loss: 3.8386 +2024-12-29 17:36:49,729 - pyskl - INFO - Epoch [96][300/3746] lr: 2.959e-02, eta: 2 days, 0:17:54, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5752, loss_cls: 3.8761, loss: 3.8761 +2024-12-29 17:38:16,708 - pyskl - INFO - Epoch [96][400/3746] lr: 2.956e-02, eta: 2 days, 0:16:31, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5870, loss_cls: 3.8356, loss: 3.8356 +2024-12-29 17:39:43,563 - pyskl - INFO - Epoch [96][500/3746] lr: 2.954e-02, eta: 2 days, 0:15:08, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5617, loss_cls: 3.9466, loss: 3.9466 +2024-12-29 17:41:10,034 - pyskl - INFO - Epoch [96][600/3746] lr: 2.951e-02, eta: 2 days, 0:13:44, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.5834, loss_cls: 3.8114, loss: 3.8114 +2024-12-29 17:42:37,509 - pyskl - INFO - Epoch [96][700/3746] lr: 2.948e-02, eta: 2 days, 0:12:21, time: 0.875, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5809, loss_cls: 3.8925, loss: 3.8925 +2024-12-29 17:44:04,671 - pyskl - INFO - Epoch [96][800/3746] lr: 2.946e-02, eta: 2 days, 0:10:58, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5695, loss_cls: 3.9092, loss: 3.9092 +2024-12-29 17:45:31,387 - pyskl - INFO - Epoch [96][900/3746] lr: 2.943e-02, eta: 2 days, 0:09:35, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5745, loss_cls: 3.9017, loss: 3.9017 +2024-12-29 17:46:58,611 - pyskl - INFO - Epoch [96][1000/3746] lr: 2.941e-02, eta: 2 days, 0:08:12, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5700, loss_cls: 3.8955, loss: 3.8955 +2024-12-29 17:48:25,645 - pyskl - INFO - Epoch [96][1100/3746] lr: 2.938e-02, eta: 2 days, 0:06:49, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5717, loss_cls: 3.8699, loss: 3.8699 +2024-12-29 17:49:53,326 - pyskl - INFO - Epoch [96][1200/3746] lr: 2.936e-02, eta: 2 days, 0:05:26, time: 0.877, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5659, loss_cls: 3.9265, loss: 3.9265 +2024-12-29 17:51:20,648 - pyskl - INFO - Epoch [96][1300/3746] lr: 2.933e-02, eta: 2 days, 0:04:04, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5737, loss_cls: 3.9138, loss: 3.9138 +2024-12-29 17:52:47,725 - pyskl - INFO - Epoch [96][1400/3746] lr: 2.931e-02, eta: 2 days, 0:02:40, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5675, loss_cls: 3.9212, loss: 3.9212 +2024-12-29 17:54:14,450 - pyskl - INFO - Epoch [96][1500/3746] lr: 2.928e-02, eta: 2 days, 0:01:17, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5673, loss_cls: 3.8999, loss: 3.8999 +2024-12-29 17:55:40,266 - pyskl - INFO - Epoch [96][1600/3746] lr: 2.926e-02, eta: 1 day, 23:59:53, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5602, loss_cls: 3.9733, loss: 3.9733 +2024-12-29 17:57:04,658 - pyskl - INFO - Epoch [96][1700/3746] lr: 2.923e-02, eta: 1 day, 23:58:29, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5645, loss_cls: 3.9608, loss: 3.9608 +2024-12-29 17:58:30,234 - pyskl - INFO - Epoch [96][1800/3746] lr: 2.920e-02, eta: 1 day, 23:57:05, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5831, loss_cls: 3.8660, loss: 3.8660 +2024-12-29 17:59:55,133 - pyskl - INFO - Epoch [96][1900/3746] lr: 2.918e-02, eta: 1 day, 23:55:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5667, loss_cls: 3.9272, loss: 3.9272 +2024-12-29 18:01:20,486 - pyskl - INFO - Epoch [96][2000/3746] lr: 2.915e-02, eta: 1 day, 23:54:17, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5709, loss_cls: 3.8964, loss: 3.8964 +2024-12-29 18:02:45,647 - pyskl - INFO - Epoch [96][2100/3746] lr: 2.913e-02, eta: 1 day, 23:52:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5717, loss_cls: 3.9296, loss: 3.9296 +2024-12-29 18:04:10,989 - pyskl - INFO - Epoch [96][2200/3746] lr: 2.910e-02, eta: 1 day, 23:51:28, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5764, loss_cls: 3.8855, loss: 3.8855 +2024-12-29 18:05:36,270 - pyskl - INFO - Epoch [96][2300/3746] lr: 2.908e-02, eta: 1 day, 23:50:04, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5694, loss_cls: 3.9115, loss: 3.9115 +2024-12-29 18:07:02,045 - pyskl - INFO - Epoch [96][2400/3746] lr: 2.905e-02, eta: 1 day, 23:48:40, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5575, loss_cls: 3.9660, loss: 3.9660 +2024-12-29 18:08:27,093 - pyskl - INFO - Epoch [96][2500/3746] lr: 2.903e-02, eta: 1 day, 23:47:16, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5733, loss_cls: 3.9056, loss: 3.9056 +2024-12-29 18:09:52,405 - pyskl - INFO - Epoch [96][2600/3746] lr: 2.900e-02, eta: 1 day, 23:45:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5786, loss_cls: 3.8786, loss: 3.8786 +2024-12-29 18:11:18,405 - pyskl - INFO - Epoch [96][2700/3746] lr: 2.898e-02, eta: 1 day, 23:44:28, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5722, loss_cls: 3.9265, loss: 3.9265 +2024-12-29 18:12:43,881 - pyskl - INFO - Epoch [96][2800/3746] lr: 2.895e-02, eta: 1 day, 23:43:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5728, loss_cls: 3.9404, loss: 3.9404 +2024-12-29 18:14:08,969 - pyskl - INFO - Epoch [96][2900/3746] lr: 2.893e-02, eta: 1 day, 23:41:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5672, loss_cls: 3.9337, loss: 3.9337 +2024-12-29 18:15:33,759 - pyskl - INFO - Epoch [96][3000/3746] lr: 2.890e-02, eta: 1 day, 23:40:16, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5713, loss_cls: 3.9177, loss: 3.9177 +2024-12-29 18:16:58,272 - pyskl - INFO - Epoch [96][3100/3746] lr: 2.887e-02, eta: 1 day, 23:38:51, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5658, loss_cls: 3.9523, loss: 3.9523 +2024-12-29 18:18:22,903 - pyskl - INFO - Epoch [96][3200/3746] lr: 2.885e-02, eta: 1 day, 23:37:27, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5748, loss_cls: 3.9017, loss: 3.9017 +2024-12-29 18:19:47,848 - pyskl - INFO - Epoch [96][3300/3746] lr: 2.882e-02, eta: 1 day, 23:36:03, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5722, loss_cls: 3.8782, loss: 3.8782 +2024-12-29 18:21:12,582 - pyskl - INFO - Epoch [96][3400/3746] lr: 2.880e-02, eta: 1 day, 23:34:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5653, loss_cls: 3.9332, loss: 3.9332 +2024-12-29 18:22:37,552 - pyskl - INFO - Epoch [96][3500/3746] lr: 2.877e-02, eta: 1 day, 23:33:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5681, loss_cls: 3.9023, loss: 3.9023 +2024-12-29 18:24:02,134 - pyskl - INFO - Epoch [96][3600/3746] lr: 2.875e-02, eta: 1 day, 23:31:49, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5663, loss_cls: 3.9401, loss: 3.9401 +2024-12-29 18:25:27,299 - pyskl - INFO - Epoch [96][3700/3746] lr: 2.872e-02, eta: 1 day, 23:30:25, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5736, loss_cls: 3.9324, loss: 3.9324 +2024-12-29 18:26:08,538 - pyskl - INFO - Saving checkpoint at 96 epochs +2024-12-29 18:28:08,000 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 18:28:08,734 - pyskl - INFO - +top1_acc 0.2604 +top5_acc 0.5040 +2024-12-29 18:28:08,734 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 18:28:08,785 - pyskl - INFO - +mean_acc 0.2603 +2024-12-29 18:28:08,790 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_91.pth was removed +2024-12-29 18:28:09,111 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_96.pth. +2024-12-29 18:28:09,113 - pyskl - INFO - Best top1_acc is 0.2604 at 96 epoch. +2024-12-29 18:28:09,129 - pyskl - INFO - Epoch(val) [96][309] top1_acc: 0.2604, top5_acc: 0.5040, mean_class_accuracy: 0.2603 +2024-12-29 18:32:30,900 - pyskl - INFO - Epoch [97][100/3746] lr: 2.869e-02, eta: 1 day, 23:29:40, time: 2.618, data_time: 1.581, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5708, loss_cls: 3.8932, loss: 3.8932 +2024-12-29 18:33:56,592 - pyskl - INFO - Epoch [97][200/3746] lr: 2.866e-02, eta: 1 day, 23:28:16, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5850, loss_cls: 3.8461, loss: 3.8461 +2024-12-29 18:35:22,076 - pyskl - INFO - Epoch [97][300/3746] lr: 2.864e-02, eta: 1 day, 23:26:52, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5852, loss_cls: 3.8438, loss: 3.8438 +2024-12-29 18:36:47,611 - pyskl - INFO - Epoch [97][400/3746] lr: 2.861e-02, eta: 1 day, 23:25:27, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5803, loss_cls: 3.8693, loss: 3.8693 +2024-12-29 18:38:12,980 - pyskl - INFO - Epoch [97][500/3746] lr: 2.858e-02, eta: 1 day, 23:24:03, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5713, loss_cls: 3.8789, loss: 3.8789 +2024-12-29 18:39:38,846 - pyskl - INFO - Epoch [97][600/3746] lr: 2.856e-02, eta: 1 day, 23:22:40, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5734, loss_cls: 3.8976, loss: 3.8976 +2024-12-29 18:41:04,419 - pyskl - INFO - Epoch [97][700/3746] lr: 2.853e-02, eta: 1 day, 23:21:16, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5773, loss_cls: 3.8563, loss: 3.8563 +2024-12-29 18:42:30,263 - pyskl - INFO - Epoch [97][800/3746] lr: 2.851e-02, eta: 1 day, 23:19:52, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5703, loss_cls: 3.8981, loss: 3.8981 +2024-12-29 18:43:55,533 - pyskl - INFO - Epoch [97][900/3746] lr: 2.848e-02, eta: 1 day, 23:18:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5739, loss_cls: 3.8875, loss: 3.8875 +2024-12-29 18:45:21,705 - pyskl - INFO - Epoch [97][1000/3746] lr: 2.846e-02, eta: 1 day, 23:17:04, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5636, loss_cls: 3.9320, loss: 3.9320 +2024-12-29 18:46:48,001 - pyskl - INFO - Epoch [97][1100/3746] lr: 2.843e-02, eta: 1 day, 23:15:40, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5705, loss_cls: 3.9048, loss: 3.9048 +2024-12-29 18:48:14,545 - pyskl - INFO - Epoch [97][1200/3746] lr: 2.841e-02, eta: 1 day, 23:14:17, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5814, loss_cls: 3.8503, loss: 3.8503 +2024-12-29 18:49:40,455 - pyskl - INFO - Epoch [97][1300/3746] lr: 2.838e-02, eta: 1 day, 23:12:53, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5750, loss_cls: 3.8895, loss: 3.8895 +2024-12-29 18:51:06,478 - pyskl - INFO - Epoch [97][1400/3746] lr: 2.836e-02, eta: 1 day, 23:11:29, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5725, loss_cls: 3.9096, loss: 3.9096 +2024-12-29 18:52:32,509 - pyskl - INFO - Epoch [97][1500/3746] lr: 2.833e-02, eta: 1 day, 23:10:05, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5759, loss_cls: 3.8803, loss: 3.8803 +2024-12-29 18:53:58,635 - pyskl - INFO - Epoch [97][1600/3746] lr: 2.831e-02, eta: 1 day, 23:08:42, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5777, loss_cls: 3.8822, loss: 3.8822 +2024-12-29 18:55:23,736 - pyskl - INFO - Epoch [97][1700/3746] lr: 2.828e-02, eta: 1 day, 23:07:17, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5709, loss_cls: 3.8994, loss: 3.8994 +2024-12-29 18:56:49,277 - pyskl - INFO - Epoch [97][1800/3746] lr: 2.826e-02, eta: 1 day, 23:05:53, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5723, loss_cls: 3.9040, loss: 3.9040 +2024-12-29 18:58:14,706 - pyskl - INFO - Epoch [97][1900/3746] lr: 2.823e-02, eta: 1 day, 23:04:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5641, loss_cls: 3.9393, loss: 3.9393 +2024-12-29 18:59:40,606 - pyskl - INFO - Epoch [97][2000/3746] lr: 2.821e-02, eta: 1 day, 23:03:05, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5814, loss_cls: 3.8537, loss: 3.8537 +2024-12-29 19:01:06,594 - pyskl - INFO - Epoch [97][2100/3746] lr: 2.818e-02, eta: 1 day, 23:01:42, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5778, loss_cls: 3.8981, loss: 3.8981 +2024-12-29 19:02:32,507 - pyskl - INFO - Epoch [97][2200/3746] lr: 2.816e-02, eta: 1 day, 23:00:18, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5681, loss_cls: 3.8976, loss: 3.8976 +2024-12-29 19:03:58,469 - pyskl - INFO - Epoch [97][2300/3746] lr: 2.813e-02, eta: 1 day, 22:58:54, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5680, loss_cls: 3.9450, loss: 3.9450 +2024-12-29 19:05:24,538 - pyskl - INFO - Epoch [97][2400/3746] lr: 2.811e-02, eta: 1 day, 22:57:30, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5822, loss_cls: 3.8790, loss: 3.8790 +2024-12-29 19:06:50,171 - pyskl - INFO - Epoch [97][2500/3746] lr: 2.808e-02, eta: 1 day, 22:56:06, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5697, loss_cls: 3.9114, loss: 3.9114 +2024-12-29 19:08:15,434 - pyskl - INFO - Epoch [97][2600/3746] lr: 2.806e-02, eta: 1 day, 22:54:42, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5687, loss_cls: 3.9400, loss: 3.9400 +2024-12-29 19:09:41,879 - pyskl - INFO - Epoch [97][2700/3746] lr: 2.803e-02, eta: 1 day, 22:53:19, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5884, loss_cls: 3.8194, loss: 3.8194 +2024-12-29 19:11:07,903 - pyskl - INFO - Epoch [97][2800/3746] lr: 2.801e-02, eta: 1 day, 22:51:55, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5692, loss_cls: 3.9093, loss: 3.9093 +2024-12-29 19:12:33,076 - pyskl - INFO - Epoch [97][2900/3746] lr: 2.798e-02, eta: 1 day, 22:50:30, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5756, loss_cls: 3.9140, loss: 3.9140 +2024-12-29 19:13:58,152 - pyskl - INFO - Epoch [97][3000/3746] lr: 2.796e-02, eta: 1 day, 22:49:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5770, loss_cls: 3.8980, loss: 3.8980 +2024-12-29 19:15:23,368 - pyskl - INFO - Epoch [97][3100/3746] lr: 2.793e-02, eta: 1 day, 22:47:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5628, loss_cls: 3.9428, loss: 3.9428 +2024-12-29 19:16:48,732 - pyskl - INFO - Epoch [97][3200/3746] lr: 2.791e-02, eta: 1 day, 22:46:18, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5667, loss_cls: 3.9249, loss: 3.9249 +2024-12-29 19:18:13,963 - pyskl - INFO - Epoch [97][3300/3746] lr: 2.788e-02, eta: 1 day, 22:44:54, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5816, loss_cls: 3.8634, loss: 3.8634 +2024-12-29 19:19:39,434 - pyskl - INFO - Epoch [97][3400/3746] lr: 2.786e-02, eta: 1 day, 22:43:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5711, loss_cls: 3.8952, loss: 3.8952 +2024-12-29 19:21:04,736 - pyskl - INFO - Epoch [97][3500/3746] lr: 2.783e-02, eta: 1 day, 22:42:05, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5723, loss_cls: 3.9126, loss: 3.9126 +2024-12-29 19:22:29,901 - pyskl - INFO - Epoch [97][3600/3746] lr: 2.781e-02, eta: 1 day, 22:40:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5678, loss_cls: 3.9134, loss: 3.9134 +2024-12-29 19:23:54,930 - pyskl - INFO - Epoch [97][3700/3746] lr: 2.778e-02, eta: 1 day, 22:39:17, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5759, loss_cls: 3.9162, loss: 3.9162 +2024-12-29 19:24:35,794 - pyskl - INFO - Saving checkpoint at 97 epochs +2024-12-29 19:26:37,808 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 19:26:38,592 - pyskl - INFO - +top1_acc 0.2630 +top5_acc 0.5096 +2024-12-29 19:26:38,592 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 19:26:38,640 - pyskl - INFO - +mean_acc 0.2627 +2024-12-29 19:26:38,644 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_96.pth was removed +2024-12-29 19:26:38,954 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_97.pth. +2024-12-29 19:26:38,954 - pyskl - INFO - Best top1_acc is 0.2630 at 97 epoch. +2024-12-29 19:26:38,973 - pyskl - INFO - Epoch(val) [97][309] top1_acc: 0.2630, top5_acc: 0.5096, mean_class_accuracy: 0.2627 +2024-12-29 19:31:03,470 - pyskl - INFO - Epoch [98][100/3746] lr: 2.774e-02, eta: 1 day, 22:38:30, time: 2.645, data_time: 1.588, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5922, loss_cls: 3.7959, loss: 3.7959 +2024-12-29 19:32:29,815 - pyskl - INFO - Epoch [98][200/3746] lr: 2.772e-02, eta: 1 day, 22:37:07, time: 0.863, data_time: 0.001, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5975, loss_cls: 3.7952, loss: 3.7952 +2024-12-29 19:33:55,886 - pyskl - INFO - Epoch [98][300/3746] lr: 2.769e-02, eta: 1 day, 22:35:43, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5906, loss_cls: 3.8074, loss: 3.8074 +2024-12-29 19:35:22,983 - pyskl - INFO - Epoch [98][400/3746] lr: 2.767e-02, eta: 1 day, 22:34:19, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5863, loss_cls: 3.8533, loss: 3.8533 +2024-12-29 19:36:48,988 - pyskl - INFO - Epoch [98][500/3746] lr: 2.764e-02, eta: 1 day, 22:32:56, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5869, loss_cls: 3.8295, loss: 3.8295 +2024-12-29 19:38:15,007 - pyskl - INFO - Epoch [98][600/3746] lr: 2.762e-02, eta: 1 day, 22:31:32, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5773, loss_cls: 3.8287, loss: 3.8287 +2024-12-29 19:39:41,258 - pyskl - INFO - Epoch [98][700/3746] lr: 2.759e-02, eta: 1 day, 22:30:08, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5814, loss_cls: 3.8916, loss: 3.8916 +2024-12-29 19:41:07,769 - pyskl - INFO - Epoch [98][800/3746] lr: 2.757e-02, eta: 1 day, 22:28:44, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5763, loss_cls: 3.8928, loss: 3.8928 +2024-12-29 19:42:33,257 - pyskl - INFO - Epoch [98][900/3746] lr: 2.754e-02, eta: 1 day, 22:27:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5664, loss_cls: 3.9142, loss: 3.9142 +2024-12-29 19:43:59,437 - pyskl - INFO - Epoch [98][1000/3746] lr: 2.752e-02, eta: 1 day, 22:25:57, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5797, loss_cls: 3.8594, loss: 3.8594 +2024-12-29 19:45:26,155 - pyskl - INFO - Epoch [98][1100/3746] lr: 2.749e-02, eta: 1 day, 22:24:33, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5809, loss_cls: 3.8635, loss: 3.8635 +2024-12-29 19:46:52,332 - pyskl - INFO - Epoch [98][1200/3746] lr: 2.747e-02, eta: 1 day, 22:23:09, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5672, loss_cls: 3.9061, loss: 3.9061 +2024-12-29 19:48:18,792 - pyskl - INFO - Epoch [98][1300/3746] lr: 2.744e-02, eta: 1 day, 22:21:46, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5803, loss_cls: 3.8549, loss: 3.8549 +2024-12-29 19:49:45,347 - pyskl - INFO - Epoch [98][1400/3746] lr: 2.742e-02, eta: 1 day, 22:20:22, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5744, loss_cls: 3.8876, loss: 3.8876 +2024-12-29 19:51:11,906 - pyskl - INFO - Epoch [98][1500/3746] lr: 2.739e-02, eta: 1 day, 22:18:58, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5784, loss_cls: 3.8910, loss: 3.8910 +2024-12-29 19:52:37,663 - pyskl - INFO - Epoch [98][1600/3746] lr: 2.737e-02, eta: 1 day, 22:17:34, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5798, loss_cls: 3.8755, loss: 3.8755 +2024-12-29 19:54:03,709 - pyskl - INFO - Epoch [98][1700/3746] lr: 2.734e-02, eta: 1 day, 22:16:11, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5831, loss_cls: 3.8680, loss: 3.8680 +2024-12-29 19:55:29,879 - pyskl - INFO - Epoch [98][1800/3746] lr: 2.732e-02, eta: 1 day, 22:14:47, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5737, loss_cls: 3.8696, loss: 3.8696 +2024-12-29 19:56:57,140 - pyskl - INFO - Epoch [98][1900/3746] lr: 2.729e-02, eta: 1 day, 22:13:24, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5613, loss_cls: 3.9785, loss: 3.9785 +2024-12-29 19:58:24,971 - pyskl - INFO - Epoch [98][2000/3746] lr: 2.727e-02, eta: 1 day, 22:12:01, time: 0.878, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5753, loss_cls: 3.8869, loss: 3.8869 +2024-12-29 19:59:52,524 - pyskl - INFO - Epoch [98][2100/3746] lr: 2.724e-02, eta: 1 day, 22:10:38, time: 0.875, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5772, loss_cls: 3.8482, loss: 3.8482 +2024-12-29 20:01:20,334 - pyskl - INFO - Epoch [98][2200/3746] lr: 2.722e-02, eta: 1 day, 22:09:15, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5719, loss_cls: 3.9338, loss: 3.9338 +2024-12-29 20:02:47,668 - pyskl - INFO - Epoch [98][2300/3746] lr: 2.719e-02, eta: 1 day, 22:07:52, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5802, loss_cls: 3.8742, loss: 3.8742 +2024-12-29 20:04:14,481 - pyskl - INFO - Epoch [98][2400/3746] lr: 2.717e-02, eta: 1 day, 22:06:28, time: 0.868, data_time: 0.001, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5761, loss_cls: 3.8731, loss: 3.8731 +2024-12-29 20:05:41,470 - pyskl - INFO - Epoch [98][2500/3746] lr: 2.714e-02, eta: 1 day, 22:05:05, time: 0.870, data_time: 0.001, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5823, loss_cls: 3.8787, loss: 3.8787 +2024-12-29 20:07:08,179 - pyskl - INFO - Epoch [98][2600/3746] lr: 2.712e-02, eta: 1 day, 22:03:41, time: 0.867, data_time: 0.001, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5736, loss_cls: 3.8950, loss: 3.8950 +2024-12-29 20:08:34,247 - pyskl - INFO - Epoch [98][2700/3746] lr: 2.709e-02, eta: 1 day, 22:02:17, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5737, loss_cls: 3.8944, loss: 3.8944 +2024-12-29 20:10:00,430 - pyskl - INFO - Epoch [98][2800/3746] lr: 2.707e-02, eta: 1 day, 22:00:54, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5705, loss_cls: 3.8836, loss: 3.8836 +2024-12-29 20:11:27,756 - pyskl - INFO - Epoch [98][2900/3746] lr: 2.705e-02, eta: 1 day, 21:59:30, time: 0.873, data_time: 0.001, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5767, loss_cls: 3.8863, loss: 3.8863 +2024-12-29 20:12:55,262 - pyskl - INFO - Epoch [98][3000/3746] lr: 2.702e-02, eta: 1 day, 21:58:07, time: 0.875, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5727, loss_cls: 3.8891, loss: 3.8891 +2024-12-29 20:14:22,569 - pyskl - INFO - Epoch [98][3100/3746] lr: 2.700e-02, eta: 1 day, 21:56:44, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5867, loss_cls: 3.8489, loss: 3.8489 +2024-12-29 20:15:50,357 - pyskl - INFO - Epoch [98][3200/3746] lr: 2.697e-02, eta: 1 day, 21:55:21, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5763, loss_cls: 3.8814, loss: 3.8814 +2024-12-29 20:17:18,118 - pyskl - INFO - Epoch [98][3300/3746] lr: 2.695e-02, eta: 1 day, 21:53:58, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5714, loss_cls: 3.9055, loss: 3.9055 +2024-12-29 20:18:44,245 - pyskl - INFO - Epoch [98][3400/3746] lr: 2.692e-02, eta: 1 day, 21:52:34, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5828, loss_cls: 3.8268, loss: 3.8268 +2024-12-29 20:20:10,521 - pyskl - INFO - Epoch [98][3500/3746] lr: 2.690e-02, eta: 1 day, 21:51:11, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5795, loss_cls: 3.8730, loss: 3.8730 +2024-12-29 20:21:36,352 - pyskl - INFO - Epoch [98][3600/3746] lr: 2.687e-02, eta: 1 day, 21:49:47, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5766, loss_cls: 3.8749, loss: 3.8749 +2024-12-29 20:23:02,547 - pyskl - INFO - Epoch [98][3700/3746] lr: 2.685e-02, eta: 1 day, 21:48:23, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5764, loss_cls: 3.9101, loss: 3.9101 +2024-12-29 20:23:44,065 - pyskl - INFO - Saving checkpoint at 98 epochs +2024-12-29 20:25:43,037 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 20:25:43,764 - pyskl - INFO - +top1_acc 0.2560 +top5_acc 0.5004 +2024-12-29 20:25:43,765 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 20:25:43,806 - pyskl - INFO - +mean_acc 0.2559 +2024-12-29 20:25:43,819 - pyskl - INFO - Epoch(val) [98][309] top1_acc: 0.2560, top5_acc: 0.5004, mean_class_accuracy: 0.2559 +2024-12-29 20:30:09,918 - pyskl - INFO - Epoch [99][100/3746] lr: 2.681e-02, eta: 1 day, 21:47:35, time: 2.661, data_time: 1.595, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5869, loss_cls: 3.7968, loss: 3.7968 +2024-12-29 20:31:37,388 - pyskl - INFO - Epoch [99][200/3746] lr: 2.679e-02, eta: 1 day, 21:46:12, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5961, loss_cls: 3.7990, loss: 3.7990 +2024-12-29 20:33:04,777 - pyskl - INFO - Epoch [99][300/3746] lr: 2.676e-02, eta: 1 day, 21:44:48, time: 0.874, data_time: 0.001, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5831, loss_cls: 3.8432, loss: 3.8432 +2024-12-29 20:34:32,494 - pyskl - INFO - Epoch [99][400/3746] lr: 2.674e-02, eta: 1 day, 21:43:25, time: 0.877, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5775, loss_cls: 3.8546, loss: 3.8546 +2024-12-29 20:36:00,378 - pyskl - INFO - Epoch [99][500/3746] lr: 2.671e-02, eta: 1 day, 21:42:02, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5848, loss_cls: 3.8441, loss: 3.8441 +2024-12-29 20:37:28,936 - pyskl - INFO - Epoch [99][600/3746] lr: 2.669e-02, eta: 1 day, 21:40:40, time: 0.886, data_time: 0.001, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5952, loss_cls: 3.7949, loss: 3.7949 +2024-12-29 20:38:56,762 - pyskl - INFO - Epoch [99][700/3746] lr: 2.666e-02, eta: 1 day, 21:39:17, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5842, loss_cls: 3.8440, loss: 3.8440 +2024-12-29 20:40:23,716 - pyskl - INFO - Epoch [99][800/3746] lr: 2.664e-02, eta: 1 day, 21:37:53, time: 0.870, data_time: 0.001, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5784, loss_cls: 3.8862, loss: 3.8862 +2024-12-29 20:41:50,972 - pyskl - INFO - Epoch [99][900/3746] lr: 2.661e-02, eta: 1 day, 21:36:30, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5855, loss_cls: 3.8457, loss: 3.8457 +2024-12-29 20:43:18,734 - pyskl - INFO - Epoch [99][1000/3746] lr: 2.659e-02, eta: 1 day, 21:35:07, time: 0.878, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5787, loss_cls: 3.8380, loss: 3.8380 +2024-12-29 20:44:46,476 - pyskl - INFO - Epoch [99][1100/3746] lr: 2.656e-02, eta: 1 day, 21:33:44, time: 0.877, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5852, loss_cls: 3.8384, loss: 3.8384 +2024-12-29 20:46:13,551 - pyskl - INFO - Epoch [99][1200/3746] lr: 2.654e-02, eta: 1 day, 21:32:20, time: 0.871, data_time: 0.001, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5836, loss_cls: 3.8290, loss: 3.8290 +2024-12-29 20:47:41,239 - pyskl - INFO - Epoch [99][1300/3746] lr: 2.651e-02, eta: 1 day, 21:30:57, time: 0.877, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5825, loss_cls: 3.8517, loss: 3.8517 +2024-12-29 20:49:08,759 - pyskl - INFO - Epoch [99][1400/3746] lr: 2.649e-02, eta: 1 day, 21:29:34, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5803, loss_cls: 3.8517, loss: 3.8517 +2024-12-29 20:50:37,099 - pyskl - INFO - Epoch [99][1500/3746] lr: 2.646e-02, eta: 1 day, 21:28:11, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5847, loss_cls: 3.8413, loss: 3.8413 +2024-12-29 20:52:03,643 - pyskl - INFO - Epoch [99][1600/3746] lr: 2.644e-02, eta: 1 day, 21:26:47, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5852, loss_cls: 3.8538, loss: 3.8538 +2024-12-29 20:53:30,209 - pyskl - INFO - Epoch [99][1700/3746] lr: 2.642e-02, eta: 1 day, 21:25:24, time: 0.866, data_time: 0.001, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5714, loss_cls: 3.8848, loss: 3.8848 +2024-12-29 20:54:56,551 - pyskl - INFO - Epoch [99][1800/3746] lr: 2.639e-02, eta: 1 day, 21:24:00, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5680, loss_cls: 3.8910, loss: 3.8910 +2024-12-29 20:56:22,727 - pyskl - INFO - Epoch [99][1900/3746] lr: 2.637e-02, eta: 1 day, 21:22:36, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5702, loss_cls: 3.8829, loss: 3.8829 +2024-12-29 20:57:49,954 - pyskl - INFO - Epoch [99][2000/3746] lr: 2.634e-02, eta: 1 day, 21:21:13, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5761, loss_cls: 3.8912, loss: 3.8912 +2024-12-29 20:59:16,989 - pyskl - INFO - Epoch [99][2100/3746] lr: 2.632e-02, eta: 1 day, 21:19:49, time: 0.870, data_time: 0.001, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5719, loss_cls: 3.8872, loss: 3.8872 +2024-12-29 21:00:44,201 - pyskl - INFO - Epoch [99][2200/3746] lr: 2.629e-02, eta: 1 day, 21:18:26, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5836, loss_cls: 3.8512, loss: 3.8512 +2024-12-29 21:02:11,392 - pyskl - INFO - Epoch [99][2300/3746] lr: 2.627e-02, eta: 1 day, 21:17:02, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5761, loss_cls: 3.8431, loss: 3.8431 +2024-12-29 21:03:39,301 - pyskl - INFO - Epoch [99][2400/3746] lr: 2.624e-02, eta: 1 day, 21:15:39, time: 0.879, data_time: 0.001, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5613, loss_cls: 3.9599, loss: 3.9599 +2024-12-29 21:05:06,935 - pyskl - INFO - Epoch [99][2500/3746] lr: 2.622e-02, eta: 1 day, 21:14:16, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5808, loss_cls: 3.8472, loss: 3.8472 +2024-12-29 21:06:33,294 - pyskl - INFO - Epoch [99][2600/3746] lr: 2.619e-02, eta: 1 day, 21:12:52, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5800, loss_cls: 3.8371, loss: 3.8371 +2024-12-29 21:07:59,459 - pyskl - INFO - Epoch [99][2700/3746] lr: 2.617e-02, eta: 1 day, 21:11:28, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5839, loss_cls: 3.8640, loss: 3.8640 +2024-12-29 21:09:26,131 - pyskl - INFO - Epoch [99][2800/3746] lr: 2.614e-02, eta: 1 day, 21:10:05, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5809, loss_cls: 3.8731, loss: 3.8731 +2024-12-29 21:10:52,499 - pyskl - INFO - Epoch [99][2900/3746] lr: 2.612e-02, eta: 1 day, 21:08:41, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5677, loss_cls: 3.9289, loss: 3.9289 +2024-12-29 21:12:19,842 - pyskl - INFO - Epoch [99][3000/3746] lr: 2.610e-02, eta: 1 day, 21:07:18, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5714, loss_cls: 3.9039, loss: 3.9039 +2024-12-29 21:13:46,408 - pyskl - INFO - Epoch [99][3100/3746] lr: 2.607e-02, eta: 1 day, 21:05:54, time: 0.866, data_time: 0.001, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5750, loss_cls: 3.8899, loss: 3.8899 +2024-12-29 21:15:13,689 - pyskl - INFO - Epoch [99][3200/3746] lr: 2.605e-02, eta: 1 day, 21:04:31, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5720, loss_cls: 3.8802, loss: 3.8802 +2024-12-29 21:16:40,499 - pyskl - INFO - Epoch [99][3300/3746] lr: 2.602e-02, eta: 1 day, 21:03:07, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5741, loss_cls: 3.8869, loss: 3.8869 +2024-12-29 21:18:06,853 - pyskl - INFO - Epoch [99][3400/3746] lr: 2.600e-02, eta: 1 day, 21:01:43, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5734, loss_cls: 3.8887, loss: 3.8887 +2024-12-29 21:19:31,896 - pyskl - INFO - Epoch [99][3500/3746] lr: 2.597e-02, eta: 1 day, 21:00:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5831, loss_cls: 3.8515, loss: 3.8515 +2024-12-29 21:20:57,092 - pyskl - INFO - Epoch [99][3600/3746] lr: 2.595e-02, eta: 1 day, 20:58:54, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5803, loss_cls: 3.8565, loss: 3.8565 +2024-12-29 21:22:22,390 - pyskl - INFO - Epoch [99][3700/3746] lr: 2.592e-02, eta: 1 day, 20:57:30, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5741, loss_cls: 3.8909, loss: 3.8909 +2024-12-29 21:23:03,789 - pyskl - INFO - Saving checkpoint at 99 epochs +2024-12-29 21:25:03,567 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 21:25:04,330 - pyskl - INFO - +top1_acc 0.2701 +top5_acc 0.5181 +2024-12-29 21:25:04,330 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 21:25:04,367 - pyskl - INFO - +mean_acc 0.2699 +2024-12-29 21:25:04,372 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_97.pth was removed +2024-12-29 21:25:04,711 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_99.pth. +2024-12-29 21:25:04,713 - pyskl - INFO - Best top1_acc is 0.2701 at 99 epoch. +2024-12-29 21:25:04,725 - pyskl - INFO - Epoch(val) [99][309] top1_acc: 0.2701, top5_acc: 0.5181, mean_class_accuracy: 0.2699 +2024-12-29 21:29:23,564 - pyskl - INFO - Epoch [100][100/3746] lr: 2.589e-02, eta: 1 day, 20:56:36, time: 2.588, data_time: 1.562, memory: 15990, top1_acc: 0.3372, top5_acc: 0.6062, loss_cls: 3.7426, loss: 3.7426 +2024-12-29 21:30:48,785 - pyskl - INFO - Epoch [100][200/3746] lr: 2.586e-02, eta: 1 day, 20:55:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5992, loss_cls: 3.7616, loss: 3.7616 +2024-12-29 21:32:14,232 - pyskl - INFO - Epoch [100][300/3746] lr: 2.584e-02, eta: 1 day, 20:53:47, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5772, loss_cls: 3.8508, loss: 3.8508 +2024-12-29 21:33:39,528 - pyskl - INFO - Epoch [100][400/3746] lr: 2.581e-02, eta: 1 day, 20:52:22, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.6003, loss_cls: 3.7888, loss: 3.7888 +2024-12-29 21:35:05,142 - pyskl - INFO - Epoch [100][500/3746] lr: 2.579e-02, eta: 1 day, 20:50:58, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5967, loss_cls: 3.7972, loss: 3.7972 +2024-12-29 21:36:30,626 - pyskl - INFO - Epoch [100][600/3746] lr: 2.577e-02, eta: 1 day, 20:49:34, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5845, loss_cls: 3.8300, loss: 3.8300 +2024-12-29 21:37:56,034 - pyskl - INFO - Epoch [100][700/3746] lr: 2.574e-02, eta: 1 day, 20:48:09, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5900, loss_cls: 3.8228, loss: 3.8228 +2024-12-29 21:39:21,560 - pyskl - INFO - Epoch [100][800/3746] lr: 2.572e-02, eta: 1 day, 20:46:45, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5833, loss_cls: 3.8212, loss: 3.8212 +2024-12-29 21:40:47,212 - pyskl - INFO - Epoch [100][900/3746] lr: 2.569e-02, eta: 1 day, 20:45:21, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5841, loss_cls: 3.8012, loss: 3.8012 +2024-12-29 21:42:12,507 - pyskl - INFO - Epoch [100][1000/3746] lr: 2.567e-02, eta: 1 day, 20:43:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.5897, loss_cls: 3.8194, loss: 3.8194 +2024-12-29 21:43:37,983 - pyskl - INFO - Epoch [100][1100/3746] lr: 2.564e-02, eta: 1 day, 20:42:32, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5919, loss_cls: 3.8004, loss: 3.8004 +2024-12-29 21:45:02,954 - pyskl - INFO - Epoch [100][1200/3746] lr: 2.562e-02, eta: 1 day, 20:41:07, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5902, loss_cls: 3.8215, loss: 3.8215 +2024-12-29 21:46:28,509 - pyskl - INFO - Epoch [100][1300/3746] lr: 2.559e-02, eta: 1 day, 20:39:43, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5816, loss_cls: 3.8529, loss: 3.8529 +2024-12-29 21:47:53,797 - pyskl - INFO - Epoch [100][1400/3746] lr: 2.557e-02, eta: 1 day, 20:38:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5787, loss_cls: 3.8846, loss: 3.8846 +2024-12-29 21:49:19,026 - pyskl - INFO - Epoch [100][1500/3746] lr: 2.555e-02, eta: 1 day, 20:36:54, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5784, loss_cls: 3.8528, loss: 3.8528 +2024-12-29 21:50:44,577 - pyskl - INFO - Epoch [100][1600/3746] lr: 2.552e-02, eta: 1 day, 20:35:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5748, loss_cls: 3.8841, loss: 3.8841 +2024-12-29 21:52:10,025 - pyskl - INFO - Epoch [100][1700/3746] lr: 2.550e-02, eta: 1 day, 20:34:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5873, loss_cls: 3.8373, loss: 3.8373 +2024-12-29 21:53:35,404 - pyskl - INFO - Epoch [100][1800/3746] lr: 2.547e-02, eta: 1 day, 20:32:41, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5731, loss_cls: 3.8702, loss: 3.8702 +2024-12-29 21:55:01,188 - pyskl - INFO - Epoch [100][1900/3746] lr: 2.545e-02, eta: 1 day, 20:31:17, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5730, loss_cls: 3.8396, loss: 3.8396 +2024-12-29 21:56:26,477 - pyskl - INFO - Epoch [100][2000/3746] lr: 2.542e-02, eta: 1 day, 20:29:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5752, loss_cls: 3.8367, loss: 3.8367 +2024-12-29 21:57:52,627 - pyskl - INFO - Epoch [100][2100/3746] lr: 2.540e-02, eta: 1 day, 20:28:28, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5741, loss_cls: 3.8323, loss: 3.8323 +2024-12-29 21:59:18,181 - pyskl - INFO - Epoch [100][2200/3746] lr: 2.538e-02, eta: 1 day, 20:27:04, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5759, loss_cls: 3.8473, loss: 3.8473 +2024-12-29 22:00:44,178 - pyskl - INFO - Epoch [100][2300/3746] lr: 2.535e-02, eta: 1 day, 20:25:40, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5792, loss_cls: 3.8452, loss: 3.8452 +2024-12-29 22:02:10,006 - pyskl - INFO - Epoch [100][2400/3746] lr: 2.533e-02, eta: 1 day, 20:24:16, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5808, loss_cls: 3.8490, loss: 3.8490 +2024-12-29 22:03:35,312 - pyskl - INFO - Epoch [100][2500/3746] lr: 2.530e-02, eta: 1 day, 20:22:51, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5787, loss_cls: 3.8522, loss: 3.8522 +2024-12-29 22:05:00,760 - pyskl - INFO - Epoch [100][2600/3746] lr: 2.528e-02, eta: 1 day, 20:21:27, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5777, loss_cls: 3.8736, loss: 3.8736 +2024-12-29 22:06:25,927 - pyskl - INFO - Epoch [100][2700/3746] lr: 2.525e-02, eta: 1 day, 20:20:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5886, loss_cls: 3.8349, loss: 3.8349 +2024-12-29 22:07:51,365 - pyskl - INFO - Epoch [100][2800/3746] lr: 2.523e-02, eta: 1 day, 20:18:38, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5806, loss_cls: 3.8572, loss: 3.8572 +2024-12-29 22:09:17,290 - pyskl - INFO - Epoch [100][2900/3746] lr: 2.521e-02, eta: 1 day, 20:17:14, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5784, loss_cls: 3.8416, loss: 3.8416 +2024-12-29 22:10:42,923 - pyskl - INFO - Epoch [100][3000/3746] lr: 2.518e-02, eta: 1 day, 20:15:49, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5687, loss_cls: 3.8872, loss: 3.8872 +2024-12-29 22:12:09,195 - pyskl - INFO - Epoch [100][3100/3746] lr: 2.516e-02, eta: 1 day, 20:14:26, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5687, loss_cls: 3.9320, loss: 3.9320 +2024-12-29 22:13:34,650 - pyskl - INFO - Epoch [100][3200/3746] lr: 2.513e-02, eta: 1 day, 20:13:01, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5852, loss_cls: 3.8334, loss: 3.8334 +2024-12-29 22:14:59,742 - pyskl - INFO - Epoch [100][3300/3746] lr: 2.511e-02, eta: 1 day, 20:11:37, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5761, loss_cls: 3.8819, loss: 3.8819 +2024-12-29 22:16:24,938 - pyskl - INFO - Epoch [100][3400/3746] lr: 2.508e-02, eta: 1 day, 20:10:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5806, loss_cls: 3.8599, loss: 3.8599 +2024-12-29 22:17:49,725 - pyskl - INFO - Epoch [100][3500/3746] lr: 2.506e-02, eta: 1 day, 20:08:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5797, loss_cls: 3.8920, loss: 3.8920 +2024-12-29 22:19:16,024 - pyskl - INFO - Epoch [100][3600/3746] lr: 2.504e-02, eta: 1 day, 20:07:23, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5825, loss_cls: 3.8171, loss: 3.8171 +2024-12-29 22:20:41,915 - pyskl - INFO - Epoch [100][3700/3746] lr: 2.501e-02, eta: 1 day, 20:05:59, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5706, loss_cls: 3.9112, loss: 3.9112 +2024-12-29 22:21:23,215 - pyskl - INFO - Saving checkpoint at 100 epochs +2024-12-29 22:23:23,766 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 22:23:24,715 - pyskl - INFO - +top1_acc 0.2683 +top5_acc 0.5140 +2024-12-29 22:23:24,715 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 22:23:24,763 - pyskl - INFO - +mean_acc 0.2681 +2024-12-29 22:23:24,776 - pyskl - INFO - Epoch(val) [100][309] top1_acc: 0.2683, top5_acc: 0.5140, mean_class_accuracy: 0.2681 +2024-12-29 22:27:40,870 - pyskl - INFO - Epoch [101][100/3746] lr: 2.498e-02, eta: 1 day, 20:05:02, time: 2.561, data_time: 1.539, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5931, loss_cls: 3.8074, loss: 3.8074 +2024-12-29 22:29:06,702 - pyskl - INFO - Epoch [101][200/3746] lr: 2.495e-02, eta: 1 day, 20:03:37, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6014, loss_cls: 3.7710, loss: 3.7710 +2024-12-29 22:30:32,362 - pyskl - INFO - Epoch [101][300/3746] lr: 2.493e-02, eta: 1 day, 20:02:13, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5889, loss_cls: 3.7746, loss: 3.7746 +2024-12-29 22:31:57,728 - pyskl - INFO - Epoch [101][400/3746] lr: 2.490e-02, eta: 1 day, 20:00:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5900, loss_cls: 3.7802, loss: 3.7802 +2024-12-29 22:33:22,826 - pyskl - INFO - Epoch [101][500/3746] lr: 2.488e-02, eta: 1 day, 19:59:24, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.5928, loss_cls: 3.7854, loss: 3.7854 +2024-12-29 22:34:47,969 - pyskl - INFO - Epoch [101][600/3746] lr: 2.486e-02, eta: 1 day, 19:57:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5767, loss_cls: 3.8467, loss: 3.8467 +2024-12-29 22:36:13,452 - pyskl - INFO - Epoch [101][700/3746] lr: 2.483e-02, eta: 1 day, 19:56:35, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5858, loss_cls: 3.8272, loss: 3.8272 +2024-12-29 22:37:38,896 - pyskl - INFO - Epoch [101][800/3746] lr: 2.481e-02, eta: 1 day, 19:55:10, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.6014, loss_cls: 3.7695, loss: 3.7695 +2024-12-29 22:39:04,242 - pyskl - INFO - Epoch [101][900/3746] lr: 2.478e-02, eta: 1 day, 19:53:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5880, loss_cls: 3.8139, loss: 3.8139 +2024-12-29 22:40:29,419 - pyskl - INFO - Epoch [101][1000/3746] lr: 2.476e-02, eta: 1 day, 19:52:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5889, loss_cls: 3.8245, loss: 3.8245 +2024-12-29 22:41:54,857 - pyskl - INFO - Epoch [101][1100/3746] lr: 2.473e-02, eta: 1 day, 19:50:57, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5972, loss_cls: 3.8304, loss: 3.8304 +2024-12-29 22:43:20,888 - pyskl - INFO - Epoch [101][1200/3746] lr: 2.471e-02, eta: 1 day, 19:49:33, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5813, loss_cls: 3.8457, loss: 3.8457 +2024-12-29 22:44:46,082 - pyskl - INFO - Epoch [101][1300/3746] lr: 2.469e-02, eta: 1 day, 19:48:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5891, loss_cls: 3.8043, loss: 3.8043 +2024-12-29 22:46:11,614 - pyskl - INFO - Epoch [101][1400/3746] lr: 2.466e-02, eta: 1 day, 19:46:44, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5852, loss_cls: 3.8320, loss: 3.8320 +2024-12-29 22:47:37,346 - pyskl - INFO - Epoch [101][1500/3746] lr: 2.464e-02, eta: 1 day, 19:45:19, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5755, loss_cls: 3.8851, loss: 3.8851 +2024-12-29 22:49:02,566 - pyskl - INFO - Epoch [101][1600/3746] lr: 2.461e-02, eta: 1 day, 19:43:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5805, loss_cls: 3.8463, loss: 3.8463 +2024-12-29 22:50:27,350 - pyskl - INFO - Epoch [101][1700/3746] lr: 2.459e-02, eta: 1 day, 19:42:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5803, loss_cls: 3.8746, loss: 3.8746 +2024-12-29 22:51:52,116 - pyskl - INFO - Epoch [101][1800/3746] lr: 2.457e-02, eta: 1 day, 19:41:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5900, loss_cls: 3.8439, loss: 3.8439 +2024-12-29 22:53:17,363 - pyskl - INFO - Epoch [101][1900/3746] lr: 2.454e-02, eta: 1 day, 19:39:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5795, loss_cls: 3.8723, loss: 3.8723 +2024-12-29 22:54:41,928 - pyskl - INFO - Epoch [101][2000/3746] lr: 2.452e-02, eta: 1 day, 19:38:16, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5897, loss_cls: 3.8054, loss: 3.8054 +2024-12-29 22:56:06,834 - pyskl - INFO - Epoch [101][2100/3746] lr: 2.449e-02, eta: 1 day, 19:36:51, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5731, loss_cls: 3.8348, loss: 3.8348 +2024-12-29 22:57:31,354 - pyskl - INFO - Epoch [101][2200/3746] lr: 2.447e-02, eta: 1 day, 19:35:26, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5889, loss_cls: 3.8184, loss: 3.8184 +2024-12-29 22:58:56,081 - pyskl - INFO - Epoch [101][2300/3746] lr: 2.445e-02, eta: 1 day, 19:34:01, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5872, loss_cls: 3.8313, loss: 3.8313 +2024-12-29 23:00:20,619 - pyskl - INFO - Epoch [101][2400/3746] lr: 2.442e-02, eta: 1 day, 19:32:37, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5844, loss_cls: 3.8417, loss: 3.8417 +2024-12-29 23:01:45,470 - pyskl - INFO - Epoch [101][2500/3746] lr: 2.440e-02, eta: 1 day, 19:31:12, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5880, loss_cls: 3.8398, loss: 3.8398 +2024-12-29 23:03:09,735 - pyskl - INFO - Epoch [101][2600/3746] lr: 2.437e-02, eta: 1 day, 19:29:47, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5948, loss_cls: 3.7743, loss: 3.7743 +2024-12-29 23:04:34,150 - pyskl - INFO - Epoch [101][2700/3746] lr: 2.435e-02, eta: 1 day, 19:28:22, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5817, loss_cls: 3.8458, loss: 3.8458 +2024-12-29 23:05:59,175 - pyskl - INFO - Epoch [101][2800/3746] lr: 2.433e-02, eta: 1 day, 19:26:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5884, loss_cls: 3.8136, loss: 3.8136 +2024-12-29 23:07:23,865 - pyskl - INFO - Epoch [101][2900/3746] lr: 2.430e-02, eta: 1 day, 19:25:32, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5842, loss_cls: 3.8315, loss: 3.8315 +2024-12-29 23:08:48,506 - pyskl - INFO - Epoch [101][3000/3746] lr: 2.428e-02, eta: 1 day, 19:24:08, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5786, loss_cls: 3.8545, loss: 3.8545 +2024-12-29 23:10:13,770 - pyskl - INFO - Epoch [101][3100/3746] lr: 2.425e-02, eta: 1 day, 19:22:43, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5933, loss_cls: 3.7948, loss: 3.7948 +2024-12-29 23:11:38,638 - pyskl - INFO - Epoch [101][3200/3746] lr: 2.423e-02, eta: 1 day, 19:21:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5941, loss_cls: 3.7969, loss: 3.7969 +2024-12-29 23:13:02,778 - pyskl - INFO - Epoch [101][3300/3746] lr: 2.421e-02, eta: 1 day, 19:19:53, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5906, loss_cls: 3.7940, loss: 3.7940 +2024-12-29 23:14:27,035 - pyskl - INFO - Epoch [101][3400/3746] lr: 2.418e-02, eta: 1 day, 19:18:28, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5864, loss_cls: 3.8122, loss: 3.8122 +2024-12-29 23:15:51,440 - pyskl - INFO - Epoch [101][3500/3746] lr: 2.416e-02, eta: 1 day, 19:17:03, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5852, loss_cls: 3.8416, loss: 3.8416 +2024-12-29 23:17:15,670 - pyskl - INFO - Epoch [101][3600/3746] lr: 2.413e-02, eta: 1 day, 19:15:38, time: 0.842, data_time: 0.001, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5805, loss_cls: 3.8489, loss: 3.8489 +2024-12-29 23:18:39,996 - pyskl - INFO - Epoch [101][3700/3746] lr: 2.411e-02, eta: 1 day, 19:14:13, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5830, loss_cls: 3.8440, loss: 3.8440 +2024-12-29 23:19:20,831 - pyskl - INFO - Saving checkpoint at 101 epochs +2024-12-29 23:21:20,553 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 23:21:21,435 - pyskl - INFO - +top1_acc 0.2716 +top5_acc 0.5164 +2024-12-29 23:21:21,436 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 23:21:21,477 - pyskl - INFO - +mean_acc 0.2715 +2024-12-29 23:21:21,481 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_99.pth was removed +2024-12-29 23:21:21,728 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_101.pth. +2024-12-29 23:21:21,729 - pyskl - INFO - Best top1_acc is 0.2716 at 101 epoch. +2024-12-29 23:21:21,741 - pyskl - INFO - Epoch(val) [101][309] top1_acc: 0.2716, top5_acc: 0.5164, mean_class_accuracy: 0.2715 +2024-12-29 23:25:28,836 - pyskl - INFO - Epoch [102][100/3746] lr: 2.407e-02, eta: 1 day, 19:13:09, time: 2.471, data_time: 1.450, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5955, loss_cls: 3.7681, loss: 3.7681 +2024-12-29 23:26:54,149 - pyskl - INFO - Epoch [102][200/3746] lr: 2.405e-02, eta: 1 day, 19:11:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5911, loss_cls: 3.7526, loss: 3.7526 +2024-12-29 23:28:19,198 - pyskl - INFO - Epoch [102][300/3746] lr: 2.403e-02, eta: 1 day, 19:10:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5897, loss_cls: 3.8009, loss: 3.8009 +2024-12-29 23:29:43,679 - pyskl - INFO - Epoch [102][400/3746] lr: 2.400e-02, eta: 1 day, 19:08:55, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.5891, loss_cls: 3.7822, loss: 3.7822 +2024-12-29 23:31:08,276 - pyskl - INFO - Epoch [102][500/3746] lr: 2.398e-02, eta: 1 day, 19:07:30, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.5992, loss_cls: 3.7751, loss: 3.7751 +2024-12-29 23:32:32,908 - pyskl - INFO - Epoch [102][600/3746] lr: 2.396e-02, eta: 1 day, 19:06:05, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5866, loss_cls: 3.7940, loss: 3.7940 +2024-12-29 23:33:57,092 - pyskl - INFO - Epoch [102][700/3746] lr: 2.393e-02, eta: 1 day, 19:04:40, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5914, loss_cls: 3.8030, loss: 3.8030 +2024-12-29 23:35:21,631 - pyskl - INFO - Epoch [102][800/3746] lr: 2.391e-02, eta: 1 day, 19:03:15, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5908, loss_cls: 3.7874, loss: 3.7874 +2024-12-29 23:36:46,142 - pyskl - INFO - Epoch [102][900/3746] lr: 2.388e-02, eta: 1 day, 19:01:50, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5813, loss_cls: 3.8466, loss: 3.8466 +2024-12-29 23:38:10,160 - pyskl - INFO - Epoch [102][1000/3746] lr: 2.386e-02, eta: 1 day, 19:00:25, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5839, loss_cls: 3.8209, loss: 3.8209 +2024-12-29 23:39:34,287 - pyskl - INFO - Epoch [102][1100/3746] lr: 2.384e-02, eta: 1 day, 18:59:00, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5898, loss_cls: 3.8018, loss: 3.8018 +2024-12-29 23:40:58,634 - pyskl - INFO - Epoch [102][1200/3746] lr: 2.381e-02, eta: 1 day, 18:57:35, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5828, loss_cls: 3.8386, loss: 3.8386 +2024-12-29 23:42:22,889 - pyskl - INFO - Epoch [102][1300/3746] lr: 2.379e-02, eta: 1 day, 18:56:10, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5948, loss_cls: 3.7644, loss: 3.7644 +2024-12-29 23:43:47,799 - pyskl - INFO - Epoch [102][1400/3746] lr: 2.376e-02, eta: 1 day, 18:54:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5831, loss_cls: 3.8069, loss: 3.8069 +2024-12-29 23:45:12,068 - pyskl - INFO - Epoch [102][1500/3746] lr: 2.374e-02, eta: 1 day, 18:53:20, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5889, loss_cls: 3.8129, loss: 3.8129 +2024-12-29 23:46:36,083 - pyskl - INFO - Epoch [102][1600/3746] lr: 2.372e-02, eta: 1 day, 18:51:55, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5861, loss_cls: 3.8202, loss: 3.8202 +2024-12-29 23:48:00,504 - pyskl - INFO - Epoch [102][1700/3746] lr: 2.369e-02, eta: 1 day, 18:50:30, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5748, loss_cls: 3.8427, loss: 3.8427 +2024-12-29 23:49:24,717 - pyskl - INFO - Epoch [102][1800/3746] lr: 2.367e-02, eta: 1 day, 18:49:04, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5858, loss_cls: 3.8098, loss: 3.8098 +2024-12-29 23:50:49,493 - pyskl - INFO - Epoch [102][1900/3746] lr: 2.365e-02, eta: 1 day, 18:47:40, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5895, loss_cls: 3.7979, loss: 3.7979 +2024-12-29 23:52:14,070 - pyskl - INFO - Epoch [102][2000/3746] lr: 2.362e-02, eta: 1 day, 18:46:15, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5877, loss_cls: 3.8003, loss: 3.8003 +2024-12-29 23:53:39,084 - pyskl - INFO - Epoch [102][2100/3746] lr: 2.360e-02, eta: 1 day, 18:44:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5848, loss_cls: 3.8553, loss: 3.8553 +2024-12-29 23:55:03,615 - pyskl - INFO - Epoch [102][2200/3746] lr: 2.357e-02, eta: 1 day, 18:43:25, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5780, loss_cls: 3.8631, loss: 3.8631 +2024-12-29 23:56:28,351 - pyskl - INFO - Epoch [102][2300/3746] lr: 2.355e-02, eta: 1 day, 18:42:00, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5878, loss_cls: 3.8474, loss: 3.8474 +2024-12-29 23:57:53,418 - pyskl - INFO - Epoch [102][2400/3746] lr: 2.353e-02, eta: 1 day, 18:40:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5831, loss_cls: 3.8046, loss: 3.8046 +2024-12-29 23:59:18,308 - pyskl - INFO - Epoch [102][2500/3746] lr: 2.350e-02, eta: 1 day, 18:39:11, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5961, loss_cls: 3.7952, loss: 3.7952 +2024-12-30 00:00:42,724 - pyskl - INFO - Epoch [102][2600/3746] lr: 2.348e-02, eta: 1 day, 18:37:46, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5842, loss_cls: 3.8236, loss: 3.8236 +2024-12-30 00:02:06,776 - pyskl - INFO - Epoch [102][2700/3746] lr: 2.346e-02, eta: 1 day, 18:36:21, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5944, loss_cls: 3.7836, loss: 3.7836 +2024-12-30 00:03:31,331 - pyskl - INFO - Epoch [102][2800/3746] lr: 2.343e-02, eta: 1 day, 18:34:56, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5900, loss_cls: 3.7853, loss: 3.7853 +2024-12-30 00:04:55,913 - pyskl - INFO - Epoch [102][2900/3746] lr: 2.341e-02, eta: 1 day, 18:33:31, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5878, loss_cls: 3.8241, loss: 3.8241 +2024-12-30 00:06:20,974 - pyskl - INFO - Epoch [102][3000/3746] lr: 2.339e-02, eta: 1 day, 18:32:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5900, loss_cls: 3.8026, loss: 3.8026 +2024-12-30 00:07:45,772 - pyskl - INFO - Epoch [102][3100/3746] lr: 2.336e-02, eta: 1 day, 18:30:41, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5947, loss_cls: 3.7762, loss: 3.7762 +2024-12-30 00:09:10,764 - pyskl - INFO - Epoch [102][3200/3746] lr: 2.334e-02, eta: 1 day, 18:29:17, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5870, loss_cls: 3.8276, loss: 3.8276 +2024-12-30 00:10:34,680 - pyskl - INFO - Epoch [102][3300/3746] lr: 2.331e-02, eta: 1 day, 18:27:51, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.5913, loss_cls: 3.7857, loss: 3.7857 +2024-12-30 00:11:58,776 - pyskl - INFO - Epoch [102][3400/3746] lr: 2.329e-02, eta: 1 day, 18:26:26, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5859, loss_cls: 3.8438, loss: 3.8438 +2024-12-30 00:13:23,043 - pyskl - INFO - Epoch [102][3500/3746] lr: 2.327e-02, eta: 1 day, 18:25:01, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5806, loss_cls: 3.8290, loss: 3.8290 +2024-12-30 00:14:47,392 - pyskl - INFO - Epoch [102][3600/3746] lr: 2.324e-02, eta: 1 day, 18:23:36, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5920, loss_cls: 3.8168, loss: 3.8168 +2024-12-30 00:16:12,264 - pyskl - INFO - Epoch [102][3700/3746] lr: 2.322e-02, eta: 1 day, 18:22:11, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5889, loss_cls: 3.7655, loss: 3.7655 +2024-12-30 00:16:52,844 - pyskl - INFO - Saving checkpoint at 102 epochs +2024-12-30 00:18:50,757 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 00:18:51,749 - pyskl - INFO - +top1_acc 0.2777 +top5_acc 0.5236 +2024-12-30 00:18:51,749 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 00:18:51,797 - pyskl - INFO - +mean_acc 0.2776 +2024-12-30 00:18:51,801 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_101.pth was removed +2024-12-30 00:18:52,275 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_102.pth. +2024-12-30 00:18:52,276 - pyskl - INFO - Best top1_acc is 0.2777 at 102 epoch. +2024-12-30 00:18:52,293 - pyskl - INFO - Epoch(val) [102][309] top1_acc: 0.2777, top5_acc: 0.5236, mean_class_accuracy: 0.2776 +2024-12-30 00:23:06,194 - pyskl - INFO - Epoch [103][100/3746] lr: 2.319e-02, eta: 1 day, 18:21:09, time: 2.539, data_time: 1.508, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6047, loss_cls: 3.6847, loss: 3.6847 +2024-12-30 00:24:31,706 - pyskl - INFO - Epoch [103][200/3746] lr: 2.316e-02, eta: 1 day, 18:19:44, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5964, loss_cls: 3.7525, loss: 3.7525 +2024-12-30 00:25:56,718 - pyskl - INFO - Epoch [103][300/3746] lr: 2.314e-02, eta: 1 day, 18:18:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6016, loss_cls: 3.7478, loss: 3.7478 +2024-12-30 00:27:20,975 - pyskl - INFO - Epoch [103][400/3746] lr: 2.311e-02, eta: 1 day, 18:16:54, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6091, loss_cls: 3.7396, loss: 3.7396 +2024-12-30 00:28:45,474 - pyskl - INFO - Epoch [103][500/3746] lr: 2.309e-02, eta: 1 day, 18:15:29, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6050, loss_cls: 3.7449, loss: 3.7449 +2024-12-30 00:30:11,006 - pyskl - INFO - Epoch [103][600/3746] lr: 2.307e-02, eta: 1 day, 18:14:05, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6012, loss_cls: 3.7251, loss: 3.7251 +2024-12-30 00:31:36,054 - pyskl - INFO - Epoch [103][700/3746] lr: 2.304e-02, eta: 1 day, 18:12:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.5972, loss_cls: 3.7704, loss: 3.7704 +2024-12-30 00:33:00,999 - pyskl - INFO - Epoch [103][800/3746] lr: 2.302e-02, eta: 1 day, 18:11:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5975, loss_cls: 3.7799, loss: 3.7799 +2024-12-30 00:34:26,004 - pyskl - INFO - Epoch [103][900/3746] lr: 2.300e-02, eta: 1 day, 18:09:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.5972, loss_cls: 3.7489, loss: 3.7489 +2024-12-30 00:35:50,954 - pyskl - INFO - Epoch [103][1000/3746] lr: 2.297e-02, eta: 1 day, 18:08:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5941, loss_cls: 3.7731, loss: 3.7731 +2024-12-30 00:37:16,228 - pyskl - INFO - Epoch [103][1100/3746] lr: 2.295e-02, eta: 1 day, 18:07:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5845, loss_cls: 3.8052, loss: 3.8052 +2024-12-30 00:38:41,800 - pyskl - INFO - Epoch [103][1200/3746] lr: 2.293e-02, eta: 1 day, 18:05:36, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5886, loss_cls: 3.8032, loss: 3.8032 +2024-12-30 00:40:07,062 - pyskl - INFO - Epoch [103][1300/3746] lr: 2.290e-02, eta: 1 day, 18:04:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5808, loss_cls: 3.8320, loss: 3.8320 +2024-12-30 00:41:32,332 - pyskl - INFO - Epoch [103][1400/3746] lr: 2.288e-02, eta: 1 day, 18:02:47, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5877, loss_cls: 3.8203, loss: 3.8203 +2024-12-30 00:42:57,153 - pyskl - INFO - Epoch [103][1500/3746] lr: 2.286e-02, eta: 1 day, 18:01:22, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5920, loss_cls: 3.7952, loss: 3.7952 +2024-12-30 00:44:21,941 - pyskl - INFO - Epoch [103][1600/3746] lr: 2.283e-02, eta: 1 day, 17:59:57, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.6014, loss_cls: 3.7471, loss: 3.7471 +2024-12-30 00:45:47,069 - pyskl - INFO - Epoch [103][1700/3746] lr: 2.281e-02, eta: 1 day, 17:58:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5869, loss_cls: 3.8150, loss: 3.8150 +2024-12-30 00:47:11,800 - pyskl - INFO - Epoch [103][1800/3746] lr: 2.279e-02, eta: 1 day, 17:57:08, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5875, loss_cls: 3.8269, loss: 3.8269 +2024-12-30 00:48:36,518 - pyskl - INFO - Epoch [103][1900/3746] lr: 2.276e-02, eta: 1 day, 17:55:43, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6017, loss_cls: 3.7704, loss: 3.7704 +2024-12-30 00:50:00,760 - pyskl - INFO - Epoch [103][2000/3746] lr: 2.274e-02, eta: 1 day, 17:54:18, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5922, loss_cls: 3.8121, loss: 3.8121 +2024-12-30 00:51:25,321 - pyskl - INFO - Epoch [103][2100/3746] lr: 2.272e-02, eta: 1 day, 17:52:53, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5942, loss_cls: 3.7688, loss: 3.7688 +2024-12-30 00:52:50,494 - pyskl - INFO - Epoch [103][2200/3746] lr: 2.269e-02, eta: 1 day, 17:51:28, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5977, loss_cls: 3.7604, loss: 3.7604 +2024-12-30 00:54:14,344 - pyskl - INFO - Epoch [103][2300/3746] lr: 2.267e-02, eta: 1 day, 17:50:03, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.5955, loss_cls: 3.7800, loss: 3.7800 +2024-12-30 00:55:38,926 - pyskl - INFO - Epoch [103][2400/3746] lr: 2.264e-02, eta: 1 day, 17:48:38, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5852, loss_cls: 3.8498, loss: 3.8498 +2024-12-30 00:57:03,779 - pyskl - INFO - Epoch [103][2500/3746] lr: 2.262e-02, eta: 1 day, 17:47:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5958, loss_cls: 3.7582, loss: 3.7582 +2024-12-30 00:58:28,532 - pyskl - INFO - Epoch [103][2600/3746] lr: 2.260e-02, eta: 1 day, 17:45:48, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5927, loss_cls: 3.7950, loss: 3.7950 +2024-12-30 00:59:53,391 - pyskl - INFO - Epoch [103][2700/3746] lr: 2.257e-02, eta: 1 day, 17:44:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5877, loss_cls: 3.7803, loss: 3.7803 +2024-12-30 01:01:18,388 - pyskl - INFO - Epoch [103][2800/3746] lr: 2.255e-02, eta: 1 day, 17:42:58, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5962, loss_cls: 3.7606, loss: 3.7606 +2024-12-30 01:02:43,486 - pyskl - INFO - Epoch [103][2900/3746] lr: 2.253e-02, eta: 1 day, 17:41:34, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5737, loss_cls: 3.8325, loss: 3.8325 +2024-12-30 01:04:08,045 - pyskl - INFO - Epoch [103][3000/3746] lr: 2.250e-02, eta: 1 day, 17:40:09, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5914, loss_cls: 3.8000, loss: 3.8000 +2024-12-30 01:05:32,803 - pyskl - INFO - Epoch [103][3100/3746] lr: 2.248e-02, eta: 1 day, 17:38:44, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5919, loss_cls: 3.8146, loss: 3.8146 +2024-12-30 01:06:57,388 - pyskl - INFO - Epoch [103][3200/3746] lr: 2.246e-02, eta: 1 day, 17:37:19, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5913, loss_cls: 3.7903, loss: 3.7903 +2024-12-30 01:08:22,132 - pyskl - INFO - Epoch [103][3300/3746] lr: 2.243e-02, eta: 1 day, 17:35:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5809, loss_cls: 3.8340, loss: 3.8340 +2024-12-30 01:09:46,762 - pyskl - INFO - Epoch [103][3400/3746] lr: 2.241e-02, eta: 1 day, 17:34:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5805, loss_cls: 3.8467, loss: 3.8467 +2024-12-30 01:11:11,907 - pyskl - INFO - Epoch [103][3500/3746] lr: 2.239e-02, eta: 1 day, 17:33:04, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5823, loss_cls: 3.8359, loss: 3.8359 +2024-12-30 01:12:35,920 - pyskl - INFO - Epoch [103][3600/3746] lr: 2.236e-02, eta: 1 day, 17:31:39, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5855, loss_cls: 3.8428, loss: 3.8428 +2024-12-30 01:14:00,517 - pyskl - INFO - Epoch [103][3700/3746] lr: 2.234e-02, eta: 1 day, 17:30:14, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.5939, loss_cls: 3.7553, loss: 3.7553 +2024-12-30 01:14:41,057 - pyskl - INFO - Saving checkpoint at 103 epochs +2024-12-30 01:16:39,675 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 01:16:40,509 - pyskl - INFO - +top1_acc 0.2818 +top5_acc 0.5214 +2024-12-30 01:16:40,509 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 01:16:40,561 - pyskl - INFO - +mean_acc 0.2816 +2024-12-30 01:16:40,565 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_102.pth was removed +2024-12-30 01:16:40,891 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_103.pth. +2024-12-30 01:16:40,892 - pyskl - INFO - Best top1_acc is 0.2818 at 103 epoch. +2024-12-30 01:16:40,917 - pyskl - INFO - Epoch(val) [103][309] top1_acc: 0.2818, top5_acc: 0.5214, mean_class_accuracy: 0.2816 +2024-12-30 01:20:54,158 - pyskl - INFO - Epoch [104][100/3746] lr: 2.231e-02, eta: 1 day, 17:29:09, time: 2.532, data_time: 1.505, memory: 15990, top1_acc: 0.3439, top5_acc: 0.5975, loss_cls: 3.7310, loss: 3.7310 +2024-12-30 01:22:19,448 - pyskl - INFO - Epoch [104][200/3746] lr: 2.228e-02, eta: 1 day, 17:27:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6019, loss_cls: 3.7069, loss: 3.7069 +2024-12-30 01:23:45,004 - pyskl - INFO - Epoch [104][300/3746] lr: 2.226e-02, eta: 1 day, 17:26:20, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5911, loss_cls: 3.7669, loss: 3.7669 +2024-12-30 01:25:10,856 - pyskl - INFO - Epoch [104][400/3746] lr: 2.224e-02, eta: 1 day, 17:24:56, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5875, loss_cls: 3.7975, loss: 3.7975 +2024-12-30 01:26:36,592 - pyskl - INFO - Epoch [104][500/3746] lr: 2.221e-02, eta: 1 day, 17:23:31, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.5978, loss_cls: 3.7316, loss: 3.7316 +2024-12-30 01:28:01,950 - pyskl - INFO - Epoch [104][600/3746] lr: 2.219e-02, eta: 1 day, 17:22:07, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6069, loss_cls: 3.6758, loss: 3.6758 +2024-12-30 01:29:27,215 - pyskl - INFO - Epoch [104][700/3746] lr: 2.217e-02, eta: 1 day, 17:20:42, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.5975, loss_cls: 3.7403, loss: 3.7403 +2024-12-30 01:30:53,335 - pyskl - INFO - Epoch [104][800/3746] lr: 2.214e-02, eta: 1 day, 17:19:17, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5916, loss_cls: 3.7789, loss: 3.7789 +2024-12-30 01:32:18,527 - pyskl - INFO - Epoch [104][900/3746] lr: 2.212e-02, eta: 1 day, 17:17:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.6047, loss_cls: 3.7219, loss: 3.7219 +2024-12-30 01:33:43,837 - pyskl - INFO - Epoch [104][1000/3746] lr: 2.210e-02, eta: 1 day, 17:16:28, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6038, loss_cls: 3.7153, loss: 3.7153 +2024-12-30 01:35:09,642 - pyskl - INFO - Epoch [104][1100/3746] lr: 2.208e-02, eta: 1 day, 17:15:04, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.5955, loss_cls: 3.7677, loss: 3.7677 +2024-12-30 01:36:35,526 - pyskl - INFO - Epoch [104][1200/3746] lr: 2.205e-02, eta: 1 day, 17:13:39, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.5872, loss_cls: 3.7803, loss: 3.7803 +2024-12-30 01:38:01,287 - pyskl - INFO - Epoch [104][1300/3746] lr: 2.203e-02, eta: 1 day, 17:12:15, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6059, loss_cls: 3.7252, loss: 3.7252 +2024-12-30 01:39:27,454 - pyskl - INFO - Epoch [104][1400/3746] lr: 2.201e-02, eta: 1 day, 17:10:50, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.5927, loss_cls: 3.7591, loss: 3.7591 +2024-12-30 01:40:53,280 - pyskl - INFO - Epoch [104][1500/3746] lr: 2.198e-02, eta: 1 day, 17:09:26, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5916, loss_cls: 3.7835, loss: 3.7835 +2024-12-30 01:42:19,083 - pyskl - INFO - Epoch [104][1600/3746] lr: 2.196e-02, eta: 1 day, 17:08:02, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5941, loss_cls: 3.7823, loss: 3.7823 +2024-12-30 01:43:43,948 - pyskl - INFO - Epoch [104][1700/3746] lr: 2.194e-02, eta: 1 day, 17:06:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.5986, loss_cls: 3.7256, loss: 3.7256 +2024-12-30 01:45:08,797 - pyskl - INFO - Epoch [104][1800/3746] lr: 2.191e-02, eta: 1 day, 17:05:12, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5959, loss_cls: 3.7873, loss: 3.7873 +2024-12-30 01:46:34,137 - pyskl - INFO - Epoch [104][1900/3746] lr: 2.189e-02, eta: 1 day, 17:03:47, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5894, loss_cls: 3.8070, loss: 3.8070 +2024-12-30 01:47:59,252 - pyskl - INFO - Epoch [104][2000/3746] lr: 2.187e-02, eta: 1 day, 17:02:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5909, loss_cls: 3.7762, loss: 3.7762 +2024-12-30 01:49:24,833 - pyskl - INFO - Epoch [104][2100/3746] lr: 2.184e-02, eta: 1 day, 17:00:58, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5967, loss_cls: 3.7857, loss: 3.7857 +2024-12-30 01:50:50,249 - pyskl - INFO - Epoch [104][2200/3746] lr: 2.182e-02, eta: 1 day, 16:59:33, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.5919, loss_cls: 3.7777, loss: 3.7777 +2024-12-30 01:52:15,454 - pyskl - INFO - Epoch [104][2300/3746] lr: 2.180e-02, eta: 1 day, 16:58:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.5972, loss_cls: 3.7512, loss: 3.7512 +2024-12-30 01:53:40,708 - pyskl - INFO - Epoch [104][2400/3746] lr: 2.177e-02, eta: 1 day, 16:56:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5945, loss_cls: 3.7951, loss: 3.7951 +2024-12-30 01:55:06,196 - pyskl - INFO - Epoch [104][2500/3746] lr: 2.175e-02, eta: 1 day, 16:55:19, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5867, loss_cls: 3.8015, loss: 3.8015 +2024-12-30 01:56:31,459 - pyskl - INFO - Epoch [104][2600/3746] lr: 2.173e-02, eta: 1 day, 16:53:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.5950, loss_cls: 3.7751, loss: 3.7751 +2024-12-30 01:57:56,472 - pyskl - INFO - Epoch [104][2700/3746] lr: 2.171e-02, eta: 1 day, 16:52:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.5902, loss_cls: 3.7783, loss: 3.7783 +2024-12-30 01:59:21,770 - pyskl - INFO - Epoch [104][2800/3746] lr: 2.168e-02, eta: 1 day, 16:51:05, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5983, loss_cls: 3.7710, loss: 3.7710 +2024-12-30 02:00:46,542 - pyskl - INFO - Epoch [104][2900/3746] lr: 2.166e-02, eta: 1 day, 16:49:40, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5905, loss_cls: 3.7904, loss: 3.7904 +2024-12-30 02:02:12,442 - pyskl - INFO - Epoch [104][3000/3746] lr: 2.164e-02, eta: 1 day, 16:48:15, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5872, loss_cls: 3.7890, loss: 3.7890 +2024-12-30 02:03:37,600 - pyskl - INFO - Epoch [104][3100/3746] lr: 2.161e-02, eta: 1 day, 16:46:51, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5961, loss_cls: 3.7968, loss: 3.7968 +2024-12-30 02:05:03,147 - pyskl - INFO - Epoch [104][3200/3746] lr: 2.159e-02, eta: 1 day, 16:45:26, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.5931, loss_cls: 3.7581, loss: 3.7581 +2024-12-30 02:06:28,085 - pyskl - INFO - Epoch [104][3300/3746] lr: 2.157e-02, eta: 1 day, 16:44:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5877, loss_cls: 3.7893, loss: 3.7893 +2024-12-30 02:07:53,151 - pyskl - INFO - Epoch [104][3400/3746] lr: 2.154e-02, eta: 1 day, 16:42:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5813, loss_cls: 3.8550, loss: 3.8550 +2024-12-30 02:09:18,652 - pyskl - INFO - Epoch [104][3500/3746] lr: 2.152e-02, eta: 1 day, 16:41:12, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5900, loss_cls: 3.7789, loss: 3.7789 +2024-12-30 02:10:43,849 - pyskl - INFO - Epoch [104][3600/3746] lr: 2.150e-02, eta: 1 day, 16:39:47, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5833, loss_cls: 3.8135, loss: 3.8135 +2024-12-30 02:12:09,057 - pyskl - INFO - Epoch [104][3700/3746] lr: 2.148e-02, eta: 1 day, 16:38:22, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5823, loss_cls: 3.8234, loss: 3.8234 +2024-12-30 02:12:49,881 - pyskl - INFO - Saving checkpoint at 104 epochs +2024-12-30 02:14:51,814 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 02:14:52,633 - pyskl - INFO - +top1_acc 0.2810 +top5_acc 0.5318 +2024-12-30 02:14:52,633 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 02:14:52,682 - pyskl - INFO - +mean_acc 0.2807 +2024-12-30 02:14:52,696 - pyskl - INFO - Epoch(val) [104][309] top1_acc: 0.2810, top5_acc: 0.5318, mean_class_accuracy: 0.2807 +2024-12-30 02:19:15,201 - pyskl - INFO - Epoch [105][100/3746] lr: 2.144e-02, eta: 1 day, 16:37:20, time: 2.625, data_time: 1.602, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6077, loss_cls: 3.6881, loss: 3.6881 +2024-12-30 02:20:39,963 - pyskl - INFO - Epoch [105][200/3746] lr: 2.142e-02, eta: 1 day, 16:35:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6109, loss_cls: 3.6977, loss: 3.6977 +2024-12-30 02:22:04,230 - pyskl - INFO - Epoch [105][300/3746] lr: 2.140e-02, eta: 1 day, 16:34:29, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6066, loss_cls: 3.7212, loss: 3.7212 +2024-12-30 02:23:29,454 - pyskl - INFO - Epoch [105][400/3746] lr: 2.137e-02, eta: 1 day, 16:33:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6017, loss_cls: 3.7053, loss: 3.7053 +2024-12-30 02:24:54,719 - pyskl - INFO - Epoch [105][500/3746] lr: 2.135e-02, eta: 1 day, 16:31:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6036, loss_cls: 3.7042, loss: 3.7042 +2024-12-30 02:26:19,541 - pyskl - INFO - Epoch [105][600/3746] lr: 2.133e-02, eta: 1 day, 16:30:15, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6050, loss_cls: 3.7286, loss: 3.7286 +2024-12-30 02:27:44,167 - pyskl - INFO - Epoch [105][700/3746] lr: 2.130e-02, eta: 1 day, 16:28:50, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5922, loss_cls: 3.7848, loss: 3.7848 +2024-12-30 02:29:08,323 - pyskl - INFO - Epoch [105][800/3746] lr: 2.128e-02, eta: 1 day, 16:27:25, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6028, loss_cls: 3.7377, loss: 3.7377 +2024-12-30 02:30:32,580 - pyskl - INFO - Epoch [105][900/3746] lr: 2.126e-02, eta: 1 day, 16:25:59, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.5970, loss_cls: 3.7502, loss: 3.7502 +2024-12-30 02:31:56,501 - pyskl - INFO - Epoch [105][1000/3746] lr: 2.124e-02, eta: 1 day, 16:24:34, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6072, loss_cls: 3.7292, loss: 3.7292 +2024-12-30 02:33:20,357 - pyskl - INFO - Epoch [105][1100/3746] lr: 2.121e-02, eta: 1 day, 16:23:09, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.5989, loss_cls: 3.7423, loss: 3.7423 +2024-12-30 02:34:44,730 - pyskl - INFO - Epoch [105][1200/3746] lr: 2.119e-02, eta: 1 day, 16:21:44, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5948, loss_cls: 3.7510, loss: 3.7510 +2024-12-30 02:36:08,713 - pyskl - INFO - Epoch [105][1300/3746] lr: 2.117e-02, eta: 1 day, 16:20:18, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.5983, loss_cls: 3.7383, loss: 3.7383 +2024-12-30 02:37:33,040 - pyskl - INFO - Epoch [105][1400/3746] lr: 2.114e-02, eta: 1 day, 16:18:53, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.6077, loss_cls: 3.7257, loss: 3.7257 +2024-12-30 02:38:57,803 - pyskl - INFO - Epoch [105][1500/3746] lr: 2.112e-02, eta: 1 day, 16:17:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5953, loss_cls: 3.7422, loss: 3.7422 +2024-12-30 02:40:22,445 - pyskl - INFO - Epoch [105][1600/3746] lr: 2.110e-02, eta: 1 day, 16:16:03, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5945, loss_cls: 3.7660, loss: 3.7660 +2024-12-30 02:41:46,690 - pyskl - INFO - Epoch [105][1700/3746] lr: 2.108e-02, eta: 1 day, 16:14:38, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5909, loss_cls: 3.7934, loss: 3.7934 +2024-12-30 02:43:11,477 - pyskl - INFO - Epoch [105][1800/3746] lr: 2.105e-02, eta: 1 day, 16:13:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.5953, loss_cls: 3.7827, loss: 3.7827 +2024-12-30 02:44:35,909 - pyskl - INFO - Epoch [105][1900/3746] lr: 2.103e-02, eta: 1 day, 16:11:48, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.5972, loss_cls: 3.7502, loss: 3.7502 +2024-12-30 02:46:00,157 - pyskl - INFO - Epoch [105][2000/3746] lr: 2.101e-02, eta: 1 day, 16:10:23, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.5902, loss_cls: 3.7604, loss: 3.7604 +2024-12-30 02:47:25,057 - pyskl - INFO - Epoch [105][2100/3746] lr: 2.098e-02, eta: 1 day, 16:08:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5909, loss_cls: 3.7915, loss: 3.7915 +2024-12-30 02:48:49,909 - pyskl - INFO - Epoch [105][2200/3746] lr: 2.096e-02, eta: 1 day, 16:07:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.6006, loss_cls: 3.7998, loss: 3.7998 +2024-12-30 02:50:14,603 - pyskl - INFO - Epoch [105][2300/3746] lr: 2.094e-02, eta: 1 day, 16:06:08, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5986, loss_cls: 3.8012, loss: 3.8012 +2024-12-30 02:51:39,427 - pyskl - INFO - Epoch [105][2400/3746] lr: 2.092e-02, eta: 1 day, 16:04:43, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5988, loss_cls: 3.7744, loss: 3.7744 +2024-12-30 02:53:03,566 - pyskl - INFO - Epoch [105][2500/3746] lr: 2.089e-02, eta: 1 day, 16:03:17, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5916, loss_cls: 3.7918, loss: 3.7918 +2024-12-30 02:54:27,521 - pyskl - INFO - Epoch [105][2600/3746] lr: 2.087e-02, eta: 1 day, 16:01:52, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.5948, loss_cls: 3.7695, loss: 3.7695 +2024-12-30 02:55:51,592 - pyskl - INFO - Epoch [105][2700/3746] lr: 2.085e-02, eta: 1 day, 16:00:27, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.5961, loss_cls: 3.7731, loss: 3.7731 +2024-12-30 02:57:16,388 - pyskl - INFO - Epoch [105][2800/3746] lr: 2.083e-02, eta: 1 day, 15:59:02, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6027, loss_cls: 3.7093, loss: 3.7093 +2024-12-30 02:58:41,479 - pyskl - INFO - Epoch [105][2900/3746] lr: 2.080e-02, eta: 1 day, 15:57:37, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5978, loss_cls: 3.7497, loss: 3.7497 +2024-12-30 03:00:06,224 - pyskl - INFO - Epoch [105][3000/3746] lr: 2.078e-02, eta: 1 day, 15:56:12, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.6048, loss_cls: 3.7636, loss: 3.7636 +2024-12-30 03:01:30,948 - pyskl - INFO - Epoch [105][3100/3746] lr: 2.076e-02, eta: 1 day, 15:54:47, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.5934, loss_cls: 3.7815, loss: 3.7815 +2024-12-30 03:02:55,158 - pyskl - INFO - Epoch [105][3200/3746] lr: 2.073e-02, eta: 1 day, 15:53:22, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.5998, loss_cls: 3.7301, loss: 3.7301 +2024-12-30 03:04:19,684 - pyskl - INFO - Epoch [105][3300/3746] lr: 2.071e-02, eta: 1 day, 15:51:57, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5930, loss_cls: 3.7713, loss: 3.7713 +2024-12-30 03:05:44,843 - pyskl - INFO - Epoch [105][3400/3746] lr: 2.069e-02, eta: 1 day, 15:50:32, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6122, loss_cls: 3.7031, loss: 3.7031 +2024-12-30 03:07:09,176 - pyskl - INFO - Epoch [105][3500/3746] lr: 2.067e-02, eta: 1 day, 15:49:07, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5877, loss_cls: 3.8093, loss: 3.8093 +2024-12-30 03:08:33,753 - pyskl - INFO - Epoch [105][3600/3746] lr: 2.064e-02, eta: 1 day, 15:47:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.5947, loss_cls: 3.7388, loss: 3.7388 +2024-12-30 03:09:57,923 - pyskl - INFO - Epoch [105][3700/3746] lr: 2.062e-02, eta: 1 day, 15:46:17, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.5953, loss_cls: 3.7207, loss: 3.7207 +2024-12-30 03:10:38,881 - pyskl - INFO - Saving checkpoint at 105 epochs +2024-12-30 03:12:38,048 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 03:12:39,081 - pyskl - INFO - +top1_acc 0.2675 +top5_acc 0.5183 +2024-12-30 03:12:39,081 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 03:12:39,130 - pyskl - INFO - +mean_acc 0.2672 +2024-12-30 03:12:39,144 - pyskl - INFO - Epoch(val) [105][309] top1_acc: 0.2675, top5_acc: 0.5183, mean_class_accuracy: 0.2672 +2024-12-30 03:16:53,072 - pyskl - INFO - Epoch [106][100/3746] lr: 2.059e-02, eta: 1 day, 15:45:08, time: 2.539, data_time: 1.502, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6134, loss_cls: 3.6954, loss: 3.6954 +2024-12-30 03:18:18,569 - pyskl - INFO - Epoch [106][200/3746] lr: 2.057e-02, eta: 1 day, 15:43:43, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6072, loss_cls: 3.6905, loss: 3.6905 +2024-12-30 03:19:43,458 - pyskl - INFO - Epoch [106][300/3746] lr: 2.054e-02, eta: 1 day, 15:42:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5936, loss_cls: 3.7540, loss: 3.7540 +2024-12-30 03:21:08,417 - pyskl - INFO - Epoch [106][400/3746] lr: 2.052e-02, eta: 1 day, 15:40:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6034, loss_cls: 3.7271, loss: 3.7271 +2024-12-30 03:22:33,563 - pyskl - INFO - Epoch [106][500/3746] lr: 2.050e-02, eta: 1 day, 15:39:29, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6147, loss_cls: 3.7033, loss: 3.7033 +2024-12-30 03:23:58,222 - pyskl - INFO - Epoch [106][600/3746] lr: 2.048e-02, eta: 1 day, 15:38:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6097, loss_cls: 3.7059, loss: 3.7059 +2024-12-30 03:25:23,678 - pyskl - INFO - Epoch [106][700/3746] lr: 2.045e-02, eta: 1 day, 15:36:39, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6058, loss_cls: 3.6851, loss: 3.6851 +2024-12-30 03:26:48,598 - pyskl - INFO - Epoch [106][800/3746] lr: 2.043e-02, eta: 1 day, 15:35:14, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6062, loss_cls: 3.7062, loss: 3.7062 +2024-12-30 03:28:13,287 - pyskl - INFO - Epoch [106][900/3746] lr: 2.041e-02, eta: 1 day, 15:33:49, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6041, loss_cls: 3.6971, loss: 3.6971 +2024-12-30 03:29:38,905 - pyskl - INFO - Epoch [106][1000/3746] lr: 2.039e-02, eta: 1 day, 15:32:24, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.5984, loss_cls: 3.7200, loss: 3.7200 +2024-12-30 03:31:04,319 - pyskl - INFO - Epoch [106][1100/3746] lr: 2.036e-02, eta: 1 day, 15:30:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6000, loss_cls: 3.7644, loss: 3.7644 +2024-12-30 03:32:29,575 - pyskl - INFO - Epoch [106][1200/3746] lr: 2.034e-02, eta: 1 day, 15:29:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6059, loss_cls: 3.7057, loss: 3.7057 +2024-12-30 03:33:54,612 - pyskl - INFO - Epoch [106][1300/3746] lr: 2.032e-02, eta: 1 day, 15:28:10, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5992, loss_cls: 3.7593, loss: 3.7593 +2024-12-30 03:35:19,931 - pyskl - INFO - Epoch [106][1400/3746] lr: 2.030e-02, eta: 1 day, 15:26:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6019, loss_cls: 3.7320, loss: 3.7320 +2024-12-30 03:36:45,098 - pyskl - INFO - Epoch [106][1500/3746] lr: 2.027e-02, eta: 1 day, 15:25:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6019, loss_cls: 3.7508, loss: 3.7508 +2024-12-30 03:38:10,629 - pyskl - INFO - Epoch [106][1600/3746] lr: 2.025e-02, eta: 1 day, 15:23:55, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6147, loss_cls: 3.6782, loss: 3.6782 +2024-12-30 03:39:35,821 - pyskl - INFO - Epoch [106][1700/3746] lr: 2.023e-02, eta: 1 day, 15:22:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5984, loss_cls: 3.7559, loss: 3.7559 +2024-12-30 03:41:00,530 - pyskl - INFO - Epoch [106][1800/3746] lr: 2.021e-02, eta: 1 day, 15:21:06, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6044, loss_cls: 3.6966, loss: 3.6966 +2024-12-30 03:42:25,199 - pyskl - INFO - Epoch [106][1900/3746] lr: 2.018e-02, eta: 1 day, 15:19:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6094, loss_cls: 3.7208, loss: 3.7208 +2024-12-30 03:43:50,724 - pyskl - INFO - Epoch [106][2000/3746] lr: 2.016e-02, eta: 1 day, 15:18:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6034, loss_cls: 3.7107, loss: 3.7107 +2024-12-30 03:45:15,791 - pyskl - INFO - Epoch [106][2100/3746] lr: 2.014e-02, eta: 1 day, 15:16:51, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.5973, loss_cls: 3.7497, loss: 3.7497 +2024-12-30 03:46:40,678 - pyskl - INFO - Epoch [106][2200/3746] lr: 2.012e-02, eta: 1 day, 15:15:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6020, loss_cls: 3.7215, loss: 3.7215 +2024-12-30 03:48:05,979 - pyskl - INFO - Epoch [106][2300/3746] lr: 2.009e-02, eta: 1 day, 15:14:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5977, loss_cls: 3.7406, loss: 3.7406 +2024-12-30 03:49:31,072 - pyskl - INFO - Epoch [106][2400/3746] lr: 2.007e-02, eta: 1 day, 15:12:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5984, loss_cls: 3.7585, loss: 3.7585 +2024-12-30 03:50:56,290 - pyskl - INFO - Epoch [106][2500/3746] lr: 2.005e-02, eta: 1 day, 15:11:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6050, loss_cls: 3.7074, loss: 3.7074 +2024-12-30 03:52:21,288 - pyskl - INFO - Epoch [106][2600/3746] lr: 2.003e-02, eta: 1 day, 15:09:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6002, loss_cls: 3.7507, loss: 3.7507 +2024-12-30 03:53:46,166 - pyskl - INFO - Epoch [106][2700/3746] lr: 2.000e-02, eta: 1 day, 15:08:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6070, loss_cls: 3.7418, loss: 3.7418 +2024-12-30 03:55:12,073 - pyskl - INFO - Epoch [106][2800/3746] lr: 1.998e-02, eta: 1 day, 15:06:57, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6067, loss_cls: 3.7310, loss: 3.7310 +2024-12-30 03:56:38,133 - pyskl - INFO - Epoch [106][2900/3746] lr: 1.996e-02, eta: 1 day, 15:05:32, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6061, loss_cls: 3.7162, loss: 3.7162 +2024-12-30 03:58:04,290 - pyskl - INFO - Epoch [106][3000/3746] lr: 1.994e-02, eta: 1 day, 15:04:08, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5953, loss_cls: 3.7798, loss: 3.7798 +2024-12-30 03:59:29,810 - pyskl - INFO - Epoch [106][3100/3746] lr: 1.991e-02, eta: 1 day, 15:02:43, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6019, loss_cls: 3.7457, loss: 3.7457 +2024-12-30 04:00:55,363 - pyskl - INFO - Epoch [106][3200/3746] lr: 1.989e-02, eta: 1 day, 15:01:19, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5950, loss_cls: 3.7729, loss: 3.7729 +2024-12-30 04:02:21,862 - pyskl - INFO - Epoch [106][3300/3746] lr: 1.987e-02, eta: 1 day, 14:59:54, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5939, loss_cls: 3.7543, loss: 3.7543 +2024-12-30 04:03:47,100 - pyskl - INFO - Epoch [106][3400/3746] lr: 1.985e-02, eta: 1 day, 14:58:29, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6105, loss_cls: 3.7131, loss: 3.7131 +2024-12-30 04:05:12,502 - pyskl - INFO - Epoch [106][3500/3746] lr: 1.983e-02, eta: 1 day, 14:57:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.5927, loss_cls: 3.7828, loss: 3.7828 +2024-12-30 04:06:37,707 - pyskl - INFO - Epoch [106][3600/3746] lr: 1.980e-02, eta: 1 day, 14:55:40, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6048, loss_cls: 3.7150, loss: 3.7150 +2024-12-30 04:08:03,775 - pyskl - INFO - Epoch [106][3700/3746] lr: 1.978e-02, eta: 1 day, 14:54:15, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6048, loss_cls: 3.7400, loss: 3.7400 +2024-12-30 04:08:45,286 - pyskl - INFO - Saving checkpoint at 106 epochs +2024-12-30 04:10:45,558 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 04:10:46,786 - pyskl - INFO - +top1_acc 0.2788 +top5_acc 0.5223 +2024-12-30 04:10:46,786 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 04:10:46,837 - pyskl - INFO - +mean_acc 0.2785 +2024-12-30 04:10:46,853 - pyskl - INFO - Epoch(val) [106][309] top1_acc: 0.2788, top5_acc: 0.5223, mean_class_accuracy: 0.2785 +2024-12-30 04:15:08,238 - pyskl - INFO - Epoch [107][100/3746] lr: 1.975e-02, eta: 1 day, 14:53:08, time: 2.614, data_time: 1.575, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6080, loss_cls: 3.6781, loss: 3.6781 +2024-12-30 04:16:33,310 - pyskl - INFO - Epoch [107][200/3746] lr: 1.973e-02, eta: 1 day, 14:51:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6091, loss_cls: 3.7034, loss: 3.7034 +2024-12-30 04:17:58,621 - pyskl - INFO - Epoch [107][300/3746] lr: 1.970e-02, eta: 1 day, 14:50:18, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6073, loss_cls: 3.6566, loss: 3.6566 +2024-12-30 04:19:23,486 - pyskl - INFO - Epoch [107][400/3746] lr: 1.968e-02, eta: 1 day, 14:48:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6197, loss_cls: 3.6298, loss: 3.6298 +2024-12-30 04:20:48,718 - pyskl - INFO - Epoch [107][500/3746] lr: 1.966e-02, eta: 1 day, 14:47:29, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6119, loss_cls: 3.7051, loss: 3.7051 +2024-12-30 04:22:14,401 - pyskl - INFO - Epoch [107][600/3746] lr: 1.964e-02, eta: 1 day, 14:46:04, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6158, loss_cls: 3.6558, loss: 3.6558 +2024-12-30 04:23:39,620 - pyskl - INFO - Epoch [107][700/3746] lr: 1.961e-02, eta: 1 day, 14:44:39, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6111, loss_cls: 3.7178, loss: 3.7178 +2024-12-30 04:25:04,739 - pyskl - INFO - Epoch [107][800/3746] lr: 1.959e-02, eta: 1 day, 14:43:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6052, loss_cls: 3.7090, loss: 3.7090 +2024-12-30 04:26:30,286 - pyskl - INFO - Epoch [107][900/3746] lr: 1.957e-02, eta: 1 day, 14:41:49, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6052, loss_cls: 3.6874, loss: 3.6874 +2024-12-30 04:27:55,562 - pyskl - INFO - Epoch [107][1000/3746] lr: 1.955e-02, eta: 1 day, 14:40:24, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6150, loss_cls: 3.6832, loss: 3.6832 +2024-12-30 04:29:20,912 - pyskl - INFO - Epoch [107][1100/3746] lr: 1.953e-02, eta: 1 day, 14:39:00, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6188, loss_cls: 3.6711, loss: 3.6711 +2024-12-30 04:30:45,862 - pyskl - INFO - Epoch [107][1200/3746] lr: 1.950e-02, eta: 1 day, 14:37:35, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6170, loss_cls: 3.6517, loss: 3.6517 +2024-12-30 04:32:10,832 - pyskl - INFO - Epoch [107][1300/3746] lr: 1.948e-02, eta: 1 day, 14:36:10, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3452, top5_acc: 0.6039, loss_cls: 3.7057, loss: 3.7057 +2024-12-30 04:33:36,139 - pyskl - INFO - Epoch [107][1400/3746] lr: 1.946e-02, eta: 1 day, 14:34:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6039, loss_cls: 3.7491, loss: 3.7491 +2024-12-30 04:35:00,856 - pyskl - INFO - Epoch [107][1500/3746] lr: 1.944e-02, eta: 1 day, 14:33:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6123, loss_cls: 3.6849, loss: 3.6849 +2024-12-30 04:36:26,272 - pyskl - INFO - Epoch [107][1600/3746] lr: 1.942e-02, eta: 1 day, 14:31:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6034, loss_cls: 3.7163, loss: 3.7163 +2024-12-30 04:37:51,130 - pyskl - INFO - Epoch [107][1700/3746] lr: 1.939e-02, eta: 1 day, 14:30:30, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5981, loss_cls: 3.7395, loss: 3.7395 +2024-12-30 04:39:16,557 - pyskl - INFO - Epoch [107][1800/3746] lr: 1.937e-02, eta: 1 day, 14:29:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6084, loss_cls: 3.7238, loss: 3.7238 +2024-12-30 04:40:41,926 - pyskl - INFO - Epoch [107][1900/3746] lr: 1.935e-02, eta: 1 day, 14:27:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6030, loss_cls: 3.7277, loss: 3.7277 +2024-12-30 04:42:06,748 - pyskl - INFO - Epoch [107][2000/3746] lr: 1.933e-02, eta: 1 day, 14:26:15, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6030, loss_cls: 3.7384, loss: 3.7384 +2024-12-30 04:43:32,132 - pyskl - INFO - Epoch [107][2100/3746] lr: 1.930e-02, eta: 1 day, 14:24:50, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5927, loss_cls: 3.7592, loss: 3.7592 +2024-12-30 04:44:57,416 - pyskl - INFO - Epoch [107][2200/3746] lr: 1.928e-02, eta: 1 day, 14:23:25, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6097, loss_cls: 3.7420, loss: 3.7420 +2024-12-30 04:46:22,919 - pyskl - INFO - Epoch [107][2300/3746] lr: 1.926e-02, eta: 1 day, 14:22:01, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6027, loss_cls: 3.7101, loss: 3.7101 +2024-12-30 04:47:47,623 - pyskl - INFO - Epoch [107][2400/3746] lr: 1.924e-02, eta: 1 day, 14:20:36, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6084, loss_cls: 3.6888, loss: 3.6888 +2024-12-30 04:49:12,338 - pyskl - INFO - Epoch [107][2500/3746] lr: 1.922e-02, eta: 1 day, 14:19:10, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5956, loss_cls: 3.7465, loss: 3.7465 +2024-12-30 04:50:37,484 - pyskl - INFO - Epoch [107][2600/3746] lr: 1.919e-02, eta: 1 day, 14:17:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.5980, loss_cls: 3.7547, loss: 3.7547 +2024-12-30 04:52:02,760 - pyskl - INFO - Epoch [107][2700/3746] lr: 1.917e-02, eta: 1 day, 14:16:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6023, loss_cls: 3.7309, loss: 3.7309 +2024-12-30 04:53:28,415 - pyskl - INFO - Epoch [107][2800/3746] lr: 1.915e-02, eta: 1 day, 14:14:56, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6042, loss_cls: 3.7170, loss: 3.7170 +2024-12-30 04:54:53,893 - pyskl - INFO - Epoch [107][2900/3746] lr: 1.913e-02, eta: 1 day, 14:13:31, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6077, loss_cls: 3.7238, loss: 3.7238 +2024-12-30 04:56:19,377 - pyskl - INFO - Epoch [107][3000/3746] lr: 1.911e-02, eta: 1 day, 14:12:06, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6022, loss_cls: 3.7127, loss: 3.7127 +2024-12-30 04:57:44,315 - pyskl - INFO - Epoch [107][3100/3746] lr: 1.908e-02, eta: 1 day, 14:10:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6006, loss_cls: 3.7342, loss: 3.7342 +2024-12-30 04:59:09,490 - pyskl - INFO - Epoch [107][3200/3746] lr: 1.906e-02, eta: 1 day, 14:09:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6031, loss_cls: 3.7180, loss: 3.7180 +2024-12-30 05:00:34,627 - pyskl - INFO - Epoch [107][3300/3746] lr: 1.904e-02, eta: 1 day, 14:07:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.5941, loss_cls: 3.7326, loss: 3.7326 +2024-12-30 05:01:59,883 - pyskl - INFO - Epoch [107][3400/3746] lr: 1.902e-02, eta: 1 day, 14:06:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6016, loss_cls: 3.7180, loss: 3.7180 +2024-12-30 05:03:25,463 - pyskl - INFO - Epoch [107][3500/3746] lr: 1.900e-02, eta: 1 day, 14:05:02, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5998, loss_cls: 3.7177, loss: 3.7177 +2024-12-30 05:04:50,761 - pyskl - INFO - Epoch [107][3600/3746] lr: 1.897e-02, eta: 1 day, 14:03:37, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6078, loss_cls: 3.6991, loss: 3.6991 +2024-12-30 05:06:16,038 - pyskl - INFO - Epoch [107][3700/3746] lr: 1.895e-02, eta: 1 day, 14:02:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.6009, loss_cls: 3.7388, loss: 3.7388 +2024-12-30 05:06:57,489 - pyskl - INFO - Saving checkpoint at 107 epochs +2024-12-30 05:08:58,447 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 05:08:59,251 - pyskl - INFO - +top1_acc 0.2914 +top5_acc 0.5436 +2024-12-30 05:08:59,251 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 05:08:59,295 - pyskl - INFO - +mean_acc 0.2912 +2024-12-30 05:08:59,300 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_103.pth was removed +2024-12-30 05:08:59,584 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_107.pth. +2024-12-30 05:08:59,585 - pyskl - INFO - Best top1_acc is 0.2914 at 107 epoch. +2024-12-30 05:08:59,607 - pyskl - INFO - Epoch(val) [107][309] top1_acc: 0.2914, top5_acc: 0.5436, mean_class_accuracy: 0.2912 +2024-12-30 05:13:24,517 - pyskl - INFO - Epoch [108][100/3746] lr: 1.892e-02, eta: 1 day, 14:01:05, time: 2.649, data_time: 1.617, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6136, loss_cls: 3.6581, loss: 3.6581 +2024-12-30 05:14:50,185 - pyskl - INFO - Epoch [108][200/3746] lr: 1.890e-02, eta: 1 day, 13:59:40, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6134, loss_cls: 3.6376, loss: 3.6376 +2024-12-30 05:16:15,484 - pyskl - INFO - Epoch [108][300/3746] lr: 1.888e-02, eta: 1 day, 13:58:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6008, loss_cls: 3.7258, loss: 3.7258 +2024-12-30 05:17:40,785 - pyskl - INFO - Epoch [108][400/3746] lr: 1.886e-02, eta: 1 day, 13:56:50, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6092, loss_cls: 3.6718, loss: 3.6718 +2024-12-30 05:19:06,231 - pyskl - INFO - Epoch [108][500/3746] lr: 1.883e-02, eta: 1 day, 13:55:25, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6205, loss_cls: 3.6349, loss: 3.6349 +2024-12-30 05:20:31,502 - pyskl - INFO - Epoch [108][600/3746] lr: 1.881e-02, eta: 1 day, 13:54:00, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6217, loss_cls: 3.6469, loss: 3.6469 +2024-12-30 05:21:57,205 - pyskl - INFO - Epoch [108][700/3746] lr: 1.879e-02, eta: 1 day, 13:52:36, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6214, loss_cls: 3.6085, loss: 3.6085 +2024-12-30 05:23:22,257 - pyskl - INFO - Epoch [108][800/3746] lr: 1.877e-02, eta: 1 day, 13:51:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6073, loss_cls: 3.7024, loss: 3.7024 +2024-12-30 05:24:47,287 - pyskl - INFO - Epoch [108][900/3746] lr: 1.875e-02, eta: 1 day, 13:49:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6089, loss_cls: 3.6737, loss: 3.6737 +2024-12-30 05:26:12,483 - pyskl - INFO - Epoch [108][1000/3746] lr: 1.872e-02, eta: 1 day, 13:48:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3541, top5_acc: 0.6059, loss_cls: 3.6767, loss: 3.6767 +2024-12-30 05:27:37,919 - pyskl - INFO - Epoch [108][1100/3746] lr: 1.870e-02, eta: 1 day, 13:46:56, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6091, loss_cls: 3.6694, loss: 3.6694 +2024-12-30 05:29:02,939 - pyskl - INFO - Epoch [108][1200/3746] lr: 1.868e-02, eta: 1 day, 13:45:31, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6153, loss_cls: 3.6779, loss: 3.6779 +2024-12-30 05:30:28,121 - pyskl - INFO - Epoch [108][1300/3746] lr: 1.866e-02, eta: 1 day, 13:44:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6136, loss_cls: 3.6544, loss: 3.6544 +2024-12-30 05:31:53,754 - pyskl - INFO - Epoch [108][1400/3746] lr: 1.864e-02, eta: 1 day, 13:42:41, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6194, loss_cls: 3.6559, loss: 3.6559 +2024-12-30 05:33:19,364 - pyskl - INFO - Epoch [108][1500/3746] lr: 1.862e-02, eta: 1 day, 13:41:16, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6128, loss_cls: 3.6542, loss: 3.6542 +2024-12-30 05:34:45,073 - pyskl - INFO - Epoch [108][1600/3746] lr: 1.859e-02, eta: 1 day, 13:39:51, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6116, loss_cls: 3.6513, loss: 3.6513 +2024-12-30 05:36:10,338 - pyskl - INFO - Epoch [108][1700/3746] lr: 1.857e-02, eta: 1 day, 13:38:27, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6027, loss_cls: 3.7034, loss: 3.7034 +2024-12-30 05:37:35,278 - pyskl - INFO - Epoch [108][1800/3746] lr: 1.855e-02, eta: 1 day, 13:37:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.5981, loss_cls: 3.7476, loss: 3.7476 +2024-12-30 05:39:00,812 - pyskl - INFO - Epoch [108][1900/3746] lr: 1.853e-02, eta: 1 day, 13:35:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6108, loss_cls: 3.6918, loss: 3.6918 +2024-12-30 05:40:26,274 - pyskl - INFO - Epoch [108][2000/3746] lr: 1.851e-02, eta: 1 day, 13:34:12, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6134, loss_cls: 3.6574, loss: 3.6574 +2024-12-30 05:41:51,479 - pyskl - INFO - Epoch [108][2100/3746] lr: 1.848e-02, eta: 1 day, 13:32:47, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6039, loss_cls: 3.7058, loss: 3.7058 +2024-12-30 05:43:16,405 - pyskl - INFO - Epoch [108][2200/3746] lr: 1.846e-02, eta: 1 day, 13:31:22, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6016, loss_cls: 3.7159, loss: 3.7159 +2024-12-30 05:44:41,391 - pyskl - INFO - Epoch [108][2300/3746] lr: 1.844e-02, eta: 1 day, 13:29:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6016, loss_cls: 3.7381, loss: 3.7381 +2024-12-30 05:46:06,753 - pyskl - INFO - Epoch [108][2400/3746] lr: 1.842e-02, eta: 1 day, 13:28:32, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6100, loss_cls: 3.6761, loss: 3.6761 +2024-12-30 05:47:31,939 - pyskl - INFO - Epoch [108][2500/3746] lr: 1.840e-02, eta: 1 day, 13:27:07, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.5959, loss_cls: 3.7400, loss: 3.7400 +2024-12-30 05:48:57,101 - pyskl - INFO - Epoch [108][2600/3746] lr: 1.838e-02, eta: 1 day, 13:25:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6078, loss_cls: 3.7051, loss: 3.7051 +2024-12-30 05:50:22,299 - pyskl - INFO - Epoch [108][2700/3746] lr: 1.835e-02, eta: 1 day, 13:24:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6003, loss_cls: 3.7186, loss: 3.7186 +2024-12-30 05:51:47,250 - pyskl - INFO - Epoch [108][2800/3746] lr: 1.833e-02, eta: 1 day, 13:22:52, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6148, loss_cls: 3.6722, loss: 3.6722 +2024-12-30 05:53:12,252 - pyskl - INFO - Epoch [108][2900/3746] lr: 1.831e-02, eta: 1 day, 13:21:27, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6000, loss_cls: 3.7151, loss: 3.7151 +2024-12-30 05:54:37,125 - pyskl - INFO - Epoch [108][3000/3746] lr: 1.829e-02, eta: 1 day, 13:20:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6025, loss_cls: 3.7188, loss: 3.7188 +2024-12-30 05:56:02,119 - pyskl - INFO - Epoch [108][3100/3746] lr: 1.827e-02, eta: 1 day, 13:18:37, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6034, loss_cls: 3.7328, loss: 3.7328 +2024-12-30 05:57:27,440 - pyskl - INFO - Epoch [108][3200/3746] lr: 1.825e-02, eta: 1 day, 13:17:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.5997, loss_cls: 3.7117, loss: 3.7117 +2024-12-30 05:58:52,901 - pyskl - INFO - Epoch [108][3300/3746] lr: 1.823e-02, eta: 1 day, 13:15:47, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5938, loss_cls: 3.7928, loss: 3.7928 +2024-12-30 06:00:17,783 - pyskl - INFO - Epoch [108][3400/3746] lr: 1.820e-02, eta: 1 day, 13:14:22, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6011, loss_cls: 3.7508, loss: 3.7508 +2024-12-30 06:01:42,127 - pyskl - INFO - Epoch [108][3500/3746] lr: 1.818e-02, eta: 1 day, 13:12:57, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6127, loss_cls: 3.6849, loss: 3.6849 +2024-12-30 06:03:07,268 - pyskl - INFO - Epoch [108][3600/3746] lr: 1.816e-02, eta: 1 day, 13:11:32, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.5956, loss_cls: 3.7200, loss: 3.7200 +2024-12-30 06:04:33,287 - pyskl - INFO - Epoch [108][3700/3746] lr: 1.814e-02, eta: 1 day, 13:10:07, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6048, loss_cls: 3.6848, loss: 3.6848 +2024-12-30 06:05:14,780 - pyskl - INFO - Saving checkpoint at 108 epochs +2024-12-30 06:07:15,000 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 06:07:15,735 - pyskl - INFO - +top1_acc 0.2886 +top5_acc 0.5417 +2024-12-30 06:07:15,735 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 06:07:15,784 - pyskl - INFO - +mean_acc 0.2883 +2024-12-30 06:07:15,803 - pyskl - INFO - Epoch(val) [108][309] top1_acc: 0.2886, top5_acc: 0.5417, mean_class_accuracy: 0.2883 +2024-12-30 06:11:34,204 - pyskl - INFO - Epoch [109][100/3746] lr: 1.811e-02, eta: 1 day, 13:08:55, time: 2.584, data_time: 1.564, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6123, loss_cls: 3.6565, loss: 3.6565 +2024-12-30 06:12:59,036 - pyskl - INFO - Epoch [109][200/3746] lr: 1.809e-02, eta: 1 day, 13:07:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6252, loss_cls: 3.5951, loss: 3.5951 +2024-12-30 06:14:24,039 - pyskl - INFO - Epoch [109][300/3746] lr: 1.806e-02, eta: 1 day, 13:06:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6178, loss_cls: 3.6631, loss: 3.6631 +2024-12-30 06:15:49,193 - pyskl - INFO - Epoch [109][400/3746] lr: 1.804e-02, eta: 1 day, 13:04:40, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6177, loss_cls: 3.6513, loss: 3.6513 +2024-12-30 06:17:15,569 - pyskl - INFO - Epoch [109][500/3746] lr: 1.802e-02, eta: 1 day, 13:03:15, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6177, loss_cls: 3.6333, loss: 3.6333 +2024-12-30 06:18:41,299 - pyskl - INFO - Epoch [109][600/3746] lr: 1.800e-02, eta: 1 day, 13:01:50, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6155, loss_cls: 3.6518, loss: 3.6518 +2024-12-30 06:20:06,965 - pyskl - INFO - Epoch [109][700/3746] lr: 1.798e-02, eta: 1 day, 13:00:26, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6172, loss_cls: 3.6459, loss: 3.6459 +2024-12-30 06:21:32,703 - pyskl - INFO - Epoch [109][800/3746] lr: 1.796e-02, eta: 1 day, 12:59:01, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6131, loss_cls: 3.6505, loss: 3.6505 +2024-12-30 06:22:58,273 - pyskl - INFO - Epoch [109][900/3746] lr: 1.794e-02, eta: 1 day, 12:57:36, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6172, loss_cls: 3.6407, loss: 3.6407 +2024-12-30 06:24:24,117 - pyskl - INFO - Epoch [109][1000/3746] lr: 1.791e-02, eta: 1 day, 12:56:11, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6164, loss_cls: 3.6504, loss: 3.6504 +2024-12-30 06:25:50,241 - pyskl - INFO - Epoch [109][1100/3746] lr: 1.789e-02, eta: 1 day, 12:54:47, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6084, loss_cls: 3.6889, loss: 3.6889 +2024-12-30 06:27:15,592 - pyskl - INFO - Epoch [109][1200/3746] lr: 1.787e-02, eta: 1 day, 12:53:22, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6067, loss_cls: 3.6631, loss: 3.6631 +2024-12-30 06:28:41,391 - pyskl - INFO - Epoch [109][1300/3746] lr: 1.785e-02, eta: 1 day, 12:51:57, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6144, loss_cls: 3.6491, loss: 3.6491 +2024-12-30 06:30:07,136 - pyskl - INFO - Epoch [109][1400/3746] lr: 1.783e-02, eta: 1 day, 12:50:32, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6052, loss_cls: 3.6883, loss: 3.6883 +2024-12-30 06:31:32,756 - pyskl - INFO - Epoch [109][1500/3746] lr: 1.781e-02, eta: 1 day, 12:49:07, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6208, loss_cls: 3.6428, loss: 3.6428 +2024-12-30 06:32:58,246 - pyskl - INFO - Epoch [109][1600/3746] lr: 1.779e-02, eta: 1 day, 12:47:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6147, loss_cls: 3.6710, loss: 3.6710 +2024-12-30 06:34:23,877 - pyskl - INFO - Epoch [109][1700/3746] lr: 1.776e-02, eta: 1 day, 12:46:18, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3588, top5_acc: 0.6205, loss_cls: 3.6265, loss: 3.6265 +2024-12-30 06:35:49,305 - pyskl - INFO - Epoch [109][1800/3746] lr: 1.774e-02, eta: 1 day, 12:44:53, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6119, loss_cls: 3.6433, loss: 3.6433 +2024-12-30 06:37:14,576 - pyskl - INFO - Epoch [109][1900/3746] lr: 1.772e-02, eta: 1 day, 12:43:28, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6139, loss_cls: 3.6487, loss: 3.6487 +2024-12-30 06:38:39,753 - pyskl - INFO - Epoch [109][2000/3746] lr: 1.770e-02, eta: 1 day, 12:42:03, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6122, loss_cls: 3.6600, loss: 3.6600 +2024-12-30 06:40:03,863 - pyskl - INFO - Epoch [109][2100/3746] lr: 1.768e-02, eta: 1 day, 12:40:37, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6016, loss_cls: 3.7192, loss: 3.7192 +2024-12-30 06:41:27,673 - pyskl - INFO - Epoch [109][2200/3746] lr: 1.766e-02, eta: 1 day, 12:39:12, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6045, loss_cls: 3.7140, loss: 3.7140 +2024-12-30 06:42:52,460 - pyskl - INFO - Epoch [109][2300/3746] lr: 1.764e-02, eta: 1 day, 12:37:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6103, loss_cls: 3.6862, loss: 3.6862 +2024-12-30 06:44:17,339 - pyskl - INFO - Epoch [109][2400/3746] lr: 1.761e-02, eta: 1 day, 12:36:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.6000, loss_cls: 3.7299, loss: 3.7299 +2024-12-30 06:45:41,735 - pyskl - INFO - Epoch [109][2500/3746] lr: 1.759e-02, eta: 1 day, 12:34:56, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6223, loss_cls: 3.6221, loss: 3.6221 +2024-12-30 06:47:06,768 - pyskl - INFO - Epoch [109][2600/3746] lr: 1.757e-02, eta: 1 day, 12:33:31, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5944, loss_cls: 3.7560, loss: 3.7560 +2024-12-30 06:48:31,745 - pyskl - INFO - Epoch [109][2700/3746] lr: 1.755e-02, eta: 1 day, 12:32:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6102, loss_cls: 3.6914, loss: 3.6914 +2024-12-30 06:49:56,206 - pyskl - INFO - Epoch [109][2800/3746] lr: 1.753e-02, eta: 1 day, 12:30:41, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6006, loss_cls: 3.7235, loss: 3.7235 +2024-12-30 06:51:20,267 - pyskl - INFO - Epoch [109][2900/3746] lr: 1.751e-02, eta: 1 day, 12:29:15, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6133, loss_cls: 3.6664, loss: 3.6664 +2024-12-30 06:52:44,560 - pyskl - INFO - Epoch [109][3000/3746] lr: 1.749e-02, eta: 1 day, 12:27:50, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6125, loss_cls: 3.6672, loss: 3.6672 +2024-12-30 06:54:08,883 - pyskl - INFO - Epoch [109][3100/3746] lr: 1.747e-02, eta: 1 day, 12:26:24, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6134, loss_cls: 3.6535, loss: 3.6535 +2024-12-30 06:55:33,271 - pyskl - INFO - Epoch [109][3200/3746] lr: 1.744e-02, eta: 1 day, 12:24:59, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3541, top5_acc: 0.6123, loss_cls: 3.6767, loss: 3.6767 +2024-12-30 06:56:58,133 - pyskl - INFO - Epoch [109][3300/3746] lr: 1.742e-02, eta: 1 day, 12:23:34, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6067, loss_cls: 3.7312, loss: 3.7312 +2024-12-30 06:58:22,803 - pyskl - INFO - Epoch [109][3400/3746] lr: 1.740e-02, eta: 1 day, 12:22:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6053, loss_cls: 3.6976, loss: 3.6976 +2024-12-30 06:59:47,832 - pyskl - INFO - Epoch [109][3500/3746] lr: 1.738e-02, eta: 1 day, 12:20:44, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3541, top5_acc: 0.6177, loss_cls: 3.6483, loss: 3.6483 +2024-12-30 07:01:12,371 - pyskl - INFO - Epoch [109][3600/3746] lr: 1.736e-02, eta: 1 day, 12:19:19, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6048, loss_cls: 3.7013, loss: 3.7013 +2024-12-30 07:02:36,933 - pyskl - INFO - Epoch [109][3700/3746] lr: 1.734e-02, eta: 1 day, 12:17:53, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6075, loss_cls: 3.7243, loss: 3.7243 +2024-12-30 07:03:18,191 - pyskl - INFO - Saving checkpoint at 109 epochs +2024-12-30 07:05:17,506 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 07:05:18,221 - pyskl - INFO - +top1_acc 0.2968 +top5_acc 0.5457 +2024-12-30 07:05:18,221 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 07:05:18,267 - pyskl - INFO - +mean_acc 0.2965 +2024-12-30 07:05:18,272 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_107.pth was removed +2024-12-30 07:05:18,602 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_109.pth. +2024-12-30 07:05:18,603 - pyskl - INFO - Best top1_acc is 0.2968 at 109 epoch. +2024-12-30 07:05:18,616 - pyskl - INFO - Epoch(val) [109][309] top1_acc: 0.2968, top5_acc: 0.5457, mean_class_accuracy: 0.2965 +2024-12-30 07:09:34,952 - pyskl - INFO - Epoch [110][100/3746] lr: 1.731e-02, eta: 1 day, 12:16:39, time: 2.563, data_time: 1.526, memory: 15990, top1_acc: 0.3731, top5_acc: 0.6252, loss_cls: 3.5970, loss: 3.5970 +2024-12-30 07:10:59,529 - pyskl - INFO - Epoch [110][200/3746] lr: 1.729e-02, eta: 1 day, 12:15:13, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6036, loss_cls: 3.6753, loss: 3.6753 +2024-12-30 07:12:24,332 - pyskl - INFO - Epoch [110][300/3746] lr: 1.727e-02, eta: 1 day, 12:13:48, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6147, loss_cls: 3.6186, loss: 3.6186 +2024-12-30 07:13:49,272 - pyskl - INFO - Epoch [110][400/3746] lr: 1.724e-02, eta: 1 day, 12:12:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6286, loss_cls: 3.6075, loss: 3.6075 +2024-12-30 07:15:13,556 - pyskl - INFO - Epoch [110][500/3746] lr: 1.722e-02, eta: 1 day, 12:10:58, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6222, loss_cls: 3.6381, loss: 3.6381 +2024-12-30 07:16:37,973 - pyskl - INFO - Epoch [110][600/3746] lr: 1.720e-02, eta: 1 day, 12:09:32, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6250, loss_cls: 3.5965, loss: 3.5965 +2024-12-30 07:18:02,259 - pyskl - INFO - Epoch [110][700/3746] lr: 1.718e-02, eta: 1 day, 12:08:07, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6141, loss_cls: 3.6609, loss: 3.6609 +2024-12-30 07:19:27,050 - pyskl - INFO - Epoch [110][800/3746] lr: 1.716e-02, eta: 1 day, 12:06:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6270, loss_cls: 3.6071, loss: 3.6071 +2024-12-30 07:20:52,579 - pyskl - INFO - Epoch [110][900/3746] lr: 1.714e-02, eta: 1 day, 12:05:17, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6161, loss_cls: 3.6621, loss: 3.6621 +2024-12-30 07:22:17,836 - pyskl - INFO - Epoch [110][1000/3746] lr: 1.712e-02, eta: 1 day, 12:03:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6136, loss_cls: 3.6344, loss: 3.6344 +2024-12-30 07:23:42,819 - pyskl - INFO - Epoch [110][1100/3746] lr: 1.710e-02, eta: 1 day, 12:02:27, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6145, loss_cls: 3.6351, loss: 3.6351 +2024-12-30 07:25:07,339 - pyskl - INFO - Epoch [110][1200/3746] lr: 1.708e-02, eta: 1 day, 12:01:01, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6164, loss_cls: 3.6387, loss: 3.6387 +2024-12-30 07:26:31,706 - pyskl - INFO - Epoch [110][1300/3746] lr: 1.705e-02, eta: 1 day, 11:59:36, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6078, loss_cls: 3.6619, loss: 3.6619 +2024-12-30 07:27:55,852 - pyskl - INFO - Epoch [110][1400/3746] lr: 1.703e-02, eta: 1 day, 11:58:11, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6108, loss_cls: 3.6701, loss: 3.6701 +2024-12-30 07:29:20,526 - pyskl - INFO - Epoch [110][1500/3746] lr: 1.701e-02, eta: 1 day, 11:56:45, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6244, loss_cls: 3.6415, loss: 3.6415 +2024-12-30 07:30:45,136 - pyskl - INFO - Epoch [110][1600/3746] lr: 1.699e-02, eta: 1 day, 11:55:20, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6152, loss_cls: 3.6684, loss: 3.6684 +2024-12-30 07:32:09,784 - pyskl - INFO - Epoch [110][1700/3746] lr: 1.697e-02, eta: 1 day, 11:53:55, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6139, loss_cls: 3.6317, loss: 3.6317 +2024-12-30 07:33:34,163 - pyskl - INFO - Epoch [110][1800/3746] lr: 1.695e-02, eta: 1 day, 11:52:30, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6103, loss_cls: 3.6787, loss: 3.6787 +2024-12-30 07:34:59,319 - pyskl - INFO - Epoch [110][1900/3746] lr: 1.693e-02, eta: 1 day, 11:51:04, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6222, loss_cls: 3.6176, loss: 3.6176 +2024-12-30 07:36:24,117 - pyskl - INFO - Epoch [110][2000/3746] lr: 1.691e-02, eta: 1 day, 11:49:39, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6139, loss_cls: 3.6784, loss: 3.6784 +2024-12-30 07:37:48,893 - pyskl - INFO - Epoch [110][2100/3746] lr: 1.689e-02, eta: 1 day, 11:48:14, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6233, loss_cls: 3.5996, loss: 3.5996 +2024-12-30 07:39:12,816 - pyskl - INFO - Epoch [110][2200/3746] lr: 1.687e-02, eta: 1 day, 11:46:49, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6094, loss_cls: 3.6653, loss: 3.6653 +2024-12-30 07:40:37,551 - pyskl - INFO - Epoch [110][2300/3746] lr: 1.685e-02, eta: 1 day, 11:45:23, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6172, loss_cls: 3.6446, loss: 3.6446 +2024-12-30 07:42:02,400 - pyskl - INFO - Epoch [110][2400/3746] lr: 1.682e-02, eta: 1 day, 11:43:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6108, loss_cls: 3.6617, loss: 3.6617 +2024-12-30 07:43:26,225 - pyskl - INFO - Epoch [110][2500/3746] lr: 1.680e-02, eta: 1 day, 11:42:33, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6133, loss_cls: 3.6651, loss: 3.6651 +2024-12-30 07:44:51,277 - pyskl - INFO - Epoch [110][2600/3746] lr: 1.678e-02, eta: 1 day, 11:41:08, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6175, loss_cls: 3.6477, loss: 3.6477 +2024-12-30 07:46:15,744 - pyskl - INFO - Epoch [110][2700/3746] lr: 1.676e-02, eta: 1 day, 11:39:42, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6148, loss_cls: 3.6663, loss: 3.6663 +2024-12-30 07:47:40,519 - pyskl - INFO - Epoch [110][2800/3746] lr: 1.674e-02, eta: 1 day, 11:38:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6114, loss_cls: 3.6615, loss: 3.6615 +2024-12-30 07:49:05,324 - pyskl - INFO - Epoch [110][2900/3746] lr: 1.672e-02, eta: 1 day, 11:36:52, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6123, loss_cls: 3.6485, loss: 3.6485 +2024-12-30 07:50:29,872 - pyskl - INFO - Epoch [110][3000/3746] lr: 1.670e-02, eta: 1 day, 11:35:27, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3541, top5_acc: 0.6138, loss_cls: 3.6818, loss: 3.6818 +2024-12-30 07:51:54,539 - pyskl - INFO - Epoch [110][3100/3746] lr: 1.668e-02, eta: 1 day, 11:34:01, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6073, loss_cls: 3.6815, loss: 3.6815 +2024-12-30 07:53:19,103 - pyskl - INFO - Epoch [110][3200/3746] lr: 1.666e-02, eta: 1 day, 11:32:36, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6220, loss_cls: 3.6120, loss: 3.6120 +2024-12-30 07:54:43,165 - pyskl - INFO - Epoch [110][3300/3746] lr: 1.664e-02, eta: 1 day, 11:31:11, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6092, loss_cls: 3.6724, loss: 3.6724 +2024-12-30 07:56:08,722 - pyskl - INFO - Epoch [110][3400/3746] lr: 1.662e-02, eta: 1 day, 11:29:46, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6162, loss_cls: 3.6612, loss: 3.6612 +2024-12-30 07:57:33,235 - pyskl - INFO - Epoch [110][3500/3746] lr: 1.659e-02, eta: 1 day, 11:28:20, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6212, loss_cls: 3.6279, loss: 3.6279 +2024-12-30 07:58:57,907 - pyskl - INFO - Epoch [110][3600/3746] lr: 1.657e-02, eta: 1 day, 11:26:55, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6197, loss_cls: 3.6317, loss: 3.6317 +2024-12-30 08:00:22,214 - pyskl - INFO - Epoch [110][3700/3746] lr: 1.655e-02, eta: 1 day, 11:25:30, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6120, loss_cls: 3.6478, loss: 3.6478 +2024-12-30 08:01:02,626 - pyskl - INFO - Saving checkpoint at 110 epochs +2024-12-30 08:03:02,345 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 08:03:03,042 - pyskl - INFO - +top1_acc 0.3007 +top5_acc 0.5512 +2024-12-30 08:03:03,043 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 08:03:03,085 - pyskl - INFO - +mean_acc 0.3004 +2024-12-30 08:03:03,090 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_109.pth was removed +2024-12-30 08:03:03,381 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_110.pth. +2024-12-30 08:03:03,381 - pyskl - INFO - Best top1_acc is 0.3007 at 110 epoch. +2024-12-30 08:03:03,404 - pyskl - INFO - Epoch(val) [110][309] top1_acc: 0.3007, top5_acc: 0.5512, mean_class_accuracy: 0.3004 +2024-12-30 08:07:21,115 - pyskl - INFO - Epoch [111][100/3746] lr: 1.652e-02, eta: 1 day, 11:24:14, time: 2.577, data_time: 1.536, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6286, loss_cls: 3.5631, loss: 3.5631 +2024-12-30 08:08:46,299 - pyskl - INFO - Epoch [111][200/3746] lr: 1.650e-02, eta: 1 day, 11:22:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6264, loss_cls: 3.5491, loss: 3.5491 +2024-12-30 08:10:11,401 - pyskl - INFO - Epoch [111][300/3746] lr: 1.648e-02, eta: 1 day, 11:21:24, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6312, loss_cls: 3.5630, loss: 3.5630 +2024-12-30 08:11:36,906 - pyskl - INFO - Epoch [111][400/3746] lr: 1.646e-02, eta: 1 day, 11:19:59, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6372, loss_cls: 3.5226, loss: 3.5226 +2024-12-30 08:13:02,192 - pyskl - INFO - Epoch [111][500/3746] lr: 1.644e-02, eta: 1 day, 11:18:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6291, loss_cls: 3.5815, loss: 3.5815 +2024-12-30 08:14:26,982 - pyskl - INFO - Epoch [111][600/3746] lr: 1.642e-02, eta: 1 day, 11:17:09, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6248, loss_cls: 3.6011, loss: 3.6011 +2024-12-30 08:15:52,016 - pyskl - INFO - Epoch [111][700/3746] lr: 1.640e-02, eta: 1 day, 11:15:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6195, loss_cls: 3.6293, loss: 3.6293 +2024-12-30 08:17:17,305 - pyskl - INFO - Epoch [111][800/3746] lr: 1.638e-02, eta: 1 day, 11:14:18, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6280, loss_cls: 3.6041, loss: 3.6041 +2024-12-30 08:18:42,707 - pyskl - INFO - Epoch [111][900/3746] lr: 1.636e-02, eta: 1 day, 11:12:53, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6253, loss_cls: 3.5788, loss: 3.5788 +2024-12-30 08:20:08,185 - pyskl - INFO - Epoch [111][1000/3746] lr: 1.634e-02, eta: 1 day, 11:11:28, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3594, top5_acc: 0.6205, loss_cls: 3.6177, loss: 3.6177 +2024-12-30 08:21:33,768 - pyskl - INFO - Epoch [111][1100/3746] lr: 1.632e-02, eta: 1 day, 11:10:03, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6267, loss_cls: 3.6135, loss: 3.6135 +2024-12-30 08:22:59,320 - pyskl - INFO - Epoch [111][1200/3746] lr: 1.630e-02, eta: 1 day, 11:08:38, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3656, top5_acc: 0.6223, loss_cls: 3.6123, loss: 3.6123 +2024-12-30 08:24:24,679 - pyskl - INFO - Epoch [111][1300/3746] lr: 1.627e-02, eta: 1 day, 11:07:13, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6194, loss_cls: 3.6286, loss: 3.6286 +2024-12-30 08:25:49,881 - pyskl - INFO - Epoch [111][1400/3746] lr: 1.625e-02, eta: 1 day, 11:05:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6147, loss_cls: 3.6630, loss: 3.6630 +2024-12-30 08:27:14,203 - pyskl - INFO - Epoch [111][1500/3746] lr: 1.623e-02, eta: 1 day, 11:04:23, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6269, loss_cls: 3.6187, loss: 3.6187 +2024-12-30 08:28:39,079 - pyskl - INFO - Epoch [111][1600/3746] lr: 1.621e-02, eta: 1 day, 11:02:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6180, loss_cls: 3.6133, loss: 3.6133 +2024-12-30 08:30:03,886 - pyskl - INFO - Epoch [111][1700/3746] lr: 1.619e-02, eta: 1 day, 11:01:32, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6108, loss_cls: 3.6787, loss: 3.6787 +2024-12-30 08:31:29,351 - pyskl - INFO - Epoch [111][1800/3746] lr: 1.617e-02, eta: 1 day, 11:00:07, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6175, loss_cls: 3.6414, loss: 3.6414 +2024-12-30 08:32:54,541 - pyskl - INFO - Epoch [111][1900/3746] lr: 1.615e-02, eta: 1 day, 10:58:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6170, loss_cls: 3.6692, loss: 3.6692 +2024-12-30 08:34:19,397 - pyskl - INFO - Epoch [111][2000/3746] lr: 1.613e-02, eta: 1 day, 10:57:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6012, loss_cls: 3.6858, loss: 3.6858 +2024-12-30 08:35:44,237 - pyskl - INFO - Epoch [111][2100/3746] lr: 1.611e-02, eta: 1 day, 10:55:52, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6078, loss_cls: 3.6866, loss: 3.6866 +2024-12-30 08:37:09,005 - pyskl - INFO - Epoch [111][2200/3746] lr: 1.609e-02, eta: 1 day, 10:54:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6108, loss_cls: 3.6501, loss: 3.6501 +2024-12-30 08:38:33,928 - pyskl - INFO - Epoch [111][2300/3746] lr: 1.607e-02, eta: 1 day, 10:53:02, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6186, loss_cls: 3.6336, loss: 3.6336 +2024-12-30 08:39:58,921 - pyskl - INFO - Epoch [111][2400/3746] lr: 1.605e-02, eta: 1 day, 10:51:36, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6044, loss_cls: 3.6977, loss: 3.6977 +2024-12-30 08:41:23,405 - pyskl - INFO - Epoch [111][2500/3746] lr: 1.603e-02, eta: 1 day, 10:50:11, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6152, loss_cls: 3.6470, loss: 3.6470 +2024-12-30 08:42:48,325 - pyskl - INFO - Epoch [111][2600/3746] lr: 1.601e-02, eta: 1 day, 10:48:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6123, loss_cls: 3.6802, loss: 3.6802 +2024-12-30 08:44:12,500 - pyskl - INFO - Epoch [111][2700/3746] lr: 1.599e-02, eta: 1 day, 10:47:20, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6192, loss_cls: 3.6334, loss: 3.6334 +2024-12-30 08:45:36,726 - pyskl - INFO - Epoch [111][2800/3746] lr: 1.597e-02, eta: 1 day, 10:45:55, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6122, loss_cls: 3.6527, loss: 3.6527 +2024-12-30 08:47:00,749 - pyskl - INFO - Epoch [111][2900/3746] lr: 1.595e-02, eta: 1 day, 10:44:29, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6162, loss_cls: 3.6310, loss: 3.6310 +2024-12-30 08:48:24,463 - pyskl - INFO - Epoch [111][3000/3746] lr: 1.593e-02, eta: 1 day, 10:43:04, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6142, loss_cls: 3.6458, loss: 3.6458 +2024-12-30 08:49:48,672 - pyskl - INFO - Epoch [111][3100/3746] lr: 1.590e-02, eta: 1 day, 10:41:38, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6186, loss_cls: 3.6366, loss: 3.6366 +2024-12-30 08:51:12,827 - pyskl - INFO - Epoch [111][3200/3746] lr: 1.588e-02, eta: 1 day, 10:40:13, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6156, loss_cls: 3.6375, loss: 3.6375 +2024-12-30 08:52:37,540 - pyskl - INFO - Epoch [111][3300/3746] lr: 1.586e-02, eta: 1 day, 10:38:48, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6184, loss_cls: 3.6044, loss: 3.6044 +2024-12-30 08:54:02,805 - pyskl - INFO - Epoch [111][3400/3746] lr: 1.584e-02, eta: 1 day, 10:37:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6138, loss_cls: 3.6562, loss: 3.6562 +2024-12-30 08:55:27,102 - pyskl - INFO - Epoch [111][3500/3746] lr: 1.582e-02, eta: 1 day, 10:35:57, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6162, loss_cls: 3.6423, loss: 3.6423 +2024-12-30 08:56:51,227 - pyskl - INFO - Epoch [111][3600/3746] lr: 1.580e-02, eta: 1 day, 10:34:32, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6258, loss_cls: 3.5858, loss: 3.5858 +2024-12-30 08:58:15,740 - pyskl - INFO - Epoch [111][3700/3746] lr: 1.578e-02, eta: 1 day, 10:33:06, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6198, loss_cls: 3.6420, loss: 3.6420 +2024-12-30 08:58:56,662 - pyskl - INFO - Saving checkpoint at 111 epochs +2024-12-30 09:00:56,726 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 09:00:57,437 - pyskl - INFO - +top1_acc 0.3094 +top5_acc 0.5564 +2024-12-30 09:00:57,437 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 09:00:57,490 - pyskl - INFO - +mean_acc 0.3091 +2024-12-30 09:00:57,494 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_110.pth was removed +2024-12-30 09:00:57,820 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_111.pth. +2024-12-30 09:00:57,820 - pyskl - INFO - Best top1_acc is 0.3094 at 111 epoch. +2024-12-30 09:00:57,841 - pyskl - INFO - Epoch(val) [111][309] top1_acc: 0.3094, top5_acc: 0.5564, mean_class_accuracy: 0.3091 +2024-12-30 09:05:12,227 - pyskl - INFO - Epoch [112][100/3746] lr: 1.575e-02, eta: 1 day, 10:31:48, time: 2.544, data_time: 1.522, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6264, loss_cls: 3.5593, loss: 3.5593 +2024-12-30 09:06:37,188 - pyskl - INFO - Epoch [112][200/3746] lr: 1.573e-02, eta: 1 day, 10:30:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6261, loss_cls: 3.5746, loss: 3.5746 +2024-12-30 09:08:01,363 - pyskl - INFO - Epoch [112][300/3746] lr: 1.571e-02, eta: 1 day, 10:28:57, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6261, loss_cls: 3.6079, loss: 3.6079 +2024-12-30 09:09:26,383 - pyskl - INFO - Epoch [112][400/3746] lr: 1.569e-02, eta: 1 day, 10:27:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6381, loss_cls: 3.5701, loss: 3.5701 +2024-12-30 09:10:51,033 - pyskl - INFO - Epoch [112][500/3746] lr: 1.567e-02, eta: 1 day, 10:26:07, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6272, loss_cls: 3.5665, loss: 3.5665 +2024-12-30 09:12:15,682 - pyskl - INFO - Epoch [112][600/3746] lr: 1.565e-02, eta: 1 day, 10:24:41, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6236, loss_cls: 3.5983, loss: 3.5983 +2024-12-30 09:13:40,064 - pyskl - INFO - Epoch [112][700/3746] lr: 1.563e-02, eta: 1 day, 10:23:16, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6209, loss_cls: 3.6292, loss: 3.6292 +2024-12-30 09:15:04,693 - pyskl - INFO - Epoch [112][800/3746] lr: 1.561e-02, eta: 1 day, 10:21:51, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6289, loss_cls: 3.5764, loss: 3.5764 +2024-12-30 09:16:29,262 - pyskl - INFO - Epoch [112][900/3746] lr: 1.559e-02, eta: 1 day, 10:20:25, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6266, loss_cls: 3.6199, loss: 3.6199 +2024-12-30 09:17:53,682 - pyskl - INFO - Epoch [112][1000/3746] lr: 1.557e-02, eta: 1 day, 10:19:00, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6170, loss_cls: 3.6438, loss: 3.6438 +2024-12-30 09:19:18,362 - pyskl - INFO - Epoch [112][1100/3746] lr: 1.555e-02, eta: 1 day, 10:17:34, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6225, loss_cls: 3.6104, loss: 3.6104 +2024-12-30 09:20:42,855 - pyskl - INFO - Epoch [112][1200/3746] lr: 1.553e-02, eta: 1 day, 10:16:09, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6278, loss_cls: 3.5715, loss: 3.5715 +2024-12-30 09:22:07,712 - pyskl - INFO - Epoch [112][1300/3746] lr: 1.551e-02, eta: 1 day, 10:14:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6238, loss_cls: 3.6405, loss: 3.6405 +2024-12-30 09:23:33,152 - pyskl - INFO - Epoch [112][1400/3746] lr: 1.549e-02, eta: 1 day, 10:13:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6220, loss_cls: 3.5779, loss: 3.5779 +2024-12-30 09:24:57,965 - pyskl - INFO - Epoch [112][1500/3746] lr: 1.547e-02, eta: 1 day, 10:11:54, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6230, loss_cls: 3.5942, loss: 3.5942 +2024-12-30 09:26:23,345 - pyskl - INFO - Epoch [112][1600/3746] lr: 1.545e-02, eta: 1 day, 10:10:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3686, top5_acc: 0.6294, loss_cls: 3.5668, loss: 3.5668 +2024-12-30 09:27:48,652 - pyskl - INFO - Epoch [112][1700/3746] lr: 1.543e-02, eta: 1 day, 10:09:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6186, loss_cls: 3.6019, loss: 3.6019 +2024-12-30 09:29:14,107 - pyskl - INFO - Epoch [112][1800/3746] lr: 1.541e-02, eta: 1 day, 10:07:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6134, loss_cls: 3.6500, loss: 3.6500 +2024-12-30 09:30:38,621 - pyskl - INFO - Epoch [112][1900/3746] lr: 1.539e-02, eta: 1 day, 10:06:13, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6261, loss_cls: 3.5913, loss: 3.5913 +2024-12-30 09:32:02,621 - pyskl - INFO - Epoch [112][2000/3746] lr: 1.537e-02, eta: 1 day, 10:04:47, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6284, loss_cls: 3.5657, loss: 3.5657 +2024-12-30 09:33:26,953 - pyskl - INFO - Epoch [112][2100/3746] lr: 1.535e-02, eta: 1 day, 10:03:22, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6270, loss_cls: 3.5852, loss: 3.5852 +2024-12-30 09:34:51,178 - pyskl - INFO - Epoch [112][2200/3746] lr: 1.533e-02, eta: 1 day, 10:01:57, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6392, loss_cls: 3.5245, loss: 3.5245 +2024-12-30 09:36:15,364 - pyskl - INFO - Epoch [112][2300/3746] lr: 1.531e-02, eta: 1 day, 10:00:31, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6172, loss_cls: 3.6294, loss: 3.6294 +2024-12-30 09:37:40,644 - pyskl - INFO - Epoch [112][2400/3746] lr: 1.529e-02, eta: 1 day, 9:59:06, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6180, loss_cls: 3.6408, loss: 3.6408 +2024-12-30 09:39:05,011 - pyskl - INFO - Epoch [112][2500/3746] lr: 1.527e-02, eta: 1 day, 9:57:41, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6209, loss_cls: 3.6082, loss: 3.6082 +2024-12-30 09:40:29,384 - pyskl - INFO - Epoch [112][2600/3746] lr: 1.525e-02, eta: 1 day, 9:56:15, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3622, top5_acc: 0.6223, loss_cls: 3.6392, loss: 3.6392 +2024-12-30 09:41:53,223 - pyskl - INFO - Epoch [112][2700/3746] lr: 1.523e-02, eta: 1 day, 9:54:50, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6314, loss_cls: 3.5900, loss: 3.5900 +2024-12-30 09:43:17,676 - pyskl - INFO - Epoch [112][2800/3746] lr: 1.521e-02, eta: 1 day, 9:53:24, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6095, loss_cls: 3.6302, loss: 3.6302 +2024-12-30 09:44:42,147 - pyskl - INFO - Epoch [112][2900/3746] lr: 1.519e-02, eta: 1 day, 9:51:59, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6186, loss_cls: 3.6318, loss: 3.6318 +2024-12-30 09:46:06,208 - pyskl - INFO - Epoch [112][3000/3746] lr: 1.517e-02, eta: 1 day, 9:50:33, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6147, loss_cls: 3.6215, loss: 3.6215 +2024-12-30 09:47:30,322 - pyskl - INFO - Epoch [112][3100/3746] lr: 1.515e-02, eta: 1 day, 9:49:08, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6159, loss_cls: 3.6411, loss: 3.6411 +2024-12-30 09:48:55,200 - pyskl - INFO - Epoch [112][3200/3746] lr: 1.513e-02, eta: 1 day, 9:47:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6094, loss_cls: 3.6587, loss: 3.6587 +2024-12-30 09:50:19,602 - pyskl - INFO - Epoch [112][3300/3746] lr: 1.511e-02, eta: 1 day, 9:46:17, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6206, loss_cls: 3.6079, loss: 3.6079 +2024-12-30 09:51:44,186 - pyskl - INFO - Epoch [112][3400/3746] lr: 1.509e-02, eta: 1 day, 9:44:52, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6211, loss_cls: 3.6308, loss: 3.6308 +2024-12-30 09:53:09,311 - pyskl - INFO - Epoch [112][3500/3746] lr: 1.507e-02, eta: 1 day, 9:43:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6269, loss_cls: 3.5847, loss: 3.5847 +2024-12-30 09:54:34,115 - pyskl - INFO - Epoch [112][3600/3746] lr: 1.505e-02, eta: 1 day, 9:42:01, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6158, loss_cls: 3.6198, loss: 3.6198 +2024-12-30 09:55:59,165 - pyskl - INFO - Epoch [112][3700/3746] lr: 1.503e-02, eta: 1 day, 9:40:36, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6195, loss_cls: 3.6304, loss: 3.6304 +2024-12-30 09:56:39,761 - pyskl - INFO - Saving checkpoint at 112 epochs +2024-12-30 09:58:39,426 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 09:58:40,190 - pyskl - INFO - +top1_acc 0.3033 +top5_acc 0.5556 +2024-12-30 09:58:40,191 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 09:58:40,247 - pyskl - INFO - +mean_acc 0.3030 +2024-12-30 09:58:40,260 - pyskl - INFO - Epoch(val) [112][309] top1_acc: 0.3033, top5_acc: 0.5556, mean_class_accuracy: 0.3030 +2024-12-30 10:02:54,232 - pyskl - INFO - Epoch [113][100/3746] lr: 1.500e-02, eta: 1 day, 9:39:16, time: 2.540, data_time: 1.493, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6362, loss_cls: 3.5165, loss: 3.5165 +2024-12-30 10:04:19,667 - pyskl - INFO - Epoch [113][200/3746] lr: 1.498e-02, eta: 1 day, 9:37:51, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6455, loss_cls: 3.5020, loss: 3.5020 +2024-12-30 10:05:45,315 - pyskl - INFO - Epoch [113][300/3746] lr: 1.496e-02, eta: 1 day, 9:36:26, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6275, loss_cls: 3.5882, loss: 3.5882 +2024-12-30 10:07:10,723 - pyskl - INFO - Epoch [113][400/3746] lr: 1.494e-02, eta: 1 day, 9:35:01, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6305, loss_cls: 3.5563, loss: 3.5563 +2024-12-30 10:08:36,354 - pyskl - INFO - Epoch [113][500/3746] lr: 1.492e-02, eta: 1 day, 9:33:36, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6272, loss_cls: 3.5579, loss: 3.5579 +2024-12-30 10:10:01,324 - pyskl - INFO - Epoch [113][600/3746] lr: 1.490e-02, eta: 1 day, 9:32:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6295, loss_cls: 3.5679, loss: 3.5679 +2024-12-30 10:11:26,553 - pyskl - INFO - Epoch [113][700/3746] lr: 1.488e-02, eta: 1 day, 9:30:45, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3723, top5_acc: 0.6312, loss_cls: 3.5603, loss: 3.5603 +2024-12-30 10:12:52,546 - pyskl - INFO - Epoch [113][800/3746] lr: 1.486e-02, eta: 1 day, 9:29:20, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6300, loss_cls: 3.5793, loss: 3.5793 +2024-12-30 10:14:18,177 - pyskl - INFO - Epoch [113][900/3746] lr: 1.484e-02, eta: 1 day, 9:27:55, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6289, loss_cls: 3.5844, loss: 3.5844 +2024-12-30 10:15:44,599 - pyskl - INFO - Epoch [113][1000/3746] lr: 1.482e-02, eta: 1 day, 9:26:31, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6244, loss_cls: 3.5860, loss: 3.5860 +2024-12-30 10:17:09,612 - pyskl - INFO - Epoch [113][1100/3746] lr: 1.480e-02, eta: 1 day, 9:25:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6267, loss_cls: 3.5763, loss: 3.5763 +2024-12-30 10:18:35,029 - pyskl - INFO - Epoch [113][1200/3746] lr: 1.478e-02, eta: 1 day, 9:23:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6302, loss_cls: 3.5721, loss: 3.5721 +2024-12-30 10:20:01,202 - pyskl - INFO - Epoch [113][1300/3746] lr: 1.476e-02, eta: 1 day, 9:22:16, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6300, loss_cls: 3.6091, loss: 3.6091 +2024-12-30 10:21:26,843 - pyskl - INFO - Epoch [113][1400/3746] lr: 1.474e-02, eta: 1 day, 9:20:51, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6223, loss_cls: 3.5935, loss: 3.5935 +2024-12-30 10:22:52,225 - pyskl - INFO - Epoch [113][1500/3746] lr: 1.472e-02, eta: 1 day, 9:19:25, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6214, loss_cls: 3.6003, loss: 3.6003 +2024-12-30 10:24:17,465 - pyskl - INFO - Epoch [113][1600/3746] lr: 1.470e-02, eta: 1 day, 9:18:00, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6255, loss_cls: 3.5616, loss: 3.5616 +2024-12-30 10:25:42,829 - pyskl - INFO - Epoch [113][1700/3746] lr: 1.468e-02, eta: 1 day, 9:16:35, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6195, loss_cls: 3.5768, loss: 3.5768 +2024-12-30 10:27:07,936 - pyskl - INFO - Epoch [113][1800/3746] lr: 1.466e-02, eta: 1 day, 9:15:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6300, loss_cls: 3.5692, loss: 3.5692 +2024-12-30 10:28:33,476 - pyskl - INFO - Epoch [113][1900/3746] lr: 1.464e-02, eta: 1 day, 9:13:45, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6300, loss_cls: 3.5843, loss: 3.5843 +2024-12-30 10:29:58,167 - pyskl - INFO - Epoch [113][2000/3746] lr: 1.462e-02, eta: 1 day, 9:12:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6123, loss_cls: 3.6240, loss: 3.6240 +2024-12-30 10:31:22,862 - pyskl - INFO - Epoch [113][2100/3746] lr: 1.460e-02, eta: 1 day, 9:10:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6295, loss_cls: 3.5768, loss: 3.5768 +2024-12-30 10:32:47,677 - pyskl - INFO - Epoch [113][2200/3746] lr: 1.458e-02, eta: 1 day, 9:09:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6295, loss_cls: 3.5675, loss: 3.5675 +2024-12-30 10:34:12,815 - pyskl - INFO - Epoch [113][2300/3746] lr: 1.456e-02, eta: 1 day, 9:08:04, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3656, top5_acc: 0.6214, loss_cls: 3.6159, loss: 3.6159 +2024-12-30 10:35:37,919 - pyskl - INFO - Epoch [113][2400/3746] lr: 1.454e-02, eta: 1 day, 9:06:39, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6125, loss_cls: 3.6572, loss: 3.6572 +2024-12-30 10:37:01,821 - pyskl - INFO - Epoch [113][2500/3746] lr: 1.452e-02, eta: 1 day, 9:05:13, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6020, loss_cls: 3.6806, loss: 3.6806 +2024-12-30 10:38:25,663 - pyskl - INFO - Epoch [113][2600/3746] lr: 1.450e-02, eta: 1 day, 9:03:47, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6305, loss_cls: 3.5610, loss: 3.5610 +2024-12-30 10:39:50,118 - pyskl - INFO - Epoch [113][2700/3746] lr: 1.448e-02, eta: 1 day, 9:02:22, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6316, loss_cls: 3.5674, loss: 3.5674 +2024-12-30 10:41:14,543 - pyskl - INFO - Epoch [113][2800/3746] lr: 1.446e-02, eta: 1 day, 9:00:57, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6206, loss_cls: 3.6204, loss: 3.6204 +2024-12-30 10:42:39,323 - pyskl - INFO - Epoch [113][2900/3746] lr: 1.444e-02, eta: 1 day, 8:59:31, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6320, loss_cls: 3.5584, loss: 3.5584 +2024-12-30 10:44:03,745 - pyskl - INFO - Epoch [113][3000/3746] lr: 1.442e-02, eta: 1 day, 8:58:06, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6317, loss_cls: 3.5668, loss: 3.5668 +2024-12-30 10:45:28,473 - pyskl - INFO - Epoch [113][3100/3746] lr: 1.440e-02, eta: 1 day, 8:56:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6234, loss_cls: 3.5900, loss: 3.5900 +2024-12-30 10:46:53,098 - pyskl - INFO - Epoch [113][3200/3746] lr: 1.438e-02, eta: 1 day, 8:55:15, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6156, loss_cls: 3.6082, loss: 3.6082 +2024-12-30 10:48:18,266 - pyskl - INFO - Epoch [113][3300/3746] lr: 1.436e-02, eta: 1 day, 8:53:50, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6211, loss_cls: 3.6161, loss: 3.6161 +2024-12-30 10:49:42,202 - pyskl - INFO - Epoch [113][3400/3746] lr: 1.434e-02, eta: 1 day, 8:52:24, time: 0.839, data_time: 0.001, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6256, loss_cls: 3.5737, loss: 3.5737 +2024-12-30 10:51:06,803 - pyskl - INFO - Epoch [113][3500/3746] lr: 1.432e-02, eta: 1 day, 8:50:59, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6178, loss_cls: 3.6176, loss: 3.6176 +2024-12-30 10:52:31,653 - pyskl - INFO - Epoch [113][3600/3746] lr: 1.431e-02, eta: 1 day, 8:49:34, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6281, loss_cls: 3.5614, loss: 3.5614 +2024-12-30 10:53:56,637 - pyskl - INFO - Epoch [113][3700/3746] lr: 1.429e-02, eta: 1 day, 8:48:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6231, loss_cls: 3.5816, loss: 3.5816 +2024-12-30 10:54:37,280 - pyskl - INFO - Saving checkpoint at 113 epochs +2024-12-30 10:56:35,550 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 10:56:36,545 - pyskl - INFO - +top1_acc 0.3122 +top5_acc 0.5618 +2024-12-30 10:56:36,545 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 10:56:36,587 - pyskl - INFO - +mean_acc 0.3119 +2024-12-30 10:56:36,592 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_111.pth was removed +2024-12-30 10:56:36,855 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_113.pth. +2024-12-30 10:56:36,856 - pyskl - INFO - Best top1_acc is 0.3122 at 113 epoch. +2024-12-30 10:56:36,869 - pyskl - INFO - Epoch(val) [113][309] top1_acc: 0.3122, top5_acc: 0.5618, mean_class_accuracy: 0.3119 +2024-12-30 11:00:48,505 - pyskl - INFO - Epoch [114][100/3746] lr: 1.426e-02, eta: 1 day, 8:46:46, time: 2.516, data_time: 1.484, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6466, loss_cls: 3.4952, loss: 3.4952 +2024-12-30 11:02:13,589 - pyskl - INFO - Epoch [114][200/3746] lr: 1.424e-02, eta: 1 day, 8:45:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6364, loss_cls: 3.5258, loss: 3.5258 +2024-12-30 11:03:38,635 - pyskl - INFO - Epoch [114][300/3746] lr: 1.422e-02, eta: 1 day, 8:43:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6414, loss_cls: 3.5218, loss: 3.5218 +2024-12-30 11:05:03,987 - pyskl - INFO - Epoch [114][400/3746] lr: 1.420e-02, eta: 1 day, 8:42:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6297, loss_cls: 3.5489, loss: 3.5489 +2024-12-30 11:06:29,435 - pyskl - INFO - Epoch [114][500/3746] lr: 1.418e-02, eta: 1 day, 8:41:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6355, loss_cls: 3.5518, loss: 3.5518 +2024-12-30 11:07:54,569 - pyskl - INFO - Epoch [114][600/3746] lr: 1.416e-02, eta: 1 day, 8:39:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6462, loss_cls: 3.4880, loss: 3.4880 +2024-12-30 11:09:20,012 - pyskl - INFO - Epoch [114][700/3746] lr: 1.414e-02, eta: 1 day, 8:38:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6266, loss_cls: 3.5737, loss: 3.5737 +2024-12-30 11:10:45,363 - pyskl - INFO - Epoch [114][800/3746] lr: 1.412e-02, eta: 1 day, 8:36:49, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6281, loss_cls: 3.5901, loss: 3.5901 +2024-12-30 11:12:10,659 - pyskl - INFO - Epoch [114][900/3746] lr: 1.410e-02, eta: 1 day, 8:35:24, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3812, top5_acc: 0.6394, loss_cls: 3.5073, loss: 3.5073 +2024-12-30 11:13:35,761 - pyskl - INFO - Epoch [114][1000/3746] lr: 1.408e-02, eta: 1 day, 8:33:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6338, loss_cls: 3.5159, loss: 3.5159 +2024-12-30 11:15:01,028 - pyskl - INFO - Epoch [114][1100/3746] lr: 1.406e-02, eta: 1 day, 8:32:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6311, loss_cls: 3.5663, loss: 3.5663 +2024-12-30 11:16:26,478 - pyskl - INFO - Epoch [114][1200/3746] lr: 1.404e-02, eta: 1 day, 8:31:09, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3812, top5_acc: 0.6302, loss_cls: 3.5377, loss: 3.5377 +2024-12-30 11:17:51,782 - pyskl - INFO - Epoch [114][1300/3746] lr: 1.402e-02, eta: 1 day, 8:29:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6355, loss_cls: 3.5200, loss: 3.5200 +2024-12-30 11:19:17,147 - pyskl - INFO - Epoch [114][1400/3746] lr: 1.400e-02, eta: 1 day, 8:28:18, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6233, loss_cls: 3.5717, loss: 3.5717 +2024-12-30 11:20:42,706 - pyskl - INFO - Epoch [114][1500/3746] lr: 1.398e-02, eta: 1 day, 8:26:53, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6358, loss_cls: 3.5259, loss: 3.5259 +2024-12-30 11:22:08,047 - pyskl - INFO - Epoch [114][1600/3746] lr: 1.397e-02, eta: 1 day, 8:25:28, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6316, loss_cls: 3.5685, loss: 3.5685 +2024-12-30 11:23:33,175 - pyskl - INFO - Epoch [114][1700/3746] lr: 1.395e-02, eta: 1 day, 8:24:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6384, loss_cls: 3.5224, loss: 3.5224 +2024-12-30 11:24:58,376 - pyskl - INFO - Epoch [114][1800/3746] lr: 1.393e-02, eta: 1 day, 8:22:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6256, loss_cls: 3.5671, loss: 3.5671 +2024-12-30 11:26:22,876 - pyskl - INFO - Epoch [114][1900/3746] lr: 1.391e-02, eta: 1 day, 8:21:12, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6316, loss_cls: 3.5356, loss: 3.5356 +2024-12-30 11:27:47,454 - pyskl - INFO - Epoch [114][2000/3746] lr: 1.389e-02, eta: 1 day, 8:19:47, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6291, loss_cls: 3.5510, loss: 3.5510 +2024-12-30 11:29:11,997 - pyskl - INFO - Epoch [114][2100/3746] lr: 1.387e-02, eta: 1 day, 8:18:22, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6248, loss_cls: 3.5713, loss: 3.5713 +2024-12-30 11:30:35,624 - pyskl - INFO - Epoch [114][2200/3746] lr: 1.385e-02, eta: 1 day, 8:16:56, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6270, loss_cls: 3.6259, loss: 3.6259 +2024-12-30 11:32:00,089 - pyskl - INFO - Epoch [114][2300/3746] lr: 1.383e-02, eta: 1 day, 8:15:30, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3723, top5_acc: 0.6258, loss_cls: 3.5878, loss: 3.5878 +2024-12-30 11:33:24,807 - pyskl - INFO - Epoch [114][2400/3746] lr: 1.381e-02, eta: 1 day, 8:14:05, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6314, loss_cls: 3.5641, loss: 3.5641 +2024-12-30 11:34:48,856 - pyskl - INFO - Epoch [114][2500/3746] lr: 1.379e-02, eta: 1 day, 8:12:39, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6191, loss_cls: 3.5810, loss: 3.5810 +2024-12-30 11:36:13,478 - pyskl - INFO - Epoch [114][2600/3746] lr: 1.377e-02, eta: 1 day, 8:11:14, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6223, loss_cls: 3.5768, loss: 3.5768 +2024-12-30 11:37:37,406 - pyskl - INFO - Epoch [114][2700/3746] lr: 1.375e-02, eta: 1 day, 8:09:48, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6373, loss_cls: 3.5476, loss: 3.5476 +2024-12-30 11:39:01,893 - pyskl - INFO - Epoch [114][2800/3746] lr: 1.373e-02, eta: 1 day, 8:08:23, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6327, loss_cls: 3.5567, loss: 3.5567 +2024-12-30 11:40:26,161 - pyskl - INFO - Epoch [114][2900/3746] lr: 1.371e-02, eta: 1 day, 8:06:58, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6270, loss_cls: 3.5439, loss: 3.5439 +2024-12-30 11:41:50,374 - pyskl - INFO - Epoch [114][3000/3746] lr: 1.369e-02, eta: 1 day, 8:05:32, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6297, loss_cls: 3.5537, loss: 3.5537 +2024-12-30 11:43:14,660 - pyskl - INFO - Epoch [114][3100/3746] lr: 1.368e-02, eta: 1 day, 8:04:07, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6256, loss_cls: 3.5688, loss: 3.5688 +2024-12-30 11:44:38,430 - pyskl - INFO - Epoch [114][3200/3746] lr: 1.366e-02, eta: 1 day, 8:02:41, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6183, loss_cls: 3.6109, loss: 3.6109 +2024-12-30 11:46:02,780 - pyskl - INFO - Epoch [114][3300/3746] lr: 1.364e-02, eta: 1 day, 8:01:15, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6261, loss_cls: 3.5986, loss: 3.5986 +2024-12-30 11:47:27,373 - pyskl - INFO - Epoch [114][3400/3746] lr: 1.362e-02, eta: 1 day, 7:59:50, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6231, loss_cls: 3.6010, loss: 3.6010 +2024-12-30 11:48:52,106 - pyskl - INFO - Epoch [114][3500/3746] lr: 1.360e-02, eta: 1 day, 7:58:25, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6220, loss_cls: 3.6326, loss: 3.6326 +2024-12-30 11:50:16,598 - pyskl - INFO - Epoch [114][3600/3746] lr: 1.358e-02, eta: 1 day, 7:56:59, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6278, loss_cls: 3.5830, loss: 3.5830 +2024-12-30 11:51:41,670 - pyskl - INFO - Epoch [114][3700/3746] lr: 1.356e-02, eta: 1 day, 7:55:34, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6284, loss_cls: 3.5639, loss: 3.5639 +2024-12-30 11:52:22,158 - pyskl - INFO - Saving checkpoint at 114 epochs +2024-12-30 11:54:20,045 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 11:54:20,757 - pyskl - INFO - +top1_acc 0.3147 +top5_acc 0.5678 +2024-12-30 11:54:20,757 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 11:54:20,802 - pyskl - INFO - +mean_acc 0.3145 +2024-12-30 11:54:20,811 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_113.pth was removed +2024-12-30 11:54:21,090 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_114.pth. +2024-12-30 11:54:21,091 - pyskl - INFO - Best top1_acc is 0.3147 at 114 epoch. +2024-12-30 11:54:21,105 - pyskl - INFO - Epoch(val) [114][309] top1_acc: 0.3147, top5_acc: 0.5678, mean_class_accuracy: 0.3145 +2024-12-30 11:58:36,355 - pyskl - INFO - Epoch [115][100/3746] lr: 1.353e-02, eta: 1 day, 7:54:11, time: 2.552, data_time: 1.519, memory: 15990, top1_acc: 0.3858, top5_acc: 0.6431, loss_cls: 3.4905, loss: 3.4905 +2024-12-30 12:00:01,392 - pyskl - INFO - Epoch [115][200/3746] lr: 1.351e-02, eta: 1 day, 7:52:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6364, loss_cls: 3.5025, loss: 3.5025 +2024-12-30 12:01:26,244 - pyskl - INFO - Epoch [115][300/3746] lr: 1.349e-02, eta: 1 day, 7:51:20, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6394, loss_cls: 3.4986, loss: 3.4986 +2024-12-30 12:02:51,527 - pyskl - INFO - Epoch [115][400/3746] lr: 1.348e-02, eta: 1 day, 7:49:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6375, loss_cls: 3.4989, loss: 3.4989 +2024-12-30 12:04:16,289 - pyskl - INFO - Epoch [115][500/3746] lr: 1.346e-02, eta: 1 day, 7:48:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6384, loss_cls: 3.4997, loss: 3.4997 +2024-12-30 12:05:41,384 - pyskl - INFO - Epoch [115][600/3746] lr: 1.344e-02, eta: 1 day, 7:47:04, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6400, loss_cls: 3.5225, loss: 3.5225 +2024-12-30 12:07:06,140 - pyskl - INFO - Epoch [115][700/3746] lr: 1.342e-02, eta: 1 day, 7:45:39, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6327, loss_cls: 3.5253, loss: 3.5253 +2024-12-30 12:08:31,193 - pyskl - INFO - Epoch [115][800/3746] lr: 1.340e-02, eta: 1 day, 7:44:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6370, loss_cls: 3.5289, loss: 3.5289 +2024-12-30 12:09:56,044 - pyskl - INFO - Epoch [115][900/3746] lr: 1.338e-02, eta: 1 day, 7:42:48, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6317, loss_cls: 3.5415, loss: 3.5415 +2024-12-30 12:11:20,662 - pyskl - INFO - Epoch [115][1000/3746] lr: 1.336e-02, eta: 1 day, 7:41:23, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6441, loss_cls: 3.4997, loss: 3.4997 +2024-12-30 12:12:45,113 - pyskl - INFO - Epoch [115][1100/3746] lr: 1.334e-02, eta: 1 day, 7:39:58, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6253, loss_cls: 3.5596, loss: 3.5596 +2024-12-30 12:14:09,076 - pyskl - INFO - Epoch [115][1200/3746] lr: 1.332e-02, eta: 1 day, 7:38:32, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6305, loss_cls: 3.5482, loss: 3.5482 +2024-12-30 12:15:33,989 - pyskl - INFO - Epoch [115][1300/3746] lr: 1.330e-02, eta: 1 day, 7:37:07, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6381, loss_cls: 3.5140, loss: 3.5140 +2024-12-30 12:16:58,348 - pyskl - INFO - Epoch [115][1400/3746] lr: 1.328e-02, eta: 1 day, 7:35:41, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6380, loss_cls: 3.5385, loss: 3.5385 +2024-12-30 12:18:23,491 - pyskl - INFO - Epoch [115][1500/3746] lr: 1.327e-02, eta: 1 day, 7:34:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6292, loss_cls: 3.5396, loss: 3.5396 +2024-12-30 12:19:48,067 - pyskl - INFO - Epoch [115][1600/3746] lr: 1.325e-02, eta: 1 day, 7:32:50, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6359, loss_cls: 3.5301, loss: 3.5301 +2024-12-30 12:21:12,594 - pyskl - INFO - Epoch [115][1700/3746] lr: 1.323e-02, eta: 1 day, 7:31:25, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6266, loss_cls: 3.5612, loss: 3.5612 +2024-12-30 12:22:36,989 - pyskl - INFO - Epoch [115][1800/3746] lr: 1.321e-02, eta: 1 day, 7:29:59, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6377, loss_cls: 3.5095, loss: 3.5095 +2024-12-30 12:24:01,432 - pyskl - INFO - Epoch [115][1900/3746] lr: 1.319e-02, eta: 1 day, 7:28:34, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6372, loss_cls: 3.5021, loss: 3.5021 +2024-12-30 12:25:26,462 - pyskl - INFO - Epoch [115][2000/3746] lr: 1.317e-02, eta: 1 day, 7:27:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6367, loss_cls: 3.5133, loss: 3.5133 +2024-12-30 12:26:50,881 - pyskl - INFO - Epoch [115][2100/3746] lr: 1.315e-02, eta: 1 day, 7:25:43, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6241, loss_cls: 3.5701, loss: 3.5701 +2024-12-30 12:28:15,669 - pyskl - INFO - Epoch [115][2200/3746] lr: 1.313e-02, eta: 1 day, 7:24:18, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6325, loss_cls: 3.5648, loss: 3.5648 +2024-12-30 12:29:40,024 - pyskl - INFO - Epoch [115][2300/3746] lr: 1.311e-02, eta: 1 day, 7:22:52, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6280, loss_cls: 3.5670, loss: 3.5670 +2024-12-30 12:31:04,550 - pyskl - INFO - Epoch [115][2400/3746] lr: 1.310e-02, eta: 1 day, 7:21:27, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6347, loss_cls: 3.5457, loss: 3.5457 +2024-12-30 12:32:28,935 - pyskl - INFO - Epoch [115][2500/3746] lr: 1.308e-02, eta: 1 day, 7:20:01, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6342, loss_cls: 3.5403, loss: 3.5403 +2024-12-30 12:33:53,044 - pyskl - INFO - Epoch [115][2600/3746] lr: 1.306e-02, eta: 1 day, 7:18:36, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6239, loss_cls: 3.5840, loss: 3.5840 +2024-12-30 12:35:17,366 - pyskl - INFO - Epoch [115][2700/3746] lr: 1.304e-02, eta: 1 day, 7:17:10, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6400, loss_cls: 3.5326, loss: 3.5326 +2024-12-30 12:36:41,390 - pyskl - INFO - Epoch [115][2800/3746] lr: 1.302e-02, eta: 1 day, 7:15:45, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3708, top5_acc: 0.6238, loss_cls: 3.5539, loss: 3.5539 +2024-12-30 12:38:06,317 - pyskl - INFO - Epoch [115][2900/3746] lr: 1.300e-02, eta: 1 day, 7:14:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6248, loss_cls: 3.5367, loss: 3.5367 +2024-12-30 12:39:30,899 - pyskl - INFO - Epoch [115][3000/3746] lr: 1.298e-02, eta: 1 day, 7:12:54, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6283, loss_cls: 3.5470, loss: 3.5470 +2024-12-30 12:40:55,290 - pyskl - INFO - Epoch [115][3100/3746] lr: 1.296e-02, eta: 1 day, 7:11:29, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3656, top5_acc: 0.6203, loss_cls: 3.6317, loss: 3.6317 +2024-12-30 12:42:19,763 - pyskl - INFO - Epoch [115][3200/3746] lr: 1.295e-02, eta: 1 day, 7:10:03, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6367, loss_cls: 3.5242, loss: 3.5242 +2024-12-30 12:43:44,189 - pyskl - INFO - Epoch [115][3300/3746] lr: 1.293e-02, eta: 1 day, 7:08:38, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6388, loss_cls: 3.5398, loss: 3.5398 +2024-12-30 12:45:08,809 - pyskl - INFO - Epoch [115][3400/3746] lr: 1.291e-02, eta: 1 day, 7:07:12, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6267, loss_cls: 3.5704, loss: 3.5704 +2024-12-30 12:46:34,073 - pyskl - INFO - Epoch [115][3500/3746] lr: 1.289e-02, eta: 1 day, 7:05:47, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6439, loss_cls: 3.4980, loss: 3.4980 +2024-12-30 12:47:58,499 - pyskl - INFO - Epoch [115][3600/3746] lr: 1.287e-02, eta: 1 day, 7:04:22, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6358, loss_cls: 3.5456, loss: 3.5456 +2024-12-30 12:49:23,221 - pyskl - INFO - Epoch [115][3700/3746] lr: 1.285e-02, eta: 1 day, 7:02:56, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6238, loss_cls: 3.5812, loss: 3.5812 +2024-12-30 12:50:04,193 - pyskl - INFO - Saving checkpoint at 115 epochs +2024-12-30 12:52:03,952 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 12:52:04,686 - pyskl - INFO - +top1_acc 0.3145 +top5_acc 0.5649 +2024-12-30 12:52:04,686 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 12:52:04,728 - pyskl - INFO - +mean_acc 0.3144 +2024-12-30 12:52:04,742 - pyskl - INFO - Epoch(val) [115][309] top1_acc: 0.3145, top5_acc: 0.5649, mean_class_accuracy: 0.3144 +2024-12-30 12:56:20,642 - pyskl - INFO - Epoch [116][100/3746] lr: 1.282e-02, eta: 1 day, 7:01:32, time: 2.559, data_time: 1.524, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6516, loss_cls: 3.4193, loss: 3.4193 +2024-12-30 12:57:46,172 - pyskl - INFO - Epoch [116][200/3746] lr: 1.281e-02, eta: 1 day, 7:00:07, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6453, loss_cls: 3.4473, loss: 3.4473 +2024-12-30 12:59:11,109 - pyskl - INFO - Epoch [116][300/3746] lr: 1.279e-02, eta: 1 day, 6:58:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6469, loss_cls: 3.4167, loss: 3.4167 +2024-12-30 13:00:35,982 - pyskl - INFO - Epoch [116][400/3746] lr: 1.277e-02, eta: 1 day, 6:57:16, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6484, loss_cls: 3.4525, loss: 3.4525 +2024-12-30 13:02:01,307 - pyskl - INFO - Epoch [116][500/3746] lr: 1.275e-02, eta: 1 day, 6:55:51, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3819, top5_acc: 0.6409, loss_cls: 3.4809, loss: 3.4809 +2024-12-30 13:03:26,573 - pyskl - INFO - Epoch [116][600/3746] lr: 1.273e-02, eta: 1 day, 6:54:25, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6455, loss_cls: 3.4968, loss: 3.4968 +2024-12-30 13:04:51,732 - pyskl - INFO - Epoch [116][700/3746] lr: 1.271e-02, eta: 1 day, 6:53:00, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6494, loss_cls: 3.4720, loss: 3.4720 +2024-12-30 13:06:16,514 - pyskl - INFO - Epoch [116][800/3746] lr: 1.269e-02, eta: 1 day, 6:51:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3748, top5_acc: 0.6325, loss_cls: 3.5409, loss: 3.5409 +2024-12-30 13:07:41,412 - pyskl - INFO - Epoch [116][900/3746] lr: 1.268e-02, eta: 1 day, 6:50:09, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3859, top5_acc: 0.6453, loss_cls: 3.4671, loss: 3.4671 +2024-12-30 13:09:06,891 - pyskl - INFO - Epoch [116][1000/3746] lr: 1.266e-02, eta: 1 day, 6:48:44, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6327, loss_cls: 3.5626, loss: 3.5626 +2024-12-30 13:10:31,699 - pyskl - INFO - Epoch [116][1100/3746] lr: 1.264e-02, eta: 1 day, 6:47:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6366, loss_cls: 3.5076, loss: 3.5076 +2024-12-30 13:11:56,895 - pyskl - INFO - Epoch [116][1200/3746] lr: 1.262e-02, eta: 1 day, 6:45:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6297, loss_cls: 3.5430, loss: 3.5430 +2024-12-30 13:13:21,826 - pyskl - INFO - Epoch [116][1300/3746] lr: 1.260e-02, eta: 1 day, 6:44:28, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3723, top5_acc: 0.6341, loss_cls: 3.5348, loss: 3.5348 +2024-12-30 13:14:47,411 - pyskl - INFO - Epoch [116][1400/3746] lr: 1.258e-02, eta: 1 day, 6:43:03, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6295, loss_cls: 3.5527, loss: 3.5527 +2024-12-30 13:16:12,638 - pyskl - INFO - Epoch [116][1500/3746] lr: 1.256e-02, eta: 1 day, 6:41:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6284, loss_cls: 3.5792, loss: 3.5792 +2024-12-30 13:17:37,570 - pyskl - INFO - Epoch [116][1600/3746] lr: 1.255e-02, eta: 1 day, 6:40:12, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6283, loss_cls: 3.5378, loss: 3.5378 +2024-12-30 13:19:02,609 - pyskl - INFO - Epoch [116][1700/3746] lr: 1.253e-02, eta: 1 day, 6:38:47, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6319, loss_cls: 3.5783, loss: 3.5783 +2024-12-30 13:20:27,389 - pyskl - INFO - Epoch [116][1800/3746] lr: 1.251e-02, eta: 1 day, 6:37:22, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6408, loss_cls: 3.4544, loss: 3.4544 +2024-12-30 13:21:52,064 - pyskl - INFO - Epoch [116][1900/3746] lr: 1.249e-02, eta: 1 day, 6:35:56, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3762, top5_acc: 0.6388, loss_cls: 3.4983, loss: 3.4983 +2024-12-30 13:23:16,839 - pyskl - INFO - Epoch [116][2000/3746] lr: 1.247e-02, eta: 1 day, 6:34:31, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6391, loss_cls: 3.5071, loss: 3.5071 +2024-12-30 13:24:40,766 - pyskl - INFO - Epoch [116][2100/3746] lr: 1.245e-02, eta: 1 day, 6:33:05, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6420, loss_cls: 3.4974, loss: 3.4974 +2024-12-30 13:26:05,552 - pyskl - INFO - Epoch [116][2200/3746] lr: 1.243e-02, eta: 1 day, 6:31:40, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6314, loss_cls: 3.5397, loss: 3.5397 +2024-12-30 13:27:29,759 - pyskl - INFO - Epoch [116][2300/3746] lr: 1.242e-02, eta: 1 day, 6:30:14, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6355, loss_cls: 3.5268, loss: 3.5268 +2024-12-30 13:28:54,584 - pyskl - INFO - Epoch [116][2400/3746] lr: 1.240e-02, eta: 1 day, 6:28:49, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6352, loss_cls: 3.5054, loss: 3.5054 +2024-12-30 13:30:18,924 - pyskl - INFO - Epoch [116][2500/3746] lr: 1.238e-02, eta: 1 day, 6:27:23, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6412, loss_cls: 3.4967, loss: 3.4967 +2024-12-30 13:31:43,571 - pyskl - INFO - Epoch [116][2600/3746] lr: 1.236e-02, eta: 1 day, 6:25:58, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6331, loss_cls: 3.5272, loss: 3.5272 +2024-12-30 13:33:07,997 - pyskl - INFO - Epoch [116][2700/3746] lr: 1.234e-02, eta: 1 day, 6:24:32, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6292, loss_cls: 3.5611, loss: 3.5611 +2024-12-30 13:34:32,395 - pyskl - INFO - Epoch [116][2800/3746] lr: 1.232e-02, eta: 1 day, 6:23:07, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6344, loss_cls: 3.5104, loss: 3.5104 +2024-12-30 13:35:57,906 - pyskl - INFO - Epoch [116][2900/3746] lr: 1.231e-02, eta: 1 day, 6:21:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6238, loss_cls: 3.5692, loss: 3.5692 +2024-12-30 13:37:22,514 - pyskl - INFO - Epoch [116][3000/3746] lr: 1.229e-02, eta: 1 day, 6:20:16, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6359, loss_cls: 3.5157, loss: 3.5157 +2024-12-30 13:38:47,863 - pyskl - INFO - Epoch [116][3100/3746] lr: 1.227e-02, eta: 1 day, 6:18:51, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6250, loss_cls: 3.5757, loss: 3.5757 +2024-12-30 13:40:12,739 - pyskl - INFO - Epoch [116][3200/3746] lr: 1.225e-02, eta: 1 day, 6:17:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6373, loss_cls: 3.5075, loss: 3.5075 +2024-12-30 13:41:38,043 - pyskl - INFO - Epoch [116][3300/3746] lr: 1.223e-02, eta: 1 day, 6:16:00, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6297, loss_cls: 3.5856, loss: 3.5856 +2024-12-30 13:43:03,527 - pyskl - INFO - Epoch [116][3400/3746] lr: 1.221e-02, eta: 1 day, 6:14:35, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6264, loss_cls: 3.5606, loss: 3.5606 +2024-12-30 13:44:28,240 - pyskl - INFO - Epoch [116][3500/3746] lr: 1.220e-02, eta: 1 day, 6:13:10, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6423, loss_cls: 3.5164, loss: 3.5164 +2024-12-30 13:45:52,555 - pyskl - INFO - Epoch [116][3600/3746] lr: 1.218e-02, eta: 1 day, 6:11:44, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6480, loss_cls: 3.4909, loss: 3.4909 +2024-12-30 13:47:16,952 - pyskl - INFO - Epoch [116][3700/3746] lr: 1.216e-02, eta: 1 day, 6:10:19, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6366, loss_cls: 3.5149, loss: 3.5149 +2024-12-30 13:47:57,867 - pyskl - INFO - Saving checkpoint at 116 epochs +2024-12-30 13:49:56,683 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 13:49:57,501 - pyskl - INFO - +top1_acc 0.3168 +top5_acc 0.5682 +2024-12-30 13:49:57,502 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 13:49:57,549 - pyskl - INFO - +mean_acc 0.3165 +2024-12-30 13:49:57,554 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_114.pth was removed +2024-12-30 13:49:57,843 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_116.pth. +2024-12-30 13:49:57,843 - pyskl - INFO - Best top1_acc is 0.3168 at 116 epoch. +2024-12-30 13:49:57,857 - pyskl - INFO - Epoch(val) [116][309] top1_acc: 0.3168, top5_acc: 0.5682, mean_class_accuracy: 0.3165 +2024-12-30 13:54:09,754 - pyskl - INFO - Epoch [117][100/3746] lr: 1.213e-02, eta: 1 day, 6:08:52, time: 2.519, data_time: 1.483, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6470, loss_cls: 3.4429, loss: 3.4429 +2024-12-30 13:55:34,613 - pyskl - INFO - Epoch [117][200/3746] lr: 1.211e-02, eta: 1 day, 6:07:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6458, loss_cls: 3.4436, loss: 3.4436 +2024-12-30 13:56:59,630 - pyskl - INFO - Epoch [117][300/3746] lr: 1.210e-02, eta: 1 day, 6:06:01, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6350, loss_cls: 3.4742, loss: 3.4742 +2024-12-30 13:58:24,517 - pyskl - INFO - Epoch [117][400/3746] lr: 1.208e-02, eta: 1 day, 6:04:35, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6552, loss_cls: 3.4083, loss: 3.4083 +2024-12-30 13:59:49,412 - pyskl - INFO - Epoch [117][500/3746] lr: 1.206e-02, eta: 1 day, 6:03:10, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6406, loss_cls: 3.4758, loss: 3.4758 +2024-12-30 14:01:14,680 - pyskl - INFO - Epoch [117][600/3746] lr: 1.204e-02, eta: 1 day, 6:01:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6508, loss_cls: 3.4493, loss: 3.4493 +2024-12-30 14:02:39,961 - pyskl - INFO - Epoch [117][700/3746] lr: 1.202e-02, eta: 1 day, 6:00:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3812, top5_acc: 0.6377, loss_cls: 3.4782, loss: 3.4782 +2024-12-30 14:04:05,427 - pyskl - INFO - Epoch [117][800/3746] lr: 1.200e-02, eta: 1 day, 5:58:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6462, loss_cls: 3.4575, loss: 3.4575 +2024-12-30 14:05:30,301 - pyskl - INFO - Epoch [117][900/3746] lr: 1.199e-02, eta: 1 day, 5:57:29, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6444, loss_cls: 3.4574, loss: 3.4574 +2024-12-30 14:06:55,088 - pyskl - INFO - Epoch [117][1000/3746] lr: 1.197e-02, eta: 1 day, 5:56:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6398, loss_cls: 3.4775, loss: 3.4775 +2024-12-30 14:08:19,655 - pyskl - INFO - Epoch [117][1100/3746] lr: 1.195e-02, eta: 1 day, 5:54:38, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3819, top5_acc: 0.6406, loss_cls: 3.5096, loss: 3.5096 +2024-12-30 14:09:44,015 - pyskl - INFO - Epoch [117][1200/3746] lr: 1.193e-02, eta: 1 day, 5:53:12, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3831, top5_acc: 0.6442, loss_cls: 3.4897, loss: 3.4897 +2024-12-30 14:11:08,416 - pyskl - INFO - Epoch [117][1300/3746] lr: 1.191e-02, eta: 1 day, 5:51:47, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3739, top5_acc: 0.6402, loss_cls: 3.5306, loss: 3.5306 +2024-12-30 14:12:33,373 - pyskl - INFO - Epoch [117][1400/3746] lr: 1.190e-02, eta: 1 day, 5:50:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6372, loss_cls: 3.5070, loss: 3.5070 +2024-12-30 14:13:58,501 - pyskl - INFO - Epoch [117][1500/3746] lr: 1.188e-02, eta: 1 day, 5:48:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6400, loss_cls: 3.4930, loss: 3.4930 +2024-12-30 14:15:23,137 - pyskl - INFO - Epoch [117][1600/3746] lr: 1.186e-02, eta: 1 day, 5:47:31, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6441, loss_cls: 3.5090, loss: 3.5090 +2024-12-30 14:16:48,461 - pyskl - INFO - Epoch [117][1700/3746] lr: 1.184e-02, eta: 1 day, 5:46:05, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3858, top5_acc: 0.6456, loss_cls: 3.4929, loss: 3.4929 +2024-12-30 14:18:13,152 - pyskl - INFO - Epoch [117][1800/3746] lr: 1.182e-02, eta: 1 day, 5:44:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6358, loss_cls: 3.4977, loss: 3.4977 +2024-12-30 14:19:38,225 - pyskl - INFO - Epoch [117][1900/3746] lr: 1.181e-02, eta: 1 day, 5:43:15, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6416, loss_cls: 3.4863, loss: 3.4863 +2024-12-30 14:21:02,612 - pyskl - INFO - Epoch [117][2000/3746] lr: 1.179e-02, eta: 1 day, 5:41:49, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6545, loss_cls: 3.4324, loss: 3.4324 +2024-12-30 14:22:26,981 - pyskl - INFO - Epoch [117][2100/3746] lr: 1.177e-02, eta: 1 day, 5:40:24, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6439, loss_cls: 3.4646, loss: 3.4646 +2024-12-30 14:23:51,266 - pyskl - INFO - Epoch [117][2200/3746] lr: 1.175e-02, eta: 1 day, 5:38:58, time: 0.843, data_time: 0.001, memory: 15990, top1_acc: 0.3741, top5_acc: 0.6286, loss_cls: 3.5313, loss: 3.5313 +2024-12-30 14:25:15,576 - pyskl - INFO - Epoch [117][2300/3746] lr: 1.173e-02, eta: 1 day, 5:37:32, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6470, loss_cls: 3.4453, loss: 3.4453 +2024-12-30 14:26:40,772 - pyskl - INFO - Epoch [117][2400/3746] lr: 1.172e-02, eta: 1 day, 5:36:07, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6469, loss_cls: 3.4400, loss: 3.4400 +2024-12-30 14:28:05,664 - pyskl - INFO - Epoch [117][2500/3746] lr: 1.170e-02, eta: 1 day, 5:34:42, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6289, loss_cls: 3.5742, loss: 3.5742 +2024-12-30 14:29:29,742 - pyskl - INFO - Epoch [117][2600/3746] lr: 1.168e-02, eta: 1 day, 5:33:16, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6403, loss_cls: 3.4854, loss: 3.4854 +2024-12-30 14:30:54,442 - pyskl - INFO - Epoch [117][2700/3746] lr: 1.166e-02, eta: 1 day, 5:31:51, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6264, loss_cls: 3.5491, loss: 3.5491 +2024-12-30 14:32:19,534 - pyskl - INFO - Epoch [117][2800/3746] lr: 1.164e-02, eta: 1 day, 5:30:25, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6388, loss_cls: 3.4746, loss: 3.4746 +2024-12-30 14:33:43,831 - pyskl - INFO - Epoch [117][2900/3746] lr: 1.163e-02, eta: 1 day, 5:29:00, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6439, loss_cls: 3.4799, loss: 3.4799 +2024-12-30 14:35:08,402 - pyskl - INFO - Epoch [117][3000/3746] lr: 1.161e-02, eta: 1 day, 5:27:34, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6452, loss_cls: 3.4877, loss: 3.4877 +2024-12-30 14:36:32,351 - pyskl - INFO - Epoch [117][3100/3746] lr: 1.159e-02, eta: 1 day, 5:26:09, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6458, loss_cls: 3.4768, loss: 3.4768 +2024-12-30 14:37:56,541 - pyskl - INFO - Epoch [117][3200/3746] lr: 1.157e-02, eta: 1 day, 5:24:43, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6303, loss_cls: 3.5308, loss: 3.5308 +2024-12-30 14:39:20,802 - pyskl - INFO - Epoch [117][3300/3746] lr: 1.155e-02, eta: 1 day, 5:23:17, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6403, loss_cls: 3.5004, loss: 3.5004 +2024-12-30 14:40:45,151 - pyskl - INFO - Epoch [117][3400/3746] lr: 1.154e-02, eta: 1 day, 5:21:52, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6384, loss_cls: 3.5281, loss: 3.5281 +2024-12-30 14:42:09,618 - pyskl - INFO - Epoch [117][3500/3746] lr: 1.152e-02, eta: 1 day, 5:20:26, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6359, loss_cls: 3.5004, loss: 3.5004 +2024-12-30 14:43:34,451 - pyskl - INFO - Epoch [117][3600/3746] lr: 1.150e-02, eta: 1 day, 5:19:01, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6512, loss_cls: 3.4560, loss: 3.4560 +2024-12-30 14:44:58,881 - pyskl - INFO - Epoch [117][3700/3746] lr: 1.148e-02, eta: 1 day, 5:17:36, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6258, loss_cls: 3.5606, loss: 3.5606 +2024-12-30 14:45:39,434 - pyskl - INFO - Saving checkpoint at 117 epochs +2024-12-30 14:47:38,321 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 14:47:39,036 - pyskl - INFO - +top1_acc 0.3155 +top5_acc 0.5693 +2024-12-30 14:47:39,036 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 14:47:39,091 - pyskl - INFO - +mean_acc 0.3152 +2024-12-30 14:47:39,110 - pyskl - INFO - Epoch(val) [117][309] top1_acc: 0.3155, top5_acc: 0.5693, mean_class_accuracy: 0.3152 +2024-12-30 14:51:54,038 - pyskl - INFO - Epoch [118][100/3746] lr: 1.146e-02, eta: 1 day, 5:16:08, time: 2.549, data_time: 1.507, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6606, loss_cls: 3.3703, loss: 3.3703 +2024-12-30 14:53:20,176 - pyskl - INFO - Epoch [118][200/3746] lr: 1.144e-02, eta: 1 day, 5:14:43, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6495, loss_cls: 3.4342, loss: 3.4342 +2024-12-30 14:54:46,285 - pyskl - INFO - Epoch [118][300/3746] lr: 1.142e-02, eta: 1 day, 5:13:18, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6478, loss_cls: 3.4110, loss: 3.4110 +2024-12-30 14:56:11,976 - pyskl - INFO - Epoch [118][400/3746] lr: 1.140e-02, eta: 1 day, 5:11:52, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6578, loss_cls: 3.4065, loss: 3.4065 +2024-12-30 14:57:37,758 - pyskl - INFO - Epoch [118][500/3746] lr: 1.139e-02, eta: 1 day, 5:10:27, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6589, loss_cls: 3.4242, loss: 3.4242 +2024-12-30 14:59:03,020 - pyskl - INFO - Epoch [118][600/3746] lr: 1.137e-02, eta: 1 day, 5:09:02, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6477, loss_cls: 3.4240, loss: 3.4240 +2024-12-30 15:00:28,672 - pyskl - INFO - Epoch [118][700/3746] lr: 1.135e-02, eta: 1 day, 5:07:37, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6494, loss_cls: 3.4415, loss: 3.4415 +2024-12-30 15:01:54,549 - pyskl - INFO - Epoch [118][800/3746] lr: 1.133e-02, eta: 1 day, 5:06:12, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6455, loss_cls: 3.4713, loss: 3.4713 +2024-12-30 15:03:20,874 - pyskl - INFO - Epoch [118][900/3746] lr: 1.131e-02, eta: 1 day, 5:04:47, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6559, loss_cls: 3.4421, loss: 3.4421 +2024-12-30 15:04:46,619 - pyskl - INFO - Epoch [118][1000/3746] lr: 1.130e-02, eta: 1 day, 5:03:21, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6444, loss_cls: 3.4603, loss: 3.4603 +2024-12-30 15:06:12,631 - pyskl - INFO - Epoch [118][1100/3746] lr: 1.128e-02, eta: 1 day, 5:01:56, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3822, top5_acc: 0.6398, loss_cls: 3.4819, loss: 3.4819 +2024-12-30 15:07:38,637 - pyskl - INFO - Epoch [118][1200/3746] lr: 1.126e-02, eta: 1 day, 5:00:31, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6508, loss_cls: 3.4156, loss: 3.4156 +2024-12-30 15:09:04,603 - pyskl - INFO - Epoch [118][1300/3746] lr: 1.124e-02, eta: 1 day, 4:59:06, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6384, loss_cls: 3.4961, loss: 3.4961 +2024-12-30 15:10:30,506 - pyskl - INFO - Epoch [118][1400/3746] lr: 1.123e-02, eta: 1 day, 4:57:41, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6383, loss_cls: 3.4773, loss: 3.4773 +2024-12-30 15:11:56,753 - pyskl - INFO - Epoch [118][1500/3746] lr: 1.121e-02, eta: 1 day, 4:56:16, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6405, loss_cls: 3.5141, loss: 3.5141 +2024-12-30 15:13:22,960 - pyskl - INFO - Epoch [118][1600/3746] lr: 1.119e-02, eta: 1 day, 4:54:51, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3898, top5_acc: 0.6516, loss_cls: 3.4584, loss: 3.4584 +2024-12-30 15:14:48,834 - pyskl - INFO - Epoch [118][1700/3746] lr: 1.117e-02, eta: 1 day, 4:53:26, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6519, loss_cls: 3.4565, loss: 3.4565 +2024-12-30 15:16:14,704 - pyskl - INFO - Epoch [118][1800/3746] lr: 1.116e-02, eta: 1 day, 4:52:00, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6402, loss_cls: 3.4807, loss: 3.4807 +2024-12-30 15:17:40,520 - pyskl - INFO - Epoch [118][1900/3746] lr: 1.114e-02, eta: 1 day, 4:50:35, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6503, loss_cls: 3.4490, loss: 3.4490 +2024-12-30 15:19:05,622 - pyskl - INFO - Epoch [118][2000/3746] lr: 1.112e-02, eta: 1 day, 4:49:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6450, loss_cls: 3.4572, loss: 3.4572 +2024-12-30 15:20:30,456 - pyskl - INFO - Epoch [118][2100/3746] lr: 1.110e-02, eta: 1 day, 4:47:44, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6367, loss_cls: 3.5084, loss: 3.5084 +2024-12-30 15:21:55,125 - pyskl - INFO - Epoch [118][2200/3746] lr: 1.109e-02, eta: 1 day, 4:46:19, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6406, loss_cls: 3.5000, loss: 3.5000 +2024-12-30 15:23:20,158 - pyskl - INFO - Epoch [118][2300/3746] lr: 1.107e-02, eta: 1 day, 4:44:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6489, loss_cls: 3.4459, loss: 3.4459 +2024-12-30 15:24:45,418 - pyskl - INFO - Epoch [118][2400/3746] lr: 1.105e-02, eta: 1 day, 4:43:28, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6394, loss_cls: 3.4771, loss: 3.4771 +2024-12-30 15:26:10,963 - pyskl - INFO - Epoch [118][2500/3746] lr: 1.103e-02, eta: 1 day, 4:42:03, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6439, loss_cls: 3.4439, loss: 3.4439 +2024-12-30 15:27:36,662 - pyskl - INFO - Epoch [118][2600/3746] lr: 1.102e-02, eta: 1 day, 4:40:38, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6416, loss_cls: 3.5157, loss: 3.5157 +2024-12-30 15:29:02,055 - pyskl - INFO - Epoch [118][2700/3746] lr: 1.100e-02, eta: 1 day, 4:39:13, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6423, loss_cls: 3.4969, loss: 3.4969 +2024-12-30 15:30:27,914 - pyskl - INFO - Epoch [118][2800/3746] lr: 1.098e-02, eta: 1 day, 4:37:47, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6522, loss_cls: 3.4390, loss: 3.4390 +2024-12-30 15:31:53,129 - pyskl - INFO - Epoch [118][2900/3746] lr: 1.096e-02, eta: 1 day, 4:36:22, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6416, loss_cls: 3.4633, loss: 3.4633 +2024-12-30 15:33:18,477 - pyskl - INFO - Epoch [118][3000/3746] lr: 1.095e-02, eta: 1 day, 4:34:57, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6306, loss_cls: 3.4944, loss: 3.4944 +2024-12-30 15:34:43,866 - pyskl - INFO - Epoch [118][3100/3746] lr: 1.093e-02, eta: 1 day, 4:33:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6550, loss_cls: 3.4355, loss: 3.4355 +2024-12-30 15:36:09,759 - pyskl - INFO - Epoch [118][3200/3746] lr: 1.091e-02, eta: 1 day, 4:32:06, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.3858, top5_acc: 0.6402, loss_cls: 3.5048, loss: 3.5048 +2024-12-30 15:37:35,108 - pyskl - INFO - Epoch [118][3300/3746] lr: 1.089e-02, eta: 1 day, 4:30:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6375, loss_cls: 3.5109, loss: 3.5109 +2024-12-30 15:39:01,001 - pyskl - INFO - Epoch [118][3400/3746] lr: 1.088e-02, eta: 1 day, 4:29:16, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3812, top5_acc: 0.6422, loss_cls: 3.5212, loss: 3.5212 +2024-12-30 15:40:26,591 - pyskl - INFO - Epoch [118][3500/3746] lr: 1.086e-02, eta: 1 day, 4:27:51, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3903, top5_acc: 0.6403, loss_cls: 3.4780, loss: 3.4780 +2024-12-30 15:41:52,037 - pyskl - INFO - Epoch [118][3600/3746] lr: 1.084e-02, eta: 1 day, 4:26:25, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6383, loss_cls: 3.4805, loss: 3.4805 +2024-12-30 15:43:17,623 - pyskl - INFO - Epoch [118][3700/3746] lr: 1.082e-02, eta: 1 day, 4:25:00, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3858, top5_acc: 0.6384, loss_cls: 3.4884, loss: 3.4884 +2024-12-30 15:43:58,824 - pyskl - INFO - Saving checkpoint at 118 epochs +2024-12-30 15:45:59,579 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 15:46:00,330 - pyskl - INFO - +top1_acc 0.3192 +top5_acc 0.5689 +2024-12-30 15:46:00,330 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 15:46:00,383 - pyskl - INFO - +mean_acc 0.3190 +2024-12-30 15:46:00,388 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_116.pth was removed +2024-12-30 15:46:00,745 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2024-12-30 15:46:00,745 - pyskl - INFO - Best top1_acc is 0.3192 at 118 epoch. +2024-12-30 15:46:00,761 - pyskl - INFO - Epoch(val) [118][309] top1_acc: 0.3192, top5_acc: 0.5689, mean_class_accuracy: 0.3190 +2024-12-30 15:50:24,005 - pyskl - INFO - Epoch [119][100/3746] lr: 1.080e-02, eta: 1 day, 4:23:33, time: 2.632, data_time: 1.598, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6586, loss_cls: 3.4021, loss: 3.4021 +2024-12-30 15:51:49,126 - pyskl - INFO - Epoch [119][200/3746] lr: 1.078e-02, eta: 1 day, 4:22:08, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6545, loss_cls: 3.4120, loss: 3.4120 +2024-12-30 15:53:14,386 - pyskl - INFO - Epoch [119][300/3746] lr: 1.076e-02, eta: 1 day, 4:20:42, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6567, loss_cls: 3.4277, loss: 3.4277 +2024-12-30 15:54:39,726 - pyskl - INFO - Epoch [119][400/3746] lr: 1.075e-02, eta: 1 day, 4:19:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3980, top5_acc: 0.6598, loss_cls: 3.3777, loss: 3.3777 +2024-12-30 15:56:04,784 - pyskl - INFO - Epoch [119][500/3746] lr: 1.073e-02, eta: 1 day, 4:17:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6542, loss_cls: 3.3992, loss: 3.3992 +2024-12-30 15:57:30,384 - pyskl - INFO - Epoch [119][600/3746] lr: 1.071e-02, eta: 1 day, 4:16:26, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4009, top5_acc: 0.6570, loss_cls: 3.3852, loss: 3.3852 +2024-12-30 15:58:55,733 - pyskl - INFO - Epoch [119][700/3746] lr: 1.069e-02, eta: 1 day, 4:15:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4044, top5_acc: 0.6575, loss_cls: 3.3957, loss: 3.3957 +2024-12-30 16:00:21,120 - pyskl - INFO - Epoch [119][800/3746] lr: 1.068e-02, eta: 1 day, 4:13:36, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6542, loss_cls: 3.4075, loss: 3.4075 +2024-12-30 16:01:46,047 - pyskl - INFO - Epoch [119][900/3746] lr: 1.066e-02, eta: 1 day, 4:12:10, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6434, loss_cls: 3.4459, loss: 3.4459 +2024-12-30 16:03:11,376 - pyskl - INFO - Epoch [119][1000/3746] lr: 1.064e-02, eta: 1 day, 4:10:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6391, loss_cls: 3.4782, loss: 3.4782 +2024-12-30 16:04:36,801 - pyskl - INFO - Epoch [119][1100/3746] lr: 1.063e-02, eta: 1 day, 4:09:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6500, loss_cls: 3.4159, loss: 3.4159 +2024-12-30 16:06:02,341 - pyskl - INFO - Epoch [119][1200/3746] lr: 1.061e-02, eta: 1 day, 4:07:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6525, loss_cls: 3.4291, loss: 3.4291 +2024-12-30 16:07:27,497 - pyskl - INFO - Epoch [119][1300/3746] lr: 1.059e-02, eta: 1 day, 4:06:29, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6452, loss_cls: 3.4568, loss: 3.4568 +2024-12-30 16:08:52,493 - pyskl - INFO - Epoch [119][1400/3746] lr: 1.057e-02, eta: 1 day, 4:05:03, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6534, loss_cls: 3.4302, loss: 3.4302 +2024-12-30 16:10:17,807 - pyskl - INFO - Epoch [119][1500/3746] lr: 1.056e-02, eta: 1 day, 4:03:38, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6489, loss_cls: 3.4481, loss: 3.4481 +2024-12-30 16:11:43,811 - pyskl - INFO - Epoch [119][1600/3746] lr: 1.054e-02, eta: 1 day, 4:02:13, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6517, loss_cls: 3.4215, loss: 3.4215 +2024-12-30 16:13:08,959 - pyskl - INFO - Epoch [119][1700/3746] lr: 1.052e-02, eta: 1 day, 4:00:48, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3945, top5_acc: 0.6430, loss_cls: 3.4380, loss: 3.4380 +2024-12-30 16:14:34,684 - pyskl - INFO - Epoch [119][1800/3746] lr: 1.050e-02, eta: 1 day, 3:59:22, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6558, loss_cls: 3.4054, loss: 3.4054 +2024-12-30 16:15:59,863 - pyskl - INFO - Epoch [119][1900/3746] lr: 1.049e-02, eta: 1 day, 3:57:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6512, loss_cls: 3.4310, loss: 3.4310 +2024-12-30 16:17:25,145 - pyskl - INFO - Epoch [119][2000/3746] lr: 1.047e-02, eta: 1 day, 3:56:32, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6350, loss_cls: 3.4949, loss: 3.4949 +2024-12-30 16:18:49,902 - pyskl - INFO - Epoch [119][2100/3746] lr: 1.045e-02, eta: 1 day, 3:55:06, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6550, loss_cls: 3.4406, loss: 3.4406 +2024-12-30 16:20:15,240 - pyskl - INFO - Epoch [119][2200/3746] lr: 1.044e-02, eta: 1 day, 3:53:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6425, loss_cls: 3.4561, loss: 3.4561 +2024-12-30 16:21:40,480 - pyskl - INFO - Epoch [119][2300/3746] lr: 1.042e-02, eta: 1 day, 3:52:15, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6519, loss_cls: 3.4463, loss: 3.4463 +2024-12-30 16:23:05,651 - pyskl - INFO - Epoch [119][2400/3746] lr: 1.040e-02, eta: 1 day, 3:50:50, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6456, loss_cls: 3.4421, loss: 3.4421 +2024-12-30 16:24:30,903 - pyskl - INFO - Epoch [119][2500/3746] lr: 1.039e-02, eta: 1 day, 3:49:25, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6506, loss_cls: 3.4442, loss: 3.4442 +2024-12-30 16:25:56,056 - pyskl - INFO - Epoch [119][2600/3746] lr: 1.037e-02, eta: 1 day, 3:47:59, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6614, loss_cls: 3.4169, loss: 3.4169 +2024-12-30 16:27:21,800 - pyskl - INFO - Epoch [119][2700/3746] lr: 1.035e-02, eta: 1 day, 3:46:34, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6467, loss_cls: 3.4596, loss: 3.4596 +2024-12-30 16:28:47,091 - pyskl - INFO - Epoch [119][2800/3746] lr: 1.033e-02, eta: 1 day, 3:45:09, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6492, loss_cls: 3.4422, loss: 3.4422 +2024-12-30 16:30:12,736 - pyskl - INFO - Epoch [119][2900/3746] lr: 1.032e-02, eta: 1 day, 3:43:43, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6494, loss_cls: 3.4296, loss: 3.4296 +2024-12-30 16:31:38,343 - pyskl - INFO - Epoch [119][3000/3746] lr: 1.030e-02, eta: 1 day, 3:42:18, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6462, loss_cls: 3.4557, loss: 3.4557 +2024-12-30 16:33:03,934 - pyskl - INFO - Epoch [119][3100/3746] lr: 1.028e-02, eta: 1 day, 3:40:53, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3992, top5_acc: 0.6525, loss_cls: 3.4106, loss: 3.4106 +2024-12-30 16:34:29,328 - pyskl - INFO - Epoch [119][3200/3746] lr: 1.027e-02, eta: 1 day, 3:39:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3859, top5_acc: 0.6464, loss_cls: 3.4638, loss: 3.4638 +2024-12-30 16:35:55,342 - pyskl - INFO - Epoch [119][3300/3746] lr: 1.025e-02, eta: 1 day, 3:38:02, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6525, loss_cls: 3.4376, loss: 3.4376 +2024-12-30 16:37:22,020 - pyskl - INFO - Epoch [119][3400/3746] lr: 1.023e-02, eta: 1 day, 3:36:37, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6314, loss_cls: 3.5452, loss: 3.5452 +2024-12-30 16:38:46,883 - pyskl - INFO - Epoch [119][3500/3746] lr: 1.022e-02, eta: 1 day, 3:35:12, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6402, loss_cls: 3.4901, loss: 3.4901 +2024-12-30 16:40:11,970 - pyskl - INFO - Epoch [119][3600/3746] lr: 1.020e-02, eta: 1 day, 3:33:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6495, loss_cls: 3.4253, loss: 3.4253 +2024-12-30 16:41:37,907 - pyskl - INFO - Epoch [119][3700/3746] lr: 1.018e-02, eta: 1 day, 3:32:21, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6397, loss_cls: 3.4931, loss: 3.4931 +2024-12-30 16:42:18,880 - pyskl - INFO - Saving checkpoint at 119 epochs +2024-12-30 16:44:20,911 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 16:44:21,659 - pyskl - INFO - +top1_acc 0.3303 +top5_acc 0.5842 +2024-12-30 16:44:21,659 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 16:44:21,715 - pyskl - INFO - +mean_acc 0.3300 +2024-12-30 16:44:21,722 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_118.pth was removed +2024-12-30 16:44:22,051 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_119.pth. +2024-12-30 16:44:22,052 - pyskl - INFO - Best top1_acc is 0.3303 at 119 epoch. +2024-12-30 16:44:22,067 - pyskl - INFO - Epoch(val) [119][309] top1_acc: 0.3303, top5_acc: 0.5842, mean_class_accuracy: 0.3300 +2024-12-30 16:48:44,460 - pyskl - INFO - Epoch [120][100/3746] lr: 1.016e-02, eta: 1 day, 3:30:53, time: 2.624, data_time: 1.592, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6606, loss_cls: 3.3483, loss: 3.3483 +2024-12-30 16:50:09,630 - pyskl - INFO - Epoch [120][200/3746] lr: 1.014e-02, eta: 1 day, 3:29:27, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6644, loss_cls: 3.3583, loss: 3.3583 +2024-12-30 16:51:34,952 - pyskl - INFO - Epoch [120][300/3746] lr: 1.012e-02, eta: 1 day, 3:28:02, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6572, loss_cls: 3.3850, loss: 3.3850 +2024-12-30 16:53:00,437 - pyskl - INFO - Epoch [120][400/3746] lr: 1.011e-02, eta: 1 day, 3:26:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6564, loss_cls: 3.3867, loss: 3.3867 +2024-12-30 16:54:25,921 - pyskl - INFO - Epoch [120][500/3746] lr: 1.009e-02, eta: 1 day, 3:25:11, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6603, loss_cls: 3.3596, loss: 3.3596 +2024-12-30 16:55:51,175 - pyskl - INFO - Epoch [120][600/3746] lr: 1.007e-02, eta: 1 day, 3:23:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6547, loss_cls: 3.3829, loss: 3.3829 +2024-12-30 16:57:16,857 - pyskl - INFO - Epoch [120][700/3746] lr: 1.006e-02, eta: 1 day, 3:22:20, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6547, loss_cls: 3.4264, loss: 3.4264 +2024-12-30 16:58:42,398 - pyskl - INFO - Epoch [120][800/3746] lr: 1.004e-02, eta: 1 day, 3:20:55, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6578, loss_cls: 3.3810, loss: 3.3810 +2024-12-30 17:00:07,689 - pyskl - INFO - Epoch [120][900/3746] lr: 1.002e-02, eta: 1 day, 3:19:30, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6498, loss_cls: 3.4436, loss: 3.4436 +2024-12-30 17:01:32,930 - pyskl - INFO - Epoch [120][1000/3746] lr: 1.001e-02, eta: 1 day, 3:18:04, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3941, top5_acc: 0.6570, loss_cls: 3.4091, loss: 3.4091 +2024-12-30 17:02:58,534 - pyskl - INFO - Epoch [120][1100/3746] lr: 9.989e-03, eta: 1 day, 3:16:39, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4102, top5_acc: 0.6616, loss_cls: 3.3559, loss: 3.3559 +2024-12-30 17:04:23,849 - pyskl - INFO - Epoch [120][1200/3746] lr: 9.972e-03, eta: 1 day, 3:15:14, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6581, loss_cls: 3.3779, loss: 3.3779 +2024-12-30 17:05:49,054 - pyskl - INFO - Epoch [120][1300/3746] lr: 9.955e-03, eta: 1 day, 3:13:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6600, loss_cls: 3.3866, loss: 3.3866 +2024-12-30 17:07:14,352 - pyskl - INFO - Epoch [120][1400/3746] lr: 9.938e-03, eta: 1 day, 3:12:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4044, top5_acc: 0.6595, loss_cls: 3.4115, loss: 3.4115 +2024-12-30 17:08:39,808 - pyskl - INFO - Epoch [120][1500/3746] lr: 9.922e-03, eta: 1 day, 3:10:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6438, loss_cls: 3.4697, loss: 3.4697 +2024-12-30 17:10:04,979 - pyskl - INFO - Epoch [120][1600/3746] lr: 9.905e-03, eta: 1 day, 3:09:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6489, loss_cls: 3.4497, loss: 3.4497 +2024-12-30 17:11:30,098 - pyskl - INFO - Epoch [120][1700/3746] lr: 9.888e-03, eta: 1 day, 3:08:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6481, loss_cls: 3.4203, loss: 3.4203 +2024-12-30 17:12:55,346 - pyskl - INFO - Epoch [120][1800/3746] lr: 9.871e-03, eta: 1 day, 3:06:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6494, loss_cls: 3.4524, loss: 3.4524 +2024-12-30 17:14:20,817 - pyskl - INFO - Epoch [120][1900/3746] lr: 9.855e-03, eta: 1 day, 3:05:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6502, loss_cls: 3.4702, loss: 3.4702 +2024-12-30 17:15:46,183 - pyskl - INFO - Epoch [120][2000/3746] lr: 9.838e-03, eta: 1 day, 3:03:50, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6584, loss_cls: 3.3735, loss: 3.3735 +2024-12-30 17:17:11,840 - pyskl - INFO - Epoch [120][2100/3746] lr: 9.821e-03, eta: 1 day, 3:02:25, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6491, loss_cls: 3.3912, loss: 3.3912 +2024-12-30 17:18:37,301 - pyskl - INFO - Epoch [120][2200/3746] lr: 9.805e-03, eta: 1 day, 3:01:00, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6514, loss_cls: 3.4314, loss: 3.4314 +2024-12-30 17:20:02,812 - pyskl - INFO - Epoch [120][2300/3746] lr: 9.788e-03, eta: 1 day, 2:59:34, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6506, loss_cls: 3.4408, loss: 3.4408 +2024-12-30 17:21:27,544 - pyskl - INFO - Epoch [120][2400/3746] lr: 9.772e-03, eta: 1 day, 2:58:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3927, top5_acc: 0.6525, loss_cls: 3.3878, loss: 3.3878 +2024-12-30 17:22:52,658 - pyskl - INFO - Epoch [120][2500/3746] lr: 9.755e-03, eta: 1 day, 2:56:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6547, loss_cls: 3.4405, loss: 3.4405 +2024-12-30 17:24:18,103 - pyskl - INFO - Epoch [120][2600/3746] lr: 9.738e-03, eta: 1 day, 2:55:18, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6556, loss_cls: 3.4053, loss: 3.4053 +2024-12-30 17:25:43,217 - pyskl - INFO - Epoch [120][2700/3746] lr: 9.722e-03, eta: 1 day, 2:53:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6544, loss_cls: 3.4082, loss: 3.4082 +2024-12-30 17:27:08,740 - pyskl - INFO - Epoch [120][2800/3746] lr: 9.705e-03, eta: 1 day, 2:52:27, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6481, loss_cls: 3.4295, loss: 3.4295 +2024-12-30 17:28:33,916 - pyskl - INFO - Epoch [120][2900/3746] lr: 9.689e-03, eta: 1 day, 2:51:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6434, loss_cls: 3.5074, loss: 3.5074 +2024-12-30 17:29:58,913 - pyskl - INFO - Epoch [120][3000/3746] lr: 9.672e-03, eta: 1 day, 2:49:36, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6552, loss_cls: 3.4186, loss: 3.4186 +2024-12-30 17:31:24,128 - pyskl - INFO - Epoch [120][3100/3746] lr: 9.656e-03, eta: 1 day, 2:48:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6527, loss_cls: 3.4297, loss: 3.4297 +2024-12-30 17:32:49,159 - pyskl - INFO - Epoch [120][3200/3746] lr: 9.639e-03, eta: 1 day, 2:46:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6462, loss_cls: 3.4381, loss: 3.4381 +2024-12-30 17:34:14,609 - pyskl - INFO - Epoch [120][3300/3746] lr: 9.623e-03, eta: 1 day, 2:45:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4005, top5_acc: 0.6550, loss_cls: 3.4259, loss: 3.4259 +2024-12-30 17:35:39,536 - pyskl - INFO - Epoch [120][3400/3746] lr: 9.606e-03, eta: 1 day, 2:43:55, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6467, loss_cls: 3.4537, loss: 3.4537 +2024-12-30 17:37:04,622 - pyskl - INFO - Epoch [120][3500/3746] lr: 9.590e-03, eta: 1 day, 2:42:29, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6591, loss_cls: 3.3952, loss: 3.3952 +2024-12-30 17:38:29,603 - pyskl - INFO - Epoch [120][3600/3746] lr: 9.573e-03, eta: 1 day, 2:41:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4059, top5_acc: 0.6680, loss_cls: 3.3530, loss: 3.3530 +2024-12-30 17:39:54,932 - pyskl - INFO - Epoch [120][3700/3746] lr: 9.557e-03, eta: 1 day, 2:39:38, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6478, loss_cls: 3.4490, loss: 3.4490 +2024-12-30 17:40:36,505 - pyskl - INFO - Saving checkpoint at 120 epochs +2024-12-30 17:42:36,486 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 17:42:37,251 - pyskl - INFO - +top1_acc 0.3294 +top5_acc 0.5871 +2024-12-30 17:42:37,251 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 17:42:37,308 - pyskl - INFO - +mean_acc 0.3292 +2024-12-30 17:42:37,323 - pyskl - INFO - Epoch(val) [120][309] top1_acc: 0.3294, top5_acc: 0.5871, mean_class_accuracy: 0.3292 +2024-12-30 17:46:55,060 - pyskl - INFO - Epoch [121][100/3746] lr: 9.533e-03, eta: 1 day, 2:38:07, time: 2.577, data_time: 1.550, memory: 15990, top1_acc: 0.4136, top5_acc: 0.6733, loss_cls: 3.2817, loss: 3.2817 +2024-12-30 17:48:19,543 - pyskl - INFO - Epoch [121][200/3746] lr: 9.516e-03, eta: 1 day, 2:36:41, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4005, top5_acc: 0.6642, loss_cls: 3.3605, loss: 3.3605 +2024-12-30 17:49:44,667 - pyskl - INFO - Epoch [121][300/3746] lr: 9.500e-03, eta: 1 day, 2:35:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6592, loss_cls: 3.3608, loss: 3.3608 +2024-12-30 17:51:09,542 - pyskl - INFO - Epoch [121][400/3746] lr: 9.484e-03, eta: 1 day, 2:33:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6747, loss_cls: 3.3071, loss: 3.3071 +2024-12-30 17:52:34,703 - pyskl - INFO - Epoch [121][500/3746] lr: 9.467e-03, eta: 1 day, 2:32:25, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4044, top5_acc: 0.6648, loss_cls: 3.3552, loss: 3.3552 +2024-12-30 17:53:59,760 - pyskl - INFO - Epoch [121][600/3746] lr: 9.451e-03, eta: 1 day, 2:30:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6627, loss_cls: 3.3576, loss: 3.3576 +2024-12-30 17:55:25,516 - pyskl - INFO - Epoch [121][700/3746] lr: 9.435e-03, eta: 1 day, 2:29:34, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6664, loss_cls: 3.3211, loss: 3.3211 +2024-12-30 17:56:50,406 - pyskl - INFO - Epoch [121][800/3746] lr: 9.418e-03, eta: 1 day, 2:28:09, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6566, loss_cls: 3.3998, loss: 3.3998 +2024-12-30 17:58:15,448 - pyskl - INFO - Epoch [121][900/3746] lr: 9.402e-03, eta: 1 day, 2:26:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3941, top5_acc: 0.6562, loss_cls: 3.4151, loss: 3.4151 +2024-12-30 17:59:40,177 - pyskl - INFO - Epoch [121][1000/3746] lr: 9.386e-03, eta: 1 day, 2:25:18, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4136, top5_acc: 0.6709, loss_cls: 3.3133, loss: 3.3133 +2024-12-30 18:01:05,347 - pyskl - INFO - Epoch [121][1100/3746] lr: 9.369e-03, eta: 1 day, 2:23:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6658, loss_cls: 3.3365, loss: 3.3365 +2024-12-30 18:02:30,581 - pyskl - INFO - Epoch [121][1200/3746] lr: 9.353e-03, eta: 1 day, 2:22:27, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4042, top5_acc: 0.6705, loss_cls: 3.3460, loss: 3.3460 +2024-12-30 18:03:56,443 - pyskl - INFO - Epoch [121][1300/3746] lr: 9.337e-03, eta: 1 day, 2:21:01, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6536, loss_cls: 3.4106, loss: 3.4106 +2024-12-30 18:05:22,239 - pyskl - INFO - Epoch [121][1400/3746] lr: 9.321e-03, eta: 1 day, 2:19:36, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4044, top5_acc: 0.6614, loss_cls: 3.3749, loss: 3.3749 +2024-12-30 18:06:47,805 - pyskl - INFO - Epoch [121][1500/3746] lr: 9.304e-03, eta: 1 day, 2:18:11, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6545, loss_cls: 3.3878, loss: 3.3878 +2024-12-30 18:08:13,182 - pyskl - INFO - Epoch [121][1600/3746] lr: 9.288e-03, eta: 1 day, 2:16:45, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6617, loss_cls: 3.3757, loss: 3.3757 +2024-12-30 18:09:38,727 - pyskl - INFO - Epoch [121][1700/3746] lr: 9.272e-03, eta: 1 day, 2:15:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6569, loss_cls: 3.3606, loss: 3.3606 +2024-12-30 18:11:04,723 - pyskl - INFO - Epoch [121][1800/3746] lr: 9.256e-03, eta: 1 day, 2:13:55, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6486, loss_cls: 3.4146, loss: 3.4146 +2024-12-30 18:12:30,643 - pyskl - INFO - Epoch [121][1900/3746] lr: 9.239e-03, eta: 1 day, 2:12:29, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3903, top5_acc: 0.6527, loss_cls: 3.4199, loss: 3.4199 +2024-12-30 18:13:56,123 - pyskl - INFO - Epoch [121][2000/3746] lr: 9.223e-03, eta: 1 day, 2:11:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6523, loss_cls: 3.3848, loss: 3.3848 +2024-12-30 18:15:21,366 - pyskl - INFO - Epoch [121][2100/3746] lr: 9.207e-03, eta: 1 day, 2:09:39, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6580, loss_cls: 3.4263, loss: 3.4263 +2024-12-30 18:16:46,173 - pyskl - INFO - Epoch [121][2200/3746] lr: 9.191e-03, eta: 1 day, 2:08:13, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.3947, top5_acc: 0.6506, loss_cls: 3.4327, loss: 3.4327 +2024-12-30 18:18:11,627 - pyskl - INFO - Epoch [121][2300/3746] lr: 9.175e-03, eta: 1 day, 2:06:48, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6495, loss_cls: 3.4285, loss: 3.4285 +2024-12-30 18:19:36,788 - pyskl - INFO - Epoch [121][2400/3746] lr: 9.159e-03, eta: 1 day, 2:05:22, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6680, loss_cls: 3.3659, loss: 3.3659 +2024-12-30 18:21:01,958 - pyskl - INFO - Epoch [121][2500/3746] lr: 9.142e-03, eta: 1 day, 2:03:57, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6592, loss_cls: 3.4382, loss: 3.4382 +2024-12-30 18:22:26,806 - pyskl - INFO - Epoch [121][2600/3746] lr: 9.126e-03, eta: 1 day, 2:02:31, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6509, loss_cls: 3.4054, loss: 3.4054 +2024-12-30 18:23:51,929 - pyskl - INFO - Epoch [121][2700/3746] lr: 9.110e-03, eta: 1 day, 2:01:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6503, loss_cls: 3.4221, loss: 3.4221 +2024-12-30 18:25:16,286 - pyskl - INFO - Epoch [121][2800/3746] lr: 9.094e-03, eta: 1 day, 1:59:40, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6531, loss_cls: 3.4076, loss: 3.4076 +2024-12-30 18:26:40,886 - pyskl - INFO - Epoch [121][2900/3746] lr: 9.078e-03, eta: 1 day, 1:58:15, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6567, loss_cls: 3.3932, loss: 3.3932 +2024-12-30 18:28:05,693 - pyskl - INFO - Epoch [121][3000/3746] lr: 9.062e-03, eta: 1 day, 1:56:49, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6489, loss_cls: 3.4157, loss: 3.4157 +2024-12-30 18:29:30,250 - pyskl - INFO - Epoch [121][3100/3746] lr: 9.046e-03, eta: 1 day, 1:55:23, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3966, top5_acc: 0.6614, loss_cls: 3.3568, loss: 3.3568 +2024-12-30 18:30:54,927 - pyskl - INFO - Epoch [121][3200/3746] lr: 9.030e-03, eta: 1 day, 1:53:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3986, top5_acc: 0.6569, loss_cls: 3.4029, loss: 3.4029 +2024-12-30 18:32:19,205 - pyskl - INFO - Epoch [121][3300/3746] lr: 9.014e-03, eta: 1 day, 1:52:32, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4095, top5_acc: 0.6558, loss_cls: 3.3849, loss: 3.3849 +2024-12-30 18:33:43,954 - pyskl - INFO - Epoch [121][3400/3746] lr: 8.998e-03, eta: 1 day, 1:51:07, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6581, loss_cls: 3.3851, loss: 3.3851 +2024-12-30 18:35:09,446 - pyskl - INFO - Epoch [121][3500/3746] lr: 8.982e-03, eta: 1 day, 1:49:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6519, loss_cls: 3.4361, loss: 3.4361 +2024-12-30 18:36:35,092 - pyskl - INFO - Epoch [121][3600/3746] lr: 8.966e-03, eta: 1 day, 1:48:16, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6522, loss_cls: 3.3938, loss: 3.3938 +2024-12-30 18:38:00,303 - pyskl - INFO - Epoch [121][3700/3746] lr: 8.950e-03, eta: 1 day, 1:46:50, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3994, top5_acc: 0.6559, loss_cls: 3.4258, loss: 3.4258 +2024-12-30 18:38:41,554 - pyskl - INFO - Saving checkpoint at 121 epochs +2024-12-30 18:40:42,013 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 18:40:42,705 - pyskl - INFO - +top1_acc 0.3352 +top5_acc 0.5897 +2024-12-30 18:40:42,705 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 18:40:42,747 - pyskl - INFO - +mean_acc 0.3350 +2024-12-30 18:40:42,753 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_119.pth was removed +2024-12-30 18:40:43,013 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_121.pth. +2024-12-30 18:40:43,014 - pyskl - INFO - Best top1_acc is 0.3352 at 121 epoch. +2024-12-30 18:40:43,027 - pyskl - INFO - Epoch(val) [121][309] top1_acc: 0.3352, top5_acc: 0.5897, mean_class_accuracy: 0.3350 +2024-12-30 18:45:03,764 - pyskl - INFO - Epoch [122][100/3746] lr: 8.927e-03, eta: 1 day, 1:45:18, time: 2.607, data_time: 1.584, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6775, loss_cls: 3.2965, loss: 3.2965 +2024-12-30 18:46:28,578 - pyskl - INFO - Epoch [122][200/3746] lr: 8.911e-03, eta: 1 day, 1:43:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4073, top5_acc: 0.6689, loss_cls: 3.3202, loss: 3.3202 +2024-12-30 18:47:53,356 - pyskl - INFO - Epoch [122][300/3746] lr: 8.895e-03, eta: 1 day, 1:42:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6663, loss_cls: 3.3266, loss: 3.3266 +2024-12-30 18:49:18,994 - pyskl - INFO - Epoch [122][400/3746] lr: 8.879e-03, eta: 1 day, 1:41:02, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6681, loss_cls: 3.3341, loss: 3.3341 +2024-12-30 18:50:44,677 - pyskl - INFO - Epoch [122][500/3746] lr: 8.863e-03, eta: 1 day, 1:39:36, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6730, loss_cls: 3.2862, loss: 3.2862 +2024-12-30 18:52:09,332 - pyskl - INFO - Epoch [122][600/3746] lr: 8.847e-03, eta: 1 day, 1:38:11, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4078, top5_acc: 0.6605, loss_cls: 3.3630, loss: 3.3630 +2024-12-30 18:53:34,371 - pyskl - INFO - Epoch [122][700/3746] lr: 8.831e-03, eta: 1 day, 1:36:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6661, loss_cls: 3.3313, loss: 3.3313 +2024-12-30 18:54:59,731 - pyskl - INFO - Epoch [122][800/3746] lr: 8.815e-03, eta: 1 day, 1:35:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6592, loss_cls: 3.3544, loss: 3.3544 +2024-12-30 18:56:24,658 - pyskl - INFO - Epoch [122][900/3746] lr: 8.800e-03, eta: 1 day, 1:33:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6695, loss_cls: 3.3179, loss: 3.3179 +2024-12-30 18:57:50,027 - pyskl - INFO - Epoch [122][1000/3746] lr: 8.784e-03, eta: 1 day, 1:32:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6623, loss_cls: 3.3613, loss: 3.3613 +2024-12-30 18:59:15,305 - pyskl - INFO - Epoch [122][1100/3746] lr: 8.768e-03, eta: 1 day, 1:31:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3980, top5_acc: 0.6605, loss_cls: 3.3854, loss: 3.3854 +2024-12-30 19:00:40,445 - pyskl - INFO - Epoch [122][1200/3746] lr: 8.752e-03, eta: 1 day, 1:29:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4156, top5_acc: 0.6734, loss_cls: 3.2874, loss: 3.2874 +2024-12-30 19:02:05,857 - pyskl - INFO - Epoch [122][1300/3746] lr: 8.736e-03, eta: 1 day, 1:28:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3994, top5_acc: 0.6603, loss_cls: 3.3627, loss: 3.3627 +2024-12-30 19:03:31,310 - pyskl - INFO - Epoch [122][1400/3746] lr: 8.721e-03, eta: 1 day, 1:26:47, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6617, loss_cls: 3.3750, loss: 3.3750 +2024-12-30 19:04:56,312 - pyskl - INFO - Epoch [122][1500/3746] lr: 8.705e-03, eta: 1 day, 1:25:22, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6777, loss_cls: 3.2667, loss: 3.2667 +2024-12-30 19:06:21,560 - pyskl - INFO - Epoch [122][1600/3746] lr: 8.689e-03, eta: 1 day, 1:23:56, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4088, top5_acc: 0.6539, loss_cls: 3.3866, loss: 3.3866 +2024-12-30 19:07:47,155 - pyskl - INFO - Epoch [122][1700/3746] lr: 8.673e-03, eta: 1 day, 1:22:31, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4006, top5_acc: 0.6523, loss_cls: 3.4019, loss: 3.4019 +2024-12-30 19:09:12,825 - pyskl - INFO - Epoch [122][1800/3746] lr: 8.658e-03, eta: 1 day, 1:21:05, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6628, loss_cls: 3.3646, loss: 3.3646 +2024-12-30 19:10:37,845 - pyskl - INFO - Epoch [122][1900/3746] lr: 8.642e-03, eta: 1 day, 1:19:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6556, loss_cls: 3.3811, loss: 3.3811 +2024-12-30 19:12:03,541 - pyskl - INFO - Epoch [122][2000/3746] lr: 8.626e-03, eta: 1 day, 1:18:14, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6600, loss_cls: 3.3785, loss: 3.3785 +2024-12-30 19:13:28,949 - pyskl - INFO - Epoch [122][2100/3746] lr: 8.610e-03, eta: 1 day, 1:16:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3970, top5_acc: 0.6511, loss_cls: 3.4126, loss: 3.4126 +2024-12-30 19:14:54,098 - pyskl - INFO - Epoch [122][2200/3746] lr: 8.595e-03, eta: 1 day, 1:15:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6634, loss_cls: 3.3524, loss: 3.3524 +2024-12-30 19:16:19,744 - pyskl - INFO - Epoch [122][2300/3746] lr: 8.579e-03, eta: 1 day, 1:13:58, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6594, loss_cls: 3.3668, loss: 3.3668 +2024-12-30 19:17:44,293 - pyskl - INFO - Epoch [122][2400/3746] lr: 8.563e-03, eta: 1 day, 1:12:32, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4066, top5_acc: 0.6631, loss_cls: 3.3848, loss: 3.3848 +2024-12-30 19:19:08,882 - pyskl - INFO - Epoch [122][2500/3746] lr: 8.548e-03, eta: 1 day, 1:11:07, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3997, top5_acc: 0.6475, loss_cls: 3.4078, loss: 3.4078 +2024-12-30 19:20:33,836 - pyskl - INFO - Epoch [122][2600/3746] lr: 8.532e-03, eta: 1 day, 1:09:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4073, top5_acc: 0.6611, loss_cls: 3.3559, loss: 3.3559 +2024-12-30 19:21:58,374 - pyskl - INFO - Epoch [122][2700/3746] lr: 8.517e-03, eta: 1 day, 1:08:16, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6522, loss_cls: 3.3945, loss: 3.3945 +2024-12-30 19:23:22,689 - pyskl - INFO - Epoch [122][2800/3746] lr: 8.501e-03, eta: 1 day, 1:06:50, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4078, top5_acc: 0.6653, loss_cls: 3.3402, loss: 3.3402 +2024-12-30 19:24:47,420 - pyskl - INFO - Epoch [122][2900/3746] lr: 8.485e-03, eta: 1 day, 1:05:24, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6573, loss_cls: 3.3710, loss: 3.3710 +2024-12-30 19:26:12,428 - pyskl - INFO - Epoch [122][3000/3746] lr: 8.470e-03, eta: 1 day, 1:03:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6566, loss_cls: 3.3939, loss: 3.3939 +2024-12-30 19:27:37,271 - pyskl - INFO - Epoch [122][3100/3746] lr: 8.454e-03, eta: 1 day, 1:02:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6533, loss_cls: 3.4215, loss: 3.4215 +2024-12-30 19:29:02,903 - pyskl - INFO - Epoch [122][3200/3746] lr: 8.439e-03, eta: 1 day, 1:01:08, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4062, top5_acc: 0.6558, loss_cls: 3.3746, loss: 3.3746 +2024-12-30 19:30:28,887 - pyskl - INFO - Epoch [122][3300/3746] lr: 8.423e-03, eta: 1 day, 0:59:43, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6600, loss_cls: 3.3387, loss: 3.3387 +2024-12-30 19:31:55,127 - pyskl - INFO - Epoch [122][3400/3746] lr: 8.408e-03, eta: 1 day, 0:58:17, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6670, loss_cls: 3.3502, loss: 3.3502 +2024-12-30 19:33:21,348 - pyskl - INFO - Epoch [122][3500/3746] lr: 8.392e-03, eta: 1 day, 0:56:52, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4059, top5_acc: 0.6614, loss_cls: 3.3532, loss: 3.3532 +2024-12-30 19:34:46,451 - pyskl - INFO - Epoch [122][3600/3746] lr: 8.377e-03, eta: 1 day, 0:55:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6598, loss_cls: 3.3679, loss: 3.3679 +2024-12-30 19:36:11,704 - pyskl - INFO - Epoch [122][3700/3746] lr: 8.361e-03, eta: 1 day, 0:54:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6600, loss_cls: 3.3577, loss: 3.3577 +2024-12-30 19:36:52,951 - pyskl - INFO - Saving checkpoint at 122 epochs +2024-12-30 19:38:52,422 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 19:38:53,184 - pyskl - INFO - +top1_acc 0.3304 +top5_acc 0.5832 +2024-12-30 19:38:53,184 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 19:38:53,239 - pyskl - INFO - +mean_acc 0.3301 +2024-12-30 19:38:53,256 - pyskl - INFO - Epoch(val) [122][309] top1_acc: 0.3304, top5_acc: 0.5832, mean_class_accuracy: 0.3301 +2024-12-30 19:43:14,972 - pyskl - INFO - Epoch [123][100/3746] lr: 8.339e-03, eta: 1 day, 0:52:28, time: 2.617, data_time: 1.567, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6725, loss_cls: 3.2937, loss: 3.2937 +2024-12-30 19:44:40,654 - pyskl - INFO - Epoch [123][200/3746] lr: 8.323e-03, eta: 1 day, 0:51:02, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6777, loss_cls: 3.2767, loss: 3.2767 +2024-12-30 19:46:06,131 - pyskl - INFO - Epoch [123][300/3746] lr: 8.308e-03, eta: 1 day, 0:49:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6667, loss_cls: 3.3465, loss: 3.3465 +2024-12-30 19:47:31,239 - pyskl - INFO - Epoch [123][400/3746] lr: 8.292e-03, eta: 1 day, 0:48:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4088, top5_acc: 0.6658, loss_cls: 3.3312, loss: 3.3312 +2024-12-30 19:48:56,115 - pyskl - INFO - Epoch [123][500/3746] lr: 8.277e-03, eta: 1 day, 0:46:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4078, top5_acc: 0.6713, loss_cls: 3.3134, loss: 3.3134 +2024-12-30 19:50:21,501 - pyskl - INFO - Epoch [123][600/3746] lr: 8.262e-03, eta: 1 day, 0:45:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6670, loss_cls: 3.3008, loss: 3.3008 +2024-12-30 19:51:46,582 - pyskl - INFO - Epoch [123][700/3746] lr: 8.246e-03, eta: 1 day, 0:43:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4119, top5_acc: 0.6695, loss_cls: 3.3048, loss: 3.3048 +2024-12-30 19:53:11,275 - pyskl - INFO - Epoch [123][800/3746] lr: 8.231e-03, eta: 1 day, 0:42:29, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4142, top5_acc: 0.6711, loss_cls: 3.3048, loss: 3.3048 +2024-12-30 19:54:36,153 - pyskl - INFO - Epoch [123][900/3746] lr: 8.215e-03, eta: 1 day, 0:41:04, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6727, loss_cls: 3.3153, loss: 3.3153 +2024-12-30 19:56:01,107 - pyskl - INFO - Epoch [123][1000/3746] lr: 8.200e-03, eta: 1 day, 0:39:38, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4016, top5_acc: 0.6583, loss_cls: 3.3568, loss: 3.3568 +2024-12-30 19:57:26,257 - pyskl - INFO - Epoch [123][1100/3746] lr: 8.185e-03, eta: 1 day, 0:38:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6769, loss_cls: 3.3042, loss: 3.3042 +2024-12-30 19:58:51,941 - pyskl - INFO - Epoch [123][1200/3746] lr: 8.169e-03, eta: 1 day, 0:36:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6728, loss_cls: 3.2861, loss: 3.2861 +2024-12-30 20:00:17,239 - pyskl - INFO - Epoch [123][1300/3746] lr: 8.154e-03, eta: 1 day, 0:35:22, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4153, top5_acc: 0.6669, loss_cls: 3.3311, loss: 3.3311 +2024-12-30 20:01:42,443 - pyskl - INFO - Epoch [123][1400/3746] lr: 8.139e-03, eta: 1 day, 0:33:56, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4091, top5_acc: 0.6714, loss_cls: 3.3253, loss: 3.3253 +2024-12-30 20:03:07,580 - pyskl - INFO - Epoch [123][1500/3746] lr: 8.124e-03, eta: 1 day, 0:32:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6641, loss_cls: 3.3184, loss: 3.3184 +2024-12-30 20:04:33,015 - pyskl - INFO - Epoch [123][1600/3746] lr: 8.108e-03, eta: 1 day, 0:31:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6611, loss_cls: 3.3250, loss: 3.3250 +2024-12-30 20:05:58,095 - pyskl - INFO - Epoch [123][1700/3746] lr: 8.093e-03, eta: 1 day, 0:29:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4106, top5_acc: 0.6619, loss_cls: 3.3511, loss: 3.3511 +2024-12-30 20:07:23,719 - pyskl - INFO - Epoch [123][1800/3746] lr: 8.078e-03, eta: 1 day, 0:28:14, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4062, top5_acc: 0.6637, loss_cls: 3.3156, loss: 3.3156 +2024-12-30 20:08:48,942 - pyskl - INFO - Epoch [123][1900/3746] lr: 8.063e-03, eta: 1 day, 0:26:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6670, loss_cls: 3.3251, loss: 3.3251 +2024-12-30 20:10:14,333 - pyskl - INFO - Epoch [123][2000/3746] lr: 8.047e-03, eta: 1 day, 0:25:23, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6512, loss_cls: 3.3926, loss: 3.3926 +2024-12-30 20:11:39,483 - pyskl - INFO - Epoch [123][2100/3746] lr: 8.032e-03, eta: 1 day, 0:23:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6575, loss_cls: 3.3815, loss: 3.3815 +2024-12-30 20:13:04,405 - pyskl - INFO - Epoch [123][2200/3746] lr: 8.017e-03, eta: 1 day, 0:22:32, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6689, loss_cls: 3.2880, loss: 3.2880 +2024-12-30 20:14:29,185 - pyskl - INFO - Epoch [123][2300/3746] lr: 8.002e-03, eta: 1 day, 0:21:06, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4142, top5_acc: 0.6673, loss_cls: 3.3177, loss: 3.3177 +2024-12-30 20:15:54,440 - pyskl - INFO - Epoch [123][2400/3746] lr: 7.987e-03, eta: 1 day, 0:19:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6602, loss_cls: 3.3583, loss: 3.3583 +2024-12-30 20:17:20,657 - pyskl - INFO - Epoch [123][2500/3746] lr: 7.971e-03, eta: 1 day, 0:18:16, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6655, loss_cls: 3.3512, loss: 3.3512 +2024-12-30 20:18:46,362 - pyskl - INFO - Epoch [123][2600/3746] lr: 7.956e-03, eta: 1 day, 0:16:50, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6602, loss_cls: 3.4157, loss: 3.4157 +2024-12-30 20:20:12,320 - pyskl - INFO - Epoch [123][2700/3746] lr: 7.941e-03, eta: 1 day, 0:15:25, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4142, top5_acc: 0.6711, loss_cls: 3.3039, loss: 3.3039 +2024-12-30 20:21:38,360 - pyskl - INFO - Epoch [123][2800/3746] lr: 7.926e-03, eta: 1 day, 0:14:00, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6655, loss_cls: 3.3592, loss: 3.3592 +2024-12-30 20:23:04,204 - pyskl - INFO - Epoch [123][2900/3746] lr: 7.911e-03, eta: 1 day, 0:12:34, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6548, loss_cls: 3.3814, loss: 3.3814 +2024-12-30 20:24:30,253 - pyskl - INFO - Epoch [123][3000/3746] lr: 7.896e-03, eta: 1 day, 0:11:09, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6680, loss_cls: 3.3339, loss: 3.3339 +2024-12-30 20:25:56,072 - pyskl - INFO - Epoch [123][3100/3746] lr: 7.881e-03, eta: 1 day, 0:09:44, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4025, top5_acc: 0.6670, loss_cls: 3.3472, loss: 3.3472 +2024-12-30 20:27:21,764 - pyskl - INFO - Epoch [123][3200/3746] lr: 7.866e-03, eta: 1 day, 0:08:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4072, top5_acc: 0.6663, loss_cls: 3.3208, loss: 3.3208 +2024-12-30 20:28:47,533 - pyskl - INFO - Epoch [123][3300/3746] lr: 7.851e-03, eta: 1 day, 0:06:53, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6694, loss_cls: 3.3473, loss: 3.3473 +2024-12-30 20:30:13,564 - pyskl - INFO - Epoch [123][3400/3746] lr: 7.836e-03, eta: 1 day, 0:05:27, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4044, top5_acc: 0.6663, loss_cls: 3.3629, loss: 3.3629 +2024-12-30 20:31:38,872 - pyskl - INFO - Epoch [123][3500/3746] lr: 7.821e-03, eta: 1 day, 0:04:02, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6714, loss_cls: 3.2874, loss: 3.2874 +2024-12-30 20:33:03,913 - pyskl - INFO - Epoch [123][3600/3746] lr: 7.806e-03, eta: 1 day, 0:02:36, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4066, top5_acc: 0.6578, loss_cls: 3.3378, loss: 3.3378 +2024-12-30 20:34:29,143 - pyskl - INFO - Epoch [123][3700/3746] lr: 7.791e-03, eta: 1 day, 0:01:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6672, loss_cls: 3.3799, loss: 3.3799 +2024-12-30 20:35:10,524 - pyskl - INFO - Saving checkpoint at 123 epochs +2024-12-30 20:37:12,252 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 20:37:12,942 - pyskl - INFO - +top1_acc 0.3415 +top5_acc 0.5951 +2024-12-30 20:37:12,942 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 20:37:13,005 - pyskl - INFO - +mean_acc 0.3413 +2024-12-30 20:37:13,011 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_121.pth was removed +2024-12-30 20:37:13,401 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_123.pth. +2024-12-30 20:37:13,402 - pyskl - INFO - Best top1_acc is 0.3415 at 123 epoch. +2024-12-30 20:37:13,414 - pyskl - INFO - Epoch(val) [123][309] top1_acc: 0.3415, top5_acc: 0.5951, mean_class_accuracy: 0.3413 +2024-12-30 20:41:39,482 - pyskl - INFO - Epoch [124][100/3746] lr: 7.769e-03, eta: 23:59:37, time: 2.661, data_time: 1.601, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6813, loss_cls: 3.2780, loss: 3.2780 +2024-12-30 20:43:04,896 - pyskl - INFO - Epoch [124][200/3746] lr: 7.754e-03, eta: 23:58:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6830, loss_cls: 3.2502, loss: 3.2502 +2024-12-30 20:44:30,219 - pyskl - INFO - Epoch [124][300/3746] lr: 7.739e-03, eta: 23:56:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4114, top5_acc: 0.6794, loss_cls: 3.2691, loss: 3.2691 +2024-12-30 20:45:55,447 - pyskl - INFO - Epoch [124][400/3746] lr: 7.724e-03, eta: 23:55:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4116, top5_acc: 0.6741, loss_cls: 3.2884, loss: 3.2884 +2024-12-30 20:47:21,017 - pyskl - INFO - Epoch [124][500/3746] lr: 7.709e-03, eta: 23:53:55, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6855, loss_cls: 3.2228, loss: 3.2228 +2024-12-30 20:48:46,538 - pyskl - INFO - Epoch [124][600/3746] lr: 7.694e-03, eta: 23:52:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4145, top5_acc: 0.6614, loss_cls: 3.2987, loss: 3.2987 +2024-12-30 20:50:12,200 - pyskl - INFO - Epoch [124][700/3746] lr: 7.679e-03, eta: 23:51:04, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4102, top5_acc: 0.6758, loss_cls: 3.3037, loss: 3.3037 +2024-12-30 20:51:37,686 - pyskl - INFO - Epoch [124][800/3746] lr: 7.664e-03, eta: 23:49:39, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4220, top5_acc: 0.6702, loss_cls: 3.2927, loss: 3.2927 +2024-12-30 20:53:03,031 - pyskl - INFO - Epoch [124][900/3746] lr: 7.649e-03, eta: 23:48:13, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6695, loss_cls: 3.2859, loss: 3.2859 +2024-12-30 20:54:28,486 - pyskl - INFO - Epoch [124][1000/3746] lr: 7.635e-03, eta: 23:46:48, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4139, top5_acc: 0.6675, loss_cls: 3.3195, loss: 3.3195 +2024-12-30 20:55:53,914 - pyskl - INFO - Epoch [124][1100/3746] lr: 7.620e-03, eta: 23:45:22, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4264, top5_acc: 0.6811, loss_cls: 3.2579, loss: 3.2579 +2024-12-30 20:57:18,809 - pyskl - INFO - Epoch [124][1200/3746] lr: 7.605e-03, eta: 23:43:57, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6744, loss_cls: 3.2713, loss: 3.2713 +2024-12-30 20:58:44,197 - pyskl - INFO - Epoch [124][1300/3746] lr: 7.590e-03, eta: 23:42:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4166, top5_acc: 0.6772, loss_cls: 3.2850, loss: 3.2850 +2024-12-30 21:00:09,636 - pyskl - INFO - Epoch [124][1400/3746] lr: 7.575e-03, eta: 23:41:06, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6709, loss_cls: 3.3122, loss: 3.3122 +2024-12-30 21:01:35,010 - pyskl - INFO - Epoch [124][1500/3746] lr: 7.561e-03, eta: 23:39:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6786, loss_cls: 3.2533, loss: 3.2533 +2024-12-30 21:03:00,081 - pyskl - INFO - Epoch [124][1600/3746] lr: 7.546e-03, eta: 23:38:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6627, loss_cls: 3.3246, loss: 3.3246 +2024-12-30 21:04:25,478 - pyskl - INFO - Epoch [124][1700/3746] lr: 7.531e-03, eta: 23:36:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6645, loss_cls: 3.3347, loss: 3.3347 +2024-12-30 21:05:50,836 - pyskl - INFO - Epoch [124][1800/3746] lr: 7.516e-03, eta: 23:35:23, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6703, loss_cls: 3.2793, loss: 3.2793 +2024-12-30 21:07:16,009 - pyskl - INFO - Epoch [124][1900/3746] lr: 7.502e-03, eta: 23:33:58, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6659, loss_cls: 3.3355, loss: 3.3355 +2024-12-30 21:08:41,294 - pyskl - INFO - Epoch [124][2000/3746] lr: 7.487e-03, eta: 23:32:32, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6694, loss_cls: 3.3090, loss: 3.3090 +2024-12-30 21:10:06,858 - pyskl - INFO - Epoch [124][2100/3746] lr: 7.472e-03, eta: 23:31:07, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6744, loss_cls: 3.2465, loss: 3.2465 +2024-12-30 21:11:32,181 - pyskl - INFO - Epoch [124][2200/3746] lr: 7.457e-03, eta: 23:29:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4114, top5_acc: 0.6631, loss_cls: 3.3215, loss: 3.3215 +2024-12-30 21:12:57,380 - pyskl - INFO - Epoch [124][2300/3746] lr: 7.443e-03, eta: 23:28:16, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6641, loss_cls: 3.3494, loss: 3.3494 +2024-12-30 21:14:22,746 - pyskl - INFO - Epoch [124][2400/3746] lr: 7.428e-03, eta: 23:26:50, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6773, loss_cls: 3.2799, loss: 3.2799 +2024-12-30 21:15:48,323 - pyskl - INFO - Epoch [124][2500/3746] lr: 7.413e-03, eta: 23:25:25, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6723, loss_cls: 3.3020, loss: 3.3020 +2024-12-30 21:17:13,607 - pyskl - INFO - Epoch [124][2600/3746] lr: 7.399e-03, eta: 23:23:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6659, loss_cls: 3.3004, loss: 3.3004 +2024-12-30 21:18:39,032 - pyskl - INFO - Epoch [124][2700/3746] lr: 7.384e-03, eta: 23:22:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6562, loss_cls: 3.4015, loss: 3.4015 +2024-12-30 21:20:04,399 - pyskl - INFO - Epoch [124][2800/3746] lr: 7.370e-03, eta: 23:21:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6700, loss_cls: 3.3026, loss: 3.3026 +2024-12-30 21:21:29,957 - pyskl - INFO - Epoch [124][2900/3746] lr: 7.355e-03, eta: 23:19:43, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6731, loss_cls: 3.2914, loss: 3.2914 +2024-12-30 21:22:55,167 - pyskl - INFO - Epoch [124][3000/3746] lr: 7.340e-03, eta: 23:18:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4152, top5_acc: 0.6750, loss_cls: 3.2817, loss: 3.2817 +2024-12-30 21:24:20,368 - pyskl - INFO - Epoch [124][3100/3746] lr: 7.326e-03, eta: 23:16:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4106, top5_acc: 0.6666, loss_cls: 3.3179, loss: 3.3179 +2024-12-30 21:25:45,943 - pyskl - INFO - Epoch [124][3200/3746] lr: 7.311e-03, eta: 23:15:26, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4109, top5_acc: 0.6697, loss_cls: 3.3072, loss: 3.3072 +2024-12-30 21:27:10,774 - pyskl - INFO - Epoch [124][3300/3746] lr: 7.297e-03, eta: 23:14:01, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6692, loss_cls: 3.3282, loss: 3.3282 +2024-12-30 21:28:36,034 - pyskl - INFO - Epoch [124][3400/3746] lr: 7.282e-03, eta: 23:12:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6647, loss_cls: 3.3135, loss: 3.3135 +2024-12-30 21:30:01,139 - pyskl - INFO - Epoch [124][3500/3746] lr: 7.268e-03, eta: 23:11:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6645, loss_cls: 3.3348, loss: 3.3348 +2024-12-30 21:31:26,383 - pyskl - INFO - Epoch [124][3600/3746] lr: 7.253e-03, eta: 23:09:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4136, top5_acc: 0.6659, loss_cls: 3.3166, loss: 3.3166 +2024-12-30 21:32:51,045 - pyskl - INFO - Epoch [124][3700/3746] lr: 7.239e-03, eta: 23:08:18, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6687, loss_cls: 3.3075, loss: 3.3075 +2024-12-30 21:33:32,281 - pyskl - INFO - Saving checkpoint at 124 epochs +2024-12-30 21:35:33,087 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 21:35:33,788 - pyskl - INFO - +top1_acc 0.3546 +top5_acc 0.6071 +2024-12-30 21:35:33,788 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 21:35:33,828 - pyskl - INFO - +mean_acc 0.3544 +2024-12-30 21:35:33,834 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_123.pth was removed +2024-12-30 21:35:34,132 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_124.pth. +2024-12-30 21:35:34,133 - pyskl - INFO - Best top1_acc is 0.3546 at 124 epoch. +2024-12-30 21:35:34,152 - pyskl - INFO - Epoch(val) [124][309] top1_acc: 0.3546, top5_acc: 0.6071, mean_class_accuracy: 0.3544 +2024-12-30 21:39:53,033 - pyskl - INFO - Epoch [125][100/3746] lr: 7.217e-03, eta: 23:06:42, time: 2.589, data_time: 1.555, memory: 15990, top1_acc: 0.4309, top5_acc: 0.6903, loss_cls: 3.1863, loss: 3.1863 +2024-12-30 21:41:18,693 - pyskl - INFO - Epoch [125][200/3746] lr: 7.203e-03, eta: 23:05:16, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6909, loss_cls: 3.2141, loss: 3.2141 +2024-12-30 21:42:44,041 - pyskl - INFO - Epoch [125][300/3746] lr: 7.189e-03, eta: 23:03:51, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6798, loss_cls: 3.2461, loss: 3.2461 +2024-12-30 21:44:09,488 - pyskl - INFO - Epoch [125][400/3746] lr: 7.174e-03, eta: 23:02:25, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4283, top5_acc: 0.6816, loss_cls: 3.2402, loss: 3.2402 +2024-12-30 21:45:35,162 - pyskl - INFO - Epoch [125][500/3746] lr: 7.160e-03, eta: 23:01:00, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6737, loss_cls: 3.2609, loss: 3.2609 +2024-12-30 21:47:00,722 - pyskl - INFO - Epoch [125][600/3746] lr: 7.145e-03, eta: 22:59:34, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4173, top5_acc: 0.6681, loss_cls: 3.3162, loss: 3.3162 +2024-12-30 21:48:26,154 - pyskl - INFO - Epoch [125][700/3746] lr: 7.131e-03, eta: 22:58:09, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4241, top5_acc: 0.6823, loss_cls: 3.2329, loss: 3.2329 +2024-12-30 21:49:51,748 - pyskl - INFO - Epoch [125][800/3746] lr: 7.117e-03, eta: 22:56:43, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6737, loss_cls: 3.2760, loss: 3.2760 +2024-12-30 21:51:17,299 - pyskl - INFO - Epoch [125][900/3746] lr: 7.102e-03, eta: 22:55:18, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6786, loss_cls: 3.2646, loss: 3.2646 +2024-12-30 21:52:42,588 - pyskl - INFO - Epoch [125][1000/3746] lr: 7.088e-03, eta: 22:53:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6809, loss_cls: 3.2292, loss: 3.2292 +2024-12-30 21:54:07,664 - pyskl - INFO - Epoch [125][1100/3746] lr: 7.073e-03, eta: 22:52:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6848, loss_cls: 3.2428, loss: 3.2428 +2024-12-30 21:55:32,680 - pyskl - INFO - Epoch [125][1200/3746] lr: 7.059e-03, eta: 22:51:01, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4134, top5_acc: 0.6753, loss_cls: 3.2842, loss: 3.2842 +2024-12-30 21:56:58,063 - pyskl - INFO - Epoch [125][1300/3746] lr: 7.045e-03, eta: 22:49:35, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6747, loss_cls: 3.2495, loss: 3.2495 +2024-12-30 21:58:23,639 - pyskl - INFO - Epoch [125][1400/3746] lr: 7.031e-03, eta: 22:48:10, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4164, top5_acc: 0.6770, loss_cls: 3.2555, loss: 3.2555 +2024-12-30 21:59:48,991 - pyskl - INFO - Epoch [125][1500/3746] lr: 7.016e-03, eta: 22:46:44, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6781, loss_cls: 3.2769, loss: 3.2769 +2024-12-30 22:01:14,417 - pyskl - INFO - Epoch [125][1600/3746] lr: 7.002e-03, eta: 22:45:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6706, loss_cls: 3.3004, loss: 3.3004 +2024-12-30 22:02:39,252 - pyskl - INFO - Epoch [125][1700/3746] lr: 6.988e-03, eta: 22:43:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4256, top5_acc: 0.6805, loss_cls: 3.2387, loss: 3.2387 +2024-12-30 22:04:04,327 - pyskl - INFO - Epoch [125][1800/3746] lr: 6.973e-03, eta: 22:42:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6778, loss_cls: 3.2300, loss: 3.2300 +2024-12-30 22:05:30,000 - pyskl - INFO - Epoch [125][1900/3746] lr: 6.959e-03, eta: 22:41:02, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4142, top5_acc: 0.6730, loss_cls: 3.3001, loss: 3.3001 +2024-12-30 22:06:55,155 - pyskl - INFO - Epoch [125][2000/3746] lr: 6.945e-03, eta: 22:39:36, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6739, loss_cls: 3.2893, loss: 3.2893 +2024-12-30 22:08:20,622 - pyskl - INFO - Epoch [125][2100/3746] lr: 6.931e-03, eta: 22:38:11, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6708, loss_cls: 3.2993, loss: 3.2993 +2024-12-30 22:09:45,754 - pyskl - INFO - Epoch [125][2200/3746] lr: 6.917e-03, eta: 22:36:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4245, top5_acc: 0.6794, loss_cls: 3.2669, loss: 3.2669 +2024-12-30 22:11:11,091 - pyskl - INFO - Epoch [125][2300/3746] lr: 6.902e-03, eta: 22:35:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6866, loss_cls: 3.2292, loss: 3.2292 +2024-12-30 22:12:36,925 - pyskl - INFO - Epoch [125][2400/3746] lr: 6.888e-03, eta: 22:33:54, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4270, top5_acc: 0.6814, loss_cls: 3.2621, loss: 3.2621 +2024-12-30 22:14:02,330 - pyskl - INFO - Epoch [125][2500/3746] lr: 6.874e-03, eta: 22:32:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4245, top5_acc: 0.6822, loss_cls: 3.2467, loss: 3.2467 +2024-12-30 22:15:27,489 - pyskl - INFO - Epoch [125][2600/3746] lr: 6.860e-03, eta: 22:31:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4161, top5_acc: 0.6769, loss_cls: 3.2703, loss: 3.2703 +2024-12-30 22:16:53,059 - pyskl - INFO - Epoch [125][2700/3746] lr: 6.846e-03, eta: 22:29:38, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6717, loss_cls: 3.3117, loss: 3.3117 +2024-12-30 22:18:18,176 - pyskl - INFO - Epoch [125][2800/3746] lr: 6.832e-03, eta: 22:28:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6752, loss_cls: 3.2458, loss: 3.2458 +2024-12-30 22:19:43,439 - pyskl - INFO - Epoch [125][2900/3746] lr: 6.818e-03, eta: 22:26:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4078, top5_acc: 0.6603, loss_cls: 3.3377, loss: 3.3377 +2024-12-30 22:21:08,567 - pyskl - INFO - Epoch [125][3000/3746] lr: 6.804e-03, eta: 22:25:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6713, loss_cls: 3.2763, loss: 3.2763 +2024-12-30 22:22:33,977 - pyskl - INFO - Epoch [125][3100/3746] lr: 6.789e-03, eta: 22:23:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4158, top5_acc: 0.6772, loss_cls: 3.2829, loss: 3.2829 +2024-12-30 22:23:59,285 - pyskl - INFO - Epoch [125][3200/3746] lr: 6.775e-03, eta: 22:22:30, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6758, loss_cls: 3.2786, loss: 3.2786 +2024-12-30 22:25:24,660 - pyskl - INFO - Epoch [125][3300/3746] lr: 6.761e-03, eta: 22:21:04, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4017, top5_acc: 0.6603, loss_cls: 3.3479, loss: 3.3479 +2024-12-30 22:26:50,160 - pyskl - INFO - Epoch [125][3400/3746] lr: 6.747e-03, eta: 22:19:39, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6639, loss_cls: 3.2900, loss: 3.2900 +2024-12-30 22:28:15,218 - pyskl - INFO - Epoch [125][3500/3746] lr: 6.733e-03, eta: 22:18:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6647, loss_cls: 3.3413, loss: 3.3413 +2024-12-30 22:29:40,536 - pyskl - INFO - Epoch [125][3600/3746] lr: 6.719e-03, eta: 22:16:47, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6823, loss_cls: 3.2428, loss: 3.2428 +2024-12-30 22:31:05,628 - pyskl - INFO - Epoch [125][3700/3746] lr: 6.705e-03, eta: 22:15:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6727, loss_cls: 3.2735, loss: 3.2735 +2024-12-30 22:31:47,079 - pyskl - INFO - Saving checkpoint at 125 epochs +2024-12-30 22:33:48,181 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 22:33:48,886 - pyskl - INFO - +top1_acc 0.3531 +top5_acc 0.6044 +2024-12-30 22:33:48,886 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 22:33:48,933 - pyskl - INFO - +mean_acc 0.3529 +2024-12-30 22:33:48,950 - pyskl - INFO - Epoch(val) [125][309] top1_acc: 0.3531, top5_acc: 0.6044, mean_class_accuracy: 0.3529 +2024-12-30 22:38:10,794 - pyskl - INFO - Epoch [126][100/3746] lr: 6.685e-03, eta: 22:13:44, time: 2.618, data_time: 1.576, memory: 15990, top1_acc: 0.4447, top5_acc: 0.6956, loss_cls: 3.1408, loss: 3.1408 +2024-12-30 22:39:36,128 - pyskl - INFO - Epoch [126][200/3746] lr: 6.671e-03, eta: 22:12:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4428, top5_acc: 0.7044, loss_cls: 3.1351, loss: 3.1351 +2024-12-30 22:41:01,417 - pyskl - INFO - Epoch [126][300/3746] lr: 6.657e-03, eta: 22:10:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6869, loss_cls: 3.2183, loss: 3.2183 +2024-12-30 22:42:26,720 - pyskl - INFO - Epoch [126][400/3746] lr: 6.643e-03, eta: 22:09:28, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4214, top5_acc: 0.6848, loss_cls: 3.2273, loss: 3.2273 +2024-12-30 22:43:51,952 - pyskl - INFO - Epoch [126][500/3746] lr: 6.629e-03, eta: 22:08:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6934, loss_cls: 3.2032, loss: 3.2032 +2024-12-30 22:45:17,078 - pyskl - INFO - Epoch [126][600/3746] lr: 6.615e-03, eta: 22:06:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4309, top5_acc: 0.6936, loss_cls: 3.1943, loss: 3.1943 +2024-12-30 22:46:42,518 - pyskl - INFO - Epoch [126][700/3746] lr: 6.601e-03, eta: 22:05:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4309, top5_acc: 0.6848, loss_cls: 3.2237, loss: 3.2237 +2024-12-30 22:48:08,132 - pyskl - INFO - Epoch [126][800/3746] lr: 6.587e-03, eta: 22:03:45, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4259, top5_acc: 0.6925, loss_cls: 3.2004, loss: 3.2004 +2024-12-30 22:49:33,317 - pyskl - INFO - Epoch [126][900/3746] lr: 6.574e-03, eta: 22:02:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4330, top5_acc: 0.6830, loss_cls: 3.2094, loss: 3.2094 +2024-12-30 22:50:59,061 - pyskl - INFO - Epoch [126][1000/3746] lr: 6.560e-03, eta: 22:00:54, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6861, loss_cls: 3.2258, loss: 3.2258 +2024-12-30 22:52:24,017 - pyskl - INFO - Epoch [126][1100/3746] lr: 6.546e-03, eta: 21:59:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6744, loss_cls: 3.2698, loss: 3.2698 +2024-12-30 22:53:49,177 - pyskl - INFO - Epoch [126][1200/3746] lr: 6.532e-03, eta: 21:58:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4211, top5_acc: 0.6827, loss_cls: 3.2538, loss: 3.2538 +2024-12-30 22:55:14,726 - pyskl - INFO - Epoch [126][1300/3746] lr: 6.518e-03, eta: 21:56:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6816, loss_cls: 3.2453, loss: 3.2453 +2024-12-30 22:56:40,059 - pyskl - INFO - Epoch [126][1400/3746] lr: 6.505e-03, eta: 21:55:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4270, top5_acc: 0.6831, loss_cls: 3.2207, loss: 3.2207 +2024-12-30 22:58:05,677 - pyskl - INFO - Epoch [126][1500/3746] lr: 6.491e-03, eta: 21:53:46, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4147, top5_acc: 0.6742, loss_cls: 3.2712, loss: 3.2712 +2024-12-30 22:59:30,894 - pyskl - INFO - Epoch [126][1600/3746] lr: 6.477e-03, eta: 21:52:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6781, loss_cls: 3.2420, loss: 3.2420 +2024-12-30 23:00:55,904 - pyskl - INFO - Epoch [126][1700/3746] lr: 6.463e-03, eta: 21:50:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4205, top5_acc: 0.6823, loss_cls: 3.2361, loss: 3.2361 +2024-12-30 23:02:21,419 - pyskl - INFO - Epoch [126][1800/3746] lr: 6.449e-03, eta: 21:49:29, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4270, top5_acc: 0.6792, loss_cls: 3.2493, loss: 3.2493 +2024-12-30 23:03:46,750 - pyskl - INFO - Epoch [126][1900/3746] lr: 6.436e-03, eta: 21:48:04, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4139, top5_acc: 0.6794, loss_cls: 3.2549, loss: 3.2549 +2024-12-30 23:05:11,953 - pyskl - INFO - Epoch [126][2000/3746] lr: 6.422e-03, eta: 21:46:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6783, loss_cls: 3.2446, loss: 3.2446 +2024-12-30 23:06:36,849 - pyskl - INFO - Epoch [126][2100/3746] lr: 6.408e-03, eta: 21:45:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6753, loss_cls: 3.2603, loss: 3.2603 +2024-12-30 23:08:02,493 - pyskl - INFO - Epoch [126][2200/3746] lr: 6.395e-03, eta: 21:43:47, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6731, loss_cls: 3.2472, loss: 3.2472 +2024-12-30 23:09:27,749 - pyskl - INFO - Epoch [126][2300/3746] lr: 6.381e-03, eta: 21:42:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4272, top5_acc: 0.6839, loss_cls: 3.2168, loss: 3.2168 +2024-12-30 23:10:53,020 - pyskl - INFO - Epoch [126][2400/3746] lr: 6.367e-03, eta: 21:40:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4277, top5_acc: 0.6837, loss_cls: 3.2078, loss: 3.2078 +2024-12-30 23:12:18,383 - pyskl - INFO - Epoch [126][2500/3746] lr: 6.354e-03, eta: 21:39:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6809, loss_cls: 3.2387, loss: 3.2387 +2024-12-30 23:13:43,848 - pyskl - INFO - Epoch [126][2600/3746] lr: 6.340e-03, eta: 21:38:05, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6827, loss_cls: 3.2578, loss: 3.2578 +2024-12-30 23:15:09,173 - pyskl - INFO - Epoch [126][2700/3746] lr: 6.326e-03, eta: 21:36:39, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4166, top5_acc: 0.6803, loss_cls: 3.2510, loss: 3.2510 +2024-12-30 23:16:34,860 - pyskl - INFO - Epoch [126][2800/3746] lr: 6.313e-03, eta: 21:35:14, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6806, loss_cls: 3.2552, loss: 3.2552 +2024-12-30 23:18:01,164 - pyskl - INFO - Epoch [126][2900/3746] lr: 6.299e-03, eta: 21:33:48, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4169, top5_acc: 0.6783, loss_cls: 3.2774, loss: 3.2774 +2024-12-30 23:19:27,129 - pyskl - INFO - Epoch [126][3000/3746] lr: 6.286e-03, eta: 21:32:23, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4189, top5_acc: 0.6809, loss_cls: 3.2620, loss: 3.2620 +2024-12-30 23:20:53,261 - pyskl - INFO - Epoch [126][3100/3746] lr: 6.272e-03, eta: 21:30:57, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4245, top5_acc: 0.6872, loss_cls: 3.2354, loss: 3.2354 +2024-12-30 23:22:18,911 - pyskl - INFO - Epoch [126][3200/3746] lr: 6.259e-03, eta: 21:29:32, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4227, top5_acc: 0.6750, loss_cls: 3.2690, loss: 3.2690 +2024-12-30 23:23:44,650 - pyskl - INFO - Epoch [126][3300/3746] lr: 6.245e-03, eta: 21:28:06, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4211, top5_acc: 0.6720, loss_cls: 3.2575, loss: 3.2575 +2024-12-30 23:25:09,836 - pyskl - INFO - Epoch [126][3400/3746] lr: 6.231e-03, eta: 21:26:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6734, loss_cls: 3.2483, loss: 3.2483 +2024-12-30 23:26:34,507 - pyskl - INFO - Epoch [126][3500/3746] lr: 6.218e-03, eta: 21:25:15, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4245, top5_acc: 0.6787, loss_cls: 3.2527, loss: 3.2527 +2024-12-30 23:27:59,546 - pyskl - INFO - Epoch [126][3600/3746] lr: 6.204e-03, eta: 21:23:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4292, top5_acc: 0.6836, loss_cls: 3.2253, loss: 3.2253 +2024-12-30 23:29:24,985 - pyskl - INFO - Epoch [126][3700/3746] lr: 6.191e-03, eta: 21:22:24, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4228, top5_acc: 0.6734, loss_cls: 3.2983, loss: 3.2983 +2024-12-30 23:30:06,646 - pyskl - INFO - Saving checkpoint at 126 epochs +2024-12-30 23:32:08,555 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 23:32:09,325 - pyskl - INFO - +top1_acc 0.3551 +top5_acc 0.6058 +2024-12-30 23:32:09,325 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 23:32:09,394 - pyskl - INFO - +mean_acc 0.3547 +2024-12-30 23:32:09,400 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_124.pth was removed +2024-12-30 23:32:09,753 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_126.pth. +2024-12-30 23:32:09,754 - pyskl - INFO - Best top1_acc is 0.3551 at 126 epoch. +2024-12-30 23:32:09,768 - pyskl - INFO - Epoch(val) [126][309] top1_acc: 0.3551, top5_acc: 0.6058, mean_class_accuracy: 0.3547 +2024-12-30 23:36:32,284 - pyskl - INFO - Epoch [127][100/3746] lr: 6.171e-03, eta: 21:20:45, time: 2.625, data_time: 1.591, memory: 15990, top1_acc: 0.4341, top5_acc: 0.6925, loss_cls: 3.1645, loss: 3.1645 +2024-12-30 23:37:57,599 - pyskl - INFO - Epoch [127][200/3746] lr: 6.158e-03, eta: 21:19:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4350, top5_acc: 0.6839, loss_cls: 3.2008, loss: 3.2008 +2024-12-30 23:39:22,966 - pyskl - INFO - Epoch [127][300/3746] lr: 6.144e-03, eta: 21:17:54, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4416, top5_acc: 0.7045, loss_cls: 3.1414, loss: 3.1414 +2024-12-30 23:40:48,243 - pyskl - INFO - Epoch [127][400/3746] lr: 6.131e-03, eta: 21:16:28, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6866, loss_cls: 3.1755, loss: 3.1755 +2024-12-30 23:42:13,100 - pyskl - INFO - Epoch [127][500/3746] lr: 6.118e-03, eta: 21:15:03, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.6936, loss_cls: 3.1656, loss: 3.1656 +2024-12-30 23:43:38,887 - pyskl - INFO - Epoch [127][600/3746] lr: 6.104e-03, eta: 21:13:37, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4437, top5_acc: 0.6961, loss_cls: 3.1688, loss: 3.1688 +2024-12-30 23:45:03,981 - pyskl - INFO - Epoch [127][700/3746] lr: 6.091e-03, eta: 21:12:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4347, top5_acc: 0.6852, loss_cls: 3.1921, loss: 3.1921 +2024-12-30 23:46:29,211 - pyskl - INFO - Epoch [127][800/3746] lr: 6.077e-03, eta: 21:10:46, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4344, top5_acc: 0.6913, loss_cls: 3.2173, loss: 3.2173 +2024-12-30 23:47:54,791 - pyskl - INFO - Epoch [127][900/3746] lr: 6.064e-03, eta: 21:09:20, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6908, loss_cls: 3.2005, loss: 3.2005 +2024-12-30 23:49:20,307 - pyskl - INFO - Epoch [127][1000/3746] lr: 6.051e-03, eta: 21:07:55, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4289, top5_acc: 0.6933, loss_cls: 3.1960, loss: 3.1960 +2024-12-30 23:50:45,950 - pyskl - INFO - Epoch [127][1100/3746] lr: 6.037e-03, eta: 21:06:29, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4389, top5_acc: 0.6848, loss_cls: 3.1960, loss: 3.1960 +2024-12-30 23:52:10,777 - pyskl - INFO - Epoch [127][1200/3746] lr: 6.024e-03, eta: 21:05:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4478, top5_acc: 0.6941, loss_cls: 3.1189, loss: 3.1189 +2024-12-30 23:53:36,316 - pyskl - INFO - Epoch [127][1300/3746] lr: 6.011e-03, eta: 21:03:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4286, top5_acc: 0.6761, loss_cls: 3.2300, loss: 3.2300 +2024-12-30 23:55:01,741 - pyskl - INFO - Epoch [127][1400/3746] lr: 5.998e-03, eta: 21:02:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4297, top5_acc: 0.6884, loss_cls: 3.2076, loss: 3.2076 +2024-12-30 23:56:26,844 - pyskl - INFO - Epoch [127][1500/3746] lr: 5.984e-03, eta: 21:00:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6953, loss_cls: 3.1712, loss: 3.1712 +2024-12-30 23:57:52,393 - pyskl - INFO - Epoch [127][1600/3746] lr: 5.971e-03, eta: 20:59:21, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4331, top5_acc: 0.6827, loss_cls: 3.2084, loss: 3.2084 +2024-12-30 23:59:18,219 - pyskl - INFO - Epoch [127][1700/3746] lr: 5.958e-03, eta: 20:57:55, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6877, loss_cls: 3.2081, loss: 3.2081 +2024-12-31 00:00:43,765 - pyskl - INFO - Epoch [127][1800/3746] lr: 5.945e-03, eta: 20:56:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4227, top5_acc: 0.6892, loss_cls: 3.2133, loss: 3.2133 +2024-12-31 00:02:08,834 - pyskl - INFO - Epoch [127][1900/3746] lr: 5.931e-03, eta: 20:55:04, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4256, top5_acc: 0.6887, loss_cls: 3.2038, loss: 3.2038 +2024-12-31 00:03:34,306 - pyskl - INFO - Epoch [127][2000/3746] lr: 5.918e-03, eta: 20:53:39, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6797, loss_cls: 3.2673, loss: 3.2673 +2024-12-31 00:04:59,249 - pyskl - INFO - Epoch [127][2100/3746] lr: 5.905e-03, eta: 20:52:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6814, loss_cls: 3.2020, loss: 3.2020 +2024-12-31 00:06:24,382 - pyskl - INFO - Epoch [127][2200/3746] lr: 5.892e-03, eta: 20:50:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4248, top5_acc: 0.6827, loss_cls: 3.2396, loss: 3.2396 +2024-12-31 00:07:49,822 - pyskl - INFO - Epoch [127][2300/3746] lr: 5.879e-03, eta: 20:49:22, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4272, top5_acc: 0.6828, loss_cls: 3.2261, loss: 3.2261 +2024-12-31 00:09:15,035 - pyskl - INFO - Epoch [127][2400/3746] lr: 5.866e-03, eta: 20:47:56, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4334, top5_acc: 0.6903, loss_cls: 3.1618, loss: 3.1618 +2024-12-31 00:10:40,529 - pyskl - INFO - Epoch [127][2500/3746] lr: 5.852e-03, eta: 20:46:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6856, loss_cls: 3.2209, loss: 3.2209 +2024-12-31 00:12:05,920 - pyskl - INFO - Epoch [127][2600/3746] lr: 5.839e-03, eta: 20:45:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6847, loss_cls: 3.2341, loss: 3.2341 +2024-12-31 00:13:32,067 - pyskl - INFO - Epoch [127][2700/3746] lr: 5.826e-03, eta: 20:43:39, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4298, top5_acc: 0.6822, loss_cls: 3.2296, loss: 3.2296 +2024-12-31 00:14:57,331 - pyskl - INFO - Epoch [127][2800/3746] lr: 5.813e-03, eta: 20:42:14, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4266, top5_acc: 0.6852, loss_cls: 3.2216, loss: 3.2216 +2024-12-31 00:16:22,712 - pyskl - INFO - Epoch [127][2900/3746] lr: 5.800e-03, eta: 20:40:48, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6855, loss_cls: 3.2263, loss: 3.2263 +2024-12-31 00:17:47,856 - pyskl - INFO - Epoch [127][3000/3746] lr: 5.787e-03, eta: 20:39:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6845, loss_cls: 3.2399, loss: 3.2399 +2024-12-31 00:19:13,378 - pyskl - INFO - Epoch [127][3100/3746] lr: 5.774e-03, eta: 20:37:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4331, top5_acc: 0.6827, loss_cls: 3.2365, loss: 3.2365 +2024-12-31 00:20:38,556 - pyskl - INFO - Epoch [127][3200/3746] lr: 5.761e-03, eta: 20:36:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4339, top5_acc: 0.6836, loss_cls: 3.2292, loss: 3.2292 +2024-12-31 00:22:03,767 - pyskl - INFO - Epoch [127][3300/3746] lr: 5.748e-03, eta: 20:35:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6897, loss_cls: 3.2285, loss: 3.2285 +2024-12-31 00:23:29,041 - pyskl - INFO - Epoch [127][3400/3746] lr: 5.735e-03, eta: 20:33:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4286, top5_acc: 0.6841, loss_cls: 3.2103, loss: 3.2103 +2024-12-31 00:24:53,925 - pyskl - INFO - Epoch [127][3500/3746] lr: 5.722e-03, eta: 20:32:14, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4205, top5_acc: 0.6775, loss_cls: 3.2627, loss: 3.2627 +2024-12-31 00:26:18,918 - pyskl - INFO - Epoch [127][3600/3746] lr: 5.709e-03, eta: 20:30:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4313, top5_acc: 0.6825, loss_cls: 3.2156, loss: 3.2156 +2024-12-31 00:27:44,073 - pyskl - INFO - Epoch [127][3700/3746] lr: 5.696e-03, eta: 20:29:23, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6848, loss_cls: 3.2160, loss: 3.2160 +2024-12-31 00:28:25,812 - pyskl - INFO - Saving checkpoint at 127 epochs +2024-12-31 00:30:27,167 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 00:30:27,965 - pyskl - INFO - +top1_acc 0.3620 +top5_acc 0.6184 +2024-12-31 00:30:27,965 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 00:30:28,018 - pyskl - INFO - +mean_acc 0.3618 +2024-12-31 00:30:28,026 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_126.pth was removed +2024-12-31 00:30:28,321 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_127.pth. +2024-12-31 00:30:28,323 - pyskl - INFO - Best top1_acc is 0.3620 at 127 epoch. +2024-12-31 00:30:28,336 - pyskl - INFO - Epoch(val) [127][309] top1_acc: 0.3620, top5_acc: 0.6184, mean_class_accuracy: 0.3618 +2024-12-31 00:34:53,500 - pyskl - INFO - Epoch [128][100/3746] lr: 5.677e-03, eta: 20:27:43, time: 2.652, data_time: 1.608, memory: 15990, top1_acc: 0.4462, top5_acc: 0.6972, loss_cls: 3.1323, loss: 3.1323 +2024-12-31 00:36:18,785 - pyskl - INFO - Epoch [128][200/3746] lr: 5.664e-03, eta: 20:26:18, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4464, top5_acc: 0.7050, loss_cls: 3.1049, loss: 3.1049 +2024-12-31 00:37:44,190 - pyskl - INFO - Epoch [128][300/3746] lr: 5.651e-03, eta: 20:24:52, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4469, top5_acc: 0.6967, loss_cls: 3.1230, loss: 3.1230 +2024-12-31 00:39:09,663 - pyskl - INFO - Epoch [128][400/3746] lr: 5.638e-03, eta: 20:23:27, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.6995, loss_cls: 3.1415, loss: 3.1415 +2024-12-31 00:40:35,396 - pyskl - INFO - Epoch [128][500/3746] lr: 5.625e-03, eta: 20:22:01, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4414, top5_acc: 0.6980, loss_cls: 3.1309, loss: 3.1309 +2024-12-31 00:42:00,390 - pyskl - INFO - Epoch [128][600/3746] lr: 5.612e-03, eta: 20:20:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.6973, loss_cls: 3.1136, loss: 3.1136 +2024-12-31 00:43:25,609 - pyskl - INFO - Epoch [128][700/3746] lr: 5.600e-03, eta: 20:19:10, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4331, top5_acc: 0.6877, loss_cls: 3.1841, loss: 3.1841 +2024-12-31 00:44:51,437 - pyskl - INFO - Epoch [128][800/3746] lr: 5.587e-03, eta: 20:17:44, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4462, top5_acc: 0.6995, loss_cls: 3.1168, loss: 3.1168 +2024-12-31 00:46:16,746 - pyskl - INFO - Epoch [128][900/3746] lr: 5.574e-03, eta: 20:16:18, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4428, top5_acc: 0.7020, loss_cls: 3.1296, loss: 3.1296 +2024-12-31 00:47:41,945 - pyskl - INFO - Epoch [128][1000/3746] lr: 5.561e-03, eta: 20:14:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4405, top5_acc: 0.7048, loss_cls: 3.1319, loss: 3.1319 +2024-12-31 00:49:07,215 - pyskl - INFO - Epoch [128][1100/3746] lr: 5.548e-03, eta: 20:13:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4384, top5_acc: 0.6936, loss_cls: 3.1386, loss: 3.1386 +2024-12-31 00:50:32,496 - pyskl - INFO - Epoch [128][1200/3746] lr: 5.536e-03, eta: 20:12:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4369, top5_acc: 0.6900, loss_cls: 3.1892, loss: 3.1892 +2024-12-31 00:51:57,782 - pyskl - INFO - Epoch [128][1300/3746] lr: 5.523e-03, eta: 20:10:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4458, top5_acc: 0.6956, loss_cls: 3.1348, loss: 3.1348 +2024-12-31 00:53:23,126 - pyskl - INFO - Epoch [128][1400/3746] lr: 5.510e-03, eta: 20:09:10, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4306, top5_acc: 0.6942, loss_cls: 3.1683, loss: 3.1683 +2024-12-31 00:54:48,847 - pyskl - INFO - Epoch [128][1500/3746] lr: 5.497e-03, eta: 20:07:45, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4272, top5_acc: 0.6800, loss_cls: 3.2422, loss: 3.2422 +2024-12-31 00:56:14,674 - pyskl - INFO - Epoch [128][1600/3746] lr: 5.485e-03, eta: 20:06:19, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4300, top5_acc: 0.6842, loss_cls: 3.1966, loss: 3.1966 +2024-12-31 00:57:39,784 - pyskl - INFO - Epoch [128][1700/3746] lr: 5.472e-03, eta: 20:04:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4402, top5_acc: 0.6836, loss_cls: 3.1899, loss: 3.1899 +2024-12-31 00:59:05,249 - pyskl - INFO - Epoch [128][1800/3746] lr: 5.459e-03, eta: 20:03:28, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4348, top5_acc: 0.6841, loss_cls: 3.2201, loss: 3.2201 +2024-12-31 01:00:30,312 - pyskl - INFO - Epoch [128][1900/3746] lr: 5.446e-03, eta: 20:02:02, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4303, top5_acc: 0.6914, loss_cls: 3.1842, loss: 3.1842 +2024-12-31 01:01:55,458 - pyskl - INFO - Epoch [128][2000/3746] lr: 5.434e-03, eta: 20:00:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4338, top5_acc: 0.6937, loss_cls: 3.1879, loss: 3.1879 +2024-12-31 01:03:20,463 - pyskl - INFO - Epoch [128][2100/3746] lr: 5.421e-03, eta: 19:59:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.6920, loss_cls: 3.1574, loss: 3.1574 +2024-12-31 01:04:46,311 - pyskl - INFO - Epoch [128][2200/3746] lr: 5.408e-03, eta: 19:57:45, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4431, top5_acc: 0.7070, loss_cls: 3.1330, loss: 3.1330 +2024-12-31 01:06:11,605 - pyskl - INFO - Epoch [128][2300/3746] lr: 5.396e-03, eta: 19:56:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4366, top5_acc: 0.6961, loss_cls: 3.1663, loss: 3.1663 +2024-12-31 01:07:36,733 - pyskl - INFO - Epoch [128][2400/3746] lr: 5.383e-03, eta: 19:54:54, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4341, top5_acc: 0.6898, loss_cls: 3.1939, loss: 3.1939 +2024-12-31 01:09:01,878 - pyskl - INFO - Epoch [128][2500/3746] lr: 5.370e-03, eta: 19:53:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4266, top5_acc: 0.6845, loss_cls: 3.2543, loss: 3.2543 +2024-12-31 01:10:27,313 - pyskl - INFO - Epoch [128][2600/3746] lr: 5.358e-03, eta: 19:52:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6931, loss_cls: 3.1984, loss: 3.1984 +2024-12-31 01:11:52,202 - pyskl - INFO - Epoch [128][2700/3746] lr: 5.345e-03, eta: 19:50:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6906, loss_cls: 3.2007, loss: 3.2007 +2024-12-31 01:13:17,541 - pyskl - INFO - Epoch [128][2800/3746] lr: 5.333e-03, eta: 19:49:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4373, top5_acc: 0.6936, loss_cls: 3.1765, loss: 3.1765 +2024-12-31 01:14:42,506 - pyskl - INFO - Epoch [128][2900/3746] lr: 5.320e-03, eta: 19:47:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4295, top5_acc: 0.6878, loss_cls: 3.2065, loss: 3.2065 +2024-12-31 01:16:07,985 - pyskl - INFO - Epoch [128][3000/3746] lr: 5.308e-03, eta: 19:46:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4433, top5_acc: 0.6972, loss_cls: 3.1513, loss: 3.1513 +2024-12-31 01:17:33,278 - pyskl - INFO - Epoch [128][3100/3746] lr: 5.295e-03, eta: 19:44:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4370, top5_acc: 0.6856, loss_cls: 3.1763, loss: 3.1763 +2024-12-31 01:18:58,648 - pyskl - INFO - Epoch [128][3200/3746] lr: 5.283e-03, eta: 19:43:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4344, top5_acc: 0.6927, loss_cls: 3.1720, loss: 3.1720 +2024-12-31 01:20:23,826 - pyskl - INFO - Epoch [128][3300/3746] lr: 5.270e-03, eta: 19:42:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4322, top5_acc: 0.6861, loss_cls: 3.2013, loss: 3.2013 +2024-12-31 01:21:49,015 - pyskl - INFO - Epoch [128][3400/3746] lr: 5.258e-03, eta: 19:40:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6872, loss_cls: 3.2073, loss: 3.2073 +2024-12-31 01:23:14,247 - pyskl - INFO - Epoch [128][3500/3746] lr: 5.245e-03, eta: 19:39:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4363, top5_acc: 0.6964, loss_cls: 3.1646, loss: 3.1646 +2024-12-31 01:24:39,358 - pyskl - INFO - Epoch [128][3600/3746] lr: 5.233e-03, eta: 19:37:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4323, top5_acc: 0.6937, loss_cls: 3.1832, loss: 3.1832 +2024-12-31 01:26:04,518 - pyskl - INFO - Epoch [128][3700/3746] lr: 5.220e-03, eta: 19:36:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4338, top5_acc: 0.6891, loss_cls: 3.1944, loss: 3.1944 +2024-12-31 01:26:46,379 - pyskl - INFO - Saving checkpoint at 128 epochs +2024-12-31 01:28:48,456 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 01:28:49,231 - pyskl - INFO - +top1_acc 0.3616 +top5_acc 0.6152 +2024-12-31 01:28:49,231 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 01:28:49,276 - pyskl - INFO - +mean_acc 0.3614 +2024-12-31 01:28:49,289 - pyskl - INFO - Epoch(val) [128][309] top1_acc: 0.3616, top5_acc: 0.6152, mean_class_accuracy: 0.3614 +2024-12-31 01:33:11,651 - pyskl - INFO - Epoch [129][100/3746] lr: 5.202e-03, eta: 19:34:39, time: 2.624, data_time: 1.559, memory: 15990, top1_acc: 0.4495, top5_acc: 0.7122, loss_cls: 3.0828, loss: 3.0828 +2024-12-31 01:34:37,107 - pyskl - INFO - Epoch [129][200/3746] lr: 5.190e-03, eta: 19:33:13, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4467, top5_acc: 0.7013, loss_cls: 3.1288, loss: 3.1288 +2024-12-31 01:36:02,217 - pyskl - INFO - Epoch [129][300/3746] lr: 5.177e-03, eta: 19:31:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4459, top5_acc: 0.7077, loss_cls: 3.0869, loss: 3.0869 +2024-12-31 01:37:27,606 - pyskl - INFO - Epoch [129][400/3746] lr: 5.165e-03, eta: 19:30:22, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4492, top5_acc: 0.7050, loss_cls: 3.0885, loss: 3.0885 +2024-12-31 01:38:52,908 - pyskl - INFO - Epoch [129][500/3746] lr: 5.153e-03, eta: 19:28:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4475, top5_acc: 0.7009, loss_cls: 3.1183, loss: 3.1183 +2024-12-31 01:40:18,557 - pyskl - INFO - Epoch [129][600/3746] lr: 5.140e-03, eta: 19:27:30, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4517, top5_acc: 0.7084, loss_cls: 3.0855, loss: 3.0855 +2024-12-31 01:41:43,810 - pyskl - INFO - Epoch [129][700/3746] lr: 5.128e-03, eta: 19:26:05, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4464, top5_acc: 0.7066, loss_cls: 3.1130, loss: 3.1130 +2024-12-31 01:43:09,690 - pyskl - INFO - Epoch [129][800/3746] lr: 5.116e-03, eta: 19:24:39, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4437, top5_acc: 0.6981, loss_cls: 3.1211, loss: 3.1211 +2024-12-31 01:44:36,578 - pyskl - INFO - Epoch [129][900/3746] lr: 5.103e-03, eta: 19:23:14, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.4425, top5_acc: 0.6964, loss_cls: 3.1161, loss: 3.1161 +2024-12-31 01:46:03,175 - pyskl - INFO - Epoch [129][1000/3746] lr: 5.091e-03, eta: 19:21:48, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.4450, top5_acc: 0.6953, loss_cls: 3.1486, loss: 3.1486 +2024-12-31 01:47:29,678 - pyskl - INFO - Epoch [129][1100/3746] lr: 5.079e-03, eta: 19:20:23, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.4437, top5_acc: 0.6984, loss_cls: 3.0963, loss: 3.0963 +2024-12-31 01:48:56,263 - pyskl - INFO - Epoch [129][1200/3746] lr: 5.066e-03, eta: 19:18:57, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.4439, top5_acc: 0.6977, loss_cls: 3.1255, loss: 3.1255 +2024-12-31 01:50:22,923 - pyskl - INFO - Epoch [129][1300/3746] lr: 5.054e-03, eta: 19:17:32, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.4406, top5_acc: 0.6889, loss_cls: 3.1626, loss: 3.1626 +2024-12-31 01:51:49,247 - pyskl - INFO - Epoch [129][1400/3746] lr: 5.042e-03, eta: 19:16:06, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4375, top5_acc: 0.6956, loss_cls: 3.1437, loss: 3.1437 +2024-12-31 01:53:15,731 - pyskl - INFO - Epoch [129][1500/3746] lr: 5.030e-03, eta: 19:14:41, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.4441, top5_acc: 0.6986, loss_cls: 3.1368, loss: 3.1368 +2024-12-31 01:54:42,095 - pyskl - INFO - Epoch [129][1600/3746] lr: 5.017e-03, eta: 19:13:15, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.4477, top5_acc: 0.7089, loss_cls: 3.0855, loss: 3.0855 +2024-12-31 01:56:08,905 - pyskl - INFO - Epoch [129][1700/3746] lr: 5.005e-03, eta: 19:11:50, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.4342, top5_acc: 0.6856, loss_cls: 3.1509, loss: 3.1509 +2024-12-31 01:57:35,192 - pyskl - INFO - Epoch [129][1800/3746] lr: 4.993e-03, eta: 19:10:24, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.6945, loss_cls: 3.1368, loss: 3.1368 +2024-12-31 01:59:01,843 - pyskl - INFO - Epoch [129][1900/3746] lr: 4.981e-03, eta: 19:08:59, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6950, loss_cls: 3.1388, loss: 3.1388 +2024-12-31 02:00:28,369 - pyskl - INFO - Epoch [129][2000/3746] lr: 4.969e-03, eta: 19:07:33, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.4366, top5_acc: 0.6917, loss_cls: 3.1708, loss: 3.1708 +2024-12-31 02:01:55,220 - pyskl - INFO - Epoch [129][2100/3746] lr: 4.957e-03, eta: 19:06:08, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.4347, top5_acc: 0.6959, loss_cls: 3.1675, loss: 3.1675 +2024-12-31 02:03:22,033 - pyskl - INFO - Epoch [129][2200/3746] lr: 4.944e-03, eta: 19:04:43, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.4450, top5_acc: 0.6947, loss_cls: 3.1366, loss: 3.1366 +2024-12-31 02:04:48,611 - pyskl - INFO - Epoch [129][2300/3746] lr: 4.932e-03, eta: 19:03:17, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.4373, top5_acc: 0.6903, loss_cls: 3.1685, loss: 3.1685 +2024-12-31 02:06:15,058 - pyskl - INFO - Epoch [129][2400/3746] lr: 4.920e-03, eta: 19:01:52, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.4445, top5_acc: 0.6936, loss_cls: 3.1542, loss: 3.1542 +2024-12-31 02:07:41,563 - pyskl - INFO - Epoch [129][2500/3746] lr: 4.908e-03, eta: 19:00:26, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.4395, top5_acc: 0.6923, loss_cls: 3.1853, loss: 3.1853 +2024-12-31 02:09:07,992 - pyskl - INFO - Epoch [129][2600/3746] lr: 4.896e-03, eta: 18:59:01, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.4345, top5_acc: 0.6911, loss_cls: 3.1866, loss: 3.1866 +2024-12-31 02:10:35,193 - pyskl - INFO - Epoch [129][2700/3746] lr: 4.884e-03, eta: 18:57:35, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.4427, top5_acc: 0.6956, loss_cls: 3.1602, loss: 3.1602 +2024-12-31 02:12:01,764 - pyskl - INFO - Epoch [129][2800/3746] lr: 4.872e-03, eta: 18:56:10, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.4467, top5_acc: 0.7005, loss_cls: 3.1248, loss: 3.1248 +2024-12-31 02:13:28,061 - pyskl - INFO - Epoch [129][2900/3746] lr: 4.860e-03, eta: 18:54:44, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4509, top5_acc: 0.7014, loss_cls: 3.1047, loss: 3.1047 +2024-12-31 02:14:54,691 - pyskl - INFO - Epoch [129][3000/3746] lr: 4.848e-03, eta: 18:53:19, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.4439, top5_acc: 0.6895, loss_cls: 3.1535, loss: 3.1535 +2024-12-31 02:16:21,236 - pyskl - INFO - Epoch [129][3100/3746] lr: 4.836e-03, eta: 18:51:53, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.4289, top5_acc: 0.6823, loss_cls: 3.2102, loss: 3.2102 +2024-12-31 02:17:47,115 - pyskl - INFO - Epoch [129][3200/3746] lr: 4.824e-03, eta: 18:50:28, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4416, top5_acc: 0.6984, loss_cls: 3.1476, loss: 3.1476 +2024-12-31 02:19:13,121 - pyskl - INFO - Epoch [129][3300/3746] lr: 4.812e-03, eta: 18:49:02, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4433, top5_acc: 0.6977, loss_cls: 3.1421, loss: 3.1421 +2024-12-31 02:20:38,546 - pyskl - INFO - Epoch [129][3400/3746] lr: 4.800e-03, eta: 18:47:37, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4263, top5_acc: 0.6844, loss_cls: 3.2036, loss: 3.2036 +2024-12-31 02:22:04,506 - pyskl - INFO - Epoch [129][3500/3746] lr: 4.788e-03, eta: 18:46:11, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.7033, loss_cls: 3.1112, loss: 3.1112 +2024-12-31 02:23:31,023 - pyskl - INFO - Epoch [129][3600/3746] lr: 4.776e-03, eta: 18:44:46, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.4447, top5_acc: 0.7069, loss_cls: 3.1191, loss: 3.1191 +2024-12-31 02:24:57,637 - pyskl - INFO - Epoch [129][3700/3746] lr: 4.764e-03, eta: 18:43:20, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.4366, top5_acc: 0.6825, loss_cls: 3.1924, loss: 3.1924 +2024-12-31 02:25:39,555 - pyskl - INFO - Saving checkpoint at 129 epochs +2024-12-31 02:27:41,872 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 02:27:42,591 - pyskl - INFO - +top1_acc 0.3626 +top5_acc 0.6177 +2024-12-31 02:27:42,591 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 02:27:42,649 - pyskl - INFO - +mean_acc 0.3622 +2024-12-31 02:27:42,656 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_127.pth was removed +2024-12-31 02:27:43,080 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_129.pth. +2024-12-31 02:27:43,082 - pyskl - INFO - Best top1_acc is 0.3626 at 129 epoch. +2024-12-31 02:27:43,100 - pyskl - INFO - Epoch(val) [129][309] top1_acc: 0.3626, top5_acc: 0.6177, mean_class_accuracy: 0.3622 +2024-12-31 02:32:10,835 - pyskl - INFO - Epoch [130][100/3746] lr: 4.747e-03, eta: 18:41:38, time: 2.677, data_time: 1.647, memory: 15990, top1_acc: 0.4584, top5_acc: 0.7055, loss_cls: 3.0525, loss: 3.0525 +2024-12-31 02:33:37,604 - pyskl - INFO - Epoch [130][200/3746] lr: 4.735e-03, eta: 18:40:13, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7139, loss_cls: 3.0566, loss: 3.0566 +2024-12-31 02:35:03,454 - pyskl - INFO - Epoch [130][300/3746] lr: 4.723e-03, eta: 18:38:47, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4511, top5_acc: 0.7092, loss_cls: 3.0680, loss: 3.0680 +2024-12-31 02:36:29,898 - pyskl - INFO - Epoch [130][400/3746] lr: 4.711e-03, eta: 18:37:22, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.4544, top5_acc: 0.7033, loss_cls: 3.1053, loss: 3.1053 +2024-12-31 02:37:56,125 - pyskl - INFO - Epoch [130][500/3746] lr: 4.699e-03, eta: 18:35:56, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7102, loss_cls: 3.0546, loss: 3.0546 +2024-12-31 02:39:22,947 - pyskl - INFO - Epoch [130][600/3746] lr: 4.688e-03, eta: 18:34:31, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.4606, top5_acc: 0.7209, loss_cls: 3.0028, loss: 3.0028 +2024-12-31 02:40:49,580 - pyskl - INFO - Epoch [130][700/3746] lr: 4.676e-03, eta: 18:33:05, time: 0.866, data_time: 0.001, memory: 15990, top1_acc: 0.4450, top5_acc: 0.7069, loss_cls: 3.0802, loss: 3.0802 +2024-12-31 02:42:16,757 - pyskl - INFO - Epoch [130][800/3746] lr: 4.664e-03, eta: 18:31:40, time: 0.872, data_time: 0.001, memory: 15990, top1_acc: 0.4459, top5_acc: 0.7039, loss_cls: 3.0995, loss: 3.0995 +2024-12-31 02:43:43,328 - pyskl - INFO - Epoch [130][900/3746] lr: 4.652e-03, eta: 18:30:14, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.7016, loss_cls: 3.1126, loss: 3.1126 +2024-12-31 02:45:10,265 - pyskl - INFO - Epoch [130][1000/3746] lr: 4.640e-03, eta: 18:28:49, time: 0.869, data_time: 0.001, memory: 15990, top1_acc: 0.4389, top5_acc: 0.7053, loss_cls: 3.0912, loss: 3.0912 +2024-12-31 02:46:37,712 - pyskl - INFO - Epoch [130][1100/3746] lr: 4.629e-03, eta: 18:27:23, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.4572, top5_acc: 0.7127, loss_cls: 3.0334, loss: 3.0334 +2024-12-31 02:48:05,543 - pyskl - INFO - Epoch [130][1200/3746] lr: 4.617e-03, eta: 18:25:58, time: 0.878, data_time: 0.000, memory: 15990, top1_acc: 0.4500, top5_acc: 0.7039, loss_cls: 3.0740, loss: 3.0740 +2024-12-31 02:49:33,480 - pyskl - INFO - Epoch [130][1300/3746] lr: 4.605e-03, eta: 18:24:33, time: 0.879, data_time: 0.001, memory: 15990, top1_acc: 0.4533, top5_acc: 0.7064, loss_cls: 3.0917, loss: 3.0917 +2024-12-31 02:51:00,553 - pyskl - INFO - Epoch [130][1400/3746] lr: 4.594e-03, eta: 18:23:07, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.4508, top5_acc: 0.7019, loss_cls: 3.0853, loss: 3.0853 +2024-12-31 02:52:28,320 - pyskl - INFO - Epoch [130][1500/3746] lr: 4.582e-03, eta: 18:21:42, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.4512, top5_acc: 0.6997, loss_cls: 3.0903, loss: 3.0903 +2024-12-31 02:53:56,457 - pyskl - INFO - Epoch [130][1600/3746] lr: 4.570e-03, eta: 18:20:17, time: 0.881, data_time: 0.001, memory: 15990, top1_acc: 0.4486, top5_acc: 0.6989, loss_cls: 3.0993, loss: 3.0993 +2024-12-31 02:55:24,336 - pyskl - INFO - Epoch [130][1700/3746] lr: 4.558e-03, eta: 18:18:51, time: 0.879, data_time: 0.001, memory: 15990, top1_acc: 0.4556, top5_acc: 0.7067, loss_cls: 3.0820, loss: 3.0820 +2024-12-31 02:56:50,863 - pyskl - INFO - Epoch [130][1800/3746] lr: 4.547e-03, eta: 18:17:26, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.4442, top5_acc: 0.6981, loss_cls: 3.1150, loss: 3.1150 +2024-12-31 02:58:17,115 - pyskl - INFO - Epoch [130][1900/3746] lr: 4.535e-03, eta: 18:16:00, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4452, top5_acc: 0.6991, loss_cls: 3.1253, loss: 3.1253 +2024-12-31 02:59:43,037 - pyskl - INFO - Epoch [130][2000/3746] lr: 4.524e-03, eta: 18:14:35, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4431, top5_acc: 0.7061, loss_cls: 3.1021, loss: 3.1021 +2024-12-31 03:01:11,043 - pyskl - INFO - Epoch [130][2100/3746] lr: 4.512e-03, eta: 18:13:09, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.4430, top5_acc: 0.7025, loss_cls: 3.1218, loss: 3.1218 +2024-12-31 03:02:38,608 - pyskl - INFO - Epoch [130][2200/3746] lr: 4.500e-03, eta: 18:11:44, time: 0.876, data_time: 0.001, memory: 15990, top1_acc: 0.4342, top5_acc: 0.7020, loss_cls: 3.1341, loss: 3.1341 +2024-12-31 03:04:06,210 - pyskl - INFO - Epoch [130][2300/3746] lr: 4.489e-03, eta: 18:10:19, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.4402, top5_acc: 0.6936, loss_cls: 3.1306, loss: 3.1306 +2024-12-31 03:05:33,640 - pyskl - INFO - Epoch [130][2400/3746] lr: 4.477e-03, eta: 18:08:53, time: 0.874, data_time: 0.001, memory: 15990, top1_acc: 0.4520, top5_acc: 0.7034, loss_cls: 3.0971, loss: 3.0971 +2024-12-31 03:07:01,508 - pyskl - INFO - Epoch [130][2500/3746] lr: 4.466e-03, eta: 18:07:28, time: 0.879, data_time: 0.001, memory: 15990, top1_acc: 0.4467, top5_acc: 0.7000, loss_cls: 3.1279, loss: 3.1279 +2024-12-31 03:08:29,725 - pyskl - INFO - Epoch [130][2600/3746] lr: 4.454e-03, eta: 18:06:03, time: 0.882, data_time: 0.001, memory: 15990, top1_acc: 0.4436, top5_acc: 0.6909, loss_cls: 3.1589, loss: 3.1589 +2024-12-31 03:09:57,793 - pyskl - INFO - Epoch [130][2700/3746] lr: 4.443e-03, eta: 18:04:37, time: 0.881, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.6987, loss_cls: 3.1526, loss: 3.1526 +2024-12-31 03:11:25,335 - pyskl - INFO - Epoch [130][2800/3746] lr: 4.431e-03, eta: 18:03:12, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.4452, top5_acc: 0.7069, loss_cls: 3.0786, loss: 3.0786 +2024-12-31 03:12:53,913 - pyskl - INFO - Epoch [130][2900/3746] lr: 4.420e-03, eta: 18:01:47, time: 0.886, data_time: 0.001, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7137, loss_cls: 3.0566, loss: 3.0566 +2024-12-31 03:14:22,036 - pyskl - INFO - Epoch [130][3000/3746] lr: 4.408e-03, eta: 18:00:21, time: 0.881, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.7139, loss_cls: 3.1053, loss: 3.1053 +2024-12-31 03:15:49,431 - pyskl - INFO - Epoch [130][3100/3746] lr: 4.397e-03, eta: 17:58:56, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.4500, top5_acc: 0.7028, loss_cls: 3.1110, loss: 3.1110 +2024-12-31 03:17:16,066 - pyskl - INFO - Epoch [130][3200/3746] lr: 4.385e-03, eta: 17:57:31, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.4462, top5_acc: 0.7019, loss_cls: 3.1404, loss: 3.1404 +2024-12-31 03:18:42,687 - pyskl - INFO - Epoch [130][3300/3746] lr: 4.374e-03, eta: 17:56:05, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.4297, top5_acc: 0.6955, loss_cls: 3.1600, loss: 3.1600 +2024-12-31 03:20:08,740 - pyskl - INFO - Epoch [130][3400/3746] lr: 4.362e-03, eta: 17:54:39, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4358, top5_acc: 0.6911, loss_cls: 3.1440, loss: 3.1440 +2024-12-31 03:21:35,964 - pyskl - INFO - Epoch [130][3500/3746] lr: 4.351e-03, eta: 17:53:14, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.4467, top5_acc: 0.7002, loss_cls: 3.1168, loss: 3.1168 +2024-12-31 03:23:01,729 - pyskl - INFO - Epoch [130][3600/3746] lr: 4.339e-03, eta: 17:51:48, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4373, top5_acc: 0.6916, loss_cls: 3.1650, loss: 3.1650 +2024-12-31 03:24:28,239 - pyskl - INFO - Epoch [130][3700/3746] lr: 4.328e-03, eta: 17:50:23, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7083, loss_cls: 3.0687, loss: 3.0687 +2024-12-31 03:25:10,482 - pyskl - INFO - Saving checkpoint at 130 epochs +2024-12-31 03:27:10,478 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 03:27:11,264 - pyskl - INFO - +top1_acc 0.3712 +top5_acc 0.6272 +2024-12-31 03:27:11,264 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 03:27:11,307 - pyskl - INFO - +mean_acc 0.3711 +2024-12-31 03:27:11,312 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_129.pth was removed +2024-12-31 03:27:11,589 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_130.pth. +2024-12-31 03:27:11,590 - pyskl - INFO - Best top1_acc is 0.3712 at 130 epoch. +2024-12-31 03:27:11,605 - pyskl - INFO - Epoch(val) [130][309] top1_acc: 0.3712, top5_acc: 0.6272, mean_class_accuracy: 0.3711 +2024-12-31 03:31:43,233 - pyskl - INFO - Epoch [131][100/3746] lr: 4.311e-03, eta: 17:48:40, time: 2.716, data_time: 1.659, memory: 15990, top1_acc: 0.4706, top5_acc: 0.7281, loss_cls: 2.9530, loss: 2.9530 +2024-12-31 03:33:10,757 - pyskl - INFO - Epoch [131][200/3746] lr: 4.300e-03, eta: 17:47:15, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.4684, top5_acc: 0.7153, loss_cls: 3.0076, loss: 3.0076 +2024-12-31 03:34:38,271 - pyskl - INFO - Epoch [131][300/3746] lr: 4.289e-03, eta: 17:45:49, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.4620, top5_acc: 0.7192, loss_cls: 2.9885, loss: 2.9885 +2024-12-31 03:36:06,146 - pyskl - INFO - Epoch [131][400/3746] lr: 4.277e-03, eta: 17:44:24, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.4628, top5_acc: 0.7094, loss_cls: 3.0439, loss: 3.0439 +2024-12-31 03:37:34,676 - pyskl - INFO - Epoch [131][500/3746] lr: 4.266e-03, eta: 17:42:59, time: 0.885, data_time: 0.001, memory: 15990, top1_acc: 0.4623, top5_acc: 0.7169, loss_cls: 3.0195, loss: 3.0195 +2024-12-31 03:39:03,027 - pyskl - INFO - Epoch [131][600/3746] lr: 4.255e-03, eta: 17:41:33, time: 0.883, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7123, loss_cls: 3.0579, loss: 3.0579 +2024-12-31 03:40:31,174 - pyskl - INFO - Epoch [131][700/3746] lr: 4.244e-03, eta: 17:40:08, time: 0.881, data_time: 0.001, memory: 15990, top1_acc: 0.4620, top5_acc: 0.7116, loss_cls: 3.0394, loss: 3.0394 +2024-12-31 03:41:59,434 - pyskl - INFO - Epoch [131][800/3746] lr: 4.232e-03, eta: 17:38:43, time: 0.883, data_time: 0.000, memory: 15990, top1_acc: 0.4445, top5_acc: 0.7072, loss_cls: 3.0863, loss: 3.0863 +2024-12-31 03:43:28,119 - pyskl - INFO - Epoch [131][900/3746] lr: 4.221e-03, eta: 17:37:18, time: 0.887, data_time: 0.001, memory: 15990, top1_acc: 0.4572, top5_acc: 0.7153, loss_cls: 3.0489, loss: 3.0489 +2024-12-31 03:44:56,274 - pyskl - INFO - Epoch [131][1000/3746] lr: 4.210e-03, eta: 17:35:52, time: 0.882, data_time: 0.001, memory: 15990, top1_acc: 0.4502, top5_acc: 0.7092, loss_cls: 3.0603, loss: 3.0603 +2024-12-31 03:46:24,658 - pyskl - INFO - Epoch [131][1100/3746] lr: 4.199e-03, eta: 17:34:27, time: 0.884, data_time: 0.001, memory: 15990, top1_acc: 0.4509, top5_acc: 0.7067, loss_cls: 3.0847, loss: 3.0847 +2024-12-31 03:47:52,969 - pyskl - INFO - Epoch [131][1200/3746] lr: 4.187e-03, eta: 17:33:02, time: 0.883, data_time: 0.000, memory: 15990, top1_acc: 0.4645, top5_acc: 0.7091, loss_cls: 3.0277, loss: 3.0277 +2024-12-31 03:49:21,557 - pyskl - INFO - Epoch [131][1300/3746] lr: 4.176e-03, eta: 17:31:36, time: 0.886, data_time: 0.000, memory: 15990, top1_acc: 0.4434, top5_acc: 0.6942, loss_cls: 3.1288, loss: 3.1288 +2024-12-31 03:50:49,893 - pyskl - INFO - Epoch [131][1400/3746] lr: 4.165e-03, eta: 17:30:11, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.4530, top5_acc: 0.7106, loss_cls: 3.0728, loss: 3.0728 +2024-12-31 03:52:18,429 - pyskl - INFO - Epoch [131][1500/3746] lr: 4.154e-03, eta: 17:28:46, time: 0.886, data_time: 0.001, memory: 15990, top1_acc: 0.4467, top5_acc: 0.7087, loss_cls: 3.0511, loss: 3.0511 +2024-12-31 03:53:46,851 - pyskl - INFO - Epoch [131][1600/3746] lr: 4.143e-03, eta: 17:27:20, time: 0.884, data_time: 0.001, memory: 15990, top1_acc: 0.4459, top5_acc: 0.7153, loss_cls: 3.0887, loss: 3.0887 +2024-12-31 03:55:13,365 - pyskl - INFO - Epoch [131][1700/3746] lr: 4.132e-03, eta: 17:25:55, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.4378, top5_acc: 0.6978, loss_cls: 3.1355, loss: 3.1355 +2024-12-31 03:56:39,911 - pyskl - INFO - Epoch [131][1800/3746] lr: 4.120e-03, eta: 17:24:29, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.4573, top5_acc: 0.7128, loss_cls: 3.0536, loss: 3.0536 +2024-12-31 03:58:05,977 - pyskl - INFO - Epoch [131][1900/3746] lr: 4.109e-03, eta: 17:23:04, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.7083, loss_cls: 3.0536, loss: 3.0536 +2024-12-31 03:59:33,532 - pyskl - INFO - Epoch [131][2000/3746] lr: 4.098e-03, eta: 17:21:38, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.4384, top5_acc: 0.7023, loss_cls: 3.0818, loss: 3.0818 +2024-12-31 04:01:01,614 - pyskl - INFO - Epoch [131][2100/3746] lr: 4.087e-03, eta: 17:20:13, time: 0.881, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7083, loss_cls: 3.0525, loss: 3.0525 +2024-12-31 04:02:30,551 - pyskl - INFO - Epoch [131][2200/3746] lr: 4.076e-03, eta: 17:18:48, time: 0.889, data_time: 0.000, memory: 15990, top1_acc: 0.4592, top5_acc: 0.7130, loss_cls: 3.0481, loss: 3.0481 +2024-12-31 04:03:58,890 - pyskl - INFO - Epoch [131][2300/3746] lr: 4.065e-03, eta: 17:17:22, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.4497, top5_acc: 0.7070, loss_cls: 3.0722, loss: 3.0722 +2024-12-31 04:05:27,118 - pyskl - INFO - Epoch [131][2400/3746] lr: 4.054e-03, eta: 17:15:57, time: 0.882, data_time: 0.000, memory: 15990, top1_acc: 0.4462, top5_acc: 0.7013, loss_cls: 3.0934, loss: 3.0934 +2024-12-31 04:06:55,390 - pyskl - INFO - Epoch [131][2500/3746] lr: 4.043e-03, eta: 17:14:32, time: 0.883, data_time: 0.000, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7087, loss_cls: 3.0637, loss: 3.0637 +2024-12-31 04:08:23,494 - pyskl - INFO - Epoch [131][2600/3746] lr: 4.032e-03, eta: 17:13:06, time: 0.881, data_time: 0.000, memory: 15990, top1_acc: 0.4505, top5_acc: 0.7059, loss_cls: 3.1001, loss: 3.1001 +2024-12-31 04:09:52,235 - pyskl - INFO - Epoch [131][2700/3746] lr: 4.021e-03, eta: 17:11:41, time: 0.887, data_time: 0.001, memory: 15990, top1_acc: 0.4564, top5_acc: 0.7106, loss_cls: 3.0723, loss: 3.0723 +2024-12-31 04:11:20,899 - pyskl - INFO - Epoch [131][2800/3746] lr: 4.010e-03, eta: 17:10:16, time: 0.887, data_time: 0.001, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7025, loss_cls: 3.0939, loss: 3.0939 +2024-12-31 04:12:49,193 - pyskl - INFO - Epoch [131][2900/3746] lr: 3.999e-03, eta: 17:08:50, time: 0.883, data_time: 0.000, memory: 15990, top1_acc: 0.4680, top5_acc: 0.7188, loss_cls: 2.9985, loss: 2.9985 +2024-12-31 04:14:17,811 - pyskl - INFO - Epoch [131][3000/3746] lr: 3.988e-03, eta: 17:07:25, time: 0.886, data_time: 0.001, memory: 15990, top1_acc: 0.4653, top5_acc: 0.7139, loss_cls: 3.0189, loss: 3.0189 +2024-12-31 04:15:45,357 - pyskl - INFO - Epoch [131][3100/3746] lr: 3.977e-03, eta: 17:06:00, time: 0.875, data_time: 0.000, memory: 15990, top1_acc: 0.4425, top5_acc: 0.6961, loss_cls: 3.0999, loss: 3.0999 +2024-12-31 04:17:11,406 - pyskl - INFO - Epoch [131][3200/3746] lr: 3.966e-03, eta: 17:04:34, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.6942, loss_cls: 3.1463, loss: 3.1463 +2024-12-31 04:18:37,368 - pyskl - INFO - Epoch [131][3300/3746] lr: 3.955e-03, eta: 17:03:08, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.6970, loss_cls: 3.1314, loss: 3.1314 +2024-12-31 04:20:04,900 - pyskl - INFO - Epoch [131][3400/3746] lr: 3.945e-03, eta: 17:01:43, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.4536, top5_acc: 0.7108, loss_cls: 3.0726, loss: 3.0726 +2024-12-31 04:21:33,198 - pyskl - INFO - Epoch [131][3500/3746] lr: 3.934e-03, eta: 17:00:17, time: 0.883, data_time: 0.000, memory: 15990, top1_acc: 0.4541, top5_acc: 0.7097, loss_cls: 3.0338, loss: 3.0338 +2024-12-31 04:23:01,328 - pyskl - INFO - Epoch [131][3600/3746] lr: 3.923e-03, eta: 16:58:52, time: 0.881, data_time: 0.001, memory: 15990, top1_acc: 0.4519, top5_acc: 0.7037, loss_cls: 3.0793, loss: 3.0793 +2024-12-31 04:24:29,780 - pyskl - INFO - Epoch [131][3700/3746] lr: 3.912e-03, eta: 16:57:27, time: 0.884, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.7030, loss_cls: 3.1107, loss: 3.1107 +2024-12-31 04:25:12,481 - pyskl - INFO - Saving checkpoint at 131 epochs +2024-12-31 04:27:17,204 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 04:27:17,971 - pyskl - INFO - +top1_acc 0.3691 +top5_acc 0.6243 +2024-12-31 04:27:17,971 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 04:27:18,009 - pyskl - INFO - +mean_acc 0.3689 +2024-12-31 04:27:18,021 - pyskl - INFO - Epoch(val) [131][309] top1_acc: 0.3691, top5_acc: 0.6243, mean_class_accuracy: 0.3689 +2024-12-31 04:31:52,810 - pyskl - INFO - Epoch [132][100/3746] lr: 3.896e-03, eta: 16:55:43, time: 2.748, data_time: 1.675, memory: 15990, top1_acc: 0.4750, top5_acc: 0.7308, loss_cls: 2.9542, loss: 2.9542 +2024-12-31 04:33:20,770 - pyskl - INFO - Epoch [132][200/3746] lr: 3.885e-03, eta: 16:54:18, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.4731, top5_acc: 0.7227, loss_cls: 2.9757, loss: 2.9757 +2024-12-31 04:34:48,738 - pyskl - INFO - Epoch [132][300/3746] lr: 3.875e-03, eta: 16:52:52, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.4647, top5_acc: 0.7219, loss_cls: 2.9919, loss: 2.9919 +2024-12-31 04:36:16,383 - pyskl - INFO - Epoch [132][400/3746] lr: 3.864e-03, eta: 16:51:27, time: 0.876, data_time: 0.001, memory: 15990, top1_acc: 0.4697, top5_acc: 0.7250, loss_cls: 2.9734, loss: 2.9734 +2024-12-31 04:37:44,377 - pyskl - INFO - Epoch [132][500/3746] lr: 3.853e-03, eta: 16:50:02, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.4595, top5_acc: 0.7167, loss_cls: 3.0071, loss: 3.0071 +2024-12-31 04:39:12,060 - pyskl - INFO - Epoch [132][600/3746] lr: 3.842e-03, eta: 16:48:36, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.4669, top5_acc: 0.7272, loss_cls: 2.9777, loss: 2.9777 +2024-12-31 04:40:39,774 - pyskl - INFO - Epoch [132][700/3746] lr: 3.831e-03, eta: 16:47:11, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7225, loss_cls: 2.9914, loss: 2.9914 +2024-12-31 04:42:07,643 - pyskl - INFO - Epoch [132][800/3746] lr: 3.821e-03, eta: 16:45:45, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.4637, top5_acc: 0.7205, loss_cls: 3.0226, loss: 3.0226 +2024-12-31 04:43:36,243 - pyskl - INFO - Epoch [132][900/3746] lr: 3.810e-03, eta: 16:44:20, time: 0.886, data_time: 0.000, memory: 15990, top1_acc: 0.4723, top5_acc: 0.7231, loss_cls: 2.9470, loss: 2.9470 +2024-12-31 04:45:03,646 - pyskl - INFO - Epoch [132][1000/3746] lr: 3.799e-03, eta: 16:42:54, time: 0.874, data_time: 0.001, memory: 15990, top1_acc: 0.4527, top5_acc: 0.7214, loss_cls: 3.0123, loss: 3.0123 +2024-12-31 04:46:31,894 - pyskl - INFO - Epoch [132][1100/3746] lr: 3.789e-03, eta: 16:41:29, time: 0.882, data_time: 0.000, memory: 15990, top1_acc: 0.4666, top5_acc: 0.7106, loss_cls: 3.0324, loss: 3.0324 +2024-12-31 04:48:00,029 - pyskl - INFO - Epoch [132][1200/3746] lr: 3.778e-03, eta: 16:40:03, time: 0.881, data_time: 0.001, memory: 15990, top1_acc: 0.4537, top5_acc: 0.7083, loss_cls: 3.0406, loss: 3.0406 +2024-12-31 04:49:28,245 - pyskl - INFO - Epoch [132][1300/3746] lr: 3.767e-03, eta: 16:38:38, time: 0.882, data_time: 0.000, memory: 15990, top1_acc: 0.4695, top5_acc: 0.7186, loss_cls: 2.9863, loss: 2.9863 +2024-12-31 04:50:55,971 - pyskl - INFO - Epoch [132][1400/3746] lr: 3.757e-03, eta: 16:37:13, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.4622, top5_acc: 0.7142, loss_cls: 3.0280, loss: 3.0280 +2024-12-31 04:52:24,528 - pyskl - INFO - Epoch [132][1500/3746] lr: 3.746e-03, eta: 16:35:47, time: 0.886, data_time: 0.001, memory: 15990, top1_acc: 0.4675, top5_acc: 0.7200, loss_cls: 2.9875, loss: 2.9875 +2024-12-31 04:53:52,104 - pyskl - INFO - Epoch [132][1600/3746] lr: 3.735e-03, eta: 16:34:22, time: 0.876, data_time: 0.001, memory: 15990, top1_acc: 0.4603, top5_acc: 0.7152, loss_cls: 3.0136, loss: 3.0136 +2024-12-31 04:55:18,496 - pyskl - INFO - Epoch [132][1700/3746] lr: 3.725e-03, eta: 16:32:56, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.4686, top5_acc: 0.7233, loss_cls: 2.9686, loss: 2.9686 +2024-12-31 04:56:44,983 - pyskl - INFO - Epoch [132][1800/3746] lr: 3.714e-03, eta: 16:31:30, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.4502, top5_acc: 0.7089, loss_cls: 3.0778, loss: 3.0778 +2024-12-31 04:58:11,964 - pyskl - INFO - Epoch [132][1900/3746] lr: 3.704e-03, eta: 16:30:05, time: 0.870, data_time: 0.001, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7122, loss_cls: 3.0624, loss: 3.0624 +2024-12-31 04:59:40,505 - pyskl - INFO - Epoch [132][2000/3746] lr: 3.693e-03, eta: 16:28:40, time: 0.885, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.7041, loss_cls: 3.0582, loss: 3.0582 +2024-12-31 05:01:08,266 - pyskl - INFO - Epoch [132][2100/3746] lr: 3.683e-03, eta: 16:27:14, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7131, loss_cls: 3.0521, loss: 3.0521 +2024-12-31 05:02:36,150 - pyskl - INFO - Epoch [132][2200/3746] lr: 3.672e-03, eta: 16:25:49, time: 0.879, data_time: 0.001, memory: 15990, top1_acc: 0.4544, top5_acc: 0.7097, loss_cls: 3.0557, loss: 3.0557 +2024-12-31 05:04:04,195 - pyskl - INFO - Epoch [132][2300/3746] lr: 3.662e-03, eta: 16:24:23, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.4575, top5_acc: 0.7150, loss_cls: 3.0292, loss: 3.0292 +2024-12-31 05:05:32,390 - pyskl - INFO - Epoch [132][2400/3746] lr: 3.651e-03, eta: 16:22:58, time: 0.882, data_time: 0.001, memory: 15990, top1_acc: 0.4683, top5_acc: 0.7195, loss_cls: 3.0032, loss: 3.0032 +2024-12-31 05:07:00,674 - pyskl - INFO - Epoch [132][2500/3746] lr: 3.641e-03, eta: 16:21:32, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.4614, top5_acc: 0.7158, loss_cls: 3.0079, loss: 3.0079 +2024-12-31 05:08:29,430 - pyskl - INFO - Epoch [132][2600/3746] lr: 3.630e-03, eta: 16:20:07, time: 0.888, data_time: 0.000, memory: 15990, top1_acc: 0.4519, top5_acc: 0.7127, loss_cls: 3.0539, loss: 3.0539 +2024-12-31 05:09:58,001 - pyskl - INFO - Epoch [132][2700/3746] lr: 3.620e-03, eta: 16:18:42, time: 0.886, data_time: 0.001, memory: 15990, top1_acc: 0.4562, top5_acc: 0.7050, loss_cls: 3.0595, loss: 3.0595 +2024-12-31 05:11:26,365 - pyskl - INFO - Epoch [132][2800/3746] lr: 3.609e-03, eta: 16:17:16, time: 0.884, data_time: 0.001, memory: 15990, top1_acc: 0.4594, top5_acc: 0.7111, loss_cls: 3.0308, loss: 3.0308 +2024-12-31 05:12:54,183 - pyskl - INFO - Epoch [132][2900/3746] lr: 3.599e-03, eta: 16:15:51, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.4539, top5_acc: 0.7055, loss_cls: 3.0424, loss: 3.0424 +2024-12-31 05:14:21,444 - pyskl - INFO - Epoch [132][3000/3746] lr: 3.588e-03, eta: 16:14:25, time: 0.873, data_time: 0.001, memory: 15990, top1_acc: 0.4534, top5_acc: 0.7052, loss_cls: 3.0720, loss: 3.0720 +2024-12-31 05:15:47,839 - pyskl - INFO - Epoch [132][3100/3746] lr: 3.578e-03, eta: 16:13:00, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7100, loss_cls: 3.0301, loss: 3.0301 +2024-12-31 05:17:13,922 - pyskl - INFO - Epoch [132][3200/3746] lr: 3.568e-03, eta: 16:11:34, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.4616, top5_acc: 0.7197, loss_cls: 3.0125, loss: 3.0125 +2024-12-31 05:18:41,185 - pyskl - INFO - Epoch [132][3300/3746] lr: 3.557e-03, eta: 16:10:08, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.4564, top5_acc: 0.7198, loss_cls: 3.0293, loss: 3.0293 +2024-12-31 05:20:08,843 - pyskl - INFO - Epoch [132][3400/3746] lr: 3.547e-03, eta: 16:08:43, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.4492, top5_acc: 0.7052, loss_cls: 3.0831, loss: 3.0831 +2024-12-31 05:21:36,636 - pyskl - INFO - Epoch [132][3500/3746] lr: 3.537e-03, eta: 16:07:17, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.4555, top5_acc: 0.7072, loss_cls: 3.0349, loss: 3.0349 +2024-12-31 05:23:04,203 - pyskl - INFO - Epoch [132][3600/3746] lr: 3.526e-03, eta: 16:05:52, time: 0.876, data_time: 0.001, memory: 15990, top1_acc: 0.4533, top5_acc: 0.7048, loss_cls: 3.0620, loss: 3.0620 +2024-12-31 05:24:31,982 - pyskl - INFO - Epoch [132][3700/3746] lr: 3.516e-03, eta: 16:04:26, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.4462, top5_acc: 0.7006, loss_cls: 3.0915, loss: 3.0915 +2024-12-31 05:25:14,500 - pyskl - INFO - Saving checkpoint at 132 epochs +2024-12-31 05:27:17,614 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 05:27:18,414 - pyskl - INFO - +top1_acc 0.3807 +top5_acc 0.6295 +2024-12-31 05:27:18,414 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 05:27:18,466 - pyskl - INFO - +mean_acc 0.3804 +2024-12-31 05:27:18,472 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_130.pth was removed +2024-12-31 05:27:18,759 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_132.pth. +2024-12-31 05:27:18,759 - pyskl - INFO - Best top1_acc is 0.3807 at 132 epoch. +2024-12-31 05:27:18,773 - pyskl - INFO - Epoch(val) [132][309] top1_acc: 0.3807, top5_acc: 0.6295, mean_class_accuracy: 0.3804 +2024-12-31 05:31:49,457 - pyskl - INFO - Epoch [133][100/3746] lr: 3.501e-03, eta: 16:02:41, time: 2.707, data_time: 1.635, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7341, loss_cls: 2.8797, loss: 2.8797 +2024-12-31 05:33:17,163 - pyskl - INFO - Epoch [133][200/3746] lr: 3.491e-03, eta: 16:01:15, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.4773, top5_acc: 0.7317, loss_cls: 2.9214, loss: 2.9214 +2024-12-31 05:34:44,634 - pyskl - INFO - Epoch [133][300/3746] lr: 3.480e-03, eta: 15:59:50, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.4675, top5_acc: 0.7244, loss_cls: 2.9738, loss: 2.9738 +2024-12-31 05:36:12,337 - pyskl - INFO - Epoch [133][400/3746] lr: 3.470e-03, eta: 15:58:24, time: 0.877, data_time: 0.000, memory: 15990, top1_acc: 0.4883, top5_acc: 0.7316, loss_cls: 2.9282, loss: 2.9282 +2024-12-31 05:37:40,082 - pyskl - INFO - Epoch [133][500/3746] lr: 3.460e-03, eta: 15:56:59, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.4673, top5_acc: 0.7186, loss_cls: 2.9694, loss: 2.9694 +2024-12-31 05:39:07,464 - pyskl - INFO - Epoch [133][600/3746] lr: 3.450e-03, eta: 15:55:33, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.4750, top5_acc: 0.7305, loss_cls: 2.9283, loss: 2.9283 +2024-12-31 05:40:35,779 - pyskl - INFO - Epoch [133][700/3746] lr: 3.440e-03, eta: 15:54:08, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.4763, top5_acc: 0.7212, loss_cls: 2.9572, loss: 2.9572 +2024-12-31 05:42:03,571 - pyskl - INFO - Epoch [133][800/3746] lr: 3.429e-03, eta: 15:52:42, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.4655, top5_acc: 0.7159, loss_cls: 3.0075, loss: 3.0075 +2024-12-31 05:43:31,495 - pyskl - INFO - Epoch [133][900/3746] lr: 3.419e-03, eta: 15:51:17, time: 0.879, data_time: 0.001, memory: 15990, top1_acc: 0.4659, top5_acc: 0.7123, loss_cls: 3.0110, loss: 3.0110 +2024-12-31 05:44:59,556 - pyskl - INFO - Epoch [133][1000/3746] lr: 3.409e-03, eta: 15:49:51, time: 0.881, data_time: 0.001, memory: 15990, top1_acc: 0.4664, top5_acc: 0.7184, loss_cls: 2.9699, loss: 2.9699 +2024-12-31 05:46:28,188 - pyskl - INFO - Epoch [133][1100/3746] lr: 3.399e-03, eta: 15:48:26, time: 0.886, data_time: 0.001, memory: 15990, top1_acc: 0.4720, top5_acc: 0.7284, loss_cls: 2.9630, loss: 2.9630 +2024-12-31 05:47:56,734 - pyskl - INFO - Epoch [133][1200/3746] lr: 3.389e-03, eta: 15:47:00, time: 0.885, data_time: 0.000, memory: 15990, top1_acc: 0.4587, top5_acc: 0.7161, loss_cls: 3.0332, loss: 3.0332 +2024-12-31 05:49:25,195 - pyskl - INFO - Epoch [133][1300/3746] lr: 3.379e-03, eta: 15:45:35, time: 0.885, data_time: 0.000, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7180, loss_cls: 2.9611, loss: 2.9611 +2024-12-31 05:50:53,556 - pyskl - INFO - Epoch [133][1400/3746] lr: 3.369e-03, eta: 15:44:09, time: 0.884, data_time: 0.001, memory: 15990, top1_acc: 0.4686, top5_acc: 0.7148, loss_cls: 3.0147, loss: 3.0147 +2024-12-31 05:52:22,417 - pyskl - INFO - Epoch [133][1500/3746] lr: 3.359e-03, eta: 15:42:44, time: 0.889, data_time: 0.001, memory: 15990, top1_acc: 0.4767, top5_acc: 0.7227, loss_cls: 2.9729, loss: 2.9729 +2024-12-31 05:53:49,263 - pyskl - INFO - Epoch [133][1600/3746] lr: 3.348e-03, eta: 15:41:18, time: 0.868, data_time: 0.001, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7134, loss_cls: 3.0133, loss: 3.0133 +2024-12-31 05:55:15,360 - pyskl - INFO - Epoch [133][1700/3746] lr: 3.338e-03, eta: 15:39:53, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4727, top5_acc: 0.7275, loss_cls: 2.9799, loss: 2.9799 +2024-12-31 05:56:41,685 - pyskl - INFO - Epoch [133][1800/3746] lr: 3.328e-03, eta: 15:38:27, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4548, top5_acc: 0.7216, loss_cls: 3.0146, loss: 3.0146 +2024-12-31 05:58:08,954 - pyskl - INFO - Epoch [133][1900/3746] lr: 3.318e-03, eta: 15:37:01, time: 0.873, data_time: 0.001, memory: 15990, top1_acc: 0.4698, top5_acc: 0.7189, loss_cls: 2.9922, loss: 2.9922 +2024-12-31 05:59:36,177 - pyskl - INFO - Epoch [133][2000/3746] lr: 3.308e-03, eta: 15:35:36, time: 0.872, data_time: 0.001, memory: 15990, top1_acc: 0.4692, top5_acc: 0.7228, loss_cls: 2.9883, loss: 2.9883 +2024-12-31 06:01:03,270 - pyskl - INFO - Epoch [133][2100/3746] lr: 3.298e-03, eta: 15:34:10, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.4589, top5_acc: 0.7156, loss_cls: 3.0016, loss: 3.0016 +2024-12-31 06:02:31,438 - pyskl - INFO - Epoch [133][2200/3746] lr: 3.288e-03, eta: 15:32:45, time: 0.882, data_time: 0.001, memory: 15990, top1_acc: 0.4594, top5_acc: 0.7114, loss_cls: 3.0475, loss: 3.0475 +2024-12-31 06:03:59,246 - pyskl - INFO - Epoch [133][2300/3746] lr: 3.278e-03, eta: 15:31:19, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.4719, top5_acc: 0.7214, loss_cls: 2.9625, loss: 2.9625 +2024-12-31 06:05:26,897 - pyskl - INFO - Epoch [133][2400/3746] lr: 3.268e-03, eta: 15:29:53, time: 0.876, data_time: 0.001, memory: 15990, top1_acc: 0.4728, top5_acc: 0.7223, loss_cls: 2.9722, loss: 2.9722 +2024-12-31 06:06:54,751 - pyskl - INFO - Epoch [133][2500/3746] lr: 3.259e-03, eta: 15:28:28, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.4681, top5_acc: 0.7253, loss_cls: 2.9766, loss: 2.9766 +2024-12-31 06:08:23,047 - pyskl - INFO - Epoch [133][2600/3746] lr: 3.249e-03, eta: 15:27:02, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.4627, top5_acc: 0.7212, loss_cls: 2.9860, loss: 2.9860 +2024-12-31 06:09:50,310 - pyskl - INFO - Epoch [133][2700/3746] lr: 3.239e-03, eta: 15:25:37, time: 0.873, data_time: 0.001, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7158, loss_cls: 3.0042, loss: 3.0042 +2024-12-31 06:11:18,200 - pyskl - INFO - Epoch [133][2800/3746] lr: 3.229e-03, eta: 15:24:11, time: 0.879, data_time: 0.001, memory: 15990, top1_acc: 0.4664, top5_acc: 0.7228, loss_cls: 2.9914, loss: 2.9914 +2024-12-31 06:12:46,064 - pyskl - INFO - Epoch [133][2900/3746] lr: 3.219e-03, eta: 15:22:46, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.4620, top5_acc: 0.7244, loss_cls: 3.0059, loss: 3.0059 +2024-12-31 06:14:11,979 - pyskl - INFO - Epoch [133][3000/3746] lr: 3.209e-03, eta: 15:21:20, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4652, top5_acc: 0.7169, loss_cls: 3.0241, loss: 3.0241 +2024-12-31 06:15:38,072 - pyskl - INFO - Epoch [133][3100/3746] lr: 3.199e-03, eta: 15:19:54, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4639, top5_acc: 0.7177, loss_cls: 2.9992, loss: 2.9992 +2024-12-31 06:17:04,527 - pyskl - INFO - Epoch [133][3200/3746] lr: 3.189e-03, eta: 15:18:28, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.4620, top5_acc: 0.7180, loss_cls: 3.0098, loss: 3.0098 +2024-12-31 06:18:32,081 - pyskl - INFO - Epoch [133][3300/3746] lr: 3.180e-03, eta: 15:17:03, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.4575, top5_acc: 0.7150, loss_cls: 3.0131, loss: 3.0131 +2024-12-31 06:20:00,338 - pyskl - INFO - Epoch [133][3400/3746] lr: 3.170e-03, eta: 15:15:37, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.4761, top5_acc: 0.7297, loss_cls: 2.9429, loss: 2.9429 +2024-12-31 06:21:27,830 - pyskl - INFO - Epoch [133][3500/3746] lr: 3.160e-03, eta: 15:14:12, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.4609, top5_acc: 0.7139, loss_cls: 3.0092, loss: 3.0092 +2024-12-31 06:22:56,341 - pyskl - INFO - Epoch [133][3600/3746] lr: 3.150e-03, eta: 15:12:46, time: 0.885, data_time: 0.001, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7123, loss_cls: 3.0441, loss: 3.0441 +2024-12-31 06:24:24,532 - pyskl - INFO - Epoch [133][3700/3746] lr: 3.140e-03, eta: 15:11:21, time: 0.882, data_time: 0.000, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7134, loss_cls: 3.0143, loss: 3.0143 +2024-12-31 06:25:07,473 - pyskl - INFO - Saving checkpoint at 133 epochs +2024-12-31 06:27:12,584 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 06:27:13,365 - pyskl - INFO - +top1_acc 0.3805 +top5_acc 0.6355 +2024-12-31 06:27:13,366 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 06:27:13,417 - pyskl - INFO - +mean_acc 0.3803 +2024-12-31 06:27:13,434 - pyskl - INFO - Epoch(val) [133][309] top1_acc: 0.3805, top5_acc: 0.6355, mean_class_accuracy: 0.3803 +2024-12-31 06:31:42,947 - pyskl - INFO - Epoch [134][100/3746] lr: 3.126e-03, eta: 15:09:34, time: 2.695, data_time: 1.629, memory: 15990, top1_acc: 0.4744, top5_acc: 0.7312, loss_cls: 2.9323, loss: 2.9323 +2024-12-31 06:33:10,713 - pyskl - INFO - Epoch [134][200/3746] lr: 3.117e-03, eta: 15:08:08, time: 0.878, data_time: 0.000, memory: 15990, top1_acc: 0.4845, top5_acc: 0.7377, loss_cls: 2.9038, loss: 2.9038 +2024-12-31 06:34:38,558 - pyskl - INFO - Epoch [134][300/3746] lr: 3.107e-03, eta: 15:06:43, time: 0.878, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7331, loss_cls: 2.9257, loss: 2.9257 +2024-12-31 06:36:06,392 - pyskl - INFO - Epoch [134][400/3746] lr: 3.097e-03, eta: 15:05:17, time: 0.878, data_time: 0.000, memory: 15990, top1_acc: 0.4728, top5_acc: 0.7256, loss_cls: 2.9525, loss: 2.9525 +2024-12-31 06:37:34,447 - pyskl - INFO - Epoch [134][500/3746] lr: 3.087e-03, eta: 15:03:52, time: 0.881, data_time: 0.001, memory: 15990, top1_acc: 0.4798, top5_acc: 0.7292, loss_cls: 2.9416, loss: 2.9416 +2024-12-31 06:39:02,757 - pyskl - INFO - Epoch [134][600/3746] lr: 3.078e-03, eta: 15:02:26, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.4698, top5_acc: 0.7273, loss_cls: 2.9439, loss: 2.9439 +2024-12-31 06:40:30,582 - pyskl - INFO - Epoch [134][700/3746] lr: 3.068e-03, eta: 15:01:01, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.4697, top5_acc: 0.7202, loss_cls: 2.9540, loss: 2.9540 +2024-12-31 06:41:59,124 - pyskl - INFO - Epoch [134][800/3746] lr: 3.059e-03, eta: 14:59:35, time: 0.885, data_time: 0.001, memory: 15990, top1_acc: 0.4719, top5_acc: 0.7327, loss_cls: 2.9247, loss: 2.9247 +2024-12-31 06:43:27,540 - pyskl - INFO - Epoch [134][900/3746] lr: 3.049e-03, eta: 14:58:10, time: 0.884, data_time: 0.000, memory: 15990, top1_acc: 0.4891, top5_acc: 0.7394, loss_cls: 2.8733, loss: 2.8733 +2024-12-31 06:44:55,601 - pyskl - INFO - Epoch [134][1000/3746] lr: 3.039e-03, eta: 14:56:44, time: 0.881, data_time: 0.001, memory: 15990, top1_acc: 0.4797, top5_acc: 0.7273, loss_cls: 2.9151, loss: 2.9151 +2024-12-31 06:46:23,955 - pyskl - INFO - Epoch [134][1100/3746] lr: 3.030e-03, eta: 14:55:19, time: 0.884, data_time: 0.000, memory: 15990, top1_acc: 0.4783, top5_acc: 0.7273, loss_cls: 2.9459, loss: 2.9459 +2024-12-31 06:47:52,195 - pyskl - INFO - Epoch [134][1200/3746] lr: 3.020e-03, eta: 14:53:53, time: 0.882, data_time: 0.001, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7247, loss_cls: 2.9520, loss: 2.9520 +2024-12-31 06:49:19,865 - pyskl - INFO - Epoch [134][1300/3746] lr: 3.011e-03, eta: 14:52:27, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.4739, top5_acc: 0.7362, loss_cls: 2.9066, loss: 2.9066 +2024-12-31 06:50:47,611 - pyskl - INFO - Epoch [134][1400/3746] lr: 3.001e-03, eta: 14:51:02, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.4741, top5_acc: 0.7300, loss_cls: 2.9097, loss: 2.9097 +2024-12-31 06:52:15,020 - pyskl - INFO - Epoch [134][1500/3746] lr: 2.991e-03, eta: 14:49:36, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.4744, top5_acc: 0.7331, loss_cls: 2.9440, loss: 2.9440 +2024-12-31 06:53:41,461 - pyskl - INFO - Epoch [134][1600/3746] lr: 2.982e-03, eta: 14:48:10, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.4778, top5_acc: 0.7259, loss_cls: 2.9601, loss: 2.9601 +2024-12-31 06:55:07,133 - pyskl - INFO - Epoch [134][1700/3746] lr: 2.972e-03, eta: 14:46:45, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4794, top5_acc: 0.7353, loss_cls: 2.9196, loss: 2.9196 +2024-12-31 06:56:33,696 - pyskl - INFO - Epoch [134][1800/3746] lr: 2.963e-03, eta: 14:45:19, time: 0.866, data_time: 0.001, memory: 15990, top1_acc: 0.4739, top5_acc: 0.7262, loss_cls: 2.9649, loss: 2.9649 +2024-12-31 06:58:01,179 - pyskl - INFO - Epoch [134][1900/3746] lr: 2.953e-03, eta: 14:43:53, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7241, loss_cls: 2.9802, loss: 2.9802 +2024-12-31 06:59:28,526 - pyskl - INFO - Epoch [134][2000/3746] lr: 2.944e-03, eta: 14:42:27, time: 0.873, data_time: 0.001, memory: 15990, top1_acc: 0.4763, top5_acc: 0.7344, loss_cls: 2.9118, loss: 2.9118 +2024-12-31 07:00:56,563 - pyskl - INFO - Epoch [134][2100/3746] lr: 2.935e-03, eta: 14:41:02, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.4784, top5_acc: 0.7309, loss_cls: 2.9339, loss: 2.9339 +2024-12-31 07:02:24,724 - pyskl - INFO - Epoch [134][2200/3746] lr: 2.925e-03, eta: 14:39:36, time: 0.882, data_time: 0.000, memory: 15990, top1_acc: 0.4745, top5_acc: 0.7202, loss_cls: 2.9479, loss: 2.9479 +2024-12-31 07:03:52,762 - pyskl - INFO - Epoch [134][2300/3746] lr: 2.916e-03, eta: 14:38:11, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.4748, top5_acc: 0.7220, loss_cls: 2.9394, loss: 2.9394 +2024-12-31 07:05:21,286 - pyskl - INFO - Epoch [134][2400/3746] lr: 2.906e-03, eta: 14:36:45, time: 0.885, data_time: 0.000, memory: 15990, top1_acc: 0.4728, top5_acc: 0.7211, loss_cls: 2.9882, loss: 2.9882 +2024-12-31 07:06:49,121 - pyskl - INFO - Epoch [134][2500/3746] lr: 2.897e-03, eta: 14:35:20, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.4764, top5_acc: 0.7294, loss_cls: 2.9194, loss: 2.9194 +2024-12-31 07:08:16,788 - pyskl - INFO - Epoch [134][2600/3746] lr: 2.888e-03, eta: 14:33:54, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.4666, top5_acc: 0.7159, loss_cls: 2.9747, loss: 2.9747 +2024-12-31 07:09:44,192 - pyskl - INFO - Epoch [134][2700/3746] lr: 2.878e-03, eta: 14:32:28, time: 0.874, data_time: 0.001, memory: 15990, top1_acc: 0.4741, top5_acc: 0.7352, loss_cls: 2.9076, loss: 2.9076 +2024-12-31 07:11:11,977 - pyskl - INFO - Epoch [134][2800/3746] lr: 2.869e-03, eta: 14:31:03, time: 0.878, data_time: 0.000, memory: 15990, top1_acc: 0.4752, top5_acc: 0.7292, loss_cls: 2.9362, loss: 2.9362 +2024-12-31 07:12:38,897 - pyskl - INFO - Epoch [134][2900/3746] lr: 2.860e-03, eta: 14:29:37, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.4692, top5_acc: 0.7158, loss_cls: 3.0004, loss: 3.0004 +2024-12-31 07:14:04,862 - pyskl - INFO - Epoch [134][3000/3746] lr: 2.850e-03, eta: 14:28:11, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4670, top5_acc: 0.7183, loss_cls: 3.0024, loss: 3.0024 +2024-12-31 07:15:31,355 - pyskl - INFO - Epoch [134][3100/3746] lr: 2.841e-03, eta: 14:26:45, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.4689, top5_acc: 0.7188, loss_cls: 2.9954, loss: 2.9954 +2024-12-31 07:16:58,381 - pyskl - INFO - Epoch [134][3200/3746] lr: 2.832e-03, eta: 14:25:20, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.4817, top5_acc: 0.7325, loss_cls: 2.9260, loss: 2.9260 +2024-12-31 07:18:26,020 - pyskl - INFO - Epoch [134][3300/3746] lr: 2.822e-03, eta: 14:23:54, time: 0.876, data_time: 0.001, memory: 15990, top1_acc: 0.4689, top5_acc: 0.7238, loss_cls: 2.9842, loss: 2.9842 +2024-12-31 07:19:54,348 - pyskl - INFO - Epoch [134][3400/3746] lr: 2.813e-03, eta: 14:22:29, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.4669, top5_acc: 0.7269, loss_cls: 2.9587, loss: 2.9587 +2024-12-31 07:21:21,663 - pyskl - INFO - Epoch [134][3500/3746] lr: 2.804e-03, eta: 14:21:03, time: 0.873, data_time: 0.001, memory: 15990, top1_acc: 0.4798, top5_acc: 0.7244, loss_cls: 2.9591, loss: 2.9591 +2024-12-31 07:22:49,807 - pyskl - INFO - Epoch [134][3600/3746] lr: 2.795e-03, eta: 14:19:37, time: 0.881, data_time: 0.001, memory: 15990, top1_acc: 0.4644, top5_acc: 0.7116, loss_cls: 3.0273, loss: 3.0273 +2024-12-31 07:24:17,839 - pyskl - INFO - Epoch [134][3700/3746] lr: 2.786e-03, eta: 14:18:12, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.4661, top5_acc: 0.7228, loss_cls: 2.9625, loss: 2.9625 +2024-12-31 07:25:00,031 - pyskl - INFO - Saving checkpoint at 134 epochs +2024-12-31 07:27:00,406 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 07:27:01,091 - pyskl - INFO - +top1_acc 0.3818 +top5_acc 0.6329 +2024-12-31 07:27:01,091 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 07:27:01,139 - pyskl - INFO - +mean_acc 0.3815 +2024-12-31 07:27:01,146 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_132.pth was removed +2024-12-31 07:27:01,459 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_134.pth. +2024-12-31 07:27:01,460 - pyskl - INFO - Best top1_acc is 0.3818 at 134 epoch. +2024-12-31 07:27:01,477 - pyskl - INFO - Epoch(val) [134][309] top1_acc: 0.3818, top5_acc: 0.6329, mean_class_accuracy: 0.3815 +2024-12-31 07:31:32,888 - pyskl - INFO - Epoch [135][100/3746] lr: 2.772e-03, eta: 14:16:24, time: 2.714, data_time: 1.642, memory: 15990, top1_acc: 0.4966, top5_acc: 0.7453, loss_cls: 2.7961, loss: 2.7961 +2024-12-31 07:33:01,228 - pyskl - INFO - Epoch [135][200/3746] lr: 2.763e-03, eta: 14:14:58, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.4892, top5_acc: 0.7430, loss_cls: 2.8598, loss: 2.8598 +2024-12-31 07:34:29,884 - pyskl - INFO - Epoch [135][300/3746] lr: 2.754e-03, eta: 14:13:33, time: 0.887, data_time: 0.001, memory: 15990, top1_acc: 0.4792, top5_acc: 0.7314, loss_cls: 2.9220, loss: 2.9220 +2024-12-31 07:35:57,541 - pyskl - INFO - Epoch [135][400/3746] lr: 2.745e-03, eta: 14:12:07, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.4883, top5_acc: 0.7378, loss_cls: 2.8791, loss: 2.8791 +2024-12-31 07:37:26,098 - pyskl - INFO - Epoch [135][500/3746] lr: 2.735e-03, eta: 14:10:41, time: 0.886, data_time: 0.000, memory: 15990, top1_acc: 0.4828, top5_acc: 0.7308, loss_cls: 2.9126, loss: 2.9126 +2024-12-31 07:38:54,721 - pyskl - INFO - Epoch [135][600/3746] lr: 2.726e-03, eta: 14:09:16, time: 0.886, data_time: 0.001, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7331, loss_cls: 2.8886, loss: 2.8886 +2024-12-31 07:40:22,327 - pyskl - INFO - Epoch [135][700/3746] lr: 2.717e-03, eta: 14:07:50, time: 0.876, data_time: 0.001, memory: 15990, top1_acc: 0.4833, top5_acc: 0.7388, loss_cls: 2.8704, loss: 2.8704 +2024-12-31 07:41:50,847 - pyskl - INFO - Epoch [135][800/3746] lr: 2.708e-03, eta: 14:06:25, time: 0.885, data_time: 0.001, memory: 15990, top1_acc: 0.4928, top5_acc: 0.7392, loss_cls: 2.8385, loss: 2.8385 +2024-12-31 07:43:18,590 - pyskl - INFO - Epoch [135][900/3746] lr: 2.699e-03, eta: 14:04:59, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.4986, top5_acc: 0.7436, loss_cls: 2.8364, loss: 2.8364 +2024-12-31 07:44:46,932 - pyskl - INFO - Epoch [135][1000/3746] lr: 2.690e-03, eta: 14:03:33, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.4881, top5_acc: 0.7453, loss_cls: 2.8585, loss: 2.8585 +2024-12-31 07:46:14,948 - pyskl - INFO - Epoch [135][1100/3746] lr: 2.681e-03, eta: 14:02:08, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.4828, top5_acc: 0.7405, loss_cls: 2.8787, loss: 2.8787 +2024-12-31 07:47:42,523 - pyskl - INFO - Epoch [135][1200/3746] lr: 2.672e-03, eta: 14:00:42, time: 0.876, data_time: 0.001, memory: 15990, top1_acc: 0.4758, top5_acc: 0.7333, loss_cls: 2.9028, loss: 2.9028 +2024-12-31 07:49:10,943 - pyskl - INFO - Epoch [135][1300/3746] lr: 2.663e-03, eta: 13:59:17, time: 0.884, data_time: 0.001, memory: 15990, top1_acc: 0.4881, top5_acc: 0.7430, loss_cls: 2.8501, loss: 2.8501 +2024-12-31 07:50:38,237 - pyskl - INFO - Epoch [135][1400/3746] lr: 2.654e-03, eta: 13:57:51, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7397, loss_cls: 2.9012, loss: 2.9012 +2024-12-31 07:52:04,613 - pyskl - INFO - Epoch [135][1500/3746] lr: 2.645e-03, eta: 13:56:25, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.4806, top5_acc: 0.7250, loss_cls: 2.9415, loss: 2.9415 +2024-12-31 07:53:31,482 - pyskl - INFO - Epoch [135][1600/3746] lr: 2.636e-03, eta: 13:54:59, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.4886, top5_acc: 0.7391, loss_cls: 2.8649, loss: 2.8649 +2024-12-31 07:54:57,828 - pyskl - INFO - Epoch [135][1700/3746] lr: 2.627e-03, eta: 13:53:33, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4834, top5_acc: 0.7312, loss_cls: 2.9159, loss: 2.9159 +2024-12-31 07:56:25,744 - pyskl - INFO - Epoch [135][1800/3746] lr: 2.618e-03, eta: 13:52:08, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.4922, top5_acc: 0.7397, loss_cls: 2.8500, loss: 2.8500 +2024-12-31 07:57:53,692 - pyskl - INFO - Epoch [135][1900/3746] lr: 2.609e-03, eta: 13:50:42, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.4842, top5_acc: 0.7388, loss_cls: 2.9093, loss: 2.9093 +2024-12-31 07:59:22,124 - pyskl - INFO - Epoch [135][2000/3746] lr: 2.600e-03, eta: 13:49:17, time: 0.884, data_time: 0.001, memory: 15990, top1_acc: 0.4859, top5_acc: 0.7342, loss_cls: 2.8785, loss: 2.8785 +2024-12-31 08:00:50,525 - pyskl - INFO - Epoch [135][2100/3746] lr: 2.591e-03, eta: 13:47:51, time: 0.884, data_time: 0.001, memory: 15990, top1_acc: 0.4870, top5_acc: 0.7358, loss_cls: 2.8851, loss: 2.8851 +2024-12-31 08:02:19,515 - pyskl - INFO - Epoch [135][2200/3746] lr: 2.583e-03, eta: 13:46:25, time: 0.890, data_time: 0.001, memory: 15990, top1_acc: 0.4689, top5_acc: 0.7336, loss_cls: 2.9231, loss: 2.9231 +2024-12-31 08:03:48,245 - pyskl - INFO - Epoch [135][2300/3746] lr: 2.574e-03, eta: 13:45:00, time: 0.887, data_time: 0.001, memory: 15990, top1_acc: 0.4659, top5_acc: 0.7166, loss_cls: 2.9947, loss: 2.9947 +2024-12-31 08:05:16,569 - pyskl - INFO - Epoch [135][2400/3746] lr: 2.565e-03, eta: 13:43:34, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.4664, top5_acc: 0.7208, loss_cls: 2.9850, loss: 2.9850 +2024-12-31 08:06:45,331 - pyskl - INFO - Epoch [135][2500/3746] lr: 2.556e-03, eta: 13:42:09, time: 0.888, data_time: 0.001, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7367, loss_cls: 2.8751, loss: 2.8751 +2024-12-31 08:08:13,625 - pyskl - INFO - Epoch [135][2600/3746] lr: 2.547e-03, eta: 13:40:43, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.4739, top5_acc: 0.7239, loss_cls: 2.9249, loss: 2.9249 +2024-12-31 08:09:42,390 - pyskl - INFO - Epoch [135][2700/3746] lr: 2.538e-03, eta: 13:39:18, time: 0.888, data_time: 0.001, memory: 15990, top1_acc: 0.4686, top5_acc: 0.7150, loss_cls: 2.9653, loss: 2.9653 +2024-12-31 08:11:09,833 - pyskl - INFO - Epoch [135][2800/3746] lr: 2.530e-03, eta: 13:37:52, time: 0.874, data_time: 0.001, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7278, loss_cls: 2.9429, loss: 2.9429 +2024-12-31 08:12:35,954 - pyskl - INFO - Epoch [135][2900/3746] lr: 2.521e-03, eta: 13:36:26, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4884, top5_acc: 0.7306, loss_cls: 2.8982, loss: 2.8982 +2024-12-31 08:14:01,885 - pyskl - INFO - Epoch [135][3000/3746] lr: 2.512e-03, eta: 13:35:00, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4784, top5_acc: 0.7277, loss_cls: 2.9434, loss: 2.9434 +2024-12-31 08:15:29,492 - pyskl - INFO - Epoch [135][3100/3746] lr: 2.503e-03, eta: 13:33:34, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.4842, top5_acc: 0.7389, loss_cls: 2.8917, loss: 2.8917 +2024-12-31 08:16:57,520 - pyskl - INFO - Epoch [135][3200/3746] lr: 2.495e-03, eta: 13:32:09, time: 0.880, data_time: 0.000, memory: 15990, top1_acc: 0.4680, top5_acc: 0.7242, loss_cls: 2.9517, loss: 2.9517 +2024-12-31 08:18:26,091 - pyskl - INFO - Epoch [135][3300/3746] lr: 2.486e-03, eta: 13:30:43, time: 0.886, data_time: 0.000, memory: 15990, top1_acc: 0.4819, top5_acc: 0.7373, loss_cls: 2.8700, loss: 2.8700 +2024-12-31 08:19:54,715 - pyskl - INFO - Epoch [135][3400/3746] lr: 2.477e-03, eta: 13:29:18, time: 0.886, data_time: 0.000, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7186, loss_cls: 2.9512, loss: 2.9512 +2024-12-31 08:21:23,845 - pyskl - INFO - Epoch [135][3500/3746] lr: 2.469e-03, eta: 13:27:52, time: 0.891, data_time: 0.001, memory: 15990, top1_acc: 0.4814, top5_acc: 0.7298, loss_cls: 2.8922, loss: 2.8922 +2024-12-31 08:22:52,867 - pyskl - INFO - Epoch [135][3600/3746] lr: 2.460e-03, eta: 13:26:26, time: 0.890, data_time: 0.001, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7280, loss_cls: 2.9108, loss: 2.9108 +2024-12-31 08:24:21,931 - pyskl - INFO - Epoch [135][3700/3746] lr: 2.451e-03, eta: 13:25:01, time: 0.891, data_time: 0.000, memory: 15990, top1_acc: 0.4900, top5_acc: 0.7402, loss_cls: 2.8575, loss: 2.8575 +2024-12-31 08:25:05,017 - pyskl - INFO - Saving checkpoint at 135 epochs +2024-12-31 08:27:05,215 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 08:27:06,026 - pyskl - INFO - +top1_acc 0.3872 +top5_acc 0.6359 +2024-12-31 08:27:06,027 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 08:27:06,078 - pyskl - INFO - +mean_acc 0.3869 +2024-12-31 08:27:06,083 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_134.pth was removed +2024-12-31 08:27:06,358 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_135.pth. +2024-12-31 08:27:06,359 - pyskl - INFO - Best top1_acc is 0.3872 at 135 epoch. +2024-12-31 08:27:06,391 - pyskl - INFO - Epoch(val) [135][309] top1_acc: 0.3872, top5_acc: 0.6359, mean_class_accuracy: 0.3869 +2024-12-31 08:31:33,665 - pyskl - INFO - Epoch [136][100/3746] lr: 2.439e-03, eta: 13:23:11, time: 2.673, data_time: 1.613, memory: 15990, top1_acc: 0.4864, top5_acc: 0.7467, loss_cls: 2.8273, loss: 2.8273 +2024-12-31 08:33:01,099 - pyskl - INFO - Epoch [136][200/3746] lr: 2.430e-03, eta: 13:21:46, time: 0.874, data_time: 0.001, memory: 15990, top1_acc: 0.4936, top5_acc: 0.7467, loss_cls: 2.8425, loss: 2.8425 +2024-12-31 08:34:29,004 - pyskl - INFO - Epoch [136][300/3746] lr: 2.421e-03, eta: 13:20:20, time: 0.879, data_time: 0.001, memory: 15990, top1_acc: 0.5008, top5_acc: 0.7533, loss_cls: 2.7558, loss: 2.7558 +2024-12-31 08:35:56,749 - pyskl - INFO - Epoch [136][400/3746] lr: 2.413e-03, eta: 13:18:54, time: 0.877, data_time: 0.000, memory: 15990, top1_acc: 0.5031, top5_acc: 0.7530, loss_cls: 2.7882, loss: 2.7882 +2024-12-31 08:37:23,923 - pyskl - INFO - Epoch [136][500/3746] lr: 2.404e-03, eta: 13:17:28, time: 0.872, data_time: 0.001, memory: 15990, top1_acc: 0.4972, top5_acc: 0.7395, loss_cls: 2.8306, loss: 2.8306 +2024-12-31 08:38:51,954 - pyskl - INFO - Epoch [136][600/3746] lr: 2.396e-03, eta: 13:16:03, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.5038, top5_acc: 0.7423, loss_cls: 2.8047, loss: 2.8047 +2024-12-31 08:40:20,040 - pyskl - INFO - Epoch [136][700/3746] lr: 2.387e-03, eta: 13:14:37, time: 0.881, data_time: 0.001, memory: 15990, top1_acc: 0.4823, top5_acc: 0.7389, loss_cls: 2.8720, loss: 2.8720 +2024-12-31 08:41:47,249 - pyskl - INFO - Epoch [136][800/3746] lr: 2.379e-03, eta: 13:13:11, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.4834, top5_acc: 0.7423, loss_cls: 2.8403, loss: 2.8403 +2024-12-31 08:43:15,341 - pyskl - INFO - Epoch [136][900/3746] lr: 2.370e-03, eta: 13:11:45, time: 0.881, data_time: 0.001, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7414, loss_cls: 2.8330, loss: 2.8330 +2024-12-31 08:44:42,723 - pyskl - INFO - Epoch [136][1000/3746] lr: 2.362e-03, eta: 13:10:20, time: 0.874, data_time: 0.001, memory: 15990, top1_acc: 0.5056, top5_acc: 0.7470, loss_cls: 2.8073, loss: 2.8073 +2024-12-31 08:46:11,147 - pyskl - INFO - Epoch [136][1100/3746] lr: 2.353e-03, eta: 13:08:54, time: 0.884, data_time: 0.001, memory: 15990, top1_acc: 0.4947, top5_acc: 0.7411, loss_cls: 2.8371, loss: 2.8371 +2024-12-31 08:47:39,283 - pyskl - INFO - Epoch [136][1200/3746] lr: 2.345e-03, eta: 13:07:28, time: 0.881, data_time: 0.001, memory: 15990, top1_acc: 0.4984, top5_acc: 0.7495, loss_cls: 2.7874, loss: 2.7874 +2024-12-31 08:49:06,494 - pyskl - INFO - Epoch [136][1300/3746] lr: 2.336e-03, eta: 13:06:03, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.4925, top5_acc: 0.7436, loss_cls: 2.8575, loss: 2.8575 +2024-12-31 08:50:32,702 - pyskl - INFO - Epoch [136][1400/3746] lr: 2.328e-03, eta: 13:04:37, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4853, top5_acc: 0.7345, loss_cls: 2.8811, loss: 2.8811 +2024-12-31 08:51:58,638 - pyskl - INFO - Epoch [136][1500/3746] lr: 2.319e-03, eta: 13:03:11, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.4861, top5_acc: 0.7342, loss_cls: 2.8773, loss: 2.8773 +2024-12-31 08:53:25,054 - pyskl - INFO - Epoch [136][1600/3746] lr: 2.311e-03, eta: 13:01:45, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.4850, top5_acc: 0.7295, loss_cls: 2.8838, loss: 2.8838 +2024-12-31 08:54:50,479 - pyskl - INFO - Epoch [136][1700/3746] lr: 2.303e-03, eta: 13:00:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4886, top5_acc: 0.7444, loss_cls: 2.8554, loss: 2.8554 +2024-12-31 08:56:16,281 - pyskl - INFO - Epoch [136][1800/3746] lr: 2.294e-03, eta: 12:58:53, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5002, top5_acc: 0.7530, loss_cls: 2.8100, loss: 2.8100 +2024-12-31 08:57:41,024 - pyskl - INFO - Epoch [136][1900/3746] lr: 2.286e-03, eta: 12:57:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4872, top5_acc: 0.7416, loss_cls: 2.8584, loss: 2.8584 +2024-12-31 08:59:07,108 - pyskl - INFO - Epoch [136][2000/3746] lr: 2.277e-03, eta: 12:56:01, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4919, top5_acc: 0.7448, loss_cls: 2.8564, loss: 2.8564 +2024-12-31 09:00:32,814 - pyskl - INFO - Epoch [136][2100/3746] lr: 2.269e-03, eta: 12:54:35, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5008, top5_acc: 0.7423, loss_cls: 2.8405, loss: 2.8405 +2024-12-31 09:01:58,107 - pyskl - INFO - Epoch [136][2200/3746] lr: 2.261e-03, eta: 12:53:09, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4878, top5_acc: 0.7442, loss_cls: 2.8527, loss: 2.8527 +2024-12-31 09:03:23,377 - pyskl - INFO - Epoch [136][2300/3746] lr: 2.253e-03, eta: 12:51:43, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4845, top5_acc: 0.7302, loss_cls: 2.8774, loss: 2.8774 +2024-12-31 09:04:47,814 - pyskl - INFO - Epoch [136][2400/3746] lr: 2.244e-03, eta: 12:50:17, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4811, top5_acc: 0.7300, loss_cls: 2.8982, loss: 2.8982 +2024-12-31 09:06:13,266 - pyskl - INFO - Epoch [136][2500/3746] lr: 2.236e-03, eta: 12:48:51, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4916, top5_acc: 0.7325, loss_cls: 2.8829, loss: 2.8829 +2024-12-31 09:07:39,355 - pyskl - INFO - Epoch [136][2600/3746] lr: 2.228e-03, eta: 12:47:25, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4825, top5_acc: 0.7408, loss_cls: 2.8529, loss: 2.8529 +2024-12-31 09:09:04,751 - pyskl - INFO - Epoch [136][2700/3746] lr: 2.219e-03, eta: 12:45:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4823, top5_acc: 0.7309, loss_cls: 2.9106, loss: 2.9106 +2024-12-31 09:10:31,186 - pyskl - INFO - Epoch [136][2800/3746] lr: 2.211e-03, eta: 12:44:33, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.4931, top5_acc: 0.7402, loss_cls: 2.8662, loss: 2.8662 +2024-12-31 09:11:56,326 - pyskl - INFO - Epoch [136][2900/3746] lr: 2.203e-03, eta: 12:43:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4880, top5_acc: 0.7430, loss_cls: 2.8779, loss: 2.8779 +2024-12-31 09:13:21,596 - pyskl - INFO - Epoch [136][3000/3746] lr: 2.195e-03, eta: 12:41:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4894, top5_acc: 0.7359, loss_cls: 2.8977, loss: 2.8977 +2024-12-31 09:14:47,361 - pyskl - INFO - Epoch [136][3100/3746] lr: 2.187e-03, eta: 12:40:15, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4741, top5_acc: 0.7308, loss_cls: 2.9182, loss: 2.9182 +2024-12-31 09:16:12,749 - pyskl - INFO - Epoch [136][3200/3746] lr: 2.178e-03, eta: 12:38:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4802, top5_acc: 0.7386, loss_cls: 2.8679, loss: 2.8679 +2024-12-31 09:17:37,966 - pyskl - INFO - Epoch [136][3300/3746] lr: 2.170e-03, eta: 12:37:23, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4938, top5_acc: 0.7358, loss_cls: 2.8687, loss: 2.8687 +2024-12-31 09:19:03,471 - pyskl - INFO - Epoch [136][3400/3746] lr: 2.162e-03, eta: 12:35:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4791, top5_acc: 0.7295, loss_cls: 2.9104, loss: 2.9104 +2024-12-31 09:20:28,704 - pyskl - INFO - Epoch [136][3500/3746] lr: 2.154e-03, eta: 12:34:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4892, top5_acc: 0.7312, loss_cls: 2.9055, loss: 2.9055 +2024-12-31 09:21:54,737 - pyskl - INFO - Epoch [136][3600/3746] lr: 2.146e-03, eta: 12:33:06, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4764, top5_acc: 0.7206, loss_cls: 2.9502, loss: 2.9502 +2024-12-31 09:23:20,063 - pyskl - INFO - Epoch [136][3700/3746] lr: 2.138e-03, eta: 12:31:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4922, top5_acc: 0.7370, loss_cls: 2.8572, loss: 2.8572 +2024-12-31 09:24:00,828 - pyskl - INFO - Saving checkpoint at 136 epochs +2024-12-31 09:26:01,709 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 09:26:02,398 - pyskl - INFO - +top1_acc 0.3931 +top5_acc 0.6415 +2024-12-31 09:26:02,399 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 09:26:02,461 - pyskl - INFO - +mean_acc 0.3928 +2024-12-31 09:26:02,467 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_135.pth was removed +2024-12-31 09:26:02,788 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_136.pth. +2024-12-31 09:26:02,789 - pyskl - INFO - Best top1_acc is 0.3931 at 136 epoch. +2024-12-31 09:26:02,807 - pyskl - INFO - Epoch(val) [136][309] top1_acc: 0.3931, top5_acc: 0.6415, mean_class_accuracy: 0.3928 +2024-12-31 09:30:20,913 - pyskl - INFO - Epoch [137][100/3746] lr: 2.126e-03, eta: 12:29:48, time: 2.581, data_time: 1.538, memory: 15990, top1_acc: 0.5095, top5_acc: 0.7577, loss_cls: 2.7340, loss: 2.7340 +2024-12-31 09:31:47,154 - pyskl - INFO - Epoch [137][200/3746] lr: 2.118e-03, eta: 12:28:22, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5058, top5_acc: 0.7564, loss_cls: 2.7709, loss: 2.7709 +2024-12-31 09:33:12,892 - pyskl - INFO - Epoch [137][300/3746] lr: 2.110e-03, eta: 12:26:56, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5045, top5_acc: 0.7502, loss_cls: 2.7694, loss: 2.7694 +2024-12-31 09:34:39,328 - pyskl - INFO - Epoch [137][400/3746] lr: 2.102e-03, eta: 12:25:30, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5119, top5_acc: 0.7583, loss_cls: 2.7382, loss: 2.7382 +2024-12-31 09:36:05,875 - pyskl - INFO - Epoch [137][500/3746] lr: 2.094e-03, eta: 12:24:04, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5091, top5_acc: 0.7522, loss_cls: 2.7741, loss: 2.7741 +2024-12-31 09:37:31,631 - pyskl - INFO - Epoch [137][600/3746] lr: 2.086e-03, eta: 12:22:38, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4963, top5_acc: 0.7539, loss_cls: 2.7670, loss: 2.7670 +2024-12-31 09:38:58,582 - pyskl - INFO - Epoch [137][700/3746] lr: 2.078e-03, eta: 12:21:12, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5070, top5_acc: 0.7655, loss_cls: 2.7386, loss: 2.7386 +2024-12-31 09:40:25,181 - pyskl - INFO - Epoch [137][800/3746] lr: 2.070e-03, eta: 12:19:46, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5078, top5_acc: 0.7559, loss_cls: 2.7820, loss: 2.7820 +2024-12-31 09:41:51,117 - pyskl - INFO - Epoch [137][900/3746] lr: 2.062e-03, eta: 12:18:20, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5080, top5_acc: 0.7462, loss_cls: 2.7810, loss: 2.7810 +2024-12-31 09:43:17,812 - pyskl - INFO - Epoch [137][1000/3746] lr: 2.054e-03, eta: 12:16:55, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.4942, top5_acc: 0.7506, loss_cls: 2.8162, loss: 2.8162 +2024-12-31 09:44:45,030 - pyskl - INFO - Epoch [137][1100/3746] lr: 2.046e-03, eta: 12:15:29, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5042, top5_acc: 0.7523, loss_cls: 2.7843, loss: 2.7843 +2024-12-31 09:46:11,669 - pyskl - INFO - Epoch [137][1200/3746] lr: 2.038e-03, eta: 12:14:03, time: 0.866, data_time: 0.001, memory: 15990, top1_acc: 0.5011, top5_acc: 0.7473, loss_cls: 2.7916, loss: 2.7916 +2024-12-31 09:47:37,706 - pyskl - INFO - Epoch [137][1300/3746] lr: 2.030e-03, eta: 12:12:37, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.4842, top5_acc: 0.7419, loss_cls: 2.8534, loss: 2.8534 +2024-12-31 09:49:02,975 - pyskl - INFO - Epoch [137][1400/3746] lr: 2.022e-03, eta: 12:11:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4903, top5_acc: 0.7406, loss_cls: 2.8327, loss: 2.8327 +2024-12-31 09:50:27,522 - pyskl - INFO - Epoch [137][1500/3746] lr: 2.015e-03, eta: 12:09:45, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4942, top5_acc: 0.7417, loss_cls: 2.8254, loss: 2.8254 +2024-12-31 09:51:52,882 - pyskl - INFO - Epoch [137][1600/3746] lr: 2.007e-03, eta: 12:08:19, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.5009, top5_acc: 0.7498, loss_cls: 2.7966, loss: 2.7966 +2024-12-31 09:53:17,728 - pyskl - INFO - Epoch [137][1700/3746] lr: 1.999e-03, eta: 12:06:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5114, top5_acc: 0.7509, loss_cls: 2.7645, loss: 2.7645 +2024-12-31 09:54:43,075 - pyskl - INFO - Epoch [137][1800/3746] lr: 1.991e-03, eta: 12:05:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4959, top5_acc: 0.7423, loss_cls: 2.8447, loss: 2.8447 +2024-12-31 09:56:08,921 - pyskl - INFO - Epoch [137][1900/3746] lr: 1.983e-03, eta: 12:04:01, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5016, top5_acc: 0.7461, loss_cls: 2.8165, loss: 2.8165 +2024-12-31 09:57:35,313 - pyskl - INFO - Epoch [137][2000/3746] lr: 1.976e-03, eta: 12:02:35, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.4902, top5_acc: 0.7352, loss_cls: 2.8294, loss: 2.8294 +2024-12-31 09:59:01,133 - pyskl - INFO - Epoch [137][2100/3746] lr: 1.968e-03, eta: 12:01:09, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4983, top5_acc: 0.7467, loss_cls: 2.7924, loss: 2.7924 +2024-12-31 10:00:27,788 - pyskl - INFO - Epoch [137][2200/3746] lr: 1.960e-03, eta: 11:59:43, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.4877, top5_acc: 0.7334, loss_cls: 2.8885, loss: 2.8885 +2024-12-31 10:01:53,828 - pyskl - INFO - Epoch [137][2300/3746] lr: 1.952e-03, eta: 11:58:17, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7458, loss_cls: 2.8443, loss: 2.8443 +2024-12-31 10:03:19,996 - pyskl - INFO - Epoch [137][2400/3746] lr: 1.944e-03, eta: 11:56:51, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5012, top5_acc: 0.7475, loss_cls: 2.8085, loss: 2.8085 +2024-12-31 10:04:46,133 - pyskl - INFO - Epoch [137][2500/3746] lr: 1.937e-03, eta: 11:55:25, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4944, top5_acc: 0.7447, loss_cls: 2.8165, loss: 2.8165 +2024-12-31 10:06:12,688 - pyskl - INFO - Epoch [137][2600/3746] lr: 1.929e-03, eta: 11:53:59, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5081, top5_acc: 0.7562, loss_cls: 2.7821, loss: 2.7821 +2024-12-31 10:07:38,974 - pyskl - INFO - Epoch [137][2700/3746] lr: 1.921e-03, eta: 11:52:33, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4863, top5_acc: 0.7450, loss_cls: 2.8446, loss: 2.8446 +2024-12-31 10:09:04,985 - pyskl - INFO - Epoch [137][2800/3746] lr: 1.914e-03, eta: 11:51:08, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4839, top5_acc: 0.7434, loss_cls: 2.8518, loss: 2.8518 +2024-12-31 10:10:30,575 - pyskl - INFO - Epoch [137][2900/3746] lr: 1.906e-03, eta: 11:49:42, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7381, loss_cls: 2.8265, loss: 2.8265 +2024-12-31 10:11:55,301 - pyskl - INFO - Epoch [137][3000/3746] lr: 1.898e-03, eta: 11:48:15, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4922, top5_acc: 0.7388, loss_cls: 2.8629, loss: 2.8629 +2024-12-31 10:13:20,014 - pyskl - INFO - Epoch [137][3100/3746] lr: 1.891e-03, eta: 11:46:49, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4973, top5_acc: 0.7480, loss_cls: 2.8168, loss: 2.8168 +2024-12-31 10:14:44,521 - pyskl - INFO - Epoch [137][3200/3746] lr: 1.883e-03, eta: 11:45:23, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7464, loss_cls: 2.8402, loss: 2.8402 +2024-12-31 10:16:09,357 - pyskl - INFO - Epoch [137][3300/3746] lr: 1.876e-03, eta: 11:43:57, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4984, top5_acc: 0.7442, loss_cls: 2.8175, loss: 2.8175 +2024-12-31 10:17:33,995 - pyskl - INFO - Epoch [137][3400/3746] lr: 1.868e-03, eta: 11:42:31, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4998, top5_acc: 0.7534, loss_cls: 2.7952, loss: 2.7952 +2024-12-31 10:18:58,749 - pyskl - INFO - Epoch [137][3500/3746] lr: 1.860e-03, eta: 11:41:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4967, top5_acc: 0.7356, loss_cls: 2.8500, loss: 2.8500 +2024-12-31 10:20:23,212 - pyskl - INFO - Epoch [137][3600/3746] lr: 1.853e-03, eta: 11:39:39, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4917, top5_acc: 0.7431, loss_cls: 2.8458, loss: 2.8458 +2024-12-31 10:21:47,732 - pyskl - INFO - Epoch [137][3700/3746] lr: 1.845e-03, eta: 11:38:13, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4872, top5_acc: 0.7434, loss_cls: 2.8450, loss: 2.8450 +2024-12-31 10:22:28,615 - pyskl - INFO - Saving checkpoint at 137 epochs +2024-12-31 10:24:27,506 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 10:24:28,444 - pyskl - INFO - +top1_acc 0.3957 +top5_acc 0.6464 +2024-12-31 10:24:28,444 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 10:24:28,498 - pyskl - INFO - +mean_acc 0.3955 +2024-12-31 10:24:28,504 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_136.pth was removed +2024-12-31 10:24:28,795 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_137.pth. +2024-12-31 10:24:28,796 - pyskl - INFO - Best top1_acc is 0.3957 at 137 epoch. +2024-12-31 10:24:28,813 - pyskl - INFO - Epoch(val) [137][309] top1_acc: 0.3957, top5_acc: 0.6464, mean_class_accuracy: 0.3955 +2024-12-31 10:28:41,594 - pyskl - INFO - Epoch [138][100/3746] lr: 1.834e-03, eta: 11:36:20, time: 2.528, data_time: 1.509, memory: 15990, top1_acc: 0.5162, top5_acc: 0.7612, loss_cls: 2.7105, loss: 2.7105 +2024-12-31 10:30:06,088 - pyskl - INFO - Epoch [138][200/3746] lr: 1.827e-03, eta: 11:34:53, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5106, top5_acc: 0.7583, loss_cls: 2.7496, loss: 2.7496 +2024-12-31 10:31:30,450 - pyskl - INFO - Epoch [138][300/3746] lr: 1.819e-03, eta: 11:33:27, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5248, top5_acc: 0.7642, loss_cls: 2.6755, loss: 2.6755 +2024-12-31 10:32:54,928 - pyskl - INFO - Epoch [138][400/3746] lr: 1.812e-03, eta: 11:32:01, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5141, top5_acc: 0.7539, loss_cls: 2.7264, loss: 2.7264 +2024-12-31 10:34:19,805 - pyskl - INFO - Epoch [138][500/3746] lr: 1.805e-03, eta: 11:30:35, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5111, top5_acc: 0.7578, loss_cls: 2.7409, loss: 2.7409 +2024-12-31 10:35:44,506 - pyskl - INFO - Epoch [138][600/3746] lr: 1.797e-03, eta: 11:29:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5195, top5_acc: 0.7552, loss_cls: 2.7228, loss: 2.7228 +2024-12-31 10:37:08,955 - pyskl - INFO - Epoch [138][700/3746] lr: 1.790e-03, eta: 11:27:43, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5136, top5_acc: 0.7625, loss_cls: 2.7124, loss: 2.7124 +2024-12-31 10:38:33,972 - pyskl - INFO - Epoch [138][800/3746] lr: 1.782e-03, eta: 11:26:17, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5102, top5_acc: 0.7534, loss_cls: 2.7548, loss: 2.7548 +2024-12-31 10:39:58,263 - pyskl - INFO - Epoch [138][900/3746] lr: 1.775e-03, eta: 11:24:51, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4944, top5_acc: 0.7495, loss_cls: 2.7933, loss: 2.7933 +2024-12-31 10:41:22,245 - pyskl - INFO - Epoch [138][1000/3746] lr: 1.768e-03, eta: 11:23:25, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5166, top5_acc: 0.7644, loss_cls: 2.7194, loss: 2.7194 +2024-12-31 10:42:46,523 - pyskl - INFO - Epoch [138][1100/3746] lr: 1.760e-03, eta: 11:21:58, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5036, top5_acc: 0.7516, loss_cls: 2.7658, loss: 2.7658 +2024-12-31 10:44:11,147 - pyskl - INFO - Epoch [138][1200/3746] lr: 1.753e-03, eta: 11:20:32, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7470, loss_cls: 2.8057, loss: 2.8057 +2024-12-31 10:45:36,425 - pyskl - INFO - Epoch [138][1300/3746] lr: 1.745e-03, eta: 11:19:06, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.5105, top5_acc: 0.7591, loss_cls: 2.7278, loss: 2.7278 +2024-12-31 10:47:01,086 - pyskl - INFO - Epoch [138][1400/3746] lr: 1.738e-03, eta: 11:17:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5119, top5_acc: 0.7591, loss_cls: 2.7421, loss: 2.7421 +2024-12-31 10:48:25,776 - pyskl - INFO - Epoch [138][1500/3746] lr: 1.731e-03, eta: 11:16:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5030, top5_acc: 0.7491, loss_cls: 2.7896, loss: 2.7896 +2024-12-31 10:49:50,225 - pyskl - INFO - Epoch [138][1600/3746] lr: 1.724e-03, eta: 11:14:48, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5105, top5_acc: 0.7622, loss_cls: 2.7540, loss: 2.7540 +2024-12-31 10:51:14,701 - pyskl - INFO - Epoch [138][1700/3746] lr: 1.716e-03, eta: 11:13:22, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5002, top5_acc: 0.7500, loss_cls: 2.7953, loss: 2.7953 +2024-12-31 10:52:38,982 - pyskl - INFO - Epoch [138][1800/3746] lr: 1.709e-03, eta: 11:11:56, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5081, top5_acc: 0.7466, loss_cls: 2.7612, loss: 2.7612 +2024-12-31 10:54:03,825 - pyskl - INFO - Epoch [138][1900/3746] lr: 1.702e-03, eta: 11:10:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5123, top5_acc: 0.7542, loss_cls: 2.7582, loss: 2.7582 +2024-12-31 10:55:27,876 - pyskl - INFO - Epoch [138][2000/3746] lr: 1.695e-03, eta: 11:09:04, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5025, top5_acc: 0.7514, loss_cls: 2.7599, loss: 2.7599 +2024-12-31 10:56:51,475 - pyskl - INFO - Epoch [138][2100/3746] lr: 1.687e-03, eta: 11:07:37, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5002, top5_acc: 0.7491, loss_cls: 2.7798, loss: 2.7798 +2024-12-31 10:58:15,344 - pyskl - INFO - Epoch [138][2200/3746] lr: 1.680e-03, eta: 11:06:11, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5098, top5_acc: 0.7586, loss_cls: 2.7395, loss: 2.7395 +2024-12-31 10:59:39,328 - pyskl - INFO - Epoch [138][2300/3746] lr: 1.673e-03, eta: 11:04:45, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.4975, top5_acc: 0.7505, loss_cls: 2.8029, loss: 2.8029 +2024-12-31 11:01:03,173 - pyskl - INFO - Epoch [138][2400/3746] lr: 1.666e-03, eta: 11:03:19, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5062, top5_acc: 0.7606, loss_cls: 2.7408, loss: 2.7408 +2024-12-31 11:02:27,445 - pyskl - INFO - Epoch [138][2500/3746] lr: 1.659e-03, eta: 11:01:53, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5020, top5_acc: 0.7438, loss_cls: 2.7870, loss: 2.7870 +2024-12-31 11:03:51,870 - pyskl - INFO - Epoch [138][2600/3746] lr: 1.652e-03, eta: 11:00:27, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5042, top5_acc: 0.7488, loss_cls: 2.7765, loss: 2.7765 +2024-12-31 11:05:16,552 - pyskl - INFO - Epoch [138][2700/3746] lr: 1.644e-03, eta: 10:59:01, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5109, top5_acc: 0.7530, loss_cls: 2.7416, loss: 2.7416 +2024-12-31 11:06:41,009 - pyskl - INFO - Epoch [138][2800/3746] lr: 1.637e-03, eta: 10:57:35, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5017, top5_acc: 0.7559, loss_cls: 2.7877, loss: 2.7877 +2024-12-31 11:08:05,440 - pyskl - INFO - Epoch [138][2900/3746] lr: 1.630e-03, eta: 10:56:08, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5134, top5_acc: 0.7592, loss_cls: 2.7394, loss: 2.7394 +2024-12-31 11:09:30,214 - pyskl - INFO - Epoch [138][3000/3746] lr: 1.623e-03, eta: 10:54:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5050, top5_acc: 0.7452, loss_cls: 2.7909, loss: 2.7909 +2024-12-31 11:10:54,681 - pyskl - INFO - Epoch [138][3100/3746] lr: 1.616e-03, eta: 10:53:16, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4959, top5_acc: 0.7503, loss_cls: 2.8162, loss: 2.8162 +2024-12-31 11:12:19,125 - pyskl - INFO - Epoch [138][3200/3746] lr: 1.609e-03, eta: 10:51:50, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4955, top5_acc: 0.7428, loss_cls: 2.8398, loss: 2.8398 +2024-12-31 11:13:43,851 - pyskl - INFO - Epoch [138][3300/3746] lr: 1.602e-03, eta: 10:50:24, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4986, top5_acc: 0.7541, loss_cls: 2.7741, loss: 2.7741 +2024-12-31 11:15:08,545 - pyskl - INFO - Epoch [138][3400/3746] lr: 1.595e-03, eta: 10:48:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5042, top5_acc: 0.7475, loss_cls: 2.8119, loss: 2.8119 +2024-12-31 11:16:33,313 - pyskl - INFO - Epoch [138][3500/3746] lr: 1.588e-03, eta: 10:47:32, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5052, top5_acc: 0.7511, loss_cls: 2.7438, loss: 2.7438 +2024-12-31 11:17:58,651 - pyskl - INFO - Epoch [138][3600/3746] lr: 1.581e-03, eta: 10:46:06, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4916, top5_acc: 0.7502, loss_cls: 2.8226, loss: 2.8226 +2024-12-31 11:19:23,385 - pyskl - INFO - Epoch [138][3700/3746] lr: 1.574e-03, eta: 10:44:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5141, top5_acc: 0.7580, loss_cls: 2.7431, loss: 2.7431 +2024-12-31 11:20:04,177 - pyskl - INFO - Saving checkpoint at 138 epochs +2024-12-31 11:22:03,266 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 11:22:04,074 - pyskl - INFO - +top1_acc 0.3974 +top5_acc 0.6461 +2024-12-31 11:22:04,075 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 11:22:04,119 - pyskl - INFO - +mean_acc 0.3971 +2024-12-31 11:22:04,123 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_137.pth was removed +2024-12-31 11:22:04,402 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_138.pth. +2024-12-31 11:22:04,403 - pyskl - INFO - Best top1_acc is 0.3974 at 138 epoch. +2024-12-31 11:22:04,420 - pyskl - INFO - Epoch(val) [138][309] top1_acc: 0.3974, top5_acc: 0.6461, mean_class_accuracy: 0.3971 +2024-12-31 11:26:19,983 - pyskl - INFO - Epoch [139][100/3746] lr: 1.564e-03, eta: 10:42:46, time: 2.556, data_time: 1.514, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7720, loss_cls: 2.6613, loss: 2.6613 +2024-12-31 11:27:45,066 - pyskl - INFO - Epoch [139][200/3746] lr: 1.557e-03, eta: 10:41:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5227, top5_acc: 0.7694, loss_cls: 2.6511, loss: 2.6511 +2024-12-31 11:29:10,412 - pyskl - INFO - Epoch [139][300/3746] lr: 1.550e-03, eta: 10:39:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5258, top5_acc: 0.7656, loss_cls: 2.6797, loss: 2.6797 +2024-12-31 11:30:35,817 - pyskl - INFO - Epoch [139][400/3746] lr: 1.543e-03, eta: 10:38:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5139, top5_acc: 0.7712, loss_cls: 2.6949, loss: 2.6949 +2024-12-31 11:32:00,941 - pyskl - INFO - Epoch [139][500/3746] lr: 1.536e-03, eta: 10:37:02, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5284, top5_acc: 0.7661, loss_cls: 2.6698, loss: 2.6698 +2024-12-31 11:33:25,969 - pyskl - INFO - Epoch [139][600/3746] lr: 1.529e-03, eta: 10:35:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5337, top5_acc: 0.7666, loss_cls: 2.6553, loss: 2.6553 +2024-12-31 11:34:51,017 - pyskl - INFO - Epoch [139][700/3746] lr: 1.523e-03, eta: 10:34:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5139, top5_acc: 0.7636, loss_cls: 2.6894, loss: 2.6894 +2024-12-31 11:36:16,203 - pyskl - INFO - Epoch [139][800/3746] lr: 1.516e-03, eta: 10:32:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5188, top5_acc: 0.7709, loss_cls: 2.6873, loss: 2.6873 +2024-12-31 11:37:41,340 - pyskl - INFO - Epoch [139][900/3746] lr: 1.509e-03, eta: 10:31:17, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5161, top5_acc: 0.7550, loss_cls: 2.7305, loss: 2.7305 +2024-12-31 11:39:06,441 - pyskl - INFO - Epoch [139][1000/3746] lr: 1.502e-03, eta: 10:29:51, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5167, top5_acc: 0.7650, loss_cls: 2.7024, loss: 2.7024 +2024-12-31 11:40:31,397 - pyskl - INFO - Epoch [139][1100/3746] lr: 1.495e-03, eta: 10:28:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5136, top5_acc: 0.7542, loss_cls: 2.7233, loss: 2.7233 +2024-12-31 11:41:56,844 - pyskl - INFO - Epoch [139][1200/3746] lr: 1.489e-03, eta: 10:26:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5347, top5_acc: 0.7736, loss_cls: 2.6240, loss: 2.6240 +2024-12-31 11:43:22,474 - pyskl - INFO - Epoch [139][1300/3746] lr: 1.482e-03, eta: 10:25:33, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5067, top5_acc: 0.7662, loss_cls: 2.7225, loss: 2.7225 +2024-12-31 11:44:47,705 - pyskl - INFO - Epoch [139][1400/3746] lr: 1.475e-03, eta: 10:24:07, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5098, top5_acc: 0.7566, loss_cls: 2.7677, loss: 2.7677 +2024-12-31 11:46:12,860 - pyskl - INFO - Epoch [139][1500/3746] lr: 1.468e-03, eta: 10:22:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5105, top5_acc: 0.7595, loss_cls: 2.7451, loss: 2.7451 +2024-12-31 11:47:38,230 - pyskl - INFO - Epoch [139][1600/3746] lr: 1.462e-03, eta: 10:21:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5194, top5_acc: 0.7608, loss_cls: 2.7229, loss: 2.7229 +2024-12-31 11:49:03,391 - pyskl - INFO - Epoch [139][1700/3746] lr: 1.455e-03, eta: 10:19:49, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.5169, top5_acc: 0.7619, loss_cls: 2.7054, loss: 2.7054 +2024-12-31 11:50:28,419 - pyskl - INFO - Epoch [139][1800/3746] lr: 1.448e-03, eta: 10:18:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5088, top5_acc: 0.7555, loss_cls: 2.7205, loss: 2.7205 +2024-12-31 11:51:53,617 - pyskl - INFO - Epoch [139][1900/3746] lr: 1.442e-03, eta: 10:16:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5162, top5_acc: 0.7600, loss_cls: 2.7198, loss: 2.7198 +2024-12-31 11:53:19,900 - pyskl - INFO - Epoch [139][2000/3746] lr: 1.435e-03, eta: 10:15:31, time: 0.863, data_time: 0.001, memory: 15990, top1_acc: 0.5128, top5_acc: 0.7683, loss_cls: 2.6972, loss: 2.6972 +2024-12-31 11:54:45,773 - pyskl - INFO - Epoch [139][2100/3746] lr: 1.428e-03, eta: 10:14:05, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5108, top5_acc: 0.7597, loss_cls: 2.7103, loss: 2.7103 +2024-12-31 11:56:12,006 - pyskl - INFO - Epoch [139][2200/3746] lr: 1.422e-03, eta: 10:12:39, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5170, top5_acc: 0.7633, loss_cls: 2.6924, loss: 2.6924 +2024-12-31 11:57:37,983 - pyskl - INFO - Epoch [139][2300/3746] lr: 1.415e-03, eta: 10:11:13, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5048, top5_acc: 0.7561, loss_cls: 2.7823, loss: 2.7823 +2024-12-31 11:59:03,673 - pyskl - INFO - Epoch [139][2400/3746] lr: 1.408e-03, eta: 10:09:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5022, top5_acc: 0.7534, loss_cls: 2.7661, loss: 2.7661 +2024-12-31 12:00:30,225 - pyskl - INFO - Epoch [139][2500/3746] lr: 1.402e-03, eta: 10:08:21, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5111, top5_acc: 0.7627, loss_cls: 2.6958, loss: 2.6958 +2024-12-31 12:01:56,451 - pyskl - INFO - Epoch [139][2600/3746] lr: 1.395e-03, eta: 10:06:55, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5186, top5_acc: 0.7675, loss_cls: 2.6904, loss: 2.6904 +2024-12-31 12:03:23,082 - pyskl - INFO - Epoch [139][2700/3746] lr: 1.389e-03, eta: 10:05:29, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5138, top5_acc: 0.7652, loss_cls: 2.7267, loss: 2.7267 +2024-12-31 12:04:48,711 - pyskl - INFO - Epoch [139][2800/3746] lr: 1.382e-03, eta: 10:04:03, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5125, top5_acc: 0.7578, loss_cls: 2.7302, loss: 2.7302 +2024-12-31 12:06:14,060 - pyskl - INFO - Epoch [139][2900/3746] lr: 1.376e-03, eta: 10:02:37, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5081, top5_acc: 0.7594, loss_cls: 2.7393, loss: 2.7393 +2024-12-31 12:07:39,227 - pyskl - INFO - Epoch [139][3000/3746] lr: 1.369e-03, eta: 10:01:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5128, top5_acc: 0.7612, loss_cls: 2.7396, loss: 2.7396 +2024-12-31 12:09:04,143 - pyskl - INFO - Epoch [139][3100/3746] lr: 1.363e-03, eta: 9:59:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5234, top5_acc: 0.7708, loss_cls: 2.6659, loss: 2.6659 +2024-12-31 12:10:29,345 - pyskl - INFO - Epoch [139][3200/3746] lr: 1.356e-03, eta: 9:58:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5152, top5_acc: 0.7611, loss_cls: 2.7122, loss: 2.7122 +2024-12-31 12:11:54,710 - pyskl - INFO - Epoch [139][3300/3746] lr: 1.350e-03, eta: 9:56:53, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5164, top5_acc: 0.7528, loss_cls: 2.7306, loss: 2.7306 +2024-12-31 12:13:19,777 - pyskl - INFO - Epoch [139][3400/3746] lr: 1.343e-03, eta: 9:55:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5077, top5_acc: 0.7512, loss_cls: 2.7729, loss: 2.7729 +2024-12-31 12:14:44,770 - pyskl - INFO - Epoch [139][3500/3746] lr: 1.337e-03, eta: 9:54:01, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5088, top5_acc: 0.7538, loss_cls: 2.7365, loss: 2.7365 +2024-12-31 12:16:09,873 - pyskl - INFO - Epoch [139][3600/3746] lr: 1.330e-03, eta: 9:52:35, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5091, top5_acc: 0.7612, loss_cls: 2.7226, loss: 2.7226 +2024-12-31 12:17:35,049 - pyskl - INFO - Epoch [139][3700/3746] lr: 1.324e-03, eta: 9:51:09, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5142, top5_acc: 0.7498, loss_cls: 2.7539, loss: 2.7539 +2024-12-31 12:18:16,242 - pyskl - INFO - Saving checkpoint at 139 epochs +2024-12-31 12:20:16,859 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 12:20:17,601 - pyskl - INFO - +top1_acc 0.4022 +top5_acc 0.6529 +2024-12-31 12:20:17,601 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 12:20:17,638 - pyskl - INFO - +mean_acc 0.4020 +2024-12-31 12:20:17,643 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_138.pth was removed +2024-12-31 12:20:17,972 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_139.pth. +2024-12-31 12:20:17,973 - pyskl - INFO - Best top1_acc is 0.4022 at 139 epoch. +2024-12-31 12:20:17,989 - pyskl - INFO - Epoch(val) [139][309] top1_acc: 0.4022, top5_acc: 0.6529, mean_class_accuracy: 0.4020 +2024-12-31 12:24:35,307 - pyskl - INFO - Epoch [140][100/3746] lr: 1.315e-03, eta: 9:49:13, time: 2.573, data_time: 1.542, memory: 15990, top1_acc: 0.5408, top5_acc: 0.7789, loss_cls: 2.5584, loss: 2.5584 +2024-12-31 12:26:00,280 - pyskl - INFO - Epoch [140][200/3746] lr: 1.308e-03, eta: 9:47:47, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5234, top5_acc: 0.7684, loss_cls: 2.6620, loss: 2.6620 +2024-12-31 12:27:25,582 - pyskl - INFO - Epoch [140][300/3746] lr: 1.302e-03, eta: 9:46:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5334, top5_acc: 0.7762, loss_cls: 2.6217, loss: 2.6217 +2024-12-31 12:28:51,160 - pyskl - INFO - Epoch [140][400/3746] lr: 1.296e-03, eta: 9:44:55, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5277, top5_acc: 0.7748, loss_cls: 2.6263, loss: 2.6263 +2024-12-31 12:30:16,596 - pyskl - INFO - Epoch [140][500/3746] lr: 1.289e-03, eta: 9:43:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5459, top5_acc: 0.7805, loss_cls: 2.5721, loss: 2.5721 +2024-12-31 12:31:41,911 - pyskl - INFO - Epoch [140][600/3746] lr: 1.283e-03, eta: 9:42:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5286, top5_acc: 0.7695, loss_cls: 2.6429, loss: 2.6429 +2024-12-31 12:33:07,224 - pyskl - INFO - Epoch [140][700/3746] lr: 1.277e-03, eta: 9:40:37, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5202, top5_acc: 0.7675, loss_cls: 2.6761, loss: 2.6761 +2024-12-31 12:34:33,179 - pyskl - INFO - Epoch [140][800/3746] lr: 1.271e-03, eta: 9:39:11, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5341, top5_acc: 0.7650, loss_cls: 2.6217, loss: 2.6217 +2024-12-31 12:35:58,533 - pyskl - INFO - Epoch [140][900/3746] lr: 1.264e-03, eta: 9:37:45, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5370, top5_acc: 0.7816, loss_cls: 2.5946, loss: 2.5946 +2024-12-31 12:37:24,109 - pyskl - INFO - Epoch [140][1000/3746] lr: 1.258e-03, eta: 9:36:19, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5380, top5_acc: 0.7758, loss_cls: 2.6169, loss: 2.6169 +2024-12-31 12:38:49,395 - pyskl - INFO - Epoch [140][1100/3746] lr: 1.252e-03, eta: 9:34:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5330, top5_acc: 0.7762, loss_cls: 2.6211, loss: 2.6211 +2024-12-31 12:40:14,585 - pyskl - INFO - Epoch [140][1200/3746] lr: 1.246e-03, eta: 9:33:27, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5234, top5_acc: 0.7703, loss_cls: 2.6691, loss: 2.6691 +2024-12-31 12:41:40,046 - pyskl - INFO - Epoch [140][1300/3746] lr: 1.239e-03, eta: 9:32:01, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5206, top5_acc: 0.7639, loss_cls: 2.6726, loss: 2.6726 +2024-12-31 12:43:04,819 - pyskl - INFO - Epoch [140][1400/3746] lr: 1.233e-03, eta: 9:30:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5230, top5_acc: 0.7680, loss_cls: 2.6847, loss: 2.6847 +2024-12-31 12:44:29,858 - pyskl - INFO - Epoch [140][1500/3746] lr: 1.227e-03, eta: 9:29:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5170, top5_acc: 0.7623, loss_cls: 2.7053, loss: 2.7053 +2024-12-31 12:45:55,306 - pyskl - INFO - Epoch [140][1600/3746] lr: 1.221e-03, eta: 9:27:43, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5261, top5_acc: 0.7720, loss_cls: 2.6402, loss: 2.6402 +2024-12-31 12:47:20,347 - pyskl - INFO - Epoch [140][1700/3746] lr: 1.215e-03, eta: 9:26:17, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5173, top5_acc: 0.7688, loss_cls: 2.6826, loss: 2.6826 +2024-12-31 12:48:45,479 - pyskl - INFO - Epoch [140][1800/3746] lr: 1.209e-03, eta: 9:24:50, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7677, loss_cls: 2.6659, loss: 2.6659 +2024-12-31 12:50:10,775 - pyskl - INFO - Epoch [140][1900/3746] lr: 1.203e-03, eta: 9:23:24, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5198, top5_acc: 0.7681, loss_cls: 2.6681, loss: 2.6681 +2024-12-31 12:51:35,781 - pyskl - INFO - Epoch [140][2000/3746] lr: 1.196e-03, eta: 9:21:58, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5355, top5_acc: 0.7697, loss_cls: 2.6399, loss: 2.6399 +2024-12-31 12:53:01,212 - pyskl - INFO - Epoch [140][2100/3746] lr: 1.190e-03, eta: 9:20:32, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5127, top5_acc: 0.7584, loss_cls: 2.7131, loss: 2.7131 +2024-12-31 12:54:27,120 - pyskl - INFO - Epoch [140][2200/3746] lr: 1.184e-03, eta: 9:19:06, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5394, top5_acc: 0.7748, loss_cls: 2.5956, loss: 2.5956 +2024-12-31 12:55:52,262 - pyskl - INFO - Epoch [140][2300/3746] lr: 1.178e-03, eta: 9:17:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5269, top5_acc: 0.7672, loss_cls: 2.6597, loss: 2.6597 +2024-12-31 12:57:17,638 - pyskl - INFO - Epoch [140][2400/3746] lr: 1.172e-03, eta: 9:16:14, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5131, top5_acc: 0.7594, loss_cls: 2.6985, loss: 2.6985 +2024-12-31 12:58:42,831 - pyskl - INFO - Epoch [140][2500/3746] lr: 1.166e-03, eta: 9:14:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5158, top5_acc: 0.7689, loss_cls: 2.6963, loss: 2.6963 +2024-12-31 13:00:07,960 - pyskl - INFO - Epoch [140][2600/3746] lr: 1.160e-03, eta: 9:13:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5152, top5_acc: 0.7597, loss_cls: 2.6926, loss: 2.6926 +2024-12-31 13:01:33,348 - pyskl - INFO - Epoch [140][2700/3746] lr: 1.154e-03, eta: 9:11:56, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5162, top5_acc: 0.7661, loss_cls: 2.7023, loss: 2.7023 +2024-12-31 13:02:58,757 - pyskl - INFO - Epoch [140][2800/3746] lr: 1.148e-03, eta: 9:10:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5177, top5_acc: 0.7623, loss_cls: 2.7001, loss: 2.7001 +2024-12-31 13:04:24,484 - pyskl - INFO - Epoch [140][2900/3746] lr: 1.142e-03, eta: 9:09:04, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5105, top5_acc: 0.7600, loss_cls: 2.7138, loss: 2.7138 +2024-12-31 13:05:50,026 - pyskl - INFO - Epoch [140][3000/3746] lr: 1.136e-03, eta: 9:07:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5184, top5_acc: 0.7711, loss_cls: 2.6790, loss: 2.6790 +2024-12-31 13:07:15,416 - pyskl - INFO - Epoch [140][3100/3746] lr: 1.131e-03, eta: 9:06:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5027, top5_acc: 0.7623, loss_cls: 2.7166, loss: 2.7166 +2024-12-31 13:08:41,460 - pyskl - INFO - Epoch [140][3200/3746] lr: 1.125e-03, eta: 9:04:46, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5188, top5_acc: 0.7691, loss_cls: 2.6708, loss: 2.6708 +2024-12-31 13:10:07,726 - pyskl - INFO - Epoch [140][3300/3746] lr: 1.119e-03, eta: 9:03:20, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5295, top5_acc: 0.7652, loss_cls: 2.6532, loss: 2.6532 +2024-12-31 13:11:34,046 - pyskl - INFO - Epoch [140][3400/3746] lr: 1.113e-03, eta: 9:01:54, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5202, top5_acc: 0.7709, loss_cls: 2.6717, loss: 2.6717 +2024-12-31 13:13:00,050 - pyskl - INFO - Epoch [140][3500/3746] lr: 1.107e-03, eta: 9:00:28, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5173, top5_acc: 0.7739, loss_cls: 2.6668, loss: 2.6668 +2024-12-31 13:14:25,659 - pyskl - INFO - Epoch [140][3600/3746] lr: 1.101e-03, eta: 8:59:02, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5130, top5_acc: 0.7659, loss_cls: 2.6868, loss: 2.6868 +2024-12-31 13:15:51,591 - pyskl - INFO - Epoch [140][3700/3746] lr: 1.095e-03, eta: 8:57:36, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5250, top5_acc: 0.7747, loss_cls: 2.6463, loss: 2.6463 +2024-12-31 13:16:32,321 - pyskl - INFO - Saving checkpoint at 140 epochs +2024-12-31 13:18:33,047 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 13:18:33,832 - pyskl - INFO - +top1_acc 0.3974 +top5_acc 0.6517 +2024-12-31 13:18:33,832 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 13:18:33,900 - pyskl - INFO - +mean_acc 0.3971 +2024-12-31 13:18:33,916 - pyskl - INFO - Epoch(val) [140][309] top1_acc: 0.3974, top5_acc: 0.6517, mean_class_accuracy: 0.3971 +2024-12-31 13:23:00,110 - pyskl - INFO - Epoch [141][100/3746] lr: 1.087e-03, eta: 8:55:40, time: 2.662, data_time: 1.627, memory: 15990, top1_acc: 0.5337, top5_acc: 0.7767, loss_cls: 2.6010, loss: 2.6010 +2024-12-31 13:24:24,723 - pyskl - INFO - Epoch [141][200/3746] lr: 1.081e-03, eta: 8:54:14, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5459, top5_acc: 0.7845, loss_cls: 2.5681, loss: 2.5681 +2024-12-31 13:25:50,193 - pyskl - INFO - Epoch [141][300/3746] lr: 1.075e-03, eta: 8:52:48, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5437, top5_acc: 0.7802, loss_cls: 2.5556, loss: 2.5556 +2024-12-31 13:27:15,229 - pyskl - INFO - Epoch [141][400/3746] lr: 1.070e-03, eta: 8:51:22, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5445, top5_acc: 0.7825, loss_cls: 2.5429, loss: 2.5429 +2024-12-31 13:28:40,688 - pyskl - INFO - Epoch [141][500/3746] lr: 1.064e-03, eta: 8:49:56, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5589, top5_acc: 0.7884, loss_cls: 2.5104, loss: 2.5104 +2024-12-31 13:30:06,275 - pyskl - INFO - Epoch [141][600/3746] lr: 1.058e-03, eta: 8:48:30, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5369, top5_acc: 0.7755, loss_cls: 2.6043, loss: 2.6043 +2024-12-31 13:31:31,899 - pyskl - INFO - Epoch [141][700/3746] lr: 1.052e-03, eta: 8:47:04, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5356, top5_acc: 0.7802, loss_cls: 2.6029, loss: 2.6029 +2024-12-31 13:32:57,239 - pyskl - INFO - Epoch [141][800/3746] lr: 1.047e-03, eta: 8:45:38, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5377, top5_acc: 0.7802, loss_cls: 2.5928, loss: 2.5928 +2024-12-31 13:34:22,708 - pyskl - INFO - Epoch [141][900/3746] lr: 1.041e-03, eta: 8:44:12, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5384, top5_acc: 0.7839, loss_cls: 2.5973, loss: 2.5973 +2024-12-31 13:35:47,820 - pyskl - INFO - Epoch [141][1000/3746] lr: 1.035e-03, eta: 8:42:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5284, top5_acc: 0.7744, loss_cls: 2.6216, loss: 2.6216 +2024-12-31 13:37:13,225 - pyskl - INFO - Epoch [141][1100/3746] lr: 1.030e-03, eta: 8:41:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5309, top5_acc: 0.7670, loss_cls: 2.6477, loss: 2.6477 +2024-12-31 13:38:38,798 - pyskl - INFO - Epoch [141][1200/3746] lr: 1.024e-03, eta: 8:39:53, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5222, top5_acc: 0.7783, loss_cls: 2.6235, loss: 2.6235 +2024-12-31 13:40:04,412 - pyskl - INFO - Epoch [141][1300/3746] lr: 1.018e-03, eta: 8:38:27, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5281, top5_acc: 0.7769, loss_cls: 2.6318, loss: 2.6318 +2024-12-31 13:41:29,672 - pyskl - INFO - Epoch [141][1400/3746] lr: 1.013e-03, eta: 8:37:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5398, top5_acc: 0.7747, loss_cls: 2.6136, loss: 2.6136 +2024-12-31 13:42:54,620 - pyskl - INFO - Epoch [141][1500/3746] lr: 1.007e-03, eta: 8:35:35, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5459, top5_acc: 0.7856, loss_cls: 2.5675, loss: 2.5675 +2024-12-31 13:44:19,490 - pyskl - INFO - Epoch [141][1600/3746] lr: 1.002e-03, eta: 8:34:09, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5316, top5_acc: 0.7761, loss_cls: 2.6324, loss: 2.6324 +2024-12-31 13:45:44,140 - pyskl - INFO - Epoch [141][1700/3746] lr: 9.961e-04, eta: 8:32:43, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.5347, top5_acc: 0.7794, loss_cls: 2.6075, loss: 2.6075 +2024-12-31 13:47:08,998 - pyskl - INFO - Epoch [141][1800/3746] lr: 9.905e-04, eta: 8:31:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5428, top5_acc: 0.7853, loss_cls: 2.5563, loss: 2.5563 +2024-12-31 13:48:33,074 - pyskl - INFO - Epoch [141][1900/3746] lr: 9.850e-04, eta: 8:29:51, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.5430, top5_acc: 0.7869, loss_cls: 2.5575, loss: 2.5575 +2024-12-31 13:49:57,833 - pyskl - INFO - Epoch [141][2000/3746] lr: 9.795e-04, eta: 8:28:25, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5314, top5_acc: 0.7788, loss_cls: 2.6088, loss: 2.6088 +2024-12-31 13:51:22,673 - pyskl - INFO - Epoch [141][2100/3746] lr: 9.740e-04, eta: 8:26:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5344, top5_acc: 0.7714, loss_cls: 2.6082, loss: 2.6082 +2024-12-31 13:52:47,701 - pyskl - INFO - Epoch [141][2200/3746] lr: 9.685e-04, eta: 8:25:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5361, top5_acc: 0.7861, loss_cls: 2.6082, loss: 2.6082 +2024-12-31 13:54:12,272 - pyskl - INFO - Epoch [141][2300/3746] lr: 9.630e-04, eta: 8:24:06, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5352, top5_acc: 0.7791, loss_cls: 2.6071, loss: 2.6071 +2024-12-31 13:55:37,072 - pyskl - INFO - Epoch [141][2400/3746] lr: 9.576e-04, eta: 8:22:40, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5138, top5_acc: 0.7672, loss_cls: 2.6750, loss: 2.6750 +2024-12-31 13:57:02,479 - pyskl - INFO - Epoch [141][2500/3746] lr: 9.522e-04, eta: 8:21:14, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5302, top5_acc: 0.7780, loss_cls: 2.6120, loss: 2.6120 +2024-12-31 13:58:27,193 - pyskl - INFO - Epoch [141][2600/3746] lr: 9.467e-04, eta: 8:19:48, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5241, top5_acc: 0.7666, loss_cls: 2.6432, loss: 2.6432 +2024-12-31 13:59:51,890 - pyskl - INFO - Epoch [141][2700/3746] lr: 9.413e-04, eta: 8:18:22, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5105, top5_acc: 0.7600, loss_cls: 2.7134, loss: 2.7134 +2024-12-31 14:01:16,853 - pyskl - INFO - Epoch [141][2800/3746] lr: 9.359e-04, eta: 8:16:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5302, top5_acc: 0.7698, loss_cls: 2.6112, loss: 2.6112 +2024-12-31 14:02:41,998 - pyskl - INFO - Epoch [141][2900/3746] lr: 9.306e-04, eta: 8:15:30, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.5286, top5_acc: 0.7722, loss_cls: 2.6222, loss: 2.6222 +2024-12-31 14:04:07,527 - pyskl - INFO - Epoch [141][3000/3746] lr: 9.252e-04, eta: 8:14:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5236, top5_acc: 0.7783, loss_cls: 2.6211, loss: 2.6211 +2024-12-31 14:05:32,519 - pyskl - INFO - Epoch [141][3100/3746] lr: 9.199e-04, eta: 8:12:38, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5350, top5_acc: 0.7714, loss_cls: 2.6174, loss: 2.6174 +2024-12-31 14:06:57,344 - pyskl - INFO - Epoch [141][3200/3746] lr: 9.145e-04, eta: 8:11:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5378, top5_acc: 0.7820, loss_cls: 2.6015, loss: 2.6015 +2024-12-31 14:08:22,582 - pyskl - INFO - Epoch [141][3300/3746] lr: 9.092e-04, eta: 8:09:45, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5339, top5_acc: 0.7741, loss_cls: 2.6023, loss: 2.6023 +2024-12-31 14:09:48,356 - pyskl - INFO - Epoch [141][3400/3746] lr: 9.039e-04, eta: 8:08:19, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.5337, top5_acc: 0.7814, loss_cls: 2.5977, loss: 2.5977 +2024-12-31 14:11:13,442 - pyskl - INFO - Epoch [141][3500/3746] lr: 8.986e-04, eta: 8:06:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5378, top5_acc: 0.7747, loss_cls: 2.5932, loss: 2.5932 +2024-12-31 14:12:38,511 - pyskl - INFO - Epoch [141][3600/3746] lr: 8.934e-04, eta: 8:05:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5348, top5_acc: 0.7773, loss_cls: 2.6343, loss: 2.6343 +2024-12-31 14:14:04,060 - pyskl - INFO - Epoch [141][3700/3746] lr: 8.881e-04, eta: 8:04:01, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5286, top5_acc: 0.7714, loss_cls: 2.6479, loss: 2.6479 +2024-12-31 14:14:45,908 - pyskl - INFO - Saving checkpoint at 141 epochs +2024-12-31 14:16:43,918 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 14:16:44,729 - pyskl - INFO - +top1_acc 0.4015 +top5_acc 0.6529 +2024-12-31 14:16:44,729 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 14:16:44,781 - pyskl - INFO - +mean_acc 0.4013 +2024-12-31 14:16:44,797 - pyskl - INFO - Epoch(val) [141][309] top1_acc: 0.4015, top5_acc: 0.6529, mean_class_accuracy: 0.4013 +2024-12-31 14:21:11,191 - pyskl - INFO - Epoch [142][100/3746] lr: 8.805e-04, eta: 8:02:04, time: 2.664, data_time: 1.631, memory: 15990, top1_acc: 0.5495, top5_acc: 0.7853, loss_cls: 2.5425, loss: 2.5425 +2024-12-31 14:22:36,290 - pyskl - INFO - Epoch [142][200/3746] lr: 8.752e-04, eta: 8:00:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5441, top5_acc: 0.7844, loss_cls: 2.5515, loss: 2.5515 +2024-12-31 14:24:00,762 - pyskl - INFO - Epoch [142][300/3746] lr: 8.700e-04, eta: 7:59:12, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.5444, top5_acc: 0.7839, loss_cls: 2.5593, loss: 2.5593 +2024-12-31 14:25:26,375 - pyskl - INFO - Epoch [142][400/3746] lr: 8.649e-04, eta: 7:57:46, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5564, top5_acc: 0.7973, loss_cls: 2.4947, loss: 2.4947 +2024-12-31 14:26:52,047 - pyskl - INFO - Epoch [142][500/3746] lr: 8.597e-04, eta: 7:56:20, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5563, top5_acc: 0.7886, loss_cls: 2.5311, loss: 2.5311 +2024-12-31 14:28:17,959 - pyskl - INFO - Epoch [142][600/3746] lr: 8.545e-04, eta: 7:54:54, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5563, top5_acc: 0.7973, loss_cls: 2.4788, loss: 2.4788 +2024-12-31 14:29:43,351 - pyskl - INFO - Epoch [142][700/3746] lr: 8.494e-04, eta: 7:53:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5561, top5_acc: 0.7900, loss_cls: 2.5087, loss: 2.5087 +2024-12-31 14:31:08,634 - pyskl - INFO - Epoch [142][800/3746] lr: 8.443e-04, eta: 7:52:02, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5441, top5_acc: 0.7937, loss_cls: 2.5342, loss: 2.5342 +2024-12-31 14:32:34,189 - pyskl - INFO - Epoch [142][900/3746] lr: 8.392e-04, eta: 7:50:36, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5373, top5_acc: 0.7831, loss_cls: 2.5745, loss: 2.5745 +2024-12-31 14:33:58,765 - pyskl - INFO - Epoch [142][1000/3746] lr: 8.341e-04, eta: 7:49:10, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5363, top5_acc: 0.7734, loss_cls: 2.6051, loss: 2.6051 +2024-12-31 14:35:23,599 - pyskl - INFO - Epoch [142][1100/3746] lr: 8.290e-04, eta: 7:47:43, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5541, top5_acc: 0.7937, loss_cls: 2.5230, loss: 2.5230 +2024-12-31 14:36:48,897 - pyskl - INFO - Epoch [142][1200/3746] lr: 8.239e-04, eta: 7:46:17, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.5333, top5_acc: 0.7794, loss_cls: 2.5998, loss: 2.5998 +2024-12-31 14:38:13,640 - pyskl - INFO - Epoch [142][1300/3746] lr: 8.189e-04, eta: 7:44:51, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5559, top5_acc: 0.7891, loss_cls: 2.5266, loss: 2.5266 +2024-12-31 14:39:38,952 - pyskl - INFO - Epoch [142][1400/3746] lr: 8.139e-04, eta: 7:43:25, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.5542, top5_acc: 0.7894, loss_cls: 2.5169, loss: 2.5169 +2024-12-31 14:41:03,721 - pyskl - INFO - Epoch [142][1500/3746] lr: 8.088e-04, eta: 7:41:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5423, top5_acc: 0.7802, loss_cls: 2.5792, loss: 2.5792 +2024-12-31 14:42:27,958 - pyskl - INFO - Epoch [142][1600/3746] lr: 8.038e-04, eta: 7:40:33, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5417, top5_acc: 0.7817, loss_cls: 2.5614, loss: 2.5614 +2024-12-31 14:43:52,752 - pyskl - INFO - Epoch [142][1700/3746] lr: 7.989e-04, eta: 7:39:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5355, top5_acc: 0.7770, loss_cls: 2.5777, loss: 2.5777 +2024-12-31 14:45:17,545 - pyskl - INFO - Epoch [142][1800/3746] lr: 7.939e-04, eta: 7:37:41, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5492, top5_acc: 0.7867, loss_cls: 2.5243, loss: 2.5243 +2024-12-31 14:46:40,882 - pyskl - INFO - Epoch [142][1900/3746] lr: 7.889e-04, eta: 7:36:14, time: 0.833, data_time: 0.001, memory: 15990, top1_acc: 0.5467, top5_acc: 0.7887, loss_cls: 2.5491, loss: 2.5491 +2024-12-31 14:48:05,702 - pyskl - INFO - Epoch [142][2000/3746] lr: 7.840e-04, eta: 7:34:48, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5447, top5_acc: 0.7816, loss_cls: 2.5548, loss: 2.5548 +2024-12-31 14:49:30,319 - pyskl - INFO - Epoch [142][2100/3746] lr: 7.791e-04, eta: 7:33:22, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5427, top5_acc: 0.7844, loss_cls: 2.5459, loss: 2.5459 +2024-12-31 14:50:54,917 - pyskl - INFO - Epoch [142][2200/3746] lr: 7.742e-04, eta: 7:31:56, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5470, top5_acc: 0.7887, loss_cls: 2.5272, loss: 2.5272 +2024-12-31 14:52:18,997 - pyskl - INFO - Epoch [142][2300/3746] lr: 7.693e-04, eta: 7:30:30, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.5375, top5_acc: 0.7822, loss_cls: 2.5535, loss: 2.5535 +2024-12-31 14:53:43,339 - pyskl - INFO - Epoch [142][2400/3746] lr: 7.644e-04, eta: 7:29:04, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5317, top5_acc: 0.7848, loss_cls: 2.5636, loss: 2.5636 +2024-12-31 14:55:07,866 - pyskl - INFO - Epoch [142][2500/3746] lr: 7.595e-04, eta: 7:27:38, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5469, top5_acc: 0.7797, loss_cls: 2.5751, loss: 2.5751 +2024-12-31 14:56:32,339 - pyskl - INFO - Epoch [142][2600/3746] lr: 7.547e-04, eta: 7:26:11, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5364, top5_acc: 0.7809, loss_cls: 2.5804, loss: 2.5804 +2024-12-31 14:57:56,310 - pyskl - INFO - Epoch [142][2700/3746] lr: 7.499e-04, eta: 7:24:45, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5431, top5_acc: 0.7845, loss_cls: 2.5592, loss: 2.5592 +2024-12-31 14:59:20,711 - pyskl - INFO - Epoch [142][2800/3746] lr: 7.450e-04, eta: 7:23:19, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5458, top5_acc: 0.7847, loss_cls: 2.5586, loss: 2.5586 +2024-12-31 15:00:44,611 - pyskl - INFO - Epoch [142][2900/3746] lr: 7.402e-04, eta: 7:21:53, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5470, top5_acc: 0.7883, loss_cls: 2.5525, loss: 2.5525 +2024-12-31 15:02:08,931 - pyskl - INFO - Epoch [142][3000/3746] lr: 7.355e-04, eta: 7:20:27, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5431, top5_acc: 0.7848, loss_cls: 2.5531, loss: 2.5531 +2024-12-31 15:03:34,071 - pyskl - INFO - Epoch [142][3100/3746] lr: 7.307e-04, eta: 7:19:01, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5409, top5_acc: 0.7827, loss_cls: 2.5609, loss: 2.5609 +2024-12-31 15:04:58,734 - pyskl - INFO - Epoch [142][3200/3746] lr: 7.259e-04, eta: 7:17:35, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5394, top5_acc: 0.7800, loss_cls: 2.5820, loss: 2.5820 +2024-12-31 15:06:23,329 - pyskl - INFO - Epoch [142][3300/3746] lr: 7.212e-04, eta: 7:16:08, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5302, top5_acc: 0.7803, loss_cls: 2.5966, loss: 2.5966 +2024-12-31 15:07:47,180 - pyskl - INFO - Epoch [142][3400/3746] lr: 7.165e-04, eta: 7:14:42, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5525, top5_acc: 0.7877, loss_cls: 2.5194, loss: 2.5194 +2024-12-31 15:09:11,947 - pyskl - INFO - Epoch [142][3500/3746] lr: 7.118e-04, eta: 7:13:16, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5413, top5_acc: 0.7837, loss_cls: 2.5533, loss: 2.5533 +2024-12-31 15:10:35,608 - pyskl - INFO - Epoch [142][3600/3746] lr: 7.071e-04, eta: 7:11:50, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5416, top5_acc: 0.7786, loss_cls: 2.5726, loss: 2.5726 +2024-12-31 15:12:00,021 - pyskl - INFO - Epoch [142][3700/3746] lr: 7.024e-04, eta: 7:10:24, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5348, top5_acc: 0.7798, loss_cls: 2.6079, loss: 2.6079 +2024-12-31 15:12:40,592 - pyskl - INFO - Saving checkpoint at 142 epochs +2024-12-31 15:14:38,577 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 15:14:39,326 - pyskl - INFO - +top1_acc 0.4082 +top5_acc 0.6566 +2024-12-31 15:14:39,326 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 15:14:39,371 - pyskl - INFO - +mean_acc 0.4079 +2024-12-31 15:14:39,376 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_139.pth was removed +2024-12-31 15:14:39,637 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_142.pth. +2024-12-31 15:14:39,638 - pyskl - INFO - Best top1_acc is 0.4082 at 142 epoch. +2024-12-31 15:14:39,656 - pyskl - INFO - Epoch(val) [142][309] top1_acc: 0.4082, top5_acc: 0.6566, mean_class_accuracy: 0.4079 +2024-12-31 15:19:00,299 - pyskl - INFO - Epoch [143][100/3746] lr: 6.956e-04, eta: 7:08:26, time: 2.606, data_time: 1.568, memory: 15990, top1_acc: 0.5645, top5_acc: 0.8031, loss_cls: 2.4288, loss: 2.4288 +2024-12-31 15:20:25,712 - pyskl - INFO - Epoch [143][200/3746] lr: 6.910e-04, eta: 7:07:00, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5650, top5_acc: 0.7970, loss_cls: 2.4637, loss: 2.4637 +2024-12-31 15:21:51,427 - pyskl - INFO - Epoch [143][300/3746] lr: 6.863e-04, eta: 7:05:34, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5600, top5_acc: 0.7998, loss_cls: 2.4524, loss: 2.4524 +2024-12-31 15:23:17,308 - pyskl - INFO - Epoch [143][400/3746] lr: 6.817e-04, eta: 7:04:08, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5663, top5_acc: 0.8027, loss_cls: 2.4280, loss: 2.4280 +2024-12-31 15:24:43,122 - pyskl - INFO - Epoch [143][500/3746] lr: 6.771e-04, eta: 7:02:41, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5537, top5_acc: 0.7880, loss_cls: 2.5179, loss: 2.5179 +2024-12-31 15:26:08,835 - pyskl - INFO - Epoch [143][600/3746] lr: 6.725e-04, eta: 7:01:15, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5698, top5_acc: 0.8034, loss_cls: 2.4290, loss: 2.4290 +2024-12-31 15:27:34,276 - pyskl - INFO - Epoch [143][700/3746] lr: 6.680e-04, eta: 6:59:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5492, top5_acc: 0.7883, loss_cls: 2.5044, loss: 2.5044 +2024-12-31 15:28:59,608 - pyskl - INFO - Epoch [143][800/3746] lr: 6.634e-04, eta: 6:58:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5577, top5_acc: 0.7967, loss_cls: 2.4991, loss: 2.4991 +2024-12-31 15:30:25,812 - pyskl - INFO - Epoch [143][900/3746] lr: 6.589e-04, eta: 6:56:57, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5555, top5_acc: 0.7887, loss_cls: 2.5072, loss: 2.5072 +2024-12-31 15:31:51,385 - pyskl - INFO - Epoch [143][1000/3746] lr: 6.544e-04, eta: 6:55:31, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5536, top5_acc: 0.7917, loss_cls: 2.4853, loss: 2.4853 +2024-12-31 15:33:17,214 - pyskl - INFO - Epoch [143][1100/3746] lr: 6.499e-04, eta: 6:54:05, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5539, top5_acc: 0.7941, loss_cls: 2.5061, loss: 2.5061 +2024-12-31 15:34:42,891 - pyskl - INFO - Epoch [143][1200/3746] lr: 6.454e-04, eta: 6:52:39, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.5436, top5_acc: 0.7864, loss_cls: 2.5524, loss: 2.5524 +2024-12-31 15:36:08,270 - pyskl - INFO - Epoch [143][1300/3746] lr: 6.409e-04, eta: 6:51:13, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5613, top5_acc: 0.7917, loss_cls: 2.4885, loss: 2.4885 +2024-12-31 15:37:32,853 - pyskl - INFO - Epoch [143][1400/3746] lr: 6.365e-04, eta: 6:49:47, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5578, top5_acc: 0.7936, loss_cls: 2.4778, loss: 2.4778 +2024-12-31 15:38:57,825 - pyskl - INFO - Epoch [143][1500/3746] lr: 6.320e-04, eta: 6:48:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5605, top5_acc: 0.7973, loss_cls: 2.4665, loss: 2.4665 +2024-12-31 15:40:22,352 - pyskl - INFO - Epoch [143][1600/3746] lr: 6.276e-04, eta: 6:46:54, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5620, top5_acc: 0.7905, loss_cls: 2.4933, loss: 2.4933 +2024-12-31 15:41:46,937 - pyskl - INFO - Epoch [143][1700/3746] lr: 6.232e-04, eta: 6:45:28, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5380, top5_acc: 0.7831, loss_cls: 2.5443, loss: 2.5443 +2024-12-31 15:43:11,469 - pyskl - INFO - Epoch [143][1800/3746] lr: 6.188e-04, eta: 6:44:02, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5586, top5_acc: 0.7950, loss_cls: 2.4848, loss: 2.4848 +2024-12-31 15:44:36,318 - pyskl - INFO - Epoch [143][1900/3746] lr: 6.144e-04, eta: 6:42:36, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5463, top5_acc: 0.7877, loss_cls: 2.5509, loss: 2.5509 +2024-12-31 15:46:01,119 - pyskl - INFO - Epoch [143][2000/3746] lr: 6.101e-04, eta: 6:41:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5595, top5_acc: 0.7953, loss_cls: 2.4792, loss: 2.4792 +2024-12-31 15:47:25,713 - pyskl - INFO - Epoch [143][2100/3746] lr: 6.057e-04, eta: 6:39:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5572, top5_acc: 0.7930, loss_cls: 2.4884, loss: 2.4884 +2024-12-31 15:48:50,348 - pyskl - INFO - Epoch [143][2200/3746] lr: 6.014e-04, eta: 6:38:18, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5534, top5_acc: 0.7945, loss_cls: 2.5068, loss: 2.5068 +2024-12-31 15:50:15,053 - pyskl - INFO - Epoch [143][2300/3746] lr: 5.971e-04, eta: 6:36:51, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5611, top5_acc: 0.7981, loss_cls: 2.4861, loss: 2.4861 +2024-12-31 15:51:39,892 - pyskl - INFO - Epoch [143][2400/3746] lr: 5.928e-04, eta: 6:35:25, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5459, top5_acc: 0.7886, loss_cls: 2.5175, loss: 2.5175 +2024-12-31 15:53:04,921 - pyskl - INFO - Epoch [143][2500/3746] lr: 5.885e-04, eta: 6:33:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5486, top5_acc: 0.7883, loss_cls: 2.5162, loss: 2.5162 +2024-12-31 15:54:30,067 - pyskl - INFO - Epoch [143][2600/3746] lr: 5.842e-04, eta: 6:32:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5464, top5_acc: 0.7952, loss_cls: 2.5108, loss: 2.5108 +2024-12-31 15:55:55,121 - pyskl - INFO - Epoch [143][2700/3746] lr: 5.800e-04, eta: 6:31:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5528, top5_acc: 0.7903, loss_cls: 2.5058, loss: 2.5058 +2024-12-31 15:57:20,041 - pyskl - INFO - Epoch [143][2800/3746] lr: 5.757e-04, eta: 6:29:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5516, top5_acc: 0.7914, loss_cls: 2.4867, loss: 2.4867 +2024-12-31 15:58:45,201 - pyskl - INFO - Epoch [143][2900/3746] lr: 5.715e-04, eta: 6:28:15, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5463, top5_acc: 0.7853, loss_cls: 2.5284, loss: 2.5284 +2024-12-31 16:00:09,582 - pyskl - INFO - Epoch [143][3000/3746] lr: 5.673e-04, eta: 6:26:49, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5513, top5_acc: 0.7861, loss_cls: 2.5206, loss: 2.5206 +2024-12-31 16:01:34,127 - pyskl - INFO - Epoch [143][3100/3746] lr: 5.631e-04, eta: 6:25:22, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5578, top5_acc: 0.7909, loss_cls: 2.4996, loss: 2.4996 +2024-12-31 16:02:58,430 - pyskl - INFO - Epoch [143][3200/3746] lr: 5.590e-04, eta: 6:23:56, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5433, top5_acc: 0.7895, loss_cls: 2.5272, loss: 2.5272 +2024-12-31 16:04:23,162 - pyskl - INFO - Epoch [143][3300/3746] lr: 5.548e-04, eta: 6:22:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5475, top5_acc: 0.7887, loss_cls: 2.5224, loss: 2.5224 +2024-12-31 16:05:47,772 - pyskl - INFO - Epoch [143][3400/3746] lr: 5.506e-04, eta: 6:21:04, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5617, top5_acc: 0.7936, loss_cls: 2.4952, loss: 2.4952 +2024-12-31 16:07:12,599 - pyskl - INFO - Epoch [143][3500/3746] lr: 5.465e-04, eta: 6:19:38, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5497, top5_acc: 0.7887, loss_cls: 2.5125, loss: 2.5125 +2024-12-31 16:08:37,095 - pyskl - INFO - Epoch [143][3600/3746] lr: 5.424e-04, eta: 6:18:12, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5513, top5_acc: 0.7916, loss_cls: 2.5208, loss: 2.5208 +2024-12-31 16:10:01,930 - pyskl - INFO - Epoch [143][3700/3746] lr: 5.383e-04, eta: 6:16:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5558, top5_acc: 0.7948, loss_cls: 2.4897, loss: 2.4897 +2024-12-31 16:10:42,743 - pyskl - INFO - Saving checkpoint at 143 epochs +2024-12-31 16:12:41,601 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 16:12:42,638 - pyskl - INFO - +top1_acc 0.4060 +top5_acc 0.6553 +2024-12-31 16:12:42,638 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 16:12:42,685 - pyskl - INFO - +mean_acc 0.4058 +2024-12-31 16:12:42,698 - pyskl - INFO - Epoch(val) [143][309] top1_acc: 0.4060, top5_acc: 0.6553, mean_class_accuracy: 0.4058 +2024-12-31 16:17:03,819 - pyskl - INFO - Epoch [144][100/3746] lr: 5.323e-04, eta: 6:14:47, time: 2.611, data_time: 1.568, memory: 15990, top1_acc: 0.5633, top5_acc: 0.8064, loss_cls: 2.4207, loss: 2.4207 +2024-12-31 16:18:29,650 - pyskl - INFO - Epoch [144][200/3746] lr: 5.283e-04, eta: 6:13:20, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5689, top5_acc: 0.7961, loss_cls: 2.4290, loss: 2.4290 +2024-12-31 16:19:55,465 - pyskl - INFO - Epoch [144][300/3746] lr: 5.242e-04, eta: 6:11:54, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5695, top5_acc: 0.8087, loss_cls: 2.3915, loss: 2.3915 +2024-12-31 16:21:21,746 - pyskl - INFO - Epoch [144][400/3746] lr: 5.202e-04, eta: 6:10:28, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5609, top5_acc: 0.7989, loss_cls: 2.4457, loss: 2.4457 +2024-12-31 16:22:47,188 - pyskl - INFO - Epoch [144][500/3746] lr: 5.162e-04, eta: 6:09:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5745, top5_acc: 0.7989, loss_cls: 2.4106, loss: 2.4106 +2024-12-31 16:24:12,634 - pyskl - INFO - Epoch [144][600/3746] lr: 5.122e-04, eta: 6:07:36, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5717, top5_acc: 0.8070, loss_cls: 2.4171, loss: 2.4171 +2024-12-31 16:25:37,956 - pyskl - INFO - Epoch [144][700/3746] lr: 5.082e-04, eta: 6:06:10, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5680, top5_acc: 0.7961, loss_cls: 2.4621, loss: 2.4621 +2024-12-31 16:27:03,585 - pyskl - INFO - Epoch [144][800/3746] lr: 5.042e-04, eta: 6:04:44, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5798, top5_acc: 0.8087, loss_cls: 2.3801, loss: 2.3801 +2024-12-31 16:28:29,576 - pyskl - INFO - Epoch [144][900/3746] lr: 5.003e-04, eta: 6:03:18, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5678, top5_acc: 0.7948, loss_cls: 2.4697, loss: 2.4697 +2024-12-31 16:29:55,469 - pyskl - INFO - Epoch [144][1000/3746] lr: 4.964e-04, eta: 6:01:52, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5566, top5_acc: 0.7884, loss_cls: 2.4941, loss: 2.4941 +2024-12-31 16:31:21,071 - pyskl - INFO - Epoch [144][1100/3746] lr: 4.924e-04, eta: 6:00:26, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5641, top5_acc: 0.7994, loss_cls: 2.4426, loss: 2.4426 +2024-12-31 16:32:46,861 - pyskl - INFO - Epoch [144][1200/3746] lr: 4.885e-04, eta: 5:58:59, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5637, top5_acc: 0.8039, loss_cls: 2.4359, loss: 2.4359 +2024-12-31 16:34:12,032 - pyskl - INFO - Epoch [144][1300/3746] lr: 4.846e-04, eta: 5:57:33, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5605, top5_acc: 0.8027, loss_cls: 2.4592, loss: 2.4592 +2024-12-31 16:35:37,716 - pyskl - INFO - Epoch [144][1400/3746] lr: 4.808e-04, eta: 5:56:07, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5591, top5_acc: 0.8006, loss_cls: 2.4619, loss: 2.4619 +2024-12-31 16:37:03,579 - pyskl - INFO - Epoch [144][1500/3746] lr: 4.769e-04, eta: 5:54:41, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5728, top5_acc: 0.7987, loss_cls: 2.4617, loss: 2.4617 +2024-12-31 16:38:28,552 - pyskl - INFO - Epoch [144][1600/3746] lr: 4.731e-04, eta: 5:53:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5553, top5_acc: 0.7955, loss_cls: 2.4880, loss: 2.4880 +2024-12-31 16:39:53,056 - pyskl - INFO - Epoch [144][1700/3746] lr: 4.692e-04, eta: 5:51:49, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5500, top5_acc: 0.7941, loss_cls: 2.5144, loss: 2.5144 +2024-12-31 16:41:17,789 - pyskl - INFO - Epoch [144][1800/3746] lr: 4.654e-04, eta: 5:50:23, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.5658, top5_acc: 0.8050, loss_cls: 2.4371, loss: 2.4371 +2024-12-31 16:42:42,462 - pyskl - INFO - Epoch [144][1900/3746] lr: 4.616e-04, eta: 5:48:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5617, top5_acc: 0.7937, loss_cls: 2.4675, loss: 2.4675 +2024-12-31 16:44:06,927 - pyskl - INFO - Epoch [144][2000/3746] lr: 4.578e-04, eta: 5:47:30, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5761, top5_acc: 0.7984, loss_cls: 2.4335, loss: 2.4335 +2024-12-31 16:45:31,405 - pyskl - INFO - Epoch [144][2100/3746] lr: 4.541e-04, eta: 5:46:04, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5681, top5_acc: 0.7956, loss_cls: 2.4576, loss: 2.4576 +2024-12-31 16:46:55,680 - pyskl - INFO - Epoch [144][2200/3746] lr: 4.503e-04, eta: 5:44:38, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5577, top5_acc: 0.8028, loss_cls: 2.4637, loss: 2.4637 +2024-12-31 16:48:20,338 - pyskl - INFO - Epoch [144][2300/3746] lr: 4.466e-04, eta: 5:43:12, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5686, top5_acc: 0.7997, loss_cls: 2.4420, loss: 2.4420 +2024-12-31 16:49:45,549 - pyskl - INFO - Epoch [144][2400/3746] lr: 4.429e-04, eta: 5:41:46, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5567, top5_acc: 0.7963, loss_cls: 2.4890, loss: 2.4890 +2024-12-31 16:51:10,788 - pyskl - INFO - Epoch [144][2500/3746] lr: 4.392e-04, eta: 5:40:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5550, top5_acc: 0.7900, loss_cls: 2.5083, loss: 2.5083 +2024-12-31 16:52:36,479 - pyskl - INFO - Epoch [144][2600/3746] lr: 4.355e-04, eta: 5:38:54, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5569, top5_acc: 0.7916, loss_cls: 2.4896, loss: 2.4896 +2024-12-31 16:54:01,277 - pyskl - INFO - Epoch [144][2700/3746] lr: 4.318e-04, eta: 5:37:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5595, top5_acc: 0.8027, loss_cls: 2.4648, loss: 2.4648 +2024-12-31 16:55:26,446 - pyskl - INFO - Epoch [144][2800/3746] lr: 4.281e-04, eta: 5:36:01, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5578, top5_acc: 0.8014, loss_cls: 2.4362, loss: 2.4362 +2024-12-31 16:56:51,609 - pyskl - INFO - Epoch [144][2900/3746] lr: 4.245e-04, eta: 5:34:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5578, top5_acc: 0.7955, loss_cls: 2.4837, loss: 2.4837 +2024-12-31 16:58:17,251 - pyskl - INFO - Epoch [144][3000/3746] lr: 4.209e-04, eta: 5:33:09, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5741, top5_acc: 0.8058, loss_cls: 2.4047, loss: 2.4047 +2024-12-31 16:59:42,573 - pyskl - INFO - Epoch [144][3100/3746] lr: 4.173e-04, eta: 5:31:43, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.5681, top5_acc: 0.7970, loss_cls: 2.4316, loss: 2.4316 +2024-12-31 17:01:06,956 - pyskl - INFO - Epoch [144][3200/3746] lr: 4.137e-04, eta: 5:30:17, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5700, top5_acc: 0.8063, loss_cls: 2.4138, loss: 2.4138 +2024-12-31 17:02:31,720 - pyskl - INFO - Epoch [144][3300/3746] lr: 4.101e-04, eta: 5:28:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5647, top5_acc: 0.7997, loss_cls: 2.4558, loss: 2.4558 +2024-12-31 17:03:56,656 - pyskl - INFO - Epoch [144][3400/3746] lr: 4.065e-04, eta: 5:27:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5603, top5_acc: 0.7972, loss_cls: 2.4413, loss: 2.4413 +2024-12-31 17:05:22,057 - pyskl - INFO - Epoch [144][3500/3746] lr: 4.030e-04, eta: 5:25:58, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5727, top5_acc: 0.7930, loss_cls: 2.4467, loss: 2.4467 +2024-12-31 17:06:47,332 - pyskl - INFO - Epoch [144][3600/3746] lr: 3.994e-04, eta: 5:24:32, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5633, top5_acc: 0.7992, loss_cls: 2.4382, loss: 2.4382 +2024-12-31 17:08:12,736 - pyskl - INFO - Epoch [144][3700/3746] lr: 3.959e-04, eta: 5:23:06, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5584, top5_acc: 0.7994, loss_cls: 2.4558, loss: 2.4558 +2024-12-31 17:08:54,187 - pyskl - INFO - Saving checkpoint at 144 epochs +2024-12-31 17:10:53,269 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 17:10:53,999 - pyskl - INFO - +top1_acc 0.4053 +top5_acc 0.6597 +2024-12-31 17:10:53,999 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 17:10:54,067 - pyskl - INFO - +mean_acc 0.4051 +2024-12-31 17:10:54,088 - pyskl - INFO - Epoch(val) [144][309] top1_acc: 0.4053, top5_acc: 0.6597, mean_class_accuracy: 0.4051 +2024-12-31 17:15:18,511 - pyskl - INFO - Epoch [145][100/3746] lr: 3.908e-04, eta: 5:21:06, time: 2.644, data_time: 1.585, memory: 15990, top1_acc: 0.5666, top5_acc: 0.7997, loss_cls: 2.4418, loss: 2.4418 +2024-12-31 17:16:44,068 - pyskl - INFO - Epoch [145][200/3746] lr: 3.873e-04, eta: 5:19:40, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5839, top5_acc: 0.8208, loss_cls: 2.3400, loss: 2.3400 +2024-12-31 17:18:09,490 - pyskl - INFO - Epoch [145][300/3746] lr: 3.839e-04, eta: 5:18:14, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5787, top5_acc: 0.8133, loss_cls: 2.3539, loss: 2.3539 +2024-12-31 17:19:35,366 - pyskl - INFO - Epoch [145][400/3746] lr: 3.804e-04, eta: 5:16:48, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5861, top5_acc: 0.8166, loss_cls: 2.3422, loss: 2.3422 +2024-12-31 17:21:00,599 - pyskl - INFO - Epoch [145][500/3746] lr: 3.770e-04, eta: 5:15:22, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5739, top5_acc: 0.7984, loss_cls: 2.4344, loss: 2.4344 +2024-12-31 17:22:26,025 - pyskl - INFO - Epoch [145][600/3746] lr: 3.736e-04, eta: 5:13:56, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5794, top5_acc: 0.8125, loss_cls: 2.3682, loss: 2.3682 +2024-12-31 17:23:51,228 - pyskl - INFO - Epoch [145][700/3746] lr: 3.702e-04, eta: 5:12:30, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5878, top5_acc: 0.8197, loss_cls: 2.3304, loss: 2.3304 +2024-12-31 17:25:17,026 - pyskl - INFO - Epoch [145][800/3746] lr: 3.668e-04, eta: 5:11:03, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5702, top5_acc: 0.8073, loss_cls: 2.3955, loss: 2.3955 +2024-12-31 17:26:41,976 - pyskl - INFO - Epoch [145][900/3746] lr: 3.634e-04, eta: 5:09:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5798, top5_acc: 0.8105, loss_cls: 2.3843, loss: 2.3843 +2024-12-31 17:28:07,357 - pyskl - INFO - Epoch [145][1000/3746] lr: 3.600e-04, eta: 5:08:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5681, top5_acc: 0.8005, loss_cls: 2.4287, loss: 2.4287 +2024-12-31 17:29:32,518 - pyskl - INFO - Epoch [145][1100/3746] lr: 3.567e-04, eta: 5:06:45, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5747, top5_acc: 0.8044, loss_cls: 2.4090, loss: 2.4090 +2024-12-31 17:30:58,560 - pyskl - INFO - Epoch [145][1200/3746] lr: 3.534e-04, eta: 5:05:19, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.5775, top5_acc: 0.8039, loss_cls: 2.3870, loss: 2.3870 +2024-12-31 17:32:23,552 - pyskl - INFO - Epoch [145][1300/3746] lr: 3.501e-04, eta: 5:03:53, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5633, top5_acc: 0.8067, loss_cls: 2.4216, loss: 2.4216 +2024-12-31 17:33:48,840 - pyskl - INFO - Epoch [145][1400/3746] lr: 3.468e-04, eta: 5:02:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5691, top5_acc: 0.8086, loss_cls: 2.3953, loss: 2.3953 +2024-12-31 17:35:14,403 - pyskl - INFO - Epoch [145][1500/3746] lr: 3.435e-04, eta: 5:01:01, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5720, top5_acc: 0.8034, loss_cls: 2.4215, loss: 2.4215 +2024-12-31 17:36:40,331 - pyskl - INFO - Epoch [145][1600/3746] lr: 3.402e-04, eta: 4:59:34, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5689, top5_acc: 0.8019, loss_cls: 2.4232, loss: 2.4232 +2024-12-31 17:38:05,889 - pyskl - INFO - Epoch [145][1700/3746] lr: 3.370e-04, eta: 4:58:08, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5742, top5_acc: 0.8084, loss_cls: 2.4250, loss: 2.4250 +2024-12-31 17:39:31,577 - pyskl - INFO - Epoch [145][1800/3746] lr: 3.337e-04, eta: 4:56:42, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5720, top5_acc: 0.8039, loss_cls: 2.4084, loss: 2.4084 +2024-12-31 17:40:56,991 - pyskl - INFO - Epoch [145][1900/3746] lr: 3.305e-04, eta: 4:55:16, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.5644, top5_acc: 0.8047, loss_cls: 2.4355, loss: 2.4355 +2024-12-31 17:42:22,238 - pyskl - INFO - Epoch [145][2000/3746] lr: 3.273e-04, eta: 4:53:50, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5763, top5_acc: 0.8064, loss_cls: 2.3967, loss: 2.3967 +2024-12-31 17:43:47,085 - pyskl - INFO - Epoch [145][2100/3746] lr: 3.241e-04, eta: 4:52:24, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5802, top5_acc: 0.8106, loss_cls: 2.3811, loss: 2.3811 +2024-12-31 17:45:12,108 - pyskl - INFO - Epoch [145][2200/3746] lr: 3.210e-04, eta: 4:50:58, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5763, top5_acc: 0.8113, loss_cls: 2.3724, loss: 2.3724 +2024-12-31 17:46:37,399 - pyskl - INFO - Epoch [145][2300/3746] lr: 3.178e-04, eta: 4:49:31, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5720, top5_acc: 0.8072, loss_cls: 2.3992, loss: 2.3992 +2024-12-31 17:48:03,067 - pyskl - INFO - Epoch [145][2400/3746] lr: 3.147e-04, eta: 4:48:05, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5780, top5_acc: 0.8034, loss_cls: 2.4002, loss: 2.4002 +2024-12-31 17:49:28,814 - pyskl - INFO - Epoch [145][2500/3746] lr: 3.116e-04, eta: 4:46:39, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5811, top5_acc: 0.8094, loss_cls: 2.3970, loss: 2.3970 +2024-12-31 17:50:53,576 - pyskl - INFO - Epoch [145][2600/3746] lr: 3.084e-04, eta: 4:45:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5694, top5_acc: 0.7983, loss_cls: 2.4323, loss: 2.4323 +2024-12-31 17:52:18,797 - pyskl - INFO - Epoch [145][2700/3746] lr: 3.054e-04, eta: 4:43:47, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5694, top5_acc: 0.7983, loss_cls: 2.4273, loss: 2.4273 +2024-12-31 17:53:43,838 - pyskl - INFO - Epoch [145][2800/3746] lr: 3.023e-04, eta: 4:42:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5736, top5_acc: 0.8105, loss_cls: 2.3885, loss: 2.3885 +2024-12-31 17:55:09,047 - pyskl - INFO - Epoch [145][2900/3746] lr: 2.992e-04, eta: 4:40:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5809, top5_acc: 0.8137, loss_cls: 2.3856, loss: 2.3856 +2024-12-31 17:56:33,856 - pyskl - INFO - Epoch [145][3000/3746] lr: 2.962e-04, eta: 4:39:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5761, top5_acc: 0.8037, loss_cls: 2.3960, loss: 2.3960 +2024-12-31 17:57:58,803 - pyskl - INFO - Epoch [145][3100/3746] lr: 2.931e-04, eta: 4:38:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5736, top5_acc: 0.8053, loss_cls: 2.3936, loss: 2.3936 +2024-12-31 17:59:23,589 - pyskl - INFO - Epoch [145][3200/3746] lr: 2.901e-04, eta: 4:36:36, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5742, top5_acc: 0.8041, loss_cls: 2.4253, loss: 2.4253 +2024-12-31 18:00:48,246 - pyskl - INFO - Epoch [145][3300/3746] lr: 2.871e-04, eta: 4:35:10, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5728, top5_acc: 0.8102, loss_cls: 2.3691, loss: 2.3691 +2024-12-31 18:02:13,356 - pyskl - INFO - Epoch [145][3400/3746] lr: 2.841e-04, eta: 4:33:44, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5670, top5_acc: 0.8006, loss_cls: 2.4310, loss: 2.4310 +2024-12-31 18:03:38,737 - pyskl - INFO - Epoch [145][3500/3746] lr: 2.812e-04, eta: 4:32:18, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5667, top5_acc: 0.7981, loss_cls: 2.4353, loss: 2.4353 +2024-12-31 18:05:03,679 - pyskl - INFO - Epoch [145][3600/3746] lr: 2.782e-04, eta: 4:30:52, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5659, top5_acc: 0.8050, loss_cls: 2.4188, loss: 2.4188 +2024-12-31 18:06:29,199 - pyskl - INFO - Epoch [145][3700/3746] lr: 2.753e-04, eta: 4:29:26, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5742, top5_acc: 0.8052, loss_cls: 2.4066, loss: 2.4066 +2024-12-31 18:07:10,555 - pyskl - INFO - Saving checkpoint at 145 epochs +2024-12-31 18:09:11,667 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 18:09:12,541 - pyskl - INFO - +top1_acc 0.4101 +top5_acc 0.6612 +2024-12-31 18:09:12,541 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 18:09:12,590 - pyskl - INFO - +mean_acc 0.4099 +2024-12-31 18:09:12,595 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_142.pth was removed +2024-12-31 18:09:12,887 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_145.pth. +2024-12-31 18:09:12,888 - pyskl - INFO - Best top1_acc is 0.4101 at 145 epoch. +2024-12-31 18:09:12,907 - pyskl - INFO - Epoch(val) [145][309] top1_acc: 0.4101, top5_acc: 0.6612, mean_class_accuracy: 0.4099 +2024-12-31 18:13:32,905 - pyskl - INFO - Epoch [146][100/3746] lr: 2.710e-04, eta: 4:27:24, time: 2.600, data_time: 1.570, memory: 15990, top1_acc: 0.5756, top5_acc: 0.8147, loss_cls: 2.3617, loss: 2.3617 +2024-12-31 18:14:57,806 - pyskl - INFO - Epoch [146][200/3746] lr: 2.681e-04, eta: 4:25:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5781, top5_acc: 0.8156, loss_cls: 2.3465, loss: 2.3465 +2024-12-31 18:16:22,789 - pyskl - INFO - Epoch [146][300/3746] lr: 2.652e-04, eta: 4:24:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5828, top5_acc: 0.8031, loss_cls: 2.3633, loss: 2.3633 +2024-12-31 18:17:47,387 - pyskl - INFO - Epoch [146][400/3746] lr: 2.624e-04, eta: 4:23:06, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5808, top5_acc: 0.8216, loss_cls: 2.3222, loss: 2.3222 +2024-12-31 18:19:12,174 - pyskl - INFO - Epoch [146][500/3746] lr: 2.595e-04, eta: 4:21:40, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5877, top5_acc: 0.8203, loss_cls: 2.3234, loss: 2.3234 +2024-12-31 18:20:37,095 - pyskl - INFO - Epoch [146][600/3746] lr: 2.567e-04, eta: 4:20:14, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5797, top5_acc: 0.8153, loss_cls: 2.3705, loss: 2.3705 +2024-12-31 18:22:01,956 - pyskl - INFO - Epoch [146][700/3746] lr: 2.539e-04, eta: 4:18:47, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5831, top5_acc: 0.8145, loss_cls: 2.3397, loss: 2.3397 +2024-12-31 18:23:26,889 - pyskl - INFO - Epoch [146][800/3746] lr: 2.511e-04, eta: 4:17:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5794, top5_acc: 0.8141, loss_cls: 2.3562, loss: 2.3562 +2024-12-31 18:24:51,909 - pyskl - INFO - Epoch [146][900/3746] lr: 2.483e-04, eta: 4:15:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5769, top5_acc: 0.8172, loss_cls: 2.3434, loss: 2.3434 +2024-12-31 18:26:16,791 - pyskl - INFO - Epoch [146][1000/3746] lr: 2.455e-04, eta: 4:14:29, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5713, top5_acc: 0.8113, loss_cls: 2.3853, loss: 2.3853 +2024-12-31 18:27:41,777 - pyskl - INFO - Epoch [146][1100/3746] lr: 2.427e-04, eta: 4:13:03, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5858, top5_acc: 0.8194, loss_cls: 2.3477, loss: 2.3477 +2024-12-31 18:29:06,710 - pyskl - INFO - Epoch [146][1200/3746] lr: 2.400e-04, eta: 4:11:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5830, top5_acc: 0.8047, loss_cls: 2.3769, loss: 2.3769 +2024-12-31 18:30:31,489 - pyskl - INFO - Epoch [146][1300/3746] lr: 2.373e-04, eta: 4:10:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5884, top5_acc: 0.8155, loss_cls: 2.3541, loss: 2.3541 +2024-12-31 18:31:55,886 - pyskl - INFO - Epoch [146][1400/3746] lr: 2.345e-04, eta: 4:08:44, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5759, top5_acc: 0.8144, loss_cls: 2.3524, loss: 2.3524 +2024-12-31 18:33:20,802 - pyskl - INFO - Epoch [146][1500/3746] lr: 2.318e-04, eta: 4:07:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5925, top5_acc: 0.8142, loss_cls: 2.3322, loss: 2.3322 +2024-12-31 18:34:45,770 - pyskl - INFO - Epoch [146][1600/3746] lr: 2.292e-04, eta: 4:05:52, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5841, top5_acc: 0.8139, loss_cls: 2.3430, loss: 2.3430 +2024-12-31 18:36:10,623 - pyskl - INFO - Epoch [146][1700/3746] lr: 2.265e-04, eta: 4:04:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5880, top5_acc: 0.8113, loss_cls: 2.3640, loss: 2.3640 +2024-12-31 18:37:35,566 - pyskl - INFO - Epoch [146][1800/3746] lr: 2.239e-04, eta: 4:03:00, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5833, top5_acc: 0.8092, loss_cls: 2.3551, loss: 2.3551 +2024-12-31 18:39:00,568 - pyskl - INFO - Epoch [146][1900/3746] lr: 2.212e-04, eta: 4:01:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5814, top5_acc: 0.8184, loss_cls: 2.3289, loss: 2.3289 +2024-12-31 18:40:25,628 - pyskl - INFO - Epoch [146][2000/3746] lr: 2.186e-04, eta: 4:00:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5802, top5_acc: 0.8127, loss_cls: 2.3703, loss: 2.3703 +2024-12-31 18:41:50,534 - pyskl - INFO - Epoch [146][2100/3746] lr: 2.160e-04, eta: 3:58:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5794, top5_acc: 0.8080, loss_cls: 2.3734, loss: 2.3734 +2024-12-31 18:43:15,707 - pyskl - INFO - Epoch [146][2200/3746] lr: 2.134e-04, eta: 3:57:15, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5805, top5_acc: 0.8055, loss_cls: 2.3853, loss: 2.3853 +2024-12-31 18:44:40,414 - pyskl - INFO - Epoch [146][2300/3746] lr: 2.108e-04, eta: 3:55:49, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5772, top5_acc: 0.8167, loss_cls: 2.3671, loss: 2.3671 +2024-12-31 18:46:04,911 - pyskl - INFO - Epoch [146][2400/3746] lr: 2.083e-04, eta: 3:54:23, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5786, top5_acc: 0.8155, loss_cls: 2.3623, loss: 2.3623 +2024-12-31 18:47:29,764 - pyskl - INFO - Epoch [146][2500/3746] lr: 2.057e-04, eta: 3:52:57, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5748, top5_acc: 0.8089, loss_cls: 2.3787, loss: 2.3787 +2024-12-31 18:48:54,621 - pyskl - INFO - Epoch [146][2600/3746] lr: 2.032e-04, eta: 3:51:30, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5848, top5_acc: 0.8125, loss_cls: 2.3462, loss: 2.3462 +2024-12-31 18:50:19,657 - pyskl - INFO - Epoch [146][2700/3746] lr: 2.007e-04, eta: 3:50:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5816, top5_acc: 0.8056, loss_cls: 2.3788, loss: 2.3788 +2024-12-31 18:51:44,737 - pyskl - INFO - Epoch [146][2800/3746] lr: 1.982e-04, eta: 3:48:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5747, top5_acc: 0.8113, loss_cls: 2.3824, loss: 2.3824 +2024-12-31 18:53:09,931 - pyskl - INFO - Epoch [146][2900/3746] lr: 1.957e-04, eta: 3:47:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5852, top5_acc: 0.8161, loss_cls: 2.3429, loss: 2.3429 +2024-12-31 18:54:35,023 - pyskl - INFO - Epoch [146][3000/3746] lr: 1.933e-04, eta: 3:45:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5802, top5_acc: 0.8089, loss_cls: 2.3559, loss: 2.3559 +2024-12-31 18:56:00,056 - pyskl - INFO - Epoch [146][3100/3746] lr: 1.908e-04, eta: 3:44:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5897, top5_acc: 0.8183, loss_cls: 2.3301, loss: 2.3301 +2024-12-31 18:57:25,147 - pyskl - INFO - Epoch [146][3200/3746] lr: 1.884e-04, eta: 3:42:54, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5669, top5_acc: 0.8020, loss_cls: 2.4181, loss: 2.4181 +2024-12-31 18:58:50,343 - pyskl - INFO - Epoch [146][3300/3746] lr: 1.860e-04, eta: 3:41:27, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5819, top5_acc: 0.8142, loss_cls: 2.3635, loss: 2.3635 +2024-12-31 19:00:15,349 - pyskl - INFO - Epoch [146][3400/3746] lr: 1.836e-04, eta: 3:40:01, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5814, top5_acc: 0.8063, loss_cls: 2.3673, loss: 2.3673 +2024-12-31 19:01:40,115 - pyskl - INFO - Epoch [146][3500/3746] lr: 1.812e-04, eta: 3:38:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5867, top5_acc: 0.8063, loss_cls: 2.3470, loss: 2.3470 +2024-12-31 19:03:04,872 - pyskl - INFO - Epoch [146][3600/3746] lr: 1.788e-04, eta: 3:37:09, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5770, top5_acc: 0.8119, loss_cls: 2.3577, loss: 2.3577 +2024-12-31 19:04:30,010 - pyskl - INFO - Epoch [146][3700/3746] lr: 1.765e-04, eta: 3:35:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5789, top5_acc: 0.8130, loss_cls: 2.3444, loss: 2.3444 +2024-12-31 19:05:11,233 - pyskl - INFO - Saving checkpoint at 146 epochs +2024-12-31 19:07:11,385 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 19:07:12,097 - pyskl - INFO - +top1_acc 0.4099 +top5_acc 0.6592 +2024-12-31 19:07:12,097 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 19:07:12,157 - pyskl - INFO - +mean_acc 0.4096 +2024-12-31 19:07:12,171 - pyskl - INFO - Epoch(val) [146][309] top1_acc: 0.4099, top5_acc: 0.6592, mean_class_accuracy: 0.4096 +2024-12-31 19:11:32,384 - pyskl - INFO - Epoch [147][100/3746] lr: 1.730e-04, eta: 3:33:41, time: 2.602, data_time: 1.548, memory: 15990, top1_acc: 0.5905, top5_acc: 0.8213, loss_cls: 2.3064, loss: 2.3064 +2024-12-31 19:12:57,517 - pyskl - INFO - Epoch [147][200/3746] lr: 1.707e-04, eta: 3:32:15, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5978, top5_acc: 0.8195, loss_cls: 2.3048, loss: 2.3048 +2024-12-31 19:14:22,719 - pyskl - INFO - Epoch [147][300/3746] lr: 1.684e-04, eta: 3:30:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5859, top5_acc: 0.8194, loss_cls: 2.3185, loss: 2.3185 +2024-12-31 19:15:47,677 - pyskl - INFO - Epoch [147][400/3746] lr: 1.661e-04, eta: 3:29:22, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5927, top5_acc: 0.8241, loss_cls: 2.2990, loss: 2.2990 +2024-12-31 19:17:12,761 - pyskl - INFO - Epoch [147][500/3746] lr: 1.639e-04, eta: 3:27:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5816, top5_acc: 0.8125, loss_cls: 2.3434, loss: 2.3434 +2024-12-31 19:18:38,068 - pyskl - INFO - Epoch [147][600/3746] lr: 1.616e-04, eta: 3:26:30, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5914, top5_acc: 0.8173, loss_cls: 2.3102, loss: 2.3102 +2024-12-31 19:20:03,114 - pyskl - INFO - Epoch [147][700/3746] lr: 1.594e-04, eta: 3:25:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5839, top5_acc: 0.8125, loss_cls: 2.3417, loss: 2.3417 +2024-12-31 19:21:28,881 - pyskl - INFO - Epoch [147][800/3746] lr: 1.572e-04, eta: 3:23:38, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5925, top5_acc: 0.8172, loss_cls: 2.3064, loss: 2.3064 +2024-12-31 19:22:54,537 - pyskl - INFO - Epoch [147][900/3746] lr: 1.550e-04, eta: 3:22:11, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5909, top5_acc: 0.8214, loss_cls: 2.2916, loss: 2.2916 +2024-12-31 19:24:20,375 - pyskl - INFO - Epoch [147][1000/3746] lr: 1.528e-04, eta: 3:20:45, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5894, top5_acc: 0.8152, loss_cls: 2.3457, loss: 2.3457 +2024-12-31 19:25:45,973 - pyskl - INFO - Epoch [147][1100/3746] lr: 1.506e-04, eta: 3:19:19, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5889, top5_acc: 0.8187, loss_cls: 2.3099, loss: 2.3099 +2024-12-31 19:27:11,577 - pyskl - INFO - Epoch [147][1200/3746] lr: 1.484e-04, eta: 3:17:53, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5850, top5_acc: 0.8217, loss_cls: 2.3182, loss: 2.3182 +2024-12-31 19:28:36,097 - pyskl - INFO - Epoch [147][1300/3746] lr: 1.463e-04, eta: 3:16:27, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.5813, top5_acc: 0.8183, loss_cls: 2.3461, loss: 2.3461 +2024-12-31 19:30:00,235 - pyskl - INFO - Epoch [147][1400/3746] lr: 1.442e-04, eta: 3:15:01, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.5897, top5_acc: 0.8166, loss_cls: 2.3224, loss: 2.3224 +2024-12-31 19:31:24,788 - pyskl - INFO - Epoch [147][1500/3746] lr: 1.420e-04, eta: 3:13:34, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5797, top5_acc: 0.8194, loss_cls: 2.3398, loss: 2.3398 +2024-12-31 19:32:49,556 - pyskl - INFO - Epoch [147][1600/3746] lr: 1.399e-04, eta: 3:12:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5848, top5_acc: 0.8103, loss_cls: 2.3654, loss: 2.3654 +2024-12-31 19:34:14,482 - pyskl - INFO - Epoch [147][1700/3746] lr: 1.379e-04, eta: 3:10:42, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5983, top5_acc: 0.8250, loss_cls: 2.2760, loss: 2.2760 +2024-12-31 19:35:39,409 - pyskl - INFO - Epoch [147][1800/3746] lr: 1.358e-04, eta: 3:09:16, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5884, top5_acc: 0.8134, loss_cls: 2.3395, loss: 2.3395 +2024-12-31 19:37:03,812 - pyskl - INFO - Epoch [147][1900/3746] lr: 1.337e-04, eta: 3:07:50, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5798, top5_acc: 0.8120, loss_cls: 2.3215, loss: 2.3215 +2024-12-31 19:38:28,436 - pyskl - INFO - Epoch [147][2000/3746] lr: 1.317e-04, eta: 3:06:24, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5942, top5_acc: 0.8222, loss_cls: 2.2986, loss: 2.2986 +2024-12-31 19:39:53,395 - pyskl - INFO - Epoch [147][2100/3746] lr: 1.297e-04, eta: 3:04:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5967, top5_acc: 0.8211, loss_cls: 2.2877, loss: 2.2877 +2024-12-31 19:41:18,081 - pyskl - INFO - Epoch [147][2200/3746] lr: 1.277e-04, eta: 3:03:31, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5880, top5_acc: 0.8144, loss_cls: 2.3330, loss: 2.3330 +2024-12-31 19:42:43,263 - pyskl - INFO - Epoch [147][2300/3746] lr: 1.257e-04, eta: 3:02:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5794, top5_acc: 0.8131, loss_cls: 2.3353, loss: 2.3353 +2024-12-31 19:44:07,615 - pyskl - INFO - Epoch [147][2400/3746] lr: 1.237e-04, eta: 3:00:39, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5944, top5_acc: 0.8175, loss_cls: 2.3173, loss: 2.3173 +2024-12-31 19:45:32,042 - pyskl - INFO - Epoch [147][2500/3746] lr: 1.218e-04, eta: 2:59:13, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5828, top5_acc: 0.8172, loss_cls: 2.3398, loss: 2.3398 +2024-12-31 19:46:56,784 - pyskl - INFO - Epoch [147][2600/3746] lr: 1.198e-04, eta: 2:57:47, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5784, top5_acc: 0.8089, loss_cls: 2.3706, loss: 2.3706 +2024-12-31 19:48:20,952 - pyskl - INFO - Epoch [147][2700/3746] lr: 1.179e-04, eta: 2:56:20, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5919, top5_acc: 0.8227, loss_cls: 2.2881, loss: 2.2881 +2024-12-31 19:49:44,698 - pyskl - INFO - Epoch [147][2800/3746] lr: 1.160e-04, eta: 2:54:54, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5856, top5_acc: 0.8105, loss_cls: 2.3349, loss: 2.3349 +2024-12-31 19:51:09,190 - pyskl - INFO - Epoch [147][2900/3746] lr: 1.141e-04, eta: 2:53:28, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5869, top5_acc: 0.8145, loss_cls: 2.3150, loss: 2.3150 +2024-12-31 19:52:33,865 - pyskl - INFO - Epoch [147][3000/3746] lr: 1.122e-04, eta: 2:52:02, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5853, top5_acc: 0.8253, loss_cls: 2.2959, loss: 2.2959 +2024-12-31 19:53:58,198 - pyskl - INFO - Epoch [147][3100/3746] lr: 1.103e-04, eta: 2:50:36, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5886, top5_acc: 0.8155, loss_cls: 2.3290, loss: 2.3290 +2024-12-31 19:55:22,496 - pyskl - INFO - Epoch [147][3200/3746] lr: 1.085e-04, eta: 2:49:09, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5881, top5_acc: 0.8133, loss_cls: 2.3280, loss: 2.3280 +2024-12-31 19:56:46,714 - pyskl - INFO - Epoch [147][3300/3746] lr: 1.067e-04, eta: 2:47:43, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5908, top5_acc: 0.8237, loss_cls: 2.3084, loss: 2.3084 +2024-12-31 19:58:11,206 - pyskl - INFO - Epoch [147][3400/3746] lr: 1.048e-04, eta: 2:46:17, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5891, top5_acc: 0.8155, loss_cls: 2.3333, loss: 2.3333 +2024-12-31 19:59:35,353 - pyskl - INFO - Epoch [147][3500/3746] lr: 1.030e-04, eta: 2:44:51, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.5930, top5_acc: 0.8094, loss_cls: 2.3380, loss: 2.3380 +2024-12-31 20:00:59,568 - pyskl - INFO - Epoch [147][3600/3746] lr: 1.013e-04, eta: 2:43:25, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5792, top5_acc: 0.8158, loss_cls: 2.3589, loss: 2.3589 +2024-12-31 20:02:24,212 - pyskl - INFO - Epoch [147][3700/3746] lr: 9.949e-05, eta: 2:41:59, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5911, top5_acc: 0.8173, loss_cls: 2.3262, loss: 2.3262 +2024-12-31 20:03:04,532 - pyskl - INFO - Saving checkpoint at 147 epochs +2024-12-31 20:05:03,843 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 20:05:04,595 - pyskl - INFO - +top1_acc 0.4122 +top5_acc 0.6605 +2024-12-31 20:05:04,595 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 20:05:04,653 - pyskl - INFO - +mean_acc 0.4120 +2024-12-31 20:05:04,665 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_145.pth was removed +2024-12-31 20:05:04,941 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_147.pth. +2024-12-31 20:05:04,942 - pyskl - INFO - Best top1_acc is 0.4122 at 147 epoch. +2024-12-31 20:05:04,960 - pyskl - INFO - Epoch(val) [147][309] top1_acc: 0.4122, top5_acc: 0.6605, mean_class_accuracy: 0.4120 +2024-12-31 20:09:21,362 - pyskl - INFO - Epoch [148][100/3746] lr: 9.693e-05, eta: 2:39:56, time: 2.564, data_time: 1.527, memory: 15990, top1_acc: 0.5842, top5_acc: 0.8178, loss_cls: 2.3281, loss: 2.3281 +2024-12-31 20:10:46,889 - pyskl - INFO - Epoch [148][200/3746] lr: 9.520e-05, eta: 2:38:29, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6033, top5_acc: 0.8280, loss_cls: 2.2709, loss: 2.2709 +2024-12-31 20:12:12,886 - pyskl - INFO - Epoch [148][300/3746] lr: 9.348e-05, eta: 2:37:03, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5991, top5_acc: 0.8173, loss_cls: 2.3145, loss: 2.3145 +2024-12-31 20:13:38,483 - pyskl - INFO - Epoch [148][400/3746] lr: 9.178e-05, eta: 2:35:37, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5867, top5_acc: 0.8161, loss_cls: 2.3289, loss: 2.3289 +2024-12-31 20:15:04,544 - pyskl - INFO - Epoch [148][500/3746] lr: 9.010e-05, eta: 2:34:11, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5858, top5_acc: 0.8170, loss_cls: 2.3318, loss: 2.3318 +2024-12-31 20:16:30,870 - pyskl - INFO - Epoch [148][600/3746] lr: 8.843e-05, eta: 2:32:45, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5928, top5_acc: 0.8159, loss_cls: 2.3064, loss: 2.3064 +2024-12-31 20:17:57,110 - pyskl - INFO - Epoch [148][700/3746] lr: 8.678e-05, eta: 2:31:19, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.6017, top5_acc: 0.8244, loss_cls: 2.2894, loss: 2.2894 +2024-12-31 20:19:23,014 - pyskl - INFO - Epoch [148][800/3746] lr: 8.514e-05, eta: 2:29:52, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5995, top5_acc: 0.8253, loss_cls: 2.2748, loss: 2.2748 +2024-12-31 20:20:48,954 - pyskl - INFO - Epoch [148][900/3746] lr: 8.351e-05, eta: 2:28:26, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6008, top5_acc: 0.8236, loss_cls: 2.2822, loss: 2.2822 +2024-12-31 20:22:14,904 - pyskl - INFO - Epoch [148][1000/3746] lr: 8.191e-05, eta: 2:27:00, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6089, top5_acc: 0.8283, loss_cls: 2.2538, loss: 2.2538 +2024-12-31 20:23:40,763 - pyskl - INFO - Epoch [148][1100/3746] lr: 8.031e-05, eta: 2:25:34, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6022, top5_acc: 0.8233, loss_cls: 2.2873, loss: 2.2873 +2024-12-31 20:25:06,000 - pyskl - INFO - Epoch [148][1200/3746] lr: 7.874e-05, eta: 2:24:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5958, top5_acc: 0.8186, loss_cls: 2.3089, loss: 2.3089 +2024-12-31 20:26:31,501 - pyskl - INFO - Epoch [148][1300/3746] lr: 7.718e-05, eta: 2:22:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5855, top5_acc: 0.8144, loss_cls: 2.3458, loss: 2.3458 +2024-12-31 20:27:56,731 - pyskl - INFO - Epoch [148][1400/3746] lr: 7.563e-05, eta: 2:21:15, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6023, top5_acc: 0.8250, loss_cls: 2.2660, loss: 2.2660 +2024-12-31 20:29:22,266 - pyskl - INFO - Epoch [148][1500/3746] lr: 7.410e-05, eta: 2:19:49, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5905, top5_acc: 0.8147, loss_cls: 2.3064, loss: 2.3064 +2024-12-31 20:30:48,166 - pyskl - INFO - Epoch [148][1600/3746] lr: 7.259e-05, eta: 2:18:23, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6042, top5_acc: 0.8283, loss_cls: 2.2788, loss: 2.2788 +2024-12-31 20:32:14,020 - pyskl - INFO - Epoch [148][1700/3746] lr: 7.109e-05, eta: 2:16:57, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5945, top5_acc: 0.8178, loss_cls: 2.3063, loss: 2.3063 +2024-12-31 20:33:39,588 - pyskl - INFO - Epoch [148][1800/3746] lr: 6.961e-05, eta: 2:15:31, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5839, top5_acc: 0.8159, loss_cls: 2.3269, loss: 2.3269 +2024-12-31 20:35:05,319 - pyskl - INFO - Epoch [148][1900/3746] lr: 6.814e-05, eta: 2:14:05, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5861, top5_acc: 0.8152, loss_cls: 2.3309, loss: 2.3309 +2024-12-31 20:36:30,576 - pyskl - INFO - Epoch [148][2000/3746] lr: 6.669e-05, eta: 2:12:38, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6005, top5_acc: 0.8245, loss_cls: 2.2819, loss: 2.2819 +2024-12-31 20:37:56,419 - pyskl - INFO - Epoch [148][2100/3746] lr: 6.526e-05, eta: 2:11:12, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5988, top5_acc: 0.8214, loss_cls: 2.2937, loss: 2.2937 +2024-12-31 20:39:21,663 - pyskl - INFO - Epoch [148][2200/3746] lr: 6.384e-05, eta: 2:09:46, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5988, top5_acc: 0.8183, loss_cls: 2.2984, loss: 2.2984 +2024-12-31 20:40:47,118 - pyskl - INFO - Epoch [148][2300/3746] lr: 6.243e-05, eta: 2:08:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5923, top5_acc: 0.8206, loss_cls: 2.2994, loss: 2.2994 +2024-12-31 20:42:12,393 - pyskl - INFO - Epoch [148][2400/3746] lr: 6.104e-05, eta: 2:06:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5998, top5_acc: 0.8225, loss_cls: 2.2649, loss: 2.2649 +2024-12-31 20:43:37,725 - pyskl - INFO - Epoch [148][2500/3746] lr: 5.967e-05, eta: 2:05:28, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5895, top5_acc: 0.8186, loss_cls: 2.2820, loss: 2.2820 +2024-12-31 20:45:03,107 - pyskl - INFO - Epoch [148][2600/3746] lr: 5.831e-05, eta: 2:04:01, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6067, top5_acc: 0.8228, loss_cls: 2.2737, loss: 2.2737 +2024-12-31 20:46:27,973 - pyskl - INFO - Epoch [148][2700/3746] lr: 5.697e-05, eta: 2:02:35, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5986, top5_acc: 0.8194, loss_cls: 2.2871, loss: 2.2871 +2024-12-31 20:47:53,251 - pyskl - INFO - Epoch [148][2800/3746] lr: 5.564e-05, eta: 2:01:09, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5917, top5_acc: 0.8237, loss_cls: 2.2803, loss: 2.2803 +2024-12-31 20:49:18,355 - pyskl - INFO - Epoch [148][2900/3746] lr: 5.433e-05, eta: 1:59:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5903, top5_acc: 0.8216, loss_cls: 2.2912, loss: 2.2912 +2024-12-31 20:50:43,540 - pyskl - INFO - Epoch [148][3000/3746] lr: 5.304e-05, eta: 1:58:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5816, top5_acc: 0.8144, loss_cls: 2.3567, loss: 2.3567 +2024-12-31 20:52:08,780 - pyskl - INFO - Epoch [148][3100/3746] lr: 5.176e-05, eta: 1:56:51, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5984, top5_acc: 0.8189, loss_cls: 2.2927, loss: 2.2927 +2024-12-31 20:53:33,762 - pyskl - INFO - Epoch [148][3200/3746] lr: 5.050e-05, eta: 1:55:24, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6020, top5_acc: 0.8275, loss_cls: 2.2566, loss: 2.2566 +2024-12-31 20:54:59,010 - pyskl - INFO - Epoch [148][3300/3746] lr: 4.925e-05, eta: 1:53:58, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5892, top5_acc: 0.8230, loss_cls: 2.3067, loss: 2.3067 +2024-12-31 20:56:23,940 - pyskl - INFO - Epoch [148][3400/3746] lr: 4.801e-05, eta: 1:52:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5981, top5_acc: 0.8242, loss_cls: 2.2816, loss: 2.2816 +2024-12-31 20:57:48,939 - pyskl - INFO - Epoch [148][3500/3746] lr: 4.680e-05, eta: 1:51:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5914, top5_acc: 0.8097, loss_cls: 2.3134, loss: 2.3134 +2024-12-31 20:59:13,831 - pyskl - INFO - Epoch [148][3600/3746] lr: 4.560e-05, eta: 1:49:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6022, top5_acc: 0.8325, loss_cls: 2.2640, loss: 2.2640 +2024-12-31 21:00:39,568 - pyskl - INFO - Epoch [148][3700/3746] lr: 4.441e-05, eta: 1:48:14, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5975, top5_acc: 0.8236, loss_cls: 2.2845, loss: 2.2845 +2024-12-31 21:01:20,457 - pyskl - INFO - Saving checkpoint at 148 epochs +2024-12-31 21:03:20,773 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 21:03:21,481 - pyskl - INFO - +top1_acc 0.4128 +top5_acc 0.6613 +2024-12-31 21:03:21,481 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 21:03:21,521 - pyskl - INFO - +mean_acc 0.4125 +2024-12-31 21:03:21,526 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_147.pth was removed +2024-12-31 21:03:21,801 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_148.pth. +2024-12-31 21:03:21,801 - pyskl - INFO - Best top1_acc is 0.4128 at 148 epoch. +2024-12-31 21:03:21,812 - pyskl - INFO - Epoch(val) [148][309] top1_acc: 0.4128, top5_acc: 0.6613, mean_class_accuracy: 0.4125 +2024-12-31 21:07:42,407 - pyskl - INFO - Epoch [149][100/3746] lr: 4.271e-05, eta: 1:46:10, time: 2.606, data_time: 1.578, memory: 15990, top1_acc: 0.5991, top5_acc: 0.8248, loss_cls: 2.2690, loss: 2.2690 +2024-12-31 21:09:07,438 - pyskl - INFO - Epoch [149][200/3746] lr: 4.156e-05, eta: 1:44:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6089, top5_acc: 0.8298, loss_cls: 2.2350, loss: 2.2350 +2024-12-31 21:10:32,195 - pyskl - INFO - Epoch [149][300/3746] lr: 4.043e-05, eta: 1:43:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5964, top5_acc: 0.8217, loss_cls: 2.2799, loss: 2.2799 +2024-12-31 21:11:56,813 - pyskl - INFO - Epoch [149][400/3746] lr: 3.931e-05, eta: 1:41:51, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5948, top5_acc: 0.8233, loss_cls: 2.2827, loss: 2.2827 +2024-12-31 21:13:22,174 - pyskl - INFO - Epoch [149][500/3746] lr: 3.821e-05, eta: 1:40:25, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5961, top5_acc: 0.8200, loss_cls: 2.2785, loss: 2.2785 +2024-12-31 21:14:46,860 - pyskl - INFO - Epoch [149][600/3746] lr: 3.713e-05, eta: 1:38:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5962, top5_acc: 0.8278, loss_cls: 2.2658, loss: 2.2658 +2024-12-31 21:16:12,216 - pyskl - INFO - Epoch [149][700/3746] lr: 3.606e-05, eta: 1:37:32, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5983, top5_acc: 0.8177, loss_cls: 2.2918, loss: 2.2918 +2024-12-31 21:17:36,934 - pyskl - INFO - Epoch [149][800/3746] lr: 3.500e-05, eta: 1:36:06, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5995, top5_acc: 0.8214, loss_cls: 2.2789, loss: 2.2789 +2024-12-31 21:19:02,329 - pyskl - INFO - Epoch [149][900/3746] lr: 3.397e-05, eta: 1:34:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5895, top5_acc: 0.8231, loss_cls: 2.2917, loss: 2.2917 +2024-12-31 21:20:27,309 - pyskl - INFO - Epoch [149][1000/3746] lr: 3.294e-05, eta: 1:33:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5900, top5_acc: 0.8256, loss_cls: 2.3111, loss: 2.3111 +2024-12-31 21:21:52,801 - pyskl - INFO - Epoch [149][1100/3746] lr: 3.194e-05, eta: 1:31:48, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.5917, top5_acc: 0.8164, loss_cls: 2.3162, loss: 2.3162 +2024-12-31 21:23:17,959 - pyskl - INFO - Epoch [149][1200/3746] lr: 3.095e-05, eta: 1:30:22, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5945, top5_acc: 0.8202, loss_cls: 2.2922, loss: 2.2922 +2024-12-31 21:24:42,751 - pyskl - INFO - Epoch [149][1300/3746] lr: 2.997e-05, eta: 1:28:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5975, top5_acc: 0.8217, loss_cls: 2.2872, loss: 2.2872 +2024-12-31 21:26:07,547 - pyskl - INFO - Epoch [149][1400/3746] lr: 2.901e-05, eta: 1:27:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5939, top5_acc: 0.8202, loss_cls: 2.3061, loss: 2.3061 +2024-12-31 21:27:32,863 - pyskl - INFO - Epoch [149][1500/3746] lr: 2.807e-05, eta: 1:26:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6036, top5_acc: 0.8331, loss_cls: 2.2558, loss: 2.2558 +2024-12-31 21:28:57,424 - pyskl - INFO - Epoch [149][1600/3746] lr: 2.714e-05, eta: 1:24:37, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5936, top5_acc: 0.8172, loss_cls: 2.3078, loss: 2.3078 +2024-12-31 21:30:22,235 - pyskl - INFO - Epoch [149][1700/3746] lr: 2.622e-05, eta: 1:23:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6045, top5_acc: 0.8219, loss_cls: 2.2776, loss: 2.2776 +2024-12-31 21:31:47,175 - pyskl - INFO - Epoch [149][1800/3746] lr: 2.533e-05, eta: 1:21:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5894, top5_acc: 0.8191, loss_cls: 2.2928, loss: 2.2928 +2024-12-31 21:33:12,727 - pyskl - INFO - Epoch [149][1900/3746] lr: 2.444e-05, eta: 1:20:18, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5855, top5_acc: 0.8255, loss_cls: 2.3077, loss: 2.3077 +2024-12-31 21:34:37,550 - pyskl - INFO - Epoch [149][2000/3746] lr: 2.358e-05, eta: 1:18:52, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.6008, top5_acc: 0.8281, loss_cls: 2.2609, loss: 2.2609 +2024-12-31 21:36:02,626 - pyskl - INFO - Epoch [149][2100/3746] lr: 2.273e-05, eta: 1:17:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5977, top5_acc: 0.8170, loss_cls: 2.3049, loss: 2.3049 +2024-12-31 21:37:27,279 - pyskl - INFO - Epoch [149][2200/3746] lr: 2.189e-05, eta: 1:16:00, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6042, top5_acc: 0.8286, loss_cls: 2.2489, loss: 2.2489 +2024-12-31 21:38:52,008 - pyskl - INFO - Epoch [149][2300/3746] lr: 2.107e-05, eta: 1:14:34, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6034, top5_acc: 0.8213, loss_cls: 2.2740, loss: 2.2740 +2024-12-31 21:40:17,202 - pyskl - INFO - Epoch [149][2400/3746] lr: 2.027e-05, eta: 1:13:07, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5911, top5_acc: 0.8231, loss_cls: 2.2972, loss: 2.2972 +2024-12-31 21:41:42,105 - pyskl - INFO - Epoch [149][2500/3746] lr: 1.948e-05, eta: 1:11:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6002, top5_acc: 0.8234, loss_cls: 2.2668, loss: 2.2668 +2024-12-31 21:43:06,632 - pyskl - INFO - Epoch [149][2600/3746] lr: 1.871e-05, eta: 1:10:15, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5920, top5_acc: 0.8234, loss_cls: 2.2990, loss: 2.2990 +2024-12-31 21:44:32,163 - pyskl - INFO - Epoch [149][2700/3746] lr: 1.795e-05, eta: 1:08:49, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6086, top5_acc: 0.8266, loss_cls: 2.2537, loss: 2.2537 +2024-12-31 21:45:57,157 - pyskl - INFO - Epoch [149][2800/3746] lr: 1.721e-05, eta: 1:07:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6005, top5_acc: 0.8348, loss_cls: 2.2296, loss: 2.2296 +2024-12-31 21:47:22,216 - pyskl - INFO - Epoch [149][2900/3746] lr: 1.649e-05, eta: 1:05:56, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.6036, top5_acc: 0.8303, loss_cls: 2.2433, loss: 2.2433 +2024-12-31 21:48:47,245 - pyskl - INFO - Epoch [149][3000/3746] lr: 1.578e-05, eta: 1:04:30, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5998, top5_acc: 0.8236, loss_cls: 2.2819, loss: 2.2819 +2024-12-31 21:50:11,772 - pyskl - INFO - Epoch [149][3100/3746] lr: 1.508e-05, eta: 1:03:04, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6042, top5_acc: 0.8269, loss_cls: 2.2458, loss: 2.2458 +2024-12-31 21:51:35,967 - pyskl - INFO - Epoch [149][3200/3746] lr: 1.440e-05, eta: 1:01:38, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6017, top5_acc: 0.8239, loss_cls: 2.2598, loss: 2.2598 +2024-12-31 21:53:00,532 - pyskl - INFO - Epoch [149][3300/3746] lr: 1.374e-05, eta: 1:00:12, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6000, top5_acc: 0.8294, loss_cls: 2.2586, loss: 2.2586 +2024-12-31 21:54:24,732 - pyskl - INFO - Epoch [149][3400/3746] lr: 1.309e-05, eta: 0:58:46, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5900, top5_acc: 0.8198, loss_cls: 2.3068, loss: 2.3068 +2024-12-31 21:55:48,878 - pyskl - INFO - Epoch [149][3500/3746] lr: 1.246e-05, eta: 0:57:19, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.6044, top5_acc: 0.8314, loss_cls: 2.2316, loss: 2.2316 +2024-12-31 21:57:12,710 - pyskl - INFO - Epoch [149][3600/3746] lr: 1.184e-05, eta: 0:55:53, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.6050, top5_acc: 0.8242, loss_cls: 2.2520, loss: 2.2520 +2024-12-31 21:58:37,275 - pyskl - INFO - Epoch [149][3700/3746] lr: 1.124e-05, eta: 0:54:27, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5948, top5_acc: 0.8189, loss_cls: 2.3021, loss: 2.3021 +2024-12-31 21:59:18,157 - pyskl - INFO - Saving checkpoint at 149 epochs +2024-12-31 22:01:16,887 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 22:01:17,664 - pyskl - INFO - +top1_acc 0.4129 +top5_acc 0.6607 +2024-12-31 22:01:17,664 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 22:01:17,704 - pyskl - INFO - +mean_acc 0.4127 +2024-12-31 22:01:17,710 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_148.pth was removed +2024-12-31 22:01:17,972 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_149.pth. +2024-12-31 22:01:17,972 - pyskl - INFO - Best top1_acc is 0.4129 at 149 epoch. +2024-12-31 22:01:17,984 - pyskl - INFO - Epoch(val) [149][309] top1_acc: 0.4129, top5_acc: 0.6607, mean_class_accuracy: 0.4127 +2024-12-31 22:05:33,434 - pyskl - INFO - Epoch [150][100/3746] lr: 1.039e-05, eta: 0:52:22, time: 2.554, data_time: 1.531, memory: 15990, top1_acc: 0.5902, top5_acc: 0.8219, loss_cls: 2.2837, loss: 2.2837 +2024-12-31 22:06:58,591 - pyskl - INFO - Epoch [150][200/3746] lr: 9.832e-06, eta: 0:50:56, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5905, top5_acc: 0.8161, loss_cls: 2.3020, loss: 2.3020 +2024-12-31 22:08:23,079 - pyskl - INFO - Epoch [150][300/3746] lr: 9.285e-06, eta: 0:49:30, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5991, top5_acc: 0.8209, loss_cls: 2.2825, loss: 2.2825 +2024-12-31 22:09:48,111 - pyskl - INFO - Epoch [150][400/3746] lr: 8.754e-06, eta: 0:48:03, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5972, top5_acc: 0.8236, loss_cls: 2.2880, loss: 2.2880 +2024-12-31 22:11:13,685 - pyskl - INFO - Epoch [150][500/3746] lr: 8.239e-06, eta: 0:46:37, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5944, top5_acc: 0.8144, loss_cls: 2.3040, loss: 2.3040 +2024-12-31 22:12:39,010 - pyskl - INFO - Epoch [150][600/3746] lr: 7.739e-06, eta: 0:45:11, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.6033, top5_acc: 0.8302, loss_cls: 2.2465, loss: 2.2465 +2024-12-31 22:14:04,107 - pyskl - INFO - Epoch [150][700/3746] lr: 7.255e-06, eta: 0:43:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6027, top5_acc: 0.8314, loss_cls: 2.2557, loss: 2.2557 +2024-12-31 22:15:28,675 - pyskl - INFO - Epoch [150][800/3746] lr: 6.787e-06, eta: 0:42:19, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5902, top5_acc: 0.8244, loss_cls: 2.2921, loss: 2.2921 +2024-12-31 22:16:53,917 - pyskl - INFO - Epoch [150][900/3746] lr: 6.334e-06, eta: 0:40:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6047, top5_acc: 0.8228, loss_cls: 2.2555, loss: 2.2555 +2024-12-31 22:18:18,690 - pyskl - INFO - Epoch [150][1000/3746] lr: 5.897e-06, eta: 0:39:26, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6048, top5_acc: 0.8230, loss_cls: 2.2627, loss: 2.2627 +2024-12-31 22:19:43,186 - pyskl - INFO - Epoch [150][1100/3746] lr: 5.475e-06, eta: 0:38:00, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5988, top5_acc: 0.8281, loss_cls: 2.2590, loss: 2.2590 +2024-12-31 22:21:08,071 - pyskl - INFO - Epoch [150][1200/3746] lr: 5.070e-06, eta: 0:36:34, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5984, top5_acc: 0.8308, loss_cls: 2.2573, loss: 2.2573 +2024-12-31 22:22:32,470 - pyskl - INFO - Epoch [150][1300/3746] lr: 4.679e-06, eta: 0:35:08, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6105, top5_acc: 0.8306, loss_cls: 2.2210, loss: 2.2210 +2024-12-31 22:23:56,730 - pyskl - INFO - Epoch [150][1400/3746] lr: 4.305e-06, eta: 0:33:41, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6038, top5_acc: 0.8187, loss_cls: 2.2745, loss: 2.2745 +2024-12-31 22:25:21,198 - pyskl - INFO - Epoch [150][1500/3746] lr: 3.946e-06, eta: 0:32:15, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5955, top5_acc: 0.8245, loss_cls: 2.2779, loss: 2.2779 +2024-12-31 22:26:45,639 - pyskl - INFO - Epoch [150][1600/3746] lr: 3.602e-06, eta: 0:30:49, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6019, top5_acc: 0.8348, loss_cls: 2.2415, loss: 2.2415 +2024-12-31 22:28:09,722 - pyskl - INFO - Epoch [150][1700/3746] lr: 3.275e-06, eta: 0:29:23, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.5944, top5_acc: 0.8237, loss_cls: 2.2705, loss: 2.2705 +2024-12-31 22:29:34,756 - pyskl - INFO - Epoch [150][1800/3746] lr: 2.962e-06, eta: 0:27:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6092, top5_acc: 0.8305, loss_cls: 2.2395, loss: 2.2395 +2024-12-31 22:30:59,625 - pyskl - INFO - Epoch [150][1900/3746] lr: 2.666e-06, eta: 0:26:30, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6047, top5_acc: 0.8263, loss_cls: 2.2390, loss: 2.2390 +2024-12-31 22:32:24,640 - pyskl - INFO - Epoch [150][2000/3746] lr: 2.385e-06, eta: 0:25:04, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.6028, top5_acc: 0.8302, loss_cls: 2.2525, loss: 2.2525 +2024-12-31 22:33:50,029 - pyskl - INFO - Epoch [150][2100/3746] lr: 2.120e-06, eta: 0:23:38, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6038, top5_acc: 0.8306, loss_cls: 2.2400, loss: 2.2400 +2024-12-31 22:35:14,605 - pyskl - INFO - Epoch [150][2200/3746] lr: 1.870e-06, eta: 0:22:12, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.6097, top5_acc: 0.8280, loss_cls: 2.2374, loss: 2.2374 +2024-12-31 22:36:39,104 - pyskl - INFO - Epoch [150][2300/3746] lr: 1.636e-06, eta: 0:20:46, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.5944, top5_acc: 0.8231, loss_cls: 2.2931, loss: 2.2931 +2024-12-31 22:38:03,434 - pyskl - INFO - Epoch [150][2400/3746] lr: 1.418e-06, eta: 0:19:20, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5939, top5_acc: 0.8213, loss_cls: 2.2955, loss: 2.2955 +2024-12-31 22:39:28,185 - pyskl - INFO - Epoch [150][2500/3746] lr: 1.215e-06, eta: 0:17:53, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6012, top5_acc: 0.8259, loss_cls: 2.2607, loss: 2.2607 +2024-12-31 22:40:52,948 - pyskl - INFO - Epoch [150][2600/3746] lr: 1.028e-06, eta: 0:16:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5947, top5_acc: 0.8247, loss_cls: 2.2642, loss: 2.2642 +2024-12-31 22:42:17,819 - pyskl - INFO - Epoch [150][2700/3746] lr: 8.567e-07, eta: 0:15:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6061, top5_acc: 0.8181, loss_cls: 2.2645, loss: 2.2645 +2024-12-31 22:43:42,629 - pyskl - INFO - Epoch [150][2800/3746] lr: 7.008e-07, eta: 0:13:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5969, top5_acc: 0.8111, loss_cls: 2.3103, loss: 2.3103 +2024-12-31 22:45:07,516 - pyskl - INFO - Epoch [150][2900/3746] lr: 5.606e-07, eta: 0:12:09, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.6058, top5_acc: 0.8250, loss_cls: 2.2510, loss: 2.2510 +2024-12-31 22:46:32,569 - pyskl - INFO - Epoch [150][3000/3746] lr: 4.361e-07, eta: 0:10:42, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5945, top5_acc: 0.8191, loss_cls: 2.2918, loss: 2.2918 +2024-12-31 22:47:56,765 - pyskl - INFO - Epoch [150][3100/3746] lr: 3.271e-07, eta: 0:09:16, time: 0.842, data_time: 0.001, memory: 15990, top1_acc: 0.6048, top5_acc: 0.8244, loss_cls: 2.2663, loss: 2.2663 +2024-12-31 22:49:21,272 - pyskl - INFO - Epoch [150][3200/3746] lr: 2.338e-07, eta: 0:07:50, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6016, top5_acc: 0.8258, loss_cls: 2.2567, loss: 2.2567 +2024-12-31 22:50:45,649 - pyskl - INFO - Epoch [150][3300/3746] lr: 1.561e-07, eta: 0:06:24, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6070, top5_acc: 0.8322, loss_cls: 2.2101, loss: 2.2101 +2024-12-31 22:52:09,139 - pyskl - INFO - Epoch [150][3400/3746] lr: 9.410e-08, eta: 0:04:58, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5945, top5_acc: 0.8195, loss_cls: 2.2753, loss: 2.2753 +2024-12-31 22:53:33,036 - pyskl - INFO - Epoch [150][3500/3746] lr: 4.768e-08, eta: 0:03:32, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.6084, top5_acc: 0.8305, loss_cls: 2.2336, loss: 2.2336 +2024-12-31 22:54:57,167 - pyskl - INFO - Epoch [150][3600/3746] lr: 1.689e-08, eta: 0:02:05, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.6073, top5_acc: 0.8339, loss_cls: 2.2433, loss: 2.2433 +2024-12-31 22:56:21,176 - pyskl - INFO - Epoch [150][3700/3746] lr: 1.726e-09, eta: 0:00:39, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.6144, top5_acc: 0.8297, loss_cls: 2.2029, loss: 2.2029 +2024-12-31 22:57:01,474 - pyskl - INFO - Saving checkpoint at 150 epochs +2024-12-31 22:58:58,000 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 22:58:58,770 - pyskl - INFO - +top1_acc 0.4124 +top5_acc 0.6602 +2024-12-31 22:58:58,770 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 22:58:58,816 - pyskl - INFO - +mean_acc 0.4122 +2024-12-31 22:58:58,830 - pyskl - INFO - Epoch(val) [150][309] top1_acc: 0.4124, top5_acc: 0.6602, mean_class_accuracy: 0.4122 +2024-12-31 22:59:14,417 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-12-31 23:12:17,471 - pyskl - INFO - Testing results of the last checkpoint +2024-12-31 23:12:17,472 - pyskl - INFO - top1_acc: 0.4273 +2024-12-31 23:12:17,472 - pyskl - INFO - top5_acc: 0.6777 +2024-12-31 23:12:17,472 - pyskl - INFO - mean_class_accuracy: 0.4271 +2024-12-31 23:12:17,473 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/k400/bm/best_top1_acc_epoch_149.pth +2024-12-31 23:25:05,561 - pyskl - INFO - Testing results of the best checkpoint +2024-12-31 23:25:05,561 - pyskl - INFO - top1_acc: 0.4294 +2024-12-31 23:25:05,561 - pyskl - INFO - top5_acc: 0.6793 +2024-12-31 23:25:05,561 - pyskl - INFO - mean_class_accuracy: 0.4291 diff --git a/k400/bm/20241226_014612.log.json b/k400/bm/20241226_014612.log.json new file mode 100644 index 0000000000000000000000000000000000000000..40144b43a4fe8358be2bed296989efd2f90c6f7e --- /dev/null +++ b/k400/bm/20241226_014612.log.json @@ -0,0 +1,5701 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 1828476928, "config_name": "bm.py", "work_dir": "bm", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.1, "memory": 15990, "data_time": 1.24313, "top1_acc": 0.00516, "top5_acc": 0.02391, "loss_cls": 6.47852, "loss": 6.47852, "time": 1.94808} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.00969, "top5_acc": 0.04359, "loss_cls": 6.4414, "loss": 6.4414, "time": 0.70189} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.1, "memory": 15990, "data_time": 0.00083, "top1_acc": 0.015, "top5_acc": 0.05812, "loss_cls": 6.26878, "loss": 6.26878, "time": 0.71055} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.1, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.01703, "top5_acc": 0.06984, "loss_cls": 6.1636, "loss": 6.1636, "time": 0.71373} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.1, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.02312, "top5_acc": 0.0825, "loss_cls": 6.11417, "loss": 6.11417, "time": 0.71385} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.02344, "top5_acc": 0.08969, "loss_cls": 6.03068, "loss": 6.03068, "time": 0.71363} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.1, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.02562, "top5_acc": 0.1, "loss_cls": 5.95252, "loss": 5.95252, "time": 0.71105} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.1, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.02781, "top5_acc": 0.10625, "loss_cls": 5.93372, "loss": 5.93372, "time": 0.71065} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.1, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.02531, "top5_acc": 0.11141, "loss_cls": 5.8775, "loss": 5.8775, "time": 0.71319} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.1, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.03328, "top5_acc": 0.12359, "loss_cls": 5.82725, "loss": 5.82725, "time": 0.71342} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.1, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.03719, "top5_acc": 0.12547, "loss_cls": 5.82949, "loss": 5.82949, "time": 0.71408} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.1, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.03734, "top5_acc": 0.12562, "loss_cls": 5.7605, "loss": 5.7605, "time": 0.71096} +{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.1, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.03828, "top5_acc": 0.13719, "loss_cls": 5.75966, "loss": 5.75966, "time": 0.71516} +{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.1, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.03422, "top5_acc": 0.12969, "loss_cls": 5.75812, "loss": 5.75812, "time": 0.71487} +{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.1, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.04188, "top5_acc": 0.14203, "loss_cls": 5.73004, "loss": 5.73004, "time": 0.71221} +{"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.1, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.04734, "top5_acc": 0.15297, "loss_cls": 5.68353, "loss": 5.68353, "time": 0.7144} +{"mode": "train", "epoch": 1, "iter": 1700, "lr": 0.1, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.03797, "top5_acc": 0.14703, "loss_cls": 5.68312, "loss": 5.68312, "time": 0.71619} +{"mode": "train", "epoch": 1, "iter": 1800, "lr": 0.1, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.04812, "top5_acc": 0.15219, "loss_cls": 5.65038, "loss": 5.65038, "time": 0.7221} +{"mode": "train", "epoch": 1, "iter": 1900, "lr": 0.1, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.04766, "top5_acc": 0.15156, "loss_cls": 5.66604, "loss": 5.66604, "time": 0.71611} +{"mode": "train", "epoch": 1, "iter": 2000, "lr": 0.1, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.04812, "top5_acc": 0.15297, "loss_cls": 5.66401, "loss": 5.66401, "time": 0.7185} +{"mode": "train", "epoch": 1, "iter": 2100, "lr": 0.1, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.05156, "top5_acc": 0.1625, "loss_cls": 5.60899, "loss": 5.60899, "time": 0.7205} +{"mode": "train", "epoch": 1, "iter": 2200, "lr": 0.1, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.04984, "top5_acc": 0.16422, "loss_cls": 5.59655, "loss": 5.59655, "time": 0.71917} +{"mode": "train", "epoch": 1, "iter": 2300, "lr": 0.1, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.05328, "top5_acc": 0.16078, "loss_cls": 5.61878, "loss": 5.61878, "time": 0.71749} +{"mode": "train", "epoch": 1, "iter": 2400, "lr": 0.1, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.05672, "top5_acc": 0.17188, "loss_cls": 5.58453, "loss": 5.58453, "time": 0.72207} +{"mode": "train", "epoch": 1, "iter": 2500, "lr": 0.1, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.05625, "top5_acc": 0.18156, "loss_cls": 5.56643, "loss": 5.56643, "time": 0.72027} +{"mode": "train", "epoch": 1, "iter": 2600, "lr": 0.09999, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.05969, "top5_acc": 0.17703, "loss_cls": 5.56233, "loss": 5.56233, "time": 0.71752} +{"mode": "train", "epoch": 1, "iter": 2700, "lr": 0.09999, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.05703, "top5_acc": 0.18391, "loss_cls": 5.53442, "loss": 5.53442, "time": 0.71779} +{"mode": "train", "epoch": 1, "iter": 2800, "lr": 0.09999, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.05969, "top5_acc": 0.18547, "loss_cls": 5.51797, "loss": 5.51797, "time": 0.71791} +{"mode": "train", "epoch": 1, "iter": 2900, "lr": 0.09999, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.05812, "top5_acc": 0.18297, "loss_cls": 5.5166, "loss": 5.5166, "time": 0.72096} +{"mode": "train", "epoch": 1, "iter": 3000, "lr": 0.09999, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.06281, "top5_acc": 0.19125, "loss_cls": 5.50402, "loss": 5.50402, "time": 0.7176} +{"mode": "train", "epoch": 1, "iter": 3100, "lr": 0.09999, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.06844, "top5_acc": 0.19484, "loss_cls": 5.47593, "loss": 5.47593, "time": 0.72147} +{"mode": "train", "epoch": 1, "iter": 3200, "lr": 0.09999, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.06031, "top5_acc": 0.19203, "loss_cls": 5.45377, "loss": 5.45377, "time": 0.71561} +{"mode": "train", "epoch": 1, "iter": 3300, "lr": 0.09999, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.06578, "top5_acc": 0.20078, "loss_cls": 5.45355, "loss": 5.45355, "time": 0.72134} +{"mode": "train", "epoch": 1, "iter": 3400, "lr": 0.09999, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.06906, "top5_acc": 0.21078, "loss_cls": 5.4374, "loss": 5.4374, "time": 0.71508} +{"mode": "train", "epoch": 1, "iter": 3500, "lr": 0.09999, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.07219, "top5_acc": 0.21, "loss_cls": 5.45333, "loss": 5.45333, "time": 0.71905} +{"mode": "train", "epoch": 1, "iter": 3600, "lr": 0.09999, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.07312, "top5_acc": 0.21438, "loss_cls": 5.42251, "loss": 5.42251, "time": 0.71927} +{"mode": "train", "epoch": 1, "iter": 3700, "lr": 0.09999, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.07062, "top5_acc": 0.21734, "loss_cls": 5.38347, "loss": 5.38347, "time": 0.71907} +{"mode": "val", "epoch": 1, "iter": 309, "lr": 0.09999, "top1_acc": 0.04624, "top5_acc": 0.14501, "mean_class_accuracy": 0.04626} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.09999, "memory": 15990, "data_time": 1.43132, "top1_acc": 0.07156, "top5_acc": 0.22375, "loss_cls": 5.32792, "loss": 5.32792, "time": 2.14718} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.09999, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.07984, "top5_acc": 0.23375, "loss_cls": 5.34959, "loss": 5.34959, "time": 0.71672} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.09999, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.08031, "top5_acc": 0.23094, "loss_cls": 5.32516, "loss": 5.32516, "time": 0.71396} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.09999, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.07688, "top5_acc": 0.23156, "loss_cls": 5.33026, "loss": 5.33026, "time": 0.71338} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.09999, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.08328, "top5_acc": 0.23578, "loss_cls": 5.30091, "loss": 5.30091, "time": 0.70925} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.09999, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.08672, "top5_acc": 0.24234, "loss_cls": 5.27426, "loss": 5.27426, "time": 0.70792} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.09998, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.0825, "top5_acc": 0.23453, "loss_cls": 5.30206, "loss": 5.30206, "time": 0.70894} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.09998, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.0875, "top5_acc": 0.24297, "loss_cls": 5.2899, "loss": 5.2899, "time": 0.71381} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.09998, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.09344, "top5_acc": 0.25031, "loss_cls": 5.24692, "loss": 5.24692, "time": 0.70891} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.09998, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.08797, "top5_acc": 0.25328, "loss_cls": 5.24798, "loss": 5.24798, "time": 0.71104} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.09998, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.09109, "top5_acc": 0.25734, "loss_cls": 5.21928, "loss": 5.21928, "time": 0.71297} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.09998, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.09484, "top5_acc": 0.26297, "loss_cls": 5.18415, "loss": 5.18415, "time": 0.71435} +{"mode": "train", "epoch": 2, "iter": 1300, "lr": 0.09998, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.09203, "top5_acc": 0.26781, "loss_cls": 5.16428, "loss": 5.16428, "time": 0.71511} +{"mode": "train", "epoch": 2, "iter": 1400, "lr": 0.09998, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.09453, "top5_acc": 0.26141, "loss_cls": 5.22139, "loss": 5.22139, "time": 0.71807} +{"mode": "train", "epoch": 2, "iter": 1500, "lr": 0.09998, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.10578, "top5_acc": 0.27609, "loss_cls": 5.1531, "loss": 5.1531, "time": 0.71887} +{"mode": "train", "epoch": 2, "iter": 1600, "lr": 0.09998, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.09641, "top5_acc": 0.26375, "loss_cls": 5.17717, "loss": 5.17717, "time": 0.71502} +{"mode": "train", "epoch": 2, "iter": 1700, "lr": 0.09998, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.09859, "top5_acc": 0.27594, "loss_cls": 5.17225, "loss": 5.17225, "time": 0.71852} +{"mode": "train", "epoch": 2, "iter": 1800, "lr": 0.09998, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.10312, "top5_acc": 0.27062, "loss_cls": 5.18046, "loss": 5.18046, "time": 0.71977} +{"mode": "train", "epoch": 2, "iter": 1900, "lr": 0.09998, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.11234, "top5_acc": 0.27734, "loss_cls": 5.10297, "loss": 5.10297, "time": 0.71959} +{"mode": "train", "epoch": 2, "iter": 2000, "lr": 0.09997, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.10531, "top5_acc": 0.27703, "loss_cls": 5.1226, "loss": 5.1226, "time": 0.71733} +{"mode": "train", "epoch": 2, "iter": 2100, "lr": 0.09997, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.1, "top5_acc": 0.28094, "loss_cls": 5.13913, "loss": 5.13913, "time": 0.71897} +{"mode": "train", "epoch": 2, "iter": 2200, "lr": 0.09997, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.10875, "top5_acc": 0.27953, "loss_cls": 5.11025, "loss": 5.11025, "time": 0.71737} +{"mode": "train", "epoch": 2, "iter": 2300, "lr": 0.09997, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.10891, "top5_acc": 0.28594, "loss_cls": 5.0964, "loss": 5.0964, "time": 0.71935} +{"mode": "train", "epoch": 2, "iter": 2400, "lr": 0.09997, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.11094, "top5_acc": 0.28797, "loss_cls": 5.081, "loss": 5.081, "time": 0.71773} +{"mode": "train", "epoch": 2, "iter": 2500, "lr": 0.09997, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.10938, "top5_acc": 0.29172, "loss_cls": 5.09371, "loss": 5.09371, "time": 0.7191} +{"mode": "train", "epoch": 2, "iter": 2600, "lr": 0.09997, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.1075, "top5_acc": 0.28922, "loss_cls": 5.06521, "loss": 5.06521, "time": 0.71748} +{"mode": "train", "epoch": 2, "iter": 2700, "lr": 0.09997, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.11094, "top5_acc": 0.30016, "loss_cls": 5.09258, "loss": 5.09258, "time": 0.71938} +{"mode": "train", "epoch": 2, "iter": 2800, "lr": 0.09997, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.10422, "top5_acc": 0.28172, "loss_cls": 5.10666, "loss": 5.10666, "time": 0.72052} +{"mode": "train", "epoch": 2, "iter": 2900, "lr": 0.09997, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.11047, "top5_acc": 0.29703, "loss_cls": 5.05509, "loss": 5.05509, "time": 0.71812} +{"mode": "train", "epoch": 2, "iter": 3000, "lr": 0.09996, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.10047, "top5_acc": 0.28734, "loss_cls": 5.05929, "loss": 5.05929, "time": 0.7177} +{"mode": "train", "epoch": 2, "iter": 3100, "lr": 0.09996, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.11766, "top5_acc": 0.30609, "loss_cls": 5.03409, "loss": 5.03409, "time": 0.7187} +{"mode": "train", "epoch": 2, "iter": 3200, "lr": 0.09996, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.11703, "top5_acc": 0.30984, "loss_cls": 5.00568, "loss": 5.00568, "time": 0.71822} +{"mode": "train", "epoch": 2, "iter": 3300, "lr": 0.09996, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.12094, "top5_acc": 0.31203, "loss_cls": 4.9935, "loss": 4.9935, "time": 0.71703} +{"mode": "train", "epoch": 2, "iter": 3400, "lr": 0.09996, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.11359, "top5_acc": 0.30188, "loss_cls": 5.04497, "loss": 5.04497, "time": 0.71673} +{"mode": "train", "epoch": 2, "iter": 3500, "lr": 0.09996, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.11844, "top5_acc": 0.30141, "loss_cls": 5.02268, "loss": 5.02268, "time": 0.7174} +{"mode": "train", "epoch": 2, "iter": 3600, "lr": 0.09996, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.12391, "top5_acc": 0.30922, "loss_cls": 4.99528, "loss": 4.99528, "time": 0.72129} +{"mode": "train", "epoch": 2, "iter": 3700, "lr": 0.09996, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.11781, "top5_acc": 0.31266, "loss_cls": 5.01751, "loss": 5.01751, "time": 0.71892} +{"mode": "val", "epoch": 2, "iter": 309, "lr": 0.09996, "top1_acc": 0.08216, "top5_acc": 0.23841, "mean_class_accuracy": 0.08203} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.09995, "memory": 15990, "data_time": 1.42702, "top1_acc": 0.12375, "top5_acc": 0.31094, "loss_cls": 5.00331, "loss": 5.00331, "time": 2.14431} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.09995, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.12281, "top5_acc": 0.31125, "loss_cls": 4.98572, "loss": 4.98572, "time": 0.71705} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.09995, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.12781, "top5_acc": 0.31734, "loss_cls": 4.94981, "loss": 4.94981, "time": 0.71312} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.09995, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.11969, "top5_acc": 0.31188, "loss_cls": 4.99037, "loss": 4.99037, "time": 0.71207} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.09995, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.12687, "top5_acc": 0.325, "loss_cls": 4.94439, "loss": 4.94439, "time": 0.70931} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.09995, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.12656, "top5_acc": 0.31656, "loss_cls": 4.96798, "loss": 4.96798, "time": 0.70782} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.09995, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.12859, "top5_acc": 0.32203, "loss_cls": 4.94217, "loss": 4.94217, "time": 0.70857} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.09995, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.12156, "top5_acc": 0.31812, "loss_cls": 4.9642, "loss": 4.9642, "time": 0.70812} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.09994, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.12172, "top5_acc": 0.31156, "loss_cls": 4.97476, "loss": 4.97476, "time": 0.71024} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.09994, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.13562, "top5_acc": 0.32797, "loss_cls": 4.90889, "loss": 4.90889, "time": 0.71419} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.09994, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.1375, "top5_acc": 0.33188, "loss_cls": 4.91082, "loss": 4.91082, "time": 0.70993} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.09994, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.13062, "top5_acc": 0.32047, "loss_cls": 4.9381, "loss": 4.9381, "time": 0.71175} +{"mode": "train", "epoch": 3, "iter": 1300, "lr": 0.09994, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.12906, "top5_acc": 0.32984, "loss_cls": 4.92084, "loss": 4.92084, "time": 0.70915} +{"mode": "train", "epoch": 3, "iter": 1400, "lr": 0.09994, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.13594, "top5_acc": 0.3325, "loss_cls": 4.88912, "loss": 4.88912, "time": 0.71295} +{"mode": "train", "epoch": 3, "iter": 1500, "lr": 0.09994, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.12609, "top5_acc": 0.32109, "loss_cls": 4.94372, "loss": 4.94372, "time": 0.71434} +{"mode": "train", "epoch": 3, "iter": 1600, "lr": 0.09994, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.13375, "top5_acc": 0.33141, "loss_cls": 4.87269, "loss": 4.87269, "time": 0.71336} +{"mode": "train", "epoch": 3, "iter": 1700, "lr": 0.09993, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.13453, "top5_acc": 0.32969, "loss_cls": 4.92782, "loss": 4.92782, "time": 0.7142} +{"mode": "train", "epoch": 3, "iter": 1800, "lr": 0.09993, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.13656, "top5_acc": 0.32781, "loss_cls": 4.89068, "loss": 4.89068, "time": 0.71391} +{"mode": "train", "epoch": 3, "iter": 1900, "lr": 0.09993, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.13125, "top5_acc": 0.33188, "loss_cls": 4.90136, "loss": 4.90136, "time": 0.71788} +{"mode": "train", "epoch": 3, "iter": 2000, "lr": 0.09993, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.1425, "top5_acc": 0.33656, "loss_cls": 4.86796, "loss": 4.86796, "time": 0.71813} +{"mode": "train", "epoch": 3, "iter": 2100, "lr": 0.09993, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.14, "top5_acc": 0.33359, "loss_cls": 4.87224, "loss": 4.87224, "time": 0.71785} +{"mode": "train", "epoch": 3, "iter": 2200, "lr": 0.09993, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.13281, "top5_acc": 0.33109, "loss_cls": 4.88637, "loss": 4.88637, "time": 0.71793} +{"mode": "train", "epoch": 3, "iter": 2300, "lr": 0.09993, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.14062, "top5_acc": 0.33922, "loss_cls": 4.86225, "loss": 4.86225, "time": 0.71982} +{"mode": "train", "epoch": 3, "iter": 2400, "lr": 0.09992, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.13484, "top5_acc": 0.32484, "loss_cls": 4.92843, "loss": 4.92843, "time": 0.71821} +{"mode": "train", "epoch": 3, "iter": 2500, "lr": 0.09992, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.13219, "top5_acc": 0.32922, "loss_cls": 4.87053, "loss": 4.87053, "time": 0.71662} +{"mode": "train", "epoch": 3, "iter": 2600, "lr": 0.09992, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.14281, "top5_acc": 0.34781, "loss_cls": 4.82645, "loss": 4.82645, "time": 0.71842} +{"mode": "train", "epoch": 3, "iter": 2700, "lr": 0.09992, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.13672, "top5_acc": 0.33734, "loss_cls": 4.90267, "loss": 4.90267, "time": 0.72129} +{"mode": "train", "epoch": 3, "iter": 2800, "lr": 0.09992, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.14141, "top5_acc": 0.34, "loss_cls": 4.86884, "loss": 4.86884, "time": 0.71636} +{"mode": "train", "epoch": 3, "iter": 2900, "lr": 0.09992, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.13141, "top5_acc": 0.33109, "loss_cls": 4.88875, "loss": 4.88875, "time": 0.72109} +{"mode": "train", "epoch": 3, "iter": 3000, "lr": 0.09991, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.13562, "top5_acc": 0.34094, "loss_cls": 4.84772, "loss": 4.84772, "time": 0.71646} +{"mode": "train", "epoch": 3, "iter": 3100, "lr": 0.09991, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.13984, "top5_acc": 0.34469, "loss_cls": 4.837, "loss": 4.837, "time": 0.71781} +{"mode": "train", "epoch": 3, "iter": 3200, "lr": 0.09991, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.14391, "top5_acc": 0.33984, "loss_cls": 4.84662, "loss": 4.84662, "time": 0.72063} +{"mode": "train", "epoch": 3, "iter": 3300, "lr": 0.09991, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.14016, "top5_acc": 0.34594, "loss_cls": 4.85326, "loss": 4.85326, "time": 0.71829} +{"mode": "train", "epoch": 3, "iter": 3400, "lr": 0.09991, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.14406, "top5_acc": 0.35031, "loss_cls": 4.80959, "loss": 4.80959, "time": 0.7184} +{"mode": "train", "epoch": 3, "iter": 3500, "lr": 0.09991, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.14047, "top5_acc": 0.34188, "loss_cls": 4.85837, "loss": 4.85837, "time": 0.7167} +{"mode": "train", "epoch": 3, "iter": 3600, "lr": 0.0999, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.14797, "top5_acc": 0.35594, "loss_cls": 4.80675, "loss": 4.80675, "time": 0.71795} +{"mode": "train", "epoch": 3, "iter": 3700, "lr": 0.0999, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.14812, "top5_acc": 0.34734, "loss_cls": 4.83678, "loss": 4.83678, "time": 0.72098} +{"mode": "val", "epoch": 3, "iter": 309, "lr": 0.0999, "top1_acc": 0.07669, "top5_acc": 0.21223, "mean_class_accuracy": 0.07653} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.0999, "memory": 15990, "data_time": 1.42719, "top1_acc": 0.1325, "top5_acc": 0.33797, "loss_cls": 4.85704, "loss": 4.85704, "time": 2.14442} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.0999, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.155, "top5_acc": 0.35969, "loss_cls": 4.80208, "loss": 4.80208, "time": 0.71977} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.0999, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.14172, "top5_acc": 0.34719, "loss_cls": 4.80584, "loss": 4.80584, "time": 0.71454} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.09989, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.14969, "top5_acc": 0.3475, "loss_cls": 4.79575, "loss": 4.79575, "time": 0.7146} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.09989, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.14375, "top5_acc": 0.34766, "loss_cls": 4.81567, "loss": 4.81567, "time": 0.71411} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.09989, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.15188, "top5_acc": 0.35375, "loss_cls": 4.79962, "loss": 4.79962, "time": 0.70785} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.09989, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.14906, "top5_acc": 0.35672, "loss_cls": 4.80606, "loss": 4.80606, "time": 0.71404} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.09989, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.14688, "top5_acc": 0.34859, "loss_cls": 4.81206, "loss": 4.81206, "time": 0.70988} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.09988, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.1525, "top5_acc": 0.35016, "loss_cls": 4.80692, "loss": 4.80692, "time": 0.71159} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.09988, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.14703, "top5_acc": 0.36141, "loss_cls": 4.78198, "loss": 4.78198, "time": 0.7098} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.09988, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.14734, "top5_acc": 0.34438, "loss_cls": 4.78816, "loss": 4.78816, "time": 0.71076} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.09988, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.15516, "top5_acc": 0.35422, "loss_cls": 4.78816, "loss": 4.78816, "time": 0.71054} +{"mode": "train", "epoch": 4, "iter": 1300, "lr": 0.09988, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.15062, "top5_acc": 0.36266, "loss_cls": 4.75695, "loss": 4.75695, "time": 0.70953} +{"mode": "train", "epoch": 4, "iter": 1400, "lr": 0.09988, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.15188, "top5_acc": 0.35172, "loss_cls": 4.80784, "loss": 4.80784, "time": 0.70844} +{"mode": "train", "epoch": 4, "iter": 1500, "lr": 0.09987, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.16062, "top5_acc": 0.37109, "loss_cls": 4.71803, "loss": 4.71803, "time": 0.71195} +{"mode": "train", "epoch": 4, "iter": 1600, "lr": 0.09987, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.15281, "top5_acc": 0.36672, "loss_cls": 4.79303, "loss": 4.79303, "time": 0.71149} +{"mode": "train", "epoch": 4, "iter": 1700, "lr": 0.09987, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.14578, "top5_acc": 0.36062, "loss_cls": 4.76583, "loss": 4.76583, "time": 0.71027} +{"mode": "train", "epoch": 4, "iter": 1800, "lr": 0.09987, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.15469, "top5_acc": 0.35703, "loss_cls": 4.7869, "loss": 4.7869, "time": 0.7143} +{"mode": "train", "epoch": 4, "iter": 1900, "lr": 0.09987, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.14703, "top5_acc": 0.35578, "loss_cls": 4.78097, "loss": 4.78097, "time": 0.70909} +{"mode": "train", "epoch": 4, "iter": 2000, "lr": 0.09986, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.15844, "top5_acc": 0.36641, "loss_cls": 4.75521, "loss": 4.75521, "time": 0.71193} +{"mode": "train", "epoch": 4, "iter": 2100, "lr": 0.09986, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.15656, "top5_acc": 0.37062, "loss_cls": 4.76956, "loss": 4.76956, "time": 0.71321} +{"mode": "train", "epoch": 4, "iter": 2200, "lr": 0.09986, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.15937, "top5_acc": 0.36406, "loss_cls": 4.74852, "loss": 4.74852, "time": 0.71365} +{"mode": "train", "epoch": 4, "iter": 2300, "lr": 0.09986, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.15047, "top5_acc": 0.35891, "loss_cls": 4.78583, "loss": 4.78583, "time": 0.71252} +{"mode": "train", "epoch": 4, "iter": 2400, "lr": 0.09985, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.1475, "top5_acc": 0.35547, "loss_cls": 4.7904, "loss": 4.7904, "time": 0.71292} +{"mode": "train", "epoch": 4, "iter": 2500, "lr": 0.09985, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.15578, "top5_acc": 0.36812, "loss_cls": 4.77735, "loss": 4.77735, "time": 0.71483} +{"mode": "train", "epoch": 4, "iter": 2600, "lr": 0.09985, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.15031, "top5_acc": 0.35047, "loss_cls": 4.79831, "loss": 4.79831, "time": 0.72152} +{"mode": "train", "epoch": 4, "iter": 2700, "lr": 0.09985, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.16219, "top5_acc": 0.37047, "loss_cls": 4.70997, "loss": 4.70997, "time": 0.7173} +{"mode": "train", "epoch": 4, "iter": 2800, "lr": 0.09985, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.16234, "top5_acc": 0.36891, "loss_cls": 4.71045, "loss": 4.71045, "time": 0.71702} +{"mode": "train", "epoch": 4, "iter": 2900, "lr": 0.09984, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.16, "top5_acc": 0.36812, "loss_cls": 4.72518, "loss": 4.72518, "time": 0.7174} +{"mode": "train", "epoch": 4, "iter": 3000, "lr": 0.09984, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.16047, "top5_acc": 0.37375, "loss_cls": 4.73081, "loss": 4.73081, "time": 0.71919} +{"mode": "train", "epoch": 4, "iter": 3100, "lr": 0.09984, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.15219, "top5_acc": 0.36125, "loss_cls": 4.75816, "loss": 4.75816, "time": 0.71807} +{"mode": "train", "epoch": 4, "iter": 3200, "lr": 0.09984, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.15844, "top5_acc": 0.36391, "loss_cls": 4.71657, "loss": 4.71657, "time": 0.71747} +{"mode": "train", "epoch": 4, "iter": 3300, "lr": 0.09983, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.15391, "top5_acc": 0.35391, "loss_cls": 4.77337, "loss": 4.77337, "time": 0.72099} +{"mode": "train", "epoch": 4, "iter": 3400, "lr": 0.09983, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.16047, "top5_acc": 0.37344, "loss_cls": 4.7467, "loss": 4.7467, "time": 0.71762} +{"mode": "train", "epoch": 4, "iter": 3500, "lr": 0.09983, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.16422, "top5_acc": 0.37297, "loss_cls": 4.71353, "loss": 4.71353, "time": 0.72051} +{"mode": "train", "epoch": 4, "iter": 3600, "lr": 0.09983, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.16219, "top5_acc": 0.37344, "loss_cls": 4.7388, "loss": 4.7388, "time": 0.71761} +{"mode": "train", "epoch": 4, "iter": 3700, "lr": 0.09983, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.17078, "top5_acc": 0.38047, "loss_cls": 4.7028, "loss": 4.7028, "time": 0.71767} +{"mode": "val", "epoch": 4, "iter": 309, "lr": 0.09982, "top1_acc": 0.09639, "top5_acc": 0.26592, "mean_class_accuracy": 0.09628} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.09982, "memory": 15990, "data_time": 1.46023, "top1_acc": 0.16062, "top5_acc": 0.37719, "loss_cls": 4.69918, "loss": 4.69918, "time": 2.18033} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.09982, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.16312, "top5_acc": 0.37766, "loss_cls": 4.69626, "loss": 4.69626, "time": 0.72023} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.09982, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.15656, "top5_acc": 0.37766, "loss_cls": 4.70233, "loss": 4.70233, "time": 0.72022} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.09982, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.17031, "top5_acc": 0.37891, "loss_cls": 4.68381, "loss": 4.68381, "time": 0.71792} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.09981, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.1625, "top5_acc": 0.36875, "loss_cls": 4.72275, "loss": 4.72275, "time": 0.71682} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.09981, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.16891, "top5_acc": 0.37969, "loss_cls": 4.67793, "loss": 4.67793, "time": 0.71183} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.09981, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.15453, "top5_acc": 0.37328, "loss_cls": 4.71901, "loss": 4.71901, "time": 0.71427} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.09981, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.15547, "top5_acc": 0.37266, "loss_cls": 4.73015, "loss": 4.73015, "time": 0.71385} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.0998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.16453, "top5_acc": 0.37547, "loss_cls": 4.68826, "loss": 4.68826, "time": 0.71759} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.0998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.16812, "top5_acc": 0.38594, "loss_cls": 4.69238, "loss": 4.69238, "time": 0.71537} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.0998, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.15391, "top5_acc": 0.36797, "loss_cls": 4.75224, "loss": 4.75224, "time": 0.71367} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.0998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.16516, "top5_acc": 0.38266, "loss_cls": 4.68545, "loss": 4.68545, "time": 0.71348} +{"mode": "train", "epoch": 5, "iter": 1300, "lr": 0.09979, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.16422, "top5_acc": 0.37578, "loss_cls": 4.72509, "loss": 4.72509, "time": 0.71144} +{"mode": "train", "epoch": 5, "iter": 1400, "lr": 0.09979, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.1675, "top5_acc": 0.3825, "loss_cls": 4.69528, "loss": 4.69528, "time": 0.71165} +{"mode": "train", "epoch": 5, "iter": 1500, "lr": 0.09979, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.16312, "top5_acc": 0.37625, "loss_cls": 4.71547, "loss": 4.71547, "time": 0.71383} +{"mode": "train", "epoch": 5, "iter": 1600, "lr": 0.09979, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.16656, "top5_acc": 0.37906, "loss_cls": 4.70288, "loss": 4.70288, "time": 0.71332} +{"mode": "train", "epoch": 5, "iter": 1700, "lr": 0.09978, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.16078, "top5_acc": 0.36625, "loss_cls": 4.71876, "loss": 4.71876, "time": 0.71819} +{"mode": "train", "epoch": 5, "iter": 1800, "lr": 0.09978, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.15453, "top5_acc": 0.37016, "loss_cls": 4.72124, "loss": 4.72124, "time": 0.71085} +{"mode": "train", "epoch": 5, "iter": 1900, "lr": 0.09978, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.16484, "top5_acc": 0.37672, "loss_cls": 4.69346, "loss": 4.69346, "time": 0.71593} +{"mode": "train", "epoch": 5, "iter": 2000, "lr": 0.09977, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.16203, "top5_acc": 0.37641, "loss_cls": 4.67558, "loss": 4.67558, "time": 0.71265} +{"mode": "train", "epoch": 5, "iter": 2100, "lr": 0.09977, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.15953, "top5_acc": 0.37516, "loss_cls": 4.69674, "loss": 4.69674, "time": 0.71477} +{"mode": "train", "epoch": 5, "iter": 2200, "lr": 0.09977, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.17359, "top5_acc": 0.385, "loss_cls": 4.66636, "loss": 4.66636, "time": 0.71011} +{"mode": "train", "epoch": 5, "iter": 2300, "lr": 0.09977, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.16938, "top5_acc": 0.38469, "loss_cls": 4.66161, "loss": 4.66161, "time": 0.71619} +{"mode": "train", "epoch": 5, "iter": 2400, "lr": 0.09976, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.17031, "top5_acc": 0.38656, "loss_cls": 4.63748, "loss": 4.63748, "time": 0.71549} +{"mode": "train", "epoch": 5, "iter": 2500, "lr": 0.09976, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.15859, "top5_acc": 0.37, "loss_cls": 4.71727, "loss": 4.71727, "time": 0.71805} +{"mode": "train", "epoch": 5, "iter": 2600, "lr": 0.09976, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.15656, "top5_acc": 0.38281, "loss_cls": 4.68596, "loss": 4.68596, "time": 0.71385} +{"mode": "train", "epoch": 5, "iter": 2700, "lr": 0.09976, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.17078, "top5_acc": 0.38562, "loss_cls": 4.66076, "loss": 4.66076, "time": 0.71274} +{"mode": "train", "epoch": 5, "iter": 2800, "lr": 0.09975, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.16578, "top5_acc": 0.38375, "loss_cls": 4.68017, "loss": 4.68017, "time": 0.72051} +{"mode": "train", "epoch": 5, "iter": 2900, "lr": 0.09975, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.17312, "top5_acc": 0.39188, "loss_cls": 4.67557, "loss": 4.67557, "time": 0.71833} +{"mode": "train", "epoch": 5, "iter": 3000, "lr": 0.09975, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.17094, "top5_acc": 0.37094, "loss_cls": 4.66739, "loss": 4.66739, "time": 0.71956} +{"mode": "train", "epoch": 5, "iter": 3100, "lr": 0.09974, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.17, "top5_acc": 0.38391, "loss_cls": 4.68209, "loss": 4.68209, "time": 0.72062} +{"mode": "train", "epoch": 5, "iter": 3200, "lr": 0.09974, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.16375, "top5_acc": 0.38188, "loss_cls": 4.68948, "loss": 4.68948, "time": 0.71955} +{"mode": "train", "epoch": 5, "iter": 3300, "lr": 0.09974, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.16797, "top5_acc": 0.37906, "loss_cls": 4.69675, "loss": 4.69675, "time": 0.71815} +{"mode": "train", "epoch": 5, "iter": 3400, "lr": 0.09974, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.16484, "top5_acc": 0.39062, "loss_cls": 4.66731, "loss": 4.66731, "time": 0.71859} +{"mode": "train", "epoch": 5, "iter": 3500, "lr": 0.09973, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.16734, "top5_acc": 0.38281, "loss_cls": 4.66952, "loss": 4.66952, "time": 0.71814} +{"mode": "train", "epoch": 5, "iter": 3600, "lr": 0.09973, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.16641, "top5_acc": 0.38203, "loss_cls": 4.6825, "loss": 4.6825, "time": 0.71802} +{"mode": "train", "epoch": 5, "iter": 3700, "lr": 0.09973, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.17484, "top5_acc": 0.39234, "loss_cls": 4.65517, "loss": 4.65517, "time": 0.7205} +{"mode": "val", "epoch": 5, "iter": 309, "lr": 0.09973, "top1_acc": 0.09902, "top5_acc": 0.26506, "mean_class_accuracy": 0.0988} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.09972, "memory": 15990, "data_time": 1.44151, "top1_acc": 0.1775, "top5_acc": 0.40766, "loss_cls": 4.59471, "loss": 4.59471, "time": 2.15924} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.09972, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.17531, "top5_acc": 0.39031, "loss_cls": 4.61159, "loss": 4.61159, "time": 0.71883} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.09972, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.16531, "top5_acc": 0.38094, "loss_cls": 4.63975, "loss": 4.63975, "time": 0.71718} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.09971, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.17094, "top5_acc": 0.37891, "loss_cls": 4.68103, "loss": 4.68103, "time": 0.71607} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.09971, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.17234, "top5_acc": 0.38938, "loss_cls": 4.66707, "loss": 4.66707, "time": 0.71418} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.09971, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.17156, "top5_acc": 0.38891, "loss_cls": 4.6372, "loss": 4.6372, "time": 0.71609} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.09971, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.17766, "top5_acc": 0.39016, "loss_cls": 4.64052, "loss": 4.64052, "time": 0.71621} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.0997, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.17438, "top5_acc": 0.4, "loss_cls": 4.64492, "loss": 4.64492, "time": 0.71519} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.0997, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.16734, "top5_acc": 0.38594, "loss_cls": 4.67106, "loss": 4.67106, "time": 0.71329} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.0997, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.16922, "top5_acc": 0.38578, "loss_cls": 4.67373, "loss": 4.67373, "time": 0.71207} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.09969, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.15891, "top5_acc": 0.37438, "loss_cls": 4.69422, "loss": 4.69422, "time": 0.71804} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.09969, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.18125, "top5_acc": 0.39688, "loss_cls": 4.62556, "loss": 4.62556, "time": 0.71208} +{"mode": "train", "epoch": 6, "iter": 1300, "lr": 0.09969, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.17078, "top5_acc": 0.38828, "loss_cls": 4.66764, "loss": 4.66764, "time": 0.71408} +{"mode": "train", "epoch": 6, "iter": 1400, "lr": 0.09968, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.16547, "top5_acc": 0.37734, "loss_cls": 4.69483, "loss": 4.69483, "time": 0.71668} +{"mode": "train", "epoch": 6, "iter": 1500, "lr": 0.09968, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.17734, "top5_acc": 0.38766, "loss_cls": 4.60968, "loss": 4.60968, "time": 0.71915} +{"mode": "train", "epoch": 6, "iter": 1600, "lr": 0.09968, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.18031, "top5_acc": 0.39547, "loss_cls": 4.63333, "loss": 4.63333, "time": 0.71678} +{"mode": "train", "epoch": 6, "iter": 1700, "lr": 0.09967, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.17328, "top5_acc": 0.38375, "loss_cls": 4.66077, "loss": 4.66077, "time": 0.71622} +{"mode": "train", "epoch": 6, "iter": 1800, "lr": 0.09967, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.17484, "top5_acc": 0.38859, "loss_cls": 4.62538, "loss": 4.62538, "time": 0.71579} +{"mode": "train", "epoch": 6, "iter": 1900, "lr": 0.09967, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.17609, "top5_acc": 0.39344, "loss_cls": 4.61437, "loss": 4.61437, "time": 0.71572} +{"mode": "train", "epoch": 6, "iter": 2000, "lr": 0.09966, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.16875, "top5_acc": 0.39234, "loss_cls": 4.63773, "loss": 4.63773, "time": 0.71636} +{"mode": "train", "epoch": 6, "iter": 2100, "lr": 0.09966, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.17016, "top5_acc": 0.39094, "loss_cls": 4.64192, "loss": 4.64192, "time": 0.71687} +{"mode": "train", "epoch": 6, "iter": 2200, "lr": 0.09966, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.17172, "top5_acc": 0.39172, "loss_cls": 4.63195, "loss": 4.63195, "time": 0.72049} +{"mode": "train", "epoch": 6, "iter": 2300, "lr": 0.09965, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.17344, "top5_acc": 0.39016, "loss_cls": 4.64264, "loss": 4.64264, "time": 0.71699} +{"mode": "train", "epoch": 6, "iter": 2400, "lr": 0.09965, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.16906, "top5_acc": 0.38891, "loss_cls": 4.66899, "loss": 4.66899, "time": 0.72334} +{"mode": "train", "epoch": 6, "iter": 2500, "lr": 0.09965, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.17203, "top5_acc": 0.39, "loss_cls": 4.64881, "loss": 4.64881, "time": 0.72069} +{"mode": "train", "epoch": 6, "iter": 2600, "lr": 0.09964, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.16266, "top5_acc": 0.37828, "loss_cls": 4.69105, "loss": 4.69105, "time": 0.72052} +{"mode": "train", "epoch": 6, "iter": 2700, "lr": 0.09964, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.17016, "top5_acc": 0.39141, "loss_cls": 4.65021, "loss": 4.65021, "time": 0.71737} +{"mode": "train", "epoch": 6, "iter": 2800, "lr": 0.09964, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.17188, "top5_acc": 0.39125, "loss_cls": 4.64363, "loss": 4.64363, "time": 0.72027} +{"mode": "train", "epoch": 6, "iter": 2900, "lr": 0.09963, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.175, "top5_acc": 0.39453, "loss_cls": 4.62992, "loss": 4.62992, "time": 0.71806} +{"mode": "train", "epoch": 6, "iter": 3000, "lr": 0.09963, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.1775, "top5_acc": 0.39078, "loss_cls": 4.6487, "loss": 4.6487, "time": 0.71917} +{"mode": "train", "epoch": 6, "iter": 3100, "lr": 0.09963, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.17219, "top5_acc": 0.39172, "loss_cls": 4.61104, "loss": 4.61104, "time": 0.72415} +{"mode": "train", "epoch": 6, "iter": 3200, "lr": 0.09962, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.175, "top5_acc": 0.39391, "loss_cls": 4.63958, "loss": 4.63958, "time": 0.71942} +{"mode": "train", "epoch": 6, "iter": 3300, "lr": 0.09962, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.17234, "top5_acc": 0.39203, "loss_cls": 4.60878, "loss": 4.60878, "time": 0.72055} +{"mode": "train", "epoch": 6, "iter": 3400, "lr": 0.09962, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.17891, "top5_acc": 0.39062, "loss_cls": 4.62354, "loss": 4.62354, "time": 0.71871} +{"mode": "train", "epoch": 6, "iter": 3500, "lr": 0.09961, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.17406, "top5_acc": 0.39484, "loss_cls": 4.60589, "loss": 4.60589, "time": 0.72245} +{"mode": "train", "epoch": 6, "iter": 3600, "lr": 0.09961, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.17484, "top5_acc": 0.38812, "loss_cls": 4.61634, "loss": 4.61634, "time": 0.72401} +{"mode": "train", "epoch": 6, "iter": 3700, "lr": 0.09961, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.17359, "top5_acc": 0.39172, "loss_cls": 4.62652, "loss": 4.62652, "time": 0.72236} +{"mode": "val", "epoch": 6, "iter": 309, "lr": 0.09961, "top1_acc": 0.08575, "top5_acc": 0.23862, "mean_class_accuracy": 0.08569} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0996, "memory": 15990, "data_time": 1.45924, "top1_acc": 0.18094, "top5_acc": 0.41156, "loss_cls": 4.55911, "loss": 4.55911, "time": 2.18079} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0996, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.185, "top5_acc": 0.40688, "loss_cls": 4.5799, "loss": 4.5799, "time": 0.71745} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.0996, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.1775, "top5_acc": 0.40156, "loss_cls": 4.58437, "loss": 4.58437, "time": 0.71795} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.09959, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.18641, "top5_acc": 0.40609, "loss_cls": 4.57633, "loss": 4.57633, "time": 0.71685} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.09959, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.17172, "top5_acc": 0.40047, "loss_cls": 4.60278, "loss": 4.60278, "time": 0.71668} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.09958, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.1725, "top5_acc": 0.38672, "loss_cls": 4.64605, "loss": 4.64605, "time": 0.71153} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.09958, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.17781, "top5_acc": 0.41016, "loss_cls": 4.57057, "loss": 4.57057, "time": 0.71372} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.09958, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.17438, "top5_acc": 0.38438, "loss_cls": 4.64909, "loss": 4.64909, "time": 0.71367} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.09957, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.18078, "top5_acc": 0.40516, "loss_cls": 4.58416, "loss": 4.58416, "time": 0.71068} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.09957, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.17688, "top5_acc": 0.39891, "loss_cls": 4.62537, "loss": 4.62537, "time": 0.71485} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.09957, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.17609, "top5_acc": 0.39016, "loss_cls": 4.63603, "loss": 4.63603, "time": 0.71262} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.09956, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.18156, "top5_acc": 0.39906, "loss_cls": 4.60088, "loss": 4.60088, "time": 0.71336} +{"mode": "train", "epoch": 7, "iter": 1300, "lr": 0.09956, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.17641, "top5_acc": 0.39078, "loss_cls": 4.63001, "loss": 4.63001, "time": 0.71154} +{"mode": "train", "epoch": 7, "iter": 1400, "lr": 0.09956, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.18281, "top5_acc": 0.39672, "loss_cls": 4.60426, "loss": 4.60426, "time": 0.71446} +{"mode": "train", "epoch": 7, "iter": 1500, "lr": 0.09955, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.18172, "top5_acc": 0.40609, "loss_cls": 4.61038, "loss": 4.61038, "time": 0.71754} +{"mode": "train", "epoch": 7, "iter": 1600, "lr": 0.09955, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.17531, "top5_acc": 0.4, "loss_cls": 4.62579, "loss": 4.62579, "time": 0.71543} +{"mode": "train", "epoch": 7, "iter": 1700, "lr": 0.09954, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.1725, "top5_acc": 0.40594, "loss_cls": 4.58526, "loss": 4.58526, "time": 0.71568} +{"mode": "train", "epoch": 7, "iter": 1800, "lr": 0.09954, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.1775, "top5_acc": 0.39359, "loss_cls": 4.60675, "loss": 4.60675, "time": 0.72065} +{"mode": "train", "epoch": 7, "iter": 1900, "lr": 0.09954, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.18125, "top5_acc": 0.39047, "loss_cls": 4.62612, "loss": 4.62612, "time": 0.71798} +{"mode": "train", "epoch": 7, "iter": 2000, "lr": 0.09953, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.18234, "top5_acc": 0.40156, "loss_cls": 4.60072, "loss": 4.60072, "time": 0.72086} +{"mode": "train", "epoch": 7, "iter": 2100, "lr": 0.09953, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.18688, "top5_acc": 0.41188, "loss_cls": 4.55077, "loss": 4.55077, "time": 0.71496} +{"mode": "train", "epoch": 7, "iter": 2200, "lr": 0.09952, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.17344, "top5_acc": 0.39031, "loss_cls": 4.62382, "loss": 4.62382, "time": 0.71781} +{"mode": "train", "epoch": 7, "iter": 2300, "lr": 0.09952, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.18172, "top5_acc": 0.39516, "loss_cls": 4.62, "loss": 4.62, "time": 0.71529} +{"mode": "train", "epoch": 7, "iter": 2400, "lr": 0.09952, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.17953, "top5_acc": 0.40156, "loss_cls": 4.57647, "loss": 4.57647, "time": 0.72027} +{"mode": "train", "epoch": 7, "iter": 2500, "lr": 0.09951, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.18, "top5_acc": 0.39781, "loss_cls": 4.58625, "loss": 4.58625, "time": 0.71622} +{"mode": "train", "epoch": 7, "iter": 2600, "lr": 0.09951, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.18297, "top5_acc": 0.40156, "loss_cls": 4.5867, "loss": 4.5867, "time": 0.71967} +{"mode": "train", "epoch": 7, "iter": 2700, "lr": 0.09951, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.18422, "top5_acc": 0.39438, "loss_cls": 4.60734, "loss": 4.60734, "time": 0.71562} +{"mode": "train", "epoch": 7, "iter": 2800, "lr": 0.0995, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.17016, "top5_acc": 0.39078, "loss_cls": 4.63172, "loss": 4.63172, "time": 0.71898} +{"mode": "train", "epoch": 7, "iter": 2900, "lr": 0.0995, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.18969, "top5_acc": 0.405, "loss_cls": 4.58066, "loss": 4.58066, "time": 0.71528} +{"mode": "train", "epoch": 7, "iter": 3000, "lr": 0.09949, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.18547, "top5_acc": 0.40625, "loss_cls": 4.54919, "loss": 4.54919, "time": 0.71832} +{"mode": "train", "epoch": 7, "iter": 3100, "lr": 0.09949, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.18922, "top5_acc": 0.40312, "loss_cls": 4.57273, "loss": 4.57273, "time": 0.72155} +{"mode": "train", "epoch": 7, "iter": 3200, "lr": 0.09949, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.17594, "top5_acc": 0.40125, "loss_cls": 4.58725, "loss": 4.58725, "time": 0.72117} +{"mode": "train", "epoch": 7, "iter": 3300, "lr": 0.09948, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.18594, "top5_acc": 0.40562, "loss_cls": 4.59448, "loss": 4.59448, "time": 0.72385} +{"mode": "train", "epoch": 7, "iter": 3400, "lr": 0.09948, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.18078, "top5_acc": 0.39531, "loss_cls": 4.59951, "loss": 4.59951, "time": 0.71885} +{"mode": "train", "epoch": 7, "iter": 3500, "lr": 0.09947, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.17859, "top5_acc": 0.40078, "loss_cls": 4.58017, "loss": 4.58017, "time": 0.72473} +{"mode": "train", "epoch": 7, "iter": 3600, "lr": 0.09947, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.17672, "top5_acc": 0.405, "loss_cls": 4.58705, "loss": 4.58705, "time": 0.71684} +{"mode": "train", "epoch": 7, "iter": 3700, "lr": 0.09947, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.17484, "top5_acc": 0.39062, "loss_cls": 4.65241, "loss": 4.65241, "time": 0.71776} +{"mode": "val", "epoch": 7, "iter": 309, "lr": 0.09946, "top1_acc": 0.12328, "top5_acc": 0.30973, "mean_class_accuracy": 0.12325} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.09946, "memory": 15990, "data_time": 1.47042, "top1_acc": 0.17953, "top5_acc": 0.40469, "loss_cls": 4.55498, "loss": 4.55498, "time": 2.18488} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.09946, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.19078, "top5_acc": 0.41609, "loss_cls": 4.51699, "loss": 4.51699, "time": 0.71843} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.09945, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.17938, "top5_acc": 0.41281, "loss_cls": 4.54043, "loss": 4.54043, "time": 0.71764} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.09945, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.18594, "top5_acc": 0.40969, "loss_cls": 4.5809, "loss": 4.5809, "time": 0.71999} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.09944, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.17734, "top5_acc": 0.40516, "loss_cls": 4.55339, "loss": 4.55339, "time": 0.71806} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.09944, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.19, "top5_acc": 0.41625, "loss_cls": 4.50773, "loss": 4.50773, "time": 0.71134} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.09943, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.17719, "top5_acc": 0.39984, "loss_cls": 4.58351, "loss": 4.58351, "time": 0.71497} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.09943, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.17781, "top5_acc": 0.40344, "loss_cls": 4.56922, "loss": 4.56922, "time": 0.71402} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.09943, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.17469, "top5_acc": 0.39688, "loss_cls": 4.59135, "loss": 4.59135, "time": 0.71074} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.09942, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.18172, "top5_acc": 0.39578, "loss_cls": 4.60785, "loss": 4.60785, "time": 0.71441} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.09942, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19625, "top5_acc": 0.41719, "loss_cls": 4.52538, "loss": 4.52538, "time": 0.71237} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.09941, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.18688, "top5_acc": 0.40656, "loss_cls": 4.57966, "loss": 4.57966, "time": 0.71382} +{"mode": "train", "epoch": 8, "iter": 1300, "lr": 0.09941, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.17969, "top5_acc": 0.39859, "loss_cls": 4.5935, "loss": 4.5935, "time": 0.7109} +{"mode": "train", "epoch": 8, "iter": 1400, "lr": 0.0994, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18797, "top5_acc": 0.40344, "loss_cls": 4.59139, "loss": 4.59139, "time": 0.71512} +{"mode": "train", "epoch": 8, "iter": 1500, "lr": 0.0994, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.18391, "top5_acc": 0.41641, "loss_cls": 4.50611, "loss": 4.50611, "time": 0.7117} +{"mode": "train", "epoch": 8, "iter": 1600, "lr": 0.0994, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.18562, "top5_acc": 0.40922, "loss_cls": 4.56479, "loss": 4.56479, "time": 0.71426} +{"mode": "train", "epoch": 8, "iter": 1700, "lr": 0.09939, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.19016, "top5_acc": 0.40984, "loss_cls": 4.53887, "loss": 4.53887, "time": 0.71525} +{"mode": "train", "epoch": 8, "iter": 1800, "lr": 0.09939, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19281, "top5_acc": 0.41516, "loss_cls": 4.52048, "loss": 4.52048, "time": 0.72143} +{"mode": "train", "epoch": 8, "iter": 1900, "lr": 0.09938, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.18969, "top5_acc": 0.40672, "loss_cls": 4.54381, "loss": 4.54381, "time": 0.71706} +{"mode": "train", "epoch": 8, "iter": 2000, "lr": 0.09938, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.18219, "top5_acc": 0.40406, "loss_cls": 4.59747, "loss": 4.59747, "time": 0.71753} +{"mode": "train", "epoch": 8, "iter": 2100, "lr": 0.09937, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.17922, "top5_acc": 0.40391, "loss_cls": 4.57263, "loss": 4.57263, "time": 0.71927} +{"mode": "train", "epoch": 8, "iter": 2200, "lr": 0.09937, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.18359, "top5_acc": 0.40219, "loss_cls": 4.5909, "loss": 4.5909, "time": 0.71655} +{"mode": "train", "epoch": 8, "iter": 2300, "lr": 0.09937, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.19031, "top5_acc": 0.41266, "loss_cls": 4.55617, "loss": 4.55617, "time": 0.71836} +{"mode": "train", "epoch": 8, "iter": 2400, "lr": 0.09936, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.17875, "top5_acc": 0.40047, "loss_cls": 4.59992, "loss": 4.59992, "time": 0.71887} +{"mode": "train", "epoch": 8, "iter": 2500, "lr": 0.09936, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.18, "top5_acc": 0.40328, "loss_cls": 4.5694, "loss": 4.5694, "time": 0.7204} +{"mode": "train", "epoch": 8, "iter": 2600, "lr": 0.09935, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.17766, "top5_acc": 0.40516, "loss_cls": 4.56609, "loss": 4.56609, "time": 0.71838} +{"mode": "train", "epoch": 8, "iter": 2700, "lr": 0.09935, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.175, "top5_acc": 0.39953, "loss_cls": 4.56798, "loss": 4.56798, "time": 0.72096} +{"mode": "train", "epoch": 8, "iter": 2800, "lr": 0.09934, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.17812, "top5_acc": 0.3975, "loss_cls": 4.58728, "loss": 4.58728, "time": 0.71657} +{"mode": "train", "epoch": 8, "iter": 2900, "lr": 0.09934, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.19062, "top5_acc": 0.41609, "loss_cls": 4.51961, "loss": 4.51961, "time": 0.71892} +{"mode": "train", "epoch": 8, "iter": 3000, "lr": 0.09933, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.18594, "top5_acc": 0.40453, "loss_cls": 4.57819, "loss": 4.57819, "time": 0.71683} +{"mode": "train", "epoch": 8, "iter": 3100, "lr": 0.09933, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.18938, "top5_acc": 0.40812, "loss_cls": 4.55401, "loss": 4.55401, "time": 0.71757} +{"mode": "train", "epoch": 8, "iter": 3200, "lr": 0.09933, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.18828, "top5_acc": 0.40391, "loss_cls": 4.59251, "loss": 4.59251, "time": 0.71749} +{"mode": "train", "epoch": 8, "iter": 3300, "lr": 0.09932, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.18438, "top5_acc": 0.4025, "loss_cls": 4.58499, "loss": 4.58499, "time": 0.72073} +{"mode": "train", "epoch": 8, "iter": 3400, "lr": 0.09932, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.18828, "top5_acc": 0.41406, "loss_cls": 4.5236, "loss": 4.5236, "time": 0.7176} +{"mode": "train", "epoch": 8, "iter": 3500, "lr": 0.09931, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19016, "top5_acc": 0.41188, "loss_cls": 4.53795, "loss": 4.53795, "time": 0.71636} +{"mode": "train", "epoch": 8, "iter": 3600, "lr": 0.09931, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.18641, "top5_acc": 0.40578, "loss_cls": 4.55832, "loss": 4.55832, "time": 0.71815} +{"mode": "train", "epoch": 8, "iter": 3700, "lr": 0.0993, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.17234, "top5_acc": 0.40125, "loss_cls": 4.5932, "loss": 4.5932, "time": 0.72} +{"mode": "val", "epoch": 8, "iter": 309, "lr": 0.0993, "top1_acc": 0.1203, "top5_acc": 0.31039, "mean_class_accuracy": 0.12009} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.0993, "memory": 15990, "data_time": 1.45132, "top1_acc": 0.1925, "top5_acc": 0.41922, "loss_cls": 4.4953, "loss": 4.4953, "time": 2.16244} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.09929, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.19422, "top5_acc": 0.42172, "loss_cls": 4.49645, "loss": 4.49645, "time": 0.72022} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.09929, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.19469, "top5_acc": 0.41344, "loss_cls": 4.50605, "loss": 4.50605, "time": 0.71734} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.09928, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.18109, "top5_acc": 0.41234, "loss_cls": 4.56175, "loss": 4.56175, "time": 0.71695} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.09928, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19141, "top5_acc": 0.42422, "loss_cls": 4.49734, "loss": 4.49734, "time": 0.71476} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.09927, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19172, "top5_acc": 0.41547, "loss_cls": 4.51974, "loss": 4.51974, "time": 0.71596} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.09927, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.18812, "top5_acc": 0.41906, "loss_cls": 4.52024, "loss": 4.52024, "time": 0.71517} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.09926, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19281, "top5_acc": 0.40875, "loss_cls": 4.55385, "loss": 4.55385, "time": 0.71212} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.09926, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19516, "top5_acc": 0.41391, "loss_cls": 4.49973, "loss": 4.49973, "time": 0.71385} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.09925, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.18469, "top5_acc": 0.40625, "loss_cls": 4.5779, "loss": 4.5779, "time": 0.70864} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.09925, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.18859, "top5_acc": 0.40906, "loss_cls": 4.53859, "loss": 4.53859, "time": 0.71439} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.09924, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.17906, "top5_acc": 0.40578, "loss_cls": 4.56137, "loss": 4.56137, "time": 0.7125} +{"mode": "train", "epoch": 9, "iter": 1300, "lr": 0.09924, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.18766, "top5_acc": 0.41453, "loss_cls": 4.53428, "loss": 4.53428, "time": 0.71587} +{"mode": "train", "epoch": 9, "iter": 1400, "lr": 0.09923, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19016, "top5_acc": 0.40953, "loss_cls": 4.52933, "loss": 4.52933, "time": 0.71223} +{"mode": "train", "epoch": 9, "iter": 1500, "lr": 0.09923, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.18453, "top5_acc": 0.40469, "loss_cls": 4.58817, "loss": 4.58817, "time": 0.71213} +{"mode": "train", "epoch": 9, "iter": 1600, "lr": 0.09922, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.18125, "top5_acc": 0.41094, "loss_cls": 4.53971, "loss": 4.53971, "time": 0.71386} +{"mode": "train", "epoch": 9, "iter": 1700, "lr": 0.09922, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.19203, "top5_acc": 0.41641, "loss_cls": 4.5406, "loss": 4.5406, "time": 0.71456} +{"mode": "train", "epoch": 9, "iter": 1800, "lr": 0.09921, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.18891, "top5_acc": 0.40766, "loss_cls": 4.54064, "loss": 4.54064, "time": 0.71337} +{"mode": "train", "epoch": 9, "iter": 1900, "lr": 0.09921, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.19, "top5_acc": 0.41844, "loss_cls": 4.51012, "loss": 4.51012, "time": 0.70969} +{"mode": "train", "epoch": 9, "iter": 2000, "lr": 0.0992, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.18656, "top5_acc": 0.4025, "loss_cls": 4.59369, "loss": 4.59369, "time": 0.71208} +{"mode": "train", "epoch": 9, "iter": 2100, "lr": 0.0992, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.18938, "top5_acc": 0.41984, "loss_cls": 4.52408, "loss": 4.52408, "time": 0.72026} +{"mode": "train", "epoch": 9, "iter": 2200, "lr": 0.09919, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.18516, "top5_acc": 0.40953, "loss_cls": 4.56217, "loss": 4.56217, "time": 0.72197} +{"mode": "train", "epoch": 9, "iter": 2300, "lr": 0.09919, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19641, "top5_acc": 0.41062, "loss_cls": 4.51536, "loss": 4.51536, "time": 0.7164} +{"mode": "train", "epoch": 9, "iter": 2400, "lr": 0.09918, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.1925, "top5_acc": 0.41844, "loss_cls": 4.52929, "loss": 4.52929, "time": 0.7173} +{"mode": "train", "epoch": 9, "iter": 2500, "lr": 0.09918, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.19547, "top5_acc": 0.40297, "loss_cls": 4.55852, "loss": 4.55852, "time": 0.71738} +{"mode": "train", "epoch": 9, "iter": 2600, "lr": 0.09917, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.18828, "top5_acc": 0.40156, "loss_cls": 4.55577, "loss": 4.55577, "time": 0.71809} +{"mode": "train", "epoch": 9, "iter": 2700, "lr": 0.09917, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.19297, "top5_acc": 0.41344, "loss_cls": 4.49999, "loss": 4.49999, "time": 0.71859} +{"mode": "train", "epoch": 9, "iter": 2800, "lr": 0.09916, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.19156, "top5_acc": 0.40328, "loss_cls": 4.54298, "loss": 4.54298, "time": 0.71811} +{"mode": "train", "epoch": 9, "iter": 2900, "lr": 0.09916, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.18375, "top5_acc": 0.40234, "loss_cls": 4.54666, "loss": 4.54666, "time": 0.71964} +{"mode": "train", "epoch": 9, "iter": 3000, "lr": 0.09915, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.19531, "top5_acc": 0.41469, "loss_cls": 4.47849, "loss": 4.47849, "time": 0.71728} +{"mode": "train", "epoch": 9, "iter": 3100, "lr": 0.09915, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.18094, "top5_acc": 0.40141, "loss_cls": 4.60598, "loss": 4.60598, "time": 0.71689} +{"mode": "train", "epoch": 9, "iter": 3200, "lr": 0.09914, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.19031, "top5_acc": 0.40797, "loss_cls": 4.5414, "loss": 4.5414, "time": 0.72025} +{"mode": "train", "epoch": 9, "iter": 3300, "lr": 0.09914, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.19125, "top5_acc": 0.40531, "loss_cls": 4.55018, "loss": 4.55018, "time": 0.72009} +{"mode": "train", "epoch": 9, "iter": 3400, "lr": 0.09913, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.19766, "top5_acc": 0.42516, "loss_cls": 4.51, "loss": 4.51, "time": 0.72098} +{"mode": "train", "epoch": 9, "iter": 3500, "lr": 0.09913, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.18703, "top5_acc": 0.41125, "loss_cls": 4.52698, "loss": 4.52698, "time": 0.72034} +{"mode": "train", "epoch": 9, "iter": 3600, "lr": 0.09912, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.18406, "top5_acc": 0.40812, "loss_cls": 4.57222, "loss": 4.57222, "time": 0.71731} +{"mode": "train", "epoch": 9, "iter": 3700, "lr": 0.09912, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.18562, "top5_acc": 0.40938, "loss_cls": 4.53875, "loss": 4.53875, "time": 0.71977} +{"mode": "val", "epoch": 9, "iter": 309, "lr": 0.09911, "top1_acc": 0.12582, "top5_acc": 0.311, "mean_class_accuracy": 0.12563} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.09911, "memory": 15990, "data_time": 1.46828, "top1_acc": 0.20281, "top5_acc": 0.43047, "loss_cls": 4.45652, "loss": 4.45652, "time": 2.18775} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.0991, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.19688, "top5_acc": 0.42578, "loss_cls": 4.47948, "loss": 4.47948, "time": 0.72116} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.0991, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.18531, "top5_acc": 0.40438, "loss_cls": 4.54738, "loss": 4.54738, "time": 0.71635} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.09909, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.18188, "top5_acc": 0.40891, "loss_cls": 4.5451, "loss": 4.5451, "time": 0.7148} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.09909, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18938, "top5_acc": 0.42141, "loss_cls": 4.49286, "loss": 4.49286, "time": 0.71444} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.09908, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19203, "top5_acc": 0.42062, "loss_cls": 4.5066, "loss": 4.5066, "time": 0.71208} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.09908, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19156, "top5_acc": 0.41812, "loss_cls": 4.50129, "loss": 4.50129, "time": 0.71037} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.09907, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.18875, "top5_acc": 0.40375, "loss_cls": 4.55137, "loss": 4.55137, "time": 0.71299} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.09907, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.19094, "top5_acc": 0.41719, "loss_cls": 4.51091, "loss": 4.51091, "time": 0.71203} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.09906, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.18797, "top5_acc": 0.42328, "loss_cls": 4.49388, "loss": 4.49388, "time": 0.71203} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.09906, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.19016, "top5_acc": 0.415, "loss_cls": 4.51784, "loss": 4.51784, "time": 0.71432} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.09905, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.18469, "top5_acc": 0.40906, "loss_cls": 4.5744, "loss": 4.5744, "time": 0.71256} +{"mode": "train", "epoch": 10, "iter": 1300, "lr": 0.09905, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18688, "top5_acc": 0.41344, "loss_cls": 4.53299, "loss": 4.53299, "time": 0.71384} +{"mode": "train", "epoch": 10, "iter": 1400, "lr": 0.09904, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.18391, "top5_acc": 0.40969, "loss_cls": 4.56041, "loss": 4.56041, "time": 0.71348} +{"mode": "train", "epoch": 10, "iter": 1500, "lr": 0.09903, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.1925, "top5_acc": 0.42562, "loss_cls": 4.46469, "loss": 4.46469, "time": 0.71294} +{"mode": "train", "epoch": 10, "iter": 1600, "lr": 0.09903, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.18703, "top5_acc": 0.41906, "loss_cls": 4.47754, "loss": 4.47754, "time": 0.71184} +{"mode": "train", "epoch": 10, "iter": 1700, "lr": 0.09902, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.19, "top5_acc": 0.41578, "loss_cls": 4.51833, "loss": 4.51833, "time": 0.71828} +{"mode": "train", "epoch": 10, "iter": 1800, "lr": 0.09902, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.18266, "top5_acc": 0.40797, "loss_cls": 4.55321, "loss": 4.55321, "time": 0.71212} +{"mode": "train", "epoch": 10, "iter": 1900, "lr": 0.09901, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.1925, "top5_acc": 0.41922, "loss_cls": 4.52624, "loss": 4.52624, "time": 0.71547} +{"mode": "train", "epoch": 10, "iter": 2000, "lr": 0.09901, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.18719, "top5_acc": 0.40344, "loss_cls": 4.55333, "loss": 4.55333, "time": 0.71129} +{"mode": "train", "epoch": 10, "iter": 2100, "lr": 0.099, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.19625, "top5_acc": 0.425, "loss_cls": 4.48405, "loss": 4.48405, "time": 0.71491} +{"mode": "train", "epoch": 10, "iter": 2200, "lr": 0.099, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.18938, "top5_acc": 0.4125, "loss_cls": 4.55508, "loss": 4.55508, "time": 0.71673} +{"mode": "train", "epoch": 10, "iter": 2300, "lr": 0.09899, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.18672, "top5_acc": 0.41031, "loss_cls": 4.54497, "loss": 4.54497, "time": 0.71908} +{"mode": "train", "epoch": 10, "iter": 2400, "lr": 0.09898, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.19391, "top5_acc": 0.41547, "loss_cls": 4.51771, "loss": 4.51771, "time": 0.71699} +{"mode": "train", "epoch": 10, "iter": 2500, "lr": 0.09898, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.18484, "top5_acc": 0.40828, "loss_cls": 4.5482, "loss": 4.5482, "time": 0.71787} +{"mode": "train", "epoch": 10, "iter": 2600, "lr": 0.09897, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.19406, "top5_acc": 0.41516, "loss_cls": 4.5408, "loss": 4.5408, "time": 0.71885} +{"mode": "train", "epoch": 10, "iter": 2700, "lr": 0.09897, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.19531, "top5_acc": 0.42719, "loss_cls": 4.46011, "loss": 4.46011, "time": 0.72091} +{"mode": "train", "epoch": 10, "iter": 2800, "lr": 0.09896, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20156, "top5_acc": 0.42422, "loss_cls": 4.46923, "loss": 4.46923, "time": 0.7186} +{"mode": "train", "epoch": 10, "iter": 2900, "lr": 0.09896, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.19656, "top5_acc": 0.41312, "loss_cls": 4.52518, "loss": 4.52518, "time": 0.71897} +{"mode": "train", "epoch": 10, "iter": 3000, "lr": 0.09895, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.19156, "top5_acc": 0.41281, "loss_cls": 4.52699, "loss": 4.52699, "time": 0.7193} +{"mode": "train", "epoch": 10, "iter": 3100, "lr": 0.09894, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.19125, "top5_acc": 0.41438, "loss_cls": 4.50617, "loss": 4.50617, "time": 0.71901} +{"mode": "train", "epoch": 10, "iter": 3200, "lr": 0.09894, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.19422, "top5_acc": 0.41953, "loss_cls": 4.50745, "loss": 4.50745, "time": 0.71869} +{"mode": "train", "epoch": 10, "iter": 3300, "lr": 0.09893, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.18656, "top5_acc": 0.41406, "loss_cls": 4.56308, "loss": 4.56308, "time": 0.71973} +{"mode": "train", "epoch": 10, "iter": 3400, "lr": 0.09893, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.195, "top5_acc": 0.4225, "loss_cls": 4.48891, "loss": 4.48891, "time": 0.72041} +{"mode": "train", "epoch": 10, "iter": 3500, "lr": 0.09892, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.19438, "top5_acc": 0.42484, "loss_cls": 4.48397, "loss": 4.48397, "time": 0.71698} +{"mode": "train", "epoch": 10, "iter": 3600, "lr": 0.09892, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.19719, "top5_acc": 0.41891, "loss_cls": 4.51868, "loss": 4.51868, "time": 0.71957} +{"mode": "train", "epoch": 10, "iter": 3700, "lr": 0.09891, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19734, "top5_acc": 0.41656, "loss_cls": 4.52299, "loss": 4.52299, "time": 0.71822} +{"mode": "val", "epoch": 10, "iter": 309, "lr": 0.09891, "top1_acc": 0.11234, "top5_acc": 0.2919, "mean_class_accuracy": 0.11212} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.0989, "memory": 15990, "data_time": 1.4271, "top1_acc": 0.1875, "top5_acc": 0.41953, "loss_cls": 4.50692, "loss": 4.50692, "time": 2.14315} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.0989, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.19156, "top5_acc": 0.41625, "loss_cls": 4.49445, "loss": 4.49445, "time": 0.71458} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.09889, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19938, "top5_acc": 0.42453, "loss_cls": 4.49246, "loss": 4.49246, "time": 0.71731} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.09888, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.18969, "top5_acc": 0.41453, "loss_cls": 4.52657, "loss": 4.52657, "time": 0.71726} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.09888, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.19, "top5_acc": 0.42297, "loss_cls": 4.49685, "loss": 4.49685, "time": 0.7159} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.09887, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19172, "top5_acc": 0.42141, "loss_cls": 4.51991, "loss": 4.51991, "time": 0.71387} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.09887, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19312, "top5_acc": 0.40359, "loss_cls": 4.5386, "loss": 4.5386, "time": 0.71461} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.09886, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.19641, "top5_acc": 0.42062, "loss_cls": 4.51101, "loss": 4.51101, "time": 0.71416} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.09885, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.18859, "top5_acc": 0.41422, "loss_cls": 4.5134, "loss": 4.5134, "time": 0.71421} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.09885, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.18641, "top5_acc": 0.41, "loss_cls": 4.51705, "loss": 4.51705, "time": 0.7206} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.09884, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.185, "top5_acc": 0.41406, "loss_cls": 4.54406, "loss": 4.54406, "time": 0.71609} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.09884, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.1875, "top5_acc": 0.42281, "loss_cls": 4.49817, "loss": 4.49817, "time": 0.7208} +{"mode": "train", "epoch": 11, "iter": 1300, "lr": 0.09883, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.18344, "top5_acc": 0.40656, "loss_cls": 4.52412, "loss": 4.52412, "time": 0.72056} +{"mode": "train", "epoch": 11, "iter": 1400, "lr": 0.09882, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.17984, "top5_acc": 0.41203, "loss_cls": 4.54411, "loss": 4.54411, "time": 0.72205} +{"mode": "train", "epoch": 11, "iter": 1500, "lr": 0.09882, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19203, "top5_acc": 0.41891, "loss_cls": 4.51526, "loss": 4.51526, "time": 0.72146} +{"mode": "train", "epoch": 11, "iter": 1600, "lr": 0.09881, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.18438, "top5_acc": 0.40969, "loss_cls": 4.54181, "loss": 4.54181, "time": 0.72326} +{"mode": "train", "epoch": 11, "iter": 1700, "lr": 0.09881, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.19906, "top5_acc": 0.42781, "loss_cls": 4.44617, "loss": 4.44617, "time": 0.72052} +{"mode": "train", "epoch": 11, "iter": 1800, "lr": 0.0988, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2025, "top5_acc": 0.43719, "loss_cls": 4.46674, "loss": 4.46674, "time": 0.72082} +{"mode": "train", "epoch": 11, "iter": 1900, "lr": 0.09879, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.19219, "top5_acc": 0.41375, "loss_cls": 4.49867, "loss": 4.49867, "time": 0.71808} +{"mode": "train", "epoch": 11, "iter": 2000, "lr": 0.09879, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.20094, "top5_acc": 0.42031, "loss_cls": 4.47339, "loss": 4.47339, "time": 0.72024} +{"mode": "train", "epoch": 11, "iter": 2100, "lr": 0.09878, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.19953, "top5_acc": 0.42297, "loss_cls": 4.4928, "loss": 4.4928, "time": 0.72033} +{"mode": "train", "epoch": 11, "iter": 2200, "lr": 0.09878, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.19828, "top5_acc": 0.42219, "loss_cls": 4.50216, "loss": 4.50216, "time": 0.72243} +{"mode": "train", "epoch": 11, "iter": 2300, "lr": 0.09877, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.19078, "top5_acc": 0.41094, "loss_cls": 4.53732, "loss": 4.53732, "time": 0.72087} +{"mode": "train", "epoch": 11, "iter": 2400, "lr": 0.09876, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.19172, "top5_acc": 0.41484, "loss_cls": 4.52353, "loss": 4.52353, "time": 0.72073} +{"mode": "train", "epoch": 11, "iter": 2500, "lr": 0.09876, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20016, "top5_acc": 0.42766, "loss_cls": 4.47175, "loss": 4.47175, "time": 0.71965} +{"mode": "train", "epoch": 11, "iter": 2600, "lr": 0.09875, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.19984, "top5_acc": 0.42172, "loss_cls": 4.47773, "loss": 4.47773, "time": 0.72025} +{"mode": "train", "epoch": 11, "iter": 2700, "lr": 0.09874, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19797, "top5_acc": 0.42609, "loss_cls": 4.4929, "loss": 4.4929, "time": 0.72261} +{"mode": "train", "epoch": 11, "iter": 2800, "lr": 0.09874, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19016, "top5_acc": 0.42219, "loss_cls": 4.49119, "loss": 4.49119, "time": 0.71925} +{"mode": "train", "epoch": 11, "iter": 2900, "lr": 0.09873, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.19016, "top5_acc": 0.41656, "loss_cls": 4.50215, "loss": 4.50215, "time": 0.72248} +{"mode": "train", "epoch": 11, "iter": 3000, "lr": 0.09873, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20219, "top5_acc": 0.42641, "loss_cls": 4.47637, "loss": 4.47637, "time": 0.72138} +{"mode": "train", "epoch": 11, "iter": 3100, "lr": 0.09872, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.19344, "top5_acc": 0.41359, "loss_cls": 4.50922, "loss": 4.50922, "time": 0.72005} +{"mode": "train", "epoch": 11, "iter": 3200, "lr": 0.09871, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.19859, "top5_acc": 0.42344, "loss_cls": 4.4894, "loss": 4.4894, "time": 0.72326} +{"mode": "train", "epoch": 11, "iter": 3300, "lr": 0.09871, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.20219, "top5_acc": 0.42891, "loss_cls": 4.455, "loss": 4.455, "time": 0.7198} +{"mode": "train", "epoch": 11, "iter": 3400, "lr": 0.0987, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.19281, "top5_acc": 0.41656, "loss_cls": 4.52418, "loss": 4.52418, "time": 0.72017} +{"mode": "train", "epoch": 11, "iter": 3500, "lr": 0.09869, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19484, "top5_acc": 0.41531, "loss_cls": 4.49383, "loss": 4.49383, "time": 0.71887} +{"mode": "train", "epoch": 11, "iter": 3600, "lr": 0.09869, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.1875, "top5_acc": 0.41547, "loss_cls": 4.52186, "loss": 4.52186, "time": 0.72697} +{"mode": "train", "epoch": 11, "iter": 3700, "lr": 0.09868, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.19312, "top5_acc": 0.41672, "loss_cls": 4.50093, "loss": 4.50093, "time": 0.7194} +{"mode": "val", "epoch": 11, "iter": 309, "lr": 0.09868, "top1_acc": 0.12171, "top5_acc": 0.30603, "mean_class_accuracy": 0.12147} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.09867, "memory": 15990, "data_time": 1.47497, "top1_acc": 0.21062, "top5_acc": 0.44062, "loss_cls": 4.39685, "loss": 4.39685, "time": 2.19207} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.09867, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.19578, "top5_acc": 0.42484, "loss_cls": 4.45026, "loss": 4.45026, "time": 0.72047} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.09866, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.19312, "top5_acc": 0.42328, "loss_cls": 4.459, "loss": 4.459, "time": 0.71615} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.09865, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.19812, "top5_acc": 0.41859, "loss_cls": 4.47532, "loss": 4.47532, "time": 0.71358} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.09865, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.19734, "top5_acc": 0.42188, "loss_cls": 4.47356, "loss": 4.47356, "time": 0.71413} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.09864, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19375, "top5_acc": 0.42094, "loss_cls": 4.47774, "loss": 4.47774, "time": 0.71278} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.09863, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20328, "top5_acc": 0.42406, "loss_cls": 4.46276, "loss": 4.46276, "time": 0.71667} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.09863, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19266, "top5_acc": 0.42719, "loss_cls": 4.48348, "loss": 4.48348, "time": 0.71301} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.09862, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.19844, "top5_acc": 0.42922, "loss_cls": 4.46475, "loss": 4.46475, "time": 0.71543} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.09861, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.19391, "top5_acc": 0.41422, "loss_cls": 4.49359, "loss": 4.49359, "time": 0.71421} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.09861, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19688, "top5_acc": 0.41859, "loss_cls": 4.49797, "loss": 4.49797, "time": 0.71179} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.0986, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20016, "top5_acc": 0.42219, "loss_cls": 4.46328, "loss": 4.46328, "time": 0.72059} +{"mode": "train", "epoch": 12, "iter": 1300, "lr": 0.09859, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19359, "top5_acc": 0.42141, "loss_cls": 4.48589, "loss": 4.48589, "time": 0.71817} +{"mode": "train", "epoch": 12, "iter": 1400, "lr": 0.09859, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20203, "top5_acc": 0.42766, "loss_cls": 4.43859, "loss": 4.43859, "time": 0.72094} +{"mode": "train", "epoch": 12, "iter": 1500, "lr": 0.09858, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.19688, "top5_acc": 0.42, "loss_cls": 4.50722, "loss": 4.50722, "time": 0.71773} +{"mode": "train", "epoch": 12, "iter": 1600, "lr": 0.09857, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.19078, "top5_acc": 0.40969, "loss_cls": 4.53853, "loss": 4.53853, "time": 0.72066} +{"mode": "train", "epoch": 12, "iter": 1700, "lr": 0.09857, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19703, "top5_acc": 0.41312, "loss_cls": 4.5316, "loss": 4.5316, "time": 0.72237} +{"mode": "train", "epoch": 12, "iter": 1800, "lr": 0.09856, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.20109, "top5_acc": 0.41531, "loss_cls": 4.5104, "loss": 4.5104, "time": 0.71738} +{"mode": "train", "epoch": 12, "iter": 1900, "lr": 0.09855, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.19688, "top5_acc": 0.42156, "loss_cls": 4.47476, "loss": 4.47476, "time": 0.71834} +{"mode": "train", "epoch": 12, "iter": 2000, "lr": 0.09855, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.18938, "top5_acc": 0.42109, "loss_cls": 4.50854, "loss": 4.50854, "time": 0.71807} +{"mode": "train", "epoch": 12, "iter": 2100, "lr": 0.09854, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.19109, "top5_acc": 0.42484, "loss_cls": 4.48663, "loss": 4.48663, "time": 0.71948} +{"mode": "train", "epoch": 12, "iter": 2200, "lr": 0.09853, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19172, "top5_acc": 0.42344, "loss_cls": 4.49835, "loss": 4.49835, "time": 0.71886} +{"mode": "train", "epoch": 12, "iter": 2300, "lr": 0.09853, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.19453, "top5_acc": 0.42078, "loss_cls": 4.49702, "loss": 4.49702, "time": 0.71791} +{"mode": "train", "epoch": 12, "iter": 2400, "lr": 0.09852, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.19922, "top5_acc": 0.41719, "loss_cls": 4.47506, "loss": 4.47506, "time": 0.72154} +{"mode": "train", "epoch": 12, "iter": 2500, "lr": 0.09851, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.19562, "top5_acc": 0.41984, "loss_cls": 4.50197, "loss": 4.50197, "time": 0.72315} +{"mode": "train", "epoch": 12, "iter": 2600, "lr": 0.09851, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19234, "top5_acc": 0.41594, "loss_cls": 4.49921, "loss": 4.49921, "time": 0.71957} +{"mode": "train", "epoch": 12, "iter": 2700, "lr": 0.0985, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20344, "top5_acc": 0.43641, "loss_cls": 4.44908, "loss": 4.44908, "time": 0.72125} +{"mode": "train", "epoch": 12, "iter": 2800, "lr": 0.09849, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.19016, "top5_acc": 0.41656, "loss_cls": 4.50646, "loss": 4.50646, "time": 0.71899} +{"mode": "train", "epoch": 12, "iter": 2900, "lr": 0.09849, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.19891, "top5_acc": 0.415, "loss_cls": 4.51367, "loss": 4.51367, "time": 0.72053} +{"mode": "train", "epoch": 12, "iter": 3000, "lr": 0.09848, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.18406, "top5_acc": 0.41219, "loss_cls": 4.53313, "loss": 4.53313, "time": 0.72138} +{"mode": "train", "epoch": 12, "iter": 3100, "lr": 0.09847, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.19859, "top5_acc": 0.42797, "loss_cls": 4.48009, "loss": 4.48009, "time": 0.71855} +{"mode": "train", "epoch": 12, "iter": 3200, "lr": 0.09847, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.19891, "top5_acc": 0.42469, "loss_cls": 4.46233, "loss": 4.46233, "time": 0.72091} +{"mode": "train", "epoch": 12, "iter": 3300, "lr": 0.09846, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20094, "top5_acc": 0.42047, "loss_cls": 4.51777, "loss": 4.51777, "time": 0.71899} +{"mode": "train", "epoch": 12, "iter": 3400, "lr": 0.09845, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19438, "top5_acc": 0.42375, "loss_cls": 4.49544, "loss": 4.49544, "time": 0.71776} +{"mode": "train", "epoch": 12, "iter": 3500, "lr": 0.09845, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19266, "top5_acc": 0.42219, "loss_cls": 4.49126, "loss": 4.49126, "time": 0.72116} +{"mode": "train", "epoch": 12, "iter": 3600, "lr": 0.09844, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19109, "top5_acc": 0.41969, "loss_cls": 4.4952, "loss": 4.4952, "time": 0.72057} +{"mode": "train", "epoch": 12, "iter": 3700, "lr": 0.09843, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.18984, "top5_acc": 0.41781, "loss_cls": 4.51906, "loss": 4.51906, "time": 0.72023} +{"mode": "val", "epoch": 12, "iter": 309, "lr": 0.09843, "top1_acc": 0.11006, "top5_acc": 0.28187, "mean_class_accuracy": 0.10981} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.09842, "memory": 15990, "data_time": 1.4295, "top1_acc": 0.18766, "top5_acc": 0.41625, "loss_cls": 4.50163, "loss": 4.50163, "time": 2.14433} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.09842, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.20484, "top5_acc": 0.435, "loss_cls": 4.44761, "loss": 4.44761, "time": 0.71464} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.09841, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.19797, "top5_acc": 0.42938, "loss_cls": 4.45633, "loss": 4.45633, "time": 0.7168} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.0984, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20906, "top5_acc": 0.43406, "loss_cls": 4.417, "loss": 4.417, "time": 0.71665} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.09839, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.1975, "top5_acc": 0.42219, "loss_cls": 4.46311, "loss": 4.46311, "time": 0.71487} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.09839, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20203, "top5_acc": 0.42422, "loss_cls": 4.45202, "loss": 4.45202, "time": 0.71472} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.09838, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.19812, "top5_acc": 0.43, "loss_cls": 4.45836, "loss": 4.45836, "time": 0.71461} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.09837, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19859, "top5_acc": 0.42359, "loss_cls": 4.48863, "loss": 4.48863, "time": 0.71524} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.09837, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.1975, "top5_acc": 0.42062, "loss_cls": 4.48494, "loss": 4.48494, "time": 0.71315} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.09836, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.19781, "top5_acc": 0.42641, "loss_cls": 4.4747, "loss": 4.4747, "time": 0.7123} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.09835, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.18812, "top5_acc": 0.41484, "loss_cls": 4.52975, "loss": 4.52975, "time": 0.71405} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.09834, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19484, "top5_acc": 0.41391, "loss_cls": 4.50224, "loss": 4.50224, "time": 0.71593} +{"mode": "train", "epoch": 13, "iter": 1300, "lr": 0.09834, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.19562, "top5_acc": 0.41375, "loss_cls": 4.51431, "loss": 4.51431, "time": 0.72009} +{"mode": "train", "epoch": 13, "iter": 1400, "lr": 0.09833, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.19797, "top5_acc": 0.43016, "loss_cls": 4.43282, "loss": 4.43282, "time": 0.71867} +{"mode": "train", "epoch": 13, "iter": 1500, "lr": 0.09832, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.19703, "top5_acc": 0.42422, "loss_cls": 4.46126, "loss": 4.46126, "time": 0.7245} +{"mode": "train", "epoch": 13, "iter": 1600, "lr": 0.09832, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.19812, "top5_acc": 0.41078, "loss_cls": 4.48737, "loss": 4.48737, "time": 0.7192} +{"mode": "train", "epoch": 13, "iter": 1700, "lr": 0.09831, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.20047, "top5_acc": 0.43531, "loss_cls": 4.4279, "loss": 4.4279, "time": 0.71931} +{"mode": "train", "epoch": 13, "iter": 1800, "lr": 0.0983, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.19875, "top5_acc": 0.43328, "loss_cls": 4.45341, "loss": 4.45341, "time": 0.71949} +{"mode": "train", "epoch": 13, "iter": 1900, "lr": 0.09829, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.20344, "top5_acc": 0.43141, "loss_cls": 4.4543, "loss": 4.4543, "time": 0.71903} +{"mode": "train", "epoch": 13, "iter": 2000, "lr": 0.09829, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20812, "top5_acc": 0.43422, "loss_cls": 4.43663, "loss": 4.43663, "time": 0.72067} +{"mode": "train", "epoch": 13, "iter": 2100, "lr": 0.09828, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.18828, "top5_acc": 0.41938, "loss_cls": 4.49491, "loss": 4.49491, "time": 0.72331} +{"mode": "train", "epoch": 13, "iter": 2200, "lr": 0.09827, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.19719, "top5_acc": 0.42, "loss_cls": 4.47523, "loss": 4.47523, "time": 0.72164} +{"mode": "train", "epoch": 13, "iter": 2300, "lr": 0.09827, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20141, "top5_acc": 0.42016, "loss_cls": 4.47276, "loss": 4.47276, "time": 0.72018} +{"mode": "train", "epoch": 13, "iter": 2400, "lr": 0.09826, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.19594, "top5_acc": 0.42219, "loss_cls": 4.49907, "loss": 4.49907, "time": 0.72144} +{"mode": "train", "epoch": 13, "iter": 2500, "lr": 0.09825, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.19547, "top5_acc": 0.42938, "loss_cls": 4.4858, "loss": 4.4858, "time": 0.71813} +{"mode": "train", "epoch": 13, "iter": 2600, "lr": 0.09824, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.19438, "top5_acc": 0.42422, "loss_cls": 4.47302, "loss": 4.47302, "time": 0.72207} +{"mode": "train", "epoch": 13, "iter": 2700, "lr": 0.09824, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19422, "top5_acc": 0.41906, "loss_cls": 4.49714, "loss": 4.49714, "time": 0.72232} +{"mode": "train", "epoch": 13, "iter": 2800, "lr": 0.09823, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.20438, "top5_acc": 0.41906, "loss_cls": 4.50317, "loss": 4.50317, "time": 0.71901} +{"mode": "train", "epoch": 13, "iter": 2900, "lr": 0.09822, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19734, "top5_acc": 0.42, "loss_cls": 4.50512, "loss": 4.50512, "time": 0.72274} +{"mode": "train", "epoch": 13, "iter": 3000, "lr": 0.09821, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.19922, "top5_acc": 0.42188, "loss_cls": 4.48807, "loss": 4.48807, "time": 0.71982} +{"mode": "train", "epoch": 13, "iter": 3100, "lr": 0.09821, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.195, "top5_acc": 0.41406, "loss_cls": 4.50688, "loss": 4.50688, "time": 0.72202} +{"mode": "train", "epoch": 13, "iter": 3200, "lr": 0.0982, "memory": 15990, "data_time": 0.00073, "top1_acc": 0.18922, "top5_acc": 0.42188, "loss_cls": 4.48272, "loss": 4.48272, "time": 0.71757} +{"mode": "train", "epoch": 13, "iter": 3300, "lr": 0.09819, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20109, "top5_acc": 0.42781, "loss_cls": 4.46533, "loss": 4.46533, "time": 0.72178} +{"mode": "train", "epoch": 13, "iter": 3400, "lr": 0.09818, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.19312, "top5_acc": 0.41266, "loss_cls": 4.50151, "loss": 4.50151, "time": 0.71767} +{"mode": "train", "epoch": 13, "iter": 3500, "lr": 0.09818, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20359, "top5_acc": 0.42766, "loss_cls": 4.43914, "loss": 4.43914, "time": 0.72041} +{"mode": "train", "epoch": 13, "iter": 3600, "lr": 0.09817, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.19625, "top5_acc": 0.41469, "loss_cls": 4.52437, "loss": 4.52437, "time": 0.71778} +{"mode": "train", "epoch": 13, "iter": 3700, "lr": 0.09816, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.19625, "top5_acc": 0.42562, "loss_cls": 4.48325, "loss": 4.48325, "time": 0.72101} +{"mode": "val", "epoch": 13, "iter": 309, "lr": 0.09816, "top1_acc": 0.14329, "top5_acc": 0.34189, "mean_class_accuracy": 0.14296} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.09815, "memory": 15990, "data_time": 1.42812, "top1_acc": 0.20359, "top5_acc": 0.43172, "loss_cls": 4.43247, "loss": 4.43247, "time": 2.14411} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.09814, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20203, "top5_acc": 0.43078, "loss_cls": 4.45048, "loss": 4.45048, "time": 0.71392} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.09814, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20172, "top5_acc": 0.4325, "loss_cls": 4.44773, "loss": 4.44773, "time": 0.71713} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.09813, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20172, "top5_acc": 0.42969, "loss_cls": 4.45315, "loss": 4.45315, "time": 0.71422} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.09812, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20266, "top5_acc": 0.42969, "loss_cls": 4.46404, "loss": 4.46404, "time": 0.71265} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.09811, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20031, "top5_acc": 0.42734, "loss_cls": 4.4528, "loss": 4.4528, "time": 0.71644} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.09811, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20516, "top5_acc": 0.42156, "loss_cls": 4.44931, "loss": 4.44931, "time": 0.71313} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.0981, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.19578, "top5_acc": 0.42391, "loss_cls": 4.48509, "loss": 4.48509, "time": 0.71961} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.09809, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19547, "top5_acc": 0.42641, "loss_cls": 4.48729, "loss": 4.48729, "time": 0.71527} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.09808, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19391, "top5_acc": 0.42641, "loss_cls": 4.48267, "loss": 4.48267, "time": 0.71342} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.09807, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20281, "top5_acc": 0.42453, "loss_cls": 4.47393, "loss": 4.47393, "time": 0.7179} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.09807, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.19922, "top5_acc": 0.42406, "loss_cls": 4.45589, "loss": 4.45589, "time": 0.72106} +{"mode": "train", "epoch": 14, "iter": 1300, "lr": 0.09806, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.20109, "top5_acc": 0.42891, "loss_cls": 4.45466, "loss": 4.45466, "time": 0.72299} +{"mode": "train", "epoch": 14, "iter": 1400, "lr": 0.09805, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20125, "top5_acc": 0.42281, "loss_cls": 4.46652, "loss": 4.46652, "time": 0.72406} +{"mode": "train", "epoch": 14, "iter": 1500, "lr": 0.09804, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.19828, "top5_acc": 0.42344, "loss_cls": 4.469, "loss": 4.469, "time": 0.7197} +{"mode": "train", "epoch": 14, "iter": 1600, "lr": 0.09804, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.19734, "top5_acc": 0.42547, "loss_cls": 4.46008, "loss": 4.46008, "time": 0.71867} +{"mode": "train", "epoch": 14, "iter": 1700, "lr": 0.09803, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.19875, "top5_acc": 0.42469, "loss_cls": 4.47384, "loss": 4.47384, "time": 0.72373} +{"mode": "train", "epoch": 14, "iter": 1800, "lr": 0.09802, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20672, "top5_acc": 0.42812, "loss_cls": 4.44981, "loss": 4.44981, "time": 0.71942} +{"mode": "train", "epoch": 14, "iter": 1900, "lr": 0.09801, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.19672, "top5_acc": 0.41828, "loss_cls": 4.48307, "loss": 4.48307, "time": 0.71672} +{"mode": "train", "epoch": 14, "iter": 2000, "lr": 0.098, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.19312, "top5_acc": 0.43125, "loss_cls": 4.47948, "loss": 4.47948, "time": 0.72177} +{"mode": "train", "epoch": 14, "iter": 2100, "lr": 0.098, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.19844, "top5_acc": 0.42266, "loss_cls": 4.48748, "loss": 4.48748, "time": 0.71892} +{"mode": "train", "epoch": 14, "iter": 2200, "lr": 0.09799, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19312, "top5_acc": 0.43188, "loss_cls": 4.48292, "loss": 4.48292, "time": 0.71919} +{"mode": "train", "epoch": 14, "iter": 2300, "lr": 0.09798, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.18906, "top5_acc": 0.42344, "loss_cls": 4.47424, "loss": 4.47424, "time": 0.7157} +{"mode": "train", "epoch": 14, "iter": 2400, "lr": 0.09797, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.19828, "top5_acc": 0.42828, "loss_cls": 4.45244, "loss": 4.45244, "time": 0.71889} +{"mode": "train", "epoch": 14, "iter": 2500, "lr": 0.09797, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.19906, "top5_acc": 0.42453, "loss_cls": 4.48376, "loss": 4.48376, "time": 0.71849} +{"mode": "train", "epoch": 14, "iter": 2600, "lr": 0.09796, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.19656, "top5_acc": 0.42125, "loss_cls": 4.47261, "loss": 4.47261, "time": 0.71877} +{"mode": "train", "epoch": 14, "iter": 2700, "lr": 0.09795, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.19547, "top5_acc": 0.42375, "loss_cls": 4.47607, "loss": 4.47607, "time": 0.71784} +{"mode": "train", "epoch": 14, "iter": 2800, "lr": 0.09794, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20281, "top5_acc": 0.42625, "loss_cls": 4.4544, "loss": 4.4544, "time": 0.71918} +{"mode": "train", "epoch": 14, "iter": 2900, "lr": 0.09793, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.19969, "top5_acc": 0.42781, "loss_cls": 4.47683, "loss": 4.47683, "time": 0.72392} +{"mode": "train", "epoch": 14, "iter": 3000, "lr": 0.09793, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.19828, "top5_acc": 0.42328, "loss_cls": 4.49405, "loss": 4.49405, "time": 0.72005} +{"mode": "train", "epoch": 14, "iter": 3100, "lr": 0.09792, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.19906, "top5_acc": 0.43625, "loss_cls": 4.44116, "loss": 4.44116, "time": 0.71737} +{"mode": "train", "epoch": 14, "iter": 3200, "lr": 0.09791, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21203, "top5_acc": 0.43047, "loss_cls": 4.43368, "loss": 4.43368, "time": 0.71766} +{"mode": "train", "epoch": 14, "iter": 3300, "lr": 0.0979, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21, "top5_acc": 0.43297, "loss_cls": 4.46282, "loss": 4.46282, "time": 0.71773} +{"mode": "train", "epoch": 14, "iter": 3400, "lr": 0.09789, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.19562, "top5_acc": 0.41969, "loss_cls": 4.48761, "loss": 4.48761, "time": 0.72106} +{"mode": "train", "epoch": 14, "iter": 3500, "lr": 0.09789, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.2025, "top5_acc": 0.43672, "loss_cls": 4.43256, "loss": 4.43256, "time": 0.71932} +{"mode": "train", "epoch": 14, "iter": 3600, "lr": 0.09788, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.19516, "top5_acc": 0.42328, "loss_cls": 4.48215, "loss": 4.48215, "time": 0.72039} +{"mode": "train", "epoch": 14, "iter": 3700, "lr": 0.09787, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.18938, "top5_acc": 0.41938, "loss_cls": 4.51322, "loss": 4.51322, "time": 0.72104} +{"mode": "val", "epoch": 14, "iter": 309, "lr": 0.09787, "top1_acc": 0.12663, "top5_acc": 0.31282, "mean_class_accuracy": 0.12649} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.09786, "memory": 15990, "data_time": 1.44342, "top1_acc": 0.20703, "top5_acc": 0.44141, "loss_cls": 4.42905, "loss": 4.42905, "time": 2.15822} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.09785, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20656, "top5_acc": 0.43562, "loss_cls": 4.44372, "loss": 4.44372, "time": 0.71347} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.09784, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20109, "top5_acc": 0.43719, "loss_cls": 4.40236, "loss": 4.40236, "time": 0.71504} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.09783, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.19969, "top5_acc": 0.43375, "loss_cls": 4.45339, "loss": 4.45339, "time": 0.71244} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.09783, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.19672, "top5_acc": 0.43297, "loss_cls": 4.45166, "loss": 4.45166, "time": 0.71681} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.09782, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.19875, "top5_acc": 0.42359, "loss_cls": 4.46571, "loss": 4.46571, "time": 0.7154} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.09781, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.19781, "top5_acc": 0.42453, "loss_cls": 4.44447, "loss": 4.44447, "time": 0.7119} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.0978, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19844, "top5_acc": 0.43078, "loss_cls": 4.45594, "loss": 4.45594, "time": 0.71486} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.09779, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20812, "top5_acc": 0.43141, "loss_cls": 4.42105, "loss": 4.42105, "time": 0.71266} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.09778, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20938, "top5_acc": 0.43453, "loss_cls": 4.39154, "loss": 4.39154, "time": 0.71494} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.09778, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.19938, "top5_acc": 0.42781, "loss_cls": 4.42976, "loss": 4.42976, "time": 0.7202} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.09777, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20328, "top5_acc": 0.43188, "loss_cls": 4.44508, "loss": 4.44508, "time": 0.71865} +{"mode": "train", "epoch": 15, "iter": 1300, "lr": 0.09776, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.19734, "top5_acc": 0.42, "loss_cls": 4.476, "loss": 4.476, "time": 0.71184} +{"mode": "train", "epoch": 15, "iter": 1400, "lr": 0.09775, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.18234, "top5_acc": 0.41656, "loss_cls": 4.49367, "loss": 4.49367, "time": 0.7184} +{"mode": "train", "epoch": 15, "iter": 1500, "lr": 0.09774, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19969, "top5_acc": 0.42547, "loss_cls": 4.46306, "loss": 4.46306, "time": 0.72063} +{"mode": "train", "epoch": 15, "iter": 1600, "lr": 0.09773, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20094, "top5_acc": 0.42875, "loss_cls": 4.44727, "loss": 4.44727, "time": 0.71637} +{"mode": "train", "epoch": 15, "iter": 1700, "lr": 0.09773, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20734, "top5_acc": 0.43938, "loss_cls": 4.4202, "loss": 4.4202, "time": 0.71764} +{"mode": "train", "epoch": 15, "iter": 1800, "lr": 0.09772, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.1975, "top5_acc": 0.42625, "loss_cls": 4.48653, "loss": 4.48653, "time": 0.72062} +{"mode": "train", "epoch": 15, "iter": 1900, "lr": 0.09771, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20422, "top5_acc": 0.43062, "loss_cls": 4.44884, "loss": 4.44884, "time": 0.72032} +{"mode": "train", "epoch": 15, "iter": 2000, "lr": 0.0977, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.19312, "top5_acc": 0.42438, "loss_cls": 4.47161, "loss": 4.47161, "time": 0.71949} +{"mode": "train", "epoch": 15, "iter": 2100, "lr": 0.09769, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.19375, "top5_acc": 0.41906, "loss_cls": 4.51579, "loss": 4.51579, "time": 0.72078} +{"mode": "train", "epoch": 15, "iter": 2200, "lr": 0.09768, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.1975, "top5_acc": 0.425, "loss_cls": 4.46009, "loss": 4.46009, "time": 0.72208} +{"mode": "train", "epoch": 15, "iter": 2300, "lr": 0.09768, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20422, "top5_acc": 0.42688, "loss_cls": 4.45441, "loss": 4.45441, "time": 0.72113} +{"mode": "train", "epoch": 15, "iter": 2400, "lr": 0.09767, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.19609, "top5_acc": 0.40875, "loss_cls": 4.51241, "loss": 4.51241, "time": 0.72074} +{"mode": "train", "epoch": 15, "iter": 2500, "lr": 0.09766, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.19766, "top5_acc": 0.42969, "loss_cls": 4.46982, "loss": 4.46982, "time": 0.72059} +{"mode": "train", "epoch": 15, "iter": 2600, "lr": 0.09765, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.20188, "top5_acc": 0.42734, "loss_cls": 4.4539, "loss": 4.4539, "time": 0.72055} +{"mode": "train", "epoch": 15, "iter": 2700, "lr": 0.09764, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20469, "top5_acc": 0.43984, "loss_cls": 4.4322, "loss": 4.4322, "time": 0.71881} +{"mode": "train", "epoch": 15, "iter": 2800, "lr": 0.09763, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20578, "top5_acc": 0.44016, "loss_cls": 4.42337, "loss": 4.42337, "time": 0.7199} +{"mode": "train", "epoch": 15, "iter": 2900, "lr": 0.09763, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.19531, "top5_acc": 0.42641, "loss_cls": 4.51016, "loss": 4.51016, "time": 0.71715} +{"mode": "train", "epoch": 15, "iter": 3000, "lr": 0.09762, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.19906, "top5_acc": 0.42, "loss_cls": 4.48574, "loss": 4.48574, "time": 0.72265} +{"mode": "train", "epoch": 15, "iter": 3100, "lr": 0.09761, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.20797, "top5_acc": 0.43062, "loss_cls": 4.40931, "loss": 4.40931, "time": 0.72035} +{"mode": "train", "epoch": 15, "iter": 3200, "lr": 0.0976, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.19234, "top5_acc": 0.42609, "loss_cls": 4.45836, "loss": 4.45836, "time": 0.72083} +{"mode": "train", "epoch": 15, "iter": 3300, "lr": 0.09759, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.19016, "top5_acc": 0.42, "loss_cls": 4.47436, "loss": 4.47436, "time": 0.71909} +{"mode": "train", "epoch": 15, "iter": 3400, "lr": 0.09758, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20312, "top5_acc": 0.43016, "loss_cls": 4.45885, "loss": 4.45885, "time": 0.71842} +{"mode": "train", "epoch": 15, "iter": 3500, "lr": 0.09757, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.19844, "top5_acc": 0.4275, "loss_cls": 4.48348, "loss": 4.48348, "time": 0.72053} +{"mode": "train", "epoch": 15, "iter": 3600, "lr": 0.09757, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.19953, "top5_acc": 0.42797, "loss_cls": 4.46579, "loss": 4.46579, "time": 0.7225} +{"mode": "train", "epoch": 15, "iter": 3700, "lr": 0.09756, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20516, "top5_acc": 0.43297, "loss_cls": 4.44044, "loss": 4.44044, "time": 0.72062} +{"mode": "val", "epoch": 15, "iter": 309, "lr": 0.09755, "top1_acc": 0.13559, "top5_acc": 0.33318, "mean_class_accuracy": 0.13564} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.09754, "memory": 15990, "data_time": 1.47229, "top1_acc": 0.20938, "top5_acc": 0.43562, "loss_cls": 4.40697, "loss": 4.40697, "time": 2.19022} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.09754, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2025, "top5_acc": 0.42984, "loss_cls": 4.48856, "loss": 4.48856, "time": 0.71698} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.09753, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.20219, "top5_acc": 0.42969, "loss_cls": 4.43373, "loss": 4.43373, "time": 0.71661} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.09752, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20234, "top5_acc": 0.43141, "loss_cls": 4.46935, "loss": 4.46935, "time": 0.71064} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.09751, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20125, "top5_acc": 0.42766, "loss_cls": 4.44628, "loss": 4.44628, "time": 0.71406} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.0975, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.1975, "top5_acc": 0.41891, "loss_cls": 4.46705, "loss": 4.46705, "time": 0.71262} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.09749, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.19859, "top5_acc": 0.42656, "loss_cls": 4.45828, "loss": 4.45828, "time": 0.71159} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.09748, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19625, "top5_acc": 0.42578, "loss_cls": 4.45861, "loss": 4.45861, "time": 0.71087} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.09747, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20938, "top5_acc": 0.43141, "loss_cls": 4.44036, "loss": 4.44036, "time": 0.71593} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.09747, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19828, "top5_acc": 0.43422, "loss_cls": 4.45634, "loss": 4.45634, "time": 0.71271} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.09746, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19453, "top5_acc": 0.42297, "loss_cls": 4.47211, "loss": 4.47211, "time": 0.7141} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.09745, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20609, "top5_acc": 0.43359, "loss_cls": 4.40994, "loss": 4.40994, "time": 0.71508} +{"mode": "train", "epoch": 16, "iter": 1300, "lr": 0.09744, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.19594, "top5_acc": 0.42859, "loss_cls": 4.44745, "loss": 4.44745, "time": 0.723} +{"mode": "train", "epoch": 16, "iter": 1400, "lr": 0.09743, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.19875, "top5_acc": 0.42531, "loss_cls": 4.4708, "loss": 4.4708, "time": 0.71817} +{"mode": "train", "epoch": 16, "iter": 1500, "lr": 0.09742, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.20547, "top5_acc": 0.42922, "loss_cls": 4.43867, "loss": 4.43867, "time": 0.71903} +{"mode": "train", "epoch": 16, "iter": 1600, "lr": 0.09741, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20391, "top5_acc": 0.43703, "loss_cls": 4.43104, "loss": 4.43104, "time": 0.71919} +{"mode": "train", "epoch": 16, "iter": 1700, "lr": 0.0974, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20781, "top5_acc": 0.42953, "loss_cls": 4.43982, "loss": 4.43982, "time": 0.72405} +{"mode": "train", "epoch": 16, "iter": 1800, "lr": 0.0974, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.19875, "top5_acc": 0.41969, "loss_cls": 4.47832, "loss": 4.47832, "time": 0.72021} +{"mode": "train", "epoch": 16, "iter": 1900, "lr": 0.09739, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20031, "top5_acc": 0.41938, "loss_cls": 4.45811, "loss": 4.45811, "time": 0.72162} +{"mode": "train", "epoch": 16, "iter": 2000, "lr": 0.09738, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20328, "top5_acc": 0.43344, "loss_cls": 4.44398, "loss": 4.44398, "time": 0.72018} +{"mode": "train", "epoch": 16, "iter": 2100, "lr": 0.09737, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20109, "top5_acc": 0.42203, "loss_cls": 4.44833, "loss": 4.44833, "time": 0.72267} +{"mode": "train", "epoch": 16, "iter": 2200, "lr": 0.09736, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20188, "top5_acc": 0.43234, "loss_cls": 4.42591, "loss": 4.42591, "time": 0.7158} +{"mode": "train", "epoch": 16, "iter": 2300, "lr": 0.09735, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.18578, "top5_acc": 0.43156, "loss_cls": 4.47816, "loss": 4.47816, "time": 0.72324} +{"mode": "train", "epoch": 16, "iter": 2400, "lr": 0.09734, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20984, "top5_acc": 0.44859, "loss_cls": 4.41328, "loss": 4.41328, "time": 0.71797} +{"mode": "train", "epoch": 16, "iter": 2500, "lr": 0.09733, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2, "top5_acc": 0.4275, "loss_cls": 4.47168, "loss": 4.47168, "time": 0.71858} +{"mode": "train", "epoch": 16, "iter": 2600, "lr": 0.09732, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20203, "top5_acc": 0.44156, "loss_cls": 4.42356, "loss": 4.42356, "time": 0.71692} +{"mode": "train", "epoch": 16, "iter": 2700, "lr": 0.09731, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20438, "top5_acc": 0.43391, "loss_cls": 4.44796, "loss": 4.44796, "time": 0.71941} +{"mode": "train", "epoch": 16, "iter": 2800, "lr": 0.09731, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21047, "top5_acc": 0.43797, "loss_cls": 4.43519, "loss": 4.43519, "time": 0.72294} +{"mode": "train", "epoch": 16, "iter": 2900, "lr": 0.0973, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20297, "top5_acc": 0.43453, "loss_cls": 4.43312, "loss": 4.43312, "time": 0.71654} +{"mode": "train", "epoch": 16, "iter": 3000, "lr": 0.09729, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.20109, "top5_acc": 0.42, "loss_cls": 4.48179, "loss": 4.48179, "time": 0.72211} +{"mode": "train", "epoch": 16, "iter": 3100, "lr": 0.09728, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.19812, "top5_acc": 0.42562, "loss_cls": 4.45836, "loss": 4.45836, "time": 0.72204} +{"mode": "train", "epoch": 16, "iter": 3200, "lr": 0.09727, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.19875, "top5_acc": 0.43438, "loss_cls": 4.43188, "loss": 4.43188, "time": 0.72272} +{"mode": "train", "epoch": 16, "iter": 3300, "lr": 0.09726, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20125, "top5_acc": 0.43594, "loss_cls": 4.42212, "loss": 4.42212, "time": 0.71915} +{"mode": "train", "epoch": 16, "iter": 3400, "lr": 0.09725, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20141, "top5_acc": 0.42297, "loss_cls": 4.46677, "loss": 4.46677, "time": 0.72038} +{"mode": "train", "epoch": 16, "iter": 3500, "lr": 0.09724, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.205, "top5_acc": 0.43172, "loss_cls": 4.46408, "loss": 4.46408, "time": 0.7185} +{"mode": "train", "epoch": 16, "iter": 3600, "lr": 0.09723, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20125, "top5_acc": 0.42656, "loss_cls": 4.47958, "loss": 4.47958, "time": 0.71787} +{"mode": "train", "epoch": 16, "iter": 3700, "lr": 0.09722, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20266, "top5_acc": 0.43453, "loss_cls": 4.4547, "loss": 4.4547, "time": 0.72151} +{"mode": "val", "epoch": 16, "iter": 309, "lr": 0.09722, "top1_acc": 0.14314, "top5_acc": 0.34265, "mean_class_accuracy": 0.14314} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.09721, "memory": 15990, "data_time": 1.42652, "top1_acc": 0.20812, "top5_acc": 0.44438, "loss_cls": 4.42015, "loss": 4.42015, "time": 2.14509} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.0972, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.19328, "top5_acc": 0.42578, "loss_cls": 4.4649, "loss": 4.4649, "time": 0.71141} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.09719, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19891, "top5_acc": 0.43188, "loss_cls": 4.43167, "loss": 4.43167, "time": 0.71527} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.09718, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19625, "top5_acc": 0.42688, "loss_cls": 4.43721, "loss": 4.43721, "time": 0.71189} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.09717, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20422, "top5_acc": 0.42688, "loss_cls": 4.46788, "loss": 4.46788, "time": 0.71477} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.09716, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20484, "top5_acc": 0.42188, "loss_cls": 4.46842, "loss": 4.46842, "time": 0.71613} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.09715, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20719, "top5_acc": 0.43219, "loss_cls": 4.44452, "loss": 4.44452, "time": 0.71262} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.09714, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21391, "top5_acc": 0.43656, "loss_cls": 4.39639, "loss": 4.39639, "time": 0.71324} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.09714, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20156, "top5_acc": 0.43688, "loss_cls": 4.40485, "loss": 4.40485, "time": 0.71488} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.09713, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20359, "top5_acc": 0.42875, "loss_cls": 4.47298, "loss": 4.47298, "time": 0.71354} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.09712, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20016, "top5_acc": 0.42344, "loss_cls": 4.45993, "loss": 4.45993, "time": 0.71337} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.09711, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20703, "top5_acc": 0.42719, "loss_cls": 4.44602, "loss": 4.44602, "time": 0.71281} +{"mode": "train", "epoch": 17, "iter": 1300, "lr": 0.0971, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.205, "top5_acc": 0.43609, "loss_cls": 4.43735, "loss": 4.43735, "time": 0.716} +{"mode": "train", "epoch": 17, "iter": 1400, "lr": 0.09709, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20875, "top5_acc": 0.43, "loss_cls": 4.43858, "loss": 4.43858, "time": 0.71976} +{"mode": "train", "epoch": 17, "iter": 1500, "lr": 0.09708, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.19703, "top5_acc": 0.42297, "loss_cls": 4.47179, "loss": 4.47179, "time": 0.72025} +{"mode": "train", "epoch": 17, "iter": 1600, "lr": 0.09707, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20188, "top5_acc": 0.43078, "loss_cls": 4.46109, "loss": 4.46109, "time": 0.71867} +{"mode": "train", "epoch": 17, "iter": 1700, "lr": 0.09706, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21234, "top5_acc": 0.42594, "loss_cls": 4.44977, "loss": 4.44977, "time": 0.72317} +{"mode": "train", "epoch": 17, "iter": 1800, "lr": 0.09705, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20734, "top5_acc": 0.43625, "loss_cls": 4.42149, "loss": 4.42149, "time": 0.71674} +{"mode": "train", "epoch": 17, "iter": 1900, "lr": 0.09704, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2075, "top5_acc": 0.43906, "loss_cls": 4.41895, "loss": 4.41895, "time": 0.72085} +{"mode": "train", "epoch": 17, "iter": 2000, "lr": 0.09703, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20734, "top5_acc": 0.42891, "loss_cls": 4.45283, "loss": 4.45283, "time": 0.71899} +{"mode": "train", "epoch": 17, "iter": 2100, "lr": 0.09702, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.19156, "top5_acc": 0.43203, "loss_cls": 4.47799, "loss": 4.47799, "time": 0.7238} +{"mode": "train", "epoch": 17, "iter": 2200, "lr": 0.09701, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.20984, "top5_acc": 0.4425, "loss_cls": 4.41315, "loss": 4.41315, "time": 0.72088} +{"mode": "train", "epoch": 17, "iter": 2300, "lr": 0.097, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20484, "top5_acc": 0.42375, "loss_cls": 4.43151, "loss": 4.43151, "time": 0.71928} +{"mode": "train", "epoch": 17, "iter": 2400, "lr": 0.09699, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20344, "top5_acc": 0.43297, "loss_cls": 4.42384, "loss": 4.42384, "time": 0.72394} +{"mode": "train", "epoch": 17, "iter": 2500, "lr": 0.09698, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.19672, "top5_acc": 0.42953, "loss_cls": 4.45302, "loss": 4.45302, "time": 0.72037} +{"mode": "train", "epoch": 17, "iter": 2600, "lr": 0.09697, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.205, "top5_acc": 0.43672, "loss_cls": 4.44234, "loss": 4.44234, "time": 0.72308} +{"mode": "train", "epoch": 17, "iter": 2700, "lr": 0.09697, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.205, "top5_acc": 0.42984, "loss_cls": 4.39979, "loss": 4.39979, "time": 0.72097} +{"mode": "train", "epoch": 17, "iter": 2800, "lr": 0.09696, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20453, "top5_acc": 0.43594, "loss_cls": 4.42209, "loss": 4.42209, "time": 0.71832} +{"mode": "train", "epoch": 17, "iter": 2900, "lr": 0.09695, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20016, "top5_acc": 0.42922, "loss_cls": 4.47366, "loss": 4.47366, "time": 0.71621} +{"mode": "train", "epoch": 17, "iter": 3000, "lr": 0.09694, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.19906, "top5_acc": 0.44484, "loss_cls": 4.42621, "loss": 4.42621, "time": 0.72017} +{"mode": "train", "epoch": 17, "iter": 3100, "lr": 0.09693, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20688, "top5_acc": 0.43328, "loss_cls": 4.40223, "loss": 4.40223, "time": 0.71817} +{"mode": "train", "epoch": 17, "iter": 3200, "lr": 0.09692, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20219, "top5_acc": 0.42234, "loss_cls": 4.47988, "loss": 4.47988, "time": 0.71759} +{"mode": "train", "epoch": 17, "iter": 3300, "lr": 0.09691, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20391, "top5_acc": 0.43328, "loss_cls": 4.42505, "loss": 4.42505, "time": 0.72027} +{"mode": "train", "epoch": 17, "iter": 3400, "lr": 0.0969, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.205, "top5_acc": 0.42844, "loss_cls": 4.45985, "loss": 4.45985, "time": 0.71962} +{"mode": "train", "epoch": 17, "iter": 3500, "lr": 0.09689, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20125, "top5_acc": 0.43969, "loss_cls": 4.4581, "loss": 4.4581, "time": 0.71877} +{"mode": "train", "epoch": 17, "iter": 3600, "lr": 0.09688, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20172, "top5_acc": 0.44375, "loss_cls": 4.42941, "loss": 4.42941, "time": 0.72102} +{"mode": "train", "epoch": 17, "iter": 3700, "lr": 0.09687, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21031, "top5_acc": 0.43703, "loss_cls": 4.41796, "loss": 4.41796, "time": 0.72155} +{"mode": "val", "epoch": 17, "iter": 309, "lr": 0.09686, "top1_acc": 0.1323, "top5_acc": 0.31292, "mean_class_accuracy": 0.13216} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.09685, "memory": 15990, "data_time": 1.45876, "top1_acc": 0.21297, "top5_acc": 0.44562, "loss_cls": 4.39248, "loss": 4.39248, "time": 2.1739} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.09684, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20672, "top5_acc": 0.43266, "loss_cls": 4.45484, "loss": 4.45484, "time": 0.71439} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.09683, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.19047, "top5_acc": 0.42578, "loss_cls": 4.47, "loss": 4.47, "time": 0.71692} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.09683, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20734, "top5_acc": 0.43594, "loss_cls": 4.41708, "loss": 4.41708, "time": 0.71731} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.09682, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20031, "top5_acc": 0.42406, "loss_cls": 4.45781, "loss": 4.45781, "time": 0.7154} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.09681, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2075, "top5_acc": 0.43297, "loss_cls": 4.43027, "loss": 4.43027, "time": 0.71615} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.0968, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2, "top5_acc": 0.42812, "loss_cls": 4.44654, "loss": 4.44654, "time": 0.71379} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.09679, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20859, "top5_acc": 0.43938, "loss_cls": 4.40123, "loss": 4.40123, "time": 0.71237} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.09678, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2025, "top5_acc": 0.44094, "loss_cls": 4.41541, "loss": 4.41541, "time": 0.71241} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.09677, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20656, "top5_acc": 0.43188, "loss_cls": 4.41364, "loss": 4.41364, "time": 0.71617} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.09676, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19844, "top5_acc": 0.42734, "loss_cls": 4.44422, "loss": 4.44422, "time": 0.71709} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.09675, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20859, "top5_acc": 0.44469, "loss_cls": 4.4071, "loss": 4.4071, "time": 0.7187} +{"mode": "train", "epoch": 18, "iter": 1300, "lr": 0.09674, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20016, "top5_acc": 0.43047, "loss_cls": 4.41664, "loss": 4.41664, "time": 0.71453} +{"mode": "train", "epoch": 18, "iter": 1400, "lr": 0.09673, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2075, "top5_acc": 0.43016, "loss_cls": 4.43552, "loss": 4.43552, "time": 0.71458} +{"mode": "train", "epoch": 18, "iter": 1500, "lr": 0.09672, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20375, "top5_acc": 0.42547, "loss_cls": 4.42822, "loss": 4.42822, "time": 0.72388} +{"mode": "train", "epoch": 18, "iter": 1600, "lr": 0.09671, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.19656, "top5_acc": 0.42891, "loss_cls": 4.46141, "loss": 4.46141, "time": 0.72244} +{"mode": "train", "epoch": 18, "iter": 1700, "lr": 0.0967, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20609, "top5_acc": 0.43781, "loss_cls": 4.43168, "loss": 4.43168, "time": 0.71781} +{"mode": "train", "epoch": 18, "iter": 1800, "lr": 0.09669, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.19484, "top5_acc": 0.42453, "loss_cls": 4.46307, "loss": 4.46307, "time": 0.72202} +{"mode": "train", "epoch": 18, "iter": 1900, "lr": 0.09668, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20828, "top5_acc": 0.44203, "loss_cls": 4.39523, "loss": 4.39523, "time": 0.71994} +{"mode": "train", "epoch": 18, "iter": 2000, "lr": 0.09667, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.19781, "top5_acc": 0.42812, "loss_cls": 4.45306, "loss": 4.45306, "time": 0.72164} +{"mode": "train", "epoch": 18, "iter": 2100, "lr": 0.09666, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.20438, "top5_acc": 0.43406, "loss_cls": 4.42237, "loss": 4.42237, "time": 0.7213} +{"mode": "train", "epoch": 18, "iter": 2200, "lr": 0.09665, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20984, "top5_acc": 0.43062, "loss_cls": 4.43617, "loss": 4.43617, "time": 0.71926} +{"mode": "train", "epoch": 18, "iter": 2300, "lr": 0.09664, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20875, "top5_acc": 0.43219, "loss_cls": 4.41325, "loss": 4.41325, "time": 0.72125} +{"mode": "train", "epoch": 18, "iter": 2400, "lr": 0.09663, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20219, "top5_acc": 0.4325, "loss_cls": 4.41782, "loss": 4.41782, "time": 0.71795} +{"mode": "train", "epoch": 18, "iter": 2500, "lr": 0.09662, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20609, "top5_acc": 0.44094, "loss_cls": 4.42126, "loss": 4.42126, "time": 0.71706} +{"mode": "train", "epoch": 18, "iter": 2600, "lr": 0.09661, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20656, "top5_acc": 0.42969, "loss_cls": 4.42819, "loss": 4.42819, "time": 0.71497} +{"mode": "train", "epoch": 18, "iter": 2700, "lr": 0.0966, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21406, "top5_acc": 0.435, "loss_cls": 4.4115, "loss": 4.4115, "time": 0.71725} +{"mode": "train", "epoch": 18, "iter": 2800, "lr": 0.09659, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20844, "top5_acc": 0.44109, "loss_cls": 4.41629, "loss": 4.41629, "time": 0.71889} +{"mode": "train", "epoch": 18, "iter": 2900, "lr": 0.09658, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20344, "top5_acc": 0.42578, "loss_cls": 4.44451, "loss": 4.44451, "time": 0.72214} +{"mode": "train", "epoch": 18, "iter": 3000, "lr": 0.09657, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19625, "top5_acc": 0.43156, "loss_cls": 4.45424, "loss": 4.45424, "time": 0.72241} +{"mode": "train", "epoch": 18, "iter": 3100, "lr": 0.09656, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20219, "top5_acc": 0.43031, "loss_cls": 4.43753, "loss": 4.43753, "time": 0.71662} +{"mode": "train", "epoch": 18, "iter": 3200, "lr": 0.09654, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.20375, "top5_acc": 0.43688, "loss_cls": 4.41635, "loss": 4.41635, "time": 0.71959} +{"mode": "train", "epoch": 18, "iter": 3300, "lr": 0.09653, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.2025, "top5_acc": 0.42781, "loss_cls": 4.45298, "loss": 4.45298, "time": 0.72164} +{"mode": "train", "epoch": 18, "iter": 3400, "lr": 0.09652, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.19859, "top5_acc": 0.43125, "loss_cls": 4.45549, "loss": 4.45549, "time": 0.72438} +{"mode": "train", "epoch": 18, "iter": 3500, "lr": 0.09651, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20703, "top5_acc": 0.43172, "loss_cls": 4.4259, "loss": 4.4259, "time": 0.72063} +{"mode": "train", "epoch": 18, "iter": 3600, "lr": 0.0965, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.2125, "top5_acc": 0.44031, "loss_cls": 4.39891, "loss": 4.39891, "time": 0.71856} +{"mode": "train", "epoch": 18, "iter": 3700, "lr": 0.09649, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21031, "top5_acc": 0.45188, "loss_cls": 4.38216, "loss": 4.38216, "time": 0.71865} +{"mode": "val", "epoch": 18, "iter": 309, "lr": 0.09649, "top1_acc": 0.13235, "top5_acc": 0.31454, "mean_class_accuracy": 0.1321} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.09648, "memory": 15990, "data_time": 1.40227, "top1_acc": 0.20969, "top5_acc": 0.43641, "loss_cls": 4.42743, "loss": 4.42743, "time": 2.11662} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.09647, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20297, "top5_acc": 0.43875, "loss_cls": 4.41309, "loss": 4.41309, "time": 0.71344} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.09646, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.21531, "top5_acc": 0.43953, "loss_cls": 4.38488, "loss": 4.38488, "time": 0.71802} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.09645, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20734, "top5_acc": 0.43312, "loss_cls": 4.43563, "loss": 4.43563, "time": 0.71302} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.09644, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20578, "top5_acc": 0.43422, "loss_cls": 4.43549, "loss": 4.43549, "time": 0.71607} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.09643, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19938, "top5_acc": 0.42938, "loss_cls": 4.45447, "loss": 4.45447, "time": 0.71512} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.09642, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20484, "top5_acc": 0.44219, "loss_cls": 4.41118, "loss": 4.41118, "time": 0.71562} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.09641, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20266, "top5_acc": 0.43922, "loss_cls": 4.41026, "loss": 4.41026, "time": 0.70937} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.0964, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2075, "top5_acc": 0.43844, "loss_cls": 4.40964, "loss": 4.40964, "time": 0.71635} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.09639, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20812, "top5_acc": 0.43672, "loss_cls": 4.43416, "loss": 4.43416, "time": 0.71411} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.09637, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.19875, "top5_acc": 0.42656, "loss_cls": 4.45366, "loss": 4.45366, "time": 0.71664} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.09636, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21562, "top5_acc": 0.44422, "loss_cls": 4.38774, "loss": 4.38774, "time": 0.71672} +{"mode": "train", "epoch": 19, "iter": 1300, "lr": 0.09635, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20641, "top5_acc": 0.43594, "loss_cls": 4.42929, "loss": 4.42929, "time": 0.71362} +{"mode": "train", "epoch": 19, "iter": 1400, "lr": 0.09634, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20531, "top5_acc": 0.43109, "loss_cls": 4.42854, "loss": 4.42854, "time": 0.71462} +{"mode": "train", "epoch": 19, "iter": 1500, "lr": 0.09633, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20125, "top5_acc": 0.42328, "loss_cls": 4.4529, "loss": 4.4529, "time": 0.71625} +{"mode": "train", "epoch": 19, "iter": 1600, "lr": 0.09632, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20172, "top5_acc": 0.43422, "loss_cls": 4.44728, "loss": 4.44728, "time": 0.71751} +{"mode": "train", "epoch": 19, "iter": 1700, "lr": 0.09631, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20234, "top5_acc": 0.43188, "loss_cls": 4.44192, "loss": 4.44192, "time": 0.72306} +{"mode": "train", "epoch": 19, "iter": 1800, "lr": 0.0963, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20516, "top5_acc": 0.43656, "loss_cls": 4.41228, "loss": 4.41228, "time": 0.72112} +{"mode": "train", "epoch": 19, "iter": 1900, "lr": 0.09629, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20469, "top5_acc": 0.42969, "loss_cls": 4.40951, "loss": 4.40951, "time": 0.71633} +{"mode": "train", "epoch": 19, "iter": 2000, "lr": 0.09628, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.20609, "top5_acc": 0.43516, "loss_cls": 4.42782, "loss": 4.42782, "time": 0.71919} +{"mode": "train", "epoch": 19, "iter": 2100, "lr": 0.09627, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.20531, "top5_acc": 0.43438, "loss_cls": 4.42079, "loss": 4.42079, "time": 0.71565} +{"mode": "train", "epoch": 19, "iter": 2200, "lr": 0.09626, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20062, "top5_acc": 0.43188, "loss_cls": 4.42709, "loss": 4.42709, "time": 0.71997} +{"mode": "train", "epoch": 19, "iter": 2300, "lr": 0.09625, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20016, "top5_acc": 0.43281, "loss_cls": 4.4241, "loss": 4.4241, "time": 0.72561} +{"mode": "train", "epoch": 19, "iter": 2400, "lr": 0.09624, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.18922, "top5_acc": 0.42672, "loss_cls": 4.46225, "loss": 4.46225, "time": 0.72019} +{"mode": "train", "epoch": 19, "iter": 2500, "lr": 0.09623, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21016, "top5_acc": 0.4375, "loss_cls": 4.40729, "loss": 4.40729, "time": 0.71873} +{"mode": "train", "epoch": 19, "iter": 2600, "lr": 0.09622, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.19516, "top5_acc": 0.43031, "loss_cls": 4.41334, "loss": 4.41334, "time": 0.71844} +{"mode": "train", "epoch": 19, "iter": 2700, "lr": 0.09621, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20344, "top5_acc": 0.43609, "loss_cls": 4.41293, "loss": 4.41293, "time": 0.71709} +{"mode": "train", "epoch": 19, "iter": 2800, "lr": 0.0962, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21328, "top5_acc": 0.43812, "loss_cls": 4.4147, "loss": 4.4147, "time": 0.71984} +{"mode": "train", "epoch": 19, "iter": 2900, "lr": 0.09618, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20516, "top5_acc": 0.43188, "loss_cls": 4.46553, "loss": 4.46553, "time": 0.72157} +{"mode": "train", "epoch": 19, "iter": 3000, "lr": 0.09617, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20875, "top5_acc": 0.43625, "loss_cls": 4.40956, "loss": 4.40956, "time": 0.71886} +{"mode": "train", "epoch": 19, "iter": 3100, "lr": 0.09616, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20797, "top5_acc": 0.43938, "loss_cls": 4.39668, "loss": 4.39668, "time": 0.72022} +{"mode": "train", "epoch": 19, "iter": 3200, "lr": 0.09615, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20359, "top5_acc": 0.43172, "loss_cls": 4.43318, "loss": 4.43318, "time": 0.71848} +{"mode": "train", "epoch": 19, "iter": 3300, "lr": 0.09614, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.20625, "top5_acc": 0.4425, "loss_cls": 4.37363, "loss": 4.37363, "time": 0.72168} +{"mode": "train", "epoch": 19, "iter": 3400, "lr": 0.09613, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.20484, "top5_acc": 0.43844, "loss_cls": 4.415, "loss": 4.415, "time": 0.71907} +{"mode": "train", "epoch": 19, "iter": 3500, "lr": 0.09612, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20562, "top5_acc": 0.42719, "loss_cls": 4.41252, "loss": 4.41252, "time": 0.71999} +{"mode": "train", "epoch": 19, "iter": 3600, "lr": 0.09611, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21203, "top5_acc": 0.44219, "loss_cls": 4.39371, "loss": 4.39371, "time": 0.71642} +{"mode": "train", "epoch": 19, "iter": 3700, "lr": 0.0961, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.20125, "top5_acc": 0.43281, "loss_cls": 4.42857, "loss": 4.42857, "time": 0.71897} +{"mode": "val", "epoch": 19, "iter": 309, "lr": 0.09609, "top1_acc": 0.14668, "top5_acc": 0.34812, "mean_class_accuracy": 0.14668} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.09608, "memory": 15990, "data_time": 1.4488, "top1_acc": 0.21328, "top5_acc": 0.43844, "loss_cls": 4.3645, "loss": 4.3645, "time": 2.16543} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.09607, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21047, "top5_acc": 0.43297, "loss_cls": 4.41125, "loss": 4.41125, "time": 0.71644} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.09606, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20703, "top5_acc": 0.43766, "loss_cls": 4.40101, "loss": 4.40101, "time": 0.71432} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.09605, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.2, "top5_acc": 0.43703, "loss_cls": 4.4363, "loss": 4.4363, "time": 0.71556} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.09604, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21469, "top5_acc": 0.44359, "loss_cls": 4.3599, "loss": 4.3599, "time": 0.71462} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.09603, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21016, "top5_acc": 0.44062, "loss_cls": 4.3926, "loss": 4.3926, "time": 0.71841} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.09602, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.19734, "top5_acc": 0.42891, "loss_cls": 4.43947, "loss": 4.43947, "time": 0.71287} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.09601, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20406, "top5_acc": 0.43484, "loss_cls": 4.43619, "loss": 4.43619, "time": 0.71483} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.096, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21344, "top5_acc": 0.44109, "loss_cls": 4.37602, "loss": 4.37602, "time": 0.71433} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.09598, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21062, "top5_acc": 0.44109, "loss_cls": 4.36938, "loss": 4.36938, "time": 0.71492} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.09597, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21094, "top5_acc": 0.43578, "loss_cls": 4.42413, "loss": 4.42413, "time": 0.71295} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.09596, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19922, "top5_acc": 0.42859, "loss_cls": 4.42638, "loss": 4.42638, "time": 0.71436} +{"mode": "train", "epoch": 20, "iter": 1300, "lr": 0.09595, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21125, "top5_acc": 0.44609, "loss_cls": 4.3993, "loss": 4.3993, "time": 0.71533} +{"mode": "train", "epoch": 20, "iter": 1400, "lr": 0.09594, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20875, "top5_acc": 0.43891, "loss_cls": 4.4062, "loss": 4.4062, "time": 0.71374} +{"mode": "train", "epoch": 20, "iter": 1500, "lr": 0.09593, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20234, "top5_acc": 0.4275, "loss_cls": 4.43267, "loss": 4.43267, "time": 0.71563} +{"mode": "train", "epoch": 20, "iter": 1600, "lr": 0.09592, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20234, "top5_acc": 0.42688, "loss_cls": 4.45752, "loss": 4.45752, "time": 0.71665} +{"mode": "train", "epoch": 20, "iter": 1700, "lr": 0.09591, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20391, "top5_acc": 0.4325, "loss_cls": 4.42823, "loss": 4.42823, "time": 0.71783} +{"mode": "train", "epoch": 20, "iter": 1800, "lr": 0.0959, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.20766, "top5_acc": 0.43906, "loss_cls": 4.4243, "loss": 4.4243, "time": 0.72372} +{"mode": "train", "epoch": 20, "iter": 1900, "lr": 0.09588, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21594, "top5_acc": 0.43688, "loss_cls": 4.39884, "loss": 4.39884, "time": 0.71839} +{"mode": "train", "epoch": 20, "iter": 2000, "lr": 0.09587, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20984, "top5_acc": 0.43891, "loss_cls": 4.41298, "loss": 4.41298, "time": 0.71864} +{"mode": "train", "epoch": 20, "iter": 2100, "lr": 0.09586, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20422, "top5_acc": 0.43203, "loss_cls": 4.42733, "loss": 4.42733, "time": 0.71773} +{"mode": "train", "epoch": 20, "iter": 2200, "lr": 0.09585, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20891, "top5_acc": 0.43094, "loss_cls": 4.44117, "loss": 4.44117, "time": 0.7197} +{"mode": "train", "epoch": 20, "iter": 2300, "lr": 0.09584, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.20375, "top5_acc": 0.43516, "loss_cls": 4.42637, "loss": 4.42637, "time": 0.72091} +{"mode": "train", "epoch": 20, "iter": 2400, "lr": 0.09583, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21438, "top5_acc": 0.43469, "loss_cls": 4.39759, "loss": 4.39759, "time": 0.72458} +{"mode": "train", "epoch": 20, "iter": 2500, "lr": 0.09582, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20609, "top5_acc": 0.43812, "loss_cls": 4.43117, "loss": 4.43117, "time": 0.71957} +{"mode": "train", "epoch": 20, "iter": 2600, "lr": 0.09581, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19953, "top5_acc": 0.42469, "loss_cls": 4.43797, "loss": 4.43797, "time": 0.72019} +{"mode": "train", "epoch": 20, "iter": 2700, "lr": 0.0958, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20672, "top5_acc": 0.42984, "loss_cls": 4.44367, "loss": 4.44367, "time": 0.721} +{"mode": "train", "epoch": 20, "iter": 2800, "lr": 0.09578, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20562, "top5_acc": 0.43219, "loss_cls": 4.41656, "loss": 4.41656, "time": 0.71994} +{"mode": "train", "epoch": 20, "iter": 2900, "lr": 0.09577, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.205, "top5_acc": 0.43734, "loss_cls": 4.41435, "loss": 4.41435, "time": 0.71746} +{"mode": "train", "epoch": 20, "iter": 3000, "lr": 0.09576, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21094, "top5_acc": 0.44078, "loss_cls": 4.40718, "loss": 4.40718, "time": 0.72059} +{"mode": "train", "epoch": 20, "iter": 3100, "lr": 0.09575, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.20438, "top5_acc": 0.44047, "loss_cls": 4.4053, "loss": 4.4053, "time": 0.72031} +{"mode": "train", "epoch": 20, "iter": 3200, "lr": 0.09574, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.20406, "top5_acc": 0.43484, "loss_cls": 4.44187, "loss": 4.44187, "time": 0.72031} +{"mode": "train", "epoch": 20, "iter": 3300, "lr": 0.09573, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.21281, "top5_acc": 0.44125, "loss_cls": 4.40524, "loss": 4.40524, "time": 0.72304} +{"mode": "train", "epoch": 20, "iter": 3400, "lr": 0.09572, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20906, "top5_acc": 0.43469, "loss_cls": 4.41608, "loss": 4.41608, "time": 0.71575} +{"mode": "train", "epoch": 20, "iter": 3500, "lr": 0.09571, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21031, "top5_acc": 0.43047, "loss_cls": 4.38988, "loss": 4.38988, "time": 0.71799} +{"mode": "train", "epoch": 20, "iter": 3600, "lr": 0.09569, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21609, "top5_acc": 0.4375, "loss_cls": 4.41494, "loss": 4.41494, "time": 0.72103} +{"mode": "train", "epoch": 20, "iter": 3700, "lr": 0.09568, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20469, "top5_acc": 0.44281, "loss_cls": 4.39668, "loss": 4.39668, "time": 0.71837} +{"mode": "val", "epoch": 20, "iter": 309, "lr": 0.09568, "top1_acc": 0.1517, "top5_acc": 0.34432, "mean_class_accuracy": 0.15161} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.09567, "memory": 15990, "data_time": 1.42619, "top1_acc": 0.20906, "top5_acc": 0.44141, "loss_cls": 4.36746, "loss": 4.36746, "time": 2.14375} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.09565, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20406, "top5_acc": 0.43672, "loss_cls": 4.41027, "loss": 4.41027, "time": 0.71435} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.09564, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20938, "top5_acc": 0.43938, "loss_cls": 4.40236, "loss": 4.40236, "time": 0.71305} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.09563, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20859, "top5_acc": 0.42797, "loss_cls": 4.43563, "loss": 4.43563, "time": 0.71189} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.09562, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19828, "top5_acc": 0.42984, "loss_cls": 4.41677, "loss": 4.41677, "time": 0.71271} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.09561, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.20672, "top5_acc": 0.42625, "loss_cls": 4.43991, "loss": 4.43991, "time": 0.7207} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.0956, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20453, "top5_acc": 0.43266, "loss_cls": 4.42912, "loss": 4.42912, "time": 0.71623} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.09559, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21469, "top5_acc": 0.43875, "loss_cls": 4.37207, "loss": 4.37207, "time": 0.7148} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.09557, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20531, "top5_acc": 0.43188, "loss_cls": 4.44473, "loss": 4.44473, "time": 0.71305} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.09556, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20672, "top5_acc": 0.44062, "loss_cls": 4.40807, "loss": 4.40807, "time": 0.71428} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.09555, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20406, "top5_acc": 0.43438, "loss_cls": 4.44042, "loss": 4.44042, "time": 0.71384} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.09554, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20625, "top5_acc": 0.43422, "loss_cls": 4.43865, "loss": 4.43865, "time": 0.71144} +{"mode": "train", "epoch": 21, "iter": 1300, "lr": 0.09553, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21984, "top5_acc": 0.44562, "loss_cls": 4.34348, "loss": 4.34348, "time": 0.71082} +{"mode": "train", "epoch": 21, "iter": 1400, "lr": 0.09552, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20688, "top5_acc": 0.43469, "loss_cls": 4.43103, "loss": 4.43103, "time": 0.72164} +{"mode": "train", "epoch": 21, "iter": 1500, "lr": 0.09551, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.21672, "top5_acc": 0.4475, "loss_cls": 4.3637, "loss": 4.3637, "time": 0.72103} +{"mode": "train", "epoch": 21, "iter": 1600, "lr": 0.09549, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20406, "top5_acc": 0.43906, "loss_cls": 4.38789, "loss": 4.38789, "time": 0.72286} +{"mode": "train", "epoch": 21, "iter": 1700, "lr": 0.09548, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.20203, "top5_acc": 0.44312, "loss_cls": 4.41536, "loss": 4.41536, "time": 0.71952} +{"mode": "train", "epoch": 21, "iter": 1800, "lr": 0.09547, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20078, "top5_acc": 0.43109, "loss_cls": 4.44687, "loss": 4.44687, "time": 0.72216} +{"mode": "train", "epoch": 21, "iter": 1900, "lr": 0.09546, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21859, "top5_acc": 0.44359, "loss_cls": 4.3897, "loss": 4.3897, "time": 0.72014} +{"mode": "train", "epoch": 21, "iter": 2000, "lr": 0.09545, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20828, "top5_acc": 0.43781, "loss_cls": 4.40289, "loss": 4.40289, "time": 0.72441} +{"mode": "train", "epoch": 21, "iter": 2100, "lr": 0.09544, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19594, "top5_acc": 0.42938, "loss_cls": 4.43894, "loss": 4.43894, "time": 0.72176} +{"mode": "train", "epoch": 21, "iter": 2200, "lr": 0.09542, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21547, "top5_acc": 0.44234, "loss_cls": 4.38326, "loss": 4.38326, "time": 0.72056} +{"mode": "train", "epoch": 21, "iter": 2300, "lr": 0.09541, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20719, "top5_acc": 0.43797, "loss_cls": 4.42487, "loss": 4.42487, "time": 0.72168} +{"mode": "train", "epoch": 21, "iter": 2400, "lr": 0.0954, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20281, "top5_acc": 0.43609, "loss_cls": 4.44101, "loss": 4.44101, "time": 0.71767} +{"mode": "train", "epoch": 21, "iter": 2500, "lr": 0.09539, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20047, "top5_acc": 0.44734, "loss_cls": 4.38352, "loss": 4.38352, "time": 0.71667} +{"mode": "train", "epoch": 21, "iter": 2600, "lr": 0.09538, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2, "top5_acc": 0.42297, "loss_cls": 4.46393, "loss": 4.46393, "time": 0.71899} +{"mode": "train", "epoch": 21, "iter": 2700, "lr": 0.09537, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20766, "top5_acc": 0.44078, "loss_cls": 4.42802, "loss": 4.42802, "time": 0.718} +{"mode": "train", "epoch": 21, "iter": 2800, "lr": 0.09535, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21047, "top5_acc": 0.43719, "loss_cls": 4.41764, "loss": 4.41764, "time": 0.71793} +{"mode": "train", "epoch": 21, "iter": 2900, "lr": 0.09534, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.20547, "top5_acc": 0.43312, "loss_cls": 4.41808, "loss": 4.41808, "time": 0.724} +{"mode": "train", "epoch": 21, "iter": 3000, "lr": 0.09533, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21406, "top5_acc": 0.45562, "loss_cls": 4.34945, "loss": 4.34945, "time": 0.72005} +{"mode": "train", "epoch": 21, "iter": 3100, "lr": 0.09532, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.20703, "top5_acc": 0.42891, "loss_cls": 4.42864, "loss": 4.42864, "time": 0.7213} +{"mode": "train", "epoch": 21, "iter": 3200, "lr": 0.09531, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20359, "top5_acc": 0.43156, "loss_cls": 4.44674, "loss": 4.44674, "time": 0.71872} +{"mode": "train", "epoch": 21, "iter": 3300, "lr": 0.09529, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20625, "top5_acc": 0.43297, "loss_cls": 4.43414, "loss": 4.43414, "time": 0.7192} +{"mode": "train", "epoch": 21, "iter": 3400, "lr": 0.09528, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21188, "top5_acc": 0.44109, "loss_cls": 4.39018, "loss": 4.39018, "time": 0.71964} +{"mode": "train", "epoch": 21, "iter": 3500, "lr": 0.09527, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.20172, "top5_acc": 0.43266, "loss_cls": 4.43055, "loss": 4.43055, "time": 0.72125} +{"mode": "train", "epoch": 21, "iter": 3600, "lr": 0.09526, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.20516, "top5_acc": 0.43312, "loss_cls": 4.38889, "loss": 4.38889, "time": 0.71886} +{"mode": "train", "epoch": 21, "iter": 3700, "lr": 0.09525, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.20656, "top5_acc": 0.42781, "loss_cls": 4.46175, "loss": 4.46175, "time": 0.71921} +{"mode": "val", "epoch": 21, "iter": 309, "lr": 0.09524, "top1_acc": 0.15413, "top5_acc": 0.35841, "mean_class_accuracy": 0.15406} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.09523, "memory": 15990, "data_time": 1.42885, "top1_acc": 0.21156, "top5_acc": 0.44391, "loss_cls": 4.39703, "loss": 4.39703, "time": 2.14463} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.09522, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21078, "top5_acc": 0.43969, "loss_cls": 4.37418, "loss": 4.37418, "time": 0.71127} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.09521, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20672, "top5_acc": 0.42969, "loss_cls": 4.43863, "loss": 4.43863, "time": 0.71436} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.09519, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21641, "top5_acc": 0.45047, "loss_cls": 4.33446, "loss": 4.33446, "time": 0.71442} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.09518, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20906, "top5_acc": 0.44359, "loss_cls": 4.39353, "loss": 4.39353, "time": 0.71414} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.09517, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21438, "top5_acc": 0.44031, "loss_cls": 4.3925, "loss": 4.3925, "time": 0.71885} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.09516, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20781, "top5_acc": 0.44359, "loss_cls": 4.39953, "loss": 4.39953, "time": 0.7169} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.09515, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.19797, "top5_acc": 0.42312, "loss_cls": 4.45272, "loss": 4.45272, "time": 0.71616} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.09513, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21531, "top5_acc": 0.44547, "loss_cls": 4.37408, "loss": 4.37408, "time": 0.71509} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.09512, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21719, "top5_acc": 0.44594, "loss_cls": 4.3674, "loss": 4.3674, "time": 0.71623} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.09511, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20938, "top5_acc": 0.43422, "loss_cls": 4.4098, "loss": 4.4098, "time": 0.71279} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0951, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20234, "top5_acc": 0.44125, "loss_cls": 4.39986, "loss": 4.39986, "time": 0.7147} +{"mode": "train", "epoch": 22, "iter": 1300, "lr": 0.09509, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21734, "top5_acc": 0.45141, "loss_cls": 4.34274, "loss": 4.34274, "time": 0.71646} +{"mode": "train", "epoch": 22, "iter": 1400, "lr": 0.09507, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20312, "top5_acc": 0.43125, "loss_cls": 4.44107, "loss": 4.44107, "time": 0.71655} +{"mode": "train", "epoch": 22, "iter": 1500, "lr": 0.09506, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20531, "top5_acc": 0.43625, "loss_cls": 4.40544, "loss": 4.40544, "time": 0.71845} +{"mode": "train", "epoch": 22, "iter": 1600, "lr": 0.09505, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21109, "top5_acc": 0.44562, "loss_cls": 4.38682, "loss": 4.38682, "time": 0.72345} +{"mode": "train", "epoch": 22, "iter": 1700, "lr": 0.09504, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21172, "top5_acc": 0.44, "loss_cls": 4.38101, "loss": 4.38101, "time": 0.72134} +{"mode": "train", "epoch": 22, "iter": 1800, "lr": 0.09502, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20859, "top5_acc": 0.43625, "loss_cls": 4.46841, "loss": 4.46841, "time": 0.72153} +{"mode": "train", "epoch": 22, "iter": 1900, "lr": 0.09501, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.19625, "top5_acc": 0.43188, "loss_cls": 4.44111, "loss": 4.44111, "time": 0.72081} +{"mode": "train", "epoch": 22, "iter": 2000, "lr": 0.095, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.19859, "top5_acc": 0.42922, "loss_cls": 4.43141, "loss": 4.43141, "time": 0.7222} +{"mode": "train", "epoch": 22, "iter": 2100, "lr": 0.09499, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20625, "top5_acc": 0.43266, "loss_cls": 4.41573, "loss": 4.41573, "time": 0.71565} +{"mode": "train", "epoch": 22, "iter": 2200, "lr": 0.09498, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20719, "top5_acc": 0.44203, "loss_cls": 4.40379, "loss": 4.40379, "time": 0.71686} +{"mode": "train", "epoch": 22, "iter": 2300, "lr": 0.09496, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20375, "top5_acc": 0.43953, "loss_cls": 4.40145, "loss": 4.40145, "time": 0.72325} +{"mode": "train", "epoch": 22, "iter": 2400, "lr": 0.09495, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20359, "top5_acc": 0.43844, "loss_cls": 4.41844, "loss": 4.41844, "time": 0.72617} +{"mode": "train", "epoch": 22, "iter": 2500, "lr": 0.09494, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20844, "top5_acc": 0.42859, "loss_cls": 4.43645, "loss": 4.43645, "time": 0.7207} +{"mode": "train", "epoch": 22, "iter": 2600, "lr": 0.09493, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.21016, "top5_acc": 0.44672, "loss_cls": 4.3961, "loss": 4.3961, "time": 0.72454} +{"mode": "train", "epoch": 22, "iter": 2700, "lr": 0.09491, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22281, "top5_acc": 0.45406, "loss_cls": 4.36589, "loss": 4.36589, "time": 0.71925} +{"mode": "train", "epoch": 22, "iter": 2800, "lr": 0.0949, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20797, "top5_acc": 0.44125, "loss_cls": 4.41905, "loss": 4.41905, "time": 0.72282} +{"mode": "train", "epoch": 22, "iter": 2900, "lr": 0.09489, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21172, "top5_acc": 0.45281, "loss_cls": 4.39726, "loss": 4.39726, "time": 0.72332} +{"mode": "train", "epoch": 22, "iter": 3000, "lr": 0.09488, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2, "top5_acc": 0.43297, "loss_cls": 4.43608, "loss": 4.43608, "time": 0.71498} +{"mode": "train", "epoch": 22, "iter": 3100, "lr": 0.09487, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.19797, "top5_acc": 0.42734, "loss_cls": 4.46674, "loss": 4.46674, "time": 0.71914} +{"mode": "train", "epoch": 22, "iter": 3200, "lr": 0.09485, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20344, "top5_acc": 0.43188, "loss_cls": 4.3991, "loss": 4.3991, "time": 0.71925} +{"mode": "train", "epoch": 22, "iter": 3300, "lr": 0.09484, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.20172, "top5_acc": 0.43172, "loss_cls": 4.43317, "loss": 4.43317, "time": 0.72632} +{"mode": "train", "epoch": 22, "iter": 3400, "lr": 0.09483, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.205, "top5_acc": 0.43344, "loss_cls": 4.42681, "loss": 4.42681, "time": 0.72116} +{"mode": "train", "epoch": 22, "iter": 3500, "lr": 0.09482, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20891, "top5_acc": 0.44406, "loss_cls": 4.38682, "loss": 4.38682, "time": 0.722} +{"mode": "train", "epoch": 22, "iter": 3600, "lr": 0.0948, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.19938, "top5_acc": 0.43688, "loss_cls": 4.43981, "loss": 4.43981, "time": 0.71819} +{"mode": "train", "epoch": 22, "iter": 3700, "lr": 0.09479, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.21641, "top5_acc": 0.43641, "loss_cls": 4.38878, "loss": 4.38878, "time": 0.72125} +{"mode": "val", "epoch": 22, "iter": 309, "lr": 0.09479, "top1_acc": 0.14967, "top5_acc": 0.34432, "mean_class_accuracy": 0.14942} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.09477, "memory": 15990, "data_time": 1.46124, "top1_acc": 0.21609, "top5_acc": 0.44719, "loss_cls": 4.36572, "loss": 4.36572, "time": 2.17745} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.09476, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21516, "top5_acc": 0.45453, "loss_cls": 4.34687, "loss": 4.34687, "time": 0.7126} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.09475, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.215, "top5_acc": 0.44391, "loss_cls": 4.38774, "loss": 4.38774, "time": 0.71293} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.09474, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.2125, "top5_acc": 0.44594, "loss_cls": 4.401, "loss": 4.401, "time": 0.71613} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.09472, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21281, "top5_acc": 0.4375, "loss_cls": 4.40986, "loss": 4.40986, "time": 0.71227} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.09471, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20562, "top5_acc": 0.44125, "loss_cls": 4.40841, "loss": 4.40841, "time": 0.71625} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.0947, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20953, "top5_acc": 0.44078, "loss_cls": 4.40085, "loss": 4.40085, "time": 0.71314} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.09469, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20844, "top5_acc": 0.43188, "loss_cls": 4.41425, "loss": 4.41425, "time": 0.7131} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.09467, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21781, "top5_acc": 0.43609, "loss_cls": 4.39665, "loss": 4.39665, "time": 0.71629} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.09466, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21156, "top5_acc": 0.44312, "loss_cls": 4.3786, "loss": 4.3786, "time": 0.71226} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.09465, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21625, "top5_acc": 0.44344, "loss_cls": 4.40481, "loss": 4.40481, "time": 0.71335} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.09464, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22328, "top5_acc": 0.45031, "loss_cls": 4.35595, "loss": 4.35595, "time": 0.71333} +{"mode": "train", "epoch": 23, "iter": 1300, "lr": 0.09462, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20781, "top5_acc": 0.43281, "loss_cls": 4.41413, "loss": 4.41413, "time": 0.71182} +{"mode": "train", "epoch": 23, "iter": 1400, "lr": 0.09461, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21438, "top5_acc": 0.45391, "loss_cls": 4.36338, "loss": 4.36338, "time": 0.71798} +{"mode": "train", "epoch": 23, "iter": 1500, "lr": 0.0946, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21328, "top5_acc": 0.45438, "loss_cls": 4.36667, "loss": 4.36667, "time": 0.71875} +{"mode": "train", "epoch": 23, "iter": 1600, "lr": 0.09459, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21234, "top5_acc": 0.44656, "loss_cls": 4.38381, "loss": 4.38381, "time": 0.71875} +{"mode": "train", "epoch": 23, "iter": 1700, "lr": 0.09457, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.2025, "top5_acc": 0.42781, "loss_cls": 4.45194, "loss": 4.45194, "time": 0.72334} +{"mode": "train", "epoch": 23, "iter": 1800, "lr": 0.09456, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20828, "top5_acc": 0.42938, "loss_cls": 4.40563, "loss": 4.40563, "time": 0.71871} +{"mode": "train", "epoch": 23, "iter": 1900, "lr": 0.09455, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21234, "top5_acc": 0.43922, "loss_cls": 4.42796, "loss": 4.42796, "time": 0.72135} +{"mode": "train", "epoch": 23, "iter": 2000, "lr": 0.09453, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19922, "top5_acc": 0.44, "loss_cls": 4.41313, "loss": 4.41313, "time": 0.72407} +{"mode": "train", "epoch": 23, "iter": 2100, "lr": 0.09452, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.21125, "top5_acc": 0.43969, "loss_cls": 4.40276, "loss": 4.40276, "time": 0.71936} +{"mode": "train", "epoch": 23, "iter": 2200, "lr": 0.09451, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21766, "top5_acc": 0.45078, "loss_cls": 4.33846, "loss": 4.33846, "time": 0.71554} +{"mode": "train", "epoch": 23, "iter": 2300, "lr": 0.0945, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21, "top5_acc": 0.44109, "loss_cls": 4.40288, "loss": 4.40288, "time": 0.71966} +{"mode": "train", "epoch": 23, "iter": 2400, "lr": 0.09448, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21188, "top5_acc": 0.44344, "loss_cls": 4.39023, "loss": 4.39023, "time": 0.71783} +{"mode": "train", "epoch": 23, "iter": 2500, "lr": 0.09447, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.20781, "top5_acc": 0.43719, "loss_cls": 4.37215, "loss": 4.37215, "time": 0.71971} +{"mode": "train", "epoch": 23, "iter": 2600, "lr": 0.09446, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.20828, "top5_acc": 0.44109, "loss_cls": 4.39292, "loss": 4.39292, "time": 0.72002} +{"mode": "train", "epoch": 23, "iter": 2700, "lr": 0.09445, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.19703, "top5_acc": 0.43844, "loss_cls": 4.42182, "loss": 4.42182, "time": 0.72192} +{"mode": "train", "epoch": 23, "iter": 2800, "lr": 0.09443, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21562, "top5_acc": 0.44109, "loss_cls": 4.36693, "loss": 4.36693, "time": 0.72071} +{"mode": "train", "epoch": 23, "iter": 2900, "lr": 0.09442, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21031, "top5_acc": 0.44109, "loss_cls": 4.40144, "loss": 4.40144, "time": 0.71976} +{"mode": "train", "epoch": 23, "iter": 3000, "lr": 0.09441, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.20859, "top5_acc": 0.43688, "loss_cls": 4.42303, "loss": 4.42303, "time": 0.72305} +{"mode": "train", "epoch": 23, "iter": 3100, "lr": 0.09439, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.20469, "top5_acc": 0.43688, "loss_cls": 4.41504, "loss": 4.41504, "time": 0.71913} +{"mode": "train", "epoch": 23, "iter": 3200, "lr": 0.09438, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20234, "top5_acc": 0.43312, "loss_cls": 4.40767, "loss": 4.40767, "time": 0.72103} +{"mode": "train", "epoch": 23, "iter": 3300, "lr": 0.09437, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20188, "top5_acc": 0.43875, "loss_cls": 4.42657, "loss": 4.42657, "time": 0.71967} +{"mode": "train", "epoch": 23, "iter": 3400, "lr": 0.09436, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20281, "top5_acc": 0.43219, "loss_cls": 4.41817, "loss": 4.41817, "time": 0.72155} +{"mode": "train", "epoch": 23, "iter": 3500, "lr": 0.09434, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20781, "top5_acc": 0.43359, "loss_cls": 4.42119, "loss": 4.42119, "time": 0.71742} +{"mode": "train", "epoch": 23, "iter": 3600, "lr": 0.09433, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.19594, "top5_acc": 0.42969, "loss_cls": 4.45847, "loss": 4.45847, "time": 0.71915} +{"mode": "train", "epoch": 23, "iter": 3700, "lr": 0.09432, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21172, "top5_acc": 0.4425, "loss_cls": 4.36398, "loss": 4.36398, "time": 0.71858} +{"mode": "val", "epoch": 23, "iter": 309, "lr": 0.09431, "top1_acc": 0.15383, "top5_acc": 0.36114, "mean_class_accuracy": 0.15377} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.0943, "memory": 15990, "data_time": 1.4423, "top1_acc": 0.20719, "top5_acc": 0.43703, "loss_cls": 4.38644, "loss": 4.38644, "time": 2.15623} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.09428, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21734, "top5_acc": 0.45062, "loss_cls": 4.36844, "loss": 4.36844, "time": 0.7158} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.09427, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22094, "top5_acc": 0.44875, "loss_cls": 4.32119, "loss": 4.32119, "time": 0.71774} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.09426, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21578, "top5_acc": 0.44016, "loss_cls": 4.41074, "loss": 4.41074, "time": 0.72079} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.09425, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21281, "top5_acc": 0.43625, "loss_cls": 4.40727, "loss": 4.40727, "time": 0.71644} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.09423, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20328, "top5_acc": 0.44203, "loss_cls": 4.4138, "loss": 4.4138, "time": 0.71473} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.09422, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21469, "top5_acc": 0.43953, "loss_cls": 4.37459, "loss": 4.37459, "time": 0.71709} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.09421, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21016, "top5_acc": 0.43781, "loss_cls": 4.39588, "loss": 4.39588, "time": 0.71335} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.09419, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21469, "top5_acc": 0.44297, "loss_cls": 4.38687, "loss": 4.38687, "time": 0.71221} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.09418, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2075, "top5_acc": 0.43625, "loss_cls": 4.40304, "loss": 4.40304, "time": 0.71406} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.09417, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21641, "top5_acc": 0.45203, "loss_cls": 4.3531, "loss": 4.3531, "time": 0.71209} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.09415, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21031, "top5_acc": 0.44188, "loss_cls": 4.3945, "loss": 4.3945, "time": 0.71157} +{"mode": "train", "epoch": 24, "iter": 1300, "lr": 0.09414, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20484, "top5_acc": 0.43594, "loss_cls": 4.39246, "loss": 4.39246, "time": 0.71491} +{"mode": "train", "epoch": 24, "iter": 1400, "lr": 0.09413, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21031, "top5_acc": 0.42891, "loss_cls": 4.41426, "loss": 4.41426, "time": 0.71479} +{"mode": "train", "epoch": 24, "iter": 1500, "lr": 0.09411, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20625, "top5_acc": 0.43562, "loss_cls": 4.42498, "loss": 4.42498, "time": 0.71186} +{"mode": "train", "epoch": 24, "iter": 1600, "lr": 0.0941, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20172, "top5_acc": 0.43891, "loss_cls": 4.38169, "loss": 4.38169, "time": 0.71308} +{"mode": "train", "epoch": 24, "iter": 1700, "lr": 0.09409, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20969, "top5_acc": 0.44578, "loss_cls": 4.35814, "loss": 4.35814, "time": 0.71664} +{"mode": "train", "epoch": 24, "iter": 1800, "lr": 0.09407, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20719, "top5_acc": 0.43469, "loss_cls": 4.39583, "loss": 4.39583, "time": 0.72051} +{"mode": "train", "epoch": 24, "iter": 1900, "lr": 0.09406, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20953, "top5_acc": 0.43984, "loss_cls": 4.41192, "loss": 4.41192, "time": 0.71748} +{"mode": "train", "epoch": 24, "iter": 2000, "lr": 0.09405, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20453, "top5_acc": 0.43891, "loss_cls": 4.38789, "loss": 4.38789, "time": 0.71656} +{"mode": "train", "epoch": 24, "iter": 2100, "lr": 0.09404, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20547, "top5_acc": 0.43453, "loss_cls": 4.42252, "loss": 4.42252, "time": 0.72009} +{"mode": "train", "epoch": 24, "iter": 2200, "lr": 0.09402, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20562, "top5_acc": 0.43703, "loss_cls": 4.38658, "loss": 4.38658, "time": 0.72086} +{"mode": "train", "epoch": 24, "iter": 2300, "lr": 0.09401, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21, "top5_acc": 0.44734, "loss_cls": 4.37481, "loss": 4.37481, "time": 0.72001} +{"mode": "train", "epoch": 24, "iter": 2400, "lr": 0.094, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20391, "top5_acc": 0.43422, "loss_cls": 4.42871, "loss": 4.42871, "time": 0.71816} +{"mode": "train", "epoch": 24, "iter": 2500, "lr": 0.09398, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20922, "top5_acc": 0.43594, "loss_cls": 4.42873, "loss": 4.42873, "time": 0.71731} +{"mode": "train", "epoch": 24, "iter": 2600, "lr": 0.09397, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20641, "top5_acc": 0.43609, "loss_cls": 4.41165, "loss": 4.41165, "time": 0.7194} +{"mode": "train", "epoch": 24, "iter": 2700, "lr": 0.09396, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20609, "top5_acc": 0.44672, "loss_cls": 4.38467, "loss": 4.38467, "time": 0.7208} +{"mode": "train", "epoch": 24, "iter": 2800, "lr": 0.09394, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20938, "top5_acc": 0.435, "loss_cls": 4.40676, "loss": 4.40676, "time": 0.72009} +{"mode": "train", "epoch": 24, "iter": 2900, "lr": 0.09393, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20703, "top5_acc": 0.43188, "loss_cls": 4.43236, "loss": 4.43236, "time": 0.71886} +{"mode": "train", "epoch": 24, "iter": 3000, "lr": 0.09392, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20609, "top5_acc": 0.4325, "loss_cls": 4.42141, "loss": 4.42141, "time": 0.72009} +{"mode": "train", "epoch": 24, "iter": 3100, "lr": 0.0939, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20422, "top5_acc": 0.43734, "loss_cls": 4.41052, "loss": 4.41052, "time": 0.71994} +{"mode": "train", "epoch": 24, "iter": 3200, "lr": 0.09389, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.21141, "top5_acc": 0.45312, "loss_cls": 4.34357, "loss": 4.34357, "time": 0.71991} +{"mode": "train", "epoch": 24, "iter": 3300, "lr": 0.09388, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21172, "top5_acc": 0.43766, "loss_cls": 4.41812, "loss": 4.41812, "time": 0.72077} +{"mode": "train", "epoch": 24, "iter": 3400, "lr": 0.09386, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.20547, "top5_acc": 0.4425, "loss_cls": 4.38752, "loss": 4.38752, "time": 0.71864} +{"mode": "train", "epoch": 24, "iter": 3500, "lr": 0.09385, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20312, "top5_acc": 0.43391, "loss_cls": 4.4188, "loss": 4.4188, "time": 0.72187} +{"mode": "train", "epoch": 24, "iter": 3600, "lr": 0.09384, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20859, "top5_acc": 0.44469, "loss_cls": 4.40871, "loss": 4.40871, "time": 0.71876} +{"mode": "train", "epoch": 24, "iter": 3700, "lr": 0.09382, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20969, "top5_acc": 0.44266, "loss_cls": 4.39735, "loss": 4.39735, "time": 0.71939} +{"mode": "val", "epoch": 24, "iter": 309, "lr": 0.09382, "top1_acc": 0.13964, "top5_acc": 0.33419, "mean_class_accuracy": 0.13974} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.0938, "memory": 15990, "data_time": 1.44877, "top1_acc": 0.21969, "top5_acc": 0.44922, "loss_cls": 4.34546, "loss": 4.34546, "time": 2.1679} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.09379, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21375, "top5_acc": 0.44797, "loss_cls": 4.3675, "loss": 4.3675, "time": 0.71276} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.09378, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21391, "top5_acc": 0.43984, "loss_cls": 4.38436, "loss": 4.38436, "time": 0.71497} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.09376, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21297, "top5_acc": 0.45266, "loss_cls": 4.36633, "loss": 4.36633, "time": 0.71407} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.09375, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21328, "top5_acc": 0.44609, "loss_cls": 4.35062, "loss": 4.35062, "time": 0.71577} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.09373, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20531, "top5_acc": 0.44141, "loss_cls": 4.40615, "loss": 4.40615, "time": 0.71667} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.09372, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21031, "top5_acc": 0.45281, "loss_cls": 4.3581, "loss": 4.3581, "time": 0.71431} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.09371, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21109, "top5_acc": 0.43891, "loss_cls": 4.37316, "loss": 4.37316, "time": 0.71351} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.09369, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21719, "top5_acc": 0.45156, "loss_cls": 4.34601, "loss": 4.34601, "time": 0.71215} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.09368, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20797, "top5_acc": 0.43547, "loss_cls": 4.42409, "loss": 4.42409, "time": 0.71152} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.09367, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21281, "top5_acc": 0.44266, "loss_cls": 4.38787, "loss": 4.38787, "time": 0.71489} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.09365, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21094, "top5_acc": 0.43891, "loss_cls": 4.38178, "loss": 4.38178, "time": 0.71805} +{"mode": "train", "epoch": 25, "iter": 1300, "lr": 0.09364, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2125, "top5_acc": 0.45062, "loss_cls": 4.37675, "loss": 4.37675, "time": 0.71723} +{"mode": "train", "epoch": 25, "iter": 1400, "lr": 0.09363, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22578, "top5_acc": 0.44859, "loss_cls": 4.34687, "loss": 4.34687, "time": 0.71289} +{"mode": "train", "epoch": 25, "iter": 1500, "lr": 0.09361, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20938, "top5_acc": 0.43188, "loss_cls": 4.41839, "loss": 4.41839, "time": 0.71166} +{"mode": "train", "epoch": 25, "iter": 1600, "lr": 0.0936, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20484, "top5_acc": 0.43984, "loss_cls": 4.39868, "loss": 4.39868, "time": 0.71487} +{"mode": "train", "epoch": 25, "iter": 1700, "lr": 0.09358, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20672, "top5_acc": 0.43781, "loss_cls": 4.40689, "loss": 4.40689, "time": 0.72123} +{"mode": "train", "epoch": 25, "iter": 1800, "lr": 0.09357, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21734, "top5_acc": 0.43766, "loss_cls": 4.36096, "loss": 4.36096, "time": 0.72029} +{"mode": "train", "epoch": 25, "iter": 1900, "lr": 0.09356, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20703, "top5_acc": 0.43859, "loss_cls": 4.4075, "loss": 4.4075, "time": 0.71948} +{"mode": "train", "epoch": 25, "iter": 2000, "lr": 0.09354, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21109, "top5_acc": 0.44531, "loss_cls": 4.39185, "loss": 4.39185, "time": 0.71831} +{"mode": "train", "epoch": 25, "iter": 2100, "lr": 0.09353, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20844, "top5_acc": 0.44391, "loss_cls": 4.4024, "loss": 4.4024, "time": 0.72235} +{"mode": "train", "epoch": 25, "iter": 2200, "lr": 0.09352, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20859, "top5_acc": 0.44094, "loss_cls": 4.40466, "loss": 4.40466, "time": 0.72124} +{"mode": "train", "epoch": 25, "iter": 2300, "lr": 0.0935, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21344, "top5_acc": 0.44625, "loss_cls": 4.37865, "loss": 4.37865, "time": 0.72217} +{"mode": "train", "epoch": 25, "iter": 2400, "lr": 0.09349, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20844, "top5_acc": 0.43828, "loss_cls": 4.43079, "loss": 4.43079, "time": 0.72218} +{"mode": "train", "epoch": 25, "iter": 2500, "lr": 0.09347, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.215, "top5_acc": 0.43547, "loss_cls": 4.41893, "loss": 4.41893, "time": 0.71741} +{"mode": "train", "epoch": 25, "iter": 2600, "lr": 0.09346, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21531, "top5_acc": 0.44688, "loss_cls": 4.39732, "loss": 4.39732, "time": 0.72245} +{"mode": "train", "epoch": 25, "iter": 2700, "lr": 0.09345, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20297, "top5_acc": 0.43031, "loss_cls": 4.42725, "loss": 4.42725, "time": 0.72042} +{"mode": "train", "epoch": 25, "iter": 2800, "lr": 0.09343, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21672, "top5_acc": 0.44719, "loss_cls": 4.3764, "loss": 4.3764, "time": 0.72309} +{"mode": "train", "epoch": 25, "iter": 2900, "lr": 0.09342, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20797, "top5_acc": 0.44578, "loss_cls": 4.37184, "loss": 4.37184, "time": 0.72355} +{"mode": "train", "epoch": 25, "iter": 3000, "lr": 0.09341, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.21562, "top5_acc": 0.44438, "loss_cls": 4.36549, "loss": 4.36549, "time": 0.71857} +{"mode": "train", "epoch": 25, "iter": 3100, "lr": 0.09339, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21203, "top5_acc": 0.43938, "loss_cls": 4.38389, "loss": 4.38389, "time": 0.72212} +{"mode": "train", "epoch": 25, "iter": 3200, "lr": 0.09338, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20281, "top5_acc": 0.44031, "loss_cls": 4.40519, "loss": 4.40519, "time": 0.72168} +{"mode": "train", "epoch": 25, "iter": 3300, "lr": 0.09336, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20609, "top5_acc": 0.44734, "loss_cls": 4.36189, "loss": 4.36189, "time": 0.71661} +{"mode": "train", "epoch": 25, "iter": 3400, "lr": 0.09335, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20203, "top5_acc": 0.43156, "loss_cls": 4.45129, "loss": 4.45129, "time": 0.71859} +{"mode": "train", "epoch": 25, "iter": 3500, "lr": 0.09334, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21438, "top5_acc": 0.44891, "loss_cls": 4.37478, "loss": 4.37478, "time": 0.71852} +{"mode": "train", "epoch": 25, "iter": 3600, "lr": 0.09332, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21141, "top5_acc": 0.44234, "loss_cls": 4.4021, "loss": 4.4021, "time": 0.71853} +{"mode": "train", "epoch": 25, "iter": 3700, "lr": 0.09331, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.21266, "top5_acc": 0.43891, "loss_cls": 4.37365, "loss": 4.37365, "time": 0.7184} +{"mode": "val", "epoch": 25, "iter": 309, "lr": 0.0933, "top1_acc": 0.14587, "top5_acc": 0.34574, "mean_class_accuracy": 0.14558} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.09329, "memory": 15990, "data_time": 1.44851, "top1_acc": 0.21203, "top5_acc": 0.45078, "loss_cls": 4.33764, "loss": 4.33764, "time": 2.16415} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.09327, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20859, "top5_acc": 0.43625, "loss_cls": 4.36765, "loss": 4.36765, "time": 0.712} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.09326, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21703, "top5_acc": 0.46109, "loss_cls": 4.33288, "loss": 4.33288, "time": 0.71184} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.09325, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21906, "top5_acc": 0.44531, "loss_cls": 4.38063, "loss": 4.38063, "time": 0.71398} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.09323, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21688, "top5_acc": 0.44609, "loss_cls": 4.34368, "loss": 4.34368, "time": 0.71506} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.09322, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20891, "top5_acc": 0.445, "loss_cls": 4.37591, "loss": 4.37591, "time": 0.71549} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.0932, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20734, "top5_acc": 0.44422, "loss_cls": 4.37704, "loss": 4.37704, "time": 0.71446} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.09319, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20703, "top5_acc": 0.44125, "loss_cls": 4.36386, "loss": 4.36386, "time": 0.71372} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.09318, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21422, "top5_acc": 0.44922, "loss_cls": 4.36924, "loss": 4.36924, "time": 0.7142} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.09316, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21859, "top5_acc": 0.44266, "loss_cls": 4.37084, "loss": 4.37084, "time": 0.71311} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.09315, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20484, "top5_acc": 0.44172, "loss_cls": 4.3881, "loss": 4.3881, "time": 0.72049} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.09313, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21188, "top5_acc": 0.44672, "loss_cls": 4.35781, "loss": 4.35781, "time": 0.71468} +{"mode": "train", "epoch": 26, "iter": 1300, "lr": 0.09312, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21016, "top5_acc": 0.43578, "loss_cls": 4.39923, "loss": 4.39923, "time": 0.71276} +{"mode": "train", "epoch": 26, "iter": 1400, "lr": 0.0931, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21156, "top5_acc": 0.44828, "loss_cls": 4.36319, "loss": 4.36319, "time": 0.71519} +{"mode": "train", "epoch": 26, "iter": 1500, "lr": 0.09309, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21984, "top5_acc": 0.44953, "loss_cls": 4.36025, "loss": 4.36025, "time": 0.71212} +{"mode": "train", "epoch": 26, "iter": 1600, "lr": 0.09308, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20938, "top5_acc": 0.43688, "loss_cls": 4.40531, "loss": 4.40531, "time": 0.71553} +{"mode": "train", "epoch": 26, "iter": 1700, "lr": 0.09306, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21703, "top5_acc": 0.46047, "loss_cls": 4.32445, "loss": 4.32445, "time": 0.71441} +{"mode": "train", "epoch": 26, "iter": 1800, "lr": 0.09305, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.215, "top5_acc": 0.44859, "loss_cls": 4.35723, "loss": 4.35723, "time": 0.71695} +{"mode": "train", "epoch": 26, "iter": 1900, "lr": 0.09303, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20656, "top5_acc": 0.44391, "loss_cls": 4.3725, "loss": 4.3725, "time": 0.71913} +{"mode": "train", "epoch": 26, "iter": 2000, "lr": 0.09302, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21234, "top5_acc": 0.43656, "loss_cls": 4.39361, "loss": 4.39361, "time": 0.72066} +{"mode": "train", "epoch": 26, "iter": 2100, "lr": 0.093, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20031, "top5_acc": 0.43203, "loss_cls": 4.42363, "loss": 4.42363, "time": 0.71913} +{"mode": "train", "epoch": 26, "iter": 2200, "lr": 0.09299, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.21547, "top5_acc": 0.44094, "loss_cls": 4.35767, "loss": 4.35767, "time": 0.71817} +{"mode": "train", "epoch": 26, "iter": 2300, "lr": 0.09298, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21375, "top5_acc": 0.43484, "loss_cls": 4.3913, "loss": 4.3913, "time": 0.71965} +{"mode": "train", "epoch": 26, "iter": 2400, "lr": 0.09296, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20344, "top5_acc": 0.42453, "loss_cls": 4.43339, "loss": 4.43339, "time": 0.71841} +{"mode": "train", "epoch": 26, "iter": 2500, "lr": 0.09295, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21125, "top5_acc": 0.45062, "loss_cls": 4.38987, "loss": 4.38987, "time": 0.7192} +{"mode": "train", "epoch": 26, "iter": 2600, "lr": 0.09293, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21047, "top5_acc": 0.44156, "loss_cls": 4.38501, "loss": 4.38501, "time": 0.71881} +{"mode": "train", "epoch": 26, "iter": 2700, "lr": 0.09292, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21234, "top5_acc": 0.44609, "loss_cls": 4.41033, "loss": 4.41033, "time": 0.71585} +{"mode": "train", "epoch": 26, "iter": 2800, "lr": 0.0929, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21453, "top5_acc": 0.44578, "loss_cls": 4.37294, "loss": 4.37294, "time": 0.72432} +{"mode": "train", "epoch": 26, "iter": 2900, "lr": 0.09289, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.20547, "top5_acc": 0.44156, "loss_cls": 4.40955, "loss": 4.40955, "time": 0.72044} +{"mode": "train", "epoch": 26, "iter": 3000, "lr": 0.09288, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20281, "top5_acc": 0.43484, "loss_cls": 4.40827, "loss": 4.40827, "time": 0.72001} +{"mode": "train", "epoch": 26, "iter": 3100, "lr": 0.09286, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21281, "top5_acc": 0.44828, "loss_cls": 4.39516, "loss": 4.39516, "time": 0.71829} +{"mode": "train", "epoch": 26, "iter": 3200, "lr": 0.09285, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.20797, "top5_acc": 0.43859, "loss_cls": 4.39588, "loss": 4.39588, "time": 0.72297} +{"mode": "train", "epoch": 26, "iter": 3300, "lr": 0.09283, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22219, "top5_acc": 0.45281, "loss_cls": 4.34459, "loss": 4.34459, "time": 0.72047} +{"mode": "train", "epoch": 26, "iter": 3400, "lr": 0.09282, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19812, "top5_acc": 0.43844, "loss_cls": 4.43043, "loss": 4.43043, "time": 0.72126} +{"mode": "train", "epoch": 26, "iter": 3500, "lr": 0.0928, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.20578, "top5_acc": 0.43094, "loss_cls": 4.40791, "loss": 4.40791, "time": 0.72138} +{"mode": "train", "epoch": 26, "iter": 3600, "lr": 0.09279, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21531, "top5_acc": 0.44641, "loss_cls": 4.37195, "loss": 4.37195, "time": 0.7185} +{"mode": "train", "epoch": 26, "iter": 3700, "lr": 0.09278, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21203, "top5_acc": 0.44328, "loss_cls": 4.39562, "loss": 4.39562, "time": 0.72034} +{"mode": "val", "epoch": 26, "iter": 309, "lr": 0.09277, "top1_acc": 0.13833, "top5_acc": 0.33278, "mean_class_accuracy": 0.13812} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.09275, "memory": 15990, "data_time": 1.54337, "top1_acc": 0.22266, "top5_acc": 0.46094, "loss_cls": 4.31398, "loss": 4.31398, "time": 2.26362} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.09274, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21094, "top5_acc": 0.4425, "loss_cls": 4.37884, "loss": 4.37884, "time": 0.72141} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.09272, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.21672, "top5_acc": 0.45266, "loss_cls": 4.36638, "loss": 4.36638, "time": 0.72194} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.09271, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.21297, "top5_acc": 0.45031, "loss_cls": 4.35002, "loss": 4.35002, "time": 0.71775} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.0927, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.21234, "top5_acc": 0.44516, "loss_cls": 4.37362, "loss": 4.37362, "time": 0.71612} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.09268, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21516, "top5_acc": 0.44438, "loss_cls": 4.36606, "loss": 4.36606, "time": 0.71842} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.09267, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21891, "top5_acc": 0.43641, "loss_cls": 4.39348, "loss": 4.39348, "time": 0.71401} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.09265, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.215, "top5_acc": 0.44062, "loss_cls": 4.38616, "loss": 4.38616, "time": 0.71943} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.09264, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.205, "top5_acc": 0.44203, "loss_cls": 4.38623, "loss": 4.38623, "time": 0.72091} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.09262, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20828, "top5_acc": 0.43781, "loss_cls": 4.39625, "loss": 4.39625, "time": 0.71754} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.09261, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20438, "top5_acc": 0.43547, "loss_cls": 4.39719, "loss": 4.39719, "time": 0.7173} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.09259, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20906, "top5_acc": 0.44391, "loss_cls": 4.39131, "loss": 4.39131, "time": 0.71841} +{"mode": "train", "epoch": 27, "iter": 1300, "lr": 0.09258, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21344, "top5_acc": 0.445, "loss_cls": 4.36827, "loss": 4.36827, "time": 0.72044} +{"mode": "train", "epoch": 27, "iter": 1400, "lr": 0.09256, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21891, "top5_acc": 0.45422, "loss_cls": 4.36374, "loss": 4.36374, "time": 0.72127} +{"mode": "train", "epoch": 27, "iter": 1500, "lr": 0.09255, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20609, "top5_acc": 0.44031, "loss_cls": 4.37735, "loss": 4.37735, "time": 0.72283} +{"mode": "train", "epoch": 27, "iter": 1600, "lr": 0.09253, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20438, "top5_acc": 0.44484, "loss_cls": 4.40616, "loss": 4.40616, "time": 0.72124} +{"mode": "train", "epoch": 27, "iter": 1700, "lr": 0.09252, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20781, "top5_acc": 0.44, "loss_cls": 4.40592, "loss": 4.40592, "time": 0.71938} +{"mode": "train", "epoch": 27, "iter": 1800, "lr": 0.09251, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22641, "top5_acc": 0.44938, "loss_cls": 4.31489, "loss": 4.31489, "time": 0.71929} +{"mode": "train", "epoch": 27, "iter": 1900, "lr": 0.09249, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2075, "top5_acc": 0.44359, "loss_cls": 4.40457, "loss": 4.40457, "time": 0.71902} +{"mode": "train", "epoch": 27, "iter": 2000, "lr": 0.09248, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21656, "top5_acc": 0.45453, "loss_cls": 4.33791, "loss": 4.33791, "time": 0.72229} +{"mode": "train", "epoch": 27, "iter": 2100, "lr": 0.09246, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20781, "top5_acc": 0.43672, "loss_cls": 4.40768, "loss": 4.40768, "time": 0.72078} +{"mode": "train", "epoch": 27, "iter": 2200, "lr": 0.09245, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2125, "top5_acc": 0.44328, "loss_cls": 4.37547, "loss": 4.37547, "time": 0.71979} +{"mode": "train", "epoch": 27, "iter": 2300, "lr": 0.09243, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21438, "top5_acc": 0.44297, "loss_cls": 4.40239, "loss": 4.40239, "time": 0.72207} +{"mode": "train", "epoch": 27, "iter": 2400, "lr": 0.09242, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21094, "top5_acc": 0.435, "loss_cls": 4.39859, "loss": 4.39859, "time": 0.71902} +{"mode": "train", "epoch": 27, "iter": 2500, "lr": 0.0924, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.21109, "top5_acc": 0.44078, "loss_cls": 4.40905, "loss": 4.40905, "time": 0.71737} +{"mode": "train", "epoch": 27, "iter": 2600, "lr": 0.09239, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20797, "top5_acc": 0.44359, "loss_cls": 4.38174, "loss": 4.38174, "time": 0.71844} +{"mode": "train", "epoch": 27, "iter": 2700, "lr": 0.09237, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.21938, "top5_acc": 0.45641, "loss_cls": 4.32603, "loss": 4.32603, "time": 0.71942} +{"mode": "train", "epoch": 27, "iter": 2800, "lr": 0.09236, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.215, "top5_acc": 0.44469, "loss_cls": 4.38442, "loss": 4.38442, "time": 0.72122} +{"mode": "train", "epoch": 27, "iter": 2900, "lr": 0.09234, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21125, "top5_acc": 0.44547, "loss_cls": 4.36354, "loss": 4.36354, "time": 0.71807} +{"mode": "train", "epoch": 27, "iter": 3000, "lr": 0.09233, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21297, "top5_acc": 0.44391, "loss_cls": 4.39585, "loss": 4.39585, "time": 0.71966} +{"mode": "train", "epoch": 27, "iter": 3100, "lr": 0.09231, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20828, "top5_acc": 0.44641, "loss_cls": 4.35493, "loss": 4.35493, "time": 0.71957} +{"mode": "train", "epoch": 27, "iter": 3200, "lr": 0.0923, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20859, "top5_acc": 0.44, "loss_cls": 4.38968, "loss": 4.38968, "time": 0.71917} +{"mode": "train", "epoch": 27, "iter": 3300, "lr": 0.09228, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.20953, "top5_acc": 0.4575, "loss_cls": 4.36345, "loss": 4.36345, "time": 0.71876} +{"mode": "train", "epoch": 27, "iter": 3400, "lr": 0.09227, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20781, "top5_acc": 0.43406, "loss_cls": 4.40019, "loss": 4.40019, "time": 0.72614} +{"mode": "train", "epoch": 27, "iter": 3500, "lr": 0.09225, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20969, "top5_acc": 0.44016, "loss_cls": 4.37452, "loss": 4.37452, "time": 0.71807} +{"mode": "train", "epoch": 27, "iter": 3600, "lr": 0.09224, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20328, "top5_acc": 0.43062, "loss_cls": 4.42782, "loss": 4.42782, "time": 0.72081} +{"mode": "train", "epoch": 27, "iter": 3700, "lr": 0.09222, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21453, "top5_acc": 0.44141, "loss_cls": 4.40052, "loss": 4.40052, "time": 0.71618} +{"mode": "val", "epoch": 27, "iter": 309, "lr": 0.09222, "top1_acc": 0.13473, "top5_acc": 0.33252, "mean_class_accuracy": 0.1347} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.0922, "memory": 15990, "data_time": 1.49399, "top1_acc": 0.22609, "top5_acc": 0.45969, "loss_cls": 4.28456, "loss": 4.28456, "time": 2.21305} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.09219, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21797, "top5_acc": 0.44297, "loss_cls": 4.36263, "loss": 4.36263, "time": 0.71285} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.09217, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21234, "top5_acc": 0.44797, "loss_cls": 4.34914, "loss": 4.34914, "time": 0.71832} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.09216, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21109, "top5_acc": 0.45031, "loss_cls": 4.34966, "loss": 4.34966, "time": 0.71536} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.09214, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20656, "top5_acc": 0.44344, "loss_cls": 4.4041, "loss": 4.4041, "time": 0.71549} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.09213, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20672, "top5_acc": 0.44297, "loss_cls": 4.37544, "loss": 4.37544, "time": 0.71518} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.09211, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21125, "top5_acc": 0.44109, "loss_cls": 4.38028, "loss": 4.38028, "time": 0.71428} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.0921, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21297, "top5_acc": 0.44484, "loss_cls": 4.36193, "loss": 4.36193, "time": 0.71488} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.09208, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21531, "top5_acc": 0.4475, "loss_cls": 4.35838, "loss": 4.35838, "time": 0.71122} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.09207, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20547, "top5_acc": 0.43484, "loss_cls": 4.42977, "loss": 4.42977, "time": 0.71485} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.09205, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20828, "top5_acc": 0.44203, "loss_cls": 4.3626, "loss": 4.3626, "time": 0.71365} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.09204, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21906, "top5_acc": 0.45172, "loss_cls": 4.32445, "loss": 4.32445, "time": 0.71133} +{"mode": "train", "epoch": 28, "iter": 1300, "lr": 0.09202, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21484, "top5_acc": 0.44, "loss_cls": 4.37429, "loss": 4.37429, "time": 0.71918} +{"mode": "train", "epoch": 28, "iter": 1400, "lr": 0.09201, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22016, "top5_acc": 0.44594, "loss_cls": 4.38995, "loss": 4.38995, "time": 0.71469} +{"mode": "train", "epoch": 28, "iter": 1500, "lr": 0.09199, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21922, "top5_acc": 0.44391, "loss_cls": 4.39204, "loss": 4.39204, "time": 0.71309} +{"mode": "train", "epoch": 28, "iter": 1600, "lr": 0.09198, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21844, "top5_acc": 0.45234, "loss_cls": 4.35645, "loss": 4.35645, "time": 0.71262} +{"mode": "train", "epoch": 28, "iter": 1700, "lr": 0.09196, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21156, "top5_acc": 0.44938, "loss_cls": 4.35275, "loss": 4.35275, "time": 0.71837} +{"mode": "train", "epoch": 28, "iter": 1800, "lr": 0.09194, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22359, "top5_acc": 0.44875, "loss_cls": 4.34508, "loss": 4.34508, "time": 0.71808} +{"mode": "train", "epoch": 28, "iter": 1900, "lr": 0.09193, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.2175, "top5_acc": 0.45344, "loss_cls": 4.33966, "loss": 4.33966, "time": 0.71762} +{"mode": "train", "epoch": 28, "iter": 2000, "lr": 0.09191, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.22109, "top5_acc": 0.44719, "loss_cls": 4.35652, "loss": 4.35652, "time": 0.72276} +{"mode": "train", "epoch": 28, "iter": 2100, "lr": 0.0919, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20906, "top5_acc": 0.445, "loss_cls": 4.38693, "loss": 4.38693, "time": 0.71725} +{"mode": "train", "epoch": 28, "iter": 2200, "lr": 0.09188, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2075, "top5_acc": 0.44297, "loss_cls": 4.39213, "loss": 4.39213, "time": 0.71973} +{"mode": "train", "epoch": 28, "iter": 2300, "lr": 0.09187, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20516, "top5_acc": 0.44375, "loss_cls": 4.39896, "loss": 4.39896, "time": 0.71852} +{"mode": "train", "epoch": 28, "iter": 2400, "lr": 0.09185, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21781, "top5_acc": 0.44828, "loss_cls": 4.36687, "loss": 4.36687, "time": 0.71926} +{"mode": "train", "epoch": 28, "iter": 2500, "lr": 0.09184, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21078, "top5_acc": 0.44812, "loss_cls": 4.3618, "loss": 4.3618, "time": 0.72131} +{"mode": "train", "epoch": 28, "iter": 2600, "lr": 0.09182, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20906, "top5_acc": 0.42812, "loss_cls": 4.42312, "loss": 4.42312, "time": 0.71956} +{"mode": "train", "epoch": 28, "iter": 2700, "lr": 0.09181, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20703, "top5_acc": 0.44391, "loss_cls": 4.36402, "loss": 4.36402, "time": 0.71837} +{"mode": "train", "epoch": 28, "iter": 2800, "lr": 0.09179, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20938, "top5_acc": 0.43391, "loss_cls": 4.40318, "loss": 4.40318, "time": 0.71827} +{"mode": "train", "epoch": 28, "iter": 2900, "lr": 0.09178, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21469, "top5_acc": 0.43609, "loss_cls": 4.40157, "loss": 4.40157, "time": 0.71885} +{"mode": "train", "epoch": 28, "iter": 3000, "lr": 0.09176, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.21109, "top5_acc": 0.44172, "loss_cls": 4.37382, "loss": 4.37382, "time": 0.72135} +{"mode": "train", "epoch": 28, "iter": 3100, "lr": 0.09175, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21531, "top5_acc": 0.44656, "loss_cls": 4.36351, "loss": 4.36351, "time": 0.72249} +{"mode": "train", "epoch": 28, "iter": 3200, "lr": 0.09173, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20141, "top5_acc": 0.44, "loss_cls": 4.39482, "loss": 4.39482, "time": 0.71771} +{"mode": "train", "epoch": 28, "iter": 3300, "lr": 0.09172, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21766, "top5_acc": 0.45672, "loss_cls": 4.33721, "loss": 4.33721, "time": 0.71948} +{"mode": "train", "epoch": 28, "iter": 3400, "lr": 0.0917, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20906, "top5_acc": 0.44172, "loss_cls": 4.39682, "loss": 4.39682, "time": 0.72096} +{"mode": "train", "epoch": 28, "iter": 3500, "lr": 0.09168, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21656, "top5_acc": 0.45703, "loss_cls": 4.33447, "loss": 4.33447, "time": 0.7209} +{"mode": "train", "epoch": 28, "iter": 3600, "lr": 0.09167, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21312, "top5_acc": 0.44984, "loss_cls": 4.36708, "loss": 4.36708, "time": 0.72043} +{"mode": "train", "epoch": 28, "iter": 3700, "lr": 0.09165, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21281, "top5_acc": 0.44172, "loss_cls": 4.41047, "loss": 4.41047, "time": 0.71893} +{"mode": "val", "epoch": 28, "iter": 309, "lr": 0.09165, "top1_acc": 0.15043, "top5_acc": 0.33566, "mean_class_accuracy": 0.15009} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.09163, "memory": 15990, "data_time": 1.43271, "top1_acc": 0.21656, "top5_acc": 0.45656, "loss_cls": 4.33458, "loss": 4.33458, "time": 2.14861} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.09162, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22109, "top5_acc": 0.45078, "loss_cls": 4.34346, "loss": 4.34346, "time": 0.71656} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.0916, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21344, "top5_acc": 0.44484, "loss_cls": 4.3891, "loss": 4.3891, "time": 0.71867} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.09158, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21734, "top5_acc": 0.4425, "loss_cls": 4.34938, "loss": 4.34938, "time": 0.71335} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.09157, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21422, "top5_acc": 0.44797, "loss_cls": 4.35407, "loss": 4.35407, "time": 0.71447} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.09155, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20766, "top5_acc": 0.445, "loss_cls": 4.37782, "loss": 4.37782, "time": 0.71217} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.09154, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20359, "top5_acc": 0.45156, "loss_cls": 4.37667, "loss": 4.37667, "time": 0.71476} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.09152, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21312, "top5_acc": 0.44906, "loss_cls": 4.34013, "loss": 4.34013, "time": 0.72173} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.09151, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20828, "top5_acc": 0.43719, "loss_cls": 4.40181, "loss": 4.40181, "time": 0.71801} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.09149, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22078, "top5_acc": 0.4525, "loss_cls": 4.32589, "loss": 4.32589, "time": 0.71692} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.09148, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21562, "top5_acc": 0.45203, "loss_cls": 4.35139, "loss": 4.35139, "time": 0.7169} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.09146, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21125, "top5_acc": 0.44422, "loss_cls": 4.38043, "loss": 4.38043, "time": 0.71664} +{"mode": "train", "epoch": 29, "iter": 1300, "lr": 0.09144, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21719, "top5_acc": 0.45234, "loss_cls": 4.34093, "loss": 4.34093, "time": 0.71302} +{"mode": "train", "epoch": 29, "iter": 1400, "lr": 0.09143, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20812, "top5_acc": 0.43422, "loss_cls": 4.3638, "loss": 4.3638, "time": 0.71745} +{"mode": "train", "epoch": 29, "iter": 1500, "lr": 0.09141, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20828, "top5_acc": 0.44078, "loss_cls": 4.40804, "loss": 4.40804, "time": 0.71156} +{"mode": "train", "epoch": 29, "iter": 1600, "lr": 0.0914, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21984, "top5_acc": 0.44844, "loss_cls": 4.35671, "loss": 4.35671, "time": 0.71606} +{"mode": "train", "epoch": 29, "iter": 1700, "lr": 0.09138, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20797, "top5_acc": 0.43406, "loss_cls": 4.43108, "loss": 4.43108, "time": 0.71713} +{"mode": "train", "epoch": 29, "iter": 1800, "lr": 0.09137, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22406, "top5_acc": 0.45672, "loss_cls": 4.31047, "loss": 4.31047, "time": 0.71981} +{"mode": "train", "epoch": 29, "iter": 1900, "lr": 0.09135, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22672, "top5_acc": 0.45547, "loss_cls": 4.29594, "loss": 4.29594, "time": 0.72247} +{"mode": "train", "epoch": 29, "iter": 2000, "lr": 0.09133, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22812, "top5_acc": 0.45922, "loss_cls": 4.31174, "loss": 4.31174, "time": 0.72028} +{"mode": "train", "epoch": 29, "iter": 2100, "lr": 0.09132, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.21422, "top5_acc": 0.44172, "loss_cls": 4.36937, "loss": 4.36937, "time": 0.71873} +{"mode": "train", "epoch": 29, "iter": 2200, "lr": 0.0913, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21344, "top5_acc": 0.44609, "loss_cls": 4.37037, "loss": 4.37037, "time": 0.71802} +{"mode": "train", "epoch": 29, "iter": 2300, "lr": 0.09129, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21156, "top5_acc": 0.43922, "loss_cls": 4.38544, "loss": 4.38544, "time": 0.71914} +{"mode": "train", "epoch": 29, "iter": 2400, "lr": 0.09127, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21141, "top5_acc": 0.44109, "loss_cls": 4.37921, "loss": 4.37921, "time": 0.71801} +{"mode": "train", "epoch": 29, "iter": 2500, "lr": 0.09126, "memory": 15990, "data_time": 0.00082, "top1_acc": 0.21516, "top5_acc": 0.455, "loss_cls": 4.35024, "loss": 4.35024, "time": 0.71822} +{"mode": "train", "epoch": 29, "iter": 2600, "lr": 0.09124, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21234, "top5_acc": 0.44062, "loss_cls": 4.37667, "loss": 4.37667, "time": 0.7182} +{"mode": "train", "epoch": 29, "iter": 2700, "lr": 0.09122, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21266, "top5_acc": 0.44172, "loss_cls": 4.38156, "loss": 4.38156, "time": 0.71929} +{"mode": "train", "epoch": 29, "iter": 2800, "lr": 0.09121, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.21109, "top5_acc": 0.43844, "loss_cls": 4.40165, "loss": 4.40165, "time": 0.72437} +{"mode": "train", "epoch": 29, "iter": 2900, "lr": 0.09119, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21172, "top5_acc": 0.44641, "loss_cls": 4.38137, "loss": 4.38137, "time": 0.72207} +{"mode": "train", "epoch": 29, "iter": 3000, "lr": 0.09118, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21219, "top5_acc": 0.44562, "loss_cls": 4.36554, "loss": 4.36554, "time": 0.71838} +{"mode": "train", "epoch": 29, "iter": 3100, "lr": 0.09116, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.21406, "top5_acc": 0.44203, "loss_cls": 4.34977, "loss": 4.34977, "time": 0.71944} +{"mode": "train", "epoch": 29, "iter": 3200, "lr": 0.09114, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21188, "top5_acc": 0.43812, "loss_cls": 4.38366, "loss": 4.38366, "time": 0.71861} +{"mode": "train", "epoch": 29, "iter": 3300, "lr": 0.09113, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20484, "top5_acc": 0.45125, "loss_cls": 4.37546, "loss": 4.37546, "time": 0.71885} +{"mode": "train", "epoch": 29, "iter": 3400, "lr": 0.09111, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21, "top5_acc": 0.45047, "loss_cls": 4.3621, "loss": 4.3621, "time": 0.71949} +{"mode": "train", "epoch": 29, "iter": 3500, "lr": 0.0911, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21703, "top5_acc": 0.45312, "loss_cls": 4.34315, "loss": 4.34315, "time": 0.71917} +{"mode": "train", "epoch": 29, "iter": 3600, "lr": 0.09108, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21672, "top5_acc": 0.44609, "loss_cls": 4.368, "loss": 4.368, "time": 0.72021} +{"mode": "train", "epoch": 29, "iter": 3700, "lr": 0.09106, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21641, "top5_acc": 0.44281, "loss_cls": 4.3633, "loss": 4.3633, "time": 0.71789} +{"mode": "val", "epoch": 29, "iter": 309, "lr": 0.09106, "top1_acc": 0.08646, "top5_acc": 0.24925, "mean_class_accuracy": 0.08648} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.09104, "memory": 15990, "data_time": 1.47049, "top1_acc": 0.21516, "top5_acc": 0.44859, "loss_cls": 4.36944, "loss": 4.36944, "time": 2.29777} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.09103, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21734, "top5_acc": 0.45031, "loss_cls": 4.33763, "loss": 4.33763, "time": 0.82534} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.09101, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21641, "top5_acc": 0.44609, "loss_cls": 4.36754, "loss": 4.36754, "time": 0.82486} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.09099, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.21062, "top5_acc": 0.45156, "loss_cls": 4.34218, "loss": 4.34218, "time": 0.82741} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.09098, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21438, "top5_acc": 0.44875, "loss_cls": 4.3551, "loss": 4.3551, "time": 0.82222} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.09096, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.21391, "top5_acc": 0.44703, "loss_cls": 4.35106, "loss": 4.35106, "time": 0.83171} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.09095, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20906, "top5_acc": 0.44906, "loss_cls": 4.37136, "loss": 4.37136, "time": 0.82774} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.09093, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20719, "top5_acc": 0.43969, "loss_cls": 4.40183, "loss": 4.40183, "time": 0.83522} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.09091, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21641, "top5_acc": 0.44453, "loss_cls": 4.35657, "loss": 4.35657, "time": 0.83307} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.0909, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20625, "top5_acc": 0.44219, "loss_cls": 4.39839, "loss": 4.39839, "time": 0.83841} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.09088, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22328, "top5_acc": 0.46094, "loss_cls": 4.3148, "loss": 4.3148, "time": 0.83471} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.09087, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.20547, "top5_acc": 0.44922, "loss_cls": 4.36793, "loss": 4.36793, "time": 0.83412} +{"mode": "train", "epoch": 30, "iter": 1300, "lr": 0.09085, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21641, "top5_acc": 0.45516, "loss_cls": 4.3249, "loss": 4.3249, "time": 0.8328} +{"mode": "train", "epoch": 30, "iter": 1400, "lr": 0.09083, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.21297, "top5_acc": 0.44688, "loss_cls": 4.37225, "loss": 4.37225, "time": 0.83261} +{"mode": "train", "epoch": 30, "iter": 1500, "lr": 0.09082, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22297, "top5_acc": 0.45328, "loss_cls": 4.31657, "loss": 4.31657, "time": 0.83864} +{"mode": "train", "epoch": 30, "iter": 1600, "lr": 0.0908, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.22031, "top5_acc": 0.45781, "loss_cls": 4.31114, "loss": 4.31114, "time": 0.83237} +{"mode": "train", "epoch": 30, "iter": 1700, "lr": 0.09078, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21484, "top5_acc": 0.45516, "loss_cls": 4.34988, "loss": 4.34988, "time": 0.83696} +{"mode": "train", "epoch": 30, "iter": 1800, "lr": 0.09077, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21562, "top5_acc": 0.44, "loss_cls": 4.35413, "loss": 4.35413, "time": 0.83821} +{"mode": "train", "epoch": 30, "iter": 1900, "lr": 0.09075, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.22156, "top5_acc": 0.45891, "loss_cls": 4.31273, "loss": 4.31273, "time": 0.83774} +{"mode": "train", "epoch": 30, "iter": 2000, "lr": 0.09074, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.21016, "top5_acc": 0.44203, "loss_cls": 4.39286, "loss": 4.39286, "time": 0.84212} +{"mode": "train", "epoch": 30, "iter": 2100, "lr": 0.09072, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.21203, "top5_acc": 0.43891, "loss_cls": 4.4086, "loss": 4.4086, "time": 0.84101} +{"mode": "train", "epoch": 30, "iter": 2200, "lr": 0.0907, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21766, "top5_acc": 0.44781, "loss_cls": 4.36351, "loss": 4.36351, "time": 0.84589} +{"mode": "train", "epoch": 30, "iter": 2300, "lr": 0.09069, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21359, "top5_acc": 0.45453, "loss_cls": 4.33413, "loss": 4.33413, "time": 0.83661} +{"mode": "train", "epoch": 30, "iter": 2400, "lr": 0.09067, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22234, "top5_acc": 0.44578, "loss_cls": 4.34253, "loss": 4.34253, "time": 0.8413} +{"mode": "train", "epoch": 30, "iter": 2500, "lr": 0.09065, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.21422, "top5_acc": 0.44875, "loss_cls": 4.34241, "loss": 4.34241, "time": 0.84329} +{"mode": "train", "epoch": 30, "iter": 2600, "lr": 0.09064, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.20625, "top5_acc": 0.44, "loss_cls": 4.38842, "loss": 4.38842, "time": 0.83616} +{"mode": "train", "epoch": 30, "iter": 2700, "lr": 0.09062, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22188, "top5_acc": 0.45703, "loss_cls": 4.31258, "loss": 4.31258, "time": 0.8337} +{"mode": "train", "epoch": 30, "iter": 2800, "lr": 0.09061, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21797, "top5_acc": 0.44547, "loss_cls": 4.36538, "loss": 4.36538, "time": 0.84001} +{"mode": "train", "epoch": 30, "iter": 2900, "lr": 0.09059, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.22062, "top5_acc": 0.45219, "loss_cls": 4.32819, "loss": 4.32819, "time": 0.83813} +{"mode": "train", "epoch": 30, "iter": 3000, "lr": 0.09057, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20734, "top5_acc": 0.43797, "loss_cls": 4.41976, "loss": 4.41976, "time": 0.8371} +{"mode": "train", "epoch": 30, "iter": 3100, "lr": 0.09056, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21016, "top5_acc": 0.44672, "loss_cls": 4.38521, "loss": 4.38521, "time": 0.83632} +{"mode": "train", "epoch": 30, "iter": 3200, "lr": 0.09054, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21234, "top5_acc": 0.44109, "loss_cls": 4.35594, "loss": 4.35594, "time": 0.84106} +{"mode": "train", "epoch": 30, "iter": 3300, "lr": 0.09052, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2075, "top5_acc": 0.44219, "loss_cls": 4.38235, "loss": 4.38235, "time": 0.83858} +{"mode": "train", "epoch": 30, "iter": 3400, "lr": 0.09051, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.2175, "top5_acc": 0.44984, "loss_cls": 4.34448, "loss": 4.34448, "time": 0.83963} +{"mode": "train", "epoch": 30, "iter": 3500, "lr": 0.09049, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.21297, "top5_acc": 0.44734, "loss_cls": 4.37233, "loss": 4.37233, "time": 0.84119} +{"mode": "train", "epoch": 30, "iter": 3600, "lr": 0.09047, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22344, "top5_acc": 0.45, "loss_cls": 4.34974, "loss": 4.34974, "time": 0.83893} +{"mode": "train", "epoch": 30, "iter": 3700, "lr": 0.09046, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21469, "top5_acc": 0.44422, "loss_cls": 4.38485, "loss": 4.38485, "time": 0.82879} +{"mode": "val", "epoch": 30, "iter": 309, "lr": 0.09045, "top1_acc": 0.1362, "top5_acc": 0.31829, "mean_class_accuracy": 0.13619} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.09043, "memory": 15990, "data_time": 1.45439, "top1_acc": 0.22625, "top5_acc": 0.46078, "loss_cls": 4.56475, "loss": 4.56475, "time": 2.4834} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.09042, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23016, "top5_acc": 0.46953, "loss_cls": 4.52181, "loss": 4.52181, "time": 0.86013} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.0904, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22062, "top5_acc": 0.45391, "loss_cls": 4.57661, "loss": 4.57661, "time": 0.86136} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.09039, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21312, "top5_acc": 0.45, "loss_cls": 4.57142, "loss": 4.57142, "time": 0.85416} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.09037, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21453, "top5_acc": 0.45297, "loss_cls": 4.57968, "loss": 4.57968, "time": 0.85004} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.09035, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21375, "top5_acc": 0.44953, "loss_cls": 4.58322, "loss": 4.58322, "time": 0.85074} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.09034, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21266, "top5_acc": 0.44734, "loss_cls": 4.62085, "loss": 4.62085, "time": 0.85169} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.09032, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21594, "top5_acc": 0.45266, "loss_cls": 4.5625, "loss": 4.5625, "time": 0.85612} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0903, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20859, "top5_acc": 0.43562, "loss_cls": 4.65243, "loss": 4.65243, "time": 0.85159} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.09029, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22375, "top5_acc": 0.46406, "loss_cls": 4.54336, "loss": 4.54336, "time": 0.84842} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.09027, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22125, "top5_acc": 0.45297, "loss_cls": 4.57379, "loss": 4.57379, "time": 0.84812} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.09025, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21859, "top5_acc": 0.45188, "loss_cls": 4.5972, "loss": 4.5972, "time": 0.85568} +{"mode": "train", "epoch": 31, "iter": 1300, "lr": 0.09024, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20703, "top5_acc": 0.44188, "loss_cls": 4.62577, "loss": 4.62577, "time": 0.85976} +{"mode": "train", "epoch": 31, "iter": 1400, "lr": 0.09022, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21594, "top5_acc": 0.45219, "loss_cls": 4.55983, "loss": 4.55983, "time": 0.85696} +{"mode": "train", "epoch": 31, "iter": 1500, "lr": 0.0902, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22406, "top5_acc": 0.45297, "loss_cls": 4.56651, "loss": 4.56651, "time": 0.85592} +{"mode": "train", "epoch": 31, "iter": 1600, "lr": 0.09019, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21391, "top5_acc": 0.45359, "loss_cls": 4.62708, "loss": 4.62708, "time": 0.86293} +{"mode": "train", "epoch": 31, "iter": 1700, "lr": 0.09017, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21375, "top5_acc": 0.44156, "loss_cls": 4.61562, "loss": 4.61562, "time": 0.85844} +{"mode": "train", "epoch": 31, "iter": 1800, "lr": 0.09015, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22062, "top5_acc": 0.44797, "loss_cls": 4.58178, "loss": 4.58178, "time": 0.85552} +{"mode": "train", "epoch": 31, "iter": 1900, "lr": 0.09014, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2275, "top5_acc": 0.465, "loss_cls": 4.53858, "loss": 4.53858, "time": 0.85894} +{"mode": "train", "epoch": 31, "iter": 2000, "lr": 0.09012, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22094, "top5_acc": 0.45281, "loss_cls": 4.54735, "loss": 4.54735, "time": 0.85398} +{"mode": "train", "epoch": 31, "iter": 2100, "lr": 0.0901, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21297, "top5_acc": 0.45281, "loss_cls": 4.57499, "loss": 4.57499, "time": 0.85691} +{"mode": "train", "epoch": 31, "iter": 2200, "lr": 0.09009, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21906, "top5_acc": 0.44625, "loss_cls": 4.5933, "loss": 4.5933, "time": 0.85665} +{"mode": "train", "epoch": 31, "iter": 2300, "lr": 0.09007, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22016, "top5_acc": 0.44938, "loss_cls": 4.60207, "loss": 4.60207, "time": 0.85649} +{"mode": "train", "epoch": 31, "iter": 2400, "lr": 0.09005, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20875, "top5_acc": 0.44047, "loss_cls": 4.62563, "loss": 4.62563, "time": 0.85752} +{"mode": "train", "epoch": 31, "iter": 2500, "lr": 0.09004, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21812, "top5_acc": 0.44609, "loss_cls": 4.59055, "loss": 4.59055, "time": 0.85764} +{"mode": "train", "epoch": 31, "iter": 2600, "lr": 0.09002, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22453, "top5_acc": 0.45328, "loss_cls": 4.58778, "loss": 4.58778, "time": 0.85634} +{"mode": "train", "epoch": 31, "iter": 2700, "lr": 0.09, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22297, "top5_acc": 0.45141, "loss_cls": 4.57903, "loss": 4.57903, "time": 0.85928} +{"mode": "train", "epoch": 31, "iter": 2800, "lr": 0.08999, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21406, "top5_acc": 0.44234, "loss_cls": 4.63383, "loss": 4.63383, "time": 0.86243} +{"mode": "train", "epoch": 31, "iter": 2900, "lr": 0.08997, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20375, "top5_acc": 0.43234, "loss_cls": 4.66821, "loss": 4.66821, "time": 0.86221} +{"mode": "train", "epoch": 31, "iter": 3000, "lr": 0.08995, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20922, "top5_acc": 0.44484, "loss_cls": 4.61848, "loss": 4.61848, "time": 0.85685} +{"mode": "train", "epoch": 31, "iter": 3100, "lr": 0.08994, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21969, "top5_acc": 0.45625, "loss_cls": 4.56466, "loss": 4.56466, "time": 0.8544} +{"mode": "train", "epoch": 31, "iter": 3200, "lr": 0.08992, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22078, "top5_acc": 0.45125, "loss_cls": 4.59114, "loss": 4.59114, "time": 0.86207} +{"mode": "train", "epoch": 31, "iter": 3300, "lr": 0.0899, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23156, "top5_acc": 0.45656, "loss_cls": 4.551, "loss": 4.551, "time": 0.85804} +{"mode": "train", "epoch": 31, "iter": 3400, "lr": 0.08989, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21656, "top5_acc": 0.44344, "loss_cls": 4.63585, "loss": 4.63585, "time": 0.86297} +{"mode": "train", "epoch": 31, "iter": 3500, "lr": 0.08987, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20969, "top5_acc": 0.44812, "loss_cls": 4.63542, "loss": 4.63542, "time": 0.85847} +{"mode": "train", "epoch": 31, "iter": 3600, "lr": 0.08985, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21875, "top5_acc": 0.45609, "loss_cls": 4.57482, "loss": 4.57482, "time": 0.85907} +{"mode": "train", "epoch": 31, "iter": 3700, "lr": 0.08983, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22219, "top5_acc": 0.46297, "loss_cls": 4.5251, "loss": 4.5251, "time": 0.85185} +{"mode": "val", "epoch": 31, "iter": 309, "lr": 0.08983, "top1_acc": 0.1638, "top5_acc": 0.36418, "mean_class_accuracy": 0.16365} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.08981, "memory": 15990, "data_time": 1.57558, "top1_acc": 0.21703, "top5_acc": 0.45344, "loss_cls": 4.57522, "loss": 4.57522, "time": 2.61462} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.08979, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22312, "top5_acc": 0.45672, "loss_cls": 4.56231, "loss": 4.56231, "time": 0.85705} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.08978, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.215, "top5_acc": 0.45016, "loss_cls": 4.58988, "loss": 4.58988, "time": 0.86186} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.08976, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.21469, "top5_acc": 0.45062, "loss_cls": 4.58996, "loss": 4.58996, "time": 0.85649} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.08974, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21266, "top5_acc": 0.45109, "loss_cls": 4.59436, "loss": 4.59436, "time": 0.85322} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.08973, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.21828, "top5_acc": 0.45828, "loss_cls": 4.54797, "loss": 4.54797, "time": 0.85556} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.08971, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.22469, "top5_acc": 0.4575, "loss_cls": 4.54867, "loss": 4.54867, "time": 0.85766} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.08969, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21234, "top5_acc": 0.44375, "loss_cls": 4.62533, "loss": 4.62533, "time": 0.85842} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.08967, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22172, "top5_acc": 0.45047, "loss_cls": 4.59782, "loss": 4.59782, "time": 0.85678} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.08966, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.21844, "top5_acc": 0.44922, "loss_cls": 4.61016, "loss": 4.61016, "time": 0.85798} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.08964, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.22594, "top5_acc": 0.44984, "loss_cls": 4.55159, "loss": 4.55159, "time": 0.8562} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.08962, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22219, "top5_acc": 0.44938, "loss_cls": 4.57418, "loss": 4.57418, "time": 0.85661} +{"mode": "train", "epoch": 32, "iter": 1300, "lr": 0.08961, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22406, "top5_acc": 0.46078, "loss_cls": 4.57767, "loss": 4.57767, "time": 0.86404} +{"mode": "train", "epoch": 32, "iter": 1400, "lr": 0.08959, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.21953, "top5_acc": 0.46109, "loss_cls": 4.55042, "loss": 4.55042, "time": 0.85956} +{"mode": "train", "epoch": 32, "iter": 1500, "lr": 0.08957, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22094, "top5_acc": 0.45312, "loss_cls": 4.57336, "loss": 4.57336, "time": 0.85911} +{"mode": "train", "epoch": 32, "iter": 1600, "lr": 0.08955, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21328, "top5_acc": 0.44016, "loss_cls": 4.65976, "loss": 4.65976, "time": 0.86327} +{"mode": "train", "epoch": 32, "iter": 1700, "lr": 0.08954, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21141, "top5_acc": 0.44391, "loss_cls": 4.61443, "loss": 4.61443, "time": 0.86239} +{"mode": "train", "epoch": 32, "iter": 1800, "lr": 0.08952, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21781, "top5_acc": 0.45297, "loss_cls": 4.57717, "loss": 4.57717, "time": 0.85971} +{"mode": "train", "epoch": 32, "iter": 1900, "lr": 0.0895, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22016, "top5_acc": 0.4525, "loss_cls": 4.55566, "loss": 4.55566, "time": 0.86267} +{"mode": "train", "epoch": 32, "iter": 2000, "lr": 0.08949, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21219, "top5_acc": 0.45484, "loss_cls": 4.54287, "loss": 4.54287, "time": 0.85957} +{"mode": "train", "epoch": 32, "iter": 2100, "lr": 0.08947, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21609, "top5_acc": 0.44922, "loss_cls": 4.57097, "loss": 4.57097, "time": 0.86349} +{"mode": "train", "epoch": 32, "iter": 2200, "lr": 0.08945, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22219, "top5_acc": 0.44766, "loss_cls": 4.57356, "loss": 4.57356, "time": 0.85808} +{"mode": "train", "epoch": 32, "iter": 2300, "lr": 0.08943, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.21594, "top5_acc": 0.45375, "loss_cls": 4.56012, "loss": 4.56012, "time": 0.86382} +{"mode": "train", "epoch": 32, "iter": 2400, "lr": 0.08942, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20734, "top5_acc": 0.44391, "loss_cls": 4.63733, "loss": 4.63733, "time": 0.86268} +{"mode": "train", "epoch": 32, "iter": 2500, "lr": 0.0894, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20578, "top5_acc": 0.44, "loss_cls": 4.62695, "loss": 4.62695, "time": 0.86208} +{"mode": "train", "epoch": 32, "iter": 2600, "lr": 0.08938, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.21781, "top5_acc": 0.44203, "loss_cls": 4.60737, "loss": 4.60737, "time": 0.86453} +{"mode": "train", "epoch": 32, "iter": 2700, "lr": 0.08937, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21047, "top5_acc": 0.44109, "loss_cls": 4.62937, "loss": 4.62937, "time": 0.85676} +{"mode": "train", "epoch": 32, "iter": 2800, "lr": 0.08935, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21641, "top5_acc": 0.45203, "loss_cls": 4.55524, "loss": 4.55524, "time": 0.86605} +{"mode": "train", "epoch": 32, "iter": 2900, "lr": 0.08933, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.21578, "top5_acc": 0.44781, "loss_cls": 4.5985, "loss": 4.5985, "time": 0.85923} +{"mode": "train", "epoch": 32, "iter": 3000, "lr": 0.08931, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21703, "top5_acc": 0.46, "loss_cls": 4.5545, "loss": 4.5545, "time": 0.86407} +{"mode": "train", "epoch": 32, "iter": 3100, "lr": 0.0893, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.2175, "top5_acc": 0.44453, "loss_cls": 4.60182, "loss": 4.60182, "time": 0.8623} +{"mode": "train", "epoch": 32, "iter": 3200, "lr": 0.08928, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2175, "top5_acc": 0.45172, "loss_cls": 4.57779, "loss": 4.57779, "time": 0.86352} +{"mode": "train", "epoch": 32, "iter": 3300, "lr": 0.08926, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.21578, "top5_acc": 0.45328, "loss_cls": 4.57361, "loss": 4.57361, "time": 0.86344} +{"mode": "train", "epoch": 32, "iter": 3400, "lr": 0.08924, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.21438, "top5_acc": 0.45, "loss_cls": 4.59439, "loss": 4.59439, "time": 0.85837} +{"mode": "train", "epoch": 32, "iter": 3500, "lr": 0.08923, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22172, "top5_acc": 0.45734, "loss_cls": 4.52826, "loss": 4.52826, "time": 0.8579} +{"mode": "train", "epoch": 32, "iter": 3600, "lr": 0.08921, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22344, "top5_acc": 0.46516, "loss_cls": 4.51908, "loss": 4.51908, "time": 0.85478} +{"mode": "train", "epoch": 32, "iter": 3700, "lr": 0.08919, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21828, "top5_acc": 0.45359, "loss_cls": 4.61678, "loss": 4.61678, "time": 0.85125} +{"mode": "val", "epoch": 32, "iter": 309, "lr": 0.08918, "top1_acc": 0.17069, "top5_acc": 0.37608, "mean_class_accuracy": 0.17058} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.08917, "memory": 15990, "data_time": 1.60841, "top1_acc": 0.22188, "top5_acc": 0.46094, "loss_cls": 4.52788, "loss": 4.52788, "time": 2.65291} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.08915, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22781, "top5_acc": 0.46641, "loss_cls": 4.53502, "loss": 4.53502, "time": 0.86142} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.08913, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.21375, "top5_acc": 0.45047, "loss_cls": 4.57918, "loss": 4.57918, "time": 0.85854} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.08912, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21891, "top5_acc": 0.45656, "loss_cls": 4.58716, "loss": 4.58716, "time": 0.85866} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.0891, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22453, "top5_acc": 0.45172, "loss_cls": 4.55978, "loss": 4.55978, "time": 0.85258} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.08908, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22406, "top5_acc": 0.46125, "loss_cls": 4.53876, "loss": 4.53876, "time": 0.85291} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.08906, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.21812, "top5_acc": 0.44453, "loss_cls": 4.59112, "loss": 4.59112, "time": 0.8596} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.08905, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.21812, "top5_acc": 0.45188, "loss_cls": 4.55872, "loss": 4.55872, "time": 0.86401} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.08903, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22469, "top5_acc": 0.45672, "loss_cls": 4.5686, "loss": 4.5686, "time": 0.86873} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.08901, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.21812, "top5_acc": 0.44547, "loss_cls": 4.60548, "loss": 4.60548, "time": 0.86056} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.08899, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21594, "top5_acc": 0.45734, "loss_cls": 4.55119, "loss": 4.55119, "time": 0.86882} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.08898, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22141, "top5_acc": 0.45438, "loss_cls": 4.56396, "loss": 4.56396, "time": 0.86603} +{"mode": "train", "epoch": 33, "iter": 1300, "lr": 0.08896, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22156, "top5_acc": 0.44562, "loss_cls": 4.57581, "loss": 4.57581, "time": 0.86581} +{"mode": "train", "epoch": 33, "iter": 1400, "lr": 0.08894, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21672, "top5_acc": 0.45219, "loss_cls": 4.57452, "loss": 4.57452, "time": 0.86474} +{"mode": "train", "epoch": 33, "iter": 1500, "lr": 0.08892, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21469, "top5_acc": 0.45719, "loss_cls": 4.5538, "loss": 4.5538, "time": 0.86128} +{"mode": "train", "epoch": 33, "iter": 1600, "lr": 0.08891, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22328, "top5_acc": 0.45188, "loss_cls": 4.57379, "loss": 4.57379, "time": 0.8605} +{"mode": "train", "epoch": 33, "iter": 1700, "lr": 0.08889, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21766, "top5_acc": 0.44734, "loss_cls": 4.60512, "loss": 4.60512, "time": 0.86102} +{"mode": "train", "epoch": 33, "iter": 1800, "lr": 0.08887, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21578, "top5_acc": 0.45734, "loss_cls": 4.55411, "loss": 4.55411, "time": 0.8653} +{"mode": "train", "epoch": 33, "iter": 1900, "lr": 0.08885, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.21625, "top5_acc": 0.44516, "loss_cls": 4.62618, "loss": 4.62618, "time": 0.86803} +{"mode": "train", "epoch": 33, "iter": 2000, "lr": 0.08884, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22391, "top5_acc": 0.45703, "loss_cls": 4.57009, "loss": 4.57009, "time": 0.86545} +{"mode": "train", "epoch": 33, "iter": 2100, "lr": 0.08882, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.2175, "top5_acc": 0.45031, "loss_cls": 4.57869, "loss": 4.57869, "time": 0.86512} +{"mode": "train", "epoch": 33, "iter": 2200, "lr": 0.0888, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22312, "top5_acc": 0.45016, "loss_cls": 4.60898, "loss": 4.60898, "time": 0.86664} +{"mode": "train", "epoch": 33, "iter": 2300, "lr": 0.08878, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21656, "top5_acc": 0.45641, "loss_cls": 4.57387, "loss": 4.57387, "time": 0.86206} +{"mode": "train", "epoch": 33, "iter": 2400, "lr": 0.08876, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21797, "top5_acc": 0.45906, "loss_cls": 4.56664, "loss": 4.56664, "time": 0.85978} +{"mode": "train", "epoch": 33, "iter": 2500, "lr": 0.08875, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21719, "top5_acc": 0.44656, "loss_cls": 4.5848, "loss": 4.5848, "time": 0.86081} +{"mode": "train", "epoch": 33, "iter": 2600, "lr": 0.08873, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22484, "top5_acc": 0.44859, "loss_cls": 4.59611, "loss": 4.59611, "time": 0.85962} +{"mode": "train", "epoch": 33, "iter": 2700, "lr": 0.08871, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21031, "top5_acc": 0.44781, "loss_cls": 4.62558, "loss": 4.62558, "time": 0.86034} +{"mode": "train", "epoch": 33, "iter": 2800, "lr": 0.08869, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21312, "top5_acc": 0.45281, "loss_cls": 4.57562, "loss": 4.57562, "time": 0.86673} +{"mode": "train", "epoch": 33, "iter": 2900, "lr": 0.08868, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22156, "top5_acc": 0.45453, "loss_cls": 4.56032, "loss": 4.56032, "time": 0.86753} +{"mode": "train", "epoch": 33, "iter": 3000, "lr": 0.08866, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22109, "top5_acc": 0.45625, "loss_cls": 4.55868, "loss": 4.55868, "time": 0.86131} +{"mode": "train", "epoch": 33, "iter": 3100, "lr": 0.08864, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22078, "top5_acc": 0.45719, "loss_cls": 4.57563, "loss": 4.57563, "time": 0.86022} +{"mode": "train", "epoch": 33, "iter": 3200, "lr": 0.08862, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22344, "top5_acc": 0.45406, "loss_cls": 4.55683, "loss": 4.55683, "time": 0.86634} +{"mode": "train", "epoch": 33, "iter": 3300, "lr": 0.08861, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21922, "top5_acc": 0.45531, "loss_cls": 4.5525, "loss": 4.5525, "time": 0.86398} +{"mode": "train", "epoch": 33, "iter": 3400, "lr": 0.08859, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21438, "top5_acc": 0.45531, "loss_cls": 4.57395, "loss": 4.57395, "time": 0.86411} +{"mode": "train", "epoch": 33, "iter": 3500, "lr": 0.08857, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21859, "top5_acc": 0.46062, "loss_cls": 4.55391, "loss": 4.55391, "time": 0.85341} +{"mode": "train", "epoch": 33, "iter": 3600, "lr": 0.08855, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21281, "top5_acc": 0.43312, "loss_cls": 4.63921, "loss": 4.63921, "time": 0.84886} +{"mode": "train", "epoch": 33, "iter": 3700, "lr": 0.08853, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22047, "top5_acc": 0.45031, "loss_cls": 4.58148, "loss": 4.58148, "time": 0.85674} +{"mode": "val", "epoch": 33, "iter": 309, "lr": 0.08853, "top1_acc": 0.14598, "top5_acc": 0.34194, "mean_class_accuracy": 0.14579} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.08851, "memory": 15990, "data_time": 1.59336, "top1_acc": 0.22344, "top5_acc": 0.45953, "loss_cls": 4.52701, "loss": 4.52701, "time": 2.63185} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.08849, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.2175, "top5_acc": 0.44547, "loss_cls": 4.55845, "loss": 4.55845, "time": 0.85974} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.08847, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21969, "top5_acc": 0.45953, "loss_cls": 4.55954, "loss": 4.55954, "time": 0.85909} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.08845, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.22375, "top5_acc": 0.45578, "loss_cls": 4.53208, "loss": 4.53208, "time": 0.8573} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.08844, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22047, "top5_acc": 0.45422, "loss_cls": 4.54962, "loss": 4.54962, "time": 0.85804} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.08842, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22469, "top5_acc": 0.45172, "loss_cls": 4.55302, "loss": 4.55302, "time": 0.85062} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.0884, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21312, "top5_acc": 0.45, "loss_cls": 4.60378, "loss": 4.60378, "time": 0.8584} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.08838, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21984, "top5_acc": 0.44922, "loss_cls": 4.58265, "loss": 4.58265, "time": 0.86112} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.08836, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.23047, "top5_acc": 0.45766, "loss_cls": 4.54007, "loss": 4.54007, "time": 0.85713} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.08835, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21469, "top5_acc": 0.44344, "loss_cls": 4.60829, "loss": 4.60829, "time": 0.86079} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.08833, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.21578, "top5_acc": 0.45094, "loss_cls": 4.5891, "loss": 4.5891, "time": 0.86301} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.08831, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.22031, "top5_acc": 0.44984, "loss_cls": 4.57568, "loss": 4.57568, "time": 0.85985} +{"mode": "train", "epoch": 34, "iter": 1300, "lr": 0.08829, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22531, "top5_acc": 0.46328, "loss_cls": 4.53759, "loss": 4.53759, "time": 0.86076} +{"mode": "train", "epoch": 34, "iter": 1400, "lr": 0.08828, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21672, "top5_acc": 0.45078, "loss_cls": 4.61459, "loss": 4.61459, "time": 0.86402} +{"mode": "train", "epoch": 34, "iter": 1500, "lr": 0.08826, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21641, "top5_acc": 0.45797, "loss_cls": 4.56486, "loss": 4.56486, "time": 0.86599} +{"mode": "train", "epoch": 34, "iter": 1600, "lr": 0.08824, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21531, "top5_acc": 0.44922, "loss_cls": 4.58819, "loss": 4.58819, "time": 0.86144} +{"mode": "train", "epoch": 34, "iter": 1700, "lr": 0.08822, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22188, "top5_acc": 0.45828, "loss_cls": 4.55413, "loss": 4.55413, "time": 0.8648} +{"mode": "train", "epoch": 34, "iter": 1800, "lr": 0.0882, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21906, "top5_acc": 0.45969, "loss_cls": 4.53837, "loss": 4.53837, "time": 0.86082} +{"mode": "train", "epoch": 34, "iter": 1900, "lr": 0.08819, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22391, "top5_acc": 0.44953, "loss_cls": 4.58263, "loss": 4.58263, "time": 0.86136} +{"mode": "train", "epoch": 34, "iter": 2000, "lr": 0.08817, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21688, "top5_acc": 0.45422, "loss_cls": 4.58001, "loss": 4.58001, "time": 0.86377} +{"mode": "train", "epoch": 34, "iter": 2100, "lr": 0.08815, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21094, "top5_acc": 0.44938, "loss_cls": 4.58641, "loss": 4.58641, "time": 0.85917} +{"mode": "train", "epoch": 34, "iter": 2200, "lr": 0.08813, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20766, "top5_acc": 0.44344, "loss_cls": 4.62761, "loss": 4.62761, "time": 0.86801} +{"mode": "train", "epoch": 34, "iter": 2300, "lr": 0.08811, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21938, "top5_acc": 0.44578, "loss_cls": 4.59258, "loss": 4.59258, "time": 0.86316} +{"mode": "train", "epoch": 34, "iter": 2400, "lr": 0.08809, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.22359, "top5_acc": 0.44906, "loss_cls": 4.55323, "loss": 4.55323, "time": 0.86552} +{"mode": "train", "epoch": 34, "iter": 2500, "lr": 0.08808, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.22469, "top5_acc": 0.45469, "loss_cls": 4.54349, "loss": 4.54349, "time": 0.86797} +{"mode": "train", "epoch": 34, "iter": 2600, "lr": 0.08806, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22344, "top5_acc": 0.45141, "loss_cls": 4.59518, "loss": 4.59518, "time": 0.8627} +{"mode": "train", "epoch": 34, "iter": 2700, "lr": 0.08804, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21828, "top5_acc": 0.45516, "loss_cls": 4.58311, "loss": 4.58311, "time": 0.86332} +{"mode": "train", "epoch": 34, "iter": 2800, "lr": 0.08802, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.215, "top5_acc": 0.43609, "loss_cls": 4.6142, "loss": 4.6142, "time": 0.86067} +{"mode": "train", "epoch": 34, "iter": 2900, "lr": 0.088, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.215, "top5_acc": 0.45531, "loss_cls": 4.54936, "loss": 4.54936, "time": 0.86339} +{"mode": "train", "epoch": 34, "iter": 3000, "lr": 0.08799, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.22094, "top5_acc": 0.45656, "loss_cls": 4.58587, "loss": 4.58587, "time": 0.86476} +{"mode": "train", "epoch": 34, "iter": 3100, "lr": 0.08797, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21375, "top5_acc": 0.44953, "loss_cls": 4.578, "loss": 4.578, "time": 0.86153} +{"mode": "train", "epoch": 34, "iter": 3200, "lr": 0.08795, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21609, "top5_acc": 0.44656, "loss_cls": 4.60164, "loss": 4.60164, "time": 0.86454} +{"mode": "train", "epoch": 34, "iter": 3300, "lr": 0.08793, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21641, "top5_acc": 0.44812, "loss_cls": 4.59154, "loss": 4.59154, "time": 0.86371} +{"mode": "train", "epoch": 34, "iter": 3400, "lr": 0.08791, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21578, "top5_acc": 0.45906, "loss_cls": 4.54433, "loss": 4.54433, "time": 0.85118} +{"mode": "train", "epoch": 34, "iter": 3500, "lr": 0.08789, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22406, "top5_acc": 0.46625, "loss_cls": 4.5457, "loss": 4.5457, "time": 0.84944} +{"mode": "train", "epoch": 34, "iter": 3600, "lr": 0.08788, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2125, "top5_acc": 0.45094, "loss_cls": 4.59093, "loss": 4.59093, "time": 0.85593} +{"mode": "train", "epoch": 34, "iter": 3700, "lr": 0.08786, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2175, "top5_acc": 0.45625, "loss_cls": 4.55895, "loss": 4.55895, "time": 0.85394} +{"mode": "val", "epoch": 34, "iter": 309, "lr": 0.08785, "top1_acc": 0.17454, "top5_acc": 0.38728, "mean_class_accuracy": 0.1745} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.08783, "memory": 15990, "data_time": 1.61766, "top1_acc": 0.22281, "top5_acc": 0.45656, "loss_cls": 4.53757, "loss": 4.53757, "time": 2.67069} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.08781, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.22219, "top5_acc": 0.45188, "loss_cls": 4.53827, "loss": 4.53827, "time": 0.86689} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.0878, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21312, "top5_acc": 0.44719, "loss_cls": 4.57442, "loss": 4.57442, "time": 0.8632} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.08778, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.23109, "top5_acc": 0.46203, "loss_cls": 4.52911, "loss": 4.52911, "time": 0.85843} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.08776, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22359, "top5_acc": 0.45797, "loss_cls": 4.55062, "loss": 4.55062, "time": 0.85306} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.08774, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21688, "top5_acc": 0.45, "loss_cls": 4.54305, "loss": 4.54305, "time": 0.85062} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.08772, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.21891, "top5_acc": 0.45359, "loss_cls": 4.56741, "loss": 4.56741, "time": 0.85857} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.0877, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22375, "top5_acc": 0.45312, "loss_cls": 4.58079, "loss": 4.58079, "time": 0.85318} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.08769, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21297, "top5_acc": 0.45062, "loss_cls": 4.56797, "loss": 4.56797, "time": 0.84641} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.08767, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21984, "top5_acc": 0.45141, "loss_cls": 4.5924, "loss": 4.5924, "time": 0.85344} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.08765, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22672, "top5_acc": 0.45812, "loss_cls": 4.57209, "loss": 4.57209, "time": 0.85187} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.08763, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22312, "top5_acc": 0.45406, "loss_cls": 4.58179, "loss": 4.58179, "time": 0.85027} +{"mode": "train", "epoch": 35, "iter": 1300, "lr": 0.08761, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22141, "top5_acc": 0.45797, "loss_cls": 4.56096, "loss": 4.56096, "time": 0.85267} +{"mode": "train", "epoch": 35, "iter": 1400, "lr": 0.08759, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21438, "top5_acc": 0.4475, "loss_cls": 4.59897, "loss": 4.59897, "time": 0.8519} +{"mode": "train", "epoch": 35, "iter": 1500, "lr": 0.08757, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21766, "top5_acc": 0.44875, "loss_cls": 4.6147, "loss": 4.6147, "time": 0.84981} +{"mode": "train", "epoch": 35, "iter": 1600, "lr": 0.08756, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21641, "top5_acc": 0.45375, "loss_cls": 4.57528, "loss": 4.57528, "time": 0.85317} +{"mode": "train", "epoch": 35, "iter": 1700, "lr": 0.08754, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20969, "top5_acc": 0.44703, "loss_cls": 4.58842, "loss": 4.58842, "time": 0.84802} +{"mode": "train", "epoch": 35, "iter": 1800, "lr": 0.08752, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22719, "top5_acc": 0.46172, "loss_cls": 4.56484, "loss": 4.56484, "time": 0.85548} +{"mode": "train", "epoch": 35, "iter": 1900, "lr": 0.0875, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21891, "top5_acc": 0.45109, "loss_cls": 4.57171, "loss": 4.57171, "time": 0.85924} +{"mode": "train", "epoch": 35, "iter": 2000, "lr": 0.08748, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21484, "top5_acc": 0.44828, "loss_cls": 4.59947, "loss": 4.59947, "time": 0.85846} +{"mode": "train", "epoch": 35, "iter": 2100, "lr": 0.08746, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22344, "top5_acc": 0.43859, "loss_cls": 4.57982, "loss": 4.57982, "time": 0.85586} +{"mode": "train", "epoch": 35, "iter": 2200, "lr": 0.08745, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21828, "top5_acc": 0.45578, "loss_cls": 4.56498, "loss": 4.56498, "time": 0.86373} +{"mode": "train", "epoch": 35, "iter": 2300, "lr": 0.08743, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22203, "top5_acc": 0.46406, "loss_cls": 4.54391, "loss": 4.54391, "time": 0.86337} +{"mode": "train", "epoch": 35, "iter": 2400, "lr": 0.08741, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21797, "top5_acc": 0.45469, "loss_cls": 4.5825, "loss": 4.5825, "time": 0.85626} +{"mode": "train", "epoch": 35, "iter": 2500, "lr": 0.08739, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21234, "top5_acc": 0.44547, "loss_cls": 4.58116, "loss": 4.58116, "time": 0.85681} +{"mode": "train", "epoch": 35, "iter": 2600, "lr": 0.08737, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22297, "top5_acc": 0.45562, "loss_cls": 4.55592, "loss": 4.55592, "time": 0.85711} +{"mode": "train", "epoch": 35, "iter": 2700, "lr": 0.08735, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22828, "top5_acc": 0.45547, "loss_cls": 4.53383, "loss": 4.53383, "time": 0.85374} +{"mode": "train", "epoch": 35, "iter": 2800, "lr": 0.08733, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22547, "top5_acc": 0.46062, "loss_cls": 4.54743, "loss": 4.54743, "time": 0.85531} +{"mode": "train", "epoch": 35, "iter": 2900, "lr": 0.08732, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21562, "top5_acc": 0.46656, "loss_cls": 4.5323, "loss": 4.5323, "time": 0.86174} +{"mode": "train", "epoch": 35, "iter": 3000, "lr": 0.0873, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21406, "top5_acc": 0.44469, "loss_cls": 4.5881, "loss": 4.5881, "time": 0.862} +{"mode": "train", "epoch": 35, "iter": 3100, "lr": 0.08728, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21672, "top5_acc": 0.45703, "loss_cls": 4.53262, "loss": 4.53262, "time": 0.86156} +{"mode": "train", "epoch": 35, "iter": 3200, "lr": 0.08726, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22234, "top5_acc": 0.455, "loss_cls": 4.56998, "loss": 4.56998, "time": 0.85925} +{"mode": "train", "epoch": 35, "iter": 3300, "lr": 0.08724, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22875, "top5_acc": 0.45922, "loss_cls": 4.53786, "loss": 4.53786, "time": 0.86076} +{"mode": "train", "epoch": 35, "iter": 3400, "lr": 0.08722, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22984, "top5_acc": 0.46375, "loss_cls": 4.54997, "loss": 4.54997, "time": 0.85042} +{"mode": "train", "epoch": 35, "iter": 3500, "lr": 0.0872, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22, "top5_acc": 0.4525, "loss_cls": 4.59202, "loss": 4.59202, "time": 0.84639} +{"mode": "train", "epoch": 35, "iter": 3600, "lr": 0.08718, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22656, "top5_acc": 0.45922, "loss_cls": 4.55641, "loss": 4.55641, "time": 0.85862} +{"mode": "train", "epoch": 35, "iter": 3700, "lr": 0.08717, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21891, "top5_acc": 0.44516, "loss_cls": 4.57967, "loss": 4.57967, "time": 0.84926} +{"mode": "val", "epoch": 35, "iter": 309, "lr": 0.08716, "top1_acc": 0.16193, "top5_acc": 0.3657, "mean_class_accuracy": 0.16171} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.08714, "memory": 15990, "data_time": 1.53136, "top1_acc": 0.22812, "top5_acc": 0.47094, "loss_cls": 4.50364, "loss": 4.50364, "time": 2.57432} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.08712, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23391, "top5_acc": 0.46484, "loss_cls": 4.50311, "loss": 4.50311, "time": 0.85479} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.0871, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22344, "top5_acc": 0.44953, "loss_cls": 4.56562, "loss": 4.56562, "time": 0.85207} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.08708, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.21938, "top5_acc": 0.46, "loss_cls": 4.55669, "loss": 4.55669, "time": 0.85485} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.08706, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22016, "top5_acc": 0.45969, "loss_cls": 4.54901, "loss": 4.54901, "time": 0.85076} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.08704, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2225, "top5_acc": 0.45781, "loss_cls": 4.55864, "loss": 4.55864, "time": 0.8576} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.08703, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22188, "top5_acc": 0.45859, "loss_cls": 4.57017, "loss": 4.57017, "time": 0.84877} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.08701, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22547, "top5_acc": 0.45797, "loss_cls": 4.53674, "loss": 4.53674, "time": 0.85391} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.08699, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22859, "top5_acc": 0.46281, "loss_cls": 4.50576, "loss": 4.50576, "time": 0.85328} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.08697, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22047, "top5_acc": 0.45734, "loss_cls": 4.5674, "loss": 4.5674, "time": 0.84985} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.08695, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22328, "top5_acc": 0.45688, "loss_cls": 4.54015, "loss": 4.54015, "time": 0.85056} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.08693, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22438, "top5_acc": 0.45922, "loss_cls": 4.55289, "loss": 4.55289, "time": 0.85581} +{"mode": "train", "epoch": 36, "iter": 1300, "lr": 0.08691, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22641, "top5_acc": 0.46406, "loss_cls": 4.52812, "loss": 4.52812, "time": 0.85547} +{"mode": "train", "epoch": 36, "iter": 1400, "lr": 0.08689, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22266, "top5_acc": 0.45266, "loss_cls": 4.55675, "loss": 4.55675, "time": 0.85219} +{"mode": "train", "epoch": 36, "iter": 1500, "lr": 0.08688, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22281, "top5_acc": 0.45547, "loss_cls": 4.54893, "loss": 4.54893, "time": 0.84616} +{"mode": "train", "epoch": 36, "iter": 1600, "lr": 0.08686, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22141, "top5_acc": 0.44828, "loss_cls": 4.6137, "loss": 4.6137, "time": 0.85025} +{"mode": "train", "epoch": 36, "iter": 1700, "lr": 0.08684, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21688, "top5_acc": 0.44812, "loss_cls": 4.58877, "loss": 4.58877, "time": 0.8492} +{"mode": "train", "epoch": 36, "iter": 1800, "lr": 0.08682, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21922, "top5_acc": 0.45453, "loss_cls": 4.57067, "loss": 4.57067, "time": 0.85337} +{"mode": "train", "epoch": 36, "iter": 1900, "lr": 0.0868, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23109, "top5_acc": 0.46469, "loss_cls": 4.55796, "loss": 4.55796, "time": 0.85402} +{"mode": "train", "epoch": 36, "iter": 2000, "lr": 0.08678, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21469, "top5_acc": 0.44203, "loss_cls": 4.60704, "loss": 4.60704, "time": 0.85631} +{"mode": "train", "epoch": 36, "iter": 2100, "lr": 0.08676, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21219, "top5_acc": 0.44797, "loss_cls": 4.58046, "loss": 4.58046, "time": 0.86273} +{"mode": "train", "epoch": 36, "iter": 2200, "lr": 0.08674, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.22109, "top5_acc": 0.45203, "loss_cls": 4.56447, "loss": 4.56447, "time": 0.85738} +{"mode": "train", "epoch": 36, "iter": 2300, "lr": 0.08672, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.22156, "top5_acc": 0.45203, "loss_cls": 4.56534, "loss": 4.56534, "time": 0.85596} +{"mode": "train", "epoch": 36, "iter": 2400, "lr": 0.08671, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22719, "top5_acc": 0.45438, "loss_cls": 4.57085, "loss": 4.57085, "time": 0.85552} +{"mode": "train", "epoch": 36, "iter": 2500, "lr": 0.08669, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22359, "top5_acc": 0.45531, "loss_cls": 4.55796, "loss": 4.55796, "time": 0.85974} +{"mode": "train", "epoch": 36, "iter": 2600, "lr": 0.08667, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21578, "top5_acc": 0.44422, "loss_cls": 4.60706, "loss": 4.60706, "time": 0.86028} +{"mode": "train", "epoch": 36, "iter": 2700, "lr": 0.08665, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22609, "top5_acc": 0.44844, "loss_cls": 4.56601, "loss": 4.56601, "time": 0.86208} +{"mode": "train", "epoch": 36, "iter": 2800, "lr": 0.08663, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22094, "top5_acc": 0.45922, "loss_cls": 4.56524, "loss": 4.56524, "time": 0.86478} +{"mode": "train", "epoch": 36, "iter": 2900, "lr": 0.08661, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21797, "top5_acc": 0.45938, "loss_cls": 4.53328, "loss": 4.53328, "time": 0.8627} +{"mode": "train", "epoch": 36, "iter": 3000, "lr": 0.08659, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21406, "top5_acc": 0.4475, "loss_cls": 4.60881, "loss": 4.60881, "time": 0.85898} +{"mode": "train", "epoch": 36, "iter": 3100, "lr": 0.08657, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22312, "top5_acc": 0.45562, "loss_cls": 4.55598, "loss": 4.55598, "time": 0.85665} +{"mode": "train", "epoch": 36, "iter": 3200, "lr": 0.08655, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.21531, "top5_acc": 0.44547, "loss_cls": 4.61397, "loss": 4.61397, "time": 0.85653} +{"mode": "train", "epoch": 36, "iter": 3300, "lr": 0.08653, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22906, "top5_acc": 0.46297, "loss_cls": 4.5381, "loss": 4.5381, "time": 0.85322} +{"mode": "train", "epoch": 36, "iter": 3400, "lr": 0.08651, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21844, "top5_acc": 0.45531, "loss_cls": 4.55003, "loss": 4.55003, "time": 0.84812} +{"mode": "train", "epoch": 36, "iter": 3500, "lr": 0.0865, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2125, "top5_acc": 0.45188, "loss_cls": 4.61978, "loss": 4.61978, "time": 0.85202} +{"mode": "train", "epoch": 36, "iter": 3600, "lr": 0.08648, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21781, "top5_acc": 0.46188, "loss_cls": 4.54069, "loss": 4.54069, "time": 0.85429} +{"mode": "train", "epoch": 36, "iter": 3700, "lr": 0.08646, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22594, "top5_acc": 0.45828, "loss_cls": 4.52886, "loss": 4.52886, "time": 0.84859} +{"mode": "val", "epoch": 36, "iter": 309, "lr": 0.08645, "top1_acc": 0.13696, "top5_acc": 0.33121, "mean_class_accuracy": 0.13679} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.08643, "memory": 15990, "data_time": 1.4963, "top1_acc": 0.22438, "top5_acc": 0.46688, "loss_cls": 4.50535, "loss": 4.50535, "time": 2.52583} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.08641, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22672, "top5_acc": 0.46219, "loss_cls": 4.52544, "loss": 4.52544, "time": 0.84629} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.08639, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22625, "top5_acc": 0.45375, "loss_cls": 4.55583, "loss": 4.55583, "time": 0.84841} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.08637, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21484, "top5_acc": 0.45109, "loss_cls": 4.57219, "loss": 4.57219, "time": 0.85136} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.08635, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22594, "top5_acc": 0.46234, "loss_cls": 4.56252, "loss": 4.56252, "time": 0.85502} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.08633, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.22906, "top5_acc": 0.46719, "loss_cls": 4.51638, "loss": 4.51638, "time": 0.84724} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.08631, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22156, "top5_acc": 0.4525, "loss_cls": 4.55327, "loss": 4.55327, "time": 0.84338} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0863, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21656, "top5_acc": 0.44719, "loss_cls": 4.60178, "loss": 4.60178, "time": 0.84498} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.08628, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21156, "top5_acc": 0.45125, "loss_cls": 4.59722, "loss": 4.59722, "time": 0.84487} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.08626, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22438, "top5_acc": 0.46297, "loss_cls": 4.52158, "loss": 4.52158, "time": 0.84172} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.08624, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23219, "top5_acc": 0.4625, "loss_cls": 4.53443, "loss": 4.53443, "time": 0.84455} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.08622, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21844, "top5_acc": 0.45203, "loss_cls": 4.58037, "loss": 4.58037, "time": 0.84187} +{"mode": "train", "epoch": 37, "iter": 1300, "lr": 0.0862, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22359, "top5_acc": 0.46016, "loss_cls": 4.53637, "loss": 4.53637, "time": 0.84478} +{"mode": "train", "epoch": 37, "iter": 1400, "lr": 0.08618, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22531, "top5_acc": 0.45984, "loss_cls": 4.5569, "loss": 4.5569, "time": 0.8497} +{"mode": "train", "epoch": 37, "iter": 1500, "lr": 0.08616, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22391, "top5_acc": 0.45688, "loss_cls": 4.56265, "loss": 4.56265, "time": 0.85066} +{"mode": "train", "epoch": 37, "iter": 1600, "lr": 0.08614, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22703, "top5_acc": 0.44844, "loss_cls": 4.5714, "loss": 4.5714, "time": 0.84561} +{"mode": "train", "epoch": 37, "iter": 1700, "lr": 0.08612, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22328, "top5_acc": 0.44734, "loss_cls": 4.59679, "loss": 4.59679, "time": 0.8431} +{"mode": "train", "epoch": 37, "iter": 1800, "lr": 0.0861, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21266, "top5_acc": 0.45438, "loss_cls": 4.56858, "loss": 4.56858, "time": 0.84407} +{"mode": "train", "epoch": 37, "iter": 1900, "lr": 0.08608, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22609, "top5_acc": 0.47016, "loss_cls": 4.52425, "loss": 4.52425, "time": 0.84601} +{"mode": "train", "epoch": 37, "iter": 2000, "lr": 0.08606, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22125, "top5_acc": 0.46516, "loss_cls": 4.56153, "loss": 4.56153, "time": 0.8524} +{"mode": "train", "epoch": 37, "iter": 2100, "lr": 0.08604, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21484, "top5_acc": 0.45734, "loss_cls": 4.57232, "loss": 4.57232, "time": 0.85865} +{"mode": "train", "epoch": 37, "iter": 2200, "lr": 0.08602, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22578, "top5_acc": 0.45859, "loss_cls": 4.53401, "loss": 4.53401, "time": 0.8566} +{"mode": "train", "epoch": 37, "iter": 2300, "lr": 0.08601, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21594, "top5_acc": 0.45156, "loss_cls": 4.56606, "loss": 4.56606, "time": 0.85242} +{"mode": "train", "epoch": 37, "iter": 2400, "lr": 0.08599, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21922, "top5_acc": 0.44969, "loss_cls": 4.5664, "loss": 4.5664, "time": 0.85569} +{"mode": "train", "epoch": 37, "iter": 2500, "lr": 0.08597, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.21891, "top5_acc": 0.46, "loss_cls": 4.53891, "loss": 4.53891, "time": 0.85679} +{"mode": "train", "epoch": 37, "iter": 2600, "lr": 0.08595, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22641, "top5_acc": 0.47078, "loss_cls": 4.51359, "loss": 4.51359, "time": 0.85251} +{"mode": "train", "epoch": 37, "iter": 2700, "lr": 0.08593, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23438, "top5_acc": 0.46344, "loss_cls": 4.50464, "loss": 4.50464, "time": 0.86294} +{"mode": "train", "epoch": 37, "iter": 2800, "lr": 0.08591, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22156, "top5_acc": 0.46062, "loss_cls": 4.56417, "loss": 4.56417, "time": 0.85617} +{"mode": "train", "epoch": 37, "iter": 2900, "lr": 0.08589, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21875, "top5_acc": 0.45672, "loss_cls": 4.55529, "loss": 4.55529, "time": 0.85727} +{"mode": "train", "epoch": 37, "iter": 3000, "lr": 0.08587, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21844, "top5_acc": 0.45031, "loss_cls": 4.5715, "loss": 4.5715, "time": 0.86038} +{"mode": "train", "epoch": 37, "iter": 3100, "lr": 0.08585, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21531, "top5_acc": 0.45375, "loss_cls": 4.58798, "loss": 4.58798, "time": 0.85503} +{"mode": "train", "epoch": 37, "iter": 3200, "lr": 0.08583, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22266, "top5_acc": 0.465, "loss_cls": 4.54162, "loss": 4.54162, "time": 0.86211} +{"mode": "train", "epoch": 37, "iter": 3300, "lr": 0.08581, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2225, "top5_acc": 0.46109, "loss_cls": 4.50731, "loss": 4.50731, "time": 0.85152} +{"mode": "train", "epoch": 37, "iter": 3400, "lr": 0.08579, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2125, "top5_acc": 0.44094, "loss_cls": 4.63549, "loss": 4.63549, "time": 0.85234} +{"mode": "train", "epoch": 37, "iter": 3500, "lr": 0.08577, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21375, "top5_acc": 0.45156, "loss_cls": 4.5943, "loss": 4.5943, "time": 0.85664} +{"mode": "train", "epoch": 37, "iter": 3600, "lr": 0.08575, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22344, "top5_acc": 0.45922, "loss_cls": 4.54243, "loss": 4.54243, "time": 0.85666} +{"mode": "train", "epoch": 37, "iter": 3700, "lr": 0.08573, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22297, "top5_acc": 0.46578, "loss_cls": 4.54084, "loss": 4.54084, "time": 0.85089} +{"mode": "val", "epoch": 37, "iter": 309, "lr": 0.08572, "top1_acc": 0.12698, "top5_acc": 0.30902, "mean_class_accuracy": 0.12684} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.0857, "memory": 15990, "data_time": 1.53973, "top1_acc": 0.22906, "top5_acc": 0.465, "loss_cls": 4.47823, "loss": 4.47823, "time": 2.55988} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.08568, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22703, "top5_acc": 0.45766, "loss_cls": 4.53128, "loss": 4.53128, "time": 0.84299} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.08567, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23359, "top5_acc": 0.47547, "loss_cls": 4.47837, "loss": 4.47837, "time": 0.85301} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.08565, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21531, "top5_acc": 0.46016, "loss_cls": 4.55562, "loss": 4.55562, "time": 0.8434} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.08563, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22578, "top5_acc": 0.45766, "loss_cls": 4.541, "loss": 4.541, "time": 0.84703} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.08561, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23, "top5_acc": 0.46453, "loss_cls": 4.52848, "loss": 4.52848, "time": 0.84867} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.08559, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22531, "top5_acc": 0.4625, "loss_cls": 4.5116, "loss": 4.5116, "time": 0.84971} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.08557, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22125, "top5_acc": 0.45156, "loss_cls": 4.57684, "loss": 4.57684, "time": 0.84573} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.08555, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22344, "top5_acc": 0.45625, "loss_cls": 4.55483, "loss": 4.55483, "time": 0.84964} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.08553, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22703, "top5_acc": 0.45969, "loss_cls": 4.5207, "loss": 4.5207, "time": 0.84784} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.08551, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22391, "top5_acc": 0.45141, "loss_cls": 4.56259, "loss": 4.56259, "time": 0.85092} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.08549, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21781, "top5_acc": 0.45875, "loss_cls": 4.53294, "loss": 4.53294, "time": 0.8552} +{"mode": "train", "epoch": 38, "iter": 1300, "lr": 0.08547, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22484, "top5_acc": 0.46406, "loss_cls": 4.52659, "loss": 4.52659, "time": 0.85057} +{"mode": "train", "epoch": 38, "iter": 1400, "lr": 0.08545, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22922, "top5_acc": 0.46812, "loss_cls": 4.52744, "loss": 4.52744, "time": 0.85164} +{"mode": "train", "epoch": 38, "iter": 1500, "lr": 0.08543, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.22219, "top5_acc": 0.45344, "loss_cls": 4.56387, "loss": 4.56387, "time": 0.85494} +{"mode": "train", "epoch": 38, "iter": 1600, "lr": 0.08541, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22891, "top5_acc": 0.47219, "loss_cls": 4.4955, "loss": 4.4955, "time": 0.85403} +{"mode": "train", "epoch": 38, "iter": 1700, "lr": 0.08539, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22297, "top5_acc": 0.46844, "loss_cls": 4.54511, "loss": 4.54511, "time": 0.85102} +{"mode": "train", "epoch": 38, "iter": 1800, "lr": 0.08537, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21578, "top5_acc": 0.45031, "loss_cls": 4.59623, "loss": 4.59623, "time": 0.85903} +{"mode": "train", "epoch": 38, "iter": 1900, "lr": 0.08535, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21453, "top5_acc": 0.45672, "loss_cls": 4.56634, "loss": 4.56634, "time": 0.85377} +{"mode": "train", "epoch": 38, "iter": 2000, "lr": 0.08533, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22578, "top5_acc": 0.46062, "loss_cls": 4.5563, "loss": 4.5563, "time": 0.85942} +{"mode": "train", "epoch": 38, "iter": 2100, "lr": 0.08531, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21891, "top5_acc": 0.45281, "loss_cls": 4.55392, "loss": 4.55392, "time": 0.85737} +{"mode": "train", "epoch": 38, "iter": 2200, "lr": 0.08529, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22281, "top5_acc": 0.46656, "loss_cls": 4.52275, "loss": 4.52275, "time": 0.85604} +{"mode": "train", "epoch": 38, "iter": 2300, "lr": 0.08527, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23141, "top5_acc": 0.46562, "loss_cls": 4.47879, "loss": 4.47879, "time": 0.85665} +{"mode": "train", "epoch": 38, "iter": 2400, "lr": 0.08525, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22938, "top5_acc": 0.46781, "loss_cls": 4.50813, "loss": 4.50813, "time": 0.85504} +{"mode": "train", "epoch": 38, "iter": 2500, "lr": 0.08523, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21797, "top5_acc": 0.45688, "loss_cls": 4.57474, "loss": 4.57474, "time": 0.85445} +{"mode": "train", "epoch": 38, "iter": 2600, "lr": 0.08521, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.22266, "top5_acc": 0.45609, "loss_cls": 4.55007, "loss": 4.55007, "time": 0.85458} +{"mode": "train", "epoch": 38, "iter": 2700, "lr": 0.08519, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22031, "top5_acc": 0.45656, "loss_cls": 4.58982, "loss": 4.58982, "time": 0.8503} +{"mode": "train", "epoch": 38, "iter": 2800, "lr": 0.08517, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21703, "top5_acc": 0.45266, "loss_cls": 4.5922, "loss": 4.5922, "time": 0.85526} +{"mode": "train", "epoch": 38, "iter": 2900, "lr": 0.08515, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.2225, "top5_acc": 0.45359, "loss_cls": 4.57939, "loss": 4.57939, "time": 0.85779} +{"mode": "train", "epoch": 38, "iter": 3000, "lr": 0.08513, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22391, "top5_acc": 0.45812, "loss_cls": 4.53193, "loss": 4.53193, "time": 0.86074} +{"mode": "train", "epoch": 38, "iter": 3100, "lr": 0.08511, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22938, "top5_acc": 0.46109, "loss_cls": 4.53788, "loss": 4.53788, "time": 0.86279} +{"mode": "train", "epoch": 38, "iter": 3200, "lr": 0.08509, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22781, "top5_acc": 0.46109, "loss_cls": 4.5174, "loss": 4.5174, "time": 0.85644} +{"mode": "train", "epoch": 38, "iter": 3300, "lr": 0.08507, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20719, "top5_acc": 0.45391, "loss_cls": 4.54941, "loss": 4.54941, "time": 0.85398} +{"mode": "train", "epoch": 38, "iter": 3400, "lr": 0.08505, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2225, "top5_acc": 0.45672, "loss_cls": 4.55212, "loss": 4.55212, "time": 0.84906} +{"mode": "train", "epoch": 38, "iter": 3500, "lr": 0.08503, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21391, "top5_acc": 0.45531, "loss_cls": 4.59559, "loss": 4.59559, "time": 0.85332} +{"mode": "train", "epoch": 38, "iter": 3600, "lr": 0.08501, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22781, "top5_acc": 0.46141, "loss_cls": 4.51459, "loss": 4.51459, "time": 0.84853} +{"mode": "train", "epoch": 38, "iter": 3700, "lr": 0.08499, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21828, "top5_acc": 0.45094, "loss_cls": 4.59331, "loss": 4.59331, "time": 0.84402} +{"mode": "val", "epoch": 38, "iter": 309, "lr": 0.08498, "top1_acc": 0.14517, "top5_acc": 0.35825, "mean_class_accuracy": 0.14501} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.08496, "memory": 15990, "data_time": 1.60262, "top1_acc": 0.23281, "top5_acc": 0.47359, "loss_cls": 4.45506, "loss": 4.45506, "time": 2.64752} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.08494, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21641, "top5_acc": 0.45453, "loss_cls": 4.54082, "loss": 4.54082, "time": 0.86368} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.08492, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.22562, "top5_acc": 0.46938, "loss_cls": 4.50536, "loss": 4.50536, "time": 0.86131} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.0849, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.22906, "top5_acc": 0.46344, "loss_cls": 4.51491, "loss": 4.51491, "time": 0.85931} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.08488, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22406, "top5_acc": 0.45875, "loss_cls": 4.5505, "loss": 4.5505, "time": 0.84971} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.08486, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22, "top5_acc": 0.46219, "loss_cls": 4.52877, "loss": 4.52877, "time": 0.85286} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.08484, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23031, "top5_acc": 0.45797, "loss_cls": 4.54745, "loss": 4.54745, "time": 0.85014} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.08482, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23469, "top5_acc": 0.45641, "loss_cls": 4.52338, "loss": 4.52338, "time": 0.84773} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.0848, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23297, "top5_acc": 0.45953, "loss_cls": 4.51155, "loss": 4.51155, "time": 0.85205} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.08478, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22516, "top5_acc": 0.46328, "loss_cls": 4.49696, "loss": 4.49696, "time": 0.84622} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.08476, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22422, "top5_acc": 0.45797, "loss_cls": 4.54859, "loss": 4.54859, "time": 0.85307} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.08474, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21906, "top5_acc": 0.45203, "loss_cls": 4.56371, "loss": 4.56371, "time": 0.85474} +{"mode": "train", "epoch": 39, "iter": 1300, "lr": 0.08472, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.23297, "top5_acc": 0.46188, "loss_cls": 4.53281, "loss": 4.53281, "time": 0.85277} +{"mode": "train", "epoch": 39, "iter": 1400, "lr": 0.0847, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21703, "top5_acc": 0.46203, "loss_cls": 4.54897, "loss": 4.54897, "time": 0.8501} +{"mode": "train", "epoch": 39, "iter": 1500, "lr": 0.08468, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21828, "top5_acc": 0.44828, "loss_cls": 4.59009, "loss": 4.59009, "time": 0.8522} +{"mode": "train", "epoch": 39, "iter": 1600, "lr": 0.08466, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22359, "top5_acc": 0.45094, "loss_cls": 4.57861, "loss": 4.57861, "time": 0.85549} +{"mode": "train", "epoch": 39, "iter": 1700, "lr": 0.08464, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22797, "top5_acc": 0.47094, "loss_cls": 4.49915, "loss": 4.49915, "time": 0.84768} +{"mode": "train", "epoch": 39, "iter": 1800, "lr": 0.08462, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22094, "top5_acc": 0.45578, "loss_cls": 4.5725, "loss": 4.5725, "time": 0.84761} +{"mode": "train", "epoch": 39, "iter": 1900, "lr": 0.0846, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23156, "top5_acc": 0.46641, "loss_cls": 4.5029, "loss": 4.5029, "time": 0.84915} +{"mode": "train", "epoch": 39, "iter": 2000, "lr": 0.08458, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22484, "top5_acc": 0.46469, "loss_cls": 4.53417, "loss": 4.53417, "time": 0.85182} +{"mode": "train", "epoch": 39, "iter": 2100, "lr": 0.08456, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23297, "top5_acc": 0.47047, "loss_cls": 4.50266, "loss": 4.50266, "time": 0.86903} +{"mode": "train", "epoch": 39, "iter": 2200, "lr": 0.08454, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21625, "top5_acc": 0.4425, "loss_cls": 4.61522, "loss": 4.61522, "time": 0.85988} +{"mode": "train", "epoch": 39, "iter": 2300, "lr": 0.08452, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2225, "top5_acc": 0.45812, "loss_cls": 4.55017, "loss": 4.55017, "time": 0.85724} +{"mode": "train", "epoch": 39, "iter": 2400, "lr": 0.0845, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22172, "top5_acc": 0.45953, "loss_cls": 4.55175, "loss": 4.55175, "time": 0.85731} +{"mode": "train", "epoch": 39, "iter": 2500, "lr": 0.08448, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21766, "top5_acc": 0.46203, "loss_cls": 4.53386, "loss": 4.53386, "time": 0.86004} +{"mode": "train", "epoch": 39, "iter": 2600, "lr": 0.08446, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22953, "top5_acc": 0.46828, "loss_cls": 4.50593, "loss": 4.50593, "time": 0.85671} +{"mode": "train", "epoch": 39, "iter": 2700, "lr": 0.08444, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21906, "top5_acc": 0.46094, "loss_cls": 4.53435, "loss": 4.53435, "time": 0.86016} +{"mode": "train", "epoch": 39, "iter": 2800, "lr": 0.08442, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22875, "top5_acc": 0.46797, "loss_cls": 4.51481, "loss": 4.51481, "time": 0.85812} +{"mode": "train", "epoch": 39, "iter": 2900, "lr": 0.0844, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23031, "top5_acc": 0.46875, "loss_cls": 4.50498, "loss": 4.50498, "time": 0.86272} +{"mode": "train", "epoch": 39, "iter": 3000, "lr": 0.08438, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22359, "top5_acc": 0.46125, "loss_cls": 4.50002, "loss": 4.50002, "time": 0.8592} +{"mode": "train", "epoch": 39, "iter": 3100, "lr": 0.08436, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22984, "top5_acc": 0.46391, "loss_cls": 4.51928, "loss": 4.51928, "time": 0.8559} +{"mode": "train", "epoch": 39, "iter": 3200, "lr": 0.08434, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.225, "top5_acc": 0.45766, "loss_cls": 4.55307, "loss": 4.55307, "time": 0.84891} +{"mode": "train", "epoch": 39, "iter": 3300, "lr": 0.08432, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21766, "top5_acc": 0.45062, "loss_cls": 4.56322, "loss": 4.56322, "time": 0.85339} +{"mode": "train", "epoch": 39, "iter": 3400, "lr": 0.0843, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22031, "top5_acc": 0.45062, "loss_cls": 4.5789, "loss": 4.5789, "time": 0.85259} +{"mode": "train", "epoch": 39, "iter": 3500, "lr": 0.08428, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22812, "top5_acc": 0.45406, "loss_cls": 4.56072, "loss": 4.56072, "time": 0.85695} +{"mode": "train", "epoch": 39, "iter": 3600, "lr": 0.08426, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22484, "top5_acc": 0.46172, "loss_cls": 4.53604, "loss": 4.53604, "time": 0.84779} +{"mode": "train", "epoch": 39, "iter": 3700, "lr": 0.08424, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23156, "top5_acc": 0.46047, "loss_cls": 4.49781, "loss": 4.49781, "time": 0.84645} +{"mode": "val", "epoch": 39, "iter": 309, "lr": 0.08423, "top1_acc": 0.16264, "top5_acc": 0.37304, "mean_class_accuracy": 0.16246} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.08421, "memory": 15990, "data_time": 1.50372, "top1_acc": 0.22984, "top5_acc": 0.47344, "loss_cls": 4.47047, "loss": 4.47047, "time": 2.53752} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.08419, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22375, "top5_acc": 0.46406, "loss_cls": 4.52882, "loss": 4.52882, "time": 0.84498} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.08417, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22, "top5_acc": 0.45562, "loss_cls": 4.54464, "loss": 4.54464, "time": 0.8527} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.08415, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22312, "top5_acc": 0.46594, "loss_cls": 4.51763, "loss": 4.51763, "time": 0.85445} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.08413, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21875, "top5_acc": 0.47016, "loss_cls": 4.52233, "loss": 4.52233, "time": 0.8551} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.08411, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23562, "top5_acc": 0.46828, "loss_cls": 4.46586, "loss": 4.46586, "time": 0.85474} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.08408, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22766, "top5_acc": 0.46156, "loss_cls": 4.55631, "loss": 4.55631, "time": 0.85138} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.08406, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22938, "top5_acc": 0.46312, "loss_cls": 4.48985, "loss": 4.48985, "time": 0.85105} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.08404, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22219, "top5_acc": 0.46359, "loss_cls": 4.51995, "loss": 4.51995, "time": 0.85093} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.08402, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.235, "top5_acc": 0.46484, "loss_cls": 4.49822, "loss": 4.49822, "time": 0.84773} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.084, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21812, "top5_acc": 0.45172, "loss_cls": 4.56175, "loss": 4.56175, "time": 0.84874} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.08398, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22984, "top5_acc": 0.46797, "loss_cls": 4.51422, "loss": 4.51422, "time": 0.85093} +{"mode": "train", "epoch": 40, "iter": 1300, "lr": 0.08396, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23188, "top5_acc": 0.47125, "loss_cls": 4.50234, "loss": 4.50234, "time": 0.84753} +{"mode": "train", "epoch": 40, "iter": 1400, "lr": 0.08394, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21188, "top5_acc": 0.44344, "loss_cls": 4.60422, "loss": 4.60422, "time": 0.85168} +{"mode": "train", "epoch": 40, "iter": 1500, "lr": 0.08392, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22797, "top5_acc": 0.46484, "loss_cls": 4.52048, "loss": 4.52048, "time": 0.85109} +{"mode": "train", "epoch": 40, "iter": 1600, "lr": 0.0839, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22734, "top5_acc": 0.47156, "loss_cls": 4.48074, "loss": 4.48074, "time": 0.84982} +{"mode": "train", "epoch": 40, "iter": 1700, "lr": 0.08388, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22141, "top5_acc": 0.46031, "loss_cls": 4.54971, "loss": 4.54971, "time": 0.85327} +{"mode": "train", "epoch": 40, "iter": 1800, "lr": 0.08386, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21969, "top5_acc": 0.45094, "loss_cls": 4.56109, "loss": 4.56109, "time": 0.84749} +{"mode": "train", "epoch": 40, "iter": 1900, "lr": 0.08384, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22297, "top5_acc": 0.46266, "loss_cls": 4.55548, "loss": 4.55548, "time": 0.85351} +{"mode": "train", "epoch": 40, "iter": 2000, "lr": 0.08382, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21922, "top5_acc": 0.45906, "loss_cls": 4.55304, "loss": 4.55304, "time": 0.85172} +{"mode": "train", "epoch": 40, "iter": 2100, "lr": 0.0838, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2225, "top5_acc": 0.45156, "loss_cls": 4.56052, "loss": 4.56052, "time": 0.85385} +{"mode": "train", "epoch": 40, "iter": 2200, "lr": 0.08378, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22656, "top5_acc": 0.45828, "loss_cls": 4.52191, "loss": 4.52191, "time": 0.84808} +{"mode": "train", "epoch": 40, "iter": 2300, "lr": 0.08376, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22406, "top5_acc": 0.45625, "loss_cls": 4.55068, "loss": 4.55068, "time": 0.85245} +{"mode": "train", "epoch": 40, "iter": 2400, "lr": 0.08374, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22391, "top5_acc": 0.46078, "loss_cls": 4.52556, "loss": 4.52556, "time": 0.8558} +{"mode": "train", "epoch": 40, "iter": 2500, "lr": 0.08371, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22219, "top5_acc": 0.45344, "loss_cls": 4.55756, "loss": 4.55756, "time": 0.85631} +{"mode": "train", "epoch": 40, "iter": 2600, "lr": 0.08369, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.225, "top5_acc": 0.46344, "loss_cls": 4.54353, "loss": 4.54353, "time": 0.85574} +{"mode": "train", "epoch": 40, "iter": 2700, "lr": 0.08367, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23219, "top5_acc": 0.45562, "loss_cls": 4.55571, "loss": 4.55571, "time": 0.86125} +{"mode": "train", "epoch": 40, "iter": 2800, "lr": 0.08365, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22656, "top5_acc": 0.47109, "loss_cls": 4.50273, "loss": 4.50273, "time": 0.85691} +{"mode": "train", "epoch": 40, "iter": 2900, "lr": 0.08363, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23172, "top5_acc": 0.46875, "loss_cls": 4.48508, "loss": 4.48508, "time": 0.86178} +{"mode": "train", "epoch": 40, "iter": 3000, "lr": 0.08361, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23969, "top5_acc": 0.47312, "loss_cls": 4.47042, "loss": 4.47042, "time": 0.85455} +{"mode": "train", "epoch": 40, "iter": 3100, "lr": 0.08359, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.225, "top5_acc": 0.45734, "loss_cls": 4.52665, "loss": 4.52665, "time": 0.85455} +{"mode": "train", "epoch": 40, "iter": 3200, "lr": 0.08357, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22578, "top5_acc": 0.46609, "loss_cls": 4.51535, "loss": 4.51535, "time": 0.85109} +{"mode": "train", "epoch": 40, "iter": 3300, "lr": 0.08355, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23031, "top5_acc": 0.46547, "loss_cls": 4.51048, "loss": 4.51048, "time": 0.84853} +{"mode": "train", "epoch": 40, "iter": 3400, "lr": 0.08353, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21703, "top5_acc": 0.45609, "loss_cls": 4.57333, "loss": 4.57333, "time": 0.85379} +{"mode": "train", "epoch": 40, "iter": 3500, "lr": 0.08351, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22828, "top5_acc": 0.46359, "loss_cls": 4.5101, "loss": 4.5101, "time": 0.85767} +{"mode": "train", "epoch": 40, "iter": 3600, "lr": 0.08349, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22031, "top5_acc": 0.46531, "loss_cls": 4.53121, "loss": 4.53121, "time": 0.84715} +{"mode": "train", "epoch": 40, "iter": 3700, "lr": 0.08347, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22062, "top5_acc": 0.45625, "loss_cls": 4.55301, "loss": 4.55301, "time": 0.84729} +{"mode": "val", "epoch": 40, "iter": 309, "lr": 0.08346, "top1_acc": 0.14856, "top5_acc": 0.34225, "mean_class_accuracy": 0.14846} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.08344, "memory": 15990, "data_time": 1.48707, "top1_acc": 0.22969, "top5_acc": 0.47, "loss_cls": 4.47005, "loss": 4.47005, "time": 2.5101} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.08342, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22469, "top5_acc": 0.46969, "loss_cls": 4.50736, "loss": 4.50736, "time": 0.8442} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.08339, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2275, "top5_acc": 0.47172, "loss_cls": 4.50686, "loss": 4.50686, "time": 0.83987} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.08337, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23703, "top5_acc": 0.46844, "loss_cls": 4.47063, "loss": 4.47063, "time": 0.84647} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.08335, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22406, "top5_acc": 0.46297, "loss_cls": 4.52658, "loss": 4.52658, "time": 0.85202} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.08333, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21953, "top5_acc": 0.45266, "loss_cls": 4.54358, "loss": 4.54358, "time": 0.85289} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.08331, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22625, "top5_acc": 0.46031, "loss_cls": 4.5187, "loss": 4.5187, "time": 0.84753} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.08329, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23609, "top5_acc": 0.48, "loss_cls": 4.46185, "loss": 4.46185, "time": 0.85423} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.08327, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23344, "top5_acc": 0.47406, "loss_cls": 4.47662, "loss": 4.47662, "time": 0.84956} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.08325, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22547, "top5_acc": 0.46203, "loss_cls": 4.52571, "loss": 4.52571, "time": 0.85287} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.08323, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22562, "top5_acc": 0.45156, "loss_cls": 4.5261, "loss": 4.5261, "time": 0.85168} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.08321, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22656, "top5_acc": 0.46688, "loss_cls": 4.51144, "loss": 4.51144, "time": 0.8464} +{"mode": "train", "epoch": 41, "iter": 1300, "lr": 0.08319, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22641, "top5_acc": 0.46547, "loss_cls": 4.53346, "loss": 4.53346, "time": 0.84661} +{"mode": "train", "epoch": 41, "iter": 1400, "lr": 0.08316, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23016, "top5_acc": 0.46734, "loss_cls": 4.50724, "loss": 4.50724, "time": 0.84627} +{"mode": "train", "epoch": 41, "iter": 1500, "lr": 0.08314, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22094, "top5_acc": 0.45891, "loss_cls": 4.55082, "loss": 4.55082, "time": 0.85088} +{"mode": "train", "epoch": 41, "iter": 1600, "lr": 0.08312, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22703, "top5_acc": 0.45609, "loss_cls": 4.5382, "loss": 4.5382, "time": 0.85424} +{"mode": "train", "epoch": 41, "iter": 1700, "lr": 0.0831, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22047, "top5_acc": 0.465, "loss_cls": 4.51881, "loss": 4.51881, "time": 0.85275} +{"mode": "train", "epoch": 41, "iter": 1800, "lr": 0.08308, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22297, "top5_acc": 0.46172, "loss_cls": 4.52572, "loss": 4.52572, "time": 0.85554} +{"mode": "train", "epoch": 41, "iter": 1900, "lr": 0.08306, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22391, "top5_acc": 0.46156, "loss_cls": 4.5288, "loss": 4.5288, "time": 0.85496} +{"mode": "train", "epoch": 41, "iter": 2000, "lr": 0.08304, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21797, "top5_acc": 0.45688, "loss_cls": 4.55551, "loss": 4.55551, "time": 0.84839} +{"mode": "train", "epoch": 41, "iter": 2100, "lr": 0.08302, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23656, "top5_acc": 0.46188, "loss_cls": 4.53363, "loss": 4.53363, "time": 0.84914} +{"mode": "train", "epoch": 41, "iter": 2200, "lr": 0.083, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22688, "top5_acc": 0.465, "loss_cls": 4.50489, "loss": 4.50489, "time": 0.85614} +{"mode": "train", "epoch": 41, "iter": 2300, "lr": 0.08298, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21484, "top5_acc": 0.455, "loss_cls": 4.56597, "loss": 4.56597, "time": 0.84985} +{"mode": "train", "epoch": 41, "iter": 2400, "lr": 0.08296, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21422, "top5_acc": 0.45906, "loss_cls": 4.54274, "loss": 4.54274, "time": 0.85187} +{"mode": "train", "epoch": 41, "iter": 2500, "lr": 0.08293, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22375, "top5_acc": 0.46562, "loss_cls": 4.53, "loss": 4.53, "time": 0.85215} +{"mode": "train", "epoch": 41, "iter": 2600, "lr": 0.08291, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22516, "top5_acc": 0.46266, "loss_cls": 4.51117, "loss": 4.51117, "time": 0.85791} +{"mode": "train", "epoch": 41, "iter": 2700, "lr": 0.08289, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23156, "top5_acc": 0.45766, "loss_cls": 4.50463, "loss": 4.50463, "time": 0.86842} +{"mode": "train", "epoch": 41, "iter": 2800, "lr": 0.08287, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22391, "top5_acc": 0.45359, "loss_cls": 4.50492, "loss": 4.50492, "time": 0.86091} +{"mode": "train", "epoch": 41, "iter": 2900, "lr": 0.08285, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23078, "top5_acc": 0.46562, "loss_cls": 4.52024, "loss": 4.52024, "time": 0.86307} +{"mode": "train", "epoch": 41, "iter": 3000, "lr": 0.08283, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22625, "top5_acc": 0.46297, "loss_cls": 4.53122, "loss": 4.53122, "time": 0.85549} +{"mode": "train", "epoch": 41, "iter": 3100, "lr": 0.08281, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22234, "top5_acc": 0.46047, "loss_cls": 4.53733, "loss": 4.53733, "time": 0.85687} +{"mode": "train", "epoch": 41, "iter": 3200, "lr": 0.08279, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21906, "top5_acc": 0.46141, "loss_cls": 4.53442, "loss": 4.53442, "time": 0.85281} +{"mode": "train", "epoch": 41, "iter": 3300, "lr": 0.08277, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22812, "top5_acc": 0.45672, "loss_cls": 4.53629, "loss": 4.53629, "time": 0.85055} +{"mode": "train", "epoch": 41, "iter": 3400, "lr": 0.08274, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.22922, "top5_acc": 0.45766, "loss_cls": 4.53251, "loss": 4.53251, "time": 0.85534} +{"mode": "train", "epoch": 41, "iter": 3500, "lr": 0.08272, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23625, "top5_acc": 0.47844, "loss_cls": 4.46362, "loss": 4.46362, "time": 0.8558} +{"mode": "train", "epoch": 41, "iter": 3600, "lr": 0.0827, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22828, "top5_acc": 0.46875, "loss_cls": 4.49061, "loss": 4.49061, "time": 0.85083} +{"mode": "train", "epoch": 41, "iter": 3700, "lr": 0.08268, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.22688, "top5_acc": 0.47125, "loss_cls": 4.50175, "loss": 4.50175, "time": 0.84674} +{"mode": "val", "epoch": 41, "iter": 309, "lr": 0.08267, "top1_acc": 0.15844, "top5_acc": 0.36676, "mean_class_accuracy": 0.15839} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.08265, "memory": 15990, "data_time": 1.47547, "top1_acc": 0.2325, "top5_acc": 0.47453, "loss_cls": 4.50669, "loss": 4.50669, "time": 2.51181} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.08263, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22547, "top5_acc": 0.46219, "loss_cls": 4.50607, "loss": 4.50607, "time": 0.8514} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.08261, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23031, "top5_acc": 0.46484, "loss_cls": 4.48529, "loss": 4.48529, "time": 0.85427} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.08259, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21875, "top5_acc": 0.44766, "loss_cls": 4.53269, "loss": 4.53269, "time": 0.85038} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.08257, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23547, "top5_acc": 0.46984, "loss_cls": 4.47616, "loss": 4.47616, "time": 0.85038} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.08254, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23109, "top5_acc": 0.46141, "loss_cls": 4.51544, "loss": 4.51544, "time": 0.85181} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.08252, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22828, "top5_acc": 0.46406, "loss_cls": 4.51133, "loss": 4.51133, "time": 0.84882} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.0825, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23703, "top5_acc": 0.47078, "loss_cls": 4.44053, "loss": 4.44053, "time": 0.84751} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.08248, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22453, "top5_acc": 0.46875, "loss_cls": 4.4916, "loss": 4.4916, "time": 0.85168} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.08246, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22172, "top5_acc": 0.45969, "loss_cls": 4.50989, "loss": 4.50989, "time": 0.85585} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.08244, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22359, "top5_acc": 0.46016, "loss_cls": 4.52117, "loss": 4.52117, "time": 0.85461} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.08242, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23203, "top5_acc": 0.46891, "loss_cls": 4.50509, "loss": 4.50509, "time": 0.8504} +{"mode": "train", "epoch": 42, "iter": 1300, "lr": 0.0824, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22938, "top5_acc": 0.46922, "loss_cls": 4.50093, "loss": 4.50093, "time": 0.85345} +{"mode": "train", "epoch": 42, "iter": 1400, "lr": 0.08237, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22688, "top5_acc": 0.45797, "loss_cls": 4.54203, "loss": 4.54203, "time": 0.85435} +{"mode": "train", "epoch": 42, "iter": 1500, "lr": 0.08235, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22734, "top5_acc": 0.46594, "loss_cls": 4.51031, "loss": 4.51031, "time": 0.85425} +{"mode": "train", "epoch": 42, "iter": 1600, "lr": 0.08233, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22422, "top5_acc": 0.46203, "loss_cls": 4.51309, "loss": 4.51309, "time": 0.85181} +{"mode": "train", "epoch": 42, "iter": 1700, "lr": 0.08231, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23531, "top5_acc": 0.46953, "loss_cls": 4.49346, "loss": 4.49346, "time": 0.85131} +{"mode": "train", "epoch": 42, "iter": 1800, "lr": 0.08229, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22734, "top5_acc": 0.46547, "loss_cls": 4.5095, "loss": 4.5095, "time": 0.8535} +{"mode": "train", "epoch": 42, "iter": 1900, "lr": 0.08227, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22562, "top5_acc": 0.46547, "loss_cls": 4.53208, "loss": 4.53208, "time": 0.85026} +{"mode": "train", "epoch": 42, "iter": 2000, "lr": 0.08225, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22812, "top5_acc": 0.46, "loss_cls": 4.52219, "loss": 4.52219, "time": 0.8544} +{"mode": "train", "epoch": 42, "iter": 2100, "lr": 0.08222, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23047, "top5_acc": 0.455, "loss_cls": 4.50253, "loss": 4.50253, "time": 0.8502} +{"mode": "train", "epoch": 42, "iter": 2200, "lr": 0.0822, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2325, "top5_acc": 0.47656, "loss_cls": 4.46869, "loss": 4.46869, "time": 0.85204} +{"mode": "train", "epoch": 42, "iter": 2300, "lr": 0.08218, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22469, "top5_acc": 0.46734, "loss_cls": 4.50296, "loss": 4.50296, "time": 0.85245} +{"mode": "train", "epoch": 42, "iter": 2400, "lr": 0.08216, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22578, "top5_acc": 0.46891, "loss_cls": 4.50475, "loss": 4.50475, "time": 0.85493} +{"mode": "train", "epoch": 42, "iter": 2500, "lr": 0.08214, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23234, "top5_acc": 0.46875, "loss_cls": 4.46358, "loss": 4.46358, "time": 0.85379} +{"mode": "train", "epoch": 42, "iter": 2600, "lr": 0.08212, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21922, "top5_acc": 0.46078, "loss_cls": 4.52669, "loss": 4.52669, "time": 0.85985} +{"mode": "train", "epoch": 42, "iter": 2700, "lr": 0.0821, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22172, "top5_acc": 0.44953, "loss_cls": 4.55423, "loss": 4.55423, "time": 0.85735} +{"mode": "train", "epoch": 42, "iter": 2800, "lr": 0.08207, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22828, "top5_acc": 0.47031, "loss_cls": 4.50018, "loss": 4.50018, "time": 0.85973} +{"mode": "train", "epoch": 42, "iter": 2900, "lr": 0.08205, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22969, "top5_acc": 0.46375, "loss_cls": 4.52115, "loss": 4.52115, "time": 0.85437} +{"mode": "train", "epoch": 42, "iter": 3000, "lr": 0.08203, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.23672, "top5_acc": 0.47391, "loss_cls": 4.48276, "loss": 4.48276, "time": 0.85893} +{"mode": "train", "epoch": 42, "iter": 3100, "lr": 0.08201, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22781, "top5_acc": 0.46391, "loss_cls": 4.5161, "loss": 4.5161, "time": 0.85036} +{"mode": "train", "epoch": 42, "iter": 3200, "lr": 0.08199, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20891, "top5_acc": 0.45109, "loss_cls": 4.60312, "loss": 4.60312, "time": 0.85014} +{"mode": "train", "epoch": 42, "iter": 3300, "lr": 0.08197, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22312, "top5_acc": 0.46922, "loss_cls": 4.54561, "loss": 4.54561, "time": 0.85718} +{"mode": "train", "epoch": 42, "iter": 3400, "lr": 0.08195, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22359, "top5_acc": 0.46078, "loss_cls": 4.57598, "loss": 4.57598, "time": 0.85207} +{"mode": "train", "epoch": 42, "iter": 3500, "lr": 0.08192, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21625, "top5_acc": 0.45406, "loss_cls": 4.56239, "loss": 4.56239, "time": 0.85232} +{"mode": "train", "epoch": 42, "iter": 3600, "lr": 0.0819, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22656, "top5_acc": 0.47016, "loss_cls": 4.48244, "loss": 4.48244, "time": 0.84561} +{"mode": "train", "epoch": 42, "iter": 3700, "lr": 0.08188, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22609, "top5_acc": 0.46219, "loss_cls": 4.52945, "loss": 4.52945, "time": 0.84929} +{"mode": "val", "epoch": 42, "iter": 309, "lr": 0.08187, "top1_acc": 0.15413, "top5_acc": 0.36023, "mean_class_accuracy": 0.1541} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.08185, "memory": 15990, "data_time": 1.57576, "top1_acc": 0.23062, "top5_acc": 0.47328, "loss_cls": 4.45507, "loss": 4.45507, "time": 2.60724} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.08183, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.23812, "top5_acc": 0.48078, "loss_cls": 4.42168, "loss": 4.42168, "time": 0.85075} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.08181, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.22328, "top5_acc": 0.45828, "loss_cls": 4.52512, "loss": 4.52512, "time": 0.85362} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.08179, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24062, "top5_acc": 0.47781, "loss_cls": 4.44571, "loss": 4.44571, "time": 0.85491} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.08176, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22625, "top5_acc": 0.46469, "loss_cls": 4.50309, "loss": 4.50309, "time": 0.8524} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.08174, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22672, "top5_acc": 0.45969, "loss_cls": 4.52801, "loss": 4.52801, "time": 0.85289} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.08172, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.24141, "top5_acc": 0.47859, "loss_cls": 4.44294, "loss": 4.44294, "time": 0.85281} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.0817, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22438, "top5_acc": 0.45734, "loss_cls": 4.50414, "loss": 4.50414, "time": 0.84838} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.08168, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22516, "top5_acc": 0.46172, "loss_cls": 4.50333, "loss": 4.50333, "time": 0.84675} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.08166, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22281, "top5_acc": 0.46375, "loss_cls": 4.5214, "loss": 4.5214, "time": 0.8464} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.08163, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21922, "top5_acc": 0.46078, "loss_cls": 4.53179, "loss": 4.53179, "time": 0.84083} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.08161, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23328, "top5_acc": 0.47359, "loss_cls": 4.47946, "loss": 4.47946, "time": 0.84236} +{"mode": "train", "epoch": 43, "iter": 1300, "lr": 0.08159, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.225, "top5_acc": 0.46141, "loss_cls": 4.51587, "loss": 4.51587, "time": 0.84045} +{"mode": "train", "epoch": 43, "iter": 1400, "lr": 0.08157, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.21953, "top5_acc": 0.45453, "loss_cls": 4.53605, "loss": 4.53605, "time": 0.84631} +{"mode": "train", "epoch": 43, "iter": 1500, "lr": 0.08155, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23047, "top5_acc": 0.465, "loss_cls": 4.51272, "loss": 4.51272, "time": 0.85014} +{"mode": "train", "epoch": 43, "iter": 1600, "lr": 0.08153, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23109, "top5_acc": 0.46938, "loss_cls": 4.492, "loss": 4.492, "time": 0.84939} +{"mode": "train", "epoch": 43, "iter": 1700, "lr": 0.0815, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23531, "top5_acc": 0.47469, "loss_cls": 4.48361, "loss": 4.48361, "time": 0.85529} +{"mode": "train", "epoch": 43, "iter": 1800, "lr": 0.08148, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22703, "top5_acc": 0.46734, "loss_cls": 4.50775, "loss": 4.50775, "time": 0.85878} +{"mode": "train", "epoch": 43, "iter": 1900, "lr": 0.08146, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24344, "top5_acc": 0.47391, "loss_cls": 4.44598, "loss": 4.44598, "time": 0.85469} +{"mode": "train", "epoch": 43, "iter": 2000, "lr": 0.08144, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23641, "top5_acc": 0.47656, "loss_cls": 4.46324, "loss": 4.46324, "time": 0.86065} +{"mode": "train", "epoch": 43, "iter": 2100, "lr": 0.08142, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22594, "top5_acc": 0.46906, "loss_cls": 4.52013, "loss": 4.52013, "time": 0.85062} +{"mode": "train", "epoch": 43, "iter": 2200, "lr": 0.0814, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22781, "top5_acc": 0.46422, "loss_cls": 4.50669, "loss": 4.50669, "time": 0.84955} +{"mode": "train", "epoch": 43, "iter": 2300, "lr": 0.08137, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.2325, "top5_acc": 0.47391, "loss_cls": 4.49436, "loss": 4.49436, "time": 0.85436} +{"mode": "train", "epoch": 43, "iter": 2400, "lr": 0.08135, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23156, "top5_acc": 0.47109, "loss_cls": 4.50151, "loss": 4.50151, "time": 0.85262} +{"mode": "train", "epoch": 43, "iter": 2500, "lr": 0.08133, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22844, "top5_acc": 0.45938, "loss_cls": 4.51929, "loss": 4.51929, "time": 0.85454} +{"mode": "train", "epoch": 43, "iter": 2600, "lr": 0.08131, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22891, "top5_acc": 0.45047, "loss_cls": 4.56307, "loss": 4.56307, "time": 0.85022} +{"mode": "train", "epoch": 43, "iter": 2700, "lr": 0.08129, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23234, "top5_acc": 0.47094, "loss_cls": 4.49866, "loss": 4.49866, "time": 0.84971} +{"mode": "train", "epoch": 43, "iter": 2800, "lr": 0.08126, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22188, "top5_acc": 0.4575, "loss_cls": 4.55172, "loss": 4.55172, "time": 0.85018} +{"mode": "train", "epoch": 43, "iter": 2900, "lr": 0.08124, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23031, "top5_acc": 0.46969, "loss_cls": 4.49714, "loss": 4.49714, "time": 0.84701} +{"mode": "train", "epoch": 43, "iter": 3000, "lr": 0.08122, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23016, "top5_acc": 0.46828, "loss_cls": 4.50449, "loss": 4.50449, "time": 0.84583} +{"mode": "train", "epoch": 43, "iter": 3100, "lr": 0.0812, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22281, "top5_acc": 0.45781, "loss_cls": 4.52579, "loss": 4.52579, "time": 0.84545} +{"mode": "train", "epoch": 43, "iter": 3200, "lr": 0.08118, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23297, "top5_acc": 0.47094, "loss_cls": 4.47526, "loss": 4.47526, "time": 0.84184} +{"mode": "train", "epoch": 43, "iter": 3300, "lr": 0.08116, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.22047, "top5_acc": 0.45703, "loss_cls": 4.57075, "loss": 4.57075, "time": 0.84113} +{"mode": "train", "epoch": 43, "iter": 3400, "lr": 0.08113, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22141, "top5_acc": 0.45906, "loss_cls": 4.54588, "loss": 4.54588, "time": 0.852} +{"mode": "train", "epoch": 43, "iter": 3500, "lr": 0.08111, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23016, "top5_acc": 0.47, "loss_cls": 4.49224, "loss": 4.49224, "time": 0.85113} +{"mode": "train", "epoch": 43, "iter": 3600, "lr": 0.08109, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22359, "top5_acc": 0.46297, "loss_cls": 4.52527, "loss": 4.52527, "time": 0.84424} +{"mode": "train", "epoch": 43, "iter": 3700, "lr": 0.08107, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22938, "top5_acc": 0.46062, "loss_cls": 4.52062, "loss": 4.52062, "time": 0.84316} +{"mode": "val", "epoch": 43, "iter": 309, "lr": 0.08106, "top1_acc": 0.16715, "top5_acc": 0.37679, "mean_class_accuracy": 0.16689} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.08104, "memory": 15990, "data_time": 1.56061, "top1_acc": 0.225, "top5_acc": 0.47375, "loss_cls": 4.48252, "loss": 4.48252, "time": 2.61852} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.08101, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23672, "top5_acc": 0.46875, "loss_cls": 4.48397, "loss": 4.48397, "time": 0.85527} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.08099, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22875, "top5_acc": 0.45984, "loss_cls": 4.50274, "loss": 4.50274, "time": 0.86213} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.08097, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.2175, "top5_acc": 0.46141, "loss_cls": 4.52807, "loss": 4.52807, "time": 0.85231} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.08095, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22656, "top5_acc": 0.46859, "loss_cls": 4.52836, "loss": 4.52836, "time": 0.85553} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.08093, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22891, "top5_acc": 0.46125, "loss_cls": 4.52227, "loss": 4.52227, "time": 0.85425} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.0809, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22969, "top5_acc": 0.46594, "loss_cls": 4.48194, "loss": 4.48194, "time": 0.86376} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.08088, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22844, "top5_acc": 0.47016, "loss_cls": 4.5014, "loss": 4.5014, "time": 0.85855} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.08086, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23312, "top5_acc": 0.47188, "loss_cls": 4.44097, "loss": 4.44097, "time": 0.86381} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.08084, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22969, "top5_acc": 0.47578, "loss_cls": 4.46247, "loss": 4.46247, "time": 0.85552} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.08082, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.23203, "top5_acc": 0.46609, "loss_cls": 4.52212, "loss": 4.52212, "time": 0.85929} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.08079, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22797, "top5_acc": 0.46828, "loss_cls": 4.45827, "loss": 4.45827, "time": 0.8556} +{"mode": "train", "epoch": 44, "iter": 1300, "lr": 0.08077, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.235, "top5_acc": 0.47625, "loss_cls": 4.50145, "loss": 4.50145, "time": 0.8556} +{"mode": "train", "epoch": 44, "iter": 1400, "lr": 0.08075, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22984, "top5_acc": 0.46047, "loss_cls": 4.52734, "loss": 4.52734, "time": 0.85809} +{"mode": "train", "epoch": 44, "iter": 1500, "lr": 0.08073, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23078, "top5_acc": 0.46859, "loss_cls": 4.51274, "loss": 4.51274, "time": 0.8606} +{"mode": "train", "epoch": 44, "iter": 1600, "lr": 0.08071, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22094, "top5_acc": 0.46781, "loss_cls": 4.49926, "loss": 4.49926, "time": 0.85712} +{"mode": "train", "epoch": 44, "iter": 1700, "lr": 0.08068, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2325, "top5_acc": 0.46484, "loss_cls": 4.4799, "loss": 4.4799, "time": 0.86433} +{"mode": "train", "epoch": 44, "iter": 1800, "lr": 0.08066, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.22859, "top5_acc": 0.46422, "loss_cls": 4.52244, "loss": 4.52244, "time": 0.85698} +{"mode": "train", "epoch": 44, "iter": 1900, "lr": 0.08064, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22703, "top5_acc": 0.46578, "loss_cls": 4.50332, "loss": 4.50332, "time": 0.86241} +{"mode": "train", "epoch": 44, "iter": 2000, "lr": 0.08062, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23531, "top5_acc": 0.47906, "loss_cls": 4.45971, "loss": 4.45971, "time": 0.86224} +{"mode": "train", "epoch": 44, "iter": 2100, "lr": 0.0806, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23047, "top5_acc": 0.46641, "loss_cls": 4.50625, "loss": 4.50625, "time": 0.86078} +{"mode": "train", "epoch": 44, "iter": 2200, "lr": 0.08057, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.23156, "top5_acc": 0.46266, "loss_cls": 4.52078, "loss": 4.52078, "time": 0.86402} +{"mode": "train", "epoch": 44, "iter": 2300, "lr": 0.08055, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22984, "top5_acc": 0.46562, "loss_cls": 4.53, "loss": 4.53, "time": 0.86127} +{"mode": "train", "epoch": 44, "iter": 2400, "lr": 0.08053, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22625, "top5_acc": 0.4725, "loss_cls": 4.46855, "loss": 4.46855, "time": 0.86099} +{"mode": "train", "epoch": 44, "iter": 2500, "lr": 0.08051, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22609, "top5_acc": 0.47016, "loss_cls": 4.51581, "loss": 4.51581, "time": 0.866} +{"mode": "train", "epoch": 44, "iter": 2600, "lr": 0.08048, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23938, "top5_acc": 0.46953, "loss_cls": 4.46718, "loss": 4.46718, "time": 0.85954} +{"mode": "train", "epoch": 44, "iter": 2700, "lr": 0.08046, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23359, "top5_acc": 0.46047, "loss_cls": 4.49446, "loss": 4.49446, "time": 0.86613} +{"mode": "train", "epoch": 44, "iter": 2800, "lr": 0.08044, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.22688, "top5_acc": 0.46969, "loss_cls": 4.49595, "loss": 4.49595, "time": 0.863} +{"mode": "train", "epoch": 44, "iter": 2900, "lr": 0.08042, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23656, "top5_acc": 0.47328, "loss_cls": 4.46021, "loss": 4.46021, "time": 0.86277} +{"mode": "train", "epoch": 44, "iter": 3000, "lr": 0.0804, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23234, "top5_acc": 0.46562, "loss_cls": 4.49811, "loss": 4.49811, "time": 0.8497} +{"mode": "train", "epoch": 44, "iter": 3100, "lr": 0.08037, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23, "top5_acc": 0.46047, "loss_cls": 4.52919, "loss": 4.52919, "time": 0.85238} +{"mode": "train", "epoch": 44, "iter": 3200, "lr": 0.08035, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2225, "top5_acc": 0.46766, "loss_cls": 4.52306, "loss": 4.52306, "time": 0.85235} +{"mode": "train", "epoch": 44, "iter": 3300, "lr": 0.08033, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22734, "top5_acc": 0.47031, "loss_cls": 4.49198, "loss": 4.49198, "time": 0.8636} +{"mode": "train", "epoch": 44, "iter": 3400, "lr": 0.08031, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23312, "top5_acc": 0.46969, "loss_cls": 4.46398, "loss": 4.46398, "time": 0.85859} +{"mode": "train", "epoch": 44, "iter": 3500, "lr": 0.08028, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23391, "top5_acc": 0.475, "loss_cls": 4.44932, "loss": 4.44932, "time": 0.8497} +{"mode": "train", "epoch": 44, "iter": 3600, "lr": 0.08026, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22812, "top5_acc": 0.46859, "loss_cls": 4.50451, "loss": 4.50451, "time": 0.85437} +{"mode": "train", "epoch": 44, "iter": 3700, "lr": 0.08024, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22578, "top5_acc": 0.46672, "loss_cls": 4.50431, "loss": 4.50431, "time": 0.85638} +{"mode": "val", "epoch": 44, "iter": 309, "lr": 0.08023, "top1_acc": 0.15818, "top5_acc": 0.36428, "mean_class_accuracy": 0.15799} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.08021, "memory": 15990, "data_time": 1.60507, "top1_acc": 0.23422, "top5_acc": 0.47281, "loss_cls": 4.45041, "loss": 4.45041, "time": 2.63533} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.08019, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23812, "top5_acc": 0.47328, "loss_cls": 4.46315, "loss": 4.46315, "time": 0.85082} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.08016, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22719, "top5_acc": 0.47641, "loss_cls": 4.48702, "loss": 4.48702, "time": 0.85059} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.08014, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.23484, "top5_acc": 0.47781, "loss_cls": 4.45128, "loss": 4.45128, "time": 0.85205} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.08012, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23453, "top5_acc": 0.47172, "loss_cls": 4.48532, "loss": 4.48532, "time": 0.85126} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.0801, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.22438, "top5_acc": 0.46219, "loss_cls": 4.5112, "loss": 4.5112, "time": 0.8484} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.08007, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23844, "top5_acc": 0.47984, "loss_cls": 4.45502, "loss": 4.45502, "time": 0.85333} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.08005, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.23375, "top5_acc": 0.46516, "loss_cls": 4.47734, "loss": 4.47734, "time": 0.84633} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.08003, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22969, "top5_acc": 0.46781, "loss_cls": 4.48645, "loss": 4.48645, "time": 0.85281} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.08001, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23078, "top5_acc": 0.46, "loss_cls": 4.49507, "loss": 4.49507, "time": 0.84616} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.07998, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22672, "top5_acc": 0.46062, "loss_cls": 4.50687, "loss": 4.50687, "time": 0.84491} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.07996, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23766, "top5_acc": 0.48016, "loss_cls": 4.45264, "loss": 4.45264, "time": 0.84162} +{"mode": "train", "epoch": 45, "iter": 1300, "lr": 0.07994, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22234, "top5_acc": 0.45734, "loss_cls": 4.53258, "loss": 4.53258, "time": 0.84762} +{"mode": "train", "epoch": 45, "iter": 1400, "lr": 0.07992, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23562, "top5_acc": 0.47734, "loss_cls": 4.46738, "loss": 4.46738, "time": 0.85268} +{"mode": "train", "epoch": 45, "iter": 1500, "lr": 0.0799, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23781, "top5_acc": 0.47047, "loss_cls": 4.45408, "loss": 4.45408, "time": 0.85041} +{"mode": "train", "epoch": 45, "iter": 1600, "lr": 0.07987, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23453, "top5_acc": 0.47125, "loss_cls": 4.46922, "loss": 4.46922, "time": 0.85098} +{"mode": "train", "epoch": 45, "iter": 1700, "lr": 0.07985, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23281, "top5_acc": 0.4725, "loss_cls": 4.45651, "loss": 4.45651, "time": 0.84827} +{"mode": "train", "epoch": 45, "iter": 1800, "lr": 0.07983, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22344, "top5_acc": 0.46234, "loss_cls": 4.52935, "loss": 4.52935, "time": 0.84984} +{"mode": "train", "epoch": 45, "iter": 1900, "lr": 0.07981, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22594, "top5_acc": 0.46109, "loss_cls": 4.51379, "loss": 4.51379, "time": 0.84846} +{"mode": "train", "epoch": 45, "iter": 2000, "lr": 0.07978, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2375, "top5_acc": 0.47047, "loss_cls": 4.46577, "loss": 4.46577, "time": 0.85163} +{"mode": "train", "epoch": 45, "iter": 2100, "lr": 0.07976, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23172, "top5_acc": 0.46891, "loss_cls": 4.5214, "loss": 4.5214, "time": 0.84907} +{"mode": "train", "epoch": 45, "iter": 2200, "lr": 0.07974, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23375, "top5_acc": 0.47562, "loss_cls": 4.46732, "loss": 4.46732, "time": 0.84902} +{"mode": "train", "epoch": 45, "iter": 2300, "lr": 0.07972, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.225, "top5_acc": 0.46219, "loss_cls": 4.51826, "loss": 4.51826, "time": 0.84677} +{"mode": "train", "epoch": 45, "iter": 2400, "lr": 0.07969, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23094, "top5_acc": 0.47062, "loss_cls": 4.47602, "loss": 4.47602, "time": 0.85368} +{"mode": "train", "epoch": 45, "iter": 2500, "lr": 0.07967, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23172, "top5_acc": 0.46844, "loss_cls": 4.47357, "loss": 4.47357, "time": 0.85664} +{"mode": "train", "epoch": 45, "iter": 2600, "lr": 0.07965, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.23109, "top5_acc": 0.46625, "loss_cls": 4.48013, "loss": 4.48013, "time": 0.85362} +{"mode": "train", "epoch": 45, "iter": 2700, "lr": 0.07963, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22469, "top5_acc": 0.45828, "loss_cls": 4.52833, "loss": 4.52833, "time": 0.85272} +{"mode": "train", "epoch": 45, "iter": 2800, "lr": 0.0796, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23578, "top5_acc": 0.46938, "loss_cls": 4.47853, "loss": 4.47853, "time": 0.85038} +{"mode": "train", "epoch": 45, "iter": 2900, "lr": 0.07958, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23688, "top5_acc": 0.48469, "loss_cls": 4.46001, "loss": 4.46001, "time": 0.84829} +{"mode": "train", "epoch": 45, "iter": 3000, "lr": 0.07956, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.21578, "top5_acc": 0.45453, "loss_cls": 4.56238, "loss": 4.56238, "time": 0.8478} +{"mode": "train", "epoch": 45, "iter": 3100, "lr": 0.07954, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23875, "top5_acc": 0.47516, "loss_cls": 4.46677, "loss": 4.46677, "time": 0.84519} +{"mode": "train", "epoch": 45, "iter": 3200, "lr": 0.07951, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22766, "top5_acc": 0.46906, "loss_cls": 4.4879, "loss": 4.4879, "time": 0.84414} +{"mode": "train", "epoch": 45, "iter": 3300, "lr": 0.07949, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22203, "top5_acc": 0.46891, "loss_cls": 4.52223, "loss": 4.52223, "time": 0.85271} +{"mode": "train", "epoch": 45, "iter": 3400, "lr": 0.07947, "memory": 15990, "data_time": 0.00086, "top1_acc": 0.23078, "top5_acc": 0.46234, "loss_cls": 4.48546, "loss": 4.48546, "time": 0.8477} +{"mode": "train", "epoch": 45, "iter": 3500, "lr": 0.07945, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23031, "top5_acc": 0.46844, "loss_cls": 4.47625, "loss": 4.47625, "time": 0.84938} +{"mode": "train", "epoch": 45, "iter": 3600, "lr": 0.07942, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22578, "top5_acc": 0.465, "loss_cls": 4.50811, "loss": 4.50811, "time": 0.83996} +{"mode": "train", "epoch": 45, "iter": 3700, "lr": 0.0794, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.23484, "top5_acc": 0.46422, "loss_cls": 4.49708, "loss": 4.49708, "time": 0.84247} +{"mode": "val", "epoch": 45, "iter": 309, "lr": 0.07939, "top1_acc": 0.14795, "top5_acc": 0.34898, "mean_class_accuracy": 0.14774} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.07937, "memory": 15990, "data_time": 1.60199, "top1_acc": 0.23203, "top5_acc": 0.46578, "loss_cls": 4.48904, "loss": 4.48904, "time": 2.65071} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.07934, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.225, "top5_acc": 0.46375, "loss_cls": 4.48161, "loss": 4.48161, "time": 0.8634} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.07932, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23391, "top5_acc": 0.47547, "loss_cls": 4.47221, "loss": 4.47221, "time": 0.86298} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.0793, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.24469, "top5_acc": 0.48594, "loss_cls": 4.38605, "loss": 4.38605, "time": 0.86317} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.07928, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23297, "top5_acc": 0.46625, "loss_cls": 4.49477, "loss": 4.49477, "time": 0.8576} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.07925, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.2375, "top5_acc": 0.48078, "loss_cls": 4.46662, "loss": 4.46662, "time": 0.85137} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.07923, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23828, "top5_acc": 0.46188, "loss_cls": 4.48186, "loss": 4.48186, "time": 0.85524} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.07921, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.23594, "top5_acc": 0.47172, "loss_cls": 4.48783, "loss": 4.48783, "time": 0.85989} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.07919, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.2375, "top5_acc": 0.47719, "loss_cls": 4.45538, "loss": 4.45538, "time": 0.8636} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.07916, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23156, "top5_acc": 0.47109, "loss_cls": 4.47179, "loss": 4.47179, "time": 0.86248} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.07914, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.24281, "top5_acc": 0.47156, "loss_cls": 4.47426, "loss": 4.47426, "time": 0.86247} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.07912, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23203, "top5_acc": 0.47547, "loss_cls": 4.47269, "loss": 4.47269, "time": 0.86123} +{"mode": "train", "epoch": 46, "iter": 1300, "lr": 0.07909, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22031, "top5_acc": 0.45797, "loss_cls": 4.54271, "loss": 4.54271, "time": 0.85587} +{"mode": "train", "epoch": 46, "iter": 1400, "lr": 0.07907, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22969, "top5_acc": 0.46531, "loss_cls": 4.48155, "loss": 4.48155, "time": 0.85408} +{"mode": "train", "epoch": 46, "iter": 1500, "lr": 0.07905, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22578, "top5_acc": 0.46922, "loss_cls": 4.48082, "loss": 4.48082, "time": 0.85809} +{"mode": "train", "epoch": 46, "iter": 1600, "lr": 0.07903, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22969, "top5_acc": 0.47281, "loss_cls": 4.50095, "loss": 4.50095, "time": 0.86131} +{"mode": "train", "epoch": 46, "iter": 1700, "lr": 0.079, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2325, "top5_acc": 0.47891, "loss_cls": 4.42467, "loss": 4.42467, "time": 0.86118} +{"mode": "train", "epoch": 46, "iter": 1800, "lr": 0.07898, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23828, "top5_acc": 0.47094, "loss_cls": 4.46902, "loss": 4.46902, "time": 0.8595} +{"mode": "train", "epoch": 46, "iter": 1900, "lr": 0.07896, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24062, "top5_acc": 0.47156, "loss_cls": 4.46337, "loss": 4.46337, "time": 0.85942} +{"mode": "train", "epoch": 46, "iter": 2000, "lr": 0.07894, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.23016, "top5_acc": 0.47875, "loss_cls": 4.4466, "loss": 4.4466, "time": 0.8625} +{"mode": "train", "epoch": 46, "iter": 2100, "lr": 0.07891, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23219, "top5_acc": 0.46531, "loss_cls": 4.4737, "loss": 4.4737, "time": 0.86984} +{"mode": "train", "epoch": 46, "iter": 2200, "lr": 0.07889, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23078, "top5_acc": 0.46625, "loss_cls": 4.50687, "loss": 4.50687, "time": 0.86574} +{"mode": "train", "epoch": 46, "iter": 2300, "lr": 0.07887, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23109, "top5_acc": 0.46203, "loss_cls": 4.50577, "loss": 4.50577, "time": 0.86177} +{"mode": "train", "epoch": 46, "iter": 2400, "lr": 0.07884, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22609, "top5_acc": 0.45609, "loss_cls": 4.51734, "loss": 4.51734, "time": 0.86225} +{"mode": "train", "epoch": 46, "iter": 2500, "lr": 0.07882, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24141, "top5_acc": 0.48406, "loss_cls": 4.42392, "loss": 4.42392, "time": 0.85704} +{"mode": "train", "epoch": 46, "iter": 2600, "lr": 0.0788, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23203, "top5_acc": 0.47078, "loss_cls": 4.47407, "loss": 4.47407, "time": 0.86588} +{"mode": "train", "epoch": 46, "iter": 2700, "lr": 0.07878, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23047, "top5_acc": 0.46875, "loss_cls": 4.52101, "loss": 4.52101, "time": 0.85996} +{"mode": "train", "epoch": 46, "iter": 2800, "lr": 0.07875, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22781, "top5_acc": 0.46219, "loss_cls": 4.4854, "loss": 4.4854, "time": 0.8581} +{"mode": "train", "epoch": 46, "iter": 2900, "lr": 0.07873, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22906, "top5_acc": 0.46094, "loss_cls": 4.51574, "loss": 4.51574, "time": 0.86015} +{"mode": "train", "epoch": 46, "iter": 3000, "lr": 0.07871, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23422, "top5_acc": 0.47609, "loss_cls": 4.46862, "loss": 4.46862, "time": 0.85307} +{"mode": "train", "epoch": 46, "iter": 3100, "lr": 0.07868, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22906, "top5_acc": 0.46797, "loss_cls": 4.50264, "loss": 4.50264, "time": 0.85335} +{"mode": "train", "epoch": 46, "iter": 3200, "lr": 0.07866, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.22703, "top5_acc": 0.45859, "loss_cls": 4.51429, "loss": 4.51429, "time": 0.86003} +{"mode": "train", "epoch": 46, "iter": 3300, "lr": 0.07864, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22422, "top5_acc": 0.46438, "loss_cls": 4.50536, "loss": 4.50536, "time": 0.85558} +{"mode": "train", "epoch": 46, "iter": 3400, "lr": 0.07862, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23609, "top5_acc": 0.47359, "loss_cls": 4.47761, "loss": 4.47761, "time": 0.85703} +{"mode": "train", "epoch": 46, "iter": 3500, "lr": 0.07859, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22922, "top5_acc": 0.46859, "loss_cls": 4.48535, "loss": 4.48535, "time": 0.84965} +{"mode": "train", "epoch": 46, "iter": 3600, "lr": 0.07857, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23344, "top5_acc": 0.47422, "loss_cls": 4.47751, "loss": 4.47751, "time": 0.8497} +{"mode": "train", "epoch": 46, "iter": 3700, "lr": 0.07855, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23312, "top5_acc": 0.46391, "loss_cls": 4.4921, "loss": 4.4921, "time": 0.85972} +{"mode": "val", "epoch": 46, "iter": 309, "lr": 0.07854, "top1_acc": 0.16958, "top5_acc": 0.37583, "mean_class_accuracy": 0.16942} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.07851, "memory": 15990, "data_time": 1.55795, "top1_acc": 0.22688, "top5_acc": 0.47781, "loss_cls": 4.44876, "loss": 4.44876, "time": 2.60176} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.07849, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23641, "top5_acc": 0.47766, "loss_cls": 4.44999, "loss": 4.44999, "time": 0.84644} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.07847, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23188, "top5_acc": 0.47625, "loss_cls": 4.44442, "loss": 4.44442, "time": 0.84674} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.07844, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22875, "top5_acc": 0.47375, "loss_cls": 4.44355, "loss": 4.44355, "time": 0.84945} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.07842, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.235, "top5_acc": 0.47859, "loss_cls": 4.43339, "loss": 4.43339, "time": 0.84609} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.0784, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.23641, "top5_acc": 0.46766, "loss_cls": 4.44006, "loss": 4.44006, "time": 0.85121} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.07838, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22625, "top5_acc": 0.46734, "loss_cls": 4.483, "loss": 4.483, "time": 0.84991} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.07835, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23391, "top5_acc": 0.47078, "loss_cls": 4.45585, "loss": 4.45585, "time": 0.85083} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.07833, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23031, "top5_acc": 0.46859, "loss_cls": 4.4752, "loss": 4.4752, "time": 0.84604} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.07831, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23203, "top5_acc": 0.46828, "loss_cls": 4.49733, "loss": 4.49733, "time": 0.84543} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.07828, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.23062, "top5_acc": 0.46734, "loss_cls": 4.4977, "loss": 4.4977, "time": 0.84761} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.07826, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23969, "top5_acc": 0.47609, "loss_cls": 4.44839, "loss": 4.44839, "time": 0.85397} +{"mode": "train", "epoch": 47, "iter": 1300, "lr": 0.07824, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23078, "top5_acc": 0.47672, "loss_cls": 4.44639, "loss": 4.44639, "time": 0.84728} +{"mode": "train", "epoch": 47, "iter": 1400, "lr": 0.07821, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23391, "top5_acc": 0.47547, "loss_cls": 4.46913, "loss": 4.46913, "time": 0.84768} +{"mode": "train", "epoch": 47, "iter": 1500, "lr": 0.07819, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22422, "top5_acc": 0.46406, "loss_cls": 4.5043, "loss": 4.5043, "time": 0.8526} +{"mode": "train", "epoch": 47, "iter": 1600, "lr": 0.07817, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23391, "top5_acc": 0.47453, "loss_cls": 4.45512, "loss": 4.45512, "time": 0.84689} +{"mode": "train", "epoch": 47, "iter": 1700, "lr": 0.07814, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23422, "top5_acc": 0.47484, "loss_cls": 4.47247, "loss": 4.47247, "time": 0.84682} +{"mode": "train", "epoch": 47, "iter": 1800, "lr": 0.07812, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24031, "top5_acc": 0.47969, "loss_cls": 4.44054, "loss": 4.44054, "time": 0.84206} +{"mode": "train", "epoch": 47, "iter": 1900, "lr": 0.0781, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23656, "top5_acc": 0.47391, "loss_cls": 4.45036, "loss": 4.45036, "time": 0.84501} +{"mode": "train", "epoch": 47, "iter": 2000, "lr": 0.07808, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22219, "top5_acc": 0.46672, "loss_cls": 4.51638, "loss": 4.51638, "time": 0.85118} +{"mode": "train", "epoch": 47, "iter": 2100, "lr": 0.07805, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24, "top5_acc": 0.46984, "loss_cls": 4.47702, "loss": 4.47702, "time": 0.84758} +{"mode": "train", "epoch": 47, "iter": 2200, "lr": 0.07803, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22844, "top5_acc": 0.4775, "loss_cls": 4.47374, "loss": 4.47374, "time": 0.84876} +{"mode": "train", "epoch": 47, "iter": 2300, "lr": 0.07801, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22562, "top5_acc": 0.46234, "loss_cls": 4.52812, "loss": 4.52812, "time": 0.8518} +{"mode": "train", "epoch": 47, "iter": 2400, "lr": 0.07798, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23641, "top5_acc": 0.47594, "loss_cls": 4.44365, "loss": 4.44365, "time": 0.85342} +{"mode": "train", "epoch": 47, "iter": 2500, "lr": 0.07796, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23109, "top5_acc": 0.46891, "loss_cls": 4.49835, "loss": 4.49835, "time": 0.85533} +{"mode": "train", "epoch": 47, "iter": 2600, "lr": 0.07794, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23547, "top5_acc": 0.47422, "loss_cls": 4.43139, "loss": 4.43139, "time": 0.84831} +{"mode": "train", "epoch": 47, "iter": 2700, "lr": 0.07791, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23594, "top5_acc": 0.47156, "loss_cls": 4.48287, "loss": 4.48287, "time": 0.84868} +{"mode": "train", "epoch": 47, "iter": 2800, "lr": 0.07789, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23828, "top5_acc": 0.46688, "loss_cls": 4.4815, "loss": 4.4815, "time": 0.85539} +{"mode": "train", "epoch": 47, "iter": 2900, "lr": 0.07787, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22984, "top5_acc": 0.46219, "loss_cls": 4.49075, "loss": 4.49075, "time": 0.85373} +{"mode": "train", "epoch": 47, "iter": 3000, "lr": 0.07784, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24188, "top5_acc": 0.47031, "loss_cls": 4.46363, "loss": 4.46363, "time": 0.84502} +{"mode": "train", "epoch": 47, "iter": 3100, "lr": 0.07782, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.23734, "top5_acc": 0.47469, "loss_cls": 4.45794, "loss": 4.45794, "time": 0.84528} +{"mode": "train", "epoch": 47, "iter": 3200, "lr": 0.0778, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23969, "top5_acc": 0.47422, "loss_cls": 4.48469, "loss": 4.48469, "time": 0.85411} +{"mode": "train", "epoch": 47, "iter": 3300, "lr": 0.07777, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23984, "top5_acc": 0.4725, "loss_cls": 4.45812, "loss": 4.45812, "time": 0.85589} +{"mode": "train", "epoch": 47, "iter": 3400, "lr": 0.07775, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22922, "top5_acc": 0.47047, "loss_cls": 4.51015, "loss": 4.51015, "time": 0.84496} +{"mode": "train", "epoch": 47, "iter": 3500, "lr": 0.07773, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22906, "top5_acc": 0.46266, "loss_cls": 4.50609, "loss": 4.50609, "time": 0.84117} +{"mode": "train", "epoch": 47, "iter": 3600, "lr": 0.0777, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24281, "top5_acc": 0.47828, "loss_cls": 4.42558, "loss": 4.42558, "time": 0.84653} +{"mode": "train", "epoch": 47, "iter": 3700, "lr": 0.07768, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22891, "top5_acc": 0.46984, "loss_cls": 4.47138, "loss": 4.47138, "time": 0.84331} +{"mode": "val", "epoch": 47, "iter": 309, "lr": 0.07767, "top1_acc": 0.17358, "top5_acc": 0.37649, "mean_class_accuracy": 0.17341} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.07765, "memory": 15990, "data_time": 1.62025, "top1_acc": 0.23969, "top5_acc": 0.47969, "loss_cls": 4.42061, "loss": 4.42061, "time": 2.67657} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.07762, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.2475, "top5_acc": 0.49359, "loss_cls": 4.38563, "loss": 4.38563, "time": 0.85642} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.0776, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23891, "top5_acc": 0.47672, "loss_cls": 4.45142, "loss": 4.45142, "time": 0.86176} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.07758, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22672, "top5_acc": 0.46922, "loss_cls": 4.50432, "loss": 4.50432, "time": 0.86016} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.07755, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24156, "top5_acc": 0.48016, "loss_cls": 4.44448, "loss": 4.44448, "time": 0.85071} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.07753, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23703, "top5_acc": 0.47344, "loss_cls": 4.45556, "loss": 4.45556, "time": 0.85403} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.07751, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.23156, "top5_acc": 0.47297, "loss_cls": 4.45916, "loss": 4.45916, "time": 0.84732} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.07748, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23203, "top5_acc": 0.47266, "loss_cls": 4.46639, "loss": 4.46639, "time": 0.85707} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.07746, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23828, "top5_acc": 0.47891, "loss_cls": 4.44419, "loss": 4.44419, "time": 0.84364} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.07744, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.23641, "top5_acc": 0.48172, "loss_cls": 4.43479, "loss": 4.43479, "time": 0.85079} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.07741, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23312, "top5_acc": 0.47703, "loss_cls": 4.47033, "loss": 4.47033, "time": 0.85059} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.07739, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22688, "top5_acc": 0.46422, "loss_cls": 4.5032, "loss": 4.5032, "time": 0.85087} +{"mode": "train", "epoch": 48, "iter": 1300, "lr": 0.07737, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.23406, "top5_acc": 0.47141, "loss_cls": 4.46676, "loss": 4.46676, "time": 0.85233} +{"mode": "train", "epoch": 48, "iter": 1400, "lr": 0.07734, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.24016, "top5_acc": 0.48078, "loss_cls": 4.4111, "loss": 4.4111, "time": 0.86282} +{"mode": "train", "epoch": 48, "iter": 1500, "lr": 0.07732, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24422, "top5_acc": 0.48484, "loss_cls": 4.4061, "loss": 4.4061, "time": 0.85841} +{"mode": "train", "epoch": 48, "iter": 1600, "lr": 0.0773, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23312, "top5_acc": 0.47078, "loss_cls": 4.45951, "loss": 4.45951, "time": 0.85677} +{"mode": "train", "epoch": 48, "iter": 1700, "lr": 0.07727, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23453, "top5_acc": 0.47953, "loss_cls": 4.47534, "loss": 4.47534, "time": 0.85471} +{"mode": "train", "epoch": 48, "iter": 1800, "lr": 0.07725, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23938, "top5_acc": 0.46656, "loss_cls": 4.46114, "loss": 4.46114, "time": 0.85889} +{"mode": "train", "epoch": 48, "iter": 1900, "lr": 0.07723, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23469, "top5_acc": 0.46719, "loss_cls": 4.4893, "loss": 4.4893, "time": 0.85624} +{"mode": "train", "epoch": 48, "iter": 2000, "lr": 0.0772, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2375, "top5_acc": 0.475, "loss_cls": 4.45095, "loss": 4.45095, "time": 0.85679} +{"mode": "train", "epoch": 48, "iter": 2100, "lr": 0.07718, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23797, "top5_acc": 0.47172, "loss_cls": 4.46368, "loss": 4.46368, "time": 0.85707} +{"mode": "train", "epoch": 48, "iter": 2200, "lr": 0.07716, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.23359, "top5_acc": 0.47406, "loss_cls": 4.46554, "loss": 4.46554, "time": 0.86117} +{"mode": "train", "epoch": 48, "iter": 2300, "lr": 0.07713, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23906, "top5_acc": 0.47109, "loss_cls": 4.48074, "loss": 4.48074, "time": 0.85931} +{"mode": "train", "epoch": 48, "iter": 2400, "lr": 0.07711, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23188, "top5_acc": 0.46688, "loss_cls": 4.4731, "loss": 4.4731, "time": 0.86072} +{"mode": "train", "epoch": 48, "iter": 2500, "lr": 0.07709, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22234, "top5_acc": 0.46125, "loss_cls": 4.53383, "loss": 4.53383, "time": 0.86629} +{"mode": "train", "epoch": 48, "iter": 2600, "lr": 0.07706, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23641, "top5_acc": 0.46609, "loss_cls": 4.48935, "loss": 4.48935, "time": 0.85919} +{"mode": "train", "epoch": 48, "iter": 2700, "lr": 0.07704, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23359, "top5_acc": 0.47344, "loss_cls": 4.47089, "loss": 4.47089, "time": 0.86085} +{"mode": "train", "epoch": 48, "iter": 2800, "lr": 0.07701, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23953, "top5_acc": 0.46875, "loss_cls": 4.46453, "loss": 4.46453, "time": 0.86145} +{"mode": "train", "epoch": 48, "iter": 2900, "lr": 0.07699, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23406, "top5_acc": 0.47156, "loss_cls": 4.48654, "loss": 4.48654, "time": 0.85517} +{"mode": "train", "epoch": 48, "iter": 3000, "lr": 0.07697, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23891, "top5_acc": 0.48219, "loss_cls": 4.41071, "loss": 4.41071, "time": 0.85753} +{"mode": "train", "epoch": 48, "iter": 3100, "lr": 0.07694, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23703, "top5_acc": 0.47594, "loss_cls": 4.43579, "loss": 4.43579, "time": 0.8563} +{"mode": "train", "epoch": 48, "iter": 3200, "lr": 0.07692, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22391, "top5_acc": 0.46047, "loss_cls": 4.51095, "loss": 4.51095, "time": 0.86084} +{"mode": "train", "epoch": 48, "iter": 3300, "lr": 0.0769, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23516, "top5_acc": 0.46844, "loss_cls": 4.46821, "loss": 4.46821, "time": 0.85389} +{"mode": "train", "epoch": 48, "iter": 3400, "lr": 0.07687, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.225, "top5_acc": 0.46656, "loss_cls": 4.50225, "loss": 4.50225, "time": 0.8523} +{"mode": "train", "epoch": 48, "iter": 3500, "lr": 0.07685, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23078, "top5_acc": 0.46266, "loss_cls": 4.51853, "loss": 4.51853, "time": 0.84749} +{"mode": "train", "epoch": 48, "iter": 3600, "lr": 0.07683, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.23766, "top5_acc": 0.47719, "loss_cls": 4.43181, "loss": 4.43181, "time": 0.85196} +{"mode": "train", "epoch": 48, "iter": 3700, "lr": 0.0768, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.235, "top5_acc": 0.46922, "loss_cls": 4.47684, "loss": 4.47684, "time": 0.85886} +{"mode": "val", "epoch": 48, "iter": 309, "lr": 0.07679, "top1_acc": 0.1752, "top5_acc": 0.39173, "mean_class_accuracy": 0.17503} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.07677, "memory": 15990, "data_time": 1.58844, "top1_acc": 0.23516, "top5_acc": 0.48156, "loss_cls": 4.43723, "loss": 4.43723, "time": 2.65263} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.07674, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.24359, "top5_acc": 0.48328, "loss_cls": 4.39179, "loss": 4.39179, "time": 0.85938} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.07672, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23266, "top5_acc": 0.47625, "loss_cls": 4.44681, "loss": 4.44681, "time": 0.86479} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.0767, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23172, "top5_acc": 0.46922, "loss_cls": 4.46715, "loss": 4.46715, "time": 0.85762} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.07667, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23266, "top5_acc": 0.47766, "loss_cls": 4.44272, "loss": 4.44272, "time": 0.85445} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.07665, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24391, "top5_acc": 0.4775, "loss_cls": 4.40748, "loss": 4.40748, "time": 0.85694} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.07663, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24172, "top5_acc": 0.47422, "loss_cls": 4.46182, "loss": 4.46182, "time": 0.85327} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.0766, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22469, "top5_acc": 0.47391, "loss_cls": 4.46533, "loss": 4.46533, "time": 0.85318} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.07658, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23484, "top5_acc": 0.47688, "loss_cls": 4.46098, "loss": 4.46098, "time": 0.85397} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.07656, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23594, "top5_acc": 0.47141, "loss_cls": 4.46763, "loss": 4.46763, "time": 0.85314} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.07653, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23109, "top5_acc": 0.47359, "loss_cls": 4.4674, "loss": 4.4674, "time": 0.85344} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.07651, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24297, "top5_acc": 0.47641, "loss_cls": 4.43371, "loss": 4.43371, "time": 0.84881} +{"mode": "train", "epoch": 49, "iter": 1300, "lr": 0.07648, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23266, "top5_acc": 0.47906, "loss_cls": 4.43638, "loss": 4.43638, "time": 0.85144} +{"mode": "train", "epoch": 49, "iter": 1400, "lr": 0.07646, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23109, "top5_acc": 0.47125, "loss_cls": 4.47855, "loss": 4.47855, "time": 0.85279} +{"mode": "train", "epoch": 49, "iter": 1500, "lr": 0.07644, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23953, "top5_acc": 0.47875, "loss_cls": 4.42861, "loss": 4.42861, "time": 0.8575} +{"mode": "train", "epoch": 49, "iter": 1600, "lr": 0.07641, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24203, "top5_acc": 0.47188, "loss_cls": 4.4364, "loss": 4.4364, "time": 0.85978} +{"mode": "train", "epoch": 49, "iter": 1700, "lr": 0.07639, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23703, "top5_acc": 0.46922, "loss_cls": 4.45335, "loss": 4.45335, "time": 0.85719} +{"mode": "train", "epoch": 49, "iter": 1800, "lr": 0.07637, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23656, "top5_acc": 0.475, "loss_cls": 4.46249, "loss": 4.46249, "time": 0.85653} +{"mode": "train", "epoch": 49, "iter": 1900, "lr": 0.07634, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23844, "top5_acc": 0.47219, "loss_cls": 4.47982, "loss": 4.47982, "time": 0.85953} +{"mode": "train", "epoch": 49, "iter": 2000, "lr": 0.07632, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23578, "top5_acc": 0.47672, "loss_cls": 4.43005, "loss": 4.43005, "time": 0.86048} +{"mode": "train", "epoch": 49, "iter": 2100, "lr": 0.07629, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23641, "top5_acc": 0.47016, "loss_cls": 4.46486, "loss": 4.46486, "time": 0.85677} +{"mode": "train", "epoch": 49, "iter": 2200, "lr": 0.07627, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22359, "top5_acc": 0.46422, "loss_cls": 4.50058, "loss": 4.50058, "time": 0.85778} +{"mode": "train", "epoch": 49, "iter": 2300, "lr": 0.07625, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23578, "top5_acc": 0.47812, "loss_cls": 4.42498, "loss": 4.42498, "time": 0.85669} +{"mode": "train", "epoch": 49, "iter": 2400, "lr": 0.07622, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23656, "top5_acc": 0.46828, "loss_cls": 4.4788, "loss": 4.4788, "time": 0.85762} +{"mode": "train", "epoch": 49, "iter": 2500, "lr": 0.0762, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23062, "top5_acc": 0.46938, "loss_cls": 4.45789, "loss": 4.45789, "time": 0.86291} +{"mode": "train", "epoch": 49, "iter": 2600, "lr": 0.07618, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22828, "top5_acc": 0.46812, "loss_cls": 4.47335, "loss": 4.47335, "time": 0.85892} +{"mode": "train", "epoch": 49, "iter": 2700, "lr": 0.07615, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23734, "top5_acc": 0.48234, "loss_cls": 4.41812, "loss": 4.41812, "time": 0.85939} +{"mode": "train", "epoch": 49, "iter": 2800, "lr": 0.07613, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22891, "top5_acc": 0.46141, "loss_cls": 4.53312, "loss": 4.53312, "time": 0.85056} +{"mode": "train", "epoch": 49, "iter": 2900, "lr": 0.0761, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23016, "top5_acc": 0.46719, "loss_cls": 4.47114, "loss": 4.47114, "time": 0.84935} +{"mode": "train", "epoch": 49, "iter": 3000, "lr": 0.07608, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22953, "top5_acc": 0.47312, "loss_cls": 4.48032, "loss": 4.48032, "time": 0.85537} +{"mode": "train", "epoch": 49, "iter": 3100, "lr": 0.07606, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.245, "top5_acc": 0.47328, "loss_cls": 4.43757, "loss": 4.43757, "time": 0.86048} +{"mode": "train", "epoch": 49, "iter": 3200, "lr": 0.07603, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23562, "top5_acc": 0.47719, "loss_cls": 4.48336, "loss": 4.48336, "time": 0.856} +{"mode": "train", "epoch": 49, "iter": 3300, "lr": 0.07601, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23109, "top5_acc": 0.46719, "loss_cls": 4.49332, "loss": 4.49332, "time": 0.85286} +{"mode": "train", "epoch": 49, "iter": 3400, "lr": 0.07598, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23219, "top5_acc": 0.46703, "loss_cls": 4.4773, "loss": 4.4773, "time": 0.85049} +{"mode": "train", "epoch": 49, "iter": 3500, "lr": 0.07596, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22469, "top5_acc": 0.45938, "loss_cls": 4.48476, "loss": 4.48476, "time": 0.84596} +{"mode": "train", "epoch": 49, "iter": 3600, "lr": 0.07594, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23281, "top5_acc": 0.47656, "loss_cls": 4.47902, "loss": 4.47902, "time": 0.85018} +{"mode": "train", "epoch": 49, "iter": 3700, "lr": 0.07591, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23609, "top5_acc": 0.47547, "loss_cls": 4.43826, "loss": 4.43826, "time": 0.86291} +{"mode": "val", "epoch": 49, "iter": 309, "lr": 0.0759, "top1_acc": 0.16472, "top5_acc": 0.36722, "mean_class_accuracy": 0.16458} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.07588, "memory": 15990, "data_time": 1.58594, "top1_acc": 0.23156, "top5_acc": 0.47719, "loss_cls": 4.41978, "loss": 4.41978, "time": 2.61245} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.07585, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24234, "top5_acc": 0.49344, "loss_cls": 4.38729, "loss": 4.38729, "time": 0.84967} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.07583, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24641, "top5_acc": 0.48781, "loss_cls": 4.39902, "loss": 4.39902, "time": 0.84846} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.07581, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23078, "top5_acc": 0.48203, "loss_cls": 4.4273, "loss": 4.4273, "time": 0.85039} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.07578, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.24359, "top5_acc": 0.48234, "loss_cls": 4.40667, "loss": 4.40667, "time": 0.84517} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.07576, "memory": 15990, "data_time": 0.00071, "top1_acc": 0.23562, "top5_acc": 0.47781, "loss_cls": 4.42131, "loss": 4.42131, "time": 0.84721} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.07573, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23422, "top5_acc": 0.47797, "loss_cls": 4.43712, "loss": 4.43712, "time": 0.84994} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.07571, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23297, "top5_acc": 0.47875, "loss_cls": 4.44081, "loss": 4.44081, "time": 0.84651} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.07569, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23734, "top5_acc": 0.47516, "loss_cls": 4.44553, "loss": 4.44553, "time": 0.84587} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.07566, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23156, "top5_acc": 0.47203, "loss_cls": 4.47731, "loss": 4.47731, "time": 0.85231} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.07564, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22797, "top5_acc": 0.47328, "loss_cls": 4.46927, "loss": 4.46927, "time": 0.84864} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.07561, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23094, "top5_acc": 0.46156, "loss_cls": 4.49808, "loss": 4.49808, "time": 0.8536} +{"mode": "train", "epoch": 50, "iter": 1300, "lr": 0.07559, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24578, "top5_acc": 0.48938, "loss_cls": 4.40418, "loss": 4.40418, "time": 0.84641} +{"mode": "train", "epoch": 50, "iter": 1400, "lr": 0.07557, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23672, "top5_acc": 0.47516, "loss_cls": 4.43124, "loss": 4.43124, "time": 0.85444} +{"mode": "train", "epoch": 50, "iter": 1500, "lr": 0.07554, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22469, "top5_acc": 0.4625, "loss_cls": 4.50296, "loss": 4.50296, "time": 0.8509} +{"mode": "train", "epoch": 50, "iter": 1600, "lr": 0.07552, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23562, "top5_acc": 0.47422, "loss_cls": 4.47006, "loss": 4.47006, "time": 0.84704} +{"mode": "train", "epoch": 50, "iter": 1700, "lr": 0.07549, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23797, "top5_acc": 0.47328, "loss_cls": 4.42991, "loss": 4.42991, "time": 0.84921} +{"mode": "train", "epoch": 50, "iter": 1800, "lr": 0.07547, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24953, "top5_acc": 0.48281, "loss_cls": 4.42363, "loss": 4.42363, "time": 0.85058} +{"mode": "train", "epoch": 50, "iter": 1900, "lr": 0.07545, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23922, "top5_acc": 0.47844, "loss_cls": 4.45312, "loss": 4.45312, "time": 0.85185} +{"mode": "train", "epoch": 50, "iter": 2000, "lr": 0.07542, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23484, "top5_acc": 0.47641, "loss_cls": 4.44723, "loss": 4.44723, "time": 0.85669} +{"mode": "train", "epoch": 50, "iter": 2100, "lr": 0.0754, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23953, "top5_acc": 0.46906, "loss_cls": 4.4518, "loss": 4.4518, "time": 0.85008} +{"mode": "train", "epoch": 50, "iter": 2200, "lr": 0.07537, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23984, "top5_acc": 0.48172, "loss_cls": 4.41956, "loss": 4.41956, "time": 0.85162} +{"mode": "train", "epoch": 50, "iter": 2300, "lr": 0.07535, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23719, "top5_acc": 0.47859, "loss_cls": 4.42817, "loss": 4.42817, "time": 0.84951} +{"mode": "train", "epoch": 50, "iter": 2400, "lr": 0.07533, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22781, "top5_acc": 0.47094, "loss_cls": 4.49256, "loss": 4.49256, "time": 0.84551} +{"mode": "train", "epoch": 50, "iter": 2500, "lr": 0.0753, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22797, "top5_acc": 0.46109, "loss_cls": 4.50942, "loss": 4.50942, "time": 0.85049} +{"mode": "train", "epoch": 50, "iter": 2600, "lr": 0.07528, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22547, "top5_acc": 0.47594, "loss_cls": 4.47949, "loss": 4.47949, "time": 0.85647} +{"mode": "train", "epoch": 50, "iter": 2700, "lr": 0.07525, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22797, "top5_acc": 0.4675, "loss_cls": 4.49279, "loss": 4.49279, "time": 0.84923} +{"mode": "train", "epoch": 50, "iter": 2800, "lr": 0.07523, "memory": 15990, "data_time": 0.00067, "top1_acc": 0.23656, "top5_acc": 0.48078, "loss_cls": 4.40688, "loss": 4.40688, "time": 0.85597} +{"mode": "train", "epoch": 50, "iter": 2900, "lr": 0.0752, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23703, "top5_acc": 0.47156, "loss_cls": 4.46499, "loss": 4.46499, "time": 0.85042} +{"mode": "train", "epoch": 50, "iter": 3000, "lr": 0.07518, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.23828, "top5_acc": 0.48062, "loss_cls": 4.43985, "loss": 4.43985, "time": 0.84097} +{"mode": "train", "epoch": 50, "iter": 3100, "lr": 0.07516, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23484, "top5_acc": 0.47859, "loss_cls": 4.45036, "loss": 4.45036, "time": 0.84855} +{"mode": "train", "epoch": 50, "iter": 3200, "lr": 0.07513, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23609, "top5_acc": 0.47297, "loss_cls": 4.47561, "loss": 4.47561, "time": 0.84712} +{"mode": "train", "epoch": 50, "iter": 3300, "lr": 0.07511, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.23516, "top5_acc": 0.46422, "loss_cls": 4.48043, "loss": 4.48043, "time": 0.84425} +{"mode": "train", "epoch": 50, "iter": 3400, "lr": 0.07508, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23047, "top5_acc": 0.4775, "loss_cls": 4.46155, "loss": 4.46155, "time": 0.8454} +{"mode": "train", "epoch": 50, "iter": 3500, "lr": 0.07506, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.22938, "top5_acc": 0.47281, "loss_cls": 4.46685, "loss": 4.46685, "time": 0.8425} +{"mode": "train", "epoch": 50, "iter": 3600, "lr": 0.07504, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23203, "top5_acc": 0.48156, "loss_cls": 4.43681, "loss": 4.43681, "time": 0.84355} +{"mode": "train", "epoch": 50, "iter": 3700, "lr": 0.07501, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.225, "top5_acc": 0.46719, "loss_cls": 4.52678, "loss": 4.52678, "time": 0.84558} +{"mode": "val", "epoch": 50, "iter": 309, "lr": 0.075, "top1_acc": 0.16269, "top5_acc": 0.37436, "mean_class_accuracy": 0.16241} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.07498, "memory": 15990, "data_time": 1.56426, "top1_acc": 0.24016, "top5_acc": 0.48562, "loss_cls": 4.42764, "loss": 4.42764, "time": 2.58359} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.07495, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23469, "top5_acc": 0.46891, "loss_cls": 4.44469, "loss": 4.44469, "time": 0.85057} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.07493, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23703, "top5_acc": 0.47875, "loss_cls": 4.40461, "loss": 4.40461, "time": 0.85043} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.0749, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23312, "top5_acc": 0.47891, "loss_cls": 4.43685, "loss": 4.43685, "time": 0.85463} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.07488, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.24047, "top5_acc": 0.47688, "loss_cls": 4.43242, "loss": 4.43242, "time": 0.84715} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.07485, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.24047, "top5_acc": 0.48109, "loss_cls": 4.42648, "loss": 4.42648, "time": 0.847} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.07483, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24438, "top5_acc": 0.47594, "loss_cls": 4.43339, "loss": 4.43339, "time": 0.84772} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.07481, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.23078, "top5_acc": 0.47125, "loss_cls": 4.47132, "loss": 4.47132, "time": 0.84567} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.07478, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24109, "top5_acc": 0.48516, "loss_cls": 4.43657, "loss": 4.43657, "time": 0.84635} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.07476, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23625, "top5_acc": 0.47859, "loss_cls": 4.41884, "loss": 4.41884, "time": 0.844} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.07473, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22688, "top5_acc": 0.46438, "loss_cls": 4.4934, "loss": 4.4934, "time": 0.84014} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.07471, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23531, "top5_acc": 0.47094, "loss_cls": 4.48431, "loss": 4.48431, "time": 0.84246} +{"mode": "train", "epoch": 51, "iter": 1300, "lr": 0.07468, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23984, "top5_acc": 0.48344, "loss_cls": 4.40801, "loss": 4.40801, "time": 0.84215} +{"mode": "train", "epoch": 51, "iter": 1400, "lr": 0.07466, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.23078, "top5_acc": 0.47703, "loss_cls": 4.4562, "loss": 4.4562, "time": 0.84679} +{"mode": "train", "epoch": 51, "iter": 1500, "lr": 0.07464, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24156, "top5_acc": 0.48047, "loss_cls": 4.40104, "loss": 4.40104, "time": 0.84573} +{"mode": "train", "epoch": 51, "iter": 1600, "lr": 0.07461, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23297, "top5_acc": 0.48156, "loss_cls": 4.44727, "loss": 4.44727, "time": 0.84831} +{"mode": "train", "epoch": 51, "iter": 1700, "lr": 0.07459, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24172, "top5_acc": 0.49, "loss_cls": 4.41411, "loss": 4.41411, "time": 0.84645} +{"mode": "train", "epoch": 51, "iter": 1800, "lr": 0.07456, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24641, "top5_acc": 0.48188, "loss_cls": 4.42937, "loss": 4.42937, "time": 0.85477} +{"mode": "train", "epoch": 51, "iter": 1900, "lr": 0.07454, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22969, "top5_acc": 0.46547, "loss_cls": 4.51663, "loss": 4.51663, "time": 0.84652} +{"mode": "train", "epoch": 51, "iter": 2000, "lr": 0.07451, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24125, "top5_acc": 0.47812, "loss_cls": 4.43165, "loss": 4.43165, "time": 0.84609} +{"mode": "train", "epoch": 51, "iter": 2100, "lr": 0.07449, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23047, "top5_acc": 0.47766, "loss_cls": 4.46496, "loss": 4.46496, "time": 0.85235} +{"mode": "train", "epoch": 51, "iter": 2200, "lr": 0.07447, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23891, "top5_acc": 0.48125, "loss_cls": 4.42665, "loss": 4.42665, "time": 0.84906} +{"mode": "train", "epoch": 51, "iter": 2300, "lr": 0.07444, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23203, "top5_acc": 0.47516, "loss_cls": 4.45189, "loss": 4.45189, "time": 0.85139} +{"mode": "train", "epoch": 51, "iter": 2400, "lr": 0.07442, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24562, "top5_acc": 0.48531, "loss_cls": 4.38743, "loss": 4.38743, "time": 0.8496} +{"mode": "train", "epoch": 51, "iter": 2500, "lr": 0.07439, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23875, "top5_acc": 0.48844, "loss_cls": 4.40818, "loss": 4.40818, "time": 0.84175} +{"mode": "train", "epoch": 51, "iter": 2600, "lr": 0.07437, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2325, "top5_acc": 0.465, "loss_cls": 4.47505, "loss": 4.47505, "time": 0.84632} +{"mode": "train", "epoch": 51, "iter": 2700, "lr": 0.07434, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23156, "top5_acc": 0.47484, "loss_cls": 4.45523, "loss": 4.45523, "time": 0.84061} +{"mode": "train", "epoch": 51, "iter": 2800, "lr": 0.07432, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.235, "top5_acc": 0.48016, "loss_cls": 4.44524, "loss": 4.44524, "time": 0.84792} +{"mode": "train", "epoch": 51, "iter": 2900, "lr": 0.07429, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23297, "top5_acc": 0.4675, "loss_cls": 4.46364, "loss": 4.46364, "time": 0.84799} +{"mode": "train", "epoch": 51, "iter": 3000, "lr": 0.07427, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.24625, "top5_acc": 0.48547, "loss_cls": 4.40054, "loss": 4.40054, "time": 0.83999} +{"mode": "train", "epoch": 51, "iter": 3100, "lr": 0.07425, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24719, "top5_acc": 0.47922, "loss_cls": 4.42514, "loss": 4.42514, "time": 0.84565} +{"mode": "train", "epoch": 51, "iter": 3200, "lr": 0.07422, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22719, "top5_acc": 0.47062, "loss_cls": 4.48829, "loss": 4.48829, "time": 0.84276} +{"mode": "train", "epoch": 51, "iter": 3300, "lr": 0.0742, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.23422, "top5_acc": 0.47375, "loss_cls": 4.47198, "loss": 4.47198, "time": 0.84038} +{"mode": "train", "epoch": 51, "iter": 3400, "lr": 0.07417, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22969, "top5_acc": 0.47609, "loss_cls": 4.46403, "loss": 4.46403, "time": 0.84817} +{"mode": "train", "epoch": 51, "iter": 3500, "lr": 0.07415, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.23234, "top5_acc": 0.47859, "loss_cls": 4.46686, "loss": 4.46686, "time": 0.84554} +{"mode": "train", "epoch": 51, "iter": 3600, "lr": 0.07412, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.24359, "top5_acc": 0.47969, "loss_cls": 4.44741, "loss": 4.44741, "time": 0.84191} +{"mode": "train", "epoch": 51, "iter": 3700, "lr": 0.0741, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24453, "top5_acc": 0.48719, "loss_cls": 4.38082, "loss": 4.38082, "time": 0.84545} +{"mode": "val", "epoch": 51, "iter": 309, "lr": 0.07409, "top1_acc": 0.17247, "top5_acc": 0.38616, "mean_class_accuracy": 0.17224} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.07406, "memory": 15990, "data_time": 1.57661, "top1_acc": 0.24828, "top5_acc": 0.49422, "loss_cls": 4.33935, "loss": 4.33935, "time": 2.60783} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.07404, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23906, "top5_acc": 0.4825, "loss_cls": 4.40538, "loss": 4.40538, "time": 0.8573} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.07401, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23984, "top5_acc": 0.47281, "loss_cls": 4.43369, "loss": 4.43369, "time": 0.85223} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.07399, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25406, "top5_acc": 0.49531, "loss_cls": 4.33599, "loss": 4.33599, "time": 0.85416} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.07397, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23672, "top5_acc": 0.47969, "loss_cls": 4.44313, "loss": 4.44313, "time": 0.85467} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.07394, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.23078, "top5_acc": 0.47719, "loss_cls": 4.41443, "loss": 4.41443, "time": 0.85285} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.07392, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23359, "top5_acc": 0.47328, "loss_cls": 4.44319, "loss": 4.44319, "time": 0.84605} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.07389, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23969, "top5_acc": 0.48234, "loss_cls": 4.41145, "loss": 4.41145, "time": 0.84091} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.07387, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23438, "top5_acc": 0.48266, "loss_cls": 4.43597, "loss": 4.43597, "time": 0.84308} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.07384, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24531, "top5_acc": 0.48234, "loss_cls": 4.40161, "loss": 4.40161, "time": 0.84751} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.07382, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23469, "top5_acc": 0.48078, "loss_cls": 4.44352, "loss": 4.44352, "time": 0.84844} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.07379, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24828, "top5_acc": 0.48703, "loss_cls": 4.40989, "loss": 4.40989, "time": 0.84235} +{"mode": "train", "epoch": 52, "iter": 1300, "lr": 0.07377, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2425, "top5_acc": 0.47625, "loss_cls": 4.41644, "loss": 4.41644, "time": 0.83913} +{"mode": "train", "epoch": 52, "iter": 1400, "lr": 0.07374, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24578, "top5_acc": 0.48938, "loss_cls": 4.37043, "loss": 4.37043, "time": 0.8422} +{"mode": "train", "epoch": 52, "iter": 1500, "lr": 0.07372, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24266, "top5_acc": 0.48156, "loss_cls": 4.40933, "loss": 4.40933, "time": 0.84577} +{"mode": "train", "epoch": 52, "iter": 1600, "lr": 0.0737, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23859, "top5_acc": 0.47625, "loss_cls": 4.44206, "loss": 4.44206, "time": 0.8472} +{"mode": "train", "epoch": 52, "iter": 1700, "lr": 0.07367, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24469, "top5_acc": 0.48078, "loss_cls": 4.39606, "loss": 4.39606, "time": 0.84277} +{"mode": "train", "epoch": 52, "iter": 1800, "lr": 0.07365, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23359, "top5_acc": 0.46781, "loss_cls": 4.47185, "loss": 4.47185, "time": 0.85126} +{"mode": "train", "epoch": 52, "iter": 1900, "lr": 0.07362, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22859, "top5_acc": 0.47219, "loss_cls": 4.43716, "loss": 4.43716, "time": 0.84615} +{"mode": "train", "epoch": 52, "iter": 2000, "lr": 0.0736, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24531, "top5_acc": 0.47812, "loss_cls": 4.4418, "loss": 4.4418, "time": 0.84476} +{"mode": "train", "epoch": 52, "iter": 2100, "lr": 0.07357, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23188, "top5_acc": 0.47328, "loss_cls": 4.46554, "loss": 4.46554, "time": 0.84937} +{"mode": "train", "epoch": 52, "iter": 2200, "lr": 0.07355, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23484, "top5_acc": 0.48125, "loss_cls": 4.43742, "loss": 4.43742, "time": 0.84497} +{"mode": "train", "epoch": 52, "iter": 2300, "lr": 0.07352, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22531, "top5_acc": 0.47266, "loss_cls": 4.48654, "loss": 4.48654, "time": 0.84928} +{"mode": "train", "epoch": 52, "iter": 2400, "lr": 0.0735, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23312, "top5_acc": 0.47, "loss_cls": 4.47667, "loss": 4.47667, "time": 0.84747} +{"mode": "train", "epoch": 52, "iter": 2500, "lr": 0.07347, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23281, "top5_acc": 0.47547, "loss_cls": 4.45503, "loss": 4.45503, "time": 0.84766} +{"mode": "train", "epoch": 52, "iter": 2600, "lr": 0.07345, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24016, "top5_acc": 0.48641, "loss_cls": 4.41158, "loss": 4.41158, "time": 0.84883} +{"mode": "train", "epoch": 52, "iter": 2700, "lr": 0.07342, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22922, "top5_acc": 0.47469, "loss_cls": 4.46538, "loss": 4.46538, "time": 0.85169} +{"mode": "train", "epoch": 52, "iter": 2800, "lr": 0.0734, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22516, "top5_acc": 0.46578, "loss_cls": 4.51569, "loss": 4.51569, "time": 0.84101} +{"mode": "train", "epoch": 52, "iter": 2900, "lr": 0.07337, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24297, "top5_acc": 0.48406, "loss_cls": 4.41001, "loss": 4.41001, "time": 0.84435} +{"mode": "train", "epoch": 52, "iter": 3000, "lr": 0.07335, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24734, "top5_acc": 0.48562, "loss_cls": 4.40041, "loss": 4.40041, "time": 0.8504} +{"mode": "train", "epoch": 52, "iter": 3100, "lr": 0.07332, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.235, "top5_acc": 0.46859, "loss_cls": 4.46934, "loss": 4.46934, "time": 0.84616} +{"mode": "train", "epoch": 52, "iter": 3200, "lr": 0.0733, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24469, "top5_acc": 0.47547, "loss_cls": 4.43157, "loss": 4.43157, "time": 0.84252} +{"mode": "train", "epoch": 52, "iter": 3300, "lr": 0.07328, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23578, "top5_acc": 0.47906, "loss_cls": 4.44995, "loss": 4.44995, "time": 0.84731} +{"mode": "train", "epoch": 52, "iter": 3400, "lr": 0.07325, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23984, "top5_acc": 0.48266, "loss_cls": 4.43178, "loss": 4.43178, "time": 0.84213} +{"mode": "train", "epoch": 52, "iter": 3500, "lr": 0.07323, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.24312, "top5_acc": 0.47766, "loss_cls": 4.4196, "loss": 4.4196, "time": 0.84331} +{"mode": "train", "epoch": 52, "iter": 3600, "lr": 0.0732, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24406, "top5_acc": 0.48062, "loss_cls": 4.44, "loss": 4.44, "time": 0.84305} +{"mode": "train", "epoch": 52, "iter": 3700, "lr": 0.07318, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24438, "top5_acc": 0.47422, "loss_cls": 4.41885, "loss": 4.41885, "time": 0.8428} +{"mode": "val", "epoch": 52, "iter": 309, "lr": 0.07317, "top1_acc": 0.1751, "top5_acc": 0.39062, "mean_class_accuracy": 0.17504} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.07314, "memory": 15990, "data_time": 1.5376, "top1_acc": 0.23312, "top5_acc": 0.47953, "loss_cls": 4.43424, "loss": 4.43424, "time": 2.56186} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.07312, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24766, "top5_acc": 0.49, "loss_cls": 4.39194, "loss": 4.39194, "time": 0.84791} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.07309, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24203, "top5_acc": 0.49219, "loss_cls": 4.36829, "loss": 4.36829, "time": 0.84546} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.07307, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.24422, "top5_acc": 0.48891, "loss_cls": 4.37331, "loss": 4.37331, "time": 0.8497} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.07304, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24094, "top5_acc": 0.47703, "loss_cls": 4.43861, "loss": 4.43861, "time": 0.84375} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.07302, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.23562, "top5_acc": 0.47875, "loss_cls": 4.45203, "loss": 4.45203, "time": 0.84437} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.07299, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.24188, "top5_acc": 0.47672, "loss_cls": 4.44334, "loss": 4.44334, "time": 0.85127} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.07297, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.24516, "top5_acc": 0.47531, "loss_cls": 4.42521, "loss": 4.42521, "time": 0.84073} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.07294, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23188, "top5_acc": 0.47609, "loss_cls": 4.4388, "loss": 4.4388, "time": 0.85226} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.07292, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23719, "top5_acc": 0.4725, "loss_cls": 4.44628, "loss": 4.44628, "time": 0.84871} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.07289, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25, "top5_acc": 0.4825, "loss_cls": 4.38643, "loss": 4.38643, "time": 0.84714} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.07287, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23438, "top5_acc": 0.47188, "loss_cls": 4.44156, "loss": 4.44156, "time": 0.84434} +{"mode": "train", "epoch": 53, "iter": 1300, "lr": 0.07284, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2375, "top5_acc": 0.47469, "loss_cls": 4.43324, "loss": 4.43324, "time": 0.84868} +{"mode": "train", "epoch": 53, "iter": 1400, "lr": 0.07282, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24219, "top5_acc": 0.48453, "loss_cls": 4.39787, "loss": 4.39787, "time": 0.84894} +{"mode": "train", "epoch": 53, "iter": 1500, "lr": 0.07279, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23828, "top5_acc": 0.47906, "loss_cls": 4.40325, "loss": 4.40325, "time": 0.85271} +{"mode": "train", "epoch": 53, "iter": 1600, "lr": 0.07277, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23391, "top5_acc": 0.47156, "loss_cls": 4.46442, "loss": 4.46442, "time": 0.85072} +{"mode": "train", "epoch": 53, "iter": 1700, "lr": 0.07274, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24406, "top5_acc": 0.48266, "loss_cls": 4.39775, "loss": 4.39775, "time": 0.84956} +{"mode": "train", "epoch": 53, "iter": 1800, "lr": 0.07272, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23359, "top5_acc": 0.48328, "loss_cls": 4.4526, "loss": 4.4526, "time": 0.85493} +{"mode": "train", "epoch": 53, "iter": 1900, "lr": 0.07269, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23656, "top5_acc": 0.47594, "loss_cls": 4.44015, "loss": 4.44015, "time": 0.84428} +{"mode": "train", "epoch": 53, "iter": 2000, "lr": 0.07267, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24844, "top5_acc": 0.485, "loss_cls": 4.42233, "loss": 4.42233, "time": 0.85138} +{"mode": "train", "epoch": 53, "iter": 2100, "lr": 0.07264, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23422, "top5_acc": 0.47828, "loss_cls": 4.42973, "loss": 4.42973, "time": 0.85189} +{"mode": "train", "epoch": 53, "iter": 2200, "lr": 0.07262, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23062, "top5_acc": 0.47016, "loss_cls": 4.44428, "loss": 4.44428, "time": 0.84731} +{"mode": "train", "epoch": 53, "iter": 2300, "lr": 0.07259, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23516, "top5_acc": 0.47516, "loss_cls": 4.43529, "loss": 4.43529, "time": 0.84943} +{"mode": "train", "epoch": 53, "iter": 2400, "lr": 0.07257, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24047, "top5_acc": 0.47234, "loss_cls": 4.44819, "loss": 4.44819, "time": 0.84998} +{"mode": "train", "epoch": 53, "iter": 2500, "lr": 0.07254, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23828, "top5_acc": 0.47922, "loss_cls": 4.41195, "loss": 4.41195, "time": 0.8543} +{"mode": "train", "epoch": 53, "iter": 2600, "lr": 0.07252, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24031, "top5_acc": 0.48531, "loss_cls": 4.41316, "loss": 4.41316, "time": 0.85168} +{"mode": "train", "epoch": 53, "iter": 2700, "lr": 0.07249, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24266, "top5_acc": 0.48547, "loss_cls": 4.41047, "loss": 4.41047, "time": 0.84979} +{"mode": "train", "epoch": 53, "iter": 2800, "lr": 0.07247, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.24469, "top5_acc": 0.48984, "loss_cls": 4.40975, "loss": 4.40975, "time": 0.84926} +{"mode": "train", "epoch": 53, "iter": 2900, "lr": 0.07244, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22734, "top5_acc": 0.46938, "loss_cls": 4.46088, "loss": 4.46088, "time": 0.85272} +{"mode": "train", "epoch": 53, "iter": 3000, "lr": 0.07242, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24234, "top5_acc": 0.48453, "loss_cls": 4.40721, "loss": 4.40721, "time": 0.8501} +{"mode": "train", "epoch": 53, "iter": 3100, "lr": 0.07239, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23781, "top5_acc": 0.47438, "loss_cls": 4.46311, "loss": 4.46311, "time": 0.8484} +{"mode": "train", "epoch": 53, "iter": 3200, "lr": 0.07237, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23703, "top5_acc": 0.48234, "loss_cls": 4.44879, "loss": 4.44879, "time": 0.85002} +{"mode": "train", "epoch": 53, "iter": 3300, "lr": 0.07234, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24281, "top5_acc": 0.48375, "loss_cls": 4.41399, "loss": 4.41399, "time": 0.8465} +{"mode": "train", "epoch": 53, "iter": 3400, "lr": 0.07232, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24484, "top5_acc": 0.48484, "loss_cls": 4.399, "loss": 4.399, "time": 0.84844} +{"mode": "train", "epoch": 53, "iter": 3500, "lr": 0.07229, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24578, "top5_acc": 0.48219, "loss_cls": 4.39621, "loss": 4.39621, "time": 0.84011} +{"mode": "train", "epoch": 53, "iter": 3600, "lr": 0.07227, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.24781, "top5_acc": 0.49484, "loss_cls": 4.37759, "loss": 4.37759, "time": 0.84494} +{"mode": "train", "epoch": 53, "iter": 3700, "lr": 0.07224, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24375, "top5_acc": 0.47266, "loss_cls": 4.4253, "loss": 4.4253, "time": 0.84414} +{"mode": "val", "epoch": 53, "iter": 309, "lr": 0.07223, "top1_acc": 0.16953, "top5_acc": 0.38353, "mean_class_accuracy": 0.16951} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.07221, "memory": 15990, "data_time": 1.53966, "top1_acc": 0.23516, "top5_acc": 0.47938, "loss_cls": 4.40685, "loss": 4.40685, "time": 2.56154} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.07218, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24562, "top5_acc": 0.48859, "loss_cls": 4.39564, "loss": 4.39564, "time": 0.84691} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.07216, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24891, "top5_acc": 0.49141, "loss_cls": 4.37183, "loss": 4.37183, "time": 0.84394} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.07213, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24375, "top5_acc": 0.48016, "loss_cls": 4.41741, "loss": 4.41741, "time": 0.84881} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.07211, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25031, "top5_acc": 0.49109, "loss_cls": 4.3895, "loss": 4.3895, "time": 0.84325} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.07208, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.24719, "top5_acc": 0.48734, "loss_cls": 4.38408, "loss": 4.38408, "time": 0.84305} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.07206, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23703, "top5_acc": 0.48156, "loss_cls": 4.42123, "loss": 4.42123, "time": 0.84511} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.07203, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.24625, "top5_acc": 0.48891, "loss_cls": 4.41508, "loss": 4.41508, "time": 0.84681} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.07201, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2325, "top5_acc": 0.47562, "loss_cls": 4.42746, "loss": 4.42746, "time": 0.84364} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.07198, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22703, "top5_acc": 0.47047, "loss_cls": 4.47429, "loss": 4.47429, "time": 0.84222} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.07196, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24219, "top5_acc": 0.48875, "loss_cls": 4.419, "loss": 4.419, "time": 0.84387} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.07193, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24, "top5_acc": 0.48172, "loss_cls": 4.40825, "loss": 4.40825, "time": 0.84322} +{"mode": "train", "epoch": 54, "iter": 1300, "lr": 0.07191, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24078, "top5_acc": 0.48156, "loss_cls": 4.42589, "loss": 4.42589, "time": 0.84165} +{"mode": "train", "epoch": 54, "iter": 1400, "lr": 0.07188, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24891, "top5_acc": 0.48047, "loss_cls": 4.37005, "loss": 4.37005, "time": 0.84237} +{"mode": "train", "epoch": 54, "iter": 1500, "lr": 0.07186, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23766, "top5_acc": 0.47766, "loss_cls": 4.43376, "loss": 4.43376, "time": 0.84021} +{"mode": "train", "epoch": 54, "iter": 1600, "lr": 0.07183, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23609, "top5_acc": 0.47, "loss_cls": 4.45611, "loss": 4.45611, "time": 0.8472} +{"mode": "train", "epoch": 54, "iter": 1700, "lr": 0.07181, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24016, "top5_acc": 0.48578, "loss_cls": 4.40194, "loss": 4.40194, "time": 0.84702} +{"mode": "train", "epoch": 54, "iter": 1800, "lr": 0.07178, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23641, "top5_acc": 0.48922, "loss_cls": 4.4043, "loss": 4.4043, "time": 0.83759} +{"mode": "train", "epoch": 54, "iter": 1900, "lr": 0.07176, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23906, "top5_acc": 0.48312, "loss_cls": 4.40089, "loss": 4.40089, "time": 0.85075} +{"mode": "train", "epoch": 54, "iter": 2000, "lr": 0.07173, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24688, "top5_acc": 0.48938, "loss_cls": 4.39485, "loss": 4.39485, "time": 0.84019} +{"mode": "train", "epoch": 54, "iter": 2100, "lr": 0.0717, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2375, "top5_acc": 0.47281, "loss_cls": 4.43488, "loss": 4.43488, "time": 0.84454} +{"mode": "train", "epoch": 54, "iter": 2200, "lr": 0.07168, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24188, "top5_acc": 0.49047, "loss_cls": 4.38768, "loss": 4.38768, "time": 0.84747} +{"mode": "train", "epoch": 54, "iter": 2300, "lr": 0.07165, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23641, "top5_acc": 0.48141, "loss_cls": 4.41716, "loss": 4.41716, "time": 0.8504} +{"mode": "train", "epoch": 54, "iter": 2400, "lr": 0.07163, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23406, "top5_acc": 0.46766, "loss_cls": 4.47995, "loss": 4.47995, "time": 0.84748} +{"mode": "train", "epoch": 54, "iter": 2500, "lr": 0.0716, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23141, "top5_acc": 0.47484, "loss_cls": 4.46377, "loss": 4.46377, "time": 0.85059} +{"mode": "train", "epoch": 54, "iter": 2600, "lr": 0.07158, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25266, "top5_acc": 0.49062, "loss_cls": 4.36993, "loss": 4.36993, "time": 0.84853} +{"mode": "train", "epoch": 54, "iter": 2700, "lr": 0.07155, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23656, "top5_acc": 0.47672, "loss_cls": 4.44209, "loss": 4.44209, "time": 0.85203} +{"mode": "train", "epoch": 54, "iter": 2800, "lr": 0.07153, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24297, "top5_acc": 0.48906, "loss_cls": 4.39861, "loss": 4.39861, "time": 0.85112} +{"mode": "train", "epoch": 54, "iter": 2900, "lr": 0.0715, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.24406, "top5_acc": 0.48062, "loss_cls": 4.4156, "loss": 4.4156, "time": 0.84812} +{"mode": "train", "epoch": 54, "iter": 3000, "lr": 0.07148, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.23984, "top5_acc": 0.47359, "loss_cls": 4.45082, "loss": 4.45082, "time": 0.85059} +{"mode": "train", "epoch": 54, "iter": 3100, "lr": 0.07145, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24359, "top5_acc": 0.48594, "loss_cls": 4.4218, "loss": 4.4218, "time": 0.85084} +{"mode": "train", "epoch": 54, "iter": 3200, "lr": 0.07143, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23922, "top5_acc": 0.48203, "loss_cls": 4.42952, "loss": 4.42952, "time": 0.85287} +{"mode": "train", "epoch": 54, "iter": 3300, "lr": 0.0714, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25406, "top5_acc": 0.49344, "loss_cls": 4.33708, "loss": 4.33708, "time": 0.84545} +{"mode": "train", "epoch": 54, "iter": 3400, "lr": 0.07138, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.23125, "top5_acc": 0.48453, "loss_cls": 4.40956, "loss": 4.40956, "time": 0.84668} +{"mode": "train", "epoch": 54, "iter": 3500, "lr": 0.07135, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24109, "top5_acc": 0.48766, "loss_cls": 4.40308, "loss": 4.40308, "time": 0.84136} +{"mode": "train", "epoch": 54, "iter": 3600, "lr": 0.07133, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23234, "top5_acc": 0.47469, "loss_cls": 4.46306, "loss": 4.46306, "time": 0.84893} +{"mode": "train", "epoch": 54, "iter": 3700, "lr": 0.0713, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23203, "top5_acc": 0.48203, "loss_cls": 4.43643, "loss": 4.43643, "time": 0.84929} +{"mode": "val", "epoch": 54, "iter": 309, "lr": 0.07129, "top1_acc": 0.17601, "top5_acc": 0.39138, "mean_class_accuracy": 0.1758} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.07126, "memory": 15990, "data_time": 1.63572, "top1_acc": 0.24938, "top5_acc": 0.49391, "loss_cls": 4.35102, "loss": 4.35102, "time": 2.66605} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.07124, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24, "top5_acc": 0.47969, "loss_cls": 4.4327, "loss": 4.4327, "time": 0.85132} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.07121, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25594, "top5_acc": 0.49969, "loss_cls": 4.34616, "loss": 4.34616, "time": 0.85212} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.07119, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23141, "top5_acc": 0.46984, "loss_cls": 4.45445, "loss": 4.45445, "time": 0.85434} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.07116, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23719, "top5_acc": 0.48125, "loss_cls": 4.43311, "loss": 4.43311, "time": 0.85272} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.07114, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23438, "top5_acc": 0.46891, "loss_cls": 4.45983, "loss": 4.45983, "time": 0.86279} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.07111, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23953, "top5_acc": 0.47625, "loss_cls": 4.40713, "loss": 4.40713, "time": 0.85739} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.07109, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24109, "top5_acc": 0.48188, "loss_cls": 4.38249, "loss": 4.38249, "time": 0.85137} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.07106, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23609, "top5_acc": 0.47781, "loss_cls": 4.44125, "loss": 4.44125, "time": 0.848} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.07104, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24641, "top5_acc": 0.48141, "loss_cls": 4.42523, "loss": 4.42523, "time": 0.84894} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.07101, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23984, "top5_acc": 0.47875, "loss_cls": 4.45639, "loss": 4.45639, "time": 0.85057} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.07099, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23844, "top5_acc": 0.48547, "loss_cls": 4.38561, "loss": 4.38561, "time": 0.84862} +{"mode": "train", "epoch": 55, "iter": 1300, "lr": 0.07096, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23703, "top5_acc": 0.48312, "loss_cls": 4.4064, "loss": 4.4064, "time": 0.84894} +{"mode": "train", "epoch": 55, "iter": 1400, "lr": 0.07093, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24453, "top5_acc": 0.49125, "loss_cls": 4.38075, "loss": 4.38075, "time": 0.8517} +{"mode": "train", "epoch": 55, "iter": 1500, "lr": 0.07091, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23922, "top5_acc": 0.48016, "loss_cls": 4.41214, "loss": 4.41214, "time": 0.85399} +{"mode": "train", "epoch": 55, "iter": 1600, "lr": 0.07088, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24719, "top5_acc": 0.47969, "loss_cls": 4.39326, "loss": 4.39326, "time": 0.84772} +{"mode": "train", "epoch": 55, "iter": 1700, "lr": 0.07086, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24344, "top5_acc": 0.48719, "loss_cls": 4.40949, "loss": 4.40949, "time": 0.85018} +{"mode": "train", "epoch": 55, "iter": 1800, "lr": 0.07083, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.24203, "top5_acc": 0.47688, "loss_cls": 4.44261, "loss": 4.44261, "time": 0.85507} +{"mode": "train", "epoch": 55, "iter": 1900, "lr": 0.07081, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.24203, "top5_acc": 0.48, "loss_cls": 4.40477, "loss": 4.40477, "time": 0.8573} +{"mode": "train", "epoch": 55, "iter": 2000, "lr": 0.07078, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24078, "top5_acc": 0.48219, "loss_cls": 4.4217, "loss": 4.4217, "time": 0.84496} +{"mode": "train", "epoch": 55, "iter": 2100, "lr": 0.07076, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23125, "top5_acc": 0.47766, "loss_cls": 4.44892, "loss": 4.44892, "time": 0.85069} +{"mode": "train", "epoch": 55, "iter": 2200, "lr": 0.07073, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23109, "top5_acc": 0.47125, "loss_cls": 4.46614, "loss": 4.46614, "time": 0.85661} +{"mode": "train", "epoch": 55, "iter": 2300, "lr": 0.07071, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24312, "top5_acc": 0.48406, "loss_cls": 4.39548, "loss": 4.39548, "time": 0.85532} +{"mode": "train", "epoch": 55, "iter": 2400, "lr": 0.07068, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23578, "top5_acc": 0.47453, "loss_cls": 4.4286, "loss": 4.4286, "time": 0.85917} +{"mode": "train", "epoch": 55, "iter": 2500, "lr": 0.07065, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24812, "top5_acc": 0.49859, "loss_cls": 4.34914, "loss": 4.34914, "time": 0.84976} +{"mode": "train", "epoch": 55, "iter": 2600, "lr": 0.07063, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24766, "top5_acc": 0.4925, "loss_cls": 4.33726, "loss": 4.33726, "time": 0.85862} +{"mode": "train", "epoch": 55, "iter": 2700, "lr": 0.0706, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24844, "top5_acc": 0.49578, "loss_cls": 4.36774, "loss": 4.36774, "time": 0.85884} +{"mode": "train", "epoch": 55, "iter": 2800, "lr": 0.07058, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24266, "top5_acc": 0.48297, "loss_cls": 4.42377, "loss": 4.42377, "time": 0.84925} +{"mode": "train", "epoch": 55, "iter": 2900, "lr": 0.07055, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23781, "top5_acc": 0.48047, "loss_cls": 4.4234, "loss": 4.4234, "time": 0.8485} +{"mode": "train", "epoch": 55, "iter": 3000, "lr": 0.07053, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23922, "top5_acc": 0.47734, "loss_cls": 4.43831, "loss": 4.43831, "time": 0.83978} +{"mode": "train", "epoch": 55, "iter": 3100, "lr": 0.0705, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24438, "top5_acc": 0.49281, "loss_cls": 4.3679, "loss": 4.3679, "time": 0.84558} +{"mode": "train", "epoch": 55, "iter": 3200, "lr": 0.07048, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23859, "top5_acc": 0.48438, "loss_cls": 4.41976, "loss": 4.41976, "time": 0.84282} +{"mode": "train", "epoch": 55, "iter": 3300, "lr": 0.07045, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24969, "top5_acc": 0.48875, "loss_cls": 4.37552, "loss": 4.37552, "time": 0.84515} +{"mode": "train", "epoch": 55, "iter": 3400, "lr": 0.07043, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24156, "top5_acc": 0.47453, "loss_cls": 4.42831, "loss": 4.42831, "time": 0.84988} +{"mode": "train", "epoch": 55, "iter": 3500, "lr": 0.0704, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24203, "top5_acc": 0.47812, "loss_cls": 4.42821, "loss": 4.42821, "time": 0.84142} +{"mode": "train", "epoch": 55, "iter": 3600, "lr": 0.07037, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24484, "top5_acc": 0.48609, "loss_cls": 4.40116, "loss": 4.40116, "time": 0.84281} +{"mode": "train", "epoch": 55, "iter": 3700, "lr": 0.07035, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23719, "top5_acc": 0.47828, "loss_cls": 4.43358, "loss": 4.43358, "time": 0.84599} +{"mode": "val", "epoch": 55, "iter": 309, "lr": 0.07034, "top1_acc": 0.18908, "top5_acc": 0.40647, "mean_class_accuracy": 0.18874} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.07031, "memory": 15990, "data_time": 1.54817, "top1_acc": 0.24641, "top5_acc": 0.49188, "loss_cls": 4.36437, "loss": 4.36437, "time": 2.58899} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.07029, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24828, "top5_acc": 0.48984, "loss_cls": 4.34569, "loss": 4.34569, "time": 0.85917} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.07026, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24344, "top5_acc": 0.48109, "loss_cls": 4.41378, "loss": 4.41378, "time": 0.85568} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.07023, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24406, "top5_acc": 0.47906, "loss_cls": 4.41179, "loss": 4.41179, "time": 0.85624} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.07021, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24594, "top5_acc": 0.48922, "loss_cls": 4.38199, "loss": 4.38199, "time": 0.86153} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.07018, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.24203, "top5_acc": 0.47719, "loss_cls": 4.43351, "loss": 4.43351, "time": 0.86183} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.07016, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25, "top5_acc": 0.50266, "loss_cls": 4.31942, "loss": 4.31942, "time": 0.85186} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.07013, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24609, "top5_acc": 0.47859, "loss_cls": 4.38956, "loss": 4.38956, "time": 0.85156} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.07011, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23797, "top5_acc": 0.47328, "loss_cls": 4.44712, "loss": 4.44712, "time": 0.84825} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.07008, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24078, "top5_acc": 0.47953, "loss_cls": 4.43221, "loss": 4.43221, "time": 0.84722} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.07006, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25031, "top5_acc": 0.49984, "loss_cls": 4.338, "loss": 4.338, "time": 0.84251} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.07003, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24312, "top5_acc": 0.48688, "loss_cls": 4.41087, "loss": 4.41087, "time": 0.84141} +{"mode": "train", "epoch": 56, "iter": 1300, "lr": 0.07, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24547, "top5_acc": 0.48016, "loss_cls": 4.39293, "loss": 4.39293, "time": 0.84453} +{"mode": "train", "epoch": 56, "iter": 1400, "lr": 0.06998, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23812, "top5_acc": 0.48047, "loss_cls": 4.41361, "loss": 4.41361, "time": 0.84563} +{"mode": "train", "epoch": 56, "iter": 1500, "lr": 0.06995, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23719, "top5_acc": 0.47875, "loss_cls": 4.42048, "loss": 4.42048, "time": 0.85119} +{"mode": "train", "epoch": 56, "iter": 1600, "lr": 0.06993, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25, "top5_acc": 0.49016, "loss_cls": 4.38857, "loss": 4.38857, "time": 0.84341} +{"mode": "train", "epoch": 56, "iter": 1700, "lr": 0.0699, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24719, "top5_acc": 0.48688, "loss_cls": 4.36585, "loss": 4.36585, "time": 0.84489} +{"mode": "train", "epoch": 56, "iter": 1800, "lr": 0.06988, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23938, "top5_acc": 0.48656, "loss_cls": 4.41027, "loss": 4.41027, "time": 0.84647} +{"mode": "train", "epoch": 56, "iter": 1900, "lr": 0.06985, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23703, "top5_acc": 0.47969, "loss_cls": 4.46373, "loss": 4.46373, "time": 0.85039} +{"mode": "train", "epoch": 56, "iter": 2000, "lr": 0.06983, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24766, "top5_acc": 0.49781, "loss_cls": 4.36209, "loss": 4.36209, "time": 0.84505} +{"mode": "train", "epoch": 56, "iter": 2100, "lr": 0.0698, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24266, "top5_acc": 0.48938, "loss_cls": 4.4096, "loss": 4.4096, "time": 0.84751} +{"mode": "train", "epoch": 56, "iter": 2200, "lr": 0.06977, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23875, "top5_acc": 0.48141, "loss_cls": 4.43717, "loss": 4.43717, "time": 0.8459} +{"mode": "train", "epoch": 56, "iter": 2300, "lr": 0.06975, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23953, "top5_acc": 0.46922, "loss_cls": 4.45343, "loss": 4.45343, "time": 0.84886} +{"mode": "train", "epoch": 56, "iter": 2400, "lr": 0.06972, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24, "top5_acc": 0.48156, "loss_cls": 4.42342, "loss": 4.42342, "time": 0.84685} +{"mode": "train", "epoch": 56, "iter": 2500, "lr": 0.0697, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23656, "top5_acc": 0.48172, "loss_cls": 4.41319, "loss": 4.41319, "time": 0.84558} +{"mode": "train", "epoch": 56, "iter": 2600, "lr": 0.06967, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24125, "top5_acc": 0.47766, "loss_cls": 4.44832, "loss": 4.44832, "time": 0.84894} +{"mode": "train", "epoch": 56, "iter": 2700, "lr": 0.06965, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23844, "top5_acc": 0.48484, "loss_cls": 4.43541, "loss": 4.43541, "time": 0.84269} +{"mode": "train", "epoch": 56, "iter": 2800, "lr": 0.06962, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25422, "top5_acc": 0.49984, "loss_cls": 4.32618, "loss": 4.32618, "time": 0.84936} +{"mode": "train", "epoch": 56, "iter": 2900, "lr": 0.06959, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.23984, "top5_acc": 0.48188, "loss_cls": 4.3865, "loss": 4.3865, "time": 0.84364} +{"mode": "train", "epoch": 56, "iter": 3000, "lr": 0.06957, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23266, "top5_acc": 0.4675, "loss_cls": 4.4481, "loss": 4.4481, "time": 0.84099} +{"mode": "train", "epoch": 56, "iter": 3100, "lr": 0.06954, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23594, "top5_acc": 0.47641, "loss_cls": 4.42145, "loss": 4.42145, "time": 0.84823} +{"mode": "train", "epoch": 56, "iter": 3200, "lr": 0.06952, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24516, "top5_acc": 0.48828, "loss_cls": 4.37575, "loss": 4.37575, "time": 0.84566} +{"mode": "train", "epoch": 56, "iter": 3300, "lr": 0.06949, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24812, "top5_acc": 0.48438, "loss_cls": 4.40944, "loss": 4.40944, "time": 0.84747} +{"mode": "train", "epoch": 56, "iter": 3400, "lr": 0.06947, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2375, "top5_acc": 0.47547, "loss_cls": 4.45568, "loss": 4.45568, "time": 0.84501} +{"mode": "train", "epoch": 56, "iter": 3500, "lr": 0.06944, "memory": 15990, "data_time": 0.00068, "top1_acc": 0.24219, "top5_acc": 0.46906, "loss_cls": 4.44339, "loss": 4.44339, "time": 0.84356} +{"mode": "train", "epoch": 56, "iter": 3600, "lr": 0.06941, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24375, "top5_acc": 0.48125, "loss_cls": 4.42991, "loss": 4.42991, "time": 0.84186} +{"mode": "train", "epoch": 56, "iter": 3700, "lr": 0.06939, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25094, "top5_acc": 0.49484, "loss_cls": 4.36232, "loss": 4.36232, "time": 0.84663} +{"mode": "val", "epoch": 56, "iter": 309, "lr": 0.06938, "top1_acc": 0.19323, "top5_acc": 0.41321, "mean_class_accuracy": 0.19297} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.06935, "memory": 15990, "data_time": 1.58364, "top1_acc": 0.24344, "top5_acc": 0.48641, "loss_cls": 4.38758, "loss": 4.38758, "time": 2.63124} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.06932, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23578, "top5_acc": 0.47766, "loss_cls": 4.40988, "loss": 4.40988, "time": 0.86395} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.0693, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24219, "top5_acc": 0.48953, "loss_cls": 4.36809, "loss": 4.36809, "time": 0.86826} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.06927, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25109, "top5_acc": 0.48172, "loss_cls": 4.37845, "loss": 4.37845, "time": 0.86313} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.06925, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.25609, "top5_acc": 0.49281, "loss_cls": 4.34531, "loss": 4.34531, "time": 0.86517} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.06922, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25531, "top5_acc": 0.5025, "loss_cls": 4.312, "loss": 4.312, "time": 0.86289} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.0692, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23562, "top5_acc": 0.49031, "loss_cls": 4.3976, "loss": 4.3976, "time": 0.86027} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.06917, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24859, "top5_acc": 0.48859, "loss_cls": 4.37154, "loss": 4.37154, "time": 0.85435} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.06914, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24406, "top5_acc": 0.48656, "loss_cls": 4.39352, "loss": 4.39352, "time": 0.84936} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.06912, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24047, "top5_acc": 0.48, "loss_cls": 4.42085, "loss": 4.42085, "time": 0.85288} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.06909, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24047, "top5_acc": 0.48359, "loss_cls": 4.40618, "loss": 4.40618, "time": 0.84929} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.06907, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25031, "top5_acc": 0.48344, "loss_cls": 4.39366, "loss": 4.39366, "time": 0.8508} +{"mode": "train", "epoch": 57, "iter": 1300, "lr": 0.06904, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24188, "top5_acc": 0.47703, "loss_cls": 4.43331, "loss": 4.43331, "time": 0.85618} +{"mode": "train", "epoch": 57, "iter": 1400, "lr": 0.06901, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25266, "top5_acc": 0.49203, "loss_cls": 4.37498, "loss": 4.37498, "time": 0.85377} +{"mode": "train", "epoch": 57, "iter": 1500, "lr": 0.06899, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25281, "top5_acc": 0.49062, "loss_cls": 4.37991, "loss": 4.37991, "time": 0.85184} +{"mode": "train", "epoch": 57, "iter": 1600, "lr": 0.06896, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23438, "top5_acc": 0.48359, "loss_cls": 4.4098, "loss": 4.4098, "time": 0.85099} +{"mode": "train", "epoch": 57, "iter": 1700, "lr": 0.06894, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24922, "top5_acc": 0.49375, "loss_cls": 4.37821, "loss": 4.37821, "time": 0.85332} +{"mode": "train", "epoch": 57, "iter": 1800, "lr": 0.06891, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24109, "top5_acc": 0.48094, "loss_cls": 4.4231, "loss": 4.4231, "time": 0.85362} +{"mode": "train", "epoch": 57, "iter": 1900, "lr": 0.06889, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25172, "top5_acc": 0.49641, "loss_cls": 4.35753, "loss": 4.35753, "time": 0.85271} +{"mode": "train", "epoch": 57, "iter": 2000, "lr": 0.06886, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25391, "top5_acc": 0.5, "loss_cls": 4.34231, "loss": 4.34231, "time": 0.85493} +{"mode": "train", "epoch": 57, "iter": 2100, "lr": 0.06883, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25047, "top5_acc": 0.48766, "loss_cls": 4.38178, "loss": 4.38178, "time": 0.84811} +{"mode": "train", "epoch": 57, "iter": 2200, "lr": 0.06881, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23359, "top5_acc": 0.47734, "loss_cls": 4.40491, "loss": 4.40491, "time": 0.85846} +{"mode": "train", "epoch": 57, "iter": 2300, "lr": 0.06878, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24844, "top5_acc": 0.49672, "loss_cls": 4.34621, "loss": 4.34621, "time": 0.85059} +{"mode": "train", "epoch": 57, "iter": 2400, "lr": 0.06876, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24469, "top5_acc": 0.47734, "loss_cls": 4.41665, "loss": 4.41665, "time": 0.85213} +{"mode": "train", "epoch": 57, "iter": 2500, "lr": 0.06873, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23703, "top5_acc": 0.46812, "loss_cls": 4.44119, "loss": 4.44119, "time": 0.85599} +{"mode": "train", "epoch": 57, "iter": 2600, "lr": 0.0687, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25125, "top5_acc": 0.48891, "loss_cls": 4.3755, "loss": 4.3755, "time": 0.85751} +{"mode": "train", "epoch": 57, "iter": 2700, "lr": 0.06868, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23938, "top5_acc": 0.48312, "loss_cls": 4.41962, "loss": 4.41962, "time": 0.84967} +{"mode": "train", "epoch": 57, "iter": 2800, "lr": 0.06865, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24359, "top5_acc": 0.49234, "loss_cls": 4.35914, "loss": 4.35914, "time": 0.85097} +{"mode": "train", "epoch": 57, "iter": 2900, "lr": 0.06863, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23422, "top5_acc": 0.47719, "loss_cls": 4.45756, "loss": 4.45756, "time": 0.8527} +{"mode": "train", "epoch": 57, "iter": 3000, "lr": 0.0686, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2375, "top5_acc": 0.48938, "loss_cls": 4.41245, "loss": 4.41245, "time": 0.85808} +{"mode": "train", "epoch": 57, "iter": 3100, "lr": 0.06857, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24594, "top5_acc": 0.47734, "loss_cls": 4.41897, "loss": 4.41897, "time": 0.85941} +{"mode": "train", "epoch": 57, "iter": 3200, "lr": 0.06855, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23906, "top5_acc": 0.48109, "loss_cls": 4.406, "loss": 4.406, "time": 0.86211} +{"mode": "train", "epoch": 57, "iter": 3300, "lr": 0.06852, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24562, "top5_acc": 0.4825, "loss_cls": 4.39297, "loss": 4.39297, "time": 0.85823} +{"mode": "train", "epoch": 57, "iter": 3400, "lr": 0.0685, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25766, "top5_acc": 0.49859, "loss_cls": 4.32944, "loss": 4.32944, "time": 0.85162} +{"mode": "train", "epoch": 57, "iter": 3500, "lr": 0.06847, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23438, "top5_acc": 0.47516, "loss_cls": 4.45306, "loss": 4.45306, "time": 0.84824} +{"mode": "train", "epoch": 57, "iter": 3600, "lr": 0.06844, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.24922, "top5_acc": 0.47141, "loss_cls": 4.40843, "loss": 4.40843, "time": 0.85696} +{"mode": "train", "epoch": 57, "iter": 3700, "lr": 0.06842, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23781, "top5_acc": 0.48594, "loss_cls": 4.42322, "loss": 4.42322, "time": 0.86092} +{"mode": "val", "epoch": 57, "iter": 309, "lr": 0.06841, "top1_acc": 0.1751, "top5_acc": 0.39128, "mean_class_accuracy": 0.17505} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.06838, "memory": 15990, "data_time": 1.56298, "top1_acc": 0.25016, "top5_acc": 0.49062, "loss_cls": 4.34618, "loss": 4.34618, "time": 2.5918} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.06835, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24734, "top5_acc": 0.49391, "loss_cls": 4.37065, "loss": 4.37065, "time": 0.84954} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.06833, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24672, "top5_acc": 0.49625, "loss_cls": 4.36486, "loss": 4.36486, "time": 0.84433} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.0683, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24188, "top5_acc": 0.49, "loss_cls": 4.36769, "loss": 4.36769, "time": 0.85037} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.06828, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24438, "top5_acc": 0.47828, "loss_cls": 4.38856, "loss": 4.38856, "time": 0.85108} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.06825, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24844, "top5_acc": 0.48141, "loss_cls": 4.3873, "loss": 4.3873, "time": 0.85082} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.06822, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24969, "top5_acc": 0.48703, "loss_cls": 4.37554, "loss": 4.37554, "time": 0.85241} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.0682, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24594, "top5_acc": 0.48578, "loss_cls": 4.38797, "loss": 4.38797, "time": 0.85118} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.06817, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2475, "top5_acc": 0.48203, "loss_cls": 4.3889, "loss": 4.3889, "time": 0.83975} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.06815, "memory": 15990, "data_time": 0.0008, "top1_acc": 0.24328, "top5_acc": 0.47875, "loss_cls": 4.41273, "loss": 4.41273, "time": 0.83998} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.06812, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25297, "top5_acc": 0.49781, "loss_cls": 4.34852, "loss": 4.34852, "time": 0.84663} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.06809, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24203, "top5_acc": 0.4925, "loss_cls": 4.37441, "loss": 4.37441, "time": 0.83658} +{"mode": "train", "epoch": 58, "iter": 1300, "lr": 0.06807, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24609, "top5_acc": 0.48625, "loss_cls": 4.37348, "loss": 4.37348, "time": 0.84162} +{"mode": "train", "epoch": 58, "iter": 1400, "lr": 0.06804, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24406, "top5_acc": 0.47859, "loss_cls": 4.4144, "loss": 4.4144, "time": 0.85219} +{"mode": "train", "epoch": 58, "iter": 1500, "lr": 0.06802, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24875, "top5_acc": 0.49328, "loss_cls": 4.36622, "loss": 4.36622, "time": 0.84544} +{"mode": "train", "epoch": 58, "iter": 1600, "lr": 0.06799, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24234, "top5_acc": 0.48703, "loss_cls": 4.41288, "loss": 4.41288, "time": 0.84416} +{"mode": "train", "epoch": 58, "iter": 1700, "lr": 0.06796, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24094, "top5_acc": 0.48797, "loss_cls": 4.36957, "loss": 4.36957, "time": 0.84097} +{"mode": "train", "epoch": 58, "iter": 1800, "lr": 0.06794, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24453, "top5_acc": 0.48219, "loss_cls": 4.38884, "loss": 4.38884, "time": 0.84296} +{"mode": "train", "epoch": 58, "iter": 1900, "lr": 0.06791, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23812, "top5_acc": 0.47984, "loss_cls": 4.41997, "loss": 4.41997, "time": 0.84689} +{"mode": "train", "epoch": 58, "iter": 2000, "lr": 0.06789, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25031, "top5_acc": 0.49203, "loss_cls": 4.36617, "loss": 4.36617, "time": 0.85117} +{"mode": "train", "epoch": 58, "iter": 2100, "lr": 0.06786, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25469, "top5_acc": 0.49875, "loss_cls": 4.35981, "loss": 4.35981, "time": 0.84146} +{"mode": "train", "epoch": 58, "iter": 2200, "lr": 0.06783, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24359, "top5_acc": 0.48953, "loss_cls": 4.39535, "loss": 4.39535, "time": 0.8413} +{"mode": "train", "epoch": 58, "iter": 2300, "lr": 0.06781, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25906, "top5_acc": 0.49688, "loss_cls": 4.3607, "loss": 4.3607, "time": 0.84193} +{"mode": "train", "epoch": 58, "iter": 2400, "lr": 0.06778, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24391, "top5_acc": 0.49281, "loss_cls": 4.35299, "loss": 4.35299, "time": 0.85081} +{"mode": "train", "epoch": 58, "iter": 2500, "lr": 0.06775, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24984, "top5_acc": 0.49281, "loss_cls": 4.39234, "loss": 4.39234, "time": 0.84851} +{"mode": "train", "epoch": 58, "iter": 2600, "lr": 0.06773, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23578, "top5_acc": 0.47531, "loss_cls": 4.42427, "loss": 4.42427, "time": 0.85372} +{"mode": "train", "epoch": 58, "iter": 2700, "lr": 0.0677, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.23719, "top5_acc": 0.47984, "loss_cls": 4.41113, "loss": 4.41113, "time": 0.84809} +{"mode": "train", "epoch": 58, "iter": 2800, "lr": 0.06768, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2425, "top5_acc": 0.48516, "loss_cls": 4.4352, "loss": 4.4352, "time": 0.85282} +{"mode": "train", "epoch": 58, "iter": 2900, "lr": 0.06765, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25609, "top5_acc": 0.48938, "loss_cls": 4.38078, "loss": 4.38078, "time": 0.85139} +{"mode": "train", "epoch": 58, "iter": 3000, "lr": 0.06762, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.23891, "top5_acc": 0.47469, "loss_cls": 4.42631, "loss": 4.42631, "time": 0.85516} +{"mode": "train", "epoch": 58, "iter": 3100, "lr": 0.0676, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23719, "top5_acc": 0.48516, "loss_cls": 4.39445, "loss": 4.39445, "time": 0.84727} +{"mode": "train", "epoch": 58, "iter": 3200, "lr": 0.06757, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23812, "top5_acc": 0.47953, "loss_cls": 4.41087, "loss": 4.41087, "time": 0.85138} +{"mode": "train", "epoch": 58, "iter": 3300, "lr": 0.06755, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25266, "top5_acc": 0.49531, "loss_cls": 4.35228, "loss": 4.35228, "time": 0.8522} +{"mode": "train", "epoch": 58, "iter": 3400, "lr": 0.06752, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24984, "top5_acc": 0.48156, "loss_cls": 4.38299, "loss": 4.38299, "time": 0.85333} +{"mode": "train", "epoch": 58, "iter": 3500, "lr": 0.06749, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24578, "top5_acc": 0.48203, "loss_cls": 4.41531, "loss": 4.41531, "time": 0.84588} +{"mode": "train", "epoch": 58, "iter": 3600, "lr": 0.06747, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24391, "top5_acc": 0.49906, "loss_cls": 4.35576, "loss": 4.35576, "time": 0.848} +{"mode": "train", "epoch": 58, "iter": 3700, "lr": 0.06744, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24672, "top5_acc": 0.48047, "loss_cls": 4.42209, "loss": 4.42209, "time": 0.84949} +{"mode": "val", "epoch": 58, "iter": 309, "lr": 0.06743, "top1_acc": 0.18979, "top5_acc": 0.40971, "mean_class_accuracy": 0.1895} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.0674, "memory": 15990, "data_time": 1.61012, "top1_acc": 0.25578, "top5_acc": 0.49812, "loss_cls": 4.31872, "loss": 4.31872, "time": 2.66029} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.06738, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.245, "top5_acc": 0.48469, "loss_cls": 4.41937, "loss": 4.41937, "time": 0.85555} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.06735, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.23781, "top5_acc": 0.49031, "loss_cls": 4.36953, "loss": 4.36953, "time": 0.8502} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.06732, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.24594, "top5_acc": 0.48969, "loss_cls": 4.36731, "loss": 4.36731, "time": 0.85677} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.0673, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24703, "top5_acc": 0.49641, "loss_cls": 4.33326, "loss": 4.33326, "time": 0.86072} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.06727, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25672, "top5_acc": 0.48891, "loss_cls": 4.35265, "loss": 4.35265, "time": 0.85844} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.06725, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.2475, "top5_acc": 0.49047, "loss_cls": 4.3558, "loss": 4.3558, "time": 0.85619} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.06722, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.24891, "top5_acc": 0.49359, "loss_cls": 4.36172, "loss": 4.36172, "time": 0.85892} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.06719, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.2525, "top5_acc": 0.48844, "loss_cls": 4.37638, "loss": 4.37638, "time": 0.85696} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.06717, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.23578, "top5_acc": 0.48609, "loss_cls": 4.41461, "loss": 4.41461, "time": 0.85116} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.06714, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24859, "top5_acc": 0.48297, "loss_cls": 4.38302, "loss": 4.38302, "time": 0.85295} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.06711, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24891, "top5_acc": 0.49188, "loss_cls": 4.35967, "loss": 4.35967, "time": 0.84932} +{"mode": "train", "epoch": 59, "iter": 1300, "lr": 0.06709, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24625, "top5_acc": 0.49453, "loss_cls": 4.37646, "loss": 4.37646, "time": 0.84911} +{"mode": "train", "epoch": 59, "iter": 1400, "lr": 0.06706, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24547, "top5_acc": 0.48391, "loss_cls": 4.37985, "loss": 4.37985, "time": 0.84509} +{"mode": "train", "epoch": 59, "iter": 1500, "lr": 0.06704, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24562, "top5_acc": 0.49516, "loss_cls": 4.36345, "loss": 4.36345, "time": 0.8524} +{"mode": "train", "epoch": 59, "iter": 1600, "lr": 0.06701, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23656, "top5_acc": 0.48266, "loss_cls": 4.42309, "loss": 4.42309, "time": 0.84844} +{"mode": "train", "epoch": 59, "iter": 1700, "lr": 0.06698, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25516, "top5_acc": 0.49547, "loss_cls": 4.32445, "loss": 4.32445, "time": 0.84943} +{"mode": "train", "epoch": 59, "iter": 1800, "lr": 0.06696, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24578, "top5_acc": 0.48406, "loss_cls": 4.40426, "loss": 4.40426, "time": 0.84687} +{"mode": "train", "epoch": 59, "iter": 1900, "lr": 0.06693, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25016, "top5_acc": 0.48688, "loss_cls": 4.37407, "loss": 4.37407, "time": 0.84777} +{"mode": "train", "epoch": 59, "iter": 2000, "lr": 0.0669, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24672, "top5_acc": 0.48906, "loss_cls": 4.38135, "loss": 4.38135, "time": 0.85209} +{"mode": "train", "epoch": 59, "iter": 2100, "lr": 0.06688, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24469, "top5_acc": 0.49062, "loss_cls": 4.38657, "loss": 4.38657, "time": 0.8486} +{"mode": "train", "epoch": 59, "iter": 2200, "lr": 0.06685, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23906, "top5_acc": 0.47984, "loss_cls": 4.39241, "loss": 4.39241, "time": 0.85154} +{"mode": "train", "epoch": 59, "iter": 2300, "lr": 0.06682, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25031, "top5_acc": 0.49219, "loss_cls": 4.35422, "loss": 4.35422, "time": 0.84601} +{"mode": "train", "epoch": 59, "iter": 2400, "lr": 0.0668, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24125, "top5_acc": 0.48734, "loss_cls": 4.38827, "loss": 4.38827, "time": 0.85204} +{"mode": "train", "epoch": 59, "iter": 2500, "lr": 0.06677, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24625, "top5_acc": 0.48453, "loss_cls": 4.38431, "loss": 4.38431, "time": 0.85401} +{"mode": "train", "epoch": 59, "iter": 2600, "lr": 0.06675, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24766, "top5_acc": 0.4875, "loss_cls": 4.37734, "loss": 4.37734, "time": 0.85323} +{"mode": "train", "epoch": 59, "iter": 2700, "lr": 0.06672, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.25062, "top5_acc": 0.49859, "loss_cls": 4.33656, "loss": 4.33656, "time": 0.8513} +{"mode": "train", "epoch": 59, "iter": 2800, "lr": 0.06669, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25156, "top5_acc": 0.48531, "loss_cls": 4.4083, "loss": 4.4083, "time": 0.83954} +{"mode": "train", "epoch": 59, "iter": 2900, "lr": 0.06667, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25312, "top5_acc": 0.48594, "loss_cls": 4.37716, "loss": 4.37716, "time": 0.83861} +{"mode": "train", "epoch": 59, "iter": 3000, "lr": 0.06664, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24875, "top5_acc": 0.48656, "loss_cls": 4.37296, "loss": 4.37296, "time": 0.84257} +{"mode": "train", "epoch": 59, "iter": 3100, "lr": 0.06661, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23578, "top5_acc": 0.47188, "loss_cls": 4.4782, "loss": 4.4782, "time": 0.84151} +{"mode": "train", "epoch": 59, "iter": 3200, "lr": 0.06659, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25531, "top5_acc": 0.49828, "loss_cls": 4.33657, "loss": 4.33657, "time": 0.84686} +{"mode": "train", "epoch": 59, "iter": 3300, "lr": 0.06656, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24766, "top5_acc": 0.48625, "loss_cls": 4.37936, "loss": 4.37936, "time": 0.84501} +{"mode": "train", "epoch": 59, "iter": 3400, "lr": 0.06653, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24406, "top5_acc": 0.48875, "loss_cls": 4.39016, "loss": 4.39016, "time": 0.84866} +{"mode": "train", "epoch": 59, "iter": 3500, "lr": 0.06651, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25062, "top5_acc": 0.49344, "loss_cls": 4.36756, "loss": 4.36756, "time": 0.84436} +{"mode": "train", "epoch": 59, "iter": 3600, "lr": 0.06648, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24406, "top5_acc": 0.48875, "loss_cls": 4.36798, "loss": 4.36798, "time": 0.84362} +{"mode": "train", "epoch": 59, "iter": 3700, "lr": 0.06646, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23641, "top5_acc": 0.48297, "loss_cls": 4.42328, "loss": 4.42328, "time": 0.84087} +{"mode": "val", "epoch": 59, "iter": 309, "lr": 0.06644, "top1_acc": 0.18417, "top5_acc": 0.40323, "mean_class_accuracy": 0.18411} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.06642, "memory": 15990, "data_time": 1.57472, "top1_acc": 0.25266, "top5_acc": 0.49953, "loss_cls": 4.32117, "loss": 4.32117, "time": 2.60415} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.06639, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25875, "top5_acc": 0.50313, "loss_cls": 4.30905, "loss": 4.30905, "time": 0.85921} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.06636, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24156, "top5_acc": 0.49281, "loss_cls": 4.37698, "loss": 4.37698, "time": 0.85438} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.06634, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24484, "top5_acc": 0.49641, "loss_cls": 4.35084, "loss": 4.35084, "time": 0.85919} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.06631, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24531, "top5_acc": 0.48672, "loss_cls": 4.36586, "loss": 4.36586, "time": 0.86126} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.06629, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25312, "top5_acc": 0.50203, "loss_cls": 4.3264, "loss": 4.3264, "time": 0.85887} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.06626, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25438, "top5_acc": 0.49203, "loss_cls": 4.34911, "loss": 4.34911, "time": 0.85414} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.06623, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24125, "top5_acc": 0.47953, "loss_cls": 4.40831, "loss": 4.40831, "time": 0.85658} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.06621, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24391, "top5_acc": 0.485, "loss_cls": 4.35293, "loss": 4.35293, "time": 0.84816} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.06618, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24328, "top5_acc": 0.48422, "loss_cls": 4.40275, "loss": 4.40275, "time": 0.84782} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.06615, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24766, "top5_acc": 0.49078, "loss_cls": 4.38127, "loss": 4.38127, "time": 0.84675} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.06613, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24188, "top5_acc": 0.49219, "loss_cls": 4.37257, "loss": 4.37257, "time": 0.85102} +{"mode": "train", "epoch": 60, "iter": 1300, "lr": 0.0661, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25312, "top5_acc": 0.49156, "loss_cls": 4.367, "loss": 4.367, "time": 0.84977} +{"mode": "train", "epoch": 60, "iter": 1400, "lr": 0.06607, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25078, "top5_acc": 0.49547, "loss_cls": 4.3656, "loss": 4.3656, "time": 0.84698} +{"mode": "train", "epoch": 60, "iter": 1500, "lr": 0.06605, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24531, "top5_acc": 0.48844, "loss_cls": 4.34618, "loss": 4.34618, "time": 0.84593} +{"mode": "train", "epoch": 60, "iter": 1600, "lr": 0.06602, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24547, "top5_acc": 0.49312, "loss_cls": 4.36031, "loss": 4.36031, "time": 0.84869} +{"mode": "train", "epoch": 60, "iter": 1700, "lr": 0.06599, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24859, "top5_acc": 0.49781, "loss_cls": 4.35168, "loss": 4.35168, "time": 0.85482} +{"mode": "train", "epoch": 60, "iter": 1800, "lr": 0.06597, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24734, "top5_acc": 0.49625, "loss_cls": 4.36293, "loss": 4.36293, "time": 0.8545} +{"mode": "train", "epoch": 60, "iter": 1900, "lr": 0.06594, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25141, "top5_acc": 0.49578, "loss_cls": 4.37833, "loss": 4.37833, "time": 0.85229} +{"mode": "train", "epoch": 60, "iter": 2000, "lr": 0.06591, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.245, "top5_acc": 0.48078, "loss_cls": 4.38674, "loss": 4.38674, "time": 0.84748} +{"mode": "train", "epoch": 60, "iter": 2100, "lr": 0.06589, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25766, "top5_acc": 0.49609, "loss_cls": 4.32523, "loss": 4.32523, "time": 0.84521} +{"mode": "train", "epoch": 60, "iter": 2200, "lr": 0.06586, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25375, "top5_acc": 0.49062, "loss_cls": 4.35083, "loss": 4.35083, "time": 0.84854} +{"mode": "train", "epoch": 60, "iter": 2300, "lr": 0.06584, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25344, "top5_acc": 0.49219, "loss_cls": 4.35692, "loss": 4.35692, "time": 0.85242} +{"mode": "train", "epoch": 60, "iter": 2400, "lr": 0.06581, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.245, "top5_acc": 0.48922, "loss_cls": 4.39866, "loss": 4.39866, "time": 0.84732} +{"mode": "train", "epoch": 60, "iter": 2500, "lr": 0.06578, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24234, "top5_acc": 0.48891, "loss_cls": 4.3961, "loss": 4.3961, "time": 0.85007} +{"mode": "train", "epoch": 60, "iter": 2600, "lr": 0.06576, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24984, "top5_acc": 0.48062, "loss_cls": 4.3813, "loss": 4.3813, "time": 0.8513} +{"mode": "train", "epoch": 60, "iter": 2700, "lr": 0.06573, "memory": 15990, "data_time": 0.00077, "top1_acc": 0.24734, "top5_acc": 0.49359, "loss_cls": 4.37213, "loss": 4.37213, "time": 0.85026} +{"mode": "train", "epoch": 60, "iter": 2800, "lr": 0.0657, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25047, "top5_acc": 0.49609, "loss_cls": 4.35011, "loss": 4.35011, "time": 0.8494} +{"mode": "train", "epoch": 60, "iter": 2900, "lr": 0.06568, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24844, "top5_acc": 0.48531, "loss_cls": 4.38799, "loss": 4.38799, "time": 0.84316} +{"mode": "train", "epoch": 60, "iter": 3000, "lr": 0.06565, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24344, "top5_acc": 0.47906, "loss_cls": 4.38173, "loss": 4.38173, "time": 0.85266} +{"mode": "train", "epoch": 60, "iter": 3100, "lr": 0.06562, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25812, "top5_acc": 0.50531, "loss_cls": 4.32236, "loss": 4.32236, "time": 0.8484} +{"mode": "train", "epoch": 60, "iter": 3200, "lr": 0.0656, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24797, "top5_acc": 0.49156, "loss_cls": 4.37058, "loss": 4.37058, "time": 0.85254} +{"mode": "train", "epoch": 60, "iter": 3300, "lr": 0.06557, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2475, "top5_acc": 0.49219, "loss_cls": 4.34217, "loss": 4.34217, "time": 0.8442} +{"mode": "train", "epoch": 60, "iter": 3400, "lr": 0.06554, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25578, "top5_acc": 0.49688, "loss_cls": 4.34281, "loss": 4.34281, "time": 0.84746} +{"mode": "train", "epoch": 60, "iter": 3500, "lr": 0.06552, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26375, "top5_acc": 0.5025, "loss_cls": 4.29628, "loss": 4.29628, "time": 0.84819} +{"mode": "train", "epoch": 60, "iter": 3600, "lr": 0.06549, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2425, "top5_acc": 0.48062, "loss_cls": 4.4013, "loss": 4.4013, "time": 0.84683} +{"mode": "train", "epoch": 60, "iter": 3700, "lr": 0.06546, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23641, "top5_acc": 0.48547, "loss_cls": 4.40163, "loss": 4.40163, "time": 0.84698} +{"mode": "val", "epoch": 60, "iter": 309, "lr": 0.06545, "top1_acc": 0.17728, "top5_acc": 0.39285, "mean_class_accuracy": 0.17698} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.06542, "memory": 15990, "data_time": 1.56762, "top1_acc": 0.25641, "top5_acc": 0.50453, "loss_cls": 4.31177, "loss": 4.31177, "time": 2.60989} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.0654, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25969, "top5_acc": 0.50484, "loss_cls": 4.29699, "loss": 4.29699, "time": 0.851} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.06537, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25062, "top5_acc": 0.49156, "loss_cls": 4.35596, "loss": 4.35596, "time": 0.85378} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.06534, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25828, "top5_acc": 0.50156, "loss_cls": 4.29651, "loss": 4.29651, "time": 0.85295} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.06532, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25188, "top5_acc": 0.48297, "loss_cls": 4.35641, "loss": 4.35641, "time": 0.85772} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.06529, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26047, "top5_acc": 0.49391, "loss_cls": 4.34527, "loss": 4.34527, "time": 0.85455} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.06526, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2525, "top5_acc": 0.49672, "loss_cls": 4.33674, "loss": 4.33674, "time": 0.85266} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.06524, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25734, "top5_acc": 0.50453, "loss_cls": 4.3322, "loss": 4.3322, "time": 0.85842} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.06521, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24625, "top5_acc": 0.4925, "loss_cls": 4.36009, "loss": 4.36009, "time": 0.85356} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.06519, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.25547, "top5_acc": 0.49109, "loss_cls": 4.38921, "loss": 4.38921, "time": 0.85227} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.06516, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24172, "top5_acc": 0.47547, "loss_cls": 4.42327, "loss": 4.42327, "time": 0.85112} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.06513, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24922, "top5_acc": 0.48906, "loss_cls": 4.34403, "loss": 4.34403, "time": 0.8514} +{"mode": "train", "epoch": 61, "iter": 1300, "lr": 0.06511, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25609, "top5_acc": 0.49938, "loss_cls": 4.34533, "loss": 4.34533, "time": 0.85107} +{"mode": "train", "epoch": 61, "iter": 1400, "lr": 0.06508, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26031, "top5_acc": 0.50047, "loss_cls": 4.30391, "loss": 4.30391, "time": 0.85399} +{"mode": "train", "epoch": 61, "iter": 1500, "lr": 0.06505, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26359, "top5_acc": 0.50438, "loss_cls": 4.30246, "loss": 4.30246, "time": 0.84912} +{"mode": "train", "epoch": 61, "iter": 1600, "lr": 0.06503, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23297, "top5_acc": 0.47969, "loss_cls": 4.44127, "loss": 4.44127, "time": 0.85595} +{"mode": "train", "epoch": 61, "iter": 1700, "lr": 0.065, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24828, "top5_acc": 0.48875, "loss_cls": 4.38216, "loss": 4.38216, "time": 0.85556} +{"mode": "train", "epoch": 61, "iter": 1800, "lr": 0.06497, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2525, "top5_acc": 0.50031, "loss_cls": 4.32472, "loss": 4.32472, "time": 0.85231} +{"mode": "train", "epoch": 61, "iter": 1900, "lr": 0.06495, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24531, "top5_acc": 0.48766, "loss_cls": 4.37494, "loss": 4.37494, "time": 0.85015} +{"mode": "train", "epoch": 61, "iter": 2000, "lr": 0.06492, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25359, "top5_acc": 0.49906, "loss_cls": 4.33701, "loss": 4.33701, "time": 0.85339} +{"mode": "train", "epoch": 61, "iter": 2100, "lr": 0.06489, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24578, "top5_acc": 0.47375, "loss_cls": 4.4027, "loss": 4.4027, "time": 0.85153} +{"mode": "train", "epoch": 61, "iter": 2200, "lr": 0.06487, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24656, "top5_acc": 0.48547, "loss_cls": 4.3556, "loss": 4.3556, "time": 0.84837} +{"mode": "train", "epoch": 61, "iter": 2300, "lr": 0.06484, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23891, "top5_acc": 0.48453, "loss_cls": 4.3877, "loss": 4.3877, "time": 0.85038} +{"mode": "train", "epoch": 61, "iter": 2400, "lr": 0.06481, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24828, "top5_acc": 0.49812, "loss_cls": 4.37205, "loss": 4.37205, "time": 0.85209} +{"mode": "train", "epoch": 61, "iter": 2500, "lr": 0.06478, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24531, "top5_acc": 0.485, "loss_cls": 4.35791, "loss": 4.35791, "time": 0.85352} +{"mode": "train", "epoch": 61, "iter": 2600, "lr": 0.06476, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24328, "top5_acc": 0.49641, "loss_cls": 4.38372, "loss": 4.38372, "time": 0.85033} +{"mode": "train", "epoch": 61, "iter": 2700, "lr": 0.06473, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24859, "top5_acc": 0.48562, "loss_cls": 4.38023, "loss": 4.38023, "time": 0.85064} +{"mode": "train", "epoch": 61, "iter": 2800, "lr": 0.0647, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.245, "top5_acc": 0.48469, "loss_cls": 4.38277, "loss": 4.38277, "time": 0.84702} +{"mode": "train", "epoch": 61, "iter": 2900, "lr": 0.06468, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2525, "top5_acc": 0.495, "loss_cls": 4.34071, "loss": 4.34071, "time": 0.84595} +{"mode": "train", "epoch": 61, "iter": 3000, "lr": 0.06465, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24812, "top5_acc": 0.49141, "loss_cls": 4.34663, "loss": 4.34663, "time": 0.86276} +{"mode": "train", "epoch": 61, "iter": 3100, "lr": 0.06462, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25406, "top5_acc": 0.49125, "loss_cls": 4.35654, "loss": 4.35654, "time": 0.85666} +{"mode": "train", "epoch": 61, "iter": 3200, "lr": 0.0646, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23953, "top5_acc": 0.49062, "loss_cls": 4.33591, "loss": 4.33591, "time": 0.86227} +{"mode": "train", "epoch": 61, "iter": 3300, "lr": 0.06457, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24609, "top5_acc": 0.49062, "loss_cls": 4.39408, "loss": 4.39408, "time": 0.85952} +{"mode": "train", "epoch": 61, "iter": 3400, "lr": 0.06454, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25438, "top5_acc": 0.48984, "loss_cls": 4.35123, "loss": 4.35123, "time": 0.85319} +{"mode": "train", "epoch": 61, "iter": 3500, "lr": 0.06452, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25031, "top5_acc": 0.48781, "loss_cls": 4.36652, "loss": 4.36652, "time": 0.85384} +{"mode": "train", "epoch": 61, "iter": 3600, "lr": 0.06449, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24844, "top5_acc": 0.49, "loss_cls": 4.3641, "loss": 4.3641, "time": 0.85761} +{"mode": "train", "epoch": 61, "iter": 3700, "lr": 0.06446, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.2575, "top5_acc": 0.49906, "loss_cls": 4.33806, "loss": 4.33806, "time": 0.85756} +{"mode": "val", "epoch": 61, "iter": 309, "lr": 0.06445, "top1_acc": 0.19658, "top5_acc": 0.41306, "mean_class_accuracy": 0.19639} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.06443, "memory": 15990, "data_time": 1.5644, "top1_acc": 0.26047, "top5_acc": 0.50734, "loss_cls": 4.29626, "loss": 4.29626, "time": 2.58732} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.0644, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24812, "top5_acc": 0.49625, "loss_cls": 4.3563, "loss": 4.3563, "time": 0.84743} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.06437, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25812, "top5_acc": 0.49328, "loss_cls": 4.30933, "loss": 4.30933, "time": 0.84528} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.06434, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25219, "top5_acc": 0.50656, "loss_cls": 4.30217, "loss": 4.30217, "time": 0.84517} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.06432, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25438, "top5_acc": 0.50688, "loss_cls": 4.29601, "loss": 4.29601, "time": 0.84823} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.06429, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24891, "top5_acc": 0.49844, "loss_cls": 4.33714, "loss": 4.33714, "time": 0.85953} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.06426, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.24859, "top5_acc": 0.495, "loss_cls": 4.31035, "loss": 4.31035, "time": 0.84994} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.06424, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.25391, "top5_acc": 0.49203, "loss_cls": 4.34448, "loss": 4.34448, "time": 0.85088} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.06421, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26203, "top5_acc": 0.50375, "loss_cls": 4.28137, "loss": 4.28137, "time": 0.85289} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.06418, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26422, "top5_acc": 0.49391, "loss_cls": 4.34099, "loss": 4.34099, "time": 0.84786} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.06416, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25516, "top5_acc": 0.50047, "loss_cls": 4.31424, "loss": 4.31424, "time": 0.85257} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.06413, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26078, "top5_acc": 0.49938, "loss_cls": 4.34413, "loss": 4.34413, "time": 0.84684} +{"mode": "train", "epoch": 62, "iter": 1300, "lr": 0.0641, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24594, "top5_acc": 0.49234, "loss_cls": 4.37351, "loss": 4.37351, "time": 0.85099} +{"mode": "train", "epoch": 62, "iter": 1400, "lr": 0.06408, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24891, "top5_acc": 0.49062, "loss_cls": 4.354, "loss": 4.354, "time": 0.84261} +{"mode": "train", "epoch": 62, "iter": 1500, "lr": 0.06405, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25844, "top5_acc": 0.49359, "loss_cls": 4.3343, "loss": 4.3343, "time": 0.84211} +{"mode": "train", "epoch": 62, "iter": 1600, "lr": 0.06402, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.25328, "top5_acc": 0.48844, "loss_cls": 4.33494, "loss": 4.33494, "time": 0.8518} +{"mode": "train", "epoch": 62, "iter": 1700, "lr": 0.064, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2475, "top5_acc": 0.4975, "loss_cls": 4.34958, "loss": 4.34958, "time": 0.85443} +{"mode": "train", "epoch": 62, "iter": 1800, "lr": 0.06397, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25078, "top5_acc": 0.50281, "loss_cls": 4.31385, "loss": 4.31385, "time": 0.84922} +{"mode": "train", "epoch": 62, "iter": 1900, "lr": 0.06394, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25109, "top5_acc": 0.49766, "loss_cls": 4.34669, "loss": 4.34669, "time": 0.84611} +{"mode": "train", "epoch": 62, "iter": 2000, "lr": 0.06392, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25109, "top5_acc": 0.49422, "loss_cls": 4.35163, "loss": 4.35163, "time": 0.84755} +{"mode": "train", "epoch": 62, "iter": 2100, "lr": 0.06389, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2475, "top5_acc": 0.49594, "loss_cls": 4.35712, "loss": 4.35712, "time": 0.8473} +{"mode": "train", "epoch": 62, "iter": 2200, "lr": 0.06386, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24844, "top5_acc": 0.48734, "loss_cls": 4.3676, "loss": 4.3676, "time": 0.85286} +{"mode": "train", "epoch": 62, "iter": 2300, "lr": 0.06384, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24344, "top5_acc": 0.48531, "loss_cls": 4.40568, "loss": 4.40568, "time": 0.85523} +{"mode": "train", "epoch": 62, "iter": 2400, "lr": 0.06381, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25578, "top5_acc": 0.49828, "loss_cls": 4.34564, "loss": 4.34564, "time": 0.84954} +{"mode": "train", "epoch": 62, "iter": 2500, "lr": 0.06378, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25578, "top5_acc": 0.49469, "loss_cls": 4.35786, "loss": 4.35786, "time": 0.85463} +{"mode": "train", "epoch": 62, "iter": 2600, "lr": 0.06375, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25047, "top5_acc": 0.49031, "loss_cls": 4.35046, "loss": 4.35046, "time": 0.85273} +{"mode": "train", "epoch": 62, "iter": 2700, "lr": 0.06373, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25516, "top5_acc": 0.49734, "loss_cls": 4.34695, "loss": 4.34695, "time": 0.84918} +{"mode": "train", "epoch": 62, "iter": 2800, "lr": 0.0637, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.25344, "top5_acc": 0.50328, "loss_cls": 4.34639, "loss": 4.34639, "time": 0.84855} +{"mode": "train", "epoch": 62, "iter": 2900, "lr": 0.06367, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25016, "top5_acc": 0.48609, "loss_cls": 4.39387, "loss": 4.39387, "time": 0.84769} +{"mode": "train", "epoch": 62, "iter": 3000, "lr": 0.06365, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25859, "top5_acc": 0.49609, "loss_cls": 4.32204, "loss": 4.32204, "time": 0.84724} +{"mode": "train", "epoch": 62, "iter": 3100, "lr": 0.06362, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24984, "top5_acc": 0.49516, "loss_cls": 4.34456, "loss": 4.34456, "time": 0.84572} +{"mode": "train", "epoch": 62, "iter": 3200, "lr": 0.06359, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25109, "top5_acc": 0.48703, "loss_cls": 4.3347, "loss": 4.3347, "time": 0.84117} +{"mode": "train", "epoch": 62, "iter": 3300, "lr": 0.06357, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.24516, "top5_acc": 0.48578, "loss_cls": 4.40903, "loss": 4.40903, "time": 0.84842} +{"mode": "train", "epoch": 62, "iter": 3400, "lr": 0.06354, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24656, "top5_acc": 0.48141, "loss_cls": 4.39241, "loss": 4.39241, "time": 0.84939} +{"mode": "train", "epoch": 62, "iter": 3500, "lr": 0.06351, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.24469, "top5_acc": 0.48719, "loss_cls": 4.37497, "loss": 4.37497, "time": 0.84492} +{"mode": "train", "epoch": 62, "iter": 3600, "lr": 0.06349, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.24766, "top5_acc": 0.48984, "loss_cls": 4.36837, "loss": 4.36837, "time": 0.84318} +{"mode": "train", "epoch": 62, "iter": 3700, "lr": 0.06346, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24781, "top5_acc": 0.49, "loss_cls": 4.36067, "loss": 4.36067, "time": 0.84815} +{"mode": "val", "epoch": 62, "iter": 309, "lr": 0.06345, "top1_acc": 0.18315, "top5_acc": 0.41118, "mean_class_accuracy": 0.18304} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.06342, "memory": 15990, "data_time": 1.53653, "top1_acc": 0.26438, "top5_acc": 0.51047, "loss_cls": 4.26474, "loss": 4.26474, "time": 2.57082} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.06339, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25969, "top5_acc": 0.50016, "loss_cls": 4.28631, "loss": 4.28631, "time": 0.85082} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.06337, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25031, "top5_acc": 0.49422, "loss_cls": 4.34934, "loss": 4.34934, "time": 0.8481} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.06334, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25641, "top5_acc": 0.5025, "loss_cls": 4.30742, "loss": 4.30742, "time": 0.84875} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.06331, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25672, "top5_acc": 0.50719, "loss_cls": 4.32209, "loss": 4.32209, "time": 0.84783} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.06328, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26078, "top5_acc": 0.50016, "loss_cls": 4.29156, "loss": 4.29156, "time": 0.85409} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.06326, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26, "top5_acc": 0.50547, "loss_cls": 4.29839, "loss": 4.29839, "time": 0.8525} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.06323, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24891, "top5_acc": 0.49438, "loss_cls": 4.33822, "loss": 4.33822, "time": 0.85384} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.0632, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.24578, "top5_acc": 0.48531, "loss_cls": 4.38721, "loss": 4.38721, "time": 0.85249} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.06318, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25547, "top5_acc": 0.49156, "loss_cls": 4.34224, "loss": 4.34224, "time": 0.84069} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.06315, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.25359, "top5_acc": 0.49281, "loss_cls": 4.34603, "loss": 4.34603, "time": 0.84583} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.06312, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25031, "top5_acc": 0.49344, "loss_cls": 4.33357, "loss": 4.33357, "time": 0.85513} +{"mode": "train", "epoch": 63, "iter": 1300, "lr": 0.0631, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25469, "top5_acc": 0.49953, "loss_cls": 4.31611, "loss": 4.31611, "time": 0.84832} +{"mode": "train", "epoch": 63, "iter": 1400, "lr": 0.06307, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25625, "top5_acc": 0.49625, "loss_cls": 4.33699, "loss": 4.33699, "time": 0.85145} +{"mode": "train", "epoch": 63, "iter": 1500, "lr": 0.06304, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24812, "top5_acc": 0.49016, "loss_cls": 4.37105, "loss": 4.37105, "time": 0.85204} +{"mode": "train", "epoch": 63, "iter": 1600, "lr": 0.06301, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25641, "top5_acc": 0.49516, "loss_cls": 4.32717, "loss": 4.32717, "time": 0.85093} +{"mode": "train", "epoch": 63, "iter": 1700, "lr": 0.06299, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25031, "top5_acc": 0.495, "loss_cls": 4.32843, "loss": 4.32843, "time": 0.85079} +{"mode": "train", "epoch": 63, "iter": 1800, "lr": 0.06296, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2525, "top5_acc": 0.50172, "loss_cls": 4.34388, "loss": 4.34388, "time": 0.8517} +{"mode": "train", "epoch": 63, "iter": 1900, "lr": 0.06293, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24844, "top5_acc": 0.48984, "loss_cls": 4.34087, "loss": 4.34087, "time": 0.8559} +{"mode": "train", "epoch": 63, "iter": 2000, "lr": 0.06291, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24016, "top5_acc": 0.48641, "loss_cls": 4.36683, "loss": 4.36683, "time": 0.85149} +{"mode": "train", "epoch": 63, "iter": 2100, "lr": 0.06288, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24969, "top5_acc": 0.495, "loss_cls": 4.35638, "loss": 4.35638, "time": 0.85254} +{"mode": "train", "epoch": 63, "iter": 2200, "lr": 0.06285, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24172, "top5_acc": 0.48594, "loss_cls": 4.37383, "loss": 4.37383, "time": 0.85038} +{"mode": "train", "epoch": 63, "iter": 2300, "lr": 0.06283, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2575, "top5_acc": 0.49, "loss_cls": 4.31144, "loss": 4.31144, "time": 0.84919} +{"mode": "train", "epoch": 63, "iter": 2400, "lr": 0.0628, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25812, "top5_acc": 0.49969, "loss_cls": 4.31179, "loss": 4.31179, "time": 0.84733} +{"mode": "train", "epoch": 63, "iter": 2500, "lr": 0.06277, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25609, "top5_acc": 0.49172, "loss_cls": 4.31607, "loss": 4.31607, "time": 0.85472} +{"mode": "train", "epoch": 63, "iter": 2600, "lr": 0.06274, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24938, "top5_acc": 0.50281, "loss_cls": 4.31474, "loss": 4.31474, "time": 0.85398} +{"mode": "train", "epoch": 63, "iter": 2700, "lr": 0.06272, "memory": 15990, "data_time": 0.0007, "top1_acc": 0.24734, "top5_acc": 0.48609, "loss_cls": 4.40177, "loss": 4.40177, "time": 0.84433} +{"mode": "train", "epoch": 63, "iter": 2800, "lr": 0.06269, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.245, "top5_acc": 0.49047, "loss_cls": 4.38942, "loss": 4.38942, "time": 0.8416} +{"mode": "train", "epoch": 63, "iter": 2900, "lr": 0.06266, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24625, "top5_acc": 0.49094, "loss_cls": 4.34082, "loss": 4.34082, "time": 0.84626} +{"mode": "train", "epoch": 63, "iter": 3000, "lr": 0.06264, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24828, "top5_acc": 0.48844, "loss_cls": 4.40834, "loss": 4.40834, "time": 0.84365} +{"mode": "train", "epoch": 63, "iter": 3100, "lr": 0.06261, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25625, "top5_acc": 0.49234, "loss_cls": 4.37019, "loss": 4.37019, "time": 0.84806} +{"mode": "train", "epoch": 63, "iter": 3200, "lr": 0.06258, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25625, "top5_acc": 0.49844, "loss_cls": 4.30793, "loss": 4.30793, "time": 0.85026} +{"mode": "train", "epoch": 63, "iter": 3300, "lr": 0.06256, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25156, "top5_acc": 0.4925, "loss_cls": 4.36746, "loss": 4.36746, "time": 0.84502} +{"mode": "train", "epoch": 63, "iter": 3400, "lr": 0.06253, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.24828, "top5_acc": 0.49625, "loss_cls": 4.31693, "loss": 4.31693, "time": 0.84428} +{"mode": "train", "epoch": 63, "iter": 3500, "lr": 0.0625, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25406, "top5_acc": 0.49672, "loss_cls": 4.31502, "loss": 4.31502, "time": 0.84588} +{"mode": "train", "epoch": 63, "iter": 3600, "lr": 0.06247, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25562, "top5_acc": 0.49234, "loss_cls": 4.35092, "loss": 4.35092, "time": 0.84667} +{"mode": "train", "epoch": 63, "iter": 3700, "lr": 0.06245, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25578, "top5_acc": 0.49828, "loss_cls": 4.3351, "loss": 4.3351, "time": 0.85019} +{"mode": "val", "epoch": 63, "iter": 309, "lr": 0.06243, "top1_acc": 0.19698, "top5_acc": 0.41883, "mean_class_accuracy": 0.19689} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.06241, "memory": 15990, "data_time": 1.575, "top1_acc": 0.26531, "top5_acc": 0.51125, "loss_cls": 4.24964, "loss": 4.24964, "time": 2.63622} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.06238, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25375, "top5_acc": 0.49906, "loss_cls": 4.31331, "loss": 4.31331, "time": 0.86744} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.06235, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26172, "top5_acc": 0.49781, "loss_cls": 4.30651, "loss": 4.30651, "time": 0.86019} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.06233, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26219, "top5_acc": 0.51031, "loss_cls": 4.26619, "loss": 4.26619, "time": 0.86365} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.0623, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.25266, "top5_acc": 0.50625, "loss_cls": 4.3305, "loss": 4.3305, "time": 0.86435} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.06227, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25656, "top5_acc": 0.49812, "loss_cls": 4.30735, "loss": 4.30735, "time": 0.86336} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.06225, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.2525, "top5_acc": 0.49844, "loss_cls": 4.32016, "loss": 4.32016, "time": 0.86333} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.06222, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24641, "top5_acc": 0.48844, "loss_cls": 4.34764, "loss": 4.34764, "time": 0.86377} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.06219, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23859, "top5_acc": 0.49328, "loss_cls": 4.38373, "loss": 4.38373, "time": 0.85566} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.06216, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25141, "top5_acc": 0.50453, "loss_cls": 4.31961, "loss": 4.31961, "time": 0.85067} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.06214, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2525, "top5_acc": 0.49172, "loss_cls": 4.31984, "loss": 4.31984, "time": 0.85201} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.06211, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24891, "top5_acc": 0.49281, "loss_cls": 4.33935, "loss": 4.33935, "time": 0.85021} +{"mode": "train", "epoch": 64, "iter": 1300, "lr": 0.06208, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25562, "top5_acc": 0.50234, "loss_cls": 4.31827, "loss": 4.31827, "time": 0.85039} +{"mode": "train", "epoch": 64, "iter": 1400, "lr": 0.06206, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25656, "top5_acc": 0.48719, "loss_cls": 4.3254, "loss": 4.3254, "time": 0.8578} +{"mode": "train", "epoch": 64, "iter": 1500, "lr": 0.06203, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25578, "top5_acc": 0.49688, "loss_cls": 4.29769, "loss": 4.29769, "time": 0.85077} +{"mode": "train", "epoch": 64, "iter": 1600, "lr": 0.062, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25312, "top5_acc": 0.49859, "loss_cls": 4.32796, "loss": 4.32796, "time": 0.85383} +{"mode": "train", "epoch": 64, "iter": 1700, "lr": 0.06197, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25391, "top5_acc": 0.50172, "loss_cls": 4.31772, "loss": 4.31772, "time": 0.85372} +{"mode": "train", "epoch": 64, "iter": 1800, "lr": 0.06195, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25375, "top5_acc": 0.50531, "loss_cls": 4.31091, "loss": 4.31091, "time": 0.85106} +{"mode": "train", "epoch": 64, "iter": 1900, "lr": 0.06192, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24438, "top5_acc": 0.49031, "loss_cls": 4.36432, "loss": 4.36432, "time": 0.85714} +{"mode": "train", "epoch": 64, "iter": 2000, "lr": 0.06189, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24875, "top5_acc": 0.49906, "loss_cls": 4.3257, "loss": 4.3257, "time": 0.85683} +{"mode": "train", "epoch": 64, "iter": 2100, "lr": 0.06187, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25391, "top5_acc": 0.49047, "loss_cls": 4.33907, "loss": 4.33907, "time": 0.85181} +{"mode": "train", "epoch": 64, "iter": 2200, "lr": 0.06184, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25344, "top5_acc": 0.49172, "loss_cls": 4.3464, "loss": 4.3464, "time": 0.8514} +{"mode": "train", "epoch": 64, "iter": 2300, "lr": 0.06181, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26375, "top5_acc": 0.50656, "loss_cls": 4.31505, "loss": 4.31505, "time": 0.85304} +{"mode": "train", "epoch": 64, "iter": 2400, "lr": 0.06178, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25422, "top5_acc": 0.49688, "loss_cls": 4.33751, "loss": 4.33751, "time": 0.85298} +{"mode": "train", "epoch": 64, "iter": 2500, "lr": 0.06176, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25062, "top5_acc": 0.49172, "loss_cls": 4.33474, "loss": 4.33474, "time": 0.85703} +{"mode": "train", "epoch": 64, "iter": 2600, "lr": 0.06173, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25516, "top5_acc": 0.50453, "loss_cls": 4.32097, "loss": 4.32097, "time": 0.85775} +{"mode": "train", "epoch": 64, "iter": 2700, "lr": 0.0617, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.24625, "top5_acc": 0.47797, "loss_cls": 4.3875, "loss": 4.3875, "time": 0.84698} +{"mode": "train", "epoch": 64, "iter": 2800, "lr": 0.06168, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25109, "top5_acc": 0.49453, "loss_cls": 4.36189, "loss": 4.36189, "time": 0.85352} +{"mode": "train", "epoch": 64, "iter": 2900, "lr": 0.06165, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25094, "top5_acc": 0.50094, "loss_cls": 4.33705, "loss": 4.33705, "time": 0.85488} +{"mode": "train", "epoch": 64, "iter": 3000, "lr": 0.06162, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25125, "top5_acc": 0.4925, "loss_cls": 4.33671, "loss": 4.33671, "time": 0.85085} +{"mode": "train", "epoch": 64, "iter": 3100, "lr": 0.06159, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2575, "top5_acc": 0.50141, "loss_cls": 4.31593, "loss": 4.31593, "time": 0.85822} +{"mode": "train", "epoch": 64, "iter": 3200, "lr": 0.06157, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24641, "top5_acc": 0.48859, "loss_cls": 4.37084, "loss": 4.37084, "time": 0.84891} +{"mode": "train", "epoch": 64, "iter": 3300, "lr": 0.06154, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25297, "top5_acc": 0.50109, "loss_cls": 4.31212, "loss": 4.31212, "time": 0.84548} +{"mode": "train", "epoch": 64, "iter": 3400, "lr": 0.06151, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25922, "top5_acc": 0.50219, "loss_cls": 4.3487, "loss": 4.3487, "time": 0.85111} +{"mode": "train", "epoch": 64, "iter": 3500, "lr": 0.06148, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27062, "top5_acc": 0.51578, "loss_cls": 4.2466, "loss": 4.2466, "time": 0.85364} +{"mode": "train", "epoch": 64, "iter": 3600, "lr": 0.06146, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25203, "top5_acc": 0.49453, "loss_cls": 4.31734, "loss": 4.31734, "time": 0.8526} +{"mode": "train", "epoch": 64, "iter": 3700, "lr": 0.06143, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25141, "top5_acc": 0.48984, "loss_cls": 4.38684, "loss": 4.38684, "time": 0.85868} +{"mode": "val", "epoch": 64, "iter": 309, "lr": 0.06142, "top1_acc": 0.20346, "top5_acc": 0.42096, "mean_class_accuracy": 0.20332} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.06139, "memory": 15990, "data_time": 1.53693, "top1_acc": 0.25266, "top5_acc": 0.50516, "loss_cls": 4.29508, "loss": 4.29508, "time": 2.56325} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.06136, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25672, "top5_acc": 0.50594, "loss_cls": 4.28493, "loss": 4.28493, "time": 0.84568} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.06134, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26047, "top5_acc": 0.50641, "loss_cls": 4.28806, "loss": 4.28806, "time": 0.8488} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.06131, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25516, "top5_acc": 0.49609, "loss_cls": 4.31464, "loss": 4.31464, "time": 0.85367} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.06128, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25594, "top5_acc": 0.50125, "loss_cls": 4.30292, "loss": 4.30292, "time": 0.85477} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.06125, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26469, "top5_acc": 0.50203, "loss_cls": 4.30671, "loss": 4.30671, "time": 0.84622} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.06123, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25453, "top5_acc": 0.49812, "loss_cls": 4.29735, "loss": 4.29735, "time": 0.84871} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0612, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25109, "top5_acc": 0.49578, "loss_cls": 4.34314, "loss": 4.34314, "time": 0.84701} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.06117, "memory": 15990, "data_time": 0.00067, "top1_acc": 0.26203, "top5_acc": 0.50625, "loss_cls": 4.31104, "loss": 4.31104, "time": 0.84541} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.06115, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25219, "top5_acc": 0.50719, "loss_cls": 4.29931, "loss": 4.29931, "time": 0.84645} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.06112, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.25609, "top5_acc": 0.50109, "loss_cls": 4.34288, "loss": 4.34288, "time": 0.84858} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.06109, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25188, "top5_acc": 0.4975, "loss_cls": 4.31762, "loss": 4.31762, "time": 0.84674} +{"mode": "train", "epoch": 65, "iter": 1300, "lr": 0.06106, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25422, "top5_acc": 0.4975, "loss_cls": 4.31192, "loss": 4.31192, "time": 0.85162} +{"mode": "train", "epoch": 65, "iter": 1400, "lr": 0.06104, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25094, "top5_acc": 0.51125, "loss_cls": 4.30697, "loss": 4.30697, "time": 0.85092} +{"mode": "train", "epoch": 65, "iter": 1500, "lr": 0.06101, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24953, "top5_acc": 0.49375, "loss_cls": 4.34893, "loss": 4.34893, "time": 0.85115} +{"mode": "train", "epoch": 65, "iter": 1600, "lr": 0.06098, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26062, "top5_acc": 0.50562, "loss_cls": 4.28682, "loss": 4.28682, "time": 0.85305} +{"mode": "train", "epoch": 65, "iter": 1700, "lr": 0.06095, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26219, "top5_acc": 0.49359, "loss_cls": 4.34184, "loss": 4.34184, "time": 0.85426} +{"mode": "train", "epoch": 65, "iter": 1800, "lr": 0.06093, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26906, "top5_acc": 0.50219, "loss_cls": 4.27699, "loss": 4.27699, "time": 0.85086} +{"mode": "train", "epoch": 65, "iter": 1900, "lr": 0.0609, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24812, "top5_acc": 0.48891, "loss_cls": 4.34734, "loss": 4.34734, "time": 0.84809} +{"mode": "train", "epoch": 65, "iter": 2000, "lr": 0.06087, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25125, "top5_acc": 0.48984, "loss_cls": 4.34518, "loss": 4.34518, "time": 0.84759} +{"mode": "train", "epoch": 65, "iter": 2100, "lr": 0.06085, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25062, "top5_acc": 0.49656, "loss_cls": 4.32847, "loss": 4.32847, "time": 0.8527} +{"mode": "train", "epoch": 65, "iter": 2200, "lr": 0.06082, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26344, "top5_acc": 0.50859, "loss_cls": 4.26028, "loss": 4.26028, "time": 0.85428} +{"mode": "train", "epoch": 65, "iter": 2300, "lr": 0.06079, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25297, "top5_acc": 0.49453, "loss_cls": 4.33786, "loss": 4.33786, "time": 0.8575} +{"mode": "train", "epoch": 65, "iter": 2400, "lr": 0.06076, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25828, "top5_acc": 0.50219, "loss_cls": 4.289, "loss": 4.289, "time": 0.85465} +{"mode": "train", "epoch": 65, "iter": 2500, "lr": 0.06074, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26469, "top5_acc": 0.49938, "loss_cls": 4.31728, "loss": 4.31728, "time": 0.8503} +{"mode": "train", "epoch": 65, "iter": 2600, "lr": 0.06071, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26297, "top5_acc": 0.50781, "loss_cls": 4.28928, "loss": 4.28928, "time": 0.8499} +{"mode": "train", "epoch": 65, "iter": 2700, "lr": 0.06068, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25469, "top5_acc": 0.50328, "loss_cls": 4.30141, "loss": 4.30141, "time": 0.84883} +{"mode": "train", "epoch": 65, "iter": 2800, "lr": 0.06065, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25297, "top5_acc": 0.49016, "loss_cls": 4.35053, "loss": 4.35053, "time": 0.84782} +{"mode": "train", "epoch": 65, "iter": 2900, "lr": 0.06063, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25812, "top5_acc": 0.49953, "loss_cls": 4.29941, "loss": 4.29941, "time": 0.84698} +{"mode": "train", "epoch": 65, "iter": 3000, "lr": 0.0606, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25359, "top5_acc": 0.50266, "loss_cls": 4.31744, "loss": 4.31744, "time": 0.84421} +{"mode": "train", "epoch": 65, "iter": 3100, "lr": 0.06057, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26297, "top5_acc": 0.50594, "loss_cls": 4.26939, "loss": 4.26939, "time": 0.83783} +{"mode": "train", "epoch": 65, "iter": 3200, "lr": 0.06055, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25609, "top5_acc": 0.50219, "loss_cls": 4.3202, "loss": 4.3202, "time": 0.84506} +{"mode": "train", "epoch": 65, "iter": 3300, "lr": 0.06052, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.24969, "top5_acc": 0.49828, "loss_cls": 4.32521, "loss": 4.32521, "time": 0.84678} +{"mode": "train", "epoch": 65, "iter": 3400, "lr": 0.06049, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25031, "top5_acc": 0.48688, "loss_cls": 4.347, "loss": 4.347, "time": 0.8422} +{"mode": "train", "epoch": 65, "iter": 3500, "lr": 0.06046, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24703, "top5_acc": 0.49156, "loss_cls": 4.36382, "loss": 4.36382, "time": 0.84607} +{"mode": "train", "epoch": 65, "iter": 3600, "lr": 0.06044, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26, "top5_acc": 0.49516, "loss_cls": 4.34455, "loss": 4.34455, "time": 0.84754} +{"mode": "train", "epoch": 65, "iter": 3700, "lr": 0.06041, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25656, "top5_acc": 0.48766, "loss_cls": 4.34574, "loss": 4.34574, "time": 0.85094} +{"mode": "val", "epoch": 65, "iter": 309, "lr": 0.0604, "top1_acc": 0.19531, "top5_acc": 0.42182, "mean_class_accuracy": 0.19522} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.06037, "memory": 15990, "data_time": 1.595, "top1_acc": 0.26094, "top5_acc": 0.50984, "loss_cls": 4.25283, "loss": 4.25283, "time": 2.64342} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.06034, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26375, "top5_acc": 0.49609, "loss_cls": 4.2707, "loss": 4.2707, "time": 0.85904} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.06031, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25844, "top5_acc": 0.50953, "loss_cls": 4.28711, "loss": 4.28711, "time": 0.85176} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.06029, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25797, "top5_acc": 0.51031, "loss_cls": 4.2967, "loss": 4.2967, "time": 0.85591} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.06026, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25984, "top5_acc": 0.50281, "loss_cls": 4.2631, "loss": 4.2631, "time": 0.85063} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.06023, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27031, "top5_acc": 0.5075, "loss_cls": 4.2607, "loss": 4.2607, "time": 0.85256} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.0602, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25688, "top5_acc": 0.50125, "loss_cls": 4.27598, "loss": 4.27598, "time": 0.85359} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.06018, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25562, "top5_acc": 0.49859, "loss_cls": 4.31807, "loss": 4.31807, "time": 0.85035} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.06015, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.25344, "top5_acc": 0.50422, "loss_cls": 4.30461, "loss": 4.30461, "time": 0.85126} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.06012, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26438, "top5_acc": 0.51062, "loss_cls": 4.24889, "loss": 4.24889, "time": 0.85031} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.06009, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25328, "top5_acc": 0.48938, "loss_cls": 4.33522, "loss": 4.33522, "time": 0.85391} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.06007, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25484, "top5_acc": 0.49922, "loss_cls": 4.30425, "loss": 4.30425, "time": 0.84934} +{"mode": "train", "epoch": 66, "iter": 1300, "lr": 0.06004, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25453, "top5_acc": 0.50719, "loss_cls": 4.27967, "loss": 4.27967, "time": 0.84646} +{"mode": "train", "epoch": 66, "iter": 1400, "lr": 0.06001, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25828, "top5_acc": 0.49938, "loss_cls": 4.30482, "loss": 4.30482, "time": 0.84507} +{"mode": "train", "epoch": 66, "iter": 1500, "lr": 0.05999, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25484, "top5_acc": 0.49797, "loss_cls": 4.29673, "loss": 4.29673, "time": 0.84741} +{"mode": "train", "epoch": 66, "iter": 1600, "lr": 0.05996, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26438, "top5_acc": 0.50734, "loss_cls": 4.27401, "loss": 4.27401, "time": 0.84247} +{"mode": "train", "epoch": 66, "iter": 1700, "lr": 0.05993, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25969, "top5_acc": 0.50938, "loss_cls": 4.27422, "loss": 4.27422, "time": 0.8478} +{"mode": "train", "epoch": 66, "iter": 1800, "lr": 0.0599, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25453, "top5_acc": 0.48922, "loss_cls": 4.35115, "loss": 4.35115, "time": 0.84982} +{"mode": "train", "epoch": 66, "iter": 1900, "lr": 0.05988, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25906, "top5_acc": 0.50578, "loss_cls": 4.30513, "loss": 4.30513, "time": 0.85184} +{"mode": "train", "epoch": 66, "iter": 2000, "lr": 0.05985, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24938, "top5_acc": 0.49562, "loss_cls": 4.32045, "loss": 4.32045, "time": 0.85263} +{"mode": "train", "epoch": 66, "iter": 2100, "lr": 0.05982, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26297, "top5_acc": 0.50875, "loss_cls": 4.27143, "loss": 4.27143, "time": 0.85451} +{"mode": "train", "epoch": 66, "iter": 2200, "lr": 0.05979, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.25812, "top5_acc": 0.50078, "loss_cls": 4.33277, "loss": 4.33277, "time": 0.85329} +{"mode": "train", "epoch": 66, "iter": 2300, "lr": 0.05977, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25828, "top5_acc": 0.50766, "loss_cls": 4.32985, "loss": 4.32985, "time": 0.85792} +{"mode": "train", "epoch": 66, "iter": 2400, "lr": 0.05974, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25766, "top5_acc": 0.49797, "loss_cls": 4.30438, "loss": 4.30438, "time": 0.85383} +{"mode": "train", "epoch": 66, "iter": 2500, "lr": 0.05971, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25328, "top5_acc": 0.49641, "loss_cls": 4.33857, "loss": 4.33857, "time": 0.85608} +{"mode": "train", "epoch": 66, "iter": 2600, "lr": 0.05968, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.25625, "top5_acc": 0.50719, "loss_cls": 4.28519, "loss": 4.28519, "time": 0.85774} +{"mode": "train", "epoch": 66, "iter": 2700, "lr": 0.05966, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25328, "top5_acc": 0.49391, "loss_cls": 4.35019, "loss": 4.35019, "time": 0.85752} +{"mode": "train", "epoch": 66, "iter": 2800, "lr": 0.05963, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25453, "top5_acc": 0.49906, "loss_cls": 4.3691, "loss": 4.3691, "time": 0.84738} +{"mode": "train", "epoch": 66, "iter": 2900, "lr": 0.0596, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25375, "top5_acc": 0.49922, "loss_cls": 4.32774, "loss": 4.32774, "time": 0.85143} +{"mode": "train", "epoch": 66, "iter": 3000, "lr": 0.05957, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25469, "top5_acc": 0.50094, "loss_cls": 4.33433, "loss": 4.33433, "time": 0.84681} +{"mode": "train", "epoch": 66, "iter": 3100, "lr": 0.05955, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25672, "top5_acc": 0.505, "loss_cls": 4.31018, "loss": 4.31018, "time": 0.84815} +{"mode": "train", "epoch": 66, "iter": 3200, "lr": 0.05952, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26062, "top5_acc": 0.50172, "loss_cls": 4.3036, "loss": 4.3036, "time": 0.84777} +{"mode": "train", "epoch": 66, "iter": 3300, "lr": 0.05949, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.26516, "top5_acc": 0.505, "loss_cls": 4.26667, "loss": 4.26667, "time": 0.85134} +{"mode": "train", "epoch": 66, "iter": 3400, "lr": 0.05946, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25125, "top5_acc": 0.49828, "loss_cls": 4.33944, "loss": 4.33944, "time": 0.8422} +{"mode": "train", "epoch": 66, "iter": 3500, "lr": 0.05944, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.24344, "top5_acc": 0.49094, "loss_cls": 4.35439, "loss": 4.35439, "time": 0.84558} +{"mode": "train", "epoch": 66, "iter": 3600, "lr": 0.05941, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25719, "top5_acc": 0.50125, "loss_cls": 4.27805, "loss": 4.27805, "time": 0.84469} +{"mode": "train", "epoch": 66, "iter": 3700, "lr": 0.05938, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25734, "top5_acc": 0.50109, "loss_cls": 4.32062, "loss": 4.32062, "time": 0.84737} +{"mode": "val", "epoch": 66, "iter": 309, "lr": 0.05937, "top1_acc": 0.17576, "top5_acc": 0.39376, "mean_class_accuracy": 0.17562} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.05934, "memory": 15990, "data_time": 1.53633, "top1_acc": 0.26312, "top5_acc": 0.50969, "loss_cls": 4.2649, "loss": 4.2649, "time": 2.57481} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.05931, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26359, "top5_acc": 0.50875, "loss_cls": 4.27923, "loss": 4.27923, "time": 0.85132} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.05929, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26062, "top5_acc": 0.51469, "loss_cls": 4.26131, "loss": 4.26131, "time": 0.86013} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.05926, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26578, "top5_acc": 0.50906, "loss_cls": 4.26314, "loss": 4.26314, "time": 0.85703} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.05923, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25406, "top5_acc": 0.50469, "loss_cls": 4.29363, "loss": 4.29363, "time": 0.85136} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.0592, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26234, "top5_acc": 0.50766, "loss_cls": 4.26938, "loss": 4.26938, "time": 0.85917} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.05918, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26609, "top5_acc": 0.5225, "loss_cls": 4.20048, "loss": 4.20048, "time": 0.85746} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.05915, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25094, "top5_acc": 0.50094, "loss_cls": 4.32888, "loss": 4.32888, "time": 0.85739} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.05912, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24734, "top5_acc": 0.50281, "loss_cls": 4.3185, "loss": 4.3185, "time": 0.85774} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.05909, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25484, "top5_acc": 0.50094, "loss_cls": 4.31866, "loss": 4.31866, "time": 0.85096} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.05907, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25469, "top5_acc": 0.49344, "loss_cls": 4.32175, "loss": 4.32175, "time": 0.8406} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.05904, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2725, "top5_acc": 0.51531, "loss_cls": 4.24657, "loss": 4.24657, "time": 0.84114} +{"mode": "train", "epoch": 67, "iter": 1300, "lr": 0.05901, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25953, "top5_acc": 0.49359, "loss_cls": 4.33312, "loss": 4.33312, "time": 0.84734} +{"mode": "train", "epoch": 67, "iter": 1400, "lr": 0.05898, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25422, "top5_acc": 0.48766, "loss_cls": 4.32004, "loss": 4.32004, "time": 0.84271} +{"mode": "train", "epoch": 67, "iter": 1500, "lr": 0.05896, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26031, "top5_acc": 0.50438, "loss_cls": 4.28306, "loss": 4.28306, "time": 0.84756} +{"mode": "train", "epoch": 67, "iter": 1600, "lr": 0.05893, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25688, "top5_acc": 0.5, "loss_cls": 4.29435, "loss": 4.29435, "time": 0.84567} +{"mode": "train", "epoch": 67, "iter": 1700, "lr": 0.0589, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25812, "top5_acc": 0.50578, "loss_cls": 4.2884, "loss": 4.2884, "time": 0.85155} +{"mode": "train", "epoch": 67, "iter": 1800, "lr": 0.05887, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25719, "top5_acc": 0.49359, "loss_cls": 4.29778, "loss": 4.29778, "time": 0.8469} +{"mode": "train", "epoch": 67, "iter": 1900, "lr": 0.05885, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25281, "top5_acc": 0.50156, "loss_cls": 4.32812, "loss": 4.32812, "time": 0.84952} +{"mode": "train", "epoch": 67, "iter": 2000, "lr": 0.05882, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24594, "top5_acc": 0.49312, "loss_cls": 4.35498, "loss": 4.35498, "time": 0.85291} +{"mode": "train", "epoch": 67, "iter": 2100, "lr": 0.05879, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24516, "top5_acc": 0.50031, "loss_cls": 4.33662, "loss": 4.33662, "time": 0.84616} +{"mode": "train", "epoch": 67, "iter": 2200, "lr": 0.05876, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25203, "top5_acc": 0.50016, "loss_cls": 4.30851, "loss": 4.30851, "time": 0.84891} +{"mode": "train", "epoch": 67, "iter": 2300, "lr": 0.05874, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25766, "top5_acc": 0.50078, "loss_cls": 4.3096, "loss": 4.3096, "time": 0.85349} +{"mode": "train", "epoch": 67, "iter": 2400, "lr": 0.05871, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.2575, "top5_acc": 0.50484, "loss_cls": 4.27228, "loss": 4.27228, "time": 0.8573} +{"mode": "train", "epoch": 67, "iter": 2500, "lr": 0.05868, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25906, "top5_acc": 0.51062, "loss_cls": 4.27827, "loss": 4.27827, "time": 0.85015} +{"mode": "train", "epoch": 67, "iter": 2600, "lr": 0.05865, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.25984, "top5_acc": 0.49938, "loss_cls": 4.28771, "loss": 4.28771, "time": 0.84829} +{"mode": "train", "epoch": 67, "iter": 2700, "lr": 0.05863, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25469, "top5_acc": 0.50438, "loss_cls": 4.29245, "loss": 4.29245, "time": 0.85178} +{"mode": "train", "epoch": 67, "iter": 2800, "lr": 0.0586, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25625, "top5_acc": 0.49594, "loss_cls": 4.30112, "loss": 4.30112, "time": 0.84662} +{"mode": "train", "epoch": 67, "iter": 2900, "lr": 0.05857, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26516, "top5_acc": 0.49891, "loss_cls": 4.29171, "loss": 4.29171, "time": 0.85045} +{"mode": "train", "epoch": 67, "iter": 3000, "lr": 0.05854, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26016, "top5_acc": 0.50688, "loss_cls": 4.28073, "loss": 4.28073, "time": 0.85274} +{"mode": "train", "epoch": 67, "iter": 3100, "lr": 0.05852, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25531, "top5_acc": 0.49812, "loss_cls": 4.32532, "loss": 4.32532, "time": 0.8509} +{"mode": "train", "epoch": 67, "iter": 3200, "lr": 0.05849, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26609, "top5_acc": 0.50375, "loss_cls": 4.2913, "loss": 4.2913, "time": 0.84942} +{"mode": "train", "epoch": 67, "iter": 3300, "lr": 0.05846, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27187, "top5_acc": 0.51219, "loss_cls": 4.26603, "loss": 4.26603, "time": 0.85056} +{"mode": "train", "epoch": 67, "iter": 3400, "lr": 0.05843, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26531, "top5_acc": 0.50953, "loss_cls": 4.26586, "loss": 4.26586, "time": 0.8478} +{"mode": "train", "epoch": 67, "iter": 3500, "lr": 0.05841, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25875, "top5_acc": 0.50813, "loss_cls": 4.27806, "loss": 4.27806, "time": 0.84275} +{"mode": "train", "epoch": 67, "iter": 3600, "lr": 0.05838, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26453, "top5_acc": 0.50328, "loss_cls": 4.3241, "loss": 4.3241, "time": 0.84071} +{"mode": "train", "epoch": 67, "iter": 3700, "lr": 0.05835, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26062, "top5_acc": 0.49875, "loss_cls": 4.32265, "loss": 4.32265, "time": 0.8468} +{"mode": "val", "epoch": 67, "iter": 309, "lr": 0.05834, "top1_acc": 0.18234, "top5_acc": 0.40171, "mean_class_accuracy": 0.18216} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.05831, "memory": 15990, "data_time": 1.50138, "top1_acc": 0.25719, "top5_acc": 0.51359, "loss_cls": 4.25817, "loss": 4.25817, "time": 2.52636} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.05828, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25875, "top5_acc": 0.51672, "loss_cls": 4.23064, "loss": 4.23064, "time": 0.85043} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.05826, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26812, "top5_acc": 0.50703, "loss_cls": 4.27723, "loss": 4.27723, "time": 0.85804} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.05823, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25844, "top5_acc": 0.50406, "loss_cls": 4.29525, "loss": 4.29525, "time": 0.84621} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.0582, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26, "top5_acc": 0.51203, "loss_cls": 4.26698, "loss": 4.26698, "time": 0.84772} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.05817, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26703, "top5_acc": 0.5075, "loss_cls": 4.2736, "loss": 4.2736, "time": 0.84567} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.05815, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25281, "top5_acc": 0.49984, "loss_cls": 4.35344, "loss": 4.35344, "time": 0.85137} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.05812, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25453, "top5_acc": 0.51016, "loss_cls": 4.29635, "loss": 4.29635, "time": 0.84413} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.05809, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25625, "top5_acc": 0.49219, "loss_cls": 4.32547, "loss": 4.32547, "time": 0.84491} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.05806, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26844, "top5_acc": 0.51234, "loss_cls": 4.26002, "loss": 4.26002, "time": 0.84493} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.05804, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.26703, "top5_acc": 0.50781, "loss_cls": 4.28624, "loss": 4.28624, "time": 0.84082} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.05801, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25328, "top5_acc": 0.49422, "loss_cls": 4.32095, "loss": 4.32095, "time": 0.84095} +{"mode": "train", "epoch": 68, "iter": 1300, "lr": 0.05798, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25906, "top5_acc": 0.50656, "loss_cls": 4.2888, "loss": 4.2888, "time": 0.84616} +{"mode": "train", "epoch": 68, "iter": 1400, "lr": 0.05795, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25922, "top5_acc": 0.50094, "loss_cls": 4.29392, "loss": 4.29392, "time": 0.84643} +{"mode": "train", "epoch": 68, "iter": 1500, "lr": 0.05792, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26812, "top5_acc": 0.51922, "loss_cls": 4.21561, "loss": 4.21561, "time": 0.84442} +{"mode": "train", "epoch": 68, "iter": 1600, "lr": 0.0579, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25203, "top5_acc": 0.50328, "loss_cls": 4.29179, "loss": 4.29179, "time": 0.84977} +{"mode": "train", "epoch": 68, "iter": 1700, "lr": 0.05787, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26188, "top5_acc": 0.52031, "loss_cls": 4.23181, "loss": 4.23181, "time": 0.85216} +{"mode": "train", "epoch": 68, "iter": 1800, "lr": 0.05784, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27047, "top5_acc": 0.51266, "loss_cls": 4.25409, "loss": 4.25409, "time": 0.85486} +{"mode": "train", "epoch": 68, "iter": 1900, "lr": 0.05781, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26344, "top5_acc": 0.50156, "loss_cls": 4.28031, "loss": 4.28031, "time": 0.84918} +{"mode": "train", "epoch": 68, "iter": 2000, "lr": 0.05779, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26219, "top5_acc": 0.515, "loss_cls": 4.25165, "loss": 4.25165, "time": 0.84837} +{"mode": "train", "epoch": 68, "iter": 2100, "lr": 0.05776, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25547, "top5_acc": 0.49516, "loss_cls": 4.31073, "loss": 4.31073, "time": 0.85244} +{"mode": "train", "epoch": 68, "iter": 2200, "lr": 0.05773, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26531, "top5_acc": 0.50797, "loss_cls": 4.25756, "loss": 4.25756, "time": 0.84881} +{"mode": "train", "epoch": 68, "iter": 2300, "lr": 0.0577, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25859, "top5_acc": 0.50125, "loss_cls": 4.29191, "loss": 4.29191, "time": 0.8506} +{"mode": "train", "epoch": 68, "iter": 2400, "lr": 0.05768, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26422, "top5_acc": 0.50562, "loss_cls": 4.27851, "loss": 4.27851, "time": 0.85191} +{"mode": "train", "epoch": 68, "iter": 2500, "lr": 0.05765, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.255, "top5_acc": 0.49828, "loss_cls": 4.30841, "loss": 4.30841, "time": 0.84895} +{"mode": "train", "epoch": 68, "iter": 2600, "lr": 0.05762, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25781, "top5_acc": 0.49391, "loss_cls": 4.29217, "loss": 4.29217, "time": 0.84644} +{"mode": "train", "epoch": 68, "iter": 2700, "lr": 0.05759, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26031, "top5_acc": 0.51328, "loss_cls": 4.28816, "loss": 4.28816, "time": 0.84654} +{"mode": "train", "epoch": 68, "iter": 2800, "lr": 0.05757, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.25547, "top5_acc": 0.51172, "loss_cls": 4.25948, "loss": 4.25948, "time": 0.84805} +{"mode": "train", "epoch": 68, "iter": 2900, "lr": 0.05754, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27031, "top5_acc": 0.5075, "loss_cls": 4.26421, "loss": 4.26421, "time": 0.848} +{"mode": "train", "epoch": 68, "iter": 3000, "lr": 0.05751, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27187, "top5_acc": 0.50406, "loss_cls": 4.26885, "loss": 4.26885, "time": 0.84748} +{"mode": "train", "epoch": 68, "iter": 3100, "lr": 0.05748, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26406, "top5_acc": 0.50891, "loss_cls": 4.27063, "loss": 4.27063, "time": 0.84345} +{"mode": "train", "epoch": 68, "iter": 3200, "lr": 0.05746, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26406, "top5_acc": 0.51359, "loss_cls": 4.27339, "loss": 4.27339, "time": 0.84509} +{"mode": "train", "epoch": 68, "iter": 3300, "lr": 0.05743, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25453, "top5_acc": 0.50359, "loss_cls": 4.30005, "loss": 4.30005, "time": 0.84563} +{"mode": "train", "epoch": 68, "iter": 3400, "lr": 0.0574, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.255, "top5_acc": 0.49844, "loss_cls": 4.33666, "loss": 4.33666, "time": 0.84595} +{"mode": "train", "epoch": 68, "iter": 3500, "lr": 0.05737, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26531, "top5_acc": 0.50781, "loss_cls": 4.27756, "loss": 4.27756, "time": 0.84244} +{"mode": "train", "epoch": 68, "iter": 3600, "lr": 0.05734, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.26937, "top5_acc": 0.51109, "loss_cls": 4.26254, "loss": 4.26254, "time": 0.8413} +{"mode": "train", "epoch": 68, "iter": 3700, "lr": 0.05732, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25875, "top5_acc": 0.50781, "loss_cls": 4.26704, "loss": 4.26704, "time": 0.84438} +{"mode": "val", "epoch": 68, "iter": 309, "lr": 0.0573, "top1_acc": 0.20367, "top5_acc": 0.42375, "mean_class_accuracy": 0.20352} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.05728, "memory": 15990, "data_time": 1.59563, "top1_acc": 0.26562, "top5_acc": 0.51906, "loss_cls": 4.22105, "loss": 4.22105, "time": 2.62417} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.05725, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26062, "top5_acc": 0.50516, "loss_cls": 4.28842, "loss": 4.28842, "time": 0.85272} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.05722, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27406, "top5_acc": 0.52047, "loss_cls": 4.20302, "loss": 4.20302, "time": 0.85178} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.05719, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26125, "top5_acc": 0.51062, "loss_cls": 4.26155, "loss": 4.26155, "time": 0.85647} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.05717, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26422, "top5_acc": 0.50609, "loss_cls": 4.26637, "loss": 4.26637, "time": 0.84431} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.05714, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26609, "top5_acc": 0.51844, "loss_cls": 4.24328, "loss": 4.24328, "time": 0.85342} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.05711, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27422, "top5_acc": 0.51938, "loss_cls": 4.20323, "loss": 4.20323, "time": 0.84893} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.05708, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26094, "top5_acc": 0.50547, "loss_cls": 4.27588, "loss": 4.27588, "time": 0.85342} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.05706, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26625, "top5_acc": 0.50203, "loss_cls": 4.26461, "loss": 4.26461, "time": 0.8549} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.05703, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.25469, "top5_acc": 0.505, "loss_cls": 4.27462, "loss": 4.27462, "time": 0.84975} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.057, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26438, "top5_acc": 0.51078, "loss_cls": 4.24653, "loss": 4.24653, "time": 0.84833} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.05697, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25875, "top5_acc": 0.50469, "loss_cls": 4.26516, "loss": 4.26516, "time": 0.85199} +{"mode": "train", "epoch": 69, "iter": 1300, "lr": 0.05694, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25531, "top5_acc": 0.49281, "loss_cls": 4.30915, "loss": 4.30915, "time": 0.84093} +{"mode": "train", "epoch": 69, "iter": 1400, "lr": 0.05692, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2575, "top5_acc": 0.50609, "loss_cls": 4.29009, "loss": 4.29009, "time": 0.84774} +{"mode": "train", "epoch": 69, "iter": 1500, "lr": 0.05689, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26266, "top5_acc": 0.50187, "loss_cls": 4.28776, "loss": 4.28776, "time": 0.84626} +{"mode": "train", "epoch": 69, "iter": 1600, "lr": 0.05686, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25781, "top5_acc": 0.51469, "loss_cls": 4.25206, "loss": 4.25206, "time": 0.84262} +{"mode": "train", "epoch": 69, "iter": 1700, "lr": 0.05683, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25672, "top5_acc": 0.50344, "loss_cls": 4.29754, "loss": 4.29754, "time": 0.84047} +{"mode": "train", "epoch": 69, "iter": 1800, "lr": 0.05681, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25312, "top5_acc": 0.50375, "loss_cls": 4.30897, "loss": 4.30897, "time": 0.8467} +{"mode": "train", "epoch": 69, "iter": 1900, "lr": 0.05678, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26188, "top5_acc": 0.50953, "loss_cls": 4.28105, "loss": 4.28105, "time": 0.84701} +{"mode": "train", "epoch": 69, "iter": 2000, "lr": 0.05675, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26453, "top5_acc": 0.50938, "loss_cls": 4.26471, "loss": 4.26471, "time": 0.84606} +{"mode": "train", "epoch": 69, "iter": 2100, "lr": 0.05672, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26656, "top5_acc": 0.52156, "loss_cls": 4.22216, "loss": 4.22216, "time": 0.84916} +{"mode": "train", "epoch": 69, "iter": 2200, "lr": 0.0567, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25594, "top5_acc": 0.49594, "loss_cls": 4.29489, "loss": 4.29489, "time": 0.84939} +{"mode": "train", "epoch": 69, "iter": 2300, "lr": 0.05667, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25719, "top5_acc": 0.49531, "loss_cls": 4.3212, "loss": 4.3212, "time": 0.84433} +{"mode": "train", "epoch": 69, "iter": 2400, "lr": 0.05664, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25656, "top5_acc": 0.49641, "loss_cls": 4.31972, "loss": 4.31972, "time": 0.85048} +{"mode": "train", "epoch": 69, "iter": 2500, "lr": 0.05661, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25094, "top5_acc": 0.49281, "loss_cls": 4.32852, "loss": 4.32852, "time": 0.85201} +{"mode": "train", "epoch": 69, "iter": 2600, "lr": 0.05658, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27141, "top5_acc": 0.50813, "loss_cls": 4.26731, "loss": 4.26731, "time": 0.8505} +{"mode": "train", "epoch": 69, "iter": 2700, "lr": 0.05656, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26344, "top5_acc": 0.51844, "loss_cls": 4.2418, "loss": 4.2418, "time": 0.85178} +{"mode": "train", "epoch": 69, "iter": 2800, "lr": 0.05653, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.25938, "top5_acc": 0.51016, "loss_cls": 4.29109, "loss": 4.29109, "time": 0.84939} +{"mode": "train", "epoch": 69, "iter": 2900, "lr": 0.0565, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25297, "top5_acc": 0.49609, "loss_cls": 4.29277, "loss": 4.29277, "time": 0.84629} +{"mode": "train", "epoch": 69, "iter": 3000, "lr": 0.05647, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26047, "top5_acc": 0.49984, "loss_cls": 4.28754, "loss": 4.28754, "time": 0.84211} +{"mode": "train", "epoch": 69, "iter": 3100, "lr": 0.05645, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27266, "top5_acc": 0.51109, "loss_cls": 4.25864, "loss": 4.25864, "time": 0.83884} +{"mode": "train", "epoch": 69, "iter": 3200, "lr": 0.05642, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26531, "top5_acc": 0.51594, "loss_cls": 4.22824, "loss": 4.22824, "time": 0.84018} +{"mode": "train", "epoch": 69, "iter": 3300, "lr": 0.05639, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25094, "top5_acc": 0.50391, "loss_cls": 4.30165, "loss": 4.30165, "time": 0.84007} +{"mode": "train", "epoch": 69, "iter": 3400, "lr": 0.05636, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26719, "top5_acc": 0.51469, "loss_cls": 4.25865, "loss": 4.25865, "time": 0.84478} +{"mode": "train", "epoch": 69, "iter": 3500, "lr": 0.05634, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.26297, "top5_acc": 0.50625, "loss_cls": 4.25006, "loss": 4.25006, "time": 0.84521} +{"mode": "train", "epoch": 69, "iter": 3600, "lr": 0.05631, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26125, "top5_acc": 0.50688, "loss_cls": 4.27784, "loss": 4.27784, "time": 0.84826} +{"mode": "train", "epoch": 69, "iter": 3700, "lr": 0.05628, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26078, "top5_acc": 0.50484, "loss_cls": 4.30429, "loss": 4.30429, "time": 0.85195} +{"mode": "val", "epoch": 69, "iter": 309, "lr": 0.05627, "top1_acc": 0.20068, "top5_acc": 0.43063, "mean_class_accuracy": 0.20041} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.05624, "memory": 15990, "data_time": 1.55937, "top1_acc": 0.27422, "top5_acc": 0.52344, "loss_cls": 4.19468, "loss": 4.19468, "time": 2.59932} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.05621, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26844, "top5_acc": 0.52047, "loss_cls": 4.21383, "loss": 4.21383, "time": 0.84697} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.05618, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27531, "top5_acc": 0.51781, "loss_cls": 4.21201, "loss": 4.21201, "time": 0.84264} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.05616, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27172, "top5_acc": 0.52438, "loss_cls": 4.2131, "loss": 4.2131, "time": 0.84853} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.05613, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26703, "top5_acc": 0.5175, "loss_cls": 4.23505, "loss": 4.23505, "time": 0.84549} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.0561, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26641, "top5_acc": 0.52141, "loss_cls": 4.2327, "loss": 4.2327, "time": 0.8553} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.05607, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26734, "top5_acc": 0.52438, "loss_cls": 4.2351, "loss": 4.2351, "time": 0.85041} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.05605, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26344, "top5_acc": 0.50953, "loss_cls": 4.281, "loss": 4.281, "time": 0.8554} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.05602, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26281, "top5_acc": 0.50953, "loss_cls": 4.24437, "loss": 4.24437, "time": 0.85527} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.05599, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26, "top5_acc": 0.51172, "loss_cls": 4.28985, "loss": 4.28985, "time": 0.85071} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.05596, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.25578, "top5_acc": 0.50187, "loss_cls": 4.29903, "loss": 4.29903, "time": 0.8578} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.05593, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25797, "top5_acc": 0.50562, "loss_cls": 4.266, "loss": 4.266, "time": 0.84894} +{"mode": "train", "epoch": 70, "iter": 1300, "lr": 0.05591, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.26266, "top5_acc": 0.51156, "loss_cls": 4.2757, "loss": 4.2757, "time": 0.85085} +{"mode": "train", "epoch": 70, "iter": 1400, "lr": 0.05588, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.275, "top5_acc": 0.51141, "loss_cls": 4.24225, "loss": 4.24225, "time": 0.84775} +{"mode": "train", "epoch": 70, "iter": 1500, "lr": 0.05585, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26625, "top5_acc": 0.51016, "loss_cls": 4.26091, "loss": 4.26091, "time": 0.84445} +{"mode": "train", "epoch": 70, "iter": 1600, "lr": 0.05582, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25781, "top5_acc": 0.50984, "loss_cls": 4.26985, "loss": 4.26985, "time": 0.84508} +{"mode": "train", "epoch": 70, "iter": 1700, "lr": 0.0558, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26875, "top5_acc": 0.50359, "loss_cls": 4.27434, "loss": 4.27434, "time": 0.83992} +{"mode": "train", "epoch": 70, "iter": 1800, "lr": 0.05577, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2725, "top5_acc": 0.50578, "loss_cls": 4.25756, "loss": 4.25756, "time": 0.84058} +{"mode": "train", "epoch": 70, "iter": 1900, "lr": 0.05574, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26016, "top5_acc": 0.50422, "loss_cls": 4.2602, "loss": 4.2602, "time": 0.84851} +{"mode": "train", "epoch": 70, "iter": 2000, "lr": 0.05571, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26875, "top5_acc": 0.51672, "loss_cls": 4.25432, "loss": 4.25432, "time": 0.84461} +{"mode": "train", "epoch": 70, "iter": 2100, "lr": 0.05568, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26797, "top5_acc": 0.51875, "loss_cls": 4.22581, "loss": 4.22581, "time": 0.84744} +{"mode": "train", "epoch": 70, "iter": 2200, "lr": 0.05566, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26969, "top5_acc": 0.51031, "loss_cls": 4.24258, "loss": 4.24258, "time": 0.85002} +{"mode": "train", "epoch": 70, "iter": 2300, "lr": 0.05563, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25938, "top5_acc": 0.50578, "loss_cls": 4.27625, "loss": 4.27625, "time": 0.85073} +{"mode": "train", "epoch": 70, "iter": 2400, "lr": 0.0556, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27062, "top5_acc": 0.51453, "loss_cls": 4.2348, "loss": 4.2348, "time": 0.85205} +{"mode": "train", "epoch": 70, "iter": 2500, "lr": 0.05557, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2575, "top5_acc": 0.49484, "loss_cls": 4.32399, "loss": 4.32399, "time": 0.84844} +{"mode": "train", "epoch": 70, "iter": 2600, "lr": 0.05555, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25938, "top5_acc": 0.50625, "loss_cls": 4.27658, "loss": 4.27658, "time": 0.84517} +{"mode": "train", "epoch": 70, "iter": 2700, "lr": 0.05552, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25562, "top5_acc": 0.49969, "loss_cls": 4.28881, "loss": 4.28881, "time": 0.85735} +{"mode": "train", "epoch": 70, "iter": 2800, "lr": 0.05549, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.26672, "top5_acc": 0.50375, "loss_cls": 4.24703, "loss": 4.24703, "time": 0.84518} +{"mode": "train", "epoch": 70, "iter": 2900, "lr": 0.05546, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25688, "top5_acc": 0.50328, "loss_cls": 4.29026, "loss": 4.29026, "time": 0.84795} +{"mode": "train", "epoch": 70, "iter": 3000, "lr": 0.05543, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26844, "top5_acc": 0.50266, "loss_cls": 4.29901, "loss": 4.29901, "time": 0.84266} +{"mode": "train", "epoch": 70, "iter": 3100, "lr": 0.05541, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26922, "top5_acc": 0.50781, "loss_cls": 4.24949, "loss": 4.24949, "time": 0.84293} +{"mode": "train", "epoch": 70, "iter": 3200, "lr": 0.05538, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25953, "top5_acc": 0.49531, "loss_cls": 4.30138, "loss": 4.30138, "time": 0.84047} +{"mode": "train", "epoch": 70, "iter": 3300, "lr": 0.05535, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25172, "top5_acc": 0.50078, "loss_cls": 4.32637, "loss": 4.32637, "time": 0.84732} +{"mode": "train", "epoch": 70, "iter": 3400, "lr": 0.05532, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26062, "top5_acc": 0.51016, "loss_cls": 4.27906, "loss": 4.27906, "time": 0.84695} +{"mode": "train", "epoch": 70, "iter": 3500, "lr": 0.0553, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26359, "top5_acc": 0.50531, "loss_cls": 4.27048, "loss": 4.27048, "time": 0.84571} +{"mode": "train", "epoch": 70, "iter": 3600, "lr": 0.05527, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26922, "top5_acc": 0.51281, "loss_cls": 4.25613, "loss": 4.25613, "time": 0.83904} +{"mode": "train", "epoch": 70, "iter": 3700, "lr": 0.05524, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26234, "top5_acc": 0.51094, "loss_cls": 4.25282, "loss": 4.25282, "time": 0.84488} +{"mode": "val", "epoch": 70, "iter": 309, "lr": 0.05523, "top1_acc": 0.20802, "top5_acc": 0.43114, "mean_class_accuracy": 0.20779} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.0552, "memory": 15990, "data_time": 1.54505, "top1_acc": 0.27047, "top5_acc": 0.52125, "loss_cls": 4.19542, "loss": 4.19542, "time": 2.5839} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.05517, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.2725, "top5_acc": 0.51859, "loss_cls": 4.21221, "loss": 4.21221, "time": 0.86095} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.05514, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26906, "top5_acc": 0.51812, "loss_cls": 4.19461, "loss": 4.19461, "time": 0.86494} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.05512, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26969, "top5_acc": 0.52062, "loss_cls": 4.21483, "loss": 4.21483, "time": 0.86254} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.05509, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25375, "top5_acc": 0.49125, "loss_cls": 4.30466, "loss": 4.30466, "time": 0.85336} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.05506, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27094, "top5_acc": 0.51516, "loss_cls": 4.22107, "loss": 4.22107, "time": 0.86073} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.05503, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26156, "top5_acc": 0.50734, "loss_cls": 4.26613, "loss": 4.26613, "time": 0.85378} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.055, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25922, "top5_acc": 0.51156, "loss_cls": 4.26516, "loss": 4.26516, "time": 0.85645} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.05498, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25766, "top5_acc": 0.51734, "loss_cls": 4.2316, "loss": 4.2316, "time": 0.8688} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.05495, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.25984, "top5_acc": 0.50813, "loss_cls": 4.26585, "loss": 4.26585, "time": 0.85885} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.05492, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26375, "top5_acc": 0.51172, "loss_cls": 4.24142, "loss": 4.24142, "time": 0.85603} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.05489, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25734, "top5_acc": 0.50484, "loss_cls": 4.29078, "loss": 4.29078, "time": 0.8541} +{"mode": "train", "epoch": 71, "iter": 1300, "lr": 0.05487, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27203, "top5_acc": 0.51688, "loss_cls": 4.24835, "loss": 4.24835, "time": 0.85137} +{"mode": "train", "epoch": 71, "iter": 1400, "lr": 0.05484, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27859, "top5_acc": 0.52125, "loss_cls": 4.21102, "loss": 4.21102, "time": 0.85118} +{"mode": "train", "epoch": 71, "iter": 1500, "lr": 0.05481, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25906, "top5_acc": 0.50953, "loss_cls": 4.24835, "loss": 4.24835, "time": 0.85401} +{"mode": "train", "epoch": 71, "iter": 1600, "lr": 0.05478, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.265, "top5_acc": 0.51609, "loss_cls": 4.23192, "loss": 4.23192, "time": 0.85034} +{"mode": "train", "epoch": 71, "iter": 1700, "lr": 0.05475, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27047, "top5_acc": 0.51484, "loss_cls": 4.23547, "loss": 4.23547, "time": 0.85218} +{"mode": "train", "epoch": 71, "iter": 1800, "lr": 0.05473, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27469, "top5_acc": 0.52641, "loss_cls": 4.2045, "loss": 4.2045, "time": 0.85424} +{"mode": "train", "epoch": 71, "iter": 1900, "lr": 0.0547, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27031, "top5_acc": 0.51625, "loss_cls": 4.25668, "loss": 4.25668, "time": 0.85222} +{"mode": "train", "epoch": 71, "iter": 2000, "lr": 0.05467, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26188, "top5_acc": 0.51094, "loss_cls": 4.26916, "loss": 4.26916, "time": 0.85689} +{"mode": "train", "epoch": 71, "iter": 2100, "lr": 0.05464, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27422, "top5_acc": 0.52141, "loss_cls": 4.19554, "loss": 4.19554, "time": 0.85129} +{"mode": "train", "epoch": 71, "iter": 2200, "lr": 0.05461, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25344, "top5_acc": 0.49438, "loss_cls": 4.32505, "loss": 4.32505, "time": 0.85486} +{"mode": "train", "epoch": 71, "iter": 2300, "lr": 0.05459, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26125, "top5_acc": 0.50672, "loss_cls": 4.2581, "loss": 4.2581, "time": 0.85319} +{"mode": "train", "epoch": 71, "iter": 2400, "lr": 0.05456, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26906, "top5_acc": 0.50422, "loss_cls": 4.25627, "loss": 4.25627, "time": 0.85462} +{"mode": "train", "epoch": 71, "iter": 2500, "lr": 0.05453, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26859, "top5_acc": 0.50562, "loss_cls": 4.21827, "loss": 4.21827, "time": 0.85078} +{"mode": "train", "epoch": 71, "iter": 2600, "lr": 0.0545, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25594, "top5_acc": 0.51172, "loss_cls": 4.27757, "loss": 4.27757, "time": 0.85481} +{"mode": "train", "epoch": 71, "iter": 2700, "lr": 0.05448, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26438, "top5_acc": 0.50375, "loss_cls": 4.27504, "loss": 4.27504, "time": 0.85157} +{"mode": "train", "epoch": 71, "iter": 2800, "lr": 0.05445, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25875, "top5_acc": 0.51078, "loss_cls": 4.28696, "loss": 4.28696, "time": 0.85197} +{"mode": "train", "epoch": 71, "iter": 2900, "lr": 0.05442, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25812, "top5_acc": 0.50328, "loss_cls": 4.29825, "loss": 4.29825, "time": 0.85065} +{"mode": "train", "epoch": 71, "iter": 3000, "lr": 0.05439, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26328, "top5_acc": 0.51062, "loss_cls": 4.24642, "loss": 4.24642, "time": 0.85337} +{"mode": "train", "epoch": 71, "iter": 3100, "lr": 0.05436, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26719, "top5_acc": 0.50062, "loss_cls": 4.24483, "loss": 4.24483, "time": 0.85169} +{"mode": "train", "epoch": 71, "iter": 3200, "lr": 0.05434, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25375, "top5_acc": 0.50062, "loss_cls": 4.29048, "loss": 4.29048, "time": 0.85147} +{"mode": "train", "epoch": 71, "iter": 3300, "lr": 0.05431, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2625, "top5_acc": 0.50953, "loss_cls": 4.24549, "loss": 4.24549, "time": 0.84827} +{"mode": "train", "epoch": 71, "iter": 3400, "lr": 0.05428, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.26625, "top5_acc": 0.51391, "loss_cls": 4.26298, "loss": 4.26298, "time": 0.85118} +{"mode": "train", "epoch": 71, "iter": 3500, "lr": 0.05425, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26, "top5_acc": 0.50813, "loss_cls": 4.29129, "loss": 4.29129, "time": 0.85077} +{"mode": "train", "epoch": 71, "iter": 3600, "lr": 0.05422, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26734, "top5_acc": 0.51141, "loss_cls": 4.26025, "loss": 4.26025, "time": 0.8505} +{"mode": "train", "epoch": 71, "iter": 3700, "lr": 0.0542, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26453, "top5_acc": 0.51078, "loss_cls": 4.24359, "loss": 4.24359, "time": 0.8522} +{"mode": "val", "epoch": 71, "iter": 309, "lr": 0.05418, "top1_acc": 0.20645, "top5_acc": 0.43965, "mean_class_accuracy": 0.20616} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.05416, "memory": 15990, "data_time": 1.55449, "top1_acc": 0.27469, "top5_acc": 0.52734, "loss_cls": 4.16495, "loss": 4.16495, "time": 2.5923} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.05413, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27234, "top5_acc": 0.52344, "loss_cls": 4.18591, "loss": 4.18591, "time": 0.85359} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.0541, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27109, "top5_acc": 0.51719, "loss_cls": 4.2097, "loss": 4.2097, "time": 0.8532} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.05407, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.27031, "top5_acc": 0.51438, "loss_cls": 4.22914, "loss": 4.22914, "time": 0.85121} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.05404, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26172, "top5_acc": 0.51203, "loss_cls": 4.23427, "loss": 4.23427, "time": 0.8541} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.05402, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27812, "top5_acc": 0.52391, "loss_cls": 4.19495, "loss": 4.19495, "time": 0.85254} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.05399, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.265, "top5_acc": 0.51531, "loss_cls": 4.23931, "loss": 4.23931, "time": 0.85618} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.05396, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25766, "top5_acc": 0.51984, "loss_cls": 4.25128, "loss": 4.25128, "time": 0.85261} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.05393, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25984, "top5_acc": 0.51156, "loss_cls": 4.27369, "loss": 4.27369, "time": 0.85563} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.05391, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26406, "top5_acc": 0.51156, "loss_cls": 4.25324, "loss": 4.25324, "time": 0.85389} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.05388, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26312, "top5_acc": 0.51734, "loss_cls": 4.23426, "loss": 4.23426, "time": 0.85248} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.05385, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26609, "top5_acc": 0.51766, "loss_cls": 4.20773, "loss": 4.20773, "time": 0.85125} +{"mode": "train", "epoch": 72, "iter": 1300, "lr": 0.05382, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.25906, "top5_acc": 0.50359, "loss_cls": 4.27978, "loss": 4.27978, "time": 0.85197} +{"mode": "train", "epoch": 72, "iter": 1400, "lr": 0.05379, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.26422, "top5_acc": 0.51812, "loss_cls": 4.21348, "loss": 4.21348, "time": 0.86321} +{"mode": "train", "epoch": 72, "iter": 1500, "lr": 0.05377, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26719, "top5_acc": 0.51078, "loss_cls": 4.25253, "loss": 4.25253, "time": 0.86003} +{"mode": "train", "epoch": 72, "iter": 1600, "lr": 0.05374, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27672, "top5_acc": 0.52422, "loss_cls": 4.18471, "loss": 4.18471, "time": 0.86124} +{"mode": "train", "epoch": 72, "iter": 1700, "lr": 0.05371, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26984, "top5_acc": 0.5125, "loss_cls": 4.23694, "loss": 4.23694, "time": 0.85747} +{"mode": "train", "epoch": 72, "iter": 1800, "lr": 0.05368, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26453, "top5_acc": 0.51344, "loss_cls": 4.25218, "loss": 4.25218, "time": 0.85851} +{"mode": "train", "epoch": 72, "iter": 1900, "lr": 0.05365, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26188, "top5_acc": 0.51422, "loss_cls": 4.23482, "loss": 4.23482, "time": 0.85977} +{"mode": "train", "epoch": 72, "iter": 2000, "lr": 0.05363, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26125, "top5_acc": 0.50953, "loss_cls": 4.26664, "loss": 4.26664, "time": 0.86189} +{"mode": "train", "epoch": 72, "iter": 2100, "lr": 0.0536, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27531, "top5_acc": 0.51875, "loss_cls": 4.21295, "loss": 4.21295, "time": 0.86353} +{"mode": "train", "epoch": 72, "iter": 2200, "lr": 0.05357, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26453, "top5_acc": 0.52359, "loss_cls": 4.20817, "loss": 4.20817, "time": 0.85806} +{"mode": "train", "epoch": 72, "iter": 2300, "lr": 0.05354, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.265, "top5_acc": 0.50422, "loss_cls": 4.24357, "loss": 4.24357, "time": 0.8585} +{"mode": "train", "epoch": 72, "iter": 2400, "lr": 0.05352, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26594, "top5_acc": 0.50422, "loss_cls": 4.28028, "loss": 4.28028, "time": 0.85569} +{"mode": "train", "epoch": 72, "iter": 2500, "lr": 0.05349, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26906, "top5_acc": 0.51062, "loss_cls": 4.25477, "loss": 4.25477, "time": 0.85703} +{"mode": "train", "epoch": 72, "iter": 2600, "lr": 0.05346, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26719, "top5_acc": 0.51328, "loss_cls": 4.25148, "loss": 4.25148, "time": 0.85175} +{"mode": "train", "epoch": 72, "iter": 2700, "lr": 0.05343, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27156, "top5_acc": 0.51797, "loss_cls": 4.21489, "loss": 4.21489, "time": 0.84967} +{"mode": "train", "epoch": 72, "iter": 2800, "lr": 0.0534, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27, "top5_acc": 0.52047, "loss_cls": 4.21366, "loss": 4.21366, "time": 0.85562} +{"mode": "train", "epoch": 72, "iter": 2900, "lr": 0.05338, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.265, "top5_acc": 0.51641, "loss_cls": 4.23177, "loss": 4.23177, "time": 0.86235} +{"mode": "train", "epoch": 72, "iter": 3000, "lr": 0.05335, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26359, "top5_acc": 0.51656, "loss_cls": 4.21239, "loss": 4.21239, "time": 0.85981} +{"mode": "train", "epoch": 72, "iter": 3100, "lr": 0.05332, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26766, "top5_acc": 0.51, "loss_cls": 4.25656, "loss": 4.25656, "time": 0.86089} +{"mode": "train", "epoch": 72, "iter": 3200, "lr": 0.05329, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26844, "top5_acc": 0.51812, "loss_cls": 4.2024, "loss": 4.2024, "time": 0.85636} +{"mode": "train", "epoch": 72, "iter": 3300, "lr": 0.05326, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25625, "top5_acc": 0.50406, "loss_cls": 4.29999, "loss": 4.29999, "time": 0.85904} +{"mode": "train", "epoch": 72, "iter": 3400, "lr": 0.05324, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27156, "top5_acc": 0.51047, "loss_cls": 4.22731, "loss": 4.22731, "time": 0.85081} +{"mode": "train", "epoch": 72, "iter": 3500, "lr": 0.05321, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26562, "top5_acc": 0.51219, "loss_cls": 4.24822, "loss": 4.24822, "time": 0.85073} +{"mode": "train", "epoch": 72, "iter": 3600, "lr": 0.05318, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26719, "top5_acc": 0.50016, "loss_cls": 4.2588, "loss": 4.2588, "time": 0.85132} +{"mode": "train", "epoch": 72, "iter": 3700, "lr": 0.05315, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25406, "top5_acc": 0.50047, "loss_cls": 4.29091, "loss": 4.29091, "time": 0.84981} +{"mode": "val", "epoch": 72, "iter": 309, "lr": 0.05314, "top1_acc": 0.2138, "top5_acc": 0.44973, "mean_class_accuracy": 0.21342} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.05311, "memory": 15990, "data_time": 1.56106, "top1_acc": 0.27641, "top5_acc": 0.52859, "loss_cls": 4.18696, "loss": 4.18696, "time": 2.60338} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.05308, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26859, "top5_acc": 0.52922, "loss_cls": 4.19325, "loss": 4.19325, "time": 0.85237} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.05306, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26953, "top5_acc": 0.5175, "loss_cls": 4.19863, "loss": 4.19863, "time": 0.85552} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.05303, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27578, "top5_acc": 0.52156, "loss_cls": 4.22307, "loss": 4.22307, "time": 0.85202} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.053, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27062, "top5_acc": 0.51719, "loss_cls": 4.22268, "loss": 4.22268, "time": 0.8499} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.05297, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26562, "top5_acc": 0.51703, "loss_cls": 4.18576, "loss": 4.18576, "time": 0.85359} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.05294, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2675, "top5_acc": 0.51984, "loss_cls": 4.21751, "loss": 4.21751, "time": 0.8519} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.05292, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26922, "top5_acc": 0.51656, "loss_cls": 4.2196, "loss": 4.2196, "time": 0.85005} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.05289, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27, "top5_acc": 0.51766, "loss_cls": 4.188, "loss": 4.188, "time": 0.85861} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.05286, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27437, "top5_acc": 0.51875, "loss_cls": 4.16951, "loss": 4.16951, "time": 0.85359} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.05283, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2775, "top5_acc": 0.50781, "loss_cls": 4.23621, "loss": 4.23621, "time": 0.8563} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.0528, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26547, "top5_acc": 0.51156, "loss_cls": 4.24319, "loss": 4.24319, "time": 0.84993} +{"mode": "train", "epoch": 73, "iter": 1300, "lr": 0.05278, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26781, "top5_acc": 0.50531, "loss_cls": 4.28493, "loss": 4.28493, "time": 0.8493} +{"mode": "train", "epoch": 73, "iter": 1400, "lr": 0.05275, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27375, "top5_acc": 0.51188, "loss_cls": 4.22656, "loss": 4.22656, "time": 0.84925} +{"mode": "train", "epoch": 73, "iter": 1500, "lr": 0.05272, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26438, "top5_acc": 0.50906, "loss_cls": 4.26261, "loss": 4.26261, "time": 0.85092} +{"mode": "train", "epoch": 73, "iter": 1600, "lr": 0.05269, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26859, "top5_acc": 0.51156, "loss_cls": 4.25145, "loss": 4.25145, "time": 0.85445} +{"mode": "train", "epoch": 73, "iter": 1700, "lr": 0.05267, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27078, "top5_acc": 0.52203, "loss_cls": 4.18092, "loss": 4.18092, "time": 0.85505} +{"mode": "train", "epoch": 73, "iter": 1800, "lr": 0.05264, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27797, "top5_acc": 0.52719, "loss_cls": 4.17732, "loss": 4.17732, "time": 0.84977} +{"mode": "train", "epoch": 73, "iter": 1900, "lr": 0.05261, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26656, "top5_acc": 0.51672, "loss_cls": 4.24659, "loss": 4.24659, "time": 0.85853} +{"mode": "train", "epoch": 73, "iter": 2000, "lr": 0.05258, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26516, "top5_acc": 0.51609, "loss_cls": 4.21399, "loss": 4.21399, "time": 0.85198} +{"mode": "train", "epoch": 73, "iter": 2100, "lr": 0.05255, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26406, "top5_acc": 0.5175, "loss_cls": 4.22509, "loss": 4.22509, "time": 0.85439} +{"mode": "train", "epoch": 73, "iter": 2200, "lr": 0.05253, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26797, "top5_acc": 0.50781, "loss_cls": 4.25687, "loss": 4.25687, "time": 0.85485} +{"mode": "train", "epoch": 73, "iter": 2300, "lr": 0.0525, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27344, "top5_acc": 0.50797, "loss_cls": 4.23753, "loss": 4.23753, "time": 0.85323} +{"mode": "train", "epoch": 73, "iter": 2400, "lr": 0.05247, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27094, "top5_acc": 0.5125, "loss_cls": 4.23775, "loss": 4.23775, "time": 0.849} +{"mode": "train", "epoch": 73, "iter": 2500, "lr": 0.05244, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25812, "top5_acc": 0.50453, "loss_cls": 4.27424, "loss": 4.27424, "time": 0.85071} +{"mode": "train", "epoch": 73, "iter": 2600, "lr": 0.05241, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26969, "top5_acc": 0.51313, "loss_cls": 4.23652, "loss": 4.23652, "time": 0.85043} +{"mode": "train", "epoch": 73, "iter": 2700, "lr": 0.05239, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26312, "top5_acc": 0.51703, "loss_cls": 4.22834, "loss": 4.22834, "time": 0.85473} +{"mode": "train", "epoch": 73, "iter": 2800, "lr": 0.05236, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26234, "top5_acc": 0.51609, "loss_cls": 4.19896, "loss": 4.19896, "time": 0.85467} +{"mode": "train", "epoch": 73, "iter": 2900, "lr": 0.05233, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27062, "top5_acc": 0.51875, "loss_cls": 4.21073, "loss": 4.21073, "time": 0.85072} +{"mode": "train", "epoch": 73, "iter": 3000, "lr": 0.0523, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26828, "top5_acc": 0.51859, "loss_cls": 4.24291, "loss": 4.24291, "time": 0.85695} +{"mode": "train", "epoch": 73, "iter": 3100, "lr": 0.05227, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26594, "top5_acc": 0.50734, "loss_cls": 4.24906, "loss": 4.24906, "time": 0.85841} +{"mode": "train", "epoch": 73, "iter": 3200, "lr": 0.05225, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26375, "top5_acc": 0.51016, "loss_cls": 4.26245, "loss": 4.26245, "time": 0.85657} +{"mode": "train", "epoch": 73, "iter": 3300, "lr": 0.05222, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26984, "top5_acc": 0.52141, "loss_cls": 4.19112, "loss": 4.19112, "time": 0.85679} +{"mode": "train", "epoch": 73, "iter": 3400, "lr": 0.05219, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26219, "top5_acc": 0.51375, "loss_cls": 4.25789, "loss": 4.25789, "time": 0.85697} +{"mode": "train", "epoch": 73, "iter": 3500, "lr": 0.05216, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2625, "top5_acc": 0.51672, "loss_cls": 4.22422, "loss": 4.22422, "time": 0.8489} +{"mode": "train", "epoch": 73, "iter": 3600, "lr": 0.05213, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26859, "top5_acc": 0.51656, "loss_cls": 4.22908, "loss": 4.22908, "time": 0.85768} +{"mode": "train", "epoch": 73, "iter": 3700, "lr": 0.05211, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27375, "top5_acc": 0.50953, "loss_cls": 4.22378, "loss": 4.22378, "time": 0.85617} +{"mode": "val", "epoch": 73, "iter": 309, "lr": 0.05209, "top1_acc": 0.20595, "top5_acc": 0.44031, "mean_class_accuracy": 0.20577} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.05207, "memory": 15990, "data_time": 1.54943, "top1_acc": 0.28422, "top5_acc": 0.54312, "loss_cls": 4.09678, "loss": 4.09678, "time": 2.58977} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.05204, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27344, "top5_acc": 0.53062, "loss_cls": 4.17261, "loss": 4.17261, "time": 0.8526} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.05201, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27375, "top5_acc": 0.52281, "loss_cls": 4.18271, "loss": 4.18271, "time": 0.85634} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.05198, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.2725, "top5_acc": 0.52844, "loss_cls": 4.16752, "loss": 4.16752, "time": 0.85209} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.05195, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27516, "top5_acc": 0.52641, "loss_cls": 4.1999, "loss": 4.1999, "time": 0.85324} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.05193, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27547, "top5_acc": 0.52797, "loss_cls": 4.17684, "loss": 4.17684, "time": 0.85757} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.0519, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.275, "top5_acc": 0.52047, "loss_cls": 4.20767, "loss": 4.20767, "time": 0.85362} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.05187, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26687, "top5_acc": 0.50922, "loss_cls": 4.24629, "loss": 4.24629, "time": 0.85583} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.05184, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26953, "top5_acc": 0.51719, "loss_cls": 4.23162, "loss": 4.23162, "time": 0.85433} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.05181, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26984, "top5_acc": 0.52312, "loss_cls": 4.16733, "loss": 4.16733, "time": 0.85131} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.05179, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.27875, "top5_acc": 0.53031, "loss_cls": 4.16072, "loss": 4.16072, "time": 0.85532} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.05176, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27078, "top5_acc": 0.51562, "loss_cls": 4.22367, "loss": 4.22367, "time": 0.85331} +{"mode": "train", "epoch": 74, "iter": 1300, "lr": 0.05173, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27688, "top5_acc": 0.52172, "loss_cls": 4.19987, "loss": 4.19987, "time": 0.85233} +{"mode": "train", "epoch": 74, "iter": 1400, "lr": 0.0517, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26281, "top5_acc": 0.51125, "loss_cls": 4.24712, "loss": 4.24712, "time": 0.85545} +{"mode": "train", "epoch": 74, "iter": 1500, "lr": 0.05168, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27828, "top5_acc": 0.51875, "loss_cls": 4.21002, "loss": 4.21002, "time": 0.85373} +{"mode": "train", "epoch": 74, "iter": 1600, "lr": 0.05165, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28141, "top5_acc": 0.52188, "loss_cls": 4.1827, "loss": 4.1827, "time": 0.84958} +{"mode": "train", "epoch": 74, "iter": 1700, "lr": 0.05162, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27234, "top5_acc": 0.51453, "loss_cls": 4.21542, "loss": 4.21542, "time": 0.85894} +{"mode": "train", "epoch": 74, "iter": 1800, "lr": 0.05159, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26687, "top5_acc": 0.50547, "loss_cls": 4.2426, "loss": 4.2426, "time": 0.85785} +{"mode": "train", "epoch": 74, "iter": 1900, "lr": 0.05156, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26859, "top5_acc": 0.52609, "loss_cls": 4.18504, "loss": 4.18504, "time": 0.84908} +{"mode": "train", "epoch": 74, "iter": 2000, "lr": 0.05154, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27547, "top5_acc": 0.51656, "loss_cls": 4.21936, "loss": 4.21936, "time": 0.85005} +{"mode": "train", "epoch": 74, "iter": 2100, "lr": 0.05151, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26984, "top5_acc": 0.51859, "loss_cls": 4.22773, "loss": 4.22773, "time": 0.84961} +{"mode": "train", "epoch": 74, "iter": 2200, "lr": 0.05148, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27187, "top5_acc": 0.51734, "loss_cls": 4.21672, "loss": 4.21672, "time": 0.85607} +{"mode": "train", "epoch": 74, "iter": 2300, "lr": 0.05145, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2725, "top5_acc": 0.51609, "loss_cls": 4.21985, "loss": 4.21985, "time": 0.8527} +{"mode": "train", "epoch": 74, "iter": 2400, "lr": 0.05142, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26625, "top5_acc": 0.52109, "loss_cls": 4.23657, "loss": 4.23657, "time": 0.8587} +{"mode": "train", "epoch": 74, "iter": 2500, "lr": 0.0514, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26891, "top5_acc": 0.51188, "loss_cls": 4.23954, "loss": 4.23954, "time": 0.85491} +{"mode": "train", "epoch": 74, "iter": 2600, "lr": 0.05137, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26516, "top5_acc": 0.51234, "loss_cls": 4.26209, "loss": 4.26209, "time": 0.84909} +{"mode": "train", "epoch": 74, "iter": 2700, "lr": 0.05134, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28031, "top5_acc": 0.52688, "loss_cls": 4.16328, "loss": 4.16328, "time": 0.85226} +{"mode": "train", "epoch": 74, "iter": 2800, "lr": 0.05131, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27391, "top5_acc": 0.51922, "loss_cls": 4.20926, "loss": 4.20926, "time": 0.85338} +{"mode": "train", "epoch": 74, "iter": 2900, "lr": 0.05128, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25797, "top5_acc": 0.51422, "loss_cls": 4.24845, "loss": 4.24845, "time": 0.85359} +{"mode": "train", "epoch": 74, "iter": 3000, "lr": 0.05126, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27062, "top5_acc": 0.51562, "loss_cls": 4.21775, "loss": 4.21775, "time": 0.84821} +{"mode": "train", "epoch": 74, "iter": 3100, "lr": 0.05123, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27187, "top5_acc": 0.52312, "loss_cls": 4.20485, "loss": 4.20485, "time": 0.85729} +{"mode": "train", "epoch": 74, "iter": 3200, "lr": 0.0512, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26734, "top5_acc": 0.50625, "loss_cls": 4.245, "loss": 4.245, "time": 0.85426} +{"mode": "train", "epoch": 74, "iter": 3300, "lr": 0.05117, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26937, "top5_acc": 0.51688, "loss_cls": 4.21462, "loss": 4.21462, "time": 0.84537} +{"mode": "train", "epoch": 74, "iter": 3400, "lr": 0.05114, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2575, "top5_acc": 0.50734, "loss_cls": 4.24817, "loss": 4.24817, "time": 0.85193} +{"mode": "train", "epoch": 74, "iter": 3500, "lr": 0.05112, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25641, "top5_acc": 0.5175, "loss_cls": 4.25776, "loss": 4.25776, "time": 0.85608} +{"mode": "train", "epoch": 74, "iter": 3600, "lr": 0.05109, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27219, "top5_acc": 0.51094, "loss_cls": 4.22495, "loss": 4.22495, "time": 0.84883} +{"mode": "train", "epoch": 74, "iter": 3700, "lr": 0.05106, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26516, "top5_acc": 0.5175, "loss_cls": 4.23919, "loss": 4.23919, "time": 0.8528} +{"mode": "val", "epoch": 74, "iter": 309, "lr": 0.05105, "top1_acc": 0.20311, "top5_acc": 0.43352, "mean_class_accuracy": 0.2028} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.05102, "memory": 15990, "data_time": 1.56665, "top1_acc": 0.27766, "top5_acc": 0.52125, "loss_cls": 4.17259, "loss": 4.17259, "time": 2.59006} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.05099, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27328, "top5_acc": 0.53109, "loss_cls": 4.15361, "loss": 4.15361, "time": 0.85638} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.05096, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26906, "top5_acc": 0.51812, "loss_cls": 4.20237, "loss": 4.20237, "time": 0.85162} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.05094, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28422, "top5_acc": 0.52656, "loss_cls": 4.16769, "loss": 4.16769, "time": 0.85229} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.05091, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25422, "top5_acc": 0.51047, "loss_cls": 4.25035, "loss": 4.25035, "time": 0.85176} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.05088, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27844, "top5_acc": 0.52953, "loss_cls": 4.17629, "loss": 4.17629, "time": 0.84748} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.05085, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27, "top5_acc": 0.52312, "loss_cls": 4.17251, "loss": 4.17251, "time": 0.853} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.05082, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26578, "top5_acc": 0.51688, "loss_cls": 4.22789, "loss": 4.22789, "time": 0.84922} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.0508, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27016, "top5_acc": 0.52828, "loss_cls": 4.17508, "loss": 4.17508, "time": 0.85175} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.05077, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26922, "top5_acc": 0.52391, "loss_cls": 4.21502, "loss": 4.21502, "time": 0.84749} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.05074, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27547, "top5_acc": 0.52406, "loss_cls": 4.17215, "loss": 4.17215, "time": 0.85158} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.05071, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27187, "top5_acc": 0.52047, "loss_cls": 4.19053, "loss": 4.19053, "time": 0.85332} +{"mode": "train", "epoch": 75, "iter": 1300, "lr": 0.05068, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26312, "top5_acc": 0.50781, "loss_cls": 4.26794, "loss": 4.26794, "time": 0.85387} +{"mode": "train", "epoch": 75, "iter": 1400, "lr": 0.05066, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26781, "top5_acc": 0.51844, "loss_cls": 4.19526, "loss": 4.19526, "time": 0.84967} +{"mode": "train", "epoch": 75, "iter": 1500, "lr": 0.05063, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27578, "top5_acc": 0.53, "loss_cls": 4.17404, "loss": 4.17404, "time": 0.84926} +{"mode": "train", "epoch": 75, "iter": 1600, "lr": 0.0506, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28125, "top5_acc": 0.53281, "loss_cls": 4.15102, "loss": 4.15102, "time": 0.85426} +{"mode": "train", "epoch": 75, "iter": 1700, "lr": 0.05057, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26859, "top5_acc": 0.5175, "loss_cls": 4.22553, "loss": 4.22553, "time": 0.85334} +{"mode": "train", "epoch": 75, "iter": 1800, "lr": 0.05054, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26516, "top5_acc": 0.52031, "loss_cls": 4.22373, "loss": 4.22373, "time": 0.84766} +{"mode": "train", "epoch": 75, "iter": 1900, "lr": 0.05052, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27203, "top5_acc": 0.51734, "loss_cls": 4.21157, "loss": 4.21157, "time": 0.85018} +{"mode": "train", "epoch": 75, "iter": 2000, "lr": 0.05049, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27891, "top5_acc": 0.52922, "loss_cls": 4.14893, "loss": 4.14893, "time": 0.85081} +{"mode": "train", "epoch": 75, "iter": 2100, "lr": 0.05046, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26891, "top5_acc": 0.51266, "loss_cls": 4.22693, "loss": 4.22693, "time": 0.85778} +{"mode": "train", "epoch": 75, "iter": 2200, "lr": 0.05043, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26812, "top5_acc": 0.52031, "loss_cls": 4.21835, "loss": 4.21835, "time": 0.85406} +{"mode": "train", "epoch": 75, "iter": 2300, "lr": 0.0504, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2675, "top5_acc": 0.52219, "loss_cls": 4.21582, "loss": 4.21582, "time": 0.85129} +{"mode": "train", "epoch": 75, "iter": 2400, "lr": 0.05038, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26469, "top5_acc": 0.51344, "loss_cls": 4.2438, "loss": 4.2438, "time": 0.856} +{"mode": "train", "epoch": 75, "iter": 2500, "lr": 0.05035, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26922, "top5_acc": 0.52094, "loss_cls": 4.23841, "loss": 4.23841, "time": 0.85447} +{"mode": "train", "epoch": 75, "iter": 2600, "lr": 0.05032, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27281, "top5_acc": 0.51484, "loss_cls": 4.2119, "loss": 4.2119, "time": 0.85443} +{"mode": "train", "epoch": 75, "iter": 2700, "lr": 0.05029, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27734, "top5_acc": 0.52219, "loss_cls": 4.17022, "loss": 4.17022, "time": 0.85476} +{"mode": "train", "epoch": 75, "iter": 2800, "lr": 0.05026, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27828, "top5_acc": 0.51891, "loss_cls": 4.18588, "loss": 4.18588, "time": 0.85377} +{"mode": "train", "epoch": 75, "iter": 2900, "lr": 0.05024, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27219, "top5_acc": 0.51641, "loss_cls": 4.22219, "loss": 4.22219, "time": 0.85278} +{"mode": "train", "epoch": 75, "iter": 3000, "lr": 0.05021, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.27641, "top5_acc": 0.52438, "loss_cls": 4.19738, "loss": 4.19738, "time": 0.85283} +{"mode": "train", "epoch": 75, "iter": 3100, "lr": 0.05018, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27437, "top5_acc": 0.52297, "loss_cls": 4.17616, "loss": 4.17616, "time": 0.85608} +{"mode": "train", "epoch": 75, "iter": 3200, "lr": 0.05015, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27437, "top5_acc": 0.51484, "loss_cls": 4.19915, "loss": 4.19915, "time": 0.85192} +{"mode": "train", "epoch": 75, "iter": 3300, "lr": 0.05012, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27812, "top5_acc": 0.52484, "loss_cls": 4.1843, "loss": 4.1843, "time": 0.85078} +{"mode": "train", "epoch": 75, "iter": 3400, "lr": 0.0501, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27109, "top5_acc": 0.51234, "loss_cls": 4.22176, "loss": 4.22176, "time": 0.85381} +{"mode": "train", "epoch": 75, "iter": 3500, "lr": 0.05007, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.27437, "top5_acc": 0.51875, "loss_cls": 4.20643, "loss": 4.20643, "time": 0.85123} +{"mode": "train", "epoch": 75, "iter": 3600, "lr": 0.05004, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27562, "top5_acc": 0.51859, "loss_cls": 4.21685, "loss": 4.21685, "time": 0.84756} +{"mode": "train", "epoch": 75, "iter": 3700, "lr": 0.05001, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27266, "top5_acc": 0.51844, "loss_cls": 4.20761, "loss": 4.20761, "time": 0.8479} +{"mode": "val", "epoch": 75, "iter": 309, "lr": 0.05, "top1_acc": 0.1987, "top5_acc": 0.41701, "mean_class_accuracy": 0.19833} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.04997, "memory": 15990, "data_time": 1.54903, "top1_acc": 0.27547, "top5_acc": 0.53125, "loss_cls": 4.13661, "loss": 4.13661, "time": 2.57498} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.04994, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28844, "top5_acc": 0.54062, "loss_cls": 4.09617, "loss": 4.09617, "time": 0.85143} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.04992, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26781, "top5_acc": 0.52375, "loss_cls": 4.19482, "loss": 4.19482, "time": 0.85188} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.04989, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27328, "top5_acc": 0.52141, "loss_cls": 4.20202, "loss": 4.20202, "time": 0.8593} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.04986, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26953, "top5_acc": 0.52109, "loss_cls": 4.18355, "loss": 4.18355, "time": 0.85083} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.04983, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28203, "top5_acc": 0.52875, "loss_cls": 4.15077, "loss": 4.15077, "time": 0.85432} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.0498, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28031, "top5_acc": 0.52906, "loss_cls": 4.12922, "loss": 4.12922, "time": 0.85063} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.04978, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27625, "top5_acc": 0.52906, "loss_cls": 4.1614, "loss": 4.1614, "time": 0.85592} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.04975, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27562, "top5_acc": 0.53266, "loss_cls": 4.17052, "loss": 4.17052, "time": 0.85585} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.04972, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27422, "top5_acc": 0.53375, "loss_cls": 4.16048, "loss": 4.16048, "time": 0.84842} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.04969, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27156, "top5_acc": 0.52219, "loss_cls": 4.19883, "loss": 4.19883, "time": 0.85387} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.04966, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28469, "top5_acc": 0.52391, "loss_cls": 4.18038, "loss": 4.18038, "time": 0.84851} +{"mode": "train", "epoch": 76, "iter": 1300, "lr": 0.04964, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27625, "top5_acc": 0.53359, "loss_cls": 4.14965, "loss": 4.14965, "time": 0.85281} +{"mode": "train", "epoch": 76, "iter": 1400, "lr": 0.04961, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27766, "top5_acc": 0.52891, "loss_cls": 4.13902, "loss": 4.13902, "time": 0.85058} +{"mode": "train", "epoch": 76, "iter": 1500, "lr": 0.04958, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27, "top5_acc": 0.51328, "loss_cls": 4.24169, "loss": 4.24169, "time": 0.85182} +{"mode": "train", "epoch": 76, "iter": 1600, "lr": 0.04955, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27422, "top5_acc": 0.52031, "loss_cls": 4.21583, "loss": 4.21583, "time": 0.85809} +{"mode": "train", "epoch": 76, "iter": 1700, "lr": 0.04953, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28, "top5_acc": 0.53297, "loss_cls": 4.16155, "loss": 4.16155, "time": 0.85543} +{"mode": "train", "epoch": 76, "iter": 1800, "lr": 0.0495, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27344, "top5_acc": 0.52266, "loss_cls": 4.20201, "loss": 4.20201, "time": 0.85612} +{"mode": "train", "epoch": 76, "iter": 1900, "lr": 0.04947, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26906, "top5_acc": 0.51672, "loss_cls": 4.21904, "loss": 4.21904, "time": 0.85605} +{"mode": "train", "epoch": 76, "iter": 2000, "lr": 0.04944, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27062, "top5_acc": 0.51781, "loss_cls": 4.24052, "loss": 4.24052, "time": 0.85388} +{"mode": "train", "epoch": 76, "iter": 2100, "lr": 0.04941, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26453, "top5_acc": 0.52047, "loss_cls": 4.21675, "loss": 4.21675, "time": 0.85566} +{"mode": "train", "epoch": 76, "iter": 2200, "lr": 0.04939, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26531, "top5_acc": 0.52016, "loss_cls": 4.20109, "loss": 4.20109, "time": 0.85113} +{"mode": "train", "epoch": 76, "iter": 2300, "lr": 0.04936, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27781, "top5_acc": 0.51406, "loss_cls": 4.20745, "loss": 4.20745, "time": 0.85306} +{"mode": "train", "epoch": 76, "iter": 2400, "lr": 0.04933, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27625, "top5_acc": 0.51828, "loss_cls": 4.18832, "loss": 4.18832, "time": 0.85266} +{"mode": "train", "epoch": 76, "iter": 2500, "lr": 0.0493, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26328, "top5_acc": 0.50844, "loss_cls": 4.23294, "loss": 4.23294, "time": 0.85857} +{"mode": "train", "epoch": 76, "iter": 2600, "lr": 0.04927, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.27516, "top5_acc": 0.52609, "loss_cls": 4.19669, "loss": 4.19669, "time": 0.85206} +{"mode": "train", "epoch": 76, "iter": 2700, "lr": 0.04925, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28141, "top5_acc": 0.52656, "loss_cls": 4.16435, "loss": 4.16435, "time": 0.8549} +{"mode": "train", "epoch": 76, "iter": 2800, "lr": 0.04922, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26359, "top5_acc": 0.51359, "loss_cls": 4.23361, "loss": 4.23361, "time": 0.85065} +{"mode": "train", "epoch": 76, "iter": 2900, "lr": 0.04919, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27125, "top5_acc": 0.52219, "loss_cls": 4.20304, "loss": 4.20304, "time": 0.84823} +{"mode": "train", "epoch": 76, "iter": 3000, "lr": 0.04916, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27891, "top5_acc": 0.52828, "loss_cls": 4.16154, "loss": 4.16154, "time": 0.85036} +{"mode": "train", "epoch": 76, "iter": 3100, "lr": 0.04913, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27031, "top5_acc": 0.51391, "loss_cls": 4.20369, "loss": 4.20369, "time": 0.85464} +{"mode": "train", "epoch": 76, "iter": 3200, "lr": 0.04911, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28156, "top5_acc": 0.52359, "loss_cls": 4.18952, "loss": 4.18952, "time": 0.85004} +{"mode": "train", "epoch": 76, "iter": 3300, "lr": 0.04908, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27719, "top5_acc": 0.53062, "loss_cls": 4.1633, "loss": 4.1633, "time": 0.85362} +{"mode": "train", "epoch": 76, "iter": 3400, "lr": 0.04905, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27531, "top5_acc": 0.52203, "loss_cls": 4.2285, "loss": 4.2285, "time": 0.85174} +{"mode": "train", "epoch": 76, "iter": 3500, "lr": 0.04902, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25984, "top5_acc": 0.50594, "loss_cls": 4.24147, "loss": 4.24147, "time": 0.84933} +{"mode": "train", "epoch": 76, "iter": 3600, "lr": 0.04899, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26609, "top5_acc": 0.51516, "loss_cls": 4.24432, "loss": 4.24432, "time": 0.85537} +{"mode": "train", "epoch": 76, "iter": 3700, "lr": 0.04897, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27187, "top5_acc": 0.51906, "loss_cls": 4.21298, "loss": 4.21298, "time": 0.85359} +{"mode": "val", "epoch": 76, "iter": 309, "lr": 0.04895, "top1_acc": 0.20179, "top5_acc": 0.43595, "mean_class_accuracy": 0.20168} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.04893, "memory": 15990, "data_time": 1.55371, "top1_acc": 0.28141, "top5_acc": 0.52984, "loss_cls": 4.13573, "loss": 4.13573, "time": 2.59285} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0489, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27781, "top5_acc": 0.52891, "loss_cls": 4.16327, "loss": 4.16327, "time": 0.84994} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.04887, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28438, "top5_acc": 0.53406, "loss_cls": 4.14956, "loss": 4.14956, "time": 0.8557} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.04884, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27578, "top5_acc": 0.52156, "loss_cls": 4.17058, "loss": 4.17058, "time": 0.85112} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.04881, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27047, "top5_acc": 0.51734, "loss_cls": 4.19062, "loss": 4.19062, "time": 0.85299} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.04879, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29063, "top5_acc": 0.53328, "loss_cls": 4.14596, "loss": 4.14596, "time": 0.84719} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.04876, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27812, "top5_acc": 0.52172, "loss_cls": 4.14098, "loss": 4.14098, "time": 0.85455} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.04873, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27609, "top5_acc": 0.52391, "loss_cls": 4.20172, "loss": 4.20172, "time": 0.85986} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.0487, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27906, "top5_acc": 0.5275, "loss_cls": 4.16525, "loss": 4.16525, "time": 0.85773} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.04867, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.28, "top5_acc": 0.52781, "loss_cls": 4.17824, "loss": 4.17824, "time": 0.84952} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.04865, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28859, "top5_acc": 0.53297, "loss_cls": 4.13253, "loss": 4.13253, "time": 0.8529} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.04862, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27281, "top5_acc": 0.52547, "loss_cls": 4.16949, "loss": 4.16949, "time": 0.84877} +{"mode": "train", "epoch": 77, "iter": 1300, "lr": 0.04859, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27094, "top5_acc": 0.51859, "loss_cls": 4.2061, "loss": 4.2061, "time": 0.85976} +{"mode": "train", "epoch": 77, "iter": 1400, "lr": 0.04856, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26547, "top5_acc": 0.52188, "loss_cls": 4.20552, "loss": 4.20552, "time": 0.85692} +{"mode": "train", "epoch": 77, "iter": 1500, "lr": 0.04853, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28203, "top5_acc": 0.51906, "loss_cls": 4.14246, "loss": 4.14246, "time": 0.85581} +{"mode": "train", "epoch": 77, "iter": 1600, "lr": 0.04851, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27938, "top5_acc": 0.53062, "loss_cls": 4.14839, "loss": 4.14839, "time": 0.85943} +{"mode": "train", "epoch": 77, "iter": 1700, "lr": 0.04848, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27984, "top5_acc": 0.52531, "loss_cls": 4.13564, "loss": 4.13564, "time": 0.8563} +{"mode": "train", "epoch": 77, "iter": 1800, "lr": 0.04845, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28312, "top5_acc": 0.53016, "loss_cls": 4.13886, "loss": 4.13886, "time": 0.85926} +{"mode": "train", "epoch": 77, "iter": 1900, "lr": 0.04842, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26219, "top5_acc": 0.51453, "loss_cls": 4.22802, "loss": 4.22802, "time": 0.85845} +{"mode": "train", "epoch": 77, "iter": 2000, "lr": 0.04839, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26969, "top5_acc": 0.52141, "loss_cls": 4.18527, "loss": 4.18527, "time": 0.859} +{"mode": "train", "epoch": 77, "iter": 2100, "lr": 0.04837, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27297, "top5_acc": 0.52359, "loss_cls": 4.1959, "loss": 4.1959, "time": 0.85411} +{"mode": "train", "epoch": 77, "iter": 2200, "lr": 0.04834, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26937, "top5_acc": 0.51688, "loss_cls": 4.21475, "loss": 4.21475, "time": 0.85702} +{"mode": "train", "epoch": 77, "iter": 2300, "lr": 0.04831, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28188, "top5_acc": 0.53266, "loss_cls": 4.14549, "loss": 4.14549, "time": 0.85712} +{"mode": "train", "epoch": 77, "iter": 2400, "lr": 0.04828, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27453, "top5_acc": 0.52766, "loss_cls": 4.15738, "loss": 4.15738, "time": 0.85007} +{"mode": "train", "epoch": 77, "iter": 2500, "lr": 0.04825, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27437, "top5_acc": 0.5225, "loss_cls": 4.18383, "loss": 4.18383, "time": 0.85291} +{"mode": "train", "epoch": 77, "iter": 2600, "lr": 0.04823, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.2675, "top5_acc": 0.515, "loss_cls": 4.19774, "loss": 4.19774, "time": 0.8504} +{"mode": "train", "epoch": 77, "iter": 2700, "lr": 0.0482, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28, "top5_acc": 0.52844, "loss_cls": 4.15846, "loss": 4.15846, "time": 0.85106} +{"mode": "train", "epoch": 77, "iter": 2800, "lr": 0.04817, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27484, "top5_acc": 0.52453, "loss_cls": 4.18604, "loss": 4.18604, "time": 0.85497} +{"mode": "train", "epoch": 77, "iter": 2900, "lr": 0.04814, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27969, "top5_acc": 0.52641, "loss_cls": 4.17084, "loss": 4.17084, "time": 0.85533} +{"mode": "train", "epoch": 77, "iter": 3000, "lr": 0.04811, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27672, "top5_acc": 0.52547, "loss_cls": 4.17236, "loss": 4.17236, "time": 0.85073} +{"mode": "train", "epoch": 77, "iter": 3100, "lr": 0.04809, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26297, "top5_acc": 0.51516, "loss_cls": 4.19323, "loss": 4.19323, "time": 0.84809} +{"mode": "train", "epoch": 77, "iter": 3200, "lr": 0.04806, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27734, "top5_acc": 0.51781, "loss_cls": 4.19945, "loss": 4.19945, "time": 0.85048} +{"mode": "train", "epoch": 77, "iter": 3300, "lr": 0.04803, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27969, "top5_acc": 0.52766, "loss_cls": 4.1641, "loss": 4.1641, "time": 0.84596} +{"mode": "train", "epoch": 77, "iter": 3400, "lr": 0.048, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28281, "top5_acc": 0.52688, "loss_cls": 4.15312, "loss": 4.15312, "time": 0.84863} +{"mode": "train", "epoch": 77, "iter": 3500, "lr": 0.04798, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26828, "top5_acc": 0.52109, "loss_cls": 4.22748, "loss": 4.22748, "time": 0.85529} +{"mode": "train", "epoch": 77, "iter": 3600, "lr": 0.04795, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27422, "top5_acc": 0.51688, "loss_cls": 4.19345, "loss": 4.19345, "time": 0.85243} +{"mode": "train", "epoch": 77, "iter": 3700, "lr": 0.04792, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27453, "top5_acc": 0.52969, "loss_cls": 4.14702, "loss": 4.14702, "time": 0.85074} +{"mode": "val", "epoch": 77, "iter": 309, "lr": 0.04791, "top1_acc": 0.18022, "top5_acc": 0.39508, "mean_class_accuracy": 0.18011} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.04788, "memory": 15990, "data_time": 1.54583, "top1_acc": 0.27484, "top5_acc": 0.53188, "loss_cls": 4.16704, "loss": 4.16704, "time": 2.59336} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.04785, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2775, "top5_acc": 0.52859, "loss_cls": 4.13539, "loss": 4.13539, "time": 0.85502} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.04782, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27453, "top5_acc": 0.52219, "loss_cls": 4.15221, "loss": 4.15221, "time": 0.8524} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.04779, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27484, "top5_acc": 0.52828, "loss_cls": 4.17709, "loss": 4.17709, "time": 0.85331} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.04777, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27594, "top5_acc": 0.53016, "loss_cls": 4.18494, "loss": 4.18494, "time": 0.84839} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.04774, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29297, "top5_acc": 0.53672, "loss_cls": 4.09975, "loss": 4.09975, "time": 0.85366} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.04771, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28156, "top5_acc": 0.53141, "loss_cls": 4.14644, "loss": 4.14644, "time": 0.85286} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.04768, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27969, "top5_acc": 0.52984, "loss_cls": 4.12872, "loss": 4.12872, "time": 0.85455} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.04766, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.285, "top5_acc": 0.52859, "loss_cls": 4.13537, "loss": 4.13537, "time": 0.8521} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.04763, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27547, "top5_acc": 0.52156, "loss_cls": 4.19894, "loss": 4.19894, "time": 0.84853} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.0476, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27766, "top5_acc": 0.52906, "loss_cls": 4.1212, "loss": 4.1212, "time": 0.85507} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.04757, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28, "top5_acc": 0.52359, "loss_cls": 4.18492, "loss": 4.18492, "time": 0.8531} +{"mode": "train", "epoch": 78, "iter": 1300, "lr": 0.04754, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28141, "top5_acc": 0.52797, "loss_cls": 4.14988, "loss": 4.14988, "time": 0.85026} +{"mode": "train", "epoch": 78, "iter": 1400, "lr": 0.04752, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28344, "top5_acc": 0.53891, "loss_cls": 4.11144, "loss": 4.11144, "time": 0.85124} +{"mode": "train", "epoch": 78, "iter": 1500, "lr": 0.04749, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27594, "top5_acc": 0.52828, "loss_cls": 4.15033, "loss": 4.15033, "time": 0.84909} +{"mode": "train", "epoch": 78, "iter": 1600, "lr": 0.04746, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27719, "top5_acc": 0.53109, "loss_cls": 4.1477, "loss": 4.1477, "time": 0.85553} +{"mode": "train", "epoch": 78, "iter": 1700, "lr": 0.04743, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28078, "top5_acc": 0.52453, "loss_cls": 4.17439, "loss": 4.17439, "time": 0.85795} +{"mode": "train", "epoch": 78, "iter": 1800, "lr": 0.0474, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27375, "top5_acc": 0.52609, "loss_cls": 4.16283, "loss": 4.16283, "time": 0.85292} +{"mode": "train", "epoch": 78, "iter": 1900, "lr": 0.04738, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27578, "top5_acc": 0.52578, "loss_cls": 4.16937, "loss": 4.16937, "time": 0.85207} +{"mode": "train", "epoch": 78, "iter": 2000, "lr": 0.04735, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28094, "top5_acc": 0.51953, "loss_cls": 4.19652, "loss": 4.19652, "time": 0.8517} +{"mode": "train", "epoch": 78, "iter": 2100, "lr": 0.04732, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26875, "top5_acc": 0.50875, "loss_cls": 4.22206, "loss": 4.22206, "time": 0.8557} +{"mode": "train", "epoch": 78, "iter": 2200, "lr": 0.04729, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27891, "top5_acc": 0.53328, "loss_cls": 4.13616, "loss": 4.13616, "time": 0.85968} +{"mode": "train", "epoch": 78, "iter": 2300, "lr": 0.04726, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28766, "top5_acc": 0.54406, "loss_cls": 4.10947, "loss": 4.10947, "time": 0.84734} +{"mode": "train", "epoch": 78, "iter": 2400, "lr": 0.04724, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26719, "top5_acc": 0.51969, "loss_cls": 4.21187, "loss": 4.21187, "time": 0.85024} +{"mode": "train", "epoch": 78, "iter": 2500, "lr": 0.04721, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28156, "top5_acc": 0.52047, "loss_cls": 4.20015, "loss": 4.20015, "time": 0.85209} +{"mode": "train", "epoch": 78, "iter": 2600, "lr": 0.04718, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28, "top5_acc": 0.52859, "loss_cls": 4.13218, "loss": 4.13218, "time": 0.8525} +{"mode": "train", "epoch": 78, "iter": 2700, "lr": 0.04715, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25828, "top5_acc": 0.51047, "loss_cls": 4.23548, "loss": 4.23548, "time": 0.85359} +{"mode": "train", "epoch": 78, "iter": 2800, "lr": 0.04712, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27281, "top5_acc": 0.51922, "loss_cls": 4.19565, "loss": 4.19565, "time": 0.85561} +{"mode": "train", "epoch": 78, "iter": 2900, "lr": 0.0471, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27484, "top5_acc": 0.52, "loss_cls": 4.20564, "loss": 4.20564, "time": 0.86202} +{"mode": "train", "epoch": 78, "iter": 3000, "lr": 0.04707, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27141, "top5_acc": 0.52984, "loss_cls": 4.18808, "loss": 4.18808, "time": 0.86219} +{"mode": "train", "epoch": 78, "iter": 3100, "lr": 0.04704, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27172, "top5_acc": 0.50828, "loss_cls": 4.23565, "loss": 4.23565, "time": 0.85368} +{"mode": "train", "epoch": 78, "iter": 3200, "lr": 0.04701, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27609, "top5_acc": 0.525, "loss_cls": 4.1751, "loss": 4.1751, "time": 0.84925} +{"mode": "train", "epoch": 78, "iter": 3300, "lr": 0.04699, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27, "top5_acc": 0.51625, "loss_cls": 4.205, "loss": 4.205, "time": 0.8501} +{"mode": "train", "epoch": 78, "iter": 3400, "lr": 0.04696, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27, "top5_acc": 0.52, "loss_cls": 4.22228, "loss": 4.22228, "time": 0.84586} +{"mode": "train", "epoch": 78, "iter": 3500, "lr": 0.04693, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28672, "top5_acc": 0.53328, "loss_cls": 4.11115, "loss": 4.11115, "time": 0.85557} +{"mode": "train", "epoch": 78, "iter": 3600, "lr": 0.0469, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28203, "top5_acc": 0.53547, "loss_cls": 4.12142, "loss": 4.12142, "time": 0.86079} +{"mode": "train", "epoch": 78, "iter": 3700, "lr": 0.04687, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27, "top5_acc": 0.52125, "loss_cls": 4.19602, "loss": 4.19602, "time": 0.8551} +{"mode": "val", "epoch": 78, "iter": 309, "lr": 0.04686, "top1_acc": 0.21633, "top5_acc": 0.44639, "mean_class_accuracy": 0.21602} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.04683, "memory": 15990, "data_time": 1.59622, "top1_acc": 0.29359, "top5_acc": 0.54141, "loss_cls": 4.09287, "loss": 4.09287, "time": 2.65412} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.0468, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27984, "top5_acc": 0.53125, "loss_cls": 4.12125, "loss": 4.12125, "time": 0.85078} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.04678, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27797, "top5_acc": 0.53047, "loss_cls": 4.14701, "loss": 4.14701, "time": 0.84825} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.04675, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28391, "top5_acc": 0.52984, "loss_cls": 4.13561, "loss": 4.13561, "time": 0.85147} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.04672, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27531, "top5_acc": 0.5275, "loss_cls": 4.16978, "loss": 4.16978, "time": 0.84619} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.04669, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28891, "top5_acc": 0.52922, "loss_cls": 4.14684, "loss": 4.14684, "time": 0.85194} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.04667, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28969, "top5_acc": 0.53656, "loss_cls": 4.10992, "loss": 4.10992, "time": 0.85214} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.04664, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28547, "top5_acc": 0.53656, "loss_cls": 4.11996, "loss": 4.11996, "time": 0.84749} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.04661, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28391, "top5_acc": 0.52297, "loss_cls": 4.16522, "loss": 4.16522, "time": 0.85593} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.04658, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28594, "top5_acc": 0.53359, "loss_cls": 4.10999, "loss": 4.10999, "time": 0.85202} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.04655, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27719, "top5_acc": 0.52125, "loss_cls": 4.1537, "loss": 4.1537, "time": 0.85314} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.04653, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27359, "top5_acc": 0.52328, "loss_cls": 4.16295, "loss": 4.16295, "time": 0.85242} +{"mode": "train", "epoch": 79, "iter": 1300, "lr": 0.0465, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27688, "top5_acc": 0.5375, "loss_cls": 4.12255, "loss": 4.12255, "time": 0.8503} +{"mode": "train", "epoch": 79, "iter": 1400, "lr": 0.04647, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28797, "top5_acc": 0.53734, "loss_cls": 4.14007, "loss": 4.14007, "time": 0.85522} +{"mode": "train", "epoch": 79, "iter": 1500, "lr": 0.04644, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28047, "top5_acc": 0.5375, "loss_cls": 4.14176, "loss": 4.14176, "time": 0.85158} +{"mode": "train", "epoch": 79, "iter": 1600, "lr": 0.04641, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28109, "top5_acc": 0.525, "loss_cls": 4.17346, "loss": 4.17346, "time": 0.85213} +{"mode": "train", "epoch": 79, "iter": 1700, "lr": 0.04639, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27219, "top5_acc": 0.52438, "loss_cls": 4.16923, "loss": 4.16923, "time": 0.85046} +{"mode": "train", "epoch": 79, "iter": 1800, "lr": 0.04636, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28109, "top5_acc": 0.53312, "loss_cls": 4.12827, "loss": 4.12827, "time": 0.85396} +{"mode": "train", "epoch": 79, "iter": 1900, "lr": 0.04633, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2725, "top5_acc": 0.5275, "loss_cls": 4.1833, "loss": 4.1833, "time": 0.85213} +{"mode": "train", "epoch": 79, "iter": 2000, "lr": 0.0463, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27156, "top5_acc": 0.52172, "loss_cls": 4.15674, "loss": 4.15674, "time": 0.852} +{"mode": "train", "epoch": 79, "iter": 2100, "lr": 0.04628, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28016, "top5_acc": 0.52828, "loss_cls": 4.16514, "loss": 4.16514, "time": 0.85444} +{"mode": "train", "epoch": 79, "iter": 2200, "lr": 0.04625, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2725, "top5_acc": 0.52156, "loss_cls": 4.18749, "loss": 4.18749, "time": 0.85315} +{"mode": "train", "epoch": 79, "iter": 2300, "lr": 0.04622, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.275, "top5_acc": 0.52562, "loss_cls": 4.20508, "loss": 4.20508, "time": 0.84594} +{"mode": "train", "epoch": 79, "iter": 2400, "lr": 0.04619, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28422, "top5_acc": 0.53609, "loss_cls": 4.12581, "loss": 4.12581, "time": 0.85324} +{"mode": "train", "epoch": 79, "iter": 2500, "lr": 0.04616, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27437, "top5_acc": 0.52141, "loss_cls": 4.15697, "loss": 4.15697, "time": 0.85519} +{"mode": "train", "epoch": 79, "iter": 2600, "lr": 0.04614, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2775, "top5_acc": 0.52594, "loss_cls": 4.1662, "loss": 4.1662, "time": 0.86038} +{"mode": "train", "epoch": 79, "iter": 2700, "lr": 0.04611, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27047, "top5_acc": 0.53625, "loss_cls": 4.15721, "loss": 4.15721, "time": 0.85425} +{"mode": "train", "epoch": 79, "iter": 2800, "lr": 0.04608, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27078, "top5_acc": 0.52875, "loss_cls": 4.18537, "loss": 4.18537, "time": 0.85797} +{"mode": "train", "epoch": 79, "iter": 2900, "lr": 0.04605, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28062, "top5_acc": 0.53484, "loss_cls": 4.14825, "loss": 4.14825, "time": 0.8558} +{"mode": "train", "epoch": 79, "iter": 3000, "lr": 0.04602, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26906, "top5_acc": 0.51266, "loss_cls": 4.22028, "loss": 4.22028, "time": 0.85791} +{"mode": "train", "epoch": 79, "iter": 3100, "lr": 0.046, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28875, "top5_acc": 0.52922, "loss_cls": 4.12786, "loss": 4.12786, "time": 0.85227} +{"mode": "train", "epoch": 79, "iter": 3200, "lr": 0.04597, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28016, "top5_acc": 0.53641, "loss_cls": 4.13224, "loss": 4.13224, "time": 0.85195} +{"mode": "train", "epoch": 79, "iter": 3300, "lr": 0.04594, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27797, "top5_acc": 0.52781, "loss_cls": 4.18192, "loss": 4.18192, "time": 0.84672} +{"mode": "train", "epoch": 79, "iter": 3400, "lr": 0.04591, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27094, "top5_acc": 0.52547, "loss_cls": 4.2069, "loss": 4.2069, "time": 0.8509} +{"mode": "train", "epoch": 79, "iter": 3500, "lr": 0.04588, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27547, "top5_acc": 0.52516, "loss_cls": 4.16634, "loss": 4.16634, "time": 0.84875} +{"mode": "train", "epoch": 79, "iter": 3600, "lr": 0.04586, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28047, "top5_acc": 0.52625, "loss_cls": 4.15348, "loss": 4.15348, "time": 0.85125} +{"mode": "train", "epoch": 79, "iter": 3700, "lr": 0.04583, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28016, "top5_acc": 0.52734, "loss_cls": 4.17298, "loss": 4.17298, "time": 0.85253} +{"mode": "val", "epoch": 79, "iter": 309, "lr": 0.04582, "top1_acc": 0.20706, "top5_acc": 0.44395, "mean_class_accuracy": 0.20692} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.04579, "memory": 15990, "data_time": 1.55941, "top1_acc": 0.28703, "top5_acc": 0.53875, "loss_cls": 4.10922, "loss": 4.10922, "time": 2.58338} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.04576, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29094, "top5_acc": 0.54656, "loss_cls": 4.07235, "loss": 4.07235, "time": 0.84722} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.04573, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27828, "top5_acc": 0.53625, "loss_cls": 4.14126, "loss": 4.14126, "time": 0.8579} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.0457, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28578, "top5_acc": 0.52859, "loss_cls": 4.11697, "loss": 4.11697, "time": 0.84993} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.04568, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28484, "top5_acc": 0.53844, "loss_cls": 4.08305, "loss": 4.08305, "time": 0.84971} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.04565, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28047, "top5_acc": 0.53359, "loss_cls": 4.13184, "loss": 4.13184, "time": 0.85482} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.04562, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27688, "top5_acc": 0.53875, "loss_cls": 4.12011, "loss": 4.12011, "time": 0.85351} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.04559, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27266, "top5_acc": 0.53281, "loss_cls": 4.14968, "loss": 4.14968, "time": 0.85502} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.04557, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28344, "top5_acc": 0.53297, "loss_cls": 4.13486, "loss": 4.13486, "time": 0.85503} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.04554, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.28188, "top5_acc": 0.52688, "loss_cls": 4.1319, "loss": 4.1319, "time": 0.85652} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.04551, "memory": 15990, "data_time": 0.00073, "top1_acc": 0.28234, "top5_acc": 0.53141, "loss_cls": 4.14607, "loss": 4.14607, "time": 0.84908} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.04548, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28609, "top5_acc": 0.52812, "loss_cls": 4.13182, "loss": 4.13182, "time": 0.84836} +{"mode": "train", "epoch": 80, "iter": 1300, "lr": 0.04545, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28344, "top5_acc": 0.53125, "loss_cls": 4.12443, "loss": 4.12443, "time": 0.8496} +{"mode": "train", "epoch": 80, "iter": 1400, "lr": 0.04543, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27406, "top5_acc": 0.51797, "loss_cls": 4.19751, "loss": 4.19751, "time": 0.84959} +{"mode": "train", "epoch": 80, "iter": 1500, "lr": 0.0454, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28047, "top5_acc": 0.53156, "loss_cls": 4.14173, "loss": 4.14173, "time": 0.84942} +{"mode": "train", "epoch": 80, "iter": 1600, "lr": 0.04537, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27828, "top5_acc": 0.53, "loss_cls": 4.15258, "loss": 4.15258, "time": 0.85393} +{"mode": "train", "epoch": 80, "iter": 1700, "lr": 0.04534, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27016, "top5_acc": 0.52281, "loss_cls": 4.1781, "loss": 4.1781, "time": 0.85615} +{"mode": "train", "epoch": 80, "iter": 1800, "lr": 0.04532, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28156, "top5_acc": 0.53062, "loss_cls": 4.12964, "loss": 4.12964, "time": 0.85283} +{"mode": "train", "epoch": 80, "iter": 1900, "lr": 0.04529, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29328, "top5_acc": 0.53219, "loss_cls": 4.10509, "loss": 4.10509, "time": 0.86085} +{"mode": "train", "epoch": 80, "iter": 2000, "lr": 0.04526, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28641, "top5_acc": 0.53141, "loss_cls": 4.13284, "loss": 4.13284, "time": 0.8592} +{"mode": "train", "epoch": 80, "iter": 2100, "lr": 0.04523, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27781, "top5_acc": 0.52906, "loss_cls": 4.14521, "loss": 4.14521, "time": 0.85682} +{"mode": "train", "epoch": 80, "iter": 2200, "lr": 0.0452, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28828, "top5_acc": 0.52922, "loss_cls": 4.15032, "loss": 4.15032, "time": 0.85603} +{"mode": "train", "epoch": 80, "iter": 2300, "lr": 0.04518, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28219, "top5_acc": 0.53422, "loss_cls": 4.13531, "loss": 4.13531, "time": 0.8598} +{"mode": "train", "epoch": 80, "iter": 2400, "lr": 0.04515, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.29156, "top5_acc": 0.53219, "loss_cls": 4.10667, "loss": 4.10667, "time": 0.85417} +{"mode": "train", "epoch": 80, "iter": 2500, "lr": 0.04512, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27859, "top5_acc": 0.52188, "loss_cls": 4.1573, "loss": 4.1573, "time": 0.84532} +{"mode": "train", "epoch": 80, "iter": 2600, "lr": 0.04509, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27875, "top5_acc": 0.53781, "loss_cls": 4.14184, "loss": 4.14184, "time": 0.84718} +{"mode": "train", "epoch": 80, "iter": 2700, "lr": 0.04506, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27172, "top5_acc": 0.51906, "loss_cls": 4.22402, "loss": 4.22402, "time": 0.84722} +{"mode": "train", "epoch": 80, "iter": 2800, "lr": 0.04504, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27219, "top5_acc": 0.52375, "loss_cls": 4.1978, "loss": 4.1978, "time": 0.85125} +{"mode": "train", "epoch": 80, "iter": 2900, "lr": 0.04501, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27844, "top5_acc": 0.53094, "loss_cls": 4.12252, "loss": 4.12252, "time": 0.84797} +{"mode": "train", "epoch": 80, "iter": 3000, "lr": 0.04498, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28469, "top5_acc": 0.52969, "loss_cls": 4.1265, "loss": 4.1265, "time": 0.84599} +{"mode": "train", "epoch": 80, "iter": 3100, "lr": 0.04495, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.28109, "top5_acc": 0.52359, "loss_cls": 4.1523, "loss": 4.1523, "time": 0.84549} +{"mode": "train", "epoch": 80, "iter": 3200, "lr": 0.04493, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28156, "top5_acc": 0.52953, "loss_cls": 4.15216, "loss": 4.15216, "time": 0.84953} +{"mode": "train", "epoch": 80, "iter": 3300, "lr": 0.0449, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28062, "top5_acc": 0.52484, "loss_cls": 4.1441, "loss": 4.1441, "time": 0.84023} +{"mode": "train", "epoch": 80, "iter": 3400, "lr": 0.04487, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28016, "top5_acc": 0.53828, "loss_cls": 4.13321, "loss": 4.13321, "time": 0.84537} +{"mode": "train", "epoch": 80, "iter": 3500, "lr": 0.04484, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27891, "top5_acc": 0.5275, "loss_cls": 4.14633, "loss": 4.14633, "time": 0.84842} +{"mode": "train", "epoch": 80, "iter": 3600, "lr": 0.04481, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27922, "top5_acc": 0.52766, "loss_cls": 4.15474, "loss": 4.15474, "time": 0.84405} +{"mode": "train", "epoch": 80, "iter": 3700, "lr": 0.04479, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27437, "top5_acc": 0.51562, "loss_cls": 4.19553, "loss": 4.19553, "time": 0.84763} +{"mode": "val", "epoch": 80, "iter": 309, "lr": 0.04477, "top1_acc": 0.21167, "top5_acc": 0.45054, "mean_class_accuracy": 0.21149} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.04475, "memory": 15990, "data_time": 1.52433, "top1_acc": 0.28406, "top5_acc": 0.53594, "loss_cls": 4.09212, "loss": 4.09212, "time": 2.56368} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.04472, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29031, "top5_acc": 0.54422, "loss_cls": 4.08196, "loss": 4.08196, "time": 0.85656} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.04469, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29094, "top5_acc": 0.53594, "loss_cls": 4.09041, "loss": 4.09041, "time": 0.85134} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.04466, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28625, "top5_acc": 0.54281, "loss_cls": 4.07725, "loss": 4.07725, "time": 0.85278} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.04463, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28188, "top5_acc": 0.53453, "loss_cls": 4.12428, "loss": 4.12428, "time": 0.86086} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.04461, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28188, "top5_acc": 0.53188, "loss_cls": 4.12979, "loss": 4.12979, "time": 0.8593} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.04458, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2825, "top5_acc": 0.53453, "loss_cls": 4.12017, "loss": 4.12017, "time": 0.85499} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.04455, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.28625, "top5_acc": 0.53359, "loss_cls": 4.12508, "loss": 4.12508, "time": 0.85856} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.04452, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.29688, "top5_acc": 0.53328, "loss_cls": 4.1199, "loss": 4.1199, "time": 0.85446} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.0445, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28109, "top5_acc": 0.53516, "loss_cls": 4.12647, "loss": 4.12647, "time": 0.84991} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.04447, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27312, "top5_acc": 0.53016, "loss_cls": 4.14192, "loss": 4.14192, "time": 0.84643} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.04444, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.28031, "top5_acc": 0.53188, "loss_cls": 4.13066, "loss": 4.13066, "time": 0.84494} +{"mode": "train", "epoch": 81, "iter": 1300, "lr": 0.04441, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28594, "top5_acc": 0.52859, "loss_cls": 4.14313, "loss": 4.14313, "time": 0.83966} +{"mode": "train", "epoch": 81, "iter": 1400, "lr": 0.04438, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29063, "top5_acc": 0.54578, "loss_cls": 4.07997, "loss": 4.07997, "time": 0.84105} +{"mode": "train", "epoch": 81, "iter": 1500, "lr": 0.04436, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28219, "top5_acc": 0.54062, "loss_cls": 4.0798, "loss": 4.0798, "time": 0.84643} +{"mode": "train", "epoch": 81, "iter": 1600, "lr": 0.04433, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29391, "top5_acc": 0.54375, "loss_cls": 4.049, "loss": 4.049, "time": 0.84512} +{"mode": "train", "epoch": 81, "iter": 1700, "lr": 0.0443, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29203, "top5_acc": 0.53562, "loss_cls": 4.10997, "loss": 4.10997, "time": 0.84632} +{"mode": "train", "epoch": 81, "iter": 1800, "lr": 0.04427, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28016, "top5_acc": 0.53609, "loss_cls": 4.13042, "loss": 4.13042, "time": 0.84177} +{"mode": "train", "epoch": 81, "iter": 1900, "lr": 0.04425, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27875, "top5_acc": 0.52266, "loss_cls": 4.17045, "loss": 4.17045, "time": 0.84149} +{"mode": "train", "epoch": 81, "iter": 2000, "lr": 0.04422, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28672, "top5_acc": 0.53234, "loss_cls": 4.11209, "loss": 4.11209, "time": 0.84663} +{"mode": "train", "epoch": 81, "iter": 2100, "lr": 0.04419, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28469, "top5_acc": 0.53234, "loss_cls": 4.12785, "loss": 4.12785, "time": 0.84816} +{"mode": "train", "epoch": 81, "iter": 2200, "lr": 0.04416, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28125, "top5_acc": 0.52891, "loss_cls": 4.12147, "loss": 4.12147, "time": 0.85167} +{"mode": "train", "epoch": 81, "iter": 2300, "lr": 0.04413, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.28281, "top5_acc": 0.52812, "loss_cls": 4.13913, "loss": 4.13913, "time": 0.85015} +{"mode": "train", "epoch": 81, "iter": 2400, "lr": 0.04411, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27656, "top5_acc": 0.52344, "loss_cls": 4.16343, "loss": 4.16343, "time": 0.8487} +{"mode": "train", "epoch": 81, "iter": 2500, "lr": 0.04408, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28484, "top5_acc": 0.53141, "loss_cls": 4.12464, "loss": 4.12464, "time": 0.85402} +{"mode": "train", "epoch": 81, "iter": 2600, "lr": 0.04405, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29203, "top5_acc": 0.53406, "loss_cls": 4.09838, "loss": 4.09838, "time": 0.85357} +{"mode": "train", "epoch": 81, "iter": 2700, "lr": 0.04402, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28375, "top5_acc": 0.53594, "loss_cls": 4.12004, "loss": 4.12004, "time": 0.84689} +{"mode": "train", "epoch": 81, "iter": 2800, "lr": 0.044, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26562, "top5_acc": 0.51828, "loss_cls": 4.20222, "loss": 4.20222, "time": 0.85109} +{"mode": "train", "epoch": 81, "iter": 2900, "lr": 0.04397, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27359, "top5_acc": 0.51531, "loss_cls": 4.2019, "loss": 4.2019, "time": 0.84898} +{"mode": "train", "epoch": 81, "iter": 3000, "lr": 0.04394, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28156, "top5_acc": 0.53375, "loss_cls": 4.14855, "loss": 4.14855, "time": 0.84871} +{"mode": "train", "epoch": 81, "iter": 3100, "lr": 0.04391, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28375, "top5_acc": 0.53922, "loss_cls": 4.12388, "loss": 4.12388, "time": 0.8453} +{"mode": "train", "epoch": 81, "iter": 3200, "lr": 0.04389, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28297, "top5_acc": 0.53625, "loss_cls": 4.11638, "loss": 4.11638, "time": 0.84689} +{"mode": "train", "epoch": 81, "iter": 3300, "lr": 0.04386, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27359, "top5_acc": 0.53141, "loss_cls": 4.14224, "loss": 4.14224, "time": 0.842} +{"mode": "train", "epoch": 81, "iter": 3400, "lr": 0.04383, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28562, "top5_acc": 0.53438, "loss_cls": 4.11738, "loss": 4.11738, "time": 0.84109} +{"mode": "train", "epoch": 81, "iter": 3500, "lr": 0.0438, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28578, "top5_acc": 0.52812, "loss_cls": 4.16106, "loss": 4.16106, "time": 0.84699} +{"mode": "train", "epoch": 81, "iter": 3600, "lr": 0.04377, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27312, "top5_acc": 0.52547, "loss_cls": 4.16282, "loss": 4.16282, "time": 0.8453} +{"mode": "train", "epoch": 81, "iter": 3700, "lr": 0.04375, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29688, "top5_acc": 0.53547, "loss_cls": 4.09712, "loss": 4.09712, "time": 0.84514} +{"mode": "val", "epoch": 81, "iter": 309, "lr": 0.04373, "top1_acc": 0.22251, "top5_acc": 0.45571, "mean_class_accuracy": 0.2223} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.04371, "memory": 15990, "data_time": 1.51273, "top1_acc": 0.30031, "top5_acc": 0.54484, "loss_cls": 4.05212, "loss": 4.05212, "time": 2.54081} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.04368, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27719, "top5_acc": 0.54141, "loss_cls": 4.11146, "loss": 4.11146, "time": 0.85177} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.04365, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28453, "top5_acc": 0.535, "loss_cls": 4.1215, "loss": 4.1215, "time": 0.85428} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.04362, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28984, "top5_acc": 0.54438, "loss_cls": 4.0694, "loss": 4.0694, "time": 0.84973} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.04359, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28719, "top5_acc": 0.54453, "loss_cls": 4.08099, "loss": 4.08099, "time": 0.85108} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.04357, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28391, "top5_acc": 0.53406, "loss_cls": 4.11922, "loss": 4.11922, "time": 0.85191} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.04354, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27922, "top5_acc": 0.53297, "loss_cls": 4.11275, "loss": 4.11275, "time": 0.85321} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.04351, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28828, "top5_acc": 0.53906, "loss_cls": 4.08592, "loss": 4.08592, "time": 0.85375} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.04348, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28719, "top5_acc": 0.54297, "loss_cls": 4.08632, "loss": 4.08632, "time": 0.85006} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.04346, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28422, "top5_acc": 0.53594, "loss_cls": 4.1066, "loss": 4.1066, "time": 0.85413} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.04343, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29906, "top5_acc": 0.53953, "loss_cls": 4.06456, "loss": 4.06456, "time": 0.84497} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.0434, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28766, "top5_acc": 0.53312, "loss_cls": 4.10267, "loss": 4.10267, "time": 0.84743} +{"mode": "train", "epoch": 82, "iter": 1300, "lr": 0.04337, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28047, "top5_acc": 0.53344, "loss_cls": 4.11437, "loss": 4.11437, "time": 0.84984} +{"mode": "train", "epoch": 82, "iter": 1400, "lr": 0.04335, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29672, "top5_acc": 0.54109, "loss_cls": 4.0695, "loss": 4.0695, "time": 0.84452} +{"mode": "train", "epoch": 82, "iter": 1500, "lr": 0.04332, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28281, "top5_acc": 0.52859, "loss_cls": 4.10599, "loss": 4.10599, "time": 0.84862} +{"mode": "train", "epoch": 82, "iter": 1600, "lr": 0.04329, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28641, "top5_acc": 0.53469, "loss_cls": 4.10966, "loss": 4.10966, "time": 0.84681} +{"mode": "train", "epoch": 82, "iter": 1700, "lr": 0.04326, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28859, "top5_acc": 0.53719, "loss_cls": 4.1079, "loss": 4.1079, "time": 0.84869} +{"mode": "train", "epoch": 82, "iter": 1800, "lr": 0.04323, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28688, "top5_acc": 0.53641, "loss_cls": 4.10643, "loss": 4.10643, "time": 0.84544} +{"mode": "train", "epoch": 82, "iter": 1900, "lr": 0.04321, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29484, "top5_acc": 0.54688, "loss_cls": 4.04075, "loss": 4.04075, "time": 0.84878} +{"mode": "train", "epoch": 82, "iter": 2000, "lr": 0.04318, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27453, "top5_acc": 0.52922, "loss_cls": 4.15076, "loss": 4.15076, "time": 0.85336} +{"mode": "train", "epoch": 82, "iter": 2100, "lr": 0.04315, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28781, "top5_acc": 0.53531, "loss_cls": 4.10074, "loss": 4.10074, "time": 0.85689} +{"mode": "train", "epoch": 82, "iter": 2200, "lr": 0.04312, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28219, "top5_acc": 0.54438, "loss_cls": 4.1047, "loss": 4.1047, "time": 0.85476} +{"mode": "train", "epoch": 82, "iter": 2300, "lr": 0.0431, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.285, "top5_acc": 0.52703, "loss_cls": 4.12899, "loss": 4.12899, "time": 0.85489} +{"mode": "train", "epoch": 82, "iter": 2400, "lr": 0.04307, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28203, "top5_acc": 0.53188, "loss_cls": 4.12291, "loss": 4.12291, "time": 0.85364} +{"mode": "train", "epoch": 82, "iter": 2500, "lr": 0.04304, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28234, "top5_acc": 0.52766, "loss_cls": 4.15058, "loss": 4.15058, "time": 0.84817} +{"mode": "train", "epoch": 82, "iter": 2600, "lr": 0.04301, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.27484, "top5_acc": 0.53141, "loss_cls": 4.13888, "loss": 4.13888, "time": 0.84688} +{"mode": "train", "epoch": 82, "iter": 2700, "lr": 0.04299, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27531, "top5_acc": 0.52891, "loss_cls": 4.16461, "loss": 4.16461, "time": 0.85642} +{"mode": "train", "epoch": 82, "iter": 2800, "lr": 0.04296, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28594, "top5_acc": 0.54016, "loss_cls": 4.10663, "loss": 4.10663, "time": 0.85244} +{"mode": "train", "epoch": 82, "iter": 2900, "lr": 0.04293, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28609, "top5_acc": 0.52844, "loss_cls": 4.12113, "loss": 4.12113, "time": 0.84976} +{"mode": "train", "epoch": 82, "iter": 3000, "lr": 0.0429, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27891, "top5_acc": 0.52203, "loss_cls": 4.16831, "loss": 4.16831, "time": 0.85405} +{"mode": "train", "epoch": 82, "iter": 3100, "lr": 0.04287, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28609, "top5_acc": 0.53703, "loss_cls": 4.11135, "loss": 4.11135, "time": 0.84718} +{"mode": "train", "epoch": 82, "iter": 3200, "lr": 0.04285, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.27922, "top5_acc": 0.52625, "loss_cls": 4.15929, "loss": 4.15929, "time": 0.85056} +{"mode": "train", "epoch": 82, "iter": 3300, "lr": 0.04282, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2875, "top5_acc": 0.54312, "loss_cls": 4.08106, "loss": 4.08106, "time": 0.84423} +{"mode": "train", "epoch": 82, "iter": 3400, "lr": 0.04279, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27141, "top5_acc": 0.52625, "loss_cls": 4.16719, "loss": 4.16719, "time": 0.84992} +{"mode": "train", "epoch": 82, "iter": 3500, "lr": 0.04276, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27906, "top5_acc": 0.52672, "loss_cls": 4.14677, "loss": 4.14677, "time": 0.85282} +{"mode": "train", "epoch": 82, "iter": 3600, "lr": 0.04274, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28906, "top5_acc": 0.53344, "loss_cls": 4.08261, "loss": 4.08261, "time": 0.85054} +{"mode": "train", "epoch": 82, "iter": 3700, "lr": 0.04271, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28406, "top5_acc": 0.53047, "loss_cls": 4.14687, "loss": 4.14687, "time": 0.84371} +{"mode": "val", "epoch": 82, "iter": 309, "lr": 0.0427, "top1_acc": 0.2292, "top5_acc": 0.46746, "mean_class_accuracy": 0.22902} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.04267, "memory": 15990, "data_time": 1.5455, "top1_acc": 0.29375, "top5_acc": 0.54453, "loss_cls": 4.03914, "loss": 4.03914, "time": 2.58163} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.04264, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28734, "top5_acc": 0.53953, "loss_cls": 4.0444, "loss": 4.0444, "time": 0.85875} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.04261, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30078, "top5_acc": 0.54891, "loss_cls": 4.04118, "loss": 4.04118, "time": 0.8551} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.04259, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28375, "top5_acc": 0.53297, "loss_cls": 4.09567, "loss": 4.09567, "time": 0.85962} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.04256, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28609, "top5_acc": 0.54141, "loss_cls": 4.10206, "loss": 4.10206, "time": 0.86048} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.04253, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28859, "top5_acc": 0.54656, "loss_cls": 4.0722, "loss": 4.0722, "time": 0.861} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.0425, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29141, "top5_acc": 0.53266, "loss_cls": 4.10772, "loss": 4.10772, "time": 0.857} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.04247, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.285, "top5_acc": 0.53594, "loss_cls": 4.11687, "loss": 4.11687, "time": 0.8597} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.04245, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28594, "top5_acc": 0.54344, "loss_cls": 4.09439, "loss": 4.09439, "time": 0.86275} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.04242, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28984, "top5_acc": 0.54047, "loss_cls": 4.08914, "loss": 4.08914, "time": 0.85615} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.04239, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29672, "top5_acc": 0.55312, "loss_cls": 4.03098, "loss": 4.03098, "time": 0.85707} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.04236, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.29734, "top5_acc": 0.545, "loss_cls": 4.03811, "loss": 4.03811, "time": 0.84426} +{"mode": "train", "epoch": 83, "iter": 1300, "lr": 0.04234, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28375, "top5_acc": 0.52906, "loss_cls": 4.13008, "loss": 4.13008, "time": 0.84854} +{"mode": "train", "epoch": 83, "iter": 1400, "lr": 0.04231, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29172, "top5_acc": 0.53219, "loss_cls": 4.11712, "loss": 4.11712, "time": 0.85533} +{"mode": "train", "epoch": 83, "iter": 1500, "lr": 0.04228, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29844, "top5_acc": 0.54484, "loss_cls": 4.04096, "loss": 4.04096, "time": 0.84973} +{"mode": "train", "epoch": 83, "iter": 1600, "lr": 0.04225, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28641, "top5_acc": 0.54516, "loss_cls": 4.06056, "loss": 4.06056, "time": 0.84691} +{"mode": "train", "epoch": 83, "iter": 1700, "lr": 0.04223, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28594, "top5_acc": 0.53, "loss_cls": 4.14376, "loss": 4.14376, "time": 0.85269} +{"mode": "train", "epoch": 83, "iter": 1800, "lr": 0.0422, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28766, "top5_acc": 0.53125, "loss_cls": 4.14891, "loss": 4.14891, "time": 0.85157} +{"mode": "train", "epoch": 83, "iter": 1900, "lr": 0.04217, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29484, "top5_acc": 0.53453, "loss_cls": 4.11841, "loss": 4.11841, "time": 0.85793} +{"mode": "train", "epoch": 83, "iter": 2000, "lr": 0.04214, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28719, "top5_acc": 0.53375, "loss_cls": 4.11178, "loss": 4.11178, "time": 0.85105} +{"mode": "train", "epoch": 83, "iter": 2100, "lr": 0.04212, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28328, "top5_acc": 0.53609, "loss_cls": 4.12099, "loss": 4.12099, "time": 0.85093} +{"mode": "train", "epoch": 83, "iter": 2200, "lr": 0.04209, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28797, "top5_acc": 0.53672, "loss_cls": 4.10682, "loss": 4.10682, "time": 0.85069} +{"mode": "train", "epoch": 83, "iter": 2300, "lr": 0.04206, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28438, "top5_acc": 0.52516, "loss_cls": 4.157, "loss": 4.157, "time": 0.84972} +{"mode": "train", "epoch": 83, "iter": 2400, "lr": 0.04203, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.29125, "top5_acc": 0.54203, "loss_cls": 4.11034, "loss": 4.11034, "time": 0.8517} +{"mode": "train", "epoch": 83, "iter": 2500, "lr": 0.04201, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27953, "top5_acc": 0.52797, "loss_cls": 4.12843, "loss": 4.12843, "time": 0.8411} +{"mode": "train", "epoch": 83, "iter": 2600, "lr": 0.04198, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28734, "top5_acc": 0.53812, "loss_cls": 4.10001, "loss": 4.10001, "time": 0.84214} +{"mode": "train", "epoch": 83, "iter": 2700, "lr": 0.04195, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28969, "top5_acc": 0.53469, "loss_cls": 4.09674, "loss": 4.09674, "time": 0.85044} +{"mode": "train", "epoch": 83, "iter": 2800, "lr": 0.04192, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28828, "top5_acc": 0.53438, "loss_cls": 4.11939, "loss": 4.11939, "time": 0.85067} +{"mode": "train", "epoch": 83, "iter": 2900, "lr": 0.0419, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28859, "top5_acc": 0.53938, "loss_cls": 4.10233, "loss": 4.10233, "time": 0.84676} +{"mode": "train", "epoch": 83, "iter": 3000, "lr": 0.04187, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2675, "top5_acc": 0.52453, "loss_cls": 4.16576, "loss": 4.16576, "time": 0.85087} +{"mode": "train", "epoch": 83, "iter": 3100, "lr": 0.04184, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29141, "top5_acc": 0.54844, "loss_cls": 4.07816, "loss": 4.07816, "time": 0.84306} +{"mode": "train", "epoch": 83, "iter": 3200, "lr": 0.04181, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28281, "top5_acc": 0.53609, "loss_cls": 4.11938, "loss": 4.11938, "time": 0.84568} +{"mode": "train", "epoch": 83, "iter": 3300, "lr": 0.04178, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.28, "top5_acc": 0.53297, "loss_cls": 4.11305, "loss": 4.11305, "time": 0.84485} +{"mode": "train", "epoch": 83, "iter": 3400, "lr": 0.04176, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.29047, "top5_acc": 0.54078, "loss_cls": 4.10843, "loss": 4.10843, "time": 0.84877} +{"mode": "train", "epoch": 83, "iter": 3500, "lr": 0.04173, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28703, "top5_acc": 0.54359, "loss_cls": 4.07902, "loss": 4.07902, "time": 0.8516} +{"mode": "train", "epoch": 83, "iter": 3600, "lr": 0.0417, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27734, "top5_acc": 0.52469, "loss_cls": 4.15964, "loss": 4.15964, "time": 0.84772} +{"mode": "train", "epoch": 83, "iter": 3700, "lr": 0.04167, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28703, "top5_acc": 0.53297, "loss_cls": 4.09666, "loss": 4.09666, "time": 0.84686} +{"mode": "val", "epoch": 83, "iter": 309, "lr": 0.04166, "top1_acc": 0.23183, "top5_acc": 0.46401, "mean_class_accuracy": 0.23166} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.04163, "memory": 15990, "data_time": 1.50375, "top1_acc": 0.29234, "top5_acc": 0.55406, "loss_cls": 4.02185, "loss": 4.02185, "time": 2.54074} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.04161, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29125, "top5_acc": 0.54516, "loss_cls": 4.07626, "loss": 4.07626, "time": 0.85505} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.04158, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30203, "top5_acc": 0.55703, "loss_cls": 4.0282, "loss": 4.0282, "time": 0.85095} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.04155, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29719, "top5_acc": 0.5425, "loss_cls": 4.03715, "loss": 4.03715, "time": 0.85317} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.04152, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30234, "top5_acc": 0.55516, "loss_cls": 4.03201, "loss": 4.03201, "time": 0.85201} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.0415, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29578, "top5_acc": 0.54266, "loss_cls": 4.08534, "loss": 4.08534, "time": 0.85385} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.04147, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28906, "top5_acc": 0.54188, "loss_cls": 4.07994, "loss": 4.07994, "time": 0.85486} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.04144, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29734, "top5_acc": 0.54594, "loss_cls": 4.05702, "loss": 4.05702, "time": 0.85842} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.04141, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29375, "top5_acc": 0.54594, "loss_cls": 4.0693, "loss": 4.0693, "time": 0.85591} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.04139, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28375, "top5_acc": 0.53734, "loss_cls": 4.11311, "loss": 4.11311, "time": 0.84637} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.04136, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28266, "top5_acc": 0.53062, "loss_cls": 4.1495, "loss": 4.1495, "time": 0.847} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.04133, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27984, "top5_acc": 0.5425, "loss_cls": 4.09413, "loss": 4.09413, "time": 0.83773} +{"mode": "train", "epoch": 84, "iter": 1300, "lr": 0.0413, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30141, "top5_acc": 0.55016, "loss_cls": 4.03706, "loss": 4.03706, "time": 0.84141} +{"mode": "train", "epoch": 84, "iter": 1400, "lr": 0.04128, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29109, "top5_acc": 0.54047, "loss_cls": 4.05907, "loss": 4.05907, "time": 0.84039} +{"mode": "train", "epoch": 84, "iter": 1500, "lr": 0.04125, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28828, "top5_acc": 0.53703, "loss_cls": 4.10381, "loss": 4.10381, "time": 0.83929} +{"mode": "train", "epoch": 84, "iter": 1600, "lr": 0.04122, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28547, "top5_acc": 0.53234, "loss_cls": 4.11461, "loss": 4.11461, "time": 0.84299} +{"mode": "train", "epoch": 84, "iter": 1700, "lr": 0.04119, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28781, "top5_acc": 0.53781, "loss_cls": 4.08603, "loss": 4.08603, "time": 0.8458} +{"mode": "train", "epoch": 84, "iter": 1800, "lr": 0.04117, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28422, "top5_acc": 0.53906, "loss_cls": 4.09074, "loss": 4.09074, "time": 0.84517} +{"mode": "train", "epoch": 84, "iter": 1900, "lr": 0.04114, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2825, "top5_acc": 0.53625, "loss_cls": 4.11892, "loss": 4.11892, "time": 0.8527} +{"mode": "train", "epoch": 84, "iter": 2000, "lr": 0.04111, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29, "top5_acc": 0.53594, "loss_cls": 4.1161, "loss": 4.1161, "time": 0.85404} +{"mode": "train", "epoch": 84, "iter": 2100, "lr": 0.04108, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28672, "top5_acc": 0.53766, "loss_cls": 4.13644, "loss": 4.13644, "time": 0.84803} +{"mode": "train", "epoch": 84, "iter": 2200, "lr": 0.04106, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27781, "top5_acc": 0.53859, "loss_cls": 4.12326, "loss": 4.12326, "time": 0.85537} +{"mode": "train", "epoch": 84, "iter": 2300, "lr": 0.04103, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30172, "top5_acc": 0.55047, "loss_cls": 4.03566, "loss": 4.03566, "time": 0.85494} +{"mode": "train", "epoch": 84, "iter": 2400, "lr": 0.041, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27891, "top5_acc": 0.525, "loss_cls": 4.16831, "loss": 4.16831, "time": 0.84964} +{"mode": "train", "epoch": 84, "iter": 2500, "lr": 0.04097, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29297, "top5_acc": 0.53891, "loss_cls": 4.07952, "loss": 4.07952, "time": 0.85737} +{"mode": "train", "epoch": 84, "iter": 2600, "lr": 0.04095, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28703, "top5_acc": 0.54219, "loss_cls": 4.08168, "loss": 4.08168, "time": 0.84654} +{"mode": "train", "epoch": 84, "iter": 2700, "lr": 0.04092, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28375, "top5_acc": 0.53156, "loss_cls": 4.11293, "loss": 4.11293, "time": 0.85332} +{"mode": "train", "epoch": 84, "iter": 2800, "lr": 0.04089, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28828, "top5_acc": 0.54109, "loss_cls": 4.1075, "loss": 4.1075, "time": 0.84905} +{"mode": "train", "epoch": 84, "iter": 2900, "lr": 0.04086, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27359, "top5_acc": 0.52594, "loss_cls": 4.12213, "loss": 4.12213, "time": 0.85221} +{"mode": "train", "epoch": 84, "iter": 3000, "lr": 0.04084, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29234, "top5_acc": 0.54266, "loss_cls": 4.09097, "loss": 4.09097, "time": 0.85318} +{"mode": "train", "epoch": 84, "iter": 3100, "lr": 0.04081, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.28844, "top5_acc": 0.53203, "loss_cls": 4.10392, "loss": 4.10392, "time": 0.84919} +{"mode": "train", "epoch": 84, "iter": 3200, "lr": 0.04078, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29547, "top5_acc": 0.55578, "loss_cls": 4.03698, "loss": 4.03698, "time": 0.84796} +{"mode": "train", "epoch": 84, "iter": 3300, "lr": 0.04075, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28531, "top5_acc": 0.53578, "loss_cls": 4.10351, "loss": 4.10351, "time": 0.85116} +{"mode": "train", "epoch": 84, "iter": 3400, "lr": 0.04073, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29594, "top5_acc": 0.55109, "loss_cls": 4.04948, "loss": 4.04948, "time": 0.84699} +{"mode": "train", "epoch": 84, "iter": 3500, "lr": 0.0407, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27844, "top5_acc": 0.53281, "loss_cls": 4.13346, "loss": 4.13346, "time": 0.84607} +{"mode": "train", "epoch": 84, "iter": 3600, "lr": 0.04067, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28484, "top5_acc": 0.53578, "loss_cls": 4.13029, "loss": 4.13029, "time": 0.84338} +{"mode": "train", "epoch": 84, "iter": 3700, "lr": 0.04064, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28469, "top5_acc": 0.53391, "loss_cls": 4.11363, "loss": 4.11363, "time": 0.84535} +{"mode": "val", "epoch": 84, "iter": 309, "lr": 0.04063, "top1_acc": 0.23345, "top5_acc": 0.47262, "mean_class_accuracy": 0.23317} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.0406, "memory": 15990, "data_time": 1.61833, "top1_acc": 0.30141, "top5_acc": 0.56469, "loss_cls": 3.97558, "loss": 3.97558, "time": 2.63977} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.04058, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30078, "top5_acc": 0.55672, "loss_cls": 4.04723, "loss": 4.04723, "time": 0.84856} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.04055, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28656, "top5_acc": 0.54062, "loss_cls": 4.09205, "loss": 4.09205, "time": 0.84511} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.04052, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30297, "top5_acc": 0.55266, "loss_cls": 4.01179, "loss": 4.01179, "time": 0.84229} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.04049, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28984, "top5_acc": 0.54516, "loss_cls": 4.0544, "loss": 4.0544, "time": 0.85014} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.04047, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28953, "top5_acc": 0.55016, "loss_cls": 4.04371, "loss": 4.04371, "time": 0.84279} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.04044, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29719, "top5_acc": 0.55203, "loss_cls": 4.04869, "loss": 4.04869, "time": 0.84457} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.04041, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28859, "top5_acc": 0.54141, "loss_cls": 4.08098, "loss": 4.08098, "time": 0.83947} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.04038, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28719, "top5_acc": 0.53703, "loss_cls": 4.08847, "loss": 4.08847, "time": 0.83734} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.04036, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29, "top5_acc": 0.53734, "loss_cls": 4.09456, "loss": 4.09456, "time": 0.84305} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.04033, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29391, "top5_acc": 0.54625, "loss_cls": 4.07915, "loss": 4.07915, "time": 0.85138} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.0403, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29953, "top5_acc": 0.54016, "loss_cls": 4.07839, "loss": 4.07839, "time": 0.83914} +{"mode": "train", "epoch": 85, "iter": 1300, "lr": 0.04027, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29219, "top5_acc": 0.54641, "loss_cls": 4.04112, "loss": 4.04112, "time": 0.84186} +{"mode": "train", "epoch": 85, "iter": 1400, "lr": 0.04025, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29656, "top5_acc": 0.55016, "loss_cls": 4.0215, "loss": 4.0215, "time": 0.84251} +{"mode": "train", "epoch": 85, "iter": 1500, "lr": 0.04022, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28391, "top5_acc": 0.53234, "loss_cls": 4.09407, "loss": 4.09407, "time": 0.84081} +{"mode": "train", "epoch": 85, "iter": 1600, "lr": 0.04019, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27766, "top5_acc": 0.52734, "loss_cls": 4.15128, "loss": 4.15128, "time": 0.84817} +{"mode": "train", "epoch": 85, "iter": 1700, "lr": 0.04016, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29078, "top5_acc": 0.55188, "loss_cls": 4.03722, "loss": 4.03722, "time": 0.84375} +{"mode": "train", "epoch": 85, "iter": 1800, "lr": 0.04014, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29156, "top5_acc": 0.53656, "loss_cls": 4.105, "loss": 4.105, "time": 0.84553} +{"mode": "train", "epoch": 85, "iter": 1900, "lr": 0.04011, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28641, "top5_acc": 0.53172, "loss_cls": 4.11679, "loss": 4.11679, "time": 0.83999} +{"mode": "train", "epoch": 85, "iter": 2000, "lr": 0.04008, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28688, "top5_acc": 0.53625, "loss_cls": 4.09387, "loss": 4.09387, "time": 0.84469} +{"mode": "train", "epoch": 85, "iter": 2100, "lr": 0.04006, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29344, "top5_acc": 0.53438, "loss_cls": 4.09937, "loss": 4.09937, "time": 0.85148} +{"mode": "train", "epoch": 85, "iter": 2200, "lr": 0.04003, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29016, "top5_acc": 0.54125, "loss_cls": 4.08375, "loss": 4.08375, "time": 0.85111} +{"mode": "train", "epoch": 85, "iter": 2300, "lr": 0.04, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30797, "top5_acc": 0.54656, "loss_cls": 4.01885, "loss": 4.01885, "time": 0.84735} +{"mode": "train", "epoch": 85, "iter": 2400, "lr": 0.03997, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28906, "top5_acc": 0.54703, "loss_cls": 4.06645, "loss": 4.06645, "time": 0.84314} +{"mode": "train", "epoch": 85, "iter": 2500, "lr": 0.03995, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29422, "top5_acc": 0.54359, "loss_cls": 4.08969, "loss": 4.08969, "time": 0.85526} +{"mode": "train", "epoch": 85, "iter": 2600, "lr": 0.03992, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29234, "top5_acc": 0.54344, "loss_cls": 4.06438, "loss": 4.06438, "time": 0.85457} +{"mode": "train", "epoch": 85, "iter": 2700, "lr": 0.03989, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.28906, "top5_acc": 0.54156, "loss_cls": 4.07945, "loss": 4.07945, "time": 0.85279} +{"mode": "train", "epoch": 85, "iter": 2800, "lr": 0.03986, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28359, "top5_acc": 0.53859, "loss_cls": 4.12292, "loss": 4.12292, "time": 0.84906} +{"mode": "train", "epoch": 85, "iter": 2900, "lr": 0.03984, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29188, "top5_acc": 0.54516, "loss_cls": 4.07374, "loss": 4.07374, "time": 0.84948} +{"mode": "train", "epoch": 85, "iter": 3000, "lr": 0.03981, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29016, "top5_acc": 0.54516, "loss_cls": 4.0569, "loss": 4.0569, "time": 0.8465} +{"mode": "train", "epoch": 85, "iter": 3100, "lr": 0.03978, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28188, "top5_acc": 0.5325, "loss_cls": 4.1202, "loss": 4.1202, "time": 0.85208} +{"mode": "train", "epoch": 85, "iter": 3200, "lr": 0.03975, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.28203, "top5_acc": 0.53031, "loss_cls": 4.12086, "loss": 4.12086, "time": 0.85211} +{"mode": "train", "epoch": 85, "iter": 3300, "lr": 0.03973, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28875, "top5_acc": 0.5375, "loss_cls": 4.10651, "loss": 4.10651, "time": 0.84584} +{"mode": "train", "epoch": 85, "iter": 3400, "lr": 0.0397, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.29656, "top5_acc": 0.54547, "loss_cls": 4.08173, "loss": 4.08173, "time": 0.8414} +{"mode": "train", "epoch": 85, "iter": 3500, "lr": 0.03967, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29422, "top5_acc": 0.54438, "loss_cls": 4.07527, "loss": 4.07527, "time": 0.85058} +{"mode": "train", "epoch": 85, "iter": 3600, "lr": 0.03964, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29328, "top5_acc": 0.55422, "loss_cls": 4.03745, "loss": 4.03745, "time": 0.85071} +{"mode": "train", "epoch": 85, "iter": 3700, "lr": 0.03962, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28922, "top5_acc": 0.54359, "loss_cls": 4.05538, "loss": 4.05538, "time": 0.85017} +{"mode": "val", "epoch": 85, "iter": 309, "lr": 0.0396, "top1_acc": 0.21972, "top5_acc": 0.45606, "mean_class_accuracy": 0.21957} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.03958, "memory": 15990, "data_time": 1.48025, "top1_acc": 0.30594, "top5_acc": 0.56031, "loss_cls": 3.98292, "loss": 3.98292, "time": 2.50646} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.03955, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30594, "top5_acc": 0.55578, "loss_cls": 4.00962, "loss": 4.00962, "time": 0.84823} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.03952, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29406, "top5_acc": 0.55078, "loss_cls": 4.04197, "loss": 4.04197, "time": 0.85141} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.0395, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30266, "top5_acc": 0.54469, "loss_cls": 4.0358, "loss": 4.0358, "time": 0.85312} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.03947, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2975, "top5_acc": 0.55516, "loss_cls": 3.99908, "loss": 3.99908, "time": 0.84969} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.03944, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30297, "top5_acc": 0.55141, "loss_cls": 4.01532, "loss": 4.01532, "time": 0.85001} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.03941, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29531, "top5_acc": 0.55094, "loss_cls": 4.03381, "loss": 4.03381, "time": 0.84518} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.03939, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28953, "top5_acc": 0.53969, "loss_cls": 4.07937, "loss": 4.07937, "time": 0.84931} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.03936, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29203, "top5_acc": 0.54125, "loss_cls": 4.07723, "loss": 4.07723, "time": 0.84498} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.03933, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30438, "top5_acc": 0.54719, "loss_cls": 4.03766, "loss": 4.03766, "time": 0.8525} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.0393, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29391, "top5_acc": 0.54906, "loss_cls": 4.03844, "loss": 4.03844, "time": 0.85038} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.03928, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28969, "top5_acc": 0.54422, "loss_cls": 4.06657, "loss": 4.06657, "time": 0.84374} +{"mode": "train", "epoch": 86, "iter": 1300, "lr": 0.03925, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29406, "top5_acc": 0.54469, "loss_cls": 4.0596, "loss": 4.0596, "time": 0.84443} +{"mode": "train", "epoch": 86, "iter": 1400, "lr": 0.03922, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29359, "top5_acc": 0.54531, "loss_cls": 4.06739, "loss": 4.06739, "time": 0.84428} +{"mode": "train", "epoch": 86, "iter": 1500, "lr": 0.03919, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29172, "top5_acc": 0.54922, "loss_cls": 4.05061, "loss": 4.05061, "time": 0.84753} +{"mode": "train", "epoch": 86, "iter": 1600, "lr": 0.03917, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29641, "top5_acc": 0.55094, "loss_cls": 4.05916, "loss": 4.05916, "time": 0.84622} +{"mode": "train", "epoch": 86, "iter": 1700, "lr": 0.03914, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29859, "top5_acc": 0.54875, "loss_cls": 4.07372, "loss": 4.07372, "time": 0.84896} +{"mode": "train", "epoch": 86, "iter": 1800, "lr": 0.03911, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30031, "top5_acc": 0.54328, "loss_cls": 4.07526, "loss": 4.07526, "time": 0.84796} +{"mode": "train", "epoch": 86, "iter": 1900, "lr": 0.03909, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29531, "top5_acc": 0.54188, "loss_cls": 4.08126, "loss": 4.08126, "time": 0.84739} +{"mode": "train", "epoch": 86, "iter": 2000, "lr": 0.03906, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29266, "top5_acc": 0.54328, "loss_cls": 4.06393, "loss": 4.06393, "time": 0.84828} +{"mode": "train", "epoch": 86, "iter": 2100, "lr": 0.03903, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29641, "top5_acc": 0.54031, "loss_cls": 4.0562, "loss": 4.0562, "time": 0.84792} +{"mode": "train", "epoch": 86, "iter": 2200, "lr": 0.039, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29563, "top5_acc": 0.54734, "loss_cls": 4.04311, "loss": 4.04311, "time": 0.84729} +{"mode": "train", "epoch": 86, "iter": 2300, "lr": 0.03898, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29656, "top5_acc": 0.55156, "loss_cls": 4.02359, "loss": 4.02359, "time": 0.84857} +{"mode": "train", "epoch": 86, "iter": 2400, "lr": 0.03895, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28344, "top5_acc": 0.54016, "loss_cls": 4.08681, "loss": 4.08681, "time": 0.85149} +{"mode": "train", "epoch": 86, "iter": 2500, "lr": 0.03892, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.29719, "top5_acc": 0.54516, "loss_cls": 4.04475, "loss": 4.04475, "time": 0.84768} +{"mode": "train", "epoch": 86, "iter": 2600, "lr": 0.03889, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30172, "top5_acc": 0.55141, "loss_cls": 4.02846, "loss": 4.02846, "time": 0.8437} +{"mode": "train", "epoch": 86, "iter": 2700, "lr": 0.03887, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29281, "top5_acc": 0.53859, "loss_cls": 4.06527, "loss": 4.06527, "time": 0.83824} +{"mode": "train", "epoch": 86, "iter": 2800, "lr": 0.03884, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.29047, "top5_acc": 0.53703, "loss_cls": 4.10463, "loss": 4.10463, "time": 0.84435} +{"mode": "train", "epoch": 86, "iter": 2900, "lr": 0.03881, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28047, "top5_acc": 0.53469, "loss_cls": 4.10078, "loss": 4.10078, "time": 0.84151} +{"mode": "train", "epoch": 86, "iter": 3000, "lr": 0.03879, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29906, "top5_acc": 0.54734, "loss_cls": 4.0353, "loss": 4.0353, "time": 0.84485} +{"mode": "train", "epoch": 86, "iter": 3100, "lr": 0.03876, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29688, "top5_acc": 0.54875, "loss_cls": 4.04936, "loss": 4.04936, "time": 0.84364} +{"mode": "train", "epoch": 86, "iter": 3200, "lr": 0.03873, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29047, "top5_acc": 0.54375, "loss_cls": 4.06803, "loss": 4.06803, "time": 0.84431} +{"mode": "train", "epoch": 86, "iter": 3300, "lr": 0.0387, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28609, "top5_acc": 0.54406, "loss_cls": 4.06691, "loss": 4.06691, "time": 0.84103} +{"mode": "train", "epoch": 86, "iter": 3400, "lr": 0.03868, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29094, "top5_acc": 0.54578, "loss_cls": 4.06274, "loss": 4.06274, "time": 0.84375} +{"mode": "train", "epoch": 86, "iter": 3500, "lr": 0.03865, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28641, "top5_acc": 0.54203, "loss_cls": 4.09585, "loss": 4.09585, "time": 0.84608} +{"mode": "train", "epoch": 86, "iter": 3600, "lr": 0.03862, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28781, "top5_acc": 0.53953, "loss_cls": 4.09732, "loss": 4.09732, "time": 0.84917} +{"mode": "train", "epoch": 86, "iter": 3700, "lr": 0.0386, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28266, "top5_acc": 0.53766, "loss_cls": 4.12971, "loss": 4.12971, "time": 0.84429} +{"mode": "val", "epoch": 86, "iter": 309, "lr": 0.03858, "top1_acc": 0.24292, "top5_acc": 0.48382, "mean_class_accuracy": 0.24271} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.03856, "memory": 15990, "data_time": 1.52846, "top1_acc": 0.31391, "top5_acc": 0.56359, "loss_cls": 3.96668, "loss": 3.96668, "time": 2.55471} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.03853, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30688, "top5_acc": 0.56172, "loss_cls": 3.96528, "loss": 3.96528, "time": 0.85024} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.0385, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3025, "top5_acc": 0.56375, "loss_cls": 3.96318, "loss": 3.96318, "time": 0.84986} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.03847, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29875, "top5_acc": 0.55281, "loss_cls": 4.04075, "loss": 4.04075, "time": 0.84692} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.03845, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29516, "top5_acc": 0.55141, "loss_cls": 4.02781, "loss": 4.02781, "time": 0.84614} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.03842, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30031, "top5_acc": 0.55328, "loss_cls": 4.00143, "loss": 4.00143, "time": 0.84926} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.03839, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29609, "top5_acc": 0.54516, "loss_cls": 4.07795, "loss": 4.07795, "time": 0.85112} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.03837, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29203, "top5_acc": 0.54484, "loss_cls": 4.07193, "loss": 4.07193, "time": 0.84968} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.03834, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29891, "top5_acc": 0.55203, "loss_cls": 4.01664, "loss": 4.01664, "time": 0.84712} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.03831, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29516, "top5_acc": 0.54203, "loss_cls": 4.0668, "loss": 4.0668, "time": 0.8518} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.03828, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.29438, "top5_acc": 0.54875, "loss_cls": 4.04444, "loss": 4.04444, "time": 0.84692} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.03826, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28828, "top5_acc": 0.55203, "loss_cls": 4.04688, "loss": 4.04688, "time": 0.85083} +{"mode": "train", "epoch": 87, "iter": 1300, "lr": 0.03823, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29641, "top5_acc": 0.54906, "loss_cls": 4.00967, "loss": 4.00967, "time": 0.84792} +{"mode": "train", "epoch": 87, "iter": 1400, "lr": 0.0382, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29734, "top5_acc": 0.56031, "loss_cls": 4.0133, "loss": 4.0133, "time": 0.85588} +{"mode": "train", "epoch": 87, "iter": 1500, "lr": 0.03817, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29609, "top5_acc": 0.54328, "loss_cls": 4.06897, "loss": 4.06897, "time": 0.84254} +{"mode": "train", "epoch": 87, "iter": 1600, "lr": 0.03815, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28594, "top5_acc": 0.54047, "loss_cls": 4.10023, "loss": 4.10023, "time": 0.84435} +{"mode": "train", "epoch": 87, "iter": 1700, "lr": 0.03812, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29109, "top5_acc": 0.53562, "loss_cls": 4.0663, "loss": 4.0663, "time": 0.84624} +{"mode": "train", "epoch": 87, "iter": 1800, "lr": 0.03809, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29703, "top5_acc": 0.54359, "loss_cls": 4.04885, "loss": 4.04885, "time": 0.84483} +{"mode": "train", "epoch": 87, "iter": 1900, "lr": 0.03807, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29422, "top5_acc": 0.55875, "loss_cls": 4.03055, "loss": 4.03055, "time": 0.85121} +{"mode": "train", "epoch": 87, "iter": 2000, "lr": 0.03804, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29578, "top5_acc": 0.54625, "loss_cls": 4.03628, "loss": 4.03628, "time": 0.84969} +{"mode": "train", "epoch": 87, "iter": 2100, "lr": 0.03801, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29547, "top5_acc": 0.5525, "loss_cls": 4.03732, "loss": 4.03732, "time": 0.84892} +{"mode": "train", "epoch": 87, "iter": 2200, "lr": 0.03798, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28719, "top5_acc": 0.54937, "loss_cls": 4.05857, "loss": 4.05857, "time": 0.84508} +{"mode": "train", "epoch": 87, "iter": 2300, "lr": 0.03796, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29703, "top5_acc": 0.54375, "loss_cls": 4.08221, "loss": 4.08221, "time": 0.8447} +{"mode": "train", "epoch": 87, "iter": 2400, "lr": 0.03793, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30016, "top5_acc": 0.55062, "loss_cls": 4.02945, "loss": 4.02945, "time": 0.85188} +{"mode": "train", "epoch": 87, "iter": 2500, "lr": 0.0379, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29938, "top5_acc": 0.54953, "loss_cls": 4.03484, "loss": 4.03484, "time": 0.84023} +{"mode": "train", "epoch": 87, "iter": 2600, "lr": 0.03788, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28828, "top5_acc": 0.54047, "loss_cls": 4.1138, "loss": 4.1138, "time": 0.85013} +{"mode": "train", "epoch": 87, "iter": 2700, "lr": 0.03785, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.29625, "top5_acc": 0.54438, "loss_cls": 4.02692, "loss": 4.02692, "time": 0.84335} +{"mode": "train", "epoch": 87, "iter": 2800, "lr": 0.03782, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29828, "top5_acc": 0.5425, "loss_cls": 4.06035, "loss": 4.06035, "time": 0.84752} +{"mode": "train", "epoch": 87, "iter": 2900, "lr": 0.03779, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29859, "top5_acc": 0.55016, "loss_cls": 4.04424, "loss": 4.04424, "time": 0.84444} +{"mode": "train", "epoch": 87, "iter": 3000, "lr": 0.03777, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29516, "top5_acc": 0.54828, "loss_cls": 4.04197, "loss": 4.04197, "time": 0.84356} +{"mode": "train", "epoch": 87, "iter": 3100, "lr": 0.03774, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29906, "top5_acc": 0.54703, "loss_cls": 4.04201, "loss": 4.04201, "time": 0.84214} +{"mode": "train", "epoch": 87, "iter": 3200, "lr": 0.03771, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29344, "top5_acc": 0.53984, "loss_cls": 4.09008, "loss": 4.09008, "time": 0.84691} +{"mode": "train", "epoch": 87, "iter": 3300, "lr": 0.03769, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29422, "top5_acc": 0.54234, "loss_cls": 4.05836, "loss": 4.05836, "time": 0.84488} +{"mode": "train", "epoch": 87, "iter": 3400, "lr": 0.03766, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28609, "top5_acc": 0.53828, "loss_cls": 4.08095, "loss": 4.08095, "time": 0.84075} +{"mode": "train", "epoch": 87, "iter": 3500, "lr": 0.03763, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29125, "top5_acc": 0.54031, "loss_cls": 4.06798, "loss": 4.06798, "time": 0.84777} +{"mode": "train", "epoch": 87, "iter": 3600, "lr": 0.03761, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29328, "top5_acc": 0.54188, "loss_cls": 4.08171, "loss": 4.08171, "time": 0.85092} +{"mode": "train", "epoch": 87, "iter": 3700, "lr": 0.03758, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29141, "top5_acc": 0.55531, "loss_cls": 4.04926, "loss": 4.04926, "time": 0.84976} +{"mode": "val", "epoch": 87, "iter": 309, "lr": 0.03757, "top1_acc": 0.23269, "top5_acc": 0.47885, "mean_class_accuracy": 0.23267} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.03754, "memory": 15990, "data_time": 1.52795, "top1_acc": 0.30719, "top5_acc": 0.56578, "loss_cls": 3.96158, "loss": 3.96158, "time": 2.56015} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.03751, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29781, "top5_acc": 0.5525, "loss_cls": 4.00487, "loss": 4.00487, "time": 0.8558} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.03748, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29875, "top5_acc": 0.55406, "loss_cls": 4.02584, "loss": 4.02584, "time": 0.84813} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.03746, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29938, "top5_acc": 0.55, "loss_cls": 4.01147, "loss": 4.01147, "time": 0.85287} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.03743, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30562, "top5_acc": 0.54891, "loss_cls": 4.01198, "loss": 4.01198, "time": 0.84882} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.0374, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29906, "top5_acc": 0.55719, "loss_cls": 4.00388, "loss": 4.00388, "time": 0.84521} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.03738, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30188, "top5_acc": 0.5575, "loss_cls": 3.97601, "loss": 3.97601, "time": 0.84606} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.03735, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29812, "top5_acc": 0.55547, "loss_cls": 3.99383, "loss": 3.99383, "time": 0.84738} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.03732, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30234, "top5_acc": 0.54875, "loss_cls": 3.99125, "loss": 3.99125, "time": 0.84484} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.0373, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29313, "top5_acc": 0.55641, "loss_cls": 4.03216, "loss": 4.03216, "time": 0.85042} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.03727, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30094, "top5_acc": 0.55203, "loss_cls": 4.02983, "loss": 4.02983, "time": 0.84288} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.03724, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.2925, "top5_acc": 0.54312, "loss_cls": 4.07218, "loss": 4.07218, "time": 0.84838} +{"mode": "train", "epoch": 88, "iter": 1300, "lr": 0.03721, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29375, "top5_acc": 0.54828, "loss_cls": 4.02102, "loss": 4.02102, "time": 0.84827} +{"mode": "train", "epoch": 88, "iter": 1400, "lr": 0.03719, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30031, "top5_acc": 0.55188, "loss_cls": 4.01809, "loss": 4.01809, "time": 0.84502} +{"mode": "train", "epoch": 88, "iter": 1500, "lr": 0.03716, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30094, "top5_acc": 0.55125, "loss_cls": 4.0283, "loss": 4.0283, "time": 0.84577} +{"mode": "train", "epoch": 88, "iter": 1600, "lr": 0.03713, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29891, "top5_acc": 0.55531, "loss_cls": 4.00713, "loss": 4.00713, "time": 0.84494} +{"mode": "train", "epoch": 88, "iter": 1700, "lr": 0.03711, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28859, "top5_acc": 0.55484, "loss_cls": 4.04408, "loss": 4.04408, "time": 0.84428} +{"mode": "train", "epoch": 88, "iter": 1800, "lr": 0.03708, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29719, "top5_acc": 0.54891, "loss_cls": 4.01789, "loss": 4.01789, "time": 0.84486} +{"mode": "train", "epoch": 88, "iter": 1900, "lr": 0.03705, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29969, "top5_acc": 0.54969, "loss_cls": 4.01518, "loss": 4.01518, "time": 0.84748} +{"mode": "train", "epoch": 88, "iter": 2000, "lr": 0.03703, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29516, "top5_acc": 0.53875, "loss_cls": 4.08461, "loss": 4.08461, "time": 0.84305} +{"mode": "train", "epoch": 88, "iter": 2100, "lr": 0.037, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29609, "top5_acc": 0.55047, "loss_cls": 4.03726, "loss": 4.03726, "time": 0.84626} +{"mode": "train", "epoch": 88, "iter": 2200, "lr": 0.03697, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28891, "top5_acc": 0.53969, "loss_cls": 4.08471, "loss": 4.08471, "time": 0.84921} +{"mode": "train", "epoch": 88, "iter": 2300, "lr": 0.03694, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29438, "top5_acc": 0.53641, "loss_cls": 4.06232, "loss": 4.06232, "time": 0.84524} +{"mode": "train", "epoch": 88, "iter": 2400, "lr": 0.03692, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30469, "top5_acc": 0.56031, "loss_cls": 3.99681, "loss": 3.99681, "time": 0.84902} +{"mode": "train", "epoch": 88, "iter": 2500, "lr": 0.03689, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29688, "top5_acc": 0.54688, "loss_cls": 4.0657, "loss": 4.0657, "time": 0.84836} +{"mode": "train", "epoch": 88, "iter": 2600, "lr": 0.03686, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29406, "top5_acc": 0.54953, "loss_cls": 4.03826, "loss": 4.03826, "time": 0.85079} +{"mode": "train", "epoch": 88, "iter": 2700, "lr": 0.03684, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29438, "top5_acc": 0.54, "loss_cls": 4.06022, "loss": 4.06022, "time": 0.84827} +{"mode": "train", "epoch": 88, "iter": 2800, "lr": 0.03681, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29844, "top5_acc": 0.55937, "loss_cls": 4.00315, "loss": 4.00315, "time": 0.84359} +{"mode": "train", "epoch": 88, "iter": 2900, "lr": 0.03678, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29828, "top5_acc": 0.55812, "loss_cls": 4.02, "loss": 4.02, "time": 0.84349} +{"mode": "train", "epoch": 88, "iter": 3000, "lr": 0.03676, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29234, "top5_acc": 0.54422, "loss_cls": 4.08382, "loss": 4.08382, "time": 0.84294} +{"mode": "train", "epoch": 88, "iter": 3100, "lr": 0.03673, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.295, "top5_acc": 0.53719, "loss_cls": 4.09875, "loss": 4.09875, "time": 0.84033} +{"mode": "train", "epoch": 88, "iter": 3200, "lr": 0.0367, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.30234, "top5_acc": 0.55062, "loss_cls": 4.04307, "loss": 4.04307, "time": 0.84766} +{"mode": "train", "epoch": 88, "iter": 3300, "lr": 0.03667, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30062, "top5_acc": 0.55156, "loss_cls": 3.9999, "loss": 3.9999, "time": 0.84229} +{"mode": "train", "epoch": 88, "iter": 3400, "lr": 0.03665, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29625, "top5_acc": 0.54609, "loss_cls": 4.06416, "loss": 4.06416, "time": 0.84336} +{"mode": "train", "epoch": 88, "iter": 3500, "lr": 0.03662, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29297, "top5_acc": 0.54578, "loss_cls": 4.08293, "loss": 4.08293, "time": 0.84382} +{"mode": "train", "epoch": 88, "iter": 3600, "lr": 0.03659, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30016, "top5_acc": 0.54625, "loss_cls": 3.99504, "loss": 3.99504, "time": 0.8382} +{"mode": "train", "epoch": 88, "iter": 3700, "lr": 0.03657, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30156, "top5_acc": 0.55156, "loss_cls": 4.05599, "loss": 4.05599, "time": 0.84119} +{"mode": "val", "epoch": 88, "iter": 309, "lr": 0.03655, "top1_acc": 0.24713, "top5_acc": 0.48767, "mean_class_accuracy": 0.24692} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.03653, "memory": 15990, "data_time": 1.5051, "top1_acc": 0.30516, "top5_acc": 0.56484, "loss_cls": 3.96121, "loss": 3.96121, "time": 2.52526} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0365, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29781, "top5_acc": 0.55359, "loss_cls": 4.01075, "loss": 4.01075, "time": 0.85327} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.03647, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29828, "top5_acc": 0.55078, "loss_cls": 4.02545, "loss": 4.02545, "time": 0.84677} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.03645, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29641, "top5_acc": 0.55188, "loss_cls": 4.02523, "loss": 4.02523, "time": 0.84671} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.03642, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29938, "top5_acc": 0.55484, "loss_cls": 4.0184, "loss": 4.0184, "time": 0.84845} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.03639, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29875, "top5_acc": 0.55016, "loss_cls": 4.04781, "loss": 4.04781, "time": 0.84723} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.03637, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29609, "top5_acc": 0.54359, "loss_cls": 4.03882, "loss": 4.03882, "time": 0.85267} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.03634, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30891, "top5_acc": 0.55391, "loss_cls": 3.95935, "loss": 3.95935, "time": 0.84916} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.03631, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29828, "top5_acc": 0.54953, "loss_cls": 4.02758, "loss": 4.02758, "time": 0.84445} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.03629, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30281, "top5_acc": 0.55656, "loss_cls": 4.00468, "loss": 4.00468, "time": 0.8438} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.03626, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30234, "top5_acc": 0.55609, "loss_cls": 4.02607, "loss": 4.02607, "time": 0.84114} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.03623, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29875, "top5_acc": 0.55188, "loss_cls": 4.03021, "loss": 4.03021, "time": 0.84799} +{"mode": "train", "epoch": 89, "iter": 1300, "lr": 0.0362, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29828, "top5_acc": 0.55875, "loss_cls": 3.99345, "loss": 3.99345, "time": 0.85016} +{"mode": "train", "epoch": 89, "iter": 1400, "lr": 0.03618, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.30734, "top5_acc": 0.54969, "loss_cls": 3.99859, "loss": 3.99859, "time": 0.84251} +{"mode": "train", "epoch": 89, "iter": 1500, "lr": 0.03615, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29703, "top5_acc": 0.54922, "loss_cls": 4.03243, "loss": 4.03243, "time": 0.84434} +{"mode": "train", "epoch": 89, "iter": 1600, "lr": 0.03612, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28953, "top5_acc": 0.54484, "loss_cls": 4.04833, "loss": 4.04833, "time": 0.84013} +{"mode": "train", "epoch": 89, "iter": 1700, "lr": 0.0361, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29953, "top5_acc": 0.56516, "loss_cls": 3.9754, "loss": 3.9754, "time": 0.8417} +{"mode": "train", "epoch": 89, "iter": 1800, "lr": 0.03607, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30141, "top5_acc": 0.55531, "loss_cls": 3.99707, "loss": 3.99707, "time": 0.84288} +{"mode": "train", "epoch": 89, "iter": 1900, "lr": 0.03604, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.30625, "top5_acc": 0.55453, "loss_cls": 4.01317, "loss": 4.01317, "time": 0.83974} +{"mode": "train", "epoch": 89, "iter": 2000, "lr": 0.03602, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29719, "top5_acc": 0.54625, "loss_cls": 4.02867, "loss": 4.02867, "time": 0.84035} +{"mode": "train", "epoch": 89, "iter": 2100, "lr": 0.03599, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29922, "top5_acc": 0.55437, "loss_cls": 4.04674, "loss": 4.04674, "time": 0.84463} +{"mode": "train", "epoch": 89, "iter": 2200, "lr": 0.03596, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30016, "top5_acc": 0.54812, "loss_cls": 4.02816, "loss": 4.02816, "time": 0.84155} +{"mode": "train", "epoch": 89, "iter": 2300, "lr": 0.03594, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30094, "top5_acc": 0.55109, "loss_cls": 4.00778, "loss": 4.00778, "time": 0.84999} +{"mode": "train", "epoch": 89, "iter": 2400, "lr": 0.03591, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29453, "top5_acc": 0.54516, "loss_cls": 4.04418, "loss": 4.04418, "time": 0.84232} +{"mode": "train", "epoch": 89, "iter": 2500, "lr": 0.03588, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30172, "top5_acc": 0.54984, "loss_cls": 4.01012, "loss": 4.01012, "time": 0.84192} +{"mode": "train", "epoch": 89, "iter": 2600, "lr": 0.03586, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.3, "top5_acc": 0.55609, "loss_cls": 4.01646, "loss": 4.01646, "time": 0.85225} +{"mode": "train", "epoch": 89, "iter": 2700, "lr": 0.03583, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29906, "top5_acc": 0.54922, "loss_cls": 4.0301, "loss": 4.0301, "time": 0.84829} +{"mode": "train", "epoch": 89, "iter": 2800, "lr": 0.0358, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.29812, "top5_acc": 0.54891, "loss_cls": 4.02046, "loss": 4.02046, "time": 0.84319} +{"mode": "train", "epoch": 89, "iter": 2900, "lr": 0.03578, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29063, "top5_acc": 0.55094, "loss_cls": 4.0626, "loss": 4.0626, "time": 0.84315} +{"mode": "train", "epoch": 89, "iter": 3000, "lr": 0.03575, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29531, "top5_acc": 0.54094, "loss_cls": 4.04641, "loss": 4.04641, "time": 0.841} +{"mode": "train", "epoch": 89, "iter": 3100, "lr": 0.03572, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29078, "top5_acc": 0.55734, "loss_cls": 4.02269, "loss": 4.02269, "time": 0.84418} +{"mode": "train", "epoch": 89, "iter": 3200, "lr": 0.03569, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.30312, "top5_acc": 0.56141, "loss_cls": 3.98726, "loss": 3.98726, "time": 0.84362} +{"mode": "train", "epoch": 89, "iter": 3300, "lr": 0.03567, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30125, "top5_acc": 0.54672, "loss_cls": 4.04817, "loss": 4.04817, "time": 0.84521} +{"mode": "train", "epoch": 89, "iter": 3400, "lr": 0.03564, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28891, "top5_acc": 0.55406, "loss_cls": 4.02594, "loss": 4.02594, "time": 0.83899} +{"mode": "train", "epoch": 89, "iter": 3500, "lr": 0.03561, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29922, "top5_acc": 0.55578, "loss_cls": 4.03079, "loss": 4.03079, "time": 0.8442} +{"mode": "train", "epoch": 89, "iter": 3600, "lr": 0.03559, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30641, "top5_acc": 0.55688, "loss_cls": 3.98636, "loss": 3.98636, "time": 0.84669} +{"mode": "train", "epoch": 89, "iter": 3700, "lr": 0.03556, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30656, "top5_acc": 0.55484, "loss_cls": 4.00439, "loss": 4.00439, "time": 0.84301} +{"mode": "val", "epoch": 89, "iter": 309, "lr": 0.03555, "top1_acc": 0.24469, "top5_acc": 0.48139, "mean_class_accuracy": 0.24462} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.03552, "memory": 15990, "data_time": 1.50051, "top1_acc": 0.31484, "top5_acc": 0.56437, "loss_cls": 3.94052, "loss": 3.94052, "time": 2.52846} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.0355, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31703, "top5_acc": 0.56938, "loss_cls": 3.92334, "loss": 3.92334, "time": 0.84826} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.03547, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30312, "top5_acc": 0.56688, "loss_cls": 3.96843, "loss": 3.96843, "time": 0.84857} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.03544, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29938, "top5_acc": 0.55328, "loss_cls": 4.0059, "loss": 4.0059, "time": 0.85031} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.03541, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3075, "top5_acc": 0.55406, "loss_cls": 4.00315, "loss": 4.00315, "time": 0.84769} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.03539, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30641, "top5_acc": 0.55406, "loss_cls": 4.00327, "loss": 4.00327, "time": 0.84874} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.03536, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29891, "top5_acc": 0.55578, "loss_cls": 4.00242, "loss": 4.00242, "time": 0.85002} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.03533, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29922, "top5_acc": 0.55312, "loss_cls": 4.00481, "loss": 4.00481, "time": 0.84687} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.03531, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30328, "top5_acc": 0.5525, "loss_cls": 3.98206, "loss": 3.98206, "time": 0.85049} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.03528, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30328, "top5_acc": 0.56156, "loss_cls": 3.97098, "loss": 3.97098, "time": 0.84525} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.03525, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29875, "top5_acc": 0.55172, "loss_cls": 4.00511, "loss": 4.00511, "time": 0.84493} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.03523, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30344, "top5_acc": 0.55266, "loss_cls": 4.02533, "loss": 4.02533, "time": 0.84814} +{"mode": "train", "epoch": 90, "iter": 1300, "lr": 0.0352, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29672, "top5_acc": 0.55609, "loss_cls": 4.02383, "loss": 4.02383, "time": 0.85025} +{"mode": "train", "epoch": 90, "iter": 1400, "lr": 0.03517, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29578, "top5_acc": 0.55281, "loss_cls": 4.01268, "loss": 4.01268, "time": 0.84506} +{"mode": "train", "epoch": 90, "iter": 1500, "lr": 0.03515, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30078, "top5_acc": 0.56063, "loss_cls": 3.99494, "loss": 3.99494, "time": 0.84927} +{"mode": "train", "epoch": 90, "iter": 1600, "lr": 0.03512, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29844, "top5_acc": 0.55609, "loss_cls": 4.01066, "loss": 4.01066, "time": 0.85081} +{"mode": "train", "epoch": 90, "iter": 1700, "lr": 0.03509, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30516, "top5_acc": 0.56172, "loss_cls": 3.95763, "loss": 3.95763, "time": 0.85399} +{"mode": "train", "epoch": 90, "iter": 1800, "lr": 0.03507, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29656, "top5_acc": 0.54062, "loss_cls": 4.06971, "loss": 4.06971, "time": 0.84885} +{"mode": "train", "epoch": 90, "iter": 1900, "lr": 0.03504, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30703, "top5_acc": 0.55391, "loss_cls": 3.98441, "loss": 3.98441, "time": 0.84718} +{"mode": "train", "epoch": 90, "iter": 2000, "lr": 0.03501, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30844, "top5_acc": 0.56078, "loss_cls": 3.99522, "loss": 3.99522, "time": 0.84388} +{"mode": "train", "epoch": 90, "iter": 2100, "lr": 0.03499, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30375, "top5_acc": 0.56094, "loss_cls": 3.95647, "loss": 3.95647, "time": 0.84936} +{"mode": "train", "epoch": 90, "iter": 2200, "lr": 0.03496, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30484, "top5_acc": 0.56016, "loss_cls": 3.97516, "loss": 3.97516, "time": 0.84923} +{"mode": "train", "epoch": 90, "iter": 2300, "lr": 0.03493, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29703, "top5_acc": 0.55391, "loss_cls": 4.03058, "loss": 4.03058, "time": 0.8508} +{"mode": "train", "epoch": 90, "iter": 2400, "lr": 0.03491, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30703, "top5_acc": 0.55031, "loss_cls": 3.98849, "loss": 3.98849, "time": 0.852} +{"mode": "train", "epoch": 90, "iter": 2500, "lr": 0.03488, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.3, "top5_acc": 0.55844, "loss_cls": 3.98396, "loss": 3.98396, "time": 0.8497} +{"mode": "train", "epoch": 90, "iter": 2600, "lr": 0.03485, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29859, "top5_acc": 0.54609, "loss_cls": 4.0312, "loss": 4.0312, "time": 0.84369} +{"mode": "train", "epoch": 90, "iter": 2700, "lr": 0.03483, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29609, "top5_acc": 0.54922, "loss_cls": 4.03708, "loss": 4.03708, "time": 0.84458} +{"mode": "train", "epoch": 90, "iter": 2800, "lr": 0.0348, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29688, "top5_acc": 0.54625, "loss_cls": 4.05164, "loss": 4.05164, "time": 0.84588} +{"mode": "train", "epoch": 90, "iter": 2900, "lr": 0.03477, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30391, "top5_acc": 0.55969, "loss_cls": 3.99551, "loss": 3.99551, "time": 0.84595} +{"mode": "train", "epoch": 90, "iter": 3000, "lr": 0.03475, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30609, "top5_acc": 0.55844, "loss_cls": 3.99236, "loss": 3.99236, "time": 0.84541} +{"mode": "train", "epoch": 90, "iter": 3100, "lr": 0.03472, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30109, "top5_acc": 0.54781, "loss_cls": 4.04132, "loss": 4.04132, "time": 0.84772} +{"mode": "train", "epoch": 90, "iter": 3200, "lr": 0.03469, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30312, "top5_acc": 0.55812, "loss_cls": 3.9914, "loss": 3.9914, "time": 0.8482} +{"mode": "train", "epoch": 90, "iter": 3300, "lr": 0.03467, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28813, "top5_acc": 0.54734, "loss_cls": 4.04012, "loss": 4.04012, "time": 0.84924} +{"mode": "train", "epoch": 90, "iter": 3400, "lr": 0.03464, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29422, "top5_acc": 0.55188, "loss_cls": 4.02629, "loss": 4.02629, "time": 0.83861} +{"mode": "train", "epoch": 90, "iter": 3500, "lr": 0.03461, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31062, "top5_acc": 0.55922, "loss_cls": 3.97766, "loss": 3.97766, "time": 0.84249} +{"mode": "train", "epoch": 90, "iter": 3600, "lr": 0.03459, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30516, "top5_acc": 0.55609, "loss_cls": 4.00774, "loss": 4.00774, "time": 0.83704} +{"mode": "train", "epoch": 90, "iter": 3700, "lr": 0.03456, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29578, "top5_acc": 0.55641, "loss_cls": 4.01253, "loss": 4.01253, "time": 0.84368} +{"mode": "val", "epoch": 90, "iter": 309, "lr": 0.03455, "top1_acc": 0.22332, "top5_acc": 0.45181, "mean_class_accuracy": 0.22313} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.03452, "memory": 15990, "data_time": 1.48278, "top1_acc": 0.3125, "top5_acc": 0.57031, "loss_cls": 3.93686, "loss": 3.93686, "time": 2.50025} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0345, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30828, "top5_acc": 0.55797, "loss_cls": 3.95227, "loss": 3.95227, "time": 0.84851} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.03447, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30531, "top5_acc": 0.56016, "loss_cls": 3.97698, "loss": 3.97698, "time": 0.84688} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.03444, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30906, "top5_acc": 0.55766, "loss_cls": 3.94168, "loss": 3.94168, "time": 0.84461} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.03442, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3175, "top5_acc": 0.56875, "loss_cls": 3.92473, "loss": 3.92473, "time": 0.85163} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.03439, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30312, "top5_acc": 0.55609, "loss_cls": 3.96619, "loss": 3.96619, "time": 0.8422} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.03436, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2975, "top5_acc": 0.55141, "loss_cls": 4.01395, "loss": 4.01395, "time": 0.84524} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.03434, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30359, "top5_acc": 0.55875, "loss_cls": 3.96829, "loss": 3.96829, "time": 0.84912} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.03431, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30141, "top5_acc": 0.55812, "loss_cls": 3.98676, "loss": 3.98676, "time": 0.84501} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.03428, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30203, "top5_acc": 0.55078, "loss_cls": 3.99993, "loss": 3.99993, "time": 0.84528} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.03426, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30609, "top5_acc": 0.55844, "loss_cls": 3.99773, "loss": 3.99773, "time": 0.84537} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.03423, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2975, "top5_acc": 0.54547, "loss_cls": 4.04801, "loss": 4.04801, "time": 0.83977} +{"mode": "train", "epoch": 91, "iter": 1300, "lr": 0.0342, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30969, "top5_acc": 0.55859, "loss_cls": 3.95551, "loss": 3.95551, "time": 0.84342} +{"mode": "train", "epoch": 91, "iter": 1400, "lr": 0.03418, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2975, "top5_acc": 0.55766, "loss_cls": 4.00099, "loss": 4.00099, "time": 0.84373} +{"mode": "train", "epoch": 91, "iter": 1500, "lr": 0.03415, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.30328, "top5_acc": 0.55844, "loss_cls": 4.00394, "loss": 4.00394, "time": 0.84167} +{"mode": "train", "epoch": 91, "iter": 1600, "lr": 0.03412, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29359, "top5_acc": 0.55641, "loss_cls": 4.00584, "loss": 4.00584, "time": 0.84015} +{"mode": "train", "epoch": 91, "iter": 1700, "lr": 0.0341, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30219, "top5_acc": 0.55641, "loss_cls": 4.00509, "loss": 4.00509, "time": 0.83967} +{"mode": "train", "epoch": 91, "iter": 1800, "lr": 0.03407, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30484, "top5_acc": 0.55297, "loss_cls": 3.99421, "loss": 3.99421, "time": 0.84563} +{"mode": "train", "epoch": 91, "iter": 1900, "lr": 0.03405, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3025, "top5_acc": 0.55594, "loss_cls": 3.99079, "loss": 3.99079, "time": 0.84633} +{"mode": "train", "epoch": 91, "iter": 2000, "lr": 0.03402, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31109, "top5_acc": 0.56437, "loss_cls": 3.96595, "loss": 3.96595, "time": 0.8439} +{"mode": "train", "epoch": 91, "iter": 2100, "lr": 0.03399, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30625, "top5_acc": 0.54906, "loss_cls": 4.0046, "loss": 4.0046, "time": 0.84332} +{"mode": "train", "epoch": 91, "iter": 2200, "lr": 0.03397, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31141, "top5_acc": 0.55828, "loss_cls": 3.97258, "loss": 3.97258, "time": 0.84194} +{"mode": "train", "epoch": 91, "iter": 2300, "lr": 0.03394, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30594, "top5_acc": 0.56141, "loss_cls": 3.98461, "loss": 3.98461, "time": 0.84696} +{"mode": "train", "epoch": 91, "iter": 2400, "lr": 0.03391, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30922, "top5_acc": 0.56563, "loss_cls": 3.97311, "loss": 3.97311, "time": 0.8496} +{"mode": "train", "epoch": 91, "iter": 2500, "lr": 0.03389, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30547, "top5_acc": 0.55875, "loss_cls": 3.9885, "loss": 3.9885, "time": 0.84676} +{"mode": "train", "epoch": 91, "iter": 2600, "lr": 0.03386, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.30109, "top5_acc": 0.55484, "loss_cls": 4.02457, "loss": 4.02457, "time": 0.85005} +{"mode": "train", "epoch": 91, "iter": 2700, "lr": 0.03383, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.30312, "top5_acc": 0.57047, "loss_cls": 3.97219, "loss": 3.97219, "time": 0.84988} +{"mode": "train", "epoch": 91, "iter": 2800, "lr": 0.03381, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29734, "top5_acc": 0.55891, "loss_cls": 4.01245, "loss": 4.01245, "time": 0.85338} +{"mode": "train", "epoch": 91, "iter": 2900, "lr": 0.03378, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30656, "top5_acc": 0.55484, "loss_cls": 3.97988, "loss": 3.97988, "time": 0.84825} +{"mode": "train", "epoch": 91, "iter": 3000, "lr": 0.03375, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30078, "top5_acc": 0.55172, "loss_cls": 4.02683, "loss": 4.02683, "time": 0.84793} +{"mode": "train", "epoch": 91, "iter": 3100, "lr": 0.03373, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29406, "top5_acc": 0.55359, "loss_cls": 4.03654, "loss": 4.03654, "time": 0.84623} +{"mode": "train", "epoch": 91, "iter": 3200, "lr": 0.0337, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30641, "top5_acc": 0.57109, "loss_cls": 3.96105, "loss": 3.96105, "time": 0.84901} +{"mode": "train", "epoch": 91, "iter": 3300, "lr": 0.03367, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30516, "top5_acc": 0.55703, "loss_cls": 3.98939, "loss": 3.98939, "time": 0.84899} +{"mode": "train", "epoch": 91, "iter": 3400, "lr": 0.03365, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31, "top5_acc": 0.55562, "loss_cls": 3.97187, "loss": 3.97187, "time": 0.84767} +{"mode": "train", "epoch": 91, "iter": 3500, "lr": 0.03362, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31156, "top5_acc": 0.55937, "loss_cls": 3.96691, "loss": 3.96691, "time": 0.84659} +{"mode": "train", "epoch": 91, "iter": 3600, "lr": 0.0336, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30359, "top5_acc": 0.55937, "loss_cls": 3.9886, "loss": 3.9886, "time": 0.85358} +{"mode": "train", "epoch": 91, "iter": 3700, "lr": 0.03357, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31188, "top5_acc": 0.56969, "loss_cls": 3.94977, "loss": 3.94977, "time": 0.84915} +{"mode": "val", "epoch": 91, "iter": 309, "lr": 0.03356, "top1_acc": 0.24946, "top5_acc": 0.49101, "mean_class_accuracy": 0.24934} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.03353, "memory": 15990, "data_time": 1.59371, "top1_acc": 0.31797, "top5_acc": 0.57328, "loss_cls": 3.92215, "loss": 3.92215, "time": 2.63102} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.0335, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.30719, "top5_acc": 0.56344, "loss_cls": 3.96987, "loss": 3.96987, "time": 0.86156} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.03348, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.31156, "top5_acc": 0.56953, "loss_cls": 3.92689, "loss": 3.92689, "time": 0.86144} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.03345, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31516, "top5_acc": 0.56078, "loss_cls": 3.94781, "loss": 3.94781, "time": 0.86482} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.03342, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30375, "top5_acc": 0.55953, "loss_cls": 3.97753, "loss": 3.97753, "time": 0.87005} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.0334, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30734, "top5_acc": 0.56656, "loss_cls": 3.93795, "loss": 3.93795, "time": 0.86577} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.03337, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.29922, "top5_acc": 0.555, "loss_cls": 3.99193, "loss": 3.99193, "time": 0.86528} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.03335, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30531, "top5_acc": 0.55437, "loss_cls": 3.97313, "loss": 3.97313, "time": 0.85759} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.03332, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.30156, "top5_acc": 0.56078, "loss_cls": 3.97292, "loss": 3.97292, "time": 0.86545} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.03329, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31266, "top5_acc": 0.56484, "loss_cls": 3.94975, "loss": 3.94975, "time": 0.86472} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.03327, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29844, "top5_acc": 0.55219, "loss_cls": 3.98276, "loss": 3.98276, "time": 0.86861} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.03324, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30266, "top5_acc": 0.55203, "loss_cls": 3.99883, "loss": 3.99883, "time": 0.85711} +{"mode": "train", "epoch": 92, "iter": 1300, "lr": 0.03321, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.3, "top5_acc": 0.56469, "loss_cls": 3.95798, "loss": 3.95798, "time": 0.87468} +{"mode": "train", "epoch": 92, "iter": 1400, "lr": 0.03319, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30719, "top5_acc": 0.55781, "loss_cls": 3.99687, "loss": 3.99687, "time": 0.86247} +{"mode": "train", "epoch": 92, "iter": 1500, "lr": 0.03316, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.29922, "top5_acc": 0.56219, "loss_cls": 3.9896, "loss": 3.9896, "time": 0.86588} +{"mode": "train", "epoch": 92, "iter": 1600, "lr": 0.03314, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.30766, "top5_acc": 0.56578, "loss_cls": 3.95642, "loss": 3.95642, "time": 0.85986} +{"mode": "train", "epoch": 92, "iter": 1700, "lr": 0.03311, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30328, "top5_acc": 0.55859, "loss_cls": 3.98436, "loss": 3.98436, "time": 0.85407} +{"mode": "train", "epoch": 92, "iter": 1800, "lr": 0.03308, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29594, "top5_acc": 0.55078, "loss_cls": 4.01705, "loss": 4.01705, "time": 0.86338} +{"mode": "train", "epoch": 92, "iter": 1900, "lr": 0.03306, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30031, "top5_acc": 0.55266, "loss_cls": 4.02551, "loss": 4.02551, "time": 0.85942} +{"mode": "train", "epoch": 92, "iter": 2000, "lr": 0.03303, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30531, "top5_acc": 0.56063, "loss_cls": 3.9654, "loss": 3.9654, "time": 0.8553} +{"mode": "train", "epoch": 92, "iter": 2100, "lr": 0.033, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30672, "top5_acc": 0.55344, "loss_cls": 3.97535, "loss": 3.97535, "time": 0.8568} +{"mode": "train", "epoch": 92, "iter": 2200, "lr": 0.03298, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30781, "top5_acc": 0.55688, "loss_cls": 3.98569, "loss": 3.98569, "time": 0.84976} +{"mode": "train", "epoch": 92, "iter": 2300, "lr": 0.03295, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29906, "top5_acc": 0.55469, "loss_cls": 3.9757, "loss": 3.9757, "time": 0.85426} +{"mode": "train", "epoch": 92, "iter": 2400, "lr": 0.03292, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3125, "top5_acc": 0.55688, "loss_cls": 3.96596, "loss": 3.96596, "time": 0.85508} +{"mode": "train", "epoch": 92, "iter": 2500, "lr": 0.0329, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30234, "top5_acc": 0.55656, "loss_cls": 3.99432, "loss": 3.99432, "time": 0.85883} +{"mode": "train", "epoch": 92, "iter": 2600, "lr": 0.03287, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.31531, "top5_acc": 0.56719, "loss_cls": 3.92323, "loss": 3.92323, "time": 0.85578} +{"mode": "train", "epoch": 92, "iter": 2700, "lr": 0.03285, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.30734, "top5_acc": 0.56172, "loss_cls": 3.98787, "loss": 3.98787, "time": 0.85153} +{"mode": "train", "epoch": 92, "iter": 2800, "lr": 0.03282, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30844, "top5_acc": 0.5575, "loss_cls": 3.96886, "loss": 3.96886, "time": 0.84982} +{"mode": "train", "epoch": 92, "iter": 2900, "lr": 0.03279, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31297, "top5_acc": 0.56547, "loss_cls": 3.95973, "loss": 3.95973, "time": 0.85146} +{"mode": "train", "epoch": 92, "iter": 3000, "lr": 0.03277, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30047, "top5_acc": 0.54297, "loss_cls": 4.03597, "loss": 4.03597, "time": 0.85025} +{"mode": "train", "epoch": 92, "iter": 3100, "lr": 0.03274, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30547, "top5_acc": 0.55984, "loss_cls": 3.9896, "loss": 3.9896, "time": 0.84754} +{"mode": "train", "epoch": 92, "iter": 3200, "lr": 0.03271, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30859, "top5_acc": 0.56234, "loss_cls": 3.96332, "loss": 3.96332, "time": 0.8488} +{"mode": "train", "epoch": 92, "iter": 3300, "lr": 0.03269, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.30062, "top5_acc": 0.55875, "loss_cls": 3.97609, "loss": 3.97609, "time": 0.84861} +{"mode": "train", "epoch": 92, "iter": 3400, "lr": 0.03266, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30547, "top5_acc": 0.56172, "loss_cls": 3.9517, "loss": 3.9517, "time": 0.85187} +{"mode": "train", "epoch": 92, "iter": 3500, "lr": 0.03264, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30719, "top5_acc": 0.56109, "loss_cls": 3.96792, "loss": 3.96792, "time": 0.84906} +{"mode": "train", "epoch": 92, "iter": 3600, "lr": 0.03261, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30562, "top5_acc": 0.55406, "loss_cls": 3.99835, "loss": 3.99835, "time": 0.85781} +{"mode": "train", "epoch": 92, "iter": 3700, "lr": 0.03258, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30188, "top5_acc": 0.56437, "loss_cls": 3.98791, "loss": 3.98791, "time": 0.8577} +{"mode": "val", "epoch": 92, "iter": 309, "lr": 0.03257, "top1_acc": 0.24621, "top5_acc": 0.49263, "mean_class_accuracy": 0.24603} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.03255, "memory": 15990, "data_time": 1.53355, "top1_acc": 0.31281, "top5_acc": 0.5625, "loss_cls": 3.93952, "loss": 3.93952, "time": 2.56828} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.03252, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3225, "top5_acc": 0.58297, "loss_cls": 3.88969, "loss": 3.88969, "time": 0.85573} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.03249, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31109, "top5_acc": 0.56984, "loss_cls": 3.95674, "loss": 3.95674, "time": 0.85288} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.03247, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31156, "top5_acc": 0.56703, "loss_cls": 3.95097, "loss": 3.95097, "time": 0.85731} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.03244, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30719, "top5_acc": 0.56453, "loss_cls": 3.94514, "loss": 3.94514, "time": 0.85865} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.03241, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30766, "top5_acc": 0.56578, "loss_cls": 3.94858, "loss": 3.94858, "time": 0.85051} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.03239, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31234, "top5_acc": 0.57312, "loss_cls": 3.92885, "loss": 3.92885, "time": 0.85259} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.03236, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32, "top5_acc": 0.56531, "loss_cls": 3.91274, "loss": 3.91274, "time": 0.85165} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.03234, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31109, "top5_acc": 0.56297, "loss_cls": 3.9362, "loss": 3.9362, "time": 0.85597} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.03231, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31172, "top5_acc": 0.56844, "loss_cls": 3.93755, "loss": 3.93755, "time": 0.85995} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.03228, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.29469, "top5_acc": 0.55453, "loss_cls": 4.02059, "loss": 4.02059, "time": 0.85507} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.03226, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30266, "top5_acc": 0.56141, "loss_cls": 3.97923, "loss": 3.97923, "time": 0.84791} +{"mode": "train", "epoch": 93, "iter": 1300, "lr": 0.03223, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30812, "top5_acc": 0.56141, "loss_cls": 3.96, "loss": 3.96, "time": 0.86215} +{"mode": "train", "epoch": 93, "iter": 1400, "lr": 0.03221, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.32047, "top5_acc": 0.56109, "loss_cls": 3.94101, "loss": 3.94101, "time": 0.85979} +{"mode": "train", "epoch": 93, "iter": 1500, "lr": 0.03218, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30891, "top5_acc": 0.57203, "loss_cls": 3.95559, "loss": 3.95559, "time": 0.85811} +{"mode": "train", "epoch": 93, "iter": 1600, "lr": 0.03215, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31625, "top5_acc": 0.56156, "loss_cls": 3.93601, "loss": 3.93601, "time": 0.84921} +{"mode": "train", "epoch": 93, "iter": 1700, "lr": 0.03213, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31703, "top5_acc": 0.57063, "loss_cls": 3.93513, "loss": 3.93513, "time": 0.85272} +{"mode": "train", "epoch": 93, "iter": 1800, "lr": 0.0321, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31344, "top5_acc": 0.56672, "loss_cls": 3.94088, "loss": 3.94088, "time": 0.84497} +{"mode": "train", "epoch": 93, "iter": 1900, "lr": 0.03207, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3, "top5_acc": 0.55562, "loss_cls": 3.95495, "loss": 3.95495, "time": 0.85266} +{"mode": "train", "epoch": 93, "iter": 2000, "lr": 0.03205, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31047, "top5_acc": 0.56656, "loss_cls": 3.92255, "loss": 3.92255, "time": 0.85721} +{"mode": "train", "epoch": 93, "iter": 2100, "lr": 0.03202, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.30641, "top5_acc": 0.55672, "loss_cls": 4.03393, "loss": 4.03393, "time": 0.8518} +{"mode": "train", "epoch": 93, "iter": 2200, "lr": 0.032, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31266, "top5_acc": 0.5625, "loss_cls": 3.92072, "loss": 3.92072, "time": 0.84865} +{"mode": "train", "epoch": 93, "iter": 2300, "lr": 0.03197, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30625, "top5_acc": 0.55266, "loss_cls": 4.01089, "loss": 4.01089, "time": 0.85307} +{"mode": "train", "epoch": 93, "iter": 2400, "lr": 0.03194, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30984, "top5_acc": 0.56969, "loss_cls": 3.94046, "loss": 3.94046, "time": 0.85519} +{"mode": "train", "epoch": 93, "iter": 2500, "lr": 0.03192, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3075, "top5_acc": 0.55937, "loss_cls": 3.95298, "loss": 3.95298, "time": 0.85896} +{"mode": "train", "epoch": 93, "iter": 2600, "lr": 0.03189, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30328, "top5_acc": 0.55484, "loss_cls": 3.97946, "loss": 3.97946, "time": 0.85768} +{"mode": "train", "epoch": 93, "iter": 2700, "lr": 0.03187, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30328, "top5_acc": 0.56625, "loss_cls": 3.95327, "loss": 3.95327, "time": 0.84843} +{"mode": "train", "epoch": 93, "iter": 2800, "lr": 0.03184, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30016, "top5_acc": 0.56156, "loss_cls": 3.99802, "loss": 3.99802, "time": 0.84638} +{"mode": "train", "epoch": 93, "iter": 2900, "lr": 0.03181, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30375, "top5_acc": 0.56656, "loss_cls": 3.94, "loss": 3.94, "time": 0.84946} +{"mode": "train", "epoch": 93, "iter": 3000, "lr": 0.03179, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30438, "top5_acc": 0.56266, "loss_cls": 3.9683, "loss": 3.9683, "time": 0.8539} +{"mode": "train", "epoch": 93, "iter": 3100, "lr": 0.03176, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30422, "top5_acc": 0.56234, "loss_cls": 3.98541, "loss": 3.98541, "time": 0.8559} +{"mode": "train", "epoch": 93, "iter": 3200, "lr": 0.03174, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30266, "top5_acc": 0.55531, "loss_cls": 3.98414, "loss": 3.98414, "time": 0.85743} +{"mode": "train", "epoch": 93, "iter": 3300, "lr": 0.03171, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31109, "top5_acc": 0.56078, "loss_cls": 3.95121, "loss": 3.95121, "time": 0.85746} +{"mode": "train", "epoch": 93, "iter": 3400, "lr": 0.03168, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.305, "top5_acc": 0.56125, "loss_cls": 3.98531, "loss": 3.98531, "time": 0.85501} +{"mode": "train", "epoch": 93, "iter": 3500, "lr": 0.03166, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31172, "top5_acc": 0.56141, "loss_cls": 3.97619, "loss": 3.97619, "time": 0.85561} +{"mode": "train", "epoch": 93, "iter": 3600, "lr": 0.03163, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30172, "top5_acc": 0.56703, "loss_cls": 3.96047, "loss": 3.96047, "time": 0.84868} +{"mode": "train", "epoch": 93, "iter": 3700, "lr": 0.03161, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31, "top5_acc": 0.55844, "loss_cls": 3.96267, "loss": 3.96267, "time": 0.85452} +{"mode": "val", "epoch": 93, "iter": 309, "lr": 0.03159, "top1_acc": 0.24946, "top5_acc": 0.49678, "mean_class_accuracy": 0.24938} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.03157, "memory": 15990, "data_time": 1.62757, "top1_acc": 0.32469, "top5_acc": 0.58016, "loss_cls": 3.86853, "loss": 3.86853, "time": 2.66753} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.03154, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30875, "top5_acc": 0.56828, "loss_cls": 3.91075, "loss": 3.91075, "time": 0.86517} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.03152, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30859, "top5_acc": 0.56453, "loss_cls": 3.93911, "loss": 3.93911, "time": 0.86165} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.03149, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.29859, "top5_acc": 0.55703, "loss_cls": 3.99021, "loss": 3.99021, "time": 0.85949} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.03146, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31516, "top5_acc": 0.58062, "loss_cls": 3.88741, "loss": 3.88741, "time": 0.86885} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.03144, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.30594, "top5_acc": 0.56422, "loss_cls": 3.95456, "loss": 3.95456, "time": 0.86841} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.03141, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.31719, "top5_acc": 0.57719, "loss_cls": 3.89561, "loss": 3.89561, "time": 0.86797} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.03139, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.31266, "top5_acc": 0.56625, "loss_cls": 3.94985, "loss": 3.94985, "time": 0.86101} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.03136, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31078, "top5_acc": 0.57688, "loss_cls": 3.91501, "loss": 3.91501, "time": 0.85581} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.03133, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29766, "top5_acc": 0.55297, "loss_cls": 4.0294, "loss": 4.0294, "time": 0.8608} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.03131, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.31828, "top5_acc": 0.57734, "loss_cls": 3.89048, "loss": 3.89048, "time": 0.86897} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.03128, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30609, "top5_acc": 0.55266, "loss_cls": 3.98327, "loss": 3.98327, "time": 0.86601} +{"mode": "train", "epoch": 94, "iter": 1300, "lr": 0.03126, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30875, "top5_acc": 0.57063, "loss_cls": 3.90869, "loss": 3.90869, "time": 0.86455} +{"mode": "train", "epoch": 94, "iter": 1400, "lr": 0.03123, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.30828, "top5_acc": 0.56203, "loss_cls": 3.92886, "loss": 3.92886, "time": 0.86727} +{"mode": "train", "epoch": 94, "iter": 1500, "lr": 0.0312, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.31453, "top5_acc": 0.56469, "loss_cls": 3.94583, "loss": 3.94583, "time": 0.86035} +{"mode": "train", "epoch": 94, "iter": 1600, "lr": 0.03118, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.31641, "top5_acc": 0.56328, "loss_cls": 3.93002, "loss": 3.93002, "time": 0.86115} +{"mode": "train", "epoch": 94, "iter": 1700, "lr": 0.03115, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31531, "top5_acc": 0.57484, "loss_cls": 3.92217, "loss": 3.92217, "time": 0.85576} +{"mode": "train", "epoch": 94, "iter": 1800, "lr": 0.03113, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.31156, "top5_acc": 0.56516, "loss_cls": 3.93564, "loss": 3.93564, "time": 0.84988} +{"mode": "train", "epoch": 94, "iter": 1900, "lr": 0.0311, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31516, "top5_acc": 0.57031, "loss_cls": 3.92918, "loss": 3.92918, "time": 0.84938} +{"mode": "train", "epoch": 94, "iter": 2000, "lr": 0.03108, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30516, "top5_acc": 0.56531, "loss_cls": 3.99595, "loss": 3.99595, "time": 0.85481} +{"mode": "train", "epoch": 94, "iter": 2100, "lr": 0.03105, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.31875, "top5_acc": 0.57328, "loss_cls": 3.92598, "loss": 3.92598, "time": 0.85574} +{"mode": "train", "epoch": 94, "iter": 2200, "lr": 0.03102, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31766, "top5_acc": 0.56516, "loss_cls": 3.92653, "loss": 3.92653, "time": 0.85011} +{"mode": "train", "epoch": 94, "iter": 2300, "lr": 0.031, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29922, "top5_acc": 0.55781, "loss_cls": 3.9828, "loss": 3.9828, "time": 0.85605} +{"mode": "train", "epoch": 94, "iter": 2400, "lr": 0.03097, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30922, "top5_acc": 0.56328, "loss_cls": 3.97743, "loss": 3.97743, "time": 0.85468} +{"mode": "train", "epoch": 94, "iter": 2500, "lr": 0.03095, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29906, "top5_acc": 0.5625, "loss_cls": 3.99432, "loss": 3.99432, "time": 0.85532} +{"mode": "train", "epoch": 94, "iter": 2600, "lr": 0.03092, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.31109, "top5_acc": 0.56172, "loss_cls": 3.94746, "loss": 3.94746, "time": 0.86234} +{"mode": "train", "epoch": 94, "iter": 2700, "lr": 0.03089, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.305, "top5_acc": 0.55812, "loss_cls": 3.99819, "loss": 3.99819, "time": 0.8582} +{"mode": "train", "epoch": 94, "iter": 2800, "lr": 0.03087, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.32594, "top5_acc": 0.57984, "loss_cls": 3.8951, "loss": 3.8951, "time": 0.85727} +{"mode": "train", "epoch": 94, "iter": 2900, "lr": 0.03084, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30672, "top5_acc": 0.55844, "loss_cls": 3.95824, "loss": 3.95824, "time": 0.85226} +{"mode": "train", "epoch": 94, "iter": 3000, "lr": 0.03082, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.30969, "top5_acc": 0.55875, "loss_cls": 3.97277, "loss": 3.97277, "time": 0.85126} +{"mode": "train", "epoch": 94, "iter": 3100, "lr": 0.03079, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31172, "top5_acc": 0.56344, "loss_cls": 3.95538, "loss": 3.95538, "time": 0.85129} +{"mode": "train", "epoch": 94, "iter": 3200, "lr": 0.03077, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31516, "top5_acc": 0.56719, "loss_cls": 3.91944, "loss": 3.91944, "time": 0.84963} +{"mode": "train", "epoch": 94, "iter": 3300, "lr": 0.03074, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30531, "top5_acc": 0.55312, "loss_cls": 3.95398, "loss": 3.95398, "time": 0.85294} +{"mode": "train", "epoch": 94, "iter": 3400, "lr": 0.03071, "memory": 15990, "data_time": 0.00074, "top1_acc": 0.30797, "top5_acc": 0.55984, "loss_cls": 3.93657, "loss": 3.93657, "time": 0.85525} +{"mode": "train", "epoch": 94, "iter": 3500, "lr": 0.03069, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31062, "top5_acc": 0.56625, "loss_cls": 3.92643, "loss": 3.92643, "time": 0.85529} +{"mode": "train", "epoch": 94, "iter": 3600, "lr": 0.03066, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30891, "top5_acc": 0.56594, "loss_cls": 3.97005, "loss": 3.97005, "time": 0.84361} +{"mode": "train", "epoch": 94, "iter": 3700, "lr": 0.03064, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.31891, "top5_acc": 0.57375, "loss_cls": 3.91603, "loss": 3.91603, "time": 0.85064} +{"mode": "val", "epoch": 94, "iter": 309, "lr": 0.03062, "top1_acc": 0.23558, "top5_acc": 0.47176, "mean_class_accuracy": 0.23533} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.0306, "memory": 15990, "data_time": 1.6145, "top1_acc": 0.32266, "top5_acc": 0.58031, "loss_cls": 3.86702, "loss": 3.86702, "time": 2.67179} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.03057, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31953, "top5_acc": 0.58078, "loss_cls": 3.85262, "loss": 3.85262, "time": 0.86589} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.03055, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.31281, "top5_acc": 0.58141, "loss_cls": 3.87837, "loss": 3.87837, "time": 0.87334} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.03052, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.30859, "top5_acc": 0.565, "loss_cls": 3.952, "loss": 3.952, "time": 0.87729} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.0305, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.31172, "top5_acc": 0.58078, "loss_cls": 3.86528, "loss": 3.86528, "time": 0.87045} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.03047, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.31031, "top5_acc": 0.57641, "loss_cls": 3.89052, "loss": 3.89052, "time": 0.86763} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.03044, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.3175, "top5_acc": 0.57, "loss_cls": 3.92208, "loss": 3.92208, "time": 0.86533} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.03042, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.31, "top5_acc": 0.56641, "loss_cls": 3.91395, "loss": 3.91395, "time": 0.86482} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.03039, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.30719, "top5_acc": 0.55859, "loss_cls": 3.97563, "loss": 3.97563, "time": 0.8739} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.03037, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.31781, "top5_acc": 0.58422, "loss_cls": 3.88206, "loss": 3.88206, "time": 0.86932} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.03034, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32141, "top5_acc": 0.57578, "loss_cls": 3.8894, "loss": 3.8894, "time": 0.86185} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.03032, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.31219, "top5_acc": 0.57063, "loss_cls": 3.93001, "loss": 3.93001, "time": 0.86739} +{"mode": "train", "epoch": 95, "iter": 1300, "lr": 0.03029, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31312, "top5_acc": 0.56984, "loss_cls": 3.92608, "loss": 3.92608, "time": 0.86084} +{"mode": "train", "epoch": 95, "iter": 1400, "lr": 0.03026, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.33531, "top5_acc": 0.59469, "loss_cls": 3.801, "loss": 3.801, "time": 0.86935} +{"mode": "train", "epoch": 95, "iter": 1500, "lr": 0.03024, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.31469, "top5_acc": 0.56891, "loss_cls": 3.9534, "loss": 3.9534, "time": 0.86382} +{"mode": "train", "epoch": 95, "iter": 1600, "lr": 0.03021, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29906, "top5_acc": 0.55969, "loss_cls": 3.97864, "loss": 3.97864, "time": 0.86289} +{"mode": "train", "epoch": 95, "iter": 1700, "lr": 0.03019, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.30688, "top5_acc": 0.56281, "loss_cls": 3.93916, "loss": 3.93916, "time": 0.86368} +{"mode": "train", "epoch": 95, "iter": 1800, "lr": 0.03016, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.30734, "top5_acc": 0.56234, "loss_cls": 3.96313, "loss": 3.96313, "time": 0.8609} +{"mode": "train", "epoch": 95, "iter": 1900, "lr": 0.03014, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32094, "top5_acc": 0.56094, "loss_cls": 3.94047, "loss": 3.94047, "time": 0.85757} +{"mode": "train", "epoch": 95, "iter": 2000, "lr": 0.03011, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31672, "top5_acc": 0.56641, "loss_cls": 3.91899, "loss": 3.91899, "time": 0.86381} +{"mode": "train", "epoch": 95, "iter": 2100, "lr": 0.03008, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30609, "top5_acc": 0.56016, "loss_cls": 3.96936, "loss": 3.96936, "time": 0.86095} +{"mode": "train", "epoch": 95, "iter": 2200, "lr": 0.03006, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30516, "top5_acc": 0.57063, "loss_cls": 3.94585, "loss": 3.94585, "time": 0.86479} +{"mode": "train", "epoch": 95, "iter": 2300, "lr": 0.03003, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30984, "top5_acc": 0.56359, "loss_cls": 3.96726, "loss": 3.96726, "time": 0.86268} +{"mode": "train", "epoch": 95, "iter": 2400, "lr": 0.03001, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31609, "top5_acc": 0.57422, "loss_cls": 3.89587, "loss": 3.89587, "time": 0.86298} +{"mode": "train", "epoch": 95, "iter": 2500, "lr": 0.02998, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.31016, "top5_acc": 0.56859, "loss_cls": 3.92538, "loss": 3.92538, "time": 0.85754} +{"mode": "train", "epoch": 95, "iter": 2600, "lr": 0.02996, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.315, "top5_acc": 0.5625, "loss_cls": 3.97185, "loss": 3.97185, "time": 0.85755} +{"mode": "train", "epoch": 95, "iter": 2700, "lr": 0.02993, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31844, "top5_acc": 0.57094, "loss_cls": 3.90989, "loss": 3.90989, "time": 0.84521} +{"mode": "train", "epoch": 95, "iter": 2800, "lr": 0.02991, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.3175, "top5_acc": 0.56125, "loss_cls": 3.947, "loss": 3.947, "time": 0.84722} +{"mode": "train", "epoch": 95, "iter": 2900, "lr": 0.02988, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30875, "top5_acc": 0.56391, "loss_cls": 3.95457, "loss": 3.95457, "time": 0.84849} +{"mode": "train", "epoch": 95, "iter": 3000, "lr": 0.02985, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32406, "top5_acc": 0.57141, "loss_cls": 3.89959, "loss": 3.89959, "time": 0.84729} +{"mode": "train", "epoch": 95, "iter": 3100, "lr": 0.02983, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31172, "top5_acc": 0.56422, "loss_cls": 3.94863, "loss": 3.94863, "time": 0.8507} +{"mode": "train", "epoch": 95, "iter": 3200, "lr": 0.0298, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30547, "top5_acc": 0.56672, "loss_cls": 3.95314, "loss": 3.95314, "time": 0.8506} +{"mode": "train", "epoch": 95, "iter": 3300, "lr": 0.02978, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31734, "top5_acc": 0.57469, "loss_cls": 3.91452, "loss": 3.91452, "time": 0.85083} +{"mode": "train", "epoch": 95, "iter": 3400, "lr": 0.02975, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.31109, "top5_acc": 0.56922, "loss_cls": 3.92987, "loss": 3.92987, "time": 0.85182} +{"mode": "train", "epoch": 95, "iter": 3500, "lr": 0.02973, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32406, "top5_acc": 0.58062, "loss_cls": 3.89815, "loss": 3.89815, "time": 0.85622} +{"mode": "train", "epoch": 95, "iter": 3600, "lr": 0.0297, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31422, "top5_acc": 0.56719, "loss_cls": 3.94477, "loss": 3.94477, "time": 0.84593} +{"mode": "train", "epoch": 95, "iter": 3700, "lr": 0.02968, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31797, "top5_acc": 0.56906, "loss_cls": 3.93927, "loss": 3.93927, "time": 0.85589} +{"mode": "val", "epoch": 95, "iter": 309, "lr": 0.02966, "top1_acc": 0.24171, "top5_acc": 0.48812, "mean_class_accuracy": 0.24153} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.02964, "memory": 15990, "data_time": 1.56596, "top1_acc": 0.32422, "top5_acc": 0.5825, "loss_cls": 3.8737, "loss": 3.8737, "time": 2.62709} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.02961, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32078, "top5_acc": 0.58016, "loss_cls": 3.83862, "loss": 3.83862, "time": 0.87014} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.02959, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.31781, "top5_acc": 0.57516, "loss_cls": 3.8761, "loss": 3.8761, "time": 0.87012} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.02956, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33344, "top5_acc": 0.58703, "loss_cls": 3.83558, "loss": 3.83558, "time": 0.86978} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.02954, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.30969, "top5_acc": 0.56172, "loss_cls": 3.94662, "loss": 3.94662, "time": 0.86853} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.02951, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34203, "top5_acc": 0.58344, "loss_cls": 3.81141, "loss": 3.81141, "time": 0.86469} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.02948, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31734, "top5_acc": 0.58094, "loss_cls": 3.89249, "loss": 3.89249, "time": 0.87473} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.02946, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.31938, "top5_acc": 0.56953, "loss_cls": 3.90918, "loss": 3.90918, "time": 0.87161} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.02943, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31469, "top5_acc": 0.57453, "loss_cls": 3.90174, "loss": 3.90174, "time": 0.86714} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.02941, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31812, "top5_acc": 0.57, "loss_cls": 3.89551, "loss": 3.89551, "time": 0.87222} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.02938, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32547, "top5_acc": 0.57172, "loss_cls": 3.8699, "loss": 3.8699, "time": 0.87033} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.02936, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.31, "top5_acc": 0.56594, "loss_cls": 3.92651, "loss": 3.92651, "time": 0.8768} +{"mode": "train", "epoch": 96, "iter": 1300, "lr": 0.02933, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.31422, "top5_acc": 0.57375, "loss_cls": 3.91378, "loss": 3.91378, "time": 0.87319} +{"mode": "train", "epoch": 96, "iter": 1400, "lr": 0.02931, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30703, "top5_acc": 0.5675, "loss_cls": 3.92116, "loss": 3.92116, "time": 0.87075} +{"mode": "train", "epoch": 96, "iter": 1500, "lr": 0.02928, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.31422, "top5_acc": 0.56734, "loss_cls": 3.89987, "loss": 3.89987, "time": 0.86724} +{"mode": "train", "epoch": 96, "iter": 1600, "lr": 0.02926, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30562, "top5_acc": 0.56016, "loss_cls": 3.97327, "loss": 3.97327, "time": 0.85814} +{"mode": "train", "epoch": 96, "iter": 1700, "lr": 0.02923, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31359, "top5_acc": 0.56453, "loss_cls": 3.96077, "loss": 3.96077, "time": 0.84392} +{"mode": "train", "epoch": 96, "iter": 1800, "lr": 0.0292, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33031, "top5_acc": 0.58312, "loss_cls": 3.86601, "loss": 3.86601, "time": 0.85575} +{"mode": "train", "epoch": 96, "iter": 1900, "lr": 0.02918, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31375, "top5_acc": 0.56672, "loss_cls": 3.92722, "loss": 3.92722, "time": 0.84898} +{"mode": "train", "epoch": 96, "iter": 2000, "lr": 0.02915, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31781, "top5_acc": 0.57094, "loss_cls": 3.8964, "loss": 3.8964, "time": 0.85352} +{"mode": "train", "epoch": 96, "iter": 2100, "lr": 0.02913, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31375, "top5_acc": 0.57172, "loss_cls": 3.92964, "loss": 3.92964, "time": 0.8516} +{"mode": "train", "epoch": 96, "iter": 2200, "lr": 0.0291, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32266, "top5_acc": 0.57641, "loss_cls": 3.88554, "loss": 3.88554, "time": 0.85341} +{"mode": "train", "epoch": 96, "iter": 2300, "lr": 0.02908, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31328, "top5_acc": 0.56938, "loss_cls": 3.91154, "loss": 3.91154, "time": 0.8528} +{"mode": "train", "epoch": 96, "iter": 2400, "lr": 0.02905, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31, "top5_acc": 0.5575, "loss_cls": 3.96603, "loss": 3.96603, "time": 0.85772} +{"mode": "train", "epoch": 96, "iter": 2500, "lr": 0.02903, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.31609, "top5_acc": 0.57328, "loss_cls": 3.90562, "loss": 3.90562, "time": 0.85047} +{"mode": "train", "epoch": 96, "iter": 2600, "lr": 0.029, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32328, "top5_acc": 0.57859, "loss_cls": 3.87856, "loss": 3.87856, "time": 0.8531} +{"mode": "train", "epoch": 96, "iter": 2700, "lr": 0.02898, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30359, "top5_acc": 0.57219, "loss_cls": 3.92647, "loss": 3.92647, "time": 0.85999} +{"mode": "train", "epoch": 96, "iter": 2800, "lr": 0.02895, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31578, "top5_acc": 0.57281, "loss_cls": 3.94044, "loss": 3.94044, "time": 0.85475} +{"mode": "train", "epoch": 96, "iter": 2900, "lr": 0.02893, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32219, "top5_acc": 0.56719, "loss_cls": 3.93365, "loss": 3.93365, "time": 0.85087} +{"mode": "train", "epoch": 96, "iter": 3000, "lr": 0.0289, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32062, "top5_acc": 0.57125, "loss_cls": 3.91774, "loss": 3.91774, "time": 0.84789} +{"mode": "train", "epoch": 96, "iter": 3100, "lr": 0.02887, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3125, "top5_acc": 0.56578, "loss_cls": 3.95226, "loss": 3.95226, "time": 0.84513} +{"mode": "train", "epoch": 96, "iter": 3200, "lr": 0.02885, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31922, "top5_acc": 0.57484, "loss_cls": 3.90175, "loss": 3.90175, "time": 0.84629} +{"mode": "train", "epoch": 96, "iter": 3300, "lr": 0.02882, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32656, "top5_acc": 0.57219, "loss_cls": 3.87815, "loss": 3.87815, "time": 0.84945} +{"mode": "train", "epoch": 96, "iter": 3400, "lr": 0.0288, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32016, "top5_acc": 0.56531, "loss_cls": 3.93317, "loss": 3.93317, "time": 0.84719} +{"mode": "train", "epoch": 96, "iter": 3500, "lr": 0.02877, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.31719, "top5_acc": 0.56812, "loss_cls": 3.90232, "loss": 3.90232, "time": 0.84968} +{"mode": "train", "epoch": 96, "iter": 3600, "lr": 0.02875, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.30812, "top5_acc": 0.56625, "loss_cls": 3.94011, "loss": 3.94011, "time": 0.84581} +{"mode": "train", "epoch": 96, "iter": 3700, "lr": 0.02872, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31375, "top5_acc": 0.57359, "loss_cls": 3.9324, "loss": 3.9324, "time": 0.85163} +{"mode": "val", "epoch": 96, "iter": 309, "lr": 0.02871, "top1_acc": 0.26045, "top5_acc": 0.50398, "mean_class_accuracy": 0.26029} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.02869, "memory": 15990, "data_time": 1.58076, "top1_acc": 0.31609, "top5_acc": 0.57078, "loss_cls": 3.89321, "loss": 3.89321, "time": 2.6176} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.02866, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32484, "top5_acc": 0.585, "loss_cls": 3.84613, "loss": 3.84613, "time": 0.85691} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.02864, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32984, "top5_acc": 0.58516, "loss_cls": 3.8438, "loss": 3.8438, "time": 0.85483} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.02861, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32578, "top5_acc": 0.58031, "loss_cls": 3.86926, "loss": 3.86926, "time": 0.85533} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.02858, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.3175, "top5_acc": 0.57125, "loss_cls": 3.87885, "loss": 3.87885, "time": 0.85367} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.02856, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31641, "top5_acc": 0.57344, "loss_cls": 3.89757, "loss": 3.89757, "time": 0.85865} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.02853, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32344, "top5_acc": 0.57734, "loss_cls": 3.8563, "loss": 3.8563, "time": 0.85572} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.02851, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32375, "top5_acc": 0.57031, "loss_cls": 3.89814, "loss": 3.89814, "time": 0.85842} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.02848, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30453, "top5_acc": 0.57391, "loss_cls": 3.88747, "loss": 3.88747, "time": 0.85268} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.02846, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.31641, "top5_acc": 0.56359, "loss_cls": 3.93196, "loss": 3.93196, "time": 0.86171} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.02843, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31266, "top5_acc": 0.57047, "loss_cls": 3.90478, "loss": 3.90478, "time": 0.86294} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.02841, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.32688, "top5_acc": 0.58141, "loss_cls": 3.85035, "loss": 3.85035, "time": 0.86543} +{"mode": "train", "epoch": 97, "iter": 1300, "lr": 0.02838, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31906, "top5_acc": 0.575, "loss_cls": 3.88948, "loss": 3.88948, "time": 0.85908} +{"mode": "train", "epoch": 97, "iter": 1400, "lr": 0.02836, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.32297, "top5_acc": 0.5725, "loss_cls": 3.90959, "loss": 3.90959, "time": 0.86023} +{"mode": "train", "epoch": 97, "iter": 1500, "lr": 0.02833, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32109, "top5_acc": 0.57594, "loss_cls": 3.8803, "loss": 3.8803, "time": 0.8603} +{"mode": "train", "epoch": 97, "iter": 1600, "lr": 0.02831, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.31953, "top5_acc": 0.57766, "loss_cls": 3.88218, "loss": 3.88218, "time": 0.86124} +{"mode": "train", "epoch": 97, "iter": 1700, "lr": 0.02828, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31172, "top5_acc": 0.57094, "loss_cls": 3.89941, "loss": 3.89941, "time": 0.851} +{"mode": "train", "epoch": 97, "iter": 1800, "lr": 0.02826, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31266, "top5_acc": 0.57234, "loss_cls": 3.90404, "loss": 3.90404, "time": 0.85538} +{"mode": "train", "epoch": 97, "iter": 1900, "lr": 0.02823, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.30828, "top5_acc": 0.56406, "loss_cls": 3.93928, "loss": 3.93928, "time": 0.85429} +{"mode": "train", "epoch": 97, "iter": 2000, "lr": 0.02821, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33281, "top5_acc": 0.58141, "loss_cls": 3.85369, "loss": 3.85369, "time": 0.85899} +{"mode": "train", "epoch": 97, "iter": 2100, "lr": 0.02818, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32422, "top5_acc": 0.57781, "loss_cls": 3.89815, "loss": 3.89815, "time": 0.85987} +{"mode": "train", "epoch": 97, "iter": 2200, "lr": 0.02816, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31734, "top5_acc": 0.56812, "loss_cls": 3.89756, "loss": 3.89756, "time": 0.85912} +{"mode": "train", "epoch": 97, "iter": 2300, "lr": 0.02813, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31328, "top5_acc": 0.56797, "loss_cls": 3.94504, "loss": 3.94504, "time": 0.85961} +{"mode": "train", "epoch": 97, "iter": 2400, "lr": 0.02811, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.32453, "top5_acc": 0.58219, "loss_cls": 3.87904, "loss": 3.87904, "time": 0.86067} +{"mode": "train", "epoch": 97, "iter": 2500, "lr": 0.02808, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.31703, "top5_acc": 0.56969, "loss_cls": 3.9114, "loss": 3.9114, "time": 0.85632} +{"mode": "train", "epoch": 97, "iter": 2600, "lr": 0.02806, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31438, "top5_acc": 0.56875, "loss_cls": 3.93997, "loss": 3.93997, "time": 0.85262} +{"mode": "train", "epoch": 97, "iter": 2700, "lr": 0.02803, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3325, "top5_acc": 0.58844, "loss_cls": 3.8194, "loss": 3.8194, "time": 0.86443} +{"mode": "train", "epoch": 97, "iter": 2800, "lr": 0.02801, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.32047, "top5_acc": 0.56922, "loss_cls": 3.90933, "loss": 3.90933, "time": 0.86024} +{"mode": "train", "epoch": 97, "iter": 2900, "lr": 0.02798, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30875, "top5_acc": 0.57563, "loss_cls": 3.91404, "loss": 3.91404, "time": 0.85171} +{"mode": "train", "epoch": 97, "iter": 3000, "lr": 0.02796, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31141, "top5_acc": 0.57703, "loss_cls": 3.89795, "loss": 3.89795, "time": 0.85075} +{"mode": "train", "epoch": 97, "iter": 3100, "lr": 0.02793, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.31703, "top5_acc": 0.56281, "loss_cls": 3.94279, "loss": 3.94279, "time": 0.85216} +{"mode": "train", "epoch": 97, "iter": 3200, "lr": 0.02791, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32109, "top5_acc": 0.56672, "loss_cls": 3.92492, "loss": 3.92492, "time": 0.85362} +{"mode": "train", "epoch": 97, "iter": 3300, "lr": 0.02788, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32047, "top5_acc": 0.58156, "loss_cls": 3.86339, "loss": 3.86339, "time": 0.8523} +{"mode": "train", "epoch": 97, "iter": 3400, "lr": 0.02786, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31812, "top5_acc": 0.57109, "loss_cls": 3.89515, "loss": 3.89515, "time": 0.85468} +{"mode": "train", "epoch": 97, "iter": 3500, "lr": 0.02783, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31875, "top5_acc": 0.57234, "loss_cls": 3.91264, "loss": 3.91264, "time": 0.85302} +{"mode": "train", "epoch": 97, "iter": 3600, "lr": 0.02781, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31281, "top5_acc": 0.56781, "loss_cls": 3.9134, "loss": 3.9134, "time": 0.85165} +{"mode": "train", "epoch": 97, "iter": 3700, "lr": 0.02778, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.32047, "top5_acc": 0.57594, "loss_cls": 3.91623, "loss": 3.91623, "time": 0.85028} +{"mode": "val", "epoch": 97, "iter": 309, "lr": 0.02777, "top1_acc": 0.26298, "top5_acc": 0.5096, "mean_class_accuracy": 0.26269} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.02774, "memory": 15990, "data_time": 1.58778, "top1_acc": 0.33594, "top5_acc": 0.59219, "loss_cls": 3.79594, "loss": 3.79594, "time": 2.64483} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.02772, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.34031, "top5_acc": 0.5975, "loss_cls": 3.79525, "loss": 3.79525, "time": 0.86344} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.02769, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.33688, "top5_acc": 0.59062, "loss_cls": 3.80737, "loss": 3.80737, "time": 0.86068} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.02767, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.32062, "top5_acc": 0.58625, "loss_cls": 3.85328, "loss": 3.85328, "time": 0.87096} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.02764, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.32906, "top5_acc": 0.58688, "loss_cls": 3.82954, "loss": 3.82954, "time": 0.86003} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.02762, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33172, "top5_acc": 0.57734, "loss_cls": 3.82872, "loss": 3.82872, "time": 0.86017} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.02759, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.31938, "top5_acc": 0.58141, "loss_cls": 3.89162, "loss": 3.89162, "time": 0.8625} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.02757, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31812, "top5_acc": 0.57625, "loss_cls": 3.89281, "loss": 3.89281, "time": 0.86509} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.02754, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.31703, "top5_acc": 0.56641, "loss_cls": 3.91424, "loss": 3.91424, "time": 0.85487} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.02752, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.32141, "top5_acc": 0.57969, "loss_cls": 3.8594, "loss": 3.8594, "time": 0.86177} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.02749, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31906, "top5_acc": 0.58094, "loss_cls": 3.86349, "loss": 3.86349, "time": 0.86716} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.02747, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32, "top5_acc": 0.56719, "loss_cls": 3.90613, "loss": 3.90613, "time": 0.86176} +{"mode": "train", "epoch": 98, "iter": 1300, "lr": 0.02744, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32672, "top5_acc": 0.58031, "loss_cls": 3.85486, "loss": 3.85486, "time": 0.86457} +{"mode": "train", "epoch": 98, "iter": 1400, "lr": 0.02742, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.31641, "top5_acc": 0.57438, "loss_cls": 3.88761, "loss": 3.88761, "time": 0.86554} +{"mode": "train", "epoch": 98, "iter": 1500, "lr": 0.02739, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.315, "top5_acc": 0.57844, "loss_cls": 3.89097, "loss": 3.89097, "time": 0.86557} +{"mode": "train", "epoch": 98, "iter": 1600, "lr": 0.02737, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.31984, "top5_acc": 0.57984, "loss_cls": 3.87553, "loss": 3.87553, "time": 0.85755} +{"mode": "train", "epoch": 98, "iter": 1700, "lr": 0.02734, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.32094, "top5_acc": 0.58312, "loss_cls": 3.86802, "loss": 3.86802, "time": 0.86043} +{"mode": "train", "epoch": 98, "iter": 1800, "lr": 0.02732, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.32594, "top5_acc": 0.57375, "loss_cls": 3.86964, "loss": 3.86964, "time": 0.86171} +{"mode": "train", "epoch": 98, "iter": 1900, "lr": 0.02729, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.31375, "top5_acc": 0.56125, "loss_cls": 3.97848, "loss": 3.97848, "time": 0.87258} +{"mode": "train", "epoch": 98, "iter": 2000, "lr": 0.02727, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.32188, "top5_acc": 0.57531, "loss_cls": 3.88686, "loss": 3.88686, "time": 0.87831} +{"mode": "train", "epoch": 98, "iter": 2100, "lr": 0.02724, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.31969, "top5_acc": 0.57719, "loss_cls": 3.84825, "loss": 3.84825, "time": 0.8755} +{"mode": "train", "epoch": 98, "iter": 2200, "lr": 0.02722, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.31203, "top5_acc": 0.57188, "loss_cls": 3.93376, "loss": 3.93376, "time": 0.87809} +{"mode": "train", "epoch": 98, "iter": 2300, "lr": 0.02719, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.32062, "top5_acc": 0.58016, "loss_cls": 3.87425, "loss": 3.87425, "time": 0.87326} +{"mode": "train", "epoch": 98, "iter": 2400, "lr": 0.02717, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.32203, "top5_acc": 0.57609, "loss_cls": 3.87314, "loss": 3.87314, "time": 0.86817} +{"mode": "train", "epoch": 98, "iter": 2500, "lr": 0.02714, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.31078, "top5_acc": 0.58234, "loss_cls": 3.87868, "loss": 3.87868, "time": 0.86988} +{"mode": "train", "epoch": 98, "iter": 2600, "lr": 0.02712, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.31641, "top5_acc": 0.57359, "loss_cls": 3.895, "loss": 3.895, "time": 0.86709} +{"mode": "train", "epoch": 98, "iter": 2700, "lr": 0.02709, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.3175, "top5_acc": 0.57375, "loss_cls": 3.8944, "loss": 3.8944, "time": 0.86066} +{"mode": "train", "epoch": 98, "iter": 2800, "lr": 0.02707, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.3225, "top5_acc": 0.57047, "loss_cls": 3.88362, "loss": 3.88362, "time": 0.86179} +{"mode": "train", "epoch": 98, "iter": 2900, "lr": 0.02705, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.31812, "top5_acc": 0.57672, "loss_cls": 3.88631, "loss": 3.88631, "time": 0.87326} +{"mode": "train", "epoch": 98, "iter": 3000, "lr": 0.02702, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.31547, "top5_acc": 0.57266, "loss_cls": 3.88914, "loss": 3.88914, "time": 0.87505} +{"mode": "train", "epoch": 98, "iter": 3100, "lr": 0.027, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.32984, "top5_acc": 0.58672, "loss_cls": 3.84892, "loss": 3.84892, "time": 0.87303} +{"mode": "train", "epoch": 98, "iter": 3200, "lr": 0.02697, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.31641, "top5_acc": 0.57625, "loss_cls": 3.88137, "loss": 3.88137, "time": 0.87789} +{"mode": "train", "epoch": 98, "iter": 3300, "lr": 0.02695, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.32188, "top5_acc": 0.57141, "loss_cls": 3.90546, "loss": 3.90546, "time": 0.87759} +{"mode": "train", "epoch": 98, "iter": 3400, "lr": 0.02692, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32906, "top5_acc": 0.58281, "loss_cls": 3.82682, "loss": 3.82682, "time": 0.86125} +{"mode": "train", "epoch": 98, "iter": 3500, "lr": 0.0269, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31938, "top5_acc": 0.57953, "loss_cls": 3.87299, "loss": 3.87299, "time": 0.86272} +{"mode": "train", "epoch": 98, "iter": 3600, "lr": 0.02687, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.31781, "top5_acc": 0.57656, "loss_cls": 3.87495, "loss": 3.87495, "time": 0.85831} +{"mode": "train", "epoch": 98, "iter": 3700, "lr": 0.02685, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.31641, "top5_acc": 0.57641, "loss_cls": 3.91013, "loss": 3.91013, "time": 0.86194} +{"mode": "val", "epoch": 98, "iter": 309, "lr": 0.02684, "top1_acc": 0.25604, "top5_acc": 0.50038, "mean_class_accuracy": 0.25591} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.02681, "memory": 15990, "data_time": 1.59467, "top1_acc": 0.34047, "top5_acc": 0.58688, "loss_cls": 3.79679, "loss": 3.79679, "time": 2.66088} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.02679, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.32672, "top5_acc": 0.59609, "loss_cls": 3.79904, "loss": 3.79904, "time": 0.87465} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.02676, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.32531, "top5_acc": 0.58312, "loss_cls": 3.84325, "loss": 3.84325, "time": 0.87387} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.02674, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.32922, "top5_acc": 0.5775, "loss_cls": 3.85461, "loss": 3.85461, "time": 0.87715} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.02671, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.32688, "top5_acc": 0.58484, "loss_cls": 3.84414, "loss": 3.84414, "time": 0.87882} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.02669, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.33391, "top5_acc": 0.59516, "loss_cls": 3.79495, "loss": 3.79495, "time": 0.88554} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.02666, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.33016, "top5_acc": 0.58422, "loss_cls": 3.84404, "loss": 3.84404, "time": 0.87822} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.02664, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.32766, "top5_acc": 0.57844, "loss_cls": 3.88621, "loss": 3.88621, "time": 0.86955} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.02661, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.32438, "top5_acc": 0.58547, "loss_cls": 3.84569, "loss": 3.84569, "time": 0.87253} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.02659, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.32438, "top5_acc": 0.57875, "loss_cls": 3.83803, "loss": 3.83803, "time": 0.87761} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.02656, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33516, "top5_acc": 0.58516, "loss_cls": 3.83837, "loss": 3.83837, "time": 0.8774} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.02654, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.32562, "top5_acc": 0.58359, "loss_cls": 3.829, "loss": 3.829, "time": 0.87073} +{"mode": "train", "epoch": 99, "iter": 1300, "lr": 0.02651, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.32375, "top5_acc": 0.5825, "loss_cls": 3.8517, "loss": 3.8517, "time": 0.87687} +{"mode": "train", "epoch": 99, "iter": 1400, "lr": 0.02649, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.3325, "top5_acc": 0.58031, "loss_cls": 3.85175, "loss": 3.85175, "time": 0.87518} +{"mode": "train", "epoch": 99, "iter": 1500, "lr": 0.02646, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.32641, "top5_acc": 0.58469, "loss_cls": 3.84128, "loss": 3.84128, "time": 0.88337} +{"mode": "train", "epoch": 99, "iter": 1600, "lr": 0.02644, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32281, "top5_acc": 0.58516, "loss_cls": 3.85379, "loss": 3.85379, "time": 0.86543} +{"mode": "train", "epoch": 99, "iter": 1700, "lr": 0.02642, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.32109, "top5_acc": 0.57141, "loss_cls": 3.88481, "loss": 3.88481, "time": 0.86564} +{"mode": "train", "epoch": 99, "iter": 1800, "lr": 0.02639, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.32031, "top5_acc": 0.56797, "loss_cls": 3.89105, "loss": 3.89105, "time": 0.8634} +{"mode": "train", "epoch": 99, "iter": 1900, "lr": 0.02637, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.3225, "top5_acc": 0.57016, "loss_cls": 3.88287, "loss": 3.88287, "time": 0.86175} +{"mode": "train", "epoch": 99, "iter": 2000, "lr": 0.02634, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.32266, "top5_acc": 0.57609, "loss_cls": 3.89125, "loss": 3.89125, "time": 0.87225} +{"mode": "train", "epoch": 99, "iter": 2100, "lr": 0.02632, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.31484, "top5_acc": 0.57188, "loss_cls": 3.88721, "loss": 3.88721, "time": 0.87034} +{"mode": "train", "epoch": 99, "iter": 2200, "lr": 0.02629, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.3225, "top5_acc": 0.58359, "loss_cls": 3.85116, "loss": 3.85116, "time": 0.87209} +{"mode": "train", "epoch": 99, "iter": 2300, "lr": 0.02627, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.32703, "top5_acc": 0.57609, "loss_cls": 3.84307, "loss": 3.84307, "time": 0.87191} +{"mode": "train", "epoch": 99, "iter": 2400, "lr": 0.02624, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.31188, "top5_acc": 0.56125, "loss_cls": 3.95987, "loss": 3.95987, "time": 0.87907} +{"mode": "train", "epoch": 99, "iter": 2500, "lr": 0.02622, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.32422, "top5_acc": 0.58078, "loss_cls": 3.8472, "loss": 3.8472, "time": 0.8763} +{"mode": "train", "epoch": 99, "iter": 2600, "lr": 0.02619, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.3275, "top5_acc": 0.58, "loss_cls": 3.83706, "loss": 3.83706, "time": 0.8636} +{"mode": "train", "epoch": 99, "iter": 2700, "lr": 0.02617, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.32641, "top5_acc": 0.58391, "loss_cls": 3.86397, "loss": 3.86397, "time": 0.86163} +{"mode": "train", "epoch": 99, "iter": 2800, "lr": 0.02614, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.32406, "top5_acc": 0.58094, "loss_cls": 3.87313, "loss": 3.87313, "time": 0.86671} +{"mode": "train", "epoch": 99, "iter": 2900, "lr": 0.02612, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31406, "top5_acc": 0.56766, "loss_cls": 3.92888, "loss": 3.92888, "time": 0.86367} +{"mode": "train", "epoch": 99, "iter": 3000, "lr": 0.0261, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.31656, "top5_acc": 0.57141, "loss_cls": 3.90391, "loss": 3.90391, "time": 0.8734} +{"mode": "train", "epoch": 99, "iter": 3100, "lr": 0.02607, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.32359, "top5_acc": 0.575, "loss_cls": 3.88987, "loss": 3.88987, "time": 0.86566} +{"mode": "train", "epoch": 99, "iter": 3200, "lr": 0.02605, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31344, "top5_acc": 0.57203, "loss_cls": 3.88018, "loss": 3.88018, "time": 0.87278} +{"mode": "train", "epoch": 99, "iter": 3300, "lr": 0.02602, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.32797, "top5_acc": 0.57406, "loss_cls": 3.88686, "loss": 3.88686, "time": 0.8681} +{"mode": "train", "epoch": 99, "iter": 3400, "lr": 0.026, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.31688, "top5_acc": 0.57344, "loss_cls": 3.88873, "loss": 3.88873, "time": 0.86352} +{"mode": "train", "epoch": 99, "iter": 3500, "lr": 0.02597, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32594, "top5_acc": 0.58312, "loss_cls": 3.85154, "loss": 3.85154, "time": 0.85041} +{"mode": "train", "epoch": 99, "iter": 3600, "lr": 0.02595, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32672, "top5_acc": 0.58031, "loss_cls": 3.85651, "loss": 3.85651, "time": 0.85193} +{"mode": "train", "epoch": 99, "iter": 3700, "lr": 0.02592, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31812, "top5_acc": 0.57406, "loss_cls": 3.89086, "loss": 3.89086, "time": 0.85298} +{"mode": "val", "epoch": 99, "iter": 309, "lr": 0.02591, "top1_acc": 0.27012, "top5_acc": 0.51811, "mean_class_accuracy": 0.26987} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.02589, "memory": 15990, "data_time": 1.56223, "top1_acc": 0.33719, "top5_acc": 0.60625, "loss_cls": 3.74256, "loss": 3.74256, "time": 2.58828} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.02586, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33625, "top5_acc": 0.59922, "loss_cls": 3.76155, "loss": 3.76155, "time": 0.85219} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.02584, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32188, "top5_acc": 0.57719, "loss_cls": 3.85085, "loss": 3.85085, "time": 0.85446} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.02581, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33188, "top5_acc": 0.60031, "loss_cls": 3.78878, "loss": 3.78878, "time": 0.85294} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.02579, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.33359, "top5_acc": 0.59672, "loss_cls": 3.79723, "loss": 3.79723, "time": 0.85612} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.02577, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33031, "top5_acc": 0.58453, "loss_cls": 3.82997, "loss": 3.82997, "time": 0.85483} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.02574, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33344, "top5_acc": 0.59, "loss_cls": 3.82282, "loss": 3.82282, "time": 0.85406} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.02572, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33125, "top5_acc": 0.58328, "loss_cls": 3.82119, "loss": 3.82119, "time": 0.85524} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.02569, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32828, "top5_acc": 0.58406, "loss_cls": 3.80117, "loss": 3.80117, "time": 0.85651} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.02567, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33766, "top5_acc": 0.58969, "loss_cls": 3.81943, "loss": 3.81943, "time": 0.85293} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.02564, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33875, "top5_acc": 0.59188, "loss_cls": 3.80042, "loss": 3.80042, "time": 0.85475} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.02562, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32875, "top5_acc": 0.59016, "loss_cls": 3.82148, "loss": 3.82148, "time": 0.8497} +{"mode": "train", "epoch": 100, "iter": 1300, "lr": 0.02559, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32641, "top5_acc": 0.58156, "loss_cls": 3.85288, "loss": 3.85288, "time": 0.85553} +{"mode": "train", "epoch": 100, "iter": 1400, "lr": 0.02557, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32078, "top5_acc": 0.57875, "loss_cls": 3.88463, "loss": 3.88463, "time": 0.85286} +{"mode": "train", "epoch": 100, "iter": 1500, "lr": 0.02555, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33359, "top5_acc": 0.57844, "loss_cls": 3.85275, "loss": 3.85275, "time": 0.85228} +{"mode": "train", "epoch": 100, "iter": 1600, "lr": 0.02552, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32719, "top5_acc": 0.57484, "loss_cls": 3.88408, "loss": 3.88408, "time": 0.85549} +{"mode": "train", "epoch": 100, "iter": 1700, "lr": 0.0255, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32531, "top5_acc": 0.58734, "loss_cls": 3.83731, "loss": 3.83731, "time": 0.85447} +{"mode": "train", "epoch": 100, "iter": 1800, "lr": 0.02547, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.32266, "top5_acc": 0.57312, "loss_cls": 3.8702, "loss": 3.8702, "time": 0.85378} +{"mode": "train", "epoch": 100, "iter": 1900, "lr": 0.02545, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32766, "top5_acc": 0.57297, "loss_cls": 3.83955, "loss": 3.83955, "time": 0.85782} +{"mode": "train", "epoch": 100, "iter": 2000, "lr": 0.02542, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32969, "top5_acc": 0.57516, "loss_cls": 3.83667, "loss": 3.83667, "time": 0.85287} +{"mode": "train", "epoch": 100, "iter": 2100, "lr": 0.0254, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.32391, "top5_acc": 0.57406, "loss_cls": 3.83227, "loss": 3.83227, "time": 0.86148} +{"mode": "train", "epoch": 100, "iter": 2200, "lr": 0.02538, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32062, "top5_acc": 0.57594, "loss_cls": 3.8473, "loss": 3.8473, "time": 0.8555} +{"mode": "train", "epoch": 100, "iter": 2300, "lr": 0.02535, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.32828, "top5_acc": 0.57922, "loss_cls": 3.84518, "loss": 3.84518, "time": 0.85997} +{"mode": "train", "epoch": 100, "iter": 2400, "lr": 0.02533, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32609, "top5_acc": 0.58078, "loss_cls": 3.84901, "loss": 3.84901, "time": 0.85826} +{"mode": "train", "epoch": 100, "iter": 2500, "lr": 0.0253, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.31984, "top5_acc": 0.57875, "loss_cls": 3.85216, "loss": 3.85216, "time": 0.85306} +{"mode": "train", "epoch": 100, "iter": 2600, "lr": 0.02528, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.33016, "top5_acc": 0.57766, "loss_cls": 3.87363, "loss": 3.87363, "time": 0.85447} +{"mode": "train", "epoch": 100, "iter": 2700, "lr": 0.02525, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.32922, "top5_acc": 0.58859, "loss_cls": 3.83486, "loss": 3.83486, "time": 0.85165} +{"mode": "train", "epoch": 100, "iter": 2800, "lr": 0.02523, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33188, "top5_acc": 0.58062, "loss_cls": 3.85723, "loss": 3.85723, "time": 0.85436} +{"mode": "train", "epoch": 100, "iter": 2900, "lr": 0.02521, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32781, "top5_acc": 0.57844, "loss_cls": 3.84162, "loss": 3.84162, "time": 0.85925} +{"mode": "train", "epoch": 100, "iter": 3000, "lr": 0.02518, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32281, "top5_acc": 0.56875, "loss_cls": 3.88725, "loss": 3.88725, "time": 0.85632} +{"mode": "train", "epoch": 100, "iter": 3100, "lr": 0.02516, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31922, "top5_acc": 0.56875, "loss_cls": 3.93199, "loss": 3.93199, "time": 0.86271} +{"mode": "train", "epoch": 100, "iter": 3200, "lr": 0.02513, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33016, "top5_acc": 0.58516, "loss_cls": 3.83339, "loss": 3.83339, "time": 0.85452} +{"mode": "train", "epoch": 100, "iter": 3300, "lr": 0.02511, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.32969, "top5_acc": 0.57609, "loss_cls": 3.88192, "loss": 3.88192, "time": 0.85092} +{"mode": "train", "epoch": 100, "iter": 3400, "lr": 0.02508, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.32922, "top5_acc": 0.58062, "loss_cls": 3.85986, "loss": 3.85986, "time": 0.85195} +{"mode": "train", "epoch": 100, "iter": 3500, "lr": 0.02506, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32234, "top5_acc": 0.57969, "loss_cls": 3.89197, "loss": 3.89197, "time": 0.84784} +{"mode": "train", "epoch": 100, "iter": 3600, "lr": 0.02504, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32562, "top5_acc": 0.5825, "loss_cls": 3.81706, "loss": 3.81706, "time": 0.86299} +{"mode": "train", "epoch": 100, "iter": 3700, "lr": 0.02501, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31906, "top5_acc": 0.57063, "loss_cls": 3.91116, "loss": 3.91116, "time": 0.8589} +{"mode": "val", "epoch": 100, "iter": 309, "lr": 0.025, "top1_acc": 0.2683, "top5_acc": 0.514, "mean_class_accuracy": 0.26807} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.02498, "memory": 15990, "data_time": 1.53887, "top1_acc": 0.32891, "top5_acc": 0.59312, "loss_cls": 3.80741, "loss": 3.80741, "time": 2.56081} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.02495, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34453, "top5_acc": 0.60141, "loss_cls": 3.77102, "loss": 3.77102, "time": 0.8583} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.02493, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3325, "top5_acc": 0.58891, "loss_cls": 3.77464, "loss": 3.77464, "time": 0.85659} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.0249, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33125, "top5_acc": 0.59, "loss_cls": 3.78021, "loss": 3.78021, "time": 0.85365} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.02488, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34438, "top5_acc": 0.59281, "loss_cls": 3.78539, "loss": 3.78539, "time": 0.85096} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.02486, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31969, "top5_acc": 0.57672, "loss_cls": 3.84666, "loss": 3.84666, "time": 0.85142} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.02483, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.32906, "top5_acc": 0.58578, "loss_cls": 3.82724, "loss": 3.82724, "time": 0.85482} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.02481, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33328, "top5_acc": 0.60141, "loss_cls": 3.76954, "loss": 3.76954, "time": 0.85443} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.02478, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33219, "top5_acc": 0.58797, "loss_cls": 3.81385, "loss": 3.81385, "time": 0.85344} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.02476, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32703, "top5_acc": 0.58891, "loss_cls": 3.82454, "loss": 3.82454, "time": 0.85175} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.02473, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33078, "top5_acc": 0.59719, "loss_cls": 3.83041, "loss": 3.83041, "time": 0.85436} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.02471, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32625, "top5_acc": 0.58125, "loss_cls": 3.84573, "loss": 3.84573, "time": 0.8603} +{"mode": "train", "epoch": 101, "iter": 1300, "lr": 0.02469, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32938, "top5_acc": 0.58906, "loss_cls": 3.80426, "loss": 3.80426, "time": 0.85191} +{"mode": "train", "epoch": 101, "iter": 1400, "lr": 0.02466, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.32828, "top5_acc": 0.58516, "loss_cls": 3.83198, "loss": 3.83198, "time": 0.85531} +{"mode": "train", "epoch": 101, "iter": 1500, "lr": 0.02464, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32016, "top5_acc": 0.57547, "loss_cls": 3.88512, "loss": 3.88512, "time": 0.85729} +{"mode": "train", "epoch": 101, "iter": 1600, "lr": 0.02461, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33188, "top5_acc": 0.58047, "loss_cls": 3.84628, "loss": 3.84628, "time": 0.85221} +{"mode": "train", "epoch": 101, "iter": 1700, "lr": 0.02459, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32047, "top5_acc": 0.58031, "loss_cls": 3.87456, "loss": 3.87456, "time": 0.84782} +{"mode": "train", "epoch": 101, "iter": 1800, "lr": 0.02457, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32391, "top5_acc": 0.59, "loss_cls": 3.84386, "loss": 3.84386, "time": 0.84765} +{"mode": "train", "epoch": 101, "iter": 1900, "lr": 0.02454, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32219, "top5_acc": 0.57953, "loss_cls": 3.87226, "loss": 3.87226, "time": 0.85245} +{"mode": "train", "epoch": 101, "iter": 2000, "lr": 0.02452, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32625, "top5_acc": 0.58969, "loss_cls": 3.80542, "loss": 3.80542, "time": 0.84565} +{"mode": "train", "epoch": 101, "iter": 2100, "lr": 0.02449, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33188, "top5_acc": 0.57312, "loss_cls": 3.83477, "loss": 3.83477, "time": 0.84905} +{"mode": "train", "epoch": 101, "iter": 2200, "lr": 0.02447, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33172, "top5_acc": 0.58891, "loss_cls": 3.81838, "loss": 3.81838, "time": 0.84519} +{"mode": "train", "epoch": 101, "iter": 2300, "lr": 0.02445, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32859, "top5_acc": 0.58719, "loss_cls": 3.83131, "loss": 3.83131, "time": 0.84726} +{"mode": "train", "epoch": 101, "iter": 2400, "lr": 0.02442, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32734, "top5_acc": 0.58438, "loss_cls": 3.8417, "loss": 3.8417, "time": 0.84537} +{"mode": "train", "epoch": 101, "iter": 2500, "lr": 0.0244, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32844, "top5_acc": 0.58797, "loss_cls": 3.83985, "loss": 3.83985, "time": 0.84849} +{"mode": "train", "epoch": 101, "iter": 2600, "lr": 0.02437, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34141, "top5_acc": 0.59484, "loss_cls": 3.77427, "loss": 3.77427, "time": 0.84264} +{"mode": "train", "epoch": 101, "iter": 2700, "lr": 0.02435, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33312, "top5_acc": 0.58172, "loss_cls": 3.84578, "loss": 3.84578, "time": 0.84414} +{"mode": "train", "epoch": 101, "iter": 2800, "lr": 0.02433, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33156, "top5_acc": 0.58844, "loss_cls": 3.81364, "loss": 3.81364, "time": 0.85024} +{"mode": "train", "epoch": 101, "iter": 2900, "lr": 0.0243, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33094, "top5_acc": 0.58422, "loss_cls": 3.83151, "loss": 3.83151, "time": 0.84688} +{"mode": "train", "epoch": 101, "iter": 3000, "lr": 0.02428, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32688, "top5_acc": 0.57859, "loss_cls": 3.8545, "loss": 3.8545, "time": 0.8464} +{"mode": "train", "epoch": 101, "iter": 3100, "lr": 0.02425, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33453, "top5_acc": 0.59328, "loss_cls": 3.79476, "loss": 3.79476, "time": 0.85263} +{"mode": "train", "epoch": 101, "iter": 3200, "lr": 0.02423, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32969, "top5_acc": 0.59406, "loss_cls": 3.79695, "loss": 3.79695, "time": 0.84867} +{"mode": "train", "epoch": 101, "iter": 3300, "lr": 0.02421, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33172, "top5_acc": 0.59062, "loss_cls": 3.79399, "loss": 3.79399, "time": 0.8414} +{"mode": "train", "epoch": 101, "iter": 3400, "lr": 0.02418, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33, "top5_acc": 0.58641, "loss_cls": 3.81215, "loss": 3.81215, "time": 0.84256} +{"mode": "train", "epoch": 101, "iter": 3500, "lr": 0.02416, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32484, "top5_acc": 0.58516, "loss_cls": 3.84162, "loss": 3.84162, "time": 0.84404} +{"mode": "train", "epoch": 101, "iter": 3600, "lr": 0.02413, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.32984, "top5_acc": 0.58047, "loss_cls": 3.84887, "loss": 3.84887, "time": 0.84229} +{"mode": "train", "epoch": 101, "iter": 3700, "lr": 0.02411, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33594, "top5_acc": 0.58297, "loss_cls": 3.84401, "loss": 3.84401, "time": 0.84325} +{"mode": "val", "epoch": 101, "iter": 309, "lr": 0.0241, "top1_acc": 0.27164, "top5_acc": 0.51639, "mean_class_accuracy": 0.27147} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.02407, "memory": 15990, "data_time": 1.45006, "top1_acc": 0.33625, "top5_acc": 0.59547, "loss_cls": 3.76807, "loss": 3.76807, "time": 2.47085} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.02405, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33703, "top5_acc": 0.59109, "loss_cls": 3.75258, "loss": 3.75258, "time": 0.85311} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.02403, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32875, "top5_acc": 0.58969, "loss_cls": 3.80094, "loss": 3.80094, "time": 0.85048} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.024, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34, "top5_acc": 0.58906, "loss_cls": 3.78222, "loss": 3.78222, "time": 0.8448} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.02398, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34094, "top5_acc": 0.59922, "loss_cls": 3.77509, "loss": 3.77509, "time": 0.84595} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.02396, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33484, "top5_acc": 0.58656, "loss_cls": 3.79403, "loss": 3.79403, "time": 0.84631} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.02393, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33344, "top5_acc": 0.59141, "loss_cls": 3.80302, "loss": 3.80302, "time": 0.84183} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.02391, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33172, "top5_acc": 0.59078, "loss_cls": 3.78739, "loss": 3.78739, "time": 0.84537} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.02388, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33391, "top5_acc": 0.58125, "loss_cls": 3.84657, "loss": 3.84657, "time": 0.8451} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.02386, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32719, "top5_acc": 0.58391, "loss_cls": 3.8209, "loss": 3.8209, "time": 0.84015} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.02384, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33875, "top5_acc": 0.58984, "loss_cls": 3.80179, "loss": 3.80179, "time": 0.84126} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.02381, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32406, "top5_acc": 0.58281, "loss_cls": 3.83859, "loss": 3.83859, "time": 0.84346} +{"mode": "train", "epoch": 102, "iter": 1300, "lr": 0.02379, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33797, "top5_acc": 0.59484, "loss_cls": 3.76436, "loss": 3.76436, "time": 0.84254} +{"mode": "train", "epoch": 102, "iter": 1400, "lr": 0.02376, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32812, "top5_acc": 0.58312, "loss_cls": 3.80687, "loss": 3.80687, "time": 0.84909} +{"mode": "train", "epoch": 102, "iter": 1500, "lr": 0.02374, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32203, "top5_acc": 0.58891, "loss_cls": 3.81289, "loss": 3.81289, "time": 0.84268} +{"mode": "train", "epoch": 102, "iter": 1600, "lr": 0.02372, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.32719, "top5_acc": 0.58609, "loss_cls": 3.82015, "loss": 3.82015, "time": 0.84014} +{"mode": "train", "epoch": 102, "iter": 1700, "lr": 0.02369, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32141, "top5_acc": 0.57484, "loss_cls": 3.84267, "loss": 3.84267, "time": 0.8442} +{"mode": "train", "epoch": 102, "iter": 1800, "lr": 0.02367, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33359, "top5_acc": 0.58578, "loss_cls": 3.80976, "loss": 3.80976, "time": 0.84212} +{"mode": "train", "epoch": 102, "iter": 1900, "lr": 0.02365, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33219, "top5_acc": 0.58953, "loss_cls": 3.79794, "loss": 3.79794, "time": 0.84775} +{"mode": "train", "epoch": 102, "iter": 2000, "lr": 0.02362, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32797, "top5_acc": 0.58766, "loss_cls": 3.80035, "loss": 3.80035, "time": 0.84576} +{"mode": "train", "epoch": 102, "iter": 2100, "lr": 0.0236, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32844, "top5_acc": 0.58484, "loss_cls": 3.85529, "loss": 3.85529, "time": 0.85013} +{"mode": "train", "epoch": 102, "iter": 2200, "lr": 0.02357, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32156, "top5_acc": 0.57797, "loss_cls": 3.8631, "loss": 3.8631, "time": 0.84531} +{"mode": "train", "epoch": 102, "iter": 2300, "lr": 0.02355, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33797, "top5_acc": 0.58781, "loss_cls": 3.84738, "loss": 3.84738, "time": 0.84735} +{"mode": "train", "epoch": 102, "iter": 2400, "lr": 0.02353, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.325, "top5_acc": 0.58312, "loss_cls": 3.80456, "loss": 3.80456, "time": 0.85066} +{"mode": "train", "epoch": 102, "iter": 2500, "lr": 0.0235, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32953, "top5_acc": 0.59609, "loss_cls": 3.79519, "loss": 3.79519, "time": 0.84888} +{"mode": "train", "epoch": 102, "iter": 2600, "lr": 0.02348, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32609, "top5_acc": 0.58422, "loss_cls": 3.82356, "loss": 3.82356, "time": 0.84416} +{"mode": "train", "epoch": 102, "iter": 2700, "lr": 0.02346, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33906, "top5_acc": 0.59438, "loss_cls": 3.78357, "loss": 3.78357, "time": 0.84051} +{"mode": "train", "epoch": 102, "iter": 2800, "lr": 0.02343, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33516, "top5_acc": 0.59, "loss_cls": 3.78528, "loss": 3.78528, "time": 0.84554} +{"mode": "train", "epoch": 102, "iter": 2900, "lr": 0.02341, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33078, "top5_acc": 0.58781, "loss_cls": 3.82413, "loss": 3.82413, "time": 0.84581} +{"mode": "train", "epoch": 102, "iter": 3000, "lr": 0.02339, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33531, "top5_acc": 0.59, "loss_cls": 3.80258, "loss": 3.80258, "time": 0.8506} +{"mode": "train", "epoch": 102, "iter": 3100, "lr": 0.02336, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33109, "top5_acc": 0.59469, "loss_cls": 3.77618, "loss": 3.77618, "time": 0.84797} +{"mode": "train", "epoch": 102, "iter": 3200, "lr": 0.02334, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33578, "top5_acc": 0.58703, "loss_cls": 3.82756, "loss": 3.82756, "time": 0.8499} +{"mode": "train", "epoch": 102, "iter": 3300, "lr": 0.02331, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34094, "top5_acc": 0.59125, "loss_cls": 3.78575, "loss": 3.78575, "time": 0.83915} +{"mode": "train", "epoch": 102, "iter": 3400, "lr": 0.02329, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.32969, "top5_acc": 0.58594, "loss_cls": 3.84379, "loss": 3.84379, "time": 0.84096} +{"mode": "train", "epoch": 102, "iter": 3500, "lr": 0.02327, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.33047, "top5_acc": 0.58062, "loss_cls": 3.82901, "loss": 3.82901, "time": 0.84265} +{"mode": "train", "epoch": 102, "iter": 3600, "lr": 0.02324, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33141, "top5_acc": 0.59203, "loss_cls": 3.81679, "loss": 3.81679, "time": 0.84349} +{"mode": "train", "epoch": 102, "iter": 3700, "lr": 0.02322, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33031, "top5_acc": 0.58891, "loss_cls": 3.76548, "loss": 3.76548, "time": 0.84871} +{"mode": "val", "epoch": 102, "iter": 309, "lr": 0.02321, "top1_acc": 0.27767, "top5_acc": 0.52363, "mean_class_accuracy": 0.27759} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.02319, "memory": 15990, "data_time": 1.50792, "top1_acc": 0.34641, "top5_acc": 0.60469, "loss_cls": 3.68466, "loss": 3.68466, "time": 2.53891} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.02316, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34109, "top5_acc": 0.59641, "loss_cls": 3.75254, "loss": 3.75254, "time": 0.8551} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.02314, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34047, "top5_acc": 0.60156, "loss_cls": 3.74785, "loss": 3.74785, "time": 0.85012} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.02311, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34547, "top5_acc": 0.60906, "loss_cls": 3.73956, "loss": 3.73956, "time": 0.84256} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.02309, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34625, "top5_acc": 0.605, "loss_cls": 3.74494, "loss": 3.74494, "time": 0.84497} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.02307, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34078, "top5_acc": 0.60125, "loss_cls": 3.72511, "loss": 3.72511, "time": 0.85528} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.02304, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34625, "top5_acc": 0.59719, "loss_cls": 3.77037, "loss": 3.77037, "time": 0.85049} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.02302, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33453, "top5_acc": 0.5975, "loss_cls": 3.77985, "loss": 3.77985, "time": 0.84943} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.023, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34484, "top5_acc": 0.59719, "loss_cls": 3.74889, "loss": 3.74889, "time": 0.85005} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.02297, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33891, "top5_acc": 0.59406, "loss_cls": 3.77307, "loss": 3.77307, "time": 0.84948} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.02295, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33531, "top5_acc": 0.58453, "loss_cls": 3.80522, "loss": 3.80522, "time": 0.85273} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.02293, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33031, "top5_acc": 0.58859, "loss_cls": 3.80321, "loss": 3.80321, "time": 0.8557} +{"mode": "train", "epoch": 103, "iter": 1300, "lr": 0.0229, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32969, "top5_acc": 0.58078, "loss_cls": 3.83196, "loss": 3.83196, "time": 0.8526} +{"mode": "train", "epoch": 103, "iter": 1400, "lr": 0.02288, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32938, "top5_acc": 0.58766, "loss_cls": 3.82025, "loss": 3.82025, "time": 0.8527} +{"mode": "train", "epoch": 103, "iter": 1500, "lr": 0.02286, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33625, "top5_acc": 0.59203, "loss_cls": 3.79518, "loss": 3.79518, "time": 0.8482} +{"mode": "train", "epoch": 103, "iter": 1600, "lr": 0.02283, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33594, "top5_acc": 0.60141, "loss_cls": 3.74713, "loss": 3.74713, "time": 0.84787} +{"mode": "train", "epoch": 103, "iter": 1700, "lr": 0.02281, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33031, "top5_acc": 0.58688, "loss_cls": 3.81501, "loss": 3.81501, "time": 0.85127} +{"mode": "train", "epoch": 103, "iter": 1800, "lr": 0.02279, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32641, "top5_acc": 0.5875, "loss_cls": 3.82692, "loss": 3.82692, "time": 0.84729} +{"mode": "train", "epoch": 103, "iter": 1900, "lr": 0.02276, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.34172, "top5_acc": 0.60172, "loss_cls": 3.77044, "loss": 3.77044, "time": 0.84717} +{"mode": "train", "epoch": 103, "iter": 2000, "lr": 0.02274, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32531, "top5_acc": 0.59219, "loss_cls": 3.81214, "loss": 3.81214, "time": 0.84241} +{"mode": "train", "epoch": 103, "iter": 2100, "lr": 0.02272, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34031, "top5_acc": 0.59422, "loss_cls": 3.76877, "loss": 3.76877, "time": 0.84559} +{"mode": "train", "epoch": 103, "iter": 2200, "lr": 0.02269, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33734, "top5_acc": 0.59766, "loss_cls": 3.76041, "loss": 3.76041, "time": 0.85172} +{"mode": "train", "epoch": 103, "iter": 2300, "lr": 0.02267, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33984, "top5_acc": 0.59547, "loss_cls": 3.78004, "loss": 3.78004, "time": 0.83849} +{"mode": "train", "epoch": 103, "iter": 2400, "lr": 0.02264, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32844, "top5_acc": 0.58516, "loss_cls": 3.8498, "loss": 3.8498, "time": 0.84581} +{"mode": "train", "epoch": 103, "iter": 2500, "lr": 0.02262, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.33312, "top5_acc": 0.59578, "loss_cls": 3.75818, "loss": 3.75818, "time": 0.84852} +{"mode": "train", "epoch": 103, "iter": 2600, "lr": 0.0226, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33281, "top5_acc": 0.59266, "loss_cls": 3.795, "loss": 3.795, "time": 0.84752} +{"mode": "train", "epoch": 103, "iter": 2700, "lr": 0.02257, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33812, "top5_acc": 0.58766, "loss_cls": 3.78029, "loss": 3.78029, "time": 0.84858} +{"mode": "train", "epoch": 103, "iter": 2800, "lr": 0.02255, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33906, "top5_acc": 0.59625, "loss_cls": 3.76063, "loss": 3.76063, "time": 0.84996} +{"mode": "train", "epoch": 103, "iter": 2900, "lr": 0.02253, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32703, "top5_acc": 0.57375, "loss_cls": 3.83251, "loss": 3.83251, "time": 0.85096} +{"mode": "train", "epoch": 103, "iter": 3000, "lr": 0.0225, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33344, "top5_acc": 0.59141, "loss_cls": 3.79999, "loss": 3.79999, "time": 0.84557} +{"mode": "train", "epoch": 103, "iter": 3100, "lr": 0.02248, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33062, "top5_acc": 0.59188, "loss_cls": 3.81456, "loss": 3.81456, "time": 0.84757} +{"mode": "train", "epoch": 103, "iter": 3200, "lr": 0.02246, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32906, "top5_acc": 0.59125, "loss_cls": 3.79027, "loss": 3.79027, "time": 0.84583} +{"mode": "train", "epoch": 103, "iter": 3300, "lr": 0.02243, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32172, "top5_acc": 0.58094, "loss_cls": 3.83395, "loss": 3.83395, "time": 0.84743} +{"mode": "train", "epoch": 103, "iter": 3400, "lr": 0.02241, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.32047, "top5_acc": 0.58047, "loss_cls": 3.84666, "loss": 3.84666, "time": 0.84629} +{"mode": "train", "epoch": 103, "iter": 3500, "lr": 0.02239, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33516, "top5_acc": 0.58234, "loss_cls": 3.83587, "loss": 3.83587, "time": 0.85144} +{"mode": "train", "epoch": 103, "iter": 3600, "lr": 0.02236, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32094, "top5_acc": 0.58547, "loss_cls": 3.84281, "loss": 3.84281, "time": 0.84012} +{"mode": "train", "epoch": 103, "iter": 3700, "lr": 0.02234, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34172, "top5_acc": 0.59391, "loss_cls": 3.75532, "loss": 3.75532, "time": 0.84596} +{"mode": "val", "epoch": 103, "iter": 309, "lr": 0.02233, "top1_acc": 0.28182, "top5_acc": 0.5214, "mean_class_accuracy": 0.28163} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.02231, "memory": 15990, "data_time": 1.50485, "top1_acc": 0.34391, "top5_acc": 0.5975, "loss_cls": 3.73096, "loss": 3.73096, "time": 2.53227} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.02228, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33891, "top5_acc": 0.60188, "loss_cls": 3.70693, "loss": 3.70693, "time": 0.85289} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.02226, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33062, "top5_acc": 0.59109, "loss_cls": 3.76686, "loss": 3.76686, "time": 0.85554} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.02224, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33234, "top5_acc": 0.5875, "loss_cls": 3.79746, "loss": 3.79746, "time": 0.8585} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.02221, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34312, "top5_acc": 0.59781, "loss_cls": 3.73165, "loss": 3.73165, "time": 0.85735} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.02219, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35172, "top5_acc": 0.60688, "loss_cls": 3.67584, "loss": 3.67584, "time": 0.85356} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.02217, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35016, "top5_acc": 0.5975, "loss_cls": 3.74028, "loss": 3.74028, "time": 0.85265} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.02214, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3375, "top5_acc": 0.59156, "loss_cls": 3.77885, "loss": 3.77885, "time": 0.86117} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.02212, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.33797, "top5_acc": 0.60469, "loss_cls": 3.72193, "loss": 3.72193, "time": 0.85191} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.0221, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34891, "top5_acc": 0.60375, "loss_cls": 3.71526, "loss": 3.71526, "time": 0.85309} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.02208, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34016, "top5_acc": 0.59547, "loss_cls": 3.76773, "loss": 3.76773, "time": 0.85804} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.02205, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34219, "top5_acc": 0.58719, "loss_cls": 3.78034, "loss": 3.78034, "time": 0.85883} +{"mode": "train", "epoch": 104, "iter": 1300, "lr": 0.02203, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34781, "top5_acc": 0.60594, "loss_cls": 3.72522, "loss": 3.72522, "time": 0.85759} +{"mode": "train", "epoch": 104, "iter": 1400, "lr": 0.02201, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34875, "top5_acc": 0.59266, "loss_cls": 3.75907, "loss": 3.75907, "time": 0.86165} +{"mode": "train", "epoch": 104, "iter": 1500, "lr": 0.02198, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.33469, "top5_acc": 0.59156, "loss_cls": 3.78355, "loss": 3.78355, "time": 0.85826} +{"mode": "train", "epoch": 104, "iter": 1600, "lr": 0.02196, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33328, "top5_acc": 0.59406, "loss_cls": 3.78225, "loss": 3.78225, "time": 0.85801} +{"mode": "train", "epoch": 104, "iter": 1700, "lr": 0.02194, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.34297, "top5_acc": 0.59859, "loss_cls": 3.7256, "loss": 3.7256, "time": 0.84864} +{"mode": "train", "epoch": 104, "iter": 1800, "lr": 0.02191, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34188, "top5_acc": 0.59594, "loss_cls": 3.78727, "loss": 3.78727, "time": 0.84848} +{"mode": "train", "epoch": 104, "iter": 1900, "lr": 0.02189, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33391, "top5_acc": 0.58938, "loss_cls": 3.80701, "loss": 3.80701, "time": 0.85339} +{"mode": "train", "epoch": 104, "iter": 2000, "lr": 0.02187, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33516, "top5_acc": 0.59094, "loss_cls": 3.77624, "loss": 3.77624, "time": 0.85113} +{"mode": "train", "epoch": 104, "iter": 2100, "lr": 0.02184, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.33016, "top5_acc": 0.59672, "loss_cls": 3.78567, "loss": 3.78567, "time": 0.85578} +{"mode": "train", "epoch": 104, "iter": 2200, "lr": 0.02182, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.34062, "top5_acc": 0.59188, "loss_cls": 3.77765, "loss": 3.77765, "time": 0.85417} +{"mode": "train", "epoch": 104, "iter": 2300, "lr": 0.0218, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34594, "top5_acc": 0.59719, "loss_cls": 3.7512, "loss": 3.7512, "time": 0.85204} +{"mode": "train", "epoch": 104, "iter": 2400, "lr": 0.02177, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33328, "top5_acc": 0.59453, "loss_cls": 3.79506, "loss": 3.79506, "time": 0.85251} +{"mode": "train", "epoch": 104, "iter": 2500, "lr": 0.02175, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33641, "top5_acc": 0.58672, "loss_cls": 3.80145, "loss": 3.80145, "time": 0.85485} +{"mode": "train", "epoch": 104, "iter": 2600, "lr": 0.02173, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34156, "top5_acc": 0.595, "loss_cls": 3.77511, "loss": 3.77511, "time": 0.85264} +{"mode": "train", "epoch": 104, "iter": 2700, "lr": 0.02171, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33938, "top5_acc": 0.59016, "loss_cls": 3.77832, "loss": 3.77832, "time": 0.85012} +{"mode": "train", "epoch": 104, "iter": 2800, "lr": 0.02168, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33281, "top5_acc": 0.59828, "loss_cls": 3.77101, "loss": 3.77101, "time": 0.85296} +{"mode": "train", "epoch": 104, "iter": 2900, "lr": 0.02166, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33812, "top5_acc": 0.59047, "loss_cls": 3.79037, "loss": 3.79037, "time": 0.84771} +{"mode": "train", "epoch": 104, "iter": 3000, "lr": 0.02164, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.335, "top5_acc": 0.58719, "loss_cls": 3.78903, "loss": 3.78903, "time": 0.85899} +{"mode": "train", "epoch": 104, "iter": 3100, "lr": 0.02161, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33609, "top5_acc": 0.59609, "loss_cls": 3.79678, "loss": 3.79678, "time": 0.85157} +{"mode": "train", "epoch": 104, "iter": 3200, "lr": 0.02159, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34469, "top5_acc": 0.59312, "loss_cls": 3.75812, "loss": 3.75812, "time": 0.85546} +{"mode": "train", "epoch": 104, "iter": 3300, "lr": 0.02157, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33391, "top5_acc": 0.58766, "loss_cls": 3.78934, "loss": 3.78934, "time": 0.84936} +{"mode": "train", "epoch": 104, "iter": 3400, "lr": 0.02154, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32562, "top5_acc": 0.58125, "loss_cls": 3.85495, "loss": 3.85495, "time": 0.85065} +{"mode": "train", "epoch": 104, "iter": 3500, "lr": 0.02152, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33516, "top5_acc": 0.59, "loss_cls": 3.7789, "loss": 3.7789, "time": 0.855} +{"mode": "train", "epoch": 104, "iter": 3600, "lr": 0.0215, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33203, "top5_acc": 0.58328, "loss_cls": 3.81349, "loss": 3.81349, "time": 0.85196} +{"mode": "train", "epoch": 104, "iter": 3700, "lr": 0.02148, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33188, "top5_acc": 0.58234, "loss_cls": 3.82339, "loss": 3.82339, "time": 0.85206} +{"mode": "val", "epoch": 104, "iter": 309, "lr": 0.02146, "top1_acc": 0.28096, "top5_acc": 0.53183, "mean_class_accuracy": 0.28072} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.02144, "memory": 15990, "data_time": 1.6024, "top1_acc": 0.34953, "top5_acc": 0.60766, "loss_cls": 3.68807, "loss": 3.68807, "time": 2.62485} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.02142, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35141, "top5_acc": 0.61094, "loss_cls": 3.69768, "loss": 3.69768, "time": 0.8476} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.0214, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34531, "top5_acc": 0.60656, "loss_cls": 3.72123, "loss": 3.72123, "time": 0.84265} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.02137, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34422, "top5_acc": 0.60172, "loss_cls": 3.70527, "loss": 3.70527, "time": 0.85223} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.02135, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34078, "top5_acc": 0.60359, "loss_cls": 3.70418, "loss": 3.70418, "time": 0.85264} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.02133, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34406, "top5_acc": 0.605, "loss_cls": 3.7286, "loss": 3.7286, "time": 0.8482} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.0213, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33469, "top5_acc": 0.59219, "loss_cls": 3.78485, "loss": 3.78485, "time": 0.84624} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.02128, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34047, "top5_acc": 0.60281, "loss_cls": 3.73773, "loss": 3.73773, "time": 0.84156} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.02126, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34125, "top5_acc": 0.59703, "loss_cls": 3.7502, "loss": 3.7502, "time": 0.84256} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.02124, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34172, "top5_acc": 0.60719, "loss_cls": 3.72925, "loss": 3.72925, "time": 0.83919} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.02121, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34641, "top5_acc": 0.59891, "loss_cls": 3.74234, "loss": 3.74234, "time": 0.83855} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.02119, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34141, "top5_acc": 0.59484, "loss_cls": 3.75096, "loss": 3.75096, "time": 0.84372} +{"mode": "train", "epoch": 105, "iter": 1300, "lr": 0.02117, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34094, "top5_acc": 0.59828, "loss_cls": 3.7383, "loss": 3.7383, "time": 0.83981} +{"mode": "train", "epoch": 105, "iter": 1400, "lr": 0.02114, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33812, "top5_acc": 0.60766, "loss_cls": 3.72568, "loss": 3.72568, "time": 0.84327} +{"mode": "train", "epoch": 105, "iter": 1500, "lr": 0.02112, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33828, "top5_acc": 0.59531, "loss_cls": 3.7422, "loss": 3.7422, "time": 0.84761} +{"mode": "train", "epoch": 105, "iter": 1600, "lr": 0.0211, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34031, "top5_acc": 0.59453, "loss_cls": 3.76602, "loss": 3.76602, "time": 0.84641} +{"mode": "train", "epoch": 105, "iter": 1700, "lr": 0.02108, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33906, "top5_acc": 0.59094, "loss_cls": 3.79339, "loss": 3.79339, "time": 0.84244} +{"mode": "train", "epoch": 105, "iter": 1800, "lr": 0.02105, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33781, "top5_acc": 0.59531, "loss_cls": 3.78266, "loss": 3.78266, "time": 0.84786} +{"mode": "train", "epoch": 105, "iter": 1900, "lr": 0.02103, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34125, "top5_acc": 0.59719, "loss_cls": 3.7502, "loss": 3.7502, "time": 0.84431} +{"mode": "train", "epoch": 105, "iter": 2000, "lr": 0.02101, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34062, "top5_acc": 0.59016, "loss_cls": 3.76039, "loss": 3.76039, "time": 0.84247} +{"mode": "train", "epoch": 105, "iter": 2100, "lr": 0.02098, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33719, "top5_acc": 0.59094, "loss_cls": 3.79155, "loss": 3.79155, "time": 0.84899} +{"mode": "train", "epoch": 105, "iter": 2200, "lr": 0.02096, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33359, "top5_acc": 0.60062, "loss_cls": 3.79984, "loss": 3.79984, "time": 0.84851} +{"mode": "train", "epoch": 105, "iter": 2300, "lr": 0.02094, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32359, "top5_acc": 0.59859, "loss_cls": 3.8012, "loss": 3.8012, "time": 0.84692} +{"mode": "train", "epoch": 105, "iter": 2400, "lr": 0.02092, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33578, "top5_acc": 0.59875, "loss_cls": 3.7744, "loss": 3.7744, "time": 0.84823} +{"mode": "train", "epoch": 105, "iter": 2500, "lr": 0.02089, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.33641, "top5_acc": 0.59156, "loss_cls": 3.79185, "loss": 3.79185, "time": 0.84138} +{"mode": "train", "epoch": 105, "iter": 2600, "lr": 0.02087, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.33969, "top5_acc": 0.59484, "loss_cls": 3.76951, "loss": 3.76951, "time": 0.83954} +{"mode": "train", "epoch": 105, "iter": 2700, "lr": 0.02085, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34016, "top5_acc": 0.59609, "loss_cls": 3.77313, "loss": 3.77313, "time": 0.8407} +{"mode": "train", "epoch": 105, "iter": 2800, "lr": 0.02083, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.34547, "top5_acc": 0.60266, "loss_cls": 3.70932, "loss": 3.70932, "time": 0.84795} +{"mode": "train", "epoch": 105, "iter": 2900, "lr": 0.0208, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33875, "top5_acc": 0.59781, "loss_cls": 3.74971, "loss": 3.74971, "time": 0.8509} +{"mode": "train", "epoch": 105, "iter": 3000, "lr": 0.02078, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34031, "top5_acc": 0.60484, "loss_cls": 3.76361, "loss": 3.76361, "time": 0.84743} +{"mode": "train", "epoch": 105, "iter": 3100, "lr": 0.02076, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34297, "top5_acc": 0.59344, "loss_cls": 3.78146, "loss": 3.78146, "time": 0.84724} +{"mode": "train", "epoch": 105, "iter": 3200, "lr": 0.02073, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34812, "top5_acc": 0.59984, "loss_cls": 3.7301, "loss": 3.7301, "time": 0.84209} +{"mode": "train", "epoch": 105, "iter": 3300, "lr": 0.02071, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33562, "top5_acc": 0.59297, "loss_cls": 3.7713, "loss": 3.7713, "time": 0.84525} +{"mode": "train", "epoch": 105, "iter": 3400, "lr": 0.02069, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.34453, "top5_acc": 0.61219, "loss_cls": 3.7031, "loss": 3.7031, "time": 0.85158} +{"mode": "train", "epoch": 105, "iter": 3500, "lr": 0.02067, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33016, "top5_acc": 0.58766, "loss_cls": 3.80928, "loss": 3.80928, "time": 0.84332} +{"mode": "train", "epoch": 105, "iter": 3600, "lr": 0.02064, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34734, "top5_acc": 0.59469, "loss_cls": 3.73883, "loss": 3.73883, "time": 0.84576} +{"mode": "train", "epoch": 105, "iter": 3700, "lr": 0.02062, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34078, "top5_acc": 0.59531, "loss_cls": 3.72075, "loss": 3.72075, "time": 0.84168} +{"mode": "val", "epoch": 105, "iter": 309, "lr": 0.02061, "top1_acc": 0.26749, "top5_acc": 0.51826, "mean_class_accuracy": 0.26724} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.02059, "memory": 15990, "data_time": 1.50162, "top1_acc": 0.34891, "top5_acc": 0.61344, "loss_cls": 3.69538, "loss": 3.69538, "time": 2.53917} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.02057, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35656, "top5_acc": 0.60719, "loss_cls": 3.69053, "loss": 3.69053, "time": 0.85495} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.02054, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.34109, "top5_acc": 0.59359, "loss_cls": 3.75397, "loss": 3.75397, "time": 0.84888} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.02052, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34641, "top5_acc": 0.60344, "loss_cls": 3.72709, "loss": 3.72709, "time": 0.84956} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.0205, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35266, "top5_acc": 0.61469, "loss_cls": 3.70326, "loss": 3.70326, "time": 0.85146} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.02048, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35047, "top5_acc": 0.60969, "loss_cls": 3.70593, "loss": 3.70593, "time": 0.84657} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.02045, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36203, "top5_acc": 0.60578, "loss_cls": 3.6851, "loss": 3.6851, "time": 0.85454} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.02043, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34453, "top5_acc": 0.60625, "loss_cls": 3.70622, "loss": 3.70622, "time": 0.84918} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.02041, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35062, "top5_acc": 0.60406, "loss_cls": 3.69708, "loss": 3.69708, "time": 0.84688} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.02039, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34594, "top5_acc": 0.59844, "loss_cls": 3.72003, "loss": 3.72003, "time": 0.85615} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.02036, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34203, "top5_acc": 0.6, "loss_cls": 3.76438, "loss": 3.76438, "time": 0.85413} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.02034, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.34438, "top5_acc": 0.60594, "loss_cls": 3.7057, "loss": 3.7057, "time": 0.85255} +{"mode": "train", "epoch": 106, "iter": 1300, "lr": 0.02032, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.34109, "top5_acc": 0.59922, "loss_cls": 3.75928, "loss": 3.75928, "time": 0.85035} +{"mode": "train", "epoch": 106, "iter": 1400, "lr": 0.0203, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35375, "top5_acc": 0.60188, "loss_cls": 3.732, "loss": 3.732, "time": 0.85317} +{"mode": "train", "epoch": 106, "iter": 1500, "lr": 0.02027, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34375, "top5_acc": 0.60188, "loss_cls": 3.75079, "loss": 3.75079, "time": 0.85166} +{"mode": "train", "epoch": 106, "iter": 1600, "lr": 0.02025, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35922, "top5_acc": 0.61469, "loss_cls": 3.67821, "loss": 3.67821, "time": 0.8553} +{"mode": "train", "epoch": 106, "iter": 1700, "lr": 0.02023, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34031, "top5_acc": 0.59844, "loss_cls": 3.75586, "loss": 3.75586, "time": 0.85191} +{"mode": "train", "epoch": 106, "iter": 1800, "lr": 0.02021, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35016, "top5_acc": 0.60438, "loss_cls": 3.69655, "loss": 3.69655, "time": 0.84708} +{"mode": "train", "epoch": 106, "iter": 1900, "lr": 0.02018, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34688, "top5_acc": 0.60938, "loss_cls": 3.72079, "loss": 3.72079, "time": 0.84667} +{"mode": "train", "epoch": 106, "iter": 2000, "lr": 0.02016, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35344, "top5_acc": 0.60344, "loss_cls": 3.71066, "loss": 3.71066, "time": 0.85524} +{"mode": "train", "epoch": 106, "iter": 2100, "lr": 0.02014, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33547, "top5_acc": 0.59734, "loss_cls": 3.7497, "loss": 3.7497, "time": 0.85066} +{"mode": "train", "epoch": 106, "iter": 2200, "lr": 0.02012, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34656, "top5_acc": 0.60203, "loss_cls": 3.72151, "loss": 3.72151, "time": 0.84886} +{"mode": "train", "epoch": 106, "iter": 2300, "lr": 0.02009, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34141, "top5_acc": 0.59766, "loss_cls": 3.74057, "loss": 3.74057, "time": 0.85299} +{"mode": "train", "epoch": 106, "iter": 2400, "lr": 0.02007, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33812, "top5_acc": 0.59844, "loss_cls": 3.75849, "loss": 3.75849, "time": 0.85093} +{"mode": "train", "epoch": 106, "iter": 2500, "lr": 0.02005, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.34656, "top5_acc": 0.605, "loss_cls": 3.70744, "loss": 3.70744, "time": 0.85217} +{"mode": "train", "epoch": 106, "iter": 2600, "lr": 0.02003, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34391, "top5_acc": 0.60016, "loss_cls": 3.75066, "loss": 3.75066, "time": 0.84996} +{"mode": "train", "epoch": 106, "iter": 2700, "lr": 0.02, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34172, "top5_acc": 0.60703, "loss_cls": 3.74184, "loss": 3.74184, "time": 0.84876} +{"mode": "train", "epoch": 106, "iter": 2800, "lr": 0.01998, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35234, "top5_acc": 0.60672, "loss_cls": 3.73101, "loss": 3.73101, "time": 0.85907} +{"mode": "train", "epoch": 106, "iter": 2900, "lr": 0.01996, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34922, "top5_acc": 0.60609, "loss_cls": 3.71616, "loss": 3.71616, "time": 0.86059} +{"mode": "train", "epoch": 106, "iter": 3000, "lr": 0.01994, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33375, "top5_acc": 0.59531, "loss_cls": 3.77975, "loss": 3.77975, "time": 0.86155} +{"mode": "train", "epoch": 106, "iter": 3100, "lr": 0.01991, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34844, "top5_acc": 0.60188, "loss_cls": 3.7457, "loss": 3.7457, "time": 0.8552} +{"mode": "train", "epoch": 106, "iter": 3200, "lr": 0.01989, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33906, "top5_acc": 0.595, "loss_cls": 3.77294, "loss": 3.77294, "time": 0.85552} +{"mode": "train", "epoch": 106, "iter": 3300, "lr": 0.01987, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.3325, "top5_acc": 0.59391, "loss_cls": 3.75433, "loss": 3.75433, "time": 0.86497} +{"mode": "train", "epoch": 106, "iter": 3400, "lr": 0.01985, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34562, "top5_acc": 0.61047, "loss_cls": 3.71311, "loss": 3.71311, "time": 0.85237} +{"mode": "train", "epoch": 106, "iter": 3500, "lr": 0.01983, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34281, "top5_acc": 0.59266, "loss_cls": 3.78277, "loss": 3.78277, "time": 0.85401} +{"mode": "train", "epoch": 106, "iter": 3600, "lr": 0.0198, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34656, "top5_acc": 0.60484, "loss_cls": 3.715, "loss": 3.715, "time": 0.85203} +{"mode": "train", "epoch": 106, "iter": 3700, "lr": 0.01978, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.34703, "top5_acc": 0.60484, "loss_cls": 3.73996, "loss": 3.73996, "time": 0.86067} +{"mode": "val", "epoch": 106, "iter": 309, "lr": 0.01977, "top1_acc": 0.27878, "top5_acc": 0.52231, "mean_class_accuracy": 0.27846} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.01975, "memory": 15990, "data_time": 1.57483, "top1_acc": 0.35359, "top5_acc": 0.60797, "loss_cls": 3.6781, "loss": 3.6781, "time": 2.61371} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.01973, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34562, "top5_acc": 0.60906, "loss_cls": 3.70337, "loss": 3.70337, "time": 0.85071} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.0197, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35391, "top5_acc": 0.60734, "loss_cls": 3.65655, "loss": 3.65655, "time": 0.85309} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.01968, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35625, "top5_acc": 0.61969, "loss_cls": 3.62975, "loss": 3.62975, "time": 0.84862} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.01966, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35078, "top5_acc": 0.61187, "loss_cls": 3.70514, "loss": 3.70514, "time": 0.85232} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.01964, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36, "top5_acc": 0.61578, "loss_cls": 3.65582, "loss": 3.65582, "time": 0.8568} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.01961, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34719, "top5_acc": 0.61109, "loss_cls": 3.71781, "loss": 3.71781, "time": 0.85216} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.01959, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34594, "top5_acc": 0.60516, "loss_cls": 3.70904, "loss": 3.70904, "time": 0.85119} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.01957, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34688, "top5_acc": 0.60516, "loss_cls": 3.68737, "loss": 3.68737, "time": 0.85545} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.01955, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34828, "top5_acc": 0.615, "loss_cls": 3.6832, "loss": 3.6832, "time": 0.85274} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.01953, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35141, "top5_acc": 0.61875, "loss_cls": 3.67109, "loss": 3.67109, "time": 0.85349} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.0195, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35547, "top5_acc": 0.61703, "loss_cls": 3.65168, "loss": 3.65168, "time": 0.84949} +{"mode": "train", "epoch": 107, "iter": 1300, "lr": 0.01948, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34516, "top5_acc": 0.60391, "loss_cls": 3.70572, "loss": 3.70572, "time": 0.84969} +{"mode": "train", "epoch": 107, "iter": 1400, "lr": 0.01946, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.34938, "top5_acc": 0.60391, "loss_cls": 3.74913, "loss": 3.74913, "time": 0.85305} +{"mode": "train", "epoch": 107, "iter": 1500, "lr": 0.01944, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35047, "top5_acc": 0.61234, "loss_cls": 3.68494, "loss": 3.68494, "time": 0.84716} +{"mode": "train", "epoch": 107, "iter": 1600, "lr": 0.01942, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34188, "top5_acc": 0.60344, "loss_cls": 3.71632, "loss": 3.71632, "time": 0.85416} +{"mode": "train", "epoch": 107, "iter": 1700, "lr": 0.01939, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34109, "top5_acc": 0.59812, "loss_cls": 3.73948, "loss": 3.73948, "time": 0.84856} +{"mode": "train", "epoch": 107, "iter": 1800, "lr": 0.01937, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35281, "top5_acc": 0.60844, "loss_cls": 3.72382, "loss": 3.72382, "time": 0.85425} +{"mode": "train", "epoch": 107, "iter": 1900, "lr": 0.01935, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34453, "top5_acc": 0.60297, "loss_cls": 3.72769, "loss": 3.72769, "time": 0.85368} +{"mode": "train", "epoch": 107, "iter": 2000, "lr": 0.01933, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35094, "top5_acc": 0.60297, "loss_cls": 3.73841, "loss": 3.73841, "time": 0.8482} +{"mode": "train", "epoch": 107, "iter": 2100, "lr": 0.0193, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34188, "top5_acc": 0.59266, "loss_cls": 3.75921, "loss": 3.75921, "time": 0.85384} +{"mode": "train", "epoch": 107, "iter": 2200, "lr": 0.01928, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34047, "top5_acc": 0.60969, "loss_cls": 3.74201, "loss": 3.74201, "time": 0.85283} +{"mode": "train", "epoch": 107, "iter": 2300, "lr": 0.01926, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35094, "top5_acc": 0.60266, "loss_cls": 3.71012, "loss": 3.71012, "time": 0.85501} +{"mode": "train", "epoch": 107, "iter": 2400, "lr": 0.01924, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35484, "top5_acc": 0.60844, "loss_cls": 3.68881, "loss": 3.68881, "time": 0.84704} +{"mode": "train", "epoch": 107, "iter": 2500, "lr": 0.01922, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34031, "top5_acc": 0.59562, "loss_cls": 3.74652, "loss": 3.74652, "time": 0.84714} +{"mode": "train", "epoch": 107, "iter": 2600, "lr": 0.01919, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34875, "top5_acc": 0.59797, "loss_cls": 3.75467, "loss": 3.75467, "time": 0.85145} +{"mode": "train", "epoch": 107, "iter": 2700, "lr": 0.01917, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34578, "top5_acc": 0.60234, "loss_cls": 3.73093, "loss": 3.73093, "time": 0.85274} +{"mode": "train", "epoch": 107, "iter": 2800, "lr": 0.01915, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35031, "top5_acc": 0.60422, "loss_cls": 3.71704, "loss": 3.71704, "time": 0.85653} +{"mode": "train", "epoch": 107, "iter": 2900, "lr": 0.01913, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34578, "top5_acc": 0.60766, "loss_cls": 3.72382, "loss": 3.72382, "time": 0.85478} +{"mode": "train", "epoch": 107, "iter": 3000, "lr": 0.01911, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34609, "top5_acc": 0.60219, "loss_cls": 3.71274, "loss": 3.71274, "time": 0.85482} +{"mode": "train", "epoch": 107, "iter": 3100, "lr": 0.01908, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35234, "top5_acc": 0.60062, "loss_cls": 3.73416, "loss": 3.73416, "time": 0.84936} +{"mode": "train", "epoch": 107, "iter": 3200, "lr": 0.01906, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35109, "top5_acc": 0.60312, "loss_cls": 3.71802, "loss": 3.71802, "time": 0.85174} +{"mode": "train", "epoch": 107, "iter": 3300, "lr": 0.01904, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.345, "top5_acc": 0.59406, "loss_cls": 3.73257, "loss": 3.73257, "time": 0.85137} +{"mode": "train", "epoch": 107, "iter": 3400, "lr": 0.01902, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34734, "top5_acc": 0.60156, "loss_cls": 3.718, "loss": 3.718, "time": 0.85254} +{"mode": "train", "epoch": 107, "iter": 3500, "lr": 0.019, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34141, "top5_acc": 0.59984, "loss_cls": 3.71773, "loss": 3.71773, "time": 0.85579} +{"mode": "train", "epoch": 107, "iter": 3600, "lr": 0.01897, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35078, "top5_acc": 0.60781, "loss_cls": 3.6991, "loss": 3.6991, "time": 0.85297} +{"mode": "train", "epoch": 107, "iter": 3700, "lr": 0.01895, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34125, "top5_acc": 0.60094, "loss_cls": 3.73883, "loss": 3.73883, "time": 0.85276} +{"mode": "val", "epoch": 107, "iter": 309, "lr": 0.01894, "top1_acc": 0.29145, "top5_acc": 0.54359, "mean_class_accuracy": 0.2912} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.01892, "memory": 15990, "data_time": 1.61653, "top1_acc": 0.35781, "top5_acc": 0.61359, "loss_cls": 3.65813, "loss": 3.65813, "time": 2.649} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0189, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.355, "top5_acc": 0.61344, "loss_cls": 3.63759, "loss": 3.63759, "time": 0.85667} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.01888, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.34875, "top5_acc": 0.60078, "loss_cls": 3.72578, "loss": 3.72578, "time": 0.85298} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.01886, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35219, "top5_acc": 0.60922, "loss_cls": 3.67179, "loss": 3.67179, "time": 0.85299} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.01883, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35656, "top5_acc": 0.62047, "loss_cls": 3.63491, "loss": 3.63491, "time": 0.85445} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.01881, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35312, "top5_acc": 0.62172, "loss_cls": 3.64686, "loss": 3.64686, "time": 0.85269} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.01879, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36266, "top5_acc": 0.62141, "loss_cls": 3.60849, "loss": 3.60849, "time": 0.85701} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.01877, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34797, "top5_acc": 0.60734, "loss_cls": 3.70239, "loss": 3.70239, "time": 0.85051} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.01875, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35438, "top5_acc": 0.60891, "loss_cls": 3.67367, "loss": 3.67367, "time": 0.85029} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.01872, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35406, "top5_acc": 0.60594, "loss_cls": 3.67665, "loss": 3.67665, "time": 0.85194} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.0187, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35812, "top5_acc": 0.60906, "loss_cls": 3.66938, "loss": 3.66938, "time": 0.85436} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.01868, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35531, "top5_acc": 0.61531, "loss_cls": 3.67792, "loss": 3.67792, "time": 0.85018} +{"mode": "train", "epoch": 108, "iter": 1300, "lr": 0.01866, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35484, "top5_acc": 0.61359, "loss_cls": 3.65438, "loss": 3.65438, "time": 0.8518} +{"mode": "train", "epoch": 108, "iter": 1400, "lr": 0.01864, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35531, "top5_acc": 0.61938, "loss_cls": 3.65593, "loss": 3.65593, "time": 0.85632} +{"mode": "train", "epoch": 108, "iter": 1500, "lr": 0.01862, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35281, "top5_acc": 0.61281, "loss_cls": 3.6542, "loss": 3.6542, "time": 0.85607} +{"mode": "train", "epoch": 108, "iter": 1600, "lr": 0.01859, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35953, "top5_acc": 0.61156, "loss_cls": 3.65129, "loss": 3.65129, "time": 0.85709} +{"mode": "train", "epoch": 108, "iter": 1700, "lr": 0.01857, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.35734, "top5_acc": 0.60266, "loss_cls": 3.70337, "loss": 3.70337, "time": 0.85264} +{"mode": "train", "epoch": 108, "iter": 1800, "lr": 0.01855, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34609, "top5_acc": 0.59812, "loss_cls": 3.74757, "loss": 3.74757, "time": 0.84938} +{"mode": "train", "epoch": 108, "iter": 1900, "lr": 0.01853, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35172, "top5_acc": 0.61078, "loss_cls": 3.69184, "loss": 3.69184, "time": 0.85532} +{"mode": "train", "epoch": 108, "iter": 2000, "lr": 0.01851, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36109, "top5_acc": 0.61344, "loss_cls": 3.6574, "loss": 3.6574, "time": 0.85462} +{"mode": "train", "epoch": 108, "iter": 2100, "lr": 0.01848, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35641, "top5_acc": 0.60391, "loss_cls": 3.70582, "loss": 3.70582, "time": 0.85204} +{"mode": "train", "epoch": 108, "iter": 2200, "lr": 0.01846, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34625, "top5_acc": 0.60156, "loss_cls": 3.71586, "loss": 3.71586, "time": 0.84924} +{"mode": "train", "epoch": 108, "iter": 2300, "lr": 0.01844, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34906, "top5_acc": 0.60156, "loss_cls": 3.7381, "loss": 3.7381, "time": 0.84985} +{"mode": "train", "epoch": 108, "iter": 2400, "lr": 0.01842, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35234, "top5_acc": 0.61, "loss_cls": 3.6761, "loss": 3.6761, "time": 0.8536} +{"mode": "train", "epoch": 108, "iter": 2500, "lr": 0.0184, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34172, "top5_acc": 0.59594, "loss_cls": 3.73998, "loss": 3.73998, "time": 0.85185} +{"mode": "train", "epoch": 108, "iter": 2600, "lr": 0.01838, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34703, "top5_acc": 0.60781, "loss_cls": 3.7051, "loss": 3.7051, "time": 0.8516} +{"mode": "train", "epoch": 108, "iter": 2700, "lr": 0.01835, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34641, "top5_acc": 0.60031, "loss_cls": 3.71863, "loss": 3.71863, "time": 0.85196} +{"mode": "train", "epoch": 108, "iter": 2800, "lr": 0.01833, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35078, "top5_acc": 0.61484, "loss_cls": 3.67215, "loss": 3.67215, "time": 0.8495} +{"mode": "train", "epoch": 108, "iter": 2900, "lr": 0.01831, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34328, "top5_acc": 0.6, "loss_cls": 3.71512, "loss": 3.71512, "time": 0.85001} +{"mode": "train", "epoch": 108, "iter": 3000, "lr": 0.01829, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34328, "top5_acc": 0.6025, "loss_cls": 3.71882, "loss": 3.71882, "time": 0.84871} +{"mode": "train", "epoch": 108, "iter": 3100, "lr": 0.01827, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34312, "top5_acc": 0.60344, "loss_cls": 3.73283, "loss": 3.73283, "time": 0.84993} +{"mode": "train", "epoch": 108, "iter": 3200, "lr": 0.01825, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35328, "top5_acc": 0.59969, "loss_cls": 3.71172, "loss": 3.71172, "time": 0.85321} +{"mode": "train", "epoch": 108, "iter": 3300, "lr": 0.01823, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33219, "top5_acc": 0.59375, "loss_cls": 3.79279, "loss": 3.79279, "time": 0.8546} +{"mode": "train", "epoch": 108, "iter": 3400, "lr": 0.0182, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34547, "top5_acc": 0.60109, "loss_cls": 3.75084, "loss": 3.75084, "time": 0.84881} +{"mode": "train", "epoch": 108, "iter": 3500, "lr": 0.01818, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35469, "top5_acc": 0.61266, "loss_cls": 3.68489, "loss": 3.68489, "time": 0.84343} +{"mode": "train", "epoch": 108, "iter": 3600, "lr": 0.01816, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35031, "top5_acc": 0.59562, "loss_cls": 3.72005, "loss": 3.72005, "time": 0.8514} +{"mode": "train", "epoch": 108, "iter": 3700, "lr": 0.01814, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35781, "top5_acc": 0.60484, "loss_cls": 3.68483, "loss": 3.68483, "time": 0.86015} +{"mode": "val", "epoch": 108, "iter": 309, "lr": 0.01813, "top1_acc": 0.28856, "top5_acc": 0.54166, "mean_class_accuracy": 0.2883} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.01811, "memory": 15990, "data_time": 1.56392, "top1_acc": 0.35672, "top5_acc": 0.61234, "loss_cls": 3.65647, "loss": 3.65647, "time": 2.58386} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.01809, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36938, "top5_acc": 0.62516, "loss_cls": 3.59514, "loss": 3.59514, "time": 0.8483} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.01806, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35062, "top5_acc": 0.61781, "loss_cls": 3.6631, "loss": 3.6631, "time": 0.85} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.01804, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35391, "top5_acc": 0.61766, "loss_cls": 3.65129, "loss": 3.65129, "time": 0.85153} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.01802, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36266, "top5_acc": 0.61766, "loss_cls": 3.63329, "loss": 3.63329, "time": 0.86374} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.018, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35984, "top5_acc": 0.61547, "loss_cls": 3.65177, "loss": 3.65177, "time": 0.85729} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.01798, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.35703, "top5_acc": 0.61719, "loss_cls": 3.64587, "loss": 3.64587, "time": 0.85665} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.01796, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35656, "top5_acc": 0.61312, "loss_cls": 3.65053, "loss": 3.65053, "time": 0.85737} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.01794, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35734, "top5_acc": 0.61719, "loss_cls": 3.64069, "loss": 3.64069, "time": 0.85568} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.01791, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35594, "top5_acc": 0.61641, "loss_cls": 3.65036, "loss": 3.65036, "time": 0.85843} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.01789, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34922, "top5_acc": 0.60844, "loss_cls": 3.68887, "loss": 3.68887, "time": 0.86118} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.01787, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35219, "top5_acc": 0.60672, "loss_cls": 3.66312, "loss": 3.66312, "time": 0.85354} +{"mode": "train", "epoch": 109, "iter": 1300, "lr": 0.01785, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35891, "top5_acc": 0.61438, "loss_cls": 3.6491, "loss": 3.6491, "time": 0.85797} +{"mode": "train", "epoch": 109, "iter": 1400, "lr": 0.01783, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35547, "top5_acc": 0.60516, "loss_cls": 3.68831, "loss": 3.68831, "time": 0.85744} +{"mode": "train", "epoch": 109, "iter": 1500, "lr": 0.01781, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36375, "top5_acc": 0.62078, "loss_cls": 3.64276, "loss": 3.64276, "time": 0.85617} +{"mode": "train", "epoch": 109, "iter": 1600, "lr": 0.01779, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35781, "top5_acc": 0.61469, "loss_cls": 3.67097, "loss": 3.67097, "time": 0.85488} +{"mode": "train", "epoch": 109, "iter": 1700, "lr": 0.01776, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.35875, "top5_acc": 0.62047, "loss_cls": 3.62653, "loss": 3.62653, "time": 0.8563} +{"mode": "train", "epoch": 109, "iter": 1800, "lr": 0.01774, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.35578, "top5_acc": 0.61187, "loss_cls": 3.64326, "loss": 3.64326, "time": 0.85426} +{"mode": "train", "epoch": 109, "iter": 1900, "lr": 0.01772, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.355, "top5_acc": 0.61391, "loss_cls": 3.6487, "loss": 3.6487, "time": 0.85271} +{"mode": "train", "epoch": 109, "iter": 2000, "lr": 0.0177, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.35531, "top5_acc": 0.61219, "loss_cls": 3.65995, "loss": 3.65995, "time": 0.85176} +{"mode": "train", "epoch": 109, "iter": 2100, "lr": 0.01768, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34672, "top5_acc": 0.60156, "loss_cls": 3.71918, "loss": 3.71918, "time": 0.84109} +{"mode": "train", "epoch": 109, "iter": 2200, "lr": 0.01766, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34078, "top5_acc": 0.60453, "loss_cls": 3.71403, "loss": 3.71403, "time": 0.83806} +{"mode": "train", "epoch": 109, "iter": 2300, "lr": 0.01764, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.34312, "top5_acc": 0.61031, "loss_cls": 3.68623, "loss": 3.68623, "time": 0.84788} +{"mode": "train", "epoch": 109, "iter": 2400, "lr": 0.01761, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.33734, "top5_acc": 0.6, "loss_cls": 3.72989, "loss": 3.72989, "time": 0.84878} +{"mode": "train", "epoch": 109, "iter": 2500, "lr": 0.01759, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36, "top5_acc": 0.62234, "loss_cls": 3.62211, "loss": 3.62211, "time": 0.84395} +{"mode": "train", "epoch": 109, "iter": 2600, "lr": 0.01757, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.33891, "top5_acc": 0.59438, "loss_cls": 3.756, "loss": 3.756, "time": 0.85032} +{"mode": "train", "epoch": 109, "iter": 2700, "lr": 0.01755, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35422, "top5_acc": 0.61016, "loss_cls": 3.69137, "loss": 3.69137, "time": 0.84977} +{"mode": "train", "epoch": 109, "iter": 2800, "lr": 0.01753, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34844, "top5_acc": 0.60062, "loss_cls": 3.72354, "loss": 3.72354, "time": 0.84459} +{"mode": "train", "epoch": 109, "iter": 2900, "lr": 0.01751, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35156, "top5_acc": 0.61328, "loss_cls": 3.66637, "loss": 3.66637, "time": 0.8406} +{"mode": "train", "epoch": 109, "iter": 3000, "lr": 0.01749, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35812, "top5_acc": 0.6125, "loss_cls": 3.66723, "loss": 3.66723, "time": 0.84291} +{"mode": "train", "epoch": 109, "iter": 3100, "lr": 0.01747, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35609, "top5_acc": 0.61344, "loss_cls": 3.6535, "loss": 3.6535, "time": 0.84322} +{"mode": "train", "epoch": 109, "iter": 3200, "lr": 0.01744, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35406, "top5_acc": 0.61234, "loss_cls": 3.67666, "loss": 3.67666, "time": 0.84387} +{"mode": "train", "epoch": 109, "iter": 3300, "lr": 0.01742, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35062, "top5_acc": 0.60672, "loss_cls": 3.73121, "loss": 3.73121, "time": 0.84861} +{"mode": "train", "epoch": 109, "iter": 3400, "lr": 0.0174, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34844, "top5_acc": 0.60531, "loss_cls": 3.69757, "loss": 3.69757, "time": 0.84668} +{"mode": "train", "epoch": 109, "iter": 3500, "lr": 0.01738, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35406, "top5_acc": 0.61766, "loss_cls": 3.64826, "loss": 3.64826, "time": 0.85029} +{"mode": "train", "epoch": 109, "iter": 3600, "lr": 0.01736, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3525, "top5_acc": 0.60484, "loss_cls": 3.70127, "loss": 3.70127, "time": 0.84538} +{"mode": "train", "epoch": 109, "iter": 3700, "lr": 0.01734, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34203, "top5_acc": 0.6075, "loss_cls": 3.72428, "loss": 3.72428, "time": 0.84561} +{"mode": "val", "epoch": 109, "iter": 309, "lr": 0.01733, "top1_acc": 0.29676, "top5_acc": 0.54566, "mean_class_accuracy": 0.29655} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.01731, "memory": 15990, "data_time": 1.52642, "top1_acc": 0.37312, "top5_acc": 0.62516, "loss_cls": 3.59699, "loss": 3.59699, "time": 2.56325} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.01729, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35625, "top5_acc": 0.60359, "loss_cls": 3.67526, "loss": 3.67526, "time": 0.84576} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.01727, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36109, "top5_acc": 0.61469, "loss_cls": 3.61862, "loss": 3.61862, "time": 0.84801} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.01724, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36828, "top5_acc": 0.62859, "loss_cls": 3.60747, "loss": 3.60747, "time": 0.84938} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.01722, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36391, "top5_acc": 0.62219, "loss_cls": 3.63808, "loss": 3.63808, "time": 0.84282} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.0172, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36375, "top5_acc": 0.625, "loss_cls": 3.5965, "loss": 3.5965, "time": 0.84417} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.01718, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34562, "top5_acc": 0.61406, "loss_cls": 3.66089, "loss": 3.66089, "time": 0.84284} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.01716, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36344, "top5_acc": 0.62703, "loss_cls": 3.60713, "loss": 3.60713, "time": 0.8479} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.01714, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35562, "top5_acc": 0.61609, "loss_cls": 3.66207, "loss": 3.66207, "time": 0.85526} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.01712, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.3625, "top5_acc": 0.61359, "loss_cls": 3.63439, "loss": 3.63439, "time": 0.85256} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.0171, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36578, "top5_acc": 0.61453, "loss_cls": 3.63508, "loss": 3.63508, "time": 0.84982} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.01708, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35578, "top5_acc": 0.61641, "loss_cls": 3.63868, "loss": 3.63868, "time": 0.84518} +{"mode": "train", "epoch": 110, "iter": 1300, "lr": 0.01705, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35891, "top5_acc": 0.60781, "loss_cls": 3.66188, "loss": 3.66188, "time": 0.84362} +{"mode": "train", "epoch": 110, "iter": 1400, "lr": 0.01703, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34828, "top5_acc": 0.61078, "loss_cls": 3.6701, "loss": 3.6701, "time": 0.84148} +{"mode": "train", "epoch": 110, "iter": 1500, "lr": 0.01701, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.3625, "top5_acc": 0.62438, "loss_cls": 3.64147, "loss": 3.64147, "time": 0.84672} +{"mode": "train", "epoch": 110, "iter": 1600, "lr": 0.01699, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35516, "top5_acc": 0.61516, "loss_cls": 3.66839, "loss": 3.66839, "time": 0.8461} +{"mode": "train", "epoch": 110, "iter": 1700, "lr": 0.01697, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.365, "top5_acc": 0.61391, "loss_cls": 3.63175, "loss": 3.63175, "time": 0.84646} +{"mode": "train", "epoch": 110, "iter": 1800, "lr": 0.01695, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35812, "top5_acc": 0.61031, "loss_cls": 3.67867, "loss": 3.67867, "time": 0.84377} +{"mode": "train", "epoch": 110, "iter": 1900, "lr": 0.01693, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.3675, "top5_acc": 0.62219, "loss_cls": 3.61762, "loss": 3.61762, "time": 0.85155} +{"mode": "train", "epoch": 110, "iter": 2000, "lr": 0.01691, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35609, "top5_acc": 0.61391, "loss_cls": 3.67845, "loss": 3.67845, "time": 0.84797} +{"mode": "train", "epoch": 110, "iter": 2100, "lr": 0.01689, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36094, "top5_acc": 0.62328, "loss_cls": 3.59964, "loss": 3.59964, "time": 0.84775} +{"mode": "train", "epoch": 110, "iter": 2200, "lr": 0.01687, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35031, "top5_acc": 0.60938, "loss_cls": 3.66526, "loss": 3.66526, "time": 0.83921} +{"mode": "train", "epoch": 110, "iter": 2300, "lr": 0.01685, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36016, "top5_acc": 0.61719, "loss_cls": 3.6446, "loss": 3.6446, "time": 0.84735} +{"mode": "train", "epoch": 110, "iter": 2400, "lr": 0.01682, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35984, "top5_acc": 0.61078, "loss_cls": 3.66166, "loss": 3.66166, "time": 0.84848} +{"mode": "train", "epoch": 110, "iter": 2500, "lr": 0.0168, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35562, "top5_acc": 0.61328, "loss_cls": 3.6651, "loss": 3.6651, "time": 0.83823} +{"mode": "train", "epoch": 110, "iter": 2600, "lr": 0.01678, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35312, "top5_acc": 0.6175, "loss_cls": 3.64767, "loss": 3.64767, "time": 0.85051} +{"mode": "train", "epoch": 110, "iter": 2700, "lr": 0.01676, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35438, "top5_acc": 0.61484, "loss_cls": 3.66632, "loss": 3.66632, "time": 0.84466} +{"mode": "train", "epoch": 110, "iter": 2800, "lr": 0.01674, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34922, "top5_acc": 0.61141, "loss_cls": 3.66145, "loss": 3.66145, "time": 0.84774} +{"mode": "train", "epoch": 110, "iter": 2900, "lr": 0.01672, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35656, "top5_acc": 0.61234, "loss_cls": 3.64848, "loss": 3.64848, "time": 0.84804} +{"mode": "train", "epoch": 110, "iter": 3000, "lr": 0.0167, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35406, "top5_acc": 0.61375, "loss_cls": 3.68181, "loss": 3.68181, "time": 0.84547} +{"mode": "train", "epoch": 110, "iter": 3100, "lr": 0.01668, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35922, "top5_acc": 0.60734, "loss_cls": 3.68151, "loss": 3.68151, "time": 0.84666} +{"mode": "train", "epoch": 110, "iter": 3200, "lr": 0.01666, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36188, "top5_acc": 0.62203, "loss_cls": 3.61195, "loss": 3.61195, "time": 0.84563} +{"mode": "train", "epoch": 110, "iter": 3300, "lr": 0.01664, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35234, "top5_acc": 0.60922, "loss_cls": 3.67241, "loss": 3.67241, "time": 0.84061} +{"mode": "train", "epoch": 110, "iter": 3400, "lr": 0.01662, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.36172, "top5_acc": 0.61625, "loss_cls": 3.66116, "loss": 3.66116, "time": 0.85556} +{"mode": "train", "epoch": 110, "iter": 3500, "lr": 0.01659, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35547, "top5_acc": 0.62125, "loss_cls": 3.62795, "loss": 3.62795, "time": 0.84513} +{"mode": "train", "epoch": 110, "iter": 3600, "lr": 0.01657, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.35859, "top5_acc": 0.61969, "loss_cls": 3.63169, "loss": 3.63169, "time": 0.8467} +{"mode": "train", "epoch": 110, "iter": 3700, "lr": 0.01655, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35719, "top5_acc": 0.61203, "loss_cls": 3.64781, "loss": 3.64781, "time": 0.84306} +{"mode": "val", "epoch": 110, "iter": 309, "lr": 0.01654, "top1_acc": 0.30066, "top5_acc": 0.55118, "mean_class_accuracy": 0.30042} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.01652, "memory": 15990, "data_time": 1.53642, "top1_acc": 0.36969, "top5_acc": 0.62859, "loss_cls": 3.56307, "loss": 3.56307, "time": 2.57697} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.0165, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3675, "top5_acc": 0.62641, "loss_cls": 3.54909, "loss": 3.54909, "time": 0.85183} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.01648, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37375, "top5_acc": 0.63125, "loss_cls": 3.56301, "loss": 3.56301, "time": 0.851} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.01646, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38062, "top5_acc": 0.63719, "loss_cls": 3.52256, "loss": 3.52256, "time": 0.85504} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.01644, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36828, "top5_acc": 0.62906, "loss_cls": 3.58154, "loss": 3.58154, "time": 0.85284} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.01642, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36484, "top5_acc": 0.62484, "loss_cls": 3.60109, "loss": 3.60109, "time": 0.84788} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.0164, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35438, "top5_acc": 0.61953, "loss_cls": 3.62929, "loss": 3.62929, "time": 0.85033} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.01638, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36766, "top5_acc": 0.62797, "loss_cls": 3.6041, "loss": 3.6041, "time": 0.85288} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.01636, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36641, "top5_acc": 0.62531, "loss_cls": 3.57884, "loss": 3.57884, "time": 0.85401} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.01634, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35938, "top5_acc": 0.62047, "loss_cls": 3.61768, "loss": 3.61768, "time": 0.85476} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.01632, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36641, "top5_acc": 0.62672, "loss_cls": 3.61355, "loss": 3.61355, "time": 0.85582} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.0163, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36562, "top5_acc": 0.62234, "loss_cls": 3.61226, "loss": 3.61226, "time": 0.8555} +{"mode": "train", "epoch": 111, "iter": 1300, "lr": 0.01627, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35422, "top5_acc": 0.61938, "loss_cls": 3.62864, "loss": 3.62864, "time": 0.85357} +{"mode": "train", "epoch": 111, "iter": 1400, "lr": 0.01625, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35234, "top5_acc": 0.61469, "loss_cls": 3.66303, "loss": 3.66303, "time": 0.85201} +{"mode": "train", "epoch": 111, "iter": 1500, "lr": 0.01623, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35844, "top5_acc": 0.62687, "loss_cls": 3.61872, "loss": 3.61872, "time": 0.84321} +{"mode": "train", "epoch": 111, "iter": 1600, "lr": 0.01621, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36953, "top5_acc": 0.61797, "loss_cls": 3.61327, "loss": 3.61327, "time": 0.84875} +{"mode": "train", "epoch": 111, "iter": 1700, "lr": 0.01619, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35891, "top5_acc": 0.61078, "loss_cls": 3.67865, "loss": 3.67865, "time": 0.84806} +{"mode": "train", "epoch": 111, "iter": 1800, "lr": 0.01617, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.36062, "top5_acc": 0.6175, "loss_cls": 3.64137, "loss": 3.64137, "time": 0.85463} +{"mode": "train", "epoch": 111, "iter": 1900, "lr": 0.01615, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35984, "top5_acc": 0.61703, "loss_cls": 3.66916, "loss": 3.66916, "time": 0.85189} +{"mode": "train", "epoch": 111, "iter": 2000, "lr": 0.01613, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35531, "top5_acc": 0.60125, "loss_cls": 3.68581, "loss": 3.68581, "time": 0.84855} +{"mode": "train", "epoch": 111, "iter": 2100, "lr": 0.01611, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34828, "top5_acc": 0.60781, "loss_cls": 3.68665, "loss": 3.68665, "time": 0.84839} +{"mode": "train", "epoch": 111, "iter": 2200, "lr": 0.01609, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35844, "top5_acc": 0.61078, "loss_cls": 3.65009, "loss": 3.65009, "time": 0.84766} +{"mode": "train", "epoch": 111, "iter": 2300, "lr": 0.01607, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.35922, "top5_acc": 0.61859, "loss_cls": 3.63362, "loss": 3.63362, "time": 0.84922} +{"mode": "train", "epoch": 111, "iter": 2400, "lr": 0.01605, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34938, "top5_acc": 0.60438, "loss_cls": 3.69774, "loss": 3.69774, "time": 0.84991} +{"mode": "train", "epoch": 111, "iter": 2500, "lr": 0.01603, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36297, "top5_acc": 0.61516, "loss_cls": 3.64699, "loss": 3.64699, "time": 0.84482} +{"mode": "train", "epoch": 111, "iter": 2600, "lr": 0.01601, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35375, "top5_acc": 0.61234, "loss_cls": 3.68021, "loss": 3.68021, "time": 0.8492} +{"mode": "train", "epoch": 111, "iter": 2700, "lr": 0.01599, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34859, "top5_acc": 0.61922, "loss_cls": 3.63339, "loss": 3.63339, "time": 0.84174} +{"mode": "train", "epoch": 111, "iter": 2800, "lr": 0.01597, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35375, "top5_acc": 0.61219, "loss_cls": 3.65267, "loss": 3.65267, "time": 0.84225} +{"mode": "train", "epoch": 111, "iter": 2900, "lr": 0.01595, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35125, "top5_acc": 0.61625, "loss_cls": 3.63095, "loss": 3.63095, "time": 0.84023} +{"mode": "train", "epoch": 111, "iter": 3000, "lr": 0.01593, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35906, "top5_acc": 0.61422, "loss_cls": 3.64578, "loss": 3.64578, "time": 0.83711} +{"mode": "train", "epoch": 111, "iter": 3100, "lr": 0.0159, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36406, "top5_acc": 0.61859, "loss_cls": 3.63657, "loss": 3.63657, "time": 0.84209} +{"mode": "train", "epoch": 111, "iter": 3200, "lr": 0.01588, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36062, "top5_acc": 0.61562, "loss_cls": 3.63752, "loss": 3.63752, "time": 0.84154} +{"mode": "train", "epoch": 111, "iter": 3300, "lr": 0.01586, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36984, "top5_acc": 0.61844, "loss_cls": 3.60435, "loss": 3.60435, "time": 0.84712} +{"mode": "train", "epoch": 111, "iter": 3400, "lr": 0.01584, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35656, "top5_acc": 0.61375, "loss_cls": 3.65624, "loss": 3.65624, "time": 0.85264} +{"mode": "train", "epoch": 111, "iter": 3500, "lr": 0.01582, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35516, "top5_acc": 0.61625, "loss_cls": 3.64234, "loss": 3.64234, "time": 0.84296} +{"mode": "train", "epoch": 111, "iter": 3600, "lr": 0.0158, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37047, "top5_acc": 0.62578, "loss_cls": 3.58578, "loss": 3.58578, "time": 0.84124} +{"mode": "train", "epoch": 111, "iter": 3700, "lr": 0.01578, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35781, "top5_acc": 0.61984, "loss_cls": 3.64195, "loss": 3.64195, "time": 0.84512} +{"mode": "val", "epoch": 111, "iter": 309, "lr": 0.01577, "top1_acc": 0.30938, "top5_acc": 0.5564, "mean_class_accuracy": 0.30906} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.01575, "memory": 15990, "data_time": 1.52191, "top1_acc": 0.36109, "top5_acc": 0.62641, "loss_cls": 3.55931, "loss": 3.55931, "time": 2.54376} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.01573, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36328, "top5_acc": 0.62609, "loss_cls": 3.57456, "loss": 3.57456, "time": 0.84959} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.01571, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36406, "top5_acc": 0.62609, "loss_cls": 3.60786, "loss": 3.60786, "time": 0.84174} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.01569, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37062, "top5_acc": 0.63813, "loss_cls": 3.57009, "loss": 3.57009, "time": 0.85018} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.01567, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37219, "top5_acc": 0.62719, "loss_cls": 3.56652, "loss": 3.56652, "time": 0.84649} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.01565, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35656, "top5_acc": 0.62359, "loss_cls": 3.59833, "loss": 3.59833, "time": 0.84648} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.01563, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35766, "top5_acc": 0.62094, "loss_cls": 3.62919, "loss": 3.62919, "time": 0.84381} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.01561, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36703, "top5_acc": 0.62891, "loss_cls": 3.57639, "loss": 3.57639, "time": 0.84627} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.01559, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36266, "top5_acc": 0.62656, "loss_cls": 3.61985, "loss": 3.61985, "time": 0.84567} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.01557, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35578, "top5_acc": 0.61703, "loss_cls": 3.64383, "loss": 3.64383, "time": 0.84419} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.01555, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36625, "top5_acc": 0.6225, "loss_cls": 3.61043, "loss": 3.61043, "time": 0.84679} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.01553, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37281, "top5_acc": 0.62781, "loss_cls": 3.57153, "loss": 3.57153, "time": 0.84492} +{"mode": "train", "epoch": 112, "iter": 1300, "lr": 0.01551, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36016, "top5_acc": 0.62375, "loss_cls": 3.64046, "loss": 3.64046, "time": 0.84856} +{"mode": "train", "epoch": 112, "iter": 1400, "lr": 0.01549, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36406, "top5_acc": 0.62203, "loss_cls": 3.57794, "loss": 3.57794, "time": 0.85438} +{"mode": "train", "epoch": 112, "iter": 1500, "lr": 0.01547, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37156, "top5_acc": 0.62297, "loss_cls": 3.59416, "loss": 3.59416, "time": 0.84812} +{"mode": "train", "epoch": 112, "iter": 1600, "lr": 0.01545, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36859, "top5_acc": 0.62938, "loss_cls": 3.56678, "loss": 3.56678, "time": 0.85377} +{"mode": "train", "epoch": 112, "iter": 1700, "lr": 0.01543, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36391, "top5_acc": 0.61859, "loss_cls": 3.6019, "loss": 3.6019, "time": 0.85307} +{"mode": "train", "epoch": 112, "iter": 1800, "lr": 0.01541, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.35984, "top5_acc": 0.61344, "loss_cls": 3.64998, "loss": 3.64998, "time": 0.85454} +{"mode": "train", "epoch": 112, "iter": 1900, "lr": 0.01539, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36547, "top5_acc": 0.62609, "loss_cls": 3.59125, "loss": 3.59125, "time": 0.84513} +{"mode": "train", "epoch": 112, "iter": 2000, "lr": 0.01537, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36812, "top5_acc": 0.62844, "loss_cls": 3.56574, "loss": 3.56574, "time": 0.83998} +{"mode": "train", "epoch": 112, "iter": 2100, "lr": 0.01535, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36203, "top5_acc": 0.62703, "loss_cls": 3.58519, "loss": 3.58519, "time": 0.84331} +{"mode": "train", "epoch": 112, "iter": 2200, "lr": 0.01533, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37812, "top5_acc": 0.63922, "loss_cls": 3.52451, "loss": 3.52451, "time": 0.84223} +{"mode": "train", "epoch": 112, "iter": 2300, "lr": 0.01531, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35625, "top5_acc": 0.61719, "loss_cls": 3.6294, "loss": 3.6294, "time": 0.84185} +{"mode": "train", "epoch": 112, "iter": 2400, "lr": 0.01529, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36172, "top5_acc": 0.61797, "loss_cls": 3.6408, "loss": 3.6408, "time": 0.85278} +{"mode": "train", "epoch": 112, "iter": 2500, "lr": 0.01527, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36578, "top5_acc": 0.62094, "loss_cls": 3.60817, "loss": 3.60817, "time": 0.84367} +{"mode": "train", "epoch": 112, "iter": 2600, "lr": 0.01525, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36219, "top5_acc": 0.62234, "loss_cls": 3.63916, "loss": 3.63916, "time": 0.84372} +{"mode": "train", "epoch": 112, "iter": 2700, "lr": 0.01523, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36781, "top5_acc": 0.63141, "loss_cls": 3.58995, "loss": 3.58995, "time": 0.83839} +{"mode": "train", "epoch": 112, "iter": 2800, "lr": 0.01521, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36578, "top5_acc": 0.60953, "loss_cls": 3.63019, "loss": 3.63019, "time": 0.84451} +{"mode": "train", "epoch": 112, "iter": 2900, "lr": 0.01519, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36, "top5_acc": 0.61859, "loss_cls": 3.63182, "loss": 3.63182, "time": 0.8447} +{"mode": "train", "epoch": 112, "iter": 3000, "lr": 0.01517, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36172, "top5_acc": 0.61469, "loss_cls": 3.62152, "loss": 3.62152, "time": 0.8406} +{"mode": "train", "epoch": 112, "iter": 3100, "lr": 0.01515, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35078, "top5_acc": 0.61594, "loss_cls": 3.64113, "loss": 3.64113, "time": 0.84112} +{"mode": "train", "epoch": 112, "iter": 3200, "lr": 0.01513, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35516, "top5_acc": 0.60938, "loss_cls": 3.6587, "loss": 3.6587, "time": 0.84877} +{"mode": "train", "epoch": 112, "iter": 3300, "lr": 0.01511, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36781, "top5_acc": 0.62062, "loss_cls": 3.6079, "loss": 3.6079, "time": 0.84401} +{"mode": "train", "epoch": 112, "iter": 3400, "lr": 0.01509, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35609, "top5_acc": 0.62109, "loss_cls": 3.63075, "loss": 3.63075, "time": 0.84583} +{"mode": "train", "epoch": 112, "iter": 3500, "lr": 0.01507, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37, "top5_acc": 0.62687, "loss_cls": 3.58471, "loss": 3.58471, "time": 0.85124} +{"mode": "train", "epoch": 112, "iter": 3600, "lr": 0.01505, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3625, "top5_acc": 0.61578, "loss_cls": 3.61976, "loss": 3.61976, "time": 0.84802} +{"mode": "train", "epoch": 112, "iter": 3700, "lr": 0.01503, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36719, "top5_acc": 0.61953, "loss_cls": 3.63042, "loss": 3.63042, "time": 0.85049} +{"mode": "val", "epoch": 112, "iter": 309, "lr": 0.01502, "top1_acc": 0.30335, "top5_acc": 0.55559, "mean_class_accuracy": 0.30305} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.015, "memory": 15990, "data_time": 1.49341, "top1_acc": 0.38094, "top5_acc": 0.63625, "loss_cls": 3.51652, "loss": 3.51652, "time": 2.53961} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.01498, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38516, "top5_acc": 0.64547, "loss_cls": 3.50202, "loss": 3.50202, "time": 0.85432} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.01496, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36922, "top5_acc": 0.6275, "loss_cls": 3.5882, "loss": 3.5882, "time": 0.85646} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.01494, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37094, "top5_acc": 0.63047, "loss_cls": 3.55634, "loss": 3.55634, "time": 0.85406} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.01492, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37344, "top5_acc": 0.62719, "loss_cls": 3.55795, "loss": 3.55795, "time": 0.85631} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.0149, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38, "top5_acc": 0.62953, "loss_cls": 3.56795, "loss": 3.56795, "time": 0.84966} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.01488, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37234, "top5_acc": 0.63125, "loss_cls": 3.56026, "loss": 3.56026, "time": 0.8523} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.01486, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36406, "top5_acc": 0.63, "loss_cls": 3.57932, "loss": 3.57932, "time": 0.8599} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.01484, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37297, "top5_acc": 0.62891, "loss_cls": 3.58436, "loss": 3.58436, "time": 0.85631} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.01482, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36469, "top5_acc": 0.62438, "loss_cls": 3.58603, "loss": 3.58603, "time": 0.86421} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0148, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37328, "top5_acc": 0.62672, "loss_cls": 3.57633, "loss": 3.57633, "time": 0.85011} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.01478, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36422, "top5_acc": 0.63016, "loss_cls": 3.57206, "loss": 3.57206, "time": 0.85416} +{"mode": "train", "epoch": 113, "iter": 1300, "lr": 0.01476, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.36844, "top5_acc": 0.63, "loss_cls": 3.60911, "loss": 3.60911, "time": 0.86171} +{"mode": "train", "epoch": 113, "iter": 1400, "lr": 0.01474, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37422, "top5_acc": 0.62234, "loss_cls": 3.59347, "loss": 3.59347, "time": 0.85639} +{"mode": "train", "epoch": 113, "iter": 1500, "lr": 0.01472, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.35734, "top5_acc": 0.62141, "loss_cls": 3.60026, "loss": 3.60026, "time": 0.8538} +{"mode": "train", "epoch": 113, "iter": 1600, "lr": 0.0147, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37344, "top5_acc": 0.62547, "loss_cls": 3.56162, "loss": 3.56162, "time": 0.85239} +{"mode": "train", "epoch": 113, "iter": 1700, "lr": 0.01468, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36688, "top5_acc": 0.61953, "loss_cls": 3.57683, "loss": 3.57683, "time": 0.85362} +{"mode": "train", "epoch": 113, "iter": 1800, "lr": 0.01466, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36797, "top5_acc": 0.63, "loss_cls": 3.56923, "loss": 3.56923, "time": 0.85106} +{"mode": "train", "epoch": 113, "iter": 1900, "lr": 0.01464, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36375, "top5_acc": 0.63, "loss_cls": 3.58433, "loss": 3.58433, "time": 0.85539} +{"mode": "train", "epoch": 113, "iter": 2000, "lr": 0.01462, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36, "top5_acc": 0.61234, "loss_cls": 3.62396, "loss": 3.62396, "time": 0.8469} +{"mode": "train", "epoch": 113, "iter": 2100, "lr": 0.0146, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37891, "top5_acc": 0.62953, "loss_cls": 3.57683, "loss": 3.57683, "time": 0.84693} +{"mode": "train", "epoch": 113, "iter": 2200, "lr": 0.01458, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37266, "top5_acc": 0.62953, "loss_cls": 3.56751, "loss": 3.56751, "time": 0.84814} +{"mode": "train", "epoch": 113, "iter": 2300, "lr": 0.01456, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36562, "top5_acc": 0.62141, "loss_cls": 3.61594, "loss": 3.61594, "time": 0.85137} +{"mode": "train", "epoch": 113, "iter": 2400, "lr": 0.01454, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3525, "top5_acc": 0.6125, "loss_cls": 3.65722, "loss": 3.65722, "time": 0.85103} +{"mode": "train", "epoch": 113, "iter": 2500, "lr": 0.01452, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34625, "top5_acc": 0.60203, "loss_cls": 3.68057, "loss": 3.68057, "time": 0.83901} +{"mode": "train", "epoch": 113, "iter": 2600, "lr": 0.0145, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36781, "top5_acc": 0.63047, "loss_cls": 3.56104, "loss": 3.56104, "time": 0.83841} +{"mode": "train", "epoch": 113, "iter": 2700, "lr": 0.01448, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37266, "top5_acc": 0.63156, "loss_cls": 3.56743, "loss": 3.56743, "time": 0.84454} +{"mode": "train", "epoch": 113, "iter": 2800, "lr": 0.01446, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36406, "top5_acc": 0.62062, "loss_cls": 3.62043, "loss": 3.62043, "time": 0.84424} +{"mode": "train", "epoch": 113, "iter": 2900, "lr": 0.01444, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37359, "top5_acc": 0.63203, "loss_cls": 3.5584, "loss": 3.5584, "time": 0.84779} +{"mode": "train", "epoch": 113, "iter": 3000, "lr": 0.01442, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37328, "top5_acc": 0.63172, "loss_cls": 3.56676, "loss": 3.56676, "time": 0.84421} +{"mode": "train", "epoch": 113, "iter": 3100, "lr": 0.0144, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36688, "top5_acc": 0.62344, "loss_cls": 3.59, "loss": 3.59, "time": 0.84726} +{"mode": "train", "epoch": 113, "iter": 3200, "lr": 0.01438, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36625, "top5_acc": 0.61562, "loss_cls": 3.60818, "loss": 3.60818, "time": 0.84624} +{"mode": "train", "epoch": 113, "iter": 3300, "lr": 0.01436, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36391, "top5_acc": 0.62109, "loss_cls": 3.61615, "loss": 3.61615, "time": 0.85167} +{"mode": "train", "epoch": 113, "iter": 3400, "lr": 0.01434, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.37922, "top5_acc": 0.62562, "loss_cls": 3.5737, "loss": 3.5737, "time": 0.83936} +{"mode": "train", "epoch": 113, "iter": 3500, "lr": 0.01432, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36438, "top5_acc": 0.61781, "loss_cls": 3.61763, "loss": 3.61763, "time": 0.846} +{"mode": "train", "epoch": 113, "iter": 3600, "lr": 0.01431, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36453, "top5_acc": 0.62813, "loss_cls": 3.56144, "loss": 3.56144, "time": 0.84849} +{"mode": "train", "epoch": 113, "iter": 3700, "lr": 0.01429, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36703, "top5_acc": 0.62313, "loss_cls": 3.58159, "loss": 3.58159, "time": 0.84983} +{"mode": "val", "epoch": 113, "iter": 309, "lr": 0.01428, "top1_acc": 0.31221, "top5_acc": 0.56182, "mean_class_accuracy": 0.31192} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.01426, "memory": 15990, "data_time": 1.48408, "top1_acc": 0.37469, "top5_acc": 0.64656, "loss_cls": 3.4952, "loss": 3.4952, "time": 2.51624} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.01424, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37094, "top5_acc": 0.63641, "loss_cls": 3.52584, "loss": 3.52584, "time": 0.85084} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.01422, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37688, "top5_acc": 0.64141, "loss_cls": 3.52176, "loss": 3.52176, "time": 0.85043} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.0142, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37828, "top5_acc": 0.62969, "loss_cls": 3.54893, "loss": 3.54893, "time": 0.85351} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.01418, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37281, "top5_acc": 0.63547, "loss_cls": 3.55179, "loss": 3.55179, "time": 0.85447} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.01416, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.385, "top5_acc": 0.64625, "loss_cls": 3.48802, "loss": 3.48802, "time": 0.85131} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.01414, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37547, "top5_acc": 0.62656, "loss_cls": 3.57366, "loss": 3.57366, "time": 0.85442} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.01412, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35531, "top5_acc": 0.62813, "loss_cls": 3.59006, "loss": 3.59006, "time": 0.85349} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.0141, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38125, "top5_acc": 0.63938, "loss_cls": 3.50735, "loss": 3.50735, "time": 0.85296} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.01408, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37766, "top5_acc": 0.63375, "loss_cls": 3.51586, "loss": 3.51586, "time": 0.85101} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.01406, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37125, "top5_acc": 0.63109, "loss_cls": 3.56629, "loss": 3.56629, "time": 0.85265} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.01404, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38125, "top5_acc": 0.63016, "loss_cls": 3.53765, "loss": 3.53765, "time": 0.85449} +{"mode": "train", "epoch": 114, "iter": 1300, "lr": 0.01402, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37672, "top5_acc": 0.63547, "loss_cls": 3.52002, "loss": 3.52002, "time": 0.85302} +{"mode": "train", "epoch": 114, "iter": 1400, "lr": 0.014, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36422, "top5_acc": 0.62328, "loss_cls": 3.57168, "loss": 3.57168, "time": 0.85364} +{"mode": "train", "epoch": 114, "iter": 1500, "lr": 0.01398, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.3725, "top5_acc": 0.63578, "loss_cls": 3.52587, "loss": 3.52587, "time": 0.85558} +{"mode": "train", "epoch": 114, "iter": 1600, "lr": 0.01397, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37141, "top5_acc": 0.63156, "loss_cls": 3.56846, "loss": 3.56846, "time": 0.8534} +{"mode": "train", "epoch": 114, "iter": 1700, "lr": 0.01395, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37922, "top5_acc": 0.63844, "loss_cls": 3.52242, "loss": 3.52242, "time": 0.85127} +{"mode": "train", "epoch": 114, "iter": 1800, "lr": 0.01393, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37047, "top5_acc": 0.62562, "loss_cls": 3.56715, "loss": 3.56715, "time": 0.852} +{"mode": "train", "epoch": 114, "iter": 1900, "lr": 0.01391, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.37703, "top5_acc": 0.63156, "loss_cls": 3.53565, "loss": 3.53565, "time": 0.84498} +{"mode": "train", "epoch": 114, "iter": 2000, "lr": 0.01389, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37844, "top5_acc": 0.62906, "loss_cls": 3.55102, "loss": 3.55102, "time": 0.84577} +{"mode": "train", "epoch": 114, "iter": 2100, "lr": 0.01387, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.36891, "top5_acc": 0.62484, "loss_cls": 3.57129, "loss": 3.57129, "time": 0.84542} +{"mode": "train", "epoch": 114, "iter": 2200, "lr": 0.01385, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36781, "top5_acc": 0.62703, "loss_cls": 3.6259, "loss": 3.6259, "time": 0.83626} +{"mode": "train", "epoch": 114, "iter": 2300, "lr": 0.01383, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37234, "top5_acc": 0.62578, "loss_cls": 3.58777, "loss": 3.58777, "time": 0.84464} +{"mode": "train", "epoch": 114, "iter": 2400, "lr": 0.01381, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36281, "top5_acc": 0.63141, "loss_cls": 3.56413, "loss": 3.56413, "time": 0.84716} +{"mode": "train", "epoch": 114, "iter": 2500, "lr": 0.01379, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37266, "top5_acc": 0.61906, "loss_cls": 3.58097, "loss": 3.58097, "time": 0.84048} +{"mode": "train", "epoch": 114, "iter": 2600, "lr": 0.01377, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36734, "top5_acc": 0.62234, "loss_cls": 3.57684, "loss": 3.57684, "time": 0.84622} +{"mode": "train", "epoch": 114, "iter": 2700, "lr": 0.01375, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37875, "top5_acc": 0.63734, "loss_cls": 3.54763, "loss": 3.54763, "time": 0.83926} +{"mode": "train", "epoch": 114, "iter": 2800, "lr": 0.01373, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.37328, "top5_acc": 0.63266, "loss_cls": 3.55671, "loss": 3.55671, "time": 0.84486} +{"mode": "train", "epoch": 114, "iter": 2900, "lr": 0.01371, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37781, "top5_acc": 0.62703, "loss_cls": 3.54386, "loss": 3.54386, "time": 0.84268} +{"mode": "train", "epoch": 114, "iter": 3000, "lr": 0.01369, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36625, "top5_acc": 0.62969, "loss_cls": 3.55366, "loss": 3.55366, "time": 0.84211} +{"mode": "train", "epoch": 114, "iter": 3100, "lr": 0.01368, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36531, "top5_acc": 0.62562, "loss_cls": 3.5688, "loss": 3.5688, "time": 0.84285} +{"mode": "train", "epoch": 114, "iter": 3200, "lr": 0.01366, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36797, "top5_acc": 0.61828, "loss_cls": 3.61087, "loss": 3.61087, "time": 0.83769} +{"mode": "train", "epoch": 114, "iter": 3300, "lr": 0.01364, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36641, "top5_acc": 0.62609, "loss_cls": 3.5986, "loss": 3.5986, "time": 0.84349} +{"mode": "train", "epoch": 114, "iter": 3400, "lr": 0.01362, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37359, "top5_acc": 0.62313, "loss_cls": 3.60098, "loss": 3.60098, "time": 0.84592} +{"mode": "train", "epoch": 114, "iter": 3500, "lr": 0.0136, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.35344, "top5_acc": 0.62203, "loss_cls": 3.6326, "loss": 3.6326, "time": 0.84732} +{"mode": "train", "epoch": 114, "iter": 3600, "lr": 0.01358, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37047, "top5_acc": 0.62781, "loss_cls": 3.58304, "loss": 3.58304, "time": 0.8449} +{"mode": "train", "epoch": 114, "iter": 3700, "lr": 0.01356, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37828, "top5_acc": 0.62844, "loss_cls": 3.56387, "loss": 3.56387, "time": 0.85071} +{"mode": "val", "epoch": 114, "iter": 309, "lr": 0.01355, "top1_acc": 0.31474, "top5_acc": 0.56785, "mean_class_accuracy": 0.31448} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.01353, "memory": 15990, "data_time": 1.51893, "top1_acc": 0.38578, "top5_acc": 0.64312, "loss_cls": 3.4905, "loss": 3.4905, "time": 2.55235} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.01351, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38547, "top5_acc": 0.63641, "loss_cls": 3.50253, "loss": 3.50253, "time": 0.85037} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.01349, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37656, "top5_acc": 0.63938, "loss_cls": 3.49861, "loss": 3.49861, "time": 0.84849} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.01348, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.37812, "top5_acc": 0.6375, "loss_cls": 3.49893, "loss": 3.49893, "time": 0.85282} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.01346, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37953, "top5_acc": 0.63844, "loss_cls": 3.49966, "loss": 3.49966, "time": 0.8476} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.01344, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38047, "top5_acc": 0.64, "loss_cls": 3.52249, "loss": 3.52249, "time": 0.85094} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.01342, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37609, "top5_acc": 0.63266, "loss_cls": 3.52532, "loss": 3.52532, "time": 0.84753} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.0134, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38266, "top5_acc": 0.63703, "loss_cls": 3.52887, "loss": 3.52887, "time": 0.85053} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.01338, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3725, "top5_acc": 0.63172, "loss_cls": 3.54149, "loss": 3.54149, "time": 0.8485} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.01336, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37969, "top5_acc": 0.64406, "loss_cls": 3.49975, "loss": 3.49975, "time": 0.84617} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.01334, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36953, "top5_acc": 0.62531, "loss_cls": 3.55961, "loss": 3.55961, "time": 0.8445} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.01332, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36938, "top5_acc": 0.63047, "loss_cls": 3.54822, "loss": 3.54822, "time": 0.83962} +{"mode": "train", "epoch": 115, "iter": 1300, "lr": 0.0133, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37641, "top5_acc": 0.63813, "loss_cls": 3.51399, "loss": 3.51399, "time": 0.84912} +{"mode": "train", "epoch": 115, "iter": 1400, "lr": 0.01328, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37875, "top5_acc": 0.63797, "loss_cls": 3.53848, "loss": 3.53848, "time": 0.84358} +{"mode": "train", "epoch": 115, "iter": 1500, "lr": 0.01327, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.3725, "top5_acc": 0.62922, "loss_cls": 3.53962, "loss": 3.53962, "time": 0.85142} +{"mode": "train", "epoch": 115, "iter": 1600, "lr": 0.01325, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37156, "top5_acc": 0.63594, "loss_cls": 3.53009, "loss": 3.53009, "time": 0.84574} +{"mode": "train", "epoch": 115, "iter": 1700, "lr": 0.01323, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36891, "top5_acc": 0.62656, "loss_cls": 3.56119, "loss": 3.56119, "time": 0.84527} +{"mode": "train", "epoch": 115, "iter": 1800, "lr": 0.01321, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38047, "top5_acc": 0.63766, "loss_cls": 3.50952, "loss": 3.50952, "time": 0.84394} +{"mode": "train", "epoch": 115, "iter": 1900, "lr": 0.01319, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38812, "top5_acc": 0.63719, "loss_cls": 3.50205, "loss": 3.50205, "time": 0.84442} +{"mode": "train", "epoch": 115, "iter": 2000, "lr": 0.01317, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38016, "top5_acc": 0.63672, "loss_cls": 3.51325, "loss": 3.51325, "time": 0.8503} +{"mode": "train", "epoch": 115, "iter": 2100, "lr": 0.01315, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37172, "top5_acc": 0.62406, "loss_cls": 3.57014, "loss": 3.57014, "time": 0.84417} +{"mode": "train", "epoch": 115, "iter": 2200, "lr": 0.01313, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37422, "top5_acc": 0.6325, "loss_cls": 3.56482, "loss": 3.56482, "time": 0.84788} +{"mode": "train", "epoch": 115, "iter": 2300, "lr": 0.01311, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37531, "top5_acc": 0.62797, "loss_cls": 3.56698, "loss": 3.56698, "time": 0.84354} +{"mode": "train", "epoch": 115, "iter": 2400, "lr": 0.0131, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37766, "top5_acc": 0.63469, "loss_cls": 3.54571, "loss": 3.54571, "time": 0.84525} +{"mode": "train", "epoch": 115, "iter": 2500, "lr": 0.01308, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37578, "top5_acc": 0.63422, "loss_cls": 3.5403, "loss": 3.5403, "time": 0.84385} +{"mode": "train", "epoch": 115, "iter": 2600, "lr": 0.01306, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37156, "top5_acc": 0.62391, "loss_cls": 3.58396, "loss": 3.58396, "time": 0.84107} +{"mode": "train", "epoch": 115, "iter": 2700, "lr": 0.01304, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37375, "top5_acc": 0.64, "loss_cls": 3.53262, "loss": 3.53262, "time": 0.84321} +{"mode": "train", "epoch": 115, "iter": 2800, "lr": 0.01302, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37078, "top5_acc": 0.62375, "loss_cls": 3.5539, "loss": 3.5539, "time": 0.84023} +{"mode": "train", "epoch": 115, "iter": 2900, "lr": 0.013, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38328, "top5_acc": 0.62484, "loss_cls": 3.53669, "loss": 3.53669, "time": 0.84926} +{"mode": "train", "epoch": 115, "iter": 3000, "lr": 0.01298, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.37297, "top5_acc": 0.62828, "loss_cls": 3.54698, "loss": 3.54698, "time": 0.84581} +{"mode": "train", "epoch": 115, "iter": 3100, "lr": 0.01296, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36562, "top5_acc": 0.62031, "loss_cls": 3.63168, "loss": 3.63168, "time": 0.84389} +{"mode": "train", "epoch": 115, "iter": 3200, "lr": 0.01295, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3725, "top5_acc": 0.63672, "loss_cls": 3.52424, "loss": 3.52424, "time": 0.84472} +{"mode": "train", "epoch": 115, "iter": 3300, "lr": 0.01293, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37703, "top5_acc": 0.63875, "loss_cls": 3.53976, "loss": 3.53976, "time": 0.84426} +{"mode": "train", "epoch": 115, "iter": 3400, "lr": 0.01291, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37, "top5_acc": 0.62672, "loss_cls": 3.57037, "loss": 3.57037, "time": 0.84619} +{"mode": "train", "epoch": 115, "iter": 3500, "lr": 0.01289, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38234, "top5_acc": 0.64391, "loss_cls": 3.49795, "loss": 3.49795, "time": 0.85262} +{"mode": "train", "epoch": 115, "iter": 3600, "lr": 0.01287, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37609, "top5_acc": 0.63578, "loss_cls": 3.54556, "loss": 3.54556, "time": 0.84426} +{"mode": "train", "epoch": 115, "iter": 3700, "lr": 0.01285, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.36375, "top5_acc": 0.62375, "loss_cls": 3.58124, "loss": 3.58124, "time": 0.84721} +{"mode": "val", "epoch": 115, "iter": 309, "lr": 0.01284, "top1_acc": 0.31454, "top5_acc": 0.56486, "mean_class_accuracy": 0.31436} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.01282, "memory": 15990, "data_time": 1.52434, "top1_acc": 0.39109, "top5_acc": 0.65156, "loss_cls": 3.41926, "loss": 3.41926, "time": 2.55888} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.01281, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39297, "top5_acc": 0.64531, "loss_cls": 3.44726, "loss": 3.44726, "time": 0.8553} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.01279, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39281, "top5_acc": 0.64688, "loss_cls": 3.41672, "loss": 3.41672, "time": 0.84935} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.01277, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38469, "top5_acc": 0.64844, "loss_cls": 3.45248, "loss": 3.45248, "time": 0.8487} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.01275, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38188, "top5_acc": 0.64094, "loss_cls": 3.48092, "loss": 3.48092, "time": 0.85324} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.01273, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38266, "top5_acc": 0.64547, "loss_cls": 3.49684, "loss": 3.49684, "time": 0.85264} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.01271, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37891, "top5_acc": 0.64938, "loss_cls": 3.47203, "loss": 3.47203, "time": 0.85159} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.01269, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37484, "top5_acc": 0.6325, "loss_cls": 3.54089, "loss": 3.54089, "time": 0.84781} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.01268, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.38594, "top5_acc": 0.64531, "loss_cls": 3.46714, "loss": 3.46714, "time": 0.84896} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.01266, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3725, "top5_acc": 0.63266, "loss_cls": 3.56263, "loss": 3.56263, "time": 0.85478} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.01264, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38141, "top5_acc": 0.63656, "loss_cls": 3.50757, "loss": 3.50757, "time": 0.84807} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.01262, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37469, "top5_acc": 0.62969, "loss_cls": 3.543, "loss": 3.543, "time": 0.85195} +{"mode": "train", "epoch": 116, "iter": 1300, "lr": 0.0126, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37234, "top5_acc": 0.63406, "loss_cls": 3.53481, "loss": 3.53481, "time": 0.84929} +{"mode": "train", "epoch": 116, "iter": 1400, "lr": 0.01258, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37141, "top5_acc": 0.62953, "loss_cls": 3.55268, "loss": 3.55268, "time": 0.85584} +{"mode": "train", "epoch": 116, "iter": 1500, "lr": 0.01256, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37141, "top5_acc": 0.62844, "loss_cls": 3.57922, "loss": 3.57922, "time": 0.85225} +{"mode": "train", "epoch": 116, "iter": 1600, "lr": 0.01255, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37172, "top5_acc": 0.62828, "loss_cls": 3.53778, "loss": 3.53778, "time": 0.84932} +{"mode": "train", "epoch": 116, "iter": 1700, "lr": 0.01253, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36812, "top5_acc": 0.63188, "loss_cls": 3.57828, "loss": 3.57828, "time": 0.85037} +{"mode": "train", "epoch": 116, "iter": 1800, "lr": 0.01251, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38359, "top5_acc": 0.64078, "loss_cls": 3.45437, "loss": 3.45437, "time": 0.8478} +{"mode": "train", "epoch": 116, "iter": 1900, "lr": 0.01249, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.37625, "top5_acc": 0.63875, "loss_cls": 3.49826, "loss": 3.49826, "time": 0.84673} +{"mode": "train", "epoch": 116, "iter": 2000, "lr": 0.01247, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37562, "top5_acc": 0.63906, "loss_cls": 3.50714, "loss": 3.50714, "time": 0.84774} +{"mode": "train", "epoch": 116, "iter": 2100, "lr": 0.01245, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3775, "top5_acc": 0.64203, "loss_cls": 3.49739, "loss": 3.49739, "time": 0.83926} +{"mode": "train", "epoch": 116, "iter": 2200, "lr": 0.01243, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37922, "top5_acc": 0.63141, "loss_cls": 3.53973, "loss": 3.53973, "time": 0.84786} +{"mode": "train", "epoch": 116, "iter": 2300, "lr": 0.01242, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37891, "top5_acc": 0.63547, "loss_cls": 3.52679, "loss": 3.52679, "time": 0.84206} +{"mode": "train", "epoch": 116, "iter": 2400, "lr": 0.0124, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.38422, "top5_acc": 0.63516, "loss_cls": 3.50544, "loss": 3.50544, "time": 0.84824} +{"mode": "train", "epoch": 116, "iter": 2500, "lr": 0.01238, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38266, "top5_acc": 0.64125, "loss_cls": 3.49672, "loss": 3.49672, "time": 0.84339} +{"mode": "train", "epoch": 116, "iter": 2600, "lr": 0.01236, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37734, "top5_acc": 0.63313, "loss_cls": 3.52723, "loss": 3.52723, "time": 0.84646} +{"mode": "train", "epoch": 116, "iter": 2700, "lr": 0.01234, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37266, "top5_acc": 0.62922, "loss_cls": 3.56108, "loss": 3.56108, "time": 0.84424} +{"mode": "train", "epoch": 116, "iter": 2800, "lr": 0.01232, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36797, "top5_acc": 0.63438, "loss_cls": 3.51042, "loss": 3.51042, "time": 0.84397} +{"mode": "train", "epoch": 116, "iter": 2900, "lr": 0.01231, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37172, "top5_acc": 0.62375, "loss_cls": 3.56924, "loss": 3.56924, "time": 0.8551} +{"mode": "train", "epoch": 116, "iter": 3000, "lr": 0.01229, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37656, "top5_acc": 0.63594, "loss_cls": 3.51568, "loss": 3.51568, "time": 0.84606} +{"mode": "train", "epoch": 116, "iter": 3100, "lr": 0.01227, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37188, "top5_acc": 0.625, "loss_cls": 3.57569, "loss": 3.57569, "time": 0.85348} +{"mode": "train", "epoch": 116, "iter": 3200, "lr": 0.01225, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37969, "top5_acc": 0.63734, "loss_cls": 3.50749, "loss": 3.50749, "time": 0.84875} +{"mode": "train", "epoch": 116, "iter": 3300, "lr": 0.01223, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36609, "top5_acc": 0.62969, "loss_cls": 3.58562, "loss": 3.58562, "time": 0.85303} +{"mode": "train", "epoch": 116, "iter": 3400, "lr": 0.01221, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37812, "top5_acc": 0.62641, "loss_cls": 3.56061, "loss": 3.56061, "time": 0.85482} +{"mode": "train", "epoch": 116, "iter": 3500, "lr": 0.0122, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37516, "top5_acc": 0.64234, "loss_cls": 3.51644, "loss": 3.51644, "time": 0.84712} +{"mode": "train", "epoch": 116, "iter": 3600, "lr": 0.01218, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38266, "top5_acc": 0.64797, "loss_cls": 3.49087, "loss": 3.49087, "time": 0.84315} +{"mode": "train", "epoch": 116, "iter": 3700, "lr": 0.01216, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38172, "top5_acc": 0.63656, "loss_cls": 3.51486, "loss": 3.51486, "time": 0.84395} +{"mode": "val", "epoch": 116, "iter": 309, "lr": 0.01215, "top1_acc": 0.31677, "top5_acc": 0.56815, "mean_class_accuracy": 0.31652} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.01213, "memory": 15990, "data_time": 1.48256, "top1_acc": 0.39109, "top5_acc": 0.64703, "loss_cls": 3.44287, "loss": 3.44287, "time": 2.51885} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.01211, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.38859, "top5_acc": 0.64578, "loss_cls": 3.44364, "loss": 3.44364, "time": 0.84859} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.0121, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38766, "top5_acc": 0.635, "loss_cls": 3.47417, "loss": 3.47417, "time": 0.85016} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.01208, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40219, "top5_acc": 0.65516, "loss_cls": 3.40826, "loss": 3.40826, "time": 0.84885} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.01206, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38781, "top5_acc": 0.64062, "loss_cls": 3.47581, "loss": 3.47581, "time": 0.84894} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.01204, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39, "top5_acc": 0.65078, "loss_cls": 3.44933, "loss": 3.44933, "time": 0.85266} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.01202, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38125, "top5_acc": 0.63766, "loss_cls": 3.47824, "loss": 3.47824, "time": 0.85279} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.012, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38641, "top5_acc": 0.64625, "loss_cls": 3.45751, "loss": 3.45751, "time": 0.85466} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.01199, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38453, "top5_acc": 0.64438, "loss_cls": 3.45744, "loss": 3.45744, "time": 0.84873} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.01197, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38891, "top5_acc": 0.63984, "loss_cls": 3.47752, "loss": 3.47752, "time": 0.84785} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.01195, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38188, "top5_acc": 0.64062, "loss_cls": 3.5096, "loss": 3.5096, "time": 0.84567} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.01193, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38312, "top5_acc": 0.64422, "loss_cls": 3.48971, "loss": 3.48971, "time": 0.84359} +{"mode": "train", "epoch": 117, "iter": 1300, "lr": 0.01191, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37391, "top5_acc": 0.64016, "loss_cls": 3.53055, "loss": 3.53055, "time": 0.844} +{"mode": "train", "epoch": 117, "iter": 1400, "lr": 0.0119, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37469, "top5_acc": 0.63719, "loss_cls": 3.50697, "loss": 3.50697, "time": 0.84956} +{"mode": "train", "epoch": 117, "iter": 1500, "lr": 0.01188, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37609, "top5_acc": 0.64, "loss_cls": 3.493, "loss": 3.493, "time": 0.85126} +{"mode": "train", "epoch": 117, "iter": 1600, "lr": 0.01186, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37906, "top5_acc": 0.64406, "loss_cls": 3.50897, "loss": 3.50897, "time": 0.84636} +{"mode": "train", "epoch": 117, "iter": 1700, "lr": 0.01184, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38578, "top5_acc": 0.64562, "loss_cls": 3.49288, "loss": 3.49288, "time": 0.85322} +{"mode": "train", "epoch": 117, "iter": 1800, "lr": 0.01182, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37688, "top5_acc": 0.63578, "loss_cls": 3.49765, "loss": 3.49765, "time": 0.8469} +{"mode": "train", "epoch": 117, "iter": 1900, "lr": 0.01181, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.385, "top5_acc": 0.64156, "loss_cls": 3.48634, "loss": 3.48634, "time": 0.85072} +{"mode": "train", "epoch": 117, "iter": 2000, "lr": 0.01179, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.39203, "top5_acc": 0.65453, "loss_cls": 3.43239, "loss": 3.43239, "time": 0.84387} +{"mode": "train", "epoch": 117, "iter": 2100, "lr": 0.01177, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38844, "top5_acc": 0.64391, "loss_cls": 3.46461, "loss": 3.46461, "time": 0.84367} +{"mode": "train", "epoch": 117, "iter": 2200, "lr": 0.01175, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.37406, "top5_acc": 0.62859, "loss_cls": 3.53129, "loss": 3.53129, "time": 0.84284} +{"mode": "train", "epoch": 117, "iter": 2300, "lr": 0.01173, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39219, "top5_acc": 0.64703, "loss_cls": 3.4453, "loss": 3.4453, "time": 0.84309} +{"mode": "train", "epoch": 117, "iter": 2400, "lr": 0.01172, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.38938, "top5_acc": 0.64688, "loss_cls": 3.43996, "loss": 3.43996, "time": 0.85195} +{"mode": "train", "epoch": 117, "iter": 2500, "lr": 0.0117, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.3725, "top5_acc": 0.62891, "loss_cls": 3.5742, "loss": 3.5742, "time": 0.84891} +{"mode": "train", "epoch": 117, "iter": 2600, "lr": 0.01168, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.3825, "top5_acc": 0.64031, "loss_cls": 3.48536, "loss": 3.48536, "time": 0.84078} +{"mode": "train", "epoch": 117, "iter": 2700, "lr": 0.01166, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37703, "top5_acc": 0.62641, "loss_cls": 3.54909, "loss": 3.54909, "time": 0.84698} +{"mode": "train", "epoch": 117, "iter": 2800, "lr": 0.01164, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38281, "top5_acc": 0.63875, "loss_cls": 3.4746, "loss": 3.4746, "time": 0.85091} +{"mode": "train", "epoch": 117, "iter": 2900, "lr": 0.01163, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38266, "top5_acc": 0.64391, "loss_cls": 3.47989, "loss": 3.47989, "time": 0.84296} +{"mode": "train", "epoch": 117, "iter": 3000, "lr": 0.01161, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38625, "top5_acc": 0.64516, "loss_cls": 3.4877, "loss": 3.4877, "time": 0.8457} +{"mode": "train", "epoch": 117, "iter": 3100, "lr": 0.01159, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38656, "top5_acc": 0.64578, "loss_cls": 3.47681, "loss": 3.47681, "time": 0.83948} +{"mode": "train", "epoch": 117, "iter": 3200, "lr": 0.01157, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37656, "top5_acc": 0.63031, "loss_cls": 3.53077, "loss": 3.53077, "time": 0.84189} +{"mode": "train", "epoch": 117, "iter": 3300, "lr": 0.01155, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37844, "top5_acc": 0.64031, "loss_cls": 3.50041, "loss": 3.50041, "time": 0.8426} +{"mode": "train", "epoch": 117, "iter": 3400, "lr": 0.01154, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37953, "top5_acc": 0.63844, "loss_cls": 3.52808, "loss": 3.52808, "time": 0.84348} +{"mode": "train", "epoch": 117, "iter": 3500, "lr": 0.01152, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37719, "top5_acc": 0.63594, "loss_cls": 3.50038, "loss": 3.50038, "time": 0.84466} +{"mode": "train", "epoch": 117, "iter": 3600, "lr": 0.0115, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38906, "top5_acc": 0.65125, "loss_cls": 3.45602, "loss": 3.45602, "time": 0.84832} +{"mode": "train", "epoch": 117, "iter": 3700, "lr": 0.01148, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36984, "top5_acc": 0.62578, "loss_cls": 3.56062, "loss": 3.56062, "time": 0.84429} +{"mode": "val", "epoch": 117, "iter": 309, "lr": 0.01147, "top1_acc": 0.31545, "top5_acc": 0.56932, "mean_class_accuracy": 0.31523} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.01146, "memory": 15990, "data_time": 1.50736, "top1_acc": 0.39562, "top5_acc": 0.66062, "loss_cls": 3.37027, "loss": 3.37027, "time": 2.54911} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.01144, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39156, "top5_acc": 0.64953, "loss_cls": 3.43422, "loss": 3.43422, "time": 0.86138} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.01142, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39203, "top5_acc": 0.64781, "loss_cls": 3.41104, "loss": 3.41104, "time": 0.86107} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.0114, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39422, "top5_acc": 0.65781, "loss_cls": 3.40646, "loss": 3.40646, "time": 0.85689} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.01139, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39672, "top5_acc": 0.65891, "loss_cls": 3.42418, "loss": 3.42418, "time": 0.85778} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.01137, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38812, "top5_acc": 0.64766, "loss_cls": 3.42402, "loss": 3.42402, "time": 0.85263} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.01135, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39172, "top5_acc": 0.64938, "loss_cls": 3.44147, "loss": 3.44147, "time": 0.8565} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.01133, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38078, "top5_acc": 0.64547, "loss_cls": 3.47129, "loss": 3.47129, "time": 0.85875} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.01131, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39391, "top5_acc": 0.65594, "loss_cls": 3.44213, "loss": 3.44213, "time": 0.86324} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.0113, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39016, "top5_acc": 0.64438, "loss_cls": 3.46028, "loss": 3.46028, "time": 0.85743} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.01128, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38219, "top5_acc": 0.63984, "loss_cls": 3.48193, "loss": 3.48193, "time": 0.86012} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.01126, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38516, "top5_acc": 0.65078, "loss_cls": 3.41561, "loss": 3.41561, "time": 0.86005} +{"mode": "train", "epoch": 118, "iter": 1300, "lr": 0.01124, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.37906, "top5_acc": 0.63844, "loss_cls": 3.49612, "loss": 3.49612, "time": 0.85964} +{"mode": "train", "epoch": 118, "iter": 1400, "lr": 0.01123, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38172, "top5_acc": 0.63828, "loss_cls": 3.47733, "loss": 3.47733, "time": 0.85901} +{"mode": "train", "epoch": 118, "iter": 1500, "lr": 0.01121, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37969, "top5_acc": 0.64047, "loss_cls": 3.51407, "loss": 3.51407, "time": 0.86244} +{"mode": "train", "epoch": 118, "iter": 1600, "lr": 0.01119, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.38984, "top5_acc": 0.65156, "loss_cls": 3.4584, "loss": 3.4584, "time": 0.86207} +{"mode": "train", "epoch": 118, "iter": 1700, "lr": 0.01117, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38547, "top5_acc": 0.65188, "loss_cls": 3.4565, "loss": 3.4565, "time": 0.85872} +{"mode": "train", "epoch": 118, "iter": 1800, "lr": 0.01116, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.3875, "top5_acc": 0.64016, "loss_cls": 3.48074, "loss": 3.48074, "time": 0.85869} +{"mode": "train", "epoch": 118, "iter": 1900, "lr": 0.01114, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.38453, "top5_acc": 0.65031, "loss_cls": 3.44896, "loss": 3.44896, "time": 0.85814} +{"mode": "train", "epoch": 118, "iter": 2000, "lr": 0.01112, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38531, "top5_acc": 0.645, "loss_cls": 3.45724, "loss": 3.45724, "time": 0.85101} +{"mode": "train", "epoch": 118, "iter": 2100, "lr": 0.0111, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.38234, "top5_acc": 0.63672, "loss_cls": 3.50843, "loss": 3.50843, "time": 0.84833} +{"mode": "train", "epoch": 118, "iter": 2200, "lr": 0.01109, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38625, "top5_acc": 0.64062, "loss_cls": 3.50003, "loss": 3.50003, "time": 0.84668} +{"mode": "train", "epoch": 118, "iter": 2300, "lr": 0.01107, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38672, "top5_acc": 0.64891, "loss_cls": 3.44595, "loss": 3.44595, "time": 0.85031} +{"mode": "train", "epoch": 118, "iter": 2400, "lr": 0.01105, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.38391, "top5_acc": 0.63938, "loss_cls": 3.47706, "loss": 3.47706, "time": 0.85259} +{"mode": "train", "epoch": 118, "iter": 2500, "lr": 0.01103, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.39984, "top5_acc": 0.64391, "loss_cls": 3.4439, "loss": 3.4439, "time": 0.85542} +{"mode": "train", "epoch": 118, "iter": 2600, "lr": 0.01102, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.38859, "top5_acc": 0.64156, "loss_cls": 3.51571, "loss": 3.51571, "time": 0.85699} +{"mode": "train", "epoch": 118, "iter": 2700, "lr": 0.011, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38781, "top5_acc": 0.64234, "loss_cls": 3.49692, "loss": 3.49692, "time": 0.85391} +{"mode": "train", "epoch": 118, "iter": 2800, "lr": 0.01098, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.39531, "top5_acc": 0.65219, "loss_cls": 3.43903, "loss": 3.43903, "time": 0.85858} +{"mode": "train", "epoch": 118, "iter": 2900, "lr": 0.01096, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.38469, "top5_acc": 0.64156, "loss_cls": 3.46326, "loss": 3.46326, "time": 0.85213} +{"mode": "train", "epoch": 118, "iter": 3000, "lr": 0.01095, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38328, "top5_acc": 0.63062, "loss_cls": 3.49442, "loss": 3.49442, "time": 0.85347} +{"mode": "train", "epoch": 118, "iter": 3100, "lr": 0.01093, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.39297, "top5_acc": 0.655, "loss_cls": 3.43553, "loss": 3.43553, "time": 0.85387} +{"mode": "train", "epoch": 118, "iter": 3200, "lr": 0.01091, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.38578, "top5_acc": 0.64016, "loss_cls": 3.50481, "loss": 3.50481, "time": 0.8589} +{"mode": "train", "epoch": 118, "iter": 3300, "lr": 0.01089, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38344, "top5_acc": 0.6375, "loss_cls": 3.51092, "loss": 3.51092, "time": 0.85349} +{"mode": "train", "epoch": 118, "iter": 3400, "lr": 0.01088, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38125, "top5_acc": 0.64219, "loss_cls": 3.52125, "loss": 3.52125, "time": 0.85892} +{"mode": "train", "epoch": 118, "iter": 3500, "lr": 0.01086, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39031, "top5_acc": 0.64031, "loss_cls": 3.47804, "loss": 3.47804, "time": 0.85588} +{"mode": "train", "epoch": 118, "iter": 3600, "lr": 0.01084, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.3875, "top5_acc": 0.63828, "loss_cls": 3.48053, "loss": 3.48053, "time": 0.85445} +{"mode": "train", "epoch": 118, "iter": 3700, "lr": 0.01082, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.38578, "top5_acc": 0.63844, "loss_cls": 3.48839, "loss": 3.48839, "time": 0.85584} +{"mode": "val", "epoch": 118, "iter": 309, "lr": 0.01082, "top1_acc": 0.31915, "top5_acc": 0.56891, "mean_class_accuracy": 0.31901} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.0108, "memory": 15990, "data_time": 1.59835, "top1_acc": 0.39734, "top5_acc": 0.65859, "loss_cls": 3.40207, "loss": 3.40207, "time": 2.63233} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.01078, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39609, "top5_acc": 0.65453, "loss_cls": 3.41198, "loss": 3.41198, "time": 0.85119} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.01076, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39609, "top5_acc": 0.65672, "loss_cls": 3.42768, "loss": 3.42768, "time": 0.85259} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.01075, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.39797, "top5_acc": 0.65984, "loss_cls": 3.37767, "loss": 3.37767, "time": 0.85339} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.01073, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39484, "top5_acc": 0.65422, "loss_cls": 3.39924, "loss": 3.39924, "time": 0.85056} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.01071, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40094, "top5_acc": 0.65703, "loss_cls": 3.38521, "loss": 3.38521, "time": 0.85598} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.01069, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40438, "top5_acc": 0.6575, "loss_cls": 3.3957, "loss": 3.3957, "time": 0.85347} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.01068, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38906, "top5_acc": 0.65422, "loss_cls": 3.4075, "loss": 3.4075, "time": 0.85386} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.01066, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39812, "top5_acc": 0.64344, "loss_cls": 3.44589, "loss": 3.44589, "time": 0.84926} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.01064, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38828, "top5_acc": 0.63906, "loss_cls": 3.47821, "loss": 3.47821, "time": 0.85326} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.01063, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.39062, "top5_acc": 0.65, "loss_cls": 3.41593, "loss": 3.41593, "time": 0.85422} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.01061, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.39906, "top5_acc": 0.6525, "loss_cls": 3.42908, "loss": 3.42908, "time": 0.85541} +{"mode": "train", "epoch": 119, "iter": 1300, "lr": 0.01059, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39281, "top5_acc": 0.64516, "loss_cls": 3.45676, "loss": 3.45676, "time": 0.85154} +{"mode": "train", "epoch": 119, "iter": 1400, "lr": 0.01057, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.39594, "top5_acc": 0.65344, "loss_cls": 3.43018, "loss": 3.43018, "time": 0.84994} +{"mode": "train", "epoch": 119, "iter": 1500, "lr": 0.01056, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39016, "top5_acc": 0.64891, "loss_cls": 3.44811, "loss": 3.44811, "time": 0.85312} +{"mode": "train", "epoch": 119, "iter": 1600, "lr": 0.01054, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39125, "top5_acc": 0.65172, "loss_cls": 3.4215, "loss": 3.4215, "time": 0.86003} +{"mode": "train", "epoch": 119, "iter": 1700, "lr": 0.01052, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39453, "top5_acc": 0.64297, "loss_cls": 3.43802, "loss": 3.43802, "time": 0.85147} +{"mode": "train", "epoch": 119, "iter": 1800, "lr": 0.0105, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39688, "top5_acc": 0.65578, "loss_cls": 3.40537, "loss": 3.40537, "time": 0.85723} +{"mode": "train", "epoch": 119, "iter": 1900, "lr": 0.01049, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.39422, "top5_acc": 0.65125, "loss_cls": 3.43096, "loss": 3.43096, "time": 0.85177} +{"mode": "train", "epoch": 119, "iter": 2000, "lr": 0.01047, "memory": 15990, "data_time": 0.0009, "top1_acc": 0.37422, "top5_acc": 0.635, "loss_cls": 3.49495, "loss": 3.49495, "time": 0.85281} +{"mode": "train", "epoch": 119, "iter": 2100, "lr": 0.01045, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38922, "top5_acc": 0.655, "loss_cls": 3.4406, "loss": 3.4406, "time": 0.84755} +{"mode": "train", "epoch": 119, "iter": 2200, "lr": 0.01044, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38156, "top5_acc": 0.6425, "loss_cls": 3.45607, "loss": 3.45607, "time": 0.85337} +{"mode": "train", "epoch": 119, "iter": 2300, "lr": 0.01042, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38703, "top5_acc": 0.65188, "loss_cls": 3.44631, "loss": 3.44631, "time": 0.85239} +{"mode": "train", "epoch": 119, "iter": 2400, "lr": 0.0104, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.38969, "top5_acc": 0.64562, "loss_cls": 3.4421, "loss": 3.4421, "time": 0.85169} +{"mode": "train", "epoch": 119, "iter": 2500, "lr": 0.01039, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38656, "top5_acc": 0.65062, "loss_cls": 3.44424, "loss": 3.44424, "time": 0.85252} +{"mode": "train", "epoch": 119, "iter": 2600, "lr": 0.01037, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39391, "top5_acc": 0.66141, "loss_cls": 3.41692, "loss": 3.41692, "time": 0.85152} +{"mode": "train", "epoch": 119, "iter": 2700, "lr": 0.01035, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.38094, "top5_acc": 0.64672, "loss_cls": 3.45964, "loss": 3.45964, "time": 0.85739} +{"mode": "train", "epoch": 119, "iter": 2800, "lr": 0.01033, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38953, "top5_acc": 0.64922, "loss_cls": 3.44222, "loss": 3.44222, "time": 0.85292} +{"mode": "train", "epoch": 119, "iter": 2900, "lr": 0.01032, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.38641, "top5_acc": 0.64938, "loss_cls": 3.4296, "loss": 3.4296, "time": 0.85644} +{"mode": "train", "epoch": 119, "iter": 3000, "lr": 0.0103, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38875, "top5_acc": 0.64625, "loss_cls": 3.45574, "loss": 3.45574, "time": 0.85603} +{"mode": "train", "epoch": 119, "iter": 3100, "lr": 0.01028, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.39922, "top5_acc": 0.6525, "loss_cls": 3.41056, "loss": 3.41056, "time": 0.85591} +{"mode": "train", "epoch": 119, "iter": 3200, "lr": 0.01027, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.38594, "top5_acc": 0.64641, "loss_cls": 3.46383, "loss": 3.46383, "time": 0.85393} +{"mode": "train", "epoch": 119, "iter": 3300, "lr": 0.01025, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38844, "top5_acc": 0.6525, "loss_cls": 3.43761, "loss": 3.43761, "time": 0.86012} +{"mode": "train", "epoch": 119, "iter": 3400, "lr": 0.01023, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37734, "top5_acc": 0.63141, "loss_cls": 3.54522, "loss": 3.54522, "time": 0.86676} +{"mode": "train", "epoch": 119, "iter": 3500, "lr": 0.01022, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38922, "top5_acc": 0.64016, "loss_cls": 3.4901, "loss": 3.4901, "time": 0.84862} +{"mode": "train", "epoch": 119, "iter": 3600, "lr": 0.0102, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39281, "top5_acc": 0.64953, "loss_cls": 3.42526, "loss": 3.42526, "time": 0.85086} +{"mode": "train", "epoch": 119, "iter": 3700, "lr": 0.01018, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38703, "top5_acc": 0.63969, "loss_cls": 3.49309, "loss": 3.49309, "time": 0.85936} +{"mode": "val", "epoch": 119, "iter": 309, "lr": 0.01017, "top1_acc": 0.33029, "top5_acc": 0.58416, "mean_class_accuracy": 0.33004} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.01016, "memory": 15990, "data_time": 1.59194, "top1_acc": 0.40453, "top5_acc": 0.66062, "loss_cls": 3.34835, "loss": 3.34835, "time": 2.62382} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.01014, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.40281, "top5_acc": 0.66438, "loss_cls": 3.35828, "loss": 3.35828, "time": 0.85168} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.01012, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40391, "top5_acc": 0.65719, "loss_cls": 3.38503, "loss": 3.38503, "time": 0.85321} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.01011, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40141, "top5_acc": 0.65641, "loss_cls": 3.38668, "loss": 3.38668, "time": 0.85484} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.01009, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.40609, "top5_acc": 0.66031, "loss_cls": 3.35961, "loss": 3.35961, "time": 0.85482} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.01007, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40391, "top5_acc": 0.65469, "loss_cls": 3.38289, "loss": 3.38289, "time": 0.85251} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.01006, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.39109, "top5_acc": 0.65469, "loss_cls": 3.42639, "loss": 3.42639, "time": 0.85682} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.01004, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.40234, "top5_acc": 0.65781, "loss_cls": 3.38098, "loss": 3.38098, "time": 0.8554} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.01002, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38547, "top5_acc": 0.64984, "loss_cls": 3.44363, "loss": 3.44363, "time": 0.8529} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.01001, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39406, "top5_acc": 0.65703, "loss_cls": 3.40914, "loss": 3.40914, "time": 0.85239} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00999, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41016, "top5_acc": 0.66156, "loss_cls": 3.35588, "loss": 3.35588, "time": 0.85603} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.00997, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39672, "top5_acc": 0.65812, "loss_cls": 3.37785, "loss": 3.37785, "time": 0.85314} +{"mode": "train", "epoch": 120, "iter": 1300, "lr": 0.00996, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39484, "top5_acc": 0.66, "loss_cls": 3.38656, "loss": 3.38656, "time": 0.85203} +{"mode": "train", "epoch": 120, "iter": 1400, "lr": 0.00994, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40438, "top5_acc": 0.65953, "loss_cls": 3.41153, "loss": 3.41153, "time": 0.85296} +{"mode": "train", "epoch": 120, "iter": 1500, "lr": 0.00992, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38953, "top5_acc": 0.64375, "loss_cls": 3.46967, "loss": 3.46967, "time": 0.85454} +{"mode": "train", "epoch": 120, "iter": 1600, "lr": 0.0099, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.38328, "top5_acc": 0.64891, "loss_cls": 3.44972, "loss": 3.44972, "time": 0.8517} +{"mode": "train", "epoch": 120, "iter": 1700, "lr": 0.00989, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39203, "top5_acc": 0.64812, "loss_cls": 3.42032, "loss": 3.42032, "time": 0.85118} +{"mode": "train", "epoch": 120, "iter": 1800, "lr": 0.00987, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39609, "top5_acc": 0.64938, "loss_cls": 3.45242, "loss": 3.45242, "time": 0.85247} +{"mode": "train", "epoch": 120, "iter": 1900, "lr": 0.00985, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38906, "top5_acc": 0.65016, "loss_cls": 3.47016, "loss": 3.47016, "time": 0.8547} +{"mode": "train", "epoch": 120, "iter": 2000, "lr": 0.00984, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39688, "top5_acc": 0.65844, "loss_cls": 3.37346, "loss": 3.37346, "time": 0.85364} +{"mode": "train", "epoch": 120, "iter": 2100, "lr": 0.00982, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39781, "top5_acc": 0.64906, "loss_cls": 3.3912, "loss": 3.3912, "time": 0.85656} +{"mode": "train", "epoch": 120, "iter": 2200, "lr": 0.0098, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39781, "top5_acc": 0.65141, "loss_cls": 3.4314, "loss": 3.4314, "time": 0.85459} +{"mode": "train", "epoch": 120, "iter": 2300, "lr": 0.00979, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39156, "top5_acc": 0.65062, "loss_cls": 3.44078, "loss": 3.44078, "time": 0.85511} +{"mode": "train", "epoch": 120, "iter": 2400, "lr": 0.00977, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39266, "top5_acc": 0.6525, "loss_cls": 3.38781, "loss": 3.38781, "time": 0.8473} +{"mode": "train", "epoch": 120, "iter": 2500, "lr": 0.00976, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.3975, "top5_acc": 0.65469, "loss_cls": 3.44045, "loss": 3.44045, "time": 0.85112} +{"mode": "train", "epoch": 120, "iter": 2600, "lr": 0.00974, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39734, "top5_acc": 0.65562, "loss_cls": 3.40526, "loss": 3.40526, "time": 0.85444} +{"mode": "train", "epoch": 120, "iter": 2700, "lr": 0.00972, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39359, "top5_acc": 0.65438, "loss_cls": 3.40821, "loss": 3.40821, "time": 0.85112} +{"mode": "train", "epoch": 120, "iter": 2800, "lr": 0.00971, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.39438, "top5_acc": 0.64812, "loss_cls": 3.42953, "loss": 3.42953, "time": 0.85522} +{"mode": "train", "epoch": 120, "iter": 2900, "lr": 0.00969, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37828, "top5_acc": 0.64344, "loss_cls": 3.50736, "loss": 3.50736, "time": 0.85176} +{"mode": "train", "epoch": 120, "iter": 3000, "lr": 0.00967, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39812, "top5_acc": 0.65516, "loss_cls": 3.41859, "loss": 3.41859, "time": 0.84995} +{"mode": "train", "epoch": 120, "iter": 3100, "lr": 0.00966, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39109, "top5_acc": 0.65266, "loss_cls": 3.4297, "loss": 3.4297, "time": 0.85215} +{"mode": "train", "epoch": 120, "iter": 3200, "lr": 0.00964, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39375, "top5_acc": 0.64625, "loss_cls": 3.43806, "loss": 3.43806, "time": 0.85028} +{"mode": "train", "epoch": 120, "iter": 3300, "lr": 0.00962, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40047, "top5_acc": 0.655, "loss_cls": 3.42585, "loss": 3.42585, "time": 0.8545} +{"mode": "train", "epoch": 120, "iter": 3400, "lr": 0.00961, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39109, "top5_acc": 0.64672, "loss_cls": 3.45367, "loss": 3.45367, "time": 0.84926} +{"mode": "train", "epoch": 120, "iter": 3500, "lr": 0.00959, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39359, "top5_acc": 0.65906, "loss_cls": 3.39523, "loss": 3.39523, "time": 0.85085} +{"mode": "train", "epoch": 120, "iter": 3600, "lr": 0.00957, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40594, "top5_acc": 0.66797, "loss_cls": 3.35297, "loss": 3.35297, "time": 0.84979} +{"mode": "train", "epoch": 120, "iter": 3700, "lr": 0.00956, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39344, "top5_acc": 0.64781, "loss_cls": 3.44897, "loss": 3.44897, "time": 0.85329} +{"mode": "val", "epoch": 120, "iter": 309, "lr": 0.00955, "top1_acc": 0.32943, "top5_acc": 0.58709, "mean_class_accuracy": 0.32919} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00953, "memory": 15990, "data_time": 1.5504, "top1_acc": 0.41359, "top5_acc": 0.67328, "loss_cls": 3.28169, "loss": 3.28169, "time": 2.5772} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00952, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40047, "top5_acc": 0.66422, "loss_cls": 3.3605, "loss": 3.3605, "time": 0.84482} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.0095, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40812, "top5_acc": 0.65922, "loss_cls": 3.36081, "loss": 3.36081, "time": 0.85122} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00948, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41281, "top5_acc": 0.67469, "loss_cls": 3.30711, "loss": 3.30711, "time": 0.84874} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00947, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40438, "top5_acc": 0.66484, "loss_cls": 3.3552, "loss": 3.3552, "time": 0.85159} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00945, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40484, "top5_acc": 0.66266, "loss_cls": 3.35755, "loss": 3.35755, "time": 0.85055} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.00943, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41219, "top5_acc": 0.66641, "loss_cls": 3.32111, "loss": 3.32111, "time": 0.85755} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00942, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39734, "top5_acc": 0.65656, "loss_cls": 3.39977, "loss": 3.39977, "time": 0.84888} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.0094, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39406, "top5_acc": 0.65625, "loss_cls": 3.41507, "loss": 3.41507, "time": 0.8504} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00939, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41359, "top5_acc": 0.67094, "loss_cls": 3.3133, "loss": 3.3133, "time": 0.84729} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00937, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41266, "top5_acc": 0.66578, "loss_cls": 3.33653, "loss": 3.33653, "time": 0.85168} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00935, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40422, "top5_acc": 0.67047, "loss_cls": 3.34596, "loss": 3.34596, "time": 0.85233} +{"mode": "train", "epoch": 121, "iter": 1300, "lr": 0.00934, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39844, "top5_acc": 0.65359, "loss_cls": 3.4106, "loss": 3.4106, "time": 0.85861} +{"mode": "train", "epoch": 121, "iter": 1400, "lr": 0.00932, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.40438, "top5_acc": 0.66141, "loss_cls": 3.37493, "loss": 3.37493, "time": 0.85794} +{"mode": "train", "epoch": 121, "iter": 1500, "lr": 0.0093, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40031, "top5_acc": 0.65453, "loss_cls": 3.38778, "loss": 3.38778, "time": 0.85564} +{"mode": "train", "epoch": 121, "iter": 1600, "lr": 0.00929, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40141, "top5_acc": 0.66172, "loss_cls": 3.3757, "loss": 3.3757, "time": 0.85375} +{"mode": "train", "epoch": 121, "iter": 1700, "lr": 0.00927, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40141, "top5_acc": 0.65688, "loss_cls": 3.36062, "loss": 3.36062, "time": 0.85543} +{"mode": "train", "epoch": 121, "iter": 1800, "lr": 0.00926, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39375, "top5_acc": 0.64859, "loss_cls": 3.41456, "loss": 3.41456, "time": 0.85995} +{"mode": "train", "epoch": 121, "iter": 1900, "lr": 0.00924, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39031, "top5_acc": 0.65266, "loss_cls": 3.41987, "loss": 3.41987, "time": 0.85919} +{"mode": "train", "epoch": 121, "iter": 2000, "lr": 0.00922, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39828, "top5_acc": 0.65234, "loss_cls": 3.3848, "loss": 3.3848, "time": 0.85479} +{"mode": "train", "epoch": 121, "iter": 2100, "lr": 0.00921, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39047, "top5_acc": 0.65797, "loss_cls": 3.42628, "loss": 3.42628, "time": 0.85241} +{"mode": "train", "epoch": 121, "iter": 2200, "lr": 0.00919, "memory": 15990, "data_time": 0.00071, "top1_acc": 0.39469, "top5_acc": 0.65062, "loss_cls": 3.43273, "loss": 3.43273, "time": 0.84805} +{"mode": "train", "epoch": 121, "iter": 2300, "lr": 0.00917, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39188, "top5_acc": 0.64953, "loss_cls": 3.42846, "loss": 3.42846, "time": 0.85453} +{"mode": "train", "epoch": 121, "iter": 2400, "lr": 0.00916, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40547, "top5_acc": 0.66797, "loss_cls": 3.36588, "loss": 3.36588, "time": 0.85159} +{"mode": "train", "epoch": 121, "iter": 2500, "lr": 0.00914, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.39781, "top5_acc": 0.65922, "loss_cls": 3.43824, "loss": 3.43824, "time": 0.8517} +{"mode": "train", "epoch": 121, "iter": 2600, "lr": 0.00913, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.395, "top5_acc": 0.65094, "loss_cls": 3.4054, "loss": 3.4054, "time": 0.84846} +{"mode": "train", "epoch": 121, "iter": 2700, "lr": 0.00911, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39203, "top5_acc": 0.65031, "loss_cls": 3.42211, "loss": 3.42211, "time": 0.85122} +{"mode": "train", "epoch": 121, "iter": 2800, "lr": 0.00909, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.395, "top5_acc": 0.65312, "loss_cls": 3.40761, "loss": 3.40761, "time": 0.84356} +{"mode": "train", "epoch": 121, "iter": 2900, "lr": 0.00908, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39422, "top5_acc": 0.65672, "loss_cls": 3.39319, "loss": 3.39319, "time": 0.84598} +{"mode": "train", "epoch": 121, "iter": 3000, "lr": 0.00906, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39188, "top5_acc": 0.64891, "loss_cls": 3.41569, "loss": 3.41569, "time": 0.84806} +{"mode": "train", "epoch": 121, "iter": 3100, "lr": 0.00905, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.39656, "top5_acc": 0.66141, "loss_cls": 3.35681, "loss": 3.35681, "time": 0.84556} +{"mode": "train", "epoch": 121, "iter": 3200, "lr": 0.00903, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39859, "top5_acc": 0.65688, "loss_cls": 3.40286, "loss": 3.40286, "time": 0.84676} +{"mode": "train", "epoch": 121, "iter": 3300, "lr": 0.00901, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40953, "top5_acc": 0.65578, "loss_cls": 3.38488, "loss": 3.38488, "time": 0.84277} +{"mode": "train", "epoch": 121, "iter": 3400, "lr": 0.009, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40188, "top5_acc": 0.65812, "loss_cls": 3.38508, "loss": 3.38508, "time": 0.84748} +{"mode": "train", "epoch": 121, "iter": 3500, "lr": 0.00898, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39422, "top5_acc": 0.65188, "loss_cls": 3.43606, "loss": 3.43606, "time": 0.85489} +{"mode": "train", "epoch": 121, "iter": 3600, "lr": 0.00897, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40109, "top5_acc": 0.65219, "loss_cls": 3.3938, "loss": 3.3938, "time": 0.85645} +{"mode": "train", "epoch": 121, "iter": 3700, "lr": 0.00895, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.39938, "top5_acc": 0.65594, "loss_cls": 3.42585, "loss": 3.42585, "time": 0.8521} +{"mode": "val", "epoch": 121, "iter": 309, "lr": 0.00894, "top1_acc": 0.33521, "top5_acc": 0.58968, "mean_class_accuracy": 0.33495} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00893, "memory": 15990, "data_time": 1.5838, "top1_acc": 0.41281, "top5_acc": 0.6775, "loss_cls": 3.29649, "loss": 3.29649, "time": 2.60724} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00891, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40734, "top5_acc": 0.66891, "loss_cls": 3.32021, "loss": 3.32021, "time": 0.84816} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.00889, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41281, "top5_acc": 0.66625, "loss_cls": 3.32656, "loss": 3.32656, "time": 0.84777} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00888, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.41234, "top5_acc": 0.66812, "loss_cls": 3.33405, "loss": 3.33405, "time": 0.85636} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00886, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.4175, "top5_acc": 0.67297, "loss_cls": 3.28624, "loss": 3.28624, "time": 0.8568} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00885, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40781, "top5_acc": 0.66047, "loss_cls": 3.36295, "loss": 3.36295, "time": 0.84655} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00883, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40547, "top5_acc": 0.66609, "loss_cls": 3.3313, "loss": 3.3313, "time": 0.85036} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00882, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40281, "top5_acc": 0.65922, "loss_cls": 3.35437, "loss": 3.35437, "time": 0.85359} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.0088, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40938, "top5_acc": 0.66953, "loss_cls": 3.3179, "loss": 3.3179, "time": 0.84925} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00878, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40141, "top5_acc": 0.66234, "loss_cls": 3.36131, "loss": 3.36131, "time": 0.85367} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00877, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.39797, "top5_acc": 0.66047, "loss_cls": 3.38542, "loss": 3.38542, "time": 0.85277} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.00875, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.41562, "top5_acc": 0.67344, "loss_cls": 3.28738, "loss": 3.28738, "time": 0.85139} +{"mode": "train", "epoch": 122, "iter": 1300, "lr": 0.00874, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39938, "top5_acc": 0.66031, "loss_cls": 3.36268, "loss": 3.36268, "time": 0.8541} +{"mode": "train", "epoch": 122, "iter": 1400, "lr": 0.00872, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40609, "top5_acc": 0.66172, "loss_cls": 3.37496, "loss": 3.37496, "time": 0.85452} +{"mode": "train", "epoch": 122, "iter": 1500, "lr": 0.0087, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42, "top5_acc": 0.67766, "loss_cls": 3.26666, "loss": 3.26666, "time": 0.85001} +{"mode": "train", "epoch": 122, "iter": 1600, "lr": 0.00869, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40875, "top5_acc": 0.65391, "loss_cls": 3.38661, "loss": 3.38661, "time": 0.85245} +{"mode": "train", "epoch": 122, "iter": 1700, "lr": 0.00867, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40062, "top5_acc": 0.65234, "loss_cls": 3.40185, "loss": 3.40185, "time": 0.85594} +{"mode": "train", "epoch": 122, "iter": 1800, "lr": 0.00866, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.40234, "top5_acc": 0.66281, "loss_cls": 3.36461, "loss": 3.36461, "time": 0.85669} +{"mode": "train", "epoch": 122, "iter": 1900, "lr": 0.00864, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39812, "top5_acc": 0.65562, "loss_cls": 3.38112, "loss": 3.38112, "time": 0.85019} +{"mode": "train", "epoch": 122, "iter": 2000, "lr": 0.00863, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.40188, "top5_acc": 0.66, "loss_cls": 3.37851, "loss": 3.37851, "time": 0.85695} +{"mode": "train", "epoch": 122, "iter": 2100, "lr": 0.00861, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39703, "top5_acc": 0.65109, "loss_cls": 3.41262, "loss": 3.41262, "time": 0.85407} +{"mode": "train", "epoch": 122, "iter": 2200, "lr": 0.00859, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39875, "top5_acc": 0.66344, "loss_cls": 3.35242, "loss": 3.35242, "time": 0.85146} +{"mode": "train", "epoch": 122, "iter": 2300, "lr": 0.00858, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.39875, "top5_acc": 0.65938, "loss_cls": 3.36684, "loss": 3.36684, "time": 0.85646} +{"mode": "train", "epoch": 122, "iter": 2400, "lr": 0.00856, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.40656, "top5_acc": 0.66312, "loss_cls": 3.38479, "loss": 3.38479, "time": 0.84548} +{"mode": "train", "epoch": 122, "iter": 2500, "lr": 0.00855, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39969, "top5_acc": 0.6475, "loss_cls": 3.40784, "loss": 3.40784, "time": 0.84588} +{"mode": "train", "epoch": 122, "iter": 2600, "lr": 0.00853, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40734, "top5_acc": 0.66109, "loss_cls": 3.35589, "loss": 3.35589, "time": 0.84954} +{"mode": "train", "epoch": 122, "iter": 2700, "lr": 0.00852, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39484, "top5_acc": 0.65219, "loss_cls": 3.39454, "loss": 3.39454, "time": 0.84536} +{"mode": "train", "epoch": 122, "iter": 2800, "lr": 0.0085, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40781, "top5_acc": 0.66531, "loss_cls": 3.34016, "loss": 3.34016, "time": 0.84314} +{"mode": "train", "epoch": 122, "iter": 2900, "lr": 0.00849, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.40531, "top5_acc": 0.65734, "loss_cls": 3.37099, "loss": 3.37099, "time": 0.8473} +{"mode": "train", "epoch": 122, "iter": 3000, "lr": 0.00847, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39812, "top5_acc": 0.65656, "loss_cls": 3.39389, "loss": 3.39389, "time": 0.85005} +{"mode": "train", "epoch": 122, "iter": 3100, "lr": 0.00845, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39391, "top5_acc": 0.65328, "loss_cls": 3.42148, "loss": 3.42148, "time": 0.84842} +{"mode": "train", "epoch": 122, "iter": 3200, "lr": 0.00844, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40625, "top5_acc": 0.65578, "loss_cls": 3.37464, "loss": 3.37464, "time": 0.85632} +{"mode": "train", "epoch": 122, "iter": 3300, "lr": 0.00842, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40312, "top5_acc": 0.66, "loss_cls": 3.33873, "loss": 3.33873, "time": 0.85981} +{"mode": "train", "epoch": 122, "iter": 3400, "lr": 0.00841, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40359, "top5_acc": 0.66703, "loss_cls": 3.3502, "loss": 3.3502, "time": 0.8624} +{"mode": "train", "epoch": 122, "iter": 3500, "lr": 0.00839, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.40594, "top5_acc": 0.66141, "loss_cls": 3.35325, "loss": 3.35325, "time": 0.8622} +{"mode": "train", "epoch": 122, "iter": 3600, "lr": 0.00838, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40812, "top5_acc": 0.65984, "loss_cls": 3.36794, "loss": 3.36794, "time": 0.85101} +{"mode": "train", "epoch": 122, "iter": 3700, "lr": 0.00836, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.41234, "top5_acc": 0.66, "loss_cls": 3.35766, "loss": 3.35766, "time": 0.85252} +{"mode": "val", "epoch": 122, "iter": 309, "lr": 0.00835, "top1_acc": 0.3304, "top5_acc": 0.58319, "mean_class_accuracy": 0.33014} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00834, "memory": 15990, "data_time": 1.56723, "top1_acc": 0.41797, "top5_acc": 0.6725, "loss_cls": 3.29369, "loss": 3.29369, "time": 2.61703} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00832, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41938, "top5_acc": 0.67766, "loss_cls": 3.27671, "loss": 3.27671, "time": 0.85679} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00831, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.40641, "top5_acc": 0.66672, "loss_cls": 3.34646, "loss": 3.34646, "time": 0.85477} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00829, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40875, "top5_acc": 0.66578, "loss_cls": 3.33119, "loss": 3.33119, "time": 0.85106} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00828, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.40781, "top5_acc": 0.67125, "loss_cls": 3.31341, "loss": 3.31341, "time": 0.84875} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00826, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.415, "top5_acc": 0.66703, "loss_cls": 3.30084, "loss": 3.30084, "time": 0.85382} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00825, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41188, "top5_acc": 0.66953, "loss_cls": 3.30482, "loss": 3.30482, "time": 0.85081} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.00823, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.41422, "top5_acc": 0.67109, "loss_cls": 3.30475, "loss": 3.30475, "time": 0.84692} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00822, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41219, "top5_acc": 0.67266, "loss_cls": 3.31528, "loss": 3.31528, "time": 0.84877} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.0082, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40156, "top5_acc": 0.65828, "loss_cls": 3.35676, "loss": 3.35676, "time": 0.84952} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00818, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41203, "top5_acc": 0.67688, "loss_cls": 3.30418, "loss": 3.30418, "time": 0.85148} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00817, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41844, "top5_acc": 0.67281, "loss_cls": 3.28611, "loss": 3.28611, "time": 0.85683} +{"mode": "train", "epoch": 123, "iter": 1300, "lr": 0.00815, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41531, "top5_acc": 0.66688, "loss_cls": 3.33106, "loss": 3.33106, "time": 0.85296} +{"mode": "train", "epoch": 123, "iter": 1400, "lr": 0.00814, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40906, "top5_acc": 0.67141, "loss_cls": 3.32533, "loss": 3.32533, "time": 0.85202} +{"mode": "train", "epoch": 123, "iter": 1500, "lr": 0.00812, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41484, "top5_acc": 0.66406, "loss_cls": 3.31844, "loss": 3.31844, "time": 0.85136} +{"mode": "train", "epoch": 123, "iter": 1600, "lr": 0.00811, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41234, "top5_acc": 0.66109, "loss_cls": 3.32496, "loss": 3.32496, "time": 0.85434} +{"mode": "train", "epoch": 123, "iter": 1700, "lr": 0.00809, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41062, "top5_acc": 0.66188, "loss_cls": 3.35111, "loss": 3.35111, "time": 0.85077} +{"mode": "train", "epoch": 123, "iter": 1800, "lr": 0.00808, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.40625, "top5_acc": 0.66375, "loss_cls": 3.31564, "loss": 3.31564, "time": 0.85623} +{"mode": "train", "epoch": 123, "iter": 1900, "lr": 0.00806, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.40797, "top5_acc": 0.66703, "loss_cls": 3.32509, "loss": 3.32509, "time": 0.85222} +{"mode": "train", "epoch": 123, "iter": 2000, "lr": 0.00805, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.40031, "top5_acc": 0.65125, "loss_cls": 3.3926, "loss": 3.3926, "time": 0.85391} +{"mode": "train", "epoch": 123, "iter": 2100, "lr": 0.00803, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40469, "top5_acc": 0.6575, "loss_cls": 3.38146, "loss": 3.38146, "time": 0.85147} +{"mode": "train", "epoch": 123, "iter": 2200, "lr": 0.00802, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.41922, "top5_acc": 0.66891, "loss_cls": 3.288, "loss": 3.288, "time": 0.84921} +{"mode": "train", "epoch": 123, "iter": 2300, "lr": 0.008, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41422, "top5_acc": 0.66734, "loss_cls": 3.31771, "loss": 3.31771, "time": 0.84779} +{"mode": "train", "epoch": 123, "iter": 2400, "lr": 0.00799, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.40266, "top5_acc": 0.66016, "loss_cls": 3.3583, "loss": 3.3583, "time": 0.85253} +{"mode": "train", "epoch": 123, "iter": 2500, "lr": 0.00797, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.40266, "top5_acc": 0.66547, "loss_cls": 3.35117, "loss": 3.35117, "time": 0.86217} +{"mode": "train", "epoch": 123, "iter": 2600, "lr": 0.00796, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39625, "top5_acc": 0.66016, "loss_cls": 3.41574, "loss": 3.41574, "time": 0.85703} +{"mode": "train", "epoch": 123, "iter": 2700, "lr": 0.00794, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41422, "top5_acc": 0.67109, "loss_cls": 3.30394, "loss": 3.30394, "time": 0.85956} +{"mode": "train", "epoch": 123, "iter": 2800, "lr": 0.00793, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40281, "top5_acc": 0.66547, "loss_cls": 3.35922, "loss": 3.35922, "time": 0.86038} +{"mode": "train", "epoch": 123, "iter": 2900, "lr": 0.00791, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.39781, "top5_acc": 0.65484, "loss_cls": 3.38143, "loss": 3.38143, "time": 0.85843} +{"mode": "train", "epoch": 123, "iter": 3000, "lr": 0.0079, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.40672, "top5_acc": 0.66797, "loss_cls": 3.33394, "loss": 3.33394, "time": 0.86048} +{"mode": "train", "epoch": 123, "iter": 3100, "lr": 0.00788, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.4025, "top5_acc": 0.66703, "loss_cls": 3.34716, "loss": 3.34716, "time": 0.85817} +{"mode": "train", "epoch": 123, "iter": 3200, "lr": 0.00787, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.40719, "top5_acc": 0.66625, "loss_cls": 3.32077, "loss": 3.32077, "time": 0.85691} +{"mode": "train", "epoch": 123, "iter": 3300, "lr": 0.00785, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.40547, "top5_acc": 0.66938, "loss_cls": 3.34733, "loss": 3.34733, "time": 0.85768} +{"mode": "train", "epoch": 123, "iter": 3400, "lr": 0.00784, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40438, "top5_acc": 0.66625, "loss_cls": 3.36293, "loss": 3.36293, "time": 0.86029} +{"mode": "train", "epoch": 123, "iter": 3500, "lr": 0.00782, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41938, "top5_acc": 0.67141, "loss_cls": 3.28742, "loss": 3.28742, "time": 0.85306} +{"mode": "train", "epoch": 123, "iter": 3600, "lr": 0.00781, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40656, "top5_acc": 0.65781, "loss_cls": 3.33777, "loss": 3.33777, "time": 0.8504} +{"mode": "train", "epoch": 123, "iter": 3700, "lr": 0.00779, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40125, "top5_acc": 0.66719, "loss_cls": 3.3799, "loss": 3.3799, "time": 0.85229} +{"mode": "val", "epoch": 123, "iter": 309, "lr": 0.00778, "top1_acc": 0.34149, "top5_acc": 0.59515, "mean_class_accuracy": 0.34128} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00777, "memory": 15990, "data_time": 1.60078, "top1_acc": 0.415, "top5_acc": 0.68125, "loss_cls": 3.27797, "loss": 3.27797, "time": 2.66058} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00775, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42234, "top5_acc": 0.68297, "loss_cls": 3.25018, "loss": 3.25018, "time": 0.85412} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00774, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41141, "top5_acc": 0.67938, "loss_cls": 3.26907, "loss": 3.26907, "time": 0.85322} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.00772, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41156, "top5_acc": 0.67406, "loss_cls": 3.28843, "loss": 3.28843, "time": 0.85226} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00771, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42297, "top5_acc": 0.68547, "loss_cls": 3.22282, "loss": 3.22282, "time": 0.85566} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00769, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41453, "top5_acc": 0.66141, "loss_cls": 3.29871, "loss": 3.29871, "time": 0.85522} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00768, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41016, "top5_acc": 0.67578, "loss_cls": 3.30371, "loss": 3.30371, "time": 0.85661} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00766, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42203, "top5_acc": 0.67016, "loss_cls": 3.29267, "loss": 3.29267, "time": 0.85484} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00765, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42, "top5_acc": 0.66953, "loss_cls": 3.28587, "loss": 3.28587, "time": 0.85342} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00763, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41391, "top5_acc": 0.6675, "loss_cls": 3.31946, "loss": 3.31946, "time": 0.85454} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00762, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42641, "top5_acc": 0.68109, "loss_cls": 3.25789, "loss": 3.25789, "time": 0.85428} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.0076, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41797, "top5_acc": 0.67438, "loss_cls": 3.27135, "loss": 3.27135, "time": 0.84893} +{"mode": "train", "epoch": 124, "iter": 1300, "lr": 0.00759, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41656, "top5_acc": 0.67719, "loss_cls": 3.28499, "loss": 3.28499, "time": 0.85387} +{"mode": "train", "epoch": 124, "iter": 1400, "lr": 0.00758, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40922, "top5_acc": 0.67094, "loss_cls": 3.31215, "loss": 3.31215, "time": 0.85436} +{"mode": "train", "epoch": 124, "iter": 1500, "lr": 0.00756, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42609, "top5_acc": 0.67859, "loss_cls": 3.25332, "loss": 3.25332, "time": 0.85373} +{"mode": "train", "epoch": 124, "iter": 1600, "lr": 0.00755, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40938, "top5_acc": 0.66266, "loss_cls": 3.32457, "loss": 3.32457, "time": 0.8507} +{"mode": "train", "epoch": 124, "iter": 1700, "lr": 0.00753, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.41797, "top5_acc": 0.66453, "loss_cls": 3.33469, "loss": 3.33469, "time": 0.85396} +{"mode": "train", "epoch": 124, "iter": 1800, "lr": 0.00752, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.425, "top5_acc": 0.67031, "loss_cls": 3.27935, "loss": 3.27935, "time": 0.85356} +{"mode": "train", "epoch": 124, "iter": 1900, "lr": 0.0075, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41078, "top5_acc": 0.66594, "loss_cls": 3.33554, "loss": 3.33554, "time": 0.85172} +{"mode": "train", "epoch": 124, "iter": 2000, "lr": 0.00749, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41234, "top5_acc": 0.66938, "loss_cls": 3.30904, "loss": 3.30904, "time": 0.85284} +{"mode": "train", "epoch": 124, "iter": 2100, "lr": 0.00747, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42297, "top5_acc": 0.67438, "loss_cls": 3.24646, "loss": 3.24646, "time": 0.85563} +{"mode": "train", "epoch": 124, "iter": 2200, "lr": 0.00746, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.41141, "top5_acc": 0.66312, "loss_cls": 3.32146, "loss": 3.32146, "time": 0.85322} +{"mode": "train", "epoch": 124, "iter": 2300, "lr": 0.00744, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40641, "top5_acc": 0.66406, "loss_cls": 3.34941, "loss": 3.34941, "time": 0.85196} +{"mode": "train", "epoch": 124, "iter": 2400, "lr": 0.00743, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42391, "top5_acc": 0.67734, "loss_cls": 3.27993, "loss": 3.27993, "time": 0.85366} +{"mode": "train", "epoch": 124, "iter": 2500, "lr": 0.00741, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.40672, "top5_acc": 0.67234, "loss_cls": 3.30204, "loss": 3.30204, "time": 0.85576} +{"mode": "train", "epoch": 124, "iter": 2600, "lr": 0.0074, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41719, "top5_acc": 0.66594, "loss_cls": 3.30044, "loss": 3.30044, "time": 0.85283} +{"mode": "train", "epoch": 124, "iter": 2700, "lr": 0.00738, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40203, "top5_acc": 0.65625, "loss_cls": 3.40148, "loss": 3.40148, "time": 0.85422} +{"mode": "train", "epoch": 124, "iter": 2800, "lr": 0.00737, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.41266, "top5_acc": 0.67, "loss_cls": 3.30263, "loss": 3.30263, "time": 0.85366} +{"mode": "train", "epoch": 124, "iter": 2900, "lr": 0.00735, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41922, "top5_acc": 0.67312, "loss_cls": 3.2914, "loss": 3.2914, "time": 0.85558} +{"mode": "train", "epoch": 124, "iter": 3000, "lr": 0.00734, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41516, "top5_acc": 0.675, "loss_cls": 3.2817, "loss": 3.2817, "time": 0.85209} +{"mode": "train", "epoch": 124, "iter": 3100, "lr": 0.00733, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41062, "top5_acc": 0.66656, "loss_cls": 3.31794, "loss": 3.31794, "time": 0.852} +{"mode": "train", "epoch": 124, "iter": 3200, "lr": 0.00731, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41094, "top5_acc": 0.66969, "loss_cls": 3.3072, "loss": 3.3072, "time": 0.85573} +{"mode": "train", "epoch": 124, "iter": 3300, "lr": 0.0073, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40828, "top5_acc": 0.66922, "loss_cls": 3.32818, "loss": 3.32818, "time": 0.84831} +{"mode": "train", "epoch": 124, "iter": 3400, "lr": 0.00728, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42062, "top5_acc": 0.66469, "loss_cls": 3.31345, "loss": 3.31345, "time": 0.85258} +{"mode": "train", "epoch": 124, "iter": 3500, "lr": 0.00727, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40312, "top5_acc": 0.66453, "loss_cls": 3.33477, "loss": 3.33477, "time": 0.85104} +{"mode": "train", "epoch": 124, "iter": 3600, "lr": 0.00725, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41359, "top5_acc": 0.66594, "loss_cls": 3.31663, "loss": 3.31663, "time": 0.8524} +{"mode": "train", "epoch": 124, "iter": 3700, "lr": 0.00724, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41234, "top5_acc": 0.66875, "loss_cls": 3.3075, "loss": 3.3075, "time": 0.84661} +{"mode": "val", "epoch": 124, "iter": 309, "lr": 0.00723, "top1_acc": 0.35461, "top5_acc": 0.60705, "mean_class_accuracy": 0.3544} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.00722, "memory": 15990, "data_time": 1.55487, "top1_acc": 0.43094, "top5_acc": 0.69031, "loss_cls": 3.18627, "loss": 3.18627, "time": 2.58867} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.0072, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42844, "top5_acc": 0.69094, "loss_cls": 3.21414, "loss": 3.21414, "time": 0.85659} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00719, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42438, "top5_acc": 0.67984, "loss_cls": 3.24607, "loss": 3.24607, "time": 0.85346} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00717, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42828, "top5_acc": 0.68156, "loss_cls": 3.24017, "loss": 3.24017, "time": 0.85445} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00716, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42031, "top5_acc": 0.67375, "loss_cls": 3.26088, "loss": 3.26088, "time": 0.85673} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00715, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41734, "top5_acc": 0.66812, "loss_cls": 3.31625, "loss": 3.31625, "time": 0.85558} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00713, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42406, "top5_acc": 0.68234, "loss_cls": 3.23294, "loss": 3.23294, "time": 0.8543} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00712, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41938, "top5_acc": 0.67375, "loss_cls": 3.27596, "loss": 3.27596, "time": 0.85593} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.0071, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42, "top5_acc": 0.67859, "loss_cls": 3.26464, "loss": 3.26464, "time": 0.85549} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.00709, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42547, "top5_acc": 0.68094, "loss_cls": 3.22923, "loss": 3.22923, "time": 0.85288} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00707, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42438, "top5_acc": 0.68484, "loss_cls": 3.24278, "loss": 3.24278, "time": 0.85076} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00706, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41344, "top5_acc": 0.67531, "loss_cls": 3.28419, "loss": 3.28419, "time": 0.85013} +{"mode": "train", "epoch": 125, "iter": 1300, "lr": 0.00704, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42781, "top5_acc": 0.67469, "loss_cls": 3.2495, "loss": 3.2495, "time": 0.85382} +{"mode": "train", "epoch": 125, "iter": 1400, "lr": 0.00703, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41641, "top5_acc": 0.67703, "loss_cls": 3.25552, "loss": 3.25552, "time": 0.85575} +{"mode": "train", "epoch": 125, "iter": 1500, "lr": 0.00702, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.42312, "top5_acc": 0.67812, "loss_cls": 3.27691, "loss": 3.27691, "time": 0.85351} +{"mode": "train", "epoch": 125, "iter": 1600, "lr": 0.007, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41266, "top5_acc": 0.67062, "loss_cls": 3.30038, "loss": 3.30038, "time": 0.85425} +{"mode": "train", "epoch": 125, "iter": 1700, "lr": 0.00699, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42562, "top5_acc": 0.68047, "loss_cls": 3.23868, "loss": 3.23868, "time": 0.84832} +{"mode": "train", "epoch": 125, "iter": 1800, "lr": 0.00697, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42438, "top5_acc": 0.67781, "loss_cls": 3.23004, "loss": 3.23004, "time": 0.85075} +{"mode": "train", "epoch": 125, "iter": 1900, "lr": 0.00696, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41422, "top5_acc": 0.67297, "loss_cls": 3.30014, "loss": 3.30014, "time": 0.85671} +{"mode": "train", "epoch": 125, "iter": 2000, "lr": 0.00694, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41109, "top5_acc": 0.67391, "loss_cls": 3.28928, "loss": 3.28928, "time": 0.85153} +{"mode": "train", "epoch": 125, "iter": 2100, "lr": 0.00693, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41328, "top5_acc": 0.67078, "loss_cls": 3.29927, "loss": 3.29927, "time": 0.85466} +{"mode": "train", "epoch": 125, "iter": 2200, "lr": 0.00692, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42453, "top5_acc": 0.67938, "loss_cls": 3.26685, "loss": 3.26685, "time": 0.85131} +{"mode": "train", "epoch": 125, "iter": 2300, "lr": 0.0069, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42078, "top5_acc": 0.68656, "loss_cls": 3.22921, "loss": 3.22921, "time": 0.85337} +{"mode": "train", "epoch": 125, "iter": 2400, "lr": 0.00689, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42703, "top5_acc": 0.68141, "loss_cls": 3.26211, "loss": 3.26211, "time": 0.85833} +{"mode": "train", "epoch": 125, "iter": 2500, "lr": 0.00687, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42453, "top5_acc": 0.68219, "loss_cls": 3.24671, "loss": 3.24671, "time": 0.85404} +{"mode": "train", "epoch": 125, "iter": 2600, "lr": 0.00686, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41609, "top5_acc": 0.67688, "loss_cls": 3.27026, "loss": 3.27026, "time": 0.85157} +{"mode": "train", "epoch": 125, "iter": 2700, "lr": 0.00685, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40969, "top5_acc": 0.67172, "loss_cls": 3.31175, "loss": 3.31175, "time": 0.85568} +{"mode": "train", "epoch": 125, "iter": 2800, "lr": 0.00683, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42219, "top5_acc": 0.67516, "loss_cls": 3.2458, "loss": 3.2458, "time": 0.85117} +{"mode": "train", "epoch": 125, "iter": 2900, "lr": 0.00682, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40781, "top5_acc": 0.66031, "loss_cls": 3.33771, "loss": 3.33771, "time": 0.85261} +{"mode": "train", "epoch": 125, "iter": 3000, "lr": 0.0068, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42219, "top5_acc": 0.67125, "loss_cls": 3.27631, "loss": 3.27631, "time": 0.85126} +{"mode": "train", "epoch": 125, "iter": 3100, "lr": 0.00679, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41578, "top5_acc": 0.67719, "loss_cls": 3.28292, "loss": 3.28292, "time": 0.85409} +{"mode": "train", "epoch": 125, "iter": 3200, "lr": 0.00678, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42172, "top5_acc": 0.67578, "loss_cls": 3.27856, "loss": 3.27856, "time": 0.85307} +{"mode": "train", "epoch": 125, "iter": 3300, "lr": 0.00676, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40172, "top5_acc": 0.66031, "loss_cls": 3.34791, "loss": 3.34791, "time": 0.85375} +{"mode": "train", "epoch": 125, "iter": 3400, "lr": 0.00675, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.415, "top5_acc": 0.66391, "loss_cls": 3.28998, "loss": 3.28998, "time": 0.85498} +{"mode": "train", "epoch": 125, "iter": 3500, "lr": 0.00673, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40375, "top5_acc": 0.66469, "loss_cls": 3.34134, "loss": 3.34134, "time": 0.85057} +{"mode": "train", "epoch": 125, "iter": 3600, "lr": 0.00672, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42312, "top5_acc": 0.68234, "loss_cls": 3.24282, "loss": 3.24282, "time": 0.85316} +{"mode": "train", "epoch": 125, "iter": 3700, "lr": 0.00671, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42328, "top5_acc": 0.67266, "loss_cls": 3.27354, "loss": 3.27354, "time": 0.85092} +{"mode": "val", "epoch": 125, "iter": 309, "lr": 0.0067, "top1_acc": 0.35314, "top5_acc": 0.60437, "mean_class_accuracy": 0.35291} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00668, "memory": 15990, "data_time": 1.57646, "top1_acc": 0.44469, "top5_acc": 0.69562, "loss_cls": 3.14084, "loss": 3.14084, "time": 2.61828} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00667, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44281, "top5_acc": 0.70438, "loss_cls": 3.13512, "loss": 3.13512, "time": 0.85332} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00666, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.425, "top5_acc": 0.68688, "loss_cls": 3.21828, "loss": 3.21828, "time": 0.85288} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00664, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42141, "top5_acc": 0.68484, "loss_cls": 3.22735, "loss": 3.22735, "time": 0.85301} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00663, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42781, "top5_acc": 0.69344, "loss_cls": 3.20319, "loss": 3.20319, "time": 0.85231} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00662, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43094, "top5_acc": 0.69359, "loss_cls": 3.19429, "loss": 3.19429, "time": 0.85125} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0066, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.43094, "top5_acc": 0.68484, "loss_cls": 3.22368, "loss": 3.22368, "time": 0.85438} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00659, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42594, "top5_acc": 0.6925, "loss_cls": 3.20042, "loss": 3.20042, "time": 0.85612} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00657, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43297, "top5_acc": 0.68297, "loss_cls": 3.20935, "loss": 3.20935, "time": 0.85184} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00656, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42547, "top5_acc": 0.68609, "loss_cls": 3.22576, "loss": 3.22576, "time": 0.85742} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00655, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42422, "top5_acc": 0.67438, "loss_cls": 3.26983, "loss": 3.26983, "time": 0.84955} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00653, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42109, "top5_acc": 0.68266, "loss_cls": 3.25376, "loss": 3.25376, "time": 0.85156} +{"mode": "train", "epoch": 126, "iter": 1300, "lr": 0.00652, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42172, "top5_acc": 0.68156, "loss_cls": 3.24525, "loss": 3.24525, "time": 0.85549} +{"mode": "train", "epoch": 126, "iter": 1400, "lr": 0.0065, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42703, "top5_acc": 0.68312, "loss_cls": 3.22073, "loss": 3.22073, "time": 0.85332} +{"mode": "train", "epoch": 126, "iter": 1500, "lr": 0.00649, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41469, "top5_acc": 0.67422, "loss_cls": 3.2712, "loss": 3.2712, "time": 0.85618} +{"mode": "train", "epoch": 126, "iter": 1600, "lr": 0.00648, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42344, "top5_acc": 0.67812, "loss_cls": 3.24199, "loss": 3.24199, "time": 0.85215} +{"mode": "train", "epoch": 126, "iter": 1700, "lr": 0.00646, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42047, "top5_acc": 0.68234, "loss_cls": 3.23615, "loss": 3.23615, "time": 0.85009} +{"mode": "train", "epoch": 126, "iter": 1800, "lr": 0.00645, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42703, "top5_acc": 0.67922, "loss_cls": 3.24927, "loss": 3.24927, "time": 0.85513} +{"mode": "train", "epoch": 126, "iter": 1900, "lr": 0.00644, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.41391, "top5_acc": 0.67938, "loss_cls": 3.25487, "loss": 3.25487, "time": 0.85329} +{"mode": "train", "epoch": 126, "iter": 2000, "lr": 0.00642, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42297, "top5_acc": 0.67828, "loss_cls": 3.24464, "loss": 3.24464, "time": 0.85203} +{"mode": "train", "epoch": 126, "iter": 2100, "lr": 0.00641, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.425, "top5_acc": 0.67531, "loss_cls": 3.26026, "loss": 3.26026, "time": 0.84893} +{"mode": "train", "epoch": 126, "iter": 2200, "lr": 0.00639, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42781, "top5_acc": 0.67312, "loss_cls": 3.24716, "loss": 3.24716, "time": 0.85643} +{"mode": "train", "epoch": 126, "iter": 2300, "lr": 0.00638, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42719, "top5_acc": 0.68391, "loss_cls": 3.21679, "loss": 3.21679, "time": 0.85255} +{"mode": "train", "epoch": 126, "iter": 2400, "lr": 0.00637, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42766, "top5_acc": 0.68375, "loss_cls": 3.20775, "loss": 3.20775, "time": 0.8527} +{"mode": "train", "epoch": 126, "iter": 2500, "lr": 0.00635, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41328, "top5_acc": 0.68094, "loss_cls": 3.23875, "loss": 3.23875, "time": 0.85361} +{"mode": "train", "epoch": 126, "iter": 2600, "lr": 0.00634, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42219, "top5_acc": 0.68266, "loss_cls": 3.25776, "loss": 3.25776, "time": 0.85465} +{"mode": "train", "epoch": 126, "iter": 2700, "lr": 0.00633, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.41656, "top5_acc": 0.68031, "loss_cls": 3.251, "loss": 3.251, "time": 0.85323} +{"mode": "train", "epoch": 126, "iter": 2800, "lr": 0.00631, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42062, "top5_acc": 0.68062, "loss_cls": 3.25519, "loss": 3.25519, "time": 0.85685} +{"mode": "train", "epoch": 126, "iter": 2900, "lr": 0.0063, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.41688, "top5_acc": 0.67828, "loss_cls": 3.27745, "loss": 3.27745, "time": 0.86304} +{"mode": "train", "epoch": 126, "iter": 3000, "lr": 0.00629, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.41891, "top5_acc": 0.68094, "loss_cls": 3.26197, "loss": 3.26197, "time": 0.85964} +{"mode": "train", "epoch": 126, "iter": 3100, "lr": 0.00627, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42453, "top5_acc": 0.68719, "loss_cls": 3.23545, "loss": 3.23545, "time": 0.8613} +{"mode": "train", "epoch": 126, "iter": 3200, "lr": 0.00626, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42266, "top5_acc": 0.675, "loss_cls": 3.26896, "loss": 3.26896, "time": 0.85649} +{"mode": "train", "epoch": 126, "iter": 3300, "lr": 0.00625, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42109, "top5_acc": 0.67203, "loss_cls": 3.25751, "loss": 3.25751, "time": 0.85738} +{"mode": "train", "epoch": 126, "iter": 3400, "lr": 0.00623, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42422, "top5_acc": 0.67344, "loss_cls": 3.24826, "loss": 3.24826, "time": 0.85185} +{"mode": "train", "epoch": 126, "iter": 3500, "lr": 0.00622, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42453, "top5_acc": 0.67875, "loss_cls": 3.25268, "loss": 3.25268, "time": 0.84669} +{"mode": "train", "epoch": 126, "iter": 3600, "lr": 0.0062, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42922, "top5_acc": 0.68359, "loss_cls": 3.22528, "loss": 3.22528, "time": 0.85038} +{"mode": "train", "epoch": 126, "iter": 3700, "lr": 0.00619, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42281, "top5_acc": 0.67344, "loss_cls": 3.29826, "loss": 3.29826, "time": 0.85439} +{"mode": "val", "epoch": 126, "iter": 309, "lr": 0.00618, "top1_acc": 0.35506, "top5_acc": 0.60583, "mean_class_accuracy": 0.35472} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00617, "memory": 15990, "data_time": 1.59093, "top1_acc": 0.43406, "top5_acc": 0.6925, "loss_cls": 3.16452, "loss": 3.16452, "time": 2.62506} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00616, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.435, "top5_acc": 0.68391, "loss_cls": 3.20077, "loss": 3.20077, "time": 0.85313} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00614, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44156, "top5_acc": 0.70453, "loss_cls": 3.14142, "loss": 3.14142, "time": 0.85365} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00613, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42781, "top5_acc": 0.68656, "loss_cls": 3.17546, "loss": 3.17546, "time": 0.85275} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.00612, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43641, "top5_acc": 0.69359, "loss_cls": 3.16561, "loss": 3.16561, "time": 0.84856} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.0061, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.44375, "top5_acc": 0.69609, "loss_cls": 3.16884, "loss": 3.16884, "time": 0.85785} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00609, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.43469, "top5_acc": 0.68516, "loss_cls": 3.19212, "loss": 3.19212, "time": 0.85093} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00608, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.43438, "top5_acc": 0.69125, "loss_cls": 3.21726, "loss": 3.21726, "time": 0.85228} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00606, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42781, "top5_acc": 0.69078, "loss_cls": 3.20054, "loss": 3.20054, "time": 0.85579} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00605, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42891, "top5_acc": 0.69328, "loss_cls": 3.196, "loss": 3.196, "time": 0.85514} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00604, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43891, "top5_acc": 0.68484, "loss_cls": 3.196, "loss": 3.196, "time": 0.85642} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00602, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44781, "top5_acc": 0.69406, "loss_cls": 3.1189, "loss": 3.1189, "time": 0.84826} +{"mode": "train", "epoch": 127, "iter": 1300, "lr": 0.00601, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42859, "top5_acc": 0.67609, "loss_cls": 3.22997, "loss": 3.22997, "time": 0.85537} +{"mode": "train", "epoch": 127, "iter": 1400, "lr": 0.006, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42969, "top5_acc": 0.68844, "loss_cls": 3.20764, "loss": 3.20764, "time": 0.85424} +{"mode": "train", "epoch": 127, "iter": 1500, "lr": 0.00598, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.43328, "top5_acc": 0.69531, "loss_cls": 3.17118, "loss": 3.17118, "time": 0.85102} +{"mode": "train", "epoch": 127, "iter": 1600, "lr": 0.00597, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43312, "top5_acc": 0.68266, "loss_cls": 3.20837, "loss": 3.20837, "time": 0.85547} +{"mode": "train", "epoch": 127, "iter": 1700, "lr": 0.00596, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42297, "top5_acc": 0.68766, "loss_cls": 3.20811, "loss": 3.20811, "time": 0.85825} +{"mode": "train", "epoch": 127, "iter": 1800, "lr": 0.00594, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42266, "top5_acc": 0.68922, "loss_cls": 3.21331, "loss": 3.21331, "time": 0.85545} +{"mode": "train", "epoch": 127, "iter": 1900, "lr": 0.00593, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42562, "top5_acc": 0.68875, "loss_cls": 3.20379, "loss": 3.20379, "time": 0.85067} +{"mode": "train", "epoch": 127, "iter": 2000, "lr": 0.00592, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.4175, "top5_acc": 0.67969, "loss_cls": 3.26726, "loss": 3.26726, "time": 0.85472} +{"mode": "train", "epoch": 127, "iter": 2100, "lr": 0.00591, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.4325, "top5_acc": 0.68141, "loss_cls": 3.20197, "loss": 3.20197, "time": 0.84941} +{"mode": "train", "epoch": 127, "iter": 2200, "lr": 0.00589, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42484, "top5_acc": 0.68266, "loss_cls": 3.23961, "loss": 3.23961, "time": 0.85132} +{"mode": "train", "epoch": 127, "iter": 2300, "lr": 0.00588, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42719, "top5_acc": 0.68281, "loss_cls": 3.22609, "loss": 3.22609, "time": 0.85439} +{"mode": "train", "epoch": 127, "iter": 2400, "lr": 0.00587, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43344, "top5_acc": 0.69031, "loss_cls": 3.16182, "loss": 3.16182, "time": 0.85212} +{"mode": "train", "epoch": 127, "iter": 2500, "lr": 0.00585, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.41844, "top5_acc": 0.68562, "loss_cls": 3.2209, "loss": 3.2209, "time": 0.85492} +{"mode": "train", "epoch": 127, "iter": 2600, "lr": 0.00584, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42422, "top5_acc": 0.68469, "loss_cls": 3.23414, "loss": 3.23414, "time": 0.8539} +{"mode": "train", "epoch": 127, "iter": 2700, "lr": 0.00583, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42984, "top5_acc": 0.68219, "loss_cls": 3.2296, "loss": 3.2296, "time": 0.86145} +{"mode": "train", "epoch": 127, "iter": 2800, "lr": 0.00581, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42656, "top5_acc": 0.68516, "loss_cls": 3.22159, "loss": 3.22159, "time": 0.85264} +{"mode": "train", "epoch": 127, "iter": 2900, "lr": 0.0058, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42156, "top5_acc": 0.68547, "loss_cls": 3.22633, "loss": 3.22633, "time": 0.85378} +{"mode": "train", "epoch": 127, "iter": 3000, "lr": 0.00579, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.42219, "top5_acc": 0.68453, "loss_cls": 3.23991, "loss": 3.23991, "time": 0.85142} +{"mode": "train", "epoch": 127, "iter": 3100, "lr": 0.00577, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.43312, "top5_acc": 0.68266, "loss_cls": 3.23651, "loss": 3.23651, "time": 0.85523} +{"mode": "train", "epoch": 127, "iter": 3200, "lr": 0.00576, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43391, "top5_acc": 0.68359, "loss_cls": 3.22916, "loss": 3.22916, "time": 0.85176} +{"mode": "train", "epoch": 127, "iter": 3300, "lr": 0.00575, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42312, "top5_acc": 0.68969, "loss_cls": 3.22854, "loss": 3.22854, "time": 0.8521} +{"mode": "train", "epoch": 127, "iter": 3400, "lr": 0.00573, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42859, "top5_acc": 0.68406, "loss_cls": 3.2103, "loss": 3.2103, "time": 0.85273} +{"mode": "train", "epoch": 127, "iter": 3500, "lr": 0.00572, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42047, "top5_acc": 0.6775, "loss_cls": 3.26268, "loss": 3.26268, "time": 0.84882} +{"mode": "train", "epoch": 127, "iter": 3600, "lr": 0.00571, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43125, "top5_acc": 0.6825, "loss_cls": 3.21562, "loss": 3.21562, "time": 0.84992} +{"mode": "train", "epoch": 127, "iter": 3700, "lr": 0.0057, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42344, "top5_acc": 0.68484, "loss_cls": 3.21603, "loss": 3.21603, "time": 0.85154} +{"mode": "val", "epoch": 127, "iter": 309, "lr": 0.00569, "top1_acc": 0.36195, "top5_acc": 0.61845, "mean_class_accuracy": 0.36179} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00568, "memory": 15990, "data_time": 1.60754, "top1_acc": 0.44625, "top5_acc": 0.69719, "loss_cls": 3.13232, "loss": 3.13232, "time": 2.65155} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.00566, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.44641, "top5_acc": 0.705, "loss_cls": 3.10492, "loss": 3.10492, "time": 0.85283} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00565, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44688, "top5_acc": 0.69672, "loss_cls": 3.123, "loss": 3.123, "time": 0.85403} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00564, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43906, "top5_acc": 0.69953, "loss_cls": 3.14152, "loss": 3.14152, "time": 0.85472} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00563, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.44141, "top5_acc": 0.69797, "loss_cls": 3.13087, "loss": 3.13087, "time": 0.8573} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00561, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44969, "top5_acc": 0.69734, "loss_cls": 3.1136, "loss": 3.1136, "time": 0.84994} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.0056, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43312, "top5_acc": 0.68766, "loss_cls": 3.1841, "loss": 3.1841, "time": 0.85217} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00559, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.44625, "top5_acc": 0.69953, "loss_cls": 3.11676, "loss": 3.11676, "time": 0.85827} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00557, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44281, "top5_acc": 0.70203, "loss_cls": 3.12956, "loss": 3.12956, "time": 0.85308} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00556, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44047, "top5_acc": 0.70484, "loss_cls": 3.13193, "loss": 3.13193, "time": 0.85197} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00555, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43844, "top5_acc": 0.69359, "loss_cls": 3.13863, "loss": 3.13863, "time": 0.85269} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00554, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43688, "top5_acc": 0.69, "loss_cls": 3.18917, "loss": 3.18917, "time": 0.8528} +{"mode": "train", "epoch": 128, "iter": 1300, "lr": 0.00552, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44578, "top5_acc": 0.69562, "loss_cls": 3.13479, "loss": 3.13479, "time": 0.85285} +{"mode": "train", "epoch": 128, "iter": 1400, "lr": 0.00551, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.43062, "top5_acc": 0.69422, "loss_cls": 3.16834, "loss": 3.16834, "time": 0.85342} +{"mode": "train", "epoch": 128, "iter": 1500, "lr": 0.0055, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42719, "top5_acc": 0.68, "loss_cls": 3.24219, "loss": 3.24219, "time": 0.8572} +{"mode": "train", "epoch": 128, "iter": 1600, "lr": 0.00548, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43, "top5_acc": 0.68422, "loss_cls": 3.19656, "loss": 3.19656, "time": 0.85826} +{"mode": "train", "epoch": 128, "iter": 1700, "lr": 0.00547, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.44016, "top5_acc": 0.68359, "loss_cls": 3.18991, "loss": 3.18991, "time": 0.85108} +{"mode": "train", "epoch": 128, "iter": 1800, "lr": 0.00546, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43484, "top5_acc": 0.68406, "loss_cls": 3.22014, "loss": 3.22014, "time": 0.85465} +{"mode": "train", "epoch": 128, "iter": 1900, "lr": 0.00545, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43031, "top5_acc": 0.69141, "loss_cls": 3.18416, "loss": 3.18416, "time": 0.8506} +{"mode": "train", "epoch": 128, "iter": 2000, "lr": 0.00543, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.43375, "top5_acc": 0.69375, "loss_cls": 3.18792, "loss": 3.18792, "time": 0.85145} +{"mode": "train", "epoch": 128, "iter": 2100, "lr": 0.00542, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44094, "top5_acc": 0.69203, "loss_cls": 3.15737, "loss": 3.15737, "time": 0.85005} +{"mode": "train", "epoch": 128, "iter": 2200, "lr": 0.00541, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44312, "top5_acc": 0.70703, "loss_cls": 3.13298, "loss": 3.13298, "time": 0.85845} +{"mode": "train", "epoch": 128, "iter": 2300, "lr": 0.0054, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43656, "top5_acc": 0.69609, "loss_cls": 3.16631, "loss": 3.16631, "time": 0.85294} +{"mode": "train", "epoch": 128, "iter": 2400, "lr": 0.00538, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43406, "top5_acc": 0.68984, "loss_cls": 3.19389, "loss": 3.19389, "time": 0.85128} +{"mode": "train", "epoch": 128, "iter": 2500, "lr": 0.00537, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42656, "top5_acc": 0.68453, "loss_cls": 3.25427, "loss": 3.25427, "time": 0.85142} +{"mode": "train", "epoch": 128, "iter": 2600, "lr": 0.00536, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42312, "top5_acc": 0.69312, "loss_cls": 3.19835, "loss": 3.19835, "time": 0.85435} +{"mode": "train", "epoch": 128, "iter": 2700, "lr": 0.00535, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42438, "top5_acc": 0.69062, "loss_cls": 3.20068, "loss": 3.20068, "time": 0.84887} +{"mode": "train", "epoch": 128, "iter": 2800, "lr": 0.00533, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43734, "top5_acc": 0.69359, "loss_cls": 3.17647, "loss": 3.17647, "time": 0.85338} +{"mode": "train", "epoch": 128, "iter": 2900, "lr": 0.00532, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42953, "top5_acc": 0.68781, "loss_cls": 3.20647, "loss": 3.20647, "time": 0.84964} +{"mode": "train", "epoch": 128, "iter": 3000, "lr": 0.00531, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44328, "top5_acc": 0.69719, "loss_cls": 3.1513, "loss": 3.1513, "time": 0.85477} +{"mode": "train", "epoch": 128, "iter": 3100, "lr": 0.0053, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43703, "top5_acc": 0.68562, "loss_cls": 3.17631, "loss": 3.17631, "time": 0.85292} +{"mode": "train", "epoch": 128, "iter": 3200, "lr": 0.00528, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43438, "top5_acc": 0.69266, "loss_cls": 3.17197, "loss": 3.17197, "time": 0.85368} +{"mode": "train", "epoch": 128, "iter": 3300, "lr": 0.00527, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43219, "top5_acc": 0.68609, "loss_cls": 3.20126, "loss": 3.20126, "time": 0.85179} +{"mode": "train", "epoch": 128, "iter": 3400, "lr": 0.00526, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42531, "top5_acc": 0.68719, "loss_cls": 3.20728, "loss": 3.20728, "time": 0.85188} +{"mode": "train", "epoch": 128, "iter": 3500, "lr": 0.00525, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43625, "top5_acc": 0.69641, "loss_cls": 3.16461, "loss": 3.16461, "time": 0.85231} +{"mode": "train", "epoch": 128, "iter": 3600, "lr": 0.00523, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43234, "top5_acc": 0.69375, "loss_cls": 3.18315, "loss": 3.18315, "time": 0.8511} +{"mode": "train", "epoch": 128, "iter": 3700, "lr": 0.00522, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43375, "top5_acc": 0.68906, "loss_cls": 3.19443, "loss": 3.19443, "time": 0.85158} +{"mode": "val", "epoch": 128, "iter": 309, "lr": 0.00521, "top1_acc": 0.36165, "top5_acc": 0.61521, "mean_class_accuracy": 0.36138} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.0052, "memory": 15990, "data_time": 1.55884, "top1_acc": 0.44953, "top5_acc": 0.71219, "loss_cls": 3.08282, "loss": 3.08282, "time": 2.62352} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00519, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44672, "top5_acc": 0.70125, "loss_cls": 3.12876, "loss": 3.12876, "time": 0.85454} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00518, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44594, "top5_acc": 0.70766, "loss_cls": 3.08688, "loss": 3.08688, "time": 0.85108} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00516, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44922, "top5_acc": 0.705, "loss_cls": 3.08855, "loss": 3.08855, "time": 0.85389} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00515, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.4475, "top5_acc": 0.70094, "loss_cls": 3.11829, "loss": 3.11829, "time": 0.853} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00514, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.45172, "top5_acc": 0.70844, "loss_cls": 3.08549, "loss": 3.08549, "time": 0.85647} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00513, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44641, "top5_acc": 0.70656, "loss_cls": 3.11301, "loss": 3.11301, "time": 0.85251} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00512, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44375, "top5_acc": 0.69812, "loss_cls": 3.12114, "loss": 3.12114, "time": 0.85879} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.0051, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.4425, "top5_acc": 0.69641, "loss_cls": 3.11614, "loss": 3.11614, "time": 0.86885} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00509, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.445, "top5_acc": 0.69531, "loss_cls": 3.14862, "loss": 3.14862, "time": 0.86595} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00508, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44375, "top5_acc": 0.69844, "loss_cls": 3.09627, "loss": 3.09627, "time": 0.86499} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.00507, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.44391, "top5_acc": 0.69766, "loss_cls": 3.12554, "loss": 3.12554, "time": 0.86586} +{"mode": "train", "epoch": 129, "iter": 1300, "lr": 0.00505, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.44062, "top5_acc": 0.68891, "loss_cls": 3.16257, "loss": 3.16257, "time": 0.86657} +{"mode": "train", "epoch": 129, "iter": 1400, "lr": 0.00504, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.4375, "top5_acc": 0.69562, "loss_cls": 3.14372, "loss": 3.14372, "time": 0.86324} +{"mode": "train", "epoch": 129, "iter": 1500, "lr": 0.00503, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.44406, "top5_acc": 0.69859, "loss_cls": 3.13682, "loss": 3.13682, "time": 0.86482} +{"mode": "train", "epoch": 129, "iter": 1600, "lr": 0.00502, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.44766, "top5_acc": 0.70891, "loss_cls": 3.0855, "loss": 3.0855, "time": 0.86362} +{"mode": "train", "epoch": 129, "iter": 1700, "lr": 0.00501, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.43422, "top5_acc": 0.68562, "loss_cls": 3.15093, "loss": 3.15093, "time": 0.86809} +{"mode": "train", "epoch": 129, "iter": 1800, "lr": 0.00499, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.43641, "top5_acc": 0.69453, "loss_cls": 3.13678, "loss": 3.13678, "time": 0.86285} +{"mode": "train", "epoch": 129, "iter": 1900, "lr": 0.00498, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.4325, "top5_acc": 0.695, "loss_cls": 3.13878, "loss": 3.13878, "time": 0.86649} +{"mode": "train", "epoch": 129, "iter": 2000, "lr": 0.00497, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.43656, "top5_acc": 0.69172, "loss_cls": 3.17078, "loss": 3.17078, "time": 0.86525} +{"mode": "train", "epoch": 129, "iter": 2100, "lr": 0.00496, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.43469, "top5_acc": 0.69594, "loss_cls": 3.16754, "loss": 3.16754, "time": 0.8685} +{"mode": "train", "epoch": 129, "iter": 2200, "lr": 0.00494, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.445, "top5_acc": 0.69469, "loss_cls": 3.13655, "loss": 3.13655, "time": 0.86812} +{"mode": "train", "epoch": 129, "iter": 2300, "lr": 0.00493, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.43734, "top5_acc": 0.69031, "loss_cls": 3.16847, "loss": 3.16847, "time": 0.86576} +{"mode": "train", "epoch": 129, "iter": 2400, "lr": 0.00492, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.44453, "top5_acc": 0.69359, "loss_cls": 3.15419, "loss": 3.15419, "time": 0.86446} +{"mode": "train", "epoch": 129, "iter": 2500, "lr": 0.00491, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.43953, "top5_acc": 0.69234, "loss_cls": 3.18528, "loss": 3.18528, "time": 0.86502} +{"mode": "train", "epoch": 129, "iter": 2600, "lr": 0.0049, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.43453, "top5_acc": 0.69109, "loss_cls": 3.18662, "loss": 3.18662, "time": 0.86429} +{"mode": "train", "epoch": 129, "iter": 2700, "lr": 0.00488, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.44266, "top5_acc": 0.69562, "loss_cls": 3.16016, "loss": 3.16016, "time": 0.87199} +{"mode": "train", "epoch": 129, "iter": 2800, "lr": 0.00487, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.44672, "top5_acc": 0.70047, "loss_cls": 3.12483, "loss": 3.12483, "time": 0.86571} +{"mode": "train", "epoch": 129, "iter": 2900, "lr": 0.00486, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.45094, "top5_acc": 0.70141, "loss_cls": 3.10471, "loss": 3.10471, "time": 0.86295} +{"mode": "train", "epoch": 129, "iter": 3000, "lr": 0.00485, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.44391, "top5_acc": 0.68953, "loss_cls": 3.15349, "loss": 3.15349, "time": 0.86628} +{"mode": "train", "epoch": 129, "iter": 3100, "lr": 0.00484, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.42891, "top5_acc": 0.68234, "loss_cls": 3.21017, "loss": 3.21017, "time": 0.86544} +{"mode": "train", "epoch": 129, "iter": 3200, "lr": 0.00482, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.44156, "top5_acc": 0.69844, "loss_cls": 3.14755, "loss": 3.14755, "time": 0.85877} +{"mode": "train", "epoch": 129, "iter": 3300, "lr": 0.00481, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.44328, "top5_acc": 0.69766, "loss_cls": 3.14212, "loss": 3.14212, "time": 0.86003} +{"mode": "train", "epoch": 129, "iter": 3400, "lr": 0.0048, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.42625, "top5_acc": 0.68438, "loss_cls": 3.20365, "loss": 3.20365, "time": 0.85425} +{"mode": "train", "epoch": 129, "iter": 3500, "lr": 0.00479, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.44109, "top5_acc": 0.70328, "loss_cls": 3.11122, "loss": 3.11122, "time": 0.85958} +{"mode": "train", "epoch": 129, "iter": 3600, "lr": 0.00478, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.44469, "top5_acc": 0.70688, "loss_cls": 3.11911, "loss": 3.11911, "time": 0.86516} +{"mode": "train", "epoch": 129, "iter": 3700, "lr": 0.00476, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.43656, "top5_acc": 0.6825, "loss_cls": 3.19239, "loss": 3.19239, "time": 0.86612} +{"mode": "val", "epoch": 129, "iter": 309, "lr": 0.00476, "top1_acc": 0.36256, "top5_acc": 0.61769, "mean_class_accuracy": 0.36223} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00475, "memory": 15990, "data_time": 1.64685, "top1_acc": 0.45844, "top5_acc": 0.70547, "loss_cls": 3.05245, "loss": 3.05245, "time": 2.67723} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00473, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.45656, "top5_acc": 0.71391, "loss_cls": 3.05656, "loss": 3.05656, "time": 0.86766} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00472, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.45109, "top5_acc": 0.70922, "loss_cls": 3.06796, "loss": 3.06796, "time": 0.85849} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00471, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.45438, "top5_acc": 0.70328, "loss_cls": 3.1053, "loss": 3.1053, "time": 0.86441} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.0047, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.46109, "top5_acc": 0.71016, "loss_cls": 3.05456, "loss": 3.05456, "time": 0.86225} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00469, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.46062, "top5_acc": 0.72094, "loss_cls": 3.00276, "loss": 3.00276, "time": 0.86822} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00468, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.445, "top5_acc": 0.70688, "loss_cls": 3.08015, "loss": 3.08015, "time": 0.86631} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00466, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.44594, "top5_acc": 0.70391, "loss_cls": 3.0995, "loss": 3.0995, "time": 0.87176} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00465, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.43953, "top5_acc": 0.70156, "loss_cls": 3.11261, "loss": 3.11261, "time": 0.86569} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.00464, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.43891, "top5_acc": 0.70531, "loss_cls": 3.09118, "loss": 3.09118, "time": 0.86934} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.00463, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.45719, "top5_acc": 0.71266, "loss_cls": 3.03345, "loss": 3.03345, "time": 0.87446} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00462, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.45, "top5_acc": 0.70391, "loss_cls": 3.07402, "loss": 3.07402, "time": 0.87828} +{"mode": "train", "epoch": 130, "iter": 1300, "lr": 0.00461, "memory": 15990, "data_time": 0.00068, "top1_acc": 0.45328, "top5_acc": 0.70641, "loss_cls": 3.09166, "loss": 3.09166, "time": 0.87934} +{"mode": "train", "epoch": 130, "iter": 1400, "lr": 0.00459, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.45078, "top5_acc": 0.70188, "loss_cls": 3.08535, "loss": 3.08535, "time": 0.87069} +{"mode": "train", "epoch": 130, "iter": 1500, "lr": 0.00458, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.45125, "top5_acc": 0.69969, "loss_cls": 3.09035, "loss": 3.09035, "time": 0.87761} +{"mode": "train", "epoch": 130, "iter": 1600, "lr": 0.00457, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.44859, "top5_acc": 0.69891, "loss_cls": 3.09934, "loss": 3.09934, "time": 0.88141} +{"mode": "train", "epoch": 130, "iter": 1700, "lr": 0.00456, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.45562, "top5_acc": 0.70672, "loss_cls": 3.08202, "loss": 3.08202, "time": 0.87876} +{"mode": "train", "epoch": 130, "iter": 1800, "lr": 0.00455, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.44422, "top5_acc": 0.69812, "loss_cls": 3.11502, "loss": 3.11502, "time": 0.86526} +{"mode": "train", "epoch": 130, "iter": 1900, "lr": 0.00454, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.44516, "top5_acc": 0.69906, "loss_cls": 3.12526, "loss": 3.12526, "time": 0.8625} +{"mode": "train", "epoch": 130, "iter": 2000, "lr": 0.00452, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.44312, "top5_acc": 0.70609, "loss_cls": 3.1021, "loss": 3.1021, "time": 0.85921} +{"mode": "train", "epoch": 130, "iter": 2100, "lr": 0.00451, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.44297, "top5_acc": 0.7025, "loss_cls": 3.12178, "loss": 3.12178, "time": 0.88004} +{"mode": "train", "epoch": 130, "iter": 2200, "lr": 0.0045, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.43422, "top5_acc": 0.70203, "loss_cls": 3.13414, "loss": 3.13414, "time": 0.87562} +{"mode": "train", "epoch": 130, "iter": 2300, "lr": 0.00449, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.44016, "top5_acc": 0.69359, "loss_cls": 3.13058, "loss": 3.13058, "time": 0.87598} +{"mode": "train", "epoch": 130, "iter": 2400, "lr": 0.00448, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.45203, "top5_acc": 0.70344, "loss_cls": 3.09711, "loss": 3.09711, "time": 0.87431} +{"mode": "train", "epoch": 130, "iter": 2500, "lr": 0.00447, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.44672, "top5_acc": 0.7, "loss_cls": 3.12791, "loss": 3.12791, "time": 0.87866} +{"mode": "train", "epoch": 130, "iter": 2600, "lr": 0.00445, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.44359, "top5_acc": 0.69094, "loss_cls": 3.15888, "loss": 3.15888, "time": 0.88215} +{"mode": "train", "epoch": 130, "iter": 2700, "lr": 0.00444, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.43969, "top5_acc": 0.69875, "loss_cls": 3.15255, "loss": 3.15255, "time": 0.88066} +{"mode": "train", "epoch": 130, "iter": 2800, "lr": 0.00443, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.44516, "top5_acc": 0.70688, "loss_cls": 3.07861, "loss": 3.07861, "time": 0.8754} +{"mode": "train", "epoch": 130, "iter": 2900, "lr": 0.00442, "memory": 15990, "data_time": 0.00068, "top1_acc": 0.45312, "top5_acc": 0.71375, "loss_cls": 3.05656, "loss": 3.05656, "time": 0.88577} +{"mode": "train", "epoch": 130, "iter": 3000, "lr": 0.00441, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.44297, "top5_acc": 0.71391, "loss_cls": 3.10532, "loss": 3.10532, "time": 0.88121} +{"mode": "train", "epoch": 130, "iter": 3100, "lr": 0.0044, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.45, "top5_acc": 0.70281, "loss_cls": 3.11098, "loss": 3.11098, "time": 0.87394} +{"mode": "train", "epoch": 130, "iter": 3200, "lr": 0.00439, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.44625, "top5_acc": 0.70188, "loss_cls": 3.14041, "loss": 3.14041, "time": 0.86633} +{"mode": "train", "epoch": 130, "iter": 3300, "lr": 0.00437, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42969, "top5_acc": 0.69547, "loss_cls": 3.15998, "loss": 3.15998, "time": 0.86619} +{"mode": "train", "epoch": 130, "iter": 3400, "lr": 0.00436, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.43578, "top5_acc": 0.69109, "loss_cls": 3.14403, "loss": 3.14403, "time": 0.86051} +{"mode": "train", "epoch": 130, "iter": 3500, "lr": 0.00435, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.44672, "top5_acc": 0.70016, "loss_cls": 3.1168, "loss": 3.1168, "time": 0.87223} +{"mode": "train", "epoch": 130, "iter": 3600, "lr": 0.00434, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.43734, "top5_acc": 0.69156, "loss_cls": 3.16496, "loss": 3.16496, "time": 0.85764} +{"mode": "train", "epoch": 130, "iter": 3700, "lr": 0.00433, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.46172, "top5_acc": 0.70828, "loss_cls": 3.06874, "loss": 3.06874, "time": 0.86508} +{"mode": "val", "epoch": 130, "iter": 309, "lr": 0.00432, "top1_acc": 0.37122, "top5_acc": 0.62721, "mean_class_accuracy": 0.37107} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00431, "memory": 15990, "data_time": 1.65877, "top1_acc": 0.47062, "top5_acc": 0.72812, "loss_cls": 2.95305, "loss": 2.95305, "time": 2.71613} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.0043, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.46844, "top5_acc": 0.71531, "loss_cls": 3.00763, "loss": 3.00763, "time": 0.87522} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00429, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.46203, "top5_acc": 0.71922, "loss_cls": 2.98848, "loss": 2.98848, "time": 0.87511} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00428, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.46281, "top5_acc": 0.70938, "loss_cls": 3.0439, "loss": 3.0439, "time": 0.87873} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00427, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.46234, "top5_acc": 0.71688, "loss_cls": 3.01953, "loss": 3.01953, "time": 0.88528} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00425, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.45219, "top5_acc": 0.71234, "loss_cls": 3.05795, "loss": 3.05795, "time": 0.88348} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00424, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.46203, "top5_acc": 0.71156, "loss_cls": 3.03937, "loss": 3.03937, "time": 0.88144} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00423, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.44453, "top5_acc": 0.70719, "loss_cls": 3.08626, "loss": 3.08626, "time": 0.8826} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00422, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.45719, "top5_acc": 0.71531, "loss_cls": 3.04886, "loss": 3.04886, "time": 0.88681} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.00421, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.45016, "top5_acc": 0.70922, "loss_cls": 3.06029, "loss": 3.06029, "time": 0.88154} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.0042, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.45094, "top5_acc": 0.70672, "loss_cls": 3.08465, "loss": 3.08465, "time": 0.88382} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00419, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.46453, "top5_acc": 0.70906, "loss_cls": 3.02767, "loss": 3.02767, "time": 0.88309} +{"mode": "train", "epoch": 131, "iter": 1300, "lr": 0.00418, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.44344, "top5_acc": 0.69422, "loss_cls": 3.12883, "loss": 3.12883, "time": 0.88586} +{"mode": "train", "epoch": 131, "iter": 1400, "lr": 0.00417, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.45297, "top5_acc": 0.71062, "loss_cls": 3.07282, "loss": 3.07282, "time": 0.88316} +{"mode": "train", "epoch": 131, "iter": 1500, "lr": 0.00415, "memory": 15990, "data_time": 0.00075, "top1_acc": 0.44672, "top5_acc": 0.70875, "loss_cls": 3.05108, "loss": 3.05108, "time": 0.88552} +{"mode": "train", "epoch": 131, "iter": 1600, "lr": 0.00414, "memory": 15990, "data_time": 0.0009, "top1_acc": 0.44594, "top5_acc": 0.71531, "loss_cls": 3.08867, "loss": 3.08867, "time": 0.88413} +{"mode": "train", "epoch": 131, "iter": 1700, "lr": 0.00413, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.43781, "top5_acc": 0.69781, "loss_cls": 3.13552, "loss": 3.13552, "time": 0.86519} +{"mode": "train", "epoch": 131, "iter": 1800, "lr": 0.00412, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45734, "top5_acc": 0.71281, "loss_cls": 3.05362, "loss": 3.05362, "time": 0.86545} +{"mode": "train", "epoch": 131, "iter": 1900, "lr": 0.00411, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44969, "top5_acc": 0.70828, "loss_cls": 3.05359, "loss": 3.05359, "time": 0.86064} +{"mode": "train", "epoch": 131, "iter": 2000, "lr": 0.0041, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.43844, "top5_acc": 0.70234, "loss_cls": 3.08175, "loss": 3.08175, "time": 0.87554} +{"mode": "train", "epoch": 131, "iter": 2100, "lr": 0.00409, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.45156, "top5_acc": 0.70828, "loss_cls": 3.05251, "loss": 3.05251, "time": 0.88079} +{"mode": "train", "epoch": 131, "iter": 2200, "lr": 0.00408, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.45922, "top5_acc": 0.71297, "loss_cls": 3.04807, "loss": 3.04807, "time": 0.88936} +{"mode": "train", "epoch": 131, "iter": 2300, "lr": 0.00407, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.44969, "top5_acc": 0.70703, "loss_cls": 3.07224, "loss": 3.07224, "time": 0.88337} +{"mode": "train", "epoch": 131, "iter": 2400, "lr": 0.00405, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.44625, "top5_acc": 0.70125, "loss_cls": 3.09341, "loss": 3.09341, "time": 0.88227} +{"mode": "train", "epoch": 131, "iter": 2500, "lr": 0.00404, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.45312, "top5_acc": 0.70875, "loss_cls": 3.06368, "loss": 3.06368, "time": 0.8827} +{"mode": "train", "epoch": 131, "iter": 2600, "lr": 0.00403, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.45047, "top5_acc": 0.70594, "loss_cls": 3.1001, "loss": 3.1001, "time": 0.881} +{"mode": "train", "epoch": 131, "iter": 2700, "lr": 0.00402, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.45641, "top5_acc": 0.71062, "loss_cls": 3.07231, "loss": 3.07231, "time": 0.88741} +{"mode": "train", "epoch": 131, "iter": 2800, "lr": 0.00401, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.45219, "top5_acc": 0.7025, "loss_cls": 3.09387, "loss": 3.09387, "time": 0.88664} +{"mode": "train", "epoch": 131, "iter": 2900, "lr": 0.004, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.46797, "top5_acc": 0.71875, "loss_cls": 2.99851, "loss": 2.99851, "time": 0.88291} +{"mode": "train", "epoch": 131, "iter": 3000, "lr": 0.00399, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.46531, "top5_acc": 0.71391, "loss_cls": 3.01886, "loss": 3.01886, "time": 0.88616} +{"mode": "train", "epoch": 131, "iter": 3100, "lr": 0.00398, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.4425, "top5_acc": 0.69609, "loss_cls": 3.09991, "loss": 3.09991, "time": 0.87545} +{"mode": "train", "epoch": 131, "iter": 3200, "lr": 0.00397, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43953, "top5_acc": 0.69422, "loss_cls": 3.14628, "loss": 3.14628, "time": 0.86048} +{"mode": "train", "epoch": 131, "iter": 3300, "lr": 0.00396, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44969, "top5_acc": 0.69703, "loss_cls": 3.13136, "loss": 3.13136, "time": 0.85962} +{"mode": "train", "epoch": 131, "iter": 3400, "lr": 0.00394, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.45359, "top5_acc": 0.71078, "loss_cls": 3.07256, "loss": 3.07256, "time": 0.8753} +{"mode": "train", "epoch": 131, "iter": 3500, "lr": 0.00393, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.45406, "top5_acc": 0.70969, "loss_cls": 3.03377, "loss": 3.03377, "time": 0.8829} +{"mode": "train", "epoch": 131, "iter": 3600, "lr": 0.00392, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.45188, "top5_acc": 0.70375, "loss_cls": 3.07935, "loss": 3.07935, "time": 0.88134} +{"mode": "train", "epoch": 131, "iter": 3700, "lr": 0.00391, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.44875, "top5_acc": 0.70297, "loss_cls": 3.11073, "loss": 3.11073, "time": 0.88449} +{"mode": "val", "epoch": 131, "iter": 309, "lr": 0.00391, "top1_acc": 0.36909, "top5_acc": 0.62432, "mean_class_accuracy": 0.36889} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.0039, "memory": 15990, "data_time": 1.6751, "top1_acc": 0.475, "top5_acc": 0.73078, "loss_cls": 2.95418, "loss": 2.95418, "time": 2.74777} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00389, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.47312, "top5_acc": 0.72266, "loss_cls": 2.97572, "loss": 2.97572, "time": 0.87958} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00387, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.46469, "top5_acc": 0.72188, "loss_cls": 2.99195, "loss": 2.99195, "time": 0.87963} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00386, "memory": 15990, "data_time": 0.00067, "top1_acc": 0.46969, "top5_acc": 0.725, "loss_cls": 2.97336, "loss": 2.97336, "time": 0.87645} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00385, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.45953, "top5_acc": 0.71672, "loss_cls": 3.00705, "loss": 3.00705, "time": 0.87992} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00384, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.46688, "top5_acc": 0.72719, "loss_cls": 2.97768, "loss": 2.97768, "time": 0.87681} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00383, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.46422, "top5_acc": 0.7225, "loss_cls": 2.99138, "loss": 2.99138, "time": 0.87713} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00382, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.46375, "top5_acc": 0.72047, "loss_cls": 3.02262, "loss": 3.02262, "time": 0.87868} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00381, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.47234, "top5_acc": 0.72312, "loss_cls": 2.94695, "loss": 2.94695, "time": 0.88598} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0038, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.45266, "top5_acc": 0.72141, "loss_cls": 3.01226, "loss": 3.01226, "time": 0.87399} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00379, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.46656, "top5_acc": 0.71062, "loss_cls": 3.03239, "loss": 3.03239, "time": 0.88248} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00378, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.45375, "top5_acc": 0.70828, "loss_cls": 3.04057, "loss": 3.04057, "time": 0.88133} +{"mode": "train", "epoch": 132, "iter": 1300, "lr": 0.00377, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.46953, "top5_acc": 0.71859, "loss_cls": 2.98626, "loss": 2.98626, "time": 0.88214} +{"mode": "train", "epoch": 132, "iter": 1400, "lr": 0.00376, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.46219, "top5_acc": 0.71422, "loss_cls": 3.02803, "loss": 3.02803, "time": 0.87724} +{"mode": "train", "epoch": 132, "iter": 1500, "lr": 0.00375, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.4675, "top5_acc": 0.72, "loss_cls": 2.98745, "loss": 2.98745, "time": 0.88554} +{"mode": "train", "epoch": 132, "iter": 1600, "lr": 0.00374, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.46031, "top5_acc": 0.71516, "loss_cls": 3.01355, "loss": 3.01355, "time": 0.87575} +{"mode": "train", "epoch": 132, "iter": 1700, "lr": 0.00372, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.46859, "top5_acc": 0.72328, "loss_cls": 2.96863, "loss": 2.96863, "time": 0.86391} +{"mode": "train", "epoch": 132, "iter": 1800, "lr": 0.00371, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.45016, "top5_acc": 0.70891, "loss_cls": 3.07778, "loss": 3.07778, "time": 0.86485} +{"mode": "train", "epoch": 132, "iter": 1900, "lr": 0.0037, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.45312, "top5_acc": 0.71219, "loss_cls": 3.06239, "loss": 3.06239, "time": 0.8698} +{"mode": "train", "epoch": 132, "iter": 2000, "lr": 0.00369, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.44984, "top5_acc": 0.70406, "loss_cls": 3.05823, "loss": 3.05823, "time": 0.88539} +{"mode": "train", "epoch": 132, "iter": 2100, "lr": 0.00368, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.46172, "top5_acc": 0.71312, "loss_cls": 3.05214, "loss": 3.05214, "time": 0.8776} +{"mode": "train", "epoch": 132, "iter": 2200, "lr": 0.00367, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.45438, "top5_acc": 0.70969, "loss_cls": 3.05572, "loss": 3.05572, "time": 0.87881} +{"mode": "train", "epoch": 132, "iter": 2300, "lr": 0.00366, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.4575, "top5_acc": 0.715, "loss_cls": 3.02921, "loss": 3.02921, "time": 0.8804} +{"mode": "train", "epoch": 132, "iter": 2400, "lr": 0.00365, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.46828, "top5_acc": 0.71953, "loss_cls": 3.0032, "loss": 3.0032, "time": 0.88195} +{"mode": "train", "epoch": 132, "iter": 2500, "lr": 0.00364, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.46141, "top5_acc": 0.71578, "loss_cls": 3.00786, "loss": 3.00786, "time": 0.88284} +{"mode": "train", "epoch": 132, "iter": 2600, "lr": 0.00363, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.45188, "top5_acc": 0.71266, "loss_cls": 3.05387, "loss": 3.05387, "time": 0.88755} +{"mode": "train", "epoch": 132, "iter": 2700, "lr": 0.00362, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.45625, "top5_acc": 0.705, "loss_cls": 3.05948, "loss": 3.05948, "time": 0.88569} +{"mode": "train", "epoch": 132, "iter": 2800, "lr": 0.00361, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.45938, "top5_acc": 0.71109, "loss_cls": 3.03084, "loss": 3.03084, "time": 0.88361} +{"mode": "train", "epoch": 132, "iter": 2900, "lr": 0.0036, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.45391, "top5_acc": 0.70547, "loss_cls": 3.04237, "loss": 3.04237, "time": 0.87817} +{"mode": "train", "epoch": 132, "iter": 3000, "lr": 0.00359, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.45344, "top5_acc": 0.70516, "loss_cls": 3.07197, "loss": 3.07197, "time": 0.87258} +{"mode": "train", "epoch": 132, "iter": 3100, "lr": 0.00358, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.46109, "top5_acc": 0.71, "loss_cls": 3.03012, "loss": 3.03012, "time": 0.86395} +{"mode": "train", "epoch": 132, "iter": 3200, "lr": 0.00357, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.46156, "top5_acc": 0.71969, "loss_cls": 3.01246, "loss": 3.01246, "time": 0.86081} +{"mode": "train", "epoch": 132, "iter": 3300, "lr": 0.00356, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.45641, "top5_acc": 0.71984, "loss_cls": 3.02926, "loss": 3.02926, "time": 0.87261} +{"mode": "train", "epoch": 132, "iter": 3400, "lr": 0.00355, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.44922, "top5_acc": 0.70516, "loss_cls": 3.08309, "loss": 3.08309, "time": 0.87656} +{"mode": "train", "epoch": 132, "iter": 3500, "lr": 0.00354, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.45547, "top5_acc": 0.70719, "loss_cls": 3.03485, "loss": 3.03485, "time": 0.87792} +{"mode": "train", "epoch": 132, "iter": 3600, "lr": 0.00353, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.45328, "top5_acc": 0.70484, "loss_cls": 3.062, "loss": 3.062, "time": 0.87564} +{"mode": "train", "epoch": 132, "iter": 3700, "lr": 0.00352, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.44625, "top5_acc": 0.70062, "loss_cls": 3.09152, "loss": 3.09152, "time": 0.87778} +{"mode": "val", "epoch": 132, "iter": 309, "lr": 0.00351, "top1_acc": 0.38069, "top5_acc": 0.62949, "mean_class_accuracy": 0.38041} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.0035, "memory": 15990, "data_time": 1.63454, "top1_acc": 0.49109, "top5_acc": 0.73406, "loss_cls": 2.87972, "loss": 2.87972, "time": 2.70671} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00349, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.47734, "top5_acc": 0.73172, "loss_cls": 2.92144, "loss": 2.92144, "time": 0.87702} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00348, "memory": 15990, "data_time": 0.00067, "top1_acc": 0.4675, "top5_acc": 0.72438, "loss_cls": 2.9738, "loss": 2.9738, "time": 0.87468} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00347, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.48828, "top5_acc": 0.73156, "loss_cls": 2.92819, "loss": 2.92819, "time": 0.87702} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00346, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.46734, "top5_acc": 0.71859, "loss_cls": 2.96939, "loss": 2.96939, "time": 0.87743} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00345, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.475, "top5_acc": 0.73047, "loss_cls": 2.92833, "loss": 2.92833, "time": 0.87379} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00344, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.47625, "top5_acc": 0.72125, "loss_cls": 2.95721, "loss": 2.95721, "time": 0.88308} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00343, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.46547, "top5_acc": 0.71594, "loss_cls": 3.00754, "loss": 3.00754, "time": 0.87795} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00342, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.46594, "top5_acc": 0.71234, "loss_cls": 3.01097, "loss": 3.01097, "time": 0.87917} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.00341, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.46641, "top5_acc": 0.71844, "loss_cls": 2.96995, "loss": 2.96995, "time": 0.88065} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0034, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.47203, "top5_acc": 0.72844, "loss_cls": 2.96296, "loss": 2.96296, "time": 0.8863} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00339, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.45875, "top5_acc": 0.71609, "loss_cls": 3.03315, "loss": 3.03315, "time": 0.88543} +{"mode": "train", "epoch": 133, "iter": 1300, "lr": 0.00338, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.47594, "top5_acc": 0.71797, "loss_cls": 2.96105, "loss": 2.96105, "time": 0.8846} +{"mode": "train", "epoch": 133, "iter": 1400, "lr": 0.00337, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.46859, "top5_acc": 0.71484, "loss_cls": 3.01474, "loss": 3.01474, "time": 0.88359} +{"mode": "train", "epoch": 133, "iter": 1500, "lr": 0.00336, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.47672, "top5_acc": 0.72266, "loss_cls": 2.97292, "loss": 2.97292, "time": 0.88858} +{"mode": "train", "epoch": 133, "iter": 1600, "lr": 0.00335, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.46109, "top5_acc": 0.71344, "loss_cls": 3.01329, "loss": 3.01329, "time": 0.86844} +{"mode": "train", "epoch": 133, "iter": 1700, "lr": 0.00334, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.47266, "top5_acc": 0.7275, "loss_cls": 2.97992, "loss": 2.97992, "time": 0.86096} +{"mode": "train", "epoch": 133, "iter": 1800, "lr": 0.00333, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.45484, "top5_acc": 0.72156, "loss_cls": 3.01459, "loss": 3.01459, "time": 0.86324} +{"mode": "train", "epoch": 133, "iter": 1900, "lr": 0.00332, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.46984, "top5_acc": 0.71891, "loss_cls": 2.99222, "loss": 2.99222, "time": 0.87267} +{"mode": "train", "epoch": 133, "iter": 2000, "lr": 0.00331, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.46922, "top5_acc": 0.72281, "loss_cls": 2.98833, "loss": 2.98833, "time": 0.87221} +{"mode": "train", "epoch": 133, "iter": 2100, "lr": 0.0033, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.45891, "top5_acc": 0.71562, "loss_cls": 3.00157, "loss": 3.00157, "time": 0.87092} +{"mode": "train", "epoch": 133, "iter": 2200, "lr": 0.00329, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.45938, "top5_acc": 0.71141, "loss_cls": 3.04748, "loss": 3.04748, "time": 0.88161} +{"mode": "train", "epoch": 133, "iter": 2300, "lr": 0.00328, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.47188, "top5_acc": 0.72141, "loss_cls": 2.96245, "loss": 2.96245, "time": 0.87811} +{"mode": "train", "epoch": 133, "iter": 2400, "lr": 0.00327, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.47281, "top5_acc": 0.72234, "loss_cls": 2.97221, "loss": 2.97221, "time": 0.87648} +{"mode": "train", "epoch": 133, "iter": 2500, "lr": 0.00326, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.46812, "top5_acc": 0.72531, "loss_cls": 2.97658, "loss": 2.97658, "time": 0.87854} +{"mode": "train", "epoch": 133, "iter": 2600, "lr": 0.00325, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.46266, "top5_acc": 0.72125, "loss_cls": 2.98601, "loss": 2.98601, "time": 0.88294} +{"mode": "train", "epoch": 133, "iter": 2700, "lr": 0.00324, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.46672, "top5_acc": 0.71578, "loss_cls": 3.00424, "loss": 3.00424, "time": 0.87262} +{"mode": "train", "epoch": 133, "iter": 2800, "lr": 0.00323, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.46641, "top5_acc": 0.72281, "loss_cls": 2.99135, "loss": 2.99135, "time": 0.87882} +{"mode": "train", "epoch": 133, "iter": 2900, "lr": 0.00322, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.46203, "top5_acc": 0.72438, "loss_cls": 3.00589, "loss": 3.00589, "time": 0.87862} +{"mode": "train", "epoch": 133, "iter": 3000, "lr": 0.00321, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.46516, "top5_acc": 0.71688, "loss_cls": 3.0241, "loss": 3.0241, "time": 0.85912} +{"mode": "train", "epoch": 133, "iter": 3100, "lr": 0.0032, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.46391, "top5_acc": 0.71766, "loss_cls": 2.9992, "loss": 2.9992, "time": 0.86092} +{"mode": "train", "epoch": 133, "iter": 3200, "lr": 0.00319, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.46203, "top5_acc": 0.71797, "loss_cls": 3.00981, "loss": 3.00981, "time": 0.86453} +{"mode": "train", "epoch": 133, "iter": 3300, "lr": 0.00318, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.4575, "top5_acc": 0.715, "loss_cls": 3.01307, "loss": 3.01307, "time": 0.87552} +{"mode": "train", "epoch": 133, "iter": 3400, "lr": 0.00317, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.47609, "top5_acc": 0.72969, "loss_cls": 2.94293, "loss": 2.94293, "time": 0.88254} +{"mode": "train", "epoch": 133, "iter": 3500, "lr": 0.00316, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.46094, "top5_acc": 0.71391, "loss_cls": 3.00917, "loss": 3.00917, "time": 0.87491} +{"mode": "train", "epoch": 133, "iter": 3600, "lr": 0.00315, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.45312, "top5_acc": 0.71234, "loss_cls": 3.04411, "loss": 3.04411, "time": 0.88509} +{"mode": "train", "epoch": 133, "iter": 3700, "lr": 0.00314, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.46172, "top5_acc": 0.71344, "loss_cls": 3.01429, "loss": 3.01429, "time": 0.88184} +{"mode": "val", "epoch": 133, "iter": 309, "lr": 0.00314, "top1_acc": 0.38054, "top5_acc": 0.63552, "mean_class_accuracy": 0.38032} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00313, "memory": 15990, "data_time": 1.62903, "top1_acc": 0.47438, "top5_acc": 0.73125, "loss_cls": 2.93226, "loss": 2.93226, "time": 2.69503} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00312, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.48453, "top5_acc": 0.73766, "loss_cls": 2.90385, "loss": 2.90385, "time": 0.87763} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00311, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.48562, "top5_acc": 0.73312, "loss_cls": 2.92573, "loss": 2.92573, "time": 0.87843} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.0031, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.47281, "top5_acc": 0.72562, "loss_cls": 2.95249, "loss": 2.95249, "time": 0.87832} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00309, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.47984, "top5_acc": 0.72922, "loss_cls": 2.94163, "loss": 2.94163, "time": 0.88052} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00308, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.46984, "top5_acc": 0.72734, "loss_cls": 2.94388, "loss": 2.94388, "time": 0.88309} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00307, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.46969, "top5_acc": 0.72016, "loss_cls": 2.95396, "loss": 2.95396, "time": 0.87822} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00306, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.47188, "top5_acc": 0.73266, "loss_cls": 2.9247, "loss": 2.9247, "time": 0.88541} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00305, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.48906, "top5_acc": 0.73938, "loss_cls": 2.8733, "loss": 2.8733, "time": 0.88412} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00304, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.47969, "top5_acc": 0.72734, "loss_cls": 2.91515, "loss": 2.91515, "time": 0.88059} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00303, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.47828, "top5_acc": 0.72734, "loss_cls": 2.94589, "loss": 2.94589, "time": 0.88352} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.00302, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.47219, "top5_acc": 0.72469, "loss_cls": 2.95198, "loss": 2.95198, "time": 0.88237} +{"mode": "train", "epoch": 134, "iter": 1300, "lr": 0.00301, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.47391, "top5_acc": 0.73625, "loss_cls": 2.90661, "loss": 2.90661, "time": 0.8767} +{"mode": "train", "epoch": 134, "iter": 1400, "lr": 0.003, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.47406, "top5_acc": 0.73, "loss_cls": 2.90975, "loss": 2.90975, "time": 0.87744} +{"mode": "train", "epoch": 134, "iter": 1500, "lr": 0.00299, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.47438, "top5_acc": 0.73312, "loss_cls": 2.94404, "loss": 2.94404, "time": 0.87407} +{"mode": "train", "epoch": 134, "iter": 1600, "lr": 0.00298, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.47781, "top5_acc": 0.72594, "loss_cls": 2.96013, "loss": 2.96013, "time": 0.8644} +{"mode": "train", "epoch": 134, "iter": 1700, "lr": 0.00297, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.47938, "top5_acc": 0.73531, "loss_cls": 2.91963, "loss": 2.91963, "time": 0.85667} +{"mode": "train", "epoch": 134, "iter": 1800, "lr": 0.00296, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.47391, "top5_acc": 0.72625, "loss_cls": 2.96486, "loss": 2.96486, "time": 0.86564} +{"mode": "train", "epoch": 134, "iter": 1900, "lr": 0.00295, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.46172, "top5_acc": 0.72406, "loss_cls": 2.98018, "loss": 2.98018, "time": 0.87482} +{"mode": "train", "epoch": 134, "iter": 2000, "lr": 0.00294, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.47625, "top5_acc": 0.73438, "loss_cls": 2.91183, "loss": 2.91183, "time": 0.87345} +{"mode": "train", "epoch": 134, "iter": 2100, "lr": 0.00293, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.47844, "top5_acc": 0.73094, "loss_cls": 2.93394, "loss": 2.93394, "time": 0.88036} +{"mode": "train", "epoch": 134, "iter": 2200, "lr": 0.00293, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.47453, "top5_acc": 0.72016, "loss_cls": 2.94789, "loss": 2.94789, "time": 0.88159} +{"mode": "train", "epoch": 134, "iter": 2300, "lr": 0.00292, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.47484, "top5_acc": 0.72203, "loss_cls": 2.93936, "loss": 2.93936, "time": 0.88036} +{"mode": "train", "epoch": 134, "iter": 2400, "lr": 0.00291, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.47281, "top5_acc": 0.72109, "loss_cls": 2.98818, "loss": 2.98818, "time": 0.88522} +{"mode": "train", "epoch": 134, "iter": 2500, "lr": 0.0029, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.47641, "top5_acc": 0.72938, "loss_cls": 2.91938, "loss": 2.91938, "time": 0.87832} +{"mode": "train", "epoch": 134, "iter": 2600, "lr": 0.00289, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.46656, "top5_acc": 0.71594, "loss_cls": 2.97468, "loss": 2.97468, "time": 0.87666} +{"mode": "train", "epoch": 134, "iter": 2700, "lr": 0.00288, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.47406, "top5_acc": 0.73516, "loss_cls": 2.90761, "loss": 2.90761, "time": 0.87403} +{"mode": "train", "epoch": 134, "iter": 2800, "lr": 0.00287, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.47516, "top5_acc": 0.72922, "loss_cls": 2.93616, "loss": 2.93616, "time": 0.87782} +{"mode": "train", "epoch": 134, "iter": 2900, "lr": 0.00286, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.46922, "top5_acc": 0.71578, "loss_cls": 3.00041, "loss": 3.00041, "time": 0.86919} +{"mode": "train", "epoch": 134, "iter": 3000, "lr": 0.00285, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.46703, "top5_acc": 0.71828, "loss_cls": 3.00243, "loss": 3.00243, "time": 0.85963} +{"mode": "train", "epoch": 134, "iter": 3100, "lr": 0.00284, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.46891, "top5_acc": 0.71875, "loss_cls": 2.99539, "loss": 2.99539, "time": 0.86492} +{"mode": "train", "epoch": 134, "iter": 3200, "lr": 0.00283, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.48172, "top5_acc": 0.7325, "loss_cls": 2.92605, "loss": 2.92605, "time": 0.87022} +{"mode": "train", "epoch": 134, "iter": 3300, "lr": 0.00282, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.46891, "top5_acc": 0.72375, "loss_cls": 2.98422, "loss": 2.98422, "time": 0.8764} +{"mode": "train", "epoch": 134, "iter": 3400, "lr": 0.00281, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.46688, "top5_acc": 0.72688, "loss_cls": 2.95867, "loss": 2.95867, "time": 0.88322} +{"mode": "train", "epoch": 134, "iter": 3500, "lr": 0.0028, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.47984, "top5_acc": 0.72438, "loss_cls": 2.95906, "loss": 2.95906, "time": 0.87316} +{"mode": "train", "epoch": 134, "iter": 3600, "lr": 0.00279, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.46438, "top5_acc": 0.71156, "loss_cls": 3.02728, "loss": 3.02728, "time": 0.88143} +{"mode": "train", "epoch": 134, "iter": 3700, "lr": 0.00279, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.46609, "top5_acc": 0.72281, "loss_cls": 2.96246, "loss": 2.96246, "time": 0.88027} +{"mode": "val", "epoch": 134, "iter": 309, "lr": 0.00278, "top1_acc": 0.38181, "top5_acc": 0.63288, "mean_class_accuracy": 0.38153} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00277, "memory": 15990, "data_time": 1.64175, "top1_acc": 0.49656, "top5_acc": 0.74531, "loss_cls": 2.79605, "loss": 2.79605, "time": 2.71386} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00276, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.48922, "top5_acc": 0.74297, "loss_cls": 2.85982, "loss": 2.85982, "time": 0.8834} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00275, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.47922, "top5_acc": 0.73141, "loss_cls": 2.92195, "loss": 2.92195, "time": 0.88653} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00274, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.48828, "top5_acc": 0.73781, "loss_cls": 2.87907, "loss": 2.87907, "time": 0.87655} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00274, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.48281, "top5_acc": 0.73078, "loss_cls": 2.91256, "loss": 2.91256, "time": 0.88552} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00273, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.48078, "top5_acc": 0.73312, "loss_cls": 2.8886, "loss": 2.8886, "time": 0.88623} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00272, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.48328, "top5_acc": 0.73875, "loss_cls": 2.87036, "loss": 2.87036, "time": 0.87605} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00271, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.49281, "top5_acc": 0.73922, "loss_cls": 2.83852, "loss": 2.83852, "time": 0.88517} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.0027, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.49859, "top5_acc": 0.74359, "loss_cls": 2.83638, "loss": 2.83638, "time": 0.87741} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00269, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.48812, "top5_acc": 0.74531, "loss_cls": 2.85848, "loss": 2.85848, "time": 0.8834} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00268, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.48281, "top5_acc": 0.74047, "loss_cls": 2.87872, "loss": 2.87872, "time": 0.88014} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00267, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.47578, "top5_acc": 0.73328, "loss_cls": 2.90276, "loss": 2.90276, "time": 0.87572} +{"mode": "train", "epoch": 135, "iter": 1300, "lr": 0.00266, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.48812, "top5_acc": 0.74297, "loss_cls": 2.85008, "loss": 2.85008, "time": 0.88418} +{"mode": "train", "epoch": 135, "iter": 1400, "lr": 0.00265, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.48484, "top5_acc": 0.73969, "loss_cls": 2.90123, "loss": 2.90123, "time": 0.87293} +{"mode": "train", "epoch": 135, "iter": 1500, "lr": 0.00265, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.48062, "top5_acc": 0.725, "loss_cls": 2.94148, "loss": 2.94148, "time": 0.86375} +{"mode": "train", "epoch": 135, "iter": 1600, "lr": 0.00264, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.48859, "top5_acc": 0.73906, "loss_cls": 2.86494, "loss": 2.86494, "time": 0.86866} +{"mode": "train", "epoch": 135, "iter": 1700, "lr": 0.00263, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.48344, "top5_acc": 0.73125, "loss_cls": 2.9159, "loss": 2.9159, "time": 0.86346} +{"mode": "train", "epoch": 135, "iter": 1800, "lr": 0.00262, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.49219, "top5_acc": 0.73969, "loss_cls": 2.85004, "loss": 2.85004, "time": 0.87912} +{"mode": "train", "epoch": 135, "iter": 1900, "lr": 0.00261, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.48422, "top5_acc": 0.73875, "loss_cls": 2.90931, "loss": 2.90931, "time": 0.87947} +{"mode": "train", "epoch": 135, "iter": 2000, "lr": 0.0026, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.48594, "top5_acc": 0.73422, "loss_cls": 2.87853, "loss": 2.87853, "time": 0.8843} +{"mode": "train", "epoch": 135, "iter": 2100, "lr": 0.00259, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.48703, "top5_acc": 0.73578, "loss_cls": 2.88505, "loss": 2.88505, "time": 0.88401} +{"mode": "train", "epoch": 135, "iter": 2200, "lr": 0.00258, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.46891, "top5_acc": 0.73359, "loss_cls": 2.92309, "loss": 2.92309, "time": 0.88986} +{"mode": "train", "epoch": 135, "iter": 2300, "lr": 0.00257, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.46594, "top5_acc": 0.71656, "loss_cls": 2.99472, "loss": 2.99472, "time": 0.8873} +{"mode": "train", "epoch": 135, "iter": 2400, "lr": 0.00256, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.46641, "top5_acc": 0.72078, "loss_cls": 2.98498, "loss": 2.98498, "time": 0.88322} +{"mode": "train", "epoch": 135, "iter": 2500, "lr": 0.00256, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.48484, "top5_acc": 0.73672, "loss_cls": 2.87508, "loss": 2.87508, "time": 0.8876} +{"mode": "train", "epoch": 135, "iter": 2600, "lr": 0.00255, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.47391, "top5_acc": 0.72391, "loss_cls": 2.92488, "loss": 2.92488, "time": 0.88291} +{"mode": "train", "epoch": 135, "iter": 2700, "lr": 0.00254, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.46859, "top5_acc": 0.715, "loss_cls": 2.96533, "loss": 2.96533, "time": 0.88763} +{"mode": "train", "epoch": 135, "iter": 2800, "lr": 0.00253, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.47219, "top5_acc": 0.72781, "loss_cls": 2.94292, "loss": 2.94292, "time": 0.87442} +{"mode": "train", "epoch": 135, "iter": 2900, "lr": 0.00252, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.48844, "top5_acc": 0.73062, "loss_cls": 2.8982, "loss": 2.8982, "time": 0.86121} +{"mode": "train", "epoch": 135, "iter": 3000, "lr": 0.00251, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.47844, "top5_acc": 0.72766, "loss_cls": 2.94338, "loss": 2.94338, "time": 0.8593} +{"mode": "train", "epoch": 135, "iter": 3100, "lr": 0.0025, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.48422, "top5_acc": 0.73891, "loss_cls": 2.89168, "loss": 2.89168, "time": 0.87606} +{"mode": "train", "epoch": 135, "iter": 3200, "lr": 0.00249, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.46797, "top5_acc": 0.72422, "loss_cls": 2.95171, "loss": 2.95171, "time": 0.88026} +{"mode": "train", "epoch": 135, "iter": 3300, "lr": 0.00249, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.48188, "top5_acc": 0.73734, "loss_cls": 2.86998, "loss": 2.86998, "time": 0.88569} +{"mode": "train", "epoch": 135, "iter": 3400, "lr": 0.00248, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.47219, "top5_acc": 0.71859, "loss_cls": 2.95122, "loss": 2.95122, "time": 0.88622} +{"mode": "train", "epoch": 135, "iter": 3500, "lr": 0.00247, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.48141, "top5_acc": 0.72984, "loss_cls": 2.8922, "loss": 2.8922, "time": 0.89125} +{"mode": "train", "epoch": 135, "iter": 3600, "lr": 0.00246, "memory": 15990, "data_time": 0.00068, "top1_acc": 0.47594, "top5_acc": 0.72797, "loss_cls": 2.91083, "loss": 2.91083, "time": 0.89024} +{"mode": "train", "epoch": 135, "iter": 3700, "lr": 0.00245, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.49, "top5_acc": 0.74016, "loss_cls": 2.8575, "loss": 2.8575, "time": 0.89063} +{"mode": "val", "epoch": 135, "iter": 309, "lr": 0.00245, "top1_acc": 0.38718, "top5_acc": 0.63592, "mean_class_accuracy": 0.38692} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00244, "memory": 15990, "data_time": 1.61264, "top1_acc": 0.48641, "top5_acc": 0.74672, "loss_cls": 2.82727, "loss": 2.82727, "time": 2.67261} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.00243, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.49359, "top5_acc": 0.74672, "loss_cls": 2.84247, "loss": 2.84247, "time": 0.87431} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00242, "memory": 15990, "data_time": 0.00071, "top1_acc": 0.50078, "top5_acc": 0.75328, "loss_cls": 2.75578, "loss": 2.75578, "time": 0.87905} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00241, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.50313, "top5_acc": 0.75297, "loss_cls": 2.78818, "loss": 2.78818, "time": 0.87744} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.0024, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.49719, "top5_acc": 0.73953, "loss_cls": 2.83062, "loss": 2.83062, "time": 0.87171} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.0024, "memory": 15990, "data_time": 0.00068, "top1_acc": 0.50375, "top5_acc": 0.74234, "loss_cls": 2.80465, "loss": 2.80465, "time": 0.88023} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00239, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.48234, "top5_acc": 0.73891, "loss_cls": 2.87199, "loss": 2.87199, "time": 0.88084} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00238, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.48344, "top5_acc": 0.74234, "loss_cls": 2.8403, "loss": 2.8403, "time": 0.87209} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00237, "memory": 15990, "data_time": 0.00067, "top1_acc": 0.49109, "top5_acc": 0.74141, "loss_cls": 2.83303, "loss": 2.83303, "time": 0.88087} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00236, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.50562, "top5_acc": 0.74703, "loss_cls": 2.80732, "loss": 2.80732, "time": 0.87379} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00235, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.49469, "top5_acc": 0.74109, "loss_cls": 2.8371, "loss": 2.8371, "time": 0.8842} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00234, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.49844, "top5_acc": 0.74953, "loss_cls": 2.78735, "loss": 2.78735, "time": 0.88133} +{"mode": "train", "epoch": 136, "iter": 1300, "lr": 0.00234, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.4925, "top5_acc": 0.74359, "loss_cls": 2.85753, "loss": 2.85753, "time": 0.87212} +{"mode": "train", "epoch": 136, "iter": 1400, "lr": 0.00233, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.48531, "top5_acc": 0.73453, "loss_cls": 2.88111, "loss": 2.88111, "time": 0.86208} +{"mode": "train", "epoch": 136, "iter": 1500, "lr": 0.00232, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.48609, "top5_acc": 0.73422, "loss_cls": 2.87732, "loss": 2.87732, "time": 0.85934} +{"mode": "train", "epoch": 136, "iter": 1600, "lr": 0.00231, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.485, "top5_acc": 0.72953, "loss_cls": 2.88378, "loss": 2.88378, "time": 0.86414} +{"mode": "train", "epoch": 136, "iter": 1700, "lr": 0.0023, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.48859, "top5_acc": 0.74438, "loss_cls": 2.85541, "loss": 2.85541, "time": 0.85424} +{"mode": "train", "epoch": 136, "iter": 1800, "lr": 0.00229, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.50016, "top5_acc": 0.75297, "loss_cls": 2.80999, "loss": 2.80999, "time": 0.858} +{"mode": "train", "epoch": 136, "iter": 1900, "lr": 0.00229, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.48719, "top5_acc": 0.74156, "loss_cls": 2.85839, "loss": 2.85839, "time": 0.84741} +{"mode": "train", "epoch": 136, "iter": 2000, "lr": 0.00228, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.49188, "top5_acc": 0.74484, "loss_cls": 2.85639, "loss": 2.85639, "time": 0.86084} +{"mode": "train", "epoch": 136, "iter": 2100, "lr": 0.00227, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.50078, "top5_acc": 0.74234, "loss_cls": 2.84052, "loss": 2.84052, "time": 0.85704} +{"mode": "train", "epoch": 136, "iter": 2200, "lr": 0.00226, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.48781, "top5_acc": 0.74422, "loss_cls": 2.85272, "loss": 2.85272, "time": 0.85292} +{"mode": "train", "epoch": 136, "iter": 2300, "lr": 0.00225, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48453, "top5_acc": 0.73016, "loss_cls": 2.87741, "loss": 2.87741, "time": 0.85269} +{"mode": "train", "epoch": 136, "iter": 2400, "lr": 0.00224, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.48109, "top5_acc": 0.73, "loss_cls": 2.89818, "loss": 2.89818, "time": 0.84434} +{"mode": "train", "epoch": 136, "iter": 2500, "lr": 0.00224, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.49156, "top5_acc": 0.7325, "loss_cls": 2.8829, "loss": 2.8829, "time": 0.85452} +{"mode": "train", "epoch": 136, "iter": 2600, "lr": 0.00223, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.4825, "top5_acc": 0.74078, "loss_cls": 2.85293, "loss": 2.85293, "time": 0.86088} +{"mode": "train", "epoch": 136, "iter": 2700, "lr": 0.00222, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48234, "top5_acc": 0.73094, "loss_cls": 2.91063, "loss": 2.91063, "time": 0.85395} +{"mode": "train", "epoch": 136, "iter": 2800, "lr": 0.00221, "memory": 15990, "data_time": 0.00075, "top1_acc": 0.49312, "top5_acc": 0.74016, "loss_cls": 2.86621, "loss": 2.86621, "time": 0.86433} +{"mode": "train", "epoch": 136, "iter": 2900, "lr": 0.0022, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48797, "top5_acc": 0.74297, "loss_cls": 2.87785, "loss": 2.87785, "time": 0.85139} +{"mode": "train", "epoch": 136, "iter": 3000, "lr": 0.00219, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.48938, "top5_acc": 0.73594, "loss_cls": 2.89766, "loss": 2.89766, "time": 0.85269} +{"mode": "train", "epoch": 136, "iter": 3100, "lr": 0.00219, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.47406, "top5_acc": 0.73078, "loss_cls": 2.91818, "loss": 2.91818, "time": 0.85764} +{"mode": "train", "epoch": 136, "iter": 3200, "lr": 0.00218, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48016, "top5_acc": 0.73859, "loss_cls": 2.86794, "loss": 2.86794, "time": 0.85386} +{"mode": "train", "epoch": 136, "iter": 3300, "lr": 0.00217, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.49375, "top5_acc": 0.73578, "loss_cls": 2.86866, "loss": 2.86866, "time": 0.85217} +{"mode": "train", "epoch": 136, "iter": 3400, "lr": 0.00216, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47906, "top5_acc": 0.72953, "loss_cls": 2.91036, "loss": 2.91036, "time": 0.85504} +{"mode": "train", "epoch": 136, "iter": 3500, "lr": 0.00215, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48922, "top5_acc": 0.73125, "loss_cls": 2.90547, "loss": 2.90547, "time": 0.85232} +{"mode": "train", "epoch": 136, "iter": 3600, "lr": 0.00215, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.47641, "top5_acc": 0.72062, "loss_cls": 2.95023, "loss": 2.95023, "time": 0.86032} +{"mode": "train", "epoch": 136, "iter": 3700, "lr": 0.00214, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49219, "top5_acc": 0.73703, "loss_cls": 2.85725, "loss": 2.85725, "time": 0.85326} +{"mode": "val", "epoch": 136, "iter": 309, "lr": 0.00213, "top1_acc": 0.39305, "top5_acc": 0.64154, "mean_class_accuracy": 0.39279} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00213, "memory": 15990, "data_time": 1.53824, "top1_acc": 0.50953, "top5_acc": 0.75766, "loss_cls": 2.73402, "loss": 2.73402, "time": 2.58092} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00212, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.50578, "top5_acc": 0.75641, "loss_cls": 2.77091, "loss": 2.77091, "time": 0.8624} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00211, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.50453, "top5_acc": 0.75016, "loss_cls": 2.76936, "loss": 2.76936, "time": 0.85736} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.0021, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.51188, "top5_acc": 0.75828, "loss_cls": 2.73822, "loss": 2.73822, "time": 0.86433} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.00209, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.50906, "top5_acc": 0.75219, "loss_cls": 2.77412, "loss": 2.77412, "time": 0.86548} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.00209, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49625, "top5_acc": 0.75391, "loss_cls": 2.76699, "loss": 2.76699, "time": 0.85754} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00208, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.50703, "top5_acc": 0.76547, "loss_cls": 2.73863, "loss": 2.73863, "time": 0.86949} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00207, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.50781, "top5_acc": 0.75594, "loss_cls": 2.78202, "loss": 2.78202, "time": 0.86598} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00206, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50797, "top5_acc": 0.74625, "loss_cls": 2.78104, "loss": 2.78104, "time": 0.85935} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00205, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.49422, "top5_acc": 0.75062, "loss_cls": 2.8162, "loss": 2.8162, "time": 0.86693} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00205, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.50422, "top5_acc": 0.75234, "loss_cls": 2.78427, "loss": 2.78427, "time": 0.87217} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00204, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.50109, "top5_acc": 0.74734, "loss_cls": 2.79165, "loss": 2.79165, "time": 0.86636} +{"mode": "train", "epoch": 137, "iter": 1300, "lr": 0.00203, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.48422, "top5_acc": 0.74188, "loss_cls": 2.8534, "loss": 2.8534, "time": 0.86036} +{"mode": "train", "epoch": 137, "iter": 1400, "lr": 0.00202, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.49031, "top5_acc": 0.74062, "loss_cls": 2.83267, "loss": 2.83267, "time": 0.85268} +{"mode": "train", "epoch": 137, "iter": 1500, "lr": 0.00201, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.49422, "top5_acc": 0.74172, "loss_cls": 2.8254, "loss": 2.8254, "time": 0.84546} +{"mode": "train", "epoch": 137, "iter": 1600, "lr": 0.00201, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.50094, "top5_acc": 0.74984, "loss_cls": 2.79657, "loss": 2.79657, "time": 0.85358} +{"mode": "train", "epoch": 137, "iter": 1700, "lr": 0.002, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.51141, "top5_acc": 0.75094, "loss_cls": 2.76452, "loss": 2.76452, "time": 0.84845} +{"mode": "train", "epoch": 137, "iter": 1800, "lr": 0.00199, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.49594, "top5_acc": 0.74234, "loss_cls": 2.84466, "loss": 2.84466, "time": 0.85346} +{"mode": "train", "epoch": 137, "iter": 1900, "lr": 0.00198, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.50156, "top5_acc": 0.74609, "loss_cls": 2.81651, "loss": 2.81651, "time": 0.85844} +{"mode": "train", "epoch": 137, "iter": 2000, "lr": 0.00198, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.49016, "top5_acc": 0.73516, "loss_cls": 2.82938, "loss": 2.82938, "time": 0.86391} +{"mode": "train", "epoch": 137, "iter": 2100, "lr": 0.00197, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49828, "top5_acc": 0.74672, "loss_cls": 2.79239, "loss": 2.79239, "time": 0.85819} +{"mode": "train", "epoch": 137, "iter": 2200, "lr": 0.00196, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48766, "top5_acc": 0.73344, "loss_cls": 2.88853, "loss": 2.88853, "time": 0.86653} +{"mode": "train", "epoch": 137, "iter": 2300, "lr": 0.00195, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49109, "top5_acc": 0.74578, "loss_cls": 2.84432, "loss": 2.84432, "time": 0.86038} +{"mode": "train", "epoch": 137, "iter": 2400, "lr": 0.00194, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50125, "top5_acc": 0.7475, "loss_cls": 2.80845, "loss": 2.80845, "time": 0.86167} +{"mode": "train", "epoch": 137, "iter": 2500, "lr": 0.00194, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49438, "top5_acc": 0.74469, "loss_cls": 2.81648, "loss": 2.81648, "time": 0.86136} +{"mode": "train", "epoch": 137, "iter": 2600, "lr": 0.00193, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.50813, "top5_acc": 0.75625, "loss_cls": 2.78213, "loss": 2.78213, "time": 0.86555} +{"mode": "train", "epoch": 137, "iter": 2700, "lr": 0.00192, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48625, "top5_acc": 0.745, "loss_cls": 2.8446, "loss": 2.8446, "time": 0.86283} +{"mode": "train", "epoch": 137, "iter": 2800, "lr": 0.00191, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48391, "top5_acc": 0.74344, "loss_cls": 2.85182, "loss": 2.85182, "time": 0.8601} +{"mode": "train", "epoch": 137, "iter": 2900, "lr": 0.00191, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.49578, "top5_acc": 0.73812, "loss_cls": 2.8265, "loss": 2.8265, "time": 0.85588} +{"mode": "train", "epoch": 137, "iter": 3000, "lr": 0.0019, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49219, "top5_acc": 0.73875, "loss_cls": 2.86285, "loss": 2.86285, "time": 0.84726} +{"mode": "train", "epoch": 137, "iter": 3100, "lr": 0.00189, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.49734, "top5_acc": 0.74797, "loss_cls": 2.81681, "loss": 2.81681, "time": 0.84712} +{"mode": "train", "epoch": 137, "iter": 3200, "lr": 0.00188, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49688, "top5_acc": 0.74641, "loss_cls": 2.84018, "loss": 2.84018, "time": 0.84507} +{"mode": "train", "epoch": 137, "iter": 3300, "lr": 0.00188, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.49844, "top5_acc": 0.74422, "loss_cls": 2.81749, "loss": 2.81749, "time": 0.84835} +{"mode": "train", "epoch": 137, "iter": 3400, "lr": 0.00187, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49984, "top5_acc": 0.75344, "loss_cls": 2.7952, "loss": 2.7952, "time": 0.84636} +{"mode": "train", "epoch": 137, "iter": 3500, "lr": 0.00186, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.49672, "top5_acc": 0.73562, "loss_cls": 2.85002, "loss": 2.85002, "time": 0.84752} +{"mode": "train", "epoch": 137, "iter": 3600, "lr": 0.00185, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.49172, "top5_acc": 0.74312, "loss_cls": 2.84581, "loss": 2.84581, "time": 0.84463} +{"mode": "train", "epoch": 137, "iter": 3700, "lr": 0.00185, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.48719, "top5_acc": 0.74344, "loss_cls": 2.84498, "loss": 2.84498, "time": 0.84519} +{"mode": "val", "epoch": 137, "iter": 309, "lr": 0.00184, "top1_acc": 0.39568, "top5_acc": 0.64636, "mean_class_accuracy": 0.39547} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00183, "memory": 15990, "data_time": 1.50935, "top1_acc": 0.51625, "top5_acc": 0.76125, "loss_cls": 2.71053, "loss": 2.71053, "time": 2.52771} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00183, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.51062, "top5_acc": 0.75828, "loss_cls": 2.74961, "loss": 2.74961, "time": 0.84492} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00182, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.52484, "top5_acc": 0.76422, "loss_cls": 2.67552, "loss": 2.67552, "time": 0.84362} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00181, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.51406, "top5_acc": 0.75391, "loss_cls": 2.72639, "loss": 2.72639, "time": 0.84477} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.0018, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.51109, "top5_acc": 0.75781, "loss_cls": 2.74088, "loss": 2.74088, "time": 0.84875} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.0018, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.51953, "top5_acc": 0.75516, "loss_cls": 2.72276, "loss": 2.72276, "time": 0.847} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00179, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.51359, "top5_acc": 0.7625, "loss_cls": 2.71236, "loss": 2.71236, "time": 0.84448} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00178, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51016, "top5_acc": 0.75344, "loss_cls": 2.75479, "loss": 2.75479, "time": 0.85015} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00177, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.49438, "top5_acc": 0.74953, "loss_cls": 2.7933, "loss": 2.7933, "time": 0.8429} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00177, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.51656, "top5_acc": 0.76438, "loss_cls": 2.71944, "loss": 2.71944, "time": 0.83981} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.00176, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.50359, "top5_acc": 0.75156, "loss_cls": 2.76577, "loss": 2.76577, "time": 0.84277} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.00175, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.49688, "top5_acc": 0.74703, "loss_cls": 2.80575, "loss": 2.80575, "time": 0.84622} +{"mode": "train", "epoch": 138, "iter": 1300, "lr": 0.00175, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.51047, "top5_acc": 0.75906, "loss_cls": 2.72784, "loss": 2.72784, "time": 0.85276} +{"mode": "train", "epoch": 138, "iter": 1400, "lr": 0.00174, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.51188, "top5_acc": 0.75906, "loss_cls": 2.74212, "loss": 2.74212, "time": 0.84661} +{"mode": "train", "epoch": 138, "iter": 1500, "lr": 0.00173, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.50297, "top5_acc": 0.74906, "loss_cls": 2.78957, "loss": 2.78957, "time": 0.84688} +{"mode": "train", "epoch": 138, "iter": 1600, "lr": 0.00172, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.51047, "top5_acc": 0.76219, "loss_cls": 2.75404, "loss": 2.75404, "time": 0.84448} +{"mode": "train", "epoch": 138, "iter": 1700, "lr": 0.00172, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.50016, "top5_acc": 0.75, "loss_cls": 2.79535, "loss": 2.79535, "time": 0.84475} +{"mode": "train", "epoch": 138, "iter": 1800, "lr": 0.00171, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.50813, "top5_acc": 0.74656, "loss_cls": 2.76124, "loss": 2.76124, "time": 0.8428} +{"mode": "train", "epoch": 138, "iter": 1900, "lr": 0.0017, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.51234, "top5_acc": 0.75422, "loss_cls": 2.75823, "loss": 2.75823, "time": 0.84842} +{"mode": "train", "epoch": 138, "iter": 2000, "lr": 0.00169, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.5025, "top5_acc": 0.75141, "loss_cls": 2.75985, "loss": 2.75985, "time": 0.84049} +{"mode": "train", "epoch": 138, "iter": 2100, "lr": 0.00169, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.50016, "top5_acc": 0.74906, "loss_cls": 2.77982, "loss": 2.77982, "time": 0.83599} +{"mode": "train", "epoch": 138, "iter": 2200, "lr": 0.00168, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.50984, "top5_acc": 0.75859, "loss_cls": 2.73953, "loss": 2.73953, "time": 0.83868} +{"mode": "train", "epoch": 138, "iter": 2300, "lr": 0.00167, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.4975, "top5_acc": 0.75047, "loss_cls": 2.80292, "loss": 2.80292, "time": 0.83983} +{"mode": "train", "epoch": 138, "iter": 2400, "lr": 0.00167, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.50625, "top5_acc": 0.76062, "loss_cls": 2.74077, "loss": 2.74077, "time": 0.83844} +{"mode": "train", "epoch": 138, "iter": 2500, "lr": 0.00166, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.50203, "top5_acc": 0.74375, "loss_cls": 2.78703, "loss": 2.78703, "time": 0.8427} +{"mode": "train", "epoch": 138, "iter": 2600, "lr": 0.00165, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.50422, "top5_acc": 0.74875, "loss_cls": 2.77652, "loss": 2.77652, "time": 0.84424} +{"mode": "train", "epoch": 138, "iter": 2700, "lr": 0.00164, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51094, "top5_acc": 0.75297, "loss_cls": 2.74155, "loss": 2.74155, "time": 0.8468} +{"mode": "train", "epoch": 138, "iter": 2800, "lr": 0.00164, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.50172, "top5_acc": 0.75594, "loss_cls": 2.78773, "loss": 2.78773, "time": 0.84457} +{"mode": "train", "epoch": 138, "iter": 2900, "lr": 0.00163, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.51344, "top5_acc": 0.75922, "loss_cls": 2.73937, "loss": 2.73937, "time": 0.8443} +{"mode": "train", "epoch": 138, "iter": 3000, "lr": 0.00162, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.505, "top5_acc": 0.74516, "loss_cls": 2.79088, "loss": 2.79088, "time": 0.84772} +{"mode": "train", "epoch": 138, "iter": 3100, "lr": 0.00162, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.49594, "top5_acc": 0.75031, "loss_cls": 2.81622, "loss": 2.81622, "time": 0.84466} +{"mode": "train", "epoch": 138, "iter": 3200, "lr": 0.00161, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.49547, "top5_acc": 0.74281, "loss_cls": 2.83976, "loss": 2.83976, "time": 0.84442} +{"mode": "train", "epoch": 138, "iter": 3300, "lr": 0.0016, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49859, "top5_acc": 0.75406, "loss_cls": 2.77414, "loss": 2.77414, "time": 0.84725} +{"mode": "train", "epoch": 138, "iter": 3400, "lr": 0.0016, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.50422, "top5_acc": 0.7475, "loss_cls": 2.81188, "loss": 2.81188, "time": 0.84692} +{"mode": "train", "epoch": 138, "iter": 3500, "lr": 0.00159, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.50516, "top5_acc": 0.75109, "loss_cls": 2.74384, "loss": 2.74384, "time": 0.84767} +{"mode": "train", "epoch": 138, "iter": 3600, "lr": 0.00158, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.49156, "top5_acc": 0.75016, "loss_cls": 2.82257, "loss": 2.82257, "time": 0.85338} +{"mode": "train", "epoch": 138, "iter": 3700, "lr": 0.00157, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.51406, "top5_acc": 0.75797, "loss_cls": 2.74311, "loss": 2.74311, "time": 0.84732} +{"mode": "val", "epoch": 138, "iter": 309, "lr": 0.00157, "top1_acc": 0.39736, "top5_acc": 0.6461, "mean_class_accuracy": 0.39715} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00156, "memory": 15990, "data_time": 1.51408, "top1_acc": 0.52281, "top5_acc": 0.77203, "loss_cls": 2.66125, "loss": 2.66125, "time": 2.55552} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00156, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.52266, "top5_acc": 0.76938, "loss_cls": 2.6511, "loss": 2.6511, "time": 0.85081} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00155, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.52578, "top5_acc": 0.76562, "loss_cls": 2.67969, "loss": 2.67969, "time": 0.85344} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00154, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.51391, "top5_acc": 0.77125, "loss_cls": 2.69489, "loss": 2.69489, "time": 0.85403} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00154, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.52844, "top5_acc": 0.76609, "loss_cls": 2.66983, "loss": 2.66983, "time": 0.85123} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00153, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53375, "top5_acc": 0.76656, "loss_cls": 2.65528, "loss": 2.65528, "time": 0.85026} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00152, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.51391, "top5_acc": 0.76359, "loss_cls": 2.6894, "loss": 2.6894, "time": 0.85047} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00152, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.51875, "top5_acc": 0.77094, "loss_cls": 2.6873, "loss": 2.6873, "time": 0.85185} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00151, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.51609, "top5_acc": 0.755, "loss_cls": 2.73047, "loss": 2.73047, "time": 0.85135} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.0015, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.51672, "top5_acc": 0.765, "loss_cls": 2.70242, "loss": 2.70242, "time": 0.85098} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.0015, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.51359, "top5_acc": 0.75422, "loss_cls": 2.72335, "loss": 2.72335, "time": 0.84956} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00149, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.53469, "top5_acc": 0.77359, "loss_cls": 2.62398, "loss": 2.62398, "time": 0.85446} +{"mode": "train", "epoch": 139, "iter": 1300, "lr": 0.00148, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.50672, "top5_acc": 0.76625, "loss_cls": 2.72247, "loss": 2.72247, "time": 0.85629} +{"mode": "train", "epoch": 139, "iter": 1400, "lr": 0.00148, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.50984, "top5_acc": 0.75656, "loss_cls": 2.76774, "loss": 2.76774, "time": 0.85229} +{"mode": "train", "epoch": 139, "iter": 1500, "lr": 0.00147, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.51047, "top5_acc": 0.75953, "loss_cls": 2.74505, "loss": 2.74505, "time": 0.85152} +{"mode": "train", "epoch": 139, "iter": 1600, "lr": 0.00146, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.51938, "top5_acc": 0.76078, "loss_cls": 2.72286, "loss": 2.72286, "time": 0.85369} +{"mode": "train", "epoch": 139, "iter": 1700, "lr": 0.00145, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.51688, "top5_acc": 0.76188, "loss_cls": 2.70542, "loss": 2.70542, "time": 0.8516} +{"mode": "train", "epoch": 139, "iter": 1800, "lr": 0.00145, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50875, "top5_acc": 0.75547, "loss_cls": 2.72053, "loss": 2.72053, "time": 0.85027} +{"mode": "train", "epoch": 139, "iter": 1900, "lr": 0.00144, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51625, "top5_acc": 0.76, "loss_cls": 2.71982, "loss": 2.71982, "time": 0.85195} +{"mode": "train", "epoch": 139, "iter": 2000, "lr": 0.00143, "memory": 15990, "data_time": 0.00077, "top1_acc": 0.51281, "top5_acc": 0.76828, "loss_cls": 2.69717, "loss": 2.69717, "time": 0.86283} +{"mode": "train", "epoch": 139, "iter": 2100, "lr": 0.00143, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.51078, "top5_acc": 0.75969, "loss_cls": 2.71033, "loss": 2.71033, "time": 0.85871} +{"mode": "train", "epoch": 139, "iter": 2200, "lr": 0.00142, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.51703, "top5_acc": 0.76328, "loss_cls": 2.69243, "loss": 2.69243, "time": 0.86231} +{"mode": "train", "epoch": 139, "iter": 2300, "lr": 0.00142, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.50484, "top5_acc": 0.75609, "loss_cls": 2.78233, "loss": 2.78233, "time": 0.85976} +{"mode": "train", "epoch": 139, "iter": 2400, "lr": 0.00141, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.50219, "top5_acc": 0.75344, "loss_cls": 2.76612, "loss": 2.76612, "time": 0.85688} +{"mode": "train", "epoch": 139, "iter": 2500, "lr": 0.0014, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.51109, "top5_acc": 0.76266, "loss_cls": 2.69581, "loss": 2.69581, "time": 0.86551} +{"mode": "train", "epoch": 139, "iter": 2600, "lr": 0.0014, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.51859, "top5_acc": 0.7675, "loss_cls": 2.69041, "loss": 2.69041, "time": 0.86225} +{"mode": "train", "epoch": 139, "iter": 2700, "lr": 0.00139, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.51375, "top5_acc": 0.76516, "loss_cls": 2.72666, "loss": 2.72666, "time": 0.86629} +{"mode": "train", "epoch": 139, "iter": 2800, "lr": 0.00138, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.5125, "top5_acc": 0.75781, "loss_cls": 2.73015, "loss": 2.73015, "time": 0.85628} +{"mode": "train", "epoch": 139, "iter": 2900, "lr": 0.00138, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.50813, "top5_acc": 0.75938, "loss_cls": 2.7393, "loss": 2.7393, "time": 0.85348} +{"mode": "train", "epoch": 139, "iter": 3000, "lr": 0.00137, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.51281, "top5_acc": 0.76125, "loss_cls": 2.73958, "loss": 2.73958, "time": 0.85165} +{"mode": "train", "epoch": 139, "iter": 3100, "lr": 0.00136, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.52344, "top5_acc": 0.77078, "loss_cls": 2.66594, "loss": 2.66594, "time": 0.84915} +{"mode": "train", "epoch": 139, "iter": 3200, "lr": 0.00136, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.51516, "top5_acc": 0.76109, "loss_cls": 2.71217, "loss": 2.71217, "time": 0.85201} +{"mode": "train", "epoch": 139, "iter": 3300, "lr": 0.00135, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.51641, "top5_acc": 0.75281, "loss_cls": 2.7306, "loss": 2.7306, "time": 0.85363} +{"mode": "train", "epoch": 139, "iter": 3400, "lr": 0.00134, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.50766, "top5_acc": 0.75125, "loss_cls": 2.77292, "loss": 2.77292, "time": 0.85067} +{"mode": "train", "epoch": 139, "iter": 3500, "lr": 0.00134, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.50875, "top5_acc": 0.75375, "loss_cls": 2.73649, "loss": 2.73649, "time": 0.84991} +{"mode": "train", "epoch": 139, "iter": 3600, "lr": 0.00133, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50906, "top5_acc": 0.76125, "loss_cls": 2.72256, "loss": 2.72256, "time": 0.85102} +{"mode": "train", "epoch": 139, "iter": 3700, "lr": 0.00132, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.51422, "top5_acc": 0.74984, "loss_cls": 2.75387, "loss": 2.75387, "time": 0.85175} +{"mode": "val", "epoch": 139, "iter": 309, "lr": 0.00132, "top1_acc": 0.40222, "top5_acc": 0.65294, "mean_class_accuracy": 0.40198} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00131, "memory": 15990, "data_time": 1.5417, "top1_acc": 0.54078, "top5_acc": 0.77891, "loss_cls": 2.55843, "loss": 2.55843, "time": 2.57304} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00131, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.52344, "top5_acc": 0.76844, "loss_cls": 2.662, "loss": 2.662, "time": 0.84971} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.0013, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.53344, "top5_acc": 0.77625, "loss_cls": 2.62171, "loss": 2.62171, "time": 0.85302} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.0013, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.52766, "top5_acc": 0.77484, "loss_cls": 2.62628, "loss": 2.62628, "time": 0.85576} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00129, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.54594, "top5_acc": 0.78047, "loss_cls": 2.57212, "loss": 2.57212, "time": 0.85435} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.00128, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.52859, "top5_acc": 0.76953, "loss_cls": 2.64292, "loss": 2.64292, "time": 0.85313} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.00128, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.52016, "top5_acc": 0.7675, "loss_cls": 2.67606, "loss": 2.67606, "time": 0.85311} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00127, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.53406, "top5_acc": 0.765, "loss_cls": 2.62173, "loss": 2.62173, "time": 0.85955} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00126, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.53703, "top5_acc": 0.78156, "loss_cls": 2.59457, "loss": 2.59457, "time": 0.85352} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00126, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53797, "top5_acc": 0.77578, "loss_cls": 2.61689, "loss": 2.61689, "time": 0.85574} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00125, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.53297, "top5_acc": 0.77625, "loss_cls": 2.62107, "loss": 2.62107, "time": 0.85286} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00125, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.52344, "top5_acc": 0.77031, "loss_cls": 2.66907, "loss": 2.66907, "time": 0.85188} +{"mode": "train", "epoch": 140, "iter": 1300, "lr": 0.00124, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.52062, "top5_acc": 0.76391, "loss_cls": 2.67262, "loss": 2.67262, "time": 0.85459} +{"mode": "train", "epoch": 140, "iter": 1400, "lr": 0.00123, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.52297, "top5_acc": 0.76797, "loss_cls": 2.6847, "loss": 2.6847, "time": 0.84772} +{"mode": "train", "epoch": 140, "iter": 1500, "lr": 0.00123, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.51703, "top5_acc": 0.76234, "loss_cls": 2.70525, "loss": 2.70525, "time": 0.85038} +{"mode": "train", "epoch": 140, "iter": 1600, "lr": 0.00122, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.52609, "top5_acc": 0.77203, "loss_cls": 2.64019, "loss": 2.64019, "time": 0.85447} +{"mode": "train", "epoch": 140, "iter": 1700, "lr": 0.00121, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51734, "top5_acc": 0.76875, "loss_cls": 2.68259, "loss": 2.68259, "time": 0.8504} +{"mode": "train", "epoch": 140, "iter": 1800, "lr": 0.00121, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.52281, "top5_acc": 0.76766, "loss_cls": 2.66591, "loss": 2.66591, "time": 0.85131} +{"mode": "train", "epoch": 140, "iter": 1900, "lr": 0.0012, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51984, "top5_acc": 0.76812, "loss_cls": 2.6681, "loss": 2.6681, "time": 0.85293} +{"mode": "train", "epoch": 140, "iter": 2000, "lr": 0.0012, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.53547, "top5_acc": 0.76969, "loss_cls": 2.63989, "loss": 2.63989, "time": 0.85005} +{"mode": "train", "epoch": 140, "iter": 2100, "lr": 0.00119, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51266, "top5_acc": 0.75844, "loss_cls": 2.71312, "loss": 2.71312, "time": 0.85429} +{"mode": "train", "epoch": 140, "iter": 2200, "lr": 0.00118, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53938, "top5_acc": 0.77484, "loss_cls": 2.59558, "loss": 2.59558, "time": 0.85905} +{"mode": "train", "epoch": 140, "iter": 2300, "lr": 0.00118, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.52688, "top5_acc": 0.76719, "loss_cls": 2.65966, "loss": 2.65966, "time": 0.85142} +{"mode": "train", "epoch": 140, "iter": 2400, "lr": 0.00117, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.51313, "top5_acc": 0.75938, "loss_cls": 2.69851, "loss": 2.69851, "time": 0.85375} +{"mode": "train", "epoch": 140, "iter": 2500, "lr": 0.00117, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.51578, "top5_acc": 0.76891, "loss_cls": 2.69633, "loss": 2.69633, "time": 0.85192} +{"mode": "train", "epoch": 140, "iter": 2600, "lr": 0.00116, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51516, "top5_acc": 0.75969, "loss_cls": 2.69258, "loss": 2.69258, "time": 0.85128} +{"mode": "train", "epoch": 140, "iter": 2700, "lr": 0.00115, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.51625, "top5_acc": 0.76609, "loss_cls": 2.70229, "loss": 2.70229, "time": 0.85384} +{"mode": "train", "epoch": 140, "iter": 2800, "lr": 0.00115, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.51766, "top5_acc": 0.76234, "loss_cls": 2.70008, "loss": 2.70008, "time": 0.8541} +{"mode": "train", "epoch": 140, "iter": 2900, "lr": 0.00114, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.51047, "top5_acc": 0.76, "loss_cls": 2.71379, "loss": 2.71379, "time": 0.85725} +{"mode": "train", "epoch": 140, "iter": 3000, "lr": 0.00114, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51844, "top5_acc": 0.77109, "loss_cls": 2.67897, "loss": 2.67897, "time": 0.8554} +{"mode": "train", "epoch": 140, "iter": 3100, "lr": 0.00113, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.50266, "top5_acc": 0.76234, "loss_cls": 2.71662, "loss": 2.71662, "time": 0.85388} +{"mode": "train", "epoch": 140, "iter": 3200, "lr": 0.00112, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.51875, "top5_acc": 0.76906, "loss_cls": 2.67082, "loss": 2.67082, "time": 0.86044} +{"mode": "train", "epoch": 140, "iter": 3300, "lr": 0.00112, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.52953, "top5_acc": 0.76516, "loss_cls": 2.65318, "loss": 2.65318, "time": 0.86265} +{"mode": "train", "epoch": 140, "iter": 3400, "lr": 0.00111, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.52016, "top5_acc": 0.77094, "loss_cls": 2.67173, "loss": 2.67173, "time": 0.86319} +{"mode": "train", "epoch": 140, "iter": 3500, "lr": 0.00111, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.51734, "top5_acc": 0.77391, "loss_cls": 2.66681, "loss": 2.66681, "time": 0.86003} +{"mode": "train", "epoch": 140, "iter": 3600, "lr": 0.0011, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.51297, "top5_acc": 0.76594, "loss_cls": 2.68678, "loss": 2.68678, "time": 0.85607} +{"mode": "train", "epoch": 140, "iter": 3700, "lr": 0.0011, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.525, "top5_acc": 0.77469, "loss_cls": 2.64634, "loss": 2.64634, "time": 0.8593} +{"mode": "val", "epoch": 140, "iter": 309, "lr": 0.00109, "top1_acc": 0.39736, "top5_acc": 0.65172, "mean_class_accuracy": 0.39709} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00109, "memory": 15990, "data_time": 1.62748, "top1_acc": 0.53375, "top5_acc": 0.77672, "loss_cls": 2.60102, "loss": 2.60102, "time": 2.66181} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00108, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.54594, "top5_acc": 0.78453, "loss_cls": 2.56812, "loss": 2.56812, "time": 0.84613} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00108, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.54375, "top5_acc": 0.78016, "loss_cls": 2.55556, "loss": 2.55556, "time": 0.85468} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00107, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.54453, "top5_acc": 0.7825, "loss_cls": 2.54285, "loss": 2.54285, "time": 0.85034} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00106, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.55891, "top5_acc": 0.78844, "loss_cls": 2.5104, "loss": 2.5104, "time": 0.85457} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00106, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.53688, "top5_acc": 0.77547, "loss_cls": 2.60433, "loss": 2.60433, "time": 0.85585} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00105, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.53562, "top5_acc": 0.78016, "loss_cls": 2.60286, "loss": 2.60286, "time": 0.85622} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00105, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.53766, "top5_acc": 0.78016, "loss_cls": 2.59282, "loss": 2.59282, "time": 0.85339} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00104, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53844, "top5_acc": 0.78391, "loss_cls": 2.59726, "loss": 2.59726, "time": 0.85468} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00104, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.52844, "top5_acc": 0.77438, "loss_cls": 2.62162, "loss": 2.62162, "time": 0.85109} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00103, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53094, "top5_acc": 0.76703, "loss_cls": 2.64767, "loss": 2.64767, "time": 0.85406} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00102, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.52219, "top5_acc": 0.77828, "loss_cls": 2.62345, "loss": 2.62345, "time": 0.85572} +{"mode": "train", "epoch": 141, "iter": 1300, "lr": 0.00102, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.52812, "top5_acc": 0.77688, "loss_cls": 2.63183, "loss": 2.63183, "time": 0.85611} +{"mode": "train", "epoch": 141, "iter": 1400, "lr": 0.00101, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.53984, "top5_acc": 0.77469, "loss_cls": 2.61357, "loss": 2.61357, "time": 0.85261} +{"mode": "train", "epoch": 141, "iter": 1500, "lr": 0.00101, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.54594, "top5_acc": 0.78562, "loss_cls": 2.56747, "loss": 2.56747, "time": 0.84946} +{"mode": "train", "epoch": 141, "iter": 1600, "lr": 0.001, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.53156, "top5_acc": 0.77609, "loss_cls": 2.63236, "loss": 2.63236, "time": 0.84868} +{"mode": "train", "epoch": 141, "iter": 1700, "lr": 0.001, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.53469, "top5_acc": 0.77938, "loss_cls": 2.60752, "loss": 2.60752, "time": 0.84649} +{"mode": "train", "epoch": 141, "iter": 1800, "lr": 0.00099, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.54281, "top5_acc": 0.78531, "loss_cls": 2.55633, "loss": 2.55633, "time": 0.84857} +{"mode": "train", "epoch": 141, "iter": 1900, "lr": 0.00099, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.54297, "top5_acc": 0.78688, "loss_cls": 2.55752, "loss": 2.55752, "time": 0.84075} +{"mode": "train", "epoch": 141, "iter": 2000, "lr": 0.00098, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.53141, "top5_acc": 0.77875, "loss_cls": 2.60879, "loss": 2.60879, "time": 0.84758} +{"mode": "train", "epoch": 141, "iter": 2100, "lr": 0.00097, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.53438, "top5_acc": 0.77141, "loss_cls": 2.60819, "loss": 2.60819, "time": 0.84838} +{"mode": "train", "epoch": 141, "iter": 2200, "lr": 0.00097, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.53609, "top5_acc": 0.78609, "loss_cls": 2.60819, "loss": 2.60819, "time": 0.85027} +{"mode": "train", "epoch": 141, "iter": 2300, "lr": 0.00096, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.53516, "top5_acc": 0.77906, "loss_cls": 2.60713, "loss": 2.60713, "time": 0.84571} +{"mode": "train", "epoch": 141, "iter": 2400, "lr": 0.00096, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51375, "top5_acc": 0.76719, "loss_cls": 2.67499, "loss": 2.67499, "time": 0.84798} +{"mode": "train", "epoch": 141, "iter": 2500, "lr": 0.00095, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.53016, "top5_acc": 0.77797, "loss_cls": 2.61202, "loss": 2.61202, "time": 0.85406} +{"mode": "train", "epoch": 141, "iter": 2600, "lr": 0.00095, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.52406, "top5_acc": 0.76656, "loss_cls": 2.64322, "loss": 2.64322, "time": 0.84713} +{"mode": "train", "epoch": 141, "iter": 2700, "lr": 0.00094, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51047, "top5_acc": 0.76, "loss_cls": 2.7134, "loss": 2.7134, "time": 0.84696} +{"mode": "train", "epoch": 141, "iter": 2800, "lr": 0.00094, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.53016, "top5_acc": 0.76984, "loss_cls": 2.61121, "loss": 2.61121, "time": 0.84961} +{"mode": "train", "epoch": 141, "iter": 2900, "lr": 0.00093, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.52859, "top5_acc": 0.77219, "loss_cls": 2.62224, "loss": 2.62224, "time": 0.85144} +{"mode": "train", "epoch": 141, "iter": 3000, "lr": 0.00093, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.52359, "top5_acc": 0.77828, "loss_cls": 2.62113, "loss": 2.62113, "time": 0.85524} +{"mode": "train", "epoch": 141, "iter": 3100, "lr": 0.00092, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.535, "top5_acc": 0.77141, "loss_cls": 2.61742, "loss": 2.61742, "time": 0.84995} +{"mode": "train", "epoch": 141, "iter": 3200, "lr": 0.00091, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.53781, "top5_acc": 0.78203, "loss_cls": 2.60151, "loss": 2.60151, "time": 0.84823} +{"mode": "train", "epoch": 141, "iter": 3300, "lr": 0.00091, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.53391, "top5_acc": 0.77406, "loss_cls": 2.60231, "loss": 2.60231, "time": 0.85238} +{"mode": "train", "epoch": 141, "iter": 3400, "lr": 0.0009, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.53375, "top5_acc": 0.78141, "loss_cls": 2.59767, "loss": 2.59767, "time": 0.85771} +{"mode": "train", "epoch": 141, "iter": 3500, "lr": 0.0009, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.53781, "top5_acc": 0.77469, "loss_cls": 2.59323, "loss": 2.59323, "time": 0.85086} +{"mode": "train", "epoch": 141, "iter": 3600, "lr": 0.00089, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.53484, "top5_acc": 0.77734, "loss_cls": 2.63428, "loss": 2.63428, "time": 0.85067} +{"mode": "train", "epoch": 141, "iter": 3700, "lr": 0.00089, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.52859, "top5_acc": 0.77141, "loss_cls": 2.64793, "loss": 2.64793, "time": 0.85549} +{"mode": "val", "epoch": 141, "iter": 309, "lr": 0.00089, "top1_acc": 0.40146, "top5_acc": 0.65294, "mean_class_accuracy": 0.40128} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00088, "memory": 15990, "data_time": 1.63129, "top1_acc": 0.54953, "top5_acc": 0.78531, "loss_cls": 2.54247, "loss": 2.54247, "time": 2.66382} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00088, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.54406, "top5_acc": 0.78438, "loss_cls": 2.55145, "loss": 2.55145, "time": 0.85098} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00087, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.54438, "top5_acc": 0.78391, "loss_cls": 2.5593, "loss": 2.5593, "time": 0.8447} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00086, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55641, "top5_acc": 0.79734, "loss_cls": 2.49473, "loss": 2.49473, "time": 0.85611} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.00086, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55625, "top5_acc": 0.78859, "loss_cls": 2.53113, "loss": 2.53113, "time": 0.85671} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.00085, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55625, "top5_acc": 0.79734, "loss_cls": 2.47883, "loss": 2.47883, "time": 0.8591} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.00085, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55609, "top5_acc": 0.79, "loss_cls": 2.50866, "loss": 2.50866, "time": 0.85391} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00084, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.54406, "top5_acc": 0.79375, "loss_cls": 2.53425, "loss": 2.53425, "time": 0.85281} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00084, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.53734, "top5_acc": 0.78312, "loss_cls": 2.57448, "loss": 2.57448, "time": 0.85554} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00083, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.53625, "top5_acc": 0.77344, "loss_cls": 2.60505, "loss": 2.60505, "time": 0.84575} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00083, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55406, "top5_acc": 0.79375, "loss_cls": 2.52297, "loss": 2.52297, "time": 0.84832} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00082, "memory": 15990, "data_time": 0.00088, "top1_acc": 0.53328, "top5_acc": 0.77938, "loss_cls": 2.59983, "loss": 2.59983, "time": 0.85297} +{"mode": "train", "epoch": 142, "iter": 1300, "lr": 0.00082, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55594, "top5_acc": 0.78906, "loss_cls": 2.52658, "loss": 2.52658, "time": 0.84741} +{"mode": "train", "epoch": 142, "iter": 1400, "lr": 0.00081, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.55422, "top5_acc": 0.78938, "loss_cls": 2.51693, "loss": 2.51693, "time": 0.8531} +{"mode": "train", "epoch": 142, "iter": 1500, "lr": 0.00081, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.54234, "top5_acc": 0.78016, "loss_cls": 2.57925, "loss": 2.57925, "time": 0.84769} +{"mode": "train", "epoch": 142, "iter": 1600, "lr": 0.0008, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.54172, "top5_acc": 0.78172, "loss_cls": 2.56138, "loss": 2.56138, "time": 0.84236} +{"mode": "train", "epoch": 142, "iter": 1700, "lr": 0.0008, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.53547, "top5_acc": 0.77703, "loss_cls": 2.57773, "loss": 2.57773, "time": 0.84793} +{"mode": "train", "epoch": 142, "iter": 1800, "lr": 0.00079, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.54922, "top5_acc": 0.78672, "loss_cls": 2.52428, "loss": 2.52428, "time": 0.84792} +{"mode": "train", "epoch": 142, "iter": 1900, "lr": 0.00079, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.54672, "top5_acc": 0.78875, "loss_cls": 2.54911, "loss": 2.54911, "time": 0.83335} +{"mode": "train", "epoch": 142, "iter": 2000, "lr": 0.00078, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.54469, "top5_acc": 0.78156, "loss_cls": 2.55482, "loss": 2.55482, "time": 0.8482} +{"mode": "train", "epoch": 142, "iter": 2100, "lr": 0.00078, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.54266, "top5_acc": 0.78438, "loss_cls": 2.54588, "loss": 2.54588, "time": 0.84614} +{"mode": "train", "epoch": 142, "iter": 2200, "lr": 0.00077, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.54703, "top5_acc": 0.78875, "loss_cls": 2.5272, "loss": 2.5272, "time": 0.84598} +{"mode": "train", "epoch": 142, "iter": 2300, "lr": 0.00077, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.5375, "top5_acc": 0.78219, "loss_cls": 2.55353, "loss": 2.55353, "time": 0.84079} +{"mode": "train", "epoch": 142, "iter": 2400, "lr": 0.00076, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.53172, "top5_acc": 0.78484, "loss_cls": 2.56363, "loss": 2.56363, "time": 0.84341} +{"mode": "train", "epoch": 142, "iter": 2500, "lr": 0.00076, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.54688, "top5_acc": 0.77969, "loss_cls": 2.57506, "loss": 2.57506, "time": 0.84525} +{"mode": "train", "epoch": 142, "iter": 2600, "lr": 0.00075, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53641, "top5_acc": 0.78094, "loss_cls": 2.58043, "loss": 2.58043, "time": 0.84472} +{"mode": "train", "epoch": 142, "iter": 2700, "lr": 0.00075, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.54312, "top5_acc": 0.78453, "loss_cls": 2.55924, "loss": 2.55924, "time": 0.8397} +{"mode": "train", "epoch": 142, "iter": 2800, "lr": 0.00075, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.54578, "top5_acc": 0.78469, "loss_cls": 2.55863, "loss": 2.55863, "time": 0.84399} +{"mode": "train", "epoch": 142, "iter": 2900, "lr": 0.00074, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.54703, "top5_acc": 0.78828, "loss_cls": 2.55254, "loss": 2.55254, "time": 0.83899} +{"mode": "train", "epoch": 142, "iter": 3000, "lr": 0.00074, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.54312, "top5_acc": 0.78484, "loss_cls": 2.55311, "loss": 2.55311, "time": 0.84319} +{"mode": "train", "epoch": 142, "iter": 3100, "lr": 0.00073, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.54094, "top5_acc": 0.78266, "loss_cls": 2.56092, "loss": 2.56092, "time": 0.85137} +{"mode": "train", "epoch": 142, "iter": 3200, "lr": 0.00073, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.53938, "top5_acc": 0.78, "loss_cls": 2.58198, "loss": 2.58198, "time": 0.84662} +{"mode": "train", "epoch": 142, "iter": 3300, "lr": 0.00072, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.53016, "top5_acc": 0.78031, "loss_cls": 2.59665, "loss": 2.59665, "time": 0.84595} +{"mode": "train", "epoch": 142, "iter": 3400, "lr": 0.00072, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.5525, "top5_acc": 0.78766, "loss_cls": 2.51938, "loss": 2.51938, "time": 0.8385} +{"mode": "train", "epoch": 142, "iter": 3500, "lr": 0.00071, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.54125, "top5_acc": 0.78375, "loss_cls": 2.55327, "loss": 2.55327, "time": 0.84766} +{"mode": "train", "epoch": 142, "iter": 3600, "lr": 0.00071, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.54156, "top5_acc": 0.77859, "loss_cls": 2.57261, "loss": 2.57261, "time": 0.8366} +{"mode": "train", "epoch": 142, "iter": 3700, "lr": 0.0007, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53484, "top5_acc": 0.77984, "loss_cls": 2.60786, "loss": 2.60786, "time": 0.84412} +{"mode": "val", "epoch": 142, "iter": 309, "lr": 0.0007, "top1_acc": 0.4082, "top5_acc": 0.65664, "mean_class_accuracy": 0.40794} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.0007, "memory": 15990, "data_time": 1.56806, "top1_acc": 0.56453, "top5_acc": 0.80312, "loss_cls": 2.42878, "loss": 2.42878, "time": 2.60633} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00069, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.565, "top5_acc": 0.79703, "loss_cls": 2.46368, "loss": 2.46368, "time": 0.85412} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00069, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.56, "top5_acc": 0.79984, "loss_cls": 2.45242, "loss": 2.45242, "time": 0.85713} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00068, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.56625, "top5_acc": 0.80266, "loss_cls": 2.42795, "loss": 2.42795, "time": 0.85879} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00068, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.55375, "top5_acc": 0.78797, "loss_cls": 2.5179, "loss": 2.5179, "time": 0.85813} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00067, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.56984, "top5_acc": 0.80344, "loss_cls": 2.42902, "loss": 2.42902, "time": 0.85711} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00067, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.54922, "top5_acc": 0.78828, "loss_cls": 2.50437, "loss": 2.50437, "time": 0.8544} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00066, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.55766, "top5_acc": 0.79672, "loss_cls": 2.49914, "loss": 2.49914, "time": 0.85329} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00066, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.55547, "top5_acc": 0.78875, "loss_cls": 2.50719, "loss": 2.50719, "time": 0.86204} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00065, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.55359, "top5_acc": 0.79172, "loss_cls": 2.48531, "loss": 2.48531, "time": 0.85571} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00065, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.55391, "top5_acc": 0.79406, "loss_cls": 2.50611, "loss": 2.50611, "time": 0.85827} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00065, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.54359, "top5_acc": 0.78641, "loss_cls": 2.55238, "loss": 2.55238, "time": 0.85677} +{"mode": "train", "epoch": 143, "iter": 1300, "lr": 0.00064, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.56125, "top5_acc": 0.79172, "loss_cls": 2.48853, "loss": 2.48853, "time": 0.85377} +{"mode": "train", "epoch": 143, "iter": 1400, "lr": 0.00064, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55781, "top5_acc": 0.79359, "loss_cls": 2.47776, "loss": 2.47776, "time": 0.84582} +{"mode": "train", "epoch": 143, "iter": 1500, "lr": 0.00063, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.56047, "top5_acc": 0.79734, "loss_cls": 2.4665, "loss": 2.4665, "time": 0.8497} +{"mode": "train", "epoch": 143, "iter": 1600, "lr": 0.00063, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.56203, "top5_acc": 0.79047, "loss_cls": 2.49326, "loss": 2.49326, "time": 0.84527} +{"mode": "train", "epoch": 143, "iter": 1700, "lr": 0.00062, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.53797, "top5_acc": 0.78312, "loss_cls": 2.54428, "loss": 2.54428, "time": 0.84582} +{"mode": "train", "epoch": 143, "iter": 1800, "lr": 0.00062, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55859, "top5_acc": 0.795, "loss_cls": 2.48478, "loss": 2.48478, "time": 0.84532} +{"mode": "train", "epoch": 143, "iter": 1900, "lr": 0.00061, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.54625, "top5_acc": 0.78766, "loss_cls": 2.55095, "loss": 2.55095, "time": 0.84848} +{"mode": "train", "epoch": 143, "iter": 2000, "lr": 0.00061, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55953, "top5_acc": 0.79531, "loss_cls": 2.47923, "loss": 2.47923, "time": 0.848} +{"mode": "train", "epoch": 143, "iter": 2100, "lr": 0.00061, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55719, "top5_acc": 0.79297, "loss_cls": 2.48839, "loss": 2.48839, "time": 0.84593} +{"mode": "train", "epoch": 143, "iter": 2200, "lr": 0.0006, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.55344, "top5_acc": 0.79453, "loss_cls": 2.50684, "loss": 2.50684, "time": 0.84633} +{"mode": "train", "epoch": 143, "iter": 2300, "lr": 0.0006, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.56109, "top5_acc": 0.79812, "loss_cls": 2.48614, "loss": 2.48614, "time": 0.84704} +{"mode": "train", "epoch": 143, "iter": 2400, "lr": 0.00059, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.54594, "top5_acc": 0.78859, "loss_cls": 2.51753, "loss": 2.51753, "time": 0.84838} +{"mode": "train", "epoch": 143, "iter": 2500, "lr": 0.00059, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.54859, "top5_acc": 0.78828, "loss_cls": 2.51618, "loss": 2.51618, "time": 0.85027} +{"mode": "train", "epoch": 143, "iter": 2600, "lr": 0.00058, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.54641, "top5_acc": 0.79516, "loss_cls": 2.51077, "loss": 2.51077, "time": 0.85145} +{"mode": "train", "epoch": 143, "iter": 2700, "lr": 0.00058, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.55281, "top5_acc": 0.79031, "loss_cls": 2.50576, "loss": 2.50576, "time": 0.85054} +{"mode": "train", "epoch": 143, "iter": 2800, "lr": 0.00058, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.55156, "top5_acc": 0.79141, "loss_cls": 2.48671, "loss": 2.48671, "time": 0.84919} +{"mode": "train", "epoch": 143, "iter": 2900, "lr": 0.00057, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.54625, "top5_acc": 0.78531, "loss_cls": 2.52839, "loss": 2.52839, "time": 0.85159} +{"mode": "train", "epoch": 143, "iter": 3000, "lr": 0.00057, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.55125, "top5_acc": 0.78609, "loss_cls": 2.52056, "loss": 2.52056, "time": 0.84379} +{"mode": "train", "epoch": 143, "iter": 3100, "lr": 0.00056, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.55781, "top5_acc": 0.79094, "loss_cls": 2.49957, "loss": 2.49957, "time": 0.84544} +{"mode": "train", "epoch": 143, "iter": 3200, "lr": 0.00056, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.54328, "top5_acc": 0.78953, "loss_cls": 2.52716, "loss": 2.52716, "time": 0.84302} +{"mode": "train", "epoch": 143, "iter": 3300, "lr": 0.00055, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.5475, "top5_acc": 0.78875, "loss_cls": 2.52241, "loss": 2.52241, "time": 0.8473} +{"mode": "train", "epoch": 143, "iter": 3400, "lr": 0.00055, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.56172, "top5_acc": 0.79359, "loss_cls": 2.49523, "loss": 2.49523, "time": 0.84609} +{"mode": "train", "epoch": 143, "iter": 3500, "lr": 0.00055, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.54969, "top5_acc": 0.78875, "loss_cls": 2.51253, "loss": 2.51253, "time": 0.84826} +{"mode": "train", "epoch": 143, "iter": 3600, "lr": 0.00054, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55125, "top5_acc": 0.79156, "loss_cls": 2.52081, "loss": 2.52081, "time": 0.84495} +{"mode": "train", "epoch": 143, "iter": 3700, "lr": 0.00054, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55578, "top5_acc": 0.79484, "loss_cls": 2.48968, "loss": 2.48968, "time": 0.84834} +{"mode": "val", "epoch": 143, "iter": 309, "lr": 0.00054, "top1_acc": 0.40597, "top5_acc": 0.65532, "mean_class_accuracy": 0.40579} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00053, "memory": 15990, "data_time": 1.56765, "top1_acc": 0.56328, "top5_acc": 0.80641, "loss_cls": 2.42074, "loss": 2.42074, "time": 2.6111} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00053, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.56891, "top5_acc": 0.79609, "loss_cls": 2.42897, "loss": 2.42897, "time": 0.85829} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00052, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.56953, "top5_acc": 0.80875, "loss_cls": 2.39151, "loss": 2.39151, "time": 0.85812} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00052, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.56094, "top5_acc": 0.79891, "loss_cls": 2.44567, "loss": 2.44567, "time": 0.8628} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00052, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.57453, "top5_acc": 0.79891, "loss_cls": 2.4106, "loss": 2.4106, "time": 0.85439} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00051, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57172, "top5_acc": 0.80703, "loss_cls": 2.4171, "loss": 2.4171, "time": 0.85444} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00051, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.56797, "top5_acc": 0.79609, "loss_cls": 2.46208, "loss": 2.46208, "time": 0.85319} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.0005, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.57984, "top5_acc": 0.80875, "loss_cls": 2.38011, "loss": 2.38011, "time": 0.85628} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.0005, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.56781, "top5_acc": 0.79484, "loss_cls": 2.46973, "loss": 2.46973, "time": 0.85991} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.0005, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.55656, "top5_acc": 0.78844, "loss_cls": 2.49413, "loss": 2.49413, "time": 0.85891} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.00049, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.56406, "top5_acc": 0.79938, "loss_cls": 2.44258, "loss": 2.44258, "time": 0.85601} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.00049, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.56375, "top5_acc": 0.80391, "loss_cls": 2.43585, "loss": 2.43585, "time": 0.85789} +{"mode": "train", "epoch": 144, "iter": 1300, "lr": 0.00048, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56047, "top5_acc": 0.80266, "loss_cls": 2.4592, "loss": 2.4592, "time": 0.8517} +{"mode": "train", "epoch": 144, "iter": 1400, "lr": 0.00048, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.55906, "top5_acc": 0.80062, "loss_cls": 2.46194, "loss": 2.46194, "time": 0.85681} +{"mode": "train", "epoch": 144, "iter": 1500, "lr": 0.00048, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.57281, "top5_acc": 0.79875, "loss_cls": 2.46171, "loss": 2.46171, "time": 0.85863} +{"mode": "train", "epoch": 144, "iter": 1600, "lr": 0.00047, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55531, "top5_acc": 0.79547, "loss_cls": 2.488, "loss": 2.488, "time": 0.84972} +{"mode": "train", "epoch": 144, "iter": 1700, "lr": 0.00047, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55, "top5_acc": 0.79406, "loss_cls": 2.51443, "loss": 2.51443, "time": 0.84503} +{"mode": "train", "epoch": 144, "iter": 1800, "lr": 0.00047, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.56578, "top5_acc": 0.805, "loss_cls": 2.43711, "loss": 2.43711, "time": 0.84732} +{"mode": "train", "epoch": 144, "iter": 1900, "lr": 0.00046, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.56172, "top5_acc": 0.79375, "loss_cls": 2.46749, "loss": 2.46749, "time": 0.84672} +{"mode": "train", "epoch": 144, "iter": 2000, "lr": 0.00046, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.57609, "top5_acc": 0.79844, "loss_cls": 2.43351, "loss": 2.43351, "time": 0.84464} +{"mode": "train", "epoch": 144, "iter": 2100, "lr": 0.00045, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.56812, "top5_acc": 0.79562, "loss_cls": 2.45758, "loss": 2.45758, "time": 0.84477} +{"mode": "train", "epoch": 144, "iter": 2200, "lr": 0.00045, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.55766, "top5_acc": 0.80281, "loss_cls": 2.46367, "loss": 2.46367, "time": 0.84273} +{"mode": "train", "epoch": 144, "iter": 2300, "lr": 0.00045, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56859, "top5_acc": 0.79969, "loss_cls": 2.44204, "loss": 2.44204, "time": 0.84657} +{"mode": "train", "epoch": 144, "iter": 2400, "lr": 0.00044, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.55672, "top5_acc": 0.79625, "loss_cls": 2.489, "loss": 2.489, "time": 0.85209} +{"mode": "train", "epoch": 144, "iter": 2500, "lr": 0.00044, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.555, "top5_acc": 0.79, "loss_cls": 2.50832, "loss": 2.50832, "time": 0.85238} +{"mode": "train", "epoch": 144, "iter": 2600, "lr": 0.00044, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.55688, "top5_acc": 0.79156, "loss_cls": 2.48962, "loss": 2.48962, "time": 0.8569} +{"mode": "train", "epoch": 144, "iter": 2700, "lr": 0.00043, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55953, "top5_acc": 0.80266, "loss_cls": 2.46478, "loss": 2.46478, "time": 0.84796} +{"mode": "train", "epoch": 144, "iter": 2800, "lr": 0.00043, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.55781, "top5_acc": 0.80141, "loss_cls": 2.43621, "loss": 2.43621, "time": 0.85168} +{"mode": "train", "epoch": 144, "iter": 2900, "lr": 0.00042, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.55781, "top5_acc": 0.79547, "loss_cls": 2.4837, "loss": 2.4837, "time": 0.85162} +{"mode": "train", "epoch": 144, "iter": 3000, "lr": 0.00042, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.57406, "top5_acc": 0.80578, "loss_cls": 2.40469, "loss": 2.40469, "time": 0.85641} +{"mode": "train", "epoch": 144, "iter": 3100, "lr": 0.00042, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.56812, "top5_acc": 0.79703, "loss_cls": 2.43163, "loss": 2.43163, "time": 0.8532} +{"mode": "train", "epoch": 144, "iter": 3200, "lr": 0.00041, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57, "top5_acc": 0.80625, "loss_cls": 2.41381, "loss": 2.41381, "time": 0.84383} +{"mode": "train", "epoch": 144, "iter": 3300, "lr": 0.00041, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.56469, "top5_acc": 0.79969, "loss_cls": 2.45584, "loss": 2.45584, "time": 0.84762} +{"mode": "train", "epoch": 144, "iter": 3400, "lr": 0.00041, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.56031, "top5_acc": 0.79719, "loss_cls": 2.44132, "loss": 2.44132, "time": 0.84935} +{"mode": "train", "epoch": 144, "iter": 3500, "lr": 0.0004, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57266, "top5_acc": 0.79297, "loss_cls": 2.44668, "loss": 2.44668, "time": 0.854} +{"mode": "train", "epoch": 144, "iter": 3600, "lr": 0.0004, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.56328, "top5_acc": 0.79922, "loss_cls": 2.43818, "loss": 2.43818, "time": 0.85273} +{"mode": "train", "epoch": 144, "iter": 3700, "lr": 0.0004, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55844, "top5_acc": 0.79938, "loss_cls": 2.45581, "loss": 2.45581, "time": 0.85403} +{"mode": "val", "epoch": 144, "iter": 309, "lr": 0.00039, "top1_acc": 0.40526, "top5_acc": 0.65973, "mean_class_accuracy": 0.40506} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.00039, "memory": 15990, "data_time": 1.58536, "top1_acc": 0.56656, "top5_acc": 0.79969, "loss_cls": 2.4418, "loss": 2.4418, "time": 2.64409} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 0.00039, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.58391, "top5_acc": 0.82078, "loss_cls": 2.34001, "loss": 2.34001, "time": 0.85555} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 0.00038, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.57875, "top5_acc": 0.81328, "loss_cls": 2.35387, "loss": 2.35387, "time": 0.85418} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 0.00038, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.58609, "top5_acc": 0.81656, "loss_cls": 2.34222, "loss": 2.34222, "time": 0.85876} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 0.00038, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.57391, "top5_acc": 0.79844, "loss_cls": 2.43438, "loss": 2.43438, "time": 0.85233} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 0.00037, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.57938, "top5_acc": 0.8125, "loss_cls": 2.36822, "loss": 2.36822, "time": 0.85424} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 0.00037, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.58781, "top5_acc": 0.81969, "loss_cls": 2.33036, "loss": 2.33036, "time": 0.85201} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 0.00037, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.57016, "top5_acc": 0.80734, "loss_cls": 2.39553, "loss": 2.39553, "time": 0.85797} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 0.00036, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.57984, "top5_acc": 0.81047, "loss_cls": 2.38428, "loss": 2.38428, "time": 0.84948} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 0.00036, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.56812, "top5_acc": 0.80047, "loss_cls": 2.42869, "loss": 2.42869, "time": 0.8538} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 0.00036, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.57469, "top5_acc": 0.80438, "loss_cls": 2.40905, "loss": 2.40905, "time": 0.85154} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 0.00035, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.5775, "top5_acc": 0.80391, "loss_cls": 2.38701, "loss": 2.38701, "time": 0.8604} +{"mode": "train", "epoch": 145, "iter": 1300, "lr": 0.00035, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.56328, "top5_acc": 0.80672, "loss_cls": 2.42163, "loss": 2.42163, "time": 0.84991} +{"mode": "train", "epoch": 145, "iter": 1400, "lr": 0.00035, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.56906, "top5_acc": 0.80859, "loss_cls": 2.39528, "loss": 2.39528, "time": 0.85286} +{"mode": "train", "epoch": 145, "iter": 1500, "lr": 0.00034, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.57203, "top5_acc": 0.80344, "loss_cls": 2.42149, "loss": 2.42149, "time": 0.85563} +{"mode": "train", "epoch": 145, "iter": 1600, "lr": 0.00034, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.56891, "top5_acc": 0.80188, "loss_cls": 2.42317, "loss": 2.42317, "time": 0.85927} +{"mode": "train", "epoch": 145, "iter": 1700, "lr": 0.00034, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.57422, "top5_acc": 0.80844, "loss_cls": 2.42499, "loss": 2.42499, "time": 0.85555} +{"mode": "train", "epoch": 145, "iter": 1800, "lr": 0.00033, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.57203, "top5_acc": 0.80391, "loss_cls": 2.40838, "loss": 2.40838, "time": 0.85686} +{"mode": "train", "epoch": 145, "iter": 1900, "lr": 0.00033, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.56437, "top5_acc": 0.80469, "loss_cls": 2.4355, "loss": 2.4355, "time": 0.85413} +{"mode": "train", "epoch": 145, "iter": 2000, "lr": 0.00033, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.57625, "top5_acc": 0.80641, "loss_cls": 2.39672, "loss": 2.39672, "time": 0.85246} +{"mode": "train", "epoch": 145, "iter": 2100, "lr": 0.00032, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.58016, "top5_acc": 0.81062, "loss_cls": 2.38115, "loss": 2.38115, "time": 0.84846} +{"mode": "train", "epoch": 145, "iter": 2200, "lr": 0.00032, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.57625, "top5_acc": 0.81125, "loss_cls": 2.37243, "loss": 2.37243, "time": 0.85021} +{"mode": "train", "epoch": 145, "iter": 2300, "lr": 0.00032, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.57203, "top5_acc": 0.80719, "loss_cls": 2.39921, "loss": 2.39921, "time": 0.8529} +{"mode": "train", "epoch": 145, "iter": 2400, "lr": 0.00031, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.57797, "top5_acc": 0.80344, "loss_cls": 2.40021, "loss": 2.40021, "time": 0.85667} +{"mode": "train", "epoch": 145, "iter": 2500, "lr": 0.00031, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.58109, "top5_acc": 0.80938, "loss_cls": 2.39698, "loss": 2.39698, "time": 0.85746} +{"mode": "train", "epoch": 145, "iter": 2600, "lr": 0.00031, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.56938, "top5_acc": 0.79828, "loss_cls": 2.4323, "loss": 2.4323, "time": 0.84761} +{"mode": "train", "epoch": 145, "iter": 2700, "lr": 0.00031, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.56938, "top5_acc": 0.79828, "loss_cls": 2.42725, "loss": 2.42725, "time": 0.85218} +{"mode": "train", "epoch": 145, "iter": 2800, "lr": 0.0003, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.57359, "top5_acc": 0.81047, "loss_cls": 2.38846, "loss": 2.38846, "time": 0.85041} +{"mode": "train", "epoch": 145, "iter": 2900, "lr": 0.0003, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.58094, "top5_acc": 0.81375, "loss_cls": 2.38557, "loss": 2.38557, "time": 0.85207} +{"mode": "train", "epoch": 145, "iter": 3000, "lr": 0.0003, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57609, "top5_acc": 0.80375, "loss_cls": 2.39599, "loss": 2.39599, "time": 0.84807} +{"mode": "train", "epoch": 145, "iter": 3100, "lr": 0.00029, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.57359, "top5_acc": 0.80531, "loss_cls": 2.39356, "loss": 2.39356, "time": 0.84947} +{"mode": "train", "epoch": 145, "iter": 3200, "lr": 0.00029, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.57422, "top5_acc": 0.80406, "loss_cls": 2.42534, "loss": 2.42534, "time": 0.84785} +{"mode": "train", "epoch": 145, "iter": 3300, "lr": 0.00029, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.57281, "top5_acc": 0.81016, "loss_cls": 2.36913, "loss": 2.36913, "time": 0.84656} +{"mode": "train", "epoch": 145, "iter": 3400, "lr": 0.00028, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.56703, "top5_acc": 0.80062, "loss_cls": 2.43099, "loss": 2.43099, "time": 0.85108} +{"mode": "train", "epoch": 145, "iter": 3500, "lr": 0.00028, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.56672, "top5_acc": 0.79812, "loss_cls": 2.43527, "loss": 2.43527, "time": 0.85381} +{"mode": "train", "epoch": 145, "iter": 3600, "lr": 0.00028, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.56594, "top5_acc": 0.805, "loss_cls": 2.41876, "loss": 2.41876, "time": 0.8494} +{"mode": "train", "epoch": 145, "iter": 3700, "lr": 0.00028, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.57422, "top5_acc": 0.80516, "loss_cls": 2.40663, "loss": 2.40663, "time": 0.85519} +{"mode": "val", "epoch": 145, "iter": 309, "lr": 0.00027, "top1_acc": 0.41012, "top5_acc": 0.66125, "mean_class_accuracy": 0.40988} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 0.00027, "memory": 15990, "data_time": 1.56979, "top1_acc": 0.57563, "top5_acc": 0.81469, "loss_cls": 2.36167, "loss": 2.36167, "time": 2.59988} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 0.00027, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.57812, "top5_acc": 0.81562, "loss_cls": 2.34649, "loss": 2.34649, "time": 0.84899} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 0.00027, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.58281, "top5_acc": 0.80312, "loss_cls": 2.36326, "loss": 2.36326, "time": 0.84982} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 0.00026, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58078, "top5_acc": 0.82156, "loss_cls": 2.32224, "loss": 2.32224, "time": 0.84596} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 0.00026, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.58766, "top5_acc": 0.82031, "loss_cls": 2.32336, "loss": 2.32336, "time": 0.84784} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 0.00026, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.57969, "top5_acc": 0.81531, "loss_cls": 2.37052, "loss": 2.37052, "time": 0.84921} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 0.00025, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.58312, "top5_acc": 0.81453, "loss_cls": 2.33969, "loss": 2.33969, "time": 0.84859} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 0.00025, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.57938, "top5_acc": 0.81406, "loss_cls": 2.35617, "loss": 2.35617, "time": 0.84932} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 0.00025, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.57688, "top5_acc": 0.81719, "loss_cls": 2.34343, "loss": 2.34343, "time": 0.85019} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 0.00025, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.57125, "top5_acc": 0.81125, "loss_cls": 2.38529, "loss": 2.38529, "time": 0.8488} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 0.00024, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.58578, "top5_acc": 0.81938, "loss_cls": 2.34768, "loss": 2.34768, "time": 0.84986} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 0.00024, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.58297, "top5_acc": 0.80469, "loss_cls": 2.37687, "loss": 2.37687, "time": 0.84931} +{"mode": "train", "epoch": 146, "iter": 1300, "lr": 0.00024, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.58844, "top5_acc": 0.81547, "loss_cls": 2.35415, "loss": 2.35415, "time": 0.84778} +{"mode": "train", "epoch": 146, "iter": 1400, "lr": 0.00023, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.57594, "top5_acc": 0.81438, "loss_cls": 2.35236, "loss": 2.35236, "time": 0.84396} +{"mode": "train", "epoch": 146, "iter": 1500, "lr": 0.00023, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.5925, "top5_acc": 0.81422, "loss_cls": 2.33216, "loss": 2.33216, "time": 0.84914} +{"mode": "train", "epoch": 146, "iter": 1600, "lr": 0.00023, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.58406, "top5_acc": 0.81391, "loss_cls": 2.34303, "loss": 2.34303, "time": 0.84967} +{"mode": "train", "epoch": 146, "iter": 1700, "lr": 0.00023, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.58797, "top5_acc": 0.81125, "loss_cls": 2.36401, "loss": 2.36401, "time": 0.84851} +{"mode": "train", "epoch": 146, "iter": 1800, "lr": 0.00022, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.58328, "top5_acc": 0.80922, "loss_cls": 2.35511, "loss": 2.35511, "time": 0.84943} +{"mode": "train", "epoch": 146, "iter": 1900, "lr": 0.00022, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.58141, "top5_acc": 0.81844, "loss_cls": 2.32892, "loss": 2.32892, "time": 0.85001} +{"mode": "train", "epoch": 146, "iter": 2000, "lr": 0.00022, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.58016, "top5_acc": 0.81266, "loss_cls": 2.3703, "loss": 2.3703, "time": 0.85056} +{"mode": "train", "epoch": 146, "iter": 2100, "lr": 0.00022, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.57938, "top5_acc": 0.80797, "loss_cls": 2.37343, "loss": 2.37343, "time": 0.84904} +{"mode": "train", "epoch": 146, "iter": 2200, "lr": 0.00021, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.58047, "top5_acc": 0.80547, "loss_cls": 2.38533, "loss": 2.38533, "time": 0.85174} +{"mode": "train", "epoch": 146, "iter": 2300, "lr": 0.00021, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57719, "top5_acc": 0.81672, "loss_cls": 2.36712, "loss": 2.36712, "time": 0.84707} +{"mode": "train", "epoch": 146, "iter": 2400, "lr": 0.00021, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.57859, "top5_acc": 0.81547, "loss_cls": 2.36232, "loss": 2.36232, "time": 0.84495} +{"mode": "train", "epoch": 146, "iter": 2500, "lr": 0.00021, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57484, "top5_acc": 0.80891, "loss_cls": 2.37874, "loss": 2.37874, "time": 0.84852} +{"mode": "train", "epoch": 146, "iter": 2600, "lr": 0.0002, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.58484, "top5_acc": 0.8125, "loss_cls": 2.34621, "loss": 2.34621, "time": 0.84856} +{"mode": "train", "epoch": 146, "iter": 2700, "lr": 0.0002, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58156, "top5_acc": 0.80562, "loss_cls": 2.37884, "loss": 2.37884, "time": 0.85035} +{"mode": "train", "epoch": 146, "iter": 2800, "lr": 0.0002, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.57469, "top5_acc": 0.81125, "loss_cls": 2.38245, "loss": 2.38245, "time": 0.85078} +{"mode": "train", "epoch": 146, "iter": 2900, "lr": 0.0002, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.58516, "top5_acc": 0.81609, "loss_cls": 2.34294, "loss": 2.34294, "time": 0.85192} +{"mode": "train", "epoch": 146, "iter": 3000, "lr": 0.00019, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.58016, "top5_acc": 0.80891, "loss_cls": 2.35586, "loss": 2.35586, "time": 0.85092} +{"mode": "train", "epoch": 146, "iter": 3100, "lr": 0.00019, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58969, "top5_acc": 0.81828, "loss_cls": 2.33005, "loss": 2.33005, "time": 0.85031} +{"mode": "train", "epoch": 146, "iter": 3200, "lr": 0.00019, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56688, "top5_acc": 0.80203, "loss_cls": 2.41808, "loss": 2.41808, "time": 0.85091} +{"mode": "train", "epoch": 146, "iter": 3300, "lr": 0.00019, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.58188, "top5_acc": 0.81422, "loss_cls": 2.36353, "loss": 2.36353, "time": 0.85195} +{"mode": "train", "epoch": 146, "iter": 3400, "lr": 0.00018, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.58141, "top5_acc": 0.80625, "loss_cls": 2.36731, "loss": 2.36731, "time": 0.85004} +{"mode": "train", "epoch": 146, "iter": 3500, "lr": 0.00018, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.58672, "top5_acc": 0.80625, "loss_cls": 2.34702, "loss": 2.34702, "time": 0.84765} +{"mode": "train", "epoch": 146, "iter": 3600, "lr": 0.00018, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.57703, "top5_acc": 0.81188, "loss_cls": 2.35775, "loss": 2.35775, "time": 0.84757} +{"mode": "train", "epoch": 146, "iter": 3700, "lr": 0.00018, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57891, "top5_acc": 0.81297, "loss_cls": 2.34442, "loss": 2.34442, "time": 0.85137} +{"mode": "val", "epoch": 146, "iter": 309, "lr": 0.00018, "top1_acc": 0.40987, "top5_acc": 0.65922, "mean_class_accuracy": 0.40962} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 0.00017, "memory": 15990, "data_time": 1.54849, "top1_acc": 0.59047, "top5_acc": 0.82125, "loss_cls": 2.30637, "loss": 2.30637, "time": 2.60202} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 0.00017, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.59781, "top5_acc": 0.81953, "loss_cls": 2.3048, "loss": 2.3048, "time": 0.8513} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 0.00017, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.58594, "top5_acc": 0.81938, "loss_cls": 2.31853, "loss": 2.31853, "time": 0.85201} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 0.00017, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.59266, "top5_acc": 0.82406, "loss_cls": 2.299, "loss": 2.299, "time": 0.84956} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 0.00016, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58156, "top5_acc": 0.8125, "loss_cls": 2.34339, "loss": 2.34339, "time": 0.85082} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 0.00016, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59141, "top5_acc": 0.81734, "loss_cls": 2.31016, "loss": 2.31016, "time": 0.85306} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 0.00016, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.58391, "top5_acc": 0.8125, "loss_cls": 2.34167, "loss": 2.34167, "time": 0.85045} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 0.00016, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.5925, "top5_acc": 0.81719, "loss_cls": 2.30643, "loss": 2.30643, "time": 0.85765} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 0.00015, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59094, "top5_acc": 0.82141, "loss_cls": 2.29156, "loss": 2.29156, "time": 0.85653} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 0.00015, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.58938, "top5_acc": 0.81516, "loss_cls": 2.34566, "loss": 2.34566, "time": 0.85838} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 0.00015, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.58891, "top5_acc": 0.81875, "loss_cls": 2.3099, "loss": 2.3099, "time": 0.85596} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 0.00015, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.585, "top5_acc": 0.82172, "loss_cls": 2.31819, "loss": 2.31819, "time": 0.85602} +{"mode": "train", "epoch": 147, "iter": 1300, "lr": 0.00015, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.58125, "top5_acc": 0.81828, "loss_cls": 2.34614, "loss": 2.34614, "time": 0.8452} +{"mode": "train", "epoch": 147, "iter": 1400, "lr": 0.00014, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.58969, "top5_acc": 0.81656, "loss_cls": 2.3224, "loss": 2.3224, "time": 0.84137} +{"mode": "train", "epoch": 147, "iter": 1500, "lr": 0.00014, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.57969, "top5_acc": 0.81938, "loss_cls": 2.33982, "loss": 2.33982, "time": 0.84551} +{"mode": "train", "epoch": 147, "iter": 1600, "lr": 0.00014, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58484, "top5_acc": 0.81031, "loss_cls": 2.36543, "loss": 2.36543, "time": 0.84762} +{"mode": "train", "epoch": 147, "iter": 1700, "lr": 0.00014, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.59828, "top5_acc": 0.825, "loss_cls": 2.27603, "loss": 2.27603, "time": 0.8493} +{"mode": "train", "epoch": 147, "iter": 1800, "lr": 0.00014, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.58844, "top5_acc": 0.81344, "loss_cls": 2.33951, "loss": 2.33951, "time": 0.84926} +{"mode": "train", "epoch": 147, "iter": 1900, "lr": 0.00013, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57984, "top5_acc": 0.81203, "loss_cls": 2.32151, "loss": 2.32151, "time": 0.84402} +{"mode": "train", "epoch": 147, "iter": 2000, "lr": 0.00013, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.59422, "top5_acc": 0.82219, "loss_cls": 2.29857, "loss": 2.29857, "time": 0.84623} +{"mode": "train", "epoch": 147, "iter": 2100, "lr": 0.00013, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.59672, "top5_acc": 0.82109, "loss_cls": 2.2877, "loss": 2.2877, "time": 0.84957} +{"mode": "train", "epoch": 147, "iter": 2200, "lr": 0.00013, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.58797, "top5_acc": 0.81438, "loss_cls": 2.33296, "loss": 2.33296, "time": 0.84686} +{"mode": "train", "epoch": 147, "iter": 2300, "lr": 0.00013, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57938, "top5_acc": 0.81312, "loss_cls": 2.33528, "loss": 2.33528, "time": 0.85181} +{"mode": "train", "epoch": 147, "iter": 2400, "lr": 0.00012, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.59438, "top5_acc": 0.8175, "loss_cls": 2.31728, "loss": 2.31728, "time": 0.84351} +{"mode": "train", "epoch": 147, "iter": 2500, "lr": 0.00012, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.58281, "top5_acc": 0.81719, "loss_cls": 2.33984, "loss": 2.33984, "time": 0.84425} +{"mode": "train", "epoch": 147, "iter": 2600, "lr": 0.00012, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.57844, "top5_acc": 0.80891, "loss_cls": 2.37064, "loss": 2.37064, "time": 0.84742} +{"mode": "train", "epoch": 147, "iter": 2700, "lr": 0.00012, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.59188, "top5_acc": 0.82266, "loss_cls": 2.28813, "loss": 2.28813, "time": 0.84167} +{"mode": "train", "epoch": 147, "iter": 2800, "lr": 0.00012, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.58562, "top5_acc": 0.81047, "loss_cls": 2.33488, "loss": 2.33488, "time": 0.83745} +{"mode": "train", "epoch": 147, "iter": 2900, "lr": 0.00011, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.58688, "top5_acc": 0.81453, "loss_cls": 2.31504, "loss": 2.31504, "time": 0.84491} +{"mode": "train", "epoch": 147, "iter": 3000, "lr": 0.00011, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.58531, "top5_acc": 0.82531, "loss_cls": 2.29592, "loss": 2.29592, "time": 0.84674} +{"mode": "train", "epoch": 147, "iter": 3100, "lr": 0.00011, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.58859, "top5_acc": 0.81547, "loss_cls": 2.32904, "loss": 2.32904, "time": 0.84331} +{"mode": "train", "epoch": 147, "iter": 3200, "lr": 0.00011, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58812, "top5_acc": 0.81328, "loss_cls": 2.32802, "loss": 2.32802, "time": 0.84298} +{"mode": "train", "epoch": 147, "iter": 3300, "lr": 0.00011, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.59078, "top5_acc": 0.82375, "loss_cls": 2.30839, "loss": 2.30839, "time": 0.84217} +{"mode": "train", "epoch": 147, "iter": 3400, "lr": 0.0001, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58906, "top5_acc": 0.81547, "loss_cls": 2.33331, "loss": 2.33331, "time": 0.84491} +{"mode": "train", "epoch": 147, "iter": 3500, "lr": 0.0001, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.59297, "top5_acc": 0.80938, "loss_cls": 2.338, "loss": 2.338, "time": 0.84147} +{"mode": "train", "epoch": 147, "iter": 3600, "lr": 0.0001, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57922, "top5_acc": 0.81578, "loss_cls": 2.35894, "loss": 2.35894, "time": 0.84213} +{"mode": "train", "epoch": 147, "iter": 3700, "lr": 0.0001, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.59109, "top5_acc": 0.81734, "loss_cls": 2.32619, "loss": 2.32619, "time": 0.84643} +{"mode": "val", "epoch": 147, "iter": 309, "lr": 0.0001, "top1_acc": 0.41225, "top5_acc": 0.66054, "mean_class_accuracy": 0.41204} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 0.0001, "memory": 15990, "data_time": 1.52698, "top1_acc": 0.58422, "top5_acc": 0.81781, "loss_cls": 2.32813, "loss": 2.32813, "time": 2.56386} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 0.0001, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.60328, "top5_acc": 0.82797, "loss_cls": 2.27087, "loss": 2.27087, "time": 0.85527} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 9e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.59906, "top5_acc": 0.81734, "loss_cls": 2.31452, "loss": 2.31452, "time": 0.85994} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 9e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.58672, "top5_acc": 0.81609, "loss_cls": 2.32889, "loss": 2.32889, "time": 0.85596} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 9e-05, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.58578, "top5_acc": 0.81703, "loss_cls": 2.33176, "loss": 2.33176, "time": 0.8606} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 9e-05, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.59281, "top5_acc": 0.81594, "loss_cls": 2.30641, "loss": 2.30641, "time": 0.86324} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 9e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.60172, "top5_acc": 0.82438, "loss_cls": 2.28944, "loss": 2.28944, "time": 0.86238} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 9e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.59953, "top5_acc": 0.82531, "loss_cls": 2.27479, "loss": 2.27479, "time": 0.85903} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 8e-05, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.60078, "top5_acc": 0.82359, "loss_cls": 2.2822, "loss": 2.2822, "time": 0.85939} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 8e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.60891, "top5_acc": 0.82828, "loss_cls": 2.25385, "loss": 2.25385, "time": 0.85947} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 8e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.60219, "top5_acc": 0.82328, "loss_cls": 2.28726, "loss": 2.28726, "time": 0.85858} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 8e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59578, "top5_acc": 0.81859, "loss_cls": 2.30892, "loss": 2.30892, "time": 0.85235} +{"mode": "train", "epoch": 148, "iter": 1300, "lr": 8e-05, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.58547, "top5_acc": 0.81438, "loss_cls": 2.34576, "loss": 2.34576, "time": 0.855} +{"mode": "train", "epoch": 148, "iter": 1400, "lr": 8e-05, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.60234, "top5_acc": 0.825, "loss_cls": 2.26598, "loss": 2.26598, "time": 0.85229} +{"mode": "train", "epoch": 148, "iter": 1500, "lr": 7e-05, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.59047, "top5_acc": 0.81469, "loss_cls": 2.30645, "loss": 2.30645, "time": 0.85533} +{"mode": "train", "epoch": 148, "iter": 1600, "lr": 7e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.60422, "top5_acc": 0.82828, "loss_cls": 2.2788, "loss": 2.2788, "time": 0.85899} +{"mode": "train", "epoch": 148, "iter": 1700, "lr": 7e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.59453, "top5_acc": 0.81781, "loss_cls": 2.30631, "loss": 2.30631, "time": 0.85852} +{"mode": "train", "epoch": 148, "iter": 1800, "lr": 7e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.58391, "top5_acc": 0.81594, "loss_cls": 2.32693, "loss": 2.32693, "time": 0.85567} +{"mode": "train", "epoch": 148, "iter": 1900, "lr": 7e-05, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.58609, "top5_acc": 0.81516, "loss_cls": 2.33093, "loss": 2.33093, "time": 0.8573} +{"mode": "train", "epoch": 148, "iter": 2000, "lr": 7e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.60047, "top5_acc": 0.82453, "loss_cls": 2.28192, "loss": 2.28192, "time": 0.85254} +{"mode": "train", "epoch": 148, "iter": 2100, "lr": 7e-05, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.59875, "top5_acc": 0.82141, "loss_cls": 2.29369, "loss": 2.29369, "time": 0.85843} +{"mode": "train", "epoch": 148, "iter": 2200, "lr": 6e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.59875, "top5_acc": 0.81828, "loss_cls": 2.29835, "loss": 2.29835, "time": 0.85242} +{"mode": "train", "epoch": 148, "iter": 2300, "lr": 6e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.59234, "top5_acc": 0.82062, "loss_cls": 2.29941, "loss": 2.29941, "time": 0.85455} +{"mode": "train", "epoch": 148, "iter": 2400, "lr": 6e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59984, "top5_acc": 0.8225, "loss_cls": 2.26489, "loss": 2.26489, "time": 0.85274} +{"mode": "train", "epoch": 148, "iter": 2500, "lr": 6e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.58953, "top5_acc": 0.81859, "loss_cls": 2.28202, "loss": 2.28202, "time": 0.85331} +{"mode": "train", "epoch": 148, "iter": 2600, "lr": 6e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.60672, "top5_acc": 0.82281, "loss_cls": 2.27369, "loss": 2.27369, "time": 0.85381} +{"mode": "train", "epoch": 148, "iter": 2700, "lr": 6e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.59859, "top5_acc": 0.81938, "loss_cls": 2.28714, "loss": 2.28714, "time": 0.84865} +{"mode": "train", "epoch": 148, "iter": 2800, "lr": 6e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.59172, "top5_acc": 0.82375, "loss_cls": 2.28031, "loss": 2.28031, "time": 0.85277} +{"mode": "train", "epoch": 148, "iter": 2900, "lr": 5e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.59031, "top5_acc": 0.82156, "loss_cls": 2.29115, "loss": 2.29115, "time": 0.85102} +{"mode": "train", "epoch": 148, "iter": 3000, "lr": 5e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58156, "top5_acc": 0.81438, "loss_cls": 2.35665, "loss": 2.35665, "time": 0.85182} +{"mode": "train", "epoch": 148, "iter": 3100, "lr": 5e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.59844, "top5_acc": 0.81891, "loss_cls": 2.29266, "loss": 2.29266, "time": 0.8524} +{"mode": "train", "epoch": 148, "iter": 3200, "lr": 5e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.60203, "top5_acc": 0.8275, "loss_cls": 2.25659, "loss": 2.25659, "time": 0.84982} +{"mode": "train", "epoch": 148, "iter": 3300, "lr": 5e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.58922, "top5_acc": 0.82297, "loss_cls": 2.30671, "loss": 2.30671, "time": 0.85247} +{"mode": "train", "epoch": 148, "iter": 3400, "lr": 5e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.59812, "top5_acc": 0.82422, "loss_cls": 2.2816, "loss": 2.2816, "time": 0.84928} +{"mode": "train", "epoch": 148, "iter": 3500, "lr": 5e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.59141, "top5_acc": 0.80969, "loss_cls": 2.31345, "loss": 2.31345, "time": 0.84998} +{"mode": "train", "epoch": 148, "iter": 3600, "lr": 5e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.60219, "top5_acc": 0.8325, "loss_cls": 2.26395, "loss": 2.26395, "time": 0.84891} +{"mode": "train", "epoch": 148, "iter": 3700, "lr": 4e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.5975, "top5_acc": 0.82359, "loss_cls": 2.28454, "loss": 2.28454, "time": 0.85736} +{"mode": "val", "epoch": 148, "iter": 309, "lr": 4e-05, "top1_acc": 0.41275, "top5_acc": 0.6613, "mean_class_accuracy": 0.41254} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 4e-05, "memory": 15990, "data_time": 1.57759, "top1_acc": 0.59906, "top5_acc": 0.82484, "loss_cls": 2.26897, "loss": 2.26897, "time": 2.60583} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 4e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.60891, "top5_acc": 0.82984, "loss_cls": 2.23504, "loss": 2.23504, "time": 0.85029} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 4e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59641, "top5_acc": 0.82172, "loss_cls": 2.27991, "loss": 2.27991, "time": 0.84755} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 4e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59484, "top5_acc": 0.82328, "loss_cls": 2.28272, "loss": 2.28272, "time": 0.84617} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 4e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.59609, "top5_acc": 0.82, "loss_cls": 2.27852, "loss": 2.27852, "time": 0.8536} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 4e-05, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.59625, "top5_acc": 0.82781, "loss_cls": 2.26579, "loss": 2.26579, "time": 0.84684} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 4e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59828, "top5_acc": 0.81766, "loss_cls": 2.29179, "loss": 2.29179, "time": 0.85355} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 4e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.59953, "top5_acc": 0.82141, "loss_cls": 2.27888, "loss": 2.27888, "time": 0.84715} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 3e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.58953, "top5_acc": 0.82312, "loss_cls": 2.29167, "loss": 2.29167, "time": 0.85395} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 3e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59, "top5_acc": 0.82562, "loss_cls": 2.3111, "loss": 2.3111, "time": 0.84979} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 3e-05, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.59172, "top5_acc": 0.81641, "loss_cls": 2.31616, "loss": 2.31616, "time": 0.8549} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 3e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59453, "top5_acc": 0.82016, "loss_cls": 2.29216, "loss": 2.29216, "time": 0.85156} +{"mode": "train", "epoch": 149, "iter": 1300, "lr": 3e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.5975, "top5_acc": 0.82172, "loss_cls": 2.28715, "loss": 2.28715, "time": 0.84791} +{"mode": "train", "epoch": 149, "iter": 1400, "lr": 3e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59391, "top5_acc": 0.82016, "loss_cls": 2.30614, "loss": 2.30614, "time": 0.84795} +{"mode": "train", "epoch": 149, "iter": 1500, "lr": 3e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.60359, "top5_acc": 0.83312, "loss_cls": 2.25577, "loss": 2.25577, "time": 0.85316} +{"mode": "train", "epoch": 149, "iter": 1600, "lr": 3e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.59359, "top5_acc": 0.81719, "loss_cls": 2.30785, "loss": 2.30785, "time": 0.84559} +{"mode": "train", "epoch": 149, "iter": 1700, "lr": 3e-05, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.60453, "top5_acc": 0.82188, "loss_cls": 2.2776, "loss": 2.2776, "time": 0.8481} +{"mode": "train", "epoch": 149, "iter": 1800, "lr": 3e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.58938, "top5_acc": 0.81906, "loss_cls": 2.29284, "loss": 2.29284, "time": 0.84939} +{"mode": "train", "epoch": 149, "iter": 1900, "lr": 2e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.58547, "top5_acc": 0.82547, "loss_cls": 2.30773, "loss": 2.30773, "time": 0.8555} +{"mode": "train", "epoch": 149, "iter": 2000, "lr": 2e-05, "memory": 15990, "data_time": 0.00071, "top1_acc": 0.60078, "top5_acc": 0.82812, "loss_cls": 2.26089, "loss": 2.26089, "time": 0.84822} +{"mode": "train", "epoch": 149, "iter": 2100, "lr": 2e-05, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.59766, "top5_acc": 0.81703, "loss_cls": 2.30487, "loss": 2.30487, "time": 0.85075} +{"mode": "train", "epoch": 149, "iter": 2200, "lr": 2e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.60422, "top5_acc": 0.82859, "loss_cls": 2.24888, "loss": 2.24888, "time": 0.84651} +{"mode": "train", "epoch": 149, "iter": 2300, "lr": 2e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.60344, "top5_acc": 0.82125, "loss_cls": 2.27397, "loss": 2.27397, "time": 0.84729} +{"mode": "train", "epoch": 149, "iter": 2400, "lr": 2e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59109, "top5_acc": 0.82312, "loss_cls": 2.29715, "loss": 2.29715, "time": 0.85193} +{"mode": "train", "epoch": 149, "iter": 2500, "lr": 2e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.60016, "top5_acc": 0.82344, "loss_cls": 2.26679, "loss": 2.26679, "time": 0.84902} +{"mode": "train", "epoch": 149, "iter": 2600, "lr": 2e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.59203, "top5_acc": 0.82344, "loss_cls": 2.29903, "loss": 2.29903, "time": 0.84526} +{"mode": "train", "epoch": 149, "iter": 2700, "lr": 2e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.60859, "top5_acc": 0.82656, "loss_cls": 2.25373, "loss": 2.25373, "time": 0.85528} +{"mode": "train", "epoch": 149, "iter": 2800, "lr": 2e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.60047, "top5_acc": 0.83484, "loss_cls": 2.22965, "loss": 2.22965, "time": 0.84994} +{"mode": "train", "epoch": 149, "iter": 2900, "lr": 2e-05, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.60359, "top5_acc": 0.83031, "loss_cls": 2.24333, "loss": 2.24333, "time": 0.85058} +{"mode": "train", "epoch": 149, "iter": 3000, "lr": 2e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59984, "top5_acc": 0.82359, "loss_cls": 2.28191, "loss": 2.28191, "time": 0.85025} +{"mode": "train", "epoch": 149, "iter": 3100, "lr": 2e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.60422, "top5_acc": 0.82688, "loss_cls": 2.24583, "loss": 2.24583, "time": 0.84528} +{"mode": "train", "epoch": 149, "iter": 3200, "lr": 1e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.60172, "top5_acc": 0.82391, "loss_cls": 2.25977, "loss": 2.25977, "time": 0.84194} +{"mode": "train", "epoch": 149, "iter": 3300, "lr": 1e-05, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.6, "top5_acc": 0.82938, "loss_cls": 2.25858, "loss": 2.25858, "time": 0.84564} +{"mode": "train", "epoch": 149, "iter": 3400, "lr": 1e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.59, "top5_acc": 0.81984, "loss_cls": 2.30682, "loss": 2.30682, "time": 0.84199} +{"mode": "train", "epoch": 149, "iter": 3500, "lr": 1e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.60438, "top5_acc": 0.83141, "loss_cls": 2.2316, "loss": 2.2316, "time": 0.84145} +{"mode": "train", "epoch": 149, "iter": 3600, "lr": 1e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.605, "top5_acc": 0.82422, "loss_cls": 2.25203, "loss": 2.25203, "time": 0.83832} +{"mode": "train", "epoch": 149, "iter": 3700, "lr": 1e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.59484, "top5_acc": 0.81891, "loss_cls": 2.30214, "loss": 2.30214, "time": 0.84564} +{"mode": "val", "epoch": 149, "iter": 309, "lr": 1e-05, "top1_acc": 0.41291, "top5_acc": 0.66069, "mean_class_accuracy": 0.41265} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 1e-05, "memory": 15990, "data_time": 1.53116, "top1_acc": 0.59016, "top5_acc": 0.82188, "loss_cls": 2.28375, "loss": 2.28375, "time": 2.5544} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 1e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.59047, "top5_acc": 0.81609, "loss_cls": 2.30202, "loss": 2.30202, "time": 0.85154} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 1e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.59906, "top5_acc": 0.82094, "loss_cls": 2.28253, "loss": 2.28253, "time": 0.84486} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 1e-05, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.59719, "top5_acc": 0.82359, "loss_cls": 2.28796, "loss": 2.28796, "time": 0.85031} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 1e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59438, "top5_acc": 0.81438, "loss_cls": 2.30395, "loss": 2.30395, "time": 0.85572} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 1e-05, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.60328, "top5_acc": 0.83016, "loss_cls": 2.24655, "loss": 2.24655, "time": 0.85325} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 1e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.60266, "top5_acc": 0.83141, "loss_cls": 2.25573, "loss": 2.25573, "time": 0.85094} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 1e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59016, "top5_acc": 0.82438, "loss_cls": 2.29211, "loss": 2.29211, "time": 0.84567} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 1e-05, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.60469, "top5_acc": 0.82281, "loss_cls": 2.25553, "loss": 2.25553, "time": 0.85241} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 1e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.60484, "top5_acc": 0.82297, "loss_cls": 2.26267, "loss": 2.26267, "time": 0.84772} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 1e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.59875, "top5_acc": 0.82812, "loss_cls": 2.25901, "loss": 2.25901, "time": 0.84494} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 1e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.59844, "top5_acc": 0.83078, "loss_cls": 2.25734, "loss": 2.25734, "time": 0.84884} +{"mode": "train", "epoch": 150, "iter": 1300, "lr": 0.0, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.61047, "top5_acc": 0.83062, "loss_cls": 2.22096, "loss": 2.22096, "time": 0.84397} +{"mode": "train", "epoch": 150, "iter": 1400, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.60375, "top5_acc": 0.81875, "loss_cls": 2.27453, "loss": 2.27453, "time": 0.84259} +{"mode": "train", "epoch": 150, "iter": 1500, "lr": 0.0, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.59547, "top5_acc": 0.82453, "loss_cls": 2.27786, "loss": 2.27786, "time": 0.84466} +{"mode": "train", "epoch": 150, "iter": 1600, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.60188, "top5_acc": 0.83484, "loss_cls": 2.24147, "loss": 2.24147, "time": 0.8444} +{"mode": "train", "epoch": 150, "iter": 1700, "lr": 0.0, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.59438, "top5_acc": 0.82375, "loss_cls": 2.27049, "loss": 2.27049, "time": 0.84082} +{"mode": "train", "epoch": 150, "iter": 1800, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.60922, "top5_acc": 0.83047, "loss_cls": 2.23946, "loss": 2.23946, "time": 0.85032} +{"mode": "train", "epoch": 150, "iter": 1900, "lr": 0.0, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.60469, "top5_acc": 0.82625, "loss_cls": 2.23901, "loss": 2.23901, "time": 0.84868} +{"mode": "train", "epoch": 150, "iter": 2000, "lr": 0.0, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.60281, "top5_acc": 0.83016, "loss_cls": 2.25251, "loss": 2.25251, "time": 0.85014} +{"mode": "train", "epoch": 150, "iter": 2100, "lr": 0.0, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.60375, "top5_acc": 0.83062, "loss_cls": 2.24004, "loss": 2.24004, "time": 0.85388} +{"mode": "train", "epoch": 150, "iter": 2200, "lr": 0.0, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.60969, "top5_acc": 0.82797, "loss_cls": 2.23742, "loss": 2.23742, "time": 0.84575} +{"mode": "train", "epoch": 150, "iter": 2300, "lr": 0.0, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.59438, "top5_acc": 0.82312, "loss_cls": 2.29312, "loss": 2.29312, "time": 0.84498} +{"mode": "train", "epoch": 150, "iter": 2400, "lr": 0.0, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.59391, "top5_acc": 0.82125, "loss_cls": 2.29548, "loss": 2.29548, "time": 0.84329} +{"mode": "train", "epoch": 150, "iter": 2500, "lr": 0.0, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.60125, "top5_acc": 0.82594, "loss_cls": 2.26066, "loss": 2.26066, "time": 0.8475} +{"mode": "train", "epoch": 150, "iter": 2600, "lr": 0.0, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59469, "top5_acc": 0.82469, "loss_cls": 2.26423, "loss": 2.26423, "time": 0.8476} +{"mode": "train", "epoch": 150, "iter": 2700, "lr": 0.0, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.60609, "top5_acc": 0.81812, "loss_cls": 2.26449, "loss": 2.26449, "time": 0.84871} +{"mode": "train", "epoch": 150, "iter": 2800, "lr": 0.0, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.59688, "top5_acc": 0.81109, "loss_cls": 2.31032, "loss": 2.31032, "time": 0.84809} +{"mode": "train", "epoch": 150, "iter": 2900, "lr": 0.0, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.60578, "top5_acc": 0.825, "loss_cls": 2.25099, "loss": 2.25099, "time": 0.84887} +{"mode": "train", "epoch": 150, "iter": 3000, "lr": 0.0, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.59453, "top5_acc": 0.81906, "loss_cls": 2.29176, "loss": 2.29176, "time": 0.8505} +{"mode": "train", "epoch": 150, "iter": 3100, "lr": 0.0, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.60484, "top5_acc": 0.82438, "loss_cls": 2.26634, "loss": 2.26634, "time": 0.84196} +{"mode": "train", "epoch": 150, "iter": 3200, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.60156, "top5_acc": 0.82578, "loss_cls": 2.25667, "loss": 2.25667, "time": 0.84506} +{"mode": "train", "epoch": 150, "iter": 3300, "lr": 0.0, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.60703, "top5_acc": 0.83219, "loss_cls": 2.21009, "loss": 2.21009, "time": 0.84375} +{"mode": "train", "epoch": 150, "iter": 3400, "lr": 0.0, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.59453, "top5_acc": 0.81953, "loss_cls": 2.27529, "loss": 2.27529, "time": 0.83489} +{"mode": "train", "epoch": 150, "iter": 3500, "lr": 0.0, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.60844, "top5_acc": 0.83047, "loss_cls": 2.23364, "loss": 2.23364, "time": 0.83896} +{"mode": "train", "epoch": 150, "iter": 3600, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.60734, "top5_acc": 0.83391, "loss_cls": 2.24334, "loss": 2.24334, "time": 0.8413} +{"mode": "train", "epoch": 150, "iter": 3700, "lr": 0.0, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.61438, "top5_acc": 0.82969, "loss_cls": 2.20287, "loss": 2.20287, "time": 0.84008} +{"mode": "val", "epoch": 150, "iter": 309, "lr": 0.0, "top1_acc": 0.41245, "top5_acc": 0.66023, "mean_class_accuracy": 0.41222} diff --git a/k400/bm/best_pred.pkl b/k400/bm/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..42c8587a3ab1e2d9c6e87f98973650843f69cae0 --- /dev/null +++ b/k400/bm/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d859a260c0f7ce026de30625a7527f0de9e05f086e1638559ae44acc29ff2f1b +size 44889778 diff --git a/k400/bm/best_top1_acc_epoch_149.pth b/k400/bm/best_top1_acc_epoch_149.pth new file mode 100644 index 0000000000000000000000000000000000000000..e98fc8aea6fc1060c03d3be3d9a177c955e59a22 --- /dev/null +++ b/k400/bm/best_top1_acc_epoch_149.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2c0997f72e3098c967069527f04370da21091d29d0d47146eae91b7dd4b2e00e +size 32926705 diff --git a/k400/bm/bm.py b/k400/bm/bm.py new file mode 100644 index 0000000000000000000000000000000000000000..11eba2390a23c9be5a28c56e4cc6200032bdad0f --- /dev/null +++ b/k400/bm/bm.py @@ -0,0 +1,133 @@ +modality = 'bm' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/bm' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['bm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/k400/j_1/20241226_014911.log b/k400/j_1/20241226_014911.log new file mode 100644 index 0000000000000000000000000000000000000000..e7d3b224d2ebcc976b9ce6886e03853c606eda23 --- /dev/null +++ b/k400/j_1/20241226_014911.log @@ -0,0 +1,7313 @@ +2024-12-26 01:49:11,588 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2024-12-26 01:49:12,029 - pyskl - INFO - Config: modality = 'j' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/j_1' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2024-12-26 01:49:12,030 - pyskl - INFO - Set random seed to 378083612, deterministic: False +2024-12-26 01:49:25,834 - pyskl - INFO - 239737 videos remain after valid thresholding +2024-12-26 01:49:44,082 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-12-26 01:49:44,084 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1 +2024-12-26 01:49:44,097 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2024-12-26 01:49:44,119 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2024-12-26 01:49:44,123 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1 by HardDiskBackend. +2024-12-26 01:53:28,605 - pyskl - INFO - Epoch [1][100/3746] lr: 1.000e-01, eta: 14 days, 14:18:15, time: 2.245, data_time: 1.521, memory: 15990, top1_acc: 0.0063, top5_acc: 0.0297, loss_cls: 6.4745, loss: 6.4745 +2024-12-26 01:54:40,555 - pyskl - INFO - Epoch [1][200/3746] lr: 1.000e-01, eta: 9 days, 15:15:04, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.0092, top5_acc: 0.0422, loss_cls: 6.4716, loss: 6.4716 +2024-12-26 01:55:52,020 - pyskl - INFO - Epoch [1][300/3746] lr: 1.000e-01, eta: 7 days, 23:18:03, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.0158, top5_acc: 0.0658, loss_cls: 6.2482, loss: 6.2482 +2024-12-26 01:57:03,181 - pyskl - INFO - Epoch [1][400/3746] lr: 1.000e-01, eta: 7 days, 3:11:50, time: 0.712, data_time: 0.001, memory: 15990, top1_acc: 0.0217, top5_acc: 0.0848, loss_cls: 6.0901, loss: 6.0901 +2024-12-26 01:58:14,504 - pyskl - INFO - Epoch [1][500/3746] lr: 1.000e-01, eta: 6 days, 15:10:39, time: 0.713, data_time: 0.001, memory: 15990, top1_acc: 0.0239, top5_acc: 0.0989, loss_cls: 5.9759, loss: 5.9759 +2024-12-26 01:59:25,574 - pyskl - INFO - Epoch [1][600/3746] lr: 1.000e-01, eta: 6 days, 7:05:33, time: 0.711, data_time: 0.001, memory: 15990, top1_acc: 0.0241, top5_acc: 0.1050, loss_cls: 5.9392, loss: 5.9392 +2024-12-26 02:00:36,822 - pyskl - INFO - Epoch [1][700/3746] lr: 1.000e-01, eta: 6 days, 1:21:05, time: 0.712, data_time: 0.001, memory: 15990, top1_acc: 0.0345, top5_acc: 0.1219, loss_cls: 5.8220, loss: 5.8220 +2024-12-26 02:01:48,230 - pyskl - INFO - Epoch [1][800/3746] lr: 1.000e-01, eta: 5 days, 21:04:18, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.0353, top5_acc: 0.1308, loss_cls: 5.7735, loss: 5.7735 +2024-12-26 02:02:59,575 - pyskl - INFO - Epoch [1][900/3746] lr: 1.000e-01, eta: 5 days, 17:43:40, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.0336, top5_acc: 0.1420, loss_cls: 5.7220, loss: 5.7220 +2024-12-26 02:04:10,934 - pyskl - INFO - Epoch [1][1000/3746] lr: 1.000e-01, eta: 5 days, 15:03:02, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.0373, top5_acc: 0.1394, loss_cls: 5.7184, loss: 5.7184 +2024-12-26 02:05:22,630 - pyskl - INFO - Epoch [1][1100/3746] lr: 1.000e-01, eta: 5 days, 12:54:17, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.0428, top5_acc: 0.1514, loss_cls: 5.6280, loss: 5.6280 +2024-12-26 02:06:33,835 - pyskl - INFO - Epoch [1][1200/3746] lr: 1.000e-01, eta: 5 days, 11:02:57, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.0520, top5_acc: 0.1634, loss_cls: 5.6264, loss: 5.6264 +2024-12-26 02:07:45,129 - pyskl - INFO - Epoch [1][1300/3746] lr: 1.000e-01, eta: 5 days, 9:29:11, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.0531, top5_acc: 0.1748, loss_cls: 5.5929, loss: 5.5929 +2024-12-26 02:08:56,477 - pyskl - INFO - Epoch [1][1400/3746] lr: 1.000e-01, eta: 5 days, 8:09:03, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0531, top5_acc: 0.1770, loss_cls: 5.5529, loss: 5.5529 +2024-12-26 02:10:07,808 - pyskl - INFO - Epoch [1][1500/3746] lr: 1.000e-01, eta: 5 days, 6:59:19, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.0550, top5_acc: 0.1877, loss_cls: 5.5002, loss: 5.5002 +2024-12-26 02:11:19,297 - pyskl - INFO - Epoch [1][1600/3746] lr: 1.000e-01, eta: 5 days, 5:59:04, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0606, top5_acc: 0.1966, loss_cls: 5.5045, loss: 5.5045 +2024-12-26 02:12:30,748 - pyskl - INFO - Epoch [1][1700/3746] lr: 1.000e-01, eta: 5 days, 5:05:34, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0575, top5_acc: 0.1939, loss_cls: 5.4662, loss: 5.4662 +2024-12-26 02:13:42,108 - pyskl - INFO - Epoch [1][1800/3746] lr: 1.000e-01, eta: 5 days, 4:17:24, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0691, top5_acc: 0.2158, loss_cls: 5.3999, loss: 5.3999 +2024-12-26 02:14:53,460 - pyskl - INFO - Epoch [1][1900/3746] lr: 1.000e-01, eta: 5 days, 3:34:08, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0798, top5_acc: 0.2220, loss_cls: 5.3497, loss: 5.3497 +2024-12-26 02:16:04,755 - pyskl - INFO - Epoch [1][2000/3746] lr: 1.000e-01, eta: 5 days, 2:54:49, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.0717, top5_acc: 0.2236, loss_cls: 5.3836, loss: 5.3836 +2024-12-26 02:17:16,211 - pyskl - INFO - Epoch [1][2100/3746] lr: 1.000e-01, eta: 5 days, 2:19:51, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0844, top5_acc: 0.2305, loss_cls: 5.3288, loss: 5.3288 +2024-12-26 02:18:27,694 - pyskl - INFO - Epoch [1][2200/3746] lr: 1.000e-01, eta: 5 days, 1:48:03, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0798, top5_acc: 0.2334, loss_cls: 5.3299, loss: 5.3299 +2024-12-26 02:19:39,030 - pyskl - INFO - Epoch [1][2300/3746] lr: 1.000e-01, eta: 5 days, 1:18:20, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.0853, top5_acc: 0.2339, loss_cls: 5.2965, loss: 5.2965 +2024-12-26 02:20:50,198 - pyskl - INFO - Epoch [1][2400/3746] lr: 1.000e-01, eta: 5 days, 0:50:20, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.0887, top5_acc: 0.2608, loss_cls: 5.2015, loss: 5.2015 +2024-12-26 02:22:01,734 - pyskl - INFO - Epoch [1][2500/3746] lr: 1.000e-01, eta: 5 days, 0:25:52, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0877, top5_acc: 0.2573, loss_cls: 5.2216, loss: 5.2216 +2024-12-26 02:23:13,148 - pyskl - INFO - Epoch [1][2600/3746] lr: 9.999e-02, eta: 5 days, 0:02:44, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0875, top5_acc: 0.2634, loss_cls: 5.1894, loss: 5.1894 +2024-12-26 02:24:24,491 - pyskl - INFO - Epoch [1][2700/3746] lr: 9.999e-02, eta: 4 days, 23:40:59, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.0953, top5_acc: 0.2636, loss_cls: 5.1731, loss: 5.1731 +2024-12-26 02:25:36,026 - pyskl - INFO - Epoch [1][2800/3746] lr: 9.999e-02, eta: 4 days, 23:21:21, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0980, top5_acc: 0.2717, loss_cls: 5.1301, loss: 5.1301 +2024-12-26 02:26:47,616 - pyskl - INFO - Epoch [1][2900/3746] lr: 9.999e-02, eta: 4 days, 23:03:09, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1042, top5_acc: 0.2830, loss_cls: 5.1258, loss: 5.1258 +2024-12-26 02:27:59,030 - pyskl - INFO - Epoch [1][3000/3746] lr: 9.999e-02, eta: 4 days, 22:45:33, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1058, top5_acc: 0.2806, loss_cls: 5.1114, loss: 5.1114 +2024-12-26 02:29:10,445 - pyskl - INFO - Epoch [1][3100/3746] lr: 9.999e-02, eta: 4 days, 22:29:01, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1117, top5_acc: 0.2861, loss_cls: 5.0891, loss: 5.0891 +2024-12-26 02:30:21,728 - pyskl - INFO - Epoch [1][3200/3746] lr: 9.999e-02, eta: 4 days, 22:13:03, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1125, top5_acc: 0.2948, loss_cls: 5.0737, loss: 5.0737 +2024-12-26 02:31:33,190 - pyskl - INFO - Epoch [1][3300/3746] lr: 9.999e-02, eta: 4 days, 21:58:29, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1102, top5_acc: 0.2975, loss_cls: 5.0362, loss: 5.0362 +2024-12-26 02:32:44,701 - pyskl - INFO - Epoch [1][3400/3746] lr: 9.999e-02, eta: 4 days, 21:44:50, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1163, top5_acc: 0.3042, loss_cls: 5.0402, loss: 5.0402 +2024-12-26 02:33:56,152 - pyskl - INFO - Epoch [1][3500/3746] lr: 9.999e-02, eta: 4 days, 21:31:45, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1205, top5_acc: 0.3058, loss_cls: 5.0068, loss: 5.0068 +2024-12-26 02:35:07,454 - pyskl - INFO - Epoch [1][3600/3746] lr: 9.999e-02, eta: 4 days, 21:18:56, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1147, top5_acc: 0.3050, loss_cls: 4.9490, loss: 4.9490 +2024-12-26 02:36:18,582 - pyskl - INFO - Epoch [1][3700/3746] lr: 9.999e-02, eta: 4 days, 21:06:18, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1153, top5_acc: 0.3106, loss_cls: 4.9986, loss: 4.9986 +2024-12-26 02:36:53,200 - pyskl - INFO - Saving checkpoint at 1 epochs +2024-12-26 02:38:48,489 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 02:38:49,351 - pyskl - INFO - +top1_acc 0.0860 +top5_acc 0.2384 +2024-12-26 02:38:49,351 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 02:38:49,391 - pyskl - INFO - +mean_acc 0.0860 +2024-12-26 02:38:49,821 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2024-12-26 02:38:49,822 - pyskl - INFO - Best top1_acc is 0.0860 at 1 epoch. +2024-12-26 02:38:49,831 - pyskl - INFO - Epoch(val) [1][309] top1_acc: 0.0860, top5_acc: 0.2384, mean_class_accuracy: 0.0860 +2024-12-26 02:42:28,010 - pyskl - INFO - Epoch [2][100/3746] lr: 9.999e-02, eta: 5 days, 1:25:25, time: 2.182, data_time: 1.466, memory: 15990, top1_acc: 0.1183, top5_acc: 0.3164, loss_cls: 4.9731, loss: 4.9731 +2024-12-26 02:43:40,117 - pyskl - INFO - Epoch [2][200/3746] lr: 9.999e-02, eta: 5 days, 1:09:26, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.1303, top5_acc: 0.3302, loss_cls: 4.9271, loss: 4.9271 +2024-12-26 02:44:51,908 - pyskl - INFO - Epoch [2][300/3746] lr: 9.999e-02, eta: 5 days, 0:53:28, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1217, top5_acc: 0.3206, loss_cls: 4.9614, loss: 4.9614 +2024-12-26 02:46:03,848 - pyskl - INFO - Epoch [2][400/3746] lr: 9.999e-02, eta: 5 days, 0:38:33, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1317, top5_acc: 0.3339, loss_cls: 4.9074, loss: 4.9074 +2024-12-26 02:47:15,330 - pyskl - INFO - Epoch [2][500/3746] lr: 9.999e-02, eta: 5 days, 0:23:16, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.1370, top5_acc: 0.3342, loss_cls: 4.8965, loss: 4.8965 +2024-12-26 02:48:26,556 - pyskl - INFO - Epoch [2][600/3746] lr: 9.999e-02, eta: 5 days, 0:08:05, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1336, top5_acc: 0.3244, loss_cls: 4.9210, loss: 4.9210 +2024-12-26 02:49:37,773 - pyskl - INFO - Epoch [2][700/3746] lr: 9.998e-02, eta: 4 days, 23:53:31, time: 0.712, data_time: 0.001, memory: 15990, top1_acc: 0.1394, top5_acc: 0.3342, loss_cls: 4.8518, loss: 4.8518 +2024-12-26 02:50:49,304 - pyskl - INFO - Epoch [2][800/3746] lr: 9.998e-02, eta: 4 days, 23:40:11, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1416, top5_acc: 0.3473, loss_cls: 4.8383, loss: 4.8383 +2024-12-26 02:52:00,818 - pyskl - INFO - Epoch [2][900/3746] lr: 9.998e-02, eta: 4 days, 23:27:20, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1342, top5_acc: 0.3467, loss_cls: 4.8741, loss: 4.8741 +2024-12-26 02:53:12,141 - pyskl - INFO - Epoch [2][1000/3746] lr: 9.998e-02, eta: 4 days, 23:14:36, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1381, top5_acc: 0.3358, loss_cls: 4.8718, loss: 4.8718 +2024-12-26 02:54:23,489 - pyskl - INFO - Epoch [2][1100/3746] lr: 9.998e-02, eta: 4 days, 23:02:24, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1363, top5_acc: 0.3445, loss_cls: 4.8404, loss: 4.8404 +2024-12-26 02:55:34,858 - pyskl - INFO - Epoch [2][1200/3746] lr: 9.998e-02, eta: 4 days, 22:50:40, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1403, top5_acc: 0.3466, loss_cls: 4.8479, loss: 4.8479 +2024-12-26 02:56:46,412 - pyskl - INFO - Epoch [2][1300/3746] lr: 9.998e-02, eta: 4 days, 22:39:42, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1392, top5_acc: 0.3411, loss_cls: 4.8358, loss: 4.8358 +2024-12-26 02:57:57,690 - pyskl - INFO - Epoch [2][1400/3746] lr: 9.998e-02, eta: 4 days, 22:28:38, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1403, top5_acc: 0.3502, loss_cls: 4.8285, loss: 4.8285 +2024-12-26 02:59:09,216 - pyskl - INFO - Epoch [2][1500/3746] lr: 9.998e-02, eta: 4 days, 22:18:22, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1414, top5_acc: 0.3475, loss_cls: 4.8180, loss: 4.8180 +2024-12-26 03:00:20,873 - pyskl - INFO - Epoch [2][1600/3746] lr: 9.998e-02, eta: 4 days, 22:08:40, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1459, top5_acc: 0.3586, loss_cls: 4.7860, loss: 4.7860 +2024-12-26 03:01:32,393 - pyskl - INFO - Epoch [2][1700/3746] lr: 9.998e-02, eta: 4 days, 21:59:02, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1383, top5_acc: 0.3539, loss_cls: 4.7881, loss: 4.7881 +2024-12-26 03:02:43,871 - pyskl - INFO - Epoch [2][1800/3746] lr: 9.998e-02, eta: 4 days, 21:49:39, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1498, top5_acc: 0.3598, loss_cls: 4.7980, loss: 4.7980 +2024-12-26 03:03:55,499 - pyskl - INFO - Epoch [2][1900/3746] lr: 9.998e-02, eta: 4 days, 21:40:48, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1444, top5_acc: 0.3498, loss_cls: 4.8096, loss: 4.8096 +2024-12-26 03:05:07,002 - pyskl - INFO - Epoch [2][2000/3746] lr: 9.997e-02, eta: 4 days, 21:32:01, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1467, top5_acc: 0.3688, loss_cls: 4.7379, loss: 4.7379 +2024-12-26 03:06:18,339 - pyskl - INFO - Epoch [2][2100/3746] lr: 9.997e-02, eta: 4 days, 21:23:14, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1434, top5_acc: 0.3661, loss_cls: 4.7793, loss: 4.7793 +2024-12-26 03:07:29,673 - pyskl - INFO - Epoch [2][2200/3746] lr: 9.997e-02, eta: 4 days, 21:14:42, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1480, top5_acc: 0.3661, loss_cls: 4.7364, loss: 4.7364 +2024-12-26 03:08:41,106 - pyskl - INFO - Epoch [2][2300/3746] lr: 9.997e-02, eta: 4 days, 21:06:33, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1545, top5_acc: 0.3680, loss_cls: 4.7283, loss: 4.7283 +2024-12-26 03:09:52,908 - pyskl - INFO - Epoch [2][2400/3746] lr: 9.997e-02, eta: 4 days, 20:59:11, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1566, top5_acc: 0.3605, loss_cls: 4.7475, loss: 4.7475 +2024-12-26 03:11:04,323 - pyskl - INFO - Epoch [2][2500/3746] lr: 9.997e-02, eta: 4 days, 20:51:27, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1541, top5_acc: 0.3778, loss_cls: 4.7095, loss: 4.7095 +2024-12-26 03:12:15,656 - pyskl - INFO - Epoch [2][2600/3746] lr: 9.997e-02, eta: 4 days, 20:43:48, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1502, top5_acc: 0.3639, loss_cls: 4.7571, loss: 4.7571 +2024-12-26 03:13:27,326 - pyskl - INFO - Epoch [2][2700/3746] lr: 9.997e-02, eta: 4 days, 20:36:50, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1606, top5_acc: 0.3773, loss_cls: 4.6882, loss: 4.6882 +2024-12-26 03:14:39,058 - pyskl - INFO - Epoch [2][2800/3746] lr: 9.997e-02, eta: 4 days, 20:30:08, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1713, top5_acc: 0.3925, loss_cls: 4.6184, loss: 4.6184 +2024-12-26 03:15:50,382 - pyskl - INFO - Epoch [2][2900/3746] lr: 9.997e-02, eta: 4 days, 20:23:02, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1642, top5_acc: 0.3900, loss_cls: 4.6546, loss: 4.6546 +2024-12-26 03:17:01,927 - pyskl - INFO - Epoch [2][3000/3746] lr: 9.996e-02, eta: 4 days, 20:16:24, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1555, top5_acc: 0.3747, loss_cls: 4.7090, loss: 4.7090 +2024-12-26 03:18:13,419 - pyskl - INFO - Epoch [2][3100/3746] lr: 9.996e-02, eta: 4 days, 20:09:52, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1700, top5_acc: 0.3909, loss_cls: 4.6377, loss: 4.6377 +2024-12-26 03:19:24,793 - pyskl - INFO - Epoch [2][3200/3746] lr: 9.996e-02, eta: 4 days, 20:03:19, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1658, top5_acc: 0.3842, loss_cls: 4.6480, loss: 4.6480 +2024-12-26 03:20:36,344 - pyskl - INFO - Epoch [2][3300/3746] lr: 9.996e-02, eta: 4 days, 19:57:10, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1603, top5_acc: 0.3787, loss_cls: 4.6558, loss: 4.6558 +2024-12-26 03:21:47,815 - pyskl - INFO - Epoch [2][3400/3746] lr: 9.996e-02, eta: 4 days, 19:51:03, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1653, top5_acc: 0.3880, loss_cls: 4.6314, loss: 4.6314 +2024-12-26 03:22:59,258 - pyskl - INFO - Epoch [2][3500/3746] lr: 9.996e-02, eta: 4 days, 19:45:01, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1619, top5_acc: 0.3777, loss_cls: 4.6881, loss: 4.6881 +2024-12-26 03:24:10,819 - pyskl - INFO - Epoch [2][3600/3746] lr: 9.996e-02, eta: 4 days, 19:39:17, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.1672, top5_acc: 0.3898, loss_cls: 4.6563, loss: 4.6563 +2024-12-26 03:25:22,336 - pyskl - INFO - Epoch [2][3700/3746] lr: 9.996e-02, eta: 4 days, 19:33:36, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1639, top5_acc: 0.3847, loss_cls: 4.6168, loss: 4.6168 +2024-12-26 03:25:56,973 - pyskl - INFO - Saving checkpoint at 2 epochs +2024-12-26 03:27:52,495 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 03:27:53,382 - pyskl - INFO - +top1_acc 0.0959 +top5_acc 0.2552 +2024-12-26 03:27:53,382 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 03:27:53,421 - pyskl - INFO - +mean_acc 0.0959 +2024-12-26 03:27:53,427 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_1.pth was removed +2024-12-26 03:27:53,710 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2024-12-26 03:27:53,711 - pyskl - INFO - Best top1_acc is 0.0959 at 2 epoch. +2024-12-26 03:27:53,721 - pyskl - INFO - Epoch(val) [2][309] top1_acc: 0.0959, top5_acc: 0.2552, mean_class_accuracy: 0.0959 +2024-12-26 03:31:31,476 - pyskl - INFO - Epoch [3][100/3746] lr: 9.995e-02, eta: 4 days, 21:43:27, time: 2.177, data_time: 1.462, memory: 15990, top1_acc: 0.1689, top5_acc: 0.3931, loss_cls: 4.6158, loss: 4.6158 +2024-12-26 03:32:43,089 - pyskl - INFO - Epoch [3][200/3746] lr: 9.995e-02, eta: 4 days, 21:36:21, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.1733, top5_acc: 0.4025, loss_cls: 4.5863, loss: 4.5863 +2024-12-26 03:33:54,552 - pyskl - INFO - Epoch [3][300/3746] lr: 9.995e-02, eta: 4 days, 21:29:14, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.1727, top5_acc: 0.3927, loss_cls: 4.5924, loss: 4.5924 +2024-12-26 03:35:05,875 - pyskl - INFO - Epoch [3][400/3746] lr: 9.995e-02, eta: 4 days, 21:22:06, time: 0.713, data_time: 0.001, memory: 15990, top1_acc: 0.1723, top5_acc: 0.3922, loss_cls: 4.6123, loss: 4.6123 +2024-12-26 03:36:17,173 - pyskl - INFO - Epoch [3][500/3746] lr: 9.995e-02, eta: 4 days, 21:15:05, time: 0.713, data_time: 0.001, memory: 15990, top1_acc: 0.1725, top5_acc: 0.3967, loss_cls: 4.6041, loss: 4.6041 +2024-12-26 03:37:28,365 - pyskl - INFO - Epoch [3][600/3746] lr: 9.995e-02, eta: 4 days, 21:08:06, time: 0.712, data_time: 0.001, memory: 15990, top1_acc: 0.1766, top5_acc: 0.3989, loss_cls: 4.5877, loss: 4.5877 +2024-12-26 03:38:40,057 - pyskl - INFO - Epoch [3][700/3746] lr: 9.995e-02, eta: 4 days, 21:01:49, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.1812, top5_acc: 0.4027, loss_cls: 4.5971, loss: 4.5971 +2024-12-26 03:39:51,350 - pyskl - INFO - Epoch [3][800/3746] lr: 9.995e-02, eta: 4 days, 20:55:12, time: 0.713, data_time: 0.001, memory: 15990, top1_acc: 0.1795, top5_acc: 0.4047, loss_cls: 4.5668, loss: 4.5668 +2024-12-26 03:41:03,010 - pyskl - INFO - Epoch [3][900/3746] lr: 9.994e-02, eta: 4 days, 20:49:08, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.1742, top5_acc: 0.4070, loss_cls: 4.5741, loss: 4.5741 +2024-12-26 03:42:14,708 - pyskl - INFO - Epoch [3][1000/3746] lr: 9.994e-02, eta: 4 days, 20:43:13, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.1752, top5_acc: 0.4084, loss_cls: 4.5684, loss: 4.5684 +2024-12-26 03:43:26,396 - pyskl - INFO - Epoch [3][1100/3746] lr: 9.994e-02, eta: 4 days, 20:37:24, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1908, top5_acc: 0.4130, loss_cls: 4.5326, loss: 4.5326 +2024-12-26 03:44:37,920 - pyskl - INFO - Epoch [3][1200/3746] lr: 9.994e-02, eta: 4 days, 20:31:31, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1850, top5_acc: 0.4108, loss_cls: 4.5487, loss: 4.5487 +2024-12-26 03:45:49,821 - pyskl - INFO - Epoch [3][1300/3746] lr: 9.994e-02, eta: 4 days, 20:26:08, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.1769, top5_acc: 0.4170, loss_cls: 4.5741, loss: 4.5741 +2024-12-26 03:47:01,679 - pyskl - INFO - Epoch [3][1400/3746] lr: 9.994e-02, eta: 4 days, 20:20:48, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1834, top5_acc: 0.3952, loss_cls: 4.5738, loss: 4.5738 +2024-12-26 03:48:13,245 - pyskl - INFO - Epoch [3][1500/3746] lr: 9.994e-02, eta: 4 days, 20:15:15, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1841, top5_acc: 0.4072, loss_cls: 4.5471, loss: 4.5471 +2024-12-26 03:49:24,777 - pyskl - INFO - Epoch [3][1600/3746] lr: 9.994e-02, eta: 4 days, 20:09:46, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1891, top5_acc: 0.4181, loss_cls: 4.5106, loss: 4.5106 +2024-12-26 03:50:36,414 - pyskl - INFO - Epoch [3][1700/3746] lr: 9.993e-02, eta: 4 days, 20:04:30, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1841, top5_acc: 0.4134, loss_cls: 4.5608, loss: 4.5608 +2024-12-26 03:51:48,056 - pyskl - INFO - Epoch [3][1800/3746] lr: 9.993e-02, eta: 4 days, 19:59:18, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1856, top5_acc: 0.4163, loss_cls: 4.5283, loss: 4.5283 +2024-12-26 03:52:59,588 - pyskl - INFO - Epoch [3][1900/3746] lr: 9.993e-02, eta: 4 days, 19:54:06, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1816, top5_acc: 0.4095, loss_cls: 4.5470, loss: 4.5470 +2024-12-26 03:54:11,218 - pyskl - INFO - Epoch [3][2000/3746] lr: 9.993e-02, eta: 4 days, 19:49:04, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1820, top5_acc: 0.4109, loss_cls: 4.5431, loss: 4.5431 +2024-12-26 03:55:22,567 - pyskl - INFO - Epoch [3][2100/3746] lr: 9.993e-02, eta: 4 days, 19:43:51, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1817, top5_acc: 0.4147, loss_cls: 4.5351, loss: 4.5351 +2024-12-26 03:56:34,024 - pyskl - INFO - Epoch [3][2200/3746] lr: 9.993e-02, eta: 4 days, 19:38:48, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1789, top5_acc: 0.4102, loss_cls: 4.5373, loss: 4.5373 +2024-12-26 03:57:45,586 - pyskl - INFO - Epoch [3][2300/3746] lr: 9.993e-02, eta: 4 days, 19:33:57, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1902, top5_acc: 0.4208, loss_cls: 4.4913, loss: 4.4913 +2024-12-26 03:58:56,974 - pyskl - INFO - Epoch [3][2400/3746] lr: 9.992e-02, eta: 4 days, 19:29:00, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1858, top5_acc: 0.4080, loss_cls: 4.5248, loss: 4.5248 +2024-12-26 04:00:08,242 - pyskl - INFO - Epoch [3][2500/3746] lr: 9.992e-02, eta: 4 days, 19:24:01, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1892, top5_acc: 0.4153, loss_cls: 4.5033, loss: 4.5033 +2024-12-26 04:01:19,825 - pyskl - INFO - Epoch [3][2600/3746] lr: 9.992e-02, eta: 4 days, 19:19:24, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1831, top5_acc: 0.4131, loss_cls: 4.5075, loss: 4.5075 +2024-12-26 04:02:31,125 - pyskl - INFO - Epoch [3][2700/3746] lr: 9.992e-02, eta: 4 days, 19:14:36, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1989, top5_acc: 0.4128, loss_cls: 4.4969, loss: 4.4969 +2024-12-26 04:03:42,496 - pyskl - INFO - Epoch [3][2800/3746] lr: 9.992e-02, eta: 4 days, 19:09:55, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1913, top5_acc: 0.4239, loss_cls: 4.4616, loss: 4.4616 +2024-12-26 04:04:53,926 - pyskl - INFO - Epoch [3][2900/3746] lr: 9.992e-02, eta: 4 days, 19:05:22, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1803, top5_acc: 0.4106, loss_cls: 4.5444, loss: 4.5444 +2024-12-26 04:06:05,579 - pyskl - INFO - Epoch [3][3000/3746] lr: 9.991e-02, eta: 4 days, 19:01:04, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1961, top5_acc: 0.4256, loss_cls: 4.4782, loss: 4.4782 +2024-12-26 04:07:16,983 - pyskl - INFO - Epoch [3][3100/3746] lr: 9.991e-02, eta: 4 days, 18:56:37, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1884, top5_acc: 0.4228, loss_cls: 4.5195, loss: 4.5195 +2024-12-26 04:08:28,433 - pyskl - INFO - Epoch [3][3200/3746] lr: 9.991e-02, eta: 4 days, 18:52:16, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1928, top5_acc: 0.4259, loss_cls: 4.4896, loss: 4.4896 +2024-12-26 04:09:39,903 - pyskl - INFO - Epoch [3][3300/3746] lr: 9.991e-02, eta: 4 days, 18:48:00, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1931, top5_acc: 0.4267, loss_cls: 4.4830, loss: 4.4830 +2024-12-26 04:10:51,270 - pyskl - INFO - Epoch [3][3400/3746] lr: 9.991e-02, eta: 4 days, 18:43:41, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1777, top5_acc: 0.4139, loss_cls: 4.5341, loss: 4.5341 +2024-12-26 04:12:02,765 - pyskl - INFO - Epoch [3][3500/3746] lr: 9.991e-02, eta: 4 days, 18:39:33, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1973, top5_acc: 0.4327, loss_cls: 4.4429, loss: 4.4429 +2024-12-26 04:13:14,265 - pyskl - INFO - Epoch [3][3600/3746] lr: 9.990e-02, eta: 4 days, 18:35:28, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1934, top5_acc: 0.4284, loss_cls: 4.4527, loss: 4.4527 +2024-12-26 04:14:25,715 - pyskl - INFO - Epoch [3][3700/3746] lr: 9.990e-02, eta: 4 days, 18:31:23, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1970, top5_acc: 0.4228, loss_cls: 4.4579, loss: 4.4579 +2024-12-26 04:15:00,071 - pyskl - INFO - Saving checkpoint at 3 epochs +2024-12-26 04:16:55,618 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 04:16:56,447 - pyskl - INFO - +top1_acc 0.1407 +top5_acc 0.3481 +2024-12-26 04:16:56,448 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 04:16:56,487 - pyskl - INFO - +mean_acc 0.1408 +2024-12-26 04:16:56,497 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_2.pth was removed +2024-12-26 04:16:56,787 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2024-12-26 04:16:56,787 - pyskl - INFO - Best top1_acc is 0.1407 at 3 epoch. +2024-12-26 04:16:56,799 - pyskl - INFO - Epoch(val) [3][309] top1_acc: 0.1407, top5_acc: 0.3481, mean_class_accuracy: 0.1408 +2024-12-26 04:20:37,019 - pyskl - INFO - Epoch [4][100/3746] lr: 9.990e-02, eta: 4 days, 19:59:20, time: 2.202, data_time: 1.485, memory: 15990, top1_acc: 0.1894, top5_acc: 0.4331, loss_cls: 4.4199, loss: 4.4199 +2024-12-26 04:21:48,588 - pyskl - INFO - Epoch [4][200/3746] lr: 9.990e-02, eta: 4 days, 19:54:38, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4441, loss_cls: 4.4046, loss: 4.4046 +2024-12-26 04:23:00,135 - pyskl - INFO - Epoch [4][300/3746] lr: 9.990e-02, eta: 4 days, 19:49:59, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2011, top5_acc: 0.4420, loss_cls: 4.4325, loss: 4.4325 +2024-12-26 04:24:11,446 - pyskl - INFO - Epoch [4][400/3746] lr: 9.989e-02, eta: 4 days, 19:45:12, time: 0.713, data_time: 0.001, memory: 15990, top1_acc: 0.1950, top5_acc: 0.4253, loss_cls: 4.4817, loss: 4.4817 +2024-12-26 04:25:22,803 - pyskl - INFO - Epoch [4][500/3746] lr: 9.989e-02, eta: 4 days, 19:40:32, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4439, loss_cls: 4.3925, loss: 4.3925 +2024-12-26 04:26:34,144 - pyskl - INFO - Epoch [4][600/3746] lr: 9.989e-02, eta: 4 days, 19:35:54, time: 0.713, data_time: 0.001, memory: 15990, top1_acc: 0.1967, top5_acc: 0.4320, loss_cls: 4.4553, loss: 4.4553 +2024-12-26 04:27:45,825 - pyskl - INFO - Epoch [4][700/3746] lr: 9.989e-02, eta: 4 days, 19:31:35, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.1956, top5_acc: 0.4311, loss_cls: 4.4327, loss: 4.4327 +2024-12-26 04:28:57,454 - pyskl - INFO - Epoch [4][800/3746] lr: 9.989e-02, eta: 4 days, 19:27:17, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4377, loss_cls: 4.4232, loss: 4.4232 +2024-12-26 04:30:08,583 - pyskl - INFO - Epoch [4][900/3746] lr: 9.988e-02, eta: 4 days, 19:22:39, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1956, top5_acc: 0.4387, loss_cls: 4.4454, loss: 4.4454 +2024-12-26 04:31:20,040 - pyskl - INFO - Epoch [4][1000/3746] lr: 9.988e-02, eta: 4 days, 19:18:20, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.1953, top5_acc: 0.4278, loss_cls: 4.4671, loss: 4.4671 +2024-12-26 04:32:31,394 - pyskl - INFO - Epoch [4][1100/3746] lr: 9.988e-02, eta: 4 days, 19:13:58, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4408, loss_cls: 4.4414, loss: 4.4414 +2024-12-26 04:33:42,750 - pyskl - INFO - Epoch [4][1200/3746] lr: 9.988e-02, eta: 4 days, 19:09:41, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4414, loss_cls: 4.3652, loss: 4.3652 +2024-12-26 04:34:53,871 - pyskl - INFO - Epoch [4][1300/3746] lr: 9.988e-02, eta: 4 days, 19:05:15, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4402, loss_cls: 4.4047, loss: 4.4047 +2024-12-26 04:36:05,234 - pyskl - INFO - Epoch [4][1400/3746] lr: 9.988e-02, eta: 4 days, 19:01:04, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.1952, top5_acc: 0.4295, loss_cls: 4.4615, loss: 4.4615 +2024-12-26 04:37:16,328 - pyskl - INFO - Epoch [4][1500/3746] lr: 9.987e-02, eta: 4 days, 18:56:43, time: 0.711, data_time: 0.001, memory: 15990, top1_acc: 0.1936, top5_acc: 0.4305, loss_cls: 4.4390, loss: 4.4390 +2024-12-26 04:38:27,428 - pyskl - INFO - Epoch [4][1600/3746] lr: 9.987e-02, eta: 4 days, 18:52:26, time: 0.711, data_time: 0.001, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4297, loss_cls: 4.4325, loss: 4.4325 +2024-12-26 04:39:38,650 - pyskl - INFO - Epoch [4][1700/3746] lr: 9.987e-02, eta: 4 days, 18:48:17, time: 0.712, data_time: 0.001, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4288, loss_cls: 4.4540, loss: 4.4540 +2024-12-26 04:40:50,141 - pyskl - INFO - Epoch [4][1800/3746] lr: 9.987e-02, eta: 4 days, 18:44:22, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4306, loss_cls: 4.4129, loss: 4.4129 +2024-12-26 04:42:01,600 - pyskl - INFO - Epoch [4][1900/3746] lr: 9.987e-02, eta: 4 days, 18:40:28, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4334, loss_cls: 4.3789, loss: 4.3789 +2024-12-26 04:43:13,263 - pyskl - INFO - Epoch [4][2000/3746] lr: 9.986e-02, eta: 4 days, 18:36:44, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4281, loss_cls: 4.4405, loss: 4.4405 +2024-12-26 04:44:25,519 - pyskl - INFO - Epoch [4][2100/3746] lr: 9.986e-02, eta: 4 days, 18:33:28, time: 0.723, data_time: 0.001, memory: 15990, top1_acc: 0.1989, top5_acc: 0.4431, loss_cls: 4.4117, loss: 4.4117 +2024-12-26 04:45:37,153 - pyskl - INFO - Epoch [4][2200/3746] lr: 9.986e-02, eta: 4 days, 18:29:48, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.1977, top5_acc: 0.4369, loss_cls: 4.4347, loss: 4.4347 +2024-12-26 04:46:49,284 - pyskl - INFO - Epoch [4][2300/3746] lr: 9.986e-02, eta: 4 days, 18:26:31, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2002, top5_acc: 0.4311, loss_cls: 4.4308, loss: 4.4308 +2024-12-26 04:48:01,000 - pyskl - INFO - Epoch [4][2400/3746] lr: 9.985e-02, eta: 4 days, 18:22:58, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4369, loss_cls: 4.4218, loss: 4.4218 +2024-12-26 04:49:12,713 - pyskl - INFO - Epoch [4][2500/3746] lr: 9.985e-02, eta: 4 days, 18:19:28, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2030, top5_acc: 0.4417, loss_cls: 4.4276, loss: 4.4276 +2024-12-26 04:50:24,228 - pyskl - INFO - Epoch [4][2600/3746] lr: 9.985e-02, eta: 4 days, 18:15:51, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2069, top5_acc: 0.4425, loss_cls: 4.3867, loss: 4.3867 +2024-12-26 04:51:35,637 - pyskl - INFO - Epoch [4][2700/3746] lr: 9.985e-02, eta: 4 days, 18:12:13, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4397, loss_cls: 4.4311, loss: 4.4311 +2024-12-26 04:52:47,023 - pyskl - INFO - Epoch [4][2800/3746] lr: 9.985e-02, eta: 4 days, 18:08:35, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1972, top5_acc: 0.4436, loss_cls: 4.4063, loss: 4.4063 +2024-12-26 04:53:58,395 - pyskl - INFO - Epoch [4][2900/3746] lr: 9.984e-02, eta: 4 days, 18:05:00, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4536, loss_cls: 4.3606, loss: 4.3606 +2024-12-26 04:55:09,794 - pyskl - INFO - Epoch [4][3000/3746] lr: 9.984e-02, eta: 4 days, 18:01:27, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4412, loss_cls: 4.4239, loss: 4.4239 +2024-12-26 04:56:21,257 - pyskl - INFO - Epoch [4][3100/3746] lr: 9.984e-02, eta: 4 days, 17:57:59, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4439, loss_cls: 4.3951, loss: 4.3951 +2024-12-26 04:57:32,943 - pyskl - INFO - Epoch [4][3200/3746] lr: 9.984e-02, eta: 4 days, 17:54:41, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4412, loss_cls: 4.3981, loss: 4.3981 +2024-12-26 04:58:44,625 - pyskl - INFO - Epoch [4][3300/3746] lr: 9.983e-02, eta: 4 days, 17:51:24, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4445, loss_cls: 4.3927, loss: 4.3927 +2024-12-26 04:59:56,097 - pyskl - INFO - Epoch [4][3400/3746] lr: 9.983e-02, eta: 4 days, 17:48:02, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4550, loss_cls: 4.3439, loss: 4.3439 +2024-12-26 05:01:07,739 - pyskl - INFO - Epoch [4][3500/3746] lr: 9.983e-02, eta: 4 days, 17:44:48, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4308, loss_cls: 4.4240, loss: 4.4240 +2024-12-26 05:02:19,353 - pyskl - INFO - Epoch [4][3600/3746] lr: 9.983e-02, eta: 4 days, 17:41:34, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4509, loss_cls: 4.3395, loss: 4.3395 +2024-12-26 05:03:30,716 - pyskl - INFO - Epoch [4][3700/3746] lr: 9.983e-02, eta: 4 days, 17:38:13, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4502, loss_cls: 4.3574, loss: 4.3574 +2024-12-26 05:04:05,009 - pyskl - INFO - Saving checkpoint at 4 epochs +2024-12-26 05:06:00,876 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 05:06:01,808 - pyskl - INFO - +top1_acc 0.1490 +top5_acc 0.3560 +2024-12-26 05:06:01,808 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 05:06:01,849 - pyskl - INFO - +mean_acc 0.1490 +2024-12-26 05:06:01,854 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_3.pth was removed +2024-12-26 05:06:02,112 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2024-12-26 05:06:02,113 - pyskl - INFO - Best top1_acc is 0.1490 at 4 epoch. +2024-12-26 05:06:02,124 - pyskl - INFO - Epoch(val) [4][309] top1_acc: 0.1490, top5_acc: 0.3560, mean_class_accuracy: 0.1490 +2024-12-26 05:09:42,542 - pyskl - INFO - Epoch [5][100/3746] lr: 9.982e-02, eta: 4 days, 18:43:35, time: 2.204, data_time: 1.487, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4502, loss_cls: 4.3366, loss: 4.3366 +2024-12-26 05:10:54,247 - pyskl - INFO - Epoch [5][200/3746] lr: 9.982e-02, eta: 4 days, 18:40:01, time: 0.717, data_time: 0.002, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4483, loss_cls: 4.3711, loss: 4.3711 +2024-12-26 05:12:05,591 - pyskl - INFO - Epoch [5][300/3746] lr: 9.982e-02, eta: 4 days, 18:36:17, time: 0.713, data_time: 0.002, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4567, loss_cls: 4.3054, loss: 4.3054 +2024-12-26 05:13:17,091 - pyskl - INFO - Epoch [5][400/3746] lr: 9.982e-02, eta: 4 days, 18:32:40, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4441, loss_cls: 4.3657, loss: 4.3657 +2024-12-26 05:14:28,668 - pyskl - INFO - Epoch [5][500/3746] lr: 9.981e-02, eta: 4 days, 18:29:08, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4537, loss_cls: 4.3591, loss: 4.3591 +2024-12-26 05:15:40,543 - pyskl - INFO - Epoch [5][600/3746] lr: 9.981e-02, eta: 4 days, 18:25:48, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4570, loss_cls: 4.3169, loss: 4.3169 +2024-12-26 05:16:52,277 - pyskl - INFO - Epoch [5][700/3746] lr: 9.981e-02, eta: 4 days, 18:22:25, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4500, loss_cls: 4.3497, loss: 4.3497 +2024-12-26 05:18:03,624 - pyskl - INFO - Epoch [5][800/3746] lr: 9.981e-02, eta: 4 days, 18:18:50, time: 0.713, data_time: 0.001, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4508, loss_cls: 4.3639, loss: 4.3639 +2024-12-26 05:19:15,009 - pyskl - INFO - Epoch [5][900/3746] lr: 9.980e-02, eta: 4 days, 18:15:18, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4564, loss_cls: 4.3417, loss: 4.3417 +2024-12-26 05:20:26,255 - pyskl - INFO - Epoch [5][1000/3746] lr: 9.980e-02, eta: 4 days, 18:11:43, time: 0.712, data_time: 0.001, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4544, loss_cls: 4.3382, loss: 4.3382 +2024-12-26 05:21:37,596 - pyskl - INFO - Epoch [5][1100/3746] lr: 9.980e-02, eta: 4 days, 18:08:13, time: 0.713, data_time: 0.001, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4600, loss_cls: 4.3489, loss: 4.3489 +2024-12-26 05:22:48,972 - pyskl - INFO - Epoch [5][1200/3746] lr: 9.980e-02, eta: 4 days, 18:04:46, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4548, loss_cls: 4.3418, loss: 4.3418 +2024-12-26 05:24:00,530 - pyskl - INFO - Epoch [5][1300/3746] lr: 9.979e-02, eta: 4 days, 18:01:27, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4544, loss_cls: 4.3569, loss: 4.3569 +2024-12-26 05:25:12,399 - pyskl - INFO - Epoch [5][1400/3746] lr: 9.979e-02, eta: 4 days, 17:58:19, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4630, loss_cls: 4.3287, loss: 4.3287 +2024-12-26 05:26:23,755 - pyskl - INFO - Epoch [5][1500/3746] lr: 9.979e-02, eta: 4 days, 17:54:57, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4591, loss_cls: 4.3276, loss: 4.3276 +2024-12-26 05:27:34,941 - pyskl - INFO - Epoch [5][1600/3746] lr: 9.979e-02, eta: 4 days, 17:51:30, time: 0.712, data_time: 0.001, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4619, loss_cls: 4.3106, loss: 4.3106 +2024-12-26 05:28:46,479 - pyskl - INFO - Epoch [5][1700/3746] lr: 9.978e-02, eta: 4 days, 17:48:16, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4544, loss_cls: 4.3365, loss: 4.3365 +2024-12-26 05:29:58,248 - pyskl - INFO - Epoch [5][1800/3746] lr: 9.978e-02, eta: 4 days, 17:45:11, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4497, loss_cls: 4.3295, loss: 4.3295 +2024-12-26 05:31:09,927 - pyskl - INFO - Epoch [5][1900/3746] lr: 9.978e-02, eta: 4 days, 17:42:05, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4520, loss_cls: 4.3301, loss: 4.3301 +2024-12-26 05:32:21,274 - pyskl - INFO - Epoch [5][2000/3746] lr: 9.977e-02, eta: 4 days, 17:38:49, time: 0.713, data_time: 0.001, memory: 15990, top1_acc: 0.2025, top5_acc: 0.4411, loss_cls: 4.3763, loss: 4.3763 +2024-12-26 05:33:32,923 - pyskl - INFO - Epoch [5][2100/3746] lr: 9.977e-02, eta: 4 days, 17:35:45, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4641, loss_cls: 4.3113, loss: 4.3113 +2024-12-26 05:34:45,264 - pyskl - INFO - Epoch [5][2200/3746] lr: 9.977e-02, eta: 4 days, 17:33:03, time: 0.723, data_time: 0.001, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4511, loss_cls: 4.3338, loss: 4.3338 +2024-12-26 05:35:56,950 - pyskl - INFO - Epoch [5][2300/3746] lr: 9.977e-02, eta: 4 days, 17:30:02, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4466, loss_cls: 4.3571, loss: 4.3571 +2024-12-26 05:37:08,404 - pyskl - INFO - Epoch [5][2400/3746] lr: 9.976e-02, eta: 4 days, 17:26:55, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4437, loss_cls: 4.3622, loss: 4.3622 +2024-12-26 05:38:20,005 - pyskl - INFO - Epoch [5][2500/3746] lr: 9.976e-02, eta: 4 days, 17:23:54, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4598, loss_cls: 4.3221, loss: 4.3221 +2024-12-26 05:39:31,590 - pyskl - INFO - Epoch [5][2600/3746] lr: 9.976e-02, eta: 4 days, 17:20:54, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4528, loss_cls: 4.3486, loss: 4.3486 +2024-12-26 05:40:43,530 - pyskl - INFO - Epoch [5][2700/3746] lr: 9.976e-02, eta: 4 days, 17:18:06, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4495, loss_cls: 4.3557, loss: 4.3557 +2024-12-26 05:41:55,351 - pyskl - INFO - Epoch [5][2800/3746] lr: 9.975e-02, eta: 4 days, 17:15:15, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4398, loss_cls: 4.4011, loss: 4.4011 +2024-12-26 05:43:07,104 - pyskl - INFO - Epoch [5][2900/3746] lr: 9.975e-02, eta: 4 days, 17:12:24, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4559, loss_cls: 4.3395, loss: 4.3395 +2024-12-26 05:44:18,876 - pyskl - INFO - Epoch [5][3000/3746] lr: 9.975e-02, eta: 4 days, 17:09:34, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4616, loss_cls: 4.3228, loss: 4.3228 +2024-12-26 05:45:30,872 - pyskl - INFO - Epoch [5][3100/3746] lr: 9.974e-02, eta: 4 days, 17:06:51, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4681, loss_cls: 4.2592, loss: 4.2592 +2024-12-26 05:46:42,632 - pyskl - INFO - Epoch [5][3200/3746] lr: 9.974e-02, eta: 4 days, 17:04:03, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4566, loss_cls: 4.3024, loss: 4.3024 +2024-12-26 05:47:54,673 - pyskl - INFO - Epoch [5][3300/3746] lr: 9.974e-02, eta: 4 days, 17:01:24, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4605, loss_cls: 4.3306, loss: 4.3306 +2024-12-26 05:49:06,601 - pyskl - INFO - Epoch [5][3400/3746] lr: 9.974e-02, eta: 4 days, 16:58:43, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4622, loss_cls: 4.3022, loss: 4.3022 +2024-12-26 05:50:18,389 - pyskl - INFO - Epoch [5][3500/3746] lr: 9.973e-02, eta: 4 days, 16:55:59, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4695, loss_cls: 4.2686, loss: 4.2686 +2024-12-26 05:51:30,052 - pyskl - INFO - Epoch [5][3600/3746] lr: 9.973e-02, eta: 4 days, 16:53:12, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4600, loss_cls: 4.3102, loss: 4.3102 +2024-12-26 05:52:41,275 - pyskl - INFO - Epoch [5][3700/3746] lr: 9.973e-02, eta: 4 days, 16:50:13, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4661, loss_cls: 4.2857, loss: 4.2857 +2024-12-26 05:53:15,996 - pyskl - INFO - Saving checkpoint at 5 epochs +2024-12-26 05:55:11,380 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 05:55:12,192 - pyskl - INFO - +top1_acc 0.1521 +top5_acc 0.3634 +2024-12-26 05:55:12,192 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 05:55:12,231 - pyskl - INFO - +mean_acc 0.1518 +2024-12-26 05:55:12,235 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_4.pth was removed +2024-12-26 05:55:12,597 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2024-12-26 05:55:12,598 - pyskl - INFO - Best top1_acc is 0.1521 at 5 epoch. +2024-12-26 05:55:12,608 - pyskl - INFO - Epoch(val) [5][309] top1_acc: 0.1521, top5_acc: 0.3634, mean_class_accuracy: 0.1518 +2024-12-26 05:58:41,574 - pyskl - INFO - Epoch [6][100/3746] lr: 9.972e-02, eta: 4 days, 17:36:22, time: 2.090, data_time: 1.373, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4564, loss_cls: 4.2912, loss: 4.2912 +2024-12-26 05:59:53,176 - pyskl - INFO - Epoch [6][200/3746] lr: 9.972e-02, eta: 4 days, 17:33:20, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4570, loss_cls: 4.3095, loss: 4.3095 +2024-12-26 06:01:04,850 - pyskl - INFO - Epoch [6][300/3746] lr: 9.972e-02, eta: 4 days, 17:30:21, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4616, loss_cls: 4.2829, loss: 4.2829 +2024-12-26 06:02:16,432 - pyskl - INFO - Epoch [6][400/3746] lr: 9.971e-02, eta: 4 days, 17:27:21, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4606, loss_cls: 4.3137, loss: 4.3137 +2024-12-26 06:03:28,197 - pyskl - INFO - Epoch [6][500/3746] lr: 9.971e-02, eta: 4 days, 17:24:28, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4622, loss_cls: 4.2980, loss: 4.2980 +2024-12-26 06:04:39,887 - pyskl - INFO - Epoch [6][600/3746] lr: 9.971e-02, eta: 4 days, 17:21:33, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4569, loss_cls: 4.2978, loss: 4.2978 +2024-12-26 06:05:51,135 - pyskl - INFO - Epoch [6][700/3746] lr: 9.971e-02, eta: 4 days, 17:18:27, time: 0.712, data_time: 0.001, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4627, loss_cls: 4.2968, loss: 4.2968 +2024-12-26 06:07:02,918 - pyskl - INFO - Epoch [6][800/3746] lr: 9.970e-02, eta: 4 days, 17:15:37, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4669, loss_cls: 4.2560, loss: 4.2560 +2024-12-26 06:08:14,846 - pyskl - INFO - Epoch [6][900/3746] lr: 9.970e-02, eta: 4 days, 17:12:52, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4537, loss_cls: 4.3244, loss: 4.3244 +2024-12-26 06:09:26,416 - pyskl - INFO - Epoch [6][1000/3746] lr: 9.970e-02, eta: 4 days, 17:09:58, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4583, loss_cls: 4.2987, loss: 4.2987 +2024-12-26 06:10:38,593 - pyskl - INFO - Epoch [6][1100/3746] lr: 9.969e-02, eta: 4 days, 17:07:22, time: 0.722, data_time: 0.001, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4558, loss_cls: 4.3291, loss: 4.3291 +2024-12-26 06:11:50,423 - pyskl - INFO - Epoch [6][1200/3746] lr: 9.969e-02, eta: 4 days, 17:04:37, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4716, loss_cls: 4.2817, loss: 4.2817 +2024-12-26 06:13:02,031 - pyskl - INFO - Epoch [6][1300/3746] lr: 9.969e-02, eta: 4 days, 17:01:47, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4606, loss_cls: 4.3243, loss: 4.3243 +2024-12-26 06:14:13,838 - pyskl - INFO - Epoch [6][1400/3746] lr: 9.968e-02, eta: 4 days, 16:59:04, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2194, top5_acc: 0.4730, loss_cls: 4.2716, loss: 4.2716 +2024-12-26 06:15:25,800 - pyskl - INFO - Epoch [6][1500/3746] lr: 9.968e-02, eta: 4 days, 16:56:25, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4662, loss_cls: 4.2733, loss: 4.2733 +2024-12-26 06:16:37,498 - pyskl - INFO - Epoch [6][1600/3746] lr: 9.968e-02, eta: 4 days, 16:53:41, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4584, loss_cls: 4.2923, loss: 4.2923 +2024-12-26 06:17:49,353 - pyskl - INFO - Epoch [6][1700/3746] lr: 9.967e-02, eta: 4 days, 16:51:01, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4622, loss_cls: 4.3365, loss: 4.3365 +2024-12-26 06:19:00,637 - pyskl - INFO - Epoch [6][1800/3746] lr: 9.967e-02, eta: 4 days, 16:48:07, time: 0.713, data_time: 0.001, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4631, loss_cls: 4.2838, loss: 4.2838 +2024-12-26 06:20:12,609 - pyskl - INFO - Epoch [6][1900/3746] lr: 9.967e-02, eta: 4 days, 16:45:32, time: 0.720, data_time: 0.001, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4570, loss_cls: 4.3041, loss: 4.3041 +2024-12-26 06:21:23,950 - pyskl - INFO - Epoch [6][2000/3746] lr: 9.966e-02, eta: 4 days, 16:42:42, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4628, loss_cls: 4.2885, loss: 4.2885 +2024-12-26 06:22:36,074 - pyskl - INFO - Epoch [6][2100/3746] lr: 9.966e-02, eta: 4 days, 16:40:13, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4667, loss_cls: 4.2808, loss: 4.2808 +2024-12-26 06:23:47,682 - pyskl - INFO - Epoch [6][2200/3746] lr: 9.966e-02, eta: 4 days, 16:37:31, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4573, loss_cls: 4.3184, loss: 4.3184 +2024-12-26 06:24:59,708 - pyskl - INFO - Epoch [6][2300/3746] lr: 9.965e-02, eta: 4 days, 16:35:01, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4702, loss_cls: 4.2642, loss: 4.2642 +2024-12-26 06:26:12,030 - pyskl - INFO - Epoch [6][2400/3746] lr: 9.965e-02, eta: 4 days, 16:32:39, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4617, loss_cls: 4.3032, loss: 4.3032 +2024-12-26 06:27:23,680 - pyskl - INFO - Epoch [6][2500/3746] lr: 9.965e-02, eta: 4 days, 16:30:01, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4747, loss_cls: 4.2393, loss: 4.2393 +2024-12-26 06:28:35,707 - pyskl - INFO - Epoch [6][2600/3746] lr: 9.964e-02, eta: 4 days, 16:27:33, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4742, loss_cls: 4.2452, loss: 4.2452 +2024-12-26 06:29:47,337 - pyskl - INFO - Epoch [6][2700/3746] lr: 9.964e-02, eta: 4 days, 16:24:55, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4658, loss_cls: 4.2862, loss: 4.2862 +2024-12-26 06:30:58,773 - pyskl - INFO - Epoch [6][2800/3746] lr: 9.964e-02, eta: 4 days, 16:22:14, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4648, loss_cls: 4.3040, loss: 4.3040 +2024-12-26 06:32:11,094 - pyskl - INFO - Epoch [6][2900/3746] lr: 9.963e-02, eta: 4 days, 16:19:56, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4681, loss_cls: 4.2946, loss: 4.2946 +2024-12-26 06:33:22,872 - pyskl - INFO - Epoch [6][3000/3746] lr: 9.963e-02, eta: 4 days, 16:17:25, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4691, loss_cls: 4.2652, loss: 4.2652 +2024-12-26 06:34:34,834 - pyskl - INFO - Epoch [6][3100/3746] lr: 9.963e-02, eta: 4 days, 16:14:59, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4664, loss_cls: 4.2846, loss: 4.2846 +2024-12-26 06:35:46,583 - pyskl - INFO - Epoch [6][3200/3746] lr: 9.962e-02, eta: 4 days, 16:12:28, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4692, loss_cls: 4.2667, loss: 4.2667 +2024-12-26 06:36:58,691 - pyskl - INFO - Epoch [6][3300/3746] lr: 9.962e-02, eta: 4 days, 16:10:07, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4698, loss_cls: 4.2479, loss: 4.2479 +2024-12-26 06:38:10,321 - pyskl - INFO - Epoch [6][3400/3746] lr: 9.962e-02, eta: 4 days, 16:07:35, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4600, loss_cls: 4.2957, loss: 4.2957 +2024-12-26 06:39:21,909 - pyskl - INFO - Epoch [6][3500/3746] lr: 9.961e-02, eta: 4 days, 16:05:03, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4697, loss_cls: 4.2448, loss: 4.2448 +2024-12-26 06:40:33,688 - pyskl - INFO - Epoch [6][3600/3746] lr: 9.961e-02, eta: 4 days, 16:02:36, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4706, loss_cls: 4.2416, loss: 4.2416 +2024-12-26 06:41:45,319 - pyskl - INFO - Epoch [6][3700/3746] lr: 9.961e-02, eta: 4 days, 16:00:06, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4689, loss_cls: 4.2755, loss: 4.2755 +2024-12-26 06:42:19,623 - pyskl - INFO - Saving checkpoint at 6 epochs +2024-12-26 06:44:14,647 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 06:44:15,543 - pyskl - INFO - +top1_acc 0.1524 +top5_acc 0.3725 +2024-12-26 06:44:15,543 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 06:44:15,582 - pyskl - INFO - +mean_acc 0.1523 +2024-12-26 06:44:15,587 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_5.pth was removed +2024-12-26 06:44:15,853 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_6.pth. +2024-12-26 06:44:15,854 - pyskl - INFO - Best top1_acc is 0.1524 at 6 epoch. +2024-12-26 06:44:15,864 - pyskl - INFO - Epoch(val) [6][309] top1_acc: 0.1524, top5_acc: 0.3725, mean_class_accuracy: 0.1523 +2024-12-26 06:47:49,447 - pyskl - INFO - Epoch [7][100/3746] lr: 9.960e-02, eta: 4 days, 16:39:52, time: 2.136, data_time: 1.419, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4783, loss_cls: 4.2456, loss: 4.2456 +2024-12-26 06:49:01,665 - pyskl - INFO - Epoch [7][200/3746] lr: 9.960e-02, eta: 4 days, 16:37:26, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4758, loss_cls: 4.2148, loss: 4.2148 +2024-12-26 06:50:13,140 - pyskl - INFO - Epoch [7][300/3746] lr: 9.960e-02, eta: 4 days, 16:34:42, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4713, loss_cls: 4.2476, loss: 4.2476 +2024-12-26 06:51:24,657 - pyskl - INFO - Epoch [7][400/3746] lr: 9.959e-02, eta: 4 days, 16:32:01, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4770, loss_cls: 4.2497, loss: 4.2497 +2024-12-26 06:52:36,189 - pyskl - INFO - Epoch [7][500/3746] lr: 9.959e-02, eta: 4 days, 16:29:21, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4708, loss_cls: 4.2627, loss: 4.2627 +2024-12-26 06:53:47,544 - pyskl - INFO - Epoch [7][600/3746] lr: 9.958e-02, eta: 4 days, 16:26:37, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4775, loss_cls: 4.2260, loss: 4.2260 +2024-12-26 06:54:58,918 - pyskl - INFO - Epoch [7][700/3746] lr: 9.958e-02, eta: 4 days, 16:23:55, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4611, loss_cls: 4.2833, loss: 4.2833 +2024-12-26 06:56:10,568 - pyskl - INFO - Epoch [7][800/3746] lr: 9.958e-02, eta: 4 days, 16:21:20, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4678, loss_cls: 4.2684, loss: 4.2684 +2024-12-26 06:57:22,419 - pyskl - INFO - Epoch [7][900/3746] lr: 9.957e-02, eta: 4 days, 16:18:50, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4692, loss_cls: 4.2476, loss: 4.2476 +2024-12-26 06:58:33,683 - pyskl - INFO - Epoch [7][1000/3746] lr: 9.957e-02, eta: 4 days, 16:16:07, time: 0.713, data_time: 0.001, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4650, loss_cls: 4.2662, loss: 4.2662 +2024-12-26 06:59:45,065 - pyskl - INFO - Epoch [7][1100/3746] lr: 9.957e-02, eta: 4 days, 16:13:28, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4736, loss_cls: 4.2317, loss: 4.2317 +2024-12-26 07:00:56,812 - pyskl - INFO - Epoch [7][1200/3746] lr: 9.956e-02, eta: 4 days, 16:10:58, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4616, loss_cls: 4.2869, loss: 4.2869 +2024-12-26 07:02:08,294 - pyskl - INFO - Epoch [7][1300/3746] lr: 9.956e-02, eta: 4 days, 16:08:22, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4811, loss_cls: 4.2191, loss: 4.2191 +2024-12-26 07:03:19,946 - pyskl - INFO - Epoch [7][1400/3746] lr: 9.956e-02, eta: 4 days, 16:05:51, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4713, loss_cls: 4.2558, loss: 4.2558 +2024-12-26 07:04:31,906 - pyskl - INFO - Epoch [7][1500/3746] lr: 9.955e-02, eta: 4 days, 16:03:28, time: 0.720, data_time: 0.001, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4847, loss_cls: 4.2208, loss: 4.2208 +2024-12-26 07:05:43,853 - pyskl - INFO - Epoch [7][1600/3746] lr: 9.955e-02, eta: 4 days, 16:01:05, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4706, loss_cls: 4.2465, loss: 4.2465 +2024-12-26 07:06:55,340 - pyskl - INFO - Epoch [7][1700/3746] lr: 9.954e-02, eta: 4 days, 15:58:32, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4623, loss_cls: 4.2944, loss: 4.2944 +2024-12-26 07:08:06,920 - pyskl - INFO - Epoch [7][1800/3746] lr: 9.954e-02, eta: 4 days, 15:56:02, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4864, loss_cls: 4.2032, loss: 4.2032 +2024-12-26 07:09:18,318 - pyskl - INFO - Epoch [7][1900/3746] lr: 9.954e-02, eta: 4 days, 15:53:29, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4753, loss_cls: 4.2367, loss: 4.2367 +2024-12-26 07:10:29,993 - pyskl - INFO - Epoch [7][2000/3746] lr: 9.953e-02, eta: 4 days, 15:51:02, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4763, loss_cls: 4.2122, loss: 4.2122 +2024-12-26 07:11:41,414 - pyskl - INFO - Epoch [7][2100/3746] lr: 9.953e-02, eta: 4 days, 15:48:31, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4686, loss_cls: 4.2626, loss: 4.2626 +2024-12-26 07:12:53,000 - pyskl - INFO - Epoch [7][2200/3746] lr: 9.952e-02, eta: 4 days, 15:46:03, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4753, loss_cls: 4.2558, loss: 4.2558 +2024-12-26 07:14:04,651 - pyskl - INFO - Epoch [7][2300/3746] lr: 9.952e-02, eta: 4 days, 15:43:38, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4819, loss_cls: 4.1869, loss: 4.1869 +2024-12-26 07:15:16,081 - pyskl - INFO - Epoch [7][2400/3746] lr: 9.952e-02, eta: 4 days, 15:41:08, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4652, loss_cls: 4.2911, loss: 4.2911 +2024-12-26 07:16:27,535 - pyskl - INFO - Epoch [7][2500/3746] lr: 9.951e-02, eta: 4 days, 15:38:40, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4677, loss_cls: 4.2393, loss: 4.2393 +2024-12-26 07:17:39,154 - pyskl - INFO - Epoch [7][2600/3746] lr: 9.951e-02, eta: 4 days, 15:36:16, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4695, loss_cls: 4.2577, loss: 4.2577 +2024-12-26 07:18:50,621 - pyskl - INFO - Epoch [7][2700/3746] lr: 9.951e-02, eta: 4 days, 15:33:49, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4677, loss_cls: 4.2616, loss: 4.2616 +2024-12-26 07:20:02,332 - pyskl - INFO - Epoch [7][2800/3746] lr: 9.950e-02, eta: 4 days, 15:31:28, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4697, loss_cls: 4.2452, loss: 4.2452 +2024-12-26 07:21:13,921 - pyskl - INFO - Epoch [7][2900/3746] lr: 9.950e-02, eta: 4 days, 15:29:05, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4664, loss_cls: 4.2711, loss: 4.2711 +2024-12-26 07:22:25,480 - pyskl - INFO - Epoch [7][3000/3746] lr: 9.949e-02, eta: 4 days, 15:26:42, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4709, loss_cls: 4.2594, loss: 4.2594 +2024-12-26 07:23:37,334 - pyskl - INFO - Epoch [7][3100/3746] lr: 9.949e-02, eta: 4 days, 15:24:25, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4673, loss_cls: 4.2632, loss: 4.2632 +2024-12-26 07:24:48,697 - pyskl - INFO - Epoch [7][3200/3746] lr: 9.949e-02, eta: 4 days, 15:21:59, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4720, loss_cls: 4.2605, loss: 4.2605 +2024-12-26 07:26:00,533 - pyskl - INFO - Epoch [7][3300/3746] lr: 9.948e-02, eta: 4 days, 15:19:43, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4713, loss_cls: 4.2625, loss: 4.2625 +2024-12-26 07:27:12,087 - pyskl - INFO - Epoch [7][3400/3746] lr: 9.948e-02, eta: 4 days, 15:17:22, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4667, loss_cls: 4.2563, loss: 4.2563 +2024-12-26 07:28:23,715 - pyskl - INFO - Epoch [7][3500/3746] lr: 9.947e-02, eta: 4 days, 15:15:03, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4709, loss_cls: 4.2583, loss: 4.2583 +2024-12-26 07:29:35,767 - pyskl - INFO - Epoch [7][3600/3746] lr: 9.947e-02, eta: 4 days, 15:12:53, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4595, loss_cls: 4.2807, loss: 4.2807 +2024-12-26 07:30:46,938 - pyskl - INFO - Epoch [7][3700/3746] lr: 9.947e-02, eta: 4 days, 15:10:26, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4761, loss_cls: 4.2243, loss: 4.2243 +2024-12-26 07:31:21,471 - pyskl - INFO - Saving checkpoint at 7 epochs +2024-12-26 07:33:17,859 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 07:33:18,771 - pyskl - INFO - +top1_acc 0.1603 +top5_acc 0.3772 +2024-12-26 07:33:18,771 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 07:33:18,813 - pyskl - INFO - +mean_acc 0.1601 +2024-12-26 07:33:18,817 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_6.pth was removed +2024-12-26 07:33:19,070 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2024-12-26 07:33:19,071 - pyskl - INFO - Best top1_acc is 0.1603 at 7 epoch. +2024-12-26 07:33:19,081 - pyskl - INFO - Epoch(val) [7][309] top1_acc: 0.1603, top5_acc: 0.3772, mean_class_accuracy: 0.1601 +2024-12-26 07:36:56,943 - pyskl - INFO - Epoch [8][100/3746] lr: 9.946e-02, eta: 4 days, 15:45:30, time: 2.179, data_time: 1.461, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4794, loss_cls: 4.1758, loss: 4.1758 +2024-12-26 07:38:09,021 - pyskl - INFO - Epoch [8][200/3746] lr: 9.946e-02, eta: 4 days, 15:43:14, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4766, loss_cls: 4.2289, loss: 4.2289 +2024-12-26 07:39:20,543 - pyskl - INFO - Epoch [8][300/3746] lr: 9.945e-02, eta: 4 days, 15:40:46, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4764, loss_cls: 4.2361, loss: 4.2361 +2024-12-26 07:40:32,071 - pyskl - INFO - Epoch [8][400/3746] lr: 9.945e-02, eta: 4 days, 15:38:19, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4831, loss_cls: 4.1897, loss: 4.1897 +2024-12-26 07:41:43,796 - pyskl - INFO - Epoch [8][500/3746] lr: 9.944e-02, eta: 4 days, 15:35:57, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4717, loss_cls: 4.2427, loss: 4.2427 +2024-12-26 07:42:55,200 - pyskl - INFO - Epoch [8][600/3746] lr: 9.944e-02, eta: 4 days, 15:33:29, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4673, loss_cls: 4.2725, loss: 4.2725 +2024-12-26 07:44:06,558 - pyskl - INFO - Epoch [8][700/3746] lr: 9.943e-02, eta: 4 days, 15:31:00, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4717, loss_cls: 4.2333, loss: 4.2333 +2024-12-26 07:45:17,799 - pyskl - INFO - Epoch [8][800/3746] lr: 9.943e-02, eta: 4 days, 15:28:30, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4662, loss_cls: 4.2863, loss: 4.2863 +2024-12-26 07:46:29,178 - pyskl - INFO - Epoch [8][900/3746] lr: 9.943e-02, eta: 4 days, 15:26:03, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4789, loss_cls: 4.2425, loss: 4.2425 +2024-12-26 07:47:40,798 - pyskl - INFO - Epoch [8][1000/3746] lr: 9.942e-02, eta: 4 days, 15:23:41, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4772, loss_cls: 4.2080, loss: 4.2080 +2024-12-26 07:48:52,152 - pyskl - INFO - Epoch [8][1100/3746] lr: 9.942e-02, eta: 4 days, 15:21:15, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4805, loss_cls: 4.2291, loss: 4.2291 +2024-12-26 07:50:03,766 - pyskl - INFO - Epoch [8][1200/3746] lr: 9.941e-02, eta: 4 days, 15:18:54, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4617, loss_cls: 4.2869, loss: 4.2869 +2024-12-26 07:51:15,232 - pyskl - INFO - Epoch [8][1300/3746] lr: 9.941e-02, eta: 4 days, 15:16:31, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4688, loss_cls: 4.2369, loss: 4.2369 +2024-12-26 07:52:26,893 - pyskl - INFO - Epoch [8][1400/3746] lr: 9.940e-02, eta: 4 days, 15:14:12, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4763, loss_cls: 4.2346, loss: 4.2346 +2024-12-26 07:53:38,775 - pyskl - INFO - Epoch [8][1500/3746] lr: 9.940e-02, eta: 4 days, 15:11:58, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4695, loss_cls: 4.2637, loss: 4.2637 +2024-12-26 07:54:50,488 - pyskl - INFO - Epoch [8][1600/3746] lr: 9.940e-02, eta: 4 days, 15:09:41, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4850, loss_cls: 4.2009, loss: 4.2009 +2024-12-26 07:56:02,057 - pyskl - INFO - Epoch [8][1700/3746] lr: 9.939e-02, eta: 4 days, 15:07:21, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4698, loss_cls: 4.2938, loss: 4.2938 +2024-12-26 07:57:14,173 - pyskl - INFO - Epoch [8][1800/3746] lr: 9.939e-02, eta: 4 days, 15:05:13, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4678, loss_cls: 4.2303, loss: 4.2303 +2024-12-26 07:58:25,959 - pyskl - INFO - Epoch [8][1900/3746] lr: 9.938e-02, eta: 4 days, 15:02:59, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4783, loss_cls: 4.2132, loss: 4.2132 +2024-12-26 07:59:37,568 - pyskl - INFO - Epoch [8][2000/3746] lr: 9.938e-02, eta: 4 days, 15:00:42, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4800, loss_cls: 4.2096, loss: 4.2096 +2024-12-26 08:00:49,315 - pyskl - INFO - Epoch [8][2100/3746] lr: 9.937e-02, eta: 4 days, 14:58:28, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4705, loss_cls: 4.2454, loss: 4.2454 +2024-12-26 08:02:01,098 - pyskl - INFO - Epoch [8][2200/3746] lr: 9.937e-02, eta: 4 days, 14:56:15, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4792, loss_cls: 4.2105, loss: 4.2105 +2024-12-26 08:03:12,568 - pyskl - INFO - Epoch [8][2300/3746] lr: 9.937e-02, eta: 4 days, 14:53:57, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4717, loss_cls: 4.2401, loss: 4.2401 +2024-12-26 08:04:23,779 - pyskl - INFO - Epoch [8][2400/3746] lr: 9.936e-02, eta: 4 days, 14:51:34, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4766, loss_cls: 4.2013, loss: 4.2013 +2024-12-26 08:05:35,346 - pyskl - INFO - Epoch [8][2500/3746] lr: 9.936e-02, eta: 4 days, 14:49:18, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4603, loss_cls: 4.2804, loss: 4.2804 +2024-12-26 08:06:46,920 - pyskl - INFO - Epoch [8][2600/3746] lr: 9.935e-02, eta: 4 days, 14:47:03, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4706, loss_cls: 4.2237, loss: 4.2237 +2024-12-26 08:07:58,451 - pyskl - INFO - Epoch [8][2700/3746] lr: 9.935e-02, eta: 4 days, 14:44:48, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4772, loss_cls: 4.1856, loss: 4.1856 +2024-12-26 08:09:10,438 - pyskl - INFO - Epoch [8][2800/3746] lr: 9.934e-02, eta: 4 days, 14:42:41, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4803, loss_cls: 4.2130, loss: 4.2130 +2024-12-26 08:10:22,179 - pyskl - INFO - Epoch [8][2900/3746] lr: 9.934e-02, eta: 4 days, 14:40:31, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4755, loss_cls: 4.2183, loss: 4.2183 +2024-12-26 08:11:33,680 - pyskl - INFO - Epoch [8][3000/3746] lr: 9.933e-02, eta: 4 days, 14:38:16, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4664, loss_cls: 4.2737, loss: 4.2737 +2024-12-26 08:12:45,315 - pyskl - INFO - Epoch [8][3100/3746] lr: 9.933e-02, eta: 4 days, 14:36:04, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4786, loss_cls: 4.2154, loss: 4.2154 +2024-12-26 08:13:56,906 - pyskl - INFO - Epoch [8][3200/3746] lr: 9.933e-02, eta: 4 days, 14:33:52, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4759, loss_cls: 4.2411, loss: 4.2411 +2024-12-26 08:15:08,534 - pyskl - INFO - Epoch [8][3300/3746] lr: 9.932e-02, eta: 4 days, 14:31:41, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4763, loss_cls: 4.2487, loss: 4.2487 +2024-12-26 08:16:20,067 - pyskl - INFO - Epoch [8][3400/3746] lr: 9.932e-02, eta: 4 days, 14:29:29, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4848, loss_cls: 4.2184, loss: 4.2184 +2024-12-26 08:17:31,824 - pyskl - INFO - Epoch [8][3500/3746] lr: 9.931e-02, eta: 4 days, 14:27:21, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4811, loss_cls: 4.2135, loss: 4.2135 +2024-12-26 08:18:43,420 - pyskl - INFO - Epoch [8][3600/3746] lr: 9.931e-02, eta: 4 days, 14:25:10, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4697, loss_cls: 4.2618, loss: 4.2618 +2024-12-26 08:19:54,938 - pyskl - INFO - Epoch [8][3700/3746] lr: 9.930e-02, eta: 4 days, 14:22:59, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4645, loss_cls: 4.2671, loss: 4.2671 +2024-12-26 08:20:29,256 - pyskl - INFO - Saving checkpoint at 8 epochs +2024-12-26 08:22:24,174 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 08:22:25,014 - pyskl - INFO - +top1_acc 0.1581 +top5_acc 0.3819 +2024-12-26 08:22:25,014 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 08:22:25,058 - pyskl - INFO - +mean_acc 0.1580 +2024-12-26 08:22:25,069 - pyskl - INFO - Epoch(val) [8][309] top1_acc: 0.1581, top5_acc: 0.3819, mean_class_accuracy: 0.1580 +2024-12-26 08:26:02,786 - pyskl - INFO - Epoch [9][100/3746] lr: 9.930e-02, eta: 4 days, 14:53:11, time: 2.177, data_time: 1.461, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4950, loss_cls: 4.1605, loss: 4.1605 +2024-12-26 08:27:14,392 - pyskl - INFO - Epoch [9][200/3746] lr: 9.929e-02, eta: 4 days, 14:50:56, time: 0.716, data_time: 0.002, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4817, loss_cls: 4.1851, loss: 4.1851 +2024-12-26 08:28:26,285 - pyskl - INFO - Epoch [9][300/3746] lr: 9.929e-02, eta: 4 days, 14:48:45, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4833, loss_cls: 4.1826, loss: 4.1826 +2024-12-26 08:29:37,528 - pyskl - INFO - Epoch [9][400/3746] lr: 9.928e-02, eta: 4 days, 14:46:24, time: 0.712, data_time: 0.001, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4744, loss_cls: 4.2291, loss: 4.2291 +2024-12-26 08:30:49,243 - pyskl - INFO - Epoch [9][500/3746] lr: 9.928e-02, eta: 4 days, 14:44:11, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4936, loss_cls: 4.1223, loss: 4.1223 +2024-12-26 08:32:00,879 - pyskl - INFO - Epoch [9][600/3746] lr: 9.927e-02, eta: 4 days, 14:41:57, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4830, loss_cls: 4.2003, loss: 4.2003 +2024-12-26 08:33:12,805 - pyskl - INFO - Epoch [9][700/3746] lr: 9.927e-02, eta: 4 days, 14:39:49, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4814, loss_cls: 4.2203, loss: 4.2203 +2024-12-26 08:34:24,236 - pyskl - INFO - Epoch [9][800/3746] lr: 9.926e-02, eta: 4 days, 14:37:32, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4730, loss_cls: 4.2343, loss: 4.2343 +2024-12-26 08:35:35,741 - pyskl - INFO - Epoch [9][900/3746] lr: 9.926e-02, eta: 4 days, 14:35:17, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4831, loss_cls: 4.2411, loss: 4.2411 +2024-12-26 08:36:47,286 - pyskl - INFO - Epoch [9][1000/3746] lr: 9.925e-02, eta: 4 days, 14:33:04, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4781, loss_cls: 4.1977, loss: 4.1977 +2024-12-26 08:37:58,831 - pyskl - INFO - Epoch [9][1100/3746] lr: 9.925e-02, eta: 4 days, 14:30:50, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4700, loss_cls: 4.2834, loss: 4.2834 +2024-12-26 08:39:10,350 - pyskl - INFO - Epoch [9][1200/3746] lr: 9.924e-02, eta: 4 days, 14:28:37, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4841, loss_cls: 4.1728, loss: 4.1728 +2024-12-26 08:40:21,843 - pyskl - INFO - Epoch [9][1300/3746] lr: 9.924e-02, eta: 4 days, 14:26:24, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4847, loss_cls: 4.1933, loss: 4.1933 +2024-12-26 08:41:33,508 - pyskl - INFO - Epoch [9][1400/3746] lr: 9.923e-02, eta: 4 days, 14:24:13, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4680, loss_cls: 4.2470, loss: 4.2470 +2024-12-26 08:42:45,027 - pyskl - INFO - Epoch [9][1500/3746] lr: 9.923e-02, eta: 4 days, 14:22:01, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4808, loss_cls: 4.1623, loss: 4.1623 +2024-12-26 08:43:56,067 - pyskl - INFO - Epoch [9][1600/3746] lr: 9.922e-02, eta: 4 days, 14:19:41, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4800, loss_cls: 4.2232, loss: 4.2232 +2024-12-26 08:45:07,284 - pyskl - INFO - Epoch [9][1700/3746] lr: 9.922e-02, eta: 4 days, 14:17:25, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4764, loss_cls: 4.2243, loss: 4.2243 +2024-12-26 08:46:18,801 - pyskl - INFO - Epoch [9][1800/3746] lr: 9.921e-02, eta: 4 days, 14:15:14, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4784, loss_cls: 4.2193, loss: 4.2193 +2024-12-26 08:47:30,450 - pyskl - INFO - Epoch [9][1900/3746] lr: 9.921e-02, eta: 4 days, 14:13:05, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4831, loss_cls: 4.2245, loss: 4.2245 +2024-12-26 08:48:42,123 - pyskl - INFO - Epoch [9][2000/3746] lr: 9.920e-02, eta: 4 days, 14:10:58, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4852, loss_cls: 4.2003, loss: 4.2003 +2024-12-26 08:49:53,784 - pyskl - INFO - Epoch [9][2100/3746] lr: 9.920e-02, eta: 4 days, 14:08:50, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4803, loss_cls: 4.1947, loss: 4.1947 +2024-12-26 08:51:05,697 - pyskl - INFO - Epoch [9][2200/3746] lr: 9.919e-02, eta: 4 days, 14:06:47, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4677, loss_cls: 4.2518, loss: 4.2518 +2024-12-26 08:52:17,219 - pyskl - INFO - Epoch [9][2300/3746] lr: 9.919e-02, eta: 4 days, 14:04:38, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4689, loss_cls: 4.2375, loss: 4.2375 +2024-12-26 08:53:28,851 - pyskl - INFO - Epoch [9][2400/3746] lr: 9.918e-02, eta: 4 days, 14:02:31, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4673, loss_cls: 4.2468, loss: 4.2468 +2024-12-26 08:54:40,424 - pyskl - INFO - Epoch [9][2500/3746] lr: 9.918e-02, eta: 4 days, 14:00:23, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4839, loss_cls: 4.2102, loss: 4.2102 +2024-12-26 08:55:52,167 - pyskl - INFO - Epoch [9][2600/3746] lr: 9.917e-02, eta: 4 days, 13:58:19, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4738, loss_cls: 4.2574, loss: 4.2574 +2024-12-26 08:57:03,860 - pyskl - INFO - Epoch [9][2700/3746] lr: 9.917e-02, eta: 4 days, 13:56:14, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4789, loss_cls: 4.2108, loss: 4.2108 +2024-12-26 08:58:15,403 - pyskl - INFO - Epoch [9][2800/3746] lr: 9.916e-02, eta: 4 days, 13:54:07, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4748, loss_cls: 4.2375, loss: 4.2375 +2024-12-26 08:59:27,499 - pyskl - INFO - Epoch [9][2900/3746] lr: 9.916e-02, eta: 4 days, 13:52:09, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4811, loss_cls: 4.1958, loss: 4.1958 +2024-12-26 09:00:39,033 - pyskl - INFO - Epoch [9][3000/3746] lr: 9.915e-02, eta: 4 days, 13:50:02, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4841, loss_cls: 4.1958, loss: 4.1958 +2024-12-26 09:01:50,951 - pyskl - INFO - Epoch [9][3100/3746] lr: 9.915e-02, eta: 4 days, 13:48:02, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4763, loss_cls: 4.2167, loss: 4.2167 +2024-12-26 09:03:03,043 - pyskl - INFO - Epoch [9][3200/3746] lr: 9.914e-02, eta: 4 days, 13:46:05, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4816, loss_cls: 4.1858, loss: 4.1858 +2024-12-26 09:04:14,686 - pyskl - INFO - Epoch [9][3300/3746] lr: 9.914e-02, eta: 4 days, 13:44:01, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4878, loss_cls: 4.1741, loss: 4.1741 +2024-12-26 09:05:26,657 - pyskl - INFO - Epoch [9][3400/3746] lr: 9.913e-02, eta: 4 days, 13:42:02, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4808, loss_cls: 4.2089, loss: 4.2089 +2024-12-26 09:06:38,821 - pyskl - INFO - Epoch [9][3500/3746] lr: 9.913e-02, eta: 4 days, 13:40:07, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4789, loss_cls: 4.2080, loss: 4.2080 +2024-12-26 09:07:50,648 - pyskl - INFO - Epoch [9][3600/3746] lr: 9.912e-02, eta: 4 days, 13:38:07, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4742, loss_cls: 4.2555, loss: 4.2555 +2024-12-26 09:09:02,222 - pyskl - INFO - Epoch [9][3700/3746] lr: 9.912e-02, eta: 4 days, 13:36:03, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4883, loss_cls: 4.1953, loss: 4.1953 +2024-12-26 09:09:36,926 - pyskl - INFO - Saving checkpoint at 9 epochs +2024-12-26 09:11:32,198 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 09:11:33,002 - pyskl - INFO - +top1_acc 0.1733 +top5_acc 0.3956 +2024-12-26 09:11:33,002 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 09:11:33,043 - pyskl - INFO - +mean_acc 0.1732 +2024-12-26 09:11:33,047 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_7.pth was removed +2024-12-26 09:11:33,312 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_9.pth. +2024-12-26 09:11:33,313 - pyskl - INFO - Best top1_acc is 0.1733 at 9 epoch. +2024-12-26 09:11:33,326 - pyskl - INFO - Epoch(val) [9][309] top1_acc: 0.1733, top5_acc: 0.3956, mean_class_accuracy: 0.1732 +2024-12-26 09:15:12,339 - pyskl - INFO - Epoch [10][100/3746] lr: 9.911e-02, eta: 4 days, 14:02:51, time: 2.190, data_time: 1.474, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4858, loss_cls: 4.1607, loss: 4.1607 +2024-12-26 09:16:23,861 - pyskl - INFO - Epoch [10][200/3746] lr: 9.910e-02, eta: 4 days, 14:00:42, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4875, loss_cls: 4.1662, loss: 4.1662 +2024-12-26 09:17:35,777 - pyskl - INFO - Epoch [10][300/3746] lr: 9.910e-02, eta: 4 days, 13:58:39, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4808, loss_cls: 4.1813, loss: 4.1813 +2024-12-26 09:18:47,365 - pyskl - INFO - Epoch [10][400/3746] lr: 9.909e-02, eta: 4 days, 13:56:31, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4694, loss_cls: 4.2286, loss: 4.2286 +2024-12-26 09:19:58,838 - pyskl - INFO - Epoch [10][500/3746] lr: 9.909e-02, eta: 4 days, 13:54:22, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4891, loss_cls: 4.1256, loss: 4.1256 +2024-12-26 09:21:10,205 - pyskl - INFO - Epoch [10][600/3746] lr: 9.908e-02, eta: 4 days, 13:52:11, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4791, loss_cls: 4.1858, loss: 4.1858 +2024-12-26 09:22:21,732 - pyskl - INFO - Epoch [10][700/3746] lr: 9.908e-02, eta: 4 days, 13:50:03, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4872, loss_cls: 4.1721, loss: 4.1721 +2024-12-26 09:23:33,180 - pyskl - INFO - Epoch [10][800/3746] lr: 9.907e-02, eta: 4 days, 13:47:55, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4909, loss_cls: 4.1600, loss: 4.1600 +2024-12-26 09:24:44,347 - pyskl - INFO - Epoch [10][900/3746] lr: 9.907e-02, eta: 4 days, 13:45:42, time: 0.712, data_time: 0.001, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4822, loss_cls: 4.2070, loss: 4.2070 +2024-12-26 09:25:55,608 - pyskl - INFO - Epoch [10][1000/3746] lr: 9.906e-02, eta: 4 days, 13:43:31, time: 0.713, data_time: 0.001, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4877, loss_cls: 4.1939, loss: 4.1939 +2024-12-26 09:27:06,841 - pyskl - INFO - Epoch [10][1100/3746] lr: 9.906e-02, eta: 4 days, 13:41:20, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4822, loss_cls: 4.1889, loss: 4.1889 +2024-12-26 09:28:18,419 - pyskl - INFO - Epoch [10][1200/3746] lr: 9.905e-02, eta: 4 days, 13:39:15, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4839, loss_cls: 4.1863, loss: 4.1863 +2024-12-26 09:29:29,661 - pyskl - INFO - Epoch [10][1300/3746] lr: 9.905e-02, eta: 4 days, 13:37:05, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4836, loss_cls: 4.1939, loss: 4.1939 +2024-12-26 09:30:41,007 - pyskl - INFO - Epoch [10][1400/3746] lr: 9.904e-02, eta: 4 days, 13:34:57, time: 0.713, data_time: 0.001, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4894, loss_cls: 4.1868, loss: 4.1868 +2024-12-26 09:31:52,980 - pyskl - INFO - Epoch [10][1500/3746] lr: 9.903e-02, eta: 4 days, 13:32:58, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4716, loss_cls: 4.2301, loss: 4.2301 +2024-12-26 09:33:04,265 - pyskl - INFO - Epoch [10][1600/3746] lr: 9.903e-02, eta: 4 days, 13:30:50, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4853, loss_cls: 4.1711, loss: 4.1711 +2024-12-26 09:34:16,005 - pyskl - INFO - Epoch [10][1700/3746] lr: 9.902e-02, eta: 4 days, 13:28:48, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4794, loss_cls: 4.2183, loss: 4.2183 +2024-12-26 09:35:27,398 - pyskl - INFO - Epoch [10][1800/3746] lr: 9.902e-02, eta: 4 days, 13:26:42, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4673, loss_cls: 4.2462, loss: 4.2462 +2024-12-26 09:36:38,697 - pyskl - INFO - Epoch [10][1900/3746] lr: 9.901e-02, eta: 4 days, 13:24:35, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4791, loss_cls: 4.2199, loss: 4.2199 +2024-12-26 09:37:50,139 - pyskl - INFO - Epoch [10][2000/3746] lr: 9.901e-02, eta: 4 days, 13:22:30, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4808, loss_cls: 4.1754, loss: 4.1754 +2024-12-26 09:39:01,755 - pyskl - INFO - Epoch [10][2100/3746] lr: 9.900e-02, eta: 4 days, 13:20:28, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4813, loss_cls: 4.1872, loss: 4.1872 +2024-12-26 09:40:13,164 - pyskl - INFO - Epoch [10][2200/3746] lr: 9.900e-02, eta: 4 days, 13:18:23, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4784, loss_cls: 4.2333, loss: 4.2333 +2024-12-26 09:41:24,505 - pyskl - INFO - Epoch [10][2300/3746] lr: 9.899e-02, eta: 4 days, 13:16:17, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4820, loss_cls: 4.2035, loss: 4.2035 +2024-12-26 09:42:35,985 - pyskl - INFO - Epoch [10][2400/3746] lr: 9.898e-02, eta: 4 days, 13:14:14, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4667, loss_cls: 4.2471, loss: 4.2471 +2024-12-26 09:43:47,651 - pyskl - INFO - Epoch [10][2500/3746] lr: 9.898e-02, eta: 4 days, 13:12:14, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4892, loss_cls: 4.1808, loss: 4.1808 +2024-12-26 09:44:59,285 - pyskl - INFO - Epoch [10][2600/3746] lr: 9.897e-02, eta: 4 days, 13:10:14, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4811, loss_cls: 4.1762, loss: 4.1762 +2024-12-26 09:46:10,751 - pyskl - INFO - Epoch [10][2700/3746] lr: 9.897e-02, eta: 4 days, 13:08:11, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4791, loss_cls: 4.2027, loss: 4.2027 +2024-12-26 09:47:22,059 - pyskl - INFO - Epoch [10][2800/3746] lr: 9.896e-02, eta: 4 days, 13:06:07, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4916, loss_cls: 4.1629, loss: 4.1629 +2024-12-26 09:48:33,985 - pyskl - INFO - Epoch [10][2900/3746] lr: 9.896e-02, eta: 4 days, 13:04:11, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4780, loss_cls: 4.2221, loss: 4.2221 +2024-12-26 09:49:45,508 - pyskl - INFO - Epoch [10][3000/3746] lr: 9.895e-02, eta: 4 days, 13:02:10, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4747, loss_cls: 4.2190, loss: 4.2190 +2024-12-26 09:50:57,520 - pyskl - INFO - Epoch [10][3100/3746] lr: 9.894e-02, eta: 4 days, 13:00:17, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4878, loss_cls: 4.1779, loss: 4.1779 +2024-12-26 09:52:09,182 - pyskl - INFO - Epoch [10][3200/3746] lr: 9.894e-02, eta: 4 days, 12:58:18, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4722, loss_cls: 4.2250, loss: 4.2250 +2024-12-26 09:53:20,907 - pyskl - INFO - Epoch [10][3300/3746] lr: 9.893e-02, eta: 4 days, 12:56:21, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4802, loss_cls: 4.2113, loss: 4.2113 +2024-12-26 09:54:32,658 - pyskl - INFO - Epoch [10][3400/3746] lr: 9.893e-02, eta: 4 days, 12:54:24, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4814, loss_cls: 4.2194, loss: 4.2194 +2024-12-26 09:55:44,256 - pyskl - INFO - Epoch [10][3500/3746] lr: 9.892e-02, eta: 4 days, 12:52:26, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4719, loss_cls: 4.2151, loss: 4.2151 +2024-12-26 09:56:55,936 - pyskl - INFO - Epoch [10][3600/3746] lr: 9.892e-02, eta: 4 days, 12:50:28, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4741, loss_cls: 4.2338, loss: 4.2338 +2024-12-26 09:58:07,211 - pyskl - INFO - Epoch [10][3700/3746] lr: 9.891e-02, eta: 4 days, 12:48:26, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4780, loss_cls: 4.2097, loss: 4.2097 +2024-12-26 09:58:41,840 - pyskl - INFO - Saving checkpoint at 10 epochs +2024-12-26 10:00:36,317 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 10:00:37,132 - pyskl - INFO - +top1_acc 0.1801 +top5_acc 0.4074 +2024-12-26 10:00:37,132 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 10:00:37,180 - pyskl - INFO - +mean_acc 0.1799 +2024-12-26 10:00:37,187 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_9.pth was removed +2024-12-26 10:00:37,449 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_10.pth. +2024-12-26 10:00:37,450 - pyskl - INFO - Best top1_acc is 0.1801 at 10 epoch. +2024-12-26 10:00:37,461 - pyskl - INFO - Epoch(val) [10][309] top1_acc: 0.1801, top5_acc: 0.4074, mean_class_accuracy: 0.1799 +2024-12-26 10:04:09,321 - pyskl - INFO - Epoch [11][100/3746] lr: 9.890e-02, eta: 4 days, 13:10:32, time: 2.118, data_time: 1.402, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4942, loss_cls: 4.1133, loss: 4.1133 +2024-12-26 10:05:20,965 - pyskl - INFO - Epoch [11][200/3746] lr: 9.890e-02, eta: 4 days, 13:08:31, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4800, loss_cls: 4.1594, loss: 4.1594 +2024-12-26 10:06:32,795 - pyskl - INFO - Epoch [11][300/3746] lr: 9.889e-02, eta: 4 days, 13:06:33, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4878, loss_cls: 4.1662, loss: 4.1662 +2024-12-26 10:07:44,168 - pyskl - INFO - Epoch [11][400/3746] lr: 9.888e-02, eta: 4 days, 13:04:28, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4788, loss_cls: 4.2043, loss: 4.2043 +2024-12-26 10:08:55,622 - pyskl - INFO - Epoch [11][500/3746] lr: 9.888e-02, eta: 4 days, 13:02:25, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4873, loss_cls: 4.1804, loss: 4.1804 +2024-12-26 10:10:07,239 - pyskl - INFO - Epoch [11][600/3746] lr: 9.887e-02, eta: 4 days, 13:00:25, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4933, loss_cls: 4.1643, loss: 4.1643 +2024-12-26 10:11:18,886 - pyskl - INFO - Epoch [11][700/3746] lr: 9.887e-02, eta: 4 days, 12:58:25, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4934, loss_cls: 4.1691, loss: 4.1691 +2024-12-26 10:12:31,008 - pyskl - INFO - Epoch [11][800/3746] lr: 9.886e-02, eta: 4 days, 12:56:32, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4795, loss_cls: 4.2202, loss: 4.2202 +2024-12-26 10:13:42,646 - pyskl - INFO - Epoch [11][900/3746] lr: 9.885e-02, eta: 4 days, 12:54:33, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4808, loss_cls: 4.2011, loss: 4.2011 +2024-12-26 10:14:54,579 - pyskl - INFO - Epoch [11][1000/3746] lr: 9.885e-02, eta: 4 days, 12:52:38, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4902, loss_cls: 4.1411, loss: 4.1411 +2024-12-26 10:16:06,277 - pyskl - INFO - Epoch [11][1100/3746] lr: 9.884e-02, eta: 4 days, 12:50:39, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4852, loss_cls: 4.1722, loss: 4.1722 +2024-12-26 10:17:17,943 - pyskl - INFO - Epoch [11][1200/3746] lr: 9.884e-02, eta: 4 days, 12:48:41, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4797, loss_cls: 4.2150, loss: 4.2150 +2024-12-26 10:18:29,312 - pyskl - INFO - Epoch [11][1300/3746] lr: 9.883e-02, eta: 4 days, 12:46:39, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4919, loss_cls: 4.1580, loss: 4.1580 +2024-12-26 10:19:41,480 - pyskl - INFO - Epoch [11][1400/3746] lr: 9.882e-02, eta: 4 days, 12:44:48, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4848, loss_cls: 4.2004, loss: 4.2004 +2024-12-26 10:20:53,138 - pyskl - INFO - Epoch [11][1500/3746] lr: 9.882e-02, eta: 4 days, 12:42:50, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4770, loss_cls: 4.1784, loss: 4.1784 +2024-12-26 10:22:04,528 - pyskl - INFO - Epoch [11][1600/3746] lr: 9.881e-02, eta: 4 days, 12:40:49, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4823, loss_cls: 4.2191, loss: 4.2191 +2024-12-26 10:23:16,048 - pyskl - INFO - Epoch [11][1700/3746] lr: 9.881e-02, eta: 4 days, 12:38:50, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4894, loss_cls: 4.1783, loss: 4.1783 +2024-12-26 10:24:27,958 - pyskl - INFO - Epoch [11][1800/3746] lr: 9.880e-02, eta: 4 days, 12:36:57, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4827, loss_cls: 4.1921, loss: 4.1921 +2024-12-26 10:25:39,995 - pyskl - INFO - Epoch [11][1900/3746] lr: 9.879e-02, eta: 4 days, 12:35:05, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4681, loss_cls: 4.2413, loss: 4.2413 +2024-12-26 10:26:51,784 - pyskl - INFO - Epoch [11][2000/3746] lr: 9.879e-02, eta: 4 days, 12:33:10, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4856, loss_cls: 4.1684, loss: 4.1684 +2024-12-26 10:28:03,697 - pyskl - INFO - Epoch [11][2100/3746] lr: 9.878e-02, eta: 4 days, 12:31:17, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4822, loss_cls: 4.1680, loss: 4.1680 +2024-12-26 10:29:15,626 - pyskl - INFO - Epoch [11][2200/3746] lr: 9.878e-02, eta: 4 days, 12:29:25, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4811, loss_cls: 4.1980, loss: 4.1980 +2024-12-26 10:30:27,878 - pyskl - INFO - Epoch [11][2300/3746] lr: 9.877e-02, eta: 4 days, 12:27:37, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4886, loss_cls: 4.2058, loss: 4.2058 +2024-12-26 10:31:39,758 - pyskl - INFO - Epoch [11][2400/3746] lr: 9.876e-02, eta: 4 days, 12:25:44, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4886, loss_cls: 4.1751, loss: 4.1751 +2024-12-26 10:32:51,506 - pyskl - INFO - Epoch [11][2500/3746] lr: 9.876e-02, eta: 4 days, 12:23:49, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4786, loss_cls: 4.1946, loss: 4.1946 +2024-12-26 10:34:03,581 - pyskl - INFO - Epoch [11][2600/3746] lr: 9.875e-02, eta: 4 days, 12:22:00, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4845, loss_cls: 4.2043, loss: 4.2043 +2024-12-26 10:35:15,802 - pyskl - INFO - Epoch [11][2700/3746] lr: 9.874e-02, eta: 4 days, 12:20:12, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4906, loss_cls: 4.1622, loss: 4.1622 +2024-12-26 10:36:27,643 - pyskl - INFO - Epoch [11][2800/3746] lr: 9.874e-02, eta: 4 days, 12:18:19, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4748, loss_cls: 4.2319, loss: 4.2319 +2024-12-26 10:37:39,561 - pyskl - INFO - Epoch [11][2900/3746] lr: 9.873e-02, eta: 4 days, 12:16:28, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4798, loss_cls: 4.1789, loss: 4.1789 +2024-12-26 10:38:51,886 - pyskl - INFO - Epoch [11][3000/3746] lr: 9.873e-02, eta: 4 days, 12:14:42, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4778, loss_cls: 4.2042, loss: 4.2042 +2024-12-26 10:40:03,885 - pyskl - INFO - Epoch [11][3100/3746] lr: 9.872e-02, eta: 4 days, 12:12:52, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4838, loss_cls: 4.1749, loss: 4.1749 +2024-12-26 10:41:16,143 - pyskl - INFO - Epoch [11][3200/3746] lr: 9.871e-02, eta: 4 days, 12:11:06, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4845, loss_cls: 4.1854, loss: 4.1854 +2024-12-26 10:42:28,115 - pyskl - INFO - Epoch [11][3300/3746] lr: 9.871e-02, eta: 4 days, 12:09:16, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4763, loss_cls: 4.2034, loss: 4.2034 +2024-12-26 10:43:40,157 - pyskl - INFO - Epoch [11][3400/3746] lr: 9.870e-02, eta: 4 days, 12:07:27, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4859, loss_cls: 4.1967, loss: 4.1967 +2024-12-26 10:44:52,145 - pyskl - INFO - Epoch [11][3500/3746] lr: 9.869e-02, eta: 4 days, 12:05:38, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4817, loss_cls: 4.1901, loss: 4.1901 +2024-12-26 10:46:04,415 - pyskl - INFO - Epoch [11][3600/3746] lr: 9.869e-02, eta: 4 days, 12:03:53, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4766, loss_cls: 4.2227, loss: 4.2227 +2024-12-26 10:47:16,243 - pyskl - INFO - Epoch [11][3700/3746] lr: 9.868e-02, eta: 4 days, 12:02:02, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4778, loss_cls: 4.2170, loss: 4.2170 +2024-12-26 10:47:50,830 - pyskl - INFO - Saving checkpoint at 11 epochs +2024-12-26 10:49:46,845 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 10:49:47,701 - pyskl - INFO - +top1_acc 0.1554 +top5_acc 0.3715 +2024-12-26 10:49:47,701 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 10:49:47,740 - pyskl - INFO - +mean_acc 0.1552 +2024-12-26 10:49:47,751 - pyskl - INFO - Epoch(val) [11][309] top1_acc: 0.1554, top5_acc: 0.3715, mean_class_accuracy: 0.1552 +2024-12-26 10:53:18,723 - pyskl - INFO - Epoch [12][100/3746] lr: 9.867e-02, eta: 4 days, 12:21:37, time: 2.110, data_time: 1.392, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4967, loss_cls: 4.1078, loss: 4.1078 +2024-12-26 10:54:30,840 - pyskl - INFO - Epoch [12][200/3746] lr: 9.867e-02, eta: 4 days, 12:19:47, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4806, loss_cls: 4.2075, loss: 4.2075 +2024-12-26 10:55:42,758 - pyskl - INFO - Epoch [12][300/3746] lr: 9.866e-02, eta: 4 days, 12:17:54, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4903, loss_cls: 4.1526, loss: 4.1526 +2024-12-26 10:56:54,293 - pyskl - INFO - Epoch [12][400/3746] lr: 9.865e-02, eta: 4 days, 12:15:57, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4975, loss_cls: 4.1327, loss: 4.1327 +2024-12-26 10:58:05,680 - pyskl - INFO - Epoch [12][500/3746] lr: 9.865e-02, eta: 4 days, 12:13:58, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4852, loss_cls: 4.1743, loss: 4.1743 +2024-12-26 10:59:17,353 - pyskl - INFO - Epoch [12][600/3746] lr: 9.864e-02, eta: 4 days, 12:12:03, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4875, loss_cls: 4.1970, loss: 4.1970 +2024-12-26 11:00:29,223 - pyskl - INFO - Epoch [12][700/3746] lr: 9.863e-02, eta: 4 days, 12:10:10, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4869, loss_cls: 4.1560, loss: 4.1560 +2024-12-26 11:01:40,714 - pyskl - INFO - Epoch [12][800/3746] lr: 9.863e-02, eta: 4 days, 12:08:13, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4983, loss_cls: 4.1150, loss: 4.1150 +2024-12-26 11:02:52,171 - pyskl - INFO - Epoch [12][900/3746] lr: 9.862e-02, eta: 4 days, 12:06:16, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4866, loss_cls: 4.1640, loss: 4.1640 +2024-12-26 11:04:04,178 - pyskl - INFO - Epoch [12][1000/3746] lr: 9.861e-02, eta: 4 days, 12:04:26, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4913, loss_cls: 4.1655, loss: 4.1655 +2024-12-26 11:05:15,676 - pyskl - INFO - Epoch [12][1100/3746] lr: 9.861e-02, eta: 4 days, 12:02:30, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4895, loss_cls: 4.1503, loss: 4.1503 +2024-12-26 11:06:27,796 - pyskl - INFO - Epoch [12][1200/3746] lr: 9.860e-02, eta: 4 days, 12:00:42, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4852, loss_cls: 4.1884, loss: 4.1884 +2024-12-26 11:07:39,232 - pyskl - INFO - Epoch [12][1300/3746] lr: 9.859e-02, eta: 4 days, 11:58:45, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4847, loss_cls: 4.1387, loss: 4.1387 +2024-12-26 11:08:50,876 - pyskl - INFO - Epoch [12][1400/3746] lr: 9.859e-02, eta: 4 days, 11:56:51, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4783, loss_cls: 4.1857, loss: 4.1857 +2024-12-26 11:10:02,301 - pyskl - INFO - Epoch [12][1500/3746] lr: 9.858e-02, eta: 4 days, 11:54:55, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4889, loss_cls: 4.1817, loss: 4.1817 +2024-12-26 11:11:13,607 - pyskl - INFO - Epoch [12][1600/3746] lr: 9.857e-02, eta: 4 days, 11:52:57, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4839, loss_cls: 4.1763, loss: 4.1763 +2024-12-26 11:12:25,456 - pyskl - INFO - Epoch [12][1700/3746] lr: 9.857e-02, eta: 4 days, 11:51:07, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4866, loss_cls: 4.1866, loss: 4.1866 +2024-12-26 11:13:36,855 - pyskl - INFO - Epoch [12][1800/3746] lr: 9.856e-02, eta: 4 days, 11:49:11, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4942, loss_cls: 4.1361, loss: 4.1361 +2024-12-26 11:14:48,401 - pyskl - INFO - Epoch [12][1900/3746] lr: 9.855e-02, eta: 4 days, 11:47:17, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4689, loss_cls: 4.2007, loss: 4.2007 +2024-12-26 11:16:00,404 - pyskl - INFO - Epoch [12][2000/3746] lr: 9.855e-02, eta: 4 days, 11:45:28, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4988, loss_cls: 4.1403, loss: 4.1403 +2024-12-26 11:17:12,175 - pyskl - INFO - Epoch [12][2100/3746] lr: 9.854e-02, eta: 4 days, 11:43:37, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4841, loss_cls: 4.2045, loss: 4.2045 +2024-12-26 11:18:23,918 - pyskl - INFO - Epoch [12][2200/3746] lr: 9.853e-02, eta: 4 days, 11:41:46, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4864, loss_cls: 4.1825, loss: 4.1825 +2024-12-26 11:19:36,343 - pyskl - INFO - Epoch [12][2300/3746] lr: 9.853e-02, eta: 4 days, 11:40:03, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4888, loss_cls: 4.1962, loss: 4.1962 +2024-12-26 11:20:47,963 - pyskl - INFO - Epoch [12][2400/3746] lr: 9.852e-02, eta: 4 days, 11:38:11, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4916, loss_cls: 4.1751, loss: 4.1751 +2024-12-26 11:21:59,748 - pyskl - INFO - Epoch [12][2500/3746] lr: 9.851e-02, eta: 4 days, 11:36:21, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4831, loss_cls: 4.1828, loss: 4.1828 +2024-12-26 11:23:11,520 - pyskl - INFO - Epoch [12][2600/3746] lr: 9.851e-02, eta: 4 days, 11:34:31, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4881, loss_cls: 4.1891, loss: 4.1891 +2024-12-26 11:24:23,109 - pyskl - INFO - Epoch [12][2700/3746] lr: 9.850e-02, eta: 4 days, 11:32:39, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4772, loss_cls: 4.2073, loss: 4.2073 +2024-12-26 11:25:34,923 - pyskl - INFO - Epoch [12][2800/3746] lr: 9.849e-02, eta: 4 days, 11:30:50, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4814, loss_cls: 4.2243, loss: 4.2243 +2024-12-26 11:26:46,433 - pyskl - INFO - Epoch [12][2900/3746] lr: 9.849e-02, eta: 4 days, 11:28:57, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4858, loss_cls: 4.1972, loss: 4.1972 +2024-12-26 11:27:58,144 - pyskl - INFO - Epoch [12][3000/3746] lr: 9.848e-02, eta: 4 days, 11:27:07, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4961, loss_cls: 4.1600, loss: 4.1600 +2024-12-26 11:29:10,073 - pyskl - INFO - Epoch [12][3100/3746] lr: 9.847e-02, eta: 4 days, 11:25:20, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4888, loss_cls: 4.1774, loss: 4.1774 +2024-12-26 11:30:21,941 - pyskl - INFO - Epoch [12][3200/3746] lr: 9.847e-02, eta: 4 days, 11:23:32, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4889, loss_cls: 4.1607, loss: 4.1607 +2024-12-26 11:31:33,700 - pyskl - INFO - Epoch [12][3300/3746] lr: 9.846e-02, eta: 4 days, 11:21:43, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4867, loss_cls: 4.1359, loss: 4.1359 +2024-12-26 11:32:45,710 - pyskl - INFO - Epoch [12][3400/3746] lr: 9.845e-02, eta: 4 days, 11:19:57, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4919, loss_cls: 4.1772, loss: 4.1772 +2024-12-26 11:33:57,767 - pyskl - INFO - Epoch [12][3500/3746] lr: 9.845e-02, eta: 4 days, 11:18:12, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4877, loss_cls: 4.1525, loss: 4.1525 +2024-12-26 11:35:09,252 - pyskl - INFO - Epoch [12][3600/3746] lr: 9.844e-02, eta: 4 days, 11:16:20, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4911, loss_cls: 4.1577, loss: 4.1577 +2024-12-26 11:36:20,403 - pyskl - INFO - Epoch [12][3700/3746] lr: 9.843e-02, eta: 4 days, 11:14:25, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4831, loss_cls: 4.1734, loss: 4.1734 +2024-12-26 11:36:55,083 - pyskl - INFO - Saving checkpoint at 12 epochs +2024-12-26 11:38:50,755 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 11:38:51,620 - pyskl - INFO - +top1_acc 0.1649 +top5_acc 0.3821 +2024-12-26 11:38:51,620 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 11:38:51,659 - pyskl - INFO - +mean_acc 0.1647 +2024-12-26 11:38:51,670 - pyskl - INFO - Epoch(val) [12][309] top1_acc: 0.1649, top5_acc: 0.3821, mean_class_accuracy: 0.1647 +2024-12-26 11:42:21,877 - pyskl - INFO - Epoch [13][100/3746] lr: 9.842e-02, eta: 4 days, 11:31:56, time: 2.102, data_time: 1.381, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4909, loss_cls: 4.1572, loss: 4.1572 +2024-12-26 11:43:33,672 - pyskl - INFO - Epoch [13][200/3746] lr: 9.842e-02, eta: 4 days, 11:30:06, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4930, loss_cls: 4.1276, loss: 4.1276 +2024-12-26 11:44:45,012 - pyskl - INFO - Epoch [13][300/3746] lr: 9.841e-02, eta: 4 days, 11:28:10, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4933, loss_cls: 4.1562, loss: 4.1562 +2024-12-26 11:45:56,372 - pyskl - INFO - Epoch [13][400/3746] lr: 9.840e-02, eta: 4 days, 11:26:15, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4988, loss_cls: 4.1321, loss: 4.1321 +2024-12-26 11:47:07,787 - pyskl - INFO - Epoch [13][500/3746] lr: 9.839e-02, eta: 4 days, 11:24:21, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4995, loss_cls: 4.1049, loss: 4.1049 +2024-12-26 11:48:19,684 - pyskl - INFO - Epoch [13][600/3746] lr: 9.839e-02, eta: 4 days, 11:22:32, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4814, loss_cls: 4.1322, loss: 4.1322 +2024-12-26 11:49:31,166 - pyskl - INFO - Epoch [13][700/3746] lr: 9.838e-02, eta: 4 days, 11:20:39, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4833, loss_cls: 4.1605, loss: 4.1605 +2024-12-26 11:50:42,575 - pyskl - INFO - Epoch [13][800/3746] lr: 9.837e-02, eta: 4 days, 11:18:46, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4900, loss_cls: 4.1865, loss: 4.1865 +2024-12-26 11:51:53,946 - pyskl - INFO - Epoch [13][900/3746] lr: 9.837e-02, eta: 4 days, 11:16:52, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4956, loss_cls: 4.1240, loss: 4.1240 +2024-12-26 11:53:05,189 - pyskl - INFO - Epoch [13][1000/3746] lr: 9.836e-02, eta: 4 days, 11:14:56, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4989, loss_cls: 4.1027, loss: 4.1027 +2024-12-26 11:54:16,981 - pyskl - INFO - Epoch [13][1100/3746] lr: 9.835e-02, eta: 4 days, 11:13:07, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4894, loss_cls: 4.1963, loss: 4.1963 +2024-12-26 11:55:28,542 - pyskl - INFO - Epoch [13][1200/3746] lr: 9.834e-02, eta: 4 days, 11:11:16, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4863, loss_cls: 4.1656, loss: 4.1656 +2024-12-26 11:56:40,689 - pyskl - INFO - Epoch [13][1300/3746] lr: 9.834e-02, eta: 4 days, 11:09:31, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4956, loss_cls: 4.1510, loss: 4.1510 +2024-12-26 11:57:52,419 - pyskl - INFO - Epoch [13][1400/3746] lr: 9.833e-02, eta: 4 days, 11:07:42, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4858, loss_cls: 4.1925, loss: 4.1925 +2024-12-26 11:59:04,160 - pyskl - INFO - Epoch [13][1500/3746] lr: 9.832e-02, eta: 4 days, 11:05:54, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4923, loss_cls: 4.1645, loss: 4.1645 +2024-12-26 12:00:15,667 - pyskl - INFO - Epoch [13][1600/3746] lr: 9.832e-02, eta: 4 days, 11:04:02, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4855, loss_cls: 4.1596, loss: 4.1596 +2024-12-26 12:01:27,413 - pyskl - INFO - Epoch [13][1700/3746] lr: 9.831e-02, eta: 4 days, 11:02:14, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4878, loss_cls: 4.1965, loss: 4.1965 +2024-12-26 12:02:39,079 - pyskl - INFO - Epoch [13][1800/3746] lr: 9.830e-02, eta: 4 days, 11:00:25, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4809, loss_cls: 4.2287, loss: 4.2287 +2024-12-26 12:03:50,649 - pyskl - INFO - Epoch [13][1900/3746] lr: 9.829e-02, eta: 4 days, 10:58:35, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4909, loss_cls: 4.1602, loss: 4.1602 +2024-12-26 12:05:02,201 - pyskl - INFO - Epoch [13][2000/3746] lr: 9.829e-02, eta: 4 days, 10:56:44, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4761, loss_cls: 4.1752, loss: 4.1752 +2024-12-26 12:06:14,376 - pyskl - INFO - Epoch [13][2100/3746] lr: 9.828e-02, eta: 4 days, 10:55:01, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4916, loss_cls: 4.1692, loss: 4.1692 +2024-12-26 12:07:25,984 - pyskl - INFO - Epoch [13][2200/3746] lr: 9.827e-02, eta: 4 days, 10:53:12, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4872, loss_cls: 4.1886, loss: 4.1886 +2024-12-26 12:08:37,630 - pyskl - INFO - Epoch [13][2300/3746] lr: 9.827e-02, eta: 4 days, 10:51:24, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4841, loss_cls: 4.1889, loss: 4.1889 +2024-12-26 12:09:49,191 - pyskl - INFO - Epoch [13][2400/3746] lr: 9.826e-02, eta: 4 days, 10:49:34, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4883, loss_cls: 4.1482, loss: 4.1482 +2024-12-26 12:11:00,542 - pyskl - INFO - Epoch [13][2500/3746] lr: 9.825e-02, eta: 4 days, 10:47:43, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4841, loss_cls: 4.2136, loss: 4.2136 +2024-12-26 12:12:12,002 - pyskl - INFO - Epoch [13][2600/3746] lr: 9.824e-02, eta: 4 days, 10:45:53, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5003, loss_cls: 4.1202, loss: 4.1202 +2024-12-26 12:13:23,811 - pyskl - INFO - Epoch [13][2700/3746] lr: 9.824e-02, eta: 4 days, 10:44:06, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4742, loss_cls: 4.2217, loss: 4.2217 +2024-12-26 12:14:35,447 - pyskl - INFO - Epoch [13][2800/3746] lr: 9.823e-02, eta: 4 days, 10:42:18, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4905, loss_cls: 4.1790, loss: 4.1790 +2024-12-26 12:15:47,594 - pyskl - INFO - Epoch [13][2900/3746] lr: 9.822e-02, eta: 4 days, 10:40:36, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4884, loss_cls: 4.1645, loss: 4.1645 +2024-12-26 12:16:59,607 - pyskl - INFO - Epoch [13][3000/3746] lr: 9.821e-02, eta: 4 days, 10:38:52, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4931, loss_cls: 4.1542, loss: 4.1542 +2024-12-26 12:18:11,415 - pyskl - INFO - Epoch [13][3100/3746] lr: 9.821e-02, eta: 4 days, 10:37:07, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4925, loss_cls: 4.1515, loss: 4.1515 +2024-12-26 12:19:23,424 - pyskl - INFO - Epoch [13][3200/3746] lr: 9.820e-02, eta: 4 days, 10:35:23, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4877, loss_cls: 4.1518, loss: 4.1518 +2024-12-26 12:20:35,095 - pyskl - INFO - Epoch [13][3300/3746] lr: 9.819e-02, eta: 4 days, 10:33:37, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4878, loss_cls: 4.1663, loss: 4.1663 +2024-12-26 12:21:46,684 - pyskl - INFO - Epoch [13][3400/3746] lr: 9.818e-02, eta: 4 days, 10:31:49, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4909, loss_cls: 4.1521, loss: 4.1521 +2024-12-26 12:22:58,509 - pyskl - INFO - Epoch [13][3500/3746] lr: 9.818e-02, eta: 4 days, 10:30:04, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4730, loss_cls: 4.2162, loss: 4.2162 +2024-12-26 12:24:10,267 - pyskl - INFO - Epoch [13][3600/3746] lr: 9.817e-02, eta: 4 days, 10:28:19, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4817, loss_cls: 4.2341, loss: 4.2341 +2024-12-26 12:25:22,103 - pyskl - INFO - Epoch [13][3700/3746] lr: 9.816e-02, eta: 4 days, 10:26:34, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4947, loss_cls: 4.1493, loss: 4.1493 +2024-12-26 12:25:56,613 - pyskl - INFO - Saving checkpoint at 13 epochs +2024-12-26 12:27:52,116 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 12:27:52,955 - pyskl - INFO - +top1_acc 0.1727 +top5_acc 0.3975 +2024-12-26 12:27:52,955 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 12:27:52,996 - pyskl - INFO - +mean_acc 0.1726 +2024-12-26 12:27:53,008 - pyskl - INFO - Epoch(val) [13][309] top1_acc: 0.1727, top5_acc: 0.3975, mean_class_accuracy: 0.1726 +2024-12-26 12:31:23,850 - pyskl - INFO - Epoch [14][100/3746] lr: 9.815e-02, eta: 4 days, 10:42:36, time: 2.108, data_time: 1.389, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5038, loss_cls: 4.0818, loss: 4.0818 +2024-12-26 12:32:35,296 - pyskl - INFO - Epoch [14][200/3746] lr: 9.814e-02, eta: 4 days, 10:40:45, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4991, loss_cls: 4.1120, loss: 4.1120 +2024-12-26 12:33:46,777 - pyskl - INFO - Epoch [14][300/3746] lr: 9.814e-02, eta: 4 days, 10:38:55, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4986, loss_cls: 4.1358, loss: 4.1358 +2024-12-26 12:34:58,402 - pyskl - INFO - Epoch [14][400/3746] lr: 9.813e-02, eta: 4 days, 10:37:06, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4845, loss_cls: 4.1662, loss: 4.1662 +2024-12-26 12:36:10,274 - pyskl - INFO - Epoch [14][500/3746] lr: 9.812e-02, eta: 4 days, 10:35:20, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4913, loss_cls: 4.1435, loss: 4.1435 +2024-12-26 12:37:21,599 - pyskl - INFO - Epoch [14][600/3746] lr: 9.811e-02, eta: 4 days, 10:33:29, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4784, loss_cls: 4.1936, loss: 4.1936 +2024-12-26 12:38:32,736 - pyskl - INFO - Epoch [14][700/3746] lr: 9.811e-02, eta: 4 days, 10:31:35, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4825, loss_cls: 4.1996, loss: 4.1996 +2024-12-26 12:39:43,858 - pyskl - INFO - Epoch [14][800/3746] lr: 9.810e-02, eta: 4 days, 10:29:42, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4945, loss_cls: 4.1368, loss: 4.1368 +2024-12-26 12:40:55,370 - pyskl - INFO - Epoch [14][900/3746] lr: 9.809e-02, eta: 4 days, 10:27:53, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4836, loss_cls: 4.1844, loss: 4.1844 +2024-12-26 12:42:06,739 - pyskl - INFO - Epoch [14][1000/3746] lr: 9.808e-02, eta: 4 days, 10:26:03, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4889, loss_cls: 4.1400, loss: 4.1400 +2024-12-26 12:43:18,239 - pyskl - INFO - Epoch [14][1100/3746] lr: 9.807e-02, eta: 4 days, 10:24:14, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4988, loss_cls: 4.1216, loss: 4.1216 +2024-12-26 12:44:29,676 - pyskl - INFO - Epoch [14][1200/3746] lr: 9.807e-02, eta: 4 days, 10:22:25, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4809, loss_cls: 4.1702, loss: 4.1702 +2024-12-26 12:45:41,965 - pyskl - INFO - Epoch [14][1300/3746] lr: 9.806e-02, eta: 4 days, 10:20:44, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4883, loss_cls: 4.1615, loss: 4.1615 +2024-12-26 12:46:53,675 - pyskl - INFO - Epoch [14][1400/3746] lr: 9.805e-02, eta: 4 days, 10:18:58, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4891, loss_cls: 4.1612, loss: 4.1612 +2024-12-26 12:48:05,289 - pyskl - INFO - Epoch [14][1500/3746] lr: 9.804e-02, eta: 4 days, 10:17:11, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4863, loss_cls: 4.1900, loss: 4.1900 +2024-12-26 12:49:16,607 - pyskl - INFO - Epoch [14][1600/3746] lr: 9.804e-02, eta: 4 days, 10:15:21, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4853, loss_cls: 4.1781, loss: 4.1781 +2024-12-26 12:50:28,752 - pyskl - INFO - Epoch [14][1700/3746] lr: 9.803e-02, eta: 4 days, 10:13:40, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4856, loss_cls: 4.1857, loss: 4.1857 +2024-12-26 12:51:40,568 - pyskl - INFO - Epoch [14][1800/3746] lr: 9.802e-02, eta: 4 days, 10:11:55, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4841, loss_cls: 4.1678, loss: 4.1678 +2024-12-26 12:52:52,092 - pyskl - INFO - Epoch [14][1900/3746] lr: 9.801e-02, eta: 4 days, 10:10:08, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4875, loss_cls: 4.1772, loss: 4.1772 +2024-12-26 12:54:03,594 - pyskl - INFO - Epoch [14][2000/3746] lr: 9.800e-02, eta: 4 days, 10:08:20, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5034, loss_cls: 4.1066, loss: 4.1066 +2024-12-26 12:55:15,208 - pyskl - INFO - Epoch [14][2100/3746] lr: 9.800e-02, eta: 4 days, 10:06:34, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4928, loss_cls: 4.1471, loss: 4.1471 +2024-12-26 12:56:26,692 - pyskl - INFO - Epoch [14][2200/3746] lr: 9.799e-02, eta: 4 days, 10:04:46, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4856, loss_cls: 4.1723, loss: 4.1723 +2024-12-26 12:57:38,236 - pyskl - INFO - Epoch [14][2300/3746] lr: 9.798e-02, eta: 4 days, 10:03:00, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4898, loss_cls: 4.1783, loss: 4.1783 +2024-12-26 12:58:49,557 - pyskl - INFO - Epoch [14][2400/3746] lr: 9.797e-02, eta: 4 days, 10:01:11, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5008, loss_cls: 4.0949, loss: 4.0949 +2024-12-26 13:00:01,610 - pyskl - INFO - Epoch [14][2500/3746] lr: 9.797e-02, eta: 4 days, 9:59:30, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4902, loss_cls: 4.1682, loss: 4.1682 +2024-12-26 13:01:13,177 - pyskl - INFO - Epoch [14][2600/3746] lr: 9.796e-02, eta: 4 days, 9:57:43, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4869, loss_cls: 4.1420, loss: 4.1420 +2024-12-26 13:02:25,176 - pyskl - INFO - Epoch [14][2700/3746] lr: 9.795e-02, eta: 4 days, 9:56:02, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4850, loss_cls: 4.1799, loss: 4.1799 +2024-12-26 13:03:37,318 - pyskl - INFO - Epoch [14][2800/3746] lr: 9.794e-02, eta: 4 days, 9:54:22, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4872, loss_cls: 4.1757, loss: 4.1757 +2024-12-26 13:04:49,251 - pyskl - INFO - Epoch [14][2900/3746] lr: 9.793e-02, eta: 4 days, 9:52:40, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4859, loss_cls: 4.1912, loss: 4.1912 +2024-12-26 13:06:00,904 - pyskl - INFO - Epoch [14][3000/3746] lr: 9.793e-02, eta: 4 days, 9:50:55, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4902, loss_cls: 4.1578, loss: 4.1578 +2024-12-26 13:07:12,614 - pyskl - INFO - Epoch [14][3100/3746] lr: 9.792e-02, eta: 4 days, 9:49:11, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4888, loss_cls: 4.1460, loss: 4.1460 +2024-12-26 13:08:24,555 - pyskl - INFO - Epoch [14][3200/3746] lr: 9.791e-02, eta: 4 days, 9:47:29, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4961, loss_cls: 4.1409, loss: 4.1409 +2024-12-26 13:09:36,389 - pyskl - INFO - Epoch [14][3300/3746] lr: 9.790e-02, eta: 4 days, 9:45:47, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4991, loss_cls: 4.1248, loss: 4.1248 +2024-12-26 13:10:48,308 - pyskl - INFO - Epoch [14][3400/3746] lr: 9.789e-02, eta: 4 days, 9:44:05, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4950, loss_cls: 4.1254, loss: 4.1254 +2024-12-26 13:12:00,793 - pyskl - INFO - Epoch [14][3500/3746] lr: 9.789e-02, eta: 4 days, 9:42:29, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4927, loss_cls: 4.1354, loss: 4.1354 +2024-12-26 13:13:12,654 - pyskl - INFO - Epoch [14][3600/3746] lr: 9.788e-02, eta: 4 days, 9:40:47, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4842, loss_cls: 4.1912, loss: 4.1912 +2024-12-26 13:14:24,121 - pyskl - INFO - Epoch [14][3700/3746] lr: 9.787e-02, eta: 4 days, 9:39:02, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4900, loss_cls: 4.1575, loss: 4.1575 +2024-12-26 13:14:58,735 - pyskl - INFO - Saving checkpoint at 14 epochs +2024-12-26 13:16:54,091 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 13:16:54,984 - pyskl - INFO - +top1_acc 0.1657 +top5_acc 0.3836 +2024-12-26 13:16:54,984 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 13:16:55,023 - pyskl - INFO - +mean_acc 0.1656 +2024-12-26 13:16:55,033 - pyskl - INFO - Epoch(val) [14][309] top1_acc: 0.1657, top5_acc: 0.3836, mean_class_accuracy: 0.1656 +2024-12-26 13:20:25,910 - pyskl - INFO - Epoch [15][100/3746] lr: 9.786e-02, eta: 4 days, 9:53:40, time: 2.109, data_time: 1.393, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4959, loss_cls: 4.1183, loss: 4.1183 +2024-12-26 13:21:37,507 - pyskl - INFO - Epoch [15][200/3746] lr: 9.785e-02, eta: 4 days, 9:51:54, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4953, loss_cls: 4.1178, loss: 4.1178 +2024-12-26 13:22:48,636 - pyskl - INFO - Epoch [15][300/3746] lr: 9.784e-02, eta: 4 days, 9:50:03, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.5038, loss_cls: 4.1234, loss: 4.1234 +2024-12-26 13:24:00,619 - pyskl - INFO - Epoch [15][400/3746] lr: 9.783e-02, eta: 4 days, 9:48:21, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4920, loss_cls: 4.1086, loss: 4.1086 +2024-12-26 13:25:12,702 - pyskl - INFO - Epoch [15][500/3746] lr: 9.783e-02, eta: 4 days, 9:46:40, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4995, loss_cls: 4.1138, loss: 4.1138 +2024-12-26 13:26:23,977 - pyskl - INFO - Epoch [15][600/3746] lr: 9.782e-02, eta: 4 days, 9:44:51, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4866, loss_cls: 4.1927, loss: 4.1927 +2024-12-26 13:27:35,710 - pyskl - INFO - Epoch [15][700/3746] lr: 9.781e-02, eta: 4 days, 9:43:07, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4898, loss_cls: 4.1454, loss: 4.1454 +2024-12-26 13:28:47,510 - pyskl - INFO - Epoch [15][800/3746] lr: 9.780e-02, eta: 4 days, 9:41:23, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4895, loss_cls: 4.1633, loss: 4.1633 +2024-12-26 13:29:59,299 - pyskl - INFO - Epoch [15][900/3746] lr: 9.779e-02, eta: 4 days, 9:39:40, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4922, loss_cls: 4.1615, loss: 4.1615 +2024-12-26 13:31:10,605 - pyskl - INFO - Epoch [15][1000/3746] lr: 9.778e-02, eta: 4 days, 9:37:52, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4863, loss_cls: 4.1689, loss: 4.1689 +2024-12-26 13:32:22,195 - pyskl - INFO - Epoch [15][1100/3746] lr: 9.778e-02, eta: 4 days, 9:36:06, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4841, loss_cls: 4.1651, loss: 4.1651 +2024-12-26 13:33:33,522 - pyskl - INFO - Epoch [15][1200/3746] lr: 9.777e-02, eta: 4 days, 9:34:19, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4883, loss_cls: 4.1724, loss: 4.1724 +2024-12-26 13:34:45,015 - pyskl - INFO - Epoch [15][1300/3746] lr: 9.776e-02, eta: 4 days, 9:32:33, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4917, loss_cls: 4.1395, loss: 4.1395 +2024-12-26 13:35:56,752 - pyskl - INFO - Epoch [15][1400/3746] lr: 9.775e-02, eta: 4 days, 9:30:50, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4931, loss_cls: 4.1338, loss: 4.1338 +2024-12-26 13:37:07,966 - pyskl - INFO - Epoch [15][1500/3746] lr: 9.774e-02, eta: 4 days, 9:29:01, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.5009, loss_cls: 4.1457, loss: 4.1457 +2024-12-26 13:38:19,800 - pyskl - INFO - Epoch [15][1600/3746] lr: 9.773e-02, eta: 4 days, 9:27:19, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4942, loss_cls: 4.1314, loss: 4.1314 +2024-12-26 13:39:31,263 - pyskl - INFO - Epoch [15][1700/3746] lr: 9.773e-02, eta: 4 days, 9:25:33, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4934, loss_cls: 4.1213, loss: 4.1213 +2024-12-26 13:40:43,120 - pyskl - INFO - Epoch [15][1800/3746] lr: 9.772e-02, eta: 4 days, 9:23:52, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4900, loss_cls: 4.1632, loss: 4.1632 +2024-12-26 13:41:54,691 - pyskl - INFO - Epoch [15][1900/3746] lr: 9.771e-02, eta: 4 days, 9:22:07, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4848, loss_cls: 4.2021, loss: 4.2021 +2024-12-26 13:43:06,003 - pyskl - INFO - Epoch [15][2000/3746] lr: 9.770e-02, eta: 4 days, 9:20:21, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4961, loss_cls: 4.1411, loss: 4.1411 +2024-12-26 13:44:17,720 - pyskl - INFO - Epoch [15][2100/3746] lr: 9.769e-02, eta: 4 days, 9:18:38, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4906, loss_cls: 4.1421, loss: 4.1421 +2024-12-26 13:45:29,383 - pyskl - INFO - Epoch [15][2200/3746] lr: 9.768e-02, eta: 4 days, 9:16:55, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4853, loss_cls: 4.1655, loss: 4.1655 +2024-12-26 13:46:40,722 - pyskl - INFO - Epoch [15][2300/3746] lr: 9.768e-02, eta: 4 days, 9:15:09, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4842, loss_cls: 4.1578, loss: 4.1578 +2024-12-26 13:47:52,604 - pyskl - INFO - Epoch [15][2400/3746] lr: 9.767e-02, eta: 4 days, 9:13:28, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.5038, loss_cls: 4.0898, loss: 4.0898 +2024-12-26 13:49:04,267 - pyskl - INFO - Epoch [15][2500/3746] lr: 9.766e-02, eta: 4 days, 9:11:45, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4966, loss_cls: 4.1244, loss: 4.1244 +2024-12-26 13:50:15,881 - pyskl - INFO - Epoch [15][2600/3746] lr: 9.765e-02, eta: 4 days, 9:10:02, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4938, loss_cls: 4.1293, loss: 4.1293 +2024-12-26 13:51:27,631 - pyskl - INFO - Epoch [15][2700/3746] lr: 9.764e-02, eta: 4 days, 9:08:20, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4900, loss_cls: 4.1355, loss: 4.1355 +2024-12-26 13:52:39,490 - pyskl - INFO - Epoch [15][2800/3746] lr: 9.763e-02, eta: 4 days, 9:06:39, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4822, loss_cls: 4.1658, loss: 4.1658 +2024-12-26 13:53:51,058 - pyskl - INFO - Epoch [15][2900/3746] lr: 9.763e-02, eta: 4 days, 9:04:56, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4998, loss_cls: 4.1212, loss: 4.1212 +2024-12-26 13:55:02,647 - pyskl - INFO - Epoch [15][3000/3746] lr: 9.762e-02, eta: 4 days, 9:03:13, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4958, loss_cls: 4.1558, loss: 4.1558 +2024-12-26 13:56:14,398 - pyskl - INFO - Epoch [15][3100/3746] lr: 9.761e-02, eta: 4 days, 9:01:32, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4964, loss_cls: 4.1780, loss: 4.1780 +2024-12-26 13:57:26,112 - pyskl - INFO - Epoch [15][3200/3746] lr: 9.760e-02, eta: 4 days, 8:59:50, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4852, loss_cls: 4.1719, loss: 4.1719 +2024-12-26 13:58:37,759 - pyskl - INFO - Epoch [15][3300/3746] lr: 9.759e-02, eta: 4 days, 8:58:08, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4864, loss_cls: 4.1633, loss: 4.1633 +2024-12-26 13:59:49,629 - pyskl - INFO - Epoch [15][3400/3746] lr: 9.758e-02, eta: 4 days, 8:56:28, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4844, loss_cls: 4.1707, loss: 4.1707 +2024-12-26 14:01:01,395 - pyskl - INFO - Epoch [15][3500/3746] lr: 9.757e-02, eta: 4 days, 8:54:48, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.5005, loss_cls: 4.1438, loss: 4.1438 +2024-12-26 14:02:13,101 - pyskl - INFO - Epoch [15][3600/3746] lr: 9.757e-02, eta: 4 days, 8:53:06, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4892, loss_cls: 4.1491, loss: 4.1491 +2024-12-26 14:03:24,458 - pyskl - INFO - Epoch [15][3700/3746] lr: 9.756e-02, eta: 4 days, 8:51:22, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4881, loss_cls: 4.1628, loss: 4.1628 +2024-12-26 14:03:59,144 - pyskl - INFO - Saving checkpoint at 15 epochs +2024-12-26 14:05:54,925 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 14:05:55,768 - pyskl - INFO - +top1_acc 0.1617 +top5_acc 0.3854 +2024-12-26 14:05:55,768 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 14:05:55,809 - pyskl - INFO - +mean_acc 0.1617 +2024-12-26 14:05:55,820 - pyskl - INFO - Epoch(val) [15][309] top1_acc: 0.1617, top5_acc: 0.3854, mean_class_accuracy: 0.1617 +2024-12-26 14:09:24,916 - pyskl - INFO - Epoch [16][100/3746] lr: 9.754e-02, eta: 4 days, 9:04:32, time: 2.091, data_time: 1.375, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4953, loss_cls: 4.1132, loss: 4.1132 +2024-12-26 14:10:36,657 - pyskl - INFO - Epoch [16][200/3746] lr: 9.754e-02, eta: 4 days, 9:02:50, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4922, loss_cls: 4.0923, loss: 4.0923 +2024-12-26 14:11:47,862 - pyskl - INFO - Epoch [16][300/3746] lr: 9.753e-02, eta: 4 days, 9:01:03, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4963, loss_cls: 4.1379, loss: 4.1379 +2024-12-26 14:12:58,783 - pyskl - INFO - Epoch [16][400/3746] lr: 9.752e-02, eta: 4 days, 8:59:13, time: 0.709, data_time: 0.001, memory: 15990, top1_acc: 0.2531, top5_acc: 0.4988, loss_cls: 4.1264, loss: 4.1264 +2024-12-26 14:14:10,165 - pyskl - INFO - Epoch [16][500/3746] lr: 9.751e-02, eta: 4 days, 8:57:28, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4953, loss_cls: 4.1332, loss: 4.1332 +2024-12-26 14:15:21,448 - pyskl - INFO - Epoch [16][600/3746] lr: 9.750e-02, eta: 4 days, 8:55:42, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.5002, loss_cls: 4.0878, loss: 4.0878 +2024-12-26 14:16:32,665 - pyskl - INFO - Epoch [16][700/3746] lr: 9.749e-02, eta: 4 days, 8:53:56, time: 0.712, data_time: 0.001, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4902, loss_cls: 4.1325, loss: 4.1325 +2024-12-26 14:17:44,360 - pyskl - INFO - Epoch [16][800/3746] lr: 9.748e-02, eta: 4 days, 8:52:14, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.5109, loss_cls: 4.1060, loss: 4.1060 +2024-12-26 14:18:55,560 - pyskl - INFO - Epoch [16][900/3746] lr: 9.747e-02, eta: 4 days, 8:50:27, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5084, loss_cls: 4.0778, loss: 4.0778 +2024-12-26 14:20:06,895 - pyskl - INFO - Epoch [16][1000/3746] lr: 9.747e-02, eta: 4 days, 8:48:42, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4939, loss_cls: 4.1424, loss: 4.1424 +2024-12-26 14:21:18,694 - pyskl - INFO - Epoch [16][1100/3746] lr: 9.746e-02, eta: 4 days, 8:47:01, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5034, loss_cls: 4.0824, loss: 4.0824 +2024-12-26 14:22:30,573 - pyskl - INFO - Epoch [16][1200/3746] lr: 9.745e-02, eta: 4 days, 8:45:21, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4931, loss_cls: 4.1228, loss: 4.1228 +2024-12-26 14:23:42,533 - pyskl - INFO - Epoch [16][1300/3746] lr: 9.744e-02, eta: 4 days, 8:43:42, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4850, loss_cls: 4.1524, loss: 4.1524 +2024-12-26 14:24:54,285 - pyskl - INFO - Epoch [16][1400/3746] lr: 9.743e-02, eta: 4 days, 8:42:01, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4913, loss_cls: 4.1474, loss: 4.1474 +2024-12-26 14:26:06,180 - pyskl - INFO - Epoch [16][1500/3746] lr: 9.742e-02, eta: 4 days, 8:40:21, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4903, loss_cls: 4.1109, loss: 4.1109 +2024-12-26 14:27:18,348 - pyskl - INFO - Epoch [16][1600/3746] lr: 9.741e-02, eta: 4 days, 8:38:44, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4998, loss_cls: 4.1232, loss: 4.1232 +2024-12-26 14:28:30,238 - pyskl - INFO - Epoch [16][1700/3746] lr: 9.740e-02, eta: 4 days, 8:37:05, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4958, loss_cls: 4.1246, loss: 4.1246 +2024-12-26 14:29:41,862 - pyskl - INFO - Epoch [16][1800/3746] lr: 9.740e-02, eta: 4 days, 8:35:23, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4828, loss_cls: 4.1712, loss: 4.1712 +2024-12-26 14:30:53,917 - pyskl - INFO - Epoch [16][1900/3746] lr: 9.739e-02, eta: 4 days, 8:33:45, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4953, loss_cls: 4.1347, loss: 4.1347 +2024-12-26 14:32:06,191 - pyskl - INFO - Epoch [16][2000/3746] lr: 9.738e-02, eta: 4 days, 8:32:09, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4892, loss_cls: 4.1550, loss: 4.1550 +2024-12-26 14:33:18,026 - pyskl - INFO - Epoch [16][2100/3746] lr: 9.737e-02, eta: 4 days, 8:30:30, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4948, loss_cls: 4.1615, loss: 4.1615 +2024-12-26 14:34:30,438 - pyskl - INFO - Epoch [16][2200/3746] lr: 9.736e-02, eta: 4 days, 8:28:55, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4941, loss_cls: 4.1235, loss: 4.1235 +2024-12-26 14:35:42,186 - pyskl - INFO - Epoch [16][2300/3746] lr: 9.735e-02, eta: 4 days, 8:27:15, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4919, loss_cls: 4.1300, loss: 4.1300 +2024-12-26 14:36:54,079 - pyskl - INFO - Epoch [16][2400/3746] lr: 9.734e-02, eta: 4 days, 8:25:36, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4894, loss_cls: 4.1343, loss: 4.1343 +2024-12-26 14:38:06,220 - pyskl - INFO - Epoch [16][2500/3746] lr: 9.733e-02, eta: 4 days, 8:24:00, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4931, loss_cls: 4.1365, loss: 4.1365 +2024-12-26 14:39:18,384 - pyskl - INFO - Epoch [16][2600/3746] lr: 9.732e-02, eta: 4 days, 8:22:24, time: 0.722, data_time: 0.001, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4919, loss_cls: 4.1586, loss: 4.1586 +2024-12-26 14:40:30,531 - pyskl - INFO - Epoch [16][2700/3746] lr: 9.731e-02, eta: 4 days, 8:20:47, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4894, loss_cls: 4.1612, loss: 4.1612 +2024-12-26 14:41:42,668 - pyskl - INFO - Epoch [16][2800/3746] lr: 9.731e-02, eta: 4 days, 8:19:11, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5028, loss_cls: 4.1050, loss: 4.1050 +2024-12-26 14:42:55,020 - pyskl - INFO - Epoch [16][2900/3746] lr: 9.730e-02, eta: 4 days, 8:17:36, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4928, loss_cls: 4.1203, loss: 4.1203 +2024-12-26 14:44:07,408 - pyskl - INFO - Epoch [16][3000/3746] lr: 9.729e-02, eta: 4 days, 8:16:02, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4938, loss_cls: 4.1632, loss: 4.1632 +2024-12-26 14:45:19,707 - pyskl - INFO - Epoch [16][3100/3746] lr: 9.728e-02, eta: 4 days, 8:14:28, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4913, loss_cls: 4.1430, loss: 4.1430 +2024-12-26 14:46:31,744 - pyskl - INFO - Epoch [16][3200/3746] lr: 9.727e-02, eta: 4 days, 8:12:51, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4800, loss_cls: 4.1913, loss: 4.1913 +2024-12-26 14:47:43,715 - pyskl - INFO - Epoch [16][3300/3746] lr: 9.726e-02, eta: 4 days, 8:11:13, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4953, loss_cls: 4.1636, loss: 4.1636 +2024-12-26 14:48:56,079 - pyskl - INFO - Epoch [16][3400/3746] lr: 9.725e-02, eta: 4 days, 8:09:39, time: 0.724, data_time: 0.001, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4964, loss_cls: 4.1300, loss: 4.1300 +2024-12-26 14:50:08,312 - pyskl - INFO - Epoch [16][3500/3746] lr: 9.724e-02, eta: 4 days, 8:08:04, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4864, loss_cls: 4.1438, loss: 4.1438 +2024-12-26 14:51:20,818 - pyskl - INFO - Epoch [16][3600/3746] lr: 9.723e-02, eta: 4 days, 8:06:32, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4886, loss_cls: 4.1715, loss: 4.1715 +2024-12-26 14:52:32,637 - pyskl - INFO - Epoch [16][3700/3746] lr: 9.722e-02, eta: 4 days, 8:04:53, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4972, loss_cls: 4.1306, loss: 4.1306 +2024-12-26 14:53:07,064 - pyskl - INFO - Saving checkpoint at 16 epochs +2024-12-26 14:55:02,765 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 14:55:03,569 - pyskl - INFO - +top1_acc 0.1828 +top5_acc 0.4100 +2024-12-26 14:55:03,569 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 14:55:03,607 - pyskl - INFO - +mean_acc 0.1826 +2024-12-26 14:55:03,612 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_10.pth was removed +2024-12-26 14:55:03,852 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_16.pth. +2024-12-26 14:55:03,852 - pyskl - INFO - Best top1_acc is 0.1828 at 16 epoch. +2024-12-26 14:55:03,863 - pyskl - INFO - Epoch(val) [16][309] top1_acc: 0.1828, top5_acc: 0.4100, mean_class_accuracy: 0.1826 +2024-12-26 14:58:34,349 - pyskl - INFO - Epoch [17][100/3746] lr: 9.721e-02, eta: 4 days, 8:17:13, time: 2.105, data_time: 1.386, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4973, loss_cls: 4.0921, loss: 4.0921 +2024-12-26 14:59:46,268 - pyskl - INFO - Epoch [17][200/3746] lr: 9.720e-02, eta: 4 days, 8:15:34, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4991, loss_cls: 4.1043, loss: 4.1043 +2024-12-26 15:00:57,684 - pyskl - INFO - Epoch [17][300/3746] lr: 9.719e-02, eta: 4 days, 8:13:51, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.4997, loss_cls: 4.1000, loss: 4.1000 +2024-12-26 15:02:08,870 - pyskl - INFO - Epoch [17][400/3746] lr: 9.718e-02, eta: 4 days, 8:12:06, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4936, loss_cls: 4.1341, loss: 4.1341 +2024-12-26 15:03:20,530 - pyskl - INFO - Epoch [17][500/3746] lr: 9.717e-02, eta: 4 days, 8:10:25, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5011, loss_cls: 4.1114, loss: 4.1114 +2024-12-26 15:04:31,789 - pyskl - INFO - Epoch [17][600/3746] lr: 9.716e-02, eta: 4 days, 8:08:41, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4870, loss_cls: 4.1559, loss: 4.1559 +2024-12-26 15:05:43,008 - pyskl - INFO - Epoch [17][700/3746] lr: 9.715e-02, eta: 4 days, 8:06:57, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5017, loss_cls: 4.1122, loss: 4.1122 +2024-12-26 15:06:54,185 - pyskl - INFO - Epoch [17][800/3746] lr: 9.714e-02, eta: 4 days, 8:05:13, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.5028, loss_cls: 4.1096, loss: 4.1096 +2024-12-26 15:08:05,732 - pyskl - INFO - Epoch [17][900/3746] lr: 9.714e-02, eta: 4 days, 8:03:31, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4908, loss_cls: 4.1560, loss: 4.1560 +2024-12-26 15:09:16,999 - pyskl - INFO - Epoch [17][1000/3746] lr: 9.713e-02, eta: 4 days, 8:01:48, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.5027, loss_cls: 4.0534, loss: 4.0534 +2024-12-26 15:10:28,135 - pyskl - INFO - Epoch [17][1100/3746] lr: 9.712e-02, eta: 4 days, 8:00:03, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4905, loss_cls: 4.1270, loss: 4.1270 +2024-12-26 15:11:39,713 - pyskl - INFO - Epoch [17][1200/3746] lr: 9.711e-02, eta: 4 days, 7:58:22, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4955, loss_cls: 4.1213, loss: 4.1213 +2024-12-26 15:12:51,427 - pyskl - INFO - Epoch [17][1300/3746] lr: 9.710e-02, eta: 4 days, 7:56:43, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4963, loss_cls: 4.1454, loss: 4.1454 +2024-12-26 15:14:02,964 - pyskl - INFO - Epoch [17][1400/3746] lr: 9.709e-02, eta: 4 days, 7:55:02, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.5009, loss_cls: 4.1138, loss: 4.1138 +2024-12-26 15:15:14,856 - pyskl - INFO - Epoch [17][1500/3746] lr: 9.708e-02, eta: 4 days, 7:53:24, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5047, loss_cls: 4.0980, loss: 4.0980 +2024-12-26 15:16:27,414 - pyskl - INFO - Epoch [17][1600/3746] lr: 9.707e-02, eta: 4 days, 7:51:52, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4972, loss_cls: 4.1217, loss: 4.1217 +2024-12-26 15:17:38,811 - pyskl - INFO - Epoch [17][1700/3746] lr: 9.706e-02, eta: 4 days, 7:50:10, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.5000, loss_cls: 4.1436, loss: 4.1436 +2024-12-26 15:18:50,559 - pyskl - INFO - Epoch [17][1800/3746] lr: 9.705e-02, eta: 4 days, 7:48:31, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4970, loss_cls: 4.1306, loss: 4.1306 +2024-12-26 15:20:02,054 - pyskl - INFO - Epoch [17][1900/3746] lr: 9.704e-02, eta: 4 days, 7:46:50, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4961, loss_cls: 4.1100, loss: 4.1100 +2024-12-26 15:21:13,324 - pyskl - INFO - Epoch [17][2000/3746] lr: 9.703e-02, eta: 4 days, 7:45:08, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4942, loss_cls: 4.1307, loss: 4.1307 +2024-12-26 15:22:25,039 - pyskl - INFO - Epoch [17][2100/3746] lr: 9.702e-02, eta: 4 days, 7:43:29, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4978, loss_cls: 4.1391, loss: 4.1391 +2024-12-26 15:23:36,572 - pyskl - INFO - Epoch [17][2200/3746] lr: 9.701e-02, eta: 4 days, 7:41:49, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4963, loss_cls: 4.1364, loss: 4.1364 +2024-12-26 15:24:48,620 - pyskl - INFO - Epoch [17][2300/3746] lr: 9.700e-02, eta: 4 days, 7:40:13, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.4963, loss_cls: 4.1179, loss: 4.1179 +2024-12-26 15:26:00,147 - pyskl - INFO - Epoch [17][2400/3746] lr: 9.699e-02, eta: 4 days, 7:38:33, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4853, loss_cls: 4.1699, loss: 4.1699 +2024-12-26 15:27:11,908 - pyskl - INFO - Epoch [17][2500/3746] lr: 9.698e-02, eta: 4 days, 7:36:55, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4853, loss_cls: 4.1782, loss: 4.1782 +2024-12-26 15:28:23,592 - pyskl - INFO - Epoch [17][2600/3746] lr: 9.697e-02, eta: 4 days, 7:35:16, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4928, loss_cls: 4.1493, loss: 4.1493 +2024-12-26 15:29:35,140 - pyskl - INFO - Epoch [17][2700/3746] lr: 9.697e-02, eta: 4 days, 7:33:36, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4980, loss_cls: 4.1220, loss: 4.1220 +2024-12-26 15:30:46,855 - pyskl - INFO - Epoch [17][2800/3746] lr: 9.696e-02, eta: 4 days, 7:31:58, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.5019, loss_cls: 4.1091, loss: 4.1091 +2024-12-26 15:31:58,200 - pyskl - INFO - Epoch [17][2900/3746] lr: 9.695e-02, eta: 4 days, 7:30:17, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4886, loss_cls: 4.1580, loss: 4.1580 +2024-12-26 15:33:10,062 - pyskl - INFO - Epoch [17][3000/3746] lr: 9.694e-02, eta: 4 days, 7:28:40, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4977, loss_cls: 4.1284, loss: 4.1284 +2024-12-26 15:34:21,486 - pyskl - INFO - Epoch [17][3100/3746] lr: 9.693e-02, eta: 4 days, 7:27:00, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4942, loss_cls: 4.1382, loss: 4.1382 +2024-12-26 15:35:33,014 - pyskl - INFO - Epoch [17][3200/3746] lr: 9.692e-02, eta: 4 days, 7:25:21, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4995, loss_cls: 4.1029, loss: 4.1029 +2024-12-26 15:36:44,402 - pyskl - INFO - Epoch [17][3300/3746] lr: 9.691e-02, eta: 4 days, 7:23:40, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4963, loss_cls: 4.1118, loss: 4.1118 +2024-12-26 15:37:56,080 - pyskl - INFO - Epoch [17][3400/3746] lr: 9.690e-02, eta: 4 days, 7:22:02, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4880, loss_cls: 4.1701, loss: 4.1701 +2024-12-26 15:39:07,863 - pyskl - INFO - Epoch [17][3500/3746] lr: 9.689e-02, eta: 4 days, 7:20:25, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4873, loss_cls: 4.1431, loss: 4.1431 +2024-12-26 15:40:19,445 - pyskl - INFO - Epoch [17][3600/3746] lr: 9.688e-02, eta: 4 days, 7:18:47, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5059, loss_cls: 4.0694, loss: 4.0694 +2024-12-26 15:41:31,070 - pyskl - INFO - Epoch [17][3700/3746] lr: 9.687e-02, eta: 4 days, 7:17:08, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4958, loss_cls: 4.1311, loss: 4.1311 +2024-12-26 15:42:05,507 - pyskl - INFO - Saving checkpoint at 17 epochs +2024-12-26 15:44:01,784 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 15:44:02,670 - pyskl - INFO - +top1_acc 0.1509 +top5_acc 0.3607 +2024-12-26 15:44:02,671 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 15:44:02,710 - pyskl - INFO - +mean_acc 0.1508 +2024-12-26 15:44:02,721 - pyskl - INFO - Epoch(val) [17][309] top1_acc: 0.1509, top5_acc: 0.3607, mean_class_accuracy: 0.1508 +2024-12-26 15:47:33,008 - pyskl - INFO - Epoch [18][100/3746] lr: 9.685e-02, eta: 4 days, 7:28:31, time: 2.103, data_time: 1.385, memory: 15990, top1_acc: 0.2405, top5_acc: 0.5000, loss_cls: 4.1039, loss: 4.1039 +2024-12-26 15:48:44,285 - pyskl - INFO - Epoch [18][200/3746] lr: 9.684e-02, eta: 4 days, 7:26:49, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4983, loss_cls: 4.1028, loss: 4.1028 +2024-12-26 15:49:56,144 - pyskl - INFO - Epoch [18][300/3746] lr: 9.683e-02, eta: 4 days, 7:25:11, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4947, loss_cls: 4.0928, loss: 4.0928 +2024-12-26 15:51:07,501 - pyskl - INFO - Epoch [18][400/3746] lr: 9.683e-02, eta: 4 days, 7:23:30, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5069, loss_cls: 4.0937, loss: 4.0937 +2024-12-26 15:52:19,393 - pyskl - INFO - Epoch [18][500/3746] lr: 9.682e-02, eta: 4 days, 7:21:53, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4942, loss_cls: 4.1443, loss: 4.1443 +2024-12-26 15:53:31,192 - pyskl - INFO - Epoch [18][600/3746] lr: 9.681e-02, eta: 4 days, 7:20:15, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4889, loss_cls: 4.1379, loss: 4.1379 +2024-12-26 15:54:42,724 - pyskl - INFO - Epoch [18][700/3746] lr: 9.680e-02, eta: 4 days, 7:18:35, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4936, loss_cls: 4.1607, loss: 4.1607 +2024-12-26 15:55:54,337 - pyskl - INFO - Epoch [18][800/3746] lr: 9.679e-02, eta: 4 days, 7:16:56, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4978, loss_cls: 4.1289, loss: 4.1289 +2024-12-26 15:57:06,012 - pyskl - INFO - Epoch [18][900/3746] lr: 9.678e-02, eta: 4 days, 7:15:18, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4964, loss_cls: 4.0950, loss: 4.0950 +2024-12-26 15:58:18,180 - pyskl - INFO - Epoch [18][1000/3746] lr: 9.677e-02, eta: 4 days, 7:13:43, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5016, loss_cls: 4.0631, loss: 4.0631 +2024-12-26 15:59:30,484 - pyskl - INFO - Epoch [18][1100/3746] lr: 9.676e-02, eta: 4 days, 7:12:10, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4903, loss_cls: 4.1591, loss: 4.1591 +2024-12-26 16:00:42,030 - pyskl - INFO - Epoch [18][1200/3746] lr: 9.675e-02, eta: 4 days, 7:10:31, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5050, loss_cls: 4.0651, loss: 4.0651 +2024-12-26 16:01:53,562 - pyskl - INFO - Epoch [18][1300/3746] lr: 9.674e-02, eta: 4 days, 7:08:51, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4877, loss_cls: 4.1679, loss: 4.1679 +2024-12-26 16:03:05,444 - pyskl - INFO - Epoch [18][1400/3746] lr: 9.673e-02, eta: 4 days, 7:07:15, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4913, loss_cls: 4.1495, loss: 4.1495 +2024-12-26 16:04:17,021 - pyskl - INFO - Epoch [18][1500/3746] lr: 9.672e-02, eta: 4 days, 7:05:36, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4930, loss_cls: 4.1352, loss: 4.1352 +2024-12-26 16:05:28,564 - pyskl - INFO - Epoch [18][1600/3746] lr: 9.671e-02, eta: 4 days, 7:03:57, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4994, loss_cls: 4.1153, loss: 4.1153 +2024-12-26 16:06:40,371 - pyskl - INFO - Epoch [18][1700/3746] lr: 9.670e-02, eta: 4 days, 7:02:21, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.4992, loss_cls: 4.1100, loss: 4.1100 +2024-12-26 16:07:51,815 - pyskl - INFO - Epoch [18][1800/3746] lr: 9.669e-02, eta: 4 days, 7:00:41, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.5058, loss_cls: 4.1121, loss: 4.1121 +2024-12-26 16:09:03,583 - pyskl - INFO - Epoch [18][1900/3746] lr: 9.668e-02, eta: 4 days, 6:59:04, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4952, loss_cls: 4.1199, loss: 4.1199 +2024-12-26 16:10:15,188 - pyskl - INFO - Epoch [18][2000/3746] lr: 9.667e-02, eta: 4 days, 6:57:26, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5078, loss_cls: 4.0755, loss: 4.0755 +2024-12-26 16:11:27,073 - pyskl - INFO - Epoch [18][2100/3746] lr: 9.666e-02, eta: 4 days, 6:55:50, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4983, loss_cls: 4.1323, loss: 4.1323 +2024-12-26 16:12:38,883 - pyskl - INFO - Epoch [18][2200/3746] lr: 9.665e-02, eta: 4 days, 6:54:14, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4983, loss_cls: 4.1063, loss: 4.1063 +2024-12-26 16:13:50,610 - pyskl - INFO - Epoch [18][2300/3746] lr: 9.664e-02, eta: 4 days, 6:52:37, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5006, loss_cls: 4.1093, loss: 4.1093 +2024-12-26 16:15:01,963 - pyskl - INFO - Epoch [18][2400/3746] lr: 9.663e-02, eta: 4 days, 6:50:57, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4938, loss_cls: 4.1514, loss: 4.1514 +2024-12-26 16:16:13,276 - pyskl - INFO - Epoch [18][2500/3746] lr: 9.662e-02, eta: 4 days, 6:49:17, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4923, loss_cls: 4.1174, loss: 4.1174 +2024-12-26 16:17:25,277 - pyskl - INFO - Epoch [18][2600/3746] lr: 9.661e-02, eta: 4 days, 6:47:43, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4933, loss_cls: 4.1238, loss: 4.1238 +2024-12-26 16:18:36,610 - pyskl - INFO - Epoch [18][2700/3746] lr: 9.660e-02, eta: 4 days, 6:46:03, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4958, loss_cls: 4.1355, loss: 4.1355 +2024-12-26 16:19:48,268 - pyskl - INFO - Epoch [18][2800/3746] lr: 9.659e-02, eta: 4 days, 6:44:26, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4933, loss_cls: 4.1203, loss: 4.1203 +2024-12-26 16:21:00,232 - pyskl - INFO - Epoch [18][2900/3746] lr: 9.658e-02, eta: 4 days, 6:42:51, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4975, loss_cls: 4.1499, loss: 4.1499 +2024-12-26 16:22:11,763 - pyskl - INFO - Epoch [18][3000/3746] lr: 9.657e-02, eta: 4 days, 6:41:14, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4925, loss_cls: 4.1418, loss: 4.1418 +2024-12-26 16:23:23,371 - pyskl - INFO - Epoch [18][3100/3746] lr: 9.656e-02, eta: 4 days, 6:39:36, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4942, loss_cls: 4.1272, loss: 4.1272 +2024-12-26 16:24:35,174 - pyskl - INFO - Epoch [18][3200/3746] lr: 9.654e-02, eta: 4 days, 6:38:01, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5038, loss_cls: 4.0987, loss: 4.0987 +2024-12-26 16:25:46,617 - pyskl - INFO - Epoch [18][3300/3746] lr: 9.653e-02, eta: 4 days, 6:36:22, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5088, loss_cls: 4.0904, loss: 4.0904 +2024-12-26 16:26:58,315 - pyskl - INFO - Epoch [18][3400/3746] lr: 9.652e-02, eta: 4 days, 6:34:46, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4931, loss_cls: 4.1222, loss: 4.1222 +2024-12-26 16:28:10,118 - pyskl - INFO - Epoch [18][3500/3746] lr: 9.651e-02, eta: 4 days, 6:33:11, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5002, loss_cls: 4.0811, loss: 4.0811 +2024-12-26 16:29:21,619 - pyskl - INFO - Epoch [18][3600/3746] lr: 9.650e-02, eta: 4 days, 6:31:33, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5003, loss_cls: 4.1030, loss: 4.1030 +2024-12-26 16:30:33,086 - pyskl - INFO - Epoch [18][3700/3746] lr: 9.649e-02, eta: 4 days, 6:29:55, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4998, loss_cls: 4.1139, loss: 4.1139 +2024-12-26 16:31:07,336 - pyskl - INFO - Saving checkpoint at 18 epochs +2024-12-26 16:33:03,444 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 16:33:04,277 - pyskl - INFO - +top1_acc 0.1838 +top5_acc 0.4132 +2024-12-26 16:33:04,277 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 16:33:04,317 - pyskl - INFO - +mean_acc 0.1835 +2024-12-26 16:33:04,322 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_16.pth was removed +2024-12-26 16:33:04,579 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_18.pth. +2024-12-26 16:33:04,580 - pyskl - INFO - Best top1_acc is 0.1838 at 18 epoch. +2024-12-26 16:33:04,592 - pyskl - INFO - Epoch(val) [18][309] top1_acc: 0.1838, top5_acc: 0.4132, mean_class_accuracy: 0.1835 +2024-12-26 16:36:32,369 - pyskl - INFO - Epoch [19][100/3746] lr: 9.648e-02, eta: 4 days, 6:40:10, time: 2.078, data_time: 1.360, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5053, loss_cls: 4.0501, loss: 4.0501 +2024-12-26 16:37:44,216 - pyskl - INFO - Epoch [19][200/3746] lr: 9.647e-02, eta: 4 days, 6:38:34, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4902, loss_cls: 4.1125, loss: 4.1125 +2024-12-26 16:38:55,882 - pyskl - INFO - Epoch [19][300/3746] lr: 9.646e-02, eta: 4 days, 6:36:56, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5012, loss_cls: 4.0874, loss: 4.0874 +2024-12-26 16:40:07,081 - pyskl - INFO - Epoch [19][400/3746] lr: 9.645e-02, eta: 4 days, 6:35:16, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5066, loss_cls: 4.0529, loss: 4.0529 +2024-12-26 16:41:18,288 - pyskl - INFO - Epoch [19][500/3746] lr: 9.644e-02, eta: 4 days, 6:33:35, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.5005, loss_cls: 4.1036, loss: 4.1036 +2024-12-26 16:42:30,107 - pyskl - INFO - Epoch [19][600/3746] lr: 9.643e-02, eta: 4 days, 6:31:59, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5123, loss_cls: 4.0480, loss: 4.0480 +2024-12-26 16:43:41,856 - pyskl - INFO - Epoch [19][700/3746] lr: 9.642e-02, eta: 4 days, 6:30:23, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5062, loss_cls: 4.0861, loss: 4.0861 +2024-12-26 16:44:53,030 - pyskl - INFO - Epoch [19][800/3746] lr: 9.641e-02, eta: 4 days, 6:28:42, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4888, loss_cls: 4.1579, loss: 4.1579 +2024-12-26 16:46:04,692 - pyskl - INFO - Epoch [19][900/3746] lr: 9.640e-02, eta: 4 days, 6:27:05, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4977, loss_cls: 4.1266, loss: 4.1266 +2024-12-26 16:47:16,007 - pyskl - INFO - Epoch [19][1000/3746] lr: 9.639e-02, eta: 4 days, 6:25:26, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.5028, loss_cls: 4.1242, loss: 4.1242 +2024-12-26 16:48:27,374 - pyskl - INFO - Epoch [19][1100/3746] lr: 9.637e-02, eta: 4 days, 6:23:47, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.5020, loss_cls: 4.0955, loss: 4.0955 +2024-12-26 16:49:38,745 - pyskl - INFO - Epoch [19][1200/3746] lr: 9.636e-02, eta: 4 days, 6:22:08, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4872, loss_cls: 4.1686, loss: 4.1686 +2024-12-26 16:50:50,009 - pyskl - INFO - Epoch [19][1300/3746] lr: 9.635e-02, eta: 4 days, 6:20:29, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4961, loss_cls: 4.1222, loss: 4.1222 +2024-12-26 16:52:01,445 - pyskl - INFO - Epoch [19][1400/3746] lr: 9.634e-02, eta: 4 days, 6:18:51, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.4983, loss_cls: 4.1004, loss: 4.1004 +2024-12-26 16:53:13,068 - pyskl - INFO - Epoch [19][1500/3746] lr: 9.633e-02, eta: 4 days, 6:17:14, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4969, loss_cls: 4.1362, loss: 4.1362 +2024-12-26 16:54:24,987 - pyskl - INFO - Epoch [19][1600/3746] lr: 9.632e-02, eta: 4 days, 6:15:40, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4955, loss_cls: 4.1038, loss: 4.1038 +2024-12-26 16:55:36,461 - pyskl - INFO - Epoch [19][1700/3746] lr: 9.631e-02, eta: 4 days, 6:14:02, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5002, loss_cls: 4.0701, loss: 4.0701 +2024-12-26 16:56:48,212 - pyskl - INFO - Epoch [19][1800/3746] lr: 9.630e-02, eta: 4 days, 6:12:26, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.5081, loss_cls: 4.1021, loss: 4.1021 +2024-12-26 16:57:59,998 - pyskl - INFO - Epoch [19][1900/3746] lr: 9.629e-02, eta: 4 days, 6:10:51, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4830, loss_cls: 4.1728, loss: 4.1728 +2024-12-26 16:59:11,499 - pyskl - INFO - Epoch [19][2000/3746] lr: 9.628e-02, eta: 4 days, 6:09:14, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.5009, loss_cls: 4.1201, loss: 4.1201 +2024-12-26 17:00:23,042 - pyskl - INFO - Epoch [19][2100/3746] lr: 9.627e-02, eta: 4 days, 6:07:37, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5083, loss_cls: 4.0783, loss: 4.0783 +2024-12-26 17:01:34,648 - pyskl - INFO - Epoch [19][2200/3746] lr: 9.626e-02, eta: 4 days, 6:06:01, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4916, loss_cls: 4.1544, loss: 4.1544 +2024-12-26 17:02:45,896 - pyskl - INFO - Epoch [19][2300/3746] lr: 9.625e-02, eta: 4 days, 6:04:22, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4930, loss_cls: 4.1313, loss: 4.1313 +2024-12-26 17:03:57,457 - pyskl - INFO - Epoch [19][2400/3746] lr: 9.624e-02, eta: 4 days, 6:02:45, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4981, loss_cls: 4.1503, loss: 4.1503 +2024-12-26 17:05:08,979 - pyskl - INFO - Epoch [19][2500/3746] lr: 9.623e-02, eta: 4 days, 6:01:09, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4939, loss_cls: 4.1369, loss: 4.1369 +2024-12-26 17:06:20,303 - pyskl - INFO - Epoch [19][2600/3746] lr: 9.622e-02, eta: 4 days, 5:59:31, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4920, loss_cls: 4.1151, loss: 4.1151 +2024-12-26 17:07:31,760 - pyskl - INFO - Epoch [19][2700/3746] lr: 9.621e-02, eta: 4 days, 5:57:53, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4975, loss_cls: 4.1004, loss: 4.1004 +2024-12-26 17:08:43,474 - pyskl - INFO - Epoch [19][2800/3746] lr: 9.620e-02, eta: 4 days, 5:56:18, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5002, loss_cls: 4.1075, loss: 4.1075 +2024-12-26 17:09:55,294 - pyskl - INFO - Epoch [19][2900/3746] lr: 9.618e-02, eta: 4 days, 5:54:44, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4942, loss_cls: 4.1460, loss: 4.1460 +2024-12-26 17:11:06,747 - pyskl - INFO - Epoch [19][3000/3746] lr: 9.617e-02, eta: 4 days, 5:53:07, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4961, loss_cls: 4.1068, loss: 4.1068 +2024-12-26 17:12:18,513 - pyskl - INFO - Epoch [19][3100/3746] lr: 9.616e-02, eta: 4 days, 5:51:33, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5102, loss_cls: 4.0732, loss: 4.0732 +2024-12-26 17:13:30,378 - pyskl - INFO - Epoch [19][3200/3746] lr: 9.615e-02, eta: 4 days, 5:49:59, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5027, loss_cls: 4.1271, loss: 4.1271 +2024-12-26 17:14:41,983 - pyskl - INFO - Epoch [19][3300/3746] lr: 9.614e-02, eta: 4 days, 5:48:23, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4900, loss_cls: 4.1413, loss: 4.1413 +2024-12-26 17:15:53,630 - pyskl - INFO - Epoch [19][3400/3746] lr: 9.613e-02, eta: 4 days, 5:46:48, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.5003, loss_cls: 4.1103, loss: 4.1103 +2024-12-26 17:17:05,208 - pyskl - INFO - Epoch [19][3500/3746] lr: 9.612e-02, eta: 4 days, 5:45:12, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5012, loss_cls: 4.0879, loss: 4.0879 +2024-12-26 17:18:16,887 - pyskl - INFO - Epoch [19][3600/3746] lr: 9.611e-02, eta: 4 days, 5:43:37, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4905, loss_cls: 4.1286, loss: 4.1286 +2024-12-26 17:19:28,749 - pyskl - INFO - Epoch [19][3700/3746] lr: 9.610e-02, eta: 4 days, 5:42:04, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4867, loss_cls: 4.1528, loss: 4.1528 +2024-12-26 17:20:03,253 - pyskl - INFO - Saving checkpoint at 19 epochs +2024-12-26 17:21:58,870 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 17:21:59,717 - pyskl - INFO - +top1_acc 0.1808 +top5_acc 0.4078 +2024-12-26 17:21:59,717 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 17:21:59,760 - pyskl - INFO - +mean_acc 0.1806 +2024-12-26 17:21:59,771 - pyskl - INFO - Epoch(val) [19][309] top1_acc: 0.1808, top5_acc: 0.4078, mean_class_accuracy: 0.1806 +2024-12-26 17:25:29,187 - pyskl - INFO - Epoch [20][100/3746] lr: 9.608e-02, eta: 4 days, 5:51:47, time: 2.094, data_time: 1.377, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5008, loss_cls: 4.0872, loss: 4.0872 +2024-12-26 17:26:40,820 - pyskl - INFO - Epoch [20][200/3746] lr: 9.607e-02, eta: 4 days, 5:50:11, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5127, loss_cls: 4.0351, loss: 4.0351 +2024-12-26 17:27:52,151 - pyskl - INFO - Epoch [20][300/3746] lr: 9.606e-02, eta: 4 days, 5:48:32, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5012, loss_cls: 4.0777, loss: 4.0777 +2024-12-26 17:29:04,046 - pyskl - INFO - Epoch [20][400/3746] lr: 9.605e-02, eta: 4 days, 5:46:58, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4972, loss_cls: 4.1085, loss: 4.1085 +2024-12-26 17:30:15,441 - pyskl - INFO - Epoch [20][500/3746] lr: 9.604e-02, eta: 4 days, 5:45:21, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.5031, loss_cls: 4.0862, loss: 4.0862 +2024-12-26 17:31:26,724 - pyskl - INFO - Epoch [20][600/3746] lr: 9.603e-02, eta: 4 days, 5:43:42, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4931, loss_cls: 4.1243, loss: 4.1243 +2024-12-26 17:32:38,424 - pyskl - INFO - Epoch [20][700/3746] lr: 9.602e-02, eta: 4 days, 5:42:07, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4991, loss_cls: 4.0971, loss: 4.0971 +2024-12-26 17:33:49,701 - pyskl - INFO - Epoch [20][800/3746] lr: 9.601e-02, eta: 4 days, 5:40:29, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5098, loss_cls: 4.0797, loss: 4.0797 +2024-12-26 17:35:01,220 - pyskl - INFO - Epoch [20][900/3746] lr: 9.600e-02, eta: 4 days, 5:38:53, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4966, loss_cls: 4.0887, loss: 4.0887 +2024-12-26 17:36:12,781 - pyskl - INFO - Epoch [20][1000/3746] lr: 9.598e-02, eta: 4 days, 5:37:16, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4973, loss_cls: 4.0998, loss: 4.0998 +2024-12-26 17:37:24,454 - pyskl - INFO - Epoch [20][1100/3746] lr: 9.597e-02, eta: 4 days, 5:35:41, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4930, loss_cls: 4.1363, loss: 4.1363 +2024-12-26 17:38:35,849 - pyskl - INFO - Epoch [20][1200/3746] lr: 9.596e-02, eta: 4 days, 5:34:04, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4875, loss_cls: 4.1449, loss: 4.1449 +2024-12-26 17:39:47,204 - pyskl - INFO - Epoch [20][1300/3746] lr: 9.595e-02, eta: 4 days, 5:32:27, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5138, loss_cls: 4.0497, loss: 4.0497 +2024-12-26 17:40:58,322 - pyskl - INFO - Epoch [20][1400/3746] lr: 9.594e-02, eta: 4 days, 5:30:48, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4992, loss_cls: 4.1229, loss: 4.1229 +2024-12-26 17:42:09,643 - pyskl - INFO - Epoch [20][1500/3746] lr: 9.593e-02, eta: 4 days, 5:29:11, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.5022, loss_cls: 4.1085, loss: 4.1085 +2024-12-26 17:43:20,903 - pyskl - INFO - Epoch [20][1600/3746] lr: 9.592e-02, eta: 4 days, 5:27:33, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.5025, loss_cls: 4.1340, loss: 4.1340 +2024-12-26 17:44:32,387 - pyskl - INFO - Epoch [20][1700/3746] lr: 9.591e-02, eta: 4 days, 5:25:57, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4998, loss_cls: 4.1147, loss: 4.1147 +2024-12-26 17:45:44,259 - pyskl - INFO - Epoch [20][1800/3746] lr: 9.590e-02, eta: 4 days, 5:24:24, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5009, loss_cls: 4.0789, loss: 4.0789 +2024-12-26 17:46:55,845 - pyskl - INFO - Epoch [20][1900/3746] lr: 9.588e-02, eta: 4 days, 5:22:48, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.5048, loss_cls: 4.1083, loss: 4.1083 +2024-12-26 17:48:07,577 - pyskl - INFO - Epoch [20][2000/3746] lr: 9.587e-02, eta: 4 days, 5:21:14, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.5003, loss_cls: 4.1182, loss: 4.1182 +2024-12-26 17:49:19,605 - pyskl - INFO - Epoch [20][2100/3746] lr: 9.586e-02, eta: 4 days, 5:19:42, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4931, loss_cls: 4.1291, loss: 4.1291 +2024-12-26 17:50:31,337 - pyskl - INFO - Epoch [20][2200/3746] lr: 9.585e-02, eta: 4 days, 5:18:08, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4950, loss_cls: 4.0951, loss: 4.0951 +2024-12-26 17:51:42,889 - pyskl - INFO - Epoch [20][2300/3746] lr: 9.584e-02, eta: 4 days, 5:16:33, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4966, loss_cls: 4.1215, loss: 4.1215 +2024-12-26 17:52:54,893 - pyskl - INFO - Epoch [20][2400/3746] lr: 9.583e-02, eta: 4 days, 5:15:00, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5084, loss_cls: 4.0626, loss: 4.0626 +2024-12-26 17:54:06,496 - pyskl - INFO - Epoch [20][2500/3746] lr: 9.582e-02, eta: 4 days, 5:13:26, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4959, loss_cls: 4.1356, loss: 4.1356 +2024-12-26 17:55:18,181 - pyskl - INFO - Epoch [20][2600/3746] lr: 9.581e-02, eta: 4 days, 5:11:51, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.5048, loss_cls: 4.1124, loss: 4.1124 +2024-12-26 17:56:29,619 - pyskl - INFO - Epoch [20][2700/3746] lr: 9.580e-02, eta: 4 days, 5:10:16, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.4928, loss_cls: 4.0989, loss: 4.0989 +2024-12-26 17:57:40,972 - pyskl - INFO - Epoch [20][2800/3746] lr: 9.578e-02, eta: 4 days, 5:08:39, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.5108, loss_cls: 4.0663, loss: 4.0663 +2024-12-26 17:58:52,357 - pyskl - INFO - Epoch [20][2900/3746] lr: 9.577e-02, eta: 4 days, 5:07:03, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5022, loss_cls: 4.0731, loss: 4.0731 +2024-12-26 18:00:04,327 - pyskl - INFO - Epoch [20][3000/3746] lr: 9.576e-02, eta: 4 days, 5:05:31, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5062, loss_cls: 4.0705, loss: 4.0705 +2024-12-26 18:01:16,293 - pyskl - INFO - Epoch [20][3100/3746] lr: 9.575e-02, eta: 4 days, 5:03:59, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4900, loss_cls: 4.1206, loss: 4.1206 +2024-12-26 18:02:28,042 - pyskl - INFO - Epoch [20][3200/3746] lr: 9.574e-02, eta: 4 days, 5:02:26, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5094, loss_cls: 4.0481, loss: 4.0481 +2024-12-26 18:03:39,434 - pyskl - INFO - Epoch [20][3300/3746] lr: 9.573e-02, eta: 4 days, 5:00:50, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4997, loss_cls: 4.1074, loss: 4.1074 +2024-12-26 18:04:51,289 - pyskl - INFO - Epoch [20][3400/3746] lr: 9.572e-02, eta: 4 days, 4:59:18, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5030, loss_cls: 4.0902, loss: 4.0902 +2024-12-26 18:06:02,812 - pyskl - INFO - Epoch [20][3500/3746] lr: 9.571e-02, eta: 4 days, 4:57:43, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4963, loss_cls: 4.1398, loss: 4.1398 +2024-12-26 18:07:14,249 - pyskl - INFO - Epoch [20][3600/3746] lr: 9.569e-02, eta: 4 days, 4:56:08, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5027, loss_cls: 4.0885, loss: 4.0885 +2024-12-26 18:08:25,727 - pyskl - INFO - Epoch [20][3700/3746] lr: 9.568e-02, eta: 4 days, 4:54:33, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4930, loss_cls: 4.1355, loss: 4.1355 +2024-12-26 18:09:00,313 - pyskl - INFO - Saving checkpoint at 20 epochs +2024-12-26 18:10:56,011 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 18:10:56,889 - pyskl - INFO - +top1_acc 0.1496 +top5_acc 0.3619 +2024-12-26 18:10:56,890 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 18:10:56,932 - pyskl - INFO - +mean_acc 0.1495 +2024-12-26 18:10:56,942 - pyskl - INFO - Epoch(val) [20][309] top1_acc: 0.1496, top5_acc: 0.3619, mean_class_accuracy: 0.1495 +2024-12-26 18:14:26,403 - pyskl - INFO - Epoch [21][100/3746] lr: 9.567e-02, eta: 4 days, 5:03:37, time: 2.095, data_time: 1.380, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5130, loss_cls: 4.0312, loss: 4.0312 +2024-12-26 18:15:38,609 - pyskl - INFO - Epoch [21][200/3746] lr: 9.565e-02, eta: 4 days, 5:02:05, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5120, loss_cls: 4.0447, loss: 4.0447 +2024-12-26 18:16:50,149 - pyskl - INFO - Epoch [21][300/3746] lr: 9.564e-02, eta: 4 days, 5:00:30, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5056, loss_cls: 4.1050, loss: 4.1050 +2024-12-26 18:18:02,002 - pyskl - INFO - Epoch [21][400/3746] lr: 9.563e-02, eta: 4 days, 4:58:57, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5069, loss_cls: 4.0572, loss: 4.0572 +2024-12-26 18:19:13,437 - pyskl - INFO - Epoch [21][500/3746] lr: 9.562e-02, eta: 4 days, 4:57:21, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5139, loss_cls: 4.0702, loss: 4.0702 +2024-12-26 18:20:25,800 - pyskl - INFO - Epoch [21][600/3746] lr: 9.561e-02, eta: 4 days, 4:55:51, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.5034, loss_cls: 4.0843, loss: 4.0843 +2024-12-26 18:21:37,702 - pyskl - INFO - Epoch [21][700/3746] lr: 9.560e-02, eta: 4 days, 4:54:18, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5055, loss_cls: 4.0923, loss: 4.0923 +2024-12-26 18:22:49,335 - pyskl - INFO - Epoch [21][800/3746] lr: 9.559e-02, eta: 4 days, 4:52:44, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4936, loss_cls: 4.1222, loss: 4.1222 +2024-12-26 18:24:01,308 - pyskl - INFO - Epoch [21][900/3746] lr: 9.557e-02, eta: 4 days, 4:51:12, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4958, loss_cls: 4.1027, loss: 4.1027 +2024-12-26 18:25:12,388 - pyskl - INFO - Epoch [21][1000/3746] lr: 9.556e-02, eta: 4 days, 4:49:34, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5069, loss_cls: 4.0844, loss: 4.0844 +2024-12-26 18:26:23,873 - pyskl - INFO - Epoch [21][1100/3746] lr: 9.555e-02, eta: 4 days, 4:47:59, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4989, loss_cls: 4.0983, loss: 4.0983 +2024-12-26 18:27:35,530 - pyskl - INFO - Epoch [21][1200/3746] lr: 9.554e-02, eta: 4 days, 4:46:25, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4972, loss_cls: 4.1152, loss: 4.1152 +2024-12-26 18:28:46,647 - pyskl - INFO - Epoch [21][1300/3746] lr: 9.553e-02, eta: 4 days, 4:44:47, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5031, loss_cls: 4.0822, loss: 4.0822 +2024-12-26 18:29:57,869 - pyskl - INFO - Epoch [21][1400/3746] lr: 9.552e-02, eta: 4 days, 4:43:11, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5002, loss_cls: 4.0905, loss: 4.0905 +2024-12-26 18:31:09,428 - pyskl - INFO - Epoch [21][1500/3746] lr: 9.551e-02, eta: 4 days, 4:41:36, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.4956, loss_cls: 4.0993, loss: 4.0993 +2024-12-26 18:32:21,612 - pyskl - INFO - Epoch [21][1600/3746] lr: 9.549e-02, eta: 4 days, 4:40:06, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5028, loss_cls: 4.0940, loss: 4.0940 +2024-12-26 18:33:32,986 - pyskl - INFO - Epoch [21][1700/3746] lr: 9.548e-02, eta: 4 days, 4:38:30, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4928, loss_cls: 4.1159, loss: 4.1159 +2024-12-26 18:34:44,936 - pyskl - INFO - Epoch [21][1800/3746] lr: 9.547e-02, eta: 4 days, 4:36:59, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.4998, loss_cls: 4.0988, loss: 4.0988 +2024-12-26 18:35:56,436 - pyskl - INFO - Epoch [21][1900/3746] lr: 9.546e-02, eta: 4 days, 4:35:24, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5098, loss_cls: 4.0770, loss: 4.0770 +2024-12-26 18:37:08,177 - pyskl - INFO - Epoch [21][2000/3746] lr: 9.545e-02, eta: 4 days, 4:33:51, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4906, loss_cls: 4.1639, loss: 4.1639 +2024-12-26 18:38:19,738 - pyskl - INFO - Epoch [21][2100/3746] lr: 9.544e-02, eta: 4 days, 4:32:17, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4972, loss_cls: 4.1277, loss: 4.1277 +2024-12-26 18:39:31,782 - pyskl - INFO - Epoch [21][2200/3746] lr: 9.542e-02, eta: 4 days, 4:30:46, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5053, loss_cls: 4.0810, loss: 4.0810 +2024-12-26 18:40:43,497 - pyskl - INFO - Epoch [21][2300/3746] lr: 9.541e-02, eta: 4 days, 4:29:13, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5150, loss_cls: 4.0665, loss: 4.0665 +2024-12-26 18:41:55,299 - pyskl - INFO - Epoch [21][2400/3746] lr: 9.540e-02, eta: 4 days, 4:27:40, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.5022, loss_cls: 4.0907, loss: 4.0907 +2024-12-26 18:43:06,842 - pyskl - INFO - Epoch [21][2500/3746] lr: 9.539e-02, eta: 4 days, 4:26:07, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4900, loss_cls: 4.1658, loss: 4.1658 +2024-12-26 18:44:18,364 - pyskl - INFO - Epoch [21][2600/3746] lr: 9.538e-02, eta: 4 days, 4:24:32, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5059, loss_cls: 4.0539, loss: 4.0539 +2024-12-26 18:45:30,112 - pyskl - INFO - Epoch [21][2700/3746] lr: 9.537e-02, eta: 4 days, 4:23:00, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5039, loss_cls: 4.0828, loss: 4.0828 +2024-12-26 18:46:41,796 - pyskl - INFO - Epoch [21][2800/3746] lr: 9.535e-02, eta: 4 days, 4:21:27, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4969, loss_cls: 4.1096, loss: 4.1096 +2024-12-26 18:47:53,532 - pyskl - INFO - Epoch [21][2900/3746] lr: 9.534e-02, eta: 4 days, 4:19:54, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5011, loss_cls: 4.0960, loss: 4.0960 +2024-12-26 18:49:05,352 - pyskl - INFO - Epoch [21][3000/3746] lr: 9.533e-02, eta: 4 days, 4:18:22, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4964, loss_cls: 4.1138, loss: 4.1138 +2024-12-26 18:50:17,119 - pyskl - INFO - Epoch [21][3100/3746] lr: 9.532e-02, eta: 4 days, 4:16:50, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4970, loss_cls: 4.1099, loss: 4.1099 +2024-12-26 18:51:28,838 - pyskl - INFO - Epoch [21][3200/3746] lr: 9.531e-02, eta: 4 days, 4:15:18, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5078, loss_cls: 4.0810, loss: 4.0810 +2024-12-26 18:52:40,243 - pyskl - INFO - Epoch [21][3300/3746] lr: 9.529e-02, eta: 4 days, 4:13:43, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.5059, loss_cls: 4.0816, loss: 4.0816 +2024-12-26 18:53:51,941 - pyskl - INFO - Epoch [21][3400/3746] lr: 9.528e-02, eta: 4 days, 4:12:11, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4956, loss_cls: 4.1270, loss: 4.1270 +2024-12-26 18:55:03,540 - pyskl - INFO - Epoch [21][3500/3746] lr: 9.527e-02, eta: 4 days, 4:10:38, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.5009, loss_cls: 4.0940, loss: 4.0940 +2024-12-26 18:56:15,089 - pyskl - INFO - Epoch [21][3600/3746] lr: 9.526e-02, eta: 4 days, 4:09:04, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5064, loss_cls: 4.0763, loss: 4.0763 +2024-12-26 18:57:26,782 - pyskl - INFO - Epoch [21][3700/3746] lr: 9.525e-02, eta: 4 days, 4:07:32, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4952, loss_cls: 4.1052, loss: 4.1052 +2024-12-26 18:58:01,180 - pyskl - INFO - Saving checkpoint at 21 epochs +2024-12-26 18:59:57,278 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 18:59:58,091 - pyskl - INFO - +top1_acc 0.1501 +top5_acc 0.3539 +2024-12-26 18:59:58,091 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 18:59:58,130 - pyskl - INFO - +mean_acc 0.1501 +2024-12-26 18:59:58,140 - pyskl - INFO - Epoch(val) [21][309] top1_acc: 0.1501, top5_acc: 0.3539, mean_class_accuracy: 0.1501 +2024-12-26 19:03:27,851 - pyskl - INFO - Epoch [22][100/3746] lr: 9.523e-02, eta: 4 days, 4:16:01, time: 2.097, data_time: 1.380, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5042, loss_cls: 4.0694, loss: 4.0694 +2024-12-26 19:04:39,620 - pyskl - INFO - Epoch [22][200/3746] lr: 9.522e-02, eta: 4 days, 4:14:29, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5088, loss_cls: 4.0687, loss: 4.0687 +2024-12-26 19:05:51,103 - pyskl - INFO - Epoch [22][300/3746] lr: 9.521e-02, eta: 4 days, 4:12:54, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4986, loss_cls: 4.1099, loss: 4.1099 +2024-12-26 19:07:02,580 - pyskl - INFO - Epoch [22][400/3746] lr: 9.519e-02, eta: 4 days, 4:11:20, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5128, loss_cls: 4.0425, loss: 4.0425 +2024-12-26 19:08:14,148 - pyskl - INFO - Epoch [22][500/3746] lr: 9.518e-02, eta: 4 days, 4:09:46, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5041, loss_cls: 4.0941, loss: 4.0941 +2024-12-26 19:09:25,390 - pyskl - INFO - Epoch [22][600/3746] lr: 9.517e-02, eta: 4 days, 4:08:10, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5069, loss_cls: 4.0613, loss: 4.0613 +2024-12-26 19:10:37,356 - pyskl - INFO - Epoch [22][700/3746] lr: 9.516e-02, eta: 4 days, 4:06:39, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4995, loss_cls: 4.1388, loss: 4.1388 +2024-12-26 19:11:48,987 - pyskl - INFO - Epoch [22][800/3746] lr: 9.515e-02, eta: 4 days, 4:05:06, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5112, loss_cls: 4.0460, loss: 4.0460 +2024-12-26 19:13:00,482 - pyskl - INFO - Epoch [22][900/3746] lr: 9.513e-02, eta: 4 days, 4:03:32, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4916, loss_cls: 4.1053, loss: 4.1053 +2024-12-26 19:14:12,110 - pyskl - INFO - Epoch [22][1000/3746] lr: 9.512e-02, eta: 4 days, 4:01:58, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5048, loss_cls: 4.0632, loss: 4.0632 +2024-12-26 19:15:23,487 - pyskl - INFO - Epoch [22][1100/3746] lr: 9.511e-02, eta: 4 days, 4:00:24, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4947, loss_cls: 4.1459, loss: 4.1459 +2024-12-26 19:16:34,596 - pyskl - INFO - Epoch [22][1200/3746] lr: 9.510e-02, eta: 4 days, 3:58:48, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5028, loss_cls: 4.0475, loss: 4.0475 +2024-12-26 19:17:46,313 - pyskl - INFO - Epoch [22][1300/3746] lr: 9.509e-02, eta: 4 days, 3:57:15, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.5039, loss_cls: 4.0774, loss: 4.0774 +2024-12-26 19:18:57,790 - pyskl - INFO - Epoch [22][1400/3746] lr: 9.507e-02, eta: 4 days, 3:55:41, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5095, loss_cls: 4.0712, loss: 4.0712 +2024-12-26 19:20:09,803 - pyskl - INFO - Epoch [22][1500/3746] lr: 9.506e-02, eta: 4 days, 3:54:11, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4986, loss_cls: 4.0759, loss: 4.0759 +2024-12-26 19:21:21,303 - pyskl - INFO - Epoch [22][1600/3746] lr: 9.505e-02, eta: 4 days, 3:52:37, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4953, loss_cls: 4.1373, loss: 4.1373 +2024-12-26 19:22:33,380 - pyskl - INFO - Epoch [22][1700/3746] lr: 9.504e-02, eta: 4 days, 3:51:07, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.5028, loss_cls: 4.0902, loss: 4.0902 +2024-12-26 19:23:45,115 - pyskl - INFO - Epoch [22][1800/3746] lr: 9.502e-02, eta: 4 days, 3:49:35, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5002, loss_cls: 4.1025, loss: 4.1025 +2024-12-26 19:24:57,158 - pyskl - INFO - Epoch [22][1900/3746] lr: 9.501e-02, eta: 4 days, 3:48:05, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5064, loss_cls: 4.0661, loss: 4.0661 +2024-12-26 19:26:09,078 - pyskl - INFO - Epoch [22][2000/3746] lr: 9.500e-02, eta: 4 days, 3:46:34, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4959, loss_cls: 4.1413, loss: 4.1413 +2024-12-26 19:27:20,663 - pyskl - INFO - Epoch [22][2100/3746] lr: 9.499e-02, eta: 4 days, 3:45:01, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4953, loss_cls: 4.1066, loss: 4.1066 +2024-12-26 19:28:32,293 - pyskl - INFO - Epoch [22][2200/3746] lr: 9.498e-02, eta: 4 days, 3:43:28, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5042, loss_cls: 4.0946, loss: 4.0946 +2024-12-26 19:29:44,205 - pyskl - INFO - Epoch [22][2300/3746] lr: 9.496e-02, eta: 4 days, 3:41:58, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4980, loss_cls: 4.1405, loss: 4.1405 +2024-12-26 19:30:56,288 - pyskl - INFO - Epoch [22][2400/3746] lr: 9.495e-02, eta: 4 days, 3:40:28, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.5103, loss_cls: 4.0913, loss: 4.0913 +2024-12-26 19:32:08,511 - pyskl - INFO - Epoch [22][2500/3746] lr: 9.494e-02, eta: 4 days, 3:38:59, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5055, loss_cls: 4.0634, loss: 4.0634 +2024-12-26 19:33:20,670 - pyskl - INFO - Epoch [22][2600/3746] lr: 9.493e-02, eta: 4 days, 3:37:30, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5069, loss_cls: 4.0841, loss: 4.0841 +2024-12-26 19:34:32,853 - pyskl - INFO - Epoch [22][2700/3746] lr: 9.491e-02, eta: 4 days, 3:36:01, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5062, loss_cls: 4.1016, loss: 4.1016 +2024-12-26 19:35:45,426 - pyskl - INFO - Epoch [22][2800/3746] lr: 9.490e-02, eta: 4 days, 3:34:34, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.5117, loss_cls: 4.0712, loss: 4.0712 +2024-12-26 19:36:57,404 - pyskl - INFO - Epoch [22][2900/3746] lr: 9.489e-02, eta: 4 days, 3:33:04, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5048, loss_cls: 4.0965, loss: 4.0965 +2024-12-26 19:38:09,287 - pyskl - INFO - Epoch [22][3000/3746] lr: 9.488e-02, eta: 4 days, 3:31:33, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.5055, loss_cls: 4.0998, loss: 4.0998 +2024-12-26 19:39:21,386 - pyskl - INFO - Epoch [22][3100/3746] lr: 9.487e-02, eta: 4 days, 3:30:04, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5109, loss_cls: 4.0531, loss: 4.0531 +2024-12-26 19:40:33,414 - pyskl - INFO - Epoch [22][3200/3746] lr: 9.485e-02, eta: 4 days, 3:28:34, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5055, loss_cls: 4.0783, loss: 4.0783 +2024-12-26 19:41:45,786 - pyskl - INFO - Epoch [22][3300/3746] lr: 9.484e-02, eta: 4 days, 3:27:07, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4994, loss_cls: 4.1193, loss: 4.1193 +2024-12-26 19:42:58,178 - pyskl - INFO - Epoch [22][3400/3746] lr: 9.483e-02, eta: 4 days, 3:25:39, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5014, loss_cls: 4.1051, loss: 4.1051 +2024-12-26 19:44:10,273 - pyskl - INFO - Epoch [22][3500/3746] lr: 9.482e-02, eta: 4 days, 3:24:10, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4994, loss_cls: 4.0837, loss: 4.0837 +2024-12-26 19:45:22,079 - pyskl - INFO - Epoch [22][3600/3746] lr: 9.480e-02, eta: 4 days, 3:22:39, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5203, loss_cls: 4.0225, loss: 4.0225 +2024-12-26 19:46:34,186 - pyskl - INFO - Epoch [22][3700/3746] lr: 9.479e-02, eta: 4 days, 3:21:10, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5100, loss_cls: 4.0712, loss: 4.0712 +2024-12-26 19:47:09,278 - pyskl - INFO - Saving checkpoint at 22 epochs +2024-12-26 19:49:05,463 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 19:49:06,387 - pyskl - INFO - +top1_acc 0.1734 +top5_acc 0.3923 +2024-12-26 19:49:06,388 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 19:49:06,427 - pyskl - INFO - +mean_acc 0.1734 +2024-12-26 19:49:06,438 - pyskl - INFO - Epoch(val) [22][309] top1_acc: 0.1734, top5_acc: 0.3923, mean_class_accuracy: 0.1734 +2024-12-26 19:52:36,949 - pyskl - INFO - Epoch [23][100/3746] lr: 9.477e-02, eta: 4 days, 3:29:11, time: 2.105, data_time: 1.386, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5205, loss_cls: 4.0183, loss: 4.0183 +2024-12-26 19:53:48,756 - pyskl - INFO - Epoch [23][200/3746] lr: 9.476e-02, eta: 4 days, 3:27:40, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5158, loss_cls: 4.0610, loss: 4.0610 +2024-12-26 19:55:00,483 - pyskl - INFO - Epoch [23][300/3746] lr: 9.475e-02, eta: 4 days, 3:26:08, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4994, loss_cls: 4.0861, loss: 4.0861 +2024-12-26 19:56:12,113 - pyskl - INFO - Epoch [23][400/3746] lr: 9.474e-02, eta: 4 days, 3:24:35, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5128, loss_cls: 4.0458, loss: 4.0458 +2024-12-26 19:57:23,745 - pyskl - INFO - Epoch [23][500/3746] lr: 9.472e-02, eta: 4 days, 3:23:03, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4936, loss_cls: 4.1147, loss: 4.1147 +2024-12-26 19:58:35,043 - pyskl - INFO - Epoch [23][600/3746] lr: 9.471e-02, eta: 4 days, 3:21:29, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5041, loss_cls: 4.1015, loss: 4.1015 +2024-12-26 19:59:46,063 - pyskl - INFO - Epoch [23][700/3746] lr: 9.470e-02, eta: 4 days, 3:19:53, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5086, loss_cls: 4.0375, loss: 4.0375 +2024-12-26 20:00:57,385 - pyskl - INFO - Epoch [23][800/3746] lr: 9.469e-02, eta: 4 days, 3:18:19, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5120, loss_cls: 4.0315, loss: 4.0315 +2024-12-26 20:02:09,058 - pyskl - INFO - Epoch [23][900/3746] lr: 9.467e-02, eta: 4 days, 3:16:47, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5047, loss_cls: 4.0734, loss: 4.0734 +2024-12-26 20:03:20,746 - pyskl - INFO - Epoch [23][1000/3746] lr: 9.466e-02, eta: 4 days, 3:15:15, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4980, loss_cls: 4.1147, loss: 4.1147 +2024-12-26 20:04:31,989 - pyskl - INFO - Epoch [23][1100/3746] lr: 9.465e-02, eta: 4 days, 3:13:41, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4939, loss_cls: 4.1194, loss: 4.1194 +2024-12-26 20:05:43,690 - pyskl - INFO - Epoch [23][1200/3746] lr: 9.464e-02, eta: 4 days, 3:12:09, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5114, loss_cls: 4.0731, loss: 4.0731 +2024-12-26 20:06:55,196 - pyskl - INFO - Epoch [23][1300/3746] lr: 9.462e-02, eta: 4 days, 3:10:36, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5098, loss_cls: 4.0765, loss: 4.0765 +2024-12-26 20:08:06,696 - pyskl - INFO - Epoch [23][1400/3746] lr: 9.461e-02, eta: 4 days, 3:09:03, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.5019, loss_cls: 4.0844, loss: 4.0844 +2024-12-26 20:09:18,321 - pyskl - INFO - Epoch [23][1500/3746] lr: 9.460e-02, eta: 4 days, 3:07:31, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5139, loss_cls: 4.0629, loss: 4.0629 +2024-12-26 20:10:29,742 - pyskl - INFO - Epoch [23][1600/3746] lr: 9.459e-02, eta: 4 days, 3:05:58, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5077, loss_cls: 4.1010, loss: 4.1010 +2024-12-26 20:11:41,311 - pyskl - INFO - Epoch [23][1700/3746] lr: 9.457e-02, eta: 4 days, 3:04:26, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4994, loss_cls: 4.0984, loss: 4.0984 +2024-12-26 20:12:53,180 - pyskl - INFO - Epoch [23][1800/3746] lr: 9.456e-02, eta: 4 days, 3:02:56, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5073, loss_cls: 4.0906, loss: 4.0906 +2024-12-26 20:14:04,882 - pyskl - INFO - Epoch [23][1900/3746] lr: 9.455e-02, eta: 4 days, 3:01:24, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5031, loss_cls: 4.0807, loss: 4.0807 +2024-12-26 20:15:16,791 - pyskl - INFO - Epoch [23][2000/3746] lr: 9.453e-02, eta: 4 days, 2:59:54, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5067, loss_cls: 4.0708, loss: 4.0708 +2024-12-26 20:16:28,803 - pyskl - INFO - Epoch [23][2100/3746] lr: 9.452e-02, eta: 4 days, 2:58:25, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4886, loss_cls: 4.1128, loss: 4.1128 +2024-12-26 20:17:40,882 - pyskl - INFO - Epoch [23][2200/3746] lr: 9.451e-02, eta: 4 days, 2:56:56, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4997, loss_cls: 4.0995, loss: 4.0995 +2024-12-26 20:18:52,968 - pyskl - INFO - Epoch [23][2300/3746] lr: 9.450e-02, eta: 4 days, 2:55:27, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5084, loss_cls: 4.0249, loss: 4.0249 +2024-12-26 20:20:04,990 - pyskl - INFO - Epoch [23][2400/3746] lr: 9.448e-02, eta: 4 days, 2:53:57, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4925, loss_cls: 4.1482, loss: 4.1482 +2024-12-26 20:21:17,091 - pyskl - INFO - Epoch [23][2500/3746] lr: 9.447e-02, eta: 4 days, 2:52:29, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5020, loss_cls: 4.0812, loss: 4.0812 +2024-12-26 20:22:29,162 - pyskl - INFO - Epoch [23][2600/3746] lr: 9.446e-02, eta: 4 days, 2:51:00, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5167, loss_cls: 4.0449, loss: 4.0449 +2024-12-26 20:23:40,994 - pyskl - INFO - Epoch [23][2700/3746] lr: 9.445e-02, eta: 4 days, 2:49:29, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.5062, loss_cls: 4.0713, loss: 4.0713 +2024-12-26 20:24:52,819 - pyskl - INFO - Epoch [23][2800/3746] lr: 9.443e-02, eta: 4 days, 2:47:59, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4845, loss_cls: 4.1274, loss: 4.1274 +2024-12-26 20:26:04,501 - pyskl - INFO - Epoch [23][2900/3746] lr: 9.442e-02, eta: 4 days, 2:46:28, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5112, loss_cls: 4.0570, loss: 4.0570 +2024-12-26 20:27:16,442 - pyskl - INFO - Epoch [23][3000/3746] lr: 9.441e-02, eta: 4 days, 2:44:59, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5052, loss_cls: 4.0979, loss: 4.0979 +2024-12-26 20:28:28,116 - pyskl - INFO - Epoch [23][3100/3746] lr: 9.439e-02, eta: 4 days, 2:43:28, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5073, loss_cls: 4.0608, loss: 4.0608 +2024-12-26 20:29:40,351 - pyskl - INFO - Epoch [23][3200/3746] lr: 9.438e-02, eta: 4 days, 2:42:00, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5053, loss_cls: 4.0860, loss: 4.0860 +2024-12-26 20:30:52,519 - pyskl - INFO - Epoch [23][3300/3746] lr: 9.437e-02, eta: 4 days, 2:40:32, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5077, loss_cls: 4.0751, loss: 4.0751 +2024-12-26 20:32:04,559 - pyskl - INFO - Epoch [23][3400/3746] lr: 9.436e-02, eta: 4 days, 2:39:03, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4994, loss_cls: 4.1286, loss: 4.1286 +2024-12-26 20:33:16,598 - pyskl - INFO - Epoch [23][3500/3746] lr: 9.434e-02, eta: 4 days, 2:37:34, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5042, loss_cls: 4.0805, loss: 4.0805 +2024-12-26 20:34:28,286 - pyskl - INFO - Epoch [23][3600/3746] lr: 9.433e-02, eta: 4 days, 2:36:04, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4977, loss_cls: 4.1216, loss: 4.1216 +2024-12-26 20:35:40,514 - pyskl - INFO - Epoch [23][3700/3746] lr: 9.432e-02, eta: 4 days, 2:34:36, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5003, loss_cls: 4.0901, loss: 4.0901 +2024-12-26 20:36:15,025 - pyskl - INFO - Saving checkpoint at 23 epochs +2024-12-26 20:38:11,172 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 20:38:11,991 - pyskl - INFO - +top1_acc 0.1798 +top5_acc 0.4004 +2024-12-26 20:38:11,991 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 20:38:12,034 - pyskl - INFO - +mean_acc 0.1798 +2024-12-26 20:38:12,048 - pyskl - INFO - Epoch(val) [23][309] top1_acc: 0.1798, top5_acc: 0.4004, mean_class_accuracy: 0.1798 +2024-12-26 20:41:42,435 - pyskl - INFO - Epoch [24][100/3746] lr: 9.430e-02, eta: 4 days, 2:42:07, time: 2.104, data_time: 1.386, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5102, loss_cls: 4.0415, loss: 4.0415 +2024-12-26 20:42:53,925 - pyskl - INFO - Epoch [24][200/3746] lr: 9.428e-02, eta: 4 days, 2:40:34, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.5111, loss_cls: 4.0534, loss: 4.0534 +2024-12-26 20:44:05,491 - pyskl - INFO - Epoch [24][300/3746] lr: 9.427e-02, eta: 4 days, 2:39:02, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5112, loss_cls: 4.0468, loss: 4.0468 +2024-12-26 20:45:16,988 - pyskl - INFO - Epoch [24][400/3746] lr: 9.426e-02, eta: 4 days, 2:37:30, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5084, loss_cls: 4.0689, loss: 4.0689 +2024-12-26 20:46:28,702 - pyskl - INFO - Epoch [24][500/3746] lr: 9.425e-02, eta: 4 days, 2:35:59, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5100, loss_cls: 4.0679, loss: 4.0679 +2024-12-26 20:47:39,825 - pyskl - INFO - Epoch [24][600/3746] lr: 9.423e-02, eta: 4 days, 2:34:25, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5034, loss_cls: 4.0761, loss: 4.0761 +2024-12-26 20:48:51,176 - pyskl - INFO - Epoch [24][700/3746] lr: 9.422e-02, eta: 4 days, 2:32:52, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.5039, loss_cls: 4.0660, loss: 4.0660 +2024-12-26 20:50:03,034 - pyskl - INFO - Epoch [24][800/3746] lr: 9.421e-02, eta: 4 days, 2:31:22, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4953, loss_cls: 4.1247, loss: 4.1247 +2024-12-26 20:51:14,362 - pyskl - INFO - Epoch [24][900/3746] lr: 9.419e-02, eta: 4 days, 2:29:49, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.5078, loss_cls: 4.0468, loss: 4.0468 +2024-12-26 20:52:25,790 - pyskl - INFO - Epoch [24][1000/3746] lr: 9.418e-02, eta: 4 days, 2:28:16, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4989, loss_cls: 4.0779, loss: 4.0779 +2024-12-26 20:53:37,508 - pyskl - INFO - Epoch [24][1100/3746] lr: 9.417e-02, eta: 4 days, 2:26:46, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.5012, loss_cls: 4.1183, loss: 4.1183 +2024-12-26 20:54:48,724 - pyskl - INFO - Epoch [24][1200/3746] lr: 9.415e-02, eta: 4 days, 2:25:12, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5039, loss_cls: 4.0928, loss: 4.0928 +2024-12-26 20:55:59,928 - pyskl - INFO - Epoch [24][1300/3746] lr: 9.414e-02, eta: 4 days, 2:23:39, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.5141, loss_cls: 4.0819, loss: 4.0819 +2024-12-26 20:57:11,007 - pyskl - INFO - Epoch [24][1400/3746] lr: 9.413e-02, eta: 4 days, 2:22:05, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5136, loss_cls: 4.0536, loss: 4.0536 +2024-12-26 20:58:22,696 - pyskl - INFO - Epoch [24][1500/3746] lr: 9.411e-02, eta: 4 days, 2:20:34, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5016, loss_cls: 4.0898, loss: 4.0898 +2024-12-26 20:59:34,188 - pyskl - INFO - Epoch [24][1600/3746] lr: 9.410e-02, eta: 4 days, 2:19:02, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5208, loss_cls: 4.0195, loss: 4.0195 +2024-12-26 21:00:46,161 - pyskl - INFO - Epoch [24][1700/3746] lr: 9.409e-02, eta: 4 days, 2:17:33, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5030, loss_cls: 4.0835, loss: 4.0835 +2024-12-26 21:01:57,938 - pyskl - INFO - Epoch [24][1800/3746] lr: 9.407e-02, eta: 4 days, 2:16:03, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5102, loss_cls: 4.0471, loss: 4.0471 +2024-12-26 21:03:09,602 - pyskl - INFO - Epoch [24][1900/3746] lr: 9.406e-02, eta: 4 days, 2:14:32, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.5006, loss_cls: 4.0930, loss: 4.0930 +2024-12-26 21:04:21,340 - pyskl - INFO - Epoch [24][2000/3746] lr: 9.405e-02, eta: 4 days, 2:13:02, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5198, loss_cls: 4.0239, loss: 4.0239 +2024-12-26 21:05:33,411 - pyskl - INFO - Epoch [24][2100/3746] lr: 9.404e-02, eta: 4 days, 2:11:34, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5064, loss_cls: 4.0681, loss: 4.0681 +2024-12-26 21:06:45,720 - pyskl - INFO - Epoch [24][2200/3746] lr: 9.402e-02, eta: 4 days, 2:10:07, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5027, loss_cls: 4.0965, loss: 4.0965 +2024-12-26 21:07:57,686 - pyskl - INFO - Epoch [24][2300/3746] lr: 9.401e-02, eta: 4 days, 2:08:38, time: 0.720, data_time: 0.001, memory: 15990, top1_acc: 0.2494, top5_acc: 0.5017, loss_cls: 4.0819, loss: 4.0819 +2024-12-26 21:09:09,324 - pyskl - INFO - Epoch [24][2400/3746] lr: 9.400e-02, eta: 4 days, 2:07:07, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5138, loss_cls: 4.0286, loss: 4.0286 +2024-12-26 21:10:21,416 - pyskl - INFO - Epoch [24][2500/3746] lr: 9.398e-02, eta: 4 days, 2:05:39, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.5095, loss_cls: 4.0688, loss: 4.0688 +2024-12-26 21:11:33,297 - pyskl - INFO - Epoch [24][2600/3746] lr: 9.397e-02, eta: 4 days, 2:04:10, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5092, loss_cls: 4.0604, loss: 4.0604 +2024-12-26 21:12:45,274 - pyskl - INFO - Epoch [24][2700/3746] lr: 9.396e-02, eta: 4 days, 2:02:41, time: 0.720, data_time: 0.001, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5072, loss_cls: 4.0722, loss: 4.0722 +2024-12-26 21:13:57,475 - pyskl - INFO - Epoch [24][2800/3746] lr: 9.394e-02, eta: 4 days, 2:01:14, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5020, loss_cls: 4.0816, loss: 4.0816 +2024-12-26 21:15:09,422 - pyskl - INFO - Epoch [24][2900/3746] lr: 9.393e-02, eta: 4 days, 1:59:45, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4905, loss_cls: 4.1689, loss: 4.1689 +2024-12-26 21:16:21,555 - pyskl - INFO - Epoch [24][3000/3746] lr: 9.392e-02, eta: 4 days, 1:58:17, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5078, loss_cls: 4.0724, loss: 4.0724 +2024-12-26 21:17:33,624 - pyskl - INFO - Epoch [24][3100/3746] lr: 9.390e-02, eta: 4 days, 1:56:49, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5059, loss_cls: 4.0668, loss: 4.0668 +2024-12-26 21:18:45,504 - pyskl - INFO - Epoch [24][3200/3746] lr: 9.389e-02, eta: 4 days, 1:55:20, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5083, loss_cls: 4.0454, loss: 4.0454 +2024-12-26 21:19:57,564 - pyskl - INFO - Epoch [24][3300/3746] lr: 9.388e-02, eta: 4 days, 1:53:52, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5041, loss_cls: 4.0860, loss: 4.0860 +2024-12-26 21:21:09,455 - pyskl - INFO - Epoch [24][3400/3746] lr: 9.386e-02, eta: 4 days, 1:52:23, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5005, loss_cls: 4.1205, loss: 4.1205 +2024-12-26 21:22:21,619 - pyskl - INFO - Epoch [24][3500/3746] lr: 9.385e-02, eta: 4 days, 1:50:56, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4988, loss_cls: 4.1031, loss: 4.1031 +2024-12-26 21:23:33,349 - pyskl - INFO - Epoch [24][3600/3746] lr: 9.384e-02, eta: 4 days, 1:49:26, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.5020, loss_cls: 4.0831, loss: 4.0831 +2024-12-26 21:24:45,048 - pyskl - INFO - Epoch [24][3700/3746] lr: 9.382e-02, eta: 4 days, 1:47:56, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5019, loss_cls: 4.0955, loss: 4.0955 +2024-12-26 21:25:19,758 - pyskl - INFO - Saving checkpoint at 24 epochs +2024-12-26 21:27:15,753 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 21:27:16,592 - pyskl - INFO - +top1_acc 0.1891 +top5_acc 0.4100 +2024-12-26 21:27:16,592 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 21:27:16,630 - pyskl - INFO - +mean_acc 0.1891 +2024-12-26 21:27:16,635 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_18.pth was removed +2024-12-26 21:27:16,887 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_24.pth. +2024-12-26 21:27:16,888 - pyskl - INFO - Best top1_acc is 0.1891 at 24 epoch. +2024-12-26 21:27:16,898 - pyskl - INFO - Epoch(val) [24][309] top1_acc: 0.1891, top5_acc: 0.4100, mean_class_accuracy: 0.1891 +2024-12-26 21:30:50,523 - pyskl - INFO - Epoch [25][100/3746] lr: 9.380e-02, eta: 4 days, 1:55:16, time: 2.136, data_time: 1.415, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5123, loss_cls: 4.0415, loss: 4.0415 +2024-12-26 21:32:02,060 - pyskl - INFO - Epoch [25][200/3746] lr: 9.379e-02, eta: 4 days, 1:53:45, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4981, loss_cls: 4.1015, loss: 4.1015 +2024-12-26 21:33:13,838 - pyskl - INFO - Epoch [25][300/3746] lr: 9.378e-02, eta: 4 days, 1:52:15, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5091, loss_cls: 4.0589, loss: 4.0589 +2024-12-26 21:34:25,134 - pyskl - INFO - Epoch [25][400/3746] lr: 9.376e-02, eta: 4 days, 1:50:43, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5103, loss_cls: 4.0807, loss: 4.0807 +2024-12-26 21:35:36,572 - pyskl - INFO - Epoch [25][500/3746] lr: 9.375e-02, eta: 4 days, 1:49:11, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5097, loss_cls: 4.0629, loss: 4.0629 +2024-12-26 21:36:48,140 - pyskl - INFO - Epoch [25][600/3746] lr: 9.373e-02, eta: 4 days, 1:47:40, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5094, loss_cls: 4.0552, loss: 4.0552 +2024-12-26 21:37:59,370 - pyskl - INFO - Epoch [25][700/3746] lr: 9.372e-02, eta: 4 days, 1:46:07, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5128, loss_cls: 4.0295, loss: 4.0295 +2024-12-26 21:39:10,890 - pyskl - INFO - Epoch [25][800/3746] lr: 9.371e-02, eta: 4 days, 1:44:36, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5081, loss_cls: 4.0978, loss: 4.0978 +2024-12-26 21:40:22,618 - pyskl - INFO - Epoch [25][900/3746] lr: 9.369e-02, eta: 4 days, 1:43:06, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5127, loss_cls: 4.0456, loss: 4.0456 +2024-12-26 21:41:34,329 - pyskl - INFO - Epoch [25][1000/3746] lr: 9.368e-02, eta: 4 days, 1:41:36, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4953, loss_cls: 4.1010, loss: 4.1010 +2024-12-26 21:42:45,603 - pyskl - INFO - Epoch [25][1100/3746] lr: 9.367e-02, eta: 4 days, 1:40:04, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5162, loss_cls: 4.0581, loss: 4.0581 +2024-12-26 21:43:57,106 - pyskl - INFO - Epoch [25][1200/3746] lr: 9.365e-02, eta: 4 days, 1:38:33, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.5028, loss_cls: 4.0870, loss: 4.0870 +2024-12-26 21:45:08,506 - pyskl - INFO - Epoch [25][1300/3746] lr: 9.364e-02, eta: 4 days, 1:37:01, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5092, loss_cls: 4.0506, loss: 4.0506 +2024-12-26 21:46:19,954 - pyskl - INFO - Epoch [25][1400/3746] lr: 9.363e-02, eta: 4 days, 1:35:30, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5056, loss_cls: 4.0549, loss: 4.0549 +2024-12-26 21:47:31,051 - pyskl - INFO - Epoch [25][1500/3746] lr: 9.361e-02, eta: 4 days, 1:33:57, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5070, loss_cls: 4.0478, loss: 4.0478 +2024-12-26 21:48:42,448 - pyskl - INFO - Epoch [25][1600/3746] lr: 9.360e-02, eta: 4 days, 1:32:26, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5025, loss_cls: 4.1085, loss: 4.1085 +2024-12-26 21:49:53,717 - pyskl - INFO - Epoch [25][1700/3746] lr: 9.358e-02, eta: 4 days, 1:30:54, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5072, loss_cls: 4.0686, loss: 4.0686 +2024-12-26 21:51:05,373 - pyskl - INFO - Epoch [25][1800/3746] lr: 9.357e-02, eta: 4 days, 1:29:24, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5105, loss_cls: 4.0518, loss: 4.0518 +2024-12-26 21:52:16,775 - pyskl - INFO - Epoch [25][1900/3746] lr: 9.356e-02, eta: 4 days, 1:27:53, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4978, loss_cls: 4.0922, loss: 4.0922 +2024-12-26 21:53:28,191 - pyskl - INFO - Epoch [25][2000/3746] lr: 9.354e-02, eta: 4 days, 1:26:21, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5088, loss_cls: 4.0540, loss: 4.0540 +2024-12-26 21:54:39,936 - pyskl - INFO - Epoch [25][2100/3746] lr: 9.353e-02, eta: 4 days, 1:24:52, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4952, loss_cls: 4.1166, loss: 4.1166 +2024-12-26 21:55:51,669 - pyskl - INFO - Epoch [25][2200/3746] lr: 9.352e-02, eta: 4 days, 1:23:23, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.5042, loss_cls: 4.1057, loss: 4.1057 +2024-12-26 21:57:03,476 - pyskl - INFO - Epoch [25][2300/3746] lr: 9.350e-02, eta: 4 days, 1:21:54, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5141, loss_cls: 4.0581, loss: 4.0581 +2024-12-26 21:58:14,943 - pyskl - INFO - Epoch [25][2400/3746] lr: 9.349e-02, eta: 4 days, 1:20:23, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.4950, loss_cls: 4.1003, loss: 4.1003 +2024-12-26 21:59:26,504 - pyskl - INFO - Epoch [25][2500/3746] lr: 9.347e-02, eta: 4 days, 1:18:53, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5039, loss_cls: 4.0741, loss: 4.0741 +2024-12-26 22:00:38,166 - pyskl - INFO - Epoch [25][2600/3746] lr: 9.346e-02, eta: 4 days, 1:17:23, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5061, loss_cls: 4.0539, loss: 4.0539 +2024-12-26 22:01:49,768 - pyskl - INFO - Epoch [25][2700/3746] lr: 9.345e-02, eta: 4 days, 1:15:53, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5120, loss_cls: 4.0186, loss: 4.0186 +2024-12-26 22:03:01,093 - pyskl - INFO - Epoch [25][2800/3746] lr: 9.343e-02, eta: 4 days, 1:14:22, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5014, loss_cls: 4.0495, loss: 4.0495 +2024-12-26 22:04:13,253 - pyskl - INFO - Epoch [25][2900/3746] lr: 9.342e-02, eta: 4 days, 1:12:55, time: 0.722, data_time: 0.001, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5022, loss_cls: 4.1084, loss: 4.1084 +2024-12-26 22:05:24,974 - pyskl - INFO - Epoch [25][3000/3746] lr: 9.341e-02, eta: 4 days, 1:11:26, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5094, loss_cls: 4.0344, loss: 4.0344 +2024-12-26 22:06:36,760 - pyskl - INFO - Epoch [25][3100/3746] lr: 9.339e-02, eta: 4 days, 1:09:57, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5028, loss_cls: 4.0898, loss: 4.0898 +2024-12-26 22:07:48,244 - pyskl - INFO - Epoch [25][3200/3746] lr: 9.338e-02, eta: 4 days, 1:08:27, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5100, loss_cls: 4.0306, loss: 4.0306 +2024-12-26 22:09:00,065 - pyskl - INFO - Epoch [25][3300/3746] lr: 9.336e-02, eta: 4 days, 1:06:58, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5091, loss_cls: 4.0451, loss: 4.0451 +2024-12-26 22:10:11,526 - pyskl - INFO - Epoch [25][3400/3746] lr: 9.335e-02, eta: 4 days, 1:05:28, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5116, loss_cls: 4.0647, loss: 4.0647 +2024-12-26 22:11:23,311 - pyskl - INFO - Epoch [25][3500/3746] lr: 9.334e-02, eta: 4 days, 1:03:59, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5045, loss_cls: 4.0579, loss: 4.0579 +2024-12-26 22:12:35,093 - pyskl - INFO - Epoch [25][3600/3746] lr: 9.332e-02, eta: 4 days, 1:02:31, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4998, loss_cls: 4.0854, loss: 4.0854 +2024-12-26 22:13:46,805 - pyskl - INFO - Epoch [25][3700/3746] lr: 9.331e-02, eta: 4 days, 1:01:02, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4989, loss_cls: 4.0936, loss: 4.0936 +2024-12-26 22:14:21,041 - pyskl - INFO - Saving checkpoint at 25 epochs +2024-12-26 22:16:17,830 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 22:16:18,830 - pyskl - INFO - +top1_acc 0.1683 +top5_acc 0.3962 +2024-12-26 22:16:18,831 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 22:16:18,880 - pyskl - INFO - +mean_acc 0.1681 +2024-12-26 22:16:18,890 - pyskl - INFO - Epoch(val) [25][309] top1_acc: 0.1683, top5_acc: 0.3962, mean_class_accuracy: 0.1681 +2024-12-26 22:19:50,546 - pyskl - INFO - Epoch [26][100/3746] lr: 9.329e-02, eta: 4 days, 1:07:46, time: 2.116, data_time: 1.400, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5175, loss_cls: 3.9949, loss: 3.9949 +2024-12-26 22:21:02,060 - pyskl - INFO - Epoch [26][200/3746] lr: 9.327e-02, eta: 4 days, 1:06:15, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5170, loss_cls: 4.0193, loss: 4.0193 +2024-12-26 22:22:13,535 - pyskl - INFO - Epoch [26][300/3746] lr: 9.326e-02, eta: 4 days, 1:04:45, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5170, loss_cls: 4.0478, loss: 4.0478 +2024-12-26 22:23:25,110 - pyskl - INFO - Epoch [26][400/3746] lr: 9.325e-02, eta: 4 days, 1:03:14, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5028, loss_cls: 4.0399, loss: 4.0399 +2024-12-26 22:24:36,534 - pyskl - INFO - Epoch [26][500/3746] lr: 9.323e-02, eta: 4 days, 1:01:44, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5158, loss_cls: 4.0379, loss: 4.0379 +2024-12-26 22:25:48,055 - pyskl - INFO - Epoch [26][600/3746] lr: 9.322e-02, eta: 4 days, 1:00:13, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5058, loss_cls: 4.0569, loss: 4.0569 +2024-12-26 22:26:59,515 - pyskl - INFO - Epoch [26][700/3746] lr: 9.320e-02, eta: 4 days, 0:58:43, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5228, loss_cls: 4.0116, loss: 4.0116 +2024-12-26 22:28:11,026 - pyskl - INFO - Epoch [26][800/3746] lr: 9.319e-02, eta: 4 days, 0:57:12, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5097, loss_cls: 4.0521, loss: 4.0521 +2024-12-26 22:29:22,519 - pyskl - INFO - Epoch [26][900/3746] lr: 9.318e-02, eta: 4 days, 0:55:42, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5083, loss_cls: 4.0558, loss: 4.0558 +2024-12-26 22:30:33,900 - pyskl - INFO - Epoch [26][1000/3746] lr: 9.316e-02, eta: 4 days, 0:54:11, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.5044, loss_cls: 4.0961, loss: 4.0961 +2024-12-26 22:31:45,191 - pyskl - INFO - Epoch [26][1100/3746] lr: 9.315e-02, eta: 4 days, 0:52:40, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5136, loss_cls: 4.0569, loss: 4.0569 +2024-12-26 22:32:57,106 - pyskl - INFO - Epoch [26][1200/3746] lr: 9.313e-02, eta: 4 days, 0:51:12, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5058, loss_cls: 4.0535, loss: 4.0535 +2024-12-26 22:34:08,397 - pyskl - INFO - Epoch [26][1300/3746] lr: 9.312e-02, eta: 4 days, 0:49:40, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5039, loss_cls: 4.0833, loss: 4.0833 +2024-12-26 22:35:19,788 - pyskl - INFO - Epoch [26][1400/3746] lr: 9.310e-02, eta: 4 days, 0:48:10, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5033, loss_cls: 4.0852, loss: 4.0852 +2024-12-26 22:36:31,249 - pyskl - INFO - Epoch [26][1500/3746] lr: 9.309e-02, eta: 4 days, 0:46:39, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4988, loss_cls: 4.1098, loss: 4.1098 +2024-12-26 22:37:42,645 - pyskl - INFO - Epoch [26][1600/3746] lr: 9.308e-02, eta: 4 days, 0:45:09, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5128, loss_cls: 4.0666, loss: 4.0666 +2024-12-26 22:38:54,362 - pyskl - INFO - Epoch [26][1700/3746] lr: 9.306e-02, eta: 4 days, 0:43:40, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5039, loss_cls: 4.0545, loss: 4.0545 +2024-12-26 22:40:06,012 - pyskl - INFO - Epoch [26][1800/3746] lr: 9.305e-02, eta: 4 days, 0:42:11, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4959, loss_cls: 4.1039, loss: 4.1039 +2024-12-26 22:41:17,365 - pyskl - INFO - Epoch [26][1900/3746] lr: 9.303e-02, eta: 4 days, 0:40:40, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4994, loss_cls: 4.1039, loss: 4.1039 +2024-12-26 22:42:28,841 - pyskl - INFO - Epoch [26][2000/3746] lr: 9.302e-02, eta: 4 days, 0:39:10, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5162, loss_cls: 4.0194, loss: 4.0194 +2024-12-26 22:43:40,256 - pyskl - INFO - Epoch [26][2100/3746] lr: 9.300e-02, eta: 4 days, 0:37:40, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5148, loss_cls: 4.0677, loss: 4.0677 +2024-12-26 22:44:51,794 - pyskl - INFO - Epoch [26][2200/3746] lr: 9.299e-02, eta: 4 days, 0:36:10, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5056, loss_cls: 4.0939, loss: 4.0939 +2024-12-26 22:46:03,417 - pyskl - INFO - Epoch [26][2300/3746] lr: 9.298e-02, eta: 4 days, 0:34:41, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5003, loss_cls: 4.1074, loss: 4.1074 +2024-12-26 22:47:15,323 - pyskl - INFO - Epoch [26][2400/3746] lr: 9.296e-02, eta: 4 days, 0:33:13, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5080, loss_cls: 4.0871, loss: 4.0871 +2024-12-26 22:48:27,221 - pyskl - INFO - Epoch [26][2500/3746] lr: 9.295e-02, eta: 4 days, 0:31:45, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5122, loss_cls: 4.0701, loss: 4.0701 +2024-12-26 22:49:38,976 - pyskl - INFO - Epoch [26][2600/3746] lr: 9.293e-02, eta: 4 days, 0:30:17, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5144, loss_cls: 4.0431, loss: 4.0431 +2024-12-26 22:50:50,900 - pyskl - INFO - Epoch [26][2700/3746] lr: 9.292e-02, eta: 4 days, 0:28:49, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5183, loss_cls: 4.0189, loss: 4.0189 +2024-12-26 22:52:02,795 - pyskl - INFO - Epoch [26][2800/3746] lr: 9.290e-02, eta: 4 days, 0:27:22, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5162, loss_cls: 4.0566, loss: 4.0566 +2024-12-26 22:53:14,961 - pyskl - INFO - Epoch [26][2900/3746] lr: 9.289e-02, eta: 4 days, 0:25:55, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5036, loss_cls: 4.0834, loss: 4.0834 +2024-12-26 22:54:26,863 - pyskl - INFO - Epoch [26][3000/3746] lr: 9.288e-02, eta: 4 days, 0:24:28, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5028, loss_cls: 4.0504, loss: 4.0504 +2024-12-26 22:55:38,896 - pyskl - INFO - Epoch [26][3100/3746] lr: 9.286e-02, eta: 4 days, 0:23:01, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5088, loss_cls: 4.0936, loss: 4.0936 +2024-12-26 22:56:51,218 - pyskl - INFO - Epoch [26][3200/3746] lr: 9.285e-02, eta: 4 days, 0:21:35, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5134, loss_cls: 4.0342, loss: 4.0342 +2024-12-26 22:58:03,413 - pyskl - INFO - Epoch [26][3300/3746] lr: 9.283e-02, eta: 4 days, 0:20:09, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5073, loss_cls: 4.0623, loss: 4.0623 +2024-12-26 22:59:15,421 - pyskl - INFO - Epoch [26][3400/3746] lr: 9.282e-02, eta: 4 days, 0:18:42, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5125, loss_cls: 4.0673, loss: 4.0673 +2024-12-26 23:00:27,637 - pyskl - INFO - Epoch [26][3500/3746] lr: 9.280e-02, eta: 4 days, 0:17:16, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5036, loss_cls: 4.0742, loss: 4.0742 +2024-12-26 23:01:39,756 - pyskl - INFO - Epoch [26][3600/3746] lr: 9.279e-02, eta: 4 days, 0:15:50, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5073, loss_cls: 4.0452, loss: 4.0452 +2024-12-26 23:02:52,070 - pyskl - INFO - Epoch [26][3700/3746] lr: 9.278e-02, eta: 4 days, 0:14:24, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5106, loss_cls: 4.0730, loss: 4.0730 +2024-12-26 23:03:26,937 - pyskl - INFO - Saving checkpoint at 26 epochs +2024-12-26 23:05:25,438 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 23:05:26,258 - pyskl - INFO - +top1_acc 0.1723 +top5_acc 0.3872 +2024-12-26 23:05:26,258 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 23:05:26,299 - pyskl - INFO - +mean_acc 0.1722 +2024-12-26 23:05:26,310 - pyskl - INFO - Epoch(val) [26][309] top1_acc: 0.1723, top5_acc: 0.3872, mean_class_accuracy: 0.1722 +2024-12-26 23:08:58,036 - pyskl - INFO - Epoch [27][100/3746] lr: 9.275e-02, eta: 4 days, 0:20:45, time: 2.117, data_time: 1.401, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5188, loss_cls: 4.0319, loss: 4.0319 +2024-12-26 23:10:09,726 - pyskl - INFO - Epoch [27][200/3746] lr: 9.274e-02, eta: 4 days, 0:19:16, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5177, loss_cls: 4.0159, loss: 4.0159 +2024-12-26 23:11:21,551 - pyskl - INFO - Epoch [27][300/3746] lr: 9.272e-02, eta: 4 days, 0:17:48, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5186, loss_cls: 4.0074, loss: 4.0074 +2024-12-26 23:12:33,339 - pyskl - INFO - Epoch [27][400/3746] lr: 9.271e-02, eta: 4 days, 0:16:20, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5053, loss_cls: 4.0456, loss: 4.0456 +2024-12-26 23:13:44,852 - pyskl - INFO - Epoch [27][500/3746] lr: 9.270e-02, eta: 4 days, 0:14:50, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5170, loss_cls: 4.0177, loss: 4.0177 +2024-12-26 23:14:56,068 - pyskl - INFO - Epoch [27][600/3746] lr: 9.268e-02, eta: 4 days, 0:13:19, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5072, loss_cls: 4.0716, loss: 4.0716 +2024-12-26 23:16:07,591 - pyskl - INFO - Epoch [27][700/3746] lr: 9.267e-02, eta: 4 days, 0:11:49, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5120, loss_cls: 4.0622, loss: 4.0622 +2024-12-26 23:17:18,758 - pyskl - INFO - Epoch [27][800/3746] lr: 9.265e-02, eta: 4 days, 0:10:18, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5084, loss_cls: 4.0429, loss: 4.0429 +2024-12-26 23:18:30,236 - pyskl - INFO - Epoch [27][900/3746] lr: 9.264e-02, eta: 4 days, 0:08:49, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5119, loss_cls: 4.0233, loss: 4.0233 +2024-12-26 23:19:41,966 - pyskl - INFO - Epoch [27][1000/3746] lr: 9.262e-02, eta: 4 days, 0:07:20, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5144, loss_cls: 4.0830, loss: 4.0830 +2024-12-26 23:20:53,296 - pyskl - INFO - Epoch [27][1100/3746] lr: 9.261e-02, eta: 4 days, 0:05:50, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5102, loss_cls: 4.0360, loss: 4.0360 +2024-12-26 23:22:04,650 - pyskl - INFO - Epoch [27][1200/3746] lr: 9.259e-02, eta: 4 days, 0:04:20, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5252, loss_cls: 4.0211, loss: 4.0211 +2024-12-26 23:23:16,043 - pyskl - INFO - Epoch [27][1300/3746] lr: 9.258e-02, eta: 4 days, 0:02:50, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5192, loss_cls: 4.0263, loss: 4.0263 +2024-12-26 23:24:27,677 - pyskl - INFO - Epoch [27][1400/3746] lr: 9.256e-02, eta: 4 days, 0:01:21, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5142, loss_cls: 4.0456, loss: 4.0456 +2024-12-26 23:25:39,429 - pyskl - INFO - Epoch [27][1500/3746] lr: 9.255e-02, eta: 3 days, 23:59:53, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5228, loss_cls: 3.9976, loss: 3.9976 +2024-12-26 23:26:50,966 - pyskl - INFO - Epoch [27][1600/3746] lr: 9.253e-02, eta: 3 days, 23:58:23, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4986, loss_cls: 4.0931, loss: 4.0931 +2024-12-26 23:28:02,659 - pyskl - INFO - Epoch [27][1700/3746] lr: 9.252e-02, eta: 3 days, 23:56:55, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5080, loss_cls: 4.0669, loss: 4.0669 +2024-12-26 23:29:14,158 - pyskl - INFO - Epoch [27][1800/3746] lr: 9.251e-02, eta: 3 days, 23:55:26, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5086, loss_cls: 4.0688, loss: 4.0688 +2024-12-26 23:30:26,101 - pyskl - INFO - Epoch [27][1900/3746] lr: 9.249e-02, eta: 3 days, 23:53:59, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5092, loss_cls: 4.0674, loss: 4.0674 +2024-12-26 23:31:37,800 - pyskl - INFO - Epoch [27][2000/3746] lr: 9.248e-02, eta: 3 days, 23:52:30, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5198, loss_cls: 4.0511, loss: 4.0511 +2024-12-26 23:32:49,388 - pyskl - INFO - Epoch [27][2100/3746] lr: 9.246e-02, eta: 3 days, 23:51:02, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4997, loss_cls: 4.0929, loss: 4.0929 +2024-12-26 23:34:00,949 - pyskl - INFO - Epoch [27][2200/3746] lr: 9.245e-02, eta: 3 days, 23:49:33, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5120, loss_cls: 4.0500, loss: 4.0500 +2024-12-26 23:35:12,485 - pyskl - INFO - Epoch [27][2300/3746] lr: 9.243e-02, eta: 3 days, 23:48:04, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5136, loss_cls: 4.0442, loss: 4.0442 +2024-12-26 23:36:24,732 - pyskl - INFO - Epoch [27][2400/3746] lr: 9.242e-02, eta: 3 days, 23:46:38, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5105, loss_cls: 4.0757, loss: 4.0757 +2024-12-26 23:37:36,990 - pyskl - INFO - Epoch [27][2500/3746] lr: 9.240e-02, eta: 3 days, 23:45:13, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5069, loss_cls: 4.0497, loss: 4.0497 +2024-12-26 23:38:48,765 - pyskl - INFO - Epoch [27][2600/3746] lr: 9.239e-02, eta: 3 days, 23:43:45, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5081, loss_cls: 4.0522, loss: 4.0522 +2024-12-26 23:40:00,493 - pyskl - INFO - Epoch [27][2700/3746] lr: 9.237e-02, eta: 3 days, 23:42:17, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5138, loss_cls: 4.0246, loss: 4.0246 +2024-12-26 23:41:12,416 - pyskl - INFO - Epoch [27][2800/3746] lr: 9.236e-02, eta: 3 days, 23:40:50, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5197, loss_cls: 4.0058, loss: 4.0058 +2024-12-26 23:42:24,687 - pyskl - INFO - Epoch [27][2900/3746] lr: 9.234e-02, eta: 3 days, 23:39:25, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5038, loss_cls: 4.0675, loss: 4.0675 +2024-12-26 23:43:36,500 - pyskl - INFO - Epoch [27][3000/3746] lr: 9.233e-02, eta: 3 days, 23:37:57, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5000, loss_cls: 4.0944, loss: 4.0944 +2024-12-26 23:44:48,478 - pyskl - INFO - Epoch [27][3100/3746] lr: 9.231e-02, eta: 3 days, 23:36:31, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5055, loss_cls: 4.0549, loss: 4.0549 +2024-12-26 23:46:00,193 - pyskl - INFO - Epoch [27][3200/3746] lr: 9.230e-02, eta: 3 days, 23:35:03, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5181, loss_cls: 4.0400, loss: 4.0400 +2024-12-26 23:47:11,988 - pyskl - INFO - Epoch [27][3300/3746] lr: 9.228e-02, eta: 3 days, 23:33:35, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5011, loss_cls: 4.0891, loss: 4.0891 +2024-12-26 23:48:23,662 - pyskl - INFO - Epoch [27][3400/3746] lr: 9.227e-02, eta: 3 days, 23:32:08, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5125, loss_cls: 4.0428, loss: 4.0428 +2024-12-26 23:49:35,576 - pyskl - INFO - Epoch [27][3500/3746] lr: 9.225e-02, eta: 3 days, 23:30:41, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5097, loss_cls: 4.0640, loss: 4.0640 +2024-12-26 23:50:47,906 - pyskl - INFO - Epoch [27][3600/3746] lr: 9.224e-02, eta: 3 days, 23:29:16, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5000, loss_cls: 4.0927, loss: 4.0927 +2024-12-26 23:51:59,811 - pyskl - INFO - Epoch [27][3700/3746] lr: 9.222e-02, eta: 3 days, 23:27:49, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5102, loss_cls: 4.0512, loss: 4.0512 +2024-12-26 23:52:34,449 - pyskl - INFO - Saving checkpoint at 27 epochs +2024-12-26 23:54:31,971 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 23:54:32,924 - pyskl - INFO - +top1_acc 0.1935 +top5_acc 0.4126 +2024-12-26 23:54:32,924 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 23:54:32,977 - pyskl - INFO - +mean_acc 0.1933 +2024-12-26 23:54:32,982 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_24.pth was removed +2024-12-26 23:54:33,504 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_27.pth. +2024-12-26 23:54:33,505 - pyskl - INFO - Best top1_acc is 0.1935 at 27 epoch. +2024-12-26 23:54:33,522 - pyskl - INFO - Epoch(val) [27][309] top1_acc: 0.1935, top5_acc: 0.4126, mean_class_accuracy: 0.1933 +2024-12-26 23:58:04,504 - pyskl - INFO - Epoch [28][100/3746] lr: 9.220e-02, eta: 3 days, 23:33:45, time: 2.110, data_time: 1.388, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5133, loss_cls: 4.0168, loss: 4.0168 +2024-12-26 23:59:16,294 - pyskl - INFO - Epoch [28][200/3746] lr: 9.219e-02, eta: 3 days, 23:32:17, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5147, loss_cls: 4.0216, loss: 4.0216 +2024-12-27 00:00:27,744 - pyskl - INFO - Epoch [28][300/3746] lr: 9.217e-02, eta: 3 days, 23:30:47, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5211, loss_cls: 3.9861, loss: 3.9861 +2024-12-27 00:01:39,152 - pyskl - INFO - Epoch [28][400/3746] lr: 9.216e-02, eta: 3 days, 23:29:18, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.4998, loss_cls: 4.0794, loss: 4.0794 +2024-12-27 00:02:50,734 - pyskl - INFO - Epoch [28][500/3746] lr: 9.214e-02, eta: 3 days, 23:27:49, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5245, loss_cls: 3.9878, loss: 3.9878 +2024-12-27 00:04:02,541 - pyskl - INFO - Epoch [28][600/3746] lr: 9.213e-02, eta: 3 days, 23:26:22, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5109, loss_cls: 4.0659, loss: 4.0659 +2024-12-27 00:05:13,957 - pyskl - INFO - Epoch [28][700/3746] lr: 9.211e-02, eta: 3 days, 23:24:52, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.5031, loss_cls: 4.0817, loss: 4.0817 +2024-12-27 00:06:25,266 - pyskl - INFO - Epoch [28][800/3746] lr: 9.210e-02, eta: 3 days, 23:23:23, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5141, loss_cls: 4.0151, loss: 4.0151 +2024-12-27 00:07:36,883 - pyskl - INFO - Epoch [28][900/3746] lr: 9.208e-02, eta: 3 days, 23:21:54, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5123, loss_cls: 4.0392, loss: 4.0392 +2024-12-27 00:08:48,169 - pyskl - INFO - Epoch [28][1000/3746] lr: 9.207e-02, eta: 3 days, 23:20:24, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5245, loss_cls: 3.9820, loss: 3.9820 +2024-12-27 00:09:59,455 - pyskl - INFO - Epoch [28][1100/3746] lr: 9.205e-02, eta: 3 days, 23:18:55, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4991, loss_cls: 4.0945, loss: 4.0945 +2024-12-27 00:11:10,902 - pyskl - INFO - Epoch [28][1200/3746] lr: 9.204e-02, eta: 3 days, 23:17:25, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5131, loss_cls: 4.0826, loss: 4.0826 +2024-12-27 00:12:22,398 - pyskl - INFO - Epoch [28][1300/3746] lr: 9.202e-02, eta: 3 days, 23:15:57, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5134, loss_cls: 4.0333, loss: 4.0333 +2024-12-27 00:13:34,255 - pyskl - INFO - Epoch [28][1400/3746] lr: 9.201e-02, eta: 3 days, 23:14:30, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5125, loss_cls: 4.0415, loss: 4.0415 +2024-12-27 00:14:46,188 - pyskl - INFO - Epoch [28][1500/3746] lr: 9.199e-02, eta: 3 days, 23:13:03, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5061, loss_cls: 4.0869, loss: 4.0869 +2024-12-27 00:15:57,940 - pyskl - INFO - Epoch [28][1600/3746] lr: 9.198e-02, eta: 3 days, 23:11:35, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5169, loss_cls: 4.0209, loss: 4.0209 +2024-12-27 00:17:09,447 - pyskl - INFO - Epoch [28][1700/3746] lr: 9.196e-02, eta: 3 days, 23:10:07, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5127, loss_cls: 4.0451, loss: 4.0451 +2024-12-27 00:18:21,247 - pyskl - INFO - Epoch [28][1800/3746] lr: 9.194e-02, eta: 3 days, 23:08:39, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5119, loss_cls: 4.0288, loss: 4.0288 +2024-12-27 00:19:32,676 - pyskl - INFO - Epoch [28][1900/3746] lr: 9.193e-02, eta: 3 days, 23:07:11, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.4955, loss_cls: 4.0811, loss: 4.0811 +2024-12-27 00:20:44,257 - pyskl - INFO - Epoch [28][2000/3746] lr: 9.191e-02, eta: 3 days, 23:05:42, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5116, loss_cls: 4.0616, loss: 4.0616 +2024-12-27 00:21:55,867 - pyskl - INFO - Epoch [28][2100/3746] lr: 9.190e-02, eta: 3 days, 23:04:14, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5153, loss_cls: 4.0161, loss: 4.0161 +2024-12-27 00:23:07,357 - pyskl - INFO - Epoch [28][2200/3746] lr: 9.188e-02, eta: 3 days, 23:02:46, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.5088, loss_cls: 4.0798, loss: 4.0798 +2024-12-27 00:24:18,961 - pyskl - INFO - Epoch [28][2300/3746] lr: 9.187e-02, eta: 3 days, 23:01:18, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5086, loss_cls: 4.0624, loss: 4.0624 +2024-12-27 00:25:30,283 - pyskl - INFO - Epoch [28][2400/3746] lr: 9.185e-02, eta: 3 days, 22:59:49, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5189, loss_cls: 3.9947, loss: 3.9947 +2024-12-27 00:26:41,614 - pyskl - INFO - Epoch [28][2500/3746] lr: 9.184e-02, eta: 3 days, 22:58:19, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5106, loss_cls: 4.0550, loss: 4.0550 +2024-12-27 00:27:53,447 - pyskl - INFO - Epoch [28][2600/3746] lr: 9.182e-02, eta: 3 days, 22:56:53, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5100, loss_cls: 4.0210, loss: 4.0210 +2024-12-27 00:29:05,149 - pyskl - INFO - Epoch [28][2700/3746] lr: 9.181e-02, eta: 3 days, 22:55:25, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5092, loss_cls: 4.0431, loss: 4.0431 +2024-12-27 00:30:16,827 - pyskl - INFO - Epoch [28][2800/3746] lr: 9.179e-02, eta: 3 days, 22:53:58, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5038, loss_cls: 4.0704, loss: 4.0704 +2024-12-27 00:31:28,801 - pyskl - INFO - Epoch [28][2900/3746] lr: 9.178e-02, eta: 3 days, 22:52:32, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5088, loss_cls: 4.0207, loss: 4.0207 +2024-12-27 00:32:40,355 - pyskl - INFO - Epoch [28][3000/3746] lr: 9.176e-02, eta: 3 days, 22:51:04, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5025, loss_cls: 4.0719, loss: 4.0719 +2024-12-27 00:33:52,275 - pyskl - INFO - Epoch [28][3100/3746] lr: 9.175e-02, eta: 3 days, 22:49:37, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5080, loss_cls: 4.0800, loss: 4.0800 +2024-12-27 00:35:03,920 - pyskl - INFO - Epoch [28][3200/3746] lr: 9.173e-02, eta: 3 days, 22:48:10, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5148, loss_cls: 4.0484, loss: 4.0484 +2024-12-27 00:36:16,141 - pyskl - INFO - Epoch [28][3300/3746] lr: 9.172e-02, eta: 3 days, 22:46:45, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5083, loss_cls: 4.0569, loss: 4.0569 +2024-12-27 00:37:28,077 - pyskl - INFO - Epoch [28][3400/3746] lr: 9.170e-02, eta: 3 days, 22:45:19, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5184, loss_cls: 4.0362, loss: 4.0362 +2024-12-27 00:38:39,724 - pyskl - INFO - Epoch [28][3500/3746] lr: 9.168e-02, eta: 3 days, 22:43:51, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5081, loss_cls: 4.0725, loss: 4.0725 +2024-12-27 00:39:51,457 - pyskl - INFO - Epoch [28][3600/3746] lr: 9.167e-02, eta: 3 days, 22:42:24, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5089, loss_cls: 4.0598, loss: 4.0598 +2024-12-27 00:41:03,426 - pyskl - INFO - Epoch [28][3700/3746] lr: 9.165e-02, eta: 3 days, 22:40:58, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5105, loss_cls: 4.0460, loss: 4.0460 +2024-12-27 00:41:38,042 - pyskl - INFO - Saving checkpoint at 28 epochs +2024-12-27 00:43:33,877 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 00:43:34,734 - pyskl - INFO - +top1_acc 0.1108 +top5_acc 0.2881 +2024-12-27 00:43:34,734 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 00:43:34,775 - pyskl - INFO - +mean_acc 0.1105 +2024-12-27 00:43:34,788 - pyskl - INFO - Epoch(val) [28][309] top1_acc: 0.1108, top5_acc: 0.2881, mean_class_accuracy: 0.1105 +2024-12-27 00:47:04,356 - pyskl - INFO - Epoch [29][100/3746] lr: 9.163e-02, eta: 3 days, 22:46:27, time: 2.096, data_time: 1.379, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5133, loss_cls: 4.0245, loss: 4.0245 +2024-12-27 00:48:15,961 - pyskl - INFO - Epoch [29][200/3746] lr: 9.162e-02, eta: 3 days, 22:44:59, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5167, loss_cls: 3.9926, loss: 3.9926 +2024-12-27 00:49:27,602 - pyskl - INFO - Epoch [29][300/3746] lr: 9.160e-02, eta: 3 days, 22:43:32, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5233, loss_cls: 3.9993, loss: 3.9993 +2024-12-27 00:50:39,219 - pyskl - INFO - Epoch [29][400/3746] lr: 9.158e-02, eta: 3 days, 22:42:04, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5095, loss_cls: 4.0527, loss: 4.0527 +2024-12-27 00:51:50,564 - pyskl - INFO - Epoch [29][500/3746] lr: 9.157e-02, eta: 3 days, 22:40:35, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5234, loss_cls: 3.9833, loss: 3.9833 +2024-12-27 00:53:02,012 - pyskl - INFO - Epoch [29][600/3746] lr: 9.155e-02, eta: 3 days, 22:39:06, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5148, loss_cls: 4.0179, loss: 4.0179 +2024-12-27 00:54:13,469 - pyskl - INFO - Epoch [29][700/3746] lr: 9.154e-02, eta: 3 days, 22:37:38, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5186, loss_cls: 3.9968, loss: 3.9968 +2024-12-27 00:55:25,072 - pyskl - INFO - Epoch [29][800/3746] lr: 9.152e-02, eta: 3 days, 22:36:10, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5111, loss_cls: 4.0400, loss: 4.0400 +2024-12-27 00:56:36,635 - pyskl - INFO - Epoch [29][900/3746] lr: 9.151e-02, eta: 3 days, 22:34:42, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5109, loss_cls: 4.0802, loss: 4.0802 +2024-12-27 00:57:48,727 - pyskl - INFO - Epoch [29][1000/3746] lr: 9.149e-02, eta: 3 days, 22:33:16, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5142, loss_cls: 4.0256, loss: 4.0256 +2024-12-27 00:59:00,461 - pyskl - INFO - Epoch [29][1100/3746] lr: 9.148e-02, eta: 3 days, 22:31:49, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5175, loss_cls: 4.0353, loss: 4.0353 +2024-12-27 01:00:11,634 - pyskl - INFO - Epoch [29][1200/3746] lr: 9.146e-02, eta: 3 days, 22:30:19, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5273, loss_cls: 3.9672, loss: 3.9672 +2024-12-27 01:01:23,209 - pyskl - INFO - Epoch [29][1300/3746] lr: 9.144e-02, eta: 3 days, 22:28:52, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5247, loss_cls: 4.0107, loss: 4.0107 +2024-12-27 01:02:34,740 - pyskl - INFO - Epoch [29][1400/3746] lr: 9.143e-02, eta: 3 days, 22:27:24, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5134, loss_cls: 4.0278, loss: 4.0278 +2024-12-27 01:03:46,020 - pyskl - INFO - Epoch [29][1500/3746] lr: 9.141e-02, eta: 3 days, 22:25:55, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5105, loss_cls: 4.0335, loss: 4.0335 +2024-12-27 01:04:58,085 - pyskl - INFO - Epoch [29][1600/3746] lr: 9.140e-02, eta: 3 days, 22:24:29, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5009, loss_cls: 4.0625, loss: 4.0625 +2024-12-27 01:06:09,855 - pyskl - INFO - Epoch [29][1700/3746] lr: 9.138e-02, eta: 3 days, 22:23:02, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5105, loss_cls: 4.0345, loss: 4.0345 +2024-12-27 01:07:21,360 - pyskl - INFO - Epoch [29][1800/3746] lr: 9.137e-02, eta: 3 days, 22:21:34, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5170, loss_cls: 4.0463, loss: 4.0463 +2024-12-27 01:08:32,831 - pyskl - INFO - Epoch [29][1900/3746] lr: 9.135e-02, eta: 3 days, 22:20:06, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5127, loss_cls: 4.0551, loss: 4.0551 +2024-12-27 01:09:44,287 - pyskl - INFO - Epoch [29][2000/3746] lr: 9.133e-02, eta: 3 days, 22:18:38, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5112, loss_cls: 4.0383, loss: 4.0383 +2024-12-27 01:10:55,953 - pyskl - INFO - Epoch [29][2100/3746] lr: 9.132e-02, eta: 3 days, 22:17:11, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4959, loss_cls: 4.0507, loss: 4.0507 +2024-12-27 01:12:07,761 - pyskl - INFO - Epoch [29][2200/3746] lr: 9.130e-02, eta: 3 days, 22:15:44, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5145, loss_cls: 4.0442, loss: 4.0442 +2024-12-27 01:13:19,166 - pyskl - INFO - Epoch [29][2300/3746] lr: 9.129e-02, eta: 3 days, 22:14:16, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5200, loss_cls: 4.0177, loss: 4.0177 +2024-12-27 01:14:31,084 - pyskl - INFO - Epoch [29][2400/3746] lr: 9.127e-02, eta: 3 days, 22:12:50, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5130, loss_cls: 4.0663, loss: 4.0663 +2024-12-27 01:15:43,214 - pyskl - INFO - Epoch [29][2500/3746] lr: 9.126e-02, eta: 3 days, 22:11:25, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5183, loss_cls: 4.0172, loss: 4.0172 +2024-12-27 01:16:54,980 - pyskl - INFO - Epoch [29][2600/3746] lr: 9.124e-02, eta: 3 days, 22:09:58, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5231, loss_cls: 3.9975, loss: 3.9975 +2024-12-27 01:18:06,783 - pyskl - INFO - Epoch [29][2700/3746] lr: 9.122e-02, eta: 3 days, 22:08:32, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5028, loss_cls: 4.0829, loss: 4.0829 +2024-12-27 01:19:18,551 - pyskl - INFO - Epoch [29][2800/3746] lr: 9.121e-02, eta: 3 days, 22:07:05, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5142, loss_cls: 4.0647, loss: 4.0647 +2024-12-27 01:20:30,343 - pyskl - INFO - Epoch [29][2900/3746] lr: 9.119e-02, eta: 3 days, 22:05:39, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5102, loss_cls: 4.0462, loss: 4.0462 +2024-12-27 01:21:42,321 - pyskl - INFO - Epoch [29][3000/3746] lr: 9.118e-02, eta: 3 days, 22:04:13, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5027, loss_cls: 4.0702, loss: 4.0702 +2024-12-27 01:22:53,994 - pyskl - INFO - Epoch [29][3100/3746] lr: 9.116e-02, eta: 3 days, 22:02:47, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5075, loss_cls: 4.0539, loss: 4.0539 +2024-12-27 01:24:05,867 - pyskl - INFO - Epoch [29][3200/3746] lr: 9.114e-02, eta: 3 days, 22:01:21, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5122, loss_cls: 4.0507, loss: 4.0507 +2024-12-27 01:25:17,591 - pyskl - INFO - Epoch [29][3300/3746] lr: 9.113e-02, eta: 3 days, 21:59:54, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5155, loss_cls: 4.0409, loss: 4.0409 +2024-12-27 01:26:29,037 - pyskl - INFO - Epoch [29][3400/3746] lr: 9.111e-02, eta: 3 days, 21:58:26, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5102, loss_cls: 4.0441, loss: 4.0441 +2024-12-27 01:27:40,512 - pyskl - INFO - Epoch [29][3500/3746] lr: 9.110e-02, eta: 3 days, 21:56:59, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4969, loss_cls: 4.1039, loss: 4.1039 +2024-12-27 01:28:52,707 - pyskl - INFO - Epoch [29][3600/3746] lr: 9.108e-02, eta: 3 days, 21:55:34, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5098, loss_cls: 4.0694, loss: 4.0694 +2024-12-27 01:30:04,898 - pyskl - INFO - Epoch [29][3700/3746] lr: 9.106e-02, eta: 3 days, 21:54:10, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5122, loss_cls: 4.0340, loss: 4.0340 +2024-12-27 01:30:39,600 - pyskl - INFO - Saving checkpoint at 29 epochs +2024-12-27 01:32:36,001 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 01:32:37,033 - pyskl - INFO - +top1_acc 0.1956 +top5_acc 0.4268 +2024-12-27 01:32:37,033 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 01:32:37,075 - pyskl - INFO - +mean_acc 0.1955 +2024-12-27 01:32:37,080 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_27.pth was removed +2024-12-27 01:32:37,340 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_29.pth. +2024-12-27 01:32:37,341 - pyskl - INFO - Best top1_acc is 0.1956 at 29 epoch. +2024-12-27 01:32:37,354 - pyskl - INFO - Epoch(val) [29][309] top1_acc: 0.1956, top5_acc: 0.4268, mean_class_accuracy: 0.1955 +2024-12-27 01:36:16,280 - pyskl - INFO - Epoch [30][100/3746] lr: 9.104e-02, eta: 3 days, 21:59:59, time: 2.189, data_time: 1.361, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5230, loss_cls: 3.9666, loss: 3.9666 +2024-12-27 01:37:39,671 - pyskl - INFO - Epoch [30][200/3746] lr: 9.103e-02, eta: 3 days, 21:59:21, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5233, loss_cls: 3.9872, loss: 3.9872 +2024-12-27 01:39:02,521 - pyskl - INFO - Epoch [30][300/3746] lr: 9.101e-02, eta: 3 days, 21:58:40, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5061, loss_cls: 4.0604, loss: 4.0604 +2024-12-27 01:40:24,949 - pyskl - INFO - Epoch [30][400/3746] lr: 9.099e-02, eta: 3 days, 21:57:58, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5156, loss_cls: 4.0560, loss: 4.0560 +2024-12-27 01:41:47,655 - pyskl - INFO - Epoch [30][500/3746] lr: 9.098e-02, eta: 3 days, 21:57:16, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5234, loss_cls: 4.0122, loss: 4.0122 +2024-12-27 01:43:09,928 - pyskl - INFO - Epoch [30][600/3746] lr: 9.096e-02, eta: 3 days, 21:56:33, time: 0.823, data_time: 0.001, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5214, loss_cls: 3.9770, loss: 3.9770 +2024-12-27 01:44:32,546 - pyskl - INFO - Epoch [30][700/3746] lr: 9.095e-02, eta: 3 days, 21:55:51, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5144, loss_cls: 4.0388, loss: 4.0388 +2024-12-27 01:45:55,197 - pyskl - INFO - Epoch [30][800/3746] lr: 9.093e-02, eta: 3 days, 21:55:09, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5094, loss_cls: 4.0418, loss: 4.0418 +2024-12-27 01:47:18,141 - pyskl - INFO - Epoch [30][900/3746] lr: 9.091e-02, eta: 3 days, 21:54:28, time: 0.829, data_time: 0.001, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5102, loss_cls: 4.0602, loss: 4.0602 +2024-12-27 01:48:41,697 - pyskl - INFO - Epoch [30][1000/3746] lr: 9.090e-02, eta: 3 days, 21:53:50, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5108, loss_cls: 4.0277, loss: 4.0277 +2024-12-27 01:50:04,795 - pyskl - INFO - Epoch [30][1100/3746] lr: 9.088e-02, eta: 3 days, 21:53:09, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5184, loss_cls: 4.0244, loss: 4.0244 +2024-12-27 01:51:27,434 - pyskl - INFO - Epoch [30][1200/3746] lr: 9.087e-02, eta: 3 days, 21:52:27, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5120, loss_cls: 4.0602, loss: 4.0602 +2024-12-27 01:52:50,264 - pyskl - INFO - Epoch [30][1300/3746] lr: 9.085e-02, eta: 3 days, 21:51:46, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5223, loss_cls: 4.0020, loss: 4.0020 +2024-12-27 01:54:13,107 - pyskl - INFO - Epoch [30][1400/3746] lr: 9.083e-02, eta: 3 days, 21:51:04, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5145, loss_cls: 4.0648, loss: 4.0648 +2024-12-27 01:55:35,749 - pyskl - INFO - Epoch [30][1500/3746] lr: 9.082e-02, eta: 3 days, 21:50:21, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5117, loss_cls: 4.0303, loss: 4.0303 +2024-12-27 01:56:58,913 - pyskl - INFO - Epoch [30][1600/3746] lr: 9.080e-02, eta: 3 days, 21:49:41, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4977, loss_cls: 4.0968, loss: 4.0968 +2024-12-27 01:58:22,009 - pyskl - INFO - Epoch [30][1700/3746] lr: 9.078e-02, eta: 3 days, 21:49:00, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5194, loss_cls: 4.0187, loss: 4.0187 +2024-12-27 01:59:44,668 - pyskl - INFO - Epoch [30][1800/3746] lr: 9.077e-02, eta: 3 days, 21:48:18, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5184, loss_cls: 3.9983, loss: 3.9983 +2024-12-27 02:01:07,322 - pyskl - INFO - Epoch [30][1900/3746] lr: 9.075e-02, eta: 3 days, 21:47:35, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.5041, loss_cls: 4.0746, loss: 4.0746 +2024-12-27 02:02:30,290 - pyskl - INFO - Epoch [30][2000/3746] lr: 9.074e-02, eta: 3 days, 21:46:53, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5219, loss_cls: 4.0220, loss: 4.0220 +2024-12-27 02:03:53,715 - pyskl - INFO - Epoch [30][2100/3746] lr: 9.072e-02, eta: 3 days, 21:46:14, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5269, loss_cls: 3.9899, loss: 3.9899 +2024-12-27 02:05:16,912 - pyskl - INFO - Epoch [30][2200/3746] lr: 9.070e-02, eta: 3 days, 21:45:33, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5125, loss_cls: 4.0148, loss: 4.0148 +2024-12-27 02:06:40,585 - pyskl - INFO - Epoch [30][2300/3746] lr: 9.069e-02, eta: 3 days, 21:44:54, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5180, loss_cls: 4.0409, loss: 4.0409 +2024-12-27 02:08:03,815 - pyskl - INFO - Epoch [30][2400/3746] lr: 9.067e-02, eta: 3 days, 21:44:13, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5227, loss_cls: 3.9970, loss: 3.9970 +2024-12-27 02:09:26,982 - pyskl - INFO - Epoch [30][2500/3746] lr: 9.065e-02, eta: 3 days, 21:43:32, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5103, loss_cls: 4.0264, loss: 4.0264 +2024-12-27 02:10:50,183 - pyskl - INFO - Epoch [30][2600/3746] lr: 9.064e-02, eta: 3 days, 21:42:51, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5083, loss_cls: 4.0485, loss: 4.0485 +2024-12-27 02:12:13,390 - pyskl - INFO - Epoch [30][2700/3746] lr: 9.062e-02, eta: 3 days, 21:42:10, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5061, loss_cls: 4.0460, loss: 4.0460 +2024-12-27 02:13:36,260 - pyskl - INFO - Epoch [30][2800/3746] lr: 9.061e-02, eta: 3 days, 21:41:27, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5186, loss_cls: 4.0474, loss: 4.0474 +2024-12-27 02:14:59,119 - pyskl - INFO - Epoch [30][2900/3746] lr: 9.059e-02, eta: 3 days, 21:40:45, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5117, loss_cls: 4.0456, loss: 4.0456 +2024-12-27 02:16:21,761 - pyskl - INFO - Epoch [30][3000/3746] lr: 9.057e-02, eta: 3 days, 21:40:01, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5048, loss_cls: 4.0799, loss: 4.0799 +2024-12-27 02:17:44,519 - pyskl - INFO - Epoch [30][3100/3746] lr: 9.056e-02, eta: 3 days, 21:39:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5153, loss_cls: 4.0333, loss: 4.0333 +2024-12-27 02:19:08,093 - pyskl - INFO - Epoch [30][3200/3746] lr: 9.054e-02, eta: 3 days, 21:38:38, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5153, loss_cls: 4.0201, loss: 4.0201 +2024-12-27 02:20:31,500 - pyskl - INFO - Epoch [30][3300/3746] lr: 9.052e-02, eta: 3 days, 21:37:57, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5052, loss_cls: 4.0764, loss: 4.0764 +2024-12-27 02:21:55,010 - pyskl - INFO - Epoch [30][3400/3746] lr: 9.051e-02, eta: 3 days, 21:37:17, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5177, loss_cls: 4.0233, loss: 4.0233 +2024-12-27 02:23:18,147 - pyskl - INFO - Epoch [30][3500/3746] lr: 9.049e-02, eta: 3 days, 21:36:35, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5166, loss_cls: 4.0221, loss: 4.0221 +2024-12-27 02:24:41,016 - pyskl - INFO - Epoch [30][3600/3746] lr: 9.047e-02, eta: 3 days, 21:35:52, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5058, loss_cls: 4.0621, loss: 4.0621 +2024-12-27 02:26:04,678 - pyskl - INFO - Epoch [30][3700/3746] lr: 9.046e-02, eta: 3 days, 21:35:12, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5067, loss_cls: 4.0366, loss: 4.0366 +2024-12-27 02:26:44,438 - pyskl - INFO - Saving checkpoint at 30 epochs +2024-12-27 02:28:41,347 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 02:28:42,345 - pyskl - INFO - +top1_acc 0.1850 +top5_acc 0.4114 +2024-12-27 02:28:42,345 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 02:28:42,391 - pyskl - INFO - +mean_acc 0.1848 +2024-12-27 02:28:42,402 - pyskl - INFO - Epoch(val) [30][309] top1_acc: 0.1850, top5_acc: 0.4114, mean_class_accuracy: 0.1848 +2024-12-27 02:32:43,544 - pyskl - INFO - Epoch [31][100/3746] lr: 9.043e-02, eta: 3 days, 21:42:09, time: 2.411, data_time: 1.387, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5258, loss_cls: 4.2086, loss: 4.2086 +2024-12-27 02:34:07,853 - pyskl - INFO - Epoch [31][200/3746] lr: 9.042e-02, eta: 3 days, 21:41:31, time: 0.843, data_time: 0.001, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5172, loss_cls: 4.2141, loss: 4.2141 +2024-12-27 02:35:32,447 - pyskl - INFO - Epoch [31][300/3746] lr: 9.040e-02, eta: 3 days, 21:40:54, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5128, loss_cls: 4.2388, loss: 4.2388 +2024-12-27 02:36:57,753 - pyskl - INFO - Epoch [31][400/3746] lr: 9.039e-02, eta: 3 days, 21:40:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5148, loss_cls: 4.2345, loss: 4.2345 +2024-12-27 02:38:22,494 - pyskl - INFO - Epoch [31][500/3746] lr: 9.037e-02, eta: 3 days, 21:39:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5125, loss_cls: 4.2609, loss: 4.2609 +2024-12-27 02:39:46,828 - pyskl - INFO - Epoch [31][600/3746] lr: 9.035e-02, eta: 3 days, 21:39:04, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5139, loss_cls: 4.2493, loss: 4.2493 +2024-12-27 02:41:11,543 - pyskl - INFO - Epoch [31][700/3746] lr: 9.034e-02, eta: 3 days, 21:38:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5102, loss_cls: 4.2368, loss: 4.2368 +2024-12-27 02:42:36,008 - pyskl - INFO - Epoch [31][800/3746] lr: 9.032e-02, eta: 3 days, 21:37:49, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5167, loss_cls: 4.2307, loss: 4.2307 +2024-12-27 02:44:00,402 - pyskl - INFO - Epoch [31][900/3746] lr: 9.030e-02, eta: 3 days, 21:37:11, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5056, loss_cls: 4.3074, loss: 4.3074 +2024-12-27 02:45:25,000 - pyskl - INFO - Epoch [31][1000/3746] lr: 9.029e-02, eta: 3 days, 21:36:33, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5180, loss_cls: 4.2179, loss: 4.2179 +2024-12-27 02:46:49,774 - pyskl - INFO - Epoch [31][1100/3746] lr: 9.027e-02, eta: 3 days, 21:35:56, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5114, loss_cls: 4.2591, loss: 4.2591 +2024-12-27 02:48:14,204 - pyskl - INFO - Epoch [31][1200/3746] lr: 9.025e-02, eta: 3 days, 21:35:18, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5098, loss_cls: 4.2500, loss: 4.2500 +2024-12-27 02:49:39,088 - pyskl - INFO - Epoch [31][1300/3746] lr: 9.024e-02, eta: 3 days, 21:34:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5200, loss_cls: 4.2892, loss: 4.2892 +2024-12-27 02:51:04,019 - pyskl - INFO - Epoch [31][1400/3746] lr: 9.022e-02, eta: 3 days, 21:34:04, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5158, loss_cls: 4.2604, loss: 4.2604 +2024-12-27 02:52:29,224 - pyskl - INFO - Epoch [31][1500/3746] lr: 9.020e-02, eta: 3 days, 21:33:28, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5055, loss_cls: 4.2573, loss: 4.2573 +2024-12-27 02:53:54,321 - pyskl - INFO - Epoch [31][1600/3746] lr: 9.019e-02, eta: 3 days, 21:32:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5175, loss_cls: 4.2425, loss: 4.2425 +2024-12-27 02:55:18,808 - pyskl - INFO - Epoch [31][1700/3746] lr: 9.017e-02, eta: 3 days, 21:32:14, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5067, loss_cls: 4.2588, loss: 4.2588 +2024-12-27 02:56:43,293 - pyskl - INFO - Epoch [31][1800/3746] lr: 9.015e-02, eta: 3 days, 21:31:35, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5156, loss_cls: 4.2225, loss: 4.2225 +2024-12-27 02:58:08,272 - pyskl - INFO - Epoch [31][1900/3746] lr: 9.014e-02, eta: 3 days, 21:30:58, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5180, loss_cls: 4.2568, loss: 4.2568 +2024-12-27 02:59:33,067 - pyskl - INFO - Epoch [31][2000/3746] lr: 9.012e-02, eta: 3 days, 21:30:20, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4956, loss_cls: 4.3362, loss: 4.3362 +2024-12-27 03:00:57,318 - pyskl - INFO - Epoch [31][2100/3746] lr: 9.010e-02, eta: 3 days, 21:29:40, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5195, loss_cls: 4.2424, loss: 4.2424 +2024-12-27 03:02:21,358 - pyskl - INFO - Epoch [31][2200/3746] lr: 9.009e-02, eta: 3 days, 21:28:59, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5127, loss_cls: 4.2763, loss: 4.2763 +2024-12-27 03:03:47,085 - pyskl - INFO - Epoch [31][2300/3746] lr: 9.007e-02, eta: 3 days, 21:28:25, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5122, loss_cls: 4.2764, loss: 4.2764 +2024-12-27 03:05:12,215 - pyskl - INFO - Epoch [31][2400/3746] lr: 9.005e-02, eta: 3 days, 21:27:48, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5200, loss_cls: 4.2341, loss: 4.2341 +2024-12-27 03:06:36,732 - pyskl - INFO - Epoch [31][2500/3746] lr: 9.004e-02, eta: 3 days, 21:27:09, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5220, loss_cls: 4.2241, loss: 4.2241 +2024-12-27 03:08:01,406 - pyskl - INFO - Epoch [31][2600/3746] lr: 9.002e-02, eta: 3 days, 21:26:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5230, loss_cls: 4.2250, loss: 4.2250 +2024-12-27 03:09:26,215 - pyskl - INFO - Epoch [31][2700/3746] lr: 9.000e-02, eta: 3 days, 21:25:52, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5097, loss_cls: 4.2870, loss: 4.2870 +2024-12-27 03:10:51,150 - pyskl - INFO - Epoch [31][2800/3746] lr: 8.999e-02, eta: 3 days, 21:25:14, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5120, loss_cls: 4.2500, loss: 4.2500 +2024-12-27 03:12:16,001 - pyskl - INFO - Epoch [31][2900/3746] lr: 8.997e-02, eta: 3 days, 21:24:36, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5133, loss_cls: 4.2623, loss: 4.2623 +2024-12-27 03:13:40,800 - pyskl - INFO - Epoch [31][3000/3746] lr: 8.995e-02, eta: 3 days, 21:23:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5145, loss_cls: 4.2328, loss: 4.2328 +2024-12-27 03:15:05,290 - pyskl - INFO - Epoch [31][3100/3746] lr: 8.994e-02, eta: 3 days, 21:23:18, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5125, loss_cls: 4.2623, loss: 4.2623 +2024-12-27 03:16:30,148 - pyskl - INFO - Epoch [31][3200/3746] lr: 8.992e-02, eta: 3 days, 21:22:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5231, loss_cls: 4.2078, loss: 4.2078 +2024-12-27 03:17:54,659 - pyskl - INFO - Epoch [31][3300/3746] lr: 8.990e-02, eta: 3 days, 21:22:00, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5133, loss_cls: 4.2730, loss: 4.2730 +2024-12-27 03:19:19,748 - pyskl - INFO - Epoch [31][3400/3746] lr: 8.989e-02, eta: 3 days, 21:21:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5075, loss_cls: 4.3013, loss: 4.3013 +2024-12-27 03:20:44,706 - pyskl - INFO - Epoch [31][3500/3746] lr: 8.987e-02, eta: 3 days, 21:20:44, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5252, loss_cls: 4.2180, loss: 4.2180 +2024-12-27 03:22:09,922 - pyskl - INFO - Epoch [31][3600/3746] lr: 8.985e-02, eta: 3 days, 21:20:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5084, loss_cls: 4.2826, loss: 4.2826 +2024-12-27 03:23:34,628 - pyskl - INFO - Epoch [31][3700/3746] lr: 8.983e-02, eta: 3 days, 21:19:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.5136, loss_cls: 4.2857, loss: 4.2857 +2024-12-27 03:24:15,561 - pyskl - INFO - Saving checkpoint at 31 epochs +2024-12-27 03:26:14,910 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 03:26:15,854 - pyskl - INFO - +top1_acc 0.1816 +top5_acc 0.4140 +2024-12-27 03:26:15,855 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 03:26:15,897 - pyskl - INFO - +mean_acc 0.1816 +2024-12-27 03:26:15,908 - pyskl - INFO - Epoch(val) [31][309] top1_acc: 0.1816, top5_acc: 0.4140, mean_class_accuracy: 0.1816 +2024-12-27 03:30:15,668 - pyskl - INFO - Epoch [32][100/3746] lr: 8.981e-02, eta: 3 days, 21:25:54, time: 2.398, data_time: 1.376, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5222, loss_cls: 4.2272, loss: 4.2272 +2024-12-27 03:31:40,270 - pyskl - INFO - Epoch [32][200/3746] lr: 8.979e-02, eta: 3 days, 21:25:14, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5252, loss_cls: 4.1897, loss: 4.1897 +2024-12-27 03:33:04,864 - pyskl - INFO - Epoch [32][300/3746] lr: 8.978e-02, eta: 3 days, 21:24:33, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5208, loss_cls: 4.1803, loss: 4.1803 +2024-12-27 03:34:29,646 - pyskl - INFO - Epoch [32][400/3746] lr: 8.976e-02, eta: 3 days, 21:23:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5275, loss_cls: 4.1740, loss: 4.1740 +2024-12-27 03:35:53,800 - pyskl - INFO - Epoch [32][500/3746] lr: 8.974e-02, eta: 3 days, 21:23:11, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5153, loss_cls: 4.2418, loss: 4.2418 +2024-12-27 03:37:18,306 - pyskl - INFO - Epoch [32][600/3746] lr: 8.973e-02, eta: 3 days, 21:22:29, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5061, loss_cls: 4.2988, loss: 4.2988 +2024-12-27 03:38:42,195 - pyskl - INFO - Epoch [32][700/3746] lr: 8.971e-02, eta: 3 days, 21:21:46, time: 0.839, data_time: 0.001, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5298, loss_cls: 4.2177, loss: 4.2177 +2024-12-27 03:40:06,793 - pyskl - INFO - Epoch [32][800/3746] lr: 8.969e-02, eta: 3 days, 21:21:05, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5164, loss_cls: 4.2113, loss: 4.2113 +2024-12-27 03:41:31,148 - pyskl - INFO - Epoch [32][900/3746] lr: 8.967e-02, eta: 3 days, 21:20:23, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5098, loss_cls: 4.2769, loss: 4.2769 +2024-12-27 03:42:55,559 - pyskl - INFO - Epoch [32][1000/3746] lr: 8.966e-02, eta: 3 days, 21:19:41, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5198, loss_cls: 4.2434, loss: 4.2434 +2024-12-27 03:44:20,439 - pyskl - INFO - Epoch [32][1100/3746] lr: 8.964e-02, eta: 3 days, 21:19:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5122, loss_cls: 4.2724, loss: 4.2724 +2024-12-27 03:45:45,146 - pyskl - INFO - Epoch [32][1200/3746] lr: 8.962e-02, eta: 3 days, 21:18:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5234, loss_cls: 4.2376, loss: 4.2376 +2024-12-27 03:47:09,638 - pyskl - INFO - Epoch [32][1300/3746] lr: 8.961e-02, eta: 3 days, 21:17:38, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5175, loss_cls: 4.2097, loss: 4.2097 +2024-12-27 03:48:34,392 - pyskl - INFO - Epoch [32][1400/3746] lr: 8.959e-02, eta: 3 days, 21:16:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5144, loss_cls: 4.2523, loss: 4.2523 +2024-12-27 03:49:59,172 - pyskl - INFO - Epoch [32][1500/3746] lr: 8.957e-02, eta: 3 days, 21:16:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5180, loss_cls: 4.2441, loss: 4.2441 +2024-12-27 03:51:23,936 - pyskl - INFO - Epoch [32][1600/3746] lr: 8.955e-02, eta: 3 days, 21:15:36, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5059, loss_cls: 4.2885, loss: 4.2885 +2024-12-27 03:52:48,196 - pyskl - INFO - Epoch [32][1700/3746] lr: 8.954e-02, eta: 3 days, 21:14:53, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5175, loss_cls: 4.1997, loss: 4.1997 +2024-12-27 03:54:12,803 - pyskl - INFO - Epoch [32][1800/3746] lr: 8.952e-02, eta: 3 days, 21:14:11, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5131, loss_cls: 4.2565, loss: 4.2565 +2024-12-27 03:55:37,301 - pyskl - INFO - Epoch [32][1900/3746] lr: 8.950e-02, eta: 3 days, 21:13:29, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5278, loss_cls: 4.1938, loss: 4.1938 +2024-12-27 03:57:01,767 - pyskl - INFO - Epoch [32][2000/3746] lr: 8.949e-02, eta: 3 days, 21:12:47, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5089, loss_cls: 4.2267, loss: 4.2267 +2024-12-27 03:58:26,475 - pyskl - INFO - Epoch [32][2100/3746] lr: 8.947e-02, eta: 3 days, 21:12:05, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5156, loss_cls: 4.2597, loss: 4.2597 +2024-12-27 03:59:51,273 - pyskl - INFO - Epoch [32][2200/3746] lr: 8.945e-02, eta: 3 days, 21:11:24, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5131, loss_cls: 4.2583, loss: 4.2583 +2024-12-27 04:01:15,585 - pyskl - INFO - Epoch [32][2300/3746] lr: 8.943e-02, eta: 3 days, 21:10:41, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5175, loss_cls: 4.2036, loss: 4.2036 +2024-12-27 04:02:40,179 - pyskl - INFO - Epoch [32][2400/3746] lr: 8.942e-02, eta: 3 days, 21:09:59, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5088, loss_cls: 4.2607, loss: 4.2607 +2024-12-27 04:04:05,167 - pyskl - INFO - Epoch [32][2500/3746] lr: 8.940e-02, eta: 3 days, 21:09:18, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5261, loss_cls: 4.2226, loss: 4.2226 +2024-12-27 04:05:29,921 - pyskl - INFO - Epoch [32][2600/3746] lr: 8.938e-02, eta: 3 days, 21:08:36, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5094, loss_cls: 4.2537, loss: 4.2537 +2024-12-27 04:06:54,985 - pyskl - INFO - Epoch [32][2700/3746] lr: 8.937e-02, eta: 3 days, 21:07:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5109, loss_cls: 4.2526, loss: 4.2526 +2024-12-27 04:08:19,991 - pyskl - INFO - Epoch [32][2800/3746] lr: 8.935e-02, eta: 3 days, 21:07:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5145, loss_cls: 4.2712, loss: 4.2712 +2024-12-27 04:09:44,551 - pyskl - INFO - Epoch [32][2900/3746] lr: 8.933e-02, eta: 3 days, 21:06:32, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5162, loss_cls: 4.2306, loss: 4.2306 +2024-12-27 04:11:09,752 - pyskl - INFO - Epoch [32][3000/3746] lr: 8.931e-02, eta: 3 days, 21:05:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5131, loss_cls: 4.2875, loss: 4.2875 +2024-12-27 04:12:34,861 - pyskl - INFO - Epoch [32][3100/3746] lr: 8.930e-02, eta: 3 days, 21:05:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5188, loss_cls: 4.2376, loss: 4.2376 +2024-12-27 04:13:59,731 - pyskl - INFO - Epoch [32][3200/3746] lr: 8.928e-02, eta: 3 days, 21:04:29, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5109, loss_cls: 4.2993, loss: 4.2993 +2024-12-27 04:15:24,582 - pyskl - INFO - Epoch [32][3300/3746] lr: 8.926e-02, eta: 3 days, 21:03:48, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5088, loss_cls: 4.2786, loss: 4.2786 +2024-12-27 04:16:49,520 - pyskl - INFO - Epoch [32][3400/3746] lr: 8.924e-02, eta: 3 days, 21:03:06, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5180, loss_cls: 4.2503, loss: 4.2503 +2024-12-27 04:18:14,581 - pyskl - INFO - Epoch [32][3500/3746] lr: 8.923e-02, eta: 3 days, 21:02:25, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5075, loss_cls: 4.2571, loss: 4.2571 +2024-12-27 04:19:39,851 - pyskl - INFO - Epoch [32][3600/3746] lr: 8.921e-02, eta: 3 days, 21:01:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5162, loss_cls: 4.2319, loss: 4.2319 +2024-12-27 04:21:04,769 - pyskl - INFO - Epoch [32][3700/3746] lr: 8.919e-02, eta: 3 days, 21:01:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5178, loss_cls: 4.2503, loss: 4.2503 +2024-12-27 04:21:45,226 - pyskl - INFO - Saving checkpoint at 32 epochs +2024-12-27 04:23:42,159 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 04:23:43,101 - pyskl - INFO - +top1_acc 0.1804 +top5_acc 0.4106 +2024-12-27 04:23:43,102 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 04:23:43,142 - pyskl - INFO - +mean_acc 0.1803 +2024-12-27 04:23:43,155 - pyskl - INFO - Epoch(val) [32][309] top1_acc: 0.1804, top5_acc: 0.4106, mean_class_accuracy: 0.1803 +2024-12-27 04:27:45,262 - pyskl - INFO - Epoch [33][100/3746] lr: 8.917e-02, eta: 3 days, 21:07:16, time: 2.421, data_time: 1.404, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5123, loss_cls: 4.2135, loss: 4.2135 +2024-12-27 04:29:10,023 - pyskl - INFO - Epoch [33][200/3746] lr: 8.915e-02, eta: 3 days, 21:06:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5267, loss_cls: 4.1699, loss: 4.1699 +2024-12-27 04:30:35,200 - pyskl - INFO - Epoch [33][300/3746] lr: 8.913e-02, eta: 3 days, 21:05:52, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5264, loss_cls: 4.1763, loss: 4.1763 +2024-12-27 04:32:00,832 - pyskl - INFO - Epoch [33][400/3746] lr: 8.912e-02, eta: 3 days, 21:05:11, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5177, loss_cls: 4.2492, loss: 4.2492 +2024-12-27 04:33:26,765 - pyskl - INFO - Epoch [33][500/3746] lr: 8.910e-02, eta: 3 days, 21:04:32, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5216, loss_cls: 4.2101, loss: 4.2101 +2024-12-27 04:34:51,697 - pyskl - INFO - Epoch [33][600/3746] lr: 8.908e-02, eta: 3 days, 21:03:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5109, loss_cls: 4.2561, loss: 4.2561 +2024-12-27 04:36:16,880 - pyskl - INFO - Epoch [33][700/3746] lr: 8.906e-02, eta: 3 days, 21:03:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5058, loss_cls: 4.2859, loss: 4.2859 +2024-12-27 04:37:41,750 - pyskl - INFO - Epoch [33][800/3746] lr: 8.905e-02, eta: 3 days, 21:02:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5162, loss_cls: 4.2368, loss: 4.2368 +2024-12-27 04:39:06,382 - pyskl - INFO - Epoch [33][900/3746] lr: 8.903e-02, eta: 3 days, 21:01:41, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5309, loss_cls: 4.1933, loss: 4.1933 +2024-12-27 04:40:31,593 - pyskl - INFO - Epoch [33][1000/3746] lr: 8.901e-02, eta: 3 days, 21:00:59, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5161, loss_cls: 4.2527, loss: 4.2527 +2024-12-27 04:41:55,919 - pyskl - INFO - Epoch [33][1100/3746] lr: 8.899e-02, eta: 3 days, 21:00:13, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5292, loss_cls: 4.2140, loss: 4.2140 +2024-12-27 04:43:20,551 - pyskl - INFO - Epoch [33][1200/3746] lr: 8.898e-02, eta: 3 days, 20:59:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5131, loss_cls: 4.2670, loss: 4.2670 +2024-12-27 04:44:45,979 - pyskl - INFO - Epoch [33][1300/3746] lr: 8.896e-02, eta: 3 days, 20:58:48, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5159, loss_cls: 4.2283, loss: 4.2283 +2024-12-27 04:46:11,007 - pyskl - INFO - Epoch [33][1400/3746] lr: 8.894e-02, eta: 3 days, 20:58:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5141, loss_cls: 4.2639, loss: 4.2639 +2024-12-27 04:47:36,008 - pyskl - INFO - Epoch [33][1500/3746] lr: 8.892e-02, eta: 3 days, 20:57:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5183, loss_cls: 4.1960, loss: 4.1960 +2024-12-27 04:49:00,937 - pyskl - INFO - Epoch [33][1600/3746] lr: 8.891e-02, eta: 3 days, 20:56:38, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5184, loss_cls: 4.2421, loss: 4.2421 +2024-12-27 04:50:25,754 - pyskl - INFO - Epoch [33][1700/3746] lr: 8.889e-02, eta: 3 days, 20:55:54, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5150, loss_cls: 4.2295, loss: 4.2295 +2024-12-27 04:51:50,445 - pyskl - INFO - Epoch [33][1800/3746] lr: 8.887e-02, eta: 3 days, 20:55:10, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5194, loss_cls: 4.2386, loss: 4.2386 +2024-12-27 04:53:15,517 - pyskl - INFO - Epoch [33][1900/3746] lr: 8.885e-02, eta: 3 days, 20:54:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5133, loss_cls: 4.2570, loss: 4.2570 +2024-12-27 04:54:40,806 - pyskl - INFO - Epoch [33][2000/3746] lr: 8.884e-02, eta: 3 days, 20:53:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5156, loss_cls: 4.2746, loss: 4.2746 +2024-12-27 04:56:06,170 - pyskl - INFO - Epoch [33][2100/3746] lr: 8.882e-02, eta: 3 days, 20:53:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5142, loss_cls: 4.2689, loss: 4.2689 +2024-12-27 04:57:31,013 - pyskl - INFO - Epoch [33][2200/3746] lr: 8.880e-02, eta: 3 days, 20:52:18, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5073, loss_cls: 4.2602, loss: 4.2602 +2024-12-27 04:58:56,359 - pyskl - INFO - Epoch [33][2300/3746] lr: 8.878e-02, eta: 3 days, 20:51:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5145, loss_cls: 4.2277, loss: 4.2277 +2024-12-27 05:00:21,154 - pyskl - INFO - Epoch [33][2400/3746] lr: 8.876e-02, eta: 3 days, 20:50:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5289, loss_cls: 4.2133, loss: 4.2133 +2024-12-27 05:01:45,711 - pyskl - INFO - Epoch [33][2500/3746] lr: 8.875e-02, eta: 3 days, 20:50:05, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5141, loss_cls: 4.2416, loss: 4.2416 +2024-12-27 05:03:11,419 - pyskl - INFO - Epoch [33][2600/3746] lr: 8.873e-02, eta: 3 days, 20:49:24, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5158, loss_cls: 4.2189, loss: 4.2189 +2024-12-27 05:04:36,261 - pyskl - INFO - Epoch [33][2700/3746] lr: 8.871e-02, eta: 3 days, 20:48:39, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5122, loss_cls: 4.2417, loss: 4.2417 +2024-12-27 05:06:00,906 - pyskl - INFO - Epoch [33][2800/3746] lr: 8.869e-02, eta: 3 days, 20:47:54, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5228, loss_cls: 4.2223, loss: 4.2223 +2024-12-27 05:07:26,061 - pyskl - INFO - Epoch [33][2900/3746] lr: 8.868e-02, eta: 3 days, 20:47:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5208, loss_cls: 4.2368, loss: 4.2368 +2024-12-27 05:08:50,807 - pyskl - INFO - Epoch [33][3000/3746] lr: 8.866e-02, eta: 3 days, 20:46:26, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5064, loss_cls: 4.2683, loss: 4.2683 +2024-12-27 05:10:15,623 - pyskl - INFO - Epoch [33][3100/3746] lr: 8.864e-02, eta: 3 days, 20:45:41, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5159, loss_cls: 4.2378, loss: 4.2378 +2024-12-27 05:11:40,999 - pyskl - INFO - Epoch [33][3200/3746] lr: 8.862e-02, eta: 3 days, 20:44:58, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5156, loss_cls: 4.2475, loss: 4.2475 +2024-12-27 05:13:05,671 - pyskl - INFO - Epoch [33][3300/3746] lr: 8.861e-02, eta: 3 days, 20:44:12, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5202, loss_cls: 4.2433, loss: 4.2433 +2024-12-27 05:14:31,062 - pyskl - INFO - Epoch [33][3400/3746] lr: 8.859e-02, eta: 3 days, 20:43:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5247, loss_cls: 4.1841, loss: 4.1841 +2024-12-27 05:15:56,116 - pyskl - INFO - Epoch [33][3500/3746] lr: 8.857e-02, eta: 3 days, 20:42:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5086, loss_cls: 4.2468, loss: 4.2468 +2024-12-27 05:17:21,453 - pyskl - INFO - Epoch [33][3600/3746] lr: 8.855e-02, eta: 3 days, 20:42:02, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5167, loss_cls: 4.2198, loss: 4.2198 +2024-12-27 05:18:46,899 - pyskl - INFO - Epoch [33][3700/3746] lr: 8.853e-02, eta: 3 days, 20:41:18, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5111, loss_cls: 4.2841, loss: 4.2841 +2024-12-27 05:19:27,741 - pyskl - INFO - Saving checkpoint at 33 epochs +2024-12-27 05:21:23,626 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 05:21:24,546 - pyskl - INFO - +top1_acc 0.2023 +top5_acc 0.4403 +2024-12-27 05:21:24,546 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 05:21:24,587 - pyskl - INFO - +mean_acc 0.2022 +2024-12-27 05:21:24,592 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_29.pth was removed +2024-12-27 05:21:24,853 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_33.pth. +2024-12-27 05:21:24,854 - pyskl - INFO - Best top1_acc is 0.2023 at 33 epoch. +2024-12-27 05:21:24,870 - pyskl - INFO - Epoch(val) [33][309] top1_acc: 0.2023, top5_acc: 0.4403, mean_class_accuracy: 0.2022 +2024-12-27 05:25:28,624 - pyskl - INFO - Epoch [34][100/3746] lr: 8.851e-02, eta: 3 days, 20:47:17, time: 2.437, data_time: 1.412, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5283, loss_cls: 4.2056, loss: 4.2056 +2024-12-27 05:26:53,455 - pyskl - INFO - Epoch [34][200/3746] lr: 8.849e-02, eta: 3 days, 20:46:31, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5247, loss_cls: 4.1841, loss: 4.1841 +2024-12-27 05:28:17,683 - pyskl - INFO - Epoch [34][300/3746] lr: 8.847e-02, eta: 3 days, 20:45:43, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5223, loss_cls: 4.1970, loss: 4.1970 +2024-12-27 05:29:42,748 - pyskl - INFO - Epoch [34][400/3746] lr: 8.845e-02, eta: 3 days, 20:44:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5228, loss_cls: 4.2082, loss: 4.2082 +2024-12-27 05:31:07,897 - pyskl - INFO - Epoch [34][500/3746] lr: 8.844e-02, eta: 3 days, 20:44:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5169, loss_cls: 4.2314, loss: 4.2314 +2024-12-27 05:32:33,120 - pyskl - INFO - Epoch [34][600/3746] lr: 8.842e-02, eta: 3 days, 20:43:28, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5178, loss_cls: 4.2331, loss: 4.2331 +2024-12-27 05:33:58,246 - pyskl - INFO - Epoch [34][700/3746] lr: 8.840e-02, eta: 3 days, 20:42:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5231, loss_cls: 4.2053, loss: 4.2053 +2024-12-27 05:35:23,159 - pyskl - INFO - Epoch [34][800/3746] lr: 8.838e-02, eta: 3 days, 20:41:57, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5183, loss_cls: 4.2498, loss: 4.2498 +2024-12-27 05:36:47,961 - pyskl - INFO - Epoch [34][900/3746] lr: 8.836e-02, eta: 3 days, 20:41:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5200, loss_cls: 4.2332, loss: 4.2332 +2024-12-27 05:38:11,902 - pyskl - INFO - Epoch [34][1000/3746] lr: 8.835e-02, eta: 3 days, 20:40:22, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5208, loss_cls: 4.2410, loss: 4.2410 +2024-12-27 05:39:35,844 - pyskl - INFO - Epoch [34][1100/3746] lr: 8.833e-02, eta: 3 days, 20:39:32, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5208, loss_cls: 4.2049, loss: 4.2049 +2024-12-27 05:41:01,243 - pyskl - INFO - Epoch [34][1200/3746] lr: 8.831e-02, eta: 3 days, 20:38:48, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5245, loss_cls: 4.1884, loss: 4.1884 +2024-12-27 05:42:26,158 - pyskl - INFO - Epoch [34][1300/3746] lr: 8.829e-02, eta: 3 days, 20:38:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5250, loss_cls: 4.2030, loss: 4.2030 +2024-12-27 05:43:51,374 - pyskl - INFO - Epoch [34][1400/3746] lr: 8.828e-02, eta: 3 days, 20:37:16, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5192, loss_cls: 4.2400, loss: 4.2400 +2024-12-27 05:45:16,529 - pyskl - INFO - Epoch [34][1500/3746] lr: 8.826e-02, eta: 3 days, 20:36:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5273, loss_cls: 4.1987, loss: 4.1987 +2024-12-27 05:46:41,570 - pyskl - INFO - Epoch [34][1600/3746] lr: 8.824e-02, eta: 3 days, 20:35:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5159, loss_cls: 4.2193, loss: 4.2193 +2024-12-27 05:48:06,546 - pyskl - INFO - Epoch [34][1700/3746] lr: 8.822e-02, eta: 3 days, 20:34:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5061, loss_cls: 4.2980, loss: 4.2980 +2024-12-27 05:49:31,240 - pyskl - INFO - Epoch [34][1800/3746] lr: 8.820e-02, eta: 3 days, 20:34:11, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5248, loss_cls: 4.2177, loss: 4.2177 +2024-12-27 05:50:56,069 - pyskl - INFO - Epoch [34][1900/3746] lr: 8.819e-02, eta: 3 days, 20:33:25, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5158, loss_cls: 4.2386, loss: 4.2386 +2024-12-27 05:52:21,132 - pyskl - INFO - Epoch [34][2000/3746] lr: 8.817e-02, eta: 3 days, 20:32:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5155, loss_cls: 4.2483, loss: 4.2483 +2024-12-27 05:53:46,349 - pyskl - INFO - Epoch [34][2100/3746] lr: 8.815e-02, eta: 3 days, 20:31:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5148, loss_cls: 4.2621, loss: 4.2621 +2024-12-27 05:55:11,163 - pyskl - INFO - Epoch [34][2200/3746] lr: 8.813e-02, eta: 3 days, 20:31:06, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5267, loss_cls: 4.1644, loss: 4.1644 +2024-12-27 05:56:36,063 - pyskl - INFO - Epoch [34][2300/3746] lr: 8.811e-02, eta: 3 days, 20:30:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5092, loss_cls: 4.2682, loss: 4.2682 +2024-12-27 05:58:00,733 - pyskl - INFO - Epoch [34][2400/3746] lr: 8.809e-02, eta: 3 days, 20:29:31, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5069, loss_cls: 4.2205, loss: 4.2205 +2024-12-27 05:59:25,817 - pyskl - INFO - Epoch [34][2500/3746] lr: 8.808e-02, eta: 3 days, 20:28:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5108, loss_cls: 4.2837, loss: 4.2837 +2024-12-27 06:00:51,185 - pyskl - INFO - Epoch [34][2600/3746] lr: 8.806e-02, eta: 3 days, 20:27:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5264, loss_cls: 4.2150, loss: 4.2150 +2024-12-27 06:02:16,278 - pyskl - INFO - Epoch [34][2700/3746] lr: 8.804e-02, eta: 3 days, 20:27:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5275, loss_cls: 4.2016, loss: 4.2016 +2024-12-27 06:03:40,785 - pyskl - INFO - Epoch [34][2800/3746] lr: 8.802e-02, eta: 3 days, 20:26:24, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5133, loss_cls: 4.2794, loss: 4.2794 +2024-12-27 06:05:05,668 - pyskl - INFO - Epoch [34][2900/3746] lr: 8.800e-02, eta: 3 days, 20:25:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5233, loss_cls: 4.2264, loss: 4.2264 +2024-12-27 06:06:31,255 - pyskl - INFO - Epoch [34][3000/3746] lr: 8.799e-02, eta: 3 days, 20:24:52, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5234, loss_cls: 4.1727, loss: 4.1727 +2024-12-27 06:07:56,818 - pyskl - INFO - Epoch [34][3100/3746] lr: 8.797e-02, eta: 3 days, 20:24:07, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5239, loss_cls: 4.2137, loss: 4.2137 +2024-12-27 06:09:21,889 - pyskl - INFO - Epoch [34][3200/3746] lr: 8.795e-02, eta: 3 days, 20:23:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5156, loss_cls: 4.2535, loss: 4.2535 +2024-12-27 06:10:46,813 - pyskl - INFO - Epoch [34][3300/3746] lr: 8.793e-02, eta: 3 days, 20:22:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5070, loss_cls: 4.2984, loss: 4.2984 +2024-12-27 06:12:11,880 - pyskl - INFO - Epoch [34][3400/3746] lr: 8.791e-02, eta: 3 days, 20:21:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5125, loss_cls: 4.2472, loss: 4.2472 +2024-12-27 06:13:36,750 - pyskl - INFO - Epoch [34][3500/3746] lr: 8.789e-02, eta: 3 days, 20:20:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5036, loss_cls: 4.2516, loss: 4.2516 +2024-12-27 06:15:02,237 - pyskl - INFO - Epoch [34][3600/3746] lr: 8.788e-02, eta: 3 days, 20:20:12, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5291, loss_cls: 4.1808, loss: 4.1808 +2024-12-27 06:16:27,184 - pyskl - INFO - Epoch [34][3700/3746] lr: 8.786e-02, eta: 3 days, 20:19:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5112, loss_cls: 4.2443, loss: 4.2443 +2024-12-27 06:17:07,943 - pyskl - INFO - Saving checkpoint at 34 epochs +2024-12-27 06:19:04,412 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 06:19:05,271 - pyskl - INFO - +top1_acc 0.1840 +top5_acc 0.4073 +2024-12-27 06:19:05,271 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 06:19:05,310 - pyskl - INFO - +mean_acc 0.1839 +2024-12-27 06:19:05,320 - pyskl - INFO - Epoch(val) [34][309] top1_acc: 0.1840, top5_acc: 0.4073, mean_class_accuracy: 0.1839 +2024-12-27 06:23:12,593 - pyskl - INFO - Epoch [35][100/3746] lr: 8.783e-02, eta: 3 days, 20:25:15, time: 2.473, data_time: 1.442, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5145, loss_cls: 4.2323, loss: 4.2323 +2024-12-27 06:24:37,324 - pyskl - INFO - Epoch [35][200/3746] lr: 8.781e-02, eta: 3 days, 20:24:27, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5250, loss_cls: 4.1932, loss: 4.1932 +2024-12-27 06:26:02,228 - pyskl - INFO - Epoch [35][300/3746] lr: 8.780e-02, eta: 3 days, 20:23:38, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5355, loss_cls: 4.1553, loss: 4.1553 +2024-12-27 06:27:26,770 - pyskl - INFO - Epoch [35][400/3746] lr: 8.778e-02, eta: 3 days, 20:22:49, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5230, loss_cls: 4.1989, loss: 4.1989 +2024-12-27 06:28:52,177 - pyskl - INFO - Epoch [35][500/3746] lr: 8.776e-02, eta: 3 days, 20:22:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5266, loss_cls: 4.1917, loss: 4.1917 +2024-12-27 06:30:17,772 - pyskl - INFO - Epoch [35][600/3746] lr: 8.774e-02, eta: 3 days, 20:21:16, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5175, loss_cls: 4.2519, loss: 4.2519 +2024-12-27 06:31:42,678 - pyskl - INFO - Epoch [35][700/3746] lr: 8.772e-02, eta: 3 days, 20:20:27, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5175, loss_cls: 4.2298, loss: 4.2298 +2024-12-27 06:33:07,835 - pyskl - INFO - Epoch [35][800/3746] lr: 8.770e-02, eta: 3 days, 20:19:39, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5223, loss_cls: 4.1990, loss: 4.1990 +2024-12-27 06:34:32,894 - pyskl - INFO - Epoch [35][900/3746] lr: 8.769e-02, eta: 3 days, 20:18:51, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5219, loss_cls: 4.1945, loss: 4.1945 +2024-12-27 06:35:57,284 - pyskl - INFO - Epoch [35][1000/3746] lr: 8.767e-02, eta: 3 days, 20:18:01, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5236, loss_cls: 4.1901, loss: 4.1901 +2024-12-27 06:37:21,500 - pyskl - INFO - Epoch [35][1100/3746] lr: 8.765e-02, eta: 3 days, 20:17:10, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5152, loss_cls: 4.2526, loss: 4.2526 +2024-12-27 06:38:45,412 - pyskl - INFO - Epoch [35][1200/3746] lr: 8.763e-02, eta: 3 days, 20:16:18, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5133, loss_cls: 4.2307, loss: 4.2307 +2024-12-27 06:40:09,692 - pyskl - INFO - Epoch [35][1300/3746] lr: 8.761e-02, eta: 3 days, 20:15:27, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5320, loss_cls: 4.1610, loss: 4.1610 +2024-12-27 06:41:34,120 - pyskl - INFO - Epoch [35][1400/3746] lr: 8.759e-02, eta: 3 days, 20:14:36, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5239, loss_cls: 4.2212, loss: 4.2212 +2024-12-27 06:42:59,168 - pyskl - INFO - Epoch [35][1500/3746] lr: 8.757e-02, eta: 3 days, 20:13:48, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5205, loss_cls: 4.2438, loss: 4.2438 +2024-12-27 06:44:23,757 - pyskl - INFO - Epoch [35][1600/3746] lr: 8.756e-02, eta: 3 days, 20:12:57, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5120, loss_cls: 4.2170, loss: 4.2170 +2024-12-27 06:45:48,746 - pyskl - INFO - Epoch [35][1700/3746] lr: 8.754e-02, eta: 3 days, 20:12:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5111, loss_cls: 4.2585, loss: 4.2585 +2024-12-27 06:47:13,324 - pyskl - INFO - Epoch [35][1800/3746] lr: 8.752e-02, eta: 3 days, 20:11:18, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5191, loss_cls: 4.2162, loss: 4.2162 +2024-12-27 06:48:37,944 - pyskl - INFO - Epoch [35][1900/3746] lr: 8.750e-02, eta: 3 days, 20:10:28, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5277, loss_cls: 4.1894, loss: 4.1894 +2024-12-27 06:50:02,009 - pyskl - INFO - Epoch [35][2000/3746] lr: 8.748e-02, eta: 3 days, 20:09:36, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5161, loss_cls: 4.2241, loss: 4.2241 +2024-12-27 06:51:26,525 - pyskl - INFO - Epoch [35][2100/3746] lr: 8.746e-02, eta: 3 days, 20:08:45, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5197, loss_cls: 4.2436, loss: 4.2436 +2024-12-27 06:52:50,809 - pyskl - INFO - Epoch [35][2200/3746] lr: 8.745e-02, eta: 3 days, 20:07:54, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5088, loss_cls: 4.2358, loss: 4.2358 +2024-12-27 06:54:15,583 - pyskl - INFO - Epoch [35][2300/3746] lr: 8.743e-02, eta: 3 days, 20:07:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5189, loss_cls: 4.2102, loss: 4.2102 +2024-12-27 06:55:40,248 - pyskl - INFO - Epoch [35][2400/3746] lr: 8.741e-02, eta: 3 days, 20:06:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5262, loss_cls: 4.2117, loss: 4.2117 +2024-12-27 06:57:05,473 - pyskl - INFO - Epoch [35][2500/3746] lr: 8.739e-02, eta: 3 days, 20:05:25, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5245, loss_cls: 4.1971, loss: 4.1971 +2024-12-27 06:58:30,285 - pyskl - INFO - Epoch [35][2600/3746] lr: 8.737e-02, eta: 3 days, 20:04:36, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5222, loss_cls: 4.2119, loss: 4.2119 +2024-12-27 06:59:55,324 - pyskl - INFO - Epoch [35][2700/3746] lr: 8.735e-02, eta: 3 days, 20:03:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5217, loss_cls: 4.2356, loss: 4.2356 +2024-12-27 07:01:20,288 - pyskl - INFO - Epoch [35][2800/3746] lr: 8.733e-02, eta: 3 days, 20:02:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5297, loss_cls: 4.1662, loss: 4.1662 +2024-12-27 07:02:45,407 - pyskl - INFO - Epoch [35][2900/3746] lr: 8.732e-02, eta: 3 days, 20:02:08, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5205, loss_cls: 4.2331, loss: 4.2331 +2024-12-27 07:04:10,669 - pyskl - INFO - Epoch [35][3000/3746] lr: 8.730e-02, eta: 3 days, 20:01:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5061, loss_cls: 4.2579, loss: 4.2579 +2024-12-27 07:05:36,009 - pyskl - INFO - Epoch [35][3100/3746] lr: 8.728e-02, eta: 3 days, 20:00:31, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5261, loss_cls: 4.1895, loss: 4.1895 +2024-12-27 07:07:01,145 - pyskl - INFO - Epoch [35][3200/3746] lr: 8.726e-02, eta: 3 days, 19:59:42, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5130, loss_cls: 4.1999, loss: 4.1999 +2024-12-27 07:08:25,926 - pyskl - INFO - Epoch [35][3300/3746] lr: 8.724e-02, eta: 3 days, 19:58:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5300, loss_cls: 4.1709, loss: 4.1709 +2024-12-27 07:09:50,841 - pyskl - INFO - Epoch [35][3400/3746] lr: 8.722e-02, eta: 3 days, 19:58:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5189, loss_cls: 4.2248, loss: 4.2248 +2024-12-27 07:11:15,710 - pyskl - INFO - Epoch [35][3500/3746] lr: 8.720e-02, eta: 3 days, 19:57:11, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5072, loss_cls: 4.2531, loss: 4.2531 +2024-12-27 07:12:40,460 - pyskl - INFO - Epoch [35][3600/3746] lr: 8.718e-02, eta: 3 days, 19:56:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5177, loss_cls: 4.2837, loss: 4.2837 +2024-12-27 07:14:05,187 - pyskl - INFO - Epoch [35][3700/3746] lr: 8.717e-02, eta: 3 days, 19:55:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5134, loss_cls: 4.2415, loss: 4.2415 +2024-12-27 07:14:45,907 - pyskl - INFO - Saving checkpoint at 35 epochs +2024-12-27 07:16:42,425 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 07:16:43,155 - pyskl - INFO - +top1_acc 0.1943 +top5_acc 0.4285 +2024-12-27 07:16:43,156 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 07:16:43,206 - pyskl - INFO - +mean_acc 0.1941 +2024-12-27 07:16:43,221 - pyskl - INFO - Epoch(val) [35][309] top1_acc: 0.1943, top5_acc: 0.4285, mean_class_accuracy: 0.1941 +2024-12-27 07:20:51,714 - pyskl - INFO - Epoch [36][100/3746] lr: 8.714e-02, eta: 3 days, 20:01:05, time: 2.485, data_time: 1.459, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5164, loss_cls: 4.2185, loss: 4.2185 +2024-12-27 07:22:17,058 - pyskl - INFO - Epoch [36][200/3746] lr: 8.712e-02, eta: 3 days, 20:00:16, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5244, loss_cls: 4.2117, loss: 4.2117 +2024-12-27 07:23:41,859 - pyskl - INFO - Epoch [36][300/3746] lr: 8.710e-02, eta: 3 days, 19:59:25, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5316, loss_cls: 4.1905, loss: 4.1905 +2024-12-27 07:25:06,578 - pyskl - INFO - Epoch [36][400/3746] lr: 8.708e-02, eta: 3 days, 19:58:33, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5298, loss_cls: 4.1692, loss: 4.1692 +2024-12-27 07:26:31,089 - pyskl - INFO - Epoch [36][500/3746] lr: 8.706e-02, eta: 3 days, 19:57:41, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5194, loss_cls: 4.2083, loss: 4.2083 +2024-12-27 07:27:55,584 - pyskl - INFO - Epoch [36][600/3746] lr: 8.704e-02, eta: 3 days, 19:56:49, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5261, loss_cls: 4.1810, loss: 4.1810 +2024-12-27 07:29:20,273 - pyskl - INFO - Epoch [36][700/3746] lr: 8.703e-02, eta: 3 days, 19:55:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5319, loss_cls: 4.1876, loss: 4.1876 +2024-12-27 07:30:44,650 - pyskl - INFO - Epoch [36][800/3746] lr: 8.701e-02, eta: 3 days, 19:55:04, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5214, loss_cls: 4.2046, loss: 4.2046 +2024-12-27 07:32:09,721 - pyskl - INFO - Epoch [36][900/3746] lr: 8.699e-02, eta: 3 days, 19:54:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5069, loss_cls: 4.2755, loss: 4.2755 +2024-12-27 07:33:34,272 - pyskl - INFO - Epoch [36][1000/3746] lr: 8.697e-02, eta: 3 days, 19:53:21, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5116, loss_cls: 4.2550, loss: 4.2550 +2024-12-27 07:34:58,821 - pyskl - INFO - Epoch [36][1100/3746] lr: 8.695e-02, eta: 3 days, 19:52:29, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5192, loss_cls: 4.2072, loss: 4.2072 +2024-12-27 07:36:23,322 - pyskl - INFO - Epoch [36][1200/3746] lr: 8.693e-02, eta: 3 days, 19:51:37, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5256, loss_cls: 4.2253, loss: 4.2253 +2024-12-27 07:37:48,100 - pyskl - INFO - Epoch [36][1300/3746] lr: 8.691e-02, eta: 3 days, 19:50:45, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5133, loss_cls: 4.2388, loss: 4.2388 +2024-12-27 07:39:13,061 - pyskl - INFO - Epoch [36][1400/3746] lr: 8.689e-02, eta: 3 days, 19:49:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5289, loss_cls: 4.1992, loss: 4.1992 +2024-12-27 07:40:38,023 - pyskl - INFO - Epoch [36][1500/3746] lr: 8.688e-02, eta: 3 days, 19:49:03, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5272, loss_cls: 4.1795, loss: 4.1795 +2024-12-27 07:42:02,778 - pyskl - INFO - Epoch [36][1600/3746] lr: 8.686e-02, eta: 3 days, 19:48:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5194, loss_cls: 4.2200, loss: 4.2200 +2024-12-27 07:43:27,845 - pyskl - INFO - Epoch [36][1700/3746] lr: 8.684e-02, eta: 3 days, 19:47:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5180, loss_cls: 4.2213, loss: 4.2213 +2024-12-27 07:44:52,480 - pyskl - INFO - Epoch [36][1800/3746] lr: 8.682e-02, eta: 3 days, 19:46:27, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5253, loss_cls: 4.1807, loss: 4.1807 +2024-12-27 07:46:16,841 - pyskl - INFO - Epoch [36][1900/3746] lr: 8.680e-02, eta: 3 days, 19:45:34, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5220, loss_cls: 4.2204, loss: 4.2204 +2024-12-27 07:47:41,726 - pyskl - INFO - Epoch [36][2000/3746] lr: 8.678e-02, eta: 3 days, 19:44:42, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5217, loss_cls: 4.2132, loss: 4.2132 +2024-12-27 07:49:06,086 - pyskl - INFO - Epoch [36][2100/3746] lr: 8.676e-02, eta: 3 days, 19:43:49, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5142, loss_cls: 4.2569, loss: 4.2569 +2024-12-27 07:50:30,610 - pyskl - INFO - Epoch [36][2200/3746] lr: 8.674e-02, eta: 3 days, 19:42:56, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5153, loss_cls: 4.2133, loss: 4.2133 +2024-12-27 07:51:55,436 - pyskl - INFO - Epoch [36][2300/3746] lr: 8.672e-02, eta: 3 days, 19:42:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5261, loss_cls: 4.1781, loss: 4.1781 +2024-12-27 07:53:19,804 - pyskl - INFO - Epoch [36][2400/3746] lr: 8.671e-02, eta: 3 days, 19:41:10, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5225, loss_cls: 4.2202, loss: 4.2202 +2024-12-27 07:54:44,745 - pyskl - INFO - Epoch [36][2500/3746] lr: 8.669e-02, eta: 3 days, 19:40:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5164, loss_cls: 4.2282, loss: 4.2282 +2024-12-27 07:56:09,281 - pyskl - INFO - Epoch [36][2600/3746] lr: 8.667e-02, eta: 3 days, 19:39:25, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5180, loss_cls: 4.2203, loss: 4.2203 +2024-12-27 07:57:34,093 - pyskl - INFO - Epoch [36][2700/3746] lr: 8.665e-02, eta: 3 days, 19:38:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5205, loss_cls: 4.1901, loss: 4.1901 +2024-12-27 07:58:59,007 - pyskl - INFO - Epoch [36][2800/3746] lr: 8.663e-02, eta: 3 days, 19:37:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5117, loss_cls: 4.2452, loss: 4.2452 +2024-12-27 08:00:23,666 - pyskl - INFO - Epoch [36][2900/3746] lr: 8.661e-02, eta: 3 days, 19:36:48, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5086, loss_cls: 4.2594, loss: 4.2594 +2024-12-27 08:01:49,042 - pyskl - INFO - Epoch [36][3000/3746] lr: 8.659e-02, eta: 3 days, 19:35:58, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5178, loss_cls: 4.2191, loss: 4.2191 +2024-12-27 08:03:13,657 - pyskl - INFO - Epoch [36][3100/3746] lr: 8.657e-02, eta: 3 days, 19:35:05, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5077, loss_cls: 4.2518, loss: 4.2518 +2024-12-27 08:04:38,601 - pyskl - INFO - Epoch [36][3200/3746] lr: 8.655e-02, eta: 3 days, 19:34:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5177, loss_cls: 4.2316, loss: 4.2316 +2024-12-27 08:06:03,460 - pyskl - INFO - Epoch [36][3300/3746] lr: 8.653e-02, eta: 3 days, 19:33:20, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5097, loss_cls: 4.2618, loss: 4.2618 +2024-12-27 08:07:28,078 - pyskl - INFO - Epoch [36][3400/3746] lr: 8.651e-02, eta: 3 days, 19:32:27, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5195, loss_cls: 4.2429, loss: 4.2429 +2024-12-27 08:08:53,356 - pyskl - INFO - Epoch [36][3500/3746] lr: 8.650e-02, eta: 3 days, 19:31:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5228, loss_cls: 4.1881, loss: 4.1881 +2024-12-27 08:10:18,318 - pyskl - INFO - Epoch [36][3600/3746] lr: 8.648e-02, eta: 3 days, 19:30:44, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5261, loss_cls: 4.1977, loss: 4.1977 +2024-12-27 08:11:43,607 - pyskl - INFO - Epoch [36][3700/3746] lr: 8.646e-02, eta: 3 days, 19:29:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5219, loss_cls: 4.2261, loss: 4.2261 +2024-12-27 08:12:24,934 - pyskl - INFO - Saving checkpoint at 36 epochs +2024-12-27 08:14:21,995 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 08:14:22,727 - pyskl - INFO - +top1_acc 0.1825 +top5_acc 0.4172 +2024-12-27 08:14:22,727 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 08:14:22,775 - pyskl - INFO - +mean_acc 0.1823 +2024-12-27 08:14:22,794 - pyskl - INFO - Epoch(val) [36][309] top1_acc: 0.1825, top5_acc: 0.4172, mean_class_accuracy: 0.1823 +2024-12-27 08:18:27,677 - pyskl - INFO - Epoch [37][100/3746] lr: 8.643e-02, eta: 3 days, 19:34:58, time: 2.449, data_time: 1.422, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5350, loss_cls: 4.1614, loss: 4.1614 +2024-12-27 08:19:52,524 - pyskl - INFO - Epoch [37][200/3746] lr: 8.641e-02, eta: 3 days, 19:34:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5248, loss_cls: 4.2044, loss: 4.2044 +2024-12-27 08:21:17,607 - pyskl - INFO - Epoch [37][300/3746] lr: 8.639e-02, eta: 3 days, 19:33:13, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5194, loss_cls: 4.2059, loss: 4.2059 +2024-12-27 08:22:42,113 - pyskl - INFO - Epoch [37][400/3746] lr: 8.637e-02, eta: 3 days, 19:32:18, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5228, loss_cls: 4.2233, loss: 4.2233 +2024-12-27 08:24:06,486 - pyskl - INFO - Epoch [37][500/3746] lr: 8.635e-02, eta: 3 days, 19:31:24, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5186, loss_cls: 4.2127, loss: 4.2127 +2024-12-27 08:25:31,398 - pyskl - INFO - Epoch [37][600/3746] lr: 8.633e-02, eta: 3 days, 19:30:30, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5156, loss_cls: 4.2147, loss: 4.2147 +2024-12-27 08:26:55,845 - pyskl - INFO - Epoch [37][700/3746] lr: 8.631e-02, eta: 3 days, 19:29:36, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5322, loss_cls: 4.1634, loss: 4.1634 +2024-12-27 08:28:19,856 - pyskl - INFO - Epoch [37][800/3746] lr: 8.630e-02, eta: 3 days, 19:28:40, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5258, loss_cls: 4.1728, loss: 4.1728 +2024-12-27 08:29:44,625 - pyskl - INFO - Epoch [37][900/3746] lr: 8.628e-02, eta: 3 days, 19:27:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5270, loss_cls: 4.1745, loss: 4.1745 +2024-12-27 08:31:09,212 - pyskl - INFO - Epoch [37][1000/3746] lr: 8.626e-02, eta: 3 days, 19:26:52, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5225, loss_cls: 4.2010, loss: 4.2010 +2024-12-27 08:32:34,057 - pyskl - INFO - Epoch [37][1100/3746] lr: 8.624e-02, eta: 3 days, 19:25:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5194, loss_cls: 4.2036, loss: 4.2036 +2024-12-27 08:33:57,828 - pyskl - INFO - Epoch [37][1200/3746] lr: 8.622e-02, eta: 3 days, 19:25:01, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5217, loss_cls: 4.2341, loss: 4.2341 +2024-12-27 08:35:22,644 - pyskl - INFO - Epoch [37][1300/3746] lr: 8.620e-02, eta: 3 days, 19:24:08, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5250, loss_cls: 4.2054, loss: 4.2054 +2024-12-27 08:36:47,362 - pyskl - INFO - Epoch [37][1400/3746] lr: 8.618e-02, eta: 3 days, 19:23:13, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5264, loss_cls: 4.1757, loss: 4.1757 +2024-12-27 08:38:12,082 - pyskl - INFO - Epoch [37][1500/3746] lr: 8.616e-02, eta: 3 days, 19:22:19, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5153, loss_cls: 4.2351, loss: 4.2351 +2024-12-27 08:39:37,018 - pyskl - INFO - Epoch [37][1600/3746] lr: 8.614e-02, eta: 3 days, 19:21:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5159, loss_cls: 4.1819, loss: 4.1819 +2024-12-27 08:41:02,424 - pyskl - INFO - Epoch [37][1700/3746] lr: 8.612e-02, eta: 3 days, 19:20:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5303, loss_cls: 4.1904, loss: 4.1904 +2024-12-27 08:42:27,848 - pyskl - INFO - Epoch [37][1800/3746] lr: 8.610e-02, eta: 3 days, 19:19:42, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5333, loss_cls: 4.1648, loss: 4.1648 +2024-12-27 08:43:53,130 - pyskl - INFO - Epoch [37][1900/3746] lr: 8.608e-02, eta: 3 days, 19:18:49, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5147, loss_cls: 4.2298, loss: 4.2298 +2024-12-27 08:45:18,688 - pyskl - INFO - Epoch [37][2000/3746] lr: 8.606e-02, eta: 3 days, 19:17:57, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5192, loss_cls: 4.2194, loss: 4.2194 +2024-12-27 08:46:44,048 - pyskl - INFO - Epoch [37][2100/3746] lr: 8.604e-02, eta: 3 days, 19:17:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5202, loss_cls: 4.2193, loss: 4.2193 +2024-12-27 08:48:09,320 - pyskl - INFO - Epoch [37][2200/3746] lr: 8.602e-02, eta: 3 days, 19:16:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5241, loss_cls: 4.2119, loss: 4.2119 +2024-12-27 08:49:34,579 - pyskl - INFO - Epoch [37][2300/3746] lr: 8.601e-02, eta: 3 days, 19:15:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5169, loss_cls: 4.2302, loss: 4.2302 +2024-12-27 08:50:59,793 - pyskl - INFO - Epoch [37][2400/3746] lr: 8.599e-02, eta: 3 days, 19:14:26, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5222, loss_cls: 4.2125, loss: 4.2125 +2024-12-27 08:52:25,365 - pyskl - INFO - Epoch [37][2500/3746] lr: 8.597e-02, eta: 3 days, 19:13:34, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5223, loss_cls: 4.1986, loss: 4.1986 +2024-12-27 08:53:50,674 - pyskl - INFO - Epoch [37][2600/3746] lr: 8.595e-02, eta: 3 days, 19:12:42, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5289, loss_cls: 4.1655, loss: 4.1655 +2024-12-27 08:55:15,602 - pyskl - INFO - Epoch [37][2700/3746] lr: 8.593e-02, eta: 3 days, 19:11:48, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5167, loss_cls: 4.2220, loss: 4.2220 +2024-12-27 08:56:40,526 - pyskl - INFO - Epoch [37][2800/3746] lr: 8.591e-02, eta: 3 days, 19:10:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5212, loss_cls: 4.2332, loss: 4.2332 +2024-12-27 08:58:05,713 - pyskl - INFO - Epoch [37][2900/3746] lr: 8.589e-02, eta: 3 days, 19:10:00, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5191, loss_cls: 4.2177, loss: 4.2177 +2024-12-27 08:59:30,849 - pyskl - INFO - Epoch [37][3000/3746] lr: 8.587e-02, eta: 3 days, 19:09:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5239, loss_cls: 4.2347, loss: 4.2347 +2024-12-27 09:00:55,935 - pyskl - INFO - Epoch [37][3100/3746] lr: 8.585e-02, eta: 3 days, 19:08:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5312, loss_cls: 4.1446, loss: 4.1446 +2024-12-27 09:02:21,287 - pyskl - INFO - Epoch [37][3200/3746] lr: 8.583e-02, eta: 3 days, 19:07:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5277, loss_cls: 4.1924, loss: 4.1924 +2024-12-27 09:03:46,666 - pyskl - INFO - Epoch [37][3300/3746] lr: 8.581e-02, eta: 3 days, 19:06:27, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5186, loss_cls: 4.2231, loss: 4.2231 +2024-12-27 09:05:11,950 - pyskl - INFO - Epoch [37][3400/3746] lr: 8.579e-02, eta: 3 days, 19:05:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5234, loss_cls: 4.2414, loss: 4.2414 +2024-12-27 09:06:37,111 - pyskl - INFO - Epoch [37][3500/3746] lr: 8.577e-02, eta: 3 days, 19:04:40, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5245, loss_cls: 4.1969, loss: 4.1969 +2024-12-27 09:08:02,343 - pyskl - INFO - Epoch [37][3600/3746] lr: 8.575e-02, eta: 3 days, 19:03:47, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5270, loss_cls: 4.2052, loss: 4.2052 +2024-12-27 09:09:27,559 - pyskl - INFO - Epoch [37][3700/3746] lr: 8.573e-02, eta: 3 days, 19:02:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5325, loss_cls: 4.1463, loss: 4.1463 +2024-12-27 09:10:08,736 - pyskl - INFO - Saving checkpoint at 37 epochs +2024-12-27 09:12:06,273 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 09:12:06,994 - pyskl - INFO - +top1_acc 0.2024 +top5_acc 0.4406 +2024-12-27 09:12:06,994 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 09:12:07,033 - pyskl - INFO - +mean_acc 0.2024 +2024-12-27 09:12:07,038 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_33.pth was removed +2024-12-27 09:12:07,300 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_37.pth. +2024-12-27 09:12:07,301 - pyskl - INFO - Best top1_acc is 0.2024 at 37 epoch. +2024-12-27 09:12:07,311 - pyskl - INFO - Epoch(val) [37][309] top1_acc: 0.2024, top5_acc: 0.4406, mean_class_accuracy: 0.2024 +2024-12-27 09:16:13,635 - pyskl - INFO - Epoch [38][100/3746] lr: 8.570e-02, eta: 3 days, 19:07:47, time: 2.463, data_time: 1.436, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5323, loss_cls: 4.1522, loss: 4.1522 +2024-12-27 09:17:38,829 - pyskl - INFO - Epoch [38][200/3746] lr: 8.568e-02, eta: 3 days, 19:06:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5214, loss_cls: 4.1962, loss: 4.1962 +2024-12-27 09:19:03,664 - pyskl - INFO - Epoch [38][300/3746] lr: 8.567e-02, eta: 3 days, 19:05:57, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5256, loss_cls: 4.1635, loss: 4.1635 +2024-12-27 09:20:28,594 - pyskl - INFO - Epoch [38][400/3746] lr: 8.565e-02, eta: 3 days, 19:05:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5262, loss_cls: 4.2134, loss: 4.2134 +2024-12-27 09:21:52,832 - pyskl - INFO - Epoch [38][500/3746] lr: 8.563e-02, eta: 3 days, 19:04:05, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5284, loss_cls: 4.1937, loss: 4.1937 +2024-12-27 09:23:16,911 - pyskl - INFO - Epoch [38][600/3746] lr: 8.561e-02, eta: 3 days, 19:03:07, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5292, loss_cls: 4.1567, loss: 4.1567 +2024-12-27 09:24:41,209 - pyskl - INFO - Epoch [38][700/3746] lr: 8.559e-02, eta: 3 days, 19:02:10, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5231, loss_cls: 4.2489, loss: 4.2489 +2024-12-27 09:26:05,808 - pyskl - INFO - Epoch [38][800/3746] lr: 8.557e-02, eta: 3 days, 19:01:14, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5223, loss_cls: 4.2067, loss: 4.2067 +2024-12-27 09:27:30,022 - pyskl - INFO - Epoch [38][900/3746] lr: 8.555e-02, eta: 3 days, 19:00:17, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5275, loss_cls: 4.1590, loss: 4.1590 +2024-12-27 09:28:54,113 - pyskl - INFO - Epoch [38][1000/3746] lr: 8.553e-02, eta: 3 days, 18:59:19, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5303, loss_cls: 4.1587, loss: 4.1587 +2024-12-27 09:30:17,869 - pyskl - INFO - Epoch [38][1100/3746] lr: 8.551e-02, eta: 3 days, 18:58:20, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5283, loss_cls: 4.1696, loss: 4.1696 +2024-12-27 09:31:42,342 - pyskl - INFO - Epoch [38][1200/3746] lr: 8.549e-02, eta: 3 days, 18:57:23, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5159, loss_cls: 4.2175, loss: 4.2175 +2024-12-27 09:33:06,559 - pyskl - INFO - Epoch [38][1300/3746] lr: 8.547e-02, eta: 3 days, 18:56:26, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5363, loss_cls: 4.1268, loss: 4.1268 +2024-12-27 09:34:31,331 - pyskl - INFO - Epoch [38][1400/3746] lr: 8.545e-02, eta: 3 days, 18:55:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5334, loss_cls: 4.1602, loss: 4.1602 +2024-12-27 09:35:56,210 - pyskl - INFO - Epoch [38][1500/3746] lr: 8.543e-02, eta: 3 days, 18:54:34, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5216, loss_cls: 4.2002, loss: 4.2002 +2024-12-27 09:37:20,951 - pyskl - INFO - Epoch [38][1600/3746] lr: 8.541e-02, eta: 3 days, 18:53:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5217, loss_cls: 4.2015, loss: 4.2015 +2024-12-27 09:38:45,938 - pyskl - INFO - Epoch [38][1700/3746] lr: 8.539e-02, eta: 3 days, 18:52:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5195, loss_cls: 4.2149, loss: 4.2149 +2024-12-27 09:40:10,458 - pyskl - INFO - Epoch [38][1800/3746] lr: 8.537e-02, eta: 3 days, 18:51:46, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5250, loss_cls: 4.2251, loss: 4.2251 +2024-12-27 09:41:34,989 - pyskl - INFO - Epoch [38][1900/3746] lr: 8.535e-02, eta: 3 days, 18:50:49, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5234, loss_cls: 4.1827, loss: 4.1827 +2024-12-27 09:43:00,057 - pyskl - INFO - Epoch [38][2000/3746] lr: 8.533e-02, eta: 3 days, 18:49:54, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5311, loss_cls: 4.1923, loss: 4.1923 +2024-12-27 09:44:24,571 - pyskl - INFO - Epoch [38][2100/3746] lr: 8.531e-02, eta: 3 days, 18:48:57, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5327, loss_cls: 4.1817, loss: 4.1817 +2024-12-27 09:45:49,225 - pyskl - INFO - Epoch [38][2200/3746] lr: 8.529e-02, eta: 3 days, 18:48:00, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5136, loss_cls: 4.2482, loss: 4.2482 +2024-12-27 09:47:14,076 - pyskl - INFO - Epoch [38][2300/3746] lr: 8.527e-02, eta: 3 days, 18:47:04, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5181, loss_cls: 4.2314, loss: 4.2314 +2024-12-27 09:48:39,047 - pyskl - INFO - Epoch [38][2400/3746] lr: 8.525e-02, eta: 3 days, 18:46:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5248, loss_cls: 4.2042, loss: 4.2042 +2024-12-27 09:50:03,775 - pyskl - INFO - Epoch [38][2500/3746] lr: 8.523e-02, eta: 3 days, 18:45:12, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5131, loss_cls: 4.2710, loss: 4.2710 +2024-12-27 09:51:28,484 - pyskl - INFO - Epoch [38][2600/3746] lr: 8.521e-02, eta: 3 days, 18:44:15, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5308, loss_cls: 4.1684, loss: 4.1684 +2024-12-27 09:52:52,967 - pyskl - INFO - Epoch [38][2700/3746] lr: 8.519e-02, eta: 3 days, 18:43:18, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5197, loss_cls: 4.1823, loss: 4.1823 +2024-12-27 09:54:17,759 - pyskl - INFO - Epoch [38][2800/3746] lr: 8.517e-02, eta: 3 days, 18:42:22, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5316, loss_cls: 4.1740, loss: 4.1740 +2024-12-27 09:55:42,463 - pyskl - INFO - Epoch [38][2900/3746] lr: 8.515e-02, eta: 3 days, 18:41:25, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5180, loss_cls: 4.2485, loss: 4.2485 +2024-12-27 09:57:07,264 - pyskl - INFO - Epoch [38][3000/3746] lr: 8.513e-02, eta: 3 days, 18:40:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5303, loss_cls: 4.1596, loss: 4.1596 +2024-12-27 09:58:32,153 - pyskl - INFO - Epoch [38][3100/3746] lr: 8.511e-02, eta: 3 days, 18:39:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5205, loss_cls: 4.2171, loss: 4.2171 +2024-12-27 09:59:57,050 - pyskl - INFO - Epoch [38][3200/3746] lr: 8.509e-02, eta: 3 days, 18:38:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5200, loss_cls: 4.2049, loss: 4.2049 +2024-12-27 10:01:22,122 - pyskl - INFO - Epoch [38][3300/3746] lr: 8.507e-02, eta: 3 days, 18:37:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5239, loss_cls: 4.2023, loss: 4.2023 +2024-12-27 10:02:47,493 - pyskl - INFO - Epoch [38][3400/3746] lr: 8.505e-02, eta: 3 days, 18:36:45, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5214, loss_cls: 4.1856, loss: 4.1856 +2024-12-27 10:04:12,867 - pyskl - INFO - Epoch [38][3500/3746] lr: 8.503e-02, eta: 3 days, 18:35:50, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5234, loss_cls: 4.1979, loss: 4.1979 +2024-12-27 10:05:37,769 - pyskl - INFO - Epoch [38][3600/3746] lr: 8.501e-02, eta: 3 days, 18:34:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5295, loss_cls: 4.1690, loss: 4.1690 +2024-12-27 10:07:03,217 - pyskl - INFO - Epoch [38][3700/3746] lr: 8.499e-02, eta: 3 days, 18:33:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5262, loss_cls: 4.2129, loss: 4.2129 +2024-12-27 10:07:44,266 - pyskl - INFO - Saving checkpoint at 38 epochs +2024-12-27 10:09:41,939 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 10:09:42,700 - pyskl - INFO - +top1_acc 0.1809 +top5_acc 0.4093 +2024-12-27 10:09:42,700 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 10:09:42,749 - pyskl - INFO - +mean_acc 0.1806 +2024-12-27 10:09:42,761 - pyskl - INFO - Epoch(val) [38][309] top1_acc: 0.1809, top5_acc: 0.4093, mean_class_accuracy: 0.1806 +2024-12-27 10:13:55,162 - pyskl - INFO - Epoch [39][100/3746] lr: 8.496e-02, eta: 3 days, 18:38:55, time: 2.524, data_time: 1.489, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5411, loss_cls: 4.1110, loss: 4.1110 +2024-12-27 10:15:20,647 - pyskl - INFO - Epoch [39][200/3746] lr: 8.494e-02, eta: 3 days, 18:37:59, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5333, loss_cls: 4.1453, loss: 4.1453 +2024-12-27 10:16:45,514 - pyskl - INFO - Epoch [39][300/3746] lr: 8.492e-02, eta: 3 days, 18:37:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5280, loss_cls: 4.1849, loss: 4.1849 +2024-12-27 10:18:10,706 - pyskl - INFO - Epoch [39][400/3746] lr: 8.490e-02, eta: 3 days, 18:36:06, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5355, loss_cls: 4.1304, loss: 4.1304 +2024-12-27 10:19:35,368 - pyskl - INFO - Epoch [39][500/3746] lr: 8.488e-02, eta: 3 days, 18:35:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5250, loss_cls: 4.2049, loss: 4.2049 +2024-12-27 10:20:59,959 - pyskl - INFO - Epoch [39][600/3746] lr: 8.486e-02, eta: 3 days, 18:34:11, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5378, loss_cls: 4.1804, loss: 4.1804 +2024-12-27 10:22:25,229 - pyskl - INFO - Epoch [39][700/3746] lr: 8.484e-02, eta: 3 days, 18:33:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5236, loss_cls: 4.1651, loss: 4.1651 +2024-12-27 10:23:50,550 - pyskl - INFO - Epoch [39][800/3746] lr: 8.482e-02, eta: 3 days, 18:32:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5120, loss_cls: 4.2153, loss: 4.2153 +2024-12-27 10:25:15,826 - pyskl - INFO - Epoch [39][900/3746] lr: 8.480e-02, eta: 3 days, 18:31:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5289, loss_cls: 4.1802, loss: 4.1802 +2024-12-27 10:26:40,486 - pyskl - INFO - Epoch [39][1000/3746] lr: 8.478e-02, eta: 3 days, 18:30:25, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5270, loss_cls: 4.2284, loss: 4.2284 +2024-12-27 10:28:04,653 - pyskl - INFO - Epoch [39][1100/3746] lr: 8.476e-02, eta: 3 days, 18:29:25, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5244, loss_cls: 4.1771, loss: 4.1771 +2024-12-27 10:29:28,751 - pyskl - INFO - Epoch [39][1200/3746] lr: 8.474e-02, eta: 3 days, 18:28:26, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5252, loss_cls: 4.1721, loss: 4.1721 +2024-12-27 10:30:53,049 - pyskl - INFO - Epoch [39][1300/3746] lr: 8.472e-02, eta: 3 days, 18:27:26, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5217, loss_cls: 4.2070, loss: 4.2070 +2024-12-27 10:32:17,769 - pyskl - INFO - Epoch [39][1400/3746] lr: 8.470e-02, eta: 3 days, 18:26:29, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5183, loss_cls: 4.2178, loss: 4.2178 +2024-12-27 10:33:42,198 - pyskl - INFO - Epoch [39][1500/3746] lr: 8.468e-02, eta: 3 days, 18:25:30, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5278, loss_cls: 4.1615, loss: 4.1615 +2024-12-27 10:35:06,646 - pyskl - INFO - Epoch [39][1600/3746] lr: 8.466e-02, eta: 3 days, 18:24:31, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5166, loss_cls: 4.2323, loss: 4.2323 +2024-12-27 10:36:31,010 - pyskl - INFO - Epoch [39][1700/3746] lr: 8.464e-02, eta: 3 days, 18:23:32, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5266, loss_cls: 4.1672, loss: 4.1672 +2024-12-27 10:37:55,685 - pyskl - INFO - Epoch [39][1800/3746] lr: 8.462e-02, eta: 3 days, 18:22:34, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5178, loss_cls: 4.2242, loss: 4.2242 +2024-12-27 10:39:20,261 - pyskl - INFO - Epoch [39][1900/3746] lr: 8.460e-02, eta: 3 days, 18:21:35, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5184, loss_cls: 4.2131, loss: 4.2131 +2024-12-27 10:40:44,561 - pyskl - INFO - Epoch [39][2000/3746] lr: 8.458e-02, eta: 3 days, 18:20:36, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5159, loss_cls: 4.2126, loss: 4.2126 +2024-12-27 10:42:08,894 - pyskl - INFO - Epoch [39][2100/3746] lr: 8.456e-02, eta: 3 days, 18:19:37, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5159, loss_cls: 4.2238, loss: 4.2238 +2024-12-27 10:43:33,630 - pyskl - INFO - Epoch [39][2200/3746] lr: 8.454e-02, eta: 3 days, 18:18:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5192, loss_cls: 4.2014, loss: 4.2014 +2024-12-27 10:44:58,398 - pyskl - INFO - Epoch [39][2300/3746] lr: 8.452e-02, eta: 3 days, 18:17:40, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5169, loss_cls: 4.2159, loss: 4.2159 +2024-12-27 10:46:22,848 - pyskl - INFO - Epoch [39][2400/3746] lr: 8.450e-02, eta: 3 days, 18:16:41, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5236, loss_cls: 4.1777, loss: 4.1777 +2024-12-27 10:47:47,640 - pyskl - INFO - Epoch [39][2500/3746] lr: 8.448e-02, eta: 3 days, 18:15:43, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5264, loss_cls: 4.1884, loss: 4.1884 +2024-12-27 10:49:12,588 - pyskl - INFO - Epoch [39][2600/3746] lr: 8.446e-02, eta: 3 days, 18:14:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5133, loss_cls: 4.2168, loss: 4.2168 +2024-12-27 10:50:37,779 - pyskl - INFO - Epoch [39][2700/3746] lr: 8.444e-02, eta: 3 days, 18:13:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5203, loss_cls: 4.1983, loss: 4.1983 +2024-12-27 10:52:02,800 - pyskl - INFO - Epoch [39][2800/3746] lr: 8.442e-02, eta: 3 days, 18:12:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5322, loss_cls: 4.1808, loss: 4.1808 +2024-12-27 10:53:27,736 - pyskl - INFO - Epoch [39][2900/3746] lr: 8.440e-02, eta: 3 days, 18:11:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5256, loss_cls: 4.1728, loss: 4.1728 +2024-12-27 10:54:52,460 - pyskl - INFO - Epoch [39][3000/3746] lr: 8.438e-02, eta: 3 days, 18:10:55, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5136, loss_cls: 4.2674, loss: 4.2674 +2024-12-27 10:56:17,296 - pyskl - INFO - Epoch [39][3100/3746] lr: 8.436e-02, eta: 3 days, 18:09:56, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5183, loss_cls: 4.2068, loss: 4.2068 +2024-12-27 10:57:42,159 - pyskl - INFO - Epoch [39][3200/3746] lr: 8.434e-02, eta: 3 days, 18:08:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5373, loss_cls: 4.1489, loss: 4.1489 +2024-12-27 10:59:07,017 - pyskl - INFO - Epoch [39][3300/3746] lr: 8.432e-02, eta: 3 days, 18:08:00, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5270, loss_cls: 4.2007, loss: 4.2007 +2024-12-27 11:00:32,028 - pyskl - INFO - Epoch [39][3400/3746] lr: 8.430e-02, eta: 3 days, 18:07:02, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5252, loss_cls: 4.1878, loss: 4.1878 +2024-12-27 11:01:57,202 - pyskl - INFO - Epoch [39][3500/3746] lr: 8.428e-02, eta: 3 days, 18:06:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5222, loss_cls: 4.2071, loss: 4.2071 +2024-12-27 11:03:22,380 - pyskl - INFO - Epoch [39][3600/3746] lr: 8.426e-02, eta: 3 days, 18:05:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5256, loss_cls: 4.2136, loss: 4.2136 +2024-12-27 11:04:47,488 - pyskl - INFO - Epoch [39][3700/3746] lr: 8.424e-02, eta: 3 days, 18:04:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5253, loss_cls: 4.1873, loss: 4.1873 +2024-12-27 11:05:28,240 - pyskl - INFO - Saving checkpoint at 39 epochs +2024-12-27 11:07:25,480 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 11:07:26,211 - pyskl - INFO - +top1_acc 0.1656 +top5_acc 0.3768 +2024-12-27 11:07:26,212 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 11:07:26,255 - pyskl - INFO - +mean_acc 0.1655 +2024-12-27 11:07:26,267 - pyskl - INFO - Epoch(val) [39][309] top1_acc: 0.1656, top5_acc: 0.3768, mean_class_accuracy: 0.1655 +2024-12-27 11:11:32,371 - pyskl - INFO - Epoch [40][100/3746] lr: 8.421e-02, eta: 3 days, 18:08:32, time: 2.461, data_time: 1.438, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5297, loss_cls: 4.1878, loss: 4.1878 +2024-12-27 11:12:57,252 - pyskl - INFO - Epoch [40][200/3746] lr: 8.419e-02, eta: 3 days, 18:07:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5283, loss_cls: 4.1926, loss: 4.1926 +2024-12-27 11:14:21,642 - pyskl - INFO - Epoch [40][300/3746] lr: 8.417e-02, eta: 3 days, 18:06:33, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5267, loss_cls: 4.1964, loss: 4.1964 +2024-12-27 11:15:46,583 - pyskl - INFO - Epoch [40][400/3746] lr: 8.415e-02, eta: 3 days, 18:05:35, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5353, loss_cls: 4.1665, loss: 4.1665 +2024-12-27 11:17:11,520 - pyskl - INFO - Epoch [40][500/3746] lr: 8.413e-02, eta: 3 days, 18:04:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5291, loss_cls: 4.1527, loss: 4.1527 +2024-12-27 11:18:35,689 - pyskl - INFO - Epoch [40][600/3746] lr: 8.411e-02, eta: 3 days, 18:03:35, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5220, loss_cls: 4.1796, loss: 4.1796 +2024-12-27 11:20:00,396 - pyskl - INFO - Epoch [40][700/3746] lr: 8.408e-02, eta: 3 days, 18:02:36, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5247, loss_cls: 4.2002, loss: 4.2002 +2024-12-27 11:21:25,095 - pyskl - INFO - Epoch [40][800/3746] lr: 8.406e-02, eta: 3 days, 18:01:36, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5170, loss_cls: 4.2256, loss: 4.2256 +2024-12-27 11:22:49,826 - pyskl - INFO - Epoch [40][900/3746] lr: 8.404e-02, eta: 3 days, 18:00:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5256, loss_cls: 4.1731, loss: 4.1731 +2024-12-27 11:24:14,252 - pyskl - INFO - Epoch [40][1000/3746] lr: 8.402e-02, eta: 3 days, 17:59:37, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5267, loss_cls: 4.1491, loss: 4.1491 +2024-12-27 11:25:38,489 - pyskl - INFO - Epoch [40][1100/3746] lr: 8.400e-02, eta: 3 days, 17:58:36, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5330, loss_cls: 4.1521, loss: 4.1521 +2024-12-27 11:27:02,691 - pyskl - INFO - Epoch [40][1200/3746] lr: 8.398e-02, eta: 3 days, 17:57:35, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5277, loss_cls: 4.2011, loss: 4.2011 +2024-12-27 11:28:26,501 - pyskl - INFO - Epoch [40][1300/3746] lr: 8.396e-02, eta: 3 days, 17:56:33, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5227, loss_cls: 4.1941, loss: 4.1941 +2024-12-27 11:29:51,111 - pyskl - INFO - Epoch [40][1400/3746] lr: 8.394e-02, eta: 3 days, 17:55:33, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5300, loss_cls: 4.1672, loss: 4.1672 +2024-12-27 11:31:16,094 - pyskl - INFO - Epoch [40][1500/3746] lr: 8.392e-02, eta: 3 days, 17:54:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5261, loss_cls: 4.1904, loss: 4.1904 +2024-12-27 11:32:41,049 - pyskl - INFO - Epoch [40][1600/3746] lr: 8.390e-02, eta: 3 days, 17:53:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5297, loss_cls: 4.1492, loss: 4.1492 +2024-12-27 11:34:05,477 - pyskl - INFO - Epoch [40][1700/3746] lr: 8.388e-02, eta: 3 days, 17:52:35, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5256, loss_cls: 4.1491, loss: 4.1491 +2024-12-27 11:35:30,321 - pyskl - INFO - Epoch [40][1800/3746] lr: 8.386e-02, eta: 3 days, 17:51:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5175, loss_cls: 4.1841, loss: 4.1841 +2024-12-27 11:36:55,251 - pyskl - INFO - Epoch [40][1900/3746] lr: 8.384e-02, eta: 3 days, 17:50:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5234, loss_cls: 4.1849, loss: 4.1849 +2024-12-27 11:38:19,817 - pyskl - INFO - Epoch [40][2000/3746] lr: 8.382e-02, eta: 3 days, 17:49:36, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5117, loss_cls: 4.2227, loss: 4.2227 +2024-12-27 11:39:44,273 - pyskl - INFO - Epoch [40][2100/3746] lr: 8.380e-02, eta: 3 days, 17:48:36, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5233, loss_cls: 4.1851, loss: 4.1851 +2024-12-27 11:41:09,064 - pyskl - INFO - Epoch [40][2200/3746] lr: 8.378e-02, eta: 3 days, 17:47:36, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5291, loss_cls: 4.1863, loss: 4.1863 +2024-12-27 11:42:32,929 - pyskl - INFO - Epoch [40][2300/3746] lr: 8.376e-02, eta: 3 days, 17:46:34, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5227, loss_cls: 4.1867, loss: 4.1867 +2024-12-27 11:43:57,388 - pyskl - INFO - Epoch [40][2400/3746] lr: 8.374e-02, eta: 3 days, 17:45:33, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5253, loss_cls: 4.2006, loss: 4.2006 +2024-12-27 11:45:21,985 - pyskl - INFO - Epoch [40][2500/3746] lr: 8.371e-02, eta: 3 days, 17:44:33, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5272, loss_cls: 4.1778, loss: 4.1778 +2024-12-27 11:46:46,594 - pyskl - INFO - Epoch [40][2600/3746] lr: 8.369e-02, eta: 3 days, 17:43:32, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5261, loss_cls: 4.2080, loss: 4.2080 +2024-12-27 11:48:11,452 - pyskl - INFO - Epoch [40][2700/3746] lr: 8.367e-02, eta: 3 days, 17:42:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5219, loss_cls: 4.2165, loss: 4.2165 +2024-12-27 11:49:35,657 - pyskl - INFO - Epoch [40][2800/3746] lr: 8.365e-02, eta: 3 days, 17:41:31, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5209, loss_cls: 4.2227, loss: 4.2227 +2024-12-27 11:51:00,135 - pyskl - INFO - Epoch [40][2900/3746] lr: 8.363e-02, eta: 3 days, 17:40:31, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5294, loss_cls: 4.1519, loss: 4.1519 +2024-12-27 11:52:24,814 - pyskl - INFO - Epoch [40][3000/3746] lr: 8.361e-02, eta: 3 days, 17:39:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5180, loss_cls: 4.2112, loss: 4.2112 +2024-12-27 11:53:49,911 - pyskl - INFO - Epoch [40][3100/3746] lr: 8.359e-02, eta: 3 days, 17:38:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5211, loss_cls: 4.1838, loss: 4.1838 +2024-12-27 11:55:14,819 - pyskl - INFO - Epoch [40][3200/3746] lr: 8.357e-02, eta: 3 days, 17:37:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5244, loss_cls: 4.1919, loss: 4.1919 +2024-12-27 11:56:39,859 - pyskl - INFO - Epoch [40][3300/3746] lr: 8.355e-02, eta: 3 days, 17:36:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5369, loss_cls: 4.1341, loss: 4.1341 +2024-12-27 11:58:05,565 - pyskl - INFO - Epoch [40][3400/3746] lr: 8.353e-02, eta: 3 days, 17:35:35, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5228, loss_cls: 4.2005, loss: 4.2005 +2024-12-27 11:59:30,057 - pyskl - INFO - Epoch [40][3500/3746] lr: 8.351e-02, eta: 3 days, 17:34:34, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5208, loss_cls: 4.1832, loss: 4.1832 +2024-12-27 12:00:54,960 - pyskl - INFO - Epoch [40][3600/3746] lr: 8.349e-02, eta: 3 days, 17:33:34, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5241, loss_cls: 4.2029, loss: 4.2029 +2024-12-27 12:02:19,762 - pyskl - INFO - Epoch [40][3700/3746] lr: 8.347e-02, eta: 3 days, 17:32:34, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5352, loss_cls: 4.1501, loss: 4.1501 +2024-12-27 12:03:00,743 - pyskl - INFO - Saving checkpoint at 40 epochs +2024-12-27 12:04:57,365 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 12:04:58,267 - pyskl - INFO - +top1_acc 0.2022 +top5_acc 0.4436 +2024-12-27 12:04:58,268 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 12:04:58,314 - pyskl - INFO - +mean_acc 0.2022 +2024-12-27 12:04:58,328 - pyskl - INFO - Epoch(val) [40][309] top1_acc: 0.2022, top5_acc: 0.4436, mean_class_accuracy: 0.2022 +2024-12-27 12:09:05,476 - pyskl - INFO - Epoch [41][100/3746] lr: 8.344e-02, eta: 3 days, 17:36:45, time: 2.471, data_time: 1.444, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5386, loss_cls: 4.1236, loss: 4.1236 +2024-12-27 12:10:30,329 - pyskl - INFO - Epoch [41][200/3746] lr: 8.342e-02, eta: 3 days, 17:35:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5247, loss_cls: 4.1615, loss: 4.1615 +2024-12-27 12:11:55,072 - pyskl - INFO - Epoch [41][300/3746] lr: 8.339e-02, eta: 3 days, 17:34:44, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5316, loss_cls: 4.1649, loss: 4.1649 +2024-12-27 12:13:19,970 - pyskl - INFO - Epoch [41][400/3746] lr: 8.337e-02, eta: 3 days, 17:33:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5217, loss_cls: 4.1866, loss: 4.1866 +2024-12-27 12:14:44,658 - pyskl - INFO - Epoch [41][500/3746] lr: 8.335e-02, eta: 3 days, 17:32:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5244, loss_cls: 4.1847, loss: 4.1847 +2024-12-27 12:16:08,874 - pyskl - INFO - Epoch [41][600/3746] lr: 8.333e-02, eta: 3 days, 17:31:41, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5363, loss_cls: 4.1473, loss: 4.1473 +2024-12-27 12:17:33,663 - pyskl - INFO - Epoch [41][700/3746] lr: 8.331e-02, eta: 3 days, 17:30:40, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5295, loss_cls: 4.1627, loss: 4.1627 +2024-12-27 12:18:58,666 - pyskl - INFO - Epoch [41][800/3746] lr: 8.329e-02, eta: 3 days, 17:29:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5267, loss_cls: 4.1597, loss: 4.1597 +2024-12-27 12:20:23,991 - pyskl - INFO - Epoch [41][900/3746] lr: 8.327e-02, eta: 3 days, 17:28:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5216, loss_cls: 4.1698, loss: 4.1698 +2024-12-27 12:21:49,160 - pyskl - INFO - Epoch [41][1000/3746] lr: 8.325e-02, eta: 3 days, 17:27:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5255, loss_cls: 4.2070, loss: 4.2070 +2024-12-27 12:23:14,237 - pyskl - INFO - Epoch [41][1100/3746] lr: 8.323e-02, eta: 3 days, 17:26:41, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5214, loss_cls: 4.1824, loss: 4.1824 +2024-12-27 12:24:39,544 - pyskl - INFO - Epoch [41][1200/3746] lr: 8.321e-02, eta: 3 days, 17:25:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5238, loss_cls: 4.1936, loss: 4.1936 +2024-12-27 12:26:04,230 - pyskl - INFO - Epoch [41][1300/3746] lr: 8.319e-02, eta: 3 days, 17:24:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5305, loss_cls: 4.1533, loss: 4.1533 +2024-12-27 12:27:28,902 - pyskl - INFO - Epoch [41][1400/3746] lr: 8.316e-02, eta: 3 days, 17:23:39, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5248, loss_cls: 4.2067, loss: 4.2067 +2024-12-27 12:28:53,869 - pyskl - INFO - Epoch [41][1500/3746] lr: 8.314e-02, eta: 3 days, 17:22:39, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5158, loss_cls: 4.2068, loss: 4.2068 +2024-12-27 12:30:18,564 - pyskl - INFO - Epoch [41][1600/3746] lr: 8.312e-02, eta: 3 days, 17:21:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5255, loss_cls: 4.1815, loss: 4.1815 +2024-12-27 12:31:43,453 - pyskl - INFO - Epoch [41][1700/3746] lr: 8.310e-02, eta: 3 days, 17:20:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5316, loss_cls: 4.1900, loss: 4.1900 +2024-12-27 12:33:08,139 - pyskl - INFO - Epoch [41][1800/3746] lr: 8.308e-02, eta: 3 days, 17:19:35, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5336, loss_cls: 4.1359, loss: 4.1359 +2024-12-27 12:34:32,363 - pyskl - INFO - Epoch [41][1900/3746] lr: 8.306e-02, eta: 3 days, 17:18:33, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5291, loss_cls: 4.1612, loss: 4.1612 +2024-12-27 12:35:56,800 - pyskl - INFO - Epoch [41][2000/3746] lr: 8.304e-02, eta: 3 days, 17:17:31, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5264, loss_cls: 4.1804, loss: 4.1804 +2024-12-27 12:37:21,284 - pyskl - INFO - Epoch [41][2100/3746] lr: 8.302e-02, eta: 3 days, 17:16:29, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5311, loss_cls: 4.1826, loss: 4.1826 +2024-12-27 12:38:45,653 - pyskl - INFO - Epoch [41][2200/3746] lr: 8.300e-02, eta: 3 days, 17:15:26, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5241, loss_cls: 4.1929, loss: 4.1929 +2024-12-27 12:40:09,882 - pyskl - INFO - Epoch [41][2300/3746] lr: 8.298e-02, eta: 3 days, 17:14:24, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5386, loss_cls: 4.1341, loss: 4.1341 +2024-12-27 12:41:34,311 - pyskl - INFO - Epoch [41][2400/3746] lr: 8.296e-02, eta: 3 days, 17:13:21, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5255, loss_cls: 4.2212, loss: 4.2212 +2024-12-27 12:42:58,991 - pyskl - INFO - Epoch [41][2500/3746] lr: 8.293e-02, eta: 3 days, 17:12:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5203, loss_cls: 4.1875, loss: 4.1875 +2024-12-27 12:44:23,786 - pyskl - INFO - Epoch [41][2600/3746] lr: 8.291e-02, eta: 3 days, 17:11:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5175, loss_cls: 4.2192, loss: 4.2192 +2024-12-27 12:45:48,643 - pyskl - INFO - Epoch [41][2700/3746] lr: 8.289e-02, eta: 3 days, 17:10:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5161, loss_cls: 4.2220, loss: 4.2220 +2024-12-27 12:47:13,082 - pyskl - INFO - Epoch [41][2800/3746] lr: 8.287e-02, eta: 3 days, 17:09:15, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5269, loss_cls: 4.1820, loss: 4.1820 +2024-12-27 12:48:37,575 - pyskl - INFO - Epoch [41][2900/3746] lr: 8.285e-02, eta: 3 days, 17:08:13, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5308, loss_cls: 4.1947, loss: 4.1947 +2024-12-27 12:50:01,988 - pyskl - INFO - Epoch [41][3000/3746] lr: 8.283e-02, eta: 3 days, 17:07:11, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5248, loss_cls: 4.1783, loss: 4.1783 +2024-12-27 12:51:26,487 - pyskl - INFO - Epoch [41][3100/3746] lr: 8.281e-02, eta: 3 days, 17:06:08, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5272, loss_cls: 4.1740, loss: 4.1740 +2024-12-27 12:52:51,494 - pyskl - INFO - Epoch [41][3200/3746] lr: 8.279e-02, eta: 3 days, 17:05:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5209, loss_cls: 4.2045, loss: 4.2045 +2024-12-27 12:54:16,298 - pyskl - INFO - Epoch [41][3300/3746] lr: 8.277e-02, eta: 3 days, 17:04:06, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5291, loss_cls: 4.1733, loss: 4.1733 +2024-12-27 12:55:40,771 - pyskl - INFO - Epoch [41][3400/3746] lr: 8.274e-02, eta: 3 days, 17:03:04, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5325, loss_cls: 4.1302, loss: 4.1302 +2024-12-27 12:57:05,531 - pyskl - INFO - Epoch [41][3500/3746] lr: 8.272e-02, eta: 3 days, 17:02:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5117, loss_cls: 4.2449, loss: 4.2449 +2024-12-27 12:58:30,308 - pyskl - INFO - Epoch [41][3600/3746] lr: 8.270e-02, eta: 3 days, 17:01:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5238, loss_cls: 4.1873, loss: 4.1873 +2024-12-27 12:59:55,099 - pyskl - INFO - Epoch [41][3700/3746] lr: 8.268e-02, eta: 3 days, 16:59:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5295, loss_cls: 4.1573, loss: 4.1573 +2024-12-27 13:00:35,970 - pyskl - INFO - Saving checkpoint at 41 epochs +2024-12-27 13:02:32,771 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 13:02:33,613 - pyskl - INFO - +top1_acc 0.2244 +top5_acc 0.4607 +2024-12-27 13:02:33,614 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 13:02:33,653 - pyskl - INFO - +mean_acc 0.2243 +2024-12-27 13:02:33,658 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_37.pth was removed +2024-12-27 13:02:33,917 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_41.pth. +2024-12-27 13:02:33,918 - pyskl - INFO - Best top1_acc is 0.2244 at 41 epoch. +2024-12-27 13:02:33,935 - pyskl - INFO - Epoch(val) [41][309] top1_acc: 0.2244, top5_acc: 0.4607, mean_class_accuracy: 0.2243 +2024-12-27 13:06:37,824 - pyskl - INFO - Epoch [42][100/3746] lr: 8.265e-02, eta: 3 days, 17:03:48, time: 2.439, data_time: 1.415, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5266, loss_cls: 4.1602, loss: 4.1602 +2024-12-27 13:08:02,917 - pyskl - INFO - Epoch [42][200/3746] lr: 8.263e-02, eta: 3 days, 17:02:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5467, loss_cls: 4.0701, loss: 4.0701 +2024-12-27 13:09:27,511 - pyskl - INFO - Epoch [42][300/3746] lr: 8.261e-02, eta: 3 days, 17:01:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5303, loss_cls: 4.1882, loss: 4.1882 +2024-12-27 13:10:52,290 - pyskl - INFO - Epoch [42][400/3746] lr: 8.259e-02, eta: 3 days, 17:00:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5158, loss_cls: 4.2004, loss: 4.2004 +2024-12-27 13:12:17,501 - pyskl - INFO - Epoch [42][500/3746] lr: 8.257e-02, eta: 3 days, 16:59:41, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5348, loss_cls: 4.1243, loss: 4.1243 +2024-12-27 13:13:41,689 - pyskl - INFO - Epoch [42][600/3746] lr: 8.254e-02, eta: 3 days, 16:58:37, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5336, loss_cls: 4.1599, loss: 4.1599 +2024-12-27 13:15:05,949 - pyskl - INFO - Epoch [42][700/3746] lr: 8.252e-02, eta: 3 days, 16:57:34, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5400, loss_cls: 4.1013, loss: 4.1013 +2024-12-27 13:16:30,496 - pyskl - INFO - Epoch [42][800/3746] lr: 8.250e-02, eta: 3 days, 16:56:31, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5256, loss_cls: 4.1857, loss: 4.1857 +2024-12-27 13:17:54,885 - pyskl - INFO - Epoch [42][900/3746] lr: 8.248e-02, eta: 3 days, 16:55:28, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5273, loss_cls: 4.1736, loss: 4.1736 +2024-12-27 13:19:19,725 - pyskl - INFO - Epoch [42][1000/3746] lr: 8.246e-02, eta: 3 days, 16:54:26, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5262, loss_cls: 4.1765, loss: 4.1765 +2024-12-27 13:20:43,612 - pyskl - INFO - Epoch [42][1100/3746] lr: 8.244e-02, eta: 3 days, 16:53:21, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5269, loss_cls: 4.1974, loss: 4.1974 +2024-12-27 13:22:08,382 - pyskl - INFO - Epoch [42][1200/3746] lr: 8.242e-02, eta: 3 days, 16:52:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5264, loss_cls: 4.1964, loss: 4.1964 +2024-12-27 13:23:32,208 - pyskl - INFO - Epoch [42][1300/3746] lr: 8.240e-02, eta: 3 days, 16:51:14, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5245, loss_cls: 4.1608, loss: 4.1608 +2024-12-27 13:24:56,212 - pyskl - INFO - Epoch [42][1400/3746] lr: 8.237e-02, eta: 3 days, 16:50:10, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5283, loss_cls: 4.1729, loss: 4.1729 +2024-12-27 13:26:20,437 - pyskl - INFO - Epoch [42][1500/3746] lr: 8.235e-02, eta: 3 days, 16:49:06, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5291, loss_cls: 4.2064, loss: 4.2064 +2024-12-27 13:27:44,908 - pyskl - INFO - Epoch [42][1600/3746] lr: 8.233e-02, eta: 3 days, 16:48:03, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5256, loss_cls: 4.2048, loss: 4.2048 +2024-12-27 13:29:09,900 - pyskl - INFO - Epoch [42][1700/3746] lr: 8.231e-02, eta: 3 days, 16:47:01, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5264, loss_cls: 4.1924, loss: 4.1924 +2024-12-27 13:30:34,177 - pyskl - INFO - Epoch [42][1800/3746] lr: 8.229e-02, eta: 3 days, 16:45:57, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5353, loss_cls: 4.1414, loss: 4.1414 +2024-12-27 13:31:58,730 - pyskl - INFO - Epoch [42][1900/3746] lr: 8.227e-02, eta: 3 days, 16:44:54, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5267, loss_cls: 4.1680, loss: 4.1680 +2024-12-27 13:33:23,448 - pyskl - INFO - Epoch [42][2000/3746] lr: 8.225e-02, eta: 3 days, 16:43:51, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5300, loss_cls: 4.1725, loss: 4.1725 +2024-12-27 13:34:47,652 - pyskl - INFO - Epoch [42][2100/3746] lr: 8.222e-02, eta: 3 days, 16:42:47, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5294, loss_cls: 4.1717, loss: 4.1717 +2024-12-27 13:36:12,459 - pyskl - INFO - Epoch [42][2200/3746] lr: 8.220e-02, eta: 3 days, 16:41:45, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5277, loss_cls: 4.1635, loss: 4.1635 +2024-12-27 13:37:36,674 - pyskl - INFO - Epoch [42][2300/3746] lr: 8.218e-02, eta: 3 days, 16:40:41, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5277, loss_cls: 4.1972, loss: 4.1972 +2024-12-27 13:39:01,450 - pyskl - INFO - Epoch [42][2400/3746] lr: 8.216e-02, eta: 3 days, 16:39:38, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5277, loss_cls: 4.2046, loss: 4.2046 +2024-12-27 13:40:25,394 - pyskl - INFO - Epoch [42][2500/3746] lr: 8.214e-02, eta: 3 days, 16:38:33, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5305, loss_cls: 4.1659, loss: 4.1659 +2024-12-27 13:41:49,839 - pyskl - INFO - Epoch [42][2600/3746] lr: 8.212e-02, eta: 3 days, 16:37:30, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5264, loss_cls: 4.1772, loss: 4.1772 +2024-12-27 13:43:14,363 - pyskl - INFO - Epoch [42][2700/3746] lr: 8.210e-02, eta: 3 days, 16:36:26, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5192, loss_cls: 4.1972, loss: 4.1972 +2024-12-27 13:44:39,005 - pyskl - INFO - Epoch [42][2800/3746] lr: 8.207e-02, eta: 3 days, 16:35:23, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5200, loss_cls: 4.1816, loss: 4.1816 +2024-12-27 13:46:04,031 - pyskl - INFO - Epoch [42][2900/3746] lr: 8.205e-02, eta: 3 days, 16:34:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5262, loss_cls: 4.1765, loss: 4.1765 +2024-12-27 13:47:29,187 - pyskl - INFO - Epoch [42][3000/3746] lr: 8.203e-02, eta: 3 days, 16:33:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5273, loss_cls: 4.1793, loss: 4.1793 +2024-12-27 13:48:54,013 - pyskl - INFO - Epoch [42][3100/3746] lr: 8.201e-02, eta: 3 days, 16:32:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5306, loss_cls: 4.1543, loss: 4.1543 +2024-12-27 13:50:18,434 - pyskl - INFO - Epoch [42][3200/3746] lr: 8.199e-02, eta: 3 days, 16:31:13, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5270, loss_cls: 4.1919, loss: 4.1919 +2024-12-27 13:51:43,272 - pyskl - INFO - Epoch [42][3300/3746] lr: 8.197e-02, eta: 3 days, 16:30:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5281, loss_cls: 4.1863, loss: 4.1863 +2024-12-27 13:53:08,992 - pyskl - INFO - Epoch [42][3400/3746] lr: 8.195e-02, eta: 3 days, 16:29:10, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5312, loss_cls: 4.1378, loss: 4.1378 +2024-12-27 13:54:34,278 - pyskl - INFO - Epoch [42][3500/3746] lr: 8.192e-02, eta: 3 days, 16:28:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5277, loss_cls: 4.1870, loss: 4.1870 +2024-12-27 13:55:59,413 - pyskl - INFO - Epoch [42][3600/3746] lr: 8.190e-02, eta: 3 days, 16:27:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5161, loss_cls: 4.2075, loss: 4.2075 +2024-12-27 13:57:24,699 - pyskl - INFO - Epoch [42][3700/3746] lr: 8.188e-02, eta: 3 days, 16:26:04, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5363, loss_cls: 4.1452, loss: 4.1452 +2024-12-27 13:58:05,171 - pyskl - INFO - Saving checkpoint at 42 epochs +2024-12-27 14:00:03,138 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 14:00:03,917 - pyskl - INFO - +top1_acc 0.1875 +top5_acc 0.4106 +2024-12-27 14:00:03,917 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 14:00:03,957 - pyskl - INFO - +mean_acc 0.1873 +2024-12-27 14:00:03,967 - pyskl - INFO - Epoch(val) [42][309] top1_acc: 0.1875, top5_acc: 0.4106, mean_class_accuracy: 0.1873 +2024-12-27 14:04:17,572 - pyskl - INFO - Epoch [43][100/3746] lr: 8.185e-02, eta: 3 days, 16:30:06, time: 2.536, data_time: 1.496, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5423, loss_cls: 4.1113, loss: 4.1113 +2024-12-27 14:05:42,880 - pyskl - INFO - Epoch [43][200/3746] lr: 8.183e-02, eta: 3 days, 16:29:04, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5397, loss_cls: 4.1163, loss: 4.1163 +2024-12-27 14:07:07,735 - pyskl - INFO - Epoch [43][300/3746] lr: 8.181e-02, eta: 3 days, 16:28:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5366, loss_cls: 4.1220, loss: 4.1220 +2024-12-27 14:08:32,457 - pyskl - INFO - Epoch [43][400/3746] lr: 8.179e-02, eta: 3 days, 16:26:57, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5247, loss_cls: 4.1602, loss: 4.1602 +2024-12-27 14:09:57,064 - pyskl - INFO - Epoch [43][500/3746] lr: 8.176e-02, eta: 3 days, 16:25:53, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5306, loss_cls: 4.1765, loss: 4.1765 +2024-12-27 14:11:22,288 - pyskl - INFO - Epoch [43][600/3746] lr: 8.174e-02, eta: 3 days, 16:24:51, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5311, loss_cls: 4.1269, loss: 4.1269 +2024-12-27 14:12:46,945 - pyskl - INFO - Epoch [43][700/3746] lr: 8.172e-02, eta: 3 days, 16:23:47, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5305, loss_cls: 4.1493, loss: 4.1493 +2024-12-27 14:14:12,259 - pyskl - INFO - Epoch [43][800/3746] lr: 8.170e-02, eta: 3 days, 16:22:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5244, loss_cls: 4.1652, loss: 4.1652 +2024-12-27 14:15:36,706 - pyskl - INFO - Epoch [43][900/3746] lr: 8.168e-02, eta: 3 days, 16:21:40, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5400, loss_cls: 4.1082, loss: 4.1082 +2024-12-27 14:17:01,030 - pyskl - INFO - Epoch [43][1000/3746] lr: 8.166e-02, eta: 3 days, 16:20:36, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5341, loss_cls: 4.1356, loss: 4.1356 +2024-12-27 14:18:25,596 - pyskl - INFO - Epoch [43][1100/3746] lr: 8.163e-02, eta: 3 days, 16:19:32, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5288, loss_cls: 4.1800, loss: 4.1800 +2024-12-27 14:19:50,089 - pyskl - INFO - Epoch [43][1200/3746] lr: 8.161e-02, eta: 3 days, 16:18:27, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5136, loss_cls: 4.2593, loss: 4.2593 +2024-12-27 14:21:15,071 - pyskl - INFO - Epoch [43][1300/3746] lr: 8.159e-02, eta: 3 days, 16:17:24, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5359, loss_cls: 4.1517, loss: 4.1517 +2024-12-27 14:22:39,175 - pyskl - INFO - Epoch [43][1400/3746] lr: 8.157e-02, eta: 3 days, 16:16:19, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5455, loss_cls: 4.1147, loss: 4.1147 +2024-12-27 14:24:03,644 - pyskl - INFO - Epoch [43][1500/3746] lr: 8.155e-02, eta: 3 days, 16:15:14, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5311, loss_cls: 4.1667, loss: 4.1667 +2024-12-27 14:25:28,384 - pyskl - INFO - Epoch [43][1600/3746] lr: 8.153e-02, eta: 3 days, 16:14:10, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5281, loss_cls: 4.1857, loss: 4.1857 +2024-12-27 14:26:53,551 - pyskl - INFO - Epoch [43][1700/3746] lr: 8.150e-02, eta: 3 days, 16:13:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5309, loss_cls: 4.1494, loss: 4.1494 +2024-12-27 14:28:18,259 - pyskl - INFO - Epoch [43][1800/3746] lr: 8.148e-02, eta: 3 days, 16:12:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5339, loss_cls: 4.1326, loss: 4.1326 +2024-12-27 14:29:43,098 - pyskl - INFO - Epoch [43][1900/3746] lr: 8.146e-02, eta: 3 days, 16:11:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5244, loss_cls: 4.1691, loss: 4.1691 +2024-12-27 14:31:08,589 - pyskl - INFO - Epoch [43][2000/3746] lr: 8.144e-02, eta: 3 days, 16:09:58, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5205, loss_cls: 4.2168, loss: 4.2168 +2024-12-27 14:32:33,252 - pyskl - INFO - Epoch [43][2100/3746] lr: 8.142e-02, eta: 3 days, 16:08:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5283, loss_cls: 4.1658, loss: 4.1658 +2024-12-27 14:33:58,453 - pyskl - INFO - Epoch [43][2200/3746] lr: 8.140e-02, eta: 3 days, 16:07:51, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5239, loss_cls: 4.1562, loss: 4.1562 +2024-12-27 14:35:23,259 - pyskl - INFO - Epoch [43][2300/3746] lr: 8.137e-02, eta: 3 days, 16:06:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5353, loss_cls: 4.1813, loss: 4.1813 +2024-12-27 14:36:47,884 - pyskl - INFO - Epoch [43][2400/3746] lr: 8.135e-02, eta: 3 days, 16:05:43, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5247, loss_cls: 4.2325, loss: 4.2325 +2024-12-27 14:38:12,013 - pyskl - INFO - Epoch [43][2500/3746] lr: 8.133e-02, eta: 3 days, 16:04:37, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5292, loss_cls: 4.1704, loss: 4.1704 +2024-12-27 14:39:37,521 - pyskl - INFO - Epoch [43][2600/3746] lr: 8.131e-02, eta: 3 days, 16:03:35, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5128, loss_cls: 4.1959, loss: 4.1959 +2024-12-27 14:41:02,634 - pyskl - INFO - Epoch [43][2700/3746] lr: 8.129e-02, eta: 3 days, 16:02:32, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5278, loss_cls: 4.1799, loss: 4.1799 +2024-12-27 14:42:28,023 - pyskl - INFO - Epoch [43][2800/3746] lr: 8.126e-02, eta: 3 days, 16:01:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5272, loss_cls: 4.1706, loss: 4.1706 +2024-12-27 14:43:52,627 - pyskl - INFO - Epoch [43][2900/3746] lr: 8.124e-02, eta: 3 days, 16:00:25, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5330, loss_cls: 4.1904, loss: 4.1904 +2024-12-27 14:45:16,999 - pyskl - INFO - Epoch [43][3000/3746] lr: 8.122e-02, eta: 3 days, 15:59:20, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5266, loss_cls: 4.1731, loss: 4.1731 +2024-12-27 14:46:41,014 - pyskl - INFO - Epoch [43][3100/3746] lr: 8.120e-02, eta: 3 days, 15:58:14, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5236, loss_cls: 4.1727, loss: 4.1727 +2024-12-27 14:48:05,471 - pyskl - INFO - Epoch [43][3200/3746] lr: 8.118e-02, eta: 3 days, 15:57:09, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5211, loss_cls: 4.2017, loss: 4.2017 +2024-12-27 14:49:29,731 - pyskl - INFO - Epoch [43][3300/3746] lr: 8.116e-02, eta: 3 days, 15:56:03, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5228, loss_cls: 4.1843, loss: 4.1843 +2024-12-27 14:50:54,611 - pyskl - INFO - Epoch [43][3400/3746] lr: 8.113e-02, eta: 3 days, 15:54:59, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5342, loss_cls: 4.1737, loss: 4.1737 +2024-12-27 14:52:20,049 - pyskl - INFO - Epoch [43][3500/3746] lr: 8.111e-02, eta: 3 days, 15:53:57, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5277, loss_cls: 4.1768, loss: 4.1768 +2024-12-27 14:53:44,860 - pyskl - INFO - Epoch [43][3600/3746] lr: 8.109e-02, eta: 3 days, 15:52:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5194, loss_cls: 4.1860, loss: 4.1860 +2024-12-27 14:55:10,208 - pyskl - INFO - Epoch [43][3700/3746] lr: 8.107e-02, eta: 3 days, 15:51:50, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5378, loss_cls: 4.1170, loss: 4.1170 +2024-12-27 14:55:51,235 - pyskl - INFO - Saving checkpoint at 43 epochs +2024-12-27 14:57:47,806 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 14:57:48,656 - pyskl - INFO - +top1_acc 0.1876 +top5_acc 0.4096 +2024-12-27 14:57:48,656 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 14:57:48,714 - pyskl - INFO - +mean_acc 0.1874 +2024-12-27 14:57:48,730 - pyskl - INFO - Epoch(val) [43][309] top1_acc: 0.1876, top5_acc: 0.4096, mean_class_accuracy: 0.1874 +2024-12-27 15:02:11,939 - pyskl - INFO - Epoch [44][100/3746] lr: 8.104e-02, eta: 3 days, 15:56:02, time: 2.632, data_time: 1.593, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5436, loss_cls: 4.0952, loss: 4.0952 +2024-12-27 15:03:37,600 - pyskl - INFO - Epoch [44][200/3746] lr: 8.101e-02, eta: 3 days, 15:55:00, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5342, loss_cls: 4.1097, loss: 4.1097 +2024-12-27 15:05:01,499 - pyskl - INFO - Epoch [44][300/3746] lr: 8.099e-02, eta: 3 days, 15:53:53, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5389, loss_cls: 4.1348, loss: 4.1348 +2024-12-27 15:06:25,844 - pyskl - INFO - Epoch [44][400/3746] lr: 8.097e-02, eta: 3 days, 15:52:47, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5381, loss_cls: 4.1045, loss: 4.1045 +2024-12-27 15:07:50,073 - pyskl - INFO - Epoch [44][500/3746] lr: 8.095e-02, eta: 3 days, 15:51:41, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5269, loss_cls: 4.1720, loss: 4.1720 +2024-12-27 15:09:15,080 - pyskl - INFO - Epoch [44][600/3746] lr: 8.093e-02, eta: 3 days, 15:50:37, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5330, loss_cls: 4.1650, loss: 4.1650 +2024-12-27 15:10:39,980 - pyskl - INFO - Epoch [44][700/3746] lr: 8.090e-02, eta: 3 days, 15:49:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5316, loss_cls: 4.1654, loss: 4.1654 +2024-12-27 15:12:04,650 - pyskl - INFO - Epoch [44][800/3746] lr: 8.088e-02, eta: 3 days, 15:48:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5383, loss_cls: 4.1356, loss: 4.1356 +2024-12-27 15:13:28,785 - pyskl - INFO - Epoch [44][900/3746] lr: 8.086e-02, eta: 3 days, 15:47:21, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5205, loss_cls: 4.1957, loss: 4.1957 +2024-12-27 15:14:53,036 - pyskl - INFO - Epoch [44][1000/3746] lr: 8.084e-02, eta: 3 days, 15:46:15, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5347, loss_cls: 4.1388, loss: 4.1388 +2024-12-27 15:16:18,189 - pyskl - INFO - Epoch [44][1100/3746] lr: 8.082e-02, eta: 3 days, 15:45:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5355, loss_cls: 4.1208, loss: 4.1208 +2024-12-27 15:17:42,582 - pyskl - INFO - Epoch [44][1200/3746] lr: 8.079e-02, eta: 3 days, 15:44:05, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5253, loss_cls: 4.1593, loss: 4.1593 +2024-12-27 15:19:07,329 - pyskl - INFO - Epoch [44][1300/3746] lr: 8.077e-02, eta: 3 days, 15:43:00, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5439, loss_cls: 4.1069, loss: 4.1069 +2024-12-27 15:20:31,292 - pyskl - INFO - Epoch [44][1400/3746] lr: 8.075e-02, eta: 3 days, 15:41:53, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5378, loss_cls: 4.1216, loss: 4.1216 +2024-12-27 15:21:55,817 - pyskl - INFO - Epoch [44][1500/3746] lr: 8.073e-02, eta: 3 days, 15:40:48, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5234, loss_cls: 4.2060, loss: 4.2060 +2024-12-27 15:23:20,538 - pyskl - INFO - Epoch [44][1600/3746] lr: 8.071e-02, eta: 3 days, 15:39:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5223, loss_cls: 4.2011, loss: 4.2011 +2024-12-27 15:24:45,179 - pyskl - INFO - Epoch [44][1700/3746] lr: 8.068e-02, eta: 3 days, 15:38:37, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5277, loss_cls: 4.1824, loss: 4.1824 +2024-12-27 15:26:09,825 - pyskl - INFO - Epoch [44][1800/3746] lr: 8.066e-02, eta: 3 days, 15:37:32, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5320, loss_cls: 4.1318, loss: 4.1318 +2024-12-27 15:27:34,761 - pyskl - INFO - Epoch [44][1900/3746] lr: 8.064e-02, eta: 3 days, 15:36:27, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5281, loss_cls: 4.1795, loss: 4.1795 +2024-12-27 15:28:59,635 - pyskl - INFO - Epoch [44][2000/3746] lr: 8.062e-02, eta: 3 days, 15:35:22, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5345, loss_cls: 4.1647, loss: 4.1647 +2024-12-27 15:30:24,098 - pyskl - INFO - Epoch [44][2100/3746] lr: 8.060e-02, eta: 3 days, 15:34:17, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5250, loss_cls: 4.1659, loss: 4.1659 +2024-12-27 15:31:49,100 - pyskl - INFO - Epoch [44][2200/3746] lr: 8.057e-02, eta: 3 days, 15:33:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5325, loss_cls: 4.1856, loss: 4.1856 +2024-12-27 15:33:13,777 - pyskl - INFO - Epoch [44][2300/3746] lr: 8.055e-02, eta: 3 days, 15:32:07, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5289, loss_cls: 4.1699, loss: 4.1699 +2024-12-27 15:34:38,195 - pyskl - INFO - Epoch [44][2400/3746] lr: 8.053e-02, eta: 3 days, 15:31:01, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5280, loss_cls: 4.1593, loss: 4.1593 +2024-12-27 15:36:03,173 - pyskl - INFO - Epoch [44][2500/3746] lr: 8.051e-02, eta: 3 days, 15:29:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5194, loss_cls: 4.2074, loss: 4.2074 +2024-12-27 15:37:27,269 - pyskl - INFO - Epoch [44][2600/3746] lr: 8.048e-02, eta: 3 days, 15:28:49, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5384, loss_cls: 4.1511, loss: 4.1511 +2024-12-27 15:38:52,047 - pyskl - INFO - Epoch [44][2700/3746] lr: 8.046e-02, eta: 3 days, 15:27:44, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5319, loss_cls: 4.1063, loss: 4.1063 +2024-12-27 15:40:16,917 - pyskl - INFO - Epoch [44][2800/3746] lr: 8.044e-02, eta: 3 days, 15:26:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5203, loss_cls: 4.1924, loss: 4.1924 +2024-12-27 15:41:41,630 - pyskl - INFO - Epoch [44][2900/3746] lr: 8.042e-02, eta: 3 days, 15:25:33, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5120, loss_cls: 4.2560, loss: 4.2560 +2024-12-27 15:43:06,734 - pyskl - INFO - Epoch [44][3000/3746] lr: 8.040e-02, eta: 3 days, 15:24:29, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5345, loss_cls: 4.1365, loss: 4.1365 +2024-12-27 15:44:31,122 - pyskl - INFO - Epoch [44][3100/3746] lr: 8.037e-02, eta: 3 days, 15:23:22, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5275, loss_cls: 4.1595, loss: 4.1595 +2024-12-27 15:45:55,607 - pyskl - INFO - Epoch [44][3200/3746] lr: 8.035e-02, eta: 3 days, 15:22:16, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5217, loss_cls: 4.2052, loss: 4.2052 +2024-12-27 15:47:20,624 - pyskl - INFO - Epoch [44][3300/3746] lr: 8.033e-02, eta: 3 days, 15:21:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5392, loss_cls: 4.1451, loss: 4.1451 +2024-12-27 15:48:45,638 - pyskl - INFO - Epoch [44][3400/3746] lr: 8.031e-02, eta: 3 days, 15:20:07, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5295, loss_cls: 4.1717, loss: 4.1717 +2024-12-27 15:50:10,685 - pyskl - INFO - Epoch [44][3500/3746] lr: 8.028e-02, eta: 3 days, 15:19:02, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5420, loss_cls: 4.0983, loss: 4.0983 +2024-12-27 15:51:35,324 - pyskl - INFO - Epoch [44][3600/3746] lr: 8.026e-02, eta: 3 days, 15:17:56, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5391, loss_cls: 4.1565, loss: 4.1565 +2024-12-27 15:53:00,292 - pyskl - INFO - Epoch [44][3700/3746] lr: 8.024e-02, eta: 3 days, 15:16:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5177, loss_cls: 4.2145, loss: 4.2145 +2024-12-27 15:53:41,567 - pyskl - INFO - Saving checkpoint at 44 epochs +2024-12-27 15:55:39,449 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 15:55:40,359 - pyskl - INFO - +top1_acc 0.1854 +top5_acc 0.4074 +2024-12-27 15:55:40,359 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 15:55:40,401 - pyskl - INFO - +mean_acc 0.1853 +2024-12-27 15:55:40,415 - pyskl - INFO - Epoch(val) [44][309] top1_acc: 0.1854, top5_acc: 0.4074, mean_class_accuracy: 0.1853 +2024-12-27 15:59:54,750 - pyskl - INFO - Epoch [45][100/3746] lr: 8.021e-02, eta: 3 days, 15:20:30, time: 2.543, data_time: 1.520, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5387, loss_cls: 4.0920, loss: 4.0920 +2024-12-27 16:01:19,954 - pyskl - INFO - Epoch [45][200/3746] lr: 8.019e-02, eta: 3 days, 15:19:25, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5289, loss_cls: 4.1600, loss: 4.1600 +2024-12-27 16:02:44,320 - pyskl - INFO - Epoch [45][300/3746] lr: 8.016e-02, eta: 3 days, 15:18:18, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5367, loss_cls: 4.0892, loss: 4.0892 +2024-12-27 16:04:08,749 - pyskl - INFO - Epoch [45][400/3746] lr: 8.014e-02, eta: 3 days, 15:17:11, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5361, loss_cls: 4.1240, loss: 4.1240 +2024-12-27 16:05:33,681 - pyskl - INFO - Epoch [45][500/3746] lr: 8.012e-02, eta: 3 days, 15:16:06, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5194, loss_cls: 4.1863, loss: 4.1863 +2024-12-27 16:06:58,787 - pyskl - INFO - Epoch [45][600/3746] lr: 8.010e-02, eta: 3 days, 15:15:01, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5364, loss_cls: 4.1506, loss: 4.1506 +2024-12-27 16:08:23,508 - pyskl - INFO - Epoch [45][700/3746] lr: 8.007e-02, eta: 3 days, 15:13:55, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5248, loss_cls: 4.1720, loss: 4.1720 +2024-12-27 16:09:47,784 - pyskl - INFO - Epoch [45][800/3746] lr: 8.005e-02, eta: 3 days, 15:12:48, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5448, loss_cls: 4.1121, loss: 4.1121 +2024-12-27 16:11:12,752 - pyskl - INFO - Epoch [45][900/3746] lr: 8.003e-02, eta: 3 days, 15:11:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5322, loss_cls: 4.1264, loss: 4.1264 +2024-12-27 16:12:37,338 - pyskl - INFO - Epoch [45][1000/3746] lr: 8.001e-02, eta: 3 days, 15:10:36, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5217, loss_cls: 4.1729, loss: 4.1729 +2024-12-27 16:14:01,645 - pyskl - INFO - Epoch [45][1100/3746] lr: 7.998e-02, eta: 3 days, 15:09:28, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5375, loss_cls: 4.1131, loss: 4.1131 +2024-12-27 16:15:25,905 - pyskl - INFO - Epoch [45][1200/3746] lr: 7.996e-02, eta: 3 days, 15:08:21, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5395, loss_cls: 4.1138, loss: 4.1138 +2024-12-27 16:16:49,885 - pyskl - INFO - Epoch [45][1300/3746] lr: 7.994e-02, eta: 3 days, 15:07:13, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5280, loss_cls: 4.1678, loss: 4.1678 +2024-12-27 16:18:13,893 - pyskl - INFO - Epoch [45][1400/3746] lr: 7.992e-02, eta: 3 days, 15:06:05, time: 0.840, data_time: 0.001, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5342, loss_cls: 4.1418, loss: 4.1418 +2024-12-27 16:19:38,048 - pyskl - INFO - Epoch [45][1500/3746] lr: 7.990e-02, eta: 3 days, 15:04:58, time: 0.842, data_time: 0.001, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5319, loss_cls: 4.1441, loss: 4.1441 +2024-12-27 16:21:02,446 - pyskl - INFO - Epoch [45][1600/3746] lr: 7.987e-02, eta: 3 days, 15:03:51, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5356, loss_cls: 4.1378, loss: 4.1378 +2024-12-27 16:22:26,728 - pyskl - INFO - Epoch [45][1700/3746] lr: 7.985e-02, eta: 3 days, 15:02:43, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5259, loss_cls: 4.1711, loss: 4.1711 +2024-12-27 16:23:51,609 - pyskl - INFO - Epoch [45][1800/3746] lr: 7.983e-02, eta: 3 days, 15:01:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5277, loss_cls: 4.1754, loss: 4.1754 +2024-12-27 16:25:16,617 - pyskl - INFO - Epoch [45][1900/3746] lr: 7.981e-02, eta: 3 days, 15:00:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5208, loss_cls: 4.2252, loss: 4.2252 +2024-12-27 16:26:41,585 - pyskl - INFO - Epoch [45][2000/3746] lr: 7.978e-02, eta: 3 days, 14:59:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5323, loss_cls: 4.1467, loss: 4.1467 +2024-12-27 16:28:05,956 - pyskl - INFO - Epoch [45][2100/3746] lr: 7.976e-02, eta: 3 days, 14:58:19, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5298, loss_cls: 4.1393, loss: 4.1393 +2024-12-27 16:29:30,606 - pyskl - INFO - Epoch [45][2200/3746] lr: 7.974e-02, eta: 3 days, 14:57:12, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5323, loss_cls: 4.1322, loss: 4.1322 +2024-12-27 16:30:55,866 - pyskl - INFO - Epoch [45][2300/3746] lr: 7.972e-02, eta: 3 days, 14:56:07, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5305, loss_cls: 4.1584, loss: 4.1584 +2024-12-27 16:32:21,740 - pyskl - INFO - Epoch [45][2400/3746] lr: 7.969e-02, eta: 3 days, 14:55:03, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5223, loss_cls: 4.1867, loss: 4.1867 +2024-12-27 16:33:47,303 - pyskl - INFO - Epoch [45][2500/3746] lr: 7.967e-02, eta: 3 days, 14:53:59, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5320, loss_cls: 4.1391, loss: 4.1391 +2024-12-27 16:35:12,635 - pyskl - INFO - Epoch [45][2600/3746] lr: 7.965e-02, eta: 3 days, 14:52:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5216, loss_cls: 4.1890, loss: 4.1890 +2024-12-27 16:36:38,294 - pyskl - INFO - Epoch [45][2700/3746] lr: 7.963e-02, eta: 3 days, 14:51:50, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5230, loss_cls: 4.1357, loss: 4.1357 +2024-12-27 16:38:03,961 - pyskl - INFO - Epoch [45][2800/3746] lr: 7.960e-02, eta: 3 days, 14:50:45, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5208, loss_cls: 4.2183, loss: 4.2183 +2024-12-27 16:39:30,082 - pyskl - INFO - Epoch [45][2900/3746] lr: 7.958e-02, eta: 3 days, 14:49:42, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5309, loss_cls: 4.1632, loss: 4.1632 +2024-12-27 16:40:55,601 - pyskl - INFO - Epoch [45][3000/3746] lr: 7.956e-02, eta: 3 days, 14:48:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5280, loss_cls: 4.2014, loss: 4.2014 +2024-12-27 16:42:21,873 - pyskl - INFO - Epoch [45][3100/3746] lr: 7.954e-02, eta: 3 days, 14:47:34, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5391, loss_cls: 4.1071, loss: 4.1071 +2024-12-27 16:43:48,037 - pyskl - INFO - Epoch [45][3200/3746] lr: 7.951e-02, eta: 3 days, 14:46:31, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5348, loss_cls: 4.1319, loss: 4.1319 +2024-12-27 16:45:13,535 - pyskl - INFO - Epoch [45][3300/3746] lr: 7.949e-02, eta: 3 days, 14:45:26, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5233, loss_cls: 4.2160, loss: 4.2160 +2024-12-27 16:46:39,530 - pyskl - INFO - Epoch [45][3400/3746] lr: 7.947e-02, eta: 3 days, 14:44:23, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5436, loss_cls: 4.0728, loss: 4.0728 +2024-12-27 16:48:05,193 - pyskl - INFO - Epoch [45][3500/3746] lr: 7.945e-02, eta: 3 days, 14:43:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5258, loss_cls: 4.2003, loss: 4.2003 +2024-12-27 16:49:31,467 - pyskl - INFO - Epoch [45][3600/3746] lr: 7.942e-02, eta: 3 days, 14:42:15, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5361, loss_cls: 4.1548, loss: 4.1548 +2024-12-27 16:50:57,277 - pyskl - INFO - Epoch [45][3700/3746] lr: 7.940e-02, eta: 3 days, 14:41:11, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5352, loss_cls: 4.1351, loss: 4.1351 +2024-12-27 16:51:38,387 - pyskl - INFO - Saving checkpoint at 45 epochs +2024-12-27 16:53:36,934 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 16:53:37,729 - pyskl - INFO - +top1_acc 0.2121 +top5_acc 0.4490 +2024-12-27 16:53:37,730 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 16:53:37,787 - pyskl - INFO - +mean_acc 0.2119 +2024-12-27 16:53:37,804 - pyskl - INFO - Epoch(val) [45][309] top1_acc: 0.2121, top5_acc: 0.4490, mean_class_accuracy: 0.2119 +2024-12-27 16:57:56,864 - pyskl - INFO - Epoch [46][100/3746] lr: 7.937e-02, eta: 3 days, 14:44:49, time: 2.590, data_time: 1.551, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5437, loss_cls: 4.0878, loss: 4.0878 +2024-12-27 16:59:22,238 - pyskl - INFO - Epoch [46][200/3746] lr: 7.934e-02, eta: 3 days, 14:43:43, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5436, loss_cls: 4.0932, loss: 4.0932 +2024-12-27 17:00:47,227 - pyskl - INFO - Epoch [46][300/3746] lr: 7.932e-02, eta: 3 days, 14:42:37, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5308, loss_cls: 4.1453, loss: 4.1453 +2024-12-27 17:02:12,453 - pyskl - INFO - Epoch [46][400/3746] lr: 7.930e-02, eta: 3 days, 14:41:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5302, loss_cls: 4.1338, loss: 4.1338 +2024-12-27 17:03:37,386 - pyskl - INFO - Epoch [46][500/3746] lr: 7.928e-02, eta: 3 days, 14:40:24, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5375, loss_cls: 4.1081, loss: 4.1081 +2024-12-27 17:05:02,190 - pyskl - INFO - Epoch [46][600/3746] lr: 7.925e-02, eta: 3 days, 14:39:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5423, loss_cls: 4.1233, loss: 4.1233 +2024-12-27 17:06:26,912 - pyskl - INFO - Epoch [46][700/3746] lr: 7.923e-02, eta: 3 days, 14:38:10, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5295, loss_cls: 4.1730, loss: 4.1730 +2024-12-27 17:07:51,246 - pyskl - INFO - Epoch [46][800/3746] lr: 7.921e-02, eta: 3 days, 14:37:02, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5292, loss_cls: 4.1727, loss: 4.1727 +2024-12-27 17:09:16,537 - pyskl - INFO - Epoch [46][900/3746] lr: 7.919e-02, eta: 3 days, 14:35:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5405, loss_cls: 4.1176, loss: 4.1176 +2024-12-27 17:10:41,774 - pyskl - INFO - Epoch [46][1000/3746] lr: 7.916e-02, eta: 3 days, 14:34:50, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5316, loss_cls: 4.1354, loss: 4.1354 +2024-12-27 17:12:07,098 - pyskl - INFO - Epoch [46][1100/3746] lr: 7.914e-02, eta: 3 days, 14:33:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5280, loss_cls: 4.1613, loss: 4.1613 +2024-12-27 17:13:31,974 - pyskl - INFO - Epoch [46][1200/3746] lr: 7.912e-02, eta: 3 days, 14:32:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5344, loss_cls: 4.1372, loss: 4.1372 +2024-12-27 17:14:57,080 - pyskl - INFO - Epoch [46][1300/3746] lr: 7.909e-02, eta: 3 days, 14:31:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5308, loss_cls: 4.1513, loss: 4.1513 +2024-12-27 17:16:21,864 - pyskl - INFO - Epoch [46][1400/3746] lr: 7.907e-02, eta: 3 days, 14:30:24, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5391, loss_cls: 4.1196, loss: 4.1196 +2024-12-27 17:17:46,027 - pyskl - INFO - Epoch [46][1500/3746] lr: 7.905e-02, eta: 3 days, 14:29:15, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5217, loss_cls: 4.1872, loss: 4.1872 +2024-12-27 17:19:10,448 - pyskl - INFO - Epoch [46][1600/3746] lr: 7.903e-02, eta: 3 days, 14:28:07, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5309, loss_cls: 4.1530, loss: 4.1530 +2024-12-27 17:20:35,233 - pyskl - INFO - Epoch [46][1700/3746] lr: 7.900e-02, eta: 3 days, 14:27:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5273, loss_cls: 4.1744, loss: 4.1744 +2024-12-27 17:21:59,648 - pyskl - INFO - Epoch [46][1800/3746] lr: 7.898e-02, eta: 3 days, 14:25:52, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5220, loss_cls: 4.2070, loss: 4.2070 +2024-12-27 17:23:24,293 - pyskl - INFO - Epoch [46][1900/3746] lr: 7.896e-02, eta: 3 days, 14:24:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5309, loss_cls: 4.1673, loss: 4.1673 +2024-12-27 17:24:49,029 - pyskl - INFO - Epoch [46][2000/3746] lr: 7.894e-02, eta: 3 days, 14:23:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5363, loss_cls: 4.1346, loss: 4.1346 +2024-12-27 17:26:13,871 - pyskl - INFO - Epoch [46][2100/3746] lr: 7.891e-02, eta: 3 days, 14:22:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5317, loss_cls: 4.1631, loss: 4.1631 +2024-12-27 17:27:38,133 - pyskl - INFO - Epoch [46][2200/3746] lr: 7.889e-02, eta: 3 days, 14:21:21, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5297, loss_cls: 4.1889, loss: 4.1889 +2024-12-27 17:29:02,954 - pyskl - INFO - Epoch [46][2300/3746] lr: 7.887e-02, eta: 3 days, 14:20:14, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5264, loss_cls: 4.1902, loss: 4.1902 +2024-12-27 17:30:27,904 - pyskl - INFO - Epoch [46][2400/3746] lr: 7.884e-02, eta: 3 days, 14:19:07, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5214, loss_cls: 4.1715, loss: 4.1715 +2024-12-27 17:31:52,594 - pyskl - INFO - Epoch [46][2500/3746] lr: 7.882e-02, eta: 3 days, 14:17:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5266, loss_cls: 4.1599, loss: 4.1599 +2024-12-27 17:33:17,579 - pyskl - INFO - Epoch [46][2600/3746] lr: 7.880e-02, eta: 3 days, 14:16:52, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5289, loss_cls: 4.1358, loss: 4.1358 +2024-12-27 17:34:41,765 - pyskl - INFO - Epoch [46][2700/3746] lr: 7.878e-02, eta: 3 days, 14:15:44, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5420, loss_cls: 4.0956, loss: 4.0956 +2024-12-27 17:36:07,133 - pyskl - INFO - Epoch [46][2800/3746] lr: 7.875e-02, eta: 3 days, 14:14:37, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5319, loss_cls: 4.1557, loss: 4.1557 +2024-12-27 17:37:31,868 - pyskl - INFO - Epoch [46][2900/3746] lr: 7.873e-02, eta: 3 days, 14:13:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5400, loss_cls: 4.1318, loss: 4.1318 +2024-12-27 17:38:57,278 - pyskl - INFO - Epoch [46][3000/3746] lr: 7.871e-02, eta: 3 days, 14:12:24, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5292, loss_cls: 4.1593, loss: 4.1593 +2024-12-27 17:40:21,970 - pyskl - INFO - Epoch [46][3100/3746] lr: 7.868e-02, eta: 3 days, 14:11:16, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5188, loss_cls: 4.1956, loss: 4.1956 +2024-12-27 17:41:46,742 - pyskl - INFO - Epoch [46][3200/3746] lr: 7.866e-02, eta: 3 days, 14:10:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5345, loss_cls: 4.1393, loss: 4.1393 +2024-12-27 17:43:11,645 - pyskl - INFO - Epoch [46][3300/3746] lr: 7.864e-02, eta: 3 days, 14:09:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5411, loss_cls: 4.1025, loss: 4.1025 +2024-12-27 17:44:36,663 - pyskl - INFO - Epoch [46][3400/3746] lr: 7.862e-02, eta: 3 days, 14:07:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5308, loss_cls: 4.1446, loss: 4.1446 +2024-12-27 17:46:01,828 - pyskl - INFO - Epoch [46][3500/3746] lr: 7.859e-02, eta: 3 days, 14:06:47, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5337, loss_cls: 4.1421, loss: 4.1421 +2024-12-27 17:47:26,645 - pyskl - INFO - Epoch [46][3600/3746] lr: 7.857e-02, eta: 3 days, 14:05:40, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5277, loss_cls: 4.1749, loss: 4.1749 +2024-12-27 17:48:51,577 - pyskl - INFO - Epoch [46][3700/3746] lr: 7.855e-02, eta: 3 days, 14:04:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5264, loss_cls: 4.1566, loss: 4.1566 +2024-12-27 17:49:32,952 - pyskl - INFO - Saving checkpoint at 46 epochs +2024-12-27 17:51:31,567 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 17:51:32,319 - pyskl - INFO - +top1_acc 0.2023 +top5_acc 0.4310 +2024-12-27 17:51:32,319 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 17:51:32,378 - pyskl - INFO - +mean_acc 0.2019 +2024-12-27 17:51:32,396 - pyskl - INFO - Epoch(val) [46][309] top1_acc: 0.2023, top5_acc: 0.4310, mean_class_accuracy: 0.2019 +2024-12-27 17:55:46,483 - pyskl - INFO - Epoch [47][100/3746] lr: 7.851e-02, eta: 3 days, 14:07:48, time: 2.541, data_time: 1.509, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5356, loss_cls: 4.1127, loss: 4.1127 +2024-12-27 17:57:11,902 - pyskl - INFO - Epoch [47][200/3746] lr: 7.849e-02, eta: 3 days, 14:06:42, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5400, loss_cls: 4.1116, loss: 4.1116 +2024-12-27 17:58:37,427 - pyskl - INFO - Epoch [47][300/3746] lr: 7.847e-02, eta: 3 days, 14:05:35, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5439, loss_cls: 4.0786, loss: 4.0786 +2024-12-27 18:00:02,331 - pyskl - INFO - Epoch [47][400/3746] lr: 7.844e-02, eta: 3 days, 14:04:28, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5345, loss_cls: 4.1174, loss: 4.1174 +2024-12-27 18:01:27,459 - pyskl - INFO - Epoch [47][500/3746] lr: 7.842e-02, eta: 3 days, 14:03:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5470, loss_cls: 4.0888, loss: 4.0888 +2024-12-27 18:02:52,569 - pyskl - INFO - Epoch [47][600/3746] lr: 7.840e-02, eta: 3 days, 14:02:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5328, loss_cls: 4.1309, loss: 4.1309 +2024-12-27 18:04:16,703 - pyskl - INFO - Epoch [47][700/3746] lr: 7.838e-02, eta: 3 days, 14:01:04, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5467, loss_cls: 4.1007, loss: 4.1007 +2024-12-27 18:05:40,994 - pyskl - INFO - Epoch [47][800/3746] lr: 7.835e-02, eta: 3 days, 13:59:54, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5419, loss_cls: 4.1353, loss: 4.1353 +2024-12-27 18:07:05,539 - pyskl - INFO - Epoch [47][900/3746] lr: 7.833e-02, eta: 3 days, 13:58:46, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5284, loss_cls: 4.1787, loss: 4.1787 +2024-12-27 18:08:29,828 - pyskl - INFO - Epoch [47][1000/3746] lr: 7.831e-02, eta: 3 days, 13:57:37, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5228, loss_cls: 4.1739, loss: 4.1739 +2024-12-27 18:09:54,276 - pyskl - INFO - Epoch [47][1100/3746] lr: 7.828e-02, eta: 3 days, 13:56:28, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5275, loss_cls: 4.1975, loss: 4.1975 +2024-12-27 18:11:18,960 - pyskl - INFO - Epoch [47][1200/3746] lr: 7.826e-02, eta: 3 days, 13:55:19, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5378, loss_cls: 4.1234, loss: 4.1234 +2024-12-27 18:12:43,435 - pyskl - INFO - Epoch [47][1300/3746] lr: 7.824e-02, eta: 3 days, 13:54:11, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5186, loss_cls: 4.2001, loss: 4.2001 +2024-12-27 18:14:07,597 - pyskl - INFO - Epoch [47][1400/3746] lr: 7.821e-02, eta: 3 days, 13:53:01, time: 0.842, data_time: 0.001, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5194, loss_cls: 4.2216, loss: 4.2216 +2024-12-27 18:15:31,790 - pyskl - INFO - Epoch [47][1500/3746] lr: 7.819e-02, eta: 3 days, 13:51:52, time: 0.842, data_time: 0.001, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5347, loss_cls: 4.1340, loss: 4.1340 +2024-12-27 18:16:55,206 - pyskl - INFO - Epoch [47][1600/3746] lr: 7.817e-02, eta: 3 days, 13:50:40, time: 0.834, data_time: 0.001, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5208, loss_cls: 4.1859, loss: 4.1859 +2024-12-27 18:18:19,118 - pyskl - INFO - Epoch [47][1700/3746] lr: 7.814e-02, eta: 3 days, 13:49:30, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5428, loss_cls: 4.0722, loss: 4.0722 +2024-12-27 18:19:43,814 - pyskl - INFO - Epoch [47][1800/3746] lr: 7.812e-02, eta: 3 days, 13:48:22, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5331, loss_cls: 4.1422, loss: 4.1422 +2024-12-27 18:21:08,299 - pyskl - INFO - Epoch [47][1900/3746] lr: 7.810e-02, eta: 3 days, 13:47:13, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5437, loss_cls: 4.1117, loss: 4.1117 +2024-12-27 18:22:32,175 - pyskl - INFO - Epoch [47][2000/3746] lr: 7.808e-02, eta: 3 days, 13:46:02, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5317, loss_cls: 4.1597, loss: 4.1597 +2024-12-27 18:23:56,563 - pyskl - INFO - Epoch [47][2100/3746] lr: 7.805e-02, eta: 3 days, 13:44:53, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5303, loss_cls: 4.1680, loss: 4.1680 +2024-12-27 18:25:20,643 - pyskl - INFO - Epoch [47][2200/3746] lr: 7.803e-02, eta: 3 days, 13:43:43, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5384, loss_cls: 4.1445, loss: 4.1445 +2024-12-27 18:26:45,372 - pyskl - INFO - Epoch [47][2300/3746] lr: 7.801e-02, eta: 3 days, 13:42:35, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5295, loss_cls: 4.1748, loss: 4.1748 +2024-12-27 18:28:09,671 - pyskl - INFO - Epoch [47][2400/3746] lr: 7.798e-02, eta: 3 days, 13:41:26, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5308, loss_cls: 4.1473, loss: 4.1473 +2024-12-27 18:29:34,144 - pyskl - INFO - Epoch [47][2500/3746] lr: 7.796e-02, eta: 3 days, 13:40:17, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5406, loss_cls: 4.1381, loss: 4.1381 +2024-12-27 18:30:58,988 - pyskl - INFO - Epoch [47][2600/3746] lr: 7.794e-02, eta: 3 days, 13:39:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5466, loss_cls: 4.0780, loss: 4.0780 +2024-12-27 18:32:23,900 - pyskl - INFO - Epoch [47][2700/3746] lr: 7.791e-02, eta: 3 days, 13:38:00, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5317, loss_cls: 4.1472, loss: 4.1472 +2024-12-27 18:33:48,456 - pyskl - INFO - Epoch [47][2800/3746] lr: 7.789e-02, eta: 3 days, 13:36:51, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5352, loss_cls: 4.1418, loss: 4.1418 +2024-12-27 18:35:13,100 - pyskl - INFO - Epoch [47][2900/3746] lr: 7.787e-02, eta: 3 days, 13:35:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5325, loss_cls: 4.1692, loss: 4.1692 +2024-12-27 18:36:37,652 - pyskl - INFO - Epoch [47][3000/3746] lr: 7.784e-02, eta: 3 days, 13:34:34, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5367, loss_cls: 4.1225, loss: 4.1225 +2024-12-27 18:38:01,793 - pyskl - INFO - Epoch [47][3100/3746] lr: 7.782e-02, eta: 3 days, 13:33:24, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5398, loss_cls: 4.0885, loss: 4.0885 +2024-12-27 18:39:26,205 - pyskl - INFO - Epoch [47][3200/3746] lr: 7.780e-02, eta: 3 days, 13:32:14, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5252, loss_cls: 4.1628, loss: 4.1628 +2024-12-27 18:40:50,382 - pyskl - INFO - Epoch [47][3300/3746] lr: 7.777e-02, eta: 3 days, 13:31:04, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5363, loss_cls: 4.1111, loss: 4.1111 +2024-12-27 18:42:15,504 - pyskl - INFO - Epoch [47][3400/3746] lr: 7.775e-02, eta: 3 days, 13:29:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5311, loss_cls: 4.1563, loss: 4.1563 +2024-12-27 18:43:40,312 - pyskl - INFO - Epoch [47][3500/3746] lr: 7.773e-02, eta: 3 days, 13:28:48, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5239, loss_cls: 4.1536, loss: 4.1536 +2024-12-27 18:45:05,222 - pyskl - INFO - Epoch [47][3600/3746] lr: 7.770e-02, eta: 3 days, 13:27:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5408, loss_cls: 4.1316, loss: 4.1316 +2024-12-27 18:46:29,812 - pyskl - INFO - Epoch [47][3700/3746] lr: 7.768e-02, eta: 3 days, 13:26:31, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5281, loss_cls: 4.1822, loss: 4.1822 +2024-12-27 18:47:10,605 - pyskl - INFO - Saving checkpoint at 47 epochs +2024-12-27 18:49:07,778 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 18:49:08,546 - pyskl - INFO - +top1_acc 0.1850 +top5_acc 0.4187 +2024-12-27 18:49:08,546 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 18:49:08,590 - pyskl - INFO - +mean_acc 0.1847 +2024-12-27 18:49:08,605 - pyskl - INFO - Epoch(val) [47][309] top1_acc: 0.1850, top5_acc: 0.4187, mean_class_accuracy: 0.1847 +2024-12-27 18:53:22,880 - pyskl - INFO - Epoch [48][100/3746] lr: 7.765e-02, eta: 3 days, 13:29:36, time: 2.543, data_time: 1.510, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5498, loss_cls: 4.0471, loss: 4.0471 +2024-12-27 18:54:48,374 - pyskl - INFO - Epoch [48][200/3746] lr: 7.762e-02, eta: 3 days, 13:28:29, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5316, loss_cls: 4.1334, loss: 4.1334 +2024-12-27 18:56:13,682 - pyskl - INFO - Epoch [48][300/3746] lr: 7.760e-02, eta: 3 days, 13:27:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5316, loss_cls: 4.1296, loss: 4.1296 +2024-12-27 18:57:39,124 - pyskl - INFO - Epoch [48][400/3746] lr: 7.758e-02, eta: 3 days, 13:26:14, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5378, loss_cls: 4.1295, loss: 4.1295 +2024-12-27 18:59:04,152 - pyskl - INFO - Epoch [48][500/3746] lr: 7.755e-02, eta: 3 days, 13:25:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5473, loss_cls: 4.0993, loss: 4.0993 +2024-12-27 19:00:29,390 - pyskl - INFO - Epoch [48][600/3746] lr: 7.753e-02, eta: 3 days, 13:23:58, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5334, loss_cls: 4.1309, loss: 4.1309 +2024-12-27 19:01:54,101 - pyskl - INFO - Epoch [48][700/3746] lr: 7.751e-02, eta: 3 days, 13:22:48, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5461, loss_cls: 4.1028, loss: 4.1028 +2024-12-27 19:03:19,049 - pyskl - INFO - Epoch [48][800/3746] lr: 7.748e-02, eta: 3 days, 13:21:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5327, loss_cls: 4.1380, loss: 4.1380 +2024-12-27 19:04:43,462 - pyskl - INFO - Epoch [48][900/3746] lr: 7.746e-02, eta: 3 days, 13:20:30, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5516, loss_cls: 4.0685, loss: 4.0685 +2024-12-27 19:06:08,103 - pyskl - INFO - Epoch [48][1000/3746] lr: 7.744e-02, eta: 3 days, 13:19:21, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5439, loss_cls: 4.1003, loss: 4.1003 +2024-12-27 19:07:32,930 - pyskl - INFO - Epoch [48][1100/3746] lr: 7.741e-02, eta: 3 days, 13:18:12, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5470, loss_cls: 4.0610, loss: 4.0610 +2024-12-27 19:08:57,558 - pyskl - INFO - Epoch [48][1200/3746] lr: 7.739e-02, eta: 3 days, 13:17:02, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5383, loss_cls: 4.1256, loss: 4.1256 +2024-12-27 19:10:22,493 - pyskl - INFO - Epoch [48][1300/3746] lr: 7.737e-02, eta: 3 days, 13:15:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5288, loss_cls: 4.1569, loss: 4.1569 +2024-12-27 19:11:47,269 - pyskl - INFO - Epoch [48][1400/3746] lr: 7.734e-02, eta: 3 days, 13:14:45, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5363, loss_cls: 4.1036, loss: 4.1036 +2024-12-27 19:13:11,537 - pyskl - INFO - Epoch [48][1500/3746] lr: 7.732e-02, eta: 3 days, 13:13:34, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5256, loss_cls: 4.1353, loss: 4.1353 +2024-12-27 19:14:36,040 - pyskl - INFO - Epoch [48][1600/3746] lr: 7.730e-02, eta: 3 days, 13:12:25, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5411, loss_cls: 4.1137, loss: 4.1137 +2024-12-27 19:16:00,241 - pyskl - INFO - Epoch [48][1700/3746] lr: 7.727e-02, eta: 3 days, 13:11:14, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5314, loss_cls: 4.1543, loss: 4.1543 +2024-12-27 19:17:25,019 - pyskl - INFO - Epoch [48][1800/3746] lr: 7.725e-02, eta: 3 days, 13:10:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5305, loss_cls: 4.1665, loss: 4.1665 +2024-12-27 19:18:49,829 - pyskl - INFO - Epoch [48][1900/3746] lr: 7.723e-02, eta: 3 days, 13:08:56, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5394, loss_cls: 4.1436, loss: 4.1436 +2024-12-27 19:20:14,460 - pyskl - INFO - Epoch [48][2000/3746] lr: 7.720e-02, eta: 3 days, 13:07:46, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5267, loss_cls: 4.2127, loss: 4.2127 +2024-12-27 19:21:38,715 - pyskl - INFO - Epoch [48][2100/3746] lr: 7.718e-02, eta: 3 days, 13:06:36, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5330, loss_cls: 4.1557, loss: 4.1557 +2024-12-27 19:23:03,144 - pyskl - INFO - Epoch [48][2200/3746] lr: 7.716e-02, eta: 3 days, 13:05:26, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5403, loss_cls: 4.1008, loss: 4.1008 +2024-12-27 19:24:28,467 - pyskl - INFO - Epoch [48][2300/3746] lr: 7.713e-02, eta: 3 days, 13:04:18, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5247, loss_cls: 4.1814, loss: 4.1814 +2024-12-27 19:25:53,064 - pyskl - INFO - Epoch [48][2400/3746] lr: 7.711e-02, eta: 3 days, 13:03:08, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5406, loss_cls: 4.1628, loss: 4.1628 +2024-12-27 19:27:18,814 - pyskl - INFO - Epoch [48][2500/3746] lr: 7.709e-02, eta: 3 days, 13:02:01, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5255, loss_cls: 4.1811, loss: 4.1811 +2024-12-27 19:28:43,426 - pyskl - INFO - Epoch [48][2600/3746] lr: 7.706e-02, eta: 3 days, 13:00:52, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5450, loss_cls: 4.0862, loss: 4.0862 +2024-12-27 19:30:08,590 - pyskl - INFO - Epoch [48][2700/3746] lr: 7.704e-02, eta: 3 days, 12:59:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5306, loss_cls: 4.1381, loss: 4.1381 +2024-12-27 19:31:33,337 - pyskl - INFO - Epoch [48][2800/3746] lr: 7.701e-02, eta: 3 days, 12:58:34, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5222, loss_cls: 4.1690, loss: 4.1690 +2024-12-27 19:32:58,642 - pyskl - INFO - Epoch [48][2900/3746] lr: 7.699e-02, eta: 3 days, 12:57:25, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5389, loss_cls: 4.1093, loss: 4.1093 +2024-12-27 19:34:23,612 - pyskl - INFO - Epoch [48][3000/3746] lr: 7.697e-02, eta: 3 days, 12:56:16, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5394, loss_cls: 4.1304, loss: 4.1304 +2024-12-27 19:35:48,804 - pyskl - INFO - Epoch [48][3100/3746] lr: 7.694e-02, eta: 3 days, 12:55:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5358, loss_cls: 4.1577, loss: 4.1577 +2024-12-27 19:37:13,737 - pyskl - INFO - Epoch [48][3200/3746] lr: 7.692e-02, eta: 3 days, 12:53:59, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5291, loss_cls: 4.2093, loss: 4.2093 +2024-12-27 19:38:39,136 - pyskl - INFO - Epoch [48][3300/3746] lr: 7.690e-02, eta: 3 days, 12:52:51, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5328, loss_cls: 4.1732, loss: 4.1732 +2024-12-27 19:40:04,341 - pyskl - INFO - Epoch [48][3400/3746] lr: 7.687e-02, eta: 3 days, 12:51:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5364, loss_cls: 4.1246, loss: 4.1246 +2024-12-27 19:41:29,733 - pyskl - INFO - Epoch [48][3500/3746] lr: 7.685e-02, eta: 3 days, 12:50:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5253, loss_cls: 4.1979, loss: 4.1979 +2024-12-27 19:42:55,624 - pyskl - INFO - Epoch [48][3600/3746] lr: 7.683e-02, eta: 3 days, 12:49:27, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5305, loss_cls: 4.1267, loss: 4.1267 +2024-12-27 19:44:20,501 - pyskl - INFO - Epoch [48][3700/3746] lr: 7.680e-02, eta: 3 days, 12:48:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5383, loss_cls: 4.1210, loss: 4.1210 +2024-12-27 19:45:01,306 - pyskl - INFO - Saving checkpoint at 48 epochs +2024-12-27 19:46:59,134 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 19:47:00,099 - pyskl - INFO - +top1_acc 0.1982 +top5_acc 0.4235 +2024-12-27 19:47:00,099 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 19:47:00,149 - pyskl - INFO - +mean_acc 0.1980 +2024-12-27 19:47:00,167 - pyskl - INFO - Epoch(val) [48][309] top1_acc: 0.1982, top5_acc: 0.4235, mean_class_accuracy: 0.1980 +2024-12-27 19:51:10,682 - pyskl - INFO - Epoch [49][100/3746] lr: 7.677e-02, eta: 3 days, 12:51:05, time: 2.505, data_time: 1.467, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5322, loss_cls: 4.1380, loss: 4.1380 +2024-12-27 19:52:36,433 - pyskl - INFO - Epoch [49][200/3746] lr: 7.674e-02, eta: 3 days, 12:49:58, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5430, loss_cls: 4.0654, loss: 4.0654 +2024-12-27 19:54:02,023 - pyskl - INFO - Epoch [49][300/3746] lr: 7.672e-02, eta: 3 days, 12:48:49, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5453, loss_cls: 4.1050, loss: 4.1050 +2024-12-27 19:55:26,309 - pyskl - INFO - Epoch [49][400/3746] lr: 7.670e-02, eta: 3 days, 12:47:39, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5405, loss_cls: 4.0924, loss: 4.0924 +2024-12-27 19:56:51,385 - pyskl - INFO - Epoch [49][500/3746] lr: 7.667e-02, eta: 3 days, 12:46:29, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5334, loss_cls: 4.1562, loss: 4.1562 +2024-12-27 19:58:16,533 - pyskl - INFO - Epoch [49][600/3746] lr: 7.665e-02, eta: 3 days, 12:45:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5463, loss_cls: 4.0977, loss: 4.0977 +2024-12-27 19:59:40,952 - pyskl - INFO - Epoch [49][700/3746] lr: 7.663e-02, eta: 3 days, 12:44:10, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5373, loss_cls: 4.1290, loss: 4.1290 +2024-12-27 20:01:05,720 - pyskl - INFO - Epoch [49][800/3746] lr: 7.660e-02, eta: 3 days, 12:43:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5391, loss_cls: 4.0977, loss: 4.0977 +2024-12-27 20:02:30,084 - pyskl - INFO - Epoch [49][900/3746] lr: 7.658e-02, eta: 3 days, 12:41:49, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5530, loss_cls: 4.0825, loss: 4.0825 +2024-12-27 20:03:55,160 - pyskl - INFO - Epoch [49][1000/3746] lr: 7.656e-02, eta: 3 days, 12:40:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5292, loss_cls: 4.1744, loss: 4.1744 +2024-12-27 20:05:19,782 - pyskl - INFO - Epoch [49][1100/3746] lr: 7.653e-02, eta: 3 days, 12:39:30, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5453, loss_cls: 4.0981, loss: 4.0981 +2024-12-27 20:06:44,423 - pyskl - INFO - Epoch [49][1200/3746] lr: 7.651e-02, eta: 3 days, 12:38:19, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5375, loss_cls: 4.1293, loss: 4.1293 +2024-12-27 20:08:09,564 - pyskl - INFO - Epoch [49][1300/3746] lr: 7.648e-02, eta: 3 days, 12:37:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5409, loss_cls: 4.1130, loss: 4.1130 +2024-12-27 20:09:34,435 - pyskl - INFO - Epoch [49][1400/3746] lr: 7.646e-02, eta: 3 days, 12:36:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5394, loss_cls: 4.0806, loss: 4.0806 +2024-12-27 20:10:59,053 - pyskl - INFO - Epoch [49][1500/3746] lr: 7.644e-02, eta: 3 days, 12:34:50, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5306, loss_cls: 4.1568, loss: 4.1568 +2024-12-27 20:12:23,732 - pyskl - INFO - Epoch [49][1600/3746] lr: 7.641e-02, eta: 3 days, 12:33:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5417, loss_cls: 4.1053, loss: 4.1053 +2024-12-27 20:13:48,498 - pyskl - INFO - Epoch [49][1700/3746] lr: 7.639e-02, eta: 3 days, 12:32:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5364, loss_cls: 4.1370, loss: 4.1370 +2024-12-27 20:15:12,878 - pyskl - INFO - Epoch [49][1800/3746] lr: 7.637e-02, eta: 3 days, 12:31:19, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5366, loss_cls: 4.1360, loss: 4.1360 +2024-12-27 20:16:37,572 - pyskl - INFO - Epoch [49][1900/3746] lr: 7.634e-02, eta: 3 days, 12:30:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5305, loss_cls: 4.1452, loss: 4.1452 +2024-12-27 20:18:01,913 - pyskl - INFO - Epoch [49][2000/3746] lr: 7.632e-02, eta: 3 days, 12:28:58, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5373, loss_cls: 4.0955, loss: 4.0955 +2024-12-27 20:19:26,367 - pyskl - INFO - Epoch [49][2100/3746] lr: 7.629e-02, eta: 3 days, 12:27:47, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5373, loss_cls: 4.1189, loss: 4.1189 +2024-12-27 20:20:50,891 - pyskl - INFO - Epoch [49][2200/3746] lr: 7.627e-02, eta: 3 days, 12:26:37, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5447, loss_cls: 4.0888, loss: 4.0888 +2024-12-27 20:22:15,273 - pyskl - INFO - Epoch [49][2300/3746] lr: 7.625e-02, eta: 3 days, 12:25:26, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5325, loss_cls: 4.1458, loss: 4.1458 +2024-12-27 20:23:39,564 - pyskl - INFO - Epoch [49][2400/3746] lr: 7.622e-02, eta: 3 days, 12:24:14, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5286, loss_cls: 4.1968, loss: 4.1968 +2024-12-27 20:25:04,296 - pyskl - INFO - Epoch [49][2500/3746] lr: 7.620e-02, eta: 3 days, 12:23:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5316, loss_cls: 4.1262, loss: 4.1262 +2024-12-27 20:26:29,012 - pyskl - INFO - Epoch [49][2600/3746] lr: 7.618e-02, eta: 3 days, 12:21:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5297, loss_cls: 4.1642, loss: 4.1642 +2024-12-27 20:27:53,798 - pyskl - INFO - Epoch [49][2700/3746] lr: 7.615e-02, eta: 3 days, 12:20:44, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5350, loss_cls: 4.1057, loss: 4.1057 +2024-12-27 20:29:18,785 - pyskl - INFO - Epoch [49][2800/3746] lr: 7.613e-02, eta: 3 days, 12:19:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5320, loss_cls: 4.1217, loss: 4.1217 +2024-12-27 20:30:43,912 - pyskl - INFO - Epoch [49][2900/3746] lr: 7.610e-02, eta: 3 days, 12:18:25, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5367, loss_cls: 4.1315, loss: 4.1315 +2024-12-27 20:32:09,036 - pyskl - INFO - Epoch [49][3000/3746] lr: 7.608e-02, eta: 3 days, 12:17:15, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5309, loss_cls: 4.1722, loss: 4.1722 +2024-12-27 20:33:34,001 - pyskl - INFO - Epoch [49][3100/3746] lr: 7.606e-02, eta: 3 days, 12:16:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5322, loss_cls: 4.1577, loss: 4.1577 +2024-12-27 20:34:59,210 - pyskl - INFO - Epoch [49][3200/3746] lr: 7.603e-02, eta: 3 days, 12:14:56, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5333, loss_cls: 4.1216, loss: 4.1216 +2024-12-27 20:36:24,241 - pyskl - INFO - Epoch [49][3300/3746] lr: 7.601e-02, eta: 3 days, 12:13:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5295, loss_cls: 4.1342, loss: 4.1342 +2024-12-27 20:37:49,595 - pyskl - INFO - Epoch [49][3400/3746] lr: 7.598e-02, eta: 3 days, 12:12:37, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5331, loss_cls: 4.1365, loss: 4.1365 +2024-12-27 20:39:14,925 - pyskl - INFO - Epoch [49][3500/3746] lr: 7.596e-02, eta: 3 days, 12:11:28, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5375, loss_cls: 4.1228, loss: 4.1228 +2024-12-27 20:40:40,295 - pyskl - INFO - Epoch [49][3600/3746] lr: 7.594e-02, eta: 3 days, 12:10:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5305, loss_cls: 4.1761, loss: 4.1761 +2024-12-27 20:42:05,416 - pyskl - INFO - Epoch [49][3700/3746] lr: 7.591e-02, eta: 3 days, 12:09:09, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5436, loss_cls: 4.0772, loss: 4.0772 +2024-12-27 20:42:46,121 - pyskl - INFO - Saving checkpoint at 49 epochs +2024-12-27 20:44:44,114 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 20:44:45,055 - pyskl - INFO - +top1_acc 0.2039 +top5_acc 0.4332 +2024-12-27 20:44:45,056 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 20:44:45,097 - pyskl - INFO - +mean_acc 0.2038 +2024-12-27 20:44:45,108 - pyskl - INFO - Epoch(val) [49][309] top1_acc: 0.2039, top5_acc: 0.4332, mean_class_accuracy: 0.2038 +2024-12-27 20:48:56,710 - pyskl - INFO - Epoch [50][100/3746] lr: 7.588e-02, eta: 3 days, 12:11:50, time: 2.516, data_time: 1.484, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5500, loss_cls: 4.0571, loss: 4.0571 +2024-12-27 20:50:21,715 - pyskl - INFO - Epoch [50][200/3746] lr: 7.585e-02, eta: 3 days, 12:10:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5409, loss_cls: 4.1262, loss: 4.1262 +2024-12-27 20:51:46,682 - pyskl - INFO - Epoch [50][300/3746] lr: 7.583e-02, eta: 3 days, 12:09:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5452, loss_cls: 4.1070, loss: 4.1070 +2024-12-27 20:53:11,506 - pyskl - INFO - Epoch [50][400/3746] lr: 7.581e-02, eta: 3 days, 12:08:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5353, loss_cls: 4.1412, loss: 4.1412 +2024-12-27 20:54:36,438 - pyskl - INFO - Epoch [50][500/3746] lr: 7.578e-02, eta: 3 days, 12:07:09, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5367, loss_cls: 4.1094, loss: 4.1094 +2024-12-27 20:56:01,459 - pyskl - INFO - Epoch [50][600/3746] lr: 7.576e-02, eta: 3 days, 12:05:58, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5262, loss_cls: 4.1799, loss: 4.1799 +2024-12-27 20:57:25,609 - pyskl - INFO - Epoch [50][700/3746] lr: 7.573e-02, eta: 3 days, 12:04:46, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5453, loss_cls: 4.0888, loss: 4.0888 +2024-12-27 20:58:50,093 - pyskl - INFO - Epoch [50][800/3746] lr: 7.571e-02, eta: 3 days, 12:03:35, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5430, loss_cls: 4.0774, loss: 4.0774 +2024-12-27 21:00:14,746 - pyskl - INFO - Epoch [50][900/3746] lr: 7.569e-02, eta: 3 days, 12:02:24, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5387, loss_cls: 4.1284, loss: 4.1284 +2024-12-27 21:01:39,296 - pyskl - INFO - Epoch [50][1000/3746] lr: 7.566e-02, eta: 3 days, 12:01:13, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5261, loss_cls: 4.1638, loss: 4.1638 +2024-12-27 21:03:03,809 - pyskl - INFO - Epoch [50][1100/3746] lr: 7.564e-02, eta: 3 days, 12:00:02, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5498, loss_cls: 4.0672, loss: 4.0672 +2024-12-27 21:04:28,906 - pyskl - INFO - Epoch [50][1200/3746] lr: 7.561e-02, eta: 3 days, 11:58:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5373, loss_cls: 4.0912, loss: 4.0912 +2024-12-27 21:05:53,456 - pyskl - INFO - Epoch [50][1300/3746] lr: 7.559e-02, eta: 3 days, 11:57:40, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5402, loss_cls: 4.1498, loss: 4.1498 +2024-12-27 21:07:18,349 - pyskl - INFO - Epoch [50][1400/3746] lr: 7.557e-02, eta: 3 days, 11:56:30, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5570, loss_cls: 4.0328, loss: 4.0328 +2024-12-27 21:08:42,744 - pyskl - INFO - Epoch [50][1500/3746] lr: 7.554e-02, eta: 3 days, 11:55:18, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5320, loss_cls: 4.1455, loss: 4.1455 +2024-12-27 21:10:07,412 - pyskl - INFO - Epoch [50][1600/3746] lr: 7.552e-02, eta: 3 days, 11:54:07, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5381, loss_cls: 4.1236, loss: 4.1236 +2024-12-27 21:11:31,841 - pyskl - INFO - Epoch [50][1700/3746] lr: 7.549e-02, eta: 3 days, 11:52:56, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5386, loss_cls: 4.1269, loss: 4.1269 +2024-12-27 21:12:56,035 - pyskl - INFO - Epoch [50][1800/3746] lr: 7.547e-02, eta: 3 days, 11:51:44, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5423, loss_cls: 4.0926, loss: 4.0926 +2024-12-27 21:14:20,390 - pyskl - INFO - Epoch [50][1900/3746] lr: 7.545e-02, eta: 3 days, 11:50:32, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5384, loss_cls: 4.1207, loss: 4.1207 +2024-12-27 21:15:44,875 - pyskl - INFO - Epoch [50][2000/3746] lr: 7.542e-02, eta: 3 days, 11:49:21, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5377, loss_cls: 4.1344, loss: 4.1344 +2024-12-27 21:17:09,238 - pyskl - INFO - Epoch [50][2100/3746] lr: 7.540e-02, eta: 3 days, 11:48:09, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5361, loss_cls: 4.1066, loss: 4.1066 +2024-12-27 21:18:34,037 - pyskl - INFO - Epoch [50][2200/3746] lr: 7.537e-02, eta: 3 days, 11:46:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5408, loss_cls: 4.1046, loss: 4.1046 +2024-12-27 21:19:58,459 - pyskl - INFO - Epoch [50][2300/3746] lr: 7.535e-02, eta: 3 days, 11:45:46, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5364, loss_cls: 4.1097, loss: 4.1097 +2024-12-27 21:21:23,262 - pyskl - INFO - Epoch [50][2400/3746] lr: 7.533e-02, eta: 3 days, 11:44:36, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5334, loss_cls: 4.0976, loss: 4.0976 +2024-12-27 21:22:47,855 - pyskl - INFO - Epoch [50][2500/3746] lr: 7.530e-02, eta: 3 days, 11:43:24, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5363, loss_cls: 4.1253, loss: 4.1253 +2024-12-27 21:24:12,145 - pyskl - INFO - Epoch [50][2600/3746] lr: 7.528e-02, eta: 3 days, 11:42:12, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5309, loss_cls: 4.1278, loss: 4.1278 +2024-12-27 21:25:36,534 - pyskl - INFO - Epoch [50][2700/3746] lr: 7.525e-02, eta: 3 days, 11:41:01, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5450, loss_cls: 4.1077, loss: 4.1077 +2024-12-27 21:27:00,776 - pyskl - INFO - Epoch [50][2800/3746] lr: 7.523e-02, eta: 3 days, 11:39:49, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5366, loss_cls: 4.1341, loss: 4.1341 +2024-12-27 21:28:25,027 - pyskl - INFO - Epoch [50][2900/3746] lr: 7.520e-02, eta: 3 days, 11:38:37, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5320, loss_cls: 4.1223, loss: 4.1223 +2024-12-27 21:29:49,738 - pyskl - INFO - Epoch [50][3000/3746] lr: 7.518e-02, eta: 3 days, 11:37:25, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5366, loss_cls: 4.1418, loss: 4.1418 +2024-12-27 21:31:15,169 - pyskl - INFO - Epoch [50][3100/3746] lr: 7.516e-02, eta: 3 days, 11:36:16, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5375, loss_cls: 4.1034, loss: 4.1034 +2024-12-27 21:32:40,168 - pyskl - INFO - Epoch [50][3200/3746] lr: 7.513e-02, eta: 3 days, 11:35:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5364, loss_cls: 4.0976, loss: 4.0976 +2024-12-27 21:34:05,489 - pyskl - INFO - Epoch [50][3300/3746] lr: 7.511e-02, eta: 3 days, 11:33:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5289, loss_cls: 4.1640, loss: 4.1640 +2024-12-27 21:35:30,496 - pyskl - INFO - Epoch [50][3400/3746] lr: 7.508e-02, eta: 3 days, 11:32:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5234, loss_cls: 4.1768, loss: 4.1768 +2024-12-27 21:36:54,894 - pyskl - INFO - Epoch [50][3500/3746] lr: 7.506e-02, eta: 3 days, 11:31:33, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5398, loss_cls: 4.1042, loss: 4.1042 +2024-12-27 21:38:19,291 - pyskl - INFO - Epoch [50][3600/3746] lr: 7.504e-02, eta: 3 days, 11:30:21, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5337, loss_cls: 4.1367, loss: 4.1367 +2024-12-27 21:39:43,994 - pyskl - INFO - Epoch [50][3700/3746] lr: 7.501e-02, eta: 3 days, 11:29:10, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5347, loss_cls: 4.1219, loss: 4.1219 +2024-12-27 21:40:24,682 - pyskl - INFO - Saving checkpoint at 50 epochs +2024-12-27 21:42:22,436 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 21:42:23,113 - pyskl - INFO - +top1_acc 0.2132 +top5_acc 0.4505 +2024-12-27 21:42:23,113 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 21:42:23,153 - pyskl - INFO - +mean_acc 0.2129 +2024-12-27 21:42:23,164 - pyskl - INFO - Epoch(val) [50][309] top1_acc: 0.2132, top5_acc: 0.4505, mean_class_accuracy: 0.2129 +2024-12-27 21:46:37,292 - pyskl - INFO - Epoch [51][100/3746] lr: 7.498e-02, eta: 3 days, 11:31:46, time: 2.541, data_time: 1.500, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5470, loss_cls: 4.0339, loss: 4.0339 +2024-12-27 21:48:02,727 - pyskl - INFO - Epoch [51][200/3746] lr: 7.495e-02, eta: 3 days, 11:30:36, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5389, loss_cls: 4.0648, loss: 4.0648 +2024-12-27 21:49:27,766 - pyskl - INFO - Epoch [51][300/3746] lr: 7.493e-02, eta: 3 days, 11:29:25, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5434, loss_cls: 4.1071, loss: 4.1071 +2024-12-27 21:50:52,460 - pyskl - INFO - Epoch [51][400/3746] lr: 7.490e-02, eta: 3 days, 11:28:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5450, loss_cls: 4.0416, loss: 4.0416 +2024-12-27 21:52:16,750 - pyskl - INFO - Epoch [51][500/3746] lr: 7.488e-02, eta: 3 days, 11:27:01, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5395, loss_cls: 4.1104, loss: 4.1104 +2024-12-27 21:53:41,568 - pyskl - INFO - Epoch [51][600/3746] lr: 7.485e-02, eta: 3 days, 11:25:50, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5458, loss_cls: 4.0825, loss: 4.0825 +2024-12-27 21:55:06,526 - pyskl - INFO - Epoch [51][700/3746] lr: 7.483e-02, eta: 3 days, 11:24:39, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5345, loss_cls: 4.1119, loss: 4.1119 +2024-12-27 21:56:31,326 - pyskl - INFO - Epoch [51][800/3746] lr: 7.481e-02, eta: 3 days, 11:23:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5420, loss_cls: 4.1075, loss: 4.1075 +2024-12-27 21:57:55,792 - pyskl - INFO - Epoch [51][900/3746] lr: 7.478e-02, eta: 3 days, 11:22:16, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5400, loss_cls: 4.0805, loss: 4.0805 +2024-12-27 21:59:19,576 - pyskl - INFO - Epoch [51][1000/3746] lr: 7.476e-02, eta: 3 days, 11:21:02, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5373, loss_cls: 4.0713, loss: 4.0713 +2024-12-27 22:00:43,936 - pyskl - INFO - Epoch [51][1100/3746] lr: 7.473e-02, eta: 3 days, 11:19:50, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5311, loss_cls: 4.1214, loss: 4.1214 +2024-12-27 22:02:08,631 - pyskl - INFO - Epoch [51][1200/3746] lr: 7.471e-02, eta: 3 days, 11:18:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5506, loss_cls: 4.0486, loss: 4.0486 +2024-12-27 22:03:33,138 - pyskl - INFO - Epoch [51][1300/3746] lr: 7.468e-02, eta: 3 days, 11:17:26, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5448, loss_cls: 4.0996, loss: 4.0996 +2024-12-27 22:04:58,024 - pyskl - INFO - Epoch [51][1400/3746] lr: 7.466e-02, eta: 3 days, 11:16:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5320, loss_cls: 4.1586, loss: 4.1586 +2024-12-27 22:06:22,488 - pyskl - INFO - Epoch [51][1500/3746] lr: 7.464e-02, eta: 3 days, 11:15:03, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5439, loss_cls: 4.0885, loss: 4.0885 +2024-12-27 22:07:46,501 - pyskl - INFO - Epoch [51][1600/3746] lr: 7.461e-02, eta: 3 days, 11:13:50, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5556, loss_cls: 4.0529, loss: 4.0529 +2024-12-27 22:09:11,015 - pyskl - INFO - Epoch [51][1700/3746] lr: 7.459e-02, eta: 3 days, 11:12:38, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5330, loss_cls: 4.1463, loss: 4.1463 +2024-12-27 22:10:35,009 - pyskl - INFO - Epoch [51][1800/3746] lr: 7.456e-02, eta: 3 days, 11:11:25, time: 0.840, data_time: 0.001, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5398, loss_cls: 4.1130, loss: 4.1130 +2024-12-27 22:11:59,070 - pyskl - INFO - Epoch [51][1900/3746] lr: 7.454e-02, eta: 3 days, 11:10:12, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5403, loss_cls: 4.0890, loss: 4.0890 +2024-12-27 22:13:23,761 - pyskl - INFO - Epoch [51][2000/3746] lr: 7.451e-02, eta: 3 days, 11:09:00, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5334, loss_cls: 4.1460, loss: 4.1460 +2024-12-27 22:14:48,349 - pyskl - INFO - Epoch [51][2100/3746] lr: 7.449e-02, eta: 3 days, 11:07:48, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5278, loss_cls: 4.1650, loss: 4.1650 +2024-12-27 22:16:12,435 - pyskl - INFO - Epoch [51][2200/3746] lr: 7.447e-02, eta: 3 days, 11:06:35, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5370, loss_cls: 4.1188, loss: 4.1188 +2024-12-27 22:17:37,147 - pyskl - INFO - Epoch [51][2300/3746] lr: 7.444e-02, eta: 3 days, 11:05:23, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5298, loss_cls: 4.1571, loss: 4.1571 +2024-12-27 22:19:02,213 - pyskl - INFO - Epoch [51][2400/3746] lr: 7.442e-02, eta: 3 days, 11:04:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5331, loss_cls: 4.1183, loss: 4.1183 +2024-12-27 22:20:27,761 - pyskl - INFO - Epoch [51][2500/3746] lr: 7.439e-02, eta: 3 days, 11:03:02, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5337, loss_cls: 4.1019, loss: 4.1019 +2024-12-27 22:21:53,045 - pyskl - INFO - Epoch [51][2600/3746] lr: 7.437e-02, eta: 3 days, 11:01:51, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5384, loss_cls: 4.0925, loss: 4.0925 +2024-12-27 22:23:18,815 - pyskl - INFO - Epoch [51][2700/3746] lr: 7.434e-02, eta: 3 days, 11:00:41, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5345, loss_cls: 4.1419, loss: 4.1419 +2024-12-27 22:24:44,031 - pyskl - INFO - Epoch [51][2800/3746] lr: 7.432e-02, eta: 3 days, 10:59:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5439, loss_cls: 4.0548, loss: 4.0548 +2024-12-27 22:26:09,755 - pyskl - INFO - Epoch [51][2900/3746] lr: 7.429e-02, eta: 3 days, 10:58:21, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5489, loss_cls: 4.1043, loss: 4.1043 +2024-12-27 22:27:34,989 - pyskl - INFO - Epoch [51][3000/3746] lr: 7.427e-02, eta: 3 days, 10:57:10, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5413, loss_cls: 4.1062, loss: 4.1062 +2024-12-27 22:29:00,325 - pyskl - INFO - Epoch [51][3100/3746] lr: 7.425e-02, eta: 3 days, 10:55:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5378, loss_cls: 4.1291, loss: 4.1291 +2024-12-27 22:30:25,967 - pyskl - INFO - Epoch [51][3200/3746] lr: 7.422e-02, eta: 3 days, 10:54:49, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5378, loss_cls: 4.1253, loss: 4.1253 +2024-12-27 22:31:51,089 - pyskl - INFO - Epoch [51][3300/3746] lr: 7.420e-02, eta: 3 days, 10:53:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5303, loss_cls: 4.1478, loss: 4.1478 +2024-12-27 22:33:16,464 - pyskl - INFO - Epoch [51][3400/3746] lr: 7.417e-02, eta: 3 days, 10:52:27, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5353, loss_cls: 4.1201, loss: 4.1201 +2024-12-27 22:34:42,378 - pyskl - INFO - Epoch [51][3500/3746] lr: 7.415e-02, eta: 3 days, 10:51:18, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5464, loss_cls: 4.1028, loss: 4.1028 +2024-12-27 22:36:07,626 - pyskl - INFO - Epoch [51][3600/3746] lr: 7.412e-02, eta: 3 days, 10:50:07, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5428, loss_cls: 4.1080, loss: 4.1080 +2024-12-27 22:37:32,899 - pyskl - INFO - Epoch [51][3700/3746] lr: 7.410e-02, eta: 3 days, 10:48:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5386, loss_cls: 4.1292, loss: 4.1292 +2024-12-27 22:38:13,679 - pyskl - INFO - Saving checkpoint at 51 epochs +2024-12-27 22:40:10,289 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 22:40:10,955 - pyskl - INFO - +top1_acc 0.2169 +top5_acc 0.4615 +2024-12-27 22:40:10,956 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 22:40:10,997 - pyskl - INFO - +mean_acc 0.2167 +2024-12-27 22:40:11,010 - pyskl - INFO - Epoch(val) [51][309] top1_acc: 0.2169, top5_acc: 0.4615, mean_class_accuracy: 0.2167 +2024-12-27 22:44:26,829 - pyskl - INFO - Epoch [52][100/3746] lr: 7.406e-02, eta: 3 days, 10:51:27, time: 2.558, data_time: 1.516, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5492, loss_cls: 4.0593, loss: 4.0593 +2024-12-27 22:45:52,257 - pyskl - INFO - Epoch [52][200/3746] lr: 7.404e-02, eta: 3 days, 10:50:16, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5459, loss_cls: 4.0980, loss: 4.0980 +2024-12-27 22:47:17,677 - pyskl - INFO - Epoch [52][300/3746] lr: 7.401e-02, eta: 3 days, 10:49:05, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5452, loss_cls: 4.0545, loss: 4.0545 +2024-12-27 22:48:43,571 - pyskl - INFO - Epoch [52][400/3746] lr: 7.399e-02, eta: 3 days, 10:47:55, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5392, loss_cls: 4.1130, loss: 4.1130 +2024-12-27 22:50:09,164 - pyskl - INFO - Epoch [52][500/3746] lr: 7.397e-02, eta: 3 days, 10:46:45, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5278, loss_cls: 4.0979, loss: 4.0979 +2024-12-27 22:51:34,572 - pyskl - INFO - Epoch [52][600/3746] lr: 7.394e-02, eta: 3 days, 10:45:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5502, loss_cls: 4.0509, loss: 4.0509 +2024-12-27 22:53:00,065 - pyskl - INFO - Epoch [52][700/3746] lr: 7.392e-02, eta: 3 days, 10:44:23, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5303, loss_cls: 4.1189, loss: 4.1189 +2024-12-27 22:54:25,658 - pyskl - INFO - Epoch [52][800/3746] lr: 7.389e-02, eta: 3 days, 10:43:13, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5422, loss_cls: 4.0973, loss: 4.0973 +2024-12-27 22:55:51,646 - pyskl - INFO - Epoch [52][900/3746] lr: 7.387e-02, eta: 3 days, 10:42:03, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5375, loss_cls: 4.0851, loss: 4.0851 +2024-12-27 22:57:17,591 - pyskl - INFO - Epoch [52][1000/3746] lr: 7.384e-02, eta: 3 days, 10:40:53, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5400, loss_cls: 4.0943, loss: 4.0943 +2024-12-27 22:58:43,548 - pyskl - INFO - Epoch [52][1100/3746] lr: 7.382e-02, eta: 3 days, 10:39:43, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5434, loss_cls: 4.0913, loss: 4.0913 +2024-12-27 23:00:09,310 - pyskl - INFO - Epoch [52][1200/3746] lr: 7.379e-02, eta: 3 days, 10:38:33, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5423, loss_cls: 4.0640, loss: 4.0640 +2024-12-27 23:01:35,224 - pyskl - INFO - Epoch [52][1300/3746] lr: 7.377e-02, eta: 3 days, 10:37:23, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5305, loss_cls: 4.1610, loss: 4.1610 +2024-12-27 23:03:01,438 - pyskl - INFO - Epoch [52][1400/3746] lr: 7.374e-02, eta: 3 days, 10:36:13, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5378, loss_cls: 4.1150, loss: 4.1150 +2024-12-27 23:04:27,206 - pyskl - INFO - Epoch [52][1500/3746] lr: 7.372e-02, eta: 3 days, 10:35:03, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5425, loss_cls: 4.0876, loss: 4.0876 +2024-12-27 23:05:53,316 - pyskl - INFO - Epoch [52][1600/3746] lr: 7.370e-02, eta: 3 days, 10:33:53, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5503, loss_cls: 4.0490, loss: 4.0490 +2024-12-27 23:07:18,896 - pyskl - INFO - Epoch [52][1700/3746] lr: 7.367e-02, eta: 3 days, 10:32:42, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5408, loss_cls: 4.1114, loss: 4.1114 +2024-12-27 23:08:43,814 - pyskl - INFO - Epoch [52][1800/3746] lr: 7.365e-02, eta: 3 days, 10:31:30, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5475, loss_cls: 4.0942, loss: 4.0942 +2024-12-27 23:10:09,137 - pyskl - INFO - Epoch [52][1900/3746] lr: 7.362e-02, eta: 3 days, 10:30:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5450, loss_cls: 4.1175, loss: 4.1175 +2024-12-27 23:11:34,467 - pyskl - INFO - Epoch [52][2000/3746] lr: 7.360e-02, eta: 3 days, 10:29:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5444, loss_cls: 4.0700, loss: 4.0700 +2024-12-27 23:12:59,991 - pyskl - INFO - Epoch [52][2100/3746] lr: 7.357e-02, eta: 3 days, 10:27:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5498, loss_cls: 4.0887, loss: 4.0887 +2024-12-27 23:14:25,398 - pyskl - INFO - Epoch [52][2200/3746] lr: 7.355e-02, eta: 3 days, 10:26:45, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5244, loss_cls: 4.1462, loss: 4.1462 +2024-12-27 23:15:50,804 - pyskl - INFO - Epoch [52][2300/3746] lr: 7.352e-02, eta: 3 days, 10:25:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5361, loss_cls: 4.1381, loss: 4.1381 +2024-12-27 23:17:16,390 - pyskl - INFO - Epoch [52][2400/3746] lr: 7.350e-02, eta: 3 days, 10:24:23, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5364, loss_cls: 4.1110, loss: 4.1110 +2024-12-27 23:18:41,737 - pyskl - INFO - Epoch [52][2500/3746] lr: 7.347e-02, eta: 3 days, 10:23:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5436, loss_cls: 4.1019, loss: 4.1019 +2024-12-27 23:20:07,668 - pyskl - INFO - Epoch [52][2600/3746] lr: 7.345e-02, eta: 3 days, 10:22:02, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5473, loss_cls: 4.0604, loss: 4.0604 +2024-12-27 23:21:33,459 - pyskl - INFO - Epoch [52][2700/3746] lr: 7.342e-02, eta: 3 days, 10:20:51, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5314, loss_cls: 4.1381, loss: 4.1381 +2024-12-27 23:22:59,373 - pyskl - INFO - Epoch [52][2800/3746] lr: 7.340e-02, eta: 3 days, 10:19:41, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5461, loss_cls: 4.0817, loss: 4.0817 +2024-12-27 23:24:25,084 - pyskl - INFO - Epoch [52][2900/3746] lr: 7.337e-02, eta: 3 days, 10:18:30, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5444, loss_cls: 4.1025, loss: 4.1025 +2024-12-27 23:25:50,997 - pyskl - INFO - Epoch [52][3000/3746] lr: 7.335e-02, eta: 3 days, 10:17:20, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5469, loss_cls: 4.1014, loss: 4.1014 +2024-12-27 23:27:16,517 - pyskl - INFO - Epoch [52][3100/3746] lr: 7.332e-02, eta: 3 days, 10:16:09, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5355, loss_cls: 4.1153, loss: 4.1153 +2024-12-27 23:28:42,542 - pyskl - INFO - Epoch [52][3200/3746] lr: 7.330e-02, eta: 3 days, 10:14:59, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5414, loss_cls: 4.1345, loss: 4.1345 +2024-12-27 23:30:08,176 - pyskl - INFO - Epoch [52][3300/3746] lr: 7.328e-02, eta: 3 days, 10:13:48, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5330, loss_cls: 4.1089, loss: 4.1089 +2024-12-27 23:31:33,635 - pyskl - INFO - Epoch [52][3400/3746] lr: 7.325e-02, eta: 3 days, 10:12:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5514, loss_cls: 4.0711, loss: 4.0711 +2024-12-27 23:32:59,410 - pyskl - INFO - Epoch [52][3500/3746] lr: 7.323e-02, eta: 3 days, 10:11:26, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5386, loss_cls: 4.1329, loss: 4.1329 +2024-12-27 23:34:25,149 - pyskl - INFO - Epoch [52][3600/3746] lr: 7.320e-02, eta: 3 days, 10:10:15, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5398, loss_cls: 4.0907, loss: 4.0907 +2024-12-27 23:35:51,001 - pyskl - INFO - Epoch [52][3700/3746] lr: 7.318e-02, eta: 3 days, 10:09:05, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5377, loss_cls: 4.1259, loss: 4.1259 +2024-12-27 23:36:32,409 - pyskl - INFO - Saving checkpoint at 52 epochs +2024-12-27 23:38:34,610 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 23:38:35,377 - pyskl - INFO - +top1_acc 0.2021 +top5_acc 0.4333 +2024-12-27 23:38:35,377 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 23:38:35,424 - pyskl - INFO - +mean_acc 0.2020 +2024-12-27 23:38:35,437 - pyskl - INFO - Epoch(val) [52][309] top1_acc: 0.2021, top5_acc: 0.4333, mean_class_accuracy: 0.2020 +2024-12-27 23:42:59,195 - pyskl - INFO - Epoch [53][100/3746] lr: 7.314e-02, eta: 3 days, 10:11:42, time: 2.637, data_time: 1.604, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5361, loss_cls: 4.1019, loss: 4.1019 +2024-12-27 23:44:25,180 - pyskl - INFO - Epoch [53][200/3746] lr: 7.312e-02, eta: 3 days, 10:10:32, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5472, loss_cls: 4.0558, loss: 4.0558 +2024-12-27 23:45:50,565 - pyskl - INFO - Epoch [53][300/3746] lr: 7.309e-02, eta: 3 days, 10:09:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5453, loss_cls: 4.0521, loss: 4.0521 +2024-12-27 23:47:15,764 - pyskl - INFO - Epoch [53][400/3746] lr: 7.307e-02, eta: 3 days, 10:08:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5578, loss_cls: 4.0311, loss: 4.0311 +2024-12-27 23:48:40,843 - pyskl - INFO - Epoch [53][500/3746] lr: 7.304e-02, eta: 3 days, 10:06:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5513, loss_cls: 4.0599, loss: 4.0599 +2024-12-27 23:50:06,106 - pyskl - INFO - Epoch [53][600/3746] lr: 7.302e-02, eta: 3 days, 10:05:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5416, loss_cls: 4.0919, loss: 4.0919 +2024-12-27 23:51:31,347 - pyskl - INFO - Epoch [53][700/3746] lr: 7.299e-02, eta: 3 days, 10:04:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5405, loss_cls: 4.1196, loss: 4.1196 +2024-12-27 23:52:56,757 - pyskl - INFO - Epoch [53][800/3746] lr: 7.297e-02, eta: 3 days, 10:03:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5333, loss_cls: 4.1156, loss: 4.1156 +2024-12-27 23:54:21,435 - pyskl - INFO - Epoch [53][900/3746] lr: 7.294e-02, eta: 3 days, 10:02:07, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5458, loss_cls: 4.0765, loss: 4.0765 +2024-12-27 23:55:45,271 - pyskl - INFO - Epoch [53][1000/3746] lr: 7.292e-02, eta: 3 days, 10:00:52, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5473, loss_cls: 4.0692, loss: 4.0692 +2024-12-27 23:57:10,242 - pyskl - INFO - Epoch [53][1100/3746] lr: 7.289e-02, eta: 3 days, 9:59:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5444, loss_cls: 4.0823, loss: 4.0823 +2024-12-27 23:58:34,712 - pyskl - INFO - Epoch [53][1200/3746] lr: 7.287e-02, eta: 3 days, 9:58:26, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5356, loss_cls: 4.1177, loss: 4.1177 +2024-12-27 23:59:58,649 - pyskl - INFO - Epoch [53][1300/3746] lr: 7.284e-02, eta: 3 days, 9:57:12, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5433, loss_cls: 4.0631, loss: 4.0631 +2024-12-28 00:01:23,176 - pyskl - INFO - Epoch [53][1400/3746] lr: 7.282e-02, eta: 3 days, 9:55:58, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5345, loss_cls: 4.1395, loss: 4.1395 +2024-12-28 00:02:47,288 - pyskl - INFO - Epoch [53][1500/3746] lr: 7.279e-02, eta: 3 days, 9:54:44, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5458, loss_cls: 4.1089, loss: 4.1089 +2024-12-28 00:04:12,365 - pyskl - INFO - Epoch [53][1600/3746] lr: 7.277e-02, eta: 3 days, 9:53:32, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5441, loss_cls: 4.0584, loss: 4.0584 +2024-12-28 00:05:37,475 - pyskl - INFO - Epoch [53][1700/3746] lr: 7.274e-02, eta: 3 days, 9:52:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5464, loss_cls: 4.0929, loss: 4.0929 +2024-12-28 00:07:01,921 - pyskl - INFO - Epoch [53][1800/3746] lr: 7.272e-02, eta: 3 days, 9:51:06, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5530, loss_cls: 4.0626, loss: 4.0626 +2024-12-28 00:08:26,441 - pyskl - INFO - Epoch [53][1900/3746] lr: 7.269e-02, eta: 3 days, 9:49:52, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5420, loss_cls: 4.0964, loss: 4.0964 +2024-12-28 00:09:50,841 - pyskl - INFO - Epoch [53][2000/3746] lr: 7.267e-02, eta: 3 days, 9:48:38, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5398, loss_cls: 4.1128, loss: 4.1128 +2024-12-28 00:11:15,322 - pyskl - INFO - Epoch [53][2100/3746] lr: 7.264e-02, eta: 3 days, 9:47:25, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5419, loss_cls: 4.0873, loss: 4.0873 +2024-12-28 00:12:39,666 - pyskl - INFO - Epoch [53][2200/3746] lr: 7.262e-02, eta: 3 days, 9:46:11, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5408, loss_cls: 4.1149, loss: 4.1149 +2024-12-28 00:14:04,302 - pyskl - INFO - Epoch [53][2300/3746] lr: 7.259e-02, eta: 3 days, 9:44:58, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5314, loss_cls: 4.0996, loss: 4.0996 +2024-12-28 00:15:28,824 - pyskl - INFO - Epoch [53][2400/3746] lr: 7.257e-02, eta: 3 days, 9:43:44, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5491, loss_cls: 4.0780, loss: 4.0780 +2024-12-28 00:16:53,501 - pyskl - INFO - Epoch [53][2500/3746] lr: 7.254e-02, eta: 3 days, 9:42:31, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5483, loss_cls: 4.0561, loss: 4.0561 +2024-12-28 00:18:17,689 - pyskl - INFO - Epoch [53][2600/3746] lr: 7.252e-02, eta: 3 days, 9:41:17, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5408, loss_cls: 4.1077, loss: 4.1077 +2024-12-28 00:19:42,235 - pyskl - INFO - Epoch [53][2700/3746] lr: 7.249e-02, eta: 3 days, 9:40:03, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5413, loss_cls: 4.0885, loss: 4.0885 +2024-12-28 00:21:06,850 - pyskl - INFO - Epoch [53][2800/3746] lr: 7.247e-02, eta: 3 days, 9:38:50, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5437, loss_cls: 4.0997, loss: 4.0997 +2024-12-28 00:22:31,024 - pyskl - INFO - Epoch [53][2900/3746] lr: 7.244e-02, eta: 3 days, 9:37:35, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5322, loss_cls: 4.1693, loss: 4.1693 +2024-12-28 00:23:56,082 - pyskl - INFO - Epoch [53][3000/3746] lr: 7.242e-02, eta: 3 days, 9:36:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5425, loss_cls: 4.0953, loss: 4.0953 +2024-12-28 00:25:20,361 - pyskl - INFO - Epoch [53][3100/3746] lr: 7.239e-02, eta: 3 days, 9:35:09, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5455, loss_cls: 4.0807, loss: 4.0807 +2024-12-28 00:26:45,070 - pyskl - INFO - Epoch [53][3200/3746] lr: 7.237e-02, eta: 3 days, 9:33:55, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5441, loss_cls: 4.0928, loss: 4.0928 +2024-12-28 00:28:09,817 - pyskl - INFO - Epoch [53][3300/3746] lr: 7.234e-02, eta: 3 days, 9:32:42, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5478, loss_cls: 4.0798, loss: 4.0798 +2024-12-28 00:29:34,039 - pyskl - INFO - Epoch [53][3400/3746] lr: 7.232e-02, eta: 3 days, 9:31:28, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5536, loss_cls: 4.0189, loss: 4.0189 +2024-12-28 00:30:58,535 - pyskl - INFO - Epoch [53][3500/3746] lr: 7.229e-02, eta: 3 days, 9:30:14, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5387, loss_cls: 4.1329, loss: 4.1329 +2024-12-28 00:32:23,419 - pyskl - INFO - Epoch [53][3600/3746] lr: 7.227e-02, eta: 3 days, 9:29:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5264, loss_cls: 4.1479, loss: 4.1479 +2024-12-28 00:33:47,896 - pyskl - INFO - Epoch [53][3700/3746] lr: 7.224e-02, eta: 3 days, 9:27:47, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5450, loss_cls: 4.0868, loss: 4.0868 +2024-12-28 00:34:28,754 - pyskl - INFO - Saving checkpoint at 53 epochs +2024-12-28 00:36:27,086 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 00:36:27,961 - pyskl - INFO - +top1_acc 0.2113 +top5_acc 0.4673 +2024-12-28 00:36:27,961 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 00:36:28,014 - pyskl - INFO - +mean_acc 0.2110 +2024-12-28 00:36:28,027 - pyskl - INFO - Epoch(val) [53][309] top1_acc: 0.2113, top5_acc: 0.4673, mean_class_accuracy: 0.2110 +2024-12-28 00:40:48,538 - pyskl - INFO - Epoch [54][100/3746] lr: 7.221e-02, eta: 3 days, 9:30:10, time: 2.605, data_time: 1.569, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5594, loss_cls: 4.0484, loss: 4.0484 +2024-12-28 00:42:14,478 - pyskl - INFO - Epoch [54][200/3746] lr: 7.218e-02, eta: 3 days, 9:28:59, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5502, loss_cls: 4.0473, loss: 4.0473 +2024-12-28 00:43:39,556 - pyskl - INFO - Epoch [54][300/3746] lr: 7.216e-02, eta: 3 days, 9:27:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5463, loss_cls: 4.0396, loss: 4.0396 +2024-12-28 00:45:04,517 - pyskl - INFO - Epoch [54][400/3746] lr: 7.213e-02, eta: 3 days, 9:26:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5480, loss_cls: 4.0475, loss: 4.0475 +2024-12-28 00:46:29,497 - pyskl - INFO - Epoch [54][500/3746] lr: 7.211e-02, eta: 3 days, 9:25:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5444, loss_cls: 4.0400, loss: 4.0400 +2024-12-28 00:47:53,883 - pyskl - INFO - Epoch [54][600/3746] lr: 7.208e-02, eta: 3 days, 9:24:06, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5409, loss_cls: 4.0810, loss: 4.0810 +2024-12-28 00:49:18,583 - pyskl - INFO - Epoch [54][700/3746] lr: 7.206e-02, eta: 3 days, 9:22:52, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5477, loss_cls: 4.0607, loss: 4.0607 +2024-12-28 00:50:44,193 - pyskl - INFO - Epoch [54][800/3746] lr: 7.203e-02, eta: 3 days, 9:21:40, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5416, loss_cls: 4.0722, loss: 4.0722 +2024-12-28 00:52:09,023 - pyskl - INFO - Epoch [54][900/3746] lr: 7.201e-02, eta: 3 days, 9:20:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5383, loss_cls: 4.0931, loss: 4.0931 +2024-12-28 00:53:33,989 - pyskl - INFO - Epoch [54][1000/3746] lr: 7.198e-02, eta: 3 days, 9:19:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5470, loss_cls: 4.0715, loss: 4.0715 +2024-12-28 00:54:58,866 - pyskl - INFO - Epoch [54][1100/3746] lr: 7.196e-02, eta: 3 days, 9:18:00, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5433, loss_cls: 4.0778, loss: 4.0778 +2024-12-28 00:56:23,643 - pyskl - INFO - Epoch [54][1200/3746] lr: 7.193e-02, eta: 3 days, 9:16:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5519, loss_cls: 4.0634, loss: 4.0634 +2024-12-28 00:57:48,220 - pyskl - INFO - Epoch [54][1300/3746] lr: 7.191e-02, eta: 3 days, 9:15:33, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5406, loss_cls: 4.1490, loss: 4.1490 +2024-12-28 00:59:13,406 - pyskl - INFO - Epoch [54][1400/3746] lr: 7.188e-02, eta: 3 days, 9:14:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5397, loss_cls: 4.0900, loss: 4.0900 +2024-12-28 01:00:38,197 - pyskl - INFO - Epoch [54][1500/3746] lr: 7.186e-02, eta: 3 days, 9:13:06, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5386, loss_cls: 4.0905, loss: 4.0905 +2024-12-28 01:02:02,999 - pyskl - INFO - Epoch [54][1600/3746] lr: 7.183e-02, eta: 3 days, 9:11:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5478, loss_cls: 4.0692, loss: 4.0692 +2024-12-28 01:03:27,646 - pyskl - INFO - Epoch [54][1700/3746] lr: 7.181e-02, eta: 3 days, 9:10:39, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5503, loss_cls: 4.0700, loss: 4.0700 +2024-12-28 01:04:52,083 - pyskl - INFO - Epoch [54][1800/3746] lr: 7.178e-02, eta: 3 days, 9:09:24, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5402, loss_cls: 4.0745, loss: 4.0745 +2024-12-28 01:06:16,905 - pyskl - INFO - Epoch [54][1900/3746] lr: 7.176e-02, eta: 3 days, 9:08:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5463, loss_cls: 4.0750, loss: 4.0750 +2024-12-28 01:07:42,131 - pyskl - INFO - Epoch [54][2000/3746] lr: 7.173e-02, eta: 3 days, 9:06:58, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5477, loss_cls: 4.0486, loss: 4.0486 +2024-12-28 01:09:07,313 - pyskl - INFO - Epoch [54][2100/3746] lr: 7.170e-02, eta: 3 days, 9:05:45, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5419, loss_cls: 4.0727, loss: 4.0727 +2024-12-28 01:10:31,914 - pyskl - INFO - Epoch [54][2200/3746] lr: 7.168e-02, eta: 3 days, 9:04:31, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5498, loss_cls: 4.0762, loss: 4.0762 +2024-12-28 01:11:56,288 - pyskl - INFO - Epoch [54][2300/3746] lr: 7.165e-02, eta: 3 days, 9:03:17, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5431, loss_cls: 4.0767, loss: 4.0767 +2024-12-28 01:13:21,287 - pyskl - INFO - Epoch [54][2400/3746] lr: 7.163e-02, eta: 3 days, 9:02:03, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5450, loss_cls: 4.0736, loss: 4.0736 +2024-12-28 01:14:45,678 - pyskl - INFO - Epoch [54][2500/3746] lr: 7.160e-02, eta: 3 days, 9:00:49, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5448, loss_cls: 4.0930, loss: 4.0930 +2024-12-28 01:16:09,904 - pyskl - INFO - Epoch [54][2600/3746] lr: 7.158e-02, eta: 3 days, 8:59:34, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5448, loss_cls: 4.0883, loss: 4.0883 +2024-12-28 01:17:34,516 - pyskl - INFO - Epoch [54][2700/3746] lr: 7.155e-02, eta: 3 days, 8:58:20, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5478, loss_cls: 4.0738, loss: 4.0738 +2024-12-28 01:18:58,573 - pyskl - INFO - Epoch [54][2800/3746] lr: 7.153e-02, eta: 3 days, 8:57:05, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5466, loss_cls: 4.0797, loss: 4.0797 +2024-12-28 01:20:22,182 - pyskl - INFO - Epoch [54][2900/3746] lr: 7.150e-02, eta: 3 days, 8:55:49, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5403, loss_cls: 4.0694, loss: 4.0694 +2024-12-28 01:21:46,990 - pyskl - INFO - Epoch [54][3000/3746] lr: 7.148e-02, eta: 3 days, 8:54:36, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5375, loss_cls: 4.1052, loss: 4.1052 +2024-12-28 01:23:12,508 - pyskl - INFO - Epoch [54][3100/3746] lr: 7.145e-02, eta: 3 days, 8:53:23, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5375, loss_cls: 4.0936, loss: 4.0936 +2024-12-28 01:24:37,502 - pyskl - INFO - Epoch [54][3200/3746] lr: 7.143e-02, eta: 3 days, 8:52:10, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5470, loss_cls: 4.0836, loss: 4.0836 +2024-12-28 01:26:02,558 - pyskl - INFO - Epoch [54][3300/3746] lr: 7.140e-02, eta: 3 days, 8:50:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5359, loss_cls: 4.1280, loss: 4.1280 +2024-12-28 01:27:27,731 - pyskl - INFO - Epoch [54][3400/3746] lr: 7.138e-02, eta: 3 days, 8:49:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5559, loss_cls: 4.0742, loss: 4.0742 +2024-12-28 01:28:52,908 - pyskl - INFO - Epoch [54][3500/3746] lr: 7.135e-02, eta: 3 days, 8:48:30, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5395, loss_cls: 4.1085, loss: 4.1085 +2024-12-28 01:30:16,760 - pyskl - INFO - Epoch [54][3600/3746] lr: 7.133e-02, eta: 3 days, 8:47:15, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5475, loss_cls: 4.0725, loss: 4.0725 +2024-12-28 01:31:41,232 - pyskl - INFO - Epoch [54][3700/3746] lr: 7.130e-02, eta: 3 days, 8:46:00, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5350, loss_cls: 4.1109, loss: 4.1109 +2024-12-28 01:32:22,131 - pyskl - INFO - Saving checkpoint at 54 epochs +2024-12-28 01:34:18,369 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 01:34:19,063 - pyskl - INFO - +top1_acc 0.2230 +top5_acc 0.4664 +2024-12-28 01:34:19,063 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 01:34:19,113 - pyskl - INFO - +mean_acc 0.2229 +2024-12-28 01:34:19,128 - pyskl - INFO - Epoch(val) [54][309] top1_acc: 0.2230, top5_acc: 0.4664, mean_class_accuracy: 0.2229 +2024-12-28 01:38:35,170 - pyskl - INFO - Epoch [55][100/3746] lr: 7.126e-02, eta: 3 days, 8:48:07, time: 2.560, data_time: 1.534, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5600, loss_cls: 3.9950, loss: 3.9950 +2024-12-28 01:40:01,084 - pyskl - INFO - Epoch [55][200/3746] lr: 7.124e-02, eta: 3 days, 8:46:55, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5509, loss_cls: 4.0638, loss: 4.0638 +2024-12-28 01:41:26,686 - pyskl - INFO - Epoch [55][300/3746] lr: 7.121e-02, eta: 3 days, 8:45:43, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5519, loss_cls: 4.0375, loss: 4.0375 +2024-12-28 01:42:52,309 - pyskl - INFO - Epoch [55][400/3746] lr: 7.119e-02, eta: 3 days, 8:44:30, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5578, loss_cls: 4.0063, loss: 4.0063 +2024-12-28 01:44:18,078 - pyskl - INFO - Epoch [55][500/3746] lr: 7.116e-02, eta: 3 days, 8:43:18, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5492, loss_cls: 4.0479, loss: 4.0479 +2024-12-28 01:45:42,881 - pyskl - INFO - Epoch [55][600/3746] lr: 7.114e-02, eta: 3 days, 8:42:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5417, loss_cls: 4.0880, loss: 4.0880 +2024-12-28 01:47:07,767 - pyskl - INFO - Epoch [55][700/3746] lr: 7.111e-02, eta: 3 days, 8:40:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5400, loss_cls: 4.0960, loss: 4.0960 +2024-12-28 01:48:33,013 - pyskl - INFO - Epoch [55][800/3746] lr: 7.109e-02, eta: 3 days, 8:39:37, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5459, loss_cls: 4.0742, loss: 4.0742 +2024-12-28 01:49:57,738 - pyskl - INFO - Epoch [55][900/3746] lr: 7.106e-02, eta: 3 days, 8:38:22, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5403, loss_cls: 4.1063, loss: 4.1063 +2024-12-28 01:51:22,355 - pyskl - INFO - Epoch [55][1000/3746] lr: 7.104e-02, eta: 3 days, 8:37:08, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5459, loss_cls: 4.0576, loss: 4.0576 +2024-12-28 01:52:47,306 - pyskl - INFO - Epoch [55][1100/3746] lr: 7.101e-02, eta: 3 days, 8:35:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5455, loss_cls: 4.0766, loss: 4.0766 +2024-12-28 01:54:11,795 - pyskl - INFO - Epoch [55][1200/3746] lr: 7.099e-02, eta: 3 days, 8:34:40, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5411, loss_cls: 4.0814, loss: 4.0814 +2024-12-28 01:55:36,174 - pyskl - INFO - Epoch [55][1300/3746] lr: 7.096e-02, eta: 3 days, 8:33:25, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5469, loss_cls: 4.0788, loss: 4.0788 +2024-12-28 01:57:00,396 - pyskl - INFO - Epoch [55][1400/3746] lr: 7.093e-02, eta: 3 days, 8:32:09, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5514, loss_cls: 4.0506, loss: 4.0506 +2024-12-28 01:58:24,927 - pyskl - INFO - Epoch [55][1500/3746] lr: 7.091e-02, eta: 3 days, 8:30:55, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5428, loss_cls: 4.0575, loss: 4.0575 +2024-12-28 01:59:49,766 - pyskl - INFO - Epoch [55][1600/3746] lr: 7.088e-02, eta: 3 days, 8:29:41, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5403, loss_cls: 4.1074, loss: 4.1074 +2024-12-28 02:01:14,319 - pyskl - INFO - Epoch [55][1700/3746] lr: 7.086e-02, eta: 3 days, 8:28:26, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5470, loss_cls: 4.0589, loss: 4.0589 +2024-12-28 02:02:39,866 - pyskl - INFO - Epoch [55][1800/3746] lr: 7.083e-02, eta: 3 days, 8:27:13, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5352, loss_cls: 4.1048, loss: 4.1048 +2024-12-28 02:04:04,765 - pyskl - INFO - Epoch [55][1900/3746] lr: 7.081e-02, eta: 3 days, 8:25:59, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5339, loss_cls: 4.1169, loss: 4.1169 +2024-12-28 02:05:29,002 - pyskl - INFO - Epoch [55][2000/3746] lr: 7.078e-02, eta: 3 days, 8:24:44, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5464, loss_cls: 4.0778, loss: 4.0778 +2024-12-28 02:06:53,340 - pyskl - INFO - Epoch [55][2100/3746] lr: 7.076e-02, eta: 3 days, 8:23:29, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5450, loss_cls: 4.0631, loss: 4.0631 +2024-12-28 02:08:17,495 - pyskl - INFO - Epoch [55][2200/3746] lr: 7.073e-02, eta: 3 days, 8:22:14, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5406, loss_cls: 4.0836, loss: 4.0836 +2024-12-28 02:09:42,240 - pyskl - INFO - Epoch [55][2300/3746] lr: 7.071e-02, eta: 3 days, 8:20:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5403, loss_cls: 4.1143, loss: 4.1143 +2024-12-28 02:11:06,640 - pyskl - INFO - Epoch [55][2400/3746] lr: 7.068e-02, eta: 3 days, 8:19:44, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5467, loss_cls: 4.0553, loss: 4.0553 +2024-12-28 02:12:31,427 - pyskl - INFO - Epoch [55][2500/3746] lr: 7.065e-02, eta: 3 days, 8:18:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5498, loss_cls: 4.0347, loss: 4.0347 +2024-12-28 02:13:55,488 - pyskl - INFO - Epoch [55][2600/3746] lr: 7.063e-02, eta: 3 days, 8:17:15, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5466, loss_cls: 4.0940, loss: 4.0940 +2024-12-28 02:15:20,921 - pyskl - INFO - Epoch [55][2700/3746] lr: 7.060e-02, eta: 3 days, 8:16:01, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5442, loss_cls: 4.0667, loss: 4.0667 +2024-12-28 02:16:45,760 - pyskl - INFO - Epoch [55][2800/3746] lr: 7.058e-02, eta: 3 days, 8:14:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5448, loss_cls: 4.0858, loss: 4.0858 +2024-12-28 02:18:10,316 - pyskl - INFO - Epoch [55][2900/3746] lr: 7.055e-02, eta: 3 days, 8:13:33, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5337, loss_cls: 4.1332, loss: 4.1332 +2024-12-28 02:19:35,258 - pyskl - INFO - Epoch [55][3000/3746] lr: 7.053e-02, eta: 3 days, 8:12:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5327, loss_cls: 4.1278, loss: 4.1278 +2024-12-28 02:21:00,114 - pyskl - INFO - Epoch [55][3100/3746] lr: 7.050e-02, eta: 3 days, 8:11:04, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5487, loss_cls: 4.0699, loss: 4.0699 +2024-12-28 02:22:25,099 - pyskl - INFO - Epoch [55][3200/3746] lr: 7.048e-02, eta: 3 days, 8:09:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5470, loss_cls: 4.0194, loss: 4.0194 +2024-12-28 02:23:49,885 - pyskl - INFO - Epoch [55][3300/3746] lr: 7.045e-02, eta: 3 days, 8:08:36, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5452, loss_cls: 4.0920, loss: 4.0920 +2024-12-28 02:25:14,761 - pyskl - INFO - Epoch [55][3400/3746] lr: 7.043e-02, eta: 3 days, 8:07:22, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5594, loss_cls: 4.0470, loss: 4.0470 +2024-12-28 02:26:39,848 - pyskl - INFO - Epoch [55][3500/3746] lr: 7.040e-02, eta: 3 days, 8:06:08, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5322, loss_cls: 4.1306, loss: 4.1306 +2024-12-28 02:28:04,760 - pyskl - INFO - Epoch [55][3600/3746] lr: 7.037e-02, eta: 3 days, 8:04:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5411, loss_cls: 4.1042, loss: 4.1042 +2024-12-28 02:29:30,402 - pyskl - INFO - Epoch [55][3700/3746] lr: 7.035e-02, eta: 3 days, 8:03:41, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5395, loss_cls: 4.0770, loss: 4.0770 +2024-12-28 02:30:11,364 - pyskl - INFO - Saving checkpoint at 55 epochs +2024-12-28 02:32:10,680 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 02:32:11,792 - pyskl - INFO - +top1_acc 0.2016 +top5_acc 0.4412 +2024-12-28 02:32:11,792 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 02:32:11,840 - pyskl - INFO - +mean_acc 0.2014 +2024-12-28 02:32:11,856 - pyskl - INFO - Epoch(val) [55][309] top1_acc: 0.2016, top5_acc: 0.4412, mean_class_accuracy: 0.2014 +2024-12-28 02:36:26,569 - pyskl - INFO - Epoch [56][100/3746] lr: 7.031e-02, eta: 3 days, 8:05:38, time: 2.547, data_time: 1.522, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5447, loss_cls: 4.0732, loss: 4.0732 +2024-12-28 02:37:52,030 - pyskl - INFO - Epoch [56][200/3746] lr: 7.029e-02, eta: 3 days, 8:04:25, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5439, loss_cls: 4.0627, loss: 4.0627 +2024-12-28 02:39:17,413 - pyskl - INFO - Epoch [56][300/3746] lr: 7.026e-02, eta: 3 days, 8:03:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5522, loss_cls: 4.0463, loss: 4.0463 +2024-12-28 02:40:41,951 - pyskl - INFO - Epoch [56][400/3746] lr: 7.023e-02, eta: 3 days, 8:01:56, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5422, loss_cls: 4.0926, loss: 4.0926 +2024-12-28 02:42:07,203 - pyskl - INFO - Epoch [56][500/3746] lr: 7.021e-02, eta: 3 days, 8:00:42, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5416, loss_cls: 4.0706, loss: 4.0706 +2024-12-28 02:43:32,184 - pyskl - INFO - Epoch [56][600/3746] lr: 7.018e-02, eta: 3 days, 7:59:28, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5541, loss_cls: 3.9924, loss: 3.9924 +2024-12-28 02:44:57,210 - pyskl - INFO - Epoch [56][700/3746] lr: 7.016e-02, eta: 3 days, 7:58:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5514, loss_cls: 4.0472, loss: 4.0472 +2024-12-28 02:46:22,133 - pyskl - INFO - Epoch [56][800/3746] lr: 7.013e-02, eta: 3 days, 7:56:59, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5611, loss_cls: 3.9985, loss: 3.9985 +2024-12-28 02:47:46,560 - pyskl - INFO - Epoch [56][900/3746] lr: 7.011e-02, eta: 3 days, 7:55:44, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5487, loss_cls: 4.0836, loss: 4.0836 +2024-12-28 02:49:10,581 - pyskl - INFO - Epoch [56][1000/3746] lr: 7.008e-02, eta: 3 days, 7:54:28, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5456, loss_cls: 4.1009, loss: 4.1009 +2024-12-28 02:50:35,171 - pyskl - INFO - Epoch [56][1100/3746] lr: 7.006e-02, eta: 3 days, 7:53:13, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5522, loss_cls: 4.0138, loss: 4.0138 +2024-12-28 02:52:00,132 - pyskl - INFO - Epoch [56][1200/3746] lr: 7.003e-02, eta: 3 days, 7:51:58, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5561, loss_cls: 4.0353, loss: 4.0353 +2024-12-28 02:53:24,796 - pyskl - INFO - Epoch [56][1300/3746] lr: 7.000e-02, eta: 3 days, 7:50:44, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5447, loss_cls: 4.1032, loss: 4.1032 +2024-12-28 02:54:49,478 - pyskl - INFO - Epoch [56][1400/3746] lr: 6.998e-02, eta: 3 days, 7:49:29, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5442, loss_cls: 4.0641, loss: 4.0641 +2024-12-28 02:56:14,352 - pyskl - INFO - Epoch [56][1500/3746] lr: 6.995e-02, eta: 3 days, 7:48:14, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5458, loss_cls: 4.0665, loss: 4.0665 +2024-12-28 02:57:39,441 - pyskl - INFO - Epoch [56][1600/3746] lr: 6.993e-02, eta: 3 days, 7:47:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5366, loss_cls: 4.1267, loss: 4.1267 +2024-12-28 02:59:04,419 - pyskl - INFO - Epoch [56][1700/3746] lr: 6.990e-02, eta: 3 days, 7:45:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5411, loss_cls: 4.0777, loss: 4.0777 +2024-12-28 03:00:29,072 - pyskl - INFO - Epoch [56][1800/3746] lr: 6.988e-02, eta: 3 days, 7:44:30, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5392, loss_cls: 4.1160, loss: 4.1160 +2024-12-28 03:01:53,736 - pyskl - INFO - Epoch [56][1900/3746] lr: 6.985e-02, eta: 3 days, 7:43:15, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5477, loss_cls: 4.0827, loss: 4.0827 +2024-12-28 03:03:18,528 - pyskl - INFO - Epoch [56][2000/3746] lr: 6.983e-02, eta: 3 days, 7:42:01, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5447, loss_cls: 4.0705, loss: 4.0705 +2024-12-28 03:04:43,125 - pyskl - INFO - Epoch [56][2100/3746] lr: 6.980e-02, eta: 3 days, 7:40:46, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5359, loss_cls: 4.0739, loss: 4.0739 +2024-12-28 03:06:07,713 - pyskl - INFO - Epoch [56][2200/3746] lr: 6.977e-02, eta: 3 days, 7:39:30, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5336, loss_cls: 4.0910, loss: 4.0910 +2024-12-28 03:07:31,888 - pyskl - INFO - Epoch [56][2300/3746] lr: 6.975e-02, eta: 3 days, 7:38:15, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5444, loss_cls: 4.0593, loss: 4.0593 +2024-12-28 03:08:56,173 - pyskl - INFO - Epoch [56][2400/3746] lr: 6.972e-02, eta: 3 days, 7:36:59, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5481, loss_cls: 4.0658, loss: 4.0658 +2024-12-28 03:10:20,928 - pyskl - INFO - Epoch [56][2500/3746] lr: 6.970e-02, eta: 3 days, 7:35:44, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5447, loss_cls: 4.0769, loss: 4.0769 +2024-12-28 03:11:45,548 - pyskl - INFO - Epoch [56][2600/3746] lr: 6.967e-02, eta: 3 days, 7:34:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5473, loss_cls: 4.0474, loss: 4.0474 +2024-12-28 03:13:10,574 - pyskl - INFO - Epoch [56][2700/3746] lr: 6.965e-02, eta: 3 days, 7:33:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5333, loss_cls: 4.1215, loss: 4.1215 +2024-12-28 03:14:35,570 - pyskl - INFO - Epoch [56][2800/3746] lr: 6.962e-02, eta: 3 days, 7:32:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5478, loss_cls: 4.0677, loss: 4.0677 +2024-12-28 03:16:00,085 - pyskl - INFO - Epoch [56][2900/3746] lr: 6.959e-02, eta: 3 days, 7:30:45, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5481, loss_cls: 4.0709, loss: 4.0709 +2024-12-28 03:17:24,805 - pyskl - INFO - Epoch [56][3000/3746] lr: 6.957e-02, eta: 3 days, 7:29:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5584, loss_cls: 4.0370, loss: 4.0370 +2024-12-28 03:18:49,767 - pyskl - INFO - Epoch [56][3100/3746] lr: 6.954e-02, eta: 3 days, 7:28:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5477, loss_cls: 4.1050, loss: 4.1050 +2024-12-28 03:20:14,827 - pyskl - INFO - Epoch [56][3200/3746] lr: 6.952e-02, eta: 3 days, 7:27:01, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5425, loss_cls: 4.0952, loss: 4.0952 +2024-12-28 03:21:39,510 - pyskl - INFO - Epoch [56][3300/3746] lr: 6.949e-02, eta: 3 days, 7:25:46, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5391, loss_cls: 4.1130, loss: 4.1130 +2024-12-28 03:23:04,057 - pyskl - INFO - Epoch [56][3400/3746] lr: 6.947e-02, eta: 3 days, 7:24:30, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5455, loss_cls: 4.0792, loss: 4.0792 +2024-12-28 03:24:28,348 - pyskl - INFO - Epoch [56][3500/3746] lr: 6.944e-02, eta: 3 days, 7:23:15, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5484, loss_cls: 4.0637, loss: 4.0637 +2024-12-28 03:25:52,950 - pyskl - INFO - Epoch [56][3600/3746] lr: 6.941e-02, eta: 3 days, 7:21:59, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5450, loss_cls: 4.0622, loss: 4.0622 +2024-12-28 03:27:18,031 - pyskl - INFO - Epoch [56][3700/3746] lr: 6.939e-02, eta: 3 days, 7:20:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5522, loss_cls: 4.0476, loss: 4.0476 +2024-12-28 03:27:59,111 - pyskl - INFO - Saving checkpoint at 56 epochs +2024-12-28 03:29:57,223 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 03:29:57,976 - pyskl - INFO - +top1_acc 0.2171 +top5_acc 0.4565 +2024-12-28 03:29:57,977 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 03:29:58,016 - pyskl - INFO - +mean_acc 0.2169 +2024-12-28 03:29:58,028 - pyskl - INFO - Epoch(val) [56][309] top1_acc: 0.2171, top5_acc: 0.4565, mean_class_accuracy: 0.2169 +2024-12-28 03:34:16,582 - pyskl - INFO - Epoch [57][100/3746] lr: 6.935e-02, eta: 3 days, 7:22:42, time: 2.585, data_time: 1.543, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5639, loss_cls: 3.9818, loss: 3.9818 +2024-12-28 03:35:41,658 - pyskl - INFO - Epoch [57][200/3746] lr: 6.932e-02, eta: 3 days, 7:21:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5505, loss_cls: 4.0540, loss: 4.0540 +2024-12-28 03:37:07,277 - pyskl - INFO - Epoch [57][300/3746] lr: 6.930e-02, eta: 3 days, 7:20:13, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5616, loss_cls: 4.0084, loss: 4.0084 +2024-12-28 03:38:32,833 - pyskl - INFO - Epoch [57][400/3746] lr: 6.927e-02, eta: 3 days, 7:18:59, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5541, loss_cls: 4.0127, loss: 4.0127 +2024-12-28 03:39:57,715 - pyskl - INFO - Epoch [57][500/3746] lr: 6.925e-02, eta: 3 days, 7:17:44, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5441, loss_cls: 4.1095, loss: 4.1095 +2024-12-28 03:41:22,806 - pyskl - INFO - Epoch [57][600/3746] lr: 6.922e-02, eta: 3 days, 7:16:30, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5602, loss_cls: 4.0340, loss: 4.0340 +2024-12-28 03:42:47,885 - pyskl - INFO - Epoch [57][700/3746] lr: 6.920e-02, eta: 3 days, 7:15:15, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5422, loss_cls: 4.0513, loss: 4.0513 +2024-12-28 03:44:12,506 - pyskl - INFO - Epoch [57][800/3746] lr: 6.917e-02, eta: 3 days, 7:13:59, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5428, loss_cls: 4.1073, loss: 4.1073 +2024-12-28 03:45:37,068 - pyskl - INFO - Epoch [57][900/3746] lr: 6.914e-02, eta: 3 days, 7:12:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5689, loss_cls: 3.9923, loss: 3.9923 +2024-12-28 03:47:00,883 - pyskl - INFO - Epoch [57][1000/3746] lr: 6.912e-02, eta: 3 days, 7:11:27, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5445, loss_cls: 4.0652, loss: 4.0652 +2024-12-28 03:48:25,522 - pyskl - INFO - Epoch [57][1100/3746] lr: 6.909e-02, eta: 3 days, 7:10:12, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5356, loss_cls: 4.1222, loss: 4.1222 +2024-12-28 03:49:50,841 - pyskl - INFO - Epoch [57][1200/3746] lr: 6.907e-02, eta: 3 days, 7:08:57, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5500, loss_cls: 4.0613, loss: 4.0613 +2024-12-28 03:51:15,580 - pyskl - INFO - Epoch [57][1300/3746] lr: 6.904e-02, eta: 3 days, 7:07:42, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5505, loss_cls: 4.0629, loss: 4.0629 +2024-12-28 03:52:40,499 - pyskl - INFO - Epoch [57][1400/3746] lr: 6.901e-02, eta: 3 days, 7:06:27, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5484, loss_cls: 3.9992, loss: 3.9992 +2024-12-28 03:54:05,590 - pyskl - INFO - Epoch [57][1500/3746] lr: 6.899e-02, eta: 3 days, 7:05:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5469, loss_cls: 4.0506, loss: 4.0506 +2024-12-28 03:55:30,617 - pyskl - INFO - Epoch [57][1600/3746] lr: 6.896e-02, eta: 3 days, 7:03:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5527, loss_cls: 4.0911, loss: 4.0911 +2024-12-28 03:56:55,063 - pyskl - INFO - Epoch [57][1700/3746] lr: 6.894e-02, eta: 3 days, 7:02:42, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5342, loss_cls: 4.1258, loss: 4.1258 +2024-12-28 03:58:19,378 - pyskl - INFO - Epoch [57][1800/3746] lr: 6.891e-02, eta: 3 days, 7:01:25, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5470, loss_cls: 4.0708, loss: 4.0708 +2024-12-28 03:59:43,938 - pyskl - INFO - Epoch [57][1900/3746] lr: 6.889e-02, eta: 3 days, 7:00:10, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5509, loss_cls: 4.0381, loss: 4.0381 +2024-12-28 04:01:07,869 - pyskl - INFO - Epoch [57][2000/3746] lr: 6.886e-02, eta: 3 days, 6:58:53, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5570, loss_cls: 4.0382, loss: 4.0382 +2024-12-28 04:02:32,137 - pyskl - INFO - Epoch [57][2100/3746] lr: 6.883e-02, eta: 3 days, 6:57:37, time: 0.843, data_time: 0.001, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5391, loss_cls: 4.1125, loss: 4.1125 +2024-12-28 04:03:55,975 - pyskl - INFO - Epoch [57][2200/3746] lr: 6.881e-02, eta: 3 days, 6:56:20, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5569, loss_cls: 4.0051, loss: 4.0051 +2024-12-28 04:05:20,398 - pyskl - INFO - Epoch [57][2300/3746] lr: 6.878e-02, eta: 3 days, 6:55:04, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5603, loss_cls: 4.0061, loss: 4.0061 +2024-12-28 04:06:44,981 - pyskl - INFO - Epoch [57][2400/3746] lr: 6.876e-02, eta: 3 days, 6:53:49, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5455, loss_cls: 4.0610, loss: 4.0610 +2024-12-28 04:08:09,439 - pyskl - INFO - Epoch [57][2500/3746] lr: 6.873e-02, eta: 3 days, 6:52:33, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5537, loss_cls: 4.0294, loss: 4.0294 +2024-12-28 04:09:34,012 - pyskl - INFO - Epoch [57][2600/3746] lr: 6.870e-02, eta: 3 days, 6:51:17, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5489, loss_cls: 4.0613, loss: 4.0613 +2024-12-28 04:10:58,382 - pyskl - INFO - Epoch [57][2700/3746] lr: 6.868e-02, eta: 3 days, 6:50:01, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5491, loss_cls: 4.0421, loss: 4.0421 +2024-12-28 04:12:22,898 - pyskl - INFO - Epoch [57][2800/3746] lr: 6.865e-02, eta: 3 days, 6:48:45, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5511, loss_cls: 4.0580, loss: 4.0580 +2024-12-28 04:13:48,014 - pyskl - INFO - Epoch [57][2900/3746] lr: 6.863e-02, eta: 3 days, 6:47:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5522, loss_cls: 4.0424, loss: 4.0424 +2024-12-28 04:15:12,777 - pyskl - INFO - Epoch [57][3000/3746] lr: 6.860e-02, eta: 3 days, 6:46:15, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5503, loss_cls: 4.0367, loss: 4.0367 +2024-12-28 04:16:37,289 - pyskl - INFO - Epoch [57][3100/3746] lr: 6.857e-02, eta: 3 days, 6:44:59, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5467, loss_cls: 4.0604, loss: 4.0604 +2024-12-28 04:18:02,047 - pyskl - INFO - Epoch [57][3200/3746] lr: 6.855e-02, eta: 3 days, 6:43:44, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5484, loss_cls: 4.0424, loss: 4.0424 +2024-12-28 04:19:26,519 - pyskl - INFO - Epoch [57][3300/3746] lr: 6.852e-02, eta: 3 days, 6:42:28, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5430, loss_cls: 4.0700, loss: 4.0700 +2024-12-28 04:20:50,691 - pyskl - INFO - Epoch [57][3400/3746] lr: 6.850e-02, eta: 3 days, 6:41:11, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5514, loss_cls: 4.0363, loss: 4.0363 +2024-12-28 04:22:15,749 - pyskl - INFO - Epoch [57][3500/3746] lr: 6.847e-02, eta: 3 days, 6:39:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5470, loss_cls: 4.0678, loss: 4.0678 +2024-12-28 04:23:40,269 - pyskl - INFO - Epoch [57][3600/3746] lr: 6.844e-02, eta: 3 days, 6:38:40, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5542, loss_cls: 4.0352, loss: 4.0352 +2024-12-28 04:25:05,093 - pyskl - INFO - Epoch [57][3700/3746] lr: 6.842e-02, eta: 3 days, 6:37:25, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5437, loss_cls: 4.0845, loss: 4.0845 +2024-12-28 04:25:45,772 - pyskl - INFO - Saving checkpoint at 57 epochs +2024-12-28 04:27:45,559 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 04:27:46,281 - pyskl - INFO - +top1_acc 0.2279 +top5_acc 0.4700 +2024-12-28 04:27:46,281 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 04:27:46,328 - pyskl - INFO - +mean_acc 0.2278 +2024-12-28 04:27:46,333 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_41.pth was removed +2024-12-28 04:27:46,682 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_57.pth. +2024-12-28 04:27:46,683 - pyskl - INFO - Best top1_acc is 0.2279 at 57 epoch. +2024-12-28 04:27:46,696 - pyskl - INFO - Epoch(val) [57][309] top1_acc: 0.2279, top5_acc: 0.4700, mean_class_accuracy: 0.2278 +2024-12-28 04:31:54,021 - pyskl - INFO - Epoch [58][100/3746] lr: 6.838e-02, eta: 3 days, 6:38:56, time: 2.473, data_time: 1.446, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5553, loss_cls: 4.0383, loss: 4.0383 +2024-12-28 04:33:19,081 - pyskl - INFO - Epoch [58][200/3746] lr: 6.835e-02, eta: 3 days, 6:37:41, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5423, loss_cls: 4.0609, loss: 4.0609 +2024-12-28 04:34:44,228 - pyskl - INFO - Epoch [58][300/3746] lr: 6.833e-02, eta: 3 days, 6:36:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5575, loss_cls: 4.0037, loss: 4.0037 +2024-12-28 04:36:09,204 - pyskl - INFO - Epoch [58][400/3746] lr: 6.830e-02, eta: 3 days, 6:35:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5656, loss_cls: 3.9671, loss: 3.9671 +2024-12-28 04:37:34,240 - pyskl - INFO - Epoch [58][500/3746] lr: 6.828e-02, eta: 3 days, 6:33:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5520, loss_cls: 4.0507, loss: 4.0507 +2024-12-28 04:38:59,164 - pyskl - INFO - Epoch [58][600/3746] lr: 6.825e-02, eta: 3 days, 6:32:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5489, loss_cls: 4.0212, loss: 4.0212 +2024-12-28 04:40:23,971 - pyskl - INFO - Epoch [58][700/3746] lr: 6.822e-02, eta: 3 days, 6:31:24, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5480, loss_cls: 4.0490, loss: 4.0490 +2024-12-28 04:41:48,545 - pyskl - INFO - Epoch [58][800/3746] lr: 6.820e-02, eta: 3 days, 6:30:08, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5413, loss_cls: 4.0669, loss: 4.0669 +2024-12-28 04:43:13,463 - pyskl - INFO - Epoch [58][900/3746] lr: 6.817e-02, eta: 3 days, 6:28:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5417, loss_cls: 4.0578, loss: 4.0578 +2024-12-28 04:44:37,604 - pyskl - INFO - Epoch [58][1000/3746] lr: 6.815e-02, eta: 3 days, 6:27:36, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5431, loss_cls: 4.0866, loss: 4.0866 +2024-12-28 04:46:02,120 - pyskl - INFO - Epoch [58][1100/3746] lr: 6.812e-02, eta: 3 days, 6:26:20, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5413, loss_cls: 4.0812, loss: 4.0812 +2024-12-28 04:47:26,784 - pyskl - INFO - Epoch [58][1200/3746] lr: 6.809e-02, eta: 3 days, 6:25:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5452, loss_cls: 4.0639, loss: 4.0639 +2024-12-28 04:48:51,551 - pyskl - INFO - Epoch [58][1300/3746] lr: 6.807e-02, eta: 3 days, 6:23:48, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5539, loss_cls: 3.9871, loss: 3.9871 +2024-12-28 04:50:16,236 - pyskl - INFO - Epoch [58][1400/3746] lr: 6.804e-02, eta: 3 days, 6:22:33, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5519, loss_cls: 4.0674, loss: 4.0674 +2024-12-28 04:51:40,774 - pyskl - INFO - Epoch [58][1500/3746] lr: 6.802e-02, eta: 3 days, 6:21:16, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5453, loss_cls: 4.0321, loss: 4.0321 +2024-12-28 04:53:05,544 - pyskl - INFO - Epoch [58][1600/3746] lr: 6.799e-02, eta: 3 days, 6:20:01, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5458, loss_cls: 4.0528, loss: 4.0528 +2024-12-28 04:54:30,157 - pyskl - INFO - Epoch [58][1700/3746] lr: 6.796e-02, eta: 3 days, 6:18:45, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5587, loss_cls: 4.0416, loss: 4.0416 +2024-12-28 04:55:54,377 - pyskl - INFO - Epoch [58][1800/3746] lr: 6.794e-02, eta: 3 days, 6:17:28, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5437, loss_cls: 4.1089, loss: 4.1089 +2024-12-28 04:57:18,964 - pyskl - INFO - Epoch [58][1900/3746] lr: 6.791e-02, eta: 3 days, 6:16:12, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5530, loss_cls: 4.0451, loss: 4.0451 +2024-12-28 04:58:43,049 - pyskl - INFO - Epoch [58][2000/3746] lr: 6.789e-02, eta: 3 days, 6:14:55, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5492, loss_cls: 4.0433, loss: 4.0433 +2024-12-28 05:00:07,436 - pyskl - INFO - Epoch [58][2100/3746] lr: 6.786e-02, eta: 3 days, 6:13:39, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5531, loss_cls: 4.0247, loss: 4.0247 +2024-12-28 05:01:32,062 - pyskl - INFO - Epoch [58][2200/3746] lr: 6.783e-02, eta: 3 days, 6:12:23, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5461, loss_cls: 4.0340, loss: 4.0340 +2024-12-28 05:02:55,968 - pyskl - INFO - Epoch [58][2300/3746] lr: 6.781e-02, eta: 3 days, 6:11:05, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5584, loss_cls: 4.0302, loss: 4.0302 +2024-12-28 05:04:20,662 - pyskl - INFO - Epoch [58][2400/3746] lr: 6.778e-02, eta: 3 days, 6:09:49, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5491, loss_cls: 4.0462, loss: 4.0462 +2024-12-28 05:05:44,669 - pyskl - INFO - Epoch [58][2500/3746] lr: 6.775e-02, eta: 3 days, 6:08:32, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5375, loss_cls: 4.1008, loss: 4.1008 +2024-12-28 05:07:08,959 - pyskl - INFO - Epoch [58][2600/3746] lr: 6.773e-02, eta: 3 days, 6:07:16, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5525, loss_cls: 4.0420, loss: 4.0420 +2024-12-28 05:08:33,262 - pyskl - INFO - Epoch [58][2700/3746] lr: 6.770e-02, eta: 3 days, 6:05:59, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5416, loss_cls: 4.1018, loss: 4.1018 +2024-12-28 05:09:58,282 - pyskl - INFO - Epoch [58][2800/3746] lr: 6.768e-02, eta: 3 days, 6:04:44, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5453, loss_cls: 4.0533, loss: 4.0533 +2024-12-28 05:11:22,943 - pyskl - INFO - Epoch [58][2900/3746] lr: 6.765e-02, eta: 3 days, 6:03:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5417, loss_cls: 4.0747, loss: 4.0747 +2024-12-28 05:12:47,123 - pyskl - INFO - Epoch [58][3000/3746] lr: 6.762e-02, eta: 3 days, 6:02:11, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5550, loss_cls: 4.0443, loss: 4.0443 +2024-12-28 05:14:11,242 - pyskl - INFO - Epoch [58][3100/3746] lr: 6.760e-02, eta: 3 days, 6:00:54, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5437, loss_cls: 4.0605, loss: 4.0605 +2024-12-28 05:15:35,432 - pyskl - INFO - Epoch [58][3200/3746] lr: 6.757e-02, eta: 3 days, 5:59:37, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5497, loss_cls: 4.0437, loss: 4.0437 +2024-12-28 05:16:59,508 - pyskl - INFO - Epoch [58][3300/3746] lr: 6.755e-02, eta: 3 days, 5:58:20, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5466, loss_cls: 4.0417, loss: 4.0417 +2024-12-28 05:18:23,893 - pyskl - INFO - Epoch [58][3400/3746] lr: 6.752e-02, eta: 3 days, 5:57:04, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5536, loss_cls: 4.0179, loss: 4.0179 +2024-12-28 05:19:48,582 - pyskl - INFO - Epoch [58][3500/3746] lr: 6.749e-02, eta: 3 days, 5:55:48, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5475, loss_cls: 4.0359, loss: 4.0359 +2024-12-28 05:21:13,718 - pyskl - INFO - Epoch [58][3600/3746] lr: 6.747e-02, eta: 3 days, 5:54:32, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5436, loss_cls: 4.0808, loss: 4.0808 +2024-12-28 05:22:39,014 - pyskl - INFO - Epoch [58][3700/3746] lr: 6.744e-02, eta: 3 days, 5:53:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5464, loss_cls: 4.0749, loss: 4.0749 +2024-12-28 05:23:19,575 - pyskl - INFO - Saving checkpoint at 58 epochs +2024-12-28 05:25:16,797 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 05:25:17,677 - pyskl - INFO - +top1_acc 0.2180 +top5_acc 0.4561 +2024-12-28 05:25:17,678 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 05:25:17,717 - pyskl - INFO - +mean_acc 0.2177 +2024-12-28 05:25:17,729 - pyskl - INFO - Epoch(val) [58][309] top1_acc: 0.2180, top5_acc: 0.4561, mean_class_accuracy: 0.2177 +2024-12-28 05:29:33,377 - pyskl - INFO - Epoch [59][100/3746] lr: 6.740e-02, eta: 3 days, 5:54:55, time: 2.556, data_time: 1.511, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5706, loss_cls: 3.9360, loss: 3.9360 +2024-12-28 05:30:59,213 - pyskl - INFO - Epoch [59][200/3746] lr: 6.738e-02, eta: 3 days, 5:53:41, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5513, loss_cls: 4.0169, loss: 4.0169 +2024-12-28 05:32:25,284 - pyskl - INFO - Epoch [59][300/3746] lr: 6.735e-02, eta: 3 days, 5:52:27, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5591, loss_cls: 4.0103, loss: 4.0103 +2024-12-28 05:33:51,063 - pyskl - INFO - Epoch [59][400/3746] lr: 6.732e-02, eta: 3 days, 5:51:12, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5453, loss_cls: 4.0577, loss: 4.0577 +2024-12-28 05:35:16,703 - pyskl - INFO - Epoch [59][500/3746] lr: 6.730e-02, eta: 3 days, 5:49:58, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5491, loss_cls: 4.0597, loss: 4.0597 +2024-12-28 05:36:42,455 - pyskl - INFO - Epoch [59][600/3746] lr: 6.727e-02, eta: 3 days, 5:48:43, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5573, loss_cls: 4.0193, loss: 4.0193 +2024-12-28 05:38:07,817 - pyskl - INFO - Epoch [59][700/3746] lr: 6.725e-02, eta: 3 days, 5:47:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5511, loss_cls: 4.0771, loss: 4.0771 +2024-12-28 05:39:32,593 - pyskl - INFO - Epoch [59][800/3746] lr: 6.722e-02, eta: 3 days, 5:46:12, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5481, loss_cls: 4.0225, loss: 4.0225 +2024-12-28 05:40:57,934 - pyskl - INFO - Epoch [59][900/3746] lr: 6.719e-02, eta: 3 days, 5:44:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5564, loss_cls: 4.0004, loss: 4.0004 +2024-12-28 05:42:22,766 - pyskl - INFO - Epoch [59][1000/3746] lr: 6.717e-02, eta: 3 days, 5:43:40, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5517, loss_cls: 4.0150, loss: 4.0150 +2024-12-28 05:43:47,337 - pyskl - INFO - Epoch [59][1100/3746] lr: 6.714e-02, eta: 3 days, 5:42:24, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5597, loss_cls: 4.0013, loss: 4.0013 +2024-12-28 05:45:11,665 - pyskl - INFO - Epoch [59][1200/3746] lr: 6.711e-02, eta: 3 days, 5:41:07, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5592, loss_cls: 4.0104, loss: 4.0104 +2024-12-28 05:46:36,270 - pyskl - INFO - Epoch [59][1300/3746] lr: 6.709e-02, eta: 3 days, 5:39:51, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5478, loss_cls: 4.0402, loss: 4.0402 +2024-12-28 05:48:00,455 - pyskl - INFO - Epoch [59][1400/3746] lr: 6.706e-02, eta: 3 days, 5:38:33, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5522, loss_cls: 4.0450, loss: 4.0450 +2024-12-28 05:49:24,775 - pyskl - INFO - Epoch [59][1500/3746] lr: 6.704e-02, eta: 3 days, 5:37:17, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5541, loss_cls: 4.0335, loss: 4.0335 +2024-12-28 05:50:49,857 - pyskl - INFO - Epoch [59][1600/3746] lr: 6.701e-02, eta: 3 days, 5:36:01, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5550, loss_cls: 4.0496, loss: 4.0496 +2024-12-28 05:52:14,789 - pyskl - INFO - Epoch [59][1700/3746] lr: 6.698e-02, eta: 3 days, 5:34:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5514, loss_cls: 4.0575, loss: 4.0575 +2024-12-28 05:53:40,024 - pyskl - INFO - Epoch [59][1800/3746] lr: 6.696e-02, eta: 3 days, 5:33:29, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5470, loss_cls: 4.0467, loss: 4.0467 +2024-12-28 05:55:04,899 - pyskl - INFO - Epoch [59][1900/3746] lr: 6.693e-02, eta: 3 days, 5:32:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5459, loss_cls: 4.0537, loss: 4.0537 +2024-12-28 05:56:29,808 - pyskl - INFO - Epoch [59][2000/3746] lr: 6.690e-02, eta: 3 days, 5:30:57, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5545, loss_cls: 4.0256, loss: 4.0256 +2024-12-28 05:57:53,888 - pyskl - INFO - Epoch [59][2100/3746] lr: 6.688e-02, eta: 3 days, 5:29:40, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5620, loss_cls: 4.0005, loss: 4.0005 +2024-12-28 05:59:18,582 - pyskl - INFO - Epoch [59][2200/3746] lr: 6.685e-02, eta: 3 days, 5:28:24, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5498, loss_cls: 4.0543, loss: 4.0543 +2024-12-28 06:00:43,004 - pyskl - INFO - Epoch [59][2300/3746] lr: 6.682e-02, eta: 3 days, 5:27:07, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5550, loss_cls: 4.0166, loss: 4.0166 +2024-12-28 06:02:07,961 - pyskl - INFO - Epoch [59][2400/3746] lr: 6.680e-02, eta: 3 days, 5:25:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5516, loss_cls: 4.0384, loss: 4.0384 +2024-12-28 06:03:32,654 - pyskl - INFO - Epoch [59][2500/3746] lr: 6.677e-02, eta: 3 days, 5:24:34, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5506, loss_cls: 4.0386, loss: 4.0386 +2024-12-28 06:04:57,255 - pyskl - INFO - Epoch [59][2600/3746] lr: 6.675e-02, eta: 3 days, 5:23:18, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5434, loss_cls: 4.0898, loss: 4.0898 +2024-12-28 06:06:21,478 - pyskl - INFO - Epoch [59][2700/3746] lr: 6.672e-02, eta: 3 days, 5:22:01, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5528, loss_cls: 4.0391, loss: 4.0391 +2024-12-28 06:07:45,720 - pyskl - INFO - Epoch [59][2800/3746] lr: 6.669e-02, eta: 3 days, 5:20:44, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5433, loss_cls: 4.0583, loss: 4.0583 +2024-12-28 06:09:10,014 - pyskl - INFO - Epoch [59][2900/3746] lr: 6.667e-02, eta: 3 days, 5:19:26, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5483, loss_cls: 4.0573, loss: 4.0573 +2024-12-28 06:10:34,727 - pyskl - INFO - Epoch [59][3000/3746] lr: 6.664e-02, eta: 3 days, 5:18:10, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5455, loss_cls: 4.0753, loss: 4.0753 +2024-12-28 06:11:59,033 - pyskl - INFO - Epoch [59][3100/3746] lr: 6.661e-02, eta: 3 days, 5:16:53, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5534, loss_cls: 4.0300, loss: 4.0300 +2024-12-28 06:13:23,973 - pyskl - INFO - Epoch [59][3200/3746] lr: 6.659e-02, eta: 3 days, 5:15:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5487, loss_cls: 4.0492, loss: 4.0492 +2024-12-28 06:14:48,553 - pyskl - INFO - Epoch [59][3300/3746] lr: 6.656e-02, eta: 3 days, 5:14:20, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5564, loss_cls: 4.0007, loss: 4.0007 +2024-12-28 06:16:13,110 - pyskl - INFO - Epoch [59][3400/3746] lr: 6.653e-02, eta: 3 days, 5:13:04, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5413, loss_cls: 4.0653, loss: 4.0653 +2024-12-28 06:17:37,867 - pyskl - INFO - Epoch [59][3500/3746] lr: 6.651e-02, eta: 3 days, 5:11:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5437, loss_cls: 4.0458, loss: 4.0458 +2024-12-28 06:19:03,265 - pyskl - INFO - Epoch [59][3600/3746] lr: 6.648e-02, eta: 3 days, 5:10:32, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5425, loss_cls: 4.0853, loss: 4.0853 +2024-12-28 06:20:28,051 - pyskl - INFO - Epoch [59][3700/3746] lr: 6.646e-02, eta: 3 days, 5:09:15, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5500, loss_cls: 4.0575, loss: 4.0575 +2024-12-28 06:21:08,643 - pyskl - INFO - Saving checkpoint at 59 epochs +2024-12-28 06:23:06,889 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 06:23:07,698 - pyskl - INFO - +top1_acc 0.2295 +top5_acc 0.4722 +2024-12-28 06:23:07,699 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 06:23:07,739 - pyskl - INFO - +mean_acc 0.2294 +2024-12-28 06:23:07,744 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_57.pth was removed +2024-12-28 06:23:08,007 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_59.pth. +2024-12-28 06:23:08,008 - pyskl - INFO - Best top1_acc is 0.2295 at 59 epoch. +2024-12-28 06:23:08,019 - pyskl - INFO - Epoch(val) [59][309] top1_acc: 0.2295, top5_acc: 0.4722, mean_class_accuracy: 0.2294 +2024-12-28 06:27:22,701 - pyskl - INFO - Epoch [60][100/3746] lr: 6.642e-02, eta: 3 days, 5:10:46, time: 2.547, data_time: 1.512, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5711, loss_cls: 3.9201, loss: 3.9201 +2024-12-28 06:28:47,547 - pyskl - INFO - Epoch [60][200/3746] lr: 6.639e-02, eta: 3 days, 5:09:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5573, loss_cls: 3.9679, loss: 3.9679 +2024-12-28 06:30:12,491 - pyskl - INFO - Epoch [60][300/3746] lr: 6.636e-02, eta: 3 days, 5:08:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5472, loss_cls: 4.0689, loss: 4.0689 +2024-12-28 06:31:38,180 - pyskl - INFO - Epoch [60][400/3746] lr: 6.634e-02, eta: 3 days, 5:06:58, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5667, loss_cls: 3.9569, loss: 3.9569 +2024-12-28 06:33:02,926 - pyskl - INFO - Epoch [60][500/3746] lr: 6.631e-02, eta: 3 days, 5:05:41, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5463, loss_cls: 4.0390, loss: 4.0390 +2024-12-28 06:34:27,877 - pyskl - INFO - Epoch [60][600/3746] lr: 6.629e-02, eta: 3 days, 5:04:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5522, loss_cls: 4.0429, loss: 4.0429 +2024-12-28 06:35:52,722 - pyskl - INFO - Epoch [60][700/3746] lr: 6.626e-02, eta: 3 days, 5:03:08, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5539, loss_cls: 3.9751, loss: 3.9751 +2024-12-28 06:37:17,439 - pyskl - INFO - Epoch [60][800/3746] lr: 6.623e-02, eta: 3 days, 5:01:52, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5628, loss_cls: 4.0040, loss: 4.0040 +2024-12-28 06:38:42,577 - pyskl - INFO - Epoch [60][900/3746] lr: 6.621e-02, eta: 3 days, 5:00:36, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5519, loss_cls: 4.0370, loss: 4.0370 +2024-12-28 06:40:07,033 - pyskl - INFO - Epoch [60][1000/3746] lr: 6.618e-02, eta: 3 days, 4:59:19, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5514, loss_cls: 4.0322, loss: 4.0322 +2024-12-28 06:41:31,987 - pyskl - INFO - Epoch [60][1100/3746] lr: 6.615e-02, eta: 3 days, 4:58:02, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5505, loss_cls: 4.0462, loss: 4.0462 +2024-12-28 06:42:56,929 - pyskl - INFO - Epoch [60][1200/3746] lr: 6.613e-02, eta: 3 days, 4:56:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5605, loss_cls: 3.9838, loss: 3.9838 +2024-12-28 06:44:21,473 - pyskl - INFO - Epoch [60][1300/3746] lr: 6.610e-02, eta: 3 days, 4:55:29, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5478, loss_cls: 4.0652, loss: 4.0652 +2024-12-28 06:45:46,499 - pyskl - INFO - Epoch [60][1400/3746] lr: 6.607e-02, eta: 3 days, 4:54:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5441, loss_cls: 4.0694, loss: 4.0694 +2024-12-28 06:47:11,486 - pyskl - INFO - Epoch [60][1500/3746] lr: 6.605e-02, eta: 3 days, 4:52:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5577, loss_cls: 3.9621, loss: 3.9621 +2024-12-28 06:48:36,479 - pyskl - INFO - Epoch [60][1600/3746] lr: 6.602e-02, eta: 3 days, 4:51:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5614, loss_cls: 4.0000, loss: 4.0000 +2024-12-28 06:50:01,193 - pyskl - INFO - Epoch [60][1700/3746] lr: 6.599e-02, eta: 3 days, 4:50:23, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5520, loss_cls: 4.0350, loss: 4.0350 +2024-12-28 06:51:26,281 - pyskl - INFO - Epoch [60][1800/3746] lr: 6.597e-02, eta: 3 days, 4:49:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5428, loss_cls: 4.0611, loss: 4.0611 +2024-12-28 06:52:51,105 - pyskl - INFO - Epoch [60][1900/3746] lr: 6.594e-02, eta: 3 days, 4:47:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5556, loss_cls: 4.0535, loss: 4.0535 +2024-12-28 06:54:15,555 - pyskl - INFO - Epoch [60][2000/3746] lr: 6.591e-02, eta: 3 days, 4:46:33, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5581, loss_cls: 4.0196, loss: 4.0196 +2024-12-28 06:55:40,048 - pyskl - INFO - Epoch [60][2100/3746] lr: 6.589e-02, eta: 3 days, 4:45:16, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5520, loss_cls: 4.0154, loss: 4.0154 +2024-12-28 06:57:04,736 - pyskl - INFO - Epoch [60][2200/3746] lr: 6.586e-02, eta: 3 days, 4:44:00, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5483, loss_cls: 4.0629, loss: 4.0629 +2024-12-28 06:58:29,535 - pyskl - INFO - Epoch [60][2300/3746] lr: 6.584e-02, eta: 3 days, 4:42:43, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5731, loss_cls: 3.9854, loss: 3.9854 +2024-12-28 06:59:54,703 - pyskl - INFO - Epoch [60][2400/3746] lr: 6.581e-02, eta: 3 days, 4:41:27, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5444, loss_cls: 4.0737, loss: 4.0737 +2024-12-28 07:01:19,448 - pyskl - INFO - Epoch [60][2500/3746] lr: 6.578e-02, eta: 3 days, 4:40:10, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5463, loss_cls: 4.0686, loss: 4.0686 +2024-12-28 07:02:45,527 - pyskl - INFO - Epoch [60][2600/3746] lr: 6.576e-02, eta: 3 days, 4:38:55, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5398, loss_cls: 4.0759, loss: 4.0759 +2024-12-28 07:04:10,877 - pyskl - INFO - Epoch [60][2700/3746] lr: 6.573e-02, eta: 3 days, 4:37:39, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5519, loss_cls: 4.0497, loss: 4.0497 +2024-12-28 07:05:36,196 - pyskl - INFO - Epoch [60][2800/3746] lr: 6.570e-02, eta: 3 days, 4:36:24, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5461, loss_cls: 4.0889, loss: 4.0889 +2024-12-28 07:07:01,817 - pyskl - INFO - Epoch [60][2900/3746] lr: 6.568e-02, eta: 3 days, 4:35:08, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5487, loss_cls: 4.0188, loss: 4.0188 +2024-12-28 07:08:27,488 - pyskl - INFO - Epoch [60][3000/3746] lr: 6.565e-02, eta: 3 days, 4:33:53, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5516, loss_cls: 4.0304, loss: 4.0304 +2024-12-28 07:09:53,640 - pyskl - INFO - Epoch [60][3100/3746] lr: 6.562e-02, eta: 3 days, 4:32:38, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5525, loss_cls: 4.0326, loss: 4.0326 +2024-12-28 07:11:19,094 - pyskl - INFO - Epoch [60][3200/3746] lr: 6.560e-02, eta: 3 days, 4:31:22, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5452, loss_cls: 4.0536, loss: 4.0536 +2024-12-28 07:12:45,176 - pyskl - INFO - Epoch [60][3300/3746] lr: 6.557e-02, eta: 3 days, 4:30:07, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5450, loss_cls: 4.0640, loss: 4.0640 +2024-12-28 07:14:10,937 - pyskl - INFO - Epoch [60][3400/3746] lr: 6.554e-02, eta: 3 days, 4:28:52, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5470, loss_cls: 4.0536, loss: 4.0536 +2024-12-28 07:15:37,087 - pyskl - INFO - Epoch [60][3500/3746] lr: 6.552e-02, eta: 3 days, 4:27:37, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5509, loss_cls: 4.0823, loss: 4.0823 +2024-12-28 07:17:02,992 - pyskl - INFO - Epoch [60][3600/3746] lr: 6.549e-02, eta: 3 days, 4:26:22, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5583, loss_cls: 4.0213, loss: 4.0213 +2024-12-28 07:18:29,009 - pyskl - INFO - Epoch [60][3700/3746] lr: 6.546e-02, eta: 3 days, 4:25:07, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5600, loss_cls: 3.9924, loss: 3.9924 +2024-12-28 07:19:10,697 - pyskl - INFO - Saving checkpoint at 60 epochs +2024-12-28 07:21:09,095 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 07:21:10,086 - pyskl - INFO - +top1_acc 0.2207 +top5_acc 0.4495 +2024-12-28 07:21:10,087 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 07:21:10,162 - pyskl - INFO - +mean_acc 0.2206 +2024-12-28 07:21:10,182 - pyskl - INFO - Epoch(val) [60][309] top1_acc: 0.2207, top5_acc: 0.4495, mean_class_accuracy: 0.2206 +2024-12-28 07:25:28,363 - pyskl - INFO - Epoch [61][100/3746] lr: 6.542e-02, eta: 3 days, 4:26:37, time: 2.582, data_time: 1.556, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5620, loss_cls: 3.9372, loss: 3.9372 +2024-12-28 07:26:54,083 - pyskl - INFO - Epoch [61][200/3746] lr: 6.540e-02, eta: 3 days, 4:25:21, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5539, loss_cls: 4.0244, loss: 4.0244 +2024-12-28 07:28:19,537 - pyskl - INFO - Epoch [61][300/3746] lr: 6.537e-02, eta: 3 days, 4:24:05, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5489, loss_cls: 4.0683, loss: 4.0683 +2024-12-28 07:29:44,724 - pyskl - INFO - Epoch [61][400/3746] lr: 6.534e-02, eta: 3 days, 4:22:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5641, loss_cls: 3.9702, loss: 3.9702 +2024-12-28 07:31:10,043 - pyskl - INFO - Epoch [61][500/3746] lr: 6.532e-02, eta: 3 days, 4:21:33, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5652, loss_cls: 3.9919, loss: 3.9919 +2024-12-28 07:32:35,128 - pyskl - INFO - Epoch [61][600/3746] lr: 6.529e-02, eta: 3 days, 4:20:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5634, loss_cls: 3.9792, loss: 3.9792 +2024-12-28 07:34:00,232 - pyskl - INFO - Epoch [61][700/3746] lr: 6.526e-02, eta: 3 days, 4:19:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5575, loss_cls: 4.0289, loss: 4.0289 +2024-12-28 07:35:25,002 - pyskl - INFO - Epoch [61][800/3746] lr: 6.524e-02, eta: 3 days, 4:17:43, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5569, loss_cls: 3.9970, loss: 3.9970 +2024-12-28 07:36:49,869 - pyskl - INFO - Epoch [61][900/3746] lr: 6.521e-02, eta: 3 days, 4:16:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5550, loss_cls: 3.9895, loss: 3.9895 +2024-12-28 07:38:13,959 - pyskl - INFO - Epoch [61][1000/3746] lr: 6.519e-02, eta: 3 days, 4:15:08, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5586, loss_cls: 4.0102, loss: 4.0102 +2024-12-28 07:39:38,741 - pyskl - INFO - Epoch [61][1100/3746] lr: 6.516e-02, eta: 3 days, 4:13:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5603, loss_cls: 3.9976, loss: 3.9976 +2024-12-28 07:41:03,287 - pyskl - INFO - Epoch [61][1200/3746] lr: 6.513e-02, eta: 3 days, 4:12:33, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5623, loss_cls: 4.0148, loss: 4.0148 +2024-12-28 07:42:28,199 - pyskl - INFO - Epoch [61][1300/3746] lr: 6.511e-02, eta: 3 days, 4:11:16, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5639, loss_cls: 3.9928, loss: 3.9928 +2024-12-28 07:43:53,160 - pyskl - INFO - Epoch [61][1400/3746] lr: 6.508e-02, eta: 3 days, 4:10:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5509, loss_cls: 4.0377, loss: 4.0377 +2024-12-28 07:45:17,953 - pyskl - INFO - Epoch [61][1500/3746] lr: 6.505e-02, eta: 3 days, 4:08:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5520, loss_cls: 4.0297, loss: 4.0297 +2024-12-28 07:46:43,104 - pyskl - INFO - Epoch [61][1600/3746] lr: 6.503e-02, eta: 3 days, 4:07:26, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5548, loss_cls: 4.0342, loss: 4.0342 +2024-12-28 07:48:07,590 - pyskl - INFO - Epoch [61][1700/3746] lr: 6.500e-02, eta: 3 days, 4:06:08, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5431, loss_cls: 4.0770, loss: 4.0770 +2024-12-28 07:49:32,444 - pyskl - INFO - Epoch [61][1800/3746] lr: 6.497e-02, eta: 3 days, 4:04:51, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5423, loss_cls: 4.0740, loss: 4.0740 +2024-12-28 07:50:57,516 - pyskl - INFO - Epoch [61][1900/3746] lr: 6.495e-02, eta: 3 days, 4:03:35, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5569, loss_cls: 4.0198, loss: 4.0198 +2024-12-28 07:52:21,493 - pyskl - INFO - Epoch [61][2000/3746] lr: 6.492e-02, eta: 3 days, 4:02:17, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5500, loss_cls: 4.0255, loss: 4.0255 +2024-12-28 07:53:45,502 - pyskl - INFO - Epoch [61][2100/3746] lr: 6.489e-02, eta: 3 days, 4:00:58, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5603, loss_cls: 3.9939, loss: 3.9939 +2024-12-28 07:55:09,850 - pyskl - INFO - Epoch [61][2200/3746] lr: 6.487e-02, eta: 3 days, 3:59:41, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5517, loss_cls: 4.0586, loss: 4.0586 +2024-12-28 07:56:34,477 - pyskl - INFO - Epoch [61][2300/3746] lr: 6.484e-02, eta: 3 days, 3:58:23, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5548, loss_cls: 4.0283, loss: 4.0283 +2024-12-28 07:57:58,945 - pyskl - INFO - Epoch [61][2400/3746] lr: 6.481e-02, eta: 3 days, 3:57:06, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5477, loss_cls: 4.0312, loss: 4.0312 +2024-12-28 07:59:23,035 - pyskl - INFO - Epoch [61][2500/3746] lr: 6.478e-02, eta: 3 days, 3:55:48, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5658, loss_cls: 4.0240, loss: 4.0240 +2024-12-28 08:00:47,866 - pyskl - INFO - Epoch [61][2600/3746] lr: 6.476e-02, eta: 3 days, 3:54:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5434, loss_cls: 4.0817, loss: 4.0817 +2024-12-28 08:02:12,473 - pyskl - INFO - Epoch [61][2700/3746] lr: 6.473e-02, eta: 3 days, 3:53:13, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5559, loss_cls: 4.0247, loss: 4.0247 +2024-12-28 08:03:36,611 - pyskl - INFO - Epoch [61][2800/3746] lr: 6.470e-02, eta: 3 days, 3:51:55, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5519, loss_cls: 4.0334, loss: 4.0334 +2024-12-28 08:05:01,567 - pyskl - INFO - Epoch [61][2900/3746] lr: 6.468e-02, eta: 3 days, 3:50:38, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5580, loss_cls: 4.0033, loss: 4.0033 +2024-12-28 08:06:26,349 - pyskl - INFO - Epoch [61][3000/3746] lr: 6.465e-02, eta: 3 days, 3:49:21, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5597, loss_cls: 4.0464, loss: 4.0464 +2024-12-28 08:07:50,995 - pyskl - INFO - Epoch [61][3100/3746] lr: 6.462e-02, eta: 3 days, 3:48:04, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5436, loss_cls: 4.0491, loss: 4.0491 +2024-12-28 08:09:15,504 - pyskl - INFO - Epoch [61][3200/3746] lr: 6.460e-02, eta: 3 days, 3:46:46, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5522, loss_cls: 4.0578, loss: 4.0578 +2024-12-28 08:10:40,606 - pyskl - INFO - Epoch [61][3300/3746] lr: 6.457e-02, eta: 3 days, 3:45:29, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5606, loss_cls: 3.9869, loss: 3.9869 +2024-12-28 08:12:05,439 - pyskl - INFO - Epoch [61][3400/3746] lr: 6.454e-02, eta: 3 days, 3:44:12, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5533, loss_cls: 4.0565, loss: 4.0565 +2024-12-28 08:13:30,540 - pyskl - INFO - Epoch [61][3500/3746] lr: 6.452e-02, eta: 3 days, 3:42:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5542, loss_cls: 4.0202, loss: 4.0202 +2024-12-28 08:14:55,237 - pyskl - INFO - Epoch [61][3600/3746] lr: 6.449e-02, eta: 3 days, 3:41:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5587, loss_cls: 4.0180, loss: 4.0180 +2024-12-28 08:16:19,938 - pyskl - INFO - Epoch [61][3700/3746] lr: 6.446e-02, eta: 3 days, 3:40:21, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5547, loss_cls: 4.0116, loss: 4.0116 +2024-12-28 08:17:00,592 - pyskl - INFO - Saving checkpoint at 61 epochs +2024-12-28 08:18:57,844 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 08:18:58,531 - pyskl - INFO - +top1_acc 0.2254 +top5_acc 0.4700 +2024-12-28 08:18:58,531 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 08:18:58,586 - pyskl - INFO - +mean_acc 0.2253 +2024-12-28 08:18:58,602 - pyskl - INFO - Epoch(val) [61][309] top1_acc: 0.2254, top5_acc: 0.4700, mean_class_accuracy: 0.2253 +2024-12-28 08:23:06,440 - pyskl - INFO - Epoch [62][100/3746] lr: 6.443e-02, eta: 3 days, 3:41:29, time: 2.478, data_time: 1.457, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5616, loss_cls: 3.9872, loss: 3.9872 +2024-12-28 08:24:31,345 - pyskl - INFO - Epoch [62][200/3746] lr: 6.440e-02, eta: 3 days, 3:40:12, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5613, loss_cls: 4.0043, loss: 4.0043 +2024-12-28 08:25:56,591 - pyskl - INFO - Epoch [62][300/3746] lr: 6.437e-02, eta: 3 days, 3:38:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5492, loss_cls: 4.0263, loss: 4.0263 +2024-12-28 08:27:21,948 - pyskl - INFO - Epoch [62][400/3746] lr: 6.434e-02, eta: 3 days, 3:37:39, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5567, loss_cls: 4.0258, loss: 4.0258 +2024-12-28 08:28:47,045 - pyskl - INFO - Epoch [62][500/3746] lr: 6.432e-02, eta: 3 days, 3:36:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5483, loss_cls: 4.0269, loss: 4.0269 +2024-12-28 08:30:12,362 - pyskl - INFO - Epoch [62][600/3746] lr: 6.429e-02, eta: 3 days, 3:35:05, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5575, loss_cls: 3.9900, loss: 3.9900 +2024-12-28 08:31:37,528 - pyskl - INFO - Epoch [62][700/3746] lr: 6.426e-02, eta: 3 days, 3:33:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5633, loss_cls: 3.9849, loss: 3.9849 +2024-12-28 08:33:02,285 - pyskl - INFO - Epoch [62][800/3746] lr: 6.424e-02, eta: 3 days, 3:32:31, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5536, loss_cls: 4.0037, loss: 4.0037 +2024-12-28 08:34:27,361 - pyskl - INFO - Epoch [62][900/3746] lr: 6.421e-02, eta: 3 days, 3:31:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5595, loss_cls: 3.9890, loss: 3.9890 +2024-12-28 08:35:52,132 - pyskl - INFO - Epoch [62][1000/3746] lr: 6.418e-02, eta: 3 days, 3:29:56, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5630, loss_cls: 3.9779, loss: 3.9779 +2024-12-28 08:37:16,756 - pyskl - INFO - Epoch [62][1100/3746] lr: 6.416e-02, eta: 3 days, 3:28:39, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5530, loss_cls: 4.0424, loss: 4.0424 +2024-12-28 08:38:41,315 - pyskl - INFO - Epoch [62][1200/3746] lr: 6.413e-02, eta: 3 days, 3:27:21, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5500, loss_cls: 4.0223, loss: 4.0223 +2024-12-28 08:40:05,975 - pyskl - INFO - Epoch [62][1300/3746] lr: 6.410e-02, eta: 3 days, 3:26:03, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5455, loss_cls: 4.0565, loss: 4.0565 +2024-12-28 08:41:30,879 - pyskl - INFO - Epoch [62][1400/3746] lr: 6.408e-02, eta: 3 days, 3:24:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5656, loss_cls: 3.9537, loss: 3.9537 +2024-12-28 08:42:55,138 - pyskl - INFO - Epoch [62][1500/3746] lr: 6.405e-02, eta: 3 days, 3:23:28, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5566, loss_cls: 4.0023, loss: 4.0023 +2024-12-28 08:44:19,741 - pyskl - INFO - Epoch [62][1600/3746] lr: 6.402e-02, eta: 3 days, 3:22:10, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5431, loss_cls: 4.0466, loss: 4.0466 +2024-12-28 08:45:44,453 - pyskl - INFO - Epoch [62][1700/3746] lr: 6.400e-02, eta: 3 days, 3:20:53, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5572, loss_cls: 4.0307, loss: 4.0307 +2024-12-28 08:47:09,530 - pyskl - INFO - Epoch [62][1800/3746] lr: 6.397e-02, eta: 3 days, 3:19:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5631, loss_cls: 3.9541, loss: 3.9541 +2024-12-28 08:48:34,100 - pyskl - INFO - Epoch [62][1900/3746] lr: 6.394e-02, eta: 3 days, 3:18:18, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5561, loss_cls: 4.0139, loss: 4.0139 +2024-12-28 08:49:58,440 - pyskl - INFO - Epoch [62][2000/3746] lr: 6.392e-02, eta: 3 days, 3:17:00, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5527, loss_cls: 4.0180, loss: 4.0180 +2024-12-28 08:51:23,255 - pyskl - INFO - Epoch [62][2100/3746] lr: 6.389e-02, eta: 3 days, 3:15:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5500, loss_cls: 4.0360, loss: 4.0360 +2024-12-28 08:52:48,203 - pyskl - INFO - Epoch [62][2200/3746] lr: 6.386e-02, eta: 3 days, 3:14:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5522, loss_cls: 4.0205, loss: 4.0205 +2024-12-28 08:54:13,077 - pyskl - INFO - Epoch [62][2300/3746] lr: 6.384e-02, eta: 3 days, 3:13:08, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5595, loss_cls: 4.0100, loss: 4.0100 +2024-12-28 08:55:37,946 - pyskl - INFO - Epoch [62][2400/3746] lr: 6.381e-02, eta: 3 days, 3:11:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5502, loss_cls: 4.0660, loss: 4.0660 +2024-12-28 08:57:02,949 - pyskl - INFO - Epoch [62][2500/3746] lr: 6.378e-02, eta: 3 days, 3:10:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5652, loss_cls: 3.9512, loss: 3.9512 +2024-12-28 08:58:27,075 - pyskl - INFO - Epoch [62][2600/3746] lr: 6.375e-02, eta: 3 days, 3:09:15, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5595, loss_cls: 3.9951, loss: 3.9951 +2024-12-28 08:59:51,680 - pyskl - INFO - Epoch [62][2700/3746] lr: 6.373e-02, eta: 3 days, 3:07:57, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5600, loss_cls: 3.9944, loss: 3.9944 +2024-12-28 09:01:16,134 - pyskl - INFO - Epoch [62][2800/3746] lr: 6.370e-02, eta: 3 days, 3:06:39, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5608, loss_cls: 3.9766, loss: 3.9766 +2024-12-28 09:02:40,373 - pyskl - INFO - Epoch [62][2900/3746] lr: 6.367e-02, eta: 3 days, 3:05:21, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5598, loss_cls: 4.0087, loss: 4.0087 +2024-12-28 09:04:04,432 - pyskl - INFO - Epoch [62][3000/3746] lr: 6.365e-02, eta: 3 days, 3:04:02, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5516, loss_cls: 4.0117, loss: 4.0117 +2024-12-28 09:05:29,011 - pyskl - INFO - Epoch [62][3100/3746] lr: 6.362e-02, eta: 3 days, 3:02:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5480, loss_cls: 4.0413, loss: 4.0413 +2024-12-28 09:06:53,727 - pyskl - INFO - Epoch [62][3200/3746] lr: 6.359e-02, eta: 3 days, 3:01:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5437, loss_cls: 4.0662, loss: 4.0662 +2024-12-28 09:08:18,387 - pyskl - INFO - Epoch [62][3300/3746] lr: 6.357e-02, eta: 3 days, 3:00:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5547, loss_cls: 4.0150, loss: 4.0150 +2024-12-28 09:09:42,803 - pyskl - INFO - Epoch [62][3400/3746] lr: 6.354e-02, eta: 3 days, 2:58:51, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5509, loss_cls: 4.0258, loss: 4.0258 +2024-12-28 09:11:07,262 - pyskl - INFO - Epoch [62][3500/3746] lr: 6.351e-02, eta: 3 days, 2:57:33, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5611, loss_cls: 3.9865, loss: 3.9865 +2024-12-28 09:12:31,626 - pyskl - INFO - Epoch [62][3600/3746] lr: 6.349e-02, eta: 3 days, 2:56:15, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5570, loss_cls: 4.0375, loss: 4.0375 +2024-12-28 09:13:55,669 - pyskl - INFO - Epoch [62][3700/3746] lr: 6.346e-02, eta: 3 days, 2:54:56, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5419, loss_cls: 4.0532, loss: 4.0532 +2024-12-28 09:14:36,741 - pyskl - INFO - Saving checkpoint at 62 epochs +2024-12-28 09:16:34,237 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 09:16:35,046 - pyskl - INFO - +top1_acc 0.2284 +top5_acc 0.4688 +2024-12-28 09:16:35,046 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 09:16:35,091 - pyskl - INFO - +mean_acc 0.2283 +2024-12-28 09:16:35,104 - pyskl - INFO - Epoch(val) [62][309] top1_acc: 0.2284, top5_acc: 0.4688, mean_class_accuracy: 0.2283 +2024-12-28 09:20:55,765 - pyskl - INFO - Epoch [63][100/3746] lr: 6.342e-02, eta: 3 days, 2:56:17, time: 2.607, data_time: 1.558, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5764, loss_cls: 3.9092, loss: 3.9092 +2024-12-28 09:22:21,884 - pyskl - INFO - Epoch [63][200/3746] lr: 6.339e-02, eta: 3 days, 2:55:01, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5730, loss_cls: 3.9461, loss: 3.9461 +2024-12-28 09:23:47,335 - pyskl - INFO - Epoch [63][300/3746] lr: 6.337e-02, eta: 3 days, 2:53:44, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5503, loss_cls: 4.0238, loss: 4.0238 +2024-12-28 09:25:12,923 - pyskl - INFO - Epoch [63][400/3746] lr: 6.334e-02, eta: 3 days, 2:52:28, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5689, loss_cls: 3.9410, loss: 3.9410 +2024-12-28 09:26:38,521 - pyskl - INFO - Epoch [63][500/3746] lr: 6.331e-02, eta: 3 days, 2:51:11, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5672, loss_cls: 3.9628, loss: 3.9628 +2024-12-28 09:28:03,849 - pyskl - INFO - Epoch [63][600/3746] lr: 6.328e-02, eta: 3 days, 2:49:54, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5591, loss_cls: 4.0204, loss: 4.0204 +2024-12-28 09:29:29,716 - pyskl - INFO - Epoch [63][700/3746] lr: 6.326e-02, eta: 3 days, 2:48:38, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5522, loss_cls: 4.0259, loss: 4.0259 +2024-12-28 09:30:55,386 - pyskl - INFO - Epoch [63][800/3746] lr: 6.323e-02, eta: 3 days, 2:47:21, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5575, loss_cls: 4.0036, loss: 4.0036 +2024-12-28 09:32:21,068 - pyskl - INFO - Epoch [63][900/3746] lr: 6.320e-02, eta: 3 days, 2:46:05, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5592, loss_cls: 4.0176, loss: 4.0176 +2024-12-28 09:33:46,453 - pyskl - INFO - Epoch [63][1000/3746] lr: 6.318e-02, eta: 3 days, 2:44:48, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5611, loss_cls: 4.0393, loss: 4.0393 +2024-12-28 09:35:11,611 - pyskl - INFO - Epoch [63][1100/3746] lr: 6.315e-02, eta: 3 days, 2:43:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5655, loss_cls: 3.9671, loss: 3.9671 +2024-12-28 09:36:37,432 - pyskl - INFO - Epoch [63][1200/3746] lr: 6.312e-02, eta: 3 days, 2:42:14, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5687, loss_cls: 3.9424, loss: 3.9424 +2024-12-28 09:38:02,924 - pyskl - INFO - Epoch [63][1300/3746] lr: 6.310e-02, eta: 3 days, 2:40:58, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5572, loss_cls: 4.0153, loss: 4.0153 +2024-12-28 09:39:28,450 - pyskl - INFO - Epoch [63][1400/3746] lr: 6.307e-02, eta: 3 days, 2:39:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5555, loss_cls: 3.9897, loss: 3.9897 +2024-12-28 09:40:53,950 - pyskl - INFO - Epoch [63][1500/3746] lr: 6.304e-02, eta: 3 days, 2:38:24, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5663, loss_cls: 3.9920, loss: 3.9920 +2024-12-28 09:42:19,443 - pyskl - INFO - Epoch [63][1600/3746] lr: 6.301e-02, eta: 3 days, 2:37:07, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5552, loss_cls: 4.0083, loss: 4.0083 +2024-12-28 09:43:44,221 - pyskl - INFO - Epoch [63][1700/3746] lr: 6.299e-02, eta: 3 days, 2:35:49, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5561, loss_cls: 4.0013, loss: 4.0013 +2024-12-28 09:45:09,891 - pyskl - INFO - Epoch [63][1800/3746] lr: 6.296e-02, eta: 3 days, 2:34:33, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5442, loss_cls: 4.0559, loss: 4.0559 +2024-12-28 09:46:36,344 - pyskl - INFO - Epoch [63][1900/3746] lr: 6.293e-02, eta: 3 days, 2:33:17, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5578, loss_cls: 3.9864, loss: 3.9864 +2024-12-28 09:48:02,003 - pyskl - INFO - Epoch [63][2000/3746] lr: 6.291e-02, eta: 3 days, 2:32:01, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5541, loss_cls: 4.0161, loss: 4.0161 +2024-12-28 09:49:27,666 - pyskl - INFO - Epoch [63][2100/3746] lr: 6.288e-02, eta: 3 days, 2:30:44, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5564, loss_cls: 4.0191, loss: 4.0191 +2024-12-28 09:50:53,004 - pyskl - INFO - Epoch [63][2200/3746] lr: 6.285e-02, eta: 3 days, 2:29:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5627, loss_cls: 3.9766, loss: 3.9766 +2024-12-28 09:52:18,355 - pyskl - INFO - Epoch [63][2300/3746] lr: 6.283e-02, eta: 3 days, 2:28:10, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5577, loss_cls: 4.0077, loss: 4.0077 +2024-12-28 09:53:44,438 - pyskl - INFO - Epoch [63][2400/3746] lr: 6.280e-02, eta: 3 days, 2:26:54, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5514, loss_cls: 4.0043, loss: 4.0043 +2024-12-28 09:55:10,815 - pyskl - INFO - Epoch [63][2500/3746] lr: 6.277e-02, eta: 3 days, 2:25:38, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5522, loss_cls: 4.0123, loss: 4.0123 +2024-12-28 09:56:37,899 - pyskl - INFO - Epoch [63][2600/3746] lr: 6.274e-02, eta: 3 days, 2:24:23, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5592, loss_cls: 4.0094, loss: 4.0094 +2024-12-28 09:58:04,427 - pyskl - INFO - Epoch [63][2700/3746] lr: 6.272e-02, eta: 3 days, 2:23:08, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5494, loss_cls: 4.0360, loss: 4.0360 +2024-12-28 09:59:30,828 - pyskl - INFO - Epoch [63][2800/3746] lr: 6.269e-02, eta: 3 days, 2:21:52, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5403, loss_cls: 4.0534, loss: 4.0534 +2024-12-28 10:00:57,432 - pyskl - INFO - Epoch [63][2900/3746] lr: 6.266e-02, eta: 3 days, 2:20:37, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5528, loss_cls: 4.0073, loss: 4.0073 +2024-12-28 10:02:23,965 - pyskl - INFO - Epoch [63][3000/3746] lr: 6.264e-02, eta: 3 days, 2:19:21, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5564, loss_cls: 4.0323, loss: 4.0323 +2024-12-28 10:03:50,322 - pyskl - INFO - Epoch [63][3100/3746] lr: 6.261e-02, eta: 3 days, 2:18:06, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5591, loss_cls: 3.9683, loss: 3.9683 +2024-12-28 10:05:16,747 - pyskl - INFO - Epoch [63][3200/3746] lr: 6.258e-02, eta: 3 days, 2:16:50, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5602, loss_cls: 4.0087, loss: 4.0087 +2024-12-28 10:06:43,442 - pyskl - INFO - Epoch [63][3300/3746] lr: 6.256e-02, eta: 3 days, 2:15:35, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5597, loss_cls: 4.0115, loss: 4.0115 +2024-12-28 10:08:09,645 - pyskl - INFO - Epoch [63][3400/3746] lr: 6.253e-02, eta: 3 days, 2:14:19, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5536, loss_cls: 4.0350, loss: 4.0350 +2024-12-28 10:09:36,437 - pyskl - INFO - Epoch [63][3500/3746] lr: 6.250e-02, eta: 3 days, 2:13:04, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5498, loss_cls: 4.0204, loss: 4.0204 +2024-12-28 10:11:02,504 - pyskl - INFO - Epoch [63][3600/3746] lr: 6.247e-02, eta: 3 days, 2:11:47, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5494, loss_cls: 4.0302, loss: 4.0302 +2024-12-28 10:12:28,826 - pyskl - INFO - Epoch [63][3700/3746] lr: 6.245e-02, eta: 3 days, 2:10:32, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5467, loss_cls: 4.0581, loss: 4.0581 +2024-12-28 10:13:10,316 - pyskl - INFO - Saving checkpoint at 63 epochs +2024-12-28 10:15:11,915 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 10:15:12,807 - pyskl - INFO - +top1_acc 0.2236 +top5_acc 0.4667 +2024-12-28 10:15:12,807 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 10:15:12,852 - pyskl - INFO - +mean_acc 0.2233 +2024-12-28 10:15:12,871 - pyskl - INFO - Epoch(val) [63][309] top1_acc: 0.2236, top5_acc: 0.4667, mean_class_accuracy: 0.2233 +2024-12-28 10:19:36,473 - pyskl - INFO - Epoch [64][100/3746] lr: 6.241e-02, eta: 3 days, 2:11:51, time: 2.636, data_time: 1.600, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5691, loss_cls: 3.9510, loss: 3.9510 +2024-12-28 10:21:01,807 - pyskl - INFO - Epoch [64][200/3746] lr: 6.238e-02, eta: 3 days, 2:10:33, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5600, loss_cls: 3.9788, loss: 3.9788 +2024-12-28 10:22:27,787 - pyskl - INFO - Epoch [64][300/3746] lr: 6.235e-02, eta: 3 days, 2:09:17, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5672, loss_cls: 3.9559, loss: 3.9559 +2024-12-28 10:23:52,958 - pyskl - INFO - Epoch [64][400/3746] lr: 6.233e-02, eta: 3 days, 2:07:59, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5637, loss_cls: 3.9672, loss: 3.9672 +2024-12-28 10:25:17,580 - pyskl - INFO - Epoch [64][500/3746] lr: 6.230e-02, eta: 3 days, 2:06:41, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5553, loss_cls: 3.9817, loss: 3.9817 +2024-12-28 10:26:42,295 - pyskl - INFO - Epoch [64][600/3746] lr: 6.227e-02, eta: 3 days, 2:05:23, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5602, loss_cls: 3.9858, loss: 3.9858 +2024-12-28 10:28:07,024 - pyskl - INFO - Epoch [64][700/3746] lr: 6.225e-02, eta: 3 days, 2:04:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5587, loss_cls: 3.9947, loss: 3.9947 +2024-12-28 10:29:32,309 - pyskl - INFO - Epoch [64][800/3746] lr: 6.222e-02, eta: 3 days, 2:02:47, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5650, loss_cls: 3.9807, loss: 3.9807 +2024-12-28 10:30:57,528 - pyskl - INFO - Epoch [64][900/3746] lr: 6.219e-02, eta: 3 days, 2:01:29, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5673, loss_cls: 3.9546, loss: 3.9546 +2024-12-28 10:32:21,967 - pyskl - INFO - Epoch [64][1000/3746] lr: 6.216e-02, eta: 3 days, 2:00:11, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5514, loss_cls: 4.0380, loss: 4.0380 +2024-12-28 10:33:46,591 - pyskl - INFO - Epoch [64][1100/3746] lr: 6.214e-02, eta: 3 days, 1:58:52, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5656, loss_cls: 3.9686, loss: 3.9686 +2024-12-28 10:35:12,068 - pyskl - INFO - Epoch [64][1200/3746] lr: 6.211e-02, eta: 3 days, 1:57:35, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5547, loss_cls: 4.0109, loss: 4.0109 +2024-12-28 10:36:37,738 - pyskl - INFO - Epoch [64][1300/3746] lr: 6.208e-02, eta: 3 days, 1:56:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5658, loss_cls: 3.9553, loss: 3.9553 +2024-12-28 10:38:03,531 - pyskl - INFO - Epoch [64][1400/3746] lr: 6.206e-02, eta: 3 days, 1:55:01, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5531, loss_cls: 4.0244, loss: 4.0244 +2024-12-28 10:39:28,427 - pyskl - INFO - Epoch [64][1500/3746] lr: 6.203e-02, eta: 3 days, 1:53:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5709, loss_cls: 3.9326, loss: 3.9326 +2024-12-28 10:40:53,787 - pyskl - INFO - Epoch [64][1600/3746] lr: 6.200e-02, eta: 3 days, 1:52:26, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5606, loss_cls: 3.9722, loss: 3.9722 +2024-12-28 10:42:18,994 - pyskl - INFO - Epoch [64][1700/3746] lr: 6.197e-02, eta: 3 days, 1:51:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5456, loss_cls: 4.0547, loss: 4.0547 +2024-12-28 10:43:44,177 - pyskl - INFO - Epoch [64][1800/3746] lr: 6.195e-02, eta: 3 days, 1:49:50, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5572, loss_cls: 4.0351, loss: 4.0351 +2024-12-28 10:45:09,824 - pyskl - INFO - Epoch [64][1900/3746] lr: 6.192e-02, eta: 3 days, 1:48:33, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5672, loss_cls: 3.9969, loss: 3.9969 +2024-12-28 10:46:34,356 - pyskl - INFO - Epoch [64][2000/3746] lr: 6.189e-02, eta: 3 days, 1:47:15, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5603, loss_cls: 3.9960, loss: 3.9960 +2024-12-28 10:47:59,013 - pyskl - INFO - Epoch [64][2100/3746] lr: 6.187e-02, eta: 3 days, 1:45:56, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5644, loss_cls: 3.9806, loss: 3.9806 +2024-12-28 10:49:23,008 - pyskl - INFO - Epoch [64][2200/3746] lr: 6.184e-02, eta: 3 days, 1:44:37, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5692, loss_cls: 3.9495, loss: 3.9495 +2024-12-28 10:50:47,598 - pyskl - INFO - Epoch [64][2300/3746] lr: 6.181e-02, eta: 3 days, 1:43:19, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5630, loss_cls: 3.9864, loss: 3.9864 +2024-12-28 10:52:12,221 - pyskl - INFO - Epoch [64][2400/3746] lr: 6.178e-02, eta: 3 days, 1:42:00, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5578, loss_cls: 3.9961, loss: 3.9961 +2024-12-28 10:53:36,864 - pyskl - INFO - Epoch [64][2500/3746] lr: 6.176e-02, eta: 3 days, 1:40:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5478, loss_cls: 4.0387, loss: 4.0387 +2024-12-28 10:55:01,231 - pyskl - INFO - Epoch [64][2600/3746] lr: 6.173e-02, eta: 3 days, 1:39:23, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5628, loss_cls: 3.9880, loss: 3.9880 +2024-12-28 10:56:25,669 - pyskl - INFO - Epoch [64][2700/3746] lr: 6.170e-02, eta: 3 days, 1:38:04, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5681, loss_cls: 3.9670, loss: 3.9670 +2024-12-28 10:57:50,087 - pyskl - INFO - Epoch [64][2800/3746] lr: 6.168e-02, eta: 3 days, 1:36:45, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5627, loss_cls: 3.9934, loss: 3.9934 +2024-12-28 10:59:14,382 - pyskl - INFO - Epoch [64][2900/3746] lr: 6.165e-02, eta: 3 days, 1:35:26, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5514, loss_cls: 4.0630, loss: 4.0630 +2024-12-28 11:00:39,014 - pyskl - INFO - Epoch [64][3000/3746] lr: 6.162e-02, eta: 3 days, 1:34:08, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5505, loss_cls: 4.0456, loss: 4.0456 +2024-12-28 11:02:03,485 - pyskl - INFO - Epoch [64][3100/3746] lr: 6.159e-02, eta: 3 days, 1:32:49, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5595, loss_cls: 3.9921, loss: 3.9921 +2024-12-28 11:03:28,051 - pyskl - INFO - Epoch [64][3200/3746] lr: 6.157e-02, eta: 3 days, 1:31:30, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5566, loss_cls: 4.0628, loss: 4.0628 +2024-12-28 11:04:52,877 - pyskl - INFO - Epoch [64][3300/3746] lr: 6.154e-02, eta: 3 days, 1:30:12, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5603, loss_cls: 4.0052, loss: 4.0052 +2024-12-28 11:06:18,394 - pyskl - INFO - Epoch [64][3400/3746] lr: 6.151e-02, eta: 3 days, 1:28:55, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5567, loss_cls: 4.0090, loss: 4.0090 +2024-12-28 11:07:43,334 - pyskl - INFO - Epoch [64][3500/3746] lr: 6.148e-02, eta: 3 days, 1:27:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5520, loss_cls: 4.0217, loss: 4.0217 +2024-12-28 11:09:07,908 - pyskl - INFO - Epoch [64][3600/3746] lr: 6.146e-02, eta: 3 days, 1:26:18, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5609, loss_cls: 3.9791, loss: 3.9791 +2024-12-28 11:10:33,233 - pyskl - INFO - Epoch [64][3700/3746] lr: 6.143e-02, eta: 3 days, 1:25:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5559, loss_cls: 4.0221, loss: 4.0221 +2024-12-28 11:11:13,893 - pyskl - INFO - Saving checkpoint at 64 epochs +2024-12-28 11:13:13,067 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 11:13:14,014 - pyskl - INFO - +top1_acc 0.2132 +top5_acc 0.4563 +2024-12-28 11:13:14,014 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 11:13:14,056 - pyskl - INFO - +mean_acc 0.2130 +2024-12-28 11:13:14,069 - pyskl - INFO - Epoch(val) [64][309] top1_acc: 0.2132, top5_acc: 0.4563, mean_class_accuracy: 0.2130 +2024-12-28 11:17:25,802 - pyskl - INFO - Epoch [65][100/3746] lr: 6.139e-02, eta: 3 days, 1:25:58, time: 2.517, data_time: 1.481, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5619, loss_cls: 3.9509, loss: 3.9509 +2024-12-28 11:18:51,184 - pyskl - INFO - Epoch [65][200/3746] lr: 6.136e-02, eta: 3 days, 1:24:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5672, loss_cls: 3.9637, loss: 3.9637 +2024-12-28 11:20:16,938 - pyskl - INFO - Epoch [65][300/3746] lr: 6.134e-02, eta: 3 days, 1:23:23, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5606, loss_cls: 3.9626, loss: 3.9626 +2024-12-28 11:21:42,431 - pyskl - INFO - Epoch [65][400/3746] lr: 6.131e-02, eta: 3 days, 1:22:06, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5583, loss_cls: 3.9660, loss: 3.9660 +2024-12-28 11:23:07,237 - pyskl - INFO - Epoch [65][500/3746] lr: 6.128e-02, eta: 3 days, 1:20:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5705, loss_cls: 3.9254, loss: 3.9254 +2024-12-28 11:24:31,700 - pyskl - INFO - Epoch [65][600/3746] lr: 6.125e-02, eta: 3 days, 1:19:28, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5630, loss_cls: 3.9873, loss: 3.9873 +2024-12-28 11:25:56,373 - pyskl - INFO - Epoch [65][700/3746] lr: 6.123e-02, eta: 3 days, 1:18:10, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5663, loss_cls: 3.9858, loss: 3.9858 +2024-12-28 11:27:20,845 - pyskl - INFO - Epoch [65][800/3746] lr: 6.120e-02, eta: 3 days, 1:16:51, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5580, loss_cls: 3.9531, loss: 3.9531 +2024-12-28 11:28:45,106 - pyskl - INFO - Epoch [65][900/3746] lr: 6.117e-02, eta: 3 days, 1:15:32, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5750, loss_cls: 3.9340, loss: 3.9340 +2024-12-28 11:30:09,871 - pyskl - INFO - Epoch [65][1000/3746] lr: 6.115e-02, eta: 3 days, 1:14:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5556, loss_cls: 3.9691, loss: 3.9691 +2024-12-28 11:31:34,528 - pyskl - INFO - Epoch [65][1100/3746] lr: 6.112e-02, eta: 3 days, 1:12:54, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5591, loss_cls: 4.0062, loss: 4.0062 +2024-12-28 11:32:58,898 - pyskl - INFO - Epoch [65][1200/3746] lr: 6.109e-02, eta: 3 days, 1:11:35, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5583, loss_cls: 4.0021, loss: 4.0021 +2024-12-28 11:34:22,900 - pyskl - INFO - Epoch [65][1300/3746] lr: 6.106e-02, eta: 3 days, 1:10:16, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5645, loss_cls: 3.9711, loss: 3.9711 +2024-12-28 11:35:47,696 - pyskl - INFO - Epoch [65][1400/3746] lr: 6.104e-02, eta: 3 days, 1:08:57, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5516, loss_cls: 4.0427, loss: 4.0427 +2024-12-28 11:37:11,677 - pyskl - INFO - Epoch [65][1500/3746] lr: 6.101e-02, eta: 3 days, 1:07:38, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5677, loss_cls: 3.9100, loss: 3.9100 +2024-12-28 11:38:36,196 - pyskl - INFO - Epoch [65][1600/3746] lr: 6.098e-02, eta: 3 days, 1:06:19, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5642, loss_cls: 3.9779, loss: 3.9779 +2024-12-28 11:40:01,283 - pyskl - INFO - Epoch [65][1700/3746] lr: 6.095e-02, eta: 3 days, 1:05:01, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5516, loss_cls: 4.0035, loss: 4.0035 +2024-12-28 11:41:25,687 - pyskl - INFO - Epoch [65][1800/3746] lr: 6.093e-02, eta: 3 days, 1:03:42, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5611, loss_cls: 4.0036, loss: 4.0036 +2024-12-28 11:42:49,886 - pyskl - INFO - Epoch [65][1900/3746] lr: 6.090e-02, eta: 3 days, 1:02:22, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5581, loss_cls: 4.0002, loss: 4.0002 +2024-12-28 11:44:14,059 - pyskl - INFO - Epoch [65][2000/3746] lr: 6.087e-02, eta: 3 days, 1:01:03, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5670, loss_cls: 3.9781, loss: 3.9781 +2024-12-28 11:45:38,333 - pyskl - INFO - Epoch [65][2100/3746] lr: 6.085e-02, eta: 3 days, 0:59:44, time: 0.843, data_time: 0.001, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5536, loss_cls: 3.9989, loss: 3.9989 +2024-12-28 11:47:02,908 - pyskl - INFO - Epoch [65][2200/3746] lr: 6.082e-02, eta: 3 days, 0:58:25, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5570, loss_cls: 3.9915, loss: 3.9915 +2024-12-28 11:48:27,607 - pyskl - INFO - Epoch [65][2300/3746] lr: 6.079e-02, eta: 3 days, 0:57:06, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5608, loss_cls: 3.9987, loss: 3.9987 +2024-12-28 11:49:52,016 - pyskl - INFO - Epoch [65][2400/3746] lr: 6.076e-02, eta: 3 days, 0:55:47, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5583, loss_cls: 3.9772, loss: 3.9772 +2024-12-28 11:51:16,545 - pyskl - INFO - Epoch [65][2500/3746] lr: 6.074e-02, eta: 3 days, 0:54:28, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5578, loss_cls: 3.9863, loss: 3.9863 +2024-12-28 11:52:41,369 - pyskl - INFO - Epoch [65][2600/3746] lr: 6.071e-02, eta: 3 days, 0:53:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5650, loss_cls: 3.9930, loss: 3.9930 +2024-12-28 11:54:06,451 - pyskl - INFO - Epoch [65][2700/3746] lr: 6.068e-02, eta: 3 days, 0:51:51, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5589, loss_cls: 3.9925, loss: 3.9925 +2024-12-28 11:55:31,256 - pyskl - INFO - Epoch [65][2800/3746] lr: 6.065e-02, eta: 3 days, 0:50:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5584, loss_cls: 4.0153, loss: 4.0153 +2024-12-28 11:56:56,109 - pyskl - INFO - Epoch [65][2900/3746] lr: 6.063e-02, eta: 3 days, 0:49:14, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5564, loss_cls: 4.0176, loss: 4.0176 +2024-12-28 11:58:20,329 - pyskl - INFO - Epoch [65][3000/3746] lr: 6.060e-02, eta: 3 days, 0:47:55, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5470, loss_cls: 4.0353, loss: 4.0353 +2024-12-28 11:59:45,154 - pyskl - INFO - Epoch [65][3100/3746] lr: 6.057e-02, eta: 3 days, 0:46:36, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5598, loss_cls: 3.9871, loss: 3.9871 +2024-12-28 12:01:09,902 - pyskl - INFO - Epoch [65][3200/3746] lr: 6.055e-02, eta: 3 days, 0:45:18, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5659, loss_cls: 3.9686, loss: 3.9686 +2024-12-28 12:02:34,912 - pyskl - INFO - Epoch [65][3300/3746] lr: 6.052e-02, eta: 3 days, 0:43:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5670, loss_cls: 3.9931, loss: 3.9931 +2024-12-28 12:03:59,755 - pyskl - INFO - Epoch [65][3400/3746] lr: 6.049e-02, eta: 3 days, 0:42:41, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5536, loss_cls: 4.0248, loss: 4.0248 +2024-12-28 12:05:23,997 - pyskl - INFO - Epoch [65][3500/3746] lr: 6.046e-02, eta: 3 days, 0:41:22, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5541, loss_cls: 4.0102, loss: 4.0102 +2024-12-28 12:06:48,400 - pyskl - INFO - Epoch [65][3600/3746] lr: 6.044e-02, eta: 3 days, 0:40:02, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5613, loss_cls: 3.9566, loss: 3.9566 +2024-12-28 12:08:12,722 - pyskl - INFO - Epoch [65][3700/3746] lr: 6.041e-02, eta: 3 days, 0:38:43, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5550, loss_cls: 3.9994, loss: 3.9994 +2024-12-28 12:08:53,127 - pyskl - INFO - Saving checkpoint at 65 epochs +2024-12-28 12:10:51,427 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 12:10:52,277 - pyskl - INFO - +top1_acc 0.2226 +top5_acc 0.4613 +2024-12-28 12:10:52,277 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 12:10:52,317 - pyskl - INFO - +mean_acc 0.2224 +2024-12-28 12:10:52,329 - pyskl - INFO - Epoch(val) [65][309] top1_acc: 0.2226, top5_acc: 0.4613, mean_class_accuracy: 0.2224 +2024-12-28 12:15:06,762 - pyskl - INFO - Epoch [66][100/3746] lr: 6.037e-02, eta: 3 days, 0:39:39, time: 2.544, data_time: 1.520, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5681, loss_cls: 3.9275, loss: 3.9275 +2024-12-28 12:16:31,590 - pyskl - INFO - Epoch [66][200/3746] lr: 6.034e-02, eta: 3 days, 0:38:20, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5586, loss_cls: 3.9365, loss: 3.9365 +2024-12-28 12:17:56,837 - pyskl - INFO - Epoch [66][300/3746] lr: 6.031e-02, eta: 3 days, 0:37:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5705, loss_cls: 3.9448, loss: 3.9448 +2024-12-28 12:19:22,092 - pyskl - INFO - Epoch [66][400/3746] lr: 6.029e-02, eta: 3 days, 0:35:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5683, loss_cls: 3.9527, loss: 3.9527 +2024-12-28 12:20:46,861 - pyskl - INFO - Epoch [66][500/3746] lr: 6.026e-02, eta: 3 days, 0:34:25, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5677, loss_cls: 3.9341, loss: 3.9341 +2024-12-28 12:22:11,300 - pyskl - INFO - Epoch [66][600/3746] lr: 6.023e-02, eta: 3 days, 0:33:06, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5552, loss_cls: 4.0056, loss: 4.0056 +2024-12-28 12:23:36,001 - pyskl - INFO - Epoch [66][700/3746] lr: 6.020e-02, eta: 3 days, 0:31:47, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5689, loss_cls: 3.9375, loss: 3.9375 +2024-12-28 12:25:00,328 - pyskl - INFO - Epoch [66][800/3746] lr: 6.018e-02, eta: 3 days, 0:30:28, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5637, loss_cls: 3.9782, loss: 3.9782 +2024-12-28 12:26:24,689 - pyskl - INFO - Epoch [66][900/3746] lr: 6.015e-02, eta: 3 days, 0:29:08, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5627, loss_cls: 3.9930, loss: 3.9930 +2024-12-28 12:27:49,808 - pyskl - INFO - Epoch [66][1000/3746] lr: 6.012e-02, eta: 3 days, 0:27:50, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5566, loss_cls: 3.9977, loss: 3.9977 +2024-12-28 12:29:13,963 - pyskl - INFO - Epoch [66][1100/3746] lr: 6.009e-02, eta: 3 days, 0:26:30, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5670, loss_cls: 3.9685, loss: 3.9685 +2024-12-28 12:30:38,377 - pyskl - INFO - Epoch [66][1200/3746] lr: 6.007e-02, eta: 3 days, 0:25:11, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5639, loss_cls: 3.9948, loss: 3.9948 +2024-12-28 12:32:03,030 - pyskl - INFO - Epoch [66][1300/3746] lr: 6.004e-02, eta: 3 days, 0:23:52, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5677, loss_cls: 3.9456, loss: 3.9456 +2024-12-28 12:33:27,671 - pyskl - INFO - Epoch [66][1400/3746] lr: 6.001e-02, eta: 3 days, 0:22:33, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5611, loss_cls: 3.9674, loss: 3.9674 +2024-12-28 12:34:52,350 - pyskl - INFO - Epoch [66][1500/3746] lr: 5.999e-02, eta: 3 days, 0:21:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5550, loss_cls: 4.0100, loss: 4.0100 +2024-12-28 12:36:16,770 - pyskl - INFO - Epoch [66][1600/3746] lr: 5.996e-02, eta: 3 days, 0:19:55, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5617, loss_cls: 3.9608, loss: 3.9608 +2024-12-28 12:37:41,719 - pyskl - INFO - Epoch [66][1700/3746] lr: 5.993e-02, eta: 3 days, 0:18:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5723, loss_cls: 3.9340, loss: 3.9340 +2024-12-28 12:39:06,660 - pyskl - INFO - Epoch [66][1800/3746] lr: 5.990e-02, eta: 3 days, 0:17:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5720, loss_cls: 3.9438, loss: 3.9438 +2024-12-28 12:40:31,444 - pyskl - INFO - Epoch [66][1900/3746] lr: 5.988e-02, eta: 3 days, 0:15:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5584, loss_cls: 4.0214, loss: 4.0214 +2024-12-28 12:41:56,009 - pyskl - INFO - Epoch [66][2000/3746] lr: 5.985e-02, eta: 3 days, 0:14:39, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5675, loss_cls: 3.9622, loss: 3.9622 +2024-12-28 12:43:20,966 - pyskl - INFO - Epoch [66][2100/3746] lr: 5.982e-02, eta: 3 days, 0:13:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5555, loss_cls: 4.0217, loss: 4.0217 +2024-12-28 12:44:45,966 - pyskl - INFO - Epoch [66][2200/3746] lr: 5.979e-02, eta: 3 days, 0:12:02, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5673, loss_cls: 3.9409, loss: 3.9409 +2024-12-28 12:46:10,580 - pyskl - INFO - Epoch [66][2300/3746] lr: 5.977e-02, eta: 3 days, 0:10:43, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5570, loss_cls: 4.0064, loss: 4.0064 +2024-12-28 12:47:35,389 - pyskl - INFO - Epoch [66][2400/3746] lr: 5.974e-02, eta: 3 days, 0:09:24, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5677, loss_cls: 3.9519, loss: 3.9519 +2024-12-28 12:48:59,841 - pyskl - INFO - Epoch [66][2500/3746] lr: 5.971e-02, eta: 3 days, 0:08:05, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5630, loss_cls: 3.9771, loss: 3.9771 +2024-12-28 12:50:24,423 - pyskl - INFO - Epoch [66][2600/3746] lr: 5.968e-02, eta: 3 days, 0:06:46, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5581, loss_cls: 3.9867, loss: 3.9867 +2024-12-28 12:51:49,273 - pyskl - INFO - Epoch [66][2700/3746] lr: 5.966e-02, eta: 3 days, 0:05:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5723, loss_cls: 3.9274, loss: 3.9274 +2024-12-28 12:53:13,757 - pyskl - INFO - Epoch [66][2800/3746] lr: 5.963e-02, eta: 3 days, 0:04:08, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5611, loss_cls: 3.9812, loss: 3.9812 +2024-12-28 12:54:38,332 - pyskl - INFO - Epoch [66][2900/3746] lr: 5.960e-02, eta: 3 days, 0:02:48, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5641, loss_cls: 3.9704, loss: 3.9704 +2024-12-28 12:56:03,115 - pyskl - INFO - Epoch [66][3000/3746] lr: 5.957e-02, eta: 3 days, 0:01:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5636, loss_cls: 3.9627, loss: 3.9627 +2024-12-28 12:57:27,854 - pyskl - INFO - Epoch [66][3100/3746] lr: 5.955e-02, eta: 3 days, 0:00:11, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5603, loss_cls: 3.9750, loss: 3.9750 +2024-12-28 12:58:52,541 - pyskl - INFO - Epoch [66][3200/3746] lr: 5.952e-02, eta: 2 days, 23:58:51, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5530, loss_cls: 4.0123, loss: 4.0123 +2024-12-28 13:00:16,839 - pyskl - INFO - Epoch [66][3300/3746] lr: 5.949e-02, eta: 2 days, 23:57:32, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5764, loss_cls: 3.9232, loss: 3.9232 +2024-12-28 13:01:40,624 - pyskl - INFO - Epoch [66][3400/3746] lr: 5.946e-02, eta: 2 days, 23:56:12, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5520, loss_cls: 4.0180, loss: 4.0180 +2024-12-28 13:03:05,055 - pyskl - INFO - Epoch [66][3500/3746] lr: 5.944e-02, eta: 2 days, 23:54:52, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5567, loss_cls: 3.9981, loss: 3.9981 +2024-12-28 13:04:29,349 - pyskl - INFO - Epoch [66][3600/3746] lr: 5.941e-02, eta: 2 days, 23:53:33, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5600, loss_cls: 3.9697, loss: 3.9697 +2024-12-28 13:05:53,801 - pyskl - INFO - Epoch [66][3700/3746] lr: 5.938e-02, eta: 2 days, 23:52:13, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5614, loss_cls: 4.0083, loss: 4.0083 +2024-12-28 13:06:34,674 - pyskl - INFO - Saving checkpoint at 66 epochs +2024-12-28 13:08:31,391 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 13:08:32,108 - pyskl - INFO - +top1_acc 0.2327 +top5_acc 0.4727 +2024-12-28 13:08:32,108 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 13:08:32,151 - pyskl - INFO - +mean_acc 0.2325 +2024-12-28 13:08:32,156 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_59.pth was removed +2024-12-28 13:08:32,440 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_66.pth. +2024-12-28 13:08:32,441 - pyskl - INFO - Best top1_acc is 0.2327 at 66 epoch. +2024-12-28 13:08:32,453 - pyskl - INFO - Epoch(val) [66][309] top1_acc: 0.2327, top5_acc: 0.4727, mean_class_accuracy: 0.2325 +2024-12-28 13:12:52,721 - pyskl - INFO - Epoch [67][100/3746] lr: 5.934e-02, eta: 2 days, 23:53:12, time: 2.603, data_time: 1.570, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5736, loss_cls: 3.9352, loss: 3.9352 +2024-12-28 13:14:17,782 - pyskl - INFO - Epoch [67][200/3746] lr: 5.931e-02, eta: 2 days, 23:51:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5730, loss_cls: 3.9321, loss: 3.9321 +2024-12-28 13:15:44,083 - pyskl - INFO - Epoch [67][300/3746] lr: 5.929e-02, eta: 2 days, 23:50:36, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5648, loss_cls: 3.9671, loss: 3.9671 +2024-12-28 13:17:09,841 - pyskl - INFO - Epoch [67][400/3746] lr: 5.926e-02, eta: 2 days, 23:49:18, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5792, loss_cls: 3.9151, loss: 3.9151 +2024-12-28 13:18:35,480 - pyskl - INFO - Epoch [67][500/3746] lr: 5.923e-02, eta: 2 days, 23:48:00, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5681, loss_cls: 3.9497, loss: 3.9497 +2024-12-28 13:20:00,493 - pyskl - INFO - Epoch [67][600/3746] lr: 5.920e-02, eta: 2 days, 23:46:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5689, loss_cls: 3.9442, loss: 3.9442 +2024-12-28 13:21:25,813 - pyskl - INFO - Epoch [67][700/3746] lr: 5.918e-02, eta: 2 days, 23:45:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5627, loss_cls: 3.9690, loss: 3.9690 +2024-12-28 13:22:51,308 - pyskl - INFO - Epoch [67][800/3746] lr: 5.915e-02, eta: 2 days, 23:44:05, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5759, loss_cls: 3.9011, loss: 3.9011 +2024-12-28 13:24:16,588 - pyskl - INFO - Epoch [67][900/3746] lr: 5.912e-02, eta: 2 days, 23:42:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5603, loss_cls: 3.9962, loss: 3.9962 +2024-12-28 13:25:42,239 - pyskl - INFO - Epoch [67][1000/3746] lr: 5.909e-02, eta: 2 days, 23:41:28, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5664, loss_cls: 3.9158, loss: 3.9158 +2024-12-28 13:27:06,713 - pyskl - INFO - Epoch [67][1100/3746] lr: 5.907e-02, eta: 2 days, 23:40:09, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5684, loss_cls: 3.9527, loss: 3.9527 +2024-12-28 13:28:31,507 - pyskl - INFO - Epoch [67][1200/3746] lr: 5.904e-02, eta: 2 days, 23:38:49, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5709, loss_cls: 3.9731, loss: 3.9731 +2024-12-28 13:29:55,580 - pyskl - INFO - Epoch [67][1300/3746] lr: 5.901e-02, eta: 2 days, 23:37:29, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5648, loss_cls: 3.9424, loss: 3.9424 +2024-12-28 13:31:20,364 - pyskl - INFO - Epoch [67][1400/3746] lr: 5.898e-02, eta: 2 days, 23:36:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5595, loss_cls: 4.0004, loss: 4.0004 +2024-12-28 13:32:45,619 - pyskl - INFO - Epoch [67][1500/3746] lr: 5.896e-02, eta: 2 days, 23:34:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5595, loss_cls: 3.9789, loss: 3.9789 +2024-12-28 13:34:10,306 - pyskl - INFO - Epoch [67][1600/3746] lr: 5.893e-02, eta: 2 days, 23:33:32, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5637, loss_cls: 3.9745, loss: 3.9745 +2024-12-28 13:35:35,245 - pyskl - INFO - Epoch [67][1700/3746] lr: 5.890e-02, eta: 2 days, 23:32:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5648, loss_cls: 3.9215, loss: 3.9215 +2024-12-28 13:36:59,947 - pyskl - INFO - Epoch [67][1800/3746] lr: 5.887e-02, eta: 2 days, 23:30:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5553, loss_cls: 3.9575, loss: 3.9575 +2024-12-28 13:38:25,120 - pyskl - INFO - Epoch [67][1900/3746] lr: 5.885e-02, eta: 2 days, 23:29:36, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5655, loss_cls: 3.9773, loss: 3.9773 +2024-12-28 13:39:50,020 - pyskl - INFO - Epoch [67][2000/3746] lr: 5.882e-02, eta: 2 days, 23:28:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5605, loss_cls: 3.9699, loss: 3.9699 +2024-12-28 13:41:14,528 - pyskl - INFO - Epoch [67][2100/3746] lr: 5.879e-02, eta: 2 days, 23:26:57, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5559, loss_cls: 4.0132, loss: 4.0132 +2024-12-28 13:42:39,306 - pyskl - INFO - Epoch [67][2200/3746] lr: 5.876e-02, eta: 2 days, 23:25:38, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5687, loss_cls: 3.9532, loss: 3.9532 +2024-12-28 13:44:03,688 - pyskl - INFO - Epoch [67][2300/3746] lr: 5.874e-02, eta: 2 days, 23:24:18, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5570, loss_cls: 3.9656, loss: 3.9656 +2024-12-28 13:45:27,844 - pyskl - INFO - Epoch [67][2400/3746] lr: 5.871e-02, eta: 2 days, 23:22:58, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5752, loss_cls: 3.9271, loss: 3.9271 +2024-12-28 13:46:52,104 - pyskl - INFO - Epoch [67][2500/3746] lr: 5.868e-02, eta: 2 days, 23:21:38, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5569, loss_cls: 3.9934, loss: 3.9934 +2024-12-28 13:48:16,599 - pyskl - INFO - Epoch [67][2600/3746] lr: 5.865e-02, eta: 2 days, 23:20:19, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5609, loss_cls: 3.9569, loss: 3.9569 +2024-12-28 13:49:41,041 - pyskl - INFO - Epoch [67][2700/3746] lr: 5.863e-02, eta: 2 days, 23:18:59, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5659, loss_cls: 3.9766, loss: 3.9766 +2024-12-28 13:51:05,638 - pyskl - INFO - Epoch [67][2800/3746] lr: 5.860e-02, eta: 2 days, 23:17:40, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5508, loss_cls: 4.0404, loss: 4.0404 +2024-12-28 13:52:31,009 - pyskl - INFO - Epoch [67][2900/3746] lr: 5.857e-02, eta: 2 days, 23:16:21, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5677, loss_cls: 3.9268, loss: 3.9268 +2024-12-28 13:53:56,163 - pyskl - INFO - Epoch [67][3000/3746] lr: 5.854e-02, eta: 2 days, 23:15:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5733, loss_cls: 3.9100, loss: 3.9100 +2024-12-28 13:55:21,369 - pyskl - INFO - Epoch [67][3100/3746] lr: 5.852e-02, eta: 2 days, 23:13:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5605, loss_cls: 3.9875, loss: 3.9875 +2024-12-28 13:56:47,076 - pyskl - INFO - Epoch [67][3200/3746] lr: 5.849e-02, eta: 2 days, 23:12:26, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5627, loss_cls: 4.0141, loss: 4.0141 +2024-12-28 13:58:12,798 - pyskl - INFO - Epoch [67][3300/3746] lr: 5.846e-02, eta: 2 days, 23:11:08, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5633, loss_cls: 3.9698, loss: 3.9698 +2024-12-28 13:59:38,432 - pyskl - INFO - Epoch [67][3400/3746] lr: 5.843e-02, eta: 2 days, 23:09:50, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5706, loss_cls: 3.9231, loss: 3.9231 +2024-12-28 14:01:04,477 - pyskl - INFO - Epoch [67][3500/3746] lr: 5.841e-02, eta: 2 days, 23:08:32, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5550, loss_cls: 3.9922, loss: 3.9922 +2024-12-28 14:02:30,554 - pyskl - INFO - Epoch [67][3600/3746] lr: 5.838e-02, eta: 2 days, 23:07:14, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5723, loss_cls: 3.9275, loss: 3.9275 +2024-12-28 14:03:56,170 - pyskl - INFO - Epoch [67][3700/3746] lr: 5.835e-02, eta: 2 days, 23:05:56, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5691, loss_cls: 3.9837, loss: 3.9837 +2024-12-28 14:04:37,943 - pyskl - INFO - Saving checkpoint at 67 epochs +2024-12-28 14:06:36,563 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 14:06:37,354 - pyskl - INFO - +top1_acc 0.2510 +top5_acc 0.4912 +2024-12-28 14:06:37,354 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 14:06:37,407 - pyskl - INFO - +mean_acc 0.2508 +2024-12-28 14:06:37,413 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_66.pth was removed +2024-12-28 14:06:37,792 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_67.pth. +2024-12-28 14:06:37,792 - pyskl - INFO - Best top1_acc is 0.2510 at 67 epoch. +2024-12-28 14:06:37,813 - pyskl - INFO - Epoch(val) [67][309] top1_acc: 0.2510, top5_acc: 0.4912, mean_class_accuracy: 0.2508 +2024-12-28 14:10:53,390 - pyskl - INFO - Epoch [68][100/3746] lr: 5.831e-02, eta: 2 days, 23:06:43, time: 2.556, data_time: 1.524, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5756, loss_cls: 3.8844, loss: 3.8844 +2024-12-28 14:12:19,336 - pyskl - INFO - Epoch [68][200/3746] lr: 5.828e-02, eta: 2 days, 23:05:26, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5719, loss_cls: 3.9134, loss: 3.9134 +2024-12-28 14:13:44,919 - pyskl - INFO - Epoch [68][300/3746] lr: 5.826e-02, eta: 2 days, 23:04:07, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5716, loss_cls: 3.9338, loss: 3.9338 +2024-12-28 14:15:10,510 - pyskl - INFO - Epoch [68][400/3746] lr: 5.823e-02, eta: 2 days, 23:02:49, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5780, loss_cls: 3.9089, loss: 3.9089 +2024-12-28 14:16:35,763 - pyskl - INFO - Epoch [68][500/3746] lr: 5.820e-02, eta: 2 days, 23:01:30, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5742, loss_cls: 3.9224, loss: 3.9224 +2024-12-28 14:18:00,721 - pyskl - INFO - Epoch [68][600/3746] lr: 5.817e-02, eta: 2 days, 23:00:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5642, loss_cls: 3.9606, loss: 3.9606 +2024-12-28 14:19:25,730 - pyskl - INFO - Epoch [68][700/3746] lr: 5.815e-02, eta: 2 days, 22:58:52, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5627, loss_cls: 3.9435, loss: 3.9435 +2024-12-28 14:20:51,259 - pyskl - INFO - Epoch [68][800/3746] lr: 5.812e-02, eta: 2 days, 22:57:33, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5692, loss_cls: 3.9541, loss: 3.9541 +2024-12-28 14:22:16,293 - pyskl - INFO - Epoch [68][900/3746] lr: 5.809e-02, eta: 2 days, 22:56:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5639, loss_cls: 3.9623, loss: 3.9623 +2024-12-28 14:23:41,439 - pyskl - INFO - Epoch [68][1000/3746] lr: 5.806e-02, eta: 2 days, 22:54:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5681, loss_cls: 3.9299, loss: 3.9299 +2024-12-28 14:25:05,955 - pyskl - INFO - Epoch [68][1100/3746] lr: 5.804e-02, eta: 2 days, 22:53:35, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5677, loss_cls: 3.9743, loss: 3.9743 +2024-12-28 14:26:30,661 - pyskl - INFO - Epoch [68][1200/3746] lr: 5.801e-02, eta: 2 days, 22:52:16, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5609, loss_cls: 3.9778, loss: 3.9778 +2024-12-28 14:27:55,157 - pyskl - INFO - Epoch [68][1300/3746] lr: 5.798e-02, eta: 2 days, 22:50:56, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5608, loss_cls: 3.9429, loss: 3.9429 +2024-12-28 14:29:20,061 - pyskl - INFO - Epoch [68][1400/3746] lr: 5.795e-02, eta: 2 days, 22:49:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5677, loss_cls: 3.9488, loss: 3.9488 +2024-12-28 14:30:44,598 - pyskl - INFO - Epoch [68][1500/3746] lr: 5.792e-02, eta: 2 days, 22:48:17, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5667, loss_cls: 3.9498, loss: 3.9498 +2024-12-28 14:32:09,213 - pyskl - INFO - Epoch [68][1600/3746] lr: 5.790e-02, eta: 2 days, 22:46:57, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5645, loss_cls: 3.9916, loss: 3.9916 +2024-12-28 14:33:33,687 - pyskl - INFO - Epoch [68][1700/3746] lr: 5.787e-02, eta: 2 days, 22:45:38, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5667, loss_cls: 3.9574, loss: 3.9574 +2024-12-28 14:34:58,044 - pyskl - INFO - Epoch [68][1800/3746] lr: 5.784e-02, eta: 2 days, 22:44:18, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5661, loss_cls: 3.9497, loss: 3.9497 +2024-12-28 14:36:23,095 - pyskl - INFO - Epoch [68][1900/3746] lr: 5.781e-02, eta: 2 days, 22:42:58, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5752, loss_cls: 3.9474, loss: 3.9474 +2024-12-28 14:37:47,632 - pyskl - INFO - Epoch [68][2000/3746] lr: 5.779e-02, eta: 2 days, 22:41:39, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5681, loss_cls: 3.9684, loss: 3.9684 +2024-12-28 14:39:12,668 - pyskl - INFO - Epoch [68][2100/3746] lr: 5.776e-02, eta: 2 days, 22:40:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5602, loss_cls: 3.9862, loss: 3.9862 +2024-12-28 14:40:38,027 - pyskl - INFO - Epoch [68][2200/3746] lr: 5.773e-02, eta: 2 days, 22:39:01, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5578, loss_cls: 3.9678, loss: 3.9678 +2024-12-28 14:42:02,731 - pyskl - INFO - Epoch [68][2300/3746] lr: 5.770e-02, eta: 2 days, 22:37:41, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5620, loss_cls: 3.9663, loss: 3.9663 +2024-12-28 14:43:27,190 - pyskl - INFO - Epoch [68][2400/3746] lr: 5.768e-02, eta: 2 days, 22:36:21, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5641, loss_cls: 3.9539, loss: 3.9539 +2024-12-28 14:44:52,403 - pyskl - INFO - Epoch [68][2500/3746] lr: 5.765e-02, eta: 2 days, 22:35:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5781, loss_cls: 3.8970, loss: 3.8970 +2024-12-28 14:46:17,286 - pyskl - INFO - Epoch [68][2600/3746] lr: 5.762e-02, eta: 2 days, 22:33:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5669, loss_cls: 3.9521, loss: 3.9521 +2024-12-28 14:47:42,015 - pyskl - INFO - Epoch [68][2700/3746] lr: 5.759e-02, eta: 2 days, 22:32:24, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5580, loss_cls: 3.9590, loss: 3.9590 +2024-12-28 14:49:07,116 - pyskl - INFO - Epoch [68][2800/3746] lr: 5.757e-02, eta: 2 days, 22:31:04, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5645, loss_cls: 3.9754, loss: 3.9754 +2024-12-28 14:50:32,369 - pyskl - INFO - Epoch [68][2900/3746] lr: 5.754e-02, eta: 2 days, 22:29:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5666, loss_cls: 3.9721, loss: 3.9721 +2024-12-28 14:51:56,728 - pyskl - INFO - Epoch [68][3000/3746] lr: 5.751e-02, eta: 2 days, 22:28:25, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5727, loss_cls: 3.9425, loss: 3.9425 +2024-12-28 14:53:21,813 - pyskl - INFO - Epoch [68][3100/3746] lr: 5.748e-02, eta: 2 days, 22:27:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5678, loss_cls: 3.9731, loss: 3.9731 +2024-12-28 14:54:46,926 - pyskl - INFO - Epoch [68][3200/3746] lr: 5.746e-02, eta: 2 days, 22:25:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5677, loss_cls: 3.9382, loss: 3.9382 +2024-12-28 14:56:11,900 - pyskl - INFO - Epoch [68][3300/3746] lr: 5.743e-02, eta: 2 days, 22:24:28, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5686, loss_cls: 3.9232, loss: 3.9232 +2024-12-28 14:57:36,829 - pyskl - INFO - Epoch [68][3400/3746] lr: 5.740e-02, eta: 2 days, 22:23:09, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5566, loss_cls: 3.9832, loss: 3.9832 +2024-12-28 14:59:01,819 - pyskl - INFO - Epoch [68][3500/3746] lr: 5.737e-02, eta: 2 days, 22:21:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5622, loss_cls: 3.9591, loss: 3.9591 +2024-12-28 15:00:27,256 - pyskl - INFO - Epoch [68][3600/3746] lr: 5.734e-02, eta: 2 days, 22:20:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5745, loss_cls: 3.9424, loss: 3.9424 +2024-12-28 15:01:52,333 - pyskl - INFO - Epoch [68][3700/3746] lr: 5.732e-02, eta: 2 days, 22:19:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5600, loss_cls: 3.9578, loss: 3.9578 +2024-12-28 15:02:33,656 - pyskl - INFO - Saving checkpoint at 68 epochs +2024-12-28 15:04:32,466 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 15:04:33,325 - pyskl - INFO - +top1_acc 0.2523 +top5_acc 0.5024 +2024-12-28 15:04:33,325 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 15:04:33,384 - pyskl - INFO - +mean_acc 0.2523 +2024-12-28 15:04:33,388 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_67.pth was removed +2024-12-28 15:04:33,688 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_68.pth. +2024-12-28 15:04:33,689 - pyskl - INFO - Best top1_acc is 0.2523 at 68 epoch. +2024-12-28 15:04:33,706 - pyskl - INFO - Epoch(val) [68][309] top1_acc: 0.2523, top5_acc: 0.5024, mean_class_accuracy: 0.2523 +2024-12-28 15:08:50,606 - pyskl - INFO - Epoch [69][100/3746] lr: 5.728e-02, eta: 2 days, 22:19:56, time: 2.569, data_time: 1.536, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5880, loss_cls: 3.8711, loss: 3.8711 +2024-12-28 15:10:16,473 - pyskl - INFO - Epoch [69][200/3746] lr: 5.725e-02, eta: 2 days, 22:18:37, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5708, loss_cls: 3.9252, loss: 3.9252 +2024-12-28 15:11:42,391 - pyskl - INFO - Epoch [69][300/3746] lr: 5.722e-02, eta: 2 days, 22:17:19, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5623, loss_cls: 3.9501, loss: 3.9501 +2024-12-28 15:13:08,066 - pyskl - INFO - Epoch [69][400/3746] lr: 5.719e-02, eta: 2 days, 22:16:00, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5706, loss_cls: 3.9192, loss: 3.9192 +2024-12-28 15:14:33,597 - pyskl - INFO - Epoch [69][500/3746] lr: 5.717e-02, eta: 2 days, 22:14:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5556, loss_cls: 3.9839, loss: 3.9839 +2024-12-28 15:15:58,339 - pyskl - INFO - Epoch [69][600/3746] lr: 5.714e-02, eta: 2 days, 22:13:22, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5683, loss_cls: 3.9335, loss: 3.9335 +2024-12-28 15:17:23,895 - pyskl - INFO - Epoch [69][700/3746] lr: 5.711e-02, eta: 2 days, 22:12:03, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5737, loss_cls: 3.9388, loss: 3.9388 +2024-12-28 15:18:49,015 - pyskl - INFO - Epoch [69][800/3746] lr: 5.708e-02, eta: 2 days, 22:10:44, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5744, loss_cls: 3.9495, loss: 3.9495 +2024-12-28 15:20:13,295 - pyskl - INFO - Epoch [69][900/3746] lr: 5.706e-02, eta: 2 days, 22:09:24, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5639, loss_cls: 3.9362, loss: 3.9362 +2024-12-28 15:21:37,839 - pyskl - INFO - Epoch [69][1000/3746] lr: 5.703e-02, eta: 2 days, 22:08:04, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5692, loss_cls: 3.9567, loss: 3.9567 +2024-12-28 15:23:02,329 - pyskl - INFO - Epoch [69][1100/3746] lr: 5.700e-02, eta: 2 days, 22:06:44, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5828, loss_cls: 3.8695, loss: 3.8695 +2024-12-28 15:24:27,589 - pyskl - INFO - Epoch [69][1200/3746] lr: 5.697e-02, eta: 2 days, 22:05:25, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5620, loss_cls: 3.9484, loss: 3.9484 +2024-12-28 15:25:52,302 - pyskl - INFO - Epoch [69][1300/3746] lr: 5.694e-02, eta: 2 days, 22:04:05, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5656, loss_cls: 3.9700, loss: 3.9700 +2024-12-28 15:27:17,484 - pyskl - INFO - Epoch [69][1400/3746] lr: 5.692e-02, eta: 2 days, 22:02:46, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5781, loss_cls: 3.8930, loss: 3.8930 +2024-12-28 15:28:42,519 - pyskl - INFO - Epoch [69][1500/3746] lr: 5.689e-02, eta: 2 days, 22:01:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5736, loss_cls: 3.9460, loss: 3.9460 +2024-12-28 15:30:07,407 - pyskl - INFO - Epoch [69][1600/3746] lr: 5.686e-02, eta: 2 days, 22:00:07, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5678, loss_cls: 3.9075, loss: 3.9075 +2024-12-28 15:31:32,510 - pyskl - INFO - Epoch [69][1700/3746] lr: 5.683e-02, eta: 2 days, 21:58:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5645, loss_cls: 3.9646, loss: 3.9646 +2024-12-28 15:32:57,002 - pyskl - INFO - Epoch [69][1800/3746] lr: 5.681e-02, eta: 2 days, 21:57:27, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5691, loss_cls: 3.9494, loss: 3.9494 +2024-12-28 15:34:22,079 - pyskl - INFO - Epoch [69][1900/3746] lr: 5.678e-02, eta: 2 days, 21:56:08, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5628, loss_cls: 3.9588, loss: 3.9588 +2024-12-28 15:35:46,128 - pyskl - INFO - Epoch [69][2000/3746] lr: 5.675e-02, eta: 2 days, 21:54:47, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5577, loss_cls: 4.0228, loss: 4.0228 +2024-12-28 15:37:10,466 - pyskl - INFO - Epoch [69][2100/3746] lr: 5.672e-02, eta: 2 days, 21:53:27, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5697, loss_cls: 3.9165, loss: 3.9165 +2024-12-28 15:38:34,776 - pyskl - INFO - Epoch [69][2200/3746] lr: 5.670e-02, eta: 2 days, 21:52:07, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5714, loss_cls: 3.9337, loss: 3.9337 +2024-12-28 15:39:58,683 - pyskl - INFO - Epoch [69][2300/3746] lr: 5.667e-02, eta: 2 days, 21:50:46, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5702, loss_cls: 3.9358, loss: 3.9358 +2024-12-28 15:41:23,298 - pyskl - INFO - Epoch [69][2400/3746] lr: 5.664e-02, eta: 2 days, 21:49:26, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5650, loss_cls: 3.9774, loss: 3.9774 +2024-12-28 15:42:47,305 - pyskl - INFO - Epoch [69][2500/3746] lr: 5.661e-02, eta: 2 days, 21:48:05, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5703, loss_cls: 3.9572, loss: 3.9572 +2024-12-28 15:44:11,971 - pyskl - INFO - Epoch [69][2600/3746] lr: 5.658e-02, eta: 2 days, 21:46:45, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5658, loss_cls: 3.9578, loss: 3.9578 +2024-12-28 15:45:36,569 - pyskl - INFO - Epoch [69][2700/3746] lr: 5.656e-02, eta: 2 days, 21:45:25, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5661, loss_cls: 3.9482, loss: 3.9482 +2024-12-28 15:47:01,073 - pyskl - INFO - Epoch [69][2800/3746] lr: 5.653e-02, eta: 2 days, 21:44:05, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5720, loss_cls: 3.9487, loss: 3.9487 +2024-12-28 15:48:25,443 - pyskl - INFO - Epoch [69][2900/3746] lr: 5.650e-02, eta: 2 days, 21:42:45, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5634, loss_cls: 3.9781, loss: 3.9781 +2024-12-28 15:49:49,798 - pyskl - INFO - Epoch [69][3000/3746] lr: 5.647e-02, eta: 2 days, 21:41:25, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5737, loss_cls: 3.9351, loss: 3.9351 +2024-12-28 15:51:14,323 - pyskl - INFO - Epoch [69][3100/3746] lr: 5.645e-02, eta: 2 days, 21:40:05, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5669, loss_cls: 3.9579, loss: 3.9579 +2024-12-28 15:52:38,763 - pyskl - INFO - Epoch [69][3200/3746] lr: 5.642e-02, eta: 2 days, 21:38:44, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5614, loss_cls: 3.9605, loss: 3.9605 +2024-12-28 15:54:03,128 - pyskl - INFO - Epoch [69][3300/3746] lr: 5.639e-02, eta: 2 days, 21:37:24, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5731, loss_cls: 3.9369, loss: 3.9369 +2024-12-28 15:55:27,664 - pyskl - INFO - Epoch [69][3400/3746] lr: 5.636e-02, eta: 2 days, 21:36:04, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5663, loss_cls: 3.9653, loss: 3.9653 +2024-12-28 15:56:52,145 - pyskl - INFO - Epoch [69][3500/3746] lr: 5.634e-02, eta: 2 days, 21:34:44, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5684, loss_cls: 3.9381, loss: 3.9381 +2024-12-28 15:58:16,364 - pyskl - INFO - Epoch [69][3600/3746] lr: 5.631e-02, eta: 2 days, 21:33:24, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5737, loss_cls: 3.8984, loss: 3.8984 +2024-12-28 15:59:40,975 - pyskl - INFO - Epoch [69][3700/3746] lr: 5.628e-02, eta: 2 days, 21:32:04, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5731, loss_cls: 3.9169, loss: 3.9169 +2024-12-28 16:00:21,851 - pyskl - INFO - Saving checkpoint at 69 epochs +2024-12-28 16:02:21,102 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 16:02:21,788 - pyskl - INFO - +top1_acc 0.2190 +top5_acc 0.4572 +2024-12-28 16:02:21,788 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 16:02:21,840 - pyskl - INFO - +mean_acc 0.2189 +2024-12-28 16:02:21,853 - pyskl - INFO - Epoch(val) [69][309] top1_acc: 0.2190, top5_acc: 0.4572, mean_class_accuracy: 0.2189 +2024-12-28 16:06:36,018 - pyskl - INFO - Epoch [70][100/3746] lr: 5.624e-02, eta: 2 days, 21:32:40, time: 2.541, data_time: 1.510, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5836, loss_cls: 3.8846, loss: 3.8846 +2024-12-28 16:08:01,125 - pyskl - INFO - Epoch [70][200/3746] lr: 5.621e-02, eta: 2 days, 21:31:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5745, loss_cls: 3.9184, loss: 3.9184 +2024-12-28 16:09:25,900 - pyskl - INFO - Epoch [70][300/3746] lr: 5.618e-02, eta: 2 days, 21:30:01, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5769, loss_cls: 3.9018, loss: 3.9018 +2024-12-28 16:10:50,694 - pyskl - INFO - Epoch [70][400/3746] lr: 5.616e-02, eta: 2 days, 21:28:41, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5844, loss_cls: 3.8619, loss: 3.8619 +2024-12-28 16:12:15,749 - pyskl - INFO - Epoch [70][500/3746] lr: 5.613e-02, eta: 2 days, 21:27:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5727, loss_cls: 3.9380, loss: 3.9380 +2024-12-28 16:13:40,592 - pyskl - INFO - Epoch [70][600/3746] lr: 5.610e-02, eta: 2 days, 21:26:01, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5705, loss_cls: 3.9051, loss: 3.9051 +2024-12-28 16:15:05,366 - pyskl - INFO - Epoch [70][700/3746] lr: 5.607e-02, eta: 2 days, 21:24:41, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5669, loss_cls: 3.9237, loss: 3.9237 +2024-12-28 16:16:30,135 - pyskl - INFO - Epoch [70][800/3746] lr: 5.605e-02, eta: 2 days, 21:23:21, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5728, loss_cls: 3.9562, loss: 3.9562 +2024-12-28 16:17:54,486 - pyskl - INFO - Epoch [70][900/3746] lr: 5.602e-02, eta: 2 days, 21:22:01, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5687, loss_cls: 3.9257, loss: 3.9257 +2024-12-28 16:19:19,017 - pyskl - INFO - Epoch [70][1000/3746] lr: 5.599e-02, eta: 2 days, 21:20:41, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5695, loss_cls: 3.9453, loss: 3.9453 +2024-12-28 16:20:43,236 - pyskl - INFO - Epoch [70][1100/3746] lr: 5.596e-02, eta: 2 days, 21:19:20, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5633, loss_cls: 3.9716, loss: 3.9716 +2024-12-28 16:22:08,210 - pyskl - INFO - Epoch [70][1200/3746] lr: 5.593e-02, eta: 2 days, 21:18:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5770, loss_cls: 3.9192, loss: 3.9192 +2024-12-28 16:23:33,228 - pyskl - INFO - Epoch [70][1300/3746] lr: 5.591e-02, eta: 2 days, 21:16:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5600, loss_cls: 3.9760, loss: 3.9760 +2024-12-28 16:24:57,745 - pyskl - INFO - Epoch [70][1400/3746] lr: 5.588e-02, eta: 2 days, 21:15:20, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5841, loss_cls: 3.8919, loss: 3.8919 +2024-12-28 16:26:21,884 - pyskl - INFO - Epoch [70][1500/3746] lr: 5.585e-02, eta: 2 days, 21:14:00, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5777, loss_cls: 3.9087, loss: 3.9087 +2024-12-28 16:27:46,298 - pyskl - INFO - Epoch [70][1600/3746] lr: 5.582e-02, eta: 2 days, 21:12:39, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5642, loss_cls: 3.9381, loss: 3.9381 +2024-12-28 16:29:10,813 - pyskl - INFO - Epoch [70][1700/3746] lr: 5.580e-02, eta: 2 days, 21:11:19, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5816, loss_cls: 3.8831, loss: 3.8831 +2024-12-28 16:30:35,118 - pyskl - INFO - Epoch [70][1800/3746] lr: 5.577e-02, eta: 2 days, 21:09:58, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5758, loss_cls: 3.9204, loss: 3.9204 +2024-12-28 16:31:59,702 - pyskl - INFO - Epoch [70][1900/3746] lr: 5.574e-02, eta: 2 days, 21:08:38, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5806, loss_cls: 3.9008, loss: 3.9008 +2024-12-28 16:33:24,484 - pyskl - INFO - Epoch [70][2000/3746] lr: 5.571e-02, eta: 2 days, 21:07:18, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5628, loss_cls: 3.9693, loss: 3.9693 +2024-12-28 16:34:48,813 - pyskl - INFO - Epoch [70][2100/3746] lr: 5.568e-02, eta: 2 days, 21:05:58, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5692, loss_cls: 3.9394, loss: 3.9394 +2024-12-28 16:36:13,086 - pyskl - INFO - Epoch [70][2200/3746] lr: 5.566e-02, eta: 2 days, 21:04:37, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5659, loss_cls: 3.9148, loss: 3.9148 +2024-12-28 16:37:36,928 - pyskl - INFO - Epoch [70][2300/3746] lr: 5.563e-02, eta: 2 days, 21:03:16, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5859, loss_cls: 3.8955, loss: 3.8955 +2024-12-28 16:39:01,123 - pyskl - INFO - Epoch [70][2400/3746] lr: 5.560e-02, eta: 2 days, 21:01:56, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5683, loss_cls: 3.9509, loss: 3.9509 +2024-12-28 16:40:25,520 - pyskl - INFO - Epoch [70][2500/3746] lr: 5.557e-02, eta: 2 days, 21:00:35, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5681, loss_cls: 3.9379, loss: 3.9379 +2024-12-28 16:41:50,021 - pyskl - INFO - Epoch [70][2600/3746] lr: 5.555e-02, eta: 2 days, 20:59:15, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5630, loss_cls: 3.9623, loss: 3.9623 +2024-12-28 16:43:14,664 - pyskl - INFO - Epoch [70][2700/3746] lr: 5.552e-02, eta: 2 days, 20:57:55, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5628, loss_cls: 3.9623, loss: 3.9623 +2024-12-28 16:44:39,903 - pyskl - INFO - Epoch [70][2800/3746] lr: 5.549e-02, eta: 2 days, 20:56:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5783, loss_cls: 3.9314, loss: 3.9314 +2024-12-28 16:46:05,204 - pyskl - INFO - Epoch [70][2900/3746] lr: 5.546e-02, eta: 2 days, 20:55:16, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5769, loss_cls: 3.8922, loss: 3.8922 +2024-12-28 16:47:30,843 - pyskl - INFO - Epoch [70][3000/3746] lr: 5.543e-02, eta: 2 days, 20:53:57, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5555, loss_cls: 4.0034, loss: 4.0034 +2024-12-28 16:48:55,892 - pyskl - INFO - Epoch [70][3100/3746] lr: 5.541e-02, eta: 2 days, 20:52:37, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5645, loss_cls: 3.9775, loss: 3.9775 +2024-12-28 16:50:21,026 - pyskl - INFO - Epoch [70][3200/3746] lr: 5.538e-02, eta: 2 days, 20:51:17, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5645, loss_cls: 3.9440, loss: 3.9440 +2024-12-28 16:51:46,112 - pyskl - INFO - Epoch [70][3300/3746] lr: 5.535e-02, eta: 2 days, 20:49:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5752, loss_cls: 3.8900, loss: 3.8900 +2024-12-28 16:53:10,910 - pyskl - INFO - Epoch [70][3400/3746] lr: 5.532e-02, eta: 2 days, 20:48:38, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5691, loss_cls: 3.9412, loss: 3.9412 +2024-12-28 16:54:35,825 - pyskl - INFO - Epoch [70][3500/3746] lr: 5.530e-02, eta: 2 days, 20:47:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5714, loss_cls: 3.9019, loss: 3.9019 +2024-12-28 16:56:00,461 - pyskl - INFO - Epoch [70][3600/3746] lr: 5.527e-02, eta: 2 days, 20:45:58, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5686, loss_cls: 3.9461, loss: 3.9461 +2024-12-28 16:57:25,332 - pyskl - INFO - Epoch [70][3700/3746] lr: 5.524e-02, eta: 2 days, 20:44:38, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5792, loss_cls: 3.8721, loss: 3.8721 +2024-12-28 16:58:06,278 - pyskl - INFO - Saving checkpoint at 70 epochs +2024-12-28 17:00:03,582 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 17:00:04,320 - pyskl - INFO - +top1_acc 0.2165 +top5_acc 0.4530 +2024-12-28 17:00:04,320 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 17:00:04,364 - pyskl - INFO - +mean_acc 0.2162 +2024-12-28 17:00:04,379 - pyskl - INFO - Epoch(val) [70][309] top1_acc: 0.2165, top5_acc: 0.4530, mean_class_accuracy: 0.2162 +2024-12-28 17:04:25,377 - pyskl - INFO - Epoch [71][100/3746] lr: 5.520e-02, eta: 2 days, 20:45:18, time: 2.610, data_time: 1.572, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5780, loss_cls: 3.8902, loss: 3.8902 +2024-12-28 17:05:50,832 - pyskl - INFO - Epoch [71][200/3746] lr: 5.517e-02, eta: 2 days, 20:43:58, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5775, loss_cls: 3.8975, loss: 3.8975 +2024-12-28 17:07:16,424 - pyskl - INFO - Epoch [71][300/3746] lr: 5.514e-02, eta: 2 days, 20:42:39, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5759, loss_cls: 3.9111, loss: 3.9111 +2024-12-28 17:08:42,128 - pyskl - INFO - Epoch [71][400/3746] lr: 5.512e-02, eta: 2 days, 20:41:20, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5800, loss_cls: 3.8807, loss: 3.8807 +2024-12-28 17:10:08,098 - pyskl - INFO - Epoch [71][500/3746] lr: 5.509e-02, eta: 2 days, 20:40:01, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5831, loss_cls: 3.8702, loss: 3.8702 +2024-12-28 17:11:33,739 - pyskl - INFO - Epoch [71][600/3746] lr: 5.506e-02, eta: 2 days, 20:38:42, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5792, loss_cls: 3.8831, loss: 3.8831 +2024-12-28 17:12:58,434 - pyskl - INFO - Epoch [71][700/3746] lr: 5.503e-02, eta: 2 days, 20:37:21, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5714, loss_cls: 3.9183, loss: 3.9183 +2024-12-28 17:14:23,760 - pyskl - INFO - Epoch [71][800/3746] lr: 5.500e-02, eta: 2 days, 20:36:02, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5744, loss_cls: 3.9102, loss: 3.9102 +2024-12-28 17:15:48,206 - pyskl - INFO - Epoch [71][900/3746] lr: 5.498e-02, eta: 2 days, 20:34:41, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5767, loss_cls: 3.8768, loss: 3.8768 +2024-12-28 17:17:13,202 - pyskl - INFO - Epoch [71][1000/3746] lr: 5.495e-02, eta: 2 days, 20:33:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5789, loss_cls: 3.9178, loss: 3.9178 +2024-12-28 17:18:37,736 - pyskl - INFO - Epoch [71][1100/3746] lr: 5.492e-02, eta: 2 days, 20:32:01, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5752, loss_cls: 3.8702, loss: 3.8702 +2024-12-28 17:20:02,797 - pyskl - INFO - Epoch [71][1200/3746] lr: 5.489e-02, eta: 2 days, 20:30:41, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5811, loss_cls: 3.8950, loss: 3.8950 +2024-12-28 17:21:27,368 - pyskl - INFO - Epoch [71][1300/3746] lr: 5.487e-02, eta: 2 days, 20:29:21, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5650, loss_cls: 3.9562, loss: 3.9562 +2024-12-28 17:22:51,911 - pyskl - INFO - Epoch [71][1400/3746] lr: 5.484e-02, eta: 2 days, 20:28:00, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5673, loss_cls: 3.9756, loss: 3.9756 +2024-12-28 17:24:16,926 - pyskl - INFO - Epoch [71][1500/3746] lr: 5.481e-02, eta: 2 days, 20:26:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5613, loss_cls: 3.9787, loss: 3.9787 +2024-12-28 17:25:42,337 - pyskl - INFO - Epoch [71][1600/3746] lr: 5.478e-02, eta: 2 days, 20:25:21, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5703, loss_cls: 3.9353, loss: 3.9353 +2024-12-28 17:27:07,427 - pyskl - INFO - Epoch [71][1700/3746] lr: 5.475e-02, eta: 2 days, 20:24:01, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5802, loss_cls: 3.8797, loss: 3.8797 +2024-12-28 17:28:31,984 - pyskl - INFO - Epoch [71][1800/3746] lr: 5.473e-02, eta: 2 days, 20:22:40, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5836, loss_cls: 3.8862, loss: 3.8862 +2024-12-28 17:29:57,083 - pyskl - INFO - Epoch [71][1900/3746] lr: 5.470e-02, eta: 2 days, 20:21:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5803, loss_cls: 3.9117, loss: 3.9117 +2024-12-28 17:31:21,933 - pyskl - INFO - Epoch [71][2000/3746] lr: 5.467e-02, eta: 2 days, 20:20:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5658, loss_cls: 3.9590, loss: 3.9590 +2024-12-28 17:32:47,019 - pyskl - INFO - Epoch [71][2100/3746] lr: 5.464e-02, eta: 2 days, 20:18:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5720, loss_cls: 3.9064, loss: 3.9064 +2024-12-28 17:34:12,471 - pyskl - INFO - Epoch [71][2200/3746] lr: 5.461e-02, eta: 2 days, 20:17:21, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5614, loss_cls: 3.9452, loss: 3.9452 +2024-12-28 17:35:37,618 - pyskl - INFO - Epoch [71][2300/3746] lr: 5.459e-02, eta: 2 days, 20:16:01, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5639, loss_cls: 3.9280, loss: 3.9280 +2024-12-28 17:37:02,015 - pyskl - INFO - Epoch [71][2400/3746] lr: 5.456e-02, eta: 2 days, 20:14:40, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5656, loss_cls: 3.9311, loss: 3.9311 +2024-12-28 17:38:26,023 - pyskl - INFO - Epoch [71][2500/3746] lr: 5.453e-02, eta: 2 days, 20:13:19, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5708, loss_cls: 3.8941, loss: 3.8941 +2024-12-28 17:39:50,533 - pyskl - INFO - Epoch [71][2600/3746] lr: 5.450e-02, eta: 2 days, 20:11:59, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5677, loss_cls: 3.9685, loss: 3.9685 +2024-12-28 17:41:14,991 - pyskl - INFO - Epoch [71][2700/3746] lr: 5.448e-02, eta: 2 days, 20:10:38, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5730, loss_cls: 3.9260, loss: 3.9260 +2024-12-28 17:42:39,534 - pyskl - INFO - Epoch [71][2800/3746] lr: 5.445e-02, eta: 2 days, 20:09:18, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5655, loss_cls: 3.9586, loss: 3.9586 +2024-12-28 17:44:04,057 - pyskl - INFO - Epoch [71][2900/3746] lr: 5.442e-02, eta: 2 days, 20:07:57, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5766, loss_cls: 3.9105, loss: 3.9105 +2024-12-28 17:45:28,264 - pyskl - INFO - Epoch [71][3000/3746] lr: 5.439e-02, eta: 2 days, 20:06:36, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5625, loss_cls: 3.9845, loss: 3.9845 +2024-12-28 17:46:52,466 - pyskl - INFO - Epoch [71][3100/3746] lr: 5.436e-02, eta: 2 days, 20:05:15, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5773, loss_cls: 3.9186, loss: 3.9186 +2024-12-28 17:48:17,292 - pyskl - INFO - Epoch [71][3200/3746] lr: 5.434e-02, eta: 2 days, 20:03:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5716, loss_cls: 3.9373, loss: 3.9373 +2024-12-28 17:49:41,711 - pyskl - INFO - Epoch [71][3300/3746] lr: 5.431e-02, eta: 2 days, 20:02:34, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5745, loss_cls: 3.9149, loss: 3.9149 +2024-12-28 17:51:06,312 - pyskl - INFO - Epoch [71][3400/3746] lr: 5.428e-02, eta: 2 days, 20:01:14, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5581, loss_cls: 3.9667, loss: 3.9667 +2024-12-28 17:52:30,732 - pyskl - INFO - Epoch [71][3500/3746] lr: 5.425e-02, eta: 2 days, 19:59:53, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5670, loss_cls: 3.9439, loss: 3.9439 +2024-12-28 17:53:55,408 - pyskl - INFO - Epoch [71][3600/3746] lr: 5.422e-02, eta: 2 days, 19:58:33, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5703, loss_cls: 3.9212, loss: 3.9212 +2024-12-28 17:55:20,021 - pyskl - INFO - Epoch [71][3700/3746] lr: 5.420e-02, eta: 2 days, 19:57:12, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5748, loss_cls: 3.9272, loss: 3.9272 +2024-12-28 17:56:00,661 - pyskl - INFO - Saving checkpoint at 71 epochs +2024-12-28 17:58:00,387 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 17:58:01,235 - pyskl - INFO - +top1_acc 0.2464 +top5_acc 0.4866 +2024-12-28 17:58:01,235 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 17:58:01,283 - pyskl - INFO - +mean_acc 0.2463 +2024-12-28 17:58:01,295 - pyskl - INFO - Epoch(val) [71][309] top1_acc: 0.2464, top5_acc: 0.4866, mean_class_accuracy: 0.2463 +2024-12-28 18:02:13,711 - pyskl - INFO - Epoch [72][100/3746] lr: 5.416e-02, eta: 2 days, 19:57:38, time: 2.524, data_time: 1.503, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5800, loss_cls: 3.8778, loss: 3.8778 +2024-12-28 18:03:38,489 - pyskl - INFO - Epoch [72][200/3746] lr: 5.413e-02, eta: 2 days, 19:56:18, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5881, loss_cls: 3.8149, loss: 3.8149 +2024-12-28 18:05:03,383 - pyskl - INFO - Epoch [72][300/3746] lr: 5.410e-02, eta: 2 days, 19:54:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5823, loss_cls: 3.8628, loss: 3.8628 +2024-12-28 18:06:28,487 - pyskl - INFO - Epoch [72][400/3746] lr: 5.407e-02, eta: 2 days, 19:53:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5711, loss_cls: 3.9079, loss: 3.9079 +2024-12-28 18:07:53,436 - pyskl - INFO - Epoch [72][500/3746] lr: 5.404e-02, eta: 2 days, 19:52:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5711, loss_cls: 3.9158, loss: 3.9158 +2024-12-28 18:09:18,616 - pyskl - INFO - Epoch [72][600/3746] lr: 5.402e-02, eta: 2 days, 19:50:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5719, loss_cls: 3.9300, loss: 3.9300 +2024-12-28 18:10:42,694 - pyskl - INFO - Epoch [72][700/3746] lr: 5.399e-02, eta: 2 days, 19:49:36, time: 0.841, data_time: 0.001, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5766, loss_cls: 3.8909, loss: 3.8909 +2024-12-28 18:12:06,924 - pyskl - INFO - Epoch [72][800/3746] lr: 5.396e-02, eta: 2 days, 19:48:15, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5752, loss_cls: 3.9148, loss: 3.9148 +2024-12-28 18:13:31,069 - pyskl - INFO - Epoch [72][900/3746] lr: 5.393e-02, eta: 2 days, 19:46:54, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5723, loss_cls: 3.9129, loss: 3.9129 +2024-12-28 18:14:55,803 - pyskl - INFO - Epoch [72][1000/3746] lr: 5.391e-02, eta: 2 days, 19:45:34, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5705, loss_cls: 3.9124, loss: 3.9124 +2024-12-28 18:16:19,742 - pyskl - INFO - Epoch [72][1100/3746] lr: 5.388e-02, eta: 2 days, 19:44:12, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5823, loss_cls: 3.8973, loss: 3.8973 +2024-12-28 18:17:44,118 - pyskl - INFO - Epoch [72][1200/3746] lr: 5.385e-02, eta: 2 days, 19:42:51, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5813, loss_cls: 3.8644, loss: 3.8644 +2024-12-28 18:19:08,626 - pyskl - INFO - Epoch [72][1300/3746] lr: 5.382e-02, eta: 2 days, 19:41:31, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5673, loss_cls: 3.9316, loss: 3.9316 +2024-12-28 18:20:33,407 - pyskl - INFO - Epoch [72][1400/3746] lr: 5.379e-02, eta: 2 days, 19:40:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5745, loss_cls: 3.8825, loss: 3.8825 +2024-12-28 18:21:58,420 - pyskl - INFO - Epoch [72][1500/3746] lr: 5.377e-02, eta: 2 days, 19:38:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5686, loss_cls: 3.9448, loss: 3.9448 +2024-12-28 18:23:23,260 - pyskl - INFO - Epoch [72][1600/3746] lr: 5.374e-02, eta: 2 days, 19:37:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5806, loss_cls: 3.8849, loss: 3.8849 +2024-12-28 18:24:47,926 - pyskl - INFO - Epoch [72][1700/3746] lr: 5.371e-02, eta: 2 days, 19:36:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5698, loss_cls: 3.9442, loss: 3.9442 +2024-12-28 18:26:12,289 - pyskl - INFO - Epoch [72][1800/3746] lr: 5.368e-02, eta: 2 days, 19:34:48, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5711, loss_cls: 3.9183, loss: 3.9183 +2024-12-28 18:27:36,885 - pyskl - INFO - Epoch [72][1900/3746] lr: 5.365e-02, eta: 2 days, 19:33:27, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5744, loss_cls: 3.9340, loss: 3.9340 +2024-12-28 18:29:01,551 - pyskl - INFO - Epoch [72][2000/3746] lr: 5.363e-02, eta: 2 days, 19:32:07, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5784, loss_cls: 3.9286, loss: 3.9286 +2024-12-28 18:30:25,573 - pyskl - INFO - Epoch [72][2100/3746] lr: 5.360e-02, eta: 2 days, 19:30:46, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5600, loss_cls: 3.9365, loss: 3.9365 +2024-12-28 18:31:49,820 - pyskl - INFO - Epoch [72][2200/3746] lr: 5.357e-02, eta: 2 days, 19:29:25, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5706, loss_cls: 3.8952, loss: 3.8952 +2024-12-28 18:33:14,273 - pyskl - INFO - Epoch [72][2300/3746] lr: 5.354e-02, eta: 2 days, 19:28:04, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5692, loss_cls: 3.9464, loss: 3.9464 +2024-12-28 18:34:38,480 - pyskl - INFO - Epoch [72][2400/3746] lr: 5.352e-02, eta: 2 days, 19:26:43, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5755, loss_cls: 3.9427, loss: 3.9427 +2024-12-28 18:36:02,774 - pyskl - INFO - Epoch [72][2500/3746] lr: 5.349e-02, eta: 2 days, 19:25:22, time: 0.843, data_time: 0.001, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5733, loss_cls: 3.9198, loss: 3.9198 +2024-12-28 18:37:27,209 - pyskl - INFO - Epoch [72][2600/3746] lr: 5.346e-02, eta: 2 days, 19:24:01, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5695, loss_cls: 3.9376, loss: 3.9376 +2024-12-28 18:38:51,379 - pyskl - INFO - Epoch [72][2700/3746] lr: 5.343e-02, eta: 2 days, 19:22:40, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5708, loss_cls: 3.9537, loss: 3.9537 +2024-12-28 18:40:16,491 - pyskl - INFO - Epoch [72][2800/3746] lr: 5.340e-02, eta: 2 days, 19:21:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5691, loss_cls: 3.8886, loss: 3.8886 +2024-12-28 18:41:40,922 - pyskl - INFO - Epoch [72][2900/3746] lr: 5.338e-02, eta: 2 days, 19:19:59, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5747, loss_cls: 3.9319, loss: 3.9319 +2024-12-28 18:43:05,635 - pyskl - INFO - Epoch [72][3000/3746] lr: 5.335e-02, eta: 2 days, 19:18:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5713, loss_cls: 3.9292, loss: 3.9292 +2024-12-28 18:44:30,757 - pyskl - INFO - Epoch [72][3100/3746] lr: 5.332e-02, eta: 2 days, 19:17:18, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5798, loss_cls: 3.8871, loss: 3.8871 +2024-12-28 18:45:55,318 - pyskl - INFO - Epoch [72][3200/3746] lr: 5.329e-02, eta: 2 days, 19:15:57, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5672, loss_cls: 3.9141, loss: 3.9141 +2024-12-28 18:47:20,246 - pyskl - INFO - Epoch [72][3300/3746] lr: 5.326e-02, eta: 2 days, 19:14:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5744, loss_cls: 3.8911, loss: 3.8911 +2024-12-28 18:48:45,033 - pyskl - INFO - Epoch [72][3400/3746] lr: 5.324e-02, eta: 2 days, 19:13:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5652, loss_cls: 3.9565, loss: 3.9565 +2024-12-28 18:50:09,974 - pyskl - INFO - Epoch [72][3500/3746] lr: 5.321e-02, eta: 2 days, 19:11:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5628, loss_cls: 3.9703, loss: 3.9703 +2024-12-28 18:51:34,791 - pyskl - INFO - Epoch [72][3600/3746] lr: 5.318e-02, eta: 2 days, 19:10:36, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5686, loss_cls: 3.9380, loss: 3.9380 +2024-12-28 18:52:59,644 - pyskl - INFO - Epoch [72][3700/3746] lr: 5.315e-02, eta: 2 days, 19:09:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5784, loss_cls: 3.8710, loss: 3.8710 +2024-12-28 18:53:40,290 - pyskl - INFO - Saving checkpoint at 72 epochs +2024-12-28 18:55:38,427 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 18:55:39,270 - pyskl - INFO - +top1_acc 0.2522 +top5_acc 0.4969 +2024-12-28 18:55:39,270 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 18:55:39,317 - pyskl - INFO - +mean_acc 0.2519 +2024-12-28 18:55:39,333 - pyskl - INFO - Epoch(val) [72][309] top1_acc: 0.2522, top5_acc: 0.4969, mean_class_accuracy: 0.2519 +2024-12-28 18:59:55,627 - pyskl - INFO - Epoch [73][100/3746] lr: 5.311e-02, eta: 2 days, 19:09:41, time: 2.563, data_time: 1.529, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5794, loss_cls: 3.8359, loss: 3.8359 +2024-12-28 19:01:21,429 - pyskl - INFO - Epoch [73][200/3746] lr: 5.308e-02, eta: 2 days, 19:08:22, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5811, loss_cls: 3.8602, loss: 3.8602 +2024-12-28 19:02:47,072 - pyskl - INFO - Epoch [73][300/3746] lr: 5.306e-02, eta: 2 days, 19:07:02, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5719, loss_cls: 3.9273, loss: 3.9273 +2024-12-28 19:04:12,237 - pyskl - INFO - Epoch [73][400/3746] lr: 5.303e-02, eta: 2 days, 19:05:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5791, loss_cls: 3.8952, loss: 3.8952 +2024-12-28 19:05:38,104 - pyskl - INFO - Epoch [73][500/3746] lr: 5.300e-02, eta: 2 days, 19:04:22, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5758, loss_cls: 3.9126, loss: 3.9126 +2024-12-28 19:07:03,235 - pyskl - INFO - Epoch [73][600/3746] lr: 5.297e-02, eta: 2 days, 19:03:02, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5761, loss_cls: 3.8729, loss: 3.8729 +2024-12-28 19:08:27,953 - pyskl - INFO - Epoch [73][700/3746] lr: 5.294e-02, eta: 2 days, 19:01:41, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5713, loss_cls: 3.8828, loss: 3.8828 +2024-12-28 19:09:52,994 - pyskl - INFO - Epoch [73][800/3746] lr: 5.292e-02, eta: 2 days, 19:00:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5745, loss_cls: 3.9002, loss: 3.9002 +2024-12-28 19:11:18,309 - pyskl - INFO - Epoch [73][900/3746] lr: 5.289e-02, eta: 2 days, 18:59:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5833, loss_cls: 3.8979, loss: 3.8979 +2024-12-28 19:12:43,345 - pyskl - INFO - Epoch [73][1000/3746] lr: 5.286e-02, eta: 2 days, 18:57:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5833, loss_cls: 3.8928, loss: 3.8928 +2024-12-28 19:14:08,416 - pyskl - INFO - Epoch [73][1100/3746] lr: 5.283e-02, eta: 2 days, 18:56:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5709, loss_cls: 3.9169, loss: 3.9169 +2024-12-28 19:15:33,466 - pyskl - INFO - Epoch [73][1200/3746] lr: 5.280e-02, eta: 2 days, 18:55:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5656, loss_cls: 3.9153, loss: 3.9153 +2024-12-28 19:16:58,703 - pyskl - INFO - Epoch [73][1300/3746] lr: 5.278e-02, eta: 2 days, 18:53:40, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5792, loss_cls: 3.8822, loss: 3.8822 +2024-12-28 19:18:23,568 - pyskl - INFO - Epoch [73][1400/3746] lr: 5.275e-02, eta: 2 days, 18:52:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5733, loss_cls: 3.9088, loss: 3.9088 +2024-12-28 19:19:48,911 - pyskl - INFO - Epoch [73][1500/3746] lr: 5.272e-02, eta: 2 days, 18:50:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5784, loss_cls: 3.9009, loss: 3.9009 +2024-12-28 19:21:14,275 - pyskl - INFO - Epoch [73][1600/3746] lr: 5.269e-02, eta: 2 days, 18:49:39, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5947, loss_cls: 3.8314, loss: 3.8314 +2024-12-28 19:22:39,390 - pyskl - INFO - Epoch [73][1700/3746] lr: 5.267e-02, eta: 2 days, 18:48:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5664, loss_cls: 3.9157, loss: 3.9157 +2024-12-28 19:24:04,943 - pyskl - INFO - Epoch [73][1800/3746] lr: 5.264e-02, eta: 2 days, 18:46:59, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5758, loss_cls: 3.8965, loss: 3.8965 +2024-12-28 19:25:29,674 - pyskl - INFO - Epoch [73][1900/3746] lr: 5.261e-02, eta: 2 days, 18:45:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5877, loss_cls: 3.8972, loss: 3.8972 +2024-12-28 19:26:54,637 - pyskl - INFO - Epoch [73][2000/3746] lr: 5.258e-02, eta: 2 days, 18:44:18, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5789, loss_cls: 3.8832, loss: 3.8832 +2024-12-28 19:28:19,517 - pyskl - INFO - Epoch [73][2100/3746] lr: 5.255e-02, eta: 2 days, 18:42:57, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5648, loss_cls: 3.9197, loss: 3.9197 +2024-12-28 19:29:44,950 - pyskl - INFO - Epoch [73][2200/3746] lr: 5.253e-02, eta: 2 days, 18:41:37, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5759, loss_cls: 3.8651, loss: 3.8651 +2024-12-28 19:31:09,800 - pyskl - INFO - Epoch [73][2300/3746] lr: 5.250e-02, eta: 2 days, 18:40:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5675, loss_cls: 3.9193, loss: 3.9193 +2024-12-28 19:32:34,379 - pyskl - INFO - Epoch [73][2400/3746] lr: 5.247e-02, eta: 2 days, 18:38:56, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5772, loss_cls: 3.9152, loss: 3.9152 +2024-12-28 19:33:58,533 - pyskl - INFO - Epoch [73][2500/3746] lr: 5.244e-02, eta: 2 days, 18:37:34, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5817, loss_cls: 3.8926, loss: 3.8926 +2024-12-28 19:35:23,070 - pyskl - INFO - Epoch [73][2600/3746] lr: 5.241e-02, eta: 2 days, 18:36:14, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5684, loss_cls: 3.9444, loss: 3.9444 +2024-12-28 19:36:47,855 - pyskl - INFO - Epoch [73][2700/3746] lr: 5.239e-02, eta: 2 days, 18:34:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5811, loss_cls: 3.8730, loss: 3.8730 +2024-12-28 19:38:12,889 - pyskl - INFO - Epoch [73][2800/3746] lr: 5.236e-02, eta: 2 days, 18:33:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5873, loss_cls: 3.8500, loss: 3.8500 +2024-12-28 19:39:37,803 - pyskl - INFO - Epoch [73][2900/3746] lr: 5.233e-02, eta: 2 days, 18:32:12, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5766, loss_cls: 3.9313, loss: 3.9313 +2024-12-28 19:41:02,254 - pyskl - INFO - Epoch [73][3000/3746] lr: 5.230e-02, eta: 2 days, 18:30:51, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5784, loss_cls: 3.8699, loss: 3.8699 +2024-12-28 19:42:27,330 - pyskl - INFO - Epoch [73][3100/3746] lr: 5.227e-02, eta: 2 days, 18:29:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5770, loss_cls: 3.8891, loss: 3.8891 +2024-12-28 19:43:51,469 - pyskl - INFO - Epoch [73][3200/3746] lr: 5.225e-02, eta: 2 days, 18:28:09, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5686, loss_cls: 3.9437, loss: 3.9437 +2024-12-28 19:45:15,955 - pyskl - INFO - Epoch [73][3300/3746] lr: 5.222e-02, eta: 2 days, 18:26:48, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5752, loss_cls: 3.9095, loss: 3.9095 +2024-12-28 19:46:40,765 - pyskl - INFO - Epoch [73][3400/3746] lr: 5.219e-02, eta: 2 days, 18:25:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5758, loss_cls: 3.9139, loss: 3.9139 +2024-12-28 19:48:05,508 - pyskl - INFO - Epoch [73][3500/3746] lr: 5.216e-02, eta: 2 days, 18:24:07, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5769, loss_cls: 3.9206, loss: 3.9206 +2024-12-28 19:49:30,006 - pyskl - INFO - Epoch [73][3600/3746] lr: 5.213e-02, eta: 2 days, 18:22:46, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5756, loss_cls: 3.9134, loss: 3.9134 +2024-12-28 19:50:55,367 - pyskl - INFO - Epoch [73][3700/3746] lr: 5.211e-02, eta: 2 days, 18:21:25, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5678, loss_cls: 3.9175, loss: 3.9175 +2024-12-28 19:51:36,938 - pyskl - INFO - Saving checkpoint at 73 epochs +2024-12-28 19:53:34,413 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 19:53:35,116 - pyskl - INFO - +top1_acc 0.2502 +top5_acc 0.5002 +2024-12-28 19:53:35,116 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 19:53:35,160 - pyskl - INFO - +mean_acc 0.2500 +2024-12-28 19:53:35,172 - pyskl - INFO - Epoch(val) [73][309] top1_acc: 0.2502, top5_acc: 0.5002, mean_class_accuracy: 0.2500 +2024-12-28 19:57:50,730 - pyskl - INFO - Epoch [74][100/3746] lr: 5.207e-02, eta: 2 days, 18:21:46, time: 2.555, data_time: 1.530, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5877, loss_cls: 3.8610, loss: 3.8610 +2024-12-28 19:59:16,625 - pyskl - INFO - Epoch [74][200/3746] lr: 5.204e-02, eta: 2 days, 18:20:27, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5733, loss_cls: 3.8774, loss: 3.8774 +2024-12-28 20:00:42,328 - pyskl - INFO - Epoch [74][300/3746] lr: 5.201e-02, eta: 2 days, 18:19:07, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5791, loss_cls: 3.8948, loss: 3.8948 +2024-12-28 20:02:08,058 - pyskl - INFO - Epoch [74][400/3746] lr: 5.198e-02, eta: 2 days, 18:17:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5886, loss_cls: 3.8378, loss: 3.8378 +2024-12-28 20:03:33,214 - pyskl - INFO - Epoch [74][500/3746] lr: 5.195e-02, eta: 2 days, 18:16:27, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5863, loss_cls: 3.8357, loss: 3.8357 +2024-12-28 20:04:57,768 - pyskl - INFO - Epoch [74][600/3746] lr: 5.193e-02, eta: 2 days, 18:15:06, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5805, loss_cls: 3.8788, loss: 3.8788 +2024-12-28 20:06:23,011 - pyskl - INFO - Epoch [74][700/3746] lr: 5.190e-02, eta: 2 days, 18:13:45, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5866, loss_cls: 3.8478, loss: 3.8478 +2024-12-28 20:07:47,752 - pyskl - INFO - Epoch [74][800/3746] lr: 5.187e-02, eta: 2 days, 18:12:24, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5813, loss_cls: 3.8435, loss: 3.8435 +2024-12-28 20:09:12,242 - pyskl - INFO - Epoch [74][900/3746] lr: 5.184e-02, eta: 2 days, 18:11:03, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5766, loss_cls: 3.8473, loss: 3.8473 +2024-12-28 20:10:36,885 - pyskl - INFO - Epoch [74][1000/3746] lr: 5.181e-02, eta: 2 days, 18:09:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5808, loss_cls: 3.8708, loss: 3.8708 +2024-12-28 20:12:01,229 - pyskl - INFO - Epoch [74][1100/3746] lr: 5.179e-02, eta: 2 days, 18:08:21, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5736, loss_cls: 3.8982, loss: 3.8982 +2024-12-28 20:13:25,564 - pyskl - INFO - Epoch [74][1200/3746] lr: 5.176e-02, eta: 2 days, 18:07:00, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5830, loss_cls: 3.8741, loss: 3.8741 +2024-12-28 20:14:50,237 - pyskl - INFO - Epoch [74][1300/3746] lr: 5.173e-02, eta: 2 days, 18:05:39, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5908, loss_cls: 3.8508, loss: 3.8508 +2024-12-28 20:16:14,374 - pyskl - INFO - Epoch [74][1400/3746] lr: 5.170e-02, eta: 2 days, 18:04:17, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5827, loss_cls: 3.8460, loss: 3.8460 +2024-12-28 20:17:38,980 - pyskl - INFO - Epoch [74][1500/3746] lr: 5.168e-02, eta: 2 days, 18:02:56, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5800, loss_cls: 3.8788, loss: 3.8788 +2024-12-28 20:19:03,074 - pyskl - INFO - Epoch [74][1600/3746] lr: 5.165e-02, eta: 2 days, 18:01:34, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5705, loss_cls: 3.9447, loss: 3.9447 +2024-12-28 20:20:27,404 - pyskl - INFO - Epoch [74][1700/3746] lr: 5.162e-02, eta: 2 days, 18:00:13, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5780, loss_cls: 3.9208, loss: 3.9208 +2024-12-28 20:21:52,158 - pyskl - INFO - Epoch [74][1800/3746] lr: 5.159e-02, eta: 2 days, 17:58:52, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5650, loss_cls: 3.9392, loss: 3.9392 +2024-12-28 20:23:16,499 - pyskl - INFO - Epoch [74][1900/3746] lr: 5.156e-02, eta: 2 days, 17:57:31, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5767, loss_cls: 3.8998, loss: 3.8998 +2024-12-28 20:24:40,498 - pyskl - INFO - Epoch [74][2000/3746] lr: 5.154e-02, eta: 2 days, 17:56:09, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5783, loss_cls: 3.8736, loss: 3.8736 +2024-12-28 20:26:05,448 - pyskl - INFO - Epoch [74][2100/3746] lr: 5.151e-02, eta: 2 days, 17:54:48, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5798, loss_cls: 3.8811, loss: 3.8811 +2024-12-28 20:27:29,842 - pyskl - INFO - Epoch [74][2200/3746] lr: 5.148e-02, eta: 2 days, 17:53:27, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5752, loss_cls: 3.8831, loss: 3.8831 +2024-12-28 20:28:54,730 - pyskl - INFO - Epoch [74][2300/3746] lr: 5.145e-02, eta: 2 days, 17:52:06, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5769, loss_cls: 3.8965, loss: 3.8965 +2024-12-28 20:30:19,750 - pyskl - INFO - Epoch [74][2400/3746] lr: 5.142e-02, eta: 2 days, 17:50:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5694, loss_cls: 3.9672, loss: 3.9672 +2024-12-28 20:31:44,298 - pyskl - INFO - Epoch [74][2500/3746] lr: 5.140e-02, eta: 2 days, 17:49:25, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5764, loss_cls: 3.9009, loss: 3.9009 +2024-12-28 20:33:08,402 - pyskl - INFO - Epoch [74][2600/3746] lr: 5.137e-02, eta: 2 days, 17:48:03, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5772, loss_cls: 3.8948, loss: 3.8948 +2024-12-28 20:34:32,610 - pyskl - INFO - Epoch [74][2700/3746] lr: 5.134e-02, eta: 2 days, 17:46:41, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5808, loss_cls: 3.8558, loss: 3.8558 +2024-12-28 20:35:56,870 - pyskl - INFO - Epoch [74][2800/3746] lr: 5.131e-02, eta: 2 days, 17:45:20, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5789, loss_cls: 3.8680, loss: 3.8680 +2024-12-28 20:37:21,586 - pyskl - INFO - Epoch [74][2900/3746] lr: 5.128e-02, eta: 2 days, 17:43:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5772, loss_cls: 3.8940, loss: 3.8940 +2024-12-28 20:38:45,978 - pyskl - INFO - Epoch [74][3000/3746] lr: 5.126e-02, eta: 2 days, 17:42:38, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5881, loss_cls: 3.8457, loss: 3.8457 +2024-12-28 20:40:10,345 - pyskl - INFO - Epoch [74][3100/3746] lr: 5.123e-02, eta: 2 days, 17:41:16, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5753, loss_cls: 3.9170, loss: 3.9170 +2024-12-28 20:41:34,714 - pyskl - INFO - Epoch [74][3200/3746] lr: 5.120e-02, eta: 2 days, 17:39:55, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5787, loss_cls: 3.8782, loss: 3.8782 +2024-12-28 20:42:58,760 - pyskl - INFO - Epoch [74][3300/3746] lr: 5.117e-02, eta: 2 days, 17:38:33, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5756, loss_cls: 3.8677, loss: 3.8677 +2024-12-28 20:44:23,063 - pyskl - INFO - Epoch [74][3400/3746] lr: 5.114e-02, eta: 2 days, 17:37:12, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5761, loss_cls: 3.9435, loss: 3.9435 +2024-12-28 20:45:47,586 - pyskl - INFO - Epoch [74][3500/3746] lr: 5.112e-02, eta: 2 days, 17:35:51, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5737, loss_cls: 3.8908, loss: 3.8908 +2024-12-28 20:47:11,992 - pyskl - INFO - Epoch [74][3600/3746] lr: 5.109e-02, eta: 2 days, 17:34:30, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5647, loss_cls: 3.9214, loss: 3.9214 +2024-12-28 20:48:36,334 - pyskl - INFO - Epoch [74][3700/3746] lr: 5.106e-02, eta: 2 days, 17:33:08, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5730, loss_cls: 3.8982, loss: 3.8982 +2024-12-28 20:49:16,701 - pyskl - INFO - Saving checkpoint at 74 epochs +2024-12-28 20:51:15,544 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 20:51:16,305 - pyskl - INFO - +top1_acc 0.2485 +top5_acc 0.4995 +2024-12-28 20:51:16,305 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 20:51:16,354 - pyskl - INFO - +mean_acc 0.2484 +2024-12-28 20:51:16,367 - pyskl - INFO - Epoch(val) [74][309] top1_acc: 0.2485, top5_acc: 0.4995, mean_class_accuracy: 0.2484 +2024-12-28 20:55:27,967 - pyskl - INFO - Epoch [75][100/3746] lr: 5.102e-02, eta: 2 days, 17:33:21, time: 2.516, data_time: 1.487, memory: 15990, top1_acc: 0.3341, top5_acc: 0.6006, loss_cls: 3.8023, loss: 3.8023 +2024-12-28 20:56:52,846 - pyskl - INFO - Epoch [75][200/3746] lr: 5.099e-02, eta: 2 days, 17:32:00, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5964, loss_cls: 3.7874, loss: 3.7874 +2024-12-28 20:58:17,077 - pyskl - INFO - Epoch [75][300/3746] lr: 5.096e-02, eta: 2 days, 17:30:39, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5867, loss_cls: 3.8371, loss: 3.8371 +2024-12-28 20:59:41,957 - pyskl - INFO - Epoch [75][400/3746] lr: 5.094e-02, eta: 2 days, 17:29:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5909, loss_cls: 3.8343, loss: 3.8343 +2024-12-28 21:01:07,040 - pyskl - INFO - Epoch [75][500/3746] lr: 5.091e-02, eta: 2 days, 17:27:57, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5858, loss_cls: 3.8600, loss: 3.8600 +2024-12-28 21:02:32,007 - pyskl - INFO - Epoch [75][600/3746] lr: 5.088e-02, eta: 2 days, 17:26:36, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5856, loss_cls: 3.8576, loss: 3.8576 +2024-12-28 21:03:56,433 - pyskl - INFO - Epoch [75][700/3746] lr: 5.085e-02, eta: 2 days, 17:25:15, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5763, loss_cls: 3.8893, loss: 3.8893 +2024-12-28 21:05:20,766 - pyskl - INFO - Epoch [75][800/3746] lr: 5.082e-02, eta: 2 days, 17:23:53, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5895, loss_cls: 3.8550, loss: 3.8550 +2024-12-28 21:06:44,916 - pyskl - INFO - Epoch [75][900/3746] lr: 5.080e-02, eta: 2 days, 17:22:32, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5823, loss_cls: 3.8833, loss: 3.8833 +2024-12-28 21:08:09,057 - pyskl - INFO - Epoch [75][1000/3746] lr: 5.077e-02, eta: 2 days, 17:21:10, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5861, loss_cls: 3.8529, loss: 3.8529 +2024-12-28 21:09:33,515 - pyskl - INFO - Epoch [75][1100/3746] lr: 5.074e-02, eta: 2 days, 17:19:48, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5763, loss_cls: 3.8855, loss: 3.8855 +2024-12-28 21:10:58,054 - pyskl - INFO - Epoch [75][1200/3746] lr: 5.071e-02, eta: 2 days, 17:18:27, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5809, loss_cls: 3.8667, loss: 3.8667 +2024-12-28 21:12:22,648 - pyskl - INFO - Epoch [75][1300/3746] lr: 5.068e-02, eta: 2 days, 17:17:06, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5797, loss_cls: 3.8731, loss: 3.8731 +2024-12-28 21:13:47,554 - pyskl - INFO - Epoch [75][1400/3746] lr: 5.066e-02, eta: 2 days, 17:15:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5720, loss_cls: 3.9066, loss: 3.9066 +2024-12-28 21:15:11,967 - pyskl - INFO - Epoch [75][1500/3746] lr: 5.063e-02, eta: 2 days, 17:14:24, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5831, loss_cls: 3.8723, loss: 3.8723 +2024-12-28 21:16:36,479 - pyskl - INFO - Epoch [75][1600/3746] lr: 5.060e-02, eta: 2 days, 17:13:02, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5805, loss_cls: 3.8418, loss: 3.8418 +2024-12-28 21:18:00,475 - pyskl - INFO - Epoch [75][1700/3746] lr: 5.057e-02, eta: 2 days, 17:11:40, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5787, loss_cls: 3.8742, loss: 3.8742 +2024-12-28 21:19:24,971 - pyskl - INFO - Epoch [75][1800/3746] lr: 5.054e-02, eta: 2 days, 17:10:19, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5781, loss_cls: 3.8951, loss: 3.8951 +2024-12-28 21:20:50,032 - pyskl - INFO - Epoch [75][1900/3746] lr: 5.052e-02, eta: 2 days, 17:08:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5864, loss_cls: 3.8611, loss: 3.8611 +2024-12-28 21:22:14,819 - pyskl - INFO - Epoch [75][2000/3746] lr: 5.049e-02, eta: 2 days, 17:07:37, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5856, loss_cls: 3.8754, loss: 3.8754 +2024-12-28 21:23:39,228 - pyskl - INFO - Epoch [75][2100/3746] lr: 5.046e-02, eta: 2 days, 17:06:16, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5789, loss_cls: 3.9148, loss: 3.9148 +2024-12-28 21:25:04,157 - pyskl - INFO - Epoch [75][2200/3746] lr: 5.043e-02, eta: 2 days, 17:04:55, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5755, loss_cls: 3.8750, loss: 3.8750 +2024-12-28 21:26:28,733 - pyskl - INFO - Epoch [75][2300/3746] lr: 5.040e-02, eta: 2 days, 17:03:34, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5766, loss_cls: 3.8843, loss: 3.8843 +2024-12-28 21:27:53,261 - pyskl - INFO - Epoch [75][2400/3746] lr: 5.038e-02, eta: 2 days, 17:02:12, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5858, loss_cls: 3.8497, loss: 3.8497 +2024-12-28 21:29:17,530 - pyskl - INFO - Epoch [75][2500/3746] lr: 5.035e-02, eta: 2 days, 17:00:51, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5759, loss_cls: 3.8777, loss: 3.8777 +2024-12-28 21:30:41,870 - pyskl - INFO - Epoch [75][2600/3746] lr: 5.032e-02, eta: 2 days, 16:59:29, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5863, loss_cls: 3.8121, loss: 3.8121 +2024-12-28 21:32:06,036 - pyskl - INFO - Epoch [75][2700/3746] lr: 5.029e-02, eta: 2 days, 16:58:07, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5711, loss_cls: 3.9025, loss: 3.9025 +2024-12-28 21:33:30,663 - pyskl - INFO - Epoch [75][2800/3746] lr: 5.026e-02, eta: 2 days, 16:56:46, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5731, loss_cls: 3.9282, loss: 3.9282 +2024-12-28 21:34:55,795 - pyskl - INFO - Epoch [75][2900/3746] lr: 5.024e-02, eta: 2 days, 16:55:25, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5848, loss_cls: 3.8411, loss: 3.8411 +2024-12-28 21:36:20,521 - pyskl - INFO - Epoch [75][3000/3746] lr: 5.021e-02, eta: 2 days, 16:54:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5741, loss_cls: 3.9147, loss: 3.9147 +2024-12-28 21:37:45,530 - pyskl - INFO - Epoch [75][3100/3746] lr: 5.018e-02, eta: 2 days, 16:52:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5794, loss_cls: 3.8987, loss: 3.8987 +2024-12-28 21:39:10,365 - pyskl - INFO - Epoch [75][3200/3746] lr: 5.015e-02, eta: 2 days, 16:51:22, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5844, loss_cls: 3.8418, loss: 3.8418 +2024-12-28 21:40:35,440 - pyskl - INFO - Epoch [75][3300/3746] lr: 5.012e-02, eta: 2 days, 16:50:02, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5823, loss_cls: 3.8844, loss: 3.8844 +2024-12-28 21:42:00,116 - pyskl - INFO - Epoch [75][3400/3746] lr: 5.010e-02, eta: 2 days, 16:48:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5773, loss_cls: 3.9219, loss: 3.9219 +2024-12-28 21:43:25,398 - pyskl - INFO - Epoch [75][3500/3746] lr: 5.007e-02, eta: 2 days, 16:47:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5752, loss_cls: 3.8957, loss: 3.8957 +2024-12-28 21:44:50,232 - pyskl - INFO - Epoch [75][3600/3746] lr: 5.004e-02, eta: 2 days, 16:45:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5783, loss_cls: 3.8746, loss: 3.8746 +2024-12-28 21:46:14,779 - pyskl - INFO - Epoch [75][3700/3746] lr: 5.001e-02, eta: 2 days, 16:44:37, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5780, loss_cls: 3.8685, loss: 3.8685 +2024-12-28 21:46:55,774 - pyskl - INFO - Saving checkpoint at 75 epochs +2024-12-28 21:48:54,211 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 21:48:54,951 - pyskl - INFO - +top1_acc 0.2480 +top5_acc 0.4927 +2024-12-28 21:48:54,951 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 21:48:54,996 - pyskl - INFO - +mean_acc 0.2478 +2024-12-28 21:48:55,017 - pyskl - INFO - Epoch(val) [75][309] top1_acc: 0.2480, top5_acc: 0.4927, mean_class_accuracy: 0.2478 +2024-12-28 21:53:07,362 - pyskl - INFO - Epoch [76][100/3746] lr: 4.997e-02, eta: 2 days, 16:44:47, time: 2.523, data_time: 1.494, memory: 15990, top1_acc: 0.3336, top5_acc: 0.6000, loss_cls: 3.7563, loss: 3.7563 +2024-12-28 21:54:32,447 - pyskl - INFO - Epoch [76][200/3746] lr: 4.994e-02, eta: 2 days, 16:43:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5931, loss_cls: 3.7937, loss: 3.7937 +2024-12-28 21:55:57,329 - pyskl - INFO - Epoch [76][300/3746] lr: 4.992e-02, eta: 2 days, 16:42:05, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5867, loss_cls: 3.8003, loss: 3.8003 +2024-12-28 21:57:22,276 - pyskl - INFO - Epoch [76][400/3746] lr: 4.989e-02, eta: 2 days, 16:40:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5867, loss_cls: 3.8181, loss: 3.8181 +2024-12-28 21:58:47,188 - pyskl - INFO - Epoch [76][500/3746] lr: 4.986e-02, eta: 2 days, 16:39:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5800, loss_cls: 3.8568, loss: 3.8568 +2024-12-28 22:00:12,071 - pyskl - INFO - Epoch [76][600/3746] lr: 4.983e-02, eta: 2 days, 16:38:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5927, loss_cls: 3.8083, loss: 3.8083 +2024-12-28 22:01:36,999 - pyskl - INFO - Epoch [76][700/3746] lr: 4.980e-02, eta: 2 days, 16:36:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5825, loss_cls: 3.8374, loss: 3.8374 +2024-12-28 22:03:01,540 - pyskl - INFO - Epoch [76][800/3746] lr: 4.978e-02, eta: 2 days, 16:35:20, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5830, loss_cls: 3.8689, loss: 3.8689 +2024-12-28 22:04:26,202 - pyskl - INFO - Epoch [76][900/3746] lr: 4.975e-02, eta: 2 days, 16:33:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5809, loss_cls: 3.8515, loss: 3.8515 +2024-12-28 22:05:50,691 - pyskl - INFO - Epoch [76][1000/3746] lr: 4.972e-02, eta: 2 days, 16:32:37, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5894, loss_cls: 3.8452, loss: 3.8452 +2024-12-28 22:07:15,114 - pyskl - INFO - Epoch [76][1100/3746] lr: 4.969e-02, eta: 2 days, 16:31:15, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5891, loss_cls: 3.8205, loss: 3.8205 +2024-12-28 22:08:40,008 - pyskl - INFO - Epoch [76][1200/3746] lr: 4.966e-02, eta: 2 days, 16:29:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5855, loss_cls: 3.8316, loss: 3.8316 +2024-12-28 22:10:04,718 - pyskl - INFO - Epoch [76][1300/3746] lr: 4.964e-02, eta: 2 days, 16:28:33, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5739, loss_cls: 3.8958, loss: 3.8958 +2024-12-28 22:11:29,950 - pyskl - INFO - Epoch [76][1400/3746] lr: 4.961e-02, eta: 2 days, 16:27:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5764, loss_cls: 3.8714, loss: 3.8714 +2024-12-28 22:12:54,426 - pyskl - INFO - Epoch [76][1500/3746] lr: 4.958e-02, eta: 2 days, 16:25:50, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5870, loss_cls: 3.8582, loss: 3.8582 +2024-12-28 22:14:19,147 - pyskl - INFO - Epoch [76][1600/3746] lr: 4.955e-02, eta: 2 days, 16:24:29, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5761, loss_cls: 3.8943, loss: 3.8943 +2024-12-28 22:15:43,914 - pyskl - INFO - Epoch [76][1700/3746] lr: 4.953e-02, eta: 2 days, 16:23:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5877, loss_cls: 3.8473, loss: 3.8473 +2024-12-28 22:17:08,583 - pyskl - INFO - Epoch [76][1800/3746] lr: 4.950e-02, eta: 2 days, 16:21:46, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5833, loss_cls: 3.8645, loss: 3.8645 +2024-12-28 22:18:33,313 - pyskl - INFO - Epoch [76][1900/3746] lr: 4.947e-02, eta: 2 days, 16:20:25, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5763, loss_cls: 3.8926, loss: 3.8926 +2024-12-28 22:19:58,100 - pyskl - INFO - Epoch [76][2000/3746] lr: 4.944e-02, eta: 2 days, 16:19:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5861, loss_cls: 3.8751, loss: 3.8751 +2024-12-28 22:21:22,830 - pyskl - INFO - Epoch [76][2100/3746] lr: 4.941e-02, eta: 2 days, 16:17:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5764, loss_cls: 3.8930, loss: 3.8930 +2024-12-28 22:22:47,765 - pyskl - INFO - Epoch [76][2200/3746] lr: 4.939e-02, eta: 2 days, 16:16:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5767, loss_cls: 3.9236, loss: 3.9236 +2024-12-28 22:24:12,866 - pyskl - INFO - Epoch [76][2300/3746] lr: 4.936e-02, eta: 2 days, 16:15:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5670, loss_cls: 3.9295, loss: 3.9295 +2024-12-28 22:25:37,587 - pyskl - INFO - Epoch [76][2400/3746] lr: 4.933e-02, eta: 2 days, 16:13:39, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5783, loss_cls: 3.8482, loss: 3.8482 +2024-12-28 22:27:02,292 - pyskl - INFO - Epoch [76][2500/3746] lr: 4.930e-02, eta: 2 days, 16:12:18, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5770, loss_cls: 3.8710, loss: 3.8710 +2024-12-28 22:28:26,744 - pyskl - INFO - Epoch [76][2600/3746] lr: 4.927e-02, eta: 2 days, 16:10:56, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5817, loss_cls: 3.8632, loss: 3.8632 +2024-12-28 22:29:51,027 - pyskl - INFO - Epoch [76][2700/3746] lr: 4.925e-02, eta: 2 days, 16:09:34, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5820, loss_cls: 3.8707, loss: 3.8707 +2024-12-28 22:31:15,436 - pyskl - INFO - Epoch [76][2800/3746] lr: 4.922e-02, eta: 2 days, 16:08:13, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5933, loss_cls: 3.8100, loss: 3.8100 +2024-12-28 22:32:39,765 - pyskl - INFO - Epoch [76][2900/3746] lr: 4.919e-02, eta: 2 days, 16:06:51, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5795, loss_cls: 3.8662, loss: 3.8662 +2024-12-28 22:34:04,570 - pyskl - INFO - Epoch [76][3000/3746] lr: 4.916e-02, eta: 2 days, 16:05:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5747, loss_cls: 3.8857, loss: 3.8857 +2024-12-28 22:35:28,682 - pyskl - INFO - Epoch [76][3100/3746] lr: 4.913e-02, eta: 2 days, 16:04:08, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5784, loss_cls: 3.8913, loss: 3.8913 +2024-12-28 22:36:53,032 - pyskl - INFO - Epoch [76][3200/3746] lr: 4.911e-02, eta: 2 days, 16:02:46, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5748, loss_cls: 3.8802, loss: 3.8802 +2024-12-28 22:38:18,134 - pyskl - INFO - Epoch [76][3300/3746] lr: 4.908e-02, eta: 2 days, 16:01:25, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5828, loss_cls: 3.8540, loss: 3.8540 +2024-12-28 22:39:43,445 - pyskl - INFO - Epoch [76][3400/3746] lr: 4.905e-02, eta: 2 days, 16:00:04, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5791, loss_cls: 3.8849, loss: 3.8849 +2024-12-28 22:41:08,032 - pyskl - INFO - Epoch [76][3500/3746] lr: 4.902e-02, eta: 2 days, 15:58:43, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5825, loss_cls: 3.8639, loss: 3.8639 +2024-12-28 22:42:32,484 - pyskl - INFO - Epoch [76][3600/3746] lr: 4.899e-02, eta: 2 days, 15:57:21, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5761, loss_cls: 3.9154, loss: 3.9154 +2024-12-28 22:43:57,688 - pyskl - INFO - Epoch [76][3700/3746] lr: 4.897e-02, eta: 2 days, 15:56:00, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5939, loss_cls: 3.8117, loss: 3.8117 +2024-12-28 22:44:38,666 - pyskl - INFO - Saving checkpoint at 76 epochs +2024-12-28 22:46:36,827 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 22:46:37,517 - pyskl - INFO - +top1_acc 0.2566 +top5_acc 0.5072 +2024-12-28 22:46:37,517 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 22:46:37,560 - pyskl - INFO - +mean_acc 0.2565 +2024-12-28 22:46:37,564 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_68.pth was removed +2024-12-28 22:46:37,848 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_76.pth. +2024-12-28 22:46:37,848 - pyskl - INFO - Best top1_acc is 0.2566 at 76 epoch. +2024-12-28 22:46:37,864 - pyskl - INFO - Epoch(val) [76][309] top1_acc: 0.2566, top5_acc: 0.5072, mean_class_accuracy: 0.2565 +2024-12-28 22:50:46,383 - pyskl - INFO - Epoch [77][100/3746] lr: 4.893e-02, eta: 2 days, 15:56:03, time: 2.485, data_time: 1.464, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5900, loss_cls: 3.8142, loss: 3.8142 +2024-12-28 22:52:11,397 - pyskl - INFO - Epoch [77][200/3746] lr: 4.890e-02, eta: 2 days, 15:54:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5919, loss_cls: 3.8166, loss: 3.8166 +2024-12-28 22:53:36,484 - pyskl - INFO - Epoch [77][300/3746] lr: 4.887e-02, eta: 2 days, 15:53:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5830, loss_cls: 3.8314, loss: 3.8314 +2024-12-28 22:55:01,335 - pyskl - INFO - Epoch [77][400/3746] lr: 4.884e-02, eta: 2 days, 15:52:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5869, loss_cls: 3.8611, loss: 3.8611 +2024-12-28 22:56:26,085 - pyskl - INFO - Epoch [77][500/3746] lr: 4.881e-02, eta: 2 days, 15:50:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5959, loss_cls: 3.7671, loss: 3.7671 +2024-12-28 22:57:50,853 - pyskl - INFO - Epoch [77][600/3746] lr: 4.879e-02, eta: 2 days, 15:49:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5878, loss_cls: 3.8181, loss: 3.8181 +2024-12-28 22:59:15,175 - pyskl - INFO - Epoch [77][700/3746] lr: 4.876e-02, eta: 2 days, 15:47:55, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5898, loss_cls: 3.7822, loss: 3.7822 +2024-12-28 23:00:39,283 - pyskl - INFO - Epoch [77][800/3746] lr: 4.873e-02, eta: 2 days, 15:46:33, time: 0.841, data_time: 0.001, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5781, loss_cls: 3.8484, loss: 3.8484 +2024-12-28 23:02:03,804 - pyskl - INFO - Epoch [77][900/3746] lr: 4.870e-02, eta: 2 days, 15:45:11, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5919, loss_cls: 3.8135, loss: 3.8135 +2024-12-28 23:03:28,285 - pyskl - INFO - Epoch [77][1000/3746] lr: 4.867e-02, eta: 2 days, 15:43:49, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5928, loss_cls: 3.8136, loss: 3.8136 +2024-12-28 23:04:52,682 - pyskl - INFO - Epoch [77][1100/3746] lr: 4.865e-02, eta: 2 days, 15:42:28, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5834, loss_cls: 3.8544, loss: 3.8544 +2024-12-28 23:06:17,426 - pyskl - INFO - Epoch [77][1200/3746] lr: 4.862e-02, eta: 2 days, 15:41:06, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5859, loss_cls: 3.8196, loss: 3.8196 +2024-12-28 23:07:41,407 - pyskl - INFO - Epoch [77][1300/3746] lr: 4.859e-02, eta: 2 days, 15:39:44, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5822, loss_cls: 3.8627, loss: 3.8627 +2024-12-28 23:09:05,323 - pyskl - INFO - Epoch [77][1400/3746] lr: 4.856e-02, eta: 2 days, 15:38:22, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5719, loss_cls: 3.9027, loss: 3.9027 +2024-12-28 23:10:29,573 - pyskl - INFO - Epoch [77][1500/3746] lr: 4.853e-02, eta: 2 days, 15:37:00, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5787, loss_cls: 3.8781, loss: 3.8781 +2024-12-28 23:11:53,919 - pyskl - INFO - Epoch [77][1600/3746] lr: 4.851e-02, eta: 2 days, 15:35:38, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5756, loss_cls: 3.8513, loss: 3.8513 +2024-12-28 23:13:18,503 - pyskl - INFO - Epoch [77][1700/3746] lr: 4.848e-02, eta: 2 days, 15:34:16, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5878, loss_cls: 3.8475, loss: 3.8475 +2024-12-28 23:14:43,266 - pyskl - INFO - Epoch [77][1800/3746] lr: 4.845e-02, eta: 2 days, 15:32:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5873, loss_cls: 3.8500, loss: 3.8500 +2024-12-28 23:16:08,430 - pyskl - INFO - Epoch [77][1900/3746] lr: 4.842e-02, eta: 2 days, 15:31:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5844, loss_cls: 3.8439, loss: 3.8439 +2024-12-28 23:17:33,186 - pyskl - INFO - Epoch [77][2000/3746] lr: 4.839e-02, eta: 2 days, 15:30:12, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5748, loss_cls: 3.8891, loss: 3.8891 +2024-12-28 23:18:57,972 - pyskl - INFO - Epoch [77][2100/3746] lr: 4.837e-02, eta: 2 days, 15:28:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5847, loss_cls: 3.8469, loss: 3.8469 +2024-12-28 23:20:22,984 - pyskl - INFO - Epoch [77][2200/3746] lr: 4.834e-02, eta: 2 days, 15:27:30, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5767, loss_cls: 3.8772, loss: 3.8772 +2024-12-28 23:21:47,770 - pyskl - INFO - Epoch [77][2300/3746] lr: 4.831e-02, eta: 2 days, 15:26:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5825, loss_cls: 3.8321, loss: 3.8321 +2024-12-28 23:23:12,862 - pyskl - INFO - Epoch [77][2400/3746] lr: 4.828e-02, eta: 2 days, 15:24:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5717, loss_cls: 3.9058, loss: 3.9058 +2024-12-28 23:24:37,608 - pyskl - INFO - Epoch [77][2500/3746] lr: 4.825e-02, eta: 2 days, 15:23:26, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5923, loss_cls: 3.8198, loss: 3.8198 +2024-12-28 23:26:01,693 - pyskl - INFO - Epoch [77][2600/3746] lr: 4.823e-02, eta: 2 days, 15:22:04, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5869, loss_cls: 3.8598, loss: 3.8598 +2024-12-28 23:27:26,312 - pyskl - INFO - Epoch [77][2700/3746] lr: 4.820e-02, eta: 2 days, 15:20:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5802, loss_cls: 3.8812, loss: 3.8812 +2024-12-28 23:28:51,032 - pyskl - INFO - Epoch [77][2800/3746] lr: 4.817e-02, eta: 2 days, 15:19:21, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5909, loss_cls: 3.8475, loss: 3.8475 +2024-12-28 23:30:15,855 - pyskl - INFO - Epoch [77][2900/3746] lr: 4.814e-02, eta: 2 days, 15:17:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5858, loss_cls: 3.8108, loss: 3.8108 +2024-12-28 23:31:40,749 - pyskl - INFO - Epoch [77][3000/3746] lr: 4.811e-02, eta: 2 days, 15:16:38, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5837, loss_cls: 3.8805, loss: 3.8805 +2024-12-28 23:33:05,639 - pyskl - INFO - Epoch [77][3100/3746] lr: 4.809e-02, eta: 2 days, 15:15:16, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5869, loss_cls: 3.8629, loss: 3.8629 +2024-12-28 23:34:30,417 - pyskl - INFO - Epoch [77][3200/3746] lr: 4.806e-02, eta: 2 days, 15:13:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5798, loss_cls: 3.8747, loss: 3.8747 +2024-12-28 23:35:54,849 - pyskl - INFO - Epoch [77][3300/3746] lr: 4.803e-02, eta: 2 days, 15:12:33, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5789, loss_cls: 3.8696, loss: 3.8696 +2024-12-28 23:37:19,160 - pyskl - INFO - Epoch [77][3400/3746] lr: 4.800e-02, eta: 2 days, 15:11:11, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5852, loss_cls: 3.8604, loss: 3.8604 +2024-12-28 23:38:43,939 - pyskl - INFO - Epoch [77][3500/3746] lr: 4.798e-02, eta: 2 days, 15:09:50, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5852, loss_cls: 3.8572, loss: 3.8572 +2024-12-28 23:40:08,479 - pyskl - INFO - Epoch [77][3600/3746] lr: 4.795e-02, eta: 2 days, 15:08:28, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5850, loss_cls: 3.8394, loss: 3.8394 +2024-12-28 23:41:33,253 - pyskl - INFO - Epoch [77][3700/3746] lr: 4.792e-02, eta: 2 days, 15:07:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5695, loss_cls: 3.9274, loss: 3.9274 +2024-12-28 23:42:13,658 - pyskl - INFO - Saving checkpoint at 77 epochs +2024-12-28 23:44:12,425 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 23:44:13,305 - pyskl - INFO - +top1_acc 0.2444 +top5_acc 0.4959 +2024-12-28 23:44:13,305 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 23:44:13,346 - pyskl - INFO - +mean_acc 0.2443 +2024-12-28 23:44:13,357 - pyskl - INFO - Epoch(val) [77][309] top1_acc: 0.2444, top5_acc: 0.4959, mean_class_accuracy: 0.2443 +2024-12-28 23:48:25,313 - pyskl - INFO - Epoch [78][100/3746] lr: 4.788e-02, eta: 2 days, 15:07:09, time: 2.519, data_time: 1.500, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5980, loss_cls: 3.7837, loss: 3.7837 +2024-12-28 23:49:49,770 - pyskl - INFO - Epoch [78][200/3746] lr: 4.785e-02, eta: 2 days, 15:05:47, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5983, loss_cls: 3.7602, loss: 3.7602 +2024-12-28 23:51:14,300 - pyskl - INFO - Epoch [78][300/3746] lr: 4.782e-02, eta: 2 days, 15:04:26, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.6047, loss_cls: 3.7845, loss: 3.7845 +2024-12-28 23:52:38,944 - pyskl - INFO - Epoch [78][400/3746] lr: 4.779e-02, eta: 2 days, 15:03:04, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.6048, loss_cls: 3.7436, loss: 3.7436 +2024-12-28 23:54:03,147 - pyskl - INFO - Epoch [78][500/3746] lr: 4.777e-02, eta: 2 days, 15:01:42, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5889, loss_cls: 3.7896, loss: 3.7896 +2024-12-28 23:55:28,211 - pyskl - INFO - Epoch [78][600/3746] lr: 4.774e-02, eta: 2 days, 15:00:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5922, loss_cls: 3.8258, loss: 3.8258 +2024-12-28 23:56:53,097 - pyskl - INFO - Epoch [78][700/3746] lr: 4.771e-02, eta: 2 days, 14:58:59, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5891, loss_cls: 3.8356, loss: 3.8356 +2024-12-28 23:58:17,759 - pyskl - INFO - Epoch [78][800/3746] lr: 4.768e-02, eta: 2 days, 14:57:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5856, loss_cls: 3.8354, loss: 3.8354 +2024-12-28 23:59:42,503 - pyskl - INFO - Epoch [78][900/3746] lr: 4.766e-02, eta: 2 days, 14:56:16, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5878, loss_cls: 3.8349, loss: 3.8349 +2024-12-29 00:01:06,964 - pyskl - INFO - Epoch [78][1000/3746] lr: 4.763e-02, eta: 2 days, 14:54:54, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5914, loss_cls: 3.8320, loss: 3.8320 +2024-12-29 00:02:31,669 - pyskl - INFO - Epoch [78][1100/3746] lr: 4.760e-02, eta: 2 days, 14:53:32, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5853, loss_cls: 3.8638, loss: 3.8638 +2024-12-29 00:03:56,258 - pyskl - INFO - Epoch [78][1200/3746] lr: 4.757e-02, eta: 2 days, 14:52:10, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5811, loss_cls: 3.8592, loss: 3.8592 +2024-12-29 00:05:20,705 - pyskl - INFO - Epoch [78][1300/3746] lr: 4.754e-02, eta: 2 days, 14:50:48, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5869, loss_cls: 3.8423, loss: 3.8423 +2024-12-29 00:06:44,445 - pyskl - INFO - Epoch [78][1400/3746] lr: 4.752e-02, eta: 2 days, 14:49:26, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5834, loss_cls: 3.8223, loss: 3.8223 +2024-12-29 00:08:08,780 - pyskl - INFO - Epoch [78][1500/3746] lr: 4.749e-02, eta: 2 days, 14:48:04, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5784, loss_cls: 3.8571, loss: 3.8571 +2024-12-29 00:09:33,744 - pyskl - INFO - Epoch [78][1600/3746] lr: 4.746e-02, eta: 2 days, 14:46:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5903, loss_cls: 3.8251, loss: 3.8251 +2024-12-29 00:10:58,448 - pyskl - INFO - Epoch [78][1700/3746] lr: 4.743e-02, eta: 2 days, 14:45:21, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5697, loss_cls: 3.8963, loss: 3.8963 +2024-12-29 00:12:23,202 - pyskl - INFO - Epoch [78][1800/3746] lr: 4.740e-02, eta: 2 days, 14:43:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5887, loss_cls: 3.8350, loss: 3.8350 +2024-12-29 00:13:47,909 - pyskl - INFO - Epoch [78][1900/3746] lr: 4.738e-02, eta: 2 days, 14:42:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5881, loss_cls: 3.8399, loss: 3.8399 +2024-12-29 00:15:12,254 - pyskl - INFO - Epoch [78][2000/3746] lr: 4.735e-02, eta: 2 days, 14:41:16, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5819, loss_cls: 3.8468, loss: 3.8468 +2024-12-29 00:16:36,845 - pyskl - INFO - Epoch [78][2100/3746] lr: 4.732e-02, eta: 2 days, 14:39:54, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5897, loss_cls: 3.7932, loss: 3.7932 +2024-12-29 00:18:01,482 - pyskl - INFO - Epoch [78][2200/3746] lr: 4.729e-02, eta: 2 days, 14:38:32, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5892, loss_cls: 3.7939, loss: 3.7939 +2024-12-29 00:19:25,682 - pyskl - INFO - Epoch [78][2300/3746] lr: 4.726e-02, eta: 2 days, 14:37:10, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5892, loss_cls: 3.8588, loss: 3.8588 +2024-12-29 00:20:50,121 - pyskl - INFO - Epoch [78][2400/3746] lr: 4.724e-02, eta: 2 days, 14:35:48, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5873, loss_cls: 3.8516, loss: 3.8516 +2024-12-29 00:22:14,339 - pyskl - INFO - Epoch [78][2500/3746] lr: 4.721e-02, eta: 2 days, 14:34:26, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.6002, loss_cls: 3.7794, loss: 3.7794 +2024-12-29 00:23:39,105 - pyskl - INFO - Epoch [78][2600/3746] lr: 4.718e-02, eta: 2 days, 14:33:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5867, loss_cls: 3.8690, loss: 3.8690 +2024-12-29 00:25:03,185 - pyskl - INFO - Epoch [78][2700/3746] lr: 4.715e-02, eta: 2 days, 14:31:42, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5811, loss_cls: 3.8534, loss: 3.8534 +2024-12-29 00:26:27,647 - pyskl - INFO - Epoch [78][2800/3746] lr: 4.712e-02, eta: 2 days, 14:30:20, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5952, loss_cls: 3.8180, loss: 3.8180 +2024-12-29 00:27:51,964 - pyskl - INFO - Epoch [78][2900/3746] lr: 4.710e-02, eta: 2 days, 14:28:58, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5913, loss_cls: 3.8242, loss: 3.8242 +2024-12-29 00:29:16,391 - pyskl - INFO - Epoch [78][3000/3746] lr: 4.707e-02, eta: 2 days, 14:27:36, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5892, loss_cls: 3.8533, loss: 3.8533 +2024-12-29 00:30:41,085 - pyskl - INFO - Epoch [78][3100/3746] lr: 4.704e-02, eta: 2 days, 14:26:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5772, loss_cls: 3.9073, loss: 3.9073 +2024-12-29 00:32:05,398 - pyskl - INFO - Epoch [78][3200/3746] lr: 4.701e-02, eta: 2 days, 14:24:52, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5836, loss_cls: 3.8521, loss: 3.8521 +2024-12-29 00:33:30,078 - pyskl - INFO - Epoch [78][3300/3746] lr: 4.699e-02, eta: 2 days, 14:23:31, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5864, loss_cls: 3.8611, loss: 3.8611 +2024-12-29 00:34:54,219 - pyskl - INFO - Epoch [78][3400/3746] lr: 4.696e-02, eta: 2 days, 14:22:08, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5867, loss_cls: 3.8298, loss: 3.8298 +2024-12-29 00:36:18,186 - pyskl - INFO - Epoch [78][3500/3746] lr: 4.693e-02, eta: 2 days, 14:20:46, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5878, loss_cls: 3.8714, loss: 3.8714 +2024-12-29 00:37:42,896 - pyskl - INFO - Epoch [78][3600/3746] lr: 4.690e-02, eta: 2 days, 14:19:24, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5916, loss_cls: 3.8450, loss: 3.8450 +2024-12-29 00:39:07,362 - pyskl - INFO - Epoch [78][3700/3746] lr: 4.687e-02, eta: 2 days, 14:18:02, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5839, loss_cls: 3.8686, loss: 3.8686 +2024-12-29 00:39:48,480 - pyskl - INFO - Saving checkpoint at 78 epochs +2024-12-29 00:41:47,023 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 00:41:47,721 - pyskl - INFO - +top1_acc 0.2536 +top5_acc 0.5082 +2024-12-29 00:41:47,721 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 00:41:47,761 - pyskl - INFO - +mean_acc 0.2532 +2024-12-29 00:41:47,775 - pyskl - INFO - Epoch(val) [78][309] top1_acc: 0.2536, top5_acc: 0.5082, mean_class_accuracy: 0.2532 +2024-12-29 00:45:57,544 - pyskl - INFO - Epoch [79][100/3746] lr: 4.683e-02, eta: 2 days, 14:17:59, time: 2.498, data_time: 1.465, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5950, loss_cls: 3.7978, loss: 3.7978 +2024-12-29 00:47:22,260 - pyskl - INFO - Epoch [79][200/3746] lr: 4.680e-02, eta: 2 days, 14:16:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6000, loss_cls: 3.7608, loss: 3.7608 +2024-12-29 00:48:47,726 - pyskl - INFO - Epoch [79][300/3746] lr: 4.678e-02, eta: 2 days, 14:15:17, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5884, loss_cls: 3.8068, loss: 3.8068 +2024-12-29 00:50:12,951 - pyskl - INFO - Epoch [79][400/3746] lr: 4.675e-02, eta: 2 days, 14:13:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5920, loss_cls: 3.7517, loss: 3.7517 +2024-12-29 00:51:37,638 - pyskl - INFO - Epoch [79][500/3746] lr: 4.672e-02, eta: 2 days, 14:12:33, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5850, loss_cls: 3.8347, loss: 3.8347 +2024-12-29 00:53:02,241 - pyskl - INFO - Epoch [79][600/3746] lr: 4.669e-02, eta: 2 days, 14:11:12, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5891, loss_cls: 3.8194, loss: 3.8194 +2024-12-29 00:54:27,120 - pyskl - INFO - Epoch [79][700/3746] lr: 4.667e-02, eta: 2 days, 14:09:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.5983, loss_cls: 3.7560, loss: 3.7560 +2024-12-29 00:55:51,539 - pyskl - INFO - Epoch [79][800/3746] lr: 4.664e-02, eta: 2 days, 14:08:28, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5916, loss_cls: 3.7653, loss: 3.7653 +2024-12-29 00:57:16,538 - pyskl - INFO - Epoch [79][900/3746] lr: 4.661e-02, eta: 2 days, 14:07:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5870, loss_cls: 3.8261, loss: 3.8261 +2024-12-29 00:58:41,053 - pyskl - INFO - Epoch [79][1000/3746] lr: 4.658e-02, eta: 2 days, 14:05:44, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5938, loss_cls: 3.8014, loss: 3.8014 +2024-12-29 01:00:05,744 - pyskl - INFO - Epoch [79][1100/3746] lr: 4.655e-02, eta: 2 days, 14:04:22, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5872, loss_cls: 3.8480, loss: 3.8480 +2024-12-29 01:01:29,910 - pyskl - INFO - Epoch [79][1200/3746] lr: 4.653e-02, eta: 2 days, 14:03:00, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5947, loss_cls: 3.7826, loss: 3.7826 +2024-12-29 01:02:54,586 - pyskl - INFO - Epoch [79][1300/3746] lr: 4.650e-02, eta: 2 days, 14:01:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5823, loss_cls: 3.8661, loss: 3.8661 +2024-12-29 01:04:18,706 - pyskl - INFO - Epoch [79][1400/3746] lr: 4.647e-02, eta: 2 days, 14:00:16, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5938, loss_cls: 3.7986, loss: 3.7986 +2024-12-29 01:05:43,192 - pyskl - INFO - Epoch [79][1500/3746] lr: 4.644e-02, eta: 2 days, 13:58:54, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5816, loss_cls: 3.8763, loss: 3.8763 +2024-12-29 01:07:07,965 - pyskl - INFO - Epoch [79][1600/3746] lr: 4.641e-02, eta: 2 days, 13:57:32, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5866, loss_cls: 3.8470, loss: 3.8470 +2024-12-29 01:08:31,892 - pyskl - INFO - Epoch [79][1700/3746] lr: 4.639e-02, eta: 2 days, 13:56:10, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5781, loss_cls: 3.9018, loss: 3.9018 +2024-12-29 01:09:56,341 - pyskl - INFO - Epoch [79][1800/3746] lr: 4.636e-02, eta: 2 days, 13:54:48, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5836, loss_cls: 3.8418, loss: 3.8418 +2024-12-29 01:11:21,010 - pyskl - INFO - Epoch [79][1900/3746] lr: 4.633e-02, eta: 2 days, 13:53:26, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5877, loss_cls: 3.8134, loss: 3.8134 +2024-12-29 01:12:45,724 - pyskl - INFO - Epoch [79][2000/3746] lr: 4.630e-02, eta: 2 days, 13:52:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5853, loss_cls: 3.8171, loss: 3.8171 +2024-12-29 01:14:09,875 - pyskl - INFO - Epoch [79][2100/3746] lr: 4.628e-02, eta: 2 days, 13:50:42, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5884, loss_cls: 3.8190, loss: 3.8190 +2024-12-29 01:15:34,162 - pyskl - INFO - Epoch [79][2200/3746] lr: 4.625e-02, eta: 2 days, 13:49:19, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5920, loss_cls: 3.8142, loss: 3.8142 +2024-12-29 01:16:58,446 - pyskl - INFO - Epoch [79][2300/3746] lr: 4.622e-02, eta: 2 days, 13:47:57, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5944, loss_cls: 3.8014, loss: 3.8014 +2024-12-29 01:18:22,969 - pyskl - INFO - Epoch [79][2400/3746] lr: 4.619e-02, eta: 2 days, 13:46:35, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5789, loss_cls: 3.8596, loss: 3.8596 +2024-12-29 01:19:47,415 - pyskl - INFO - Epoch [79][2500/3746] lr: 4.616e-02, eta: 2 days, 13:45:13, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5809, loss_cls: 3.8544, loss: 3.8544 +2024-12-29 01:21:11,546 - pyskl - INFO - Epoch [79][2600/3746] lr: 4.614e-02, eta: 2 days, 13:43:51, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5861, loss_cls: 3.8503, loss: 3.8503 +2024-12-29 01:22:35,687 - pyskl - INFO - Epoch [79][2700/3746] lr: 4.611e-02, eta: 2 days, 13:42:28, time: 0.841, data_time: 0.001, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5848, loss_cls: 3.8390, loss: 3.8390 +2024-12-29 01:24:00,316 - pyskl - INFO - Epoch [79][2800/3746] lr: 4.608e-02, eta: 2 days, 13:41:07, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5889, loss_cls: 3.8175, loss: 3.8175 +2024-12-29 01:25:24,324 - pyskl - INFO - Epoch [79][2900/3746] lr: 4.605e-02, eta: 2 days, 13:39:44, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5887, loss_cls: 3.8372, loss: 3.8372 +2024-12-29 01:26:49,166 - pyskl - INFO - Epoch [79][3000/3746] lr: 4.602e-02, eta: 2 days, 13:38:22, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5906, loss_cls: 3.8207, loss: 3.8207 +2024-12-29 01:28:13,882 - pyskl - INFO - Epoch [79][3100/3746] lr: 4.600e-02, eta: 2 days, 13:37:01, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5853, loss_cls: 3.8422, loss: 3.8422 +2024-12-29 01:29:38,751 - pyskl - INFO - Epoch [79][3200/3746] lr: 4.597e-02, eta: 2 days, 13:35:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5856, loss_cls: 3.8283, loss: 3.8283 +2024-12-29 01:31:02,776 - pyskl - INFO - Epoch [79][3300/3746] lr: 4.594e-02, eta: 2 days, 13:34:16, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5866, loss_cls: 3.8161, loss: 3.8161 +2024-12-29 01:32:27,223 - pyskl - INFO - Epoch [79][3400/3746] lr: 4.591e-02, eta: 2 days, 13:32:54, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5789, loss_cls: 3.8533, loss: 3.8533 +2024-12-29 01:33:51,779 - pyskl - INFO - Epoch [79][3500/3746] lr: 4.588e-02, eta: 2 days, 13:31:32, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5928, loss_cls: 3.8078, loss: 3.8078 +2024-12-29 01:35:16,743 - pyskl - INFO - Epoch [79][3600/3746] lr: 4.586e-02, eta: 2 days, 13:30:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5820, loss_cls: 3.8472, loss: 3.8472 +2024-12-29 01:36:42,084 - pyskl - INFO - Epoch [79][3700/3746] lr: 4.583e-02, eta: 2 days, 13:28:49, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5911, loss_cls: 3.8297, loss: 3.8297 +2024-12-29 01:37:23,177 - pyskl - INFO - Saving checkpoint at 79 epochs +2024-12-29 01:39:20,876 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 01:39:21,597 - pyskl - INFO - +top1_acc 0.2647 +top5_acc 0.5099 +2024-12-29 01:39:21,597 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 01:39:21,639 - pyskl - INFO - +mean_acc 0.2643 +2024-12-29 01:39:21,644 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_76.pth was removed +2024-12-29 01:39:21,903 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_79.pth. +2024-12-29 01:39:21,904 - pyskl - INFO - Best top1_acc is 0.2647 at 79 epoch. +2024-12-29 01:39:21,916 - pyskl - INFO - Epoch(val) [79][309] top1_acc: 0.2647, top5_acc: 0.5099, mean_class_accuracy: 0.2643 +2024-12-29 01:43:32,898 - pyskl - INFO - Epoch [80][100/3746] lr: 4.579e-02, eta: 2 days, 13:28:44, time: 2.510, data_time: 1.485, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5941, loss_cls: 3.7862, loss: 3.7862 +2024-12-29 01:44:58,099 - pyskl - INFO - Epoch [80][200/3746] lr: 4.576e-02, eta: 2 days, 13:27:23, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5863, loss_cls: 3.8184, loss: 3.8184 +2024-12-29 01:46:24,042 - pyskl - INFO - Epoch [80][300/3746] lr: 4.573e-02, eta: 2 days, 13:26:02, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6038, loss_cls: 3.7590, loss: 3.7590 +2024-12-29 01:47:49,425 - pyskl - INFO - Epoch [80][400/3746] lr: 4.570e-02, eta: 2 days, 13:24:41, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5900, loss_cls: 3.7930, loss: 3.7930 +2024-12-29 01:49:14,402 - pyskl - INFO - Epoch [80][500/3746] lr: 4.568e-02, eta: 2 days, 13:23:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5962, loss_cls: 3.7670, loss: 3.7670 +2024-12-29 01:50:38,899 - pyskl - INFO - Epoch [80][600/3746] lr: 4.565e-02, eta: 2 days, 13:21:57, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.6030, loss_cls: 3.7538, loss: 3.7538 +2024-12-29 01:52:03,606 - pyskl - INFO - Epoch [80][700/3746] lr: 4.562e-02, eta: 2 days, 13:20:35, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5855, loss_cls: 3.8598, loss: 3.8598 +2024-12-29 01:53:27,626 - pyskl - INFO - Epoch [80][800/3746] lr: 4.559e-02, eta: 2 days, 13:19:12, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.6009, loss_cls: 3.7632, loss: 3.7632 +2024-12-29 01:54:52,274 - pyskl - INFO - Epoch [80][900/3746] lr: 4.557e-02, eta: 2 days, 13:17:50, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.6005, loss_cls: 3.7495, loss: 3.7495 +2024-12-29 01:56:16,738 - pyskl - INFO - Epoch [80][1000/3746] lr: 4.554e-02, eta: 2 days, 13:16:28, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.6091, loss_cls: 3.7349, loss: 3.7349 +2024-12-29 01:57:41,118 - pyskl - INFO - Epoch [80][1100/3746] lr: 4.551e-02, eta: 2 days, 13:15:06, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5972, loss_cls: 3.7729, loss: 3.7729 +2024-12-29 01:59:05,795 - pyskl - INFO - Epoch [80][1200/3746] lr: 4.548e-02, eta: 2 days, 13:13:44, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5819, loss_cls: 3.8562, loss: 3.8562 +2024-12-29 02:00:30,209 - pyskl - INFO - Epoch [80][1300/3746] lr: 4.545e-02, eta: 2 days, 13:12:22, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5798, loss_cls: 3.8891, loss: 3.8891 +2024-12-29 02:01:54,791 - pyskl - INFO - Epoch [80][1400/3746] lr: 4.543e-02, eta: 2 days, 13:11:00, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5845, loss_cls: 3.8378, loss: 3.8378 +2024-12-29 02:03:19,372 - pyskl - INFO - Epoch [80][1500/3746] lr: 4.540e-02, eta: 2 days, 13:09:38, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5878, loss_cls: 3.8313, loss: 3.8313 +2024-12-29 02:04:43,481 - pyskl - INFO - Epoch [80][1600/3746] lr: 4.537e-02, eta: 2 days, 13:08:15, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5962, loss_cls: 3.8245, loss: 3.8245 +2024-12-29 02:06:07,908 - pyskl - INFO - Epoch [80][1700/3746] lr: 4.534e-02, eta: 2 days, 13:06:53, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5916, loss_cls: 3.8042, loss: 3.8042 +2024-12-29 02:07:32,384 - pyskl - INFO - Epoch [80][1800/3746] lr: 4.532e-02, eta: 2 days, 13:05:31, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5870, loss_cls: 3.8046, loss: 3.8046 +2024-12-29 02:08:56,970 - pyskl - INFO - Epoch [80][1900/3746] lr: 4.529e-02, eta: 2 days, 13:04:09, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5920, loss_cls: 3.8144, loss: 3.8144 +2024-12-29 02:10:21,389 - pyskl - INFO - Epoch [80][2000/3746] lr: 4.526e-02, eta: 2 days, 13:02:46, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5941, loss_cls: 3.8005, loss: 3.8005 +2024-12-29 02:11:46,597 - pyskl - INFO - Epoch [80][2100/3746] lr: 4.523e-02, eta: 2 days, 13:01:25, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5938, loss_cls: 3.8066, loss: 3.8066 +2024-12-29 02:13:11,235 - pyskl - INFO - Epoch [80][2200/3746] lr: 4.520e-02, eta: 2 days, 13:00:03, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5988, loss_cls: 3.8014, loss: 3.8014 +2024-12-29 02:14:36,810 - pyskl - INFO - Epoch [80][2300/3746] lr: 4.518e-02, eta: 2 days, 12:58:42, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5920, loss_cls: 3.8158, loss: 3.8158 +2024-12-29 02:16:02,308 - pyskl - INFO - Epoch [80][2400/3746] lr: 4.515e-02, eta: 2 days, 12:57:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5903, loss_cls: 3.8176, loss: 3.8176 +2024-12-29 02:17:27,122 - pyskl - INFO - Epoch [80][2500/3746] lr: 4.512e-02, eta: 2 days, 12:55:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5778, loss_cls: 3.8433, loss: 3.8433 +2024-12-29 02:18:51,895 - pyskl - INFO - Epoch [80][2600/3746] lr: 4.509e-02, eta: 2 days, 12:54:36, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5934, loss_cls: 3.8066, loss: 3.8066 +2024-12-29 02:20:16,613 - pyskl - INFO - Epoch [80][2700/3746] lr: 4.506e-02, eta: 2 days, 12:53:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6027, loss_cls: 3.7655, loss: 3.7655 +2024-12-29 02:21:41,673 - pyskl - INFO - Epoch [80][2800/3746] lr: 4.504e-02, eta: 2 days, 12:51:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5928, loss_cls: 3.8316, loss: 3.8316 +2024-12-29 02:23:06,319 - pyskl - INFO - Epoch [80][2900/3746] lr: 4.501e-02, eta: 2 days, 12:50:31, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5866, loss_cls: 3.8186, loss: 3.8186 +2024-12-29 02:24:30,910 - pyskl - INFO - Epoch [80][3000/3746] lr: 4.498e-02, eta: 2 days, 12:49:09, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5906, loss_cls: 3.8014, loss: 3.8014 +2024-12-29 02:25:55,725 - pyskl - INFO - Epoch [80][3100/3746] lr: 4.495e-02, eta: 2 days, 12:47:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5817, loss_cls: 3.8412, loss: 3.8412 +2024-12-29 02:27:19,925 - pyskl - INFO - Epoch [80][3200/3746] lr: 4.493e-02, eta: 2 days, 12:46:24, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5908, loss_cls: 3.7972, loss: 3.7972 +2024-12-29 02:28:45,276 - pyskl - INFO - Epoch [80][3300/3746] lr: 4.490e-02, eta: 2 days, 12:45:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5928, loss_cls: 3.8046, loss: 3.8046 +2024-12-29 02:30:10,234 - pyskl - INFO - Epoch [80][3400/3746] lr: 4.487e-02, eta: 2 days, 12:43:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5845, loss_cls: 3.8389, loss: 3.8389 +2024-12-29 02:31:34,517 - pyskl - INFO - Epoch [80][3500/3746] lr: 4.484e-02, eta: 2 days, 12:42:19, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5859, loss_cls: 3.8177, loss: 3.8177 +2024-12-29 02:32:59,246 - pyskl - INFO - Epoch [80][3600/3746] lr: 4.481e-02, eta: 2 days, 12:40:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5709, loss_cls: 3.9118, loss: 3.9118 +2024-12-29 02:34:24,294 - pyskl - INFO - Epoch [80][3700/3746] lr: 4.479e-02, eta: 2 days, 12:39:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5811, loss_cls: 3.8450, loss: 3.8450 +2024-12-29 02:35:04,898 - pyskl - INFO - Saving checkpoint at 80 epochs +2024-12-29 02:37:02,242 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 02:37:02,923 - pyskl - INFO - +top1_acc 0.2750 +top5_acc 0.5301 +2024-12-29 02:37:02,923 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 02:37:02,988 - pyskl - INFO - +mean_acc 0.2746 +2024-12-29 02:37:02,993 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_79.pth was removed +2024-12-29 02:37:03,292 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_80.pth. +2024-12-29 02:37:03,293 - pyskl - INFO - Best top1_acc is 0.2750 at 80 epoch. +2024-12-29 02:37:03,305 - pyskl - INFO - Epoch(val) [80][309] top1_acc: 0.2750, top5_acc: 0.5301, mean_class_accuracy: 0.2746 +2024-12-29 02:41:24,031 - pyskl - INFO - Epoch [81][100/3746] lr: 4.475e-02, eta: 2 days, 12:39:35, time: 2.607, data_time: 1.561, memory: 15990, top1_acc: 0.3377, top5_acc: 0.5986, loss_cls: 3.7475, loss: 3.7475 +2024-12-29 02:42:49,583 - pyskl - INFO - Epoch [81][200/3746] lr: 4.472e-02, eta: 2 days, 12:38:14, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.6023, loss_cls: 3.7556, loss: 3.7556 +2024-12-29 02:44:15,448 - pyskl - INFO - Epoch [81][300/3746] lr: 4.469e-02, eta: 2 days, 12:36:53, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3425, top5_acc: 0.6102, loss_cls: 3.7427, loss: 3.7427 +2024-12-29 02:45:41,341 - pyskl - INFO - Epoch [81][400/3746] lr: 4.466e-02, eta: 2 days, 12:35:32, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.5978, loss_cls: 3.7666, loss: 3.7666 +2024-12-29 02:47:06,946 - pyskl - INFO - Epoch [81][500/3746] lr: 4.463e-02, eta: 2 days, 12:34:10, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5942, loss_cls: 3.7730, loss: 3.7730 +2024-12-29 02:48:32,936 - pyskl - INFO - Epoch [81][600/3746] lr: 4.461e-02, eta: 2 days, 12:32:49, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5980, loss_cls: 3.8018, loss: 3.8018 +2024-12-29 02:49:58,401 - pyskl - INFO - Epoch [81][700/3746] lr: 4.458e-02, eta: 2 days, 12:31:28, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5955, loss_cls: 3.7900, loss: 3.7900 +2024-12-29 02:51:23,567 - pyskl - INFO - Epoch [81][800/3746] lr: 4.455e-02, eta: 2 days, 12:30:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5967, loss_cls: 3.7912, loss: 3.7912 +2024-12-29 02:52:48,419 - pyskl - INFO - Epoch [81][900/3746] lr: 4.452e-02, eta: 2 days, 12:28:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.6033, loss_cls: 3.7483, loss: 3.7483 +2024-12-29 02:54:13,678 - pyskl - INFO - Epoch [81][1000/3746] lr: 4.450e-02, eta: 2 days, 12:27:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5928, loss_cls: 3.8259, loss: 3.8259 +2024-12-29 02:55:39,515 - pyskl - INFO - Epoch [81][1100/3746] lr: 4.447e-02, eta: 2 days, 12:26:01, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.6059, loss_cls: 3.7373, loss: 3.7373 +2024-12-29 02:57:05,375 - pyskl - INFO - Epoch [81][1200/3746] lr: 4.444e-02, eta: 2 days, 12:24:40, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5962, loss_cls: 3.7771, loss: 3.7771 +2024-12-29 02:58:31,062 - pyskl - INFO - Epoch [81][1300/3746] lr: 4.441e-02, eta: 2 days, 12:23:19, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.6014, loss_cls: 3.7633, loss: 3.7633 +2024-12-29 02:59:56,782 - pyskl - INFO - Epoch [81][1400/3746] lr: 4.438e-02, eta: 2 days, 12:21:58, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5853, loss_cls: 3.8166, loss: 3.8166 +2024-12-29 03:01:22,109 - pyskl - INFO - Epoch [81][1500/3746] lr: 4.436e-02, eta: 2 days, 12:20:36, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5883, loss_cls: 3.8102, loss: 3.8102 +2024-12-29 03:02:48,359 - pyskl - INFO - Epoch [81][1600/3746] lr: 4.433e-02, eta: 2 days, 12:19:15, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5939, loss_cls: 3.8198, loss: 3.8198 +2024-12-29 03:04:14,366 - pyskl - INFO - Epoch [81][1700/3746] lr: 4.430e-02, eta: 2 days, 12:17:54, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5898, loss_cls: 3.7921, loss: 3.7921 +2024-12-29 03:05:39,602 - pyskl - INFO - Epoch [81][1800/3746] lr: 4.427e-02, eta: 2 days, 12:16:33, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5977, loss_cls: 3.7595, loss: 3.7595 +2024-12-29 03:07:05,342 - pyskl - INFO - Epoch [81][1900/3746] lr: 4.425e-02, eta: 2 days, 12:15:12, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5933, loss_cls: 3.7782, loss: 3.7782 +2024-12-29 03:08:31,396 - pyskl - INFO - Epoch [81][2000/3746] lr: 4.422e-02, eta: 2 days, 12:13:51, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5850, loss_cls: 3.8385, loss: 3.8385 +2024-12-29 03:09:57,443 - pyskl - INFO - Epoch [81][2100/3746] lr: 4.419e-02, eta: 2 days, 12:12:30, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5906, loss_cls: 3.7785, loss: 3.7785 +2024-12-29 03:11:23,227 - pyskl - INFO - Epoch [81][2200/3746] lr: 4.416e-02, eta: 2 days, 12:11:08, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.6003, loss_cls: 3.7641, loss: 3.7641 +2024-12-29 03:12:49,331 - pyskl - INFO - Epoch [81][2300/3746] lr: 4.413e-02, eta: 2 days, 12:09:47, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5928, loss_cls: 3.8370, loss: 3.8370 +2024-12-29 03:14:14,961 - pyskl - INFO - Epoch [81][2400/3746] lr: 4.411e-02, eta: 2 days, 12:08:26, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5913, loss_cls: 3.8168, loss: 3.8168 +2024-12-29 03:15:40,721 - pyskl - INFO - Epoch [81][2500/3746] lr: 4.408e-02, eta: 2 days, 12:07:05, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5913, loss_cls: 3.8156, loss: 3.8156 +2024-12-29 03:17:05,664 - pyskl - INFO - Epoch [81][2600/3746] lr: 4.405e-02, eta: 2 days, 12:05:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5975, loss_cls: 3.7845, loss: 3.7845 +2024-12-29 03:18:31,466 - pyskl - INFO - Epoch [81][2700/3746] lr: 4.402e-02, eta: 2 days, 12:04:22, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5886, loss_cls: 3.7903, loss: 3.7903 +2024-12-29 03:19:56,451 - pyskl - INFO - Epoch [81][2800/3746] lr: 4.400e-02, eta: 2 days, 12:03:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5869, loss_cls: 3.8427, loss: 3.8427 +2024-12-29 03:21:21,972 - pyskl - INFO - Epoch [81][2900/3746] lr: 4.397e-02, eta: 2 days, 12:01:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5891, loss_cls: 3.8217, loss: 3.8217 +2024-12-29 03:22:47,436 - pyskl - INFO - Epoch [81][3000/3746] lr: 4.394e-02, eta: 2 days, 12:00:17, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5828, loss_cls: 3.8600, loss: 3.8600 +2024-12-29 03:24:12,982 - pyskl - INFO - Epoch [81][3100/3746] lr: 4.391e-02, eta: 2 days, 11:58:55, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5984, loss_cls: 3.7706, loss: 3.7706 +2024-12-29 03:25:38,605 - pyskl - INFO - Epoch [81][3200/3746] lr: 4.389e-02, eta: 2 days, 11:57:34, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5784, loss_cls: 3.8439, loss: 3.8439 +2024-12-29 03:27:04,483 - pyskl - INFO - Epoch [81][3300/3746] lr: 4.386e-02, eta: 2 days, 11:56:13, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5989, loss_cls: 3.7916, loss: 3.7916 +2024-12-29 03:28:30,080 - pyskl - INFO - Epoch [81][3400/3746] lr: 4.383e-02, eta: 2 days, 11:54:51, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5861, loss_cls: 3.8030, loss: 3.8030 +2024-12-29 03:29:55,489 - pyskl - INFO - Epoch [81][3500/3746] lr: 4.380e-02, eta: 2 days, 11:53:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5834, loss_cls: 3.8499, loss: 3.8499 +2024-12-29 03:31:21,484 - pyskl - INFO - Epoch [81][3600/3746] lr: 4.377e-02, eta: 2 days, 11:52:09, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5802, loss_cls: 3.8373, loss: 3.8373 +2024-12-29 03:32:47,158 - pyskl - INFO - Epoch [81][3700/3746] lr: 4.375e-02, eta: 2 days, 11:50:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5953, loss_cls: 3.7939, loss: 3.7939 +2024-12-29 03:33:28,660 - pyskl - INFO - Saving checkpoint at 81 epochs +2024-12-29 03:35:30,105 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 03:35:30,854 - pyskl - INFO - +top1_acc 0.2695 +top5_acc 0.5204 +2024-12-29 03:35:30,854 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 03:35:30,904 - pyskl - INFO - +mean_acc 0.2692 +2024-12-29 03:35:30,919 - pyskl - INFO - Epoch(val) [81][309] top1_acc: 0.2695, top5_acc: 0.5204, mean_class_accuracy: 0.2692 +2024-12-29 03:39:57,265 - pyskl - INFO - Epoch [82][100/3746] lr: 4.371e-02, eta: 2 days, 11:50:49, time: 2.663, data_time: 1.611, memory: 15990, top1_acc: 0.3394, top5_acc: 0.6088, loss_cls: 3.7029, loss: 3.7029 +2024-12-29 03:41:23,164 - pyskl - INFO - Epoch [82][200/3746] lr: 4.368e-02, eta: 2 days, 11:49:28, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.6009, loss_cls: 3.7503, loss: 3.7503 +2024-12-29 03:42:49,150 - pyskl - INFO - Epoch [82][300/3746] lr: 4.365e-02, eta: 2 days, 11:48:06, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.6073, loss_cls: 3.7267, loss: 3.7267 +2024-12-29 03:44:14,482 - pyskl - INFO - Epoch [82][400/3746] lr: 4.362e-02, eta: 2 days, 11:46:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5970, loss_cls: 3.7947, loss: 3.7947 +2024-12-29 03:45:40,582 - pyskl - INFO - Epoch [82][500/3746] lr: 4.359e-02, eta: 2 days, 11:45:24, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3425, top5_acc: 0.6012, loss_cls: 3.7475, loss: 3.7475 +2024-12-29 03:47:06,492 - pyskl - INFO - Epoch [82][600/3746] lr: 4.357e-02, eta: 2 days, 11:44:02, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.6020, loss_cls: 3.7697, loss: 3.7697 +2024-12-29 03:48:31,972 - pyskl - INFO - Epoch [82][700/3746] lr: 4.354e-02, eta: 2 days, 11:42:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5845, loss_cls: 3.8499, loss: 3.8499 +2024-12-29 03:49:57,926 - pyskl - INFO - Epoch [82][800/3746] lr: 4.351e-02, eta: 2 days, 11:41:20, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.5894, loss_cls: 3.7866, loss: 3.7866 +2024-12-29 03:51:23,764 - pyskl - INFO - Epoch [82][900/3746] lr: 4.348e-02, eta: 2 days, 11:39:58, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5962, loss_cls: 3.7940, loss: 3.7940 +2024-12-29 03:52:49,630 - pyskl - INFO - Epoch [82][1000/3746] lr: 4.346e-02, eta: 2 days, 11:38:37, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6091, loss_cls: 3.7279, loss: 3.7279 +2024-12-29 03:54:15,109 - pyskl - INFO - Epoch [82][1100/3746] lr: 4.343e-02, eta: 2 days, 11:37:15, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5827, loss_cls: 3.8309, loss: 3.8309 +2024-12-29 03:55:40,676 - pyskl - INFO - Epoch [82][1200/3746] lr: 4.340e-02, eta: 2 days, 11:35:54, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5922, loss_cls: 3.7821, loss: 3.7821 +2024-12-29 03:57:05,958 - pyskl - INFO - Epoch [82][1300/3746] lr: 4.337e-02, eta: 2 days, 11:34:32, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.5975, loss_cls: 3.7547, loss: 3.7547 +2024-12-29 03:58:31,505 - pyskl - INFO - Epoch [82][1400/3746] lr: 4.335e-02, eta: 2 days, 11:33:10, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5945, loss_cls: 3.7906, loss: 3.7906 +2024-12-29 03:59:56,467 - pyskl - INFO - Epoch [82][1500/3746] lr: 4.332e-02, eta: 2 days, 11:31:48, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5975, loss_cls: 3.7966, loss: 3.7966 +2024-12-29 04:01:21,859 - pyskl - INFO - Epoch [82][1600/3746] lr: 4.329e-02, eta: 2 days, 11:30:27, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5898, loss_cls: 3.8107, loss: 3.8107 +2024-12-29 04:02:47,964 - pyskl - INFO - Epoch [82][1700/3746] lr: 4.326e-02, eta: 2 days, 11:29:05, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5889, loss_cls: 3.7961, loss: 3.7961 +2024-12-29 04:04:13,696 - pyskl - INFO - Epoch [82][1800/3746] lr: 4.323e-02, eta: 2 days, 11:27:44, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.6017, loss_cls: 3.7452, loss: 3.7452 +2024-12-29 04:05:39,506 - pyskl - INFO - Epoch [82][1900/3746] lr: 4.321e-02, eta: 2 days, 11:26:23, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5917, loss_cls: 3.7939, loss: 3.7939 +2024-12-29 04:07:05,095 - pyskl - INFO - Epoch [82][2000/3746] lr: 4.318e-02, eta: 2 days, 11:25:01, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5908, loss_cls: 3.8029, loss: 3.8029 +2024-12-29 04:08:30,525 - pyskl - INFO - Epoch [82][2100/3746] lr: 4.315e-02, eta: 2 days, 11:23:39, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.5992, loss_cls: 3.7824, loss: 3.7824 +2024-12-29 04:09:55,739 - pyskl - INFO - Epoch [82][2200/3746] lr: 4.312e-02, eta: 2 days, 11:22:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.6027, loss_cls: 3.8004, loss: 3.8004 +2024-12-29 04:11:21,437 - pyskl - INFO - Epoch [82][2300/3746] lr: 4.310e-02, eta: 2 days, 11:20:56, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5975, loss_cls: 3.7751, loss: 3.7751 +2024-12-29 04:12:46,919 - pyskl - INFO - Epoch [82][2400/3746] lr: 4.307e-02, eta: 2 days, 11:19:34, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5913, loss_cls: 3.7753, loss: 3.7753 +2024-12-29 04:14:12,444 - pyskl - INFO - Epoch [82][2500/3746] lr: 4.304e-02, eta: 2 days, 11:18:13, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5925, loss_cls: 3.8434, loss: 3.8434 +2024-12-29 04:15:37,969 - pyskl - INFO - Epoch [82][2600/3746] lr: 4.301e-02, eta: 2 days, 11:16:51, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.5995, loss_cls: 3.7608, loss: 3.7608 +2024-12-29 04:17:02,731 - pyskl - INFO - Epoch [82][2700/3746] lr: 4.299e-02, eta: 2 days, 11:15:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5992, loss_cls: 3.8233, loss: 3.8233 +2024-12-29 04:18:27,789 - pyskl - INFO - Epoch [82][2800/3746] lr: 4.296e-02, eta: 2 days, 11:14:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5958, loss_cls: 3.7879, loss: 3.7879 +2024-12-29 04:19:53,546 - pyskl - INFO - Epoch [82][2900/3746] lr: 4.293e-02, eta: 2 days, 11:12:45, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5914, loss_cls: 3.8210, loss: 3.8210 +2024-12-29 04:21:19,008 - pyskl - INFO - Epoch [82][3000/3746] lr: 4.290e-02, eta: 2 days, 11:11:24, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5837, loss_cls: 3.8488, loss: 3.8488 +2024-12-29 04:22:44,106 - pyskl - INFO - Epoch [82][3100/3746] lr: 4.287e-02, eta: 2 days, 11:10:02, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5916, loss_cls: 3.7775, loss: 3.7775 +2024-12-29 04:24:09,970 - pyskl - INFO - Epoch [82][3200/3746] lr: 4.285e-02, eta: 2 days, 11:08:40, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5905, loss_cls: 3.8400, loss: 3.8400 +2024-12-29 04:25:35,447 - pyskl - INFO - Epoch [82][3300/3746] lr: 4.282e-02, eta: 2 days, 11:07:19, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.5966, loss_cls: 3.7692, loss: 3.7692 +2024-12-29 04:27:01,206 - pyskl - INFO - Epoch [82][3400/3746] lr: 4.279e-02, eta: 2 days, 11:05:57, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5967, loss_cls: 3.7681, loss: 3.7681 +2024-12-29 04:28:27,286 - pyskl - INFO - Epoch [82][3500/3746] lr: 4.276e-02, eta: 2 days, 11:04:36, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5969, loss_cls: 3.7919, loss: 3.7919 +2024-12-29 04:29:53,574 - pyskl - INFO - Epoch [82][3600/3746] lr: 4.274e-02, eta: 2 days, 11:03:15, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5952, loss_cls: 3.8058, loss: 3.8058 +2024-12-29 04:31:19,565 - pyskl - INFO - Epoch [82][3700/3746] lr: 4.271e-02, eta: 2 days, 11:01:54, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5958, loss_cls: 3.7766, loss: 3.7766 +2024-12-29 04:32:01,040 - pyskl - INFO - Saving checkpoint at 82 epochs +2024-12-29 04:34:00,331 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 04:34:01,106 - pyskl - INFO - +top1_acc 0.2714 +top5_acc 0.5294 +2024-12-29 04:34:01,106 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 04:34:01,148 - pyskl - INFO - +mean_acc 0.2712 +2024-12-29 04:34:01,161 - pyskl - INFO - Epoch(val) [82][309] top1_acc: 0.2714, top5_acc: 0.5294, mean_class_accuracy: 0.2712 +2024-12-29 04:38:27,016 - pyskl - INFO - Epoch [83][100/3746] lr: 4.267e-02, eta: 2 days, 11:01:51, time: 2.658, data_time: 1.599, memory: 15990, top1_acc: 0.3344, top5_acc: 0.6070, loss_cls: 3.7174, loss: 3.7174 +2024-12-29 04:39:53,146 - pyskl - INFO - Epoch [83][200/3746] lr: 4.264e-02, eta: 2 days, 11:00:30, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6061, loss_cls: 3.6983, loss: 3.6983 +2024-12-29 04:41:18,995 - pyskl - INFO - Epoch [83][300/3746] lr: 4.261e-02, eta: 2 days, 10:59:08, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.6028, loss_cls: 3.7645, loss: 3.7645 +2024-12-29 04:42:44,926 - pyskl - INFO - Epoch [83][400/3746] lr: 4.259e-02, eta: 2 days, 10:57:47, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5936, loss_cls: 3.7821, loss: 3.7821 +2024-12-29 04:44:10,612 - pyskl - INFO - Epoch [83][500/3746] lr: 4.256e-02, eta: 2 days, 10:56:25, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6161, loss_cls: 3.6889, loss: 3.6889 +2024-12-29 04:45:36,337 - pyskl - INFO - Epoch [83][600/3746] lr: 4.253e-02, eta: 2 days, 10:55:04, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.6011, loss_cls: 3.7849, loss: 3.7849 +2024-12-29 04:47:01,689 - pyskl - INFO - Epoch [83][700/3746] lr: 4.250e-02, eta: 2 days, 10:53:42, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5945, loss_cls: 3.7762, loss: 3.7762 +2024-12-29 04:48:27,151 - pyskl - INFO - Epoch [83][800/3746] lr: 4.247e-02, eta: 2 days, 10:52:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5867, loss_cls: 3.8046, loss: 3.8046 +2024-12-29 04:49:53,464 - pyskl - INFO - Epoch [83][900/3746] lr: 4.245e-02, eta: 2 days, 10:50:59, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6078, loss_cls: 3.7227, loss: 3.7227 +2024-12-29 04:51:18,997 - pyskl - INFO - Epoch [83][1000/3746] lr: 4.242e-02, eta: 2 days, 10:49:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.6005, loss_cls: 3.7603, loss: 3.7603 +2024-12-29 04:52:44,917 - pyskl - INFO - Epoch [83][1100/3746] lr: 4.239e-02, eta: 2 days, 10:48:16, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5972, loss_cls: 3.7750, loss: 3.7750 +2024-12-29 04:54:10,678 - pyskl - INFO - Epoch [83][1200/3746] lr: 4.236e-02, eta: 2 days, 10:46:54, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.6000, loss_cls: 3.7608, loss: 3.7608 +2024-12-29 04:55:36,281 - pyskl - INFO - Epoch [83][1300/3746] lr: 4.234e-02, eta: 2 days, 10:45:32, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5933, loss_cls: 3.7807, loss: 3.7807 +2024-12-29 04:57:01,688 - pyskl - INFO - Epoch [83][1400/3746] lr: 4.231e-02, eta: 2 days, 10:44:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5994, loss_cls: 3.7904, loss: 3.7904 +2024-12-29 04:58:27,675 - pyskl - INFO - Epoch [83][1500/3746] lr: 4.228e-02, eta: 2 days, 10:42:49, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.6039, loss_cls: 3.7393, loss: 3.7393 +2024-12-29 04:59:53,547 - pyskl - INFO - Epoch [83][1600/3746] lr: 4.225e-02, eta: 2 days, 10:41:28, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5908, loss_cls: 3.8234, loss: 3.8234 +2024-12-29 05:01:19,415 - pyskl - INFO - Epoch [83][1700/3746] lr: 4.223e-02, eta: 2 days, 10:40:06, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.6006, loss_cls: 3.7686, loss: 3.7686 +2024-12-29 05:02:45,128 - pyskl - INFO - Epoch [83][1800/3746] lr: 4.220e-02, eta: 2 days, 10:38:45, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.5920, loss_cls: 3.7872, loss: 3.7872 +2024-12-29 05:04:11,797 - pyskl - INFO - Epoch [83][1900/3746] lr: 4.217e-02, eta: 2 days, 10:37:24, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.6047, loss_cls: 3.7595, loss: 3.7595 +2024-12-29 05:05:37,464 - pyskl - INFO - Epoch [83][2000/3746] lr: 4.214e-02, eta: 2 days, 10:36:02, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5966, loss_cls: 3.8020, loss: 3.8020 +2024-12-29 05:07:03,219 - pyskl - INFO - Epoch [83][2100/3746] lr: 4.212e-02, eta: 2 days, 10:34:40, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.6056, loss_cls: 3.7282, loss: 3.7282 +2024-12-29 05:08:29,373 - pyskl - INFO - Epoch [83][2200/3746] lr: 4.209e-02, eta: 2 days, 10:33:19, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5972, loss_cls: 3.7796, loss: 3.7796 +2024-12-29 05:09:55,881 - pyskl - INFO - Epoch [83][2300/3746] lr: 4.206e-02, eta: 2 days, 10:31:58, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5870, loss_cls: 3.8013, loss: 3.8013 +2024-12-29 05:11:22,255 - pyskl - INFO - Epoch [83][2400/3746] lr: 4.203e-02, eta: 2 days, 10:30:37, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5950, loss_cls: 3.7532, loss: 3.7532 +2024-12-29 05:12:47,194 - pyskl - INFO - Epoch [83][2500/3746] lr: 4.201e-02, eta: 2 days, 10:29:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5933, loss_cls: 3.7696, loss: 3.7696 +2024-12-29 05:14:12,423 - pyskl - INFO - Epoch [83][2600/3746] lr: 4.198e-02, eta: 2 days, 10:27:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5978, loss_cls: 3.7838, loss: 3.7838 +2024-12-29 05:15:37,565 - pyskl - INFO - Epoch [83][2700/3746] lr: 4.195e-02, eta: 2 days, 10:26:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.6017, loss_cls: 3.7690, loss: 3.7690 +2024-12-29 05:17:03,538 - pyskl - INFO - Epoch [83][2800/3746] lr: 4.192e-02, eta: 2 days, 10:25:09, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6025, loss_cls: 3.7294, loss: 3.7294 +2024-12-29 05:18:29,642 - pyskl - INFO - Epoch [83][2900/3746] lr: 4.190e-02, eta: 2 days, 10:23:48, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5952, loss_cls: 3.7857, loss: 3.7857 +2024-12-29 05:19:55,949 - pyskl - INFO - Epoch [83][3000/3746] lr: 4.187e-02, eta: 2 days, 10:22:27, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5877, loss_cls: 3.8050, loss: 3.8050 +2024-12-29 05:21:21,879 - pyskl - INFO - Epoch [83][3100/3746] lr: 4.184e-02, eta: 2 days, 10:21:05, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5923, loss_cls: 3.8016, loss: 3.8016 +2024-12-29 05:22:48,330 - pyskl - INFO - Epoch [83][3200/3746] lr: 4.181e-02, eta: 2 days, 10:19:44, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5919, loss_cls: 3.7893, loss: 3.7893 +2024-12-29 05:24:14,466 - pyskl - INFO - Epoch [83][3300/3746] lr: 4.178e-02, eta: 2 days, 10:18:23, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5966, loss_cls: 3.7866, loss: 3.7866 +2024-12-29 05:25:40,618 - pyskl - INFO - Epoch [83][3400/3746] lr: 4.176e-02, eta: 2 days, 10:17:01, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.6003, loss_cls: 3.7555, loss: 3.7555 +2024-12-29 05:27:06,364 - pyskl - INFO - Epoch [83][3500/3746] lr: 4.173e-02, eta: 2 days, 10:15:40, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.6005, loss_cls: 3.7787, loss: 3.7787 +2024-12-29 05:28:32,442 - pyskl - INFO - Epoch [83][3600/3746] lr: 4.170e-02, eta: 2 days, 10:14:18, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5916, loss_cls: 3.8105, loss: 3.8105 +2024-12-29 05:29:58,744 - pyskl - INFO - Epoch [83][3700/3746] lr: 4.167e-02, eta: 2 days, 10:12:57, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.6038, loss_cls: 3.7652, loss: 3.7652 +2024-12-29 05:30:40,222 - pyskl - INFO - Saving checkpoint at 83 epochs +2024-12-29 05:32:41,337 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 05:32:42,185 - pyskl - INFO - +top1_acc 0.2668 +top5_acc 0.5206 +2024-12-29 05:32:42,185 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 05:32:42,229 - pyskl - INFO - +mean_acc 0.2667 +2024-12-29 05:32:42,240 - pyskl - INFO - Epoch(val) [83][309] top1_acc: 0.2668, top5_acc: 0.5206, mean_class_accuracy: 0.2667 +2024-12-29 05:37:05,956 - pyskl - INFO - Epoch [84][100/3746] lr: 4.163e-02, eta: 2 days, 10:12:50, time: 2.637, data_time: 1.583, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6159, loss_cls: 3.6913, loss: 3.6913 +2024-12-29 05:38:32,023 - pyskl - INFO - Epoch [84][200/3746] lr: 4.161e-02, eta: 2 days, 10:11:28, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.6070, loss_cls: 3.6971, loss: 3.6971 +2024-12-29 05:39:58,029 - pyskl - INFO - Epoch [84][300/3746] lr: 4.158e-02, eta: 2 days, 10:10:06, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.6053, loss_cls: 3.7217, loss: 3.7217 +2024-12-29 05:41:24,208 - pyskl - INFO - Epoch [84][400/3746] lr: 4.155e-02, eta: 2 days, 10:08:45, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6080, loss_cls: 3.7276, loss: 3.7276 +2024-12-29 05:42:50,174 - pyskl - INFO - Epoch [84][500/3746] lr: 4.152e-02, eta: 2 days, 10:07:23, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.6164, loss_cls: 3.6859, loss: 3.6859 +2024-12-29 05:44:15,715 - pyskl - INFO - Epoch [84][600/3746] lr: 4.150e-02, eta: 2 days, 10:06:02, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.6047, loss_cls: 3.7258, loss: 3.7258 +2024-12-29 05:45:41,199 - pyskl - INFO - Epoch [84][700/3746] lr: 4.147e-02, eta: 2 days, 10:04:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.6002, loss_cls: 3.7519, loss: 3.7519 +2024-12-29 05:47:07,058 - pyskl - INFO - Epoch [84][800/3746] lr: 4.144e-02, eta: 2 days, 10:03:18, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6019, loss_cls: 3.7523, loss: 3.7523 +2024-12-29 05:48:33,135 - pyskl - INFO - Epoch [84][900/3746] lr: 4.141e-02, eta: 2 days, 10:01:56, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5933, loss_cls: 3.7710, loss: 3.7710 +2024-12-29 05:49:58,882 - pyskl - INFO - Epoch [84][1000/3746] lr: 4.139e-02, eta: 2 days, 10:00:35, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.6066, loss_cls: 3.7466, loss: 3.7466 +2024-12-29 05:51:25,248 - pyskl - INFO - Epoch [84][1100/3746] lr: 4.136e-02, eta: 2 days, 9:59:13, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5952, loss_cls: 3.7822, loss: 3.7822 +2024-12-29 05:52:51,129 - pyskl - INFO - Epoch [84][1200/3746] lr: 4.133e-02, eta: 2 days, 9:57:52, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.6045, loss_cls: 3.7566, loss: 3.7566 +2024-12-29 05:54:16,560 - pyskl - INFO - Epoch [84][1300/3746] lr: 4.130e-02, eta: 2 days, 9:56:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.6025, loss_cls: 3.7466, loss: 3.7466 +2024-12-29 05:55:41,721 - pyskl - INFO - Epoch [84][1400/3746] lr: 4.128e-02, eta: 2 days, 9:55:07, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.6008, loss_cls: 3.7844, loss: 3.7844 +2024-12-29 05:57:07,625 - pyskl - INFO - Epoch [84][1500/3746] lr: 4.125e-02, eta: 2 days, 9:53:46, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5991, loss_cls: 3.7956, loss: 3.7956 +2024-12-29 05:58:33,465 - pyskl - INFO - Epoch [84][1600/3746] lr: 4.122e-02, eta: 2 days, 9:52:24, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6066, loss_cls: 3.7386, loss: 3.7386 +2024-12-29 05:59:59,893 - pyskl - INFO - Epoch [84][1700/3746] lr: 4.119e-02, eta: 2 days, 9:51:03, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6034, loss_cls: 3.7189, loss: 3.7189 +2024-12-29 06:01:25,701 - pyskl - INFO - Epoch [84][1800/3746] lr: 4.117e-02, eta: 2 days, 9:49:41, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.6005, loss_cls: 3.7435, loss: 3.7435 +2024-12-29 06:02:51,908 - pyskl - INFO - Epoch [84][1900/3746] lr: 4.114e-02, eta: 2 days, 9:48:20, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.6027, loss_cls: 3.7435, loss: 3.7435 +2024-12-29 06:04:18,040 - pyskl - INFO - Epoch [84][2000/3746] lr: 4.111e-02, eta: 2 days, 9:46:58, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5944, loss_cls: 3.7694, loss: 3.7694 +2024-12-29 06:05:44,484 - pyskl - INFO - Epoch [84][2100/3746] lr: 4.108e-02, eta: 2 days, 9:45:37, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6108, loss_cls: 3.7030, loss: 3.7030 +2024-12-29 06:07:11,226 - pyskl - INFO - Epoch [84][2200/3746] lr: 4.106e-02, eta: 2 days, 9:44:16, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5988, loss_cls: 3.7759, loss: 3.7759 +2024-12-29 06:08:37,688 - pyskl - INFO - Epoch [84][2300/3746] lr: 4.103e-02, eta: 2 days, 9:42:55, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5922, loss_cls: 3.7929, loss: 3.7929 +2024-12-29 06:10:03,677 - pyskl - INFO - Epoch [84][2400/3746] lr: 4.100e-02, eta: 2 days, 9:41:33, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5931, loss_cls: 3.7800, loss: 3.7800 +2024-12-29 06:11:29,074 - pyskl - INFO - Epoch [84][2500/3746] lr: 4.097e-02, eta: 2 days, 9:40:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5989, loss_cls: 3.7812, loss: 3.7812 +2024-12-29 06:12:54,549 - pyskl - INFO - Epoch [84][2600/3746] lr: 4.095e-02, eta: 2 days, 9:38:49, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.5945, loss_cls: 3.7426, loss: 3.7426 +2024-12-29 06:14:19,885 - pyskl - INFO - Epoch [84][2700/3746] lr: 4.092e-02, eta: 2 days, 9:37:27, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3380, top5_acc: 0.6072, loss_cls: 3.7243, loss: 3.7243 +2024-12-29 06:15:44,980 - pyskl - INFO - Epoch [84][2800/3746] lr: 4.089e-02, eta: 2 days, 9:36:04, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5992, loss_cls: 3.7607, loss: 3.7607 +2024-12-29 06:17:10,551 - pyskl - INFO - Epoch [84][2900/3746] lr: 4.086e-02, eta: 2 days, 9:34:42, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5850, loss_cls: 3.8294, loss: 3.8294 +2024-12-29 06:18:36,452 - pyskl - INFO - Epoch [84][3000/3746] lr: 4.084e-02, eta: 2 days, 9:33:21, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5930, loss_cls: 3.7809, loss: 3.7809 +2024-12-29 06:20:01,966 - pyskl - INFO - Epoch [84][3100/3746] lr: 4.081e-02, eta: 2 days, 9:31:59, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5970, loss_cls: 3.7817, loss: 3.7817 +2024-12-29 06:21:27,932 - pyskl - INFO - Epoch [84][3200/3746] lr: 4.078e-02, eta: 2 days, 9:30:37, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5880, loss_cls: 3.8026, loss: 3.8026 +2024-12-29 06:22:53,029 - pyskl - INFO - Epoch [84][3300/3746] lr: 4.075e-02, eta: 2 days, 9:29:15, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6047, loss_cls: 3.7371, loss: 3.7371 +2024-12-29 06:24:18,465 - pyskl - INFO - Epoch [84][3400/3746] lr: 4.073e-02, eta: 2 days, 9:27:53, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6070, loss_cls: 3.7239, loss: 3.7239 +2024-12-29 06:25:44,012 - pyskl - INFO - Epoch [84][3500/3746] lr: 4.070e-02, eta: 2 days, 9:26:31, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5884, loss_cls: 3.8443, loss: 3.8443 +2024-12-29 06:27:09,582 - pyskl - INFO - Epoch [84][3600/3746] lr: 4.067e-02, eta: 2 days, 9:25:09, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5981, loss_cls: 3.7744, loss: 3.7744 +2024-12-29 06:28:35,053 - pyskl - INFO - Epoch [84][3700/3746] lr: 4.064e-02, eta: 2 days, 9:23:47, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.6011, loss_cls: 3.7400, loss: 3.7400 +2024-12-29 06:29:16,721 - pyskl - INFO - Saving checkpoint at 84 epochs +2024-12-29 06:31:17,095 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 06:31:17,937 - pyskl - INFO - +top1_acc 0.2654 +top5_acc 0.5122 +2024-12-29 06:31:17,937 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 06:31:18,007 - pyskl - INFO - +mean_acc 0.2651 +2024-12-29 06:31:18,028 - pyskl - INFO - Epoch(val) [84][309] top1_acc: 0.2654, top5_acc: 0.5122, mean_class_accuracy: 0.2651 +2024-12-29 06:35:40,072 - pyskl - INFO - Epoch [85][100/3746] lr: 4.060e-02, eta: 2 days, 9:23:35, time: 2.620, data_time: 1.580, memory: 15990, top1_acc: 0.3452, top5_acc: 0.6102, loss_cls: 3.6704, loss: 3.6704 +2024-12-29 06:37:05,252 - pyskl - INFO - Epoch [85][200/3746] lr: 4.058e-02, eta: 2 days, 9:22:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6016, loss_cls: 3.7267, loss: 3.7267 +2024-12-29 06:38:30,611 - pyskl - INFO - Epoch [85][300/3746] lr: 4.055e-02, eta: 2 days, 9:20:50, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6136, loss_cls: 3.7023, loss: 3.7023 +2024-12-29 06:39:56,743 - pyskl - INFO - Epoch [85][400/3746] lr: 4.052e-02, eta: 2 days, 9:19:28, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6161, loss_cls: 3.6560, loss: 3.6560 +2024-12-29 06:41:22,101 - pyskl - INFO - Epoch [85][500/3746] lr: 4.049e-02, eta: 2 days, 9:18:06, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6083, loss_cls: 3.7008, loss: 3.7008 +2024-12-29 06:42:47,159 - pyskl - INFO - Epoch [85][600/3746] lr: 4.047e-02, eta: 2 days, 9:16:44, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6038, loss_cls: 3.7140, loss: 3.7140 +2024-12-29 06:44:13,177 - pyskl - INFO - Epoch [85][700/3746] lr: 4.044e-02, eta: 2 days, 9:15:22, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.6047, loss_cls: 3.7541, loss: 3.7541 +2024-12-29 06:45:38,511 - pyskl - INFO - Epoch [85][800/3746] lr: 4.041e-02, eta: 2 days, 9:14:00, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6041, loss_cls: 3.7221, loss: 3.7221 +2024-12-29 06:47:04,277 - pyskl - INFO - Epoch [85][900/3746] lr: 4.038e-02, eta: 2 days, 9:12:38, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6084, loss_cls: 3.7328, loss: 3.7328 +2024-12-29 06:48:29,433 - pyskl - INFO - Epoch [85][1000/3746] lr: 4.036e-02, eta: 2 days, 9:11:15, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6052, loss_cls: 3.7323, loss: 3.7323 +2024-12-29 06:49:54,683 - pyskl - INFO - Epoch [85][1100/3746] lr: 4.033e-02, eta: 2 days, 9:09:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5986, loss_cls: 3.7508, loss: 3.7508 +2024-12-29 06:51:20,174 - pyskl - INFO - Epoch [85][1200/3746] lr: 4.030e-02, eta: 2 days, 9:08:31, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5980, loss_cls: 3.7683, loss: 3.7683 +2024-12-29 06:52:45,621 - pyskl - INFO - Epoch [85][1300/3746] lr: 4.027e-02, eta: 2 days, 9:07:09, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.6031, loss_cls: 3.7514, loss: 3.7514 +2024-12-29 06:54:10,935 - pyskl - INFO - Epoch [85][1400/3746] lr: 4.025e-02, eta: 2 days, 9:05:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6045, loss_cls: 3.7379, loss: 3.7379 +2024-12-29 06:55:36,610 - pyskl - INFO - Epoch [85][1500/3746] lr: 4.022e-02, eta: 2 days, 9:04:24, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5920, loss_cls: 3.7999, loss: 3.7999 +2024-12-29 06:57:02,402 - pyskl - INFO - Epoch [85][1600/3746] lr: 4.019e-02, eta: 2 days, 9:03:02, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.6075, loss_cls: 3.7537, loss: 3.7537 +2024-12-29 06:58:27,922 - pyskl - INFO - Epoch [85][1700/3746] lr: 4.016e-02, eta: 2 days, 9:01:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5966, loss_cls: 3.7444, loss: 3.7444 +2024-12-29 06:59:53,584 - pyskl - INFO - Epoch [85][1800/3746] lr: 4.014e-02, eta: 2 days, 9:00:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6103, loss_cls: 3.6902, loss: 3.6902 +2024-12-29 07:01:19,123 - pyskl - INFO - Epoch [85][1900/3746] lr: 4.011e-02, eta: 2 days, 8:58:56, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.6019, loss_cls: 3.7432, loss: 3.7432 +2024-12-29 07:02:44,443 - pyskl - INFO - Epoch [85][2000/3746] lr: 4.008e-02, eta: 2 days, 8:57:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.5956, loss_cls: 3.7763, loss: 3.7763 +2024-12-29 07:04:09,941 - pyskl - INFO - Epoch [85][2100/3746] lr: 4.006e-02, eta: 2 days, 8:56:12, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5972, loss_cls: 3.7624, loss: 3.7624 +2024-12-29 07:05:35,637 - pyskl - INFO - Epoch [85][2200/3746] lr: 4.003e-02, eta: 2 days, 8:54:50, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5916, loss_cls: 3.8162, loss: 3.8162 +2024-12-29 07:07:01,127 - pyskl - INFO - Epoch [85][2300/3746] lr: 4.000e-02, eta: 2 days, 8:53:28, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6142, loss_cls: 3.6888, loss: 3.6888 +2024-12-29 07:08:26,158 - pyskl - INFO - Epoch [85][2400/3746] lr: 3.997e-02, eta: 2 days, 8:52:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5988, loss_cls: 3.7905, loss: 3.7905 +2024-12-29 07:09:52,178 - pyskl - INFO - Epoch [85][2500/3746] lr: 3.995e-02, eta: 2 days, 8:50:43, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.6041, loss_cls: 3.7354, loss: 3.7354 +2024-12-29 07:11:17,278 - pyskl - INFO - Epoch [85][2600/3746] lr: 3.992e-02, eta: 2 days, 8:49:21, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6114, loss_cls: 3.6952, loss: 3.6952 +2024-12-29 07:12:42,206 - pyskl - INFO - Epoch [85][2700/3746] lr: 3.989e-02, eta: 2 days, 8:47:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.6006, loss_cls: 3.7601, loss: 3.7601 +2024-12-29 07:14:07,602 - pyskl - INFO - Epoch [85][2800/3746] lr: 3.986e-02, eta: 2 days, 8:46:36, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5953, loss_cls: 3.8016, loss: 3.8016 +2024-12-29 07:15:33,162 - pyskl - INFO - Epoch [85][2900/3746] lr: 3.984e-02, eta: 2 days, 8:45:14, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5961, loss_cls: 3.7675, loss: 3.7675 +2024-12-29 07:16:58,542 - pyskl - INFO - Epoch [85][3000/3746] lr: 3.981e-02, eta: 2 days, 8:43:52, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6067, loss_cls: 3.7208, loss: 3.7208 +2024-12-29 07:18:24,111 - pyskl - INFO - Epoch [85][3100/3746] lr: 3.978e-02, eta: 2 days, 8:42:29, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.5961, loss_cls: 3.7545, loss: 3.7545 +2024-12-29 07:19:49,261 - pyskl - INFO - Epoch [85][3200/3746] lr: 3.975e-02, eta: 2 days, 8:41:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.6058, loss_cls: 3.7378, loss: 3.7378 +2024-12-29 07:21:14,391 - pyskl - INFO - Epoch [85][3300/3746] lr: 3.973e-02, eta: 2 days, 8:39:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6066, loss_cls: 3.7497, loss: 3.7497 +2024-12-29 07:22:40,060 - pyskl - INFO - Epoch [85][3400/3746] lr: 3.970e-02, eta: 2 days, 8:38:22, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.6075, loss_cls: 3.7336, loss: 3.7336 +2024-12-29 07:24:05,481 - pyskl - INFO - Epoch [85][3500/3746] lr: 3.967e-02, eta: 2 days, 8:37:00, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6091, loss_cls: 3.7155, loss: 3.7155 +2024-12-29 07:25:31,398 - pyskl - INFO - Epoch [85][3600/3746] lr: 3.964e-02, eta: 2 days, 8:35:38, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5994, loss_cls: 3.7590, loss: 3.7590 +2024-12-29 07:26:56,922 - pyskl - INFO - Epoch [85][3700/3746] lr: 3.962e-02, eta: 2 days, 8:34:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.6030, loss_cls: 3.7765, loss: 3.7765 +2024-12-29 07:27:38,407 - pyskl - INFO - Saving checkpoint at 85 epochs +2024-12-29 07:29:40,646 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 07:29:41,435 - pyskl - INFO - +top1_acc 0.2757 +top5_acc 0.5318 +2024-12-29 07:29:41,435 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 07:29:41,479 - pyskl - INFO - +mean_acc 0.2754 +2024-12-29 07:29:41,486 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_80.pth was removed +2024-12-29 07:29:41,772 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_85.pth. +2024-12-29 07:29:41,773 - pyskl - INFO - Best top1_acc is 0.2757 at 85 epoch. +2024-12-29 07:29:41,795 - pyskl - INFO - Epoch(val) [85][309] top1_acc: 0.2757, top5_acc: 0.5318, mean_class_accuracy: 0.2754 +2024-12-29 07:34:07,750 - pyskl - INFO - Epoch [86][100/3746] lr: 3.958e-02, eta: 2 days, 8:34:04, time: 2.659, data_time: 1.622, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6142, loss_cls: 3.6461, loss: 3.6461 +2024-12-29 07:35:32,864 - pyskl - INFO - Epoch [86][200/3746] lr: 3.955e-02, eta: 2 days, 8:32:41, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6100, loss_cls: 3.6729, loss: 3.6729 +2024-12-29 07:36:58,764 - pyskl - INFO - Epoch [86][300/3746] lr: 3.952e-02, eta: 2 days, 8:31:19, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6156, loss_cls: 3.7062, loss: 3.7062 +2024-12-29 07:38:24,644 - pyskl - INFO - Epoch [86][400/3746] lr: 3.950e-02, eta: 2 days, 8:29:57, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.6045, loss_cls: 3.7577, loss: 3.7577 +2024-12-29 07:39:50,322 - pyskl - INFO - Epoch [86][500/3746] lr: 3.947e-02, eta: 2 days, 8:28:35, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.6014, loss_cls: 3.7706, loss: 3.7706 +2024-12-29 07:41:16,056 - pyskl - INFO - Epoch [86][600/3746] lr: 3.944e-02, eta: 2 days, 8:27:13, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6119, loss_cls: 3.6786, loss: 3.6786 +2024-12-29 07:42:41,526 - pyskl - INFO - Epoch [86][700/3746] lr: 3.941e-02, eta: 2 days, 8:25:51, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6094, loss_cls: 3.6932, loss: 3.6932 +2024-12-29 07:44:07,385 - pyskl - INFO - Epoch [86][800/3746] lr: 3.939e-02, eta: 2 days, 8:24:29, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6070, loss_cls: 3.7280, loss: 3.7280 +2024-12-29 07:45:33,192 - pyskl - INFO - Epoch [86][900/3746] lr: 3.936e-02, eta: 2 days, 8:23:07, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.6042, loss_cls: 3.7460, loss: 3.7460 +2024-12-29 07:46:58,565 - pyskl - INFO - Epoch [86][1000/3746] lr: 3.933e-02, eta: 2 days, 8:21:44, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6008, loss_cls: 3.7395, loss: 3.7395 +2024-12-29 07:48:23,783 - pyskl - INFO - Epoch [86][1100/3746] lr: 3.930e-02, eta: 2 days, 8:20:22, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3345, top5_acc: 0.6070, loss_cls: 3.7463, loss: 3.7463 +2024-12-29 07:49:49,063 - pyskl - INFO - Epoch [86][1200/3746] lr: 3.928e-02, eta: 2 days, 8:18:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.5969, loss_cls: 3.7479, loss: 3.7479 +2024-12-29 07:51:14,899 - pyskl - INFO - Epoch [86][1300/3746] lr: 3.925e-02, eta: 2 days, 8:17:37, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3425, top5_acc: 0.5984, loss_cls: 3.7349, loss: 3.7349 +2024-12-29 07:52:40,076 - pyskl - INFO - Epoch [86][1400/3746] lr: 3.922e-02, eta: 2 days, 8:16:15, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6117, loss_cls: 3.6945, loss: 3.6945 +2024-12-29 07:54:05,586 - pyskl - INFO - Epoch [86][1500/3746] lr: 3.919e-02, eta: 2 days, 8:14:53, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.6059, loss_cls: 3.7262, loss: 3.7262 +2024-12-29 07:55:31,450 - pyskl - INFO - Epoch [86][1600/3746] lr: 3.917e-02, eta: 2 days, 8:13:31, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6119, loss_cls: 3.7213, loss: 3.7213 +2024-12-29 07:56:57,189 - pyskl - INFO - Epoch [86][1700/3746] lr: 3.914e-02, eta: 2 days, 8:12:08, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.5983, loss_cls: 3.7140, loss: 3.7140 +2024-12-29 07:58:22,652 - pyskl - INFO - Epoch [86][1800/3746] lr: 3.911e-02, eta: 2 days, 8:10:46, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6177, loss_cls: 3.6749, loss: 3.6749 +2024-12-29 07:59:48,626 - pyskl - INFO - Epoch [86][1900/3746] lr: 3.909e-02, eta: 2 days, 8:09:24, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5952, loss_cls: 3.7644, loss: 3.7644 +2024-12-29 08:01:14,149 - pyskl - INFO - Epoch [86][2000/3746] lr: 3.906e-02, eta: 2 days, 8:08:02, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.6091, loss_cls: 3.7346, loss: 3.7346 +2024-12-29 08:02:39,726 - pyskl - INFO - Epoch [86][2100/3746] lr: 3.903e-02, eta: 2 days, 8:06:40, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.6017, loss_cls: 3.7687, loss: 3.7687 +2024-12-29 08:04:05,505 - pyskl - INFO - Epoch [86][2200/3746] lr: 3.900e-02, eta: 2 days, 8:05:18, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6108, loss_cls: 3.7170, loss: 3.7170 +2024-12-29 08:05:31,276 - pyskl - INFO - Epoch [86][2300/3746] lr: 3.898e-02, eta: 2 days, 8:03:55, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6092, loss_cls: 3.7057, loss: 3.7057 +2024-12-29 08:06:57,012 - pyskl - INFO - Epoch [86][2400/3746] lr: 3.895e-02, eta: 2 days, 8:02:33, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.6056, loss_cls: 3.7423, loss: 3.7423 +2024-12-29 08:08:22,705 - pyskl - INFO - Epoch [86][2500/3746] lr: 3.892e-02, eta: 2 days, 8:01:11, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.3352, top5_acc: 0.6044, loss_cls: 3.7408, loss: 3.7408 +2024-12-29 08:09:48,175 - pyskl - INFO - Epoch [86][2600/3746] lr: 3.889e-02, eta: 2 days, 7:59:49, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5970, loss_cls: 3.7766, loss: 3.7766 +2024-12-29 08:11:13,201 - pyskl - INFO - Epoch [86][2700/3746] lr: 3.887e-02, eta: 2 days, 7:58:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5922, loss_cls: 3.7807, loss: 3.7807 +2024-12-29 08:12:39,061 - pyskl - INFO - Epoch [86][2800/3746] lr: 3.884e-02, eta: 2 days, 7:57:04, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6045, loss_cls: 3.7237, loss: 3.7237 +2024-12-29 08:14:04,605 - pyskl - INFO - Epoch [86][2900/3746] lr: 3.881e-02, eta: 2 days, 7:55:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.6030, loss_cls: 3.7103, loss: 3.7103 +2024-12-29 08:15:30,431 - pyskl - INFO - Epoch [86][3000/3746] lr: 3.879e-02, eta: 2 days, 7:54:20, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.6012, loss_cls: 3.7526, loss: 3.7526 +2024-12-29 08:16:56,907 - pyskl - INFO - Epoch [86][3100/3746] lr: 3.876e-02, eta: 2 days, 7:52:58, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5923, loss_cls: 3.7781, loss: 3.7781 +2024-12-29 08:18:23,068 - pyskl - INFO - Epoch [86][3200/3746] lr: 3.873e-02, eta: 2 days, 7:51:36, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5983, loss_cls: 3.7429, loss: 3.7429 +2024-12-29 08:19:49,072 - pyskl - INFO - Epoch [86][3300/3746] lr: 3.870e-02, eta: 2 days, 7:50:14, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5950, loss_cls: 3.7592, loss: 3.7592 +2024-12-29 08:21:14,746 - pyskl - INFO - Epoch [86][3400/3746] lr: 3.868e-02, eta: 2 days, 7:48:52, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.6028, loss_cls: 3.7441, loss: 3.7441 +2024-12-29 08:22:41,187 - pyskl - INFO - Epoch [86][3500/3746] lr: 3.865e-02, eta: 2 days, 7:47:30, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6036, loss_cls: 3.7284, loss: 3.7284 +2024-12-29 08:24:07,647 - pyskl - INFO - Epoch [86][3600/3746] lr: 3.862e-02, eta: 2 days, 7:46:09, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.6019, loss_cls: 3.7343, loss: 3.7343 +2024-12-29 08:25:33,622 - pyskl - INFO - Epoch [86][3700/3746] lr: 3.860e-02, eta: 2 days, 7:44:47, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6044, loss_cls: 3.7258, loss: 3.7258 +2024-12-29 08:26:15,488 - pyskl - INFO - Saving checkpoint at 86 epochs +2024-12-29 08:28:16,441 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 08:28:17,484 - pyskl - INFO - +top1_acc 0.2797 +top5_acc 0.5384 +2024-12-29 08:28:17,484 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 08:28:17,530 - pyskl - INFO - +mean_acc 0.2794 +2024-12-29 08:28:17,537 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_85.pth was removed +2024-12-29 08:28:17,892 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_86.pth. +2024-12-29 08:28:17,892 - pyskl - INFO - Best top1_acc is 0.2797 at 86 epoch. +2024-12-29 08:28:17,903 - pyskl - INFO - Epoch(val) [86][309] top1_acc: 0.2797, top5_acc: 0.5384, mean_class_accuracy: 0.2794 +2024-12-29 08:32:39,568 - pyskl - INFO - Epoch [87][100/3746] lr: 3.856e-02, eta: 2 days, 7:44:28, time: 2.617, data_time: 1.583, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6258, loss_cls: 3.6060, loss: 3.6060 +2024-12-29 08:34:04,968 - pyskl - INFO - Epoch [87][200/3746] lr: 3.853e-02, eta: 2 days, 7:43:06, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6164, loss_cls: 3.6591, loss: 3.6591 +2024-12-29 08:35:30,910 - pyskl - INFO - Epoch [87][300/3746] lr: 3.850e-02, eta: 2 days, 7:41:44, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.6091, loss_cls: 3.7364, loss: 3.7364 +2024-12-29 08:36:55,934 - pyskl - INFO - Epoch [87][400/3746] lr: 3.847e-02, eta: 2 days, 7:40:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6145, loss_cls: 3.6579, loss: 3.6579 +2024-12-29 08:38:21,191 - pyskl - INFO - Epoch [87][500/3746] lr: 3.845e-02, eta: 2 days, 7:38:58, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6170, loss_cls: 3.6571, loss: 3.6571 +2024-12-29 08:39:45,999 - pyskl - INFO - Epoch [87][600/3746] lr: 3.842e-02, eta: 2 days, 7:37:35, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.3378, top5_acc: 0.6014, loss_cls: 3.7444, loss: 3.7444 +2024-12-29 08:41:10,989 - pyskl - INFO - Epoch [87][700/3746] lr: 3.839e-02, eta: 2 days, 7:36:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6055, loss_cls: 3.7388, loss: 3.7388 +2024-12-29 08:42:36,024 - pyskl - INFO - Epoch [87][800/3746] lr: 3.837e-02, eta: 2 days, 7:34:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6062, loss_cls: 3.7232, loss: 3.7232 +2024-12-29 08:44:00,758 - pyskl - INFO - Epoch [87][900/3746] lr: 3.834e-02, eta: 2 days, 7:33:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6091, loss_cls: 3.7069, loss: 3.7069 +2024-12-29 08:45:25,741 - pyskl - INFO - Epoch [87][1000/3746] lr: 3.831e-02, eta: 2 days, 7:32:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.5994, loss_cls: 3.7359, loss: 3.7359 +2024-12-29 08:46:51,068 - pyskl - INFO - Epoch [87][1100/3746] lr: 3.828e-02, eta: 2 days, 7:30:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6177, loss_cls: 3.6737, loss: 3.6737 +2024-12-29 08:48:15,784 - pyskl - INFO - Epoch [87][1200/3746] lr: 3.826e-02, eta: 2 days, 7:29:18, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6095, loss_cls: 3.7090, loss: 3.7090 +2024-12-29 08:49:40,441 - pyskl - INFO - Epoch [87][1300/3746] lr: 3.823e-02, eta: 2 days, 7:27:55, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6144, loss_cls: 3.6942, loss: 3.6942 +2024-12-29 08:51:04,764 - pyskl - INFO - Epoch [87][1400/3746] lr: 3.820e-02, eta: 2 days, 7:26:32, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6097, loss_cls: 3.6649, loss: 3.6649 +2024-12-29 08:52:29,534 - pyskl - INFO - Epoch [87][1500/3746] lr: 3.817e-02, eta: 2 days, 7:25:09, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.6041, loss_cls: 3.7381, loss: 3.7381 +2024-12-29 08:53:54,017 - pyskl - INFO - Epoch [87][1600/3746] lr: 3.815e-02, eta: 2 days, 7:23:46, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5900, loss_cls: 3.7945, loss: 3.7945 +2024-12-29 08:55:19,051 - pyskl - INFO - Epoch [87][1700/3746] lr: 3.812e-02, eta: 2 days, 7:22:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6022, loss_cls: 3.7158, loss: 3.7158 +2024-12-29 08:56:43,917 - pyskl - INFO - Epoch [87][1800/3746] lr: 3.809e-02, eta: 2 days, 7:21:00, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.5967, loss_cls: 3.7577, loss: 3.7577 +2024-12-29 08:58:08,323 - pyskl - INFO - Epoch [87][1900/3746] lr: 3.807e-02, eta: 2 days, 7:19:37, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6119, loss_cls: 3.6722, loss: 3.6722 +2024-12-29 08:59:33,009 - pyskl - INFO - Epoch [87][2000/3746] lr: 3.804e-02, eta: 2 days, 7:18:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6159, loss_cls: 3.7050, loss: 3.7050 +2024-12-29 09:00:57,990 - pyskl - INFO - Epoch [87][2100/3746] lr: 3.801e-02, eta: 2 days, 7:16:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6008, loss_cls: 3.7096, loss: 3.7096 +2024-12-29 09:02:22,808 - pyskl - INFO - Epoch [87][2200/3746] lr: 3.798e-02, eta: 2 days, 7:15:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6108, loss_cls: 3.6873, loss: 3.6873 +2024-12-29 09:03:47,860 - pyskl - INFO - Epoch [87][2300/3746] lr: 3.796e-02, eta: 2 days, 7:14:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6130, loss_cls: 3.7051, loss: 3.7051 +2024-12-29 09:05:12,277 - pyskl - INFO - Epoch [87][2400/3746] lr: 3.793e-02, eta: 2 days, 7:12:42, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6127, loss_cls: 3.7161, loss: 3.7161 +2024-12-29 09:06:37,132 - pyskl - INFO - Epoch [87][2500/3746] lr: 3.790e-02, eta: 2 days, 7:11:19, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6083, loss_cls: 3.7133, loss: 3.7133 +2024-12-29 09:08:02,041 - pyskl - INFO - Epoch [87][2600/3746] lr: 3.788e-02, eta: 2 days, 7:09:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5969, loss_cls: 3.7443, loss: 3.7443 +2024-12-29 09:09:28,008 - pyskl - INFO - Epoch [87][2700/3746] lr: 3.785e-02, eta: 2 days, 7:08:34, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6116, loss_cls: 3.6960, loss: 3.6960 +2024-12-29 09:10:53,074 - pyskl - INFO - Epoch [87][2800/3746] lr: 3.782e-02, eta: 2 days, 7:07:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6061, loss_cls: 3.7157, loss: 3.7157 +2024-12-29 09:12:17,894 - pyskl - INFO - Epoch [87][2900/3746] lr: 3.779e-02, eta: 2 days, 7:05:48, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6009, loss_cls: 3.6998, loss: 3.6998 +2024-12-29 09:13:43,313 - pyskl - INFO - Epoch [87][3000/3746] lr: 3.777e-02, eta: 2 days, 7:04:26, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5916, loss_cls: 3.7838, loss: 3.7838 +2024-12-29 09:15:08,124 - pyskl - INFO - Epoch [87][3100/3746] lr: 3.774e-02, eta: 2 days, 7:03:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.6011, loss_cls: 3.7451, loss: 3.7451 +2024-12-29 09:16:32,858 - pyskl - INFO - Epoch [87][3200/3746] lr: 3.771e-02, eta: 2 days, 7:01:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.6095, loss_cls: 3.7286, loss: 3.7286 +2024-12-29 09:17:58,030 - pyskl - INFO - Epoch [87][3300/3746] lr: 3.769e-02, eta: 2 days, 7:00:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6047, loss_cls: 3.7327, loss: 3.7327 +2024-12-29 09:19:22,902 - pyskl - INFO - Epoch [87][3400/3746] lr: 3.766e-02, eta: 2 days, 6:58:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.6042, loss_cls: 3.7437, loss: 3.7437 +2024-12-29 09:20:47,416 - pyskl - INFO - Epoch [87][3500/3746] lr: 3.763e-02, eta: 2 days, 6:57:31, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.5970, loss_cls: 3.7319, loss: 3.7319 +2024-12-29 09:22:12,945 - pyskl - INFO - Epoch [87][3600/3746] lr: 3.761e-02, eta: 2 days, 6:56:08, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.6108, loss_cls: 3.7008, loss: 3.7008 +2024-12-29 09:23:38,049 - pyskl - INFO - Epoch [87][3700/3746] lr: 3.758e-02, eta: 2 days, 6:54:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6050, loss_cls: 3.7490, loss: 3.7490 +2024-12-29 09:24:19,371 - pyskl - INFO - Saving checkpoint at 87 epochs +2024-12-29 09:26:17,985 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 09:26:18,682 - pyskl - INFO - +top1_acc 0.2626 +top5_acc 0.5116 +2024-12-29 09:26:18,682 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 09:26:18,726 - pyskl - INFO - +mean_acc 0.2623 +2024-12-29 09:26:18,739 - pyskl - INFO - Epoch(val) [87][309] top1_acc: 0.2626, top5_acc: 0.5116, mean_class_accuracy: 0.2623 +2024-12-29 09:30:31,100 - pyskl - INFO - Epoch [88][100/3746] lr: 3.754e-02, eta: 2 days, 6:54:17, time: 2.524, data_time: 1.484, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6231, loss_cls: 3.6454, loss: 3.6454 +2024-12-29 09:31:56,429 - pyskl - INFO - Epoch [88][200/3746] lr: 3.751e-02, eta: 2 days, 6:52:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6205, loss_cls: 3.6461, loss: 3.6461 +2024-12-29 09:33:22,034 - pyskl - INFO - Epoch [88][300/3746] lr: 3.748e-02, eta: 2 days, 6:51:32, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6184, loss_cls: 3.6663, loss: 3.6663 +2024-12-29 09:34:47,349 - pyskl - INFO - Epoch [88][400/3746] lr: 3.746e-02, eta: 2 days, 6:50:10, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6189, loss_cls: 3.6477, loss: 3.6477 +2024-12-29 09:36:12,700 - pyskl - INFO - Epoch [88][500/3746] lr: 3.743e-02, eta: 2 days, 6:48:47, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.6084, loss_cls: 3.7198, loss: 3.7198 +2024-12-29 09:37:36,719 - pyskl - INFO - Epoch [88][600/3746] lr: 3.740e-02, eta: 2 days, 6:47:23, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6181, loss_cls: 3.6484, loss: 3.6484 +2024-12-29 09:39:01,836 - pyskl - INFO - Epoch [88][700/3746] lr: 3.738e-02, eta: 2 days, 6:46:00, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6184, loss_cls: 3.6209, loss: 3.6209 +2024-12-29 09:40:26,458 - pyskl - INFO - Epoch [88][800/3746] lr: 3.735e-02, eta: 2 days, 6:44:37, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6117, loss_cls: 3.6767, loss: 3.6767 +2024-12-29 09:41:50,952 - pyskl - INFO - Epoch [88][900/3746] lr: 3.732e-02, eta: 2 days, 6:43:14, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6109, loss_cls: 3.6920, loss: 3.6920 +2024-12-29 09:43:15,635 - pyskl - INFO - Epoch [88][1000/3746] lr: 3.730e-02, eta: 2 days, 6:41:51, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6248, loss_cls: 3.6189, loss: 3.6189 +2024-12-29 09:44:40,695 - pyskl - INFO - Epoch [88][1100/3746] lr: 3.727e-02, eta: 2 days, 6:40:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.6069, loss_cls: 3.7146, loss: 3.7146 +2024-12-29 09:46:05,503 - pyskl - INFO - Epoch [88][1200/3746] lr: 3.724e-02, eta: 2 days, 6:39:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6038, loss_cls: 3.7097, loss: 3.7097 +2024-12-29 09:47:30,099 - pyskl - INFO - Epoch [88][1300/3746] lr: 3.721e-02, eta: 2 days, 6:37:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.6038, loss_cls: 3.7367, loss: 3.7367 +2024-12-29 09:48:54,465 - pyskl - INFO - Epoch [88][1400/3746] lr: 3.719e-02, eta: 2 days, 6:36:18, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.6092, loss_cls: 3.7116, loss: 3.7116 +2024-12-29 09:50:19,434 - pyskl - INFO - Epoch [88][1500/3746] lr: 3.716e-02, eta: 2 days, 6:34:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6094, loss_cls: 3.7167, loss: 3.7167 +2024-12-29 09:51:44,721 - pyskl - INFO - Epoch [88][1600/3746] lr: 3.713e-02, eta: 2 days, 6:33:33, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6050, loss_cls: 3.7128, loss: 3.7128 +2024-12-29 09:53:09,558 - pyskl - INFO - Epoch [88][1700/3746] lr: 3.711e-02, eta: 2 days, 6:32:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.6059, loss_cls: 3.7132, loss: 3.7132 +2024-12-29 09:54:33,974 - pyskl - INFO - Epoch [88][1800/3746] lr: 3.708e-02, eta: 2 days, 6:30:46, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6067, loss_cls: 3.6952, loss: 3.6952 +2024-12-29 09:55:58,467 - pyskl - INFO - Epoch [88][1900/3746] lr: 3.705e-02, eta: 2 days, 6:29:23, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6039, loss_cls: 3.7003, loss: 3.7003 +2024-12-29 09:57:23,155 - pyskl - INFO - Epoch [88][2000/3746] lr: 3.703e-02, eta: 2 days, 6:28:00, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6198, loss_cls: 3.6782, loss: 3.6782 +2024-12-29 09:58:47,542 - pyskl - INFO - Epoch [88][2100/3746] lr: 3.700e-02, eta: 2 days, 6:26:36, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6130, loss_cls: 3.6862, loss: 3.6862 +2024-12-29 10:00:11,910 - pyskl - INFO - Epoch [88][2200/3746] lr: 3.697e-02, eta: 2 days, 6:25:13, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6105, loss_cls: 3.6888, loss: 3.6888 +2024-12-29 10:01:36,398 - pyskl - INFO - Epoch [88][2300/3746] lr: 3.694e-02, eta: 2 days, 6:23:50, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6130, loss_cls: 3.6731, loss: 3.6731 +2024-12-29 10:03:01,135 - pyskl - INFO - Epoch [88][2400/3746] lr: 3.692e-02, eta: 2 days, 6:22:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6030, loss_cls: 3.7390, loss: 3.7390 +2024-12-29 10:04:25,492 - pyskl - INFO - Epoch [88][2500/3746] lr: 3.689e-02, eta: 2 days, 6:21:03, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.5991, loss_cls: 3.7572, loss: 3.7572 +2024-12-29 10:05:49,676 - pyskl - INFO - Epoch [88][2600/3746] lr: 3.686e-02, eta: 2 days, 6:19:40, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6038, loss_cls: 3.7074, loss: 3.7074 +2024-12-29 10:07:13,450 - pyskl - INFO - Epoch [88][2700/3746] lr: 3.684e-02, eta: 2 days, 6:18:16, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.6017, loss_cls: 3.7482, loss: 3.7482 +2024-12-29 10:08:38,251 - pyskl - INFO - Epoch [88][2800/3746] lr: 3.681e-02, eta: 2 days, 6:16:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6153, loss_cls: 3.6723, loss: 3.6723 +2024-12-29 10:10:02,945 - pyskl - INFO - Epoch [88][2900/3746] lr: 3.678e-02, eta: 2 days, 6:15:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6175, loss_cls: 3.6619, loss: 3.6619 +2024-12-29 10:11:27,563 - pyskl - INFO - Epoch [88][3000/3746] lr: 3.676e-02, eta: 2 days, 6:14:06, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6078, loss_cls: 3.7229, loss: 3.7229 +2024-12-29 10:12:52,001 - pyskl - INFO - Epoch [88][3100/3746] lr: 3.673e-02, eta: 2 days, 6:12:43, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.6027, loss_cls: 3.7317, loss: 3.7317 +2024-12-29 10:14:17,035 - pyskl - INFO - Epoch [88][3200/3746] lr: 3.670e-02, eta: 2 days, 6:11:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6117, loss_cls: 3.6992, loss: 3.6992 +2024-12-29 10:15:41,740 - pyskl - INFO - Epoch [88][3300/3746] lr: 3.667e-02, eta: 2 days, 6:09:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5948, loss_cls: 3.7638, loss: 3.7638 +2024-12-29 10:17:07,240 - pyskl - INFO - Epoch [88][3400/3746] lr: 3.665e-02, eta: 2 days, 6:08:34, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6081, loss_cls: 3.7003, loss: 3.7003 +2024-12-29 10:18:32,159 - pyskl - INFO - Epoch [88][3500/3746] lr: 3.662e-02, eta: 2 days, 6:07:11, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5942, loss_cls: 3.7822, loss: 3.7822 +2024-12-29 10:19:57,223 - pyskl - INFO - Epoch [88][3600/3746] lr: 3.659e-02, eta: 2 days, 6:05:48, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6130, loss_cls: 3.7071, loss: 3.7071 +2024-12-29 10:21:22,698 - pyskl - INFO - Epoch [88][3700/3746] lr: 3.657e-02, eta: 2 days, 6:04:26, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6070, loss_cls: 3.6792, loss: 3.6792 +2024-12-29 10:22:03,930 - pyskl - INFO - Saving checkpoint at 88 epochs +2024-12-29 10:24:03,296 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 10:24:04,067 - pyskl - INFO - +top1_acc 0.2610 +top5_acc 0.5157 +2024-12-29 10:24:04,068 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 10:24:04,114 - pyskl - INFO - +mean_acc 0.2608 +2024-12-29 10:24:04,133 - pyskl - INFO - Epoch(val) [88][309] top1_acc: 0.2610, top5_acc: 0.5157, mean_class_accuracy: 0.2608 +2024-12-29 10:28:20,198 - pyskl - INFO - Epoch [89][100/3746] lr: 3.653e-02, eta: 2 days, 6:03:58, time: 2.561, data_time: 1.530, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6247, loss_cls: 3.6284, loss: 3.6284 +2024-12-29 10:29:44,833 - pyskl - INFO - Epoch [89][200/3746] lr: 3.650e-02, eta: 2 days, 6:02:34, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6112, loss_cls: 3.6737, loss: 3.6737 +2024-12-29 10:31:10,148 - pyskl - INFO - Epoch [89][300/3746] lr: 3.647e-02, eta: 2 days, 6:01:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6225, loss_cls: 3.6540, loss: 3.6540 +2024-12-29 10:32:35,879 - pyskl - INFO - Epoch [89][400/3746] lr: 3.645e-02, eta: 2 days, 5:59:49, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6288, loss_cls: 3.6131, loss: 3.6131 +2024-12-29 10:34:00,876 - pyskl - INFO - Epoch [89][500/3746] lr: 3.642e-02, eta: 2 days, 5:58:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6225, loss_cls: 3.6423, loss: 3.6423 +2024-12-29 10:35:25,973 - pyskl - INFO - Epoch [89][600/3746] lr: 3.639e-02, eta: 2 days, 5:57:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6066, loss_cls: 3.7126, loss: 3.7126 +2024-12-29 10:36:50,706 - pyskl - INFO - Epoch [89][700/3746] lr: 3.637e-02, eta: 2 days, 5:55:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6086, loss_cls: 3.7024, loss: 3.7024 +2024-12-29 10:38:15,362 - pyskl - INFO - Epoch [89][800/3746] lr: 3.634e-02, eta: 2 days, 5:54:16, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6136, loss_cls: 3.7062, loss: 3.7062 +2024-12-29 10:39:39,985 - pyskl - INFO - Epoch [89][900/3746] lr: 3.631e-02, eta: 2 days, 5:52:53, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6092, loss_cls: 3.6793, loss: 3.6793 +2024-12-29 10:41:04,211 - pyskl - INFO - Epoch [89][1000/3746] lr: 3.629e-02, eta: 2 days, 5:51:30, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3425, top5_acc: 0.6122, loss_cls: 3.7063, loss: 3.7063 +2024-12-29 10:42:28,857 - pyskl - INFO - Epoch [89][1100/3746] lr: 3.626e-02, eta: 2 days, 5:50:06, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6080, loss_cls: 3.7016, loss: 3.7016 +2024-12-29 10:43:53,367 - pyskl - INFO - Epoch [89][1200/3746] lr: 3.623e-02, eta: 2 days, 5:48:43, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6133, loss_cls: 3.6634, loss: 3.6634 +2024-12-29 10:45:17,503 - pyskl - INFO - Epoch [89][1300/3746] lr: 3.620e-02, eta: 2 days, 5:47:19, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6112, loss_cls: 3.7040, loss: 3.7040 +2024-12-29 10:46:42,170 - pyskl - INFO - Epoch [89][1400/3746] lr: 3.618e-02, eta: 2 days, 5:45:56, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6162, loss_cls: 3.6920, loss: 3.6920 +2024-12-29 10:48:07,177 - pyskl - INFO - Epoch [89][1500/3746] lr: 3.615e-02, eta: 2 days, 5:44:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6177, loss_cls: 3.6679, loss: 3.6679 +2024-12-29 10:49:31,778 - pyskl - INFO - Epoch [89][1600/3746] lr: 3.612e-02, eta: 2 days, 5:43:10, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6106, loss_cls: 3.6768, loss: 3.6768 +2024-12-29 10:50:56,620 - pyskl - INFO - Epoch [89][1700/3746] lr: 3.610e-02, eta: 2 days, 5:41:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.6150, loss_cls: 3.6733, loss: 3.6733 +2024-12-29 10:52:21,228 - pyskl - INFO - Epoch [89][1800/3746] lr: 3.607e-02, eta: 2 days, 5:40:23, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6097, loss_cls: 3.6985, loss: 3.6985 +2024-12-29 10:53:46,043 - pyskl - INFO - Epoch [89][1900/3746] lr: 3.604e-02, eta: 2 days, 5:39:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6111, loss_cls: 3.6846, loss: 3.6846 +2024-12-29 10:55:11,369 - pyskl - INFO - Epoch [89][2000/3746] lr: 3.602e-02, eta: 2 days, 5:37:37, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6148, loss_cls: 3.6886, loss: 3.6886 +2024-12-29 10:56:36,357 - pyskl - INFO - Epoch [89][2100/3746] lr: 3.599e-02, eta: 2 days, 5:36:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6128, loss_cls: 3.6646, loss: 3.6646 +2024-12-29 10:58:01,771 - pyskl - INFO - Epoch [89][2200/3746] lr: 3.596e-02, eta: 2 days, 5:34:51, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6062, loss_cls: 3.7168, loss: 3.7168 +2024-12-29 10:59:26,656 - pyskl - INFO - Epoch [89][2300/3746] lr: 3.594e-02, eta: 2 days, 5:33:28, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.6022, loss_cls: 3.7139, loss: 3.7139 +2024-12-29 11:00:51,746 - pyskl - INFO - Epoch [89][2400/3746] lr: 3.591e-02, eta: 2 days, 5:32:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6131, loss_cls: 3.6855, loss: 3.6855 +2024-12-29 11:02:16,801 - pyskl - INFO - Epoch [89][2500/3746] lr: 3.588e-02, eta: 2 days, 5:30:42, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6052, loss_cls: 3.6891, loss: 3.6891 +2024-12-29 11:03:41,408 - pyskl - INFO - Epoch [89][2600/3746] lr: 3.586e-02, eta: 2 days, 5:29:19, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6170, loss_cls: 3.6645, loss: 3.6645 +2024-12-29 11:05:05,825 - pyskl - INFO - Epoch [89][2700/3746] lr: 3.583e-02, eta: 2 days, 5:27:55, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6195, loss_cls: 3.6481, loss: 3.6481 +2024-12-29 11:06:30,600 - pyskl - INFO - Epoch [89][2800/3746] lr: 3.580e-02, eta: 2 days, 5:26:32, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6239, loss_cls: 3.6601, loss: 3.6601 +2024-12-29 11:07:55,058 - pyskl - INFO - Epoch [89][2900/3746] lr: 3.578e-02, eta: 2 days, 5:25:09, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6164, loss_cls: 3.6716, loss: 3.6716 +2024-12-29 11:09:20,418 - pyskl - INFO - Epoch [89][3000/3746] lr: 3.575e-02, eta: 2 days, 5:23:46, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6117, loss_cls: 3.7189, loss: 3.7189 +2024-12-29 11:10:45,350 - pyskl - INFO - Epoch [89][3100/3746] lr: 3.572e-02, eta: 2 days, 5:22:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6080, loss_cls: 3.7123, loss: 3.7123 +2024-12-29 11:12:11,111 - pyskl - INFO - Epoch [89][3200/3746] lr: 3.569e-02, eta: 2 days, 5:21:00, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6092, loss_cls: 3.6904, loss: 3.6904 +2024-12-29 11:13:36,603 - pyskl - INFO - Epoch [89][3300/3746] lr: 3.567e-02, eta: 2 days, 5:19:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6139, loss_cls: 3.6805, loss: 3.6805 +2024-12-29 11:15:01,638 - pyskl - INFO - Epoch [89][3400/3746] lr: 3.564e-02, eta: 2 days, 5:18:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6067, loss_cls: 3.7078, loss: 3.7078 +2024-12-29 11:16:26,950 - pyskl - INFO - Epoch [89][3500/3746] lr: 3.561e-02, eta: 2 days, 5:16:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6183, loss_cls: 3.6710, loss: 3.6710 +2024-12-29 11:17:52,186 - pyskl - INFO - Epoch [89][3600/3746] lr: 3.559e-02, eta: 2 days, 5:15:29, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6066, loss_cls: 3.7139, loss: 3.7139 +2024-12-29 11:19:17,548 - pyskl - INFO - Epoch [89][3700/3746] lr: 3.556e-02, eta: 2 days, 5:14:06, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6134, loss_cls: 3.6941, loss: 3.6941 +2024-12-29 11:19:58,248 - pyskl - INFO - Saving checkpoint at 89 epochs +2024-12-29 11:21:57,735 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 11:21:58,418 - pyskl - INFO - +top1_acc 0.2817 +top5_acc 0.5319 +2024-12-29 11:21:58,419 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 11:21:58,470 - pyskl - INFO - +mean_acc 0.2814 +2024-12-29 11:21:58,477 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_86.pth was removed +2024-12-29 11:21:58,774 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_89.pth. +2024-12-29 11:21:58,775 - pyskl - INFO - Best top1_acc is 0.2817 at 89 epoch. +2024-12-29 11:21:58,794 - pyskl - INFO - Epoch(val) [89][309] top1_acc: 0.2817, top5_acc: 0.5319, mean_class_accuracy: 0.2814 +2024-12-29 11:26:09,743 - pyskl - INFO - Epoch [90][100/3746] lr: 3.552e-02, eta: 2 days, 5:13:32, time: 2.509, data_time: 1.475, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6202, loss_cls: 3.6326, loss: 3.6326 +2024-12-29 11:27:35,758 - pyskl - INFO - Epoch [90][200/3746] lr: 3.550e-02, eta: 2 days, 5:12:09, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6127, loss_cls: 3.6691, loss: 3.6691 +2024-12-29 11:29:01,253 - pyskl - INFO - Epoch [90][300/3746] lr: 3.547e-02, eta: 2 days, 5:10:46, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6089, loss_cls: 3.6989, loss: 3.6989 +2024-12-29 11:30:26,419 - pyskl - INFO - Epoch [90][400/3746] lr: 3.544e-02, eta: 2 days, 5:09:23, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6184, loss_cls: 3.6503, loss: 3.6503 +2024-12-29 11:31:51,469 - pyskl - INFO - Epoch [90][500/3746] lr: 3.541e-02, eta: 2 days, 5:08:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6278, loss_cls: 3.5991, loss: 3.5991 +2024-12-29 11:33:15,849 - pyskl - INFO - Epoch [90][600/3746] lr: 3.539e-02, eta: 2 days, 5:06:37, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6211, loss_cls: 3.6364, loss: 3.6364 +2024-12-29 11:34:40,679 - pyskl - INFO - Epoch [90][700/3746] lr: 3.536e-02, eta: 2 days, 5:05:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.6084, loss_cls: 3.6954, loss: 3.6954 +2024-12-29 11:36:05,181 - pyskl - INFO - Epoch [90][800/3746] lr: 3.533e-02, eta: 2 days, 5:03:50, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6092, loss_cls: 3.7054, loss: 3.7054 +2024-12-29 11:37:29,944 - pyskl - INFO - Epoch [90][900/3746] lr: 3.531e-02, eta: 2 days, 5:02:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6011, loss_cls: 3.6985, loss: 3.6985 +2024-12-29 11:38:54,849 - pyskl - INFO - Epoch [90][1000/3746] lr: 3.528e-02, eta: 2 days, 5:01:03, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6130, loss_cls: 3.6641, loss: 3.6641 +2024-12-29 11:40:19,738 - pyskl - INFO - Epoch [90][1100/3746] lr: 3.525e-02, eta: 2 days, 4:59:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6169, loss_cls: 3.6538, loss: 3.6538 +2024-12-29 11:41:44,550 - pyskl - INFO - Epoch [90][1200/3746] lr: 3.523e-02, eta: 2 days, 4:58:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6195, loss_cls: 3.6345, loss: 3.6345 +2024-12-29 11:43:09,041 - pyskl - INFO - Epoch [90][1300/3746] lr: 3.520e-02, eta: 2 days, 4:56:53, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.6091, loss_cls: 3.7128, loss: 3.7128 +2024-12-29 11:44:33,872 - pyskl - INFO - Epoch [90][1400/3746] lr: 3.517e-02, eta: 2 days, 4:55:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6145, loss_cls: 3.7086, loss: 3.7086 +2024-12-29 11:45:58,687 - pyskl - INFO - Epoch [90][1500/3746] lr: 3.515e-02, eta: 2 days, 4:54:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6272, loss_cls: 3.6209, loss: 3.6209 +2024-12-29 11:47:23,604 - pyskl - INFO - Epoch [90][1600/3746] lr: 3.512e-02, eta: 2 days, 4:52:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6122, loss_cls: 3.6655, loss: 3.6655 +2024-12-29 11:48:48,386 - pyskl - INFO - Epoch [90][1700/3746] lr: 3.509e-02, eta: 2 days, 4:51:20, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6141, loss_cls: 3.6547, loss: 3.6547 +2024-12-29 11:50:12,733 - pyskl - INFO - Epoch [90][1800/3746] lr: 3.507e-02, eta: 2 days, 4:49:57, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6138, loss_cls: 3.6618, loss: 3.6618 +2024-12-29 11:51:37,568 - pyskl - INFO - Epoch [90][1900/3746] lr: 3.504e-02, eta: 2 days, 4:48:34, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6064, loss_cls: 3.7047, loss: 3.7047 +2024-12-29 11:53:02,187 - pyskl - INFO - Epoch [90][2000/3746] lr: 3.501e-02, eta: 2 days, 4:47:10, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6009, loss_cls: 3.7016, loss: 3.7016 +2024-12-29 11:54:26,799 - pyskl - INFO - Epoch [90][2100/3746] lr: 3.499e-02, eta: 2 days, 4:45:47, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6136, loss_cls: 3.6787, loss: 3.6787 +2024-12-29 11:55:52,278 - pyskl - INFO - Epoch [90][2200/3746] lr: 3.496e-02, eta: 2 days, 4:44:24, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6089, loss_cls: 3.6901, loss: 3.6901 +2024-12-29 11:57:16,479 - pyskl - INFO - Epoch [90][2300/3746] lr: 3.493e-02, eta: 2 days, 4:43:00, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6219, loss_cls: 3.6474, loss: 3.6474 +2024-12-29 11:58:41,185 - pyskl - INFO - Epoch [90][2400/3746] lr: 3.491e-02, eta: 2 days, 4:41:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6155, loss_cls: 3.6572, loss: 3.6572 +2024-12-29 12:00:05,774 - pyskl - INFO - Epoch [90][2500/3746] lr: 3.488e-02, eta: 2 days, 4:40:13, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6125, loss_cls: 3.6761, loss: 3.6761 +2024-12-29 12:01:30,532 - pyskl - INFO - Epoch [90][2600/3746] lr: 3.485e-02, eta: 2 days, 4:38:50, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6258, loss_cls: 3.6220, loss: 3.6220 +2024-12-29 12:02:54,955 - pyskl - INFO - Epoch [90][2700/3746] lr: 3.483e-02, eta: 2 days, 4:37:27, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6056, loss_cls: 3.7032, loss: 3.7032 +2024-12-29 12:04:19,078 - pyskl - INFO - Epoch [90][2800/3746] lr: 3.480e-02, eta: 2 days, 4:36:03, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6112, loss_cls: 3.6600, loss: 3.6600 +2024-12-29 12:05:43,882 - pyskl - INFO - Epoch [90][2900/3746] lr: 3.477e-02, eta: 2 days, 4:34:40, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6158, loss_cls: 3.6697, loss: 3.6697 +2024-12-29 12:07:08,457 - pyskl - INFO - Epoch [90][3000/3746] lr: 3.475e-02, eta: 2 days, 4:33:16, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6111, loss_cls: 3.6946, loss: 3.6946 +2024-12-29 12:08:33,377 - pyskl - INFO - Epoch [90][3100/3746] lr: 3.472e-02, eta: 2 days, 4:31:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6134, loss_cls: 3.6734, loss: 3.6734 +2024-12-29 12:09:58,006 - pyskl - INFO - Epoch [90][3200/3746] lr: 3.469e-02, eta: 2 days, 4:30:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6159, loss_cls: 3.6494, loss: 3.6494 +2024-12-29 12:11:22,674 - pyskl - INFO - Epoch [90][3300/3746] lr: 3.467e-02, eta: 2 days, 4:29:06, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6053, loss_cls: 3.6940, loss: 3.6940 +2024-12-29 12:12:46,883 - pyskl - INFO - Epoch [90][3400/3746] lr: 3.464e-02, eta: 2 days, 4:27:42, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6094, loss_cls: 3.6789, loss: 3.6789 +2024-12-29 12:14:11,746 - pyskl - INFO - Epoch [90][3500/3746] lr: 3.461e-02, eta: 2 days, 4:26:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6116, loss_cls: 3.6821, loss: 3.6821 +2024-12-29 12:15:36,631 - pyskl - INFO - Epoch [90][3600/3746] lr: 3.459e-02, eta: 2 days, 4:24:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6138, loss_cls: 3.6788, loss: 3.6788 +2024-12-29 12:17:01,343 - pyskl - INFO - Epoch [90][3700/3746] lr: 3.456e-02, eta: 2 days, 4:23:33, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6177, loss_cls: 3.6833, loss: 3.6833 +2024-12-29 12:17:41,821 - pyskl - INFO - Saving checkpoint at 90 epochs +2024-12-29 12:19:40,276 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 12:19:41,081 - pyskl - INFO - +top1_acc 0.2953 +top5_acc 0.5489 +2024-12-29 12:19:41,081 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 12:19:41,128 - pyskl - INFO - +mean_acc 0.2949 +2024-12-29 12:19:41,133 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_89.pth was removed +2024-12-29 12:19:41,420 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_90.pth. +2024-12-29 12:19:41,421 - pyskl - INFO - Best top1_acc is 0.2953 at 90 epoch. +2024-12-29 12:19:41,432 - pyskl - INFO - Epoch(val) [90][309] top1_acc: 0.2953, top5_acc: 0.5489, mean_class_accuracy: 0.2949 +2024-12-29 12:23:57,806 - pyskl - INFO - Epoch [91][100/3746] lr: 3.452e-02, eta: 2 days, 4:22:59, time: 2.564, data_time: 1.513, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6212, loss_cls: 3.6034, loss: 3.6034 +2024-12-29 12:25:23,154 - pyskl - INFO - Epoch [91][200/3746] lr: 3.450e-02, eta: 2 days, 4:21:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6325, loss_cls: 3.5893, loss: 3.5893 +2024-12-29 12:26:48,378 - pyskl - INFO - Epoch [91][300/3746] lr: 3.447e-02, eta: 2 days, 4:20:13, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6272, loss_cls: 3.6286, loss: 3.6286 +2024-12-29 12:28:13,837 - pyskl - INFO - Epoch [91][400/3746] lr: 3.444e-02, eta: 2 days, 4:18:50, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6248, loss_cls: 3.6048, loss: 3.6048 +2024-12-29 12:29:39,364 - pyskl - INFO - Epoch [91][500/3746] lr: 3.442e-02, eta: 2 days, 4:17:27, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6167, loss_cls: 3.6321, loss: 3.6321 +2024-12-29 12:31:04,257 - pyskl - INFO - Epoch [91][600/3746] lr: 3.439e-02, eta: 2 days, 4:16:04, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6078, loss_cls: 3.6711, loss: 3.6711 +2024-12-29 12:32:29,441 - pyskl - INFO - Epoch [91][700/3746] lr: 3.436e-02, eta: 2 days, 4:14:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6158, loss_cls: 3.6679, loss: 3.6679 +2024-12-29 12:33:54,356 - pyskl - INFO - Epoch [91][800/3746] lr: 3.434e-02, eta: 2 days, 4:13:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6189, loss_cls: 3.6350, loss: 3.6350 +2024-12-29 12:35:19,612 - pyskl - INFO - Epoch [91][900/3746] lr: 3.431e-02, eta: 2 days, 4:11:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6214, loss_cls: 3.6138, loss: 3.6138 +2024-12-29 12:36:44,288 - pyskl - INFO - Epoch [91][1000/3746] lr: 3.428e-02, eta: 2 days, 4:10:31, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6177, loss_cls: 3.6669, loss: 3.6669 +2024-12-29 12:38:09,571 - pyskl - INFO - Epoch [91][1100/3746] lr: 3.426e-02, eta: 2 days, 4:09:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6175, loss_cls: 3.6856, loss: 3.6856 +2024-12-29 12:39:34,609 - pyskl - INFO - Epoch [91][1200/3746] lr: 3.423e-02, eta: 2 days, 4:07:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6081, loss_cls: 3.6900, loss: 3.6900 +2024-12-29 12:40:59,471 - pyskl - INFO - Epoch [91][1300/3746] lr: 3.420e-02, eta: 2 days, 4:06:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6181, loss_cls: 3.6538, loss: 3.6538 +2024-12-29 12:42:24,475 - pyskl - INFO - Epoch [91][1400/3746] lr: 3.418e-02, eta: 2 days, 4:04:58, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6250, loss_cls: 3.6173, loss: 3.6173 +2024-12-29 12:43:49,420 - pyskl - INFO - Epoch [91][1500/3746] lr: 3.415e-02, eta: 2 days, 4:03:35, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6175, loss_cls: 3.6535, loss: 3.6535 +2024-12-29 12:45:14,507 - pyskl - INFO - Epoch [91][1600/3746] lr: 3.412e-02, eta: 2 days, 4:02:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6206, loss_cls: 3.6123, loss: 3.6123 +2024-12-29 12:46:39,073 - pyskl - INFO - Epoch [91][1700/3746] lr: 3.410e-02, eta: 2 days, 4:00:48, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6167, loss_cls: 3.6169, loss: 3.6169 +2024-12-29 12:48:03,494 - pyskl - INFO - Epoch [91][1800/3746] lr: 3.407e-02, eta: 2 days, 3:59:25, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6147, loss_cls: 3.6848, loss: 3.6848 +2024-12-29 12:49:28,322 - pyskl - INFO - Epoch [91][1900/3746] lr: 3.405e-02, eta: 2 days, 3:58:01, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6152, loss_cls: 3.6644, loss: 3.6644 +2024-12-29 12:50:52,482 - pyskl - INFO - Epoch [91][2000/3746] lr: 3.402e-02, eta: 2 days, 3:56:37, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6200, loss_cls: 3.6580, loss: 3.6580 +2024-12-29 12:52:17,223 - pyskl - INFO - Epoch [91][2100/3746] lr: 3.399e-02, eta: 2 days, 3:55:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6152, loss_cls: 3.6705, loss: 3.6705 +2024-12-29 12:53:41,572 - pyskl - INFO - Epoch [91][2200/3746] lr: 3.397e-02, eta: 2 days, 3:53:50, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6067, loss_cls: 3.6700, loss: 3.6700 +2024-12-29 12:55:06,280 - pyskl - INFO - Epoch [91][2300/3746] lr: 3.394e-02, eta: 2 days, 3:52:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6281, loss_cls: 3.5775, loss: 3.5775 +2024-12-29 12:56:31,101 - pyskl - INFO - Epoch [91][2400/3746] lr: 3.391e-02, eta: 2 days, 3:51:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6183, loss_cls: 3.6448, loss: 3.6448 +2024-12-29 12:57:55,370 - pyskl - INFO - Epoch [91][2500/3746] lr: 3.389e-02, eta: 2 days, 3:49:40, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6123, loss_cls: 3.6860, loss: 3.6860 +2024-12-29 12:59:20,251 - pyskl - INFO - Epoch [91][2600/3746] lr: 3.386e-02, eta: 2 days, 3:48:16, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6200, loss_cls: 3.6311, loss: 3.6311 +2024-12-29 13:00:44,503 - pyskl - INFO - Epoch [91][2700/3746] lr: 3.383e-02, eta: 2 days, 3:46:53, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6161, loss_cls: 3.6507, loss: 3.6507 +2024-12-29 13:02:09,024 - pyskl - INFO - Epoch [91][2800/3746] lr: 3.381e-02, eta: 2 days, 3:45:29, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6008, loss_cls: 3.7010, loss: 3.7010 +2024-12-29 13:03:33,673 - pyskl - INFO - Epoch [91][2900/3746] lr: 3.378e-02, eta: 2 days, 3:44:06, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6159, loss_cls: 3.6804, loss: 3.6804 +2024-12-29 13:04:57,790 - pyskl - INFO - Epoch [91][3000/3746] lr: 3.375e-02, eta: 2 days, 3:42:42, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6148, loss_cls: 3.6791, loss: 3.6791 +2024-12-29 13:06:22,895 - pyskl - INFO - Epoch [91][3100/3746] lr: 3.373e-02, eta: 2 days, 3:41:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6152, loss_cls: 3.6561, loss: 3.6561 +2024-12-29 13:07:47,933 - pyskl - INFO - Epoch [91][3200/3746] lr: 3.370e-02, eta: 2 days, 3:39:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6022, loss_cls: 3.7278, loss: 3.7278 +2024-12-29 13:09:12,426 - pyskl - INFO - Epoch [91][3300/3746] lr: 3.367e-02, eta: 2 days, 3:38:32, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6103, loss_cls: 3.7041, loss: 3.7041 +2024-12-29 13:10:36,721 - pyskl - INFO - Epoch [91][3400/3746] lr: 3.365e-02, eta: 2 days, 3:37:08, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6158, loss_cls: 3.6733, loss: 3.6733 +2024-12-29 13:12:02,291 - pyskl - INFO - Epoch [91][3500/3746] lr: 3.362e-02, eta: 2 days, 3:35:45, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6180, loss_cls: 3.6462, loss: 3.6462 +2024-12-29 13:13:27,788 - pyskl - INFO - Epoch [91][3600/3746] lr: 3.360e-02, eta: 2 days, 3:34:22, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6114, loss_cls: 3.7146, loss: 3.7146 +2024-12-29 13:14:52,753 - pyskl - INFO - Epoch [91][3700/3746] lr: 3.357e-02, eta: 2 days, 3:32:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6092, loss_cls: 3.6868, loss: 3.6868 +2024-12-29 13:15:33,861 - pyskl - INFO - Saving checkpoint at 91 epochs +2024-12-29 13:17:33,305 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 13:17:33,997 - pyskl - INFO - +top1_acc 0.2834 +top5_acc 0.5394 +2024-12-29 13:17:33,997 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 13:17:34,037 - pyskl - INFO - +mean_acc 0.2833 +2024-12-29 13:17:34,052 - pyskl - INFO - Epoch(val) [91][309] top1_acc: 0.2834, top5_acc: 0.5394, mean_class_accuracy: 0.2833 +2024-12-29 13:21:51,407 - pyskl - INFO - Epoch [92][100/3746] lr: 3.353e-02, eta: 2 days, 3:32:24, time: 2.573, data_time: 1.534, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6212, loss_cls: 3.6387, loss: 3.6387 +2024-12-29 13:23:17,568 - pyskl - INFO - Epoch [92][200/3746] lr: 3.350e-02, eta: 2 days, 3:31:01, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6247, loss_cls: 3.5830, loss: 3.5830 +2024-12-29 13:24:43,563 - pyskl - INFO - Epoch [92][300/3746] lr: 3.348e-02, eta: 2 days, 3:29:38, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6159, loss_cls: 3.6257, loss: 3.6257 +2024-12-29 13:26:08,983 - pyskl - INFO - Epoch [92][400/3746] lr: 3.345e-02, eta: 2 days, 3:28:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6211, loss_cls: 3.6454, loss: 3.6454 +2024-12-29 13:27:35,086 - pyskl - INFO - Epoch [92][500/3746] lr: 3.342e-02, eta: 2 days, 3:26:53, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6275, loss_cls: 3.5995, loss: 3.5995 +2024-12-29 13:29:00,759 - pyskl - INFO - Epoch [92][600/3746] lr: 3.340e-02, eta: 2 days, 3:25:30, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6258, loss_cls: 3.6060, loss: 3.6060 +2024-12-29 13:30:26,679 - pyskl - INFO - Epoch [92][700/3746] lr: 3.337e-02, eta: 2 days, 3:24:07, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6188, loss_cls: 3.6407, loss: 3.6407 +2024-12-29 13:31:52,553 - pyskl - INFO - Epoch [92][800/3746] lr: 3.335e-02, eta: 2 days, 3:22:44, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6153, loss_cls: 3.6664, loss: 3.6664 +2024-12-29 13:33:18,231 - pyskl - INFO - Epoch [92][900/3746] lr: 3.332e-02, eta: 2 days, 3:21:21, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6259, loss_cls: 3.6436, loss: 3.6436 +2024-12-29 13:34:43,615 - pyskl - INFO - Epoch [92][1000/3746] lr: 3.329e-02, eta: 2 days, 3:19:58, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6242, loss_cls: 3.6173, loss: 3.6173 +2024-12-29 13:36:09,244 - pyskl - INFO - Epoch [92][1100/3746] lr: 3.327e-02, eta: 2 days, 3:18:35, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6239, loss_cls: 3.6459, loss: 3.6459 +2024-12-29 13:37:34,337 - pyskl - INFO - Epoch [92][1200/3746] lr: 3.324e-02, eta: 2 days, 3:17:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6225, loss_cls: 3.6249, loss: 3.6249 +2024-12-29 13:38:59,821 - pyskl - INFO - Epoch [92][1300/3746] lr: 3.321e-02, eta: 2 days, 3:15:49, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6177, loss_cls: 3.6227, loss: 3.6227 +2024-12-29 13:40:24,806 - pyskl - INFO - Epoch [92][1400/3746] lr: 3.319e-02, eta: 2 days, 3:14:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6109, loss_cls: 3.6408, loss: 3.6408 +2024-12-29 13:41:49,994 - pyskl - INFO - Epoch [92][1500/3746] lr: 3.316e-02, eta: 2 days, 3:13:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6236, loss_cls: 3.6182, loss: 3.6182 +2024-12-29 13:43:15,429 - pyskl - INFO - Epoch [92][1600/3746] lr: 3.314e-02, eta: 2 days, 3:11:39, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6186, loss_cls: 3.6674, loss: 3.6674 +2024-12-29 13:44:40,680 - pyskl - INFO - Epoch [92][1700/3746] lr: 3.311e-02, eta: 2 days, 3:10:16, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6242, loss_cls: 3.6430, loss: 3.6430 +2024-12-29 13:46:05,549 - pyskl - INFO - Epoch [92][1800/3746] lr: 3.308e-02, eta: 2 days, 3:08:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6183, loss_cls: 3.6557, loss: 3.6557 +2024-12-29 13:47:30,576 - pyskl - INFO - Epoch [92][1900/3746] lr: 3.306e-02, eta: 2 days, 3:07:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3708, top5_acc: 0.6250, loss_cls: 3.5972, loss: 3.5972 +2024-12-29 13:48:56,013 - pyskl - INFO - Epoch [92][2000/3746] lr: 3.303e-02, eta: 2 days, 3:06:06, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.6091, loss_cls: 3.6949, loss: 3.6949 +2024-12-29 13:50:21,553 - pyskl - INFO - Epoch [92][2100/3746] lr: 3.300e-02, eta: 2 days, 3:04:43, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6170, loss_cls: 3.6195, loss: 3.6195 +2024-12-29 13:51:46,243 - pyskl - INFO - Epoch [92][2200/3746] lr: 3.298e-02, eta: 2 days, 3:03:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6116, loss_cls: 3.6763, loss: 3.6763 +2024-12-29 13:53:10,995 - pyskl - INFO - Epoch [92][2300/3746] lr: 3.295e-02, eta: 2 days, 3:01:56, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6205, loss_cls: 3.6322, loss: 3.6322 +2024-12-29 13:54:35,634 - pyskl - INFO - Epoch [92][2400/3746] lr: 3.292e-02, eta: 2 days, 3:00:32, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6253, loss_cls: 3.6041, loss: 3.6041 +2024-12-29 13:56:00,439 - pyskl - INFO - Epoch [92][2500/3746] lr: 3.290e-02, eta: 2 days, 2:59:09, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6227, loss_cls: 3.6124, loss: 3.6124 +2024-12-29 13:57:25,252 - pyskl - INFO - Epoch [92][2600/3746] lr: 3.287e-02, eta: 2 days, 2:57:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6138, loss_cls: 3.6704, loss: 3.6704 +2024-12-29 13:58:50,229 - pyskl - INFO - Epoch [92][2700/3746] lr: 3.285e-02, eta: 2 days, 2:56:22, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6280, loss_cls: 3.6313, loss: 3.6313 +2024-12-29 14:00:14,594 - pyskl - INFO - Epoch [92][2800/3746] lr: 3.282e-02, eta: 2 days, 2:54:58, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6258, loss_cls: 3.6133, loss: 3.6133 +2024-12-29 14:01:39,224 - pyskl - INFO - Epoch [92][2900/3746] lr: 3.279e-02, eta: 2 days, 2:53:35, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6269, loss_cls: 3.6230, loss: 3.6230 +2024-12-29 14:03:04,008 - pyskl - INFO - Epoch [92][3000/3746] lr: 3.277e-02, eta: 2 days, 2:52:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6222, loss_cls: 3.6394, loss: 3.6394 +2024-12-29 14:04:28,655 - pyskl - INFO - Epoch [92][3100/3746] lr: 3.274e-02, eta: 2 days, 2:50:48, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6203, loss_cls: 3.6442, loss: 3.6442 +2024-12-29 14:05:54,115 - pyskl - INFO - Epoch [92][3200/3746] lr: 3.271e-02, eta: 2 days, 2:49:25, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.6002, loss_cls: 3.7273, loss: 3.7273 +2024-12-29 14:07:19,411 - pyskl - INFO - Epoch [92][3300/3746] lr: 3.269e-02, eta: 2 days, 2:48:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6116, loss_cls: 3.6742, loss: 3.6742 +2024-12-29 14:08:44,090 - pyskl - INFO - Epoch [92][3400/3746] lr: 3.266e-02, eta: 2 days, 2:46:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6053, loss_cls: 3.7012, loss: 3.7012 +2024-12-29 14:10:08,933 - pyskl - INFO - Epoch [92][3500/3746] lr: 3.264e-02, eta: 2 days, 2:45:14, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6252, loss_cls: 3.6046, loss: 3.6046 +2024-12-29 14:11:34,670 - pyskl - INFO - Epoch [92][3600/3746] lr: 3.261e-02, eta: 2 days, 2:43:51, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6100, loss_cls: 3.6508, loss: 3.6508 +2024-12-29 14:13:01,380 - pyskl - INFO - Epoch [92][3700/3746] lr: 3.258e-02, eta: 2 days, 2:42:29, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6083, loss_cls: 3.6795, loss: 3.6795 +2024-12-29 14:13:42,216 - pyskl - INFO - Saving checkpoint at 92 epochs +2024-12-29 14:15:39,921 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 14:15:40,708 - pyskl - INFO - +top1_acc 0.2818 +top5_acc 0.5373 +2024-12-29 14:15:40,708 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 14:15:40,756 - pyskl - INFO - +mean_acc 0.2815 +2024-12-29 14:15:40,771 - pyskl - INFO - Epoch(val) [92][309] top1_acc: 0.2818, top5_acc: 0.5373, mean_class_accuracy: 0.2815 +2024-12-29 14:20:01,819 - pyskl - INFO - Epoch [93][100/3746] lr: 3.255e-02, eta: 2 days, 2:41:54, time: 2.610, data_time: 1.555, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6370, loss_cls: 3.5343, loss: 3.5343 +2024-12-29 14:21:29,616 - pyskl - INFO - Epoch [93][200/3746] lr: 3.252e-02, eta: 2 days, 2:40:32, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6308, loss_cls: 3.5697, loss: 3.5697 +2024-12-29 14:22:56,866 - pyskl - INFO - Epoch [93][300/3746] lr: 3.249e-02, eta: 2 days, 2:39:10, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6145, loss_cls: 3.6376, loss: 3.6376 +2024-12-29 14:24:23,390 - pyskl - INFO - Epoch [93][400/3746] lr: 3.247e-02, eta: 2 days, 2:37:48, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6236, loss_cls: 3.5990, loss: 3.5990 +2024-12-29 14:25:50,293 - pyskl - INFO - Epoch [93][500/3746] lr: 3.244e-02, eta: 2 days, 2:36:25, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6170, loss_cls: 3.6210, loss: 3.6210 +2024-12-29 14:27:16,500 - pyskl - INFO - Epoch [93][600/3746] lr: 3.241e-02, eta: 2 days, 2:35:03, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6209, loss_cls: 3.6339, loss: 3.6339 +2024-12-29 14:28:42,374 - pyskl - INFO - Epoch [93][700/3746] lr: 3.239e-02, eta: 2 days, 2:33:40, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6130, loss_cls: 3.6681, loss: 3.6681 +2024-12-29 14:30:08,464 - pyskl - INFO - Epoch [93][800/3746] lr: 3.236e-02, eta: 2 days, 2:32:17, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6214, loss_cls: 3.6401, loss: 3.6401 +2024-12-29 14:31:35,279 - pyskl - INFO - Epoch [93][900/3746] lr: 3.234e-02, eta: 2 days, 2:30:55, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6253, loss_cls: 3.5683, loss: 3.5683 +2024-12-29 14:33:00,561 - pyskl - INFO - Epoch [93][1000/3746] lr: 3.231e-02, eta: 2 days, 2:29:31, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6162, loss_cls: 3.6436, loss: 3.6436 +2024-12-29 14:34:25,966 - pyskl - INFO - Epoch [93][1100/3746] lr: 3.228e-02, eta: 2 days, 2:28:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6297, loss_cls: 3.6174, loss: 3.6174 +2024-12-29 14:35:51,430 - pyskl - INFO - Epoch [93][1200/3746] lr: 3.226e-02, eta: 2 days, 2:26:45, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6256, loss_cls: 3.6249, loss: 3.6249 +2024-12-29 14:37:17,006 - pyskl - INFO - Epoch [93][1300/3746] lr: 3.223e-02, eta: 2 days, 2:25:22, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6158, loss_cls: 3.6414, loss: 3.6414 +2024-12-29 14:38:42,906 - pyskl - INFO - Epoch [93][1400/3746] lr: 3.221e-02, eta: 2 days, 2:23:59, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6125, loss_cls: 3.6472, loss: 3.6472 +2024-12-29 14:40:08,481 - pyskl - INFO - Epoch [93][1500/3746] lr: 3.218e-02, eta: 2 days, 2:22:36, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6198, loss_cls: 3.6271, loss: 3.6271 +2024-12-29 14:41:33,698 - pyskl - INFO - Epoch [93][1600/3746] lr: 3.215e-02, eta: 2 days, 2:21:13, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6270, loss_cls: 3.6031, loss: 3.6031 +2024-12-29 14:42:59,306 - pyskl - INFO - Epoch [93][1700/3746] lr: 3.213e-02, eta: 2 days, 2:19:49, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6302, loss_cls: 3.5866, loss: 3.5866 +2024-12-29 14:44:24,632 - pyskl - INFO - Epoch [93][1800/3746] lr: 3.210e-02, eta: 2 days, 2:18:26, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6238, loss_cls: 3.5632, loss: 3.5632 +2024-12-29 14:45:49,661 - pyskl - INFO - Epoch [93][1900/3746] lr: 3.207e-02, eta: 2 days, 2:17:03, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6233, loss_cls: 3.6045, loss: 3.6045 +2024-12-29 14:47:14,548 - pyskl - INFO - Epoch [93][2000/3746] lr: 3.205e-02, eta: 2 days, 2:15:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6311, loss_cls: 3.6115, loss: 3.6115 +2024-12-29 14:48:40,213 - pyskl - INFO - Epoch [93][2100/3746] lr: 3.202e-02, eta: 2 days, 2:14:16, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3594, top5_acc: 0.6208, loss_cls: 3.6331, loss: 3.6331 +2024-12-29 14:50:06,565 - pyskl - INFO - Epoch [93][2200/3746] lr: 3.200e-02, eta: 2 days, 2:12:54, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6230, loss_cls: 3.6347, loss: 3.6347 +2024-12-29 14:51:33,085 - pyskl - INFO - Epoch [93][2300/3746] lr: 3.197e-02, eta: 2 days, 2:11:31, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6134, loss_cls: 3.6605, loss: 3.6605 +2024-12-29 14:52:59,343 - pyskl - INFO - Epoch [93][2400/3746] lr: 3.194e-02, eta: 2 days, 2:10:08, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6220, loss_cls: 3.6095, loss: 3.6095 +2024-12-29 14:54:25,675 - pyskl - INFO - Epoch [93][2500/3746] lr: 3.192e-02, eta: 2 days, 2:08:46, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6273, loss_cls: 3.6406, loss: 3.6406 +2024-12-29 14:55:52,356 - pyskl - INFO - Epoch [93][2600/3746] lr: 3.189e-02, eta: 2 days, 2:07:23, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6220, loss_cls: 3.6003, loss: 3.6003 +2024-12-29 14:57:19,206 - pyskl - INFO - Epoch [93][2700/3746] lr: 3.187e-02, eta: 2 days, 2:06:01, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6266, loss_cls: 3.6295, loss: 3.6295 +2024-12-29 14:58:45,815 - pyskl - INFO - Epoch [93][2800/3746] lr: 3.184e-02, eta: 2 days, 2:04:38, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6361, loss_cls: 3.5828, loss: 3.5828 +2024-12-29 15:00:11,608 - pyskl - INFO - Epoch [93][2900/3746] lr: 3.181e-02, eta: 2 days, 2:03:15, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6250, loss_cls: 3.6021, loss: 3.6021 +2024-12-29 15:01:37,011 - pyskl - INFO - Epoch [93][3000/3746] lr: 3.179e-02, eta: 2 days, 2:01:52, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6314, loss_cls: 3.6104, loss: 3.6104 +2024-12-29 15:03:02,623 - pyskl - INFO - Epoch [93][3100/3746] lr: 3.176e-02, eta: 2 days, 2:00:29, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6041, loss_cls: 3.6802, loss: 3.6802 +2024-12-29 15:04:28,793 - pyskl - INFO - Epoch [93][3200/3746] lr: 3.174e-02, eta: 2 days, 1:59:06, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6169, loss_cls: 3.6481, loss: 3.6481 +2024-12-29 15:05:53,701 - pyskl - INFO - Epoch [93][3300/3746] lr: 3.171e-02, eta: 2 days, 1:57:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6148, loss_cls: 3.6531, loss: 3.6531 +2024-12-29 15:07:18,846 - pyskl - INFO - Epoch [93][3400/3746] lr: 3.168e-02, eta: 2 days, 1:56:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6200, loss_cls: 3.6469, loss: 3.6469 +2024-12-29 15:08:43,938 - pyskl - INFO - Epoch [93][3500/3746] lr: 3.166e-02, eta: 2 days, 1:54:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6197, loss_cls: 3.6606, loss: 3.6606 +2024-12-29 15:10:08,950 - pyskl - INFO - Epoch [93][3600/3746] lr: 3.163e-02, eta: 2 days, 1:53:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6111, loss_cls: 3.6969, loss: 3.6969 +2024-12-29 15:11:34,215 - pyskl - INFO - Epoch [93][3700/3746] lr: 3.161e-02, eta: 2 days, 1:52:09, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6255, loss_cls: 3.6039, loss: 3.6039 +2024-12-29 15:12:15,319 - pyskl - INFO - Saving checkpoint at 93 epochs +2024-12-29 15:14:14,083 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 15:14:14,865 - pyskl - INFO - +top1_acc 0.2956 +top5_acc 0.5532 +2024-12-29 15:14:14,865 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 15:14:14,910 - pyskl - INFO - +mean_acc 0.2955 +2024-12-29 15:14:14,914 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_90.pth was removed +2024-12-29 15:14:15,192 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_93.pth. +2024-12-29 15:14:15,193 - pyskl - INFO - Best top1_acc is 0.2956 at 93 epoch. +2024-12-29 15:14:15,208 - pyskl - INFO - Epoch(val) [93][309] top1_acc: 0.2956, top5_acc: 0.5532, mean_class_accuracy: 0.2955 +2024-12-29 15:18:44,302 - pyskl - INFO - Epoch [94][100/3746] lr: 3.157e-02, eta: 2 days, 1:51:36, time: 2.691, data_time: 1.647, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6278, loss_cls: 3.5769, loss: 3.5769 +2024-12-29 15:20:10,978 - pyskl - INFO - Epoch [94][200/3746] lr: 3.154e-02, eta: 2 days, 1:50:14, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6331, loss_cls: 3.5709, loss: 3.5709 +2024-12-29 15:21:36,861 - pyskl - INFO - Epoch [94][300/3746] lr: 3.152e-02, eta: 2 days, 1:48:50, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6261, loss_cls: 3.5836, loss: 3.5836 +2024-12-29 15:23:04,023 - pyskl - INFO - Epoch [94][400/3746] lr: 3.149e-02, eta: 2 days, 1:47:28, time: 0.872, data_time: 0.001, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6356, loss_cls: 3.5592, loss: 3.5592 +2024-12-29 15:24:30,719 - pyskl - INFO - Epoch [94][500/3746] lr: 3.146e-02, eta: 2 days, 1:46:06, time: 0.867, data_time: 0.001, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6362, loss_cls: 3.5336, loss: 3.5336 +2024-12-29 15:25:57,656 - pyskl - INFO - Epoch [94][600/3746] lr: 3.144e-02, eta: 2 days, 1:44:43, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6217, loss_cls: 3.6368, loss: 3.6368 +2024-12-29 15:27:24,087 - pyskl - INFO - Epoch [94][700/3746] lr: 3.141e-02, eta: 2 days, 1:43:21, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6309, loss_cls: 3.5528, loss: 3.5528 +2024-12-29 15:28:50,393 - pyskl - INFO - Epoch [94][800/3746] lr: 3.139e-02, eta: 2 days, 1:41:58, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6302, loss_cls: 3.5590, loss: 3.5590 +2024-12-29 15:30:16,275 - pyskl - INFO - Epoch [94][900/3746] lr: 3.136e-02, eta: 2 days, 1:40:35, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6292, loss_cls: 3.6083, loss: 3.6083 +2024-12-29 15:31:41,827 - pyskl - INFO - Epoch [94][1000/3746] lr: 3.133e-02, eta: 2 days, 1:39:12, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6275, loss_cls: 3.5724, loss: 3.5724 +2024-12-29 15:33:06,943 - pyskl - INFO - Epoch [94][1100/3746] lr: 3.131e-02, eta: 2 days, 1:37:48, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6147, loss_cls: 3.6558, loss: 3.6558 +2024-12-29 15:34:32,425 - pyskl - INFO - Epoch [94][1200/3746] lr: 3.128e-02, eta: 2 days, 1:36:25, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6230, loss_cls: 3.6212, loss: 3.6212 +2024-12-29 15:35:58,392 - pyskl - INFO - Epoch [94][1300/3746] lr: 3.126e-02, eta: 2 days, 1:35:02, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6219, loss_cls: 3.6335, loss: 3.6335 +2024-12-29 15:37:23,854 - pyskl - INFO - Epoch [94][1400/3746] lr: 3.123e-02, eta: 2 days, 1:33:39, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6289, loss_cls: 3.5821, loss: 3.5821 +2024-12-29 15:38:49,136 - pyskl - INFO - Epoch [94][1500/3746] lr: 3.120e-02, eta: 2 days, 1:32:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6236, loss_cls: 3.6514, loss: 3.6514 +2024-12-29 15:40:14,108 - pyskl - INFO - Epoch [94][1600/3746] lr: 3.118e-02, eta: 2 days, 1:30:52, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6206, loss_cls: 3.6330, loss: 3.6330 +2024-12-29 15:41:39,714 - pyskl - INFO - Epoch [94][1700/3746] lr: 3.115e-02, eta: 2 days, 1:29:28, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6292, loss_cls: 3.5963, loss: 3.5963 +2024-12-29 15:43:05,113 - pyskl - INFO - Epoch [94][1800/3746] lr: 3.113e-02, eta: 2 days, 1:28:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6150, loss_cls: 3.6289, loss: 3.6289 +2024-12-29 15:44:31,423 - pyskl - INFO - Epoch [94][1900/3746] lr: 3.110e-02, eta: 2 days, 1:26:42, time: 0.863, data_time: 0.001, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6162, loss_cls: 3.6739, loss: 3.6739 +2024-12-29 15:45:55,995 - pyskl - INFO - Epoch [94][2000/3746] lr: 3.108e-02, eta: 2 days, 1:25:18, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6330, loss_cls: 3.5596, loss: 3.5596 +2024-12-29 15:47:21,122 - pyskl - INFO - Epoch [94][2100/3746] lr: 3.105e-02, eta: 2 days, 1:23:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6156, loss_cls: 3.6081, loss: 3.6081 +2024-12-29 15:48:46,461 - pyskl - INFO - Epoch [94][2200/3746] lr: 3.102e-02, eta: 2 days, 1:22:32, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6212, loss_cls: 3.6065, loss: 3.6065 +2024-12-29 15:50:11,678 - pyskl - INFO - Epoch [94][2300/3746] lr: 3.100e-02, eta: 2 days, 1:21:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6292, loss_cls: 3.5660, loss: 3.5660 +2024-12-29 15:51:36,765 - pyskl - INFO - Epoch [94][2400/3746] lr: 3.097e-02, eta: 2 days, 1:19:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6247, loss_cls: 3.6032, loss: 3.6032 +2024-12-29 15:53:01,421 - pyskl - INFO - Epoch [94][2500/3746] lr: 3.095e-02, eta: 2 days, 1:18:21, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6236, loss_cls: 3.6049, loss: 3.6049 +2024-12-29 15:54:26,728 - pyskl - INFO - Epoch [94][2600/3746] lr: 3.092e-02, eta: 2 days, 1:16:57, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6289, loss_cls: 3.6241, loss: 3.6241 +2024-12-29 15:55:51,725 - pyskl - INFO - Epoch [94][2700/3746] lr: 3.089e-02, eta: 2 days, 1:15:34, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6264, loss_cls: 3.6092, loss: 3.6092 +2024-12-29 15:57:17,259 - pyskl - INFO - Epoch [94][2800/3746] lr: 3.087e-02, eta: 2 days, 1:14:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6222, loss_cls: 3.6003, loss: 3.6003 +2024-12-29 15:58:41,920 - pyskl - INFO - Epoch [94][2900/3746] lr: 3.084e-02, eta: 2 days, 1:12:47, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6231, loss_cls: 3.6373, loss: 3.6373 +2024-12-29 16:00:06,983 - pyskl - INFO - Epoch [94][3000/3746] lr: 3.082e-02, eta: 2 days, 1:11:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6145, loss_cls: 3.6702, loss: 3.6702 +2024-12-29 16:01:32,263 - pyskl - INFO - Epoch [94][3100/3746] lr: 3.079e-02, eta: 2 days, 1:10:00, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6109, loss_cls: 3.6861, loss: 3.6861 +2024-12-29 16:02:57,932 - pyskl - INFO - Epoch [94][3200/3746] lr: 3.077e-02, eta: 2 days, 1:08:37, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6302, loss_cls: 3.5724, loss: 3.5724 +2024-12-29 16:04:23,483 - pyskl - INFO - Epoch [94][3300/3746] lr: 3.074e-02, eta: 2 days, 1:07:13, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6208, loss_cls: 3.6492, loss: 3.6492 +2024-12-29 16:05:48,301 - pyskl - INFO - Epoch [94][3400/3746] lr: 3.071e-02, eta: 2 days, 1:05:50, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6277, loss_cls: 3.6203, loss: 3.6203 +2024-12-29 16:07:14,002 - pyskl - INFO - Epoch [94][3500/3746] lr: 3.069e-02, eta: 2 days, 1:04:26, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6167, loss_cls: 3.6243, loss: 3.6243 +2024-12-29 16:08:39,720 - pyskl - INFO - Epoch [94][3600/3746] lr: 3.066e-02, eta: 2 days, 1:03:03, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6194, loss_cls: 3.6130, loss: 3.6130 +2024-12-29 16:10:05,217 - pyskl - INFO - Epoch [94][3700/3746] lr: 3.064e-02, eta: 2 days, 1:01:40, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6219, loss_cls: 3.6084, loss: 3.6084 +2024-12-29 16:10:46,250 - pyskl - INFO - Saving checkpoint at 94 epochs +2024-12-29 16:12:44,436 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 16:12:45,105 - pyskl - INFO - +top1_acc 0.3066 +top5_acc 0.5556 +2024-12-29 16:12:45,105 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 16:12:45,147 - pyskl - INFO - +mean_acc 0.3063 +2024-12-29 16:12:45,152 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_93.pth was removed +2024-12-29 16:12:45,422 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_94.pth. +2024-12-29 16:12:45,422 - pyskl - INFO - Best top1_acc is 0.3066 at 94 epoch. +2024-12-29 16:12:45,437 - pyskl - INFO - Epoch(val) [94][309] top1_acc: 0.3066, top5_acc: 0.5556, mean_class_accuracy: 0.3063 +2024-12-29 16:17:05,751 - pyskl - INFO - Epoch [95][100/3746] lr: 3.060e-02, eta: 2 days, 1:00:59, time: 2.603, data_time: 1.536, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6369, loss_cls: 3.5362, loss: 3.5362 +2024-12-29 16:18:33,144 - pyskl - INFO - Epoch [95][200/3746] lr: 3.057e-02, eta: 2 days, 0:59:37, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6373, loss_cls: 3.5246, loss: 3.5246 +2024-12-29 16:20:00,070 - pyskl - INFO - Epoch [95][300/3746] lr: 3.055e-02, eta: 2 days, 0:58:14, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6403, loss_cls: 3.5256, loss: 3.5256 +2024-12-29 16:21:26,829 - pyskl - INFO - Epoch [95][400/3746] lr: 3.052e-02, eta: 2 days, 0:56:52, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6391, loss_cls: 3.5621, loss: 3.5621 +2024-12-29 16:22:53,153 - pyskl - INFO - Epoch [95][500/3746] lr: 3.050e-02, eta: 2 days, 0:55:29, time: 0.863, data_time: 0.001, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6277, loss_cls: 3.5914, loss: 3.5914 +2024-12-29 16:24:19,716 - pyskl - INFO - Epoch [95][600/3746] lr: 3.047e-02, eta: 2 days, 0:54:06, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6202, loss_cls: 3.6306, loss: 3.6306 +2024-12-29 16:25:46,029 - pyskl - INFO - Epoch [95][700/3746] lr: 3.044e-02, eta: 2 days, 0:52:43, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6273, loss_cls: 3.5727, loss: 3.5727 +2024-12-29 16:27:11,331 - pyskl - INFO - Epoch [95][800/3746] lr: 3.042e-02, eta: 2 days, 0:51:20, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6266, loss_cls: 3.5807, loss: 3.5807 +2024-12-29 16:28:37,122 - pyskl - INFO - Epoch [95][900/3746] lr: 3.039e-02, eta: 2 days, 0:49:56, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6338, loss_cls: 3.5470, loss: 3.5470 +2024-12-29 16:30:02,262 - pyskl - INFO - Epoch [95][1000/3746] lr: 3.037e-02, eta: 2 days, 0:48:33, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6306, loss_cls: 3.5590, loss: 3.5590 +2024-12-29 16:31:27,667 - pyskl - INFO - Epoch [95][1100/3746] lr: 3.034e-02, eta: 2 days, 0:47:09, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6147, loss_cls: 3.6046, loss: 3.6046 +2024-12-29 16:32:53,391 - pyskl - INFO - Epoch [95][1200/3746] lr: 3.032e-02, eta: 2 days, 0:45:46, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6183, loss_cls: 3.6174, loss: 3.6174 +2024-12-29 16:34:19,404 - pyskl - INFO - Epoch [95][1300/3746] lr: 3.029e-02, eta: 2 days, 0:44:23, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6327, loss_cls: 3.5617, loss: 3.5617 +2024-12-29 16:35:44,750 - pyskl - INFO - Epoch [95][1400/3746] lr: 3.026e-02, eta: 2 days, 0:43:00, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6180, loss_cls: 3.6282, loss: 3.6282 +2024-12-29 16:37:11,065 - pyskl - INFO - Epoch [95][1500/3746] lr: 3.024e-02, eta: 2 days, 0:41:37, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6198, loss_cls: 3.6242, loss: 3.6242 +2024-12-29 16:38:37,434 - pyskl - INFO - Epoch [95][1600/3746] lr: 3.021e-02, eta: 2 days, 0:40:14, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6236, loss_cls: 3.5868, loss: 3.5868 +2024-12-29 16:40:03,722 - pyskl - INFO - Epoch [95][1700/3746] lr: 3.019e-02, eta: 2 days, 0:38:51, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6291, loss_cls: 3.5513, loss: 3.5513 +2024-12-29 16:41:29,553 - pyskl - INFO - Epoch [95][1800/3746] lr: 3.016e-02, eta: 2 days, 0:37:28, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6192, loss_cls: 3.6237, loss: 3.6237 +2024-12-29 16:42:55,835 - pyskl - INFO - Epoch [95][1900/3746] lr: 3.014e-02, eta: 2 days, 0:36:05, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6309, loss_cls: 3.5623, loss: 3.5623 +2024-12-29 16:44:21,066 - pyskl - INFO - Epoch [95][2000/3746] lr: 3.011e-02, eta: 2 days, 0:34:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6184, loss_cls: 3.5986, loss: 3.5986 +2024-12-29 16:45:46,558 - pyskl - INFO - Epoch [95][2100/3746] lr: 3.008e-02, eta: 2 days, 0:33:18, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6286, loss_cls: 3.6269, loss: 3.6269 +2024-12-29 16:47:11,153 - pyskl - INFO - Epoch [95][2200/3746] lr: 3.006e-02, eta: 2 days, 0:31:54, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6203, loss_cls: 3.6379, loss: 3.6379 +2024-12-29 16:48:36,544 - pyskl - INFO - Epoch [95][2300/3746] lr: 3.003e-02, eta: 2 days, 0:30:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6180, loss_cls: 3.6250, loss: 3.6250 +2024-12-29 16:50:01,855 - pyskl - INFO - Epoch [95][2400/3746] lr: 3.001e-02, eta: 2 days, 0:29:07, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6356, loss_cls: 3.5713, loss: 3.5713 +2024-12-29 16:51:27,314 - pyskl - INFO - Epoch [95][2500/3746] lr: 2.998e-02, eta: 2 days, 0:27:44, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6214, loss_cls: 3.6284, loss: 3.6284 +2024-12-29 16:52:53,412 - pyskl - INFO - Epoch [95][2600/3746] lr: 2.996e-02, eta: 2 days, 0:26:21, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6206, loss_cls: 3.5886, loss: 3.5886 +2024-12-29 16:54:19,345 - pyskl - INFO - Epoch [95][2700/3746] lr: 2.993e-02, eta: 2 days, 0:24:57, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6255, loss_cls: 3.5922, loss: 3.5922 +2024-12-29 16:55:43,966 - pyskl - INFO - Epoch [95][2800/3746] lr: 2.991e-02, eta: 2 days, 0:23:33, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6308, loss_cls: 3.5946, loss: 3.5946 +2024-12-29 16:57:08,810 - pyskl - INFO - Epoch [95][2900/3746] lr: 2.988e-02, eta: 2 days, 0:22:10, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6211, loss_cls: 3.6264, loss: 3.6264 +2024-12-29 16:58:33,630 - pyskl - INFO - Epoch [95][3000/3746] lr: 2.985e-02, eta: 2 days, 0:20:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6191, loss_cls: 3.6394, loss: 3.6394 +2024-12-29 16:59:59,305 - pyskl - INFO - Epoch [95][3100/3746] lr: 2.983e-02, eta: 2 days, 0:19:23, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6308, loss_cls: 3.5616, loss: 3.5616 +2024-12-29 17:01:24,952 - pyskl - INFO - Epoch [95][3200/3746] lr: 2.980e-02, eta: 2 days, 0:17:59, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6162, loss_cls: 3.6285, loss: 3.6285 +2024-12-29 17:02:50,325 - pyskl - INFO - Epoch [95][3300/3746] lr: 2.978e-02, eta: 2 days, 0:16:36, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3720, top5_acc: 0.6358, loss_cls: 3.5454, loss: 3.5454 +2024-12-29 17:04:16,094 - pyskl - INFO - Epoch [95][3400/3746] lr: 2.975e-02, eta: 2 days, 0:15:13, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6198, loss_cls: 3.6372, loss: 3.6372 +2024-12-29 17:05:42,111 - pyskl - INFO - Epoch [95][3500/3746] lr: 2.973e-02, eta: 2 days, 0:13:49, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6402, loss_cls: 3.5539, loss: 3.5539 +2024-12-29 17:07:08,063 - pyskl - INFO - Epoch [95][3600/3746] lr: 2.970e-02, eta: 2 days, 0:12:26, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6323, loss_cls: 3.5556, loss: 3.5556 +2024-12-29 17:08:33,727 - pyskl - INFO - Epoch [95][3700/3746] lr: 2.968e-02, eta: 2 days, 0:11:03, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6220, loss_cls: 3.6284, loss: 3.6284 +2024-12-29 17:09:15,062 - pyskl - INFO - Saving checkpoint at 95 epochs +2024-12-29 17:11:15,519 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 17:11:16,376 - pyskl - INFO - +top1_acc 0.2634 +top5_acc 0.5113 +2024-12-29 17:11:16,376 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 17:11:16,433 - pyskl - INFO - +mean_acc 0.2634 +2024-12-29 17:11:16,449 - pyskl - INFO - Epoch(val) [95][309] top1_acc: 0.2634, top5_acc: 0.5113, mean_class_accuracy: 0.2634 +2024-12-29 17:15:36,493 - pyskl - INFO - Epoch [96][100/3746] lr: 2.964e-02, eta: 2 days, 0:10:19, time: 2.600, data_time: 1.564, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6414, loss_cls: 3.5049, loss: 3.5049 +2024-12-29 17:17:02,303 - pyskl - INFO - Epoch [96][200/3746] lr: 2.961e-02, eta: 2 days, 0:08:56, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6411, loss_cls: 3.4959, loss: 3.4959 +2024-12-29 17:18:28,171 - pyskl - INFO - Epoch [96][300/3746] lr: 2.959e-02, eta: 2 days, 0:07:33, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6361, loss_cls: 3.5200, loss: 3.5200 +2024-12-29 17:19:53,498 - pyskl - INFO - Epoch [96][400/3746] lr: 2.956e-02, eta: 2 days, 0:06:09, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6416, loss_cls: 3.5067, loss: 3.5067 +2024-12-29 17:21:18,947 - pyskl - INFO - Epoch [96][500/3746] lr: 2.954e-02, eta: 2 days, 0:04:46, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6309, loss_cls: 3.5671, loss: 3.5671 +2024-12-29 17:22:44,060 - pyskl - INFO - Epoch [96][600/3746] lr: 2.951e-02, eta: 2 days, 0:03:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6366, loss_cls: 3.5533, loss: 3.5533 +2024-12-29 17:24:09,439 - pyskl - INFO - Epoch [96][700/3746] lr: 2.948e-02, eta: 2 days, 0:01:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6269, loss_cls: 3.5888, loss: 3.5888 +2024-12-29 17:25:35,205 - pyskl - INFO - Epoch [96][800/3746] lr: 2.946e-02, eta: 2 days, 0:00:35, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6219, loss_cls: 3.6210, loss: 3.6210 +2024-12-29 17:27:00,582 - pyskl - INFO - Epoch [96][900/3746] lr: 2.943e-02, eta: 1 day, 23:59:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6352, loss_cls: 3.5838, loss: 3.5838 +2024-12-29 17:28:25,447 - pyskl - INFO - Epoch [96][1000/3746] lr: 2.941e-02, eta: 1 day, 23:57:48, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6258, loss_cls: 3.6113, loss: 3.6113 +2024-12-29 17:29:50,828 - pyskl - INFO - Epoch [96][1100/3746] lr: 2.938e-02, eta: 1 day, 23:56:24, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6400, loss_cls: 3.5655, loss: 3.5655 +2024-12-29 17:31:15,586 - pyskl - INFO - Epoch [96][1200/3746] lr: 2.936e-02, eta: 1 day, 23:55:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6309, loss_cls: 3.5740, loss: 3.5740 +2024-12-29 17:32:40,532 - pyskl - INFO - Epoch [96][1300/3746] lr: 2.933e-02, eta: 1 day, 23:53:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6391, loss_cls: 3.5331, loss: 3.5331 +2024-12-29 17:34:06,086 - pyskl - INFO - Epoch [96][1400/3746] lr: 2.931e-02, eta: 1 day, 23:52:13, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6216, loss_cls: 3.6128, loss: 3.6128 +2024-12-29 17:35:31,136 - pyskl - INFO - Epoch [96][1500/3746] lr: 2.928e-02, eta: 1 day, 23:50:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6392, loss_cls: 3.5320, loss: 3.5320 +2024-12-29 17:36:56,446 - pyskl - INFO - Epoch [96][1600/3746] lr: 2.926e-02, eta: 1 day, 23:49:26, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6358, loss_cls: 3.5813, loss: 3.5813 +2024-12-29 17:38:21,551 - pyskl - INFO - Epoch [96][1700/3746] lr: 2.923e-02, eta: 1 day, 23:48:02, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6298, loss_cls: 3.5901, loss: 3.5901 +2024-12-29 17:39:47,247 - pyskl - INFO - Epoch [96][1800/3746] lr: 2.920e-02, eta: 1 day, 23:46:39, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6264, loss_cls: 3.5933, loss: 3.5933 +2024-12-29 17:41:12,821 - pyskl - INFO - Epoch [96][1900/3746] lr: 2.918e-02, eta: 1 day, 23:45:15, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6302, loss_cls: 3.5563, loss: 3.5563 +2024-12-29 17:42:38,979 - pyskl - INFO - Epoch [96][2000/3746] lr: 2.915e-02, eta: 1 day, 23:43:52, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6322, loss_cls: 3.5748, loss: 3.5748 +2024-12-29 17:44:05,331 - pyskl - INFO - Epoch [96][2100/3746] lr: 2.913e-02, eta: 1 day, 23:42:29, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6211, loss_cls: 3.6485, loss: 3.6485 +2024-12-29 17:45:31,504 - pyskl - INFO - Epoch [96][2200/3746] lr: 2.910e-02, eta: 1 day, 23:41:06, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6322, loss_cls: 3.5626, loss: 3.5626 +2024-12-29 17:46:56,795 - pyskl - INFO - Epoch [96][2300/3746] lr: 2.908e-02, eta: 1 day, 23:39:42, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6350, loss_cls: 3.5546, loss: 3.5546 +2024-12-29 17:48:22,328 - pyskl - INFO - Epoch [96][2400/3746] lr: 2.905e-02, eta: 1 day, 23:38:19, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6311, loss_cls: 3.5759, loss: 3.5759 +2024-12-29 17:49:48,491 - pyskl - INFO - Epoch [96][2500/3746] lr: 2.903e-02, eta: 1 day, 23:36:56, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6252, loss_cls: 3.5689, loss: 3.5689 +2024-12-29 17:51:14,059 - pyskl - INFO - Epoch [96][2600/3746] lr: 2.900e-02, eta: 1 day, 23:35:32, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6298, loss_cls: 3.6162, loss: 3.6162 +2024-12-29 17:52:39,672 - pyskl - INFO - Epoch [96][2700/3746] lr: 2.898e-02, eta: 1 day, 23:34:09, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6303, loss_cls: 3.5661, loss: 3.5661 +2024-12-29 17:54:05,337 - pyskl - INFO - Epoch [96][2800/3746] lr: 2.895e-02, eta: 1 day, 23:32:46, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3622, top5_acc: 0.6219, loss_cls: 3.6032, loss: 3.6032 +2024-12-29 17:55:30,629 - pyskl - INFO - Epoch [96][2900/3746] lr: 2.893e-02, eta: 1 day, 23:31:22, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6267, loss_cls: 3.5731, loss: 3.5731 +2024-12-29 17:56:54,851 - pyskl - INFO - Epoch [96][3000/3746] lr: 2.890e-02, eta: 1 day, 23:29:58, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6244, loss_cls: 3.6070, loss: 3.6070 +2024-12-29 17:58:19,608 - pyskl - INFO - Epoch [96][3100/3746] lr: 2.887e-02, eta: 1 day, 23:28:34, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6380, loss_cls: 3.5176, loss: 3.5176 +2024-12-29 17:59:44,771 - pyskl - INFO - Epoch [96][3200/3746] lr: 2.885e-02, eta: 1 day, 23:27:10, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6194, loss_cls: 3.6327, loss: 3.6327 +2024-12-29 18:01:09,728 - pyskl - INFO - Epoch [96][3300/3746] lr: 2.882e-02, eta: 1 day, 23:25:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6314, loss_cls: 3.5952, loss: 3.5952 +2024-12-29 18:02:35,097 - pyskl - INFO - Epoch [96][3400/3746] lr: 2.880e-02, eta: 1 day, 23:24:23, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6386, loss_cls: 3.5226, loss: 3.5226 +2024-12-29 18:03:59,559 - pyskl - INFO - Epoch [96][3500/3746] lr: 2.877e-02, eta: 1 day, 23:22:59, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6256, loss_cls: 3.5840, loss: 3.5840 +2024-12-29 18:05:24,618 - pyskl - INFO - Epoch [96][3600/3746] lr: 2.875e-02, eta: 1 day, 23:21:35, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3686, top5_acc: 0.6316, loss_cls: 3.5989, loss: 3.5989 +2024-12-29 18:06:49,894 - pyskl - INFO - Epoch [96][3700/3746] lr: 2.872e-02, eta: 1 day, 23:20:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6247, loss_cls: 3.6127, loss: 3.6127 +2024-12-29 18:07:31,753 - pyskl - INFO - Saving checkpoint at 96 epochs +2024-12-29 18:09:30,536 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 18:09:31,506 - pyskl - INFO - +top1_acc 0.2860 +top5_acc 0.5382 +2024-12-29 18:09:31,506 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 18:09:31,581 - pyskl - INFO - +mean_acc 0.2858 +2024-12-29 18:09:31,601 - pyskl - INFO - Epoch(val) [96][309] top1_acc: 0.2860, top5_acc: 0.5382, mean_class_accuracy: 0.2858 +2024-12-29 18:13:58,776 - pyskl - INFO - Epoch [97][100/3746] lr: 2.869e-02, eta: 1 day, 23:19:29, time: 2.672, data_time: 1.618, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6545, loss_cls: 3.4635, loss: 3.4635 +2024-12-29 18:15:25,268 - pyskl - INFO - Epoch [97][200/3746] lr: 2.866e-02, eta: 1 day, 23:18:06, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6408, loss_cls: 3.5072, loss: 3.5072 +2024-12-29 18:16:52,156 - pyskl - INFO - Epoch [97][300/3746] lr: 2.864e-02, eta: 1 day, 23:16:44, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6416, loss_cls: 3.5239, loss: 3.5239 +2024-12-29 18:18:18,588 - pyskl - INFO - Epoch [97][400/3746] lr: 2.861e-02, eta: 1 day, 23:15:20, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6414, loss_cls: 3.5158, loss: 3.5158 +2024-12-29 18:19:45,011 - pyskl - INFO - Epoch [97][500/3746] lr: 2.858e-02, eta: 1 day, 23:13:57, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6312, loss_cls: 3.5510, loss: 3.5510 +2024-12-29 18:21:11,844 - pyskl - INFO - Epoch [97][600/3746] lr: 2.856e-02, eta: 1 day, 23:12:35, time: 0.868, data_time: 0.001, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6395, loss_cls: 3.5235, loss: 3.5235 +2024-12-29 18:22:38,857 - pyskl - INFO - Epoch [97][700/3746] lr: 2.853e-02, eta: 1 day, 23:11:12, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6353, loss_cls: 3.5421, loss: 3.5421 +2024-12-29 18:24:04,801 - pyskl - INFO - Epoch [97][800/3746] lr: 2.851e-02, eta: 1 day, 23:09:49, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6419, loss_cls: 3.5021, loss: 3.5021 +2024-12-29 18:25:31,264 - pyskl - INFO - Epoch [97][900/3746] lr: 2.848e-02, eta: 1 day, 23:08:25, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6367, loss_cls: 3.5381, loss: 3.5381 +2024-12-29 18:26:58,131 - pyskl - INFO - Epoch [97][1000/3746] lr: 2.846e-02, eta: 1 day, 23:07:03, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6362, loss_cls: 3.5654, loss: 3.5654 +2024-12-29 18:28:24,503 - pyskl - INFO - Epoch [97][1100/3746] lr: 2.843e-02, eta: 1 day, 23:05:40, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6383, loss_cls: 3.5395, loss: 3.5395 +2024-12-29 18:29:50,863 - pyskl - INFO - Epoch [97][1200/3746] lr: 2.841e-02, eta: 1 day, 23:04:16, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6402, loss_cls: 3.4973, loss: 3.4973 +2024-12-29 18:31:17,200 - pyskl - INFO - Epoch [97][1300/3746] lr: 2.838e-02, eta: 1 day, 23:02:53, time: 0.863, data_time: 0.001, memory: 15990, top1_acc: 0.3748, top5_acc: 0.6375, loss_cls: 3.5504, loss: 3.5504 +2024-12-29 18:32:43,866 - pyskl - INFO - Epoch [97][1400/3746] lr: 2.836e-02, eta: 1 day, 23:01:30, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6178, loss_cls: 3.6389, loss: 3.6389 +2024-12-29 18:34:10,436 - pyskl - INFO - Epoch [97][1500/3746] lr: 2.833e-02, eta: 1 day, 23:00:07, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6373, loss_cls: 3.5359, loss: 3.5359 +2024-12-29 18:35:37,049 - pyskl - INFO - Epoch [97][1600/3746] lr: 2.831e-02, eta: 1 day, 22:58:44, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6303, loss_cls: 3.5632, loss: 3.5632 +2024-12-29 18:37:03,660 - pyskl - INFO - Epoch [97][1700/3746] lr: 2.828e-02, eta: 1 day, 22:57:21, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6334, loss_cls: 3.5673, loss: 3.5673 +2024-12-29 18:38:29,845 - pyskl - INFO - Epoch [97][1800/3746] lr: 2.826e-02, eta: 1 day, 22:55:58, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6383, loss_cls: 3.5118, loss: 3.5118 +2024-12-29 18:39:56,212 - pyskl - INFO - Epoch [97][1900/3746] lr: 2.823e-02, eta: 1 day, 22:54:35, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6423, loss_cls: 3.5317, loss: 3.5317 +2024-12-29 18:41:22,456 - pyskl - INFO - Epoch [97][2000/3746] lr: 2.821e-02, eta: 1 day, 22:53:12, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6277, loss_cls: 3.5670, loss: 3.5670 +2024-12-29 18:42:49,107 - pyskl - INFO - Epoch [97][2100/3746] lr: 2.818e-02, eta: 1 day, 22:51:49, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6233, loss_cls: 3.5609, loss: 3.5609 +2024-12-29 18:44:15,765 - pyskl - INFO - Epoch [97][2200/3746] lr: 2.816e-02, eta: 1 day, 22:50:26, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6259, loss_cls: 3.5749, loss: 3.5749 +2024-12-29 18:45:42,288 - pyskl - INFO - Epoch [97][2300/3746] lr: 2.813e-02, eta: 1 day, 22:49:03, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6269, loss_cls: 3.5961, loss: 3.5961 +2024-12-29 18:47:09,076 - pyskl - INFO - Epoch [97][2400/3746] lr: 2.811e-02, eta: 1 day, 22:47:40, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6423, loss_cls: 3.5719, loss: 3.5719 +2024-12-29 18:48:35,686 - pyskl - INFO - Epoch [97][2500/3746] lr: 2.808e-02, eta: 1 day, 22:46:17, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6231, loss_cls: 3.6125, loss: 3.6125 +2024-12-29 18:50:02,127 - pyskl - INFO - Epoch [97][2600/3746] lr: 2.806e-02, eta: 1 day, 22:44:54, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3656, top5_acc: 0.6275, loss_cls: 3.5846, loss: 3.5846 +2024-12-29 18:51:28,427 - pyskl - INFO - Epoch [97][2700/3746] lr: 2.803e-02, eta: 1 day, 22:43:31, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6361, loss_cls: 3.5350, loss: 3.5350 +2024-12-29 18:52:54,618 - pyskl - INFO - Epoch [97][2800/3746] lr: 2.801e-02, eta: 1 day, 22:42:08, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6302, loss_cls: 3.5638, loss: 3.5638 +2024-12-29 18:54:20,664 - pyskl - INFO - Epoch [97][2900/3746] lr: 2.798e-02, eta: 1 day, 22:40:44, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3720, top5_acc: 0.6236, loss_cls: 3.5830, loss: 3.5830 +2024-12-29 18:55:47,095 - pyskl - INFO - Epoch [97][3000/3746] lr: 2.796e-02, eta: 1 day, 22:39:21, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6302, loss_cls: 3.5802, loss: 3.5802 +2024-12-29 18:57:13,748 - pyskl - INFO - Epoch [97][3100/3746] lr: 2.793e-02, eta: 1 day, 22:37:58, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6267, loss_cls: 3.5495, loss: 3.5495 +2024-12-29 18:58:41,165 - pyskl - INFO - Epoch [97][3200/3746] lr: 2.791e-02, eta: 1 day, 22:36:36, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6347, loss_cls: 3.5735, loss: 3.5735 +2024-12-29 19:00:08,864 - pyskl - INFO - Epoch [97][3300/3746] lr: 2.788e-02, eta: 1 day, 22:35:13, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6373, loss_cls: 3.5377, loss: 3.5377 +2024-12-29 19:01:36,157 - pyskl - INFO - Epoch [97][3400/3746] lr: 2.786e-02, eta: 1 day, 22:33:51, time: 0.873, data_time: 0.001, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6320, loss_cls: 3.5664, loss: 3.5664 +2024-12-29 19:03:03,784 - pyskl - INFO - Epoch [97][3500/3746] lr: 2.783e-02, eta: 1 day, 22:32:28, time: 0.876, data_time: 0.001, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6342, loss_cls: 3.5641, loss: 3.5641 +2024-12-29 19:04:32,246 - pyskl - INFO - Epoch [97][3600/3746] lr: 2.781e-02, eta: 1 day, 22:31:06, time: 0.885, data_time: 0.001, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6409, loss_cls: 3.5409, loss: 3.5409 +2024-12-29 19:06:00,610 - pyskl - INFO - Epoch [97][3700/3746] lr: 2.778e-02, eta: 1 day, 22:29:44, time: 0.884, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6352, loss_cls: 3.5802, loss: 3.5802 +2024-12-29 19:06:42,759 - pyskl - INFO - Saving checkpoint at 97 epochs +2024-12-29 19:08:43,530 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 19:08:44,286 - pyskl - INFO - +top1_acc 0.3041 +top5_acc 0.5701 +2024-12-29 19:08:44,286 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 19:08:44,335 - pyskl - INFO - +mean_acc 0.3040 +2024-12-29 19:08:44,355 - pyskl - INFO - Epoch(val) [97][309] top1_acc: 0.3041, top5_acc: 0.5701, mean_class_accuracy: 0.3040 +2024-12-29 19:13:17,807 - pyskl - INFO - Epoch [98][100/3746] lr: 2.774e-02, eta: 1 day, 22:29:03, time: 2.734, data_time: 1.684, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6425, loss_cls: 3.4794, loss: 3.4794 +2024-12-29 19:14:44,941 - pyskl - INFO - Epoch [98][200/3746] lr: 2.772e-02, eta: 1 day, 22:27:40, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.3872, top5_acc: 0.6473, loss_cls: 3.4748, loss: 3.4748 +2024-12-29 19:16:12,024 - pyskl - INFO - Epoch [98][300/3746] lr: 2.769e-02, eta: 1 day, 22:26:17, time: 0.871, data_time: 0.001, memory: 15990, top1_acc: 0.3858, top5_acc: 0.6431, loss_cls: 3.4786, loss: 3.4786 +2024-12-29 19:17:39,059 - pyskl - INFO - Epoch [98][400/3746] lr: 2.767e-02, eta: 1 day, 22:24:55, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6498, loss_cls: 3.4874, loss: 3.4874 +2024-12-29 19:19:05,722 - pyskl - INFO - Epoch [98][500/3746] lr: 2.764e-02, eta: 1 day, 22:23:31, time: 0.867, data_time: 0.001, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6281, loss_cls: 3.5608, loss: 3.5608 +2024-12-29 19:20:32,728 - pyskl - INFO - Epoch [98][600/3746] lr: 2.762e-02, eta: 1 day, 22:22:09, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6358, loss_cls: 3.5808, loss: 3.5808 +2024-12-29 19:21:59,233 - pyskl - INFO - Epoch [98][700/3746] lr: 2.759e-02, eta: 1 day, 22:20:45, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6370, loss_cls: 3.5134, loss: 3.5134 +2024-12-29 19:23:25,523 - pyskl - INFO - Epoch [98][800/3746] lr: 2.757e-02, eta: 1 day, 22:19:22, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6294, loss_cls: 3.5428, loss: 3.5428 +2024-12-29 19:24:52,609 - pyskl - INFO - Epoch [98][900/3746] lr: 2.754e-02, eta: 1 day, 22:17:59, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6414, loss_cls: 3.5283, loss: 3.5283 +2024-12-29 19:26:19,099 - pyskl - INFO - Epoch [98][1000/3746] lr: 2.752e-02, eta: 1 day, 22:16:36, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6388, loss_cls: 3.5101, loss: 3.5101 +2024-12-29 19:27:45,257 - pyskl - INFO - Epoch [98][1100/3746] lr: 2.749e-02, eta: 1 day, 22:15:13, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6330, loss_cls: 3.5468, loss: 3.5468 +2024-12-29 19:29:11,303 - pyskl - INFO - Epoch [98][1200/3746] lr: 2.747e-02, eta: 1 day, 22:13:49, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3741, top5_acc: 0.6377, loss_cls: 3.5538, loss: 3.5538 +2024-12-29 19:30:36,875 - pyskl - INFO - Epoch [98][1300/3746] lr: 2.744e-02, eta: 1 day, 22:12:26, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6375, loss_cls: 3.5149, loss: 3.5149 +2024-12-29 19:32:02,483 - pyskl - INFO - Epoch [98][1400/3746] lr: 2.742e-02, eta: 1 day, 22:11:02, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6273, loss_cls: 3.5729, loss: 3.5729 +2024-12-29 19:33:27,976 - pyskl - INFO - Epoch [98][1500/3746] lr: 2.739e-02, eta: 1 day, 22:09:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3739, top5_acc: 0.6350, loss_cls: 3.4982, loss: 3.4982 +2024-12-29 19:34:53,184 - pyskl - INFO - Epoch [98][1600/3746] lr: 2.737e-02, eta: 1 day, 22:08:15, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6412, loss_cls: 3.5135, loss: 3.5135 +2024-12-29 19:36:19,040 - pyskl - INFO - Epoch [98][1700/3746] lr: 2.734e-02, eta: 1 day, 22:06:51, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3588, top5_acc: 0.6294, loss_cls: 3.5895, loss: 3.5895 +2024-12-29 19:37:44,810 - pyskl - INFO - Epoch [98][1800/3746] lr: 2.732e-02, eta: 1 day, 22:05:27, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6366, loss_cls: 3.5190, loss: 3.5190 +2024-12-29 19:39:10,604 - pyskl - INFO - Epoch [98][1900/3746] lr: 2.729e-02, eta: 1 day, 22:04:04, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6395, loss_cls: 3.5035, loss: 3.5035 +2024-12-29 19:40:36,212 - pyskl - INFO - Epoch [98][2000/3746] lr: 2.727e-02, eta: 1 day, 22:02:40, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6344, loss_cls: 3.5497, loss: 3.5497 +2024-12-29 19:42:02,247 - pyskl - INFO - Epoch [98][2100/3746] lr: 2.724e-02, eta: 1 day, 22:01:17, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6352, loss_cls: 3.5438, loss: 3.5438 +2024-12-29 19:43:28,312 - pyskl - INFO - Epoch [98][2200/3746] lr: 2.722e-02, eta: 1 day, 21:59:53, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6366, loss_cls: 3.5511, loss: 3.5511 +2024-12-29 19:44:54,188 - pyskl - INFO - Epoch [98][2300/3746] lr: 2.719e-02, eta: 1 day, 21:58:30, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6328, loss_cls: 3.5413, loss: 3.5413 +2024-12-29 19:46:20,270 - pyskl - INFO - Epoch [98][2400/3746] lr: 2.717e-02, eta: 1 day, 21:57:06, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6219, loss_cls: 3.5887, loss: 3.5887 +2024-12-29 19:47:46,297 - pyskl - INFO - Epoch [98][2500/3746] lr: 2.714e-02, eta: 1 day, 21:55:43, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6248, loss_cls: 3.5585, loss: 3.5585 +2024-12-29 19:49:11,400 - pyskl - INFO - Epoch [98][2600/3746] lr: 2.712e-02, eta: 1 day, 21:54:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3656, top5_acc: 0.6297, loss_cls: 3.5546, loss: 3.5546 +2024-12-29 19:50:37,251 - pyskl - INFO - Epoch [98][2700/3746] lr: 2.709e-02, eta: 1 day, 21:52:56, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6422, loss_cls: 3.4982, loss: 3.4982 +2024-12-29 19:52:03,068 - pyskl - INFO - Epoch [98][2800/3746] lr: 2.707e-02, eta: 1 day, 21:51:32, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6166, loss_cls: 3.6568, loss: 3.6568 +2024-12-29 19:53:28,267 - pyskl - INFO - Epoch [98][2900/3746] lr: 2.705e-02, eta: 1 day, 21:50:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6236, loss_cls: 3.5657, loss: 3.5657 +2024-12-29 19:54:53,484 - pyskl - INFO - Epoch [98][3000/3746] lr: 2.702e-02, eta: 1 day, 21:48:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6494, loss_cls: 3.4833, loss: 3.4833 +2024-12-29 19:56:18,435 - pyskl - INFO - Epoch [98][3100/3746] lr: 2.700e-02, eta: 1 day, 21:47:20, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6348, loss_cls: 3.5441, loss: 3.5441 +2024-12-29 19:57:44,039 - pyskl - INFO - Epoch [98][3200/3746] lr: 2.697e-02, eta: 1 day, 21:45:57, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6373, loss_cls: 3.5128, loss: 3.5128 +2024-12-29 19:59:09,675 - pyskl - INFO - Epoch [98][3300/3746] lr: 2.695e-02, eta: 1 day, 21:44:33, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6417, loss_cls: 3.5347, loss: 3.5347 +2024-12-29 20:00:35,937 - pyskl - INFO - Epoch [98][3400/3746] lr: 2.692e-02, eta: 1 day, 21:43:10, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6334, loss_cls: 3.5510, loss: 3.5510 +2024-12-29 20:02:01,467 - pyskl - INFO - Epoch [98][3500/3746] lr: 2.690e-02, eta: 1 day, 21:41:46, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6362, loss_cls: 3.5530, loss: 3.5530 +2024-12-29 20:03:26,722 - pyskl - INFO - Epoch [98][3600/3746] lr: 2.687e-02, eta: 1 day, 21:40:22, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6441, loss_cls: 3.5147, loss: 3.5147 +2024-12-29 20:04:52,409 - pyskl - INFO - Epoch [98][3700/3746] lr: 2.685e-02, eta: 1 day, 21:38:58, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6231, loss_cls: 3.5797, loss: 3.5797 +2024-12-29 20:05:34,093 - pyskl - INFO - Saving checkpoint at 98 epochs +2024-12-29 20:07:34,055 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 20:07:35,025 - pyskl - INFO - +top1_acc 0.3063 +top5_acc 0.5628 +2024-12-29 20:07:35,026 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 20:07:35,076 - pyskl - INFO - +mean_acc 0.3061 +2024-12-29 20:07:35,095 - pyskl - INFO - Epoch(val) [98][309] top1_acc: 0.3063, top5_acc: 0.5628, mean_class_accuracy: 0.3061 +2024-12-29 20:12:07,618 - pyskl - INFO - Epoch [99][100/3746] lr: 2.681e-02, eta: 1 day, 21:38:14, time: 2.725, data_time: 1.661, memory: 15990, top1_acc: 0.3762, top5_acc: 0.6398, loss_cls: 3.5471, loss: 3.5471 +2024-12-29 20:13:34,573 - pyskl - INFO - Epoch [99][200/3746] lr: 2.679e-02, eta: 1 day, 21:36:51, time: 0.870, data_time: 0.001, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6655, loss_cls: 3.3883, loss: 3.3883 +2024-12-29 20:15:01,589 - pyskl - INFO - Epoch [99][300/3746] lr: 2.676e-02, eta: 1 day, 21:35:28, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6470, loss_cls: 3.4871, loss: 3.4871 +2024-12-29 20:16:29,055 - pyskl - INFO - Epoch [99][400/3746] lr: 2.674e-02, eta: 1 day, 21:34:06, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6389, loss_cls: 3.5101, loss: 3.5101 +2024-12-29 20:17:55,402 - pyskl - INFO - Epoch [99][500/3746] lr: 2.671e-02, eta: 1 day, 21:32:42, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6331, loss_cls: 3.5448, loss: 3.5448 +2024-12-29 20:19:21,660 - pyskl - INFO - Epoch [99][600/3746] lr: 2.669e-02, eta: 1 day, 21:31:19, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6292, loss_cls: 3.5592, loss: 3.5592 +2024-12-29 20:20:47,017 - pyskl - INFO - Epoch [99][700/3746] lr: 2.666e-02, eta: 1 day, 21:29:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6425, loss_cls: 3.5061, loss: 3.5061 +2024-12-29 20:22:12,116 - pyskl - INFO - Epoch [99][800/3746] lr: 2.664e-02, eta: 1 day, 21:28:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6381, loss_cls: 3.5435, loss: 3.5435 +2024-12-29 20:23:37,109 - pyskl - INFO - Epoch [99][900/3746] lr: 2.661e-02, eta: 1 day, 21:27:07, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6362, loss_cls: 3.5720, loss: 3.5720 +2024-12-29 20:25:02,461 - pyskl - INFO - Epoch [99][1000/3746] lr: 2.659e-02, eta: 1 day, 21:25:43, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6395, loss_cls: 3.4956, loss: 3.4956 +2024-12-29 20:26:28,567 - pyskl - INFO - Epoch [99][1100/3746] lr: 2.656e-02, eta: 1 day, 21:24:19, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6439, loss_cls: 3.4881, loss: 3.4881 +2024-12-29 20:27:54,447 - pyskl - INFO - Epoch [99][1200/3746] lr: 2.654e-02, eta: 1 day, 21:22:56, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6408, loss_cls: 3.5014, loss: 3.5014 +2024-12-29 20:29:20,511 - pyskl - INFO - Epoch [99][1300/3746] lr: 2.651e-02, eta: 1 day, 21:21:32, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6456, loss_cls: 3.4907, loss: 3.4907 +2024-12-29 20:30:47,326 - pyskl - INFO - Epoch [99][1400/3746] lr: 2.649e-02, eta: 1 day, 21:20:09, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6377, loss_cls: 3.5284, loss: 3.5284 +2024-12-29 20:32:13,426 - pyskl - INFO - Epoch [99][1500/3746] lr: 2.646e-02, eta: 1 day, 21:18:46, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3748, top5_acc: 0.6380, loss_cls: 3.5120, loss: 3.5120 +2024-12-29 20:33:40,155 - pyskl - INFO - Epoch [99][1600/3746] lr: 2.644e-02, eta: 1 day, 21:17:23, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6477, loss_cls: 3.4773, loss: 3.4773 +2024-12-29 20:35:07,131 - pyskl - INFO - Epoch [99][1700/3746] lr: 2.642e-02, eta: 1 day, 21:15:59, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6461, loss_cls: 3.4995, loss: 3.4995 +2024-12-29 20:36:33,968 - pyskl - INFO - Epoch [99][1800/3746] lr: 2.639e-02, eta: 1 day, 21:14:36, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6406, loss_cls: 3.5444, loss: 3.5444 +2024-12-29 20:38:00,343 - pyskl - INFO - Epoch [99][1900/3746] lr: 2.637e-02, eta: 1 day, 21:13:13, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6439, loss_cls: 3.4923, loss: 3.4923 +2024-12-29 20:39:26,831 - pyskl - INFO - Epoch [99][2000/3746] lr: 2.634e-02, eta: 1 day, 21:11:50, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6342, loss_cls: 3.5626, loss: 3.5626 +2024-12-29 20:40:54,034 - pyskl - INFO - Epoch [99][2100/3746] lr: 2.632e-02, eta: 1 day, 21:10:27, time: 0.872, data_time: 0.001, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6336, loss_cls: 3.5791, loss: 3.5791 +2024-12-29 20:42:20,517 - pyskl - INFO - Epoch [99][2200/3746] lr: 2.629e-02, eta: 1 day, 21:09:03, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6298, loss_cls: 3.5359, loss: 3.5359 +2024-12-29 20:43:47,437 - pyskl - INFO - Epoch [99][2300/3746] lr: 2.627e-02, eta: 1 day, 21:07:40, time: 0.869, data_time: 0.001, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6414, loss_cls: 3.4910, loss: 3.4910 +2024-12-29 20:45:14,089 - pyskl - INFO - Epoch [99][2400/3746] lr: 2.624e-02, eta: 1 day, 21:06:17, time: 0.867, data_time: 0.001, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6397, loss_cls: 3.5258, loss: 3.5258 +2024-12-29 20:46:40,982 - pyskl - INFO - Epoch [99][2500/3746] lr: 2.622e-02, eta: 1 day, 21:04:54, time: 0.869, data_time: 0.001, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6406, loss_cls: 3.5424, loss: 3.5424 +2024-12-29 20:48:07,711 - pyskl - INFO - Epoch [99][2600/3746] lr: 2.619e-02, eta: 1 day, 21:03:31, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6498, loss_cls: 3.4905, loss: 3.4905 +2024-12-29 20:49:34,192 - pyskl - INFO - Epoch [99][2700/3746] lr: 2.617e-02, eta: 1 day, 21:02:08, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6436, loss_cls: 3.4970, loss: 3.4970 +2024-12-29 20:51:01,945 - pyskl - INFO - Epoch [99][2800/3746] lr: 2.614e-02, eta: 1 day, 21:00:45, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6427, loss_cls: 3.4857, loss: 3.4857 +2024-12-29 20:52:27,731 - pyskl - INFO - Epoch [99][2900/3746] lr: 2.612e-02, eta: 1 day, 20:59:21, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6286, loss_cls: 3.5273, loss: 3.5273 +2024-12-29 20:53:52,446 - pyskl - INFO - Epoch [99][3000/3746] lr: 2.610e-02, eta: 1 day, 20:57:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6398, loss_cls: 3.4988, loss: 3.4988 +2024-12-29 20:55:17,183 - pyskl - INFO - Epoch [99][3100/3746] lr: 2.607e-02, eta: 1 day, 20:56:33, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6305, loss_cls: 3.5613, loss: 3.5613 +2024-12-29 20:56:42,847 - pyskl - INFO - Epoch [99][3200/3746] lr: 2.605e-02, eta: 1 day, 20:55:09, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6261, loss_cls: 3.5828, loss: 3.5828 +2024-12-29 20:58:08,584 - pyskl - INFO - Epoch [99][3300/3746] lr: 2.602e-02, eta: 1 day, 20:53:45, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6238, loss_cls: 3.5683, loss: 3.5683 +2024-12-29 20:59:33,816 - pyskl - INFO - Epoch [99][3400/3746] lr: 2.600e-02, eta: 1 day, 20:52:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6362, loss_cls: 3.5592, loss: 3.5592 +2024-12-29 21:00:59,202 - pyskl - INFO - Epoch [99][3500/3746] lr: 2.597e-02, eta: 1 day, 20:50:57, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3762, top5_acc: 0.6367, loss_cls: 3.5291, loss: 3.5291 +2024-12-29 21:02:24,861 - pyskl - INFO - Epoch [99][3600/3746] lr: 2.595e-02, eta: 1 day, 20:49:34, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6391, loss_cls: 3.5254, loss: 3.5254 +2024-12-29 21:03:50,462 - pyskl - INFO - Epoch [99][3700/3746] lr: 2.592e-02, eta: 1 day, 20:48:10, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6392, loss_cls: 3.5387, loss: 3.5387 +2024-12-29 21:04:32,021 - pyskl - INFO - Saving checkpoint at 99 epochs +2024-12-29 21:06:33,049 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 21:06:33,988 - pyskl - INFO - +top1_acc 0.3041 +top5_acc 0.5616 +2024-12-29 21:06:33,988 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 21:06:34,037 - pyskl - INFO - +mean_acc 0.3038 +2024-12-29 21:06:34,050 - pyskl - INFO - Epoch(val) [99][309] top1_acc: 0.3041, top5_acc: 0.5616, mean_class_accuracy: 0.3038 +2024-12-29 21:11:01,319 - pyskl - INFO - Epoch [100][100/3746] lr: 2.589e-02, eta: 1 day, 20:47:21, time: 2.673, data_time: 1.618, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6586, loss_cls: 3.4433, loss: 3.4433 +2024-12-29 21:12:27,212 - pyskl - INFO - Epoch [100][200/3746] lr: 2.586e-02, eta: 1 day, 20:45:57, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6466, loss_cls: 3.4447, loss: 3.4447 +2024-12-29 21:13:53,465 - pyskl - INFO - Epoch [100][300/3746] lr: 2.584e-02, eta: 1 day, 20:44:34, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6523, loss_cls: 3.4557, loss: 3.4557 +2024-12-29 21:15:19,923 - pyskl - INFO - Epoch [100][400/3746] lr: 2.581e-02, eta: 1 day, 20:43:10, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6547, loss_cls: 3.4635, loss: 3.4635 +2024-12-29 21:16:45,917 - pyskl - INFO - Epoch [100][500/3746] lr: 2.579e-02, eta: 1 day, 20:41:46, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6367, loss_cls: 3.5178, loss: 3.5178 +2024-12-29 21:18:11,736 - pyskl - INFO - Epoch [100][600/3746] lr: 2.577e-02, eta: 1 day, 20:40:23, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6503, loss_cls: 3.4291, loss: 3.4291 +2024-12-29 21:19:36,758 - pyskl - INFO - Epoch [100][700/3746] lr: 2.574e-02, eta: 1 day, 20:38:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6394, loss_cls: 3.4946, loss: 3.4946 +2024-12-29 21:21:01,344 - pyskl - INFO - Epoch [100][800/3746] lr: 2.572e-02, eta: 1 day, 20:37:34, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6386, loss_cls: 3.4955, loss: 3.4955 +2024-12-29 21:22:26,661 - pyskl - INFO - Epoch [100][900/3746] lr: 2.569e-02, eta: 1 day, 20:36:10, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6397, loss_cls: 3.4865, loss: 3.4865 +2024-12-29 21:23:51,283 - pyskl - INFO - Epoch [100][1000/3746] lr: 2.567e-02, eta: 1 day, 20:34:46, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6372, loss_cls: 3.5397, loss: 3.5397 +2024-12-29 21:25:16,632 - pyskl - INFO - Epoch [100][1100/3746] lr: 2.564e-02, eta: 1 day, 20:33:22, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6445, loss_cls: 3.4734, loss: 3.4734 +2024-12-29 21:26:42,240 - pyskl - INFO - Epoch [100][1200/3746] lr: 2.562e-02, eta: 1 day, 20:31:58, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6342, loss_cls: 3.5037, loss: 3.5037 +2024-12-29 21:28:07,349 - pyskl - INFO - Epoch [100][1300/3746] lr: 2.559e-02, eta: 1 day, 20:30:34, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6488, loss_cls: 3.4768, loss: 3.4768 +2024-12-29 21:29:32,810 - pyskl - INFO - Epoch [100][1400/3746] lr: 2.557e-02, eta: 1 day, 20:29:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3859, top5_acc: 0.6442, loss_cls: 3.5057, loss: 3.5057 +2024-12-29 21:30:57,834 - pyskl - INFO - Epoch [100][1500/3746] lr: 2.555e-02, eta: 1 day, 20:27:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6283, loss_cls: 3.5465, loss: 3.5465 +2024-12-29 21:32:22,776 - pyskl - INFO - Epoch [100][1600/3746] lr: 2.552e-02, eta: 1 day, 20:26:22, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6533, loss_cls: 3.4247, loss: 3.4247 +2024-12-29 21:33:48,280 - pyskl - INFO - Epoch [100][1700/3746] lr: 2.550e-02, eta: 1 day, 20:24:58, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6270, loss_cls: 3.5544, loss: 3.5544 +2024-12-29 21:35:13,538 - pyskl - INFO - Epoch [100][1800/3746] lr: 2.547e-02, eta: 1 day, 20:23:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6364, loss_cls: 3.5253, loss: 3.5253 +2024-12-29 21:36:38,597 - pyskl - INFO - Epoch [100][1900/3746] lr: 2.545e-02, eta: 1 day, 20:22:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6459, loss_cls: 3.4839, loss: 3.4839 +2024-12-29 21:38:03,792 - pyskl - INFO - Epoch [100][2000/3746] lr: 2.542e-02, eta: 1 day, 20:20:45, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6394, loss_cls: 3.5124, loss: 3.5124 +2024-12-29 21:39:29,217 - pyskl - INFO - Epoch [100][2100/3746] lr: 2.540e-02, eta: 1 day, 20:19:22, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6395, loss_cls: 3.5182, loss: 3.5182 +2024-12-29 21:40:54,712 - pyskl - INFO - Epoch [100][2200/3746] lr: 2.538e-02, eta: 1 day, 20:17:58, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6291, loss_cls: 3.5524, loss: 3.5524 +2024-12-29 21:42:20,171 - pyskl - INFO - Epoch [100][2300/3746] lr: 2.535e-02, eta: 1 day, 20:16:34, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3831, top5_acc: 0.6472, loss_cls: 3.4552, loss: 3.4552 +2024-12-29 21:43:45,294 - pyskl - INFO - Epoch [100][2400/3746] lr: 2.533e-02, eta: 1 day, 20:15:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6441, loss_cls: 3.4842, loss: 3.4842 +2024-12-29 21:45:10,675 - pyskl - INFO - Epoch [100][2500/3746] lr: 2.530e-02, eta: 1 day, 20:13:46, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6327, loss_cls: 3.5619, loss: 3.5619 +2024-12-29 21:46:35,643 - pyskl - INFO - Epoch [100][2600/3746] lr: 2.528e-02, eta: 1 day, 20:12:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6395, loss_cls: 3.5208, loss: 3.5208 +2024-12-29 21:48:00,650 - pyskl - INFO - Epoch [100][2700/3746] lr: 2.525e-02, eta: 1 day, 20:10:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6500, loss_cls: 3.4612, loss: 3.4612 +2024-12-29 21:49:25,064 - pyskl - INFO - Epoch [100][2800/3746] lr: 2.523e-02, eta: 1 day, 20:09:33, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6434, loss_cls: 3.5262, loss: 3.5262 +2024-12-29 21:50:50,293 - pyskl - INFO - Epoch [100][2900/3746] lr: 2.521e-02, eta: 1 day, 20:08:09, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6414, loss_cls: 3.5256, loss: 3.5256 +2024-12-29 21:52:14,974 - pyskl - INFO - Epoch [100][3000/3746] lr: 2.518e-02, eta: 1 day, 20:06:44, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6309, loss_cls: 3.5718, loss: 3.5718 +2024-12-29 21:53:38,946 - pyskl - INFO - Epoch [100][3100/3746] lr: 2.516e-02, eta: 1 day, 20:05:20, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6438, loss_cls: 3.4985, loss: 3.4985 +2024-12-29 21:55:03,911 - pyskl - INFO - Epoch [100][3200/3746] lr: 2.513e-02, eta: 1 day, 20:03:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6427, loss_cls: 3.5056, loss: 3.5056 +2024-12-29 21:56:29,092 - pyskl - INFO - Epoch [100][3300/3746] lr: 2.511e-02, eta: 1 day, 20:02:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6420, loss_cls: 3.5363, loss: 3.5363 +2024-12-29 21:57:54,178 - pyskl - INFO - Epoch [100][3400/3746] lr: 2.508e-02, eta: 1 day, 20:01:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6255, loss_cls: 3.5891, loss: 3.5891 +2024-12-29 21:59:19,296 - pyskl - INFO - Epoch [100][3500/3746] lr: 2.506e-02, eta: 1 day, 19:59:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6400, loss_cls: 3.5412, loss: 3.5412 +2024-12-29 22:00:44,915 - pyskl - INFO - Epoch [100][3600/3746] lr: 2.504e-02, eta: 1 day, 19:58:19, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6442, loss_cls: 3.4989, loss: 3.4989 +2024-12-29 22:02:09,819 - pyskl - INFO - Epoch [100][3700/3746] lr: 2.501e-02, eta: 1 day, 19:56:55, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6428, loss_cls: 3.5023, loss: 3.5023 +2024-12-29 22:02:50,605 - pyskl - INFO - Saving checkpoint at 100 epochs +2024-12-29 22:04:50,321 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 22:04:51,156 - pyskl - INFO - +top1_acc 0.3111 +top5_acc 0.5659 +2024-12-29 22:04:51,157 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 22:04:51,198 - pyskl - INFO - +mean_acc 0.3109 +2024-12-29 22:04:51,203 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_94.pth was removed +2024-12-29 22:04:51,470 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_100.pth. +2024-12-29 22:04:51,471 - pyskl - INFO - Best top1_acc is 0.3111 at 100 epoch. +2024-12-29 22:04:51,483 - pyskl - INFO - Epoch(val) [100][309] top1_acc: 0.3111, top5_acc: 0.5659, mean_class_accuracy: 0.3109 +2024-12-29 22:09:06,968 - pyskl - INFO - Epoch [101][100/3746] lr: 2.498e-02, eta: 1 day, 19:55:58, time: 2.555, data_time: 1.517, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6564, loss_cls: 3.4247, loss: 3.4247 +2024-12-29 22:10:32,925 - pyskl - INFO - Epoch [101][200/3746] lr: 2.495e-02, eta: 1 day, 19:54:34, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6470, loss_cls: 3.4747, loss: 3.4747 +2024-12-29 22:11:58,731 - pyskl - INFO - Epoch [101][300/3746] lr: 2.493e-02, eta: 1 day, 19:53:10, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6484, loss_cls: 3.4714, loss: 3.4714 +2024-12-29 22:13:23,613 - pyskl - INFO - Epoch [101][400/3746] lr: 2.490e-02, eta: 1 day, 19:51:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6617, loss_cls: 3.4165, loss: 3.4165 +2024-12-29 22:14:49,214 - pyskl - INFO - Epoch [101][500/3746] lr: 2.488e-02, eta: 1 day, 19:50:22, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6548, loss_cls: 3.4294, loss: 3.4294 +2024-12-29 22:16:14,381 - pyskl - INFO - Epoch [101][600/3746] lr: 2.486e-02, eta: 1 day, 19:48:58, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6406, loss_cls: 3.4769, loss: 3.4769 +2024-12-29 22:17:39,220 - pyskl - INFO - Epoch [101][700/3746] lr: 2.483e-02, eta: 1 day, 19:47:34, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6475, loss_cls: 3.4688, loss: 3.4688 +2024-12-29 22:19:04,204 - pyskl - INFO - Epoch [101][800/3746] lr: 2.481e-02, eta: 1 day, 19:46:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6478, loss_cls: 3.4487, loss: 3.4487 +2024-12-29 22:20:29,185 - pyskl - INFO - Epoch [101][900/3746] lr: 2.478e-02, eta: 1 day, 19:44:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6503, loss_cls: 3.4750, loss: 3.4750 +2024-12-29 22:21:54,061 - pyskl - INFO - Epoch [101][1000/3746] lr: 2.476e-02, eta: 1 day, 19:43:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6373, loss_cls: 3.5382, loss: 3.5382 +2024-12-29 22:23:19,224 - pyskl - INFO - Epoch [101][1100/3746] lr: 2.473e-02, eta: 1 day, 19:41:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6433, loss_cls: 3.4732, loss: 3.4732 +2024-12-29 22:24:43,984 - pyskl - INFO - Epoch [101][1200/3746] lr: 2.471e-02, eta: 1 day, 19:40:32, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6477, loss_cls: 3.4685, loss: 3.4685 +2024-12-29 22:26:08,673 - pyskl - INFO - Epoch [101][1300/3746] lr: 2.469e-02, eta: 1 day, 19:39:08, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3748, top5_acc: 0.6412, loss_cls: 3.5196, loss: 3.5196 +2024-12-29 22:27:34,010 - pyskl - INFO - Epoch [101][1400/3746] lr: 2.466e-02, eta: 1 day, 19:37:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6434, loss_cls: 3.4652, loss: 3.4652 +2024-12-29 22:28:58,878 - pyskl - INFO - Epoch [101][1500/3746] lr: 2.464e-02, eta: 1 day, 19:36:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6478, loss_cls: 3.4785, loss: 3.4785 +2024-12-29 22:30:24,047 - pyskl - INFO - Epoch [101][1600/3746] lr: 2.461e-02, eta: 1 day, 19:34:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6442, loss_cls: 3.4942, loss: 3.4942 +2024-12-29 22:31:48,791 - pyskl - INFO - Epoch [101][1700/3746] lr: 2.459e-02, eta: 1 day, 19:33:31, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6502, loss_cls: 3.4903, loss: 3.4903 +2024-12-29 22:33:13,868 - pyskl - INFO - Epoch [101][1800/3746] lr: 2.457e-02, eta: 1 day, 19:32:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6491, loss_cls: 3.4532, loss: 3.4532 +2024-12-29 22:34:38,657 - pyskl - INFO - Epoch [101][1900/3746] lr: 2.454e-02, eta: 1 day, 19:30:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3970, top5_acc: 0.6534, loss_cls: 3.4153, loss: 3.4153 +2024-12-29 22:36:03,799 - pyskl - INFO - Epoch [101][2000/3746] lr: 2.452e-02, eta: 1 day, 19:29:18, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6431, loss_cls: 3.5065, loss: 3.5065 +2024-12-29 22:37:28,181 - pyskl - INFO - Epoch [101][2100/3746] lr: 2.449e-02, eta: 1 day, 19:27:54, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6430, loss_cls: 3.4999, loss: 3.4999 +2024-12-29 22:38:52,804 - pyskl - INFO - Epoch [101][2200/3746] lr: 2.447e-02, eta: 1 day, 19:26:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6416, loss_cls: 3.5049, loss: 3.5049 +2024-12-29 22:40:18,423 - pyskl - INFO - Epoch [101][2300/3746] lr: 2.445e-02, eta: 1 day, 19:25:05, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3731, top5_acc: 0.6405, loss_cls: 3.5468, loss: 3.5468 +2024-12-29 22:41:42,965 - pyskl - INFO - Epoch [101][2400/3746] lr: 2.442e-02, eta: 1 day, 19:23:41, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6384, loss_cls: 3.5070, loss: 3.5070 +2024-12-29 22:43:07,584 - pyskl - INFO - Epoch [101][2500/3746] lr: 2.440e-02, eta: 1 day, 19:22:16, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6388, loss_cls: 3.5350, loss: 3.5350 +2024-12-29 22:44:32,422 - pyskl - INFO - Epoch [101][2600/3746] lr: 2.437e-02, eta: 1 day, 19:20:52, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6361, loss_cls: 3.5124, loss: 3.5124 +2024-12-29 22:45:57,004 - pyskl - INFO - Epoch [101][2700/3746] lr: 2.435e-02, eta: 1 day, 19:19:28, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6430, loss_cls: 3.4583, loss: 3.4583 +2024-12-29 22:47:21,655 - pyskl - INFO - Epoch [101][2800/3746] lr: 2.433e-02, eta: 1 day, 19:18:03, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6411, loss_cls: 3.4974, loss: 3.4974 +2024-12-29 22:48:46,505 - pyskl - INFO - Epoch [101][2900/3746] lr: 2.430e-02, eta: 1 day, 19:16:39, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6339, loss_cls: 3.5375, loss: 3.5375 +2024-12-29 22:50:10,476 - pyskl - INFO - Epoch [101][3000/3746] lr: 2.428e-02, eta: 1 day, 19:15:14, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6498, loss_cls: 3.4735, loss: 3.4735 +2024-12-29 22:51:34,433 - pyskl - INFO - Epoch [101][3100/3746] lr: 2.425e-02, eta: 1 day, 19:13:49, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6328, loss_cls: 3.5186, loss: 3.5186 +2024-12-29 22:52:58,889 - pyskl - INFO - Epoch [101][3200/3746] lr: 2.423e-02, eta: 1 day, 19:12:25, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6409, loss_cls: 3.5183, loss: 3.5183 +2024-12-29 22:54:23,332 - pyskl - INFO - Epoch [101][3300/3746] lr: 2.421e-02, eta: 1 day, 19:11:00, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6492, loss_cls: 3.4565, loss: 3.4565 +2024-12-29 22:55:47,696 - pyskl - INFO - Epoch [101][3400/3746] lr: 2.418e-02, eta: 1 day, 19:09:36, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6444, loss_cls: 3.5091, loss: 3.5091 +2024-12-29 22:57:12,378 - pyskl - INFO - Epoch [101][3500/3746] lr: 2.416e-02, eta: 1 day, 19:08:11, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6472, loss_cls: 3.4825, loss: 3.4825 +2024-12-29 22:58:36,743 - pyskl - INFO - Epoch [101][3600/3746] lr: 2.413e-02, eta: 1 day, 19:06:47, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6506, loss_cls: 3.4462, loss: 3.4462 +2024-12-29 23:00:01,615 - pyskl - INFO - Epoch [101][3700/3746] lr: 2.411e-02, eta: 1 day, 19:05:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3812, top5_acc: 0.6400, loss_cls: 3.5080, loss: 3.5080 +2024-12-29 23:00:42,368 - pyskl - INFO - Saving checkpoint at 101 epochs +2024-12-29 23:02:40,276 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 23:02:41,111 - pyskl - INFO - +top1_acc 0.3050 +top5_acc 0.5647 +2024-12-29 23:02:41,111 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 23:02:41,159 - pyskl - INFO - +mean_acc 0.3049 +2024-12-29 23:02:41,171 - pyskl - INFO - Epoch(val) [101][309] top1_acc: 0.3050, top5_acc: 0.5647, mean_class_accuracy: 0.3049 +2024-12-29 23:06:55,229 - pyskl - INFO - Epoch [102][100/3746] lr: 2.407e-02, eta: 1 day, 19:04:23, time: 2.540, data_time: 1.515, memory: 15990, top1_acc: 0.3986, top5_acc: 0.6728, loss_cls: 3.3532, loss: 3.3532 +2024-12-29 23:08:20,729 - pyskl - INFO - Epoch [102][200/3746] lr: 2.405e-02, eta: 1 day, 19:02:59, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6591, loss_cls: 3.4048, loss: 3.4048 +2024-12-29 23:09:46,215 - pyskl - INFO - Epoch [102][300/3746] lr: 2.403e-02, eta: 1 day, 19:01:35, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3837, top5_acc: 0.6505, loss_cls: 3.4685, loss: 3.4685 +2024-12-29 23:11:11,678 - pyskl - INFO - Epoch [102][400/3746] lr: 2.400e-02, eta: 1 day, 19:00:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6523, loss_cls: 3.4393, loss: 3.4393 +2024-12-29 23:12:36,780 - pyskl - INFO - Epoch [102][500/3746] lr: 2.398e-02, eta: 1 day, 18:58:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6520, loss_cls: 3.4345, loss: 3.4345 +2024-12-29 23:14:01,876 - pyskl - INFO - Epoch [102][600/3746] lr: 2.396e-02, eta: 1 day, 18:57:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6503, loss_cls: 3.4448, loss: 3.4448 +2024-12-29 23:15:26,618 - pyskl - INFO - Epoch [102][700/3746] lr: 2.393e-02, eta: 1 day, 18:55:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6444, loss_cls: 3.4888, loss: 3.4888 +2024-12-29 23:16:51,294 - pyskl - INFO - Epoch [102][800/3746] lr: 2.391e-02, eta: 1 day, 18:54:33, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6544, loss_cls: 3.4304, loss: 3.4304 +2024-12-29 23:18:15,943 - pyskl - INFO - Epoch [102][900/3746] lr: 2.388e-02, eta: 1 day, 18:53:09, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6591, loss_cls: 3.4106, loss: 3.4106 +2024-12-29 23:19:41,177 - pyskl - INFO - Epoch [102][1000/3746] lr: 2.386e-02, eta: 1 day, 18:51:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6355, loss_cls: 3.5177, loss: 3.5177 +2024-12-29 23:21:06,305 - pyskl - INFO - Epoch [102][1100/3746] lr: 2.384e-02, eta: 1 day, 18:50:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6548, loss_cls: 3.4752, loss: 3.4752 +2024-12-29 23:22:31,187 - pyskl - INFO - Epoch [102][1200/3746] lr: 2.381e-02, eta: 1 day, 18:48:56, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6583, loss_cls: 3.4121, loss: 3.4121 +2024-12-29 23:23:55,703 - pyskl - INFO - Epoch [102][1300/3746] lr: 2.379e-02, eta: 1 day, 18:47:31, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6577, loss_cls: 3.4212, loss: 3.4212 +2024-12-29 23:25:20,463 - pyskl - INFO - Epoch [102][1400/3746] lr: 2.376e-02, eta: 1 day, 18:46:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6372, loss_cls: 3.5084, loss: 3.5084 +2024-12-29 23:26:45,176 - pyskl - INFO - Epoch [102][1500/3746] lr: 2.374e-02, eta: 1 day, 18:44:42, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6427, loss_cls: 3.4746, loss: 3.4746 +2024-12-29 23:28:09,585 - pyskl - INFO - Epoch [102][1600/3746] lr: 2.372e-02, eta: 1 day, 18:43:18, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6402, loss_cls: 3.4903, loss: 3.4903 +2024-12-29 23:29:33,920 - pyskl - INFO - Epoch [102][1700/3746] lr: 2.369e-02, eta: 1 day, 18:41:53, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6439, loss_cls: 3.4508, loss: 3.4508 +2024-12-29 23:30:58,480 - pyskl - INFO - Epoch [102][1800/3746] lr: 2.367e-02, eta: 1 day, 18:40:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6469, loss_cls: 3.4667, loss: 3.4667 +2024-12-29 23:32:23,578 - pyskl - INFO - Epoch [102][1900/3746] lr: 2.365e-02, eta: 1 day, 18:39:04, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6422, loss_cls: 3.4987, loss: 3.4987 +2024-12-29 23:33:48,514 - pyskl - INFO - Epoch [102][2000/3746] lr: 2.362e-02, eta: 1 day, 18:37:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6530, loss_cls: 3.4490, loss: 3.4490 +2024-12-29 23:35:13,889 - pyskl - INFO - Epoch [102][2100/3746] lr: 2.360e-02, eta: 1 day, 18:36:16, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6422, loss_cls: 3.5031, loss: 3.5031 +2024-12-29 23:36:38,641 - pyskl - INFO - Epoch [102][2200/3746] lr: 2.357e-02, eta: 1 day, 18:34:52, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6420, loss_cls: 3.4979, loss: 3.4979 +2024-12-29 23:38:03,321 - pyskl - INFO - Epoch [102][2300/3746] lr: 2.355e-02, eta: 1 day, 18:33:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6564, loss_cls: 3.4315, loss: 3.4315 +2024-12-29 23:39:28,237 - pyskl - INFO - Epoch [102][2400/3746] lr: 2.353e-02, eta: 1 day, 18:32:03, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3898, top5_acc: 0.6497, loss_cls: 3.4423, loss: 3.4423 +2024-12-29 23:40:52,985 - pyskl - INFO - Epoch [102][2500/3746] lr: 2.350e-02, eta: 1 day, 18:30:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6306, loss_cls: 3.5332, loss: 3.5332 +2024-12-29 23:42:17,940 - pyskl - INFO - Epoch [102][2600/3746] lr: 2.348e-02, eta: 1 day, 18:29:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6455, loss_cls: 3.4887, loss: 3.4887 +2024-12-29 23:43:42,737 - pyskl - INFO - Epoch [102][2700/3746] lr: 2.346e-02, eta: 1 day, 18:27:50, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6584, loss_cls: 3.4398, loss: 3.4398 +2024-12-29 23:45:07,553 - pyskl - INFO - Epoch [102][2800/3746] lr: 2.343e-02, eta: 1 day, 18:26:25, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6452, loss_cls: 3.4922, loss: 3.4922 +2024-12-29 23:46:31,916 - pyskl - INFO - Epoch [102][2900/3746] lr: 2.341e-02, eta: 1 day, 18:25:01, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6522, loss_cls: 3.4513, loss: 3.4513 +2024-12-29 23:47:56,190 - pyskl - INFO - Epoch [102][3000/3746] lr: 2.339e-02, eta: 1 day, 18:23:36, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6461, loss_cls: 3.4791, loss: 3.4791 +2024-12-29 23:49:20,444 - pyskl - INFO - Epoch [102][3100/3746] lr: 2.336e-02, eta: 1 day, 18:22:11, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6450, loss_cls: 3.4734, loss: 3.4734 +2024-12-29 23:50:45,589 - pyskl - INFO - Epoch [102][3200/3746] lr: 2.334e-02, eta: 1 day, 18:20:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6475, loss_cls: 3.4817, loss: 3.4817 +2024-12-29 23:52:10,798 - pyskl - INFO - Epoch [102][3300/3746] lr: 2.331e-02, eta: 1 day, 18:19:23, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3741, top5_acc: 0.6492, loss_cls: 3.4987, loss: 3.4987 +2024-12-29 23:53:36,183 - pyskl - INFO - Epoch [102][3400/3746] lr: 2.329e-02, eta: 1 day, 18:17:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6425, loss_cls: 3.4937, loss: 3.4937 +2024-12-29 23:55:02,063 - pyskl - INFO - Epoch [102][3500/3746] lr: 2.327e-02, eta: 1 day, 18:16:35, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6525, loss_cls: 3.4752, loss: 3.4752 +2024-12-29 23:56:27,014 - pyskl - INFO - Epoch [102][3600/3746] lr: 2.324e-02, eta: 1 day, 18:15:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6430, loss_cls: 3.4902, loss: 3.4902 +2024-12-29 23:57:52,609 - pyskl - INFO - Epoch [102][3700/3746] lr: 2.322e-02, eta: 1 day, 18:13:47, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6459, loss_cls: 3.4745, loss: 3.4745 +2024-12-29 23:58:34,040 - pyskl - INFO - Saving checkpoint at 102 epochs +2024-12-30 00:00:32,131 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 00:00:32,979 - pyskl - INFO - +top1_acc 0.3133 +top5_acc 0.5686 +2024-12-30 00:00:32,980 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 00:00:33,024 - pyskl - INFO - +mean_acc 0.3132 +2024-12-30 00:00:33,029 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_100.pth was removed +2024-12-30 00:00:33,324 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_102.pth. +2024-12-30 00:00:33,324 - pyskl - INFO - Best top1_acc is 0.3133 at 102 epoch. +2024-12-30 00:00:33,340 - pyskl - INFO - Epoch(val) [102][309] top1_acc: 0.3133, top5_acc: 0.5686, mean_class_accuracy: 0.3132 +2024-12-30 00:04:42,144 - pyskl - INFO - Epoch [103][100/3746] lr: 2.319e-02, eta: 1 day, 18:12:42, time: 2.488, data_time: 1.460, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6573, loss_cls: 3.4135, loss: 3.4135 +2024-12-30 00:06:07,509 - pyskl - INFO - Epoch [103][200/3746] lr: 2.316e-02, eta: 1 day, 18:11:18, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3898, top5_acc: 0.6587, loss_cls: 3.4389, loss: 3.4389 +2024-12-30 00:07:33,339 - pyskl - INFO - Epoch [103][300/3746] lr: 2.314e-02, eta: 1 day, 18:09:54, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3933, top5_acc: 0.6639, loss_cls: 3.3793, loss: 3.3793 +2024-12-30 00:08:59,012 - pyskl - INFO - Epoch [103][400/3746] lr: 2.311e-02, eta: 1 day, 18:08:30, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6587, loss_cls: 3.4051, loss: 3.4051 +2024-12-30 00:10:24,448 - pyskl - INFO - Epoch [103][500/3746] lr: 2.309e-02, eta: 1 day, 18:07:06, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6570, loss_cls: 3.4357, loss: 3.4357 +2024-12-30 00:11:49,924 - pyskl - INFO - Epoch [103][600/3746] lr: 2.307e-02, eta: 1 day, 18:05:42, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6606, loss_cls: 3.4060, loss: 3.4060 +2024-12-30 00:13:14,205 - pyskl - INFO - Epoch [103][700/3746] lr: 2.304e-02, eta: 1 day, 18:04:17, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6580, loss_cls: 3.4025, loss: 3.4025 +2024-12-30 00:14:39,079 - pyskl - INFO - Epoch [103][800/3746] lr: 2.302e-02, eta: 1 day, 18:02:53, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6600, loss_cls: 3.3991, loss: 3.3991 +2024-12-30 00:16:03,864 - pyskl - INFO - Epoch [103][900/3746] lr: 2.300e-02, eta: 1 day, 18:01:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6584, loss_cls: 3.3820, loss: 3.3820 +2024-12-30 00:17:28,607 - pyskl - INFO - Epoch [103][1000/3746] lr: 2.297e-02, eta: 1 day, 18:00:04, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6542, loss_cls: 3.4338, loss: 3.4338 +2024-12-30 00:18:53,449 - pyskl - INFO - Epoch [103][1100/3746] lr: 2.295e-02, eta: 1 day, 17:58:39, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6495, loss_cls: 3.4635, loss: 3.4635 +2024-12-30 00:20:17,959 - pyskl - INFO - Epoch [103][1200/3746] lr: 2.293e-02, eta: 1 day, 17:57:15, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6455, loss_cls: 3.4853, loss: 3.4853 +2024-12-30 00:21:42,258 - pyskl - INFO - Epoch [103][1300/3746] lr: 2.290e-02, eta: 1 day, 17:55:50, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6431, loss_cls: 3.4951, loss: 3.4951 +2024-12-30 00:23:06,684 - pyskl - INFO - Epoch [103][1400/3746] lr: 2.288e-02, eta: 1 day, 17:54:25, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6600, loss_cls: 3.4099, loss: 3.4099 +2024-12-30 00:24:31,422 - pyskl - INFO - Epoch [103][1500/3746] lr: 2.286e-02, eta: 1 day, 17:53:01, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6613, loss_cls: 3.4080, loss: 3.4080 +2024-12-30 00:25:56,280 - pyskl - INFO - Epoch [103][1600/3746] lr: 2.283e-02, eta: 1 day, 17:51:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6455, loss_cls: 3.4355, loss: 3.4355 +2024-12-30 00:27:20,571 - pyskl - INFO - Epoch [103][1700/3746] lr: 2.281e-02, eta: 1 day, 17:50:12, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6422, loss_cls: 3.4925, loss: 3.4925 +2024-12-30 00:28:45,402 - pyskl - INFO - Epoch [103][1800/3746] lr: 2.279e-02, eta: 1 day, 17:48:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6519, loss_cls: 3.4657, loss: 3.4657 +2024-12-30 00:30:09,773 - pyskl - INFO - Epoch [103][1900/3746] lr: 2.276e-02, eta: 1 day, 17:47:23, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6509, loss_cls: 3.4559, loss: 3.4559 +2024-12-30 00:31:34,200 - pyskl - INFO - Epoch [103][2000/3746] lr: 2.274e-02, eta: 1 day, 17:45:58, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6541, loss_cls: 3.4290, loss: 3.4290 +2024-12-30 00:32:58,802 - pyskl - INFO - Epoch [103][2100/3746] lr: 2.272e-02, eta: 1 day, 17:44:33, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6597, loss_cls: 3.3866, loss: 3.3866 +2024-12-30 00:34:23,217 - pyskl - INFO - Epoch [103][2200/3746] lr: 2.269e-02, eta: 1 day, 17:43:09, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6458, loss_cls: 3.4372, loss: 3.4372 +2024-12-30 00:35:47,557 - pyskl - INFO - Epoch [103][2300/3746] lr: 2.267e-02, eta: 1 day, 17:41:44, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6423, loss_cls: 3.4833, loss: 3.4833 +2024-12-30 00:37:11,944 - pyskl - INFO - Epoch [103][2400/3746] lr: 2.264e-02, eta: 1 day, 17:40:19, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6497, loss_cls: 3.4483, loss: 3.4483 +2024-12-30 00:38:37,138 - pyskl - INFO - Epoch [103][2500/3746] lr: 2.262e-02, eta: 1 day, 17:38:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6538, loss_cls: 3.4752, loss: 3.4752 +2024-12-30 00:40:01,691 - pyskl - INFO - Epoch [103][2600/3746] lr: 2.260e-02, eta: 1 day, 17:37:31, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6492, loss_cls: 3.4480, loss: 3.4480 +2024-12-30 00:41:26,330 - pyskl - INFO - Epoch [103][2700/3746] lr: 2.257e-02, eta: 1 day, 17:36:06, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6450, loss_cls: 3.4594, loss: 3.4594 +2024-12-30 00:42:52,362 - pyskl - INFO - Epoch [103][2800/3746] lr: 2.255e-02, eta: 1 day, 17:34:42, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6444, loss_cls: 3.4778, loss: 3.4778 +2024-12-30 00:44:17,403 - pyskl - INFO - Epoch [103][2900/3746] lr: 2.253e-02, eta: 1 day, 17:33:18, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3908, top5_acc: 0.6478, loss_cls: 3.4391, loss: 3.4391 +2024-12-30 00:45:42,703 - pyskl - INFO - Epoch [103][3000/3746] lr: 2.250e-02, eta: 1 day, 17:31:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6331, loss_cls: 3.4887, loss: 3.4887 +2024-12-30 00:47:06,704 - pyskl - INFO - Epoch [103][3100/3746] lr: 2.248e-02, eta: 1 day, 17:30:29, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6366, loss_cls: 3.4972, loss: 3.4972 +2024-12-30 00:48:31,522 - pyskl - INFO - Epoch [103][3200/3746] lr: 2.246e-02, eta: 1 day, 17:29:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6578, loss_cls: 3.4177, loss: 3.4177 +2024-12-30 00:49:56,294 - pyskl - INFO - Epoch [103][3300/3746] lr: 2.243e-02, eta: 1 day, 17:27:40, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3859, top5_acc: 0.6469, loss_cls: 3.4507, loss: 3.4507 +2024-12-30 00:51:21,209 - pyskl - INFO - Epoch [103][3400/3746] lr: 2.241e-02, eta: 1 day, 17:26:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6486, loss_cls: 3.4652, loss: 3.4652 +2024-12-30 00:52:46,211 - pyskl - INFO - Epoch [103][3500/3746] lr: 2.239e-02, eta: 1 day, 17:24:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6308, loss_cls: 3.5517, loss: 3.5517 +2024-12-30 00:54:11,168 - pyskl - INFO - Epoch [103][3600/3746] lr: 2.236e-02, eta: 1 day, 17:23:27, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3831, top5_acc: 0.6459, loss_cls: 3.4469, loss: 3.4469 +2024-12-30 00:55:36,439 - pyskl - INFO - Epoch [103][3700/3746] lr: 2.234e-02, eta: 1 day, 17:22:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3837, top5_acc: 0.6450, loss_cls: 3.4504, loss: 3.4504 +2024-12-30 00:56:16,986 - pyskl - INFO - Saving checkpoint at 103 epochs +2024-12-30 00:58:15,066 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 00:58:15,857 - pyskl - INFO - +top1_acc 0.3227 +top5_acc 0.5829 +2024-12-30 00:58:15,858 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 00:58:15,900 - pyskl - INFO - +mean_acc 0.3226 +2024-12-30 00:58:15,905 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_102.pth was removed +2024-12-30 00:58:16,182 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_103.pth. +2024-12-30 00:58:16,183 - pyskl - INFO - Best top1_acc is 0.3227 at 103 epoch. +2024-12-30 00:58:16,195 - pyskl - INFO - Epoch(val) [103][309] top1_acc: 0.3227, top5_acc: 0.5829, mean_class_accuracy: 0.3226 +2024-12-30 01:02:24,809 - pyskl - INFO - Epoch [104][100/3746] lr: 2.231e-02, eta: 1 day, 17:20:56, time: 2.486, data_time: 1.451, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6641, loss_cls: 3.3780, loss: 3.3780 +2024-12-30 01:03:50,481 - pyskl - INFO - Epoch [104][200/3746] lr: 2.228e-02, eta: 1 day, 17:19:32, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6591, loss_cls: 3.4256, loss: 3.4256 +2024-12-30 01:05:15,856 - pyskl - INFO - Epoch [104][300/3746] lr: 2.226e-02, eta: 1 day, 17:18:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6681, loss_cls: 3.3570, loss: 3.3570 +2024-12-30 01:06:41,347 - pyskl - INFO - Epoch [104][400/3746] lr: 2.224e-02, eta: 1 day, 17:16:44, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3945, top5_acc: 0.6620, loss_cls: 3.3986, loss: 3.3986 +2024-12-30 01:08:07,033 - pyskl - INFO - Epoch [104][500/3746] lr: 2.221e-02, eta: 1 day, 17:15:19, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3989, top5_acc: 0.6592, loss_cls: 3.3639, loss: 3.3639 +2024-12-30 01:09:32,913 - pyskl - INFO - Epoch [104][600/3746] lr: 2.219e-02, eta: 1 day, 17:13:55, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.3980, top5_acc: 0.6584, loss_cls: 3.3714, loss: 3.3714 +2024-12-30 01:10:57,845 - pyskl - INFO - Epoch [104][700/3746] lr: 2.217e-02, eta: 1 day, 17:12:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3923, top5_acc: 0.6509, loss_cls: 3.4140, loss: 3.4140 +2024-12-30 01:12:22,179 - pyskl - INFO - Epoch [104][800/3746] lr: 2.214e-02, eta: 1 day, 17:11:06, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6586, loss_cls: 3.3999, loss: 3.3999 +2024-12-30 01:13:46,771 - pyskl - INFO - Epoch [104][900/3746] lr: 2.212e-02, eta: 1 day, 17:09:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6594, loss_cls: 3.4071, loss: 3.4071 +2024-12-30 01:15:10,925 - pyskl - INFO - Epoch [104][1000/3746] lr: 2.210e-02, eta: 1 day, 17:08:17, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6616, loss_cls: 3.4117, loss: 3.4117 +2024-12-30 01:16:35,674 - pyskl - INFO - Epoch [104][1100/3746] lr: 2.208e-02, eta: 1 day, 17:06:52, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6527, loss_cls: 3.4384, loss: 3.4384 +2024-12-30 01:18:00,216 - pyskl - INFO - Epoch [104][1200/3746] lr: 2.205e-02, eta: 1 day, 17:05:28, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3898, top5_acc: 0.6572, loss_cls: 3.3971, loss: 3.3971 +2024-12-30 01:19:24,727 - pyskl - INFO - Epoch [104][1300/3746] lr: 2.203e-02, eta: 1 day, 17:04:03, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6406, loss_cls: 3.4925, loss: 3.4925 +2024-12-30 01:20:49,653 - pyskl - INFO - Epoch [104][1400/3746] lr: 2.201e-02, eta: 1 day, 17:02:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6477, loss_cls: 3.4666, loss: 3.4666 +2024-12-30 01:22:14,173 - pyskl - INFO - Epoch [104][1500/3746] lr: 2.198e-02, eta: 1 day, 17:01:14, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6558, loss_cls: 3.4096, loss: 3.4096 +2024-12-30 01:23:39,383 - pyskl - INFO - Epoch [104][1600/3746] lr: 2.196e-02, eta: 1 day, 16:59:50, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6520, loss_cls: 3.4265, loss: 3.4265 +2024-12-30 01:25:03,986 - pyskl - INFO - Epoch [104][1700/3746] lr: 2.194e-02, eta: 1 day, 16:58:25, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6569, loss_cls: 3.4065, loss: 3.4065 +2024-12-30 01:26:29,325 - pyskl - INFO - Epoch [104][1800/3746] lr: 2.191e-02, eta: 1 day, 16:57:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6539, loss_cls: 3.4447, loss: 3.4447 +2024-12-30 01:27:54,033 - pyskl - INFO - Epoch [104][1900/3746] lr: 2.189e-02, eta: 1 day, 16:55:36, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6547, loss_cls: 3.4470, loss: 3.4470 +2024-12-30 01:29:18,589 - pyskl - INFO - Epoch [104][2000/3746] lr: 2.187e-02, eta: 1 day, 16:54:12, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6589, loss_cls: 3.4384, loss: 3.4384 +2024-12-30 01:30:43,841 - pyskl - INFO - Epoch [104][2100/3746] lr: 2.184e-02, eta: 1 day, 16:52:47, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6417, loss_cls: 3.4741, loss: 3.4741 +2024-12-30 01:32:08,645 - pyskl - INFO - Epoch [104][2200/3746] lr: 2.182e-02, eta: 1 day, 16:51:23, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3933, top5_acc: 0.6572, loss_cls: 3.4253, loss: 3.4253 +2024-12-30 01:33:33,465 - pyskl - INFO - Epoch [104][2300/3746] lr: 2.180e-02, eta: 1 day, 16:49:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6502, loss_cls: 3.4266, loss: 3.4266 +2024-12-30 01:34:58,315 - pyskl - INFO - Epoch [104][2400/3746] lr: 2.177e-02, eta: 1 day, 16:48:34, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3914, top5_acc: 0.6520, loss_cls: 3.4160, loss: 3.4160 +2024-12-30 01:36:23,321 - pyskl - INFO - Epoch [104][2500/3746] lr: 2.175e-02, eta: 1 day, 16:47:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3945, top5_acc: 0.6531, loss_cls: 3.4008, loss: 3.4008 +2024-12-30 01:37:48,042 - pyskl - INFO - Epoch [104][2600/3746] lr: 2.173e-02, eta: 1 day, 16:45:45, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6473, loss_cls: 3.4658, loss: 3.4658 +2024-12-30 01:39:13,357 - pyskl - INFO - Epoch [104][2700/3746] lr: 2.171e-02, eta: 1 day, 16:44:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6475, loss_cls: 3.4350, loss: 3.4350 +2024-12-30 01:40:38,706 - pyskl - INFO - Epoch [104][2800/3746] lr: 2.168e-02, eta: 1 day, 16:42:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6483, loss_cls: 3.4630, loss: 3.4630 +2024-12-30 01:42:03,628 - pyskl - INFO - Epoch [104][2900/3746] lr: 2.166e-02, eta: 1 day, 16:41:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6458, loss_cls: 3.4877, loss: 3.4877 +2024-12-30 01:43:28,421 - pyskl - INFO - Epoch [104][3000/3746] lr: 2.164e-02, eta: 1 day, 16:40:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6445, loss_cls: 3.4700, loss: 3.4700 +2024-12-30 01:44:53,305 - pyskl - INFO - Epoch [104][3100/3746] lr: 2.161e-02, eta: 1 day, 16:38:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6417, loss_cls: 3.4847, loss: 3.4847 +2024-12-30 01:46:17,482 - pyskl - INFO - Epoch [104][3200/3746] lr: 2.159e-02, eta: 1 day, 16:37:18, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3903, top5_acc: 0.6492, loss_cls: 3.4222, loss: 3.4222 +2024-12-30 01:47:41,830 - pyskl - INFO - Epoch [104][3300/3746] lr: 2.157e-02, eta: 1 day, 16:35:53, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6580, loss_cls: 3.4311, loss: 3.4311 +2024-12-30 01:49:06,222 - pyskl - INFO - Epoch [104][3400/3746] lr: 2.154e-02, eta: 1 day, 16:34:29, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6456, loss_cls: 3.4715, loss: 3.4715 +2024-12-30 01:50:30,371 - pyskl - INFO - Epoch [104][3500/3746] lr: 2.152e-02, eta: 1 day, 16:33:04, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6481, loss_cls: 3.4315, loss: 3.4315 +2024-12-30 01:51:54,891 - pyskl - INFO - Epoch [104][3600/3746] lr: 2.150e-02, eta: 1 day, 16:31:39, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6519, loss_cls: 3.4375, loss: 3.4375 +2024-12-30 01:53:19,517 - pyskl - INFO - Epoch [104][3700/3746] lr: 2.148e-02, eta: 1 day, 16:30:15, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6466, loss_cls: 3.4558, loss: 3.4558 +2024-12-30 01:53:59,979 - pyskl - INFO - Saving checkpoint at 104 epochs +2024-12-30 01:55:57,772 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 01:55:58,632 - pyskl - INFO - +top1_acc 0.3193 +top5_acc 0.5748 +2024-12-30 01:55:58,632 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 01:55:58,683 - pyskl - INFO - +mean_acc 0.3190 +2024-12-30 01:55:58,696 - pyskl - INFO - Epoch(val) [104][309] top1_acc: 0.3193, top5_acc: 0.5748, mean_class_accuracy: 0.3190 +2024-12-30 02:00:12,172 - pyskl - INFO - Epoch [105][100/3746] lr: 2.144e-02, eta: 1 day, 16:29:09, time: 2.535, data_time: 1.500, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6734, loss_cls: 3.3513, loss: 3.3513 +2024-12-30 02:01:36,859 - pyskl - INFO - Epoch [105][200/3746] lr: 2.142e-02, eta: 1 day, 16:27:44, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6761, loss_cls: 3.3150, loss: 3.3150 +2024-12-30 02:03:01,561 - pyskl - INFO - Epoch [105][300/3746] lr: 2.140e-02, eta: 1 day, 16:26:19, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3992, top5_acc: 0.6597, loss_cls: 3.3720, loss: 3.3720 +2024-12-30 02:04:26,429 - pyskl - INFO - Epoch [105][400/3746] lr: 2.137e-02, eta: 1 day, 16:24:55, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6694, loss_cls: 3.3347, loss: 3.3347 +2024-12-30 02:05:50,817 - pyskl - INFO - Epoch [105][500/3746] lr: 2.135e-02, eta: 1 day, 16:23:30, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3992, top5_acc: 0.6614, loss_cls: 3.3828, loss: 3.3828 +2024-12-30 02:07:15,170 - pyskl - INFO - Epoch [105][600/3746] lr: 2.133e-02, eta: 1 day, 16:22:05, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6559, loss_cls: 3.4354, loss: 3.4354 +2024-12-30 02:08:40,148 - pyskl - INFO - Epoch [105][700/3746] lr: 2.130e-02, eta: 1 day, 16:20:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6553, loss_cls: 3.4023, loss: 3.4023 +2024-12-30 02:10:05,067 - pyskl - INFO - Epoch [105][800/3746] lr: 2.128e-02, eta: 1 day, 16:19:16, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6641, loss_cls: 3.4101, loss: 3.4101 +2024-12-30 02:11:29,864 - pyskl - INFO - Epoch [105][900/3746] lr: 2.126e-02, eta: 1 day, 16:17:52, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3898, top5_acc: 0.6562, loss_cls: 3.3920, loss: 3.3920 +2024-12-30 02:12:54,846 - pyskl - INFO - Epoch [105][1000/3746] lr: 2.124e-02, eta: 1 day, 16:16:27, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6497, loss_cls: 3.4317, loss: 3.4317 +2024-12-30 02:14:19,060 - pyskl - INFO - Epoch [105][1100/3746] lr: 2.121e-02, eta: 1 day, 16:15:03, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6534, loss_cls: 3.4125, loss: 3.4125 +2024-12-30 02:15:43,250 - pyskl - INFO - Epoch [105][1200/3746] lr: 2.119e-02, eta: 1 day, 16:13:38, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6589, loss_cls: 3.3812, loss: 3.3812 +2024-12-30 02:17:07,663 - pyskl - INFO - Epoch [105][1300/3746] lr: 2.117e-02, eta: 1 day, 16:12:13, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6680, loss_cls: 3.3839, loss: 3.3839 +2024-12-30 02:18:32,669 - pyskl - INFO - Epoch [105][1400/3746] lr: 2.114e-02, eta: 1 day, 16:10:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6608, loss_cls: 3.3822, loss: 3.3822 +2024-12-30 02:19:56,828 - pyskl - INFO - Epoch [105][1500/3746] lr: 2.112e-02, eta: 1 day, 16:09:24, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6491, loss_cls: 3.4807, loss: 3.4807 +2024-12-30 02:21:21,126 - pyskl - INFO - Epoch [105][1600/3746] lr: 2.110e-02, eta: 1 day, 16:07:59, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4016, top5_acc: 0.6648, loss_cls: 3.3518, loss: 3.3518 +2024-12-30 02:22:46,137 - pyskl - INFO - Epoch [105][1700/3746] lr: 2.108e-02, eta: 1 day, 16:06:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6552, loss_cls: 3.4211, loss: 3.4211 +2024-12-30 02:24:10,641 - pyskl - INFO - Epoch [105][1800/3746] lr: 2.105e-02, eta: 1 day, 16:05:10, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6448, loss_cls: 3.4273, loss: 3.4273 +2024-12-30 02:25:34,832 - pyskl - INFO - Epoch [105][1900/3746] lr: 2.103e-02, eta: 1 day, 16:03:45, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3986, top5_acc: 0.6538, loss_cls: 3.4229, loss: 3.4229 +2024-12-30 02:26:59,548 - pyskl - INFO - Epoch [105][2000/3746] lr: 2.101e-02, eta: 1 day, 16:02:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3977, top5_acc: 0.6658, loss_cls: 3.3529, loss: 3.3529 +2024-12-30 02:28:24,243 - pyskl - INFO - Epoch [105][2100/3746] lr: 2.098e-02, eta: 1 day, 16:00:56, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6578, loss_cls: 3.4200, loss: 3.4200 +2024-12-30 02:29:48,672 - pyskl - INFO - Epoch [105][2200/3746] lr: 2.096e-02, eta: 1 day, 15:59:31, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6605, loss_cls: 3.3974, loss: 3.3974 +2024-12-30 02:31:12,954 - pyskl - INFO - Epoch [105][2300/3746] lr: 2.094e-02, eta: 1 day, 15:58:06, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6587, loss_cls: 3.4050, loss: 3.4050 +2024-12-30 02:32:37,197 - pyskl - INFO - Epoch [105][2400/3746] lr: 2.092e-02, eta: 1 day, 15:56:41, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6619, loss_cls: 3.3999, loss: 3.3999 +2024-12-30 02:34:01,298 - pyskl - INFO - Epoch [105][2500/3746] lr: 2.089e-02, eta: 1 day, 15:55:17, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6530, loss_cls: 3.4435, loss: 3.4435 +2024-12-30 02:35:25,307 - pyskl - INFO - Epoch [105][2600/3746] lr: 2.087e-02, eta: 1 day, 15:53:52, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6542, loss_cls: 3.4521, loss: 3.4521 +2024-12-30 02:36:50,061 - pyskl - INFO - Epoch [105][2700/3746] lr: 2.085e-02, eta: 1 day, 15:52:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6647, loss_cls: 3.3699, loss: 3.3699 +2024-12-30 02:38:14,820 - pyskl - INFO - Epoch [105][2800/3746] lr: 2.083e-02, eta: 1 day, 15:51:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6525, loss_cls: 3.4311, loss: 3.4311 +2024-12-30 02:39:39,204 - pyskl - INFO - Epoch [105][2900/3746] lr: 2.080e-02, eta: 1 day, 15:49:38, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6503, loss_cls: 3.4582, loss: 3.4582 +2024-12-30 02:41:03,932 - pyskl - INFO - Epoch [105][3000/3746] lr: 2.078e-02, eta: 1 day, 15:48:13, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6630, loss_cls: 3.4140, loss: 3.4140 +2024-12-30 02:42:28,799 - pyskl - INFO - Epoch [105][3100/3746] lr: 2.076e-02, eta: 1 day, 15:46:49, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6567, loss_cls: 3.4226, loss: 3.4226 +2024-12-30 02:43:53,346 - pyskl - INFO - Epoch [105][3200/3746] lr: 2.073e-02, eta: 1 day, 15:45:24, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6556, loss_cls: 3.4197, loss: 3.4197 +2024-12-30 02:45:18,348 - pyskl - INFO - Epoch [105][3300/3746] lr: 2.071e-02, eta: 1 day, 15:44:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6433, loss_cls: 3.4514, loss: 3.4514 +2024-12-30 02:46:43,395 - pyskl - INFO - Epoch [105][3400/3746] lr: 2.069e-02, eta: 1 day, 15:42:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6516, loss_cls: 3.4205, loss: 3.4205 +2024-12-30 02:48:08,173 - pyskl - INFO - Epoch [105][3500/3746] lr: 2.067e-02, eta: 1 day, 15:41:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6592, loss_cls: 3.4042, loss: 3.4042 +2024-12-30 02:49:32,887 - pyskl - INFO - Epoch [105][3600/3746] lr: 2.064e-02, eta: 1 day, 15:39:46, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6548, loss_cls: 3.4353, loss: 3.4353 +2024-12-30 02:50:57,746 - pyskl - INFO - Epoch [105][3700/3746] lr: 2.062e-02, eta: 1 day, 15:38:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6534, loss_cls: 3.4522, loss: 3.4522 +2024-12-30 02:51:39,046 - pyskl - INFO - Saving checkpoint at 105 epochs +2024-12-30 02:53:37,530 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 02:53:38,534 - pyskl - INFO - +top1_acc 0.3183 +top5_acc 0.5736 +2024-12-30 02:53:38,534 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 02:53:38,575 - pyskl - INFO - +mean_acc 0.3181 +2024-12-30 02:53:38,588 - pyskl - INFO - Epoch(val) [105][309] top1_acc: 0.3183, top5_acc: 0.5736, mean_class_accuracy: 0.3181 +2024-12-30 02:57:54,375 - pyskl - INFO - Epoch [106][100/3746] lr: 2.059e-02, eta: 1 day, 15:37:14, time: 2.558, data_time: 1.527, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6714, loss_cls: 3.3317, loss: 3.3317 +2024-12-30 02:59:20,070 - pyskl - INFO - Epoch [106][200/3746] lr: 2.057e-02, eta: 1 day, 15:35:50, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3947, top5_acc: 0.6622, loss_cls: 3.4097, loss: 3.4097 +2024-12-30 03:00:46,311 - pyskl - INFO - Epoch [106][300/3746] lr: 2.054e-02, eta: 1 day, 15:34:26, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3927, top5_acc: 0.6562, loss_cls: 3.3947, loss: 3.3947 +2024-12-30 03:02:11,707 - pyskl - INFO - Epoch [106][400/3746] lr: 2.052e-02, eta: 1 day, 15:33:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3977, top5_acc: 0.6695, loss_cls: 3.3467, loss: 3.3467 +2024-12-30 03:03:37,246 - pyskl - INFO - Epoch [106][500/3746] lr: 2.050e-02, eta: 1 day, 15:31:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4006, top5_acc: 0.6594, loss_cls: 3.3547, loss: 3.3547 +2024-12-30 03:05:02,862 - pyskl - INFO - Epoch [106][600/3746] lr: 2.048e-02, eta: 1 day, 15:30:13, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4033, top5_acc: 0.6634, loss_cls: 3.3576, loss: 3.3576 +2024-12-30 03:06:28,198 - pyskl - INFO - Epoch [106][700/3746] lr: 2.045e-02, eta: 1 day, 15:28:49, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6614, loss_cls: 3.3836, loss: 3.3836 +2024-12-30 03:07:53,630 - pyskl - INFO - Epoch [106][800/3746] lr: 2.043e-02, eta: 1 day, 15:27:25, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6627, loss_cls: 3.3696, loss: 3.3696 +2024-12-30 03:09:19,476 - pyskl - INFO - Epoch [106][900/3746] lr: 2.041e-02, eta: 1 day, 15:26:00, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3941, top5_acc: 0.6558, loss_cls: 3.4120, loss: 3.4120 +2024-12-30 03:10:44,556 - pyskl - INFO - Epoch [106][1000/3746] lr: 2.039e-02, eta: 1 day, 15:24:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6503, loss_cls: 3.3645, loss: 3.3645 +2024-12-30 03:12:09,297 - pyskl - INFO - Epoch [106][1100/3746] lr: 2.036e-02, eta: 1 day, 15:23:11, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6652, loss_cls: 3.3756, loss: 3.3756 +2024-12-30 03:13:34,223 - pyskl - INFO - Epoch [106][1200/3746] lr: 2.034e-02, eta: 1 day, 15:21:47, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6497, loss_cls: 3.4245, loss: 3.4245 +2024-12-30 03:14:59,252 - pyskl - INFO - Epoch [106][1300/3746] lr: 2.032e-02, eta: 1 day, 15:20:22, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6534, loss_cls: 3.4169, loss: 3.4169 +2024-12-30 03:16:24,411 - pyskl - INFO - Epoch [106][1400/3746] lr: 2.030e-02, eta: 1 day, 15:18:58, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6572, loss_cls: 3.4051, loss: 3.4051 +2024-12-30 03:17:49,187 - pyskl - INFO - Epoch [106][1500/3746] lr: 2.027e-02, eta: 1 day, 15:17:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6675, loss_cls: 3.3670, loss: 3.3670 +2024-12-30 03:19:13,667 - pyskl - INFO - Epoch [106][1600/3746] lr: 2.025e-02, eta: 1 day, 15:16:08, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6538, loss_cls: 3.4373, loss: 3.4373 +2024-12-30 03:20:37,841 - pyskl - INFO - Epoch [106][1700/3746] lr: 2.023e-02, eta: 1 day, 15:14:44, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6542, loss_cls: 3.4010, loss: 3.4010 +2024-12-30 03:22:02,028 - pyskl - INFO - Epoch [106][1800/3746] lr: 2.021e-02, eta: 1 day, 15:13:19, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6700, loss_cls: 3.3294, loss: 3.3294 +2024-12-30 03:23:26,541 - pyskl - INFO - Epoch [106][1900/3746] lr: 2.018e-02, eta: 1 day, 15:11:54, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6514, loss_cls: 3.4248, loss: 3.4248 +2024-12-30 03:24:51,353 - pyskl - INFO - Epoch [106][2000/3746] lr: 2.016e-02, eta: 1 day, 15:10:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6569, loss_cls: 3.3757, loss: 3.3757 +2024-12-30 03:26:16,089 - pyskl - INFO - Epoch [106][2100/3746] lr: 2.014e-02, eta: 1 day, 15:09:05, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6552, loss_cls: 3.4452, loss: 3.4452 +2024-12-30 03:27:40,879 - pyskl - INFO - Epoch [106][2200/3746] lr: 2.012e-02, eta: 1 day, 15:07:40, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6605, loss_cls: 3.3967, loss: 3.3967 +2024-12-30 03:29:05,444 - pyskl - INFO - Epoch [106][2300/3746] lr: 2.009e-02, eta: 1 day, 15:06:15, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6591, loss_cls: 3.4233, loss: 3.4233 +2024-12-30 03:30:29,746 - pyskl - INFO - Epoch [106][2400/3746] lr: 2.007e-02, eta: 1 day, 15:04:51, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3972, top5_acc: 0.6583, loss_cls: 3.3713, loss: 3.3713 +2024-12-30 03:31:54,651 - pyskl - INFO - Epoch [106][2500/3746] lr: 2.005e-02, eta: 1 day, 15:03:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3941, top5_acc: 0.6578, loss_cls: 3.4189, loss: 3.4189 +2024-12-30 03:33:18,905 - pyskl - INFO - Epoch [106][2600/3746] lr: 2.003e-02, eta: 1 day, 15:02:01, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6575, loss_cls: 3.4014, loss: 3.4014 +2024-12-30 03:34:43,205 - pyskl - INFO - Epoch [106][2700/3746] lr: 2.000e-02, eta: 1 day, 15:00:36, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6605, loss_cls: 3.3717, loss: 3.3717 +2024-12-30 03:36:07,943 - pyskl - INFO - Epoch [106][2800/3746] lr: 1.998e-02, eta: 1 day, 14:59:12, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6677, loss_cls: 3.3766, loss: 3.3766 +2024-12-30 03:37:32,697 - pyskl - INFO - Epoch [106][2900/3746] lr: 1.996e-02, eta: 1 day, 14:57:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6548, loss_cls: 3.4349, loss: 3.4349 +2024-12-30 03:38:56,976 - pyskl - INFO - Epoch [106][3000/3746] lr: 1.994e-02, eta: 1 day, 14:56:22, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6594, loss_cls: 3.4053, loss: 3.4053 +2024-12-30 03:40:21,400 - pyskl - INFO - Epoch [106][3100/3746] lr: 1.991e-02, eta: 1 day, 14:54:57, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3994, top5_acc: 0.6700, loss_cls: 3.3477, loss: 3.3477 +2024-12-30 03:41:45,577 - pyskl - INFO - Epoch [106][3200/3746] lr: 1.989e-02, eta: 1 day, 14:53:33, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6623, loss_cls: 3.3984, loss: 3.3984 +2024-12-30 03:43:10,411 - pyskl - INFO - Epoch [106][3300/3746] lr: 1.987e-02, eta: 1 day, 14:52:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6587, loss_cls: 3.4222, loss: 3.4222 +2024-12-30 03:44:35,151 - pyskl - INFO - Epoch [106][3400/3746] lr: 1.985e-02, eta: 1 day, 14:50:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6575, loss_cls: 3.3651, loss: 3.3651 +2024-12-30 03:45:59,912 - pyskl - INFO - Epoch [106][3500/3746] lr: 1.983e-02, eta: 1 day, 14:49:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6577, loss_cls: 3.3943, loss: 3.3943 +2024-12-30 03:47:24,973 - pyskl - INFO - Epoch [106][3600/3746] lr: 1.980e-02, eta: 1 day, 14:47:54, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6500, loss_cls: 3.4270, loss: 3.4270 +2024-12-30 03:48:49,475 - pyskl - INFO - Epoch [106][3700/3746] lr: 1.978e-02, eta: 1 day, 14:46:29, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6647, loss_cls: 3.3629, loss: 3.3629 +2024-12-30 03:49:30,246 - pyskl - INFO - Saving checkpoint at 106 epochs +2024-12-30 03:51:28,559 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 03:51:29,260 - pyskl - INFO - +top1_acc 0.3403 +top5_acc 0.6018 +2024-12-30 03:51:29,260 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 03:51:29,304 - pyskl - INFO - +mean_acc 0.3399 +2024-12-30 03:51:29,309 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_103.pth was removed +2024-12-30 03:51:29,575 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_106.pth. +2024-12-30 03:51:29,575 - pyskl - INFO - Best top1_acc is 0.3403 at 106 epoch. +2024-12-30 03:51:29,592 - pyskl - INFO - Epoch(val) [106][309] top1_acc: 0.3403, top5_acc: 0.6018, mean_class_accuracy: 0.3399 +2024-12-30 03:55:36,524 - pyskl - INFO - Epoch [107][100/3746] lr: 1.975e-02, eta: 1 day, 14:45:17, time: 2.469, data_time: 1.443, memory: 15990, top1_acc: 0.4044, top5_acc: 0.6716, loss_cls: 3.3245, loss: 3.3245 +2024-12-30 03:57:02,084 - pyskl - INFO - Epoch [107][200/3746] lr: 1.973e-02, eta: 1 day, 14:43:53, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4088, top5_acc: 0.6761, loss_cls: 3.2920, loss: 3.2920 +2024-12-30 03:58:26,784 - pyskl - INFO - Epoch [107][300/3746] lr: 1.970e-02, eta: 1 day, 14:42:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6731, loss_cls: 3.3357, loss: 3.3357 +2024-12-30 03:59:51,537 - pyskl - INFO - Epoch [107][400/3746] lr: 1.968e-02, eta: 1 day, 14:41:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6681, loss_cls: 3.3021, loss: 3.3021 +2024-12-30 04:01:16,374 - pyskl - INFO - Epoch [107][500/3746] lr: 1.966e-02, eta: 1 day, 14:39:39, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6595, loss_cls: 3.3657, loss: 3.3657 +2024-12-30 04:02:41,478 - pyskl - INFO - Epoch [107][600/3746] lr: 1.964e-02, eta: 1 day, 14:38:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6623, loss_cls: 3.3917, loss: 3.3917 +2024-12-30 04:04:06,306 - pyskl - INFO - Epoch [107][700/3746] lr: 1.961e-02, eta: 1 day, 14:36:49, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4066, top5_acc: 0.6672, loss_cls: 3.3577, loss: 3.3577 +2024-12-30 04:05:31,223 - pyskl - INFO - Epoch [107][800/3746] lr: 1.959e-02, eta: 1 day, 14:35:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6684, loss_cls: 3.3220, loss: 3.3220 +2024-12-30 04:06:55,818 - pyskl - INFO - Epoch [107][900/3746] lr: 1.957e-02, eta: 1 day, 14:34:00, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6589, loss_cls: 3.3990, loss: 3.3990 +2024-12-30 04:08:21,070 - pyskl - INFO - Epoch [107][1000/3746] lr: 1.955e-02, eta: 1 day, 14:32:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4070, top5_acc: 0.6713, loss_cls: 3.3084, loss: 3.3084 +2024-12-30 04:09:45,920 - pyskl - INFO - Epoch [107][1100/3746] lr: 1.953e-02, eta: 1 day, 14:31:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6652, loss_cls: 3.3469, loss: 3.3469 +2024-12-30 04:11:10,898 - pyskl - INFO - Epoch [107][1200/3746] lr: 1.950e-02, eta: 1 day, 14:29:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6630, loss_cls: 3.3594, loss: 3.3594 +2024-12-30 04:12:35,404 - pyskl - INFO - Epoch [107][1300/3746] lr: 1.948e-02, eta: 1 day, 14:28:22, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6694, loss_cls: 3.3482, loss: 3.3482 +2024-12-30 04:13:59,922 - pyskl - INFO - Epoch [107][1400/3746] lr: 1.946e-02, eta: 1 day, 14:26:57, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6653, loss_cls: 3.3557, loss: 3.3557 +2024-12-30 04:15:24,345 - pyskl - INFO - Epoch [107][1500/3746] lr: 1.944e-02, eta: 1 day, 14:25:32, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6663, loss_cls: 3.3717, loss: 3.3717 +2024-12-30 04:16:49,047 - pyskl - INFO - Epoch [107][1600/3746] lr: 1.942e-02, eta: 1 day, 14:24:07, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6562, loss_cls: 3.4168, loss: 3.4168 +2024-12-30 04:18:13,668 - pyskl - INFO - Epoch [107][1700/3746] lr: 1.939e-02, eta: 1 day, 14:22:43, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6633, loss_cls: 3.3635, loss: 3.3635 +2024-12-30 04:19:38,231 - pyskl - INFO - Epoch [107][1800/3746] lr: 1.937e-02, eta: 1 day, 14:21:18, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6663, loss_cls: 3.3618, loss: 3.3618 +2024-12-30 04:21:03,136 - pyskl - INFO - Epoch [107][1900/3746] lr: 1.935e-02, eta: 1 day, 14:19:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6723, loss_cls: 3.3195, loss: 3.3195 +2024-12-30 04:22:27,708 - pyskl - INFO - Epoch [107][2000/3746] lr: 1.933e-02, eta: 1 day, 14:18:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6617, loss_cls: 3.3623, loss: 3.3623 +2024-12-30 04:23:52,010 - pyskl - INFO - Epoch [107][2100/3746] lr: 1.930e-02, eta: 1 day, 14:17:04, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4006, top5_acc: 0.6700, loss_cls: 3.3504, loss: 3.3504 +2024-12-30 04:25:16,699 - pyskl - INFO - Epoch [107][2200/3746] lr: 1.928e-02, eta: 1 day, 14:15:39, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6653, loss_cls: 3.3732, loss: 3.3732 +2024-12-30 04:26:41,593 - pyskl - INFO - Epoch [107][2300/3746] lr: 1.926e-02, eta: 1 day, 14:14:14, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4006, top5_acc: 0.6623, loss_cls: 3.3679, loss: 3.3679 +2024-12-30 04:28:05,956 - pyskl - INFO - Epoch [107][2400/3746] lr: 1.924e-02, eta: 1 day, 14:12:49, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6583, loss_cls: 3.3945, loss: 3.3945 +2024-12-30 04:29:30,228 - pyskl - INFO - Epoch [107][2500/3746] lr: 1.922e-02, eta: 1 day, 14:11:25, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6561, loss_cls: 3.3667, loss: 3.3667 +2024-12-30 04:30:54,798 - pyskl - INFO - Epoch [107][2600/3746] lr: 1.919e-02, eta: 1 day, 14:10:00, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6512, loss_cls: 3.4653, loss: 3.4653 +2024-12-30 04:32:19,335 - pyskl - INFO - Epoch [107][2700/3746] lr: 1.917e-02, eta: 1 day, 14:08:35, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6587, loss_cls: 3.4150, loss: 3.4150 +2024-12-30 04:33:43,788 - pyskl - INFO - Epoch [107][2800/3746] lr: 1.915e-02, eta: 1 day, 14:07:10, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6567, loss_cls: 3.4075, loss: 3.4075 +2024-12-30 04:35:08,664 - pyskl - INFO - Epoch [107][2900/3746] lr: 1.913e-02, eta: 1 day, 14:05:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6717, loss_cls: 3.3266, loss: 3.3266 +2024-12-30 04:36:33,397 - pyskl - INFO - Epoch [107][3000/3746] lr: 1.911e-02, eta: 1 day, 14:04:21, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3997, top5_acc: 0.6680, loss_cls: 3.3747, loss: 3.3747 +2024-12-30 04:37:58,089 - pyskl - INFO - Epoch [107][3100/3746] lr: 1.908e-02, eta: 1 day, 14:02:56, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3992, top5_acc: 0.6611, loss_cls: 3.3626, loss: 3.3626 +2024-12-30 04:39:22,981 - pyskl - INFO - Epoch [107][3200/3746] lr: 1.906e-02, eta: 1 day, 14:01:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6609, loss_cls: 3.3845, loss: 3.3845 +2024-12-30 04:40:47,160 - pyskl - INFO - Epoch [107][3300/3746] lr: 1.904e-02, eta: 1 day, 14:00:07, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6606, loss_cls: 3.3830, loss: 3.3830 +2024-12-30 04:42:11,683 - pyskl - INFO - Epoch [107][3400/3746] lr: 1.902e-02, eta: 1 day, 13:58:42, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3933, top5_acc: 0.6538, loss_cls: 3.3856, loss: 3.3856 +2024-12-30 04:43:36,329 - pyskl - INFO - Epoch [107][3500/3746] lr: 1.900e-02, eta: 1 day, 13:57:17, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6650, loss_cls: 3.3613, loss: 3.3613 +2024-12-30 04:45:01,071 - pyskl - INFO - Epoch [107][3600/3746] lr: 1.897e-02, eta: 1 day, 13:55:53, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3914, top5_acc: 0.6538, loss_cls: 3.4480, loss: 3.4480 +2024-12-30 04:46:25,689 - pyskl - INFO - Epoch [107][3700/3746] lr: 1.895e-02, eta: 1 day, 13:54:28, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6586, loss_cls: 3.4403, loss: 3.4403 +2024-12-30 04:47:05,883 - pyskl - INFO - Saving checkpoint at 107 epochs +2024-12-30 04:49:03,780 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 04:49:04,454 - pyskl - INFO - +top1_acc 0.3245 +top5_acc 0.5847 +2024-12-30 04:49:04,454 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 04:49:04,502 - pyskl - INFO - +mean_acc 0.3241 +2024-12-30 04:49:04,514 - pyskl - INFO - Epoch(val) [107][309] top1_acc: 0.3245, top5_acc: 0.5847, mean_class_accuracy: 0.3241 +2024-12-30 04:53:12,236 - pyskl - INFO - Epoch [108][100/3746] lr: 1.892e-02, eta: 1 day, 13:53:14, time: 2.477, data_time: 1.455, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6775, loss_cls: 3.2796, loss: 3.2796 +2024-12-30 04:54:37,200 - pyskl - INFO - Epoch [108][200/3746] lr: 1.890e-02, eta: 1 day, 13:51:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4113, top5_acc: 0.6697, loss_cls: 3.3311, loss: 3.3311 +2024-12-30 04:56:02,112 - pyskl - INFO - Epoch [108][300/3746] lr: 1.888e-02, eta: 1 day, 13:50:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3994, top5_acc: 0.6678, loss_cls: 3.3418, loss: 3.3418 +2024-12-30 04:57:26,982 - pyskl - INFO - Epoch [108][400/3746] lr: 1.886e-02, eta: 1 day, 13:49:00, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6641, loss_cls: 3.3279, loss: 3.3279 +2024-12-30 04:58:51,463 - pyskl - INFO - Epoch [108][500/3746] lr: 1.883e-02, eta: 1 day, 13:47:35, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4062, top5_acc: 0.6661, loss_cls: 3.3433, loss: 3.3433 +2024-12-30 05:00:16,496 - pyskl - INFO - Epoch [108][600/3746] lr: 1.881e-02, eta: 1 day, 13:46:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6686, loss_cls: 3.3271, loss: 3.3271 +2024-12-30 05:01:40,992 - pyskl - INFO - Epoch [108][700/3746] lr: 1.879e-02, eta: 1 day, 13:44:46, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6694, loss_cls: 3.3136, loss: 3.3136 +2024-12-30 05:03:05,764 - pyskl - INFO - Epoch [108][800/3746] lr: 1.877e-02, eta: 1 day, 13:43:21, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6684, loss_cls: 3.3294, loss: 3.3294 +2024-12-30 05:04:30,036 - pyskl - INFO - Epoch [108][900/3746] lr: 1.875e-02, eta: 1 day, 13:41:56, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4016, top5_acc: 0.6661, loss_cls: 3.3608, loss: 3.3608 +2024-12-30 05:05:54,319 - pyskl - INFO - Epoch [108][1000/3746] lr: 1.872e-02, eta: 1 day, 13:40:31, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6686, loss_cls: 3.3180, loss: 3.3180 +2024-12-30 05:07:19,026 - pyskl - INFO - Epoch [108][1100/3746] lr: 1.870e-02, eta: 1 day, 13:39:07, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6573, loss_cls: 3.3752, loss: 3.3752 +2024-12-30 05:08:43,845 - pyskl - INFO - Epoch [108][1200/3746] lr: 1.868e-02, eta: 1 day, 13:37:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6752, loss_cls: 3.3461, loss: 3.3461 +2024-12-30 05:10:08,393 - pyskl - INFO - Epoch [108][1300/3746] lr: 1.866e-02, eta: 1 day, 13:36:17, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6648, loss_cls: 3.3681, loss: 3.3681 +2024-12-30 05:11:32,701 - pyskl - INFO - Epoch [108][1400/3746] lr: 1.864e-02, eta: 1 day, 13:34:52, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6700, loss_cls: 3.3421, loss: 3.3421 +2024-12-30 05:12:57,443 - pyskl - INFO - Epoch [108][1500/3746] lr: 1.862e-02, eta: 1 day, 13:33:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6763, loss_cls: 3.3238, loss: 3.3238 +2024-12-30 05:14:22,500 - pyskl - INFO - Epoch [108][1600/3746] lr: 1.859e-02, eta: 1 day, 13:32:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6619, loss_cls: 3.3579, loss: 3.3579 +2024-12-30 05:15:47,283 - pyskl - INFO - Epoch [108][1700/3746] lr: 1.857e-02, eta: 1 day, 13:30:38, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6650, loss_cls: 3.3705, loss: 3.3705 +2024-12-30 05:17:11,331 - pyskl - INFO - Epoch [108][1800/3746] lr: 1.855e-02, eta: 1 day, 13:29:13, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6592, loss_cls: 3.3849, loss: 3.3849 +2024-12-30 05:18:35,757 - pyskl - INFO - Epoch [108][1900/3746] lr: 1.853e-02, eta: 1 day, 13:27:48, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6675, loss_cls: 3.3239, loss: 3.3239 +2024-12-30 05:19:59,914 - pyskl - INFO - Epoch [108][2000/3746] lr: 1.851e-02, eta: 1 day, 13:26:23, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6678, loss_cls: 3.3167, loss: 3.3167 +2024-12-30 05:21:23,810 - pyskl - INFO - Epoch [108][2100/3746] lr: 1.848e-02, eta: 1 day, 13:24:58, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6756, loss_cls: 3.3332, loss: 3.3332 +2024-12-30 05:22:48,610 - pyskl - INFO - Epoch [108][2200/3746] lr: 1.846e-02, eta: 1 day, 13:23:34, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6672, loss_cls: 3.3540, loss: 3.3540 +2024-12-30 05:24:12,708 - pyskl - INFO - Epoch [108][2300/3746] lr: 1.844e-02, eta: 1 day, 13:22:09, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6717, loss_cls: 3.3056, loss: 3.3056 +2024-12-30 05:25:37,355 - pyskl - INFO - Epoch [108][2400/3746] lr: 1.842e-02, eta: 1 day, 13:20:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4195, top5_acc: 0.6745, loss_cls: 3.2954, loss: 3.2954 +2024-12-30 05:27:01,814 - pyskl - INFO - Epoch [108][2500/3746] lr: 1.840e-02, eta: 1 day, 13:19:19, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3941, top5_acc: 0.6587, loss_cls: 3.4018, loss: 3.4018 +2024-12-30 05:28:26,330 - pyskl - INFO - Epoch [108][2600/3746] lr: 1.838e-02, eta: 1 day, 13:17:54, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6644, loss_cls: 3.3687, loss: 3.3687 +2024-12-30 05:29:51,448 - pyskl - INFO - Epoch [108][2700/3746] lr: 1.835e-02, eta: 1 day, 13:16:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6702, loss_cls: 3.3180, loss: 3.3180 +2024-12-30 05:31:16,084 - pyskl - INFO - Epoch [108][2800/3746] lr: 1.833e-02, eta: 1 day, 13:15:05, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6698, loss_cls: 3.3391, loss: 3.3391 +2024-12-30 05:32:40,163 - pyskl - INFO - Epoch [108][2900/3746] lr: 1.831e-02, eta: 1 day, 13:13:40, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6611, loss_cls: 3.3907, loss: 3.3907 +2024-12-30 05:34:04,408 - pyskl - INFO - Epoch [108][3000/3746] lr: 1.829e-02, eta: 1 day, 13:12:15, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3945, top5_acc: 0.6617, loss_cls: 3.3727, loss: 3.3727 +2024-12-30 05:35:28,769 - pyskl - INFO - Epoch [108][3100/3746] lr: 1.827e-02, eta: 1 day, 13:10:50, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6631, loss_cls: 3.3637, loss: 3.3637 +2024-12-30 05:36:53,357 - pyskl - INFO - Epoch [108][3200/3746] lr: 1.825e-02, eta: 1 day, 13:09:25, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6730, loss_cls: 3.3307, loss: 3.3307 +2024-12-30 05:38:17,486 - pyskl - INFO - Epoch [108][3300/3746] lr: 1.823e-02, eta: 1 day, 13:08:00, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6562, loss_cls: 3.3748, loss: 3.3748 +2024-12-30 05:39:41,760 - pyskl - INFO - Epoch [108][3400/3746] lr: 1.820e-02, eta: 1 day, 13:06:35, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6611, loss_cls: 3.3759, loss: 3.3759 +2024-12-30 05:41:06,470 - pyskl - INFO - Epoch [108][3500/3746] lr: 1.818e-02, eta: 1 day, 13:05:11, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3872, top5_acc: 0.6602, loss_cls: 3.4090, loss: 3.4090 +2024-12-30 05:42:30,848 - pyskl - INFO - Epoch [108][3600/3746] lr: 1.816e-02, eta: 1 day, 13:03:46, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6681, loss_cls: 3.3372, loss: 3.3372 +2024-12-30 05:43:55,308 - pyskl - INFO - Epoch [108][3700/3746] lr: 1.814e-02, eta: 1 day, 13:02:21, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4117, top5_acc: 0.6723, loss_cls: 3.3213, loss: 3.3213 +2024-12-30 05:44:36,081 - pyskl - INFO - Saving checkpoint at 108 epochs +2024-12-30 05:46:34,530 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 05:46:35,243 - pyskl - INFO - +top1_acc 0.3364 +top5_acc 0.5917 +2024-12-30 05:46:35,243 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 05:46:35,291 - pyskl - INFO - +mean_acc 0.3362 +2024-12-30 05:46:35,305 - pyskl - INFO - Epoch(val) [108][309] top1_acc: 0.3364, top5_acc: 0.5917, mean_class_accuracy: 0.3362 +2024-12-30 05:50:42,031 - pyskl - INFO - Epoch [109][100/3746] lr: 1.811e-02, eta: 1 day, 13:01:05, time: 2.467, data_time: 1.449, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6755, loss_cls: 3.2822, loss: 3.2822 +2024-12-30 05:52:06,301 - pyskl - INFO - Epoch [109][200/3746] lr: 1.809e-02, eta: 1 day, 12:59:40, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4113, top5_acc: 0.6742, loss_cls: 3.2718, loss: 3.2718 +2024-12-30 05:53:31,131 - pyskl - INFO - Epoch [109][300/3746] lr: 1.806e-02, eta: 1 day, 12:58:15, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6905, loss_cls: 3.2153, loss: 3.2153 +2024-12-30 05:54:55,746 - pyskl - INFO - Epoch [109][400/3746] lr: 1.804e-02, eta: 1 day, 12:56:51, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4173, top5_acc: 0.6767, loss_cls: 3.2680, loss: 3.2680 +2024-12-30 05:56:20,267 - pyskl - INFO - Epoch [109][500/3746] lr: 1.802e-02, eta: 1 day, 12:55:26, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6714, loss_cls: 3.3339, loss: 3.3339 +2024-12-30 05:57:44,505 - pyskl - INFO - Epoch [109][600/3746] lr: 1.800e-02, eta: 1 day, 12:54:01, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6778, loss_cls: 3.2614, loss: 3.2614 +2024-12-30 05:59:08,697 - pyskl - INFO - Epoch [109][700/3746] lr: 1.798e-02, eta: 1 day, 12:52:36, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6848, loss_cls: 3.2586, loss: 3.2586 +2024-12-30 06:00:34,006 - pyskl - INFO - Epoch [109][800/3746] lr: 1.796e-02, eta: 1 day, 12:51:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6695, loss_cls: 3.3060, loss: 3.3060 +2024-12-30 06:01:58,451 - pyskl - INFO - Epoch [109][900/3746] lr: 1.794e-02, eta: 1 day, 12:49:46, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4009, top5_acc: 0.6655, loss_cls: 3.3591, loss: 3.3591 +2024-12-30 06:03:22,905 - pyskl - INFO - Epoch [109][1000/3746] lr: 1.791e-02, eta: 1 day, 12:48:22, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6620, loss_cls: 3.3880, loss: 3.3880 +2024-12-30 06:04:47,959 - pyskl - INFO - Epoch [109][1100/3746] lr: 1.789e-02, eta: 1 day, 12:46:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6756, loss_cls: 3.3111, loss: 3.3111 +2024-12-30 06:06:12,755 - pyskl - INFO - Epoch [109][1200/3746] lr: 1.787e-02, eta: 1 day, 12:45:32, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6717, loss_cls: 3.3294, loss: 3.3294 +2024-12-30 06:07:37,943 - pyskl - INFO - Epoch [109][1300/3746] lr: 1.785e-02, eta: 1 day, 12:44:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6659, loss_cls: 3.3592, loss: 3.3592 +2024-12-30 06:09:02,243 - pyskl - INFO - Epoch [109][1400/3746] lr: 1.783e-02, eta: 1 day, 12:42:43, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6675, loss_cls: 3.3547, loss: 3.3547 +2024-12-30 06:10:26,937 - pyskl - INFO - Epoch [109][1500/3746] lr: 1.781e-02, eta: 1 day, 12:41:18, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6583, loss_cls: 3.3840, loss: 3.3840 +2024-12-30 06:11:51,943 - pyskl - INFO - Epoch [109][1600/3746] lr: 1.779e-02, eta: 1 day, 12:39:53, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6616, loss_cls: 3.3569, loss: 3.3569 +2024-12-30 06:13:16,337 - pyskl - INFO - Epoch [109][1700/3746] lr: 1.776e-02, eta: 1 day, 12:38:28, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6714, loss_cls: 3.3417, loss: 3.3417 +2024-12-30 06:14:40,601 - pyskl - INFO - Epoch [109][1800/3746] lr: 1.774e-02, eta: 1 day, 12:37:03, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6625, loss_cls: 3.3567, loss: 3.3567 +2024-12-30 06:16:05,714 - pyskl - INFO - Epoch [109][1900/3746] lr: 1.772e-02, eta: 1 day, 12:35:39, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4117, top5_acc: 0.6722, loss_cls: 3.2912, loss: 3.2912 +2024-12-30 06:17:29,897 - pyskl - INFO - Epoch [109][2000/3746] lr: 1.770e-02, eta: 1 day, 12:34:14, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6787, loss_cls: 3.2883, loss: 3.2883 +2024-12-30 06:18:54,675 - pyskl - INFO - Epoch [109][2100/3746] lr: 1.768e-02, eta: 1 day, 12:32:49, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6609, loss_cls: 3.3563, loss: 3.3563 +2024-12-30 06:20:19,319 - pyskl - INFO - Epoch [109][2200/3746] lr: 1.766e-02, eta: 1 day, 12:31:24, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6758, loss_cls: 3.2903, loss: 3.2903 +2024-12-30 06:21:44,383 - pyskl - INFO - Epoch [109][2300/3746] lr: 1.764e-02, eta: 1 day, 12:30:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6631, loss_cls: 3.3424, loss: 3.3424 +2024-12-30 06:23:09,550 - pyskl - INFO - Epoch [109][2400/3746] lr: 1.761e-02, eta: 1 day, 12:28:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3992, top5_acc: 0.6647, loss_cls: 3.3491, loss: 3.3491 +2024-12-30 06:24:34,616 - pyskl - INFO - Epoch [109][2500/3746] lr: 1.759e-02, eta: 1 day, 12:27:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4078, top5_acc: 0.6697, loss_cls: 3.3374, loss: 3.3374 +2024-12-30 06:26:00,206 - pyskl - INFO - Epoch [109][2600/3746] lr: 1.757e-02, eta: 1 day, 12:25:46, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6594, loss_cls: 3.3726, loss: 3.3726 +2024-12-30 06:27:25,405 - pyskl - INFO - Epoch [109][2700/3746] lr: 1.755e-02, eta: 1 day, 12:24:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6730, loss_cls: 3.3200, loss: 3.3200 +2024-12-30 06:28:50,665 - pyskl - INFO - Epoch [109][2800/3746] lr: 1.753e-02, eta: 1 day, 12:22:57, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4088, top5_acc: 0.6680, loss_cls: 3.3292, loss: 3.3292 +2024-12-30 06:30:15,630 - pyskl - INFO - Epoch [109][2900/3746] lr: 1.751e-02, eta: 1 day, 12:21:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6717, loss_cls: 3.3394, loss: 3.3394 +2024-12-30 06:31:40,859 - pyskl - INFO - Epoch [109][3000/3746] lr: 1.749e-02, eta: 1 day, 12:20:07, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4044, top5_acc: 0.6684, loss_cls: 3.3219, loss: 3.3219 +2024-12-30 06:33:06,137 - pyskl - INFO - Epoch [109][3100/3746] lr: 1.747e-02, eta: 1 day, 12:18:43, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6739, loss_cls: 3.3201, loss: 3.3201 +2024-12-30 06:34:31,019 - pyskl - INFO - Epoch [109][3200/3746] lr: 1.744e-02, eta: 1 day, 12:17:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3947, top5_acc: 0.6611, loss_cls: 3.3703, loss: 3.3703 +2024-12-30 06:35:55,872 - pyskl - INFO - Epoch [109][3300/3746] lr: 1.742e-02, eta: 1 day, 12:15:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6613, loss_cls: 3.3774, loss: 3.3774 +2024-12-30 06:37:20,050 - pyskl - INFO - Epoch [109][3400/3746] lr: 1.740e-02, eta: 1 day, 12:14:28, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4098, top5_acc: 0.6755, loss_cls: 3.3409, loss: 3.3409 +2024-12-30 06:38:44,317 - pyskl - INFO - Epoch [109][3500/3746] lr: 1.738e-02, eta: 1 day, 12:13:04, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6619, loss_cls: 3.3825, loss: 3.3825 +2024-12-30 06:40:09,162 - pyskl - INFO - Epoch [109][3600/3746] lr: 1.736e-02, eta: 1 day, 12:11:39, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6669, loss_cls: 3.3655, loss: 3.3655 +2024-12-30 06:41:34,004 - pyskl - INFO - Epoch [109][3700/3746] lr: 1.734e-02, eta: 1 day, 12:10:14, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6622, loss_cls: 3.3601, loss: 3.3601 +2024-12-30 06:42:14,581 - pyskl - INFO - Saving checkpoint at 109 epochs +2024-12-30 06:44:13,940 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 06:44:14,951 - pyskl - INFO - +top1_acc 0.3473 +top5_acc 0.6063 +2024-12-30 06:44:14,952 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 06:44:14,993 - pyskl - INFO - +mean_acc 0.3471 +2024-12-30 06:44:14,997 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_106.pth was removed +2024-12-30 06:44:15,261 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_109.pth. +2024-12-30 06:44:15,261 - pyskl - INFO - Best top1_acc is 0.3473 at 109 epoch. +2024-12-30 06:44:15,273 - pyskl - INFO - Epoch(val) [109][309] top1_acc: 0.3473, top5_acc: 0.6063, mean_class_accuracy: 0.3471 +2024-12-30 06:48:30,668 - pyskl - INFO - Epoch [110][100/3746] lr: 1.731e-02, eta: 1 day, 12:09:00, time: 2.554, data_time: 1.519, memory: 15990, top1_acc: 0.4339, top5_acc: 0.6919, loss_cls: 3.2057, loss: 3.2057 +2024-12-30 06:49:56,091 - pyskl - INFO - Epoch [110][200/3746] lr: 1.729e-02, eta: 1 day, 12:07:35, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6891, loss_cls: 3.2554, loss: 3.2554 +2024-12-30 06:51:20,822 - pyskl - INFO - Epoch [110][300/3746] lr: 1.727e-02, eta: 1 day, 12:06:10, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6863, loss_cls: 3.2428, loss: 3.2428 +2024-12-30 06:52:45,921 - pyskl - INFO - Epoch [110][400/3746] lr: 1.724e-02, eta: 1 day, 12:04:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4164, top5_acc: 0.6795, loss_cls: 3.2532, loss: 3.2532 +2024-12-30 06:54:11,633 - pyskl - INFO - Epoch [110][500/3746] lr: 1.722e-02, eta: 1 day, 12:03:21, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4134, top5_acc: 0.6858, loss_cls: 3.2638, loss: 3.2638 +2024-12-30 06:55:37,118 - pyskl - INFO - Epoch [110][600/3746] lr: 1.720e-02, eta: 1 day, 12:01:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4205, top5_acc: 0.6816, loss_cls: 3.2774, loss: 3.2774 +2024-12-30 06:57:02,144 - pyskl - INFO - Epoch [110][700/3746] lr: 1.718e-02, eta: 1 day, 12:00:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6687, loss_cls: 3.3026, loss: 3.3026 +2024-12-30 06:58:27,002 - pyskl - INFO - Epoch [110][800/3746] lr: 1.716e-02, eta: 1 day, 11:59:07, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6745, loss_cls: 3.2938, loss: 3.2938 +2024-12-30 06:59:51,424 - pyskl - INFO - Epoch [110][900/3746] lr: 1.714e-02, eta: 1 day, 11:57:42, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6794, loss_cls: 3.2586, loss: 3.2586 +2024-12-30 07:01:15,760 - pyskl - INFO - Epoch [110][1000/3746] lr: 1.712e-02, eta: 1 day, 11:56:17, time: 0.843, data_time: 0.001, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6614, loss_cls: 3.3648, loss: 3.3648 +2024-12-30 07:02:40,171 - pyskl - INFO - Epoch [110][1100/3746] lr: 1.710e-02, eta: 1 day, 11:54:53, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6684, loss_cls: 3.3420, loss: 3.3420 +2024-12-30 07:04:04,965 - pyskl - INFO - Epoch [110][1200/3746] lr: 1.708e-02, eta: 1 day, 11:53:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6825, loss_cls: 3.2764, loss: 3.2764 +2024-12-30 07:05:29,360 - pyskl - INFO - Epoch [110][1300/3746] lr: 1.705e-02, eta: 1 day, 11:52:03, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4142, top5_acc: 0.6789, loss_cls: 3.2784, loss: 3.2784 +2024-12-30 07:06:53,162 - pyskl - INFO - Epoch [110][1400/3746] lr: 1.703e-02, eta: 1 day, 11:50:38, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6669, loss_cls: 3.3361, loss: 3.3361 +2024-12-30 07:08:17,707 - pyskl - INFO - Epoch [110][1500/3746] lr: 1.701e-02, eta: 1 day, 11:49:13, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6761, loss_cls: 3.2933, loss: 3.2933 +2024-12-30 07:09:42,864 - pyskl - INFO - Epoch [110][1600/3746] lr: 1.699e-02, eta: 1 day, 11:47:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6797, loss_cls: 3.2631, loss: 3.2631 +2024-12-30 07:11:07,591 - pyskl - INFO - Epoch [110][1700/3746] lr: 1.697e-02, eta: 1 day, 11:46:23, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6739, loss_cls: 3.3024, loss: 3.3024 +2024-12-30 07:12:32,378 - pyskl - INFO - Epoch [110][1800/3746] lr: 1.695e-02, eta: 1 day, 11:44:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4042, top5_acc: 0.6656, loss_cls: 3.3524, loss: 3.3524 +2024-12-30 07:13:56,972 - pyskl - INFO - Epoch [110][1900/3746] lr: 1.693e-02, eta: 1 day, 11:43:34, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3945, top5_acc: 0.6616, loss_cls: 3.3685, loss: 3.3685 +2024-12-30 07:15:21,609 - pyskl - INFO - Epoch [110][2000/3746] lr: 1.691e-02, eta: 1 day, 11:42:09, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6759, loss_cls: 3.2682, loss: 3.2682 +2024-12-30 07:16:45,917 - pyskl - INFO - Epoch [110][2100/3746] lr: 1.689e-02, eta: 1 day, 11:40:44, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6647, loss_cls: 3.3635, loss: 3.3635 +2024-12-30 07:18:10,448 - pyskl - INFO - Epoch [110][2200/3746] lr: 1.687e-02, eta: 1 day, 11:39:19, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6669, loss_cls: 3.3139, loss: 3.3139 +2024-12-30 07:19:35,198 - pyskl - INFO - Epoch [110][2300/3746] lr: 1.685e-02, eta: 1 day, 11:37:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6858, loss_cls: 3.2653, loss: 3.2653 +2024-12-30 07:20:59,832 - pyskl - INFO - Epoch [110][2400/3746] lr: 1.682e-02, eta: 1 day, 11:36:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6636, loss_cls: 3.3934, loss: 3.3934 +2024-12-30 07:22:24,529 - pyskl - INFO - Epoch [110][2500/3746] lr: 1.680e-02, eta: 1 day, 11:35:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6770, loss_cls: 3.2684, loss: 3.2684 +2024-12-30 07:23:48,944 - pyskl - INFO - Epoch [110][2600/3746] lr: 1.678e-02, eta: 1 day, 11:33:40, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6778, loss_cls: 3.3342, loss: 3.3342 +2024-12-30 07:25:13,465 - pyskl - INFO - Epoch [110][2700/3746] lr: 1.676e-02, eta: 1 day, 11:32:15, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4088, top5_acc: 0.6742, loss_cls: 3.2702, loss: 3.2702 +2024-12-30 07:26:38,011 - pyskl - INFO - Epoch [110][2800/3746] lr: 1.674e-02, eta: 1 day, 11:30:50, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4066, top5_acc: 0.6673, loss_cls: 3.3408, loss: 3.3408 +2024-12-30 07:28:02,593 - pyskl - INFO - Epoch [110][2900/3746] lr: 1.672e-02, eta: 1 day, 11:29:25, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6728, loss_cls: 3.3124, loss: 3.3124 +2024-12-30 07:29:27,196 - pyskl - INFO - Epoch [110][3000/3746] lr: 1.670e-02, eta: 1 day, 11:28:00, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6647, loss_cls: 3.3441, loss: 3.3441 +2024-12-30 07:30:51,890 - pyskl - INFO - Epoch [110][3100/3746] lr: 1.668e-02, eta: 1 day, 11:26:35, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6733, loss_cls: 3.2915, loss: 3.2915 +2024-12-30 07:32:16,087 - pyskl - INFO - Epoch [110][3200/3746] lr: 1.666e-02, eta: 1 day, 11:25:10, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6673, loss_cls: 3.3417, loss: 3.3417 +2024-12-30 07:33:39,908 - pyskl - INFO - Epoch [110][3300/3746] lr: 1.664e-02, eta: 1 day, 11:23:45, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4147, top5_acc: 0.6597, loss_cls: 3.3532, loss: 3.3532 +2024-12-30 07:35:04,362 - pyskl - INFO - Epoch [110][3400/3746] lr: 1.662e-02, eta: 1 day, 11:22:20, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6678, loss_cls: 3.3010, loss: 3.3010 +2024-12-30 07:36:28,164 - pyskl - INFO - Epoch [110][3500/3746] lr: 1.659e-02, eta: 1 day, 11:20:55, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6664, loss_cls: 3.3730, loss: 3.3730 +2024-12-30 07:37:52,442 - pyskl - INFO - Epoch [110][3600/3746] lr: 1.657e-02, eta: 1 day, 11:19:30, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3972, top5_acc: 0.6677, loss_cls: 3.3241, loss: 3.3241 +2024-12-30 07:39:17,017 - pyskl - INFO - Epoch [110][3700/3746] lr: 1.655e-02, eta: 1 day, 11:18:05, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6614, loss_cls: 3.3419, loss: 3.3419 +2024-12-30 07:39:57,539 - pyskl - INFO - Saving checkpoint at 110 epochs +2024-12-30 07:41:55,344 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 07:41:56,076 - pyskl - INFO - +top1_acc 0.3453 +top5_acc 0.6032 +2024-12-30 07:41:56,076 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 07:41:56,124 - pyskl - INFO - +mean_acc 0.3451 +2024-12-30 07:41:56,138 - pyskl - INFO - Epoch(val) [110][309] top1_acc: 0.3453, top5_acc: 0.6032, mean_class_accuracy: 0.3451 +2024-12-30 07:46:10,093 - pyskl - INFO - Epoch [111][100/3746] lr: 1.652e-02, eta: 1 day, 11:16:49, time: 2.539, data_time: 1.507, memory: 15990, top1_acc: 0.4238, top5_acc: 0.6827, loss_cls: 3.2518, loss: 3.2518 +2024-12-30 07:47:36,157 - pyskl - INFO - Epoch [111][200/3746] lr: 1.650e-02, eta: 1 day, 11:15:24, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4275, top5_acc: 0.6867, loss_cls: 3.2070, loss: 3.2070 +2024-12-30 07:49:01,080 - pyskl - INFO - Epoch [111][300/3746] lr: 1.648e-02, eta: 1 day, 11:13:59, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6809, loss_cls: 3.2667, loss: 3.2667 +2024-12-30 07:50:26,383 - pyskl - INFO - Epoch [111][400/3746] lr: 1.646e-02, eta: 1 day, 11:12:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6716, loss_cls: 3.3073, loss: 3.3073 +2024-12-30 07:51:51,947 - pyskl - INFO - Epoch [111][500/3746] lr: 1.644e-02, eta: 1 day, 11:11:10, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6891, loss_cls: 3.2377, loss: 3.2377 +2024-12-30 07:53:17,263 - pyskl - INFO - Epoch [111][600/3746] lr: 1.642e-02, eta: 1 day, 11:09:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6697, loss_cls: 3.2933, loss: 3.2933 +2024-12-30 07:54:43,089 - pyskl - INFO - Epoch [111][700/3746] lr: 1.640e-02, eta: 1 day, 11:08:21, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4277, top5_acc: 0.6842, loss_cls: 3.2282, loss: 3.2282 +2024-12-30 07:56:08,350 - pyskl - INFO - Epoch [111][800/3746] lr: 1.638e-02, eta: 1 day, 11:06:57, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4183, top5_acc: 0.6791, loss_cls: 3.2789, loss: 3.2789 +2024-12-30 07:57:32,960 - pyskl - INFO - Epoch [111][900/3746] lr: 1.636e-02, eta: 1 day, 11:05:32, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6773, loss_cls: 3.2585, loss: 3.2585 +2024-12-30 07:58:57,732 - pyskl - INFO - Epoch [111][1000/3746] lr: 1.634e-02, eta: 1 day, 11:04:07, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.4178, top5_acc: 0.6853, loss_cls: 3.2262, loss: 3.2262 +2024-12-30 08:00:22,106 - pyskl - INFO - Epoch [111][1100/3746] lr: 1.632e-02, eta: 1 day, 11:02:42, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4142, top5_acc: 0.6795, loss_cls: 3.2623, loss: 3.2623 +2024-12-30 08:01:47,233 - pyskl - INFO - Epoch [111][1200/3746] lr: 1.630e-02, eta: 1 day, 11:01:17, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6692, loss_cls: 3.3050, loss: 3.3050 +2024-12-30 08:03:12,222 - pyskl - INFO - Epoch [111][1300/3746] lr: 1.627e-02, eta: 1 day, 10:59:52, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4259, top5_acc: 0.6887, loss_cls: 3.2288, loss: 3.2288 +2024-12-30 08:04:36,750 - pyskl - INFO - Epoch [111][1400/3746] lr: 1.625e-02, eta: 1 day, 10:58:27, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6730, loss_cls: 3.2832, loss: 3.2832 +2024-12-30 08:06:01,457 - pyskl - INFO - Epoch [111][1500/3746] lr: 1.623e-02, eta: 1 day, 10:57:03, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6758, loss_cls: 3.3033, loss: 3.3033 +2024-12-30 08:07:25,774 - pyskl - INFO - Epoch [111][1600/3746] lr: 1.621e-02, eta: 1 day, 10:55:38, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4086, top5_acc: 0.6787, loss_cls: 3.2919, loss: 3.2919 +2024-12-30 08:08:50,279 - pyskl - INFO - Epoch [111][1700/3746] lr: 1.619e-02, eta: 1 day, 10:54:13, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4214, top5_acc: 0.6770, loss_cls: 3.2424, loss: 3.2424 +2024-12-30 08:10:14,233 - pyskl - INFO - Epoch [111][1800/3746] lr: 1.617e-02, eta: 1 day, 10:52:47, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6722, loss_cls: 3.3040, loss: 3.3040 +2024-12-30 08:11:39,100 - pyskl - INFO - Epoch [111][1900/3746] lr: 1.615e-02, eta: 1 day, 10:51:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6752, loss_cls: 3.2854, loss: 3.2854 +2024-12-30 08:13:03,408 - pyskl - INFO - Epoch [111][2000/3746] lr: 1.613e-02, eta: 1 day, 10:49:58, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6745, loss_cls: 3.3184, loss: 3.3184 +2024-12-30 08:14:28,057 - pyskl - INFO - Epoch [111][2100/3746] lr: 1.611e-02, eta: 1 day, 10:48:33, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6698, loss_cls: 3.3404, loss: 3.3404 +2024-12-30 08:15:52,469 - pyskl - INFO - Epoch [111][2200/3746] lr: 1.609e-02, eta: 1 day, 10:47:08, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6736, loss_cls: 3.2938, loss: 3.2938 +2024-12-30 08:17:17,160 - pyskl - INFO - Epoch [111][2300/3746] lr: 1.607e-02, eta: 1 day, 10:45:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6687, loss_cls: 3.2930, loss: 3.2930 +2024-12-30 08:18:42,347 - pyskl - INFO - Epoch [111][2400/3746] lr: 1.605e-02, eta: 1 day, 10:44:18, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4181, top5_acc: 0.6792, loss_cls: 3.2816, loss: 3.2816 +2024-12-30 08:20:06,884 - pyskl - INFO - Epoch [111][2500/3746] lr: 1.603e-02, eta: 1 day, 10:42:53, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6737, loss_cls: 3.3208, loss: 3.3208 +2024-12-30 08:21:32,055 - pyskl - INFO - Epoch [111][2600/3746] lr: 1.601e-02, eta: 1 day, 10:41:29, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4134, top5_acc: 0.6773, loss_cls: 3.3042, loss: 3.3042 +2024-12-30 08:22:56,720 - pyskl - INFO - Epoch [111][2700/3746] lr: 1.599e-02, eta: 1 day, 10:40:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4153, top5_acc: 0.6795, loss_cls: 3.2666, loss: 3.2666 +2024-12-30 08:24:21,567 - pyskl - INFO - Epoch [111][2800/3746] lr: 1.597e-02, eta: 1 day, 10:38:39, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4119, top5_acc: 0.6759, loss_cls: 3.2901, loss: 3.2901 +2024-12-30 08:25:46,788 - pyskl - INFO - Epoch [111][2900/3746] lr: 1.595e-02, eta: 1 day, 10:37:14, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6791, loss_cls: 3.2833, loss: 3.2833 +2024-12-30 08:27:11,222 - pyskl - INFO - Epoch [111][3000/3746] lr: 1.593e-02, eta: 1 day, 10:35:49, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4134, top5_acc: 0.6722, loss_cls: 3.2844, loss: 3.2844 +2024-12-30 08:28:35,760 - pyskl - INFO - Epoch [111][3100/3746] lr: 1.590e-02, eta: 1 day, 10:34:24, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4086, top5_acc: 0.6722, loss_cls: 3.2914, loss: 3.2914 +2024-12-30 08:29:59,644 - pyskl - INFO - Epoch [111][3200/3746] lr: 1.588e-02, eta: 1 day, 10:32:59, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6739, loss_cls: 3.2901, loss: 3.2901 +2024-12-30 08:31:24,142 - pyskl - INFO - Epoch [111][3300/3746] lr: 1.586e-02, eta: 1 day, 10:31:34, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6700, loss_cls: 3.3091, loss: 3.3091 +2024-12-30 08:32:47,848 - pyskl - INFO - Epoch [111][3400/3746] lr: 1.584e-02, eta: 1 day, 10:30:09, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4106, top5_acc: 0.6689, loss_cls: 3.3108, loss: 3.3108 +2024-12-30 08:34:12,334 - pyskl - INFO - Epoch [111][3500/3746] lr: 1.582e-02, eta: 1 day, 10:28:44, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6772, loss_cls: 3.3084, loss: 3.3084 +2024-12-30 08:35:36,446 - pyskl - INFO - Epoch [111][3600/3746] lr: 1.580e-02, eta: 1 day, 10:27:19, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4086, top5_acc: 0.6727, loss_cls: 3.3090, loss: 3.3090 +2024-12-30 08:37:00,977 - pyskl - INFO - Epoch [111][3700/3746] lr: 1.578e-02, eta: 1 day, 10:25:54, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6692, loss_cls: 3.3116, loss: 3.3116 +2024-12-30 08:37:41,613 - pyskl - INFO - Saving checkpoint at 111 epochs +2024-12-30 08:39:39,520 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 08:39:40,208 - pyskl - INFO - +top1_acc 0.3469 +top5_acc 0.6067 +2024-12-30 08:39:40,208 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 08:39:40,266 - pyskl - INFO - +mean_acc 0.3465 +2024-12-30 08:39:40,287 - pyskl - INFO - Epoch(val) [111][309] top1_acc: 0.3469, top5_acc: 0.6067, mean_class_accuracy: 0.3465 +2024-12-30 08:43:57,527 - pyskl - INFO - Epoch [112][100/3746] lr: 1.575e-02, eta: 1 day, 10:24:37, time: 2.572, data_time: 1.555, memory: 15990, top1_acc: 0.4283, top5_acc: 0.6934, loss_cls: 3.1780, loss: 3.1780 +2024-12-30 08:45:22,357 - pyskl - INFO - Epoch [112][200/3746] lr: 1.573e-02, eta: 1 day, 10:23:12, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6861, loss_cls: 3.2252, loss: 3.2252 +2024-12-30 08:46:47,113 - pyskl - INFO - Epoch [112][300/3746] lr: 1.571e-02, eta: 1 day, 10:21:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4205, top5_acc: 0.6795, loss_cls: 3.2715, loss: 3.2715 +2024-12-30 08:48:12,365 - pyskl - INFO - Epoch [112][400/3746] lr: 1.569e-02, eta: 1 day, 10:20:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4195, top5_acc: 0.6752, loss_cls: 3.2716, loss: 3.2716 +2024-12-30 08:49:38,025 - pyskl - INFO - Epoch [112][500/3746] lr: 1.567e-02, eta: 1 day, 10:18:58, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6845, loss_cls: 3.2164, loss: 3.2164 +2024-12-30 08:51:03,449 - pyskl - INFO - Epoch [112][600/3746] lr: 1.565e-02, eta: 1 day, 10:17:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4330, top5_acc: 0.6877, loss_cls: 3.1843, loss: 3.1843 +2024-12-30 08:52:29,054 - pyskl - INFO - Epoch [112][700/3746] lr: 1.563e-02, eta: 1 day, 10:16:09, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4280, top5_acc: 0.6920, loss_cls: 3.1900, loss: 3.1900 +2024-12-30 08:53:54,797 - pyskl - INFO - Epoch [112][800/3746] lr: 1.561e-02, eta: 1 day, 10:14:44, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6752, loss_cls: 3.2903, loss: 3.2903 +2024-12-30 08:55:19,910 - pyskl - INFO - Epoch [112][900/3746] lr: 1.559e-02, eta: 1 day, 10:13:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4153, top5_acc: 0.6697, loss_cls: 3.2734, loss: 3.2734 +2024-12-30 08:56:44,911 - pyskl - INFO - Epoch [112][1000/3746] lr: 1.557e-02, eta: 1 day, 10:11:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4189, top5_acc: 0.6853, loss_cls: 3.2490, loss: 3.2490 +2024-12-30 08:58:09,687 - pyskl - INFO - Epoch [112][1100/3746] lr: 1.555e-02, eta: 1 day, 10:10:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6808, loss_cls: 3.2493, loss: 3.2493 +2024-12-30 08:59:34,976 - pyskl - INFO - Epoch [112][1200/3746] lr: 1.553e-02, eta: 1 day, 10:09:05, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6778, loss_cls: 3.2833, loss: 3.2833 +2024-12-30 09:00:59,930 - pyskl - INFO - Epoch [112][1300/3746] lr: 1.551e-02, eta: 1 day, 10:07:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4139, top5_acc: 0.6750, loss_cls: 3.3090, loss: 3.3090 +2024-12-30 09:02:24,473 - pyskl - INFO - Epoch [112][1400/3746] lr: 1.549e-02, eta: 1 day, 10:06:15, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6925, loss_cls: 3.2125, loss: 3.2125 +2024-12-30 09:03:48,734 - pyskl - INFO - Epoch [112][1500/3746] lr: 1.547e-02, eta: 1 day, 10:04:50, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4205, top5_acc: 0.6791, loss_cls: 3.2671, loss: 3.2671 +2024-12-30 09:05:13,405 - pyskl - INFO - Epoch [112][1600/3746] lr: 1.545e-02, eta: 1 day, 10:03:25, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4164, top5_acc: 0.6894, loss_cls: 3.2368, loss: 3.2368 +2024-12-30 09:06:37,995 - pyskl - INFO - Epoch [112][1700/3746] lr: 1.543e-02, eta: 1 day, 10:02:01, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6755, loss_cls: 3.2750, loss: 3.2750 +2024-12-30 09:08:02,634 - pyskl - INFO - Epoch [112][1800/3746] lr: 1.541e-02, eta: 1 day, 10:00:36, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6839, loss_cls: 3.2679, loss: 3.2679 +2024-12-30 09:09:27,481 - pyskl - INFO - Epoch [112][1900/3746] lr: 1.539e-02, eta: 1 day, 9:59:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4195, top5_acc: 0.6856, loss_cls: 3.2396, loss: 3.2396 +2024-12-30 09:10:52,442 - pyskl - INFO - Epoch [112][2000/3746] lr: 1.537e-02, eta: 1 day, 9:57:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4078, top5_acc: 0.6809, loss_cls: 3.2798, loss: 3.2798 +2024-12-30 09:12:18,095 - pyskl - INFO - Epoch [112][2100/3746] lr: 1.535e-02, eta: 1 day, 9:56:21, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4266, top5_acc: 0.6773, loss_cls: 3.2355, loss: 3.2355 +2024-12-30 09:13:43,364 - pyskl - INFO - Epoch [112][2200/3746] lr: 1.533e-02, eta: 1 day, 9:54:57, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6792, loss_cls: 3.2605, loss: 3.2605 +2024-12-30 09:15:09,200 - pyskl - INFO - Epoch [112][2300/3746] lr: 1.531e-02, eta: 1 day, 9:53:32, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4117, top5_acc: 0.6784, loss_cls: 3.2706, loss: 3.2706 +2024-12-30 09:16:34,801 - pyskl - INFO - Epoch [112][2400/3746] lr: 1.529e-02, eta: 1 day, 9:52:08, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4214, top5_acc: 0.6864, loss_cls: 3.2204, loss: 3.2204 +2024-12-30 09:18:00,075 - pyskl - INFO - Epoch [112][2500/3746] lr: 1.527e-02, eta: 1 day, 9:50:43, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6822, loss_cls: 3.2402, loss: 3.2402 +2024-12-30 09:19:25,523 - pyskl - INFO - Epoch [112][2600/3746] lr: 1.525e-02, eta: 1 day, 9:49:18, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6817, loss_cls: 3.2478, loss: 3.2478 +2024-12-30 09:20:50,812 - pyskl - INFO - Epoch [112][2700/3746] lr: 1.523e-02, eta: 1 day, 9:47:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6692, loss_cls: 3.3415, loss: 3.3415 +2024-12-30 09:22:15,627 - pyskl - INFO - Epoch [112][2800/3746] lr: 1.521e-02, eta: 1 day, 9:46:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4197, top5_acc: 0.6748, loss_cls: 3.2766, loss: 3.2766 +2024-12-30 09:23:41,185 - pyskl - INFO - Epoch [112][2900/3746] lr: 1.519e-02, eta: 1 day, 9:45:04, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4220, top5_acc: 0.6834, loss_cls: 3.2521, loss: 3.2521 +2024-12-30 09:25:06,340 - pyskl - INFO - Epoch [112][3000/3746] lr: 1.517e-02, eta: 1 day, 9:43:39, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4166, top5_acc: 0.6775, loss_cls: 3.2634, loss: 3.2634 +2024-12-30 09:26:31,419 - pyskl - INFO - Epoch [112][3100/3746] lr: 1.515e-02, eta: 1 day, 9:42:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4163, top5_acc: 0.6841, loss_cls: 3.2499, loss: 3.2499 +2024-12-30 09:27:56,207 - pyskl - INFO - Epoch [112][3200/3746] lr: 1.513e-02, eta: 1 day, 9:40:50, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6836, loss_cls: 3.2965, loss: 3.2965 +2024-12-30 09:29:21,091 - pyskl - INFO - Epoch [112][3300/3746] lr: 1.511e-02, eta: 1 day, 9:39:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4136, top5_acc: 0.6797, loss_cls: 3.2810, loss: 3.2810 +2024-12-30 09:30:45,535 - pyskl - INFO - Epoch [112][3400/3746] lr: 1.509e-02, eta: 1 day, 9:38:00, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4334, top5_acc: 0.6778, loss_cls: 3.2302, loss: 3.2302 +2024-12-30 09:32:09,853 - pyskl - INFO - Epoch [112][3500/3746] lr: 1.507e-02, eta: 1 day, 9:36:35, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6848, loss_cls: 3.2369, loss: 3.2369 +2024-12-30 09:33:34,196 - pyskl - INFO - Epoch [112][3600/3746] lr: 1.505e-02, eta: 1 day, 9:35:10, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6833, loss_cls: 3.2536, loss: 3.2536 +2024-12-30 09:34:58,722 - pyskl - INFO - Epoch [112][3700/3746] lr: 1.503e-02, eta: 1 day, 9:33:45, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4114, top5_acc: 0.6711, loss_cls: 3.3111, loss: 3.3111 +2024-12-30 09:35:39,613 - pyskl - INFO - Saving checkpoint at 112 epochs +2024-12-30 09:37:37,321 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 09:37:38,022 - pyskl - INFO - +top1_acc 0.3599 +top5_acc 0.6150 +2024-12-30 09:37:38,022 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 09:37:38,067 - pyskl - INFO - +mean_acc 0.3596 +2024-12-30 09:37:38,072 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_109.pth was removed +2024-12-30 09:37:38,351 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_112.pth. +2024-12-30 09:37:38,352 - pyskl - INFO - Best top1_acc is 0.3599 at 112 epoch. +2024-12-30 09:37:38,365 - pyskl - INFO - Epoch(val) [112][309] top1_acc: 0.3599, top5_acc: 0.6150, mean_class_accuracy: 0.3596 +2024-12-30 09:41:53,541 - pyskl - INFO - Epoch [113][100/3746] lr: 1.500e-02, eta: 1 day, 9:32:25, time: 2.552, data_time: 1.514, memory: 15990, top1_acc: 0.4345, top5_acc: 0.6977, loss_cls: 3.1555, loss: 3.1555 +2024-12-30 09:43:18,728 - pyskl - INFO - Epoch [113][200/3746] lr: 1.498e-02, eta: 1 day, 9:31:01, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4178, top5_acc: 0.6789, loss_cls: 3.2452, loss: 3.2452 +2024-12-30 09:44:44,574 - pyskl - INFO - Epoch [113][300/3746] lr: 1.496e-02, eta: 1 day, 9:29:36, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.7020, loss_cls: 3.1399, loss: 3.1399 +2024-12-30 09:46:10,749 - pyskl - INFO - Epoch [113][400/3746] lr: 1.494e-02, eta: 1 day, 9:28:12, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.4373, top5_acc: 0.7005, loss_cls: 3.1467, loss: 3.1467 +2024-12-30 09:47:36,343 - pyskl - INFO - Epoch [113][500/3746] lr: 1.492e-02, eta: 1 day, 9:26:47, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4195, top5_acc: 0.6945, loss_cls: 3.2016, loss: 3.2016 +2024-12-30 09:49:02,573 - pyskl - INFO - Epoch [113][600/3746] lr: 1.490e-02, eta: 1 day, 9:25:22, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4316, top5_acc: 0.6931, loss_cls: 3.1860, loss: 3.1860 +2024-12-30 09:50:28,876 - pyskl - INFO - Epoch [113][700/3746] lr: 1.488e-02, eta: 1 day, 9:23:58, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6852, loss_cls: 3.2287, loss: 3.2287 +2024-12-30 09:51:54,724 - pyskl - INFO - Epoch [113][800/3746] lr: 1.486e-02, eta: 1 day, 9:22:34, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4141, top5_acc: 0.6887, loss_cls: 3.2364, loss: 3.2364 +2024-12-30 09:53:19,934 - pyskl - INFO - Epoch [113][900/3746] lr: 1.484e-02, eta: 1 day, 9:21:09, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4289, top5_acc: 0.6947, loss_cls: 3.1715, loss: 3.1715 +2024-12-30 09:54:45,697 - pyskl - INFO - Epoch [113][1000/3746] lr: 1.482e-02, eta: 1 day, 9:19:44, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4309, top5_acc: 0.6881, loss_cls: 3.1850, loss: 3.1850 +2024-12-30 09:56:11,545 - pyskl - INFO - Epoch [113][1100/3746] lr: 1.480e-02, eta: 1 day, 9:18:20, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6823, loss_cls: 3.2449, loss: 3.2449 +2024-12-30 09:57:36,270 - pyskl - INFO - Epoch [113][1200/3746] lr: 1.478e-02, eta: 1 day, 9:16:55, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6787, loss_cls: 3.2185, loss: 3.2185 +2024-12-30 09:59:00,545 - pyskl - INFO - Epoch [113][1300/3746] lr: 1.476e-02, eta: 1 day, 9:15:30, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4189, top5_acc: 0.6847, loss_cls: 3.2521, loss: 3.2521 +2024-12-30 10:00:24,540 - pyskl - INFO - Epoch [113][1400/3746] lr: 1.474e-02, eta: 1 day, 9:14:04, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.4291, top5_acc: 0.6934, loss_cls: 3.1943, loss: 3.1943 +2024-12-30 10:01:48,835 - pyskl - INFO - Epoch [113][1500/3746] lr: 1.472e-02, eta: 1 day, 9:12:39, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6773, loss_cls: 3.2523, loss: 3.2523 +2024-12-30 10:03:13,652 - pyskl - INFO - Epoch [113][1600/3746] lr: 1.470e-02, eta: 1 day, 9:11:14, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4248, top5_acc: 0.6905, loss_cls: 3.2283, loss: 3.2283 +2024-12-30 10:04:38,804 - pyskl - INFO - Epoch [113][1700/3746] lr: 1.468e-02, eta: 1 day, 9:09:50, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6789, loss_cls: 3.2811, loss: 3.2811 +2024-12-30 10:06:04,670 - pyskl - INFO - Epoch [113][1800/3746] lr: 1.466e-02, eta: 1 day, 9:08:25, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6823, loss_cls: 3.2571, loss: 3.2571 +2024-12-30 10:07:29,597 - pyskl - INFO - Epoch [113][1900/3746] lr: 1.464e-02, eta: 1 day, 9:07:00, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4173, top5_acc: 0.6802, loss_cls: 3.2406, loss: 3.2406 +2024-12-30 10:08:55,001 - pyskl - INFO - Epoch [113][2000/3746] lr: 1.462e-02, eta: 1 day, 9:05:35, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6825, loss_cls: 3.2411, loss: 3.2411 +2024-12-30 10:10:21,033 - pyskl - INFO - Epoch [113][2100/3746] lr: 1.460e-02, eta: 1 day, 9:04:11, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6763, loss_cls: 3.2514, loss: 3.2514 +2024-12-30 10:11:45,863 - pyskl - INFO - Epoch [113][2200/3746] lr: 1.458e-02, eta: 1 day, 9:02:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6848, loss_cls: 3.2177, loss: 3.2177 +2024-12-30 10:13:11,388 - pyskl - INFO - Epoch [113][2300/3746] lr: 1.456e-02, eta: 1 day, 9:01:21, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6858, loss_cls: 3.2417, loss: 3.2417 +2024-12-30 10:14:36,806 - pyskl - INFO - Epoch [113][2400/3746] lr: 1.454e-02, eta: 1 day, 8:59:57, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6922, loss_cls: 3.2006, loss: 3.2006 +2024-12-30 10:16:02,354 - pyskl - INFO - Epoch [113][2500/3746] lr: 1.452e-02, eta: 1 day, 8:58:32, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6703, loss_cls: 3.2857, loss: 3.2857 +2024-12-30 10:17:27,112 - pyskl - INFO - Epoch [113][2600/3746] lr: 1.450e-02, eta: 1 day, 8:57:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6834, loss_cls: 3.2514, loss: 3.2514 +2024-12-30 10:18:52,125 - pyskl - INFO - Epoch [113][2700/3746] lr: 1.448e-02, eta: 1 day, 8:55:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6670, loss_cls: 3.3110, loss: 3.3110 +2024-12-30 10:20:16,777 - pyskl - INFO - Epoch [113][2800/3746] lr: 1.446e-02, eta: 1 day, 8:54:17, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6833, loss_cls: 3.2778, loss: 3.2778 +2024-12-30 10:21:41,889 - pyskl - INFO - Epoch [113][2900/3746] lr: 1.444e-02, eta: 1 day, 8:52:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6783, loss_cls: 3.2898, loss: 3.2898 +2024-12-30 10:23:07,066 - pyskl - INFO - Epoch [113][3000/3746] lr: 1.442e-02, eta: 1 day, 8:51:28, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4169, top5_acc: 0.6872, loss_cls: 3.2228, loss: 3.2228 +2024-12-30 10:24:31,232 - pyskl - INFO - Epoch [113][3100/3746] lr: 1.440e-02, eta: 1 day, 8:50:03, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6797, loss_cls: 3.2659, loss: 3.2659 +2024-12-30 10:25:56,121 - pyskl - INFO - Epoch [113][3200/3746] lr: 1.438e-02, eta: 1 day, 8:48:38, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4256, top5_acc: 0.6808, loss_cls: 3.2290, loss: 3.2290 +2024-12-30 10:27:20,767 - pyskl - INFO - Epoch [113][3300/3746] lr: 1.436e-02, eta: 1 day, 8:47:13, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4181, top5_acc: 0.6791, loss_cls: 3.2512, loss: 3.2512 +2024-12-30 10:28:45,858 - pyskl - INFO - Epoch [113][3400/3746] lr: 1.434e-02, eta: 1 day, 8:45:48, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6825, loss_cls: 3.2680, loss: 3.2680 +2024-12-30 10:30:10,455 - pyskl - INFO - Epoch [113][3500/3746] lr: 1.432e-02, eta: 1 day, 8:44:23, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4188, top5_acc: 0.6792, loss_cls: 3.2629, loss: 3.2629 +2024-12-30 10:31:34,020 - pyskl - INFO - Epoch [113][3600/3746] lr: 1.431e-02, eta: 1 day, 8:42:58, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4166, top5_acc: 0.6833, loss_cls: 3.2574, loss: 3.2574 +2024-12-30 10:32:58,874 - pyskl - INFO - Epoch [113][3700/3746] lr: 1.429e-02, eta: 1 day, 8:41:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4188, top5_acc: 0.6797, loss_cls: 3.2509, loss: 3.2509 +2024-12-30 10:33:39,757 - pyskl - INFO - Saving checkpoint at 113 epochs +2024-12-30 10:35:38,003 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 10:35:38,692 - pyskl - INFO - +top1_acc 0.3670 +top5_acc 0.6271 +2024-12-30 10:35:38,692 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 10:35:38,746 - pyskl - INFO - +mean_acc 0.3667 +2024-12-30 10:35:38,753 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_112.pth was removed +2024-12-30 10:35:39,031 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_113.pth. +2024-12-30 10:35:39,032 - pyskl - INFO - Best top1_acc is 0.3670 at 113 epoch. +2024-12-30 10:35:39,045 - pyskl - INFO - Epoch(val) [113][309] top1_acc: 0.3670, top5_acc: 0.6271, mean_class_accuracy: 0.3667 +2024-12-30 10:39:53,965 - pyskl - INFO - Epoch [114][100/3746] lr: 1.426e-02, eta: 1 day, 8:40:12, time: 2.549, data_time: 1.513, memory: 15990, top1_acc: 0.4397, top5_acc: 0.7005, loss_cls: 3.1127, loss: 3.1127 +2024-12-30 10:41:19,040 - pyskl - INFO - Epoch [114][200/3746] lr: 1.424e-02, eta: 1 day, 8:38:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4309, top5_acc: 0.6952, loss_cls: 3.1485, loss: 3.1485 +2024-12-30 10:42:44,177 - pyskl - INFO - Epoch [114][300/3746] lr: 1.422e-02, eta: 1 day, 8:37:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.7020, loss_cls: 3.1451, loss: 3.1451 +2024-12-30 10:44:09,688 - pyskl - INFO - Epoch [114][400/3746] lr: 1.420e-02, eta: 1 day, 8:35:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4412, top5_acc: 0.6969, loss_cls: 3.1442, loss: 3.1442 +2024-12-30 10:45:34,845 - pyskl - INFO - Epoch [114][500/3746] lr: 1.418e-02, eta: 1 day, 8:34:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4248, top5_acc: 0.6869, loss_cls: 3.1952, loss: 3.1952 +2024-12-30 10:46:59,958 - pyskl - INFO - Epoch [114][600/3746] lr: 1.416e-02, eta: 1 day, 8:33:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4381, top5_acc: 0.6984, loss_cls: 3.1447, loss: 3.1447 +2024-12-30 10:48:25,320 - pyskl - INFO - Epoch [114][700/3746] lr: 1.414e-02, eta: 1 day, 8:31:43, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4286, top5_acc: 0.6933, loss_cls: 3.2008, loss: 3.2008 +2024-12-30 10:49:49,971 - pyskl - INFO - Epoch [114][800/3746] lr: 1.412e-02, eta: 1 day, 8:30:18, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6919, loss_cls: 3.1830, loss: 3.1830 +2024-12-30 10:51:14,856 - pyskl - INFO - Epoch [114][900/3746] lr: 1.410e-02, eta: 1 day, 8:28:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4273, top5_acc: 0.6875, loss_cls: 3.2282, loss: 3.2282 +2024-12-30 10:52:39,368 - pyskl - INFO - Epoch [114][1000/3746] lr: 1.408e-02, eta: 1 day, 8:27:28, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4295, top5_acc: 0.6981, loss_cls: 3.1739, loss: 3.1739 +2024-12-30 10:54:04,147 - pyskl - INFO - Epoch [114][1100/3746] lr: 1.406e-02, eta: 1 day, 8:26:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4270, top5_acc: 0.6809, loss_cls: 3.2144, loss: 3.2144 +2024-12-30 10:55:28,567 - pyskl - INFO - Epoch [114][1200/3746] lr: 1.404e-02, eta: 1 day, 8:24:38, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.4297, top5_acc: 0.6873, loss_cls: 3.2217, loss: 3.2217 +2024-12-30 10:56:53,645 - pyskl - INFO - Epoch [114][1300/3746] lr: 1.402e-02, eta: 1 day, 8:23:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6831, loss_cls: 3.2582, loss: 3.2582 +2024-12-30 10:58:18,173 - pyskl - INFO - Epoch [114][1400/3746] lr: 1.400e-02, eta: 1 day, 8:21:48, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.4197, top5_acc: 0.6880, loss_cls: 3.2289, loss: 3.2289 +2024-12-30 10:59:43,022 - pyskl - INFO - Epoch [114][1500/3746] lr: 1.398e-02, eta: 1 day, 8:20:23, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4350, top5_acc: 0.7037, loss_cls: 3.1321, loss: 3.1321 +2024-12-30 11:01:07,991 - pyskl - INFO - Epoch [114][1600/3746] lr: 1.397e-02, eta: 1 day, 8:18:58, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6781, loss_cls: 3.2727, loss: 3.2727 +2024-12-30 11:02:33,099 - pyskl - INFO - Epoch [114][1700/3746] lr: 1.395e-02, eta: 1 day, 8:17:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6914, loss_cls: 3.2294, loss: 3.2294 +2024-12-30 11:03:57,747 - pyskl - INFO - Epoch [114][1800/3746] lr: 1.393e-02, eta: 1 day, 8:16:08, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4263, top5_acc: 0.6891, loss_cls: 3.2396, loss: 3.2396 +2024-12-30 11:05:22,653 - pyskl - INFO - Epoch [114][1900/3746] lr: 1.391e-02, eta: 1 day, 8:14:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6825, loss_cls: 3.2280, loss: 3.2280 +2024-12-30 11:06:47,755 - pyskl - INFO - Epoch [114][2000/3746] lr: 1.389e-02, eta: 1 day, 8:13:18, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6853, loss_cls: 3.1908, loss: 3.1908 +2024-12-30 11:08:13,023 - pyskl - INFO - Epoch [114][2100/3746] lr: 1.387e-02, eta: 1 day, 8:11:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6911, loss_cls: 3.1852, loss: 3.1852 +2024-12-30 11:09:37,854 - pyskl - INFO - Epoch [114][2200/3746] lr: 1.385e-02, eta: 1 day, 8:10:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6861, loss_cls: 3.2163, loss: 3.2163 +2024-12-30 11:11:02,686 - pyskl - INFO - Epoch [114][2300/3746] lr: 1.383e-02, eta: 1 day, 8:09:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4241, top5_acc: 0.6783, loss_cls: 3.2560, loss: 3.2560 +2024-12-30 11:12:27,445 - pyskl - INFO - Epoch [114][2400/3746] lr: 1.381e-02, eta: 1 day, 8:07:39, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6723, loss_cls: 3.2903, loss: 3.2903 +2024-12-30 11:13:52,578 - pyskl - INFO - Epoch [114][2500/3746] lr: 1.379e-02, eta: 1 day, 8:06:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6798, loss_cls: 3.2601, loss: 3.2601 +2024-12-30 11:15:17,363 - pyskl - INFO - Epoch [114][2600/3746] lr: 1.377e-02, eta: 1 day, 8:04:49, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4181, top5_acc: 0.6841, loss_cls: 3.2474, loss: 3.2474 +2024-12-30 11:16:42,194 - pyskl - INFO - Epoch [114][2700/3746] lr: 1.375e-02, eta: 1 day, 8:03:24, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4189, top5_acc: 0.6872, loss_cls: 3.2466, loss: 3.2466 +2024-12-30 11:18:07,147 - pyskl - INFO - Epoch [114][2800/3746] lr: 1.373e-02, eta: 1 day, 8:01:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6869, loss_cls: 3.2115, loss: 3.2115 +2024-12-30 11:19:32,367 - pyskl - INFO - Epoch [114][2900/3746] lr: 1.371e-02, eta: 1 day, 8:00:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4247, top5_acc: 0.6956, loss_cls: 3.1831, loss: 3.1831 +2024-12-30 11:20:57,250 - pyskl - INFO - Epoch [114][3000/3746] lr: 1.369e-02, eta: 1 day, 7:59:09, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6856, loss_cls: 3.2194, loss: 3.2194 +2024-12-30 11:22:22,343 - pyskl - INFO - Epoch [114][3100/3746] lr: 1.368e-02, eta: 1 day, 7:57:44, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4263, top5_acc: 0.6934, loss_cls: 3.1834, loss: 3.1834 +2024-12-30 11:23:47,130 - pyskl - INFO - Epoch [114][3200/3746] lr: 1.366e-02, eta: 1 day, 7:56:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6973, loss_cls: 3.1624, loss: 3.1624 +2024-12-30 11:25:11,899 - pyskl - INFO - Epoch [114][3300/3746] lr: 1.364e-02, eta: 1 day, 7:54:54, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4272, top5_acc: 0.6844, loss_cls: 3.2261, loss: 3.2261 +2024-12-30 11:26:36,218 - pyskl - INFO - Epoch [114][3400/3746] lr: 1.362e-02, eta: 1 day, 7:53:29, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4259, top5_acc: 0.6861, loss_cls: 3.2097, loss: 3.2097 +2024-12-30 11:28:00,357 - pyskl - INFO - Epoch [114][3500/3746] lr: 1.360e-02, eta: 1 day, 7:52:04, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6881, loss_cls: 3.2347, loss: 3.2347 +2024-12-30 11:29:24,920 - pyskl - INFO - Epoch [114][3600/3746] lr: 1.358e-02, eta: 1 day, 7:50:39, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4214, top5_acc: 0.6873, loss_cls: 3.2331, loss: 3.2331 +2024-12-30 11:30:49,405 - pyskl - INFO - Epoch [114][3700/3746] lr: 1.356e-02, eta: 1 day, 7:49:14, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4275, top5_acc: 0.6916, loss_cls: 3.1960, loss: 3.1960 +2024-12-30 11:31:30,098 - pyskl - INFO - Saving checkpoint at 114 epochs +2024-12-30 11:33:28,922 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 11:33:29,697 - pyskl - INFO - +top1_acc 0.3479 +top5_acc 0.6101 +2024-12-30 11:33:29,698 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 11:33:29,740 - pyskl - INFO - +mean_acc 0.3477 +2024-12-30 11:33:29,753 - pyskl - INFO - Epoch(val) [114][309] top1_acc: 0.3479, top5_acc: 0.6101, mean_class_accuracy: 0.3477 +2024-12-30 11:37:41,268 - pyskl - INFO - Epoch [115][100/3746] lr: 1.353e-02, eta: 1 day, 7:47:50, time: 2.515, data_time: 1.484, memory: 15990, top1_acc: 0.4348, top5_acc: 0.6977, loss_cls: 3.1340, loss: 3.1340 +2024-12-30 11:39:06,387 - pyskl - INFO - Epoch [115][200/3746] lr: 1.351e-02, eta: 1 day, 7:46:25, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4433, top5_acc: 0.7017, loss_cls: 3.1268, loss: 3.1268 +2024-12-30 11:40:31,949 - pyskl - INFO - Epoch [115][300/3746] lr: 1.349e-02, eta: 1 day, 7:45:01, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4380, top5_acc: 0.7005, loss_cls: 3.1139, loss: 3.1139 +2024-12-30 11:41:56,811 - pyskl - INFO - Epoch [115][400/3746] lr: 1.348e-02, eta: 1 day, 7:43:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4344, top5_acc: 0.7027, loss_cls: 3.1501, loss: 3.1501 +2024-12-30 11:43:22,383 - pyskl - INFO - Epoch [115][500/3746] lr: 1.346e-02, eta: 1 day, 7:42:11, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4436, top5_acc: 0.7100, loss_cls: 3.0976, loss: 3.0976 +2024-12-30 11:44:48,317 - pyskl - INFO - Epoch [115][600/3746] lr: 1.344e-02, eta: 1 day, 7:40:46, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4281, top5_acc: 0.6898, loss_cls: 3.1958, loss: 3.1958 +2024-12-30 11:46:13,482 - pyskl - INFO - Epoch [115][700/3746] lr: 1.342e-02, eta: 1 day, 7:39:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4355, top5_acc: 0.6916, loss_cls: 3.1662, loss: 3.1662 +2024-12-30 11:47:38,469 - pyskl - INFO - Epoch [115][800/3746] lr: 1.340e-02, eta: 1 day, 7:37:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4317, top5_acc: 0.6947, loss_cls: 3.1753, loss: 3.1753 +2024-12-30 11:49:03,860 - pyskl - INFO - Epoch [115][900/3746] lr: 1.338e-02, eta: 1 day, 7:36:32, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4441, top5_acc: 0.7073, loss_cls: 3.0980, loss: 3.0980 +2024-12-30 11:50:29,288 - pyskl - INFO - Epoch [115][1000/3746] lr: 1.336e-02, eta: 1 day, 7:35:07, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4348, top5_acc: 0.6994, loss_cls: 3.1533, loss: 3.1533 +2024-12-30 11:51:54,712 - pyskl - INFO - Epoch [115][1100/3746] lr: 1.334e-02, eta: 1 day, 7:33:42, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4366, top5_acc: 0.6937, loss_cls: 3.1426, loss: 3.1426 +2024-12-30 11:53:19,826 - pyskl - INFO - Epoch [115][1200/3746] lr: 1.332e-02, eta: 1 day, 7:32:17, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4270, top5_acc: 0.6937, loss_cls: 3.1728, loss: 3.1728 +2024-12-30 11:54:44,518 - pyskl - INFO - Epoch [115][1300/3746] lr: 1.330e-02, eta: 1 day, 7:30:52, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4264, top5_acc: 0.6792, loss_cls: 3.2277, loss: 3.2277 +2024-12-30 11:56:09,482 - pyskl - INFO - Epoch [115][1400/3746] lr: 1.328e-02, eta: 1 day, 7:29:27, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6852, loss_cls: 3.2398, loss: 3.2398 +2024-12-30 11:57:34,304 - pyskl - INFO - Epoch [115][1500/3746] lr: 1.327e-02, eta: 1 day, 7:28:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6945, loss_cls: 3.1736, loss: 3.1736 +2024-12-30 11:58:58,900 - pyskl - INFO - Epoch [115][1600/3746] lr: 1.325e-02, eta: 1 day, 7:26:37, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4345, top5_acc: 0.7030, loss_cls: 3.1424, loss: 3.1424 +2024-12-30 12:00:23,917 - pyskl - INFO - Epoch [115][1700/3746] lr: 1.323e-02, eta: 1 day, 7:25:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4258, top5_acc: 0.6952, loss_cls: 3.1885, loss: 3.1885 +2024-12-30 12:01:48,862 - pyskl - INFO - Epoch [115][1800/3746] lr: 1.321e-02, eta: 1 day, 7:23:47, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4275, top5_acc: 0.6911, loss_cls: 3.2037, loss: 3.2037 +2024-12-30 12:03:13,906 - pyskl - INFO - Epoch [115][1900/3746] lr: 1.319e-02, eta: 1 day, 7:22:22, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4334, top5_acc: 0.6930, loss_cls: 3.1877, loss: 3.1877 +2024-12-30 12:04:38,441 - pyskl - INFO - Epoch [115][2000/3746] lr: 1.317e-02, eta: 1 day, 7:20:57, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6916, loss_cls: 3.1911, loss: 3.1911 +2024-12-30 12:06:03,836 - pyskl - INFO - Epoch [115][2100/3746] lr: 1.315e-02, eta: 1 day, 7:19:32, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4416, top5_acc: 0.6953, loss_cls: 3.1513, loss: 3.1513 +2024-12-30 12:07:28,660 - pyskl - INFO - Epoch [115][2200/3746] lr: 1.313e-02, eta: 1 day, 7:18:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6878, loss_cls: 3.2041, loss: 3.2041 +2024-12-30 12:08:53,653 - pyskl - INFO - Epoch [115][2300/3746] lr: 1.311e-02, eta: 1 day, 7:16:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4341, top5_acc: 0.7011, loss_cls: 3.1763, loss: 3.1763 +2024-12-30 12:10:18,589 - pyskl - INFO - Epoch [115][2400/3746] lr: 1.310e-02, eta: 1 day, 7:15:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6861, loss_cls: 3.2055, loss: 3.2055 +2024-12-30 12:11:42,924 - pyskl - INFO - Epoch [115][2500/3746] lr: 1.308e-02, eta: 1 day, 7:13:52, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4183, top5_acc: 0.6852, loss_cls: 3.2102, loss: 3.2102 +2024-12-30 12:13:07,842 - pyskl - INFO - Epoch [115][2600/3746] lr: 1.306e-02, eta: 1 day, 7:12:27, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4348, top5_acc: 0.6945, loss_cls: 3.1837, loss: 3.1837 +2024-12-30 12:14:32,842 - pyskl - INFO - Epoch [115][2700/3746] lr: 1.304e-02, eta: 1 day, 7:11:03, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6791, loss_cls: 3.2355, loss: 3.2355 +2024-12-30 12:15:57,600 - pyskl - INFO - Epoch [115][2800/3746] lr: 1.302e-02, eta: 1 day, 7:09:38, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6877, loss_cls: 3.2266, loss: 3.2266 +2024-12-30 12:17:22,697 - pyskl - INFO - Epoch [115][2900/3746] lr: 1.300e-02, eta: 1 day, 7:08:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6898, loss_cls: 3.2129, loss: 3.2129 +2024-12-30 12:18:48,728 - pyskl - INFO - Epoch [115][3000/3746] lr: 1.298e-02, eta: 1 day, 7:06:48, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.4269, top5_acc: 0.6936, loss_cls: 3.1987, loss: 3.1987 +2024-12-30 12:20:14,101 - pyskl - INFO - Epoch [115][3100/3746] lr: 1.296e-02, eta: 1 day, 7:05:23, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6794, loss_cls: 3.2421, loss: 3.2421 +2024-12-30 12:21:39,109 - pyskl - INFO - Epoch [115][3200/3746] lr: 1.295e-02, eta: 1 day, 7:03:58, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6841, loss_cls: 3.2203, loss: 3.2203 +2024-12-30 12:23:04,356 - pyskl - INFO - Epoch [115][3300/3746] lr: 1.293e-02, eta: 1 day, 7:02:33, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4322, top5_acc: 0.6956, loss_cls: 3.1468, loss: 3.1468 +2024-12-30 12:24:29,203 - pyskl - INFO - Epoch [115][3400/3746] lr: 1.291e-02, eta: 1 day, 7:01:08, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.4163, top5_acc: 0.6827, loss_cls: 3.2485, loss: 3.2485 +2024-12-30 12:25:54,384 - pyskl - INFO - Epoch [115][3500/3746] lr: 1.289e-02, eta: 1 day, 6:59:44, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.4263, top5_acc: 0.6827, loss_cls: 3.2204, loss: 3.2204 +2024-12-30 12:27:19,974 - pyskl - INFO - Epoch [115][3600/3746] lr: 1.287e-02, eta: 1 day, 6:58:19, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.4125, top5_acc: 0.6886, loss_cls: 3.2240, loss: 3.2240 +2024-12-30 12:28:44,805 - pyskl - INFO - Epoch [115][3700/3746] lr: 1.285e-02, eta: 1 day, 6:56:54, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6841, loss_cls: 3.2293, loss: 3.2293 +2024-12-30 12:29:25,840 - pyskl - INFO - Saving checkpoint at 115 epochs +2024-12-30 12:31:24,057 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 12:31:24,949 - pyskl - INFO - +top1_acc 0.3511 +top5_acc 0.6101 +2024-12-30 12:31:24,949 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 12:31:24,991 - pyskl - INFO - +mean_acc 0.3509 +2024-12-30 12:31:25,004 - pyskl - INFO - Epoch(val) [115][309] top1_acc: 0.3511, top5_acc: 0.6101, mean_class_accuracy: 0.3509 +2024-12-30 12:35:43,714 - pyskl - INFO - Epoch [116][100/3746] lr: 1.282e-02, eta: 1 day, 6:55:31, time: 2.587, data_time: 1.562, memory: 15990, top1_acc: 0.4414, top5_acc: 0.7089, loss_cls: 3.0967, loss: 3.0967 +2024-12-30 12:37:08,946 - pyskl - INFO - Epoch [116][200/3746] lr: 1.281e-02, eta: 1 day, 6:54:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4403, top5_acc: 0.7067, loss_cls: 3.1004, loss: 3.1004 +2024-12-30 12:38:33,700 - pyskl - INFO - Epoch [116][300/3746] lr: 1.279e-02, eta: 1 day, 6:52:41, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.7005, loss_cls: 3.1358, loss: 3.1358 +2024-12-30 12:39:58,988 - pyskl - INFO - Epoch [116][400/3746] lr: 1.277e-02, eta: 1 day, 6:51:16, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4422, top5_acc: 0.6984, loss_cls: 3.1059, loss: 3.1059 +2024-12-30 12:41:24,008 - pyskl - INFO - Epoch [116][500/3746] lr: 1.275e-02, eta: 1 day, 6:49:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4316, top5_acc: 0.6942, loss_cls: 3.1568, loss: 3.1568 +2024-12-30 12:42:49,105 - pyskl - INFO - Epoch [116][600/3746] lr: 1.273e-02, eta: 1 day, 6:48:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4275, top5_acc: 0.7006, loss_cls: 3.1579, loss: 3.1579 +2024-12-30 12:44:13,965 - pyskl - INFO - Epoch [116][700/3746] lr: 1.271e-02, eta: 1 day, 6:47:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.7098, loss_cls: 3.1054, loss: 3.1054 +2024-12-30 12:45:39,237 - pyskl - INFO - Epoch [116][800/3746] lr: 1.269e-02, eta: 1 day, 6:45:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4455, top5_acc: 0.7087, loss_cls: 3.0987, loss: 3.0987 +2024-12-30 12:47:04,586 - pyskl - INFO - Epoch [116][900/3746] lr: 1.268e-02, eta: 1 day, 6:44:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.6992, loss_cls: 3.1470, loss: 3.1470 +2024-12-30 12:48:29,627 - pyskl - INFO - Epoch [116][1000/3746] lr: 1.266e-02, eta: 1 day, 6:42:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4511, top5_acc: 0.7078, loss_cls: 3.0913, loss: 3.0913 +2024-12-30 12:49:54,552 - pyskl - INFO - Epoch [116][1100/3746] lr: 1.264e-02, eta: 1 day, 6:41:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4342, top5_acc: 0.6952, loss_cls: 3.1514, loss: 3.1514 +2024-12-30 12:51:19,600 - pyskl - INFO - Epoch [116][1200/3746] lr: 1.262e-02, eta: 1 day, 6:39:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4428, top5_acc: 0.7045, loss_cls: 3.1157, loss: 3.1157 +2024-12-30 12:52:44,423 - pyskl - INFO - Epoch [116][1300/3746] lr: 1.260e-02, eta: 1 day, 6:38:31, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4369, top5_acc: 0.6958, loss_cls: 3.1643, loss: 3.1643 +2024-12-30 12:54:09,444 - pyskl - INFO - Epoch [116][1400/3746] lr: 1.258e-02, eta: 1 day, 6:37:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4437, top5_acc: 0.7031, loss_cls: 3.1159, loss: 3.1159 +2024-12-30 12:55:34,627 - pyskl - INFO - Epoch [116][1500/3746] lr: 1.256e-02, eta: 1 day, 6:35:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4286, top5_acc: 0.6920, loss_cls: 3.1776, loss: 3.1776 +2024-12-30 12:56:59,876 - pyskl - INFO - Epoch [116][1600/3746] lr: 1.255e-02, eta: 1 day, 6:34:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4298, top5_acc: 0.6977, loss_cls: 3.1569, loss: 3.1569 +2024-12-30 12:58:25,090 - pyskl - INFO - Epoch [116][1700/3746] lr: 1.253e-02, eta: 1 day, 6:32:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4298, top5_acc: 0.6952, loss_cls: 3.1721, loss: 3.1721 +2024-12-30 12:59:49,517 - pyskl - INFO - Epoch [116][1800/3746] lr: 1.251e-02, eta: 1 day, 6:31:26, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6855, loss_cls: 3.2150, loss: 3.2150 +2024-12-30 13:01:14,280 - pyskl - INFO - Epoch [116][1900/3746] lr: 1.249e-02, eta: 1 day, 6:30:01, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4306, top5_acc: 0.6950, loss_cls: 3.1872, loss: 3.1872 +2024-12-30 13:02:38,916 - pyskl - INFO - Epoch [116][2000/3746] lr: 1.247e-02, eta: 1 day, 6:28:36, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4405, top5_acc: 0.7003, loss_cls: 3.1427, loss: 3.1427 +2024-12-30 13:04:04,001 - pyskl - INFO - Epoch [116][2100/3746] lr: 1.245e-02, eta: 1 day, 6:27:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4272, top5_acc: 0.6902, loss_cls: 3.2079, loss: 3.2079 +2024-12-30 13:05:28,483 - pyskl - INFO - Epoch [116][2200/3746] lr: 1.243e-02, eta: 1 day, 6:25:46, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6894, loss_cls: 3.1864, loss: 3.1864 +2024-12-30 13:06:53,185 - pyskl - INFO - Epoch [116][2300/3746] lr: 1.242e-02, eta: 1 day, 6:24:21, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6756, loss_cls: 3.2672, loss: 3.2672 +2024-12-30 13:08:18,036 - pyskl - INFO - Epoch [116][2400/3746] lr: 1.240e-02, eta: 1 day, 6:22:56, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4317, top5_acc: 0.6898, loss_cls: 3.1963, loss: 3.1963 +2024-12-30 13:09:43,107 - pyskl - INFO - Epoch [116][2500/3746] lr: 1.238e-02, eta: 1 day, 6:21:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4348, top5_acc: 0.6969, loss_cls: 3.1668, loss: 3.1668 +2024-12-30 13:11:08,161 - pyskl - INFO - Epoch [116][2600/3746] lr: 1.236e-02, eta: 1 day, 6:20:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4322, top5_acc: 0.6987, loss_cls: 3.1612, loss: 3.1612 +2024-12-30 13:12:32,473 - pyskl - INFO - Epoch [116][2700/3746] lr: 1.234e-02, eta: 1 day, 6:18:41, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4328, top5_acc: 0.6928, loss_cls: 3.1860, loss: 3.1860 +2024-12-30 13:13:57,218 - pyskl - INFO - Epoch [116][2800/3746] lr: 1.232e-02, eta: 1 day, 6:17:16, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4356, top5_acc: 0.6900, loss_cls: 3.1883, loss: 3.1883 +2024-12-30 13:15:22,208 - pyskl - INFO - Epoch [116][2900/3746] lr: 1.231e-02, eta: 1 day, 6:15:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4323, top5_acc: 0.6936, loss_cls: 3.1489, loss: 3.1489 +2024-12-30 13:16:47,212 - pyskl - INFO - Epoch [116][3000/3746] lr: 1.229e-02, eta: 1 day, 6:14:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6944, loss_cls: 3.1822, loss: 3.1822 +2024-12-30 13:18:12,500 - pyskl - INFO - Epoch [116][3100/3746] lr: 1.227e-02, eta: 1 day, 6:13:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4264, top5_acc: 0.6909, loss_cls: 3.1869, loss: 3.1869 +2024-12-30 13:19:37,364 - pyskl - INFO - Epoch [116][3200/3746] lr: 1.225e-02, eta: 1 day, 6:11:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6937, loss_cls: 3.1681, loss: 3.1681 +2024-12-30 13:21:02,225 - pyskl - INFO - Epoch [116][3300/3746] lr: 1.223e-02, eta: 1 day, 6:10:11, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.6906, loss_cls: 3.1803, loss: 3.1803 +2024-12-30 13:22:26,244 - pyskl - INFO - Epoch [116][3400/3746] lr: 1.221e-02, eta: 1 day, 6:08:46, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6850, loss_cls: 3.2384, loss: 3.2384 +2024-12-30 13:23:51,127 - pyskl - INFO - Epoch [116][3500/3746] lr: 1.220e-02, eta: 1 day, 6:07:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6964, loss_cls: 3.1511, loss: 3.1511 +2024-12-30 13:25:15,518 - pyskl - INFO - Epoch [116][3600/3746] lr: 1.218e-02, eta: 1 day, 6:05:56, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6902, loss_cls: 3.1964, loss: 3.1964 +2024-12-30 13:26:40,042 - pyskl - INFO - Epoch [116][3700/3746] lr: 1.216e-02, eta: 1 day, 6:04:31, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4275, top5_acc: 0.6861, loss_cls: 3.2050, loss: 3.2050 +2024-12-30 13:27:20,786 - pyskl - INFO - Saving checkpoint at 116 epochs +2024-12-30 13:29:20,031 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 13:29:20,706 - pyskl - INFO - +top1_acc 0.3641 +top5_acc 0.6256 +2024-12-30 13:29:20,707 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 13:29:20,763 - pyskl - INFO - +mean_acc 0.3638 +2024-12-30 13:29:20,775 - pyskl - INFO - Epoch(val) [116][309] top1_acc: 0.3641, top5_acc: 0.6256, mean_class_accuracy: 0.3638 +2024-12-30 13:33:30,917 - pyskl - INFO - Epoch [117][100/3746] lr: 1.213e-02, eta: 1 day, 6:03:03, time: 2.501, data_time: 1.478, memory: 15990, top1_acc: 0.4444, top5_acc: 0.7075, loss_cls: 3.0708, loss: 3.0708 +2024-12-30 13:34:55,973 - pyskl - INFO - Epoch [117][200/3746] lr: 1.211e-02, eta: 1 day, 6:01:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4434, top5_acc: 0.7127, loss_cls: 3.0828, loss: 3.0828 +2024-12-30 13:36:21,223 - pyskl - INFO - Epoch [117][300/3746] lr: 1.210e-02, eta: 1 day, 6:00:14, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4417, top5_acc: 0.6995, loss_cls: 3.1179, loss: 3.1179 +2024-12-30 13:37:46,426 - pyskl - INFO - Epoch [117][400/3746] lr: 1.208e-02, eta: 1 day, 5:58:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4530, top5_acc: 0.7003, loss_cls: 3.1222, loss: 3.1222 +2024-12-30 13:39:11,319 - pyskl - INFO - Epoch [117][500/3746] lr: 1.206e-02, eta: 1 day, 5:57:24, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4458, top5_acc: 0.7047, loss_cls: 3.0929, loss: 3.0929 +2024-12-30 13:40:36,619 - pyskl - INFO - Epoch [117][600/3746] lr: 1.204e-02, eta: 1 day, 5:55:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4491, top5_acc: 0.7117, loss_cls: 3.0844, loss: 3.0844 +2024-12-30 13:42:01,708 - pyskl - INFO - Epoch [117][700/3746] lr: 1.202e-02, eta: 1 day, 5:54:34, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4348, top5_acc: 0.6880, loss_cls: 3.1471, loss: 3.1471 +2024-12-30 13:43:26,704 - pyskl - INFO - Epoch [117][800/3746] lr: 1.200e-02, eta: 1 day, 5:53:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4387, top5_acc: 0.7056, loss_cls: 3.1063, loss: 3.1063 +2024-12-30 13:44:51,157 - pyskl - INFO - Epoch [117][900/3746] lr: 1.199e-02, eta: 1 day, 5:51:43, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4412, top5_acc: 0.7094, loss_cls: 3.0882, loss: 3.0882 +2024-12-30 13:46:16,019 - pyskl - INFO - Epoch [117][1000/3746] lr: 1.197e-02, eta: 1 day, 5:50:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.7000, loss_cls: 3.1145, loss: 3.1145 +2024-12-30 13:47:40,676 - pyskl - INFO - Epoch [117][1100/3746] lr: 1.195e-02, eta: 1 day, 5:48:53, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4380, top5_acc: 0.7028, loss_cls: 3.1273, loss: 3.1273 +2024-12-30 13:49:05,745 - pyskl - INFO - Epoch [117][1200/3746] lr: 1.193e-02, eta: 1 day, 5:47:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4386, top5_acc: 0.6941, loss_cls: 3.1671, loss: 3.1671 +2024-12-30 13:50:30,517 - pyskl - INFO - Epoch [117][1300/3746] lr: 1.191e-02, eta: 1 day, 5:46:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4439, top5_acc: 0.7017, loss_cls: 3.1029, loss: 3.1029 +2024-12-30 13:51:54,878 - pyskl - INFO - Epoch [117][1400/3746] lr: 1.190e-02, eta: 1 day, 5:44:38, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4331, top5_acc: 0.6953, loss_cls: 3.1731, loss: 3.1731 +2024-12-30 13:53:19,075 - pyskl - INFO - Epoch [117][1500/3746] lr: 1.188e-02, eta: 1 day, 5:43:13, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.6986, loss_cls: 3.1499, loss: 3.1499 +2024-12-30 13:54:43,560 - pyskl - INFO - Epoch [117][1600/3746] lr: 1.186e-02, eta: 1 day, 5:41:48, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4356, top5_acc: 0.6956, loss_cls: 3.1757, loss: 3.1757 +2024-12-30 13:56:08,199 - pyskl - INFO - Epoch [117][1700/3746] lr: 1.184e-02, eta: 1 day, 5:40:23, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4419, top5_acc: 0.7020, loss_cls: 3.1116, loss: 3.1116 +2024-12-30 13:57:32,248 - pyskl - INFO - Epoch [117][1800/3746] lr: 1.182e-02, eta: 1 day, 5:38:57, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.4339, top5_acc: 0.6952, loss_cls: 3.1805, loss: 3.1805 +2024-12-30 13:58:56,703 - pyskl - INFO - Epoch [117][1900/3746] lr: 1.181e-02, eta: 1 day, 5:37:32, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4281, top5_acc: 0.6870, loss_cls: 3.2130, loss: 3.2130 +2024-12-30 14:00:21,419 - pyskl - INFO - Epoch [117][2000/3746] lr: 1.179e-02, eta: 1 day, 5:36:07, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.6991, loss_cls: 3.1305, loss: 3.1305 +2024-12-30 14:01:45,975 - pyskl - INFO - Epoch [117][2100/3746] lr: 1.177e-02, eta: 1 day, 5:34:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4344, top5_acc: 0.6944, loss_cls: 3.1362, loss: 3.1362 +2024-12-30 14:03:11,264 - pyskl - INFO - Epoch [117][2200/3746] lr: 1.175e-02, eta: 1 day, 5:33:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6936, loss_cls: 3.1520, loss: 3.1520 +2024-12-30 14:04:35,498 - pyskl - INFO - Epoch [117][2300/3746] lr: 1.173e-02, eta: 1 day, 5:31:52, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4456, top5_acc: 0.7017, loss_cls: 3.1226, loss: 3.1226 +2024-12-30 14:06:00,491 - pyskl - INFO - Epoch [117][2400/3746] lr: 1.172e-02, eta: 1 day, 5:30:27, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.6978, loss_cls: 3.1339, loss: 3.1339 +2024-12-30 14:07:25,798 - pyskl - INFO - Epoch [117][2500/3746] lr: 1.170e-02, eta: 1 day, 5:29:02, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4484, top5_acc: 0.7072, loss_cls: 3.0919, loss: 3.0919 +2024-12-30 14:08:51,326 - pyskl - INFO - Epoch [117][2600/3746] lr: 1.168e-02, eta: 1 day, 5:27:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4431, top5_acc: 0.7037, loss_cls: 3.1039, loss: 3.1039 +2024-12-30 14:10:16,063 - pyskl - INFO - Epoch [117][2700/3746] lr: 1.166e-02, eta: 1 day, 5:26:12, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4344, top5_acc: 0.6964, loss_cls: 3.1588, loss: 3.1588 +2024-12-30 14:11:41,296 - pyskl - INFO - Epoch [117][2800/3746] lr: 1.164e-02, eta: 1 day, 5:24:47, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4375, top5_acc: 0.7008, loss_cls: 3.1473, loss: 3.1473 +2024-12-30 14:13:06,747 - pyskl - INFO - Epoch [117][2900/3746] lr: 1.163e-02, eta: 1 day, 5:23:22, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4272, top5_acc: 0.6930, loss_cls: 3.2143, loss: 3.2143 +2024-12-30 14:14:31,757 - pyskl - INFO - Epoch [117][3000/3746] lr: 1.161e-02, eta: 1 day, 5:21:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4316, top5_acc: 0.6913, loss_cls: 3.1773, loss: 3.1773 +2024-12-30 14:15:57,138 - pyskl - INFO - Epoch [117][3100/3746] lr: 1.159e-02, eta: 1 day, 5:20:32, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4408, top5_acc: 0.6970, loss_cls: 3.1607, loss: 3.1607 +2024-12-30 14:17:22,836 - pyskl - INFO - Epoch [117][3200/3746] lr: 1.157e-02, eta: 1 day, 5:19:07, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4417, top5_acc: 0.7037, loss_cls: 3.1166, loss: 3.1166 +2024-12-30 14:18:48,087 - pyskl - INFO - Epoch [117][3300/3746] lr: 1.155e-02, eta: 1 day, 5:17:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4402, top5_acc: 0.6928, loss_cls: 3.1304, loss: 3.1304 +2024-12-30 14:20:13,105 - pyskl - INFO - Epoch [117][3400/3746] lr: 1.154e-02, eta: 1 day, 5:16:17, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4523, top5_acc: 0.7097, loss_cls: 3.0779, loss: 3.0779 +2024-12-30 14:21:38,216 - pyskl - INFO - Epoch [117][3500/3746] lr: 1.152e-02, eta: 1 day, 5:14:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4328, top5_acc: 0.6989, loss_cls: 3.1241, loss: 3.1241 +2024-12-30 14:23:02,833 - pyskl - INFO - Epoch [117][3600/3746] lr: 1.150e-02, eta: 1 day, 5:13:27, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.4427, top5_acc: 0.6981, loss_cls: 3.1516, loss: 3.1516 +2024-12-30 14:24:27,458 - pyskl - INFO - Epoch [117][3700/3746] lr: 1.148e-02, eta: 1 day, 5:12:02, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4334, top5_acc: 0.6928, loss_cls: 3.1677, loss: 3.1677 +2024-12-30 14:25:08,278 - pyskl - INFO - Saving checkpoint at 117 epochs +2024-12-30 14:27:06,027 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 14:27:06,801 - pyskl - INFO - +top1_acc 0.3659 +top5_acc 0.6234 +2024-12-30 14:27:06,802 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 14:27:06,846 - pyskl - INFO - +mean_acc 0.3658 +2024-12-30 14:27:06,858 - pyskl - INFO - Epoch(val) [117][309] top1_acc: 0.3659, top5_acc: 0.6234, mean_class_accuracy: 0.3658 +2024-12-30 14:31:19,748 - pyskl - INFO - Epoch [118][100/3746] lr: 1.146e-02, eta: 1 day, 5:10:34, time: 2.529, data_time: 1.503, memory: 15990, top1_acc: 0.4564, top5_acc: 0.7166, loss_cls: 3.0382, loss: 3.0382 +2024-12-30 14:32:44,756 - pyskl - INFO - Epoch [118][200/3746] lr: 1.144e-02, eta: 1 day, 5:09:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4441, top5_acc: 0.7023, loss_cls: 3.1262, loss: 3.1262 +2024-12-30 14:34:10,084 - pyskl - INFO - Epoch [118][300/3746] lr: 1.142e-02, eta: 1 day, 5:07:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4595, top5_acc: 0.7230, loss_cls: 2.9921, loss: 2.9921 +2024-12-30 14:35:36,030 - pyskl - INFO - Epoch [118][400/3746] lr: 1.140e-02, eta: 1 day, 5:06:20, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4433, top5_acc: 0.7109, loss_cls: 3.0633, loss: 3.0633 +2024-12-30 14:37:01,848 - pyskl - INFO - Epoch [118][500/3746] lr: 1.139e-02, eta: 1 day, 5:04:55, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4480, top5_acc: 0.7092, loss_cls: 3.0466, loss: 3.0466 +2024-12-30 14:38:27,103 - pyskl - INFO - Epoch [118][600/3746] lr: 1.137e-02, eta: 1 day, 5:03:30, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4448, top5_acc: 0.7078, loss_cls: 3.0918, loss: 3.0918 +2024-12-30 14:39:52,593 - pyskl - INFO - Epoch [118][700/3746] lr: 1.135e-02, eta: 1 day, 5:02:05, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4389, top5_acc: 0.6964, loss_cls: 3.1404, loss: 3.1404 +2024-12-30 14:41:18,140 - pyskl - INFO - Epoch [118][800/3746] lr: 1.133e-02, eta: 1 day, 5:00:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4358, top5_acc: 0.7014, loss_cls: 3.1224, loss: 3.1224 +2024-12-30 14:42:43,397 - pyskl - INFO - Epoch [118][900/3746] lr: 1.131e-02, eta: 1 day, 4:59:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4425, top5_acc: 0.7034, loss_cls: 3.0865, loss: 3.0865 +2024-12-30 14:44:08,364 - pyskl - INFO - Epoch [118][1000/3746] lr: 1.130e-02, eta: 1 day, 4:57:50, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.4434, top5_acc: 0.7091, loss_cls: 3.1106, loss: 3.1106 +2024-12-30 14:45:32,790 - pyskl - INFO - Epoch [118][1100/3746] lr: 1.128e-02, eta: 1 day, 4:56:25, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6986, loss_cls: 3.1281, loss: 3.1281 +2024-12-30 14:46:58,099 - pyskl - INFO - Epoch [118][1200/3746] lr: 1.126e-02, eta: 1 day, 4:55:00, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.4419, top5_acc: 0.7019, loss_cls: 3.1423, loss: 3.1423 +2024-12-30 14:48:22,749 - pyskl - INFO - Epoch [118][1300/3746] lr: 1.124e-02, eta: 1 day, 4:53:35, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.7053, loss_cls: 3.1103, loss: 3.1103 +2024-12-30 14:49:47,416 - pyskl - INFO - Epoch [118][1400/3746] lr: 1.123e-02, eta: 1 day, 4:52:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4373, top5_acc: 0.7061, loss_cls: 3.1106, loss: 3.1106 +2024-12-30 14:51:12,692 - pyskl - INFO - Epoch [118][1500/3746] lr: 1.121e-02, eta: 1 day, 4:50:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4356, top5_acc: 0.7016, loss_cls: 3.1367, loss: 3.1367 +2024-12-30 14:52:38,055 - pyskl - INFO - Epoch [118][1600/3746] lr: 1.119e-02, eta: 1 day, 4:49:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.7045, loss_cls: 3.0975, loss: 3.0975 +2024-12-30 14:54:02,550 - pyskl - INFO - Epoch [118][1700/3746] lr: 1.117e-02, eta: 1 day, 4:47:54, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7095, loss_cls: 3.0513, loss: 3.0513 +2024-12-30 14:55:27,189 - pyskl - INFO - Epoch [118][1800/3746] lr: 1.116e-02, eta: 1 day, 4:46:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.7003, loss_cls: 3.1256, loss: 3.1256 +2024-12-30 14:56:51,729 - pyskl - INFO - Epoch [118][1900/3746] lr: 1.114e-02, eta: 1 day, 4:45:04, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.6995, loss_cls: 3.1165, loss: 3.1165 +2024-12-30 14:58:16,690 - pyskl - INFO - Epoch [118][2000/3746] lr: 1.112e-02, eta: 1 day, 4:43:39, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4345, top5_acc: 0.6984, loss_cls: 3.1220, loss: 3.1220 +2024-12-30 14:59:40,933 - pyskl - INFO - Epoch [118][2100/3746] lr: 1.110e-02, eta: 1 day, 4:42:14, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.6994, loss_cls: 3.1333, loss: 3.1333 +2024-12-30 15:01:05,534 - pyskl - INFO - Epoch [118][2200/3746] lr: 1.109e-02, eta: 1 day, 4:40:49, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4447, top5_acc: 0.6989, loss_cls: 3.0881, loss: 3.0881 +2024-12-30 15:02:30,093 - pyskl - INFO - Epoch [118][2300/3746] lr: 1.107e-02, eta: 1 day, 4:39:23, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4466, top5_acc: 0.7077, loss_cls: 3.0921, loss: 3.0921 +2024-12-30 15:03:55,282 - pyskl - INFO - Epoch [118][2400/3746] lr: 1.105e-02, eta: 1 day, 4:37:58, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4398, top5_acc: 0.7106, loss_cls: 3.1206, loss: 3.1206 +2024-12-30 15:05:20,466 - pyskl - INFO - Epoch [118][2500/3746] lr: 1.103e-02, eta: 1 day, 4:36:33, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4470, top5_acc: 0.6956, loss_cls: 3.1279, loss: 3.1279 +2024-12-30 15:06:45,257 - pyskl - INFO - Epoch [118][2600/3746] lr: 1.102e-02, eta: 1 day, 4:35:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.7063, loss_cls: 3.0903, loss: 3.0903 +2024-12-30 15:08:09,607 - pyskl - INFO - Epoch [118][2700/3746] lr: 1.100e-02, eta: 1 day, 4:33:43, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4302, top5_acc: 0.7045, loss_cls: 3.1274, loss: 3.1274 +2024-12-30 15:09:34,569 - pyskl - INFO - Epoch [118][2800/3746] lr: 1.098e-02, eta: 1 day, 4:32:18, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4277, top5_acc: 0.6927, loss_cls: 3.1699, loss: 3.1699 +2024-12-30 15:10:59,064 - pyskl - INFO - Epoch [118][2900/3746] lr: 1.096e-02, eta: 1 day, 4:30:53, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4444, top5_acc: 0.6964, loss_cls: 3.1311, loss: 3.1311 +2024-12-30 15:12:23,903 - pyskl - INFO - Epoch [118][3000/3746] lr: 1.095e-02, eta: 1 day, 4:29:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.6964, loss_cls: 3.1441, loss: 3.1441 +2024-12-30 15:13:49,440 - pyskl - INFO - Epoch [118][3100/3746] lr: 1.093e-02, eta: 1 day, 4:28:03, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4369, top5_acc: 0.7048, loss_cls: 3.1283, loss: 3.1283 +2024-12-30 15:15:14,871 - pyskl - INFO - Epoch [118][3200/3746] lr: 1.091e-02, eta: 1 day, 4:26:38, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4342, top5_acc: 0.6998, loss_cls: 3.1531, loss: 3.1531 +2024-12-30 15:16:39,628 - pyskl - INFO - Epoch [118][3300/3746] lr: 1.089e-02, eta: 1 day, 4:25:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.7039, loss_cls: 3.1038, loss: 3.1038 +2024-12-30 15:18:04,409 - pyskl - INFO - Epoch [118][3400/3746] lr: 1.088e-02, eta: 1 day, 4:23:48, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4459, top5_acc: 0.7017, loss_cls: 3.1211, loss: 3.1211 +2024-12-30 15:19:28,774 - pyskl - INFO - Epoch [118][3500/3746] lr: 1.086e-02, eta: 1 day, 4:22:22, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4373, top5_acc: 0.7033, loss_cls: 3.1017, loss: 3.1017 +2024-12-30 15:20:53,727 - pyskl - INFO - Epoch [118][3600/3746] lr: 1.084e-02, eta: 1 day, 4:20:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.7034, loss_cls: 3.0829, loss: 3.0829 +2024-12-30 15:22:18,564 - pyskl - INFO - Epoch [118][3700/3746] lr: 1.082e-02, eta: 1 day, 4:19:32, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4375, top5_acc: 0.7053, loss_cls: 3.1149, loss: 3.1149 +2024-12-30 15:22:59,381 - pyskl - INFO - Saving checkpoint at 118 epochs +2024-12-30 15:24:57,628 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 15:24:58,463 - pyskl - INFO - +top1_acc 0.3810 +top5_acc 0.6419 +2024-12-30 15:24:58,463 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 15:24:58,512 - pyskl - INFO - +mean_acc 0.3808 +2024-12-30 15:24:58,519 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_113.pth was removed +2024-12-30 15:24:58,830 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2024-12-30 15:24:58,831 - pyskl - INFO - Best top1_acc is 0.3810 at 118 epoch. +2024-12-30 15:24:58,854 - pyskl - INFO - Epoch(val) [118][309] top1_acc: 0.3810, top5_acc: 0.6419, mean_class_accuracy: 0.3808 +2024-12-30 15:29:09,750 - pyskl - INFO - Epoch [119][100/3746] lr: 1.080e-02, eta: 1 day, 4:18:03, time: 2.509, data_time: 1.482, memory: 15990, top1_acc: 0.4733, top5_acc: 0.7264, loss_cls: 2.9743, loss: 2.9743 +2024-12-30 15:30:35,005 - pyskl - INFO - Epoch [119][200/3746] lr: 1.078e-02, eta: 1 day, 4:16:38, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4452, top5_acc: 0.7050, loss_cls: 3.0548, loss: 3.0548 +2024-12-30 15:32:00,249 - pyskl - INFO - Epoch [119][300/3746] lr: 1.076e-02, eta: 1 day, 4:15:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4505, top5_acc: 0.7152, loss_cls: 3.0735, loss: 3.0735 +2024-12-30 15:33:24,996 - pyskl - INFO - Epoch [119][400/3746] lr: 1.075e-02, eta: 1 day, 4:13:47, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4539, top5_acc: 0.7153, loss_cls: 3.0529, loss: 3.0529 +2024-12-30 15:34:50,329 - pyskl - INFO - Epoch [119][500/3746] lr: 1.073e-02, eta: 1 day, 4:12:22, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4591, top5_acc: 0.7206, loss_cls: 3.0212, loss: 3.0212 +2024-12-30 15:36:15,250 - pyskl - INFO - Epoch [119][600/3746] lr: 1.071e-02, eta: 1 day, 4:10:57, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4591, top5_acc: 0.7216, loss_cls: 3.0162, loss: 3.0162 +2024-12-30 15:37:40,099 - pyskl - INFO - Epoch [119][700/3746] lr: 1.069e-02, eta: 1 day, 4:09:32, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4452, top5_acc: 0.7052, loss_cls: 3.0759, loss: 3.0759 +2024-12-30 15:39:04,521 - pyskl - INFO - Epoch [119][800/3746] lr: 1.068e-02, eta: 1 day, 4:08:07, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4464, top5_acc: 0.7166, loss_cls: 3.0477, loss: 3.0477 +2024-12-30 15:40:28,918 - pyskl - INFO - Epoch [119][900/3746] lr: 1.066e-02, eta: 1 day, 4:06:42, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.7114, loss_cls: 3.0694, loss: 3.0694 +2024-12-30 15:41:54,367 - pyskl - INFO - Epoch [119][1000/3746] lr: 1.064e-02, eta: 1 day, 4:05:17, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4486, top5_acc: 0.7098, loss_cls: 3.0611, loss: 3.0611 +2024-12-30 15:43:19,017 - pyskl - INFO - Epoch [119][1100/3746] lr: 1.063e-02, eta: 1 day, 4:03:52, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4534, top5_acc: 0.7105, loss_cls: 3.0799, loss: 3.0799 +2024-12-30 15:44:43,803 - pyskl - INFO - Epoch [119][1200/3746] lr: 1.061e-02, eta: 1 day, 4:02:26, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4408, top5_acc: 0.7095, loss_cls: 3.0882, loss: 3.0882 +2024-12-30 15:46:08,993 - pyskl - INFO - Epoch [119][1300/3746] lr: 1.059e-02, eta: 1 day, 4:01:01, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4431, top5_acc: 0.7045, loss_cls: 3.1230, loss: 3.1230 +2024-12-30 15:47:33,368 - pyskl - INFO - Epoch [119][1400/3746] lr: 1.057e-02, eta: 1 day, 3:59:36, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.4411, top5_acc: 0.7022, loss_cls: 3.0843, loss: 3.0843 +2024-12-30 15:48:58,139 - pyskl - INFO - Epoch [119][1500/3746] lr: 1.056e-02, eta: 1 day, 3:58:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4475, top5_acc: 0.7050, loss_cls: 3.0920, loss: 3.0920 +2024-12-30 15:50:22,826 - pyskl - INFO - Epoch [119][1600/3746] lr: 1.054e-02, eta: 1 day, 3:56:46, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4486, top5_acc: 0.7019, loss_cls: 3.1139, loss: 3.1139 +2024-12-30 15:51:47,284 - pyskl - INFO - Epoch [119][1700/3746] lr: 1.052e-02, eta: 1 day, 3:55:21, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4525, top5_acc: 0.7105, loss_cls: 3.0479, loss: 3.0479 +2024-12-30 15:53:11,431 - pyskl - INFO - Epoch [119][1800/3746] lr: 1.050e-02, eta: 1 day, 3:53:55, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4442, top5_acc: 0.7036, loss_cls: 3.0839, loss: 3.0839 +2024-12-30 15:54:36,146 - pyskl - INFO - Epoch [119][1900/3746] lr: 1.049e-02, eta: 1 day, 3:52:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4514, top5_acc: 0.7155, loss_cls: 3.0536, loss: 3.0536 +2024-12-30 15:56:00,638 - pyskl - INFO - Epoch [119][2000/3746] lr: 1.047e-02, eta: 1 day, 3:51:05, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4414, top5_acc: 0.6973, loss_cls: 3.1346, loss: 3.1346 +2024-12-30 15:57:25,505 - pyskl - INFO - Epoch [119][2100/3746] lr: 1.045e-02, eta: 1 day, 3:49:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4558, top5_acc: 0.7080, loss_cls: 3.0678, loss: 3.0678 +2024-12-30 15:58:50,932 - pyskl - INFO - Epoch [119][2200/3746] lr: 1.044e-02, eta: 1 day, 3:48:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4502, top5_acc: 0.7069, loss_cls: 3.0831, loss: 3.0831 +2024-12-30 16:00:15,375 - pyskl - INFO - Epoch [119][2300/3746] lr: 1.042e-02, eta: 1 day, 3:46:50, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4469, top5_acc: 0.7139, loss_cls: 3.0464, loss: 3.0464 +2024-12-30 16:01:39,551 - pyskl - INFO - Epoch [119][2400/3746] lr: 1.040e-02, eta: 1 day, 3:45:24, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4466, top5_acc: 0.7078, loss_cls: 3.1025, loss: 3.1025 +2024-12-30 16:03:04,191 - pyskl - INFO - Epoch [119][2500/3746] lr: 1.039e-02, eta: 1 day, 3:43:59, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4470, top5_acc: 0.7008, loss_cls: 3.0926, loss: 3.0926 +2024-12-30 16:04:28,833 - pyskl - INFO - Epoch [119][2600/3746] lr: 1.037e-02, eta: 1 day, 3:42:34, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.6994, loss_cls: 3.1266, loss: 3.1266 +2024-12-30 16:05:53,247 - pyskl - INFO - Epoch [119][2700/3746] lr: 1.035e-02, eta: 1 day, 3:41:09, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4455, top5_acc: 0.7023, loss_cls: 3.0862, loss: 3.0862 +2024-12-30 16:07:17,786 - pyskl - INFO - Epoch [119][2800/3746] lr: 1.033e-02, eta: 1 day, 3:39:43, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4478, top5_acc: 0.7069, loss_cls: 3.0791, loss: 3.0791 +2024-12-30 16:08:42,390 - pyskl - INFO - Epoch [119][2900/3746] lr: 1.032e-02, eta: 1 day, 3:38:18, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.7022, loss_cls: 3.0968, loss: 3.0968 +2024-12-30 16:10:07,617 - pyskl - INFO - Epoch [119][3000/3746] lr: 1.030e-02, eta: 1 day, 3:36:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4442, top5_acc: 0.7034, loss_cls: 3.1030, loss: 3.1030 +2024-12-30 16:11:32,457 - pyskl - INFO - Epoch [119][3100/3746] lr: 1.028e-02, eta: 1 day, 3:35:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4473, top5_acc: 0.7111, loss_cls: 3.0707, loss: 3.0707 +2024-12-30 16:12:56,933 - pyskl - INFO - Epoch [119][3200/3746] lr: 1.027e-02, eta: 1 day, 3:34:03, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4359, top5_acc: 0.6959, loss_cls: 3.1485, loss: 3.1485 +2024-12-30 16:14:21,326 - pyskl - INFO - Epoch [119][3300/3746] lr: 1.025e-02, eta: 1 day, 3:32:38, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4503, top5_acc: 0.7148, loss_cls: 3.0337, loss: 3.0337 +2024-12-30 16:15:46,154 - pyskl - INFO - Epoch [119][3400/3746] lr: 1.023e-02, eta: 1 day, 3:31:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4377, top5_acc: 0.7017, loss_cls: 3.1539, loss: 3.1539 +2024-12-30 16:17:10,918 - pyskl - INFO - Epoch [119][3500/3746] lr: 1.022e-02, eta: 1 day, 3:29:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4339, top5_acc: 0.6970, loss_cls: 3.1706, loss: 3.1706 +2024-12-30 16:18:35,139 - pyskl - INFO - Epoch [119][3600/3746] lr: 1.020e-02, eta: 1 day, 3:28:22, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4512, top5_acc: 0.7013, loss_cls: 3.1070, loss: 3.1070 +2024-12-30 16:19:59,267 - pyskl - INFO - Epoch [119][3700/3746] lr: 1.018e-02, eta: 1 day, 3:26:57, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4436, top5_acc: 0.7050, loss_cls: 3.0928, loss: 3.0928 +2024-12-30 16:20:40,282 - pyskl - INFO - Saving checkpoint at 119 epochs +2024-12-30 16:22:39,284 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 16:22:40,086 - pyskl - INFO - +top1_acc 0.3759 +top5_acc 0.6340 +2024-12-30 16:22:40,086 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 16:22:40,130 - pyskl - INFO - +mean_acc 0.3757 +2024-12-30 16:22:40,143 - pyskl - INFO - Epoch(val) [119][309] top1_acc: 0.3759, top5_acc: 0.6340, mean_class_accuracy: 0.3757 +2024-12-30 16:26:55,886 - pyskl - INFO - Epoch [120][100/3746] lr: 1.016e-02, eta: 1 day, 3:25:27, time: 2.557, data_time: 1.530, memory: 15990, top1_acc: 0.4561, top5_acc: 0.7236, loss_cls: 2.9987, loss: 2.9987 +2024-12-30 16:28:22,003 - pyskl - INFO - Epoch [120][200/3746] lr: 1.014e-02, eta: 1 day, 3:24:02, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4622, top5_acc: 0.7358, loss_cls: 2.9480, loss: 2.9480 +2024-12-30 16:29:47,752 - pyskl - INFO - Epoch [120][300/3746] lr: 1.012e-02, eta: 1 day, 3:22:37, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4634, top5_acc: 0.7163, loss_cls: 3.0083, loss: 3.0083 +2024-12-30 16:31:13,412 - pyskl - INFO - Epoch [120][400/3746] lr: 1.011e-02, eta: 1 day, 3:21:12, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4550, top5_acc: 0.7153, loss_cls: 3.0219, loss: 3.0219 +2024-12-30 16:32:39,151 - pyskl - INFO - Epoch [120][500/3746] lr: 1.009e-02, eta: 1 day, 3:19:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4456, top5_acc: 0.7122, loss_cls: 3.0652, loss: 3.0652 +2024-12-30 16:34:04,019 - pyskl - INFO - Epoch [120][600/3746] lr: 1.007e-02, eta: 1 day, 3:18:22, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4508, top5_acc: 0.7142, loss_cls: 3.0299, loss: 3.0299 +2024-12-30 16:35:29,088 - pyskl - INFO - Epoch [120][700/3746] lr: 1.006e-02, eta: 1 day, 3:16:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4605, top5_acc: 0.7222, loss_cls: 3.0187, loss: 3.0187 +2024-12-30 16:36:54,403 - pyskl - INFO - Epoch [120][800/3746] lr: 1.004e-02, eta: 1 day, 3:15:32, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.7228, loss_cls: 3.0327, loss: 3.0327 +2024-12-30 16:38:19,356 - pyskl - INFO - Epoch [120][900/3746] lr: 1.002e-02, eta: 1 day, 3:14:07, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4530, top5_acc: 0.7113, loss_cls: 3.0581, loss: 3.0581 +2024-12-30 16:39:43,893 - pyskl - INFO - Epoch [120][1000/3746] lr: 1.001e-02, eta: 1 day, 3:12:42, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7072, loss_cls: 3.0655, loss: 3.0655 +2024-12-30 16:41:08,939 - pyskl - INFO - Epoch [120][1100/3746] lr: 9.989e-03, eta: 1 day, 3:11:17, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4462, top5_acc: 0.7052, loss_cls: 3.0911, loss: 3.0911 +2024-12-30 16:42:33,964 - pyskl - INFO - Epoch [120][1200/3746] lr: 9.972e-03, eta: 1 day, 3:09:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4441, top5_acc: 0.7063, loss_cls: 3.0620, loss: 3.0620 +2024-12-30 16:43:58,710 - pyskl - INFO - Epoch [120][1300/3746] lr: 9.955e-03, eta: 1 day, 3:08:26, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.4591, top5_acc: 0.7130, loss_cls: 3.0275, loss: 3.0275 +2024-12-30 16:45:23,064 - pyskl - INFO - Epoch [120][1400/3746] lr: 9.938e-03, eta: 1 day, 3:07:01, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4542, top5_acc: 0.7186, loss_cls: 3.0316, loss: 3.0316 +2024-12-30 16:46:48,182 - pyskl - INFO - Epoch [120][1500/3746] lr: 9.922e-03, eta: 1 day, 3:05:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4506, top5_acc: 0.7081, loss_cls: 3.0573, loss: 3.0573 +2024-12-30 16:48:12,909 - pyskl - INFO - Epoch [120][1600/3746] lr: 9.905e-03, eta: 1 day, 3:04:11, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4512, top5_acc: 0.7128, loss_cls: 3.0271, loss: 3.0271 +2024-12-30 16:49:37,464 - pyskl - INFO - Epoch [120][1700/3746] lr: 9.888e-03, eta: 1 day, 3:02:45, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4558, top5_acc: 0.7105, loss_cls: 3.0644, loss: 3.0644 +2024-12-30 16:51:02,176 - pyskl - INFO - Epoch [120][1800/3746] lr: 9.871e-03, eta: 1 day, 3:01:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4545, top5_acc: 0.7198, loss_cls: 2.9985, loss: 2.9985 +2024-12-30 16:52:27,038 - pyskl - INFO - Epoch [120][1900/3746] lr: 9.855e-03, eta: 1 day, 2:59:55, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4567, top5_acc: 0.7209, loss_cls: 3.0359, loss: 3.0359 +2024-12-30 16:53:51,874 - pyskl - INFO - Epoch [120][2000/3746] lr: 9.838e-03, eta: 1 day, 2:58:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4494, top5_acc: 0.7169, loss_cls: 3.0265, loss: 3.0265 +2024-12-30 16:55:16,189 - pyskl - INFO - Epoch [120][2100/3746] lr: 9.821e-03, eta: 1 day, 2:57:05, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4508, top5_acc: 0.7097, loss_cls: 3.0482, loss: 3.0482 +2024-12-30 16:56:40,399 - pyskl - INFO - Epoch [120][2200/3746] lr: 9.805e-03, eta: 1 day, 2:55:39, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4502, top5_acc: 0.7137, loss_cls: 3.0507, loss: 3.0507 +2024-12-30 16:58:04,682 - pyskl - INFO - Epoch [120][2300/3746] lr: 9.788e-03, eta: 1 day, 2:54:14, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4655, top5_acc: 0.7222, loss_cls: 2.9958, loss: 2.9958 +2024-12-30 16:59:29,410 - pyskl - INFO - Epoch [120][2400/3746] lr: 9.772e-03, eta: 1 day, 2:52:49, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4403, top5_acc: 0.7042, loss_cls: 3.0937, loss: 3.0937 +2024-12-30 17:00:54,255 - pyskl - INFO - Epoch [120][2500/3746] lr: 9.755e-03, eta: 1 day, 2:51:24, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4539, top5_acc: 0.7167, loss_cls: 3.0422, loss: 3.0422 +2024-12-30 17:02:18,470 - pyskl - INFO - Epoch [120][2600/3746] lr: 9.738e-03, eta: 1 day, 2:49:58, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4470, top5_acc: 0.7064, loss_cls: 3.0780, loss: 3.0780 +2024-12-30 17:03:42,907 - pyskl - INFO - Epoch [120][2700/3746] lr: 9.722e-03, eta: 1 day, 2:48:33, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4503, top5_acc: 0.7200, loss_cls: 3.0337, loss: 3.0337 +2024-12-30 17:05:07,302 - pyskl - INFO - Epoch [120][2800/3746] lr: 9.705e-03, eta: 1 day, 2:47:08, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4380, top5_acc: 0.7069, loss_cls: 3.0969, loss: 3.0969 +2024-12-30 17:06:31,385 - pyskl - INFO - Epoch [120][2900/3746] lr: 9.689e-03, eta: 1 day, 2:45:43, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7223, loss_cls: 2.9947, loss: 2.9947 +2024-12-30 17:07:55,638 - pyskl - INFO - Epoch [120][3000/3746] lr: 9.672e-03, eta: 1 day, 2:44:17, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4405, top5_acc: 0.6983, loss_cls: 3.1041, loss: 3.1041 +2024-12-30 17:09:20,733 - pyskl - INFO - Epoch [120][3100/3746] lr: 9.656e-03, eta: 1 day, 2:42:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4381, top5_acc: 0.7002, loss_cls: 3.1048, loss: 3.1048 +2024-12-30 17:10:45,415 - pyskl - INFO - Epoch [120][3200/3746] lr: 9.639e-03, eta: 1 day, 2:41:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4514, top5_acc: 0.7173, loss_cls: 3.0308, loss: 3.0308 +2024-12-30 17:12:10,116 - pyskl - INFO - Epoch [120][3300/3746] lr: 9.623e-03, eta: 1 day, 2:40:02, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4361, top5_acc: 0.7020, loss_cls: 3.1280, loss: 3.1280 +2024-12-30 17:13:34,662 - pyskl - INFO - Epoch [120][3400/3746] lr: 9.606e-03, eta: 1 day, 2:38:37, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.7061, loss_cls: 3.0990, loss: 3.0990 +2024-12-30 17:14:59,027 - pyskl - INFO - Epoch [120][3500/3746] lr: 9.590e-03, eta: 1 day, 2:37:11, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4427, top5_acc: 0.7000, loss_cls: 3.1196, loss: 3.1196 +2024-12-30 17:16:24,563 - pyskl - INFO - Epoch [120][3600/3746] lr: 9.573e-03, eta: 1 day, 2:35:46, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4448, top5_acc: 0.7119, loss_cls: 3.0689, loss: 3.0689 +2024-12-30 17:17:48,413 - pyskl - INFO - Epoch [120][3700/3746] lr: 9.557e-03, eta: 1 day, 2:34:21, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4459, top5_acc: 0.7023, loss_cls: 3.0901, loss: 3.0901 +2024-12-30 17:18:28,920 - pyskl - INFO - Saving checkpoint at 120 epochs +2024-12-30 17:20:27,406 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 17:20:28,157 - pyskl - INFO - +top1_acc 0.3792 +top5_acc 0.6327 +2024-12-30 17:20:28,157 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 17:20:28,199 - pyskl - INFO - +mean_acc 0.3789 +2024-12-30 17:20:28,211 - pyskl - INFO - Epoch(val) [120][309] top1_acc: 0.3792, top5_acc: 0.6327, mean_class_accuracy: 0.3789 +2024-12-30 17:24:37,604 - pyskl - INFO - Epoch [121][100/3746] lr: 9.533e-03, eta: 1 day, 2:32:48, time: 2.494, data_time: 1.466, memory: 15990, top1_acc: 0.4608, top5_acc: 0.7314, loss_cls: 2.9786, loss: 2.9786 +2024-12-30 17:26:02,174 - pyskl - INFO - Epoch [121][200/3746] lr: 9.516e-03, eta: 1 day, 2:31:23, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4684, top5_acc: 0.7269, loss_cls: 2.9378, loss: 2.9378 +2024-12-30 17:27:27,451 - pyskl - INFO - Epoch [121][300/3746] lr: 9.500e-03, eta: 1 day, 2:29:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4745, top5_acc: 0.7294, loss_cls: 2.9306, loss: 2.9306 +2024-12-30 17:28:52,169 - pyskl - INFO - Epoch [121][400/3746] lr: 9.484e-03, eta: 1 day, 2:28:32, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4662, top5_acc: 0.7225, loss_cls: 2.9684, loss: 2.9684 +2024-12-30 17:30:17,221 - pyskl - INFO - Epoch [121][500/3746] lr: 9.467e-03, eta: 1 day, 2:27:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4595, top5_acc: 0.7191, loss_cls: 3.0217, loss: 3.0217 +2024-12-30 17:31:42,053 - pyskl - INFO - Epoch [121][600/3746] lr: 9.451e-03, eta: 1 day, 2:25:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7105, loss_cls: 3.0492, loss: 3.0492 +2024-12-30 17:33:06,159 - pyskl - INFO - Epoch [121][700/3746] lr: 9.435e-03, eta: 1 day, 2:24:17, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4645, top5_acc: 0.7222, loss_cls: 2.9935, loss: 2.9935 +2024-12-30 17:34:31,081 - pyskl - INFO - Epoch [121][800/3746] lr: 9.418e-03, eta: 1 day, 2:22:52, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4570, top5_acc: 0.7166, loss_cls: 3.0060, loss: 3.0060 +2024-12-30 17:35:56,055 - pyskl - INFO - Epoch [121][900/3746] lr: 9.402e-03, eta: 1 day, 2:21:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4569, top5_acc: 0.7233, loss_cls: 2.9752, loss: 2.9752 +2024-12-30 17:37:21,236 - pyskl - INFO - Epoch [121][1000/3746] lr: 9.386e-03, eta: 1 day, 2:20:01, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.4550, top5_acc: 0.7100, loss_cls: 3.0402, loss: 3.0402 +2024-12-30 17:38:45,829 - pyskl - INFO - Epoch [121][1100/3746] lr: 9.369e-03, eta: 1 day, 2:18:36, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4628, top5_acc: 0.7191, loss_cls: 3.0239, loss: 3.0239 +2024-12-30 17:40:10,435 - pyskl - INFO - Epoch [121][1200/3746] lr: 9.353e-03, eta: 1 day, 2:17:11, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4641, top5_acc: 0.7217, loss_cls: 3.0042, loss: 3.0042 +2024-12-30 17:41:34,942 - pyskl - INFO - Epoch [121][1300/3746] lr: 9.337e-03, eta: 1 day, 2:15:46, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4564, top5_acc: 0.7100, loss_cls: 3.0353, loss: 3.0353 +2024-12-30 17:43:00,316 - pyskl - INFO - Epoch [121][1400/3746] lr: 9.321e-03, eta: 1 day, 2:14:21, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4478, top5_acc: 0.7120, loss_cls: 3.0418, loss: 3.0418 +2024-12-30 17:44:24,553 - pyskl - INFO - Epoch [121][1500/3746] lr: 9.304e-03, eta: 1 day, 2:12:55, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4550, top5_acc: 0.7203, loss_cls: 3.0086, loss: 3.0086 +2024-12-30 17:45:49,292 - pyskl - INFO - Epoch [121][1600/3746] lr: 9.288e-03, eta: 1 day, 2:11:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4552, top5_acc: 0.7273, loss_cls: 2.9974, loss: 2.9974 +2024-12-30 17:47:14,240 - pyskl - INFO - Epoch [121][1700/3746] lr: 9.272e-03, eta: 1 day, 2:10:05, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4619, top5_acc: 0.7231, loss_cls: 2.9926, loss: 2.9926 +2024-12-30 17:48:38,937 - pyskl - INFO - Epoch [121][1800/3746] lr: 9.256e-03, eta: 1 day, 2:08:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4625, top5_acc: 0.7161, loss_cls: 3.0096, loss: 3.0096 +2024-12-30 17:50:03,322 - pyskl - INFO - Epoch [121][1900/3746] lr: 9.239e-03, eta: 1 day, 2:07:14, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4661, top5_acc: 0.7188, loss_cls: 3.0007, loss: 3.0007 +2024-12-30 17:51:27,818 - pyskl - INFO - Epoch [121][2000/3746] lr: 9.223e-03, eta: 1 day, 2:05:49, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4527, top5_acc: 0.7191, loss_cls: 3.0306, loss: 3.0306 +2024-12-30 17:52:52,213 - pyskl - INFO - Epoch [121][2100/3746] lr: 9.207e-03, eta: 1 day, 2:04:24, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4527, top5_acc: 0.7067, loss_cls: 3.0429, loss: 3.0429 +2024-12-30 17:54:17,009 - pyskl - INFO - Epoch [121][2200/3746] lr: 9.191e-03, eta: 1 day, 2:02:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4561, top5_acc: 0.7155, loss_cls: 3.0219, loss: 3.0219 +2024-12-30 17:55:42,034 - pyskl - INFO - Epoch [121][2300/3746] lr: 9.175e-03, eta: 1 day, 2:01:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4431, top5_acc: 0.7053, loss_cls: 3.0950, loss: 3.0950 +2024-12-30 17:57:06,968 - pyskl - INFO - Epoch [121][2400/3746] lr: 9.159e-03, eta: 1 day, 2:00:08, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4597, top5_acc: 0.7206, loss_cls: 2.9938, loss: 2.9938 +2024-12-30 17:58:31,990 - pyskl - INFO - Epoch [121][2500/3746] lr: 9.142e-03, eta: 1 day, 1:58:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4520, top5_acc: 0.7159, loss_cls: 3.0461, loss: 3.0461 +2024-12-30 17:59:56,817 - pyskl - INFO - Epoch [121][2600/3746] lr: 9.126e-03, eta: 1 day, 1:57:18, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4545, top5_acc: 0.7137, loss_cls: 3.0436, loss: 3.0436 +2024-12-30 18:01:21,227 - pyskl - INFO - Epoch [121][2700/3746] lr: 9.110e-03, eta: 1 day, 1:55:53, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4616, top5_acc: 0.7202, loss_cls: 3.0228, loss: 3.0228 +2024-12-30 18:02:45,964 - pyskl - INFO - Epoch [121][2800/3746] lr: 9.094e-03, eta: 1 day, 1:54:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4584, top5_acc: 0.7166, loss_cls: 3.0321, loss: 3.0321 +2024-12-30 18:04:10,935 - pyskl - INFO - Epoch [121][2900/3746] lr: 9.078e-03, eta: 1 day, 1:53:02, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4506, top5_acc: 0.7131, loss_cls: 3.0537, loss: 3.0537 +2024-12-30 18:05:36,206 - pyskl - INFO - Epoch [121][3000/3746] lr: 9.062e-03, eta: 1 day, 1:51:37, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4530, top5_acc: 0.7173, loss_cls: 3.0256, loss: 3.0256 +2024-12-30 18:07:00,785 - pyskl - INFO - Epoch [121][3100/3746] lr: 9.046e-03, eta: 1 day, 1:50:12, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7108, loss_cls: 3.0458, loss: 3.0458 +2024-12-30 18:08:25,938 - pyskl - INFO - Epoch [121][3200/3746] lr: 9.030e-03, eta: 1 day, 1:48:47, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4570, top5_acc: 0.7169, loss_cls: 3.0328, loss: 3.0328 +2024-12-30 18:09:50,686 - pyskl - INFO - Epoch [121][3300/3746] lr: 9.014e-03, eta: 1 day, 1:47:22, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4567, top5_acc: 0.7116, loss_cls: 3.0570, loss: 3.0570 +2024-12-30 18:11:15,561 - pyskl - INFO - Epoch [121][3400/3746] lr: 8.998e-03, eta: 1 day, 1:45:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4544, top5_acc: 0.7144, loss_cls: 3.0248, loss: 3.0248 +2024-12-30 18:12:40,683 - pyskl - INFO - Epoch [121][3500/3746] lr: 8.982e-03, eta: 1 day, 1:44:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4508, top5_acc: 0.7111, loss_cls: 3.0565, loss: 3.0565 +2024-12-30 18:14:04,685 - pyskl - INFO - Epoch [121][3600/3746] lr: 8.966e-03, eta: 1 day, 1:43:06, time: 0.840, data_time: 0.001, memory: 15990, top1_acc: 0.4455, top5_acc: 0.7127, loss_cls: 3.0852, loss: 3.0852 +2024-12-30 18:15:29,002 - pyskl - INFO - Epoch [121][3700/3746] lr: 8.950e-03, eta: 1 day, 1:41:41, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4453, top5_acc: 0.7072, loss_cls: 3.0867, loss: 3.0867 +2024-12-30 18:16:09,579 - pyskl - INFO - Saving checkpoint at 121 epochs +2024-12-30 18:18:05,495 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 18:18:06,201 - pyskl - INFO - +top1_acc 0.3811 +top5_acc 0.6285 +2024-12-30 18:18:06,202 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 18:18:06,241 - pyskl - INFO - +mean_acc 0.3809 +2024-12-30 18:18:06,245 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_118.pth was removed +2024-12-30 18:18:06,520 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_121.pth. +2024-12-30 18:18:06,521 - pyskl - INFO - Best top1_acc is 0.3811 at 121 epoch. +2024-12-30 18:18:06,534 - pyskl - INFO - Epoch(val) [121][309] top1_acc: 0.3811, top5_acc: 0.6285, mean_class_accuracy: 0.3809 +2024-12-30 18:22:15,285 - pyskl - INFO - Epoch [122][100/3746] lr: 8.927e-03, eta: 1 day, 1:40:06, time: 2.487, data_time: 1.458, memory: 15990, top1_acc: 0.4739, top5_acc: 0.7383, loss_cls: 2.9015, loss: 2.9015 +2024-12-30 18:23:40,955 - pyskl - INFO - Epoch [122][200/3746] lr: 8.911e-03, eta: 1 day, 1:38:41, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4653, top5_acc: 0.7262, loss_cls: 2.9614, loss: 2.9614 +2024-12-30 18:25:07,006 - pyskl - INFO - Epoch [122][300/3746] lr: 8.895e-03, eta: 1 day, 1:37:16, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7280, loss_cls: 2.9554, loss: 2.9554 +2024-12-30 18:26:32,471 - pyskl - INFO - Epoch [122][400/3746] lr: 8.879e-03, eta: 1 day, 1:35:51, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4584, top5_acc: 0.7212, loss_cls: 2.9803, loss: 2.9803 +2024-12-30 18:27:58,370 - pyskl - INFO - Epoch [122][500/3746] lr: 8.863e-03, eta: 1 day, 1:34:26, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4637, top5_acc: 0.7137, loss_cls: 2.9915, loss: 2.9915 +2024-12-30 18:29:23,972 - pyskl - INFO - Epoch [122][600/3746] lr: 8.847e-03, eta: 1 day, 1:33:01, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4683, top5_acc: 0.7214, loss_cls: 2.9621, loss: 2.9621 +2024-12-30 18:30:49,849 - pyskl - INFO - Epoch [122][700/3746] lr: 8.831e-03, eta: 1 day, 1:31:36, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4672, top5_acc: 0.7248, loss_cls: 2.9736, loss: 2.9736 +2024-12-30 18:32:15,472 - pyskl - INFO - Epoch [122][800/3746] lr: 8.815e-03, eta: 1 day, 1:30:11, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4597, top5_acc: 0.7139, loss_cls: 3.0232, loss: 3.0232 +2024-12-30 18:33:40,866 - pyskl - INFO - Epoch [122][900/3746] lr: 8.800e-03, eta: 1 day, 1:28:46, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7203, loss_cls: 3.0045, loss: 3.0045 +2024-12-30 18:35:06,781 - pyskl - INFO - Epoch [122][1000/3746] lr: 8.784e-03, eta: 1 day, 1:27:21, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4697, top5_acc: 0.7292, loss_cls: 2.9617, loss: 2.9617 +2024-12-30 18:36:32,020 - pyskl - INFO - Epoch [122][1100/3746] lr: 8.768e-03, eta: 1 day, 1:25:56, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4639, top5_acc: 0.7277, loss_cls: 2.9759, loss: 2.9759 +2024-12-30 18:37:56,771 - pyskl - INFO - Epoch [122][1200/3746] lr: 8.752e-03, eta: 1 day, 1:24:31, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4603, top5_acc: 0.7278, loss_cls: 2.9809, loss: 2.9809 +2024-12-30 18:39:21,428 - pyskl - INFO - Epoch [122][1300/3746] lr: 8.736e-03, eta: 1 day, 1:23:06, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4561, top5_acc: 0.7270, loss_cls: 3.0092, loss: 3.0092 +2024-12-30 18:40:46,403 - pyskl - INFO - Epoch [122][1400/3746] lr: 8.721e-03, eta: 1 day, 1:21:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4620, top5_acc: 0.7264, loss_cls: 2.9695, loss: 2.9695 +2024-12-30 18:42:11,206 - pyskl - INFO - Epoch [122][1500/3746] lr: 8.705e-03, eta: 1 day, 1:20:15, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4689, top5_acc: 0.7256, loss_cls: 2.9962, loss: 2.9962 +2024-12-30 18:43:35,437 - pyskl - INFO - Epoch [122][1600/3746] lr: 8.689e-03, eta: 1 day, 1:18:50, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4677, top5_acc: 0.7250, loss_cls: 2.9739, loss: 2.9739 +2024-12-30 18:45:00,359 - pyskl - INFO - Epoch [122][1700/3746] lr: 8.673e-03, eta: 1 day, 1:17:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4612, top5_acc: 0.7150, loss_cls: 3.0126, loss: 3.0126 +2024-12-30 18:46:24,891 - pyskl - INFO - Epoch [122][1800/3746] lr: 8.658e-03, eta: 1 day, 1:15:59, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4595, top5_acc: 0.7169, loss_cls: 3.0106, loss: 3.0106 +2024-12-30 18:47:48,819 - pyskl - INFO - Epoch [122][1900/3746] lr: 8.642e-03, eta: 1 day, 1:14:34, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4561, top5_acc: 0.7117, loss_cls: 3.0056, loss: 3.0056 +2024-12-30 18:49:13,203 - pyskl - INFO - Epoch [122][2000/3746] lr: 8.626e-03, eta: 1 day, 1:13:09, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4511, top5_acc: 0.7166, loss_cls: 3.0295, loss: 3.0295 +2024-12-30 18:50:38,114 - pyskl - INFO - Epoch [122][2100/3746] lr: 8.610e-03, eta: 1 day, 1:11:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4678, top5_acc: 0.7212, loss_cls: 3.0031, loss: 3.0031 +2024-12-30 18:52:02,404 - pyskl - INFO - Epoch [122][2200/3746] lr: 8.595e-03, eta: 1 day, 1:10:18, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4627, top5_acc: 0.7184, loss_cls: 2.9725, loss: 2.9725 +2024-12-30 18:53:26,979 - pyskl - INFO - Epoch [122][2300/3746] lr: 8.579e-03, eta: 1 day, 1:08:53, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4645, top5_acc: 0.7220, loss_cls: 2.9790, loss: 2.9790 +2024-12-30 18:54:51,558 - pyskl - INFO - Epoch [122][2400/3746] lr: 8.563e-03, eta: 1 day, 1:07:28, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4577, top5_acc: 0.7209, loss_cls: 2.9989, loss: 2.9989 +2024-12-30 18:56:15,642 - pyskl - INFO - Epoch [122][2500/3746] lr: 8.548e-03, eta: 1 day, 1:06:02, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4500, top5_acc: 0.7133, loss_cls: 3.0555, loss: 3.0555 +2024-12-30 18:57:39,664 - pyskl - INFO - Epoch [122][2600/3746] lr: 8.532e-03, eta: 1 day, 1:04:37, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.4583, top5_acc: 0.7148, loss_cls: 3.0106, loss: 3.0106 +2024-12-30 18:59:04,180 - pyskl - INFO - Epoch [122][2700/3746] lr: 8.517e-03, eta: 1 day, 1:03:11, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4631, top5_acc: 0.7247, loss_cls: 2.9740, loss: 2.9740 +2024-12-30 19:00:28,067 - pyskl - INFO - Epoch [122][2800/3746] lr: 8.501e-03, eta: 1 day, 1:01:46, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4708, top5_acc: 0.7242, loss_cls: 2.9480, loss: 2.9480 +2024-12-30 19:01:53,187 - pyskl - INFO - Epoch [122][2900/3746] lr: 8.485e-03, eta: 1 day, 1:00:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4519, top5_acc: 0.7097, loss_cls: 3.0573, loss: 3.0573 +2024-12-30 19:03:17,760 - pyskl - INFO - Epoch [122][3000/3746] lr: 8.470e-03, eta: 1 day, 0:58:56, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4559, top5_acc: 0.7067, loss_cls: 3.0265, loss: 3.0265 +2024-12-30 19:04:41,961 - pyskl - INFO - Epoch [122][3100/3746] lr: 8.454e-03, eta: 1 day, 0:57:30, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7189, loss_cls: 2.9853, loss: 2.9853 +2024-12-30 19:06:06,857 - pyskl - INFO - Epoch [122][3200/3746] lr: 8.439e-03, eta: 1 day, 0:56:05, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4527, top5_acc: 0.7314, loss_cls: 2.9815, loss: 2.9815 +2024-12-30 19:07:31,971 - pyskl - INFO - Epoch [122][3300/3746] lr: 8.423e-03, eta: 1 day, 0:54:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4536, top5_acc: 0.7141, loss_cls: 3.0342, loss: 3.0342 +2024-12-30 19:08:57,080 - pyskl - INFO - Epoch [122][3400/3746] lr: 8.408e-03, eta: 1 day, 0:53:15, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4512, top5_acc: 0.7070, loss_cls: 3.0595, loss: 3.0595 +2024-12-30 19:10:21,701 - pyskl - INFO - Epoch [122][3500/3746] lr: 8.392e-03, eta: 1 day, 0:51:50, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4598, top5_acc: 0.7212, loss_cls: 2.9886, loss: 2.9886 +2024-12-30 19:11:46,118 - pyskl - INFO - Epoch [122][3600/3746] lr: 8.377e-03, eta: 1 day, 0:50:24, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.4606, top5_acc: 0.7134, loss_cls: 3.0272, loss: 3.0272 +2024-12-30 19:13:11,245 - pyskl - INFO - Epoch [122][3700/3746] lr: 8.361e-03, eta: 1 day, 0:48:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4575, top5_acc: 0.7191, loss_cls: 3.0115, loss: 3.0115 +2024-12-30 19:13:51,570 - pyskl - INFO - Saving checkpoint at 122 epochs +2024-12-30 19:15:48,467 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 19:15:49,150 - pyskl - INFO - +top1_acc 0.3863 +top5_acc 0.6486 +2024-12-30 19:15:49,150 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 19:15:49,192 - pyskl - INFO - +mean_acc 0.3861 +2024-12-30 19:15:49,197 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_121.pth was removed +2024-12-30 19:15:49,459 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_122.pth. +2024-12-30 19:15:49,460 - pyskl - INFO - Best top1_acc is 0.3863 at 122 epoch. +2024-12-30 19:15:49,473 - pyskl - INFO - Epoch(val) [122][309] top1_acc: 0.3863, top5_acc: 0.6486, mean_class_accuracy: 0.3861 +2024-12-30 19:20:04,688 - pyskl - INFO - Epoch [123][100/3746] lr: 8.339e-03, eta: 1 day, 0:47:25, time: 2.552, data_time: 1.519, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7420, loss_cls: 2.8839, loss: 2.8839 +2024-12-30 19:21:30,668 - pyskl - INFO - Epoch [123][200/3746] lr: 8.323e-03, eta: 1 day, 0:46:00, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4750, top5_acc: 0.7320, loss_cls: 2.9043, loss: 2.9043 +2024-12-30 19:22:56,450 - pyskl - INFO - Epoch [123][300/3746] lr: 8.308e-03, eta: 1 day, 0:44:35, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4697, top5_acc: 0.7331, loss_cls: 2.9069, loss: 2.9069 +2024-12-30 19:24:21,602 - pyskl - INFO - Epoch [123][400/3746] lr: 8.292e-03, eta: 1 day, 0:43:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4780, top5_acc: 0.7336, loss_cls: 2.9004, loss: 2.9004 +2024-12-30 19:25:47,240 - pyskl - INFO - Epoch [123][500/3746] lr: 8.277e-03, eta: 1 day, 0:41:45, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4691, top5_acc: 0.7320, loss_cls: 2.9327, loss: 2.9327 +2024-12-30 19:27:12,887 - pyskl - INFO - Epoch [123][600/3746] lr: 8.262e-03, eta: 1 day, 0:40:19, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4728, top5_acc: 0.7309, loss_cls: 2.9477, loss: 2.9477 +2024-12-30 19:28:39,276 - pyskl - INFO - Epoch [123][700/3746] lr: 8.246e-03, eta: 1 day, 0:38:55, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.4709, top5_acc: 0.7294, loss_cls: 2.9235, loss: 2.9235 +2024-12-30 19:30:04,830 - pyskl - INFO - Epoch [123][800/3746] lr: 8.231e-03, eta: 1 day, 0:37:29, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4730, top5_acc: 0.7319, loss_cls: 2.9061, loss: 2.9061 +2024-12-30 19:31:30,506 - pyskl - INFO - Epoch [123][900/3746] lr: 8.215e-03, eta: 1 day, 0:36:04, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4744, top5_acc: 0.7258, loss_cls: 2.9508, loss: 2.9508 +2024-12-30 19:32:55,565 - pyskl - INFO - Epoch [123][1000/3746] lr: 8.200e-03, eta: 1 day, 0:34:39, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4606, top5_acc: 0.7205, loss_cls: 3.0053, loss: 3.0053 +2024-12-30 19:34:20,114 - pyskl - INFO - Epoch [123][1100/3746] lr: 8.185e-03, eta: 1 day, 0:33:14, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4652, top5_acc: 0.7303, loss_cls: 2.9266, loss: 2.9266 +2024-12-30 19:35:45,047 - pyskl - INFO - Epoch [123][1200/3746] lr: 8.169e-03, eta: 1 day, 0:31:49, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.4703, top5_acc: 0.7294, loss_cls: 2.9308, loss: 2.9308 +2024-12-30 19:37:09,571 - pyskl - INFO - Epoch [123][1300/3746] lr: 8.154e-03, eta: 1 day, 0:30:23, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4727, top5_acc: 0.7256, loss_cls: 2.9534, loss: 2.9534 +2024-12-30 19:38:34,938 - pyskl - INFO - Epoch [123][1400/3746] lr: 8.139e-03, eta: 1 day, 0:28:58, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4694, top5_acc: 0.7291, loss_cls: 2.9338, loss: 2.9338 +2024-12-30 19:39:59,704 - pyskl - INFO - Epoch [123][1500/3746] lr: 8.124e-03, eta: 1 day, 0:27:33, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.4480, top5_acc: 0.7042, loss_cls: 3.0605, loss: 3.0605 +2024-12-30 19:41:24,602 - pyskl - INFO - Epoch [123][1600/3746] lr: 8.108e-03, eta: 1 day, 0:26:08, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4670, top5_acc: 0.7253, loss_cls: 2.9603, loss: 2.9603 +2024-12-30 19:42:49,690 - pyskl - INFO - Epoch [123][1700/3746] lr: 8.093e-03, eta: 1 day, 0:24:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4656, top5_acc: 0.7255, loss_cls: 2.9761, loss: 2.9761 +2024-12-30 19:44:14,283 - pyskl - INFO - Epoch [123][1800/3746] lr: 8.078e-03, eta: 1 day, 0:23:17, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4661, top5_acc: 0.7369, loss_cls: 2.9263, loss: 2.9263 +2024-12-30 19:45:39,618 - pyskl - INFO - Epoch [123][1900/3746] lr: 8.063e-03, eta: 1 day, 0:21:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4678, top5_acc: 0.7239, loss_cls: 2.9649, loss: 2.9649 +2024-12-30 19:47:04,606 - pyskl - INFO - Epoch [123][2000/3746] lr: 8.047e-03, eta: 1 day, 0:20:27, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4723, top5_acc: 0.7220, loss_cls: 2.9506, loss: 2.9506 +2024-12-30 19:48:29,043 - pyskl - INFO - Epoch [123][2100/3746] lr: 8.032e-03, eta: 1 day, 0:19:02, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4664, top5_acc: 0.7197, loss_cls: 2.9729, loss: 2.9729 +2024-12-30 19:49:54,009 - pyskl - INFO - Epoch [123][2200/3746] lr: 8.017e-03, eta: 1 day, 0:17:36, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4677, top5_acc: 0.7245, loss_cls: 2.9684, loss: 2.9684 +2024-12-30 19:51:18,234 - pyskl - INFO - Epoch [123][2300/3746] lr: 8.002e-03, eta: 1 day, 0:16:11, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4681, top5_acc: 0.7231, loss_cls: 2.9590, loss: 2.9590 +2024-12-30 19:52:43,393 - pyskl - INFO - Epoch [123][2400/3746] lr: 7.987e-03, eta: 1 day, 0:14:46, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4702, top5_acc: 0.7258, loss_cls: 2.9608, loss: 2.9608 +2024-12-30 19:54:08,271 - pyskl - INFO - Epoch [123][2500/3746] lr: 7.971e-03, eta: 1 day, 0:13:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4666, top5_acc: 0.7217, loss_cls: 2.9792, loss: 2.9792 +2024-12-30 19:55:32,938 - pyskl - INFO - Epoch [123][2600/3746] lr: 7.956e-03, eta: 1 day, 0:11:55, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4545, top5_acc: 0.7217, loss_cls: 3.0134, loss: 3.0134 +2024-12-30 19:56:57,815 - pyskl - INFO - Epoch [123][2700/3746] lr: 7.941e-03, eta: 1 day, 0:10:30, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4675, top5_acc: 0.7328, loss_cls: 2.9456, loss: 2.9456 +2024-12-30 19:58:23,086 - pyskl - INFO - Epoch [123][2800/3746] lr: 7.926e-03, eta: 1 day, 0:09:05, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4616, top5_acc: 0.7220, loss_cls: 2.9625, loss: 2.9625 +2024-12-30 19:59:49,213 - pyskl - INFO - Epoch [123][2900/3746] lr: 7.911e-03, eta: 1 day, 0:07:40, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4595, top5_acc: 0.7211, loss_cls: 3.0159, loss: 3.0159 +2024-12-30 20:01:14,555 - pyskl - INFO - Epoch [123][3000/3746] lr: 7.896e-03, eta: 1 day, 0:06:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4708, top5_acc: 0.7238, loss_cls: 2.9486, loss: 2.9486 +2024-12-30 20:02:39,770 - pyskl - INFO - Epoch [123][3100/3746] lr: 7.881e-03, eta: 1 day, 0:04:50, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4553, top5_acc: 0.7198, loss_cls: 3.0125, loss: 3.0125 +2024-12-30 20:04:04,790 - pyskl - INFO - Epoch [123][3200/3746] lr: 7.866e-03, eta: 1 day, 0:03:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4694, top5_acc: 0.7255, loss_cls: 2.9576, loss: 2.9576 +2024-12-30 20:05:29,793 - pyskl - INFO - Epoch [123][3300/3746] lr: 7.851e-03, eta: 1 day, 0:01:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4658, top5_acc: 0.7188, loss_cls: 3.0160, loss: 3.0160 +2024-12-30 20:06:54,354 - pyskl - INFO - Epoch [123][3400/3746] lr: 7.836e-03, eta: 1 day, 0:00:34, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4587, top5_acc: 0.7180, loss_cls: 3.0256, loss: 3.0256 +2024-12-30 20:08:19,197 - pyskl - INFO - Epoch [123][3500/3746] lr: 7.821e-03, eta: 23:59:09, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4628, top5_acc: 0.7209, loss_cls: 3.0085, loss: 3.0085 +2024-12-30 20:09:43,449 - pyskl - INFO - Epoch [123][3600/3746] lr: 7.806e-03, eta: 23:57:43, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4514, top5_acc: 0.7120, loss_cls: 3.0480, loss: 3.0480 +2024-12-30 20:11:07,946 - pyskl - INFO - Epoch [123][3700/3746] lr: 7.791e-03, eta: 23:56:18, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7175, loss_cls: 3.0298, loss: 3.0298 +2024-12-30 20:11:48,236 - pyskl - INFO - Saving checkpoint at 123 epochs +2024-12-30 20:13:45,612 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 20:13:46,357 - pyskl - INFO - +top1_acc 0.4024 +top5_acc 0.6574 +2024-12-30 20:13:46,358 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 20:13:46,410 - pyskl - INFO - +mean_acc 0.4021 +2024-12-30 20:13:46,415 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_122.pth was removed +2024-12-30 20:13:46,698 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_123.pth. +2024-12-30 20:13:46,699 - pyskl - INFO - Best top1_acc is 0.4024 at 123 epoch. +2024-12-30 20:13:46,711 - pyskl - INFO - Epoch(val) [123][309] top1_acc: 0.4024, top5_acc: 0.6574, mean_class_accuracy: 0.4021 +2024-12-30 20:18:04,987 - pyskl - INFO - Epoch [124][100/3746] lr: 7.769e-03, eta: 23:54:43, time: 2.583, data_time: 1.533, memory: 15990, top1_acc: 0.4884, top5_acc: 0.7480, loss_cls: 2.8317, loss: 2.8317 +2024-12-30 20:19:30,645 - pyskl - INFO - Epoch [124][200/3746] lr: 7.754e-03, eta: 23:53:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4766, top5_acc: 0.7356, loss_cls: 2.9057, loss: 2.9057 +2024-12-30 20:20:56,931 - pyskl - INFO - Epoch [124][300/3746] lr: 7.739e-03, eta: 23:51:53, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4809, top5_acc: 0.7358, loss_cls: 2.8940, loss: 2.8940 +2024-12-30 20:22:22,533 - pyskl - INFO - Epoch [124][400/3746] lr: 7.724e-03, eta: 23:50:28, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4750, top5_acc: 0.7391, loss_cls: 2.8981, loss: 2.8981 +2024-12-30 20:23:48,571 - pyskl - INFO - Epoch [124][500/3746] lr: 7.709e-03, eta: 23:49:03, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4800, top5_acc: 0.7309, loss_cls: 2.9167, loss: 2.9167 +2024-12-30 20:25:14,223 - pyskl - INFO - Epoch [124][600/3746] lr: 7.694e-03, eta: 23:47:38, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4748, top5_acc: 0.7391, loss_cls: 2.8970, loss: 2.8970 +2024-12-30 20:26:40,254 - pyskl - INFO - Epoch [124][700/3746] lr: 7.679e-03, eta: 23:46:13, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4805, top5_acc: 0.7298, loss_cls: 2.9247, loss: 2.9247 +2024-12-30 20:28:06,108 - pyskl - INFO - Epoch [124][800/3746] lr: 7.664e-03, eta: 23:44:48, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4741, top5_acc: 0.7336, loss_cls: 2.9090, loss: 2.9090 +2024-12-30 20:29:31,795 - pyskl - INFO - Epoch [124][900/3746] lr: 7.649e-03, eta: 23:43:23, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.4677, top5_acc: 0.7248, loss_cls: 2.9272, loss: 2.9272 +2024-12-30 20:30:57,059 - pyskl - INFO - Epoch [124][1000/3746] lr: 7.635e-03, eta: 23:41:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4741, top5_acc: 0.7450, loss_cls: 2.9028, loss: 2.9028 +2024-12-30 20:32:22,260 - pyskl - INFO - Epoch [124][1100/3746] lr: 7.620e-03, eta: 23:40:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4773, top5_acc: 0.7395, loss_cls: 2.8822, loss: 2.8822 +2024-12-30 20:33:47,260 - pyskl - INFO - Epoch [124][1200/3746] lr: 7.605e-03, eta: 23:39:07, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.4714, top5_acc: 0.7317, loss_cls: 2.9326, loss: 2.9326 +2024-12-30 20:35:12,229 - pyskl - INFO - Epoch [124][1300/3746] lr: 7.590e-03, eta: 23:37:42, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.4697, top5_acc: 0.7325, loss_cls: 2.9312, loss: 2.9312 +2024-12-30 20:36:36,759 - pyskl - INFO - Epoch [124][1400/3746] lr: 7.575e-03, eta: 23:36:17, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4739, top5_acc: 0.7322, loss_cls: 2.9328, loss: 2.9328 +2024-12-30 20:38:01,635 - pyskl - INFO - Epoch [124][1500/3746] lr: 7.561e-03, eta: 23:34:51, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7306, loss_cls: 2.9273, loss: 2.9273 +2024-12-30 20:39:26,953 - pyskl - INFO - Epoch [124][1600/3746] lr: 7.546e-03, eta: 23:33:26, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4727, top5_acc: 0.7317, loss_cls: 2.9227, loss: 2.9227 +2024-12-30 20:40:51,675 - pyskl - INFO - Epoch [124][1700/3746] lr: 7.531e-03, eta: 23:32:01, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4727, top5_acc: 0.7323, loss_cls: 2.9271, loss: 2.9271 +2024-12-30 20:42:16,791 - pyskl - INFO - Epoch [124][1800/3746] lr: 7.516e-03, eta: 23:30:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4694, top5_acc: 0.7278, loss_cls: 2.9518, loss: 2.9518 +2024-12-30 20:43:41,067 - pyskl - INFO - Epoch [124][1900/3746] lr: 7.502e-03, eta: 23:29:10, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4750, top5_acc: 0.7336, loss_cls: 2.9146, loss: 2.9146 +2024-12-30 20:45:05,634 - pyskl - INFO - Epoch [124][2000/3746] lr: 7.487e-03, eta: 23:27:45, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4823, top5_acc: 0.7394, loss_cls: 2.8511, loss: 2.8511 +2024-12-30 20:46:30,600 - pyskl - INFO - Epoch [124][2100/3746] lr: 7.472e-03, eta: 23:26:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4711, top5_acc: 0.7288, loss_cls: 2.9378, loss: 2.9378 +2024-12-30 20:47:55,389 - pyskl - INFO - Epoch [124][2200/3746] lr: 7.457e-03, eta: 23:24:54, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7289, loss_cls: 2.9182, loss: 2.9182 +2024-12-30 20:49:19,686 - pyskl - INFO - Epoch [124][2300/3746] lr: 7.443e-03, eta: 23:23:29, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4750, top5_acc: 0.7280, loss_cls: 2.9460, loss: 2.9460 +2024-12-30 20:50:43,625 - pyskl - INFO - Epoch [124][2400/3746] lr: 7.428e-03, eta: 23:22:03, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4606, top5_acc: 0.7228, loss_cls: 2.9848, loss: 2.9848 +2024-12-30 20:52:08,211 - pyskl - INFO - Epoch [124][2500/3746] lr: 7.413e-03, eta: 23:20:38, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4655, top5_acc: 0.7247, loss_cls: 2.9642, loss: 2.9642 +2024-12-30 20:53:32,684 - pyskl - INFO - Epoch [124][2600/3746] lr: 7.399e-03, eta: 23:19:13, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7300, loss_cls: 2.9326, loss: 2.9326 +2024-12-30 20:54:57,237 - pyskl - INFO - Epoch [124][2700/3746] lr: 7.384e-03, eta: 23:17:47, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7231, loss_cls: 2.9642, loss: 2.9642 +2024-12-30 20:56:21,488 - pyskl - INFO - Epoch [124][2800/3746] lr: 7.370e-03, eta: 23:16:22, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4605, top5_acc: 0.7231, loss_cls: 2.9859, loss: 2.9859 +2024-12-30 20:57:45,984 - pyskl - INFO - Epoch [124][2900/3746] lr: 7.355e-03, eta: 23:14:57, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4778, top5_acc: 0.7294, loss_cls: 2.9299, loss: 2.9299 +2024-12-30 20:59:11,059 - pyskl - INFO - Epoch [124][3000/3746] lr: 7.340e-03, eta: 23:13:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4605, top5_acc: 0.7189, loss_cls: 2.9898, loss: 2.9898 +2024-12-30 21:00:35,988 - pyskl - INFO - Epoch [124][3100/3746] lr: 7.326e-03, eta: 23:12:06, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4747, top5_acc: 0.7344, loss_cls: 2.9131, loss: 2.9131 +2024-12-30 21:02:00,408 - pyskl - INFO - Epoch [124][3200/3746] lr: 7.311e-03, eta: 23:10:41, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4653, top5_acc: 0.7306, loss_cls: 2.9428, loss: 2.9428 +2024-12-30 21:03:24,696 - pyskl - INFO - Epoch [124][3300/3746] lr: 7.297e-03, eta: 23:09:15, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4702, top5_acc: 0.7308, loss_cls: 2.9484, loss: 2.9484 +2024-12-30 21:04:48,950 - pyskl - INFO - Epoch [124][3400/3746] lr: 7.282e-03, eta: 23:07:50, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7269, loss_cls: 2.9368, loss: 2.9368 +2024-12-30 21:06:12,888 - pyskl - INFO - Epoch [124][3500/3746] lr: 7.268e-03, eta: 23:06:25, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4602, top5_acc: 0.7164, loss_cls: 3.0164, loss: 3.0164 +2024-12-30 21:07:37,501 - pyskl - INFO - Epoch [124][3600/3746] lr: 7.253e-03, eta: 23:04:59, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4745, top5_acc: 0.7262, loss_cls: 2.9339, loss: 2.9339 +2024-12-30 21:09:01,903 - pyskl - INFO - Epoch [124][3700/3746] lr: 7.239e-03, eta: 23:03:34, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4680, top5_acc: 0.7270, loss_cls: 2.9427, loss: 2.9427 +2024-12-30 21:09:42,416 - pyskl - INFO - Saving checkpoint at 124 epochs +2024-12-30 21:11:40,302 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 21:11:41,056 - pyskl - INFO - +top1_acc 0.3960 +top5_acc 0.6502 +2024-12-30 21:11:41,057 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 21:11:41,100 - pyskl - INFO - +mean_acc 0.3958 +2024-12-30 21:11:41,114 - pyskl - INFO - Epoch(val) [124][309] top1_acc: 0.3960, top5_acc: 0.6502, mean_class_accuracy: 0.3958 +2024-12-30 21:15:49,565 - pyskl - INFO - Epoch [125][100/3746] lr: 7.217e-03, eta: 23:01:56, time: 2.484, data_time: 1.451, memory: 15990, top1_acc: 0.4908, top5_acc: 0.7492, loss_cls: 2.8074, loss: 2.8074 +2024-12-30 21:17:14,568 - pyskl - INFO - Epoch [125][200/3746] lr: 7.203e-03, eta: 23:00:30, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4905, top5_acc: 0.7438, loss_cls: 2.8148, loss: 2.8148 +2024-12-30 21:18:39,863 - pyskl - INFO - Epoch [125][300/3746] lr: 7.189e-03, eta: 22:59:05, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4794, top5_acc: 0.7361, loss_cls: 2.8736, loss: 2.8736 +2024-12-30 21:20:04,887 - pyskl - INFO - Epoch [125][400/3746] lr: 7.174e-03, eta: 22:57:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4947, top5_acc: 0.7533, loss_cls: 2.8076, loss: 2.8076 +2024-12-30 21:21:29,919 - pyskl - INFO - Epoch [125][500/3746] lr: 7.160e-03, eta: 22:56:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7441, loss_cls: 2.8651, loss: 2.8651 +2024-12-30 21:22:54,533 - pyskl - INFO - Epoch [125][600/3746] lr: 7.145e-03, eta: 22:54:49, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4905, top5_acc: 0.7472, loss_cls: 2.8114, loss: 2.8114 +2024-12-30 21:24:19,526 - pyskl - INFO - Epoch [125][700/3746] lr: 7.131e-03, eta: 22:53:24, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4738, top5_acc: 0.7478, loss_cls: 2.8671, loss: 2.8671 +2024-12-30 21:25:44,091 - pyskl - INFO - Epoch [125][800/3746] lr: 7.117e-03, eta: 22:51:59, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4870, top5_acc: 0.7373, loss_cls: 2.8967, loss: 2.8967 +2024-12-30 21:27:08,953 - pyskl - INFO - Epoch [125][900/3746] lr: 7.102e-03, eta: 22:50:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4892, top5_acc: 0.7391, loss_cls: 2.8709, loss: 2.8709 +2024-12-30 21:28:33,328 - pyskl - INFO - Epoch [125][1000/3746] lr: 7.088e-03, eta: 22:49:08, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4728, top5_acc: 0.7317, loss_cls: 2.9057, loss: 2.9057 +2024-12-30 21:29:58,211 - pyskl - INFO - Epoch [125][1100/3746] lr: 7.073e-03, eta: 22:47:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4819, top5_acc: 0.7359, loss_cls: 2.9014, loss: 2.9014 +2024-12-30 21:31:22,895 - pyskl - INFO - Epoch [125][1200/3746] lr: 7.059e-03, eta: 22:46:17, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.4753, top5_acc: 0.7383, loss_cls: 2.9031, loss: 2.9031 +2024-12-30 21:32:47,667 - pyskl - INFO - Epoch [125][1300/3746] lr: 7.045e-03, eta: 22:44:52, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.4769, top5_acc: 0.7400, loss_cls: 2.8709, loss: 2.8709 +2024-12-30 21:34:11,900 - pyskl - INFO - Epoch [125][1400/3746] lr: 7.031e-03, eta: 22:43:27, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4680, top5_acc: 0.7317, loss_cls: 2.9160, loss: 2.9160 +2024-12-30 21:35:36,469 - pyskl - INFO - Epoch [125][1500/3746] lr: 7.016e-03, eta: 22:42:01, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.4756, top5_acc: 0.7400, loss_cls: 2.9031, loss: 2.9031 +2024-12-30 21:37:00,129 - pyskl - INFO - Epoch [125][1600/3746] lr: 7.002e-03, eta: 22:40:36, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7408, loss_cls: 2.8713, loss: 2.8713 +2024-12-30 21:38:24,480 - pyskl - INFO - Epoch [125][1700/3746] lr: 6.988e-03, eta: 22:39:10, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4744, top5_acc: 0.7416, loss_cls: 2.8781, loss: 2.8781 +2024-12-30 21:39:49,164 - pyskl - INFO - Epoch [125][1800/3746] lr: 6.973e-03, eta: 22:37:45, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4753, top5_acc: 0.7184, loss_cls: 2.9435, loss: 2.9435 +2024-12-30 21:41:13,785 - pyskl - INFO - Epoch [125][1900/3746] lr: 6.959e-03, eta: 22:36:20, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4816, top5_acc: 0.7384, loss_cls: 2.8901, loss: 2.8901 +2024-12-30 21:42:38,173 - pyskl - INFO - Epoch [125][2000/3746] lr: 6.945e-03, eta: 22:34:54, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4744, top5_acc: 0.7355, loss_cls: 2.9270, loss: 2.9270 +2024-12-30 21:44:02,751 - pyskl - INFO - Epoch [125][2100/3746] lr: 6.931e-03, eta: 22:33:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7344, loss_cls: 2.8914, loss: 2.8914 +2024-12-30 21:45:27,330 - pyskl - INFO - Epoch [125][2200/3746] lr: 6.917e-03, eta: 22:32:03, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4731, top5_acc: 0.7338, loss_cls: 2.9136, loss: 2.9136 +2024-12-30 21:46:51,866 - pyskl - INFO - Epoch [125][2300/3746] lr: 6.902e-03, eta: 22:30:38, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4570, top5_acc: 0.7189, loss_cls: 2.9892, loss: 2.9892 +2024-12-30 21:48:16,300 - pyskl - INFO - Epoch [125][2400/3746] lr: 6.888e-03, eta: 22:29:13, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4753, top5_acc: 0.7306, loss_cls: 2.9149, loss: 2.9149 +2024-12-30 21:49:40,912 - pyskl - INFO - Epoch [125][2500/3746] lr: 6.874e-03, eta: 22:27:47, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4791, top5_acc: 0.7300, loss_cls: 2.9118, loss: 2.9118 +2024-12-30 21:51:05,958 - pyskl - INFO - Epoch [125][2600/3746] lr: 6.860e-03, eta: 22:26:22, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4716, top5_acc: 0.7289, loss_cls: 2.9224, loss: 2.9224 +2024-12-30 21:52:30,509 - pyskl - INFO - Epoch [125][2700/3746] lr: 6.846e-03, eta: 22:24:57, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4761, top5_acc: 0.7352, loss_cls: 2.9070, loss: 2.9070 +2024-12-30 21:53:54,871 - pyskl - INFO - Epoch [125][2800/3746] lr: 6.832e-03, eta: 22:23:31, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4748, top5_acc: 0.7372, loss_cls: 2.8953, loss: 2.8953 +2024-12-30 21:55:19,558 - pyskl - INFO - Epoch [125][2900/3746] lr: 6.818e-03, eta: 22:22:06, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4752, top5_acc: 0.7280, loss_cls: 2.9302, loss: 2.9302 +2024-12-30 21:56:44,623 - pyskl - INFO - Epoch [125][3000/3746] lr: 6.804e-03, eta: 22:20:41, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4652, top5_acc: 0.7228, loss_cls: 2.9513, loss: 2.9513 +2024-12-30 21:58:09,736 - pyskl - INFO - Epoch [125][3100/3746] lr: 6.789e-03, eta: 22:19:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4763, top5_acc: 0.7370, loss_cls: 2.8892, loss: 2.8892 +2024-12-30 21:59:34,696 - pyskl - INFO - Epoch [125][3200/3746] lr: 6.775e-03, eta: 22:17:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4730, top5_acc: 0.7306, loss_cls: 2.9043, loss: 2.9043 +2024-12-30 22:00:59,664 - pyskl - INFO - Epoch [125][3300/3746] lr: 6.761e-03, eta: 22:16:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4719, top5_acc: 0.7256, loss_cls: 2.9362, loss: 2.9362 +2024-12-30 22:02:24,721 - pyskl - INFO - Epoch [125][3400/3746] lr: 6.747e-03, eta: 22:15:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4755, top5_acc: 0.7272, loss_cls: 2.8935, loss: 2.8935 +2024-12-30 22:03:49,351 - pyskl - INFO - Epoch [125][3500/3746] lr: 6.733e-03, eta: 22:13:34, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4713, top5_acc: 0.7242, loss_cls: 2.9431, loss: 2.9431 +2024-12-30 22:05:14,304 - pyskl - INFO - Epoch [125][3600/3746] lr: 6.719e-03, eta: 22:12:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4636, top5_acc: 0.7245, loss_cls: 2.9675, loss: 2.9675 +2024-12-30 22:06:38,845 - pyskl - INFO - Epoch [125][3700/3746] lr: 6.705e-03, eta: 22:10:44, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4770, top5_acc: 0.7288, loss_cls: 2.9452, loss: 2.9452 +2024-12-30 22:07:19,455 - pyskl - INFO - Saving checkpoint at 125 epochs +2024-12-30 22:09:16,878 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 22:09:17,576 - pyskl - INFO - +top1_acc 0.4029 +top5_acc 0.6608 +2024-12-30 22:09:17,576 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 22:09:17,627 - pyskl - INFO - +mean_acc 0.4027 +2024-12-30 22:09:17,632 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_123.pth was removed +2024-12-30 22:09:17,937 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_125.pth. +2024-12-30 22:09:17,938 - pyskl - INFO - Best top1_acc is 0.4029 at 125 epoch. +2024-12-30 22:09:17,956 - pyskl - INFO - Epoch(val) [125][309] top1_acc: 0.4029, top5_acc: 0.6608, mean_class_accuracy: 0.4027 +2024-12-30 22:13:28,788 - pyskl - INFO - Epoch [126][100/3746] lr: 6.685e-03, eta: 22:09:05, time: 2.508, data_time: 1.482, memory: 15990, top1_acc: 0.4875, top5_acc: 0.7478, loss_cls: 2.7935, loss: 2.7935 +2024-12-30 22:14:53,753 - pyskl - INFO - Epoch [126][200/3746] lr: 6.671e-03, eta: 22:07:39, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5039, top5_acc: 0.7509, loss_cls: 2.7974, loss: 2.7974 +2024-12-30 22:16:18,501 - pyskl - INFO - Epoch [126][300/3746] lr: 6.657e-03, eta: 22:06:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4863, top5_acc: 0.7478, loss_cls: 2.8296, loss: 2.8296 +2024-12-30 22:17:43,448 - pyskl - INFO - Epoch [126][400/3746] lr: 6.643e-03, eta: 22:04:49, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4961, top5_acc: 0.7477, loss_cls: 2.8110, loss: 2.8110 +2024-12-30 22:19:07,499 - pyskl - INFO - Epoch [126][500/3746] lr: 6.629e-03, eta: 22:03:23, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4920, top5_acc: 0.7539, loss_cls: 2.7902, loss: 2.7902 +2024-12-30 22:20:31,697 - pyskl - INFO - Epoch [126][600/3746] lr: 6.615e-03, eta: 22:01:58, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4916, top5_acc: 0.7455, loss_cls: 2.8206, loss: 2.8206 +2024-12-30 22:21:56,461 - pyskl - INFO - Epoch [126][700/3746] lr: 6.601e-03, eta: 22:00:32, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4894, top5_acc: 0.7412, loss_cls: 2.8372, loss: 2.8372 +2024-12-30 22:23:20,946 - pyskl - INFO - Epoch [126][800/3746] lr: 6.587e-03, eta: 21:59:07, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4972, top5_acc: 0.7522, loss_cls: 2.8000, loss: 2.8000 +2024-12-30 22:24:45,667 - pyskl - INFO - Epoch [126][900/3746] lr: 6.574e-03, eta: 21:57:42, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7392, loss_cls: 2.8703, loss: 2.8703 +2024-12-30 22:26:10,706 - pyskl - INFO - Epoch [126][1000/3746] lr: 6.560e-03, eta: 21:56:16, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4841, top5_acc: 0.7383, loss_cls: 2.8802, loss: 2.8802 +2024-12-30 22:27:35,817 - pyskl - INFO - Epoch [126][1100/3746] lr: 6.546e-03, eta: 21:54:51, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4814, top5_acc: 0.7391, loss_cls: 2.8736, loss: 2.8736 +2024-12-30 22:29:00,660 - pyskl - INFO - Epoch [126][1200/3746] lr: 6.532e-03, eta: 21:53:26, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4802, top5_acc: 0.7323, loss_cls: 2.9173, loss: 2.9173 +2024-12-30 22:30:25,046 - pyskl - INFO - Epoch [126][1300/3746] lr: 6.518e-03, eta: 21:52:00, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4809, top5_acc: 0.7373, loss_cls: 2.8811, loss: 2.8811 +2024-12-30 22:31:49,530 - pyskl - INFO - Epoch [126][1400/3746] lr: 6.505e-03, eta: 21:50:35, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4838, top5_acc: 0.7430, loss_cls: 2.8629, loss: 2.8629 +2024-12-30 22:33:14,223 - pyskl - INFO - Epoch [126][1500/3746] lr: 6.491e-03, eta: 21:49:10, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4714, top5_acc: 0.7373, loss_cls: 2.8882, loss: 2.8882 +2024-12-30 22:34:38,304 - pyskl - INFO - Epoch [126][1600/3746] lr: 6.477e-03, eta: 21:47:44, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4828, top5_acc: 0.7469, loss_cls: 2.8314, loss: 2.8314 +2024-12-30 22:36:02,848 - pyskl - INFO - Epoch [126][1700/3746] lr: 6.463e-03, eta: 21:46:19, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4778, top5_acc: 0.7398, loss_cls: 2.8962, loss: 2.8962 +2024-12-30 22:37:27,735 - pyskl - INFO - Epoch [126][1800/3746] lr: 6.449e-03, eta: 21:44:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4650, top5_acc: 0.7316, loss_cls: 2.9198, loss: 2.9198 +2024-12-30 22:38:52,114 - pyskl - INFO - Epoch [126][1900/3746] lr: 6.436e-03, eta: 21:43:28, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4817, top5_acc: 0.7411, loss_cls: 2.8581, loss: 2.8581 +2024-12-30 22:40:16,390 - pyskl - INFO - Epoch [126][2000/3746] lr: 6.422e-03, eta: 21:42:03, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4769, top5_acc: 0.7455, loss_cls: 2.8550, loss: 2.8550 +2024-12-30 22:41:40,870 - pyskl - INFO - Epoch [126][2100/3746] lr: 6.408e-03, eta: 21:40:37, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4898, top5_acc: 0.7448, loss_cls: 2.8548, loss: 2.8548 +2024-12-30 22:43:05,581 - pyskl - INFO - Epoch [126][2200/3746] lr: 6.395e-03, eta: 21:39:12, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4827, top5_acc: 0.7366, loss_cls: 2.8813, loss: 2.8813 +2024-12-30 22:44:29,910 - pyskl - INFO - Epoch [126][2300/3746] lr: 6.381e-03, eta: 21:37:47, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4803, top5_acc: 0.7408, loss_cls: 2.8722, loss: 2.8722 +2024-12-30 22:45:54,819 - pyskl - INFO - Epoch [126][2400/3746] lr: 6.367e-03, eta: 21:36:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4819, top5_acc: 0.7355, loss_cls: 2.8844, loss: 2.8844 +2024-12-30 22:47:19,227 - pyskl - INFO - Epoch [126][2500/3746] lr: 6.354e-03, eta: 21:34:56, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4730, top5_acc: 0.7395, loss_cls: 2.8889, loss: 2.8889 +2024-12-30 22:48:43,670 - pyskl - INFO - Epoch [126][2600/3746] lr: 6.340e-03, eta: 21:33:30, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4792, top5_acc: 0.7428, loss_cls: 2.8394, loss: 2.8394 +2024-12-30 22:50:08,786 - pyskl - INFO - Epoch [126][2700/3746] lr: 6.326e-03, eta: 21:32:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4723, top5_acc: 0.7303, loss_cls: 2.9018, loss: 2.9018 +2024-12-30 22:51:33,956 - pyskl - INFO - Epoch [126][2800/3746] lr: 6.313e-03, eta: 21:30:40, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4761, top5_acc: 0.7306, loss_cls: 2.9028, loss: 2.9028 +2024-12-30 22:52:58,402 - pyskl - INFO - Epoch [126][2900/3746] lr: 6.299e-03, eta: 21:29:15, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4859, top5_acc: 0.7367, loss_cls: 2.8905, loss: 2.8905 +2024-12-30 22:54:23,419 - pyskl - INFO - Epoch [126][3000/3746] lr: 6.286e-03, eta: 21:27:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4841, top5_acc: 0.7342, loss_cls: 2.8663, loss: 2.8663 +2024-12-30 22:55:48,013 - pyskl - INFO - Epoch [126][3100/3746] lr: 6.272e-03, eta: 21:26:24, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4783, top5_acc: 0.7344, loss_cls: 2.8903, loss: 2.8903 +2024-12-30 22:57:13,069 - pyskl - INFO - Epoch [126][3200/3746] lr: 6.259e-03, eta: 21:24:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4788, top5_acc: 0.7333, loss_cls: 2.9126, loss: 2.9126 +2024-12-30 22:58:38,018 - pyskl - INFO - Epoch [126][3300/3746] lr: 6.245e-03, eta: 21:23:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4806, top5_acc: 0.7364, loss_cls: 2.8660, loss: 2.8660 +2024-12-30 23:00:02,454 - pyskl - INFO - Epoch [126][3400/3746] lr: 6.231e-03, eta: 21:22:08, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4756, top5_acc: 0.7361, loss_cls: 2.8948, loss: 2.8948 +2024-12-30 23:01:26,629 - pyskl - INFO - Epoch [126][3500/3746] lr: 6.218e-03, eta: 21:20:43, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4758, top5_acc: 0.7280, loss_cls: 2.9239, loss: 2.9239 +2024-12-30 23:02:51,485 - pyskl - INFO - Epoch [126][3600/3746] lr: 6.204e-03, eta: 21:19:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4888, top5_acc: 0.7461, loss_cls: 2.8581, loss: 2.8581 +2024-12-30 23:04:15,767 - pyskl - INFO - Epoch [126][3700/3746] lr: 6.191e-03, eta: 21:17:52, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4805, top5_acc: 0.7380, loss_cls: 2.8860, loss: 2.8860 +2024-12-30 23:04:56,292 - pyskl - INFO - Saving checkpoint at 126 epochs +2024-12-30 23:06:54,360 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 23:06:55,083 - pyskl - INFO - +top1_acc 0.4103 +top5_acc 0.6625 +2024-12-30 23:06:55,083 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 23:06:55,127 - pyskl - INFO - +mean_acc 0.4100 +2024-12-30 23:06:55,132 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_125.pth was removed +2024-12-30 23:06:55,409 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_126.pth. +2024-12-30 23:06:55,409 - pyskl - INFO - Best top1_acc is 0.4103 at 126 epoch. +2024-12-30 23:06:55,422 - pyskl - INFO - Epoch(val) [126][309] top1_acc: 0.4103, top5_acc: 0.6625, mean_class_accuracy: 0.4100 +2024-12-30 23:11:02,413 - pyskl - INFO - Epoch [127][100/3746] lr: 6.171e-03, eta: 21:16:11, time: 2.470, data_time: 1.446, memory: 15990, top1_acc: 0.5009, top5_acc: 0.7523, loss_cls: 2.7802, loss: 2.7802 +2024-12-30 23:12:27,355 - pyskl - INFO - Epoch [127][200/3746] lr: 6.158e-03, eta: 21:14:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4905, top5_acc: 0.7516, loss_cls: 2.8014, loss: 2.8014 +2024-12-30 23:13:52,240 - pyskl - INFO - Epoch [127][300/3746] lr: 6.144e-03, eta: 21:13:20, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4941, top5_acc: 0.7434, loss_cls: 2.7928, loss: 2.7928 +2024-12-30 23:15:17,441 - pyskl - INFO - Epoch [127][400/3746] lr: 6.131e-03, eta: 21:11:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4980, top5_acc: 0.7517, loss_cls: 2.7660, loss: 2.7660 +2024-12-30 23:16:42,437 - pyskl - INFO - Epoch [127][500/3746] lr: 6.118e-03, eta: 21:10:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4939, top5_acc: 0.7544, loss_cls: 2.7764, loss: 2.7764 +2024-12-30 23:18:07,396 - pyskl - INFO - Epoch [127][600/3746] lr: 6.104e-03, eta: 21:09:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7403, loss_cls: 2.8493, loss: 2.8493 +2024-12-30 23:19:32,110 - pyskl - INFO - Epoch [127][700/3746] lr: 6.091e-03, eta: 21:07:39, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4863, top5_acc: 0.7414, loss_cls: 2.8419, loss: 2.8419 +2024-12-30 23:20:57,028 - pyskl - INFO - Epoch [127][800/3746] lr: 6.077e-03, eta: 21:06:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4995, top5_acc: 0.7491, loss_cls: 2.8046, loss: 2.8046 +2024-12-30 23:22:21,955 - pyskl - INFO - Epoch [127][900/3746] lr: 6.064e-03, eta: 21:04:48, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4927, top5_acc: 0.7523, loss_cls: 2.8039, loss: 2.8039 +2024-12-30 23:23:46,763 - pyskl - INFO - Epoch [127][1000/3746] lr: 6.051e-03, eta: 21:03:23, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4872, top5_acc: 0.7414, loss_cls: 2.8224, loss: 2.8224 +2024-12-30 23:25:11,457 - pyskl - INFO - Epoch [127][1100/3746] lr: 6.037e-03, eta: 21:01:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4813, top5_acc: 0.7503, loss_cls: 2.8241, loss: 2.8241 +2024-12-30 23:26:35,660 - pyskl - INFO - Epoch [127][1200/3746] lr: 6.024e-03, eta: 21:00:32, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4833, top5_acc: 0.7383, loss_cls: 2.8604, loss: 2.8604 +2024-12-30 23:28:00,327 - pyskl - INFO - Epoch [127][1300/3746] lr: 6.011e-03, eta: 20:59:06, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4855, top5_acc: 0.7397, loss_cls: 2.8252, loss: 2.8252 +2024-12-30 23:29:24,476 - pyskl - INFO - Epoch [127][1400/3746] lr: 5.998e-03, eta: 20:57:41, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4953, top5_acc: 0.7391, loss_cls: 2.8263, loss: 2.8263 +2024-12-30 23:30:49,044 - pyskl - INFO - Epoch [127][1500/3746] lr: 5.984e-03, eta: 20:56:16, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4908, top5_acc: 0.7523, loss_cls: 2.7971, loss: 2.7971 +2024-12-30 23:32:13,507 - pyskl - INFO - Epoch [127][1600/3746] lr: 5.971e-03, eta: 20:54:50, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4927, top5_acc: 0.7466, loss_cls: 2.8416, loss: 2.8416 +2024-12-30 23:33:37,679 - pyskl - INFO - Epoch [127][1700/3746] lr: 5.958e-03, eta: 20:53:25, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4870, top5_acc: 0.7452, loss_cls: 2.8267, loss: 2.8267 +2024-12-30 23:35:01,957 - pyskl - INFO - Epoch [127][1800/3746] lr: 5.945e-03, eta: 20:51:59, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4941, top5_acc: 0.7534, loss_cls: 2.8051, loss: 2.8051 +2024-12-30 23:36:26,155 - pyskl - INFO - Epoch [127][1900/3746] lr: 5.931e-03, eta: 20:50:34, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4997, top5_acc: 0.7567, loss_cls: 2.7568, loss: 2.7568 +2024-12-30 23:37:50,670 - pyskl - INFO - Epoch [127][2000/3746] lr: 5.918e-03, eta: 20:49:08, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4813, top5_acc: 0.7400, loss_cls: 2.8469, loss: 2.8469 +2024-12-30 23:39:14,797 - pyskl - INFO - Epoch [127][2100/3746] lr: 5.905e-03, eta: 20:47:43, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4855, top5_acc: 0.7398, loss_cls: 2.8432, loss: 2.8432 +2024-12-30 23:40:38,650 - pyskl - INFO - Epoch [127][2200/3746] lr: 5.892e-03, eta: 20:46:17, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4842, top5_acc: 0.7370, loss_cls: 2.8668, loss: 2.8668 +2024-12-30 23:42:03,204 - pyskl - INFO - Epoch [127][2300/3746] lr: 5.879e-03, eta: 20:44:52, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4816, top5_acc: 0.7422, loss_cls: 2.8896, loss: 2.8896 +2024-12-30 23:43:27,804 - pyskl - INFO - Epoch [127][2400/3746] lr: 5.866e-03, eta: 20:43:27, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4859, top5_acc: 0.7367, loss_cls: 2.8653, loss: 2.8653 +2024-12-30 23:44:52,325 - pyskl - INFO - Epoch [127][2500/3746] lr: 5.852e-03, eta: 20:42:01, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4841, top5_acc: 0.7433, loss_cls: 2.8484, loss: 2.8484 +2024-12-30 23:46:16,683 - pyskl - INFO - Epoch [127][2600/3746] lr: 5.839e-03, eta: 20:40:36, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4816, top5_acc: 0.7388, loss_cls: 2.8590, loss: 2.8590 +2024-12-30 23:47:41,796 - pyskl - INFO - Epoch [127][2700/3746] lr: 5.826e-03, eta: 20:39:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4766, top5_acc: 0.7373, loss_cls: 2.9048, loss: 2.9048 +2024-12-30 23:49:06,873 - pyskl - INFO - Epoch [127][2800/3746] lr: 5.813e-03, eta: 20:37:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4953, top5_acc: 0.7470, loss_cls: 2.8301, loss: 2.8301 +2024-12-30 23:50:31,365 - pyskl - INFO - Epoch [127][2900/3746] lr: 5.800e-03, eta: 20:36:20, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7522, loss_cls: 2.7877, loss: 2.7877 +2024-12-30 23:51:56,189 - pyskl - INFO - Epoch [127][3000/3746] lr: 5.787e-03, eta: 20:34:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4963, top5_acc: 0.7450, loss_cls: 2.8285, loss: 2.8285 +2024-12-30 23:53:20,610 - pyskl - INFO - Epoch [127][3100/3746] lr: 5.774e-03, eta: 20:33:29, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4809, top5_acc: 0.7347, loss_cls: 2.8608, loss: 2.8608 +2024-12-30 23:54:45,367 - pyskl - INFO - Epoch [127][3200/3746] lr: 5.761e-03, eta: 20:32:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7436, loss_cls: 2.8315, loss: 2.8315 +2024-12-30 23:56:09,537 - pyskl - INFO - Epoch [127][3300/3746] lr: 5.748e-03, eta: 20:30:38, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4850, top5_acc: 0.7403, loss_cls: 2.8341, loss: 2.8341 +2024-12-30 23:57:33,900 - pyskl - INFO - Epoch [127][3400/3746] lr: 5.735e-03, eta: 20:29:13, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4920, top5_acc: 0.7441, loss_cls: 2.8449, loss: 2.8449 +2024-12-30 23:58:58,234 - pyskl - INFO - Epoch [127][3500/3746] lr: 5.722e-03, eta: 20:27:47, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4792, top5_acc: 0.7364, loss_cls: 2.8767, loss: 2.8767 +2024-12-31 00:00:22,942 - pyskl - INFO - Epoch [127][3600/3746] lr: 5.709e-03, eta: 20:26:22, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4903, top5_acc: 0.7422, loss_cls: 2.8416, loss: 2.8416 +2024-12-31 00:01:47,315 - pyskl - INFO - Epoch [127][3700/3746] lr: 5.696e-03, eta: 20:24:57, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4830, top5_acc: 0.7398, loss_cls: 2.8849, loss: 2.8849 +2024-12-31 00:02:28,229 - pyskl - INFO - Saving checkpoint at 127 epochs +2024-12-31 00:04:26,495 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 00:04:27,232 - pyskl - INFO - +top1_acc 0.4035 +top5_acc 0.6579 +2024-12-31 00:04:27,232 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 00:04:27,280 - pyskl - INFO - +mean_acc 0.4033 +2024-12-31 00:04:27,291 - pyskl - INFO - Epoch(val) [127][309] top1_acc: 0.4035, top5_acc: 0.6579, mean_class_accuracy: 0.4033 +2024-12-31 00:08:34,263 - pyskl - INFO - Epoch [128][100/3746] lr: 5.677e-03, eta: 20:23:14, time: 2.470, data_time: 1.441, memory: 15990, top1_acc: 0.5078, top5_acc: 0.7616, loss_cls: 2.7164, loss: 2.7164 +2024-12-31 00:09:58,852 - pyskl - INFO - Epoch [128][200/3746] lr: 5.664e-03, eta: 20:21:49, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5134, top5_acc: 0.7627, loss_cls: 2.7184, loss: 2.7184 +2024-12-31 00:11:23,458 - pyskl - INFO - Epoch [128][300/3746] lr: 5.651e-03, eta: 20:20:23, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4933, top5_acc: 0.7475, loss_cls: 2.8242, loss: 2.8242 +2024-12-31 00:12:47,818 - pyskl - INFO - Epoch [128][400/3746] lr: 5.638e-03, eta: 20:18:58, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4992, top5_acc: 0.7525, loss_cls: 2.8063, loss: 2.8063 +2024-12-31 00:14:12,440 - pyskl - INFO - Epoch [128][500/3746] lr: 5.625e-03, eta: 20:17:33, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5033, top5_acc: 0.7502, loss_cls: 2.7918, loss: 2.7918 +2024-12-31 00:15:36,872 - pyskl - INFO - Epoch [128][600/3746] lr: 5.612e-03, eta: 20:16:07, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4986, top5_acc: 0.7525, loss_cls: 2.7731, loss: 2.7731 +2024-12-31 00:17:01,488 - pyskl - INFO - Epoch [128][700/3746] lr: 5.600e-03, eta: 20:14:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5039, top5_acc: 0.7534, loss_cls: 2.7808, loss: 2.7808 +2024-12-31 00:18:26,246 - pyskl - INFO - Epoch [128][800/3746] lr: 5.587e-03, eta: 20:13:16, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4984, top5_acc: 0.7541, loss_cls: 2.7507, loss: 2.7507 +2024-12-31 00:19:50,872 - pyskl - INFO - Epoch [128][900/3746] lr: 5.574e-03, eta: 20:11:51, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5003, top5_acc: 0.7541, loss_cls: 2.7683, loss: 2.7683 +2024-12-31 00:21:15,650 - pyskl - INFO - Epoch [128][1000/3746] lr: 5.561e-03, eta: 20:10:26, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4961, top5_acc: 0.7506, loss_cls: 2.8068, loss: 2.8068 +2024-12-31 00:22:40,682 - pyskl - INFO - Epoch [128][1100/3746] lr: 5.548e-03, eta: 20:09:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5081, top5_acc: 0.7527, loss_cls: 2.7512, loss: 2.7512 +2024-12-31 00:24:04,838 - pyskl - INFO - Epoch [128][1200/3746] lr: 5.536e-03, eta: 20:07:35, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7483, loss_cls: 2.7949, loss: 2.7949 +2024-12-31 00:25:29,683 - pyskl - INFO - Epoch [128][1300/3746] lr: 5.523e-03, eta: 20:06:09, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5014, top5_acc: 0.7514, loss_cls: 2.7836, loss: 2.7836 +2024-12-31 00:26:54,238 - pyskl - INFO - Epoch [128][1400/3746] lr: 5.510e-03, eta: 20:04:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4888, top5_acc: 0.7408, loss_cls: 2.8478, loss: 2.8478 +2024-12-31 00:28:18,323 - pyskl - INFO - Epoch [128][1500/3746] lr: 5.497e-03, eta: 20:03:19, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4873, top5_acc: 0.7378, loss_cls: 2.8698, loss: 2.8698 +2024-12-31 00:29:43,388 - pyskl - INFO - Epoch [128][1600/3746] lr: 5.485e-03, eta: 20:01:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5027, top5_acc: 0.7539, loss_cls: 2.7556, loss: 2.7556 +2024-12-31 00:31:07,930 - pyskl - INFO - Epoch [128][1700/3746] lr: 5.472e-03, eta: 20:00:28, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4964, top5_acc: 0.7470, loss_cls: 2.7946, loss: 2.7946 +2024-12-31 00:32:32,750 - pyskl - INFO - Epoch [128][1800/3746] lr: 5.459e-03, eta: 19:59:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4967, top5_acc: 0.7530, loss_cls: 2.7826, loss: 2.7826 +2024-12-31 00:33:57,290 - pyskl - INFO - Epoch [128][1900/3746] lr: 5.446e-03, eta: 19:57:37, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4984, top5_acc: 0.7561, loss_cls: 2.7707, loss: 2.7707 +2024-12-31 00:35:21,514 - pyskl - INFO - Epoch [128][2000/3746] lr: 5.434e-03, eta: 19:56:12, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4908, top5_acc: 0.7564, loss_cls: 2.7985, loss: 2.7985 +2024-12-31 00:36:46,130 - pyskl - INFO - Epoch [128][2100/3746] lr: 5.421e-03, eta: 19:54:46, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4892, top5_acc: 0.7506, loss_cls: 2.8057, loss: 2.8057 +2024-12-31 00:38:11,097 - pyskl - INFO - Epoch [128][2200/3746] lr: 5.408e-03, eta: 19:53:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4973, top5_acc: 0.7486, loss_cls: 2.8133, loss: 2.8133 +2024-12-31 00:39:35,910 - pyskl - INFO - Epoch [128][2300/3746] lr: 5.396e-03, eta: 19:51:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4889, top5_acc: 0.7430, loss_cls: 2.8491, loss: 2.8491 +2024-12-31 00:41:00,435 - pyskl - INFO - Epoch [128][2400/3746] lr: 5.383e-03, eta: 19:50:30, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4944, top5_acc: 0.7603, loss_cls: 2.7608, loss: 2.7608 +2024-12-31 00:42:25,049 - pyskl - INFO - Epoch [128][2500/3746] lr: 5.370e-03, eta: 19:49:05, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4928, top5_acc: 0.7495, loss_cls: 2.8019, loss: 2.8019 +2024-12-31 00:43:49,379 - pyskl - INFO - Epoch [128][2600/3746] lr: 5.358e-03, eta: 19:47:39, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4995, top5_acc: 0.7512, loss_cls: 2.7790, loss: 2.7790 +2024-12-31 00:45:13,658 - pyskl - INFO - Epoch [128][2700/3746] lr: 5.345e-03, eta: 19:46:14, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5003, top5_acc: 0.7545, loss_cls: 2.7670, loss: 2.7670 +2024-12-31 00:46:38,114 - pyskl - INFO - Epoch [128][2800/3746] lr: 5.333e-03, eta: 19:44:48, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4886, top5_acc: 0.7430, loss_cls: 2.8462, loss: 2.8462 +2024-12-31 00:48:02,701 - pyskl - INFO - Epoch [128][2900/3746] lr: 5.320e-03, eta: 19:43:23, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4866, top5_acc: 0.7422, loss_cls: 2.8278, loss: 2.8278 +2024-12-31 00:49:26,937 - pyskl - INFO - Epoch [128][3000/3746] lr: 5.308e-03, eta: 19:41:57, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4959, top5_acc: 0.7522, loss_cls: 2.8131, loss: 2.8131 +2024-12-31 00:50:50,836 - pyskl - INFO - Epoch [128][3100/3746] lr: 5.295e-03, eta: 19:40:32, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4866, top5_acc: 0.7472, loss_cls: 2.8243, loss: 2.8243 +2024-12-31 00:52:15,334 - pyskl - INFO - Epoch [128][3200/3746] lr: 5.283e-03, eta: 19:39:07, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4945, top5_acc: 0.7475, loss_cls: 2.8067, loss: 2.8067 +2024-12-31 00:53:39,729 - pyskl - INFO - Epoch [128][3300/3746] lr: 5.270e-03, eta: 19:37:41, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5005, top5_acc: 0.7527, loss_cls: 2.7809, loss: 2.7809 +2024-12-31 00:55:04,415 - pyskl - INFO - Epoch [128][3400/3746] lr: 5.258e-03, eta: 19:36:16, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4822, top5_acc: 0.7398, loss_cls: 2.8449, loss: 2.8449 +2024-12-31 00:56:28,920 - pyskl - INFO - Epoch [128][3500/3746] lr: 5.245e-03, eta: 19:34:50, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4950, top5_acc: 0.7483, loss_cls: 2.7955, loss: 2.7955 +2024-12-31 00:57:53,335 - pyskl - INFO - Epoch [128][3600/3746] lr: 5.233e-03, eta: 19:33:25, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4978, top5_acc: 0.7498, loss_cls: 2.8050, loss: 2.8050 +2024-12-31 00:59:18,024 - pyskl - INFO - Epoch [128][3700/3746] lr: 5.220e-03, eta: 19:31:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4847, top5_acc: 0.7488, loss_cls: 2.8398, loss: 2.8398 +2024-12-31 00:59:58,579 - pyskl - INFO - Saving checkpoint at 128 epochs +2024-12-31 01:01:56,565 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 01:01:57,254 - pyskl - INFO - +top1_acc 0.4153 +top5_acc 0.6678 +2024-12-31 01:01:57,254 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 01:01:57,298 - pyskl - INFO - +mean_acc 0.4150 +2024-12-31 01:01:57,302 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_126.pth was removed +2024-12-31 01:01:57,557 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_128.pth. +2024-12-31 01:01:57,557 - pyskl - INFO - Best top1_acc is 0.4153 at 128 epoch. +2024-12-31 01:01:57,569 - pyskl - INFO - Epoch(val) [128][309] top1_acc: 0.4153, top5_acc: 0.6678, mean_class_accuracy: 0.4150 +2024-12-31 01:06:03,549 - pyskl - INFO - Epoch [129][100/3746] lr: 5.202e-03, eta: 19:30:16, time: 2.460, data_time: 1.438, memory: 15990, top1_acc: 0.5197, top5_acc: 0.7666, loss_cls: 2.6796, loss: 2.6796 +2024-12-31 01:07:28,062 - pyskl - INFO - Epoch [129][200/3746] lr: 5.190e-03, eta: 19:28:50, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5172, top5_acc: 0.7670, loss_cls: 2.6853, loss: 2.6853 +2024-12-31 01:08:52,574 - pyskl - INFO - Epoch [129][300/3746] lr: 5.177e-03, eta: 19:27:25, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5153, top5_acc: 0.7662, loss_cls: 2.6755, loss: 2.6755 +2024-12-31 01:10:17,788 - pyskl - INFO - Epoch [129][400/3746] lr: 5.165e-03, eta: 19:26:00, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5048, top5_acc: 0.7612, loss_cls: 2.7185, loss: 2.7185 +2024-12-31 01:11:43,045 - pyskl - INFO - Epoch [129][500/3746] lr: 5.153e-03, eta: 19:24:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5114, top5_acc: 0.7639, loss_cls: 2.6983, loss: 2.6983 +2024-12-31 01:13:08,064 - pyskl - INFO - Epoch [129][600/3746] lr: 5.140e-03, eta: 19:23:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5116, top5_acc: 0.7647, loss_cls: 2.6921, loss: 2.6921 +2024-12-31 01:14:32,485 - pyskl - INFO - Epoch [129][700/3746] lr: 5.128e-03, eta: 19:21:43, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5097, top5_acc: 0.7664, loss_cls: 2.7199, loss: 2.7199 +2024-12-31 01:15:56,827 - pyskl - INFO - Epoch [129][800/3746] lr: 5.116e-03, eta: 19:20:18, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5088, top5_acc: 0.7597, loss_cls: 2.7294, loss: 2.7294 +2024-12-31 01:17:21,088 - pyskl - INFO - Epoch [129][900/3746] lr: 5.103e-03, eta: 19:18:53, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4963, top5_acc: 0.7519, loss_cls: 2.7963, loss: 2.7963 +2024-12-31 01:18:45,423 - pyskl - INFO - Epoch [129][1000/3746] lr: 5.091e-03, eta: 19:17:27, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5052, top5_acc: 0.7578, loss_cls: 2.7656, loss: 2.7656 +2024-12-31 01:20:10,192 - pyskl - INFO - Epoch [129][1100/3746] lr: 5.079e-03, eta: 19:16:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5095, top5_acc: 0.7661, loss_cls: 2.7019, loss: 2.7019 +2024-12-31 01:21:34,666 - pyskl - INFO - Epoch [129][1200/3746] lr: 5.066e-03, eta: 19:14:36, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.5070, top5_acc: 0.7633, loss_cls: 2.7123, loss: 2.7123 +2024-12-31 01:22:59,025 - pyskl - INFO - Epoch [129][1300/3746] lr: 5.054e-03, eta: 19:13:11, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5006, top5_acc: 0.7453, loss_cls: 2.7956, loss: 2.7956 +2024-12-31 01:24:23,381 - pyskl - INFO - Epoch [129][1400/3746] lr: 5.042e-03, eta: 19:11:45, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4988, top5_acc: 0.7562, loss_cls: 2.8048, loss: 2.8048 +2024-12-31 01:25:47,712 - pyskl - INFO - Epoch [129][1500/3746] lr: 5.030e-03, eta: 19:10:20, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5055, top5_acc: 0.7466, loss_cls: 2.7811, loss: 2.7811 +2024-12-31 01:27:11,913 - pyskl - INFO - Epoch [129][1600/3746] lr: 5.017e-03, eta: 19:08:54, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4984, top5_acc: 0.7584, loss_cls: 2.7656, loss: 2.7656 +2024-12-31 01:28:36,621 - pyskl - INFO - Epoch [129][1700/3746] lr: 5.005e-03, eta: 19:07:29, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4961, top5_acc: 0.7550, loss_cls: 2.7764, loss: 2.7764 +2024-12-31 01:30:00,697 - pyskl - INFO - Epoch [129][1800/3746] lr: 4.993e-03, eta: 19:06:03, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.5016, top5_acc: 0.7573, loss_cls: 2.7718, loss: 2.7718 +2024-12-31 01:31:25,040 - pyskl - INFO - Epoch [129][1900/3746] lr: 4.981e-03, eta: 19:04:38, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4889, top5_acc: 0.7516, loss_cls: 2.8048, loss: 2.8048 +2024-12-31 01:32:49,537 - pyskl - INFO - Epoch [129][2000/3746] lr: 4.969e-03, eta: 19:03:13, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4986, top5_acc: 0.7555, loss_cls: 2.7513, loss: 2.7513 +2024-12-31 01:34:14,165 - pyskl - INFO - Epoch [129][2100/3746] lr: 4.957e-03, eta: 19:01:47, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4919, top5_acc: 0.7483, loss_cls: 2.8023, loss: 2.8023 +2024-12-31 01:35:38,874 - pyskl - INFO - Epoch [129][2200/3746] lr: 4.944e-03, eta: 19:00:22, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4902, top5_acc: 0.7581, loss_cls: 2.7697, loss: 2.7697 +2024-12-31 01:37:03,267 - pyskl - INFO - Epoch [129][2300/3746] lr: 4.932e-03, eta: 18:58:56, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4905, top5_acc: 0.7525, loss_cls: 2.7782, loss: 2.7782 +2024-12-31 01:38:27,990 - pyskl - INFO - Epoch [129][2400/3746] lr: 4.920e-03, eta: 18:57:31, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4891, top5_acc: 0.7500, loss_cls: 2.8015, loss: 2.8015 +2024-12-31 01:39:52,217 - pyskl - INFO - Epoch [129][2500/3746] lr: 4.908e-03, eta: 18:56:05, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5017, top5_acc: 0.7516, loss_cls: 2.7816, loss: 2.7816 +2024-12-31 01:41:16,873 - pyskl - INFO - Epoch [129][2600/3746] lr: 4.896e-03, eta: 18:54:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4956, top5_acc: 0.7469, loss_cls: 2.8094, loss: 2.8094 +2024-12-31 01:42:41,514 - pyskl - INFO - Epoch [129][2700/3746] lr: 4.884e-03, eta: 18:53:15, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5033, top5_acc: 0.7517, loss_cls: 2.7752, loss: 2.7752 +2024-12-31 01:44:07,149 - pyskl - INFO - Epoch [129][2800/3746] lr: 4.872e-03, eta: 18:51:49, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5014, top5_acc: 0.7561, loss_cls: 2.7528, loss: 2.7528 +2024-12-31 01:45:32,077 - pyskl - INFO - Epoch [129][2900/3746] lr: 4.860e-03, eta: 18:50:24, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5078, top5_acc: 0.7545, loss_cls: 2.7553, loss: 2.7553 +2024-12-31 01:46:57,087 - pyskl - INFO - Epoch [129][3000/3746] lr: 4.848e-03, eta: 18:48:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4875, top5_acc: 0.7508, loss_cls: 2.8249, loss: 2.8249 +2024-12-31 01:48:21,863 - pyskl - INFO - Epoch [129][3100/3746] lr: 4.836e-03, eta: 18:47:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4878, top5_acc: 0.7517, loss_cls: 2.8130, loss: 2.8130 +2024-12-31 01:49:46,941 - pyskl - INFO - Epoch [129][3200/3746] lr: 4.824e-03, eta: 18:46:08, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5167, top5_acc: 0.7567, loss_cls: 2.7217, loss: 2.7217 +2024-12-31 01:51:12,350 - pyskl - INFO - Epoch [129][3300/3746] lr: 4.812e-03, eta: 18:44:43, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4956, top5_acc: 0.7470, loss_cls: 2.7844, loss: 2.7844 +2024-12-31 01:52:37,826 - pyskl - INFO - Epoch [129][3400/3746] lr: 4.800e-03, eta: 18:43:17, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4983, top5_acc: 0.7528, loss_cls: 2.7614, loss: 2.7614 +2024-12-31 01:54:03,380 - pyskl - INFO - Epoch [129][3500/3746] lr: 4.788e-03, eta: 18:41:52, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4931, top5_acc: 0.7505, loss_cls: 2.8128, loss: 2.8128 +2024-12-31 01:55:28,451 - pyskl - INFO - Epoch [129][3600/3746] lr: 4.776e-03, eta: 18:40:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5042, top5_acc: 0.7531, loss_cls: 2.7460, loss: 2.7460 +2024-12-31 01:56:53,468 - pyskl - INFO - Epoch [129][3700/3746] lr: 4.764e-03, eta: 18:39:01, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5175, top5_acc: 0.7645, loss_cls: 2.7090, loss: 2.7090 +2024-12-31 01:57:34,210 - pyskl - INFO - Saving checkpoint at 129 epochs +2024-12-31 01:59:33,516 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 01:59:34,300 - pyskl - INFO - +top1_acc 0.4139 +top5_acc 0.6641 +2024-12-31 01:59:34,300 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 01:59:34,346 - pyskl - INFO - +mean_acc 0.4136 +2024-12-31 01:59:34,366 - pyskl - INFO - Epoch(val) [129][309] top1_acc: 0.4139, top5_acc: 0.6641, mean_class_accuracy: 0.4136 +2024-12-31 02:03:47,399 - pyskl - INFO - Epoch [130][100/3746] lr: 4.747e-03, eta: 18:37:18, time: 2.530, data_time: 1.486, memory: 15990, top1_acc: 0.5209, top5_acc: 0.7752, loss_cls: 2.6308, loss: 2.6308 +2024-12-31 02:05:12,771 - pyskl - INFO - Epoch [130][200/3746] lr: 4.735e-03, eta: 18:35:52, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5206, top5_acc: 0.7689, loss_cls: 2.6837, loss: 2.6837 +2024-12-31 02:06:39,332 - pyskl - INFO - Epoch [130][300/3746] lr: 4.723e-03, eta: 18:34:27, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5162, top5_acc: 0.7644, loss_cls: 2.6823, loss: 2.6823 +2024-12-31 02:08:05,722 - pyskl - INFO - Epoch [130][400/3746] lr: 4.711e-03, eta: 18:33:02, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5261, top5_acc: 0.7736, loss_cls: 2.6129, loss: 2.6129 +2024-12-31 02:09:31,428 - pyskl - INFO - Epoch [130][500/3746] lr: 4.699e-03, eta: 18:31:37, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5169, top5_acc: 0.7641, loss_cls: 2.6651, loss: 2.6651 +2024-12-31 02:10:57,493 - pyskl - INFO - Epoch [130][600/3746] lr: 4.688e-03, eta: 18:30:12, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5122, top5_acc: 0.7628, loss_cls: 2.6768, loss: 2.6768 +2024-12-31 02:12:23,636 - pyskl - INFO - Epoch [130][700/3746] lr: 4.676e-03, eta: 18:28:46, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5172, top5_acc: 0.7594, loss_cls: 2.6865, loss: 2.6865 +2024-12-31 02:13:49,504 - pyskl - INFO - Epoch [130][800/3746] lr: 4.664e-03, eta: 18:27:21, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5162, top5_acc: 0.7630, loss_cls: 2.7065, loss: 2.7065 +2024-12-31 02:15:14,489 - pyskl - INFO - Epoch [130][900/3746] lr: 4.652e-03, eta: 18:25:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5052, top5_acc: 0.7598, loss_cls: 2.7315, loss: 2.7315 +2024-12-31 02:16:40,524 - pyskl - INFO - Epoch [130][1000/3746] lr: 4.640e-03, eta: 18:24:31, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5094, top5_acc: 0.7595, loss_cls: 2.6949, loss: 2.6949 +2024-12-31 02:18:06,185 - pyskl - INFO - Epoch [130][1100/3746] lr: 4.629e-03, eta: 18:23:05, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5020, top5_acc: 0.7617, loss_cls: 2.7472, loss: 2.7472 +2024-12-31 02:19:32,544 - pyskl - INFO - Epoch [130][1200/3746] lr: 4.617e-03, eta: 18:21:40, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5125, top5_acc: 0.7688, loss_cls: 2.6989, loss: 2.6989 +2024-12-31 02:20:57,454 - pyskl - INFO - Epoch [130][1300/3746] lr: 4.605e-03, eta: 18:20:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5064, top5_acc: 0.7608, loss_cls: 2.7275, loss: 2.7275 +2024-12-31 02:22:22,642 - pyskl - INFO - Epoch [130][1400/3746] lr: 4.594e-03, eta: 18:18:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5041, top5_acc: 0.7688, loss_cls: 2.7174, loss: 2.7174 +2024-12-31 02:23:48,294 - pyskl - INFO - Epoch [130][1500/3746] lr: 4.582e-03, eta: 18:17:24, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5092, top5_acc: 0.7594, loss_cls: 2.7252, loss: 2.7252 +2024-12-31 02:25:13,634 - pyskl - INFO - Epoch [130][1600/3746] lr: 4.570e-03, eta: 18:15:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5184, top5_acc: 0.7686, loss_cls: 2.6626, loss: 2.6626 +2024-12-31 02:26:39,176 - pyskl - INFO - Epoch [130][1700/3746] lr: 4.558e-03, eta: 18:14:34, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5117, top5_acc: 0.7631, loss_cls: 2.6998, loss: 2.6998 +2024-12-31 02:28:05,005 - pyskl - INFO - Epoch [130][1800/3746] lr: 4.547e-03, eta: 18:13:08, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4986, top5_acc: 0.7575, loss_cls: 2.7427, loss: 2.7427 +2024-12-31 02:29:30,149 - pyskl - INFO - Epoch [130][1900/3746] lr: 4.535e-03, eta: 18:11:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4983, top5_acc: 0.7597, loss_cls: 2.7561, loss: 2.7561 +2024-12-31 02:30:56,074 - pyskl - INFO - Epoch [130][2000/3746] lr: 4.524e-03, eta: 18:10:18, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.5109, top5_acc: 0.7606, loss_cls: 2.7193, loss: 2.7193 +2024-12-31 02:32:21,924 - pyskl - INFO - Epoch [130][2100/3746] lr: 4.512e-03, eta: 18:08:52, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5058, top5_acc: 0.7606, loss_cls: 2.7303, loss: 2.7303 +2024-12-31 02:33:47,316 - pyskl - INFO - Epoch [130][2200/3746] lr: 4.500e-03, eta: 18:07:27, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5044, top5_acc: 0.7634, loss_cls: 2.7367, loss: 2.7367 +2024-12-31 02:35:12,740 - pyskl - INFO - Epoch [130][2300/3746] lr: 4.489e-03, eta: 18:06:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5084, top5_acc: 0.7612, loss_cls: 2.7432, loss: 2.7432 +2024-12-31 02:36:38,839 - pyskl - INFO - Epoch [130][2400/3746] lr: 4.477e-03, eta: 18:04:37, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5141, top5_acc: 0.7612, loss_cls: 2.7209, loss: 2.7209 +2024-12-31 02:38:03,859 - pyskl - INFO - Epoch [130][2500/3746] lr: 4.466e-03, eta: 18:03:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5011, top5_acc: 0.7600, loss_cls: 2.7568, loss: 2.7568 +2024-12-31 02:39:29,328 - pyskl - INFO - Epoch [130][2600/3746] lr: 4.454e-03, eta: 18:01:46, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.5080, top5_acc: 0.7605, loss_cls: 2.7390, loss: 2.7390 +2024-12-31 02:40:54,626 - pyskl - INFO - Epoch [130][2700/3746] lr: 4.443e-03, eta: 18:00:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5023, top5_acc: 0.7558, loss_cls: 2.7441, loss: 2.7441 +2024-12-31 02:42:20,516 - pyskl - INFO - Epoch [130][2800/3746] lr: 4.431e-03, eta: 17:58:55, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5095, top5_acc: 0.7581, loss_cls: 2.7422, loss: 2.7422 +2024-12-31 02:43:45,914 - pyskl - INFO - Epoch [130][2900/3746] lr: 4.420e-03, eta: 17:57:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5089, top5_acc: 0.7673, loss_cls: 2.6887, loss: 2.6887 +2024-12-31 02:45:11,714 - pyskl - INFO - Epoch [130][3000/3746] lr: 4.408e-03, eta: 17:56:05, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4981, top5_acc: 0.7552, loss_cls: 2.7477, loss: 2.7477 +2024-12-31 02:46:37,154 - pyskl - INFO - Epoch [130][3100/3746] lr: 4.397e-03, eta: 17:54:39, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4978, top5_acc: 0.7584, loss_cls: 2.7644, loss: 2.7644 +2024-12-31 02:48:02,791 - pyskl - INFO - Epoch [130][3200/3746] lr: 4.385e-03, eta: 17:53:14, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4923, top5_acc: 0.7500, loss_cls: 2.7907, loss: 2.7907 +2024-12-31 02:49:28,710 - pyskl - INFO - Epoch [130][3300/3746] lr: 4.374e-03, eta: 17:51:49, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5012, top5_acc: 0.7528, loss_cls: 2.7433, loss: 2.7433 +2024-12-31 02:50:55,393 - pyskl - INFO - Epoch [130][3400/3746] lr: 4.362e-03, eta: 17:50:24, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5064, top5_acc: 0.7484, loss_cls: 2.7699, loss: 2.7699 +2024-12-31 02:52:21,523 - pyskl - INFO - Epoch [130][3500/3746] lr: 4.351e-03, eta: 17:48:59, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5033, top5_acc: 0.7550, loss_cls: 2.7691, loss: 2.7691 +2024-12-31 02:53:47,940 - pyskl - INFO - Epoch [130][3600/3746] lr: 4.339e-03, eta: 17:47:33, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5078, top5_acc: 0.7612, loss_cls: 2.7364, loss: 2.7364 +2024-12-31 02:55:14,024 - pyskl - INFO - Epoch [130][3700/3746] lr: 4.328e-03, eta: 17:46:08, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5062, top5_acc: 0.7569, loss_cls: 2.7391, loss: 2.7391 +2024-12-31 02:55:55,017 - pyskl - INFO - Saving checkpoint at 130 epochs +2024-12-31 02:57:54,364 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 02:57:55,043 - pyskl - INFO - +top1_acc 0.4197 +top5_acc 0.6784 +2024-12-31 02:57:55,043 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 02:57:55,083 - pyskl - INFO - +mean_acc 0.4194 +2024-12-31 02:57:55,088 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_128.pth was removed +2024-12-31 02:57:55,350 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_130.pth. +2024-12-31 02:57:55,351 - pyskl - INFO - Best top1_acc is 0.4197 at 130 epoch. +2024-12-31 02:57:55,362 - pyskl - INFO - Epoch(val) [130][309] top1_acc: 0.4197, top5_acc: 0.6784, mean_class_accuracy: 0.4194 +2024-12-31 03:02:08,025 - pyskl - INFO - Epoch [131][100/3746] lr: 4.311e-03, eta: 17:44:23, time: 2.527, data_time: 1.491, memory: 15990, top1_acc: 0.5211, top5_acc: 0.7711, loss_cls: 2.6660, loss: 2.6660 +2024-12-31 03:03:33,880 - pyskl - INFO - Epoch [131][200/3746] lr: 4.300e-03, eta: 17:42:58, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5192, top5_acc: 0.7706, loss_cls: 2.6483, loss: 2.6483 +2024-12-31 03:05:00,012 - pyskl - INFO - Epoch [131][300/3746] lr: 4.289e-03, eta: 17:41:33, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5264, top5_acc: 0.7769, loss_cls: 2.6276, loss: 2.6276 +2024-12-31 03:06:25,665 - pyskl - INFO - Epoch [131][400/3746] lr: 4.277e-03, eta: 17:40:07, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5255, top5_acc: 0.7753, loss_cls: 2.6338, loss: 2.6338 +2024-12-31 03:07:51,517 - pyskl - INFO - Epoch [131][500/3746] lr: 4.266e-03, eta: 17:38:42, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5088, top5_acc: 0.7625, loss_cls: 2.7023, loss: 2.7023 +2024-12-31 03:09:17,393 - pyskl - INFO - Epoch [131][600/3746] lr: 4.255e-03, eta: 17:37:17, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5255, top5_acc: 0.7738, loss_cls: 2.6424, loss: 2.6424 +2024-12-31 03:10:43,175 - pyskl - INFO - Epoch [131][700/3746] lr: 4.244e-03, eta: 17:35:52, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5134, top5_acc: 0.7648, loss_cls: 2.6890, loss: 2.6890 +2024-12-31 03:12:09,144 - pyskl - INFO - Epoch [131][800/3746] lr: 4.232e-03, eta: 17:34:26, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5172, top5_acc: 0.7692, loss_cls: 2.6803, loss: 2.6803 +2024-12-31 03:13:35,250 - pyskl - INFO - Epoch [131][900/3746] lr: 4.221e-03, eta: 17:33:01, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5164, top5_acc: 0.7703, loss_cls: 2.6559, loss: 2.6559 +2024-12-31 03:15:01,520 - pyskl - INFO - Epoch [131][1000/3746] lr: 4.210e-03, eta: 17:31:36, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5195, top5_acc: 0.7694, loss_cls: 2.6582, loss: 2.6582 +2024-12-31 03:16:27,200 - pyskl - INFO - Epoch [131][1100/3746] lr: 4.199e-03, eta: 17:30:11, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.5158, top5_acc: 0.7623, loss_cls: 2.6858, loss: 2.6858 +2024-12-31 03:17:53,113 - pyskl - INFO - Epoch [131][1200/3746] lr: 4.187e-03, eta: 17:28:45, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5031, top5_acc: 0.7631, loss_cls: 2.7001, loss: 2.7001 +2024-12-31 03:19:18,777 - pyskl - INFO - Epoch [131][1300/3746] lr: 4.176e-03, eta: 17:27:20, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5164, top5_acc: 0.7597, loss_cls: 2.7132, loss: 2.7132 +2024-12-31 03:20:44,036 - pyskl - INFO - Epoch [131][1400/3746] lr: 4.165e-03, eta: 17:25:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5072, top5_acc: 0.7588, loss_cls: 2.7200, loss: 2.7200 +2024-12-31 03:22:09,147 - pyskl - INFO - Epoch [131][1500/3746] lr: 4.154e-03, eta: 17:24:29, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5150, top5_acc: 0.7708, loss_cls: 2.6798, loss: 2.6798 +2024-12-31 03:23:34,668 - pyskl - INFO - Epoch [131][1600/3746] lr: 4.143e-03, eta: 17:23:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5136, top5_acc: 0.7673, loss_cls: 2.6886, loss: 2.6886 +2024-12-31 03:25:00,386 - pyskl - INFO - Epoch [131][1700/3746] lr: 4.132e-03, eta: 17:21:39, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5159, top5_acc: 0.7645, loss_cls: 2.6503, loss: 2.6503 +2024-12-31 03:26:26,754 - pyskl - INFO - Epoch [131][1800/3746] lr: 4.120e-03, eta: 17:20:13, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5158, top5_acc: 0.7672, loss_cls: 2.6692, loss: 2.6692 +2024-12-31 03:27:53,267 - pyskl - INFO - Epoch [131][1900/3746] lr: 4.109e-03, eta: 17:18:48, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5111, top5_acc: 0.7569, loss_cls: 2.7282, loss: 2.7282 +2024-12-31 03:29:19,097 - pyskl - INFO - Epoch [131][2000/3746] lr: 4.098e-03, eta: 17:17:23, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5116, top5_acc: 0.7667, loss_cls: 2.6964, loss: 2.6964 +2024-12-31 03:30:45,059 - pyskl - INFO - Epoch [131][2100/3746] lr: 4.087e-03, eta: 17:15:58, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5091, top5_acc: 0.7620, loss_cls: 2.6980, loss: 2.6980 +2024-12-31 03:32:10,490 - pyskl - INFO - Epoch [131][2200/3746] lr: 4.076e-03, eta: 17:14:32, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5019, top5_acc: 0.7628, loss_cls: 2.7116, loss: 2.7116 +2024-12-31 03:33:36,402 - pyskl - INFO - Epoch [131][2300/3746] lr: 4.065e-03, eta: 17:13:07, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5194, top5_acc: 0.7678, loss_cls: 2.6679, loss: 2.6679 +2024-12-31 03:35:01,841 - pyskl - INFO - Epoch [131][2400/3746] lr: 4.054e-03, eta: 17:11:42, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5128, top5_acc: 0.7630, loss_cls: 2.6741, loss: 2.6741 +2024-12-31 03:36:27,680 - pyskl - INFO - Epoch [131][2500/3746] lr: 4.043e-03, eta: 17:10:16, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5178, top5_acc: 0.7648, loss_cls: 2.6799, loss: 2.6799 +2024-12-31 03:37:53,962 - pyskl - INFO - Epoch [131][2600/3746] lr: 4.032e-03, eta: 17:08:51, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5103, top5_acc: 0.7584, loss_cls: 2.7350, loss: 2.7350 +2024-12-31 03:39:19,691 - pyskl - INFO - Epoch [131][2700/3746] lr: 4.021e-03, eta: 17:07:26, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5311, top5_acc: 0.7761, loss_cls: 2.6451, loss: 2.6451 +2024-12-31 03:40:45,346 - pyskl - INFO - Epoch [131][2800/3746] lr: 4.010e-03, eta: 17:06:01, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5066, top5_acc: 0.7570, loss_cls: 2.7273, loss: 2.7273 +2024-12-31 03:42:11,211 - pyskl - INFO - Epoch [131][2900/3746] lr: 3.999e-03, eta: 17:04:35, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5220, top5_acc: 0.7616, loss_cls: 2.6781, loss: 2.6781 +2024-12-31 03:43:36,776 - pyskl - INFO - Epoch [131][3000/3746] lr: 3.988e-03, eta: 17:03:10, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5150, top5_acc: 0.7698, loss_cls: 2.6897, loss: 2.6897 +2024-12-31 03:45:02,662 - pyskl - INFO - Epoch [131][3100/3746] lr: 3.977e-03, eta: 17:01:45, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5158, top5_acc: 0.7636, loss_cls: 2.7097, loss: 2.7097 +2024-12-31 03:46:28,757 - pyskl - INFO - Epoch [131][3200/3746] lr: 3.966e-03, eta: 17:00:19, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5152, top5_acc: 0.7695, loss_cls: 2.6433, loss: 2.6433 +2024-12-31 03:47:54,706 - pyskl - INFO - Epoch [131][3300/3746] lr: 3.955e-03, eta: 16:58:54, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5159, top5_acc: 0.7669, loss_cls: 2.6870, loss: 2.6870 +2024-12-31 03:49:20,778 - pyskl - INFO - Epoch [131][3400/3746] lr: 3.945e-03, eta: 16:57:29, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5102, top5_acc: 0.7652, loss_cls: 2.6931, loss: 2.6931 +2024-12-31 03:50:46,681 - pyskl - INFO - Epoch [131][3500/3746] lr: 3.934e-03, eta: 16:56:04, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4931, top5_acc: 0.7620, loss_cls: 2.7773, loss: 2.7773 +2024-12-31 03:52:12,162 - pyskl - INFO - Epoch [131][3600/3746] lr: 3.923e-03, eta: 16:54:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5125, top5_acc: 0.7630, loss_cls: 2.7193, loss: 2.7193 +2024-12-31 03:53:38,119 - pyskl - INFO - Epoch [131][3700/3746] lr: 3.912e-03, eta: 16:53:13, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5070, top5_acc: 0.7611, loss_cls: 2.7153, loss: 2.7153 +2024-12-31 03:54:19,407 - pyskl - INFO - Saving checkpoint at 131 epochs +2024-12-31 03:56:17,920 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 03:56:18,821 - pyskl - INFO - +top1_acc 0.4287 +top5_acc 0.6817 +2024-12-31 03:56:18,822 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 03:56:18,870 - pyskl - INFO - +mean_acc 0.4285 +2024-12-31 03:56:18,875 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_130.pth was removed +2024-12-31 03:56:19,169 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_131.pth. +2024-12-31 03:56:19,170 - pyskl - INFO - Best top1_acc is 0.4287 at 131 epoch. +2024-12-31 03:56:19,184 - pyskl - INFO - Epoch(val) [131][309] top1_acc: 0.4287, top5_acc: 0.6817, mean_class_accuracy: 0.4285 +2024-12-31 04:00:35,536 - pyskl - INFO - Epoch [132][100/3746] lr: 3.896e-03, eta: 16:51:27, time: 2.563, data_time: 1.510, memory: 15990, top1_acc: 0.5286, top5_acc: 0.7847, loss_cls: 2.5917, loss: 2.5917 +2024-12-31 04:02:02,622 - pyskl - INFO - Epoch [132][200/3746] lr: 3.885e-03, eta: 16:50:02, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5427, top5_acc: 0.7873, loss_cls: 2.5494, loss: 2.5494 +2024-12-31 04:03:30,051 - pyskl - INFO - Epoch [132][300/3746] lr: 3.875e-03, eta: 16:48:37, time: 0.874, data_time: 0.001, memory: 15990, top1_acc: 0.5250, top5_acc: 0.7773, loss_cls: 2.6243, loss: 2.6243 +2024-12-31 04:04:57,577 - pyskl - INFO - Epoch [132][400/3746] lr: 3.864e-03, eta: 16:47:12, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.5258, top5_acc: 0.7800, loss_cls: 2.5904, loss: 2.5904 +2024-12-31 04:06:24,719 - pyskl - INFO - Epoch [132][500/3746] lr: 3.853e-03, eta: 16:45:47, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5342, top5_acc: 0.7872, loss_cls: 2.5685, loss: 2.5685 +2024-12-31 04:07:51,869 - pyskl - INFO - Epoch [132][600/3746] lr: 3.842e-03, eta: 16:44:22, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5222, top5_acc: 0.7794, loss_cls: 2.6136, loss: 2.6136 +2024-12-31 04:09:18,862 - pyskl - INFO - Epoch [132][700/3746] lr: 3.831e-03, eta: 16:42:57, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5245, top5_acc: 0.7691, loss_cls: 2.6247, loss: 2.6247 +2024-12-31 04:10:46,279 - pyskl - INFO - Epoch [132][800/3746] lr: 3.821e-03, eta: 16:41:32, time: 0.874, data_time: 0.001, memory: 15990, top1_acc: 0.5261, top5_acc: 0.7756, loss_cls: 2.6345, loss: 2.6345 +2024-12-31 04:12:14,312 - pyskl - INFO - Epoch [132][900/3746] lr: 3.810e-03, eta: 16:40:07, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.5336, top5_acc: 0.7834, loss_cls: 2.5970, loss: 2.5970 +2024-12-31 04:13:42,407 - pyskl - INFO - Epoch [132][1000/3746] lr: 3.799e-03, eta: 16:38:42, time: 0.881, data_time: 0.001, memory: 15990, top1_acc: 0.5353, top5_acc: 0.7780, loss_cls: 2.5953, loss: 2.5953 +2024-12-31 04:15:09,757 - pyskl - INFO - Epoch [132][1100/3746] lr: 3.789e-03, eta: 16:37:16, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.5348, top5_acc: 0.7836, loss_cls: 2.5886, loss: 2.5886 +2024-12-31 04:16:36,495 - pyskl - INFO - Epoch [132][1200/3746] lr: 3.778e-03, eta: 16:35:51, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5180, top5_acc: 0.7719, loss_cls: 2.6541, loss: 2.6541 +2024-12-31 04:18:02,993 - pyskl - INFO - Epoch [132][1300/3746] lr: 3.767e-03, eta: 16:34:26, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5288, top5_acc: 0.7750, loss_cls: 2.6192, loss: 2.6192 +2024-12-31 04:19:30,144 - pyskl - INFO - Epoch [132][1400/3746] lr: 3.757e-03, eta: 16:33:01, time: 0.871, data_time: 0.001, memory: 15990, top1_acc: 0.5278, top5_acc: 0.7719, loss_cls: 2.6283, loss: 2.6283 +2024-12-31 04:20:58,444 - pyskl - INFO - Epoch [132][1500/3746] lr: 3.746e-03, eta: 16:31:36, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.5234, top5_acc: 0.7741, loss_cls: 2.6363, loss: 2.6363 +2024-12-31 04:22:26,658 - pyskl - INFO - Epoch [132][1600/3746] lr: 3.735e-03, eta: 16:30:11, time: 0.882, data_time: 0.001, memory: 15990, top1_acc: 0.5186, top5_acc: 0.7739, loss_cls: 2.6468, loss: 2.6468 +2024-12-31 04:23:54,923 - pyskl - INFO - Epoch [132][1700/3746] lr: 3.725e-03, eta: 16:28:46, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.5303, top5_acc: 0.7767, loss_cls: 2.5988, loss: 2.5988 +2024-12-31 04:25:22,259 - pyskl - INFO - Epoch [132][1800/3746] lr: 3.714e-03, eta: 16:27:21, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.5148, top5_acc: 0.7762, loss_cls: 2.6315, loss: 2.6315 +2024-12-31 04:26:48,917 - pyskl - INFO - Epoch [132][1900/3746] lr: 3.704e-03, eta: 16:25:56, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5233, top5_acc: 0.7742, loss_cls: 2.6621, loss: 2.6621 +2024-12-31 04:28:14,604 - pyskl - INFO - Epoch [132][2000/3746] lr: 3.693e-03, eta: 16:24:30, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5134, top5_acc: 0.7669, loss_cls: 2.6990, loss: 2.6990 +2024-12-31 04:29:40,596 - pyskl - INFO - Epoch [132][2100/3746] lr: 3.683e-03, eta: 16:23:05, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5169, top5_acc: 0.7758, loss_cls: 2.6659, loss: 2.6659 +2024-12-31 04:31:07,367 - pyskl - INFO - Epoch [132][2200/3746] lr: 3.672e-03, eta: 16:21:40, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5128, top5_acc: 0.7706, loss_cls: 2.6766, loss: 2.6766 +2024-12-31 04:32:33,654 - pyskl - INFO - Epoch [132][2300/3746] lr: 3.662e-03, eta: 16:20:14, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5327, top5_acc: 0.7786, loss_cls: 2.6129, loss: 2.6129 +2024-12-31 04:33:59,920 - pyskl - INFO - Epoch [132][2400/3746] lr: 3.651e-03, eta: 16:18:49, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5067, top5_acc: 0.7620, loss_cls: 2.6796, loss: 2.6796 +2024-12-31 04:35:26,821 - pyskl - INFO - Epoch [132][2500/3746] lr: 3.641e-03, eta: 16:17:24, time: 0.869, data_time: 0.001, memory: 15990, top1_acc: 0.5050, top5_acc: 0.7655, loss_cls: 2.7091, loss: 2.7091 +2024-12-31 04:36:53,295 - pyskl - INFO - Epoch [132][2600/3746] lr: 3.630e-03, eta: 16:15:59, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7712, loss_cls: 2.6319, loss: 2.6319 +2024-12-31 04:38:19,255 - pyskl - INFO - Epoch [132][2700/3746] lr: 3.620e-03, eta: 16:14:33, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5016, top5_acc: 0.7600, loss_cls: 2.7232, loss: 2.7232 +2024-12-31 04:39:45,918 - pyskl - INFO - Epoch [132][2800/3746] lr: 3.609e-03, eta: 16:13:08, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5233, top5_acc: 0.7698, loss_cls: 2.6354, loss: 2.6354 +2024-12-31 04:41:12,236 - pyskl - INFO - Epoch [132][2900/3746] lr: 3.599e-03, eta: 16:11:43, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5233, top5_acc: 0.7708, loss_cls: 2.6403, loss: 2.6403 +2024-12-31 04:42:38,688 - pyskl - INFO - Epoch [132][3000/3746] lr: 3.588e-03, eta: 16:10:18, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5180, top5_acc: 0.7702, loss_cls: 2.6904, loss: 2.6904 +2024-12-31 04:44:05,066 - pyskl - INFO - Epoch [132][3100/3746] lr: 3.578e-03, eta: 16:08:52, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5325, top5_acc: 0.7756, loss_cls: 2.6163, loss: 2.6163 +2024-12-31 04:45:30,802 - pyskl - INFO - Epoch [132][3200/3746] lr: 3.568e-03, eta: 16:07:27, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5238, top5_acc: 0.7727, loss_cls: 2.6402, loss: 2.6402 +2024-12-31 04:46:56,702 - pyskl - INFO - Epoch [132][3300/3746] lr: 3.557e-03, eta: 16:06:02, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5192, top5_acc: 0.7602, loss_cls: 2.6826, loss: 2.6826 +2024-12-31 04:48:23,926 - pyskl - INFO - Epoch [132][3400/3746] lr: 3.547e-03, eta: 16:04:37, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5134, top5_acc: 0.7703, loss_cls: 2.6567, loss: 2.6567 +2024-12-31 04:49:49,940 - pyskl - INFO - Epoch [132][3500/3746] lr: 3.537e-03, eta: 16:03:11, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5158, top5_acc: 0.7677, loss_cls: 2.6882, loss: 2.6882 +2024-12-31 04:51:16,030 - pyskl - INFO - Epoch [132][3600/3746] lr: 3.526e-03, eta: 16:01:46, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5059, top5_acc: 0.7688, loss_cls: 2.6982, loss: 2.6982 +2024-12-31 04:52:43,018 - pyskl - INFO - Epoch [132][3700/3746] lr: 3.516e-03, eta: 16:00:21, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5114, top5_acc: 0.7611, loss_cls: 2.6697, loss: 2.6697 +2024-12-31 04:53:24,662 - pyskl - INFO - Saving checkpoint at 132 epochs +2024-12-31 04:55:23,384 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 04:55:24,135 - pyskl - INFO - +top1_acc 0.4276 +top5_acc 0.6830 +2024-12-31 04:55:24,136 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 04:55:24,177 - pyskl - INFO - +mean_acc 0.4274 +2024-12-31 04:55:24,189 - pyskl - INFO - Epoch(val) [132][309] top1_acc: 0.4276, top5_acc: 0.6830, mean_class_accuracy: 0.4274 +2024-12-31 04:59:38,387 - pyskl - INFO - Epoch [133][100/3746] lr: 3.501e-03, eta: 15:58:34, time: 2.542, data_time: 1.491, memory: 15990, top1_acc: 0.5361, top5_acc: 0.7820, loss_cls: 2.5298, loss: 2.5298 +2024-12-31 05:01:06,260 - pyskl - INFO - Epoch [133][200/3746] lr: 3.491e-03, eta: 15:57:09, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.5317, top5_acc: 0.7767, loss_cls: 2.5887, loss: 2.5887 +2024-12-31 05:02:34,026 - pyskl - INFO - Epoch [133][300/3746] lr: 3.480e-03, eta: 15:55:44, time: 0.878, data_time: 0.000, memory: 15990, top1_acc: 0.5442, top5_acc: 0.7908, loss_cls: 2.5164, loss: 2.5164 +2024-12-31 05:04:01,287 - pyskl - INFO - Epoch [133][400/3746] lr: 3.470e-03, eta: 15:54:18, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.5266, top5_acc: 0.7825, loss_cls: 2.5909, loss: 2.5909 +2024-12-31 05:05:29,613 - pyskl - INFO - Epoch [133][500/3746] lr: 3.460e-03, eta: 15:52:53, time: 0.883, data_time: 0.000, memory: 15990, top1_acc: 0.5548, top5_acc: 0.7950, loss_cls: 2.4738, loss: 2.4738 +2024-12-31 05:06:57,986 - pyskl - INFO - Epoch [133][600/3746] lr: 3.450e-03, eta: 15:51:28, time: 0.884, data_time: 0.000, memory: 15990, top1_acc: 0.5342, top5_acc: 0.7839, loss_cls: 2.5896, loss: 2.5896 +2024-12-31 05:08:25,896 - pyskl - INFO - Epoch [133][700/3746] lr: 3.440e-03, eta: 15:50:03, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.5256, top5_acc: 0.7742, loss_cls: 2.6339, loss: 2.6339 +2024-12-31 05:09:53,715 - pyskl - INFO - Epoch [133][800/3746] lr: 3.429e-03, eta: 15:48:38, time: 0.878, data_time: 0.000, memory: 15990, top1_acc: 0.5352, top5_acc: 0.7806, loss_cls: 2.5951, loss: 2.5951 +2024-12-31 05:11:21,356 - pyskl - INFO - Epoch [133][900/3746] lr: 3.419e-03, eta: 15:47:13, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.5339, top5_acc: 0.7845, loss_cls: 2.5531, loss: 2.5531 +2024-12-31 05:12:48,594 - pyskl - INFO - Epoch [133][1000/3746] lr: 3.409e-03, eta: 15:45:48, time: 0.872, data_time: 0.001, memory: 15990, top1_acc: 0.5392, top5_acc: 0.7806, loss_cls: 2.5825, loss: 2.5825 +2024-12-31 05:14:16,064 - pyskl - INFO - Epoch [133][1100/3746] lr: 3.399e-03, eta: 15:44:23, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.5347, top5_acc: 0.7803, loss_cls: 2.5979, loss: 2.5979 +2024-12-31 05:15:42,199 - pyskl - INFO - Epoch [133][1200/3746] lr: 3.389e-03, eta: 15:42:57, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5350, top5_acc: 0.7770, loss_cls: 2.5843, loss: 2.5843 +2024-12-31 05:17:08,049 - pyskl - INFO - Epoch [133][1300/3746] lr: 3.379e-03, eta: 15:41:32, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5314, top5_acc: 0.7739, loss_cls: 2.5906, loss: 2.5906 +2024-12-31 05:18:34,960 - pyskl - INFO - Epoch [133][1400/3746] lr: 3.369e-03, eta: 15:40:07, time: 0.869, data_time: 0.001, memory: 15990, top1_acc: 0.5328, top5_acc: 0.7788, loss_cls: 2.5840, loss: 2.5840 +2024-12-31 05:20:03,716 - pyskl - INFO - Epoch [133][1500/3746] lr: 3.359e-03, eta: 15:38:42, time: 0.888, data_time: 0.001, memory: 15990, top1_acc: 0.5272, top5_acc: 0.7717, loss_cls: 2.6199, loss: 2.6199 +2024-12-31 05:21:31,376 - pyskl - INFO - Epoch [133][1600/3746] lr: 3.348e-03, eta: 15:37:17, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.5358, top5_acc: 0.7834, loss_cls: 2.5635, loss: 2.5635 +2024-12-31 05:22:59,641 - pyskl - INFO - Epoch [133][1700/3746] lr: 3.338e-03, eta: 15:35:52, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.5273, top5_acc: 0.7719, loss_cls: 2.6350, loss: 2.6350 +2024-12-31 05:24:27,419 - pyskl - INFO - Epoch [133][1800/3746] lr: 3.328e-03, eta: 15:34:26, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.5330, top5_acc: 0.7808, loss_cls: 2.5828, loss: 2.5828 +2024-12-31 05:25:54,432 - pyskl - INFO - Epoch [133][1900/3746] lr: 3.318e-03, eta: 15:33:01, time: 0.870, data_time: 0.001, memory: 15990, top1_acc: 0.5262, top5_acc: 0.7767, loss_cls: 2.6003, loss: 2.6003 +2024-12-31 05:27:21,383 - pyskl - INFO - Epoch [133][2000/3746] lr: 3.308e-03, eta: 15:31:36, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5241, top5_acc: 0.7770, loss_cls: 2.6291, loss: 2.6291 +2024-12-31 05:28:47,699 - pyskl - INFO - Epoch [133][2100/3746] lr: 3.298e-03, eta: 15:30:11, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5264, top5_acc: 0.7869, loss_cls: 2.5960, loss: 2.5960 +2024-12-31 05:30:14,244 - pyskl - INFO - Epoch [133][2200/3746] lr: 3.288e-03, eta: 15:28:45, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5259, top5_acc: 0.7770, loss_cls: 2.6282, loss: 2.6282 +2024-12-31 05:31:40,789 - pyskl - INFO - Epoch [133][2300/3746] lr: 3.278e-03, eta: 15:27:20, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5278, top5_acc: 0.7767, loss_cls: 2.6074, loss: 2.6074 +2024-12-31 05:33:07,957 - pyskl - INFO - Epoch [133][2400/3746] lr: 3.268e-03, eta: 15:25:55, time: 0.872, data_time: 0.001, memory: 15990, top1_acc: 0.5203, top5_acc: 0.7775, loss_cls: 2.6257, loss: 2.6257 +2024-12-31 05:34:35,367 - pyskl - INFO - Epoch [133][2500/3746] lr: 3.259e-03, eta: 15:24:30, time: 0.874, data_time: 0.001, memory: 15990, top1_acc: 0.5372, top5_acc: 0.7856, loss_cls: 2.5707, loss: 2.5707 +2024-12-31 05:36:03,447 - pyskl - INFO - Epoch [133][2600/3746] lr: 3.249e-03, eta: 15:23:05, time: 0.881, data_time: 0.001, memory: 15990, top1_acc: 0.5395, top5_acc: 0.7788, loss_cls: 2.5753, loss: 2.5753 +2024-12-31 05:37:30,900 - pyskl - INFO - Epoch [133][2700/3746] lr: 3.239e-03, eta: 15:21:39, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.5316, top5_acc: 0.7823, loss_cls: 2.5954, loss: 2.5954 +2024-12-31 05:38:58,192 - pyskl - INFO - Epoch [133][2800/3746] lr: 3.229e-03, eta: 15:20:14, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.5184, top5_acc: 0.7706, loss_cls: 2.6440, loss: 2.6440 +2024-12-31 05:40:26,737 - pyskl - INFO - Epoch [133][2900/3746] lr: 3.219e-03, eta: 15:18:49, time: 0.885, data_time: 0.001, memory: 15990, top1_acc: 0.5198, top5_acc: 0.7800, loss_cls: 2.6043, loss: 2.6043 +2024-12-31 05:41:54,553 - pyskl - INFO - Epoch [133][3000/3746] lr: 3.209e-03, eta: 15:17:24, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.5262, top5_acc: 0.7762, loss_cls: 2.6296, loss: 2.6296 +2024-12-31 05:43:23,309 - pyskl - INFO - Epoch [133][3100/3746] lr: 3.199e-03, eta: 15:15:59, time: 0.888, data_time: 0.001, memory: 15990, top1_acc: 0.5170, top5_acc: 0.7725, loss_cls: 2.6400, loss: 2.6400 +2024-12-31 05:44:51,757 - pyskl - INFO - Epoch [133][3200/3746] lr: 3.189e-03, eta: 15:14:34, time: 0.884, data_time: 0.001, memory: 15990, top1_acc: 0.5320, top5_acc: 0.7878, loss_cls: 2.5787, loss: 2.5787 +2024-12-31 05:46:19,625 - pyskl - INFO - Epoch [133][3300/3746] lr: 3.180e-03, eta: 15:13:09, time: 0.879, data_time: 0.001, memory: 15990, top1_acc: 0.5303, top5_acc: 0.7795, loss_cls: 2.5986, loss: 2.5986 +2024-12-31 05:47:47,639 - pyskl - INFO - Epoch [133][3400/3746] lr: 3.170e-03, eta: 15:11:44, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.5255, top5_acc: 0.7750, loss_cls: 2.6318, loss: 2.6318 +2024-12-31 05:49:16,290 - pyskl - INFO - Epoch [133][3500/3746] lr: 3.160e-03, eta: 15:10:19, time: 0.886, data_time: 0.000, memory: 15990, top1_acc: 0.5181, top5_acc: 0.7741, loss_cls: 2.6431, loss: 2.6431 +2024-12-31 05:50:44,549 - pyskl - INFO - Epoch [133][3600/3746] lr: 3.150e-03, eta: 15:08:54, time: 0.883, data_time: 0.000, memory: 15990, top1_acc: 0.5286, top5_acc: 0.7756, loss_cls: 2.5983, loss: 2.5983 +2024-12-31 05:52:12,445 - pyskl - INFO - Epoch [133][3700/3746] lr: 3.140e-03, eta: 15:07:29, time: 0.879, data_time: 0.001, memory: 15990, top1_acc: 0.5147, top5_acc: 0.7622, loss_cls: 2.6817, loss: 2.6817 +2024-12-31 05:52:54,822 - pyskl - INFO - Saving checkpoint at 133 epochs +2024-12-31 05:54:57,128 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 05:54:57,852 - pyskl - INFO - +top1_acc 0.4333 +top5_acc 0.6885 +2024-12-31 05:54:57,852 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 05:54:57,898 - pyskl - INFO - +mean_acc 0.4331 +2024-12-31 05:54:57,902 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_131.pth was removed +2024-12-31 05:54:58,190 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_133.pth. +2024-12-31 05:54:58,191 - pyskl - INFO - Best top1_acc is 0.4333 at 133 epoch. +2024-12-31 05:54:58,212 - pyskl - INFO - Epoch(val) [133][309] top1_acc: 0.4333, top5_acc: 0.6885, mean_class_accuracy: 0.4331 +2024-12-31 05:59:12,397 - pyskl - INFO - Epoch [134][100/3746] lr: 3.126e-03, eta: 15:05:40, time: 2.542, data_time: 1.509, memory: 15990, top1_acc: 0.5423, top5_acc: 0.7900, loss_cls: 2.5323, loss: 2.5323 +2024-12-31 06:00:38,367 - pyskl - INFO - Epoch [134][200/3746] lr: 3.117e-03, eta: 15:04:15, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.5347, top5_acc: 0.7864, loss_cls: 2.5372, loss: 2.5372 +2024-12-31 06:02:03,789 - pyskl - INFO - Epoch [134][300/3746] lr: 3.107e-03, eta: 15:02:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5519, top5_acc: 0.7963, loss_cls: 2.4932, loss: 2.4932 +2024-12-31 06:03:29,820 - pyskl - INFO - Epoch [134][400/3746] lr: 3.097e-03, eta: 15:01:24, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5370, top5_acc: 0.7817, loss_cls: 2.5483, loss: 2.5483 +2024-12-31 06:04:56,026 - pyskl - INFO - Epoch [134][500/3746] lr: 3.087e-03, eta: 14:59:59, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5369, top5_acc: 0.7864, loss_cls: 2.5400, loss: 2.5400 +2024-12-31 06:06:22,152 - pyskl - INFO - Epoch [134][600/3746] lr: 3.078e-03, eta: 14:58:33, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5337, top5_acc: 0.7841, loss_cls: 2.5410, loss: 2.5410 +2024-12-31 06:07:49,227 - pyskl - INFO - Epoch [134][700/3746] lr: 3.068e-03, eta: 14:57:08, time: 0.871, data_time: 0.001, memory: 15990, top1_acc: 0.5420, top5_acc: 0.7894, loss_cls: 2.5244, loss: 2.5244 +2024-12-31 06:09:15,314 - pyskl - INFO - Epoch [134][800/3746] lr: 3.059e-03, eta: 14:55:43, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.5386, top5_acc: 0.7903, loss_cls: 2.5228, loss: 2.5228 +2024-12-31 06:10:42,178 - pyskl - INFO - Epoch [134][900/3746] lr: 3.049e-03, eta: 14:54:17, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5350, top5_acc: 0.7947, loss_cls: 2.5053, loss: 2.5053 +2024-12-31 06:12:08,068 - pyskl - INFO - Epoch [134][1000/3746] lr: 3.039e-03, eta: 14:52:52, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5478, top5_acc: 0.7941, loss_cls: 2.5173, loss: 2.5173 +2024-12-31 06:13:34,184 - pyskl - INFO - Epoch [134][1100/3746] lr: 3.030e-03, eta: 14:51:27, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5305, top5_acc: 0.7811, loss_cls: 2.5653, loss: 2.5653 +2024-12-31 06:14:59,095 - pyskl - INFO - Epoch [134][1200/3746] lr: 3.020e-03, eta: 14:50:01, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.5391, top5_acc: 0.7880, loss_cls: 2.5524, loss: 2.5524 +2024-12-31 06:16:24,379 - pyskl - INFO - Epoch [134][1300/3746] lr: 3.011e-03, eta: 14:48:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5437, top5_acc: 0.7848, loss_cls: 2.5336, loss: 2.5336 +2024-12-31 06:17:49,377 - pyskl - INFO - Epoch [134][1400/3746] lr: 3.001e-03, eta: 14:47:10, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5295, top5_acc: 0.7806, loss_cls: 2.6071, loss: 2.6071 +2024-12-31 06:19:14,485 - pyskl - INFO - Epoch [134][1500/3746] lr: 2.991e-03, eta: 14:45:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5547, top5_acc: 0.7880, loss_cls: 2.4959, loss: 2.4959 +2024-12-31 06:20:39,323 - pyskl - INFO - Epoch [134][1600/3746] lr: 2.982e-03, eta: 14:44:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5286, top5_acc: 0.7775, loss_cls: 2.6017, loss: 2.6017 +2024-12-31 06:22:04,611 - pyskl - INFO - Epoch [134][1700/3746] lr: 2.972e-03, eta: 14:42:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5455, top5_acc: 0.7911, loss_cls: 2.5187, loss: 2.5187 +2024-12-31 06:23:30,670 - pyskl - INFO - Epoch [134][1800/3746] lr: 2.963e-03, eta: 14:41:28, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5427, top5_acc: 0.7914, loss_cls: 2.5137, loss: 2.5137 +2024-12-31 06:24:56,640 - pyskl - INFO - Epoch [134][1900/3746] lr: 2.953e-03, eta: 14:40:03, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5256, top5_acc: 0.7800, loss_cls: 2.6154, loss: 2.6154 +2024-12-31 06:26:21,701 - pyskl - INFO - Epoch [134][2000/3746] lr: 2.944e-03, eta: 14:38:37, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.5337, top5_acc: 0.7825, loss_cls: 2.5716, loss: 2.5716 +2024-12-31 06:27:46,831 - pyskl - INFO - Epoch [134][2100/3746] lr: 2.935e-03, eta: 14:37:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5375, top5_acc: 0.7780, loss_cls: 2.5798, loss: 2.5798 +2024-12-31 06:29:11,410 - pyskl - INFO - Epoch [134][2200/3746] lr: 2.925e-03, eta: 14:35:46, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.5277, top5_acc: 0.7786, loss_cls: 2.6057, loss: 2.6057 +2024-12-31 06:30:36,702 - pyskl - INFO - Epoch [134][2300/3746] lr: 2.916e-03, eta: 14:34:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5387, top5_acc: 0.7872, loss_cls: 2.5320, loss: 2.5320 +2024-12-31 06:32:03,257 - pyskl - INFO - Epoch [134][2400/3746] lr: 2.906e-03, eta: 14:32:55, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5392, top5_acc: 0.7903, loss_cls: 2.5187, loss: 2.5187 +2024-12-31 06:33:29,764 - pyskl - INFO - Epoch [134][2500/3746] lr: 2.897e-03, eta: 14:31:30, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5342, top5_acc: 0.7858, loss_cls: 2.5357, loss: 2.5357 +2024-12-31 06:34:55,926 - pyskl - INFO - Epoch [134][2600/3746] lr: 2.888e-03, eta: 14:30:05, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5316, top5_acc: 0.7903, loss_cls: 2.5442, loss: 2.5442 +2024-12-31 06:36:22,551 - pyskl - INFO - Epoch [134][2700/3746] lr: 2.878e-03, eta: 14:28:39, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5231, top5_acc: 0.7822, loss_cls: 2.6086, loss: 2.6086 +2024-12-31 06:37:48,758 - pyskl - INFO - Epoch [134][2800/3746] lr: 2.869e-03, eta: 14:27:14, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.5366, top5_acc: 0.7880, loss_cls: 2.5952, loss: 2.5952 +2024-12-31 06:39:15,396 - pyskl - INFO - Epoch [134][2900/3746] lr: 2.860e-03, eta: 14:25:48, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5416, top5_acc: 0.7863, loss_cls: 2.5381, loss: 2.5381 +2024-12-31 06:40:41,454 - pyskl - INFO - Epoch [134][3000/3746] lr: 2.850e-03, eta: 14:24:23, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5317, top5_acc: 0.7803, loss_cls: 2.5623, loss: 2.5623 +2024-12-31 06:42:07,972 - pyskl - INFO - Epoch [134][3100/3746] lr: 2.841e-03, eta: 14:22:58, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5273, top5_acc: 0.7883, loss_cls: 2.5782, loss: 2.5782 +2024-12-31 06:43:34,908 - pyskl - INFO - Epoch [134][3200/3746] lr: 2.832e-03, eta: 14:21:32, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5289, top5_acc: 0.7730, loss_cls: 2.6100, loss: 2.6100 +2024-12-31 06:45:02,281 - pyskl - INFO - Epoch [134][3300/3746] lr: 2.822e-03, eta: 14:20:07, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.5284, top5_acc: 0.7884, loss_cls: 2.5655, loss: 2.5655 +2024-12-31 06:46:28,986 - pyskl - INFO - Epoch [134][3400/3746] lr: 2.813e-03, eta: 14:18:42, time: 0.867, data_time: 0.001, memory: 15990, top1_acc: 0.5322, top5_acc: 0.7727, loss_cls: 2.5944, loss: 2.5944 +2024-12-31 06:47:55,564 - pyskl - INFO - Epoch [134][3500/3746] lr: 2.804e-03, eta: 14:17:17, time: 0.866, data_time: 0.001, memory: 15990, top1_acc: 0.5336, top5_acc: 0.7809, loss_cls: 2.5756, loss: 2.5756 +2024-12-31 06:49:22,093 - pyskl - INFO - Epoch [134][3600/3746] lr: 2.795e-03, eta: 14:15:51, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5306, top5_acc: 0.7802, loss_cls: 2.5544, loss: 2.5544 +2024-12-31 06:50:49,244 - pyskl - INFO - Epoch [134][3700/3746] lr: 2.786e-03, eta: 14:14:26, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5367, top5_acc: 0.7792, loss_cls: 2.5931, loss: 2.5931 +2024-12-31 06:51:31,380 - pyskl - INFO - Saving checkpoint at 134 epochs +2024-12-31 06:53:30,592 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 06:53:31,357 - pyskl - INFO - +top1_acc 0.4359 +top5_acc 0.6917 +2024-12-31 06:53:31,357 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 06:53:31,398 - pyskl - INFO - +mean_acc 0.4357 +2024-12-31 06:53:31,402 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_133.pth was removed +2024-12-31 06:53:31,794 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_134.pth. +2024-12-31 06:53:31,795 - pyskl - INFO - Best top1_acc is 0.4359 at 134 epoch. +2024-12-31 06:53:31,817 - pyskl - INFO - Epoch(val) [134][309] top1_acc: 0.4359, top5_acc: 0.6917, mean_class_accuracy: 0.4357 +2024-12-31 06:57:38,856 - pyskl - INFO - Epoch [135][100/3746] lr: 2.772e-03, eta: 14:12:36, time: 2.470, data_time: 1.447, memory: 15990, top1_acc: 0.5620, top5_acc: 0.7973, loss_cls: 2.4592, loss: 2.4592 +2024-12-31 06:59:03,733 - pyskl - INFO - Epoch [135][200/3746] lr: 2.763e-03, eta: 14:11:10, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5586, top5_acc: 0.8025, loss_cls: 2.4453, loss: 2.4453 +2024-12-31 07:00:29,079 - pyskl - INFO - Epoch [135][300/3746] lr: 2.754e-03, eta: 14:09:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5586, top5_acc: 0.7913, loss_cls: 2.4750, loss: 2.4750 +2024-12-31 07:01:54,223 - pyskl - INFO - Epoch [135][400/3746] lr: 2.745e-03, eta: 14:08:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5545, top5_acc: 0.7986, loss_cls: 2.4425, loss: 2.4425 +2024-12-31 07:03:19,756 - pyskl - INFO - Epoch [135][500/3746] lr: 2.735e-03, eta: 14:06:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5594, top5_acc: 0.8016, loss_cls: 2.4406, loss: 2.4406 +2024-12-31 07:04:45,548 - pyskl - INFO - Epoch [135][600/3746] lr: 2.726e-03, eta: 14:05:28, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5513, top5_acc: 0.7972, loss_cls: 2.4886, loss: 2.4886 +2024-12-31 07:06:11,162 - pyskl - INFO - Epoch [135][700/3746] lr: 2.717e-03, eta: 14:04:03, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5497, top5_acc: 0.7944, loss_cls: 2.5092, loss: 2.5092 +2024-12-31 07:07:37,117 - pyskl - INFO - Epoch [135][800/3746] lr: 2.708e-03, eta: 14:02:37, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5494, top5_acc: 0.7927, loss_cls: 2.4927, loss: 2.4927 +2024-12-31 07:09:03,454 - pyskl - INFO - Epoch [135][900/3746] lr: 2.699e-03, eta: 14:01:12, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5533, top5_acc: 0.7906, loss_cls: 2.4912, loss: 2.4912 +2024-12-31 07:10:28,921 - pyskl - INFO - Epoch [135][1000/3746] lr: 2.690e-03, eta: 13:59:46, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5434, top5_acc: 0.7877, loss_cls: 2.5388, loss: 2.5388 +2024-12-31 07:11:54,462 - pyskl - INFO - Epoch [135][1100/3746] lr: 2.681e-03, eta: 13:58:21, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5498, top5_acc: 0.7997, loss_cls: 2.4809, loss: 2.4809 +2024-12-31 07:13:20,398 - pyskl - INFO - Epoch [135][1200/3746] lr: 2.672e-03, eta: 13:56:55, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5570, top5_acc: 0.8058, loss_cls: 2.4394, loss: 2.4394 +2024-12-31 07:14:44,884 - pyskl - INFO - Epoch [135][1300/3746] lr: 2.663e-03, eta: 13:55:30, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5472, top5_acc: 0.7923, loss_cls: 2.5051, loss: 2.5051 +2024-12-31 07:16:09,800 - pyskl - INFO - Epoch [135][1400/3746] lr: 2.654e-03, eta: 13:54:04, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5400, top5_acc: 0.7864, loss_cls: 2.5346, loss: 2.5346 +2024-12-31 07:17:34,914 - pyskl - INFO - Epoch [135][1500/3746] lr: 2.645e-03, eta: 13:52:39, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5517, top5_acc: 0.7966, loss_cls: 2.4917, loss: 2.4917 +2024-12-31 07:19:00,183 - pyskl - INFO - Epoch [135][1600/3746] lr: 2.636e-03, eta: 13:51:13, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5533, top5_acc: 0.7919, loss_cls: 2.4867, loss: 2.4867 +2024-12-31 07:20:25,680 - pyskl - INFO - Epoch [135][1700/3746] lr: 2.627e-03, eta: 13:49:48, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5473, top5_acc: 0.7991, loss_cls: 2.4863, loss: 2.4863 +2024-12-31 07:21:50,713 - pyskl - INFO - Epoch [135][1800/3746] lr: 2.618e-03, eta: 13:48:22, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5378, top5_acc: 0.7872, loss_cls: 2.5466, loss: 2.5466 +2024-12-31 07:23:15,775 - pyskl - INFO - Epoch [135][1900/3746] lr: 2.609e-03, eta: 13:46:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5492, top5_acc: 0.7909, loss_cls: 2.5090, loss: 2.5090 +2024-12-31 07:24:41,226 - pyskl - INFO - Epoch [135][2000/3746] lr: 2.600e-03, eta: 13:45:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5484, top5_acc: 0.7898, loss_cls: 2.5134, loss: 2.5134 +2024-12-31 07:26:06,239 - pyskl - INFO - Epoch [135][2100/3746] lr: 2.591e-03, eta: 13:44:06, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.5511, top5_acc: 0.7922, loss_cls: 2.4952, loss: 2.4952 +2024-12-31 07:27:31,646 - pyskl - INFO - Epoch [135][2200/3746] lr: 2.583e-03, eta: 13:42:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5492, top5_acc: 0.7983, loss_cls: 2.4704, loss: 2.4704 +2024-12-31 07:28:56,433 - pyskl - INFO - Epoch [135][2300/3746] lr: 2.574e-03, eta: 13:41:14, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5367, top5_acc: 0.7884, loss_cls: 2.5158, loss: 2.5158 +2024-12-31 07:30:21,890 - pyskl - INFO - Epoch [135][2400/3746] lr: 2.565e-03, eta: 13:39:49, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5477, top5_acc: 0.7952, loss_cls: 2.4994, loss: 2.4994 +2024-12-31 07:31:47,138 - pyskl - INFO - Epoch [135][2500/3746] lr: 2.556e-03, eta: 13:38:23, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5348, top5_acc: 0.7883, loss_cls: 2.5345, loss: 2.5345 +2024-12-31 07:33:13,452 - pyskl - INFO - Epoch [135][2600/3746] lr: 2.547e-03, eta: 13:36:58, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5389, top5_acc: 0.7872, loss_cls: 2.5354, loss: 2.5354 +2024-12-31 07:34:39,603 - pyskl - INFO - Epoch [135][2700/3746] lr: 2.538e-03, eta: 13:35:33, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5472, top5_acc: 0.7873, loss_cls: 2.5363, loss: 2.5363 +2024-12-31 07:36:06,282 - pyskl - INFO - Epoch [135][2800/3746] lr: 2.530e-03, eta: 13:34:07, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5334, top5_acc: 0.7797, loss_cls: 2.5515, loss: 2.5515 +2024-12-31 07:37:32,449 - pyskl - INFO - Epoch [135][2900/3746] lr: 2.521e-03, eta: 13:32:42, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5411, top5_acc: 0.7878, loss_cls: 2.5303, loss: 2.5303 +2024-12-31 07:38:58,476 - pyskl - INFO - Epoch [135][3000/3746] lr: 2.512e-03, eta: 13:31:16, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5417, top5_acc: 0.7872, loss_cls: 2.5185, loss: 2.5185 +2024-12-31 07:40:24,239 - pyskl - INFO - Epoch [135][3100/3746] lr: 2.503e-03, eta: 13:29:51, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5333, top5_acc: 0.7845, loss_cls: 2.5658, loss: 2.5658 +2024-12-31 07:41:50,213 - pyskl - INFO - Epoch [135][3200/3746] lr: 2.495e-03, eta: 13:28:26, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5458, top5_acc: 0.7891, loss_cls: 2.5298, loss: 2.5298 +2024-12-31 07:43:16,638 - pyskl - INFO - Epoch [135][3300/3746] lr: 2.486e-03, eta: 13:27:00, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5355, top5_acc: 0.7795, loss_cls: 2.5680, loss: 2.5680 +2024-12-31 07:44:42,860 - pyskl - INFO - Epoch [135][3400/3746] lr: 2.477e-03, eta: 13:25:35, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5386, top5_acc: 0.7906, loss_cls: 2.5359, loss: 2.5359 +2024-12-31 07:46:09,013 - pyskl - INFO - Epoch [135][3500/3746] lr: 2.469e-03, eta: 13:24:09, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5402, top5_acc: 0.7867, loss_cls: 2.5251, loss: 2.5251 +2024-12-31 07:47:34,648 - pyskl - INFO - Epoch [135][3600/3746] lr: 2.460e-03, eta: 13:22:44, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5431, top5_acc: 0.7875, loss_cls: 2.5327, loss: 2.5327 +2024-12-31 07:49:01,060 - pyskl - INFO - Epoch [135][3700/3746] lr: 2.451e-03, eta: 13:21:18, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5439, top5_acc: 0.7872, loss_cls: 2.5399, loss: 2.5399 +2024-12-31 07:49:42,802 - pyskl - INFO - Saving checkpoint at 135 epochs +2024-12-31 07:51:41,537 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 07:51:42,254 - pyskl - INFO - +top1_acc 0.4385 +top5_acc 0.6933 +2024-12-31 07:51:42,254 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 07:51:42,302 - pyskl - INFO - +mean_acc 0.4383 +2024-12-31 07:51:42,309 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_134.pth was removed +2024-12-31 07:51:42,650 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_135.pth. +2024-12-31 07:51:42,655 - pyskl - INFO - Best top1_acc is 0.4385 at 135 epoch. +2024-12-31 07:51:42,672 - pyskl - INFO - Epoch(val) [135][309] top1_acc: 0.4385, top5_acc: 0.6933, mean_class_accuracy: 0.4383 +2024-12-31 07:55:46,667 - pyskl - INFO - Epoch [136][100/3746] lr: 2.439e-03, eta: 13:19:27, time: 2.440, data_time: 1.418, memory: 15990, top1_acc: 0.5737, top5_acc: 0.8133, loss_cls: 2.3447, loss: 2.3447 +2024-12-31 07:57:12,102 - pyskl - INFO - Epoch [136][200/3746] lr: 2.430e-03, eta: 13:18:01, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5663, top5_acc: 0.8092, loss_cls: 2.3944, loss: 2.3944 +2024-12-31 07:58:37,851 - pyskl - INFO - Epoch [136][300/3746] lr: 2.421e-03, eta: 13:16:36, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5595, top5_acc: 0.8002, loss_cls: 2.4572, loss: 2.4572 +2024-12-31 08:00:03,180 - pyskl - INFO - Epoch [136][400/3746] lr: 2.413e-03, eta: 13:15:10, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5453, top5_acc: 0.8006, loss_cls: 2.4748, loss: 2.4748 +2024-12-31 08:01:28,974 - pyskl - INFO - Epoch [136][500/3746] lr: 2.404e-03, eta: 13:13:45, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5578, top5_acc: 0.7987, loss_cls: 2.4616, loss: 2.4616 +2024-12-31 08:02:55,146 - pyskl - INFO - Epoch [136][600/3746] lr: 2.396e-03, eta: 13:12:19, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5716, top5_acc: 0.8111, loss_cls: 2.3812, loss: 2.3812 +2024-12-31 08:04:21,343 - pyskl - INFO - Epoch [136][700/3746] lr: 2.387e-03, eta: 13:10:54, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5630, top5_acc: 0.8091, loss_cls: 2.4125, loss: 2.4125 +2024-12-31 08:05:48,044 - pyskl - INFO - Epoch [136][800/3746] lr: 2.379e-03, eta: 13:09:29, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5592, top5_acc: 0.8073, loss_cls: 2.4410, loss: 2.4410 +2024-12-31 08:07:14,232 - pyskl - INFO - Epoch [136][900/3746] lr: 2.370e-03, eta: 13:08:03, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5630, top5_acc: 0.8039, loss_cls: 2.4193, loss: 2.4193 +2024-12-31 08:08:40,595 - pyskl - INFO - Epoch [136][1000/3746] lr: 2.362e-03, eta: 13:06:38, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5608, top5_acc: 0.7991, loss_cls: 2.4572, loss: 2.4572 +2024-12-31 08:10:06,835 - pyskl - INFO - Epoch [136][1100/3746] lr: 2.353e-03, eta: 13:05:12, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5406, top5_acc: 0.7913, loss_cls: 2.5237, loss: 2.5237 +2024-12-31 08:11:32,069 - pyskl - INFO - Epoch [136][1200/3746] lr: 2.345e-03, eta: 13:03:47, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5567, top5_acc: 0.7945, loss_cls: 2.4642, loss: 2.4642 +2024-12-31 08:12:57,293 - pyskl - INFO - Epoch [136][1300/3746] lr: 2.336e-03, eta: 13:02:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5652, top5_acc: 0.8042, loss_cls: 2.4152, loss: 2.4152 +2024-12-31 08:14:22,413 - pyskl - INFO - Epoch [136][1400/3746] lr: 2.328e-03, eta: 13:00:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5545, top5_acc: 0.7955, loss_cls: 2.4789, loss: 2.4789 +2024-12-31 08:15:48,153 - pyskl - INFO - Epoch [136][1500/3746] lr: 2.319e-03, eta: 12:59:30, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5444, top5_acc: 0.7864, loss_cls: 2.4912, loss: 2.4912 +2024-12-31 08:17:14,141 - pyskl - INFO - Epoch [136][1600/3746] lr: 2.311e-03, eta: 12:58:05, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5677, top5_acc: 0.8044, loss_cls: 2.4351, loss: 2.4351 +2024-12-31 08:18:40,391 - pyskl - INFO - Epoch [136][1700/3746] lr: 2.303e-03, eta: 12:56:39, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5523, top5_acc: 0.7969, loss_cls: 2.4643, loss: 2.4643 +2024-12-31 08:20:06,292 - pyskl - INFO - Epoch [136][1800/3746] lr: 2.294e-03, eta: 12:55:14, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5592, top5_acc: 0.7977, loss_cls: 2.4378, loss: 2.4378 +2024-12-31 08:21:33,087 - pyskl - INFO - Epoch [136][1900/3746] lr: 2.286e-03, eta: 12:53:48, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5542, top5_acc: 0.8003, loss_cls: 2.4501, loss: 2.4501 +2024-12-31 08:22:59,312 - pyskl - INFO - Epoch [136][2000/3746] lr: 2.277e-03, eta: 12:52:23, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5487, top5_acc: 0.7955, loss_cls: 2.4835, loss: 2.4835 +2024-12-31 08:24:25,770 - pyskl - INFO - Epoch [136][2100/3746] lr: 2.269e-03, eta: 12:50:57, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5416, top5_acc: 0.7944, loss_cls: 2.4991, loss: 2.4991 +2024-12-31 08:25:51,301 - pyskl - INFO - Epoch [136][2200/3746] lr: 2.261e-03, eta: 12:49:32, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.5609, top5_acc: 0.8013, loss_cls: 2.4452, loss: 2.4452 +2024-12-31 08:27:16,616 - pyskl - INFO - Epoch [136][2300/3746] lr: 2.253e-03, eta: 12:48:06, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5517, top5_acc: 0.7897, loss_cls: 2.4989, loss: 2.4989 +2024-12-31 08:28:41,138 - pyskl - INFO - Epoch [136][2400/3746] lr: 2.244e-03, eta: 12:46:41, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.5563, top5_acc: 0.7983, loss_cls: 2.4497, loss: 2.4497 +2024-12-31 08:30:06,513 - pyskl - INFO - Epoch [136][2500/3746] lr: 2.236e-03, eta: 12:45:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5473, top5_acc: 0.7855, loss_cls: 2.5187, loss: 2.5187 +2024-12-31 08:31:31,978 - pyskl - INFO - Epoch [136][2600/3746] lr: 2.228e-03, eta: 12:43:50, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5552, top5_acc: 0.7944, loss_cls: 2.4703, loss: 2.4703 +2024-12-31 08:32:57,717 - pyskl - INFO - Epoch [136][2700/3746] lr: 2.219e-03, eta: 12:42:24, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5456, top5_acc: 0.7863, loss_cls: 2.5110, loss: 2.5110 +2024-12-31 08:34:23,351 - pyskl - INFO - Epoch [136][2800/3746] lr: 2.211e-03, eta: 12:40:59, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5452, top5_acc: 0.7931, loss_cls: 2.4855, loss: 2.4855 +2024-12-31 08:35:49,647 - pyskl - INFO - Epoch [136][2900/3746] lr: 2.203e-03, eta: 12:39:33, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5420, top5_acc: 0.7855, loss_cls: 2.5262, loss: 2.5262 +2024-12-31 08:37:15,918 - pyskl - INFO - Epoch [136][3000/3746] lr: 2.195e-03, eta: 12:38:08, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5491, top5_acc: 0.7984, loss_cls: 2.4592, loss: 2.4592 +2024-12-31 08:38:41,681 - pyskl - INFO - Epoch [136][3100/3746] lr: 2.187e-03, eta: 12:36:42, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5511, top5_acc: 0.7963, loss_cls: 2.4729, loss: 2.4729 +2024-12-31 08:40:07,693 - pyskl - INFO - Epoch [136][3200/3746] lr: 2.178e-03, eta: 12:35:17, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5495, top5_acc: 0.7889, loss_cls: 2.5044, loss: 2.5044 +2024-12-31 08:41:33,724 - pyskl - INFO - Epoch [136][3300/3746] lr: 2.170e-03, eta: 12:33:51, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5536, top5_acc: 0.7941, loss_cls: 2.4694, loss: 2.4694 +2024-12-31 08:42:59,262 - pyskl - INFO - Epoch [136][3400/3746] lr: 2.162e-03, eta: 12:32:26, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5514, top5_acc: 0.7945, loss_cls: 2.4775, loss: 2.4775 +2024-12-31 08:44:24,835 - pyskl - INFO - Epoch [136][3500/3746] lr: 2.154e-03, eta: 12:31:00, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5483, top5_acc: 0.7895, loss_cls: 2.5114, loss: 2.5114 +2024-12-31 08:45:51,068 - pyskl - INFO - Epoch [136][3600/3746] lr: 2.146e-03, eta: 12:29:35, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5519, top5_acc: 0.7969, loss_cls: 2.4796, loss: 2.4796 +2024-12-31 08:47:17,302 - pyskl - INFO - Epoch [136][3700/3746] lr: 2.138e-03, eta: 12:28:09, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5544, top5_acc: 0.8041, loss_cls: 2.4371, loss: 2.4371 +2024-12-31 08:47:58,530 - pyskl - INFO - Saving checkpoint at 136 epochs +2024-12-31 08:49:56,851 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 08:49:57,915 - pyskl - INFO - +top1_acc 0.4366 +top5_acc 0.6917 +2024-12-31 08:49:57,916 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 08:49:57,971 - pyskl - INFO - +mean_acc 0.4364 +2024-12-31 08:49:57,982 - pyskl - INFO - Epoch(val) [136][309] top1_acc: 0.4366, top5_acc: 0.6917, mean_class_accuracy: 0.4364 +2024-12-31 08:54:04,661 - pyskl - INFO - Epoch [137][100/3746] lr: 2.126e-03, eta: 12:26:17, time: 2.467, data_time: 1.442, memory: 15990, top1_acc: 0.5706, top5_acc: 0.8059, loss_cls: 2.3820, loss: 2.3820 +2024-12-31 08:55:29,303 - pyskl - INFO - Epoch [137][200/3746] lr: 2.118e-03, eta: 12:24:51, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5878, top5_acc: 0.8177, loss_cls: 2.3084, loss: 2.3084 +2024-12-31 08:56:54,289 - pyskl - INFO - Epoch [137][300/3746] lr: 2.110e-03, eta: 12:23:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5720, top5_acc: 0.8108, loss_cls: 2.3606, loss: 2.3606 +2024-12-31 08:58:19,394 - pyskl - INFO - Epoch [137][400/3746] lr: 2.102e-03, eta: 12:22:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5652, top5_acc: 0.8008, loss_cls: 2.4021, loss: 2.4021 +2024-12-31 08:59:44,677 - pyskl - INFO - Epoch [137][500/3746] lr: 2.094e-03, eta: 12:20:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5597, top5_acc: 0.7995, loss_cls: 2.4519, loss: 2.4519 +2024-12-31 09:01:10,354 - pyskl - INFO - Epoch [137][600/3746] lr: 2.086e-03, eta: 12:19:09, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5711, top5_acc: 0.8100, loss_cls: 2.3866, loss: 2.3866 +2024-12-31 09:02:36,399 - pyskl - INFO - Epoch [137][700/3746] lr: 2.078e-03, eta: 12:17:44, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5641, top5_acc: 0.8047, loss_cls: 2.4187, loss: 2.4187 +2024-12-31 09:04:03,066 - pyskl - INFO - Epoch [137][800/3746] lr: 2.070e-03, eta: 12:16:18, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5731, top5_acc: 0.8148, loss_cls: 2.3681, loss: 2.3681 +2024-12-31 09:05:28,978 - pyskl - INFO - Epoch [137][900/3746] lr: 2.062e-03, eta: 12:14:53, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5672, top5_acc: 0.8072, loss_cls: 2.3769, loss: 2.3769 +2024-12-31 09:06:55,659 - pyskl - INFO - Epoch [137][1000/3746] lr: 2.054e-03, eta: 12:13:27, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5611, top5_acc: 0.8063, loss_cls: 2.4201, loss: 2.4201 +2024-12-31 09:08:22,458 - pyskl - INFO - Epoch [137][1100/3746] lr: 2.046e-03, eta: 12:12:02, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5545, top5_acc: 0.8044, loss_cls: 2.4178, loss: 2.4178 +2024-12-31 09:09:49,000 - pyskl - INFO - Epoch [137][1200/3746] lr: 2.038e-03, eta: 12:10:36, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5773, top5_acc: 0.8144, loss_cls: 2.3618, loss: 2.3618 +2024-12-31 09:11:14,664 - pyskl - INFO - Epoch [137][1300/3746] lr: 2.030e-03, eta: 12:09:11, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.5617, top5_acc: 0.8034, loss_cls: 2.3974, loss: 2.3974 +2024-12-31 09:12:40,304 - pyskl - INFO - Epoch [137][1400/3746] lr: 2.022e-03, eta: 12:07:45, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5595, top5_acc: 0.8041, loss_cls: 2.4329, loss: 2.4329 +2024-12-31 09:14:05,247 - pyskl - INFO - Epoch [137][1500/3746] lr: 2.015e-03, eta: 12:06:20, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5706, top5_acc: 0.8039, loss_cls: 2.3918, loss: 2.3918 +2024-12-31 09:15:30,394 - pyskl - INFO - Epoch [137][1600/3746] lr: 2.007e-03, eta: 12:04:54, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5592, top5_acc: 0.8025, loss_cls: 2.4199, loss: 2.4199 +2024-12-31 09:16:55,939 - pyskl - INFO - Epoch [137][1700/3746] lr: 1.999e-03, eta: 12:03:29, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5700, top5_acc: 0.8091, loss_cls: 2.3967, loss: 2.3967 +2024-12-31 09:18:20,023 - pyskl - INFO - Epoch [137][1800/3746] lr: 1.991e-03, eta: 12:02:03, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.5628, top5_acc: 0.8098, loss_cls: 2.3727, loss: 2.3727 +2024-12-31 09:19:45,257 - pyskl - INFO - Epoch [137][1900/3746] lr: 1.983e-03, eta: 12:00:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5581, top5_acc: 0.8078, loss_cls: 2.4061, loss: 2.4061 +2024-12-31 09:21:10,527 - pyskl - INFO - Epoch [137][2000/3746] lr: 1.976e-03, eta: 11:59:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5483, top5_acc: 0.8025, loss_cls: 2.4465, loss: 2.4465 +2024-12-31 09:22:35,386 - pyskl - INFO - Epoch [137][2100/3746] lr: 1.968e-03, eta: 11:57:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5644, top5_acc: 0.8042, loss_cls: 2.4095, loss: 2.4095 +2024-12-31 09:24:00,312 - pyskl - INFO - Epoch [137][2200/3746] lr: 1.960e-03, eta: 11:56:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5541, top5_acc: 0.8000, loss_cls: 2.4316, loss: 2.4316 +2024-12-31 09:25:25,737 - pyskl - INFO - Epoch [137][2300/3746] lr: 1.952e-03, eta: 11:54:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5617, top5_acc: 0.8002, loss_cls: 2.4232, loss: 2.4232 +2024-12-31 09:26:50,671 - pyskl - INFO - Epoch [137][2400/3746] lr: 1.944e-03, eta: 11:53:29, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5595, top5_acc: 0.7981, loss_cls: 2.4473, loss: 2.4473 +2024-12-31 09:28:16,303 - pyskl - INFO - Epoch [137][2500/3746] lr: 1.937e-03, eta: 11:52:04, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5627, top5_acc: 0.8025, loss_cls: 2.4499, loss: 2.4499 +2024-12-31 09:29:42,194 - pyskl - INFO - Epoch [137][2600/3746] lr: 1.929e-03, eta: 11:50:38, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5586, top5_acc: 0.8048, loss_cls: 2.4132, loss: 2.4132 +2024-12-31 09:31:07,797 - pyskl - INFO - Epoch [137][2700/3746] lr: 1.921e-03, eta: 11:49:13, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5687, top5_acc: 0.8022, loss_cls: 2.4089, loss: 2.4089 +2024-12-31 09:32:33,463 - pyskl - INFO - Epoch [137][2800/3746] lr: 1.914e-03, eta: 11:47:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5670, top5_acc: 0.8058, loss_cls: 2.4015, loss: 2.4015 +2024-12-31 09:33:59,573 - pyskl - INFO - Epoch [137][2900/3746] lr: 1.906e-03, eta: 11:46:22, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5533, top5_acc: 0.7984, loss_cls: 2.4647, loss: 2.4647 +2024-12-31 09:35:25,359 - pyskl - INFO - Epoch [137][3000/3746] lr: 1.898e-03, eta: 11:44:56, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5723, top5_acc: 0.8095, loss_cls: 2.4020, loss: 2.4020 +2024-12-31 09:36:50,490 - pyskl - INFO - Epoch [137][3100/3746] lr: 1.891e-03, eta: 11:43:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5611, top5_acc: 0.7967, loss_cls: 2.4253, loss: 2.4253 +2024-12-31 09:38:16,838 - pyskl - INFO - Epoch [137][3200/3746] lr: 1.883e-03, eta: 11:42:05, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5641, top5_acc: 0.7975, loss_cls: 2.4415, loss: 2.4415 +2024-12-31 09:39:43,173 - pyskl - INFO - Epoch [137][3300/3746] lr: 1.876e-03, eta: 11:40:40, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5592, top5_acc: 0.7995, loss_cls: 2.4486, loss: 2.4486 +2024-12-31 09:41:08,825 - pyskl - INFO - Epoch [137][3400/3746] lr: 1.868e-03, eta: 11:39:14, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5570, top5_acc: 0.7934, loss_cls: 2.4591, loss: 2.4591 +2024-12-31 09:42:34,793 - pyskl - INFO - Epoch [137][3500/3746] lr: 1.860e-03, eta: 11:37:49, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5664, top5_acc: 0.8103, loss_cls: 2.4068, loss: 2.4068 +2024-12-31 09:44:00,690 - pyskl - INFO - Epoch [137][3600/3746] lr: 1.853e-03, eta: 11:36:23, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5567, top5_acc: 0.8011, loss_cls: 2.4295, loss: 2.4295 +2024-12-31 09:45:26,280 - pyskl - INFO - Epoch [137][3700/3746] lr: 1.845e-03, eta: 11:34:58, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5505, top5_acc: 0.7928, loss_cls: 2.4868, loss: 2.4868 +2024-12-31 09:46:07,142 - pyskl - INFO - Saving checkpoint at 137 epochs +2024-12-31 09:48:05,330 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 09:48:06,345 - pyskl - INFO - +top1_acc 0.4532 +top5_acc 0.7016 +2024-12-31 09:48:06,345 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 09:48:06,396 - pyskl - INFO - +mean_acc 0.4530 +2024-12-31 09:48:06,401 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_135.pth was removed +2024-12-31 09:48:06,719 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_137.pth. +2024-12-31 09:48:06,720 - pyskl - INFO - Best top1_acc is 0.4532 at 137 epoch. +2024-12-31 09:48:06,732 - pyskl - INFO - Epoch(val) [137][309] top1_acc: 0.4532, top5_acc: 0.7016, mean_class_accuracy: 0.4530 +2024-12-31 09:52:23,513 - pyskl - INFO - Epoch [138][100/3746] lr: 1.834e-03, eta: 11:33:05, time: 2.568, data_time: 1.545, memory: 15990, top1_acc: 0.5794, top5_acc: 0.8163, loss_cls: 2.3083, loss: 2.3083 +2024-12-31 09:53:48,529 - pyskl - INFO - Epoch [138][200/3746] lr: 1.827e-03, eta: 11:31:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5850, top5_acc: 0.8245, loss_cls: 2.3061, loss: 2.3061 +2024-12-31 09:55:14,291 - pyskl - INFO - Epoch [138][300/3746] lr: 1.819e-03, eta: 11:30:14, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5831, top5_acc: 0.8198, loss_cls: 2.3081, loss: 2.3081 +2024-12-31 09:56:40,122 - pyskl - INFO - Epoch [138][400/3746] lr: 1.812e-03, eta: 11:28:48, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5995, top5_acc: 0.8222, loss_cls: 2.2801, loss: 2.2801 +2024-12-31 09:58:05,746 - pyskl - INFO - Epoch [138][500/3746] lr: 1.805e-03, eta: 11:27:23, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5758, top5_acc: 0.8158, loss_cls: 2.3393, loss: 2.3393 +2024-12-31 09:59:31,046 - pyskl - INFO - Epoch [138][600/3746] lr: 1.797e-03, eta: 11:25:57, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5759, top5_acc: 0.8155, loss_cls: 2.3397, loss: 2.3397 +2024-12-31 10:00:56,953 - pyskl - INFO - Epoch [138][700/3746] lr: 1.790e-03, eta: 11:24:32, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5775, top5_acc: 0.8144, loss_cls: 2.3267, loss: 2.3267 +2024-12-31 10:02:22,692 - pyskl - INFO - Epoch [138][800/3746] lr: 1.782e-03, eta: 11:23:06, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5667, top5_acc: 0.8091, loss_cls: 2.3907, loss: 2.3907 +2024-12-31 10:03:49,024 - pyskl - INFO - Epoch [138][900/3746] lr: 1.775e-03, eta: 11:21:41, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5697, top5_acc: 0.8148, loss_cls: 2.3304, loss: 2.3304 +2024-12-31 10:05:14,811 - pyskl - INFO - Epoch [138][1000/3746] lr: 1.768e-03, eta: 11:20:15, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5775, top5_acc: 0.8120, loss_cls: 2.3461, loss: 2.3461 +2024-12-31 10:06:40,339 - pyskl - INFO - Epoch [138][1100/3746] lr: 1.760e-03, eta: 11:18:50, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5723, top5_acc: 0.8078, loss_cls: 2.3576, loss: 2.3576 +2024-12-31 10:08:05,864 - pyskl - INFO - Epoch [138][1200/3746] lr: 1.753e-03, eta: 11:17:24, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5773, top5_acc: 0.8091, loss_cls: 2.3571, loss: 2.3571 +2024-12-31 10:09:31,516 - pyskl - INFO - Epoch [138][1300/3746] lr: 1.745e-03, eta: 11:15:58, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.5772, top5_acc: 0.8147, loss_cls: 2.3334, loss: 2.3334 +2024-12-31 10:10:56,425 - pyskl - INFO - Epoch [138][1400/3746] lr: 1.738e-03, eta: 11:14:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5839, top5_acc: 0.8133, loss_cls: 2.3270, loss: 2.3270 +2024-12-31 10:12:21,176 - pyskl - INFO - Epoch [138][1500/3746] lr: 1.731e-03, eta: 11:13:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5745, top5_acc: 0.8164, loss_cls: 2.3354, loss: 2.3354 +2024-12-31 10:13:46,051 - pyskl - INFO - Epoch [138][1600/3746] lr: 1.724e-03, eta: 11:11:42, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5786, top5_acc: 0.8109, loss_cls: 2.3519, loss: 2.3519 +2024-12-31 10:15:11,042 - pyskl - INFO - Epoch [138][1700/3746] lr: 1.716e-03, eta: 11:10:16, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5741, top5_acc: 0.8102, loss_cls: 2.3712, loss: 2.3712 +2024-12-31 10:16:35,257 - pyskl - INFO - Epoch [138][1800/3746] lr: 1.709e-03, eta: 11:08:50, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5673, top5_acc: 0.8086, loss_cls: 2.3830, loss: 2.3830 +2024-12-31 10:17:59,863 - pyskl - INFO - Epoch [138][1900/3746] lr: 1.702e-03, eta: 11:07:25, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5695, top5_acc: 0.8103, loss_cls: 2.3609, loss: 2.3609 +2024-12-31 10:19:24,686 - pyskl - INFO - Epoch [138][2000/3746] lr: 1.695e-03, eta: 11:05:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5800, top5_acc: 0.8141, loss_cls: 2.3602, loss: 2.3602 +2024-12-31 10:20:48,960 - pyskl - INFO - Epoch [138][2100/3746] lr: 1.687e-03, eta: 11:04:33, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5733, top5_acc: 0.8119, loss_cls: 2.3655, loss: 2.3655 +2024-12-31 10:22:13,502 - pyskl - INFO - Epoch [138][2200/3746] lr: 1.680e-03, eta: 11:03:08, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5795, top5_acc: 0.8177, loss_cls: 2.3451, loss: 2.3451 +2024-12-31 10:23:38,111 - pyskl - INFO - Epoch [138][2300/3746] lr: 1.673e-03, eta: 11:01:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5725, top5_acc: 0.8091, loss_cls: 2.3816, loss: 2.3816 +2024-12-31 10:25:02,812 - pyskl - INFO - Epoch [138][2400/3746] lr: 1.666e-03, eta: 11:00:16, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5728, top5_acc: 0.8175, loss_cls: 2.3308, loss: 2.3308 +2024-12-31 10:26:27,429 - pyskl - INFO - Epoch [138][2500/3746] lr: 1.659e-03, eta: 10:58:51, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5741, top5_acc: 0.8098, loss_cls: 2.3576, loss: 2.3576 +2024-12-31 10:27:51,659 - pyskl - INFO - Epoch [138][2600/3746] lr: 1.652e-03, eta: 10:57:25, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5667, top5_acc: 0.8014, loss_cls: 2.4214, loss: 2.4214 +2024-12-31 10:29:16,357 - pyskl - INFO - Epoch [138][2700/3746] lr: 1.644e-03, eta: 10:55:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5816, top5_acc: 0.8106, loss_cls: 2.3586, loss: 2.3586 +2024-12-31 10:30:41,352 - pyskl - INFO - Epoch [138][2800/3746] lr: 1.637e-03, eta: 10:54:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5541, top5_acc: 0.8047, loss_cls: 2.4406, loss: 2.4406 +2024-12-31 10:32:05,697 - pyskl - INFO - Epoch [138][2900/3746] lr: 1.630e-03, eta: 10:53:08, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5667, top5_acc: 0.8037, loss_cls: 2.4128, loss: 2.4128 +2024-12-31 10:33:30,391 - pyskl - INFO - Epoch [138][3000/3746] lr: 1.623e-03, eta: 10:51:42, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5730, top5_acc: 0.8167, loss_cls: 2.3327, loss: 2.3327 +2024-12-31 10:34:55,552 - pyskl - INFO - Epoch [138][3100/3746] lr: 1.616e-03, eta: 10:50:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5708, top5_acc: 0.8095, loss_cls: 2.3696, loss: 2.3696 +2024-12-31 10:36:20,775 - pyskl - INFO - Epoch [138][3200/3746] lr: 1.609e-03, eta: 10:48:51, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5698, top5_acc: 0.8102, loss_cls: 2.3744, loss: 2.3744 +2024-12-31 10:37:45,785 - pyskl - INFO - Epoch [138][3300/3746] lr: 1.602e-03, eta: 10:47:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5602, top5_acc: 0.8053, loss_cls: 2.4136, loss: 2.4136 +2024-12-31 10:39:10,844 - pyskl - INFO - Epoch [138][3400/3746] lr: 1.595e-03, eta: 10:46:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5700, top5_acc: 0.8069, loss_cls: 2.4011, loss: 2.4011 +2024-12-31 10:40:35,567 - pyskl - INFO - Epoch [138][3500/3746] lr: 1.588e-03, eta: 10:44:34, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5686, top5_acc: 0.8130, loss_cls: 2.3514, loss: 2.3514 +2024-12-31 10:42:00,180 - pyskl - INFO - Epoch [138][3600/3746] lr: 1.581e-03, eta: 10:43:09, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5706, top5_acc: 0.8137, loss_cls: 2.3695, loss: 2.3695 +2024-12-31 10:43:24,935 - pyskl - INFO - Epoch [138][3700/3746] lr: 1.574e-03, eta: 10:41:43, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5587, top5_acc: 0.8027, loss_cls: 2.3941, loss: 2.3941 +2024-12-31 10:44:05,778 - pyskl - INFO - Saving checkpoint at 138 epochs +2024-12-31 10:46:04,002 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 10:46:05,244 - pyskl - INFO - +top1_acc 0.4470 +top5_acc 0.7045 +2024-12-31 10:46:05,245 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 10:46:05,286 - pyskl - INFO - +mean_acc 0.4467 +2024-12-31 10:46:05,301 - pyskl - INFO - Epoch(val) [138][309] top1_acc: 0.4470, top5_acc: 0.7045, mean_class_accuracy: 0.4467 +2024-12-31 10:50:21,718 - pyskl - INFO - Epoch [139][100/3746] lr: 1.564e-03, eta: 10:39:49, time: 2.564, data_time: 1.547, memory: 15990, top1_acc: 0.5894, top5_acc: 0.8225, loss_cls: 2.2884, loss: 2.2884 +2024-12-31 10:51:46,513 - pyskl - INFO - Epoch [139][200/3746] lr: 1.557e-03, eta: 10:38:24, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5884, top5_acc: 0.8252, loss_cls: 2.2706, loss: 2.2706 +2024-12-31 10:53:10,783 - pyskl - INFO - Epoch [139][300/3746] lr: 1.550e-03, eta: 10:36:58, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5970, top5_acc: 0.8250, loss_cls: 2.2582, loss: 2.2582 +2024-12-31 10:54:35,189 - pyskl - INFO - Epoch [139][400/3746] lr: 1.543e-03, eta: 10:35:32, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5870, top5_acc: 0.8253, loss_cls: 2.2783, loss: 2.2783 +2024-12-31 10:55:59,832 - pyskl - INFO - Epoch [139][500/3746] lr: 1.536e-03, eta: 10:34:07, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5897, top5_acc: 0.8289, loss_cls: 2.2779, loss: 2.2779 +2024-12-31 10:57:25,029 - pyskl - INFO - Epoch [139][600/3746] lr: 1.529e-03, eta: 10:32:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5852, top5_acc: 0.8253, loss_cls: 2.2858, loss: 2.2858 +2024-12-31 10:58:49,979 - pyskl - INFO - Epoch [139][700/3746] lr: 1.523e-03, eta: 10:31:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5923, top5_acc: 0.8309, loss_cls: 2.2675, loss: 2.2675 +2024-12-31 11:00:15,719 - pyskl - INFO - Epoch [139][800/3746] lr: 1.516e-03, eta: 10:29:50, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5870, top5_acc: 0.8237, loss_cls: 2.2799, loss: 2.2799 +2024-12-31 11:01:41,019 - pyskl - INFO - Epoch [139][900/3746] lr: 1.509e-03, eta: 10:28:24, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5848, top5_acc: 0.8317, loss_cls: 2.2499, loss: 2.2499 +2024-12-31 11:03:06,875 - pyskl - INFO - Epoch [139][1000/3746] lr: 1.502e-03, eta: 10:26:59, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5856, top5_acc: 0.8244, loss_cls: 2.2844, loss: 2.2844 +2024-12-31 11:04:31,841 - pyskl - INFO - Epoch [139][1100/3746] lr: 1.495e-03, eta: 10:25:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5861, top5_acc: 0.8145, loss_cls: 2.3155, loss: 2.3155 +2024-12-31 11:05:56,683 - pyskl - INFO - Epoch [139][1200/3746] lr: 1.489e-03, eta: 10:24:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5784, top5_acc: 0.8120, loss_cls: 2.3179, loss: 2.3179 +2024-12-31 11:07:21,637 - pyskl - INFO - Epoch [139][1300/3746] lr: 1.482e-03, eta: 10:22:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5764, top5_acc: 0.8198, loss_cls: 2.3084, loss: 2.3084 +2024-12-31 11:08:47,209 - pyskl - INFO - Epoch [139][1400/3746] lr: 1.475e-03, eta: 10:21:16, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.5727, top5_acc: 0.8125, loss_cls: 2.3599, loss: 2.3599 +2024-12-31 11:10:12,643 - pyskl - INFO - Epoch [139][1500/3746] lr: 1.468e-03, eta: 10:19:51, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5875, top5_acc: 0.8234, loss_cls: 2.2650, loss: 2.2650 +2024-12-31 11:11:37,576 - pyskl - INFO - Epoch [139][1600/3746] lr: 1.462e-03, eta: 10:18:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5845, top5_acc: 0.8222, loss_cls: 2.2750, loss: 2.2750 +2024-12-31 11:13:02,494 - pyskl - INFO - Epoch [139][1700/3746] lr: 1.455e-03, eta: 10:16:59, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5902, top5_acc: 0.8144, loss_cls: 2.2877, loss: 2.2877 +2024-12-31 11:14:27,929 - pyskl - INFO - Epoch [139][1800/3746] lr: 1.448e-03, eta: 10:15:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5892, top5_acc: 0.8213, loss_cls: 2.2836, loss: 2.2836 +2024-12-31 11:15:53,233 - pyskl - INFO - Epoch [139][1900/3746] lr: 1.442e-03, eta: 10:14:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5806, top5_acc: 0.8211, loss_cls: 2.3094, loss: 2.3094 +2024-12-31 11:17:17,568 - pyskl - INFO - Epoch [139][2000/3746] lr: 1.435e-03, eta: 10:12:42, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5806, top5_acc: 0.8150, loss_cls: 2.3203, loss: 2.3203 +2024-12-31 11:18:43,156 - pyskl - INFO - Epoch [139][2100/3746] lr: 1.428e-03, eta: 10:11:17, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5800, top5_acc: 0.8222, loss_cls: 2.3084, loss: 2.3084 +2024-12-31 11:20:07,929 - pyskl - INFO - Epoch [139][2200/3746] lr: 1.422e-03, eta: 10:09:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5823, top5_acc: 0.8233, loss_cls: 2.2790, loss: 2.2790 +2024-12-31 11:21:32,700 - pyskl - INFO - Epoch [139][2300/3746] lr: 1.415e-03, eta: 10:08:25, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.5694, top5_acc: 0.8103, loss_cls: 2.3741, loss: 2.3741 +2024-12-31 11:22:57,541 - pyskl - INFO - Epoch [139][2400/3746] lr: 1.408e-03, eta: 10:07:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5791, top5_acc: 0.8183, loss_cls: 2.3235, loss: 2.3235 +2024-12-31 11:24:21,781 - pyskl - INFO - Epoch [139][2500/3746] lr: 1.402e-03, eta: 10:05:34, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5773, top5_acc: 0.8086, loss_cls: 2.3390, loss: 2.3390 +2024-12-31 11:25:46,788 - pyskl - INFO - Epoch [139][2600/3746] lr: 1.395e-03, eta: 10:04:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5847, top5_acc: 0.8136, loss_cls: 2.3182, loss: 2.3182 +2024-12-31 11:27:11,531 - pyskl - INFO - Epoch [139][2700/3746] lr: 1.389e-03, eta: 10:02:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5766, top5_acc: 0.8145, loss_cls: 2.3512, loss: 2.3512 +2024-12-31 11:28:36,135 - pyskl - INFO - Epoch [139][2800/3746] lr: 1.382e-03, eta: 10:01:17, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5819, top5_acc: 0.8183, loss_cls: 2.2951, loss: 2.2951 +2024-12-31 11:30:00,771 - pyskl - INFO - Epoch [139][2900/3746] lr: 1.376e-03, eta: 9:59:51, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5864, top5_acc: 0.8191, loss_cls: 2.3099, loss: 2.3099 +2024-12-31 11:31:25,480 - pyskl - INFO - Epoch [139][3000/3746] lr: 1.369e-03, eta: 9:58:26, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5673, top5_acc: 0.8152, loss_cls: 2.3602, loss: 2.3602 +2024-12-31 11:32:50,416 - pyskl - INFO - Epoch [139][3100/3746] lr: 1.363e-03, eta: 9:57:00, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5820, top5_acc: 0.8158, loss_cls: 2.3100, loss: 2.3100 +2024-12-31 11:34:15,452 - pyskl - INFO - Epoch [139][3200/3746] lr: 1.356e-03, eta: 9:55:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5717, top5_acc: 0.8125, loss_cls: 2.3383, loss: 2.3383 +2024-12-31 11:35:40,168 - pyskl - INFO - Epoch [139][3300/3746] lr: 1.350e-03, eta: 9:54:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5803, top5_acc: 0.8233, loss_cls: 2.3141, loss: 2.3141 +2024-12-31 11:37:04,518 - pyskl - INFO - Epoch [139][3400/3746] lr: 1.343e-03, eta: 9:52:43, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5837, top5_acc: 0.8219, loss_cls: 2.2872, loss: 2.2872 +2024-12-31 11:38:28,915 - pyskl - INFO - Epoch [139][3500/3746] lr: 1.337e-03, eta: 9:51:17, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5759, top5_acc: 0.8064, loss_cls: 2.3565, loss: 2.3565 +2024-12-31 11:39:53,950 - pyskl - INFO - Epoch [139][3600/3746] lr: 1.330e-03, eta: 9:49:52, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5828, top5_acc: 0.8172, loss_cls: 2.3191, loss: 2.3191 +2024-12-31 11:41:19,030 - pyskl - INFO - Epoch [139][3700/3746] lr: 1.324e-03, eta: 9:48:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5766, top5_acc: 0.8242, loss_cls: 2.3175, loss: 2.3175 +2024-12-31 11:41:59,909 - pyskl - INFO - Saving checkpoint at 139 epochs +2024-12-31 11:43:57,250 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 11:43:58,024 - pyskl - INFO - +top1_acc 0.4538 +top5_acc 0.7062 +2024-12-31 11:43:58,024 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 11:43:58,071 - pyskl - INFO - +mean_acc 0.4536 +2024-12-31 11:43:58,079 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_137.pth was removed +2024-12-31 11:43:58,375 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_139.pth. +2024-12-31 11:43:58,376 - pyskl - INFO - Best top1_acc is 0.4538 at 139 epoch. +2024-12-31 11:43:58,389 - pyskl - INFO - Epoch(val) [139][309] top1_acc: 0.4538, top5_acc: 0.7062, mean_class_accuracy: 0.4536 +2024-12-31 11:48:04,975 - pyskl - INFO - Epoch [140][100/3746] lr: 1.315e-03, eta: 9:46:31, time: 2.466, data_time: 1.444, memory: 15990, top1_acc: 0.6017, top5_acc: 0.8342, loss_cls: 2.2026, loss: 2.2026 +2024-12-31 11:49:29,552 - pyskl - INFO - Epoch [140][200/3746] lr: 1.308e-03, eta: 9:45:05, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.5931, top5_acc: 0.8398, loss_cls: 2.1948, loss: 2.1948 +2024-12-31 11:50:53,899 - pyskl - INFO - Epoch [140][300/3746] lr: 1.302e-03, eta: 9:43:39, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5980, top5_acc: 0.8298, loss_cls: 2.2317, loss: 2.2317 +2024-12-31 11:52:18,772 - pyskl - INFO - Epoch [140][400/3746] lr: 1.296e-03, eta: 9:42:14, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5913, top5_acc: 0.8275, loss_cls: 2.2515, loss: 2.2515 +2024-12-31 11:53:43,203 - pyskl - INFO - Epoch [140][500/3746] lr: 1.289e-03, eta: 9:40:48, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5991, top5_acc: 0.8345, loss_cls: 2.2168, loss: 2.2168 +2024-12-31 11:55:07,836 - pyskl - INFO - Epoch [140][600/3746] lr: 1.283e-03, eta: 9:39:22, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5948, top5_acc: 0.8263, loss_cls: 2.2411, loss: 2.2411 +2024-12-31 11:56:32,666 - pyskl - INFO - Epoch [140][700/3746] lr: 1.277e-03, eta: 9:37:57, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5992, top5_acc: 0.8263, loss_cls: 2.2210, loss: 2.2210 +2024-12-31 11:57:57,351 - pyskl - INFO - Epoch [140][800/3746] lr: 1.271e-03, eta: 9:36:31, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5978, top5_acc: 0.8287, loss_cls: 2.2513, loss: 2.2513 +2024-12-31 11:59:21,921 - pyskl - INFO - Epoch [140][900/3746] lr: 1.264e-03, eta: 9:35:05, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6005, top5_acc: 0.8270, loss_cls: 2.2196, loss: 2.2196 +2024-12-31 12:00:46,382 - pyskl - INFO - Epoch [140][1000/3746] lr: 1.258e-03, eta: 9:33:40, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5928, top5_acc: 0.8278, loss_cls: 2.2523, loss: 2.2523 +2024-12-31 12:02:11,577 - pyskl - INFO - Epoch [140][1100/3746] lr: 1.252e-03, eta: 9:32:14, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5798, top5_acc: 0.8170, loss_cls: 2.2943, loss: 2.2943 +2024-12-31 12:03:36,342 - pyskl - INFO - Epoch [140][1200/3746] lr: 1.246e-03, eta: 9:30:48, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5877, top5_acc: 0.8139, loss_cls: 2.2868, loss: 2.2868 +2024-12-31 12:05:01,209 - pyskl - INFO - Epoch [140][1300/3746] lr: 1.239e-03, eta: 9:29:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6027, top5_acc: 0.8342, loss_cls: 2.2232, loss: 2.2232 +2024-12-31 12:06:25,864 - pyskl - INFO - Epoch [140][1400/3746] lr: 1.233e-03, eta: 9:27:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5958, top5_acc: 0.8264, loss_cls: 2.2470, loss: 2.2470 +2024-12-31 12:07:49,862 - pyskl - INFO - Epoch [140][1500/3746] lr: 1.227e-03, eta: 9:26:31, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5978, top5_acc: 0.8339, loss_cls: 2.2185, loss: 2.2185 +2024-12-31 12:09:13,929 - pyskl - INFO - Epoch [140][1600/3746] lr: 1.221e-03, eta: 9:25:06, time: 0.841, data_time: 0.001, memory: 15990, top1_acc: 0.6038, top5_acc: 0.8303, loss_cls: 2.2296, loss: 2.2296 +2024-12-31 12:10:38,234 - pyskl - INFO - Epoch [140][1700/3746] lr: 1.215e-03, eta: 9:23:40, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5797, top5_acc: 0.8139, loss_cls: 2.3020, loss: 2.3020 +2024-12-31 12:12:03,128 - pyskl - INFO - Epoch [140][1800/3746] lr: 1.209e-03, eta: 9:22:14, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5848, top5_acc: 0.8209, loss_cls: 2.2764, loss: 2.2764 +2024-12-31 12:13:27,639 - pyskl - INFO - Epoch [140][1900/3746] lr: 1.203e-03, eta: 9:20:48, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5902, top5_acc: 0.8281, loss_cls: 2.2469, loss: 2.2469 +2024-12-31 12:14:52,489 - pyskl - INFO - Epoch [140][2000/3746] lr: 1.196e-03, eta: 9:19:23, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5908, top5_acc: 0.8302, loss_cls: 2.2392, loss: 2.2392 +2024-12-31 12:16:16,864 - pyskl - INFO - Epoch [140][2100/3746] lr: 1.190e-03, eta: 9:17:57, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5928, top5_acc: 0.8227, loss_cls: 2.2879, loss: 2.2879 +2024-12-31 12:17:41,098 - pyskl - INFO - Epoch [140][2200/3746] lr: 1.184e-03, eta: 9:16:31, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5917, top5_acc: 0.8269, loss_cls: 2.2529, loss: 2.2529 +2024-12-31 12:19:05,509 - pyskl - INFO - Epoch [140][2300/3746] lr: 1.178e-03, eta: 9:15:06, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.5863, top5_acc: 0.8178, loss_cls: 2.3037, loss: 2.3037 +2024-12-31 12:20:30,461 - pyskl - INFO - Epoch [140][2400/3746] lr: 1.172e-03, eta: 9:13:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5891, top5_acc: 0.8206, loss_cls: 2.2905, loss: 2.2905 +2024-12-31 12:21:54,826 - pyskl - INFO - Epoch [140][2500/3746] lr: 1.166e-03, eta: 9:12:14, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5806, top5_acc: 0.8152, loss_cls: 2.3020, loss: 2.3020 +2024-12-31 12:23:19,764 - pyskl - INFO - Epoch [140][2600/3746] lr: 1.160e-03, eta: 9:10:49, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5992, top5_acc: 0.8303, loss_cls: 2.2366, loss: 2.2366 +2024-12-31 12:24:43,932 - pyskl - INFO - Epoch [140][2700/3746] lr: 1.154e-03, eta: 9:09:23, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5833, top5_acc: 0.8266, loss_cls: 2.2870, loss: 2.2870 +2024-12-31 12:26:08,103 - pyskl - INFO - Epoch [140][2800/3746] lr: 1.148e-03, eta: 9:07:57, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5980, top5_acc: 0.8277, loss_cls: 2.2165, loss: 2.2165 +2024-12-31 12:27:32,453 - pyskl - INFO - Epoch [140][2900/3746] lr: 1.142e-03, eta: 9:06:32, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5945, top5_acc: 0.8313, loss_cls: 2.2296, loss: 2.2296 +2024-12-31 12:28:57,307 - pyskl - INFO - Epoch [140][3000/3746] lr: 1.136e-03, eta: 9:05:06, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5962, top5_acc: 0.8322, loss_cls: 2.2466, loss: 2.2466 +2024-12-31 12:30:21,963 - pyskl - INFO - Epoch [140][3100/3746] lr: 1.131e-03, eta: 9:03:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6014, top5_acc: 0.8294, loss_cls: 2.2194, loss: 2.2194 +2024-12-31 12:31:46,358 - pyskl - INFO - Epoch [140][3200/3746] lr: 1.125e-03, eta: 9:02:15, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5808, top5_acc: 0.8186, loss_cls: 2.2966, loss: 2.2966 +2024-12-31 12:33:11,676 - pyskl - INFO - Epoch [140][3300/3746] lr: 1.119e-03, eta: 9:00:49, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5953, top5_acc: 0.8203, loss_cls: 2.2944, loss: 2.2944 +2024-12-31 12:34:36,556 - pyskl - INFO - Epoch [140][3400/3746] lr: 1.113e-03, eta: 8:59:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5834, top5_acc: 0.8203, loss_cls: 2.3109, loss: 2.3109 +2024-12-31 12:36:01,697 - pyskl - INFO - Epoch [140][3500/3746] lr: 1.107e-03, eta: 8:57:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5988, top5_acc: 0.8228, loss_cls: 2.2225, loss: 2.2225 +2024-12-31 12:37:27,382 - pyskl - INFO - Epoch [140][3600/3746] lr: 1.101e-03, eta: 8:56:32, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5934, top5_acc: 0.8236, loss_cls: 2.2618, loss: 2.2618 +2024-12-31 12:38:51,546 - pyskl - INFO - Epoch [140][3700/3746] lr: 1.095e-03, eta: 8:55:06, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5950, top5_acc: 0.8186, loss_cls: 2.2682, loss: 2.2682 +2024-12-31 12:39:31,904 - pyskl - INFO - Saving checkpoint at 140 epochs +2024-12-31 12:41:30,322 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 12:41:31,133 - pyskl - INFO - +top1_acc 0.4578 +top5_acc 0.7095 +2024-12-31 12:41:31,133 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 12:41:31,194 - pyskl - INFO - +mean_acc 0.4575 +2024-12-31 12:41:31,198 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_139.pth was removed +2024-12-31 12:41:31,590 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_140.pth. +2024-12-31 12:41:31,591 - pyskl - INFO - Best top1_acc is 0.4578 at 140 epoch. +2024-12-31 12:41:31,610 - pyskl - INFO - Epoch(val) [140][309] top1_acc: 0.4578, top5_acc: 0.7095, mean_class_accuracy: 0.4575 +2024-12-31 12:45:48,374 - pyskl - INFO - Epoch [141][100/3746] lr: 1.087e-03, eta: 8:53:11, time: 2.568, data_time: 1.539, memory: 15990, top1_acc: 0.6106, top5_acc: 0.8331, loss_cls: 2.1806, loss: 2.1806 +2024-12-31 12:47:13,491 - pyskl - INFO - Epoch [141][200/3746] lr: 1.081e-03, eta: 8:51:45, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.6298, top5_acc: 0.8453, loss_cls: 2.1229, loss: 2.1229 +2024-12-31 12:48:37,649 - pyskl - INFO - Epoch [141][300/3746] lr: 1.075e-03, eta: 8:50:19, time: 0.842, data_time: 0.001, memory: 15990, top1_acc: 0.6227, top5_acc: 0.8403, loss_cls: 2.1301, loss: 2.1301 +2024-12-31 12:50:01,886 - pyskl - INFO - Epoch [141][400/3746] lr: 1.070e-03, eta: 8:48:54, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6105, top5_acc: 0.8369, loss_cls: 2.1717, loss: 2.1717 +2024-12-31 12:51:26,233 - pyskl - INFO - Epoch [141][500/3746] lr: 1.064e-03, eta: 8:47:28, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6138, top5_acc: 0.8428, loss_cls: 2.1377, loss: 2.1377 +2024-12-31 12:52:51,082 - pyskl - INFO - Epoch [141][600/3746] lr: 1.058e-03, eta: 8:46:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6078, top5_acc: 0.8386, loss_cls: 2.1671, loss: 2.1671 +2024-12-31 12:54:15,747 - pyskl - INFO - Epoch [141][700/3746] lr: 1.052e-03, eta: 8:44:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5944, top5_acc: 0.8241, loss_cls: 2.2417, loss: 2.2417 +2024-12-31 12:55:40,376 - pyskl - INFO - Epoch [141][800/3746] lr: 1.047e-03, eta: 8:43:11, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6016, top5_acc: 0.8342, loss_cls: 2.2128, loss: 2.2128 +2024-12-31 12:57:05,336 - pyskl - INFO - Epoch [141][900/3746] lr: 1.041e-03, eta: 8:41:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6088, top5_acc: 0.8391, loss_cls: 2.1566, loss: 2.1566 +2024-12-31 12:58:30,507 - pyskl - INFO - Epoch [141][1000/3746] lr: 1.035e-03, eta: 8:40:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6075, top5_acc: 0.8341, loss_cls: 2.1914, loss: 2.1914 +2024-12-31 12:59:55,495 - pyskl - INFO - Epoch [141][1100/3746] lr: 1.030e-03, eta: 8:38:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6012, top5_acc: 0.8319, loss_cls: 2.2087, loss: 2.2087 +2024-12-31 13:01:20,460 - pyskl - INFO - Epoch [141][1200/3746] lr: 1.024e-03, eta: 8:37:28, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6161, top5_acc: 0.8323, loss_cls: 2.1527, loss: 2.1527 +2024-12-31 13:02:45,051 - pyskl - INFO - Epoch [141][1300/3746] lr: 1.018e-03, eta: 8:36:02, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6038, top5_acc: 0.8348, loss_cls: 2.1973, loss: 2.1973 +2024-12-31 13:04:09,996 - pyskl - INFO - Epoch [141][1400/3746] lr: 1.013e-03, eta: 8:34:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5952, top5_acc: 0.8320, loss_cls: 2.2395, loss: 2.2395 +2024-12-31 13:05:35,285 - pyskl - INFO - Epoch [141][1500/3746] lr: 1.007e-03, eta: 8:33:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6031, top5_acc: 0.8422, loss_cls: 2.1649, loss: 2.1649 +2024-12-31 13:06:59,441 - pyskl - INFO - Epoch [141][1600/3746] lr: 1.002e-03, eta: 8:31:45, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5962, top5_acc: 0.8342, loss_cls: 2.1999, loss: 2.1999 +2024-12-31 13:08:24,686 - pyskl - INFO - Epoch [141][1700/3746] lr: 9.961e-04, eta: 8:30:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5977, top5_acc: 0.8325, loss_cls: 2.2110, loss: 2.2110 +2024-12-31 13:09:50,162 - pyskl - INFO - Epoch [141][1800/3746] lr: 9.905e-04, eta: 8:28:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6103, top5_acc: 0.8372, loss_cls: 2.1717, loss: 2.1717 +2024-12-31 13:11:15,615 - pyskl - INFO - Epoch [141][1900/3746] lr: 9.850e-04, eta: 8:27:28, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6019, top5_acc: 0.8328, loss_cls: 2.2045, loss: 2.2045 +2024-12-31 13:12:40,723 - pyskl - INFO - Epoch [141][2000/3746] lr: 9.795e-04, eta: 8:26:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5994, top5_acc: 0.8364, loss_cls: 2.2248, loss: 2.2248 +2024-12-31 13:14:05,902 - pyskl - INFO - Epoch [141][2100/3746] lr: 9.740e-04, eta: 8:24:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6014, top5_acc: 0.8341, loss_cls: 2.2241, loss: 2.2241 +2024-12-31 13:15:30,820 - pyskl - INFO - Epoch [141][2200/3746] lr: 9.685e-04, eta: 8:23:11, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6033, top5_acc: 0.8336, loss_cls: 2.1781, loss: 2.1781 +2024-12-31 13:16:55,588 - pyskl - INFO - Epoch [141][2300/3746] lr: 9.630e-04, eta: 8:21:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6009, top5_acc: 0.8358, loss_cls: 2.1912, loss: 2.1912 +2024-12-31 13:18:20,822 - pyskl - INFO - Epoch [141][2400/3746] lr: 9.576e-04, eta: 8:20:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6073, top5_acc: 0.8386, loss_cls: 2.1782, loss: 2.1782 +2024-12-31 13:19:45,158 - pyskl - INFO - Epoch [141][2500/3746] lr: 9.522e-04, eta: 8:18:54, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6070, top5_acc: 0.8333, loss_cls: 2.2212, loss: 2.2212 +2024-12-31 13:21:10,169 - pyskl - INFO - Epoch [141][2600/3746] lr: 9.467e-04, eta: 8:17:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5955, top5_acc: 0.8241, loss_cls: 2.2297, loss: 2.2297 +2024-12-31 13:22:35,058 - pyskl - INFO - Epoch [141][2700/3746] lr: 9.413e-04, eta: 8:16:03, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5988, top5_acc: 0.8287, loss_cls: 2.2314, loss: 2.2314 +2024-12-31 13:23:59,507 - pyskl - INFO - Epoch [141][2800/3746] lr: 9.359e-04, eta: 8:14:37, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6078, top5_acc: 0.8355, loss_cls: 2.1821, loss: 2.1821 +2024-12-31 13:25:24,758 - pyskl - INFO - Epoch [141][2900/3746] lr: 9.306e-04, eta: 8:13:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6059, top5_acc: 0.8353, loss_cls: 2.1758, loss: 2.1758 +2024-12-31 13:26:49,676 - pyskl - INFO - Epoch [141][3000/3746] lr: 9.252e-04, eta: 8:11:46, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.6075, top5_acc: 0.8339, loss_cls: 2.2059, loss: 2.2059 +2024-12-31 13:28:14,335 - pyskl - INFO - Epoch [141][3100/3746] lr: 9.199e-04, eta: 8:10:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6012, top5_acc: 0.8389, loss_cls: 2.1948, loss: 2.1948 +2024-12-31 13:29:39,018 - pyskl - INFO - Epoch [141][3200/3746] lr: 9.145e-04, eta: 8:08:55, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6000, top5_acc: 0.8295, loss_cls: 2.2122, loss: 2.2122 +2024-12-31 13:31:04,053 - pyskl - INFO - Epoch [141][3300/3746] lr: 9.092e-04, eta: 8:07:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6034, top5_acc: 0.8350, loss_cls: 2.2126, loss: 2.2126 +2024-12-31 13:32:29,855 - pyskl - INFO - Epoch [141][3400/3746] lr: 9.039e-04, eta: 8:06:03, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6039, top5_acc: 0.8358, loss_cls: 2.1914, loss: 2.1914 +2024-12-31 13:33:56,206 - pyskl - INFO - Epoch [141][3500/3746] lr: 8.986e-04, eta: 8:04:38, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.6045, top5_acc: 0.8402, loss_cls: 2.1601, loss: 2.1601 +2024-12-31 13:35:21,326 - pyskl - INFO - Epoch [141][3600/3746] lr: 8.934e-04, eta: 8:03:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6064, top5_acc: 0.8297, loss_cls: 2.2176, loss: 2.2176 +2024-12-31 13:36:46,452 - pyskl - INFO - Epoch [141][3700/3746] lr: 8.881e-04, eta: 8:01:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5992, top5_acc: 0.8303, loss_cls: 2.2243, loss: 2.2243 +2024-12-31 13:37:27,180 - pyskl - INFO - Saving checkpoint at 141 epochs +2024-12-31 13:39:24,271 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 13:39:25,276 - pyskl - INFO - +top1_acc 0.4589 +top5_acc 0.7118 +2024-12-31 13:39:25,276 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 13:39:25,321 - pyskl - INFO - +mean_acc 0.4587 +2024-12-31 13:39:25,327 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_140.pth was removed +2024-12-31 13:39:25,623 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_141.pth. +2024-12-31 13:39:25,624 - pyskl - INFO - Best top1_acc is 0.4589 at 141 epoch. +2024-12-31 13:39:25,638 - pyskl - INFO - Epoch(val) [141][309] top1_acc: 0.4589, top5_acc: 0.7118, mean_class_accuracy: 0.4587 +2024-12-31 13:43:43,469 - pyskl - INFO - Epoch [142][100/3746] lr: 8.805e-04, eta: 7:59:50, time: 2.578, data_time: 1.543, memory: 15990, top1_acc: 0.6212, top5_acc: 0.8400, loss_cls: 2.1123, loss: 2.1123 +2024-12-31 13:45:08,278 - pyskl - INFO - Epoch [142][200/3746] lr: 8.752e-04, eta: 7:58:24, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6119, top5_acc: 0.8411, loss_cls: 2.1384, loss: 2.1384 +2024-12-31 13:46:33,477 - pyskl - INFO - Epoch [142][300/3746] lr: 8.700e-04, eta: 7:56:59, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6222, top5_acc: 0.8408, loss_cls: 2.1349, loss: 2.1349 +2024-12-31 13:47:58,444 - pyskl - INFO - Epoch [142][400/3746] lr: 8.649e-04, eta: 7:55:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6145, top5_acc: 0.8344, loss_cls: 2.1611, loss: 2.1611 +2024-12-31 13:49:23,711 - pyskl - INFO - Epoch [142][500/3746] lr: 8.597e-04, eta: 7:54:07, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6214, top5_acc: 0.8470, loss_cls: 2.1029, loss: 2.1029 +2024-12-31 13:50:48,722 - pyskl - INFO - Epoch [142][600/3746] lr: 8.545e-04, eta: 7:52:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6183, top5_acc: 0.8464, loss_cls: 2.1067, loss: 2.1067 +2024-12-31 13:52:14,026 - pyskl - INFO - Epoch [142][700/3746] lr: 8.494e-04, eta: 7:51:16, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6081, top5_acc: 0.8413, loss_cls: 2.1700, loss: 2.1700 +2024-12-31 13:53:38,688 - pyskl - INFO - Epoch [142][800/3746] lr: 8.443e-04, eta: 7:49:50, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6228, top5_acc: 0.8469, loss_cls: 2.0964, loss: 2.0964 +2024-12-31 13:55:04,267 - pyskl - INFO - Epoch [142][900/3746] lr: 8.392e-04, eta: 7:48:24, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6103, top5_acc: 0.8403, loss_cls: 2.1473, loss: 2.1473 +2024-12-31 13:56:28,828 - pyskl - INFO - Epoch [142][1000/3746] lr: 8.341e-04, eta: 7:46:59, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6242, top5_acc: 0.8491, loss_cls: 2.1008, loss: 2.1008 +2024-12-31 13:57:53,856 - pyskl - INFO - Epoch [142][1100/3746] lr: 8.290e-04, eta: 7:45:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6206, top5_acc: 0.8458, loss_cls: 2.1085, loss: 2.1085 +2024-12-31 13:59:19,355 - pyskl - INFO - Epoch [142][1200/3746] lr: 8.239e-04, eta: 7:44:07, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6266, top5_acc: 0.8531, loss_cls: 2.0668, loss: 2.0668 +2024-12-31 14:00:44,409 - pyskl - INFO - Epoch [142][1300/3746] lr: 8.189e-04, eta: 7:42:42, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6238, top5_acc: 0.8472, loss_cls: 2.1058, loss: 2.1058 +2024-12-31 14:02:08,989 - pyskl - INFO - Epoch [142][1400/3746] lr: 8.139e-04, eta: 7:41:16, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6053, top5_acc: 0.8383, loss_cls: 2.1829, loss: 2.1829 +2024-12-31 14:03:34,628 - pyskl - INFO - Epoch [142][1500/3746] lr: 8.088e-04, eta: 7:39:50, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6197, top5_acc: 0.8427, loss_cls: 2.1293, loss: 2.1293 +2024-12-31 14:04:58,339 - pyskl - INFO - Epoch [142][1600/3746] lr: 8.038e-04, eta: 7:38:25, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.6094, top5_acc: 0.8347, loss_cls: 2.1692, loss: 2.1692 +2024-12-31 14:06:22,895 - pyskl - INFO - Epoch [142][1700/3746] lr: 7.989e-04, eta: 7:36:59, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6195, top5_acc: 0.8377, loss_cls: 2.1329, loss: 2.1329 +2024-12-31 14:07:47,516 - pyskl - INFO - Epoch [142][1800/3746] lr: 7.939e-04, eta: 7:35:33, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6098, top5_acc: 0.8458, loss_cls: 2.1288, loss: 2.1288 +2024-12-31 14:09:12,190 - pyskl - INFO - Epoch [142][1900/3746] lr: 7.889e-04, eta: 7:34:07, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6116, top5_acc: 0.8431, loss_cls: 2.1284, loss: 2.1284 +2024-12-31 14:10:37,320 - pyskl - INFO - Epoch [142][2000/3746] lr: 7.840e-04, eta: 7:32:42, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6211, top5_acc: 0.8441, loss_cls: 2.1178, loss: 2.1178 +2024-12-31 14:12:02,255 - pyskl - INFO - Epoch [142][2100/3746] lr: 7.791e-04, eta: 7:31:16, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6211, top5_acc: 0.8377, loss_cls: 2.1306, loss: 2.1306 +2024-12-31 14:13:27,017 - pyskl - INFO - Epoch [142][2200/3746] lr: 7.742e-04, eta: 7:29:50, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6067, top5_acc: 0.8445, loss_cls: 2.1381, loss: 2.1381 +2024-12-31 14:14:51,670 - pyskl - INFO - Epoch [142][2300/3746] lr: 7.693e-04, eta: 7:28:25, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6161, top5_acc: 0.8408, loss_cls: 2.1472, loss: 2.1472 +2024-12-31 14:16:17,088 - pyskl - INFO - Epoch [142][2400/3746] lr: 7.644e-04, eta: 7:26:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6108, top5_acc: 0.8409, loss_cls: 2.1394, loss: 2.1394 +2024-12-31 14:17:41,681 - pyskl - INFO - Epoch [142][2500/3746] lr: 7.595e-04, eta: 7:25:33, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6039, top5_acc: 0.8292, loss_cls: 2.2030, loss: 2.2030 +2024-12-31 14:19:06,763 - pyskl - INFO - Epoch [142][2600/3746] lr: 7.547e-04, eta: 7:24:08, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6048, top5_acc: 0.8400, loss_cls: 2.1611, loss: 2.1611 +2024-12-31 14:20:31,532 - pyskl - INFO - Epoch [142][2700/3746] lr: 7.499e-04, eta: 7:22:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6194, top5_acc: 0.8400, loss_cls: 2.1563, loss: 2.1563 +2024-12-31 14:21:55,836 - pyskl - INFO - Epoch [142][2800/3746] lr: 7.450e-04, eta: 7:21:16, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6170, top5_acc: 0.8439, loss_cls: 2.1375, loss: 2.1375 +2024-12-31 14:23:20,743 - pyskl - INFO - Epoch [142][2900/3746] lr: 7.402e-04, eta: 7:19:51, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6120, top5_acc: 0.8405, loss_cls: 2.1395, loss: 2.1395 +2024-12-31 14:24:45,179 - pyskl - INFO - Epoch [142][3000/3746] lr: 7.355e-04, eta: 7:18:25, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6147, top5_acc: 0.8381, loss_cls: 2.1502, loss: 2.1502 +2024-12-31 14:26:10,268 - pyskl - INFO - Epoch [142][3100/3746] lr: 7.307e-04, eta: 7:16:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6167, top5_acc: 0.8386, loss_cls: 2.1368, loss: 2.1368 +2024-12-31 14:27:35,093 - pyskl - INFO - Epoch [142][3200/3746] lr: 7.259e-04, eta: 7:15:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6055, top5_acc: 0.8363, loss_cls: 2.1592, loss: 2.1592 +2024-12-31 14:29:00,090 - pyskl - INFO - Epoch [142][3300/3746] lr: 7.212e-04, eta: 7:14:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6020, top5_acc: 0.8369, loss_cls: 2.1893, loss: 2.1893 +2024-12-31 14:30:25,544 - pyskl - INFO - Epoch [142][3400/3746] lr: 7.165e-04, eta: 7:12:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6075, top5_acc: 0.8381, loss_cls: 2.1525, loss: 2.1525 +2024-12-31 14:31:50,904 - pyskl - INFO - Epoch [142][3500/3746] lr: 7.118e-04, eta: 7:11:16, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6083, top5_acc: 0.8389, loss_cls: 2.1954, loss: 2.1954 +2024-12-31 14:33:15,494 - pyskl - INFO - Epoch [142][3600/3746] lr: 7.071e-04, eta: 7:09:51, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5986, top5_acc: 0.8314, loss_cls: 2.1876, loss: 2.1876 +2024-12-31 14:34:40,005 - pyskl - INFO - Epoch [142][3700/3746] lr: 7.024e-04, eta: 7:08:25, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6100, top5_acc: 0.8402, loss_cls: 2.1524, loss: 2.1524 +2024-12-31 14:35:21,013 - pyskl - INFO - Saving checkpoint at 142 epochs +2024-12-31 14:37:20,957 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 14:37:21,631 - pyskl - INFO - +top1_acc 0.4649 +top5_acc 0.7117 +2024-12-31 14:37:21,631 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 14:37:21,672 - pyskl - INFO - +mean_acc 0.4647 +2024-12-31 14:37:21,676 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_141.pth was removed +2024-12-31 14:37:21,997 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_142.pth. +2024-12-31 14:37:21,998 - pyskl - INFO - Best top1_acc is 0.4649 at 142 epoch. +2024-12-31 14:37:22,013 - pyskl - INFO - Epoch(val) [142][309] top1_acc: 0.4649, top5_acc: 0.7117, mean_class_accuracy: 0.4647 +2024-12-31 14:41:38,718 - pyskl - INFO - Epoch [143][100/3746] lr: 6.956e-04, eta: 7:06:27, time: 2.567, data_time: 1.533, memory: 15990, top1_acc: 0.6436, top5_acc: 0.8573, loss_cls: 2.0158, loss: 2.0158 +2024-12-31 14:43:04,555 - pyskl - INFO - Epoch [143][200/3746] lr: 6.910e-04, eta: 7:05:02, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.6333, top5_acc: 0.8545, loss_cls: 2.0569, loss: 2.0569 +2024-12-31 14:44:30,097 - pyskl - INFO - Epoch [143][300/3746] lr: 6.863e-04, eta: 7:03:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6348, top5_acc: 0.8631, loss_cls: 2.0291, loss: 2.0291 +2024-12-31 14:45:54,940 - pyskl - INFO - Epoch [143][400/3746] lr: 6.817e-04, eta: 7:02:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6189, top5_acc: 0.8414, loss_cls: 2.1240, loss: 2.1240 +2024-12-31 14:47:20,041 - pyskl - INFO - Epoch [143][500/3746] lr: 6.771e-04, eta: 7:00:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6208, top5_acc: 0.8539, loss_cls: 2.0771, loss: 2.0771 +2024-12-31 14:48:44,509 - pyskl - INFO - Epoch [143][600/3746] lr: 6.725e-04, eta: 6:59:19, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6353, top5_acc: 0.8559, loss_cls: 2.0369, loss: 2.0369 +2024-12-31 14:50:09,490 - pyskl - INFO - Epoch [143][700/3746] lr: 6.680e-04, eta: 6:57:53, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6409, top5_acc: 0.8556, loss_cls: 2.0318, loss: 2.0318 +2024-12-31 14:51:33,873 - pyskl - INFO - Epoch [143][800/3746] lr: 6.634e-04, eta: 6:56:27, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6250, top5_acc: 0.8480, loss_cls: 2.0890, loss: 2.0890 +2024-12-31 14:52:58,671 - pyskl - INFO - Epoch [143][900/3746] lr: 6.589e-04, eta: 6:55:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6311, top5_acc: 0.8497, loss_cls: 2.0866, loss: 2.0866 +2024-12-31 14:54:22,662 - pyskl - INFO - Epoch [143][1000/3746] lr: 6.544e-04, eta: 6:53:36, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.6273, top5_acc: 0.8527, loss_cls: 2.0731, loss: 2.0731 +2024-12-31 14:55:46,336 - pyskl - INFO - Epoch [143][1100/3746] lr: 6.499e-04, eta: 6:52:10, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.6300, top5_acc: 0.8577, loss_cls: 2.0662, loss: 2.0662 +2024-12-31 14:57:10,383 - pyskl - INFO - Epoch [143][1200/3746] lr: 6.454e-04, eta: 6:50:44, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.6372, top5_acc: 0.8478, loss_cls: 2.0795, loss: 2.0795 +2024-12-31 14:58:35,432 - pyskl - INFO - Epoch [143][1300/3746] lr: 6.409e-04, eta: 6:49:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6258, top5_acc: 0.8419, loss_cls: 2.0975, loss: 2.0975 +2024-12-31 14:59:59,806 - pyskl - INFO - Epoch [143][1400/3746] lr: 6.365e-04, eta: 6:47:53, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6367, top5_acc: 0.8492, loss_cls: 2.0686, loss: 2.0686 +2024-12-31 15:01:25,365 - pyskl - INFO - Epoch [143][1500/3746] lr: 6.320e-04, eta: 6:46:27, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6269, top5_acc: 0.8505, loss_cls: 2.0649, loss: 2.0649 +2024-12-31 15:02:50,117 - pyskl - INFO - Epoch [143][1600/3746] lr: 6.276e-04, eta: 6:45:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6309, top5_acc: 0.8502, loss_cls: 2.0805, loss: 2.0805 +2024-12-31 15:04:14,295 - pyskl - INFO - Epoch [143][1700/3746] lr: 6.232e-04, eta: 6:43:36, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6191, top5_acc: 0.8447, loss_cls: 2.1335, loss: 2.1335 +2024-12-31 15:05:39,179 - pyskl - INFO - Epoch [143][1800/3746] lr: 6.188e-04, eta: 6:42:10, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6270, top5_acc: 0.8494, loss_cls: 2.0862, loss: 2.0862 +2024-12-31 15:07:04,178 - pyskl - INFO - Epoch [143][1900/3746] lr: 6.144e-04, eta: 6:40:44, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6281, top5_acc: 0.8489, loss_cls: 2.0927, loss: 2.0927 +2024-12-31 15:08:28,968 - pyskl - INFO - Epoch [143][2000/3746] lr: 6.101e-04, eta: 6:39:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6270, top5_acc: 0.8508, loss_cls: 2.0870, loss: 2.0870 +2024-12-31 15:09:53,315 - pyskl - INFO - Epoch [143][2100/3746] lr: 6.057e-04, eta: 6:37:53, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6359, top5_acc: 0.8509, loss_cls: 2.0476, loss: 2.0476 +2024-12-31 15:11:18,572 - pyskl - INFO - Epoch [143][2200/3746] lr: 6.014e-04, eta: 6:36:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6261, top5_acc: 0.8511, loss_cls: 2.0679, loss: 2.0679 +2024-12-31 15:12:42,715 - pyskl - INFO - Epoch [143][2300/3746] lr: 5.971e-04, eta: 6:35:02, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.6181, top5_acc: 0.8445, loss_cls: 2.1183, loss: 2.1183 +2024-12-31 15:14:06,886 - pyskl - INFO - Epoch [143][2400/3746] lr: 5.928e-04, eta: 6:33:36, time: 0.842, data_time: 0.001, memory: 15990, top1_acc: 0.6225, top5_acc: 0.8431, loss_cls: 2.1001, loss: 2.1001 +2024-12-31 15:15:32,067 - pyskl - INFO - Epoch [143][2500/3746] lr: 5.885e-04, eta: 6:32:10, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6362, top5_acc: 0.8573, loss_cls: 2.0283, loss: 2.0283 +2024-12-31 15:16:57,105 - pyskl - INFO - Epoch [143][2600/3746] lr: 5.842e-04, eta: 6:30:44, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6242, top5_acc: 0.8472, loss_cls: 2.0878, loss: 2.0878 +2024-12-31 15:18:21,725 - pyskl - INFO - Epoch [143][2700/3746] lr: 5.800e-04, eta: 6:29:19, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6203, top5_acc: 0.8411, loss_cls: 2.1200, loss: 2.1200 +2024-12-31 15:19:45,880 - pyskl - INFO - Epoch [143][2800/3746] lr: 5.757e-04, eta: 6:27:53, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6148, top5_acc: 0.8400, loss_cls: 2.1454, loss: 2.1454 +2024-12-31 15:21:10,157 - pyskl - INFO - Epoch [143][2900/3746] lr: 5.715e-04, eta: 6:26:27, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6203, top5_acc: 0.8478, loss_cls: 2.0822, loss: 2.0822 +2024-12-31 15:22:35,533 - pyskl - INFO - Epoch [143][3000/3746] lr: 5.673e-04, eta: 6:25:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6289, top5_acc: 0.8439, loss_cls: 2.1026, loss: 2.1026 +2024-12-31 15:24:00,260 - pyskl - INFO - Epoch [143][3100/3746] lr: 5.631e-04, eta: 6:23:36, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6208, top5_acc: 0.8431, loss_cls: 2.0892, loss: 2.0892 +2024-12-31 15:25:24,505 - pyskl - INFO - Epoch [143][3200/3746] lr: 5.590e-04, eta: 6:22:10, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6241, top5_acc: 0.8484, loss_cls: 2.1000, loss: 2.1000 +2024-12-31 15:26:49,339 - pyskl - INFO - Epoch [143][3300/3746] lr: 5.548e-04, eta: 6:20:44, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6173, top5_acc: 0.8348, loss_cls: 2.1316, loss: 2.1316 +2024-12-31 15:28:13,967 - pyskl - INFO - Epoch [143][3400/3746] lr: 5.506e-04, eta: 6:19:19, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6242, top5_acc: 0.8505, loss_cls: 2.0817, loss: 2.0817 +2024-12-31 15:29:38,907 - pyskl - INFO - Epoch [143][3500/3746] lr: 5.465e-04, eta: 6:17:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6242, top5_acc: 0.8503, loss_cls: 2.0919, loss: 2.0919 +2024-12-31 15:31:04,128 - pyskl - INFO - Epoch [143][3600/3746] lr: 5.424e-04, eta: 6:16:27, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6203, top5_acc: 0.8484, loss_cls: 2.0867, loss: 2.0867 +2024-12-31 15:32:28,562 - pyskl - INFO - Epoch [143][3700/3746] lr: 5.383e-04, eta: 6:15:02, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6328, top5_acc: 0.8550, loss_cls: 2.0490, loss: 2.0490 +2024-12-31 15:33:09,713 - pyskl - INFO - Saving checkpoint at 143 epochs +2024-12-31 15:35:07,571 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 15:35:08,257 - pyskl - INFO - +top1_acc 0.4656 +top5_acc 0.7142 +2024-12-31 15:35:08,257 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 15:35:08,300 - pyskl - INFO - +mean_acc 0.4654 +2024-12-31 15:35:08,306 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_142.pth was removed +2024-12-31 15:35:08,605 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_143.pth. +2024-12-31 15:35:08,605 - pyskl - INFO - Best top1_acc is 0.4656 at 143 epoch. +2024-12-31 15:35:08,617 - pyskl - INFO - Epoch(val) [143][309] top1_acc: 0.4656, top5_acc: 0.7142, mean_class_accuracy: 0.4654 +2024-12-31 15:39:25,394 - pyskl - INFO - Epoch [144][100/3746] lr: 5.323e-04, eta: 6:13:03, time: 2.568, data_time: 1.519, memory: 15990, top1_acc: 0.6442, top5_acc: 0.8600, loss_cls: 2.0014, loss: 2.0014 +2024-12-31 15:40:51,248 - pyskl - INFO - Epoch [144][200/3746] lr: 5.283e-04, eta: 6:11:37, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6498, top5_acc: 0.8614, loss_cls: 1.9836, loss: 1.9836 +2024-12-31 15:42:16,238 - pyskl - INFO - Epoch [144][300/3746] lr: 5.242e-04, eta: 6:10:12, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.6412, top5_acc: 0.8616, loss_cls: 2.0041, loss: 2.0041 +2024-12-31 15:43:41,430 - pyskl - INFO - Epoch [144][400/3746] lr: 5.202e-04, eta: 6:08:46, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6422, top5_acc: 0.8628, loss_cls: 2.0104, loss: 2.0104 +2024-12-31 15:45:06,567 - pyskl - INFO - Epoch [144][500/3746] lr: 5.162e-04, eta: 6:07:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6447, top5_acc: 0.8634, loss_cls: 2.0080, loss: 2.0080 +2024-12-31 15:46:32,558 - pyskl - INFO - Epoch [144][600/3746] lr: 5.122e-04, eta: 6:05:54, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.6370, top5_acc: 0.8556, loss_cls: 2.0350, loss: 2.0350 +2024-12-31 15:47:58,325 - pyskl - INFO - Epoch [144][700/3746] lr: 5.082e-04, eta: 6:04:29, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6411, top5_acc: 0.8536, loss_cls: 2.0272, loss: 2.0272 +2024-12-31 15:49:24,358 - pyskl - INFO - Epoch [144][800/3746] lr: 5.042e-04, eta: 6:03:03, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6383, top5_acc: 0.8555, loss_cls: 2.0323, loss: 2.0323 +2024-12-31 15:50:50,096 - pyskl - INFO - Epoch [144][900/3746] lr: 5.003e-04, eta: 6:01:37, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6334, top5_acc: 0.8530, loss_cls: 2.0267, loss: 2.0267 +2024-12-31 15:52:15,795 - pyskl - INFO - Epoch [144][1000/3746] lr: 4.964e-04, eta: 6:00:12, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6509, top5_acc: 0.8603, loss_cls: 1.9866, loss: 1.9866 +2024-12-31 15:53:41,769 - pyskl - INFO - Epoch [144][1100/3746] lr: 4.924e-04, eta: 5:58:46, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6473, top5_acc: 0.8642, loss_cls: 1.9842, loss: 1.9842 +2024-12-31 15:55:07,825 - pyskl - INFO - Epoch [144][1200/3746] lr: 4.885e-04, eta: 5:57:20, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.6420, top5_acc: 0.8625, loss_cls: 2.0097, loss: 2.0097 +2024-12-31 15:56:33,634 - pyskl - INFO - Epoch [144][1300/3746] lr: 4.846e-04, eta: 5:55:55, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6320, top5_acc: 0.8520, loss_cls: 2.0367, loss: 2.0367 +2024-12-31 15:57:59,278 - pyskl - INFO - Epoch [144][1400/3746] lr: 4.808e-04, eta: 5:54:29, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6378, top5_acc: 0.8588, loss_cls: 2.0202, loss: 2.0202 +2024-12-31 15:59:25,077 - pyskl - INFO - Epoch [144][1500/3746] lr: 4.769e-04, eta: 5:53:03, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6428, top5_acc: 0.8536, loss_cls: 2.0147, loss: 2.0147 +2024-12-31 16:00:50,626 - pyskl - INFO - Epoch [144][1600/3746] lr: 4.731e-04, eta: 5:51:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6402, top5_acc: 0.8569, loss_cls: 2.0205, loss: 2.0205 +2024-12-31 16:02:15,832 - pyskl - INFO - Epoch [144][1700/3746] lr: 4.692e-04, eta: 5:50:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6389, top5_acc: 0.8592, loss_cls: 2.0075, loss: 2.0075 +2024-12-31 16:03:41,928 - pyskl - INFO - Epoch [144][1800/3746] lr: 4.654e-04, eta: 5:48:46, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.6273, top5_acc: 0.8527, loss_cls: 2.0538, loss: 2.0538 +2024-12-31 16:05:07,882 - pyskl - INFO - Epoch [144][1900/3746] lr: 4.616e-04, eta: 5:47:21, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6169, top5_acc: 0.8545, loss_cls: 2.0876, loss: 2.0876 +2024-12-31 16:06:33,508 - pyskl - INFO - Epoch [144][2000/3746] lr: 4.578e-04, eta: 5:45:55, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6370, top5_acc: 0.8556, loss_cls: 2.0303, loss: 2.0303 +2024-12-31 16:07:59,246 - pyskl - INFO - Epoch [144][2100/3746] lr: 4.541e-04, eta: 5:44:29, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6403, top5_acc: 0.8622, loss_cls: 2.0202, loss: 2.0202 +2024-12-31 16:09:25,348 - pyskl - INFO - Epoch [144][2200/3746] lr: 4.503e-04, eta: 5:43:03, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.6358, top5_acc: 0.8544, loss_cls: 2.0500, loss: 2.0500 +2024-12-31 16:10:51,156 - pyskl - INFO - Epoch [144][2300/3746] lr: 4.466e-04, eta: 5:41:38, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6388, top5_acc: 0.8509, loss_cls: 2.0379, loss: 2.0379 +2024-12-31 16:12:16,682 - pyskl - INFO - Epoch [144][2400/3746] lr: 4.429e-04, eta: 5:40:12, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6391, top5_acc: 0.8588, loss_cls: 2.0326, loss: 2.0326 +2024-12-31 16:13:42,433 - pyskl - INFO - Epoch [144][2500/3746] lr: 4.392e-04, eta: 5:38:46, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6391, top5_acc: 0.8458, loss_cls: 2.0788, loss: 2.0788 +2024-12-31 16:15:07,774 - pyskl - INFO - Epoch [144][2600/3746] lr: 4.355e-04, eta: 5:37:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6386, top5_acc: 0.8531, loss_cls: 2.0344, loss: 2.0344 +2024-12-31 16:16:33,486 - pyskl - INFO - Epoch [144][2700/3746] lr: 4.318e-04, eta: 5:35:55, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6297, top5_acc: 0.8489, loss_cls: 2.0581, loss: 2.0581 +2024-12-31 16:17:59,159 - pyskl - INFO - Epoch [144][2800/3746] lr: 4.281e-04, eta: 5:34:29, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6405, top5_acc: 0.8545, loss_cls: 2.0272, loss: 2.0272 +2024-12-31 16:19:25,202 - pyskl - INFO - Epoch [144][2900/3746] lr: 4.245e-04, eta: 5:33:04, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6375, top5_acc: 0.8550, loss_cls: 2.0270, loss: 2.0270 +2024-12-31 16:20:51,446 - pyskl - INFO - Epoch [144][3000/3746] lr: 4.209e-04, eta: 5:31:38, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.6281, top5_acc: 0.8534, loss_cls: 2.0559, loss: 2.0559 +2024-12-31 16:22:17,269 - pyskl - INFO - Epoch [144][3100/3746] lr: 4.173e-04, eta: 5:30:12, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6352, top5_acc: 0.8511, loss_cls: 2.0442, loss: 2.0442 +2024-12-31 16:23:43,469 - pyskl - INFO - Epoch [144][3200/3746] lr: 4.137e-04, eta: 5:28:47, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.6470, top5_acc: 0.8616, loss_cls: 1.9828, loss: 1.9828 +2024-12-31 16:25:09,722 - pyskl - INFO - Epoch [144][3300/3746] lr: 4.101e-04, eta: 5:27:21, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.6270, top5_acc: 0.8533, loss_cls: 2.0721, loss: 2.0721 +2024-12-31 16:26:35,999 - pyskl - INFO - Epoch [144][3400/3746] lr: 4.065e-04, eta: 5:25:55, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.6414, top5_acc: 0.8527, loss_cls: 2.0142, loss: 2.0142 +2024-12-31 16:28:01,975 - pyskl - INFO - Epoch [144][3500/3746] lr: 4.030e-04, eta: 5:24:30, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6355, top5_acc: 0.8516, loss_cls: 2.0364, loss: 2.0364 +2024-12-31 16:29:27,876 - pyskl - INFO - Epoch [144][3600/3746] lr: 3.994e-04, eta: 5:23:04, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6403, top5_acc: 0.8531, loss_cls: 2.0242, loss: 2.0242 +2024-12-31 16:30:53,870 - pyskl - INFO - Epoch [144][3700/3746] lr: 3.959e-04, eta: 5:21:38, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6234, top5_acc: 0.8497, loss_cls: 2.0688, loss: 2.0688 +2024-12-31 16:31:35,108 - pyskl - INFO - Saving checkpoint at 144 epochs +2024-12-31 16:33:34,870 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 16:33:35,736 - pyskl - INFO - +top1_acc 0.4667 +top5_acc 0.7166 +2024-12-31 16:33:35,737 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 16:33:35,792 - pyskl - INFO - +mean_acc 0.4665 +2024-12-31 16:33:35,797 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_143.pth was removed +2024-12-31 16:33:36,199 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_144.pth. +2024-12-31 16:33:36,200 - pyskl - INFO - Best top1_acc is 0.4667 at 144 epoch. +2024-12-31 16:33:36,216 - pyskl - INFO - Epoch(val) [144][309] top1_acc: 0.4667, top5_acc: 0.7166, mean_class_accuracy: 0.4665 +2024-12-31 16:38:04,411 - pyskl - INFO - Epoch [145][100/3746] lr: 3.908e-04, eta: 5:19:39, time: 2.682, data_time: 1.642, memory: 15990, top1_acc: 0.6623, top5_acc: 0.8712, loss_cls: 1.9231, loss: 1.9231 +2024-12-31 16:39:29,694 - pyskl - INFO - Epoch [145][200/3746] lr: 3.873e-04, eta: 5:18:13, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6577, top5_acc: 0.8766, loss_cls: 1.9037, loss: 1.9037 +2024-12-31 16:40:54,305 - pyskl - INFO - Epoch [145][300/3746] lr: 3.839e-04, eta: 5:16:48, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.6427, top5_acc: 0.8577, loss_cls: 2.0112, loss: 2.0112 +2024-12-31 16:42:19,077 - pyskl - INFO - Epoch [145][400/3746] lr: 3.804e-04, eta: 5:15:22, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6572, top5_acc: 0.8702, loss_cls: 1.9327, loss: 1.9327 +2024-12-31 16:43:43,746 - pyskl - INFO - Epoch [145][500/3746] lr: 3.770e-04, eta: 5:13:56, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6456, top5_acc: 0.8653, loss_cls: 1.9498, loss: 1.9498 +2024-12-31 16:45:07,935 - pyskl - INFO - Epoch [145][600/3746] lr: 3.736e-04, eta: 5:12:30, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6475, top5_acc: 0.8653, loss_cls: 1.9698, loss: 1.9698 +2024-12-31 16:46:33,127 - pyskl - INFO - Epoch [145][700/3746] lr: 3.702e-04, eta: 5:11:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6502, top5_acc: 0.8648, loss_cls: 1.9687, loss: 1.9687 +2024-12-31 16:47:58,825 - pyskl - INFO - Epoch [145][800/3746] lr: 3.668e-04, eta: 5:09:39, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6462, top5_acc: 0.8639, loss_cls: 1.9874, loss: 1.9874 +2024-12-31 16:49:23,625 - pyskl - INFO - Epoch [145][900/3746] lr: 3.634e-04, eta: 5:08:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6572, top5_acc: 0.8627, loss_cls: 1.9651, loss: 1.9651 +2024-12-31 16:50:48,885 - pyskl - INFO - Epoch [145][1000/3746] lr: 3.600e-04, eta: 5:06:47, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6531, top5_acc: 0.8656, loss_cls: 1.9590, loss: 1.9590 +2024-12-31 16:52:14,232 - pyskl - INFO - Epoch [145][1100/3746] lr: 3.567e-04, eta: 5:05:22, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6466, top5_acc: 0.8606, loss_cls: 1.9795, loss: 1.9795 +2024-12-31 16:53:39,635 - pyskl - INFO - Epoch [145][1200/3746] lr: 3.534e-04, eta: 5:03:56, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6439, top5_acc: 0.8642, loss_cls: 1.9831, loss: 1.9831 +2024-12-31 16:55:04,201 - pyskl - INFO - Epoch [145][1300/3746] lr: 3.501e-04, eta: 5:02:30, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6505, top5_acc: 0.8636, loss_cls: 1.9621, loss: 1.9621 +2024-12-31 16:56:28,963 - pyskl - INFO - Epoch [145][1400/3746] lr: 3.468e-04, eta: 5:01:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6498, top5_acc: 0.8616, loss_cls: 1.9646, loss: 1.9646 +2024-12-31 16:57:53,854 - pyskl - INFO - Epoch [145][1500/3746] lr: 3.435e-04, eta: 4:59:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6477, top5_acc: 0.8634, loss_cls: 1.9854, loss: 1.9854 +2024-12-31 16:59:18,262 - pyskl - INFO - Epoch [145][1600/3746] lr: 3.402e-04, eta: 4:58:13, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6459, top5_acc: 0.8588, loss_cls: 1.9842, loss: 1.9842 +2024-12-31 17:00:43,080 - pyskl - INFO - Epoch [145][1700/3746] lr: 3.370e-04, eta: 4:56:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6434, top5_acc: 0.8594, loss_cls: 1.9887, loss: 1.9887 +2024-12-31 17:02:08,164 - pyskl - INFO - Epoch [145][1800/3746] lr: 3.337e-04, eta: 4:55:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6481, top5_acc: 0.8623, loss_cls: 1.9718, loss: 1.9718 +2024-12-31 17:03:32,459 - pyskl - INFO - Epoch [145][1900/3746] lr: 3.305e-04, eta: 4:53:56, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6397, top5_acc: 0.8586, loss_cls: 2.0059, loss: 2.0059 +2024-12-31 17:04:58,211 - pyskl - INFO - Epoch [145][2000/3746] lr: 3.273e-04, eta: 4:52:30, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6431, top5_acc: 0.8583, loss_cls: 1.9995, loss: 1.9995 +2024-12-31 17:06:23,411 - pyskl - INFO - Epoch [145][2100/3746] lr: 3.241e-04, eta: 4:51:04, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6525, top5_acc: 0.8602, loss_cls: 1.9731, loss: 1.9731 +2024-12-31 17:07:47,932 - pyskl - INFO - Epoch [145][2200/3746] lr: 3.210e-04, eta: 4:49:38, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6417, top5_acc: 0.8598, loss_cls: 2.0135, loss: 2.0135 +2024-12-31 17:09:12,654 - pyskl - INFO - Epoch [145][2300/3746] lr: 3.178e-04, eta: 4:48:13, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6473, top5_acc: 0.8600, loss_cls: 1.9940, loss: 1.9940 +2024-12-31 17:10:38,192 - pyskl - INFO - Epoch [145][2400/3746] lr: 3.147e-04, eta: 4:46:47, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6466, top5_acc: 0.8584, loss_cls: 1.9897, loss: 1.9897 +2024-12-31 17:12:03,339 - pyskl - INFO - Epoch [145][2500/3746] lr: 3.116e-04, eta: 4:45:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6417, top5_acc: 0.8611, loss_cls: 2.0277, loss: 2.0277 +2024-12-31 17:13:27,785 - pyskl - INFO - Epoch [145][2600/3746] lr: 3.084e-04, eta: 4:43:56, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6438, top5_acc: 0.8594, loss_cls: 1.9816, loss: 1.9816 +2024-12-31 17:14:52,899 - pyskl - INFO - Epoch [145][2700/3746] lr: 3.054e-04, eta: 4:42:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6542, top5_acc: 0.8603, loss_cls: 1.9702, loss: 1.9702 +2024-12-31 17:16:17,599 - pyskl - INFO - Epoch [145][2800/3746] lr: 3.023e-04, eta: 4:41:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6564, top5_acc: 0.8609, loss_cls: 1.9638, loss: 1.9638 +2024-12-31 17:17:42,308 - pyskl - INFO - Epoch [145][2900/3746] lr: 2.992e-04, eta: 4:39:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6478, top5_acc: 0.8586, loss_cls: 2.0098, loss: 2.0098 +2024-12-31 17:19:07,257 - pyskl - INFO - Epoch [145][3000/3746] lr: 2.962e-04, eta: 4:38:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6520, top5_acc: 0.8689, loss_cls: 1.9550, loss: 1.9550 +2024-12-31 17:20:32,136 - pyskl - INFO - Epoch [145][3100/3746] lr: 2.931e-04, eta: 4:36:47, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6489, top5_acc: 0.8570, loss_cls: 1.9783, loss: 1.9783 +2024-12-31 17:21:56,901 - pyskl - INFO - Epoch [145][3200/3746] lr: 2.901e-04, eta: 4:35:21, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6502, top5_acc: 0.8597, loss_cls: 1.9955, loss: 1.9955 +2024-12-31 17:23:21,657 - pyskl - INFO - Epoch [145][3300/3746] lr: 2.871e-04, eta: 4:33:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6505, top5_acc: 0.8611, loss_cls: 1.9828, loss: 1.9828 +2024-12-31 17:24:46,529 - pyskl - INFO - Epoch [145][3400/3746] lr: 2.841e-04, eta: 4:32:30, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6552, top5_acc: 0.8645, loss_cls: 1.9600, loss: 1.9600 +2024-12-31 17:26:11,641 - pyskl - INFO - Epoch [145][3500/3746] lr: 2.812e-04, eta: 4:31:04, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6375, top5_acc: 0.8597, loss_cls: 1.9840, loss: 1.9840 +2024-12-31 17:27:35,954 - pyskl - INFO - Epoch [145][3600/3746] lr: 2.782e-04, eta: 4:29:38, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6472, top5_acc: 0.8612, loss_cls: 1.9956, loss: 1.9956 +2024-12-31 17:29:00,228 - pyskl - INFO - Epoch [145][3700/3746] lr: 2.753e-04, eta: 4:28:12, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6388, top5_acc: 0.8586, loss_cls: 1.9999, loss: 1.9999 +2024-12-31 17:29:41,310 - pyskl - INFO - Saving checkpoint at 145 epochs +2024-12-31 17:31:40,304 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 17:31:40,975 - pyskl - INFO - +top1_acc 0.4703 +top5_acc 0.7193 +2024-12-31 17:31:40,975 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 17:31:41,014 - pyskl - INFO - +mean_acc 0.4701 +2024-12-31 17:31:41,019 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_144.pth was removed +2024-12-31 17:31:41,299 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_145.pth. +2024-12-31 17:31:41,299 - pyskl - INFO - Best top1_acc is 0.4703 at 145 epoch. +2024-12-31 17:31:41,311 - pyskl - INFO - Epoch(val) [145][309] top1_acc: 0.4703, top5_acc: 0.7193, mean_class_accuracy: 0.4701 +2024-12-31 17:36:00,771 - pyskl - INFO - Epoch [146][100/3746] lr: 2.710e-04, eta: 4:26:12, time: 2.595, data_time: 1.555, memory: 15990, top1_acc: 0.6653, top5_acc: 0.8675, loss_cls: 1.9248, loss: 1.9248 +2024-12-31 17:37:26,436 - pyskl - INFO - Epoch [146][200/3746] lr: 2.681e-04, eta: 4:24:46, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6491, top5_acc: 0.8694, loss_cls: 1.9355, loss: 1.9355 +2024-12-31 17:38:51,874 - pyskl - INFO - Epoch [146][300/3746] lr: 2.652e-04, eta: 4:23:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6566, top5_acc: 0.8700, loss_cls: 1.9097, loss: 1.9097 +2024-12-31 17:40:16,986 - pyskl - INFO - Epoch [146][400/3746] lr: 2.624e-04, eta: 4:21:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6545, top5_acc: 0.8672, loss_cls: 1.9576, loss: 1.9576 +2024-12-31 17:41:41,450 - pyskl - INFO - Epoch [146][500/3746] lr: 2.595e-04, eta: 4:20:29, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6637, top5_acc: 0.8750, loss_cls: 1.8910, loss: 1.8910 +2024-12-31 17:43:05,908 - pyskl - INFO - Epoch [146][600/3746] lr: 2.567e-04, eta: 4:19:03, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6573, top5_acc: 0.8683, loss_cls: 1.9490, loss: 1.9490 +2024-12-31 17:44:30,267 - pyskl - INFO - Epoch [146][700/3746] lr: 2.539e-04, eta: 4:17:37, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6542, top5_acc: 0.8656, loss_cls: 1.9370, loss: 1.9370 +2024-12-31 17:45:55,557 - pyskl - INFO - Epoch [146][800/3746] lr: 2.511e-04, eta: 4:16:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6639, top5_acc: 0.8725, loss_cls: 1.9242, loss: 1.9242 +2024-12-31 17:47:20,177 - pyskl - INFO - Epoch [146][900/3746] lr: 2.483e-04, eta: 4:14:46, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6678, top5_acc: 0.8748, loss_cls: 1.8720, loss: 1.8720 +2024-12-31 17:48:45,112 - pyskl - INFO - Epoch [146][1000/3746] lr: 2.455e-04, eta: 4:13:20, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6598, top5_acc: 0.8677, loss_cls: 1.9338, loss: 1.9338 +2024-12-31 17:50:09,726 - pyskl - INFO - Epoch [146][1100/3746] lr: 2.427e-04, eta: 4:11:54, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6616, top5_acc: 0.8761, loss_cls: 1.9166, loss: 1.9166 +2024-12-31 17:51:34,321 - pyskl - INFO - Epoch [146][1200/3746] lr: 2.400e-04, eta: 4:10:28, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6519, top5_acc: 0.8633, loss_cls: 1.9553, loss: 1.9553 +2024-12-31 17:52:59,041 - pyskl - INFO - Epoch [146][1300/3746] lr: 2.373e-04, eta: 4:09:03, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6592, top5_acc: 0.8708, loss_cls: 1.9393, loss: 1.9393 +2024-12-31 17:54:23,765 - pyskl - INFO - Epoch [146][1400/3746] lr: 2.345e-04, eta: 4:07:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6570, top5_acc: 0.8659, loss_cls: 1.9366, loss: 1.9366 +2024-12-31 17:55:49,153 - pyskl - INFO - Epoch [146][1500/3746] lr: 2.318e-04, eta: 4:06:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6622, top5_acc: 0.8684, loss_cls: 1.8991, loss: 1.8991 +2024-12-31 17:57:14,205 - pyskl - INFO - Epoch [146][1600/3746] lr: 2.292e-04, eta: 4:04:45, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.6577, top5_acc: 0.8712, loss_cls: 1.9146, loss: 1.9146 +2024-12-31 17:58:39,022 - pyskl - INFO - Epoch [146][1700/3746] lr: 2.265e-04, eta: 4:03:20, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6650, top5_acc: 0.8667, loss_cls: 1.9163, loss: 1.9163 +2024-12-31 18:00:03,641 - pyskl - INFO - Epoch [146][1800/3746] lr: 2.239e-04, eta: 4:01:54, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6494, top5_acc: 0.8761, loss_cls: 1.9208, loss: 1.9208 +2024-12-31 18:01:27,937 - pyskl - INFO - Epoch [146][1900/3746] lr: 2.212e-04, eta: 4:00:28, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6522, top5_acc: 0.8684, loss_cls: 1.9410, loss: 1.9410 +2024-12-31 18:02:52,544 - pyskl - INFO - Epoch [146][2000/3746] lr: 2.186e-04, eta: 3:59:02, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6522, top5_acc: 0.8650, loss_cls: 1.9628, loss: 1.9628 +2024-12-31 18:04:17,392 - pyskl - INFO - Epoch [146][2100/3746] lr: 2.160e-04, eta: 3:57:37, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6600, top5_acc: 0.8748, loss_cls: 1.8992, loss: 1.8992 +2024-12-31 18:05:42,153 - pyskl - INFO - Epoch [146][2200/3746] lr: 2.134e-04, eta: 3:56:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6613, top5_acc: 0.8636, loss_cls: 1.9290, loss: 1.9290 +2024-12-31 18:07:07,368 - pyskl - INFO - Epoch [146][2300/3746] lr: 2.108e-04, eta: 3:54:45, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6603, top5_acc: 0.8683, loss_cls: 1.9295, loss: 1.9295 +2024-12-31 18:08:32,694 - pyskl - INFO - Epoch [146][2400/3746] lr: 2.083e-04, eta: 3:53:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6558, top5_acc: 0.8662, loss_cls: 1.9514, loss: 1.9514 +2024-12-31 18:09:57,358 - pyskl - INFO - Epoch [146][2500/3746] lr: 2.057e-04, eta: 3:51:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6606, top5_acc: 0.8656, loss_cls: 1.9399, loss: 1.9399 +2024-12-31 18:11:22,196 - pyskl - INFO - Epoch [146][2600/3746] lr: 2.032e-04, eta: 3:50:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6556, top5_acc: 0.8630, loss_cls: 1.9489, loss: 1.9489 +2024-12-31 18:12:47,212 - pyskl - INFO - Epoch [146][2700/3746] lr: 2.007e-04, eta: 3:49:02, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6550, top5_acc: 0.8616, loss_cls: 1.9496, loss: 1.9496 +2024-12-31 18:14:11,421 - pyskl - INFO - Epoch [146][2800/3746] lr: 1.982e-04, eta: 3:47:36, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6525, top5_acc: 0.8673, loss_cls: 1.9384, loss: 1.9384 +2024-12-31 18:15:35,859 - pyskl - INFO - Epoch [146][2900/3746] lr: 1.957e-04, eta: 3:46:11, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6627, top5_acc: 0.8762, loss_cls: 1.9039, loss: 1.9039 +2024-12-31 18:17:00,318 - pyskl - INFO - Epoch [146][3000/3746] lr: 1.933e-04, eta: 3:44:45, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6475, top5_acc: 0.8670, loss_cls: 1.9799, loss: 1.9799 +2024-12-31 18:18:25,329 - pyskl - INFO - Epoch [146][3100/3746] lr: 1.908e-04, eta: 3:43:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6491, top5_acc: 0.8630, loss_cls: 1.9612, loss: 1.9612 +2024-12-31 18:19:50,221 - pyskl - INFO - Epoch [146][3200/3746] lr: 1.884e-04, eta: 3:41:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6492, top5_acc: 0.8620, loss_cls: 1.9562, loss: 1.9562 +2024-12-31 18:21:15,034 - pyskl - INFO - Epoch [146][3300/3746] lr: 1.860e-04, eta: 3:40:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6555, top5_acc: 0.8641, loss_cls: 1.9491, loss: 1.9491 +2024-12-31 18:22:39,687 - pyskl - INFO - Epoch [146][3400/3746] lr: 1.836e-04, eta: 3:39:02, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6578, top5_acc: 0.8745, loss_cls: 1.8942, loss: 1.8942 +2024-12-31 18:24:04,779 - pyskl - INFO - Epoch [146][3500/3746] lr: 1.812e-04, eta: 3:37:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6539, top5_acc: 0.8683, loss_cls: 1.9391, loss: 1.9391 +2024-12-31 18:25:30,134 - pyskl - INFO - Epoch [146][3600/3746] lr: 1.788e-04, eta: 3:36:10, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6475, top5_acc: 0.8689, loss_cls: 1.9617, loss: 1.9617 +2024-12-31 18:26:55,208 - pyskl - INFO - Epoch [146][3700/3746] lr: 1.765e-04, eta: 3:34:44, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6634, top5_acc: 0.8734, loss_cls: 1.9001, loss: 1.9001 +2024-12-31 18:27:36,445 - pyskl - INFO - Saving checkpoint at 146 epochs +2024-12-31 18:29:35,618 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 18:29:36,340 - pyskl - INFO - +top1_acc 0.4710 +top5_acc 0.7204 +2024-12-31 18:29:36,340 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 18:29:36,389 - pyskl - INFO - +mean_acc 0.4708 +2024-12-31 18:29:36,394 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_145.pth was removed +2024-12-31 18:29:36,696 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_146.pth. +2024-12-31 18:29:36,698 - pyskl - INFO - Best top1_acc is 0.4710 at 146 epoch. +2024-12-31 18:29:36,714 - pyskl - INFO - Epoch(val) [146][309] top1_acc: 0.4710, top5_acc: 0.7204, mean_class_accuracy: 0.4708 +2024-12-31 18:33:51,951 - pyskl - INFO - Epoch [147][100/3746] lr: 1.730e-04, eta: 3:32:43, time: 2.552, data_time: 1.517, memory: 15990, top1_acc: 0.6709, top5_acc: 0.8714, loss_cls: 1.8880, loss: 1.8880 +2024-12-31 18:35:17,420 - pyskl - INFO - Epoch [147][200/3746] lr: 1.707e-04, eta: 3:31:17, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6736, top5_acc: 0.8736, loss_cls: 1.8801, loss: 1.8801 +2024-12-31 18:36:42,393 - pyskl - INFO - Epoch [147][300/3746] lr: 1.684e-04, eta: 3:29:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6723, top5_acc: 0.8748, loss_cls: 1.8634, loss: 1.8634 +2024-12-31 18:38:06,780 - pyskl - INFO - Epoch [147][400/3746] lr: 1.661e-04, eta: 3:28:26, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6859, top5_acc: 0.8816, loss_cls: 1.8445, loss: 1.8445 +2024-12-31 18:39:31,109 - pyskl - INFO - Epoch [147][500/3746] lr: 1.639e-04, eta: 3:27:00, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6697, top5_acc: 0.8759, loss_cls: 1.8829, loss: 1.8829 +2024-12-31 18:40:55,582 - pyskl - INFO - Epoch [147][600/3746] lr: 1.616e-04, eta: 3:25:34, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6641, top5_acc: 0.8659, loss_cls: 1.9161, loss: 1.9161 +2024-12-31 18:42:20,154 - pyskl - INFO - Epoch [147][700/3746] lr: 1.594e-04, eta: 3:24:08, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6745, top5_acc: 0.8805, loss_cls: 1.8528, loss: 1.8528 +2024-12-31 18:43:44,598 - pyskl - INFO - Epoch [147][800/3746] lr: 1.572e-04, eta: 3:22:42, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6567, top5_acc: 0.8708, loss_cls: 1.9160, loss: 1.9160 +2024-12-31 18:45:08,826 - pyskl - INFO - Epoch [147][900/3746] lr: 1.550e-04, eta: 3:21:17, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6605, top5_acc: 0.8661, loss_cls: 1.9144, loss: 1.9144 +2024-12-31 18:46:33,998 - pyskl - INFO - Epoch [147][1000/3746] lr: 1.528e-04, eta: 3:19:51, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6606, top5_acc: 0.8672, loss_cls: 1.9138, loss: 1.9138 +2024-12-31 18:47:58,498 - pyskl - INFO - Epoch [147][1100/3746] lr: 1.506e-04, eta: 3:18:25, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6639, top5_acc: 0.8734, loss_cls: 1.8960, loss: 1.8960 +2024-12-31 18:49:23,222 - pyskl - INFO - Epoch [147][1200/3746] lr: 1.484e-04, eta: 3:16:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6755, top5_acc: 0.8738, loss_cls: 1.8981, loss: 1.8981 +2024-12-31 18:50:48,082 - pyskl - INFO - Epoch [147][1300/3746] lr: 1.463e-04, eta: 3:15:34, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6708, top5_acc: 0.8722, loss_cls: 1.8840, loss: 1.8840 +2024-12-31 18:52:12,566 - pyskl - INFO - Epoch [147][1400/3746] lr: 1.442e-04, eta: 3:14:08, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6591, top5_acc: 0.8705, loss_cls: 1.9057, loss: 1.9057 +2024-12-31 18:53:37,588 - pyskl - INFO - Epoch [147][1500/3746] lr: 1.420e-04, eta: 3:12:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6669, top5_acc: 0.8781, loss_cls: 1.8822, loss: 1.8822 +2024-12-31 18:55:02,205 - pyskl - INFO - Epoch [147][1600/3746] lr: 1.399e-04, eta: 3:11:16, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6647, top5_acc: 0.8717, loss_cls: 1.9152, loss: 1.9152 +2024-12-31 18:56:27,117 - pyskl - INFO - Epoch [147][1700/3746] lr: 1.379e-04, eta: 3:09:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6577, top5_acc: 0.8662, loss_cls: 1.9382, loss: 1.9382 +2024-12-31 18:57:51,949 - pyskl - INFO - Epoch [147][1800/3746] lr: 1.358e-04, eta: 3:08:25, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6636, top5_acc: 0.8733, loss_cls: 1.8906, loss: 1.8906 +2024-12-31 18:59:16,138 - pyskl - INFO - Epoch [147][1900/3746] lr: 1.337e-04, eta: 3:06:59, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6647, top5_acc: 0.8717, loss_cls: 1.9066, loss: 1.9066 +2024-12-31 19:00:40,015 - pyskl - INFO - Epoch [147][2000/3746] lr: 1.317e-04, eta: 3:05:33, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.6742, top5_acc: 0.8697, loss_cls: 1.8761, loss: 1.8761 +2024-12-31 19:02:04,619 - pyskl - INFO - Epoch [147][2100/3746] lr: 1.297e-04, eta: 3:04:07, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6527, top5_acc: 0.8730, loss_cls: 1.9267, loss: 1.9267 +2024-12-31 19:03:29,115 - pyskl - INFO - Epoch [147][2200/3746] lr: 1.277e-04, eta: 3:02:42, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6617, top5_acc: 0.8664, loss_cls: 1.9185, loss: 1.9185 +2024-12-31 19:04:53,563 - pyskl - INFO - Epoch [147][2300/3746] lr: 1.257e-04, eta: 3:01:16, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6683, top5_acc: 0.8748, loss_cls: 1.8808, loss: 1.8808 +2024-12-31 19:06:18,287 - pyskl - INFO - Epoch [147][2400/3746] lr: 1.237e-04, eta: 2:59:50, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.6622, top5_acc: 0.8769, loss_cls: 1.8901, loss: 1.8901 +2024-12-31 19:07:43,031 - pyskl - INFO - Epoch [147][2500/3746] lr: 1.218e-04, eta: 2:58:24, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6656, top5_acc: 0.8773, loss_cls: 1.8706, loss: 1.8706 +2024-12-31 19:09:07,360 - pyskl - INFO - Epoch [147][2600/3746] lr: 1.198e-04, eta: 2:56:58, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6625, top5_acc: 0.8731, loss_cls: 1.9100, loss: 1.9100 +2024-12-31 19:10:31,853 - pyskl - INFO - Epoch [147][2700/3746] lr: 1.179e-04, eta: 2:55:33, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6422, top5_acc: 0.8638, loss_cls: 1.9812, loss: 1.9812 +2024-12-31 19:11:56,517 - pyskl - INFO - Epoch [147][2800/3746] lr: 1.160e-04, eta: 2:54:07, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6469, top5_acc: 0.8709, loss_cls: 1.9371, loss: 1.9371 +2024-12-31 19:13:21,674 - pyskl - INFO - Epoch [147][2900/3746] lr: 1.141e-04, eta: 2:52:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6689, top5_acc: 0.8708, loss_cls: 1.8899, loss: 1.8899 +2024-12-31 19:14:46,596 - pyskl - INFO - Epoch [147][3000/3746] lr: 1.122e-04, eta: 2:51:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6592, top5_acc: 0.8688, loss_cls: 1.9334, loss: 1.9334 +2024-12-31 19:16:11,066 - pyskl - INFO - Epoch [147][3100/3746] lr: 1.103e-04, eta: 2:49:50, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6581, top5_acc: 0.8717, loss_cls: 1.9103, loss: 1.9103 +2024-12-31 19:17:35,690 - pyskl - INFO - Epoch [147][3200/3746] lr: 1.085e-04, eta: 2:48:24, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6669, top5_acc: 0.8695, loss_cls: 1.8996, loss: 1.8996 +2024-12-31 19:19:00,458 - pyskl - INFO - Epoch [147][3300/3746] lr: 1.067e-04, eta: 2:46:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6639, top5_acc: 0.8780, loss_cls: 1.8779, loss: 1.8779 +2024-12-31 19:20:25,044 - pyskl - INFO - Epoch [147][3400/3746] lr: 1.048e-04, eta: 2:45:32, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6591, top5_acc: 0.8705, loss_cls: 1.8791, loss: 1.8791 +2024-12-31 19:21:50,000 - pyskl - INFO - Epoch [147][3500/3746] lr: 1.030e-04, eta: 2:44:07, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6755, top5_acc: 0.8764, loss_cls: 1.8527, loss: 1.8527 +2024-12-31 19:23:15,041 - pyskl - INFO - Epoch [147][3600/3746] lr: 1.013e-04, eta: 2:42:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6727, top5_acc: 0.8784, loss_cls: 1.8515, loss: 1.8515 +2024-12-31 19:24:39,982 - pyskl - INFO - Epoch [147][3700/3746] lr: 9.949e-05, eta: 2:41:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6666, top5_acc: 0.8780, loss_cls: 1.8770, loss: 1.8770 +2024-12-31 19:25:20,638 - pyskl - INFO - Saving checkpoint at 147 epochs +2024-12-31 19:27:19,731 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 19:27:20,865 - pyskl - INFO - +top1_acc 0.4693 +top5_acc 0.7185 +2024-12-31 19:27:20,865 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 19:27:20,908 - pyskl - INFO - +mean_acc 0.4691 +2024-12-31 19:27:20,925 - pyskl - INFO - Epoch(val) [147][309] top1_acc: 0.4693, top5_acc: 0.7185, mean_class_accuracy: 0.4691 +2024-12-31 19:31:33,725 - pyskl - INFO - Epoch [148][100/3746] lr: 9.693e-05, eta: 2:39:12, time: 2.528, data_time: 1.502, memory: 15990, top1_acc: 0.6642, top5_acc: 0.8716, loss_cls: 1.8898, loss: 1.8898 +2024-12-31 19:32:59,766 - pyskl - INFO - Epoch [148][200/3746] lr: 9.520e-05, eta: 2:37:47, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6709, top5_acc: 0.8764, loss_cls: 1.8709, loss: 1.8709 +2024-12-31 19:34:25,459 - pyskl - INFO - Epoch [148][300/3746] lr: 9.348e-05, eta: 2:36:21, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6722, top5_acc: 0.8752, loss_cls: 1.8746, loss: 1.8746 +2024-12-31 19:35:50,482 - pyskl - INFO - Epoch [148][400/3746] lr: 9.178e-05, eta: 2:34:55, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.6720, top5_acc: 0.8756, loss_cls: 1.8782, loss: 1.8782 +2024-12-31 19:37:16,140 - pyskl - INFO - Epoch [148][500/3746] lr: 9.010e-05, eta: 2:33:29, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6702, top5_acc: 0.8792, loss_cls: 1.8522, loss: 1.8522 +2024-12-31 19:38:40,507 - pyskl - INFO - Epoch [148][600/3746] lr: 8.843e-05, eta: 2:32:03, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6784, top5_acc: 0.8773, loss_cls: 1.8607, loss: 1.8607 +2024-12-31 19:40:05,533 - pyskl - INFO - Epoch [148][700/3746] lr: 8.678e-05, eta: 2:30:38, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6725, top5_acc: 0.8753, loss_cls: 1.8605, loss: 1.8605 +2024-12-31 19:41:30,097 - pyskl - INFO - Epoch [148][800/3746] lr: 8.514e-05, eta: 2:29:12, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6758, top5_acc: 0.8794, loss_cls: 1.8515, loss: 1.8515 +2024-12-31 19:42:54,610 - pyskl - INFO - Epoch [148][900/3746] lr: 8.351e-05, eta: 2:27:46, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6756, top5_acc: 0.8798, loss_cls: 1.8639, loss: 1.8639 +2024-12-31 19:44:19,484 - pyskl - INFO - Epoch [148][1000/3746] lr: 8.191e-05, eta: 2:26:20, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6656, top5_acc: 0.8803, loss_cls: 1.8451, loss: 1.8451 +2024-12-31 19:45:43,852 - pyskl - INFO - Epoch [148][1100/3746] lr: 8.031e-05, eta: 2:24:55, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6687, top5_acc: 0.8741, loss_cls: 1.8690, loss: 1.8690 +2024-12-31 19:47:08,024 - pyskl - INFO - Epoch [148][1200/3746] lr: 7.874e-05, eta: 2:23:29, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6677, top5_acc: 0.8784, loss_cls: 1.8618, loss: 1.8618 +2024-12-31 19:48:32,524 - pyskl - INFO - Epoch [148][1300/3746] lr: 7.718e-05, eta: 2:22:03, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6769, top5_acc: 0.8756, loss_cls: 1.8366, loss: 1.8366 +2024-12-31 19:49:56,917 - pyskl - INFO - Epoch [148][1400/3746] lr: 7.563e-05, eta: 2:20:37, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6742, top5_acc: 0.8808, loss_cls: 1.8681, loss: 1.8681 +2024-12-31 19:51:21,551 - pyskl - INFO - Epoch [148][1500/3746] lr: 7.410e-05, eta: 2:19:11, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6713, top5_acc: 0.8769, loss_cls: 1.8698, loss: 1.8698 +2024-12-31 19:52:46,644 - pyskl - INFO - Epoch [148][1600/3746] lr: 7.259e-05, eta: 2:17:46, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.6697, top5_acc: 0.8755, loss_cls: 1.8931, loss: 1.8931 +2024-12-31 19:54:11,484 - pyskl - INFO - Epoch [148][1700/3746] lr: 7.109e-05, eta: 2:16:20, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.6695, top5_acc: 0.8750, loss_cls: 1.8523, loss: 1.8523 +2024-12-31 19:55:35,982 - pyskl - INFO - Epoch [148][1800/3746] lr: 6.961e-05, eta: 2:14:54, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6698, top5_acc: 0.8791, loss_cls: 1.8538, loss: 1.8538 +2024-12-31 19:57:00,781 - pyskl - INFO - Epoch [148][1900/3746] lr: 6.814e-05, eta: 2:13:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6678, top5_acc: 0.8744, loss_cls: 1.9025, loss: 1.9025 +2024-12-31 19:58:26,196 - pyskl - INFO - Epoch [148][2000/3746] lr: 6.669e-05, eta: 2:12:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6733, top5_acc: 0.8773, loss_cls: 1.8590, loss: 1.8590 +2024-12-31 19:59:51,586 - pyskl - INFO - Epoch [148][2100/3746] lr: 6.526e-05, eta: 2:10:37, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6634, top5_acc: 0.8755, loss_cls: 1.8908, loss: 1.8908 +2024-12-31 20:01:17,304 - pyskl - INFO - Epoch [148][2200/3746] lr: 6.384e-05, eta: 2:09:11, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6753, top5_acc: 0.8794, loss_cls: 1.8362, loss: 1.8362 +2024-12-31 20:02:42,961 - pyskl - INFO - Epoch [148][2300/3746] lr: 6.243e-05, eta: 2:07:45, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6609, top5_acc: 0.8798, loss_cls: 1.8835, loss: 1.8835 +2024-12-31 20:04:08,229 - pyskl - INFO - Epoch [148][2400/3746] lr: 6.104e-05, eta: 2:06:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6581, top5_acc: 0.8692, loss_cls: 1.9138, loss: 1.9138 +2024-12-31 20:05:33,109 - pyskl - INFO - Epoch [148][2500/3746] lr: 5.967e-05, eta: 2:04:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6664, top5_acc: 0.8741, loss_cls: 1.8869, loss: 1.8869 +2024-12-31 20:06:57,870 - pyskl - INFO - Epoch [148][2600/3746] lr: 5.831e-05, eta: 2:03:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6742, top5_acc: 0.8756, loss_cls: 1.8884, loss: 1.8884 +2024-12-31 20:08:22,782 - pyskl - INFO - Epoch [148][2700/3746] lr: 5.697e-05, eta: 2:02:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6809, top5_acc: 0.8811, loss_cls: 1.8263, loss: 1.8263 +2024-12-31 20:09:47,914 - pyskl - INFO - Epoch [148][2800/3746] lr: 5.564e-05, eta: 2:00:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6697, top5_acc: 0.8791, loss_cls: 1.8643, loss: 1.8643 +2024-12-31 20:11:12,642 - pyskl - INFO - Epoch [148][2900/3746] lr: 5.433e-05, eta: 1:59:11, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6664, top5_acc: 0.8727, loss_cls: 1.8756, loss: 1.8756 +2024-12-31 20:12:37,115 - pyskl - INFO - Epoch [148][3000/3746] lr: 5.304e-05, eta: 1:57:45, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6667, top5_acc: 0.8698, loss_cls: 1.8956, loss: 1.8956 +2024-12-31 20:14:01,571 - pyskl - INFO - Epoch [148][3100/3746] lr: 5.176e-05, eta: 1:56:19, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6664, top5_acc: 0.8734, loss_cls: 1.8805, loss: 1.8805 +2024-12-31 20:15:26,484 - pyskl - INFO - Epoch [148][3200/3746] lr: 5.050e-05, eta: 1:54:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6747, top5_acc: 0.8850, loss_cls: 1.8359, loss: 1.8359 +2024-12-31 20:16:51,525 - pyskl - INFO - Epoch [148][3300/3746] lr: 4.925e-05, eta: 1:53:27, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6728, top5_acc: 0.8772, loss_cls: 1.8443, loss: 1.8443 +2024-12-31 20:18:16,304 - pyskl - INFO - Epoch [148][3400/3746] lr: 4.801e-05, eta: 1:52:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6653, top5_acc: 0.8716, loss_cls: 1.8823, loss: 1.8823 +2024-12-31 20:19:40,930 - pyskl - INFO - Epoch [148][3500/3746] lr: 4.680e-05, eta: 1:50:36, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6720, top5_acc: 0.8750, loss_cls: 1.8827, loss: 1.8827 +2024-12-31 20:21:06,142 - pyskl - INFO - Epoch [148][3600/3746] lr: 4.560e-05, eta: 1:49:10, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6717, top5_acc: 0.8816, loss_cls: 1.8424, loss: 1.8424 +2024-12-31 20:22:31,446 - pyskl - INFO - Epoch [148][3700/3746] lr: 4.441e-05, eta: 1:47:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6708, top5_acc: 0.8777, loss_cls: 1.8483, loss: 1.8483 +2024-12-31 20:23:12,312 - pyskl - INFO - Saving checkpoint at 148 epochs +2024-12-31 20:25:10,246 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 20:25:11,282 - pyskl - INFO - +top1_acc 0.4716 +top5_acc 0.7182 +2024-12-31 20:25:11,282 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 20:25:11,322 - pyskl - INFO - +mean_acc 0.4714 +2024-12-31 20:25:11,329 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_146.pth was removed +2024-12-31 20:25:11,593 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_148.pth. +2024-12-31 20:25:11,593 - pyskl - INFO - Best top1_acc is 0.4716 at 148 epoch. +2024-12-31 20:25:11,606 - pyskl - INFO - Epoch(val) [148][309] top1_acc: 0.4716, top5_acc: 0.7182, mean_class_accuracy: 0.4714 +2024-12-31 20:29:23,537 - pyskl - INFO - Epoch [149][100/3746] lr: 4.271e-05, eta: 1:45:41, time: 2.519, data_time: 1.496, memory: 15990, top1_acc: 0.6763, top5_acc: 0.8861, loss_cls: 1.8117, loss: 1.8117 +2024-12-31 20:30:49,171 - pyskl - INFO - Epoch [149][200/3746] lr: 4.156e-05, eta: 1:44:15, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6714, top5_acc: 0.8811, loss_cls: 1.8417, loss: 1.8417 +2024-12-31 20:32:14,424 - pyskl - INFO - Epoch [149][300/3746] lr: 4.043e-05, eta: 1:42:49, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6794, top5_acc: 0.8828, loss_cls: 1.8230, loss: 1.8230 +2024-12-31 20:33:39,473 - pyskl - INFO - Epoch [149][400/3746] lr: 3.931e-05, eta: 1:41:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6763, top5_acc: 0.8836, loss_cls: 1.8330, loss: 1.8330 +2024-12-31 20:35:03,961 - pyskl - INFO - Epoch [149][500/3746] lr: 3.821e-05, eta: 1:39:58, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6784, top5_acc: 0.8816, loss_cls: 1.8250, loss: 1.8250 +2024-12-31 20:36:27,756 - pyskl - INFO - Epoch [149][600/3746] lr: 3.713e-05, eta: 1:38:32, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.6709, top5_acc: 0.8773, loss_cls: 1.8543, loss: 1.8543 +2024-12-31 20:37:52,020 - pyskl - INFO - Epoch [149][700/3746] lr: 3.606e-05, eta: 1:37:06, time: 0.843, data_time: 0.001, memory: 15990, top1_acc: 0.6823, top5_acc: 0.8795, loss_cls: 1.8405, loss: 1.8405 +2024-12-31 20:39:17,344 - pyskl - INFO - Epoch [149][800/3746] lr: 3.500e-05, eta: 1:35:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6686, top5_acc: 0.8773, loss_cls: 1.8718, loss: 1.8718 +2024-12-31 20:40:41,973 - pyskl - INFO - Epoch [149][900/3746] lr: 3.397e-05, eta: 1:34:14, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6803, top5_acc: 0.8852, loss_cls: 1.8142, loss: 1.8142 +2024-12-31 20:42:06,739 - pyskl - INFO - Epoch [149][1000/3746] lr: 3.294e-05, eta: 1:32:49, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6716, top5_acc: 0.8805, loss_cls: 1.8624, loss: 1.8624 +2024-12-31 20:43:31,244 - pyskl - INFO - Epoch [149][1100/3746] lr: 3.194e-05, eta: 1:31:23, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6764, top5_acc: 0.8791, loss_cls: 1.8330, loss: 1.8330 +2024-12-31 20:44:55,916 - pyskl - INFO - Epoch [149][1200/3746] lr: 3.095e-05, eta: 1:29:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6789, top5_acc: 0.8784, loss_cls: 1.8430, loss: 1.8430 +2024-12-31 20:46:20,664 - pyskl - INFO - Epoch [149][1300/3746] lr: 2.997e-05, eta: 1:28:31, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6748, top5_acc: 0.8792, loss_cls: 1.8534, loss: 1.8534 +2024-12-31 20:47:45,495 - pyskl - INFO - Epoch [149][1400/3746] lr: 2.901e-05, eta: 1:27:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6700, top5_acc: 0.8752, loss_cls: 1.8576, loss: 1.8576 +2024-12-31 20:49:10,912 - pyskl - INFO - Epoch [149][1500/3746] lr: 2.807e-05, eta: 1:25:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6766, top5_acc: 0.8734, loss_cls: 1.8733, loss: 1.8733 +2024-12-31 20:50:35,998 - pyskl - INFO - Epoch [149][1600/3746] lr: 2.714e-05, eta: 1:24:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6736, top5_acc: 0.8781, loss_cls: 1.8619, loss: 1.8619 +2024-12-31 20:52:00,641 - pyskl - INFO - Epoch [149][1700/3746] lr: 2.622e-05, eta: 1:22:48, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.6819, top5_acc: 0.8817, loss_cls: 1.8144, loss: 1.8144 +2024-12-31 20:53:25,663 - pyskl - INFO - Epoch [149][1800/3746] lr: 2.533e-05, eta: 1:21:22, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6708, top5_acc: 0.8752, loss_cls: 1.8689, loss: 1.8689 +2024-12-31 20:54:50,273 - pyskl - INFO - Epoch [149][1900/3746] lr: 2.444e-05, eta: 1:19:56, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6717, top5_acc: 0.8822, loss_cls: 1.8423, loss: 1.8423 +2024-12-31 20:56:14,540 - pyskl - INFO - Epoch [149][2000/3746] lr: 2.358e-05, eta: 1:18:31, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6727, top5_acc: 0.8764, loss_cls: 1.8663, loss: 1.8663 +2024-12-31 20:57:39,057 - pyskl - INFO - Epoch [149][2100/3746] lr: 2.273e-05, eta: 1:17:05, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6719, top5_acc: 0.8767, loss_cls: 1.8940, loss: 1.8940 +2024-12-31 20:59:03,394 - pyskl - INFO - Epoch [149][2200/3746] lr: 2.189e-05, eta: 1:15:39, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6794, top5_acc: 0.8814, loss_cls: 1.8279, loss: 1.8279 +2024-12-31 21:00:27,933 - pyskl - INFO - Epoch [149][2300/3746] lr: 2.107e-05, eta: 1:14:13, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6752, top5_acc: 0.8755, loss_cls: 1.8664, loss: 1.8664 +2024-12-31 21:01:52,374 - pyskl - INFO - Epoch [149][2400/3746] lr: 2.027e-05, eta: 1:12:47, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6748, top5_acc: 0.8758, loss_cls: 1.8674, loss: 1.8674 +2024-12-31 21:03:17,130 - pyskl - INFO - Epoch [149][2500/3746] lr: 1.948e-05, eta: 1:11:22, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6794, top5_acc: 0.8770, loss_cls: 1.8596, loss: 1.8596 +2024-12-31 21:04:41,547 - pyskl - INFO - Epoch [149][2600/3746] lr: 1.871e-05, eta: 1:09:56, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.6802, top5_acc: 0.8770, loss_cls: 1.8446, loss: 1.8446 +2024-12-31 21:06:06,522 - pyskl - INFO - Epoch [149][2700/3746] lr: 1.795e-05, eta: 1:08:30, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6720, top5_acc: 0.8744, loss_cls: 1.8648, loss: 1.8648 +2024-12-31 21:07:30,711 - pyskl - INFO - Epoch [149][2800/3746] lr: 1.721e-05, eta: 1:07:04, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6747, top5_acc: 0.8817, loss_cls: 1.8598, loss: 1.8598 +2024-12-31 21:08:55,166 - pyskl - INFO - Epoch [149][2900/3746] lr: 1.649e-05, eta: 1:05:39, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6905, top5_acc: 0.8838, loss_cls: 1.7896, loss: 1.7896 +2024-12-31 21:10:19,607 - pyskl - INFO - Epoch [149][3000/3746] lr: 1.578e-05, eta: 1:04:13, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6770, top5_acc: 0.8823, loss_cls: 1.8404, loss: 1.8404 +2024-12-31 21:11:44,457 - pyskl - INFO - Epoch [149][3100/3746] lr: 1.508e-05, eta: 1:02:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6713, top5_acc: 0.8777, loss_cls: 1.8493, loss: 1.8493 +2024-12-31 21:13:09,077 - pyskl - INFO - Epoch [149][3200/3746] lr: 1.440e-05, eta: 1:01:21, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6791, top5_acc: 0.8812, loss_cls: 1.8413, loss: 1.8413 +2024-12-31 21:14:33,618 - pyskl - INFO - Epoch [149][3300/3746] lr: 1.374e-05, eta: 0:59:55, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6692, top5_acc: 0.8834, loss_cls: 1.8683, loss: 1.8683 +2024-12-31 21:15:57,928 - pyskl - INFO - Epoch [149][3400/3746] lr: 1.309e-05, eta: 0:58:30, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6702, top5_acc: 0.8709, loss_cls: 1.9014, loss: 1.9014 +2024-12-31 21:17:22,448 - pyskl - INFO - Epoch [149][3500/3746] lr: 1.246e-05, eta: 0:57:04, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6761, top5_acc: 0.8806, loss_cls: 1.8467, loss: 1.8467 +2024-12-31 21:18:47,351 - pyskl - INFO - Epoch [149][3600/3746] lr: 1.184e-05, eta: 0:55:38, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6805, top5_acc: 0.8803, loss_cls: 1.8241, loss: 1.8241 +2024-12-31 21:20:12,325 - pyskl - INFO - Epoch [149][3700/3746] lr: 1.124e-05, eta: 0:54:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6705, top5_acc: 0.8755, loss_cls: 1.8591, loss: 1.8591 +2024-12-31 21:20:53,029 - pyskl - INFO - Saving checkpoint at 149 epochs +2024-12-31 21:22:49,782 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 21:22:50,476 - pyskl - INFO - +top1_acc 0.4726 +top5_acc 0.7188 +2024-12-31 21:22:50,476 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 21:22:50,523 - pyskl - INFO - +mean_acc 0.4723 +2024-12-31 21:22:50,528 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_148.pth was removed +2024-12-31 21:22:50,817 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_149.pth. +2024-12-31 21:22:50,817 - pyskl - INFO - Best top1_acc is 0.4726 at 149 epoch. +2024-12-31 21:22:50,847 - pyskl - INFO - Epoch(val) [149][309] top1_acc: 0.4726, top5_acc: 0.7188, mean_class_accuracy: 0.4723 +2024-12-31 21:27:02,898 - pyskl - INFO - Epoch [150][100/3746] lr: 1.039e-05, eta: 0:52:08, time: 2.520, data_time: 1.479, memory: 15990, top1_acc: 0.6775, top5_acc: 0.8803, loss_cls: 1.8161, loss: 1.8161 +2024-12-31 21:28:28,621 - pyskl - INFO - Epoch [150][200/3746] lr: 9.832e-06, eta: 0:50:42, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6795, top5_acc: 0.8870, loss_cls: 1.8301, loss: 1.8301 +2024-12-31 21:29:53,404 - pyskl - INFO - Epoch [150][300/3746] lr: 9.285e-06, eta: 0:49:16, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6755, top5_acc: 0.8812, loss_cls: 1.8421, loss: 1.8421 +2024-12-31 21:31:18,154 - pyskl - INFO - Epoch [150][400/3746] lr: 8.754e-06, eta: 0:47:50, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6778, top5_acc: 0.8788, loss_cls: 1.8455, loss: 1.8455 +2024-12-31 21:32:43,826 - pyskl - INFO - Epoch [150][500/3746] lr: 8.239e-06, eta: 0:46:25, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.6770, top5_acc: 0.8786, loss_cls: 1.8466, loss: 1.8466 +2024-12-31 21:34:08,247 - pyskl - INFO - Epoch [150][600/3746] lr: 7.739e-06, eta: 0:44:59, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6763, top5_acc: 0.8811, loss_cls: 1.8310, loss: 1.8310 +2024-12-31 21:35:32,883 - pyskl - INFO - Epoch [150][700/3746] lr: 7.255e-06, eta: 0:43:33, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6737, top5_acc: 0.8812, loss_cls: 1.8458, loss: 1.8458 +2024-12-31 21:36:57,099 - pyskl - INFO - Epoch [150][800/3746] lr: 6.787e-06, eta: 0:42:07, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6773, top5_acc: 0.8805, loss_cls: 1.8254, loss: 1.8254 +2024-12-31 21:38:21,373 - pyskl - INFO - Epoch [150][900/3746] lr: 6.334e-06, eta: 0:40:41, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6848, top5_acc: 0.8855, loss_cls: 1.8075, loss: 1.8075 +2024-12-31 21:39:46,224 - pyskl - INFO - Epoch [150][1000/3746] lr: 5.897e-06, eta: 0:39:16, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6819, top5_acc: 0.8762, loss_cls: 1.8290, loss: 1.8290 +2024-12-31 21:41:10,951 - pyskl - INFO - Epoch [150][1100/3746] lr: 5.475e-06, eta: 0:37:50, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6881, top5_acc: 0.8897, loss_cls: 1.8145, loss: 1.8145 +2024-12-31 21:42:35,356 - pyskl - INFO - Epoch [150][1200/3746] lr: 5.070e-06, eta: 0:36:24, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6723, top5_acc: 0.8788, loss_cls: 1.8454, loss: 1.8454 +2024-12-31 21:44:00,229 - pyskl - INFO - Epoch [150][1300/3746] lr: 4.679e-06, eta: 0:34:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6831, top5_acc: 0.8784, loss_cls: 1.8211, loss: 1.8211 +2024-12-31 21:45:24,561 - pyskl - INFO - Epoch [150][1400/3746] lr: 4.305e-06, eta: 0:33:32, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6875, top5_acc: 0.8855, loss_cls: 1.7745, loss: 1.7745 +2024-12-31 21:46:49,052 - pyskl - INFO - Epoch [150][1500/3746] lr: 3.946e-06, eta: 0:32:07, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6723, top5_acc: 0.8747, loss_cls: 1.8664, loss: 1.8664 +2024-12-31 21:48:13,827 - pyskl - INFO - Epoch [150][1600/3746] lr: 3.602e-06, eta: 0:30:41, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6755, top5_acc: 0.8742, loss_cls: 1.8699, loss: 1.8699 +2024-12-31 21:49:38,173 - pyskl - INFO - Epoch [150][1700/3746] lr: 3.275e-06, eta: 0:29:15, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6750, top5_acc: 0.8788, loss_cls: 1.8508, loss: 1.8508 +2024-12-31 21:51:02,840 - pyskl - INFO - Epoch [150][1800/3746] lr: 2.962e-06, eta: 0:27:49, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6742, top5_acc: 0.8766, loss_cls: 1.8651, loss: 1.8651 +2024-12-31 21:52:27,687 - pyskl - INFO - Epoch [150][1900/3746] lr: 2.666e-06, eta: 0:26:23, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6773, top5_acc: 0.8839, loss_cls: 1.8425, loss: 1.8425 +2024-12-31 21:53:52,174 - pyskl - INFO - Epoch [150][2000/3746] lr: 2.385e-06, eta: 0:24:58, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6781, top5_acc: 0.8778, loss_cls: 1.8603, loss: 1.8603 +2024-12-31 21:55:16,198 - pyskl - INFO - Epoch [150][2100/3746] lr: 2.120e-06, eta: 0:23:32, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.6848, top5_acc: 0.8838, loss_cls: 1.7962, loss: 1.7962 +2024-12-31 21:56:40,735 - pyskl - INFO - Epoch [150][2200/3746] lr: 1.870e-06, eta: 0:22:06, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6827, top5_acc: 0.8775, loss_cls: 1.8509, loss: 1.8509 +2024-12-31 21:58:05,633 - pyskl - INFO - Epoch [150][2300/3746] lr: 1.636e-06, eta: 0:20:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6739, top5_acc: 0.8798, loss_cls: 1.8477, loss: 1.8477 +2024-12-31 21:59:30,794 - pyskl - INFO - Epoch [150][2400/3746] lr: 1.418e-06, eta: 0:19:14, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6759, top5_acc: 0.8759, loss_cls: 1.8618, loss: 1.8618 +2024-12-31 22:00:55,697 - pyskl - INFO - Epoch [150][2500/3746] lr: 1.215e-06, eta: 0:17:49, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6697, top5_acc: 0.8758, loss_cls: 1.8756, loss: 1.8756 +2024-12-31 22:02:19,849 - pyskl - INFO - Epoch [150][2600/3746] lr: 1.028e-06, eta: 0:16:23, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6734, top5_acc: 0.8789, loss_cls: 1.8368, loss: 1.8368 +2024-12-31 22:03:44,198 - pyskl - INFO - Epoch [150][2700/3746] lr: 8.567e-07, eta: 0:14:57, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6769, top5_acc: 0.8833, loss_cls: 1.8043, loss: 1.8043 +2024-12-31 22:05:08,558 - pyskl - INFO - Epoch [150][2800/3746] lr: 7.008e-07, eta: 0:13:31, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6791, top5_acc: 0.8733, loss_cls: 1.8610, loss: 1.8610 +2024-12-31 22:06:33,186 - pyskl - INFO - Epoch [150][2900/3746] lr: 5.606e-07, eta: 0:12:05, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6756, top5_acc: 0.8773, loss_cls: 1.8441, loss: 1.8441 +2024-12-31 22:07:57,295 - pyskl - INFO - Epoch [150][3000/3746] lr: 4.361e-07, eta: 0:10:40, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.6892, top5_acc: 0.8870, loss_cls: 1.7864, loss: 1.7864 +2024-12-31 22:09:22,037 - pyskl - INFO - Epoch [150][3100/3746] lr: 3.271e-07, eta: 0:09:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6780, top5_acc: 0.8784, loss_cls: 1.8312, loss: 1.8312 +2024-12-31 22:10:46,931 - pyskl - INFO - Epoch [150][3200/3746] lr: 2.338e-07, eta: 0:07:48, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6683, top5_acc: 0.8730, loss_cls: 1.8982, loss: 1.8982 +2024-12-31 22:12:11,460 - pyskl - INFO - Epoch [150][3300/3746] lr: 1.561e-07, eta: 0:06:22, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6809, top5_acc: 0.8848, loss_cls: 1.8298, loss: 1.8298 +2024-12-31 22:13:35,964 - pyskl - INFO - Epoch [150][3400/3746] lr: 9.410e-08, eta: 0:04:56, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6852, top5_acc: 0.8795, loss_cls: 1.8117, loss: 1.8117 +2024-12-31 22:15:00,269 - pyskl - INFO - Epoch [150][3500/3746] lr: 4.768e-08, eta: 0:03:31, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6725, top5_acc: 0.8803, loss_cls: 1.8461, loss: 1.8461 +2024-12-31 22:16:24,914 - pyskl - INFO - Epoch [150][3600/3746] lr: 1.689e-08, eta: 0:02:05, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6905, top5_acc: 0.8744, loss_cls: 1.8067, loss: 1.8067 +2024-12-31 22:17:49,520 - pyskl - INFO - Epoch [150][3700/3746] lr: 1.726e-09, eta: 0:00:39, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6752, top5_acc: 0.8797, loss_cls: 1.8382, loss: 1.8382 +2024-12-31 22:18:30,162 - pyskl - INFO - Saving checkpoint at 150 epochs +2024-12-31 22:20:27,862 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 22:20:28,539 - pyskl - INFO - +top1_acc 0.4721 +top5_acc 0.7180 +2024-12-31 22:20:28,539 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 22:20:28,585 - pyskl - INFO - +mean_acc 0.4718 +2024-12-31 22:20:28,600 - pyskl - INFO - Epoch(val) [150][309] top1_acc: 0.4721, top5_acc: 0.7180, mean_class_accuracy: 0.4718 +2024-12-31 22:20:47,104 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-12-31 22:33:31,828 - pyskl - INFO - Testing results of the last checkpoint +2024-12-31 22:33:31,829 - pyskl - INFO - top1_acc: 0.4818 +2024-12-31 22:33:31,829 - pyskl - INFO - top5_acc: 0.7286 +2024-12-31 22:33:31,829 - pyskl - INFO - mean_class_accuracy: 0.4816 +2024-12-31 22:33:31,830 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/k400/j_1/best_top1_acc_epoch_149.pth +2024-12-31 22:46:21,434 - pyskl - INFO - Testing results of the best checkpoint +2024-12-31 22:46:21,435 - pyskl - INFO - top1_acc: 0.4837 +2024-12-31 22:46:21,435 - pyskl - INFO - top5_acc: 0.7279 +2024-12-31 22:46:21,435 - pyskl - INFO - mean_class_accuracy: 0.4834 diff --git a/k400/j_1/20241226_014911.log.json b/k400/j_1/20241226_014911.log.json new file mode 100644 index 0000000000000000000000000000000000000000..4afeabc729f1add00a6d5913adf5dfb2e746c59a --- /dev/null +++ b/k400/j_1/20241226_014911.log.json @@ -0,0 +1,5701 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 378083612, "config_name": "j_1.py", "work_dir": "j_1", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.1, "memory": 15990, "data_time": 1.5211, "top1_acc": 0.00625, "top5_acc": 0.02969, "loss_cls": 6.47446, "loss": 6.47446, "time": 2.24474} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.1, "memory": 15990, "data_time": 0.00089, "top1_acc": 0.00922, "top5_acc": 0.04219, "loss_cls": 6.47161, "loss": 6.47161, "time": 0.71949} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.1, "memory": 15990, "data_time": 0.00074, "top1_acc": 0.01578, "top5_acc": 0.06578, "loss_cls": 6.24817, "loss": 6.24817, "time": 0.71463} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.1, "memory": 15990, "data_time": 0.00104, "top1_acc": 0.02172, "top5_acc": 0.08484, "loss_cls": 6.09014, "loss": 6.09014, "time": 0.7116} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.1, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.02391, "top5_acc": 0.09891, "loss_cls": 5.97588, "loss": 5.97588, "time": 0.71321} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.1, "memory": 15990, "data_time": 0.00078, "top1_acc": 0.02406, "top5_acc": 0.105, "loss_cls": 5.9392, "loss": 5.9392, "time": 0.71069} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.1, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.03453, "top5_acc": 0.12188, "loss_cls": 5.82203, "loss": 5.82203, "time": 0.71247} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.1, "memory": 15990, "data_time": 0.00111, "top1_acc": 0.03531, "top5_acc": 0.13078, "loss_cls": 5.77354, "loss": 5.77354, "time": 0.71407} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.1, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.03359, "top5_acc": 0.14203, "loss_cls": 5.72204, "loss": 5.72204, "time": 0.71344} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.1, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.03734, "top5_acc": 0.13938, "loss_cls": 5.71844, "loss": 5.71844, "time": 0.71357} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.1, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.04281, "top5_acc": 0.15141, "loss_cls": 5.62804, "loss": 5.62804, "time": 0.71696} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.1, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.05203, "top5_acc": 0.16344, "loss_cls": 5.62641, "loss": 5.62641, "time": 0.71204} +{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.1, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.05312, "top5_acc": 0.17484, "loss_cls": 5.59292, "loss": 5.59292, "time": 0.71289} +{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.1, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.05312, "top5_acc": 0.17703, "loss_cls": 5.55288, "loss": 5.55288, "time": 0.71351} +{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.1, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.055, "top5_acc": 0.18766, "loss_cls": 5.50022, "loss": 5.50022, "time": 0.71329} +{"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.1, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.06062, "top5_acc": 0.19656, "loss_cls": 5.50453, "loss": 5.50453, "time": 0.71489} +{"mode": "train", "epoch": 1, "iter": 1700, "lr": 0.1, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.0575, "top5_acc": 0.19391, "loss_cls": 5.46619, "loss": 5.46619, "time": 0.7145} +{"mode": "train", "epoch": 1, "iter": 1800, "lr": 0.1, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.06906, "top5_acc": 0.21578, "loss_cls": 5.39989, "loss": 5.39989, "time": 0.71359} +{"mode": "train", "epoch": 1, "iter": 1900, "lr": 0.1, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.07984, "top5_acc": 0.22203, "loss_cls": 5.34966, "loss": 5.34966, "time": 0.71351} +{"mode": "train", "epoch": 1, "iter": 2000, "lr": 0.1, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.07172, "top5_acc": 0.22359, "loss_cls": 5.38356, "loss": 5.38356, "time": 0.71294} +{"mode": "train", "epoch": 1, "iter": 2100, "lr": 0.1, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.08438, "top5_acc": 0.23047, "loss_cls": 5.32882, "loss": 5.32882, "time": 0.71456} +{"mode": "train", "epoch": 1, "iter": 2200, "lr": 0.1, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.07984, "top5_acc": 0.23344, "loss_cls": 5.32989, "loss": 5.32989, "time": 0.71481} +{"mode": "train", "epoch": 1, "iter": 2300, "lr": 0.1, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.08531, "top5_acc": 0.23391, "loss_cls": 5.29648, "loss": 5.29648, "time": 0.71335} +{"mode": "train", "epoch": 1, "iter": 2400, "lr": 0.1, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.08875, "top5_acc": 0.26078, "loss_cls": 5.20152, "loss": 5.20152, "time": 0.71168} +{"mode": "train", "epoch": 1, "iter": 2500, "lr": 0.1, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.08766, "top5_acc": 0.25734, "loss_cls": 5.22156, "loss": 5.22156, "time": 0.71535} +{"mode": "train", "epoch": 1, "iter": 2600, "lr": 0.09999, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.0875, "top5_acc": 0.26344, "loss_cls": 5.18935, "loss": 5.18935, "time": 0.71413} +{"mode": "train", "epoch": 1, "iter": 2700, "lr": 0.09999, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.09531, "top5_acc": 0.26359, "loss_cls": 5.17312, "loss": 5.17312, "time": 0.71342} +{"mode": "train", "epoch": 1, "iter": 2800, "lr": 0.09999, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.09797, "top5_acc": 0.27172, "loss_cls": 5.13008, "loss": 5.13008, "time": 0.71535} +{"mode": "train", "epoch": 1, "iter": 2900, "lr": 0.09999, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.10422, "top5_acc": 0.28297, "loss_cls": 5.12579, "loss": 5.12579, "time": 0.71588} +{"mode": "train", "epoch": 1, "iter": 3000, "lr": 0.09999, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.10578, "top5_acc": 0.28062, "loss_cls": 5.11141, "loss": 5.11141, "time": 0.71413} +{"mode": "train", "epoch": 1, "iter": 3100, "lr": 0.09999, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.11172, "top5_acc": 0.28609, "loss_cls": 5.08914, "loss": 5.08914, "time": 0.71414} +{"mode": "train", "epoch": 1, "iter": 3200, "lr": 0.09999, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.1125, "top5_acc": 0.29484, "loss_cls": 5.07369, "loss": 5.07369, "time": 0.71282} +{"mode": "train", "epoch": 1, "iter": 3300, "lr": 0.09999, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.11016, "top5_acc": 0.2975, "loss_cls": 5.03618, "loss": 5.03618, "time": 0.71461} +{"mode": "train", "epoch": 1, "iter": 3400, "lr": 0.09999, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.11625, "top5_acc": 0.30422, "loss_cls": 5.04017, "loss": 5.04017, "time": 0.71509} +{"mode": "train", "epoch": 1, "iter": 3500, "lr": 0.09999, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.12047, "top5_acc": 0.30578, "loss_cls": 5.0068, "loss": 5.0068, "time": 0.7145} +{"mode": "train", "epoch": 1, "iter": 3600, "lr": 0.09999, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.11469, "top5_acc": 0.305, "loss_cls": 4.949, "loss": 4.949, "time": 0.713} +{"mode": "train", "epoch": 1, "iter": 3700, "lr": 0.09999, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.11531, "top5_acc": 0.31062, "loss_cls": 4.99864, "loss": 4.99864, "time": 0.71128} +{"mode": "val", "epoch": 1, "iter": 309, "lr": 0.09999, "top1_acc": 0.08595, "top5_acc": 0.23841, "mean_class_accuracy": 0.08595} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.09999, "memory": 15990, "data_time": 1.46551, "top1_acc": 0.11828, "top5_acc": 0.31641, "loss_cls": 4.97312, "loss": 4.97312, "time": 2.18169} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.09999, "memory": 15990, "data_time": 0.00102, "top1_acc": 0.13031, "top5_acc": 0.33016, "loss_cls": 4.9271, "loss": 4.9271, "time": 0.72106} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.09999, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.12172, "top5_acc": 0.32062, "loss_cls": 4.96141, "loss": 4.96141, "time": 0.71789} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.09999, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.13172, "top5_acc": 0.33391, "loss_cls": 4.90739, "loss": 4.90739, "time": 0.71939} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.09999, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.13703, "top5_acc": 0.33422, "loss_cls": 4.89647, "loss": 4.89647, "time": 0.7148} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.09999, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.13359, "top5_acc": 0.32438, "loss_cls": 4.92103, "loss": 4.92103, "time": 0.71224} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.09998, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.13938, "top5_acc": 0.33422, "loss_cls": 4.85179, "loss": 4.85179, "time": 0.71216} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.09998, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.14156, "top5_acc": 0.34734, "loss_cls": 4.83828, "loss": 4.83828, "time": 0.7153} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.09998, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.13422, "top5_acc": 0.34672, "loss_cls": 4.87407, "loss": 4.87407, "time": 0.71512} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.09998, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.13812, "top5_acc": 0.33578, "loss_cls": 4.87183, "loss": 4.87183, "time": 0.71322} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.09998, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.13625, "top5_acc": 0.34453, "loss_cls": 4.84039, "loss": 4.84039, "time": 0.71347} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.09998, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.14031, "top5_acc": 0.34656, "loss_cls": 4.84788, "loss": 4.84788, "time": 0.71368} +{"mode": "train", "epoch": 2, "iter": 1300, "lr": 0.09998, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.13922, "top5_acc": 0.34109, "loss_cls": 4.83577, "loss": 4.83577, "time": 0.71553} +{"mode": "train", "epoch": 2, "iter": 1400, "lr": 0.09998, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.14031, "top5_acc": 0.35016, "loss_cls": 4.82845, "loss": 4.82845, "time": 0.71277} +{"mode": "train", "epoch": 2, "iter": 1500, "lr": 0.09998, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.14141, "top5_acc": 0.3475, "loss_cls": 4.81796, "loss": 4.81796, "time": 0.71525} +{"mode": "train", "epoch": 2, "iter": 1600, "lr": 0.09998, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.14594, "top5_acc": 0.35859, "loss_cls": 4.78603, "loss": 4.78603, "time": 0.71656} +{"mode": "train", "epoch": 2, "iter": 1700, "lr": 0.09998, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.13828, "top5_acc": 0.35391, "loss_cls": 4.7881, "loss": 4.7881, "time": 0.71518} +{"mode": "train", "epoch": 2, "iter": 1800, "lr": 0.09998, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.14984, "top5_acc": 0.35984, "loss_cls": 4.79803, "loss": 4.79803, "time": 0.71478} +{"mode": "train", "epoch": 2, "iter": 1900, "lr": 0.09998, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.14438, "top5_acc": 0.34984, "loss_cls": 4.80956, "loss": 4.80956, "time": 0.71627} +{"mode": "train", "epoch": 2, "iter": 2000, "lr": 0.09997, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.14672, "top5_acc": 0.36875, "loss_cls": 4.73786, "loss": 4.73786, "time": 0.71501} +{"mode": "train", "epoch": 2, "iter": 2100, "lr": 0.09997, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.14344, "top5_acc": 0.36609, "loss_cls": 4.77931, "loss": 4.77931, "time": 0.71336} +{"mode": "train", "epoch": 2, "iter": 2200, "lr": 0.09997, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.14797, "top5_acc": 0.36609, "loss_cls": 4.73641, "loss": 4.73641, "time": 0.71333} +{"mode": "train", "epoch": 2, "iter": 2300, "lr": 0.09997, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.15453, "top5_acc": 0.36797, "loss_cls": 4.72828, "loss": 4.72828, "time": 0.71432} +{"mode": "train", "epoch": 2, "iter": 2400, "lr": 0.09997, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.15656, "top5_acc": 0.36047, "loss_cls": 4.74751, "loss": 4.74751, "time": 0.71801} +{"mode": "train", "epoch": 2, "iter": 2500, "lr": 0.09997, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.15406, "top5_acc": 0.37781, "loss_cls": 4.70953, "loss": 4.70953, "time": 0.71415} +{"mode": "train", "epoch": 2, "iter": 2600, "lr": 0.09997, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.15016, "top5_acc": 0.36391, "loss_cls": 4.75711, "loss": 4.75711, "time": 0.71325} +{"mode": "train", "epoch": 2, "iter": 2700, "lr": 0.09997, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.16062, "top5_acc": 0.37734, "loss_cls": 4.68816, "loss": 4.68816, "time": 0.71675} +{"mode": "train", "epoch": 2, "iter": 2800, "lr": 0.09997, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.17125, "top5_acc": 0.3925, "loss_cls": 4.61841, "loss": 4.61841, "time": 0.71731} +{"mode": "train", "epoch": 2, "iter": 2900, "lr": 0.09997, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.16422, "top5_acc": 0.39, "loss_cls": 4.65455, "loss": 4.65455, "time": 0.71323} +{"mode": "train", "epoch": 2, "iter": 3000, "lr": 0.09996, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.15547, "top5_acc": 0.37469, "loss_cls": 4.70897, "loss": 4.70897, "time": 0.71545} +{"mode": "train", "epoch": 2, "iter": 3100, "lr": 0.09996, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.17, "top5_acc": 0.39094, "loss_cls": 4.63769, "loss": 4.63769, "time": 0.7149} +{"mode": "train", "epoch": 2, "iter": 3200, "lr": 0.09996, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.16578, "top5_acc": 0.38422, "loss_cls": 4.64796, "loss": 4.64796, "time": 0.71373} +{"mode": "train", "epoch": 2, "iter": 3300, "lr": 0.09996, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.16031, "top5_acc": 0.37875, "loss_cls": 4.65582, "loss": 4.65582, "time": 0.71551} +{"mode": "train", "epoch": 2, "iter": 3400, "lr": 0.09996, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.16531, "top5_acc": 0.38797, "loss_cls": 4.63144, "loss": 4.63144, "time": 0.7147} +{"mode": "train", "epoch": 2, "iter": 3500, "lr": 0.09996, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.16188, "top5_acc": 0.37766, "loss_cls": 4.6881, "loss": 4.6881, "time": 0.71442} +{"mode": "train", "epoch": 2, "iter": 3600, "lr": 0.09996, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.16719, "top5_acc": 0.38984, "loss_cls": 4.65627, "loss": 4.65627, "time": 0.7156} +{"mode": "train", "epoch": 2, "iter": 3700, "lr": 0.09996, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.16391, "top5_acc": 0.38469, "loss_cls": 4.61677, "loss": 4.61677, "time": 0.71515} +{"mode": "val", "epoch": 2, "iter": 309, "lr": 0.09996, "top1_acc": 0.09593, "top5_acc": 0.25523, "mean_class_accuracy": 0.09591} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.09995, "memory": 15990, "data_time": 1.46184, "top1_acc": 0.16891, "top5_acc": 0.39312, "loss_cls": 4.61577, "loss": 4.61577, "time": 2.17745} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.09995, "memory": 15990, "data_time": 0.00097, "top1_acc": 0.17328, "top5_acc": 0.4025, "loss_cls": 4.5863, "loss": 4.5863, "time": 0.71611} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.09995, "memory": 15990, "data_time": 0.00117, "top1_acc": 0.17266, "top5_acc": 0.39266, "loss_cls": 4.59241, "loss": 4.59241, "time": 0.71461} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.09995, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.17234, "top5_acc": 0.39219, "loss_cls": 4.61228, "loss": 4.61228, "time": 0.71321} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.09995, "memory": 15990, "data_time": 0.00097, "top1_acc": 0.1725, "top5_acc": 0.39672, "loss_cls": 4.60413, "loss": 4.60413, "time": 0.71297} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.09995, "memory": 15990, "data_time": 0.00108, "top1_acc": 0.17656, "top5_acc": 0.39891, "loss_cls": 4.58768, "loss": 4.58768, "time": 0.71191} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.09995, "memory": 15990, "data_time": 0.00082, "top1_acc": 0.18125, "top5_acc": 0.40266, "loss_cls": 4.59709, "loss": 4.59709, "time": 0.7169} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.09995, "memory": 15990, "data_time": 0.0008, "top1_acc": 0.17953, "top5_acc": 0.40469, "loss_cls": 4.5668, "loss": 4.5668, "time": 0.71293} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.09994, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.17422, "top5_acc": 0.40703, "loss_cls": 4.57413, "loss": 4.57413, "time": 0.71655} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.09994, "memory": 15990, "data_time": 0.00081, "top1_acc": 0.17516, "top5_acc": 0.40844, "loss_cls": 4.56836, "loss": 4.56836, "time": 0.717} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.09994, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.19078, "top5_acc": 0.41297, "loss_cls": 4.53257, "loss": 4.53257, "time": 0.71687} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.09994, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.185, "top5_acc": 0.41078, "loss_cls": 4.5487, "loss": 4.5487, "time": 0.7152} +{"mode": "train", "epoch": 3, "iter": 1300, "lr": 0.09994, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.17688, "top5_acc": 0.41703, "loss_cls": 4.57409, "loss": 4.57409, "time": 0.71903} +{"mode": "train", "epoch": 3, "iter": 1400, "lr": 0.09994, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.18344, "top5_acc": 0.39516, "loss_cls": 4.5738, "loss": 4.5738, "time": 0.71857} +{"mode": "train", "epoch": 3, "iter": 1500, "lr": 0.09994, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.18406, "top5_acc": 0.40719, "loss_cls": 4.54712, "loss": 4.54712, "time": 0.71565} +{"mode": "train", "epoch": 3, "iter": 1600, "lr": 0.09994, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.18906, "top5_acc": 0.41812, "loss_cls": 4.51057, "loss": 4.51057, "time": 0.7153} +{"mode": "train", "epoch": 3, "iter": 1700, "lr": 0.09993, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.18406, "top5_acc": 0.41344, "loss_cls": 4.56081, "loss": 4.56081, "time": 0.71637} +{"mode": "train", "epoch": 3, "iter": 1800, "lr": 0.09993, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.18562, "top5_acc": 0.41625, "loss_cls": 4.5283, "loss": 4.5283, "time": 0.71641} +{"mode": "train", "epoch": 3, "iter": 1900, "lr": 0.09993, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.18156, "top5_acc": 0.40953, "loss_cls": 4.54696, "loss": 4.54696, "time": 0.7153} +{"mode": "train", "epoch": 3, "iter": 2000, "lr": 0.09993, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.18203, "top5_acc": 0.41094, "loss_cls": 4.54309, "loss": 4.54309, "time": 0.7163} +{"mode": "train", "epoch": 3, "iter": 2100, "lr": 0.09993, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.18172, "top5_acc": 0.41469, "loss_cls": 4.53513, "loss": 4.53513, "time": 0.71347} +{"mode": "train", "epoch": 3, "iter": 2200, "lr": 0.09993, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.17891, "top5_acc": 0.41016, "loss_cls": 4.53734, "loss": 4.53734, "time": 0.71457} +{"mode": "train", "epoch": 3, "iter": 2300, "lr": 0.09993, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.19016, "top5_acc": 0.42078, "loss_cls": 4.49128, "loss": 4.49128, "time": 0.7156} +{"mode": "train", "epoch": 3, "iter": 2400, "lr": 0.09992, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.18578, "top5_acc": 0.40797, "loss_cls": 4.52483, "loss": 4.52483, "time": 0.71388} +{"mode": "train", "epoch": 3, "iter": 2500, "lr": 0.09992, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.18922, "top5_acc": 0.41531, "loss_cls": 4.50333, "loss": 4.50333, "time": 0.71267} +{"mode": "train", "epoch": 3, "iter": 2600, "lr": 0.09992, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.18312, "top5_acc": 0.41312, "loss_cls": 4.50753, "loss": 4.50753, "time": 0.71582} +{"mode": "train", "epoch": 3, "iter": 2700, "lr": 0.09992, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.19891, "top5_acc": 0.41281, "loss_cls": 4.49693, "loss": 4.49693, "time": 0.71298} +{"mode": "train", "epoch": 3, "iter": 2800, "lr": 0.09992, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.19125, "top5_acc": 0.42391, "loss_cls": 4.46161, "loss": 4.46161, "time": 0.71371} +{"mode": "train", "epoch": 3, "iter": 2900, "lr": 0.09992, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.18031, "top5_acc": 0.41062, "loss_cls": 4.54438, "loss": 4.54438, "time": 0.71429} +{"mode": "train", "epoch": 3, "iter": 3000, "lr": 0.09991, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19609, "top5_acc": 0.42562, "loss_cls": 4.47817, "loss": 4.47817, "time": 0.71651} +{"mode": "train", "epoch": 3, "iter": 3100, "lr": 0.09991, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.18844, "top5_acc": 0.42281, "loss_cls": 4.51948, "loss": 4.51948, "time": 0.71403} +{"mode": "train", "epoch": 3, "iter": 3200, "lr": 0.09991, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.19281, "top5_acc": 0.42594, "loss_cls": 4.48959, "loss": 4.48959, "time": 0.71449} +{"mode": "train", "epoch": 3, "iter": 3300, "lr": 0.09991, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.19312, "top5_acc": 0.42672, "loss_cls": 4.48296, "loss": 4.48296, "time": 0.71469} +{"mode": "train", "epoch": 3, "iter": 3400, "lr": 0.09991, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.17766, "top5_acc": 0.41391, "loss_cls": 4.53409, "loss": 4.53409, "time": 0.71366} +{"mode": "train", "epoch": 3, "iter": 3500, "lr": 0.09991, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19734, "top5_acc": 0.43266, "loss_cls": 4.44293, "loss": 4.44293, "time": 0.71494} +{"mode": "train", "epoch": 3, "iter": 3600, "lr": 0.0999, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.19344, "top5_acc": 0.42844, "loss_cls": 4.45268, "loss": 4.45268, "time": 0.715} +{"mode": "train", "epoch": 3, "iter": 3700, "lr": 0.0999, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19703, "top5_acc": 0.42281, "loss_cls": 4.45794, "loss": 4.45794, "time": 0.71448} +{"mode": "val", "epoch": 3, "iter": 309, "lr": 0.0999, "top1_acc": 0.14071, "top5_acc": 0.34812, "mean_class_accuracy": 0.14083} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.0999, "memory": 15990, "data_time": 1.48546, "top1_acc": 0.18938, "top5_acc": 0.43312, "loss_cls": 4.41987, "loss": 4.41987, "time": 2.2021} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.0999, "memory": 15990, "data_time": 0.00071, "top1_acc": 0.20844, "top5_acc": 0.44406, "loss_cls": 4.40456, "loss": 4.40456, "time": 0.71567} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.0999, "memory": 15990, "data_time": 0.00076, "top1_acc": 0.20109, "top5_acc": 0.44203, "loss_cls": 4.43253, "loss": 4.43253, "time": 0.71545} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.09989, "memory": 15990, "data_time": 0.00086, "top1_acc": 0.195, "top5_acc": 0.42531, "loss_cls": 4.48166, "loss": 4.48166, "time": 0.71311} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.09989, "memory": 15990, "data_time": 0.00097, "top1_acc": 0.20406, "top5_acc": 0.44391, "loss_cls": 4.3925, "loss": 4.3925, "time": 0.71354} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.09989, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.19672, "top5_acc": 0.43203, "loss_cls": 4.45529, "loss": 4.45529, "time": 0.71341} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.09989, "memory": 15990, "data_time": 0.00119, "top1_acc": 0.19562, "top5_acc": 0.43109, "loss_cls": 4.43274, "loss": 4.43274, "time": 0.7168} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.09989, "memory": 15990, "data_time": 0.00108, "top1_acc": 0.19812, "top5_acc": 0.43766, "loss_cls": 4.42325, "loss": 4.42325, "time": 0.71628} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.09988, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.19562, "top5_acc": 0.43875, "loss_cls": 4.44539, "loss": 4.44539, "time": 0.71129} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.09988, "memory": 15990, "data_time": 0.00089, "top1_acc": 0.19531, "top5_acc": 0.42781, "loss_cls": 4.46705, "loss": 4.46705, "time": 0.71456} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.09988, "memory": 15990, "data_time": 0.00097, "top1_acc": 0.20219, "top5_acc": 0.44078, "loss_cls": 4.4414, "loss": 4.4414, "time": 0.71352} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.09988, "memory": 15990, "data_time": 0.0008, "top1_acc": 0.21172, "top5_acc": 0.44141, "loss_cls": 4.36516, "loss": 4.36516, "time": 0.71355} +{"mode": "train", "epoch": 4, "iter": 1300, "lr": 0.09988, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20734, "top5_acc": 0.44016, "loss_cls": 4.40466, "loss": 4.40466, "time": 0.71121} +{"mode": "train", "epoch": 4, "iter": 1400, "lr": 0.09988, "memory": 15990, "data_time": 0.00109, "top1_acc": 0.19516, "top5_acc": 0.42953, "loss_cls": 4.46148, "loss": 4.46148, "time": 0.71362} +{"mode": "train", "epoch": 4, "iter": 1500, "lr": 0.09987, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.19359, "top5_acc": 0.43047, "loss_cls": 4.43905, "loss": 4.43905, "time": 0.71093} +{"mode": "train", "epoch": 4, "iter": 1600, "lr": 0.09987, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.20156, "top5_acc": 0.42969, "loss_cls": 4.4325, "loss": 4.4325, "time": 0.71099} +{"mode": "train", "epoch": 4, "iter": 1700, "lr": 0.09987, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.19797, "top5_acc": 0.42875, "loss_cls": 4.45402, "loss": 4.45402, "time": 0.71221} +{"mode": "train", "epoch": 4, "iter": 1800, "lr": 0.09987, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.20531, "top5_acc": 0.43062, "loss_cls": 4.41292, "loss": 4.41292, "time": 0.71491} +{"mode": "train", "epoch": 4, "iter": 1900, "lr": 0.09987, "memory": 15990, "data_time": 0.00079, "top1_acc": 0.20516, "top5_acc": 0.43344, "loss_cls": 4.37889, "loss": 4.37889, "time": 0.71457} +{"mode": "train", "epoch": 4, "iter": 2000, "lr": 0.09986, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.19688, "top5_acc": 0.42812, "loss_cls": 4.44051, "loss": 4.44051, "time": 0.71662} +{"mode": "train", "epoch": 4, "iter": 2100, "lr": 0.09986, "memory": 15990, "data_time": 0.00076, "top1_acc": 0.19891, "top5_acc": 0.44312, "loss_cls": 4.41168, "loss": 4.41168, "time": 0.72255} +{"mode": "train", "epoch": 4, "iter": 2200, "lr": 0.09986, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.19766, "top5_acc": 0.43688, "loss_cls": 4.43474, "loss": 4.43474, "time": 0.71633} +{"mode": "train", "epoch": 4, "iter": 2300, "lr": 0.09986, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.20016, "top5_acc": 0.43109, "loss_cls": 4.43075, "loss": 4.43075, "time": 0.7213} +{"mode": "train", "epoch": 4, "iter": 2400, "lr": 0.09985, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20234, "top5_acc": 0.43688, "loss_cls": 4.42178, "loss": 4.42178, "time": 0.71714} +{"mode": "train", "epoch": 4, "iter": 2500, "lr": 0.09985, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20297, "top5_acc": 0.44172, "loss_cls": 4.42758, "loss": 4.42758, "time": 0.71713} +{"mode": "train", "epoch": 4, "iter": 2600, "lr": 0.09985, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20688, "top5_acc": 0.4425, "loss_cls": 4.38668, "loss": 4.38668, "time": 0.71514} +{"mode": "train", "epoch": 4, "iter": 2700, "lr": 0.09985, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20156, "top5_acc": 0.43969, "loss_cls": 4.43115, "loss": 4.43115, "time": 0.71407} +{"mode": "train", "epoch": 4, "iter": 2800, "lr": 0.09985, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19719, "top5_acc": 0.44359, "loss_cls": 4.40631, "loss": 4.40631, "time": 0.71385} +{"mode": "train", "epoch": 4, "iter": 2900, "lr": 0.09984, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21031, "top5_acc": 0.45359, "loss_cls": 4.36059, "loss": 4.36059, "time": 0.71372} +{"mode": "train", "epoch": 4, "iter": 3000, "lr": 0.09984, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20203, "top5_acc": 0.44125, "loss_cls": 4.42389, "loss": 4.42389, "time": 0.71398} +{"mode": "train", "epoch": 4, "iter": 3100, "lr": 0.09984, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20406, "top5_acc": 0.44391, "loss_cls": 4.39514, "loss": 4.39514, "time": 0.71462} +{"mode": "train", "epoch": 4, "iter": 3200, "lr": 0.09984, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20578, "top5_acc": 0.44125, "loss_cls": 4.39807, "loss": 4.39807, "time": 0.71685} +{"mode": "train", "epoch": 4, "iter": 3300, "lr": 0.09983, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21047, "top5_acc": 0.44453, "loss_cls": 4.39267, "loss": 4.39267, "time": 0.7168} +{"mode": "train", "epoch": 4, "iter": 3400, "lr": 0.09983, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21234, "top5_acc": 0.455, "loss_cls": 4.3439, "loss": 4.3439, "time": 0.71472} +{"mode": "train", "epoch": 4, "iter": 3500, "lr": 0.09983, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20156, "top5_acc": 0.43078, "loss_cls": 4.42399, "loss": 4.42399, "time": 0.71641} +{"mode": "train", "epoch": 4, "iter": 3600, "lr": 0.09983, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.20844, "top5_acc": 0.45094, "loss_cls": 4.3395, "loss": 4.3395, "time": 0.71612} +{"mode": "train", "epoch": 4, "iter": 3700, "lr": 0.09983, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.21688, "top5_acc": 0.45016, "loss_cls": 4.35736, "loss": 4.35736, "time": 0.71363} +{"mode": "val", "epoch": 4, "iter": 309, "lr": 0.09982, "top1_acc": 0.14901, "top5_acc": 0.35602, "mean_class_accuracy": 0.149} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.09982, "memory": 15990, "data_time": 1.48729, "top1_acc": 0.21016, "top5_acc": 0.45016, "loss_cls": 4.3366, "loss": 4.3366, "time": 2.20407} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.09982, "memory": 15990, "data_time": 0.00152, "top1_acc": 0.20609, "top5_acc": 0.44828, "loss_cls": 4.37106, "loss": 4.37106, "time": 0.71704} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.09982, "memory": 15990, "data_time": 0.00158, "top1_acc": 0.21438, "top5_acc": 0.45672, "loss_cls": 4.30542, "loss": 4.30542, "time": 0.71342} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.09982, "memory": 15990, "data_time": 0.00081, "top1_acc": 0.20719, "top5_acc": 0.44406, "loss_cls": 4.36568, "loss": 4.36568, "time": 0.715} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.09981, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.21328, "top5_acc": 0.45375, "loss_cls": 4.35907, "loss": 4.35907, "time": 0.71575} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.09981, "memory": 15990, "data_time": 0.00082, "top1_acc": 0.21672, "top5_acc": 0.45703, "loss_cls": 4.31688, "loss": 4.31688, "time": 0.71874} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.09981, "memory": 15990, "data_time": 0.00131, "top1_acc": 0.20547, "top5_acc": 0.45, "loss_cls": 4.34975, "loss": 4.34975, "time": 0.71733} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.09981, "memory": 15990, "data_time": 0.00096, "top1_acc": 0.20844, "top5_acc": 0.45078, "loss_cls": 4.36393, "loss": 4.36393, "time": 0.71345} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.0998, "memory": 15990, "data_time": 0.00098, "top1_acc": 0.21672, "top5_acc": 0.45641, "loss_cls": 4.34169, "loss": 4.34169, "time": 0.71385} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.0998, "memory": 15990, "data_time": 0.00071, "top1_acc": 0.21297, "top5_acc": 0.45438, "loss_cls": 4.33822, "loss": 4.33822, "time": 0.71244} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.0998, "memory": 15990, "data_time": 0.00135, "top1_acc": 0.21172, "top5_acc": 0.46, "loss_cls": 4.34888, "loss": 4.34888, "time": 0.71341} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.0998, "memory": 15990, "data_time": 0.0007, "top1_acc": 0.21594, "top5_acc": 0.45484, "loss_cls": 4.3418, "loss": 4.3418, "time": 0.71375} +{"mode": "train", "epoch": 5, "iter": 1300, "lr": 0.09979, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.20984, "top5_acc": 0.45438, "loss_cls": 4.35693, "loss": 4.35693, "time": 0.71557} +{"mode": "train", "epoch": 5, "iter": 1400, "lr": 0.09979, "memory": 15990, "data_time": 0.00086, "top1_acc": 0.21844, "top5_acc": 0.46297, "loss_cls": 4.32871, "loss": 4.32871, "time": 0.71868} +{"mode": "train", "epoch": 5, "iter": 1500, "lr": 0.09979, "memory": 15990, "data_time": 0.00127, "top1_acc": 0.21578, "top5_acc": 0.45906, "loss_cls": 4.32764, "loss": 4.32764, "time": 0.71355} +{"mode": "train", "epoch": 5, "iter": 1600, "lr": 0.09979, "memory": 15990, "data_time": 0.00079, "top1_acc": 0.21891, "top5_acc": 0.46188, "loss_cls": 4.31062, "loss": 4.31062, "time": 0.71185} +{"mode": "train", "epoch": 5, "iter": 1700, "lr": 0.09978, "memory": 15990, "data_time": 0.00098, "top1_acc": 0.21188, "top5_acc": 0.45438, "loss_cls": 4.33653, "loss": 4.33653, "time": 0.71537} +{"mode": "train", "epoch": 5, "iter": 1800, "lr": 0.09978, "memory": 15990, "data_time": 0.001, "top1_acc": 0.21234, "top5_acc": 0.44969, "loss_cls": 4.3295, "loss": 4.3295, "time": 0.71768} +{"mode": "train", "epoch": 5, "iter": 1900, "lr": 0.09978, "memory": 15990, "data_time": 0.0012, "top1_acc": 0.21516, "top5_acc": 0.45203, "loss_cls": 4.33008, "loss": 4.33008, "time": 0.71678} +{"mode": "train", "epoch": 5, "iter": 2000, "lr": 0.09977, "memory": 15990, "data_time": 0.00094, "top1_acc": 0.2025, "top5_acc": 0.44109, "loss_cls": 4.37629, "loss": 4.37629, "time": 0.71345} +{"mode": "train", "epoch": 5, "iter": 2100, "lr": 0.09977, "memory": 15990, "data_time": 0.00082, "top1_acc": 0.21609, "top5_acc": 0.46406, "loss_cls": 4.31132, "loss": 4.31132, "time": 0.71649} +{"mode": "train", "epoch": 5, "iter": 2200, "lr": 0.09977, "memory": 15990, "data_time": 0.00067, "top1_acc": 0.21641, "top5_acc": 0.45109, "loss_cls": 4.33382, "loss": 4.33382, "time": 0.72339} +{"mode": "train", "epoch": 5, "iter": 2300, "lr": 0.09977, "memory": 15990, "data_time": 0.00085, "top1_acc": 0.20906, "top5_acc": 0.44656, "loss_cls": 4.3571, "loss": 4.3571, "time": 0.71685} +{"mode": "train", "epoch": 5, "iter": 2400, "lr": 0.09976, "memory": 15990, "data_time": 0.00092, "top1_acc": 0.21125, "top5_acc": 0.44375, "loss_cls": 4.36223, "loss": 4.36223, "time": 0.71453} +{"mode": "train", "epoch": 5, "iter": 2500, "lr": 0.09976, "memory": 15990, "data_time": 0.00087, "top1_acc": 0.22328, "top5_acc": 0.45984, "loss_cls": 4.32206, "loss": 4.32206, "time": 0.71599} +{"mode": "train", "epoch": 5, "iter": 2600, "lr": 0.09976, "memory": 15990, "data_time": 0.00081, "top1_acc": 0.21125, "top5_acc": 0.45281, "loss_cls": 4.34863, "loss": 4.34863, "time": 0.71584} +{"mode": "train", "epoch": 5, "iter": 2700, "lr": 0.09976, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.21641, "top5_acc": 0.44953, "loss_cls": 4.35567, "loss": 4.35567, "time": 0.71939} +{"mode": "train", "epoch": 5, "iter": 2800, "lr": 0.09975, "memory": 15990, "data_time": 0.00106, "top1_acc": 0.20703, "top5_acc": 0.43984, "loss_cls": 4.40106, "loss": 4.40106, "time": 0.7182} +{"mode": "train", "epoch": 5, "iter": 2900, "lr": 0.09975, "memory": 15990, "data_time": 0.00077, "top1_acc": 0.20672, "top5_acc": 0.45594, "loss_cls": 4.33954, "loss": 4.33954, "time": 0.71753} +{"mode": "train", "epoch": 5, "iter": 3000, "lr": 0.09975, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22141, "top5_acc": 0.46156, "loss_cls": 4.32285, "loss": 4.32285, "time": 0.7177} +{"mode": "train", "epoch": 5, "iter": 3100, "lr": 0.09974, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22547, "top5_acc": 0.46812, "loss_cls": 4.25924, "loss": 4.25924, "time": 0.71995} +{"mode": "train", "epoch": 5, "iter": 3200, "lr": 0.09974, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22047, "top5_acc": 0.45656, "loss_cls": 4.30244, "loss": 4.30244, "time": 0.7176} +{"mode": "train", "epoch": 5, "iter": 3300, "lr": 0.09974, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21422, "top5_acc": 0.46047, "loss_cls": 4.33059, "loss": 4.33059, "time": 0.72039} +{"mode": "train", "epoch": 5, "iter": 3400, "lr": 0.09974, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21812, "top5_acc": 0.46219, "loss_cls": 4.30218, "loss": 4.30218, "time": 0.71927} +{"mode": "train", "epoch": 5, "iter": 3500, "lr": 0.09973, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22172, "top5_acc": 0.46953, "loss_cls": 4.26861, "loss": 4.26861, "time": 0.71787} +{"mode": "train", "epoch": 5, "iter": 3600, "lr": 0.09973, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21516, "top5_acc": 0.46, "loss_cls": 4.31023, "loss": 4.31023, "time": 0.71662} +{"mode": "train", "epoch": 5, "iter": 3700, "lr": 0.09973, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21734, "top5_acc": 0.46609, "loss_cls": 4.2857, "loss": 4.2857, "time": 0.71221} +{"mode": "val", "epoch": 5, "iter": 309, "lr": 0.09973, "top1_acc": 0.15205, "top5_acc": 0.36337, "mean_class_accuracy": 0.15182} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.09972, "memory": 15990, "data_time": 1.37251, "top1_acc": 0.22422, "top5_acc": 0.45641, "loss_cls": 4.29122, "loss": 4.29122, "time": 2.08956} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.09972, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.21625, "top5_acc": 0.45703, "loss_cls": 4.30953, "loss": 4.30953, "time": 0.71602} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.09972, "memory": 15990, "data_time": 0.00108, "top1_acc": 0.21094, "top5_acc": 0.46156, "loss_cls": 4.28286, "loss": 4.28286, "time": 0.71672} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.09971, "memory": 15990, "data_time": 0.00082, "top1_acc": 0.21688, "top5_acc": 0.46062, "loss_cls": 4.31375, "loss": 4.31375, "time": 0.7158} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.09971, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.2175, "top5_acc": 0.46219, "loss_cls": 4.29803, "loss": 4.29803, "time": 0.71764} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.09971, "memory": 15990, "data_time": 0.00087, "top1_acc": 0.22219, "top5_acc": 0.45688, "loss_cls": 4.29777, "loss": 4.29777, "time": 0.71689} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.09971, "memory": 15990, "data_time": 0.00075, "top1_acc": 0.22609, "top5_acc": 0.46266, "loss_cls": 4.29679, "loss": 4.29679, "time": 0.71247} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.0997, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.22281, "top5_acc": 0.46688, "loss_cls": 4.25604, "loss": 4.25604, "time": 0.71782} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.0997, "memory": 15990, "data_time": 0.00113, "top1_acc": 0.21703, "top5_acc": 0.45375, "loss_cls": 4.32438, "loss": 4.32438, "time": 0.71926} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.0997, "memory": 15990, "data_time": 0.0008, "top1_acc": 0.21922, "top5_acc": 0.45828, "loss_cls": 4.29873, "loss": 4.29873, "time": 0.71569} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.09969, "memory": 15990, "data_time": 0.0013, "top1_acc": 0.21109, "top5_acc": 0.45578, "loss_cls": 4.32905, "loss": 4.32905, "time": 0.72176} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.09969, "memory": 15990, "data_time": 0.00081, "top1_acc": 0.22891, "top5_acc": 0.47156, "loss_cls": 4.2817, "loss": 4.2817, "time": 0.7183} +{"mode": "train", "epoch": 6, "iter": 1300, "lr": 0.09969, "memory": 15990, "data_time": 0.00089, "top1_acc": 0.22766, "top5_acc": 0.46062, "loss_cls": 4.32427, "loss": 4.32427, "time": 0.71606} +{"mode": "train", "epoch": 6, "iter": 1400, "lr": 0.09968, "memory": 15990, "data_time": 0.00073, "top1_acc": 0.21938, "top5_acc": 0.47297, "loss_cls": 4.27159, "loss": 4.27159, "time": 0.71806} +{"mode": "train", "epoch": 6, "iter": 1500, "lr": 0.09968, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.22109, "top5_acc": 0.46625, "loss_cls": 4.27334, "loss": 4.27334, "time": 0.71961} +{"mode": "train", "epoch": 6, "iter": 1600, "lr": 0.09968, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.22312, "top5_acc": 0.45844, "loss_cls": 4.29231, "loss": 4.29231, "time": 0.71697} +{"mode": "train", "epoch": 6, "iter": 1700, "lr": 0.09967, "memory": 15990, "data_time": 0.00081, "top1_acc": 0.2175, "top5_acc": 0.46219, "loss_cls": 4.33647, "loss": 4.33647, "time": 0.71854} +{"mode": "train", "epoch": 6, "iter": 1800, "lr": 0.09967, "memory": 15990, "data_time": 0.0008, "top1_acc": 0.22688, "top5_acc": 0.46312, "loss_cls": 4.28379, "loss": 4.28379, "time": 0.71282} +{"mode": "train", "epoch": 6, "iter": 1900, "lr": 0.09967, "memory": 15990, "data_time": 0.00103, "top1_acc": 0.21828, "top5_acc": 0.45703, "loss_cls": 4.30407, "loss": 4.30407, "time": 0.71971} +{"mode": "train", "epoch": 6, "iter": 2000, "lr": 0.09966, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22188, "top5_acc": 0.46281, "loss_cls": 4.28851, "loss": 4.28851, "time": 0.71341} +{"mode": "train", "epoch": 6, "iter": 2100, "lr": 0.09966, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21719, "top5_acc": 0.46672, "loss_cls": 4.28076, "loss": 4.28076, "time": 0.72123} +{"mode": "train", "epoch": 6, "iter": 2200, "lr": 0.09966, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21562, "top5_acc": 0.45734, "loss_cls": 4.31844, "loss": 4.31844, "time": 0.71606} +{"mode": "train", "epoch": 6, "iter": 2300, "lr": 0.09965, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22578, "top5_acc": 0.47016, "loss_cls": 4.26423, "loss": 4.26423, "time": 0.72026} +{"mode": "train", "epoch": 6, "iter": 2400, "lr": 0.09965, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21844, "top5_acc": 0.46172, "loss_cls": 4.30323, "loss": 4.30323, "time": 0.72321} +{"mode": "train", "epoch": 6, "iter": 2500, "lr": 0.09965, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22844, "top5_acc": 0.47469, "loss_cls": 4.23934, "loss": 4.23934, "time": 0.71649} +{"mode": "train", "epoch": 6, "iter": 2600, "lr": 0.09964, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23359, "top5_acc": 0.47422, "loss_cls": 4.24524, "loss": 4.24524, "time": 0.72024} +{"mode": "train", "epoch": 6, "iter": 2700, "lr": 0.09964, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21891, "top5_acc": 0.46578, "loss_cls": 4.28616, "loss": 4.28616, "time": 0.7163} +{"mode": "train", "epoch": 6, "iter": 2800, "lr": 0.09964, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21609, "top5_acc": 0.46484, "loss_cls": 4.304, "loss": 4.304, "time": 0.71436} +{"mode": "train", "epoch": 6, "iter": 2900, "lr": 0.09963, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22156, "top5_acc": 0.46812, "loss_cls": 4.29456, "loss": 4.29456, "time": 0.72319} +{"mode": "train", "epoch": 6, "iter": 3000, "lr": 0.09963, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22969, "top5_acc": 0.46906, "loss_cls": 4.26516, "loss": 4.26516, "time": 0.71778} +{"mode": "train", "epoch": 6, "iter": 3100, "lr": 0.09963, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22281, "top5_acc": 0.46641, "loss_cls": 4.28464, "loss": 4.28464, "time": 0.71961} +{"mode": "train", "epoch": 6, "iter": 3200, "lr": 0.09962, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23266, "top5_acc": 0.46922, "loss_cls": 4.26672, "loss": 4.26672, "time": 0.71748} +{"mode": "train", "epoch": 6, "iter": 3300, "lr": 0.09962, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.225, "top5_acc": 0.46984, "loss_cls": 4.24795, "loss": 4.24795, "time": 0.72107} +{"mode": "train", "epoch": 6, "iter": 3400, "lr": 0.09962, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21484, "top5_acc": 0.46, "loss_cls": 4.29571, "loss": 4.29571, "time": 0.7163} +{"mode": "train", "epoch": 6, "iter": 3500, "lr": 0.09961, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23047, "top5_acc": 0.46969, "loss_cls": 4.24475, "loss": 4.24475, "time": 0.71586} +{"mode": "train", "epoch": 6, "iter": 3600, "lr": 0.09961, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.22516, "top5_acc": 0.47062, "loss_cls": 4.24159, "loss": 4.24159, "time": 0.71779} +{"mode": "train", "epoch": 6, "iter": 3700, "lr": 0.09961, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21922, "top5_acc": 0.46891, "loss_cls": 4.2755, "loss": 4.2755, "time": 0.71629} +{"mode": "val", "epoch": 6, "iter": 309, "lr": 0.09961, "top1_acc": 0.15241, "top5_acc": 0.37254, "mean_class_accuracy": 0.15231} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0996, "memory": 15990, "data_time": 1.41918, "top1_acc": 0.23578, "top5_acc": 0.47828, "loss_cls": 4.24562, "loss": 4.24562, "time": 2.13574} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0996, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22984, "top5_acc": 0.47578, "loss_cls": 4.21479, "loss": 4.21479, "time": 0.72217} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.0996, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.22891, "top5_acc": 0.47125, "loss_cls": 4.24757, "loss": 4.24757, "time": 0.71473} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.09959, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22953, "top5_acc": 0.47703, "loss_cls": 4.24972, "loss": 4.24972, "time": 0.71516} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.09959, "memory": 15990, "data_time": 0.0008, "top1_acc": 0.22875, "top5_acc": 0.47078, "loss_cls": 4.2627, "loss": 4.2627, "time": 0.71531} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.09958, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.23094, "top5_acc": 0.4775, "loss_cls": 4.22599, "loss": 4.22599, "time": 0.71354} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.09958, "memory": 15990, "data_time": 0.00082, "top1_acc": 0.22609, "top5_acc": 0.46109, "loss_cls": 4.28328, "loss": 4.28328, "time": 0.71373} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.09958, "memory": 15990, "data_time": 0.00082, "top1_acc": 0.22062, "top5_acc": 0.46781, "loss_cls": 4.26842, "loss": 4.26842, "time": 0.71648} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.09957, "memory": 15990, "data_time": 0.00092, "top1_acc": 0.22016, "top5_acc": 0.46922, "loss_cls": 4.24764, "loss": 4.24764, "time": 0.71851} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.09957, "memory": 15990, "data_time": 0.00081, "top1_acc": 0.21516, "top5_acc": 0.465, "loss_cls": 4.2662, "loss": 4.2662, "time": 0.71262} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.09957, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.23359, "top5_acc": 0.47359, "loss_cls": 4.23167, "loss": 4.23167, "time": 0.7138} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.09956, "memory": 15990, "data_time": 0.00092, "top1_acc": 0.22375, "top5_acc": 0.46156, "loss_cls": 4.28693, "loss": 4.28693, "time": 0.71746} +{"mode": "train", "epoch": 7, "iter": 1300, "lr": 0.09956, "memory": 15990, "data_time": 0.0009, "top1_acc": 0.23562, "top5_acc": 0.48109, "loss_cls": 4.21909, "loss": 4.21909, "time": 0.7148} +{"mode": "train", "epoch": 7, "iter": 1400, "lr": 0.09956, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.22672, "top5_acc": 0.47125, "loss_cls": 4.25578, "loss": 4.25578, "time": 0.71651} +{"mode": "train", "epoch": 7, "iter": 1500, "lr": 0.09955, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.23609, "top5_acc": 0.48469, "loss_cls": 4.22085, "loss": 4.22085, "time": 0.71959} +{"mode": "train", "epoch": 7, "iter": 1600, "lr": 0.09955, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.22391, "top5_acc": 0.47062, "loss_cls": 4.24649, "loss": 4.24649, "time": 0.71946} +{"mode": "train", "epoch": 7, "iter": 1700, "lr": 0.09954, "memory": 15990, "data_time": 0.00079, "top1_acc": 0.21828, "top5_acc": 0.46234, "loss_cls": 4.29436, "loss": 4.29436, "time": 0.71486} +{"mode": "train", "epoch": 7, "iter": 1800, "lr": 0.09954, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.23891, "top5_acc": 0.48641, "loss_cls": 4.2032, "loss": 4.2032, "time": 0.71577} +{"mode": "train", "epoch": 7, "iter": 1900, "lr": 0.09954, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23891, "top5_acc": 0.47531, "loss_cls": 4.23666, "loss": 4.23666, "time": 0.71398} +{"mode": "train", "epoch": 7, "iter": 2000, "lr": 0.09953, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23531, "top5_acc": 0.47625, "loss_cls": 4.21223, "loss": 4.21223, "time": 0.71675} +{"mode": "train", "epoch": 7, "iter": 2100, "lr": 0.09953, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22297, "top5_acc": 0.46859, "loss_cls": 4.26263, "loss": 4.26263, "time": 0.71419} +{"mode": "train", "epoch": 7, "iter": 2200, "lr": 0.09952, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21969, "top5_acc": 0.47531, "loss_cls": 4.25579, "loss": 4.25579, "time": 0.71585} +{"mode": "train", "epoch": 7, "iter": 2300, "lr": 0.09952, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23094, "top5_acc": 0.48188, "loss_cls": 4.18688, "loss": 4.18688, "time": 0.71651} +{"mode": "train", "epoch": 7, "iter": 2400, "lr": 0.09952, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21562, "top5_acc": 0.46516, "loss_cls": 4.29105, "loss": 4.29105, "time": 0.71429} +{"mode": "train", "epoch": 7, "iter": 2500, "lr": 0.09951, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22734, "top5_acc": 0.46766, "loss_cls": 4.23931, "loss": 4.23931, "time": 0.71453} +{"mode": "train", "epoch": 7, "iter": 2600, "lr": 0.09951, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21703, "top5_acc": 0.46953, "loss_cls": 4.25765, "loss": 4.25765, "time": 0.71617} +{"mode": "train", "epoch": 7, "iter": 2700, "lr": 0.09951, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22984, "top5_acc": 0.46766, "loss_cls": 4.26162, "loss": 4.26162, "time": 0.71467} +{"mode": "train", "epoch": 7, "iter": 2800, "lr": 0.0995, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22859, "top5_acc": 0.46969, "loss_cls": 4.24518, "loss": 4.24518, "time": 0.7171} +{"mode": "train", "epoch": 7, "iter": 2900, "lr": 0.0995, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22484, "top5_acc": 0.46641, "loss_cls": 4.27115, "loss": 4.27115, "time": 0.71588} +{"mode": "train", "epoch": 7, "iter": 3000, "lr": 0.09949, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23047, "top5_acc": 0.47094, "loss_cls": 4.25939, "loss": 4.25939, "time": 0.71558} +{"mode": "train", "epoch": 7, "iter": 3100, "lr": 0.09949, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22625, "top5_acc": 0.46734, "loss_cls": 4.26316, "loss": 4.26316, "time": 0.71853} +{"mode": "train", "epoch": 7, "iter": 3200, "lr": 0.09949, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22219, "top5_acc": 0.47203, "loss_cls": 4.26049, "loss": 4.26049, "time": 0.71362} +{"mode": "train", "epoch": 7, "iter": 3300, "lr": 0.09948, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22203, "top5_acc": 0.47125, "loss_cls": 4.26245, "loss": 4.26245, "time": 0.71836} +{"mode": "train", "epoch": 7, "iter": 3400, "lr": 0.09948, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23156, "top5_acc": 0.46672, "loss_cls": 4.2563, "loss": 4.2563, "time": 0.71552} +{"mode": "train", "epoch": 7, "iter": 3500, "lr": 0.09947, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22484, "top5_acc": 0.47094, "loss_cls": 4.25832, "loss": 4.25832, "time": 0.71627} +{"mode": "train", "epoch": 7, "iter": 3600, "lr": 0.09947, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22297, "top5_acc": 0.45953, "loss_cls": 4.28075, "loss": 4.28075, "time": 0.7205} +{"mode": "train", "epoch": 7, "iter": 3700, "lr": 0.09947, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22766, "top5_acc": 0.47609, "loss_cls": 4.22433, "loss": 4.22433, "time": 0.71171} +{"mode": "val", "epoch": 7, "iter": 309, "lr": 0.09946, "top1_acc": 0.16026, "top5_acc": 0.37725, "mean_class_accuracy": 0.16008} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.09946, "memory": 15990, "data_time": 1.46116, "top1_acc": 0.23844, "top5_acc": 0.47938, "loss_cls": 4.17575, "loss": 4.17575, "time": 2.17853} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.09946, "memory": 15990, "data_time": 0.00115, "top1_acc": 0.22906, "top5_acc": 0.47656, "loss_cls": 4.22893, "loss": 4.22893, "time": 0.72076} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.09945, "memory": 15990, "data_time": 0.0013, "top1_acc": 0.22906, "top5_acc": 0.47641, "loss_cls": 4.23611, "loss": 4.23611, "time": 0.7152} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.09945, "memory": 15990, "data_time": 0.00114, "top1_acc": 0.24156, "top5_acc": 0.48312, "loss_cls": 4.18969, "loss": 4.18969, "time": 0.71527} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.09944, "memory": 15990, "data_time": 0.00103, "top1_acc": 0.22953, "top5_acc": 0.47172, "loss_cls": 4.24267, "loss": 4.24267, "time": 0.71723} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.09944, "memory": 15990, "data_time": 0.00074, "top1_acc": 0.22188, "top5_acc": 0.46734, "loss_cls": 4.27247, "loss": 4.27247, "time": 0.71403} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.09943, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22531, "top5_acc": 0.47172, "loss_cls": 4.23335, "loss": 4.23335, "time": 0.71357} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.09943, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.22375, "top5_acc": 0.46625, "loss_cls": 4.28635, "loss": 4.28635, "time": 0.71239} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.09943, "memory": 15990, "data_time": 0.00083, "top1_acc": 0.22734, "top5_acc": 0.47891, "loss_cls": 4.24246, "loss": 4.24246, "time": 0.71378} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.09942, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23156, "top5_acc": 0.47719, "loss_cls": 4.20798, "loss": 4.20798, "time": 0.71618} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.09942, "memory": 15990, "data_time": 0.00087, "top1_acc": 0.23203, "top5_acc": 0.48047, "loss_cls": 4.22915, "loss": 4.22915, "time": 0.71354} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.09941, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.22156, "top5_acc": 0.46172, "loss_cls": 4.28688, "loss": 4.28688, "time": 0.71613} +{"mode": "train", "epoch": 8, "iter": 1300, "lr": 0.09941, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.22547, "top5_acc": 0.46875, "loss_cls": 4.23689, "loss": 4.23689, "time": 0.71465} +{"mode": "train", "epoch": 8, "iter": 1400, "lr": 0.0994, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.23312, "top5_acc": 0.47625, "loss_cls": 4.23456, "loss": 4.23456, "time": 0.7166} +{"mode": "train", "epoch": 8, "iter": 1500, "lr": 0.0994, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.22062, "top5_acc": 0.46953, "loss_cls": 4.26375, "loss": 4.26375, "time": 0.7188} +{"mode": "train", "epoch": 8, "iter": 1600, "lr": 0.0994, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.23125, "top5_acc": 0.485, "loss_cls": 4.20094, "loss": 4.20094, "time": 0.71712} +{"mode": "train", "epoch": 8, "iter": 1700, "lr": 0.09939, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.22547, "top5_acc": 0.46984, "loss_cls": 4.29383, "loss": 4.29383, "time": 0.71567} +{"mode": "train", "epoch": 8, "iter": 1800, "lr": 0.09939, "memory": 15990, "data_time": 0.0007, "top1_acc": 0.2275, "top5_acc": 0.46781, "loss_cls": 4.23034, "loss": 4.23034, "time": 0.72116} +{"mode": "train", "epoch": 8, "iter": 1900, "lr": 0.09938, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.23625, "top5_acc": 0.47828, "loss_cls": 4.21324, "loss": 4.21324, "time": 0.71783} +{"mode": "train", "epoch": 8, "iter": 2000, "lr": 0.09938, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23172, "top5_acc": 0.48, "loss_cls": 4.20964, "loss": 4.20964, "time": 0.71607} +{"mode": "train", "epoch": 8, "iter": 2100, "lr": 0.09937, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23484, "top5_acc": 0.47047, "loss_cls": 4.24536, "loss": 4.24536, "time": 0.71748} +{"mode": "train", "epoch": 8, "iter": 2200, "lr": 0.09937, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23156, "top5_acc": 0.47922, "loss_cls": 4.21046, "loss": 4.21046, "time": 0.71783} +{"mode": "train", "epoch": 8, "iter": 2300, "lr": 0.09937, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23266, "top5_acc": 0.47172, "loss_cls": 4.24014, "loss": 4.24014, "time": 0.71468} +{"mode": "train", "epoch": 8, "iter": 2400, "lr": 0.09936, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23609, "top5_acc": 0.47656, "loss_cls": 4.20126, "loss": 4.20126, "time": 0.7121} +{"mode": "train", "epoch": 8, "iter": 2500, "lr": 0.09936, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22266, "top5_acc": 0.46031, "loss_cls": 4.28043, "loss": 4.28043, "time": 0.71566} +{"mode": "train", "epoch": 8, "iter": 2600, "lr": 0.09935, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2275, "top5_acc": 0.47062, "loss_cls": 4.22368, "loss": 4.22368, "time": 0.71573} +{"mode": "train", "epoch": 8, "iter": 2700, "lr": 0.09935, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23578, "top5_acc": 0.47719, "loss_cls": 4.18559, "loss": 4.18559, "time": 0.71531} +{"mode": "train", "epoch": 8, "iter": 2800, "lr": 0.09934, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22469, "top5_acc": 0.48031, "loss_cls": 4.21298, "loss": 4.21298, "time": 0.71986} +{"mode": "train", "epoch": 8, "iter": 2900, "lr": 0.09934, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22781, "top5_acc": 0.47547, "loss_cls": 4.21826, "loss": 4.21826, "time": 0.7174} +{"mode": "train", "epoch": 8, "iter": 3000, "lr": 0.09933, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21797, "top5_acc": 0.46641, "loss_cls": 4.27371, "loss": 4.27371, "time": 0.715} +{"mode": "train", "epoch": 8, "iter": 3100, "lr": 0.09933, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22891, "top5_acc": 0.47859, "loss_cls": 4.21544, "loss": 4.21544, "time": 0.71634} +{"mode": "train", "epoch": 8, "iter": 3200, "lr": 0.09933, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22562, "top5_acc": 0.47594, "loss_cls": 4.24107, "loss": 4.24107, "time": 0.71591} +{"mode": "train", "epoch": 8, "iter": 3300, "lr": 0.09932, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22266, "top5_acc": 0.47625, "loss_cls": 4.24868, "loss": 4.24868, "time": 0.71627} +{"mode": "train", "epoch": 8, "iter": 3400, "lr": 0.09932, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23469, "top5_acc": 0.48484, "loss_cls": 4.21844, "loss": 4.21844, "time": 0.71532} +{"mode": "train", "epoch": 8, "iter": 3500, "lr": 0.09931, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23672, "top5_acc": 0.48109, "loss_cls": 4.21351, "loss": 4.21351, "time": 0.71755} +{"mode": "train", "epoch": 8, "iter": 3600, "lr": 0.09931, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23156, "top5_acc": 0.46969, "loss_cls": 4.26183, "loss": 4.26183, "time": 0.71596} +{"mode": "train", "epoch": 8, "iter": 3700, "lr": 0.0993, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.22203, "top5_acc": 0.46453, "loss_cls": 4.26712, "loss": 4.26712, "time": 0.71517} +{"mode": "val", "epoch": 8, "iter": 309, "lr": 0.0993, "top1_acc": 0.15813, "top5_acc": 0.38186, "mean_class_accuracy": 0.15798} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.0993, "memory": 15990, "data_time": 1.46099, "top1_acc": 0.22906, "top5_acc": 0.495, "loss_cls": 4.16049, "loss": 4.16049, "time": 2.17708} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.09929, "memory": 15990, "data_time": 0.00155, "top1_acc": 0.23406, "top5_acc": 0.48172, "loss_cls": 4.18512, "loss": 4.18512, "time": 0.71605} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.09929, "memory": 15990, "data_time": 0.00083, "top1_acc": 0.23672, "top5_acc": 0.48328, "loss_cls": 4.18262, "loss": 4.18262, "time": 0.7189} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.09928, "memory": 15990, "data_time": 0.00105, "top1_acc": 0.23094, "top5_acc": 0.47438, "loss_cls": 4.22909, "loss": 4.22909, "time": 0.71243} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.09928, "memory": 15990, "data_time": 0.00104, "top1_acc": 0.25375, "top5_acc": 0.49359, "loss_cls": 4.1223, "loss": 4.1223, "time": 0.71714} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.09927, "memory": 15990, "data_time": 0.0015, "top1_acc": 0.23609, "top5_acc": 0.48297, "loss_cls": 4.20032, "loss": 4.20032, "time": 0.71634} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.09927, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.23234, "top5_acc": 0.48141, "loss_cls": 4.22029, "loss": 4.22029, "time": 0.71925} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.09926, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.22359, "top5_acc": 0.47297, "loss_cls": 4.23427, "loss": 4.23427, "time": 0.71431} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.09926, "memory": 15990, "data_time": 0.00081, "top1_acc": 0.2275, "top5_acc": 0.48312, "loss_cls": 4.24108, "loss": 4.24108, "time": 0.715} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.09925, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.23281, "top5_acc": 0.47812, "loss_cls": 4.1977, "loss": 4.1977, "time": 0.71546} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.09925, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.21953, "top5_acc": 0.47, "loss_cls": 4.28344, "loss": 4.28344, "time": 0.71543} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.09924, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.24031, "top5_acc": 0.48406, "loss_cls": 4.17276, "loss": 4.17276, "time": 0.71519} +{"mode": "train", "epoch": 9, "iter": 1300, "lr": 0.09924, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.23391, "top5_acc": 0.48469, "loss_cls": 4.1933, "loss": 4.1933, "time": 0.71492} +{"mode": "train", "epoch": 9, "iter": 1400, "lr": 0.09923, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.22641, "top5_acc": 0.46797, "loss_cls": 4.247, "loss": 4.247, "time": 0.71664} +{"mode": "train", "epoch": 9, "iter": 1500, "lr": 0.09923, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24156, "top5_acc": 0.48078, "loss_cls": 4.16226, "loss": 4.16226, "time": 0.71518} +{"mode": "train", "epoch": 9, "iter": 1600, "lr": 0.09922, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23188, "top5_acc": 0.48, "loss_cls": 4.22322, "loss": 4.22322, "time": 0.7104} +{"mode": "train", "epoch": 9, "iter": 1700, "lr": 0.09922, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2325, "top5_acc": 0.47641, "loss_cls": 4.22428, "loss": 4.22428, "time": 0.71215} +{"mode": "train", "epoch": 9, "iter": 1800, "lr": 0.09921, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23625, "top5_acc": 0.47844, "loss_cls": 4.21927, "loss": 4.21927, "time": 0.71517} +{"mode": "train", "epoch": 9, "iter": 1900, "lr": 0.09921, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23062, "top5_acc": 0.48312, "loss_cls": 4.22454, "loss": 4.22454, "time": 0.71648} +{"mode": "train", "epoch": 9, "iter": 2000, "lr": 0.0992, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23188, "top5_acc": 0.48516, "loss_cls": 4.20029, "loss": 4.20029, "time": 0.71672} +{"mode": "train", "epoch": 9, "iter": 2100, "lr": 0.0992, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23453, "top5_acc": 0.48031, "loss_cls": 4.19475, "loss": 4.19475, "time": 0.7166} +{"mode": "train", "epoch": 9, "iter": 2200, "lr": 0.09919, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22641, "top5_acc": 0.46766, "loss_cls": 4.25178, "loss": 4.25178, "time": 0.71912} +{"mode": "train", "epoch": 9, "iter": 2300, "lr": 0.09919, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23078, "top5_acc": 0.46891, "loss_cls": 4.23755, "loss": 4.23755, "time": 0.71521} +{"mode": "train", "epoch": 9, "iter": 2400, "lr": 0.09918, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22828, "top5_acc": 0.46734, "loss_cls": 4.24676, "loss": 4.24676, "time": 0.71631} +{"mode": "train", "epoch": 9, "iter": 2500, "lr": 0.09918, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22969, "top5_acc": 0.48391, "loss_cls": 4.21021, "loss": 4.21021, "time": 0.71571} +{"mode": "train", "epoch": 9, "iter": 2600, "lr": 0.09917, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22375, "top5_acc": 0.47375, "loss_cls": 4.25741, "loss": 4.25741, "time": 0.71743} +{"mode": "train", "epoch": 9, "iter": 2700, "lr": 0.09917, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22781, "top5_acc": 0.47891, "loss_cls": 4.2108, "loss": 4.2108, "time": 0.71691} +{"mode": "train", "epoch": 9, "iter": 2800, "lr": 0.09916, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23062, "top5_acc": 0.47484, "loss_cls": 4.23747, "loss": 4.23747, "time": 0.71542} +{"mode": "train", "epoch": 9, "iter": 2900, "lr": 0.09916, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23922, "top5_acc": 0.48109, "loss_cls": 4.19575, "loss": 4.19575, "time": 0.72096} +{"mode": "train", "epoch": 9, "iter": 3000, "lr": 0.09915, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23125, "top5_acc": 0.48406, "loss_cls": 4.19576, "loss": 4.19576, "time": 0.71533} +{"mode": "train", "epoch": 9, "iter": 3100, "lr": 0.09915, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23172, "top5_acc": 0.47625, "loss_cls": 4.21668, "loss": 4.21668, "time": 0.71916} +{"mode": "train", "epoch": 9, "iter": 3200, "lr": 0.09914, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23797, "top5_acc": 0.48156, "loss_cls": 4.18578, "loss": 4.18578, "time": 0.72092} +{"mode": "train", "epoch": 9, "iter": 3300, "lr": 0.09914, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24094, "top5_acc": 0.48781, "loss_cls": 4.17407, "loss": 4.17407, "time": 0.71643} +{"mode": "train", "epoch": 9, "iter": 3400, "lr": 0.09913, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23469, "top5_acc": 0.48078, "loss_cls": 4.20892, "loss": 4.20892, "time": 0.71969} +{"mode": "train", "epoch": 9, "iter": 3500, "lr": 0.09913, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23578, "top5_acc": 0.47891, "loss_cls": 4.20796, "loss": 4.20796, "time": 0.72163} +{"mode": "train", "epoch": 9, "iter": 3600, "lr": 0.09912, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.23109, "top5_acc": 0.47422, "loss_cls": 4.25547, "loss": 4.25547, "time": 0.71825} +{"mode": "train", "epoch": 9, "iter": 3700, "lr": 0.09912, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.235, "top5_acc": 0.48828, "loss_cls": 4.19531, "loss": 4.19531, "time": 0.71573} +{"mode": "val", "epoch": 9, "iter": 309, "lr": 0.09911, "top1_acc": 0.17328, "top5_acc": 0.39558, "mean_class_accuracy": 0.17318} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.09911, "memory": 15990, "data_time": 1.47403, "top1_acc": 0.24078, "top5_acc": 0.48578, "loss_cls": 4.16068, "loss": 4.16068, "time": 2.19001} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.0991, "memory": 15990, "data_time": 0.00099, "top1_acc": 0.23422, "top5_acc": 0.4875, "loss_cls": 4.1662, "loss": 4.1662, "time": 0.71522} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.0991, "memory": 15990, "data_time": 0.00115, "top1_acc": 0.24375, "top5_acc": 0.48078, "loss_cls": 4.1813, "loss": 4.1813, "time": 0.71914} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.09909, "memory": 15990, "data_time": 0.00145, "top1_acc": 0.23031, "top5_acc": 0.46938, "loss_cls": 4.22857, "loss": 4.22857, "time": 0.71587} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.09909, "memory": 15990, "data_time": 0.00094, "top1_acc": 0.24219, "top5_acc": 0.48906, "loss_cls": 4.12564, "loss": 4.12564, "time": 0.71472} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.09908, "memory": 15990, "data_time": 0.00077, "top1_acc": 0.23156, "top5_acc": 0.47906, "loss_cls": 4.18575, "loss": 4.18575, "time": 0.71365} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.09908, "memory": 15990, "data_time": 0.00097, "top1_acc": 0.24016, "top5_acc": 0.48719, "loss_cls": 4.17214, "loss": 4.17214, "time": 0.71526} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.09907, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24469, "top5_acc": 0.49094, "loss_cls": 4.16003, "loss": 4.16003, "time": 0.71446} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.09907, "memory": 15990, "data_time": 0.00081, "top1_acc": 0.23578, "top5_acc": 0.48219, "loss_cls": 4.20703, "loss": 4.20703, "time": 0.71167} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.09906, "memory": 15990, "data_time": 0.00073, "top1_acc": 0.23344, "top5_acc": 0.48766, "loss_cls": 4.19387, "loss": 4.19387, "time": 0.7126} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.09906, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.23453, "top5_acc": 0.48219, "loss_cls": 4.18887, "loss": 4.18887, "time": 0.71232} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.09905, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.23969, "top5_acc": 0.48391, "loss_cls": 4.18627, "loss": 4.18627, "time": 0.71576} +{"mode": "train", "epoch": 10, "iter": 1300, "lr": 0.09905, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23234, "top5_acc": 0.48359, "loss_cls": 4.19386, "loss": 4.19386, "time": 0.71242} +{"mode": "train", "epoch": 10, "iter": 1400, "lr": 0.09904, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.24047, "top5_acc": 0.48938, "loss_cls": 4.18679, "loss": 4.18679, "time": 0.71345} +{"mode": "train", "epoch": 10, "iter": 1500, "lr": 0.09903, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22062, "top5_acc": 0.47156, "loss_cls": 4.23012, "loss": 4.23012, "time": 0.71972} +{"mode": "train", "epoch": 10, "iter": 1600, "lr": 0.09903, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23062, "top5_acc": 0.48531, "loss_cls": 4.17111, "loss": 4.17111, "time": 0.71284} +{"mode": "train", "epoch": 10, "iter": 1700, "lr": 0.09902, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2375, "top5_acc": 0.47938, "loss_cls": 4.21831, "loss": 4.21831, "time": 0.71739} +{"mode": "train", "epoch": 10, "iter": 1800, "lr": 0.09902, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23141, "top5_acc": 0.46734, "loss_cls": 4.24622, "loss": 4.24622, "time": 0.71393} +{"mode": "train", "epoch": 10, "iter": 1900, "lr": 0.09901, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22703, "top5_acc": 0.47906, "loss_cls": 4.21991, "loss": 4.21991, "time": 0.71297} +{"mode": "train", "epoch": 10, "iter": 2000, "lr": 0.09901, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23594, "top5_acc": 0.48078, "loss_cls": 4.17543, "loss": 4.17543, "time": 0.71442} +{"mode": "train", "epoch": 10, "iter": 2100, "lr": 0.099, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.235, "top5_acc": 0.48125, "loss_cls": 4.18724, "loss": 4.18724, "time": 0.71615} +{"mode": "train", "epoch": 10, "iter": 2200, "lr": 0.099, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23516, "top5_acc": 0.47844, "loss_cls": 4.2333, "loss": 4.2333, "time": 0.71408} +{"mode": "train", "epoch": 10, "iter": 2300, "lr": 0.09899, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23812, "top5_acc": 0.48203, "loss_cls": 4.20353, "loss": 4.20353, "time": 0.7134} +{"mode": "train", "epoch": 10, "iter": 2400, "lr": 0.09898, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22344, "top5_acc": 0.46672, "loss_cls": 4.24709, "loss": 4.24709, "time": 0.71477} +{"mode": "train", "epoch": 10, "iter": 2500, "lr": 0.09898, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24125, "top5_acc": 0.48922, "loss_cls": 4.18082, "loss": 4.18082, "time": 0.71668} +{"mode": "train", "epoch": 10, "iter": 2600, "lr": 0.09897, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23078, "top5_acc": 0.48109, "loss_cls": 4.17616, "loss": 4.17616, "time": 0.71632} +{"mode": "train", "epoch": 10, "iter": 2700, "lr": 0.09897, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23656, "top5_acc": 0.47906, "loss_cls": 4.20267, "loss": 4.20267, "time": 0.71466} +{"mode": "train", "epoch": 10, "iter": 2800, "lr": 0.09896, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23906, "top5_acc": 0.49156, "loss_cls": 4.16293, "loss": 4.16293, "time": 0.71307} +{"mode": "train", "epoch": 10, "iter": 2900, "lr": 0.09896, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22781, "top5_acc": 0.47797, "loss_cls": 4.22211, "loss": 4.22211, "time": 0.71924} +{"mode": "train", "epoch": 10, "iter": 3000, "lr": 0.09895, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23453, "top5_acc": 0.47469, "loss_cls": 4.21897, "loss": 4.21897, "time": 0.71522} +{"mode": "train", "epoch": 10, "iter": 3100, "lr": 0.09894, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23172, "top5_acc": 0.48781, "loss_cls": 4.17786, "loss": 4.17786, "time": 0.72011} +{"mode": "train", "epoch": 10, "iter": 3200, "lr": 0.09894, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23234, "top5_acc": 0.47219, "loss_cls": 4.22495, "loss": 4.22495, "time": 0.71662} +{"mode": "train", "epoch": 10, "iter": 3300, "lr": 0.09893, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23875, "top5_acc": 0.48016, "loss_cls": 4.21132, "loss": 4.21132, "time": 0.71724} +{"mode": "train", "epoch": 10, "iter": 3400, "lr": 0.09893, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23125, "top5_acc": 0.48141, "loss_cls": 4.2194, "loss": 4.2194, "time": 0.7175} +{"mode": "train", "epoch": 10, "iter": 3500, "lr": 0.09892, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23062, "top5_acc": 0.47188, "loss_cls": 4.21511, "loss": 4.21511, "time": 0.71597} +{"mode": "train", "epoch": 10, "iter": 3600, "lr": 0.09892, "memory": 15990, "data_time": 0.00078, "top1_acc": 0.22719, "top5_acc": 0.47406, "loss_cls": 4.23382, "loss": 4.23382, "time": 0.71678} +{"mode": "train", "epoch": 10, "iter": 3700, "lr": 0.09891, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24031, "top5_acc": 0.47797, "loss_cls": 4.20975, "loss": 4.20975, "time": 0.71274} +{"mode": "val", "epoch": 10, "iter": 309, "lr": 0.09891, "top1_acc": 0.18011, "top5_acc": 0.40738, "mean_class_accuracy": 0.1799} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.0989, "memory": 15990, "data_time": 1.40158, "top1_acc": 0.23984, "top5_acc": 0.49422, "loss_cls": 4.11332, "loss": 4.11332, "time": 2.11849} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.0989, "memory": 15990, "data_time": 0.00078, "top1_acc": 0.24203, "top5_acc": 0.48, "loss_cls": 4.15944, "loss": 4.15944, "time": 0.71642} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.09889, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23875, "top5_acc": 0.48781, "loss_cls": 4.16618, "loss": 4.16618, "time": 0.71829} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.09888, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23188, "top5_acc": 0.47875, "loss_cls": 4.20426, "loss": 4.20426, "time": 0.71371} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.09888, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23312, "top5_acc": 0.48734, "loss_cls": 4.18037, "loss": 4.18037, "time": 0.71453} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.09887, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23891, "top5_acc": 0.49328, "loss_cls": 4.16429, "loss": 4.16429, "time": 0.71616} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.09887, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23812, "top5_acc": 0.49344, "loss_cls": 4.16907, "loss": 4.16907, "time": 0.71646} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.09886, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23359, "top5_acc": 0.47953, "loss_cls": 4.2202, "loss": 4.2202, "time": 0.72121} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.09885, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23, "top5_acc": 0.48078, "loss_cls": 4.20107, "loss": 4.20107, "time": 0.71635} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.09885, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24594, "top5_acc": 0.49016, "loss_cls": 4.14109, "loss": 4.14109, "time": 0.71933} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.09884, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23844, "top5_acc": 0.48516, "loss_cls": 4.17216, "loss": 4.17216, "time": 0.71696} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.09884, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22656, "top5_acc": 0.47969, "loss_cls": 4.215, "loss": 4.215, "time": 0.71666} +{"mode": "train", "epoch": 11, "iter": 1300, "lr": 0.09883, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24125, "top5_acc": 0.49188, "loss_cls": 4.15799, "loss": 4.15799, "time": 0.71367} +{"mode": "train", "epoch": 11, "iter": 1400, "lr": 0.09882, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23281, "top5_acc": 0.48484, "loss_cls": 4.20038, "loss": 4.20038, "time": 0.72168} +{"mode": "train", "epoch": 11, "iter": 1500, "lr": 0.09882, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23422, "top5_acc": 0.47703, "loss_cls": 4.17838, "loss": 4.17838, "time": 0.71656} +{"mode": "train", "epoch": 11, "iter": 1600, "lr": 0.09881, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23109, "top5_acc": 0.48234, "loss_cls": 4.21905, "loss": 4.21905, "time": 0.71388} +{"mode": "train", "epoch": 11, "iter": 1700, "lr": 0.09881, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23594, "top5_acc": 0.48938, "loss_cls": 4.17829, "loss": 4.17829, "time": 0.7152} +{"mode": "train", "epoch": 11, "iter": 1800, "lr": 0.0988, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23391, "top5_acc": 0.48266, "loss_cls": 4.19209, "loss": 4.19209, "time": 0.71908} +{"mode": "train", "epoch": 11, "iter": 1900, "lr": 0.09879, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22547, "top5_acc": 0.46812, "loss_cls": 4.24131, "loss": 4.24131, "time": 0.72037} +{"mode": "train", "epoch": 11, "iter": 2000, "lr": 0.09879, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23641, "top5_acc": 0.48562, "loss_cls": 4.16843, "loss": 4.16843, "time": 0.71787} +{"mode": "train", "epoch": 11, "iter": 2100, "lr": 0.09878, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.23891, "top5_acc": 0.48219, "loss_cls": 4.16803, "loss": 4.16803, "time": 0.71912} +{"mode": "train", "epoch": 11, "iter": 2200, "lr": 0.09878, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23438, "top5_acc": 0.48109, "loss_cls": 4.19799, "loss": 4.19799, "time": 0.71927} +{"mode": "train", "epoch": 11, "iter": 2300, "lr": 0.09877, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22219, "top5_acc": 0.48859, "loss_cls": 4.20578, "loss": 4.20578, "time": 0.72251} +{"mode": "train", "epoch": 11, "iter": 2400, "lr": 0.09876, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23453, "top5_acc": 0.48859, "loss_cls": 4.1751, "loss": 4.1751, "time": 0.71879} +{"mode": "train", "epoch": 11, "iter": 2500, "lr": 0.09876, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22906, "top5_acc": 0.47859, "loss_cls": 4.19463, "loss": 4.19463, "time": 0.71739} +{"mode": "train", "epoch": 11, "iter": 2600, "lr": 0.09875, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.2375, "top5_acc": 0.48453, "loss_cls": 4.20431, "loss": 4.20431, "time": 0.72081} +{"mode": "train", "epoch": 11, "iter": 2700, "lr": 0.09874, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24312, "top5_acc": 0.49062, "loss_cls": 4.16223, "loss": 4.16223, "time": 0.7222} +{"mode": "train", "epoch": 11, "iter": 2800, "lr": 0.09874, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.22828, "top5_acc": 0.47484, "loss_cls": 4.23188, "loss": 4.23188, "time": 0.71839} +{"mode": "train", "epoch": 11, "iter": 2900, "lr": 0.09873, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23953, "top5_acc": 0.47984, "loss_cls": 4.17887, "loss": 4.17887, "time": 0.71917} +{"mode": "train", "epoch": 11, "iter": 3000, "lr": 0.09873, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23125, "top5_acc": 0.47781, "loss_cls": 4.20419, "loss": 4.20419, "time": 0.72323} +{"mode": "train", "epoch": 11, "iter": 3100, "lr": 0.09872, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23484, "top5_acc": 0.48375, "loss_cls": 4.17488, "loss": 4.17488, "time": 0.71998} +{"mode": "train", "epoch": 11, "iter": 3200, "lr": 0.09871, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23609, "top5_acc": 0.48453, "loss_cls": 4.18542, "loss": 4.18542, "time": 0.72257} +{"mode": "train", "epoch": 11, "iter": 3300, "lr": 0.09871, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22547, "top5_acc": 0.47625, "loss_cls": 4.20343, "loss": 4.20343, "time": 0.7197} +{"mode": "train", "epoch": 11, "iter": 3400, "lr": 0.0987, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23844, "top5_acc": 0.48594, "loss_cls": 4.19666, "loss": 4.19666, "time": 0.72041} +{"mode": "train", "epoch": 11, "iter": 3500, "lr": 0.09869, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24203, "top5_acc": 0.48172, "loss_cls": 4.19009, "loss": 4.19009, "time": 0.71987} +{"mode": "train", "epoch": 11, "iter": 3600, "lr": 0.09869, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23344, "top5_acc": 0.47656, "loss_cls": 4.2227, "loss": 4.2227, "time": 0.72269} +{"mode": "train", "epoch": 11, "iter": 3700, "lr": 0.09868, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22688, "top5_acc": 0.47781, "loss_cls": 4.21698, "loss": 4.21698, "time": 0.71827} +{"mode": "val", "epoch": 11, "iter": 309, "lr": 0.09868, "top1_acc": 0.1554, "top5_acc": 0.37152, "mean_class_accuracy": 0.1552} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.09867, "memory": 15990, "data_time": 1.39183, "top1_acc": 0.2425, "top5_acc": 0.49672, "loss_cls": 4.1078, "loss": 4.1078, "time": 2.1096} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.09867, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.23031, "top5_acc": 0.48062, "loss_cls": 4.20749, "loss": 4.20749, "time": 0.72116} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.09866, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23688, "top5_acc": 0.49031, "loss_cls": 4.15261, "loss": 4.15261, "time": 0.71917} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.09865, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24469, "top5_acc": 0.4975, "loss_cls": 4.13268, "loss": 4.13268, "time": 0.71534} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.09865, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23797, "top5_acc": 0.48516, "loss_cls": 4.17435, "loss": 4.17435, "time": 0.71386} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.09864, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22938, "top5_acc": 0.4875, "loss_cls": 4.19696, "loss": 4.19696, "time": 0.71672} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.09863, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23844, "top5_acc": 0.48688, "loss_cls": 4.15599, "loss": 4.15599, "time": 0.71869} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.09863, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24438, "top5_acc": 0.49828, "loss_cls": 4.11499, "loss": 4.11499, "time": 0.7149} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.09862, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22906, "top5_acc": 0.48656, "loss_cls": 4.16397, "loss": 4.16397, "time": 0.71455} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.09861, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25062, "top5_acc": 0.49125, "loss_cls": 4.16545, "loss": 4.16545, "time": 0.72006} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.09861, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23266, "top5_acc": 0.48953, "loss_cls": 4.15027, "loss": 4.15027, "time": 0.71497} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.0986, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23953, "top5_acc": 0.48516, "loss_cls": 4.18836, "loss": 4.18836, "time": 0.72119} +{"mode": "train", "epoch": 12, "iter": 1300, "lr": 0.09859, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24047, "top5_acc": 0.48469, "loss_cls": 4.1387, "loss": 4.1387, "time": 0.71435} +{"mode": "train", "epoch": 12, "iter": 1400, "lr": 0.09859, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23141, "top5_acc": 0.47828, "loss_cls": 4.18568, "loss": 4.18568, "time": 0.71643} +{"mode": "train", "epoch": 12, "iter": 1500, "lr": 0.09858, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23594, "top5_acc": 0.48891, "loss_cls": 4.18168, "loss": 4.18168, "time": 0.71424} +{"mode": "train", "epoch": 12, "iter": 1600, "lr": 0.09857, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23875, "top5_acc": 0.48391, "loss_cls": 4.17627, "loss": 4.17627, "time": 0.71306} +{"mode": "train", "epoch": 12, "iter": 1700, "lr": 0.09857, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23719, "top5_acc": 0.48656, "loss_cls": 4.18665, "loss": 4.18665, "time": 0.71847} +{"mode": "train", "epoch": 12, "iter": 1800, "lr": 0.09856, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24141, "top5_acc": 0.49422, "loss_cls": 4.13613, "loss": 4.13613, "time": 0.71398} +{"mode": "train", "epoch": 12, "iter": 1900, "lr": 0.09855, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23812, "top5_acc": 0.46891, "loss_cls": 4.20065, "loss": 4.20065, "time": 0.71544} +{"mode": "train", "epoch": 12, "iter": 2000, "lr": 0.09855, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24719, "top5_acc": 0.49875, "loss_cls": 4.14029, "loss": 4.14029, "time": 0.72003} +{"mode": "train", "epoch": 12, "iter": 2100, "lr": 0.09854, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24203, "top5_acc": 0.48406, "loss_cls": 4.20455, "loss": 4.20455, "time": 0.71769} +{"mode": "train", "epoch": 12, "iter": 2200, "lr": 0.09853, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2375, "top5_acc": 0.48641, "loss_cls": 4.1825, "loss": 4.1825, "time": 0.71743} +{"mode": "train", "epoch": 12, "iter": 2300, "lr": 0.09853, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24016, "top5_acc": 0.48875, "loss_cls": 4.19624, "loss": 4.19624, "time": 0.72424} +{"mode": "train", "epoch": 12, "iter": 2400, "lr": 0.09852, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23875, "top5_acc": 0.49156, "loss_cls": 4.17506, "loss": 4.17506, "time": 0.71618} +{"mode": "train", "epoch": 12, "iter": 2500, "lr": 0.09851, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23938, "top5_acc": 0.48312, "loss_cls": 4.18279, "loss": 4.18279, "time": 0.71784} +{"mode": "train", "epoch": 12, "iter": 2600, "lr": 0.09851, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23938, "top5_acc": 0.48812, "loss_cls": 4.18906, "loss": 4.18906, "time": 0.71771} +{"mode": "train", "epoch": 12, "iter": 2700, "lr": 0.0985, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2325, "top5_acc": 0.47719, "loss_cls": 4.20726, "loss": 4.20726, "time": 0.71587} +{"mode": "train", "epoch": 12, "iter": 2800, "lr": 0.09849, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22953, "top5_acc": 0.48141, "loss_cls": 4.22429, "loss": 4.22429, "time": 0.71813} +{"mode": "train", "epoch": 12, "iter": 2900, "lr": 0.09849, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23547, "top5_acc": 0.48578, "loss_cls": 4.19719, "loss": 4.19719, "time": 0.71509} +{"mode": "train", "epoch": 12, "iter": 3000, "lr": 0.09848, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24281, "top5_acc": 0.49609, "loss_cls": 4.15999, "loss": 4.15999, "time": 0.71711} +{"mode": "train", "epoch": 12, "iter": 3100, "lr": 0.09847, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23578, "top5_acc": 0.48875, "loss_cls": 4.17739, "loss": 4.17739, "time": 0.71928} +{"mode": "train", "epoch": 12, "iter": 3200, "lr": 0.09847, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24125, "top5_acc": 0.48891, "loss_cls": 4.16069, "loss": 4.16069, "time": 0.71866} +{"mode": "train", "epoch": 12, "iter": 3300, "lr": 0.09846, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23953, "top5_acc": 0.48672, "loss_cls": 4.13593, "loss": 4.13593, "time": 0.71758} +{"mode": "train", "epoch": 12, "iter": 3400, "lr": 0.09845, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23828, "top5_acc": 0.49188, "loss_cls": 4.17721, "loss": 4.17721, "time": 0.72008} +{"mode": "train", "epoch": 12, "iter": 3500, "lr": 0.09845, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24375, "top5_acc": 0.48766, "loss_cls": 4.15251, "loss": 4.15251, "time": 0.72056} +{"mode": "train", "epoch": 12, "iter": 3600, "lr": 0.09844, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.24062, "top5_acc": 0.49109, "loss_cls": 4.15771, "loss": 4.15771, "time": 0.71484} +{"mode": "train", "epoch": 12, "iter": 3700, "lr": 0.09843, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23594, "top5_acc": 0.48312, "loss_cls": 4.17339, "loss": 4.17339, "time": 0.7115} +{"mode": "val", "epoch": 12, "iter": 309, "lr": 0.09843, "top1_acc": 0.16487, "top5_acc": 0.38211, "mean_class_accuracy": 0.16467} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.09842, "memory": 15990, "data_time": 1.38115, "top1_acc": 0.2375, "top5_acc": 0.49094, "loss_cls": 4.15717, "loss": 4.15717, "time": 2.10197} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.09842, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24125, "top5_acc": 0.49297, "loss_cls": 4.12757, "loss": 4.12757, "time": 0.71794} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.09841, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24094, "top5_acc": 0.49328, "loss_cls": 4.15623, "loss": 4.15623, "time": 0.71338} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.0984, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24531, "top5_acc": 0.49875, "loss_cls": 4.13213, "loss": 4.13213, "time": 0.71358} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.09839, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24906, "top5_acc": 0.49953, "loss_cls": 4.10487, "loss": 4.10487, "time": 0.71414} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.09839, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24516, "top5_acc": 0.48141, "loss_cls": 4.13225, "loss": 4.13225, "time": 0.71896} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.09838, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22875, "top5_acc": 0.48328, "loss_cls": 4.16049, "loss": 4.16049, "time": 0.7148} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.09837, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23016, "top5_acc": 0.49, "loss_cls": 4.18654, "loss": 4.18654, "time": 0.71408} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.09837, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24297, "top5_acc": 0.49562, "loss_cls": 4.12401, "loss": 4.12401, "time": 0.7137} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.09836, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24484, "top5_acc": 0.49891, "loss_cls": 4.10274, "loss": 4.10274, "time": 0.71242} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.09835, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23781, "top5_acc": 0.48938, "loss_cls": 4.19629, "loss": 4.19629, "time": 0.71791} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.09834, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24344, "top5_acc": 0.48625, "loss_cls": 4.16556, "loss": 4.16556, "time": 0.7156} +{"mode": "train", "epoch": 13, "iter": 1300, "lr": 0.09834, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.235, "top5_acc": 0.49562, "loss_cls": 4.15099, "loss": 4.15099, "time": 0.72146} +{"mode": "train", "epoch": 13, "iter": 1400, "lr": 0.09833, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22891, "top5_acc": 0.48578, "loss_cls": 4.19254, "loss": 4.19254, "time": 0.71728} +{"mode": "train", "epoch": 13, "iter": 1500, "lr": 0.09832, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23797, "top5_acc": 0.49234, "loss_cls": 4.16451, "loss": 4.16451, "time": 0.71739} +{"mode": "train", "epoch": 13, "iter": 1600, "lr": 0.09832, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24906, "top5_acc": 0.48547, "loss_cls": 4.1596, "loss": 4.1596, "time": 0.71507} +{"mode": "train", "epoch": 13, "iter": 1700, "lr": 0.09831, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24, "top5_acc": 0.48781, "loss_cls": 4.19651, "loss": 4.19651, "time": 0.71744} +{"mode": "train", "epoch": 13, "iter": 1800, "lr": 0.0983, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22891, "top5_acc": 0.48094, "loss_cls": 4.22873, "loss": 4.22873, "time": 0.71665} +{"mode": "train", "epoch": 13, "iter": 1900, "lr": 0.09829, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23906, "top5_acc": 0.49094, "loss_cls": 4.16022, "loss": 4.16022, "time": 0.71569} +{"mode": "train", "epoch": 13, "iter": 2000, "lr": 0.09829, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23234, "top5_acc": 0.47609, "loss_cls": 4.17517, "loss": 4.17517, "time": 0.71552} +{"mode": "train", "epoch": 13, "iter": 2100, "lr": 0.09828, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24, "top5_acc": 0.49156, "loss_cls": 4.16919, "loss": 4.16919, "time": 0.72174} +{"mode": "train", "epoch": 13, "iter": 2200, "lr": 0.09827, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23641, "top5_acc": 0.48719, "loss_cls": 4.18856, "loss": 4.18856, "time": 0.71607} +{"mode": "train", "epoch": 13, "iter": 2300, "lr": 0.09827, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23906, "top5_acc": 0.48406, "loss_cls": 4.18886, "loss": 4.18886, "time": 0.71645} +{"mode": "train", "epoch": 13, "iter": 2400, "lr": 0.09826, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24219, "top5_acc": 0.48828, "loss_cls": 4.1482, "loss": 4.1482, "time": 0.71559} +{"mode": "train", "epoch": 13, "iter": 2500, "lr": 0.09825, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24031, "top5_acc": 0.48406, "loss_cls": 4.21363, "loss": 4.21363, "time": 0.71351} +{"mode": "train", "epoch": 13, "iter": 2600, "lr": 0.09824, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25156, "top5_acc": 0.50031, "loss_cls": 4.12024, "loss": 4.12024, "time": 0.71459} +{"mode": "train", "epoch": 13, "iter": 2700, "lr": 0.09824, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22562, "top5_acc": 0.47422, "loss_cls": 4.22172, "loss": 4.22172, "time": 0.71808} +{"mode": "train", "epoch": 13, "iter": 2800, "lr": 0.09823, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23438, "top5_acc": 0.49047, "loss_cls": 4.17898, "loss": 4.17898, "time": 0.71635} +{"mode": "train", "epoch": 13, "iter": 2900, "lr": 0.09822, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23516, "top5_acc": 0.48844, "loss_cls": 4.1645, "loss": 4.1645, "time": 0.72146} +{"mode": "train", "epoch": 13, "iter": 3000, "lr": 0.09821, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23719, "top5_acc": 0.49312, "loss_cls": 4.15419, "loss": 4.15419, "time": 0.72012} +{"mode": "train", "epoch": 13, "iter": 3100, "lr": 0.09821, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24375, "top5_acc": 0.4925, "loss_cls": 4.15152, "loss": 4.15152, "time": 0.71807} +{"mode": "train", "epoch": 13, "iter": 3200, "lr": 0.0982, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23438, "top5_acc": 0.48766, "loss_cls": 4.15184, "loss": 4.15184, "time": 0.72008} +{"mode": "train", "epoch": 13, "iter": 3300, "lr": 0.09819, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24438, "top5_acc": 0.48781, "loss_cls": 4.16633, "loss": 4.16633, "time": 0.71669} +{"mode": "train", "epoch": 13, "iter": 3400, "lr": 0.09818, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23766, "top5_acc": 0.49094, "loss_cls": 4.15209, "loss": 4.15209, "time": 0.71588} +{"mode": "train", "epoch": 13, "iter": 3500, "lr": 0.09818, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23281, "top5_acc": 0.47297, "loss_cls": 4.21622, "loss": 4.21622, "time": 0.71825} +{"mode": "train", "epoch": 13, "iter": 3600, "lr": 0.09817, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22766, "top5_acc": 0.48172, "loss_cls": 4.23407, "loss": 4.23407, "time": 0.71756} +{"mode": "train", "epoch": 13, "iter": 3700, "lr": 0.09816, "memory": 15990, "data_time": 0.00071, "top1_acc": 0.235, "top5_acc": 0.49469, "loss_cls": 4.1493, "loss": 4.1493, "time": 0.71835} +{"mode": "val", "epoch": 13, "iter": 309, "lr": 0.09816, "top1_acc": 0.17272, "top5_acc": 0.39751, "mean_class_accuracy": 0.1726} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.09815, "memory": 15990, "data_time": 1.38869, "top1_acc": 0.25375, "top5_acc": 0.50375, "loss_cls": 4.08176, "loss": 4.08176, "time": 2.10833} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.09814, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24953, "top5_acc": 0.49906, "loss_cls": 4.11199, "loss": 4.11199, "time": 0.71444} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.09814, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24016, "top5_acc": 0.49859, "loss_cls": 4.13575, "loss": 4.13575, "time": 0.7148} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.09813, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23891, "top5_acc": 0.48453, "loss_cls": 4.16623, "loss": 4.16623, "time": 0.71624} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.09812, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24359, "top5_acc": 0.49125, "loss_cls": 4.14347, "loss": 4.14347, "time": 0.71871} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.09811, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23844, "top5_acc": 0.47844, "loss_cls": 4.19356, "loss": 4.19356, "time": 0.71324} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.09811, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23359, "top5_acc": 0.4825, "loss_cls": 4.19961, "loss": 4.19961, "time": 0.71135} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.0981, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24078, "top5_acc": 0.49453, "loss_cls": 4.13683, "loss": 4.13683, "time": 0.71122} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.09809, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23875, "top5_acc": 0.48359, "loss_cls": 4.18442, "loss": 4.18442, "time": 0.7151} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.09808, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24672, "top5_acc": 0.48891, "loss_cls": 4.14002, "loss": 4.14002, "time": 0.71368} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.09807, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2425, "top5_acc": 0.49875, "loss_cls": 4.12162, "loss": 4.12162, "time": 0.71499} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.09807, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24328, "top5_acc": 0.48094, "loss_cls": 4.17022, "loss": 4.17022, "time": 0.71435} +{"mode": "train", "epoch": 14, "iter": 1300, "lr": 0.09806, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24391, "top5_acc": 0.48828, "loss_cls": 4.16152, "loss": 4.16152, "time": 0.72288} +{"mode": "train", "epoch": 14, "iter": 1400, "lr": 0.09805, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23656, "top5_acc": 0.48906, "loss_cls": 4.16122, "loss": 4.16122, "time": 0.71708} +{"mode": "train", "epoch": 14, "iter": 1500, "lr": 0.09804, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23812, "top5_acc": 0.48625, "loss_cls": 4.19003, "loss": 4.19003, "time": 0.71612} +{"mode": "train", "epoch": 14, "iter": 1600, "lr": 0.09804, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.235, "top5_acc": 0.48531, "loss_cls": 4.17814, "loss": 4.17814, "time": 0.71318} +{"mode": "train", "epoch": 14, "iter": 1700, "lr": 0.09803, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23812, "top5_acc": 0.48562, "loss_cls": 4.18569, "loss": 4.18569, "time": 0.72143} +{"mode": "train", "epoch": 14, "iter": 1800, "lr": 0.09802, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24, "top5_acc": 0.48406, "loss_cls": 4.16781, "loss": 4.16781, "time": 0.71815} +{"mode": "train", "epoch": 14, "iter": 1900, "lr": 0.09801, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23703, "top5_acc": 0.4875, "loss_cls": 4.1772, "loss": 4.1772, "time": 0.71523} +{"mode": "train", "epoch": 14, "iter": 2000, "lr": 0.098, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25297, "top5_acc": 0.50344, "loss_cls": 4.10659, "loss": 4.10659, "time": 0.71501} +{"mode": "train", "epoch": 14, "iter": 2100, "lr": 0.098, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24359, "top5_acc": 0.49281, "loss_cls": 4.14714, "loss": 4.14714, "time": 0.71613} +{"mode": "train", "epoch": 14, "iter": 2200, "lr": 0.09799, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24156, "top5_acc": 0.48562, "loss_cls": 4.17234, "loss": 4.17234, "time": 0.71484} +{"mode": "train", "epoch": 14, "iter": 2300, "lr": 0.09798, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24266, "top5_acc": 0.48984, "loss_cls": 4.17833, "loss": 4.17833, "time": 0.71542} +{"mode": "train", "epoch": 14, "iter": 2400, "lr": 0.09797, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25297, "top5_acc": 0.50078, "loss_cls": 4.09494, "loss": 4.09494, "time": 0.7132} +{"mode": "train", "epoch": 14, "iter": 2500, "lr": 0.09797, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23469, "top5_acc": 0.49016, "loss_cls": 4.16825, "loss": 4.16825, "time": 0.72052} +{"mode": "train", "epoch": 14, "iter": 2600, "lr": 0.09796, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24094, "top5_acc": 0.48688, "loss_cls": 4.14205, "loss": 4.14205, "time": 0.71565} +{"mode": "train", "epoch": 14, "iter": 2700, "lr": 0.09795, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24281, "top5_acc": 0.485, "loss_cls": 4.17991, "loss": 4.17991, "time": 0.71998} +{"mode": "train", "epoch": 14, "iter": 2800, "lr": 0.09794, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23641, "top5_acc": 0.48719, "loss_cls": 4.17569, "loss": 4.17569, "time": 0.7214} +{"mode": "train", "epoch": 14, "iter": 2900, "lr": 0.09793, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24141, "top5_acc": 0.48594, "loss_cls": 4.19119, "loss": 4.19119, "time": 0.71932} +{"mode": "train", "epoch": 14, "iter": 3000, "lr": 0.09793, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.23781, "top5_acc": 0.49016, "loss_cls": 4.15782, "loss": 4.15782, "time": 0.71652} +{"mode": "train", "epoch": 14, "iter": 3100, "lr": 0.09792, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23469, "top5_acc": 0.48875, "loss_cls": 4.146, "loss": 4.146, "time": 0.71709} +{"mode": "train", "epoch": 14, "iter": 3200, "lr": 0.09791, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23984, "top5_acc": 0.49609, "loss_cls": 4.14091, "loss": 4.14091, "time": 0.71941} +{"mode": "train", "epoch": 14, "iter": 3300, "lr": 0.0979, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23906, "top5_acc": 0.49906, "loss_cls": 4.12479, "loss": 4.12479, "time": 0.71833} +{"mode": "train", "epoch": 14, "iter": 3400, "lr": 0.09789, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24453, "top5_acc": 0.495, "loss_cls": 4.12544, "loss": 4.12544, "time": 0.71917} +{"mode": "train", "epoch": 14, "iter": 3500, "lr": 0.09789, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24938, "top5_acc": 0.49266, "loss_cls": 4.1354, "loss": 4.1354, "time": 0.72485} +{"mode": "train", "epoch": 14, "iter": 3600, "lr": 0.09788, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23797, "top5_acc": 0.48422, "loss_cls": 4.19125, "loss": 4.19125, "time": 0.7186} +{"mode": "train", "epoch": 14, "iter": 3700, "lr": 0.09787, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.24359, "top5_acc": 0.49, "loss_cls": 4.15752, "loss": 4.15752, "time": 0.71466} +{"mode": "val", "epoch": 14, "iter": 309, "lr": 0.09787, "top1_acc": 0.16568, "top5_acc": 0.38363, "mean_class_accuracy": 0.16563} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.09786, "memory": 15990, "data_time": 1.39323, "top1_acc": 0.24625, "top5_acc": 0.49594, "loss_cls": 4.11827, "loss": 4.11827, "time": 2.10868} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.09785, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24594, "top5_acc": 0.49531, "loss_cls": 4.11775, "loss": 4.11775, "time": 0.71595} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.09784, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24891, "top5_acc": 0.50375, "loss_cls": 4.12342, "loss": 4.12342, "time": 0.71128} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.09783, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24578, "top5_acc": 0.49203, "loss_cls": 4.10859, "loss": 4.10859, "time": 0.71981} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.09783, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25141, "top5_acc": 0.49953, "loss_cls": 4.11381, "loss": 4.11381, "time": 0.72083} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.09782, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23984, "top5_acc": 0.48656, "loss_cls": 4.19267, "loss": 4.19267, "time": 0.71273} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.09781, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24453, "top5_acc": 0.48984, "loss_cls": 4.14543, "loss": 4.14543, "time": 0.71732} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.0978, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24531, "top5_acc": 0.48953, "loss_cls": 4.16328, "loss": 4.16328, "time": 0.71799} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.09779, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24625, "top5_acc": 0.49219, "loss_cls": 4.16147, "loss": 4.16147, "time": 0.71788} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.09778, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24406, "top5_acc": 0.48625, "loss_cls": 4.16893, "loss": 4.16893, "time": 0.71305} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.09778, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23703, "top5_acc": 0.48406, "loss_cls": 4.16514, "loss": 4.16514, "time": 0.71589} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.09777, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24328, "top5_acc": 0.48828, "loss_cls": 4.17238, "loss": 4.17238, "time": 0.71326} +{"mode": "train", "epoch": 15, "iter": 1300, "lr": 0.09776, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24375, "top5_acc": 0.49172, "loss_cls": 4.13949, "loss": 4.13949, "time": 0.71492} +{"mode": "train", "epoch": 15, "iter": 1400, "lr": 0.09775, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24328, "top5_acc": 0.49312, "loss_cls": 4.13382, "loss": 4.13382, "time": 0.71737} +{"mode": "train", "epoch": 15, "iter": 1500, "lr": 0.09774, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24453, "top5_acc": 0.50094, "loss_cls": 4.14572, "loss": 4.14572, "time": 0.71213} +{"mode": "train", "epoch": 15, "iter": 1600, "lr": 0.09773, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24578, "top5_acc": 0.49422, "loss_cls": 4.13145, "loss": 4.13145, "time": 0.71834} +{"mode": "train", "epoch": 15, "iter": 1700, "lr": 0.09773, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24594, "top5_acc": 0.49344, "loss_cls": 4.12126, "loss": 4.12126, "time": 0.71461} +{"mode": "train", "epoch": 15, "iter": 1800, "lr": 0.09772, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24203, "top5_acc": 0.49, "loss_cls": 4.16325, "loss": 4.16325, "time": 0.71855} +{"mode": "train", "epoch": 15, "iter": 1900, "lr": 0.09771, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23062, "top5_acc": 0.48484, "loss_cls": 4.20211, "loss": 4.20211, "time": 0.71571} +{"mode": "train", "epoch": 15, "iter": 2000, "lr": 0.0977, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25, "top5_acc": 0.49609, "loss_cls": 4.14114, "loss": 4.14114, "time": 0.71311} +{"mode": "train", "epoch": 15, "iter": 2100, "lr": 0.09769, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.24578, "top5_acc": 0.49062, "loss_cls": 4.14213, "loss": 4.14213, "time": 0.71716} +{"mode": "train", "epoch": 15, "iter": 2200, "lr": 0.09768, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24531, "top5_acc": 0.48531, "loss_cls": 4.16553, "loss": 4.16553, "time": 0.71662} +{"mode": "train", "epoch": 15, "iter": 2300, "lr": 0.09768, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24234, "top5_acc": 0.48422, "loss_cls": 4.15783, "loss": 4.15783, "time": 0.71337} +{"mode": "train", "epoch": 15, "iter": 2400, "lr": 0.09767, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24266, "top5_acc": 0.50375, "loss_cls": 4.08976, "loss": 4.08976, "time": 0.71882} +{"mode": "train", "epoch": 15, "iter": 2500, "lr": 0.09766, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24469, "top5_acc": 0.49656, "loss_cls": 4.12443, "loss": 4.12443, "time": 0.71662} +{"mode": "train", "epoch": 15, "iter": 2600, "lr": 0.09765, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25203, "top5_acc": 0.49375, "loss_cls": 4.12934, "loss": 4.12934, "time": 0.71613} +{"mode": "train", "epoch": 15, "iter": 2700, "lr": 0.09764, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23844, "top5_acc": 0.49, "loss_cls": 4.13552, "loss": 4.13552, "time": 0.71748} +{"mode": "train", "epoch": 15, "iter": 2800, "lr": 0.09763, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23391, "top5_acc": 0.48219, "loss_cls": 4.16581, "loss": 4.16581, "time": 0.71858} +{"mode": "train", "epoch": 15, "iter": 2900, "lr": 0.09763, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24688, "top5_acc": 0.49984, "loss_cls": 4.12123, "loss": 4.12123, "time": 0.71568} +{"mode": "train", "epoch": 15, "iter": 3000, "lr": 0.09762, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24062, "top5_acc": 0.49578, "loss_cls": 4.15583, "loss": 4.15583, "time": 0.71588} +{"mode": "train", "epoch": 15, "iter": 3100, "lr": 0.09761, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23516, "top5_acc": 0.49641, "loss_cls": 4.17804, "loss": 4.17804, "time": 0.7175} +{"mode": "train", "epoch": 15, "iter": 3200, "lr": 0.0976, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23781, "top5_acc": 0.48516, "loss_cls": 4.17195, "loss": 4.17195, "time": 0.71711} +{"mode": "train", "epoch": 15, "iter": 3300, "lr": 0.09759, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23812, "top5_acc": 0.48641, "loss_cls": 4.16325, "loss": 4.16325, "time": 0.71647} +{"mode": "train", "epoch": 15, "iter": 3400, "lr": 0.09758, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24875, "top5_acc": 0.48438, "loss_cls": 4.17069, "loss": 4.17069, "time": 0.71869} +{"mode": "train", "epoch": 15, "iter": 3500, "lr": 0.09757, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24094, "top5_acc": 0.50047, "loss_cls": 4.14379, "loss": 4.14379, "time": 0.71765} +{"mode": "train", "epoch": 15, "iter": 3600, "lr": 0.09757, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24719, "top5_acc": 0.48922, "loss_cls": 4.1491, "loss": 4.1491, "time": 0.71705} +{"mode": "train", "epoch": 15, "iter": 3700, "lr": 0.09756, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23531, "top5_acc": 0.48812, "loss_cls": 4.16275, "loss": 4.16275, "time": 0.71356} +{"mode": "val", "epoch": 15, "iter": 309, "lr": 0.09755, "top1_acc": 0.16168, "top5_acc": 0.3854, "mean_class_accuracy": 0.16171} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.09754, "memory": 15990, "data_time": 1.37503, "top1_acc": 0.2525, "top5_acc": 0.49531, "loss_cls": 4.11316, "loss": 4.11316, "time": 2.09087} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.09754, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24922, "top5_acc": 0.49219, "loss_cls": 4.09226, "loss": 4.09226, "time": 0.7174} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.09753, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2425, "top5_acc": 0.49625, "loss_cls": 4.13792, "loss": 4.13792, "time": 0.71204} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.09752, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.25312, "top5_acc": 0.49875, "loss_cls": 4.1264, "loss": 4.1264, "time": 0.70919} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.09751, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.23688, "top5_acc": 0.49531, "loss_cls": 4.13321, "loss": 4.13321, "time": 0.71382} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.0975, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24188, "top5_acc": 0.50016, "loss_cls": 4.08778, "loss": 4.08778, "time": 0.71281} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.09749, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.24547, "top5_acc": 0.49016, "loss_cls": 4.13251, "loss": 4.13251, "time": 0.71216} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.09748, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24406, "top5_acc": 0.51094, "loss_cls": 4.10595, "loss": 4.10595, "time": 0.71693} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.09747, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25, "top5_acc": 0.50844, "loss_cls": 4.07779, "loss": 4.07779, "time": 0.712} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.09747, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24234, "top5_acc": 0.49391, "loss_cls": 4.14243, "loss": 4.14243, "time": 0.71334} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.09746, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25125, "top5_acc": 0.50344, "loss_cls": 4.08245, "loss": 4.08245, "time": 0.71797} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.09745, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24672, "top5_acc": 0.49312, "loss_cls": 4.12281, "loss": 4.12281, "time": 0.71878} +{"mode": "train", "epoch": 16, "iter": 1300, "lr": 0.09744, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23625, "top5_acc": 0.485, "loss_cls": 4.15238, "loss": 4.15238, "time": 0.71959} +{"mode": "train", "epoch": 16, "iter": 1400, "lr": 0.09743, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23078, "top5_acc": 0.49125, "loss_cls": 4.14744, "loss": 4.14744, "time": 0.71752} +{"mode": "train", "epoch": 16, "iter": 1500, "lr": 0.09742, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25094, "top5_acc": 0.49031, "loss_cls": 4.11088, "loss": 4.11088, "time": 0.71894} +{"mode": "train", "epoch": 16, "iter": 1600, "lr": 0.09741, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24781, "top5_acc": 0.49984, "loss_cls": 4.12323, "loss": 4.12323, "time": 0.72165} +{"mode": "train", "epoch": 16, "iter": 1700, "lr": 0.0974, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24812, "top5_acc": 0.49578, "loss_cls": 4.12459, "loss": 4.12459, "time": 0.7189} +{"mode": "train", "epoch": 16, "iter": 1800, "lr": 0.0974, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23688, "top5_acc": 0.48281, "loss_cls": 4.17119, "loss": 4.17119, "time": 0.71624} +{"mode": "train", "epoch": 16, "iter": 1900, "lr": 0.09739, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23734, "top5_acc": 0.49531, "loss_cls": 4.13468, "loss": 4.13468, "time": 0.72053} +{"mode": "train", "epoch": 16, "iter": 2000, "lr": 0.09738, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2425, "top5_acc": 0.48922, "loss_cls": 4.15502, "loss": 4.15502, "time": 0.72274} +{"mode": "train", "epoch": 16, "iter": 2100, "lr": 0.09737, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24781, "top5_acc": 0.49484, "loss_cls": 4.16152, "loss": 4.16152, "time": 0.71832} +{"mode": "train", "epoch": 16, "iter": 2200, "lr": 0.09736, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24172, "top5_acc": 0.49406, "loss_cls": 4.1235, "loss": 4.1235, "time": 0.72412} +{"mode": "train", "epoch": 16, "iter": 2300, "lr": 0.09735, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24266, "top5_acc": 0.49188, "loss_cls": 4.13004, "loss": 4.13004, "time": 0.71746} +{"mode": "train", "epoch": 16, "iter": 2400, "lr": 0.09734, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24391, "top5_acc": 0.48938, "loss_cls": 4.13432, "loss": 4.13432, "time": 0.71892} +{"mode": "train", "epoch": 16, "iter": 2500, "lr": 0.09733, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24188, "top5_acc": 0.49312, "loss_cls": 4.13653, "loss": 4.13653, "time": 0.72141} +{"mode": "train", "epoch": 16, "iter": 2600, "lr": 0.09732, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.23641, "top5_acc": 0.49188, "loss_cls": 4.15855, "loss": 4.15855, "time": 0.72162} +{"mode": "train", "epoch": 16, "iter": 2700, "lr": 0.09731, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.23734, "top5_acc": 0.48938, "loss_cls": 4.16124, "loss": 4.16124, "time": 0.72146} +{"mode": "train", "epoch": 16, "iter": 2800, "lr": 0.09731, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25469, "top5_acc": 0.50281, "loss_cls": 4.10501, "loss": 4.10501, "time": 0.72136} +{"mode": "train", "epoch": 16, "iter": 2900, "lr": 0.0973, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24594, "top5_acc": 0.49281, "loss_cls": 4.12026, "loss": 4.12026, "time": 0.7235} +{"mode": "train", "epoch": 16, "iter": 3000, "lr": 0.09729, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.23844, "top5_acc": 0.49375, "loss_cls": 4.16323, "loss": 4.16323, "time": 0.72387} +{"mode": "train", "epoch": 16, "iter": 3100, "lr": 0.09728, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24422, "top5_acc": 0.49125, "loss_cls": 4.14296, "loss": 4.14296, "time": 0.72298} +{"mode": "train", "epoch": 16, "iter": 3200, "lr": 0.09727, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24656, "top5_acc": 0.48, "loss_cls": 4.19125, "loss": 4.19125, "time": 0.72035} +{"mode": "train", "epoch": 16, "iter": 3300, "lr": 0.09726, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24375, "top5_acc": 0.49531, "loss_cls": 4.16362, "loss": 4.16362, "time": 0.71969} +{"mode": "train", "epoch": 16, "iter": 3400, "lr": 0.09725, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.24688, "top5_acc": 0.49641, "loss_cls": 4.13001, "loss": 4.13001, "time": 0.72364} +{"mode": "train", "epoch": 16, "iter": 3500, "lr": 0.09724, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24188, "top5_acc": 0.48641, "loss_cls": 4.14377, "loss": 4.14377, "time": 0.72231} +{"mode": "train", "epoch": 16, "iter": 3600, "lr": 0.09723, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.23562, "top5_acc": 0.48859, "loss_cls": 4.17149, "loss": 4.17149, "time": 0.72506} +{"mode": "train", "epoch": 16, "iter": 3700, "lr": 0.09722, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24625, "top5_acc": 0.49719, "loss_cls": 4.13062, "loss": 4.13062, "time": 0.71816} +{"mode": "val", "epoch": 16, "iter": 309, "lr": 0.09722, "top1_acc": 0.18285, "top5_acc": 0.41002, "mean_class_accuracy": 0.18265} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.09721, "memory": 15990, "data_time": 1.38577, "top1_acc": 0.25375, "top5_acc": 0.49734, "loss_cls": 4.09206, "loss": 4.09206, "time": 2.10477} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.0972, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.25094, "top5_acc": 0.49906, "loss_cls": 4.10427, "loss": 4.10427, "time": 0.71917} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.09719, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25484, "top5_acc": 0.49969, "loss_cls": 4.09997, "loss": 4.09997, "time": 0.71415} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.09718, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23766, "top5_acc": 0.49359, "loss_cls": 4.13409, "loss": 4.13409, "time": 0.71184} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.09717, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25547, "top5_acc": 0.50109, "loss_cls": 4.11136, "loss": 4.11136, "time": 0.7166} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.09716, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23953, "top5_acc": 0.48703, "loss_cls": 4.15587, "loss": 4.15587, "time": 0.71257} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.09715, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25062, "top5_acc": 0.50172, "loss_cls": 4.11223, "loss": 4.11223, "time": 0.71219} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.09714, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24938, "top5_acc": 0.50281, "loss_cls": 4.10963, "loss": 4.10963, "time": 0.71176} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.09714, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23594, "top5_acc": 0.49078, "loss_cls": 4.15597, "loss": 4.15597, "time": 0.71545} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.09713, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24516, "top5_acc": 0.50266, "loss_cls": 4.05335, "loss": 4.05335, "time": 0.71266} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.09712, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24203, "top5_acc": 0.49047, "loss_cls": 4.12702, "loss": 4.12702, "time": 0.71135} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.09711, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2475, "top5_acc": 0.49547, "loss_cls": 4.12131, "loss": 4.12131, "time": 0.71577} +{"mode": "train", "epoch": 17, "iter": 1300, "lr": 0.0971, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24484, "top5_acc": 0.49625, "loss_cls": 4.14536, "loss": 4.14536, "time": 0.71713} +{"mode": "train", "epoch": 17, "iter": 1400, "lr": 0.09709, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2475, "top5_acc": 0.50094, "loss_cls": 4.11377, "loss": 4.11377, "time": 0.71536} +{"mode": "train", "epoch": 17, "iter": 1500, "lr": 0.09708, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25141, "top5_acc": 0.50469, "loss_cls": 4.09802, "loss": 4.09802, "time": 0.71891} +{"mode": "train", "epoch": 17, "iter": 1600, "lr": 0.09707, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23531, "top5_acc": 0.49719, "loss_cls": 4.1217, "loss": 4.1217, "time": 0.72558} +{"mode": "train", "epoch": 17, "iter": 1700, "lr": 0.09706, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24703, "top5_acc": 0.5, "loss_cls": 4.14364, "loss": 4.14364, "time": 0.71395} +{"mode": "train", "epoch": 17, "iter": 1800, "lr": 0.09705, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24703, "top5_acc": 0.49703, "loss_cls": 4.13065, "loss": 4.13065, "time": 0.71748} +{"mode": "train", "epoch": 17, "iter": 1900, "lr": 0.09704, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24859, "top5_acc": 0.49609, "loss_cls": 4.11003, "loss": 4.11003, "time": 0.71493} +{"mode": "train", "epoch": 17, "iter": 2000, "lr": 0.09703, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24297, "top5_acc": 0.49422, "loss_cls": 4.13074, "loss": 4.13074, "time": 0.71269} +{"mode": "train", "epoch": 17, "iter": 2100, "lr": 0.09702, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24359, "top5_acc": 0.49781, "loss_cls": 4.1391, "loss": 4.1391, "time": 0.71714} +{"mode": "train", "epoch": 17, "iter": 2200, "lr": 0.09701, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24, "top5_acc": 0.49625, "loss_cls": 4.13637, "loss": 4.13637, "time": 0.71532} +{"mode": "train", "epoch": 17, "iter": 2300, "lr": 0.097, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25594, "top5_acc": 0.49625, "loss_cls": 4.11792, "loss": 4.11792, "time": 0.72047} +{"mode": "train", "epoch": 17, "iter": 2400, "lr": 0.09699, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24047, "top5_acc": 0.48531, "loss_cls": 4.16992, "loss": 4.16992, "time": 0.71526} +{"mode": "train", "epoch": 17, "iter": 2500, "lr": 0.09698, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2375, "top5_acc": 0.48531, "loss_cls": 4.1782, "loss": 4.1782, "time": 0.7176} +{"mode": "train", "epoch": 17, "iter": 2600, "lr": 0.09697, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24578, "top5_acc": 0.49281, "loss_cls": 4.14931, "loss": 4.14931, "time": 0.71683} +{"mode": "train", "epoch": 17, "iter": 2700, "lr": 0.09697, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24234, "top5_acc": 0.49797, "loss_cls": 4.12203, "loss": 4.12203, "time": 0.71546} +{"mode": "train", "epoch": 17, "iter": 2800, "lr": 0.09696, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24406, "top5_acc": 0.50187, "loss_cls": 4.10909, "loss": 4.10909, "time": 0.71715} +{"mode": "train", "epoch": 17, "iter": 2900, "lr": 0.09695, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24297, "top5_acc": 0.48859, "loss_cls": 4.15801, "loss": 4.15801, "time": 0.71342} +{"mode": "train", "epoch": 17, "iter": 3000, "lr": 0.09694, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24109, "top5_acc": 0.49766, "loss_cls": 4.12843, "loss": 4.12843, "time": 0.71862} +{"mode": "train", "epoch": 17, "iter": 3100, "lr": 0.09693, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24844, "top5_acc": 0.49422, "loss_cls": 4.13824, "loss": 4.13824, "time": 0.71423} +{"mode": "train", "epoch": 17, "iter": 3200, "lr": 0.09692, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24812, "top5_acc": 0.49953, "loss_cls": 4.10291, "loss": 4.10291, "time": 0.71527} +{"mode": "train", "epoch": 17, "iter": 3300, "lr": 0.09691, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25156, "top5_acc": 0.49625, "loss_cls": 4.11183, "loss": 4.11183, "time": 0.71387} +{"mode": "train", "epoch": 17, "iter": 3400, "lr": 0.0969, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24391, "top5_acc": 0.48797, "loss_cls": 4.17015, "loss": 4.17015, "time": 0.71677} +{"mode": "train", "epoch": 17, "iter": 3500, "lr": 0.09689, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24312, "top5_acc": 0.48734, "loss_cls": 4.14306, "loss": 4.14306, "time": 0.71782} +{"mode": "train", "epoch": 17, "iter": 3600, "lr": 0.09688, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25328, "top5_acc": 0.50594, "loss_cls": 4.0694, "loss": 4.0694, "time": 0.71581} +{"mode": "train", "epoch": 17, "iter": 3700, "lr": 0.09687, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24594, "top5_acc": 0.49578, "loss_cls": 4.1311, "loss": 4.1311, "time": 0.71625} +{"mode": "val", "epoch": 17, "iter": 309, "lr": 0.09686, "top1_acc": 0.15089, "top5_acc": 0.36068, "mean_class_accuracy": 0.15079} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.09685, "memory": 15990, "data_time": 1.38482, "top1_acc": 0.24047, "top5_acc": 0.5, "loss_cls": 4.10386, "loss": 4.10386, "time": 2.10277} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.09684, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24812, "top5_acc": 0.49828, "loss_cls": 4.10276, "loss": 4.10276, "time": 0.71275} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.09683, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24859, "top5_acc": 0.49469, "loss_cls": 4.09281, "loss": 4.09281, "time": 0.71859} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.09683, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25234, "top5_acc": 0.50688, "loss_cls": 4.09371, "loss": 4.09371, "time": 0.71355} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.09682, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24312, "top5_acc": 0.49422, "loss_cls": 4.14431, "loss": 4.14431, "time": 0.71891} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.09681, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2425, "top5_acc": 0.48891, "loss_cls": 4.1379, "loss": 4.1379, "time": 0.71798} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.0968, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24562, "top5_acc": 0.49359, "loss_cls": 4.16074, "loss": 4.16074, "time": 0.7153} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.09679, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24203, "top5_acc": 0.49781, "loss_cls": 4.12887, "loss": 4.12887, "time": 0.71612} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.09678, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25109, "top5_acc": 0.49641, "loss_cls": 4.095, "loss": 4.095, "time": 0.71675} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.09677, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25031, "top5_acc": 0.50156, "loss_cls": 4.06315, "loss": 4.06315, "time": 0.72166} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.09676, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24172, "top5_acc": 0.49031, "loss_cls": 4.1591, "loss": 4.1591, "time": 0.72302} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.09675, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25656, "top5_acc": 0.505, "loss_cls": 4.06507, "loss": 4.06507, "time": 0.71546} +{"mode": "train", "epoch": 18, "iter": 1300, "lr": 0.09674, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23469, "top5_acc": 0.48766, "loss_cls": 4.16792, "loss": 4.16792, "time": 0.71531} +{"mode": "train", "epoch": 18, "iter": 1400, "lr": 0.09673, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23766, "top5_acc": 0.49125, "loss_cls": 4.14946, "loss": 4.14946, "time": 0.71882} +{"mode": "train", "epoch": 18, "iter": 1500, "lr": 0.09672, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.24625, "top5_acc": 0.49297, "loss_cls": 4.13518, "loss": 4.13518, "time": 0.71573} +{"mode": "train", "epoch": 18, "iter": 1600, "lr": 0.09671, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.25, "top5_acc": 0.49938, "loss_cls": 4.11531, "loss": 4.11531, "time": 0.71543} +{"mode": "train", "epoch": 18, "iter": 1700, "lr": 0.0967, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25438, "top5_acc": 0.49922, "loss_cls": 4.11001, "loss": 4.11001, "time": 0.71806} +{"mode": "train", "epoch": 18, "iter": 1800, "lr": 0.09669, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24359, "top5_acc": 0.50578, "loss_cls": 4.11209, "loss": 4.11209, "time": 0.71443} +{"mode": "train", "epoch": 18, "iter": 1900, "lr": 0.09668, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24641, "top5_acc": 0.49516, "loss_cls": 4.11995, "loss": 4.11995, "time": 0.71766} +{"mode": "train", "epoch": 18, "iter": 2000, "lr": 0.09667, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25031, "top5_acc": 0.50781, "loss_cls": 4.07547, "loss": 4.07547, "time": 0.71605} +{"mode": "train", "epoch": 18, "iter": 2100, "lr": 0.09666, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23984, "top5_acc": 0.49828, "loss_cls": 4.13227, "loss": 4.13227, "time": 0.71883} +{"mode": "train", "epoch": 18, "iter": 2200, "lr": 0.09665, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25, "top5_acc": 0.49828, "loss_cls": 4.10626, "loss": 4.10626, "time": 0.71809} +{"mode": "train", "epoch": 18, "iter": 2300, "lr": 0.09664, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25, "top5_acc": 0.50062, "loss_cls": 4.10932, "loss": 4.10932, "time": 0.71726} +{"mode": "train", "epoch": 18, "iter": 2400, "lr": 0.09663, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24375, "top5_acc": 0.49375, "loss_cls": 4.15135, "loss": 4.15135, "time": 0.71353} +{"mode": "train", "epoch": 18, "iter": 2500, "lr": 0.09662, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25109, "top5_acc": 0.49234, "loss_cls": 4.11736, "loss": 4.11736, "time": 0.71312} +{"mode": "train", "epoch": 18, "iter": 2600, "lr": 0.09661, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24594, "top5_acc": 0.49328, "loss_cls": 4.12385, "loss": 4.12385, "time": 0.71999} +{"mode": "train", "epoch": 18, "iter": 2700, "lr": 0.0966, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23234, "top5_acc": 0.49578, "loss_cls": 4.13546, "loss": 4.13546, "time": 0.71332} +{"mode": "train", "epoch": 18, "iter": 2800, "lr": 0.09659, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24312, "top5_acc": 0.49328, "loss_cls": 4.12027, "loss": 4.12027, "time": 0.71657} +{"mode": "train", "epoch": 18, "iter": 2900, "lr": 0.09658, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24656, "top5_acc": 0.4975, "loss_cls": 4.14992, "loss": 4.14992, "time": 0.71963} +{"mode": "train", "epoch": 18, "iter": 3000, "lr": 0.09657, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25094, "top5_acc": 0.4925, "loss_cls": 4.14178, "loss": 4.14178, "time": 0.71529} +{"mode": "train", "epoch": 18, "iter": 3100, "lr": 0.09656, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24484, "top5_acc": 0.49422, "loss_cls": 4.12722, "loss": 4.12722, "time": 0.71607} +{"mode": "train", "epoch": 18, "iter": 3200, "lr": 0.09654, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25156, "top5_acc": 0.50375, "loss_cls": 4.09867, "loss": 4.09867, "time": 0.71802} +{"mode": "train", "epoch": 18, "iter": 3300, "lr": 0.09653, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25219, "top5_acc": 0.50875, "loss_cls": 4.09042, "loss": 4.09042, "time": 0.71442} +{"mode": "train", "epoch": 18, "iter": 3400, "lr": 0.09652, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24484, "top5_acc": 0.49312, "loss_cls": 4.12216, "loss": 4.12216, "time": 0.71698} +{"mode": "train", "epoch": 18, "iter": 3500, "lr": 0.09651, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24953, "top5_acc": 0.50016, "loss_cls": 4.08114, "loss": 4.08114, "time": 0.71802} +{"mode": "train", "epoch": 18, "iter": 3600, "lr": 0.0965, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25438, "top5_acc": 0.50031, "loss_cls": 4.10297, "loss": 4.10297, "time": 0.71501} +{"mode": "train", "epoch": 18, "iter": 3700, "lr": 0.09649, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25016, "top5_acc": 0.49984, "loss_cls": 4.11385, "loss": 4.11385, "time": 0.71465} +{"mode": "val", "epoch": 18, "iter": 309, "lr": 0.09649, "top1_acc": 0.18381, "top5_acc": 0.41321, "mean_class_accuracy": 0.18353} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.09648, "memory": 15990, "data_time": 1.36044, "top1_acc": 0.25219, "top5_acc": 0.50531, "loss_cls": 4.05006, "loss": 4.05006, "time": 2.07767} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.09647, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.23891, "top5_acc": 0.49016, "loss_cls": 4.11248, "loss": 4.11248, "time": 0.71846} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.09646, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.25062, "top5_acc": 0.50125, "loss_cls": 4.08739, "loss": 4.08739, "time": 0.71665} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.09645, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26109, "top5_acc": 0.50656, "loss_cls": 4.05286, "loss": 4.05286, "time": 0.71198} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.09644, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24344, "top5_acc": 0.50047, "loss_cls": 4.1036, "loss": 4.1036, "time": 0.71206} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.09643, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26281, "top5_acc": 0.51234, "loss_cls": 4.04795, "loss": 4.04795, "time": 0.71817} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.09642, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25125, "top5_acc": 0.50625, "loss_cls": 4.08608, "loss": 4.08608, "time": 0.71749} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.09641, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24375, "top5_acc": 0.48875, "loss_cls": 4.15794, "loss": 4.15794, "time": 0.71173} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.0964, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24641, "top5_acc": 0.49766, "loss_cls": 4.12655, "loss": 4.12655, "time": 0.71661} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.09639, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24547, "top5_acc": 0.50281, "loss_cls": 4.12419, "loss": 4.12419, "time": 0.71314} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.09637, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24906, "top5_acc": 0.50203, "loss_cls": 4.09549, "loss": 4.09549, "time": 0.71366} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.09636, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24266, "top5_acc": 0.48719, "loss_cls": 4.16859, "loss": 4.16859, "time": 0.71371} +{"mode": "train", "epoch": 19, "iter": 1300, "lr": 0.09635, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24703, "top5_acc": 0.49609, "loss_cls": 4.12221, "loss": 4.12221, "time": 0.71263} +{"mode": "train", "epoch": 19, "iter": 1400, "lr": 0.09634, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25359, "top5_acc": 0.49828, "loss_cls": 4.1004, "loss": 4.1004, "time": 0.71434} +{"mode": "train", "epoch": 19, "iter": 1500, "lr": 0.09633, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2425, "top5_acc": 0.49688, "loss_cls": 4.13621, "loss": 4.13621, "time": 0.71623} +{"mode": "train", "epoch": 19, "iter": 1600, "lr": 0.09632, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24578, "top5_acc": 0.49547, "loss_cls": 4.1038, "loss": 4.1038, "time": 0.71917} +{"mode": "train", "epoch": 19, "iter": 1700, "lr": 0.09631, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25594, "top5_acc": 0.50016, "loss_cls": 4.07007, "loss": 4.07007, "time": 0.71474} +{"mode": "train", "epoch": 19, "iter": 1800, "lr": 0.0963, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24906, "top5_acc": 0.50813, "loss_cls": 4.10214, "loss": 4.10214, "time": 0.7175} +{"mode": "train", "epoch": 19, "iter": 1900, "lr": 0.09629, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23391, "top5_acc": 0.48297, "loss_cls": 4.17277, "loss": 4.17277, "time": 0.71786} +{"mode": "train", "epoch": 19, "iter": 2000, "lr": 0.09628, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24672, "top5_acc": 0.50094, "loss_cls": 4.12013, "loss": 4.12013, "time": 0.71499} +{"mode": "train", "epoch": 19, "iter": 2100, "lr": 0.09627, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25281, "top5_acc": 0.50828, "loss_cls": 4.07826, "loss": 4.07826, "time": 0.71542} +{"mode": "train", "epoch": 19, "iter": 2200, "lr": 0.09626, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24375, "top5_acc": 0.49156, "loss_cls": 4.15437, "loss": 4.15437, "time": 0.71606} +{"mode": "train", "epoch": 19, "iter": 2300, "lr": 0.09625, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24734, "top5_acc": 0.49297, "loss_cls": 4.13126, "loss": 4.13126, "time": 0.71247} +{"mode": "train", "epoch": 19, "iter": 2400, "lr": 0.09624, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24609, "top5_acc": 0.49812, "loss_cls": 4.15026, "loss": 4.15026, "time": 0.71559} +{"mode": "train", "epoch": 19, "iter": 2500, "lr": 0.09623, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23797, "top5_acc": 0.49391, "loss_cls": 4.13689, "loss": 4.13689, "time": 0.71522} +{"mode": "train", "epoch": 19, "iter": 2600, "lr": 0.09622, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24891, "top5_acc": 0.49203, "loss_cls": 4.11509, "loss": 4.11509, "time": 0.71323} +{"mode": "train", "epoch": 19, "iter": 2700, "lr": 0.09621, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24984, "top5_acc": 0.4975, "loss_cls": 4.10045, "loss": 4.10045, "time": 0.71456} +{"mode": "train", "epoch": 19, "iter": 2800, "lr": 0.0962, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25406, "top5_acc": 0.50016, "loss_cls": 4.10753, "loss": 4.10753, "time": 0.71714} +{"mode": "train", "epoch": 19, "iter": 2900, "lr": 0.09618, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25516, "top5_acc": 0.49422, "loss_cls": 4.14599, "loss": 4.14599, "time": 0.71818} +{"mode": "train", "epoch": 19, "iter": 3000, "lr": 0.09617, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24438, "top5_acc": 0.49609, "loss_cls": 4.10683, "loss": 4.10683, "time": 0.71452} +{"mode": "train", "epoch": 19, "iter": 3100, "lr": 0.09616, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25406, "top5_acc": 0.51016, "loss_cls": 4.07315, "loss": 4.07315, "time": 0.71765} +{"mode": "train", "epoch": 19, "iter": 3200, "lr": 0.09615, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25375, "top5_acc": 0.50266, "loss_cls": 4.12706, "loss": 4.12706, "time": 0.71864} +{"mode": "train", "epoch": 19, "iter": 3300, "lr": 0.09614, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23859, "top5_acc": 0.49, "loss_cls": 4.14134, "loss": 4.14134, "time": 0.71604} +{"mode": "train", "epoch": 19, "iter": 3400, "lr": 0.09613, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24688, "top5_acc": 0.50031, "loss_cls": 4.11029, "loss": 4.11029, "time": 0.71645} +{"mode": "train", "epoch": 19, "iter": 3500, "lr": 0.09612, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24953, "top5_acc": 0.50125, "loss_cls": 4.08794, "loss": 4.08794, "time": 0.71577} +{"mode": "train", "epoch": 19, "iter": 3600, "lr": 0.09611, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24422, "top5_acc": 0.49047, "loss_cls": 4.12858, "loss": 4.12858, "time": 0.71679} +{"mode": "train", "epoch": 19, "iter": 3700, "lr": 0.0961, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23422, "top5_acc": 0.48672, "loss_cls": 4.15284, "loss": 4.15284, "time": 0.7186} +{"mode": "val", "epoch": 19, "iter": 309, "lr": 0.09609, "top1_acc": 0.18077, "top5_acc": 0.40779, "mean_class_accuracy": 0.18062} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.09608, "memory": 15990, "data_time": 1.37711, "top1_acc": 0.25312, "top5_acc": 0.50078, "loss_cls": 4.08721, "loss": 4.08721, "time": 2.09407} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.09607, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26703, "top5_acc": 0.51266, "loss_cls": 4.03514, "loss": 4.03514, "time": 0.71632} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.09606, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25609, "top5_acc": 0.50125, "loss_cls": 4.07774, "loss": 4.07774, "time": 0.7133} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.09605, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.24875, "top5_acc": 0.49719, "loss_cls": 4.10854, "loss": 4.10854, "time": 0.71894} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.09604, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24172, "top5_acc": 0.50313, "loss_cls": 4.08621, "loss": 4.08621, "time": 0.71394} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.09603, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24656, "top5_acc": 0.49312, "loss_cls": 4.12428, "loss": 4.12428, "time": 0.71282} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.09602, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24562, "top5_acc": 0.49906, "loss_cls": 4.09714, "loss": 4.09714, "time": 0.71698} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.09601, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25516, "top5_acc": 0.50984, "loss_cls": 4.07967, "loss": 4.07967, "time": 0.71276} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.096, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25094, "top5_acc": 0.49656, "loss_cls": 4.08866, "loss": 4.08866, "time": 0.71519} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.09598, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24641, "top5_acc": 0.49734, "loss_cls": 4.09983, "loss": 4.09983, "time": 0.71559} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.09597, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24859, "top5_acc": 0.49297, "loss_cls": 4.1363, "loss": 4.1363, "time": 0.71672} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.09596, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24156, "top5_acc": 0.4875, "loss_cls": 4.1449, "loss": 4.1449, "time": 0.71394} +{"mode": "train", "epoch": 20, "iter": 1300, "lr": 0.09595, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25297, "top5_acc": 0.51375, "loss_cls": 4.0497, "loss": 4.0497, "time": 0.71354} +{"mode": "train", "epoch": 20, "iter": 1400, "lr": 0.09594, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24828, "top5_acc": 0.49922, "loss_cls": 4.12287, "loss": 4.12287, "time": 0.71117} +{"mode": "train", "epoch": 20, "iter": 1500, "lr": 0.09593, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24734, "top5_acc": 0.50219, "loss_cls": 4.10847, "loss": 4.10847, "time": 0.7132} +{"mode": "train", "epoch": 20, "iter": 1600, "lr": 0.09592, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24375, "top5_acc": 0.5025, "loss_cls": 4.13398, "loss": 4.13398, "time": 0.71259} +{"mode": "train", "epoch": 20, "iter": 1700, "lr": 0.09591, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24438, "top5_acc": 0.49984, "loss_cls": 4.11465, "loss": 4.11465, "time": 0.71483} +{"mode": "train", "epoch": 20, "iter": 1800, "lr": 0.0959, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25, "top5_acc": 0.50094, "loss_cls": 4.07885, "loss": 4.07885, "time": 0.71871} +{"mode": "train", "epoch": 20, "iter": 1900, "lr": 0.09588, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24328, "top5_acc": 0.50484, "loss_cls": 4.10831, "loss": 4.10831, "time": 0.71585} +{"mode": "train", "epoch": 20, "iter": 2000, "lr": 0.09587, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24609, "top5_acc": 0.50031, "loss_cls": 4.11818, "loss": 4.11818, "time": 0.71731} +{"mode": "train", "epoch": 20, "iter": 2100, "lr": 0.09586, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24234, "top5_acc": 0.49312, "loss_cls": 4.12912, "loss": 4.12912, "time": 0.72027} +{"mode": "train", "epoch": 20, "iter": 2200, "lr": 0.09585, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25125, "top5_acc": 0.495, "loss_cls": 4.09514, "loss": 4.09514, "time": 0.7173} +{"mode": "train", "epoch": 20, "iter": 2300, "lr": 0.09584, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24469, "top5_acc": 0.49656, "loss_cls": 4.12147, "loss": 4.12147, "time": 0.71551} +{"mode": "train", "epoch": 20, "iter": 2400, "lr": 0.09583, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25578, "top5_acc": 0.50844, "loss_cls": 4.06257, "loss": 4.06257, "time": 0.72003} +{"mode": "train", "epoch": 20, "iter": 2500, "lr": 0.09582, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25297, "top5_acc": 0.49594, "loss_cls": 4.13564, "loss": 4.13564, "time": 0.71602} +{"mode": "train", "epoch": 20, "iter": 2600, "lr": 0.09581, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24578, "top5_acc": 0.50484, "loss_cls": 4.11242, "loss": 4.11242, "time": 0.71684} +{"mode": "train", "epoch": 20, "iter": 2700, "lr": 0.0958, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25438, "top5_acc": 0.49281, "loss_cls": 4.09886, "loss": 4.09886, "time": 0.71437} +{"mode": "train", "epoch": 20, "iter": 2800, "lr": 0.09578, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24812, "top5_acc": 0.51078, "loss_cls": 4.06631, "loss": 4.06631, "time": 0.71352} +{"mode": "train", "epoch": 20, "iter": 2900, "lr": 0.09577, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25188, "top5_acc": 0.50219, "loss_cls": 4.07313, "loss": 4.07313, "time": 0.71385} +{"mode": "train", "epoch": 20, "iter": 3000, "lr": 0.09576, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25344, "top5_acc": 0.50625, "loss_cls": 4.07048, "loss": 4.07048, "time": 0.71968} +{"mode": "train", "epoch": 20, "iter": 3100, "lr": 0.09575, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24203, "top5_acc": 0.49, "loss_cls": 4.12062, "loss": 4.12062, "time": 0.71965} +{"mode": "train", "epoch": 20, "iter": 3200, "lr": 0.09574, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26, "top5_acc": 0.50938, "loss_cls": 4.04814, "loss": 4.04814, "time": 0.71748} +{"mode": "train", "epoch": 20, "iter": 3300, "lr": 0.09573, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24625, "top5_acc": 0.49969, "loss_cls": 4.10745, "loss": 4.10745, "time": 0.71391} +{"mode": "train", "epoch": 20, "iter": 3400, "lr": 0.09572, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25672, "top5_acc": 0.50297, "loss_cls": 4.09025, "loss": 4.09025, "time": 0.71854} +{"mode": "train", "epoch": 20, "iter": 3500, "lr": 0.09571, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24578, "top5_acc": 0.49625, "loss_cls": 4.13978, "loss": 4.13978, "time": 0.71522} +{"mode": "train", "epoch": 20, "iter": 3600, "lr": 0.09569, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25219, "top5_acc": 0.50266, "loss_cls": 4.08849, "loss": 4.08849, "time": 0.71437} +{"mode": "train", "epoch": 20, "iter": 3700, "lr": 0.09568, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24438, "top5_acc": 0.49297, "loss_cls": 4.13548, "loss": 4.13548, "time": 0.71476} +{"mode": "val", "epoch": 20, "iter": 309, "lr": 0.09568, "top1_acc": 0.14957, "top5_acc": 0.3619, "mean_class_accuracy": 0.14947} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.09567, "memory": 15990, "data_time": 1.37964, "top1_acc": 0.26156, "top5_acc": 0.51297, "loss_cls": 4.03124, "loss": 4.03124, "time": 2.09452} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.09565, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25703, "top5_acc": 0.51203, "loss_cls": 4.0447, "loss": 4.0447, "time": 0.72204} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.09564, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25125, "top5_acc": 0.50562, "loss_cls": 4.105, "loss": 4.105, "time": 0.71538} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.09563, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.25516, "top5_acc": 0.50688, "loss_cls": 4.05725, "loss": 4.05725, "time": 0.71852} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.09562, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.25688, "top5_acc": 0.51391, "loss_cls": 4.07016, "loss": 4.07016, "time": 0.71434} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.09561, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24625, "top5_acc": 0.50344, "loss_cls": 4.08433, "loss": 4.08433, "time": 0.72362} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.0956, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25312, "top5_acc": 0.50547, "loss_cls": 4.09234, "loss": 4.09234, "time": 0.71901} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.09559, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25047, "top5_acc": 0.49359, "loss_cls": 4.12216, "loss": 4.12216, "time": 0.71632} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.09557, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25078, "top5_acc": 0.49578, "loss_cls": 4.10269, "loss": 4.10269, "time": 0.71972} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.09556, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25562, "top5_acc": 0.50688, "loss_cls": 4.08438, "loss": 4.08438, "time": 0.71079} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.09555, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24797, "top5_acc": 0.49891, "loss_cls": 4.09831, "loss": 4.09831, "time": 0.71485} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.09554, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24156, "top5_acc": 0.49719, "loss_cls": 4.11516, "loss": 4.11516, "time": 0.71656} +{"mode": "train", "epoch": 21, "iter": 1300, "lr": 0.09553, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25609, "top5_acc": 0.50313, "loss_cls": 4.08217, "loss": 4.08217, "time": 0.71116} +{"mode": "train", "epoch": 21, "iter": 1400, "lr": 0.09552, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25344, "top5_acc": 0.50016, "loss_cls": 4.09055, "loss": 4.09055, "time": 0.71221} +{"mode": "train", "epoch": 21, "iter": 1500, "lr": 0.09551, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25719, "top5_acc": 0.49562, "loss_cls": 4.09928, "loss": 4.09928, "time": 0.71558} +{"mode": "train", "epoch": 21, "iter": 1600, "lr": 0.09549, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25266, "top5_acc": 0.50281, "loss_cls": 4.094, "loss": 4.094, "time": 0.72183} +{"mode": "train", "epoch": 21, "iter": 1700, "lr": 0.09548, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24969, "top5_acc": 0.49281, "loss_cls": 4.11587, "loss": 4.11587, "time": 0.71373} +{"mode": "train", "epoch": 21, "iter": 1800, "lr": 0.09547, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25453, "top5_acc": 0.49984, "loss_cls": 4.09879, "loss": 4.09879, "time": 0.71945} +{"mode": "train", "epoch": 21, "iter": 1900, "lr": 0.09546, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25156, "top5_acc": 0.50984, "loss_cls": 4.07703, "loss": 4.07703, "time": 0.71503} +{"mode": "train", "epoch": 21, "iter": 2000, "lr": 0.09545, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24219, "top5_acc": 0.49062, "loss_cls": 4.16387, "loss": 4.16387, "time": 0.7174} +{"mode": "train", "epoch": 21, "iter": 2100, "lr": 0.09544, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23984, "top5_acc": 0.49719, "loss_cls": 4.12769, "loss": 4.12769, "time": 0.7156} +{"mode": "train", "epoch": 21, "iter": 2200, "lr": 0.09542, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25234, "top5_acc": 0.50531, "loss_cls": 4.08097, "loss": 4.08097, "time": 0.72042} +{"mode": "train", "epoch": 21, "iter": 2300, "lr": 0.09541, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2575, "top5_acc": 0.515, "loss_cls": 4.06648, "loss": 4.06648, "time": 0.71714} +{"mode": "train", "epoch": 21, "iter": 2400, "lr": 0.0954, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24672, "top5_acc": 0.50219, "loss_cls": 4.09071, "loss": 4.09071, "time": 0.71801} +{"mode": "train", "epoch": 21, "iter": 2500, "lr": 0.09539, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23562, "top5_acc": 0.49, "loss_cls": 4.16577, "loss": 4.16577, "time": 0.71541} +{"mode": "train", "epoch": 21, "iter": 2600, "lr": 0.09538, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26219, "top5_acc": 0.50594, "loss_cls": 4.05394, "loss": 4.05394, "time": 0.71521} +{"mode": "train", "epoch": 21, "iter": 2700, "lr": 0.09537, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26031, "top5_acc": 0.50391, "loss_cls": 4.08276, "loss": 4.08276, "time": 0.71747} +{"mode": "train", "epoch": 21, "iter": 2800, "lr": 0.09535, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25062, "top5_acc": 0.49688, "loss_cls": 4.10962, "loss": 4.10962, "time": 0.71683} +{"mode": "train", "epoch": 21, "iter": 2900, "lr": 0.09534, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25484, "top5_acc": 0.50109, "loss_cls": 4.09602, "loss": 4.09602, "time": 0.71735} +{"mode": "train", "epoch": 21, "iter": 3000, "lr": 0.09533, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24703, "top5_acc": 0.49641, "loss_cls": 4.11378, "loss": 4.11378, "time": 0.71819} +{"mode": "train", "epoch": 21, "iter": 3100, "lr": 0.09532, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24703, "top5_acc": 0.49703, "loss_cls": 4.10994, "loss": 4.10994, "time": 0.71766} +{"mode": "train", "epoch": 21, "iter": 3200, "lr": 0.09531, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25734, "top5_acc": 0.50781, "loss_cls": 4.08097, "loss": 4.08097, "time": 0.71718} +{"mode": "train", "epoch": 21, "iter": 3300, "lr": 0.09529, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24531, "top5_acc": 0.50594, "loss_cls": 4.08158, "loss": 4.08158, "time": 0.71403} +{"mode": "train", "epoch": 21, "iter": 3400, "lr": 0.09528, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24609, "top5_acc": 0.49562, "loss_cls": 4.127, "loss": 4.127, "time": 0.71698} +{"mode": "train", "epoch": 21, "iter": 3500, "lr": 0.09527, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24922, "top5_acc": 0.50094, "loss_cls": 4.09398, "loss": 4.09398, "time": 0.71598} +{"mode": "train", "epoch": 21, "iter": 3600, "lr": 0.09526, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25516, "top5_acc": 0.50641, "loss_cls": 4.0763, "loss": 4.0763, "time": 0.71547} +{"mode": "train", "epoch": 21, "iter": 3700, "lr": 0.09525, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24609, "top5_acc": 0.49516, "loss_cls": 4.10519, "loss": 4.10519, "time": 0.71693} +{"mode": "val", "epoch": 21, "iter": 309, "lr": 0.09524, "top1_acc": 0.15008, "top5_acc": 0.3539, "mean_class_accuracy": 0.15014} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.09523, "memory": 15990, "data_time": 1.37962, "top1_acc": 0.25953, "top5_acc": 0.50422, "loss_cls": 4.0694, "loss": 4.0694, "time": 2.09703} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.09522, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25844, "top5_acc": 0.50875, "loss_cls": 4.06867, "loss": 4.06867, "time": 0.71767} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.09521, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24828, "top5_acc": 0.49859, "loss_cls": 4.10985, "loss": 4.10985, "time": 0.71481} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.09519, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.25391, "top5_acc": 0.51281, "loss_cls": 4.04255, "loss": 4.04255, "time": 0.71476} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.09518, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25531, "top5_acc": 0.50406, "loss_cls": 4.09409, "loss": 4.09409, "time": 0.71567} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.09517, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25766, "top5_acc": 0.50688, "loss_cls": 4.06129, "loss": 4.06129, "time": 0.71241} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.09516, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24078, "top5_acc": 0.49953, "loss_cls": 4.1388, "loss": 4.1388, "time": 0.71965} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.09515, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25469, "top5_acc": 0.51125, "loss_cls": 4.04595, "loss": 4.04595, "time": 0.71631} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.09513, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24719, "top5_acc": 0.49156, "loss_cls": 4.10533, "loss": 4.10533, "time": 0.71493} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.09512, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25578, "top5_acc": 0.50484, "loss_cls": 4.06324, "loss": 4.06324, "time": 0.71628} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.09511, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24078, "top5_acc": 0.49469, "loss_cls": 4.14589, "loss": 4.14589, "time": 0.71376} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0951, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25484, "top5_acc": 0.50281, "loss_cls": 4.04753, "loss": 4.04753, "time": 0.71107} +{"mode": "train", "epoch": 22, "iter": 1300, "lr": 0.09509, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24516, "top5_acc": 0.50391, "loss_cls": 4.0774, "loss": 4.0774, "time": 0.71716} +{"mode": "train", "epoch": 22, "iter": 1400, "lr": 0.09507, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25688, "top5_acc": 0.50953, "loss_cls": 4.07118, "loss": 4.07118, "time": 0.71477} +{"mode": "train", "epoch": 22, "iter": 1500, "lr": 0.09506, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24859, "top5_acc": 0.49859, "loss_cls": 4.07589, "loss": 4.07589, "time": 0.72011} +{"mode": "train", "epoch": 22, "iter": 1600, "lr": 0.09505, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25094, "top5_acc": 0.49531, "loss_cls": 4.13734, "loss": 4.13734, "time": 0.71499} +{"mode": "train", "epoch": 22, "iter": 1700, "lr": 0.09504, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24438, "top5_acc": 0.50281, "loss_cls": 4.09022, "loss": 4.09022, "time": 0.72076} +{"mode": "train", "epoch": 22, "iter": 1800, "lr": 0.09502, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25047, "top5_acc": 0.50016, "loss_cls": 4.1025, "loss": 4.1025, "time": 0.71734} +{"mode": "train", "epoch": 22, "iter": 1900, "lr": 0.09501, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26188, "top5_acc": 0.50641, "loss_cls": 4.06608, "loss": 4.06608, "time": 0.72042} +{"mode": "train", "epoch": 22, "iter": 2000, "lr": 0.095, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24094, "top5_acc": 0.49594, "loss_cls": 4.14132, "loss": 4.14132, "time": 0.71919} +{"mode": "train", "epoch": 22, "iter": 2100, "lr": 0.09499, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25, "top5_acc": 0.49531, "loss_cls": 4.1066, "loss": 4.1066, "time": 0.71583} +{"mode": "train", "epoch": 22, "iter": 2200, "lr": 0.09498, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25453, "top5_acc": 0.50422, "loss_cls": 4.09464, "loss": 4.09464, "time": 0.71629} +{"mode": "train", "epoch": 22, "iter": 2300, "lr": 0.09496, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25156, "top5_acc": 0.49797, "loss_cls": 4.14047, "loss": 4.14047, "time": 0.71912} +{"mode": "train", "epoch": 22, "iter": 2400, "lr": 0.09495, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24719, "top5_acc": 0.51031, "loss_cls": 4.09126, "loss": 4.09126, "time": 0.72081} +{"mode": "train", "epoch": 22, "iter": 2500, "lr": 0.09494, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25828, "top5_acc": 0.50547, "loss_cls": 4.0634, "loss": 4.0634, "time": 0.72222} +{"mode": "train", "epoch": 22, "iter": 2600, "lr": 0.09493, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25359, "top5_acc": 0.50688, "loss_cls": 4.08415, "loss": 4.08415, "time": 0.72157} +{"mode": "train", "epoch": 22, "iter": 2700, "lr": 0.09491, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25, "top5_acc": 0.50625, "loss_cls": 4.10155, "loss": 4.10155, "time": 0.72182} +{"mode": "train", "epoch": 22, "iter": 2800, "lr": 0.0949, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2475, "top5_acc": 0.51172, "loss_cls": 4.07124, "loss": 4.07124, "time": 0.72572} +{"mode": "train", "epoch": 22, "iter": 2900, "lr": 0.09489, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25766, "top5_acc": 0.50484, "loss_cls": 4.09651, "loss": 4.09651, "time": 0.71974} +{"mode": "train", "epoch": 22, "iter": 3000, "lr": 0.09488, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24672, "top5_acc": 0.50547, "loss_cls": 4.09977, "loss": 4.09977, "time": 0.71885} +{"mode": "train", "epoch": 22, "iter": 3100, "lr": 0.09487, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26078, "top5_acc": 0.51094, "loss_cls": 4.0531, "loss": 4.0531, "time": 0.72098} +{"mode": "train", "epoch": 22, "iter": 3200, "lr": 0.09485, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25047, "top5_acc": 0.50547, "loss_cls": 4.07831, "loss": 4.07831, "time": 0.72027} +{"mode": "train", "epoch": 22, "iter": 3300, "lr": 0.09484, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.24172, "top5_acc": 0.49938, "loss_cls": 4.11928, "loss": 4.11928, "time": 0.72371} +{"mode": "train", "epoch": 22, "iter": 3400, "lr": 0.09483, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24953, "top5_acc": 0.50141, "loss_cls": 4.10506, "loss": 4.10506, "time": 0.72391} +{"mode": "train", "epoch": 22, "iter": 3500, "lr": 0.09482, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24516, "top5_acc": 0.49938, "loss_cls": 4.08366, "loss": 4.08366, "time": 0.72093} +{"mode": "train", "epoch": 22, "iter": 3600, "lr": 0.0948, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26906, "top5_acc": 0.52031, "loss_cls": 4.02245, "loss": 4.02245, "time": 0.71804} +{"mode": "train", "epoch": 22, "iter": 3700, "lr": 0.09479, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25781, "top5_acc": 0.51, "loss_cls": 4.07119, "loss": 4.07119, "time": 0.72106} +{"mode": "val", "epoch": 22, "iter": 309, "lr": 0.09479, "top1_acc": 0.17338, "top5_acc": 0.39234, "mean_class_accuracy": 0.17345} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.09477, "memory": 15990, "data_time": 1.38647, "top1_acc": 0.25891, "top5_acc": 0.52047, "loss_cls": 4.01829, "loss": 4.01829, "time": 2.10503} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.09476, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25859, "top5_acc": 0.51578, "loss_cls": 4.06099, "loss": 4.06099, "time": 0.71805} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.09475, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25328, "top5_acc": 0.49938, "loss_cls": 4.0861, "loss": 4.0861, "time": 0.71725} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.09474, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2525, "top5_acc": 0.51281, "loss_cls": 4.04585, "loss": 4.04585, "time": 0.71629} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.09472, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24719, "top5_acc": 0.49359, "loss_cls": 4.11475, "loss": 4.11475, "time": 0.71631} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.09471, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25312, "top5_acc": 0.50406, "loss_cls": 4.1015, "loss": 4.1015, "time": 0.71296} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.0947, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25844, "top5_acc": 0.50859, "loss_cls": 4.03751, "loss": 4.03751, "time": 0.71019} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.09469, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26219, "top5_acc": 0.51203, "loss_cls": 4.03148, "loss": 4.03148, "time": 0.71321} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.09467, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24984, "top5_acc": 0.50469, "loss_cls": 4.07345, "loss": 4.07345, "time": 0.71672} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.09466, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24406, "top5_acc": 0.49797, "loss_cls": 4.11468, "loss": 4.11468, "time": 0.71687} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.09465, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24672, "top5_acc": 0.49391, "loss_cls": 4.11939, "loss": 4.11939, "time": 0.71242} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.09464, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25797, "top5_acc": 0.51141, "loss_cls": 4.07311, "loss": 4.07311, "time": 0.71699} +{"mode": "train", "epoch": 23, "iter": 1300, "lr": 0.09462, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25594, "top5_acc": 0.50984, "loss_cls": 4.07651, "loss": 4.07651, "time": 0.71506} +{"mode": "train", "epoch": 23, "iter": 1400, "lr": 0.09461, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24938, "top5_acc": 0.50187, "loss_cls": 4.0844, "loss": 4.0844, "time": 0.71499} +{"mode": "train", "epoch": 23, "iter": 1500, "lr": 0.0946, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25328, "top5_acc": 0.51391, "loss_cls": 4.0629, "loss": 4.0629, "time": 0.71624} +{"mode": "train", "epoch": 23, "iter": 1600, "lr": 0.09459, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25562, "top5_acc": 0.50766, "loss_cls": 4.10104, "loss": 4.10104, "time": 0.71419} +{"mode": "train", "epoch": 23, "iter": 1700, "lr": 0.09457, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24641, "top5_acc": 0.49938, "loss_cls": 4.09842, "loss": 4.09842, "time": 0.71567} +{"mode": "train", "epoch": 23, "iter": 1800, "lr": 0.09456, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25438, "top5_acc": 0.50734, "loss_cls": 4.09063, "loss": 4.09063, "time": 0.71868} +{"mode": "train", "epoch": 23, "iter": 1900, "lr": 0.09455, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25594, "top5_acc": 0.50313, "loss_cls": 4.08066, "loss": 4.08066, "time": 0.71701} +{"mode": "train", "epoch": 23, "iter": 2000, "lr": 0.09453, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24953, "top5_acc": 0.50672, "loss_cls": 4.07084, "loss": 4.07084, "time": 0.71908} +{"mode": "train", "epoch": 23, "iter": 2100, "lr": 0.09452, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24875, "top5_acc": 0.48859, "loss_cls": 4.11278, "loss": 4.11278, "time": 0.7201} +{"mode": "train", "epoch": 23, "iter": 2200, "lr": 0.09451, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24922, "top5_acc": 0.49969, "loss_cls": 4.09954, "loss": 4.09954, "time": 0.72078} +{"mode": "train", "epoch": 23, "iter": 2300, "lr": 0.0945, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25859, "top5_acc": 0.50844, "loss_cls": 4.02491, "loss": 4.02491, "time": 0.72085} +{"mode": "train", "epoch": 23, "iter": 2400, "lr": 0.09448, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24016, "top5_acc": 0.4925, "loss_cls": 4.14824, "loss": 4.14824, "time": 0.72021} +{"mode": "train", "epoch": 23, "iter": 2500, "lr": 0.09447, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25469, "top5_acc": 0.50203, "loss_cls": 4.0812, "loss": 4.0812, "time": 0.72099} +{"mode": "train", "epoch": 23, "iter": 2600, "lr": 0.09446, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26094, "top5_acc": 0.51672, "loss_cls": 4.04488, "loss": 4.04488, "time": 0.7207} +{"mode": "train", "epoch": 23, "iter": 2700, "lr": 0.09445, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24859, "top5_acc": 0.50625, "loss_cls": 4.07129, "loss": 4.07129, "time": 0.7183} +{"mode": "train", "epoch": 23, "iter": 2800, "lr": 0.09443, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24703, "top5_acc": 0.48453, "loss_cls": 4.1274, "loss": 4.1274, "time": 0.71824} +{"mode": "train", "epoch": 23, "iter": 2900, "lr": 0.09442, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25875, "top5_acc": 0.51125, "loss_cls": 4.05704, "loss": 4.05704, "time": 0.71681} +{"mode": "train", "epoch": 23, "iter": 3000, "lr": 0.09441, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25391, "top5_acc": 0.50516, "loss_cls": 4.09789, "loss": 4.09789, "time": 0.71938} +{"mode": "train", "epoch": 23, "iter": 3100, "lr": 0.09439, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25344, "top5_acc": 0.50734, "loss_cls": 4.06081, "loss": 4.06081, "time": 0.71674} +{"mode": "train", "epoch": 23, "iter": 3200, "lr": 0.09438, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25703, "top5_acc": 0.50531, "loss_cls": 4.086, "loss": 4.086, "time": 0.72233} +{"mode": "train", "epoch": 23, "iter": 3300, "lr": 0.09437, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25516, "top5_acc": 0.50766, "loss_cls": 4.07515, "loss": 4.07515, "time": 0.72167} +{"mode": "train", "epoch": 23, "iter": 3400, "lr": 0.09436, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24719, "top5_acc": 0.49938, "loss_cls": 4.12865, "loss": 4.12865, "time": 0.72038} +{"mode": "train", "epoch": 23, "iter": 3500, "lr": 0.09434, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25344, "top5_acc": 0.50422, "loss_cls": 4.08054, "loss": 4.08054, "time": 0.72039} +{"mode": "train", "epoch": 23, "iter": 3600, "lr": 0.09433, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25219, "top5_acc": 0.49766, "loss_cls": 4.1216, "loss": 4.1216, "time": 0.71687} +{"mode": "train", "epoch": 23, "iter": 3700, "lr": 0.09432, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25062, "top5_acc": 0.50031, "loss_cls": 4.0901, "loss": 4.0901, "time": 0.72227} +{"mode": "val", "epoch": 23, "iter": 309, "lr": 0.09431, "top1_acc": 0.17981, "top5_acc": 0.40045, "mean_class_accuracy": 0.17979} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.0943, "memory": 15990, "data_time": 1.38568, "top1_acc": 0.25438, "top5_acc": 0.51016, "loss_cls": 4.04145, "loss": 4.04145, "time": 2.10378} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.09428, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24703, "top5_acc": 0.51109, "loss_cls": 4.05336, "loss": 4.05336, "time": 0.71489} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.09427, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26797, "top5_acc": 0.51125, "loss_cls": 4.04679, "loss": 4.04679, "time": 0.71564} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.09426, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25891, "top5_acc": 0.50844, "loss_cls": 4.06888, "loss": 4.06888, "time": 0.71496} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.09425, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.26016, "top5_acc": 0.51, "loss_cls": 4.06791, "loss": 4.06791, "time": 0.71714} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.09423, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25172, "top5_acc": 0.50344, "loss_cls": 4.07608, "loss": 4.07608, "time": 0.71122} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.09422, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24859, "top5_acc": 0.50391, "loss_cls": 4.06601, "loss": 4.06601, "time": 0.7135} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.09421, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2425, "top5_acc": 0.49531, "loss_cls": 4.12467, "loss": 4.12467, "time": 0.71856} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.09419, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24891, "top5_acc": 0.50781, "loss_cls": 4.04679, "loss": 4.04679, "time": 0.71327} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.09418, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25031, "top5_acc": 0.49891, "loss_cls": 4.07792, "loss": 4.07792, "time": 0.71427} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.09417, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24453, "top5_acc": 0.50125, "loss_cls": 4.11825, "loss": 4.11825, "time": 0.71717} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.09415, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25109, "top5_acc": 0.50391, "loss_cls": 4.09282, "loss": 4.09282, "time": 0.71216} +{"mode": "train", "epoch": 24, "iter": 1300, "lr": 0.09414, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.245, "top5_acc": 0.51406, "loss_cls": 4.0819, "loss": 4.0819, "time": 0.71203} +{"mode": "train", "epoch": 24, "iter": 1400, "lr": 0.09413, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25375, "top5_acc": 0.51359, "loss_cls": 4.05359, "loss": 4.05359, "time": 0.71078} +{"mode": "train", "epoch": 24, "iter": 1500, "lr": 0.09411, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.25516, "top5_acc": 0.50156, "loss_cls": 4.08976, "loss": 4.08976, "time": 0.71687} +{"mode": "train", "epoch": 24, "iter": 1600, "lr": 0.0941, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26484, "top5_acc": 0.52078, "loss_cls": 4.01946, "loss": 4.01946, "time": 0.71492} +{"mode": "train", "epoch": 24, "iter": 1700, "lr": 0.09409, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25281, "top5_acc": 0.50297, "loss_cls": 4.08346, "loss": 4.08346, "time": 0.71971} +{"mode": "train", "epoch": 24, "iter": 1800, "lr": 0.09407, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25641, "top5_acc": 0.51016, "loss_cls": 4.04708, "loss": 4.04708, "time": 0.71776} +{"mode": "train", "epoch": 24, "iter": 1900, "lr": 0.09406, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24812, "top5_acc": 0.50062, "loss_cls": 4.09303, "loss": 4.09303, "time": 0.71663} +{"mode": "train", "epoch": 24, "iter": 2000, "lr": 0.09405, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26016, "top5_acc": 0.51984, "loss_cls": 4.02386, "loss": 4.02386, "time": 0.71737} +{"mode": "train", "epoch": 24, "iter": 2100, "lr": 0.09404, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25188, "top5_acc": 0.50641, "loss_cls": 4.06813, "loss": 4.06813, "time": 0.72069} +{"mode": "train", "epoch": 24, "iter": 2200, "lr": 0.09402, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25281, "top5_acc": 0.50266, "loss_cls": 4.09649, "loss": 4.09649, "time": 0.72309} +{"mode": "train", "epoch": 24, "iter": 2300, "lr": 0.09401, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.24938, "top5_acc": 0.50172, "loss_cls": 4.0819, "loss": 4.0819, "time": 0.71964} +{"mode": "train", "epoch": 24, "iter": 2400, "lr": 0.094, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26844, "top5_acc": 0.51375, "loss_cls": 4.02859, "loss": 4.02859, "time": 0.71637} +{"mode": "train", "epoch": 24, "iter": 2500, "lr": 0.09398, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24828, "top5_acc": 0.50953, "loss_cls": 4.06878, "loss": 4.06878, "time": 0.72091} +{"mode": "train", "epoch": 24, "iter": 2600, "lr": 0.09397, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25156, "top5_acc": 0.50922, "loss_cls": 4.0604, "loss": 4.0604, "time": 0.7188} +{"mode": "train", "epoch": 24, "iter": 2700, "lr": 0.09396, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.25375, "top5_acc": 0.50719, "loss_cls": 4.0722, "loss": 4.0722, "time": 0.71975} +{"mode": "train", "epoch": 24, "iter": 2800, "lr": 0.09394, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25141, "top5_acc": 0.50203, "loss_cls": 4.08156, "loss": 4.08156, "time": 0.72201} +{"mode": "train", "epoch": 24, "iter": 2900, "lr": 0.09393, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24266, "top5_acc": 0.49047, "loss_cls": 4.16889, "loss": 4.16889, "time": 0.71946} +{"mode": "train", "epoch": 24, "iter": 3000, "lr": 0.09392, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25656, "top5_acc": 0.50781, "loss_cls": 4.0724, "loss": 4.0724, "time": 0.72131} +{"mode": "train", "epoch": 24, "iter": 3100, "lr": 0.0939, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25312, "top5_acc": 0.50594, "loss_cls": 4.06682, "loss": 4.06682, "time": 0.72069} +{"mode": "train", "epoch": 24, "iter": 3200, "lr": 0.09389, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26234, "top5_acc": 0.50828, "loss_cls": 4.04539, "loss": 4.04539, "time": 0.71878} +{"mode": "train", "epoch": 24, "iter": 3300, "lr": 0.09388, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25594, "top5_acc": 0.50406, "loss_cls": 4.08603, "loss": 4.08603, "time": 0.72058} +{"mode": "train", "epoch": 24, "iter": 3400, "lr": 0.09386, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24953, "top5_acc": 0.50047, "loss_cls": 4.12049, "loss": 4.12049, "time": 0.7189} +{"mode": "train", "epoch": 24, "iter": 3500, "lr": 0.09385, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25094, "top5_acc": 0.49875, "loss_cls": 4.10314, "loss": 4.10314, "time": 0.72164} +{"mode": "train", "epoch": 24, "iter": 3600, "lr": 0.09384, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24672, "top5_acc": 0.50203, "loss_cls": 4.08313, "loss": 4.08313, "time": 0.71728} +{"mode": "train", "epoch": 24, "iter": 3700, "lr": 0.09382, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25734, "top5_acc": 0.50187, "loss_cls": 4.09546, "loss": 4.09546, "time": 0.71698} +{"mode": "val", "epoch": 24, "iter": 309, "lr": 0.09382, "top1_acc": 0.18913, "top5_acc": 0.40997, "mean_class_accuracy": 0.18906} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.0938, "memory": 15990, "data_time": 1.41468, "top1_acc": 0.25359, "top5_acc": 0.51234, "loss_cls": 4.04146, "loss": 4.04146, "time": 2.13616} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.09379, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25094, "top5_acc": 0.49812, "loss_cls": 4.10153, "loss": 4.10153, "time": 0.71537} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.09378, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25656, "top5_acc": 0.50906, "loss_cls": 4.05892, "loss": 4.05892, "time": 0.71776} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.09376, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25797, "top5_acc": 0.51031, "loss_cls": 4.08074, "loss": 4.08074, "time": 0.71295} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.09375, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.26219, "top5_acc": 0.50969, "loss_cls": 4.06293, "loss": 4.06293, "time": 0.71435} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.09373, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.25609, "top5_acc": 0.50938, "loss_cls": 4.05525, "loss": 4.05525, "time": 0.71568} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.09372, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25844, "top5_acc": 0.51281, "loss_cls": 4.02947, "loss": 4.02947, "time": 0.71229} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.09371, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.25875, "top5_acc": 0.50813, "loss_cls": 4.09783, "loss": 4.09783, "time": 0.71518} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.09369, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25016, "top5_acc": 0.51266, "loss_cls": 4.04564, "loss": 4.04564, "time": 0.71728} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.09368, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25062, "top5_acc": 0.49531, "loss_cls": 4.10096, "loss": 4.10096, "time": 0.7171} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.09367, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25891, "top5_acc": 0.51625, "loss_cls": 4.05811, "loss": 4.05811, "time": 0.71274} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.09365, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24172, "top5_acc": 0.50281, "loss_cls": 4.08703, "loss": 4.08703, "time": 0.71501} +{"mode": "train", "epoch": 25, "iter": 1300, "lr": 0.09364, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26219, "top5_acc": 0.50922, "loss_cls": 4.05056, "loss": 4.05056, "time": 0.71399} +{"mode": "train", "epoch": 25, "iter": 1400, "lr": 0.09363, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26062, "top5_acc": 0.50562, "loss_cls": 4.05495, "loss": 4.05495, "time": 0.71447} +{"mode": "train", "epoch": 25, "iter": 1500, "lr": 0.09361, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25188, "top5_acc": 0.50703, "loss_cls": 4.04781, "loss": 4.04781, "time": 0.71096} +{"mode": "train", "epoch": 25, "iter": 1600, "lr": 0.0936, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25609, "top5_acc": 0.5025, "loss_cls": 4.10846, "loss": 4.10846, "time": 0.71396} +{"mode": "train", "epoch": 25, "iter": 1700, "lr": 0.09358, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25438, "top5_acc": 0.50719, "loss_cls": 4.06863, "loss": 4.06863, "time": 0.71267} +{"mode": "train", "epoch": 25, "iter": 1800, "lr": 0.09357, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.24953, "top5_acc": 0.51047, "loss_cls": 4.05179, "loss": 4.05179, "time": 0.71656} +{"mode": "train", "epoch": 25, "iter": 1900, "lr": 0.09356, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25047, "top5_acc": 0.49781, "loss_cls": 4.09222, "loss": 4.09222, "time": 0.71401} +{"mode": "train", "epoch": 25, "iter": 2000, "lr": 0.09354, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26109, "top5_acc": 0.50875, "loss_cls": 4.05397, "loss": 4.05397, "time": 0.71415} +{"mode": "train", "epoch": 25, "iter": 2100, "lr": 0.09353, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24203, "top5_acc": 0.49516, "loss_cls": 4.1166, "loss": 4.1166, "time": 0.71743} +{"mode": "train", "epoch": 25, "iter": 2200, "lr": 0.09352, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24406, "top5_acc": 0.50422, "loss_cls": 4.10574, "loss": 4.10574, "time": 0.71732} +{"mode": "train", "epoch": 25, "iter": 2300, "lr": 0.0935, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.26062, "top5_acc": 0.51406, "loss_cls": 4.05815, "loss": 4.05815, "time": 0.71807} +{"mode": "train", "epoch": 25, "iter": 2400, "lr": 0.09349, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25844, "top5_acc": 0.495, "loss_cls": 4.10026, "loss": 4.10026, "time": 0.71465} +{"mode": "train", "epoch": 25, "iter": 2500, "lr": 0.09347, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25562, "top5_acc": 0.50391, "loss_cls": 4.0741, "loss": 4.0741, "time": 0.7156} +{"mode": "train", "epoch": 25, "iter": 2600, "lr": 0.09346, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25312, "top5_acc": 0.50609, "loss_cls": 4.05385, "loss": 4.05385, "time": 0.71661} +{"mode": "train", "epoch": 25, "iter": 2700, "lr": 0.09345, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26266, "top5_acc": 0.51203, "loss_cls": 4.01857, "loss": 4.01857, "time": 0.71601} +{"mode": "train", "epoch": 25, "iter": 2800, "lr": 0.09343, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25328, "top5_acc": 0.50141, "loss_cls": 4.04947, "loss": 4.04947, "time": 0.71324} +{"mode": "train", "epoch": 25, "iter": 2900, "lr": 0.09342, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.25438, "top5_acc": 0.50219, "loss_cls": 4.10838, "loss": 4.10838, "time": 0.72159} +{"mode": "train", "epoch": 25, "iter": 3000, "lr": 0.09341, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25594, "top5_acc": 0.50938, "loss_cls": 4.0344, "loss": 4.0344, "time": 0.7172} +{"mode": "train", "epoch": 25, "iter": 3100, "lr": 0.09339, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25109, "top5_acc": 0.50281, "loss_cls": 4.08977, "loss": 4.08977, "time": 0.71785} +{"mode": "train", "epoch": 25, "iter": 3200, "lr": 0.09338, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26141, "top5_acc": 0.51, "loss_cls": 4.03063, "loss": 4.03063, "time": 0.71483} +{"mode": "train", "epoch": 25, "iter": 3300, "lr": 0.09336, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2575, "top5_acc": 0.50906, "loss_cls": 4.04506, "loss": 4.04506, "time": 0.71821} +{"mode": "train", "epoch": 25, "iter": 3400, "lr": 0.09335, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25672, "top5_acc": 0.51156, "loss_cls": 4.06465, "loss": 4.06465, "time": 0.7146} +{"mode": "train", "epoch": 25, "iter": 3500, "lr": 0.09334, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25141, "top5_acc": 0.50453, "loss_cls": 4.05791, "loss": 4.05791, "time": 0.71783} +{"mode": "train", "epoch": 25, "iter": 3600, "lr": 0.09332, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.255, "top5_acc": 0.49984, "loss_cls": 4.08538, "loss": 4.08538, "time": 0.71782} +{"mode": "train", "epoch": 25, "iter": 3700, "lr": 0.09331, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25172, "top5_acc": 0.49891, "loss_cls": 4.09362, "loss": 4.09362, "time": 0.71711} +{"mode": "val", "epoch": 25, "iter": 309, "lr": 0.0933, "top1_acc": 0.16831, "top5_acc": 0.39624, "mean_class_accuracy": 0.16814} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.09329, "memory": 15990, "data_time": 1.39973, "top1_acc": 0.26, "top5_acc": 0.5175, "loss_cls": 3.99491, "loss": 3.99491, "time": 2.11647} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.09327, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25609, "top5_acc": 0.51703, "loss_cls": 4.01928, "loss": 4.01928, "time": 0.71513} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.09326, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25594, "top5_acc": 0.51703, "loss_cls": 4.0478, "loss": 4.0478, "time": 0.71474} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.09325, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25844, "top5_acc": 0.50281, "loss_cls": 4.03989, "loss": 4.03989, "time": 0.71574} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.09323, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.25891, "top5_acc": 0.51578, "loss_cls": 4.03789, "loss": 4.03789, "time": 0.71423} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.09322, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.25875, "top5_acc": 0.50578, "loss_cls": 4.05689, "loss": 4.05689, "time": 0.7152} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.0932, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2575, "top5_acc": 0.52281, "loss_cls": 4.01164, "loss": 4.01164, "time": 0.71458} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.09319, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25766, "top5_acc": 0.50969, "loss_cls": 4.05207, "loss": 4.05207, "time": 0.71511} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.09318, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26266, "top5_acc": 0.50828, "loss_cls": 4.05579, "loss": 4.05579, "time": 0.71491} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.09316, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24812, "top5_acc": 0.50438, "loss_cls": 4.09607, "loss": 4.09607, "time": 0.7138} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.09315, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25297, "top5_acc": 0.51359, "loss_cls": 4.0569, "loss": 4.0569, "time": 0.7129} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.09313, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24953, "top5_acc": 0.50578, "loss_cls": 4.05347, "loss": 4.05347, "time": 0.71914} +{"mode": "train", "epoch": 26, "iter": 1300, "lr": 0.09312, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25016, "top5_acc": 0.50391, "loss_cls": 4.08325, "loss": 4.08325, "time": 0.7129} +{"mode": "train", "epoch": 26, "iter": 1400, "lr": 0.0931, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25359, "top5_acc": 0.50328, "loss_cls": 4.08524, "loss": 4.08524, "time": 0.7139} +{"mode": "train", "epoch": 26, "iter": 1500, "lr": 0.09309, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2475, "top5_acc": 0.49875, "loss_cls": 4.1098, "loss": 4.1098, "time": 0.7146} +{"mode": "train", "epoch": 26, "iter": 1600, "lr": 0.09308, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25781, "top5_acc": 0.51281, "loss_cls": 4.06663, "loss": 4.06663, "time": 0.71394} +{"mode": "train", "epoch": 26, "iter": 1700, "lr": 0.09306, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26031, "top5_acc": 0.50391, "loss_cls": 4.0545, "loss": 4.0545, "time": 0.71717} +{"mode": "train", "epoch": 26, "iter": 1800, "lr": 0.09305, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25203, "top5_acc": 0.49594, "loss_cls": 4.10393, "loss": 4.10393, "time": 0.71647} +{"mode": "train", "epoch": 26, "iter": 1900, "lr": 0.09303, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24844, "top5_acc": 0.49938, "loss_cls": 4.10387, "loss": 4.10387, "time": 0.71354} +{"mode": "train", "epoch": 26, "iter": 2000, "lr": 0.09302, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25969, "top5_acc": 0.51625, "loss_cls": 4.01942, "loss": 4.01942, "time": 0.71475} +{"mode": "train", "epoch": 26, "iter": 2100, "lr": 0.093, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25875, "top5_acc": 0.51484, "loss_cls": 4.06766, "loss": 4.06766, "time": 0.71413} +{"mode": "train", "epoch": 26, "iter": 2200, "lr": 0.09299, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25375, "top5_acc": 0.50562, "loss_cls": 4.09394, "loss": 4.09394, "time": 0.71537} +{"mode": "train", "epoch": 26, "iter": 2300, "lr": 0.09298, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25016, "top5_acc": 0.50031, "loss_cls": 4.10738, "loss": 4.10738, "time": 0.71623} +{"mode": "train", "epoch": 26, "iter": 2400, "lr": 0.09296, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25031, "top5_acc": 0.50797, "loss_cls": 4.08708, "loss": 4.08708, "time": 0.71904} +{"mode": "train", "epoch": 26, "iter": 2500, "lr": 0.09295, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25422, "top5_acc": 0.51219, "loss_cls": 4.07009, "loss": 4.07009, "time": 0.71897} +{"mode": "train", "epoch": 26, "iter": 2600, "lr": 0.09293, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.255, "top5_acc": 0.51438, "loss_cls": 4.04308, "loss": 4.04308, "time": 0.71754} +{"mode": "train", "epoch": 26, "iter": 2700, "lr": 0.09292, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25688, "top5_acc": 0.51828, "loss_cls": 4.01889, "loss": 4.01889, "time": 0.71923} +{"mode": "train", "epoch": 26, "iter": 2800, "lr": 0.0929, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25562, "top5_acc": 0.51625, "loss_cls": 4.05661, "loss": 4.05661, "time": 0.71893} +{"mode": "train", "epoch": 26, "iter": 2900, "lr": 0.09289, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25656, "top5_acc": 0.50359, "loss_cls": 4.08345, "loss": 4.08345, "time": 0.72165} +{"mode": "train", "epoch": 26, "iter": 3000, "lr": 0.09288, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25531, "top5_acc": 0.50281, "loss_cls": 4.05045, "loss": 4.05045, "time": 0.71901} +{"mode": "train", "epoch": 26, "iter": 3100, "lr": 0.09286, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24953, "top5_acc": 0.50875, "loss_cls": 4.0936, "loss": 4.0936, "time": 0.72032} +{"mode": "train", "epoch": 26, "iter": 3200, "lr": 0.09285, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26078, "top5_acc": 0.51344, "loss_cls": 4.03416, "loss": 4.03416, "time": 0.72321} +{"mode": "train", "epoch": 26, "iter": 3300, "lr": 0.09283, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25953, "top5_acc": 0.50734, "loss_cls": 4.0623, "loss": 4.0623, "time": 0.72191} +{"mode": "train", "epoch": 26, "iter": 3400, "lr": 0.09282, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25203, "top5_acc": 0.5125, "loss_cls": 4.06733, "loss": 4.06733, "time": 0.72009} +{"mode": "train", "epoch": 26, "iter": 3500, "lr": 0.0928, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25312, "top5_acc": 0.50359, "loss_cls": 4.07425, "loss": 4.07425, "time": 0.72214} +{"mode": "train", "epoch": 26, "iter": 3600, "lr": 0.09279, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25875, "top5_acc": 0.50734, "loss_cls": 4.04523, "loss": 4.04523, "time": 0.72117} +{"mode": "train", "epoch": 26, "iter": 3700, "lr": 0.09278, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25281, "top5_acc": 0.51062, "loss_cls": 4.07302, "loss": 4.07302, "time": 0.72313} +{"mode": "val", "epoch": 26, "iter": 309, "lr": 0.09277, "top1_acc": 0.17226, "top5_acc": 0.38718, "mean_class_accuracy": 0.17217} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.09275, "memory": 15990, "data_time": 1.40067, "top1_acc": 0.26625, "top5_acc": 0.51875, "loss_cls": 4.03192, "loss": 4.03192, "time": 2.11717} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.09274, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25922, "top5_acc": 0.51766, "loss_cls": 4.01594, "loss": 4.01594, "time": 0.71687} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.09272, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26547, "top5_acc": 0.51859, "loss_cls": 4.00739, "loss": 4.00739, "time": 0.71825} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.09271, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25578, "top5_acc": 0.50531, "loss_cls": 4.0456, "loss": 4.0456, "time": 0.71787} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.0927, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.26547, "top5_acc": 0.51703, "loss_cls": 4.01771, "loss": 4.01771, "time": 0.71512} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.09268, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.25984, "top5_acc": 0.50719, "loss_cls": 4.07156, "loss": 4.07156, "time": 0.71215} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.09267, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.25922, "top5_acc": 0.51203, "loss_cls": 4.06215, "loss": 4.06215, "time": 0.71522} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.09265, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26109, "top5_acc": 0.50844, "loss_cls": 4.04288, "loss": 4.04288, "time": 0.71165} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.09264, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25797, "top5_acc": 0.51188, "loss_cls": 4.02325, "loss": 4.02325, "time": 0.71477} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.09262, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25219, "top5_acc": 0.51438, "loss_cls": 4.08304, "loss": 4.08304, "time": 0.71729} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.09261, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2575, "top5_acc": 0.51016, "loss_cls": 4.03595, "loss": 4.03595, "time": 0.71329} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.09259, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26422, "top5_acc": 0.52516, "loss_cls": 4.02108, "loss": 4.02108, "time": 0.71353} +{"mode": "train", "epoch": 27, "iter": 1300, "lr": 0.09258, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26906, "top5_acc": 0.51922, "loss_cls": 4.02628, "loss": 4.02628, "time": 0.71392} +{"mode": "train", "epoch": 27, "iter": 1400, "lr": 0.09256, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26297, "top5_acc": 0.51422, "loss_cls": 4.04557, "loss": 4.04557, "time": 0.71633} +{"mode": "train", "epoch": 27, "iter": 1500, "lr": 0.09255, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26562, "top5_acc": 0.52281, "loss_cls": 3.99755, "loss": 3.99755, "time": 0.71751} +{"mode": "train", "epoch": 27, "iter": 1600, "lr": 0.09253, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24766, "top5_acc": 0.49859, "loss_cls": 4.09305, "loss": 4.09305, "time": 0.71536} +{"mode": "train", "epoch": 27, "iter": 1700, "lr": 0.09252, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25953, "top5_acc": 0.50797, "loss_cls": 4.06691, "loss": 4.06691, "time": 0.71692} +{"mode": "train", "epoch": 27, "iter": 1800, "lr": 0.09251, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25391, "top5_acc": 0.50859, "loss_cls": 4.06877, "loss": 4.06877, "time": 0.71498} +{"mode": "train", "epoch": 27, "iter": 1900, "lr": 0.09249, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24969, "top5_acc": 0.50922, "loss_cls": 4.06741, "loss": 4.06741, "time": 0.71941} +{"mode": "train", "epoch": 27, "iter": 2000, "lr": 0.09248, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25594, "top5_acc": 0.51984, "loss_cls": 4.05111, "loss": 4.05111, "time": 0.71698} +{"mode": "train", "epoch": 27, "iter": 2100, "lr": 0.09246, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24781, "top5_acc": 0.49969, "loss_cls": 4.09293, "loss": 4.09293, "time": 0.71587} +{"mode": "train", "epoch": 27, "iter": 2200, "lr": 0.09245, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25688, "top5_acc": 0.51203, "loss_cls": 4.05003, "loss": 4.05003, "time": 0.7156} +{"mode": "train", "epoch": 27, "iter": 2300, "lr": 0.09243, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.25781, "top5_acc": 0.51359, "loss_cls": 4.04417, "loss": 4.04417, "time": 0.71534} +{"mode": "train", "epoch": 27, "iter": 2400, "lr": 0.09242, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25375, "top5_acc": 0.51047, "loss_cls": 4.07565, "loss": 4.07565, "time": 0.72242} +{"mode": "train", "epoch": 27, "iter": 2500, "lr": 0.0924, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25547, "top5_acc": 0.50688, "loss_cls": 4.04965, "loss": 4.04965, "time": 0.72255} +{"mode": "train", "epoch": 27, "iter": 2600, "lr": 0.09239, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25516, "top5_acc": 0.50813, "loss_cls": 4.05224, "loss": 4.05224, "time": 0.71775} +{"mode": "train", "epoch": 27, "iter": 2700, "lr": 0.09237, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26328, "top5_acc": 0.51375, "loss_cls": 4.02461, "loss": 4.02461, "time": 0.71724} +{"mode": "train", "epoch": 27, "iter": 2800, "lr": 0.09236, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26391, "top5_acc": 0.51969, "loss_cls": 4.00575, "loss": 4.00575, "time": 0.71924} +{"mode": "train", "epoch": 27, "iter": 2900, "lr": 0.09234, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26672, "top5_acc": 0.50375, "loss_cls": 4.0675, "loss": 4.0675, "time": 0.7227} +{"mode": "train", "epoch": 27, "iter": 3000, "lr": 0.09233, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25359, "top5_acc": 0.5, "loss_cls": 4.09439, "loss": 4.09439, "time": 0.7181} +{"mode": "train", "epoch": 27, "iter": 3100, "lr": 0.09231, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25969, "top5_acc": 0.50547, "loss_cls": 4.05491, "loss": 4.05491, "time": 0.71977} +{"mode": "train", "epoch": 27, "iter": 3200, "lr": 0.0923, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.25812, "top5_acc": 0.51812, "loss_cls": 4.04003, "loss": 4.04003, "time": 0.71714} +{"mode": "train", "epoch": 27, "iter": 3300, "lr": 0.09228, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25281, "top5_acc": 0.50109, "loss_cls": 4.08911, "loss": 4.08911, "time": 0.71793} +{"mode": "train", "epoch": 27, "iter": 3400, "lr": 0.09227, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.255, "top5_acc": 0.5125, "loss_cls": 4.04285, "loss": 4.04285, "time": 0.71672} +{"mode": "train", "epoch": 27, "iter": 3500, "lr": 0.09225, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26, "top5_acc": 0.50969, "loss_cls": 4.064, "loss": 4.064, "time": 0.71913} +{"mode": "train", "epoch": 27, "iter": 3600, "lr": 0.09224, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25625, "top5_acc": 0.5, "loss_cls": 4.09272, "loss": 4.09272, "time": 0.7233} +{"mode": "train", "epoch": 27, "iter": 3700, "lr": 0.09222, "memory": 15990, "data_time": 0.00074, "top1_acc": 0.25703, "top5_acc": 0.51016, "loss_cls": 4.05116, "loss": 4.05116, "time": 0.71903} +{"mode": "val", "epoch": 27, "iter": 309, "lr": 0.09222, "top1_acc": 0.19349, "top5_acc": 0.4126, "mean_class_accuracy": 0.19332} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.0922, "memory": 15990, "data_time": 1.38846, "top1_acc": 0.26047, "top5_acc": 0.51328, "loss_cls": 4.01681, "loss": 4.01681, "time": 2.10972} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.09219, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26625, "top5_acc": 0.51469, "loss_cls": 4.02164, "loss": 4.02164, "time": 0.71789} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.09217, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.27219, "top5_acc": 0.52109, "loss_cls": 3.98606, "loss": 3.98606, "time": 0.71449} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.09216, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25656, "top5_acc": 0.49984, "loss_cls": 4.07938, "loss": 4.07938, "time": 0.71406} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.09214, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26281, "top5_acc": 0.52453, "loss_cls": 3.98783, "loss": 3.98783, "time": 0.71581} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.09213, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.26125, "top5_acc": 0.51094, "loss_cls": 4.06593, "loss": 4.06593, "time": 0.71806} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.09211, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24906, "top5_acc": 0.50313, "loss_cls": 4.08173, "loss": 4.08173, "time": 0.71415} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.0921, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25891, "top5_acc": 0.51406, "loss_cls": 4.01511, "loss": 4.01511, "time": 0.71308} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.09208, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26062, "top5_acc": 0.51234, "loss_cls": 4.03924, "loss": 4.03924, "time": 0.71616} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.09207, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26828, "top5_acc": 0.52453, "loss_cls": 3.98201, "loss": 3.98201, "time": 0.71285} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.09205, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24594, "top5_acc": 0.49906, "loss_cls": 4.09453, "loss": 4.09453, "time": 0.71285} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.09204, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25016, "top5_acc": 0.51313, "loss_cls": 4.0826, "loss": 4.0826, "time": 0.71447} +{"mode": "train", "epoch": 28, "iter": 1300, "lr": 0.09202, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26219, "top5_acc": 0.51344, "loss_cls": 4.03328, "loss": 4.03328, "time": 0.71495} +{"mode": "train", "epoch": 28, "iter": 1400, "lr": 0.09201, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25219, "top5_acc": 0.5125, "loss_cls": 4.04153, "loss": 4.04153, "time": 0.71856} +{"mode": "train", "epoch": 28, "iter": 1500, "lr": 0.09199, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25438, "top5_acc": 0.50609, "loss_cls": 4.08687, "loss": 4.08687, "time": 0.71931} +{"mode": "train", "epoch": 28, "iter": 1600, "lr": 0.09198, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26641, "top5_acc": 0.51688, "loss_cls": 4.02094, "loss": 4.02094, "time": 0.71752} +{"mode": "train", "epoch": 28, "iter": 1700, "lr": 0.09196, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25562, "top5_acc": 0.51266, "loss_cls": 4.04509, "loss": 4.04509, "time": 0.71505} +{"mode": "train", "epoch": 28, "iter": 1800, "lr": 0.09194, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26641, "top5_acc": 0.51188, "loss_cls": 4.02883, "loss": 4.02883, "time": 0.71799} +{"mode": "train", "epoch": 28, "iter": 1900, "lr": 0.09193, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25828, "top5_acc": 0.49547, "loss_cls": 4.08113, "loss": 4.08113, "time": 0.71428} +{"mode": "train", "epoch": 28, "iter": 2000, "lr": 0.09191, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25266, "top5_acc": 0.51156, "loss_cls": 4.06161, "loss": 4.06161, "time": 0.7158} +{"mode": "train", "epoch": 28, "iter": 2100, "lr": 0.0919, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25812, "top5_acc": 0.51531, "loss_cls": 4.01611, "loss": 4.01611, "time": 0.71609} +{"mode": "train", "epoch": 28, "iter": 2200, "lr": 0.09188, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24547, "top5_acc": 0.50875, "loss_cls": 4.07979, "loss": 4.07979, "time": 0.7149} +{"mode": "train", "epoch": 28, "iter": 2300, "lr": 0.09187, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25469, "top5_acc": 0.50859, "loss_cls": 4.06236, "loss": 4.06236, "time": 0.71603} +{"mode": "train", "epoch": 28, "iter": 2400, "lr": 0.09185, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26484, "top5_acc": 0.51891, "loss_cls": 3.9947, "loss": 3.9947, "time": 0.7132} +{"mode": "train", "epoch": 28, "iter": 2500, "lr": 0.09184, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26141, "top5_acc": 0.51062, "loss_cls": 4.05503, "loss": 4.05503, "time": 0.7133} +{"mode": "train", "epoch": 28, "iter": 2600, "lr": 0.09182, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.26422, "top5_acc": 0.51, "loss_cls": 4.02095, "loss": 4.02095, "time": 0.71832} +{"mode": "train", "epoch": 28, "iter": 2700, "lr": 0.09181, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25938, "top5_acc": 0.50922, "loss_cls": 4.04314, "loss": 4.04314, "time": 0.71701} +{"mode": "train", "epoch": 28, "iter": 2800, "lr": 0.09179, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25516, "top5_acc": 0.50375, "loss_cls": 4.0704, "loss": 4.0704, "time": 0.71677} +{"mode": "train", "epoch": 28, "iter": 2900, "lr": 0.09178, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26172, "top5_acc": 0.50875, "loss_cls": 4.02068, "loss": 4.02068, "time": 0.71974} +{"mode": "train", "epoch": 28, "iter": 3000, "lr": 0.09176, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26047, "top5_acc": 0.5025, "loss_cls": 4.07192, "loss": 4.07192, "time": 0.71552} +{"mode": "train", "epoch": 28, "iter": 3100, "lr": 0.09175, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25766, "top5_acc": 0.50797, "loss_cls": 4.08004, "loss": 4.08004, "time": 0.71919} +{"mode": "train", "epoch": 28, "iter": 3200, "lr": 0.09173, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.25797, "top5_acc": 0.51484, "loss_cls": 4.04842, "loss": 4.04842, "time": 0.71644} +{"mode": "train", "epoch": 28, "iter": 3300, "lr": 0.09172, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25453, "top5_acc": 0.50828, "loss_cls": 4.05689, "loss": 4.05689, "time": 0.72218} +{"mode": "train", "epoch": 28, "iter": 3400, "lr": 0.0917, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.25875, "top5_acc": 0.51844, "loss_cls": 4.03623, "loss": 4.03623, "time": 0.71937} +{"mode": "train", "epoch": 28, "iter": 3500, "lr": 0.09168, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25406, "top5_acc": 0.50813, "loss_cls": 4.07253, "loss": 4.07253, "time": 0.71645} +{"mode": "train", "epoch": 28, "iter": 3600, "lr": 0.09167, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25172, "top5_acc": 0.50891, "loss_cls": 4.05979, "loss": 4.05979, "time": 0.71732} +{"mode": "train", "epoch": 28, "iter": 3700, "lr": 0.09165, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25422, "top5_acc": 0.51047, "loss_cls": 4.04604, "loss": 4.04604, "time": 0.71969} +{"mode": "val", "epoch": 28, "iter": 309, "lr": 0.09165, "top1_acc": 0.11077, "top5_acc": 0.2881, "mean_class_accuracy": 0.11053} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.09163, "memory": 15990, "data_time": 1.37923, "top1_acc": 0.25734, "top5_acc": 0.51328, "loss_cls": 4.02447, "loss": 4.02447, "time": 2.09558} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.09162, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26438, "top5_acc": 0.51672, "loss_cls": 3.9926, "loss": 3.9926, "time": 0.71602} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.0916, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26812, "top5_acc": 0.52328, "loss_cls": 3.99931, "loss": 3.99931, "time": 0.71642} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.09158, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26281, "top5_acc": 0.50953, "loss_cls": 4.05275, "loss": 4.05275, "time": 0.71616} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.09157, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26625, "top5_acc": 0.52344, "loss_cls": 3.98328, "loss": 3.98328, "time": 0.71342} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.09155, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26156, "top5_acc": 0.51484, "loss_cls": 4.01792, "loss": 4.01792, "time": 0.71447} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.09154, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.26687, "top5_acc": 0.51859, "loss_cls": 3.9968, "loss": 3.9968, "time": 0.71456} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.09152, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26047, "top5_acc": 0.51109, "loss_cls": 4.03997, "loss": 4.03997, "time": 0.71601} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.09151, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.25375, "top5_acc": 0.51094, "loss_cls": 4.08018, "loss": 4.08018, "time": 0.71562} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.09149, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26422, "top5_acc": 0.51422, "loss_cls": 4.02559, "loss": 4.02559, "time": 0.72091} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.09148, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25516, "top5_acc": 0.5175, "loss_cls": 4.03534, "loss": 4.03534, "time": 0.71733} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.09146, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27359, "top5_acc": 0.52734, "loss_cls": 3.96724, "loss": 3.96724, "time": 0.71171} +{"mode": "train", "epoch": 29, "iter": 1300, "lr": 0.09144, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26828, "top5_acc": 0.52469, "loss_cls": 4.01072, "loss": 4.01072, "time": 0.71574} +{"mode": "train", "epoch": 29, "iter": 1400, "lr": 0.09143, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25875, "top5_acc": 0.51344, "loss_cls": 4.02785, "loss": 4.02785, "time": 0.7153} +{"mode": "train", "epoch": 29, "iter": 1500, "lr": 0.09141, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25344, "top5_acc": 0.51047, "loss_cls": 4.03352, "loss": 4.03352, "time": 0.71279} +{"mode": "train", "epoch": 29, "iter": 1600, "lr": 0.0914, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25266, "top5_acc": 0.50094, "loss_cls": 4.06252, "loss": 4.06252, "time": 0.72064} +{"mode": "train", "epoch": 29, "iter": 1700, "lr": 0.09138, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26781, "top5_acc": 0.51047, "loss_cls": 4.03446, "loss": 4.03446, "time": 0.71769} +{"mode": "train", "epoch": 29, "iter": 1800, "lr": 0.09137, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26078, "top5_acc": 0.51703, "loss_cls": 4.0463, "loss": 4.0463, "time": 0.71504} +{"mode": "train", "epoch": 29, "iter": 1900, "lr": 0.09135, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.265, "top5_acc": 0.51266, "loss_cls": 4.05508, "loss": 4.05508, "time": 0.71469} +{"mode": "train", "epoch": 29, "iter": 2000, "lr": 0.09133, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26297, "top5_acc": 0.51125, "loss_cls": 4.03834, "loss": 4.03834, "time": 0.71455} +{"mode": "train", "epoch": 29, "iter": 2100, "lr": 0.09132, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24547, "top5_acc": 0.49594, "loss_cls": 4.05069, "loss": 4.05069, "time": 0.71665} +{"mode": "train", "epoch": 29, "iter": 2200, "lr": 0.0913, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26797, "top5_acc": 0.51453, "loss_cls": 4.04419, "loss": 4.04419, "time": 0.71807} +{"mode": "train", "epoch": 29, "iter": 2300, "lr": 0.09129, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.265, "top5_acc": 0.52, "loss_cls": 4.01766, "loss": 4.01766, "time": 0.71404} +{"mode": "train", "epoch": 29, "iter": 2400, "lr": 0.09127, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25938, "top5_acc": 0.51297, "loss_cls": 4.06634, "loss": 4.06634, "time": 0.71915} +{"mode": "train", "epoch": 29, "iter": 2500, "lr": 0.09126, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25562, "top5_acc": 0.51828, "loss_cls": 4.01718, "loss": 4.01718, "time": 0.7213} +{"mode": "train", "epoch": 29, "iter": 2600, "lr": 0.09124, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26328, "top5_acc": 0.52312, "loss_cls": 3.99746, "loss": 3.99746, "time": 0.71766} +{"mode": "train", "epoch": 29, "iter": 2700, "lr": 0.09122, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25766, "top5_acc": 0.50281, "loss_cls": 4.08289, "loss": 4.08289, "time": 0.71801} +{"mode": "train", "epoch": 29, "iter": 2800, "lr": 0.09121, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26156, "top5_acc": 0.51422, "loss_cls": 4.06467, "loss": 4.06467, "time": 0.71767} +{"mode": "train", "epoch": 29, "iter": 2900, "lr": 0.09119, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25625, "top5_acc": 0.51016, "loss_cls": 4.04616, "loss": 4.04616, "time": 0.71792} +{"mode": "train", "epoch": 29, "iter": 3000, "lr": 0.09118, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25, "top5_acc": 0.50266, "loss_cls": 4.0702, "loss": 4.0702, "time": 0.71977} +{"mode": "train", "epoch": 29, "iter": 3100, "lr": 0.09116, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25953, "top5_acc": 0.5075, "loss_cls": 4.05391, "loss": 4.05391, "time": 0.71672} +{"mode": "train", "epoch": 29, "iter": 3200, "lr": 0.09114, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25859, "top5_acc": 0.51219, "loss_cls": 4.05071, "loss": 4.05071, "time": 0.71872} +{"mode": "train", "epoch": 29, "iter": 3300, "lr": 0.09113, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25562, "top5_acc": 0.51547, "loss_cls": 4.04093, "loss": 4.04093, "time": 0.71724} +{"mode": "train", "epoch": 29, "iter": 3400, "lr": 0.09111, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25688, "top5_acc": 0.51016, "loss_cls": 4.04407, "loss": 4.04407, "time": 0.71444} +{"mode": "train", "epoch": 29, "iter": 3500, "lr": 0.0911, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24938, "top5_acc": 0.49688, "loss_cls": 4.10393, "loss": 4.10393, "time": 0.71475} +{"mode": "train", "epoch": 29, "iter": 3600, "lr": 0.09108, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25828, "top5_acc": 0.50984, "loss_cls": 4.06944, "loss": 4.06944, "time": 0.72193} +{"mode": "train", "epoch": 29, "iter": 3700, "lr": 0.09106, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26156, "top5_acc": 0.51219, "loss_cls": 4.03403, "loss": 4.03403, "time": 0.7219} +{"mode": "val", "epoch": 29, "iter": 309, "lr": 0.09106, "top1_acc": 0.19556, "top5_acc": 0.42683, "mean_class_accuracy": 0.19554} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.09104, "memory": 15990, "data_time": 1.36115, "top1_acc": 0.26828, "top5_acc": 0.52297, "loss_cls": 3.96664, "loss": 3.96664, "time": 2.18917} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.09103, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26516, "top5_acc": 0.52328, "loss_cls": 3.9872, "loss": 3.9872, "time": 0.83389} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.09101, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25766, "top5_acc": 0.50609, "loss_cls": 4.0604, "loss": 4.0604, "time": 0.82849} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.09099, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25766, "top5_acc": 0.51562, "loss_cls": 4.05601, "loss": 4.05601, "time": 0.82427} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.09098, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26297, "top5_acc": 0.52344, "loss_cls": 4.01222, "loss": 4.01222, "time": 0.82705} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.09096, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.2725, "top5_acc": 0.52141, "loss_cls": 3.97697, "loss": 3.97697, "time": 0.82272} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.09095, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25578, "top5_acc": 0.51438, "loss_cls": 4.03884, "loss": 4.03884, "time": 0.82617} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.09093, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25922, "top5_acc": 0.50938, "loss_cls": 4.04183, "loss": 4.04183, "time": 0.82649} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.09091, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.25438, "top5_acc": 0.51016, "loss_cls": 4.06025, "loss": 4.06025, "time": 0.82944} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.0909, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26203, "top5_acc": 0.51078, "loss_cls": 4.02773, "loss": 4.02773, "time": 0.83554} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.09088, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26703, "top5_acc": 0.51844, "loss_cls": 4.02435, "loss": 4.02435, "time": 0.83097} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.09087, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26047, "top5_acc": 0.51203, "loss_cls": 4.06025, "loss": 4.06025, "time": 0.82638} +{"mode": "train", "epoch": 30, "iter": 1300, "lr": 0.09085, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26531, "top5_acc": 0.52234, "loss_cls": 4.00195, "loss": 4.00195, "time": 0.82829} +{"mode": "train", "epoch": 30, "iter": 1400, "lr": 0.09083, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25203, "top5_acc": 0.51453, "loss_cls": 4.06482, "loss": 4.06482, "time": 0.82842} +{"mode": "train", "epoch": 30, "iter": 1500, "lr": 0.09082, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25719, "top5_acc": 0.51172, "loss_cls": 4.03029, "loss": 4.03029, "time": 0.82641} +{"mode": "train", "epoch": 30, "iter": 1600, "lr": 0.0908, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25609, "top5_acc": 0.49766, "loss_cls": 4.09676, "loss": 4.09676, "time": 0.83162} +{"mode": "train", "epoch": 30, "iter": 1700, "lr": 0.09078, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26203, "top5_acc": 0.51938, "loss_cls": 4.01869, "loss": 4.01869, "time": 0.83096} +{"mode": "train", "epoch": 30, "iter": 1800, "lr": 0.09077, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26578, "top5_acc": 0.51844, "loss_cls": 3.99833, "loss": 3.99833, "time": 0.82658} +{"mode": "train", "epoch": 30, "iter": 1900, "lr": 0.09075, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24703, "top5_acc": 0.50406, "loss_cls": 4.07464, "loss": 4.07464, "time": 0.82653} +{"mode": "train", "epoch": 30, "iter": 2000, "lr": 0.09074, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26406, "top5_acc": 0.52188, "loss_cls": 4.02204, "loss": 4.02204, "time": 0.82967} +{"mode": "train", "epoch": 30, "iter": 2100, "lr": 0.09072, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26469, "top5_acc": 0.52688, "loss_cls": 3.98993, "loss": 3.98993, "time": 0.83424} +{"mode": "train", "epoch": 30, "iter": 2200, "lr": 0.0907, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26344, "top5_acc": 0.5125, "loss_cls": 4.01477, "loss": 4.01477, "time": 0.83196} +{"mode": "train", "epoch": 30, "iter": 2300, "lr": 0.09069, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25953, "top5_acc": 0.51797, "loss_cls": 4.04088, "loss": 4.04088, "time": 0.83672} +{"mode": "train", "epoch": 30, "iter": 2400, "lr": 0.09067, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2675, "top5_acc": 0.52266, "loss_cls": 3.99695, "loss": 3.99695, "time": 0.83229} +{"mode": "train", "epoch": 30, "iter": 2500, "lr": 0.09065, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2575, "top5_acc": 0.51031, "loss_cls": 4.02636, "loss": 4.02636, "time": 0.83166} +{"mode": "train", "epoch": 30, "iter": 2600, "lr": 0.09064, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.265, "top5_acc": 0.50828, "loss_cls": 4.04848, "loss": 4.04848, "time": 0.83199} +{"mode": "train", "epoch": 30, "iter": 2700, "lr": 0.09062, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26, "top5_acc": 0.50609, "loss_cls": 4.046, "loss": 4.046, "time": 0.83206} +{"mode": "train", "epoch": 30, "iter": 2800, "lr": 0.09061, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26609, "top5_acc": 0.51859, "loss_cls": 4.04743, "loss": 4.04743, "time": 0.82869} +{"mode": "train", "epoch": 30, "iter": 2900, "lr": 0.09059, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26953, "top5_acc": 0.51172, "loss_cls": 4.0456, "loss": 4.0456, "time": 0.82858} +{"mode": "train", "epoch": 30, "iter": 3000, "lr": 0.09057, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25062, "top5_acc": 0.50484, "loss_cls": 4.07985, "loss": 4.07985, "time": 0.82641} +{"mode": "train", "epoch": 30, "iter": 3100, "lr": 0.09056, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2575, "top5_acc": 0.51531, "loss_cls": 4.03331, "loss": 4.03331, "time": 0.82757} +{"mode": "train", "epoch": 30, "iter": 3200, "lr": 0.09054, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25625, "top5_acc": 0.51531, "loss_cls": 4.02012, "loss": 4.02012, "time": 0.83573} +{"mode": "train", "epoch": 30, "iter": 3300, "lr": 0.09052, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25516, "top5_acc": 0.50516, "loss_cls": 4.07642, "loss": 4.07642, "time": 0.83406} +{"mode": "train", "epoch": 30, "iter": 3400, "lr": 0.09051, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26891, "top5_acc": 0.51766, "loss_cls": 4.02332, "loss": 4.02332, "time": 0.83508} +{"mode": "train", "epoch": 30, "iter": 3500, "lr": 0.09049, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26156, "top5_acc": 0.51656, "loss_cls": 4.02214, "loss": 4.02214, "time": 0.83136} +{"mode": "train", "epoch": 30, "iter": 3600, "lr": 0.09047, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25984, "top5_acc": 0.50578, "loss_cls": 4.06209, "loss": 4.06209, "time": 0.82869} +{"mode": "train", "epoch": 30, "iter": 3700, "lr": 0.09046, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26219, "top5_acc": 0.50672, "loss_cls": 4.03662, "loss": 4.03662, "time": 0.8366} +{"mode": "val", "epoch": 30, "iter": 309, "lr": 0.09045, "top1_acc": 0.18498, "top5_acc": 0.41139, "mean_class_accuracy": 0.18475} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.09043, "memory": 15990, "data_time": 1.38727, "top1_acc": 0.27, "top5_acc": 0.52578, "loss_cls": 4.20858, "loss": 4.20858, "time": 2.41132} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.09042, "memory": 15990, "data_time": 0.00109, "top1_acc": 0.27, "top5_acc": 0.51719, "loss_cls": 4.21412, "loss": 4.21412, "time": 0.84308} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.0904, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26016, "top5_acc": 0.51281, "loss_cls": 4.23885, "loss": 4.23885, "time": 0.84592} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.09039, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26109, "top5_acc": 0.51484, "loss_cls": 4.23447, "loss": 4.23447, "time": 0.85305} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.09037, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25938, "top5_acc": 0.5125, "loss_cls": 4.26092, "loss": 4.26092, "time": 0.8474} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.09035, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27062, "top5_acc": 0.51391, "loss_cls": 4.24927, "loss": 4.24927, "time": 0.84332} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.09034, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26625, "top5_acc": 0.51016, "loss_cls": 4.23678, "loss": 4.23678, "time": 0.84714} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.09032, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26203, "top5_acc": 0.51672, "loss_cls": 4.23073, "loss": 4.23073, "time": 0.84464} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0903, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25297, "top5_acc": 0.50562, "loss_cls": 4.30738, "loss": 4.30738, "time": 0.84392} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.09029, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26469, "top5_acc": 0.51797, "loss_cls": 4.21793, "loss": 4.21793, "time": 0.84597} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.09027, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25891, "top5_acc": 0.51141, "loss_cls": 4.25911, "loss": 4.25911, "time": 0.84773} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.09025, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26234, "top5_acc": 0.50984, "loss_cls": 4.25003, "loss": 4.25003, "time": 0.8443} +{"mode": "train", "epoch": 31, "iter": 1300, "lr": 0.09024, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25781, "top5_acc": 0.52, "loss_cls": 4.28924, "loss": 4.28924, "time": 0.84882} +{"mode": "train", "epoch": 31, "iter": 1400, "lr": 0.09022, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26312, "top5_acc": 0.51578, "loss_cls": 4.26042, "loss": 4.26042, "time": 0.8493} +{"mode": "train", "epoch": 31, "iter": 1500, "lr": 0.0902, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26016, "top5_acc": 0.50547, "loss_cls": 4.25729, "loss": 4.25729, "time": 0.85204} +{"mode": "train", "epoch": 31, "iter": 1600, "lr": 0.09019, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26, "top5_acc": 0.5175, "loss_cls": 4.24247, "loss": 4.24247, "time": 0.85096} +{"mode": "train", "epoch": 31, "iter": 1700, "lr": 0.09017, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25844, "top5_acc": 0.50672, "loss_cls": 4.25878, "loss": 4.25878, "time": 0.84485} +{"mode": "train", "epoch": 31, "iter": 1800, "lr": 0.09015, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26281, "top5_acc": 0.51562, "loss_cls": 4.2225, "loss": 4.2225, "time": 0.84484} +{"mode": "train", "epoch": 31, "iter": 1900, "lr": 0.09014, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25859, "top5_acc": 0.51797, "loss_cls": 4.25676, "loss": 4.25676, "time": 0.84978} +{"mode": "train", "epoch": 31, "iter": 2000, "lr": 0.09012, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24797, "top5_acc": 0.49562, "loss_cls": 4.33622, "loss": 4.33622, "time": 0.84794} +{"mode": "train", "epoch": 31, "iter": 2100, "lr": 0.0901, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26203, "top5_acc": 0.51953, "loss_cls": 4.24239, "loss": 4.24239, "time": 0.84251} +{"mode": "train", "epoch": 31, "iter": 2200, "lr": 0.09009, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25781, "top5_acc": 0.51266, "loss_cls": 4.2763, "loss": 4.2763, "time": 0.84038} +{"mode": "train", "epoch": 31, "iter": 2300, "lr": 0.09007, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26109, "top5_acc": 0.51219, "loss_cls": 4.2764, "loss": 4.2764, "time": 0.85727} +{"mode": "train", "epoch": 31, "iter": 2400, "lr": 0.09005, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2675, "top5_acc": 0.52, "loss_cls": 4.23408, "loss": 4.23408, "time": 0.85129} +{"mode": "train", "epoch": 31, "iter": 2500, "lr": 0.09004, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26328, "top5_acc": 0.52203, "loss_cls": 4.22409, "loss": 4.22409, "time": 0.84516} +{"mode": "train", "epoch": 31, "iter": 2600, "lr": 0.09002, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26234, "top5_acc": 0.52297, "loss_cls": 4.22502, "loss": 4.22502, "time": 0.84673} +{"mode": "train", "epoch": 31, "iter": 2700, "lr": 0.09, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2525, "top5_acc": 0.50969, "loss_cls": 4.28698, "loss": 4.28698, "time": 0.84808} +{"mode": "train", "epoch": 31, "iter": 2800, "lr": 0.08999, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26609, "top5_acc": 0.51203, "loss_cls": 4.24995, "loss": 4.24995, "time": 0.84934} +{"mode": "train", "epoch": 31, "iter": 2900, "lr": 0.08997, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26328, "top5_acc": 0.51328, "loss_cls": 4.26232, "loss": 4.26232, "time": 0.8485} +{"mode": "train", "epoch": 31, "iter": 3000, "lr": 0.08995, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25984, "top5_acc": 0.51453, "loss_cls": 4.23281, "loss": 4.23281, "time": 0.84798} +{"mode": "train", "epoch": 31, "iter": 3100, "lr": 0.08994, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26312, "top5_acc": 0.5125, "loss_cls": 4.26234, "loss": 4.26234, "time": 0.8449} +{"mode": "train", "epoch": 31, "iter": 3200, "lr": 0.08992, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26641, "top5_acc": 0.52312, "loss_cls": 4.20781, "loss": 4.20781, "time": 0.84856} +{"mode": "train", "epoch": 31, "iter": 3300, "lr": 0.0899, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25844, "top5_acc": 0.51328, "loss_cls": 4.27304, "loss": 4.27304, "time": 0.8451} +{"mode": "train", "epoch": 31, "iter": 3400, "lr": 0.08989, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25078, "top5_acc": 0.5075, "loss_cls": 4.30135, "loss": 4.30135, "time": 0.85087} +{"mode": "train", "epoch": 31, "iter": 3500, "lr": 0.08987, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25922, "top5_acc": 0.52516, "loss_cls": 4.21805, "loss": 4.21805, "time": 0.84958} +{"mode": "train", "epoch": 31, "iter": 3600, "lr": 0.08985, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25359, "top5_acc": 0.50844, "loss_cls": 4.28257, "loss": 4.28257, "time": 0.85215} +{"mode": "train", "epoch": 31, "iter": 3700, "lr": 0.08983, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24859, "top5_acc": 0.51359, "loss_cls": 4.28568, "loss": 4.28568, "time": 0.84705} +{"mode": "val", "epoch": 31, "iter": 309, "lr": 0.08983, "top1_acc": 0.18158, "top5_acc": 0.41397, "mean_class_accuracy": 0.18163} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.08981, "memory": 15990, "data_time": 1.37618, "top1_acc": 0.26234, "top5_acc": 0.52219, "loss_cls": 4.22718, "loss": 4.22718, "time": 2.39751} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.08979, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27547, "top5_acc": 0.52516, "loss_cls": 4.18971, "loss": 4.18971, "time": 0.846} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.08978, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26406, "top5_acc": 0.52078, "loss_cls": 4.18032, "loss": 4.18032, "time": 0.84592} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.08976, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27109, "top5_acc": 0.5275, "loss_cls": 4.17405, "loss": 4.17405, "time": 0.84781} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.08974, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26234, "top5_acc": 0.51531, "loss_cls": 4.24182, "loss": 4.24182, "time": 0.84154} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.08973, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26, "top5_acc": 0.50609, "loss_cls": 4.29881, "loss": 4.29881, "time": 0.84505} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.08971, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.26422, "top5_acc": 0.52984, "loss_cls": 4.21772, "loss": 4.21772, "time": 0.83887} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.08969, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.265, "top5_acc": 0.51641, "loss_cls": 4.21127, "loss": 4.21127, "time": 0.84597} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.08967, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25016, "top5_acc": 0.50984, "loss_cls": 4.27689, "loss": 4.27689, "time": 0.84353} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.08966, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27281, "top5_acc": 0.51984, "loss_cls": 4.24338, "loss": 4.24338, "time": 0.84412} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.08964, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26125, "top5_acc": 0.51219, "loss_cls": 4.27238, "loss": 4.27238, "time": 0.84879} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.08962, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26594, "top5_acc": 0.52344, "loss_cls": 4.23758, "loss": 4.23758, "time": 0.84705} +{"mode": "train", "epoch": 32, "iter": 1300, "lr": 0.08961, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25828, "top5_acc": 0.5175, "loss_cls": 4.20974, "loss": 4.20974, "time": 0.84491} +{"mode": "train", "epoch": 32, "iter": 1400, "lr": 0.08959, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25875, "top5_acc": 0.51438, "loss_cls": 4.25234, "loss": 4.25234, "time": 0.84753} +{"mode": "train", "epoch": 32, "iter": 1500, "lr": 0.08957, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26297, "top5_acc": 0.51797, "loss_cls": 4.2441, "loss": 4.2441, "time": 0.84778} +{"mode": "train", "epoch": 32, "iter": 1600, "lr": 0.08955, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25406, "top5_acc": 0.50594, "loss_cls": 4.28854, "loss": 4.28854, "time": 0.84763} +{"mode": "train", "epoch": 32, "iter": 1700, "lr": 0.08954, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26516, "top5_acc": 0.5175, "loss_cls": 4.19966, "loss": 4.19966, "time": 0.84259} +{"mode": "train", "epoch": 32, "iter": 1800, "lr": 0.08952, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26094, "top5_acc": 0.51313, "loss_cls": 4.25653, "loss": 4.25653, "time": 0.84606} +{"mode": "train", "epoch": 32, "iter": 1900, "lr": 0.0895, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26922, "top5_acc": 0.52781, "loss_cls": 4.19379, "loss": 4.19379, "time": 0.84497} +{"mode": "train", "epoch": 32, "iter": 2000, "lr": 0.08949, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26656, "top5_acc": 0.50891, "loss_cls": 4.22667, "loss": 4.22667, "time": 0.84465} +{"mode": "train", "epoch": 32, "iter": 2100, "lr": 0.08947, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25203, "top5_acc": 0.51562, "loss_cls": 4.25967, "loss": 4.25967, "time": 0.84707} +{"mode": "train", "epoch": 32, "iter": 2200, "lr": 0.08945, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26094, "top5_acc": 0.51313, "loss_cls": 4.25829, "loss": 4.25829, "time": 0.84797} +{"mode": "train", "epoch": 32, "iter": 2300, "lr": 0.08943, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26969, "top5_acc": 0.5175, "loss_cls": 4.20358, "loss": 4.20358, "time": 0.84312} +{"mode": "train", "epoch": 32, "iter": 2400, "lr": 0.08942, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25734, "top5_acc": 0.50875, "loss_cls": 4.26066, "loss": 4.26066, "time": 0.84592} +{"mode": "train", "epoch": 32, "iter": 2500, "lr": 0.0894, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25969, "top5_acc": 0.52609, "loss_cls": 4.22256, "loss": 4.22256, "time": 0.84987} +{"mode": "train", "epoch": 32, "iter": 2600, "lr": 0.08938, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26281, "top5_acc": 0.50938, "loss_cls": 4.25367, "loss": 4.25367, "time": 0.84754} +{"mode": "train", "epoch": 32, "iter": 2700, "lr": 0.08937, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26391, "top5_acc": 0.51094, "loss_cls": 4.25255, "loss": 4.25255, "time": 0.85062} +{"mode": "train", "epoch": 32, "iter": 2800, "lr": 0.08935, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26078, "top5_acc": 0.51453, "loss_cls": 4.27117, "loss": 4.27117, "time": 0.85006} +{"mode": "train", "epoch": 32, "iter": 2900, "lr": 0.08933, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26594, "top5_acc": 0.51625, "loss_cls": 4.23055, "loss": 4.23055, "time": 0.84559} +{"mode": "train", "epoch": 32, "iter": 3000, "lr": 0.08931, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25781, "top5_acc": 0.51313, "loss_cls": 4.28748, "loss": 4.28748, "time": 0.852} +{"mode": "train", "epoch": 32, "iter": 3100, "lr": 0.0893, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25656, "top5_acc": 0.51875, "loss_cls": 4.23759, "loss": 4.23759, "time": 0.85108} +{"mode": "train", "epoch": 32, "iter": 3200, "lr": 0.08928, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25422, "top5_acc": 0.51094, "loss_cls": 4.29931, "loss": 4.29931, "time": 0.84869} +{"mode": "train", "epoch": 32, "iter": 3300, "lr": 0.08926, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25703, "top5_acc": 0.50875, "loss_cls": 4.2786, "loss": 4.2786, "time": 0.8485} +{"mode": "train", "epoch": 32, "iter": 3400, "lr": 0.08924, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26203, "top5_acc": 0.51797, "loss_cls": 4.25034, "loss": 4.25034, "time": 0.84937} +{"mode": "train", "epoch": 32, "iter": 3500, "lr": 0.08923, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26, "top5_acc": 0.5075, "loss_cls": 4.25714, "loss": 4.25714, "time": 0.8506} +{"mode": "train", "epoch": 32, "iter": 3600, "lr": 0.08921, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26234, "top5_acc": 0.51625, "loss_cls": 4.23186, "loss": 4.23186, "time": 0.85269} +{"mode": "train", "epoch": 32, "iter": 3700, "lr": 0.08919, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26578, "top5_acc": 0.51781, "loss_cls": 4.25027, "loss": 4.25027, "time": 0.84917} +{"mode": "val", "epoch": 32, "iter": 309, "lr": 0.08918, "top1_acc": 0.18042, "top5_acc": 0.41058, "mean_class_accuracy": 0.18029} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.08917, "memory": 15990, "data_time": 1.40366, "top1_acc": 0.26422, "top5_acc": 0.51234, "loss_cls": 4.21352, "loss": 4.21352, "time": 2.42098} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.08915, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27797, "top5_acc": 0.52672, "loss_cls": 4.16991, "loss": 4.16991, "time": 0.84759} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.08913, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.27187, "top5_acc": 0.52641, "loss_cls": 4.17634, "loss": 4.17634, "time": 0.85176} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.08912, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26125, "top5_acc": 0.51766, "loss_cls": 4.24916, "loss": 4.24916, "time": 0.85631} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.0891, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25703, "top5_acc": 0.52156, "loss_cls": 4.2101, "loss": 4.2101, "time": 0.85931} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.08908, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25969, "top5_acc": 0.51094, "loss_cls": 4.25607, "loss": 4.25607, "time": 0.8493} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.08906, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25156, "top5_acc": 0.50578, "loss_cls": 4.28593, "loss": 4.28593, "time": 0.85182} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.08905, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.25391, "top5_acc": 0.51625, "loss_cls": 4.23685, "loss": 4.23685, "time": 0.84869} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.08903, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27484, "top5_acc": 0.53094, "loss_cls": 4.19327, "loss": 4.19327, "time": 0.84631} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.08901, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26766, "top5_acc": 0.51609, "loss_cls": 4.25268, "loss": 4.25268, "time": 0.8521} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.08899, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27203, "top5_acc": 0.52922, "loss_cls": 4.21399, "loss": 4.21399, "time": 0.84325} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.08898, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26484, "top5_acc": 0.51313, "loss_cls": 4.26697, "loss": 4.26697, "time": 0.84631} +{"mode": "train", "epoch": 33, "iter": 1300, "lr": 0.08896, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27203, "top5_acc": 0.51594, "loss_cls": 4.22833, "loss": 4.22833, "time": 0.85426} +{"mode": "train", "epoch": 33, "iter": 1400, "lr": 0.08894, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25844, "top5_acc": 0.51406, "loss_cls": 4.26393, "loss": 4.26393, "time": 0.85028} +{"mode": "train", "epoch": 33, "iter": 1500, "lr": 0.08892, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26875, "top5_acc": 0.51828, "loss_cls": 4.19604, "loss": 4.19604, "time": 0.85} +{"mode": "train", "epoch": 33, "iter": 1600, "lr": 0.08891, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25734, "top5_acc": 0.51844, "loss_cls": 4.24205, "loss": 4.24205, "time": 0.84928} +{"mode": "train", "epoch": 33, "iter": 1700, "lr": 0.08889, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26344, "top5_acc": 0.515, "loss_cls": 4.22952, "loss": 4.22952, "time": 0.84815} +{"mode": "train", "epoch": 33, "iter": 1800, "lr": 0.08887, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26344, "top5_acc": 0.51938, "loss_cls": 4.23857, "loss": 4.23857, "time": 0.84691} +{"mode": "train", "epoch": 33, "iter": 1900, "lr": 0.08885, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26406, "top5_acc": 0.51328, "loss_cls": 4.25696, "loss": 4.25696, "time": 0.85071} +{"mode": "train", "epoch": 33, "iter": 2000, "lr": 0.08884, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26141, "top5_acc": 0.51562, "loss_cls": 4.27457, "loss": 4.27457, "time": 0.85288} +{"mode": "train", "epoch": 33, "iter": 2100, "lr": 0.08882, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26203, "top5_acc": 0.51422, "loss_cls": 4.26889, "loss": 4.26889, "time": 0.85363} +{"mode": "train", "epoch": 33, "iter": 2200, "lr": 0.0888, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25828, "top5_acc": 0.50734, "loss_cls": 4.26021, "loss": 4.26021, "time": 0.84842} +{"mode": "train", "epoch": 33, "iter": 2300, "lr": 0.08878, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26109, "top5_acc": 0.51453, "loss_cls": 4.2277, "loss": 4.2277, "time": 0.85344} +{"mode": "train", "epoch": 33, "iter": 2400, "lr": 0.08876, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27078, "top5_acc": 0.52891, "loss_cls": 4.21328, "loss": 4.21328, "time": 0.84795} +{"mode": "train", "epoch": 33, "iter": 2500, "lr": 0.08875, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26172, "top5_acc": 0.51406, "loss_cls": 4.24157, "loss": 4.24157, "time": 0.84556} +{"mode": "train", "epoch": 33, "iter": 2600, "lr": 0.08873, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26937, "top5_acc": 0.51578, "loss_cls": 4.21887, "loss": 4.21887, "time": 0.85707} +{"mode": "train", "epoch": 33, "iter": 2700, "lr": 0.08871, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26047, "top5_acc": 0.51219, "loss_cls": 4.24165, "loss": 4.24165, "time": 0.84842} +{"mode": "train", "epoch": 33, "iter": 2800, "lr": 0.08869, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26344, "top5_acc": 0.52281, "loss_cls": 4.22229, "loss": 4.22229, "time": 0.84644} +{"mode": "train", "epoch": 33, "iter": 2900, "lr": 0.08868, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26047, "top5_acc": 0.52078, "loss_cls": 4.2368, "loss": 4.2368, "time": 0.85153} +{"mode": "train", "epoch": 33, "iter": 3000, "lr": 0.08866, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26125, "top5_acc": 0.50641, "loss_cls": 4.26833, "loss": 4.26833, "time": 0.84745} +{"mode": "train", "epoch": 33, "iter": 3100, "lr": 0.08864, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26281, "top5_acc": 0.51594, "loss_cls": 4.23781, "loss": 4.23781, "time": 0.84815} +{"mode": "train", "epoch": 33, "iter": 3200, "lr": 0.08862, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26578, "top5_acc": 0.51562, "loss_cls": 4.24753, "loss": 4.24753, "time": 0.85376} +{"mode": "train", "epoch": 33, "iter": 3300, "lr": 0.08861, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27062, "top5_acc": 0.52016, "loss_cls": 4.24333, "loss": 4.24333, "time": 0.84671} +{"mode": "train", "epoch": 33, "iter": 3400, "lr": 0.08859, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27187, "top5_acc": 0.52469, "loss_cls": 4.18407, "loss": 4.18407, "time": 0.85389} +{"mode": "train", "epoch": 33, "iter": 3500, "lr": 0.08857, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25875, "top5_acc": 0.50859, "loss_cls": 4.24678, "loss": 4.24678, "time": 0.85053} +{"mode": "train", "epoch": 33, "iter": 3600, "lr": 0.08855, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26516, "top5_acc": 0.51672, "loss_cls": 4.2198, "loss": 4.2198, "time": 0.85337} +{"mode": "train", "epoch": 33, "iter": 3700, "lr": 0.08853, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25156, "top5_acc": 0.51109, "loss_cls": 4.28407, "loss": 4.28407, "time": 0.85444} +{"mode": "val", "epoch": 33, "iter": 309, "lr": 0.08853, "top1_acc": 0.2023, "top5_acc": 0.44026, "mean_class_accuracy": 0.20222} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.08851, "memory": 15990, "data_time": 1.41168, "top1_acc": 0.26969, "top5_acc": 0.52828, "loss_cls": 4.20562, "loss": 4.20562, "time": 2.43743} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.08849, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27328, "top5_acc": 0.52469, "loss_cls": 4.18408, "loss": 4.18408, "time": 0.84829} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.08847, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26719, "top5_acc": 0.52234, "loss_cls": 4.19696, "loss": 4.19696, "time": 0.84227} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.08845, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26094, "top5_acc": 0.52281, "loss_cls": 4.2082, "loss": 4.2082, "time": 0.85063} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.08844, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26594, "top5_acc": 0.51688, "loss_cls": 4.23144, "loss": 4.23144, "time": 0.85148} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.08842, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26062, "top5_acc": 0.51781, "loss_cls": 4.23314, "loss": 4.23314, "time": 0.85222} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.0884, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27281, "top5_acc": 0.52312, "loss_cls": 4.2053, "loss": 4.2053, "time": 0.85124} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.08838, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26203, "top5_acc": 0.51828, "loss_cls": 4.2498, "loss": 4.2498, "time": 0.84913} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.08836, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26781, "top5_acc": 0.52, "loss_cls": 4.23319, "loss": 4.23319, "time": 0.84801} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.08835, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26281, "top5_acc": 0.52078, "loss_cls": 4.24101, "loss": 4.24101, "time": 0.83939} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.08833, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26266, "top5_acc": 0.52078, "loss_cls": 4.20486, "loss": 4.20486, "time": 0.83942} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.08831, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27562, "top5_acc": 0.52453, "loss_cls": 4.18841, "loss": 4.18841, "time": 0.85397} +{"mode": "train", "epoch": 34, "iter": 1300, "lr": 0.08829, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26969, "top5_acc": 0.525, "loss_cls": 4.20296, "loss": 4.20296, "time": 0.84914} +{"mode": "train", "epoch": 34, "iter": 1400, "lr": 0.08828, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27078, "top5_acc": 0.51922, "loss_cls": 4.24, "loss": 4.24, "time": 0.85214} +{"mode": "train", "epoch": 34, "iter": 1500, "lr": 0.08826, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27234, "top5_acc": 0.52734, "loss_cls": 4.19874, "loss": 4.19874, "time": 0.85154} +{"mode": "train", "epoch": 34, "iter": 1600, "lr": 0.08824, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26766, "top5_acc": 0.51594, "loss_cls": 4.21929, "loss": 4.21929, "time": 0.8504} +{"mode": "train", "epoch": 34, "iter": 1700, "lr": 0.08822, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25344, "top5_acc": 0.50609, "loss_cls": 4.29801, "loss": 4.29801, "time": 0.84975} +{"mode": "train", "epoch": 34, "iter": 1800, "lr": 0.0882, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27016, "top5_acc": 0.52484, "loss_cls": 4.21768, "loss": 4.21768, "time": 0.84694} +{"mode": "train", "epoch": 34, "iter": 1900, "lr": 0.08819, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26516, "top5_acc": 0.51578, "loss_cls": 4.23863, "loss": 4.23863, "time": 0.84828} +{"mode": "train", "epoch": 34, "iter": 2000, "lr": 0.08817, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26016, "top5_acc": 0.51547, "loss_cls": 4.24828, "loss": 4.24828, "time": 0.85062} +{"mode": "train", "epoch": 34, "iter": 2100, "lr": 0.08815, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25812, "top5_acc": 0.51484, "loss_cls": 4.26209, "loss": 4.26209, "time": 0.85216} +{"mode": "train", "epoch": 34, "iter": 2200, "lr": 0.08813, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27719, "top5_acc": 0.52672, "loss_cls": 4.16438, "loss": 4.16438, "time": 0.84813} +{"mode": "train", "epoch": 34, "iter": 2300, "lr": 0.08811, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26312, "top5_acc": 0.50922, "loss_cls": 4.26823, "loss": 4.26823, "time": 0.849} +{"mode": "train", "epoch": 34, "iter": 2400, "lr": 0.08809, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26562, "top5_acc": 0.50688, "loss_cls": 4.22051, "loss": 4.22051, "time": 0.84668} +{"mode": "train", "epoch": 34, "iter": 2500, "lr": 0.08808, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2625, "top5_acc": 0.51078, "loss_cls": 4.28372, "loss": 4.28372, "time": 0.85083} +{"mode": "train", "epoch": 34, "iter": 2600, "lr": 0.08806, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26766, "top5_acc": 0.52641, "loss_cls": 4.21501, "loss": 4.21501, "time": 0.85367} +{"mode": "train", "epoch": 34, "iter": 2700, "lr": 0.08804, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27125, "top5_acc": 0.5275, "loss_cls": 4.20156, "loss": 4.20156, "time": 0.85092} +{"mode": "train", "epoch": 34, "iter": 2800, "lr": 0.08802, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25656, "top5_acc": 0.51328, "loss_cls": 4.27935, "loss": 4.27935, "time": 0.84506} +{"mode": "train", "epoch": 34, "iter": 2900, "lr": 0.088, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26391, "top5_acc": 0.52328, "loss_cls": 4.22642, "loss": 4.22642, "time": 0.84882} +{"mode": "train", "epoch": 34, "iter": 3000, "lr": 0.08799, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27219, "top5_acc": 0.52344, "loss_cls": 4.17267, "loss": 4.17267, "time": 0.85585} +{"mode": "train", "epoch": 34, "iter": 3100, "lr": 0.08797, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26859, "top5_acc": 0.52391, "loss_cls": 4.2137, "loss": 4.2137, "time": 0.85562} +{"mode": "train", "epoch": 34, "iter": 3200, "lr": 0.08795, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25766, "top5_acc": 0.51562, "loss_cls": 4.25351, "loss": 4.25351, "time": 0.8507} +{"mode": "train", "epoch": 34, "iter": 3300, "lr": 0.08793, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24969, "top5_acc": 0.50703, "loss_cls": 4.29842, "loss": 4.29842, "time": 0.84924} +{"mode": "train", "epoch": 34, "iter": 3400, "lr": 0.08791, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26281, "top5_acc": 0.5125, "loss_cls": 4.24717, "loss": 4.24717, "time": 0.85065} +{"mode": "train", "epoch": 34, "iter": 3500, "lr": 0.08789, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27297, "top5_acc": 0.50359, "loss_cls": 4.25161, "loss": 4.25161, "time": 0.84871} +{"mode": "train", "epoch": 34, "iter": 3600, "lr": 0.08788, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26891, "top5_acc": 0.52906, "loss_cls": 4.18078, "loss": 4.18078, "time": 0.85485} +{"mode": "train", "epoch": 34, "iter": 3700, "lr": 0.08786, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26656, "top5_acc": 0.51125, "loss_cls": 4.24435, "loss": 4.24435, "time": 0.84946} +{"mode": "val", "epoch": 34, "iter": 309, "lr": 0.08785, "top1_acc": 0.18396, "top5_acc": 0.40733, "mean_class_accuracy": 0.18388} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.08783, "memory": 15990, "data_time": 1.44198, "top1_acc": 0.26984, "top5_acc": 0.51453, "loss_cls": 4.23226, "loss": 4.23226, "time": 2.47265} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.08781, "memory": 15990, "data_time": 0.00106, "top1_acc": 0.27047, "top5_acc": 0.525, "loss_cls": 4.19316, "loss": 4.19316, "time": 0.84729} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.0878, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28219, "top5_acc": 0.53547, "loss_cls": 4.1553, "loss": 4.1553, "time": 0.84902} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.08778, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27297, "top5_acc": 0.52297, "loss_cls": 4.19893, "loss": 4.19893, "time": 0.84541} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.08776, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26609, "top5_acc": 0.52656, "loss_cls": 4.19172, "loss": 4.19172, "time": 0.85406} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.08774, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26062, "top5_acc": 0.5175, "loss_cls": 4.25192, "loss": 4.25192, "time": 0.85593} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.08772, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26375, "top5_acc": 0.5175, "loss_cls": 4.2298, "loss": 4.2298, "time": 0.84905} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.0877, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2675, "top5_acc": 0.52234, "loss_cls": 4.19898, "loss": 4.19898, "time": 0.85157} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.08769, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.26891, "top5_acc": 0.52188, "loss_cls": 4.19447, "loss": 4.19447, "time": 0.85058} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.08767, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26484, "top5_acc": 0.52359, "loss_cls": 4.19008, "loss": 4.19008, "time": 0.84388} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.08765, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26109, "top5_acc": 0.51516, "loss_cls": 4.25261, "loss": 4.25261, "time": 0.84216} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.08763, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26328, "top5_acc": 0.51328, "loss_cls": 4.23069, "loss": 4.23069, "time": 0.83911} +{"mode": "train", "epoch": 35, "iter": 1300, "lr": 0.08761, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27703, "top5_acc": 0.53203, "loss_cls": 4.16098, "loss": 4.16098, "time": 0.84279} +{"mode": "train", "epoch": 35, "iter": 1400, "lr": 0.08759, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26484, "top5_acc": 0.52391, "loss_cls": 4.22123, "loss": 4.22123, "time": 0.84427} +{"mode": "train", "epoch": 35, "iter": 1500, "lr": 0.08757, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25922, "top5_acc": 0.52047, "loss_cls": 4.24378, "loss": 4.24378, "time": 0.85048} +{"mode": "train", "epoch": 35, "iter": 1600, "lr": 0.08756, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26047, "top5_acc": 0.51203, "loss_cls": 4.21702, "loss": 4.21702, "time": 0.84587} +{"mode": "train", "epoch": 35, "iter": 1700, "lr": 0.08754, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26453, "top5_acc": 0.51109, "loss_cls": 4.25855, "loss": 4.25855, "time": 0.84988} +{"mode": "train", "epoch": 35, "iter": 1800, "lr": 0.08752, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26797, "top5_acc": 0.51906, "loss_cls": 4.21618, "loss": 4.21618, "time": 0.84577} +{"mode": "train", "epoch": 35, "iter": 1900, "lr": 0.0875, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26547, "top5_acc": 0.52766, "loss_cls": 4.18937, "loss": 4.18937, "time": 0.8462} +{"mode": "train", "epoch": 35, "iter": 2000, "lr": 0.08748, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26141, "top5_acc": 0.51609, "loss_cls": 4.22411, "loss": 4.22411, "time": 0.84063} +{"mode": "train", "epoch": 35, "iter": 2100, "lr": 0.08746, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26062, "top5_acc": 0.51969, "loss_cls": 4.24355, "loss": 4.24355, "time": 0.84515} +{"mode": "train", "epoch": 35, "iter": 2200, "lr": 0.08745, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26047, "top5_acc": 0.50875, "loss_cls": 4.23579, "loss": 4.23579, "time": 0.84284} +{"mode": "train", "epoch": 35, "iter": 2300, "lr": 0.08743, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26625, "top5_acc": 0.51891, "loss_cls": 4.21016, "loss": 4.21016, "time": 0.84773} +{"mode": "train", "epoch": 35, "iter": 2400, "lr": 0.08741, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26859, "top5_acc": 0.52625, "loss_cls": 4.21165, "loss": 4.21165, "time": 0.84663} +{"mode": "train", "epoch": 35, "iter": 2500, "lr": 0.08739, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26891, "top5_acc": 0.52453, "loss_cls": 4.19708, "loss": 4.19708, "time": 0.85224} +{"mode": "train", "epoch": 35, "iter": 2600, "lr": 0.08737, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26906, "top5_acc": 0.52219, "loss_cls": 4.2119, "loss": 4.2119, "time": 0.84812} +{"mode": "train", "epoch": 35, "iter": 2700, "lr": 0.08735, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25922, "top5_acc": 0.52172, "loss_cls": 4.23556, "loss": 4.23556, "time": 0.85038} +{"mode": "train", "epoch": 35, "iter": 2800, "lr": 0.08733, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27125, "top5_acc": 0.52969, "loss_cls": 4.16615, "loss": 4.16615, "time": 0.84962} +{"mode": "train", "epoch": 35, "iter": 2900, "lr": 0.08732, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26438, "top5_acc": 0.52047, "loss_cls": 4.23309, "loss": 4.23309, "time": 0.85117} +{"mode": "train", "epoch": 35, "iter": 3000, "lr": 0.0873, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26234, "top5_acc": 0.50609, "loss_cls": 4.25789, "loss": 4.25789, "time": 0.85262} +{"mode": "train", "epoch": 35, "iter": 3100, "lr": 0.08728, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27016, "top5_acc": 0.52609, "loss_cls": 4.18947, "loss": 4.18947, "time": 0.85339} +{"mode": "train", "epoch": 35, "iter": 3200, "lr": 0.08726, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26844, "top5_acc": 0.51297, "loss_cls": 4.19994, "loss": 4.19994, "time": 0.85135} +{"mode": "train", "epoch": 35, "iter": 3300, "lr": 0.08724, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27062, "top5_acc": 0.53, "loss_cls": 4.17092, "loss": 4.17092, "time": 0.8478} +{"mode": "train", "epoch": 35, "iter": 3400, "lr": 0.08722, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26234, "top5_acc": 0.51891, "loss_cls": 4.2248, "loss": 4.2248, "time": 0.84913} +{"mode": "train", "epoch": 35, "iter": 3500, "lr": 0.0872, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26312, "top5_acc": 0.50719, "loss_cls": 4.25312, "loss": 4.25312, "time": 0.84868} +{"mode": "train", "epoch": 35, "iter": 3600, "lr": 0.08718, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25312, "top5_acc": 0.51766, "loss_cls": 4.28371, "loss": 4.28371, "time": 0.84749} +{"mode": "train", "epoch": 35, "iter": 3700, "lr": 0.08717, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26531, "top5_acc": 0.51344, "loss_cls": 4.24148, "loss": 4.24148, "time": 0.84726} +{"mode": "val", "epoch": 35, "iter": 309, "lr": 0.08716, "top1_acc": 0.1943, "top5_acc": 0.42846, "mean_class_accuracy": 0.19405} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.08714, "memory": 15990, "data_time": 1.4588, "top1_acc": 0.26219, "top5_acc": 0.51641, "loss_cls": 4.21847, "loss": 4.21847, "time": 2.48479} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.08712, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26828, "top5_acc": 0.52438, "loss_cls": 4.21169, "loss": 4.21169, "time": 0.85343} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.0871, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.26906, "top5_acc": 0.53156, "loss_cls": 4.19054, "loss": 4.19054, "time": 0.84799} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.08708, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27187, "top5_acc": 0.52984, "loss_cls": 4.16918, "loss": 4.16918, "time": 0.84717} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.08706, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.26641, "top5_acc": 0.51938, "loss_cls": 4.20827, "loss": 4.20827, "time": 0.8451} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.08704, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27094, "top5_acc": 0.52609, "loss_cls": 4.18104, "loss": 4.18104, "time": 0.84493} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.08703, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27125, "top5_acc": 0.53188, "loss_cls": 4.18764, "loss": 4.18764, "time": 0.84688} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.08701, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26516, "top5_acc": 0.52141, "loss_cls": 4.20455, "loss": 4.20455, "time": 0.84376} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.08699, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25312, "top5_acc": 0.50688, "loss_cls": 4.27547, "loss": 4.27547, "time": 0.8507} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.08697, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25359, "top5_acc": 0.51156, "loss_cls": 4.25498, "loss": 4.25498, "time": 0.84551} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.08695, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26219, "top5_acc": 0.51922, "loss_cls": 4.20717, "loss": 4.20717, "time": 0.84547} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.08693, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26609, "top5_acc": 0.52562, "loss_cls": 4.22534, "loss": 4.22534, "time": 0.845} +{"mode": "train", "epoch": 36, "iter": 1300, "lr": 0.08691, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26031, "top5_acc": 0.51328, "loss_cls": 4.23877, "loss": 4.23877, "time": 0.84777} +{"mode": "train", "epoch": 36, "iter": 1400, "lr": 0.08689, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27109, "top5_acc": 0.52891, "loss_cls": 4.19925, "loss": 4.19925, "time": 0.8496} +{"mode": "train", "epoch": 36, "iter": 1500, "lr": 0.08688, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26844, "top5_acc": 0.52719, "loss_cls": 4.17953, "loss": 4.17953, "time": 0.8496} +{"mode": "train", "epoch": 36, "iter": 1600, "lr": 0.08686, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26484, "top5_acc": 0.51938, "loss_cls": 4.21997, "loss": 4.21997, "time": 0.84755} +{"mode": "train", "epoch": 36, "iter": 1700, "lr": 0.08684, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26609, "top5_acc": 0.51797, "loss_cls": 4.22127, "loss": 4.22127, "time": 0.85065} +{"mode": "train", "epoch": 36, "iter": 1800, "lr": 0.08682, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28312, "top5_acc": 0.52531, "loss_cls": 4.18067, "loss": 4.18067, "time": 0.84634} +{"mode": "train", "epoch": 36, "iter": 1900, "lr": 0.0868, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26297, "top5_acc": 0.52203, "loss_cls": 4.2204, "loss": 4.2204, "time": 0.8436} +{"mode": "train", "epoch": 36, "iter": 2000, "lr": 0.08678, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27031, "top5_acc": 0.52172, "loss_cls": 4.21321, "loss": 4.21321, "time": 0.84885} +{"mode": "train", "epoch": 36, "iter": 2100, "lr": 0.08676, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26234, "top5_acc": 0.51422, "loss_cls": 4.25694, "loss": 4.25694, "time": 0.84358} +{"mode": "train", "epoch": 36, "iter": 2200, "lr": 0.08674, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2675, "top5_acc": 0.51531, "loss_cls": 4.21332, "loss": 4.21332, "time": 0.84523} +{"mode": "train", "epoch": 36, "iter": 2300, "lr": 0.08672, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26875, "top5_acc": 0.52609, "loss_cls": 4.17813, "loss": 4.17813, "time": 0.84825} +{"mode": "train", "epoch": 36, "iter": 2400, "lr": 0.08671, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26937, "top5_acc": 0.5225, "loss_cls": 4.2202, "loss": 4.2202, "time": 0.84367} +{"mode": "train", "epoch": 36, "iter": 2500, "lr": 0.08669, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26469, "top5_acc": 0.51641, "loss_cls": 4.22819, "loss": 4.22819, "time": 0.8494} +{"mode": "train", "epoch": 36, "iter": 2600, "lr": 0.08667, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2675, "top5_acc": 0.51797, "loss_cls": 4.22027, "loss": 4.22027, "time": 0.84535} +{"mode": "train", "epoch": 36, "iter": 2700, "lr": 0.08665, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27141, "top5_acc": 0.52047, "loss_cls": 4.19014, "loss": 4.19014, "time": 0.84811} +{"mode": "train", "epoch": 36, "iter": 2800, "lr": 0.08663, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.255, "top5_acc": 0.51172, "loss_cls": 4.24523, "loss": 4.24523, "time": 0.84913} +{"mode": "train", "epoch": 36, "iter": 2900, "lr": 0.08661, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26234, "top5_acc": 0.50859, "loss_cls": 4.25937, "loss": 4.25937, "time": 0.84658} +{"mode": "train", "epoch": 36, "iter": 3000, "lr": 0.08659, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26578, "top5_acc": 0.51781, "loss_cls": 4.21912, "loss": 4.21912, "time": 0.85375} +{"mode": "train", "epoch": 36, "iter": 3100, "lr": 0.08657, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26188, "top5_acc": 0.50766, "loss_cls": 4.25185, "loss": 4.25185, "time": 0.84613} +{"mode": "train", "epoch": 36, "iter": 3200, "lr": 0.08655, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25469, "top5_acc": 0.51766, "loss_cls": 4.23155, "loss": 4.23155, "time": 0.84943} +{"mode": "train", "epoch": 36, "iter": 3300, "lr": 0.08653, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26391, "top5_acc": 0.50969, "loss_cls": 4.2618, "loss": 4.2618, "time": 0.84858} +{"mode": "train", "epoch": 36, "iter": 3400, "lr": 0.08651, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26422, "top5_acc": 0.51953, "loss_cls": 4.24295, "loss": 4.24295, "time": 0.84617} +{"mode": "train", "epoch": 36, "iter": 3500, "lr": 0.0865, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26578, "top5_acc": 0.52281, "loss_cls": 4.18812, "loss": 4.18812, "time": 0.85277} +{"mode": "train", "epoch": 36, "iter": 3600, "lr": 0.08648, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2675, "top5_acc": 0.52609, "loss_cls": 4.1977, "loss": 4.1977, "time": 0.84962} +{"mode": "train", "epoch": 36, "iter": 3700, "lr": 0.08646, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26125, "top5_acc": 0.52188, "loss_cls": 4.22609, "loss": 4.22609, "time": 0.85288} +{"mode": "val", "epoch": 36, "iter": 309, "lr": 0.08645, "top1_acc": 0.1825, "top5_acc": 0.41716, "mean_class_accuracy": 0.18231} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.08643, "memory": 15990, "data_time": 1.42221, "top1_acc": 0.27672, "top5_acc": 0.535, "loss_cls": 4.1614, "loss": 4.1614, "time": 2.44871} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.08641, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26656, "top5_acc": 0.52484, "loss_cls": 4.20441, "loss": 4.20441, "time": 0.84845} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.08639, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.27125, "top5_acc": 0.51938, "loss_cls": 4.20589, "loss": 4.20589, "time": 0.85082} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.08637, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26453, "top5_acc": 0.52281, "loss_cls": 4.22326, "loss": 4.22326, "time": 0.84504} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.08635, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26203, "top5_acc": 0.51859, "loss_cls": 4.21266, "loss": 4.21266, "time": 0.84372} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.08633, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26625, "top5_acc": 0.51562, "loss_cls": 4.21475, "loss": 4.21475, "time": 0.8491} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.08631, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28203, "top5_acc": 0.53219, "loss_cls": 4.16341, "loss": 4.16341, "time": 0.84446} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0863, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27031, "top5_acc": 0.52578, "loss_cls": 4.17282, "loss": 4.17282, "time": 0.8401} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.08628, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26859, "top5_acc": 0.52703, "loss_cls": 4.1745, "loss": 4.1745, "time": 0.84768} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.08626, "memory": 15990, "data_time": 0.00077, "top1_acc": 0.27375, "top5_acc": 0.5225, "loss_cls": 4.20097, "loss": 4.20097, "time": 0.84586} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.08624, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26656, "top5_acc": 0.51938, "loss_cls": 4.20357, "loss": 4.20357, "time": 0.84844} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.08622, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26734, "top5_acc": 0.52172, "loss_cls": 4.23414, "loss": 4.23414, "time": 0.8377} +{"mode": "train", "epoch": 37, "iter": 1300, "lr": 0.0862, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.26312, "top5_acc": 0.525, "loss_cls": 4.20543, "loss": 4.20543, "time": 0.84815} +{"mode": "train", "epoch": 37, "iter": 1400, "lr": 0.08618, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.275, "top5_acc": 0.52641, "loss_cls": 4.17574, "loss": 4.17574, "time": 0.84717} +{"mode": "train", "epoch": 37, "iter": 1500, "lr": 0.08616, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25812, "top5_acc": 0.51531, "loss_cls": 4.23512, "loss": 4.23512, "time": 0.84718} +{"mode": "train", "epoch": 37, "iter": 1600, "lr": 0.08614, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26844, "top5_acc": 0.51594, "loss_cls": 4.18187, "loss": 4.18187, "time": 0.84936} +{"mode": "train", "epoch": 37, "iter": 1700, "lr": 0.08612, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27609, "top5_acc": 0.53031, "loss_cls": 4.19043, "loss": 4.19043, "time": 0.85403} +{"mode": "train", "epoch": 37, "iter": 1800, "lr": 0.0861, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27437, "top5_acc": 0.53328, "loss_cls": 4.16481, "loss": 4.16481, "time": 0.85424} +{"mode": "train", "epoch": 37, "iter": 1900, "lr": 0.08608, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26469, "top5_acc": 0.51469, "loss_cls": 4.22981, "loss": 4.22981, "time": 0.85282} +{"mode": "train", "epoch": 37, "iter": 2000, "lr": 0.08606, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26031, "top5_acc": 0.51922, "loss_cls": 4.21936, "loss": 4.21936, "time": 0.85557} +{"mode": "train", "epoch": 37, "iter": 2100, "lr": 0.08604, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26156, "top5_acc": 0.52016, "loss_cls": 4.21926, "loss": 4.21926, "time": 0.85358} +{"mode": "train", "epoch": 37, "iter": 2200, "lr": 0.08602, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26344, "top5_acc": 0.52406, "loss_cls": 4.21193, "loss": 4.21193, "time": 0.85271} +{"mode": "train", "epoch": 37, "iter": 2300, "lr": 0.08601, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26547, "top5_acc": 0.51688, "loss_cls": 4.23023, "loss": 4.23023, "time": 0.85259} +{"mode": "train", "epoch": 37, "iter": 2400, "lr": 0.08599, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26359, "top5_acc": 0.52219, "loss_cls": 4.21255, "loss": 4.21255, "time": 0.85212} +{"mode": "train", "epoch": 37, "iter": 2500, "lr": 0.08597, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26312, "top5_acc": 0.52234, "loss_cls": 4.19858, "loss": 4.19858, "time": 0.85559} +{"mode": "train", "epoch": 37, "iter": 2600, "lr": 0.08595, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27156, "top5_acc": 0.52891, "loss_cls": 4.1655, "loss": 4.1655, "time": 0.8532} +{"mode": "train", "epoch": 37, "iter": 2700, "lr": 0.08593, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25797, "top5_acc": 0.51672, "loss_cls": 4.22204, "loss": 4.22204, "time": 0.84927} +{"mode": "train", "epoch": 37, "iter": 2800, "lr": 0.08591, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26328, "top5_acc": 0.52125, "loss_cls": 4.23325, "loss": 4.23325, "time": 0.84924} +{"mode": "train", "epoch": 37, "iter": 2900, "lr": 0.08589, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27047, "top5_acc": 0.51906, "loss_cls": 4.21774, "loss": 4.21774, "time": 0.85184} +{"mode": "train", "epoch": 37, "iter": 3000, "lr": 0.08587, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27109, "top5_acc": 0.52391, "loss_cls": 4.23467, "loss": 4.23467, "time": 0.85136} +{"mode": "train", "epoch": 37, "iter": 3100, "lr": 0.08585, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27297, "top5_acc": 0.53125, "loss_cls": 4.1446, "loss": 4.1446, "time": 0.85085} +{"mode": "train", "epoch": 37, "iter": 3200, "lr": 0.08583, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27, "top5_acc": 0.52766, "loss_cls": 4.19243, "loss": 4.19243, "time": 0.85351} +{"mode": "train", "epoch": 37, "iter": 3300, "lr": 0.08581, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26781, "top5_acc": 0.51859, "loss_cls": 4.22313, "loss": 4.22313, "time": 0.85377} +{"mode": "train", "epoch": 37, "iter": 3400, "lr": 0.08579, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26031, "top5_acc": 0.52344, "loss_cls": 4.24136, "loss": 4.24136, "time": 0.85282} +{"mode": "train", "epoch": 37, "iter": 3500, "lr": 0.08577, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26859, "top5_acc": 0.52453, "loss_cls": 4.19694, "loss": 4.19694, "time": 0.8516} +{"mode": "train", "epoch": 37, "iter": 3600, "lr": 0.08575, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.255, "top5_acc": 0.52703, "loss_cls": 4.20519, "loss": 4.20519, "time": 0.85231} +{"mode": "train", "epoch": 37, "iter": 3700, "lr": 0.08573, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27312, "top5_acc": 0.5325, "loss_cls": 4.14628, "loss": 4.14628, "time": 0.85215} +{"mode": "val", "epoch": 37, "iter": 309, "lr": 0.08572, "top1_acc": 0.2024, "top5_acc": 0.44056, "mean_class_accuracy": 0.20236} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.0857, "memory": 15990, "data_time": 1.4356, "top1_acc": 0.27531, "top5_acc": 0.53234, "loss_cls": 4.15218, "loss": 4.15218, "time": 2.46315} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.08568, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26828, "top5_acc": 0.52141, "loss_cls": 4.19623, "loss": 4.19623, "time": 0.85192} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.08567, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.27125, "top5_acc": 0.52562, "loss_cls": 4.16354, "loss": 4.16354, "time": 0.84833} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.08565, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27047, "top5_acc": 0.52625, "loss_cls": 4.21344, "loss": 4.21344, "time": 0.8493} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.08563, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26953, "top5_acc": 0.52844, "loss_cls": 4.19366, "loss": 4.19366, "time": 0.84237} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.08561, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27484, "top5_acc": 0.52922, "loss_cls": 4.15667, "loss": 4.15667, "time": 0.84078} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.08559, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26219, "top5_acc": 0.52312, "loss_cls": 4.24886, "loss": 4.24886, "time": 0.84297} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.08557, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27109, "top5_acc": 0.52234, "loss_cls": 4.20671, "loss": 4.20671, "time": 0.84598} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.08555, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27594, "top5_acc": 0.5275, "loss_cls": 4.15896, "loss": 4.15896, "time": 0.84214} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.08553, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27016, "top5_acc": 0.53031, "loss_cls": 4.15875, "loss": 4.15875, "time": 0.84089} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.08551, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.27609, "top5_acc": 0.52828, "loss_cls": 4.16961, "loss": 4.16961, "time": 0.83756} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.08549, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27391, "top5_acc": 0.51594, "loss_cls": 4.21749, "loss": 4.21749, "time": 0.84472} +{"mode": "train", "epoch": 38, "iter": 1300, "lr": 0.08547, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28703, "top5_acc": 0.53625, "loss_cls": 4.12681, "loss": 4.12681, "time": 0.84216} +{"mode": "train", "epoch": 38, "iter": 1400, "lr": 0.08545, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26937, "top5_acc": 0.53344, "loss_cls": 4.16024, "loss": 4.16024, "time": 0.84771} +{"mode": "train", "epoch": 38, "iter": 1500, "lr": 0.08543, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26391, "top5_acc": 0.52156, "loss_cls": 4.20024, "loss": 4.20024, "time": 0.84877} +{"mode": "train", "epoch": 38, "iter": 1600, "lr": 0.08541, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26734, "top5_acc": 0.52172, "loss_cls": 4.2015, "loss": 4.2015, "time": 0.84741} +{"mode": "train", "epoch": 38, "iter": 1700, "lr": 0.08539, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26812, "top5_acc": 0.51953, "loss_cls": 4.21488, "loss": 4.21488, "time": 0.84986} +{"mode": "train", "epoch": 38, "iter": 1800, "lr": 0.08537, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2675, "top5_acc": 0.525, "loss_cls": 4.22509, "loss": 4.22509, "time": 0.84519} +{"mode": "train", "epoch": 38, "iter": 1900, "lr": 0.08535, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27328, "top5_acc": 0.52344, "loss_cls": 4.18272, "loss": 4.18272, "time": 0.8453} +{"mode": "train", "epoch": 38, "iter": 2000, "lr": 0.08533, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26766, "top5_acc": 0.53109, "loss_cls": 4.19235, "loss": 4.19235, "time": 0.85067} +{"mode": "train", "epoch": 38, "iter": 2100, "lr": 0.08531, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27422, "top5_acc": 0.53266, "loss_cls": 4.18167, "loss": 4.18167, "time": 0.84512} +{"mode": "train", "epoch": 38, "iter": 2200, "lr": 0.08529, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26516, "top5_acc": 0.51359, "loss_cls": 4.24824, "loss": 4.24824, "time": 0.84652} +{"mode": "train", "epoch": 38, "iter": 2300, "lr": 0.08527, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26984, "top5_acc": 0.51812, "loss_cls": 4.23136, "loss": 4.23136, "time": 0.84851} +{"mode": "train", "epoch": 38, "iter": 2400, "lr": 0.08525, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26859, "top5_acc": 0.52484, "loss_cls": 4.20419, "loss": 4.20419, "time": 0.8497} +{"mode": "train", "epoch": 38, "iter": 2500, "lr": 0.08523, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26484, "top5_acc": 0.51313, "loss_cls": 4.27104, "loss": 4.27104, "time": 0.84727} +{"mode": "train", "epoch": 38, "iter": 2600, "lr": 0.08521, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27406, "top5_acc": 0.53078, "loss_cls": 4.16843, "loss": 4.16843, "time": 0.84707} +{"mode": "train", "epoch": 38, "iter": 2700, "lr": 0.08519, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26828, "top5_acc": 0.51969, "loss_cls": 4.18229, "loss": 4.18229, "time": 0.84482} +{"mode": "train", "epoch": 38, "iter": 2800, "lr": 0.08517, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26781, "top5_acc": 0.53156, "loss_cls": 4.17396, "loss": 4.17396, "time": 0.84791} +{"mode": "train", "epoch": 38, "iter": 2900, "lr": 0.08515, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25734, "top5_acc": 0.51797, "loss_cls": 4.24849, "loss": 4.24849, "time": 0.84704} +{"mode": "train", "epoch": 38, "iter": 3000, "lr": 0.08513, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27844, "top5_acc": 0.53031, "loss_cls": 4.15964, "loss": 4.15964, "time": 0.84799} +{"mode": "train", "epoch": 38, "iter": 3100, "lr": 0.08511, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26359, "top5_acc": 0.52047, "loss_cls": 4.21708, "loss": 4.21708, "time": 0.84888} +{"mode": "train", "epoch": 38, "iter": 3200, "lr": 0.08509, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26672, "top5_acc": 0.52, "loss_cls": 4.20493, "loss": 4.20493, "time": 0.84896} +{"mode": "train", "epoch": 38, "iter": 3300, "lr": 0.08507, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26656, "top5_acc": 0.52391, "loss_cls": 4.20226, "loss": 4.20226, "time": 0.85071} +{"mode": "train", "epoch": 38, "iter": 3400, "lr": 0.08505, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26625, "top5_acc": 0.52141, "loss_cls": 4.18562, "loss": 4.18562, "time": 0.85369} +{"mode": "train", "epoch": 38, "iter": 3500, "lr": 0.08503, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26469, "top5_acc": 0.52344, "loss_cls": 4.19787, "loss": 4.19787, "time": 0.85373} +{"mode": "train", "epoch": 38, "iter": 3600, "lr": 0.08501, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2775, "top5_acc": 0.52953, "loss_cls": 4.16897, "loss": 4.16897, "time": 0.849} +{"mode": "train", "epoch": 38, "iter": 3700, "lr": 0.08499, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26859, "top5_acc": 0.52625, "loss_cls": 4.2129, "loss": 4.2129, "time": 0.85447} +{"mode": "val", "epoch": 38, "iter": 309, "lr": 0.08498, "top1_acc": 0.18087, "top5_acc": 0.40931, "mean_class_accuracy": 0.18057} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.08496, "memory": 15990, "data_time": 1.48875, "top1_acc": 0.28328, "top5_acc": 0.54109, "loss_cls": 4.11101, "loss": 4.11101, "time": 2.52392} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.08494, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27375, "top5_acc": 0.53328, "loss_cls": 4.14528, "loss": 4.14528, "time": 0.85483} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.08492, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26891, "top5_acc": 0.52797, "loss_cls": 4.18488, "loss": 4.18488, "time": 0.84866} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.0849, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.27922, "top5_acc": 0.53547, "loss_cls": 4.13043, "loss": 4.13043, "time": 0.85191} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.08488, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26922, "top5_acc": 0.525, "loss_cls": 4.2049, "loss": 4.2049, "time": 0.8466} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.08486, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27359, "top5_acc": 0.53781, "loss_cls": 4.18036, "loss": 4.18036, "time": 0.8459} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.08484, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26453, "top5_acc": 0.52359, "loss_cls": 4.16511, "loss": 4.16511, "time": 0.85269} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.08482, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26703, "top5_acc": 0.51203, "loss_cls": 4.21529, "loss": 4.21529, "time": 0.8532} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.0848, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27125, "top5_acc": 0.52891, "loss_cls": 4.18023, "loss": 4.18023, "time": 0.85275} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.08478, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27047, "top5_acc": 0.52703, "loss_cls": 4.2284, "loss": 4.2284, "time": 0.84659} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.08476, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.27297, "top5_acc": 0.52438, "loss_cls": 4.17713, "loss": 4.17713, "time": 0.84167} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.08474, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26609, "top5_acc": 0.52516, "loss_cls": 4.17206, "loss": 4.17206, "time": 0.84097} +{"mode": "train", "epoch": 39, "iter": 1300, "lr": 0.08472, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27016, "top5_acc": 0.52172, "loss_cls": 4.20698, "loss": 4.20698, "time": 0.84297} +{"mode": "train", "epoch": 39, "iter": 1400, "lr": 0.0847, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26578, "top5_acc": 0.51828, "loss_cls": 4.21777, "loss": 4.21777, "time": 0.84719} +{"mode": "train", "epoch": 39, "iter": 1500, "lr": 0.08468, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27969, "top5_acc": 0.52781, "loss_cls": 4.16151, "loss": 4.16151, "time": 0.84427} +{"mode": "train", "epoch": 39, "iter": 1600, "lr": 0.08466, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26984, "top5_acc": 0.51656, "loss_cls": 4.23226, "loss": 4.23226, "time": 0.84447} +{"mode": "train", "epoch": 39, "iter": 1700, "lr": 0.08464, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27437, "top5_acc": 0.52656, "loss_cls": 4.16724, "loss": 4.16724, "time": 0.84362} +{"mode": "train", "epoch": 39, "iter": 1800, "lr": 0.08462, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26453, "top5_acc": 0.51781, "loss_cls": 4.22416, "loss": 4.22416, "time": 0.84675} +{"mode": "train", "epoch": 39, "iter": 1900, "lr": 0.0846, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27047, "top5_acc": 0.51844, "loss_cls": 4.21309, "loss": 4.21309, "time": 0.84575} +{"mode": "train", "epoch": 39, "iter": 2000, "lr": 0.08458, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26625, "top5_acc": 0.51594, "loss_cls": 4.21259, "loss": 4.21259, "time": 0.84299} +{"mode": "train", "epoch": 39, "iter": 2100, "lr": 0.08456, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27016, "top5_acc": 0.51594, "loss_cls": 4.22383, "loss": 4.22383, "time": 0.84332} +{"mode": "train", "epoch": 39, "iter": 2200, "lr": 0.08454, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26344, "top5_acc": 0.51922, "loss_cls": 4.20142, "loss": 4.20142, "time": 0.84735} +{"mode": "train", "epoch": 39, "iter": 2300, "lr": 0.08452, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26531, "top5_acc": 0.51688, "loss_cls": 4.21592, "loss": 4.21592, "time": 0.84767} +{"mode": "train", "epoch": 39, "iter": 2400, "lr": 0.0845, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26937, "top5_acc": 0.52359, "loss_cls": 4.1777, "loss": 4.1777, "time": 0.84449} +{"mode": "train", "epoch": 39, "iter": 2500, "lr": 0.08448, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27359, "top5_acc": 0.52641, "loss_cls": 4.18844, "loss": 4.18844, "time": 0.84791} +{"mode": "train", "epoch": 39, "iter": 2600, "lr": 0.08446, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26672, "top5_acc": 0.51328, "loss_cls": 4.21676, "loss": 4.21676, "time": 0.84947} +{"mode": "train", "epoch": 39, "iter": 2700, "lr": 0.08444, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26906, "top5_acc": 0.52031, "loss_cls": 4.19832, "loss": 4.19832, "time": 0.8519} +{"mode": "train", "epoch": 39, "iter": 2800, "lr": 0.08442, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27016, "top5_acc": 0.53219, "loss_cls": 4.18078, "loss": 4.18078, "time": 0.8502} +{"mode": "train", "epoch": 39, "iter": 2900, "lr": 0.0844, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.275, "top5_acc": 0.52562, "loss_cls": 4.1728, "loss": 4.1728, "time": 0.84935} +{"mode": "train", "epoch": 39, "iter": 3000, "lr": 0.08438, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26406, "top5_acc": 0.51359, "loss_cls": 4.26737, "loss": 4.26737, "time": 0.84724} +{"mode": "train", "epoch": 39, "iter": 3100, "lr": 0.08436, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26219, "top5_acc": 0.51828, "loss_cls": 4.20675, "loss": 4.20675, "time": 0.84834} +{"mode": "train", "epoch": 39, "iter": 3200, "lr": 0.08434, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27594, "top5_acc": 0.53734, "loss_cls": 4.14888, "loss": 4.14888, "time": 0.84862} +{"mode": "train", "epoch": 39, "iter": 3300, "lr": 0.08432, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27172, "top5_acc": 0.52703, "loss_cls": 4.20074, "loss": 4.20074, "time": 0.84857} +{"mode": "train", "epoch": 39, "iter": 3400, "lr": 0.0843, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26719, "top5_acc": 0.52516, "loss_cls": 4.18784, "loss": 4.18784, "time": 0.85011} +{"mode": "train", "epoch": 39, "iter": 3500, "lr": 0.08428, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26516, "top5_acc": 0.52219, "loss_cls": 4.20706, "loss": 4.20706, "time": 0.85173} +{"mode": "train", "epoch": 39, "iter": 3600, "lr": 0.08426, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26594, "top5_acc": 0.52562, "loss_cls": 4.2136, "loss": 4.2136, "time": 0.85176} +{"mode": "train", "epoch": 39, "iter": 3700, "lr": 0.08424, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27141, "top5_acc": 0.52531, "loss_cls": 4.18729, "loss": 4.18729, "time": 0.85107} +{"mode": "val", "epoch": 39, "iter": 309, "lr": 0.08423, "top1_acc": 0.16558, "top5_acc": 0.37679, "mean_class_accuracy": 0.1655} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.08421, "memory": 15990, "data_time": 1.43818, "top1_acc": 0.26891, "top5_acc": 0.52969, "loss_cls": 4.18782, "loss": 4.18782, "time": 2.46096} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.08419, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27062, "top5_acc": 0.52828, "loss_cls": 4.19262, "loss": 4.19262, "time": 0.84879} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.08417, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26859, "top5_acc": 0.52672, "loss_cls": 4.1964, "loss": 4.1964, "time": 0.84388} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.08415, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.27375, "top5_acc": 0.53531, "loss_cls": 4.16649, "loss": 4.16649, "time": 0.84939} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.08413, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26766, "top5_acc": 0.52906, "loss_cls": 4.15272, "loss": 4.15272, "time": 0.84937} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.08411, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27016, "top5_acc": 0.52203, "loss_cls": 4.17959, "loss": 4.17959, "time": 0.84167} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.08408, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26484, "top5_acc": 0.52469, "loss_cls": 4.20019, "loss": 4.20019, "time": 0.84706} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.08406, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27312, "top5_acc": 0.51703, "loss_cls": 4.22557, "loss": 4.22557, "time": 0.84699} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.08404, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26516, "top5_acc": 0.52562, "loss_cls": 4.17312, "loss": 4.17312, "time": 0.8473} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.08402, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27734, "top5_acc": 0.52672, "loss_cls": 4.14907, "loss": 4.14907, "time": 0.84425} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.084, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27781, "top5_acc": 0.53297, "loss_cls": 4.15207, "loss": 4.15207, "time": 0.84236} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.08398, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27297, "top5_acc": 0.52766, "loss_cls": 4.20113, "loss": 4.20113, "time": 0.84201} +{"mode": "train", "epoch": 40, "iter": 1300, "lr": 0.08396, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27, "top5_acc": 0.52266, "loss_cls": 4.19408, "loss": 4.19408, "time": 0.8381} +{"mode": "train", "epoch": 40, "iter": 1400, "lr": 0.08394, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.28047, "top5_acc": 0.53, "loss_cls": 4.1672, "loss": 4.1672, "time": 0.84609} +{"mode": "train", "epoch": 40, "iter": 1500, "lr": 0.08392, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27328, "top5_acc": 0.52609, "loss_cls": 4.19039, "loss": 4.19039, "time": 0.84982} +{"mode": "train", "epoch": 40, "iter": 1600, "lr": 0.0839, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28109, "top5_acc": 0.52969, "loss_cls": 4.14922, "loss": 4.14922, "time": 0.84953} +{"mode": "train", "epoch": 40, "iter": 1700, "lr": 0.08388, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26937, "top5_acc": 0.52562, "loss_cls": 4.14908, "loss": 4.14908, "time": 0.84428} +{"mode": "train", "epoch": 40, "iter": 1800, "lr": 0.08386, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27187, "top5_acc": 0.5175, "loss_cls": 4.18409, "loss": 4.18409, "time": 0.84844} +{"mode": "train", "epoch": 40, "iter": 1900, "lr": 0.08384, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27344, "top5_acc": 0.52344, "loss_cls": 4.18485, "loss": 4.18485, "time": 0.84929} +{"mode": "train", "epoch": 40, "iter": 2000, "lr": 0.08382, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26406, "top5_acc": 0.51172, "loss_cls": 4.22272, "loss": 4.22272, "time": 0.84564} +{"mode": "train", "epoch": 40, "iter": 2100, "lr": 0.0838, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27, "top5_acc": 0.52328, "loss_cls": 4.18515, "loss": 4.18515, "time": 0.84456} +{"mode": "train", "epoch": 40, "iter": 2200, "lr": 0.08378, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27156, "top5_acc": 0.52906, "loss_cls": 4.18632, "loss": 4.18632, "time": 0.8479} +{"mode": "train", "epoch": 40, "iter": 2300, "lr": 0.08376, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26766, "top5_acc": 0.52266, "loss_cls": 4.18671, "loss": 4.18671, "time": 0.83863} +{"mode": "train", "epoch": 40, "iter": 2400, "lr": 0.08374, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26953, "top5_acc": 0.52531, "loss_cls": 4.20057, "loss": 4.20057, "time": 0.84458} +{"mode": "train", "epoch": 40, "iter": 2500, "lr": 0.08371, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27625, "top5_acc": 0.52719, "loss_cls": 4.17778, "loss": 4.17778, "time": 0.84596} +{"mode": "train", "epoch": 40, "iter": 2600, "lr": 0.08369, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.275, "top5_acc": 0.52609, "loss_cls": 4.208, "loss": 4.208, "time": 0.84609} +{"mode": "train", "epoch": 40, "iter": 2700, "lr": 0.08367, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2625, "top5_acc": 0.52188, "loss_cls": 4.21647, "loss": 4.21647, "time": 0.84857} +{"mode": "train", "epoch": 40, "iter": 2800, "lr": 0.08365, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26812, "top5_acc": 0.52094, "loss_cls": 4.22272, "loss": 4.22272, "time": 0.84204} +{"mode": "train", "epoch": 40, "iter": 2900, "lr": 0.08363, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26875, "top5_acc": 0.52938, "loss_cls": 4.15195, "loss": 4.15195, "time": 0.84478} +{"mode": "train", "epoch": 40, "iter": 3000, "lr": 0.08361, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25922, "top5_acc": 0.51797, "loss_cls": 4.21123, "loss": 4.21123, "time": 0.84677} +{"mode": "train", "epoch": 40, "iter": 3100, "lr": 0.08359, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26875, "top5_acc": 0.52109, "loss_cls": 4.18377, "loss": 4.18377, "time": 0.85096} +{"mode": "train", "epoch": 40, "iter": 3200, "lr": 0.08357, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26937, "top5_acc": 0.52438, "loss_cls": 4.19191, "loss": 4.19191, "time": 0.84907} +{"mode": "train", "epoch": 40, "iter": 3300, "lr": 0.08355, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27203, "top5_acc": 0.53688, "loss_cls": 4.13412, "loss": 4.13412, "time": 0.8504} +{"mode": "train", "epoch": 40, "iter": 3400, "lr": 0.08353, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26531, "top5_acc": 0.52281, "loss_cls": 4.20047, "loss": 4.20047, "time": 0.85705} +{"mode": "train", "epoch": 40, "iter": 3500, "lr": 0.08351, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27047, "top5_acc": 0.52078, "loss_cls": 4.18315, "loss": 4.18315, "time": 0.84491} +{"mode": "train", "epoch": 40, "iter": 3600, "lr": 0.08349, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26859, "top5_acc": 0.52406, "loss_cls": 4.20292, "loss": 4.20292, "time": 0.84903} +{"mode": "train", "epoch": 40, "iter": 3700, "lr": 0.08347, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27859, "top5_acc": 0.53516, "loss_cls": 4.15009, "loss": 4.15009, "time": 0.84801} +{"mode": "val", "epoch": 40, "iter": 309, "lr": 0.08346, "top1_acc": 0.2022, "top5_acc": 0.4436, "mean_class_accuracy": 0.20222} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.08344, "memory": 15990, "data_time": 1.44355, "top1_acc": 0.27875, "top5_acc": 0.53859, "loss_cls": 4.12361, "loss": 4.12361, "time": 2.47139} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.08342, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27812, "top5_acc": 0.52469, "loss_cls": 4.16149, "loss": 4.16149, "time": 0.84851} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.08339, "memory": 15990, "data_time": 0.0008, "top1_acc": 0.27, "top5_acc": 0.53156, "loss_cls": 4.16493, "loss": 4.16493, "time": 0.84742} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.08337, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2725, "top5_acc": 0.52172, "loss_cls": 4.18664, "loss": 4.18664, "time": 0.84897} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.08335, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26594, "top5_acc": 0.52438, "loss_cls": 4.1847, "loss": 4.1847, "time": 0.84687} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.08333, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27047, "top5_acc": 0.53625, "loss_cls": 4.14729, "loss": 4.14729, "time": 0.84215} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.08331, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.27172, "top5_acc": 0.52953, "loss_cls": 4.16269, "loss": 4.16269, "time": 0.84788} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.08329, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28062, "top5_acc": 0.52672, "loss_cls": 4.1597, "loss": 4.1597, "time": 0.85002} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.08327, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27734, "top5_acc": 0.52156, "loss_cls": 4.16979, "loss": 4.16979, "time": 0.85322} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.08325, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.2625, "top5_acc": 0.52547, "loss_cls": 4.20699, "loss": 4.20699, "time": 0.85169} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.08323, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.2725, "top5_acc": 0.52141, "loss_cls": 4.18237, "loss": 4.18237, "time": 0.85076} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.08321, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27297, "top5_acc": 0.52375, "loss_cls": 4.19362, "loss": 4.19362, "time": 0.85306} +{"mode": "train", "epoch": 41, "iter": 1300, "lr": 0.08319, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26625, "top5_acc": 0.53047, "loss_cls": 4.1533, "loss": 4.1533, "time": 0.84685} +{"mode": "train", "epoch": 41, "iter": 1400, "lr": 0.08316, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26531, "top5_acc": 0.52484, "loss_cls": 4.20672, "loss": 4.20672, "time": 0.8467} +{"mode": "train", "epoch": 41, "iter": 1500, "lr": 0.08314, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26812, "top5_acc": 0.51578, "loss_cls": 4.20683, "loss": 4.20683, "time": 0.84966} +{"mode": "train", "epoch": 41, "iter": 1600, "lr": 0.08312, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.265, "top5_acc": 0.52547, "loss_cls": 4.18149, "loss": 4.18149, "time": 0.84694} +{"mode": "train", "epoch": 41, "iter": 1700, "lr": 0.0831, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27047, "top5_acc": 0.53156, "loss_cls": 4.18998, "loss": 4.18998, "time": 0.84888} +{"mode": "train", "epoch": 41, "iter": 1800, "lr": 0.08308, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28297, "top5_acc": 0.53359, "loss_cls": 4.13593, "loss": 4.13593, "time": 0.84686} +{"mode": "train", "epoch": 41, "iter": 1900, "lr": 0.08306, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27641, "top5_acc": 0.52906, "loss_cls": 4.1612, "loss": 4.1612, "time": 0.84222} +{"mode": "train", "epoch": 41, "iter": 2000, "lr": 0.08304, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27016, "top5_acc": 0.52641, "loss_cls": 4.18041, "loss": 4.18041, "time": 0.84437} +{"mode": "train", "epoch": 41, "iter": 2100, "lr": 0.08302, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26937, "top5_acc": 0.53109, "loss_cls": 4.18258, "loss": 4.18258, "time": 0.84482} +{"mode": "train", "epoch": 41, "iter": 2200, "lr": 0.083, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26953, "top5_acc": 0.52406, "loss_cls": 4.19289, "loss": 4.19289, "time": 0.84368} +{"mode": "train", "epoch": 41, "iter": 2300, "lr": 0.08298, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27625, "top5_acc": 0.53859, "loss_cls": 4.13406, "loss": 4.13406, "time": 0.84229} +{"mode": "train", "epoch": 41, "iter": 2400, "lr": 0.08296, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26891, "top5_acc": 0.52547, "loss_cls": 4.22115, "loss": 4.22115, "time": 0.84428} +{"mode": "train", "epoch": 41, "iter": 2500, "lr": 0.08293, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27047, "top5_acc": 0.52031, "loss_cls": 4.18751, "loss": 4.18751, "time": 0.84678} +{"mode": "train", "epoch": 41, "iter": 2600, "lr": 0.08291, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26219, "top5_acc": 0.5175, "loss_cls": 4.2192, "loss": 4.2192, "time": 0.84795} +{"mode": "train", "epoch": 41, "iter": 2700, "lr": 0.08289, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26984, "top5_acc": 0.51609, "loss_cls": 4.22201, "loss": 4.22201, "time": 0.84856} +{"mode": "train", "epoch": 41, "iter": 2800, "lr": 0.08287, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27703, "top5_acc": 0.52688, "loss_cls": 4.18203, "loss": 4.18203, "time": 0.84439} +{"mode": "train", "epoch": 41, "iter": 2900, "lr": 0.08285, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27312, "top5_acc": 0.53078, "loss_cls": 4.19468, "loss": 4.19468, "time": 0.84491} +{"mode": "train", "epoch": 41, "iter": 3000, "lr": 0.08283, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26953, "top5_acc": 0.52484, "loss_cls": 4.17833, "loss": 4.17833, "time": 0.84413} +{"mode": "train", "epoch": 41, "iter": 3100, "lr": 0.08281, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27266, "top5_acc": 0.52719, "loss_cls": 4.174, "loss": 4.174, "time": 0.84498} +{"mode": "train", "epoch": 41, "iter": 3200, "lr": 0.08279, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27, "top5_acc": 0.52094, "loss_cls": 4.20449, "loss": 4.20449, "time": 0.85006} +{"mode": "train", "epoch": 41, "iter": 3300, "lr": 0.08277, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27359, "top5_acc": 0.52906, "loss_cls": 4.17334, "loss": 4.17334, "time": 0.84803} +{"mode": "train", "epoch": 41, "iter": 3400, "lr": 0.08274, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28438, "top5_acc": 0.5325, "loss_cls": 4.13015, "loss": 4.13015, "time": 0.84472} +{"mode": "train", "epoch": 41, "iter": 3500, "lr": 0.08272, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26125, "top5_acc": 0.51172, "loss_cls": 4.24494, "loss": 4.24494, "time": 0.84758} +{"mode": "train", "epoch": 41, "iter": 3600, "lr": 0.0827, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26234, "top5_acc": 0.52375, "loss_cls": 4.18726, "loss": 4.18726, "time": 0.84777} +{"mode": "train", "epoch": 41, "iter": 3700, "lr": 0.08268, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27703, "top5_acc": 0.52953, "loss_cls": 4.15727, "loss": 4.15727, "time": 0.8479} +{"mode": "val", "epoch": 41, "iter": 309, "lr": 0.08267, "top1_acc": 0.22438, "top5_acc": 0.46067, "mean_class_accuracy": 0.22432} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.08265, "memory": 15990, "data_time": 1.41499, "top1_acc": 0.27578, "top5_acc": 0.52656, "loss_cls": 4.16018, "loss": 4.16018, "time": 2.43879} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.08263, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28813, "top5_acc": 0.54672, "loss_cls": 4.0701, "loss": 4.0701, "time": 0.8509} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.08261, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27062, "top5_acc": 0.53031, "loss_cls": 4.18818, "loss": 4.18818, "time": 0.84593} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.08259, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26531, "top5_acc": 0.51578, "loss_cls": 4.20036, "loss": 4.20036, "time": 0.84778} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.08257, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.28109, "top5_acc": 0.53484, "loss_cls": 4.12428, "loss": 4.12428, "time": 0.85209} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.08254, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27266, "top5_acc": 0.53359, "loss_cls": 4.15988, "loss": 4.15988, "time": 0.84188} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.08252, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28828, "top5_acc": 0.54, "loss_cls": 4.10132, "loss": 4.10132, "time": 0.84259} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.0825, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2725, "top5_acc": 0.52562, "loss_cls": 4.18573, "loss": 4.18573, "time": 0.84545} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.08248, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27234, "top5_acc": 0.52734, "loss_cls": 4.17362, "loss": 4.17362, "time": 0.84388} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.08246, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27141, "top5_acc": 0.52625, "loss_cls": 4.17654, "loss": 4.17654, "time": 0.84838} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.08244, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26641, "top5_acc": 0.52688, "loss_cls": 4.19737, "loss": 4.19737, "time": 0.83887} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.08242, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27359, "top5_acc": 0.52641, "loss_cls": 4.19643, "loss": 4.19643, "time": 0.84768} +{"mode": "train", "epoch": 42, "iter": 1300, "lr": 0.0824, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27859, "top5_acc": 0.52453, "loss_cls": 4.16084, "loss": 4.16084, "time": 0.83824} +{"mode": "train", "epoch": 42, "iter": 1400, "lr": 0.08237, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26891, "top5_acc": 0.52828, "loss_cls": 4.17293, "loss": 4.17293, "time": 0.84003} +{"mode": "train", "epoch": 42, "iter": 1500, "lr": 0.08235, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26781, "top5_acc": 0.52906, "loss_cls": 4.20641, "loss": 4.20641, "time": 0.84224} +{"mode": "train", "epoch": 42, "iter": 1600, "lr": 0.08233, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26984, "top5_acc": 0.52562, "loss_cls": 4.20483, "loss": 4.20483, "time": 0.84469} +{"mode": "train", "epoch": 42, "iter": 1700, "lr": 0.08231, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26859, "top5_acc": 0.52641, "loss_cls": 4.19243, "loss": 4.19243, "time": 0.84991} +{"mode": "train", "epoch": 42, "iter": 1800, "lr": 0.08229, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28625, "top5_acc": 0.53531, "loss_cls": 4.1414, "loss": 4.1414, "time": 0.84275} +{"mode": "train", "epoch": 42, "iter": 1900, "lr": 0.08227, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27328, "top5_acc": 0.52672, "loss_cls": 4.16799, "loss": 4.16799, "time": 0.84553} +{"mode": "train", "epoch": 42, "iter": 2000, "lr": 0.08225, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27516, "top5_acc": 0.53, "loss_cls": 4.17248, "loss": 4.17248, "time": 0.84717} +{"mode": "train", "epoch": 42, "iter": 2100, "lr": 0.08222, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27547, "top5_acc": 0.52938, "loss_cls": 4.1717, "loss": 4.1717, "time": 0.84203} +{"mode": "train", "epoch": 42, "iter": 2200, "lr": 0.0822, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27984, "top5_acc": 0.52766, "loss_cls": 4.16355, "loss": 4.16355, "time": 0.84805} +{"mode": "train", "epoch": 42, "iter": 2300, "lr": 0.08218, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27187, "top5_acc": 0.52766, "loss_cls": 4.19721, "loss": 4.19721, "time": 0.84214} +{"mode": "train", "epoch": 42, "iter": 2400, "lr": 0.08216, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26937, "top5_acc": 0.52766, "loss_cls": 4.2046, "loss": 4.2046, "time": 0.84775} +{"mode": "train", "epoch": 42, "iter": 2500, "lr": 0.08214, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27547, "top5_acc": 0.53047, "loss_cls": 4.16592, "loss": 4.16592, "time": 0.83944} +{"mode": "train", "epoch": 42, "iter": 2600, "lr": 0.08212, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27688, "top5_acc": 0.52641, "loss_cls": 4.17725, "loss": 4.17725, "time": 0.84443} +{"mode": "train", "epoch": 42, "iter": 2700, "lr": 0.0821, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2625, "top5_acc": 0.51922, "loss_cls": 4.19716, "loss": 4.19716, "time": 0.84523} +{"mode": "train", "epoch": 42, "iter": 2800, "lr": 0.08207, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27187, "top5_acc": 0.52, "loss_cls": 4.18164, "loss": 4.18164, "time": 0.84641} +{"mode": "train", "epoch": 42, "iter": 2900, "lr": 0.08205, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26641, "top5_acc": 0.52625, "loss_cls": 4.17646, "loss": 4.17646, "time": 0.85024} +{"mode": "train", "epoch": 42, "iter": 3000, "lr": 0.08203, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27484, "top5_acc": 0.52734, "loss_cls": 4.17935, "loss": 4.17935, "time": 0.85156} +{"mode": "train", "epoch": 42, "iter": 3100, "lr": 0.08201, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27594, "top5_acc": 0.53062, "loss_cls": 4.15426, "loss": 4.15426, "time": 0.84826} +{"mode": "train", "epoch": 42, "iter": 3200, "lr": 0.08199, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27281, "top5_acc": 0.52703, "loss_cls": 4.19187, "loss": 4.19187, "time": 0.84419} +{"mode": "train", "epoch": 42, "iter": 3300, "lr": 0.08197, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26641, "top5_acc": 0.52812, "loss_cls": 4.18626, "loss": 4.18626, "time": 0.84836} +{"mode": "train", "epoch": 42, "iter": 3400, "lr": 0.08195, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27734, "top5_acc": 0.53125, "loss_cls": 4.13779, "loss": 4.13779, "time": 0.85718} +{"mode": "train", "epoch": 42, "iter": 3500, "lr": 0.08192, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27062, "top5_acc": 0.52766, "loss_cls": 4.18699, "loss": 4.18699, "time": 0.85286} +{"mode": "train", "epoch": 42, "iter": 3600, "lr": 0.0819, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27109, "top5_acc": 0.51609, "loss_cls": 4.2075, "loss": 4.2075, "time": 0.85133} +{"mode": "train", "epoch": 42, "iter": 3700, "lr": 0.08188, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27328, "top5_acc": 0.53625, "loss_cls": 4.14518, "loss": 4.14518, "time": 0.85285} +{"mode": "val", "epoch": 42, "iter": 309, "lr": 0.08187, "top1_acc": 0.18751, "top5_acc": 0.41058, "mean_class_accuracy": 0.18729} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.08185, "memory": 15990, "data_time": 1.49569, "top1_acc": 0.28625, "top5_acc": 0.54234, "loss_cls": 4.11132, "loss": 4.11132, "time": 2.53596} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.08183, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.28047, "top5_acc": 0.53969, "loss_cls": 4.11633, "loss": 4.11633, "time": 0.85305} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.08181, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27391, "top5_acc": 0.53656, "loss_cls": 4.12203, "loss": 4.12203, "time": 0.84854} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.08179, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.27625, "top5_acc": 0.52469, "loss_cls": 4.16016, "loss": 4.16016, "time": 0.8472} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.08176, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27328, "top5_acc": 0.53062, "loss_cls": 4.1765, "loss": 4.1765, "time": 0.84606} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.08174, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28562, "top5_acc": 0.53109, "loss_cls": 4.1269, "loss": 4.1269, "time": 0.85223} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.08172, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.275, "top5_acc": 0.53047, "loss_cls": 4.14928, "loss": 4.14928, "time": 0.84656} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.0817, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27219, "top5_acc": 0.52438, "loss_cls": 4.16524, "loss": 4.16524, "time": 0.85313} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.08168, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28219, "top5_acc": 0.54, "loss_cls": 4.10822, "loss": 4.10822, "time": 0.84445} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.08166, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28531, "top5_acc": 0.53406, "loss_cls": 4.13563, "loss": 4.13563, "time": 0.84323} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.08163, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27094, "top5_acc": 0.52875, "loss_cls": 4.17998, "loss": 4.17998, "time": 0.84564} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.08161, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26703, "top5_acc": 0.51359, "loss_cls": 4.25928, "loss": 4.25928, "time": 0.84493} +{"mode": "train", "epoch": 43, "iter": 1300, "lr": 0.08159, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27672, "top5_acc": 0.53594, "loss_cls": 4.15173, "loss": 4.15173, "time": 0.84981} +{"mode": "train", "epoch": 43, "iter": 1400, "lr": 0.08157, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.28453, "top5_acc": 0.54547, "loss_cls": 4.11465, "loss": 4.11465, "time": 0.84103} +{"mode": "train", "epoch": 43, "iter": 1500, "lr": 0.08155, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27578, "top5_acc": 0.53109, "loss_cls": 4.16668, "loss": 4.16668, "time": 0.84467} +{"mode": "train", "epoch": 43, "iter": 1600, "lr": 0.08153, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27187, "top5_acc": 0.52812, "loss_cls": 4.18569, "loss": 4.18569, "time": 0.8474} +{"mode": "train", "epoch": 43, "iter": 1700, "lr": 0.0815, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26969, "top5_acc": 0.53094, "loss_cls": 4.1494, "loss": 4.1494, "time": 0.85166} +{"mode": "train", "epoch": 43, "iter": 1800, "lr": 0.08148, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27953, "top5_acc": 0.53391, "loss_cls": 4.13257, "loss": 4.13257, "time": 0.84706} +{"mode": "train", "epoch": 43, "iter": 1900, "lr": 0.08146, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26797, "top5_acc": 0.52438, "loss_cls": 4.16912, "loss": 4.16912, "time": 0.84839} +{"mode": "train", "epoch": 43, "iter": 2000, "lr": 0.08144, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26453, "top5_acc": 0.52047, "loss_cls": 4.21682, "loss": 4.21682, "time": 0.85488} +{"mode": "train", "epoch": 43, "iter": 2100, "lr": 0.08142, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27203, "top5_acc": 0.52828, "loss_cls": 4.16581, "loss": 4.16581, "time": 0.84664} +{"mode": "train", "epoch": 43, "iter": 2200, "lr": 0.0814, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27891, "top5_acc": 0.52391, "loss_cls": 4.15623, "loss": 4.15623, "time": 0.852} +{"mode": "train", "epoch": 43, "iter": 2300, "lr": 0.08137, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26844, "top5_acc": 0.53531, "loss_cls": 4.1813, "loss": 4.1813, "time": 0.84804} +{"mode": "train", "epoch": 43, "iter": 2400, "lr": 0.08135, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25859, "top5_acc": 0.52469, "loss_cls": 4.23253, "loss": 4.23253, "time": 0.84625} +{"mode": "train", "epoch": 43, "iter": 2500, "lr": 0.08133, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26984, "top5_acc": 0.52922, "loss_cls": 4.17042, "loss": 4.17042, "time": 0.84129} +{"mode": "train", "epoch": 43, "iter": 2600, "lr": 0.08131, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26719, "top5_acc": 0.51281, "loss_cls": 4.19592, "loss": 4.19592, "time": 0.85507} +{"mode": "train", "epoch": 43, "iter": 2700, "lr": 0.08129, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26406, "top5_acc": 0.52781, "loss_cls": 4.17991, "loss": 4.17991, "time": 0.85111} +{"mode": "train", "epoch": 43, "iter": 2800, "lr": 0.08126, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27734, "top5_acc": 0.52719, "loss_cls": 4.17061, "loss": 4.17061, "time": 0.85387} +{"mode": "train", "epoch": 43, "iter": 2900, "lr": 0.08124, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26766, "top5_acc": 0.53297, "loss_cls": 4.19044, "loss": 4.19044, "time": 0.84605} +{"mode": "train", "epoch": 43, "iter": 3000, "lr": 0.08122, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27, "top5_acc": 0.52656, "loss_cls": 4.17307, "loss": 4.17307, "time": 0.8437} +{"mode": "train", "epoch": 43, "iter": 3100, "lr": 0.0812, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26969, "top5_acc": 0.52359, "loss_cls": 4.17273, "loss": 4.17273, "time": 0.84014} +{"mode": "train", "epoch": 43, "iter": 3200, "lr": 0.08118, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26547, "top5_acc": 0.52109, "loss_cls": 4.20167, "loss": 4.20167, "time": 0.84456} +{"mode": "train", "epoch": 43, "iter": 3300, "lr": 0.08116, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27062, "top5_acc": 0.52281, "loss_cls": 4.1843, "loss": 4.1843, "time": 0.84259} +{"mode": "train", "epoch": 43, "iter": 3400, "lr": 0.08113, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26766, "top5_acc": 0.53422, "loss_cls": 4.1737, "loss": 4.1737, "time": 0.8488} +{"mode": "train", "epoch": 43, "iter": 3500, "lr": 0.08111, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27078, "top5_acc": 0.52766, "loss_cls": 4.17685, "loss": 4.17685, "time": 0.85437} +{"mode": "train", "epoch": 43, "iter": 3600, "lr": 0.08109, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26797, "top5_acc": 0.51938, "loss_cls": 4.18597, "loss": 4.18597, "time": 0.84807} +{"mode": "train", "epoch": 43, "iter": 3700, "lr": 0.08107, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27703, "top5_acc": 0.53781, "loss_cls": 4.117, "loss": 4.117, "time": 0.8535} +{"mode": "val", "epoch": 43, "iter": 309, "lr": 0.08106, "top1_acc": 0.18756, "top5_acc": 0.40956, "mean_class_accuracy": 0.18744} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.08104, "memory": 15990, "data_time": 1.59328, "top1_acc": 0.27641, "top5_acc": 0.54359, "loss_cls": 4.09521, "loss": 4.09521, "time": 2.63199} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.08101, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28, "top5_acc": 0.53422, "loss_cls": 4.10968, "loss": 4.10968, "time": 0.8566} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.08099, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28156, "top5_acc": 0.53891, "loss_cls": 4.13483, "loss": 4.13483, "time": 0.83897} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.08097, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28375, "top5_acc": 0.53812, "loss_cls": 4.10445, "loss": 4.10445, "time": 0.84344} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.08095, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27391, "top5_acc": 0.52688, "loss_cls": 4.172, "loss": 4.172, "time": 0.84227} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.08093, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28016, "top5_acc": 0.53297, "loss_cls": 4.16501, "loss": 4.16501, "time": 0.85007} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.0809, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28188, "top5_acc": 0.53156, "loss_cls": 4.16539, "loss": 4.16539, "time": 0.84899} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.08088, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27391, "top5_acc": 0.53828, "loss_cls": 4.13559, "loss": 4.13559, "time": 0.84669} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.08086, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26453, "top5_acc": 0.52047, "loss_cls": 4.19567, "loss": 4.19567, "time": 0.84133} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.08084, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27203, "top5_acc": 0.53469, "loss_cls": 4.13882, "loss": 4.13882, "time": 0.8425} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.08082, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28062, "top5_acc": 0.53547, "loss_cls": 4.12082, "loss": 4.12082, "time": 0.85152} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.08079, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.28016, "top5_acc": 0.52531, "loss_cls": 4.15926, "loss": 4.15926, "time": 0.84392} +{"mode": "train", "epoch": 44, "iter": 1300, "lr": 0.08077, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.285, "top5_acc": 0.54391, "loss_cls": 4.10688, "loss": 4.10688, "time": 0.84745} +{"mode": "train", "epoch": 44, "iter": 1400, "lr": 0.08075, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28453, "top5_acc": 0.53781, "loss_cls": 4.12161, "loss": 4.12161, "time": 0.83963} +{"mode": "train", "epoch": 44, "iter": 1500, "lr": 0.08073, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27562, "top5_acc": 0.52344, "loss_cls": 4.20602, "loss": 4.20602, "time": 0.84523} +{"mode": "train", "epoch": 44, "iter": 1600, "lr": 0.08071, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27141, "top5_acc": 0.52234, "loss_cls": 4.20113, "loss": 4.20113, "time": 0.8472} +{"mode": "train", "epoch": 44, "iter": 1700, "lr": 0.08068, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27922, "top5_acc": 0.52766, "loss_cls": 4.18238, "loss": 4.18238, "time": 0.8464} +{"mode": "train", "epoch": 44, "iter": 1800, "lr": 0.08066, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2775, "top5_acc": 0.53203, "loss_cls": 4.13177, "loss": 4.13177, "time": 0.84645} +{"mode": "train", "epoch": 44, "iter": 1900, "lr": 0.08064, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27359, "top5_acc": 0.52812, "loss_cls": 4.17952, "loss": 4.17952, "time": 0.84934} +{"mode": "train", "epoch": 44, "iter": 2000, "lr": 0.08062, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2725, "top5_acc": 0.53453, "loss_cls": 4.16469, "loss": 4.16469, "time": 0.84874} +{"mode": "train", "epoch": 44, "iter": 2100, "lr": 0.0806, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26516, "top5_acc": 0.525, "loss_cls": 4.16587, "loss": 4.16587, "time": 0.84461} +{"mode": "train", "epoch": 44, "iter": 2200, "lr": 0.08057, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27766, "top5_acc": 0.5325, "loss_cls": 4.18565, "loss": 4.18565, "time": 0.85002} +{"mode": "train", "epoch": 44, "iter": 2300, "lr": 0.08055, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27531, "top5_acc": 0.52891, "loss_cls": 4.16989, "loss": 4.16989, "time": 0.84674} +{"mode": "train", "epoch": 44, "iter": 2400, "lr": 0.08053, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27422, "top5_acc": 0.52797, "loss_cls": 4.15927, "loss": 4.15927, "time": 0.84418} +{"mode": "train", "epoch": 44, "iter": 2500, "lr": 0.08051, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26906, "top5_acc": 0.51938, "loss_cls": 4.20739, "loss": 4.20739, "time": 0.84977} +{"mode": "train", "epoch": 44, "iter": 2600, "lr": 0.08048, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28281, "top5_acc": 0.53844, "loss_cls": 4.15107, "loss": 4.15107, "time": 0.84096} +{"mode": "train", "epoch": 44, "iter": 2700, "lr": 0.08046, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27953, "top5_acc": 0.53188, "loss_cls": 4.10631, "loss": 4.10631, "time": 0.84777} +{"mode": "train", "epoch": 44, "iter": 2800, "lr": 0.08044, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27391, "top5_acc": 0.52031, "loss_cls": 4.19238, "loss": 4.19238, "time": 0.84868} +{"mode": "train", "epoch": 44, "iter": 2900, "lr": 0.08042, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25828, "top5_acc": 0.51203, "loss_cls": 4.25598, "loss": 4.25598, "time": 0.84713} +{"mode": "train", "epoch": 44, "iter": 3000, "lr": 0.0804, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27406, "top5_acc": 0.53453, "loss_cls": 4.1365, "loss": 4.1365, "time": 0.85103} +{"mode": "train", "epoch": 44, "iter": 3100, "lr": 0.08037, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27547, "top5_acc": 0.5275, "loss_cls": 4.15946, "loss": 4.15946, "time": 0.84388} +{"mode": "train", "epoch": 44, "iter": 3200, "lr": 0.08035, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26969, "top5_acc": 0.52172, "loss_cls": 4.20523, "loss": 4.20523, "time": 0.84484} +{"mode": "train", "epoch": 44, "iter": 3300, "lr": 0.08033, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27359, "top5_acc": 0.53922, "loss_cls": 4.14506, "loss": 4.14506, "time": 0.85015} +{"mode": "train", "epoch": 44, "iter": 3400, "lr": 0.08031, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27531, "top5_acc": 0.52953, "loss_cls": 4.17169, "loss": 4.17169, "time": 0.85013} +{"mode": "train", "epoch": 44, "iter": 3500, "lr": 0.08028, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28688, "top5_acc": 0.54203, "loss_cls": 4.09833, "loss": 4.09833, "time": 0.85046} +{"mode": "train", "epoch": 44, "iter": 3600, "lr": 0.08026, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27719, "top5_acc": 0.53906, "loss_cls": 4.15653, "loss": 4.15653, "time": 0.84638} +{"mode": "train", "epoch": 44, "iter": 3700, "lr": 0.08024, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27078, "top5_acc": 0.51766, "loss_cls": 4.21452, "loss": 4.21452, "time": 0.84967} +{"mode": "val", "epoch": 44, "iter": 309, "lr": 0.08023, "top1_acc": 0.18538, "top5_acc": 0.40744, "mean_class_accuracy": 0.1853} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.08021, "memory": 15990, "data_time": 1.52025, "top1_acc": 0.27906, "top5_acc": 0.53875, "loss_cls": 4.09197, "loss": 4.09197, "time": 2.54325} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.08019, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.2775, "top5_acc": 0.52891, "loss_cls": 4.16, "loss": 4.16, "time": 0.85203} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.08016, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29, "top5_acc": 0.53672, "loss_cls": 4.0892, "loss": 4.0892, "time": 0.84365} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.08014, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.27969, "top5_acc": 0.53609, "loss_cls": 4.12402, "loss": 4.12402, "time": 0.84427} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.08012, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27312, "top5_acc": 0.51938, "loss_cls": 4.18631, "loss": 4.18631, "time": 0.84931} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.0801, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.27781, "top5_acc": 0.53641, "loss_cls": 4.15057, "loss": 4.15057, "time": 0.85104} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.08007, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27109, "top5_acc": 0.52484, "loss_cls": 4.17197, "loss": 4.17197, "time": 0.8472} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.08005, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27766, "top5_acc": 0.54484, "loss_cls": 4.11212, "loss": 4.11212, "time": 0.84276} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.08003, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27344, "top5_acc": 0.53219, "loss_cls": 4.12636, "loss": 4.12636, "time": 0.84967} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.08001, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27766, "top5_acc": 0.52172, "loss_cls": 4.17287, "loss": 4.17287, "time": 0.84584} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.07998, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28641, "top5_acc": 0.5375, "loss_cls": 4.11312, "loss": 4.11312, "time": 0.84306} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.07996, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.285, "top5_acc": 0.53953, "loss_cls": 4.11376, "loss": 4.11376, "time": 0.84259} +{"mode": "train", "epoch": 45, "iter": 1300, "lr": 0.07994, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27969, "top5_acc": 0.52797, "loss_cls": 4.16784, "loss": 4.16784, "time": 0.83979} +{"mode": "train", "epoch": 45, "iter": 1400, "lr": 0.07992, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.27266, "top5_acc": 0.53422, "loss_cls": 4.14179, "loss": 4.14179, "time": 0.84007} +{"mode": "train", "epoch": 45, "iter": 1500, "lr": 0.0799, "memory": 15990, "data_time": 0.00075, "top1_acc": 0.27672, "top5_acc": 0.53188, "loss_cls": 4.14408, "loss": 4.14408, "time": 0.84154} +{"mode": "train", "epoch": 45, "iter": 1600, "lr": 0.07987, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27688, "top5_acc": 0.53562, "loss_cls": 4.13782, "loss": 4.13782, "time": 0.84397} +{"mode": "train", "epoch": 45, "iter": 1700, "lr": 0.07985, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27094, "top5_acc": 0.52594, "loss_cls": 4.17107, "loss": 4.17107, "time": 0.84281} +{"mode": "train", "epoch": 45, "iter": 1800, "lr": 0.07983, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27437, "top5_acc": 0.52766, "loss_cls": 4.1754, "loss": 4.1754, "time": 0.84881} +{"mode": "train", "epoch": 45, "iter": 1900, "lr": 0.07981, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27016, "top5_acc": 0.52078, "loss_cls": 4.22523, "loss": 4.22523, "time": 0.85006} +{"mode": "train", "epoch": 45, "iter": 2000, "lr": 0.07978, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27391, "top5_acc": 0.53234, "loss_cls": 4.14666, "loss": 4.14666, "time": 0.84967} +{"mode": "train", "epoch": 45, "iter": 2100, "lr": 0.07976, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2825, "top5_acc": 0.52984, "loss_cls": 4.1393, "loss": 4.1393, "time": 0.84369} +{"mode": "train", "epoch": 45, "iter": 2200, "lr": 0.07974, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27781, "top5_acc": 0.53234, "loss_cls": 4.13225, "loss": 4.13225, "time": 0.84649} +{"mode": "train", "epoch": 45, "iter": 2300, "lr": 0.07972, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27703, "top5_acc": 0.53047, "loss_cls": 4.15836, "loss": 4.15836, "time": 0.8526} +{"mode": "train", "epoch": 45, "iter": 2400, "lr": 0.07969, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26641, "top5_acc": 0.52234, "loss_cls": 4.18673, "loss": 4.18673, "time": 0.85871} +{"mode": "train", "epoch": 45, "iter": 2500, "lr": 0.07967, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28266, "top5_acc": 0.53203, "loss_cls": 4.1391, "loss": 4.1391, "time": 0.85564} +{"mode": "train", "epoch": 45, "iter": 2600, "lr": 0.07965, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27406, "top5_acc": 0.52156, "loss_cls": 4.18901, "loss": 4.18901, "time": 0.85331} +{"mode": "train", "epoch": 45, "iter": 2700, "lr": 0.07963, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27297, "top5_acc": 0.52297, "loss_cls": 4.13571, "loss": 4.13571, "time": 0.85658} +{"mode": "train", "epoch": 45, "iter": 2800, "lr": 0.0796, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27156, "top5_acc": 0.52078, "loss_cls": 4.21832, "loss": 4.21832, "time": 0.85664} +{"mode": "train", "epoch": 45, "iter": 2900, "lr": 0.07958, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27156, "top5_acc": 0.53094, "loss_cls": 4.16318, "loss": 4.16318, "time": 0.86122} +{"mode": "train", "epoch": 45, "iter": 3000, "lr": 0.07956, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26641, "top5_acc": 0.52797, "loss_cls": 4.20135, "loss": 4.20135, "time": 0.85517} +{"mode": "train", "epoch": 45, "iter": 3100, "lr": 0.07954, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28109, "top5_acc": 0.53906, "loss_cls": 4.10714, "loss": 4.10714, "time": 0.86271} +{"mode": "train", "epoch": 45, "iter": 3200, "lr": 0.07951, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28, "top5_acc": 0.53484, "loss_cls": 4.13189, "loss": 4.13189, "time": 0.86163} +{"mode": "train", "epoch": 45, "iter": 3300, "lr": 0.07949, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26641, "top5_acc": 0.52328, "loss_cls": 4.21604, "loss": 4.21604, "time": 0.85497} +{"mode": "train", "epoch": 45, "iter": 3400, "lr": 0.07947, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28125, "top5_acc": 0.54359, "loss_cls": 4.07282, "loss": 4.07282, "time": 0.85994} +{"mode": "train", "epoch": 45, "iter": 3500, "lr": 0.07945, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26984, "top5_acc": 0.52578, "loss_cls": 4.2003, "loss": 4.2003, "time": 0.85662} +{"mode": "train", "epoch": 45, "iter": 3600, "lr": 0.07942, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26859, "top5_acc": 0.53609, "loss_cls": 4.1548, "loss": 4.1548, "time": 0.86272} +{"mode": "train", "epoch": 45, "iter": 3700, "lr": 0.0794, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28406, "top5_acc": 0.53516, "loss_cls": 4.13513, "loss": 4.13513, "time": 0.85807} +{"mode": "val", "epoch": 45, "iter": 309, "lr": 0.07939, "top1_acc": 0.21208, "top5_acc": 0.44897, "mean_class_accuracy": 0.21195} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.07937, "memory": 15990, "data_time": 1.55088, "top1_acc": 0.28203, "top5_acc": 0.54375, "loss_cls": 4.08782, "loss": 4.08782, "time": 2.59048} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.07934, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29109, "top5_acc": 0.54359, "loss_cls": 4.09323, "loss": 4.09323, "time": 0.85372} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.07932, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27875, "top5_acc": 0.53078, "loss_cls": 4.14527, "loss": 4.14527, "time": 0.84988} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.0793, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27125, "top5_acc": 0.53016, "loss_cls": 4.13384, "loss": 4.13384, "time": 0.85225} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.07928, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28156, "top5_acc": 0.5375, "loss_cls": 4.10814, "loss": 4.10814, "time": 0.84932} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.07925, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.27641, "top5_acc": 0.54234, "loss_cls": 4.12333, "loss": 4.12333, "time": 0.84803} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.07923, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.27688, "top5_acc": 0.52953, "loss_cls": 4.173, "loss": 4.173, "time": 0.8472} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.07921, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26734, "top5_acc": 0.52922, "loss_cls": 4.17273, "loss": 4.17273, "time": 0.84333} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.07919, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28, "top5_acc": 0.54047, "loss_cls": 4.11764, "loss": 4.11764, "time": 0.8529} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.07916, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27656, "top5_acc": 0.53156, "loss_cls": 4.13541, "loss": 4.13541, "time": 0.85236} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.07914, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27328, "top5_acc": 0.52797, "loss_cls": 4.1613, "loss": 4.1613, "time": 0.85323} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.07912, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27719, "top5_acc": 0.53438, "loss_cls": 4.13716, "loss": 4.13716, "time": 0.84874} +{"mode": "train", "epoch": 46, "iter": 1300, "lr": 0.07909, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27359, "top5_acc": 0.53078, "loss_cls": 4.15128, "loss": 4.15128, "time": 0.85105} +{"mode": "train", "epoch": 46, "iter": 1400, "lr": 0.07907, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27734, "top5_acc": 0.53906, "loss_cls": 4.11964, "loss": 4.11964, "time": 0.84783} +{"mode": "train", "epoch": 46, "iter": 1500, "lr": 0.07905, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.27562, "top5_acc": 0.52172, "loss_cls": 4.18716, "loss": 4.18716, "time": 0.84162} +{"mode": "train", "epoch": 46, "iter": 1600, "lr": 0.07903, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27562, "top5_acc": 0.53094, "loss_cls": 4.15304, "loss": 4.15304, "time": 0.8442} +{"mode": "train", "epoch": 46, "iter": 1700, "lr": 0.079, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27297, "top5_acc": 0.52734, "loss_cls": 4.17437, "loss": 4.17437, "time": 0.84783} +{"mode": "train", "epoch": 46, "iter": 1800, "lr": 0.07898, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26562, "top5_acc": 0.52203, "loss_cls": 4.20704, "loss": 4.20704, "time": 0.84414} +{"mode": "train", "epoch": 46, "iter": 1900, "lr": 0.07896, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27141, "top5_acc": 0.53094, "loss_cls": 4.16725, "loss": 4.16725, "time": 0.84645} +{"mode": "train", "epoch": 46, "iter": 2000, "lr": 0.07894, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28141, "top5_acc": 0.53625, "loss_cls": 4.13457, "loss": 4.13457, "time": 0.84735} +{"mode": "train", "epoch": 46, "iter": 2100, "lr": 0.07891, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26937, "top5_acc": 0.53172, "loss_cls": 4.16314, "loss": 4.16314, "time": 0.84841} +{"mode": "train", "epoch": 46, "iter": 2200, "lr": 0.07889, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26891, "top5_acc": 0.52969, "loss_cls": 4.18887, "loss": 4.18887, "time": 0.84261} +{"mode": "train", "epoch": 46, "iter": 2300, "lr": 0.07887, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26984, "top5_acc": 0.52641, "loss_cls": 4.19016, "loss": 4.19016, "time": 0.8482} +{"mode": "train", "epoch": 46, "iter": 2400, "lr": 0.07884, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27328, "top5_acc": 0.52141, "loss_cls": 4.17155, "loss": 4.17155, "time": 0.84949} +{"mode": "train", "epoch": 46, "iter": 2500, "lr": 0.07882, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26766, "top5_acc": 0.52656, "loss_cls": 4.15992, "loss": 4.15992, "time": 0.84689} +{"mode": "train", "epoch": 46, "iter": 2600, "lr": 0.0788, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27437, "top5_acc": 0.52891, "loss_cls": 4.13582, "loss": 4.13582, "time": 0.84985} +{"mode": "train", "epoch": 46, "iter": 2700, "lr": 0.07878, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27969, "top5_acc": 0.54203, "loss_cls": 4.09562, "loss": 4.09562, "time": 0.84184} +{"mode": "train", "epoch": 46, "iter": 2800, "lr": 0.07875, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27734, "top5_acc": 0.53188, "loss_cls": 4.15575, "loss": 4.15575, "time": 0.85366} +{"mode": "train", "epoch": 46, "iter": 2900, "lr": 0.07873, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27844, "top5_acc": 0.54, "loss_cls": 4.13178, "loss": 4.13178, "time": 0.84734} +{"mode": "train", "epoch": 46, "iter": 3000, "lr": 0.07871, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27641, "top5_acc": 0.52922, "loss_cls": 4.15927, "loss": 4.15927, "time": 0.8541} +{"mode": "train", "epoch": 46, "iter": 3100, "lr": 0.07868, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27172, "top5_acc": 0.51875, "loss_cls": 4.19558, "loss": 4.19558, "time": 0.8469} +{"mode": "train", "epoch": 46, "iter": 3200, "lr": 0.07866, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28062, "top5_acc": 0.53453, "loss_cls": 4.13931, "loss": 4.13931, "time": 0.84771} +{"mode": "train", "epoch": 46, "iter": 3300, "lr": 0.07864, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27891, "top5_acc": 0.54109, "loss_cls": 4.10253, "loss": 4.10253, "time": 0.84901} +{"mode": "train", "epoch": 46, "iter": 3400, "lr": 0.07862, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27234, "top5_acc": 0.53078, "loss_cls": 4.14455, "loss": 4.14455, "time": 0.85018} +{"mode": "train", "epoch": 46, "iter": 3500, "lr": 0.07859, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27359, "top5_acc": 0.53375, "loss_cls": 4.14215, "loss": 4.14215, "time": 0.85164} +{"mode": "train", "epoch": 46, "iter": 3600, "lr": 0.07857, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27516, "top5_acc": 0.52766, "loss_cls": 4.17492, "loss": 4.17492, "time": 0.84816} +{"mode": "train", "epoch": 46, "iter": 3700, "lr": 0.07855, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27594, "top5_acc": 0.52641, "loss_cls": 4.15658, "loss": 4.15658, "time": 0.84931} +{"mode": "val", "epoch": 46, "iter": 309, "lr": 0.07854, "top1_acc": 0.2023, "top5_acc": 0.43104, "mean_class_accuracy": 0.20192} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.07851, "memory": 15990, "data_time": 1.50883, "top1_acc": 0.28172, "top5_acc": 0.53562, "loss_cls": 4.11266, "loss": 4.11266, "time": 2.54077} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.07849, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27906, "top5_acc": 0.54, "loss_cls": 4.11156, "loss": 4.11156, "time": 0.85417} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.07847, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.28734, "top5_acc": 0.54391, "loss_cls": 4.07859, "loss": 4.07859, "time": 0.85524} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.07844, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28219, "top5_acc": 0.53453, "loss_cls": 4.11743, "loss": 4.11743, "time": 0.84903} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.07842, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28125, "top5_acc": 0.54703, "loss_cls": 4.08878, "loss": 4.08878, "time": 0.85126} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.0784, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27219, "top5_acc": 0.53281, "loss_cls": 4.13094, "loss": 4.13094, "time": 0.85109} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.07838, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28703, "top5_acc": 0.54672, "loss_cls": 4.10072, "loss": 4.10072, "time": 0.84133} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.07835, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27953, "top5_acc": 0.54188, "loss_cls": 4.13534, "loss": 4.13534, "time": 0.84289} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.07833, "memory": 15990, "data_time": 0.00076, "top1_acc": 0.26812, "top5_acc": 0.52844, "loss_cls": 4.17868, "loss": 4.17868, "time": 0.84544} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.07831, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27094, "top5_acc": 0.52281, "loss_cls": 4.17391, "loss": 4.17391, "time": 0.84287} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.07828, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27047, "top5_acc": 0.5275, "loss_cls": 4.19752, "loss": 4.19752, "time": 0.84447} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.07826, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27125, "top5_acc": 0.53781, "loss_cls": 4.12345, "loss": 4.12345, "time": 0.84684} +{"mode": "train", "epoch": 47, "iter": 1300, "lr": 0.07824, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26969, "top5_acc": 0.51859, "loss_cls": 4.20008, "loss": 4.20008, "time": 0.84473} +{"mode": "train", "epoch": 47, "iter": 1400, "lr": 0.07821, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.26266, "top5_acc": 0.51938, "loss_cls": 4.22161, "loss": 4.22161, "time": 0.84161} +{"mode": "train", "epoch": 47, "iter": 1500, "lr": 0.07819, "memory": 15990, "data_time": 0.00071, "top1_acc": 0.27641, "top5_acc": 0.53469, "loss_cls": 4.13404, "loss": 4.13404, "time": 0.84192} +{"mode": "train", "epoch": 47, "iter": 1600, "lr": 0.07817, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.27094, "top5_acc": 0.52078, "loss_cls": 4.18593, "loss": 4.18593, "time": 0.83414} +{"mode": "train", "epoch": 47, "iter": 1700, "lr": 0.07814, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29031, "top5_acc": 0.54281, "loss_cls": 4.07219, "loss": 4.07219, "time": 0.8391} +{"mode": "train", "epoch": 47, "iter": 1800, "lr": 0.07812, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27172, "top5_acc": 0.53312, "loss_cls": 4.14219, "loss": 4.14219, "time": 0.84695} +{"mode": "train", "epoch": 47, "iter": 1900, "lr": 0.0781, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27875, "top5_acc": 0.54375, "loss_cls": 4.11166, "loss": 4.11166, "time": 0.84485} +{"mode": "train", "epoch": 47, "iter": 2000, "lr": 0.07808, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26812, "top5_acc": 0.53172, "loss_cls": 4.15968, "loss": 4.15968, "time": 0.83875} +{"mode": "train", "epoch": 47, "iter": 2100, "lr": 0.07805, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27234, "top5_acc": 0.53031, "loss_cls": 4.16797, "loss": 4.16797, "time": 0.84386} +{"mode": "train", "epoch": 47, "iter": 2200, "lr": 0.07803, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28266, "top5_acc": 0.53844, "loss_cls": 4.14446, "loss": 4.14446, "time": 0.8408} +{"mode": "train", "epoch": 47, "iter": 2300, "lr": 0.07801, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2725, "top5_acc": 0.52953, "loss_cls": 4.17483, "loss": 4.17483, "time": 0.84728} +{"mode": "train", "epoch": 47, "iter": 2400, "lr": 0.07798, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28, "top5_acc": 0.53078, "loss_cls": 4.14726, "loss": 4.14726, "time": 0.84298} +{"mode": "train", "epoch": 47, "iter": 2500, "lr": 0.07796, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28219, "top5_acc": 0.54062, "loss_cls": 4.13813, "loss": 4.13813, "time": 0.84471} +{"mode": "train", "epoch": 47, "iter": 2600, "lr": 0.07794, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28266, "top5_acc": 0.54656, "loss_cls": 4.07805, "loss": 4.07805, "time": 0.84843} +{"mode": "train", "epoch": 47, "iter": 2700, "lr": 0.07791, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27781, "top5_acc": 0.53172, "loss_cls": 4.1472, "loss": 4.1472, "time": 0.84912} +{"mode": "train", "epoch": 47, "iter": 2800, "lr": 0.07789, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28141, "top5_acc": 0.53516, "loss_cls": 4.14178, "loss": 4.14178, "time": 0.84555} +{"mode": "train", "epoch": 47, "iter": 2900, "lr": 0.07787, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27141, "top5_acc": 0.5325, "loss_cls": 4.16917, "loss": 4.16917, "time": 0.84642} +{"mode": "train", "epoch": 47, "iter": 3000, "lr": 0.07784, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27766, "top5_acc": 0.53672, "loss_cls": 4.12251, "loss": 4.12251, "time": 0.84552} +{"mode": "train", "epoch": 47, "iter": 3100, "lr": 0.07782, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28656, "top5_acc": 0.53984, "loss_cls": 4.08847, "loss": 4.08847, "time": 0.84139} +{"mode": "train", "epoch": 47, "iter": 3200, "lr": 0.0778, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27281, "top5_acc": 0.52516, "loss_cls": 4.16278, "loss": 4.16278, "time": 0.84412} +{"mode": "train", "epoch": 47, "iter": 3300, "lr": 0.07777, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29078, "top5_acc": 0.53625, "loss_cls": 4.11113, "loss": 4.11113, "time": 0.84176} +{"mode": "train", "epoch": 47, "iter": 3400, "lr": 0.07775, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27672, "top5_acc": 0.53109, "loss_cls": 4.15629, "loss": 4.15629, "time": 0.85121} +{"mode": "train", "epoch": 47, "iter": 3500, "lr": 0.07773, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27547, "top5_acc": 0.52391, "loss_cls": 4.15357, "loss": 4.15357, "time": 0.84807} +{"mode": "train", "epoch": 47, "iter": 3600, "lr": 0.0777, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28062, "top5_acc": 0.54078, "loss_cls": 4.13162, "loss": 4.13162, "time": 0.84909} +{"mode": "train", "epoch": 47, "iter": 3700, "lr": 0.07768, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27109, "top5_acc": 0.52812, "loss_cls": 4.18218, "loss": 4.18218, "time": 0.84589} +{"mode": "val", "epoch": 47, "iter": 309, "lr": 0.07767, "top1_acc": 0.18503, "top5_acc": 0.41868, "mean_class_accuracy": 0.18475} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.07765, "memory": 15990, "data_time": 1.51006, "top1_acc": 0.29344, "top5_acc": 0.54984, "loss_cls": 4.0471, "loss": 4.0471, "time": 2.54263} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.07762, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27906, "top5_acc": 0.53156, "loss_cls": 4.13336, "loss": 4.13336, "time": 0.85493} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.0776, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28438, "top5_acc": 0.53156, "loss_cls": 4.1296, "loss": 4.1296, "time": 0.85306} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.07758, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.27812, "top5_acc": 0.53781, "loss_cls": 4.12955, "loss": 4.12955, "time": 0.85442} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.07755, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28562, "top5_acc": 0.54734, "loss_cls": 4.0993, "loss": 4.0993, "time": 0.85026} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.07753, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27672, "top5_acc": 0.53344, "loss_cls": 4.13089, "loss": 4.13089, "time": 0.85236} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.07751, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.27469, "top5_acc": 0.54609, "loss_cls": 4.10284, "loss": 4.10284, "time": 0.8471} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.07748, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27656, "top5_acc": 0.53266, "loss_cls": 4.13804, "loss": 4.13804, "time": 0.84947} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.07746, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28734, "top5_acc": 0.55156, "loss_cls": 4.06845, "loss": 4.06845, "time": 0.84412} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.07744, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28281, "top5_acc": 0.54391, "loss_cls": 4.10025, "loss": 4.10025, "time": 0.8464} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.07741, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29453, "top5_acc": 0.54703, "loss_cls": 4.06098, "loss": 4.06098, "time": 0.84826} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.07739, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28078, "top5_acc": 0.53828, "loss_cls": 4.12557, "loss": 4.12557, "time": 0.84627} +{"mode": "train", "epoch": 48, "iter": 1300, "lr": 0.07737, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26625, "top5_acc": 0.52875, "loss_cls": 4.15687, "loss": 4.15687, "time": 0.84934} +{"mode": "train", "epoch": 48, "iter": 1400, "lr": 0.07734, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27766, "top5_acc": 0.53625, "loss_cls": 4.10362, "loss": 4.10362, "time": 0.84775} +{"mode": "train", "epoch": 48, "iter": 1500, "lr": 0.07732, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27938, "top5_acc": 0.52562, "loss_cls": 4.13528, "loss": 4.13528, "time": 0.84266} +{"mode": "train", "epoch": 48, "iter": 1600, "lr": 0.0773, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28484, "top5_acc": 0.54109, "loss_cls": 4.1137, "loss": 4.1137, "time": 0.84502} +{"mode": "train", "epoch": 48, "iter": 1700, "lr": 0.07727, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28125, "top5_acc": 0.53141, "loss_cls": 4.15433, "loss": 4.15433, "time": 0.84201} +{"mode": "train", "epoch": 48, "iter": 1800, "lr": 0.07725, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27594, "top5_acc": 0.53047, "loss_cls": 4.16645, "loss": 4.16645, "time": 0.84776} +{"mode": "train", "epoch": 48, "iter": 1900, "lr": 0.07723, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.275, "top5_acc": 0.53938, "loss_cls": 4.14363, "loss": 4.14363, "time": 0.84809} +{"mode": "train", "epoch": 48, "iter": 2000, "lr": 0.0772, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26641, "top5_acc": 0.52672, "loss_cls": 4.21267, "loss": 4.21267, "time": 0.84629} +{"mode": "train", "epoch": 48, "iter": 2100, "lr": 0.07718, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28062, "top5_acc": 0.53297, "loss_cls": 4.15573, "loss": 4.15573, "time": 0.84254} +{"mode": "train", "epoch": 48, "iter": 2200, "lr": 0.07716, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28359, "top5_acc": 0.54031, "loss_cls": 4.10076, "loss": 4.10076, "time": 0.84427} +{"mode": "train", "epoch": 48, "iter": 2300, "lr": 0.07713, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26625, "top5_acc": 0.52469, "loss_cls": 4.1814, "loss": 4.1814, "time": 0.85323} +{"mode": "train", "epoch": 48, "iter": 2400, "lr": 0.07711, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27984, "top5_acc": 0.54062, "loss_cls": 4.16275, "loss": 4.16275, "time": 0.84596} +{"mode": "train", "epoch": 48, "iter": 2500, "lr": 0.07709, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27047, "top5_acc": 0.52547, "loss_cls": 4.18106, "loss": 4.18106, "time": 0.8575} +{"mode": "train", "epoch": 48, "iter": 2600, "lr": 0.07706, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28375, "top5_acc": 0.545, "loss_cls": 4.08623, "loss": 4.08623, "time": 0.84611} +{"mode": "train", "epoch": 48, "iter": 2700, "lr": 0.07704, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28031, "top5_acc": 0.53062, "loss_cls": 4.13807, "loss": 4.13807, "time": 0.85163} +{"mode": "train", "epoch": 48, "iter": 2800, "lr": 0.07701, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27187, "top5_acc": 0.52219, "loss_cls": 4.16896, "loss": 4.16896, "time": 0.84746} +{"mode": "train", "epoch": 48, "iter": 2900, "lr": 0.07699, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28562, "top5_acc": 0.53891, "loss_cls": 4.10935, "loss": 4.10935, "time": 0.85305} +{"mode": "train", "epoch": 48, "iter": 3000, "lr": 0.07697, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2775, "top5_acc": 0.53938, "loss_cls": 4.13037, "loss": 4.13037, "time": 0.84969} +{"mode": "train", "epoch": 48, "iter": 3100, "lr": 0.07694, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27766, "top5_acc": 0.53578, "loss_cls": 4.15766, "loss": 4.15766, "time": 0.85191} +{"mode": "train", "epoch": 48, "iter": 3200, "lr": 0.07692, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26937, "top5_acc": 0.52906, "loss_cls": 4.20931, "loss": 4.20931, "time": 0.84932} +{"mode": "train", "epoch": 48, "iter": 3300, "lr": 0.0769, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27406, "top5_acc": 0.53281, "loss_cls": 4.17318, "loss": 4.17318, "time": 0.85398} +{"mode": "train", "epoch": 48, "iter": 3400, "lr": 0.07687, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28062, "top5_acc": 0.53641, "loss_cls": 4.1246, "loss": 4.1246, "time": 0.85203} +{"mode": "train", "epoch": 48, "iter": 3500, "lr": 0.07685, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26922, "top5_acc": 0.52531, "loss_cls": 4.19788, "loss": 4.19788, "time": 0.85391} +{"mode": "train", "epoch": 48, "iter": 3600, "lr": 0.07683, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28469, "top5_acc": 0.53047, "loss_cls": 4.12665, "loss": 4.12665, "time": 0.85889} +{"mode": "train", "epoch": 48, "iter": 3700, "lr": 0.0768, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28406, "top5_acc": 0.53828, "loss_cls": 4.12099, "loss": 4.12099, "time": 0.84877} +{"mode": "val", "epoch": 48, "iter": 309, "lr": 0.07679, "top1_acc": 0.19825, "top5_acc": 0.42354, "mean_class_accuracy": 0.19799} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.07677, "memory": 15990, "data_time": 1.46736, "top1_acc": 0.28516, "top5_acc": 0.53219, "loss_cls": 4.13805, "loss": 4.13805, "time": 2.50503} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.07674, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28781, "top5_acc": 0.54297, "loss_cls": 4.06541, "loss": 4.06541, "time": 0.8575} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.07672, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28094, "top5_acc": 0.54531, "loss_cls": 4.10495, "loss": 4.10495, "time": 0.85587} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.0767, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29219, "top5_acc": 0.54047, "loss_cls": 4.09237, "loss": 4.09237, "time": 0.84286} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.07667, "memory": 15990, "data_time": 0.0008, "top1_acc": 0.27688, "top5_acc": 0.53344, "loss_cls": 4.15617, "loss": 4.15617, "time": 0.85074} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.07665, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28109, "top5_acc": 0.54625, "loss_cls": 4.09772, "loss": 4.09772, "time": 0.85147} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.07663, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27719, "top5_acc": 0.53734, "loss_cls": 4.12902, "loss": 4.12902, "time": 0.84417} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.0766, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27547, "top5_acc": 0.53906, "loss_cls": 4.09773, "loss": 4.09773, "time": 0.84767} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.07658, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27875, "top5_acc": 0.55297, "loss_cls": 4.08252, "loss": 4.08252, "time": 0.84363} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.07656, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27141, "top5_acc": 0.52922, "loss_cls": 4.17441, "loss": 4.17441, "time": 0.85075} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.07653, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2875, "top5_acc": 0.54531, "loss_cls": 4.09813, "loss": 4.09813, "time": 0.84621} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.07651, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27609, "top5_acc": 0.5375, "loss_cls": 4.1293, "loss": 4.1293, "time": 0.8464} +{"mode": "train", "epoch": 49, "iter": 1300, "lr": 0.07648, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28234, "top5_acc": 0.54094, "loss_cls": 4.11305, "loss": 4.11305, "time": 0.85139} +{"mode": "train", "epoch": 49, "iter": 1400, "lr": 0.07646, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29281, "top5_acc": 0.53938, "loss_cls": 4.0806, "loss": 4.0806, "time": 0.8487} +{"mode": "train", "epoch": 49, "iter": 1500, "lr": 0.07644, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27922, "top5_acc": 0.53062, "loss_cls": 4.15678, "loss": 4.15678, "time": 0.84617} +{"mode": "train", "epoch": 49, "iter": 1600, "lr": 0.07641, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28188, "top5_acc": 0.54172, "loss_cls": 4.10527, "loss": 4.10527, "time": 0.84679} +{"mode": "train", "epoch": 49, "iter": 1700, "lr": 0.07639, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27875, "top5_acc": 0.53641, "loss_cls": 4.13698, "loss": 4.13698, "time": 0.84764} +{"mode": "train", "epoch": 49, "iter": 1800, "lr": 0.07637, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27359, "top5_acc": 0.53656, "loss_cls": 4.13601, "loss": 4.13601, "time": 0.8438} +{"mode": "train", "epoch": 49, "iter": 1900, "lr": 0.07634, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27672, "top5_acc": 0.53047, "loss_cls": 4.14519, "loss": 4.14519, "time": 0.84693} +{"mode": "train", "epoch": 49, "iter": 2000, "lr": 0.07632, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28969, "top5_acc": 0.53734, "loss_cls": 4.09549, "loss": 4.09549, "time": 0.8434} +{"mode": "train", "epoch": 49, "iter": 2100, "lr": 0.07629, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27641, "top5_acc": 0.53734, "loss_cls": 4.11894, "loss": 4.11894, "time": 0.84453} +{"mode": "train", "epoch": 49, "iter": 2200, "lr": 0.07627, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28672, "top5_acc": 0.54469, "loss_cls": 4.08883, "loss": 4.08883, "time": 0.84522} +{"mode": "train", "epoch": 49, "iter": 2300, "lr": 0.07625, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28188, "top5_acc": 0.5325, "loss_cls": 4.1458, "loss": 4.1458, "time": 0.84382} +{"mode": "train", "epoch": 49, "iter": 2400, "lr": 0.07622, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27516, "top5_acc": 0.52859, "loss_cls": 4.19683, "loss": 4.19683, "time": 0.84289} +{"mode": "train", "epoch": 49, "iter": 2500, "lr": 0.0762, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28438, "top5_acc": 0.53156, "loss_cls": 4.1262, "loss": 4.1262, "time": 0.84732} +{"mode": "train", "epoch": 49, "iter": 2600, "lr": 0.07618, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27516, "top5_acc": 0.52969, "loss_cls": 4.16425, "loss": 4.16425, "time": 0.84714} +{"mode": "train", "epoch": 49, "iter": 2700, "lr": 0.07615, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27938, "top5_acc": 0.535, "loss_cls": 4.10566, "loss": 4.10566, "time": 0.84785} +{"mode": "train", "epoch": 49, "iter": 2800, "lr": 0.07613, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27938, "top5_acc": 0.53203, "loss_cls": 4.12171, "loss": 4.12171, "time": 0.84984} +{"mode": "train", "epoch": 49, "iter": 2900, "lr": 0.0761, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28094, "top5_acc": 0.53672, "loss_cls": 4.13148, "loss": 4.13148, "time": 0.85128} +{"mode": "train", "epoch": 49, "iter": 3000, "lr": 0.07608, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27906, "top5_acc": 0.53094, "loss_cls": 4.17221, "loss": 4.17221, "time": 0.85123} +{"mode": "train", "epoch": 49, "iter": 3100, "lr": 0.07606, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27828, "top5_acc": 0.53219, "loss_cls": 4.15774, "loss": 4.15774, "time": 0.84965} +{"mode": "train", "epoch": 49, "iter": 3200, "lr": 0.07603, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27609, "top5_acc": 0.53328, "loss_cls": 4.12157, "loss": 4.12157, "time": 0.85207} +{"mode": "train", "epoch": 49, "iter": 3300, "lr": 0.07601, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28016, "top5_acc": 0.52953, "loss_cls": 4.1342, "loss": 4.1342, "time": 0.8503} +{"mode": "train", "epoch": 49, "iter": 3400, "lr": 0.07598, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.275, "top5_acc": 0.53312, "loss_cls": 4.13647, "loss": 4.13647, "time": 0.85353} +{"mode": "train", "epoch": 49, "iter": 3500, "lr": 0.07596, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28078, "top5_acc": 0.5375, "loss_cls": 4.12277, "loss": 4.12277, "time": 0.8533} +{"mode": "train", "epoch": 49, "iter": 3600, "lr": 0.07594, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27781, "top5_acc": 0.53047, "loss_cls": 4.1761, "loss": 4.1761, "time": 0.85368} +{"mode": "train", "epoch": 49, "iter": 3700, "lr": 0.07591, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28953, "top5_acc": 0.54359, "loss_cls": 4.07721, "loss": 4.07721, "time": 0.85121} +{"mode": "val", "epoch": 49, "iter": 309, "lr": 0.0759, "top1_acc": 0.20392, "top5_acc": 0.43317, "mean_class_accuracy": 0.20377} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.07588, "memory": 15990, "data_time": 1.48423, "top1_acc": 0.29469, "top5_acc": 0.55, "loss_cls": 4.0571, "loss": 4.0571, "time": 2.51593} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.07585, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27562, "top5_acc": 0.54094, "loss_cls": 4.1262, "loss": 4.1262, "time": 0.85003} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.07583, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28281, "top5_acc": 0.54516, "loss_cls": 4.10695, "loss": 4.10695, "time": 0.84966} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.07581, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28219, "top5_acc": 0.53531, "loss_cls": 4.14118, "loss": 4.14118, "time": 0.84822} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.07578, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27781, "top5_acc": 0.53672, "loss_cls": 4.10938, "loss": 4.10938, "time": 0.84931} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.07576, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27656, "top5_acc": 0.52625, "loss_cls": 4.17992, "loss": 4.17992, "time": 0.85018} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.07573, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27906, "top5_acc": 0.54531, "loss_cls": 4.08882, "loss": 4.08882, "time": 0.8415} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.07571, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29063, "top5_acc": 0.54297, "loss_cls": 4.07745, "loss": 4.07745, "time": 0.84483} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.07569, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27938, "top5_acc": 0.53875, "loss_cls": 4.1284, "loss": 4.1284, "time": 0.84651} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.07566, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27984, "top5_acc": 0.52609, "loss_cls": 4.16377, "loss": 4.16377, "time": 0.8455} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.07564, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29063, "top5_acc": 0.54984, "loss_cls": 4.06718, "loss": 4.06718, "time": 0.84512} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.07561, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28062, "top5_acc": 0.53734, "loss_cls": 4.09116, "loss": 4.09116, "time": 0.85095} +{"mode": "train", "epoch": 50, "iter": 1300, "lr": 0.07559, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2775, "top5_acc": 0.54016, "loss_cls": 4.14984, "loss": 4.14984, "time": 0.84548} +{"mode": "train", "epoch": 50, "iter": 1400, "lr": 0.07557, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28922, "top5_acc": 0.55703, "loss_cls": 4.03281, "loss": 4.03281, "time": 0.84893} +{"mode": "train", "epoch": 50, "iter": 1500, "lr": 0.07554, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28062, "top5_acc": 0.53203, "loss_cls": 4.14552, "loss": 4.14552, "time": 0.84393} +{"mode": "train", "epoch": 50, "iter": 1600, "lr": 0.07552, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.28328, "top5_acc": 0.53812, "loss_cls": 4.12356, "loss": 4.12356, "time": 0.84666} +{"mode": "train", "epoch": 50, "iter": 1700, "lr": 0.07549, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28, "top5_acc": 0.53859, "loss_cls": 4.12695, "loss": 4.12695, "time": 0.84428} +{"mode": "train", "epoch": 50, "iter": 1800, "lr": 0.07547, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28984, "top5_acc": 0.54234, "loss_cls": 4.09264, "loss": 4.09264, "time": 0.84192} +{"mode": "train", "epoch": 50, "iter": 1900, "lr": 0.07545, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28516, "top5_acc": 0.53844, "loss_cls": 4.12073, "loss": 4.12073, "time": 0.84355} +{"mode": "train", "epoch": 50, "iter": 2000, "lr": 0.07542, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28031, "top5_acc": 0.53766, "loss_cls": 4.13437, "loss": 4.13437, "time": 0.84484} +{"mode": "train", "epoch": 50, "iter": 2100, "lr": 0.0754, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28656, "top5_acc": 0.53609, "loss_cls": 4.10663, "loss": 4.10663, "time": 0.84361} +{"mode": "train", "epoch": 50, "iter": 2200, "lr": 0.07537, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28703, "top5_acc": 0.54078, "loss_cls": 4.1046, "loss": 4.1046, "time": 0.84798} +{"mode": "train", "epoch": 50, "iter": 2300, "lr": 0.07535, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28344, "top5_acc": 0.53641, "loss_cls": 4.10966, "loss": 4.10966, "time": 0.84422} +{"mode": "train", "epoch": 50, "iter": 2400, "lr": 0.07533, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28484, "top5_acc": 0.53344, "loss_cls": 4.09761, "loss": 4.09761, "time": 0.84801} +{"mode": "train", "epoch": 50, "iter": 2500, "lr": 0.0753, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27781, "top5_acc": 0.53625, "loss_cls": 4.12529, "loss": 4.12529, "time": 0.84592} +{"mode": "train", "epoch": 50, "iter": 2600, "lr": 0.07528, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28594, "top5_acc": 0.53094, "loss_cls": 4.12781, "loss": 4.12781, "time": 0.84289} +{"mode": "train", "epoch": 50, "iter": 2700, "lr": 0.07525, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27719, "top5_acc": 0.545, "loss_cls": 4.10775, "loss": 4.10775, "time": 0.84388} +{"mode": "train", "epoch": 50, "iter": 2800, "lr": 0.07523, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28, "top5_acc": 0.53656, "loss_cls": 4.1341, "loss": 4.1341, "time": 0.84241} +{"mode": "train", "epoch": 50, "iter": 2900, "lr": 0.0752, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27688, "top5_acc": 0.53203, "loss_cls": 4.12228, "loss": 4.12228, "time": 0.8425} +{"mode": "train", "epoch": 50, "iter": 3000, "lr": 0.07518, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27844, "top5_acc": 0.53656, "loss_cls": 4.1418, "loss": 4.1418, "time": 0.8471} +{"mode": "train", "epoch": 50, "iter": 3100, "lr": 0.07516, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28656, "top5_acc": 0.5375, "loss_cls": 4.10336, "loss": 4.10336, "time": 0.85429} +{"mode": "train", "epoch": 50, "iter": 3200, "lr": 0.07513, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28094, "top5_acc": 0.53641, "loss_cls": 4.09761, "loss": 4.09761, "time": 0.84998} +{"mode": "train", "epoch": 50, "iter": 3300, "lr": 0.07511, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28109, "top5_acc": 0.52891, "loss_cls": 4.164, "loss": 4.164, "time": 0.8532} +{"mode": "train", "epoch": 50, "iter": 3400, "lr": 0.07508, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26937, "top5_acc": 0.52344, "loss_cls": 4.17682, "loss": 4.17682, "time": 0.85007} +{"mode": "train", "epoch": 50, "iter": 3500, "lr": 0.07506, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28641, "top5_acc": 0.53984, "loss_cls": 4.10419, "loss": 4.10419, "time": 0.84396} +{"mode": "train", "epoch": 50, "iter": 3600, "lr": 0.07504, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27437, "top5_acc": 0.53375, "loss_cls": 4.13671, "loss": 4.13671, "time": 0.84396} +{"mode": "train", "epoch": 50, "iter": 3700, "lr": 0.07501, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2775, "top5_acc": 0.53469, "loss_cls": 4.12192, "loss": 4.12192, "time": 0.84703} +{"mode": "val", "epoch": 50, "iter": 309, "lr": 0.075, "top1_acc": 0.21319, "top5_acc": 0.45049, "mean_class_accuracy": 0.21286} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.07498, "memory": 15990, "data_time": 1.50023, "top1_acc": 0.29156, "top5_acc": 0.54703, "loss_cls": 4.03393, "loss": 4.03393, "time": 2.54118} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.07495, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28766, "top5_acc": 0.53891, "loss_cls": 4.06481, "loss": 4.06481, "time": 0.85433} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.07493, "memory": 15990, "data_time": 0.00068, "top1_acc": 0.28328, "top5_acc": 0.54344, "loss_cls": 4.10707, "loss": 4.10707, "time": 0.85038} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.0749, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28859, "top5_acc": 0.545, "loss_cls": 4.04156, "loss": 4.04156, "time": 0.84693} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.07488, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.27812, "top5_acc": 0.53953, "loss_cls": 4.11039, "loss": 4.11039, "time": 0.84289} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.07485, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28953, "top5_acc": 0.54578, "loss_cls": 4.08248, "loss": 4.08248, "time": 0.84817} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.07483, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28, "top5_acc": 0.53453, "loss_cls": 4.11189, "loss": 4.11189, "time": 0.84957} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.07481, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28203, "top5_acc": 0.54203, "loss_cls": 4.10753, "loss": 4.10753, "time": 0.84799} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.07478, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28422, "top5_acc": 0.54, "loss_cls": 4.08054, "loss": 4.08054, "time": 0.84466} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.07476, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28625, "top5_acc": 0.53734, "loss_cls": 4.07134, "loss": 4.07134, "time": 0.83783} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.07473, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27969, "top5_acc": 0.53109, "loss_cls": 4.12143, "loss": 4.12143, "time": 0.84359} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.07471, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28844, "top5_acc": 0.55062, "loss_cls": 4.04861, "loss": 4.04861, "time": 0.84694} +{"mode": "train", "epoch": 51, "iter": 1300, "lr": 0.07468, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28516, "top5_acc": 0.54484, "loss_cls": 4.09956, "loss": 4.09956, "time": 0.84505} +{"mode": "train", "epoch": 51, "iter": 1400, "lr": 0.07466, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27187, "top5_acc": 0.53203, "loss_cls": 4.15863, "loss": 4.15863, "time": 0.84886} +{"mode": "train", "epoch": 51, "iter": 1500, "lr": 0.07464, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28938, "top5_acc": 0.54391, "loss_cls": 4.08852, "loss": 4.08852, "time": 0.84463} +{"mode": "train", "epoch": 51, "iter": 1600, "lr": 0.07461, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29, "top5_acc": 0.55562, "loss_cls": 4.05286, "loss": 4.05286, "time": 0.84012} +{"mode": "train", "epoch": 51, "iter": 1700, "lr": 0.07459, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27422, "top5_acc": 0.53297, "loss_cls": 4.14633, "loss": 4.14633, "time": 0.84513} +{"mode": "train", "epoch": 51, "iter": 1800, "lr": 0.07456, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.28422, "top5_acc": 0.53984, "loss_cls": 4.11303, "loss": 4.11303, "time": 0.83993} +{"mode": "train", "epoch": 51, "iter": 1900, "lr": 0.07454, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28484, "top5_acc": 0.54031, "loss_cls": 4.089, "loss": 4.089, "time": 0.8406} +{"mode": "train", "epoch": 51, "iter": 2000, "lr": 0.07451, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28, "top5_acc": 0.53344, "loss_cls": 4.14604, "loss": 4.14604, "time": 0.8469} +{"mode": "train", "epoch": 51, "iter": 2100, "lr": 0.07449, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27234, "top5_acc": 0.52781, "loss_cls": 4.16505, "loss": 4.16505, "time": 0.84587} +{"mode": "train", "epoch": 51, "iter": 2200, "lr": 0.07447, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28297, "top5_acc": 0.53703, "loss_cls": 4.11883, "loss": 4.11883, "time": 0.84085} +{"mode": "train", "epoch": 51, "iter": 2300, "lr": 0.07444, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27562, "top5_acc": 0.52984, "loss_cls": 4.15713, "loss": 4.15713, "time": 0.8471} +{"mode": "train", "epoch": 51, "iter": 2400, "lr": 0.07442, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27969, "top5_acc": 0.53312, "loss_cls": 4.11826, "loss": 4.11826, "time": 0.85065} +{"mode": "train", "epoch": 51, "iter": 2500, "lr": 0.07439, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27906, "top5_acc": 0.53375, "loss_cls": 4.10194, "loss": 4.10194, "time": 0.85548} +{"mode": "train", "epoch": 51, "iter": 2600, "lr": 0.07437, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28375, "top5_acc": 0.53844, "loss_cls": 4.0925, "loss": 4.0925, "time": 0.85282} +{"mode": "train", "epoch": 51, "iter": 2700, "lr": 0.07434, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.27875, "top5_acc": 0.53453, "loss_cls": 4.14185, "loss": 4.14185, "time": 0.85769} +{"mode": "train", "epoch": 51, "iter": 2800, "lr": 0.07432, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29359, "top5_acc": 0.54391, "loss_cls": 4.05485, "loss": 4.05485, "time": 0.85216} +{"mode": "train", "epoch": 51, "iter": 2900, "lr": 0.07429, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.28266, "top5_acc": 0.54891, "loss_cls": 4.10426, "loss": 4.10426, "time": 0.85722} +{"mode": "train", "epoch": 51, "iter": 3000, "lr": 0.07427, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28391, "top5_acc": 0.54125, "loss_cls": 4.10616, "loss": 4.10616, "time": 0.85232} +{"mode": "train", "epoch": 51, "iter": 3100, "lr": 0.07425, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28547, "top5_acc": 0.53781, "loss_cls": 4.12908, "loss": 4.12908, "time": 0.85336} +{"mode": "train", "epoch": 51, "iter": 3200, "lr": 0.07422, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27594, "top5_acc": 0.53781, "loss_cls": 4.12534, "loss": 4.12534, "time": 0.85641} +{"mode": "train", "epoch": 51, "iter": 3300, "lr": 0.0742, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2775, "top5_acc": 0.53031, "loss_cls": 4.1478, "loss": 4.1478, "time": 0.85121} +{"mode": "train", "epoch": 51, "iter": 3400, "lr": 0.07417, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28203, "top5_acc": 0.53531, "loss_cls": 4.12012, "loss": 4.12012, "time": 0.85374} +{"mode": "train", "epoch": 51, "iter": 3500, "lr": 0.07415, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28375, "top5_acc": 0.54641, "loss_cls": 4.10281, "loss": 4.10281, "time": 0.85912} +{"mode": "train", "epoch": 51, "iter": 3600, "lr": 0.07412, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28516, "top5_acc": 0.54281, "loss_cls": 4.10805, "loss": 4.10805, "time": 0.85247} +{"mode": "train", "epoch": 51, "iter": 3700, "lr": 0.0741, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27344, "top5_acc": 0.53859, "loss_cls": 4.12916, "loss": 4.12916, "time": 0.85272} +{"mode": "val", "epoch": 51, "iter": 309, "lr": 0.07409, "top1_acc": 0.21689, "top5_acc": 0.46148, "mean_class_accuracy": 0.21666} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.07406, "memory": 15990, "data_time": 1.51568, "top1_acc": 0.28438, "top5_acc": 0.54922, "loss_cls": 4.05934, "loss": 4.05934, "time": 2.55808} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.07404, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.28656, "top5_acc": 0.54594, "loss_cls": 4.09795, "loss": 4.09795, "time": 0.85426} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.07401, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.29219, "top5_acc": 0.54516, "loss_cls": 4.05452, "loss": 4.05452, "time": 0.85418} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.07399, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28641, "top5_acc": 0.53922, "loss_cls": 4.11302, "loss": 4.11302, "time": 0.85892} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.07397, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28547, "top5_acc": 0.52781, "loss_cls": 4.09793, "loss": 4.09793, "time": 0.85591} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.07394, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28938, "top5_acc": 0.55016, "loss_cls": 4.05093, "loss": 4.05093, "time": 0.85407} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.07392, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27938, "top5_acc": 0.53031, "loss_cls": 4.11891, "loss": 4.11891, "time": 0.85491} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.07389, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29031, "top5_acc": 0.54219, "loss_cls": 4.09729, "loss": 4.09729, "time": 0.85591} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.07387, "memory": 15990, "data_time": 0.00076, "top1_acc": 0.285, "top5_acc": 0.5375, "loss_cls": 4.0851, "loss": 4.0851, "time": 0.85987} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.07384, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28328, "top5_acc": 0.54, "loss_cls": 4.09427, "loss": 4.09427, "time": 0.85942} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.07382, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28469, "top5_acc": 0.54344, "loss_cls": 4.09125, "loss": 4.09125, "time": 0.85956} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.07379, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28531, "top5_acc": 0.54234, "loss_cls": 4.06398, "loss": 4.06398, "time": 0.8576} +{"mode": "train", "epoch": 52, "iter": 1300, "lr": 0.07377, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27531, "top5_acc": 0.53047, "loss_cls": 4.16098, "loss": 4.16098, "time": 0.85913} +{"mode": "train", "epoch": 52, "iter": 1400, "lr": 0.07374, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2825, "top5_acc": 0.53781, "loss_cls": 4.11501, "loss": 4.11501, "time": 0.86213} +{"mode": "train", "epoch": 52, "iter": 1500, "lr": 0.07372, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28906, "top5_acc": 0.5425, "loss_cls": 4.08756, "loss": 4.08756, "time": 0.85762} +{"mode": "train", "epoch": 52, "iter": 1600, "lr": 0.0737, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.28625, "top5_acc": 0.55031, "loss_cls": 4.04901, "loss": 4.04901, "time": 0.86108} +{"mode": "train", "epoch": 52, "iter": 1700, "lr": 0.07367, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28297, "top5_acc": 0.54078, "loss_cls": 4.11138, "loss": 4.11138, "time": 0.85578} +{"mode": "train", "epoch": 52, "iter": 1800, "lr": 0.07365, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27906, "top5_acc": 0.5475, "loss_cls": 4.09419, "loss": 4.09419, "time": 0.84917} +{"mode": "train", "epoch": 52, "iter": 1900, "lr": 0.07362, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28672, "top5_acc": 0.545, "loss_cls": 4.11747, "loss": 4.11747, "time": 0.85321} +{"mode": "train", "epoch": 52, "iter": 2000, "lr": 0.0736, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29422, "top5_acc": 0.54438, "loss_cls": 4.07003, "loss": 4.07003, "time": 0.85329} +{"mode": "train", "epoch": 52, "iter": 2100, "lr": 0.07357, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28172, "top5_acc": 0.54984, "loss_cls": 4.08868, "loss": 4.08868, "time": 0.85522} +{"mode": "train", "epoch": 52, "iter": 2200, "lr": 0.07355, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28453, "top5_acc": 0.52438, "loss_cls": 4.14621, "loss": 4.14621, "time": 0.85407} +{"mode": "train", "epoch": 52, "iter": 2300, "lr": 0.07352, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27625, "top5_acc": 0.53609, "loss_cls": 4.13809, "loss": 4.13809, "time": 0.85405} +{"mode": "train", "epoch": 52, "iter": 2400, "lr": 0.0735, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28172, "top5_acc": 0.53641, "loss_cls": 4.111, "loss": 4.111, "time": 0.85584} +{"mode": "train", "epoch": 52, "iter": 2500, "lr": 0.07347, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28641, "top5_acc": 0.54359, "loss_cls": 4.10189, "loss": 4.10189, "time": 0.85345} +{"mode": "train", "epoch": 52, "iter": 2600, "lr": 0.07345, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29953, "top5_acc": 0.54734, "loss_cls": 4.06045, "loss": 4.06045, "time": 0.8593} +{"mode": "train", "epoch": 52, "iter": 2700, "lr": 0.07342, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28094, "top5_acc": 0.53141, "loss_cls": 4.13813, "loss": 4.13813, "time": 0.85789} +{"mode": "train", "epoch": 52, "iter": 2800, "lr": 0.0734, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28578, "top5_acc": 0.54609, "loss_cls": 4.08166, "loss": 4.08166, "time": 0.85913} +{"mode": "train", "epoch": 52, "iter": 2900, "lr": 0.07337, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27922, "top5_acc": 0.54438, "loss_cls": 4.10255, "loss": 4.10255, "time": 0.85709} +{"mode": "train", "epoch": 52, "iter": 3000, "lr": 0.07335, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.295, "top5_acc": 0.54688, "loss_cls": 4.10139, "loss": 4.10139, "time": 0.85912} +{"mode": "train", "epoch": 52, "iter": 3100, "lr": 0.07332, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28578, "top5_acc": 0.53547, "loss_cls": 4.11533, "loss": 4.11533, "time": 0.85519} +{"mode": "train", "epoch": 52, "iter": 3200, "lr": 0.0733, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27203, "top5_acc": 0.54141, "loss_cls": 4.13447, "loss": 4.13447, "time": 0.86024} +{"mode": "train", "epoch": 52, "iter": 3300, "lr": 0.07328, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28391, "top5_acc": 0.53297, "loss_cls": 4.10894, "loss": 4.10894, "time": 0.85632} +{"mode": "train", "epoch": 52, "iter": 3400, "lr": 0.07325, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28875, "top5_acc": 0.55141, "loss_cls": 4.07107, "loss": 4.07107, "time": 0.85459} +{"mode": "train", "epoch": 52, "iter": 3500, "lr": 0.07323, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27984, "top5_acc": 0.53859, "loss_cls": 4.13292, "loss": 4.13292, "time": 0.85773} +{"mode": "train", "epoch": 52, "iter": 3600, "lr": 0.0732, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28234, "top5_acc": 0.53984, "loss_cls": 4.09066, "loss": 4.09066, "time": 0.85736} +{"mode": "train", "epoch": 52, "iter": 3700, "lr": 0.07318, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.28016, "top5_acc": 0.53766, "loss_cls": 4.12593, "loss": 4.12593, "time": 0.85852} +{"mode": "val", "epoch": 52, "iter": 309, "lr": 0.07317, "top1_acc": 0.2021, "top5_acc": 0.43332, "mean_class_accuracy": 0.20198} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.07314, "memory": 15990, "data_time": 1.60424, "top1_acc": 0.28219, "top5_acc": 0.53609, "loss_cls": 4.10191, "loss": 4.10191, "time": 2.63737} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.07312, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.28906, "top5_acc": 0.54719, "loss_cls": 4.05576, "loss": 4.05576, "time": 0.85984} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.07309, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29328, "top5_acc": 0.54531, "loss_cls": 4.05211, "loss": 4.05211, "time": 0.85383} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.07307, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.29469, "top5_acc": 0.55781, "loss_cls": 4.03111, "loss": 4.03111, "time": 0.85198} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.07304, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28625, "top5_acc": 0.55125, "loss_cls": 4.0599, "loss": 4.0599, "time": 0.85078} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.07302, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28344, "top5_acc": 0.54156, "loss_cls": 4.09191, "loss": 4.09191, "time": 0.85262} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.07299, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28203, "top5_acc": 0.54047, "loss_cls": 4.11958, "loss": 4.11958, "time": 0.85239} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.07297, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.2875, "top5_acc": 0.53328, "loss_cls": 4.11557, "loss": 4.11557, "time": 0.8541} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.07294, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28281, "top5_acc": 0.54578, "loss_cls": 4.07654, "loss": 4.07654, "time": 0.84676} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.07292, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28703, "top5_acc": 0.54734, "loss_cls": 4.06916, "loss": 4.06916, "time": 0.83835} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.07289, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28703, "top5_acc": 0.54438, "loss_cls": 4.08231, "loss": 4.08231, "time": 0.8497} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.07287, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28516, "top5_acc": 0.53562, "loss_cls": 4.11765, "loss": 4.11765, "time": 0.84468} +{"mode": "train", "epoch": 53, "iter": 1300, "lr": 0.07284, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28766, "top5_acc": 0.54328, "loss_cls": 4.06306, "loss": 4.06306, "time": 0.83936} +{"mode": "train", "epoch": 53, "iter": 1400, "lr": 0.07282, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.27938, "top5_acc": 0.53453, "loss_cls": 4.13949, "loss": 4.13949, "time": 0.84526} +{"mode": "train", "epoch": 53, "iter": 1500, "lr": 0.07279, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28578, "top5_acc": 0.54578, "loss_cls": 4.10889, "loss": 4.10889, "time": 0.84111} +{"mode": "train", "epoch": 53, "iter": 1600, "lr": 0.07277, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.28938, "top5_acc": 0.54406, "loss_cls": 4.05843, "loss": 4.05843, "time": 0.85076} +{"mode": "train", "epoch": 53, "iter": 1700, "lr": 0.07274, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.28828, "top5_acc": 0.54641, "loss_cls": 4.09295, "loss": 4.09295, "time": 0.85109} +{"mode": "train", "epoch": 53, "iter": 1800, "lr": 0.07272, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28484, "top5_acc": 0.55297, "loss_cls": 4.06262, "loss": 4.06262, "time": 0.84445} +{"mode": "train", "epoch": 53, "iter": 1900, "lr": 0.07269, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28734, "top5_acc": 0.54203, "loss_cls": 4.09638, "loss": 4.09638, "time": 0.84519} +{"mode": "train", "epoch": 53, "iter": 2000, "lr": 0.07267, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28422, "top5_acc": 0.53984, "loss_cls": 4.11285, "loss": 4.11285, "time": 0.84399} +{"mode": "train", "epoch": 53, "iter": 2100, "lr": 0.07264, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28484, "top5_acc": 0.54188, "loss_cls": 4.08729, "loss": 4.08729, "time": 0.8448} +{"mode": "train", "epoch": 53, "iter": 2200, "lr": 0.07262, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28609, "top5_acc": 0.54078, "loss_cls": 4.11495, "loss": 4.11495, "time": 0.84343} +{"mode": "train", "epoch": 53, "iter": 2300, "lr": 0.07259, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28156, "top5_acc": 0.53141, "loss_cls": 4.0996, "loss": 4.0996, "time": 0.84635} +{"mode": "train", "epoch": 53, "iter": 2400, "lr": 0.07257, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28641, "top5_acc": 0.54906, "loss_cls": 4.07804, "loss": 4.07804, "time": 0.84522} +{"mode": "train", "epoch": 53, "iter": 2500, "lr": 0.07254, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28703, "top5_acc": 0.54828, "loss_cls": 4.05614, "loss": 4.05614, "time": 0.84675} +{"mode": "train", "epoch": 53, "iter": 2600, "lr": 0.07252, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28594, "top5_acc": 0.54078, "loss_cls": 4.10773, "loss": 4.10773, "time": 0.84187} +{"mode": "train", "epoch": 53, "iter": 2700, "lr": 0.07249, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28813, "top5_acc": 0.54125, "loss_cls": 4.08847, "loss": 4.08847, "time": 0.84545} +{"mode": "train", "epoch": 53, "iter": 2800, "lr": 0.07247, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27953, "top5_acc": 0.54375, "loss_cls": 4.09968, "loss": 4.09968, "time": 0.84615} +{"mode": "train", "epoch": 53, "iter": 2900, "lr": 0.07244, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27266, "top5_acc": 0.53219, "loss_cls": 4.16932, "loss": 4.16932, "time": 0.84173} +{"mode": "train", "epoch": 53, "iter": 3000, "lr": 0.07242, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27938, "top5_acc": 0.5425, "loss_cls": 4.09526, "loss": 4.09526, "time": 0.85056} +{"mode": "train", "epoch": 53, "iter": 3100, "lr": 0.07239, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28484, "top5_acc": 0.54547, "loss_cls": 4.08068, "loss": 4.08068, "time": 0.84279} +{"mode": "train", "epoch": 53, "iter": 3200, "lr": 0.07237, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28219, "top5_acc": 0.54406, "loss_cls": 4.09282, "loss": 4.09282, "time": 0.84708} +{"mode": "train", "epoch": 53, "iter": 3300, "lr": 0.07234, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28641, "top5_acc": 0.54781, "loss_cls": 4.07982, "loss": 4.07982, "time": 0.84746} +{"mode": "train", "epoch": 53, "iter": 3400, "lr": 0.07232, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28375, "top5_acc": 0.55359, "loss_cls": 4.01893, "loss": 4.01893, "time": 0.84222} +{"mode": "train", "epoch": 53, "iter": 3500, "lr": 0.07229, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27297, "top5_acc": 0.53875, "loss_cls": 4.13289, "loss": 4.13289, "time": 0.84494} +{"mode": "train", "epoch": 53, "iter": 3600, "lr": 0.07227, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27672, "top5_acc": 0.52641, "loss_cls": 4.14787, "loss": 4.14787, "time": 0.84883} +{"mode": "train", "epoch": 53, "iter": 3700, "lr": 0.07224, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28875, "top5_acc": 0.545, "loss_cls": 4.08682, "loss": 4.08682, "time": 0.84476} +{"mode": "val", "epoch": 53, "iter": 309, "lr": 0.07223, "top1_acc": 0.21132, "top5_acc": 0.4673, "mean_class_accuracy": 0.211} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.07221, "memory": 15990, "data_time": 1.56877, "top1_acc": 0.28875, "top5_acc": 0.55937, "loss_cls": 4.04842, "loss": 4.04842, "time": 2.60501} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.07218, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29141, "top5_acc": 0.55016, "loss_cls": 4.0473, "loss": 4.0473, "time": 0.85937} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.07216, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29016, "top5_acc": 0.54625, "loss_cls": 4.0396, "loss": 4.0396, "time": 0.85076} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.07213, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29188, "top5_acc": 0.54797, "loss_cls": 4.0475, "loss": 4.0475, "time": 0.8496} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.07211, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30047, "top5_acc": 0.54438, "loss_cls": 4.03995, "loss": 4.03995, "time": 0.84978} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.07208, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28953, "top5_acc": 0.54094, "loss_cls": 4.08103, "loss": 4.08103, "time": 0.84386} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.07206, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28062, "top5_acc": 0.54766, "loss_cls": 4.06075, "loss": 4.06075, "time": 0.84698} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.07203, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29188, "top5_acc": 0.54156, "loss_cls": 4.07224, "loss": 4.07224, "time": 0.85609} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.07201, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28594, "top5_acc": 0.53828, "loss_cls": 4.09306, "loss": 4.09306, "time": 0.84829} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.07198, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28672, "top5_acc": 0.54703, "loss_cls": 4.07149, "loss": 4.07149, "time": 0.84965} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.07196, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29125, "top5_acc": 0.54328, "loss_cls": 4.07781, "loss": 4.07781, "time": 0.84876} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.07193, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28781, "top5_acc": 0.55188, "loss_cls": 4.06339, "loss": 4.06339, "time": 0.84776} +{"mode": "train", "epoch": 54, "iter": 1300, "lr": 0.07191, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27641, "top5_acc": 0.54062, "loss_cls": 4.14897, "loss": 4.14897, "time": 0.84575} +{"mode": "train", "epoch": 54, "iter": 1400, "lr": 0.07188, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28797, "top5_acc": 0.53969, "loss_cls": 4.09005, "loss": 4.09005, "time": 0.85186} +{"mode": "train", "epoch": 54, "iter": 1500, "lr": 0.07186, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28125, "top5_acc": 0.53859, "loss_cls": 4.09046, "loss": 4.09046, "time": 0.84788} +{"mode": "train", "epoch": 54, "iter": 1600, "lr": 0.07183, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28844, "top5_acc": 0.54781, "loss_cls": 4.06916, "loss": 4.06916, "time": 0.84803} +{"mode": "train", "epoch": 54, "iter": 1700, "lr": 0.07181, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29188, "top5_acc": 0.55031, "loss_cls": 4.07004, "loss": 4.07004, "time": 0.84647} +{"mode": "train", "epoch": 54, "iter": 1800, "lr": 0.07178, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28766, "top5_acc": 0.54016, "loss_cls": 4.07453, "loss": 4.07453, "time": 0.84435} +{"mode": "train", "epoch": 54, "iter": 1900, "lr": 0.07176, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29688, "top5_acc": 0.54625, "loss_cls": 4.07496, "loss": 4.07496, "time": 0.8482} +{"mode": "train", "epoch": 54, "iter": 2000, "lr": 0.07173, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28844, "top5_acc": 0.54766, "loss_cls": 4.04859, "loss": 4.04859, "time": 0.85226} +{"mode": "train", "epoch": 54, "iter": 2100, "lr": 0.0717, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28203, "top5_acc": 0.54188, "loss_cls": 4.07268, "loss": 4.07268, "time": 0.8518} +{"mode": "train", "epoch": 54, "iter": 2200, "lr": 0.07168, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28922, "top5_acc": 0.54984, "loss_cls": 4.07618, "loss": 4.07618, "time": 0.846} +{"mode": "train", "epoch": 54, "iter": 2300, "lr": 0.07165, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28594, "top5_acc": 0.54312, "loss_cls": 4.07673, "loss": 4.07673, "time": 0.84372} +{"mode": "train", "epoch": 54, "iter": 2400, "lr": 0.07163, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28562, "top5_acc": 0.545, "loss_cls": 4.07361, "loss": 4.07361, "time": 0.84999} +{"mode": "train", "epoch": 54, "iter": 2500, "lr": 0.0716, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28172, "top5_acc": 0.54484, "loss_cls": 4.09298, "loss": 4.09298, "time": 0.8439} +{"mode": "train", "epoch": 54, "iter": 2600, "lr": 0.07158, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28813, "top5_acc": 0.54484, "loss_cls": 4.08834, "loss": 4.08834, "time": 0.84225} +{"mode": "train", "epoch": 54, "iter": 2700, "lr": 0.07155, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28922, "top5_acc": 0.54781, "loss_cls": 4.07381, "loss": 4.07381, "time": 0.84611} +{"mode": "train", "epoch": 54, "iter": 2800, "lr": 0.07153, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28438, "top5_acc": 0.54656, "loss_cls": 4.07965, "loss": 4.07965, "time": 0.84056} +{"mode": "train", "epoch": 54, "iter": 2900, "lr": 0.0715, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29063, "top5_acc": 0.54031, "loss_cls": 4.0694, "loss": 4.0694, "time": 0.83608} +{"mode": "train", "epoch": 54, "iter": 3000, "lr": 0.07148, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28875, "top5_acc": 0.5375, "loss_cls": 4.10524, "loss": 4.10524, "time": 0.84807} +{"mode": "train", "epoch": 54, "iter": 3100, "lr": 0.07145, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28016, "top5_acc": 0.5375, "loss_cls": 4.09363, "loss": 4.09363, "time": 0.85517} +{"mode": "train", "epoch": 54, "iter": 3200, "lr": 0.07143, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29, "top5_acc": 0.54703, "loss_cls": 4.08357, "loss": 4.08357, "time": 0.84993} +{"mode": "train", "epoch": 54, "iter": 3300, "lr": 0.0714, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27719, "top5_acc": 0.53594, "loss_cls": 4.12802, "loss": 4.12802, "time": 0.85055} +{"mode": "train", "epoch": 54, "iter": 3400, "lr": 0.07138, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29078, "top5_acc": 0.55594, "loss_cls": 4.07421, "loss": 4.07421, "time": 0.85171} +{"mode": "train", "epoch": 54, "iter": 3500, "lr": 0.07135, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28297, "top5_acc": 0.53953, "loss_cls": 4.10846, "loss": 4.10846, "time": 0.85176} +{"mode": "train", "epoch": 54, "iter": 3600, "lr": 0.07133, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2875, "top5_acc": 0.5475, "loss_cls": 4.0725, "loss": 4.0725, "time": 0.83851} +{"mode": "train", "epoch": 54, "iter": 3700, "lr": 0.0713, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28188, "top5_acc": 0.535, "loss_cls": 4.1109, "loss": 4.1109, "time": 0.8447} +{"mode": "val", "epoch": 54, "iter": 309, "lr": 0.07129, "top1_acc": 0.22302, "top5_acc": 0.46644, "mean_class_accuracy": 0.22289} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.07126, "memory": 15990, "data_time": 1.53356, "top1_acc": 0.30219, "top5_acc": 0.56, "loss_cls": 3.99505, "loss": 3.99505, "time": 2.56032} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.07124, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29609, "top5_acc": 0.55094, "loss_cls": 4.0638, "loss": 4.0638, "time": 0.85913} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.07121, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29078, "top5_acc": 0.55188, "loss_cls": 4.03749, "loss": 4.03749, "time": 0.85601} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.07119, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30641, "top5_acc": 0.55781, "loss_cls": 4.00634, "loss": 4.00634, "time": 0.85622} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.07116, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29531, "top5_acc": 0.54922, "loss_cls": 4.04785, "loss": 4.04785, "time": 0.85766} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.07114, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28672, "top5_acc": 0.54172, "loss_cls": 4.08803, "loss": 4.08803, "time": 0.84802} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.07111, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.29047, "top5_acc": 0.54, "loss_cls": 4.09603, "loss": 4.09603, "time": 0.84884} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.07109, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.28875, "top5_acc": 0.54594, "loss_cls": 4.07417, "loss": 4.07417, "time": 0.85245} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.07106, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.27906, "top5_acc": 0.54031, "loss_cls": 4.10627, "loss": 4.10627, "time": 0.84724} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.07104, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29234, "top5_acc": 0.54594, "loss_cls": 4.05761, "loss": 4.05761, "time": 0.84615} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.07101, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29063, "top5_acc": 0.54547, "loss_cls": 4.07657, "loss": 4.07657, "time": 0.84951} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.07099, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.28484, "top5_acc": 0.54109, "loss_cls": 4.08138, "loss": 4.08138, "time": 0.84488} +{"mode": "train", "epoch": 55, "iter": 1300, "lr": 0.07096, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29, "top5_acc": 0.54688, "loss_cls": 4.07885, "loss": 4.07885, "time": 0.84378} +{"mode": "train", "epoch": 55, "iter": 1400, "lr": 0.07093, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29125, "top5_acc": 0.55141, "loss_cls": 4.05061, "loss": 4.05061, "time": 0.84221} +{"mode": "train", "epoch": 55, "iter": 1500, "lr": 0.07091, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28859, "top5_acc": 0.54281, "loss_cls": 4.05747, "loss": 4.05747, "time": 0.84528} +{"mode": "train", "epoch": 55, "iter": 1600, "lr": 0.07088, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27953, "top5_acc": 0.54031, "loss_cls": 4.10737, "loss": 4.10737, "time": 0.8484} +{"mode": "train", "epoch": 55, "iter": 1700, "lr": 0.07086, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.29438, "top5_acc": 0.54703, "loss_cls": 4.05887, "loss": 4.05887, "time": 0.84552} +{"mode": "train", "epoch": 55, "iter": 1800, "lr": 0.07083, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28359, "top5_acc": 0.53516, "loss_cls": 4.1048, "loss": 4.1048, "time": 0.85545} +{"mode": "train", "epoch": 55, "iter": 1900, "lr": 0.07081, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27719, "top5_acc": 0.53391, "loss_cls": 4.11689, "loss": 4.11689, "time": 0.84899} +{"mode": "train", "epoch": 55, "iter": 2000, "lr": 0.07078, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28281, "top5_acc": 0.54641, "loss_cls": 4.07778, "loss": 4.07778, "time": 0.84236} +{"mode": "train", "epoch": 55, "iter": 2100, "lr": 0.07076, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29219, "top5_acc": 0.545, "loss_cls": 4.06315, "loss": 4.06315, "time": 0.84337} +{"mode": "train", "epoch": 55, "iter": 2200, "lr": 0.07073, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28469, "top5_acc": 0.54062, "loss_cls": 4.08364, "loss": 4.08364, "time": 0.84153} +{"mode": "train", "epoch": 55, "iter": 2300, "lr": 0.07071, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27875, "top5_acc": 0.54031, "loss_cls": 4.11428, "loss": 4.11428, "time": 0.84744} +{"mode": "train", "epoch": 55, "iter": 2400, "lr": 0.07068, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29531, "top5_acc": 0.54672, "loss_cls": 4.05531, "loss": 4.05531, "time": 0.84399} +{"mode": "train", "epoch": 55, "iter": 2500, "lr": 0.07065, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29063, "top5_acc": 0.54984, "loss_cls": 4.03474, "loss": 4.03474, "time": 0.84786} +{"mode": "train", "epoch": 55, "iter": 2600, "lr": 0.07063, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28, "top5_acc": 0.54656, "loss_cls": 4.09396, "loss": 4.09396, "time": 0.8406} +{"mode": "train", "epoch": 55, "iter": 2700, "lr": 0.0706, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29125, "top5_acc": 0.54422, "loss_cls": 4.0667, "loss": 4.0667, "time": 0.85431} +{"mode": "train", "epoch": 55, "iter": 2800, "lr": 0.07058, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28156, "top5_acc": 0.54484, "loss_cls": 4.08584, "loss": 4.08584, "time": 0.84837} +{"mode": "train", "epoch": 55, "iter": 2900, "lr": 0.07055, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27859, "top5_acc": 0.53375, "loss_cls": 4.13318, "loss": 4.13318, "time": 0.84556} +{"mode": "train", "epoch": 55, "iter": 3000, "lr": 0.07053, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27969, "top5_acc": 0.53266, "loss_cls": 4.12778, "loss": 4.12778, "time": 0.8494} +{"mode": "train", "epoch": 55, "iter": 3100, "lr": 0.0705, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28828, "top5_acc": 0.54875, "loss_cls": 4.06993, "loss": 4.06993, "time": 0.84855} +{"mode": "train", "epoch": 55, "iter": 3200, "lr": 0.07048, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29141, "top5_acc": 0.54703, "loss_cls": 4.01941, "loss": 4.01941, "time": 0.84984} +{"mode": "train", "epoch": 55, "iter": 3300, "lr": 0.07045, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28688, "top5_acc": 0.54516, "loss_cls": 4.09201, "loss": 4.09201, "time": 0.84785} +{"mode": "train", "epoch": 55, "iter": 3400, "lr": 0.07043, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29344, "top5_acc": 0.55937, "loss_cls": 4.04699, "loss": 4.04699, "time": 0.84874} +{"mode": "train", "epoch": 55, "iter": 3500, "lr": 0.0704, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27797, "top5_acc": 0.53219, "loss_cls": 4.13055, "loss": 4.13055, "time": 0.85086} +{"mode": "train", "epoch": 55, "iter": 3600, "lr": 0.07037, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28469, "top5_acc": 0.54109, "loss_cls": 4.10419, "loss": 4.10419, "time": 0.84911} +{"mode": "train", "epoch": 55, "iter": 3700, "lr": 0.07035, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28703, "top5_acc": 0.53953, "loss_cls": 4.07697, "loss": 4.07697, "time": 0.85641} +{"mode": "val", "epoch": 55, "iter": 309, "lr": 0.07034, "top1_acc": 0.20164, "top5_acc": 0.44122, "mean_class_accuracy": 0.20137} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.07031, "memory": 15990, "data_time": 1.5223, "top1_acc": 0.29125, "top5_acc": 0.54469, "loss_cls": 4.07319, "loss": 4.07319, "time": 2.54703} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.07029, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29266, "top5_acc": 0.54391, "loss_cls": 4.0627, "loss": 4.0627, "time": 0.85459} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.07026, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28734, "top5_acc": 0.55219, "loss_cls": 4.04633, "loss": 4.04633, "time": 0.85381} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.07023, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.28516, "top5_acc": 0.54219, "loss_cls": 4.0926, "loss": 4.0926, "time": 0.84537} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.07021, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.29125, "top5_acc": 0.54156, "loss_cls": 4.07063, "loss": 4.07063, "time": 0.85251} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.07018, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29922, "top5_acc": 0.55406, "loss_cls": 3.99239, "loss": 3.99239, "time": 0.84979} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.07016, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29688, "top5_acc": 0.55141, "loss_cls": 4.04715, "loss": 4.04715, "time": 0.85025} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.07013, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.30109, "top5_acc": 0.56109, "loss_cls": 3.99851, "loss": 3.99851, "time": 0.84921} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.07011, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28281, "top5_acc": 0.54875, "loss_cls": 4.08364, "loss": 4.08364, "time": 0.84426} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.07008, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28422, "top5_acc": 0.54562, "loss_cls": 4.10088, "loss": 4.10088, "time": 0.8402} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.07006, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29234, "top5_acc": 0.55219, "loss_cls": 4.01381, "loss": 4.01381, "time": 0.84589} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.07003, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29, "top5_acc": 0.55609, "loss_cls": 4.03533, "loss": 4.03533, "time": 0.8496} +{"mode": "train", "epoch": 56, "iter": 1300, "lr": 0.07, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2775, "top5_acc": 0.54469, "loss_cls": 4.10319, "loss": 4.10319, "time": 0.84662} +{"mode": "train", "epoch": 56, "iter": 1400, "lr": 0.06998, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29313, "top5_acc": 0.54422, "loss_cls": 4.06405, "loss": 4.06405, "time": 0.84681} +{"mode": "train", "epoch": 56, "iter": 1500, "lr": 0.06995, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28781, "top5_acc": 0.54578, "loss_cls": 4.06647, "loss": 4.06647, "time": 0.84873} +{"mode": "train", "epoch": 56, "iter": 1600, "lr": 0.06993, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27641, "top5_acc": 0.53656, "loss_cls": 4.12669, "loss": 4.12669, "time": 0.85086} +{"mode": "train", "epoch": 56, "iter": 1700, "lr": 0.0699, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28922, "top5_acc": 0.54109, "loss_cls": 4.07769, "loss": 4.07769, "time": 0.84977} +{"mode": "train", "epoch": 56, "iter": 1800, "lr": 0.06988, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.28109, "top5_acc": 0.53922, "loss_cls": 4.116, "loss": 4.116, "time": 0.84653} +{"mode": "train", "epoch": 56, "iter": 1900, "lr": 0.06985, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28328, "top5_acc": 0.54766, "loss_cls": 4.08273, "loss": 4.08273, "time": 0.84663} +{"mode": "train", "epoch": 56, "iter": 2000, "lr": 0.06983, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28828, "top5_acc": 0.54469, "loss_cls": 4.07052, "loss": 4.07052, "time": 0.84791} +{"mode": "train", "epoch": 56, "iter": 2100, "lr": 0.0698, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28719, "top5_acc": 0.53594, "loss_cls": 4.07392, "loss": 4.07392, "time": 0.84595} +{"mode": "train", "epoch": 56, "iter": 2200, "lr": 0.06977, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28391, "top5_acc": 0.53359, "loss_cls": 4.09096, "loss": 4.09096, "time": 0.84588} +{"mode": "train", "epoch": 56, "iter": 2300, "lr": 0.06975, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.295, "top5_acc": 0.54438, "loss_cls": 4.05929, "loss": 4.05929, "time": 0.84174} +{"mode": "train", "epoch": 56, "iter": 2400, "lr": 0.06972, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29359, "top5_acc": 0.54812, "loss_cls": 4.06579, "loss": 4.06579, "time": 0.84284} +{"mode": "train", "epoch": 56, "iter": 2500, "lr": 0.0697, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28141, "top5_acc": 0.54469, "loss_cls": 4.07691, "loss": 4.07691, "time": 0.84754} +{"mode": "train", "epoch": 56, "iter": 2600, "lr": 0.06967, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2875, "top5_acc": 0.54734, "loss_cls": 4.04744, "loss": 4.04744, "time": 0.84619} +{"mode": "train", "epoch": 56, "iter": 2700, "lr": 0.06965, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28312, "top5_acc": 0.53328, "loss_cls": 4.1215, "loss": 4.1215, "time": 0.85025} +{"mode": "train", "epoch": 56, "iter": 2800, "lr": 0.06962, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29359, "top5_acc": 0.54781, "loss_cls": 4.06774, "loss": 4.06774, "time": 0.84995} +{"mode": "train", "epoch": 56, "iter": 2900, "lr": 0.06959, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29, "top5_acc": 0.54812, "loss_cls": 4.07086, "loss": 4.07086, "time": 0.84513} +{"mode": "train", "epoch": 56, "iter": 3000, "lr": 0.06957, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29609, "top5_acc": 0.55844, "loss_cls": 4.03702, "loss": 4.03702, "time": 0.84719} +{"mode": "train", "epoch": 56, "iter": 3100, "lr": 0.06954, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27922, "top5_acc": 0.54766, "loss_cls": 4.10503, "loss": 4.10503, "time": 0.84961} +{"mode": "train", "epoch": 56, "iter": 3200, "lr": 0.06952, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28516, "top5_acc": 0.5425, "loss_cls": 4.09518, "loss": 4.09518, "time": 0.85059} +{"mode": "train", "epoch": 56, "iter": 3300, "lr": 0.06949, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28703, "top5_acc": 0.53906, "loss_cls": 4.11298, "loss": 4.11298, "time": 0.84682} +{"mode": "train", "epoch": 56, "iter": 3400, "lr": 0.06947, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29078, "top5_acc": 0.54547, "loss_cls": 4.07922, "loss": 4.07922, "time": 0.84545} +{"mode": "train", "epoch": 56, "iter": 3500, "lr": 0.06944, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29266, "top5_acc": 0.54844, "loss_cls": 4.0637, "loss": 4.0637, "time": 0.8429} +{"mode": "train", "epoch": 56, "iter": 3600, "lr": 0.06941, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29531, "top5_acc": 0.545, "loss_cls": 4.06221, "loss": 4.06221, "time": 0.846} +{"mode": "train", "epoch": 56, "iter": 3700, "lr": 0.06939, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29875, "top5_acc": 0.55219, "loss_cls": 4.04765, "loss": 4.04765, "time": 0.85081} +{"mode": "val", "epoch": 56, "iter": 309, "lr": 0.06938, "top1_acc": 0.21709, "top5_acc": 0.45652, "mean_class_accuracy": 0.21694} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.06935, "memory": 15990, "data_time": 1.54319, "top1_acc": 0.29266, "top5_acc": 0.56391, "loss_cls": 3.98181, "loss": 3.98181, "time": 2.58544} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.06932, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29078, "top5_acc": 0.55047, "loss_cls": 4.05397, "loss": 4.05397, "time": 0.85074} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.0693, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30109, "top5_acc": 0.56156, "loss_cls": 4.00841, "loss": 4.00841, "time": 0.85617} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.06927, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.29844, "top5_acc": 0.55406, "loss_cls": 4.01272, "loss": 4.01272, "time": 0.85555} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.06925, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.27781, "top5_acc": 0.54406, "loss_cls": 4.10947, "loss": 4.10947, "time": 0.8488} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.06922, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.29188, "top5_acc": 0.56016, "loss_cls": 4.03398, "loss": 4.03398, "time": 0.8509} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.0692, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.29297, "top5_acc": 0.54219, "loss_cls": 4.05131, "loss": 4.05131, "time": 0.85078} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.06917, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28125, "top5_acc": 0.54281, "loss_cls": 4.10726, "loss": 4.10726, "time": 0.8462} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.06914, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30062, "top5_acc": 0.56891, "loss_cls": 3.99233, "loss": 3.99233, "time": 0.8456} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.06912, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28672, "top5_acc": 0.54453, "loss_cls": 4.06521, "loss": 4.06521, "time": 0.83813} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.06909, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28141, "top5_acc": 0.53562, "loss_cls": 4.12222, "loss": 4.12222, "time": 0.84639} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.06907, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29047, "top5_acc": 0.55, "loss_cls": 4.06131, "loss": 4.06131, "time": 0.85317} +{"mode": "train", "epoch": 57, "iter": 1300, "lr": 0.06904, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29188, "top5_acc": 0.55047, "loss_cls": 4.06292, "loss": 4.06292, "time": 0.84737} +{"mode": "train", "epoch": 57, "iter": 1400, "lr": 0.06901, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28828, "top5_acc": 0.54844, "loss_cls": 3.99918, "loss": 3.99918, "time": 0.84918} +{"mode": "train", "epoch": 57, "iter": 1500, "lr": 0.06899, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28625, "top5_acc": 0.54688, "loss_cls": 4.0506, "loss": 4.0506, "time": 0.8509} +{"mode": "train", "epoch": 57, "iter": 1600, "lr": 0.06896, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27969, "top5_acc": 0.55266, "loss_cls": 4.09113, "loss": 4.09113, "time": 0.85026} +{"mode": "train", "epoch": 57, "iter": 1700, "lr": 0.06894, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28062, "top5_acc": 0.53422, "loss_cls": 4.12584, "loss": 4.12584, "time": 0.84446} +{"mode": "train", "epoch": 57, "iter": 1800, "lr": 0.06891, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.29047, "top5_acc": 0.54703, "loss_cls": 4.07085, "loss": 4.07085, "time": 0.84314} +{"mode": "train", "epoch": 57, "iter": 1900, "lr": 0.06889, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28781, "top5_acc": 0.55094, "loss_cls": 4.03814, "loss": 4.03814, "time": 0.84559} +{"mode": "train", "epoch": 57, "iter": 2000, "lr": 0.06886, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29844, "top5_acc": 0.55703, "loss_cls": 4.03818, "loss": 4.03818, "time": 0.8393} +{"mode": "train", "epoch": 57, "iter": 2100, "lr": 0.06883, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.27734, "top5_acc": 0.53906, "loss_cls": 4.11246, "loss": 4.11246, "time": 0.84266} +{"mode": "train", "epoch": 57, "iter": 2200, "lr": 0.06881, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29563, "top5_acc": 0.55688, "loss_cls": 4.00514, "loss": 4.00514, "time": 0.83837} +{"mode": "train", "epoch": 57, "iter": 2300, "lr": 0.06878, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30656, "top5_acc": 0.56031, "loss_cls": 4.0061, "loss": 4.0061, "time": 0.84422} +{"mode": "train", "epoch": 57, "iter": 2400, "lr": 0.06876, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29266, "top5_acc": 0.54547, "loss_cls": 4.06104, "loss": 4.06104, "time": 0.84583} +{"mode": "train", "epoch": 57, "iter": 2500, "lr": 0.06873, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29375, "top5_acc": 0.55375, "loss_cls": 4.02938, "loss": 4.02938, "time": 0.84457} +{"mode": "train", "epoch": 57, "iter": 2600, "lr": 0.0687, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29313, "top5_acc": 0.54891, "loss_cls": 4.06131, "loss": 4.06131, "time": 0.84572} +{"mode": "train", "epoch": 57, "iter": 2700, "lr": 0.06868, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28953, "top5_acc": 0.54906, "loss_cls": 4.04209, "loss": 4.04209, "time": 0.84368} +{"mode": "train", "epoch": 57, "iter": 2800, "lr": 0.06865, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29109, "top5_acc": 0.55109, "loss_cls": 4.05801, "loss": 4.05801, "time": 0.84515} +{"mode": "train", "epoch": 57, "iter": 2900, "lr": 0.06863, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29328, "top5_acc": 0.55219, "loss_cls": 4.04241, "loss": 4.04241, "time": 0.85115} +{"mode": "train", "epoch": 57, "iter": 3000, "lr": 0.0686, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28906, "top5_acc": 0.55031, "loss_cls": 4.03667, "loss": 4.03667, "time": 0.84762} +{"mode": "train", "epoch": 57, "iter": 3100, "lr": 0.06857, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28891, "top5_acc": 0.54672, "loss_cls": 4.06045, "loss": 4.06045, "time": 0.84511} +{"mode": "train", "epoch": 57, "iter": 3200, "lr": 0.06855, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29109, "top5_acc": 0.54844, "loss_cls": 4.04238, "loss": 4.04238, "time": 0.84757} +{"mode": "train", "epoch": 57, "iter": 3300, "lr": 0.06852, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28781, "top5_acc": 0.54297, "loss_cls": 4.07, "loss": 4.07, "time": 0.84471} +{"mode": "train", "epoch": 57, "iter": 3400, "lr": 0.0685, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28672, "top5_acc": 0.55141, "loss_cls": 4.03634, "loss": 4.03634, "time": 0.84171} +{"mode": "train", "epoch": 57, "iter": 3500, "lr": 0.06847, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28859, "top5_acc": 0.54703, "loss_cls": 4.06779, "loss": 4.06779, "time": 0.85057} +{"mode": "train", "epoch": 57, "iter": 3600, "lr": 0.06844, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29375, "top5_acc": 0.55422, "loss_cls": 4.03524, "loss": 4.03524, "time": 0.84519} +{"mode": "train", "epoch": 57, "iter": 3700, "lr": 0.06842, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28062, "top5_acc": 0.54375, "loss_cls": 4.08451, "loss": 4.08451, "time": 0.84822} +{"mode": "val", "epoch": 57, "iter": 309, "lr": 0.06841, "top1_acc": 0.22793, "top5_acc": 0.46999, "mean_class_accuracy": 0.22784} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.06838, "memory": 15990, "data_time": 1.44565, "top1_acc": 0.29266, "top5_acc": 0.55531, "loss_cls": 4.03827, "loss": 4.03827, "time": 2.47314} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.06835, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28844, "top5_acc": 0.54234, "loss_cls": 4.06093, "loss": 4.06093, "time": 0.85059} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.06833, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29234, "top5_acc": 0.5575, "loss_cls": 4.00372, "loss": 4.00372, "time": 0.85145} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.0683, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30156, "top5_acc": 0.56563, "loss_cls": 3.96711, "loss": 3.96711, "time": 0.84975} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.06828, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28688, "top5_acc": 0.55203, "loss_cls": 4.05072, "loss": 4.05072, "time": 0.85035} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.06825, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29547, "top5_acc": 0.54891, "loss_cls": 4.02115, "loss": 4.02115, "time": 0.84922} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.06822, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29172, "top5_acc": 0.54797, "loss_cls": 4.04902, "loss": 4.04902, "time": 0.84807} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.0682, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28406, "top5_acc": 0.54125, "loss_cls": 4.06691, "loss": 4.06691, "time": 0.84572} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.06817, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28688, "top5_acc": 0.54172, "loss_cls": 4.05778, "loss": 4.05778, "time": 0.84917} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.06815, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28578, "top5_acc": 0.54312, "loss_cls": 4.08663, "loss": 4.08663, "time": 0.8414} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.06812, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2775, "top5_acc": 0.54125, "loss_cls": 4.08122, "loss": 4.08122, "time": 0.84516} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.06809, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28516, "top5_acc": 0.54516, "loss_cls": 4.06392, "loss": 4.06392, "time": 0.84663} +{"mode": "train", "epoch": 58, "iter": 1300, "lr": 0.06807, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30719, "top5_acc": 0.55391, "loss_cls": 3.98714, "loss": 3.98714, "time": 0.84765} +{"mode": "train", "epoch": 58, "iter": 1400, "lr": 0.06804, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28125, "top5_acc": 0.55188, "loss_cls": 4.06741, "loss": 4.06741, "time": 0.84684} +{"mode": "train", "epoch": 58, "iter": 1500, "lr": 0.06802, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29531, "top5_acc": 0.54531, "loss_cls": 4.03208, "loss": 4.03208, "time": 0.84538} +{"mode": "train", "epoch": 58, "iter": 1600, "lr": 0.06799, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29188, "top5_acc": 0.54578, "loss_cls": 4.05282, "loss": 4.05282, "time": 0.84769} +{"mode": "train", "epoch": 58, "iter": 1700, "lr": 0.06796, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29406, "top5_acc": 0.55875, "loss_cls": 4.04157, "loss": 4.04157, "time": 0.84612} +{"mode": "train", "epoch": 58, "iter": 1800, "lr": 0.06794, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28094, "top5_acc": 0.54375, "loss_cls": 4.10895, "loss": 4.10895, "time": 0.84219} +{"mode": "train", "epoch": 58, "iter": 1900, "lr": 0.06791, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.28688, "top5_acc": 0.55297, "loss_cls": 4.04507, "loss": 4.04507, "time": 0.84586} +{"mode": "train", "epoch": 58, "iter": 2000, "lr": 0.06789, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29438, "top5_acc": 0.54922, "loss_cls": 4.04331, "loss": 4.04331, "time": 0.84084} +{"mode": "train", "epoch": 58, "iter": 2100, "lr": 0.06786, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29156, "top5_acc": 0.55312, "loss_cls": 4.02471, "loss": 4.02471, "time": 0.84386} +{"mode": "train", "epoch": 58, "iter": 2200, "lr": 0.06783, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29203, "top5_acc": 0.54609, "loss_cls": 4.03404, "loss": 4.03404, "time": 0.84626} +{"mode": "train", "epoch": 58, "iter": 2300, "lr": 0.06781, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29547, "top5_acc": 0.55844, "loss_cls": 4.03018, "loss": 4.03018, "time": 0.83905} +{"mode": "train", "epoch": 58, "iter": 2400, "lr": 0.06778, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28672, "top5_acc": 0.54906, "loss_cls": 4.04618, "loss": 4.04618, "time": 0.84693} +{"mode": "train", "epoch": 58, "iter": 2500, "lr": 0.06775, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28516, "top5_acc": 0.5375, "loss_cls": 4.10076, "loss": 4.10076, "time": 0.84005} +{"mode": "train", "epoch": 58, "iter": 2600, "lr": 0.06773, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28984, "top5_acc": 0.5525, "loss_cls": 4.04202, "loss": 4.04202, "time": 0.84288} +{"mode": "train", "epoch": 58, "iter": 2700, "lr": 0.0677, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28859, "top5_acc": 0.54156, "loss_cls": 4.10177, "loss": 4.10177, "time": 0.84303} +{"mode": "train", "epoch": 58, "iter": 2800, "lr": 0.06768, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28422, "top5_acc": 0.54531, "loss_cls": 4.05333, "loss": 4.05333, "time": 0.85018} +{"mode": "train", "epoch": 58, "iter": 2900, "lr": 0.06765, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28703, "top5_acc": 0.54172, "loss_cls": 4.07469, "loss": 4.07469, "time": 0.84661} +{"mode": "train", "epoch": 58, "iter": 3000, "lr": 0.06762, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29375, "top5_acc": 0.555, "loss_cls": 4.04432, "loss": 4.04432, "time": 0.84179} +{"mode": "train", "epoch": 58, "iter": 3100, "lr": 0.0676, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29344, "top5_acc": 0.54375, "loss_cls": 4.06048, "loss": 4.06048, "time": 0.84118} +{"mode": "train", "epoch": 58, "iter": 3200, "lr": 0.06757, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29016, "top5_acc": 0.54969, "loss_cls": 4.04372, "loss": 4.04372, "time": 0.84189} +{"mode": "train", "epoch": 58, "iter": 3300, "lr": 0.06755, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29109, "top5_acc": 0.54656, "loss_cls": 4.04166, "loss": 4.04166, "time": 0.84075} +{"mode": "train", "epoch": 58, "iter": 3400, "lr": 0.06752, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29125, "top5_acc": 0.55359, "loss_cls": 4.0179, "loss": 4.0179, "time": 0.84384} +{"mode": "train", "epoch": 58, "iter": 3500, "lr": 0.06749, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28875, "top5_acc": 0.5475, "loss_cls": 4.03588, "loss": 4.03588, "time": 0.84687} +{"mode": "train", "epoch": 58, "iter": 3600, "lr": 0.06747, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29188, "top5_acc": 0.54359, "loss_cls": 4.08079, "loss": 4.08079, "time": 0.85135} +{"mode": "train", "epoch": 58, "iter": 3700, "lr": 0.06744, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28047, "top5_acc": 0.54641, "loss_cls": 4.07495, "loss": 4.07495, "time": 0.85294} +{"mode": "val", "epoch": 58, "iter": 309, "lr": 0.06743, "top1_acc": 0.21795, "top5_acc": 0.45611, "mean_class_accuracy": 0.21774} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.0674, "memory": 15990, "data_time": 1.51084, "top1_acc": 0.30422, "top5_acc": 0.57063, "loss_cls": 3.93597, "loss": 3.93597, "time": 2.55638} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.06738, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29063, "top5_acc": 0.55125, "loss_cls": 4.01691, "loss": 4.01691, "time": 0.85833} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.06735, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29547, "top5_acc": 0.55906, "loss_cls": 4.01035, "loss": 4.01035, "time": 0.8607} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.06732, "memory": 15990, "data_time": 0.00068, "top1_acc": 0.28234, "top5_acc": 0.54531, "loss_cls": 4.05769, "loss": 4.05769, "time": 0.85778} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.0673, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28422, "top5_acc": 0.54906, "loss_cls": 4.05967, "loss": 4.05967, "time": 0.85638} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.06727, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29453, "top5_acc": 0.55734, "loss_cls": 4.01935, "loss": 4.01935, "time": 0.85751} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.06725, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28453, "top5_acc": 0.55109, "loss_cls": 4.07713, "loss": 4.07713, "time": 0.85361} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.06722, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.28641, "top5_acc": 0.54812, "loss_cls": 4.02253, "loss": 4.02253, "time": 0.84776} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.06719, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.29516, "top5_acc": 0.55641, "loss_cls": 4.00041, "loss": 4.00041, "time": 0.85339} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.06717, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30125, "top5_acc": 0.55172, "loss_cls": 4.01503, "loss": 4.01503, "time": 0.84832} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.06714, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30188, "top5_acc": 0.55969, "loss_cls": 4.00128, "loss": 4.00128, "time": 0.8457} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.06711, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29422, "top5_acc": 0.55922, "loss_cls": 4.01041, "loss": 4.01041, "time": 0.84326} +{"mode": "train", "epoch": 59, "iter": 1300, "lr": 0.06709, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28406, "top5_acc": 0.54781, "loss_cls": 4.04017, "loss": 4.04017, "time": 0.84604} +{"mode": "train", "epoch": 59, "iter": 1400, "lr": 0.06706, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29641, "top5_acc": 0.55219, "loss_cls": 4.04498, "loss": 4.04498, "time": 0.84183} +{"mode": "train", "epoch": 59, "iter": 1500, "lr": 0.06704, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29297, "top5_acc": 0.55406, "loss_cls": 4.03352, "loss": 4.03352, "time": 0.8432} +{"mode": "train", "epoch": 59, "iter": 1600, "lr": 0.06701, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29641, "top5_acc": 0.555, "loss_cls": 4.04961, "loss": 4.04961, "time": 0.85081} +{"mode": "train", "epoch": 59, "iter": 1700, "lr": 0.06698, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2825, "top5_acc": 0.55141, "loss_cls": 4.05753, "loss": 4.05753, "time": 0.84931} +{"mode": "train", "epoch": 59, "iter": 1800, "lr": 0.06696, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.2875, "top5_acc": 0.54703, "loss_cls": 4.04667, "loss": 4.04667, "time": 0.85234} +{"mode": "train", "epoch": 59, "iter": 1900, "lr": 0.06693, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28984, "top5_acc": 0.54594, "loss_cls": 4.05372, "loss": 4.05372, "time": 0.84874} +{"mode": "train", "epoch": 59, "iter": 2000, "lr": 0.0669, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28984, "top5_acc": 0.55453, "loss_cls": 4.02558, "loss": 4.02558, "time": 0.84907} +{"mode": "train", "epoch": 59, "iter": 2100, "lr": 0.06688, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29453, "top5_acc": 0.56203, "loss_cls": 4.0005, "loss": 4.0005, "time": 0.84078} +{"mode": "train", "epoch": 59, "iter": 2200, "lr": 0.06685, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28484, "top5_acc": 0.54984, "loss_cls": 4.0543, "loss": 4.0543, "time": 0.84693} +{"mode": "train", "epoch": 59, "iter": 2300, "lr": 0.06682, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30375, "top5_acc": 0.555, "loss_cls": 4.01664, "loss": 4.01664, "time": 0.84421} +{"mode": "train", "epoch": 59, "iter": 2400, "lr": 0.0668, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29688, "top5_acc": 0.55156, "loss_cls": 4.03837, "loss": 4.03837, "time": 0.84957} +{"mode": "train", "epoch": 59, "iter": 2500, "lr": 0.06677, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29609, "top5_acc": 0.55062, "loss_cls": 4.03857, "loss": 4.03857, "time": 0.84692} +{"mode": "train", "epoch": 59, "iter": 2600, "lr": 0.06675, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28828, "top5_acc": 0.54344, "loss_cls": 4.08981, "loss": 4.08981, "time": 0.846} +{"mode": "train", "epoch": 59, "iter": 2700, "lr": 0.06672, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30266, "top5_acc": 0.55281, "loss_cls": 4.03915, "loss": 4.03915, "time": 0.84222} +{"mode": "train", "epoch": 59, "iter": 2800, "lr": 0.06669, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29344, "top5_acc": 0.54328, "loss_cls": 4.05827, "loss": 4.05827, "time": 0.8424} +{"mode": "train", "epoch": 59, "iter": 2900, "lr": 0.06667, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28859, "top5_acc": 0.54828, "loss_cls": 4.05734, "loss": 4.05734, "time": 0.84294} +{"mode": "train", "epoch": 59, "iter": 3000, "lr": 0.06664, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28359, "top5_acc": 0.54547, "loss_cls": 4.07528, "loss": 4.07528, "time": 0.84712} +{"mode": "train", "epoch": 59, "iter": 3100, "lr": 0.06661, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28688, "top5_acc": 0.55344, "loss_cls": 4.02996, "loss": 4.02996, "time": 0.84304} +{"mode": "train", "epoch": 59, "iter": 3200, "lr": 0.06659, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29359, "top5_acc": 0.54875, "loss_cls": 4.04925, "loss": 4.04925, "time": 0.84939} +{"mode": "train", "epoch": 59, "iter": 3300, "lr": 0.06656, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29656, "top5_acc": 0.55641, "loss_cls": 4.00067, "loss": 4.00067, "time": 0.8458} +{"mode": "train", "epoch": 59, "iter": 3400, "lr": 0.06653, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28859, "top5_acc": 0.54125, "loss_cls": 4.0653, "loss": 4.0653, "time": 0.84555} +{"mode": "train", "epoch": 59, "iter": 3500, "lr": 0.06651, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.295, "top5_acc": 0.54375, "loss_cls": 4.04577, "loss": 4.04577, "time": 0.84757} +{"mode": "train", "epoch": 59, "iter": 3600, "lr": 0.06648, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28875, "top5_acc": 0.5425, "loss_cls": 4.08533, "loss": 4.08533, "time": 0.85397} +{"mode": "train", "epoch": 59, "iter": 3700, "lr": 0.06646, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28141, "top5_acc": 0.55, "loss_cls": 4.05753, "loss": 4.05753, "time": 0.84783} +{"mode": "val", "epoch": 59, "iter": 309, "lr": 0.06644, "top1_acc": 0.22955, "top5_acc": 0.47222, "mean_class_accuracy": 0.22944} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.06642, "memory": 15990, "data_time": 1.51213, "top1_acc": 0.31219, "top5_acc": 0.57109, "loss_cls": 3.92011, "loss": 3.92011, "time": 2.54672} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.06639, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30422, "top5_acc": 0.55734, "loss_cls": 3.96788, "loss": 3.96788, "time": 0.84845} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.06636, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28922, "top5_acc": 0.54719, "loss_cls": 4.06893, "loss": 4.06893, "time": 0.84942} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.06634, "memory": 15990, "data_time": 0.00083, "top1_acc": 0.30125, "top5_acc": 0.56672, "loss_cls": 3.95687, "loss": 3.95687, "time": 0.85688} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.06631, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28734, "top5_acc": 0.54625, "loss_cls": 4.03902, "loss": 4.03902, "time": 0.84745} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.06629, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29266, "top5_acc": 0.55219, "loss_cls": 4.04294, "loss": 4.04294, "time": 0.8495} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.06626, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.29547, "top5_acc": 0.55391, "loss_cls": 3.97509, "loss": 3.97509, "time": 0.84844} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.06623, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29484, "top5_acc": 0.56281, "loss_cls": 4.00403, "loss": 4.00403, "time": 0.84715} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.06621, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.29063, "top5_acc": 0.55188, "loss_cls": 4.03704, "loss": 4.03704, "time": 0.85137} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.06618, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29359, "top5_acc": 0.55141, "loss_cls": 4.03222, "loss": 4.03222, "time": 0.84455} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.06615, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.28813, "top5_acc": 0.55047, "loss_cls": 4.04622, "loss": 4.04622, "time": 0.84954} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.06613, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29891, "top5_acc": 0.56047, "loss_cls": 3.98382, "loss": 3.98382, "time": 0.8494} +{"mode": "train", "epoch": 60, "iter": 1300, "lr": 0.0661, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28875, "top5_acc": 0.54781, "loss_cls": 4.06525, "loss": 4.06525, "time": 0.84542} +{"mode": "train", "epoch": 60, "iter": 1400, "lr": 0.06607, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2925, "top5_acc": 0.54406, "loss_cls": 4.06944, "loss": 4.06944, "time": 0.85024} +{"mode": "train", "epoch": 60, "iter": 1500, "lr": 0.06605, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30344, "top5_acc": 0.55766, "loss_cls": 3.96211, "loss": 3.96211, "time": 0.84986} +{"mode": "train", "epoch": 60, "iter": 1600, "lr": 0.06602, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29594, "top5_acc": 0.56141, "loss_cls": 4.00003, "loss": 4.00003, "time": 0.84993} +{"mode": "train", "epoch": 60, "iter": 1700, "lr": 0.06599, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29234, "top5_acc": 0.55203, "loss_cls": 4.03501, "loss": 4.03501, "time": 0.84713} +{"mode": "train", "epoch": 60, "iter": 1800, "lr": 0.06597, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28781, "top5_acc": 0.54281, "loss_cls": 4.0611, "loss": 4.0611, "time": 0.85087} +{"mode": "train", "epoch": 60, "iter": 1900, "lr": 0.06594, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.29109, "top5_acc": 0.55562, "loss_cls": 4.05355, "loss": 4.05355, "time": 0.84822} +{"mode": "train", "epoch": 60, "iter": 2000, "lr": 0.06591, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.29172, "top5_acc": 0.55812, "loss_cls": 4.01964, "loss": 4.01964, "time": 0.8445} +{"mode": "train", "epoch": 60, "iter": 2100, "lr": 0.06589, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29906, "top5_acc": 0.55203, "loss_cls": 4.01536, "loss": 4.01536, "time": 0.84491} +{"mode": "train", "epoch": 60, "iter": 2200, "lr": 0.06586, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29625, "top5_acc": 0.54828, "loss_cls": 4.06287, "loss": 4.06287, "time": 0.84686} +{"mode": "train", "epoch": 60, "iter": 2300, "lr": 0.06584, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30422, "top5_acc": 0.57312, "loss_cls": 3.98543, "loss": 3.98543, "time": 0.84799} +{"mode": "train", "epoch": 60, "iter": 2400, "lr": 0.06581, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28859, "top5_acc": 0.54438, "loss_cls": 4.07373, "loss": 4.07373, "time": 0.85166} +{"mode": "train", "epoch": 60, "iter": 2500, "lr": 0.06578, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28641, "top5_acc": 0.54625, "loss_cls": 4.06857, "loss": 4.06857, "time": 0.84743} +{"mode": "train", "epoch": 60, "iter": 2600, "lr": 0.06576, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28969, "top5_acc": 0.53984, "loss_cls": 4.07586, "loss": 4.07586, "time": 0.86079} +{"mode": "train", "epoch": 60, "iter": 2700, "lr": 0.06573, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29969, "top5_acc": 0.55188, "loss_cls": 4.04965, "loss": 4.04965, "time": 0.85349} +{"mode": "train", "epoch": 60, "iter": 2800, "lr": 0.0657, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27609, "top5_acc": 0.54609, "loss_cls": 4.08886, "loss": 4.08886, "time": 0.85316} +{"mode": "train", "epoch": 60, "iter": 2900, "lr": 0.06568, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29641, "top5_acc": 0.54875, "loss_cls": 4.01879, "loss": 4.01879, "time": 0.85622} +{"mode": "train", "epoch": 60, "iter": 3000, "lr": 0.06565, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29391, "top5_acc": 0.55156, "loss_cls": 4.03041, "loss": 4.03041, "time": 0.85669} +{"mode": "train", "epoch": 60, "iter": 3100, "lr": 0.06562, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29563, "top5_acc": 0.5525, "loss_cls": 4.03264, "loss": 4.03264, "time": 0.86151} +{"mode": "train", "epoch": 60, "iter": 3200, "lr": 0.0656, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28703, "top5_acc": 0.54516, "loss_cls": 4.05357, "loss": 4.05357, "time": 0.85453} +{"mode": "train", "epoch": 60, "iter": 3300, "lr": 0.06557, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28828, "top5_acc": 0.545, "loss_cls": 4.064, "loss": 4.064, "time": 0.86081} +{"mode": "train", "epoch": 60, "iter": 3400, "lr": 0.06554, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29219, "top5_acc": 0.54703, "loss_cls": 4.05364, "loss": 4.05364, "time": 0.8576} +{"mode": "train", "epoch": 60, "iter": 3500, "lr": 0.06552, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29109, "top5_acc": 0.55094, "loss_cls": 4.08234, "loss": 4.08234, "time": 0.86149} +{"mode": "train", "epoch": 60, "iter": 3600, "lr": 0.06549, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30297, "top5_acc": 0.55828, "loss_cls": 4.02126, "loss": 4.02126, "time": 0.85904} +{"mode": "train", "epoch": 60, "iter": 3700, "lr": 0.06546, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30031, "top5_acc": 0.56, "loss_cls": 3.99244, "loss": 3.99244, "time": 0.86015} +{"mode": "val", "epoch": 60, "iter": 309, "lr": 0.06545, "top1_acc": 0.22069, "top5_acc": 0.44953, "mean_class_accuracy": 0.22062} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.06542, "memory": 15990, "data_time": 1.55577, "top1_acc": 0.30422, "top5_acc": 0.56203, "loss_cls": 3.9372, "loss": 3.9372, "time": 2.58169} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.0654, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29656, "top5_acc": 0.55391, "loss_cls": 4.02439, "loss": 4.02439, "time": 0.85718} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.06537, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29141, "top5_acc": 0.54891, "loss_cls": 4.06831, "loss": 4.06831, "time": 0.85452} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.06534, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30062, "top5_acc": 0.56406, "loss_cls": 3.97021, "loss": 3.97021, "time": 0.85186} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.06532, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30141, "top5_acc": 0.56516, "loss_cls": 3.99189, "loss": 3.99189, "time": 0.85318} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.06529, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.30094, "top5_acc": 0.56344, "loss_cls": 3.97924, "loss": 3.97924, "time": 0.85083} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.06526, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29453, "top5_acc": 0.5575, "loss_cls": 4.02888, "loss": 4.02888, "time": 0.85102} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.06524, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.30047, "top5_acc": 0.55688, "loss_cls": 3.99701, "loss": 3.99701, "time": 0.84769} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.06521, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30016, "top5_acc": 0.555, "loss_cls": 3.98952, "loss": 3.98952, "time": 0.84866} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.06519, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29188, "top5_acc": 0.55859, "loss_cls": 4.01019, "loss": 4.01019, "time": 0.84089} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.06516, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29625, "top5_acc": 0.56031, "loss_cls": 3.99759, "loss": 3.99759, "time": 0.84782} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.06513, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30219, "top5_acc": 0.56234, "loss_cls": 4.01482, "loss": 4.01482, "time": 0.84544} +{"mode": "train", "epoch": 61, "iter": 1300, "lr": 0.06511, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31031, "top5_acc": 0.56391, "loss_cls": 3.99281, "loss": 3.99281, "time": 0.84912} +{"mode": "train", "epoch": 61, "iter": 1400, "lr": 0.06508, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29672, "top5_acc": 0.55094, "loss_cls": 4.03774, "loss": 4.03774, "time": 0.8496} +{"mode": "train", "epoch": 61, "iter": 1500, "lr": 0.06505, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29281, "top5_acc": 0.55203, "loss_cls": 4.0297, "loss": 4.0297, "time": 0.8479} +{"mode": "train", "epoch": 61, "iter": 1600, "lr": 0.06503, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29313, "top5_acc": 0.55484, "loss_cls": 4.03418, "loss": 4.03418, "time": 0.85152} +{"mode": "train", "epoch": 61, "iter": 1700, "lr": 0.065, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29172, "top5_acc": 0.54312, "loss_cls": 4.07701, "loss": 4.07701, "time": 0.84485} +{"mode": "train", "epoch": 61, "iter": 1800, "lr": 0.06497, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28672, "top5_acc": 0.54234, "loss_cls": 4.07401, "loss": 4.07401, "time": 0.84852} +{"mode": "train", "epoch": 61, "iter": 1900, "lr": 0.06495, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30391, "top5_acc": 0.55688, "loss_cls": 4.01981, "loss": 4.01981, "time": 0.85071} +{"mode": "train", "epoch": 61, "iter": 2000, "lr": 0.06492, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.29578, "top5_acc": 0.55, "loss_cls": 4.02549, "loss": 4.02549, "time": 0.83976} +{"mode": "train", "epoch": 61, "iter": 2100, "lr": 0.06489, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.29844, "top5_acc": 0.56031, "loss_cls": 3.99386, "loss": 3.99386, "time": 0.84007} +{"mode": "train", "epoch": 61, "iter": 2200, "lr": 0.06487, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.29109, "top5_acc": 0.55172, "loss_cls": 4.05861, "loss": 4.05861, "time": 0.84348} +{"mode": "train", "epoch": 61, "iter": 2300, "lr": 0.06484, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29078, "top5_acc": 0.55484, "loss_cls": 4.02833, "loss": 4.02833, "time": 0.84626} +{"mode": "train", "epoch": 61, "iter": 2400, "lr": 0.06481, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29953, "top5_acc": 0.54766, "loss_cls": 4.03124, "loss": 4.03124, "time": 0.84467} +{"mode": "train", "epoch": 61, "iter": 2500, "lr": 0.06478, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29203, "top5_acc": 0.56578, "loss_cls": 4.02398, "loss": 4.02398, "time": 0.84089} +{"mode": "train", "epoch": 61, "iter": 2600, "lr": 0.06476, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28047, "top5_acc": 0.54344, "loss_cls": 4.08168, "loss": 4.08168, "time": 0.8483} +{"mode": "train", "epoch": 61, "iter": 2700, "lr": 0.06473, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29516, "top5_acc": 0.55594, "loss_cls": 4.02474, "loss": 4.02474, "time": 0.84605} +{"mode": "train", "epoch": 61, "iter": 2800, "lr": 0.0647, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2875, "top5_acc": 0.55188, "loss_cls": 4.03338, "loss": 4.03338, "time": 0.84137} +{"mode": "train", "epoch": 61, "iter": 2900, "lr": 0.06468, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29344, "top5_acc": 0.55797, "loss_cls": 4.00326, "loss": 4.00326, "time": 0.84955} +{"mode": "train", "epoch": 61, "iter": 3000, "lr": 0.06465, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29547, "top5_acc": 0.55969, "loss_cls": 4.04644, "loss": 4.04644, "time": 0.84782} +{"mode": "train", "epoch": 61, "iter": 3100, "lr": 0.06462, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29344, "top5_acc": 0.54359, "loss_cls": 4.04912, "loss": 4.04912, "time": 0.84644} +{"mode": "train", "epoch": 61, "iter": 3200, "lr": 0.0646, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28375, "top5_acc": 0.55219, "loss_cls": 4.05779, "loss": 4.05779, "time": 0.84507} +{"mode": "train", "epoch": 61, "iter": 3300, "lr": 0.06457, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29063, "top5_acc": 0.56063, "loss_cls": 3.98692, "loss": 3.98692, "time": 0.85102} +{"mode": "train", "epoch": 61, "iter": 3400, "lr": 0.06454, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28859, "top5_acc": 0.55328, "loss_cls": 4.05652, "loss": 4.05652, "time": 0.84833} +{"mode": "train", "epoch": 61, "iter": 3500, "lr": 0.06452, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29781, "top5_acc": 0.55422, "loss_cls": 4.0202, "loss": 4.0202, "time": 0.851} +{"mode": "train", "epoch": 61, "iter": 3600, "lr": 0.06449, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30125, "top5_acc": 0.55875, "loss_cls": 4.01796, "loss": 4.01796, "time": 0.84697} +{"mode": "train", "epoch": 61, "iter": 3700, "lr": 0.06446, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29844, "top5_acc": 0.55469, "loss_cls": 4.01156, "loss": 4.01156, "time": 0.847} +{"mode": "val", "epoch": 61, "iter": 309, "lr": 0.06445, "top1_acc": 0.22545, "top5_acc": 0.46999, "mean_class_accuracy": 0.22531} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.06443, "memory": 15990, "data_time": 1.45662, "top1_acc": 0.30312, "top5_acc": 0.56156, "loss_cls": 3.98725, "loss": 3.98725, "time": 2.47824} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.0644, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30125, "top5_acc": 0.56125, "loss_cls": 4.00431, "loss": 4.00431, "time": 0.84903} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.06437, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29219, "top5_acc": 0.54922, "loss_cls": 4.0263, "loss": 4.0263, "time": 0.85246} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.06434, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.29094, "top5_acc": 0.55672, "loss_cls": 4.02582, "loss": 4.02582, "time": 0.85356} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.06432, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28797, "top5_acc": 0.54828, "loss_cls": 4.0269, "loss": 4.0269, "time": 0.85095} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.06429, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29688, "top5_acc": 0.5575, "loss_cls": 3.98996, "loss": 3.98996, "time": 0.85317} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.06426, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29844, "top5_acc": 0.56328, "loss_cls": 3.98494, "loss": 3.98494, "time": 0.85164} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.06424, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29516, "top5_acc": 0.55359, "loss_cls": 4.00368, "loss": 4.00368, "time": 0.84756} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.06421, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30281, "top5_acc": 0.55953, "loss_cls": 3.98897, "loss": 3.98897, "time": 0.85073} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.06418, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30266, "top5_acc": 0.56297, "loss_cls": 3.9779, "loss": 3.9779, "time": 0.84771} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.06416, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29172, "top5_acc": 0.55297, "loss_cls": 4.04236, "loss": 4.04236, "time": 0.84623} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.06413, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30203, "top5_acc": 0.55, "loss_cls": 4.02225, "loss": 4.02225, "time": 0.84558} +{"mode": "train", "epoch": 62, "iter": 1300, "lr": 0.0641, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.285, "top5_acc": 0.54547, "loss_cls": 4.05646, "loss": 4.05646, "time": 0.84659} +{"mode": "train", "epoch": 62, "iter": 1400, "lr": 0.06408, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29688, "top5_acc": 0.56563, "loss_cls": 3.95365, "loss": 3.95365, "time": 0.84902} +{"mode": "train", "epoch": 62, "iter": 1500, "lr": 0.06405, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29281, "top5_acc": 0.55656, "loss_cls": 4.00229, "loss": 4.00229, "time": 0.84258} +{"mode": "train", "epoch": 62, "iter": 1600, "lr": 0.06402, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28797, "top5_acc": 0.54312, "loss_cls": 4.04658, "loss": 4.04658, "time": 0.84602} +{"mode": "train", "epoch": 62, "iter": 1700, "lr": 0.064, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.29656, "top5_acc": 0.55719, "loss_cls": 4.03072, "loss": 4.03072, "time": 0.84711} +{"mode": "train", "epoch": 62, "iter": 1800, "lr": 0.06397, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30406, "top5_acc": 0.56312, "loss_cls": 3.95411, "loss": 3.95411, "time": 0.85076} +{"mode": "train", "epoch": 62, "iter": 1900, "lr": 0.06394, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29516, "top5_acc": 0.55609, "loss_cls": 4.01385, "loss": 4.01385, "time": 0.8457} +{"mode": "train", "epoch": 62, "iter": 2000, "lr": 0.06392, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29641, "top5_acc": 0.55266, "loss_cls": 4.01802, "loss": 4.01802, "time": 0.84338} +{"mode": "train", "epoch": 62, "iter": 2100, "lr": 0.06389, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29281, "top5_acc": 0.55, "loss_cls": 4.03602, "loss": 4.03602, "time": 0.84814} +{"mode": "train", "epoch": 62, "iter": 2200, "lr": 0.06386, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30281, "top5_acc": 0.55219, "loss_cls": 4.02051, "loss": 4.02051, "time": 0.84945} +{"mode": "train", "epoch": 62, "iter": 2300, "lr": 0.06384, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.29875, "top5_acc": 0.55953, "loss_cls": 4.00999, "loss": 4.00999, "time": 0.84875} +{"mode": "train", "epoch": 62, "iter": 2400, "lr": 0.06381, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29703, "top5_acc": 0.55016, "loss_cls": 4.06599, "loss": 4.06599, "time": 0.84867} +{"mode": "train", "epoch": 62, "iter": 2500, "lr": 0.06378, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31125, "top5_acc": 0.56516, "loss_cls": 3.95117, "loss": 3.95117, "time": 0.85003} +{"mode": "train", "epoch": 62, "iter": 2600, "lr": 0.06375, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30172, "top5_acc": 0.55953, "loss_cls": 3.99505, "loss": 3.99505, "time": 0.84125} +{"mode": "train", "epoch": 62, "iter": 2700, "lr": 0.06373, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30203, "top5_acc": 0.56, "loss_cls": 3.99436, "loss": 3.99436, "time": 0.84604} +{"mode": "train", "epoch": 62, "iter": 2800, "lr": 0.0637, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30531, "top5_acc": 0.56078, "loss_cls": 3.97663, "loss": 3.97663, "time": 0.84453} +{"mode": "train", "epoch": 62, "iter": 2900, "lr": 0.06367, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30047, "top5_acc": 0.55984, "loss_cls": 4.00866, "loss": 4.00866, "time": 0.84238} +{"mode": "train", "epoch": 62, "iter": 3000, "lr": 0.06365, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30141, "top5_acc": 0.55156, "loss_cls": 4.01165, "loss": 4.01165, "time": 0.84058} +{"mode": "train", "epoch": 62, "iter": 3100, "lr": 0.06362, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28938, "top5_acc": 0.54797, "loss_cls": 4.04127, "loss": 4.04127, "time": 0.84577} +{"mode": "train", "epoch": 62, "iter": 3200, "lr": 0.06359, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29266, "top5_acc": 0.54375, "loss_cls": 4.06623, "loss": 4.06623, "time": 0.84716} +{"mode": "train", "epoch": 62, "iter": 3300, "lr": 0.06357, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29125, "top5_acc": 0.55469, "loss_cls": 4.01496, "loss": 4.01496, "time": 0.84659} +{"mode": "train", "epoch": 62, "iter": 3400, "lr": 0.06354, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29094, "top5_acc": 0.55094, "loss_cls": 4.0258, "loss": 4.0258, "time": 0.84415} +{"mode": "train", "epoch": 62, "iter": 3500, "lr": 0.06351, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30391, "top5_acc": 0.56109, "loss_cls": 3.9865, "loss": 3.9865, "time": 0.84457} +{"mode": "train", "epoch": 62, "iter": 3600, "lr": 0.06349, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29484, "top5_acc": 0.55703, "loss_cls": 4.03748, "loss": 4.03748, "time": 0.84363} +{"mode": "train", "epoch": 62, "iter": 3700, "lr": 0.06346, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29172, "top5_acc": 0.54188, "loss_cls": 4.05322, "loss": 4.05322, "time": 0.84042} +{"mode": "val", "epoch": 62, "iter": 309, "lr": 0.06345, "top1_acc": 0.22838, "top5_acc": 0.46882, "mean_class_accuracy": 0.22831} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.06342, "memory": 15990, "data_time": 1.55835, "top1_acc": 0.30953, "top5_acc": 0.57641, "loss_cls": 3.90924, "loss": 3.90924, "time": 2.60651} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.06339, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30391, "top5_acc": 0.57297, "loss_cls": 3.94607, "loss": 3.94607, "time": 0.86116} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.06337, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30031, "top5_acc": 0.55031, "loss_cls": 4.02378, "loss": 4.02378, "time": 0.85449} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.06334, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.30484, "top5_acc": 0.56891, "loss_cls": 3.94101, "loss": 3.94101, "time": 0.85587} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.06331, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30031, "top5_acc": 0.56719, "loss_cls": 3.96281, "loss": 3.96281, "time": 0.85597} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.06328, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.2925, "top5_acc": 0.55906, "loss_cls": 4.0204, "loss": 4.0204, "time": 0.85326} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.06326, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30219, "top5_acc": 0.55219, "loss_cls": 4.0259, "loss": 4.0259, "time": 0.85865} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.06323, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30016, "top5_acc": 0.5575, "loss_cls": 4.00363, "loss": 4.00363, "time": 0.85669} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.0632, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29812, "top5_acc": 0.55922, "loss_cls": 4.01759, "loss": 4.01759, "time": 0.85678} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.06318, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28969, "top5_acc": 0.56109, "loss_cls": 4.03934, "loss": 4.03934, "time": 0.85386} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.06315, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30656, "top5_acc": 0.56547, "loss_cls": 3.96709, "loss": 3.96709, "time": 0.85156} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.06312, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.3075, "top5_acc": 0.56875, "loss_cls": 3.94242, "loss": 3.94242, "time": 0.8582} +{"mode": "train", "epoch": 63, "iter": 1300, "lr": 0.0631, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30109, "top5_acc": 0.55719, "loss_cls": 4.01526, "loss": 4.01526, "time": 0.85491} +{"mode": "train", "epoch": 63, "iter": 1400, "lr": 0.06307, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.30891, "top5_acc": 0.55547, "loss_cls": 3.9897, "loss": 3.9897, "time": 0.85525} +{"mode": "train", "epoch": 63, "iter": 1500, "lr": 0.06304, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29359, "top5_acc": 0.56625, "loss_cls": 3.99205, "loss": 3.99205, "time": 0.85498} +{"mode": "train", "epoch": 63, "iter": 1600, "lr": 0.06301, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2975, "top5_acc": 0.55516, "loss_cls": 4.00825, "loss": 4.00825, "time": 0.85493} +{"mode": "train", "epoch": 63, "iter": 1700, "lr": 0.06299, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.29797, "top5_acc": 0.55609, "loss_cls": 4.0013, "loss": 4.0013, "time": 0.84776} +{"mode": "train", "epoch": 63, "iter": 1800, "lr": 0.06296, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29156, "top5_acc": 0.54422, "loss_cls": 4.05586, "loss": 4.05586, "time": 0.85669} +{"mode": "train", "epoch": 63, "iter": 1900, "lr": 0.06293, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30219, "top5_acc": 0.55781, "loss_cls": 3.98644, "loss": 3.98644, "time": 0.86451} +{"mode": "train", "epoch": 63, "iter": 2000, "lr": 0.06291, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29344, "top5_acc": 0.55406, "loss_cls": 4.01615, "loss": 4.01615, "time": 0.85658} +{"mode": "train", "epoch": 63, "iter": 2100, "lr": 0.06288, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29766, "top5_acc": 0.55641, "loss_cls": 4.01909, "loss": 4.01909, "time": 0.85661} +{"mode": "train", "epoch": 63, "iter": 2200, "lr": 0.06285, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29734, "top5_acc": 0.56266, "loss_cls": 3.97662, "loss": 3.97662, "time": 0.85338} +{"mode": "train", "epoch": 63, "iter": 2300, "lr": 0.06283, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30078, "top5_acc": 0.55766, "loss_cls": 4.0077, "loss": 4.0077, "time": 0.8535} +{"mode": "train", "epoch": 63, "iter": 2400, "lr": 0.0628, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30375, "top5_acc": 0.55141, "loss_cls": 4.0043, "loss": 4.0043, "time": 0.86082} +{"mode": "train", "epoch": 63, "iter": 2500, "lr": 0.06277, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29234, "top5_acc": 0.55219, "loss_cls": 4.01231, "loss": 4.01231, "time": 0.86375} +{"mode": "train", "epoch": 63, "iter": 2600, "lr": 0.06274, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29625, "top5_acc": 0.55922, "loss_cls": 4.00944, "loss": 4.00944, "time": 0.8708} +{"mode": "train", "epoch": 63, "iter": 2700, "lr": 0.06272, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.29078, "top5_acc": 0.54937, "loss_cls": 4.03601, "loss": 4.03601, "time": 0.86529} +{"mode": "train", "epoch": 63, "iter": 2800, "lr": 0.06269, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29219, "top5_acc": 0.54031, "loss_cls": 4.05345, "loss": 4.05345, "time": 0.864} +{"mode": "train", "epoch": 63, "iter": 2900, "lr": 0.06266, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.29688, "top5_acc": 0.55281, "loss_cls": 4.00735, "loss": 4.00735, "time": 0.86602} +{"mode": "train", "epoch": 63, "iter": 3000, "lr": 0.06264, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.29531, "top5_acc": 0.55641, "loss_cls": 4.03226, "loss": 4.03226, "time": 0.86532} +{"mode": "train", "epoch": 63, "iter": 3100, "lr": 0.06261, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.2975, "top5_acc": 0.55906, "loss_cls": 3.96833, "loss": 3.96833, "time": 0.86355} +{"mode": "train", "epoch": 63, "iter": 3200, "lr": 0.06258, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.29891, "top5_acc": 0.56016, "loss_cls": 4.00866, "loss": 4.00866, "time": 0.86423} +{"mode": "train", "epoch": 63, "iter": 3300, "lr": 0.06256, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29469, "top5_acc": 0.55969, "loss_cls": 4.01151, "loss": 4.01151, "time": 0.86694} +{"mode": "train", "epoch": 63, "iter": 3400, "lr": 0.06253, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28625, "top5_acc": 0.55359, "loss_cls": 4.03505, "loss": 4.03505, "time": 0.86202} +{"mode": "train", "epoch": 63, "iter": 3500, "lr": 0.0625, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29922, "top5_acc": 0.54984, "loss_cls": 4.02037, "loss": 4.02037, "time": 0.86791} +{"mode": "train", "epoch": 63, "iter": 3600, "lr": 0.06247, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28797, "top5_acc": 0.54937, "loss_cls": 4.03023, "loss": 4.03023, "time": 0.86065} +{"mode": "train", "epoch": 63, "iter": 3700, "lr": 0.06245, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.29766, "top5_acc": 0.54672, "loss_cls": 4.0581, "loss": 4.0581, "time": 0.8632} +{"mode": "val", "epoch": 63, "iter": 309, "lr": 0.06243, "top1_acc": 0.22357, "top5_acc": 0.46675, "mean_class_accuracy": 0.22334} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.06241, "memory": 15990, "data_time": 1.5997, "top1_acc": 0.30797, "top5_acc": 0.56906, "loss_cls": 3.95103, "loss": 3.95103, "time": 2.63587} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.06238, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30047, "top5_acc": 0.56, "loss_cls": 3.97877, "loss": 3.97877, "time": 0.85332} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.06235, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30844, "top5_acc": 0.56719, "loss_cls": 3.95587, "loss": 3.95587, "time": 0.85979} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.06233, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30562, "top5_acc": 0.56375, "loss_cls": 3.96724, "loss": 3.96724, "time": 0.85169} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.0623, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30438, "top5_acc": 0.55531, "loss_cls": 3.98173, "loss": 3.98173, "time": 0.84621} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.06227, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.295, "top5_acc": 0.56016, "loss_cls": 3.98584, "loss": 3.98584, "time": 0.84713} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.06225, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30094, "top5_acc": 0.55875, "loss_cls": 3.99468, "loss": 3.99468, "time": 0.84728} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.06222, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30266, "top5_acc": 0.565, "loss_cls": 3.98071, "loss": 3.98071, "time": 0.85283} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.06219, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.30734, "top5_acc": 0.56734, "loss_cls": 3.9546, "loss": 3.9546, "time": 0.85218} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.06216, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29141, "top5_acc": 0.55141, "loss_cls": 4.03805, "loss": 4.03805, "time": 0.84437} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.06214, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30594, "top5_acc": 0.56563, "loss_cls": 3.96864, "loss": 3.96864, "time": 0.84623} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.06211, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29625, "top5_acc": 0.55469, "loss_cls": 4.01088, "loss": 4.01088, "time": 0.85476} +{"mode": "train", "epoch": 64, "iter": 1300, "lr": 0.06208, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31219, "top5_acc": 0.56578, "loss_cls": 3.95528, "loss": 3.95528, "time": 0.8567} +{"mode": "train", "epoch": 64, "iter": 1400, "lr": 0.06206, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29438, "top5_acc": 0.55312, "loss_cls": 4.02444, "loss": 4.02444, "time": 0.85791} +{"mode": "train", "epoch": 64, "iter": 1500, "lr": 0.06203, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30922, "top5_acc": 0.57094, "loss_cls": 3.93261, "loss": 3.93261, "time": 0.84894} +{"mode": "train", "epoch": 64, "iter": 1600, "lr": 0.062, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29812, "top5_acc": 0.56063, "loss_cls": 3.97223, "loss": 3.97223, "time": 0.8536} +{"mode": "train", "epoch": 64, "iter": 1700, "lr": 0.06197, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28938, "top5_acc": 0.54562, "loss_cls": 4.05466, "loss": 4.05466, "time": 0.85205} +{"mode": "train", "epoch": 64, "iter": 1800, "lr": 0.06195, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29438, "top5_acc": 0.55719, "loss_cls": 4.03513, "loss": 4.03513, "time": 0.85181} +{"mode": "train", "epoch": 64, "iter": 1900, "lr": 0.06192, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.3025, "top5_acc": 0.56719, "loss_cls": 3.99694, "loss": 3.99694, "time": 0.85647} +{"mode": "train", "epoch": 64, "iter": 2000, "lr": 0.06189, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.30078, "top5_acc": 0.56031, "loss_cls": 3.99602, "loss": 3.99602, "time": 0.8453} +{"mode": "train", "epoch": 64, "iter": 2100, "lr": 0.06187, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30156, "top5_acc": 0.56437, "loss_cls": 3.98062, "loss": 3.98062, "time": 0.84656} +{"mode": "train", "epoch": 64, "iter": 2200, "lr": 0.06184, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30719, "top5_acc": 0.56922, "loss_cls": 3.94947, "loss": 3.94947, "time": 0.83995} +{"mode": "train", "epoch": 64, "iter": 2300, "lr": 0.06181, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30312, "top5_acc": 0.56297, "loss_cls": 3.98642, "loss": 3.98642, "time": 0.84588} +{"mode": "train", "epoch": 64, "iter": 2400, "lr": 0.06178, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30375, "top5_acc": 0.55781, "loss_cls": 3.99612, "loss": 3.99612, "time": 0.84622} +{"mode": "train", "epoch": 64, "iter": 2500, "lr": 0.06176, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29047, "top5_acc": 0.54781, "loss_cls": 4.03865, "loss": 4.03865, "time": 0.84642} +{"mode": "train", "epoch": 64, "iter": 2600, "lr": 0.06173, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30344, "top5_acc": 0.56281, "loss_cls": 3.98803, "loss": 3.98803, "time": 0.84366} +{"mode": "train", "epoch": 64, "iter": 2700, "lr": 0.0617, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30609, "top5_acc": 0.56812, "loss_cls": 3.96704, "loss": 3.96704, "time": 0.84437} +{"mode": "train", "epoch": 64, "iter": 2800, "lr": 0.06168, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30078, "top5_acc": 0.56266, "loss_cls": 3.99341, "loss": 3.99341, "time": 0.84417} +{"mode": "train", "epoch": 64, "iter": 2900, "lr": 0.06165, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29234, "top5_acc": 0.55141, "loss_cls": 4.063, "loss": 4.063, "time": 0.84293} +{"mode": "train", "epoch": 64, "iter": 3000, "lr": 0.06162, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29984, "top5_acc": 0.55047, "loss_cls": 4.04563, "loss": 4.04563, "time": 0.84631} +{"mode": "train", "epoch": 64, "iter": 3100, "lr": 0.06159, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30812, "top5_acc": 0.55953, "loss_cls": 3.99206, "loss": 3.99206, "time": 0.8447} +{"mode": "train", "epoch": 64, "iter": 3200, "lr": 0.06157, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28719, "top5_acc": 0.55656, "loss_cls": 4.06285, "loss": 4.06285, "time": 0.84565} +{"mode": "train", "epoch": 64, "iter": 3300, "lr": 0.06154, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30203, "top5_acc": 0.56031, "loss_cls": 4.00523, "loss": 4.00523, "time": 0.84824} +{"mode": "train", "epoch": 64, "iter": 3400, "lr": 0.06151, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30062, "top5_acc": 0.55672, "loss_cls": 4.00897, "loss": 4.00897, "time": 0.85516} +{"mode": "train", "epoch": 64, "iter": 3500, "lr": 0.06148, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29094, "top5_acc": 0.55203, "loss_cls": 4.02173, "loss": 4.02173, "time": 0.84939} +{"mode": "train", "epoch": 64, "iter": 3600, "lr": 0.06146, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30859, "top5_acc": 0.56094, "loss_cls": 3.97907, "loss": 3.97907, "time": 0.84574} +{"mode": "train", "epoch": 64, "iter": 3700, "lr": 0.06143, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29375, "top5_acc": 0.55594, "loss_cls": 4.02206, "loss": 4.02206, "time": 0.85323} +{"mode": "val", "epoch": 64, "iter": 309, "lr": 0.06142, "top1_acc": 0.21324, "top5_acc": 0.45631, "mean_class_accuracy": 0.21304} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.06139, "memory": 15990, "data_time": 1.48079, "top1_acc": 0.30031, "top5_acc": 0.56188, "loss_cls": 3.9509, "loss": 3.9509, "time": 2.51723} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.06136, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3025, "top5_acc": 0.56719, "loss_cls": 3.96374, "loss": 3.96374, "time": 0.8538} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.06134, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30406, "top5_acc": 0.56063, "loss_cls": 3.9626, "loss": 3.9626, "time": 0.85752} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.06131, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30766, "top5_acc": 0.55828, "loss_cls": 3.96604, "loss": 3.96604, "time": 0.85491} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.06128, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30688, "top5_acc": 0.57047, "loss_cls": 3.92538, "loss": 3.92538, "time": 0.84806} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.06125, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29391, "top5_acc": 0.56297, "loss_cls": 3.98732, "loss": 3.98732, "time": 0.84462} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.06123, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30125, "top5_acc": 0.56625, "loss_cls": 3.98576, "loss": 3.98576, "time": 0.84671} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0612, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30594, "top5_acc": 0.55797, "loss_cls": 3.9531, "loss": 3.9531, "time": 0.84471} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.06117, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31078, "top5_acc": 0.575, "loss_cls": 3.93401, "loss": 3.93401, "time": 0.84259} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.06115, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29906, "top5_acc": 0.55562, "loss_cls": 3.96913, "loss": 3.96913, "time": 0.84764} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.06112, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.29438, "top5_acc": 0.55906, "loss_cls": 4.00616, "loss": 4.00616, "time": 0.84656} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.06109, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29313, "top5_acc": 0.55828, "loss_cls": 4.00206, "loss": 4.00206, "time": 0.84369} +{"mode": "train", "epoch": 65, "iter": 1300, "lr": 0.06106, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30016, "top5_acc": 0.56453, "loss_cls": 3.97113, "loss": 3.97113, "time": 0.84} +{"mode": "train", "epoch": 65, "iter": 1400, "lr": 0.06104, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29188, "top5_acc": 0.55156, "loss_cls": 4.04272, "loss": 4.04272, "time": 0.84795} +{"mode": "train", "epoch": 65, "iter": 1500, "lr": 0.06101, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31891, "top5_acc": 0.56766, "loss_cls": 3.90999, "loss": 3.90999, "time": 0.8398} +{"mode": "train", "epoch": 65, "iter": 1600, "lr": 0.06098, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31062, "top5_acc": 0.56422, "loss_cls": 3.97787, "loss": 3.97787, "time": 0.84518} +{"mode": "train", "epoch": 65, "iter": 1700, "lr": 0.06095, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29563, "top5_acc": 0.55156, "loss_cls": 4.00353, "loss": 4.00353, "time": 0.85086} +{"mode": "train", "epoch": 65, "iter": 1800, "lr": 0.06093, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29484, "top5_acc": 0.56109, "loss_cls": 4.00359, "loss": 4.00359, "time": 0.84402} +{"mode": "train", "epoch": 65, "iter": 1900, "lr": 0.0609, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30547, "top5_acc": 0.55812, "loss_cls": 4.00015, "loss": 4.00015, "time": 0.84198} +{"mode": "train", "epoch": 65, "iter": 2000, "lr": 0.06087, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29641, "top5_acc": 0.56703, "loss_cls": 3.97809, "loss": 3.97809, "time": 0.84173} +{"mode": "train", "epoch": 65, "iter": 2100, "lr": 0.06085, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.29406, "top5_acc": 0.55359, "loss_cls": 3.99893, "loss": 3.99893, "time": 0.84272} +{"mode": "train", "epoch": 65, "iter": 2200, "lr": 0.06082, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29563, "top5_acc": 0.55703, "loss_cls": 3.99145, "loss": 3.99145, "time": 0.84574} +{"mode": "train", "epoch": 65, "iter": 2300, "lr": 0.06079, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.30188, "top5_acc": 0.56078, "loss_cls": 3.99874, "loss": 3.99874, "time": 0.84697} +{"mode": "train", "epoch": 65, "iter": 2400, "lr": 0.06076, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29531, "top5_acc": 0.55828, "loss_cls": 3.97723, "loss": 3.97723, "time": 0.84409} +{"mode": "train", "epoch": 65, "iter": 2500, "lr": 0.06074, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30141, "top5_acc": 0.55781, "loss_cls": 3.98627, "loss": 3.98627, "time": 0.84528} +{"mode": "train", "epoch": 65, "iter": 2600, "lr": 0.06071, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30438, "top5_acc": 0.565, "loss_cls": 3.99302, "loss": 3.99302, "time": 0.84823} +{"mode": "train", "epoch": 65, "iter": 2700, "lr": 0.06068, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29609, "top5_acc": 0.55891, "loss_cls": 3.99248, "loss": 3.99248, "time": 0.85081} +{"mode": "train", "epoch": 65, "iter": 2800, "lr": 0.06065, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29859, "top5_acc": 0.55844, "loss_cls": 4.01527, "loss": 4.01527, "time": 0.84803} +{"mode": "train", "epoch": 65, "iter": 2900, "lr": 0.06063, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29641, "top5_acc": 0.55641, "loss_cls": 4.01765, "loss": 4.01765, "time": 0.84852} +{"mode": "train", "epoch": 65, "iter": 3000, "lr": 0.0606, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30172, "top5_acc": 0.54703, "loss_cls": 4.03529, "loss": 4.03529, "time": 0.84218} +{"mode": "train", "epoch": 65, "iter": 3100, "lr": 0.06057, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30156, "top5_acc": 0.55984, "loss_cls": 3.98709, "loss": 3.98709, "time": 0.84823} +{"mode": "train", "epoch": 65, "iter": 3200, "lr": 0.06055, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30438, "top5_acc": 0.56594, "loss_cls": 3.96858, "loss": 3.96858, "time": 0.84748} +{"mode": "train", "epoch": 65, "iter": 3300, "lr": 0.06052, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31203, "top5_acc": 0.56703, "loss_cls": 3.99311, "loss": 3.99311, "time": 0.8501} +{"mode": "train", "epoch": 65, "iter": 3400, "lr": 0.06049, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29438, "top5_acc": 0.55359, "loss_cls": 4.02483, "loss": 4.02483, "time": 0.84842} +{"mode": "train", "epoch": 65, "iter": 3500, "lr": 0.06046, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29875, "top5_acc": 0.55406, "loss_cls": 4.01016, "loss": 4.01016, "time": 0.84241} +{"mode": "train", "epoch": 65, "iter": 3600, "lr": 0.06044, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30594, "top5_acc": 0.56125, "loss_cls": 3.95657, "loss": 3.95657, "time": 0.84402} +{"mode": "train", "epoch": 65, "iter": 3700, "lr": 0.06041, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30156, "top5_acc": 0.555, "loss_cls": 3.99939, "loss": 3.99939, "time": 0.8432} +{"mode": "val", "epoch": 65, "iter": 309, "lr": 0.0604, "top1_acc": 0.22261, "top5_acc": 0.46133, "mean_class_accuracy": 0.22241} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.06037, "memory": 15990, "data_time": 1.52035, "top1_acc": 0.30406, "top5_acc": 0.56812, "loss_cls": 3.92748, "loss": 3.92748, "time": 2.54424} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.06034, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30906, "top5_acc": 0.55859, "loss_cls": 3.9365, "loss": 3.9365, "time": 0.84826} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.06031, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29563, "top5_acc": 0.57047, "loss_cls": 3.94484, "loss": 3.94484, "time": 0.85246} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.06029, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30781, "top5_acc": 0.56828, "loss_cls": 3.95268, "loss": 3.95268, "time": 0.85254} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.06026, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30688, "top5_acc": 0.56766, "loss_cls": 3.93412, "loss": 3.93412, "time": 0.84767} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.06023, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.30375, "top5_acc": 0.55516, "loss_cls": 4.00558, "loss": 4.00558, "time": 0.84438} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.0602, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30594, "top5_acc": 0.56891, "loss_cls": 3.93746, "loss": 3.93746, "time": 0.84699} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.06018, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29953, "top5_acc": 0.56375, "loss_cls": 3.97817, "loss": 3.97817, "time": 0.84326} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.06015, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.29875, "top5_acc": 0.56266, "loss_cls": 3.99304, "loss": 3.99304, "time": 0.84359} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.06012, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29625, "top5_acc": 0.55656, "loss_cls": 3.99766, "loss": 3.99766, "time": 0.85119} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.06009, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30062, "top5_acc": 0.56703, "loss_cls": 3.96845, "loss": 3.96845, "time": 0.84154} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.06007, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30422, "top5_acc": 0.56391, "loss_cls": 3.9948, "loss": 3.9948, "time": 0.84413} +{"mode": "train", "epoch": 66, "iter": 1300, "lr": 0.06004, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30812, "top5_acc": 0.56766, "loss_cls": 3.94556, "loss": 3.94556, "time": 0.84651} +{"mode": "train", "epoch": 66, "iter": 1400, "lr": 0.06001, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30469, "top5_acc": 0.56109, "loss_cls": 3.96736, "loss": 3.96736, "time": 0.8464} +{"mode": "train", "epoch": 66, "iter": 1500, "lr": 0.05999, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29641, "top5_acc": 0.555, "loss_cls": 4.01001, "loss": 4.01001, "time": 0.84678} +{"mode": "train", "epoch": 66, "iter": 1600, "lr": 0.05996, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30625, "top5_acc": 0.56172, "loss_cls": 3.96083, "loss": 3.96083, "time": 0.84419} +{"mode": "train", "epoch": 66, "iter": 1700, "lr": 0.05993, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31359, "top5_acc": 0.57234, "loss_cls": 3.93396, "loss": 3.93396, "time": 0.84949} +{"mode": "train", "epoch": 66, "iter": 1800, "lr": 0.0599, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31219, "top5_acc": 0.57203, "loss_cls": 3.94375, "loss": 3.94375, "time": 0.8494} +{"mode": "train", "epoch": 66, "iter": 1900, "lr": 0.05988, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29469, "top5_acc": 0.55844, "loss_cls": 4.02141, "loss": 4.02141, "time": 0.84783} +{"mode": "train", "epoch": 66, "iter": 2000, "lr": 0.05985, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30484, "top5_acc": 0.5675, "loss_cls": 3.96221, "loss": 3.96221, "time": 0.84563} +{"mode": "train", "epoch": 66, "iter": 2100, "lr": 0.05982, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.29063, "top5_acc": 0.55547, "loss_cls": 4.02165, "loss": 4.02165, "time": 0.84956} +{"mode": "train", "epoch": 66, "iter": 2200, "lr": 0.05979, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30594, "top5_acc": 0.56734, "loss_cls": 3.94092, "loss": 3.94092, "time": 0.84999} +{"mode": "train", "epoch": 66, "iter": 2300, "lr": 0.05977, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.30047, "top5_acc": 0.55703, "loss_cls": 4.00638, "loss": 4.00638, "time": 0.84612} +{"mode": "train", "epoch": 66, "iter": 2400, "lr": 0.05974, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30641, "top5_acc": 0.56766, "loss_cls": 3.9519, "loss": 3.9519, "time": 0.84808} +{"mode": "train", "epoch": 66, "iter": 2500, "lr": 0.05971, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29891, "top5_acc": 0.56297, "loss_cls": 3.9771, "loss": 3.9771, "time": 0.84451} +{"mode": "train", "epoch": 66, "iter": 2600, "lr": 0.05968, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30906, "top5_acc": 0.55812, "loss_cls": 3.98669, "loss": 3.98669, "time": 0.84582} +{"mode": "train", "epoch": 66, "iter": 2700, "lr": 0.05966, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30828, "top5_acc": 0.57234, "loss_cls": 3.9274, "loss": 3.9274, "time": 0.84849} +{"mode": "train", "epoch": 66, "iter": 2800, "lr": 0.05963, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30172, "top5_acc": 0.56109, "loss_cls": 3.98116, "loss": 3.98116, "time": 0.84484} +{"mode": "train", "epoch": 66, "iter": 2900, "lr": 0.0596, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30766, "top5_acc": 0.56406, "loss_cls": 3.97037, "loss": 3.97037, "time": 0.84573} +{"mode": "train", "epoch": 66, "iter": 3000, "lr": 0.05957, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30219, "top5_acc": 0.56359, "loss_cls": 3.96267, "loss": 3.96267, "time": 0.84782} +{"mode": "train", "epoch": 66, "iter": 3100, "lr": 0.05955, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29938, "top5_acc": 0.56031, "loss_cls": 3.97505, "loss": 3.97505, "time": 0.84738} +{"mode": "train", "epoch": 66, "iter": 3200, "lr": 0.05952, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29063, "top5_acc": 0.55297, "loss_cls": 4.01226, "loss": 4.01226, "time": 0.84686} +{"mode": "train", "epoch": 66, "iter": 3300, "lr": 0.05949, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30703, "top5_acc": 0.57641, "loss_cls": 3.92322, "loss": 3.92322, "time": 0.84298} +{"mode": "train", "epoch": 66, "iter": 3400, "lr": 0.05946, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29328, "top5_acc": 0.55203, "loss_cls": 4.01801, "loss": 4.01801, "time": 0.83784} +{"mode": "train", "epoch": 66, "iter": 3500, "lr": 0.05944, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30594, "top5_acc": 0.55672, "loss_cls": 3.9981, "loss": 3.9981, "time": 0.8443} +{"mode": "train", "epoch": 66, "iter": 3600, "lr": 0.05941, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30688, "top5_acc": 0.56, "loss_cls": 3.96967, "loss": 3.96967, "time": 0.84292} +{"mode": "train", "epoch": 66, "iter": 3700, "lr": 0.05938, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29, "top5_acc": 0.56141, "loss_cls": 4.00835, "loss": 4.00835, "time": 0.84451} +{"mode": "val", "epoch": 66, "iter": 309, "lr": 0.05937, "top1_acc": 0.23269, "top5_acc": 0.47267, "mean_class_accuracy": 0.23252} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.05934, "memory": 15990, "data_time": 1.57032, "top1_acc": 0.31312, "top5_acc": 0.57359, "loss_cls": 3.93523, "loss": 3.93523, "time": 2.60258} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.05931, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30734, "top5_acc": 0.57297, "loss_cls": 3.9321, "loss": 3.9321, "time": 0.85058} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.05929, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30469, "top5_acc": 0.56484, "loss_cls": 3.96708, "loss": 3.96708, "time": 0.86301} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.05926, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31391, "top5_acc": 0.57922, "loss_cls": 3.91506, "loss": 3.91506, "time": 0.85756} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.05923, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31141, "top5_acc": 0.56812, "loss_cls": 3.94966, "loss": 3.94966, "time": 0.85639} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.0592, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30953, "top5_acc": 0.56891, "loss_cls": 3.94416, "loss": 3.94416, "time": 0.8501} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.05918, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30328, "top5_acc": 0.56266, "loss_cls": 3.96897, "loss": 3.96897, "time": 0.8532} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.05915, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31719, "top5_acc": 0.57594, "loss_cls": 3.90107, "loss": 3.90107, "time": 0.85493} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.05912, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30281, "top5_acc": 0.56031, "loss_cls": 3.99623, "loss": 3.99623, "time": 0.85279} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.05909, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.31266, "top5_acc": 0.56641, "loss_cls": 3.91578, "loss": 3.91578, "time": 0.8565} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.05907, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30031, "top5_acc": 0.56844, "loss_cls": 3.95268, "loss": 3.95268, "time": 0.84473} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.05904, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30469, "top5_acc": 0.57094, "loss_cls": 3.97309, "loss": 3.97309, "time": 0.84793} +{"mode": "train", "epoch": 67, "iter": 1300, "lr": 0.05901, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30531, "top5_acc": 0.56484, "loss_cls": 3.94244, "loss": 3.94244, "time": 0.84072} +{"mode": "train", "epoch": 67, "iter": 1400, "lr": 0.05898, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3, "top5_acc": 0.55953, "loss_cls": 4.00039, "loss": 4.00039, "time": 0.84783} +{"mode": "train", "epoch": 67, "iter": 1500, "lr": 0.05896, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30359, "top5_acc": 0.55953, "loss_cls": 3.97887, "loss": 3.97887, "time": 0.85253} +{"mode": "train", "epoch": 67, "iter": 1600, "lr": 0.05893, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30438, "top5_acc": 0.56375, "loss_cls": 3.97447, "loss": 3.97447, "time": 0.84687} +{"mode": "train", "epoch": 67, "iter": 1700, "lr": 0.0589, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30641, "top5_acc": 0.56484, "loss_cls": 3.9215, "loss": 3.9215, "time": 0.84938} +{"mode": "train", "epoch": 67, "iter": 1800, "lr": 0.05887, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30484, "top5_acc": 0.55531, "loss_cls": 3.95746, "loss": 3.95746, "time": 0.84701} +{"mode": "train", "epoch": 67, "iter": 1900, "lr": 0.05885, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30109, "top5_acc": 0.56547, "loss_cls": 3.97727, "loss": 3.97727, "time": 0.85172} +{"mode": "train", "epoch": 67, "iter": 2000, "lr": 0.05882, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30391, "top5_acc": 0.56047, "loss_cls": 3.96985, "loss": 3.96985, "time": 0.84898} +{"mode": "train", "epoch": 67, "iter": 2100, "lr": 0.05879, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29781, "top5_acc": 0.55594, "loss_cls": 4.01323, "loss": 4.01323, "time": 0.84507} +{"mode": "train", "epoch": 67, "iter": 2200, "lr": 0.05876, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30891, "top5_acc": 0.56875, "loss_cls": 3.95319, "loss": 3.95319, "time": 0.84778} +{"mode": "train", "epoch": 67, "iter": 2300, "lr": 0.05874, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30516, "top5_acc": 0.55703, "loss_cls": 3.9656, "loss": 3.9656, "time": 0.8438} +{"mode": "train", "epoch": 67, "iter": 2400, "lr": 0.05871, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31406, "top5_acc": 0.57516, "loss_cls": 3.92706, "loss": 3.92706, "time": 0.84156} +{"mode": "train", "epoch": 67, "iter": 2500, "lr": 0.05868, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29484, "top5_acc": 0.55688, "loss_cls": 3.99337, "loss": 3.99337, "time": 0.84259} +{"mode": "train", "epoch": 67, "iter": 2600, "lr": 0.05865, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.30234, "top5_acc": 0.56094, "loss_cls": 3.95688, "loss": 3.95688, "time": 0.84495} +{"mode": "train", "epoch": 67, "iter": 2700, "lr": 0.05863, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30047, "top5_acc": 0.56594, "loss_cls": 3.97661, "loss": 3.97661, "time": 0.84439} +{"mode": "train", "epoch": 67, "iter": 2800, "lr": 0.0586, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28766, "top5_acc": 0.55078, "loss_cls": 4.04042, "loss": 4.04042, "time": 0.84597} +{"mode": "train", "epoch": 67, "iter": 2900, "lr": 0.05857, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31125, "top5_acc": 0.56766, "loss_cls": 3.92684, "loss": 3.92684, "time": 0.8537} +{"mode": "train", "epoch": 67, "iter": 3000, "lr": 0.05854, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31375, "top5_acc": 0.57328, "loss_cls": 3.90997, "loss": 3.90997, "time": 0.85152} +{"mode": "train", "epoch": 67, "iter": 3100, "lr": 0.05852, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30812, "top5_acc": 0.56047, "loss_cls": 3.98751, "loss": 3.98751, "time": 0.85205} +{"mode": "train", "epoch": 67, "iter": 3200, "lr": 0.05849, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29859, "top5_acc": 0.56266, "loss_cls": 4.01409, "loss": 4.01409, "time": 0.85706} +{"mode": "train", "epoch": 67, "iter": 3300, "lr": 0.05846, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29953, "top5_acc": 0.56328, "loss_cls": 3.96976, "loss": 3.96976, "time": 0.85721} +{"mode": "train", "epoch": 67, "iter": 3400, "lr": 0.05843, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31172, "top5_acc": 0.57063, "loss_cls": 3.92308, "loss": 3.92308, "time": 0.85632} +{"mode": "train", "epoch": 67, "iter": 3500, "lr": 0.05841, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3025, "top5_acc": 0.555, "loss_cls": 3.99223, "loss": 3.99223, "time": 0.86044} +{"mode": "train", "epoch": 67, "iter": 3600, "lr": 0.05838, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31375, "top5_acc": 0.57234, "loss_cls": 3.92754, "loss": 3.92754, "time": 0.86075} +{"mode": "train", "epoch": 67, "iter": 3700, "lr": 0.05835, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29984, "top5_acc": 0.56906, "loss_cls": 3.98368, "loss": 3.98368, "time": 0.85616} +{"mode": "val", "epoch": 67, "iter": 309, "lr": 0.05834, "top1_acc": 0.25098, "top5_acc": 0.49116, "mean_class_accuracy": 0.25085} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.05831, "memory": 15990, "data_time": 1.52434, "top1_acc": 0.31719, "top5_acc": 0.57563, "loss_cls": 3.88437, "loss": 3.88437, "time": 2.55565} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.05828, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.30766, "top5_acc": 0.57188, "loss_cls": 3.91337, "loss": 3.91337, "time": 0.85945} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.05826, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30516, "top5_acc": 0.57156, "loss_cls": 3.93377, "loss": 3.93377, "time": 0.85581} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.05823, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29844, "top5_acc": 0.57797, "loss_cls": 3.9089, "loss": 3.9089, "time": 0.85589} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.0582, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31188, "top5_acc": 0.57422, "loss_cls": 3.92236, "loss": 3.92236, "time": 0.85251} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.05817, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30891, "top5_acc": 0.56422, "loss_cls": 3.96064, "loss": 3.96064, "time": 0.84957} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.05815, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.30562, "top5_acc": 0.56266, "loss_cls": 3.94354, "loss": 3.94354, "time": 0.85008} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.05812, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30203, "top5_acc": 0.56922, "loss_cls": 3.9541, "loss": 3.9541, "time": 0.85526} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.05809, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.305, "top5_acc": 0.56391, "loss_cls": 3.96226, "loss": 3.96226, "time": 0.85035} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.05806, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.30438, "top5_acc": 0.56812, "loss_cls": 3.92987, "loss": 3.92987, "time": 0.85145} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.05804, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30359, "top5_acc": 0.56766, "loss_cls": 3.97426, "loss": 3.97426, "time": 0.84515} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.05801, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29859, "top5_acc": 0.56094, "loss_cls": 3.97781, "loss": 3.97781, "time": 0.84705} +{"mode": "train", "epoch": 68, "iter": 1300, "lr": 0.05798, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30609, "top5_acc": 0.56078, "loss_cls": 3.94292, "loss": 3.94292, "time": 0.84495} +{"mode": "train", "epoch": 68, "iter": 1400, "lr": 0.05795, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30281, "top5_acc": 0.56766, "loss_cls": 3.94875, "loss": 3.94875, "time": 0.84903} +{"mode": "train", "epoch": 68, "iter": 1500, "lr": 0.05792, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31031, "top5_acc": 0.56672, "loss_cls": 3.94982, "loss": 3.94982, "time": 0.84536} +{"mode": "train", "epoch": 68, "iter": 1600, "lr": 0.0579, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30328, "top5_acc": 0.56453, "loss_cls": 3.99163, "loss": 3.99163, "time": 0.84614} +{"mode": "train", "epoch": 68, "iter": 1700, "lr": 0.05787, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30797, "top5_acc": 0.56672, "loss_cls": 3.95741, "loss": 3.95741, "time": 0.84472} +{"mode": "train", "epoch": 68, "iter": 1800, "lr": 0.05784, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31453, "top5_acc": 0.56609, "loss_cls": 3.94974, "loss": 3.94974, "time": 0.84357} +{"mode": "train", "epoch": 68, "iter": 1900, "lr": 0.05781, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31188, "top5_acc": 0.57516, "loss_cls": 3.94743, "loss": 3.94743, "time": 0.85049} +{"mode": "train", "epoch": 68, "iter": 2000, "lr": 0.05779, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30375, "top5_acc": 0.56812, "loss_cls": 3.96843, "loss": 3.96843, "time": 0.84536} +{"mode": "train", "epoch": 68, "iter": 2100, "lr": 0.05776, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30016, "top5_acc": 0.56016, "loss_cls": 3.98618, "loss": 3.98618, "time": 0.85034} +{"mode": "train", "epoch": 68, "iter": 2200, "lr": 0.05773, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30578, "top5_acc": 0.55781, "loss_cls": 3.96776, "loss": 3.96776, "time": 0.85358} +{"mode": "train", "epoch": 68, "iter": 2300, "lr": 0.0577, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.30672, "top5_acc": 0.56203, "loss_cls": 3.96634, "loss": 3.96634, "time": 0.84703} +{"mode": "train", "epoch": 68, "iter": 2400, "lr": 0.05768, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.30031, "top5_acc": 0.56406, "loss_cls": 3.95394, "loss": 3.95394, "time": 0.84458} +{"mode": "train", "epoch": 68, "iter": 2500, "lr": 0.05765, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30969, "top5_acc": 0.57812, "loss_cls": 3.89703, "loss": 3.89703, "time": 0.85212} +{"mode": "train", "epoch": 68, "iter": 2600, "lr": 0.05762, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30594, "top5_acc": 0.56688, "loss_cls": 3.95211, "loss": 3.95211, "time": 0.84883} +{"mode": "train", "epoch": 68, "iter": 2700, "lr": 0.05759, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30641, "top5_acc": 0.55797, "loss_cls": 3.95903, "loss": 3.95903, "time": 0.84727} +{"mode": "train", "epoch": 68, "iter": 2800, "lr": 0.05757, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30516, "top5_acc": 0.56453, "loss_cls": 3.97544, "loss": 3.97544, "time": 0.85101} +{"mode": "train", "epoch": 68, "iter": 2900, "lr": 0.05754, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30453, "top5_acc": 0.56656, "loss_cls": 3.97206, "loss": 3.97206, "time": 0.85252} +{"mode": "train", "epoch": 68, "iter": 3000, "lr": 0.05751, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30594, "top5_acc": 0.57266, "loss_cls": 3.94248, "loss": 3.94248, "time": 0.84357} +{"mode": "train", "epoch": 68, "iter": 3100, "lr": 0.05748, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29953, "top5_acc": 0.56781, "loss_cls": 3.97311, "loss": 3.97311, "time": 0.85085} +{"mode": "train", "epoch": 68, "iter": 3200, "lr": 0.05746, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30516, "top5_acc": 0.56766, "loss_cls": 3.93824, "loss": 3.93824, "time": 0.85111} +{"mode": "train", "epoch": 68, "iter": 3300, "lr": 0.05743, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30859, "top5_acc": 0.56859, "loss_cls": 3.92322, "loss": 3.92322, "time": 0.84974} +{"mode": "train", "epoch": 68, "iter": 3400, "lr": 0.0574, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29766, "top5_acc": 0.55656, "loss_cls": 3.98323, "loss": 3.98323, "time": 0.84927} +{"mode": "train", "epoch": 68, "iter": 3500, "lr": 0.05737, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30906, "top5_acc": 0.56219, "loss_cls": 3.95906, "loss": 3.95906, "time": 0.84989} +{"mode": "train", "epoch": 68, "iter": 3600, "lr": 0.05734, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31688, "top5_acc": 0.57453, "loss_cls": 3.94243, "loss": 3.94243, "time": 0.85436} +{"mode": "train", "epoch": 68, "iter": 3700, "lr": 0.05732, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30562, "top5_acc": 0.56, "loss_cls": 3.95782, "loss": 3.95782, "time": 0.85075} +{"mode": "val", "epoch": 68, "iter": 309, "lr": 0.0573, "top1_acc": 0.25234, "top5_acc": 0.50241, "mean_class_accuracy": 0.25229} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.05728, "memory": 15990, "data_time": 1.5357, "top1_acc": 0.32062, "top5_acc": 0.58797, "loss_cls": 3.87114, "loss": 3.87114, "time": 2.5689} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.05725, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31078, "top5_acc": 0.57078, "loss_cls": 3.92518, "loss": 3.92518, "time": 0.85865} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.05722, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30406, "top5_acc": 0.56234, "loss_cls": 3.95006, "loss": 3.95006, "time": 0.85916} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.05719, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31344, "top5_acc": 0.57063, "loss_cls": 3.91924, "loss": 3.91924, "time": 0.85674} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.05717, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30234, "top5_acc": 0.55562, "loss_cls": 3.98395, "loss": 3.98395, "time": 0.8553} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.05714, "memory": 15990, "data_time": 0.00071, "top1_acc": 0.31312, "top5_acc": 0.56828, "loss_cls": 3.93345, "loss": 3.93345, "time": 0.84741} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.05711, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30828, "top5_acc": 0.57375, "loss_cls": 3.93876, "loss": 3.93876, "time": 0.85555} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.05708, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30875, "top5_acc": 0.57438, "loss_cls": 3.9495, "loss": 3.9495, "time": 0.85119} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.05706, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.30656, "top5_acc": 0.56391, "loss_cls": 3.93623, "loss": 3.93623, "time": 0.84279} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.05703, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30859, "top5_acc": 0.56922, "loss_cls": 3.95668, "loss": 3.95668, "time": 0.84543} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.057, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31719, "top5_acc": 0.58281, "loss_cls": 3.86947, "loss": 3.86947, "time": 0.84489} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.05697, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.30547, "top5_acc": 0.56203, "loss_cls": 3.9484, "loss": 3.9484, "time": 0.85258} +{"mode": "train", "epoch": 69, "iter": 1300, "lr": 0.05694, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30438, "top5_acc": 0.56563, "loss_cls": 3.96998, "loss": 3.96998, "time": 0.84712} +{"mode": "train", "epoch": 69, "iter": 1400, "lr": 0.05692, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.31281, "top5_acc": 0.57812, "loss_cls": 3.89303, "loss": 3.89303, "time": 0.85181} +{"mode": "train", "epoch": 69, "iter": 1500, "lr": 0.05689, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30594, "top5_acc": 0.57359, "loss_cls": 3.94603, "loss": 3.94603, "time": 0.85033} +{"mode": "train", "epoch": 69, "iter": 1600, "lr": 0.05686, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30719, "top5_acc": 0.56781, "loss_cls": 3.9075, "loss": 3.9075, "time": 0.84886} +{"mode": "train", "epoch": 69, "iter": 1700, "lr": 0.05683, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29859, "top5_acc": 0.56453, "loss_cls": 3.96458, "loss": 3.96458, "time": 0.85103} +{"mode": "train", "epoch": 69, "iter": 1800, "lr": 0.05681, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.31344, "top5_acc": 0.56906, "loss_cls": 3.94939, "loss": 3.94939, "time": 0.84491} +{"mode": "train", "epoch": 69, "iter": 1900, "lr": 0.05678, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31562, "top5_acc": 0.56281, "loss_cls": 3.95883, "loss": 3.95883, "time": 0.85076} +{"mode": "train", "epoch": 69, "iter": 2000, "lr": 0.05675, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29422, "top5_acc": 0.55766, "loss_cls": 4.02285, "loss": 4.02285, "time": 0.84048} +{"mode": "train", "epoch": 69, "iter": 2100, "lr": 0.05672, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30625, "top5_acc": 0.56969, "loss_cls": 3.9165, "loss": 3.9165, "time": 0.84336} +{"mode": "train", "epoch": 69, "iter": 2200, "lr": 0.0567, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.315, "top5_acc": 0.57141, "loss_cls": 3.93367, "loss": 3.93367, "time": 0.84309} +{"mode": "train", "epoch": 69, "iter": 2300, "lr": 0.05667, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30609, "top5_acc": 0.57016, "loss_cls": 3.93583, "loss": 3.93583, "time": 0.83906} +{"mode": "train", "epoch": 69, "iter": 2400, "lr": 0.05664, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.30828, "top5_acc": 0.565, "loss_cls": 3.97742, "loss": 3.97742, "time": 0.84614} +{"mode": "train", "epoch": 69, "iter": 2500, "lr": 0.05661, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30969, "top5_acc": 0.57031, "loss_cls": 3.95718, "loss": 3.95718, "time": 0.84006} +{"mode": "train", "epoch": 69, "iter": 2600, "lr": 0.05658, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31641, "top5_acc": 0.56578, "loss_cls": 3.95784, "loss": 3.95784, "time": 0.84665} +{"mode": "train", "epoch": 69, "iter": 2700, "lr": 0.05656, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30188, "top5_acc": 0.56609, "loss_cls": 3.94819, "loss": 3.94819, "time": 0.84597} +{"mode": "train", "epoch": 69, "iter": 2800, "lr": 0.05653, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30703, "top5_acc": 0.57203, "loss_cls": 3.94865, "loss": 3.94865, "time": 0.84503} +{"mode": "train", "epoch": 69, "iter": 2900, "lr": 0.0565, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30297, "top5_acc": 0.56344, "loss_cls": 3.97812, "loss": 3.97812, "time": 0.84369} +{"mode": "train", "epoch": 69, "iter": 3000, "lr": 0.05647, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31594, "top5_acc": 0.57375, "loss_cls": 3.93514, "loss": 3.93514, "time": 0.84354} +{"mode": "train", "epoch": 69, "iter": 3100, "lr": 0.05645, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31078, "top5_acc": 0.56688, "loss_cls": 3.95792, "loss": 3.95792, "time": 0.84524} +{"mode": "train", "epoch": 69, "iter": 3200, "lr": 0.05642, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30453, "top5_acc": 0.56141, "loss_cls": 3.9605, "loss": 3.9605, "time": 0.84439} +{"mode": "train", "epoch": 69, "iter": 3300, "lr": 0.05639, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31047, "top5_acc": 0.57312, "loss_cls": 3.9369, "loss": 3.9369, "time": 0.84364} +{"mode": "train", "epoch": 69, "iter": 3400, "lr": 0.05636, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31078, "top5_acc": 0.56625, "loss_cls": 3.96533, "loss": 3.96533, "time": 0.84535} +{"mode": "train", "epoch": 69, "iter": 3500, "lr": 0.05634, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.3125, "top5_acc": 0.56844, "loss_cls": 3.93814, "loss": 3.93814, "time": 0.8448} +{"mode": "train", "epoch": 69, "iter": 3600, "lr": 0.05631, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30844, "top5_acc": 0.57375, "loss_cls": 3.89843, "loss": 3.89843, "time": 0.84218} +{"mode": "train", "epoch": 69, "iter": 3700, "lr": 0.05628, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31219, "top5_acc": 0.57312, "loss_cls": 3.91694, "loss": 3.91694, "time": 0.8461} +{"mode": "val", "epoch": 69, "iter": 309, "lr": 0.05627, "top1_acc": 0.21901, "top5_acc": 0.45723, "mean_class_accuracy": 0.2189} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.05624, "memory": 15990, "data_time": 1.50971, "top1_acc": 0.31391, "top5_acc": 0.58359, "loss_cls": 3.88456, "loss": 3.88456, "time": 2.54149} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.05621, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30812, "top5_acc": 0.57453, "loss_cls": 3.91838, "loss": 3.91838, "time": 0.85106} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.05618, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31531, "top5_acc": 0.57688, "loss_cls": 3.90177, "loss": 3.90177, "time": 0.84774} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.05616, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31734, "top5_acc": 0.58438, "loss_cls": 3.86194, "loss": 3.86194, "time": 0.84793} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.05613, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31453, "top5_acc": 0.57266, "loss_cls": 3.93801, "loss": 3.93801, "time": 0.85054} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.0561, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.31844, "top5_acc": 0.57047, "loss_cls": 3.90513, "loss": 3.90513, "time": 0.84842} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.05607, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30656, "top5_acc": 0.56688, "loss_cls": 3.9237, "loss": 3.9237, "time": 0.84773} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.05605, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31328, "top5_acc": 0.57281, "loss_cls": 3.95619, "loss": 3.95619, "time": 0.84767} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.05602, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.31359, "top5_acc": 0.56875, "loss_cls": 3.92571, "loss": 3.92571, "time": 0.84351} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.05599, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30797, "top5_acc": 0.56953, "loss_cls": 3.94527, "loss": 3.94527, "time": 0.8453} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.05596, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29969, "top5_acc": 0.56328, "loss_cls": 3.97159, "loss": 3.97159, "time": 0.84218} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.05593, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31078, "top5_acc": 0.57703, "loss_cls": 3.91916, "loss": 3.91916, "time": 0.84973} +{"mode": "train", "epoch": 70, "iter": 1300, "lr": 0.05591, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30391, "top5_acc": 0.56, "loss_cls": 3.97598, "loss": 3.97598, "time": 0.85015} +{"mode": "train", "epoch": 70, "iter": 1400, "lr": 0.05588, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31703, "top5_acc": 0.58406, "loss_cls": 3.89188, "loss": 3.89188, "time": 0.84517} +{"mode": "train", "epoch": 70, "iter": 1500, "lr": 0.05585, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31297, "top5_acc": 0.57766, "loss_cls": 3.9087, "loss": 3.9087, "time": 0.84138} +{"mode": "train", "epoch": 70, "iter": 1600, "lr": 0.05582, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31, "top5_acc": 0.56422, "loss_cls": 3.9381, "loss": 3.9381, "time": 0.84413} +{"mode": "train", "epoch": 70, "iter": 1700, "lr": 0.0558, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31812, "top5_acc": 0.58156, "loss_cls": 3.88309, "loss": 3.88309, "time": 0.84514} +{"mode": "train", "epoch": 70, "iter": 1800, "lr": 0.05577, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31328, "top5_acc": 0.57578, "loss_cls": 3.92038, "loss": 3.92038, "time": 0.84304} +{"mode": "train", "epoch": 70, "iter": 1900, "lr": 0.05574, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31516, "top5_acc": 0.58062, "loss_cls": 3.90083, "loss": 3.90083, "time": 0.84583} +{"mode": "train", "epoch": 70, "iter": 2000, "lr": 0.05571, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30734, "top5_acc": 0.56281, "loss_cls": 3.96927, "loss": 3.96927, "time": 0.84782} +{"mode": "train", "epoch": 70, "iter": 2100, "lr": 0.05568, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30453, "top5_acc": 0.56922, "loss_cls": 3.9394, "loss": 3.9394, "time": 0.84328} +{"mode": "train", "epoch": 70, "iter": 2200, "lr": 0.05566, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31094, "top5_acc": 0.56594, "loss_cls": 3.91483, "loss": 3.91483, "time": 0.84272} +{"mode": "train", "epoch": 70, "iter": 2300, "lr": 0.05563, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32078, "top5_acc": 0.58594, "loss_cls": 3.89548, "loss": 3.89548, "time": 0.83841} +{"mode": "train", "epoch": 70, "iter": 2400, "lr": 0.0556, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30609, "top5_acc": 0.56828, "loss_cls": 3.95093, "loss": 3.95093, "time": 0.84194} +{"mode": "train", "epoch": 70, "iter": 2500, "lr": 0.05557, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.30844, "top5_acc": 0.56812, "loss_cls": 3.93786, "loss": 3.93786, "time": 0.84396} +{"mode": "train", "epoch": 70, "iter": 2600, "lr": 0.05555, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30016, "top5_acc": 0.56297, "loss_cls": 3.96228, "loss": 3.96228, "time": 0.84501} +{"mode": "train", "epoch": 70, "iter": 2700, "lr": 0.05552, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.305, "top5_acc": 0.56281, "loss_cls": 3.96232, "loss": 3.96232, "time": 0.84641} +{"mode": "train", "epoch": 70, "iter": 2800, "lr": 0.05549, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3125, "top5_acc": 0.57828, "loss_cls": 3.93142, "loss": 3.93142, "time": 0.85236} +{"mode": "train", "epoch": 70, "iter": 2900, "lr": 0.05546, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31797, "top5_acc": 0.57688, "loss_cls": 3.89218, "loss": 3.89218, "time": 0.85302} +{"mode": "train", "epoch": 70, "iter": 3000, "lr": 0.05543, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30609, "top5_acc": 0.55547, "loss_cls": 4.00338, "loss": 4.00338, "time": 0.85638} +{"mode": "train", "epoch": 70, "iter": 3100, "lr": 0.05541, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30547, "top5_acc": 0.56453, "loss_cls": 3.97754, "loss": 3.97754, "time": 0.85045} +{"mode": "train", "epoch": 70, "iter": 3200, "lr": 0.05538, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30078, "top5_acc": 0.56453, "loss_cls": 3.94401, "loss": 3.94401, "time": 0.85136} +{"mode": "train", "epoch": 70, "iter": 3300, "lr": 0.05535, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32109, "top5_acc": 0.57516, "loss_cls": 3.89003, "loss": 3.89003, "time": 0.85085} +{"mode": "train", "epoch": 70, "iter": 3400, "lr": 0.05532, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30844, "top5_acc": 0.56906, "loss_cls": 3.94116, "loss": 3.94116, "time": 0.84797} +{"mode": "train", "epoch": 70, "iter": 3500, "lr": 0.0553, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31281, "top5_acc": 0.57141, "loss_cls": 3.90194, "loss": 3.90194, "time": 0.84914} +{"mode": "train", "epoch": 70, "iter": 3600, "lr": 0.05527, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30812, "top5_acc": 0.56859, "loss_cls": 3.94613, "loss": 3.94613, "time": 0.84628} +{"mode": "train", "epoch": 70, "iter": 3700, "lr": 0.05524, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31953, "top5_acc": 0.57922, "loss_cls": 3.87208, "loss": 3.87208, "time": 0.84877} +{"mode": "val", "epoch": 70, "iter": 309, "lr": 0.05523, "top1_acc": 0.21648, "top5_acc": 0.45297, "mean_class_accuracy": 0.21619} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.0552, "memory": 15990, "data_time": 1.57153, "top1_acc": 0.31625, "top5_acc": 0.57797, "loss_cls": 3.89017, "loss": 3.89017, "time": 2.60984} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.05517, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31531, "top5_acc": 0.5775, "loss_cls": 3.89749, "loss": 3.89749, "time": 0.85453} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.05514, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31422, "top5_acc": 0.57594, "loss_cls": 3.9111, "loss": 3.9111, "time": 0.8559} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.05512, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.32406, "top5_acc": 0.58, "loss_cls": 3.88065, "loss": 3.88065, "time": 0.85703} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.05509, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.325, "top5_acc": 0.58312, "loss_cls": 3.87023, "loss": 3.87023, "time": 0.85968} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.05506, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.31766, "top5_acc": 0.57922, "loss_cls": 3.88306, "loss": 3.88306, "time": 0.85638} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.05503, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.30906, "top5_acc": 0.57141, "loss_cls": 3.91832, "loss": 3.91832, "time": 0.84696} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.055, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31578, "top5_acc": 0.57438, "loss_cls": 3.91022, "loss": 3.91022, "time": 0.85324} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.05498, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.31594, "top5_acc": 0.57672, "loss_cls": 3.87675, "loss": 3.87675, "time": 0.84445} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.05495, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31781, "top5_acc": 0.57891, "loss_cls": 3.91779, "loss": 3.91779, "time": 0.84995} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.05492, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.32109, "top5_acc": 0.57516, "loss_cls": 3.87021, "loss": 3.87021, "time": 0.84532} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.05489, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31125, "top5_acc": 0.58109, "loss_cls": 3.895, "loss": 3.895, "time": 0.8506} +{"mode": "train", "epoch": 71, "iter": 1300, "lr": 0.05487, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30906, "top5_acc": 0.565, "loss_cls": 3.95624, "loss": 3.95624, "time": 0.8457} +{"mode": "train", "epoch": 71, "iter": 1400, "lr": 0.05484, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29734, "top5_acc": 0.56734, "loss_cls": 3.97561, "loss": 3.97561, "time": 0.84542} +{"mode": "train", "epoch": 71, "iter": 1500, "lr": 0.05481, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29766, "top5_acc": 0.56125, "loss_cls": 3.97874, "loss": 3.97874, "time": 0.85014} +{"mode": "train", "epoch": 71, "iter": 1600, "lr": 0.05478, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31141, "top5_acc": 0.57031, "loss_cls": 3.93532, "loss": 3.93532, "time": 0.8541} +{"mode": "train", "epoch": 71, "iter": 1700, "lr": 0.05475, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31781, "top5_acc": 0.58016, "loss_cls": 3.87972, "loss": 3.87972, "time": 0.85089} +{"mode": "train", "epoch": 71, "iter": 1800, "lr": 0.05473, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31641, "top5_acc": 0.58359, "loss_cls": 3.88616, "loss": 3.88616, "time": 0.84555} +{"mode": "train", "epoch": 71, "iter": 1900, "lr": 0.0547, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31875, "top5_acc": 0.58031, "loss_cls": 3.91165, "loss": 3.91165, "time": 0.85098} +{"mode": "train", "epoch": 71, "iter": 2000, "lr": 0.05467, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31094, "top5_acc": 0.56578, "loss_cls": 3.959, "loss": 3.959, "time": 0.84849} +{"mode": "train", "epoch": 71, "iter": 2100, "lr": 0.05464, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31062, "top5_acc": 0.57203, "loss_cls": 3.90645, "loss": 3.90645, "time": 0.85084} +{"mode": "train", "epoch": 71, "iter": 2200, "lr": 0.05461, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31156, "top5_acc": 0.56141, "loss_cls": 3.94524, "loss": 3.94524, "time": 0.85451} +{"mode": "train", "epoch": 71, "iter": 2300, "lr": 0.05459, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.31281, "top5_acc": 0.56391, "loss_cls": 3.92803, "loss": 3.92803, "time": 0.85146} +{"mode": "train", "epoch": 71, "iter": 2400, "lr": 0.05456, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.31047, "top5_acc": 0.56563, "loss_cls": 3.93112, "loss": 3.93112, "time": 0.84396} +{"mode": "train", "epoch": 71, "iter": 2500, "lr": 0.05453, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.30953, "top5_acc": 0.57078, "loss_cls": 3.89414, "loss": 3.89414, "time": 0.84007} +{"mode": "train", "epoch": 71, "iter": 2600, "lr": 0.0545, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30969, "top5_acc": 0.56766, "loss_cls": 3.96847, "loss": 3.96847, "time": 0.84509} +{"mode": "train", "epoch": 71, "iter": 2700, "lr": 0.05448, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30906, "top5_acc": 0.57297, "loss_cls": 3.92599, "loss": 3.92599, "time": 0.84456} +{"mode": "train", "epoch": 71, "iter": 2800, "lr": 0.05445, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3075, "top5_acc": 0.56547, "loss_cls": 3.95856, "loss": 3.95856, "time": 0.84542} +{"mode": "train", "epoch": 71, "iter": 2900, "lr": 0.05442, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3125, "top5_acc": 0.57656, "loss_cls": 3.91046, "loss": 3.91046, "time": 0.84522} +{"mode": "train", "epoch": 71, "iter": 3000, "lr": 0.05439, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30922, "top5_acc": 0.5625, "loss_cls": 3.98448, "loss": 3.98448, "time": 0.84206} +{"mode": "train", "epoch": 71, "iter": 3100, "lr": 0.05436, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30953, "top5_acc": 0.57734, "loss_cls": 3.91863, "loss": 3.91863, "time": 0.84201} +{"mode": "train", "epoch": 71, "iter": 3200, "lr": 0.05434, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30828, "top5_acc": 0.57156, "loss_cls": 3.93732, "loss": 3.93732, "time": 0.84825} +{"mode": "train", "epoch": 71, "iter": 3300, "lr": 0.05431, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31359, "top5_acc": 0.57453, "loss_cls": 3.91494, "loss": 3.91494, "time": 0.84418} +{"mode": "train", "epoch": 71, "iter": 3400, "lr": 0.05428, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30984, "top5_acc": 0.55812, "loss_cls": 3.96671, "loss": 3.96671, "time": 0.846} +{"mode": "train", "epoch": 71, "iter": 3500, "lr": 0.05425, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30719, "top5_acc": 0.56703, "loss_cls": 3.94389, "loss": 3.94389, "time": 0.84419} +{"mode": "train", "epoch": 71, "iter": 3600, "lr": 0.05422, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31312, "top5_acc": 0.57031, "loss_cls": 3.9212, "loss": 3.9212, "time": 0.84675} +{"mode": "train", "epoch": 71, "iter": 3700, "lr": 0.0542, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31078, "top5_acc": 0.57484, "loss_cls": 3.92722, "loss": 3.92722, "time": 0.84612} +{"mode": "val", "epoch": 71, "iter": 309, "lr": 0.05418, "top1_acc": 0.24637, "top5_acc": 0.4866, "mean_class_accuracy": 0.24629} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.05416, "memory": 15990, "data_time": 1.50294, "top1_acc": 0.31625, "top5_acc": 0.58, "loss_cls": 3.87781, "loss": 3.87781, "time": 2.52405} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.05413, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32703, "top5_acc": 0.58812, "loss_cls": 3.81487, "loss": 3.81487, "time": 0.84777} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.0541, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31797, "top5_acc": 0.58234, "loss_cls": 3.86284, "loss": 3.86284, "time": 0.84891} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.05407, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30266, "top5_acc": 0.57109, "loss_cls": 3.90788, "loss": 3.90788, "time": 0.85104} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.05404, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31031, "top5_acc": 0.57109, "loss_cls": 3.91581, "loss": 3.91581, "time": 0.84948} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.05402, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30984, "top5_acc": 0.57188, "loss_cls": 3.93001, "loss": 3.93001, "time": 0.85178} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.05399, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.31484, "top5_acc": 0.57656, "loss_cls": 3.8909, "loss": 3.8909, "time": 0.84077} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.05396, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31281, "top5_acc": 0.57516, "loss_cls": 3.91484, "loss": 3.91484, "time": 0.8423} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.05393, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31062, "top5_acc": 0.57234, "loss_cls": 3.91291, "loss": 3.91291, "time": 0.84144} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.05391, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31375, "top5_acc": 0.57047, "loss_cls": 3.91241, "loss": 3.91241, "time": 0.84732} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.05388, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31562, "top5_acc": 0.58234, "loss_cls": 3.89735, "loss": 3.89735, "time": 0.83939} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.05385, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32406, "top5_acc": 0.58125, "loss_cls": 3.86437, "loss": 3.86437, "time": 0.84374} +{"mode": "train", "epoch": 72, "iter": 1300, "lr": 0.05382, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31391, "top5_acc": 0.56734, "loss_cls": 3.9316, "loss": 3.9316, "time": 0.84507} +{"mode": "train", "epoch": 72, "iter": 1400, "lr": 0.05379, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32062, "top5_acc": 0.57453, "loss_cls": 3.88248, "loss": 3.88248, "time": 0.84781} +{"mode": "train", "epoch": 72, "iter": 1500, "lr": 0.05377, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30094, "top5_acc": 0.56859, "loss_cls": 3.94476, "loss": 3.94476, "time": 0.85011} +{"mode": "train", "epoch": 72, "iter": 1600, "lr": 0.05374, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31641, "top5_acc": 0.58062, "loss_cls": 3.88494, "loss": 3.88494, "time": 0.84839} +{"mode": "train", "epoch": 72, "iter": 1700, "lr": 0.05371, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30406, "top5_acc": 0.56984, "loss_cls": 3.94418, "loss": 3.94418, "time": 0.84665} +{"mode": "train", "epoch": 72, "iter": 1800, "lr": 0.05368, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31516, "top5_acc": 0.57109, "loss_cls": 3.9183, "loss": 3.9183, "time": 0.84362} +{"mode": "train", "epoch": 72, "iter": 1900, "lr": 0.05365, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.3075, "top5_acc": 0.57438, "loss_cls": 3.93397, "loss": 3.93397, "time": 0.84595} +{"mode": "train", "epoch": 72, "iter": 2000, "lr": 0.05363, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31406, "top5_acc": 0.57844, "loss_cls": 3.92856, "loss": 3.92856, "time": 0.84665} +{"mode": "train", "epoch": 72, "iter": 2100, "lr": 0.0536, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30672, "top5_acc": 0.56, "loss_cls": 3.93648, "loss": 3.93648, "time": 0.84021} +{"mode": "train", "epoch": 72, "iter": 2200, "lr": 0.05357, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31672, "top5_acc": 0.57063, "loss_cls": 3.89523, "loss": 3.89523, "time": 0.84246} +{"mode": "train", "epoch": 72, "iter": 2300, "lr": 0.05354, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.31281, "top5_acc": 0.56922, "loss_cls": 3.94637, "loss": 3.94637, "time": 0.84453} +{"mode": "train", "epoch": 72, "iter": 2400, "lr": 0.05352, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31062, "top5_acc": 0.57547, "loss_cls": 3.94266, "loss": 3.94266, "time": 0.84205} +{"mode": "train", "epoch": 72, "iter": 2500, "lr": 0.05349, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.31438, "top5_acc": 0.57328, "loss_cls": 3.91981, "loss": 3.91981, "time": 0.84293} +{"mode": "train", "epoch": 72, "iter": 2600, "lr": 0.05346, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30719, "top5_acc": 0.56953, "loss_cls": 3.93763, "loss": 3.93763, "time": 0.84434} +{"mode": "train", "epoch": 72, "iter": 2700, "lr": 0.05343, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30312, "top5_acc": 0.57078, "loss_cls": 3.95373, "loss": 3.95373, "time": 0.84169} +{"mode": "train", "epoch": 72, "iter": 2800, "lr": 0.0534, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31438, "top5_acc": 0.56906, "loss_cls": 3.88857, "loss": 3.88857, "time": 0.85111} +{"mode": "train", "epoch": 72, "iter": 2900, "lr": 0.05338, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31125, "top5_acc": 0.57469, "loss_cls": 3.9319, "loss": 3.9319, "time": 0.8443} +{"mode": "train", "epoch": 72, "iter": 3000, "lr": 0.05335, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30609, "top5_acc": 0.57125, "loss_cls": 3.92918, "loss": 3.92918, "time": 0.84712} +{"mode": "train", "epoch": 72, "iter": 3100, "lr": 0.05332, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31781, "top5_acc": 0.57984, "loss_cls": 3.8871, "loss": 3.8871, "time": 0.85122} +{"mode": "train", "epoch": 72, "iter": 3200, "lr": 0.05329, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30844, "top5_acc": 0.56719, "loss_cls": 3.91411, "loss": 3.91411, "time": 0.84559} +{"mode": "train", "epoch": 72, "iter": 3300, "lr": 0.05326, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31969, "top5_acc": 0.57438, "loss_cls": 3.89107, "loss": 3.89107, "time": 0.84928} +{"mode": "train", "epoch": 72, "iter": 3400, "lr": 0.05324, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31172, "top5_acc": 0.56516, "loss_cls": 3.95651, "loss": 3.95651, "time": 0.84786} +{"mode": "train", "epoch": 72, "iter": 3500, "lr": 0.05321, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30375, "top5_acc": 0.56281, "loss_cls": 3.97025, "loss": 3.97025, "time": 0.84939} +{"mode": "train", "epoch": 72, "iter": 3600, "lr": 0.05318, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31047, "top5_acc": 0.56859, "loss_cls": 3.93797, "loss": 3.93797, "time": 0.84817} +{"mode": "train", "epoch": 72, "iter": 3700, "lr": 0.05315, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32, "top5_acc": 0.57844, "loss_cls": 3.87104, "loss": 3.87104, "time": 0.84851} +{"mode": "val", "epoch": 72, "iter": 309, "lr": 0.05314, "top1_acc": 0.25224, "top5_acc": 0.49694, "mean_class_accuracy": 0.25192} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.05311, "memory": 15990, "data_time": 1.52908, "top1_acc": 0.32719, "top5_acc": 0.57938, "loss_cls": 3.83589, "loss": 3.83589, "time": 2.56282} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.05308, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31625, "top5_acc": 0.58109, "loss_cls": 3.86017, "loss": 3.86017, "time": 0.858} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.05306, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.31125, "top5_acc": 0.57188, "loss_cls": 3.92728, "loss": 3.92728, "time": 0.85642} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.05303, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31125, "top5_acc": 0.57906, "loss_cls": 3.89525, "loss": 3.89525, "time": 0.85164} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.053, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31359, "top5_acc": 0.57578, "loss_cls": 3.91255, "loss": 3.91255, "time": 0.85865} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.05297, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31891, "top5_acc": 0.57609, "loss_cls": 3.87294, "loss": 3.87294, "time": 0.85131} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.05294, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.31297, "top5_acc": 0.57125, "loss_cls": 3.88279, "loss": 3.88279, "time": 0.84717} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.05292, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31469, "top5_acc": 0.57453, "loss_cls": 3.90016, "loss": 3.90016, "time": 0.85038} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.05289, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31297, "top5_acc": 0.58328, "loss_cls": 3.89792, "loss": 3.89792, "time": 0.85315} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.05286, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.31734, "top5_acc": 0.58328, "loss_cls": 3.89284, "loss": 3.89284, "time": 0.85035} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.05283, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30547, "top5_acc": 0.57094, "loss_cls": 3.91688, "loss": 3.91688, "time": 0.85071} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.0528, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31109, "top5_acc": 0.56563, "loss_cls": 3.91531, "loss": 3.91531, "time": 0.85048} +{"mode": "train", "epoch": 73, "iter": 1300, "lr": 0.05278, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31422, "top5_acc": 0.57922, "loss_cls": 3.8822, "loss": 3.8822, "time": 0.85237} +{"mode": "train", "epoch": 73, "iter": 1400, "lr": 0.05275, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30969, "top5_acc": 0.57328, "loss_cls": 3.90879, "loss": 3.90879, "time": 0.84863} +{"mode": "train", "epoch": 73, "iter": 1500, "lr": 0.05272, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31484, "top5_acc": 0.57844, "loss_cls": 3.90087, "loss": 3.90087, "time": 0.85343} +{"mode": "train", "epoch": 73, "iter": 1600, "lr": 0.05269, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32156, "top5_acc": 0.59469, "loss_cls": 3.83143, "loss": 3.83143, "time": 0.85361} +{"mode": "train", "epoch": 73, "iter": 1700, "lr": 0.05267, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31766, "top5_acc": 0.56641, "loss_cls": 3.91566, "loss": 3.91566, "time": 0.85115} +{"mode": "train", "epoch": 73, "iter": 1800, "lr": 0.05264, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31516, "top5_acc": 0.57578, "loss_cls": 3.89646, "loss": 3.89646, "time": 0.85551} +{"mode": "train", "epoch": 73, "iter": 1900, "lr": 0.05261, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31219, "top5_acc": 0.58766, "loss_cls": 3.89716, "loss": 3.89716, "time": 0.84731} +{"mode": "train", "epoch": 73, "iter": 2000, "lr": 0.05258, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30812, "top5_acc": 0.57891, "loss_cls": 3.88325, "loss": 3.88325, "time": 0.84961} +{"mode": "train", "epoch": 73, "iter": 2100, "lr": 0.05255, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31875, "top5_acc": 0.56484, "loss_cls": 3.91969, "loss": 3.91969, "time": 0.84879} +{"mode": "train", "epoch": 73, "iter": 2200, "lr": 0.05253, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32109, "top5_acc": 0.57594, "loss_cls": 3.86511, "loss": 3.86511, "time": 0.85432} +{"mode": "train", "epoch": 73, "iter": 2300, "lr": 0.0525, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31, "top5_acc": 0.5675, "loss_cls": 3.91932, "loss": 3.91932, "time": 0.84849} +{"mode": "train", "epoch": 73, "iter": 2400, "lr": 0.05247, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30156, "top5_acc": 0.57719, "loss_cls": 3.91515, "loss": 3.91515, "time": 0.84578} +{"mode": "train", "epoch": 73, "iter": 2500, "lr": 0.05244, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.315, "top5_acc": 0.58172, "loss_cls": 3.8926, "loss": 3.8926, "time": 0.84153} +{"mode": "train", "epoch": 73, "iter": 2600, "lr": 0.05241, "memory": 15990, "data_time": 0.00085, "top1_acc": 0.31453, "top5_acc": 0.56844, "loss_cls": 3.9444, "loss": 3.9444, "time": 0.84536} +{"mode": "train", "epoch": 73, "iter": 2700, "lr": 0.05239, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32094, "top5_acc": 0.58109, "loss_cls": 3.87298, "loss": 3.87298, "time": 0.84784} +{"mode": "train", "epoch": 73, "iter": 2800, "lr": 0.05236, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32141, "top5_acc": 0.58734, "loss_cls": 3.85, "loss": 3.85, "time": 0.85033} +{"mode": "train", "epoch": 73, "iter": 2900, "lr": 0.05233, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31234, "top5_acc": 0.57656, "loss_cls": 3.93133, "loss": 3.93133, "time": 0.84914} +{"mode": "train", "epoch": 73, "iter": 3000, "lr": 0.0523, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32141, "top5_acc": 0.57844, "loss_cls": 3.8699, "loss": 3.8699, "time": 0.8445} +{"mode": "train", "epoch": 73, "iter": 3100, "lr": 0.05227, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31781, "top5_acc": 0.57703, "loss_cls": 3.88915, "loss": 3.88915, "time": 0.85075} +{"mode": "train", "epoch": 73, "iter": 3200, "lr": 0.05225, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30984, "top5_acc": 0.56859, "loss_cls": 3.94365, "loss": 3.94365, "time": 0.84138} +{"mode": "train", "epoch": 73, "iter": 3300, "lr": 0.05222, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31719, "top5_acc": 0.57516, "loss_cls": 3.90948, "loss": 3.90948, "time": 0.84485} +{"mode": "train", "epoch": 73, "iter": 3400, "lr": 0.05219, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30734, "top5_acc": 0.57578, "loss_cls": 3.91386, "loss": 3.91386, "time": 0.84809} +{"mode": "train", "epoch": 73, "iter": 3500, "lr": 0.05216, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30812, "top5_acc": 0.57688, "loss_cls": 3.92056, "loss": 3.92056, "time": 0.84741} +{"mode": "train", "epoch": 73, "iter": 3600, "lr": 0.05213, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31609, "top5_acc": 0.57563, "loss_cls": 3.91336, "loss": 3.91336, "time": 0.84497} +{"mode": "train", "epoch": 73, "iter": 3700, "lr": 0.05211, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31219, "top5_acc": 0.56781, "loss_cls": 3.91754, "loss": 3.91754, "time": 0.8536} +{"mode": "val", "epoch": 73, "iter": 309, "lr": 0.05209, "top1_acc": 0.25016, "top5_acc": 0.50023, "mean_class_accuracy": 0.25005} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.05207, "memory": 15990, "data_time": 1.52963, "top1_acc": 0.31719, "top5_acc": 0.58766, "loss_cls": 3.86098, "loss": 3.86098, "time": 2.55546} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.05204, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31891, "top5_acc": 0.57328, "loss_cls": 3.87735, "loss": 3.87735, "time": 0.85893} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.05201, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31266, "top5_acc": 0.57906, "loss_cls": 3.89485, "loss": 3.89485, "time": 0.85702} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.05198, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.315, "top5_acc": 0.58859, "loss_cls": 3.83775, "loss": 3.83775, "time": 0.85728} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.05195, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.32484, "top5_acc": 0.58625, "loss_cls": 3.83575, "loss": 3.83575, "time": 0.85154} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.05193, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32531, "top5_acc": 0.58047, "loss_cls": 3.8788, "loss": 3.8788, "time": 0.84553} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.0519, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.325, "top5_acc": 0.58656, "loss_cls": 3.84782, "loss": 3.84782, "time": 0.85241} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.05187, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31922, "top5_acc": 0.58125, "loss_cls": 3.84345, "loss": 3.84345, "time": 0.8474} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.05184, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32297, "top5_acc": 0.57656, "loss_cls": 3.84734, "loss": 3.84734, "time": 0.84488} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.05181, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32016, "top5_acc": 0.58078, "loss_cls": 3.87085, "loss": 3.87085, "time": 0.84642} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.05179, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.32188, "top5_acc": 0.57359, "loss_cls": 3.89822, "loss": 3.89822, "time": 0.84343} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.05176, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31844, "top5_acc": 0.58297, "loss_cls": 3.87409, "loss": 3.87409, "time": 0.84334} +{"mode": "train", "epoch": 74, "iter": 1300, "lr": 0.05173, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.31297, "top5_acc": 0.59078, "loss_cls": 3.85077, "loss": 3.85077, "time": 0.84672} +{"mode": "train", "epoch": 74, "iter": 1400, "lr": 0.0517, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32641, "top5_acc": 0.58266, "loss_cls": 3.84597, "loss": 3.84597, "time": 0.84136} +{"mode": "train", "epoch": 74, "iter": 1500, "lr": 0.05168, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32188, "top5_acc": 0.58, "loss_cls": 3.87877, "loss": 3.87877, "time": 0.84604} +{"mode": "train", "epoch": 74, "iter": 1600, "lr": 0.05165, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31016, "top5_acc": 0.57047, "loss_cls": 3.94474, "loss": 3.94474, "time": 0.84094} +{"mode": "train", "epoch": 74, "iter": 1700, "lr": 0.05162, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31078, "top5_acc": 0.57797, "loss_cls": 3.92077, "loss": 3.92077, "time": 0.84328} +{"mode": "train", "epoch": 74, "iter": 1800, "lr": 0.05159, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31891, "top5_acc": 0.565, "loss_cls": 3.93922, "loss": 3.93922, "time": 0.84753} +{"mode": "train", "epoch": 74, "iter": 1900, "lr": 0.05156, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31859, "top5_acc": 0.57672, "loss_cls": 3.8998, "loss": 3.8998, "time": 0.8434} +{"mode": "train", "epoch": 74, "iter": 2000, "lr": 0.05154, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31609, "top5_acc": 0.57828, "loss_cls": 3.87361, "loss": 3.87361, "time": 0.83998} +{"mode": "train", "epoch": 74, "iter": 2100, "lr": 0.05151, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31969, "top5_acc": 0.57984, "loss_cls": 3.88112, "loss": 3.88112, "time": 0.84949} +{"mode": "train", "epoch": 74, "iter": 2200, "lr": 0.05148, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31562, "top5_acc": 0.57516, "loss_cls": 3.88307, "loss": 3.88307, "time": 0.84393} +{"mode": "train", "epoch": 74, "iter": 2300, "lr": 0.05145, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31438, "top5_acc": 0.57688, "loss_cls": 3.89654, "loss": 3.89654, "time": 0.84887} +{"mode": "train", "epoch": 74, "iter": 2400, "lr": 0.05142, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30094, "top5_acc": 0.56938, "loss_cls": 3.96716, "loss": 3.96716, "time": 0.85019} +{"mode": "train", "epoch": 74, "iter": 2500, "lr": 0.0514, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.31266, "top5_acc": 0.57641, "loss_cls": 3.90091, "loss": 3.90091, "time": 0.84548} +{"mode": "train", "epoch": 74, "iter": 2600, "lr": 0.05137, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31359, "top5_acc": 0.57719, "loss_cls": 3.89479, "loss": 3.89479, "time": 0.84103} +{"mode": "train", "epoch": 74, "iter": 2700, "lr": 0.05134, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31828, "top5_acc": 0.58078, "loss_cls": 3.85581, "loss": 3.85581, "time": 0.84207} +{"mode": "train", "epoch": 74, "iter": 2800, "lr": 0.05131, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32516, "top5_acc": 0.57891, "loss_cls": 3.86802, "loss": 3.86802, "time": 0.84258} +{"mode": "train", "epoch": 74, "iter": 2900, "lr": 0.05128, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31141, "top5_acc": 0.57719, "loss_cls": 3.89396, "loss": 3.89396, "time": 0.84715} +{"mode": "train", "epoch": 74, "iter": 3000, "lr": 0.05126, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31922, "top5_acc": 0.58812, "loss_cls": 3.84569, "loss": 3.84569, "time": 0.84391} +{"mode": "train", "epoch": 74, "iter": 3100, "lr": 0.05123, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31172, "top5_acc": 0.57531, "loss_cls": 3.91698, "loss": 3.91698, "time": 0.84367} +{"mode": "train", "epoch": 74, "iter": 3200, "lr": 0.0512, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31891, "top5_acc": 0.57875, "loss_cls": 3.87825, "loss": 3.87825, "time": 0.84368} +{"mode": "train", "epoch": 74, "iter": 3300, "lr": 0.05117, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31188, "top5_acc": 0.57563, "loss_cls": 3.8677, "loss": 3.8677, "time": 0.84045} +{"mode": "train", "epoch": 74, "iter": 3400, "lr": 0.05114, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31391, "top5_acc": 0.57609, "loss_cls": 3.94351, "loss": 3.94351, "time": 0.84302} +{"mode": "train", "epoch": 74, "iter": 3500, "lr": 0.05112, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31875, "top5_acc": 0.57375, "loss_cls": 3.89084, "loss": 3.89084, "time": 0.84522} +{"mode": "train", "epoch": 74, "iter": 3600, "lr": 0.05109, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31547, "top5_acc": 0.56469, "loss_cls": 3.92141, "loss": 3.92141, "time": 0.84404} +{"mode": "train", "epoch": 74, "iter": 3700, "lr": 0.05106, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32281, "top5_acc": 0.57297, "loss_cls": 3.89821, "loss": 3.89821, "time": 0.84341} +{"mode": "val", "epoch": 74, "iter": 309, "lr": 0.05105, "top1_acc": 0.24849, "top5_acc": 0.49947, "mean_class_accuracy": 0.24843} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.05102, "memory": 15990, "data_time": 1.48681, "top1_acc": 0.33406, "top5_acc": 0.60062, "loss_cls": 3.80233, "loss": 3.80233, "time": 2.51589} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.05099, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32781, "top5_acc": 0.59641, "loss_cls": 3.78736, "loss": 3.78736, "time": 0.84878} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.05096, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31875, "top5_acc": 0.58672, "loss_cls": 3.83706, "loss": 3.83706, "time": 0.84228} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.05094, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32125, "top5_acc": 0.59094, "loss_cls": 3.83432, "loss": 3.83432, "time": 0.84879} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.05091, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.31969, "top5_acc": 0.58578, "loss_cls": 3.86005, "loss": 3.86005, "time": 0.85082} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.05088, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32312, "top5_acc": 0.58562, "loss_cls": 3.85765, "loss": 3.85765, "time": 0.84966} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.05085, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.31453, "top5_acc": 0.57625, "loss_cls": 3.88929, "loss": 3.88929, "time": 0.84425} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.05082, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32578, "top5_acc": 0.58953, "loss_cls": 3.85497, "loss": 3.85497, "time": 0.84331} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.0508, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32656, "top5_acc": 0.58234, "loss_cls": 3.88334, "loss": 3.88334, "time": 0.84149} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.05077, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32297, "top5_acc": 0.58609, "loss_cls": 3.85291, "loss": 3.85291, "time": 0.84135} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.05074, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31969, "top5_acc": 0.57625, "loss_cls": 3.88549, "loss": 3.88549, "time": 0.84462} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.05071, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31281, "top5_acc": 0.58094, "loss_cls": 3.8667, "loss": 3.8667, "time": 0.84537} +{"mode": "train", "epoch": 75, "iter": 1300, "lr": 0.05068, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31578, "top5_acc": 0.57969, "loss_cls": 3.87312, "loss": 3.87312, "time": 0.84593} +{"mode": "train", "epoch": 75, "iter": 1400, "lr": 0.05066, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30656, "top5_acc": 0.57203, "loss_cls": 3.9066, "loss": 3.9066, "time": 0.84905} +{"mode": "train", "epoch": 75, "iter": 1500, "lr": 0.05063, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31969, "top5_acc": 0.58312, "loss_cls": 3.87232, "loss": 3.87232, "time": 0.84412} +{"mode": "train", "epoch": 75, "iter": 1600, "lr": 0.0506, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32328, "top5_acc": 0.58047, "loss_cls": 3.84176, "loss": 3.84176, "time": 0.84511} +{"mode": "train", "epoch": 75, "iter": 1700, "lr": 0.05057, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32266, "top5_acc": 0.57875, "loss_cls": 3.87416, "loss": 3.87416, "time": 0.83994} +{"mode": "train", "epoch": 75, "iter": 1800, "lr": 0.05054, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32062, "top5_acc": 0.57812, "loss_cls": 3.89514, "loss": 3.89514, "time": 0.84495} +{"mode": "train", "epoch": 75, "iter": 1900, "lr": 0.05052, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32234, "top5_acc": 0.58641, "loss_cls": 3.86114, "loss": 3.86114, "time": 0.85059} +{"mode": "train", "epoch": 75, "iter": 2000, "lr": 0.05049, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32078, "top5_acc": 0.58562, "loss_cls": 3.87536, "loss": 3.87536, "time": 0.84787} +{"mode": "train", "epoch": 75, "iter": 2100, "lr": 0.05046, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31875, "top5_acc": 0.57891, "loss_cls": 3.91483, "loss": 3.91483, "time": 0.84407} +{"mode": "train", "epoch": 75, "iter": 2200, "lr": 0.05043, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32016, "top5_acc": 0.57547, "loss_cls": 3.87501, "loss": 3.87501, "time": 0.84929} +{"mode": "train", "epoch": 75, "iter": 2300, "lr": 0.0504, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31359, "top5_acc": 0.57656, "loss_cls": 3.88427, "loss": 3.88427, "time": 0.84574} +{"mode": "train", "epoch": 75, "iter": 2400, "lr": 0.05038, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32406, "top5_acc": 0.58578, "loss_cls": 3.84974, "loss": 3.84974, "time": 0.84527} +{"mode": "train", "epoch": 75, "iter": 2500, "lr": 0.05035, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31016, "top5_acc": 0.57594, "loss_cls": 3.87767, "loss": 3.87767, "time": 0.84269} +{"mode": "train", "epoch": 75, "iter": 2600, "lr": 0.05032, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32547, "top5_acc": 0.58625, "loss_cls": 3.81213, "loss": 3.81213, "time": 0.84339} +{"mode": "train", "epoch": 75, "iter": 2700, "lr": 0.05029, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32016, "top5_acc": 0.57109, "loss_cls": 3.90247, "loss": 3.90247, "time": 0.84164} +{"mode": "train", "epoch": 75, "iter": 2800, "lr": 0.05026, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30391, "top5_acc": 0.57312, "loss_cls": 3.92817, "loss": 3.92817, "time": 0.84626} +{"mode": "train", "epoch": 75, "iter": 2900, "lr": 0.05024, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32328, "top5_acc": 0.58484, "loss_cls": 3.84105, "loss": 3.84105, "time": 0.85132} +{"mode": "train", "epoch": 75, "iter": 3000, "lr": 0.05021, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31328, "top5_acc": 0.57406, "loss_cls": 3.91475, "loss": 3.91475, "time": 0.84724} +{"mode": "train", "epoch": 75, "iter": 3100, "lr": 0.05018, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31828, "top5_acc": 0.57938, "loss_cls": 3.8987, "loss": 3.8987, "time": 0.85008} +{"mode": "train", "epoch": 75, "iter": 3200, "lr": 0.05015, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32125, "top5_acc": 0.58438, "loss_cls": 3.84182, "loss": 3.84182, "time": 0.84834} +{"mode": "train", "epoch": 75, "iter": 3300, "lr": 0.05012, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32047, "top5_acc": 0.58234, "loss_cls": 3.88441, "loss": 3.88441, "time": 0.85074} +{"mode": "train", "epoch": 75, "iter": 3400, "lr": 0.0501, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31844, "top5_acc": 0.57734, "loss_cls": 3.92189, "loss": 3.92189, "time": 0.84675} +{"mode": "train", "epoch": 75, "iter": 3500, "lr": 0.05007, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32266, "top5_acc": 0.57516, "loss_cls": 3.89572, "loss": 3.89572, "time": 0.85282} +{"mode": "train", "epoch": 75, "iter": 3600, "lr": 0.05004, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31844, "top5_acc": 0.57828, "loss_cls": 3.87459, "loss": 3.87459, "time": 0.84832} +{"mode": "train", "epoch": 75, "iter": 3700, "lr": 0.05001, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32375, "top5_acc": 0.57797, "loss_cls": 3.86854, "loss": 3.86854, "time": 0.84546} +{"mode": "val", "epoch": 75, "iter": 309, "lr": 0.05, "top1_acc": 0.24799, "top5_acc": 0.49268, "mean_class_accuracy": 0.24783} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.04997, "memory": 15990, "data_time": 1.49412, "top1_acc": 0.33359, "top5_acc": 0.6, "loss_cls": 3.75635, "loss": 3.75635, "time": 2.5233} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.04994, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33312, "top5_acc": 0.59312, "loss_cls": 3.7937, "loss": 3.7937, "time": 0.85083} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.04992, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32781, "top5_acc": 0.58672, "loss_cls": 3.80033, "loss": 3.80033, "time": 0.84881} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.04989, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32984, "top5_acc": 0.58672, "loss_cls": 3.81814, "loss": 3.81814, "time": 0.84945} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.04986, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31672, "top5_acc": 0.58, "loss_cls": 3.85683, "loss": 3.85683, "time": 0.84911} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.04983, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32953, "top5_acc": 0.59266, "loss_cls": 3.80832, "loss": 3.80832, "time": 0.84881} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.0498, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32188, "top5_acc": 0.5825, "loss_cls": 3.83737, "loss": 3.83737, "time": 0.84927} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.04978, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.31562, "top5_acc": 0.58297, "loss_cls": 3.86887, "loss": 3.86887, "time": 0.8454} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.04975, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32719, "top5_acc": 0.58094, "loss_cls": 3.85153, "loss": 3.85153, "time": 0.84661} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.04972, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31875, "top5_acc": 0.58938, "loss_cls": 3.84522, "loss": 3.84522, "time": 0.84487} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.04969, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32703, "top5_acc": 0.58906, "loss_cls": 3.82051, "loss": 3.82051, "time": 0.84422} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.04966, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32375, "top5_acc": 0.58547, "loss_cls": 3.83157, "loss": 3.83157, "time": 0.84893} +{"mode": "train", "epoch": 76, "iter": 1300, "lr": 0.04964, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30875, "top5_acc": 0.57391, "loss_cls": 3.89585, "loss": 3.89585, "time": 0.84709} +{"mode": "train", "epoch": 76, "iter": 1400, "lr": 0.04961, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32453, "top5_acc": 0.57641, "loss_cls": 3.87139, "loss": 3.87139, "time": 0.85231} +{"mode": "train", "epoch": 76, "iter": 1500, "lr": 0.04958, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32672, "top5_acc": 0.58703, "loss_cls": 3.85816, "loss": 3.85816, "time": 0.84475} +{"mode": "train", "epoch": 76, "iter": 1600, "lr": 0.04955, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31328, "top5_acc": 0.57609, "loss_cls": 3.89427, "loss": 3.89427, "time": 0.84721} +{"mode": "train", "epoch": 76, "iter": 1700, "lr": 0.04953, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32297, "top5_acc": 0.58766, "loss_cls": 3.84725, "loss": 3.84725, "time": 0.84765} +{"mode": "train", "epoch": 76, "iter": 1800, "lr": 0.0495, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32, "top5_acc": 0.58328, "loss_cls": 3.86454, "loss": 3.86454, "time": 0.84668} +{"mode": "train", "epoch": 76, "iter": 1900, "lr": 0.04947, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31375, "top5_acc": 0.57625, "loss_cls": 3.89257, "loss": 3.89257, "time": 0.84729} +{"mode": "train", "epoch": 76, "iter": 2000, "lr": 0.04944, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31891, "top5_acc": 0.58609, "loss_cls": 3.8751, "loss": 3.8751, "time": 0.84786} +{"mode": "train", "epoch": 76, "iter": 2100, "lr": 0.04941, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31391, "top5_acc": 0.57641, "loss_cls": 3.89296, "loss": 3.89296, "time": 0.84728} +{"mode": "train", "epoch": 76, "iter": 2200, "lr": 0.04939, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30906, "top5_acc": 0.57672, "loss_cls": 3.92358, "loss": 3.92358, "time": 0.84935} +{"mode": "train", "epoch": 76, "iter": 2300, "lr": 0.04936, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30734, "top5_acc": 0.56703, "loss_cls": 3.92954, "loss": 3.92954, "time": 0.85098} +{"mode": "train", "epoch": 76, "iter": 2400, "lr": 0.04933, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.32438, "top5_acc": 0.57828, "loss_cls": 3.84819, "loss": 3.84819, "time": 0.84721} +{"mode": "train", "epoch": 76, "iter": 2500, "lr": 0.0493, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32203, "top5_acc": 0.57703, "loss_cls": 3.87098, "loss": 3.87098, "time": 0.84704} +{"mode": "train", "epoch": 76, "iter": 2600, "lr": 0.04927, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32188, "top5_acc": 0.58172, "loss_cls": 3.86319, "loss": 3.86319, "time": 0.84451} +{"mode": "train", "epoch": 76, "iter": 2700, "lr": 0.04925, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31828, "top5_acc": 0.58203, "loss_cls": 3.87072, "loss": 3.87072, "time": 0.84282} +{"mode": "train", "epoch": 76, "iter": 2800, "lr": 0.04922, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33422, "top5_acc": 0.59328, "loss_cls": 3.80999, "loss": 3.80999, "time": 0.84408} +{"mode": "train", "epoch": 76, "iter": 2900, "lr": 0.04919, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32328, "top5_acc": 0.57953, "loss_cls": 3.86621, "loss": 3.86621, "time": 0.84328} +{"mode": "train", "epoch": 76, "iter": 3000, "lr": 0.04916, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.325, "top5_acc": 0.57469, "loss_cls": 3.88568, "loss": 3.88568, "time": 0.84804} +{"mode": "train", "epoch": 76, "iter": 3100, "lr": 0.04913, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30984, "top5_acc": 0.57844, "loss_cls": 3.89131, "loss": 3.89131, "time": 0.84111} +{"mode": "train", "epoch": 76, "iter": 3200, "lr": 0.04911, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32375, "top5_acc": 0.57484, "loss_cls": 3.88018, "loss": 3.88018, "time": 0.84349} +{"mode": "train", "epoch": 76, "iter": 3300, "lr": 0.04908, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32797, "top5_acc": 0.58281, "loss_cls": 3.85404, "loss": 3.85404, "time": 0.85101} +{"mode": "train", "epoch": 76, "iter": 3400, "lr": 0.04905, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31906, "top5_acc": 0.57906, "loss_cls": 3.88486, "loss": 3.88486, "time": 0.85309} +{"mode": "train", "epoch": 76, "iter": 3500, "lr": 0.04902, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.3175, "top5_acc": 0.5825, "loss_cls": 3.8639, "loss": 3.8639, "time": 0.84586} +{"mode": "train", "epoch": 76, "iter": 3600, "lr": 0.04899, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31281, "top5_acc": 0.57609, "loss_cls": 3.91542, "loss": 3.91542, "time": 0.84451} +{"mode": "train", "epoch": 76, "iter": 3700, "lr": 0.04897, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33031, "top5_acc": 0.59391, "loss_cls": 3.81175, "loss": 3.81175, "time": 0.85203} +{"mode": "val", "epoch": 76, "iter": 309, "lr": 0.04895, "top1_acc": 0.2566, "top5_acc": 0.50722, "mean_class_accuracy": 0.25652} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.04893, "memory": 15990, "data_time": 1.46366, "top1_acc": 0.32703, "top5_acc": 0.59, "loss_cls": 3.81417, "loss": 3.81417, "time": 2.48508} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0489, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31953, "top5_acc": 0.59188, "loss_cls": 3.81665, "loss": 3.81665, "time": 0.85012} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.04887, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32609, "top5_acc": 0.58297, "loss_cls": 3.83138, "loss": 3.83138, "time": 0.85086} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.04884, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32125, "top5_acc": 0.58688, "loss_cls": 3.86112, "loss": 3.86112, "time": 0.84849} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.04881, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33266, "top5_acc": 0.59594, "loss_cls": 3.76709, "loss": 3.76709, "time": 0.84748} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.04879, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32922, "top5_acc": 0.58781, "loss_cls": 3.81813, "loss": 3.81813, "time": 0.84767} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.04876, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33359, "top5_acc": 0.58984, "loss_cls": 3.78218, "loss": 3.78218, "time": 0.8432} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.04873, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.3225, "top5_acc": 0.57812, "loss_cls": 3.84843, "loss": 3.84843, "time": 0.84106} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.0487, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32797, "top5_acc": 0.59188, "loss_cls": 3.81354, "loss": 3.81354, "time": 0.84521} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.04867, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33297, "top5_acc": 0.59281, "loss_cls": 3.81364, "loss": 3.81364, "time": 0.8448} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.04865, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32703, "top5_acc": 0.58344, "loss_cls": 3.8544, "loss": 3.8544, "time": 0.84396} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.04862, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33062, "top5_acc": 0.58594, "loss_cls": 3.81963, "loss": 3.81963, "time": 0.84743} +{"mode": "train", "epoch": 77, "iter": 1300, "lr": 0.04859, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32688, "top5_acc": 0.58219, "loss_cls": 3.8627, "loss": 3.8627, "time": 0.83979} +{"mode": "train", "epoch": 77, "iter": 1400, "lr": 0.04856, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31531, "top5_acc": 0.57188, "loss_cls": 3.90273, "loss": 3.90273, "time": 0.83915} +{"mode": "train", "epoch": 77, "iter": 1500, "lr": 0.04853, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31844, "top5_acc": 0.57875, "loss_cls": 3.87809, "loss": 3.87809, "time": 0.84249} +{"mode": "train", "epoch": 77, "iter": 1600, "lr": 0.04851, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32312, "top5_acc": 0.57563, "loss_cls": 3.85127, "loss": 3.85127, "time": 0.84345} +{"mode": "train", "epoch": 77, "iter": 1700, "lr": 0.04848, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32562, "top5_acc": 0.58781, "loss_cls": 3.84753, "loss": 3.84753, "time": 0.84583} +{"mode": "train", "epoch": 77, "iter": 1800, "lr": 0.04845, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32594, "top5_acc": 0.58734, "loss_cls": 3.84998, "loss": 3.84998, "time": 0.84761} +{"mode": "train", "epoch": 77, "iter": 1900, "lr": 0.04842, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32484, "top5_acc": 0.58438, "loss_cls": 3.84388, "loss": 3.84388, "time": 0.85163} +{"mode": "train", "epoch": 77, "iter": 2000, "lr": 0.04839, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31516, "top5_acc": 0.57484, "loss_cls": 3.88909, "loss": 3.88909, "time": 0.84754} +{"mode": "train", "epoch": 77, "iter": 2100, "lr": 0.04837, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32547, "top5_acc": 0.58469, "loss_cls": 3.84686, "loss": 3.84686, "time": 0.84785} +{"mode": "train", "epoch": 77, "iter": 2200, "lr": 0.04834, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32031, "top5_acc": 0.57672, "loss_cls": 3.87717, "loss": 3.87717, "time": 0.85012} +{"mode": "train", "epoch": 77, "iter": 2300, "lr": 0.04831, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32281, "top5_acc": 0.5825, "loss_cls": 3.8321, "loss": 3.8321, "time": 0.84784} +{"mode": "train", "epoch": 77, "iter": 2400, "lr": 0.04828, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31328, "top5_acc": 0.57172, "loss_cls": 3.9058, "loss": 3.9058, "time": 0.85092} +{"mode": "train", "epoch": 77, "iter": 2500, "lr": 0.04825, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.33281, "top5_acc": 0.59234, "loss_cls": 3.81978, "loss": 3.81978, "time": 0.84744} +{"mode": "train", "epoch": 77, "iter": 2600, "lr": 0.04823, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.3125, "top5_acc": 0.58688, "loss_cls": 3.85983, "loss": 3.85983, "time": 0.84085} +{"mode": "train", "epoch": 77, "iter": 2700, "lr": 0.0482, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31812, "top5_acc": 0.58016, "loss_cls": 3.88122, "loss": 3.88122, "time": 0.84617} +{"mode": "train", "epoch": 77, "iter": 2800, "lr": 0.04817, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32203, "top5_acc": 0.59094, "loss_cls": 3.84753, "loss": 3.84753, "time": 0.84719} +{"mode": "train", "epoch": 77, "iter": 2900, "lr": 0.04814, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32812, "top5_acc": 0.58578, "loss_cls": 3.81081, "loss": 3.81081, "time": 0.84823} +{"mode": "train", "epoch": 77, "iter": 3000, "lr": 0.04811, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32344, "top5_acc": 0.58375, "loss_cls": 3.88046, "loss": 3.88046, "time": 0.84892} +{"mode": "train", "epoch": 77, "iter": 3100, "lr": 0.04809, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32, "top5_acc": 0.58688, "loss_cls": 3.86293, "loss": 3.86293, "time": 0.84888} +{"mode": "train", "epoch": 77, "iter": 3200, "lr": 0.04806, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31641, "top5_acc": 0.57984, "loss_cls": 3.87469, "loss": 3.87469, "time": 0.84779} +{"mode": "train", "epoch": 77, "iter": 3300, "lr": 0.04803, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32062, "top5_acc": 0.57891, "loss_cls": 3.86956, "loss": 3.86956, "time": 0.84431} +{"mode": "train", "epoch": 77, "iter": 3400, "lr": 0.048, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31422, "top5_acc": 0.58516, "loss_cls": 3.86044, "loss": 3.86044, "time": 0.84311} +{"mode": "train", "epoch": 77, "iter": 3500, "lr": 0.04798, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32547, "top5_acc": 0.58516, "loss_cls": 3.85718, "loss": 3.85718, "time": 0.84778} +{"mode": "train", "epoch": 77, "iter": 3600, "lr": 0.04795, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32297, "top5_acc": 0.585, "loss_cls": 3.83942, "loss": 3.83942, "time": 0.84539} +{"mode": "train", "epoch": 77, "iter": 3700, "lr": 0.04792, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30547, "top5_acc": 0.56953, "loss_cls": 3.92737, "loss": 3.92737, "time": 0.84773} +{"mode": "val", "epoch": 77, "iter": 309, "lr": 0.04791, "top1_acc": 0.24439, "top5_acc": 0.49592, "mean_class_accuracy": 0.24434} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.04788, "memory": 15990, "data_time": 1.50039, "top1_acc": 0.32328, "top5_acc": 0.59797, "loss_cls": 3.78368, "loss": 3.78368, "time": 2.51947} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.04785, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33141, "top5_acc": 0.59828, "loss_cls": 3.7602, "loss": 3.7602, "time": 0.84455} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.04782, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32578, "top5_acc": 0.60469, "loss_cls": 3.78447, "loss": 3.78447, "time": 0.84528} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.04779, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33547, "top5_acc": 0.60484, "loss_cls": 3.74359, "loss": 3.74359, "time": 0.84644} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.04777, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.33094, "top5_acc": 0.58891, "loss_cls": 3.78962, "loss": 3.78962, "time": 0.84201} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.04774, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32906, "top5_acc": 0.59219, "loss_cls": 3.82582, "loss": 3.82582, "time": 0.85063} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.04771, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32234, "top5_acc": 0.58906, "loss_cls": 3.83561, "loss": 3.83561, "time": 0.84885} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.04768, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32391, "top5_acc": 0.58562, "loss_cls": 3.83543, "loss": 3.83543, "time": 0.84661} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.04766, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32484, "top5_acc": 0.58781, "loss_cls": 3.83489, "loss": 3.83489, "time": 0.84742} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.04763, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32422, "top5_acc": 0.59141, "loss_cls": 3.83203, "loss": 3.83203, "time": 0.8446} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.0476, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31812, "top5_acc": 0.58531, "loss_cls": 3.86382, "loss": 3.86382, "time": 0.84705} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.04757, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32188, "top5_acc": 0.58109, "loss_cls": 3.85919, "loss": 3.85919, "time": 0.84588} +{"mode": "train", "epoch": 78, "iter": 1300, "lr": 0.04754, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32453, "top5_acc": 0.58688, "loss_cls": 3.8423, "loss": 3.8423, "time": 0.84447} +{"mode": "train", "epoch": 78, "iter": 1400, "lr": 0.04752, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32734, "top5_acc": 0.58344, "loss_cls": 3.82231, "loss": 3.82231, "time": 0.83739} +{"mode": "train", "epoch": 78, "iter": 1500, "lr": 0.04749, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32234, "top5_acc": 0.57844, "loss_cls": 3.85713, "loss": 3.85713, "time": 0.84333} +{"mode": "train", "epoch": 78, "iter": 1600, "lr": 0.04746, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33031, "top5_acc": 0.59031, "loss_cls": 3.82511, "loss": 3.82511, "time": 0.84963} +{"mode": "train", "epoch": 78, "iter": 1700, "lr": 0.04743, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31172, "top5_acc": 0.56969, "loss_cls": 3.8963, "loss": 3.8963, "time": 0.84703} +{"mode": "train", "epoch": 78, "iter": 1800, "lr": 0.0474, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.3275, "top5_acc": 0.58875, "loss_cls": 3.83498, "loss": 3.83498, "time": 0.84753} +{"mode": "train", "epoch": 78, "iter": 1900, "lr": 0.04738, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32688, "top5_acc": 0.58812, "loss_cls": 3.83991, "loss": 3.83991, "time": 0.84706} +{"mode": "train", "epoch": 78, "iter": 2000, "lr": 0.04735, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32469, "top5_acc": 0.58188, "loss_cls": 3.84676, "loss": 3.84676, "time": 0.84344} +{"mode": "train", "epoch": 78, "iter": 2100, "lr": 0.04732, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33, "top5_acc": 0.58969, "loss_cls": 3.79321, "loss": 3.79321, "time": 0.84589} +{"mode": "train", "epoch": 78, "iter": 2200, "lr": 0.04729, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33719, "top5_acc": 0.58922, "loss_cls": 3.79394, "loss": 3.79394, "time": 0.84636} +{"mode": "train", "epoch": 78, "iter": 2300, "lr": 0.04726, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32797, "top5_acc": 0.58922, "loss_cls": 3.85878, "loss": 3.85878, "time": 0.842} +{"mode": "train", "epoch": 78, "iter": 2400, "lr": 0.04724, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32922, "top5_acc": 0.58734, "loss_cls": 3.85161, "loss": 3.85161, "time": 0.84437} +{"mode": "train", "epoch": 78, "iter": 2500, "lr": 0.04721, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.33078, "top5_acc": 0.60016, "loss_cls": 3.77944, "loss": 3.77944, "time": 0.84217} +{"mode": "train", "epoch": 78, "iter": 2600, "lr": 0.04718, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.32375, "top5_acc": 0.58672, "loss_cls": 3.86895, "loss": 3.86895, "time": 0.84765} +{"mode": "train", "epoch": 78, "iter": 2700, "lr": 0.04715, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31969, "top5_acc": 0.58109, "loss_cls": 3.85338, "loss": 3.85338, "time": 0.8408} +{"mode": "train", "epoch": 78, "iter": 2800, "lr": 0.04712, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33094, "top5_acc": 0.59516, "loss_cls": 3.81797, "loss": 3.81797, "time": 0.8446} +{"mode": "train", "epoch": 78, "iter": 2900, "lr": 0.0471, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32484, "top5_acc": 0.59125, "loss_cls": 3.82423, "loss": 3.82423, "time": 0.84316} +{"mode": "train", "epoch": 78, "iter": 3000, "lr": 0.04707, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32047, "top5_acc": 0.58922, "loss_cls": 3.85327, "loss": 3.85327, "time": 0.84426} +{"mode": "train", "epoch": 78, "iter": 3100, "lr": 0.04704, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32406, "top5_acc": 0.57719, "loss_cls": 3.90727, "loss": 3.90727, "time": 0.84693} +{"mode": "train", "epoch": 78, "iter": 3200, "lr": 0.04701, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31594, "top5_acc": 0.58359, "loss_cls": 3.85211, "loss": 3.85211, "time": 0.84313} +{"mode": "train", "epoch": 78, "iter": 3300, "lr": 0.04699, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30766, "top5_acc": 0.58641, "loss_cls": 3.8611, "loss": 3.8611, "time": 0.84678} +{"mode": "train", "epoch": 78, "iter": 3400, "lr": 0.04696, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32812, "top5_acc": 0.58672, "loss_cls": 3.82983, "loss": 3.82983, "time": 0.84141} +{"mode": "train", "epoch": 78, "iter": 3500, "lr": 0.04693, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31719, "top5_acc": 0.58781, "loss_cls": 3.87144, "loss": 3.87144, "time": 0.83966} +{"mode": "train", "epoch": 78, "iter": 3600, "lr": 0.0469, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32266, "top5_acc": 0.59156, "loss_cls": 3.84498, "loss": 3.84498, "time": 0.84708} +{"mode": "train", "epoch": 78, "iter": 3700, "lr": 0.04687, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32688, "top5_acc": 0.58391, "loss_cls": 3.86863, "loss": 3.86863, "time": 0.84465} +{"mode": "val", "epoch": 78, "iter": 309, "lr": 0.04686, "top1_acc": 0.25361, "top5_acc": 0.50823, "mean_class_accuracy": 0.25323} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.04683, "memory": 15990, "data_time": 1.46454, "top1_acc": 0.3325, "top5_acc": 0.595, "loss_cls": 3.7978, "loss": 3.7978, "time": 2.49759} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.0468, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34203, "top5_acc": 0.6, "loss_cls": 3.76083, "loss": 3.76083, "time": 0.84715} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.04678, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32344, "top5_acc": 0.58844, "loss_cls": 3.80681, "loss": 3.80681, "time": 0.85465} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.04675, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33422, "top5_acc": 0.59203, "loss_cls": 3.7517, "loss": 3.7517, "time": 0.85224} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.04672, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32234, "top5_acc": 0.585, "loss_cls": 3.83468, "loss": 3.83468, "time": 0.84684} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.04669, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32234, "top5_acc": 0.58906, "loss_cls": 3.81939, "loss": 3.81939, "time": 0.84602} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.04667, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33969, "top5_acc": 0.59828, "loss_cls": 3.75598, "loss": 3.75598, "time": 0.84878} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.04664, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33234, "top5_acc": 0.59156, "loss_cls": 3.76531, "loss": 3.76531, "time": 0.84418} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.04661, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32125, "top5_acc": 0.58703, "loss_cls": 3.82609, "loss": 3.82609, "time": 0.84998} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.04658, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32906, "top5_acc": 0.59375, "loss_cls": 3.80138, "loss": 3.80138, "time": 0.84514} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.04655, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32484, "top5_acc": 0.58719, "loss_cls": 3.84796, "loss": 3.84796, "time": 0.8469} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.04653, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.33906, "top5_acc": 0.59469, "loss_cls": 3.78265, "loss": 3.78265, "time": 0.84164} +{"mode": "train", "epoch": 79, "iter": 1300, "lr": 0.0465, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31812, "top5_acc": 0.58234, "loss_cls": 3.86612, "loss": 3.86612, "time": 0.84675} +{"mode": "train", "epoch": 79, "iter": 1400, "lr": 0.04647, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33, "top5_acc": 0.59375, "loss_cls": 3.79856, "loss": 3.79856, "time": 0.84119} +{"mode": "train", "epoch": 79, "iter": 1500, "lr": 0.04644, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.32344, "top5_acc": 0.58156, "loss_cls": 3.87631, "loss": 3.87631, "time": 0.84484} +{"mode": "train", "epoch": 79, "iter": 1600, "lr": 0.04641, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31938, "top5_acc": 0.58656, "loss_cls": 3.84703, "loss": 3.84703, "time": 0.84772} +{"mode": "train", "epoch": 79, "iter": 1700, "lr": 0.04639, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32328, "top5_acc": 0.57812, "loss_cls": 3.90178, "loss": 3.90178, "time": 0.83926} +{"mode": "train", "epoch": 79, "iter": 1800, "lr": 0.04636, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32109, "top5_acc": 0.58359, "loss_cls": 3.8418, "loss": 3.8418, "time": 0.84448} +{"mode": "train", "epoch": 79, "iter": 1900, "lr": 0.04633, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.3275, "top5_acc": 0.58766, "loss_cls": 3.8134, "loss": 3.8134, "time": 0.84668} +{"mode": "train", "epoch": 79, "iter": 2000, "lr": 0.0463, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33328, "top5_acc": 0.58531, "loss_cls": 3.81712, "loss": 3.81712, "time": 0.84712} +{"mode": "train", "epoch": 79, "iter": 2100, "lr": 0.04628, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3275, "top5_acc": 0.58844, "loss_cls": 3.81895, "loss": 3.81895, "time": 0.84151} +{"mode": "train", "epoch": 79, "iter": 2200, "lr": 0.04625, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.325, "top5_acc": 0.59203, "loss_cls": 3.81422, "loss": 3.81422, "time": 0.84287} +{"mode": "train", "epoch": 79, "iter": 2300, "lr": 0.04622, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.3225, "top5_acc": 0.59438, "loss_cls": 3.80139, "loss": 3.80139, "time": 0.84283} +{"mode": "train", "epoch": 79, "iter": 2400, "lr": 0.04619, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32375, "top5_acc": 0.57891, "loss_cls": 3.85955, "loss": 3.85955, "time": 0.84521} +{"mode": "train", "epoch": 79, "iter": 2500, "lr": 0.04616, "memory": 15990, "data_time": 0.00102, "top1_acc": 0.32375, "top5_acc": 0.58094, "loss_cls": 3.85439, "loss": 3.85439, "time": 0.84445} +{"mode": "train", "epoch": 79, "iter": 2600, "lr": 0.04614, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32094, "top5_acc": 0.58609, "loss_cls": 3.8503, "loss": 3.8503, "time": 0.8413} +{"mode": "train", "epoch": 79, "iter": 2700, "lr": 0.04611, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.32844, "top5_acc": 0.58484, "loss_cls": 3.83902, "loss": 3.83902, "time": 0.8414} +{"mode": "train", "epoch": 79, "iter": 2800, "lr": 0.04608, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33125, "top5_acc": 0.58891, "loss_cls": 3.81753, "loss": 3.81753, "time": 0.84629} +{"mode": "train", "epoch": 79, "iter": 2900, "lr": 0.04605, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32281, "top5_acc": 0.58875, "loss_cls": 3.83717, "loss": 3.83717, "time": 0.84007} +{"mode": "train", "epoch": 79, "iter": 3000, "lr": 0.04602, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33156, "top5_acc": 0.59062, "loss_cls": 3.82069, "loss": 3.82069, "time": 0.84841} +{"mode": "train", "epoch": 79, "iter": 3100, "lr": 0.046, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31969, "top5_acc": 0.58531, "loss_cls": 3.84221, "loss": 3.84221, "time": 0.84715} +{"mode": "train", "epoch": 79, "iter": 3200, "lr": 0.04597, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33297, "top5_acc": 0.58562, "loss_cls": 3.82835, "loss": 3.82835, "time": 0.84868} +{"mode": "train", "epoch": 79, "iter": 3300, "lr": 0.04594, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3275, "top5_acc": 0.58656, "loss_cls": 3.81606, "loss": 3.81606, "time": 0.84024} +{"mode": "train", "epoch": 79, "iter": 3400, "lr": 0.04591, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31719, "top5_acc": 0.57891, "loss_cls": 3.85334, "loss": 3.85334, "time": 0.84446} +{"mode": "train", "epoch": 79, "iter": 3500, "lr": 0.04588, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32984, "top5_acc": 0.59281, "loss_cls": 3.80779, "loss": 3.80779, "time": 0.84555} +{"mode": "train", "epoch": 79, "iter": 3600, "lr": 0.04586, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32172, "top5_acc": 0.58203, "loss_cls": 3.84715, "loss": 3.84715, "time": 0.84962} +{"mode": "train", "epoch": 79, "iter": 3700, "lr": 0.04583, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31938, "top5_acc": 0.59109, "loss_cls": 3.82967, "loss": 3.82967, "time": 0.8534} +{"mode": "val", "epoch": 79, "iter": 309, "lr": 0.04582, "top1_acc": 0.2647, "top5_acc": 0.5099, "mean_class_accuracy": 0.26428} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.04579, "memory": 15990, "data_time": 1.48527, "top1_acc": 0.33391, "top5_acc": 0.59406, "loss_cls": 3.78616, "loss": 3.78616, "time": 2.50973} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.04576, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33016, "top5_acc": 0.58625, "loss_cls": 3.81838, "loss": 3.81838, "time": 0.85198} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.04573, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.34078, "top5_acc": 0.60375, "loss_cls": 3.75896, "loss": 3.75896, "time": 0.85942} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.0457, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32594, "top5_acc": 0.59, "loss_cls": 3.79296, "loss": 3.79296, "time": 0.85382} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.04568, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33719, "top5_acc": 0.59625, "loss_cls": 3.76696, "loss": 3.76696, "time": 0.84976} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.04565, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33422, "top5_acc": 0.60297, "loss_cls": 3.75384, "loss": 3.75384, "time": 0.84496} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.04562, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31656, "top5_acc": 0.58547, "loss_cls": 3.85982, "loss": 3.85982, "time": 0.84707} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.04559, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33469, "top5_acc": 0.60094, "loss_cls": 3.76322, "loss": 3.76322, "time": 0.84018} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.04557, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.335, "top5_acc": 0.60047, "loss_cls": 3.74946, "loss": 3.74946, "time": 0.84648} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.04554, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33297, "top5_acc": 0.60906, "loss_cls": 3.73495, "loss": 3.73495, "time": 0.84462} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.04551, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33469, "top5_acc": 0.59719, "loss_cls": 3.7729, "loss": 3.7729, "time": 0.84379} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.04548, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31781, "top5_acc": 0.58188, "loss_cls": 3.85619, "loss": 3.85619, "time": 0.84675} +{"mode": "train", "epoch": 80, "iter": 1300, "lr": 0.04545, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31469, "top5_acc": 0.57984, "loss_cls": 3.88906, "loss": 3.88906, "time": 0.84413} +{"mode": "train", "epoch": 80, "iter": 1400, "lr": 0.04543, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32359, "top5_acc": 0.58453, "loss_cls": 3.83785, "loss": 3.83785, "time": 0.84582} +{"mode": "train", "epoch": 80, "iter": 1500, "lr": 0.0454, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32078, "top5_acc": 0.58781, "loss_cls": 3.83131, "loss": 3.83131, "time": 0.8458} +{"mode": "train", "epoch": 80, "iter": 1600, "lr": 0.04537, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3175, "top5_acc": 0.59625, "loss_cls": 3.82446, "loss": 3.82446, "time": 0.84108} +{"mode": "train", "epoch": 80, "iter": 1700, "lr": 0.04534, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32609, "top5_acc": 0.59156, "loss_cls": 3.80419, "loss": 3.80419, "time": 0.84425} +{"mode": "train", "epoch": 80, "iter": 1800, "lr": 0.04532, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32672, "top5_acc": 0.58703, "loss_cls": 3.80459, "loss": 3.80459, "time": 0.84475} +{"mode": "train", "epoch": 80, "iter": 1900, "lr": 0.04529, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32828, "top5_acc": 0.59203, "loss_cls": 3.81437, "loss": 3.81437, "time": 0.84585} +{"mode": "train", "epoch": 80, "iter": 2000, "lr": 0.04526, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32953, "top5_acc": 0.59406, "loss_cls": 3.80046, "loss": 3.80046, "time": 0.84418} +{"mode": "train", "epoch": 80, "iter": 2100, "lr": 0.04523, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32891, "top5_acc": 0.59375, "loss_cls": 3.80659, "loss": 3.80659, "time": 0.85207} +{"mode": "train", "epoch": 80, "iter": 2200, "lr": 0.0452, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33109, "top5_acc": 0.59875, "loss_cls": 3.80143, "loss": 3.80143, "time": 0.84632} +{"mode": "train", "epoch": 80, "iter": 2300, "lr": 0.04518, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32703, "top5_acc": 0.59203, "loss_cls": 3.81581, "loss": 3.81581, "time": 0.85579} +{"mode": "train", "epoch": 80, "iter": 2400, "lr": 0.04515, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33328, "top5_acc": 0.59031, "loss_cls": 3.8176, "loss": 3.8176, "time": 0.85497} +{"mode": "train", "epoch": 80, "iter": 2500, "lr": 0.04512, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.32297, "top5_acc": 0.57781, "loss_cls": 3.84331, "loss": 3.84331, "time": 0.84813} +{"mode": "train", "epoch": 80, "iter": 2600, "lr": 0.04509, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.33312, "top5_acc": 0.59344, "loss_cls": 3.80661, "loss": 3.80661, "time": 0.84771} +{"mode": "train", "epoch": 80, "iter": 2700, "lr": 0.04506, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33891, "top5_acc": 0.60266, "loss_cls": 3.76547, "loss": 3.76547, "time": 0.84716} +{"mode": "train", "epoch": 80, "iter": 2800, "lr": 0.04504, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32828, "top5_acc": 0.59281, "loss_cls": 3.83162, "loss": 3.83162, "time": 0.85059} +{"mode": "train", "epoch": 80, "iter": 2900, "lr": 0.04501, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33172, "top5_acc": 0.58656, "loss_cls": 3.81856, "loss": 3.81856, "time": 0.84645} +{"mode": "train", "epoch": 80, "iter": 3000, "lr": 0.04498, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33375, "top5_acc": 0.59062, "loss_cls": 3.80142, "loss": 3.80142, "time": 0.8459} +{"mode": "train", "epoch": 80, "iter": 3100, "lr": 0.04495, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33031, "top5_acc": 0.58172, "loss_cls": 3.84123, "loss": 3.84123, "time": 0.84813} +{"mode": "train", "epoch": 80, "iter": 3200, "lr": 0.04493, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33297, "top5_acc": 0.59078, "loss_cls": 3.79722, "loss": 3.79722, "time": 0.842} +{"mode": "train", "epoch": 80, "iter": 3300, "lr": 0.0449, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32438, "top5_acc": 0.59281, "loss_cls": 3.80461, "loss": 3.80461, "time": 0.8535} +{"mode": "train", "epoch": 80, "iter": 3400, "lr": 0.04487, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32406, "top5_acc": 0.58453, "loss_cls": 3.83892, "loss": 3.83892, "time": 0.84957} +{"mode": "train", "epoch": 80, "iter": 3500, "lr": 0.04484, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32719, "top5_acc": 0.58594, "loss_cls": 3.81775, "loss": 3.81775, "time": 0.84282} +{"mode": "train", "epoch": 80, "iter": 3600, "lr": 0.04481, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31062, "top5_acc": 0.57094, "loss_cls": 3.91185, "loss": 3.91185, "time": 0.84728} +{"mode": "train", "epoch": 80, "iter": 3700, "lr": 0.04479, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32594, "top5_acc": 0.58109, "loss_cls": 3.84499, "loss": 3.84499, "time": 0.85047} +{"mode": "val", "epoch": 80, "iter": 309, "lr": 0.04477, "top1_acc": 0.27498, "top5_acc": 0.53011, "mean_class_accuracy": 0.27462} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.04475, "memory": 15990, "data_time": 1.56072, "top1_acc": 0.33766, "top5_acc": 0.59859, "loss_cls": 3.74745, "loss": 3.74745, "time": 2.60716} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.04472, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33328, "top5_acc": 0.60234, "loss_cls": 3.75559, "loss": 3.75559, "time": 0.85551} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.04469, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3425, "top5_acc": 0.61016, "loss_cls": 3.74273, "loss": 3.74273, "time": 0.85861} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.04466, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33984, "top5_acc": 0.59781, "loss_cls": 3.76663, "loss": 3.76663, "time": 0.85891} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.04463, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33562, "top5_acc": 0.59422, "loss_cls": 3.77297, "loss": 3.77297, "time": 0.85604} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.04461, "memory": 15990, "data_time": 0.00078, "top1_acc": 0.32844, "top5_acc": 0.59797, "loss_cls": 3.80182, "loss": 3.80182, "time": 0.85988} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.04458, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33016, "top5_acc": 0.59547, "loss_cls": 3.79004, "loss": 3.79004, "time": 0.85461} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.04455, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32781, "top5_acc": 0.59672, "loss_cls": 3.79121, "loss": 3.79121, "time": 0.85167} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.04452, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32797, "top5_acc": 0.60328, "loss_cls": 3.74826, "loss": 3.74826, "time": 0.84851} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.0445, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32453, "top5_acc": 0.59281, "loss_cls": 3.82588, "loss": 3.82588, "time": 0.85258} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.04447, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34219, "top5_acc": 0.60594, "loss_cls": 3.73733, "loss": 3.73733, "time": 0.85836} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.04444, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32719, "top5_acc": 0.59625, "loss_cls": 3.77705, "loss": 3.77705, "time": 0.85858} +{"mode": "train", "epoch": 81, "iter": 1300, "lr": 0.04441, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34141, "top5_acc": 0.60141, "loss_cls": 3.76328, "loss": 3.76328, "time": 0.85685} +{"mode": "train", "epoch": 81, "iter": 1400, "lr": 0.04438, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32484, "top5_acc": 0.58531, "loss_cls": 3.81661, "loss": 3.81661, "time": 0.85719} +{"mode": "train", "epoch": 81, "iter": 1500, "lr": 0.04436, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.32688, "top5_acc": 0.58828, "loss_cls": 3.81017, "loss": 3.81017, "time": 0.85325} +{"mode": "train", "epoch": 81, "iter": 1600, "lr": 0.04433, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.32656, "top5_acc": 0.59391, "loss_cls": 3.81977, "loss": 3.81977, "time": 0.86249} +{"mode": "train", "epoch": 81, "iter": 1700, "lr": 0.0443, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.32453, "top5_acc": 0.58984, "loss_cls": 3.79213, "loss": 3.79213, "time": 0.86004} +{"mode": "train", "epoch": 81, "iter": 1800, "lr": 0.04427, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.34047, "top5_acc": 0.59766, "loss_cls": 3.75951, "loss": 3.75951, "time": 0.85236} +{"mode": "train", "epoch": 81, "iter": 1900, "lr": 0.04425, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33641, "top5_acc": 0.59328, "loss_cls": 3.77825, "loss": 3.77825, "time": 0.85736} +{"mode": "train", "epoch": 81, "iter": 2000, "lr": 0.04422, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32266, "top5_acc": 0.585, "loss_cls": 3.83854, "loss": 3.83854, "time": 0.86055} +{"mode": "train", "epoch": 81, "iter": 2100, "lr": 0.04419, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33109, "top5_acc": 0.59062, "loss_cls": 3.77846, "loss": 3.77846, "time": 0.86045} +{"mode": "train", "epoch": 81, "iter": 2200, "lr": 0.04416, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33719, "top5_acc": 0.60031, "loss_cls": 3.76405, "loss": 3.76405, "time": 0.85784} +{"mode": "train", "epoch": 81, "iter": 2300, "lr": 0.04413, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.33578, "top5_acc": 0.59281, "loss_cls": 3.83699, "loss": 3.83699, "time": 0.86102} +{"mode": "train", "epoch": 81, "iter": 2400, "lr": 0.04411, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32391, "top5_acc": 0.59125, "loss_cls": 3.81676, "loss": 3.81676, "time": 0.85628} +{"mode": "train", "epoch": 81, "iter": 2500, "lr": 0.04408, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31734, "top5_acc": 0.59125, "loss_cls": 3.81557, "loss": 3.81557, "time": 0.85758} +{"mode": "train", "epoch": 81, "iter": 2600, "lr": 0.04405, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32906, "top5_acc": 0.5975, "loss_cls": 3.78446, "loss": 3.78446, "time": 0.84942} +{"mode": "train", "epoch": 81, "iter": 2700, "lr": 0.04402, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.33281, "top5_acc": 0.58859, "loss_cls": 3.7903, "loss": 3.7903, "time": 0.85801} +{"mode": "train", "epoch": 81, "iter": 2800, "lr": 0.044, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32656, "top5_acc": 0.58688, "loss_cls": 3.84272, "loss": 3.84272, "time": 0.84984} +{"mode": "train", "epoch": 81, "iter": 2900, "lr": 0.04397, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32688, "top5_acc": 0.58906, "loss_cls": 3.82165, "loss": 3.82165, "time": 0.8552} +{"mode": "train", "epoch": 81, "iter": 3000, "lr": 0.04394, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32078, "top5_acc": 0.58281, "loss_cls": 3.86002, "loss": 3.86002, "time": 0.85461} +{"mode": "train", "epoch": 81, "iter": 3100, "lr": 0.04391, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33078, "top5_acc": 0.59844, "loss_cls": 3.77065, "loss": 3.77065, "time": 0.85547} +{"mode": "train", "epoch": 81, "iter": 3200, "lr": 0.04389, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.32469, "top5_acc": 0.57844, "loss_cls": 3.84386, "loss": 3.84386, "time": 0.85621} +{"mode": "train", "epoch": 81, "iter": 3300, "lr": 0.04386, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.33484, "top5_acc": 0.59891, "loss_cls": 3.79158, "loss": 3.79158, "time": 0.85876} +{"mode": "train", "epoch": 81, "iter": 3400, "lr": 0.04383, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.32688, "top5_acc": 0.58609, "loss_cls": 3.80304, "loss": 3.80304, "time": 0.85596} +{"mode": "train", "epoch": 81, "iter": 3500, "lr": 0.0438, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.31703, "top5_acc": 0.58344, "loss_cls": 3.84988, "loss": 3.84988, "time": 0.85408} +{"mode": "train", "epoch": 81, "iter": 3600, "lr": 0.04377, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.32219, "top5_acc": 0.58016, "loss_cls": 3.83732, "loss": 3.83732, "time": 0.85994} +{"mode": "train", "epoch": 81, "iter": 3700, "lr": 0.04375, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33734, "top5_acc": 0.59531, "loss_cls": 3.79387, "loss": 3.79387, "time": 0.85672} +{"mode": "val", "epoch": 81, "iter": 309, "lr": 0.04373, "top1_acc": 0.26946, "top5_acc": 0.52039, "mean_class_accuracy": 0.26922} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.04371, "memory": 15990, "data_time": 1.61124, "top1_acc": 0.33938, "top5_acc": 0.60875, "loss_cls": 3.70286, "loss": 3.70286, "time": 2.66333} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.04368, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33547, "top5_acc": 0.60094, "loss_cls": 3.75029, "loss": 3.75029, "time": 0.85896} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.04365, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33484, "top5_acc": 0.60734, "loss_cls": 3.72672, "loss": 3.72672, "time": 0.85985} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.04362, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.32922, "top5_acc": 0.59703, "loss_cls": 3.7947, "loss": 3.7947, "time": 0.85331} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.04359, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.3425, "top5_acc": 0.60125, "loss_cls": 3.74745, "loss": 3.74745, "time": 0.86096} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.04357, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.33516, "top5_acc": 0.60203, "loss_cls": 3.76974, "loss": 3.76974, "time": 0.85911} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.04354, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32625, "top5_acc": 0.58453, "loss_cls": 3.84994, "loss": 3.84994, "time": 0.85479} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.04351, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34234, "top5_acc": 0.58938, "loss_cls": 3.78662, "loss": 3.78662, "time": 0.85947} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.04348, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32859, "top5_acc": 0.59625, "loss_cls": 3.79402, "loss": 3.79402, "time": 0.85836} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.04346, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.345, "top5_acc": 0.60906, "loss_cls": 3.72787, "loss": 3.72787, "time": 0.85865} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.04343, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.32688, "top5_acc": 0.58266, "loss_cls": 3.83094, "loss": 3.83094, "time": 0.85477} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.0434, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33797, "top5_acc": 0.59219, "loss_cls": 3.78209, "loss": 3.78209, "time": 0.85565} +{"mode": "train", "epoch": 82, "iter": 1300, "lr": 0.04337, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.33766, "top5_acc": 0.5975, "loss_cls": 3.75465, "loss": 3.75465, "time": 0.85282} +{"mode": "train", "epoch": 82, "iter": 1400, "lr": 0.04335, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.33531, "top5_acc": 0.59453, "loss_cls": 3.79056, "loss": 3.79056, "time": 0.85545} +{"mode": "train", "epoch": 82, "iter": 1500, "lr": 0.04332, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.33438, "top5_acc": 0.5975, "loss_cls": 3.79664, "loss": 3.79664, "time": 0.84961} +{"mode": "train", "epoch": 82, "iter": 1600, "lr": 0.04329, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32484, "top5_acc": 0.58984, "loss_cls": 3.81075, "loss": 3.81075, "time": 0.85391} +{"mode": "train", "epoch": 82, "iter": 1700, "lr": 0.04326, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33328, "top5_acc": 0.58891, "loss_cls": 3.79611, "loss": 3.79611, "time": 0.86104} +{"mode": "train", "epoch": 82, "iter": 1800, "lr": 0.04323, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34094, "top5_acc": 0.60172, "loss_cls": 3.74518, "loss": 3.74518, "time": 0.8573} +{"mode": "train", "epoch": 82, "iter": 1900, "lr": 0.04321, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32609, "top5_acc": 0.59172, "loss_cls": 3.7939, "loss": 3.7939, "time": 0.85809} +{"mode": "train", "epoch": 82, "iter": 2000, "lr": 0.04318, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.32484, "top5_acc": 0.59078, "loss_cls": 3.8029, "loss": 3.8029, "time": 0.85588} +{"mode": "train", "epoch": 82, "iter": 2100, "lr": 0.04315, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33766, "top5_acc": 0.59922, "loss_cls": 3.78241, "loss": 3.78241, "time": 0.85428} +{"mode": "train", "epoch": 82, "iter": 2200, "lr": 0.04312, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.3275, "top5_acc": 0.60266, "loss_cls": 3.80044, "loss": 3.80044, "time": 0.85214} +{"mode": "train", "epoch": 82, "iter": 2300, "lr": 0.0431, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33047, "top5_acc": 0.5975, "loss_cls": 3.77507, "loss": 3.77507, "time": 0.85696} +{"mode": "train", "epoch": 82, "iter": 2400, "lr": 0.04307, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34047, "top5_acc": 0.59125, "loss_cls": 3.77526, "loss": 3.77526, "time": 0.85481} +{"mode": "train", "epoch": 82, "iter": 2500, "lr": 0.04304, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.31156, "top5_acc": 0.5925, "loss_cls": 3.84343, "loss": 3.84343, "time": 0.85524} +{"mode": "train", "epoch": 82, "iter": 2600, "lr": 0.04301, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34562, "top5_acc": 0.59953, "loss_cls": 3.76084, "loss": 3.76084, "time": 0.85524} +{"mode": "train", "epoch": 82, "iter": 2700, "lr": 0.04299, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32688, "top5_acc": 0.59922, "loss_cls": 3.82335, "loss": 3.82335, "time": 0.8476} +{"mode": "train", "epoch": 82, "iter": 2800, "lr": 0.04296, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33266, "top5_acc": 0.59578, "loss_cls": 3.78791, "loss": 3.78791, "time": 0.85057} +{"mode": "train", "epoch": 82, "iter": 2900, "lr": 0.04293, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33125, "top5_acc": 0.59141, "loss_cls": 3.821, "loss": 3.821, "time": 0.85756} +{"mode": "train", "epoch": 82, "iter": 3000, "lr": 0.0429, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32031, "top5_acc": 0.58375, "loss_cls": 3.84883, "loss": 3.84883, "time": 0.8546} +{"mode": "train", "epoch": 82, "iter": 3100, "lr": 0.04287, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33422, "top5_acc": 0.59156, "loss_cls": 3.77748, "loss": 3.77748, "time": 0.85097} +{"mode": "train", "epoch": 82, "iter": 3200, "lr": 0.04285, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32734, "top5_acc": 0.59047, "loss_cls": 3.83999, "loss": 3.83999, "time": 0.85862} +{"mode": "train", "epoch": 82, "iter": 3300, "lr": 0.04282, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.34562, "top5_acc": 0.59656, "loss_cls": 3.76919, "loss": 3.76919, "time": 0.85477} +{"mode": "train", "epoch": 82, "iter": 3400, "lr": 0.04279, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33281, "top5_acc": 0.59672, "loss_cls": 3.76815, "loss": 3.76815, "time": 0.85757} +{"mode": "train", "epoch": 82, "iter": 3500, "lr": 0.04276, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33281, "top5_acc": 0.59688, "loss_cls": 3.79192, "loss": 3.79192, "time": 0.86079} +{"mode": "train", "epoch": 82, "iter": 3600, "lr": 0.04274, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33234, "top5_acc": 0.59516, "loss_cls": 3.80575, "loss": 3.80575, "time": 0.86285} +{"mode": "train", "epoch": 82, "iter": 3700, "lr": 0.04271, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.33219, "top5_acc": 0.59578, "loss_cls": 3.77661, "loss": 3.77661, "time": 0.85991} +{"mode": "val", "epoch": 82, "iter": 309, "lr": 0.0427, "top1_acc": 0.27139, "top5_acc": 0.52935, "mean_class_accuracy": 0.2712} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.04267, "memory": 15990, "data_time": 1.59939, "top1_acc": 0.33438, "top5_acc": 0.60703, "loss_cls": 3.7174, "loss": 3.7174, "time": 2.65843} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.04264, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34594, "top5_acc": 0.60609, "loss_cls": 3.69833, "loss": 3.69833, "time": 0.86126} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.04261, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.33547, "top5_acc": 0.60281, "loss_cls": 3.76453, "loss": 3.76453, "time": 0.85846} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.04259, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.3325, "top5_acc": 0.59359, "loss_cls": 3.78207, "loss": 3.78207, "time": 0.85931} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.04256, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.33906, "top5_acc": 0.61609, "loss_cls": 3.68889, "loss": 3.68889, "time": 0.85685} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.04253, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.33266, "top5_acc": 0.60109, "loss_cls": 3.78493, "loss": 3.78493, "time": 0.85723} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.0425, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33344, "top5_acc": 0.59453, "loss_cls": 3.77618, "loss": 3.77618, "time": 0.8535} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.04247, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32734, "top5_acc": 0.58672, "loss_cls": 3.80456, "loss": 3.80456, "time": 0.8546} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.04245, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34812, "top5_acc": 0.60781, "loss_cls": 3.72274, "loss": 3.72274, "time": 0.86311} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.04242, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.33266, "top5_acc": 0.60047, "loss_cls": 3.76028, "loss": 3.76028, "time": 0.85533} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.04239, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33125, "top5_acc": 0.59719, "loss_cls": 3.77499, "loss": 3.77499, "time": 0.8592} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.04236, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33844, "top5_acc": 0.6, "loss_cls": 3.76082, "loss": 3.76082, "time": 0.85758} +{"mode": "train", "epoch": 83, "iter": 1300, "lr": 0.04234, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33203, "top5_acc": 0.59328, "loss_cls": 3.78065, "loss": 3.78065, "time": 0.85602} +{"mode": "train", "epoch": 83, "iter": 1400, "lr": 0.04231, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33219, "top5_acc": 0.59938, "loss_cls": 3.7904, "loss": 3.7904, "time": 0.85405} +{"mode": "train", "epoch": 83, "iter": 1500, "lr": 0.04228, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33844, "top5_acc": 0.60391, "loss_cls": 3.73927, "loss": 3.73927, "time": 0.85985} +{"mode": "train", "epoch": 83, "iter": 1600, "lr": 0.04225, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.32766, "top5_acc": 0.59078, "loss_cls": 3.82338, "loss": 3.82338, "time": 0.85871} +{"mode": "train", "epoch": 83, "iter": 1700, "lr": 0.04223, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.33375, "top5_acc": 0.60062, "loss_cls": 3.76864, "loss": 3.76864, "time": 0.85866} +{"mode": "train", "epoch": 83, "iter": 1800, "lr": 0.0422, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.34125, "top5_acc": 0.59203, "loss_cls": 3.78721, "loss": 3.78721, "time": 0.85711} +{"mode": "train", "epoch": 83, "iter": 1900, "lr": 0.04217, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.33609, "top5_acc": 0.60469, "loss_cls": 3.75947, "loss": 3.75947, "time": 0.86668} +{"mode": "train", "epoch": 83, "iter": 2000, "lr": 0.04214, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32938, "top5_acc": 0.59656, "loss_cls": 3.80203, "loss": 3.80203, "time": 0.85666} +{"mode": "train", "epoch": 83, "iter": 2100, "lr": 0.04212, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33984, "top5_acc": 0.60562, "loss_cls": 3.72819, "loss": 3.72819, "time": 0.85753} +{"mode": "train", "epoch": 83, "iter": 2200, "lr": 0.04209, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32891, "top5_acc": 0.59719, "loss_cls": 3.77965, "loss": 3.77965, "time": 0.86153} +{"mode": "train", "epoch": 83, "iter": 2300, "lr": 0.04206, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33875, "top5_acc": 0.58703, "loss_cls": 3.80127, "loss": 3.80127, "time": 0.86506} +{"mode": "train", "epoch": 83, "iter": 2400, "lr": 0.04203, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.33031, "top5_acc": 0.595, "loss_cls": 3.7532, "loss": 3.7532, "time": 0.86372} +{"mode": "train", "epoch": 83, "iter": 2500, "lr": 0.04201, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33438, "top5_acc": 0.59328, "loss_cls": 3.76964, "loss": 3.76964, "time": 0.84937} +{"mode": "train", "epoch": 83, "iter": 2600, "lr": 0.04198, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33453, "top5_acc": 0.59781, "loss_cls": 3.78376, "loss": 3.78376, "time": 0.85228} +{"mode": "train", "epoch": 83, "iter": 2700, "lr": 0.04195, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33672, "top5_acc": 0.60172, "loss_cls": 3.76899, "loss": 3.76899, "time": 0.85142} +{"mode": "train", "epoch": 83, "iter": 2800, "lr": 0.04192, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34484, "top5_acc": 0.6025, "loss_cls": 3.72939, "loss": 3.72939, "time": 0.85971} +{"mode": "train", "epoch": 83, "iter": 2900, "lr": 0.0419, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32562, "top5_acc": 0.59516, "loss_cls": 3.78568, "loss": 3.78568, "time": 0.86102} +{"mode": "train", "epoch": 83, "iter": 3000, "lr": 0.04187, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33297, "top5_acc": 0.58766, "loss_cls": 3.80501, "loss": 3.80501, "time": 0.86306} +{"mode": "train", "epoch": 83, "iter": 3100, "lr": 0.04184, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32469, "top5_acc": 0.59234, "loss_cls": 3.80157, "loss": 3.80157, "time": 0.85929} +{"mode": "train", "epoch": 83, "iter": 3200, "lr": 0.04181, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33609, "top5_acc": 0.59188, "loss_cls": 3.78927, "loss": 3.78927, "time": 0.8645} +{"mode": "train", "epoch": 83, "iter": 3300, "lr": 0.04178, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32359, "top5_acc": 0.59656, "loss_cls": 3.78664, "loss": 3.78664, "time": 0.86135} +{"mode": "train", "epoch": 83, "iter": 3400, "lr": 0.04176, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33828, "top5_acc": 0.60031, "loss_cls": 3.75553, "loss": 3.75553, "time": 0.86151} +{"mode": "train", "epoch": 83, "iter": 3500, "lr": 0.04173, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33797, "top5_acc": 0.60047, "loss_cls": 3.77874, "loss": 3.77874, "time": 0.85743} +{"mode": "train", "epoch": 83, "iter": 3600, "lr": 0.0417, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32547, "top5_acc": 0.59156, "loss_cls": 3.81048, "loss": 3.81048, "time": 0.86075} +{"mode": "train", "epoch": 83, "iter": 3700, "lr": 0.04167, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.33656, "top5_acc": 0.60375, "loss_cls": 3.76524, "loss": 3.76524, "time": 0.86296} +{"mode": "val", "epoch": 83, "iter": 309, "lr": 0.04166, "top1_acc": 0.26683, "top5_acc": 0.52064, "mean_class_accuracy": 0.26671} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.04163, "memory": 15990, "data_time": 1.58275, "top1_acc": 0.34938, "top5_acc": 0.61594, "loss_cls": 3.69128, "loss": 3.69128, "time": 2.63705} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.04161, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33984, "top5_acc": 0.60703, "loss_cls": 3.69713, "loss": 3.69713, "time": 0.86066} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.04158, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3375, "top5_acc": 0.60531, "loss_cls": 3.72175, "loss": 3.72175, "time": 0.86004} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.04155, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.34078, "top5_acc": 0.60797, "loss_cls": 3.72763, "loss": 3.72763, "time": 0.86177} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.04152, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34219, "top5_acc": 0.61641, "loss_cls": 3.68589, "loss": 3.68589, "time": 0.85965} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.0415, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33953, "top5_acc": 0.60469, "loss_cls": 3.7258, "loss": 3.7258, "time": 0.85539} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.04147, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33516, "top5_acc": 0.60016, "loss_cls": 3.75191, "loss": 3.75191, "time": 0.85483} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.04144, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.33906, "top5_acc": 0.60188, "loss_cls": 3.75227, "loss": 3.75227, "time": 0.85857} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.04141, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.33078, "top5_acc": 0.59328, "loss_cls": 3.771, "loss": 3.771, "time": 0.86076} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.04139, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33828, "top5_acc": 0.60656, "loss_cls": 3.74656, "loss": 3.74656, "time": 0.85745} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.04136, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33641, "top5_acc": 0.59516, "loss_cls": 3.78223, "loss": 3.78223, "time": 0.86365} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.04133, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.33875, "top5_acc": 0.60453, "loss_cls": 3.75661, "loss": 3.75661, "time": 0.85879} +{"mode": "train", "epoch": 84, "iter": 1300, "lr": 0.0413, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33469, "top5_acc": 0.6025, "loss_cls": 3.74658, "loss": 3.74658, "time": 0.8543} +{"mode": "train", "epoch": 84, "iter": 1400, "lr": 0.04128, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33047, "top5_acc": 0.60078, "loss_cls": 3.78445, "loss": 3.78445, "time": 0.85159} +{"mode": "train", "epoch": 84, "iter": 1500, "lr": 0.04125, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.3325, "top5_acc": 0.59906, "loss_cls": 3.79561, "loss": 3.79561, "time": 0.85902} +{"mode": "train", "epoch": 84, "iter": 1600, "lr": 0.04122, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33906, "top5_acc": 0.60656, "loss_cls": 3.7386, "loss": 3.7386, "time": 0.85838} +{"mode": "train", "epoch": 84, "iter": 1700, "lr": 0.04119, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34172, "top5_acc": 0.60344, "loss_cls": 3.71891, "loss": 3.71891, "time": 0.86427} +{"mode": "train", "epoch": 84, "iter": 1800, "lr": 0.04117, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33359, "top5_acc": 0.60047, "loss_cls": 3.74349, "loss": 3.74349, "time": 0.85806} +{"mode": "train", "epoch": 84, "iter": 1900, "lr": 0.04114, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33422, "top5_acc": 0.60266, "loss_cls": 3.74345, "loss": 3.74345, "time": 0.86205} +{"mode": "train", "epoch": 84, "iter": 2000, "lr": 0.04111, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33562, "top5_acc": 0.59438, "loss_cls": 3.76939, "loss": 3.76939, "time": 0.86128} +{"mode": "train", "epoch": 84, "iter": 2100, "lr": 0.04108, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.34766, "top5_acc": 0.61078, "loss_cls": 3.70303, "loss": 3.70303, "time": 0.86444} +{"mode": "train", "epoch": 84, "iter": 2200, "lr": 0.04106, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33031, "top5_acc": 0.59875, "loss_cls": 3.77588, "loss": 3.77588, "time": 0.86742} +{"mode": "train", "epoch": 84, "iter": 2300, "lr": 0.04103, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33703, "top5_acc": 0.59219, "loss_cls": 3.79289, "loss": 3.79289, "time": 0.86461} +{"mode": "train", "epoch": 84, "iter": 2400, "lr": 0.041, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33578, "top5_acc": 0.59312, "loss_cls": 3.78002, "loss": 3.78002, "time": 0.85988} +{"mode": "train", "epoch": 84, "iter": 2500, "lr": 0.04097, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.33719, "top5_acc": 0.59891, "loss_cls": 3.78121, "loss": 3.78121, "time": 0.85396} +{"mode": "train", "epoch": 84, "iter": 2600, "lr": 0.04095, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34562, "top5_acc": 0.59453, "loss_cls": 3.7426, "loss": 3.7426, "time": 0.85473} +{"mode": "train", "epoch": 84, "iter": 2700, "lr": 0.04092, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.33797, "top5_acc": 0.60719, "loss_cls": 3.72427, "loss": 3.72427, "time": 0.85335} +{"mode": "train", "epoch": 84, "iter": 2800, "lr": 0.04089, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33172, "top5_acc": 0.59922, "loss_cls": 3.76068, "loss": 3.76068, "time": 0.85094} +{"mode": "train", "epoch": 84, "iter": 2900, "lr": 0.04086, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32375, "top5_acc": 0.585, "loss_cls": 3.8294, "loss": 3.8294, "time": 0.8557} +{"mode": "train", "epoch": 84, "iter": 3000, "lr": 0.04084, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33328, "top5_acc": 0.59297, "loss_cls": 3.78088, "loss": 3.78088, "time": 0.859} +{"mode": "train", "epoch": 84, "iter": 3100, "lr": 0.04081, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33297, "top5_acc": 0.59703, "loss_cls": 3.78175, "loss": 3.78175, "time": 0.85512} +{"mode": "train", "epoch": 84, "iter": 3200, "lr": 0.04078, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32562, "top5_acc": 0.58797, "loss_cls": 3.8026, "loss": 3.8026, "time": 0.85965} +{"mode": "train", "epoch": 84, "iter": 3300, "lr": 0.04075, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33891, "top5_acc": 0.60469, "loss_cls": 3.73715, "loss": 3.73715, "time": 0.85096} +{"mode": "train", "epoch": 84, "iter": 3400, "lr": 0.04073, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34391, "top5_acc": 0.60703, "loss_cls": 3.72387, "loss": 3.72387, "time": 0.85435} +{"mode": "train", "epoch": 84, "iter": 3500, "lr": 0.0407, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32766, "top5_acc": 0.58844, "loss_cls": 3.84428, "loss": 3.84428, "time": 0.85546} +{"mode": "train", "epoch": 84, "iter": 3600, "lr": 0.04067, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33719, "top5_acc": 0.59812, "loss_cls": 3.77437, "loss": 3.77437, "time": 0.85568} +{"mode": "train", "epoch": 84, "iter": 3700, "lr": 0.04064, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32812, "top5_acc": 0.60109, "loss_cls": 3.74, "loss": 3.74, "time": 0.8547} +{"mode": "val", "epoch": 84, "iter": 309, "lr": 0.04063, "top1_acc": 0.26541, "top5_acc": 0.51218, "mean_class_accuracy": 0.26515} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.0406, "memory": 15990, "data_time": 1.58026, "top1_acc": 0.34516, "top5_acc": 0.61016, "loss_cls": 3.67038, "loss": 3.67038, "time": 2.6203} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.04058, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34, "top5_acc": 0.60156, "loss_cls": 3.72672, "loss": 3.72672, "time": 0.85178} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.04055, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34875, "top5_acc": 0.61359, "loss_cls": 3.70231, "loss": 3.70231, "time": 0.85356} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.04052, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34734, "top5_acc": 0.61609, "loss_cls": 3.65599, "loss": 3.65599, "time": 0.8613} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.04049, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35297, "top5_acc": 0.60828, "loss_cls": 3.70078, "loss": 3.70078, "time": 0.85358} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.04047, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33891, "top5_acc": 0.60375, "loss_cls": 3.71404, "loss": 3.71404, "time": 0.85055} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.04044, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33344, "top5_acc": 0.60469, "loss_cls": 3.75412, "loss": 3.75412, "time": 0.86018} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.04041, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35031, "top5_acc": 0.60406, "loss_cls": 3.72214, "loss": 3.72214, "time": 0.85331} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.04038, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.33891, "top5_acc": 0.60844, "loss_cls": 3.73275, "loss": 3.73275, "time": 0.85765} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.04036, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.33906, "top5_acc": 0.60516, "loss_cls": 3.73227, "loss": 3.73227, "time": 0.85155} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.04033, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33734, "top5_acc": 0.59859, "loss_cls": 3.75084, "loss": 3.75084, "time": 0.85248} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.0403, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33438, "top5_acc": 0.59797, "loss_cls": 3.76834, "loss": 3.76834, "time": 0.85491} +{"mode": "train", "epoch": 85, "iter": 1300, "lr": 0.04027, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.33656, "top5_acc": 0.60312, "loss_cls": 3.75136, "loss": 3.75136, "time": 0.85445} +{"mode": "train", "epoch": 85, "iter": 1400, "lr": 0.04025, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34406, "top5_acc": 0.60453, "loss_cls": 3.73791, "loss": 3.73791, "time": 0.85313} +{"mode": "train", "epoch": 85, "iter": 1500, "lr": 0.04022, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32797, "top5_acc": 0.59203, "loss_cls": 3.79993, "loss": 3.79993, "time": 0.85673} +{"mode": "train", "epoch": 85, "iter": 1600, "lr": 0.04019, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34141, "top5_acc": 0.6075, "loss_cls": 3.75365, "loss": 3.75365, "time": 0.85791} +{"mode": "train", "epoch": 85, "iter": 1700, "lr": 0.04016, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33562, "top5_acc": 0.59656, "loss_cls": 3.74442, "loss": 3.74442, "time": 0.85518} +{"mode": "train", "epoch": 85, "iter": 1800, "lr": 0.04014, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34875, "top5_acc": 0.61031, "loss_cls": 3.69019, "loss": 3.69019, "time": 0.85661} +{"mode": "train", "epoch": 85, "iter": 1900, "lr": 0.04011, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33031, "top5_acc": 0.60188, "loss_cls": 3.74324, "loss": 3.74324, "time": 0.85537} +{"mode": "train", "epoch": 85, "iter": 2000, "lr": 0.04008, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33969, "top5_acc": 0.59562, "loss_cls": 3.7763, "loss": 3.7763, "time": 0.85319} +{"mode": "train", "epoch": 85, "iter": 2100, "lr": 0.04006, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33188, "top5_acc": 0.59719, "loss_cls": 3.76238, "loss": 3.76238, "time": 0.85496} +{"mode": "train", "epoch": 85, "iter": 2200, "lr": 0.04003, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33578, "top5_acc": 0.59156, "loss_cls": 3.81624, "loss": 3.81624, "time": 0.85695} +{"mode": "train", "epoch": 85, "iter": 2300, "lr": 0.04, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35125, "top5_acc": 0.61422, "loss_cls": 3.68876, "loss": 3.68876, "time": 0.85489} +{"mode": "train", "epoch": 85, "iter": 2400, "lr": 0.03997, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32844, "top5_acc": 0.59875, "loss_cls": 3.79053, "loss": 3.79053, "time": 0.85028} +{"mode": "train", "epoch": 85, "iter": 2500, "lr": 0.03995, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34156, "top5_acc": 0.60406, "loss_cls": 3.73545, "loss": 3.73545, "time": 0.86018} +{"mode": "train", "epoch": 85, "iter": 2600, "lr": 0.03992, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.35312, "top5_acc": 0.61141, "loss_cls": 3.6952, "loss": 3.6952, "time": 0.851} +{"mode": "train", "epoch": 85, "iter": 2700, "lr": 0.03989, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33016, "top5_acc": 0.60062, "loss_cls": 3.76014, "loss": 3.76014, "time": 0.84927} +{"mode": "train", "epoch": 85, "iter": 2800, "lr": 0.03986, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32641, "top5_acc": 0.59531, "loss_cls": 3.80163, "loss": 3.80163, "time": 0.85395} +{"mode": "train", "epoch": 85, "iter": 2900, "lr": 0.03984, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33812, "top5_acc": 0.59609, "loss_cls": 3.76751, "loss": 3.76751, "time": 0.85558} +{"mode": "train", "epoch": 85, "iter": 3000, "lr": 0.03981, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34344, "top5_acc": 0.60672, "loss_cls": 3.72077, "loss": 3.72077, "time": 0.85378} +{"mode": "train", "epoch": 85, "iter": 3100, "lr": 0.03978, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33922, "top5_acc": 0.59609, "loss_cls": 3.75447, "loss": 3.75447, "time": 0.85567} +{"mode": "train", "epoch": 85, "iter": 3200, "lr": 0.03975, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33953, "top5_acc": 0.60578, "loss_cls": 3.73779, "loss": 3.73779, "time": 0.85149} +{"mode": "train", "epoch": 85, "iter": 3300, "lr": 0.03973, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34266, "top5_acc": 0.60656, "loss_cls": 3.74971, "loss": 3.74971, "time": 0.8513} +{"mode": "train", "epoch": 85, "iter": 3400, "lr": 0.0397, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33312, "top5_acc": 0.6075, "loss_cls": 3.73364, "loss": 3.73364, "time": 0.85667} +{"mode": "train", "epoch": 85, "iter": 3500, "lr": 0.03967, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34984, "top5_acc": 0.60906, "loss_cls": 3.7155, "loss": 3.7155, "time": 0.8542} +{"mode": "train", "epoch": 85, "iter": 3600, "lr": 0.03964, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33, "top5_acc": 0.59938, "loss_cls": 3.75901, "loss": 3.75901, "time": 0.85916} +{"mode": "train", "epoch": 85, "iter": 3700, "lr": 0.03962, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33609, "top5_acc": 0.60297, "loss_cls": 3.77654, "loss": 3.77654, "time": 0.85522} +{"mode": "val", "epoch": 85, "iter": 309, "lr": 0.0396, "top1_acc": 0.27569, "top5_acc": 0.53178, "mean_class_accuracy": 0.27541} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.03958, "memory": 15990, "data_time": 1.62197, "top1_acc": 0.34672, "top5_acc": 0.61422, "loss_cls": 3.64612, "loss": 3.64612, "time": 2.6594} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.03955, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35484, "top5_acc": 0.61, "loss_cls": 3.67294, "loss": 3.67294, "time": 0.85112} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.03952, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35297, "top5_acc": 0.61562, "loss_cls": 3.70619, "loss": 3.70619, "time": 0.85898} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.0395, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33875, "top5_acc": 0.60453, "loss_cls": 3.75769, "loss": 3.75769, "time": 0.85879} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.03947, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33359, "top5_acc": 0.60141, "loss_cls": 3.77057, "loss": 3.77057, "time": 0.85676} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.03944, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34844, "top5_acc": 0.61187, "loss_cls": 3.67858, "loss": 3.67858, "time": 0.85733} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.03941, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34484, "top5_acc": 0.60938, "loss_cls": 3.69319, "loss": 3.69319, "time": 0.85468} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.03939, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34453, "top5_acc": 0.60703, "loss_cls": 3.72796, "loss": 3.72796, "time": 0.85856} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.03936, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33625, "top5_acc": 0.60422, "loss_cls": 3.74599, "loss": 3.74599, "time": 0.85806} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.03933, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34, "top5_acc": 0.60078, "loss_cls": 3.73952, "loss": 3.73952, "time": 0.85372} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.0393, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.33453, "top5_acc": 0.60703, "loss_cls": 3.74627, "loss": 3.74627, "time": 0.85217} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.03928, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34234, "top5_acc": 0.59688, "loss_cls": 3.74791, "loss": 3.74791, "time": 0.85277} +{"mode": "train", "epoch": 86, "iter": 1300, "lr": 0.03925, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3425, "top5_acc": 0.59844, "loss_cls": 3.73486, "loss": 3.73486, "time": 0.85835} +{"mode": "train", "epoch": 86, "iter": 1400, "lr": 0.03922, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.34328, "top5_acc": 0.61172, "loss_cls": 3.69452, "loss": 3.69452, "time": 0.85176} +{"mode": "train", "epoch": 86, "iter": 1500, "lr": 0.03919, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33781, "top5_acc": 0.60594, "loss_cls": 3.72623, "loss": 3.72623, "time": 0.85507} +{"mode": "train", "epoch": 86, "iter": 1600, "lr": 0.03917, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34766, "top5_acc": 0.61187, "loss_cls": 3.72128, "loss": 3.72128, "time": 0.85865} +{"mode": "train", "epoch": 86, "iter": 1700, "lr": 0.03914, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34438, "top5_acc": 0.59828, "loss_cls": 3.71395, "loss": 3.71395, "time": 0.85736} +{"mode": "train", "epoch": 86, "iter": 1800, "lr": 0.03911, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.35047, "top5_acc": 0.61766, "loss_cls": 3.67486, "loss": 3.67486, "time": 0.85463} +{"mode": "train", "epoch": 86, "iter": 1900, "lr": 0.03909, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.3325, "top5_acc": 0.59516, "loss_cls": 3.76443, "loss": 3.76443, "time": 0.85971} +{"mode": "train", "epoch": 86, "iter": 2000, "lr": 0.03906, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33734, "top5_acc": 0.60906, "loss_cls": 3.73456, "loss": 3.73456, "time": 0.85521} +{"mode": "train", "epoch": 86, "iter": 2100, "lr": 0.03903, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33531, "top5_acc": 0.60172, "loss_cls": 3.76871, "loss": 3.76871, "time": 0.85575} +{"mode": "train", "epoch": 86, "iter": 2200, "lr": 0.039, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34453, "top5_acc": 0.61078, "loss_cls": 3.71696, "loss": 3.71696, "time": 0.85778} +{"mode": "train", "epoch": 86, "iter": 2300, "lr": 0.03898, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34531, "top5_acc": 0.60922, "loss_cls": 3.70572, "loss": 3.70572, "time": 0.8577} +{"mode": "train", "epoch": 86, "iter": 2400, "lr": 0.03895, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33391, "top5_acc": 0.60562, "loss_cls": 3.74226, "loss": 3.74226, "time": 0.85735} +{"mode": "train", "epoch": 86, "iter": 2500, "lr": 0.03892, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.33516, "top5_acc": 0.60438, "loss_cls": 3.74079, "loss": 3.74079, "time": 0.8569} +{"mode": "train", "epoch": 86, "iter": 2600, "lr": 0.03889, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33578, "top5_acc": 0.59703, "loss_cls": 3.77665, "loss": 3.77665, "time": 0.85471} +{"mode": "train", "epoch": 86, "iter": 2700, "lr": 0.03887, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33688, "top5_acc": 0.59219, "loss_cls": 3.78071, "loss": 3.78071, "time": 0.85025} +{"mode": "train", "epoch": 86, "iter": 2800, "lr": 0.03884, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34531, "top5_acc": 0.60453, "loss_cls": 3.72375, "loss": 3.72375, "time": 0.85858} +{"mode": "train", "epoch": 86, "iter": 2900, "lr": 0.03881, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33766, "top5_acc": 0.60297, "loss_cls": 3.71026, "loss": 3.71026, "time": 0.85542} +{"mode": "train", "epoch": 86, "iter": 3000, "lr": 0.03879, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33844, "top5_acc": 0.60125, "loss_cls": 3.75265, "loss": 3.75265, "time": 0.85825} +{"mode": "train", "epoch": 86, "iter": 3100, "lr": 0.03876, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.32969, "top5_acc": 0.59234, "loss_cls": 3.77807, "loss": 3.77807, "time": 0.86473} +{"mode": "train", "epoch": 86, "iter": 3200, "lr": 0.03873, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33562, "top5_acc": 0.59828, "loss_cls": 3.74286, "loss": 3.74286, "time": 0.86161} +{"mode": "train", "epoch": 86, "iter": 3300, "lr": 0.0387, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.3375, "top5_acc": 0.595, "loss_cls": 3.7592, "loss": 3.7592, "time": 0.86002} +{"mode": "train", "epoch": 86, "iter": 3400, "lr": 0.03868, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34031, "top5_acc": 0.60281, "loss_cls": 3.74413, "loss": 3.74413, "time": 0.85672} +{"mode": "train", "epoch": 86, "iter": 3500, "lr": 0.03865, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34266, "top5_acc": 0.60359, "loss_cls": 3.7284, "loss": 3.7284, "time": 0.8644} +{"mode": "train", "epoch": 86, "iter": 3600, "lr": 0.03862, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33984, "top5_acc": 0.60188, "loss_cls": 3.73433, "loss": 3.73433, "time": 0.86459} +{"mode": "train", "epoch": 86, "iter": 3700, "lr": 0.0386, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34266, "top5_acc": 0.60438, "loss_cls": 3.72578, "loss": 3.72578, "time": 0.85974} +{"mode": "val", "epoch": 86, "iter": 309, "lr": 0.03858, "top1_acc": 0.27974, "top5_acc": 0.53842, "mean_class_accuracy": 0.27943} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.03856, "memory": 15990, "data_time": 1.58278, "top1_acc": 0.35562, "top5_acc": 0.62578, "loss_cls": 3.60601, "loss": 3.60601, "time": 2.61654} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.03853, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35266, "top5_acc": 0.61641, "loss_cls": 3.65906, "loss": 3.65906, "time": 0.854} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.0385, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33203, "top5_acc": 0.60906, "loss_cls": 3.73644, "loss": 3.73644, "time": 0.8594} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.03847, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34641, "top5_acc": 0.61453, "loss_cls": 3.65791, "loss": 3.65791, "time": 0.85022} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.03845, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.35234, "top5_acc": 0.61703, "loss_cls": 3.65707, "loss": 3.65707, "time": 0.85256} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.03842, "memory": 15990, "data_time": 0.00077, "top1_acc": 0.33781, "top5_acc": 0.60141, "loss_cls": 3.74438, "loss": 3.74438, "time": 0.84806} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.03839, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34609, "top5_acc": 0.60547, "loss_cls": 3.73878, "loss": 3.73878, "time": 0.8499} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.03837, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34438, "top5_acc": 0.60625, "loss_cls": 3.72323, "loss": 3.72323, "time": 0.85033} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.03834, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34297, "top5_acc": 0.60906, "loss_cls": 3.70687, "loss": 3.70687, "time": 0.84733} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.03831, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34094, "top5_acc": 0.59938, "loss_cls": 3.73593, "loss": 3.73593, "time": 0.84982} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.03828, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35219, "top5_acc": 0.61766, "loss_cls": 3.67368, "loss": 3.67368, "time": 0.85326} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.03826, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.34719, "top5_acc": 0.60953, "loss_cls": 3.70902, "loss": 3.70902, "time": 0.84713} +{"mode": "train", "epoch": 87, "iter": 1300, "lr": 0.03823, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.34812, "top5_acc": 0.61438, "loss_cls": 3.69417, "loss": 3.69417, "time": 0.84657} +{"mode": "train", "epoch": 87, "iter": 1400, "lr": 0.0382, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3525, "top5_acc": 0.60969, "loss_cls": 3.66487, "loss": 3.66487, "time": 0.84322} +{"mode": "train", "epoch": 87, "iter": 1500, "lr": 0.03817, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33422, "top5_acc": 0.60406, "loss_cls": 3.73815, "loss": 3.73815, "time": 0.84769} +{"mode": "train", "epoch": 87, "iter": 1600, "lr": 0.03815, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32531, "top5_acc": 0.59, "loss_cls": 3.79452, "loss": 3.79452, "time": 0.84482} +{"mode": "train", "epoch": 87, "iter": 1700, "lr": 0.03812, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34688, "top5_acc": 0.60219, "loss_cls": 3.71579, "loss": 3.71579, "time": 0.85032} +{"mode": "train", "epoch": 87, "iter": 1800, "lr": 0.03809, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33938, "top5_acc": 0.59672, "loss_cls": 3.75768, "loss": 3.75768, "time": 0.84867} +{"mode": "train", "epoch": 87, "iter": 1900, "lr": 0.03807, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34719, "top5_acc": 0.61187, "loss_cls": 3.67222, "loss": 3.67222, "time": 0.84404} +{"mode": "train", "epoch": 87, "iter": 2000, "lr": 0.03804, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34844, "top5_acc": 0.61594, "loss_cls": 3.70502, "loss": 3.70502, "time": 0.84685} +{"mode": "train", "epoch": 87, "iter": 2100, "lr": 0.03801, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34109, "top5_acc": 0.60078, "loss_cls": 3.70958, "loss": 3.70958, "time": 0.84979} +{"mode": "train", "epoch": 87, "iter": 2200, "lr": 0.03798, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35688, "top5_acc": 0.61078, "loss_cls": 3.68726, "loss": 3.68726, "time": 0.84818} +{"mode": "train", "epoch": 87, "iter": 2300, "lr": 0.03796, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34109, "top5_acc": 0.61297, "loss_cls": 3.7051, "loss": 3.7051, "time": 0.8505} +{"mode": "train", "epoch": 87, "iter": 2400, "lr": 0.03793, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34391, "top5_acc": 0.61266, "loss_cls": 3.71608, "loss": 3.71608, "time": 0.84416} +{"mode": "train", "epoch": 87, "iter": 2500, "lr": 0.0379, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.34484, "top5_acc": 0.60828, "loss_cls": 3.71333, "loss": 3.71333, "time": 0.84854} +{"mode": "train", "epoch": 87, "iter": 2600, "lr": 0.03788, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33516, "top5_acc": 0.59688, "loss_cls": 3.74435, "loss": 3.74435, "time": 0.84908} +{"mode": "train", "epoch": 87, "iter": 2700, "lr": 0.03785, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34484, "top5_acc": 0.61156, "loss_cls": 3.696, "loss": 3.696, "time": 0.85966} +{"mode": "train", "epoch": 87, "iter": 2800, "lr": 0.03782, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34328, "top5_acc": 0.60609, "loss_cls": 3.71571, "loss": 3.71571, "time": 0.85065} +{"mode": "train", "epoch": 87, "iter": 2900, "lr": 0.03779, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35609, "top5_acc": 0.60094, "loss_cls": 3.69981, "loss": 3.69981, "time": 0.84819} +{"mode": "train", "epoch": 87, "iter": 3000, "lr": 0.03777, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33234, "top5_acc": 0.59156, "loss_cls": 3.78383, "loss": 3.78383, "time": 0.85416} +{"mode": "train", "epoch": 87, "iter": 3100, "lr": 0.03774, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33859, "top5_acc": 0.60109, "loss_cls": 3.74512, "loss": 3.74512, "time": 0.84812} +{"mode": "train", "epoch": 87, "iter": 3200, "lr": 0.03771, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34219, "top5_acc": 0.60953, "loss_cls": 3.72864, "loss": 3.72864, "time": 0.84733} +{"mode": "train", "epoch": 87, "iter": 3300, "lr": 0.03769, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34422, "top5_acc": 0.60469, "loss_cls": 3.73268, "loss": 3.73268, "time": 0.85171} +{"mode": "train", "epoch": 87, "iter": 3400, "lr": 0.03766, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33781, "top5_acc": 0.60422, "loss_cls": 3.74372, "loss": 3.74372, "time": 0.8487} +{"mode": "train", "epoch": 87, "iter": 3500, "lr": 0.03763, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33859, "top5_acc": 0.59703, "loss_cls": 3.73187, "loss": 3.73187, "time": 0.84514} +{"mode": "train", "epoch": 87, "iter": 3600, "lr": 0.03761, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34125, "top5_acc": 0.61078, "loss_cls": 3.70081, "loss": 3.70081, "time": 0.85528} +{"mode": "train", "epoch": 87, "iter": 3700, "lr": 0.03758, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34109, "top5_acc": 0.605, "loss_cls": 3.74898, "loss": 3.74898, "time": 0.85102} +{"mode": "val", "epoch": 87, "iter": 309, "lr": 0.03757, "top1_acc": 0.26257, "top5_acc": 0.51157, "mean_class_accuracy": 0.26234} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.03754, "memory": 15990, "data_time": 1.48367, "top1_acc": 0.35703, "top5_acc": 0.62313, "loss_cls": 3.64544, "loss": 3.64544, "time": 2.52351} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.03751, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35, "top5_acc": 0.62047, "loss_cls": 3.6461, "loss": 3.6461, "time": 0.85327} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.03748, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3475, "top5_acc": 0.61844, "loss_cls": 3.66627, "loss": 3.66627, "time": 0.85604} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.03746, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35312, "top5_acc": 0.61891, "loss_cls": 3.64765, "loss": 3.64765, "time": 0.85312} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.03743, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33953, "top5_acc": 0.60844, "loss_cls": 3.71983, "loss": 3.71983, "time": 0.85351} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.0374, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.3525, "top5_acc": 0.61812, "loss_cls": 3.64841, "loss": 3.64841, "time": 0.84018} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.03738, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.35328, "top5_acc": 0.61844, "loss_cls": 3.62087, "loss": 3.62087, "time": 0.85116} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.03735, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34766, "top5_acc": 0.61172, "loss_cls": 3.67674, "loss": 3.67674, "time": 0.84621} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.03732, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34766, "top5_acc": 0.61094, "loss_cls": 3.69196, "loss": 3.69196, "time": 0.84492} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.0373, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36406, "top5_acc": 0.62484, "loss_cls": 3.61889, "loss": 3.61889, "time": 0.84682} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.03727, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33938, "top5_acc": 0.60688, "loss_cls": 3.71455, "loss": 3.71455, "time": 0.85059} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.03724, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33891, "top5_acc": 0.60375, "loss_cls": 3.70966, "loss": 3.70966, "time": 0.84806} +{"mode": "train", "epoch": 88, "iter": 1300, "lr": 0.03721, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33422, "top5_acc": 0.60375, "loss_cls": 3.73667, "loss": 3.73667, "time": 0.84595} +{"mode": "train", "epoch": 88, "iter": 1400, "lr": 0.03719, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33562, "top5_acc": 0.60922, "loss_cls": 3.71158, "loss": 3.71158, "time": 0.84364} +{"mode": "train", "epoch": 88, "iter": 1500, "lr": 0.03716, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34, "top5_acc": 0.60938, "loss_cls": 3.71666, "loss": 3.71666, "time": 0.84969} +{"mode": "train", "epoch": 88, "iter": 1600, "lr": 0.03713, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34312, "top5_acc": 0.605, "loss_cls": 3.71279, "loss": 3.71279, "time": 0.85287} +{"mode": "train", "epoch": 88, "iter": 1700, "lr": 0.03711, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33766, "top5_acc": 0.60594, "loss_cls": 3.71324, "loss": 3.71324, "time": 0.84836} +{"mode": "train", "epoch": 88, "iter": 1800, "lr": 0.03708, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34297, "top5_acc": 0.60672, "loss_cls": 3.69519, "loss": 3.69519, "time": 0.84414} +{"mode": "train", "epoch": 88, "iter": 1900, "lr": 0.03705, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34734, "top5_acc": 0.60391, "loss_cls": 3.70028, "loss": 3.70028, "time": 0.84492} +{"mode": "train", "epoch": 88, "iter": 2000, "lr": 0.03703, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35734, "top5_acc": 0.61984, "loss_cls": 3.6782, "loss": 3.6782, "time": 0.84687} +{"mode": "train", "epoch": 88, "iter": 2100, "lr": 0.037, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34641, "top5_acc": 0.61297, "loss_cls": 3.68616, "loss": 3.68616, "time": 0.84386} +{"mode": "train", "epoch": 88, "iter": 2200, "lr": 0.03697, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34344, "top5_acc": 0.61047, "loss_cls": 3.68878, "loss": 3.68878, "time": 0.84367} +{"mode": "train", "epoch": 88, "iter": 2300, "lr": 0.03694, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34984, "top5_acc": 0.61297, "loss_cls": 3.6731, "loss": 3.6731, "time": 0.84487} +{"mode": "train", "epoch": 88, "iter": 2400, "lr": 0.03692, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34203, "top5_acc": 0.60297, "loss_cls": 3.73899, "loss": 3.73899, "time": 0.84736} +{"mode": "train", "epoch": 88, "iter": 2500, "lr": 0.03689, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34, "top5_acc": 0.59906, "loss_cls": 3.7572, "loss": 3.7572, "time": 0.84355} +{"mode": "train", "epoch": 88, "iter": 2600, "lr": 0.03686, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34453, "top5_acc": 0.60375, "loss_cls": 3.70742, "loss": 3.70742, "time": 0.84185} +{"mode": "train", "epoch": 88, "iter": 2700, "lr": 0.03684, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33734, "top5_acc": 0.60172, "loss_cls": 3.74824, "loss": 3.74824, "time": 0.83773} +{"mode": "train", "epoch": 88, "iter": 2800, "lr": 0.03681, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35188, "top5_acc": 0.61531, "loss_cls": 3.67225, "loss": 3.67225, "time": 0.848} +{"mode": "train", "epoch": 88, "iter": 2900, "lr": 0.03678, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35719, "top5_acc": 0.6175, "loss_cls": 3.6619, "loss": 3.6619, "time": 0.84693} +{"mode": "train", "epoch": 88, "iter": 3000, "lr": 0.03676, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34438, "top5_acc": 0.60781, "loss_cls": 3.72293, "loss": 3.72293, "time": 0.84616} +{"mode": "train", "epoch": 88, "iter": 3100, "lr": 0.03673, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33531, "top5_acc": 0.60266, "loss_cls": 3.73167, "loss": 3.73167, "time": 0.84436} +{"mode": "train", "epoch": 88, "iter": 3200, "lr": 0.0367, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35312, "top5_acc": 0.61172, "loss_cls": 3.69924, "loss": 3.69924, "time": 0.85034} +{"mode": "train", "epoch": 88, "iter": 3300, "lr": 0.03667, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33578, "top5_acc": 0.59484, "loss_cls": 3.76381, "loss": 3.76381, "time": 0.84704} +{"mode": "train", "epoch": 88, "iter": 3400, "lr": 0.03665, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34438, "top5_acc": 0.60812, "loss_cls": 3.70029, "loss": 3.70029, "time": 0.85499} +{"mode": "train", "epoch": 88, "iter": 3500, "lr": 0.03662, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3325, "top5_acc": 0.59422, "loss_cls": 3.78217, "loss": 3.78217, "time": 0.84918} +{"mode": "train", "epoch": 88, "iter": 3600, "lr": 0.03659, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34438, "top5_acc": 0.61297, "loss_cls": 3.70714, "loss": 3.70714, "time": 0.85063} +{"mode": "train", "epoch": 88, "iter": 3700, "lr": 0.03657, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35656, "top5_acc": 0.60703, "loss_cls": 3.67925, "loss": 3.67925, "time": 0.85474} +{"mode": "val", "epoch": 88, "iter": 309, "lr": 0.03655, "top1_acc": 0.261, "top5_acc": 0.51568, "mean_class_accuracy": 0.2608} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.03653, "memory": 15990, "data_time": 1.52968, "top1_acc": 0.36203, "top5_acc": 0.62469, "loss_cls": 3.62838, "loss": 3.62838, "time": 2.56051} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0365, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34812, "top5_acc": 0.61125, "loss_cls": 3.67369, "loss": 3.67369, "time": 0.84632} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.03647, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35203, "top5_acc": 0.6225, "loss_cls": 3.65405, "loss": 3.65405, "time": 0.85315} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.03645, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36797, "top5_acc": 0.62875, "loss_cls": 3.61312, "loss": 3.61312, "time": 0.85728} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.03642, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.355, "top5_acc": 0.6225, "loss_cls": 3.64225, "loss": 3.64225, "time": 0.84995} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.03639, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.34016, "top5_acc": 0.60656, "loss_cls": 3.71256, "loss": 3.71256, "time": 0.85097} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.03637, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34781, "top5_acc": 0.60859, "loss_cls": 3.70239, "loss": 3.70239, "time": 0.84731} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.03634, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34719, "top5_acc": 0.61359, "loss_cls": 3.70622, "loss": 3.70622, "time": 0.84655} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.03631, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35641, "top5_acc": 0.60922, "loss_cls": 3.67932, "loss": 3.67932, "time": 0.84622} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.03629, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.3425, "top5_acc": 0.61219, "loss_cls": 3.70635, "loss": 3.70635, "time": 0.84225} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.03626, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34375, "top5_acc": 0.60797, "loss_cls": 3.7016, "loss": 3.7016, "time": 0.84646} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.03623, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35109, "top5_acc": 0.61328, "loss_cls": 3.66343, "loss": 3.66343, "time": 0.84508} +{"mode": "train", "epoch": 89, "iter": 1300, "lr": 0.0362, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34484, "top5_acc": 0.61125, "loss_cls": 3.70397, "loss": 3.70397, "time": 0.84135} +{"mode": "train", "epoch": 89, "iter": 1400, "lr": 0.03618, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34562, "top5_acc": 0.61625, "loss_cls": 3.69201, "loss": 3.69201, "time": 0.84666} +{"mode": "train", "epoch": 89, "iter": 1500, "lr": 0.03615, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34891, "top5_acc": 0.61766, "loss_cls": 3.66794, "loss": 3.66794, "time": 0.85006} +{"mode": "train", "epoch": 89, "iter": 1600, "lr": 0.03612, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34281, "top5_acc": 0.61062, "loss_cls": 3.67678, "loss": 3.67678, "time": 0.846} +{"mode": "train", "epoch": 89, "iter": 1700, "lr": 0.0361, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33984, "top5_acc": 0.615, "loss_cls": 3.67335, "loss": 3.67335, "time": 0.84841} +{"mode": "train", "epoch": 89, "iter": 1800, "lr": 0.03607, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34781, "top5_acc": 0.60969, "loss_cls": 3.69848, "loss": 3.69848, "time": 0.84607} +{"mode": "train", "epoch": 89, "iter": 1900, "lr": 0.03604, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35609, "top5_acc": 0.61109, "loss_cls": 3.68463, "loss": 3.68463, "time": 0.84814} +{"mode": "train", "epoch": 89, "iter": 2000, "lr": 0.03602, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3525, "top5_acc": 0.61484, "loss_cls": 3.68864, "loss": 3.68864, "time": 0.85324} +{"mode": "train", "epoch": 89, "iter": 2100, "lr": 0.03599, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35234, "top5_acc": 0.61281, "loss_cls": 3.66462, "loss": 3.66462, "time": 0.84987} +{"mode": "train", "epoch": 89, "iter": 2200, "lr": 0.03596, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34359, "top5_acc": 0.60625, "loss_cls": 3.71682, "loss": 3.71682, "time": 0.85413} +{"mode": "train", "epoch": 89, "iter": 2300, "lr": 0.03594, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34031, "top5_acc": 0.60219, "loss_cls": 3.71393, "loss": 3.71393, "time": 0.84884} +{"mode": "train", "epoch": 89, "iter": 2400, "lr": 0.03591, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34859, "top5_acc": 0.61312, "loss_cls": 3.68546, "loss": 3.68546, "time": 0.85089} +{"mode": "train", "epoch": 89, "iter": 2500, "lr": 0.03588, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34344, "top5_acc": 0.60516, "loss_cls": 3.68906, "loss": 3.68906, "time": 0.85054} +{"mode": "train", "epoch": 89, "iter": 2600, "lr": 0.03586, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35, "top5_acc": 0.61703, "loss_cls": 3.66454, "loss": 3.66454, "time": 0.84605} +{"mode": "train", "epoch": 89, "iter": 2700, "lr": 0.03583, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35375, "top5_acc": 0.61953, "loss_cls": 3.64805, "loss": 3.64805, "time": 0.84415} +{"mode": "train", "epoch": 89, "iter": 2800, "lr": 0.0358, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.34703, "top5_acc": 0.62391, "loss_cls": 3.66006, "loss": 3.66006, "time": 0.84774} +{"mode": "train", "epoch": 89, "iter": 2900, "lr": 0.03578, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33891, "top5_acc": 0.61641, "loss_cls": 3.6716, "loss": 3.6716, "time": 0.84457} +{"mode": "train", "epoch": 89, "iter": 3000, "lr": 0.03575, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34422, "top5_acc": 0.61172, "loss_cls": 3.71886, "loss": 3.71886, "time": 0.85359} +{"mode": "train", "epoch": 89, "iter": 3100, "lr": 0.03572, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35547, "top5_acc": 0.60797, "loss_cls": 3.71228, "loss": 3.71228, "time": 0.84931} +{"mode": "train", "epoch": 89, "iter": 3200, "lr": 0.03569, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35219, "top5_acc": 0.60922, "loss_cls": 3.69039, "loss": 3.69039, "time": 0.85759} +{"mode": "train", "epoch": 89, "iter": 3300, "lr": 0.03567, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35172, "top5_acc": 0.61391, "loss_cls": 3.68055, "loss": 3.68055, "time": 0.85491} +{"mode": "train", "epoch": 89, "iter": 3400, "lr": 0.03564, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34484, "top5_acc": 0.60672, "loss_cls": 3.7078, "loss": 3.7078, "time": 0.85034} +{"mode": "train", "epoch": 89, "iter": 3500, "lr": 0.03561, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34953, "top5_acc": 0.61828, "loss_cls": 3.67096, "loss": 3.67096, "time": 0.85311} +{"mode": "train", "epoch": 89, "iter": 3600, "lr": 0.03559, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34484, "top5_acc": 0.60656, "loss_cls": 3.7139, "loss": 3.7139, "time": 0.85235} +{"mode": "train", "epoch": 89, "iter": 3700, "lr": 0.03556, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34578, "top5_acc": 0.61344, "loss_cls": 3.69405, "loss": 3.69405, "time": 0.8536} +{"mode": "val", "epoch": 89, "iter": 309, "lr": 0.03555, "top1_acc": 0.28172, "top5_acc": 0.53188, "mean_class_accuracy": 0.28144} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.03552, "memory": 15990, "data_time": 1.47474, "top1_acc": 0.36594, "top5_acc": 0.62016, "loss_cls": 3.63259, "loss": 3.63259, "time": 2.50937} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.0355, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34719, "top5_acc": 0.61266, "loss_cls": 3.66915, "loss": 3.66915, "time": 0.86014} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.03547, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34266, "top5_acc": 0.60891, "loss_cls": 3.69889, "loss": 3.69889, "time": 0.85493} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.03544, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.35297, "top5_acc": 0.61844, "loss_cls": 3.65028, "loss": 3.65028, "time": 0.85164} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.03541, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.36031, "top5_acc": 0.62781, "loss_cls": 3.59913, "loss": 3.59913, "time": 0.85049} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.03539, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34875, "top5_acc": 0.62109, "loss_cls": 3.63636, "loss": 3.63636, "time": 0.84379} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.03536, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.34031, "top5_acc": 0.60844, "loss_cls": 3.69542, "loss": 3.69542, "time": 0.84829} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.03533, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34625, "top5_acc": 0.60922, "loss_cls": 3.70544, "loss": 3.70544, "time": 0.84502} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.03531, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34547, "top5_acc": 0.60109, "loss_cls": 3.69853, "loss": 3.69853, "time": 0.84761} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.03528, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.36094, "top5_acc": 0.61297, "loss_cls": 3.66409, "loss": 3.66409, "time": 0.84903} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.03525, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35688, "top5_acc": 0.61687, "loss_cls": 3.65379, "loss": 3.65379, "time": 0.84888} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.03523, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36328, "top5_acc": 0.61953, "loss_cls": 3.63452, "loss": 3.63452, "time": 0.84812} +{"mode": "train", "epoch": 90, "iter": 1300, "lr": 0.0352, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34219, "top5_acc": 0.60906, "loss_cls": 3.71279, "loss": 3.71279, "time": 0.84489} +{"mode": "train", "epoch": 90, "iter": 1400, "lr": 0.03517, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34906, "top5_acc": 0.61453, "loss_cls": 3.70859, "loss": 3.70859, "time": 0.84831} +{"mode": "train", "epoch": 90, "iter": 1500, "lr": 0.03515, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3525, "top5_acc": 0.62719, "loss_cls": 3.62094, "loss": 3.62094, "time": 0.84813} +{"mode": "train", "epoch": 90, "iter": 1600, "lr": 0.03512, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.34703, "top5_acc": 0.61219, "loss_cls": 3.66553, "loss": 3.66553, "time": 0.84916} +{"mode": "train", "epoch": 90, "iter": 1700, "lr": 0.03509, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35344, "top5_acc": 0.61406, "loss_cls": 3.65469, "loss": 3.65469, "time": 0.84781} +{"mode": "train", "epoch": 90, "iter": 1800, "lr": 0.03507, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.3525, "top5_acc": 0.61375, "loss_cls": 3.66178, "loss": 3.66178, "time": 0.84347} +{"mode": "train", "epoch": 90, "iter": 1900, "lr": 0.03504, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34609, "top5_acc": 0.60641, "loss_cls": 3.7047, "loss": 3.7047, "time": 0.84833} +{"mode": "train", "epoch": 90, "iter": 2000, "lr": 0.03501, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34469, "top5_acc": 0.60094, "loss_cls": 3.70158, "loss": 3.70158, "time": 0.84619} +{"mode": "train", "epoch": 90, "iter": 2100, "lr": 0.03499, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35172, "top5_acc": 0.61359, "loss_cls": 3.67874, "loss": 3.67874, "time": 0.84609} +{"mode": "train", "epoch": 90, "iter": 2200, "lr": 0.03496, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34562, "top5_acc": 0.60891, "loss_cls": 3.69013, "loss": 3.69013, "time": 0.85479} +{"mode": "train", "epoch": 90, "iter": 2300, "lr": 0.03493, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35047, "top5_acc": 0.62187, "loss_cls": 3.64741, "loss": 3.64741, "time": 0.842} +{"mode": "train", "epoch": 90, "iter": 2400, "lr": 0.03491, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34844, "top5_acc": 0.61547, "loss_cls": 3.65717, "loss": 3.65717, "time": 0.84704} +{"mode": "train", "epoch": 90, "iter": 2500, "lr": 0.03488, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.35688, "top5_acc": 0.6125, "loss_cls": 3.67611, "loss": 3.67611, "time": 0.84589} +{"mode": "train", "epoch": 90, "iter": 2600, "lr": 0.03485, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35328, "top5_acc": 0.62578, "loss_cls": 3.62199, "loss": 3.62199, "time": 0.84757} +{"mode": "train", "epoch": 90, "iter": 2700, "lr": 0.03483, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34703, "top5_acc": 0.60562, "loss_cls": 3.70324, "loss": 3.70324, "time": 0.84421} +{"mode": "train", "epoch": 90, "iter": 2800, "lr": 0.0348, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36047, "top5_acc": 0.61125, "loss_cls": 3.65999, "loss": 3.65999, "time": 0.84122} +{"mode": "train", "epoch": 90, "iter": 2900, "lr": 0.03477, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35422, "top5_acc": 0.61578, "loss_cls": 3.66968, "loss": 3.66968, "time": 0.84804} +{"mode": "train", "epoch": 90, "iter": 3000, "lr": 0.03475, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34609, "top5_acc": 0.61109, "loss_cls": 3.69464, "loss": 3.69464, "time": 0.84573} +{"mode": "train", "epoch": 90, "iter": 3100, "lr": 0.03472, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34609, "top5_acc": 0.61344, "loss_cls": 3.67336, "loss": 3.67336, "time": 0.8492} +{"mode": "train", "epoch": 90, "iter": 3200, "lr": 0.03469, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35516, "top5_acc": 0.61594, "loss_cls": 3.64941, "loss": 3.64941, "time": 0.84628} +{"mode": "train", "epoch": 90, "iter": 3300, "lr": 0.03467, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3475, "top5_acc": 0.60531, "loss_cls": 3.69399, "loss": 3.69399, "time": 0.84667} +{"mode": "train", "epoch": 90, "iter": 3400, "lr": 0.03464, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34312, "top5_acc": 0.60938, "loss_cls": 3.67892, "loss": 3.67892, "time": 0.84208} +{"mode": "train", "epoch": 90, "iter": 3500, "lr": 0.03461, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34828, "top5_acc": 0.61156, "loss_cls": 3.68209, "loss": 3.68209, "time": 0.84862} +{"mode": "train", "epoch": 90, "iter": 3600, "lr": 0.03459, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35234, "top5_acc": 0.61375, "loss_cls": 3.6788, "loss": 3.6788, "time": 0.84884} +{"mode": "train", "epoch": 90, "iter": 3700, "lr": 0.03456, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.34672, "top5_acc": 0.61766, "loss_cls": 3.68334, "loss": 3.68334, "time": 0.8471} +{"mode": "val", "epoch": 90, "iter": 309, "lr": 0.03455, "top1_acc": 0.29535, "top5_acc": 0.5489, "mean_class_accuracy": 0.2949} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.03452, "memory": 15990, "data_time": 1.51251, "top1_acc": 0.35781, "top5_acc": 0.62125, "loss_cls": 3.60339, "loss": 3.60339, "time": 2.56363} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0345, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.3625, "top5_acc": 0.6325, "loss_cls": 3.58935, "loss": 3.58935, "time": 0.85348} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.03447, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35344, "top5_acc": 0.62719, "loss_cls": 3.62863, "loss": 3.62863, "time": 0.85222} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.03444, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.35484, "top5_acc": 0.62484, "loss_cls": 3.60479, "loss": 3.60479, "time": 0.85458} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.03442, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35531, "top5_acc": 0.61672, "loss_cls": 3.63207, "loss": 3.63207, "time": 0.85526} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.03439, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.34891, "top5_acc": 0.60781, "loss_cls": 3.67111, "loss": 3.67111, "time": 0.84891} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.03436, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34984, "top5_acc": 0.61578, "loss_cls": 3.66792, "loss": 3.66792, "time": 0.85183} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.03434, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35844, "top5_acc": 0.61891, "loss_cls": 3.63501, "loss": 3.63501, "time": 0.84914} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.03431, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36031, "top5_acc": 0.62141, "loss_cls": 3.61377, "loss": 3.61377, "time": 0.85254} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.03428, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.35203, "top5_acc": 0.61766, "loss_cls": 3.66687, "loss": 3.66687, "time": 0.84676} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.03426, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35328, "top5_acc": 0.6175, "loss_cls": 3.68563, "loss": 3.68563, "time": 0.85282} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.03423, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34266, "top5_acc": 0.60812, "loss_cls": 3.69001, "loss": 3.69001, "time": 0.85038} +{"mode": "train", "epoch": 91, "iter": 1300, "lr": 0.0342, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35375, "top5_acc": 0.61812, "loss_cls": 3.65383, "loss": 3.65383, "time": 0.8486} +{"mode": "train", "epoch": 91, "iter": 1400, "lr": 0.03418, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35906, "top5_acc": 0.625, "loss_cls": 3.61733, "loss": 3.61733, "time": 0.85004} +{"mode": "train", "epoch": 91, "iter": 1500, "lr": 0.03415, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36141, "top5_acc": 0.6175, "loss_cls": 3.65353, "loss": 3.65353, "time": 0.84944} +{"mode": "train", "epoch": 91, "iter": 1600, "lr": 0.03412, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35641, "top5_acc": 0.62062, "loss_cls": 3.61233, "loss": 3.61233, "time": 0.85085} +{"mode": "train", "epoch": 91, "iter": 1700, "lr": 0.0341, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35234, "top5_acc": 0.61672, "loss_cls": 3.61688, "loss": 3.61688, "time": 0.84565} +{"mode": "train", "epoch": 91, "iter": 1800, "lr": 0.03407, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35391, "top5_acc": 0.61469, "loss_cls": 3.68481, "loss": 3.68481, "time": 0.84419} +{"mode": "train", "epoch": 91, "iter": 1900, "lr": 0.03405, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35609, "top5_acc": 0.61516, "loss_cls": 3.66441, "loss": 3.66441, "time": 0.84827} +{"mode": "train", "epoch": 91, "iter": 2000, "lr": 0.03402, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34828, "top5_acc": 0.62, "loss_cls": 3.65796, "loss": 3.65796, "time": 0.84159} +{"mode": "train", "epoch": 91, "iter": 2100, "lr": 0.03399, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34531, "top5_acc": 0.61516, "loss_cls": 3.67052, "loss": 3.67052, "time": 0.8474} +{"mode": "train", "epoch": 91, "iter": 2200, "lr": 0.03397, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35078, "top5_acc": 0.60672, "loss_cls": 3.67002, "loss": 3.67002, "time": 0.84348} +{"mode": "train", "epoch": 91, "iter": 2300, "lr": 0.03394, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36828, "top5_acc": 0.62813, "loss_cls": 3.57747, "loss": 3.57747, "time": 0.84708} +{"mode": "train", "epoch": 91, "iter": 2400, "lr": 0.03391, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35578, "top5_acc": 0.61828, "loss_cls": 3.64484, "loss": 3.64484, "time": 0.84819} +{"mode": "train", "epoch": 91, "iter": 2500, "lr": 0.03389, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34906, "top5_acc": 0.61234, "loss_cls": 3.68599, "loss": 3.68599, "time": 0.84268} +{"mode": "train", "epoch": 91, "iter": 2600, "lr": 0.03386, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36141, "top5_acc": 0.62, "loss_cls": 3.63105, "loss": 3.63105, "time": 0.8488} +{"mode": "train", "epoch": 91, "iter": 2700, "lr": 0.03383, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36031, "top5_acc": 0.61609, "loss_cls": 3.65071, "loss": 3.65071, "time": 0.84251} +{"mode": "train", "epoch": 91, "iter": 2800, "lr": 0.03381, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.34641, "top5_acc": 0.60078, "loss_cls": 3.70104, "loss": 3.70104, "time": 0.84519} +{"mode": "train", "epoch": 91, "iter": 2900, "lr": 0.03378, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35, "top5_acc": 0.61594, "loss_cls": 3.68041, "loss": 3.68041, "time": 0.84648} +{"mode": "train", "epoch": 91, "iter": 3000, "lr": 0.03375, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35531, "top5_acc": 0.61484, "loss_cls": 3.67911, "loss": 3.67911, "time": 0.84117} +{"mode": "train", "epoch": 91, "iter": 3100, "lr": 0.03373, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35625, "top5_acc": 0.61516, "loss_cls": 3.65613, "loss": 3.65613, "time": 0.85104} +{"mode": "train", "epoch": 91, "iter": 3200, "lr": 0.0337, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3475, "top5_acc": 0.60219, "loss_cls": 3.72785, "loss": 3.72785, "time": 0.85037} +{"mode": "train", "epoch": 91, "iter": 3300, "lr": 0.03367, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34047, "top5_acc": 0.61031, "loss_cls": 3.70407, "loss": 3.70407, "time": 0.84492} +{"mode": "train", "epoch": 91, "iter": 3400, "lr": 0.03365, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35016, "top5_acc": 0.61578, "loss_cls": 3.67328, "loss": 3.67328, "time": 0.84294} +{"mode": "train", "epoch": 91, "iter": 3500, "lr": 0.03362, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36047, "top5_acc": 0.61797, "loss_cls": 3.64621, "loss": 3.64621, "time": 0.85569} +{"mode": "train", "epoch": 91, "iter": 3600, "lr": 0.0336, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34109, "top5_acc": 0.61141, "loss_cls": 3.7146, "loss": 3.7146, "time": 0.85497} +{"mode": "train", "epoch": 91, "iter": 3700, "lr": 0.03357, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34531, "top5_acc": 0.60922, "loss_cls": 3.68679, "loss": 3.68679, "time": 0.84964} +{"mode": "val", "epoch": 91, "iter": 309, "lr": 0.03356, "top1_acc": 0.28339, "top5_acc": 0.53943, "mean_class_accuracy": 0.28332} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.03353, "memory": 15990, "data_time": 1.53364, "top1_acc": 0.35172, "top5_acc": 0.62125, "loss_cls": 3.63866, "loss": 3.63866, "time": 2.57342} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.0335, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35891, "top5_acc": 0.62469, "loss_cls": 3.58298, "loss": 3.58298, "time": 0.86159} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.03348, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34984, "top5_acc": 0.61594, "loss_cls": 3.6257, "loss": 3.6257, "time": 0.85993} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.03345, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35297, "top5_acc": 0.62109, "loss_cls": 3.64543, "loss": 3.64543, "time": 0.85419} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.03342, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.3575, "top5_acc": 0.6275, "loss_cls": 3.59949, "loss": 3.59949, "time": 0.86101} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.0334, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35812, "top5_acc": 0.62578, "loss_cls": 3.60602, "loss": 3.60602, "time": 0.85672} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.03337, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35641, "top5_acc": 0.61875, "loss_cls": 3.64072, "loss": 3.64072, "time": 0.8591} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.03335, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.35141, "top5_acc": 0.61531, "loss_cls": 3.66641, "loss": 3.66641, "time": 0.8588} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.03332, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36156, "top5_acc": 0.62594, "loss_cls": 3.6436, "loss": 3.6436, "time": 0.85676} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.03329, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35656, "top5_acc": 0.62422, "loss_cls": 3.61726, "loss": 3.61726, "time": 0.85384} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.03327, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35703, "top5_acc": 0.62391, "loss_cls": 3.64588, "loss": 3.64588, "time": 0.85627} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.03324, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35797, "top5_acc": 0.6225, "loss_cls": 3.62494, "loss": 3.62494, "time": 0.85091} +{"mode": "train", "epoch": 92, "iter": 1300, "lr": 0.03321, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35578, "top5_acc": 0.61766, "loss_cls": 3.62267, "loss": 3.62267, "time": 0.85485} +{"mode": "train", "epoch": 92, "iter": 1400, "lr": 0.03319, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35234, "top5_acc": 0.61094, "loss_cls": 3.6408, "loss": 3.6408, "time": 0.84984} +{"mode": "train", "epoch": 92, "iter": 1500, "lr": 0.03316, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36156, "top5_acc": 0.62359, "loss_cls": 3.61817, "loss": 3.61817, "time": 0.85187} +{"mode": "train", "epoch": 92, "iter": 1600, "lr": 0.03314, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3475, "top5_acc": 0.61859, "loss_cls": 3.66742, "loss": 3.66742, "time": 0.85434} +{"mode": "train", "epoch": 92, "iter": 1700, "lr": 0.03311, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36, "top5_acc": 0.62422, "loss_cls": 3.64304, "loss": 3.64304, "time": 0.85249} +{"mode": "train", "epoch": 92, "iter": 1800, "lr": 0.03308, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35141, "top5_acc": 0.61828, "loss_cls": 3.65571, "loss": 3.65571, "time": 0.84868} +{"mode": "train", "epoch": 92, "iter": 1900, "lr": 0.03306, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37078, "top5_acc": 0.625, "loss_cls": 3.59718, "loss": 3.59718, "time": 0.85025} +{"mode": "train", "epoch": 92, "iter": 2000, "lr": 0.03303, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33656, "top5_acc": 0.60906, "loss_cls": 3.69494, "loss": 3.69494, "time": 0.85436} +{"mode": "train", "epoch": 92, "iter": 2100, "lr": 0.033, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35734, "top5_acc": 0.61703, "loss_cls": 3.61947, "loss": 3.61947, "time": 0.85538} +{"mode": "train", "epoch": 92, "iter": 2200, "lr": 0.03298, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34953, "top5_acc": 0.61156, "loss_cls": 3.67629, "loss": 3.67629, "time": 0.84689} +{"mode": "train", "epoch": 92, "iter": 2300, "lr": 0.03295, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36172, "top5_acc": 0.62047, "loss_cls": 3.63222, "loss": 3.63222, "time": 0.84751} +{"mode": "train", "epoch": 92, "iter": 2400, "lr": 0.03292, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36078, "top5_acc": 0.62531, "loss_cls": 3.60411, "loss": 3.60411, "time": 0.84637} +{"mode": "train", "epoch": 92, "iter": 2500, "lr": 0.0329, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35344, "top5_acc": 0.62266, "loss_cls": 3.61236, "loss": 3.61236, "time": 0.84804} +{"mode": "train", "epoch": 92, "iter": 2600, "lr": 0.03287, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35188, "top5_acc": 0.61375, "loss_cls": 3.67044, "loss": 3.67044, "time": 0.84812} +{"mode": "train", "epoch": 92, "iter": 2700, "lr": 0.03285, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36062, "top5_acc": 0.62797, "loss_cls": 3.63135, "loss": 3.63135, "time": 0.84977} +{"mode": "train", "epoch": 92, "iter": 2800, "lr": 0.03282, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35812, "top5_acc": 0.62578, "loss_cls": 3.61327, "loss": 3.61327, "time": 0.84363} +{"mode": "train", "epoch": 92, "iter": 2900, "lr": 0.03279, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35688, "top5_acc": 0.62687, "loss_cls": 3.62296, "loss": 3.62296, "time": 0.84631} +{"mode": "train", "epoch": 92, "iter": 3000, "lr": 0.03277, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35188, "top5_acc": 0.62219, "loss_cls": 3.63941, "loss": 3.63941, "time": 0.84782} +{"mode": "train", "epoch": 92, "iter": 3100, "lr": 0.03274, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35125, "top5_acc": 0.62031, "loss_cls": 3.64419, "loss": 3.64419, "time": 0.84647} +{"mode": "train", "epoch": 92, "iter": 3200, "lr": 0.03271, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34062, "top5_acc": 0.60016, "loss_cls": 3.72725, "loss": 3.72725, "time": 0.85458} +{"mode": "train", "epoch": 92, "iter": 3300, "lr": 0.03269, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34578, "top5_acc": 0.61156, "loss_cls": 3.67415, "loss": 3.67415, "time": 0.85295} +{"mode": "train", "epoch": 92, "iter": 3400, "lr": 0.03266, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34922, "top5_acc": 0.60531, "loss_cls": 3.70117, "loss": 3.70117, "time": 0.84678} +{"mode": "train", "epoch": 92, "iter": 3500, "lr": 0.03264, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36594, "top5_acc": 0.62516, "loss_cls": 3.60459, "loss": 3.60459, "time": 0.84842} +{"mode": "train", "epoch": 92, "iter": 3600, "lr": 0.03261, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35578, "top5_acc": 0.61, "loss_cls": 3.65081, "loss": 3.65081, "time": 0.85736} +{"mode": "train", "epoch": 92, "iter": 3700, "lr": 0.03258, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.35109, "top5_acc": 0.60828, "loss_cls": 3.67951, "loss": 3.67951, "time": 0.86708} +{"mode": "val", "epoch": 92, "iter": 309, "lr": 0.03257, "top1_acc": 0.28182, "top5_acc": 0.53725, "mean_class_accuracy": 0.28152} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.03255, "memory": 15990, "data_time": 1.55536, "top1_acc": 0.36938, "top5_acc": 0.63703, "loss_cls": 3.53427, "loss": 3.53427, "time": 2.61033} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.03252, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.37047, "top5_acc": 0.63078, "loss_cls": 3.56974, "loss": 3.56974, "time": 0.87798} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.03249, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.35359, "top5_acc": 0.61453, "loss_cls": 3.63761, "loss": 3.63761, "time": 0.87248} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.03247, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.36047, "top5_acc": 0.62359, "loss_cls": 3.59901, "loss": 3.59901, "time": 0.86523} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.03244, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.35547, "top5_acc": 0.61703, "loss_cls": 3.62105, "loss": 3.62105, "time": 0.86899} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.03241, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36438, "top5_acc": 0.62094, "loss_cls": 3.63389, "loss": 3.63389, "time": 0.86205} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.03239, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.35438, "top5_acc": 0.61297, "loss_cls": 3.66806, "loss": 3.66806, "time": 0.85872} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.03236, "memory": 15990, "data_time": 0.00077, "top1_acc": 0.36, "top5_acc": 0.62141, "loss_cls": 3.64006, "loss": 3.64006, "time": 0.86089} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.03234, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36703, "top5_acc": 0.62531, "loss_cls": 3.56829, "loss": 3.56829, "time": 0.86814} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.03231, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.35125, "top5_acc": 0.61625, "loss_cls": 3.64356, "loss": 3.64356, "time": 0.85281} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.03228, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36109, "top5_acc": 0.62969, "loss_cls": 3.61741, "loss": 3.61741, "time": 0.85403} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.03226, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.35453, "top5_acc": 0.62562, "loss_cls": 3.6249, "loss": 3.6249, "time": 0.85463} +{"mode": "train", "epoch": 93, "iter": 1300, "lr": 0.03223, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35516, "top5_acc": 0.61578, "loss_cls": 3.64142, "loss": 3.64142, "time": 0.85576} +{"mode": "train", "epoch": 93, "iter": 1400, "lr": 0.03221, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35109, "top5_acc": 0.6125, "loss_cls": 3.64718, "loss": 3.64718, "time": 0.85899} +{"mode": "train", "epoch": 93, "iter": 1500, "lr": 0.03218, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35422, "top5_acc": 0.61984, "loss_cls": 3.62712, "loss": 3.62712, "time": 0.85574} +{"mode": "train", "epoch": 93, "iter": 1600, "lr": 0.03215, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36406, "top5_acc": 0.62703, "loss_cls": 3.60306, "loss": 3.60306, "time": 0.85216} +{"mode": "train", "epoch": 93, "iter": 1700, "lr": 0.03213, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37172, "top5_acc": 0.63016, "loss_cls": 3.58662, "loss": 3.58662, "time": 0.85606} +{"mode": "train", "epoch": 93, "iter": 1800, "lr": 0.0321, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37453, "top5_acc": 0.62375, "loss_cls": 3.56324, "loss": 3.56324, "time": 0.85326} +{"mode": "train", "epoch": 93, "iter": 1900, "lr": 0.03207, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.3525, "top5_acc": 0.62328, "loss_cls": 3.60451, "loss": 3.60451, "time": 0.85027} +{"mode": "train", "epoch": 93, "iter": 2000, "lr": 0.03205, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35766, "top5_acc": 0.63109, "loss_cls": 3.61154, "loss": 3.61154, "time": 0.84886} +{"mode": "train", "epoch": 93, "iter": 2100, "lr": 0.03202, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35938, "top5_acc": 0.62078, "loss_cls": 3.6331, "loss": 3.6331, "time": 0.85663} +{"mode": "train", "epoch": 93, "iter": 2200, "lr": 0.032, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35703, "top5_acc": 0.62297, "loss_cls": 3.63471, "loss": 3.63471, "time": 0.8635} +{"mode": "train", "epoch": 93, "iter": 2300, "lr": 0.03197, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34609, "top5_acc": 0.61344, "loss_cls": 3.66045, "loss": 3.66045, "time": 0.86519} +{"mode": "train", "epoch": 93, "iter": 2400, "lr": 0.03194, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.36578, "top5_acc": 0.62203, "loss_cls": 3.60946, "loss": 3.60946, "time": 0.86257} +{"mode": "train", "epoch": 93, "iter": 2500, "lr": 0.03192, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35438, "top5_acc": 0.62734, "loss_cls": 3.64065, "loss": 3.64065, "time": 0.8633} +{"mode": "train", "epoch": 93, "iter": 2600, "lr": 0.03189, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35891, "top5_acc": 0.62203, "loss_cls": 3.60026, "loss": 3.60026, "time": 0.86681} +{"mode": "train", "epoch": 93, "iter": 2700, "lr": 0.03187, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.35547, "top5_acc": 0.62656, "loss_cls": 3.62949, "loss": 3.62949, "time": 0.86849} +{"mode": "train", "epoch": 93, "iter": 2800, "lr": 0.03184, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36375, "top5_acc": 0.63609, "loss_cls": 3.58279, "loss": 3.58279, "time": 0.86607} +{"mode": "train", "epoch": 93, "iter": 2900, "lr": 0.03181, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.36453, "top5_acc": 0.625, "loss_cls": 3.60211, "loss": 3.60211, "time": 0.85792} +{"mode": "train", "epoch": 93, "iter": 3000, "lr": 0.03179, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36953, "top5_acc": 0.63141, "loss_cls": 3.6104, "loss": 3.6104, "time": 0.85402} +{"mode": "train", "epoch": 93, "iter": 3100, "lr": 0.03176, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34828, "top5_acc": 0.60406, "loss_cls": 3.68024, "loss": 3.68024, "time": 0.85611} +{"mode": "train", "epoch": 93, "iter": 3200, "lr": 0.03174, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35516, "top5_acc": 0.61687, "loss_cls": 3.64814, "loss": 3.64814, "time": 0.86169} +{"mode": "train", "epoch": 93, "iter": 3300, "lr": 0.03171, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35109, "top5_acc": 0.61484, "loss_cls": 3.65314, "loss": 3.65314, "time": 0.84908} +{"mode": "train", "epoch": 93, "iter": 3400, "lr": 0.03168, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35625, "top5_acc": 0.62, "loss_cls": 3.64691, "loss": 3.64691, "time": 0.85144} +{"mode": "train", "epoch": 93, "iter": 3500, "lr": 0.03166, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35047, "top5_acc": 0.61969, "loss_cls": 3.66059, "loss": 3.66059, "time": 0.85091} +{"mode": "train", "epoch": 93, "iter": 3600, "lr": 0.03163, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34578, "top5_acc": 0.61109, "loss_cls": 3.69687, "loss": 3.69687, "time": 0.85011} +{"mode": "train", "epoch": 93, "iter": 3700, "lr": 0.03161, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36203, "top5_acc": 0.62547, "loss_cls": 3.60389, "loss": 3.60389, "time": 0.85258} +{"mode": "val", "epoch": 93, "iter": 309, "lr": 0.03159, "top1_acc": 0.29565, "top5_acc": 0.55316, "mean_class_accuracy": 0.29549} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.03157, "memory": 15990, "data_time": 1.6471, "top1_acc": 0.36078, "top5_acc": 0.62781, "loss_cls": 3.57688, "loss": 3.57688, "time": 2.69085} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.03154, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36203, "top5_acc": 0.63313, "loss_cls": 3.57086, "loss": 3.57086, "time": 0.86673} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.03152, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36031, "top5_acc": 0.62609, "loss_cls": 3.58361, "loss": 3.58361, "time": 0.85882} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.03149, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.36172, "top5_acc": 0.63562, "loss_cls": 3.55923, "loss": 3.55923, "time": 0.87161} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.03146, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.38094, "top5_acc": 0.63625, "loss_cls": 3.5336, "loss": 3.5336, "time": 0.86694} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.03144, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36234, "top5_acc": 0.62172, "loss_cls": 3.63679, "loss": 3.63679, "time": 0.86931} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.03141, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.36875, "top5_acc": 0.63094, "loss_cls": 3.55277, "loss": 3.55277, "time": 0.86433} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.03139, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.37359, "top5_acc": 0.63016, "loss_cls": 3.55902, "loss": 3.55902, "time": 0.86303} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.03136, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36016, "top5_acc": 0.62922, "loss_cls": 3.60832, "loss": 3.60832, "time": 0.85883} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.03133, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36797, "top5_acc": 0.6275, "loss_cls": 3.57241, "loss": 3.57241, "time": 0.8555} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.03131, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35281, "top5_acc": 0.61469, "loss_cls": 3.65577, "loss": 3.65577, "time": 0.85115} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.03128, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35703, "top5_acc": 0.62297, "loss_cls": 3.62116, "loss": 3.62116, "time": 0.85481} +{"mode": "train", "epoch": 94, "iter": 1300, "lr": 0.03126, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35297, "top5_acc": 0.62187, "loss_cls": 3.63353, "loss": 3.63353, "time": 0.85966} +{"mode": "train", "epoch": 94, "iter": 1400, "lr": 0.03123, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36203, "top5_acc": 0.62891, "loss_cls": 3.58215, "loss": 3.58215, "time": 0.85461} +{"mode": "train", "epoch": 94, "iter": 1500, "lr": 0.0312, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35578, "top5_acc": 0.62359, "loss_cls": 3.65137, "loss": 3.65137, "time": 0.85281} +{"mode": "train", "epoch": 94, "iter": 1600, "lr": 0.03118, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36172, "top5_acc": 0.62062, "loss_cls": 3.63299, "loss": 3.63299, "time": 0.84972} +{"mode": "train", "epoch": 94, "iter": 1700, "lr": 0.03115, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36266, "top5_acc": 0.62922, "loss_cls": 3.59629, "loss": 3.59629, "time": 0.85603} +{"mode": "train", "epoch": 94, "iter": 1800, "lr": 0.03113, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35344, "top5_acc": 0.615, "loss_cls": 3.62894, "loss": 3.62894, "time": 0.854} +{"mode": "train", "epoch": 94, "iter": 1900, "lr": 0.0311, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.34828, "top5_acc": 0.61625, "loss_cls": 3.67392, "loss": 3.67392, "time": 0.86309} +{"mode": "train", "epoch": 94, "iter": 2000, "lr": 0.03108, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36609, "top5_acc": 0.63297, "loss_cls": 3.55962, "loss": 3.55962, "time": 0.84571} +{"mode": "train", "epoch": 94, "iter": 2100, "lr": 0.03105, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36141, "top5_acc": 0.61562, "loss_cls": 3.60813, "loss": 3.60813, "time": 0.85125} +{"mode": "train", "epoch": 94, "iter": 2200, "lr": 0.03102, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35859, "top5_acc": 0.62125, "loss_cls": 3.60647, "loss": 3.60647, "time": 0.85338} +{"mode": "train", "epoch": 94, "iter": 2300, "lr": 0.031, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36953, "top5_acc": 0.62922, "loss_cls": 3.56595, "loss": 3.56595, "time": 0.85216} +{"mode": "train", "epoch": 94, "iter": 2400, "lr": 0.03097, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36547, "top5_acc": 0.62469, "loss_cls": 3.60317, "loss": 3.60317, "time": 0.85086} +{"mode": "train", "epoch": 94, "iter": 2500, "lr": 0.03095, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35328, "top5_acc": 0.62359, "loss_cls": 3.60485, "loss": 3.60485, "time": 0.84654} +{"mode": "train", "epoch": 94, "iter": 2600, "lr": 0.03092, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35328, "top5_acc": 0.62891, "loss_cls": 3.62408, "loss": 3.62408, "time": 0.85307} +{"mode": "train", "epoch": 94, "iter": 2700, "lr": 0.03089, "memory": 15990, "data_time": 0.00078, "top1_acc": 0.36359, "top5_acc": 0.62641, "loss_cls": 3.60924, "loss": 3.60924, "time": 0.84995} +{"mode": "train", "epoch": 94, "iter": 2800, "lr": 0.03087, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35688, "top5_acc": 0.62219, "loss_cls": 3.60034, "loss": 3.60034, "time": 0.85534} +{"mode": "train", "epoch": 94, "iter": 2900, "lr": 0.03084, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35703, "top5_acc": 0.62313, "loss_cls": 3.63734, "loss": 3.63734, "time": 0.8466} +{"mode": "train", "epoch": 94, "iter": 3000, "lr": 0.03082, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34844, "top5_acc": 0.61453, "loss_cls": 3.67023, "loss": 3.67023, "time": 0.85061} +{"mode": "train", "epoch": 94, "iter": 3100, "lr": 0.03079, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35062, "top5_acc": 0.61094, "loss_cls": 3.68608, "loss": 3.68608, "time": 0.85279} +{"mode": "train", "epoch": 94, "iter": 3200, "lr": 0.03077, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36812, "top5_acc": 0.63016, "loss_cls": 3.57242, "loss": 3.57242, "time": 0.85669} +{"mode": "train", "epoch": 94, "iter": 3300, "lr": 0.03074, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35547, "top5_acc": 0.62078, "loss_cls": 3.64917, "loss": 3.64917, "time": 0.85549} +{"mode": "train", "epoch": 94, "iter": 3400, "lr": 0.03071, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36016, "top5_acc": 0.62766, "loss_cls": 3.62033, "loss": 3.62033, "time": 0.84817} +{"mode": "train", "epoch": 94, "iter": 3500, "lr": 0.03069, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.365, "top5_acc": 0.61672, "loss_cls": 3.62425, "loss": 3.62425, "time": 0.857} +{"mode": "train", "epoch": 94, "iter": 3600, "lr": 0.03066, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35797, "top5_acc": 0.61938, "loss_cls": 3.61297, "loss": 3.61297, "time": 0.85713} +{"mode": "train", "epoch": 94, "iter": 3700, "lr": 0.03064, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.35219, "top5_acc": 0.62187, "loss_cls": 3.60844, "loss": 3.60844, "time": 0.855} +{"mode": "val", "epoch": 94, "iter": 309, "lr": 0.03062, "top1_acc": 0.30664, "top5_acc": 0.55564, "mean_class_accuracy": 0.3063} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.0306, "memory": 15990, "data_time": 1.53648, "top1_acc": 0.36391, "top5_acc": 0.63688, "loss_cls": 3.53618, "loss": 3.53618, "time": 2.60303} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.03057, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.37609, "top5_acc": 0.63734, "loss_cls": 3.5246, "loss": 3.5246, "time": 0.87391} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.03055, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.37656, "top5_acc": 0.64031, "loss_cls": 3.52557, "loss": 3.52557, "time": 0.86924} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.03052, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.37016, "top5_acc": 0.63906, "loss_cls": 3.56213, "loss": 3.56213, "time": 0.86757} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.0305, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.36016, "top5_acc": 0.62766, "loss_cls": 3.5914, "loss": 3.5914, "time": 0.86323} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.03047, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.35188, "top5_acc": 0.62016, "loss_cls": 3.63059, "loss": 3.63059, "time": 0.8656} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.03044, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.36672, "top5_acc": 0.62734, "loss_cls": 3.57273, "loss": 3.57273, "time": 0.86312} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.03042, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.36688, "top5_acc": 0.62656, "loss_cls": 3.5807, "loss": 3.5807, "time": 0.85301} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.03039, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37156, "top5_acc": 0.63375, "loss_cls": 3.54699, "loss": 3.54699, "time": 0.8579} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.03037, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.36328, "top5_acc": 0.63062, "loss_cls": 3.559, "loss": 3.559, "time": 0.85138} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.03034, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.3525, "top5_acc": 0.61469, "loss_cls": 3.60463, "loss": 3.60463, "time": 0.85404} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.03032, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35641, "top5_acc": 0.61828, "loss_cls": 3.61737, "loss": 3.61737, "time": 0.85722} +{"mode": "train", "epoch": 95, "iter": 1300, "lr": 0.03029, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37422, "top5_acc": 0.63266, "loss_cls": 3.56171, "loss": 3.56171, "time": 0.86011} +{"mode": "train", "epoch": 95, "iter": 1400, "lr": 0.03026, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35828, "top5_acc": 0.61797, "loss_cls": 3.62818, "loss": 3.62818, "time": 0.85346} +{"mode": "train", "epoch": 95, "iter": 1500, "lr": 0.03024, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.36328, "top5_acc": 0.61984, "loss_cls": 3.62422, "loss": 3.62422, "time": 0.86314} +{"mode": "train", "epoch": 95, "iter": 1600, "lr": 0.03021, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.36625, "top5_acc": 0.62359, "loss_cls": 3.58684, "loss": 3.58684, "time": 0.86367} +{"mode": "train", "epoch": 95, "iter": 1700, "lr": 0.03019, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.36734, "top5_acc": 0.62906, "loss_cls": 3.55128, "loss": 3.55128, "time": 0.86286} +{"mode": "train", "epoch": 95, "iter": 1800, "lr": 0.03016, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35281, "top5_acc": 0.61922, "loss_cls": 3.62374, "loss": 3.62374, "time": 0.85832} +{"mode": "train", "epoch": 95, "iter": 1900, "lr": 0.03014, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36109, "top5_acc": 0.63094, "loss_cls": 3.56226, "loss": 3.56226, "time": 0.8628} +{"mode": "train", "epoch": 95, "iter": 2000, "lr": 0.03011, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35844, "top5_acc": 0.61844, "loss_cls": 3.59859, "loss": 3.59859, "time": 0.85231} +{"mode": "train", "epoch": 95, "iter": 2100, "lr": 0.03008, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35062, "top5_acc": 0.62859, "loss_cls": 3.62695, "loss": 3.62695, "time": 0.8549} +{"mode": "train", "epoch": 95, "iter": 2200, "lr": 0.03006, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35562, "top5_acc": 0.62031, "loss_cls": 3.6379, "loss": 3.6379, "time": 0.84595} +{"mode": "train", "epoch": 95, "iter": 2300, "lr": 0.03003, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36172, "top5_acc": 0.61797, "loss_cls": 3.62504, "loss": 3.62504, "time": 0.85389} +{"mode": "train", "epoch": 95, "iter": 2400, "lr": 0.03001, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36016, "top5_acc": 0.63562, "loss_cls": 3.57129, "loss": 3.57129, "time": 0.85309} +{"mode": "train", "epoch": 95, "iter": 2500, "lr": 0.02998, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.35047, "top5_acc": 0.62141, "loss_cls": 3.62844, "loss": 3.62844, "time": 0.85459} +{"mode": "train", "epoch": 95, "iter": 2600, "lr": 0.02996, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36344, "top5_acc": 0.62062, "loss_cls": 3.58859, "loss": 3.58859, "time": 0.86096} +{"mode": "train", "epoch": 95, "iter": 2700, "lr": 0.02993, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.35812, "top5_acc": 0.62547, "loss_cls": 3.59217, "loss": 3.59217, "time": 0.85933} +{"mode": "train", "epoch": 95, "iter": 2800, "lr": 0.02991, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.36141, "top5_acc": 0.63078, "loss_cls": 3.59464, "loss": 3.59464, "time": 0.84619} +{"mode": "train", "epoch": 95, "iter": 2900, "lr": 0.02988, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.36578, "top5_acc": 0.62109, "loss_cls": 3.62637, "loss": 3.62637, "time": 0.84844} +{"mode": "train", "epoch": 95, "iter": 3000, "lr": 0.02985, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.35766, "top5_acc": 0.61906, "loss_cls": 3.63941, "loss": 3.63941, "time": 0.84819} +{"mode": "train", "epoch": 95, "iter": 3100, "lr": 0.02983, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37531, "top5_acc": 0.63078, "loss_cls": 3.56157, "loss": 3.56157, "time": 0.85673} +{"mode": "train", "epoch": 95, "iter": 3200, "lr": 0.0298, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35281, "top5_acc": 0.61625, "loss_cls": 3.62853, "loss": 3.62853, "time": 0.85646} +{"mode": "train", "epoch": 95, "iter": 3300, "lr": 0.02978, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37203, "top5_acc": 0.63578, "loss_cls": 3.54539, "loss": 3.54539, "time": 0.85371} +{"mode": "train", "epoch": 95, "iter": 3400, "lr": 0.02975, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35547, "top5_acc": 0.61984, "loss_cls": 3.63721, "loss": 3.63721, "time": 0.85768} +{"mode": "train", "epoch": 95, "iter": 3500, "lr": 0.02973, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37188, "top5_acc": 0.64016, "loss_cls": 3.55387, "loss": 3.55387, "time": 0.86015} +{"mode": "train", "epoch": 95, "iter": 3600, "lr": 0.0297, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37422, "top5_acc": 0.63234, "loss_cls": 3.55564, "loss": 3.55564, "time": 0.85952} +{"mode": "train", "epoch": 95, "iter": 3700, "lr": 0.02968, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35656, "top5_acc": 0.62203, "loss_cls": 3.62843, "loss": 3.62843, "time": 0.85663} +{"mode": "val", "epoch": 95, "iter": 309, "lr": 0.02966, "top1_acc": 0.26344, "top5_acc": 0.51127, "mean_class_accuracy": 0.26337} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.02964, "memory": 15990, "data_time": 1.56443, "top1_acc": 0.37906, "top5_acc": 0.64141, "loss_cls": 3.50486, "loss": 3.50486, "time": 2.60026} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.02961, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38547, "top5_acc": 0.64109, "loss_cls": 3.49595, "loss": 3.49595, "time": 0.85808} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.02959, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36344, "top5_acc": 0.63609, "loss_cls": 3.51999, "loss": 3.51999, "time": 0.85866} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.02956, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38547, "top5_acc": 0.64156, "loss_cls": 3.50673, "loss": 3.50673, "time": 0.85326} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.02954, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36484, "top5_acc": 0.63094, "loss_cls": 3.56711, "loss": 3.56711, "time": 0.85447} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.02951, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36766, "top5_acc": 0.63656, "loss_cls": 3.55327, "loss": 3.55327, "time": 0.85111} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.02948, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36375, "top5_acc": 0.62687, "loss_cls": 3.58884, "loss": 3.58884, "time": 0.85378} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.02946, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36047, "top5_acc": 0.62187, "loss_cls": 3.621, "loss": 3.621, "time": 0.85764} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.02943, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37062, "top5_acc": 0.63516, "loss_cls": 3.58385, "loss": 3.58385, "time": 0.85375} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.02941, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.36078, "top5_acc": 0.62578, "loss_cls": 3.61134, "loss": 3.61134, "time": 0.84865} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.02938, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36891, "top5_acc": 0.64, "loss_cls": 3.56551, "loss": 3.56551, "time": 0.8538} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.02936, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36484, "top5_acc": 0.63094, "loss_cls": 3.57401, "loss": 3.57401, "time": 0.84757} +{"mode": "train", "epoch": 96, "iter": 1300, "lr": 0.02933, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.37281, "top5_acc": 0.63906, "loss_cls": 3.53306, "loss": 3.53306, "time": 0.84945} +{"mode": "train", "epoch": 96, "iter": 1400, "lr": 0.02931, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36, "top5_acc": 0.62156, "loss_cls": 3.61277, "loss": 3.61277, "time": 0.85554} +{"mode": "train", "epoch": 96, "iter": 1500, "lr": 0.02928, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36875, "top5_acc": 0.63922, "loss_cls": 3.53204, "loss": 3.53204, "time": 0.85048} +{"mode": "train", "epoch": 96, "iter": 1600, "lr": 0.02926, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36266, "top5_acc": 0.63578, "loss_cls": 3.58127, "loss": 3.58127, "time": 0.85308} +{"mode": "train", "epoch": 96, "iter": 1700, "lr": 0.02923, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35922, "top5_acc": 0.62984, "loss_cls": 3.59006, "loss": 3.59006, "time": 0.85104} +{"mode": "train", "epoch": 96, "iter": 1800, "lr": 0.0292, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36375, "top5_acc": 0.62641, "loss_cls": 3.59332, "loss": 3.59332, "time": 0.85694} +{"mode": "train", "epoch": 96, "iter": 1900, "lr": 0.02918, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.36062, "top5_acc": 0.63016, "loss_cls": 3.55631, "loss": 3.55631, "time": 0.85573} +{"mode": "train", "epoch": 96, "iter": 2000, "lr": 0.02915, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.365, "top5_acc": 0.63219, "loss_cls": 3.57478, "loss": 3.57478, "time": 0.86157} +{"mode": "train", "epoch": 96, "iter": 2100, "lr": 0.02913, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35734, "top5_acc": 0.62109, "loss_cls": 3.64853, "loss": 3.64853, "time": 0.8635} +{"mode": "train", "epoch": 96, "iter": 2200, "lr": 0.0291, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37156, "top5_acc": 0.63219, "loss_cls": 3.56264, "loss": 3.56264, "time": 0.86173} +{"mode": "train", "epoch": 96, "iter": 2300, "lr": 0.02908, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36516, "top5_acc": 0.635, "loss_cls": 3.55456, "loss": 3.55456, "time": 0.8529} +{"mode": "train", "epoch": 96, "iter": 2400, "lr": 0.02905, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36797, "top5_acc": 0.63109, "loss_cls": 3.57586, "loss": 3.57586, "time": 0.85531} +{"mode": "train", "epoch": 96, "iter": 2500, "lr": 0.02903, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36703, "top5_acc": 0.62516, "loss_cls": 3.56894, "loss": 3.56894, "time": 0.86149} +{"mode": "train", "epoch": 96, "iter": 2600, "lr": 0.029, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.35859, "top5_acc": 0.62984, "loss_cls": 3.61624, "loss": 3.61624, "time": 0.8558} +{"mode": "train", "epoch": 96, "iter": 2700, "lr": 0.02898, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36734, "top5_acc": 0.63031, "loss_cls": 3.56613, "loss": 3.56613, "time": 0.85612} +{"mode": "train", "epoch": 96, "iter": 2800, "lr": 0.02895, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36219, "top5_acc": 0.62187, "loss_cls": 3.60325, "loss": 3.60325, "time": 0.85663} +{"mode": "train", "epoch": 96, "iter": 2900, "lr": 0.02893, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36719, "top5_acc": 0.62672, "loss_cls": 3.57306, "loss": 3.57306, "time": 0.85292} +{"mode": "train", "epoch": 96, "iter": 3000, "lr": 0.0289, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35672, "top5_acc": 0.62438, "loss_cls": 3.60704, "loss": 3.60704, "time": 0.84222} +{"mode": "train", "epoch": 96, "iter": 3100, "lr": 0.02887, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37281, "top5_acc": 0.63797, "loss_cls": 3.51763, "loss": 3.51763, "time": 0.84755} +{"mode": "train", "epoch": 96, "iter": 3200, "lr": 0.02885, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36344, "top5_acc": 0.61938, "loss_cls": 3.63269, "loss": 3.63269, "time": 0.85163} +{"mode": "train", "epoch": 96, "iter": 3300, "lr": 0.02882, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36281, "top5_acc": 0.63141, "loss_cls": 3.59519, "loss": 3.59519, "time": 0.84956} +{"mode": "train", "epoch": 96, "iter": 3400, "lr": 0.0288, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37578, "top5_acc": 0.63859, "loss_cls": 3.52264, "loss": 3.52264, "time": 0.85367} +{"mode": "train", "epoch": 96, "iter": 3500, "lr": 0.02877, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35766, "top5_acc": 0.62562, "loss_cls": 3.584, "loss": 3.584, "time": 0.84461} +{"mode": "train", "epoch": 96, "iter": 3600, "lr": 0.02875, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36859, "top5_acc": 0.63156, "loss_cls": 3.59891, "loss": 3.59891, "time": 0.85058} +{"mode": "train", "epoch": 96, "iter": 3700, "lr": 0.02872, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36094, "top5_acc": 0.62469, "loss_cls": 3.61274, "loss": 3.61274, "time": 0.85275} +{"mode": "val", "epoch": 96, "iter": 309, "lr": 0.02871, "top1_acc": 0.28597, "top5_acc": 0.53822, "mean_class_accuracy": 0.28583} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.02869, "memory": 15990, "data_time": 1.61772, "top1_acc": 0.37609, "top5_acc": 0.65453, "loss_cls": 3.4635, "loss": 3.4635, "time": 2.67163} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.02866, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.38062, "top5_acc": 0.64078, "loss_cls": 3.50716, "loss": 3.50716, "time": 0.86489} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.02864, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.37875, "top5_acc": 0.64156, "loss_cls": 3.52387, "loss": 3.52387, "time": 0.86886} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.02861, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.37938, "top5_acc": 0.64141, "loss_cls": 3.51582, "loss": 3.51582, "time": 0.86431} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.02858, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37516, "top5_acc": 0.63125, "loss_cls": 3.55102, "loss": 3.55102, "time": 0.86422} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.02856, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.37562, "top5_acc": 0.63953, "loss_cls": 3.52348, "loss": 3.52348, "time": 0.86831} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.02853, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36359, "top5_acc": 0.63531, "loss_cls": 3.54205, "loss": 3.54205, "time": 0.87011} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.02851, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.38406, "top5_acc": 0.64188, "loss_cls": 3.50213, "loss": 3.50213, "time": 0.85941} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.02848, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.36453, "top5_acc": 0.63672, "loss_cls": 3.53812, "loss": 3.53812, "time": 0.86461} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.02846, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.36938, "top5_acc": 0.63625, "loss_cls": 3.56541, "loss": 3.56541, "time": 0.86866} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.02843, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.35953, "top5_acc": 0.63828, "loss_cls": 3.53949, "loss": 3.53949, "time": 0.8637} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.02841, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37438, "top5_acc": 0.64016, "loss_cls": 3.49731, "loss": 3.49731, "time": 0.86358} +{"mode": "train", "epoch": 97, "iter": 1300, "lr": 0.02838, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.37484, "top5_acc": 0.6375, "loss_cls": 3.55035, "loss": 3.55035, "time": 0.86337} +{"mode": "train", "epoch": 97, "iter": 1400, "lr": 0.02836, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35953, "top5_acc": 0.61781, "loss_cls": 3.63887, "loss": 3.63887, "time": 0.86665} +{"mode": "train", "epoch": 97, "iter": 1500, "lr": 0.02833, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.36703, "top5_acc": 0.63734, "loss_cls": 3.53591, "loss": 3.53591, "time": 0.86568} +{"mode": "train", "epoch": 97, "iter": 1600, "lr": 0.02831, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.36609, "top5_acc": 0.63031, "loss_cls": 3.56315, "loss": 3.56315, "time": 0.86611} +{"mode": "train", "epoch": 97, "iter": 1700, "lr": 0.02828, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.36516, "top5_acc": 0.63344, "loss_cls": 3.56734, "loss": 3.56734, "time": 0.8661} +{"mode": "train", "epoch": 97, "iter": 1800, "lr": 0.02826, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.36797, "top5_acc": 0.63828, "loss_cls": 3.51178, "loss": 3.51178, "time": 0.86182} +{"mode": "train", "epoch": 97, "iter": 1900, "lr": 0.02823, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.37156, "top5_acc": 0.64234, "loss_cls": 3.53173, "loss": 3.53173, "time": 0.86366} +{"mode": "train", "epoch": 97, "iter": 2000, "lr": 0.02821, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37422, "top5_acc": 0.62766, "loss_cls": 3.56699, "loss": 3.56699, "time": 0.86244} +{"mode": "train", "epoch": 97, "iter": 2100, "lr": 0.02818, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36656, "top5_acc": 0.62328, "loss_cls": 3.56086, "loss": 3.56086, "time": 0.86648} +{"mode": "train", "epoch": 97, "iter": 2200, "lr": 0.02816, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.36125, "top5_acc": 0.62594, "loss_cls": 3.57491, "loss": 3.57491, "time": 0.86657} +{"mode": "train", "epoch": 97, "iter": 2300, "lr": 0.02813, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.35688, "top5_acc": 0.62687, "loss_cls": 3.59609, "loss": 3.59609, "time": 0.86519} +{"mode": "train", "epoch": 97, "iter": 2400, "lr": 0.02811, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.36891, "top5_acc": 0.64234, "loss_cls": 3.57191, "loss": 3.57191, "time": 0.86787} +{"mode": "train", "epoch": 97, "iter": 2500, "lr": 0.02808, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36203, "top5_acc": 0.62313, "loss_cls": 3.61251, "loss": 3.61251, "time": 0.86608} +{"mode": "train", "epoch": 97, "iter": 2600, "lr": 0.02806, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36562, "top5_acc": 0.6275, "loss_cls": 3.58459, "loss": 3.58459, "time": 0.86441} +{"mode": "train", "epoch": 97, "iter": 2700, "lr": 0.02803, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.37344, "top5_acc": 0.63609, "loss_cls": 3.53496, "loss": 3.53496, "time": 0.86298} +{"mode": "train", "epoch": 97, "iter": 2800, "lr": 0.02801, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.36828, "top5_acc": 0.63016, "loss_cls": 3.56383, "loss": 3.56383, "time": 0.86191} +{"mode": "train", "epoch": 97, "iter": 2900, "lr": 0.02798, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.37203, "top5_acc": 0.62359, "loss_cls": 3.58295, "loss": 3.58295, "time": 0.86044} +{"mode": "train", "epoch": 97, "iter": 3000, "lr": 0.02796, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36422, "top5_acc": 0.63016, "loss_cls": 3.58019, "loss": 3.58019, "time": 0.86429} +{"mode": "train", "epoch": 97, "iter": 3100, "lr": 0.02793, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.36656, "top5_acc": 0.62672, "loss_cls": 3.54946, "loss": 3.54946, "time": 0.86652} +{"mode": "train", "epoch": 97, "iter": 3200, "lr": 0.02791, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.36953, "top5_acc": 0.63469, "loss_cls": 3.5735, "loss": 3.5735, "time": 0.87414} +{"mode": "train", "epoch": 97, "iter": 3300, "lr": 0.02788, "memory": 15990, "data_time": 0.00088, "top1_acc": 0.36438, "top5_acc": 0.63734, "loss_cls": 3.53767, "loss": 3.53767, "time": 0.87695} +{"mode": "train", "epoch": 97, "iter": 3400, "lr": 0.02786, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.36469, "top5_acc": 0.63203, "loss_cls": 3.5664, "loss": 3.5664, "time": 0.87293} +{"mode": "train", "epoch": 97, "iter": 3500, "lr": 0.02783, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.36625, "top5_acc": 0.63422, "loss_cls": 3.56412, "loss": 3.56412, "time": 0.87622} +{"mode": "train", "epoch": 97, "iter": 3600, "lr": 0.02781, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.36891, "top5_acc": 0.64094, "loss_cls": 3.54095, "loss": 3.54095, "time": 0.88463} +{"mode": "train", "epoch": 97, "iter": 3700, "lr": 0.02778, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.36203, "top5_acc": 0.63516, "loss_cls": 3.58025, "loss": 3.58025, "time": 0.88362} +{"mode": "val", "epoch": 97, "iter": 309, "lr": 0.02777, "top1_acc": 0.30406, "top5_acc": 0.57008, "mean_class_accuracy": 0.30397} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.02774, "memory": 15990, "data_time": 1.68384, "top1_acc": 0.37594, "top5_acc": 0.6425, "loss_cls": 3.4794, "loss": 3.4794, "time": 2.73439} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.02772, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.38719, "top5_acc": 0.64734, "loss_cls": 3.47477, "loss": 3.47477, "time": 0.87131} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.02769, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.38578, "top5_acc": 0.64312, "loss_cls": 3.47857, "loss": 3.47857, "time": 0.87082} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.02767, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.37547, "top5_acc": 0.64984, "loss_cls": 3.48739, "loss": 3.48739, "time": 0.87034} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.02764, "memory": 15990, "data_time": 0.00067, "top1_acc": 0.36156, "top5_acc": 0.62813, "loss_cls": 3.56081, "loss": 3.56081, "time": 0.86659} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.02762, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.35859, "top5_acc": 0.63578, "loss_cls": 3.58082, "loss": 3.58082, "time": 0.87006} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.02759, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.37578, "top5_acc": 0.63703, "loss_cls": 3.51342, "loss": 3.51342, "time": 0.86504} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.02757, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.37422, "top5_acc": 0.62938, "loss_cls": 3.54278, "loss": 3.54278, "time": 0.86288} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.02754, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.37062, "top5_acc": 0.64141, "loss_cls": 3.52834, "loss": 3.52834, "time": 0.87085} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.02752, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.38203, "top5_acc": 0.63875, "loss_cls": 3.51009, "loss": 3.51009, "time": 0.86488} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.02749, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.37188, "top5_acc": 0.63297, "loss_cls": 3.54682, "loss": 3.54682, "time": 0.86156} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.02747, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.37406, "top5_acc": 0.63766, "loss_cls": 3.55383, "loss": 3.55383, "time": 0.86044} +{"mode": "train", "epoch": 98, "iter": 1300, "lr": 0.02744, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38, "top5_acc": 0.6375, "loss_cls": 3.51487, "loss": 3.51487, "time": 0.85571} +{"mode": "train", "epoch": 98, "iter": 1400, "lr": 0.02742, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37156, "top5_acc": 0.62734, "loss_cls": 3.5729, "loss": 3.5729, "time": 0.85606} +{"mode": "train", "epoch": 98, "iter": 1500, "lr": 0.02739, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37391, "top5_acc": 0.635, "loss_cls": 3.49818, "loss": 3.49818, "time": 0.85493} +{"mode": "train", "epoch": 98, "iter": 1600, "lr": 0.02737, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37891, "top5_acc": 0.64125, "loss_cls": 3.5135, "loss": 3.5135, "time": 0.85206} +{"mode": "train", "epoch": 98, "iter": 1700, "lr": 0.02734, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35875, "top5_acc": 0.62938, "loss_cls": 3.5895, "loss": 3.5895, "time": 0.85855} +{"mode": "train", "epoch": 98, "iter": 1800, "lr": 0.02732, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37219, "top5_acc": 0.63656, "loss_cls": 3.519, "loss": 3.519, "time": 0.8577} +{"mode": "train", "epoch": 98, "iter": 1900, "lr": 0.02729, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37281, "top5_acc": 0.63953, "loss_cls": 3.50347, "loss": 3.50347, "time": 0.85792} +{"mode": "train", "epoch": 98, "iter": 2000, "lr": 0.02727, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37578, "top5_acc": 0.63438, "loss_cls": 3.54967, "loss": 3.54967, "time": 0.85607} +{"mode": "train", "epoch": 98, "iter": 2100, "lr": 0.02724, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36812, "top5_acc": 0.63516, "loss_cls": 3.5438, "loss": 3.5438, "time": 0.86034} +{"mode": "train", "epoch": 98, "iter": 2200, "lr": 0.02722, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37672, "top5_acc": 0.63656, "loss_cls": 3.55108, "loss": 3.55108, "time": 0.86063} +{"mode": "train", "epoch": 98, "iter": 2300, "lr": 0.02719, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37453, "top5_acc": 0.63281, "loss_cls": 3.54131, "loss": 3.54131, "time": 0.85876} +{"mode": "train", "epoch": 98, "iter": 2400, "lr": 0.02717, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35906, "top5_acc": 0.62187, "loss_cls": 3.58869, "loss": 3.58869, "time": 0.8608} +{"mode": "train", "epoch": 98, "iter": 2500, "lr": 0.02714, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.36781, "top5_acc": 0.62484, "loss_cls": 3.55847, "loss": 3.55847, "time": 0.86025} +{"mode": "train", "epoch": 98, "iter": 2600, "lr": 0.02712, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36562, "top5_acc": 0.62969, "loss_cls": 3.55459, "loss": 3.55459, "time": 0.85099} +{"mode": "train", "epoch": 98, "iter": 2700, "lr": 0.02709, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38172, "top5_acc": 0.64219, "loss_cls": 3.49817, "loss": 3.49817, "time": 0.85851} +{"mode": "train", "epoch": 98, "iter": 2800, "lr": 0.02707, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35172, "top5_acc": 0.61656, "loss_cls": 3.65676, "loss": 3.65676, "time": 0.85817} +{"mode": "train", "epoch": 98, "iter": 2900, "lr": 0.02705, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36828, "top5_acc": 0.62359, "loss_cls": 3.56572, "loss": 3.56572, "time": 0.85198} +{"mode": "train", "epoch": 98, "iter": 3000, "lr": 0.02702, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37875, "top5_acc": 0.64938, "loss_cls": 3.48331, "loss": 3.48331, "time": 0.85216} +{"mode": "train", "epoch": 98, "iter": 3100, "lr": 0.027, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.37062, "top5_acc": 0.63484, "loss_cls": 3.54415, "loss": 3.54415, "time": 0.8495} +{"mode": "train", "epoch": 98, "iter": 3200, "lr": 0.02697, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37875, "top5_acc": 0.63734, "loss_cls": 3.51284, "loss": 3.51284, "time": 0.85603} +{"mode": "train", "epoch": 98, "iter": 3300, "lr": 0.02695, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37141, "top5_acc": 0.64172, "loss_cls": 3.53471, "loss": 3.53471, "time": 0.85635} +{"mode": "train", "epoch": 98, "iter": 3400, "lr": 0.02692, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37859, "top5_acc": 0.63344, "loss_cls": 3.551, "loss": 3.551, "time": 0.8626} +{"mode": "train", "epoch": 98, "iter": 3500, "lr": 0.0269, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3725, "top5_acc": 0.63625, "loss_cls": 3.55295, "loss": 3.55295, "time": 0.8553} +{"mode": "train", "epoch": 98, "iter": 3600, "lr": 0.02687, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3675, "top5_acc": 0.64406, "loss_cls": 3.51473, "loss": 3.51473, "time": 0.85253} +{"mode": "train", "epoch": 98, "iter": 3700, "lr": 0.02685, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36359, "top5_acc": 0.62313, "loss_cls": 3.57971, "loss": 3.57971, "time": 0.85686} +{"mode": "val", "epoch": 98, "iter": 309, "lr": 0.02684, "top1_acc": 0.30634, "top5_acc": 0.56283, "mean_class_accuracy": 0.30606} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.02681, "memory": 15990, "data_time": 1.66094, "top1_acc": 0.37625, "top5_acc": 0.63984, "loss_cls": 3.54707, "loss": 3.54707, "time": 2.72503} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.02679, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.39672, "top5_acc": 0.66547, "loss_cls": 3.3883, "loss": 3.3883, "time": 0.86956} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.02676, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.37891, "top5_acc": 0.64703, "loss_cls": 3.4871, "loss": 3.4871, "time": 0.87013} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.02674, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.37562, "top5_acc": 0.63891, "loss_cls": 3.51013, "loss": 3.51013, "time": 0.87464} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.02671, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.36547, "top5_acc": 0.63313, "loss_cls": 3.54476, "loss": 3.54476, "time": 0.86344} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.02669, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36188, "top5_acc": 0.62922, "loss_cls": 3.55923, "loss": 3.55923, "time": 0.86257} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.02666, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37656, "top5_acc": 0.6425, "loss_cls": 3.50606, "loss": 3.50606, "time": 0.85355} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.02664, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36688, "top5_acc": 0.63813, "loss_cls": 3.54354, "loss": 3.54354, "time": 0.85098} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.02661, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36719, "top5_acc": 0.63625, "loss_cls": 3.57197, "loss": 3.57197, "time": 0.84991} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.02659, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.385, "top5_acc": 0.63953, "loss_cls": 3.4956, "loss": 3.4956, "time": 0.8535} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.02656, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37766, "top5_acc": 0.64391, "loss_cls": 3.48814, "loss": 3.48814, "time": 0.86106} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.02654, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37125, "top5_acc": 0.64078, "loss_cls": 3.50141, "loss": 3.50141, "time": 0.85879} +{"mode": "train", "epoch": 99, "iter": 1300, "lr": 0.02651, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.37906, "top5_acc": 0.64562, "loss_cls": 3.49066, "loss": 3.49066, "time": 0.86062} +{"mode": "train", "epoch": 99, "iter": 1400, "lr": 0.02649, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36812, "top5_acc": 0.63766, "loss_cls": 3.52838, "loss": 3.52838, "time": 0.86814} +{"mode": "train", "epoch": 99, "iter": 1500, "lr": 0.02646, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37484, "top5_acc": 0.63797, "loss_cls": 3.51197, "loss": 3.51197, "time": 0.86098} +{"mode": "train", "epoch": 99, "iter": 1600, "lr": 0.02644, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38109, "top5_acc": 0.64766, "loss_cls": 3.47728, "loss": 3.47728, "time": 0.86729} +{"mode": "train", "epoch": 99, "iter": 1700, "lr": 0.02642, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37875, "top5_acc": 0.64609, "loss_cls": 3.49952, "loss": 3.49952, "time": 0.86974} +{"mode": "train", "epoch": 99, "iter": 1800, "lr": 0.02639, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37562, "top5_acc": 0.64062, "loss_cls": 3.54439, "loss": 3.54439, "time": 0.86836} +{"mode": "train", "epoch": 99, "iter": 1900, "lr": 0.02637, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36781, "top5_acc": 0.64391, "loss_cls": 3.4923, "loss": 3.4923, "time": 0.8637} +{"mode": "train", "epoch": 99, "iter": 2000, "lr": 0.02634, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.36625, "top5_acc": 0.63422, "loss_cls": 3.56256, "loss": 3.56256, "time": 0.8649} +{"mode": "train", "epoch": 99, "iter": 2100, "lr": 0.02632, "memory": 15990, "data_time": 0.00083, "top1_acc": 0.36281, "top5_acc": 0.63359, "loss_cls": 3.57908, "loss": 3.57908, "time": 0.872} +{"mode": "train", "epoch": 99, "iter": 2200, "lr": 0.02629, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.3725, "top5_acc": 0.62984, "loss_cls": 3.53592, "loss": 3.53592, "time": 0.86482} +{"mode": "train", "epoch": 99, "iter": 2300, "lr": 0.02627, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.37688, "top5_acc": 0.64141, "loss_cls": 3.49102, "loss": 3.49102, "time": 0.86917} +{"mode": "train", "epoch": 99, "iter": 2400, "lr": 0.02624, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.36953, "top5_acc": 0.63969, "loss_cls": 3.52579, "loss": 3.52579, "time": 0.86651} +{"mode": "train", "epoch": 99, "iter": 2500, "lr": 0.02622, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.37453, "top5_acc": 0.64062, "loss_cls": 3.54243, "loss": 3.54243, "time": 0.86892} +{"mode": "train", "epoch": 99, "iter": 2600, "lr": 0.02619, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.375, "top5_acc": 0.64984, "loss_cls": 3.49051, "loss": 3.49051, "time": 0.86727} +{"mode": "train", "epoch": 99, "iter": 2700, "lr": 0.02617, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.38078, "top5_acc": 0.64359, "loss_cls": 3.49696, "loss": 3.49696, "time": 0.86481} +{"mode": "train", "epoch": 99, "iter": 2800, "lr": 0.02614, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.37766, "top5_acc": 0.64266, "loss_cls": 3.48574, "loss": 3.48574, "time": 0.8775} +{"mode": "train", "epoch": 99, "iter": 2900, "lr": 0.02612, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37922, "top5_acc": 0.62859, "loss_cls": 3.52734, "loss": 3.52734, "time": 0.85784} +{"mode": "train", "epoch": 99, "iter": 3000, "lr": 0.0261, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38203, "top5_acc": 0.63984, "loss_cls": 3.4988, "loss": 3.4988, "time": 0.84716} +{"mode": "train", "epoch": 99, "iter": 3100, "lr": 0.02607, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36672, "top5_acc": 0.63047, "loss_cls": 3.56135, "loss": 3.56135, "time": 0.84735} +{"mode": "train", "epoch": 99, "iter": 3200, "lr": 0.02605, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35766, "top5_acc": 0.62609, "loss_cls": 3.5828, "loss": 3.5828, "time": 0.85663} +{"mode": "train", "epoch": 99, "iter": 3300, "lr": 0.02602, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36953, "top5_acc": 0.62375, "loss_cls": 3.5683, "loss": 3.5683, "time": 0.85736} +{"mode": "train", "epoch": 99, "iter": 3400, "lr": 0.026, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36031, "top5_acc": 0.63625, "loss_cls": 3.55922, "loss": 3.55922, "time": 0.85231} +{"mode": "train", "epoch": 99, "iter": 3500, "lr": 0.02597, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37625, "top5_acc": 0.63672, "loss_cls": 3.52912, "loss": 3.52912, "time": 0.85385} +{"mode": "train", "epoch": 99, "iter": 3600, "lr": 0.02595, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37109, "top5_acc": 0.63906, "loss_cls": 3.52539, "loss": 3.52539, "time": 0.85658} +{"mode": "train", "epoch": 99, "iter": 3700, "lr": 0.02592, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36109, "top5_acc": 0.63922, "loss_cls": 3.53872, "loss": 3.53872, "time": 0.856} +{"mode": "val", "epoch": 99, "iter": 309, "lr": 0.02591, "top1_acc": 0.30411, "top5_acc": 0.56162, "mean_class_accuracy": 0.30383} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.02589, "memory": 15990, "data_time": 1.61784, "top1_acc": 0.38141, "top5_acc": 0.65859, "loss_cls": 3.44326, "loss": 3.44326, "time": 2.67257} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.02586, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38766, "top5_acc": 0.64656, "loss_cls": 3.44475, "loss": 3.44475, "time": 0.85891} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.02584, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38344, "top5_acc": 0.65234, "loss_cls": 3.45565, "loss": 3.45565, "time": 0.86251} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.02581, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38078, "top5_acc": 0.65469, "loss_cls": 3.46355, "loss": 3.46355, "time": 0.86457} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.02579, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37281, "top5_acc": 0.63672, "loss_cls": 3.51784, "loss": 3.51784, "time": 0.85993} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.02577, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38516, "top5_acc": 0.65031, "loss_cls": 3.42911, "loss": 3.42911, "time": 0.85818} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.02574, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37469, "top5_acc": 0.63938, "loss_cls": 3.49463, "loss": 3.49463, "time": 0.8502} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.02572, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37844, "top5_acc": 0.63859, "loss_cls": 3.4955, "loss": 3.4955, "time": 0.84585} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.02569, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37453, "top5_acc": 0.63969, "loss_cls": 3.48647, "loss": 3.48647, "time": 0.85315} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.02567, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36531, "top5_acc": 0.63719, "loss_cls": 3.53971, "loss": 3.53971, "time": 0.84621} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.02564, "memory": 15990, "data_time": 0.00077, "top1_acc": 0.38281, "top5_acc": 0.64453, "loss_cls": 3.47345, "loss": 3.47345, "time": 0.85348} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.02562, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.3825, "top5_acc": 0.63422, "loss_cls": 3.50374, "loss": 3.50374, "time": 0.85607} +{"mode": "train", "epoch": 100, "iter": 1300, "lr": 0.02559, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38, "top5_acc": 0.64875, "loss_cls": 3.47683, "loss": 3.47683, "time": 0.85108} +{"mode": "train", "epoch": 100, "iter": 1400, "lr": 0.02557, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38594, "top5_acc": 0.64422, "loss_cls": 3.50572, "loss": 3.50572, "time": 0.8546} +{"mode": "train", "epoch": 100, "iter": 1500, "lr": 0.02555, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37109, "top5_acc": 0.62828, "loss_cls": 3.54649, "loss": 3.54649, "time": 0.85022} +{"mode": "train", "epoch": 100, "iter": 1600, "lr": 0.02552, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38516, "top5_acc": 0.65328, "loss_cls": 3.42468, "loss": 3.42468, "time": 0.84941} +{"mode": "train", "epoch": 100, "iter": 1700, "lr": 0.0255, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37031, "top5_acc": 0.62703, "loss_cls": 3.55437, "loss": 3.55437, "time": 0.85503} +{"mode": "train", "epoch": 100, "iter": 1800, "lr": 0.02547, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37109, "top5_acc": 0.63641, "loss_cls": 3.52528, "loss": 3.52528, "time": 0.85256} +{"mode": "train", "epoch": 100, "iter": 1900, "lr": 0.02545, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37891, "top5_acc": 0.64594, "loss_cls": 3.4839, "loss": 3.4839, "time": 0.85059} +{"mode": "train", "epoch": 100, "iter": 2000, "lr": 0.02542, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37422, "top5_acc": 0.63938, "loss_cls": 3.51245, "loss": 3.51245, "time": 0.85193} +{"mode": "train", "epoch": 100, "iter": 2100, "lr": 0.0254, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37, "top5_acc": 0.63953, "loss_cls": 3.51823, "loss": 3.51823, "time": 0.85425} +{"mode": "train", "epoch": 100, "iter": 2200, "lr": 0.02538, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37125, "top5_acc": 0.62906, "loss_cls": 3.55244, "loss": 3.55244, "time": 0.85493} +{"mode": "train", "epoch": 100, "iter": 2300, "lr": 0.02535, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38312, "top5_acc": 0.64719, "loss_cls": 3.45517, "loss": 3.45517, "time": 0.85458} +{"mode": "train", "epoch": 100, "iter": 2400, "lr": 0.02533, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38094, "top5_acc": 0.64406, "loss_cls": 3.48418, "loss": 3.48418, "time": 0.85121} +{"mode": "train", "epoch": 100, "iter": 2500, "lr": 0.0253, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37109, "top5_acc": 0.63266, "loss_cls": 3.56195, "loss": 3.56195, "time": 0.85381} +{"mode": "train", "epoch": 100, "iter": 2600, "lr": 0.02528, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37562, "top5_acc": 0.63953, "loss_cls": 3.52079, "loss": 3.52079, "time": 0.84967} +{"mode": "train", "epoch": 100, "iter": 2700, "lr": 0.02525, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.385, "top5_acc": 0.65, "loss_cls": 3.4612, "loss": 3.4612, "time": 0.85006} +{"mode": "train", "epoch": 100, "iter": 2800, "lr": 0.02523, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38109, "top5_acc": 0.64344, "loss_cls": 3.52616, "loss": 3.52616, "time": 0.84413} +{"mode": "train", "epoch": 100, "iter": 2900, "lr": 0.02521, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37891, "top5_acc": 0.64141, "loss_cls": 3.52563, "loss": 3.52563, "time": 0.85228} +{"mode": "train", "epoch": 100, "iter": 3000, "lr": 0.02518, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37172, "top5_acc": 0.63094, "loss_cls": 3.57184, "loss": 3.57184, "time": 0.84679} +{"mode": "train", "epoch": 100, "iter": 3100, "lr": 0.02516, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37266, "top5_acc": 0.64375, "loss_cls": 3.49854, "loss": 3.49854, "time": 0.83971} +{"mode": "train", "epoch": 100, "iter": 3200, "lr": 0.02513, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37672, "top5_acc": 0.64266, "loss_cls": 3.50561, "loss": 3.50561, "time": 0.84963} +{"mode": "train", "epoch": 100, "iter": 3300, "lr": 0.02511, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37125, "top5_acc": 0.64203, "loss_cls": 3.53632, "loss": 3.53632, "time": 0.85181} +{"mode": "train", "epoch": 100, "iter": 3400, "lr": 0.02508, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36531, "top5_acc": 0.62547, "loss_cls": 3.58905, "loss": 3.58905, "time": 0.85084} +{"mode": "train", "epoch": 100, "iter": 3500, "lr": 0.02506, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.36938, "top5_acc": 0.64, "loss_cls": 3.54123, "loss": 3.54123, "time": 0.85118} +{"mode": "train", "epoch": 100, "iter": 3600, "lr": 0.02504, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38516, "top5_acc": 0.64422, "loss_cls": 3.49894, "loss": 3.49894, "time": 0.85617} +{"mode": "train", "epoch": 100, "iter": 3700, "lr": 0.02501, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37828, "top5_acc": 0.64281, "loss_cls": 3.50227, "loss": 3.50227, "time": 0.84903} +{"mode": "val", "epoch": 100, "iter": 309, "lr": 0.025, "top1_acc": 0.3111, "top5_acc": 0.56587, "mean_class_accuracy": 0.31094} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.02498, "memory": 15990, "data_time": 1.51664, "top1_acc": 0.38859, "top5_acc": 0.65641, "loss_cls": 3.42468, "loss": 3.42468, "time": 2.55475} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.02495, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38391, "top5_acc": 0.64703, "loss_cls": 3.47475, "loss": 3.47475, "time": 0.85955} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.02493, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37875, "top5_acc": 0.64844, "loss_cls": 3.47139, "loss": 3.47139, "time": 0.85804} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.0249, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39, "top5_acc": 0.66172, "loss_cls": 3.41647, "loss": 3.41647, "time": 0.84881} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.02488, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38672, "top5_acc": 0.65484, "loss_cls": 3.42935, "loss": 3.42935, "time": 0.85599} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.02486, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.37594, "top5_acc": 0.64062, "loss_cls": 3.47691, "loss": 3.47691, "time": 0.85166} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.02483, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38109, "top5_acc": 0.6475, "loss_cls": 3.46877, "loss": 3.46877, "time": 0.84838} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.02481, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38391, "top5_acc": 0.64781, "loss_cls": 3.44873, "loss": 3.44873, "time": 0.84983} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.02478, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37797, "top5_acc": 0.65031, "loss_cls": 3.47502, "loss": 3.47502, "time": 0.8498} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.02476, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36953, "top5_acc": 0.63734, "loss_cls": 3.53818, "loss": 3.53818, "time": 0.84875} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.02473, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38922, "top5_acc": 0.64328, "loss_cls": 3.47318, "loss": 3.47318, "time": 0.85163} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.02471, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.38359, "top5_acc": 0.64766, "loss_cls": 3.46854, "loss": 3.46854, "time": 0.84758} +{"mode": "train", "epoch": 101, "iter": 1300, "lr": 0.02469, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37484, "top5_acc": 0.64125, "loss_cls": 3.51961, "loss": 3.51961, "time": 0.84687} +{"mode": "train", "epoch": 101, "iter": 1400, "lr": 0.02466, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38766, "top5_acc": 0.64344, "loss_cls": 3.46517, "loss": 3.46517, "time": 0.85336} +{"mode": "train", "epoch": 101, "iter": 1500, "lr": 0.02464, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37281, "top5_acc": 0.64781, "loss_cls": 3.47853, "loss": 3.47853, "time": 0.84867} +{"mode": "train", "epoch": 101, "iter": 1600, "lr": 0.02461, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38328, "top5_acc": 0.64422, "loss_cls": 3.49417, "loss": 3.49417, "time": 0.85167} +{"mode": "train", "epoch": 101, "iter": 1700, "lr": 0.02459, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3825, "top5_acc": 0.65016, "loss_cls": 3.49035, "loss": 3.49035, "time": 0.84744} +{"mode": "train", "epoch": 101, "iter": 1800, "lr": 0.02457, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38688, "top5_acc": 0.64906, "loss_cls": 3.4532, "loss": 3.4532, "time": 0.85076} +{"mode": "train", "epoch": 101, "iter": 1900, "lr": 0.02454, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39703, "top5_acc": 0.65344, "loss_cls": 3.41526, "loss": 3.41526, "time": 0.84788} +{"mode": "train", "epoch": 101, "iter": 2000, "lr": 0.02452, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37344, "top5_acc": 0.64312, "loss_cls": 3.50649, "loss": 3.50649, "time": 0.85141} +{"mode": "train", "epoch": 101, "iter": 2100, "lr": 0.02449, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37781, "top5_acc": 0.64297, "loss_cls": 3.49986, "loss": 3.49986, "time": 0.84381} +{"mode": "train", "epoch": 101, "iter": 2200, "lr": 0.02447, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37719, "top5_acc": 0.64156, "loss_cls": 3.50491, "loss": 3.50491, "time": 0.84622} +{"mode": "train", "epoch": 101, "iter": 2300, "lr": 0.02445, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37312, "top5_acc": 0.64047, "loss_cls": 3.54677, "loss": 3.54677, "time": 0.85618} +{"mode": "train", "epoch": 101, "iter": 2400, "lr": 0.02442, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37891, "top5_acc": 0.63844, "loss_cls": 3.50702, "loss": 3.50702, "time": 0.84541} +{"mode": "train", "epoch": 101, "iter": 2500, "lr": 0.0244, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37781, "top5_acc": 0.63875, "loss_cls": 3.53497, "loss": 3.53497, "time": 0.84618} +{"mode": "train", "epoch": 101, "iter": 2600, "lr": 0.02437, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37797, "top5_acc": 0.63609, "loss_cls": 3.51239, "loss": 3.51239, "time": 0.84837} +{"mode": "train", "epoch": 101, "iter": 2700, "lr": 0.02435, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38266, "top5_acc": 0.64297, "loss_cls": 3.45834, "loss": 3.45834, "time": 0.84581} +{"mode": "train", "epoch": 101, "iter": 2800, "lr": 0.02433, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37531, "top5_acc": 0.64109, "loss_cls": 3.49741, "loss": 3.49741, "time": 0.84651} +{"mode": "train", "epoch": 101, "iter": 2900, "lr": 0.0243, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36766, "top5_acc": 0.63391, "loss_cls": 3.53752, "loss": 3.53752, "time": 0.84848} +{"mode": "train", "epoch": 101, "iter": 3000, "lr": 0.02428, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38234, "top5_acc": 0.64984, "loss_cls": 3.47351, "loss": 3.47351, "time": 0.83969} +{"mode": "train", "epoch": 101, "iter": 3100, "lr": 0.02425, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.36766, "top5_acc": 0.63281, "loss_cls": 3.51859, "loss": 3.51859, "time": 0.83957} +{"mode": "train", "epoch": 101, "iter": 3200, "lr": 0.02423, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37703, "top5_acc": 0.64094, "loss_cls": 3.51832, "loss": 3.51832, "time": 0.84455} +{"mode": "train", "epoch": 101, "iter": 3300, "lr": 0.02421, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38734, "top5_acc": 0.64922, "loss_cls": 3.45654, "loss": 3.45654, "time": 0.84442} +{"mode": "train", "epoch": 101, "iter": 3400, "lr": 0.02418, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36953, "top5_acc": 0.64438, "loss_cls": 3.50911, "loss": 3.50911, "time": 0.84362} +{"mode": "train", "epoch": 101, "iter": 3500, "lr": 0.02416, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38656, "top5_acc": 0.64719, "loss_cls": 3.48246, "loss": 3.48246, "time": 0.84682} +{"mode": "train", "epoch": 101, "iter": 3600, "lr": 0.02413, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39375, "top5_acc": 0.65062, "loss_cls": 3.44615, "loss": 3.44615, "time": 0.84364} +{"mode": "train", "epoch": 101, "iter": 3700, "lr": 0.02411, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38125, "top5_acc": 0.64, "loss_cls": 3.50803, "loss": 3.50803, "time": 0.84871} +{"mode": "val", "epoch": 101, "iter": 309, "lr": 0.0241, "top1_acc": 0.30497, "top5_acc": 0.56466, "mean_class_accuracy": 0.30486} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.02407, "memory": 15990, "data_time": 1.51473, "top1_acc": 0.39859, "top5_acc": 0.67281, "loss_cls": 3.35321, "loss": 3.35321, "time": 2.54047} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.02405, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38672, "top5_acc": 0.65906, "loss_cls": 3.40485, "loss": 3.40485, "time": 0.855} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.02403, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38375, "top5_acc": 0.65047, "loss_cls": 3.46851, "loss": 3.46851, "time": 0.85484} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.024, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38688, "top5_acc": 0.65234, "loss_cls": 3.43928, "loss": 3.43928, "time": 0.85462} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.02398, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.3825, "top5_acc": 0.65203, "loss_cls": 3.43454, "loss": 3.43454, "time": 0.851} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.02396, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.38812, "top5_acc": 0.65031, "loss_cls": 3.44484, "loss": 3.44484, "time": 0.85094} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.02393, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38297, "top5_acc": 0.64438, "loss_cls": 3.48878, "loss": 3.48878, "time": 0.84741} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.02391, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38141, "top5_acc": 0.65438, "loss_cls": 3.43038, "loss": 3.43038, "time": 0.84675} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.02388, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39219, "top5_acc": 0.65906, "loss_cls": 3.41057, "loss": 3.41057, "time": 0.84649} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.02386, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3725, "top5_acc": 0.63547, "loss_cls": 3.51769, "loss": 3.51769, "time": 0.85232} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.02384, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38, "top5_acc": 0.65484, "loss_cls": 3.47523, "loss": 3.47523, "time": 0.85128} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.02381, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.39062, "top5_acc": 0.65828, "loss_cls": 3.41214, "loss": 3.41214, "time": 0.84881} +{"mode": "train", "epoch": 102, "iter": 1300, "lr": 0.02379, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38859, "top5_acc": 0.65766, "loss_cls": 3.4212, "loss": 3.4212, "time": 0.84515} +{"mode": "train", "epoch": 102, "iter": 1400, "lr": 0.02376, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37422, "top5_acc": 0.63719, "loss_cls": 3.50837, "loss": 3.50837, "time": 0.84759} +{"mode": "train", "epoch": 102, "iter": 1500, "lr": 0.02374, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38062, "top5_acc": 0.64266, "loss_cls": 3.47455, "loss": 3.47455, "time": 0.84711} +{"mode": "train", "epoch": 102, "iter": 1600, "lr": 0.02372, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37969, "top5_acc": 0.64016, "loss_cls": 3.49032, "loss": 3.49032, "time": 0.84409} +{"mode": "train", "epoch": 102, "iter": 1700, "lr": 0.02369, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38766, "top5_acc": 0.64391, "loss_cls": 3.45083, "loss": 3.45083, "time": 0.84333} +{"mode": "train", "epoch": 102, "iter": 1800, "lr": 0.02367, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37453, "top5_acc": 0.64688, "loss_cls": 3.46671, "loss": 3.46671, "time": 0.84559} +{"mode": "train", "epoch": 102, "iter": 1900, "lr": 0.02365, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38062, "top5_acc": 0.64219, "loss_cls": 3.49871, "loss": 3.49871, "time": 0.85096} +{"mode": "train", "epoch": 102, "iter": 2000, "lr": 0.02362, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37938, "top5_acc": 0.65297, "loss_cls": 3.44903, "loss": 3.44903, "time": 0.84936} +{"mode": "train", "epoch": 102, "iter": 2100, "lr": 0.0236, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37547, "top5_acc": 0.64219, "loss_cls": 3.50309, "loss": 3.50309, "time": 0.85374} +{"mode": "train", "epoch": 102, "iter": 2200, "lr": 0.02357, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.375, "top5_acc": 0.64203, "loss_cls": 3.49793, "loss": 3.49793, "time": 0.84751} +{"mode": "train", "epoch": 102, "iter": 2300, "lr": 0.02355, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39953, "top5_acc": 0.65641, "loss_cls": 3.43146, "loss": 3.43146, "time": 0.84679} +{"mode": "train", "epoch": 102, "iter": 2400, "lr": 0.02353, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38984, "top5_acc": 0.64969, "loss_cls": 3.4423, "loss": 3.4423, "time": 0.84915} +{"mode": "train", "epoch": 102, "iter": 2500, "lr": 0.0235, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37156, "top5_acc": 0.63062, "loss_cls": 3.53324, "loss": 3.53324, "time": 0.84747} +{"mode": "train", "epoch": 102, "iter": 2600, "lr": 0.02348, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37953, "top5_acc": 0.64547, "loss_cls": 3.48866, "loss": 3.48866, "time": 0.84955} +{"mode": "train", "epoch": 102, "iter": 2700, "lr": 0.02346, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.385, "top5_acc": 0.65844, "loss_cls": 3.4398, "loss": 3.4398, "time": 0.84796} +{"mode": "train", "epoch": 102, "iter": 2800, "lr": 0.02343, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38469, "top5_acc": 0.64516, "loss_cls": 3.49219, "loss": 3.49219, "time": 0.84815} +{"mode": "train", "epoch": 102, "iter": 2900, "lr": 0.02341, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.38812, "top5_acc": 0.65219, "loss_cls": 3.45131, "loss": 3.45131, "time": 0.84362} +{"mode": "train", "epoch": 102, "iter": 3000, "lr": 0.02339, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38047, "top5_acc": 0.64609, "loss_cls": 3.4791, "loss": 3.4791, "time": 0.84274} +{"mode": "train", "epoch": 102, "iter": 3100, "lr": 0.02336, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38469, "top5_acc": 0.645, "loss_cls": 3.47336, "loss": 3.47336, "time": 0.84253} +{"mode": "train", "epoch": 102, "iter": 3200, "lr": 0.02334, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38688, "top5_acc": 0.6475, "loss_cls": 3.48165, "loss": 3.48165, "time": 0.85144} +{"mode": "train", "epoch": 102, "iter": 3300, "lr": 0.02331, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37406, "top5_acc": 0.64922, "loss_cls": 3.4987, "loss": 3.4987, "time": 0.85207} +{"mode": "train", "epoch": 102, "iter": 3400, "lr": 0.02329, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37656, "top5_acc": 0.6425, "loss_cls": 3.4937, "loss": 3.4937, "time": 0.85384} +{"mode": "train", "epoch": 102, "iter": 3500, "lr": 0.02327, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37938, "top5_acc": 0.6525, "loss_cls": 3.47523, "loss": 3.47523, "time": 0.85878} +{"mode": "train", "epoch": 102, "iter": 3600, "lr": 0.02324, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38703, "top5_acc": 0.64297, "loss_cls": 3.49016, "loss": 3.49016, "time": 0.84951} +{"mode": "train", "epoch": 102, "iter": 3700, "lr": 0.02322, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39172, "top5_acc": 0.64594, "loss_cls": 3.47453, "loss": 3.47453, "time": 0.85594} +{"mode": "val", "epoch": 102, "iter": 309, "lr": 0.02321, "top1_acc": 0.31333, "top5_acc": 0.56861, "mean_class_accuracy": 0.31315} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.02319, "memory": 15990, "data_time": 1.45999, "top1_acc": 0.39125, "top5_acc": 0.65734, "loss_cls": 3.41347, "loss": 3.41347, "time": 2.48793} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.02316, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38984, "top5_acc": 0.65875, "loss_cls": 3.43886, "loss": 3.43886, "time": 0.85363} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.02314, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39328, "top5_acc": 0.66391, "loss_cls": 3.37926, "loss": 3.37926, "time": 0.85818} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.02311, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.395, "top5_acc": 0.65875, "loss_cls": 3.4051, "loss": 3.4051, "time": 0.85681} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.02309, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38172, "top5_acc": 0.65703, "loss_cls": 3.43568, "loss": 3.43568, "time": 0.85435} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.02307, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.40141, "top5_acc": 0.66062, "loss_cls": 3.406, "loss": 3.406, "time": 0.85474} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.02304, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39609, "top5_acc": 0.65797, "loss_cls": 3.40252, "loss": 3.40252, "time": 0.84281} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.02302, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.39547, "top5_acc": 0.66, "loss_cls": 3.39909, "loss": 3.39909, "time": 0.84873} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.023, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39844, "top5_acc": 0.65844, "loss_cls": 3.382, "loss": 3.382, "time": 0.84784} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.02297, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.38828, "top5_acc": 0.65422, "loss_cls": 3.43376, "loss": 3.43376, "time": 0.84742} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.02295, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.38859, "top5_acc": 0.64953, "loss_cls": 3.46352, "loss": 3.46352, "time": 0.84841} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.02293, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38062, "top5_acc": 0.64547, "loss_cls": 3.48531, "loss": 3.48531, "time": 0.84509} +{"mode": "train", "epoch": 103, "iter": 1300, "lr": 0.0229, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37688, "top5_acc": 0.64312, "loss_cls": 3.49509, "loss": 3.49509, "time": 0.84298} +{"mode": "train", "epoch": 103, "iter": 1400, "lr": 0.02288, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39016, "top5_acc": 0.66, "loss_cls": 3.40989, "loss": 3.40989, "time": 0.84425} +{"mode": "train", "epoch": 103, "iter": 1500, "lr": 0.02286, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39, "top5_acc": 0.66125, "loss_cls": 3.40803, "loss": 3.40803, "time": 0.84737} +{"mode": "train", "epoch": 103, "iter": 1600, "lr": 0.02283, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39375, "top5_acc": 0.64547, "loss_cls": 3.43553, "loss": 3.43553, "time": 0.84857} +{"mode": "train", "epoch": 103, "iter": 1700, "lr": 0.02281, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37812, "top5_acc": 0.64219, "loss_cls": 3.4925, "loss": 3.4925, "time": 0.84289} +{"mode": "train", "epoch": 103, "iter": 1800, "lr": 0.02279, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38359, "top5_acc": 0.65188, "loss_cls": 3.46571, "loss": 3.46571, "time": 0.8483} +{"mode": "train", "epoch": 103, "iter": 1900, "lr": 0.02276, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39203, "top5_acc": 0.65094, "loss_cls": 3.45587, "loss": 3.45587, "time": 0.8437} +{"mode": "train", "epoch": 103, "iter": 2000, "lr": 0.02274, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39062, "top5_acc": 0.65406, "loss_cls": 3.42896, "loss": 3.42896, "time": 0.84425} +{"mode": "train", "epoch": 103, "iter": 2100, "lr": 0.02272, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38703, "top5_acc": 0.65969, "loss_cls": 3.38659, "loss": 3.38659, "time": 0.84602} +{"mode": "train", "epoch": 103, "iter": 2200, "lr": 0.02269, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38797, "top5_acc": 0.64578, "loss_cls": 3.43715, "loss": 3.43715, "time": 0.84414} +{"mode": "train", "epoch": 103, "iter": 2300, "lr": 0.02267, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.37297, "top5_acc": 0.64234, "loss_cls": 3.48331, "loss": 3.48331, "time": 0.84339} +{"mode": "train", "epoch": 103, "iter": 2400, "lr": 0.02264, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39312, "top5_acc": 0.64969, "loss_cls": 3.44828, "loss": 3.44828, "time": 0.84386} +{"mode": "train", "epoch": 103, "iter": 2500, "lr": 0.02262, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.385, "top5_acc": 0.65375, "loss_cls": 3.4752, "loss": 3.4752, "time": 0.85193} +{"mode": "train", "epoch": 103, "iter": 2600, "lr": 0.0226, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38562, "top5_acc": 0.64922, "loss_cls": 3.44795, "loss": 3.44795, "time": 0.84552} +{"mode": "train", "epoch": 103, "iter": 2700, "lr": 0.02257, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37797, "top5_acc": 0.645, "loss_cls": 3.45945, "loss": 3.45945, "time": 0.84638} +{"mode": "train", "epoch": 103, "iter": 2800, "lr": 0.02255, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38203, "top5_acc": 0.64438, "loss_cls": 3.47784, "loss": 3.47784, "time": 0.8603} +{"mode": "train", "epoch": 103, "iter": 2900, "lr": 0.02253, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39078, "top5_acc": 0.64781, "loss_cls": 3.43907, "loss": 3.43907, "time": 0.8504} +{"mode": "train", "epoch": 103, "iter": 3000, "lr": 0.0225, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38453, "top5_acc": 0.63313, "loss_cls": 3.48869, "loss": 3.48869, "time": 0.85298} +{"mode": "train", "epoch": 103, "iter": 3100, "lr": 0.02248, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3775, "top5_acc": 0.63656, "loss_cls": 3.49724, "loss": 3.49724, "time": 0.84001} +{"mode": "train", "epoch": 103, "iter": 3200, "lr": 0.02246, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.39516, "top5_acc": 0.65781, "loss_cls": 3.41766, "loss": 3.41766, "time": 0.84815} +{"mode": "train", "epoch": 103, "iter": 3300, "lr": 0.02243, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38594, "top5_acc": 0.64688, "loss_cls": 3.45071, "loss": 3.45071, "time": 0.84772} +{"mode": "train", "epoch": 103, "iter": 3400, "lr": 0.02241, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38516, "top5_acc": 0.64859, "loss_cls": 3.46523, "loss": 3.46523, "time": 0.84914} +{"mode": "train", "epoch": 103, "iter": 3500, "lr": 0.02239, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37188, "top5_acc": 0.63078, "loss_cls": 3.55172, "loss": 3.55172, "time": 0.85} +{"mode": "train", "epoch": 103, "iter": 3600, "lr": 0.02236, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38312, "top5_acc": 0.64594, "loss_cls": 3.44692, "loss": 3.44692, "time": 0.84957} +{"mode": "train", "epoch": 103, "iter": 3700, "lr": 0.02234, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38375, "top5_acc": 0.645, "loss_cls": 3.4504, "loss": 3.4504, "time": 0.8527} +{"mode": "val", "epoch": 103, "iter": 309, "lr": 0.02233, "top1_acc": 0.32275, "top5_acc": 0.58289, "mean_class_accuracy": 0.3226} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.02231, "memory": 15990, "data_time": 1.45143, "top1_acc": 0.39516, "top5_acc": 0.66406, "loss_cls": 3.37801, "loss": 3.37801, "time": 2.48605} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.02228, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38828, "top5_acc": 0.65906, "loss_cls": 3.42556, "loss": 3.42556, "time": 0.8567} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.02226, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40016, "top5_acc": 0.66812, "loss_cls": 3.35696, "loss": 3.35696, "time": 0.85372} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.02224, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39453, "top5_acc": 0.66203, "loss_cls": 3.39856, "loss": 3.39856, "time": 0.8549} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.02221, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39891, "top5_acc": 0.65922, "loss_cls": 3.3639, "loss": 3.3639, "time": 0.85684} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.02219, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.39797, "top5_acc": 0.65844, "loss_cls": 3.37144, "loss": 3.37144, "time": 0.85879} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.02217, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39234, "top5_acc": 0.65094, "loss_cls": 3.41395, "loss": 3.41395, "time": 0.84931} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.02214, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38547, "top5_acc": 0.65859, "loss_cls": 3.3999, "loss": 3.3999, "time": 0.84333} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.02212, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39125, "top5_acc": 0.65938, "loss_cls": 3.40714, "loss": 3.40714, "time": 0.8459} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.0221, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39109, "top5_acc": 0.66156, "loss_cls": 3.4117, "loss": 3.4117, "time": 0.84153} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.02208, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.38266, "top5_acc": 0.65266, "loss_cls": 3.4384, "loss": 3.4384, "time": 0.84748} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.02205, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38984, "top5_acc": 0.65719, "loss_cls": 3.39714, "loss": 3.39714, "time": 0.84541} +{"mode": "train", "epoch": 104, "iter": 1300, "lr": 0.02203, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.38078, "top5_acc": 0.64062, "loss_cls": 3.49247, "loss": 3.49247, "time": 0.8451} +{"mode": "train", "epoch": 104, "iter": 1400, "lr": 0.02201, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38422, "top5_acc": 0.64766, "loss_cls": 3.46656, "loss": 3.46656, "time": 0.84925} +{"mode": "train", "epoch": 104, "iter": 1500, "lr": 0.02198, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38547, "top5_acc": 0.65578, "loss_cls": 3.40955, "loss": 3.40955, "time": 0.84519} +{"mode": "train", "epoch": 104, "iter": 1600, "lr": 0.02196, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39062, "top5_acc": 0.65203, "loss_cls": 3.42646, "loss": 3.42646, "time": 0.85208} +{"mode": "train", "epoch": 104, "iter": 1700, "lr": 0.02194, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39516, "top5_acc": 0.65688, "loss_cls": 3.40648, "loss": 3.40648, "time": 0.84602} +{"mode": "train", "epoch": 104, "iter": 1800, "lr": 0.02191, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38547, "top5_acc": 0.65391, "loss_cls": 3.4447, "loss": 3.4447, "time": 0.85339} +{"mode": "train", "epoch": 104, "iter": 1900, "lr": 0.02189, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38641, "top5_acc": 0.65469, "loss_cls": 3.44704, "loss": 3.44704, "time": 0.84706} +{"mode": "train", "epoch": 104, "iter": 2000, "lr": 0.02187, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3875, "top5_acc": 0.65891, "loss_cls": 3.43837, "loss": 3.43837, "time": 0.84555} +{"mode": "train", "epoch": 104, "iter": 2100, "lr": 0.02184, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38703, "top5_acc": 0.64172, "loss_cls": 3.4741, "loss": 3.4741, "time": 0.85251} +{"mode": "train", "epoch": 104, "iter": 2200, "lr": 0.02182, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39328, "top5_acc": 0.65719, "loss_cls": 3.4253, "loss": 3.4253, "time": 0.84803} +{"mode": "train", "epoch": 104, "iter": 2300, "lr": 0.0218, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38172, "top5_acc": 0.65016, "loss_cls": 3.42659, "loss": 3.42659, "time": 0.84819} +{"mode": "train", "epoch": 104, "iter": 2400, "lr": 0.02177, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39141, "top5_acc": 0.65203, "loss_cls": 3.41603, "loss": 3.41603, "time": 0.84848} +{"mode": "train", "epoch": 104, "iter": 2500, "lr": 0.02175, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39453, "top5_acc": 0.65312, "loss_cls": 3.40079, "loss": 3.40079, "time": 0.85005} +{"mode": "train", "epoch": 104, "iter": 2600, "lr": 0.02173, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37719, "top5_acc": 0.64734, "loss_cls": 3.46576, "loss": 3.46576, "time": 0.8472} +{"mode": "train", "epoch": 104, "iter": 2700, "lr": 0.02171, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38938, "top5_acc": 0.6475, "loss_cls": 3.43503, "loss": 3.43503, "time": 0.85314} +{"mode": "train", "epoch": 104, "iter": 2800, "lr": 0.02168, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36828, "top5_acc": 0.64828, "loss_cls": 3.46296, "loss": 3.46296, "time": 0.85347} +{"mode": "train", "epoch": 104, "iter": 2900, "lr": 0.02166, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37688, "top5_acc": 0.64578, "loss_cls": 3.48772, "loss": 3.48772, "time": 0.84921} +{"mode": "train", "epoch": 104, "iter": 3000, "lr": 0.02164, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38766, "top5_acc": 0.64453, "loss_cls": 3.47003, "loss": 3.47003, "time": 0.84792} +{"mode": "train", "epoch": 104, "iter": 3100, "lr": 0.02161, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.37938, "top5_acc": 0.64172, "loss_cls": 3.48475, "loss": 3.48475, "time": 0.84883} +{"mode": "train", "epoch": 104, "iter": 3200, "lr": 0.02159, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39031, "top5_acc": 0.64922, "loss_cls": 3.42225, "loss": 3.42225, "time": 0.84176} +{"mode": "train", "epoch": 104, "iter": 3300, "lr": 0.02157, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38844, "top5_acc": 0.65797, "loss_cls": 3.43109, "loss": 3.43109, "time": 0.84347} +{"mode": "train", "epoch": 104, "iter": 3400, "lr": 0.02154, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38094, "top5_acc": 0.64562, "loss_cls": 3.4715, "loss": 3.4715, "time": 0.8439} +{"mode": "train", "epoch": 104, "iter": 3500, "lr": 0.02152, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39312, "top5_acc": 0.64812, "loss_cls": 3.43154, "loss": 3.43154, "time": 0.84149} +{"mode": "train", "epoch": 104, "iter": 3600, "lr": 0.0215, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38938, "top5_acc": 0.65188, "loss_cls": 3.43748, "loss": 3.43748, "time": 0.84519} +{"mode": "train", "epoch": 104, "iter": 3700, "lr": 0.02148, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38469, "top5_acc": 0.64656, "loss_cls": 3.45578, "loss": 3.45578, "time": 0.84625} +{"mode": "val", "epoch": 104, "iter": 309, "lr": 0.02146, "top1_acc": 0.31925, "top5_acc": 0.57479, "mean_class_accuracy": 0.31903} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.02144, "memory": 15990, "data_time": 1.49985, "top1_acc": 0.40281, "top5_acc": 0.67344, "loss_cls": 3.35126, "loss": 3.35126, "time": 2.53466} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.02142, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40844, "top5_acc": 0.67609, "loss_cls": 3.31501, "loss": 3.31501, "time": 0.84685} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.0214, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39922, "top5_acc": 0.65969, "loss_cls": 3.37204, "loss": 3.37204, "time": 0.84701} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.02137, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40016, "top5_acc": 0.66938, "loss_cls": 3.33467, "loss": 3.33467, "time": 0.84867} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.02135, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39922, "top5_acc": 0.66141, "loss_cls": 3.38282, "loss": 3.38282, "time": 0.84387} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.02133, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38328, "top5_acc": 0.65594, "loss_cls": 3.43535, "loss": 3.43535, "time": 0.84352} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.0213, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39359, "top5_acc": 0.65531, "loss_cls": 3.40232, "loss": 3.40232, "time": 0.84965} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.02128, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39312, "top5_acc": 0.66406, "loss_cls": 3.41014, "loss": 3.41014, "time": 0.84917} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.02126, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38984, "top5_acc": 0.65625, "loss_cls": 3.39197, "loss": 3.39197, "time": 0.84797} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.02124, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39312, "top5_acc": 0.64969, "loss_cls": 3.43169, "loss": 3.43169, "time": 0.84981} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.02121, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.39531, "top5_acc": 0.65344, "loss_cls": 3.41247, "loss": 3.41247, "time": 0.84212} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.02119, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39906, "top5_acc": 0.65891, "loss_cls": 3.38125, "loss": 3.38125, "time": 0.84189} +{"mode": "train", "epoch": 105, "iter": 1300, "lr": 0.02117, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.39219, "top5_acc": 0.66797, "loss_cls": 3.38394, "loss": 3.38394, "time": 0.84412} +{"mode": "train", "epoch": 105, "iter": 1400, "lr": 0.02114, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40188, "top5_acc": 0.66078, "loss_cls": 3.38224, "loss": 3.38224, "time": 0.85004} +{"mode": "train", "epoch": 105, "iter": 1500, "lr": 0.02112, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37734, "top5_acc": 0.64906, "loss_cls": 3.48073, "loss": 3.48073, "time": 0.84158} +{"mode": "train", "epoch": 105, "iter": 1600, "lr": 0.0211, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40156, "top5_acc": 0.66484, "loss_cls": 3.35185, "loss": 3.35185, "time": 0.84297} +{"mode": "train", "epoch": 105, "iter": 1700, "lr": 0.02108, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39906, "top5_acc": 0.65516, "loss_cls": 3.42109, "loss": 3.42109, "time": 0.85009} +{"mode": "train", "epoch": 105, "iter": 1800, "lr": 0.02105, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39109, "top5_acc": 0.64484, "loss_cls": 3.42735, "loss": 3.42735, "time": 0.84503} +{"mode": "train", "epoch": 105, "iter": 1900, "lr": 0.02103, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39859, "top5_acc": 0.65375, "loss_cls": 3.42288, "loss": 3.42288, "time": 0.84191} +{"mode": "train", "epoch": 105, "iter": 2000, "lr": 0.02101, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39766, "top5_acc": 0.66578, "loss_cls": 3.3529, "loss": 3.3529, "time": 0.84715} +{"mode": "train", "epoch": 105, "iter": 2100, "lr": 0.02098, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38516, "top5_acc": 0.65781, "loss_cls": 3.41996, "loss": 3.41996, "time": 0.84693} +{"mode": "train", "epoch": 105, "iter": 2200, "lr": 0.02096, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40203, "top5_acc": 0.66047, "loss_cls": 3.39738, "loss": 3.39738, "time": 0.84428} +{"mode": "train", "epoch": 105, "iter": 2300, "lr": 0.02094, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39625, "top5_acc": 0.65875, "loss_cls": 3.40501, "loss": 3.40501, "time": 0.84281} +{"mode": "train", "epoch": 105, "iter": 2400, "lr": 0.02092, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39672, "top5_acc": 0.66188, "loss_cls": 3.39989, "loss": 3.39989, "time": 0.84242} +{"mode": "train", "epoch": 105, "iter": 2500, "lr": 0.02089, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38625, "top5_acc": 0.65297, "loss_cls": 3.44351, "loss": 3.44351, "time": 0.841} +{"mode": "train", "epoch": 105, "iter": 2600, "lr": 0.02087, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38922, "top5_acc": 0.65422, "loss_cls": 3.45214, "loss": 3.45214, "time": 0.84007} +{"mode": "train", "epoch": 105, "iter": 2700, "lr": 0.02085, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40359, "top5_acc": 0.66469, "loss_cls": 3.36987, "loss": 3.36987, "time": 0.84753} +{"mode": "train", "epoch": 105, "iter": 2800, "lr": 0.02083, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39297, "top5_acc": 0.6525, "loss_cls": 3.43106, "loss": 3.43106, "time": 0.84758} +{"mode": "train", "epoch": 105, "iter": 2900, "lr": 0.0208, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38453, "top5_acc": 0.65031, "loss_cls": 3.45816, "loss": 3.45816, "time": 0.84382} +{"mode": "train", "epoch": 105, "iter": 3000, "lr": 0.02078, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38797, "top5_acc": 0.66297, "loss_cls": 3.41399, "loss": 3.41399, "time": 0.84728} +{"mode": "train", "epoch": 105, "iter": 3100, "lr": 0.02076, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.39062, "top5_acc": 0.65672, "loss_cls": 3.42264, "loss": 3.42264, "time": 0.84866} +{"mode": "train", "epoch": 105, "iter": 3200, "lr": 0.02073, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38812, "top5_acc": 0.65562, "loss_cls": 3.41971, "loss": 3.41971, "time": 0.84547} +{"mode": "train", "epoch": 105, "iter": 3300, "lr": 0.02071, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38625, "top5_acc": 0.64328, "loss_cls": 3.45137, "loss": 3.45137, "time": 0.85001} +{"mode": "train", "epoch": 105, "iter": 3400, "lr": 0.02069, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38734, "top5_acc": 0.65156, "loss_cls": 3.42054, "loss": 3.42054, "time": 0.85044} +{"mode": "train", "epoch": 105, "iter": 3500, "lr": 0.02067, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39297, "top5_acc": 0.65922, "loss_cls": 3.40425, "loss": 3.40425, "time": 0.84779} +{"mode": "train", "epoch": 105, "iter": 3600, "lr": 0.02064, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38797, "top5_acc": 0.65484, "loss_cls": 3.43535, "loss": 3.43535, "time": 0.84712} +{"mode": "train", "epoch": 105, "iter": 3700, "lr": 0.02062, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38047, "top5_acc": 0.65344, "loss_cls": 3.45224, "loss": 3.45224, "time": 0.84857} +{"mode": "val", "epoch": 105, "iter": 309, "lr": 0.02061, "top1_acc": 0.31829, "top5_acc": 0.57362, "mean_class_accuracy": 0.31807} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.02059, "memory": 15990, "data_time": 1.52694, "top1_acc": 0.40141, "top5_acc": 0.67141, "loss_cls": 3.33172, "loss": 3.33172, "time": 2.55776} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.02057, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39469, "top5_acc": 0.66219, "loss_cls": 3.40971, "loss": 3.40971, "time": 0.85692} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.02054, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39266, "top5_acc": 0.65625, "loss_cls": 3.3947, "loss": 3.3947, "time": 0.86241} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.02052, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39766, "top5_acc": 0.66953, "loss_cls": 3.3467, "loss": 3.3467, "time": 0.85393} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.0205, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40062, "top5_acc": 0.65938, "loss_cls": 3.35468, "loss": 3.35468, "time": 0.85538} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.02048, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.40328, "top5_acc": 0.66344, "loss_cls": 3.3576, "loss": 3.3576, "time": 0.85611} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.02045, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38625, "top5_acc": 0.66141, "loss_cls": 3.3836, "loss": 3.3836, "time": 0.85338} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.02043, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40203, "top5_acc": 0.66266, "loss_cls": 3.36958, "loss": 3.36958, "time": 0.85431} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.02041, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39406, "top5_acc": 0.65578, "loss_cls": 3.412, "loss": 3.412, "time": 0.85846} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.02039, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39875, "top5_acc": 0.65031, "loss_cls": 3.36455, "loss": 3.36455, "time": 0.85078} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.02036, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39906, "top5_acc": 0.66516, "loss_cls": 3.37558, "loss": 3.37558, "time": 0.84741} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.02034, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38656, "top5_acc": 0.64969, "loss_cls": 3.42454, "loss": 3.42454, "time": 0.84924} +{"mode": "train", "epoch": 106, "iter": 1300, "lr": 0.02032, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.38781, "top5_acc": 0.65344, "loss_cls": 3.41686, "loss": 3.41686, "time": 0.85027} +{"mode": "train", "epoch": 106, "iter": 1400, "lr": 0.0203, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38812, "top5_acc": 0.65719, "loss_cls": 3.40514, "loss": 3.40514, "time": 0.85159} +{"mode": "train", "epoch": 106, "iter": 1500, "lr": 0.02027, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40688, "top5_acc": 0.6675, "loss_cls": 3.36696, "loss": 3.36696, "time": 0.84775} +{"mode": "train", "epoch": 106, "iter": 1600, "lr": 0.02025, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38609, "top5_acc": 0.65375, "loss_cls": 3.43729, "loss": 3.43729, "time": 0.84479} +{"mode": "train", "epoch": 106, "iter": 1700, "lr": 0.02023, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39422, "top5_acc": 0.65422, "loss_cls": 3.40104, "loss": 3.40104, "time": 0.84172} +{"mode": "train", "epoch": 106, "iter": 1800, "lr": 0.02021, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40344, "top5_acc": 0.67, "loss_cls": 3.32935, "loss": 3.32935, "time": 0.84186} +{"mode": "train", "epoch": 106, "iter": 1900, "lr": 0.02018, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38734, "top5_acc": 0.65141, "loss_cls": 3.42478, "loss": 3.42478, "time": 0.84513} +{"mode": "train", "epoch": 106, "iter": 2000, "lr": 0.02016, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39734, "top5_acc": 0.65688, "loss_cls": 3.37574, "loss": 3.37574, "time": 0.84811} +{"mode": "train", "epoch": 106, "iter": 2100, "lr": 0.02014, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39625, "top5_acc": 0.65516, "loss_cls": 3.44517, "loss": 3.44517, "time": 0.84734} +{"mode": "train", "epoch": 106, "iter": 2200, "lr": 0.02012, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39641, "top5_acc": 0.66047, "loss_cls": 3.39673, "loss": 3.39673, "time": 0.8479} +{"mode": "train", "epoch": 106, "iter": 2300, "lr": 0.02009, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39422, "top5_acc": 0.65906, "loss_cls": 3.42332, "loss": 3.42332, "time": 0.84564} +{"mode": "train", "epoch": 106, "iter": 2400, "lr": 0.02007, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39719, "top5_acc": 0.65828, "loss_cls": 3.37129, "loss": 3.37129, "time": 0.84301} +{"mode": "train", "epoch": 106, "iter": 2500, "lr": 0.02005, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39406, "top5_acc": 0.65781, "loss_cls": 3.41894, "loss": 3.41894, "time": 0.84904} +{"mode": "train", "epoch": 106, "iter": 2600, "lr": 0.02003, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39984, "top5_acc": 0.6575, "loss_cls": 3.40143, "loss": 3.40143, "time": 0.84253} +{"mode": "train", "epoch": 106, "iter": 2700, "lr": 0.02, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39484, "top5_acc": 0.66047, "loss_cls": 3.37171, "loss": 3.37171, "time": 0.84299} +{"mode": "train", "epoch": 106, "iter": 2800, "lr": 0.01998, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39609, "top5_acc": 0.66766, "loss_cls": 3.37656, "loss": 3.37656, "time": 0.84737} +{"mode": "train", "epoch": 106, "iter": 2900, "lr": 0.01996, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38531, "top5_acc": 0.65484, "loss_cls": 3.43489, "loss": 3.43489, "time": 0.84754} +{"mode": "train", "epoch": 106, "iter": 3000, "lr": 0.01994, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39375, "top5_acc": 0.65938, "loss_cls": 3.40528, "loss": 3.40528, "time": 0.84278} +{"mode": "train", "epoch": 106, "iter": 3100, "lr": 0.01991, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39938, "top5_acc": 0.67, "loss_cls": 3.34772, "loss": 3.34772, "time": 0.84423} +{"mode": "train", "epoch": 106, "iter": 3200, "lr": 0.01989, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.38734, "top5_acc": 0.66234, "loss_cls": 3.39844, "loss": 3.39844, "time": 0.84176} +{"mode": "train", "epoch": 106, "iter": 3300, "lr": 0.01987, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37859, "top5_acc": 0.65875, "loss_cls": 3.42223, "loss": 3.42223, "time": 0.84833} +{"mode": "train", "epoch": 106, "iter": 3400, "lr": 0.01985, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40109, "top5_acc": 0.6575, "loss_cls": 3.36509, "loss": 3.36509, "time": 0.84739} +{"mode": "train", "epoch": 106, "iter": 3500, "lr": 0.01983, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39016, "top5_acc": 0.65766, "loss_cls": 3.39429, "loss": 3.39429, "time": 0.84761} +{"mode": "train", "epoch": 106, "iter": 3600, "lr": 0.0198, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39188, "top5_acc": 0.65, "loss_cls": 3.42701, "loss": 3.42701, "time": 0.8506} +{"mode": "train", "epoch": 106, "iter": 3700, "lr": 0.01978, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40484, "top5_acc": 0.66469, "loss_cls": 3.3629, "loss": 3.3629, "time": 0.84501} +{"mode": "val", "epoch": 106, "iter": 309, "lr": 0.01977, "top1_acc": 0.34027, "top5_acc": 0.60178, "mean_class_accuracy": 0.33994} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.01975, "memory": 15990, "data_time": 1.44318, "top1_acc": 0.40438, "top5_acc": 0.67156, "loss_cls": 3.32453, "loss": 3.32453, "time": 2.46922} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.01973, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40875, "top5_acc": 0.67609, "loss_cls": 3.29202, "loss": 3.29202, "time": 0.8556} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.0197, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40297, "top5_acc": 0.67312, "loss_cls": 3.33569, "loss": 3.33569, "time": 0.84698} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.01968, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40766, "top5_acc": 0.66812, "loss_cls": 3.30205, "loss": 3.30205, "time": 0.84752} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.01966, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39828, "top5_acc": 0.65953, "loss_cls": 3.36569, "loss": 3.36569, "time": 0.84836} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.01964, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40141, "top5_acc": 0.66234, "loss_cls": 3.3917, "loss": 3.3917, "time": 0.85103} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.01961, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40656, "top5_acc": 0.66719, "loss_cls": 3.35772, "loss": 3.35772, "time": 0.84826} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.01959, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40688, "top5_acc": 0.66844, "loss_cls": 3.32199, "loss": 3.32199, "time": 0.84916} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.01957, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.39156, "top5_acc": 0.65891, "loss_cls": 3.399, "loss": 3.399, "time": 0.84593} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.01955, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40703, "top5_acc": 0.67125, "loss_cls": 3.30841, "loss": 3.30841, "time": 0.85252} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.01953, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40531, "top5_acc": 0.66516, "loss_cls": 3.34686, "loss": 3.34686, "time": 0.84849} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.0195, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.405, "top5_acc": 0.66297, "loss_cls": 3.35942, "loss": 3.35942, "time": 0.84976} +{"mode": "train", "epoch": 107, "iter": 1300, "lr": 0.01948, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40812, "top5_acc": 0.66938, "loss_cls": 3.34819, "loss": 3.34819, "time": 0.84505} +{"mode": "train", "epoch": 107, "iter": 1400, "lr": 0.01946, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40266, "top5_acc": 0.66531, "loss_cls": 3.35568, "loss": 3.35568, "time": 0.84517} +{"mode": "train", "epoch": 107, "iter": 1500, "lr": 0.01944, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40375, "top5_acc": 0.66625, "loss_cls": 3.37168, "loss": 3.37168, "time": 0.84423} +{"mode": "train", "epoch": 107, "iter": 1600, "lr": 0.01942, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38922, "top5_acc": 0.65625, "loss_cls": 3.4168, "loss": 3.4168, "time": 0.847} +{"mode": "train", "epoch": 107, "iter": 1700, "lr": 0.01939, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40219, "top5_acc": 0.66328, "loss_cls": 3.36351, "loss": 3.36351, "time": 0.8462} +{"mode": "train", "epoch": 107, "iter": 1800, "lr": 0.01937, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40344, "top5_acc": 0.66625, "loss_cls": 3.36176, "loss": 3.36176, "time": 0.84562} +{"mode": "train", "epoch": 107, "iter": 1900, "lr": 0.01935, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40938, "top5_acc": 0.67234, "loss_cls": 3.31947, "loss": 3.31947, "time": 0.84903} +{"mode": "train", "epoch": 107, "iter": 2000, "lr": 0.01933, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39516, "top5_acc": 0.66172, "loss_cls": 3.36234, "loss": 3.36234, "time": 0.84571} +{"mode": "train", "epoch": 107, "iter": 2100, "lr": 0.0193, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40062, "top5_acc": 0.67, "loss_cls": 3.35041, "loss": 3.35041, "time": 0.843} +{"mode": "train", "epoch": 107, "iter": 2200, "lr": 0.01928, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40312, "top5_acc": 0.66531, "loss_cls": 3.37324, "loss": 3.37324, "time": 0.84688} +{"mode": "train", "epoch": 107, "iter": 2300, "lr": 0.01926, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40062, "top5_acc": 0.66234, "loss_cls": 3.36788, "loss": 3.36788, "time": 0.84894} +{"mode": "train", "epoch": 107, "iter": 2400, "lr": 0.01924, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39219, "top5_acc": 0.65828, "loss_cls": 3.39453, "loss": 3.39453, "time": 0.84361} +{"mode": "train", "epoch": 107, "iter": 2500, "lr": 0.01922, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40234, "top5_acc": 0.65609, "loss_cls": 3.3667, "loss": 3.3667, "time": 0.84271} +{"mode": "train", "epoch": 107, "iter": 2600, "lr": 0.01919, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38516, "top5_acc": 0.65125, "loss_cls": 3.46529, "loss": 3.46529, "time": 0.84569} +{"mode": "train", "epoch": 107, "iter": 2700, "lr": 0.01917, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39391, "top5_acc": 0.65875, "loss_cls": 3.41501, "loss": 3.41501, "time": 0.84536} +{"mode": "train", "epoch": 107, "iter": 2800, "lr": 0.01915, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.4, "top5_acc": 0.65672, "loss_cls": 3.40749, "loss": 3.40749, "time": 0.84452} +{"mode": "train", "epoch": 107, "iter": 2900, "lr": 0.01913, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40469, "top5_acc": 0.67172, "loss_cls": 3.32665, "loss": 3.32665, "time": 0.84875} +{"mode": "train", "epoch": 107, "iter": 3000, "lr": 0.01911, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39969, "top5_acc": 0.66797, "loss_cls": 3.37465, "loss": 3.37465, "time": 0.84732} +{"mode": "train", "epoch": 107, "iter": 3100, "lr": 0.01908, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39922, "top5_acc": 0.66109, "loss_cls": 3.36264, "loss": 3.36264, "time": 0.84691} +{"mode": "train", "epoch": 107, "iter": 3200, "lr": 0.01906, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39641, "top5_acc": 0.66094, "loss_cls": 3.38447, "loss": 3.38447, "time": 0.84891} +{"mode": "train", "epoch": 107, "iter": 3300, "lr": 0.01904, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39875, "top5_acc": 0.66062, "loss_cls": 3.38299, "loss": 3.38299, "time": 0.84179} +{"mode": "train", "epoch": 107, "iter": 3400, "lr": 0.01902, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39328, "top5_acc": 0.65375, "loss_cls": 3.38556, "loss": 3.38556, "time": 0.84521} +{"mode": "train", "epoch": 107, "iter": 3500, "lr": 0.019, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40609, "top5_acc": 0.665, "loss_cls": 3.36125, "loss": 3.36125, "time": 0.84645} +{"mode": "train", "epoch": 107, "iter": 3600, "lr": 0.01897, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.39141, "top5_acc": 0.65375, "loss_cls": 3.44797, "loss": 3.44797, "time": 0.84741} +{"mode": "train", "epoch": 107, "iter": 3700, "lr": 0.01895, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38859, "top5_acc": 0.65859, "loss_cls": 3.44034, "loss": 3.44034, "time": 0.84617} +{"mode": "val", "epoch": 107, "iter": 309, "lr": 0.01894, "top1_acc": 0.32447, "top5_acc": 0.58471, "mean_class_accuracy": 0.3241} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.01892, "memory": 15990, "data_time": 1.45518, "top1_acc": 0.41031, "top5_acc": 0.6775, "loss_cls": 3.2796, "loss": 3.2796, "time": 2.47711} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0189, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41125, "top5_acc": 0.66969, "loss_cls": 3.33108, "loss": 3.33108, "time": 0.84962} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.01888, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39938, "top5_acc": 0.66781, "loss_cls": 3.34175, "loss": 3.34175, "time": 0.84912} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.01886, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40828, "top5_acc": 0.66406, "loss_cls": 3.32787, "loss": 3.32787, "time": 0.84869} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.01883, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40625, "top5_acc": 0.66609, "loss_cls": 3.34333, "loss": 3.34333, "time": 0.84479} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.01881, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40312, "top5_acc": 0.66859, "loss_cls": 3.3271, "loss": 3.3271, "time": 0.85032} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.01879, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.41266, "top5_acc": 0.66938, "loss_cls": 3.31357, "loss": 3.31357, "time": 0.84495} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.01877, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40641, "top5_acc": 0.66844, "loss_cls": 3.32938, "loss": 3.32938, "time": 0.84771} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.01875, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40156, "top5_acc": 0.66609, "loss_cls": 3.36082, "loss": 3.36082, "time": 0.84271} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.01872, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40688, "top5_acc": 0.66859, "loss_cls": 3.31798, "loss": 3.31798, "time": 0.84281} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.0187, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39188, "top5_acc": 0.65734, "loss_cls": 3.37522, "loss": 3.37522, "time": 0.84706} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.01868, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39391, "top5_acc": 0.67516, "loss_cls": 3.34606, "loss": 3.34606, "time": 0.84818} +{"mode": "train", "epoch": 108, "iter": 1300, "lr": 0.01866, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40234, "top5_acc": 0.66484, "loss_cls": 3.36812, "loss": 3.36812, "time": 0.84547} +{"mode": "train", "epoch": 108, "iter": 1400, "lr": 0.01864, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40266, "top5_acc": 0.67, "loss_cls": 3.3421, "loss": 3.3421, "time": 0.84307} +{"mode": "train", "epoch": 108, "iter": 1500, "lr": 0.01862, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40672, "top5_acc": 0.67625, "loss_cls": 3.32376, "loss": 3.32376, "time": 0.84741} +{"mode": "train", "epoch": 108, "iter": 1600, "lr": 0.01859, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.40016, "top5_acc": 0.66188, "loss_cls": 3.35793, "loss": 3.35793, "time": 0.85056} +{"mode": "train", "epoch": 108, "iter": 1700, "lr": 0.01857, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40516, "top5_acc": 0.665, "loss_cls": 3.37047, "loss": 3.37047, "time": 0.84782} +{"mode": "train", "epoch": 108, "iter": 1800, "lr": 0.01855, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41031, "top5_acc": 0.65922, "loss_cls": 3.38493, "loss": 3.38493, "time": 0.84047} +{"mode": "train", "epoch": 108, "iter": 1900, "lr": 0.01853, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40812, "top5_acc": 0.6675, "loss_cls": 3.32388, "loss": 3.32388, "time": 0.84424} +{"mode": "train", "epoch": 108, "iter": 2000, "lr": 0.01851, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41047, "top5_acc": 0.66781, "loss_cls": 3.31675, "loss": 3.31675, "time": 0.84156} +{"mode": "train", "epoch": 108, "iter": 2100, "lr": 0.01848, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40359, "top5_acc": 0.67562, "loss_cls": 3.33325, "loss": 3.33325, "time": 0.83895} +{"mode": "train", "epoch": 108, "iter": 2200, "lr": 0.01846, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40203, "top5_acc": 0.66719, "loss_cls": 3.35399, "loss": 3.35399, "time": 0.84799} +{"mode": "train", "epoch": 108, "iter": 2300, "lr": 0.01844, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.405, "top5_acc": 0.67172, "loss_cls": 3.30561, "loss": 3.30561, "time": 0.84097} +{"mode": "train", "epoch": 108, "iter": 2400, "lr": 0.01842, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41953, "top5_acc": 0.67453, "loss_cls": 3.29544, "loss": 3.29544, "time": 0.84646} +{"mode": "train", "epoch": 108, "iter": 2500, "lr": 0.0184, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39406, "top5_acc": 0.65875, "loss_cls": 3.40183, "loss": 3.40183, "time": 0.84458} +{"mode": "train", "epoch": 108, "iter": 2600, "lr": 0.01838, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40266, "top5_acc": 0.66438, "loss_cls": 3.36871, "loss": 3.36871, "time": 0.84515} +{"mode": "train", "epoch": 108, "iter": 2700, "lr": 0.01835, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40797, "top5_acc": 0.67016, "loss_cls": 3.31795, "loss": 3.31795, "time": 0.85117} +{"mode": "train", "epoch": 108, "iter": 2800, "lr": 0.01833, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40828, "top5_acc": 0.66984, "loss_cls": 3.33909, "loss": 3.33909, "time": 0.84635} +{"mode": "train", "epoch": 108, "iter": 2900, "lr": 0.01831, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39109, "top5_acc": 0.66109, "loss_cls": 3.39066, "loss": 3.39066, "time": 0.84078} +{"mode": "train", "epoch": 108, "iter": 3000, "lr": 0.01829, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39453, "top5_acc": 0.66172, "loss_cls": 3.37268, "loss": 3.37268, "time": 0.84244} +{"mode": "train", "epoch": 108, "iter": 3100, "lr": 0.01827, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.40391, "top5_acc": 0.66312, "loss_cls": 3.36365, "loss": 3.36365, "time": 0.8436} +{"mode": "train", "epoch": 108, "iter": 3200, "lr": 0.01825, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40203, "top5_acc": 0.67297, "loss_cls": 3.33066, "loss": 3.33066, "time": 0.84586} +{"mode": "train", "epoch": 108, "iter": 3300, "lr": 0.01823, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39875, "top5_acc": 0.65625, "loss_cls": 3.37482, "loss": 3.37482, "time": 0.84128} +{"mode": "train", "epoch": 108, "iter": 3400, "lr": 0.0182, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39688, "top5_acc": 0.66109, "loss_cls": 3.37594, "loss": 3.37594, "time": 0.84273} +{"mode": "train", "epoch": 108, "iter": 3500, "lr": 0.01818, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38719, "top5_acc": 0.66016, "loss_cls": 3.40902, "loss": 3.40902, "time": 0.84708} +{"mode": "train", "epoch": 108, "iter": 3600, "lr": 0.01816, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40484, "top5_acc": 0.66812, "loss_cls": 3.33721, "loss": 3.33721, "time": 0.84378} +{"mode": "train", "epoch": 108, "iter": 3700, "lr": 0.01814, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41172, "top5_acc": 0.67234, "loss_cls": 3.32131, "loss": 3.32131, "time": 0.84459} +{"mode": "val", "epoch": 108, "iter": 309, "lr": 0.01813, "top1_acc": 0.33637, "top5_acc": 0.59165, "mean_class_accuracy": 0.33619} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.01811, "memory": 15990, "data_time": 1.44885, "top1_acc": 0.41484, "top5_acc": 0.67547, "loss_cls": 3.28219, "loss": 3.28219, "time": 2.46717} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.01809, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41125, "top5_acc": 0.67422, "loss_cls": 3.2718, "loss": 3.2718, "time": 0.84268} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.01806, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42234, "top5_acc": 0.69047, "loss_cls": 3.21527, "loss": 3.21527, "time": 0.84828} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.01804, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41734, "top5_acc": 0.67672, "loss_cls": 3.268, "loss": 3.268, "time": 0.84615} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.01802, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40547, "top5_acc": 0.67141, "loss_cls": 3.3339, "loss": 3.3339, "time": 0.84519} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.018, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41203, "top5_acc": 0.67781, "loss_cls": 3.26144, "loss": 3.26144, "time": 0.84237} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.01798, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41594, "top5_acc": 0.68484, "loss_cls": 3.25858, "loss": 3.25858, "time": 0.84191} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.01796, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40844, "top5_acc": 0.66953, "loss_cls": 3.30601, "loss": 3.30601, "time": 0.85308} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.01794, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40094, "top5_acc": 0.66547, "loss_cls": 3.35908, "loss": 3.35908, "time": 0.84444} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.01791, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39203, "top5_acc": 0.66203, "loss_cls": 3.38798, "loss": 3.38798, "time": 0.84453} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.01789, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40562, "top5_acc": 0.67562, "loss_cls": 3.31114, "loss": 3.31114, "time": 0.85053} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.01787, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.41109, "top5_acc": 0.67172, "loss_cls": 3.32944, "loss": 3.32944, "time": 0.84795} +{"mode": "train", "epoch": 109, "iter": 1300, "lr": 0.01785, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39688, "top5_acc": 0.66594, "loss_cls": 3.35923, "loss": 3.35923, "time": 0.85187} +{"mode": "train", "epoch": 109, "iter": 1400, "lr": 0.01783, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39219, "top5_acc": 0.6675, "loss_cls": 3.35474, "loss": 3.35474, "time": 0.84299} +{"mode": "train", "epoch": 109, "iter": 1500, "lr": 0.01781, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40641, "top5_acc": 0.65828, "loss_cls": 3.38397, "loss": 3.38397, "time": 0.84693} +{"mode": "train", "epoch": 109, "iter": 1600, "lr": 0.01779, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40406, "top5_acc": 0.66156, "loss_cls": 3.35692, "loss": 3.35692, "time": 0.85004} +{"mode": "train", "epoch": 109, "iter": 1700, "lr": 0.01776, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.4, "top5_acc": 0.67141, "loss_cls": 3.34167, "loss": 3.34167, "time": 0.84392} +{"mode": "train", "epoch": 109, "iter": 1800, "lr": 0.01774, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40938, "top5_acc": 0.6625, "loss_cls": 3.35667, "loss": 3.35667, "time": 0.84265} +{"mode": "train", "epoch": 109, "iter": 1900, "lr": 0.01772, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41172, "top5_acc": 0.67219, "loss_cls": 3.29119, "loss": 3.29119, "time": 0.85111} +{"mode": "train", "epoch": 109, "iter": 2000, "lr": 0.0177, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41031, "top5_acc": 0.67875, "loss_cls": 3.28833, "loss": 3.28833, "time": 0.84183} +{"mode": "train", "epoch": 109, "iter": 2100, "lr": 0.01768, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.405, "top5_acc": 0.66094, "loss_cls": 3.35629, "loss": 3.35629, "time": 0.84777} +{"mode": "train", "epoch": 109, "iter": 2200, "lr": 0.01766, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40828, "top5_acc": 0.67578, "loss_cls": 3.2903, "loss": 3.2903, "time": 0.84643} +{"mode": "train", "epoch": 109, "iter": 2300, "lr": 0.01764, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40359, "top5_acc": 0.66312, "loss_cls": 3.3424, "loss": 3.3424, "time": 0.85063} +{"mode": "train", "epoch": 109, "iter": 2400, "lr": 0.01761, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39922, "top5_acc": 0.66469, "loss_cls": 3.34907, "loss": 3.34907, "time": 0.85166} +{"mode": "train", "epoch": 109, "iter": 2500, "lr": 0.01759, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40781, "top5_acc": 0.66969, "loss_cls": 3.33741, "loss": 3.33741, "time": 0.85064} +{"mode": "train", "epoch": 109, "iter": 2600, "lr": 0.01757, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39812, "top5_acc": 0.65938, "loss_cls": 3.37259, "loss": 3.37259, "time": 0.8559} +{"mode": "train", "epoch": 109, "iter": 2700, "lr": 0.01755, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.405, "top5_acc": 0.67297, "loss_cls": 3.31998, "loss": 3.31998, "time": 0.85198} +{"mode": "train", "epoch": 109, "iter": 2800, "lr": 0.01753, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40875, "top5_acc": 0.66797, "loss_cls": 3.32924, "loss": 3.32924, "time": 0.85258} +{"mode": "train", "epoch": 109, "iter": 2900, "lr": 0.01751, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40922, "top5_acc": 0.67172, "loss_cls": 3.33945, "loss": 3.33945, "time": 0.84964} +{"mode": "train", "epoch": 109, "iter": 3000, "lr": 0.01749, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40438, "top5_acc": 0.66844, "loss_cls": 3.32186, "loss": 3.32186, "time": 0.85227} +{"mode": "train", "epoch": 109, "iter": 3100, "lr": 0.01747, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39953, "top5_acc": 0.67391, "loss_cls": 3.32015, "loss": 3.32015, "time": 0.85278} +{"mode": "train", "epoch": 109, "iter": 3200, "lr": 0.01744, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39469, "top5_acc": 0.66109, "loss_cls": 3.37035, "loss": 3.37035, "time": 0.8488} +{"mode": "train", "epoch": 109, "iter": 3300, "lr": 0.01742, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3925, "top5_acc": 0.66125, "loss_cls": 3.37735, "loss": 3.37735, "time": 0.84851} +{"mode": "train", "epoch": 109, "iter": 3400, "lr": 0.0174, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.40984, "top5_acc": 0.67547, "loss_cls": 3.34087, "loss": 3.34087, "time": 0.84177} +{"mode": "train", "epoch": 109, "iter": 3500, "lr": 0.01738, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.395, "top5_acc": 0.66188, "loss_cls": 3.38248, "loss": 3.38248, "time": 0.84266} +{"mode": "train", "epoch": 109, "iter": 3600, "lr": 0.01736, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39781, "top5_acc": 0.66688, "loss_cls": 3.36551, "loss": 3.36551, "time": 0.84844} +{"mode": "train", "epoch": 109, "iter": 3700, "lr": 0.01734, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40016, "top5_acc": 0.66219, "loss_cls": 3.3601, "loss": 3.3601, "time": 0.84841} +{"mode": "val", "epoch": 109, "iter": 309, "lr": 0.01733, "top1_acc": 0.34726, "top5_acc": 0.60634, "mean_class_accuracy": 0.34715} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.01731, "memory": 15990, "data_time": 1.51886, "top1_acc": 0.43391, "top5_acc": 0.69188, "loss_cls": 3.20575, "loss": 3.20575, "time": 2.55386} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.01729, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41, "top5_acc": 0.68906, "loss_cls": 3.2554, "loss": 3.2554, "time": 0.85421} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.01727, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42328, "top5_acc": 0.68625, "loss_cls": 3.24278, "loss": 3.24278, "time": 0.84729} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.01724, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41641, "top5_acc": 0.67953, "loss_cls": 3.25323, "loss": 3.25323, "time": 0.85097} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.01722, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41344, "top5_acc": 0.68578, "loss_cls": 3.26375, "loss": 3.26375, "time": 0.8571} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.0172, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42047, "top5_acc": 0.68156, "loss_cls": 3.27736, "loss": 3.27736, "time": 0.85485} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.01718, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.40672, "top5_acc": 0.66875, "loss_cls": 3.30259, "loss": 3.30259, "time": 0.85025} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.01716, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40891, "top5_acc": 0.67453, "loss_cls": 3.29378, "loss": 3.29378, "time": 0.84855} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.01714, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42438, "top5_acc": 0.67938, "loss_cls": 3.25857, "loss": 3.25857, "time": 0.84422} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.01712, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.40609, "top5_acc": 0.66141, "loss_cls": 3.36483, "loss": 3.36483, "time": 0.84335} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.0171, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40812, "top5_acc": 0.66844, "loss_cls": 3.342, "loss": 3.342, "time": 0.84408} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.01708, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.42156, "top5_acc": 0.6825, "loss_cls": 3.27639, "loss": 3.27639, "time": 0.84795} +{"mode": "train", "epoch": 110, "iter": 1300, "lr": 0.01705, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41422, "top5_acc": 0.67891, "loss_cls": 3.27841, "loss": 3.27841, "time": 0.84393} +{"mode": "train", "epoch": 110, "iter": 1400, "lr": 0.01703, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40922, "top5_acc": 0.66688, "loss_cls": 3.33607, "loss": 3.33607, "time": 0.83801} +{"mode": "train", "epoch": 110, "iter": 1500, "lr": 0.01701, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41219, "top5_acc": 0.67609, "loss_cls": 3.29325, "loss": 3.29325, "time": 0.84544} +{"mode": "train", "epoch": 110, "iter": 1600, "lr": 0.01699, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42156, "top5_acc": 0.67969, "loss_cls": 3.26309, "loss": 3.26309, "time": 0.85156} +{"mode": "train", "epoch": 110, "iter": 1700, "lr": 0.01697, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40188, "top5_acc": 0.67391, "loss_cls": 3.30242, "loss": 3.30242, "time": 0.84726} +{"mode": "train", "epoch": 110, "iter": 1800, "lr": 0.01695, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40422, "top5_acc": 0.66562, "loss_cls": 3.35239, "loss": 3.35239, "time": 0.84785} +{"mode": "train", "epoch": 110, "iter": 1900, "lr": 0.01693, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39453, "top5_acc": 0.66156, "loss_cls": 3.36846, "loss": 3.36846, "time": 0.84594} +{"mode": "train", "epoch": 110, "iter": 2000, "lr": 0.01691, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42031, "top5_acc": 0.67594, "loss_cls": 3.26822, "loss": 3.26822, "time": 0.84636} +{"mode": "train", "epoch": 110, "iter": 2100, "lr": 0.01689, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39812, "top5_acc": 0.66469, "loss_cls": 3.36347, "loss": 3.36347, "time": 0.84307} +{"mode": "train", "epoch": 110, "iter": 2200, "lr": 0.01687, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40797, "top5_acc": 0.66688, "loss_cls": 3.31392, "loss": 3.31392, "time": 0.84531} +{"mode": "train", "epoch": 110, "iter": 2300, "lr": 0.01685, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42156, "top5_acc": 0.68578, "loss_cls": 3.26528, "loss": 3.26528, "time": 0.84748} +{"mode": "train", "epoch": 110, "iter": 2400, "lr": 0.01682, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39594, "top5_acc": 0.66359, "loss_cls": 3.39342, "loss": 3.39342, "time": 0.84632} +{"mode": "train", "epoch": 110, "iter": 2500, "lr": 0.0168, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.415, "top5_acc": 0.67703, "loss_cls": 3.26841, "loss": 3.26841, "time": 0.84697} +{"mode": "train", "epoch": 110, "iter": 2600, "lr": 0.01678, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40891, "top5_acc": 0.67781, "loss_cls": 3.33421, "loss": 3.33421, "time": 0.84414} +{"mode": "train", "epoch": 110, "iter": 2700, "lr": 0.01676, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40875, "top5_acc": 0.67422, "loss_cls": 3.27016, "loss": 3.27016, "time": 0.84521} +{"mode": "train", "epoch": 110, "iter": 2800, "lr": 0.01674, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40656, "top5_acc": 0.66734, "loss_cls": 3.34083, "loss": 3.34083, "time": 0.84544} +{"mode": "train", "epoch": 110, "iter": 2900, "lr": 0.01672, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40578, "top5_acc": 0.67281, "loss_cls": 3.31239, "loss": 3.31239, "time": 0.84581} +{"mode": "train", "epoch": 110, "iter": 3000, "lr": 0.0167, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40562, "top5_acc": 0.66469, "loss_cls": 3.34412, "loss": 3.34412, "time": 0.84602} +{"mode": "train", "epoch": 110, "iter": 3100, "lr": 0.01668, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40938, "top5_acc": 0.67328, "loss_cls": 3.29152, "loss": 3.29152, "time": 0.84693} +{"mode": "train", "epoch": 110, "iter": 3200, "lr": 0.01666, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40891, "top5_acc": 0.66734, "loss_cls": 3.34168, "loss": 3.34168, "time": 0.84197} +{"mode": "train", "epoch": 110, "iter": 3300, "lr": 0.01664, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41469, "top5_acc": 0.65969, "loss_cls": 3.35316, "loss": 3.35316, "time": 0.83821} +{"mode": "train", "epoch": 110, "iter": 3400, "lr": 0.01662, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.41047, "top5_acc": 0.66781, "loss_cls": 3.30105, "loss": 3.30105, "time": 0.84452} +{"mode": "train", "epoch": 110, "iter": 3500, "lr": 0.01659, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39438, "top5_acc": 0.66641, "loss_cls": 3.37303, "loss": 3.37303, "time": 0.83801} +{"mode": "train", "epoch": 110, "iter": 3600, "lr": 0.01657, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39719, "top5_acc": 0.66766, "loss_cls": 3.32407, "loss": 3.32407, "time": 0.84277} +{"mode": "train", "epoch": 110, "iter": 3700, "lr": 0.01655, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40359, "top5_acc": 0.66141, "loss_cls": 3.3419, "loss": 3.3419, "time": 0.84573} +{"mode": "val", "epoch": 110, "iter": 309, "lr": 0.01654, "top1_acc": 0.34529, "top5_acc": 0.6032, "mean_class_accuracy": 0.34505} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.01652, "memory": 15990, "data_time": 1.50726, "top1_acc": 0.42375, "top5_acc": 0.68266, "loss_cls": 3.25182, "loss": 3.25182, "time": 2.53944} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.0165, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.4275, "top5_acc": 0.68672, "loss_cls": 3.20703, "loss": 3.20703, "time": 0.86061} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.01648, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41281, "top5_acc": 0.68094, "loss_cls": 3.26667, "loss": 3.26667, "time": 0.84922} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.01646, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.40469, "top5_acc": 0.67156, "loss_cls": 3.30729, "loss": 3.30729, "time": 0.85301} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.01644, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42359, "top5_acc": 0.68906, "loss_cls": 3.23766, "loss": 3.23766, "time": 0.85561} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.01642, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41047, "top5_acc": 0.66969, "loss_cls": 3.29326, "loss": 3.29326, "time": 0.85315} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.0164, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42766, "top5_acc": 0.68422, "loss_cls": 3.22817, "loss": 3.22817, "time": 0.85825} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.01638, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.41828, "top5_acc": 0.67906, "loss_cls": 3.27892, "loss": 3.27892, "time": 0.85259} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.01636, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41594, "top5_acc": 0.67734, "loss_cls": 3.25852, "loss": 3.25852, "time": 0.8461} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.01634, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.41781, "top5_acc": 0.68531, "loss_cls": 3.22619, "loss": 3.22619, "time": 0.8477} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.01632, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41422, "top5_acc": 0.67953, "loss_cls": 3.2623, "loss": 3.2623, "time": 0.84373} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.0163, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.41312, "top5_acc": 0.66922, "loss_cls": 3.30505, "loss": 3.30505, "time": 0.85127} +{"mode": "train", "epoch": 111, "iter": 1300, "lr": 0.01627, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42594, "top5_acc": 0.68875, "loss_cls": 3.22878, "loss": 3.22878, "time": 0.84988} +{"mode": "train", "epoch": 111, "iter": 1400, "lr": 0.01625, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.41312, "top5_acc": 0.67297, "loss_cls": 3.28317, "loss": 3.28317, "time": 0.84527} +{"mode": "train", "epoch": 111, "iter": 1500, "lr": 0.01623, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.40516, "top5_acc": 0.67578, "loss_cls": 3.30328, "loss": 3.30328, "time": 0.84706} +{"mode": "train", "epoch": 111, "iter": 1600, "lr": 0.01621, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40859, "top5_acc": 0.67875, "loss_cls": 3.29193, "loss": 3.29193, "time": 0.84317} +{"mode": "train", "epoch": 111, "iter": 1700, "lr": 0.01619, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42141, "top5_acc": 0.67703, "loss_cls": 3.24242, "loss": 3.24242, "time": 0.84504} +{"mode": "train", "epoch": 111, "iter": 1800, "lr": 0.01617, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40797, "top5_acc": 0.67219, "loss_cls": 3.30402, "loss": 3.30402, "time": 0.83952} +{"mode": "train", "epoch": 111, "iter": 1900, "lr": 0.01615, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41438, "top5_acc": 0.67516, "loss_cls": 3.28545, "loss": 3.28545, "time": 0.84867} +{"mode": "train", "epoch": 111, "iter": 2000, "lr": 0.01613, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.405, "top5_acc": 0.67453, "loss_cls": 3.31843, "loss": 3.31843, "time": 0.84306} +{"mode": "train", "epoch": 111, "iter": 2100, "lr": 0.01611, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40344, "top5_acc": 0.66984, "loss_cls": 3.34043, "loss": 3.34043, "time": 0.84648} +{"mode": "train", "epoch": 111, "iter": 2200, "lr": 0.01609, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41078, "top5_acc": 0.67359, "loss_cls": 3.29375, "loss": 3.29375, "time": 0.84411} +{"mode": "train", "epoch": 111, "iter": 2300, "lr": 0.01607, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41922, "top5_acc": 0.66875, "loss_cls": 3.29303, "loss": 3.29303, "time": 0.8469} +{"mode": "train", "epoch": 111, "iter": 2400, "lr": 0.01605, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41812, "top5_acc": 0.67922, "loss_cls": 3.28158, "loss": 3.28158, "time": 0.85186} +{"mode": "train", "epoch": 111, "iter": 2500, "lr": 0.01603, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40891, "top5_acc": 0.67375, "loss_cls": 3.32082, "loss": 3.32082, "time": 0.84536} +{"mode": "train", "epoch": 111, "iter": 2600, "lr": 0.01601, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41344, "top5_acc": 0.67734, "loss_cls": 3.30417, "loss": 3.30417, "time": 0.8517} +{"mode": "train", "epoch": 111, "iter": 2700, "lr": 0.01599, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41531, "top5_acc": 0.67953, "loss_cls": 3.26657, "loss": 3.26657, "time": 0.84664} +{"mode": "train", "epoch": 111, "iter": 2800, "lr": 0.01597, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41188, "top5_acc": 0.67594, "loss_cls": 3.29011, "loss": 3.29011, "time": 0.84846} +{"mode": "train", "epoch": 111, "iter": 2900, "lr": 0.01595, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41109, "top5_acc": 0.67906, "loss_cls": 3.28331, "loss": 3.28331, "time": 0.8522} +{"mode": "train", "epoch": 111, "iter": 3000, "lr": 0.01593, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41344, "top5_acc": 0.67219, "loss_cls": 3.28437, "loss": 3.28437, "time": 0.84433} +{"mode": "train", "epoch": 111, "iter": 3100, "lr": 0.0159, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40859, "top5_acc": 0.67219, "loss_cls": 3.2914, "loss": 3.2914, "time": 0.84538} +{"mode": "train", "epoch": 111, "iter": 3200, "lr": 0.01588, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.415, "top5_acc": 0.67391, "loss_cls": 3.29006, "loss": 3.29006, "time": 0.83882} +{"mode": "train", "epoch": 111, "iter": 3300, "lr": 0.01586, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.41328, "top5_acc": 0.67, "loss_cls": 3.30915, "loss": 3.30915, "time": 0.84498} +{"mode": "train", "epoch": 111, "iter": 3400, "lr": 0.01584, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41062, "top5_acc": 0.66891, "loss_cls": 3.31081, "loss": 3.31081, "time": 0.83705} +{"mode": "train", "epoch": 111, "iter": 3500, "lr": 0.01582, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40359, "top5_acc": 0.67719, "loss_cls": 3.30835, "loss": 3.30835, "time": 0.84485} +{"mode": "train", "epoch": 111, "iter": 3600, "lr": 0.0158, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40859, "top5_acc": 0.67266, "loss_cls": 3.30898, "loss": 3.30898, "time": 0.84112} +{"mode": "train", "epoch": 111, "iter": 3700, "lr": 0.01578, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40469, "top5_acc": 0.66922, "loss_cls": 3.31164, "loss": 3.31164, "time": 0.84529} +{"mode": "val", "epoch": 111, "iter": 309, "lr": 0.01577, "top1_acc": 0.34686, "top5_acc": 0.60675, "mean_class_accuracy": 0.34651} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.01575, "memory": 15990, "data_time": 1.55541, "top1_acc": 0.42828, "top5_acc": 0.69344, "loss_cls": 3.17803, "loss": 3.17803, "time": 2.57227} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.01573, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42422, "top5_acc": 0.68609, "loss_cls": 3.22523, "loss": 3.22523, "time": 0.84829} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.01571, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42047, "top5_acc": 0.67953, "loss_cls": 3.27151, "loss": 3.27151, "time": 0.84754} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.01569, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41953, "top5_acc": 0.67516, "loss_cls": 3.27159, "loss": 3.27159, "time": 0.85251} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.01567, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42297, "top5_acc": 0.68453, "loss_cls": 3.2164, "loss": 3.2164, "time": 0.85659} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.01565, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.43297, "top5_acc": 0.68766, "loss_cls": 3.18428, "loss": 3.18428, "time": 0.85423} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.01563, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42797, "top5_acc": 0.69203, "loss_cls": 3.18998, "loss": 3.18998, "time": 0.85603} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.01561, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.41219, "top5_acc": 0.67516, "loss_cls": 3.29035, "loss": 3.29035, "time": 0.85742} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.01559, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.41531, "top5_acc": 0.66969, "loss_cls": 3.27338, "loss": 3.27338, "time": 0.85113} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.01557, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41891, "top5_acc": 0.68531, "loss_cls": 3.24899, "loss": 3.24899, "time": 0.84999} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.01555, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41266, "top5_acc": 0.68078, "loss_cls": 3.24932, "loss": 3.24932, "time": 0.84775} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.01553, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41219, "top5_acc": 0.67781, "loss_cls": 3.28328, "loss": 3.28328, "time": 0.85289} +{"mode": "train", "epoch": 112, "iter": 1300, "lr": 0.01551, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41391, "top5_acc": 0.675, "loss_cls": 3.30903, "loss": 3.30903, "time": 0.84952} +{"mode": "train", "epoch": 112, "iter": 1400, "lr": 0.01549, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42422, "top5_acc": 0.6925, "loss_cls": 3.2125, "loss": 3.2125, "time": 0.84543} +{"mode": "train", "epoch": 112, "iter": 1500, "lr": 0.01547, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.42047, "top5_acc": 0.67906, "loss_cls": 3.26708, "loss": 3.26708, "time": 0.8426} +{"mode": "train", "epoch": 112, "iter": 1600, "lr": 0.01545, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41641, "top5_acc": 0.68938, "loss_cls": 3.23679, "loss": 3.23679, "time": 0.8467} +{"mode": "train", "epoch": 112, "iter": 1700, "lr": 0.01543, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42078, "top5_acc": 0.67547, "loss_cls": 3.27501, "loss": 3.27501, "time": 0.84589} +{"mode": "train", "epoch": 112, "iter": 1800, "lr": 0.01541, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41109, "top5_acc": 0.68391, "loss_cls": 3.26794, "loss": 3.26794, "time": 0.84638} +{"mode": "train", "epoch": 112, "iter": 1900, "lr": 0.01539, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41953, "top5_acc": 0.68562, "loss_cls": 3.23957, "loss": 3.23957, "time": 0.84834} +{"mode": "train", "epoch": 112, "iter": 2000, "lr": 0.01537, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.40781, "top5_acc": 0.68094, "loss_cls": 3.27983, "loss": 3.27983, "time": 0.84971} +{"mode": "train", "epoch": 112, "iter": 2100, "lr": 0.01535, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42656, "top5_acc": 0.67734, "loss_cls": 3.23547, "loss": 3.23547, "time": 0.85651} +{"mode": "train", "epoch": 112, "iter": 2200, "lr": 0.01533, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41797, "top5_acc": 0.67922, "loss_cls": 3.2605, "loss": 3.2605, "time": 0.85269} +{"mode": "train", "epoch": 112, "iter": 2300, "lr": 0.01531, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41172, "top5_acc": 0.67844, "loss_cls": 3.27059, "loss": 3.27059, "time": 0.85834} +{"mode": "train", "epoch": 112, "iter": 2400, "lr": 0.01529, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.42141, "top5_acc": 0.68641, "loss_cls": 3.22041, "loss": 3.22041, "time": 0.85599} +{"mode": "train", "epoch": 112, "iter": 2500, "lr": 0.01527, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42094, "top5_acc": 0.68219, "loss_cls": 3.2402, "loss": 3.2402, "time": 0.85273} +{"mode": "train", "epoch": 112, "iter": 2600, "lr": 0.01525, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.4175, "top5_acc": 0.68172, "loss_cls": 3.24778, "loss": 3.24778, "time": 0.85446} +{"mode": "train", "epoch": 112, "iter": 2700, "lr": 0.01523, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41, "top5_acc": 0.66922, "loss_cls": 3.34146, "loss": 3.34146, "time": 0.85287} +{"mode": "train", "epoch": 112, "iter": 2800, "lr": 0.01521, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41969, "top5_acc": 0.67484, "loss_cls": 3.27664, "loss": 3.27664, "time": 0.84815} +{"mode": "train", "epoch": 112, "iter": 2900, "lr": 0.01519, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42203, "top5_acc": 0.68344, "loss_cls": 3.25208, "loss": 3.25208, "time": 0.85557} +{"mode": "train", "epoch": 112, "iter": 3000, "lr": 0.01517, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41656, "top5_acc": 0.6775, "loss_cls": 3.26338, "loss": 3.26338, "time": 0.85154} +{"mode": "train", "epoch": 112, "iter": 3100, "lr": 0.01515, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41625, "top5_acc": 0.68406, "loss_cls": 3.24991, "loss": 3.24991, "time": 0.85078} +{"mode": "train", "epoch": 112, "iter": 3200, "lr": 0.01513, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41312, "top5_acc": 0.68359, "loss_cls": 3.29654, "loss": 3.29654, "time": 0.84787} +{"mode": "train", "epoch": 112, "iter": 3300, "lr": 0.01511, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.41359, "top5_acc": 0.67969, "loss_cls": 3.28098, "loss": 3.28098, "time": 0.84882} +{"mode": "train", "epoch": 112, "iter": 3400, "lr": 0.01509, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.43344, "top5_acc": 0.67781, "loss_cls": 3.23019, "loss": 3.23019, "time": 0.84443} +{"mode": "train", "epoch": 112, "iter": 3500, "lr": 0.01507, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.41938, "top5_acc": 0.68484, "loss_cls": 3.2369, "loss": 3.2369, "time": 0.84316} +{"mode": "train", "epoch": 112, "iter": 3600, "lr": 0.01505, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.415, "top5_acc": 0.68328, "loss_cls": 3.25361, "loss": 3.25361, "time": 0.84344} +{"mode": "train", "epoch": 112, "iter": 3700, "lr": 0.01503, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41141, "top5_acc": 0.67109, "loss_cls": 3.31112, "loss": 3.31112, "time": 0.84525} +{"mode": "val", "epoch": 112, "iter": 309, "lr": 0.01502, "top1_acc": 0.35987, "top5_acc": 0.61495, "mean_class_accuracy": 0.35964} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.015, "memory": 15990, "data_time": 1.5141, "top1_acc": 0.43453, "top5_acc": 0.69766, "loss_cls": 3.15553, "loss": 3.15553, "time": 2.55163} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.01498, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41781, "top5_acc": 0.67891, "loss_cls": 3.24522, "loss": 3.24522, "time": 0.85186} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.01496, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44203, "top5_acc": 0.70203, "loss_cls": 3.13995, "loss": 3.13995, "time": 0.85844} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.01494, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.43734, "top5_acc": 0.70047, "loss_cls": 3.14666, "loss": 3.14666, "time": 0.86174} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.01492, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41953, "top5_acc": 0.69453, "loss_cls": 3.20159, "loss": 3.20159, "time": 0.85593} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.0149, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.43156, "top5_acc": 0.69312, "loss_cls": 3.18603, "loss": 3.18603, "time": 0.86228} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.01488, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42078, "top5_acc": 0.68516, "loss_cls": 3.22869, "loss": 3.22869, "time": 0.86301} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.01486, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.41406, "top5_acc": 0.68875, "loss_cls": 3.23638, "loss": 3.23638, "time": 0.85847} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.01484, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42891, "top5_acc": 0.69469, "loss_cls": 3.17147, "loss": 3.17147, "time": 0.85207} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.01482, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43094, "top5_acc": 0.68812, "loss_cls": 3.18499, "loss": 3.18499, "time": 0.85763} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0148, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42062, "top5_acc": 0.68234, "loss_cls": 3.24489, "loss": 3.24489, "time": 0.85847} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.01478, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42312, "top5_acc": 0.67875, "loss_cls": 3.21845, "loss": 3.21845, "time": 0.84724} +{"mode": "train", "epoch": 113, "iter": 1300, "lr": 0.01476, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41891, "top5_acc": 0.68469, "loss_cls": 3.25205, "loss": 3.25205, "time": 0.84274} +{"mode": "train", "epoch": 113, "iter": 1400, "lr": 0.01474, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.42906, "top5_acc": 0.69344, "loss_cls": 3.19431, "loss": 3.19431, "time": 0.83993} +{"mode": "train", "epoch": 113, "iter": 1500, "lr": 0.01472, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42422, "top5_acc": 0.67734, "loss_cls": 3.2523, "loss": 3.2523, "time": 0.84293} +{"mode": "train", "epoch": 113, "iter": 1600, "lr": 0.0147, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42484, "top5_acc": 0.69047, "loss_cls": 3.22825, "loss": 3.22825, "time": 0.84817} +{"mode": "train", "epoch": 113, "iter": 1700, "lr": 0.01468, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42062, "top5_acc": 0.67891, "loss_cls": 3.28108, "loss": 3.28108, "time": 0.85151} +{"mode": "train", "epoch": 113, "iter": 1800, "lr": 0.01466, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41984, "top5_acc": 0.68234, "loss_cls": 3.25714, "loss": 3.25714, "time": 0.85864} +{"mode": "train", "epoch": 113, "iter": 1900, "lr": 0.01464, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41734, "top5_acc": 0.68016, "loss_cls": 3.24061, "loss": 3.24061, "time": 0.84927} +{"mode": "train", "epoch": 113, "iter": 2000, "lr": 0.01462, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42312, "top5_acc": 0.6825, "loss_cls": 3.2411, "loss": 3.2411, "time": 0.85402} +{"mode": "train", "epoch": 113, "iter": 2100, "lr": 0.0146, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42172, "top5_acc": 0.67625, "loss_cls": 3.25142, "loss": 3.25142, "time": 0.8603} +{"mode": "train", "epoch": 113, "iter": 2200, "lr": 0.01458, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41719, "top5_acc": 0.68484, "loss_cls": 3.21769, "loss": 3.21769, "time": 0.8483} +{"mode": "train", "epoch": 113, "iter": 2300, "lr": 0.01456, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42172, "top5_acc": 0.68578, "loss_cls": 3.24174, "loss": 3.24174, "time": 0.85524} +{"mode": "train", "epoch": 113, "iter": 2400, "lr": 0.01454, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42359, "top5_acc": 0.69219, "loss_cls": 3.20057, "loss": 3.20057, "time": 0.85416} +{"mode": "train", "epoch": 113, "iter": 2500, "lr": 0.01452, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41312, "top5_acc": 0.67031, "loss_cls": 3.28571, "loss": 3.28571, "time": 0.85548} +{"mode": "train", "epoch": 113, "iter": 2600, "lr": 0.0145, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.4175, "top5_acc": 0.68344, "loss_cls": 3.25143, "loss": 3.25143, "time": 0.84756} +{"mode": "train", "epoch": 113, "iter": 2700, "lr": 0.01448, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40969, "top5_acc": 0.66703, "loss_cls": 3.31102, "loss": 3.31102, "time": 0.85013} +{"mode": "train", "epoch": 113, "iter": 2800, "lr": 0.01446, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41203, "top5_acc": 0.68328, "loss_cls": 3.27783, "loss": 3.27783, "time": 0.84651} +{"mode": "train", "epoch": 113, "iter": 2900, "lr": 0.01444, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41109, "top5_acc": 0.67828, "loss_cls": 3.28983, "loss": 3.28983, "time": 0.85111} +{"mode": "train", "epoch": 113, "iter": 3000, "lr": 0.01442, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41688, "top5_acc": 0.68719, "loss_cls": 3.22284, "loss": 3.22284, "time": 0.85175} +{"mode": "train", "epoch": 113, "iter": 3100, "lr": 0.0144, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41109, "top5_acc": 0.67969, "loss_cls": 3.26592, "loss": 3.26592, "time": 0.84166} +{"mode": "train", "epoch": 113, "iter": 3200, "lr": 0.01438, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42562, "top5_acc": 0.68078, "loss_cls": 3.22902, "loss": 3.22902, "time": 0.84887} +{"mode": "train", "epoch": 113, "iter": 3300, "lr": 0.01436, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41812, "top5_acc": 0.67906, "loss_cls": 3.25117, "loss": 3.25117, "time": 0.84646} +{"mode": "train", "epoch": 113, "iter": 3400, "lr": 0.01434, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41938, "top5_acc": 0.6825, "loss_cls": 3.26805, "loss": 3.26805, "time": 0.8509} +{"mode": "train", "epoch": 113, "iter": 3500, "lr": 0.01432, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41875, "top5_acc": 0.67922, "loss_cls": 3.26285, "loss": 3.26285, "time": 0.84597} +{"mode": "train", "epoch": 113, "iter": 3600, "lr": 0.01431, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41656, "top5_acc": 0.68328, "loss_cls": 3.25742, "loss": 3.25742, "time": 0.83564} +{"mode": "train", "epoch": 113, "iter": 3700, "lr": 0.01429, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41875, "top5_acc": 0.67969, "loss_cls": 3.25085, "loss": 3.25085, "time": 0.84853} +{"mode": "val", "epoch": 113, "iter": 309, "lr": 0.01428, "top1_acc": 0.36702, "top5_acc": 0.62706, "mean_class_accuracy": 0.36671} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.01426, "memory": 15990, "data_time": 1.51316, "top1_acc": 0.43969, "top5_acc": 0.70047, "loss_cls": 3.11272, "loss": 3.11272, "time": 2.54911} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.01424, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43094, "top5_acc": 0.69516, "loss_cls": 3.14851, "loss": 3.14851, "time": 0.85073} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.01422, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.4325, "top5_acc": 0.70203, "loss_cls": 3.14514, "loss": 3.14514, "time": 0.85136} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.0142, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.44125, "top5_acc": 0.69688, "loss_cls": 3.14422, "loss": 3.14422, "time": 0.85509} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.01418, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42484, "top5_acc": 0.68688, "loss_cls": 3.19516, "loss": 3.19516, "time": 0.85156} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.01416, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43812, "top5_acc": 0.69844, "loss_cls": 3.14465, "loss": 3.14465, "time": 0.85112} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.01414, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42859, "top5_acc": 0.69328, "loss_cls": 3.20077, "loss": 3.20077, "time": 0.85361} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.01412, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.4325, "top5_acc": 0.69188, "loss_cls": 3.18303, "loss": 3.18303, "time": 0.8465} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.0141, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42734, "top5_acc": 0.6875, "loss_cls": 3.22815, "loss": 3.22815, "time": 0.84884} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.01408, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42953, "top5_acc": 0.69812, "loss_cls": 3.17389, "loss": 3.17389, "time": 0.84511} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.01406, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42703, "top5_acc": 0.68094, "loss_cls": 3.21435, "loss": 3.21435, "time": 0.84778} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.01404, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.42969, "top5_acc": 0.68734, "loss_cls": 3.22172, "loss": 3.22172, "time": 0.84417} +{"mode": "train", "epoch": 114, "iter": 1300, "lr": 0.01402, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42156, "top5_acc": 0.68312, "loss_cls": 3.25819, "loss": 3.25819, "time": 0.85079} +{"mode": "train", "epoch": 114, "iter": 1400, "lr": 0.014, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.41969, "top5_acc": 0.68797, "loss_cls": 3.22886, "loss": 3.22886, "time": 0.84527} +{"mode": "train", "epoch": 114, "iter": 1500, "lr": 0.01398, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.435, "top5_acc": 0.70375, "loss_cls": 3.13206, "loss": 3.13206, "time": 0.84848} +{"mode": "train", "epoch": 114, "iter": 1600, "lr": 0.01397, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41328, "top5_acc": 0.67812, "loss_cls": 3.27272, "loss": 3.27272, "time": 0.84968} +{"mode": "train", "epoch": 114, "iter": 1700, "lr": 0.01395, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42031, "top5_acc": 0.69141, "loss_cls": 3.22945, "loss": 3.22945, "time": 0.85107} +{"mode": "train", "epoch": 114, "iter": 1800, "lr": 0.01393, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42625, "top5_acc": 0.68906, "loss_cls": 3.23964, "loss": 3.23964, "time": 0.84647} +{"mode": "train", "epoch": 114, "iter": 1900, "lr": 0.01391, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42391, "top5_acc": 0.6825, "loss_cls": 3.22801, "loss": 3.22801, "time": 0.84904} +{"mode": "train", "epoch": 114, "iter": 2000, "lr": 0.01389, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42781, "top5_acc": 0.68531, "loss_cls": 3.19079, "loss": 3.19079, "time": 0.85101} +{"mode": "train", "epoch": 114, "iter": 2100, "lr": 0.01387, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42938, "top5_acc": 0.69109, "loss_cls": 3.18518, "loss": 3.18518, "time": 0.85267} +{"mode": "train", "epoch": 114, "iter": 2200, "lr": 0.01385, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42156, "top5_acc": 0.68609, "loss_cls": 3.21633, "loss": 3.21633, "time": 0.8483} +{"mode": "train", "epoch": 114, "iter": 2300, "lr": 0.01383, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42406, "top5_acc": 0.67828, "loss_cls": 3.256, "loss": 3.256, "time": 0.84832} +{"mode": "train", "epoch": 114, "iter": 2400, "lr": 0.01381, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40562, "top5_acc": 0.67234, "loss_cls": 3.29027, "loss": 3.29027, "time": 0.84758} +{"mode": "train", "epoch": 114, "iter": 2500, "lr": 0.01379, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42078, "top5_acc": 0.67984, "loss_cls": 3.26013, "loss": 3.26013, "time": 0.85132} +{"mode": "train", "epoch": 114, "iter": 2600, "lr": 0.01377, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41812, "top5_acc": 0.68406, "loss_cls": 3.24743, "loss": 3.24743, "time": 0.84784} +{"mode": "train", "epoch": 114, "iter": 2700, "lr": 0.01375, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41891, "top5_acc": 0.68719, "loss_cls": 3.24662, "loss": 3.24662, "time": 0.84829} +{"mode": "train", "epoch": 114, "iter": 2800, "lr": 0.01373, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42172, "top5_acc": 0.68688, "loss_cls": 3.21147, "loss": 3.21147, "time": 0.84953} +{"mode": "train", "epoch": 114, "iter": 2900, "lr": 0.01371, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42469, "top5_acc": 0.69562, "loss_cls": 3.18308, "loss": 3.18308, "time": 0.85219} +{"mode": "train", "epoch": 114, "iter": 3000, "lr": 0.01369, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43328, "top5_acc": 0.68562, "loss_cls": 3.21942, "loss": 3.21942, "time": 0.84881} +{"mode": "train", "epoch": 114, "iter": 3100, "lr": 0.01368, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42625, "top5_acc": 0.69344, "loss_cls": 3.18341, "loss": 3.18341, "time": 0.85092} +{"mode": "train", "epoch": 114, "iter": 3200, "lr": 0.01366, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43328, "top5_acc": 0.69734, "loss_cls": 3.16236, "loss": 3.16236, "time": 0.84786} +{"mode": "train", "epoch": 114, "iter": 3300, "lr": 0.01364, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42719, "top5_acc": 0.68438, "loss_cls": 3.2261, "loss": 3.2261, "time": 0.84768} +{"mode": "train", "epoch": 114, "iter": 3400, "lr": 0.01362, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42594, "top5_acc": 0.68609, "loss_cls": 3.20968, "loss": 3.20968, "time": 0.84318} +{"mode": "train", "epoch": 114, "iter": 3500, "lr": 0.0136, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41922, "top5_acc": 0.68812, "loss_cls": 3.23473, "loss": 3.23473, "time": 0.84138} +{"mode": "train", "epoch": 114, "iter": 3600, "lr": 0.01358, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.42141, "top5_acc": 0.68734, "loss_cls": 3.23312, "loss": 3.23312, "time": 0.84562} +{"mode": "train", "epoch": 114, "iter": 3700, "lr": 0.01356, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.4275, "top5_acc": 0.69156, "loss_cls": 3.19599, "loss": 3.19599, "time": 0.84484} +{"mode": "val", "epoch": 114, "iter": 309, "lr": 0.01355, "top1_acc": 0.34787, "top5_acc": 0.61014, "mean_class_accuracy": 0.34766} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.01353, "memory": 15990, "data_time": 1.48413, "top1_acc": 0.43484, "top5_acc": 0.69766, "loss_cls": 3.13395, "loss": 3.13395, "time": 2.51504} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.01351, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44328, "top5_acc": 0.70172, "loss_cls": 3.12685, "loss": 3.12685, "time": 0.85118} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.01349, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43797, "top5_acc": 0.70047, "loss_cls": 3.11393, "loss": 3.11393, "time": 0.8556} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.01348, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43438, "top5_acc": 0.70266, "loss_cls": 3.15005, "loss": 3.15005, "time": 0.8486} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.01346, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44359, "top5_acc": 0.71, "loss_cls": 3.0976, "loss": 3.0976, "time": 0.85571} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.01344, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42812, "top5_acc": 0.68984, "loss_cls": 3.19578, "loss": 3.19578, "time": 0.85932} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.01342, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43547, "top5_acc": 0.69156, "loss_cls": 3.16619, "loss": 3.16619, "time": 0.85163} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.0134, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43172, "top5_acc": 0.69469, "loss_cls": 3.17528, "loss": 3.17528, "time": 0.84986} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.01338, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44406, "top5_acc": 0.70734, "loss_cls": 3.09799, "loss": 3.09799, "time": 0.8539} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.01336, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.43484, "top5_acc": 0.69938, "loss_cls": 3.15329, "loss": 3.15329, "time": 0.85426} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.01334, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43656, "top5_acc": 0.69375, "loss_cls": 3.14257, "loss": 3.14257, "time": 0.85423} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.01332, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42703, "top5_acc": 0.69375, "loss_cls": 3.17277, "loss": 3.17277, "time": 0.85112} +{"mode": "train", "epoch": 115, "iter": 1300, "lr": 0.0133, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42641, "top5_acc": 0.67922, "loss_cls": 3.22767, "loss": 3.22767, "time": 0.84691} +{"mode": "train", "epoch": 115, "iter": 1400, "lr": 0.01328, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41719, "top5_acc": 0.68516, "loss_cls": 3.23979, "loss": 3.23979, "time": 0.84964} +{"mode": "train", "epoch": 115, "iter": 1500, "lr": 0.01327, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.4325, "top5_acc": 0.69453, "loss_cls": 3.17361, "loss": 3.17361, "time": 0.8482} +{"mode": "train", "epoch": 115, "iter": 1600, "lr": 0.01325, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43453, "top5_acc": 0.70297, "loss_cls": 3.14238, "loss": 3.14238, "time": 0.84596} +{"mode": "train", "epoch": 115, "iter": 1700, "lr": 0.01323, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42578, "top5_acc": 0.69516, "loss_cls": 3.18852, "loss": 3.18852, "time": 0.85015} +{"mode": "train", "epoch": 115, "iter": 1800, "lr": 0.01321, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.4275, "top5_acc": 0.69109, "loss_cls": 3.20375, "loss": 3.20375, "time": 0.84945} +{"mode": "train", "epoch": 115, "iter": 1900, "lr": 0.01319, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43344, "top5_acc": 0.69297, "loss_cls": 3.18767, "loss": 3.18767, "time": 0.85042} +{"mode": "train", "epoch": 115, "iter": 2000, "lr": 0.01317, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42844, "top5_acc": 0.69156, "loss_cls": 3.19115, "loss": 3.19115, "time": 0.84534} +{"mode": "train", "epoch": 115, "iter": 2100, "lr": 0.01315, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44156, "top5_acc": 0.69531, "loss_cls": 3.15135, "loss": 3.15135, "time": 0.85394} +{"mode": "train", "epoch": 115, "iter": 2200, "lr": 0.01313, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42781, "top5_acc": 0.68781, "loss_cls": 3.2041, "loss": 3.2041, "time": 0.84823} +{"mode": "train", "epoch": 115, "iter": 2300, "lr": 0.01311, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43406, "top5_acc": 0.70109, "loss_cls": 3.17631, "loss": 3.17631, "time": 0.84993} +{"mode": "train", "epoch": 115, "iter": 2400, "lr": 0.0131, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42234, "top5_acc": 0.68609, "loss_cls": 3.20547, "loss": 3.20547, "time": 0.84935} +{"mode": "train", "epoch": 115, "iter": 2500, "lr": 0.01308, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41828, "top5_acc": 0.68516, "loss_cls": 3.21021, "loss": 3.21021, "time": 0.84334} +{"mode": "train", "epoch": 115, "iter": 2600, "lr": 0.01306, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43484, "top5_acc": 0.69453, "loss_cls": 3.1837, "loss": 3.1837, "time": 0.84917} +{"mode": "train", "epoch": 115, "iter": 2700, "lr": 0.01304, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.4225, "top5_acc": 0.67906, "loss_cls": 3.23553, "loss": 3.23553, "time": 0.84998} +{"mode": "train", "epoch": 115, "iter": 2800, "lr": 0.01302, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42219, "top5_acc": 0.68766, "loss_cls": 3.22659, "loss": 3.22659, "time": 0.84758} +{"mode": "train", "epoch": 115, "iter": 2900, "lr": 0.013, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42312, "top5_acc": 0.68984, "loss_cls": 3.21292, "loss": 3.21292, "time": 0.85095} +{"mode": "train", "epoch": 115, "iter": 3000, "lr": 0.01298, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.42688, "top5_acc": 0.69359, "loss_cls": 3.19874, "loss": 3.19874, "time": 0.8603} +{"mode": "train", "epoch": 115, "iter": 3100, "lr": 0.01296, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42, "top5_acc": 0.67938, "loss_cls": 3.24209, "loss": 3.24209, "time": 0.85373} +{"mode": "train", "epoch": 115, "iter": 3200, "lr": 0.01295, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42359, "top5_acc": 0.68406, "loss_cls": 3.22034, "loss": 3.22034, "time": 0.85007} +{"mode": "train", "epoch": 115, "iter": 3300, "lr": 0.01293, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43219, "top5_acc": 0.69562, "loss_cls": 3.14683, "loss": 3.14683, "time": 0.85246} +{"mode": "train", "epoch": 115, "iter": 3400, "lr": 0.01291, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.41625, "top5_acc": 0.68266, "loss_cls": 3.24852, "loss": 3.24852, "time": 0.84846} +{"mode": "train", "epoch": 115, "iter": 3500, "lr": 0.01289, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.42625, "top5_acc": 0.68266, "loss_cls": 3.22039, "loss": 3.22039, "time": 0.8518} +{"mode": "train", "epoch": 115, "iter": 3600, "lr": 0.01287, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.4125, "top5_acc": 0.68859, "loss_cls": 3.22397, "loss": 3.22397, "time": 0.85588} +{"mode": "train", "epoch": 115, "iter": 3700, "lr": 0.01285, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42391, "top5_acc": 0.68406, "loss_cls": 3.22933, "loss": 3.22933, "time": 0.84832} +{"mode": "val", "epoch": 115, "iter": 309, "lr": 0.01284, "top1_acc": 0.35111, "top5_acc": 0.61014, "mean_class_accuracy": 0.35093} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.01282, "memory": 15990, "data_time": 1.56179, "top1_acc": 0.44141, "top5_acc": 0.70891, "loss_cls": 3.09667, "loss": 3.09667, "time": 2.58699} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.01281, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.44031, "top5_acc": 0.70672, "loss_cls": 3.10042, "loss": 3.10042, "time": 0.8523} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.01279, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43531, "top5_acc": 0.70047, "loss_cls": 3.13583, "loss": 3.13583, "time": 0.84751} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.01277, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44219, "top5_acc": 0.69844, "loss_cls": 3.10587, "loss": 3.10587, "time": 0.85288} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.01275, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43156, "top5_acc": 0.69422, "loss_cls": 3.15682, "loss": 3.15682, "time": 0.85019} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.01273, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.4275, "top5_acc": 0.70062, "loss_cls": 3.15787, "loss": 3.15787, "time": 0.85096} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.01271, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44297, "top5_acc": 0.70984, "loss_cls": 3.10539, "loss": 3.10539, "time": 0.84859} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.01269, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.44547, "top5_acc": 0.70875, "loss_cls": 3.09868, "loss": 3.09868, "time": 0.85271} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.01268, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43906, "top5_acc": 0.69922, "loss_cls": 3.14696, "loss": 3.14696, "time": 0.85347} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.01266, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45109, "top5_acc": 0.70781, "loss_cls": 3.09126, "loss": 3.09126, "time": 0.8504} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.01264, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43422, "top5_acc": 0.69516, "loss_cls": 3.1514, "loss": 3.1514, "time": 0.84924} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.01262, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44281, "top5_acc": 0.70453, "loss_cls": 3.11567, "loss": 3.11567, "time": 0.85047} +{"mode": "train", "epoch": 116, "iter": 1300, "lr": 0.0126, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43688, "top5_acc": 0.69578, "loss_cls": 3.16434, "loss": 3.16434, "time": 0.84821} +{"mode": "train", "epoch": 116, "iter": 1400, "lr": 0.01258, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.44375, "top5_acc": 0.70312, "loss_cls": 3.11586, "loss": 3.11586, "time": 0.85019} +{"mode": "train", "epoch": 116, "iter": 1500, "lr": 0.01256, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42859, "top5_acc": 0.69203, "loss_cls": 3.17755, "loss": 3.17755, "time": 0.85182} +{"mode": "train", "epoch": 116, "iter": 1600, "lr": 0.01255, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42984, "top5_acc": 0.69766, "loss_cls": 3.15688, "loss": 3.15688, "time": 0.85248} +{"mode": "train", "epoch": 116, "iter": 1700, "lr": 0.01253, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42984, "top5_acc": 0.69516, "loss_cls": 3.17212, "loss": 3.17212, "time": 0.85212} +{"mode": "train", "epoch": 116, "iter": 1800, "lr": 0.01251, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42312, "top5_acc": 0.68547, "loss_cls": 3.21504, "loss": 3.21504, "time": 0.84427} +{"mode": "train", "epoch": 116, "iter": 1900, "lr": 0.01249, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43062, "top5_acc": 0.695, "loss_cls": 3.18724, "loss": 3.18724, "time": 0.84761} +{"mode": "train", "epoch": 116, "iter": 2000, "lr": 0.01247, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44047, "top5_acc": 0.70031, "loss_cls": 3.14267, "loss": 3.14267, "time": 0.84635} +{"mode": "train", "epoch": 116, "iter": 2100, "lr": 0.01245, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.42719, "top5_acc": 0.69016, "loss_cls": 3.20791, "loss": 3.20791, "time": 0.85084} +{"mode": "train", "epoch": 116, "iter": 2200, "lr": 0.01243, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43188, "top5_acc": 0.68938, "loss_cls": 3.18643, "loss": 3.18643, "time": 0.8448} +{"mode": "train", "epoch": 116, "iter": 2300, "lr": 0.01242, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42359, "top5_acc": 0.67562, "loss_cls": 3.26715, "loss": 3.26715, "time": 0.84701} +{"mode": "train", "epoch": 116, "iter": 2400, "lr": 0.0124, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.43172, "top5_acc": 0.68984, "loss_cls": 3.19627, "loss": 3.19627, "time": 0.8485} +{"mode": "train", "epoch": 116, "iter": 2500, "lr": 0.01238, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43484, "top5_acc": 0.69688, "loss_cls": 3.16684, "loss": 3.16684, "time": 0.8507} +{"mode": "train", "epoch": 116, "iter": 2600, "lr": 0.01236, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43219, "top5_acc": 0.69875, "loss_cls": 3.16122, "loss": 3.16122, "time": 0.85054} +{"mode": "train", "epoch": 116, "iter": 2700, "lr": 0.01234, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43281, "top5_acc": 0.69281, "loss_cls": 3.18603, "loss": 3.18603, "time": 0.8431} +{"mode": "train", "epoch": 116, "iter": 2800, "lr": 0.01232, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43562, "top5_acc": 0.69, "loss_cls": 3.18828, "loss": 3.18828, "time": 0.84744} +{"mode": "train", "epoch": 116, "iter": 2900, "lr": 0.01231, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43234, "top5_acc": 0.69359, "loss_cls": 3.14895, "loss": 3.14895, "time": 0.84989} +{"mode": "train", "epoch": 116, "iter": 3000, "lr": 0.01229, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43188, "top5_acc": 0.69438, "loss_cls": 3.18224, "loss": 3.18224, "time": 0.85002} +{"mode": "train", "epoch": 116, "iter": 3100, "lr": 0.01227, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42641, "top5_acc": 0.69094, "loss_cls": 3.18691, "loss": 3.18691, "time": 0.85287} +{"mode": "train", "epoch": 116, "iter": 3200, "lr": 0.01225, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42422, "top5_acc": 0.69375, "loss_cls": 3.16807, "loss": 3.16807, "time": 0.84864} +{"mode": "train", "epoch": 116, "iter": 3300, "lr": 0.01223, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43047, "top5_acc": 0.69062, "loss_cls": 3.1803, "loss": 3.1803, "time": 0.8486} +{"mode": "train", "epoch": 116, "iter": 3400, "lr": 0.01221, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42219, "top5_acc": 0.685, "loss_cls": 3.23839, "loss": 3.23839, "time": 0.84017} +{"mode": "train", "epoch": 116, "iter": 3500, "lr": 0.0122, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43359, "top5_acc": 0.69641, "loss_cls": 3.15115, "loss": 3.15115, "time": 0.84882} +{"mode": "train", "epoch": 116, "iter": 3600, "lr": 0.01218, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.42297, "top5_acc": 0.69016, "loss_cls": 3.19642, "loss": 3.19642, "time": 0.8439} +{"mode": "train", "epoch": 116, "iter": 3700, "lr": 0.01216, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.4275, "top5_acc": 0.68609, "loss_cls": 3.20499, "loss": 3.20499, "time": 0.84524} +{"mode": "val", "epoch": 116, "iter": 309, "lr": 0.01215, "top1_acc": 0.36408, "top5_acc": 0.62559, "mean_class_accuracy": 0.36385} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.01213, "memory": 15990, "data_time": 1.47846, "top1_acc": 0.44438, "top5_acc": 0.7075, "loss_cls": 3.07083, "loss": 3.07083, "time": 2.50131} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.01211, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44344, "top5_acc": 0.71266, "loss_cls": 3.08281, "loss": 3.08281, "time": 0.85053} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.0121, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44172, "top5_acc": 0.69953, "loss_cls": 3.11786, "loss": 3.11786, "time": 0.8525} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.01208, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45297, "top5_acc": 0.70031, "loss_cls": 3.12216, "loss": 3.12216, "time": 0.85201} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.01206, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44578, "top5_acc": 0.70469, "loss_cls": 3.09294, "loss": 3.09294, "time": 0.84892} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.01204, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44906, "top5_acc": 0.71172, "loss_cls": 3.08438, "loss": 3.08438, "time": 0.85299} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.01202, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43484, "top5_acc": 0.68797, "loss_cls": 3.14714, "loss": 3.14714, "time": 0.85088} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.012, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43875, "top5_acc": 0.70562, "loss_cls": 3.10633, "loss": 3.10633, "time": 0.84993} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.01199, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44125, "top5_acc": 0.70938, "loss_cls": 3.08818, "loss": 3.08818, "time": 0.84453} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.01197, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44109, "top5_acc": 0.7, "loss_cls": 3.11447, "loss": 3.11447, "time": 0.84861} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.01195, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.43797, "top5_acc": 0.70281, "loss_cls": 3.12728, "loss": 3.12728, "time": 0.84656} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.01193, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.43859, "top5_acc": 0.69406, "loss_cls": 3.16711, "loss": 3.16711, "time": 0.85069} +{"mode": "train", "epoch": 117, "iter": 1300, "lr": 0.01191, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44391, "top5_acc": 0.70172, "loss_cls": 3.10295, "loss": 3.10295, "time": 0.8477} +{"mode": "train", "epoch": 117, "iter": 1400, "lr": 0.0119, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43312, "top5_acc": 0.69531, "loss_cls": 3.17311, "loss": 3.17311, "time": 0.8436} +{"mode": "train", "epoch": 117, "iter": 1500, "lr": 0.01188, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43641, "top5_acc": 0.69859, "loss_cls": 3.14986, "loss": 3.14986, "time": 0.84196} +{"mode": "train", "epoch": 117, "iter": 1600, "lr": 0.01186, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43562, "top5_acc": 0.69562, "loss_cls": 3.17575, "loss": 3.17575, "time": 0.84484} +{"mode": "train", "epoch": 117, "iter": 1700, "lr": 0.01184, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44188, "top5_acc": 0.70203, "loss_cls": 3.11164, "loss": 3.11164, "time": 0.84638} +{"mode": "train", "epoch": 117, "iter": 1800, "lr": 0.01182, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43391, "top5_acc": 0.69516, "loss_cls": 3.18048, "loss": 3.18048, "time": 0.84049} +{"mode": "train", "epoch": 117, "iter": 1900, "lr": 0.01181, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42812, "top5_acc": 0.68703, "loss_cls": 3.21297, "loss": 3.21297, "time": 0.84454} +{"mode": "train", "epoch": 117, "iter": 2000, "lr": 0.01179, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43969, "top5_acc": 0.69906, "loss_cls": 3.13055, "loss": 3.13055, "time": 0.84714} +{"mode": "train", "epoch": 117, "iter": 2100, "lr": 0.01177, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43438, "top5_acc": 0.69438, "loss_cls": 3.13617, "loss": 3.13617, "time": 0.84555} +{"mode": "train", "epoch": 117, "iter": 2200, "lr": 0.01175, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42938, "top5_acc": 0.69359, "loss_cls": 3.15205, "loss": 3.15205, "time": 0.85288} +{"mode": "train", "epoch": 117, "iter": 2300, "lr": 0.01173, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44562, "top5_acc": 0.70172, "loss_cls": 3.12262, "loss": 3.12262, "time": 0.84234} +{"mode": "train", "epoch": 117, "iter": 2400, "lr": 0.01172, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43641, "top5_acc": 0.69781, "loss_cls": 3.13386, "loss": 3.13386, "time": 0.84992} +{"mode": "train", "epoch": 117, "iter": 2500, "lr": 0.0117, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44844, "top5_acc": 0.70719, "loss_cls": 3.09191, "loss": 3.09191, "time": 0.85304} +{"mode": "train", "epoch": 117, "iter": 2600, "lr": 0.01168, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.44312, "top5_acc": 0.70375, "loss_cls": 3.10388, "loss": 3.10388, "time": 0.85527} +{"mode": "train", "epoch": 117, "iter": 2700, "lr": 0.01166, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43438, "top5_acc": 0.69641, "loss_cls": 3.15882, "loss": 3.15882, "time": 0.84736} +{"mode": "train", "epoch": 117, "iter": 2800, "lr": 0.01164, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.4375, "top5_acc": 0.70078, "loss_cls": 3.14733, "loss": 3.14733, "time": 0.85232} +{"mode": "train", "epoch": 117, "iter": 2900, "lr": 0.01163, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42719, "top5_acc": 0.69297, "loss_cls": 3.2143, "loss": 3.2143, "time": 0.85449} +{"mode": "train", "epoch": 117, "iter": 3000, "lr": 0.01161, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43156, "top5_acc": 0.69125, "loss_cls": 3.17727, "loss": 3.17727, "time": 0.85009} +{"mode": "train", "epoch": 117, "iter": 3100, "lr": 0.01159, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.44078, "top5_acc": 0.69703, "loss_cls": 3.16068, "loss": 3.16068, "time": 0.8538} +{"mode": "train", "epoch": 117, "iter": 3200, "lr": 0.01157, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44172, "top5_acc": 0.70375, "loss_cls": 3.11656, "loss": 3.11656, "time": 0.85697} +{"mode": "train", "epoch": 117, "iter": 3300, "lr": 0.01155, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44016, "top5_acc": 0.69281, "loss_cls": 3.13036, "loss": 3.13036, "time": 0.8525} +{"mode": "train", "epoch": 117, "iter": 3400, "lr": 0.01154, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.45234, "top5_acc": 0.70969, "loss_cls": 3.07789, "loss": 3.07789, "time": 0.85017} +{"mode": "train", "epoch": 117, "iter": 3500, "lr": 0.01152, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43281, "top5_acc": 0.69891, "loss_cls": 3.1241, "loss": 3.1241, "time": 0.8511} +{"mode": "train", "epoch": 117, "iter": 3600, "lr": 0.0115, "memory": 15990, "data_time": 0.00074, "top1_acc": 0.44266, "top5_acc": 0.69812, "loss_cls": 3.15157, "loss": 3.15157, "time": 0.84616} +{"mode": "train", "epoch": 117, "iter": 3700, "lr": 0.01148, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.43344, "top5_acc": 0.69281, "loss_cls": 3.16773, "loss": 3.16773, "time": 0.84621} +{"mode": "val", "epoch": 117, "iter": 309, "lr": 0.01147, "top1_acc": 0.36585, "top5_acc": 0.62341, "mean_class_accuracy": 0.3658} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.01146, "memory": 15990, "data_time": 1.50293, "top1_acc": 0.45641, "top5_acc": 0.71656, "loss_cls": 3.03821, "loss": 3.03821, "time": 2.5288} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.01144, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.44406, "top5_acc": 0.70234, "loss_cls": 3.12624, "loss": 3.12624, "time": 0.85007} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.01142, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45953, "top5_acc": 0.72297, "loss_cls": 2.99211, "loss": 2.99211, "time": 0.85326} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.0114, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44328, "top5_acc": 0.71094, "loss_cls": 3.06334, "loss": 3.06334, "time": 0.85945} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.01139, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44797, "top5_acc": 0.70922, "loss_cls": 3.0466, "loss": 3.0466, "time": 0.85816} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.01137, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44484, "top5_acc": 0.70781, "loss_cls": 3.0918, "loss": 3.0918, "time": 0.85254} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.01135, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43891, "top5_acc": 0.69641, "loss_cls": 3.14039, "loss": 3.14039, "time": 0.85489} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.01133, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43578, "top5_acc": 0.70141, "loss_cls": 3.12241, "loss": 3.12241, "time": 0.85545} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.01131, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.4425, "top5_acc": 0.70344, "loss_cls": 3.0865, "loss": 3.0865, "time": 0.85256} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.0113, "memory": 15990, "data_time": 0.00068, "top1_acc": 0.44344, "top5_acc": 0.70906, "loss_cls": 3.1106, "loss": 3.1106, "time": 0.84966} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.01128, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.43516, "top5_acc": 0.69859, "loss_cls": 3.1281, "loss": 3.1281, "time": 0.84425} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.01126, "memory": 15990, "data_time": 0.00093, "top1_acc": 0.44188, "top5_acc": 0.70188, "loss_cls": 3.14226, "loss": 3.14226, "time": 0.85295} +{"mode": "train", "epoch": 118, "iter": 1300, "lr": 0.01124, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43641, "top5_acc": 0.70531, "loss_cls": 3.11026, "loss": 3.11026, "time": 0.8465} +{"mode": "train", "epoch": 118, "iter": 1400, "lr": 0.01123, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43734, "top5_acc": 0.70609, "loss_cls": 3.11055, "loss": 3.11055, "time": 0.84666} +{"mode": "train", "epoch": 118, "iter": 1500, "lr": 0.01121, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43562, "top5_acc": 0.70156, "loss_cls": 3.13666, "loss": 3.13666, "time": 0.85274} +{"mode": "train", "epoch": 118, "iter": 1600, "lr": 0.01119, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43672, "top5_acc": 0.70453, "loss_cls": 3.09749, "loss": 3.09749, "time": 0.85362} +{"mode": "train", "epoch": 118, "iter": 1700, "lr": 0.01117, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45156, "top5_acc": 0.70953, "loss_cls": 3.05131, "loss": 3.05131, "time": 0.84494} +{"mode": "train", "epoch": 118, "iter": 1800, "lr": 0.01116, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43906, "top5_acc": 0.70031, "loss_cls": 3.1256, "loss": 3.1256, "time": 0.84638} +{"mode": "train", "epoch": 118, "iter": 1900, "lr": 0.01114, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44875, "top5_acc": 0.69953, "loss_cls": 3.11646, "loss": 3.11646, "time": 0.84539} +{"mode": "train", "epoch": 118, "iter": 2000, "lr": 0.01112, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43453, "top5_acc": 0.69844, "loss_cls": 3.12197, "loss": 3.12197, "time": 0.8496} +{"mode": "train", "epoch": 118, "iter": 2100, "lr": 0.0111, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.44297, "top5_acc": 0.69938, "loss_cls": 3.13327, "loss": 3.13327, "time": 0.84241} +{"mode": "train", "epoch": 118, "iter": 2200, "lr": 0.01109, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44469, "top5_acc": 0.69891, "loss_cls": 3.08809, "loss": 3.08809, "time": 0.846} +{"mode": "train", "epoch": 118, "iter": 2300, "lr": 0.01107, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44656, "top5_acc": 0.70766, "loss_cls": 3.09214, "loss": 3.09214, "time": 0.84559} +{"mode": "train", "epoch": 118, "iter": 2400, "lr": 0.01105, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43984, "top5_acc": 0.71062, "loss_cls": 3.12057, "loss": 3.12057, "time": 0.85186} +{"mode": "train", "epoch": 118, "iter": 2500, "lr": 0.01103, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44703, "top5_acc": 0.69562, "loss_cls": 3.12786, "loss": 3.12786, "time": 0.85184} +{"mode": "train", "epoch": 118, "iter": 2600, "lr": 0.01102, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44094, "top5_acc": 0.70625, "loss_cls": 3.09028, "loss": 3.09028, "time": 0.8479} +{"mode": "train", "epoch": 118, "iter": 2700, "lr": 0.011, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43016, "top5_acc": 0.70453, "loss_cls": 3.1274, "loss": 3.1274, "time": 0.84349} +{"mode": "train", "epoch": 118, "iter": 2800, "lr": 0.01098, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42766, "top5_acc": 0.69266, "loss_cls": 3.16994, "loss": 3.16994, "time": 0.84962} +{"mode": "train", "epoch": 118, "iter": 2900, "lr": 0.01096, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44438, "top5_acc": 0.69641, "loss_cls": 3.13111, "loss": 3.13111, "time": 0.84493} +{"mode": "train", "epoch": 118, "iter": 3000, "lr": 0.01095, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.43953, "top5_acc": 0.69641, "loss_cls": 3.14413, "loss": 3.14413, "time": 0.84839} +{"mode": "train", "epoch": 118, "iter": 3100, "lr": 0.01093, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43688, "top5_acc": 0.70484, "loss_cls": 3.12834, "loss": 3.12834, "time": 0.85535} +{"mode": "train", "epoch": 118, "iter": 3200, "lr": 0.01091, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43422, "top5_acc": 0.69984, "loss_cls": 3.15307, "loss": 3.15307, "time": 0.8543} +{"mode": "train", "epoch": 118, "iter": 3300, "lr": 0.01089, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43906, "top5_acc": 0.70391, "loss_cls": 3.10376, "loss": 3.10376, "time": 0.84757} +{"mode": "train", "epoch": 118, "iter": 3400, "lr": 0.01088, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44594, "top5_acc": 0.70172, "loss_cls": 3.12108, "loss": 3.12108, "time": 0.84779} +{"mode": "train", "epoch": 118, "iter": 3500, "lr": 0.01086, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43734, "top5_acc": 0.70328, "loss_cls": 3.10166, "loss": 3.10166, "time": 0.84365} +{"mode": "train", "epoch": 118, "iter": 3600, "lr": 0.01084, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44719, "top5_acc": 0.70344, "loss_cls": 3.0829, "loss": 3.0829, "time": 0.84951} +{"mode": "train", "epoch": 118, "iter": 3700, "lr": 0.01082, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.4375, "top5_acc": 0.70531, "loss_cls": 3.11486, "loss": 3.11486, "time": 0.84837} +{"mode": "val", "epoch": 118, "iter": 309, "lr": 0.01082, "top1_acc": 0.38105, "top5_acc": 0.6419, "mean_class_accuracy": 0.38079} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.0108, "memory": 15990, "data_time": 1.48181, "top1_acc": 0.47328, "top5_acc": 0.72641, "loss_cls": 2.97435, "loss": 2.97435, "time": 2.50885} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.01078, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44516, "top5_acc": 0.705, "loss_cls": 3.05478, "loss": 3.05478, "time": 0.85251} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.01076, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45047, "top5_acc": 0.71516, "loss_cls": 3.07351, "loss": 3.07351, "time": 0.85244} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.01075, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45391, "top5_acc": 0.71531, "loss_cls": 3.05287, "loss": 3.05287, "time": 0.84745} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.01073, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45906, "top5_acc": 0.72062, "loss_cls": 3.02121, "loss": 3.02121, "time": 0.85332} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.01071, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.45906, "top5_acc": 0.72156, "loss_cls": 3.01623, "loss": 3.01623, "time": 0.8492} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.01069, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44516, "top5_acc": 0.70516, "loss_cls": 3.0759, "loss": 3.0759, "time": 0.84848} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.01068, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44641, "top5_acc": 0.71656, "loss_cls": 3.04766, "loss": 3.04766, "time": 0.84422} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.01066, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44109, "top5_acc": 0.71141, "loss_cls": 3.06941, "loss": 3.06941, "time": 0.84396} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.01064, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44859, "top5_acc": 0.70984, "loss_cls": 3.06112, "loss": 3.06112, "time": 0.85446} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.01063, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45344, "top5_acc": 0.71047, "loss_cls": 3.07987, "loss": 3.07987, "time": 0.8465} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.01061, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44078, "top5_acc": 0.70953, "loss_cls": 3.08822, "loss": 3.08822, "time": 0.84784} +{"mode": "train", "epoch": 119, "iter": 1300, "lr": 0.01059, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44312, "top5_acc": 0.70453, "loss_cls": 3.12302, "loss": 3.12302, "time": 0.8519} +{"mode": "train", "epoch": 119, "iter": 1400, "lr": 0.01057, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.44109, "top5_acc": 0.70219, "loss_cls": 3.08431, "loss": 3.08431, "time": 0.84374} +{"mode": "train", "epoch": 119, "iter": 1500, "lr": 0.01056, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.4475, "top5_acc": 0.705, "loss_cls": 3.09196, "loss": 3.09196, "time": 0.8477} +{"mode": "train", "epoch": 119, "iter": 1600, "lr": 0.01054, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44859, "top5_acc": 0.70188, "loss_cls": 3.11394, "loss": 3.11394, "time": 0.84685} +{"mode": "train", "epoch": 119, "iter": 1700, "lr": 0.01052, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.4525, "top5_acc": 0.71047, "loss_cls": 3.0479, "loss": 3.0479, "time": 0.84458} +{"mode": "train", "epoch": 119, "iter": 1800, "lr": 0.0105, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44422, "top5_acc": 0.70359, "loss_cls": 3.08395, "loss": 3.08395, "time": 0.84146} +{"mode": "train", "epoch": 119, "iter": 1900, "lr": 0.01049, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45141, "top5_acc": 0.71547, "loss_cls": 3.05361, "loss": 3.05361, "time": 0.84714} +{"mode": "train", "epoch": 119, "iter": 2000, "lr": 0.01047, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44141, "top5_acc": 0.69734, "loss_cls": 3.13462, "loss": 3.13462, "time": 0.8449} +{"mode": "train", "epoch": 119, "iter": 2100, "lr": 0.01045, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45578, "top5_acc": 0.70797, "loss_cls": 3.06777, "loss": 3.06777, "time": 0.84866} +{"mode": "train", "epoch": 119, "iter": 2200, "lr": 0.01044, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45016, "top5_acc": 0.70688, "loss_cls": 3.08306, "loss": 3.08306, "time": 0.85426} +{"mode": "train", "epoch": 119, "iter": 2300, "lr": 0.01042, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44688, "top5_acc": 0.71391, "loss_cls": 3.04643, "loss": 3.04643, "time": 0.84442} +{"mode": "train", "epoch": 119, "iter": 2400, "lr": 0.0104, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44656, "top5_acc": 0.70781, "loss_cls": 3.10253, "loss": 3.10253, "time": 0.84175} +{"mode": "train", "epoch": 119, "iter": 2500, "lr": 0.01039, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44703, "top5_acc": 0.70078, "loss_cls": 3.09258, "loss": 3.09258, "time": 0.84639} +{"mode": "train", "epoch": 119, "iter": 2600, "lr": 0.01037, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44094, "top5_acc": 0.69938, "loss_cls": 3.12661, "loss": 3.12661, "time": 0.8464} +{"mode": "train", "epoch": 119, "iter": 2700, "lr": 0.01035, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44547, "top5_acc": 0.70234, "loss_cls": 3.08625, "loss": 3.08625, "time": 0.84413} +{"mode": "train", "epoch": 119, "iter": 2800, "lr": 0.01033, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44781, "top5_acc": 0.70688, "loss_cls": 3.07915, "loss": 3.07915, "time": 0.84538} +{"mode": "train", "epoch": 119, "iter": 2900, "lr": 0.01032, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44109, "top5_acc": 0.70219, "loss_cls": 3.09682, "loss": 3.09682, "time": 0.84603} +{"mode": "train", "epoch": 119, "iter": 3000, "lr": 0.0103, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44422, "top5_acc": 0.70344, "loss_cls": 3.10304, "loss": 3.10304, "time": 0.85225} +{"mode": "train", "epoch": 119, "iter": 3100, "lr": 0.01028, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.44734, "top5_acc": 0.71109, "loss_cls": 3.07067, "loss": 3.07067, "time": 0.84838} +{"mode": "train", "epoch": 119, "iter": 3200, "lr": 0.01027, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43594, "top5_acc": 0.69594, "loss_cls": 3.14846, "loss": 3.14846, "time": 0.84476} +{"mode": "train", "epoch": 119, "iter": 3300, "lr": 0.01025, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45031, "top5_acc": 0.71484, "loss_cls": 3.03365, "loss": 3.03365, "time": 0.84392} +{"mode": "train", "epoch": 119, "iter": 3400, "lr": 0.01023, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43766, "top5_acc": 0.70172, "loss_cls": 3.15385, "loss": 3.15385, "time": 0.84827} +{"mode": "train", "epoch": 119, "iter": 3500, "lr": 0.01022, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43391, "top5_acc": 0.69703, "loss_cls": 3.1706, "loss": 3.1706, "time": 0.84763} +{"mode": "train", "epoch": 119, "iter": 3600, "lr": 0.0102, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45125, "top5_acc": 0.70125, "loss_cls": 3.10699, "loss": 3.10699, "time": 0.8422} +{"mode": "train", "epoch": 119, "iter": 3700, "lr": 0.01018, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44359, "top5_acc": 0.705, "loss_cls": 3.09281, "loss": 3.09281, "time": 0.84127} +{"mode": "val", "epoch": 119, "iter": 309, "lr": 0.01017, "top1_acc": 0.37588, "top5_acc": 0.63405, "mean_class_accuracy": 0.37565} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.01016, "memory": 15990, "data_time": 1.5295, "top1_acc": 0.45609, "top5_acc": 0.72359, "loss_cls": 2.99873, "loss": 2.99873, "time": 2.55726} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.01014, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.46219, "top5_acc": 0.73578, "loss_cls": 2.94799, "loss": 2.94799, "time": 0.86115} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.01012, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.46344, "top5_acc": 0.71625, "loss_cls": 3.00832, "loss": 3.00832, "time": 0.85747} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.01011, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.455, "top5_acc": 0.71531, "loss_cls": 3.02188, "loss": 3.02188, "time": 0.85657} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.01009, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44562, "top5_acc": 0.71219, "loss_cls": 3.06517, "loss": 3.06517, "time": 0.85738} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.01007, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45078, "top5_acc": 0.71422, "loss_cls": 3.02994, "loss": 3.02994, "time": 0.84867} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.01006, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46047, "top5_acc": 0.72219, "loss_cls": 3.01867, "loss": 3.01867, "time": 0.85067} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.01004, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44875, "top5_acc": 0.72281, "loss_cls": 3.03266, "loss": 3.03266, "time": 0.85314} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.01002, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45297, "top5_acc": 0.71125, "loss_cls": 3.0581, "loss": 3.0581, "time": 0.84952} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.01001, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45156, "top5_acc": 0.70719, "loss_cls": 3.06546, "loss": 3.06546, "time": 0.84536} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00999, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44625, "top5_acc": 0.70516, "loss_cls": 3.09113, "loss": 3.09113, "time": 0.85045} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.00997, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44406, "top5_acc": 0.70625, "loss_cls": 3.06197, "loss": 3.06197, "time": 0.85022} +{"mode": "train", "epoch": 120, "iter": 1300, "lr": 0.00996, "memory": 15990, "data_time": 0.0008, "top1_acc": 0.45906, "top5_acc": 0.71297, "loss_cls": 3.02754, "loss": 3.02754, "time": 0.84746} +{"mode": "train", "epoch": 120, "iter": 1400, "lr": 0.00994, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45422, "top5_acc": 0.71859, "loss_cls": 3.03165, "loss": 3.03165, "time": 0.84354} +{"mode": "train", "epoch": 120, "iter": 1500, "lr": 0.00992, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.45062, "top5_acc": 0.70812, "loss_cls": 3.05733, "loss": 3.05733, "time": 0.85116} +{"mode": "train", "epoch": 120, "iter": 1600, "lr": 0.0099, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45125, "top5_acc": 0.71281, "loss_cls": 3.02706, "loss": 3.02706, "time": 0.84726} +{"mode": "train", "epoch": 120, "iter": 1700, "lr": 0.00989, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45578, "top5_acc": 0.71047, "loss_cls": 3.06442, "loss": 3.06442, "time": 0.84554} +{"mode": "train", "epoch": 120, "iter": 1800, "lr": 0.00987, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45453, "top5_acc": 0.71984, "loss_cls": 2.99848, "loss": 2.99848, "time": 0.84711} +{"mode": "train", "epoch": 120, "iter": 1900, "lr": 0.00985, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45672, "top5_acc": 0.72094, "loss_cls": 3.03592, "loss": 3.03592, "time": 0.8486} +{"mode": "train", "epoch": 120, "iter": 2000, "lr": 0.00984, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44938, "top5_acc": 0.71688, "loss_cls": 3.02654, "loss": 3.02654, "time": 0.84835} +{"mode": "train", "epoch": 120, "iter": 2100, "lr": 0.00982, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45078, "top5_acc": 0.70969, "loss_cls": 3.04822, "loss": 3.04822, "time": 0.84314} +{"mode": "train", "epoch": 120, "iter": 2200, "lr": 0.0098, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45016, "top5_acc": 0.71375, "loss_cls": 3.05066, "loss": 3.05066, "time": 0.84209} +{"mode": "train", "epoch": 120, "iter": 2300, "lr": 0.00979, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46547, "top5_acc": 0.72219, "loss_cls": 2.99585, "loss": 2.99585, "time": 0.84282} +{"mode": "train", "epoch": 120, "iter": 2400, "lr": 0.00977, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44031, "top5_acc": 0.70422, "loss_cls": 3.09366, "loss": 3.09366, "time": 0.84726} +{"mode": "train", "epoch": 120, "iter": 2500, "lr": 0.00976, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45391, "top5_acc": 0.71672, "loss_cls": 3.04223, "loss": 3.04223, "time": 0.84844} +{"mode": "train", "epoch": 120, "iter": 2600, "lr": 0.00974, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44703, "top5_acc": 0.70641, "loss_cls": 3.07799, "loss": 3.07799, "time": 0.84214} +{"mode": "train", "epoch": 120, "iter": 2700, "lr": 0.00972, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45031, "top5_acc": 0.72, "loss_cls": 3.03367, "loss": 3.03367, "time": 0.84436} +{"mode": "train", "epoch": 120, "iter": 2800, "lr": 0.00971, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43797, "top5_acc": 0.70688, "loss_cls": 3.09694, "loss": 3.09694, "time": 0.84394} +{"mode": "train", "epoch": 120, "iter": 2900, "lr": 0.00969, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46109, "top5_acc": 0.72234, "loss_cls": 2.99467, "loss": 2.99467, "time": 0.84082} +{"mode": "train", "epoch": 120, "iter": 3000, "lr": 0.00967, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44047, "top5_acc": 0.69828, "loss_cls": 3.1041, "loss": 3.1041, "time": 0.84252} +{"mode": "train", "epoch": 120, "iter": 3100, "lr": 0.00966, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43812, "top5_acc": 0.70016, "loss_cls": 3.1048, "loss": 3.1048, "time": 0.85095} +{"mode": "train", "epoch": 120, "iter": 3200, "lr": 0.00964, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45141, "top5_acc": 0.71734, "loss_cls": 3.03083, "loss": 3.03083, "time": 0.84681} +{"mode": "train", "epoch": 120, "iter": 3300, "lr": 0.00962, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43609, "top5_acc": 0.70203, "loss_cls": 3.12799, "loss": 3.12799, "time": 0.847} +{"mode": "train", "epoch": 120, "iter": 3400, "lr": 0.00961, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44297, "top5_acc": 0.70609, "loss_cls": 3.09902, "loss": 3.09902, "time": 0.84545} +{"mode": "train", "epoch": 120, "iter": 3500, "lr": 0.00959, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44266, "top5_acc": 0.7, "loss_cls": 3.11961, "loss": 3.11961, "time": 0.84363} +{"mode": "train", "epoch": 120, "iter": 3600, "lr": 0.00957, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44484, "top5_acc": 0.71188, "loss_cls": 3.06893, "loss": 3.06893, "time": 0.85535} +{"mode": "train", "epoch": 120, "iter": 3700, "lr": 0.00956, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44594, "top5_acc": 0.70234, "loss_cls": 3.09011, "loss": 3.09011, "time": 0.83849} +{"mode": "val", "epoch": 120, "iter": 309, "lr": 0.00955, "top1_acc": 0.37922, "top5_acc": 0.63268, "mean_class_accuracy": 0.37887} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00953, "memory": 15990, "data_time": 1.4662, "top1_acc": 0.46078, "top5_acc": 0.73141, "loss_cls": 2.97862, "loss": 2.97862, "time": 2.49383} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00952, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46844, "top5_acc": 0.72688, "loss_cls": 2.93776, "loss": 2.93776, "time": 0.84568} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.0095, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47453, "top5_acc": 0.72938, "loss_cls": 2.93059, "loss": 2.93059, "time": 0.85276} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00948, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46625, "top5_acc": 0.7225, "loss_cls": 2.96839, "loss": 2.96839, "time": 0.84717} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00947, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45953, "top5_acc": 0.71906, "loss_cls": 3.02168, "loss": 3.02168, "time": 0.85051} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00945, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45312, "top5_acc": 0.71047, "loss_cls": 3.04923, "loss": 3.04923, "time": 0.8483} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.00943, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46453, "top5_acc": 0.72219, "loss_cls": 2.99348, "loss": 2.99348, "time": 0.84105} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00942, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45703, "top5_acc": 0.71656, "loss_cls": 3.00602, "loss": 3.00602, "time": 0.84921} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.0094, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45688, "top5_acc": 0.72328, "loss_cls": 2.97521, "loss": 2.97521, "time": 0.84972} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00939, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.455, "top5_acc": 0.71, "loss_cls": 3.04018, "loss": 3.04018, "time": 0.8518} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00937, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46281, "top5_acc": 0.71906, "loss_cls": 3.02387, "loss": 3.02387, "time": 0.84593} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00935, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46406, "top5_acc": 0.72172, "loss_cls": 3.0042, "loss": 3.0042, "time": 0.84605} +{"mode": "train", "epoch": 121, "iter": 1300, "lr": 0.00934, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45641, "top5_acc": 0.71, "loss_cls": 3.03527, "loss": 3.03527, "time": 0.84506} +{"mode": "train", "epoch": 121, "iter": 1400, "lr": 0.00932, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44781, "top5_acc": 0.71203, "loss_cls": 3.0418, "loss": 3.0418, "time": 0.85373} +{"mode": "train", "epoch": 121, "iter": 1500, "lr": 0.0093, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.455, "top5_acc": 0.72031, "loss_cls": 3.00858, "loss": 3.00858, "time": 0.84236} +{"mode": "train", "epoch": 121, "iter": 1600, "lr": 0.00929, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45516, "top5_acc": 0.72734, "loss_cls": 2.9974, "loss": 2.9974, "time": 0.84738} +{"mode": "train", "epoch": 121, "iter": 1700, "lr": 0.00927, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.46188, "top5_acc": 0.72312, "loss_cls": 2.99262, "loss": 2.99262, "time": 0.84947} +{"mode": "train", "epoch": 121, "iter": 1800, "lr": 0.00926, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.4625, "top5_acc": 0.71609, "loss_cls": 3.00963, "loss": 3.00963, "time": 0.84696} +{"mode": "train", "epoch": 121, "iter": 1900, "lr": 0.00924, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46609, "top5_acc": 0.71875, "loss_cls": 3.00073, "loss": 3.00073, "time": 0.84383} +{"mode": "train", "epoch": 121, "iter": 2000, "lr": 0.00922, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45266, "top5_acc": 0.71906, "loss_cls": 3.03062, "loss": 3.03062, "time": 0.84494} +{"mode": "train", "epoch": 121, "iter": 2100, "lr": 0.00921, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45266, "top5_acc": 0.70672, "loss_cls": 3.04293, "loss": 3.04293, "time": 0.84394} +{"mode": "train", "epoch": 121, "iter": 2200, "lr": 0.00919, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45609, "top5_acc": 0.71547, "loss_cls": 3.02194, "loss": 3.02194, "time": 0.84796} +{"mode": "train", "epoch": 121, "iter": 2300, "lr": 0.00917, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44312, "top5_acc": 0.70531, "loss_cls": 3.09495, "loss": 3.09495, "time": 0.85023} +{"mode": "train", "epoch": 121, "iter": 2400, "lr": 0.00916, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45969, "top5_acc": 0.72062, "loss_cls": 2.99377, "loss": 2.99377, "time": 0.84932} +{"mode": "train", "epoch": 121, "iter": 2500, "lr": 0.00914, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.45203, "top5_acc": 0.71594, "loss_cls": 3.04606, "loss": 3.04606, "time": 0.85023} +{"mode": "train", "epoch": 121, "iter": 2600, "lr": 0.00913, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45453, "top5_acc": 0.71375, "loss_cls": 3.04361, "loss": 3.04361, "time": 0.84826} +{"mode": "train", "epoch": 121, "iter": 2700, "lr": 0.00911, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46156, "top5_acc": 0.72016, "loss_cls": 3.0228, "loss": 3.0228, "time": 0.84409} +{"mode": "train", "epoch": 121, "iter": 2800, "lr": 0.00909, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45844, "top5_acc": 0.71656, "loss_cls": 3.03208, "loss": 3.03208, "time": 0.84736} +{"mode": "train", "epoch": 121, "iter": 2900, "lr": 0.00908, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45062, "top5_acc": 0.71312, "loss_cls": 3.0537, "loss": 3.0537, "time": 0.84969} +{"mode": "train", "epoch": 121, "iter": 3000, "lr": 0.00906, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.45297, "top5_acc": 0.71734, "loss_cls": 3.02556, "loss": 3.02556, "time": 0.8527} +{"mode": "train", "epoch": 121, "iter": 3100, "lr": 0.00905, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45312, "top5_acc": 0.71078, "loss_cls": 3.04579, "loss": 3.04579, "time": 0.84578} +{"mode": "train", "epoch": 121, "iter": 3200, "lr": 0.00903, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45703, "top5_acc": 0.71688, "loss_cls": 3.03285, "loss": 3.03285, "time": 0.85152} +{"mode": "train", "epoch": 121, "iter": 3300, "lr": 0.00901, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45672, "top5_acc": 0.71156, "loss_cls": 3.05699, "loss": 3.05699, "time": 0.84747} +{"mode": "train", "epoch": 121, "iter": 3400, "lr": 0.009, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45438, "top5_acc": 0.71438, "loss_cls": 3.02476, "loss": 3.02476, "time": 0.84874} +{"mode": "train", "epoch": 121, "iter": 3500, "lr": 0.00898, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.45078, "top5_acc": 0.71109, "loss_cls": 3.05654, "loss": 3.05654, "time": 0.8512} +{"mode": "train", "epoch": 121, "iter": 3600, "lr": 0.00897, "memory": 15990, "data_time": 0.00091, "top1_acc": 0.44547, "top5_acc": 0.71266, "loss_cls": 3.08522, "loss": 3.08522, "time": 0.84002} +{"mode": "train", "epoch": 121, "iter": 3700, "lr": 0.00895, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44531, "top5_acc": 0.70719, "loss_cls": 3.08665, "loss": 3.08665, "time": 0.84316} +{"mode": "val", "epoch": 121, "iter": 309, "lr": 0.00894, "top1_acc": 0.3811, "top5_acc": 0.62853, "mean_class_accuracy": 0.38088} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00893, "memory": 15990, "data_time": 1.45754, "top1_acc": 0.47391, "top5_acc": 0.73828, "loss_cls": 2.90151, "loss": 2.90151, "time": 2.48741} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00891, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.46531, "top5_acc": 0.72625, "loss_cls": 2.96136, "loss": 2.96136, "time": 0.85668} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.00889, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.46672, "top5_acc": 0.72797, "loss_cls": 2.95537, "loss": 2.95537, "time": 0.8605} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00888, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45844, "top5_acc": 0.72125, "loss_cls": 2.98031, "loss": 2.98031, "time": 0.85462} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00886, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.46375, "top5_acc": 0.71375, "loss_cls": 2.99154, "loss": 2.99154, "time": 0.85899} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00885, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.46828, "top5_acc": 0.72141, "loss_cls": 2.96212, "loss": 2.96212, "time": 0.856} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00883, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.46719, "top5_acc": 0.72484, "loss_cls": 2.97363, "loss": 2.97363, "time": 0.85876} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00882, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.45969, "top5_acc": 0.71391, "loss_cls": 3.02316, "loss": 3.02316, "time": 0.85622} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.0088, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.46109, "top5_acc": 0.72031, "loss_cls": 3.00449, "loss": 3.00449, "time": 0.85394} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00878, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.46969, "top5_acc": 0.72922, "loss_cls": 2.96171, "loss": 2.96171, "time": 0.85913} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00877, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.46391, "top5_acc": 0.72766, "loss_cls": 2.97589, "loss": 2.97589, "time": 0.85238} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.00875, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46031, "top5_acc": 0.72781, "loss_cls": 2.98089, "loss": 2.98089, "time": 0.84751} +{"mode": "train", "epoch": 122, "iter": 1300, "lr": 0.00874, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45609, "top5_acc": 0.72703, "loss_cls": 3.00915, "loss": 3.00915, "time": 0.84656} +{"mode": "train", "epoch": 122, "iter": 1400, "lr": 0.00872, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.46203, "top5_acc": 0.72641, "loss_cls": 2.96954, "loss": 2.96954, "time": 0.84974} +{"mode": "train", "epoch": 122, "iter": 1500, "lr": 0.0087, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.46891, "top5_acc": 0.72562, "loss_cls": 2.99623, "loss": 2.99623, "time": 0.84802} +{"mode": "train", "epoch": 122, "iter": 1600, "lr": 0.00869, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46766, "top5_acc": 0.725, "loss_cls": 2.97392, "loss": 2.97392, "time": 0.8423} +{"mode": "train", "epoch": 122, "iter": 1700, "lr": 0.00867, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46125, "top5_acc": 0.715, "loss_cls": 3.01256, "loss": 3.01256, "time": 0.84921} +{"mode": "train", "epoch": 122, "iter": 1800, "lr": 0.00866, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45953, "top5_acc": 0.71688, "loss_cls": 3.01064, "loss": 3.01064, "time": 0.84531} +{"mode": "train", "epoch": 122, "iter": 1900, "lr": 0.00864, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45609, "top5_acc": 0.71172, "loss_cls": 3.00556, "loss": 3.00556, "time": 0.83927} +{"mode": "train", "epoch": 122, "iter": 2000, "lr": 0.00863, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45109, "top5_acc": 0.71656, "loss_cls": 3.02949, "loss": 3.02949, "time": 0.84383} +{"mode": "train", "epoch": 122, "iter": 2100, "lr": 0.00861, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46781, "top5_acc": 0.72125, "loss_cls": 3.00308, "loss": 3.00308, "time": 0.84909} +{"mode": "train", "epoch": 122, "iter": 2200, "lr": 0.00859, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.46266, "top5_acc": 0.71844, "loss_cls": 2.97246, "loss": 2.97246, "time": 0.8429} +{"mode": "train", "epoch": 122, "iter": 2300, "lr": 0.00858, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46453, "top5_acc": 0.72203, "loss_cls": 2.97897, "loss": 2.97897, "time": 0.84575} +{"mode": "train", "epoch": 122, "iter": 2400, "lr": 0.00856, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45766, "top5_acc": 0.72094, "loss_cls": 2.99888, "loss": 2.99888, "time": 0.84577} +{"mode": "train", "epoch": 122, "iter": 2500, "lr": 0.00855, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45, "top5_acc": 0.71328, "loss_cls": 3.05551, "loss": 3.05551, "time": 0.84083} +{"mode": "train", "epoch": 122, "iter": 2600, "lr": 0.00853, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45828, "top5_acc": 0.71484, "loss_cls": 3.01056, "loss": 3.01056, "time": 0.84021} +{"mode": "train", "epoch": 122, "iter": 2700, "lr": 0.00852, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46312, "top5_acc": 0.72469, "loss_cls": 2.97402, "loss": 2.97402, "time": 0.84515} +{"mode": "train", "epoch": 122, "iter": 2800, "lr": 0.0085, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47078, "top5_acc": 0.72422, "loss_cls": 2.94802, "loss": 2.94802, "time": 0.83886} +{"mode": "train", "epoch": 122, "iter": 2900, "lr": 0.00849, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45188, "top5_acc": 0.70969, "loss_cls": 3.05734, "loss": 3.05734, "time": 0.85119} +{"mode": "train", "epoch": 122, "iter": 3000, "lr": 0.00847, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45594, "top5_acc": 0.70672, "loss_cls": 3.0265, "loss": 3.0265, "time": 0.84572} +{"mode": "train", "epoch": 122, "iter": 3100, "lr": 0.00845, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.46672, "top5_acc": 0.71891, "loss_cls": 2.98528, "loss": 2.98528, "time": 0.84199} +{"mode": "train", "epoch": 122, "iter": 3200, "lr": 0.00844, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45266, "top5_acc": 0.73141, "loss_cls": 2.9815, "loss": 2.9815, "time": 0.84895} +{"mode": "train", "epoch": 122, "iter": 3300, "lr": 0.00842, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45359, "top5_acc": 0.71406, "loss_cls": 3.0342, "loss": 3.0342, "time": 0.85113} +{"mode": "train", "epoch": 122, "iter": 3400, "lr": 0.00841, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45125, "top5_acc": 0.70703, "loss_cls": 3.05951, "loss": 3.05951, "time": 0.85108} +{"mode": "train", "epoch": 122, "iter": 3500, "lr": 0.00839, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45984, "top5_acc": 0.72125, "loss_cls": 2.98861, "loss": 2.98861, "time": 0.8462} +{"mode": "train", "epoch": 122, "iter": 3600, "lr": 0.00838, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.46062, "top5_acc": 0.71344, "loss_cls": 3.02719, "loss": 3.02719, "time": 0.84416} +{"mode": "train", "epoch": 122, "iter": 3700, "lr": 0.00836, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.4575, "top5_acc": 0.71906, "loss_cls": 3.01153, "loss": 3.01153, "time": 0.85127} +{"mode": "val", "epoch": 122, "iter": 309, "lr": 0.00835, "top1_acc": 0.38626, "top5_acc": 0.64863, "mean_class_accuracy": 0.38608} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00834, "memory": 15990, "data_time": 1.51912, "top1_acc": 0.48484, "top5_acc": 0.74203, "loss_cls": 2.88387, "loss": 2.88387, "time": 2.55204} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00832, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.475, "top5_acc": 0.73203, "loss_cls": 2.90427, "loss": 2.90427, "time": 0.85977} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00831, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.46969, "top5_acc": 0.73312, "loss_cls": 2.90693, "loss": 2.90693, "time": 0.85781} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00829, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.47797, "top5_acc": 0.73359, "loss_cls": 2.90042, "loss": 2.90042, "time": 0.85148} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00828, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.46906, "top5_acc": 0.73203, "loss_cls": 2.93271, "loss": 2.93271, "time": 0.85639} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00826, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47281, "top5_acc": 0.73094, "loss_cls": 2.94766, "loss": 2.94766, "time": 0.85645} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00825, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.47094, "top5_acc": 0.72938, "loss_cls": 2.92347, "loss": 2.92347, "time": 0.86389} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.00823, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47297, "top5_acc": 0.73188, "loss_cls": 2.90614, "loss": 2.90614, "time": 0.85552} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00822, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.47438, "top5_acc": 0.72578, "loss_cls": 2.95079, "loss": 2.95079, "time": 0.85674} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.0082, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.46062, "top5_acc": 0.72047, "loss_cls": 3.00534, "loss": 3.00534, "time": 0.85058} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00818, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46516, "top5_acc": 0.73031, "loss_cls": 2.92659, "loss": 2.92659, "time": 0.84549} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00817, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.47031, "top5_acc": 0.72938, "loss_cls": 2.93079, "loss": 2.93079, "time": 0.84931} +{"mode": "train", "epoch": 123, "iter": 1300, "lr": 0.00815, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47266, "top5_acc": 0.72562, "loss_cls": 2.95337, "loss": 2.95337, "time": 0.84523} +{"mode": "train", "epoch": 123, "iter": 1400, "lr": 0.00814, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.46938, "top5_acc": 0.72906, "loss_cls": 2.93385, "loss": 2.93385, "time": 0.85366} +{"mode": "train", "epoch": 123, "iter": 1500, "lr": 0.00812, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.44797, "top5_acc": 0.70422, "loss_cls": 3.06054, "loss": 3.06054, "time": 0.84764} +{"mode": "train", "epoch": 123, "iter": 1600, "lr": 0.00811, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46703, "top5_acc": 0.72531, "loss_cls": 2.96032, "loss": 2.96032, "time": 0.84898} +{"mode": "train", "epoch": 123, "iter": 1700, "lr": 0.00809, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46562, "top5_acc": 0.72547, "loss_cls": 2.97606, "loss": 2.97606, "time": 0.85087} +{"mode": "train", "epoch": 123, "iter": 1800, "lr": 0.00808, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46609, "top5_acc": 0.73688, "loss_cls": 2.92631, "loss": 2.92631, "time": 0.84592} +{"mode": "train", "epoch": 123, "iter": 1900, "lr": 0.00806, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.46781, "top5_acc": 0.72391, "loss_cls": 2.96492, "loss": 2.96492, "time": 0.85333} +{"mode": "train", "epoch": 123, "iter": 2000, "lr": 0.00805, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47234, "top5_acc": 0.72203, "loss_cls": 2.95059, "loss": 2.95059, "time": 0.84986} +{"mode": "train", "epoch": 123, "iter": 2100, "lr": 0.00803, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46641, "top5_acc": 0.71969, "loss_cls": 2.97289, "loss": 2.97289, "time": 0.84437} +{"mode": "train", "epoch": 123, "iter": 2200, "lr": 0.00802, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46766, "top5_acc": 0.72453, "loss_cls": 2.96839, "loss": 2.96839, "time": 0.84964} +{"mode": "train", "epoch": 123, "iter": 2300, "lr": 0.008, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46812, "top5_acc": 0.72312, "loss_cls": 2.95902, "loss": 2.95902, "time": 0.84224} +{"mode": "train", "epoch": 123, "iter": 2400, "lr": 0.00799, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.47016, "top5_acc": 0.72578, "loss_cls": 2.96084, "loss": 2.96084, "time": 0.85157} +{"mode": "train", "epoch": 123, "iter": 2500, "lr": 0.00797, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46656, "top5_acc": 0.72172, "loss_cls": 2.97922, "loss": 2.97922, "time": 0.84878} +{"mode": "train", "epoch": 123, "iter": 2600, "lr": 0.00796, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45453, "top5_acc": 0.72172, "loss_cls": 3.01344, "loss": 3.01344, "time": 0.84666} +{"mode": "train", "epoch": 123, "iter": 2700, "lr": 0.00794, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.4675, "top5_acc": 0.73281, "loss_cls": 2.94555, "loss": 2.94555, "time": 0.84876} +{"mode": "train", "epoch": 123, "iter": 2800, "lr": 0.00793, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46156, "top5_acc": 0.72203, "loss_cls": 2.96253, "loss": 2.96253, "time": 0.85269} +{"mode": "train", "epoch": 123, "iter": 2900, "lr": 0.00791, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45953, "top5_acc": 0.72109, "loss_cls": 3.01586, "loss": 3.01586, "time": 0.86127} +{"mode": "train", "epoch": 123, "iter": 3000, "lr": 0.0079, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47078, "top5_acc": 0.72375, "loss_cls": 2.94855, "loss": 2.94855, "time": 0.8534} +{"mode": "train", "epoch": 123, "iter": 3100, "lr": 0.00788, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45531, "top5_acc": 0.71984, "loss_cls": 3.01248, "loss": 3.01248, "time": 0.85214} +{"mode": "train", "epoch": 123, "iter": 3200, "lr": 0.00787, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46938, "top5_acc": 0.72547, "loss_cls": 2.95764, "loss": 2.95764, "time": 0.85018} +{"mode": "train", "epoch": 123, "iter": 3300, "lr": 0.00785, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46578, "top5_acc": 0.71875, "loss_cls": 3.01601, "loss": 3.01601, "time": 0.85004} +{"mode": "train", "epoch": 123, "iter": 3400, "lr": 0.00784, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45875, "top5_acc": 0.71797, "loss_cls": 3.02561, "loss": 3.02561, "time": 0.84558} +{"mode": "train", "epoch": 123, "iter": 3500, "lr": 0.00782, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46281, "top5_acc": 0.72094, "loss_cls": 3.00848, "loss": 3.00848, "time": 0.84842} +{"mode": "train", "epoch": 123, "iter": 3600, "lr": 0.00781, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.45141, "top5_acc": 0.71203, "loss_cls": 3.04804, "loss": 3.04804, "time": 0.84251} +{"mode": "train", "epoch": 123, "iter": 3700, "lr": 0.00779, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.45656, "top5_acc": 0.7175, "loss_cls": 3.02977, "loss": 3.02977, "time": 0.84496} +{"mode": "val", "epoch": 123, "iter": 309, "lr": 0.00778, "top1_acc": 0.40242, "top5_acc": 0.6574, "mean_class_accuracy": 0.40205} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00777, "memory": 15990, "data_time": 1.5332, "top1_acc": 0.48844, "top5_acc": 0.74797, "loss_cls": 2.83175, "loss": 2.83175, "time": 2.58264} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00775, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47656, "top5_acc": 0.73562, "loss_cls": 2.90567, "loss": 2.90567, "time": 0.85656} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00774, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48094, "top5_acc": 0.73578, "loss_cls": 2.89401, "loss": 2.89401, "time": 0.86283} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.00772, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.475, "top5_acc": 0.73906, "loss_cls": 2.89809, "loss": 2.89809, "time": 0.85601} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00771, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.48, "top5_acc": 0.73094, "loss_cls": 2.91665, "loss": 2.91665, "time": 0.86036} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00769, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.47484, "top5_acc": 0.73906, "loss_cls": 2.89698, "loss": 2.89698, "time": 0.8565} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00768, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.48047, "top5_acc": 0.72984, "loss_cls": 2.92467, "loss": 2.92467, "time": 0.86031} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00766, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.47406, "top5_acc": 0.73359, "loss_cls": 2.90899, "loss": 2.90899, "time": 0.85852} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00765, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.46766, "top5_acc": 0.72484, "loss_cls": 2.92721, "loss": 2.92721, "time": 0.85686} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00763, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.47406, "top5_acc": 0.745, "loss_cls": 2.90277, "loss": 2.90277, "time": 0.85262} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00762, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47734, "top5_acc": 0.73953, "loss_cls": 2.88222, "loss": 2.88222, "time": 0.852} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.0076, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.47141, "top5_acc": 0.73172, "loss_cls": 2.93258, "loss": 2.93258, "time": 0.84999} +{"mode": "train", "epoch": 124, "iter": 1300, "lr": 0.00759, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.46969, "top5_acc": 0.7325, "loss_cls": 2.93117, "loss": 2.93117, "time": 0.84967} +{"mode": "train", "epoch": 124, "iter": 1400, "lr": 0.00758, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47391, "top5_acc": 0.73219, "loss_cls": 2.93285, "loss": 2.93285, "time": 0.8453} +{"mode": "train", "epoch": 124, "iter": 1500, "lr": 0.00756, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47219, "top5_acc": 0.73062, "loss_cls": 2.92729, "loss": 2.92729, "time": 0.84874} +{"mode": "train", "epoch": 124, "iter": 1600, "lr": 0.00755, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.47266, "top5_acc": 0.73172, "loss_cls": 2.92271, "loss": 2.92271, "time": 0.85317} +{"mode": "train", "epoch": 124, "iter": 1700, "lr": 0.00753, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47266, "top5_acc": 0.73234, "loss_cls": 2.92709, "loss": 2.92709, "time": 0.84721} +{"mode": "train", "epoch": 124, "iter": 1800, "lr": 0.00752, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46938, "top5_acc": 0.72781, "loss_cls": 2.95182, "loss": 2.95182, "time": 0.85115} +{"mode": "train", "epoch": 124, "iter": 1900, "lr": 0.0075, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.475, "top5_acc": 0.73359, "loss_cls": 2.91456, "loss": 2.91456, "time": 0.84276} +{"mode": "train", "epoch": 124, "iter": 2000, "lr": 0.00749, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48234, "top5_acc": 0.73938, "loss_cls": 2.8511, "loss": 2.8511, "time": 0.84565} +{"mode": "train", "epoch": 124, "iter": 2100, "lr": 0.00747, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47109, "top5_acc": 0.72875, "loss_cls": 2.93783, "loss": 2.93783, "time": 0.84965} +{"mode": "train", "epoch": 124, "iter": 2200, "lr": 0.00746, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47219, "top5_acc": 0.72891, "loss_cls": 2.91818, "loss": 2.91818, "time": 0.84789} +{"mode": "train", "epoch": 124, "iter": 2300, "lr": 0.00744, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.475, "top5_acc": 0.72797, "loss_cls": 2.94599, "loss": 2.94599, "time": 0.84296} +{"mode": "train", "epoch": 124, "iter": 2400, "lr": 0.00743, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46062, "top5_acc": 0.72281, "loss_cls": 2.98484, "loss": 2.98484, "time": 0.83937} +{"mode": "train", "epoch": 124, "iter": 2500, "lr": 0.00741, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46547, "top5_acc": 0.72469, "loss_cls": 2.96418, "loss": 2.96418, "time": 0.84586} +{"mode": "train", "epoch": 124, "iter": 2600, "lr": 0.0074, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46422, "top5_acc": 0.73, "loss_cls": 2.93258, "loss": 2.93258, "time": 0.84472} +{"mode": "train", "epoch": 124, "iter": 2700, "lr": 0.00738, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46672, "top5_acc": 0.72312, "loss_cls": 2.96416, "loss": 2.96416, "time": 0.84552} +{"mode": "train", "epoch": 124, "iter": 2800, "lr": 0.00737, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46047, "top5_acc": 0.72312, "loss_cls": 2.98591, "loss": 2.98591, "time": 0.84249} +{"mode": "train", "epoch": 124, "iter": 2900, "lr": 0.00735, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47781, "top5_acc": 0.72938, "loss_cls": 2.92995, "loss": 2.92995, "time": 0.84495} +{"mode": "train", "epoch": 124, "iter": 3000, "lr": 0.00734, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46047, "top5_acc": 0.71891, "loss_cls": 2.9898, "loss": 2.9898, "time": 0.85072} +{"mode": "train", "epoch": 124, "iter": 3100, "lr": 0.00733, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47469, "top5_acc": 0.73438, "loss_cls": 2.91311, "loss": 2.91311, "time": 0.8493} +{"mode": "train", "epoch": 124, "iter": 3200, "lr": 0.00731, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46531, "top5_acc": 0.73062, "loss_cls": 2.94284, "loss": 2.94284, "time": 0.84419} +{"mode": "train", "epoch": 124, "iter": 3300, "lr": 0.0073, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47016, "top5_acc": 0.73078, "loss_cls": 2.94836, "loss": 2.94836, "time": 0.84288} +{"mode": "train", "epoch": 124, "iter": 3400, "lr": 0.00728, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47219, "top5_acc": 0.72688, "loss_cls": 2.93682, "loss": 2.93682, "time": 0.84253} +{"mode": "train", "epoch": 124, "iter": 3500, "lr": 0.00727, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46016, "top5_acc": 0.71641, "loss_cls": 3.01636, "loss": 3.01636, "time": 0.83937} +{"mode": "train", "epoch": 124, "iter": 3600, "lr": 0.00725, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47453, "top5_acc": 0.72625, "loss_cls": 2.93391, "loss": 2.93391, "time": 0.84612} +{"mode": "train", "epoch": 124, "iter": 3700, "lr": 0.00724, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46797, "top5_acc": 0.72703, "loss_cls": 2.94266, "loss": 2.94266, "time": 0.84401} +{"mode": "val", "epoch": 124, "iter": 309, "lr": 0.00723, "top1_acc": 0.39604, "top5_acc": 0.65015, "mean_class_accuracy": 0.39583} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.00722, "memory": 15990, "data_time": 1.4512, "top1_acc": 0.49078, "top5_acc": 0.74922, "loss_cls": 2.80738, "loss": 2.80738, "time": 2.48439} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.0072, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.49047, "top5_acc": 0.74375, "loss_cls": 2.81481, "loss": 2.81481, "time": 0.85002} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00719, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47938, "top5_acc": 0.73609, "loss_cls": 2.87357, "loss": 2.87357, "time": 0.85292} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00717, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49469, "top5_acc": 0.75328, "loss_cls": 2.80764, "loss": 2.80764, "time": 0.85023} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00716, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48484, "top5_acc": 0.74406, "loss_cls": 2.86508, "loss": 2.86508, "time": 0.85031} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00715, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49047, "top5_acc": 0.74719, "loss_cls": 2.8114, "loss": 2.8114, "time": 0.84613} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00713, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47375, "top5_acc": 0.74781, "loss_cls": 2.86706, "loss": 2.86706, "time": 0.84992} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00712, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.48703, "top5_acc": 0.73734, "loss_cls": 2.89672, "loss": 2.89672, "time": 0.84563} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.0071, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48922, "top5_acc": 0.73906, "loss_cls": 2.87092, "loss": 2.87092, "time": 0.84861} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.00709, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.47281, "top5_acc": 0.73172, "loss_cls": 2.90566, "loss": 2.90566, "time": 0.84374} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00707, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48188, "top5_acc": 0.73594, "loss_cls": 2.90143, "loss": 2.90143, "time": 0.84881} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00706, "memory": 15990, "data_time": 0.00067, "top1_acc": 0.47531, "top5_acc": 0.73828, "loss_cls": 2.90306, "loss": 2.90306, "time": 0.84684} +{"mode": "train", "epoch": 125, "iter": 1300, "lr": 0.00704, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.47688, "top5_acc": 0.74, "loss_cls": 2.87091, "loss": 2.87091, "time": 0.8477} +{"mode": "train", "epoch": 125, "iter": 1400, "lr": 0.00703, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46797, "top5_acc": 0.73172, "loss_cls": 2.916, "loss": 2.916, "time": 0.84233} +{"mode": "train", "epoch": 125, "iter": 1500, "lr": 0.00702, "memory": 15990, "data_time": 0.00067, "top1_acc": 0.47562, "top5_acc": 0.74, "loss_cls": 2.90306, "loss": 2.90306, "time": 0.84567} +{"mode": "train", "epoch": 125, "iter": 1600, "lr": 0.007, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48562, "top5_acc": 0.74078, "loss_cls": 2.87126, "loss": 2.87126, "time": 0.83659} +{"mode": "train", "epoch": 125, "iter": 1700, "lr": 0.00699, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.47438, "top5_acc": 0.74156, "loss_cls": 2.87807, "loss": 2.87807, "time": 0.84349} +{"mode": "train", "epoch": 125, "iter": 1800, "lr": 0.00697, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47531, "top5_acc": 0.71844, "loss_cls": 2.94353, "loss": 2.94353, "time": 0.84684} +{"mode": "train", "epoch": 125, "iter": 1900, "lr": 0.00696, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.48156, "top5_acc": 0.73844, "loss_cls": 2.89012, "loss": 2.89012, "time": 0.8462} +{"mode": "train", "epoch": 125, "iter": 2000, "lr": 0.00694, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47438, "top5_acc": 0.73547, "loss_cls": 2.92697, "loss": 2.92697, "time": 0.84387} +{"mode": "train", "epoch": 125, "iter": 2100, "lr": 0.00693, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48078, "top5_acc": 0.73438, "loss_cls": 2.89137, "loss": 2.89137, "time": 0.84577} +{"mode": "train", "epoch": 125, "iter": 2200, "lr": 0.00692, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47312, "top5_acc": 0.73375, "loss_cls": 2.91356, "loss": 2.91356, "time": 0.84577} +{"mode": "train", "epoch": 125, "iter": 2300, "lr": 0.0069, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45703, "top5_acc": 0.71891, "loss_cls": 2.98921, "loss": 2.98921, "time": 0.84536} +{"mode": "train", "epoch": 125, "iter": 2400, "lr": 0.00689, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47531, "top5_acc": 0.73062, "loss_cls": 2.91486, "loss": 2.91486, "time": 0.84433} +{"mode": "train", "epoch": 125, "iter": 2500, "lr": 0.00687, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47906, "top5_acc": 0.73, "loss_cls": 2.91179, "loss": 2.91179, "time": 0.84611} +{"mode": "train", "epoch": 125, "iter": 2600, "lr": 0.00686, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47156, "top5_acc": 0.72891, "loss_cls": 2.92241, "loss": 2.92241, "time": 0.85044} +{"mode": "train", "epoch": 125, "iter": 2700, "lr": 0.00685, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47609, "top5_acc": 0.73516, "loss_cls": 2.90701, "loss": 2.90701, "time": 0.84551} +{"mode": "train", "epoch": 125, "iter": 2800, "lr": 0.00683, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47484, "top5_acc": 0.73719, "loss_cls": 2.89532, "loss": 2.89532, "time": 0.84361} +{"mode": "train", "epoch": 125, "iter": 2900, "lr": 0.00682, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47516, "top5_acc": 0.72797, "loss_cls": 2.93019, "loss": 2.93019, "time": 0.84686} +{"mode": "train", "epoch": 125, "iter": 3000, "lr": 0.0068, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46516, "top5_acc": 0.72281, "loss_cls": 2.95134, "loss": 2.95134, "time": 0.85064} +{"mode": "train", "epoch": 125, "iter": 3100, "lr": 0.00679, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47625, "top5_acc": 0.73703, "loss_cls": 2.88922, "loss": 2.88922, "time": 0.85113} +{"mode": "train", "epoch": 125, "iter": 3200, "lr": 0.00678, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47297, "top5_acc": 0.73062, "loss_cls": 2.90434, "loss": 2.90434, "time": 0.84959} +{"mode": "train", "epoch": 125, "iter": 3300, "lr": 0.00676, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47188, "top5_acc": 0.72562, "loss_cls": 2.93622, "loss": 2.93622, "time": 0.84966} +{"mode": "train", "epoch": 125, "iter": 3400, "lr": 0.00675, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47547, "top5_acc": 0.72719, "loss_cls": 2.89349, "loss": 2.89349, "time": 0.85056} +{"mode": "train", "epoch": 125, "iter": 3500, "lr": 0.00673, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47125, "top5_acc": 0.72422, "loss_cls": 2.94311, "loss": 2.94311, "time": 0.84629} +{"mode": "train", "epoch": 125, "iter": 3600, "lr": 0.00672, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46359, "top5_acc": 0.72453, "loss_cls": 2.9675, "loss": 2.9675, "time": 0.84951} +{"mode": "train", "epoch": 125, "iter": 3700, "lr": 0.00671, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47703, "top5_acc": 0.72875, "loss_cls": 2.94517, "loss": 2.94517, "time": 0.84541} +{"mode": "val", "epoch": 125, "iter": 309, "lr": 0.0067, "top1_acc": 0.40293, "top5_acc": 0.66079, "mean_class_accuracy": 0.40273} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00668, "memory": 15990, "data_time": 1.48223, "top1_acc": 0.4875, "top5_acc": 0.74781, "loss_cls": 2.79355, "loss": 2.79355, "time": 2.50817} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00667, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.50391, "top5_acc": 0.75094, "loss_cls": 2.79741, "loss": 2.79741, "time": 0.84963} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00666, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.48625, "top5_acc": 0.74781, "loss_cls": 2.82957, "loss": 2.82957, "time": 0.84747} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00664, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.49609, "top5_acc": 0.74766, "loss_cls": 2.811, "loss": 2.811, "time": 0.84946} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00663, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49203, "top5_acc": 0.75391, "loss_cls": 2.79017, "loss": 2.79017, "time": 0.8405} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00662, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49156, "top5_acc": 0.74547, "loss_cls": 2.82064, "loss": 2.82064, "time": 0.84196} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0066, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.48938, "top5_acc": 0.74125, "loss_cls": 2.83724, "loss": 2.83724, "time": 0.84764} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00659, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.49719, "top5_acc": 0.75219, "loss_cls": 2.8, "loss": 2.8, "time": 0.84484} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00657, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48484, "top5_acc": 0.73922, "loss_cls": 2.87025, "loss": 2.87025, "time": 0.8472} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00656, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48406, "top5_acc": 0.73828, "loss_cls": 2.8802, "loss": 2.8802, "time": 0.85038} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00655, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.48141, "top5_acc": 0.73906, "loss_cls": 2.87359, "loss": 2.87359, "time": 0.85108} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00653, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.48016, "top5_acc": 0.73234, "loss_cls": 2.91733, "loss": 2.91733, "time": 0.84843} +{"mode": "train", "epoch": 126, "iter": 1300, "lr": 0.00652, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48094, "top5_acc": 0.73734, "loss_cls": 2.88105, "loss": 2.88105, "time": 0.84384} +{"mode": "train", "epoch": 126, "iter": 1400, "lr": 0.0065, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.48375, "top5_acc": 0.74297, "loss_cls": 2.86288, "loss": 2.86288, "time": 0.84483} +{"mode": "train", "epoch": 126, "iter": 1500, "lr": 0.00649, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.47141, "top5_acc": 0.73734, "loss_cls": 2.88815, "loss": 2.88815, "time": 0.84692} +{"mode": "train", "epoch": 126, "iter": 1600, "lr": 0.00648, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.48281, "top5_acc": 0.74688, "loss_cls": 2.83136, "loss": 2.83136, "time": 0.8408} +{"mode": "train", "epoch": 126, "iter": 1700, "lr": 0.00646, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47781, "top5_acc": 0.73984, "loss_cls": 2.89621, "loss": 2.89621, "time": 0.84543} +{"mode": "train", "epoch": 126, "iter": 1800, "lr": 0.00645, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.465, "top5_acc": 0.73156, "loss_cls": 2.91981, "loss": 2.91981, "time": 0.84886} +{"mode": "train", "epoch": 126, "iter": 1900, "lr": 0.00644, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48172, "top5_acc": 0.74109, "loss_cls": 2.85813, "loss": 2.85813, "time": 0.84378} +{"mode": "train", "epoch": 126, "iter": 2000, "lr": 0.00642, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47688, "top5_acc": 0.74547, "loss_cls": 2.85496, "loss": 2.85496, "time": 0.84275} +{"mode": "train", "epoch": 126, "iter": 2100, "lr": 0.00641, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48984, "top5_acc": 0.74484, "loss_cls": 2.85479, "loss": 2.85479, "time": 0.84479} +{"mode": "train", "epoch": 126, "iter": 2200, "lr": 0.00639, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.48266, "top5_acc": 0.73656, "loss_cls": 2.88129, "loss": 2.88129, "time": 0.8471} +{"mode": "train", "epoch": 126, "iter": 2300, "lr": 0.00638, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48031, "top5_acc": 0.74078, "loss_cls": 2.87223, "loss": 2.87223, "time": 0.84328} +{"mode": "train", "epoch": 126, "iter": 2400, "lr": 0.00637, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48188, "top5_acc": 0.73547, "loss_cls": 2.8844, "loss": 2.8844, "time": 0.84908} +{"mode": "train", "epoch": 126, "iter": 2500, "lr": 0.00635, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47297, "top5_acc": 0.73953, "loss_cls": 2.88894, "loss": 2.88894, "time": 0.84407} +{"mode": "train", "epoch": 126, "iter": 2600, "lr": 0.00634, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47922, "top5_acc": 0.74281, "loss_cls": 2.83938, "loss": 2.83938, "time": 0.84442} +{"mode": "train", "epoch": 126, "iter": 2700, "lr": 0.00633, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.47234, "top5_acc": 0.73031, "loss_cls": 2.90181, "loss": 2.90181, "time": 0.85113} +{"mode": "train", "epoch": 126, "iter": 2800, "lr": 0.00631, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.47609, "top5_acc": 0.73062, "loss_cls": 2.90283, "loss": 2.90283, "time": 0.8517} +{"mode": "train", "epoch": 126, "iter": 2900, "lr": 0.0063, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.48594, "top5_acc": 0.73672, "loss_cls": 2.89052, "loss": 2.89052, "time": 0.84444} +{"mode": "train", "epoch": 126, "iter": 3000, "lr": 0.00629, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.48406, "top5_acc": 0.73422, "loss_cls": 2.86631, "loss": 2.86631, "time": 0.85016} +{"mode": "train", "epoch": 126, "iter": 3100, "lr": 0.00627, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47828, "top5_acc": 0.73438, "loss_cls": 2.89034, "loss": 2.89034, "time": 0.84593} +{"mode": "train", "epoch": 126, "iter": 3200, "lr": 0.00626, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47875, "top5_acc": 0.73328, "loss_cls": 2.91261, "loss": 2.91261, "time": 0.85056} +{"mode": "train", "epoch": 126, "iter": 3300, "lr": 0.00625, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48062, "top5_acc": 0.73641, "loss_cls": 2.86603, "loss": 2.86603, "time": 0.84948} +{"mode": "train", "epoch": 126, "iter": 3400, "lr": 0.00623, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47562, "top5_acc": 0.73609, "loss_cls": 2.8948, "loss": 2.8948, "time": 0.84434} +{"mode": "train", "epoch": 126, "iter": 3500, "lr": 0.00622, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47578, "top5_acc": 0.72797, "loss_cls": 2.92392, "loss": 2.92392, "time": 0.84174} +{"mode": "train", "epoch": 126, "iter": 3600, "lr": 0.0062, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48875, "top5_acc": 0.74609, "loss_cls": 2.8581, "loss": 2.8581, "time": 0.84855} +{"mode": "train", "epoch": 126, "iter": 3700, "lr": 0.00619, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48047, "top5_acc": 0.73797, "loss_cls": 2.88599, "loss": 2.88599, "time": 0.84281} +{"mode": "val", "epoch": 126, "iter": 309, "lr": 0.00618, "top1_acc": 0.41032, "top5_acc": 0.66246, "mean_class_accuracy": 0.40998} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00617, "memory": 15990, "data_time": 1.44643, "top1_acc": 0.50094, "top5_acc": 0.75234, "loss_cls": 2.78022, "loss": 2.78022, "time": 2.46979} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00616, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49047, "top5_acc": 0.75156, "loss_cls": 2.80139, "loss": 2.80139, "time": 0.84941} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00614, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.49406, "top5_acc": 0.74344, "loss_cls": 2.79284, "loss": 2.79284, "time": 0.84884} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00613, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49797, "top5_acc": 0.75172, "loss_cls": 2.76598, "loss": 2.76598, "time": 0.852} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.00612, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.49391, "top5_acc": 0.75438, "loss_cls": 2.77642, "loss": 2.77642, "time": 0.84994} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.0061, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.48562, "top5_acc": 0.74031, "loss_cls": 2.84929, "loss": 2.84929, "time": 0.84957} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00609, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48625, "top5_acc": 0.74141, "loss_cls": 2.84194, "loss": 2.84194, "time": 0.84714} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00608, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49953, "top5_acc": 0.74906, "loss_cls": 2.80462, "loss": 2.80462, "time": 0.84916} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00606, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49266, "top5_acc": 0.75234, "loss_cls": 2.80386, "loss": 2.80386, "time": 0.84926} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00605, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48719, "top5_acc": 0.74141, "loss_cls": 2.82239, "loss": 2.82239, "time": 0.84807} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00604, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48125, "top5_acc": 0.75031, "loss_cls": 2.82408, "loss": 2.82408, "time": 0.84693} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00602, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.48328, "top5_acc": 0.73828, "loss_cls": 2.86041, "loss": 2.86041, "time": 0.84201} +{"mode": "train", "epoch": 127, "iter": 1300, "lr": 0.00601, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48547, "top5_acc": 0.73969, "loss_cls": 2.82518, "loss": 2.82518, "time": 0.84666} +{"mode": "train", "epoch": 127, "iter": 1400, "lr": 0.006, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.49531, "top5_acc": 0.73906, "loss_cls": 2.82633, "loss": 2.82633, "time": 0.84148} +{"mode": "train", "epoch": 127, "iter": 1500, "lr": 0.00598, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.49078, "top5_acc": 0.75234, "loss_cls": 2.79711, "loss": 2.79711, "time": 0.84566} +{"mode": "train", "epoch": 127, "iter": 1600, "lr": 0.00597, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.49266, "top5_acc": 0.74656, "loss_cls": 2.84158, "loss": 2.84158, "time": 0.84462} +{"mode": "train", "epoch": 127, "iter": 1700, "lr": 0.00596, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.48703, "top5_acc": 0.74516, "loss_cls": 2.82671, "loss": 2.82671, "time": 0.84171} +{"mode": "train", "epoch": 127, "iter": 1800, "lr": 0.00594, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.49406, "top5_acc": 0.75344, "loss_cls": 2.80512, "loss": 2.80512, "time": 0.84277} +{"mode": "train", "epoch": 127, "iter": 1900, "lr": 0.00593, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49969, "top5_acc": 0.75672, "loss_cls": 2.75684, "loss": 2.75684, "time": 0.84196} +{"mode": "train", "epoch": 127, "iter": 2000, "lr": 0.00592, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48125, "top5_acc": 0.74, "loss_cls": 2.84688, "loss": 2.84688, "time": 0.84514} +{"mode": "train", "epoch": 127, "iter": 2100, "lr": 0.00591, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48547, "top5_acc": 0.73984, "loss_cls": 2.84324, "loss": 2.84324, "time": 0.84125} +{"mode": "train", "epoch": 127, "iter": 2200, "lr": 0.00589, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48422, "top5_acc": 0.73703, "loss_cls": 2.86683, "loss": 2.86683, "time": 0.83853} +{"mode": "train", "epoch": 127, "iter": 2300, "lr": 0.00588, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48156, "top5_acc": 0.74219, "loss_cls": 2.88961, "loss": 2.88961, "time": 0.84552} +{"mode": "train", "epoch": 127, "iter": 2400, "lr": 0.00587, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48594, "top5_acc": 0.73672, "loss_cls": 2.8653, "loss": 2.8653, "time": 0.84599} +{"mode": "train", "epoch": 127, "iter": 2500, "lr": 0.00585, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48406, "top5_acc": 0.74328, "loss_cls": 2.84844, "loss": 2.84844, "time": 0.8452} +{"mode": "train", "epoch": 127, "iter": 2600, "lr": 0.00584, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48156, "top5_acc": 0.73875, "loss_cls": 2.85903, "loss": 2.85903, "time": 0.84358} +{"mode": "train", "epoch": 127, "iter": 2700, "lr": 0.00583, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47656, "top5_acc": 0.73734, "loss_cls": 2.90483, "loss": 2.90483, "time": 0.85111} +{"mode": "train", "epoch": 127, "iter": 2800, "lr": 0.00581, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49531, "top5_acc": 0.74703, "loss_cls": 2.83006, "loss": 2.83006, "time": 0.85076} +{"mode": "train", "epoch": 127, "iter": 2900, "lr": 0.0058, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49578, "top5_acc": 0.75219, "loss_cls": 2.78774, "loss": 2.78774, "time": 0.84491} +{"mode": "train", "epoch": 127, "iter": 3000, "lr": 0.00579, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49625, "top5_acc": 0.745, "loss_cls": 2.82853, "loss": 2.82853, "time": 0.84823} +{"mode": "train", "epoch": 127, "iter": 3100, "lr": 0.00577, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48094, "top5_acc": 0.73469, "loss_cls": 2.86078, "loss": 2.86078, "time": 0.8442} +{"mode": "train", "epoch": 127, "iter": 3200, "lr": 0.00576, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48484, "top5_acc": 0.74359, "loss_cls": 2.83154, "loss": 2.83154, "time": 0.84756} +{"mode": "train", "epoch": 127, "iter": 3300, "lr": 0.00575, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.485, "top5_acc": 0.74031, "loss_cls": 2.83409, "loss": 2.83409, "time": 0.8417} +{"mode": "train", "epoch": 127, "iter": 3400, "lr": 0.00573, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49203, "top5_acc": 0.74406, "loss_cls": 2.8449, "loss": 2.8449, "time": 0.84361} +{"mode": "train", "epoch": 127, "iter": 3500, "lr": 0.00572, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47922, "top5_acc": 0.73641, "loss_cls": 2.87671, "loss": 2.87671, "time": 0.84334} +{"mode": "train", "epoch": 127, "iter": 3600, "lr": 0.00571, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49031, "top5_acc": 0.74219, "loss_cls": 2.84156, "loss": 2.84156, "time": 0.84707} +{"mode": "train", "epoch": 127, "iter": 3700, "lr": 0.0057, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48297, "top5_acc": 0.73984, "loss_cls": 2.88485, "loss": 2.88485, "time": 0.84372} +{"mode": "val", "epoch": 127, "iter": 309, "lr": 0.00569, "top1_acc": 0.40348, "top5_acc": 0.6579, "mean_class_accuracy": 0.40329} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00568, "memory": 15990, "data_time": 1.4411, "top1_acc": 0.50781, "top5_acc": 0.76156, "loss_cls": 2.71639, "loss": 2.71639, "time": 2.46962} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.00566, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.51344, "top5_acc": 0.76266, "loss_cls": 2.71842, "loss": 2.71842, "time": 0.84588} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00565, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49328, "top5_acc": 0.7475, "loss_cls": 2.8242, "loss": 2.8242, "time": 0.84604} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00564, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49922, "top5_acc": 0.7525, "loss_cls": 2.80629, "loss": 2.80629, "time": 0.84359} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00563, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.50328, "top5_acc": 0.75016, "loss_cls": 2.79185, "loss": 2.79185, "time": 0.84621} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00561, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49859, "top5_acc": 0.7525, "loss_cls": 2.77311, "loss": 2.77311, "time": 0.8443} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.0056, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.50391, "top5_acc": 0.75344, "loss_cls": 2.7808, "loss": 2.7808, "time": 0.84616} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00559, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.49844, "top5_acc": 0.75406, "loss_cls": 2.7507, "loss": 2.7507, "time": 0.84757} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00557, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.50031, "top5_acc": 0.75406, "loss_cls": 2.76835, "loss": 2.76835, "time": 0.84625} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00556, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49609, "top5_acc": 0.75062, "loss_cls": 2.80679, "loss": 2.80679, "time": 0.84776} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00555, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.50813, "top5_acc": 0.75266, "loss_cls": 2.75117, "loss": 2.75117, "time": 0.85032} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00554, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49578, "top5_acc": 0.74828, "loss_cls": 2.79493, "loss": 2.79493, "time": 0.84155} +{"mode": "train", "epoch": 128, "iter": 1300, "lr": 0.00552, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.50141, "top5_acc": 0.75141, "loss_cls": 2.78363, "loss": 2.78363, "time": 0.84843} +{"mode": "train", "epoch": 128, "iter": 1400, "lr": 0.00551, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48875, "top5_acc": 0.74078, "loss_cls": 2.84778, "loss": 2.84778, "time": 0.84555} +{"mode": "train", "epoch": 128, "iter": 1500, "lr": 0.0055, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48734, "top5_acc": 0.73781, "loss_cls": 2.86978, "loss": 2.86978, "time": 0.84083} +{"mode": "train", "epoch": 128, "iter": 1600, "lr": 0.00548, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.50266, "top5_acc": 0.75391, "loss_cls": 2.75562, "loss": 2.75562, "time": 0.85064} +{"mode": "train", "epoch": 128, "iter": 1700, "lr": 0.00547, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49641, "top5_acc": 0.74703, "loss_cls": 2.79455, "loss": 2.79455, "time": 0.84541} +{"mode": "train", "epoch": 128, "iter": 1800, "lr": 0.00546, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.49672, "top5_acc": 0.75297, "loss_cls": 2.78255, "loss": 2.78255, "time": 0.84818} +{"mode": "train", "epoch": 128, "iter": 1900, "lr": 0.00545, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49844, "top5_acc": 0.75609, "loss_cls": 2.77072, "loss": 2.77072, "time": 0.84539} +{"mode": "train", "epoch": 128, "iter": 2000, "lr": 0.00543, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49078, "top5_acc": 0.75641, "loss_cls": 2.79846, "loss": 2.79846, "time": 0.84221} +{"mode": "train", "epoch": 128, "iter": 2100, "lr": 0.00542, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48922, "top5_acc": 0.75062, "loss_cls": 2.80568, "loss": 2.80568, "time": 0.84616} +{"mode": "train", "epoch": 128, "iter": 2200, "lr": 0.00541, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49734, "top5_acc": 0.74859, "loss_cls": 2.81334, "loss": 2.81334, "time": 0.84967} +{"mode": "train", "epoch": 128, "iter": 2300, "lr": 0.0054, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48891, "top5_acc": 0.74297, "loss_cls": 2.84911, "loss": 2.84911, "time": 0.84812} +{"mode": "train", "epoch": 128, "iter": 2400, "lr": 0.00538, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49438, "top5_acc": 0.76031, "loss_cls": 2.76081, "loss": 2.76081, "time": 0.84524} +{"mode": "train", "epoch": 128, "iter": 2500, "lr": 0.00537, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.49281, "top5_acc": 0.74953, "loss_cls": 2.80188, "loss": 2.80188, "time": 0.84613} +{"mode": "train", "epoch": 128, "iter": 2600, "lr": 0.00536, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.49953, "top5_acc": 0.75125, "loss_cls": 2.779, "loss": 2.779, "time": 0.84328} +{"mode": "train", "epoch": 128, "iter": 2700, "lr": 0.00535, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.50031, "top5_acc": 0.75453, "loss_cls": 2.76703, "loss": 2.76703, "time": 0.84279} +{"mode": "train", "epoch": 128, "iter": 2800, "lr": 0.00533, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48859, "top5_acc": 0.74297, "loss_cls": 2.84616, "loss": 2.84616, "time": 0.84454} +{"mode": "train", "epoch": 128, "iter": 2900, "lr": 0.00532, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48656, "top5_acc": 0.74219, "loss_cls": 2.82784, "loss": 2.82784, "time": 0.84586} +{"mode": "train", "epoch": 128, "iter": 3000, "lr": 0.00531, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49594, "top5_acc": 0.75219, "loss_cls": 2.81307, "loss": 2.81307, "time": 0.84235} +{"mode": "train", "epoch": 128, "iter": 3100, "lr": 0.0053, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48656, "top5_acc": 0.74719, "loss_cls": 2.82434, "loss": 2.82434, "time": 0.83897} +{"mode": "train", "epoch": 128, "iter": 3200, "lr": 0.00528, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49453, "top5_acc": 0.7475, "loss_cls": 2.80667, "loss": 2.80667, "time": 0.84498} +{"mode": "train", "epoch": 128, "iter": 3300, "lr": 0.00527, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.50047, "top5_acc": 0.75266, "loss_cls": 2.78088, "loss": 2.78088, "time": 0.84394} +{"mode": "train", "epoch": 128, "iter": 3400, "lr": 0.00526, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48219, "top5_acc": 0.73984, "loss_cls": 2.84492, "loss": 2.84492, "time": 0.84685} +{"mode": "train", "epoch": 128, "iter": 3500, "lr": 0.00525, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.495, "top5_acc": 0.74828, "loss_cls": 2.79548, "loss": 2.79548, "time": 0.84503} +{"mode": "train", "epoch": 128, "iter": 3600, "lr": 0.00523, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49781, "top5_acc": 0.74984, "loss_cls": 2.80498, "loss": 2.80498, "time": 0.84415} +{"mode": "train", "epoch": 128, "iter": 3700, "lr": 0.00522, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48469, "top5_acc": 0.74875, "loss_cls": 2.83979, "loss": 2.83979, "time": 0.84687} +{"mode": "val", "epoch": 128, "iter": 309, "lr": 0.00521, "top1_acc": 0.41529, "top5_acc": 0.66783, "mean_class_accuracy": 0.41503} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.0052, "memory": 15990, "data_time": 1.43771, "top1_acc": 0.51969, "top5_acc": 0.76656, "loss_cls": 2.67956, "loss": 2.67956, "time": 2.45969} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00519, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.51719, "top5_acc": 0.76703, "loss_cls": 2.68534, "loss": 2.68534, "time": 0.84511} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00518, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.51531, "top5_acc": 0.76625, "loss_cls": 2.67553, "loss": 2.67553, "time": 0.84511} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00516, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.50484, "top5_acc": 0.76125, "loss_cls": 2.71853, "loss": 2.71853, "time": 0.85212} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00515, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.51141, "top5_acc": 0.76391, "loss_cls": 2.69826, "loss": 2.69826, "time": 0.85257} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00514, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.51156, "top5_acc": 0.76469, "loss_cls": 2.69209, "loss": 2.69209, "time": 0.85017} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00513, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.50969, "top5_acc": 0.76641, "loss_cls": 2.71986, "loss": 2.71986, "time": 0.8442} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00512, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.50875, "top5_acc": 0.75969, "loss_cls": 2.72937, "loss": 2.72937, "time": 0.84341} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.0051, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49625, "top5_acc": 0.75188, "loss_cls": 2.7963, "loss": 2.7963, "time": 0.8426} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00509, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.50516, "top5_acc": 0.75781, "loss_cls": 2.76562, "loss": 2.76562, "time": 0.84334} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00508, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.50953, "top5_acc": 0.76609, "loss_cls": 2.70194, "loss": 2.70194, "time": 0.84767} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.00507, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.50703, "top5_acc": 0.76328, "loss_cls": 2.71228, "loss": 2.71228, "time": 0.84473} +{"mode": "train", "epoch": 129, "iter": 1300, "lr": 0.00505, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.50062, "top5_acc": 0.74531, "loss_cls": 2.79559, "loss": 2.79559, "time": 0.84358} +{"mode": "train", "epoch": 129, "iter": 1400, "lr": 0.00504, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49875, "top5_acc": 0.75625, "loss_cls": 2.8048, "loss": 2.8048, "time": 0.84355} +{"mode": "train", "epoch": 129, "iter": 1500, "lr": 0.00503, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.50547, "top5_acc": 0.74656, "loss_cls": 2.78113, "loss": 2.78113, "time": 0.8433} +{"mode": "train", "epoch": 129, "iter": 1600, "lr": 0.00502, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49844, "top5_acc": 0.75844, "loss_cls": 2.76557, "loss": 2.76557, "time": 0.842} +{"mode": "train", "epoch": 129, "iter": 1700, "lr": 0.00501, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49609, "top5_acc": 0.755, "loss_cls": 2.77637, "loss": 2.77637, "time": 0.84706} +{"mode": "train", "epoch": 129, "iter": 1800, "lr": 0.00499, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.50156, "top5_acc": 0.75734, "loss_cls": 2.77183, "loss": 2.77183, "time": 0.84075} +{"mode": "train", "epoch": 129, "iter": 1900, "lr": 0.00498, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.48891, "top5_acc": 0.75156, "loss_cls": 2.80483, "loss": 2.80483, "time": 0.84342} +{"mode": "train", "epoch": 129, "iter": 2000, "lr": 0.00497, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49859, "top5_acc": 0.75547, "loss_cls": 2.75133, "loss": 2.75133, "time": 0.84497} +{"mode": "train", "epoch": 129, "iter": 2100, "lr": 0.00496, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49188, "top5_acc": 0.74828, "loss_cls": 2.80233, "loss": 2.80233, "time": 0.84627} +{"mode": "train", "epoch": 129, "iter": 2200, "lr": 0.00494, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49016, "top5_acc": 0.75812, "loss_cls": 2.76968, "loss": 2.76968, "time": 0.84708} +{"mode": "train", "epoch": 129, "iter": 2300, "lr": 0.00493, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49047, "top5_acc": 0.7525, "loss_cls": 2.77819, "loss": 2.77819, "time": 0.84392} +{"mode": "train", "epoch": 129, "iter": 2400, "lr": 0.00492, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48906, "top5_acc": 0.75, "loss_cls": 2.80149, "loss": 2.80149, "time": 0.84722} +{"mode": "train", "epoch": 129, "iter": 2500, "lr": 0.00491, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.50172, "top5_acc": 0.75156, "loss_cls": 2.78162, "loss": 2.78162, "time": 0.84226} +{"mode": "train", "epoch": 129, "iter": 2600, "lr": 0.0049, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49562, "top5_acc": 0.74688, "loss_cls": 2.80942, "loss": 2.80942, "time": 0.84654} +{"mode": "train", "epoch": 129, "iter": 2700, "lr": 0.00488, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.50328, "top5_acc": 0.75172, "loss_cls": 2.77516, "loss": 2.77516, "time": 0.8464} +{"mode": "train", "epoch": 129, "iter": 2800, "lr": 0.00487, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.50141, "top5_acc": 0.75609, "loss_cls": 2.75279, "loss": 2.75279, "time": 0.85634} +{"mode": "train", "epoch": 129, "iter": 2900, "lr": 0.00486, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.50781, "top5_acc": 0.75453, "loss_cls": 2.75533, "loss": 2.75533, "time": 0.84927} +{"mode": "train", "epoch": 129, "iter": 3000, "lr": 0.00485, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.4875, "top5_acc": 0.75078, "loss_cls": 2.82486, "loss": 2.82486, "time": 0.8501} +{"mode": "train", "epoch": 129, "iter": 3100, "lr": 0.00484, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48781, "top5_acc": 0.75172, "loss_cls": 2.81304, "loss": 2.81304, "time": 0.84774} +{"mode": "train", "epoch": 129, "iter": 3200, "lr": 0.00482, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51672, "top5_acc": 0.75672, "loss_cls": 2.72173, "loss": 2.72173, "time": 0.85078} +{"mode": "train", "epoch": 129, "iter": 3300, "lr": 0.00481, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49562, "top5_acc": 0.74703, "loss_cls": 2.78444, "loss": 2.78444, "time": 0.85408} +{"mode": "train", "epoch": 129, "iter": 3400, "lr": 0.0048, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49828, "top5_acc": 0.75281, "loss_cls": 2.76142, "loss": 2.76142, "time": 0.85475} +{"mode": "train", "epoch": 129, "iter": 3500, "lr": 0.00479, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.49312, "top5_acc": 0.75047, "loss_cls": 2.81282, "loss": 2.81282, "time": 0.85553} +{"mode": "train", "epoch": 129, "iter": 3600, "lr": 0.00478, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.50422, "top5_acc": 0.75312, "loss_cls": 2.74602, "loss": 2.74602, "time": 0.8507} +{"mode": "train", "epoch": 129, "iter": 3700, "lr": 0.00476, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.5175, "top5_acc": 0.76453, "loss_cls": 2.70903, "loss": 2.70903, "time": 0.85016} +{"mode": "val", "epoch": 129, "iter": 309, "lr": 0.00476, "top1_acc": 0.41387, "top5_acc": 0.66413, "mean_class_accuracy": 0.41358} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00475, "memory": 15990, "data_time": 1.48602, "top1_acc": 0.52094, "top5_acc": 0.77516, "loss_cls": 2.63083, "loss": 2.63083, "time": 2.53023} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00473, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.52062, "top5_acc": 0.76891, "loss_cls": 2.68374, "loss": 2.68374, "time": 0.8537} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00472, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51625, "top5_acc": 0.76438, "loss_cls": 2.68226, "loss": 2.68226, "time": 0.86559} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00471, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.52609, "top5_acc": 0.77359, "loss_cls": 2.61287, "loss": 2.61287, "time": 0.86389} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.0047, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.51688, "top5_acc": 0.76406, "loss_cls": 2.66506, "loss": 2.66506, "time": 0.85703} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00469, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.51219, "top5_acc": 0.76281, "loss_cls": 2.67675, "loss": 2.67675, "time": 0.86063} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00468, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.51719, "top5_acc": 0.75938, "loss_cls": 2.68646, "loss": 2.68646, "time": 0.86142} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00466, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.51625, "top5_acc": 0.76297, "loss_cls": 2.70649, "loss": 2.70649, "time": 0.85867} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00465, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.50516, "top5_acc": 0.75984, "loss_cls": 2.73146, "loss": 2.73146, "time": 0.84984} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.00464, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50938, "top5_acc": 0.75953, "loss_cls": 2.69493, "loss": 2.69493, "time": 0.86033} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.00463, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.50203, "top5_acc": 0.76172, "loss_cls": 2.74715, "loss": 2.74715, "time": 0.8566} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00462, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.5125, "top5_acc": 0.76875, "loss_cls": 2.69894, "loss": 2.69894, "time": 0.86358} +{"mode": "train", "epoch": 130, "iter": 1300, "lr": 0.00461, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.50641, "top5_acc": 0.76078, "loss_cls": 2.7275, "loss": 2.7275, "time": 0.84909} +{"mode": "train", "epoch": 130, "iter": 1400, "lr": 0.00459, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.50406, "top5_acc": 0.76875, "loss_cls": 2.71735, "loss": 2.71735, "time": 0.85188} +{"mode": "train", "epoch": 130, "iter": 1500, "lr": 0.00458, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.50922, "top5_acc": 0.75938, "loss_cls": 2.72525, "loss": 2.72525, "time": 0.8565} +{"mode": "train", "epoch": 130, "iter": 1600, "lr": 0.00457, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.51844, "top5_acc": 0.76859, "loss_cls": 2.66262, "loss": 2.66262, "time": 0.85339} +{"mode": "train", "epoch": 130, "iter": 1700, "lr": 0.00456, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.51172, "top5_acc": 0.76312, "loss_cls": 2.69978, "loss": 2.69978, "time": 0.85541} +{"mode": "train", "epoch": 130, "iter": 1800, "lr": 0.00455, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49859, "top5_acc": 0.7575, "loss_cls": 2.74273, "loss": 2.74273, "time": 0.85828} +{"mode": "train", "epoch": 130, "iter": 1900, "lr": 0.00454, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49828, "top5_acc": 0.75969, "loss_cls": 2.75612, "loss": 2.75612, "time": 0.85143} +{"mode": "train", "epoch": 130, "iter": 2000, "lr": 0.00452, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.51094, "top5_acc": 0.76062, "loss_cls": 2.7193, "loss": 2.7193, "time": 0.85924} +{"mode": "train", "epoch": 130, "iter": 2100, "lr": 0.00451, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.50578, "top5_acc": 0.76062, "loss_cls": 2.73025, "loss": 2.73025, "time": 0.85848} +{"mode": "train", "epoch": 130, "iter": 2200, "lr": 0.0045, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.50438, "top5_acc": 0.76344, "loss_cls": 2.73668, "loss": 2.73668, "time": 0.85391} +{"mode": "train", "epoch": 130, "iter": 2300, "lr": 0.00449, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.50844, "top5_acc": 0.76125, "loss_cls": 2.74321, "loss": 2.74321, "time": 0.85423} +{"mode": "train", "epoch": 130, "iter": 2400, "lr": 0.00448, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.51406, "top5_acc": 0.76125, "loss_cls": 2.72089, "loss": 2.72089, "time": 0.86098} +{"mode": "train", "epoch": 130, "iter": 2500, "lr": 0.00447, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.50109, "top5_acc": 0.76, "loss_cls": 2.75678, "loss": 2.75678, "time": 0.85019} +{"mode": "train", "epoch": 130, "iter": 2600, "lr": 0.00445, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.50797, "top5_acc": 0.76047, "loss_cls": 2.739, "loss": 2.739, "time": 0.85468} +{"mode": "train", "epoch": 130, "iter": 2700, "lr": 0.00444, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.50234, "top5_acc": 0.75578, "loss_cls": 2.74411, "loss": 2.74411, "time": 0.85297} +{"mode": "train", "epoch": 130, "iter": 2800, "lr": 0.00443, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.50953, "top5_acc": 0.75812, "loss_cls": 2.74218, "loss": 2.74218, "time": 0.85888} +{"mode": "train", "epoch": 130, "iter": 2900, "lr": 0.00442, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.50891, "top5_acc": 0.76734, "loss_cls": 2.68869, "loss": 2.68869, "time": 0.85397} +{"mode": "train", "epoch": 130, "iter": 3000, "lr": 0.00441, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.49812, "top5_acc": 0.75516, "loss_cls": 2.74767, "loss": 2.74767, "time": 0.85799} +{"mode": "train", "epoch": 130, "iter": 3100, "lr": 0.0044, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49781, "top5_acc": 0.75844, "loss_cls": 2.76443, "loss": 2.76443, "time": 0.85439} +{"mode": "train", "epoch": 130, "iter": 3200, "lr": 0.00439, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49234, "top5_acc": 0.75, "loss_cls": 2.79067, "loss": 2.79067, "time": 0.85637} +{"mode": "train", "epoch": 130, "iter": 3300, "lr": 0.00437, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.50125, "top5_acc": 0.75281, "loss_cls": 2.74334, "loss": 2.74334, "time": 0.85918} +{"mode": "train", "epoch": 130, "iter": 3400, "lr": 0.00436, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50641, "top5_acc": 0.74844, "loss_cls": 2.76986, "loss": 2.76986, "time": 0.86682} +{"mode": "train", "epoch": 130, "iter": 3500, "lr": 0.00435, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50328, "top5_acc": 0.755, "loss_cls": 2.76911, "loss": 2.76911, "time": 0.86128} +{"mode": "train", "epoch": 130, "iter": 3600, "lr": 0.00434, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.50781, "top5_acc": 0.76125, "loss_cls": 2.73642, "loss": 2.73642, "time": 0.86416} +{"mode": "train", "epoch": 130, "iter": 3700, "lr": 0.00433, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.50625, "top5_acc": 0.75688, "loss_cls": 2.73908, "loss": 2.73908, "time": 0.86083} +{"mode": "val", "epoch": 130, "iter": 309, "lr": 0.00432, "top1_acc": 0.41969, "top5_acc": 0.67842, "mean_class_accuracy": 0.41944} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00431, "memory": 15990, "data_time": 1.49084, "top1_acc": 0.52109, "top5_acc": 0.77109, "loss_cls": 2.66601, "loss": 2.66601, "time": 2.52653} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.0043, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.51922, "top5_acc": 0.77062, "loss_cls": 2.6483, "loss": 2.6483, "time": 0.85854} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00429, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.52641, "top5_acc": 0.77688, "loss_cls": 2.62764, "loss": 2.62764, "time": 0.8613} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00428, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.52547, "top5_acc": 0.77531, "loss_cls": 2.63378, "loss": 2.63378, "time": 0.85651} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00427, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.50875, "top5_acc": 0.7625, "loss_cls": 2.70231, "loss": 2.70231, "time": 0.8585} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00425, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.52547, "top5_acc": 0.77375, "loss_cls": 2.64237, "loss": 2.64237, "time": 0.85874} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00424, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51344, "top5_acc": 0.76484, "loss_cls": 2.68899, "loss": 2.68899, "time": 0.85781} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00423, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.51719, "top5_acc": 0.76922, "loss_cls": 2.68034, "loss": 2.68034, "time": 0.85967} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00422, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51641, "top5_acc": 0.77031, "loss_cls": 2.65591, "loss": 2.65591, "time": 0.86105} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.00421, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.51953, "top5_acc": 0.76938, "loss_cls": 2.65819, "loss": 2.65819, "time": 0.86268} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.0042, "memory": 15990, "data_time": 0.00075, "top1_acc": 0.51578, "top5_acc": 0.76234, "loss_cls": 2.68575, "loss": 2.68575, "time": 0.85679} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00419, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.50313, "top5_acc": 0.76312, "loss_cls": 2.70008, "loss": 2.70008, "time": 0.85911} +{"mode": "train", "epoch": 131, "iter": 1300, "lr": 0.00418, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.51641, "top5_acc": 0.75969, "loss_cls": 2.71324, "loss": 2.71324, "time": 0.85663} +{"mode": "train", "epoch": 131, "iter": 1400, "lr": 0.00417, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.50719, "top5_acc": 0.75875, "loss_cls": 2.72002, "loss": 2.72002, "time": 0.85258} +{"mode": "train", "epoch": 131, "iter": 1500, "lr": 0.00415, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.515, "top5_acc": 0.77078, "loss_cls": 2.67977, "loss": 2.67977, "time": 0.8511} +{"mode": "train", "epoch": 131, "iter": 1600, "lr": 0.00414, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.51359, "top5_acc": 0.76734, "loss_cls": 2.68859, "loss": 2.68859, "time": 0.8552} +{"mode": "train", "epoch": 131, "iter": 1700, "lr": 0.00413, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.51594, "top5_acc": 0.76453, "loss_cls": 2.6503, "loss": 2.6503, "time": 0.85717} +{"mode": "train", "epoch": 131, "iter": 1800, "lr": 0.00412, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.51578, "top5_acc": 0.76719, "loss_cls": 2.66924, "loss": 2.66924, "time": 0.86367} +{"mode": "train", "epoch": 131, "iter": 1900, "lr": 0.00411, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.51109, "top5_acc": 0.75688, "loss_cls": 2.72824, "loss": 2.72824, "time": 0.86511} +{"mode": "train", "epoch": 131, "iter": 2000, "lr": 0.0041, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.51156, "top5_acc": 0.76672, "loss_cls": 2.6964, "loss": 2.6964, "time": 0.85829} +{"mode": "train", "epoch": 131, "iter": 2100, "lr": 0.00409, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.50906, "top5_acc": 0.76203, "loss_cls": 2.69803, "loss": 2.69803, "time": 0.85961} +{"mode": "train", "epoch": 131, "iter": 2200, "lr": 0.00408, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.50187, "top5_acc": 0.76281, "loss_cls": 2.71162, "loss": 2.71162, "time": 0.8543} +{"mode": "train", "epoch": 131, "iter": 2300, "lr": 0.00407, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.51938, "top5_acc": 0.76781, "loss_cls": 2.66793, "loss": 2.66793, "time": 0.8591} +{"mode": "train", "epoch": 131, "iter": 2400, "lr": 0.00405, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.51281, "top5_acc": 0.76297, "loss_cls": 2.67414, "loss": 2.67414, "time": 0.85437} +{"mode": "train", "epoch": 131, "iter": 2500, "lr": 0.00404, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.51781, "top5_acc": 0.76484, "loss_cls": 2.67994, "loss": 2.67994, "time": 0.85838} +{"mode": "train", "epoch": 131, "iter": 2600, "lr": 0.00403, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51031, "top5_acc": 0.75844, "loss_cls": 2.73497, "loss": 2.73497, "time": 0.86281} +{"mode": "train", "epoch": 131, "iter": 2700, "lr": 0.00402, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.53109, "top5_acc": 0.77609, "loss_cls": 2.64512, "loss": 2.64512, "time": 0.85728} +{"mode": "train", "epoch": 131, "iter": 2800, "lr": 0.00401, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.50656, "top5_acc": 0.75703, "loss_cls": 2.72734, "loss": 2.72734, "time": 0.85654} +{"mode": "train", "epoch": 131, "iter": 2900, "lr": 0.004, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.52203, "top5_acc": 0.76156, "loss_cls": 2.67808, "loss": 2.67808, "time": 0.85863} +{"mode": "train", "epoch": 131, "iter": 3000, "lr": 0.00399, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.515, "top5_acc": 0.76984, "loss_cls": 2.68975, "loss": 2.68975, "time": 0.85564} +{"mode": "train", "epoch": 131, "iter": 3100, "lr": 0.00398, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.51578, "top5_acc": 0.76359, "loss_cls": 2.70968, "loss": 2.70968, "time": 0.85885} +{"mode": "train", "epoch": 131, "iter": 3200, "lr": 0.00397, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.51516, "top5_acc": 0.76953, "loss_cls": 2.64332, "loss": 2.64332, "time": 0.86094} +{"mode": "train", "epoch": 131, "iter": 3300, "lr": 0.00396, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.51594, "top5_acc": 0.76688, "loss_cls": 2.687, "loss": 2.687, "time": 0.85948} +{"mode": "train", "epoch": 131, "iter": 3400, "lr": 0.00394, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51016, "top5_acc": 0.76516, "loss_cls": 2.69314, "loss": 2.69314, "time": 0.8607} +{"mode": "train", "epoch": 131, "iter": 3500, "lr": 0.00393, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.49312, "top5_acc": 0.76203, "loss_cls": 2.77733, "loss": 2.77733, "time": 0.85903} +{"mode": "train", "epoch": 131, "iter": 3600, "lr": 0.00392, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.5125, "top5_acc": 0.76297, "loss_cls": 2.71935, "loss": 2.71935, "time": 0.85479} +{"mode": "train", "epoch": 131, "iter": 3700, "lr": 0.00391, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.50703, "top5_acc": 0.76109, "loss_cls": 2.71534, "loss": 2.71534, "time": 0.85956} +{"mode": "val", "epoch": 131, "iter": 309, "lr": 0.00391, "top1_acc": 0.42871, "top5_acc": 0.68171, "mean_class_accuracy": 0.42849} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.0039, "memory": 15990, "data_time": 1.50989, "top1_acc": 0.52859, "top5_acc": 0.78469, "loss_cls": 2.59166, "loss": 2.59166, "time": 2.5634} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00389, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.54266, "top5_acc": 0.78734, "loss_cls": 2.5494, "loss": 2.5494, "time": 0.87081} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00387, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.525, "top5_acc": 0.77734, "loss_cls": 2.62431, "loss": 2.62431, "time": 0.87429} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00386, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.52578, "top5_acc": 0.78, "loss_cls": 2.59039, "loss": 2.59039, "time": 0.87524} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00385, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.53422, "top5_acc": 0.78719, "loss_cls": 2.56849, "loss": 2.56849, "time": 0.87139} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00384, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.52219, "top5_acc": 0.77938, "loss_cls": 2.61362, "loss": 2.61362, "time": 0.87148} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00383, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.52453, "top5_acc": 0.76906, "loss_cls": 2.62474, "loss": 2.62474, "time": 0.86984} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00382, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.52609, "top5_acc": 0.77562, "loss_cls": 2.6345, "loss": 2.6345, "time": 0.87418} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00381, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.53359, "top5_acc": 0.78344, "loss_cls": 2.59704, "loss": 2.59704, "time": 0.88036} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0038, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.53531, "top5_acc": 0.77797, "loss_cls": 2.59528, "loss": 2.59528, "time": 0.88093} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00379, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.53484, "top5_acc": 0.78359, "loss_cls": 2.58861, "loss": 2.58861, "time": 0.87347} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00378, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.51797, "top5_acc": 0.77188, "loss_cls": 2.65406, "loss": 2.65406, "time": 0.86738} +{"mode": "train", "epoch": 132, "iter": 1300, "lr": 0.00377, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.52875, "top5_acc": 0.775, "loss_cls": 2.61918, "loss": 2.61918, "time": 0.86496} +{"mode": "train", "epoch": 132, "iter": 1400, "lr": 0.00376, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.52781, "top5_acc": 0.77188, "loss_cls": 2.62832, "loss": 2.62832, "time": 0.87149} +{"mode": "train", "epoch": 132, "iter": 1500, "lr": 0.00375, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.52344, "top5_acc": 0.77406, "loss_cls": 2.63632, "loss": 2.63632, "time": 0.88297} +{"mode": "train", "epoch": 132, "iter": 1600, "lr": 0.00374, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.51859, "top5_acc": 0.77391, "loss_cls": 2.64684, "loss": 2.64684, "time": 0.88213} +{"mode": "train", "epoch": 132, "iter": 1700, "lr": 0.00372, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.53031, "top5_acc": 0.77672, "loss_cls": 2.5988, "loss": 2.5988, "time": 0.88261} +{"mode": "train", "epoch": 132, "iter": 1800, "lr": 0.00371, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.51484, "top5_acc": 0.77625, "loss_cls": 2.63148, "loss": 2.63148, "time": 0.87335} +{"mode": "train", "epoch": 132, "iter": 1900, "lr": 0.0037, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.52328, "top5_acc": 0.77422, "loss_cls": 2.66206, "loss": 2.66206, "time": 0.86655} +{"mode": "train", "epoch": 132, "iter": 2000, "lr": 0.00369, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.51344, "top5_acc": 0.76688, "loss_cls": 2.69897, "loss": 2.69897, "time": 0.85687} +{"mode": "train", "epoch": 132, "iter": 2100, "lr": 0.00368, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.51688, "top5_acc": 0.77578, "loss_cls": 2.66595, "loss": 2.66595, "time": 0.8599} +{"mode": "train", "epoch": 132, "iter": 2200, "lr": 0.00367, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.51281, "top5_acc": 0.77062, "loss_cls": 2.67657, "loss": 2.67657, "time": 0.86768} +{"mode": "train", "epoch": 132, "iter": 2300, "lr": 0.00366, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.53266, "top5_acc": 0.77859, "loss_cls": 2.6129, "loss": 2.6129, "time": 0.86287} +{"mode": "train", "epoch": 132, "iter": 2400, "lr": 0.00365, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.50672, "top5_acc": 0.76203, "loss_cls": 2.67961, "loss": 2.67961, "time": 0.86264} +{"mode": "train", "epoch": 132, "iter": 2500, "lr": 0.00364, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.505, "top5_acc": 0.76547, "loss_cls": 2.70907, "loss": 2.70907, "time": 0.86898} +{"mode": "train", "epoch": 132, "iter": 2600, "lr": 0.00363, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.52281, "top5_acc": 0.77125, "loss_cls": 2.63189, "loss": 2.63189, "time": 0.86472} +{"mode": "train", "epoch": 132, "iter": 2700, "lr": 0.00362, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.50156, "top5_acc": 0.76, "loss_cls": 2.72318, "loss": 2.72318, "time": 0.85959} +{"mode": "train", "epoch": 132, "iter": 2800, "lr": 0.00361, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.52328, "top5_acc": 0.76984, "loss_cls": 2.63536, "loss": 2.63536, "time": 0.8666} +{"mode": "train", "epoch": 132, "iter": 2900, "lr": 0.0036, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.52328, "top5_acc": 0.77078, "loss_cls": 2.64034, "loss": 2.64034, "time": 0.86318} +{"mode": "train", "epoch": 132, "iter": 3000, "lr": 0.00359, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.51797, "top5_acc": 0.77016, "loss_cls": 2.69037, "loss": 2.69037, "time": 0.8645} +{"mode": "train", "epoch": 132, "iter": 3100, "lr": 0.00358, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.5325, "top5_acc": 0.77562, "loss_cls": 2.61626, "loss": 2.61626, "time": 0.86376} +{"mode": "train", "epoch": 132, "iter": 3200, "lr": 0.00357, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.52375, "top5_acc": 0.77266, "loss_cls": 2.64021, "loss": 2.64021, "time": 0.85735} +{"mode": "train", "epoch": 132, "iter": 3300, "lr": 0.00356, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.51922, "top5_acc": 0.76016, "loss_cls": 2.68255, "loss": 2.68255, "time": 0.85898} +{"mode": "train", "epoch": 132, "iter": 3400, "lr": 0.00355, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.51344, "top5_acc": 0.77031, "loss_cls": 2.65668, "loss": 2.65668, "time": 0.87221} +{"mode": "train", "epoch": 132, "iter": 3500, "lr": 0.00354, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.51578, "top5_acc": 0.76766, "loss_cls": 2.68823, "loss": 2.68823, "time": 0.86014} +{"mode": "train", "epoch": 132, "iter": 3600, "lr": 0.00353, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.50594, "top5_acc": 0.76875, "loss_cls": 2.69822, "loss": 2.69822, "time": 0.86089} +{"mode": "train", "epoch": 132, "iter": 3700, "lr": 0.00352, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.51141, "top5_acc": 0.76109, "loss_cls": 2.66975, "loss": 2.66975, "time": 0.86985} +{"mode": "val", "epoch": 132, "iter": 309, "lr": 0.00351, "top1_acc": 0.42765, "top5_acc": 0.68298, "mean_class_accuracy": 0.42739} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.0035, "memory": 15990, "data_time": 1.49112, "top1_acc": 0.53609, "top5_acc": 0.78203, "loss_cls": 2.52978, "loss": 2.52978, "time": 2.54188} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00349, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.53172, "top5_acc": 0.77672, "loss_cls": 2.58874, "loss": 2.58874, "time": 0.8787} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00348, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.54422, "top5_acc": 0.79078, "loss_cls": 2.51645, "loss": 2.51645, "time": 0.87764} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00347, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.52656, "top5_acc": 0.7825, "loss_cls": 2.59093, "loss": 2.59093, "time": 0.87259} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00346, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.55484, "top5_acc": 0.795, "loss_cls": 2.47376, "loss": 2.47376, "time": 0.8832} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00345, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.53422, "top5_acc": 0.78391, "loss_cls": 2.58959, "loss": 2.58959, "time": 0.88375} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00344, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.52562, "top5_acc": 0.77422, "loss_cls": 2.63389, "loss": 2.63389, "time": 0.87906} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00343, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.53516, "top5_acc": 0.78062, "loss_cls": 2.59515, "loss": 2.59515, "time": 0.87817} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00342, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.53391, "top5_acc": 0.78453, "loss_cls": 2.55315, "loss": 2.55315, "time": 0.8764} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.00341, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.53922, "top5_acc": 0.78062, "loss_cls": 2.58245, "loss": 2.58245, "time": 0.87236} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0034, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.53469, "top5_acc": 0.78031, "loss_cls": 2.5979, "loss": 2.5979, "time": 0.87469} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00339, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.535, "top5_acc": 0.77703, "loss_cls": 2.58428, "loss": 2.58428, "time": 0.86134} +{"mode": "train", "epoch": 133, "iter": 1300, "lr": 0.00338, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.53141, "top5_acc": 0.77391, "loss_cls": 2.5906, "loss": 2.5906, "time": 0.85848} +{"mode": "train", "epoch": 133, "iter": 1400, "lr": 0.00337, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.53281, "top5_acc": 0.77875, "loss_cls": 2.584, "loss": 2.584, "time": 0.86907} +{"mode": "train", "epoch": 133, "iter": 1500, "lr": 0.00336, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.52719, "top5_acc": 0.77172, "loss_cls": 2.61989, "loss": 2.61989, "time": 0.88755} +{"mode": "train", "epoch": 133, "iter": 1600, "lr": 0.00335, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.53578, "top5_acc": 0.78344, "loss_cls": 2.56354, "loss": 2.56354, "time": 0.87659} +{"mode": "train", "epoch": 133, "iter": 1700, "lr": 0.00334, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.52734, "top5_acc": 0.77188, "loss_cls": 2.63497, "loss": 2.63497, "time": 0.88263} +{"mode": "train", "epoch": 133, "iter": 1800, "lr": 0.00333, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.53297, "top5_acc": 0.78078, "loss_cls": 2.58281, "loss": 2.58281, "time": 0.87778} +{"mode": "train", "epoch": 133, "iter": 1900, "lr": 0.00332, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.52625, "top5_acc": 0.77672, "loss_cls": 2.6003, "loss": 2.6003, "time": 0.87011} +{"mode": "train", "epoch": 133, "iter": 2000, "lr": 0.00331, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.52406, "top5_acc": 0.77703, "loss_cls": 2.62915, "loss": 2.62915, "time": 0.86948} +{"mode": "train", "epoch": 133, "iter": 2100, "lr": 0.0033, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.52641, "top5_acc": 0.78688, "loss_cls": 2.596, "loss": 2.596, "time": 0.86315} +{"mode": "train", "epoch": 133, "iter": 2200, "lr": 0.00329, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.52594, "top5_acc": 0.77703, "loss_cls": 2.62822, "loss": 2.62822, "time": 0.86544} +{"mode": "train", "epoch": 133, "iter": 2300, "lr": 0.00328, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.52781, "top5_acc": 0.77672, "loss_cls": 2.60743, "loss": 2.60743, "time": 0.86544} +{"mode": "train", "epoch": 133, "iter": 2400, "lr": 0.00327, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.52031, "top5_acc": 0.7775, "loss_cls": 2.62569, "loss": 2.62569, "time": 0.87165} +{"mode": "train", "epoch": 133, "iter": 2500, "lr": 0.00326, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.53719, "top5_acc": 0.78562, "loss_cls": 2.57068, "loss": 2.57068, "time": 0.87409} +{"mode": "train", "epoch": 133, "iter": 2600, "lr": 0.00325, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.53953, "top5_acc": 0.77875, "loss_cls": 2.57529, "loss": 2.57529, "time": 0.88078} +{"mode": "train", "epoch": 133, "iter": 2700, "lr": 0.00324, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.53156, "top5_acc": 0.78234, "loss_cls": 2.59542, "loss": 2.59542, "time": 0.87451} +{"mode": "train", "epoch": 133, "iter": 2800, "lr": 0.00323, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.51844, "top5_acc": 0.77062, "loss_cls": 2.64395, "loss": 2.64395, "time": 0.87291} +{"mode": "train", "epoch": 133, "iter": 2900, "lr": 0.00322, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.51984, "top5_acc": 0.78, "loss_cls": 2.60431, "loss": 2.60431, "time": 0.88542} +{"mode": "train", "epoch": 133, "iter": 3000, "lr": 0.00321, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.52625, "top5_acc": 0.77625, "loss_cls": 2.62957, "loss": 2.62957, "time": 0.87813} +{"mode": "train", "epoch": 133, "iter": 3100, "lr": 0.0032, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.51703, "top5_acc": 0.7725, "loss_cls": 2.64004, "loss": 2.64004, "time": 0.88755} +{"mode": "train", "epoch": 133, "iter": 3200, "lr": 0.00319, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.53203, "top5_acc": 0.78781, "loss_cls": 2.57874, "loss": 2.57874, "time": 0.88445} +{"mode": "train", "epoch": 133, "iter": 3300, "lr": 0.00318, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.53031, "top5_acc": 0.77953, "loss_cls": 2.59864, "loss": 2.59864, "time": 0.87862} +{"mode": "train", "epoch": 133, "iter": 3400, "lr": 0.00317, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.52547, "top5_acc": 0.775, "loss_cls": 2.63177, "loss": 2.63177, "time": 0.88018} +{"mode": "train", "epoch": 133, "iter": 3500, "lr": 0.00316, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.51812, "top5_acc": 0.77406, "loss_cls": 2.64307, "loss": 2.64307, "time": 0.88648} +{"mode": "train", "epoch": 133, "iter": 3600, "lr": 0.00315, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.52859, "top5_acc": 0.77562, "loss_cls": 2.59835, "loss": 2.59835, "time": 0.88255} +{"mode": "train", "epoch": 133, "iter": 3700, "lr": 0.00314, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.51469, "top5_acc": 0.76219, "loss_cls": 2.68173, "loss": 2.68173, "time": 0.87897} +{"mode": "val", "epoch": 133, "iter": 309, "lr": 0.00314, "top1_acc": 0.43332, "top5_acc": 0.68855, "mean_class_accuracy": 0.43312} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00313, "memory": 15990, "data_time": 1.50896, "top1_acc": 0.54234, "top5_acc": 0.79, "loss_cls": 2.53227, "loss": 2.53227, "time": 2.54168} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00312, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.53469, "top5_acc": 0.78641, "loss_cls": 2.53717, "loss": 2.53717, "time": 0.85967} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00311, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.55188, "top5_acc": 0.79625, "loss_cls": 2.49316, "loss": 2.49316, "time": 0.8542} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.0031, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.53703, "top5_acc": 0.78172, "loss_cls": 2.54835, "loss": 2.54835, "time": 0.8603} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00309, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.53688, "top5_acc": 0.78641, "loss_cls": 2.54, "loss": 2.54, "time": 0.86204} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00308, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.53375, "top5_acc": 0.78406, "loss_cls": 2.541, "loss": 2.541, "time": 0.8612} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00307, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.54203, "top5_acc": 0.78938, "loss_cls": 2.52442, "loss": 2.52442, "time": 0.87077} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00306, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.53859, "top5_acc": 0.79031, "loss_cls": 2.52281, "loss": 2.52281, "time": 0.86086} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00305, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.535, "top5_acc": 0.79469, "loss_cls": 2.50529, "loss": 2.50529, "time": 0.86863} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00304, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.54781, "top5_acc": 0.79406, "loss_cls": 2.51732, "loss": 2.51732, "time": 0.85888} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00303, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53047, "top5_acc": 0.78109, "loss_cls": 2.56531, "loss": 2.56531, "time": 0.86115} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.00302, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.53906, "top5_acc": 0.78797, "loss_cls": 2.55241, "loss": 2.55241, "time": 0.8491} +{"mode": "train", "epoch": 134, "iter": 1300, "lr": 0.00301, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.54375, "top5_acc": 0.78484, "loss_cls": 2.53356, "loss": 2.53356, "time": 0.85283} +{"mode": "train", "epoch": 134, "iter": 1400, "lr": 0.003, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.52953, "top5_acc": 0.78062, "loss_cls": 2.60709, "loss": 2.60709, "time": 0.84997} +{"mode": "train", "epoch": 134, "iter": 1500, "lr": 0.00299, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.55469, "top5_acc": 0.78797, "loss_cls": 2.49586, "loss": 2.49586, "time": 0.85107} +{"mode": "train", "epoch": 134, "iter": 1600, "lr": 0.00298, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.52859, "top5_acc": 0.7775, "loss_cls": 2.60167, "loss": 2.60167, "time": 0.84837} +{"mode": "train", "epoch": 134, "iter": 1700, "lr": 0.00297, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.54547, "top5_acc": 0.79109, "loss_cls": 2.51873, "loss": 2.51873, "time": 0.85287} +{"mode": "train", "epoch": 134, "iter": 1800, "lr": 0.00296, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.54266, "top5_acc": 0.79141, "loss_cls": 2.51368, "loss": 2.51368, "time": 0.86058} +{"mode": "train", "epoch": 134, "iter": 1900, "lr": 0.00295, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.52562, "top5_acc": 0.78, "loss_cls": 2.61537, "loss": 2.61537, "time": 0.85969} +{"mode": "train", "epoch": 134, "iter": 2000, "lr": 0.00294, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.53375, "top5_acc": 0.7825, "loss_cls": 2.57157, "loss": 2.57157, "time": 0.8506} +{"mode": "train", "epoch": 134, "iter": 2100, "lr": 0.00293, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.5375, "top5_acc": 0.77797, "loss_cls": 2.5798, "loss": 2.5798, "time": 0.85128} +{"mode": "train", "epoch": 134, "iter": 2200, "lr": 0.00293, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.52766, "top5_acc": 0.77859, "loss_cls": 2.60573, "loss": 2.60573, "time": 0.84577} +{"mode": "train", "epoch": 134, "iter": 2300, "lr": 0.00292, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53875, "top5_acc": 0.78719, "loss_cls": 2.532, "loss": 2.532, "time": 0.85293} +{"mode": "train", "epoch": 134, "iter": 2400, "lr": 0.00291, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.53922, "top5_acc": 0.79031, "loss_cls": 2.51867, "loss": 2.51867, "time": 0.86553} +{"mode": "train", "epoch": 134, "iter": 2500, "lr": 0.0029, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.53422, "top5_acc": 0.78578, "loss_cls": 2.53571, "loss": 2.53571, "time": 0.86507} +{"mode": "train", "epoch": 134, "iter": 2600, "lr": 0.00289, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.53156, "top5_acc": 0.79031, "loss_cls": 2.54423, "loss": 2.54423, "time": 0.86161} +{"mode": "train", "epoch": 134, "iter": 2700, "lr": 0.00288, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.52312, "top5_acc": 0.78219, "loss_cls": 2.60858, "loss": 2.60858, "time": 0.86622} +{"mode": "train", "epoch": 134, "iter": 2800, "lr": 0.00287, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.53656, "top5_acc": 0.78797, "loss_cls": 2.59523, "loss": 2.59523, "time": 0.86206} +{"mode": "train", "epoch": 134, "iter": 2900, "lr": 0.00286, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.54156, "top5_acc": 0.78625, "loss_cls": 2.53809, "loss": 2.53809, "time": 0.86636} +{"mode": "train", "epoch": 134, "iter": 3000, "lr": 0.00285, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.53172, "top5_acc": 0.78031, "loss_cls": 2.56233, "loss": 2.56233, "time": 0.86057} +{"mode": "train", "epoch": 134, "iter": 3100, "lr": 0.00284, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.52734, "top5_acc": 0.78828, "loss_cls": 2.57815, "loss": 2.57815, "time": 0.86516} +{"mode": "train", "epoch": 134, "iter": 3200, "lr": 0.00283, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.52891, "top5_acc": 0.77297, "loss_cls": 2.60998, "loss": 2.60998, "time": 0.86935} +{"mode": "train", "epoch": 134, "iter": 3300, "lr": 0.00282, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.52844, "top5_acc": 0.78844, "loss_cls": 2.5655, "loss": 2.5655, "time": 0.87371} +{"mode": "train", "epoch": 134, "iter": 3400, "lr": 0.00281, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.53219, "top5_acc": 0.77266, "loss_cls": 2.59443, "loss": 2.59443, "time": 0.86704} +{"mode": "train", "epoch": 134, "iter": 3500, "lr": 0.0028, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.53359, "top5_acc": 0.78094, "loss_cls": 2.57562, "loss": 2.57562, "time": 0.86576} +{"mode": "train", "epoch": 134, "iter": 3600, "lr": 0.00279, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.53062, "top5_acc": 0.78016, "loss_cls": 2.55437, "loss": 2.55437, "time": 0.86527} +{"mode": "train", "epoch": 134, "iter": 3700, "lr": 0.00279, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.53672, "top5_acc": 0.77922, "loss_cls": 2.59305, "loss": 2.59305, "time": 0.87149} +{"mode": "val", "epoch": 134, "iter": 309, "lr": 0.00278, "top1_acc": 0.43585, "top5_acc": 0.69169, "mean_class_accuracy": 0.43566} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00277, "memory": 15990, "data_time": 1.44706, "top1_acc": 0.56203, "top5_acc": 0.79734, "loss_cls": 2.45921, "loss": 2.45921, "time": 2.47026} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00276, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.55859, "top5_acc": 0.8025, "loss_cls": 2.44533, "loss": 2.44533, "time": 0.84876} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00275, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.55859, "top5_acc": 0.79125, "loss_cls": 2.47505, "loss": 2.47505, "time": 0.85344} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00274, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.55453, "top5_acc": 0.79859, "loss_cls": 2.44249, "loss": 2.44249, "time": 0.85142} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00274, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55937, "top5_acc": 0.80156, "loss_cls": 2.4406, "loss": 2.4406, "time": 0.85532} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00273, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55125, "top5_acc": 0.79719, "loss_cls": 2.4886, "loss": 2.4886, "time": 0.8579} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00272, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.54969, "top5_acc": 0.79438, "loss_cls": 2.50921, "loss": 2.50921, "time": 0.85613} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00271, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.54937, "top5_acc": 0.79266, "loss_cls": 2.49273, "loss": 2.49273, "time": 0.85952} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.0027, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.55328, "top5_acc": 0.79062, "loss_cls": 2.49122, "loss": 2.49122, "time": 0.86336} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00269, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.54344, "top5_acc": 0.78766, "loss_cls": 2.53878, "loss": 2.53878, "time": 0.85467} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00268, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.54984, "top5_acc": 0.79969, "loss_cls": 2.48094, "loss": 2.48094, "time": 0.85538} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00267, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55703, "top5_acc": 0.80578, "loss_cls": 2.43935, "loss": 2.43935, "time": 0.85937} +{"mode": "train", "epoch": 135, "iter": 1300, "lr": 0.00266, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.54719, "top5_acc": 0.79234, "loss_cls": 2.50507, "loss": 2.50507, "time": 0.84484} +{"mode": "train", "epoch": 135, "iter": 1400, "lr": 0.00265, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54, "top5_acc": 0.78641, "loss_cls": 2.53457, "loss": 2.53457, "time": 0.84915} +{"mode": "train", "epoch": 135, "iter": 1500, "lr": 0.00265, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.55172, "top5_acc": 0.79656, "loss_cls": 2.49169, "loss": 2.49169, "time": 0.85114} +{"mode": "train", "epoch": 135, "iter": 1600, "lr": 0.00264, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.55328, "top5_acc": 0.79188, "loss_cls": 2.48668, "loss": 2.48668, "time": 0.85267} +{"mode": "train", "epoch": 135, "iter": 1700, "lr": 0.00263, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.54734, "top5_acc": 0.79906, "loss_cls": 2.48633, "loss": 2.48633, "time": 0.85496} +{"mode": "train", "epoch": 135, "iter": 1800, "lr": 0.00262, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.53781, "top5_acc": 0.78719, "loss_cls": 2.5466, "loss": 2.5466, "time": 0.85031} +{"mode": "train", "epoch": 135, "iter": 1900, "lr": 0.00261, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.54922, "top5_acc": 0.79094, "loss_cls": 2.50897, "loss": 2.50897, "time": 0.85061} +{"mode": "train", "epoch": 135, "iter": 2000, "lr": 0.0026, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.54844, "top5_acc": 0.78984, "loss_cls": 2.5134, "loss": 2.5134, "time": 0.85449} +{"mode": "train", "epoch": 135, "iter": 2100, "lr": 0.00259, "memory": 15990, "data_time": 0.00078, "top1_acc": 0.55109, "top5_acc": 0.79219, "loss_cls": 2.49515, "loss": 2.49515, "time": 0.85014} +{"mode": "train", "epoch": 135, "iter": 2200, "lr": 0.00258, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.54922, "top5_acc": 0.79828, "loss_cls": 2.47039, "loss": 2.47039, "time": 0.85405} +{"mode": "train", "epoch": 135, "iter": 2300, "lr": 0.00257, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.53672, "top5_acc": 0.78844, "loss_cls": 2.51578, "loss": 2.51578, "time": 0.84787} +{"mode": "train", "epoch": 135, "iter": 2400, "lr": 0.00256, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.54766, "top5_acc": 0.79516, "loss_cls": 2.49937, "loss": 2.49937, "time": 0.85456} +{"mode": "train", "epoch": 135, "iter": 2500, "lr": 0.00256, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53484, "top5_acc": 0.78828, "loss_cls": 2.53452, "loss": 2.53452, "time": 0.85247} +{"mode": "train", "epoch": 135, "iter": 2600, "lr": 0.00255, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53891, "top5_acc": 0.78719, "loss_cls": 2.53535, "loss": 2.53535, "time": 0.86309} +{"mode": "train", "epoch": 135, "iter": 2700, "lr": 0.00254, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.54719, "top5_acc": 0.78734, "loss_cls": 2.53632, "loss": 2.53632, "time": 0.86152} +{"mode": "train", "epoch": 135, "iter": 2800, "lr": 0.00253, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.53344, "top5_acc": 0.77969, "loss_cls": 2.55155, "loss": 2.55155, "time": 0.86675} +{"mode": "train", "epoch": 135, "iter": 2900, "lr": 0.00252, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.54109, "top5_acc": 0.78781, "loss_cls": 2.53035, "loss": 2.53035, "time": 0.86169} +{"mode": "train", "epoch": 135, "iter": 3000, "lr": 0.00251, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.54172, "top5_acc": 0.78719, "loss_cls": 2.51853, "loss": 2.51853, "time": 0.86025} +{"mode": "train", "epoch": 135, "iter": 3100, "lr": 0.0025, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.53328, "top5_acc": 0.78453, "loss_cls": 2.56583, "loss": 2.56583, "time": 0.85763} +{"mode": "train", "epoch": 135, "iter": 3200, "lr": 0.00249, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.54578, "top5_acc": 0.78906, "loss_cls": 2.52982, "loss": 2.52982, "time": 0.85972} +{"mode": "train", "epoch": 135, "iter": 3300, "lr": 0.00249, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.53547, "top5_acc": 0.77953, "loss_cls": 2.56798, "loss": 2.56798, "time": 0.86423} +{"mode": "train", "epoch": 135, "iter": 3400, "lr": 0.00248, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.53859, "top5_acc": 0.79062, "loss_cls": 2.53591, "loss": 2.53591, "time": 0.86221} +{"mode": "train", "epoch": 135, "iter": 3500, "lr": 0.00247, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.54016, "top5_acc": 0.78672, "loss_cls": 2.52508, "loss": 2.52508, "time": 0.86152} +{"mode": "train", "epoch": 135, "iter": 3600, "lr": 0.00246, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.54312, "top5_acc": 0.7875, "loss_cls": 2.53269, "loss": 2.53269, "time": 0.85633} +{"mode": "train", "epoch": 135, "iter": 3700, "lr": 0.00245, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.54391, "top5_acc": 0.78719, "loss_cls": 2.53995, "loss": 2.53995, "time": 0.86412} +{"mode": "val", "epoch": 135, "iter": 309, "lr": 0.00245, "top1_acc": 0.43854, "top5_acc": 0.69326, "mean_class_accuracy": 0.43827} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00244, "memory": 15990, "data_time": 1.41777, "top1_acc": 0.57375, "top5_acc": 0.81328, "loss_cls": 2.34466, "loss": 2.34466, "time": 2.43983} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.00243, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.56625, "top5_acc": 0.80922, "loss_cls": 2.39441, "loss": 2.39441, "time": 0.85435} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00242, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.55953, "top5_acc": 0.80016, "loss_cls": 2.4572, "loss": 2.4572, "time": 0.85747} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00241, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.54531, "top5_acc": 0.80062, "loss_cls": 2.47479, "loss": 2.47479, "time": 0.85328} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.0024, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55781, "top5_acc": 0.79875, "loss_cls": 2.46164, "loss": 2.46164, "time": 0.85792} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.0024, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.57156, "top5_acc": 0.81109, "loss_cls": 2.38116, "loss": 2.38116, "time": 0.86171} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00239, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.56297, "top5_acc": 0.80906, "loss_cls": 2.41245, "loss": 2.41245, "time": 0.86195} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00238, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55922, "top5_acc": 0.80734, "loss_cls": 2.44099, "loss": 2.44099, "time": 0.86699} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00237, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.56297, "top5_acc": 0.80391, "loss_cls": 2.41926, "loss": 2.41926, "time": 0.86187} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00236, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56078, "top5_acc": 0.79906, "loss_cls": 2.45719, "loss": 2.45719, "time": 0.86362} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00235, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.54062, "top5_acc": 0.79125, "loss_cls": 2.5237, "loss": 2.5237, "time": 0.86239} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00234, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55672, "top5_acc": 0.79453, "loss_cls": 2.46418, "loss": 2.46418, "time": 0.85233} +{"mode": "train", "epoch": 136, "iter": 1300, "lr": 0.00234, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.56516, "top5_acc": 0.80422, "loss_cls": 2.41522, "loss": 2.41522, "time": 0.85223} +{"mode": "train", "epoch": 136, "iter": 1400, "lr": 0.00233, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.55453, "top5_acc": 0.79547, "loss_cls": 2.47893, "loss": 2.47893, "time": 0.85119} +{"mode": "train", "epoch": 136, "iter": 1500, "lr": 0.00232, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.54438, "top5_acc": 0.78641, "loss_cls": 2.49122, "loss": 2.49122, "time": 0.85739} +{"mode": "train", "epoch": 136, "iter": 1600, "lr": 0.00231, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.56766, "top5_acc": 0.80438, "loss_cls": 2.43513, "loss": 2.43513, "time": 0.85986} +{"mode": "train", "epoch": 136, "iter": 1700, "lr": 0.0023, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.55234, "top5_acc": 0.79688, "loss_cls": 2.46434, "loss": 2.46434, "time": 0.86249} +{"mode": "train", "epoch": 136, "iter": 1800, "lr": 0.00229, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.55922, "top5_acc": 0.79766, "loss_cls": 2.43782, "loss": 2.43782, "time": 0.859} +{"mode": "train", "epoch": 136, "iter": 1900, "lr": 0.00229, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55422, "top5_acc": 0.80031, "loss_cls": 2.45008, "loss": 2.45008, "time": 0.86793} +{"mode": "train", "epoch": 136, "iter": 2000, "lr": 0.00228, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.54875, "top5_acc": 0.79547, "loss_cls": 2.48349, "loss": 2.48349, "time": 0.86223} +{"mode": "train", "epoch": 136, "iter": 2100, "lr": 0.00227, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.54156, "top5_acc": 0.79438, "loss_cls": 2.49907, "loss": 2.49907, "time": 0.86457} +{"mode": "train", "epoch": 136, "iter": 2200, "lr": 0.00226, "memory": 15990, "data_time": 0.00073, "top1_acc": 0.56094, "top5_acc": 0.80125, "loss_cls": 2.44519, "loss": 2.44519, "time": 0.8553} +{"mode": "train", "epoch": 136, "iter": 2300, "lr": 0.00225, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.55172, "top5_acc": 0.78969, "loss_cls": 2.49891, "loss": 2.49891, "time": 0.85313} +{"mode": "train", "epoch": 136, "iter": 2400, "lr": 0.00224, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.55625, "top5_acc": 0.79828, "loss_cls": 2.44975, "loss": 2.44975, "time": 0.84521} +{"mode": "train", "epoch": 136, "iter": 2500, "lr": 0.00224, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.54734, "top5_acc": 0.78547, "loss_cls": 2.51872, "loss": 2.51872, "time": 0.85374} +{"mode": "train", "epoch": 136, "iter": 2600, "lr": 0.00223, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.55516, "top5_acc": 0.79438, "loss_cls": 2.47027, "loss": 2.47027, "time": 0.85464} +{"mode": "train", "epoch": 136, "iter": 2700, "lr": 0.00222, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.54562, "top5_acc": 0.78625, "loss_cls": 2.51101, "loss": 2.51101, "time": 0.85738} +{"mode": "train", "epoch": 136, "iter": 2800, "lr": 0.00221, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.54516, "top5_acc": 0.79312, "loss_cls": 2.48547, "loss": 2.48547, "time": 0.85633} +{"mode": "train", "epoch": 136, "iter": 2900, "lr": 0.0022, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.54203, "top5_acc": 0.78547, "loss_cls": 2.52617, "loss": 2.52617, "time": 0.86295} +{"mode": "train", "epoch": 136, "iter": 3000, "lr": 0.00219, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.54906, "top5_acc": 0.79844, "loss_cls": 2.4592, "loss": 2.4592, "time": 0.8627} +{"mode": "train", "epoch": 136, "iter": 3100, "lr": 0.00219, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55109, "top5_acc": 0.79625, "loss_cls": 2.47288, "loss": 2.47288, "time": 0.85762} +{"mode": "train", "epoch": 136, "iter": 3200, "lr": 0.00218, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.54953, "top5_acc": 0.78891, "loss_cls": 2.5044, "loss": 2.5044, "time": 0.8601} +{"mode": "train", "epoch": 136, "iter": 3300, "lr": 0.00217, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.55359, "top5_acc": 0.79406, "loss_cls": 2.4694, "loss": 2.4694, "time": 0.8603} +{"mode": "train", "epoch": 136, "iter": 3400, "lr": 0.00216, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.55141, "top5_acc": 0.79453, "loss_cls": 2.4775, "loss": 2.4775, "time": 0.85538} +{"mode": "train", "epoch": 136, "iter": 3500, "lr": 0.00215, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.54828, "top5_acc": 0.78953, "loss_cls": 2.51138, "loss": 2.51138, "time": 0.85571} +{"mode": "train", "epoch": 136, "iter": 3600, "lr": 0.00215, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.55188, "top5_acc": 0.79688, "loss_cls": 2.47962, "loss": 2.47962, "time": 0.86233} +{"mode": "train", "epoch": 136, "iter": 3700, "lr": 0.00214, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.55437, "top5_acc": 0.80406, "loss_cls": 2.43708, "loss": 2.43708, "time": 0.86232} +{"mode": "val", "epoch": 136, "iter": 309, "lr": 0.00213, "top1_acc": 0.43661, "top5_acc": 0.69169, "mean_class_accuracy": 0.43639} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00213, "memory": 15990, "data_time": 1.44183, "top1_acc": 0.57063, "top5_acc": 0.80594, "loss_cls": 2.38195, "loss": 2.38195, "time": 2.4667} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00212, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58781, "top5_acc": 0.81766, "loss_cls": 2.3084, "loss": 2.3084, "time": 0.84641} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00211, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.57203, "top5_acc": 0.81078, "loss_cls": 2.36064, "loss": 2.36064, "time": 0.84984} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.0021, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.56516, "top5_acc": 0.80078, "loss_cls": 2.40207, "loss": 2.40207, "time": 0.85104} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.00209, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55969, "top5_acc": 0.79953, "loss_cls": 2.45195, "loss": 2.45195, "time": 0.85281} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.00209, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57109, "top5_acc": 0.81, "loss_cls": 2.38664, "loss": 2.38664, "time": 0.85675} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00208, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.56406, "top5_acc": 0.80469, "loss_cls": 2.41871, "loss": 2.41871, "time": 0.86043} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00207, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.57312, "top5_acc": 0.81484, "loss_cls": 2.36808, "loss": 2.36808, "time": 0.86667} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00206, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.56719, "top5_acc": 0.80719, "loss_cls": 2.37685, "loss": 2.37685, "time": 0.8591} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00205, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.56109, "top5_acc": 0.80625, "loss_cls": 2.42013, "loss": 2.42013, "time": 0.86676} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00205, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.55453, "top5_acc": 0.80438, "loss_cls": 2.41775, "loss": 2.41775, "time": 0.86801} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00204, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.57734, "top5_acc": 0.81438, "loss_cls": 2.36185, "loss": 2.36185, "time": 0.86541} +{"mode": "train", "epoch": 137, "iter": 1300, "lr": 0.00203, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.56172, "top5_acc": 0.80344, "loss_cls": 2.39736, "loss": 2.39736, "time": 0.85662} +{"mode": "train", "epoch": 137, "iter": 1400, "lr": 0.00202, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.55953, "top5_acc": 0.80406, "loss_cls": 2.43288, "loss": 2.43288, "time": 0.8564} +{"mode": "train", "epoch": 137, "iter": 1500, "lr": 0.00201, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.57063, "top5_acc": 0.80391, "loss_cls": 2.3918, "loss": 2.3918, "time": 0.84941} +{"mode": "train", "epoch": 137, "iter": 1600, "lr": 0.00201, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.55922, "top5_acc": 0.8025, "loss_cls": 2.41989, "loss": 2.41989, "time": 0.85146} +{"mode": "train", "epoch": 137, "iter": 1700, "lr": 0.002, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.57, "top5_acc": 0.80906, "loss_cls": 2.39666, "loss": 2.39666, "time": 0.85544} +{"mode": "train", "epoch": 137, "iter": 1800, "lr": 0.00199, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56281, "top5_acc": 0.80984, "loss_cls": 2.37273, "loss": 2.37273, "time": 0.84083} +{"mode": "train", "epoch": 137, "iter": 1900, "lr": 0.00198, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55812, "top5_acc": 0.80781, "loss_cls": 2.40614, "loss": 2.40614, "time": 0.85232} +{"mode": "train", "epoch": 137, "iter": 2000, "lr": 0.00198, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.54828, "top5_acc": 0.8025, "loss_cls": 2.44647, "loss": 2.44647, "time": 0.8527} +{"mode": "train", "epoch": 137, "iter": 2100, "lr": 0.00197, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56437, "top5_acc": 0.80422, "loss_cls": 2.40951, "loss": 2.40951, "time": 0.84857} +{"mode": "train", "epoch": 137, "iter": 2200, "lr": 0.00196, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55406, "top5_acc": 0.8, "loss_cls": 2.43163, "loss": 2.43163, "time": 0.84925} +{"mode": "train", "epoch": 137, "iter": 2300, "lr": 0.00195, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.56172, "top5_acc": 0.80016, "loss_cls": 2.42321, "loss": 2.42321, "time": 0.85424} +{"mode": "train", "epoch": 137, "iter": 2400, "lr": 0.00194, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.55953, "top5_acc": 0.79812, "loss_cls": 2.44727, "loss": 2.44727, "time": 0.84933} +{"mode": "train", "epoch": 137, "iter": 2500, "lr": 0.00194, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.56266, "top5_acc": 0.8025, "loss_cls": 2.44989, "loss": 2.44989, "time": 0.85631} +{"mode": "train", "epoch": 137, "iter": 2600, "lr": 0.00193, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.55859, "top5_acc": 0.80484, "loss_cls": 2.41319, "loss": 2.41319, "time": 0.8589} +{"mode": "train", "epoch": 137, "iter": 2700, "lr": 0.00192, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56875, "top5_acc": 0.80219, "loss_cls": 2.40894, "loss": 2.40894, "time": 0.85602} +{"mode": "train", "epoch": 137, "iter": 2800, "lr": 0.00191, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.56703, "top5_acc": 0.80578, "loss_cls": 2.40153, "loss": 2.40153, "time": 0.85664} +{"mode": "train", "epoch": 137, "iter": 2900, "lr": 0.00191, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.55328, "top5_acc": 0.79844, "loss_cls": 2.46467, "loss": 2.46467, "time": 0.8611} +{"mode": "train", "epoch": 137, "iter": 3000, "lr": 0.0019, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57234, "top5_acc": 0.80953, "loss_cls": 2.40196, "loss": 2.40196, "time": 0.85783} +{"mode": "train", "epoch": 137, "iter": 3100, "lr": 0.00189, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.56109, "top5_acc": 0.79672, "loss_cls": 2.42533, "loss": 2.42533, "time": 0.85132} +{"mode": "train", "epoch": 137, "iter": 3200, "lr": 0.00188, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56406, "top5_acc": 0.7975, "loss_cls": 2.44148, "loss": 2.44148, "time": 0.86346} +{"mode": "train", "epoch": 137, "iter": 3300, "lr": 0.00188, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55922, "top5_acc": 0.79953, "loss_cls": 2.44858, "loss": 2.44858, "time": 0.86334} +{"mode": "train", "epoch": 137, "iter": 3400, "lr": 0.00187, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.55703, "top5_acc": 0.79344, "loss_cls": 2.45908, "loss": 2.45908, "time": 0.8565} +{"mode": "train", "epoch": 137, "iter": 3500, "lr": 0.00186, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.56641, "top5_acc": 0.81031, "loss_cls": 2.40677, "loss": 2.40677, "time": 0.85968} +{"mode": "train", "epoch": 137, "iter": 3600, "lr": 0.00185, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55672, "top5_acc": 0.80109, "loss_cls": 2.42948, "loss": 2.42948, "time": 0.85895} +{"mode": "train", "epoch": 137, "iter": 3700, "lr": 0.00185, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55047, "top5_acc": 0.79281, "loss_cls": 2.48678, "loss": 2.48678, "time": 0.85589} +{"mode": "val", "epoch": 137, "iter": 309, "lr": 0.00184, "top1_acc": 0.45322, "top5_acc": 0.70162, "mean_class_accuracy": 0.45304} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00183, "memory": 15990, "data_time": 1.54453, "top1_acc": 0.57938, "top5_acc": 0.81625, "loss_cls": 2.30828, "loss": 2.30828, "time": 2.56771} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00183, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.585, "top5_acc": 0.82453, "loss_cls": 2.30608, "loss": 2.30608, "time": 0.85014} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00182, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58312, "top5_acc": 0.81984, "loss_cls": 2.30806, "loss": 2.30806, "time": 0.85759} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00181, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.59953, "top5_acc": 0.82219, "loss_cls": 2.28005, "loss": 2.28005, "time": 0.85831} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.0018, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57578, "top5_acc": 0.81578, "loss_cls": 2.33926, "loss": 2.33926, "time": 0.85622} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.0018, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.57594, "top5_acc": 0.81547, "loss_cls": 2.33967, "loss": 2.33967, "time": 0.85298} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00179, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.5775, "top5_acc": 0.81438, "loss_cls": 2.32675, "loss": 2.32675, "time": 0.85907} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00178, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.56672, "top5_acc": 0.80906, "loss_cls": 2.39069, "loss": 2.39069, "time": 0.85737} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00177, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56969, "top5_acc": 0.81484, "loss_cls": 2.33035, "loss": 2.33035, "time": 0.86329} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00177, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.5775, "top5_acc": 0.81203, "loss_cls": 2.34606, "loss": 2.34606, "time": 0.85786} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.00176, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.57234, "top5_acc": 0.80781, "loss_cls": 2.35763, "loss": 2.35763, "time": 0.85528} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.00175, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.57734, "top5_acc": 0.80906, "loss_cls": 2.3571, "loss": 2.3571, "time": 0.85523} +{"mode": "train", "epoch": 138, "iter": 1300, "lr": 0.00175, "memory": 15990, "data_time": 0.00074, "top1_acc": 0.57719, "top5_acc": 0.81469, "loss_cls": 2.33343, "loss": 2.33343, "time": 0.85652} +{"mode": "train", "epoch": 138, "iter": 1400, "lr": 0.00174, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.58391, "top5_acc": 0.81328, "loss_cls": 2.32697, "loss": 2.32697, "time": 0.84907} +{"mode": "train", "epoch": 138, "iter": 1500, "lr": 0.00173, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57453, "top5_acc": 0.81641, "loss_cls": 2.33536, "loss": 2.33536, "time": 0.8475} +{"mode": "train", "epoch": 138, "iter": 1600, "lr": 0.00172, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.57859, "top5_acc": 0.81094, "loss_cls": 2.35188, "loss": 2.35188, "time": 0.84873} +{"mode": "train", "epoch": 138, "iter": 1700, "lr": 0.00172, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.57406, "top5_acc": 0.81016, "loss_cls": 2.37123, "loss": 2.37123, "time": 0.84991} +{"mode": "train", "epoch": 138, "iter": 1800, "lr": 0.00171, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.56734, "top5_acc": 0.80859, "loss_cls": 2.38304, "loss": 2.38304, "time": 0.84214} +{"mode": "train", "epoch": 138, "iter": 1900, "lr": 0.0017, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.56953, "top5_acc": 0.81031, "loss_cls": 2.36086, "loss": 2.36086, "time": 0.84605} +{"mode": "train", "epoch": 138, "iter": 2000, "lr": 0.00169, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.58, "top5_acc": 0.81406, "loss_cls": 2.36018, "loss": 2.36018, "time": 0.84822} +{"mode": "train", "epoch": 138, "iter": 2100, "lr": 0.00169, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.57328, "top5_acc": 0.81188, "loss_cls": 2.3655, "loss": 2.3655, "time": 0.84271} +{"mode": "train", "epoch": 138, "iter": 2200, "lr": 0.00168, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.57953, "top5_acc": 0.81766, "loss_cls": 2.34511, "loss": 2.34511, "time": 0.84543} +{"mode": "train", "epoch": 138, "iter": 2300, "lr": 0.00167, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.5725, "top5_acc": 0.80906, "loss_cls": 2.38163, "loss": 2.38163, "time": 0.84607} +{"mode": "train", "epoch": 138, "iter": 2400, "lr": 0.00167, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.57281, "top5_acc": 0.8175, "loss_cls": 2.33082, "loss": 2.33082, "time": 0.84701} +{"mode": "train", "epoch": 138, "iter": 2500, "lr": 0.00166, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.57406, "top5_acc": 0.80984, "loss_cls": 2.35756, "loss": 2.35756, "time": 0.84616} +{"mode": "train", "epoch": 138, "iter": 2600, "lr": 0.00165, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.56672, "top5_acc": 0.80141, "loss_cls": 2.42136, "loss": 2.42136, "time": 0.84229} +{"mode": "train", "epoch": 138, "iter": 2700, "lr": 0.00164, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.58156, "top5_acc": 0.81062, "loss_cls": 2.35856, "loss": 2.35856, "time": 0.84697} +{"mode": "train", "epoch": 138, "iter": 2800, "lr": 0.00164, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55406, "top5_acc": 0.80469, "loss_cls": 2.44057, "loss": 2.44057, "time": 0.84994} +{"mode": "train", "epoch": 138, "iter": 2900, "lr": 0.00163, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.56672, "top5_acc": 0.80375, "loss_cls": 2.41282, "loss": 2.41282, "time": 0.84344} +{"mode": "train", "epoch": 138, "iter": 3000, "lr": 0.00162, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.57297, "top5_acc": 0.81672, "loss_cls": 2.33273, "loss": 2.33273, "time": 0.84693} +{"mode": "train", "epoch": 138, "iter": 3100, "lr": 0.00162, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57078, "top5_acc": 0.80953, "loss_cls": 2.36957, "loss": 2.36957, "time": 0.8516} +{"mode": "train", "epoch": 138, "iter": 3200, "lr": 0.00161, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.56984, "top5_acc": 0.81016, "loss_cls": 2.37443, "loss": 2.37443, "time": 0.85222} +{"mode": "train", "epoch": 138, "iter": 3300, "lr": 0.0016, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.56016, "top5_acc": 0.80531, "loss_cls": 2.41356, "loss": 2.41356, "time": 0.85009} +{"mode": "train", "epoch": 138, "iter": 3400, "lr": 0.0016, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57, "top5_acc": 0.80688, "loss_cls": 2.40106, "loss": 2.40106, "time": 0.85058} +{"mode": "train", "epoch": 138, "iter": 3500, "lr": 0.00159, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.56859, "top5_acc": 0.81297, "loss_cls": 2.35137, "loss": 2.35137, "time": 0.84722} +{"mode": "train", "epoch": 138, "iter": 3600, "lr": 0.00158, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.57063, "top5_acc": 0.81375, "loss_cls": 2.3695, "loss": 2.3695, "time": 0.84613} +{"mode": "train", "epoch": 138, "iter": 3700, "lr": 0.00157, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.55875, "top5_acc": 0.80266, "loss_cls": 2.39406, "loss": 2.39406, "time": 0.84753} +{"mode": "val", "epoch": 138, "iter": 309, "lr": 0.00157, "top1_acc": 0.44699, "top5_acc": 0.70445, "mean_class_accuracy": 0.44668} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00156, "memory": 15990, "data_time": 1.5473, "top1_acc": 0.58938, "top5_acc": 0.8225, "loss_cls": 2.28843, "loss": 2.28843, "time": 2.56408} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00156, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58844, "top5_acc": 0.82516, "loss_cls": 2.27062, "loss": 2.27062, "time": 0.84794} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00155, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.59703, "top5_acc": 0.825, "loss_cls": 2.25821, "loss": 2.25821, "time": 0.84268} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00154, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.58703, "top5_acc": 0.82531, "loss_cls": 2.27831, "loss": 2.27831, "time": 0.84405} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00154, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.58969, "top5_acc": 0.82891, "loss_cls": 2.27793, "loss": 2.27793, "time": 0.84641} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00153, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58516, "top5_acc": 0.82531, "loss_cls": 2.28584, "loss": 2.28584, "time": 0.85196} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00152, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59234, "top5_acc": 0.83094, "loss_cls": 2.26745, "loss": 2.26745, "time": 0.84949} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00152, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.58703, "top5_acc": 0.82375, "loss_cls": 2.27988, "loss": 2.27988, "time": 0.85738} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00151, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.58484, "top5_acc": 0.83172, "loss_cls": 2.24989, "loss": 2.24989, "time": 0.85298} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.0015, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.58562, "top5_acc": 0.82438, "loss_cls": 2.2844, "loss": 2.2844, "time": 0.85855} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.0015, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.58609, "top5_acc": 0.81453, "loss_cls": 2.3155, "loss": 2.3155, "time": 0.84966} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00149, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.57844, "top5_acc": 0.81203, "loss_cls": 2.31789, "loss": 2.31789, "time": 0.84841} +{"mode": "train", "epoch": 139, "iter": 1300, "lr": 0.00148, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57641, "top5_acc": 0.81984, "loss_cls": 2.30842, "loss": 2.30842, "time": 0.8495} +{"mode": "train", "epoch": 139, "iter": 1400, "lr": 0.00148, "memory": 15990, "data_time": 0.00077, "top1_acc": 0.57266, "top5_acc": 0.8125, "loss_cls": 2.35993, "loss": 2.35993, "time": 0.85572} +{"mode": "train", "epoch": 139, "iter": 1500, "lr": 0.00147, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.5875, "top5_acc": 0.82344, "loss_cls": 2.26502, "loss": 2.26502, "time": 0.85434} +{"mode": "train", "epoch": 139, "iter": 1600, "lr": 0.00146, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.58453, "top5_acc": 0.82219, "loss_cls": 2.27501, "loss": 2.27501, "time": 0.84931} +{"mode": "train", "epoch": 139, "iter": 1700, "lr": 0.00145, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.59016, "top5_acc": 0.81438, "loss_cls": 2.2877, "loss": 2.2877, "time": 0.84917} +{"mode": "train", "epoch": 139, "iter": 1800, "lr": 0.00145, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.58922, "top5_acc": 0.82125, "loss_cls": 2.2836, "loss": 2.2836, "time": 0.85434} +{"mode": "train", "epoch": 139, "iter": 1900, "lr": 0.00144, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58062, "top5_acc": 0.82109, "loss_cls": 2.30938, "loss": 2.30938, "time": 0.85303} +{"mode": "train", "epoch": 139, "iter": 2000, "lr": 0.00143, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.58062, "top5_acc": 0.815, "loss_cls": 2.32026, "loss": 2.32026, "time": 0.84334} +{"mode": "train", "epoch": 139, "iter": 2100, "lr": 0.00143, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58, "top5_acc": 0.82219, "loss_cls": 2.30845, "loss": 2.30845, "time": 0.85586} +{"mode": "train", "epoch": 139, "iter": 2200, "lr": 0.00142, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.58234, "top5_acc": 0.82328, "loss_cls": 2.27902, "loss": 2.27902, "time": 0.84772} +{"mode": "train", "epoch": 139, "iter": 2300, "lr": 0.00142, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.56938, "top5_acc": 0.81031, "loss_cls": 2.3741, "loss": 2.3741, "time": 0.8477} +{"mode": "train", "epoch": 139, "iter": 2400, "lr": 0.00141, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.57906, "top5_acc": 0.81828, "loss_cls": 2.32349, "loss": 2.32349, "time": 0.8484} +{"mode": "train", "epoch": 139, "iter": 2500, "lr": 0.0014, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.57734, "top5_acc": 0.80859, "loss_cls": 2.33895, "loss": 2.33895, "time": 0.84239} +{"mode": "train", "epoch": 139, "iter": 2600, "lr": 0.0014, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.58469, "top5_acc": 0.81359, "loss_cls": 2.31822, "loss": 2.31822, "time": 0.85006} +{"mode": "train", "epoch": 139, "iter": 2700, "lr": 0.00139, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.57656, "top5_acc": 0.81453, "loss_cls": 2.35118, "loss": 2.35118, "time": 0.84742} +{"mode": "train", "epoch": 139, "iter": 2800, "lr": 0.00138, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.58188, "top5_acc": 0.81828, "loss_cls": 2.29515, "loss": 2.29515, "time": 0.84603} +{"mode": "train", "epoch": 139, "iter": 2900, "lr": 0.00138, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.58641, "top5_acc": 0.81906, "loss_cls": 2.30986, "loss": 2.30986, "time": 0.84634} +{"mode": "train", "epoch": 139, "iter": 3000, "lr": 0.00137, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.56734, "top5_acc": 0.81516, "loss_cls": 2.36018, "loss": 2.36018, "time": 0.84708} +{"mode": "train", "epoch": 139, "iter": 3100, "lr": 0.00136, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.58203, "top5_acc": 0.81578, "loss_cls": 2.31002, "loss": 2.31002, "time": 0.84936} +{"mode": "train", "epoch": 139, "iter": 3200, "lr": 0.00136, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57172, "top5_acc": 0.8125, "loss_cls": 2.33831, "loss": 2.33831, "time": 0.85035} +{"mode": "train", "epoch": 139, "iter": 3300, "lr": 0.00135, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.58031, "top5_acc": 0.82328, "loss_cls": 2.31414, "loss": 2.31414, "time": 0.84715} +{"mode": "train", "epoch": 139, "iter": 3400, "lr": 0.00134, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.58375, "top5_acc": 0.82188, "loss_cls": 2.28717, "loss": 2.28717, "time": 0.84348} +{"mode": "train", "epoch": 139, "iter": 3500, "lr": 0.00134, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.57594, "top5_acc": 0.80641, "loss_cls": 2.35646, "loss": 2.35646, "time": 0.84397} +{"mode": "train", "epoch": 139, "iter": 3600, "lr": 0.00133, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.58281, "top5_acc": 0.81719, "loss_cls": 2.31914, "loss": 2.31914, "time": 0.85034} +{"mode": "train", "epoch": 139, "iter": 3700, "lr": 0.00132, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57656, "top5_acc": 0.82422, "loss_cls": 2.31747, "loss": 2.31747, "time": 0.85079} +{"mode": "val", "epoch": 139, "iter": 309, "lr": 0.00132, "top1_acc": 0.45378, "top5_acc": 0.70617, "mean_class_accuracy": 0.45362} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00131, "memory": 15990, "data_time": 1.44414, "top1_acc": 0.60172, "top5_acc": 0.83422, "loss_cls": 2.20262, "loss": 2.20262, "time": 2.46573} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00131, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.59312, "top5_acc": 0.83984, "loss_cls": 2.19481, "loss": 2.19481, "time": 0.84576} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.0013, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.59797, "top5_acc": 0.82984, "loss_cls": 2.23166, "loss": 2.23166, "time": 0.84345} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.0013, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.59125, "top5_acc": 0.8275, "loss_cls": 2.25153, "loss": 2.25153, "time": 0.84872} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00129, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.59906, "top5_acc": 0.83453, "loss_cls": 2.21681, "loss": 2.21681, "time": 0.8443} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.00128, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.59484, "top5_acc": 0.82625, "loss_cls": 2.2411, "loss": 2.2411, "time": 0.84631} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.00128, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.59922, "top5_acc": 0.82625, "loss_cls": 2.221, "loss": 2.221, "time": 0.84829} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00127, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.59781, "top5_acc": 0.82875, "loss_cls": 2.25127, "loss": 2.25127, "time": 0.84685} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00126, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.60047, "top5_acc": 0.82703, "loss_cls": 2.21965, "loss": 2.21965, "time": 0.84569} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00126, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.59281, "top5_acc": 0.82781, "loss_cls": 2.25234, "loss": 2.25234, "time": 0.8446} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00125, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57984, "top5_acc": 0.81703, "loss_cls": 2.29427, "loss": 2.29427, "time": 0.85195} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00125, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58766, "top5_acc": 0.81391, "loss_cls": 2.28681, "loss": 2.28681, "time": 0.84763} +{"mode": "train", "epoch": 140, "iter": 1300, "lr": 0.00124, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.60266, "top5_acc": 0.83422, "loss_cls": 2.22321, "loss": 2.22321, "time": 0.84866} +{"mode": "train", "epoch": 140, "iter": 1400, "lr": 0.00123, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.59578, "top5_acc": 0.82641, "loss_cls": 2.24705, "loss": 2.24705, "time": 0.84653} +{"mode": "train", "epoch": 140, "iter": 1500, "lr": 0.00123, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.59781, "top5_acc": 0.83391, "loss_cls": 2.21848, "loss": 2.21848, "time": 0.83997} +{"mode": "train", "epoch": 140, "iter": 1600, "lr": 0.00122, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.60375, "top5_acc": 0.83031, "loss_cls": 2.22958, "loss": 2.22958, "time": 0.84065} +{"mode": "train", "epoch": 140, "iter": 1700, "lr": 0.00121, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57969, "top5_acc": 0.81391, "loss_cls": 2.30198, "loss": 2.30198, "time": 0.84304} +{"mode": "train", "epoch": 140, "iter": 1800, "lr": 0.00121, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.58484, "top5_acc": 0.82094, "loss_cls": 2.27639, "loss": 2.27639, "time": 0.84893} +{"mode": "train", "epoch": 140, "iter": 1900, "lr": 0.0012, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.59016, "top5_acc": 0.82812, "loss_cls": 2.2469, "loss": 2.2469, "time": 0.8451} +{"mode": "train", "epoch": 140, "iter": 2000, "lr": 0.0012, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.59078, "top5_acc": 0.83016, "loss_cls": 2.23915, "loss": 2.23915, "time": 0.84847} +{"mode": "train", "epoch": 140, "iter": 2100, "lr": 0.00119, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.59281, "top5_acc": 0.82266, "loss_cls": 2.28788, "loss": 2.28788, "time": 0.84376} +{"mode": "train", "epoch": 140, "iter": 2200, "lr": 0.00118, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.59172, "top5_acc": 0.82688, "loss_cls": 2.25292, "loss": 2.25292, "time": 0.84233} +{"mode": "train", "epoch": 140, "iter": 2300, "lr": 0.00118, "memory": 15990, "data_time": 0.00087, "top1_acc": 0.58625, "top5_acc": 0.81781, "loss_cls": 2.30372, "loss": 2.30372, "time": 0.84409} +{"mode": "train", "epoch": 140, "iter": 2400, "lr": 0.00117, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.58906, "top5_acc": 0.82062, "loss_cls": 2.29048, "loss": 2.29048, "time": 0.84951} +{"mode": "train", "epoch": 140, "iter": 2500, "lr": 0.00117, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.58062, "top5_acc": 0.81516, "loss_cls": 2.30203, "loss": 2.30203, "time": 0.84364} +{"mode": "train", "epoch": 140, "iter": 2600, "lr": 0.00116, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.59922, "top5_acc": 0.83031, "loss_cls": 2.23664, "loss": 2.23664, "time": 0.84937} +{"mode": "train", "epoch": 140, "iter": 2700, "lr": 0.00115, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.58328, "top5_acc": 0.82656, "loss_cls": 2.28699, "loss": 2.28699, "time": 0.84168} +{"mode": "train", "epoch": 140, "iter": 2800, "lr": 0.00115, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.59797, "top5_acc": 0.82766, "loss_cls": 2.21652, "loss": 2.21652, "time": 0.84169} +{"mode": "train", "epoch": 140, "iter": 2900, "lr": 0.00114, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.59453, "top5_acc": 0.83125, "loss_cls": 2.22961, "loss": 2.22961, "time": 0.84349} +{"mode": "train", "epoch": 140, "iter": 3000, "lr": 0.00114, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.59625, "top5_acc": 0.83219, "loss_cls": 2.24659, "loss": 2.24659, "time": 0.84854} +{"mode": "train", "epoch": 140, "iter": 3100, "lr": 0.00113, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.60141, "top5_acc": 0.82938, "loss_cls": 2.21936, "loss": 2.21936, "time": 0.84654} +{"mode": "train", "epoch": 140, "iter": 3200, "lr": 0.00112, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.58078, "top5_acc": 0.81859, "loss_cls": 2.29659, "loss": 2.29659, "time": 0.84393} +{"mode": "train", "epoch": 140, "iter": 3300, "lr": 0.00112, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.59531, "top5_acc": 0.82031, "loss_cls": 2.2944, "loss": 2.2944, "time": 0.85318} +{"mode": "train", "epoch": 140, "iter": 3400, "lr": 0.00111, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.58344, "top5_acc": 0.82031, "loss_cls": 2.31094, "loss": 2.31094, "time": 0.84879} +{"mode": "train", "epoch": 140, "iter": 3500, "lr": 0.00111, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.59875, "top5_acc": 0.82281, "loss_cls": 2.22246, "loss": 2.22246, "time": 0.8514} +{"mode": "train", "epoch": 140, "iter": 3600, "lr": 0.0011, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.59344, "top5_acc": 0.82359, "loss_cls": 2.26175, "loss": 2.26175, "time": 0.85684} +{"mode": "train", "epoch": 140, "iter": 3700, "lr": 0.0011, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.595, "top5_acc": 0.81859, "loss_cls": 2.26824, "loss": 2.26824, "time": 0.84164} +{"mode": "val", "epoch": 140, "iter": 309, "lr": 0.00109, "top1_acc": 0.45778, "top5_acc": 0.70947, "mean_class_accuracy": 0.45754} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00109, "memory": 15990, "data_time": 1.53872, "top1_acc": 0.61062, "top5_acc": 0.83312, "loss_cls": 2.18058, "loss": 2.18058, "time": 2.56752} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00108, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.62984, "top5_acc": 0.84531, "loss_cls": 2.12293, "loss": 2.12293, "time": 0.85116} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00108, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.62266, "top5_acc": 0.84031, "loss_cls": 2.13007, "loss": 2.13007, "time": 0.84157} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00107, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.61047, "top5_acc": 0.83688, "loss_cls": 2.17167, "loss": 2.17167, "time": 0.84236} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00106, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.61375, "top5_acc": 0.84281, "loss_cls": 2.13774, "loss": 2.13774, "time": 0.84345} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00106, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.60781, "top5_acc": 0.83859, "loss_cls": 2.16709, "loss": 2.16709, "time": 0.84849} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00105, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.59438, "top5_acc": 0.82406, "loss_cls": 2.24172, "loss": 2.24172, "time": 0.84664} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00105, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.60156, "top5_acc": 0.83422, "loss_cls": 2.21278, "loss": 2.21278, "time": 0.84628} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00104, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.60875, "top5_acc": 0.83906, "loss_cls": 2.1566, "loss": 2.1566, "time": 0.84959} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00104, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.6075, "top5_acc": 0.83406, "loss_cls": 2.19138, "loss": 2.19138, "time": 0.8517} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00103, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.60125, "top5_acc": 0.83188, "loss_cls": 2.20871, "loss": 2.20871, "time": 0.84987} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00102, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.61609, "top5_acc": 0.83234, "loss_cls": 2.15273, "loss": 2.15273, "time": 0.84964} +{"mode": "train", "epoch": 141, "iter": 1300, "lr": 0.00102, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.60375, "top5_acc": 0.83484, "loss_cls": 2.19729, "loss": 2.19729, "time": 0.84589} +{"mode": "train", "epoch": 141, "iter": 1400, "lr": 0.00101, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.59516, "top5_acc": 0.83203, "loss_cls": 2.23953, "loss": 2.23953, "time": 0.84944} +{"mode": "train", "epoch": 141, "iter": 1500, "lr": 0.00101, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.60312, "top5_acc": 0.84219, "loss_cls": 2.16485, "loss": 2.16485, "time": 0.85288} +{"mode": "train", "epoch": 141, "iter": 1600, "lr": 0.001, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.59625, "top5_acc": 0.83422, "loss_cls": 2.19985, "loss": 2.19985, "time": 0.84155} +{"mode": "train", "epoch": 141, "iter": 1700, "lr": 0.001, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.59766, "top5_acc": 0.8325, "loss_cls": 2.21096, "loss": 2.21096, "time": 0.85244} +{"mode": "train", "epoch": 141, "iter": 1800, "lr": 0.00099, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.61031, "top5_acc": 0.83719, "loss_cls": 2.17168, "loss": 2.17168, "time": 0.85474} +{"mode": "train", "epoch": 141, "iter": 1900, "lr": 0.00099, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.60188, "top5_acc": 0.83281, "loss_cls": 2.20455, "loss": 2.20455, "time": 0.85452} +{"mode": "train", "epoch": 141, "iter": 2000, "lr": 0.00098, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59938, "top5_acc": 0.83641, "loss_cls": 2.22478, "loss": 2.22478, "time": 0.85107} +{"mode": "train", "epoch": 141, "iter": 2100, "lr": 0.00097, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.60141, "top5_acc": 0.83406, "loss_cls": 2.22407, "loss": 2.22407, "time": 0.85178} +{"mode": "train", "epoch": 141, "iter": 2200, "lr": 0.00097, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.60328, "top5_acc": 0.83359, "loss_cls": 2.1781, "loss": 2.1781, "time": 0.84916} +{"mode": "train", "epoch": 141, "iter": 2300, "lr": 0.00096, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.60094, "top5_acc": 0.83578, "loss_cls": 2.19115, "loss": 2.19115, "time": 0.84768} +{"mode": "train", "epoch": 141, "iter": 2400, "lr": 0.00096, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.60734, "top5_acc": 0.83859, "loss_cls": 2.17823, "loss": 2.17823, "time": 0.85233} +{"mode": "train", "epoch": 141, "iter": 2500, "lr": 0.00095, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.60703, "top5_acc": 0.83328, "loss_cls": 2.22122, "loss": 2.22122, "time": 0.84335} +{"mode": "train", "epoch": 141, "iter": 2600, "lr": 0.00095, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.59547, "top5_acc": 0.82406, "loss_cls": 2.2297, "loss": 2.2297, "time": 0.8501} +{"mode": "train", "epoch": 141, "iter": 2700, "lr": 0.00094, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.59875, "top5_acc": 0.82875, "loss_cls": 2.23136, "loss": 2.23136, "time": 0.84888} +{"mode": "train", "epoch": 141, "iter": 2800, "lr": 0.00094, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.60781, "top5_acc": 0.83547, "loss_cls": 2.18206, "loss": 2.18206, "time": 0.84447} +{"mode": "train", "epoch": 141, "iter": 2900, "lr": 0.00093, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.60594, "top5_acc": 0.83531, "loss_cls": 2.17578, "loss": 2.17578, "time": 0.8525} +{"mode": "train", "epoch": 141, "iter": 3000, "lr": 0.00093, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.6075, "top5_acc": 0.83391, "loss_cls": 2.20593, "loss": 2.20593, "time": 0.84918} +{"mode": "train", "epoch": 141, "iter": 3100, "lr": 0.00092, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.60125, "top5_acc": 0.83891, "loss_cls": 2.19479, "loss": 2.19479, "time": 0.84658} +{"mode": "train", "epoch": 141, "iter": 3200, "lr": 0.00091, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.6, "top5_acc": 0.82953, "loss_cls": 2.21221, "loss": 2.21221, "time": 0.8468} +{"mode": "train", "epoch": 141, "iter": 3300, "lr": 0.00091, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.60344, "top5_acc": 0.835, "loss_cls": 2.21262, "loss": 2.21262, "time": 0.85034} +{"mode": "train", "epoch": 141, "iter": 3400, "lr": 0.0009, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.60391, "top5_acc": 0.83578, "loss_cls": 2.19142, "loss": 2.19142, "time": 0.85801} +{"mode": "train", "epoch": 141, "iter": 3500, "lr": 0.0009, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.60453, "top5_acc": 0.84016, "loss_cls": 2.16011, "loss": 2.16011, "time": 0.86349} +{"mode": "train", "epoch": 141, "iter": 3600, "lr": 0.00089, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.60641, "top5_acc": 0.82969, "loss_cls": 2.2176, "loss": 2.2176, "time": 0.85119} +{"mode": "train", "epoch": 141, "iter": 3700, "lr": 0.00089, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59922, "top5_acc": 0.83031, "loss_cls": 2.22427, "loss": 2.22427, "time": 0.85125} +{"mode": "val", "epoch": 141, "iter": 309, "lr": 0.00089, "top1_acc": 0.4589, "top5_acc": 0.7118, "mean_class_accuracy": 0.45866} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00088, "memory": 15990, "data_time": 1.54279, "top1_acc": 0.62125, "top5_acc": 0.84, "loss_cls": 2.11231, "loss": 2.11231, "time": 2.5782} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00088, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.61187, "top5_acc": 0.84109, "loss_cls": 2.13843, "loss": 2.13843, "time": 0.84807} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00087, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.62219, "top5_acc": 0.84078, "loss_cls": 2.13487, "loss": 2.13487, "time": 0.85198} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00086, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.61453, "top5_acc": 0.83438, "loss_cls": 2.16109, "loss": 2.16109, "time": 0.84965} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.00086, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.62141, "top5_acc": 0.84703, "loss_cls": 2.1029, "loss": 2.1029, "time": 0.85266} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.00085, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.61828, "top5_acc": 0.84641, "loss_cls": 2.10668, "loss": 2.10668, "time": 0.8501} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.00085, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.60812, "top5_acc": 0.84125, "loss_cls": 2.17003, "loss": 2.17003, "time": 0.85302} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00084, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.62281, "top5_acc": 0.84688, "loss_cls": 2.09643, "loss": 2.09643, "time": 0.84659} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00084, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.61031, "top5_acc": 0.84031, "loss_cls": 2.1473, "loss": 2.1473, "time": 0.85579} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00083, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.62422, "top5_acc": 0.84906, "loss_cls": 2.10084, "loss": 2.10084, "time": 0.84559} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00083, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.62062, "top5_acc": 0.84578, "loss_cls": 2.10849, "loss": 2.10849, "time": 0.85027} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00082, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.62656, "top5_acc": 0.85312, "loss_cls": 2.06685, "loss": 2.06685, "time": 0.85498} +{"mode": "train", "epoch": 142, "iter": 1300, "lr": 0.00082, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.62375, "top5_acc": 0.84719, "loss_cls": 2.10577, "loss": 2.10577, "time": 0.85054} +{"mode": "train", "epoch": 142, "iter": 1400, "lr": 0.00081, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.60531, "top5_acc": 0.83828, "loss_cls": 2.18295, "loss": 2.18295, "time": 0.84579} +{"mode": "train", "epoch": 142, "iter": 1500, "lr": 0.00081, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.61969, "top5_acc": 0.84266, "loss_cls": 2.12928, "loss": 2.12928, "time": 0.85638} +{"mode": "train", "epoch": 142, "iter": 1600, "lr": 0.0008, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.60938, "top5_acc": 0.83469, "loss_cls": 2.16918, "loss": 2.16918, "time": 0.8371} +{"mode": "train", "epoch": 142, "iter": 1700, "lr": 0.0008, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.61953, "top5_acc": 0.83766, "loss_cls": 2.13287, "loss": 2.13287, "time": 0.84555} +{"mode": "train", "epoch": 142, "iter": 1800, "lr": 0.00079, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.60984, "top5_acc": 0.84578, "loss_cls": 2.12883, "loss": 2.12883, "time": 0.8462} +{"mode": "train", "epoch": 142, "iter": 1900, "lr": 0.00079, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.61156, "top5_acc": 0.84312, "loss_cls": 2.12839, "loss": 2.12839, "time": 0.84671} +{"mode": "train", "epoch": 142, "iter": 2000, "lr": 0.00078, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.62109, "top5_acc": 0.84406, "loss_cls": 2.11779, "loss": 2.11779, "time": 0.8513} +{"mode": "train", "epoch": 142, "iter": 2100, "lr": 0.00078, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.62109, "top5_acc": 0.83766, "loss_cls": 2.13056, "loss": 2.13056, "time": 0.84934} +{"mode": "train", "epoch": 142, "iter": 2200, "lr": 0.00077, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.60672, "top5_acc": 0.84453, "loss_cls": 2.13811, "loss": 2.13811, "time": 0.84761} +{"mode": "train", "epoch": 142, "iter": 2300, "lr": 0.00077, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.61609, "top5_acc": 0.84078, "loss_cls": 2.14724, "loss": 2.14724, "time": 0.84652} +{"mode": "train", "epoch": 142, "iter": 2400, "lr": 0.00076, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.61078, "top5_acc": 0.84094, "loss_cls": 2.13935, "loss": 2.13935, "time": 0.85416} +{"mode": "train", "epoch": 142, "iter": 2500, "lr": 0.00076, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.60391, "top5_acc": 0.82922, "loss_cls": 2.20304, "loss": 2.20304, "time": 0.84593} +{"mode": "train", "epoch": 142, "iter": 2600, "lr": 0.00075, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.60484, "top5_acc": 0.84, "loss_cls": 2.16108, "loss": 2.16108, "time": 0.8508} +{"mode": "train", "epoch": 142, "iter": 2700, "lr": 0.00075, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.61938, "top5_acc": 0.84, "loss_cls": 2.15627, "loss": 2.15627, "time": 0.84769} +{"mode": "train", "epoch": 142, "iter": 2800, "lr": 0.00075, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.61703, "top5_acc": 0.84391, "loss_cls": 2.13753, "loss": 2.13753, "time": 0.84302} +{"mode": "train", "epoch": 142, "iter": 2900, "lr": 0.00074, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.61203, "top5_acc": 0.84047, "loss_cls": 2.13946, "loss": 2.13946, "time": 0.84907} +{"mode": "train", "epoch": 142, "iter": 3000, "lr": 0.00074, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.61469, "top5_acc": 0.83812, "loss_cls": 2.15025, "loss": 2.15025, "time": 0.84435} +{"mode": "train", "epoch": 142, "iter": 3100, "lr": 0.00073, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.61672, "top5_acc": 0.83859, "loss_cls": 2.1368, "loss": 2.1368, "time": 0.85087} +{"mode": "train", "epoch": 142, "iter": 3200, "lr": 0.00073, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.60547, "top5_acc": 0.83625, "loss_cls": 2.15915, "loss": 2.15915, "time": 0.84824} +{"mode": "train", "epoch": 142, "iter": 3300, "lr": 0.00072, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.60203, "top5_acc": 0.83688, "loss_cls": 2.18927, "loss": 2.18927, "time": 0.84994} +{"mode": "train", "epoch": 142, "iter": 3400, "lr": 0.00072, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.6075, "top5_acc": 0.83812, "loss_cls": 2.15253, "loss": 2.15253, "time": 0.85455} +{"mode": "train", "epoch": 142, "iter": 3500, "lr": 0.00071, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.60828, "top5_acc": 0.83891, "loss_cls": 2.19538, "loss": 2.19538, "time": 0.85359} +{"mode": "train", "epoch": 142, "iter": 3600, "lr": 0.00071, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.59859, "top5_acc": 0.83141, "loss_cls": 2.1876, "loss": 2.1876, "time": 0.84589} +{"mode": "train", "epoch": 142, "iter": 3700, "lr": 0.0007, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.61, "top5_acc": 0.84016, "loss_cls": 2.15239, "loss": 2.15239, "time": 0.8451} +{"mode": "val", "epoch": 142, "iter": 309, "lr": 0.0007, "top1_acc": 0.46492, "top5_acc": 0.7117, "mean_class_accuracy": 0.46467} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.0007, "memory": 15990, "data_time": 1.53341, "top1_acc": 0.64359, "top5_acc": 0.85734, "loss_cls": 2.01584, "loss": 2.01584, "time": 2.56693} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00069, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.63328, "top5_acc": 0.85453, "loss_cls": 2.05694, "loss": 2.05694, "time": 0.85836} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00069, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.63484, "top5_acc": 0.86312, "loss_cls": 2.02909, "loss": 2.02909, "time": 0.85541} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00068, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.61891, "top5_acc": 0.84141, "loss_cls": 2.12402, "loss": 2.12402, "time": 0.84841} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00068, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.62078, "top5_acc": 0.85391, "loss_cls": 2.07705, "loss": 2.07705, "time": 0.85101} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00067, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.63531, "top5_acc": 0.85594, "loss_cls": 2.03685, "loss": 2.03685, "time": 0.84467} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00067, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.64094, "top5_acc": 0.85562, "loss_cls": 2.03185, "loss": 2.03185, "time": 0.84979} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00066, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.625, "top5_acc": 0.84797, "loss_cls": 2.08897, "loss": 2.08897, "time": 0.84382} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00066, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.63109, "top5_acc": 0.84969, "loss_cls": 2.08658, "loss": 2.08658, "time": 0.84798} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00065, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.62734, "top5_acc": 0.85266, "loss_cls": 2.07306, "loss": 2.07306, "time": 0.83989} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00065, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.63, "top5_acc": 0.85766, "loss_cls": 2.06624, "loss": 2.06624, "time": 0.83673} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00065, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.63719, "top5_acc": 0.84781, "loss_cls": 2.07953, "loss": 2.07953, "time": 0.84045} +{"mode": "train", "epoch": 143, "iter": 1300, "lr": 0.00064, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.62578, "top5_acc": 0.84188, "loss_cls": 2.09752, "loss": 2.09752, "time": 0.85049} +{"mode": "train", "epoch": 143, "iter": 1400, "lr": 0.00064, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.63672, "top5_acc": 0.84922, "loss_cls": 2.06859, "loss": 2.06859, "time": 0.84372} +{"mode": "train", "epoch": 143, "iter": 1500, "lr": 0.00063, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.62687, "top5_acc": 0.85047, "loss_cls": 2.06491, "loss": 2.06491, "time": 0.85558} +{"mode": "train", "epoch": 143, "iter": 1600, "lr": 0.00063, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.63094, "top5_acc": 0.85016, "loss_cls": 2.08049, "loss": 2.08049, "time": 0.84751} +{"mode": "train", "epoch": 143, "iter": 1700, "lr": 0.00062, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.61906, "top5_acc": 0.84469, "loss_cls": 2.13351, "loss": 2.13351, "time": 0.84176} +{"mode": "train", "epoch": 143, "iter": 1800, "lr": 0.00062, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.62703, "top5_acc": 0.84938, "loss_cls": 2.08623, "loss": 2.08623, "time": 0.84884} +{"mode": "train", "epoch": 143, "iter": 1900, "lr": 0.00061, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.62813, "top5_acc": 0.84891, "loss_cls": 2.0927, "loss": 2.0927, "time": 0.84997} +{"mode": "train", "epoch": 143, "iter": 2000, "lr": 0.00061, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.62703, "top5_acc": 0.85078, "loss_cls": 2.08698, "loss": 2.08698, "time": 0.84789} +{"mode": "train", "epoch": 143, "iter": 2100, "lr": 0.00061, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.63594, "top5_acc": 0.85094, "loss_cls": 2.04761, "loss": 2.04761, "time": 0.84345} +{"mode": "train", "epoch": 143, "iter": 2200, "lr": 0.0006, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.62609, "top5_acc": 0.85109, "loss_cls": 2.06792, "loss": 2.06792, "time": 0.85257} +{"mode": "train", "epoch": 143, "iter": 2300, "lr": 0.0006, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.61812, "top5_acc": 0.84453, "loss_cls": 2.11825, "loss": 2.11825, "time": 0.84142} +{"mode": "train", "epoch": 143, "iter": 2400, "lr": 0.00059, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.6225, "top5_acc": 0.84312, "loss_cls": 2.10013, "loss": 2.10013, "time": 0.8417} +{"mode": "train", "epoch": 143, "iter": 2500, "lr": 0.00059, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.63625, "top5_acc": 0.85734, "loss_cls": 2.02833, "loss": 2.02833, "time": 0.85179} +{"mode": "train", "epoch": 143, "iter": 2600, "lr": 0.00058, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.62422, "top5_acc": 0.84719, "loss_cls": 2.0878, "loss": 2.0878, "time": 0.85037} +{"mode": "train", "epoch": 143, "iter": 2700, "lr": 0.00058, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.62031, "top5_acc": 0.84109, "loss_cls": 2.12002, "loss": 2.12002, "time": 0.8462} +{"mode": "train", "epoch": 143, "iter": 2800, "lr": 0.00058, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.61484, "top5_acc": 0.84, "loss_cls": 2.14541, "loss": 2.14541, "time": 0.84154} +{"mode": "train", "epoch": 143, "iter": 2900, "lr": 0.00057, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.62031, "top5_acc": 0.84781, "loss_cls": 2.08218, "loss": 2.08218, "time": 0.84276} +{"mode": "train", "epoch": 143, "iter": 3000, "lr": 0.00057, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.62891, "top5_acc": 0.84391, "loss_cls": 2.10259, "loss": 2.10259, "time": 0.85375} +{"mode": "train", "epoch": 143, "iter": 3100, "lr": 0.00056, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.62078, "top5_acc": 0.84312, "loss_cls": 2.08923, "loss": 2.08923, "time": 0.84726} +{"mode": "train", "epoch": 143, "iter": 3200, "lr": 0.00056, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.62406, "top5_acc": 0.84844, "loss_cls": 2.10004, "loss": 2.10004, "time": 0.84244} +{"mode": "train", "epoch": 143, "iter": 3300, "lr": 0.00055, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.61734, "top5_acc": 0.83484, "loss_cls": 2.13156, "loss": 2.13156, "time": 0.84833} +{"mode": "train", "epoch": 143, "iter": 3400, "lr": 0.00055, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.62422, "top5_acc": 0.85047, "loss_cls": 2.08171, "loss": 2.08171, "time": 0.84627} +{"mode": "train", "epoch": 143, "iter": 3500, "lr": 0.00055, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.62422, "top5_acc": 0.85031, "loss_cls": 2.09186, "loss": 2.09186, "time": 0.84938} +{"mode": "train", "epoch": 143, "iter": 3600, "lr": 0.00054, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.62031, "top5_acc": 0.84844, "loss_cls": 2.08672, "loss": 2.08672, "time": 0.85221} +{"mode": "train", "epoch": 143, "iter": 3700, "lr": 0.00054, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.63281, "top5_acc": 0.855, "loss_cls": 2.04902, "loss": 2.04902, "time": 0.84433} +{"mode": "val", "epoch": 143, "iter": 309, "lr": 0.00054, "top1_acc": 0.46563, "top5_acc": 0.71418, "mean_class_accuracy": 0.4654} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00053, "memory": 15990, "data_time": 1.51883, "top1_acc": 0.64422, "top5_acc": 0.86, "loss_cls": 2.00141, "loss": 2.00141, "time": 2.56764} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00053, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.64984, "top5_acc": 0.86141, "loss_cls": 1.98363, "loss": 1.98363, "time": 0.85851} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00052, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.64125, "top5_acc": 0.86156, "loss_cls": 2.00412, "loss": 2.00412, "time": 0.8499} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00052, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.64219, "top5_acc": 0.86281, "loss_cls": 2.01039, "loss": 2.01039, "time": 0.8519} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00052, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.64469, "top5_acc": 0.86344, "loss_cls": 2.00805, "loss": 2.00805, "time": 0.85136} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00051, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.63703, "top5_acc": 0.85562, "loss_cls": 2.03502, "loss": 2.03502, "time": 0.85988} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00051, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.64109, "top5_acc": 0.85359, "loss_cls": 2.02724, "loss": 2.02724, "time": 0.85766} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.0005, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.63828, "top5_acc": 0.85547, "loss_cls": 2.03229, "loss": 2.03229, "time": 0.86032} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.0005, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.63344, "top5_acc": 0.85297, "loss_cls": 2.02666, "loss": 2.02666, "time": 0.85736} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.0005, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.65094, "top5_acc": 0.86031, "loss_cls": 1.98661, "loss": 1.98661, "time": 0.85697} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.00049, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.64734, "top5_acc": 0.86422, "loss_cls": 1.9842, "loss": 1.9842, "time": 0.85973} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.00049, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.64203, "top5_acc": 0.8625, "loss_cls": 2.00966, "loss": 2.00966, "time": 0.86054} +{"mode": "train", "epoch": 144, "iter": 1300, "lr": 0.00048, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.63203, "top5_acc": 0.85203, "loss_cls": 2.03666, "loss": 2.03666, "time": 0.85808} +{"mode": "train", "epoch": 144, "iter": 1400, "lr": 0.00048, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.63781, "top5_acc": 0.85875, "loss_cls": 2.02016, "loss": 2.02016, "time": 0.8564} +{"mode": "train", "epoch": 144, "iter": 1500, "lr": 0.00048, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.64281, "top5_acc": 0.85359, "loss_cls": 2.01472, "loss": 2.01472, "time": 0.85799} +{"mode": "train", "epoch": 144, "iter": 1600, "lr": 0.00047, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.64016, "top5_acc": 0.85688, "loss_cls": 2.02048, "loss": 2.02048, "time": 0.85547} +{"mode": "train", "epoch": 144, "iter": 1700, "lr": 0.00047, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.63891, "top5_acc": 0.85922, "loss_cls": 2.00749, "loss": 2.00749, "time": 0.85205} +{"mode": "train", "epoch": 144, "iter": 1800, "lr": 0.00047, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.62734, "top5_acc": 0.85266, "loss_cls": 2.05384, "loss": 2.05384, "time": 0.86095} +{"mode": "train", "epoch": 144, "iter": 1900, "lr": 0.00046, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.61687, "top5_acc": 0.85453, "loss_cls": 2.08764, "loss": 2.08764, "time": 0.85951} +{"mode": "train", "epoch": 144, "iter": 2000, "lr": 0.00046, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.63703, "top5_acc": 0.85562, "loss_cls": 2.03032, "loss": 2.03032, "time": 0.85625} +{"mode": "train", "epoch": 144, "iter": 2100, "lr": 0.00045, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.64031, "top5_acc": 0.86219, "loss_cls": 2.02025, "loss": 2.02025, "time": 0.85737} +{"mode": "train", "epoch": 144, "iter": 2200, "lr": 0.00045, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.63578, "top5_acc": 0.85438, "loss_cls": 2.05001, "loss": 2.05001, "time": 0.861} +{"mode": "train", "epoch": 144, "iter": 2300, "lr": 0.00045, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.63875, "top5_acc": 0.85094, "loss_cls": 2.03794, "loss": 2.03794, "time": 0.85806} +{"mode": "train", "epoch": 144, "iter": 2400, "lr": 0.00044, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.63906, "top5_acc": 0.85875, "loss_cls": 2.0326, "loss": 2.0326, "time": 0.85526} +{"mode": "train", "epoch": 144, "iter": 2500, "lr": 0.00044, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.63906, "top5_acc": 0.84578, "loss_cls": 2.07883, "loss": 2.07883, "time": 0.8575} +{"mode": "train", "epoch": 144, "iter": 2600, "lr": 0.00044, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.63859, "top5_acc": 0.85312, "loss_cls": 2.03439, "loss": 2.03439, "time": 0.85339} +{"mode": "train", "epoch": 144, "iter": 2700, "lr": 0.00043, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.62969, "top5_acc": 0.84891, "loss_cls": 2.05806, "loss": 2.05806, "time": 0.85711} +{"mode": "train", "epoch": 144, "iter": 2800, "lr": 0.00043, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.64047, "top5_acc": 0.85453, "loss_cls": 2.02721, "loss": 2.02721, "time": 0.85672} +{"mode": "train", "epoch": 144, "iter": 2900, "lr": 0.00042, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.6375, "top5_acc": 0.855, "loss_cls": 2.02696, "loss": 2.02696, "time": 0.8604} +{"mode": "train", "epoch": 144, "iter": 3000, "lr": 0.00042, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.62813, "top5_acc": 0.85344, "loss_cls": 2.05594, "loss": 2.05594, "time": 0.86241} +{"mode": "train", "epoch": 144, "iter": 3100, "lr": 0.00042, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.63516, "top5_acc": 0.85109, "loss_cls": 2.04422, "loss": 2.04422, "time": 0.85822} +{"mode": "train", "epoch": 144, "iter": 3200, "lr": 0.00041, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.64703, "top5_acc": 0.86156, "loss_cls": 1.98275, "loss": 1.98275, "time": 0.86199} +{"mode": "train", "epoch": 144, "iter": 3300, "lr": 0.00041, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.62703, "top5_acc": 0.85328, "loss_cls": 2.07208, "loss": 2.07208, "time": 0.86252} +{"mode": "train", "epoch": 144, "iter": 3400, "lr": 0.00041, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.64141, "top5_acc": 0.85266, "loss_cls": 2.01417, "loss": 2.01417, "time": 0.86273} +{"mode": "train", "epoch": 144, "iter": 3500, "lr": 0.0004, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.63547, "top5_acc": 0.85156, "loss_cls": 2.0364, "loss": 2.0364, "time": 0.85977} +{"mode": "train", "epoch": 144, "iter": 3600, "lr": 0.0004, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.64031, "top5_acc": 0.85312, "loss_cls": 2.0242, "loss": 2.0242, "time": 0.859} +{"mode": "train", "epoch": 144, "iter": 3700, "lr": 0.0004, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.62344, "top5_acc": 0.84969, "loss_cls": 2.06876, "loss": 2.06876, "time": 0.85992} +{"mode": "val", "epoch": 144, "iter": 309, "lr": 0.00039, "top1_acc": 0.4667, "top5_acc": 0.71661, "mean_class_accuracy": 0.46646} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.00039, "memory": 15990, "data_time": 1.64155, "top1_acc": 0.66234, "top5_acc": 0.87125, "loss_cls": 1.92305, "loss": 1.92305, "time": 2.68185} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 0.00039, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.65766, "top5_acc": 0.87656, "loss_cls": 1.90369, "loss": 1.90369, "time": 0.85282} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 0.00038, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.64266, "top5_acc": 0.85766, "loss_cls": 2.01116, "loss": 2.01116, "time": 0.84609} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 0.00038, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.65719, "top5_acc": 0.87016, "loss_cls": 1.93269, "loss": 1.93269, "time": 0.8477} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 0.00038, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.64562, "top5_acc": 0.86531, "loss_cls": 1.94985, "loss": 1.94985, "time": 0.84668} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 0.00037, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.6475, "top5_acc": 0.86531, "loss_cls": 1.96983, "loss": 1.96983, "time": 0.84187} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 0.00037, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.65016, "top5_acc": 0.86484, "loss_cls": 1.96866, "loss": 1.96866, "time": 0.85191} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 0.00037, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.64625, "top5_acc": 0.86391, "loss_cls": 1.98743, "loss": 1.98743, "time": 0.85695} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 0.00036, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.65719, "top5_acc": 0.86266, "loss_cls": 1.96511, "loss": 1.96511, "time": 0.84801} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 0.00036, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.65312, "top5_acc": 0.86562, "loss_cls": 1.95895, "loss": 1.95895, "time": 0.85258} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 0.00036, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.64656, "top5_acc": 0.86062, "loss_cls": 1.97949, "loss": 1.97949, "time": 0.85347} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 0.00035, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.64391, "top5_acc": 0.86422, "loss_cls": 1.98315, "loss": 1.98315, "time": 0.85402} +{"mode": "train", "epoch": 145, "iter": 1300, "lr": 0.00035, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.65047, "top5_acc": 0.86359, "loss_cls": 1.96211, "loss": 1.96211, "time": 0.84564} +{"mode": "train", "epoch": 145, "iter": 1400, "lr": 0.00035, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.64984, "top5_acc": 0.86156, "loss_cls": 1.96456, "loss": 1.96456, "time": 0.84762} +{"mode": "train", "epoch": 145, "iter": 1500, "lr": 0.00034, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.64766, "top5_acc": 0.86344, "loss_cls": 1.98538, "loss": 1.98538, "time": 0.84889} +{"mode": "train", "epoch": 145, "iter": 1600, "lr": 0.00034, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.64594, "top5_acc": 0.85875, "loss_cls": 1.98424, "loss": 1.98424, "time": 0.84408} +{"mode": "train", "epoch": 145, "iter": 1700, "lr": 0.00034, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.64344, "top5_acc": 0.85938, "loss_cls": 1.98867, "loss": 1.98867, "time": 0.84817} +{"mode": "train", "epoch": 145, "iter": 1800, "lr": 0.00033, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64812, "top5_acc": 0.86234, "loss_cls": 1.97184, "loss": 1.97184, "time": 0.85083} +{"mode": "train", "epoch": 145, "iter": 1900, "lr": 0.00033, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.63969, "top5_acc": 0.85859, "loss_cls": 2.00589, "loss": 2.00589, "time": 0.84294} +{"mode": "train", "epoch": 145, "iter": 2000, "lr": 0.00033, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.64312, "top5_acc": 0.85828, "loss_cls": 1.99952, "loss": 1.99952, "time": 0.85751} +{"mode": "train", "epoch": 145, "iter": 2100, "lr": 0.00032, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.6525, "top5_acc": 0.86016, "loss_cls": 1.97309, "loss": 1.97309, "time": 0.85199} +{"mode": "train", "epoch": 145, "iter": 2200, "lr": 0.00032, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64172, "top5_acc": 0.85984, "loss_cls": 2.01353, "loss": 2.01353, "time": 0.84519} +{"mode": "train", "epoch": 145, "iter": 2300, "lr": 0.00032, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.64734, "top5_acc": 0.86, "loss_cls": 1.99396, "loss": 1.99396, "time": 0.84721} +{"mode": "train", "epoch": 145, "iter": 2400, "lr": 0.00031, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.64656, "top5_acc": 0.85844, "loss_cls": 1.98968, "loss": 1.98968, "time": 0.85537} +{"mode": "train", "epoch": 145, "iter": 2500, "lr": 0.00031, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.64172, "top5_acc": 0.86109, "loss_cls": 2.02774, "loss": 2.02774, "time": 0.85146} +{"mode": "train", "epoch": 145, "iter": 2600, "lr": 0.00031, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.64375, "top5_acc": 0.85938, "loss_cls": 1.98158, "loss": 1.98158, "time": 0.84445} +{"mode": "train", "epoch": 145, "iter": 2700, "lr": 0.00031, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.65422, "top5_acc": 0.86031, "loss_cls": 1.97025, "loss": 1.97025, "time": 0.85112} +{"mode": "train", "epoch": 145, "iter": 2800, "lr": 0.0003, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65641, "top5_acc": 0.86094, "loss_cls": 1.96376, "loss": 1.96376, "time": 0.84699} +{"mode": "train", "epoch": 145, "iter": 2900, "lr": 0.0003, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.64781, "top5_acc": 0.85859, "loss_cls": 2.00981, "loss": 2.00981, "time": 0.84708} +{"mode": "train", "epoch": 145, "iter": 3000, "lr": 0.0003, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.65203, "top5_acc": 0.86891, "loss_cls": 1.95497, "loss": 1.95497, "time": 0.84947} +{"mode": "train", "epoch": 145, "iter": 3100, "lr": 0.00029, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.64891, "top5_acc": 0.85703, "loss_cls": 1.97825, "loss": 1.97825, "time": 0.84878} +{"mode": "train", "epoch": 145, "iter": 3200, "lr": 0.00029, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65016, "top5_acc": 0.85969, "loss_cls": 1.99546, "loss": 1.99546, "time": 0.84764} +{"mode": "train", "epoch": 145, "iter": 3300, "lr": 0.00029, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65047, "top5_acc": 0.86109, "loss_cls": 1.98276, "loss": 1.98276, "time": 0.84756} +{"mode": "train", "epoch": 145, "iter": 3400, "lr": 0.00028, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.65516, "top5_acc": 0.86453, "loss_cls": 1.96001, "loss": 1.96001, "time": 0.8487} +{"mode": "train", "epoch": 145, "iter": 3500, "lr": 0.00028, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.6375, "top5_acc": 0.85969, "loss_cls": 1.98395, "loss": 1.98395, "time": 0.85111} +{"mode": "train", "epoch": 145, "iter": 3600, "lr": 0.00028, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.64719, "top5_acc": 0.86125, "loss_cls": 1.99564, "loss": 1.99564, "time": 0.84312} +{"mode": "train", "epoch": 145, "iter": 3700, "lr": 0.00028, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.63875, "top5_acc": 0.85859, "loss_cls": 1.99992, "loss": 1.99992, "time": 0.84273} +{"mode": "val", "epoch": 145, "iter": 309, "lr": 0.00027, "top1_acc": 0.47034, "top5_acc": 0.71929, "mean_class_accuracy": 0.47015} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 0.00027, "memory": 15990, "data_time": 1.55486, "top1_acc": 0.66531, "top5_acc": 0.8675, "loss_cls": 1.92477, "loss": 1.92477, "time": 2.5945} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 0.00027, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64906, "top5_acc": 0.86938, "loss_cls": 1.93548, "loss": 1.93548, "time": 0.85663} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 0.00027, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65656, "top5_acc": 0.87, "loss_cls": 1.90969, "loss": 1.90969, "time": 0.85436} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 0.00026, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65453, "top5_acc": 0.86719, "loss_cls": 1.95763, "loss": 1.95763, "time": 0.85111} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 0.00026, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66375, "top5_acc": 0.875, "loss_cls": 1.89105, "loss": 1.89105, "time": 0.84463} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 0.00026, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.65734, "top5_acc": 0.86828, "loss_cls": 1.94896, "loss": 1.94896, "time": 0.84457} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 0.00025, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.65422, "top5_acc": 0.86562, "loss_cls": 1.93699, "loss": 1.93699, "time": 0.84358} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 0.00025, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66391, "top5_acc": 0.8725, "loss_cls": 1.92415, "loss": 1.92415, "time": 0.85289} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 0.00025, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.66781, "top5_acc": 0.87484, "loss_cls": 1.87198, "loss": 1.87198, "time": 0.84619} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 0.00025, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.65984, "top5_acc": 0.86766, "loss_cls": 1.93381, "loss": 1.93381, "time": 0.84934} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 0.00024, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66156, "top5_acc": 0.87609, "loss_cls": 1.91662, "loss": 1.91662, "time": 0.84613} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 0.00024, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65188, "top5_acc": 0.86328, "loss_cls": 1.95532, "loss": 1.95532, "time": 0.84594} +{"mode": "train", "epoch": 146, "iter": 1300, "lr": 0.00024, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65922, "top5_acc": 0.87078, "loss_cls": 1.93927, "loss": 1.93927, "time": 0.84719} +{"mode": "train", "epoch": 146, "iter": 1400, "lr": 0.00023, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.65703, "top5_acc": 0.86594, "loss_cls": 1.93662, "loss": 1.93662, "time": 0.84723} +{"mode": "train", "epoch": 146, "iter": 1500, "lr": 0.00023, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66219, "top5_acc": 0.86844, "loss_cls": 1.8991, "loss": 1.8991, "time": 0.85387} +{"mode": "train", "epoch": 146, "iter": 1600, "lr": 0.00023, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.65766, "top5_acc": 0.87125, "loss_cls": 1.91463, "loss": 1.91463, "time": 0.85051} +{"mode": "train", "epoch": 146, "iter": 1700, "lr": 0.00023, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.665, "top5_acc": 0.86672, "loss_cls": 1.91626, "loss": 1.91626, "time": 0.84816} +{"mode": "train", "epoch": 146, "iter": 1800, "lr": 0.00022, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.64938, "top5_acc": 0.87609, "loss_cls": 1.92075, "loss": 1.92075, "time": 0.84618} +{"mode": "train", "epoch": 146, "iter": 1900, "lr": 0.00022, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.65219, "top5_acc": 0.86844, "loss_cls": 1.94104, "loss": 1.94104, "time": 0.84294} +{"mode": "train", "epoch": 146, "iter": 2000, "lr": 0.00022, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.65219, "top5_acc": 0.865, "loss_cls": 1.9628, "loss": 1.9628, "time": 0.84607} +{"mode": "train", "epoch": 146, "iter": 2100, "lr": 0.00022, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66, "top5_acc": 0.87484, "loss_cls": 1.89917, "loss": 1.89917, "time": 0.84846} +{"mode": "train", "epoch": 146, "iter": 2200, "lr": 0.00021, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66125, "top5_acc": 0.86359, "loss_cls": 1.92904, "loss": 1.92904, "time": 0.8476} +{"mode": "train", "epoch": 146, "iter": 2300, "lr": 0.00021, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66031, "top5_acc": 0.86828, "loss_cls": 1.9295, "loss": 1.9295, "time": 0.85214} +{"mode": "train", "epoch": 146, "iter": 2400, "lr": 0.00021, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65578, "top5_acc": 0.86625, "loss_cls": 1.9514, "loss": 1.9514, "time": 0.85324} +{"mode": "train", "epoch": 146, "iter": 2500, "lr": 0.00021, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.66062, "top5_acc": 0.86562, "loss_cls": 1.93988, "loss": 1.93988, "time": 0.84663} +{"mode": "train", "epoch": 146, "iter": 2600, "lr": 0.0002, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65562, "top5_acc": 0.86297, "loss_cls": 1.9489, "loss": 1.9489, "time": 0.84838} +{"mode": "train", "epoch": 146, "iter": 2700, "lr": 0.0002, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.655, "top5_acc": 0.86156, "loss_cls": 1.94956, "loss": 1.94956, "time": 0.85015} +{"mode": "train", "epoch": 146, "iter": 2800, "lr": 0.0002, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.6525, "top5_acc": 0.86734, "loss_cls": 1.93843, "loss": 1.93843, "time": 0.84208} +{"mode": "train", "epoch": 146, "iter": 2900, "lr": 0.0002, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.66266, "top5_acc": 0.87625, "loss_cls": 1.9039, "loss": 1.9039, "time": 0.84437} +{"mode": "train", "epoch": 146, "iter": 3000, "lr": 0.00019, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.6475, "top5_acc": 0.86703, "loss_cls": 1.97992, "loss": 1.97992, "time": 0.84458} +{"mode": "train", "epoch": 146, "iter": 3100, "lr": 0.00019, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.64906, "top5_acc": 0.86297, "loss_cls": 1.96121, "loss": 1.96121, "time": 0.8501} +{"mode": "train", "epoch": 146, "iter": 3200, "lr": 0.00019, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64922, "top5_acc": 0.86203, "loss_cls": 1.9562, "loss": 1.9562, "time": 0.84892} +{"mode": "train", "epoch": 146, "iter": 3300, "lr": 0.00019, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65547, "top5_acc": 0.86406, "loss_cls": 1.94909, "loss": 1.94909, "time": 0.84811} +{"mode": "train", "epoch": 146, "iter": 3400, "lr": 0.00018, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.65781, "top5_acc": 0.87453, "loss_cls": 1.89418, "loss": 1.89418, "time": 0.84652} +{"mode": "train", "epoch": 146, "iter": 3500, "lr": 0.00018, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.65391, "top5_acc": 0.86828, "loss_cls": 1.93906, "loss": 1.93906, "time": 0.85091} +{"mode": "train", "epoch": 146, "iter": 3600, "lr": 0.00018, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.6475, "top5_acc": 0.86891, "loss_cls": 1.96171, "loss": 1.96171, "time": 0.85354} +{"mode": "train", "epoch": 146, "iter": 3700, "lr": 0.00018, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.66344, "top5_acc": 0.87344, "loss_cls": 1.90006, "loss": 1.90006, "time": 0.85073} +{"mode": "val", "epoch": 146, "iter": 309, "lr": 0.00018, "top1_acc": 0.471, "top5_acc": 0.72041, "mean_class_accuracy": 0.47078} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 0.00017, "memory": 15990, "data_time": 1.51685, "top1_acc": 0.67094, "top5_acc": 0.87141, "loss_cls": 1.88798, "loss": 1.88798, "time": 2.55223} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 0.00017, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.67359, "top5_acc": 0.87359, "loss_cls": 1.88012, "loss": 1.88012, "time": 0.85467} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 0.00017, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.67234, "top5_acc": 0.87484, "loss_cls": 1.86341, "loss": 1.86341, "time": 0.84972} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 0.00017, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.68594, "top5_acc": 0.88156, "loss_cls": 1.84449, "loss": 1.84449, "time": 0.84386} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 0.00016, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66969, "top5_acc": 0.87594, "loss_cls": 1.88287, "loss": 1.88287, "time": 0.84327} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 0.00016, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66406, "top5_acc": 0.86594, "loss_cls": 1.91612, "loss": 1.91612, "time": 0.84472} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 0.00016, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.67453, "top5_acc": 0.88047, "loss_cls": 1.85281, "loss": 1.85281, "time": 0.84571} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 0.00016, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.65672, "top5_acc": 0.87078, "loss_cls": 1.91596, "loss": 1.91596, "time": 0.84443} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 0.00015, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66047, "top5_acc": 0.86609, "loss_cls": 1.91439, "loss": 1.91439, "time": 0.84227} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 0.00015, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66062, "top5_acc": 0.86719, "loss_cls": 1.91379, "loss": 1.91379, "time": 0.85172} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 0.00015, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66391, "top5_acc": 0.87344, "loss_cls": 1.89599, "loss": 1.89599, "time": 0.84499} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 0.00015, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67547, "top5_acc": 0.87375, "loss_cls": 1.89813, "loss": 1.89813, "time": 0.84722} +{"mode": "train", "epoch": 147, "iter": 1300, "lr": 0.00015, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67078, "top5_acc": 0.87219, "loss_cls": 1.88401, "loss": 1.88401, "time": 0.8486} +{"mode": "train", "epoch": 147, "iter": 1400, "lr": 0.00014, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.65906, "top5_acc": 0.87047, "loss_cls": 1.90574, "loss": 1.90574, "time": 0.84483} +{"mode": "train", "epoch": 147, "iter": 1500, "lr": 0.00014, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.66688, "top5_acc": 0.87812, "loss_cls": 1.88223, "loss": 1.88223, "time": 0.85021} +{"mode": "train", "epoch": 147, "iter": 1600, "lr": 0.00014, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.66469, "top5_acc": 0.87172, "loss_cls": 1.91519, "loss": 1.91519, "time": 0.84617} +{"mode": "train", "epoch": 147, "iter": 1700, "lr": 0.00014, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65766, "top5_acc": 0.86625, "loss_cls": 1.93819, "loss": 1.93819, "time": 0.8491} +{"mode": "train", "epoch": 147, "iter": 1800, "lr": 0.00014, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.66359, "top5_acc": 0.87328, "loss_cls": 1.89061, "loss": 1.89061, "time": 0.84831} +{"mode": "train", "epoch": 147, "iter": 1900, "lr": 0.00013, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66469, "top5_acc": 0.87172, "loss_cls": 1.90657, "loss": 1.90657, "time": 0.84188} +{"mode": "train", "epoch": 147, "iter": 2000, "lr": 0.00013, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67422, "top5_acc": 0.86969, "loss_cls": 1.87608, "loss": 1.87608, "time": 0.83877} +{"mode": "train", "epoch": 147, "iter": 2100, "lr": 0.00013, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.65266, "top5_acc": 0.87297, "loss_cls": 1.92672, "loss": 1.92672, "time": 0.84602} +{"mode": "train", "epoch": 147, "iter": 2200, "lr": 0.00013, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66172, "top5_acc": 0.86641, "loss_cls": 1.91849, "loss": 1.91849, "time": 0.84495} +{"mode": "train", "epoch": 147, "iter": 2300, "lr": 0.00013, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66828, "top5_acc": 0.87484, "loss_cls": 1.88084, "loss": 1.88084, "time": 0.84448} +{"mode": "train", "epoch": 147, "iter": 2400, "lr": 0.00012, "memory": 15990, "data_time": 0.00074, "top1_acc": 0.66219, "top5_acc": 0.87688, "loss_cls": 1.89005, "loss": 1.89005, "time": 0.84722} +{"mode": "train", "epoch": 147, "iter": 2500, "lr": 0.00012, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.66562, "top5_acc": 0.87734, "loss_cls": 1.87059, "loss": 1.87059, "time": 0.84743} +{"mode": "train", "epoch": 147, "iter": 2600, "lr": 0.00012, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.6625, "top5_acc": 0.87312, "loss_cls": 1.90996, "loss": 1.90996, "time": 0.84328} +{"mode": "train", "epoch": 147, "iter": 2700, "lr": 0.00012, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.64219, "top5_acc": 0.86375, "loss_cls": 1.9812, "loss": 1.9812, "time": 0.84491} +{"mode": "train", "epoch": 147, "iter": 2800, "lr": 0.00012, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.64688, "top5_acc": 0.87094, "loss_cls": 1.93706, "loss": 1.93706, "time": 0.84664} +{"mode": "train", "epoch": 147, "iter": 2900, "lr": 0.00011, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66891, "top5_acc": 0.87078, "loss_cls": 1.88994, "loss": 1.88994, "time": 0.85156} +{"mode": "train", "epoch": 147, "iter": 3000, "lr": 0.00011, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65922, "top5_acc": 0.86875, "loss_cls": 1.93342, "loss": 1.93342, "time": 0.84921} +{"mode": "train", "epoch": 147, "iter": 3100, "lr": 0.00011, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.65812, "top5_acc": 0.87172, "loss_cls": 1.91031, "loss": 1.91031, "time": 0.8447} +{"mode": "train", "epoch": 147, "iter": 3200, "lr": 0.00011, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66688, "top5_acc": 0.86953, "loss_cls": 1.89961, "loss": 1.89961, "time": 0.84622} +{"mode": "train", "epoch": 147, "iter": 3300, "lr": 0.00011, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.66391, "top5_acc": 0.87797, "loss_cls": 1.87787, "loss": 1.87787, "time": 0.84768} +{"mode": "train", "epoch": 147, "iter": 3400, "lr": 0.0001, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.65906, "top5_acc": 0.87047, "loss_cls": 1.87909, "loss": 1.87909, "time": 0.84585} +{"mode": "train", "epoch": 147, "iter": 3500, "lr": 0.0001, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67547, "top5_acc": 0.87641, "loss_cls": 1.85266, "loss": 1.85266, "time": 0.84955} +{"mode": "train", "epoch": 147, "iter": 3600, "lr": 0.0001, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67266, "top5_acc": 0.87844, "loss_cls": 1.85151, "loss": 1.85151, "time": 0.8504} +{"mode": "train", "epoch": 147, "iter": 3700, "lr": 0.0001, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66656, "top5_acc": 0.87797, "loss_cls": 1.87699, "loss": 1.87699, "time": 0.8494} +{"mode": "val", "epoch": 147, "iter": 309, "lr": 0.0001, "top1_acc": 0.46933, "top5_acc": 0.71848, "mean_class_accuracy": 0.46906} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 0.0001, "memory": 15990, "data_time": 1.50195, "top1_acc": 0.66422, "top5_acc": 0.87156, "loss_cls": 1.88984, "loss": 1.88984, "time": 2.52787} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 0.0001, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.67094, "top5_acc": 0.87641, "loss_cls": 1.87087, "loss": 1.87087, "time": 0.8604} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 9e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.67219, "top5_acc": 0.87516, "loss_cls": 1.87459, "loss": 1.87459, "time": 0.85692} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 9e-05, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.67203, "top5_acc": 0.87562, "loss_cls": 1.87817, "loss": 1.87817, "time": 0.85022} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 9e-05, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.67016, "top5_acc": 0.87922, "loss_cls": 1.85216, "loss": 1.85216, "time": 0.85656} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 9e-05, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.67844, "top5_acc": 0.87734, "loss_cls": 1.86067, "loss": 1.86067, "time": 0.84366} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 9e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.6725, "top5_acc": 0.87531, "loss_cls": 1.86054, "loss": 1.86054, "time": 0.85025} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 9e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67578, "top5_acc": 0.87938, "loss_cls": 1.85153, "loss": 1.85153, "time": 0.84563} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 8e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.67562, "top5_acc": 0.87984, "loss_cls": 1.86394, "loss": 1.86394, "time": 0.84513} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 8e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66562, "top5_acc": 0.88031, "loss_cls": 1.84513, "loss": 1.84513, "time": 0.84873} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 8e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.66875, "top5_acc": 0.87406, "loss_cls": 1.86896, "loss": 1.86896, "time": 0.84366} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 8e-05, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.66766, "top5_acc": 0.87844, "loss_cls": 1.86181, "loss": 1.86181, "time": 0.84171} +{"mode": "train", "epoch": 148, "iter": 1300, "lr": 8e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67688, "top5_acc": 0.87562, "loss_cls": 1.83659, "loss": 1.83659, "time": 0.84499} +{"mode": "train", "epoch": 148, "iter": 1400, "lr": 8e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67422, "top5_acc": 0.88078, "loss_cls": 1.86806, "loss": 1.86806, "time": 0.84392} +{"mode": "train", "epoch": 148, "iter": 1500, "lr": 7e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67125, "top5_acc": 0.87688, "loss_cls": 1.86985, "loss": 1.86985, "time": 0.84633} +{"mode": "train", "epoch": 148, "iter": 1600, "lr": 7e-05, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.66969, "top5_acc": 0.87547, "loss_cls": 1.89311, "loss": 1.89311, "time": 0.85093} +{"mode": "train", "epoch": 148, "iter": 1700, "lr": 7e-05, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.66953, "top5_acc": 0.875, "loss_cls": 1.85226, "loss": 1.85226, "time": 0.84838} +{"mode": "train", "epoch": 148, "iter": 1800, "lr": 7e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66984, "top5_acc": 0.87906, "loss_cls": 1.85379, "loss": 1.85379, "time": 0.84497} +{"mode": "train", "epoch": 148, "iter": 1900, "lr": 7e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66781, "top5_acc": 0.87438, "loss_cls": 1.90247, "loss": 1.90247, "time": 0.84797} +{"mode": "train", "epoch": 148, "iter": 2000, "lr": 7e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.67328, "top5_acc": 0.87734, "loss_cls": 1.85897, "loss": 1.85897, "time": 0.85415} +{"mode": "train", "epoch": 148, "iter": 2100, "lr": 7e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.66344, "top5_acc": 0.87547, "loss_cls": 1.89075, "loss": 1.89075, "time": 0.85387} +{"mode": "train", "epoch": 148, "iter": 2200, "lr": 6e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.67531, "top5_acc": 0.87938, "loss_cls": 1.8362, "loss": 1.8362, "time": 0.85718} +{"mode": "train", "epoch": 148, "iter": 2300, "lr": 6e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.66094, "top5_acc": 0.87984, "loss_cls": 1.88349, "loss": 1.88349, "time": 0.85656} +{"mode": "train", "epoch": 148, "iter": 2400, "lr": 6e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.65812, "top5_acc": 0.86922, "loss_cls": 1.9138, "loss": 1.9138, "time": 0.85265} +{"mode": "train", "epoch": 148, "iter": 2500, "lr": 6e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66641, "top5_acc": 0.87406, "loss_cls": 1.88693, "loss": 1.88693, "time": 0.8488} +{"mode": "train", "epoch": 148, "iter": 2600, "lr": 6e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.67422, "top5_acc": 0.87562, "loss_cls": 1.8884, "loss": 1.8884, "time": 0.8476} +{"mode": "train", "epoch": 148, "iter": 2700, "lr": 6e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.68094, "top5_acc": 0.88109, "loss_cls": 1.82635, "loss": 1.82635, "time": 0.8491} +{"mode": "train", "epoch": 148, "iter": 2800, "lr": 6e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.66969, "top5_acc": 0.87906, "loss_cls": 1.86433, "loss": 1.86433, "time": 0.85131} +{"mode": "train", "epoch": 148, "iter": 2900, "lr": 5e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66641, "top5_acc": 0.87266, "loss_cls": 1.8756, "loss": 1.8756, "time": 0.84727} +{"mode": "train", "epoch": 148, "iter": 3000, "lr": 5e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66672, "top5_acc": 0.86984, "loss_cls": 1.89555, "loss": 1.89555, "time": 0.84472} +{"mode": "train", "epoch": 148, "iter": 3100, "lr": 5e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66641, "top5_acc": 0.87344, "loss_cls": 1.88052, "loss": 1.88052, "time": 0.84455} +{"mode": "train", "epoch": 148, "iter": 3200, "lr": 5e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67469, "top5_acc": 0.885, "loss_cls": 1.83587, "loss": 1.83587, "time": 0.84911} +{"mode": "train", "epoch": 148, "iter": 3300, "lr": 5e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67281, "top5_acc": 0.87719, "loss_cls": 1.84429, "loss": 1.84429, "time": 0.8504} +{"mode": "train", "epoch": 148, "iter": 3400, "lr": 5e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66531, "top5_acc": 0.87156, "loss_cls": 1.88227, "loss": 1.88227, "time": 0.84778} +{"mode": "train", "epoch": 148, "iter": 3500, "lr": 5e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67203, "top5_acc": 0.875, "loss_cls": 1.88269, "loss": 1.88269, "time": 0.84625} +{"mode": "train", "epoch": 148, "iter": 3600, "lr": 5e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.67172, "top5_acc": 0.88156, "loss_cls": 1.84238, "loss": 1.84238, "time": 0.85211} +{"mode": "train", "epoch": 148, "iter": 3700, "lr": 4e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67078, "top5_acc": 0.87766, "loss_cls": 1.84832, "loss": 1.84832, "time": 0.85303} +{"mode": "val", "epoch": 148, "iter": 309, "lr": 4e-05, "top1_acc": 0.47161, "top5_acc": 0.71823, "mean_class_accuracy": 0.47135} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 4e-05, "memory": 15990, "data_time": 1.49616, "top1_acc": 0.67625, "top5_acc": 0.88609, "loss_cls": 1.81166, "loss": 1.81166, "time": 2.51921} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 4e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.67141, "top5_acc": 0.88109, "loss_cls": 1.84169, "loss": 1.84169, "time": 0.85634} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 4e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.67938, "top5_acc": 0.88281, "loss_cls": 1.82297, "loss": 1.82297, "time": 0.85251} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 4e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67625, "top5_acc": 0.88359, "loss_cls": 1.83295, "loss": 1.83295, "time": 0.85048} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 4e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67844, "top5_acc": 0.88156, "loss_cls": 1.82501, "loss": 1.82501, "time": 0.84488} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 4e-05, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.67094, "top5_acc": 0.87734, "loss_cls": 1.85433, "loss": 1.85433, "time": 0.83793} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 4e-05, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.68234, "top5_acc": 0.87953, "loss_cls": 1.84053, "loss": 1.84053, "time": 0.84263} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 4e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.66859, "top5_acc": 0.87734, "loss_cls": 1.87175, "loss": 1.87175, "time": 0.85323} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 3e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.68031, "top5_acc": 0.88516, "loss_cls": 1.81419, "loss": 1.81419, "time": 0.84628} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 3e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.67156, "top5_acc": 0.88047, "loss_cls": 1.86243, "loss": 1.86243, "time": 0.84765} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 3e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.67641, "top5_acc": 0.87906, "loss_cls": 1.83296, "loss": 1.83296, "time": 0.84504} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 3e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67891, "top5_acc": 0.87844, "loss_cls": 1.84295, "loss": 1.84295, "time": 0.8467} +{"mode": "train", "epoch": 149, "iter": 1300, "lr": 3e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.67484, "top5_acc": 0.87922, "loss_cls": 1.85336, "loss": 1.85336, "time": 0.84748} +{"mode": "train", "epoch": 149, "iter": 1400, "lr": 3e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67, "top5_acc": 0.87516, "loss_cls": 1.85758, "loss": 1.85758, "time": 0.8483} +{"mode": "train", "epoch": 149, "iter": 1500, "lr": 3e-05, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.67656, "top5_acc": 0.87344, "loss_cls": 1.87327, "loss": 1.87327, "time": 0.85416} +{"mode": "train", "epoch": 149, "iter": 1600, "lr": 3e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67359, "top5_acc": 0.87812, "loss_cls": 1.8619, "loss": 1.8619, "time": 0.85085} +{"mode": "train", "epoch": 149, "iter": 1700, "lr": 3e-05, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.68188, "top5_acc": 0.88172, "loss_cls": 1.81438, "loss": 1.81438, "time": 0.84642} +{"mode": "train", "epoch": 149, "iter": 1800, "lr": 3e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.67078, "top5_acc": 0.87516, "loss_cls": 1.86887, "loss": 1.86887, "time": 0.85021} +{"mode": "train", "epoch": 149, "iter": 1900, "lr": 2e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67172, "top5_acc": 0.88219, "loss_cls": 1.8423, "loss": 1.8423, "time": 0.8461} +{"mode": "train", "epoch": 149, "iter": 2000, "lr": 2e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67266, "top5_acc": 0.87641, "loss_cls": 1.86631, "loss": 1.86631, "time": 0.84266} +{"mode": "train", "epoch": 149, "iter": 2100, "lr": 2e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67188, "top5_acc": 0.87672, "loss_cls": 1.89399, "loss": 1.89399, "time": 0.84516} +{"mode": "train", "epoch": 149, "iter": 2200, "lr": 2e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.67938, "top5_acc": 0.88141, "loss_cls": 1.82792, "loss": 1.82792, "time": 0.84336} +{"mode": "train", "epoch": 149, "iter": 2300, "lr": 2e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67516, "top5_acc": 0.87547, "loss_cls": 1.86641, "loss": 1.86641, "time": 0.84538} +{"mode": "train", "epoch": 149, "iter": 2400, "lr": 2e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.67484, "top5_acc": 0.87578, "loss_cls": 1.8674, "loss": 1.8674, "time": 0.8444} +{"mode": "train", "epoch": 149, "iter": 2500, "lr": 2e-05, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.67938, "top5_acc": 0.87703, "loss_cls": 1.85958, "loss": 1.85958, "time": 0.84754} +{"mode": "train", "epoch": 149, "iter": 2600, "lr": 2e-05, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.68016, "top5_acc": 0.87703, "loss_cls": 1.84458, "loss": 1.84458, "time": 0.84416} +{"mode": "train", "epoch": 149, "iter": 2700, "lr": 2e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67203, "top5_acc": 0.87438, "loss_cls": 1.86482, "loss": 1.86482, "time": 0.84973} +{"mode": "train", "epoch": 149, "iter": 2800, "lr": 2e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67469, "top5_acc": 0.88172, "loss_cls": 1.85976, "loss": 1.85976, "time": 0.84188} +{"mode": "train", "epoch": 149, "iter": 2900, "lr": 2e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.69047, "top5_acc": 0.88375, "loss_cls": 1.78962, "loss": 1.78962, "time": 0.84454} +{"mode": "train", "epoch": 149, "iter": 3000, "lr": 2e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.67703, "top5_acc": 0.88234, "loss_cls": 1.84039, "loss": 1.84039, "time": 0.8444} +{"mode": "train", "epoch": 149, "iter": 3100, "lr": 2e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67125, "top5_acc": 0.87766, "loss_cls": 1.84932, "loss": 1.84932, "time": 0.84849} +{"mode": "train", "epoch": 149, "iter": 3200, "lr": 1e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67906, "top5_acc": 0.88125, "loss_cls": 1.84132, "loss": 1.84132, "time": 0.84619} +{"mode": "train", "epoch": 149, "iter": 3300, "lr": 1e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.66922, "top5_acc": 0.88344, "loss_cls": 1.86829, "loss": 1.86829, "time": 0.8454} +{"mode": "train", "epoch": 149, "iter": 3400, "lr": 1e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.67016, "top5_acc": 0.87094, "loss_cls": 1.90138, "loss": 1.90138, "time": 0.84309} +{"mode": "train", "epoch": 149, "iter": 3500, "lr": 1e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.67609, "top5_acc": 0.88062, "loss_cls": 1.84665, "loss": 1.84665, "time": 0.84519} +{"mode": "train", "epoch": 149, "iter": 3600, "lr": 1e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.68047, "top5_acc": 0.88031, "loss_cls": 1.82405, "loss": 1.82405, "time": 0.84901} +{"mode": "train", "epoch": 149, "iter": 3700, "lr": 1e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.67047, "top5_acc": 0.87547, "loss_cls": 1.85909, "loss": 1.85909, "time": 0.84975} +{"mode": "val", "epoch": 149, "iter": 309, "lr": 1e-05, "top1_acc": 0.47257, "top5_acc": 0.71884, "mean_class_accuracy": 0.4723} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 1e-05, "memory": 15990, "data_time": 1.47946, "top1_acc": 0.6775, "top5_acc": 0.88031, "loss_cls": 1.81614, "loss": 1.81614, "time": 2.52036} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 1e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.67953, "top5_acc": 0.88703, "loss_cls": 1.83014, "loss": 1.83014, "time": 0.85721} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 1e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.67547, "top5_acc": 0.88125, "loss_cls": 1.84208, "loss": 1.84208, "time": 0.84782} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 1e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67781, "top5_acc": 0.87875, "loss_cls": 1.84553, "loss": 1.84553, "time": 0.84749} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 1e-05, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.67703, "top5_acc": 0.87859, "loss_cls": 1.84655, "loss": 1.84655, "time": 0.8567} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 1e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.67625, "top5_acc": 0.88109, "loss_cls": 1.83095, "loss": 1.83095, "time": 0.84421} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 1e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.67375, "top5_acc": 0.88125, "loss_cls": 1.84582, "loss": 1.84582, "time": 0.84635} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 1e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.67734, "top5_acc": 0.88047, "loss_cls": 1.82542, "loss": 1.82542, "time": 0.84215} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 1e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.68484, "top5_acc": 0.88547, "loss_cls": 1.80747, "loss": 1.80747, "time": 0.84272} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 1e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.68188, "top5_acc": 0.87625, "loss_cls": 1.829, "loss": 1.829, "time": 0.8485} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 1e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.68812, "top5_acc": 0.88969, "loss_cls": 1.81446, "loss": 1.81446, "time": 0.84725} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 1e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67234, "top5_acc": 0.87875, "loss_cls": 1.84544, "loss": 1.84544, "time": 0.84405} +{"mode": "train", "epoch": 150, "iter": 1300, "lr": 0.0, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.68312, "top5_acc": 0.87844, "loss_cls": 1.8211, "loss": 1.8211, "time": 0.84872} +{"mode": "train", "epoch": 150, "iter": 1400, "lr": 0.0, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.6875, "top5_acc": 0.88547, "loss_cls": 1.77449, "loss": 1.77449, "time": 0.8433} +{"mode": "train", "epoch": 150, "iter": 1500, "lr": 0.0, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.67234, "top5_acc": 0.87469, "loss_cls": 1.8664, "loss": 1.8664, "time": 0.84489} +{"mode": "train", "epoch": 150, "iter": 1600, "lr": 0.0, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.67547, "top5_acc": 0.87422, "loss_cls": 1.86995, "loss": 1.86995, "time": 0.84775} +{"mode": "train", "epoch": 150, "iter": 1700, "lr": 0.0, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.675, "top5_acc": 0.87875, "loss_cls": 1.85078, "loss": 1.85078, "time": 0.84345} +{"mode": "train", "epoch": 150, "iter": 1800, "lr": 0.0, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67422, "top5_acc": 0.87656, "loss_cls": 1.8651, "loss": 1.8651, "time": 0.84667} +{"mode": "train", "epoch": 150, "iter": 1900, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67734, "top5_acc": 0.88391, "loss_cls": 1.84249, "loss": 1.84249, "time": 0.84845} +{"mode": "train", "epoch": 150, "iter": 2000, "lr": 0.0, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67812, "top5_acc": 0.87781, "loss_cls": 1.86032, "loss": 1.86032, "time": 0.84486} +{"mode": "train", "epoch": 150, "iter": 2100, "lr": 0.0, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.68484, "top5_acc": 0.88375, "loss_cls": 1.79621, "loss": 1.79621, "time": 0.84022} +{"mode": "train", "epoch": 150, "iter": 2200, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.68266, "top5_acc": 0.8775, "loss_cls": 1.85094, "loss": 1.85094, "time": 0.84537} +{"mode": "train", "epoch": 150, "iter": 2300, "lr": 0.0, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.67391, "top5_acc": 0.87984, "loss_cls": 1.84768, "loss": 1.84768, "time": 0.84896} +{"mode": "train", "epoch": 150, "iter": 2400, "lr": 0.0, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.67594, "top5_acc": 0.87594, "loss_cls": 1.86183, "loss": 1.86183, "time": 0.8516} +{"mode": "train", "epoch": 150, "iter": 2500, "lr": 0.0, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.66969, "top5_acc": 0.87578, "loss_cls": 1.87562, "loss": 1.87562, "time": 0.84902} +{"mode": "train", "epoch": 150, "iter": 2600, "lr": 0.0, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67344, "top5_acc": 0.87891, "loss_cls": 1.83676, "loss": 1.83676, "time": 0.84152} +{"mode": "train", "epoch": 150, "iter": 2700, "lr": 0.0, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.67688, "top5_acc": 0.88328, "loss_cls": 1.80429, "loss": 1.80429, "time": 0.84348} +{"mode": "train", "epoch": 150, "iter": 2800, "lr": 0.0, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.67906, "top5_acc": 0.87328, "loss_cls": 1.86103, "loss": 1.86103, "time": 0.84358} +{"mode": "train", "epoch": 150, "iter": 2900, "lr": 0.0, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.67562, "top5_acc": 0.87734, "loss_cls": 1.84407, "loss": 1.84407, "time": 0.84628} +{"mode": "train", "epoch": 150, "iter": 3000, "lr": 0.0, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.68922, "top5_acc": 0.88703, "loss_cls": 1.78636, "loss": 1.78636, "time": 0.84108} +{"mode": "train", "epoch": 150, "iter": 3100, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67797, "top5_acc": 0.87844, "loss_cls": 1.83116, "loss": 1.83116, "time": 0.84741} +{"mode": "train", "epoch": 150, "iter": 3200, "lr": 0.0, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.66828, "top5_acc": 0.87297, "loss_cls": 1.89819, "loss": 1.89819, "time": 0.84892} +{"mode": "train", "epoch": 150, "iter": 3300, "lr": 0.0, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.68094, "top5_acc": 0.88484, "loss_cls": 1.82984, "loss": 1.82984, "time": 0.84528} +{"mode": "train", "epoch": 150, "iter": 3400, "lr": 0.0, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.68516, "top5_acc": 0.87953, "loss_cls": 1.81169, "loss": 1.81169, "time": 0.84503} +{"mode": "train", "epoch": 150, "iter": 3500, "lr": 0.0, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.6725, "top5_acc": 0.88031, "loss_cls": 1.84607, "loss": 1.84607, "time": 0.84305} +{"mode": "train", "epoch": 150, "iter": 3600, "lr": 0.0, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.69047, "top5_acc": 0.87438, "loss_cls": 1.80669, "loss": 1.80669, "time": 0.84644} +{"mode": "train", "epoch": 150, "iter": 3700, "lr": 0.0, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.67516, "top5_acc": 0.87969, "loss_cls": 1.83823, "loss": 1.83823, "time": 0.84605} +{"mode": "val", "epoch": 150, "iter": 309, "lr": 0.0, "top1_acc": 0.47207, "top5_acc": 0.71798, "mean_class_accuracy": 0.47184} diff --git a/k400/j_1/best_pred.pkl b/k400/j_1/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..dfb99fb38911613268443e08e3df856e21853ce9 --- /dev/null +++ b/k400/j_1/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c31005411298d3f653135edf5654ba0049052592291ccbb8ac436ec30d603e62 +size 44892099 diff --git a/k400/j_1/best_top1_acc_epoch_149.pth b/k400/j_1/best_top1_acc_epoch_149.pth new file mode 100644 index 0000000000000000000000000000000000000000..c51002b1cc9963ef817eee7aba72742bd9c0e034 --- /dev/null +++ b/k400/j_1/best_top1_acc_epoch_149.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24888529064179c4bc95dbe3a0aca6cf55d4c966beb57366bd442af8815f7dcd +size 32926705 diff --git a/k400/j_1/j_1.py b/k400/j_1/j_1.py new file mode 100644 index 0000000000000000000000000000000000000000..f019d93d504dc604118fe2f712e1e96faaa39256 --- /dev/null +++ b/k400/j_1/j_1.py @@ -0,0 +1,133 @@ +modality = 'j' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/j_1' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/k400/j_2/20240722_022822.log b/k400/j_2/20240722_022822.log new file mode 100644 index 0000000000000000000000000000000000000000..94b2139a3e3840cba1337d8d8e60b54242381f2d --- /dev/null +++ b/k400/j_2/20240722_022822.log @@ -0,0 +1,7331 @@ +2024-07-22 02:28:22,626 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2024-07-22 02:28:23,079 - pyskl - INFO - Config: modality = 'j' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/j_2' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2024-07-22 02:28:23,080 - pyskl - INFO - Set random seed to 1837183614, deterministic: False +2024-07-22 02:28:33,915 - pyskl - INFO - 239737 videos remain after valid thresholding +2024-07-22 02:28:51,184 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-07-22 02:28:51,187 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2 +2024-07-22 02:28:51,191 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2024-07-22 02:28:51,215 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2024-07-22 02:28:51,221 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2 by HardDiskBackend. +2024-07-22 02:32:08,914 - pyskl - INFO - Epoch [1][100/3746] lr: 1.000e-01, eta: 12 days, 20:29:53, time: 1.977, data_time: 1.259, memory: 15990, top1_acc: 0.0050, top5_acc: 0.0256, loss_cls: 6.4992, loss: 6.4992 +2024-07-22 02:33:20,300 - pyskl - INFO - Epoch [1][200/3746] lr: 1.000e-01, eta: 8 days, 17:54:42, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0100, top5_acc: 0.0453, loss_cls: 6.4574, loss: 6.4574 +2024-07-22 02:34:31,622 - pyskl - INFO - Epoch [1][300/3746] lr: 1.000e-01, eta: 7 days, 9:00:11, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.0131, top5_acc: 0.0622, loss_cls: 6.2790, loss: 6.2790 +2024-07-22 02:35:42,547 - pyskl - INFO - Epoch [1][400/3746] lr: 1.000e-01, eta: 6 days, 16:23:02, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.0184, top5_acc: 0.0711, loss_cls: 6.1215, loss: 6.1215 +2024-07-22 02:36:53,164 - pyskl - INFO - Epoch [1][500/3746] lr: 1.000e-01, eta: 6 days, 6:18:31, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.0195, top5_acc: 0.0853, loss_cls: 6.0202, loss: 6.0202 +2024-07-22 02:38:03,675 - pyskl - INFO - Epoch [1][600/3746] lr: 1.000e-01, eta: 5 days, 23:33:27, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0264, top5_acc: 0.0994, loss_cls: 5.9298, loss: 5.9298 +2024-07-22 02:39:14,087 - pyskl - INFO - Epoch [1][700/3746] lr: 1.000e-01, eta: 5 days, 18:42:29, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0250, top5_acc: 0.1056, loss_cls: 5.8675, loss: 5.8675 +2024-07-22 02:40:24,709 - pyskl - INFO - Epoch [1][800/3746] lr: 1.000e-01, eta: 5 days, 15:06:24, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.0266, top5_acc: 0.1077, loss_cls: 5.8480, loss: 5.8480 +2024-07-22 02:41:35,217 - pyskl - INFO - Epoch [1][900/3746] lr: 1.000e-01, eta: 5 days, 12:16:53, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0338, top5_acc: 0.1183, loss_cls: 5.8059, loss: 5.8059 +2024-07-22 02:42:45,639 - pyskl - INFO - Epoch [1][1000/3746] lr: 1.000e-01, eta: 5 days, 10:00:15, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0394, top5_acc: 0.1350, loss_cls: 5.7678, loss: 5.7678 +2024-07-22 02:43:56,344 - pyskl - INFO - Epoch [1][1100/3746] lr: 1.000e-01, eta: 5 days, 8:10:38, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.0350, top5_acc: 0.1325, loss_cls: 5.7369, loss: 5.7369 +2024-07-22 02:45:06,698 - pyskl - INFO - Epoch [1][1200/3746] lr: 1.000e-01, eta: 5 days, 6:36:22, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0395, top5_acc: 0.1430, loss_cls: 5.6848, loss: 5.6848 +2024-07-22 02:46:16,856 - pyskl - INFO - Epoch [1][1300/3746] lr: 1.000e-01, eta: 5 days, 5:15:01, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0480, top5_acc: 0.1520, loss_cls: 5.6394, loss: 5.6394 +2024-07-22 02:47:27,183 - pyskl - INFO - Epoch [1][1400/3746] lr: 1.000e-01, eta: 5 days, 4:06:14, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0517, top5_acc: 0.1648, loss_cls: 5.6171, loss: 5.6171 +2024-07-22 02:48:37,391 - pyskl - INFO - Epoch [1][1500/3746] lr: 1.000e-01, eta: 5 days, 3:05:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0462, top5_acc: 0.1634, loss_cls: 5.6079, loss: 5.6079 +2024-07-22 02:49:47,490 - pyskl - INFO - Epoch [1][1600/3746] lr: 1.000e-01, eta: 5 days, 2:12:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0483, top5_acc: 0.1647, loss_cls: 5.5617, loss: 5.5617 +2024-07-22 02:50:58,294 - pyskl - INFO - Epoch [1][1700/3746] lr: 1.000e-01, eta: 5 days, 1:28:21, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.0502, top5_acc: 0.1706, loss_cls: 5.5459, loss: 5.5459 +2024-07-22 02:52:08,832 - pyskl - INFO - Epoch [1][1800/3746] lr: 1.000e-01, eta: 5 days, 0:48:02, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0577, top5_acc: 0.1822, loss_cls: 5.5283, loss: 5.5283 +2024-07-22 02:53:19,287 - pyskl - INFO - Epoch [1][1900/3746] lr: 1.000e-01, eta: 5 days, 0:11:25, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0572, top5_acc: 0.1875, loss_cls: 5.4638, loss: 5.4638 +2024-07-22 02:54:31,077 - pyskl - INFO - Epoch [1][2000/3746] lr: 1.000e-01, eta: 4 days, 23:44:35, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.0591, top5_acc: 0.1934, loss_cls: 5.4771, loss: 5.4771 +2024-07-22 02:55:44,183 - pyskl - INFO - Epoch [1][2100/3746] lr: 1.000e-01, eta: 4 days, 23:26:02, time: 0.731, data_time: 0.000, memory: 15990, top1_acc: 0.0728, top5_acc: 0.2092, loss_cls: 5.4117, loss: 5.4117 +2024-07-22 02:56:55,417 - pyskl - INFO - Epoch [1][2200/3746] lr: 1.000e-01, eta: 4 days, 23:01:07, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.0725, top5_acc: 0.2116, loss_cls: 5.3996, loss: 5.3996 +2024-07-22 02:58:05,766 - pyskl - INFO - Epoch [1][2300/3746] lr: 1.000e-01, eta: 4 days, 22:34:41, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0697, top5_acc: 0.2189, loss_cls: 5.4007, loss: 5.4007 +2024-07-22 02:59:16,367 - pyskl - INFO - Epoch [1][2400/3746] lr: 1.000e-01, eta: 4 days, 22:11:19, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.0734, top5_acc: 0.2248, loss_cls: 5.3457, loss: 5.3457 +2024-07-22 03:00:27,041 - pyskl - INFO - Epoch [1][2500/3746] lr: 1.000e-01, eta: 4 days, 21:50:01, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.0861, top5_acc: 0.2417, loss_cls: 5.2823, loss: 5.2823 +2024-07-22 03:01:37,563 - pyskl - INFO - Epoch [1][2600/3746] lr: 9.999e-02, eta: 4 days, 21:29:43, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0820, top5_acc: 0.2417, loss_cls: 5.2854, loss: 5.2854 +2024-07-22 03:02:48,412 - pyskl - INFO - Epoch [1][2700/3746] lr: 9.999e-02, eta: 4 days, 21:11:57, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.0873, top5_acc: 0.2522, loss_cls: 5.2397, loss: 5.2397 +2024-07-22 03:03:58,621 - pyskl - INFO - Epoch [1][2800/3746] lr: 9.999e-02, eta: 4 days, 20:53:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0903, top5_acc: 0.2448, loss_cls: 5.2436, loss: 5.2436 +2024-07-22 03:05:08,923 - pyskl - INFO - Epoch [1][2900/3746] lr: 9.999e-02, eta: 4 days, 20:36:04, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0955, top5_acc: 0.2583, loss_cls: 5.2167, loss: 5.2167 +2024-07-22 03:06:19,082 - pyskl - INFO - Epoch [1][3000/3746] lr: 9.999e-02, eta: 4 days, 20:19:29, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0939, top5_acc: 0.2692, loss_cls: 5.1797, loss: 5.1797 +2024-07-22 03:07:29,046 - pyskl - INFO - Epoch [1][3100/3746] lr: 9.999e-02, eta: 4 days, 20:03:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0984, top5_acc: 0.2659, loss_cls: 5.1897, loss: 5.1897 +2024-07-22 03:08:39,034 - pyskl - INFO - Epoch [1][3200/3746] lr: 9.999e-02, eta: 4 days, 19:48:10, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0998, top5_acc: 0.2814, loss_cls: 5.1204, loss: 5.1204 +2024-07-22 03:09:48,854 - pyskl - INFO - Epoch [1][3300/3746] lr: 9.999e-02, eta: 4 days, 19:33:23, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1019, top5_acc: 0.2745, loss_cls: 5.1207, loss: 5.1207 +2024-07-22 03:10:59,097 - pyskl - INFO - Epoch [1][3400/3746] lr: 9.999e-02, eta: 4 days, 19:20:34, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1094, top5_acc: 0.2817, loss_cls: 5.0932, loss: 5.0932 +2024-07-22 03:12:09,067 - pyskl - INFO - Epoch [1][3500/3746] lr: 9.999e-02, eta: 4 days, 19:07:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1000, top5_acc: 0.2911, loss_cls: 5.0574, loss: 5.0574 +2024-07-22 03:13:19,152 - pyskl - INFO - Epoch [1][3600/3746] lr: 9.999e-02, eta: 4 days, 18:55:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1056, top5_acc: 0.2877, loss_cls: 5.0769, loss: 5.0769 +2024-07-22 03:14:29,422 - pyskl - INFO - Epoch [1][3700/3746] lr: 9.999e-02, eta: 4 days, 18:44:51, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1056, top5_acc: 0.2881, loss_cls: 5.0404, loss: 5.0404 +2024-07-22 03:15:03,324 - pyskl - INFO - Saving checkpoint at 1 epochs +2024-07-22 03:16:59,566 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 03:17:00,235 - pyskl - INFO - +top1_acc 0.0621 +top5_acc 0.1915 +2024-07-22 03:17:00,235 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 03:17:00,277 - pyskl - INFO - +mean_acc 0.0619 +2024-07-22 03:17:00,642 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2024-07-22 03:17:00,642 - pyskl - INFO - Best top1_acc is 0.0621 at 1 epoch. +2024-07-22 03:17:00,654 - pyskl - INFO - Epoch(val) [1][309] top1_acc: 0.0621, top5_acc: 0.1915, mean_class_accuracy: 0.0619 +2024-07-22 03:20:27,787 - pyskl - INFO - Epoch [2][100/3746] lr: 9.999e-02, eta: 4 days, 22:42:39, time: 2.071, data_time: 1.358, memory: 15990, top1_acc: 0.1155, top5_acc: 0.3059, loss_cls: 5.0084, loss: 5.0084 +2024-07-22 03:21:39,017 - pyskl - INFO - Epoch [2][200/3746] lr: 9.999e-02, eta: 4 days, 22:28:46, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1187, top5_acc: 0.3058, loss_cls: 5.0076, loss: 5.0076 +2024-07-22 03:22:49,573 - pyskl - INFO - Epoch [2][300/3746] lr: 9.999e-02, eta: 4 days, 22:13:58, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1152, top5_acc: 0.3014, loss_cls: 5.0285, loss: 5.0285 +2024-07-22 03:23:59,884 - pyskl - INFO - Epoch [2][400/3746] lr: 9.999e-02, eta: 4 days, 21:59:16, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1231, top5_acc: 0.3153, loss_cls: 4.9667, loss: 4.9667 +2024-07-22 03:25:10,117 - pyskl - INFO - Epoch [2][500/3746] lr: 9.999e-02, eta: 4 days, 21:45:02, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1211, top5_acc: 0.3159, loss_cls: 4.9635, loss: 4.9635 +2024-07-22 03:26:20,171 - pyskl - INFO - Epoch [2][600/3746] lr: 9.999e-02, eta: 4 days, 21:31:00, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1283, top5_acc: 0.3225, loss_cls: 4.9539, loss: 4.9539 +2024-07-22 03:27:30,653 - pyskl - INFO - Epoch [2][700/3746] lr: 9.998e-02, eta: 4 days, 21:18:28, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1252, top5_acc: 0.3264, loss_cls: 4.9179, loss: 4.9179 +2024-07-22 03:28:40,881 - pyskl - INFO - Epoch [2][800/3746] lr: 9.998e-02, eta: 4 days, 21:05:54, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1200, top5_acc: 0.3233, loss_cls: 4.9258, loss: 4.9258 +2024-07-22 03:29:51,312 - pyskl - INFO - Epoch [2][900/3746] lr: 9.998e-02, eta: 4 days, 20:54:14, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1191, top5_acc: 0.3159, loss_cls: 4.9623, loss: 4.9623 +2024-07-22 03:31:01,680 - pyskl - INFO - Epoch [2][1000/3746] lr: 9.998e-02, eta: 4 days, 20:42:54, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1305, top5_acc: 0.3316, loss_cls: 4.9043, loss: 4.9043 +2024-07-22 03:32:11,939 - pyskl - INFO - Epoch [2][1100/3746] lr: 9.998e-02, eta: 4 days, 20:31:45, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1292, top5_acc: 0.3336, loss_cls: 4.8711, loss: 4.8711 +2024-07-22 03:33:22,140 - pyskl - INFO - Epoch [2][1200/3746] lr: 9.998e-02, eta: 4 days, 20:20:55, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1344, top5_acc: 0.3461, loss_cls: 4.8849, loss: 4.8849 +2024-07-22 03:34:32,127 - pyskl - INFO - Epoch [2][1300/3746] lr: 9.998e-02, eta: 4 days, 20:10:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1294, top5_acc: 0.3208, loss_cls: 4.9350, loss: 4.9350 +2024-07-22 03:35:41,850 - pyskl - INFO - Epoch [2][1400/3746] lr: 9.998e-02, eta: 4 days, 19:59:07, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1317, top5_acc: 0.3337, loss_cls: 4.8978, loss: 4.8978 +2024-07-22 03:36:51,735 - pyskl - INFO - Epoch [2][1500/3746] lr: 9.998e-02, eta: 4 days, 19:48:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1314, top5_acc: 0.3400, loss_cls: 4.8674, loss: 4.8674 +2024-07-22 03:38:01,618 - pyskl - INFO - Epoch [2][1600/3746] lr: 9.998e-02, eta: 4 days, 19:38:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1348, top5_acc: 0.3320, loss_cls: 4.8844, loss: 4.8844 +2024-07-22 03:39:11,321 - pyskl - INFO - Epoch [2][1700/3746] lr: 9.998e-02, eta: 4 days, 19:28:56, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1352, top5_acc: 0.3441, loss_cls: 4.8532, loss: 4.8532 +2024-07-22 03:40:21,283 - pyskl - INFO - Epoch [2][1800/3746] lr: 9.998e-02, eta: 4 days, 19:19:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1333, top5_acc: 0.3494, loss_cls: 4.8206, loss: 4.8206 +2024-07-22 03:41:30,948 - pyskl - INFO - Epoch [2][1900/3746] lr: 9.998e-02, eta: 4 days, 19:10:21, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1419, top5_acc: 0.3494, loss_cls: 4.8329, loss: 4.8329 +2024-07-22 03:42:40,870 - pyskl - INFO - Epoch [2][2000/3746] lr: 9.997e-02, eta: 4 days, 19:01:40, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1494, top5_acc: 0.3539, loss_cls: 4.7807, loss: 4.7807 +2024-07-22 03:43:50,967 - pyskl - INFO - Epoch [2][2100/3746] lr: 9.997e-02, eta: 4 days, 18:53:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1456, top5_acc: 0.3567, loss_cls: 4.7945, loss: 4.7945 +2024-07-22 03:45:00,607 - pyskl - INFO - Epoch [2][2200/3746] lr: 9.997e-02, eta: 4 days, 18:44:52, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1522, top5_acc: 0.3530, loss_cls: 4.8058, loss: 4.8058 +2024-07-22 03:46:10,332 - pyskl - INFO - Epoch [2][2300/3746] lr: 9.997e-02, eta: 4 days, 18:36:37, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1431, top5_acc: 0.3409, loss_cls: 4.8421, loss: 4.8421 +2024-07-22 03:47:20,086 - pyskl - INFO - Epoch [2][2400/3746] lr: 9.997e-02, eta: 4 days, 18:28:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1462, top5_acc: 0.3634, loss_cls: 4.7659, loss: 4.7659 +2024-07-22 03:48:29,934 - pyskl - INFO - Epoch [2][2500/3746] lr: 9.997e-02, eta: 4 days, 18:21:01, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1500, top5_acc: 0.3639, loss_cls: 4.7764, loss: 4.7764 +2024-07-22 03:49:39,827 - pyskl - INFO - Epoch [2][2600/3746] lr: 9.997e-02, eta: 4 days, 18:13:39, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1564, top5_acc: 0.3711, loss_cls: 4.7413, loss: 4.7413 +2024-07-22 03:50:49,516 - pyskl - INFO - Epoch [2][2700/3746] lr: 9.997e-02, eta: 4 days, 18:06:12, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1567, top5_acc: 0.3636, loss_cls: 4.7409, loss: 4.7409 +2024-07-22 03:51:59,436 - pyskl - INFO - Epoch [2][2800/3746] lr: 9.997e-02, eta: 4 days, 17:59:16, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1456, top5_acc: 0.3600, loss_cls: 4.7730, loss: 4.7730 +2024-07-22 03:53:09,434 - pyskl - INFO - Epoch [2][2900/3746] lr: 9.997e-02, eta: 4 days, 17:52:37, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1550, top5_acc: 0.3688, loss_cls: 4.7263, loss: 4.7263 +2024-07-22 03:54:19,163 - pyskl - INFO - Epoch [2][3000/3746] lr: 9.996e-02, eta: 4 days, 17:45:45, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1552, top5_acc: 0.3636, loss_cls: 4.7364, loss: 4.7364 +2024-07-22 03:55:28,955 - pyskl - INFO - Epoch [2][3100/3746] lr: 9.996e-02, eta: 4 days, 17:39:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1573, top5_acc: 0.3702, loss_cls: 4.7569, loss: 4.7569 +2024-07-22 03:56:38,823 - pyskl - INFO - Epoch [2][3200/3746] lr: 9.996e-02, eta: 4 days, 17:32:48, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1545, top5_acc: 0.3702, loss_cls: 4.7195, loss: 4.7195 +2024-07-22 03:57:48,570 - pyskl - INFO - Epoch [2][3300/3746] lr: 9.996e-02, eta: 4 days, 17:26:26, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1537, top5_acc: 0.3659, loss_cls: 4.7377, loss: 4.7377 +2024-07-22 03:58:59,107 - pyskl - INFO - Epoch [2][3400/3746] lr: 9.996e-02, eta: 4 days, 17:21:14, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1480, top5_acc: 0.3573, loss_cls: 4.7869, loss: 4.7869 +2024-07-22 04:00:09,239 - pyskl - INFO - Epoch [2][3500/3746] lr: 9.996e-02, eta: 4 days, 17:15:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1603, top5_acc: 0.3787, loss_cls: 4.6835, loss: 4.6835 +2024-07-22 04:01:19,531 - pyskl - INFO - Epoch [2][3600/3746] lr: 9.996e-02, eta: 4 days, 17:10:22, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1630, top5_acc: 0.3769, loss_cls: 4.7031, loss: 4.7031 +2024-07-22 04:02:29,911 - pyskl - INFO - Epoch [2][3700/3746] lr: 9.996e-02, eta: 4 days, 17:05:18, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1562, top5_acc: 0.3730, loss_cls: 4.6863, loss: 4.6863 +2024-07-22 04:03:03,976 - pyskl - INFO - Saving checkpoint at 2 epochs +2024-07-22 04:04:59,554 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 04:05:00,237 - pyskl - INFO - +top1_acc 0.0984 +top5_acc 0.2740 +2024-07-22 04:05:00,238 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 04:05:00,282 - pyskl - INFO - +mean_acc 0.0983 +2024-07-22 04:05:00,287 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_1.pth was removed +2024-07-22 04:05:00,583 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2024-07-22 04:05:00,584 - pyskl - INFO - Best top1_acc is 0.0984 at 2 epoch. +2024-07-22 04:05:00,598 - pyskl - INFO - Epoch(val) [2][309] top1_acc: 0.0984, top5_acc: 0.2740, mean_class_accuracy: 0.0983 +2024-07-22 04:08:24,171 - pyskl - INFO - Epoch [3][100/3746] lr: 9.995e-02, eta: 4 days, 19:00:47, time: 2.036, data_time: 1.323, memory: 15990, top1_acc: 0.1606, top5_acc: 0.3787, loss_cls: 4.6929, loss: 4.6929 +2024-07-22 04:09:35,629 - pyskl - INFO - Epoch [3][200/3746] lr: 9.995e-02, eta: 4 days, 18:55:38, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1673, top5_acc: 0.3892, loss_cls: 4.6281, loss: 4.6281 +2024-07-22 04:10:46,356 - pyskl - INFO - Epoch [3][300/3746] lr: 9.995e-02, eta: 4 days, 18:49:44, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1600, top5_acc: 0.3739, loss_cls: 4.6702, loss: 4.6702 +2024-07-22 04:11:56,942 - pyskl - INFO - Epoch [3][400/3746] lr: 9.995e-02, eta: 4 days, 18:43:48, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1677, top5_acc: 0.3831, loss_cls: 4.6737, loss: 4.6737 +2024-07-22 04:13:07,295 - pyskl - INFO - Epoch [3][500/3746] lr: 9.995e-02, eta: 4 days, 18:37:42, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1647, top5_acc: 0.3792, loss_cls: 4.6360, loss: 4.6360 +2024-07-22 04:14:17,611 - pyskl - INFO - Epoch [3][600/3746] lr: 9.995e-02, eta: 4 days, 18:31:41, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1663, top5_acc: 0.3861, loss_cls: 4.6536, loss: 4.6536 +2024-07-22 04:15:27,709 - pyskl - INFO - Epoch [3][700/3746] lr: 9.995e-02, eta: 4 days, 18:25:32, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1616, top5_acc: 0.3880, loss_cls: 4.6651, loss: 4.6651 +2024-07-22 04:16:37,748 - pyskl - INFO - Epoch [3][800/3746] lr: 9.995e-02, eta: 4 days, 18:19:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1673, top5_acc: 0.3909, loss_cls: 4.6299, loss: 4.6299 +2024-07-22 04:17:47,764 - pyskl - INFO - Epoch [3][900/3746] lr: 9.994e-02, eta: 4 days, 18:13:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1664, top5_acc: 0.3845, loss_cls: 4.6363, loss: 4.6363 +2024-07-22 04:18:57,803 - pyskl - INFO - Epoch [3][1000/3746] lr: 9.994e-02, eta: 4 days, 18:07:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1759, top5_acc: 0.3950, loss_cls: 4.6321, loss: 4.6321 +2024-07-22 04:20:08,148 - pyskl - INFO - Epoch [3][1100/3746] lr: 9.994e-02, eta: 4 days, 18:02:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1652, top5_acc: 0.3980, loss_cls: 4.6280, loss: 4.6280 +2024-07-22 04:21:18,291 - pyskl - INFO - Epoch [3][1200/3746] lr: 9.994e-02, eta: 4 days, 17:56:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1764, top5_acc: 0.3997, loss_cls: 4.5808, loss: 4.5808 +2024-07-22 04:22:27,936 - pyskl - INFO - Epoch [3][1300/3746] lr: 9.994e-02, eta: 4 days, 17:50:41, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1719, top5_acc: 0.3977, loss_cls: 4.6090, loss: 4.6090 +2024-07-22 04:23:37,780 - pyskl - INFO - Epoch [3][1400/3746] lr: 9.994e-02, eta: 4 days, 17:45:02, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1819, top5_acc: 0.4097, loss_cls: 4.5501, loss: 4.5501 +2024-07-22 04:24:47,728 - pyskl - INFO - Epoch [3][1500/3746] lr: 9.994e-02, eta: 4 days, 17:39:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1684, top5_acc: 0.3892, loss_cls: 4.6033, loss: 4.6033 +2024-07-22 04:25:57,478 - pyskl - INFO - Epoch [3][1600/3746] lr: 9.994e-02, eta: 4 days, 17:34:03, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1819, top5_acc: 0.3991, loss_cls: 4.5934, loss: 4.5934 +2024-07-22 04:27:07,328 - pyskl - INFO - Epoch [3][1700/3746] lr: 9.993e-02, eta: 4 days, 17:28:42, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1739, top5_acc: 0.3908, loss_cls: 4.6323, loss: 4.6323 +2024-07-22 04:28:17,170 - pyskl - INFO - Epoch [3][1800/3746] lr: 9.993e-02, eta: 4 days, 17:23:26, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1673, top5_acc: 0.3955, loss_cls: 4.6153, loss: 4.6153 +2024-07-22 04:29:26,975 - pyskl - INFO - Epoch [3][1900/3746] lr: 9.993e-02, eta: 4 days, 17:18:13, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1728, top5_acc: 0.3939, loss_cls: 4.5959, loss: 4.5959 +2024-07-22 04:30:36,795 - pyskl - INFO - Epoch [3][2000/3746] lr: 9.993e-02, eta: 4 days, 17:13:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1805, top5_acc: 0.4034, loss_cls: 4.5895, loss: 4.5895 +2024-07-22 04:31:46,725 - pyskl - INFO - Epoch [3][2100/3746] lr: 9.993e-02, eta: 4 days, 17:08:10, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1697, top5_acc: 0.3948, loss_cls: 4.6014, loss: 4.6014 +2024-07-22 04:32:56,563 - pyskl - INFO - Epoch [3][2200/3746] lr: 9.993e-02, eta: 4 days, 17:03:14, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1753, top5_acc: 0.3987, loss_cls: 4.5738, loss: 4.5738 +2024-07-22 04:34:06,099 - pyskl - INFO - Epoch [3][2300/3746] lr: 9.993e-02, eta: 4 days, 16:58:05, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.1789, top5_acc: 0.4008, loss_cls: 4.5500, loss: 4.5500 +2024-07-22 04:35:15,821 - pyskl - INFO - Epoch [3][2400/3746] lr: 9.992e-02, eta: 4 days, 16:53:12, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1886, top5_acc: 0.4095, loss_cls: 4.5285, loss: 4.5285 +2024-07-22 04:36:25,525 - pyskl - INFO - Epoch [3][2500/3746] lr: 9.992e-02, eta: 4 days, 16:48:22, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1741, top5_acc: 0.4089, loss_cls: 4.5719, loss: 4.5719 +2024-07-22 04:37:35,518 - pyskl - INFO - Epoch [3][2600/3746] lr: 9.992e-02, eta: 4 days, 16:43:52, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1884, top5_acc: 0.4214, loss_cls: 4.4921, loss: 4.4921 +2024-07-22 04:38:45,084 - pyskl - INFO - Epoch [3][2700/3746] lr: 9.992e-02, eta: 4 days, 16:39:03, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1812, top5_acc: 0.4123, loss_cls: 4.5327, loss: 4.5327 +2024-07-22 04:39:54,760 - pyskl - INFO - Epoch [3][2800/3746] lr: 9.992e-02, eta: 4 days, 16:34:24, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1920, top5_acc: 0.4258, loss_cls: 4.5004, loss: 4.5004 +2024-07-22 04:41:04,411 - pyskl - INFO - Epoch [3][2900/3746] lr: 9.992e-02, eta: 4 days, 16:29:48, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1791, top5_acc: 0.4075, loss_cls: 4.5908, loss: 4.5908 +2024-07-22 04:42:14,221 - pyskl - INFO - Epoch [3][3000/3746] lr: 9.991e-02, eta: 4 days, 16:25:24, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1911, top5_acc: 0.4269, loss_cls: 4.5026, loss: 4.5026 +2024-07-22 04:43:23,898 - pyskl - INFO - Epoch [3][3100/3746] lr: 9.991e-02, eta: 4 days, 16:20:57, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1762, top5_acc: 0.4033, loss_cls: 4.5413, loss: 4.5413 +2024-07-22 04:44:33,584 - pyskl - INFO - Epoch [3][3200/3746] lr: 9.991e-02, eta: 4 days, 16:16:34, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1819, top5_acc: 0.4100, loss_cls: 4.5351, loss: 4.5351 +2024-07-22 04:45:43,279 - pyskl - INFO - Epoch [3][3300/3746] lr: 9.991e-02, eta: 4 days, 16:12:15, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1947, top5_acc: 0.4177, loss_cls: 4.5115, loss: 4.5115 +2024-07-22 04:46:53,359 - pyskl - INFO - Epoch [3][3400/3746] lr: 9.991e-02, eta: 4 days, 16:08:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1802, top5_acc: 0.4080, loss_cls: 4.5454, loss: 4.5454 +2024-07-22 04:48:03,090 - pyskl - INFO - Epoch [3][3500/3746] lr: 9.991e-02, eta: 4 days, 16:04:09, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1848, top5_acc: 0.4191, loss_cls: 4.4970, loss: 4.4970 +2024-07-22 04:49:13,064 - pyskl - INFO - Epoch [3][3600/3746] lr: 9.990e-02, eta: 4 days, 16:00:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1797, top5_acc: 0.4163, loss_cls: 4.5230, loss: 4.5230 +2024-07-22 04:50:23,269 - pyskl - INFO - Epoch [3][3700/3746] lr: 9.990e-02, eta: 4 days, 15:56:33, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1800, top5_acc: 0.4200, loss_cls: 4.5057, loss: 4.5057 +2024-07-22 04:50:57,749 - pyskl - INFO - Saving checkpoint at 3 epochs +2024-07-22 04:52:52,167 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 04:52:52,843 - pyskl - INFO - +top1_acc 0.1300 +top5_acc 0.3177 +2024-07-22 04:52:52,843 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 04:52:52,887 - pyskl - INFO - +mean_acc 0.1298 +2024-07-22 04:52:52,892 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_2.pth was removed +2024-07-22 04:52:53,149 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2024-07-22 04:52:53,149 - pyskl - INFO - Best top1_acc is 0.1300 at 3 epoch. +2024-07-22 04:52:53,162 - pyskl - INFO - Epoch(val) [3][309] top1_acc: 0.1300, top5_acc: 0.3177, mean_class_accuracy: 0.1298 +2024-07-22 04:56:19,449 - pyskl - INFO - Epoch [4][100/3746] lr: 9.990e-02, eta: 4 days, 17:15:14, time: 2.063, data_time: 1.359, memory: 15990, top1_acc: 0.1945, top5_acc: 0.4186, loss_cls: 4.4497, loss: 4.4497 +2024-07-22 04:57:29,500 - pyskl - INFO - Epoch [4][200/3746] lr: 9.990e-02, eta: 4 days, 17:10:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1903, top5_acc: 0.4167, loss_cls: 4.4736, loss: 4.4736 +2024-07-22 04:58:39,632 - pyskl - INFO - Epoch [4][300/3746] lr: 9.990e-02, eta: 4 days, 17:06:28, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2045, top5_acc: 0.4281, loss_cls: 4.4125, loss: 4.4125 +2024-07-22 04:59:49,701 - pyskl - INFO - Epoch [4][400/3746] lr: 9.989e-02, eta: 4 days, 17:02:09, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1961, top5_acc: 0.4164, loss_cls: 4.5013, loss: 4.5013 +2024-07-22 05:00:59,693 - pyskl - INFO - Epoch [4][500/3746] lr: 9.989e-02, eta: 4 days, 16:57:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1842, top5_acc: 0.4245, loss_cls: 4.4972, loss: 4.4972 +2024-07-22 05:02:09,520 - pyskl - INFO - Epoch [4][600/3746] lr: 9.989e-02, eta: 4 days, 16:53:25, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1998, top5_acc: 0.4403, loss_cls: 4.4166, loss: 4.4166 +2024-07-22 05:03:19,442 - pyskl - INFO - Epoch [4][700/3746] lr: 9.989e-02, eta: 4 days, 16:49:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1847, top5_acc: 0.4258, loss_cls: 4.4664, loss: 4.4664 +2024-07-22 05:04:29,381 - pyskl - INFO - Epoch [4][800/3746] lr: 9.989e-02, eta: 4 days, 16:44:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1927, top5_acc: 0.4316, loss_cls: 4.4465, loss: 4.4465 +2024-07-22 05:05:39,325 - pyskl - INFO - Epoch [4][900/3746] lr: 9.988e-02, eta: 4 days, 16:40:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1897, top5_acc: 0.4255, loss_cls: 4.4641, loss: 4.4641 +2024-07-22 05:06:49,338 - pyskl - INFO - Epoch [4][1000/3746] lr: 9.988e-02, eta: 4 days, 16:36:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1948, top5_acc: 0.4313, loss_cls: 4.4555, loss: 4.4555 +2024-07-22 05:07:59,521 - pyskl - INFO - Epoch [4][1100/3746] lr: 9.988e-02, eta: 4 days, 16:32:51, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4437, loss_cls: 4.3920, loss: 4.3920 +2024-07-22 05:09:09,393 - pyskl - INFO - Epoch [4][1200/3746] lr: 9.988e-02, eta: 4 days, 16:28:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1895, top5_acc: 0.4230, loss_cls: 4.4849, loss: 4.4849 +2024-07-22 05:10:19,351 - pyskl - INFO - Epoch [4][1300/3746] lr: 9.988e-02, eta: 4 days, 16:24:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4400, loss_cls: 4.4052, loss: 4.4052 +2024-07-22 05:11:29,653 - pyskl - INFO - Epoch [4][1400/3746] lr: 9.988e-02, eta: 4 days, 16:21:09, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1984, top5_acc: 0.4278, loss_cls: 4.4718, loss: 4.4718 +2024-07-22 05:12:39,418 - pyskl - INFO - Epoch [4][1500/3746] lr: 9.987e-02, eta: 4 days, 16:17:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1941, top5_acc: 0.4213, loss_cls: 4.4767, loss: 4.4767 +2024-07-22 05:13:49,172 - pyskl - INFO - Epoch [4][1600/3746] lr: 9.987e-02, eta: 4 days, 16:13:10, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1945, top5_acc: 0.4314, loss_cls: 4.4457, loss: 4.4457 +2024-07-22 05:14:59,008 - pyskl - INFO - Epoch [4][1700/3746] lr: 9.987e-02, eta: 4 days, 16:09:18, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2014, top5_acc: 0.4408, loss_cls: 4.4119, loss: 4.4119 +2024-07-22 05:16:09,047 - pyskl - INFO - Epoch [4][1800/3746] lr: 9.987e-02, eta: 4 days, 16:05:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4508, loss_cls: 4.3628, loss: 4.3628 +2024-07-22 05:17:19,130 - pyskl - INFO - Epoch [4][1900/3746] lr: 9.987e-02, eta: 4 days, 16:01:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2028, top5_acc: 0.4372, loss_cls: 4.4335, loss: 4.4335 +2024-07-22 05:18:28,892 - pyskl - INFO - Epoch [4][2000/3746] lr: 9.986e-02, eta: 4 days, 15:58:11, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4373, loss_cls: 4.4329, loss: 4.4329 +2024-07-22 05:19:39,052 - pyskl - INFO - Epoch [4][2100/3746] lr: 9.986e-02, eta: 4 days, 15:54:41, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4369, loss_cls: 4.4203, loss: 4.4203 +2024-07-22 05:20:49,022 - pyskl - INFO - Epoch [4][2200/3746] lr: 9.986e-02, eta: 4 days, 15:51:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2014, top5_acc: 0.4327, loss_cls: 4.4374, loss: 4.4374 +2024-07-22 05:21:59,036 - pyskl - INFO - Epoch [4][2300/3746] lr: 9.986e-02, eta: 4 days, 15:47:35, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1978, top5_acc: 0.4381, loss_cls: 4.4112, loss: 4.4112 +2024-07-22 05:23:09,108 - pyskl - INFO - Epoch [4][2400/3746] lr: 9.985e-02, eta: 4 days, 15:44:08, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1961, top5_acc: 0.4352, loss_cls: 4.4332, loss: 4.4332 +2024-07-22 05:24:19,173 - pyskl - INFO - Epoch [4][2500/3746] lr: 9.985e-02, eta: 4 days, 15:40:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4394, loss_cls: 4.3773, loss: 4.3773 +2024-07-22 05:25:29,008 - pyskl - INFO - Epoch [4][2600/3746] lr: 9.985e-02, eta: 4 days, 15:37:10, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4450, loss_cls: 4.4161, loss: 4.4161 +2024-07-22 05:26:38,809 - pyskl - INFO - Epoch [4][2700/3746] lr: 9.985e-02, eta: 4 days, 15:33:39, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2025, top5_acc: 0.4291, loss_cls: 4.4383, loss: 4.4383 +2024-07-22 05:27:48,601 - pyskl - INFO - Epoch [4][2800/3746] lr: 9.985e-02, eta: 4 days, 15:30:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1970, top5_acc: 0.4300, loss_cls: 4.4266, loss: 4.4266 +2024-07-22 05:28:58,441 - pyskl - INFO - Epoch [4][2900/3746] lr: 9.984e-02, eta: 4 days, 15:26:43, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4384, loss_cls: 4.3797, loss: 4.3797 +2024-07-22 05:30:08,174 - pyskl - INFO - Epoch [4][3000/3746] lr: 9.984e-02, eta: 4 days, 15:23:14, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4373, loss_cls: 4.4173, loss: 4.4173 +2024-07-22 05:31:18,343 - pyskl - INFO - Epoch [4][3100/3746] lr: 9.984e-02, eta: 4 days, 15:20:05, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4341, loss_cls: 4.4110, loss: 4.4110 +2024-07-22 05:32:28,208 - pyskl - INFO - Epoch [4][3200/3746] lr: 9.984e-02, eta: 4 days, 15:16:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4406, loss_cls: 4.3780, loss: 4.3780 +2024-07-22 05:33:37,823 - pyskl - INFO - Epoch [4][3300/3746] lr: 9.983e-02, eta: 4 days, 15:13:18, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4519, loss_cls: 4.3562, loss: 4.3562 +2024-07-22 05:34:48,600 - pyskl - INFO - Epoch [4][3400/3746] lr: 9.983e-02, eta: 4 days, 15:10:36, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1913, top5_acc: 0.4328, loss_cls: 4.4302, loss: 4.4302 +2024-07-22 05:35:58,431 - pyskl - INFO - Epoch [4][3500/3746] lr: 9.983e-02, eta: 4 days, 15:07:20, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4503, loss_cls: 4.3509, loss: 4.3509 +2024-07-22 05:37:08,793 - pyskl - INFO - Epoch [4][3600/3746] lr: 9.983e-02, eta: 4 days, 15:04:26, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4311, loss_cls: 4.3945, loss: 4.3945 +2024-07-22 05:38:19,084 - pyskl - INFO - Epoch [4][3700/3746] lr: 9.983e-02, eta: 4 days, 15:01:30, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4525, loss_cls: 4.3514, loss: 4.3514 +2024-07-22 05:38:54,085 - pyskl - INFO - Saving checkpoint at 4 epochs +2024-07-22 05:40:45,687 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 05:40:46,357 - pyskl - INFO - +top1_acc 0.1429 +top5_acc 0.3568 +2024-07-22 05:40:46,358 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 05:40:46,399 - pyskl - INFO - +mean_acc 0.1427 +2024-07-22 05:40:46,403 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_3.pth was removed +2024-07-22 05:40:46,648 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2024-07-22 05:40:46,649 - pyskl - INFO - Best top1_acc is 0.1429 at 4 epoch. +2024-07-22 05:40:46,660 - pyskl - INFO - Epoch(val) [4][309] top1_acc: 0.1429, top5_acc: 0.3568, mean_class_accuracy: 0.1427 +2024-07-22 05:44:06,049 - pyskl - INFO - Epoch [5][100/3746] lr: 9.982e-02, eta: 4 days, 15:55:43, time: 1.994, data_time: 1.289, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4569, loss_cls: 4.3054, loss: 4.3054 +2024-07-22 05:45:16,250 - pyskl - INFO - Epoch [5][200/3746] lr: 9.982e-02, eta: 4 days, 15:52:24, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4355, loss_cls: 4.3920, loss: 4.3920 +2024-07-22 05:46:26,102 - pyskl - INFO - Epoch [5][300/3746] lr: 9.982e-02, eta: 4 days, 15:48:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4512, loss_cls: 4.3748, loss: 4.3748 +2024-07-22 05:47:36,092 - pyskl - INFO - Epoch [5][400/3746] lr: 9.982e-02, eta: 4 days, 15:45:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4497, loss_cls: 4.3459, loss: 4.3459 +2024-07-22 05:48:46,255 - pyskl - INFO - Epoch [5][500/3746] lr: 9.981e-02, eta: 4 days, 15:42:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4444, loss_cls: 4.3560, loss: 4.3560 +2024-07-22 05:49:56,377 - pyskl - INFO - Epoch [5][600/3746] lr: 9.981e-02, eta: 4 days, 15:39:00, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4497, loss_cls: 4.3189, loss: 4.3189 +2024-07-22 05:51:06,495 - pyskl - INFO - Epoch [5][700/3746] lr: 9.981e-02, eta: 4 days, 15:35:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4537, loss_cls: 4.3386, loss: 4.3386 +2024-07-22 05:52:16,316 - pyskl - INFO - Epoch [5][800/3746] lr: 9.981e-02, eta: 4 days, 15:32:23, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4558, loss_cls: 4.3313, loss: 4.3313 +2024-07-22 05:53:26,212 - pyskl - INFO - Epoch [5][900/3746] lr: 9.980e-02, eta: 4 days, 15:29:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4542, loss_cls: 4.3504, loss: 4.3504 +2024-07-22 05:54:36,514 - pyskl - INFO - Epoch [5][1000/3746] lr: 9.980e-02, eta: 4 days, 15:26:02, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4552, loss_cls: 4.3396, loss: 4.3396 +2024-07-22 05:55:46,772 - pyskl - INFO - Epoch [5][1100/3746] lr: 9.980e-02, eta: 4 days, 15:22:59, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4502, loss_cls: 4.3278, loss: 4.3278 +2024-07-22 05:56:56,973 - pyskl - INFO - Epoch [5][1200/3746] lr: 9.980e-02, eta: 4 days, 15:19:55, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4478, loss_cls: 4.3636, loss: 4.3636 +2024-07-22 05:58:06,691 - pyskl - INFO - Epoch [5][1300/3746] lr: 9.979e-02, eta: 4 days, 15:16:37, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4605, loss_cls: 4.3125, loss: 4.3125 +2024-07-22 05:59:16,507 - pyskl - INFO - Epoch [5][1400/3746] lr: 9.979e-02, eta: 4 days, 15:13:23, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4622, loss_cls: 4.3072, loss: 4.3072 +2024-07-22 06:00:26,453 - pyskl - INFO - Epoch [5][1500/3746] lr: 9.979e-02, eta: 4 days, 15:10:16, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4548, loss_cls: 4.3285, loss: 4.3285 +2024-07-22 06:01:36,442 - pyskl - INFO - Epoch [5][1600/3746] lr: 9.979e-02, eta: 4 days, 15:07:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4637, loss_cls: 4.3030, loss: 4.3030 +2024-07-22 06:02:46,327 - pyskl - INFO - Epoch [5][1700/3746] lr: 9.978e-02, eta: 4 days, 15:04:04, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4492, loss_cls: 4.3563, loss: 4.3563 +2024-07-22 06:03:56,543 - pyskl - INFO - Epoch [5][1800/3746] lr: 9.978e-02, eta: 4 days, 15:01:09, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4544, loss_cls: 4.3299, loss: 4.3299 +2024-07-22 06:05:06,661 - pyskl - INFO - Epoch [5][1900/3746] lr: 9.978e-02, eta: 4 days, 14:58:13, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4516, loss_cls: 4.3430, loss: 4.3430 +2024-07-22 06:06:16,709 - pyskl - INFO - Epoch [5][2000/3746] lr: 9.977e-02, eta: 4 days, 14:55:15, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4580, loss_cls: 4.3389, loss: 4.3389 +2024-07-22 06:07:26,732 - pyskl - INFO - Epoch [5][2100/3746] lr: 9.977e-02, eta: 4 days, 14:52:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4573, loss_cls: 4.3217, loss: 4.3217 +2024-07-22 06:08:36,886 - pyskl - INFO - Epoch [5][2200/3746] lr: 9.977e-02, eta: 4 days, 14:49:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4523, loss_cls: 4.3493, loss: 4.3493 +2024-07-22 06:09:46,789 - pyskl - INFO - Epoch [5][2300/3746] lr: 9.977e-02, eta: 4 days, 14:46:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4530, loss_cls: 4.3497, loss: 4.3497 +2024-07-22 06:10:56,456 - pyskl - INFO - Epoch [5][2400/3746] lr: 9.976e-02, eta: 4 days, 14:43:23, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4609, loss_cls: 4.3011, loss: 4.3011 +2024-07-22 06:12:06,411 - pyskl - INFO - Epoch [5][2500/3746] lr: 9.976e-02, eta: 4 days, 14:40:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4555, loss_cls: 4.3645, loss: 4.3645 +2024-07-22 06:13:16,486 - pyskl - INFO - Epoch [5][2600/3746] lr: 9.976e-02, eta: 4 days, 14:37:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4462, loss_cls: 4.3543, loss: 4.3543 +2024-07-22 06:14:27,042 - pyskl - INFO - Epoch [5][2700/3746] lr: 9.976e-02, eta: 4 days, 14:35:05, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4542, loss_cls: 4.3293, loss: 4.3293 +2024-07-22 06:15:37,052 - pyskl - INFO - Epoch [5][2800/3746] lr: 9.975e-02, eta: 4 days, 14:32:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4600, loss_cls: 4.3165, loss: 4.3165 +2024-07-22 06:16:46,683 - pyskl - INFO - Epoch [5][2900/3746] lr: 9.975e-02, eta: 4 days, 14:29:16, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4572, loss_cls: 4.3405, loss: 4.3405 +2024-07-22 06:17:56,555 - pyskl - INFO - Epoch [5][3000/3746] lr: 9.975e-02, eta: 4 days, 14:26:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4602, loss_cls: 4.3059, loss: 4.3059 +2024-07-22 06:19:06,743 - pyskl - INFO - Epoch [5][3100/3746] lr: 9.974e-02, eta: 4 days, 14:23:45, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4645, loss_cls: 4.2856, loss: 4.2856 +2024-07-22 06:20:16,331 - pyskl - INFO - Epoch [5][3200/3746] lr: 9.974e-02, eta: 4 days, 14:20:47, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4575, loss_cls: 4.3012, loss: 4.3012 +2024-07-22 06:21:26,048 - pyskl - INFO - Epoch [5][3300/3746] lr: 9.974e-02, eta: 4 days, 14:17:55, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4686, loss_cls: 4.2774, loss: 4.2774 +2024-07-22 06:22:36,438 - pyskl - INFO - Epoch [5][3400/3746] lr: 9.974e-02, eta: 4 days, 14:15:23, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4675, loss_cls: 4.2753, loss: 4.2753 +2024-07-22 06:23:46,669 - pyskl - INFO - Epoch [5][3500/3746] lr: 9.973e-02, eta: 4 days, 14:12:48, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2194, top5_acc: 0.4558, loss_cls: 4.3099, loss: 4.3099 +2024-07-22 06:24:56,634 - pyskl - INFO - Epoch [5][3600/3746] lr: 9.973e-02, eta: 4 days, 14:10:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4647, loss_cls: 4.3074, loss: 4.3074 +2024-07-22 06:26:06,774 - pyskl - INFO - Epoch [5][3700/3746] lr: 9.973e-02, eta: 4 days, 14:07:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4619, loss_cls: 4.2871, loss: 4.2871 +2024-07-22 06:26:41,392 - pyskl - INFO - Saving checkpoint at 5 epochs +2024-07-22 06:28:33,391 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 06:28:34,086 - pyskl - INFO - +top1_acc 0.1211 +top5_acc 0.3143 +2024-07-22 06:28:34,086 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 06:28:34,126 - pyskl - INFO - +mean_acc 0.1210 +2024-07-22 06:28:34,138 - pyskl - INFO - Epoch(val) [5][309] top1_acc: 0.1211, top5_acc: 0.3143, mean_class_accuracy: 0.1210 +2024-07-22 06:31:54,826 - pyskl - INFO - Epoch [6][100/3746] lr: 9.972e-02, eta: 4 days, 14:50:58, time: 2.007, data_time: 1.304, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4683, loss_cls: 4.2553, loss: 4.2553 +2024-07-22 06:33:04,836 - pyskl - INFO - Epoch [6][200/3746] lr: 9.972e-02, eta: 4 days, 14:48:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4675, loss_cls: 4.2728, loss: 4.2728 +2024-07-22 06:34:14,761 - pyskl - INFO - Epoch [6][300/3746] lr: 9.972e-02, eta: 4 days, 14:45:10, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4639, loss_cls: 4.2795, loss: 4.2795 +2024-07-22 06:35:24,609 - pyskl - INFO - Epoch [6][400/3746] lr: 9.971e-02, eta: 4 days, 14:42:15, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4597, loss_cls: 4.2714, loss: 4.2714 +2024-07-22 06:36:34,508 - pyskl - INFO - Epoch [6][500/3746] lr: 9.971e-02, eta: 4 days, 14:39:21, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4648, loss_cls: 4.2999, loss: 4.2999 +2024-07-22 06:37:44,433 - pyskl - INFO - Epoch [6][600/3746] lr: 9.971e-02, eta: 4 days, 14:36:30, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4770, loss_cls: 4.2627, loss: 4.2627 +2024-07-22 06:38:54,066 - pyskl - INFO - Epoch [6][700/3746] lr: 9.971e-02, eta: 4 days, 14:33:32, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4669, loss_cls: 4.2832, loss: 4.2832 +2024-07-22 06:40:04,349 - pyskl - INFO - Epoch [6][800/3746] lr: 9.970e-02, eta: 4 days, 14:30:53, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4698, loss_cls: 4.2829, loss: 4.2829 +2024-07-22 06:41:14,549 - pyskl - INFO - Epoch [6][900/3746] lr: 9.970e-02, eta: 4 days, 14:28:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4647, loss_cls: 4.2687, loss: 4.2687 +2024-07-22 06:42:24,297 - pyskl - INFO - Epoch [6][1000/3746] lr: 9.970e-02, eta: 4 days, 14:25:20, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4680, loss_cls: 4.2668, loss: 4.2668 +2024-07-22 06:43:34,135 - pyskl - INFO - Epoch [6][1100/3746] lr: 9.969e-02, eta: 4 days, 14:22:32, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4645, loss_cls: 4.2867, loss: 4.2867 +2024-07-22 06:44:44,056 - pyskl - INFO - Epoch [6][1200/3746] lr: 9.969e-02, eta: 4 days, 14:19:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4670, loss_cls: 4.2957, loss: 4.2957 +2024-07-22 06:45:54,218 - pyskl - INFO - Epoch [6][1300/3746] lr: 9.969e-02, eta: 4 days, 14:17:09, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4598, loss_cls: 4.2953, loss: 4.2953 +2024-07-22 06:47:04,499 - pyskl - INFO - Epoch [6][1400/3746] lr: 9.968e-02, eta: 4 days, 14:14:35, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4759, loss_cls: 4.2558, loss: 4.2558 +2024-07-22 06:48:14,103 - pyskl - INFO - Epoch [6][1500/3746] lr: 9.968e-02, eta: 4 days, 14:11:44, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4711, loss_cls: 4.2490, loss: 4.2490 +2024-07-22 06:49:23,831 - pyskl - INFO - Epoch [6][1600/3746] lr: 9.968e-02, eta: 4 days, 14:08:57, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4692, loss_cls: 4.2778, loss: 4.2778 +2024-07-22 06:50:33,860 - pyskl - INFO - Epoch [6][1700/3746] lr: 9.967e-02, eta: 4 days, 14:06:19, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4706, loss_cls: 4.2490, loss: 4.2490 +2024-07-22 06:51:43,578 - pyskl - INFO - Epoch [6][1800/3746] lr: 9.967e-02, eta: 4 days, 14:03:34, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4545, loss_cls: 4.3268, loss: 4.3268 +2024-07-22 06:52:53,213 - pyskl - INFO - Epoch [6][1900/3746] lr: 9.967e-02, eta: 4 days, 14:00:48, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4691, loss_cls: 4.2557, loss: 4.2557 +2024-07-22 06:54:03,109 - pyskl - INFO - Epoch [6][2000/3746] lr: 9.966e-02, eta: 4 days, 13:58:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4650, loss_cls: 4.2827, loss: 4.2827 +2024-07-22 06:55:13,027 - pyskl - INFO - Epoch [6][2100/3746] lr: 9.966e-02, eta: 4 days, 13:55:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4709, loss_cls: 4.2951, loss: 4.2951 +2024-07-22 06:56:22,852 - pyskl - INFO - Epoch [6][2200/3746] lr: 9.966e-02, eta: 4 days, 13:52:53, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4666, loss_cls: 4.2614, loss: 4.2614 +2024-07-22 06:57:32,777 - pyskl - INFO - Epoch [6][2300/3746] lr: 9.965e-02, eta: 4 days, 13:50:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4656, loss_cls: 4.2810, loss: 4.2810 +2024-07-22 06:58:42,460 - pyskl - INFO - Epoch [6][2400/3746] lr: 9.965e-02, eta: 4 days, 13:47:37, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4614, loss_cls: 4.2946, loss: 4.2946 +2024-07-22 06:59:52,567 - pyskl - INFO - Epoch [6][2500/3746] lr: 9.965e-02, eta: 4 days, 13:45:08, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4750, loss_cls: 4.2451, loss: 4.2451 +2024-07-22 07:01:02,434 - pyskl - INFO - Epoch [6][2600/3746] lr: 9.964e-02, eta: 4 days, 13:42:33, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4698, loss_cls: 4.2275, loss: 4.2275 +2024-07-22 07:02:12,322 - pyskl - INFO - Epoch [6][2700/3746] lr: 9.964e-02, eta: 4 days, 13:40:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4602, loss_cls: 4.3017, loss: 4.3017 +2024-07-22 07:03:22,334 - pyskl - INFO - Epoch [6][2800/3746] lr: 9.964e-02, eta: 4 days, 13:37:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4733, loss_cls: 4.2781, loss: 4.2781 +2024-07-22 07:04:32,083 - pyskl - INFO - Epoch [6][2900/3746] lr: 9.963e-02, eta: 4 days, 13:34:56, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4661, loss_cls: 4.2832, loss: 4.2832 +2024-07-22 07:05:41,910 - pyskl - INFO - Epoch [6][3000/3746] lr: 9.963e-02, eta: 4 days, 13:32:23, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4595, loss_cls: 4.3275, loss: 4.3275 +2024-07-22 07:06:51,924 - pyskl - INFO - Epoch [6][3100/3746] lr: 9.963e-02, eta: 4 days, 13:29:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4617, loss_cls: 4.2818, loss: 4.2818 +2024-07-22 07:08:01,851 - pyskl - INFO - Epoch [6][3200/3746] lr: 9.962e-02, eta: 4 days, 13:27:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4786, loss_cls: 4.2019, loss: 4.2019 +2024-07-22 07:09:11,764 - pyskl - INFO - Epoch [6][3300/3746] lr: 9.962e-02, eta: 4 days, 13:25:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4594, loss_cls: 4.2747, loss: 4.2747 +2024-07-22 07:10:22,071 - pyskl - INFO - Epoch [6][3400/3746] lr: 9.962e-02, eta: 4 days, 13:22:42, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4661, loss_cls: 4.2606, loss: 4.2606 +2024-07-22 07:11:32,197 - pyskl - INFO - Epoch [6][3500/3746] lr: 9.961e-02, eta: 4 days, 13:20:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4905, loss_cls: 4.1899, loss: 4.1899 +2024-07-22 07:12:42,584 - pyskl - INFO - Epoch [6][3600/3746] lr: 9.961e-02, eta: 4 days, 13:18:06, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4703, loss_cls: 4.2627, loss: 4.2627 +2024-07-22 07:13:52,602 - pyskl - INFO - Epoch [6][3700/3746] lr: 9.961e-02, eta: 4 days, 13:15:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4595, loss_cls: 4.3053, loss: 4.3053 +2024-07-22 07:14:27,420 - pyskl - INFO - Saving checkpoint at 6 epochs +2024-07-22 07:16:21,173 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 07:16:21,994 - pyskl - INFO - +top1_acc 0.1425 +top5_acc 0.3429 +2024-07-22 07:16:21,994 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 07:16:22,045 - pyskl - INFO - +mean_acc 0.1424 +2024-07-22 07:16:22,063 - pyskl - INFO - Epoch(val) [6][309] top1_acc: 0.1425, top5_acc: 0.3429, mean_class_accuracy: 0.1424 +2024-07-22 07:19:47,513 - pyskl - INFO - Epoch [7][100/3746] lr: 9.960e-02, eta: 4 days, 13:53:21, time: 2.054, data_time: 1.348, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4723, loss_cls: 4.2620, loss: 4.2620 +2024-07-22 07:20:57,988 - pyskl - INFO - Epoch [7][200/3746] lr: 9.960e-02, eta: 4 days, 13:51:00, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4816, loss_cls: 4.1989, loss: 4.1989 +2024-07-22 07:22:08,203 - pyskl - INFO - Epoch [7][300/3746] lr: 9.960e-02, eta: 4 days, 13:48:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4817, loss_cls: 4.1961, loss: 4.1961 +2024-07-22 07:23:18,920 - pyskl - INFO - Epoch [7][400/3746] lr: 9.959e-02, eta: 4 days, 13:46:18, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4867, loss_cls: 4.1776, loss: 4.1776 +2024-07-22 07:24:28,817 - pyskl - INFO - Epoch [7][500/3746] lr: 9.959e-02, eta: 4 days, 13:43:44, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4755, loss_cls: 4.2432, loss: 4.2432 +2024-07-22 07:25:39,108 - pyskl - INFO - Epoch [7][600/3746] lr: 9.958e-02, eta: 4 days, 13:41:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4755, loss_cls: 4.2299, loss: 4.2299 +2024-07-22 07:26:49,327 - pyskl - INFO - Epoch [7][700/3746] lr: 9.958e-02, eta: 4 days, 13:38:56, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4644, loss_cls: 4.2265, loss: 4.2265 +2024-07-22 07:27:59,215 - pyskl - INFO - Epoch [7][800/3746] lr: 9.958e-02, eta: 4 days, 13:36:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4783, loss_cls: 4.2194, loss: 4.2194 +2024-07-22 07:29:09,518 - pyskl - INFO - Epoch [7][900/3746] lr: 9.957e-02, eta: 4 days, 13:34:03, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4652, loss_cls: 4.2584, loss: 4.2584 +2024-07-22 07:30:19,621 - pyskl - INFO - Epoch [7][1000/3746] lr: 9.957e-02, eta: 4 days, 13:31:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4678, loss_cls: 4.2475, loss: 4.2475 +2024-07-22 07:31:29,270 - pyskl - INFO - Epoch [7][1100/3746] lr: 9.957e-02, eta: 4 days, 13:29:03, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4780, loss_cls: 4.2078, loss: 4.2078 +2024-07-22 07:32:39,373 - pyskl - INFO - Epoch [7][1200/3746] lr: 9.956e-02, eta: 4 days, 13:26:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4703, loss_cls: 4.2416, loss: 4.2416 +2024-07-22 07:33:49,434 - pyskl - INFO - Epoch [7][1300/3746] lr: 9.956e-02, eta: 4 days, 13:24:14, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4719, loss_cls: 4.2329, loss: 4.2329 +2024-07-22 07:34:59,404 - pyskl - INFO - Epoch [7][1400/3746] lr: 9.956e-02, eta: 4 days, 13:21:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4669, loss_cls: 4.2634, loss: 4.2634 +2024-07-22 07:36:09,410 - pyskl - INFO - Epoch [7][1500/3746] lr: 9.955e-02, eta: 4 days, 13:19:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4833, loss_cls: 4.2194, loss: 4.2194 +2024-07-22 07:37:19,756 - pyskl - INFO - Epoch [7][1600/3746] lr: 9.955e-02, eta: 4 days, 13:17:08, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4780, loss_cls: 4.2435, loss: 4.2435 +2024-07-22 07:38:29,528 - pyskl - INFO - Epoch [7][1700/3746] lr: 9.954e-02, eta: 4 days, 13:14:40, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4780, loss_cls: 4.2066, loss: 4.2066 +2024-07-22 07:39:39,492 - pyskl - INFO - Epoch [7][1800/3746] lr: 9.954e-02, eta: 4 days, 13:12:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4748, loss_cls: 4.2431, loss: 4.2431 +2024-07-22 07:40:49,687 - pyskl - INFO - Epoch [7][1900/3746] lr: 9.954e-02, eta: 4 days, 13:09:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4744, loss_cls: 4.2379, loss: 4.2379 +2024-07-22 07:41:59,918 - pyskl - INFO - Epoch [7][2000/3746] lr: 9.953e-02, eta: 4 days, 13:07:42, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4681, loss_cls: 4.2612, loss: 4.2612 +2024-07-22 07:43:10,029 - pyskl - INFO - Epoch [7][2100/3746] lr: 9.953e-02, eta: 4 days, 13:05:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4808, loss_cls: 4.1850, loss: 4.1850 +2024-07-22 07:44:20,054 - pyskl - INFO - Epoch [7][2200/3746] lr: 9.952e-02, eta: 4 days, 13:03:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4742, loss_cls: 4.2485, loss: 4.2485 +2024-07-22 07:45:30,087 - pyskl - INFO - Epoch [7][2300/3746] lr: 9.952e-02, eta: 4 days, 13:00:45, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4711, loss_cls: 4.2449, loss: 4.2449 +2024-07-22 07:46:39,858 - pyskl - INFO - Epoch [7][2400/3746] lr: 9.952e-02, eta: 4 days, 12:58:21, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4769, loss_cls: 4.2192, loss: 4.2192 +2024-07-22 07:47:49,578 - pyskl - INFO - Epoch [7][2500/3746] lr: 9.951e-02, eta: 4 days, 12:55:56, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4845, loss_cls: 4.1922, loss: 4.1922 +2024-07-22 07:48:59,401 - pyskl - INFO - Epoch [7][2600/3746] lr: 9.951e-02, eta: 4 days, 12:53:34, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4739, loss_cls: 4.2490, loss: 4.2490 +2024-07-22 07:50:09,247 - pyskl - INFO - Epoch [7][2700/3746] lr: 9.951e-02, eta: 4 days, 12:51:13, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4738, loss_cls: 4.2725, loss: 4.2725 +2024-07-22 07:51:19,324 - pyskl - INFO - Epoch [7][2800/3746] lr: 9.950e-02, eta: 4 days, 12:48:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4694, loss_cls: 4.2361, loss: 4.2361 +2024-07-22 07:52:29,292 - pyskl - INFO - Epoch [7][2900/3746] lr: 9.950e-02, eta: 4 days, 12:46:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4733, loss_cls: 4.2399, loss: 4.2399 +2024-07-22 07:53:39,222 - pyskl - INFO - Epoch [7][3000/3746] lr: 9.949e-02, eta: 4 days, 12:44:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4734, loss_cls: 4.2072, loss: 4.2072 +2024-07-22 07:54:49,277 - pyskl - INFO - Epoch [7][3100/3746] lr: 9.949e-02, eta: 4 days, 12:42:09, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4727, loss_cls: 4.2392, loss: 4.2392 +2024-07-22 07:55:59,556 - pyskl - INFO - Epoch [7][3200/3746] lr: 9.949e-02, eta: 4 days, 12:40:00, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4719, loss_cls: 4.2434, loss: 4.2434 +2024-07-22 07:57:09,558 - pyskl - INFO - Epoch [7][3300/3746] lr: 9.948e-02, eta: 4 days, 12:37:46, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4875, loss_cls: 4.2216, loss: 4.2216 +2024-07-22 07:58:20,443 - pyskl - INFO - Epoch [7][3400/3746] lr: 9.948e-02, eta: 4 days, 12:35:50, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4628, loss_cls: 4.2681, loss: 4.2681 +2024-07-22 07:59:30,535 - pyskl - INFO - Epoch [7][3500/3746] lr: 9.947e-02, eta: 4 days, 12:33:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4684, loss_cls: 4.2643, loss: 4.2643 +2024-07-22 08:00:40,599 - pyskl - INFO - Epoch [7][3600/3746] lr: 9.947e-02, eta: 4 days, 12:31:27, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4633, loss_cls: 4.2455, loss: 4.2455 +2024-07-22 08:01:50,797 - pyskl - INFO - Epoch [7][3700/3746] lr: 9.947e-02, eta: 4 days, 12:29:18, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4977, loss_cls: 4.1466, loss: 4.1466 +2024-07-22 08:02:25,261 - pyskl - INFO - Saving checkpoint at 7 epochs +2024-07-22 08:04:18,636 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 08:04:19,303 - pyskl - INFO - +top1_acc 0.1478 +top5_acc 0.3634 +2024-07-22 08:04:19,303 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 08:04:19,344 - pyskl - INFO - +mean_acc 0.1477 +2024-07-22 08:04:19,348 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_4.pth was removed +2024-07-22 08:04:19,597 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2024-07-22 08:04:19,598 - pyskl - INFO - Best top1_acc is 0.1478 at 7 epoch. +2024-07-22 08:04:19,610 - pyskl - INFO - Epoch(val) [7][309] top1_acc: 0.1478, top5_acc: 0.3634, mean_class_accuracy: 0.1477 +2024-07-22 08:07:40,295 - pyskl - INFO - Epoch [8][100/3746] lr: 9.946e-02, eta: 4 days, 12:59:29, time: 2.007, data_time: 1.306, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4828, loss_cls: 4.1922, loss: 4.1922 +2024-07-22 08:08:50,486 - pyskl - INFO - Epoch [8][200/3746] lr: 9.946e-02, eta: 4 days, 12:57:14, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4816, loss_cls: 4.2035, loss: 4.2035 +2024-07-22 08:10:00,376 - pyskl - INFO - Epoch [8][300/3746] lr: 9.945e-02, eta: 4 days, 12:54:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4891, loss_cls: 4.1639, loss: 4.1639 +2024-07-22 08:11:10,254 - pyskl - INFO - Epoch [8][400/3746] lr: 9.945e-02, eta: 4 days, 12:52:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4781, loss_cls: 4.2218, loss: 4.2218 +2024-07-22 08:12:19,901 - pyskl - INFO - Epoch [8][500/3746] lr: 9.944e-02, eta: 4 days, 12:50:07, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4750, loss_cls: 4.1972, loss: 4.1972 +2024-07-22 08:13:30,052 - pyskl - INFO - Epoch [8][600/3746] lr: 9.944e-02, eta: 4 days, 12:47:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4711, loss_cls: 4.2209, loss: 4.2209 +2024-07-22 08:14:40,190 - pyskl - INFO - Epoch [8][700/3746] lr: 9.943e-02, eta: 4 days, 12:45:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4742, loss_cls: 4.2068, loss: 4.2068 +2024-07-22 08:15:49,934 - pyskl - INFO - Epoch [8][800/3746] lr: 9.943e-02, eta: 4 days, 12:43:17, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4864, loss_cls: 4.1635, loss: 4.1635 +2024-07-22 08:16:59,794 - pyskl - INFO - Epoch [8][900/3746] lr: 9.943e-02, eta: 4 days, 12:40:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4780, loss_cls: 4.2029, loss: 4.2029 +2024-07-22 08:18:10,089 - pyskl - INFO - Epoch [8][1000/3746] lr: 9.942e-02, eta: 4 days, 12:38:49, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4841, loss_cls: 4.1886, loss: 4.1886 +2024-07-22 08:19:19,975 - pyskl - INFO - Epoch [8][1100/3746] lr: 9.942e-02, eta: 4 days, 12:36:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4886, loss_cls: 4.1696, loss: 4.1696 +2024-07-22 08:20:29,792 - pyskl - INFO - Epoch [8][1200/3746] lr: 9.941e-02, eta: 4 days, 12:34:14, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4839, loss_cls: 4.1789, loss: 4.1789 +2024-07-22 08:21:39,849 - pyskl - INFO - Epoch [8][1300/3746] lr: 9.941e-02, eta: 4 days, 12:32:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4797, loss_cls: 4.2187, loss: 4.2187 +2024-07-22 08:22:49,780 - pyskl - INFO - Epoch [8][1400/3746] lr: 9.940e-02, eta: 4 days, 12:29:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4888, loss_cls: 4.1937, loss: 4.1937 +2024-07-22 08:23:59,547 - pyskl - INFO - Epoch [8][1500/3746] lr: 9.940e-02, eta: 4 days, 12:27:29, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4806, loss_cls: 4.2023, loss: 4.2023 +2024-07-22 08:25:09,541 - pyskl - INFO - Epoch [8][1600/3746] lr: 9.940e-02, eta: 4 days, 12:25:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4827, loss_cls: 4.1899, loss: 4.1899 +2024-07-22 08:26:19,506 - pyskl - INFO - Epoch [8][1700/3746] lr: 9.939e-02, eta: 4 days, 12:23:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4791, loss_cls: 4.2065, loss: 4.2065 +2024-07-22 08:27:29,579 - pyskl - INFO - Epoch [8][1800/3746] lr: 9.939e-02, eta: 4 days, 12:20:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4830, loss_cls: 4.1896, loss: 4.1896 +2024-07-22 08:28:39,443 - pyskl - INFO - Epoch [8][1900/3746] lr: 9.938e-02, eta: 4 days, 12:18:40, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4863, loss_cls: 4.1639, loss: 4.1639 +2024-07-22 08:29:49,555 - pyskl - INFO - Epoch [8][2000/3746] lr: 9.938e-02, eta: 4 days, 12:16:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4813, loss_cls: 4.1977, loss: 4.1977 +2024-07-22 08:30:59,494 - pyskl - INFO - Epoch [8][2100/3746] lr: 9.937e-02, eta: 4 days, 12:14:20, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4831, loss_cls: 4.1981, loss: 4.1981 +2024-07-22 08:32:09,199 - pyskl - INFO - Epoch [8][2200/3746] lr: 9.937e-02, eta: 4 days, 12:12:04, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4931, loss_cls: 4.1375, loss: 4.1375 +2024-07-22 08:33:19,075 - pyskl - INFO - Epoch [8][2300/3746] lr: 9.937e-02, eta: 4 days, 12:09:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4808, loss_cls: 4.1936, loss: 4.1936 +2024-07-22 08:34:29,121 - pyskl - INFO - Epoch [8][2400/3746] lr: 9.936e-02, eta: 4 days, 12:07:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4747, loss_cls: 4.2306, loss: 4.2306 +2024-07-22 08:35:39,413 - pyskl - INFO - Epoch [8][2500/3746] lr: 9.936e-02, eta: 4 days, 12:05:41, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4786, loss_cls: 4.2110, loss: 4.2110 +2024-07-22 08:36:49,257 - pyskl - INFO - Epoch [8][2600/3746] lr: 9.935e-02, eta: 4 days, 12:03:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4870, loss_cls: 4.1883, loss: 4.1883 +2024-07-22 08:37:59,149 - pyskl - INFO - Epoch [8][2700/3746] lr: 9.935e-02, eta: 4 days, 12:01:20, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4881, loss_cls: 4.1785, loss: 4.1785 +2024-07-22 08:39:09,171 - pyskl - INFO - Epoch [8][2800/3746] lr: 9.934e-02, eta: 4 days, 11:59:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4761, loss_cls: 4.2208, loss: 4.2208 +2024-07-22 08:40:19,213 - pyskl - INFO - Epoch [8][2900/3746] lr: 9.934e-02, eta: 4 days, 11:57:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4905, loss_cls: 4.1632, loss: 4.1632 +2024-07-22 08:41:29,060 - pyskl - INFO - Epoch [8][3000/3746] lr: 9.933e-02, eta: 4 days, 11:54:58, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4719, loss_cls: 4.2389, loss: 4.2389 +2024-07-22 08:42:38,829 - pyskl - INFO - Epoch [8][3100/3746] lr: 9.933e-02, eta: 4 days, 11:52:47, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4755, loss_cls: 4.2491, loss: 4.2491 +2024-07-22 08:43:48,994 - pyskl - INFO - Epoch [8][3200/3746] lr: 9.933e-02, eta: 4 days, 11:50:45, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4763, loss_cls: 4.2202, loss: 4.2202 +2024-07-22 08:44:58,831 - pyskl - INFO - Epoch [8][3300/3746] lr: 9.932e-02, eta: 4 days, 11:48:36, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4870, loss_cls: 4.1948, loss: 4.1948 +2024-07-22 08:46:09,190 - pyskl - INFO - Epoch [8][3400/3746] lr: 9.932e-02, eta: 4 days, 11:46:37, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4814, loss_cls: 4.2019, loss: 4.2019 +2024-07-22 08:47:19,180 - pyskl - INFO - Epoch [8][3500/3746] lr: 9.931e-02, eta: 4 days, 11:44:33, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4845, loss_cls: 4.1817, loss: 4.1817 +2024-07-22 08:48:29,264 - pyskl - INFO - Epoch [8][3600/3746] lr: 9.931e-02, eta: 4 days, 11:42:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4852, loss_cls: 4.1788, loss: 4.1788 +2024-07-22 08:49:39,585 - pyskl - INFO - Epoch [8][3700/3746] lr: 9.930e-02, eta: 4 days, 11:40:31, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4739, loss_cls: 4.2340, loss: 4.2340 +2024-07-22 08:50:13,952 - pyskl - INFO - Saving checkpoint at 8 epochs +2024-07-22 08:52:07,828 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 08:52:08,500 - pyskl - INFO - +top1_acc 0.1689 +top5_acc 0.3894 +2024-07-22 08:52:08,500 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 08:52:08,548 - pyskl - INFO - +mean_acc 0.1688 +2024-07-22 08:52:08,553 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_7.pth was removed +2024-07-22 08:52:08,805 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_8.pth. +2024-07-22 08:52:08,806 - pyskl - INFO - Best top1_acc is 0.1689 at 8 epoch. +2024-07-22 08:52:08,821 - pyskl - INFO - Epoch(val) [8][309] top1_acc: 0.1689, top5_acc: 0.3894, mean_class_accuracy: 0.1688 +2024-07-22 08:55:35,416 - pyskl - INFO - Epoch [9][100/3746] lr: 9.930e-02, eta: 4 days, 12:08:17, time: 2.066, data_time: 1.361, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4827, loss_cls: 4.1552, loss: 4.1552 +2024-07-22 08:56:45,958 - pyskl - INFO - Epoch [9][200/3746] lr: 9.929e-02, eta: 4 days, 12:06:17, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4773, loss_cls: 4.2045, loss: 4.2045 +2024-07-22 08:57:56,253 - pyskl - INFO - Epoch [9][300/3746] lr: 9.929e-02, eta: 4 days, 12:04:13, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4814, loss_cls: 4.1712, loss: 4.1712 +2024-07-22 08:59:06,216 - pyskl - INFO - Epoch [9][400/3746] lr: 9.928e-02, eta: 4 days, 12:02:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4959, loss_cls: 4.1600, loss: 4.1600 +2024-07-22 09:00:16,478 - pyskl - INFO - Epoch [9][500/3746] lr: 9.928e-02, eta: 4 days, 12:00:00, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4922, loss_cls: 4.1313, loss: 4.1313 +2024-07-22 09:01:26,727 - pyskl - INFO - Epoch [9][600/3746] lr: 9.927e-02, eta: 4 days, 11:57:56, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4806, loss_cls: 4.1991, loss: 4.1991 +2024-07-22 09:02:36,916 - pyskl - INFO - Epoch [9][700/3746] lr: 9.927e-02, eta: 4 days, 11:55:52, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4873, loss_cls: 4.1827, loss: 4.1827 +2024-07-22 09:03:46,888 - pyskl - INFO - Epoch [9][800/3746] lr: 9.926e-02, eta: 4 days, 11:53:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4963, loss_cls: 4.1416, loss: 4.1416 +2024-07-22 09:04:56,676 - pyskl - INFO - Epoch [9][900/3746] lr: 9.926e-02, eta: 4 days, 11:51:33, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4863, loss_cls: 4.1898, loss: 4.1898 +2024-07-22 09:06:06,393 - pyskl - INFO - Epoch [9][1000/3746] lr: 9.925e-02, eta: 4 days, 11:49:22, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4842, loss_cls: 4.1850, loss: 4.1850 +2024-07-22 09:07:16,624 - pyskl - INFO - Epoch [9][1100/3746] lr: 9.925e-02, eta: 4 days, 11:47:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4911, loss_cls: 4.1719, loss: 4.1719 +2024-07-22 09:08:26,770 - pyskl - INFO - Epoch [9][1200/3746] lr: 9.924e-02, eta: 4 days, 11:45:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4753, loss_cls: 4.2313, loss: 4.2313 +2024-07-22 09:09:36,691 - pyskl - INFO - Epoch [9][1300/3746] lr: 9.924e-02, eta: 4 days, 11:43:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4975, loss_cls: 4.1370, loss: 4.1370 +2024-07-22 09:10:46,660 - pyskl - INFO - Epoch [9][1400/3746] lr: 9.923e-02, eta: 4 days, 11:41:03, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4828, loss_cls: 4.1865, loss: 4.1865 +2024-07-22 09:11:56,394 - pyskl - INFO - Epoch [9][1500/3746] lr: 9.923e-02, eta: 4 days, 11:38:54, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4942, loss_cls: 4.1179, loss: 4.1179 +2024-07-22 09:13:06,464 - pyskl - INFO - Epoch [9][1600/3746] lr: 9.922e-02, eta: 4 days, 11:36:51, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4928, loss_cls: 4.1708, loss: 4.1708 +2024-07-22 09:14:16,709 - pyskl - INFO - Epoch [9][1700/3746] lr: 9.922e-02, eta: 4 days, 11:34:50, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4891, loss_cls: 4.1603, loss: 4.1603 +2024-07-22 09:15:26,856 - pyskl - INFO - Epoch [9][1800/3746] lr: 9.921e-02, eta: 4 days, 11:32:49, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4814, loss_cls: 4.1971, loss: 4.1971 +2024-07-22 09:16:37,167 - pyskl - INFO - Epoch [9][1900/3746] lr: 9.921e-02, eta: 4 days, 11:30:51, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4792, loss_cls: 4.1963, loss: 4.1963 +2024-07-22 09:17:47,271 - pyskl - INFO - Epoch [9][2000/3746] lr: 9.920e-02, eta: 4 days, 11:28:49, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4858, loss_cls: 4.1881, loss: 4.1881 +2024-07-22 09:18:57,569 - pyskl - INFO - Epoch [9][2100/3746] lr: 9.920e-02, eta: 4 days, 11:26:51, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4852, loss_cls: 4.2070, loss: 4.2070 +2024-07-22 09:20:07,494 - pyskl - INFO - Epoch [9][2200/3746] lr: 9.919e-02, eta: 4 days, 11:24:48, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4933, loss_cls: 4.1415, loss: 4.1415 +2024-07-22 09:21:17,630 - pyskl - INFO - Epoch [9][2300/3746] lr: 9.919e-02, eta: 4 days, 11:22:48, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4936, loss_cls: 4.1578, loss: 4.1578 +2024-07-22 09:22:27,400 - pyskl - INFO - Epoch [9][2400/3746] lr: 9.918e-02, eta: 4 days, 11:20:42, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4891, loss_cls: 4.1623, loss: 4.1623 +2024-07-22 09:23:37,338 - pyskl - INFO - Epoch [9][2500/3746] lr: 9.918e-02, eta: 4 days, 11:18:40, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4744, loss_cls: 4.1929, loss: 4.1929 +2024-07-22 09:24:47,250 - pyskl - INFO - Epoch [9][2600/3746] lr: 9.917e-02, eta: 4 days, 11:16:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4877, loss_cls: 4.1885, loss: 4.1885 +2024-07-22 09:25:57,216 - pyskl - INFO - Epoch [9][2700/3746] lr: 9.917e-02, eta: 4 days, 11:14:35, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4863, loss_cls: 4.1782, loss: 4.1782 +2024-07-22 09:27:06,939 - pyskl - INFO - Epoch [9][2800/3746] lr: 9.916e-02, eta: 4 days, 11:12:30, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5050, loss_cls: 4.1034, loss: 4.1034 +2024-07-22 09:28:16,921 - pyskl - INFO - Epoch [9][2900/3746] lr: 9.916e-02, eta: 4 days, 11:10:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4800, loss_cls: 4.1924, loss: 4.1924 +2024-07-22 09:29:26,850 - pyskl - INFO - Epoch [9][3000/3746] lr: 9.915e-02, eta: 4 days, 11:08:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4863, loss_cls: 4.1552, loss: 4.1552 +2024-07-22 09:30:36,825 - pyskl - INFO - Epoch [9][3100/3746] lr: 9.915e-02, eta: 4 days, 11:06:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4813, loss_cls: 4.1913, loss: 4.1913 +2024-07-22 09:31:46,594 - pyskl - INFO - Epoch [9][3200/3746] lr: 9.914e-02, eta: 4 days, 11:04:26, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4819, loss_cls: 4.1874, loss: 4.1874 +2024-07-22 09:32:56,529 - pyskl - INFO - Epoch [9][3300/3746] lr: 9.914e-02, eta: 4 days, 11:02:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4927, loss_cls: 4.1440, loss: 4.1440 +2024-07-22 09:34:06,935 - pyskl - INFO - Epoch [9][3400/3746] lr: 9.913e-02, eta: 4 days, 11:00:33, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4931, loss_cls: 4.1532, loss: 4.1532 +2024-07-22 09:35:17,185 - pyskl - INFO - Epoch [9][3500/3746] lr: 9.913e-02, eta: 4 days, 10:58:39, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4798, loss_cls: 4.2057, loss: 4.2057 +2024-07-22 09:36:27,700 - pyskl - INFO - Epoch [9][3600/3746] lr: 9.912e-02, eta: 4 days, 10:56:48, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4972, loss_cls: 4.1384, loss: 4.1384 +2024-07-22 09:37:38,274 - pyskl - INFO - Epoch [9][3700/3746] lr: 9.912e-02, eta: 4 days, 10:54:59, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4867, loss_cls: 4.1471, loss: 4.1471 +2024-07-22 09:38:13,003 - pyskl - INFO - Saving checkpoint at 9 epochs +2024-07-22 09:40:05,662 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 09:40:06,336 - pyskl - INFO - +top1_acc 0.1542 +top5_acc 0.3728 +2024-07-22 09:40:06,336 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 09:40:06,379 - pyskl - INFO - +mean_acc 0.1541 +2024-07-22 09:40:06,391 - pyskl - INFO - Epoch(val) [9][309] top1_acc: 0.1542, top5_acc: 0.3728, mean_class_accuracy: 0.1541 +2024-07-22 09:43:27,385 - pyskl - INFO - Epoch [10][100/3746] lr: 9.911e-02, eta: 4 days, 11:17:51, time: 2.010, data_time: 1.300, memory: 15990, top1_acc: 0.2486, top5_acc: 0.5009, loss_cls: 4.1169, loss: 4.1169 +2024-07-22 09:44:37,669 - pyskl - INFO - Epoch [10][200/3746] lr: 9.910e-02, eta: 4 days, 11:15:53, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4983, loss_cls: 4.1182, loss: 4.1182 +2024-07-22 09:45:47,670 - pyskl - INFO - Epoch [10][300/3746] lr: 9.910e-02, eta: 4 days, 11:13:51, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4970, loss_cls: 4.1278, loss: 4.1278 +2024-07-22 09:46:58,009 - pyskl - INFO - Epoch [10][400/3746] lr: 9.909e-02, eta: 4 days, 11:11:55, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4919, loss_cls: 4.1604, loss: 4.1604 +2024-07-22 09:48:08,104 - pyskl - INFO - Epoch [10][500/3746] lr: 9.909e-02, eta: 4 days, 11:09:55, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4900, loss_cls: 4.1289, loss: 4.1289 +2024-07-22 09:49:18,351 - pyskl - INFO - Epoch [10][600/3746] lr: 9.908e-02, eta: 4 days, 11:07:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4992, loss_cls: 4.1157, loss: 4.1157 +2024-07-22 09:50:28,388 - pyskl - INFO - Epoch [10][700/3746] lr: 9.908e-02, eta: 4 days, 11:05:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4888, loss_cls: 4.1374, loss: 4.1374 +2024-07-22 09:51:38,395 - pyskl - INFO - Epoch [10][800/3746] lr: 9.907e-02, eta: 4 days, 11:03:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4838, loss_cls: 4.1738, loss: 4.1738 +2024-07-22 09:52:48,518 - pyskl - INFO - Epoch [10][900/3746] lr: 9.907e-02, eta: 4 days, 11:01:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4947, loss_cls: 4.1323, loss: 4.1323 +2024-07-22 09:53:58,446 - pyskl - INFO - Epoch [10][1000/3746] lr: 9.906e-02, eta: 4 days, 10:59:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4981, loss_cls: 4.1388, loss: 4.1388 +2024-07-22 09:55:08,350 - pyskl - INFO - Epoch [10][1100/3746] lr: 9.906e-02, eta: 4 days, 10:57:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4969, loss_cls: 4.1317, loss: 4.1317 +2024-07-22 09:56:18,377 - pyskl - INFO - Epoch [10][1200/3746] lr: 9.905e-02, eta: 4 days, 10:55:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4895, loss_cls: 4.1238, loss: 4.1238 +2024-07-22 09:57:28,135 - pyskl - INFO - Epoch [10][1300/3746] lr: 9.905e-02, eta: 4 days, 10:53:56, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4827, loss_cls: 4.1918, loss: 4.1918 +2024-07-22 09:58:38,548 - pyskl - INFO - Epoch [10][1400/3746] lr: 9.904e-02, eta: 4 days, 10:52:03, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4883, loss_cls: 4.1564, loss: 4.1564 +2024-07-22 09:59:48,339 - pyskl - INFO - Epoch [10][1500/3746] lr: 9.903e-02, eta: 4 days, 10:50:02, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4953, loss_cls: 4.1349, loss: 4.1349 +2024-07-22 10:00:58,551 - pyskl - INFO - Epoch [10][1600/3746] lr: 9.903e-02, eta: 4 days, 10:48:07, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4870, loss_cls: 4.1816, loss: 4.1816 +2024-07-22 10:02:08,424 - pyskl - INFO - Epoch [10][1700/3746] lr: 9.902e-02, eta: 4 days, 10:46:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4930, loss_cls: 4.1587, loss: 4.1587 +2024-07-22 10:03:18,240 - pyskl - INFO - Epoch [10][1800/3746] lr: 9.902e-02, eta: 4 days, 10:44:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4873, loss_cls: 4.1611, loss: 4.1611 +2024-07-22 10:04:28,674 - pyskl - INFO - Epoch [10][1900/3746] lr: 9.901e-02, eta: 4 days, 10:42:16, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.4997, loss_cls: 4.0985, loss: 4.0985 +2024-07-22 10:05:39,167 - pyskl - INFO - Epoch [10][2000/3746] lr: 9.901e-02, eta: 4 days, 10:40:26, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4925, loss_cls: 4.1332, loss: 4.1332 +2024-07-22 10:06:49,200 - pyskl - INFO - Epoch [10][2100/3746] lr: 9.900e-02, eta: 4 days, 10:38:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4880, loss_cls: 4.1525, loss: 4.1525 +2024-07-22 10:07:59,042 - pyskl - INFO - Epoch [10][2200/3746] lr: 9.900e-02, eta: 4 days, 10:36:31, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4869, loss_cls: 4.1639, loss: 4.1639 +2024-07-22 10:09:09,398 - pyskl - INFO - Epoch [10][2300/3746] lr: 9.899e-02, eta: 4 days, 10:34:40, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4867, loss_cls: 4.1881, loss: 4.1881 +2024-07-22 10:10:19,457 - pyskl - INFO - Epoch [10][2400/3746] lr: 9.898e-02, eta: 4 days, 10:32:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4833, loss_cls: 4.2112, loss: 4.2112 +2024-07-22 10:11:29,774 - pyskl - INFO - Epoch [10][2500/3746] lr: 9.898e-02, eta: 4 days, 10:30:54, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4891, loss_cls: 4.1323, loss: 4.1323 +2024-07-22 10:12:39,645 - pyskl - INFO - Epoch [10][2600/3746] lr: 9.897e-02, eta: 4 days, 10:28:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4958, loss_cls: 4.1530, loss: 4.1530 +2024-07-22 10:13:49,582 - pyskl - INFO - Epoch [10][2700/3746] lr: 9.897e-02, eta: 4 days, 10:27:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4878, loss_cls: 4.1611, loss: 4.1611 +2024-07-22 10:14:59,544 - pyskl - INFO - Epoch [10][2800/3746] lr: 9.896e-02, eta: 4 days, 10:25:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4961, loss_cls: 4.1437, loss: 4.1437 +2024-07-22 10:16:09,914 - pyskl - INFO - Epoch [10][2900/3746] lr: 9.896e-02, eta: 4 days, 10:23:15, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4884, loss_cls: 4.1658, loss: 4.1658 +2024-07-22 10:17:19,866 - pyskl - INFO - Epoch [10][3000/3746] lr: 9.895e-02, eta: 4 days, 10:21:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4895, loss_cls: 4.1523, loss: 4.1523 +2024-07-22 10:18:29,504 - pyskl - INFO - Epoch [10][3100/3746] lr: 9.894e-02, eta: 4 days, 10:19:20, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4909, loss_cls: 4.1493, loss: 4.1493 +2024-07-22 10:19:39,246 - pyskl - INFO - Epoch [10][3200/3746] lr: 9.894e-02, eta: 4 days, 10:17:22, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4875, loss_cls: 4.1622, loss: 4.1622 +2024-07-22 10:20:49,483 - pyskl - INFO - Epoch [10][3300/3746] lr: 9.893e-02, eta: 4 days, 10:15:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4923, loss_cls: 4.1325, loss: 4.1325 +2024-07-22 10:21:59,940 - pyskl - INFO - Epoch [10][3400/3746] lr: 9.893e-02, eta: 4 days, 10:13:45, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4889, loss_cls: 4.1590, loss: 4.1590 +2024-07-22 10:23:09,966 - pyskl - INFO - Epoch [10][3500/3746] lr: 9.892e-02, eta: 4 days, 10:11:52, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4877, loss_cls: 4.1415, loss: 4.1415 +2024-07-22 10:24:20,141 - pyskl - INFO - Epoch [10][3600/3746] lr: 9.892e-02, eta: 4 days, 10:10:01, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4784, loss_cls: 4.1966, loss: 4.1966 +2024-07-22 10:25:30,853 - pyskl - INFO - Epoch [10][3700/3746] lr: 9.891e-02, eta: 4 days, 10:08:18, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4927, loss_cls: 4.0997, loss: 4.0997 +2024-07-22 10:26:05,421 - pyskl - INFO - Saving checkpoint at 10 epochs +2024-07-22 10:27:58,244 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 10:27:58,930 - pyskl - INFO - +top1_acc 0.1348 +top5_acc 0.3391 +2024-07-22 10:27:58,931 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 10:27:58,979 - pyskl - INFO - +mean_acc 0.1348 +2024-07-22 10:27:58,996 - pyskl - INFO - Epoch(val) [10][309] top1_acc: 0.1348, top5_acc: 0.3391, mean_class_accuracy: 0.1348 +2024-07-22 10:31:26,706 - pyskl - INFO - Epoch [11][100/3746] lr: 9.890e-02, eta: 4 days, 10:30:06, time: 2.077, data_time: 1.363, memory: 15990, top1_acc: 0.2470, top5_acc: 0.5022, loss_cls: 4.0985, loss: 4.0985 +2024-07-22 10:32:37,446 - pyskl - INFO - Epoch [11][200/3746] lr: 9.890e-02, eta: 4 days, 10:28:20, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4969, loss_cls: 4.1231, loss: 4.1231 +2024-07-22 10:33:47,307 - pyskl - INFO - Epoch [11][300/3746] lr: 9.889e-02, eta: 4 days, 10:26:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4886, loss_cls: 4.1416, loss: 4.1416 +2024-07-22 10:34:57,558 - pyskl - INFO - Epoch [11][400/3746] lr: 9.888e-02, eta: 4 days, 10:24:29, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5002, loss_cls: 4.1031, loss: 4.1031 +2024-07-22 10:36:07,729 - pyskl - INFO - Epoch [11][500/3746] lr: 9.888e-02, eta: 4 days, 10:22:36, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5044, loss_cls: 4.0790, loss: 4.0790 +2024-07-22 10:37:17,620 - pyskl - INFO - Epoch [11][600/3746] lr: 9.887e-02, eta: 4 days, 10:20:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4983, loss_cls: 4.1284, loss: 4.1284 +2024-07-22 10:38:27,952 - pyskl - INFO - Epoch [11][700/3746] lr: 9.887e-02, eta: 4 days, 10:18:48, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5078, loss_cls: 4.0513, loss: 4.0513 +2024-07-22 10:39:37,989 - pyskl - INFO - Epoch [11][800/3746] lr: 9.886e-02, eta: 4 days, 10:16:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4800, loss_cls: 4.1650, loss: 4.1650 +2024-07-22 10:40:47,887 - pyskl - INFO - Epoch [11][900/3746] lr: 9.885e-02, eta: 4 days, 10:14:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4913, loss_cls: 4.1373, loss: 4.1373 +2024-07-22 10:41:58,030 - pyskl - INFO - Epoch [11][1000/3746] lr: 9.885e-02, eta: 4 days, 10:13:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.5094, loss_cls: 4.0715, loss: 4.0715 +2024-07-22 10:43:08,101 - pyskl - INFO - Epoch [11][1100/3746] lr: 9.884e-02, eta: 4 days, 10:11:10, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4911, loss_cls: 4.1418, loss: 4.1418 +2024-07-22 10:44:17,734 - pyskl - INFO - Epoch [11][1200/3746] lr: 9.884e-02, eta: 4 days, 10:09:11, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4981, loss_cls: 4.1516, loss: 4.1516 +2024-07-22 10:45:27,935 - pyskl - INFO - Epoch [11][1300/3746] lr: 9.883e-02, eta: 4 days, 10:07:20, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4858, loss_cls: 4.1671, loss: 4.1671 +2024-07-22 10:46:38,031 - pyskl - INFO - Epoch [11][1400/3746] lr: 9.882e-02, eta: 4 days, 10:05:27, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4916, loss_cls: 4.1488, loss: 4.1488 +2024-07-22 10:47:47,849 - pyskl - INFO - Epoch [11][1500/3746] lr: 9.882e-02, eta: 4 days, 10:03:31, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4853, loss_cls: 4.1463, loss: 4.1463 +2024-07-22 10:48:57,697 - pyskl - INFO - Epoch [11][1600/3746] lr: 9.881e-02, eta: 4 days, 10:01:36, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4872, loss_cls: 4.1275, loss: 4.1275 +2024-07-22 10:50:07,606 - pyskl - INFO - Epoch [11][1700/3746] lr: 9.881e-02, eta: 4 days, 9:59:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4905, loss_cls: 4.1665, loss: 4.1665 +2024-07-22 10:51:17,590 - pyskl - INFO - Epoch [11][1800/3746] lr: 9.880e-02, eta: 4 days, 9:57:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4931, loss_cls: 4.1399, loss: 4.1399 +2024-07-22 10:52:27,524 - pyskl - INFO - Epoch [11][1900/3746] lr: 9.879e-02, eta: 4 days, 9:55:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4989, loss_cls: 4.1321, loss: 4.1321 +2024-07-22 10:53:37,373 - pyskl - INFO - Epoch [11][2000/3746] lr: 9.879e-02, eta: 4 days, 9:54:00, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4869, loss_cls: 4.1425, loss: 4.1425 +2024-07-22 10:54:47,554 - pyskl - INFO - Epoch [11][2100/3746] lr: 9.878e-02, eta: 4 days, 9:52:10, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4866, loss_cls: 4.1695, loss: 4.1695 +2024-07-22 10:55:57,627 - pyskl - INFO - Epoch [11][2200/3746] lr: 9.878e-02, eta: 4 days, 9:50:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4916, loss_cls: 4.1393, loss: 4.1393 +2024-07-22 10:57:07,654 - pyskl - INFO - Epoch [11][2300/3746] lr: 9.877e-02, eta: 4 days, 9:48:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4856, loss_cls: 4.1805, loss: 4.1805 +2024-07-22 10:58:17,583 - pyskl - INFO - Epoch [11][2400/3746] lr: 9.876e-02, eta: 4 days, 9:46:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4791, loss_cls: 4.1960, loss: 4.1960 +2024-07-22 10:59:27,604 - pyskl - INFO - Epoch [11][2500/3746] lr: 9.876e-02, eta: 4 days, 9:44:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4914, loss_cls: 4.1580, loss: 4.1580 +2024-07-22 11:00:37,486 - pyskl - INFO - Epoch [11][2600/3746] lr: 9.875e-02, eta: 4 days, 9:42:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4948, loss_cls: 4.1448, loss: 4.1448 +2024-07-22 11:01:47,272 - pyskl - INFO - Epoch [11][2700/3746] lr: 9.874e-02, eta: 4 days, 9:40:58, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4950, loss_cls: 4.1323, loss: 4.1323 +2024-07-22 11:02:57,699 - pyskl - INFO - Epoch [11][2800/3746] lr: 9.874e-02, eta: 4 days, 9:39:12, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4983, loss_cls: 4.1288, loss: 4.1288 +2024-07-22 11:04:07,355 - pyskl - INFO - Epoch [11][2900/3746] lr: 9.873e-02, eta: 4 days, 9:37:17, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4903, loss_cls: 4.1530, loss: 4.1530 +2024-07-22 11:05:17,298 - pyskl - INFO - Epoch [11][3000/3746] lr: 9.873e-02, eta: 4 days, 9:35:26, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4959, loss_cls: 4.1351, loss: 4.1351 +2024-07-22 11:06:27,592 - pyskl - INFO - Epoch [11][3100/3746] lr: 9.872e-02, eta: 4 days, 9:33:40, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4975, loss_cls: 4.1391, loss: 4.1391 +2024-07-22 11:07:37,153 - pyskl - INFO - Epoch [11][3200/3746] lr: 9.871e-02, eta: 4 days, 9:31:44, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4788, loss_cls: 4.1893, loss: 4.1893 +2024-07-22 11:08:47,071 - pyskl - INFO - Epoch [11][3300/3746] lr: 9.871e-02, eta: 4 days, 9:29:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4981, loss_cls: 4.1170, loss: 4.1170 +2024-07-22 11:09:57,668 - pyskl - INFO - Epoch [11][3400/3746] lr: 9.870e-02, eta: 4 days, 9:28:12, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4950, loss_cls: 4.1282, loss: 4.1282 +2024-07-22 11:11:07,456 - pyskl - INFO - Epoch [11][3500/3746] lr: 9.869e-02, eta: 4 days, 9:26:19, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4798, loss_cls: 4.1975, loss: 4.1975 +2024-07-22 11:12:17,409 - pyskl - INFO - Epoch [11][3600/3746] lr: 9.869e-02, eta: 4 days, 9:24:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4956, loss_cls: 4.1195, loss: 4.1195 +2024-07-22 11:13:27,586 - pyskl - INFO - Epoch [11][3700/3746] lr: 9.868e-02, eta: 4 days, 9:22:43, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.4975, loss_cls: 4.1237, loss: 4.1237 +2024-07-22 11:14:02,117 - pyskl - INFO - Saving checkpoint at 11 epochs +2024-07-22 11:15:54,687 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 11:15:55,355 - pyskl - INFO - +top1_acc 0.1705 +top5_acc 0.3919 +2024-07-22 11:15:55,355 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 11:15:55,396 - pyskl - INFO - +mean_acc 0.1703 +2024-07-22 11:15:55,400 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_8.pth was removed +2024-07-22 11:15:55,660 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_11.pth. +2024-07-22 11:15:55,661 - pyskl - INFO - Best top1_acc is 0.1705 at 11 epoch. +2024-07-22 11:15:55,672 - pyskl - INFO - Epoch(val) [11][309] top1_acc: 0.1705, top5_acc: 0.3919, mean_class_accuracy: 0.1703 +2024-07-22 11:19:17,768 - pyskl - INFO - Epoch [12][100/3746] lr: 9.867e-02, eta: 4 days, 9:41:03, time: 2.021, data_time: 1.313, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4936, loss_cls: 4.1204, loss: 4.1204 +2024-07-22 11:20:28,161 - pyskl - INFO - Epoch [12][200/3746] lr: 9.867e-02, eta: 4 days, 9:39:16, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.5041, loss_cls: 4.0898, loss: 4.0898 +2024-07-22 11:21:38,352 - pyskl - INFO - Epoch [12][300/3746] lr: 9.866e-02, eta: 4 days, 9:37:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4888, loss_cls: 4.1217, loss: 4.1217 +2024-07-22 11:22:48,746 - pyskl - INFO - Epoch [12][400/3746] lr: 9.865e-02, eta: 4 days, 9:35:40, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4933, loss_cls: 4.1171, loss: 4.1171 +2024-07-22 11:23:59,213 - pyskl - INFO - Epoch [12][500/3746] lr: 9.865e-02, eta: 4 days, 9:33:55, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5072, loss_cls: 4.0497, loss: 4.0497 +2024-07-22 11:25:09,075 - pyskl - INFO - Epoch [12][600/3746] lr: 9.864e-02, eta: 4 days, 9:32:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4947, loss_cls: 4.1096, loss: 4.1096 +2024-07-22 11:26:19,018 - pyskl - INFO - Epoch [12][700/3746] lr: 9.863e-02, eta: 4 days, 9:30:10, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5058, loss_cls: 4.0647, loss: 4.0647 +2024-07-22 11:27:29,044 - pyskl - INFO - Epoch [12][800/3746] lr: 9.863e-02, eta: 4 days, 9:28:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5002, loss_cls: 4.0977, loss: 4.0977 +2024-07-22 11:28:39,345 - pyskl - INFO - Epoch [12][900/3746] lr: 9.862e-02, eta: 4 days, 9:26:33, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5002, loss_cls: 4.0865, loss: 4.0865 +2024-07-22 11:29:49,299 - pyskl - INFO - Epoch [12][1000/3746] lr: 9.861e-02, eta: 4 days, 9:24:42, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4953, loss_cls: 4.1195, loss: 4.1195 +2024-07-22 11:30:59,387 - pyskl - INFO - Epoch [12][1100/3746] lr: 9.861e-02, eta: 4 days, 9:22:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4956, loss_cls: 4.1293, loss: 4.1293 +2024-07-22 11:32:09,646 - pyskl - INFO - Epoch [12][1200/3746] lr: 9.860e-02, eta: 4 days, 9:21:06, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.5016, loss_cls: 4.1367, loss: 4.1367 +2024-07-22 11:33:19,733 - pyskl - INFO - Epoch [12][1300/3746] lr: 9.859e-02, eta: 4 days, 9:19:18, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4861, loss_cls: 4.1172, loss: 4.1172 +2024-07-22 11:34:30,223 - pyskl - INFO - Epoch [12][1400/3746] lr: 9.859e-02, eta: 4 days, 9:17:34, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4967, loss_cls: 4.1687, loss: 4.1687 +2024-07-22 11:35:40,218 - pyskl - INFO - Epoch [12][1500/3746] lr: 9.858e-02, eta: 4 days, 9:15:45, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4977, loss_cls: 4.1447, loss: 4.1447 +2024-07-22 11:36:50,158 - pyskl - INFO - Epoch [12][1600/3746] lr: 9.857e-02, eta: 4 days, 9:13:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4948, loss_cls: 4.1486, loss: 4.1486 +2024-07-22 11:38:00,243 - pyskl - INFO - Epoch [12][1700/3746] lr: 9.857e-02, eta: 4 days, 9:12:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5020, loss_cls: 4.0980, loss: 4.0980 +2024-07-22 11:39:10,062 - pyskl - INFO - Epoch [12][1800/3746] lr: 9.856e-02, eta: 4 days, 9:10:16, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4886, loss_cls: 4.1648, loss: 4.1648 +2024-07-22 11:40:19,982 - pyskl - INFO - Epoch [12][1900/3746] lr: 9.855e-02, eta: 4 days, 9:08:26, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5077, loss_cls: 4.0953, loss: 4.0953 +2024-07-22 11:41:29,876 - pyskl - INFO - Epoch [12][2000/3746] lr: 9.855e-02, eta: 4 days, 9:06:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4928, loss_cls: 4.1255, loss: 4.1255 +2024-07-22 11:42:39,919 - pyskl - INFO - Epoch [12][2100/3746] lr: 9.854e-02, eta: 4 days, 9:04:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4811, loss_cls: 4.1887, loss: 4.1887 +2024-07-22 11:43:50,052 - pyskl - INFO - Epoch [12][2200/3746] lr: 9.853e-02, eta: 4 days, 9:03:02, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.5028, loss_cls: 4.1170, loss: 4.1170 +2024-07-22 11:45:00,178 - pyskl - INFO - Epoch [12][2300/3746] lr: 9.853e-02, eta: 4 days, 9:01:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4963, loss_cls: 4.1234, loss: 4.1234 +2024-07-22 11:46:10,051 - pyskl - INFO - Epoch [12][2400/3746] lr: 9.852e-02, eta: 4 days, 8:59:26, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4941, loss_cls: 4.1307, loss: 4.1307 +2024-07-22 11:47:19,834 - pyskl - INFO - Epoch [12][2500/3746] lr: 9.851e-02, eta: 4 days, 8:57:36, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4858, loss_cls: 4.1740, loss: 4.1740 +2024-07-22 11:48:29,665 - pyskl - INFO - Epoch [12][2600/3746] lr: 9.851e-02, eta: 4 days, 8:55:47, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4867, loss_cls: 4.1688, loss: 4.1688 +2024-07-22 11:49:39,816 - pyskl - INFO - Epoch [12][2700/3746] lr: 9.850e-02, eta: 4 days, 8:54:01, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4902, loss_cls: 4.1512, loss: 4.1512 +2024-07-22 11:50:49,730 - pyskl - INFO - Epoch [12][2800/3746] lr: 9.849e-02, eta: 4 days, 8:52:13, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4841, loss_cls: 4.1799, loss: 4.1799 +2024-07-22 11:51:59,663 - pyskl - INFO - Epoch [12][2900/3746] lr: 9.849e-02, eta: 4 days, 8:50:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4919, loss_cls: 4.1693, loss: 4.1693 +2024-07-22 11:53:09,461 - pyskl - INFO - Epoch [12][3000/3746] lr: 9.848e-02, eta: 4 days, 8:48:36, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4766, loss_cls: 4.1873, loss: 4.1873 +2024-07-22 11:54:19,547 - pyskl - INFO - Epoch [12][3100/3746] lr: 9.847e-02, eta: 4 days, 8:46:51, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4928, loss_cls: 4.1243, loss: 4.1243 +2024-07-22 11:55:29,443 - pyskl - INFO - Epoch [12][3200/3746] lr: 9.847e-02, eta: 4 days, 8:45:03, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.5031, loss_cls: 4.1042, loss: 4.1042 +2024-07-22 11:56:39,346 - pyskl - INFO - Epoch [12][3300/3746] lr: 9.846e-02, eta: 4 days, 8:43:16, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4861, loss_cls: 4.1764, loss: 4.1764 +2024-07-22 11:57:49,815 - pyskl - INFO - Epoch [12][3400/3746] lr: 9.845e-02, eta: 4 days, 8:41:35, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.5011, loss_cls: 4.1170, loss: 4.1170 +2024-07-22 11:59:00,190 - pyskl - INFO - Epoch [12][3500/3746] lr: 9.845e-02, eta: 4 days, 8:39:53, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.5027, loss_cls: 4.1055, loss: 4.1055 +2024-07-22 12:00:10,344 - pyskl - INFO - Epoch [12][3600/3746] lr: 9.844e-02, eta: 4 days, 8:38:09, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4973, loss_cls: 4.1185, loss: 4.1185 +2024-07-22 12:01:20,328 - pyskl - INFO - Epoch [12][3700/3746] lr: 9.843e-02, eta: 4 days, 8:36:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4964, loss_cls: 4.1427, loss: 4.1427 +2024-07-22 12:01:55,063 - pyskl - INFO - Saving checkpoint at 12 epochs +2024-07-22 12:03:47,777 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 12:03:48,472 - pyskl - INFO - +top1_acc 0.1608 +top5_acc 0.3836 +2024-07-22 12:03:48,472 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 12:03:48,513 - pyskl - INFO - +mean_acc 0.1608 +2024-07-22 12:03:48,525 - pyskl - INFO - Epoch(val) [12][309] top1_acc: 0.1608, top5_acc: 0.3836, mean_class_accuracy: 0.1608 +2024-07-22 12:07:09,210 - pyskl - INFO - Epoch [13][100/3746] lr: 9.842e-02, eta: 4 days, 8:52:39, time: 2.007, data_time: 1.298, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5006, loss_cls: 4.0683, loss: 4.0683 +2024-07-22 12:08:19,866 - pyskl - INFO - Epoch [13][200/3746] lr: 9.842e-02, eta: 4 days, 8:50:59, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4977, loss_cls: 4.0988, loss: 4.0988 +2024-07-22 12:09:30,654 - pyskl - INFO - Epoch [13][300/3746] lr: 9.841e-02, eta: 4 days, 8:49:20, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5016, loss_cls: 4.0997, loss: 4.0997 +2024-07-22 12:10:40,729 - pyskl - INFO - Epoch [13][400/3746] lr: 9.840e-02, eta: 4 days, 8:47:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4995, loss_cls: 4.1139, loss: 4.1139 +2024-07-22 12:11:50,792 - pyskl - INFO - Epoch [13][500/3746] lr: 9.839e-02, eta: 4 days, 8:45:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4977, loss_cls: 4.0965, loss: 4.0965 +2024-07-22 12:13:01,174 - pyskl - INFO - Epoch [13][600/3746] lr: 9.839e-02, eta: 4 days, 8:44:03, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4873, loss_cls: 4.1433, loss: 4.1433 +2024-07-22 12:14:11,357 - pyskl - INFO - Epoch [13][700/3746] lr: 9.838e-02, eta: 4 days, 8:42:18, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5028, loss_cls: 4.0776, loss: 4.0776 +2024-07-22 12:15:21,369 - pyskl - INFO - Epoch [13][800/3746] lr: 9.837e-02, eta: 4 days, 8:40:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4988, loss_cls: 4.1224, loss: 4.1224 +2024-07-22 12:16:31,151 - pyskl - INFO - Epoch [13][900/3746] lr: 9.837e-02, eta: 4 days, 8:38:42, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5081, loss_cls: 4.0666, loss: 4.0666 +2024-07-22 12:17:41,229 - pyskl - INFO - Epoch [13][1000/3746] lr: 9.836e-02, eta: 4 days, 8:36:56, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4992, loss_cls: 4.1363, loss: 4.1363 +2024-07-22 12:18:51,449 - pyskl - INFO - Epoch [13][1100/3746] lr: 9.835e-02, eta: 4 days, 8:35:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.5069, loss_cls: 4.1009, loss: 4.1009 +2024-07-22 12:20:01,472 - pyskl - INFO - Epoch [13][1200/3746] lr: 9.834e-02, eta: 4 days, 8:33:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.5009, loss_cls: 4.1003, loss: 4.1003 +2024-07-22 12:21:11,625 - pyskl - INFO - Epoch [13][1300/3746] lr: 9.834e-02, eta: 4 days, 8:31:41, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4970, loss_cls: 4.1122, loss: 4.1122 +2024-07-22 12:22:21,699 - pyskl - INFO - Epoch [13][1400/3746] lr: 9.833e-02, eta: 4 days, 8:29:56, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4995, loss_cls: 4.0827, loss: 4.0827 +2024-07-22 12:23:31,780 - pyskl - INFO - Epoch [13][1500/3746] lr: 9.832e-02, eta: 4 days, 8:28:11, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4898, loss_cls: 4.1401, loss: 4.1401 +2024-07-22 12:24:41,782 - pyskl - INFO - Epoch [13][1600/3746] lr: 9.832e-02, eta: 4 days, 8:26:25, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4969, loss_cls: 4.1129, loss: 4.1129 +2024-07-22 12:25:51,565 - pyskl - INFO - Epoch [13][1700/3746] lr: 9.831e-02, eta: 4 days, 8:24:37, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4859, loss_cls: 4.1598, loss: 4.1598 +2024-07-22 12:27:01,497 - pyskl - INFO - Epoch [13][1800/3746] lr: 9.830e-02, eta: 4 days, 8:22:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4886, loss_cls: 4.1478, loss: 4.1478 +2024-07-22 12:28:11,456 - pyskl - INFO - Epoch [13][1900/3746] lr: 9.829e-02, eta: 4 days, 8:21:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4942, loss_cls: 4.1350, loss: 4.1350 +2024-07-22 12:29:21,543 - pyskl - INFO - Epoch [13][2000/3746] lr: 9.829e-02, eta: 4 days, 8:19:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5034, loss_cls: 4.1110, loss: 4.1110 +2024-07-22 12:30:31,752 - pyskl - INFO - Epoch [13][2100/3746] lr: 9.828e-02, eta: 4 days, 8:17:38, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4966, loss_cls: 4.1070, loss: 4.1070 +2024-07-22 12:31:41,998 - pyskl - INFO - Epoch [13][2200/3746] lr: 9.827e-02, eta: 4 days, 8:15:56, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5059, loss_cls: 4.0980, loss: 4.0980 +2024-07-22 12:32:52,192 - pyskl - INFO - Epoch [13][2300/3746] lr: 9.827e-02, eta: 4 days, 8:14:13, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4903, loss_cls: 4.1327, loss: 4.1327 +2024-07-22 12:34:02,184 - pyskl - INFO - Epoch [13][2400/3746] lr: 9.826e-02, eta: 4 days, 8:12:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4861, loss_cls: 4.1672, loss: 4.1672 +2024-07-22 12:35:11,947 - pyskl - INFO - Epoch [13][2500/3746] lr: 9.825e-02, eta: 4 days, 8:10:42, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4975, loss_cls: 4.1143, loss: 4.1143 +2024-07-22 12:36:21,856 - pyskl - INFO - Epoch [13][2600/3746] lr: 9.824e-02, eta: 4 days, 8:08:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4936, loss_cls: 4.1533, loss: 4.1533 +2024-07-22 12:37:31,640 - pyskl - INFO - Epoch [13][2700/3746] lr: 9.824e-02, eta: 4 days, 8:07:10, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5038, loss_cls: 4.0877, loss: 4.0877 +2024-07-22 12:38:41,618 - pyskl - INFO - Epoch [13][2800/3746] lr: 9.823e-02, eta: 4 days, 8:05:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4923, loss_cls: 4.1474, loss: 4.1474 +2024-07-22 12:39:51,575 - pyskl - INFO - Epoch [13][2900/3746] lr: 9.822e-02, eta: 4 days, 8:03:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4864, loss_cls: 4.1334, loss: 4.1334 +2024-07-22 12:41:01,632 - pyskl - INFO - Epoch [13][3000/3746] lr: 9.821e-02, eta: 4 days, 8:01:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4981, loss_cls: 4.1231, loss: 4.1231 +2024-07-22 12:42:11,526 - pyskl - INFO - Epoch [13][3100/3746] lr: 9.821e-02, eta: 4 days, 8:00:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4992, loss_cls: 4.1003, loss: 4.1003 +2024-07-22 12:43:21,396 - pyskl - INFO - Epoch [13][3200/3746] lr: 9.820e-02, eta: 4 days, 7:58:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5041, loss_cls: 4.0752, loss: 4.0752 +2024-07-22 12:44:31,400 - pyskl - INFO - Epoch [13][3300/3746] lr: 9.819e-02, eta: 4 days, 7:56:46, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4973, loss_cls: 4.1317, loss: 4.1317 +2024-07-22 12:45:42,087 - pyskl - INFO - Epoch [13][3400/3746] lr: 9.818e-02, eta: 4 days, 7:55:10, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4978, loss_cls: 4.1433, loss: 4.1433 +2024-07-22 12:46:52,381 - pyskl - INFO - Epoch [13][3500/3746] lr: 9.818e-02, eta: 4 days, 7:53:30, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.5036, loss_cls: 4.0940, loss: 4.0940 +2024-07-22 12:48:02,576 - pyskl - INFO - Epoch [13][3600/3746] lr: 9.817e-02, eta: 4 days, 7:51:49, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4948, loss_cls: 4.1397, loss: 4.1397 +2024-07-22 12:49:12,695 - pyskl - INFO - Epoch [13][3700/3746] lr: 9.816e-02, eta: 4 days, 7:50:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4944, loss_cls: 4.1129, loss: 4.1129 +2024-07-22 12:49:47,354 - pyskl - INFO - Saving checkpoint at 13 epochs +2024-07-22 12:51:38,545 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 12:51:39,209 - pyskl - INFO - +top1_acc 0.1678 +top5_acc 0.3932 +2024-07-22 12:51:39,210 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 12:51:39,251 - pyskl - INFO - +mean_acc 0.1677 +2024-07-22 12:51:39,263 - pyskl - INFO - Epoch(val) [13][309] top1_acc: 0.1678, top5_acc: 0.3932, mean_class_accuracy: 0.1677 +2024-07-22 12:55:01,183 - pyskl - INFO - Epoch [14][100/3746] lr: 9.815e-02, eta: 4 days, 8:05:06, time: 2.019, data_time: 1.315, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5066, loss_cls: 4.0805, loss: 4.0805 +2024-07-22 12:56:12,024 - pyskl - INFO - Epoch [14][200/3746] lr: 9.814e-02, eta: 4 days, 8:03:30, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5030, loss_cls: 4.0855, loss: 4.0855 +2024-07-22 12:57:22,159 - pyskl - INFO - Epoch [14][300/3746] lr: 9.814e-02, eta: 4 days, 8:01:47, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.5062, loss_cls: 4.0727, loss: 4.0727 +2024-07-22 12:58:32,146 - pyskl - INFO - Epoch [14][400/3746] lr: 9.813e-02, eta: 4 days, 8:00:02, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5091, loss_cls: 4.0771, loss: 4.0771 +2024-07-22 12:59:42,264 - pyskl - INFO - Epoch [14][500/3746] lr: 9.812e-02, eta: 4 days, 7:58:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5098, loss_cls: 4.0604, loss: 4.0604 +2024-07-22 13:00:52,367 - pyskl - INFO - Epoch [14][600/3746] lr: 9.811e-02, eta: 4 days, 7:56:36, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.5038, loss_cls: 4.0920, loss: 4.0920 +2024-07-22 13:02:02,266 - pyskl - INFO - Epoch [14][700/3746] lr: 9.811e-02, eta: 4 days, 7:54:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5025, loss_cls: 4.0618, loss: 4.0618 +2024-07-22 13:03:12,496 - pyskl - INFO - Epoch [14][800/3746] lr: 9.810e-02, eta: 4 days, 7:53:09, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4980, loss_cls: 4.0951, loss: 4.0951 +2024-07-22 13:04:22,495 - pyskl - INFO - Epoch [14][900/3746] lr: 9.809e-02, eta: 4 days, 7:51:25, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4913, loss_cls: 4.1535, loss: 4.1535 +2024-07-22 13:05:32,479 - pyskl - INFO - Epoch [14][1000/3746] lr: 9.808e-02, eta: 4 days, 7:49:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4864, loss_cls: 4.1359, loss: 4.1359 +2024-07-22 13:06:42,446 - pyskl - INFO - Epoch [14][1100/3746] lr: 9.807e-02, eta: 4 days, 7:47:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4975, loss_cls: 4.1347, loss: 4.1347 +2024-07-22 13:07:52,276 - pyskl - INFO - Epoch [14][1200/3746] lr: 9.807e-02, eta: 4 days, 7:46:12, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5086, loss_cls: 4.0766, loss: 4.0766 +2024-07-22 13:09:02,311 - pyskl - INFO - Epoch [14][1300/3746] lr: 9.806e-02, eta: 4 days, 7:44:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5050, loss_cls: 4.0857, loss: 4.0857 +2024-07-22 13:10:12,725 - pyskl - INFO - Epoch [14][1400/3746] lr: 9.805e-02, eta: 4 days, 7:42:50, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4938, loss_cls: 4.1293, loss: 4.1293 +2024-07-22 13:11:23,234 - pyskl - INFO - Epoch [14][1500/3746] lr: 9.804e-02, eta: 4 days, 7:41:12, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4973, loss_cls: 4.0882, loss: 4.0882 +2024-07-22 13:12:33,376 - pyskl - INFO - Epoch [14][1600/3746] lr: 9.804e-02, eta: 4 days, 7:39:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5008, loss_cls: 4.1036, loss: 4.1036 +2024-07-22 13:13:43,408 - pyskl - INFO - Epoch [14][1700/3746] lr: 9.803e-02, eta: 4 days, 7:37:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.5017, loss_cls: 4.0982, loss: 4.0982 +2024-07-22 13:14:53,541 - pyskl - INFO - Epoch [14][1800/3746] lr: 9.802e-02, eta: 4 days, 7:36:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4975, loss_cls: 4.1109, loss: 4.1109 +2024-07-22 13:16:03,546 - pyskl - INFO - Epoch [14][1900/3746] lr: 9.801e-02, eta: 4 days, 7:34:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4955, loss_cls: 4.1377, loss: 4.1377 +2024-07-22 13:17:13,702 - pyskl - INFO - Epoch [14][2000/3746] lr: 9.800e-02, eta: 4 days, 7:32:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4933, loss_cls: 4.1343, loss: 4.1343 +2024-07-22 13:18:23,927 - pyskl - INFO - Epoch [14][2100/3746] lr: 9.800e-02, eta: 4 days, 7:31:04, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4933, loss_cls: 4.1246, loss: 4.1246 +2024-07-22 13:19:33,884 - pyskl - INFO - Epoch [14][2200/3746] lr: 9.799e-02, eta: 4 days, 7:29:21, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5156, loss_cls: 4.0472, loss: 4.0472 +2024-07-22 13:20:43,935 - pyskl - INFO - Epoch [14][2300/3746] lr: 9.798e-02, eta: 4 days, 7:27:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4884, loss_cls: 4.1577, loss: 4.1577 +2024-07-22 13:21:54,178 - pyskl - INFO - Epoch [14][2400/3746] lr: 9.797e-02, eta: 4 days, 7:26:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.5061, loss_cls: 4.0850, loss: 4.0850 +2024-07-22 13:23:04,429 - pyskl - INFO - Epoch [14][2500/3746] lr: 9.797e-02, eta: 4 days, 7:24:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4909, loss_cls: 4.1256, loss: 4.1256 +2024-07-22 13:24:14,500 - pyskl - INFO - Epoch [14][2600/3746] lr: 9.796e-02, eta: 4 days, 7:22:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4923, loss_cls: 4.1340, loss: 4.1340 +2024-07-22 13:25:24,724 - pyskl - INFO - Epoch [14][2700/3746] lr: 9.795e-02, eta: 4 days, 7:21:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4970, loss_cls: 4.1175, loss: 4.1175 +2024-07-22 13:26:34,711 - pyskl - INFO - Epoch [14][2800/3746] lr: 9.794e-02, eta: 4 days, 7:19:19, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5100, loss_cls: 4.0607, loss: 4.0607 +2024-07-22 13:27:44,838 - pyskl - INFO - Epoch [14][2900/3746] lr: 9.793e-02, eta: 4 days, 7:17:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.5020, loss_cls: 4.1038, loss: 4.1038 +2024-07-22 13:28:55,426 - pyskl - INFO - Epoch [14][3000/3746] lr: 9.793e-02, eta: 4 days, 7:16:03, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4988, loss_cls: 4.0728, loss: 4.0728 +2024-07-22 13:30:05,621 - pyskl - INFO - Epoch [14][3100/3746] lr: 9.792e-02, eta: 4 days, 7:14:24, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5111, loss_cls: 4.0693, loss: 4.0693 +2024-07-22 13:31:15,409 - pyskl - INFO - Epoch [14][3200/3746] lr: 9.791e-02, eta: 4 days, 7:12:41, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5052, loss_cls: 4.1137, loss: 4.1137 +2024-07-22 13:32:25,778 - pyskl - INFO - Epoch [14][3300/3746] lr: 9.790e-02, eta: 4 days, 7:11:04, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4992, loss_cls: 4.1040, loss: 4.1040 +2024-07-22 13:33:35,817 - pyskl - INFO - Epoch [14][3400/3746] lr: 9.789e-02, eta: 4 days, 7:09:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5041, loss_cls: 4.0850, loss: 4.0850 +2024-07-22 13:34:46,198 - pyskl - INFO - Epoch [14][3500/3746] lr: 9.789e-02, eta: 4 days, 7:07:47, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5008, loss_cls: 4.0927, loss: 4.0927 +2024-07-22 13:35:56,315 - pyskl - INFO - Epoch [14][3600/3746] lr: 9.788e-02, eta: 4 days, 7:06:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5066, loss_cls: 4.0778, loss: 4.0778 +2024-07-22 13:37:06,340 - pyskl - INFO - Epoch [14][3700/3746] lr: 9.787e-02, eta: 4 days, 7:04:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4891, loss_cls: 4.1406, loss: 4.1406 +2024-07-22 13:37:41,008 - pyskl - INFO - Saving checkpoint at 14 epochs +2024-07-22 13:39:33,638 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 13:39:34,292 - pyskl - INFO - +top1_acc 0.1605 +top5_acc 0.3718 +2024-07-22 13:39:34,292 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 13:39:34,338 - pyskl - INFO - +mean_acc 0.1604 +2024-07-22 13:39:34,351 - pyskl - INFO - Epoch(val) [14][309] top1_acc: 0.1605, top5_acc: 0.3718, mean_class_accuracy: 0.1604 +2024-07-22 13:42:54,280 - pyskl - INFO - Epoch [15][100/3746] lr: 9.786e-02, eta: 4 days, 7:17:48, time: 1.999, data_time: 1.297, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5009, loss_cls: 4.0942, loss: 4.0942 +2024-07-22 13:44:05,512 - pyskl - INFO - Epoch [15][200/3746] lr: 9.785e-02, eta: 4 days, 7:16:18, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5028, loss_cls: 4.0910, loss: 4.0910 +2024-07-22 13:45:15,541 - pyskl - INFO - Epoch [15][300/3746] lr: 9.784e-02, eta: 4 days, 7:14:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4991, loss_cls: 4.0996, loss: 4.0996 +2024-07-22 13:46:25,620 - pyskl - INFO - Epoch [15][400/3746] lr: 9.783e-02, eta: 4 days, 7:12:55, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5089, loss_cls: 4.0332, loss: 4.0332 +2024-07-22 13:47:35,828 - pyskl - INFO - Epoch [15][500/3746] lr: 9.783e-02, eta: 4 days, 7:11:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5055, loss_cls: 4.0541, loss: 4.0541 +2024-07-22 13:48:45,721 - pyskl - INFO - Epoch [15][600/3746] lr: 9.782e-02, eta: 4 days, 7:09:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5128, loss_cls: 4.0466, loss: 4.0466 +2024-07-22 13:49:55,618 - pyskl - INFO - Epoch [15][700/3746] lr: 9.781e-02, eta: 4 days, 7:07:50, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.5034, loss_cls: 4.0917, loss: 4.0917 +2024-07-22 13:51:05,484 - pyskl - INFO - Epoch [15][800/3746] lr: 9.780e-02, eta: 4 days, 7:06:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5058, loss_cls: 4.0813, loss: 4.0813 +2024-07-22 13:52:15,171 - pyskl - INFO - Epoch [15][900/3746] lr: 9.779e-02, eta: 4 days, 7:04:23, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.5095, loss_cls: 4.0570, loss: 4.0570 +2024-07-22 13:53:24,890 - pyskl - INFO - Epoch [15][1000/3746] lr: 9.778e-02, eta: 4 days, 7:02:39, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5072, loss_cls: 4.0664, loss: 4.0664 +2024-07-22 13:54:34,880 - pyskl - INFO - Epoch [15][1100/3746] lr: 9.778e-02, eta: 4 days, 7:00:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5134, loss_cls: 4.0439, loss: 4.0439 +2024-07-22 13:55:44,687 - pyskl - INFO - Epoch [15][1200/3746] lr: 9.777e-02, eta: 4 days, 6:59:15, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5017, loss_cls: 4.0593, loss: 4.0593 +2024-07-22 13:56:54,518 - pyskl - INFO - Epoch [15][1300/3746] lr: 9.776e-02, eta: 4 days, 6:57:33, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.5088, loss_cls: 4.0830, loss: 4.0830 +2024-07-22 13:58:04,248 - pyskl - INFO - Epoch [15][1400/3746] lr: 9.775e-02, eta: 4 days, 6:55:49, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4827, loss_cls: 4.1730, loss: 4.1730 +2024-07-22 13:59:13,972 - pyskl - INFO - Epoch [15][1500/3746] lr: 9.774e-02, eta: 4 days, 6:54:06, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5053, loss_cls: 4.0818, loss: 4.0818 +2024-07-22 14:00:23,774 - pyskl - INFO - Epoch [15][1600/3746] lr: 9.773e-02, eta: 4 days, 6:52:24, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4998, loss_cls: 4.1240, loss: 4.1240 +2024-07-22 14:01:33,519 - pyskl - INFO - Epoch [15][1700/3746] lr: 9.773e-02, eta: 4 days, 6:50:41, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5014, loss_cls: 4.0779, loss: 4.0779 +2024-07-22 14:02:43,779 - pyskl - INFO - Epoch [15][1800/3746] lr: 9.772e-02, eta: 4 days, 6:49:03, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5031, loss_cls: 4.1013, loss: 4.1013 +2024-07-22 14:03:53,909 - pyskl - INFO - Epoch [15][1900/3746] lr: 9.771e-02, eta: 4 days, 6:47:25, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5055, loss_cls: 4.0596, loss: 4.0596 +2024-07-22 14:05:03,583 - pyskl - INFO - Epoch [15][2000/3746] lr: 9.770e-02, eta: 4 days, 6:45:41, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4919, loss_cls: 4.1284, loss: 4.1284 +2024-07-22 14:06:13,434 - pyskl - INFO - Epoch [15][2100/3746] lr: 9.769e-02, eta: 4 days, 6:44:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4958, loss_cls: 4.1286, loss: 4.1286 +2024-07-22 14:07:23,003 - pyskl - INFO - Epoch [15][2200/3746] lr: 9.768e-02, eta: 4 days, 6:42:16, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.5089, loss_cls: 4.0845, loss: 4.0845 +2024-07-22 14:08:32,818 - pyskl - INFO - Epoch [15][2300/3746] lr: 9.768e-02, eta: 4 days, 6:40:35, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4989, loss_cls: 4.1026, loss: 4.1026 +2024-07-22 14:09:42,432 - pyskl - INFO - Epoch [15][2400/3746] lr: 9.767e-02, eta: 4 days, 6:38:52, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4992, loss_cls: 4.1110, loss: 4.1110 +2024-07-22 14:10:52,585 - pyskl - INFO - Epoch [15][2500/3746] lr: 9.766e-02, eta: 4 days, 6:37:14, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5088, loss_cls: 4.0764, loss: 4.0764 +2024-07-22 14:12:02,272 - pyskl - INFO - Epoch [15][2600/3746] lr: 9.765e-02, eta: 4 days, 6:35:32, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.5005, loss_cls: 4.1161, loss: 4.1161 +2024-07-22 14:13:12,177 - pyskl - INFO - Epoch [15][2700/3746] lr: 9.764e-02, eta: 4 days, 6:33:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5073, loss_cls: 4.0771, loss: 4.0771 +2024-07-22 14:14:22,029 - pyskl - INFO - Epoch [15][2800/3746] lr: 9.763e-02, eta: 4 days, 6:32:11, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5058, loss_cls: 4.0677, loss: 4.0677 +2024-07-22 14:15:32,244 - pyskl - INFO - Epoch [15][2900/3746] lr: 9.763e-02, eta: 4 days, 6:30:34, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5098, loss_cls: 4.0678, loss: 4.0678 +2024-07-22 14:16:42,071 - pyskl - INFO - Epoch [15][3000/3746] lr: 9.762e-02, eta: 4 days, 6:28:54, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4952, loss_cls: 4.1335, loss: 4.1335 +2024-07-22 14:17:51,812 - pyskl - INFO - Epoch [15][3100/3746] lr: 9.761e-02, eta: 4 days, 6:27:12, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4923, loss_cls: 4.1063, loss: 4.1063 +2024-07-22 14:19:01,709 - pyskl - INFO - Epoch [15][3200/3746] lr: 9.760e-02, eta: 4 days, 6:25:33, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5020, loss_cls: 4.0837, loss: 4.0837 +2024-07-22 14:20:11,437 - pyskl - INFO - Epoch [15][3300/3746] lr: 9.759e-02, eta: 4 days, 6:23:52, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5059, loss_cls: 4.0788, loss: 4.0788 +2024-07-22 14:21:21,690 - pyskl - INFO - Epoch [15][3400/3746] lr: 9.758e-02, eta: 4 days, 6:22:16, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4983, loss_cls: 4.1153, loss: 4.1153 +2024-07-22 14:22:31,581 - pyskl - INFO - Epoch [15][3500/3746] lr: 9.757e-02, eta: 4 days, 6:20:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.5078, loss_cls: 4.0731, loss: 4.0731 +2024-07-22 14:23:41,864 - pyskl - INFO - Epoch [15][3600/3746] lr: 9.757e-02, eta: 4 days, 6:19:00, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4958, loss_cls: 4.1127, loss: 4.1127 +2024-07-22 14:24:51,896 - pyskl - INFO - Epoch [15][3700/3746] lr: 9.756e-02, eta: 4 days, 6:17:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5058, loss_cls: 4.0989, loss: 4.0989 +2024-07-22 14:25:26,517 - pyskl - INFO - Saving checkpoint at 15 epochs +2024-07-22 14:27:18,734 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 14:27:19,399 - pyskl - INFO - +top1_acc 0.1767 +top5_acc 0.3997 +2024-07-22 14:27:19,400 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 14:27:19,442 - pyskl - INFO - +mean_acc 0.1768 +2024-07-22 14:27:19,446 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_11.pth was removed +2024-07-22 14:27:19,703 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_15.pth. +2024-07-22 14:27:19,704 - pyskl - INFO - Best top1_acc is 0.1767 at 15 epoch. +2024-07-22 14:27:19,715 - pyskl - INFO - Epoch(val) [15][309] top1_acc: 0.1767, top5_acc: 0.3997, mean_class_accuracy: 0.1768 +2024-07-22 14:30:39,952 - pyskl - INFO - Epoch [16][100/3746] lr: 9.754e-02, eta: 4 days, 6:29:40, time: 2.002, data_time: 1.301, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5081, loss_cls: 4.0747, loss: 4.0747 +2024-07-22 14:31:50,867 - pyskl - INFO - Epoch [16][200/3746] lr: 9.754e-02, eta: 4 days, 6:28:08, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5094, loss_cls: 4.0453, loss: 4.0453 +2024-07-22 14:33:01,516 - pyskl - INFO - Epoch [16][300/3746] lr: 9.753e-02, eta: 4 days, 6:26:35, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5139, loss_cls: 4.0066, loss: 4.0066 +2024-07-22 14:34:12,118 - pyskl - INFO - Epoch [16][400/3746] lr: 9.752e-02, eta: 4 days, 6:25:00, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5058, loss_cls: 4.0631, loss: 4.0631 +2024-07-22 14:35:22,177 - pyskl - INFO - Epoch [16][500/3746] lr: 9.751e-02, eta: 4 days, 6:23:22, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5058, loss_cls: 4.0421, loss: 4.0421 +2024-07-22 14:36:32,239 - pyskl - INFO - Epoch [16][600/3746] lr: 9.750e-02, eta: 4 days, 6:21:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5034, loss_cls: 4.0732, loss: 4.0732 +2024-07-22 14:37:41,836 - pyskl - INFO - Epoch [16][700/3746] lr: 9.749e-02, eta: 4 days, 6:20:00, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5112, loss_cls: 4.0786, loss: 4.0786 +2024-07-22 14:38:51,764 - pyskl - INFO - Epoch [16][800/3746] lr: 9.748e-02, eta: 4 days, 6:18:20, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4886, loss_cls: 4.1222, loss: 4.1222 +2024-07-22 14:40:01,363 - pyskl - INFO - Epoch [16][900/3746] lr: 9.747e-02, eta: 4 days, 6:16:38, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5131, loss_cls: 4.0435, loss: 4.0435 +2024-07-22 14:41:10,989 - pyskl - INFO - Epoch [16][1000/3746] lr: 9.747e-02, eta: 4 days, 6:14:56, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5006, loss_cls: 4.0976, loss: 4.0976 +2024-07-22 14:42:20,854 - pyskl - INFO - Epoch [16][1100/3746] lr: 9.746e-02, eta: 4 days, 6:13:16, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4986, loss_cls: 4.0835, loss: 4.0835 +2024-07-22 14:43:30,452 - pyskl - INFO - Epoch [16][1200/3746] lr: 9.745e-02, eta: 4 days, 6:11:34, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5048, loss_cls: 4.0644, loss: 4.0644 +2024-07-22 14:44:40,219 - pyskl - INFO - Epoch [16][1300/3746] lr: 9.744e-02, eta: 4 days, 6:09:53, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5127, loss_cls: 4.0535, loss: 4.0535 +2024-07-22 14:45:49,975 - pyskl - INFO - Epoch [16][1400/3746] lr: 9.743e-02, eta: 4 days, 6:08:12, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4928, loss_cls: 4.1054, loss: 4.1054 +2024-07-22 14:46:59,938 - pyskl - INFO - Epoch [16][1500/3746] lr: 9.742e-02, eta: 4 days, 6:06:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4892, loss_cls: 4.1401, loss: 4.1401 +2024-07-22 14:48:09,976 - pyskl - INFO - Epoch [16][1600/3746] lr: 9.741e-02, eta: 4 days, 6:04:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4998, loss_cls: 4.0925, loss: 4.0925 +2024-07-22 14:49:19,723 - pyskl - INFO - Epoch [16][1700/3746] lr: 9.740e-02, eta: 4 days, 6:03:15, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5080, loss_cls: 4.0888, loss: 4.0888 +2024-07-22 14:50:29,448 - pyskl - INFO - Epoch [16][1800/3746] lr: 9.740e-02, eta: 4 days, 6:01:35, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4906, loss_cls: 4.1486, loss: 4.1486 +2024-07-22 14:51:39,271 - pyskl - INFO - Epoch [16][1900/3746] lr: 9.739e-02, eta: 4 days, 5:59:55, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.5133, loss_cls: 4.0568, loss: 4.0568 +2024-07-22 14:52:49,320 - pyskl - INFO - Epoch [16][2000/3746] lr: 9.738e-02, eta: 4 days, 5:58:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5053, loss_cls: 4.0698, loss: 4.0698 +2024-07-22 14:53:59,155 - pyskl - INFO - Epoch [16][2100/3746] lr: 9.737e-02, eta: 4 days, 5:56:39, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5038, loss_cls: 4.0667, loss: 4.0667 +2024-07-22 14:55:08,947 - pyskl - INFO - Epoch [16][2200/3746] lr: 9.736e-02, eta: 4 days, 5:54:59, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4902, loss_cls: 4.1371, loss: 4.1371 +2024-07-22 14:56:18,412 - pyskl - INFO - Epoch [16][2300/3746] lr: 9.735e-02, eta: 4 days, 5:53:17, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.5112, loss_cls: 4.0649, loss: 4.0649 +2024-07-22 14:57:28,185 - pyskl - INFO - Epoch [16][2400/3746] lr: 9.734e-02, eta: 4 days, 5:51:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.5036, loss_cls: 4.0987, loss: 4.0987 +2024-07-22 14:58:38,007 - pyskl - INFO - Epoch [16][2500/3746] lr: 9.733e-02, eta: 4 days, 5:49:59, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5111, loss_cls: 4.0227, loss: 4.0227 +2024-07-22 14:59:47,702 - pyskl - INFO - Epoch [16][2600/3746] lr: 9.732e-02, eta: 4 days, 5:48:19, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4936, loss_cls: 4.0972, loss: 4.0972 +2024-07-22 15:00:57,610 - pyskl - INFO - Epoch [16][2700/3746] lr: 9.731e-02, eta: 4 days, 5:46:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5050, loss_cls: 4.0917, loss: 4.0917 +2024-07-22 15:02:07,113 - pyskl - INFO - Epoch [16][2800/3746] lr: 9.731e-02, eta: 4 days, 5:45:00, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5002, loss_cls: 4.1061, loss: 4.1061 +2024-07-22 15:03:16,945 - pyskl - INFO - Epoch [16][2900/3746] lr: 9.730e-02, eta: 4 days, 5:43:21, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5069, loss_cls: 4.0802, loss: 4.0802 +2024-07-22 15:04:26,932 - pyskl - INFO - Epoch [16][3000/3746] lr: 9.729e-02, eta: 4 days, 5:41:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4988, loss_cls: 4.1119, loss: 4.1119 +2024-07-22 15:05:36,699 - pyskl - INFO - Epoch [16][3100/3746] lr: 9.728e-02, eta: 4 days, 5:40:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4975, loss_cls: 4.1028, loss: 4.1028 +2024-07-22 15:06:46,570 - pyskl - INFO - Epoch [16][3200/3746] lr: 9.727e-02, eta: 4 days, 5:38:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5078, loss_cls: 4.0595, loss: 4.0595 +2024-07-22 15:07:56,799 - pyskl - INFO - Epoch [16][3300/3746] lr: 9.726e-02, eta: 4 days, 5:36:53, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.5009, loss_cls: 4.1149, loss: 4.1149 +2024-07-22 15:09:07,478 - pyskl - INFO - Epoch [16][3400/3746] lr: 9.725e-02, eta: 4 days, 5:35:22, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5084, loss_cls: 4.0547, loss: 4.0547 +2024-07-22 15:10:17,377 - pyskl - INFO - Epoch [16][3500/3746] lr: 9.724e-02, eta: 4 days, 5:33:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5048, loss_cls: 4.0543, loss: 4.0543 +2024-07-22 15:11:27,648 - pyskl - INFO - Epoch [16][3600/3746] lr: 9.723e-02, eta: 4 days, 5:32:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5027, loss_cls: 4.0943, loss: 4.0943 +2024-07-22 15:12:37,773 - pyskl - INFO - Epoch [16][3700/3746] lr: 9.722e-02, eta: 4 days, 5:30:36, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.5112, loss_cls: 4.0768, loss: 4.0768 +2024-07-22 15:13:12,085 - pyskl - INFO - Saving checkpoint at 16 epochs +2024-07-22 15:15:04,172 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 15:15:04,835 - pyskl - INFO - +top1_acc 0.1855 +top5_acc 0.4064 +2024-07-22 15:15:04,836 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 15:15:04,877 - pyskl - INFO - +mean_acc 0.1855 +2024-07-22 15:15:04,882 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_15.pth was removed +2024-07-22 15:15:05,140 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_16.pth. +2024-07-22 15:15:05,141 - pyskl - INFO - Best top1_acc is 0.1855 at 16 epoch. +2024-07-22 15:15:05,153 - pyskl - INFO - Epoch(val) [16][309] top1_acc: 0.1855, top5_acc: 0.4064, mean_class_accuracy: 0.1855 +2024-07-22 15:18:26,094 - pyskl - INFO - Epoch [17][100/3746] lr: 9.721e-02, eta: 4 days, 5:42:01, time: 2.009, data_time: 1.307, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5166, loss_cls: 4.0107, loss: 4.0107 +2024-07-22 15:19:37,154 - pyskl - INFO - Epoch [17][200/3746] lr: 9.720e-02, eta: 4 days, 5:40:32, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5103, loss_cls: 4.0518, loss: 4.0518 +2024-07-22 15:20:47,070 - pyskl - INFO - Epoch [17][300/3746] lr: 9.719e-02, eta: 4 days, 5:38:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5178, loss_cls: 3.9889, loss: 3.9889 +2024-07-22 15:21:57,344 - pyskl - INFO - Epoch [17][400/3746] lr: 9.718e-02, eta: 4 days, 5:37:19, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5025, loss_cls: 4.0518, loss: 4.0518 +2024-07-22 15:23:07,378 - pyskl - INFO - Epoch [17][500/3746] lr: 9.717e-02, eta: 4 days, 5:35:42, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5105, loss_cls: 4.0426, loss: 4.0426 +2024-07-22 15:24:17,200 - pyskl - INFO - Epoch [17][600/3746] lr: 9.716e-02, eta: 4 days, 5:34:03, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4994, loss_cls: 4.0942, loss: 4.0942 +2024-07-22 15:25:27,181 - pyskl - INFO - Epoch [17][700/3746] lr: 9.715e-02, eta: 4 days, 5:32:25, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5108, loss_cls: 4.0497, loss: 4.0497 +2024-07-22 15:26:36,812 - pyskl - INFO - Epoch [17][800/3746] lr: 9.714e-02, eta: 4 days, 5:30:45, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5109, loss_cls: 4.0492, loss: 4.0492 +2024-07-22 15:27:46,932 - pyskl - INFO - Epoch [17][900/3746] lr: 9.714e-02, eta: 4 days, 5:29:09, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5131, loss_cls: 4.0262, loss: 4.0262 +2024-07-22 15:28:56,713 - pyskl - INFO - Epoch [17][1000/3746] lr: 9.713e-02, eta: 4 days, 5:27:31, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5070, loss_cls: 4.0568, loss: 4.0568 +2024-07-22 15:30:06,420 - pyskl - INFO - Epoch [17][1100/3746] lr: 9.712e-02, eta: 4 days, 5:25:51, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.5077, loss_cls: 4.0696, loss: 4.0696 +2024-07-22 15:31:16,167 - pyskl - INFO - Epoch [17][1200/3746] lr: 9.711e-02, eta: 4 days, 5:24:13, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5112, loss_cls: 4.0572, loss: 4.0572 +2024-07-22 15:32:25,833 - pyskl - INFO - Epoch [17][1300/3746] lr: 9.710e-02, eta: 4 days, 5:22:33, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.5044, loss_cls: 4.1008, loss: 4.1008 +2024-07-22 15:33:35,582 - pyskl - INFO - Epoch [17][1400/3746] lr: 9.709e-02, eta: 4 days, 5:20:55, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5045, loss_cls: 4.0843, loss: 4.0843 +2024-07-22 15:34:45,549 - pyskl - INFO - Epoch [17][1500/3746] lr: 9.708e-02, eta: 4 days, 5:19:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5041, loss_cls: 4.0539, loss: 4.0539 +2024-07-22 15:35:55,272 - pyskl - INFO - Epoch [17][1600/3746] lr: 9.707e-02, eta: 4 days, 5:17:40, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5050, loss_cls: 4.0883, loss: 4.0883 +2024-07-22 15:37:05,333 - pyskl - INFO - Epoch [17][1700/3746] lr: 9.706e-02, eta: 4 days, 5:16:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5081, loss_cls: 4.0671, loss: 4.0671 +2024-07-22 15:38:15,115 - pyskl - INFO - Epoch [17][1800/3746] lr: 9.705e-02, eta: 4 days, 5:14:26, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5044, loss_cls: 4.0941, loss: 4.0941 +2024-07-22 15:39:24,777 - pyskl - INFO - Epoch [17][1900/3746] lr: 9.704e-02, eta: 4 days, 5:12:47, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4970, loss_cls: 4.0954, loss: 4.0954 +2024-07-22 15:40:34,548 - pyskl - INFO - Epoch [17][2000/3746] lr: 9.703e-02, eta: 4 days, 5:11:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5081, loss_cls: 4.0907, loss: 4.0907 +2024-07-22 15:41:44,161 - pyskl - INFO - Epoch [17][2100/3746] lr: 9.702e-02, eta: 4 days, 5:09:30, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4922, loss_cls: 4.1073, loss: 4.1073 +2024-07-22 15:42:54,054 - pyskl - INFO - Epoch [17][2200/3746] lr: 9.701e-02, eta: 4 days, 5:07:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.5050, loss_cls: 4.0746, loss: 4.0746 +2024-07-22 15:44:03,983 - pyskl - INFO - Epoch [17][2300/3746] lr: 9.700e-02, eta: 4 days, 5:06:17, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5012, loss_cls: 4.0654, loss: 4.0654 +2024-07-22 15:45:13,589 - pyskl - INFO - Epoch [17][2400/3746] lr: 9.699e-02, eta: 4 days, 5:04:38, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4992, loss_cls: 4.1108, loss: 4.1108 +2024-07-22 15:46:23,531 - pyskl - INFO - Epoch [17][2500/3746] lr: 9.698e-02, eta: 4 days, 5:03:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5184, loss_cls: 4.0600, loss: 4.0600 +2024-07-22 15:47:33,442 - pyskl - INFO - Epoch [17][2600/3746] lr: 9.697e-02, eta: 4 days, 5:01:26, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5041, loss_cls: 4.0967, loss: 4.0967 +2024-07-22 15:48:43,653 - pyskl - INFO - Epoch [17][2700/3746] lr: 9.697e-02, eta: 4 days, 4:59:52, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4992, loss_cls: 4.1169, loss: 4.1169 +2024-07-22 15:49:53,583 - pyskl - INFO - Epoch [17][2800/3746] lr: 9.696e-02, eta: 4 days, 4:58:17, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5142, loss_cls: 4.0437, loss: 4.0437 +2024-07-22 15:51:03,523 - pyskl - INFO - Epoch [17][2900/3746] lr: 9.695e-02, eta: 4 days, 4:56:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5102, loss_cls: 4.0315, loss: 4.0315 +2024-07-22 15:52:13,380 - pyskl - INFO - Epoch [17][3000/3746] lr: 9.694e-02, eta: 4 days, 4:55:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4847, loss_cls: 4.1420, loss: 4.1420 +2024-07-22 15:53:23,700 - pyskl - INFO - Epoch [17][3100/3746] lr: 9.693e-02, eta: 4 days, 4:53:32, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.5006, loss_cls: 4.0807, loss: 4.0807 +2024-07-22 15:54:33,564 - pyskl - INFO - Epoch [17][3200/3746] lr: 9.692e-02, eta: 4 days, 4:51:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5056, loss_cls: 4.0753, loss: 4.0753 +2024-07-22 15:55:43,324 - pyskl - INFO - Epoch [17][3300/3746] lr: 9.691e-02, eta: 4 days, 4:50:19, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5034, loss_cls: 4.0661, loss: 4.0661 +2024-07-22 15:56:53,740 - pyskl - INFO - Epoch [17][3400/3746] lr: 9.690e-02, eta: 4 days, 4:48:48, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5033, loss_cls: 4.0704, loss: 4.0704 +2024-07-22 15:58:04,035 - pyskl - INFO - Epoch [17][3500/3746] lr: 9.689e-02, eta: 4 days, 4:47:15, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5008, loss_cls: 4.0554, loss: 4.0554 +2024-07-22 15:59:14,028 - pyskl - INFO - Epoch [17][3600/3746] lr: 9.688e-02, eta: 4 days, 4:45:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5114, loss_cls: 4.0577, loss: 4.0577 +2024-07-22 16:00:23,831 - pyskl - INFO - Epoch [17][3700/3746] lr: 9.687e-02, eta: 4 days, 4:44:04, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5030, loss_cls: 4.0701, loss: 4.0701 +2024-07-22 16:00:58,289 - pyskl - INFO - Saving checkpoint at 17 epochs +2024-07-22 16:02:50,360 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 16:02:51,034 - pyskl - INFO - +top1_acc 0.1867 +top5_acc 0.4108 +2024-07-22 16:02:51,035 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 16:02:51,077 - pyskl - INFO - +mean_acc 0.1865 +2024-07-22 16:02:51,082 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_16.pth was removed +2024-07-22 16:02:51,345 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_17.pth. +2024-07-22 16:02:51,346 - pyskl - INFO - Best top1_acc is 0.1867 at 17 epoch. +2024-07-22 16:02:51,359 - pyskl - INFO - Epoch(val) [17][309] top1_acc: 0.1867, top5_acc: 0.4108, mean_class_accuracy: 0.1865 +2024-07-22 16:06:18,667 - pyskl - INFO - Epoch [18][100/3746] lr: 9.685e-02, eta: 4 days, 4:55:27, time: 2.073, data_time: 1.369, memory: 15990, top1_acc: 0.2455, top5_acc: 0.5142, loss_cls: 4.0510, loss: 4.0510 +2024-07-22 16:07:29,705 - pyskl - INFO - Epoch [18][200/3746] lr: 9.684e-02, eta: 4 days, 4:53:59, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5153, loss_cls: 4.0294, loss: 4.0294 +2024-07-22 16:08:40,105 - pyskl - INFO - Epoch [18][300/3746] lr: 9.683e-02, eta: 4 days, 4:52:27, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5036, loss_cls: 4.0654, loss: 4.0654 +2024-07-22 16:09:51,444 - pyskl - INFO - Epoch [18][400/3746] lr: 9.683e-02, eta: 4 days, 4:51:01, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5116, loss_cls: 4.0292, loss: 4.0292 +2024-07-22 16:11:01,786 - pyskl - INFO - Epoch [18][500/3746] lr: 9.682e-02, eta: 4 days, 4:49:28, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.5033, loss_cls: 4.0797, loss: 4.0797 +2024-07-22 16:12:11,729 - pyskl - INFO - Epoch [18][600/3746] lr: 9.681e-02, eta: 4 days, 4:47:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5075, loss_cls: 4.0744, loss: 4.0744 +2024-07-22 16:13:21,552 - pyskl - INFO - Epoch [18][700/3746] lr: 9.680e-02, eta: 4 days, 4:46:15, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5000, loss_cls: 4.0636, loss: 4.0636 +2024-07-22 16:14:31,290 - pyskl - INFO - Epoch [18][800/3746] lr: 9.679e-02, eta: 4 days, 4:44:38, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5083, loss_cls: 4.1005, loss: 4.1005 +2024-07-22 16:15:40,978 - pyskl - INFO - Epoch [18][900/3746] lr: 9.678e-02, eta: 4 days, 4:43:00, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4978, loss_cls: 4.0987, loss: 4.0987 +2024-07-22 16:16:50,899 - pyskl - INFO - Epoch [18][1000/3746] lr: 9.677e-02, eta: 4 days, 4:41:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5052, loss_cls: 4.0665, loss: 4.0665 +2024-07-22 16:18:00,963 - pyskl - INFO - Epoch [18][1100/3746] lr: 9.676e-02, eta: 4 days, 4:39:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5019, loss_cls: 4.0693, loss: 4.0693 +2024-07-22 16:19:10,912 - pyskl - INFO - Epoch [18][1200/3746] lr: 9.675e-02, eta: 4 days, 4:38:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5058, loss_cls: 4.0485, loss: 4.0485 +2024-07-22 16:20:21,007 - pyskl - INFO - Epoch [18][1300/3746] lr: 9.674e-02, eta: 4 days, 4:36:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5130, loss_cls: 4.0410, loss: 4.0410 +2024-07-22 16:21:30,932 - pyskl - INFO - Epoch [18][1400/3746] lr: 9.673e-02, eta: 4 days, 4:35:04, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5170, loss_cls: 4.0288, loss: 4.0288 +2024-07-22 16:22:40,618 - pyskl - INFO - Epoch [18][1500/3746] lr: 9.672e-02, eta: 4 days, 4:33:27, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5070, loss_cls: 4.0483, loss: 4.0483 +2024-07-22 16:23:50,533 - pyskl - INFO - Epoch [18][1600/3746] lr: 9.671e-02, eta: 4 days, 4:31:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.5058, loss_cls: 4.0871, loss: 4.0871 +2024-07-22 16:25:00,333 - pyskl - INFO - Epoch [18][1700/3746] lr: 9.670e-02, eta: 4 days, 4:30:15, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5048, loss_cls: 4.0778, loss: 4.0778 +2024-07-22 16:26:10,058 - pyskl - INFO - Epoch [18][1800/3746] lr: 9.669e-02, eta: 4 days, 4:28:39, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5045, loss_cls: 4.0793, loss: 4.0793 +2024-07-22 16:27:19,842 - pyskl - INFO - Epoch [18][1900/3746] lr: 9.668e-02, eta: 4 days, 4:27:02, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5077, loss_cls: 4.0570, loss: 4.0570 +2024-07-22 16:28:29,654 - pyskl - INFO - Epoch [18][2000/3746] lr: 9.667e-02, eta: 4 days, 4:25:27, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4991, loss_cls: 4.1102, loss: 4.1102 +2024-07-22 16:29:39,503 - pyskl - INFO - Epoch [18][2100/3746] lr: 9.666e-02, eta: 4 days, 4:23:51, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4997, loss_cls: 4.1063, loss: 4.1063 +2024-07-22 16:30:49,251 - pyskl - INFO - Epoch [18][2200/3746] lr: 9.665e-02, eta: 4 days, 4:22:15, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5095, loss_cls: 4.0718, loss: 4.0718 +2024-07-22 16:31:59,198 - pyskl - INFO - Epoch [18][2300/3746] lr: 9.664e-02, eta: 4 days, 4:20:40, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5033, loss_cls: 4.0941, loss: 4.0941 +2024-07-22 16:33:08,975 - pyskl - INFO - Epoch [18][2400/3746] lr: 9.663e-02, eta: 4 days, 4:19:04, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5105, loss_cls: 4.0654, loss: 4.0654 +2024-07-22 16:34:18,726 - pyskl - INFO - Epoch [18][2500/3746] lr: 9.662e-02, eta: 4 days, 4:17:28, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5025, loss_cls: 4.0839, loss: 4.0839 +2024-07-22 16:35:28,469 - pyskl - INFO - Epoch [18][2600/3746] lr: 9.661e-02, eta: 4 days, 4:15:52, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5112, loss_cls: 4.0611, loss: 4.0611 +2024-07-22 16:36:38,379 - pyskl - INFO - Epoch [18][2700/3746] lr: 9.660e-02, eta: 4 days, 4:14:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5075, loss_cls: 4.0707, loss: 4.0707 +2024-07-22 16:37:48,094 - pyskl - INFO - Epoch [18][2800/3746] lr: 9.659e-02, eta: 4 days, 4:12:42, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5005, loss_cls: 4.0714, loss: 4.0714 +2024-07-22 16:38:57,957 - pyskl - INFO - Epoch [18][2900/3746] lr: 9.658e-02, eta: 4 days, 4:11:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5006, loss_cls: 4.0974, loss: 4.0974 +2024-07-22 16:40:07,639 - pyskl - INFO - Epoch [18][3000/3746] lr: 9.657e-02, eta: 4 days, 4:09:31, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5109, loss_cls: 4.0515, loss: 4.0515 +2024-07-22 16:41:17,514 - pyskl - INFO - Epoch [18][3100/3746] lr: 9.656e-02, eta: 4 days, 4:07:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5102, loss_cls: 4.0362, loss: 4.0362 +2024-07-22 16:42:27,071 - pyskl - INFO - Epoch [18][3200/3746] lr: 9.654e-02, eta: 4 days, 4:06:19, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5023, loss_cls: 4.0718, loss: 4.0718 +2024-07-22 16:43:37,037 - pyskl - INFO - Epoch [18][3300/3746] lr: 9.653e-02, eta: 4 days, 4:04:46, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5091, loss_cls: 4.0570, loss: 4.0570 +2024-07-22 16:44:47,461 - pyskl - INFO - Epoch [18][3400/3746] lr: 9.652e-02, eta: 4 days, 4:03:15, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5131, loss_cls: 4.0434, loss: 4.0434 +2024-07-22 16:45:57,265 - pyskl - INFO - Epoch [18][3500/3746] lr: 9.651e-02, eta: 4 days, 4:01:40, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5097, loss_cls: 4.0531, loss: 4.0531 +2024-07-22 16:47:07,275 - pyskl - INFO - Epoch [18][3600/3746] lr: 9.650e-02, eta: 4 days, 4:00:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4992, loss_cls: 4.0645, loss: 4.0645 +2024-07-22 16:48:17,675 - pyskl - INFO - Epoch [18][3700/3746] lr: 9.649e-02, eta: 4 days, 3:58:37, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5059, loss_cls: 4.0460, loss: 4.0460 +2024-07-22 16:48:51,869 - pyskl - INFO - Saving checkpoint at 18 epochs +2024-07-22 16:50:43,697 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 16:50:44,369 - pyskl - INFO - +top1_acc 0.1649 +top5_acc 0.3800 +2024-07-22 16:50:44,370 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 16:50:44,411 - pyskl - INFO - +mean_acc 0.1647 +2024-07-22 16:50:44,424 - pyskl - INFO - Epoch(val) [18][309] top1_acc: 0.1649, top5_acc: 0.3800, mean_class_accuracy: 0.1647 +2024-07-22 16:54:12,975 - pyskl - INFO - Epoch [19][100/3746] lr: 9.648e-02, eta: 4 days, 4:09:19, time: 2.085, data_time: 1.378, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5141, loss_cls: 4.0379, loss: 4.0379 +2024-07-22 16:55:24,087 - pyskl - INFO - Epoch [19][200/3746] lr: 9.647e-02, eta: 4 days, 4:07:53, time: 0.711, data_time: 0.001, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5000, loss_cls: 4.0667, loss: 4.0667 +2024-07-22 16:56:34,274 - pyskl - INFO - Epoch [19][300/3746] lr: 9.646e-02, eta: 4 days, 4:06:20, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5075, loss_cls: 4.0660, loss: 4.0660 +2024-07-22 16:57:44,213 - pyskl - INFO - Epoch [19][400/3746] lr: 9.645e-02, eta: 4 days, 4:04:46, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5042, loss_cls: 4.0584, loss: 4.0584 +2024-07-22 16:58:55,046 - pyskl - INFO - Epoch [19][500/3746] lr: 9.644e-02, eta: 4 days, 4:03:17, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5111, loss_cls: 4.0135, loss: 4.0135 +2024-07-22 17:00:05,239 - pyskl - INFO - Epoch [19][600/3746] lr: 9.643e-02, eta: 4 days, 4:01:45, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5159, loss_cls: 4.0281, loss: 4.0281 +2024-07-22 17:01:15,339 - pyskl - INFO - Epoch [19][700/3746] lr: 9.642e-02, eta: 4 days, 4:00:11, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5088, loss_cls: 4.0817, loss: 4.0817 +2024-07-22 17:02:25,411 - pyskl - INFO - Epoch [19][800/3746] lr: 9.641e-02, eta: 4 days, 3:58:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5117, loss_cls: 4.0381, loss: 4.0381 +2024-07-22 17:03:35,081 - pyskl - INFO - Epoch [19][900/3746] lr: 9.640e-02, eta: 4 days, 3:57:02, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5130, loss_cls: 4.0648, loss: 4.0648 +2024-07-22 17:04:45,404 - pyskl - INFO - Epoch [19][1000/3746] lr: 9.639e-02, eta: 4 days, 3:55:30, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.5012, loss_cls: 4.0979, loss: 4.0979 +2024-07-22 17:05:55,237 - pyskl - INFO - Epoch [19][1100/3746] lr: 9.637e-02, eta: 4 days, 3:53:55, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5183, loss_cls: 4.0140, loss: 4.0140 +2024-07-22 17:07:05,203 - pyskl - INFO - Epoch [19][1200/3746] lr: 9.636e-02, eta: 4 days, 3:52:21, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.5052, loss_cls: 4.0673, loss: 4.0673 +2024-07-22 17:08:15,043 - pyskl - INFO - Epoch [19][1300/3746] lr: 9.635e-02, eta: 4 days, 3:50:46, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5109, loss_cls: 4.0504, loss: 4.0504 +2024-07-22 17:09:25,010 - pyskl - INFO - Epoch [19][1400/3746] lr: 9.634e-02, eta: 4 days, 3:49:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5098, loss_cls: 4.0010, loss: 4.0010 +2024-07-22 17:10:34,887 - pyskl - INFO - Epoch [19][1500/3746] lr: 9.633e-02, eta: 4 days, 3:47:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5078, loss_cls: 4.0669, loss: 4.0669 +2024-07-22 17:11:45,026 - pyskl - INFO - Epoch [19][1600/3746] lr: 9.632e-02, eta: 4 days, 3:46:06, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5041, loss_cls: 4.0657, loss: 4.0657 +2024-07-22 17:12:54,852 - pyskl - INFO - Epoch [19][1700/3746] lr: 9.631e-02, eta: 4 days, 3:44:31, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5112, loss_cls: 4.0614, loss: 4.0614 +2024-07-22 17:14:04,812 - pyskl - INFO - Epoch [19][1800/3746] lr: 9.630e-02, eta: 4 days, 3:42:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5170, loss_cls: 4.0003, loss: 4.0003 +2024-07-22 17:15:14,429 - pyskl - INFO - Epoch [19][1900/3746] lr: 9.629e-02, eta: 4 days, 3:41:22, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4992, loss_cls: 4.0793, loss: 4.0793 +2024-07-22 17:16:24,357 - pyskl - INFO - Epoch [19][2000/3746] lr: 9.628e-02, eta: 4 days, 3:39:48, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5111, loss_cls: 4.0718, loss: 4.0718 +2024-07-22 17:17:34,239 - pyskl - INFO - Epoch [19][2100/3746] lr: 9.627e-02, eta: 4 days, 3:38:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5095, loss_cls: 4.0928, loss: 4.0928 +2024-07-22 17:18:43,853 - pyskl - INFO - Epoch [19][2200/3746] lr: 9.626e-02, eta: 4 days, 3:36:38, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5136, loss_cls: 4.0431, loss: 4.0431 +2024-07-22 17:19:53,575 - pyskl - INFO - Epoch [19][2300/3746] lr: 9.625e-02, eta: 4 days, 3:35:04, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5178, loss_cls: 4.0292, loss: 4.0292 +2024-07-22 17:21:03,383 - pyskl - INFO - Epoch [19][2400/3746] lr: 9.624e-02, eta: 4 days, 3:33:29, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5052, loss_cls: 4.0503, loss: 4.0503 +2024-07-22 17:22:13,101 - pyskl - INFO - Epoch [19][2500/3746] lr: 9.623e-02, eta: 4 days, 3:31:55, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5152, loss_cls: 4.0489, loss: 4.0489 +2024-07-22 17:23:22,862 - pyskl - INFO - Epoch [19][2600/3746] lr: 9.622e-02, eta: 4 days, 3:30:20, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5064, loss_cls: 4.0671, loss: 4.0671 +2024-07-22 17:24:32,585 - pyskl - INFO - Epoch [19][2700/3746] lr: 9.621e-02, eta: 4 days, 3:28:46, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.5028, loss_cls: 4.0843, loss: 4.0843 +2024-07-22 17:25:42,286 - pyskl - INFO - Epoch [19][2800/3746] lr: 9.620e-02, eta: 4 days, 3:27:11, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5139, loss_cls: 4.0202, loss: 4.0202 +2024-07-22 17:26:51,861 - pyskl - INFO - Epoch [19][2900/3746] lr: 9.618e-02, eta: 4 days, 3:25:35, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5112, loss_cls: 4.0334, loss: 4.0334 +2024-07-22 17:28:01,995 - pyskl - INFO - Epoch [19][3000/3746] lr: 9.617e-02, eta: 4 days, 3:24:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5105, loss_cls: 4.0572, loss: 4.0572 +2024-07-22 17:29:11,759 - pyskl - INFO - Epoch [19][3100/3746] lr: 9.616e-02, eta: 4 days, 3:22:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5092, loss_cls: 4.0495, loss: 4.0495 +2024-07-22 17:30:21,855 - pyskl - INFO - Epoch [19][3200/3746] lr: 9.615e-02, eta: 4 days, 3:20:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.5038, loss_cls: 4.0936, loss: 4.0936 +2024-07-22 17:31:31,919 - pyskl - INFO - Epoch [19][3300/3746] lr: 9.614e-02, eta: 4 days, 3:19:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5047, loss_cls: 4.0458, loss: 4.0458 +2024-07-22 17:32:42,462 - pyskl - INFO - Epoch [19][3400/3746] lr: 9.613e-02, eta: 4 days, 3:17:58, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5055, loss_cls: 4.0642, loss: 4.0642 +2024-07-22 17:33:52,578 - pyskl - INFO - Epoch [19][3500/3746] lr: 9.612e-02, eta: 4 days, 3:16:27, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.4972, loss_cls: 4.0573, loss: 4.0573 +2024-07-22 17:35:02,532 - pyskl - INFO - Epoch [19][3600/3746] lr: 9.611e-02, eta: 4 days, 3:14:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4970, loss_cls: 4.0950, loss: 4.0950 +2024-07-22 17:36:12,645 - pyskl - INFO - Epoch [19][3700/3746] lr: 9.610e-02, eta: 4 days, 3:13:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.5044, loss_cls: 4.0658, loss: 4.0658 +2024-07-22 17:36:47,141 - pyskl - INFO - Saving checkpoint at 19 epochs +2024-07-22 17:38:39,090 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 17:38:39,760 - pyskl - INFO - +top1_acc 0.1686 +top5_acc 0.3864 +2024-07-22 17:38:39,761 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 17:38:39,803 - pyskl - INFO - +mean_acc 0.1685 +2024-07-22 17:38:39,816 - pyskl - INFO - Epoch(val) [19][309] top1_acc: 0.1686, top5_acc: 0.3864, mean_class_accuracy: 0.1685 +2024-07-22 17:42:06,006 - pyskl - INFO - Epoch [20][100/3746] lr: 9.608e-02, eta: 4 days, 3:23:04, time: 2.062, data_time: 1.354, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5178, loss_cls: 3.9745, loss: 3.9745 +2024-07-22 17:43:16,494 - pyskl - INFO - Epoch [20][200/3746] lr: 9.607e-02, eta: 4 days, 3:21:35, time: 0.705, data_time: 0.001, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5070, loss_cls: 4.0501, loss: 4.0501 +2024-07-22 17:44:26,566 - pyskl - INFO - Epoch [20][300/3746] lr: 9.606e-02, eta: 4 days, 3:20:02, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5119, loss_cls: 4.0307, loss: 4.0307 +2024-07-22 17:45:36,885 - pyskl - INFO - Epoch [20][400/3746] lr: 9.605e-02, eta: 4 days, 3:18:32, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4947, loss_cls: 4.1071, loss: 4.1071 +2024-07-22 17:46:47,434 - pyskl - INFO - Epoch [20][500/3746] lr: 9.604e-02, eta: 4 days, 3:17:02, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5178, loss_cls: 4.0034, loss: 4.0034 +2024-07-22 17:47:57,558 - pyskl - INFO - Epoch [20][600/3746] lr: 9.603e-02, eta: 4 days, 3:15:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5211, loss_cls: 4.0366, loss: 4.0366 +2024-07-22 17:49:07,610 - pyskl - INFO - Epoch [20][700/3746] lr: 9.602e-02, eta: 4 days, 3:13:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5081, loss_cls: 4.0431, loss: 4.0431 +2024-07-22 17:50:17,467 - pyskl - INFO - Epoch [20][800/3746] lr: 9.601e-02, eta: 4 days, 3:12:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5128, loss_cls: 4.0257, loss: 4.0257 +2024-07-22 17:51:27,366 - pyskl - INFO - Epoch [20][900/3746] lr: 9.600e-02, eta: 4 days, 3:10:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5083, loss_cls: 4.0363, loss: 4.0363 +2024-07-22 17:52:37,249 - pyskl - INFO - Epoch [20][1000/3746] lr: 9.598e-02, eta: 4 days, 3:09:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.5047, loss_cls: 4.0965, loss: 4.0965 +2024-07-22 17:53:46,874 - pyskl - INFO - Epoch [20][1100/3746] lr: 9.597e-02, eta: 4 days, 3:07:43, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.5078, loss_cls: 4.0808, loss: 4.0808 +2024-07-22 17:54:56,781 - pyskl - INFO - Epoch [20][1200/3746] lr: 9.596e-02, eta: 4 days, 3:06:10, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5116, loss_cls: 4.0475, loss: 4.0475 +2024-07-22 17:56:06,687 - pyskl - INFO - Epoch [20][1300/3746] lr: 9.595e-02, eta: 4 days, 3:04:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5186, loss_cls: 4.0150, loss: 4.0150 +2024-07-22 17:57:16,428 - pyskl - INFO - Epoch [20][1400/3746] lr: 9.594e-02, eta: 4 days, 3:03:03, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5159, loss_cls: 4.0083, loss: 4.0083 +2024-07-22 17:58:26,086 - pyskl - INFO - Epoch [20][1500/3746] lr: 9.593e-02, eta: 4 days, 3:01:28, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5125, loss_cls: 4.0509, loss: 4.0509 +2024-07-22 17:59:35,862 - pyskl - INFO - Epoch [20][1600/3746] lr: 9.592e-02, eta: 4 days, 2:59:55, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5058, loss_cls: 4.0612, loss: 4.0612 +2024-07-22 18:00:45,836 - pyskl - INFO - Epoch [20][1700/3746] lr: 9.591e-02, eta: 4 days, 2:58:22, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5058, loss_cls: 4.0632, loss: 4.0632 +2024-07-22 18:01:55,454 - pyskl - INFO - Epoch [20][1800/3746] lr: 9.590e-02, eta: 4 days, 2:56:48, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5066, loss_cls: 4.0672, loss: 4.0672 +2024-07-22 18:03:05,159 - pyskl - INFO - Epoch [20][1900/3746] lr: 9.588e-02, eta: 4 days, 2:55:14, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5070, loss_cls: 4.0909, loss: 4.0909 +2024-07-22 18:04:15,011 - pyskl - INFO - Epoch [20][2000/3746] lr: 9.587e-02, eta: 4 days, 2:53:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5108, loss_cls: 4.0385, loss: 4.0385 +2024-07-22 18:05:24,895 - pyskl - INFO - Epoch [20][2100/3746] lr: 9.586e-02, eta: 4 days, 2:52:08, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5012, loss_cls: 4.0586, loss: 4.0586 +2024-07-22 18:06:34,850 - pyskl - INFO - Epoch [20][2200/3746] lr: 9.585e-02, eta: 4 days, 2:50:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5105, loss_cls: 4.0511, loss: 4.0511 +2024-07-22 18:07:44,983 - pyskl - INFO - Epoch [20][2300/3746] lr: 9.584e-02, eta: 4 days, 2:49:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5053, loss_cls: 4.0587, loss: 4.0587 +2024-07-22 18:08:54,649 - pyskl - INFO - Epoch [20][2400/3746] lr: 9.583e-02, eta: 4 days, 2:47:32, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5092, loss_cls: 4.0276, loss: 4.0276 +2024-07-22 18:10:04,235 - pyskl - INFO - Epoch [20][2500/3746] lr: 9.582e-02, eta: 4 days, 2:45:57, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5147, loss_cls: 3.9974, loss: 3.9974 +2024-07-22 18:11:14,304 - pyskl - INFO - Epoch [20][2600/3746] lr: 9.581e-02, eta: 4 days, 2:44:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5105, loss_cls: 4.0058, loss: 4.0058 +2024-07-22 18:12:24,107 - pyskl - INFO - Epoch [20][2700/3746] lr: 9.580e-02, eta: 4 days, 2:42:53, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5164, loss_cls: 4.0441, loss: 4.0441 +2024-07-22 18:13:34,354 - pyskl - INFO - Epoch [20][2800/3746] lr: 9.578e-02, eta: 4 days, 2:41:24, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5055, loss_cls: 4.0829, loss: 4.0829 +2024-07-22 18:14:44,226 - pyskl - INFO - Epoch [20][2900/3746] lr: 9.577e-02, eta: 4 days, 2:39:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5061, loss_cls: 4.0298, loss: 4.0298 +2024-07-22 18:15:54,237 - pyskl - INFO - Epoch [20][3000/3746] lr: 9.576e-02, eta: 4 days, 2:38:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5097, loss_cls: 4.0301, loss: 4.0301 +2024-07-22 18:17:03,932 - pyskl - INFO - Epoch [20][3100/3746] lr: 9.575e-02, eta: 4 days, 2:36:47, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5061, loss_cls: 4.0579, loss: 4.0579 +2024-07-22 18:18:13,915 - pyskl - INFO - Epoch [20][3200/3746] lr: 9.574e-02, eta: 4 days, 2:35:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5017, loss_cls: 4.1088, loss: 4.1088 +2024-07-22 18:19:23,906 - pyskl - INFO - Epoch [20][3300/3746] lr: 9.573e-02, eta: 4 days, 2:33:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5052, loss_cls: 4.0869, loss: 4.0869 +2024-07-22 18:20:33,916 - pyskl - INFO - Epoch [20][3400/3746] lr: 9.572e-02, eta: 4 days, 2:32:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5178, loss_cls: 4.0419, loss: 4.0419 +2024-07-22 18:21:44,340 - pyskl - INFO - Epoch [20][3500/3746] lr: 9.571e-02, eta: 4 days, 2:30:45, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5050, loss_cls: 4.0664, loss: 4.0664 +2024-07-22 18:22:54,180 - pyskl - INFO - Epoch [20][3600/3746] lr: 9.569e-02, eta: 4 days, 2:29:13, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5188, loss_cls: 4.0194, loss: 4.0194 +2024-07-22 18:24:04,426 - pyskl - INFO - Epoch [20][3700/3746] lr: 9.568e-02, eta: 4 days, 2:27:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5116, loss_cls: 4.0454, loss: 4.0454 +2024-07-22 18:24:39,172 - pyskl - INFO - Saving checkpoint at 20 epochs +2024-07-22 18:26:30,907 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 18:26:31,582 - pyskl - INFO - +top1_acc 0.1901 +top5_acc 0.4193 +2024-07-22 18:26:31,582 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 18:26:31,625 - pyskl - INFO - +mean_acc 0.1900 +2024-07-22 18:26:31,629 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_17.pth was removed +2024-07-22 18:26:31,885 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_20.pth. +2024-07-22 18:26:31,886 - pyskl - INFO - Best top1_acc is 0.1901 at 20 epoch. +2024-07-22 18:26:31,899 - pyskl - INFO - Epoch(val) [20][309] top1_acc: 0.1901, top5_acc: 0.4193, mean_class_accuracy: 0.1900 +2024-07-22 18:29:54,111 - pyskl - INFO - Epoch [21][100/3746] lr: 9.567e-02, eta: 4 days, 2:36:20, time: 2.022, data_time: 1.320, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5156, loss_cls: 4.0337, loss: 4.0337 +2024-07-22 18:31:04,394 - pyskl - INFO - Epoch [21][200/3746] lr: 9.565e-02, eta: 4 days, 2:34:50, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5316, loss_cls: 3.9581, loss: 3.9581 +2024-07-22 18:32:14,705 - pyskl - INFO - Epoch [21][300/3746] lr: 9.564e-02, eta: 4 days, 2:33:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5183, loss_cls: 4.0049, loss: 4.0049 +2024-07-22 18:33:24,801 - pyskl - INFO - Epoch [21][400/3746] lr: 9.563e-02, eta: 4 days, 2:31:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5308, loss_cls: 3.9675, loss: 3.9675 +2024-07-22 18:34:34,646 - pyskl - INFO - Epoch [21][500/3746] lr: 9.562e-02, eta: 4 days, 2:30:17, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5161, loss_cls: 4.0339, loss: 4.0339 +2024-07-22 18:35:44,825 - pyskl - INFO - Epoch [21][600/3746] lr: 9.561e-02, eta: 4 days, 2:28:47, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5123, loss_cls: 4.0233, loss: 4.0233 +2024-07-22 18:36:54,788 - pyskl - INFO - Epoch [21][700/3746] lr: 9.560e-02, eta: 4 days, 2:27:15, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5091, loss_cls: 4.0528, loss: 4.0528 +2024-07-22 18:38:04,864 - pyskl - INFO - Epoch [21][800/3746] lr: 9.559e-02, eta: 4 days, 2:25:44, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.5081, loss_cls: 4.0878, loss: 4.0878 +2024-07-22 18:39:14,898 - pyskl - INFO - Epoch [21][900/3746] lr: 9.557e-02, eta: 4 days, 2:24:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5114, loss_cls: 4.0430, loss: 4.0430 +2024-07-22 18:40:24,614 - pyskl - INFO - Epoch [21][1000/3746] lr: 9.556e-02, eta: 4 days, 2:22:40, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5080, loss_cls: 4.0345, loss: 4.0345 +2024-07-22 18:41:34,481 - pyskl - INFO - Epoch [21][1100/3746] lr: 9.555e-02, eta: 4 days, 2:21:08, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4995, loss_cls: 4.0859, loss: 4.0859 +2024-07-22 18:42:44,338 - pyskl - INFO - Epoch [21][1200/3746] lr: 9.554e-02, eta: 4 days, 2:19:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.5030, loss_cls: 4.0917, loss: 4.0917 +2024-07-22 18:43:54,499 - pyskl - INFO - Epoch [21][1300/3746] lr: 9.553e-02, eta: 4 days, 2:18:05, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.4986, loss_cls: 4.0755, loss: 4.0755 +2024-07-22 18:45:04,443 - pyskl - INFO - Epoch [21][1400/3746] lr: 9.552e-02, eta: 4 days, 2:16:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5142, loss_cls: 4.0443, loss: 4.0443 +2024-07-22 18:46:14,373 - pyskl - INFO - Epoch [21][1500/3746] lr: 9.551e-02, eta: 4 days, 2:15:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5145, loss_cls: 4.0404, loss: 4.0404 +2024-07-22 18:47:24,176 - pyskl - INFO - Epoch [21][1600/3746] lr: 9.549e-02, eta: 4 days, 2:13:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5098, loss_cls: 4.0293, loss: 4.0293 +2024-07-22 18:48:34,103 - pyskl - INFO - Epoch [21][1700/3746] lr: 9.548e-02, eta: 4 days, 2:11:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5175, loss_cls: 4.0439, loss: 4.0439 +2024-07-22 18:49:44,201 - pyskl - INFO - Epoch [21][1800/3746] lr: 9.547e-02, eta: 4 days, 2:10:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5025, loss_cls: 4.0767, loss: 4.0767 +2024-07-22 18:50:53,907 - pyskl - INFO - Epoch [21][1900/3746] lr: 9.546e-02, eta: 4 days, 2:08:56, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5192, loss_cls: 4.0256, loss: 4.0256 +2024-07-22 18:52:03,783 - pyskl - INFO - Epoch [21][2000/3746] lr: 9.545e-02, eta: 4 days, 2:07:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5111, loss_cls: 4.0731, loss: 4.0731 +2024-07-22 18:53:13,505 - pyskl - INFO - Epoch [21][2100/3746] lr: 9.544e-02, eta: 4 days, 2:05:52, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5044, loss_cls: 4.0693, loss: 4.0693 +2024-07-22 18:54:23,315 - pyskl - INFO - Epoch [21][2200/3746] lr: 9.542e-02, eta: 4 days, 2:04:20, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5103, loss_cls: 4.0109, loss: 4.0109 +2024-07-22 18:55:32,990 - pyskl - INFO - Epoch [21][2300/3746] lr: 9.541e-02, eta: 4 days, 2:02:47, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5091, loss_cls: 4.0378, loss: 4.0378 +2024-07-22 18:56:42,656 - pyskl - INFO - Epoch [21][2400/3746] lr: 9.540e-02, eta: 4 days, 2:01:15, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5122, loss_cls: 4.0702, loss: 4.0702 +2024-07-22 18:57:52,586 - pyskl - INFO - Epoch [21][2500/3746] lr: 9.539e-02, eta: 4 days, 1:59:44, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.5108, loss_cls: 4.0869, loss: 4.0869 +2024-07-22 18:59:02,675 - pyskl - INFO - Epoch [21][2600/3746] lr: 9.538e-02, eta: 4 days, 1:58:14, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5112, loss_cls: 4.0478, loss: 4.0478 +2024-07-22 19:00:12,564 - pyskl - INFO - Epoch [21][2700/3746] lr: 9.537e-02, eta: 4 days, 1:56:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5155, loss_cls: 4.0232, loss: 4.0232 +2024-07-22 19:01:22,597 - pyskl - INFO - Epoch [21][2800/3746] lr: 9.535e-02, eta: 4 days, 1:55:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5147, loss_cls: 4.0217, loss: 4.0217 +2024-07-22 19:02:32,434 - pyskl - INFO - Epoch [21][2900/3746] lr: 9.534e-02, eta: 4 days, 1:53:42, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5106, loss_cls: 4.0172, loss: 4.0172 +2024-07-22 19:03:42,161 - pyskl - INFO - Epoch [21][3000/3746] lr: 9.533e-02, eta: 4 days, 1:52:10, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5094, loss_cls: 4.0222, loss: 4.0222 +2024-07-22 19:04:51,948 - pyskl - INFO - Epoch [21][3100/3746] lr: 9.532e-02, eta: 4 days, 1:50:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5091, loss_cls: 4.0342, loss: 4.0342 +2024-07-22 19:06:01,829 - pyskl - INFO - Epoch [21][3200/3746] lr: 9.531e-02, eta: 4 days, 1:49:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5089, loss_cls: 4.0376, loss: 4.0376 +2024-07-22 19:07:11,764 - pyskl - INFO - Epoch [21][3300/3746] lr: 9.529e-02, eta: 4 days, 1:47:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5070, loss_cls: 4.0539, loss: 4.0539 +2024-07-22 19:08:22,419 - pyskl - INFO - Epoch [21][3400/3746] lr: 9.528e-02, eta: 4 days, 1:46:11, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4958, loss_cls: 4.1091, loss: 4.1091 +2024-07-22 19:09:32,626 - pyskl - INFO - Epoch [21][3500/3746] lr: 9.527e-02, eta: 4 days, 1:44:42, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5120, loss_cls: 4.0479, loss: 4.0479 +2024-07-22 19:10:42,796 - pyskl - INFO - Epoch [21][3600/3746] lr: 9.526e-02, eta: 4 days, 1:43:13, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5027, loss_cls: 4.0762, loss: 4.0762 +2024-07-22 19:11:53,040 - pyskl - INFO - Epoch [21][3700/3746] lr: 9.525e-02, eta: 4 days, 1:41:45, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5053, loss_cls: 4.0581, loss: 4.0581 +2024-07-22 19:12:27,803 - pyskl - INFO - Saving checkpoint at 21 epochs +2024-07-22 19:14:20,524 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 19:14:21,183 - pyskl - INFO - +top1_acc 0.1726 +top5_acc 0.3971 +2024-07-22 19:14:21,184 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 19:14:21,224 - pyskl - INFO - +mean_acc 0.1725 +2024-07-22 19:14:21,235 - pyskl - INFO - Epoch(val) [21][309] top1_acc: 0.1726, top5_acc: 0.3971, mean_class_accuracy: 0.1725 +2024-07-22 19:17:49,745 - pyskl - INFO - Epoch [22][100/3746] lr: 9.523e-02, eta: 4 days, 1:50:26, time: 2.085, data_time: 1.379, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5169, loss_cls: 4.0307, loss: 4.0307 +2024-07-22 19:19:00,156 - pyskl - INFO - Epoch [22][200/3746] lr: 9.522e-02, eta: 4 days, 1:48:58, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5131, loss_cls: 4.0289, loss: 4.0289 +2024-07-22 19:20:11,031 - pyskl - INFO - Epoch [22][300/3746] lr: 9.521e-02, eta: 4 days, 1:47:32, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5131, loss_cls: 4.0147, loss: 4.0147 +2024-07-22 19:21:21,798 - pyskl - INFO - Epoch [22][400/3746] lr: 9.519e-02, eta: 4 days, 1:46:07, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5206, loss_cls: 3.9948, loss: 3.9948 +2024-07-22 19:22:32,232 - pyskl - INFO - Epoch [22][500/3746] lr: 9.518e-02, eta: 4 days, 1:44:39, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5120, loss_cls: 4.0321, loss: 4.0321 +2024-07-22 19:23:42,437 - pyskl - INFO - Epoch [22][600/3746] lr: 9.517e-02, eta: 4 days, 1:43:09, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5077, loss_cls: 4.0550, loss: 4.0550 +2024-07-22 19:24:52,701 - pyskl - INFO - Epoch [22][700/3746] lr: 9.516e-02, eta: 4 days, 1:41:40, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5136, loss_cls: 4.0352, loss: 4.0352 +2024-07-22 19:26:02,536 - pyskl - INFO - Epoch [22][800/3746] lr: 9.515e-02, eta: 4 days, 1:40:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5139, loss_cls: 4.0412, loss: 4.0412 +2024-07-22 19:27:12,373 - pyskl - INFO - Epoch [22][900/3746] lr: 9.513e-02, eta: 4 days, 1:38:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5188, loss_cls: 4.0224, loss: 4.0224 +2024-07-22 19:28:22,171 - pyskl - INFO - Epoch [22][1000/3746] lr: 9.512e-02, eta: 4 days, 1:37:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5127, loss_cls: 4.0489, loss: 4.0489 +2024-07-22 19:29:31,849 - pyskl - INFO - Epoch [22][1100/3746] lr: 9.511e-02, eta: 4 days, 1:35:34, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5186, loss_cls: 4.0315, loss: 4.0315 +2024-07-22 19:30:41,626 - pyskl - INFO - Epoch [22][1200/3746] lr: 9.510e-02, eta: 4 days, 1:34:02, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5058, loss_cls: 4.0684, loss: 4.0684 +2024-07-22 19:31:51,473 - pyskl - INFO - Epoch [22][1300/3746] lr: 9.509e-02, eta: 4 days, 1:32:31, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5131, loss_cls: 4.0841, loss: 4.0841 +2024-07-22 19:33:01,281 - pyskl - INFO - Epoch [22][1400/3746] lr: 9.507e-02, eta: 4 days, 1:31:00, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5128, loss_cls: 3.9919, loss: 3.9919 +2024-07-22 19:34:10,938 - pyskl - INFO - Epoch [22][1500/3746] lr: 9.506e-02, eta: 4 days, 1:29:28, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5048, loss_cls: 4.0574, loss: 4.0574 +2024-07-22 19:35:21,012 - pyskl - INFO - Epoch [22][1600/3746] lr: 9.505e-02, eta: 4 days, 1:27:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5089, loss_cls: 4.0579, loss: 4.0579 +2024-07-22 19:36:30,860 - pyskl - INFO - Epoch [22][1700/3746] lr: 9.504e-02, eta: 4 days, 1:26:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5241, loss_cls: 4.0010, loss: 4.0010 +2024-07-22 19:37:40,839 - pyskl - INFO - Epoch [22][1800/3746] lr: 9.502e-02, eta: 4 days, 1:24:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5053, loss_cls: 4.0871, loss: 4.0871 +2024-07-22 19:38:50,908 - pyskl - INFO - Epoch [22][1900/3746] lr: 9.501e-02, eta: 4 days, 1:23:28, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5164, loss_cls: 4.0345, loss: 4.0345 +2024-07-22 19:40:00,905 - pyskl - INFO - Epoch [22][2000/3746] lr: 9.500e-02, eta: 4 days, 1:21:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4991, loss_cls: 4.1045, loss: 4.1045 +2024-07-22 19:41:10,812 - pyskl - INFO - Epoch [22][2100/3746] lr: 9.499e-02, eta: 4 days, 1:20:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5108, loss_cls: 4.0485, loss: 4.0485 +2024-07-22 19:42:20,816 - pyskl - INFO - Epoch [22][2200/3746] lr: 9.498e-02, eta: 4 days, 1:18:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5028, loss_cls: 4.0732, loss: 4.0732 +2024-07-22 19:43:30,407 - pyskl - INFO - Epoch [22][2300/3746] lr: 9.496e-02, eta: 4 days, 1:17:26, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5078, loss_cls: 4.0526, loss: 4.0526 +2024-07-22 19:44:40,148 - pyskl - INFO - Epoch [22][2400/3746] lr: 9.495e-02, eta: 4 days, 1:15:55, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5169, loss_cls: 4.0236, loss: 4.0236 +2024-07-22 19:45:50,221 - pyskl - INFO - Epoch [22][2500/3746] lr: 9.494e-02, eta: 4 days, 1:14:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5103, loss_cls: 4.0083, loss: 4.0083 +2024-07-22 19:47:00,051 - pyskl - INFO - Epoch [22][2600/3746] lr: 9.493e-02, eta: 4 days, 1:12:56, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5006, loss_cls: 4.0428, loss: 4.0428 +2024-07-22 19:48:10,007 - pyskl - INFO - Epoch [22][2700/3746] lr: 9.491e-02, eta: 4 days, 1:11:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5097, loss_cls: 4.0300, loss: 4.0300 +2024-07-22 19:49:19,760 - pyskl - INFO - Epoch [22][2800/3746] lr: 9.490e-02, eta: 4 days, 1:09:55, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5058, loss_cls: 4.0678, loss: 4.0678 +2024-07-22 19:50:29,941 - pyskl - INFO - Epoch [22][2900/3746] lr: 9.489e-02, eta: 4 days, 1:08:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5142, loss_cls: 4.0623, loss: 4.0623 +2024-07-22 19:51:39,908 - pyskl - INFO - Epoch [22][3000/3746] lr: 9.488e-02, eta: 4 days, 1:06:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5169, loss_cls: 4.0405, loss: 4.0405 +2024-07-22 19:52:49,616 - pyskl - INFO - Epoch [22][3100/3746] lr: 9.487e-02, eta: 4 days, 1:05:26, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5136, loss_cls: 4.0225, loss: 4.0225 +2024-07-22 19:53:59,411 - pyskl - INFO - Epoch [22][3200/3746] lr: 9.485e-02, eta: 4 days, 1:03:56, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5167, loss_cls: 4.0243, loss: 4.0243 +2024-07-22 19:55:09,350 - pyskl - INFO - Epoch [22][3300/3746] lr: 9.484e-02, eta: 4 days, 1:02:26, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5083, loss_cls: 4.0264, loss: 4.0264 +2024-07-22 19:56:19,409 - pyskl - INFO - Epoch [22][3400/3746] lr: 9.483e-02, eta: 4 days, 1:00:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5067, loss_cls: 4.0600, loss: 4.0600 +2024-07-22 19:57:29,431 - pyskl - INFO - Epoch [22][3500/3746] lr: 9.482e-02, eta: 4 days, 0:59:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5120, loss_cls: 4.0505, loss: 4.0505 +2024-07-22 19:58:40,275 - pyskl - INFO - Epoch [22][3600/3746] lr: 9.480e-02, eta: 4 days, 0:58:04, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5208, loss_cls: 4.0223, loss: 4.0223 +2024-07-22 19:59:50,620 - pyskl - INFO - Epoch [22][3700/3746] lr: 9.479e-02, eta: 4 days, 0:56:38, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5083, loss_cls: 4.0381, loss: 4.0381 +2024-07-22 20:00:25,112 - pyskl - INFO - Saving checkpoint at 22 epochs +2024-07-22 20:02:18,134 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 20:02:18,871 - pyskl - INFO - +top1_acc 0.1858 +top5_acc 0.4139 +2024-07-22 20:02:18,871 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 20:02:18,919 - pyskl - INFO - +mean_acc 0.1855 +2024-07-22 20:02:18,933 - pyskl - INFO - Epoch(val) [22][309] top1_acc: 0.1858, top5_acc: 0.4139, mean_class_accuracy: 0.1855 +2024-07-22 20:05:43,296 - pyskl - INFO - Epoch [23][100/3746] lr: 9.477e-02, eta: 4 days, 1:04:21, time: 2.044, data_time: 1.342, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5275, loss_cls: 3.9541, loss: 3.9541 +2024-07-22 20:06:53,863 - pyskl - INFO - Epoch [23][200/3746] lr: 9.476e-02, eta: 4 days, 1:02:55, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5252, loss_cls: 3.9529, loss: 3.9529 +2024-07-22 20:08:03,981 - pyskl - INFO - Epoch [23][300/3746] lr: 9.475e-02, eta: 4 days, 1:01:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5166, loss_cls: 4.0137, loss: 4.0137 +2024-07-22 20:09:14,737 - pyskl - INFO - Epoch [23][400/3746] lr: 9.474e-02, eta: 4 days, 1:00:01, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5117, loss_cls: 4.0227, loss: 4.0227 +2024-07-22 20:10:24,901 - pyskl - INFO - Epoch [23][500/3746] lr: 9.472e-02, eta: 4 days, 0:58:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5023, loss_cls: 4.0928, loss: 4.0928 +2024-07-22 20:11:34,992 - pyskl - INFO - Epoch [23][600/3746] lr: 9.471e-02, eta: 4 days, 0:57:03, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5217, loss_cls: 3.9719, loss: 3.9719 +2024-07-22 20:12:44,998 - pyskl - INFO - Epoch [23][700/3746] lr: 9.470e-02, eta: 4 days, 0:55:33, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5172, loss_cls: 4.0033, loss: 4.0033 +2024-07-22 20:13:54,875 - pyskl - INFO - Epoch [23][800/3746] lr: 9.469e-02, eta: 4 days, 0:54:03, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.4981, loss_cls: 4.0661, loss: 4.0661 +2024-07-22 20:15:04,840 - pyskl - INFO - Epoch [23][900/3746] lr: 9.467e-02, eta: 4 days, 0:52:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5164, loss_cls: 4.0039, loss: 4.0039 +2024-07-22 20:16:14,616 - pyskl - INFO - Epoch [23][1000/3746] lr: 9.466e-02, eta: 4 days, 0:51:03, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5097, loss_cls: 4.0605, loss: 4.0605 +2024-07-22 20:17:24,394 - pyskl - INFO - Epoch [23][1100/3746] lr: 9.465e-02, eta: 4 days, 0:49:32, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5138, loss_cls: 4.0079, loss: 4.0079 +2024-07-22 20:18:33,954 - pyskl - INFO - Epoch [23][1200/3746] lr: 9.464e-02, eta: 4 days, 0:48:01, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5081, loss_cls: 4.0557, loss: 4.0557 +2024-07-22 20:19:43,874 - pyskl - INFO - Epoch [23][1300/3746] lr: 9.462e-02, eta: 4 days, 0:46:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5159, loss_cls: 4.0062, loss: 4.0062 +2024-07-22 20:20:53,601 - pyskl - INFO - Epoch [23][1400/3746] lr: 9.461e-02, eta: 4 days, 0:45:00, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5134, loss_cls: 4.0375, loss: 4.0375 +2024-07-22 20:22:03,273 - pyskl - INFO - Epoch [23][1500/3746] lr: 9.460e-02, eta: 4 days, 0:43:29, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5061, loss_cls: 4.0594, loss: 4.0594 +2024-07-22 20:23:13,111 - pyskl - INFO - Epoch [23][1600/3746] lr: 9.459e-02, eta: 4 days, 0:41:59, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5086, loss_cls: 4.0491, loss: 4.0491 +2024-07-22 20:24:23,135 - pyskl - INFO - Epoch [23][1700/3746] lr: 9.457e-02, eta: 4 days, 0:40:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5061, loss_cls: 4.0488, loss: 4.0488 +2024-07-22 20:25:33,056 - pyskl - INFO - Epoch [23][1800/3746] lr: 9.456e-02, eta: 4 days, 0:39:01, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5227, loss_cls: 4.0406, loss: 4.0406 +2024-07-22 20:26:42,638 - pyskl - INFO - Epoch [23][1900/3746] lr: 9.455e-02, eta: 4 days, 0:37:30, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5138, loss_cls: 4.0592, loss: 4.0592 +2024-07-22 20:27:52,474 - pyskl - INFO - Epoch [23][2000/3746] lr: 9.453e-02, eta: 4 days, 0:36:00, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5103, loss_cls: 4.0530, loss: 4.0530 +2024-07-22 20:29:02,121 - pyskl - INFO - Epoch [23][2100/3746] lr: 9.452e-02, eta: 4 days, 0:34:29, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5083, loss_cls: 4.0636, loss: 4.0636 +2024-07-22 20:30:12,198 - pyskl - INFO - Epoch [23][2200/3746] lr: 9.451e-02, eta: 4 days, 0:33:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5161, loss_cls: 4.0330, loss: 4.0330 +2024-07-22 20:31:21,898 - pyskl - INFO - Epoch [23][2300/3746] lr: 9.450e-02, eta: 4 days, 0:31:30, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5083, loss_cls: 4.0472, loss: 4.0472 +2024-07-22 20:32:31,783 - pyskl - INFO - Epoch [23][2400/3746] lr: 9.448e-02, eta: 4 days, 0:30:01, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5025, loss_cls: 4.0521, loss: 4.0521 +2024-07-22 20:33:41,466 - pyskl - INFO - Epoch [23][2500/3746] lr: 9.447e-02, eta: 4 days, 0:28:31, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5117, loss_cls: 4.0595, loss: 4.0595 +2024-07-22 20:34:51,419 - pyskl - INFO - Epoch [23][2600/3746] lr: 9.446e-02, eta: 4 days, 0:27:02, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5167, loss_cls: 3.9858, loss: 3.9858 +2024-07-22 20:36:01,119 - pyskl - INFO - Epoch [23][2700/3746] lr: 9.445e-02, eta: 4 days, 0:25:32, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5103, loss_cls: 4.0731, loss: 4.0731 +2024-07-22 20:37:10,909 - pyskl - INFO - Epoch [23][2800/3746] lr: 9.443e-02, eta: 4 days, 0:24:02, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.5119, loss_cls: 4.0442, loss: 4.0442 +2024-07-22 20:38:20,677 - pyskl - INFO - Epoch [23][2900/3746] lr: 9.442e-02, eta: 4 days, 0:22:32, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5158, loss_cls: 4.0320, loss: 4.0320 +2024-07-22 20:39:30,402 - pyskl - INFO - Epoch [23][3000/3746] lr: 9.441e-02, eta: 4 days, 0:21:02, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5214, loss_cls: 4.0136, loss: 4.0136 +2024-07-22 20:40:40,012 - pyskl - INFO - Epoch [23][3100/3746] lr: 9.439e-02, eta: 4 days, 0:19:32, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5069, loss_cls: 4.0644, loss: 4.0644 +2024-07-22 20:41:49,733 - pyskl - INFO - Epoch [23][3200/3746] lr: 9.438e-02, eta: 4 days, 0:18:02, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5088, loss_cls: 4.0634, loss: 4.0634 +2024-07-22 20:42:59,948 - pyskl - INFO - Epoch [23][3300/3746] lr: 9.437e-02, eta: 4 days, 0:16:35, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5045, loss_cls: 4.0821, loss: 4.0821 +2024-07-22 20:44:10,121 - pyskl - INFO - Epoch [23][3400/3746] lr: 9.436e-02, eta: 4 days, 0:15:07, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5155, loss_cls: 4.0390, loss: 4.0390 +2024-07-22 20:45:20,265 - pyskl - INFO - Epoch [23][3500/3746] lr: 9.434e-02, eta: 4 days, 0:13:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5136, loss_cls: 4.0534, loss: 4.0534 +2024-07-22 20:46:30,035 - pyskl - INFO - Epoch [23][3600/3746] lr: 9.433e-02, eta: 4 days, 0:12:11, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5148, loss_cls: 4.0292, loss: 4.0292 +2024-07-22 20:47:40,255 - pyskl - INFO - Epoch [23][3700/3746] lr: 9.432e-02, eta: 4 days, 0:10:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5014, loss_cls: 4.0693, loss: 4.0693 +2024-07-22 20:48:14,776 - pyskl - INFO - Saving checkpoint at 23 epochs +2024-07-22 20:50:05,567 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 20:50:06,233 - pyskl - INFO - +top1_acc 0.1804 +top5_acc 0.4032 +2024-07-22 20:50:06,234 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 20:50:06,276 - pyskl - INFO - +mean_acc 0.1803 +2024-07-22 20:50:06,288 - pyskl - INFO - Epoch(val) [23][309] top1_acc: 0.1804, top5_acc: 0.4032, mean_class_accuracy: 0.1803 +2024-07-22 20:53:25,996 - pyskl - INFO - Epoch [24][100/3746] lr: 9.430e-02, eta: 4 days, 0:17:33, time: 1.997, data_time: 1.294, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5337, loss_cls: 3.9377, loss: 3.9377 +2024-07-22 20:54:37,081 - pyskl - INFO - Epoch [24][200/3746] lr: 9.428e-02, eta: 4 days, 0:16:10, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5222, loss_cls: 3.9691, loss: 3.9691 +2024-07-22 20:55:47,990 - pyskl - INFO - Epoch [24][300/3746] lr: 9.427e-02, eta: 4 days, 0:14:46, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5138, loss_cls: 4.0609, loss: 4.0609 +2024-07-22 20:56:58,183 - pyskl - INFO - Epoch [24][400/3746] lr: 9.426e-02, eta: 4 days, 0:13:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5231, loss_cls: 3.9753, loss: 3.9753 +2024-07-22 20:58:08,630 - pyskl - INFO - Epoch [24][500/3746] lr: 9.425e-02, eta: 4 days, 0:11:52, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5109, loss_cls: 4.0072, loss: 4.0072 +2024-07-22 20:59:18,668 - pyskl - INFO - Epoch [24][600/3746] lr: 9.423e-02, eta: 4 days, 0:10:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5138, loss_cls: 4.0136, loss: 4.0136 +2024-07-22 21:00:28,943 - pyskl - INFO - Epoch [24][700/3746] lr: 9.422e-02, eta: 4 days, 0:08:57, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5247, loss_cls: 3.9891, loss: 3.9891 +2024-07-22 21:01:38,743 - pyskl - INFO - Epoch [24][800/3746] lr: 9.421e-02, eta: 4 days, 0:07:27, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5116, loss_cls: 4.0317, loss: 4.0317 +2024-07-22 21:02:48,603 - pyskl - INFO - Epoch [24][900/3746] lr: 9.419e-02, eta: 4 days, 0:05:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5100, loss_cls: 4.0725, loss: 4.0725 +2024-07-22 21:03:58,294 - pyskl - INFO - Epoch [24][1000/3746] lr: 9.418e-02, eta: 4 days, 0:04:28, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5123, loss_cls: 4.0241, loss: 4.0241 +2024-07-22 21:05:08,142 - pyskl - INFO - Epoch [24][1100/3746] lr: 9.417e-02, eta: 4 days, 0:02:59, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.5031, loss_cls: 4.0974, loss: 4.0974 +2024-07-22 21:06:17,987 - pyskl - INFO - Epoch [24][1200/3746] lr: 9.415e-02, eta: 4 days, 0:01:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5119, loss_cls: 4.0267, loss: 4.0267 +2024-07-22 21:07:27,785 - pyskl - INFO - Epoch [24][1300/3746] lr: 9.414e-02, eta: 4 days, 0:00:00, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5106, loss_cls: 4.0361, loss: 4.0361 +2024-07-22 21:08:37,423 - pyskl - INFO - Epoch [24][1400/3746] lr: 9.413e-02, eta: 3 days, 23:58:30, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5203, loss_cls: 4.0093, loss: 4.0093 +2024-07-22 21:09:47,396 - pyskl - INFO - Epoch [24][1500/3746] lr: 9.411e-02, eta: 3 days, 23:57:02, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5217, loss_cls: 4.0202, loss: 4.0202 +2024-07-22 21:10:57,272 - pyskl - INFO - Epoch [24][1600/3746] lr: 9.410e-02, eta: 3 days, 23:55:33, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5123, loss_cls: 4.0532, loss: 4.0532 +2024-07-22 21:12:07,115 - pyskl - INFO - Epoch [24][1700/3746] lr: 9.409e-02, eta: 3 days, 23:54:04, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5225, loss_cls: 3.9979, loss: 3.9979 +2024-07-22 21:13:16,885 - pyskl - INFO - Epoch [24][1800/3746] lr: 9.407e-02, eta: 3 days, 23:52:34, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5161, loss_cls: 4.0021, loss: 4.0021 +2024-07-22 21:14:26,687 - pyskl - INFO - Epoch [24][1900/3746] lr: 9.406e-02, eta: 3 days, 23:51:05, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5181, loss_cls: 4.0229, loss: 4.0229 +2024-07-22 21:15:36,256 - pyskl - INFO - Epoch [24][2000/3746] lr: 9.405e-02, eta: 3 days, 23:49:35, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5195, loss_cls: 4.0048, loss: 4.0048 +2024-07-22 21:16:45,904 - pyskl - INFO - Epoch [24][2100/3746] lr: 9.404e-02, eta: 3 days, 23:48:05, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5227, loss_cls: 4.0284, loss: 4.0284 +2024-07-22 21:17:55,679 - pyskl - INFO - Epoch [24][2200/3746] lr: 9.402e-02, eta: 3 days, 23:46:36, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5147, loss_cls: 4.0367, loss: 4.0367 +2024-07-22 21:19:05,420 - pyskl - INFO - Epoch [24][2300/3746] lr: 9.401e-02, eta: 3 days, 23:45:07, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5138, loss_cls: 4.0421, loss: 4.0421 +2024-07-22 21:20:15,083 - pyskl - INFO - Epoch [24][2400/3746] lr: 9.400e-02, eta: 3 days, 23:43:37, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5041, loss_cls: 4.0504, loss: 4.0504 +2024-07-22 21:21:24,752 - pyskl - INFO - Epoch [24][2500/3746] lr: 9.398e-02, eta: 3 days, 23:42:08, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5147, loss_cls: 4.0291, loss: 4.0291 +2024-07-22 21:22:34,417 - pyskl - INFO - Epoch [24][2600/3746] lr: 9.397e-02, eta: 3 days, 23:40:38, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5055, loss_cls: 4.0427, loss: 4.0427 +2024-07-22 21:23:44,133 - pyskl - INFO - Epoch [24][2700/3746] lr: 9.396e-02, eta: 3 days, 23:39:09, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5191, loss_cls: 4.0187, loss: 4.0187 +2024-07-22 21:24:53,837 - pyskl - INFO - Epoch [24][2800/3746] lr: 9.394e-02, eta: 3 days, 23:37:40, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5175, loss_cls: 4.0275, loss: 4.0275 +2024-07-22 21:26:03,568 - pyskl - INFO - Epoch [24][2900/3746] lr: 9.393e-02, eta: 3 days, 23:36:11, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5008, loss_cls: 4.0600, loss: 4.0600 +2024-07-22 21:27:13,569 - pyskl - INFO - Epoch [24][3000/3746] lr: 9.392e-02, eta: 3 days, 23:34:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5092, loss_cls: 4.0294, loss: 4.0294 +2024-07-22 21:28:23,370 - pyskl - INFO - Epoch [24][3100/3746] lr: 9.390e-02, eta: 3 days, 23:33:14, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5266, loss_cls: 3.9800, loss: 3.9800 +2024-07-22 21:29:33,041 - pyskl - INFO - Epoch [24][3200/3746] lr: 9.389e-02, eta: 3 days, 23:31:45, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5209, loss_cls: 4.0077, loss: 4.0077 +2024-07-22 21:30:43,402 - pyskl - INFO - Epoch [24][3300/3746] lr: 9.388e-02, eta: 3 days, 23:30:20, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5108, loss_cls: 4.0447, loss: 4.0447 +2024-07-22 21:31:53,550 - pyskl - INFO - Epoch [24][3400/3746] lr: 9.386e-02, eta: 3 days, 23:28:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5164, loss_cls: 4.0327, loss: 4.0327 +2024-07-22 21:33:03,832 - pyskl - INFO - Epoch [24][3500/3746] lr: 9.385e-02, eta: 3 days, 23:27:27, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5073, loss_cls: 4.0364, loss: 4.0364 +2024-07-22 21:34:13,815 - pyskl - INFO - Epoch [24][3600/3746] lr: 9.384e-02, eta: 3 days, 23:26:00, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5047, loss_cls: 4.0918, loss: 4.0918 +2024-07-22 21:35:24,086 - pyskl - INFO - Epoch [24][3700/3746] lr: 9.382e-02, eta: 3 days, 23:24:34, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5084, loss_cls: 4.0240, loss: 4.0240 +2024-07-22 21:35:58,655 - pyskl - INFO - Saving checkpoint at 24 epochs +2024-07-22 21:37:49,765 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 21:37:50,432 - pyskl - INFO - +top1_acc 0.1948 +top5_acc 0.4212 +2024-07-22 21:37:50,432 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 21:37:50,474 - pyskl - INFO - +mean_acc 0.1947 +2024-07-22 21:37:50,479 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_20.pth was removed +2024-07-22 21:37:50,741 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_24.pth. +2024-07-22 21:37:50,742 - pyskl - INFO - Best top1_acc is 0.1948 at 24 epoch. +2024-07-22 21:37:50,754 - pyskl - INFO - Epoch(val) [24][309] top1_acc: 0.1948, top5_acc: 0.4212, mean_class_accuracy: 0.1947 +2024-07-22 21:41:12,676 - pyskl - INFO - Epoch [25][100/3746] lr: 9.380e-02, eta: 3 days, 23:31:09, time: 2.019, data_time: 1.316, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5239, loss_cls: 3.9700, loss: 3.9700 +2024-07-22 21:42:23,565 - pyskl - INFO - Epoch [25][200/3746] lr: 9.379e-02, eta: 3 days, 23:29:46, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5117, loss_cls: 4.0258, loss: 4.0258 +2024-07-22 21:43:34,114 - pyskl - INFO - Epoch [25][300/3746] lr: 9.378e-02, eta: 3 days, 23:28:21, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5244, loss_cls: 3.9624, loss: 3.9624 +2024-07-22 21:44:44,255 - pyskl - INFO - Epoch [25][400/3746] lr: 9.376e-02, eta: 3 days, 23:26:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.5095, loss_cls: 4.0390, loss: 4.0390 +2024-07-22 21:45:54,490 - pyskl - INFO - Epoch [25][500/3746] lr: 9.375e-02, eta: 3 days, 23:25:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5141, loss_cls: 4.0367, loss: 4.0367 +2024-07-22 21:47:04,460 - pyskl - INFO - Epoch [25][600/3746] lr: 9.373e-02, eta: 3 days, 23:23:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5170, loss_cls: 4.0408, loss: 4.0408 +2024-07-22 21:48:14,383 - pyskl - INFO - Epoch [25][700/3746] lr: 9.372e-02, eta: 3 days, 23:22:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5244, loss_cls: 3.9715, loss: 3.9715 +2024-07-22 21:49:24,413 - pyskl - INFO - Epoch [25][800/3746] lr: 9.371e-02, eta: 3 days, 23:21:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5219, loss_cls: 4.0174, loss: 4.0174 +2024-07-22 21:50:34,189 - pyskl - INFO - Epoch [25][900/3746] lr: 9.369e-02, eta: 3 days, 23:19:35, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5220, loss_cls: 3.9999, loss: 3.9999 +2024-07-22 21:51:44,042 - pyskl - INFO - Epoch [25][1000/3746] lr: 9.368e-02, eta: 3 days, 23:18:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5219, loss_cls: 3.9878, loss: 3.9878 +2024-07-22 21:52:53,962 - pyskl - INFO - Epoch [25][1100/3746] lr: 9.367e-02, eta: 3 days, 23:16:39, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5117, loss_cls: 4.0295, loss: 4.0295 +2024-07-22 21:54:03,832 - pyskl - INFO - Epoch [25][1200/3746] lr: 9.365e-02, eta: 3 days, 23:15:11, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5222, loss_cls: 3.9630, loss: 3.9630 +2024-07-22 21:55:13,768 - pyskl - INFO - Epoch [25][1300/3746] lr: 9.364e-02, eta: 3 days, 23:13:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5233, loss_cls: 4.0067, loss: 4.0067 +2024-07-22 21:56:23,476 - pyskl - INFO - Epoch [25][1400/3746] lr: 9.363e-02, eta: 3 days, 23:12:14, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5119, loss_cls: 4.0410, loss: 4.0410 +2024-07-22 21:57:33,358 - pyskl - INFO - Epoch [25][1500/3746] lr: 9.361e-02, eta: 3 days, 23:10:46, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5183, loss_cls: 4.0219, loss: 4.0219 +2024-07-22 21:58:43,192 - pyskl - INFO - Epoch [25][1600/3746] lr: 9.360e-02, eta: 3 days, 23:09:18, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5241, loss_cls: 4.0106, loss: 4.0106 +2024-07-22 21:59:53,041 - pyskl - INFO - Epoch [25][1700/3746] lr: 9.358e-02, eta: 3 days, 23:07:49, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5112, loss_cls: 4.0591, loss: 4.0591 +2024-07-22 22:01:02,635 - pyskl - INFO - Epoch [25][1800/3746] lr: 9.357e-02, eta: 3 days, 23:06:20, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5089, loss_cls: 4.0106, loss: 4.0106 +2024-07-22 22:02:12,360 - pyskl - INFO - Epoch [25][1900/3746] lr: 9.356e-02, eta: 3 days, 23:04:51, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5266, loss_cls: 3.9726, loss: 3.9726 +2024-07-22 22:03:22,336 - pyskl - INFO - Epoch [25][2000/3746] lr: 9.354e-02, eta: 3 days, 23:03:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5131, loss_cls: 4.0270, loss: 4.0270 +2024-07-22 22:04:32,250 - pyskl - INFO - Epoch [25][2100/3746] lr: 9.353e-02, eta: 3 days, 23:01:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5133, loss_cls: 4.0352, loss: 4.0352 +2024-07-22 22:05:41,962 - pyskl - INFO - Epoch [25][2200/3746] lr: 9.352e-02, eta: 3 days, 23:00:28, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5188, loss_cls: 4.0230, loss: 4.0230 +2024-07-22 22:06:51,587 - pyskl - INFO - Epoch [25][2300/3746] lr: 9.350e-02, eta: 3 days, 22:58:59, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5095, loss_cls: 4.0558, loss: 4.0558 +2024-07-22 22:08:01,608 - pyskl - INFO - Epoch [25][2400/3746] lr: 9.349e-02, eta: 3 days, 22:57:32, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5158, loss_cls: 4.0209, loss: 4.0209 +2024-07-22 22:09:11,255 - pyskl - INFO - Epoch [25][2500/3746] lr: 9.347e-02, eta: 3 days, 22:56:03, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5122, loss_cls: 4.0630, loss: 4.0630 +2024-07-22 22:10:21,028 - pyskl - INFO - Epoch [25][2600/3746] lr: 9.346e-02, eta: 3 days, 22:54:35, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5139, loss_cls: 4.0245, loss: 4.0245 +2024-07-22 22:11:30,809 - pyskl - INFO - Epoch [25][2700/3746] lr: 9.345e-02, eta: 3 days, 22:53:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5100, loss_cls: 4.0421, loss: 4.0421 +2024-07-22 22:12:40,601 - pyskl - INFO - Epoch [25][2800/3746] lr: 9.343e-02, eta: 3 days, 22:51:39, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.4998, loss_cls: 4.0648, loss: 4.0648 +2024-07-22 22:13:50,604 - pyskl - INFO - Epoch [25][2900/3746] lr: 9.342e-02, eta: 3 days, 22:50:12, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5083, loss_cls: 4.0663, loss: 4.0663 +2024-07-22 22:15:00,409 - pyskl - INFO - Epoch [25][3000/3746] lr: 9.341e-02, eta: 3 days, 22:48:44, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5112, loss_cls: 4.0251, loss: 4.0251 +2024-07-22 22:16:10,224 - pyskl - INFO - Epoch [25][3100/3746] lr: 9.339e-02, eta: 3 days, 22:47:17, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5202, loss_cls: 4.0135, loss: 4.0135 +2024-07-22 22:17:20,034 - pyskl - INFO - Epoch [25][3200/3746] lr: 9.338e-02, eta: 3 days, 22:45:49, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5197, loss_cls: 4.0182, loss: 4.0182 +2024-07-22 22:18:29,999 - pyskl - INFO - Epoch [25][3300/3746] lr: 9.336e-02, eta: 3 days, 22:44:22, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5175, loss_cls: 3.9943, loss: 3.9943 +2024-07-22 22:19:40,269 - pyskl - INFO - Epoch [25][3400/3746] lr: 9.335e-02, eta: 3 days, 22:42:57, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5100, loss_cls: 4.0715, loss: 4.0715 +2024-07-22 22:20:50,425 - pyskl - INFO - Epoch [25][3500/3746] lr: 9.334e-02, eta: 3 days, 22:41:31, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5133, loss_cls: 4.0320, loss: 4.0320 +2024-07-22 22:22:00,472 - pyskl - INFO - Epoch [25][3600/3746] lr: 9.332e-02, eta: 3 days, 22:40:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5109, loss_cls: 4.0334, loss: 4.0334 +2024-07-22 22:23:10,325 - pyskl - INFO - Epoch [25][3700/3746] lr: 9.331e-02, eta: 3 days, 22:38:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5161, loss_cls: 4.0190, loss: 4.0190 +2024-07-22 22:23:45,296 - pyskl - INFO - Saving checkpoint at 25 epochs +2024-07-22 22:25:36,795 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 22:25:37,480 - pyskl - INFO - +top1_acc 0.1783 +top5_acc 0.3951 +2024-07-22 22:25:37,480 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 22:25:37,521 - pyskl - INFO - +mean_acc 0.1782 +2024-07-22 22:25:37,533 - pyskl - INFO - Epoch(val) [25][309] top1_acc: 0.1783, top5_acc: 0.3951, mean_class_accuracy: 0.1782 +2024-07-22 22:28:59,988 - pyskl - INFO - Epoch [26][100/3746] lr: 9.329e-02, eta: 3 days, 22:44:51, time: 2.024, data_time: 1.322, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5081, loss_cls: 4.0297, loss: 4.0297 +2024-07-22 22:30:10,452 - pyskl - INFO - Epoch [26][200/3746] lr: 9.327e-02, eta: 3 days, 22:43:27, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5270, loss_cls: 3.9702, loss: 3.9702 +2024-07-22 22:31:20,836 - pyskl - INFO - Epoch [26][300/3746] lr: 9.326e-02, eta: 3 days, 22:42:01, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5170, loss_cls: 4.0061, loss: 4.0061 +2024-07-22 22:32:31,441 - pyskl - INFO - Epoch [26][400/3746] lr: 9.325e-02, eta: 3 days, 22:40:37, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5202, loss_cls: 4.0231, loss: 4.0231 +2024-07-22 22:33:41,470 - pyskl - INFO - Epoch [26][500/3746] lr: 9.323e-02, eta: 3 days, 22:39:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5162, loss_cls: 3.9987, loss: 3.9987 +2024-07-22 22:34:51,776 - pyskl - INFO - Epoch [26][600/3746] lr: 9.322e-02, eta: 3 days, 22:37:45, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5150, loss_cls: 4.0190, loss: 4.0190 +2024-07-22 22:36:01,779 - pyskl - INFO - Epoch [26][700/3746] lr: 9.320e-02, eta: 3 days, 22:36:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5142, loss_cls: 3.9932, loss: 3.9932 +2024-07-22 22:37:11,577 - pyskl - INFO - Epoch [26][800/3746] lr: 9.319e-02, eta: 3 days, 22:34:50, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5080, loss_cls: 4.0420, loss: 4.0420 +2024-07-22 22:38:21,474 - pyskl - INFO - Epoch [26][900/3746] lr: 9.318e-02, eta: 3 days, 22:33:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5094, loss_cls: 4.0100, loss: 4.0100 +2024-07-22 22:39:31,477 - pyskl - INFO - Epoch [26][1000/3746] lr: 9.316e-02, eta: 3 days, 22:31:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5212, loss_cls: 4.0339, loss: 4.0339 +2024-07-22 22:40:41,377 - pyskl - INFO - Epoch [26][1100/3746] lr: 9.315e-02, eta: 3 days, 22:30:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5086, loss_cls: 4.0245, loss: 4.0245 +2024-07-22 22:41:51,172 - pyskl - INFO - Epoch [26][1200/3746] lr: 9.313e-02, eta: 3 days, 22:29:01, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5209, loss_cls: 4.0225, loss: 4.0225 +2024-07-22 22:43:01,088 - pyskl - INFO - Epoch [26][1300/3746] lr: 9.312e-02, eta: 3 days, 22:27:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5191, loss_cls: 4.0164, loss: 4.0164 +2024-07-22 22:44:11,021 - pyskl - INFO - Epoch [26][1400/3746] lr: 9.310e-02, eta: 3 days, 22:26:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5267, loss_cls: 4.0156, loss: 4.0156 +2024-07-22 22:45:20,639 - pyskl - INFO - Epoch [26][1500/3746] lr: 9.309e-02, eta: 3 days, 22:24:38, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5045, loss_cls: 4.0672, loss: 4.0672 +2024-07-22 22:46:30,493 - pyskl - INFO - Epoch [26][1600/3746] lr: 9.308e-02, eta: 3 days, 22:23:11, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5123, loss_cls: 4.0319, loss: 4.0319 +2024-07-22 22:47:40,294 - pyskl - INFO - Epoch [26][1700/3746] lr: 9.306e-02, eta: 3 days, 22:21:43, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5198, loss_cls: 4.0195, loss: 4.0195 +2024-07-22 22:48:50,115 - pyskl - INFO - Epoch [26][1800/3746] lr: 9.305e-02, eta: 3 days, 22:20:16, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5144, loss_cls: 4.0256, loss: 4.0256 +2024-07-22 22:50:00,253 - pyskl - INFO - Epoch [26][1900/3746] lr: 9.303e-02, eta: 3 days, 22:18:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5220, loss_cls: 3.9945, loss: 3.9945 +2024-07-22 22:51:10,067 - pyskl - INFO - Epoch [26][2000/3746] lr: 9.302e-02, eta: 3 days, 22:17:23, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5217, loss_cls: 3.9866, loss: 3.9866 +2024-07-22 22:52:20,069 - pyskl - INFO - Epoch [26][2100/3746] lr: 9.300e-02, eta: 3 days, 22:15:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5069, loss_cls: 4.0630, loss: 4.0630 +2024-07-22 22:53:30,219 - pyskl - INFO - Epoch [26][2200/3746] lr: 9.299e-02, eta: 3 days, 22:14:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5161, loss_cls: 3.9973, loss: 3.9973 +2024-07-22 22:54:40,181 - pyskl - INFO - Epoch [26][2300/3746] lr: 9.298e-02, eta: 3 days, 22:13:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5080, loss_cls: 4.0596, loss: 4.0596 +2024-07-22 22:55:50,502 - pyskl - INFO - Epoch [26][2400/3746] lr: 9.296e-02, eta: 3 days, 22:11:39, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5067, loss_cls: 4.0547, loss: 4.0547 +2024-07-22 22:57:00,278 - pyskl - INFO - Epoch [26][2500/3746] lr: 9.295e-02, eta: 3 days, 22:10:12, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5136, loss_cls: 4.0502, loss: 4.0502 +2024-07-22 22:58:10,069 - pyskl - INFO - Epoch [26][2600/3746] lr: 9.293e-02, eta: 3 days, 22:08:44, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5127, loss_cls: 4.0393, loss: 4.0393 +2024-07-22 22:59:19,853 - pyskl - INFO - Epoch [26][2700/3746] lr: 9.292e-02, eta: 3 days, 22:07:17, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5169, loss_cls: 4.0371, loss: 4.0371 +2024-07-22 23:00:29,658 - pyskl - INFO - Epoch [26][2800/3746] lr: 9.290e-02, eta: 3 days, 22:05:50, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5250, loss_cls: 4.0012, loss: 4.0012 +2024-07-22 23:01:39,521 - pyskl - INFO - Epoch [26][2900/3746] lr: 9.289e-02, eta: 3 days, 22:04:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5258, loss_cls: 3.9739, loss: 3.9739 +2024-07-22 23:02:49,455 - pyskl - INFO - Epoch [26][3000/3746] lr: 9.288e-02, eta: 3 days, 22:02:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5172, loss_cls: 3.9872, loss: 3.9872 +2024-07-22 23:03:59,193 - pyskl - INFO - Epoch [26][3100/3746] lr: 9.286e-02, eta: 3 days, 22:01:29, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5139, loss_cls: 4.0215, loss: 4.0215 +2024-07-22 23:05:09,338 - pyskl - INFO - Epoch [26][3200/3746] lr: 9.285e-02, eta: 3 days, 22:00:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5106, loss_cls: 4.0153, loss: 4.0153 +2024-07-22 23:06:19,452 - pyskl - INFO - Epoch [26][3300/3746] lr: 9.283e-02, eta: 3 days, 21:58:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5311, loss_cls: 3.9701, loss: 3.9701 +2024-07-22 23:07:29,659 - pyskl - INFO - Epoch [26][3400/3746] lr: 9.282e-02, eta: 3 days, 21:57:13, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5228, loss_cls: 4.0007, loss: 4.0007 +2024-07-22 23:08:40,222 - pyskl - INFO - Epoch [26][3500/3746] lr: 9.280e-02, eta: 3 days, 21:55:50, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5128, loss_cls: 4.0150, loss: 4.0150 +2024-07-22 23:09:50,319 - pyskl - INFO - Epoch [26][3600/3746] lr: 9.279e-02, eta: 3 days, 21:54:25, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5255, loss_cls: 3.9738, loss: 3.9738 +2024-07-22 23:11:00,294 - pyskl - INFO - Epoch [26][3700/3746] lr: 9.278e-02, eta: 3 days, 21:52:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5173, loss_cls: 4.0098, loss: 4.0098 +2024-07-22 23:11:35,300 - pyskl - INFO - Saving checkpoint at 26 epochs +2024-07-22 23:13:26,336 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 23:13:27,001 - pyskl - INFO - +top1_acc 0.1863 +top5_acc 0.4095 +2024-07-22 23:13:27,001 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 23:13:27,041 - pyskl - INFO - +mean_acc 0.1862 +2024-07-22 23:13:27,053 - pyskl - INFO - Epoch(val) [26][309] top1_acc: 0.1863, top5_acc: 0.4095, mean_class_accuracy: 0.1862 +2024-07-22 23:16:49,255 - pyskl - INFO - Epoch [27][100/3746] lr: 9.275e-02, eta: 3 days, 21:58:49, time: 2.022, data_time: 1.315, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5225, loss_cls: 3.9936, loss: 3.9936 +2024-07-22 23:17:59,503 - pyskl - INFO - Epoch [27][200/3746] lr: 9.274e-02, eta: 3 days, 21:57:24, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5166, loss_cls: 4.0186, loss: 4.0186 +2024-07-22 23:19:10,201 - pyskl - INFO - Epoch [27][300/3746] lr: 9.272e-02, eta: 3 days, 21:56:01, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5130, loss_cls: 4.0290, loss: 4.0290 +2024-07-22 23:20:20,310 - pyskl - INFO - Epoch [27][400/3746] lr: 9.271e-02, eta: 3 days, 21:54:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5150, loss_cls: 4.0021, loss: 4.0021 +2024-07-22 23:21:31,128 - pyskl - INFO - Epoch [27][500/3746] lr: 9.270e-02, eta: 3 days, 21:53:13, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5103, loss_cls: 4.0335, loss: 4.0335 +2024-07-22 23:22:41,404 - pyskl - INFO - Epoch [27][600/3746] lr: 9.268e-02, eta: 3 days, 21:51:48, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5136, loss_cls: 4.0419, loss: 4.0419 +2024-07-22 23:23:51,434 - pyskl - INFO - Epoch [27][700/3746] lr: 9.267e-02, eta: 3 days, 21:50:22, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5130, loss_cls: 3.9943, loss: 3.9943 +2024-07-22 23:25:01,291 - pyskl - INFO - Epoch [27][800/3746] lr: 9.265e-02, eta: 3 days, 21:48:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5197, loss_cls: 4.0014, loss: 4.0014 +2024-07-22 23:26:11,039 - pyskl - INFO - Epoch [27][900/3746] lr: 9.264e-02, eta: 3 days, 21:47:27, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5228, loss_cls: 3.9985, loss: 3.9985 +2024-07-22 23:27:20,727 - pyskl - INFO - Epoch [27][1000/3746] lr: 9.262e-02, eta: 3 days, 21:46:00, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5206, loss_cls: 4.0190, loss: 4.0190 +2024-07-22 23:28:30,422 - pyskl - INFO - Epoch [27][1100/3746] lr: 9.261e-02, eta: 3 days, 21:44:32, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5191, loss_cls: 4.0208, loss: 4.0208 +2024-07-22 23:29:40,131 - pyskl - INFO - Epoch [27][1200/3746] lr: 9.259e-02, eta: 3 days, 21:43:05, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5270, loss_cls: 3.9745, loss: 3.9745 +2024-07-22 23:30:49,793 - pyskl - INFO - Epoch [27][1300/3746] lr: 9.258e-02, eta: 3 days, 21:41:37, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5167, loss_cls: 4.0486, loss: 4.0486 +2024-07-22 23:31:59,816 - pyskl - INFO - Epoch [27][1400/3746] lr: 9.256e-02, eta: 3 days, 21:40:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5123, loss_cls: 4.0403, loss: 4.0403 +2024-07-22 23:33:09,550 - pyskl - INFO - Epoch [27][1500/3746] lr: 9.255e-02, eta: 3 days, 21:38:44, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5205, loss_cls: 4.0018, loss: 4.0018 +2024-07-22 23:34:19,274 - pyskl - INFO - Epoch [27][1600/3746] lr: 9.253e-02, eta: 3 days, 21:37:17, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5156, loss_cls: 4.0079, loss: 4.0079 +2024-07-22 23:35:28,815 - pyskl - INFO - Epoch [27][1700/3746] lr: 9.252e-02, eta: 3 days, 21:35:49, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5167, loss_cls: 4.0129, loss: 4.0129 +2024-07-22 23:36:38,475 - pyskl - INFO - Epoch [27][1800/3746] lr: 9.251e-02, eta: 3 days, 21:34:21, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5097, loss_cls: 4.0686, loss: 4.0686 +2024-07-22 23:37:48,337 - pyskl - INFO - Epoch [27][1900/3746] lr: 9.249e-02, eta: 3 days, 21:32:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5175, loss_cls: 3.9988, loss: 3.9988 +2024-07-22 23:38:58,163 - pyskl - INFO - Epoch [27][2000/3746] lr: 9.248e-02, eta: 3 days, 21:31:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5280, loss_cls: 3.9634, loss: 3.9634 +2024-07-22 23:40:07,954 - pyskl - INFO - Epoch [27][2100/3746] lr: 9.246e-02, eta: 3 days, 21:30:01, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5186, loss_cls: 3.9655, loss: 3.9655 +2024-07-22 23:41:17,694 - pyskl - INFO - Epoch [27][2200/3746] lr: 9.245e-02, eta: 3 days, 21:28:34, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5144, loss_cls: 4.0221, loss: 4.0221 +2024-07-22 23:42:27,468 - pyskl - INFO - Epoch [27][2300/3746] lr: 9.243e-02, eta: 3 days, 21:27:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5172, loss_cls: 4.0383, loss: 4.0383 +2024-07-22 23:43:37,071 - pyskl - INFO - Epoch [27][2400/3746] lr: 9.242e-02, eta: 3 days, 21:25:40, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5223, loss_cls: 4.0091, loss: 4.0091 +2024-07-22 23:44:46,919 - pyskl - INFO - Epoch [27][2500/3746] lr: 9.240e-02, eta: 3 days, 21:24:14, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5209, loss_cls: 3.9744, loss: 3.9744 +2024-07-22 23:45:56,613 - pyskl - INFO - Epoch [27][2600/3746] lr: 9.239e-02, eta: 3 days, 21:22:46, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5302, loss_cls: 3.9967, loss: 3.9967 +2024-07-22 23:47:06,383 - pyskl - INFO - Epoch [27][2700/3746] lr: 9.237e-02, eta: 3 days, 21:21:20, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5167, loss_cls: 3.9964, loss: 3.9964 +2024-07-22 23:48:16,152 - pyskl - INFO - Epoch [27][2800/3746] lr: 9.236e-02, eta: 3 days, 21:19:53, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5231, loss_cls: 4.0051, loss: 4.0051 +2024-07-22 23:49:25,922 - pyskl - INFO - Epoch [27][2900/3746] lr: 9.234e-02, eta: 3 days, 21:18:27, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5202, loss_cls: 3.9965, loss: 3.9965 +2024-07-22 23:50:35,627 - pyskl - INFO - Epoch [27][3000/3746] lr: 9.233e-02, eta: 3 days, 21:17:00, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5184, loss_cls: 4.0395, loss: 4.0395 +2024-07-22 23:51:45,459 - pyskl - INFO - Epoch [27][3100/3746] lr: 9.231e-02, eta: 3 days, 21:15:33, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5123, loss_cls: 4.0573, loss: 4.0573 +2024-07-22 23:52:54,965 - pyskl - INFO - Epoch [27][3200/3746] lr: 9.230e-02, eta: 3 days, 21:14:06, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5164, loss_cls: 4.0209, loss: 4.0209 +2024-07-22 23:54:05,044 - pyskl - INFO - Epoch [27][3300/3746] lr: 9.228e-02, eta: 3 days, 21:12:41, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5327, loss_cls: 3.9278, loss: 3.9278 +2024-07-22 23:55:15,231 - pyskl - INFO - Epoch [27][3400/3746] lr: 9.227e-02, eta: 3 days, 21:11:16, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5153, loss_cls: 4.0340, loss: 4.0340 +2024-07-22 23:56:25,356 - pyskl - INFO - Epoch [27][3500/3746] lr: 9.225e-02, eta: 3 days, 21:09:51, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5166, loss_cls: 3.9931, loss: 3.9931 +2024-07-22 23:57:35,282 - pyskl - INFO - Epoch [27][3600/3746] lr: 9.224e-02, eta: 3 days, 21:08:26, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5169, loss_cls: 4.0089, loss: 4.0089 +2024-07-22 23:58:45,380 - pyskl - INFO - Epoch [27][3700/3746] lr: 9.222e-02, eta: 3 days, 21:07:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5164, loss_cls: 4.0269, loss: 4.0269 +2024-07-22 23:59:20,203 - pyskl - INFO - Saving checkpoint at 27 epochs +2024-07-23 00:01:12,445 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 00:01:13,123 - pyskl - INFO - +top1_acc 0.1839 +top5_acc 0.4080 +2024-07-23 00:01:13,123 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 00:01:13,168 - pyskl - INFO - +mean_acc 0.1834 +2024-07-23 00:01:13,181 - pyskl - INFO - Epoch(val) [27][309] top1_acc: 0.1839, top5_acc: 0.4080, mean_class_accuracy: 0.1834 +2024-07-23 00:04:35,226 - pyskl - INFO - Epoch [28][100/3746] lr: 9.220e-02, eta: 3 days, 21:12:31, time: 2.020, data_time: 1.320, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5264, loss_cls: 3.9451, loss: 3.9451 +2024-07-23 00:05:45,315 - pyskl - INFO - Epoch [28][200/3746] lr: 9.219e-02, eta: 3 days, 21:11:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5155, loss_cls: 3.9798, loss: 3.9798 +2024-07-23 00:06:55,844 - pyskl - INFO - Epoch [28][300/3746] lr: 9.217e-02, eta: 3 days, 21:09:42, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5264, loss_cls: 3.9716, loss: 3.9716 +2024-07-23 00:08:05,873 - pyskl - INFO - Epoch [28][400/3746] lr: 9.216e-02, eta: 3 days, 21:08:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5142, loss_cls: 3.9999, loss: 3.9999 +2024-07-23 00:09:16,127 - pyskl - INFO - Epoch [28][500/3746] lr: 9.214e-02, eta: 3 days, 21:06:52, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5278, loss_cls: 3.9658, loss: 3.9658 +2024-07-23 00:10:26,335 - pyskl - INFO - Epoch [28][600/3746] lr: 9.213e-02, eta: 3 days, 21:05:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5209, loss_cls: 4.0022, loss: 4.0022 +2024-07-23 00:11:36,315 - pyskl - INFO - Epoch [28][700/3746] lr: 9.211e-02, eta: 3 days, 21:04:02, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5238, loss_cls: 3.9948, loss: 3.9948 +2024-07-23 00:12:46,168 - pyskl - INFO - Epoch [28][800/3746] lr: 9.210e-02, eta: 3 days, 21:02:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5197, loss_cls: 3.9943, loss: 3.9943 +2024-07-23 00:13:56,005 - pyskl - INFO - Epoch [28][900/3746] lr: 9.208e-02, eta: 3 days, 21:01:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5164, loss_cls: 4.0184, loss: 4.0184 +2024-07-23 00:15:05,765 - pyskl - INFO - Epoch [28][1000/3746] lr: 9.207e-02, eta: 3 days, 20:59:42, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5209, loss_cls: 3.9778, loss: 3.9778 +2024-07-23 00:16:15,550 - pyskl - INFO - Epoch [28][1100/3746] lr: 9.205e-02, eta: 3 days, 20:58:16, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5227, loss_cls: 3.9847, loss: 3.9847 +2024-07-23 00:17:25,289 - pyskl - INFO - Epoch [28][1200/3746] lr: 9.204e-02, eta: 3 days, 20:56:49, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5172, loss_cls: 4.0031, loss: 4.0031 +2024-07-23 00:18:34,940 - pyskl - INFO - Epoch [28][1300/3746] lr: 9.202e-02, eta: 3 days, 20:55:22, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5156, loss_cls: 4.0218, loss: 4.0218 +2024-07-23 00:19:44,933 - pyskl - INFO - Epoch [28][1400/3746] lr: 9.201e-02, eta: 3 days, 20:53:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5139, loss_cls: 4.0006, loss: 4.0006 +2024-07-23 00:20:54,637 - pyskl - INFO - Epoch [28][1500/3746] lr: 9.199e-02, eta: 3 days, 20:52:30, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5162, loss_cls: 4.0261, loss: 4.0261 +2024-07-23 00:22:04,666 - pyskl - INFO - Epoch [28][1600/3746] lr: 9.198e-02, eta: 3 days, 20:51:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5269, loss_cls: 3.9519, loss: 3.9519 +2024-07-23 00:23:14,492 - pyskl - INFO - Epoch [28][1700/3746] lr: 9.196e-02, eta: 3 days, 20:49:39, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5073, loss_cls: 4.0359, loss: 4.0359 +2024-07-23 00:24:24,061 - pyskl - INFO - Epoch [28][1800/3746] lr: 9.194e-02, eta: 3 days, 20:48:12, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5075, loss_cls: 4.0469, loss: 4.0469 +2024-07-23 00:25:33,688 - pyskl - INFO - Epoch [28][1900/3746] lr: 9.193e-02, eta: 3 days, 20:46:45, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5145, loss_cls: 4.0068, loss: 4.0068 +2024-07-23 00:26:43,618 - pyskl - INFO - Epoch [28][2000/3746] lr: 9.191e-02, eta: 3 days, 20:45:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5119, loss_cls: 4.0174, loss: 4.0174 +2024-07-23 00:27:53,632 - pyskl - INFO - Epoch [28][2100/3746] lr: 9.190e-02, eta: 3 days, 20:43:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5092, loss_cls: 4.0358, loss: 4.0358 +2024-07-23 00:29:03,455 - pyskl - INFO - Epoch [28][2200/3746] lr: 9.188e-02, eta: 3 days, 20:42:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5267, loss_cls: 3.9854, loss: 3.9854 +2024-07-23 00:30:13,188 - pyskl - INFO - Epoch [28][2300/3746] lr: 9.187e-02, eta: 3 days, 20:41:02, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5189, loss_cls: 4.0017, loss: 4.0017 +2024-07-23 00:31:23,278 - pyskl - INFO - Epoch [28][2400/3746] lr: 9.185e-02, eta: 3 days, 20:39:37, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5088, loss_cls: 4.0435, loss: 4.0435 +2024-07-23 00:32:33,093 - pyskl - INFO - Epoch [28][2500/3746] lr: 9.184e-02, eta: 3 days, 20:38:11, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5173, loss_cls: 3.9930, loss: 3.9930 +2024-07-23 00:33:42,713 - pyskl - INFO - Epoch [28][2600/3746] lr: 9.182e-02, eta: 3 days, 20:36:45, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5083, loss_cls: 4.0267, loss: 4.0267 +2024-07-23 00:34:52,495 - pyskl - INFO - Epoch [28][2700/3746] lr: 9.181e-02, eta: 3 days, 20:35:19, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5186, loss_cls: 3.9969, loss: 3.9969 +2024-07-23 00:36:02,485 - pyskl - INFO - Epoch [28][2800/3746] lr: 9.179e-02, eta: 3 days, 20:33:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5162, loss_cls: 4.0157, loss: 4.0157 +2024-07-23 00:37:12,501 - pyskl - INFO - Epoch [28][2900/3746] lr: 9.178e-02, eta: 3 days, 20:32:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5139, loss_cls: 4.0149, loss: 4.0149 +2024-07-23 00:38:22,520 - pyskl - INFO - Epoch [28][3000/3746] lr: 9.176e-02, eta: 3 days, 20:31:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5203, loss_cls: 3.9900, loss: 3.9900 +2024-07-23 00:39:32,591 - pyskl - INFO - Epoch [28][3100/3746] lr: 9.175e-02, eta: 3 days, 20:29:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5194, loss_cls: 4.0169, loss: 4.0169 +2024-07-23 00:40:42,806 - pyskl - INFO - Epoch [28][3200/3746] lr: 9.173e-02, eta: 3 days, 20:28:16, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5231, loss_cls: 3.9705, loss: 3.9705 +2024-07-23 00:41:53,048 - pyskl - INFO - Epoch [28][3300/3746] lr: 9.172e-02, eta: 3 days, 20:26:52, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5134, loss_cls: 4.0427, loss: 4.0427 +2024-07-23 00:43:02,982 - pyskl - INFO - Epoch [28][3400/3746] lr: 9.170e-02, eta: 3 days, 20:25:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5306, loss_cls: 3.9719, loss: 3.9719 +2024-07-23 00:44:13,066 - pyskl - INFO - Epoch [28][3500/3746] lr: 9.168e-02, eta: 3 days, 20:24:02, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5125, loss_cls: 4.0488, loss: 4.0488 +2024-07-23 00:45:23,551 - pyskl - INFO - Epoch [28][3600/3746] lr: 9.167e-02, eta: 3 days, 20:22:40, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5222, loss_cls: 3.9949, loss: 3.9949 +2024-07-23 00:46:33,575 - pyskl - INFO - Epoch [28][3700/3746] lr: 9.165e-02, eta: 3 days, 20:21:15, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5236, loss_cls: 3.9959, loss: 3.9959 +2024-07-23 00:47:07,996 - pyskl - INFO - Saving checkpoint at 28 epochs +2024-07-23 00:48:59,470 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 00:49:00,183 - pyskl - INFO - +top1_acc 0.2027 +top5_acc 0.4429 +2024-07-23 00:49:00,183 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 00:49:00,224 - pyskl - INFO - +mean_acc 0.2025 +2024-07-23 00:49:00,228 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_24.pth was removed +2024-07-23 00:49:00,468 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_28.pth. +2024-07-23 00:49:00,468 - pyskl - INFO - Best top1_acc is 0.2027 at 28 epoch. +2024-07-23 00:49:00,480 - pyskl - INFO - Epoch(val) [28][309] top1_acc: 0.2027, top5_acc: 0.4429, mean_class_accuracy: 0.2025 +2024-07-23 00:52:21,099 - pyskl - INFO - Epoch [29][100/3746] lr: 9.163e-02, eta: 3 days, 20:26:20, time: 2.006, data_time: 1.305, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5336, loss_cls: 3.9492, loss: 3.9492 +2024-07-23 00:53:31,452 - pyskl - INFO - Epoch [29][200/3746] lr: 9.162e-02, eta: 3 days, 20:24:56, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5222, loss_cls: 3.9932, loss: 3.9932 +2024-07-23 00:54:42,104 - pyskl - INFO - Epoch [29][300/3746] lr: 9.160e-02, eta: 3 days, 20:23:34, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5234, loss_cls: 3.9674, loss: 3.9674 +2024-07-23 00:55:52,452 - pyskl - INFO - Epoch [29][400/3746] lr: 9.158e-02, eta: 3 days, 20:22:10, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5167, loss_cls: 4.0022, loss: 4.0022 +2024-07-23 00:57:02,888 - pyskl - INFO - Epoch [29][500/3746] lr: 9.157e-02, eta: 3 days, 20:20:47, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5281, loss_cls: 3.9571, loss: 3.9571 +2024-07-23 00:58:13,139 - pyskl - INFO - Epoch [29][600/3746] lr: 9.155e-02, eta: 3 days, 20:19:23, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5219, loss_cls: 4.0043, loss: 4.0043 +2024-07-23 00:59:23,174 - pyskl - INFO - Epoch [29][700/3746] lr: 9.154e-02, eta: 3 days, 20:17:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5297, loss_cls: 3.9657, loss: 3.9657 +2024-07-23 01:00:33,111 - pyskl - INFO - Epoch [29][800/3746] lr: 9.152e-02, eta: 3 days, 20:16:33, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5238, loss_cls: 3.9792, loss: 3.9792 +2024-07-23 01:01:42,911 - pyskl - INFO - Epoch [29][900/3746] lr: 9.151e-02, eta: 3 days, 20:15:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5145, loss_cls: 4.0285, loss: 4.0285 +2024-07-23 01:02:52,785 - pyskl - INFO - Epoch [29][1000/3746] lr: 9.149e-02, eta: 3 days, 20:13:42, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5148, loss_cls: 4.0196, loss: 4.0196 +2024-07-23 01:04:02,690 - pyskl - INFO - Epoch [29][1100/3746] lr: 9.148e-02, eta: 3 days, 20:12:16, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5244, loss_cls: 3.9917, loss: 3.9917 +2024-07-23 01:05:12,782 - pyskl - INFO - Epoch [29][1200/3746] lr: 9.146e-02, eta: 3 days, 20:10:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5123, loss_cls: 4.0214, loss: 4.0214 +2024-07-23 01:06:22,679 - pyskl - INFO - Epoch [29][1300/3746] lr: 9.144e-02, eta: 3 days, 20:09:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5161, loss_cls: 4.0272, loss: 4.0272 +2024-07-23 01:07:32,329 - pyskl - INFO - Epoch [29][1400/3746] lr: 9.143e-02, eta: 3 days, 20:08:00, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5256, loss_cls: 3.9820, loss: 3.9820 +2024-07-23 01:08:42,234 - pyskl - INFO - Epoch [29][1500/3746] lr: 9.141e-02, eta: 3 days, 20:06:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5248, loss_cls: 3.9960, loss: 3.9960 +2024-07-23 01:09:52,150 - pyskl - INFO - Epoch [29][1600/3746] lr: 9.140e-02, eta: 3 days, 20:05:10, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5227, loss_cls: 3.9878, loss: 3.9878 +2024-07-23 01:11:02,010 - pyskl - INFO - Epoch [29][1700/3746] lr: 9.138e-02, eta: 3 days, 20:03:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5150, loss_cls: 4.0150, loss: 4.0150 +2024-07-23 01:12:12,272 - pyskl - INFO - Epoch [29][1800/3746] lr: 9.137e-02, eta: 3 days, 20:02:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5147, loss_cls: 4.0214, loss: 4.0214 +2024-07-23 01:13:22,023 - pyskl - INFO - Epoch [29][1900/3746] lr: 9.135e-02, eta: 3 days, 20:00:55, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5244, loss_cls: 3.9729, loss: 3.9729 +2024-07-23 01:14:31,774 - pyskl - INFO - Epoch [29][2000/3746] lr: 9.133e-02, eta: 3 days, 19:59:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5167, loss_cls: 4.0059, loss: 4.0059 +2024-07-23 01:15:41,646 - pyskl - INFO - Epoch [29][2100/3746] lr: 9.132e-02, eta: 3 days, 19:58:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5261, loss_cls: 3.9910, loss: 3.9910 +2024-07-23 01:16:51,747 - pyskl - INFO - Epoch [29][2200/3746] lr: 9.130e-02, eta: 3 days, 19:56:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5177, loss_cls: 4.0032, loss: 4.0032 +2024-07-23 01:18:01,825 - pyskl - INFO - Epoch [29][2300/3746] lr: 9.129e-02, eta: 3 days, 19:55:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5225, loss_cls: 4.0076, loss: 4.0076 +2024-07-23 01:19:11,571 - pyskl - INFO - Epoch [29][2400/3746] lr: 9.127e-02, eta: 3 days, 19:53:50, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5194, loss_cls: 4.0093, loss: 4.0093 +2024-07-23 01:20:21,596 - pyskl - INFO - Epoch [29][2500/3746] lr: 9.126e-02, eta: 3 days, 19:52:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5191, loss_cls: 3.9678, loss: 3.9678 +2024-07-23 01:21:31,310 - pyskl - INFO - Epoch [29][2600/3746] lr: 9.124e-02, eta: 3 days, 19:51:00, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5242, loss_cls: 3.9898, loss: 3.9898 +2024-07-23 01:22:41,104 - pyskl - INFO - Epoch [29][2700/3746] lr: 9.122e-02, eta: 3 days, 19:49:35, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5077, loss_cls: 4.0479, loss: 4.0479 +2024-07-23 01:23:50,914 - pyskl - INFO - Epoch [29][2800/3746] lr: 9.121e-02, eta: 3 days, 19:48:10, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5133, loss_cls: 4.0170, loss: 4.0170 +2024-07-23 01:25:00,949 - pyskl - INFO - Epoch [29][2900/3746] lr: 9.119e-02, eta: 3 days, 19:46:46, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5181, loss_cls: 4.0020, loss: 4.0020 +2024-07-23 01:26:10,573 - pyskl - INFO - Epoch [29][3000/3746] lr: 9.118e-02, eta: 3 days, 19:45:20, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5194, loss_cls: 3.9895, loss: 3.9895 +2024-07-23 01:27:20,315 - pyskl - INFO - Epoch [29][3100/3746] lr: 9.116e-02, eta: 3 days, 19:43:54, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5100, loss_cls: 4.0383, loss: 4.0383 +2024-07-23 01:28:30,360 - pyskl - INFO - Epoch [29][3200/3746] lr: 9.114e-02, eta: 3 days, 19:42:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5188, loss_cls: 3.9819, loss: 3.9819 +2024-07-23 01:29:40,412 - pyskl - INFO - Epoch [29][3300/3746] lr: 9.113e-02, eta: 3 days, 19:41:06, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5181, loss_cls: 4.0120, loss: 4.0120 +2024-07-23 01:30:50,287 - pyskl - INFO - Epoch [29][3400/3746] lr: 9.111e-02, eta: 3 days, 19:39:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5159, loss_cls: 4.0322, loss: 4.0322 +2024-07-23 01:32:00,273 - pyskl - INFO - Epoch [29][3500/3746] lr: 9.110e-02, eta: 3 days, 19:38:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5247, loss_cls: 3.9851, loss: 3.9851 +2024-07-23 01:33:10,467 - pyskl - INFO - Epoch [29][3600/3746] lr: 9.108e-02, eta: 3 days, 19:36:54, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5114, loss_cls: 4.0342, loss: 4.0342 +2024-07-23 01:34:20,627 - pyskl - INFO - Epoch [29][3700/3746] lr: 9.106e-02, eta: 3 days, 19:35:30, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5167, loss_cls: 4.0164, loss: 4.0164 +2024-07-23 01:34:55,141 - pyskl - INFO - Saving checkpoint at 29 epochs +2024-07-23 01:36:46,107 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 01:36:46,769 - pyskl - INFO - +top1_acc 0.1886 +top5_acc 0.4159 +2024-07-23 01:36:46,769 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 01:36:46,810 - pyskl - INFO - +mean_acc 0.1886 +2024-07-23 01:36:46,822 - pyskl - INFO - Epoch(val) [29][309] top1_acc: 0.1886, top5_acc: 0.4159, mean_class_accuracy: 0.1886 +2024-07-23 01:40:23,357 - pyskl - INFO - Epoch [30][100/3746] lr: 9.104e-02, eta: 3 days, 19:41:24, time: 2.165, data_time: 1.346, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5241, loss_cls: 3.9573, loss: 3.9573 +2024-07-23 01:41:44,726 - pyskl - INFO - Epoch [30][200/3746] lr: 9.103e-02, eta: 3 days, 19:40:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5262, loss_cls: 3.9537, loss: 3.9537 +2024-07-23 01:43:06,707 - pyskl - INFO - Epoch [30][300/3746] lr: 9.101e-02, eta: 3 days, 19:40:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5364, loss_cls: 3.9084, loss: 3.9084 +2024-07-23 01:44:27,656 - pyskl - INFO - Epoch [30][400/3746] lr: 9.099e-02, eta: 3 days, 19:39:32, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5327, loss_cls: 3.9462, loss: 3.9462 +2024-07-23 01:45:48,912 - pyskl - INFO - Epoch [30][500/3746] lr: 9.098e-02, eta: 3 days, 19:38:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5114, loss_cls: 4.0244, loss: 4.0244 +2024-07-23 01:47:10,762 - pyskl - INFO - Epoch [30][600/3746] lr: 9.096e-02, eta: 3 days, 19:38:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5212, loss_cls: 3.9604, loss: 3.9604 +2024-07-23 01:48:32,711 - pyskl - INFO - Epoch [30][700/3746] lr: 9.095e-02, eta: 3 days, 19:37:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5277, loss_cls: 3.9764, loss: 3.9764 +2024-07-23 01:49:54,412 - pyskl - INFO - Epoch [30][800/3746] lr: 9.093e-02, eta: 3 days, 19:37:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5150, loss_cls: 4.0280, loss: 4.0280 +2024-07-23 01:51:15,585 - pyskl - INFO - Epoch [30][900/3746] lr: 9.091e-02, eta: 3 days, 19:36:28, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5128, loss_cls: 4.0083, loss: 4.0083 +2024-07-23 01:52:36,954 - pyskl - INFO - Epoch [30][1000/3746] lr: 9.090e-02, eta: 3 days, 19:35:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5172, loss_cls: 3.9923, loss: 3.9923 +2024-07-23 01:53:58,075 - pyskl - INFO - Epoch [30][1100/3746] lr: 9.088e-02, eta: 3 days, 19:35:11, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5219, loss_cls: 3.9605, loss: 3.9605 +2024-07-23 01:55:18,630 - pyskl - INFO - Epoch [30][1200/3746] lr: 9.087e-02, eta: 3 days, 19:34:29, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5170, loss_cls: 4.0215, loss: 4.0215 +2024-07-23 01:56:39,147 - pyskl - INFO - Epoch [30][1300/3746] lr: 9.085e-02, eta: 3 days, 19:33:48, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5203, loss_cls: 4.0088, loss: 4.0088 +2024-07-23 01:57:59,417 - pyskl - INFO - Epoch [30][1400/3746] lr: 9.083e-02, eta: 3 days, 19:33:05, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5186, loss_cls: 3.9838, loss: 3.9838 +2024-07-23 01:59:19,376 - pyskl - INFO - Epoch [30][1500/3746] lr: 9.082e-02, eta: 3 days, 19:32:21, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5150, loss_cls: 4.0030, loss: 4.0030 +2024-07-23 02:00:39,840 - pyskl - INFO - Epoch [30][1600/3746] lr: 9.080e-02, eta: 3 days, 19:31:39, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5220, loss_cls: 3.9544, loss: 3.9544 +2024-07-23 02:02:00,369 - pyskl - INFO - Epoch [30][1700/3746] lr: 9.078e-02, eta: 3 days, 19:30:57, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5148, loss_cls: 4.0286, loss: 4.0286 +2024-07-23 02:03:20,350 - pyskl - INFO - Epoch [30][1800/3746] lr: 9.077e-02, eta: 3 days, 19:30:12, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5295, loss_cls: 3.9576, loss: 3.9576 +2024-07-23 02:04:41,050 - pyskl - INFO - Epoch [30][1900/3746] lr: 9.075e-02, eta: 3 days, 19:29:31, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5208, loss_cls: 4.0134, loss: 4.0134 +2024-07-23 02:06:01,409 - pyskl - INFO - Epoch [30][2000/3746] lr: 9.074e-02, eta: 3 days, 19:28:48, time: 0.804, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5145, loss_cls: 4.0496, loss: 4.0496 +2024-07-23 02:07:21,434 - pyskl - INFO - Epoch [30][2100/3746] lr: 9.072e-02, eta: 3 days, 19:28:04, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5225, loss_cls: 3.9692, loss: 3.9692 +2024-07-23 02:08:41,006 - pyskl - INFO - Epoch [30][2200/3746] lr: 9.070e-02, eta: 3 days, 19:27:18, time: 0.796, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5127, loss_cls: 4.0191, loss: 4.0191 +2024-07-23 02:10:01,163 - pyskl - INFO - Epoch [30][2300/3746] lr: 9.069e-02, eta: 3 days, 19:26:34, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5297, loss_cls: 3.9827, loss: 3.9827 +2024-07-23 02:11:20,995 - pyskl - INFO - Epoch [30][2400/3746] lr: 9.067e-02, eta: 3 days, 19:25:49, time: 0.798, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5161, loss_cls: 4.0413, loss: 4.0413 +2024-07-23 02:12:41,840 - pyskl - INFO - Epoch [30][2500/3746] lr: 9.065e-02, eta: 3 days, 19:25:07, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5305, loss_cls: 3.9724, loss: 3.9724 +2024-07-23 02:14:02,264 - pyskl - INFO - Epoch [30][2600/3746] lr: 9.064e-02, eta: 3 days, 19:24:24, time: 0.804, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5259, loss_cls: 3.9585, loss: 3.9585 +2024-07-23 02:15:22,436 - pyskl - INFO - Epoch [30][2700/3746] lr: 9.062e-02, eta: 3 days, 19:23:40, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5259, loss_cls: 3.9913, loss: 3.9913 +2024-07-23 02:16:42,151 - pyskl - INFO - Epoch [30][2800/3746] lr: 9.061e-02, eta: 3 days, 19:22:54, time: 0.797, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5252, loss_cls: 3.9694, loss: 3.9694 +2024-07-23 02:18:02,417 - pyskl - INFO - Epoch [30][2900/3746] lr: 9.059e-02, eta: 3 days, 19:22:11, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5130, loss_cls: 4.0528, loss: 4.0528 +2024-07-23 02:19:22,132 - pyskl - INFO - Epoch [30][3000/3746] lr: 9.057e-02, eta: 3 days, 19:21:24, time: 0.797, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5194, loss_cls: 3.9971, loss: 3.9971 +2024-07-23 02:20:42,243 - pyskl - INFO - Epoch [30][3100/3746] lr: 9.056e-02, eta: 3 days, 19:20:40, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5208, loss_cls: 3.9820, loss: 3.9820 +2024-07-23 02:22:01,865 - pyskl - INFO - Epoch [30][3200/3746] lr: 9.054e-02, eta: 3 days, 19:19:53, time: 0.796, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5220, loss_cls: 4.0087, loss: 4.0087 +2024-07-23 02:23:21,975 - pyskl - INFO - Epoch [30][3300/3746] lr: 9.052e-02, eta: 3 days, 19:19:09, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5200, loss_cls: 3.9821, loss: 3.9821 +2024-07-23 02:24:41,621 - pyskl - INFO - Epoch [30][3400/3746] lr: 9.051e-02, eta: 3 days, 19:18:22, time: 0.796, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5234, loss_cls: 4.0090, loss: 4.0090 +2024-07-23 02:26:02,051 - pyskl - INFO - Epoch [30][3500/3746] lr: 9.049e-02, eta: 3 days, 19:17:38, time: 0.804, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5173, loss_cls: 3.9953, loss: 3.9953 +2024-07-23 02:27:22,904 - pyskl - INFO - Epoch [30][3600/3746] lr: 9.047e-02, eta: 3 days, 19:16:57, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5233, loss_cls: 4.0064, loss: 4.0064 +2024-07-23 02:28:43,516 - pyskl - INFO - Epoch [30][3700/3746] lr: 9.046e-02, eta: 3 days, 19:16:14, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5202, loss_cls: 3.9826, loss: 3.9826 +2024-07-23 02:29:22,937 - pyskl - INFO - Saving checkpoint at 30 epochs +2024-07-23 02:31:16,109 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 02:31:16,795 - pyskl - INFO - +top1_acc 0.2008 +top5_acc 0.4336 +2024-07-23 02:31:16,796 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 02:31:16,845 - pyskl - INFO - +mean_acc 0.2007 +2024-07-23 02:31:16,860 - pyskl - INFO - Epoch(val) [30][309] top1_acc: 0.2008, top5_acc: 0.4336, mean_class_accuracy: 0.2007 +2024-07-23 02:35:10,992 - pyskl - INFO - Epoch [31][100/3746] lr: 9.043e-02, eta: 3 days, 19:22:56, time: 2.341, data_time: 1.357, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5303, loss_cls: 4.1632, loss: 4.1632 +2024-07-23 02:36:34,066 - pyskl - INFO - Epoch [31][200/3746] lr: 9.042e-02, eta: 3 days, 19:22:22, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5216, loss_cls: 4.2206, loss: 4.2206 +2024-07-23 02:37:56,645 - pyskl - INFO - Epoch [31][300/3746] lr: 9.040e-02, eta: 3 days, 19:21:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5344, loss_cls: 4.1996, loss: 4.1996 +2024-07-23 02:39:19,270 - pyskl - INFO - Epoch [31][400/3746] lr: 9.039e-02, eta: 3 days, 19:21:11, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5230, loss_cls: 4.1856, loss: 4.1856 +2024-07-23 02:40:42,292 - pyskl - INFO - Epoch [31][500/3746] lr: 9.037e-02, eta: 3 days, 19:20:36, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5178, loss_cls: 4.2280, loss: 4.2280 +2024-07-23 02:42:05,283 - pyskl - INFO - Epoch [31][600/3746] lr: 9.035e-02, eta: 3 days, 19:20:02, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5223, loss_cls: 4.2108, loss: 4.2108 +2024-07-23 02:43:28,330 - pyskl - INFO - Epoch [31][700/3746] lr: 9.034e-02, eta: 3 days, 19:19:27, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5097, loss_cls: 4.2528, loss: 4.2528 +2024-07-23 02:44:50,514 - pyskl - INFO - Epoch [31][800/3746] lr: 9.032e-02, eta: 3 days, 19:18:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5208, loss_cls: 4.2122, loss: 4.2122 +2024-07-23 02:46:13,195 - pyskl - INFO - Epoch [31][900/3746] lr: 9.030e-02, eta: 3 days, 19:18:14, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5142, loss_cls: 4.2054, loss: 4.2054 +2024-07-23 02:47:35,517 - pyskl - INFO - Epoch [31][1000/3746] lr: 9.029e-02, eta: 3 days, 19:17:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5234, loss_cls: 4.2027, loss: 4.2027 +2024-07-23 02:48:58,023 - pyskl - INFO - Epoch [31][1100/3746] lr: 9.027e-02, eta: 3 days, 19:16:59, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5264, loss_cls: 4.2079, loss: 4.2079 +2024-07-23 02:50:20,107 - pyskl - INFO - Epoch [31][1200/3746] lr: 9.025e-02, eta: 3 days, 19:16:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5162, loss_cls: 4.2157, loss: 4.2157 +2024-07-23 02:51:42,638 - pyskl - INFO - Epoch [31][1300/3746] lr: 9.024e-02, eta: 3 days, 19:15:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5205, loss_cls: 4.1895, loss: 4.1895 +2024-07-23 02:53:04,128 - pyskl - INFO - Epoch [31][1400/3746] lr: 9.022e-02, eta: 3 days, 19:15:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5273, loss_cls: 4.1796, loss: 4.1796 +2024-07-23 02:54:26,411 - pyskl - INFO - Epoch [31][1500/3746] lr: 9.020e-02, eta: 3 days, 19:14:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5247, loss_cls: 4.2127, loss: 4.2127 +2024-07-23 02:55:48,827 - pyskl - INFO - Epoch [31][1600/3746] lr: 9.019e-02, eta: 3 days, 19:13:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5211, loss_cls: 4.2291, loss: 4.2291 +2024-07-23 02:57:11,479 - pyskl - INFO - Epoch [31][1700/3746] lr: 9.017e-02, eta: 3 days, 19:13:11, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5270, loss_cls: 4.2039, loss: 4.2039 +2024-07-23 02:58:34,309 - pyskl - INFO - Epoch [31][1800/3746] lr: 9.015e-02, eta: 3 days, 19:12:35, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5211, loss_cls: 4.2071, loss: 4.2071 +2024-07-23 02:59:56,947 - pyskl - INFO - Epoch [31][1900/3746] lr: 9.014e-02, eta: 3 days, 19:11:58, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5188, loss_cls: 4.2236, loss: 4.2236 +2024-07-23 03:01:18,888 - pyskl - INFO - Epoch [31][2000/3746] lr: 9.012e-02, eta: 3 days, 19:11:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5292, loss_cls: 4.1893, loss: 4.1893 +2024-07-23 03:02:41,644 - pyskl - INFO - Epoch [31][2100/3746] lr: 9.010e-02, eta: 3 days, 19:10:41, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5334, loss_cls: 4.1708, loss: 4.1708 +2024-07-23 03:04:05,096 - pyskl - INFO - Epoch [31][2200/3746] lr: 9.009e-02, eta: 3 days, 19:10:07, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5269, loss_cls: 4.2035, loss: 4.2035 +2024-07-23 03:05:27,494 - pyskl - INFO - Epoch [31][2300/3746] lr: 9.007e-02, eta: 3 days, 19:09:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5214, loss_cls: 4.2041, loss: 4.2041 +2024-07-23 03:06:49,685 - pyskl - INFO - Epoch [31][2400/3746] lr: 9.005e-02, eta: 3 days, 19:08:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5069, loss_cls: 4.2720, loss: 4.2720 +2024-07-23 03:08:12,026 - pyskl - INFO - Epoch [31][2500/3746] lr: 9.004e-02, eta: 3 days, 19:08:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5114, loss_cls: 4.2366, loss: 4.2366 +2024-07-23 03:09:34,175 - pyskl - INFO - Epoch [31][2600/3746] lr: 9.002e-02, eta: 3 days, 19:07:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5245, loss_cls: 4.2007, loss: 4.2007 +2024-07-23 03:10:57,153 - pyskl - INFO - Epoch [31][2700/3746] lr: 9.000e-02, eta: 3 days, 19:06:56, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5320, loss_cls: 4.1635, loss: 4.1635 +2024-07-23 03:12:19,038 - pyskl - INFO - Epoch [31][2800/3746] lr: 8.999e-02, eta: 3 days, 19:06:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5200, loss_cls: 4.2247, loss: 4.2247 +2024-07-23 03:13:40,345 - pyskl - INFO - Epoch [31][2900/3746] lr: 8.997e-02, eta: 3 days, 19:05:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5191, loss_cls: 4.2187, loss: 4.2187 +2024-07-23 03:15:02,022 - pyskl - INFO - Epoch [31][3000/3746] lr: 8.995e-02, eta: 3 days, 19:04:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5150, loss_cls: 4.2470, loss: 4.2470 +2024-07-23 03:16:24,235 - pyskl - INFO - Epoch [31][3100/3746] lr: 8.994e-02, eta: 3 days, 19:04:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5142, loss_cls: 4.2094, loss: 4.2094 +2024-07-23 03:17:45,803 - pyskl - INFO - Epoch [31][3200/3746] lr: 8.992e-02, eta: 3 days, 19:03:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5250, loss_cls: 4.2156, loss: 4.2156 +2024-07-23 03:19:07,769 - pyskl - INFO - Epoch [31][3300/3746] lr: 8.990e-02, eta: 3 days, 19:02:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5262, loss_cls: 4.2028, loss: 4.2028 +2024-07-23 03:20:29,926 - pyskl - INFO - Epoch [31][3400/3746] lr: 8.989e-02, eta: 3 days, 19:02:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5238, loss_cls: 4.2409, loss: 4.2409 +2024-07-23 03:21:52,368 - pyskl - INFO - Epoch [31][3500/3746] lr: 8.987e-02, eta: 3 days, 19:01:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5161, loss_cls: 4.2273, loss: 4.2273 +2024-07-23 03:23:14,039 - pyskl - INFO - Epoch [31][3600/3746] lr: 8.985e-02, eta: 3 days, 19:00:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5192, loss_cls: 4.2323, loss: 4.2323 +2024-07-23 03:24:36,301 - pyskl - INFO - Epoch [31][3700/3746] lr: 8.983e-02, eta: 3 days, 19:00:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5147, loss_cls: 4.2370, loss: 4.2370 +2024-07-23 03:25:15,570 - pyskl - INFO - Saving checkpoint at 31 epochs +2024-07-23 03:27:08,447 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 03:27:09,204 - pyskl - INFO - +top1_acc 0.1963 +top5_acc 0.4347 +2024-07-23 03:27:09,204 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 03:27:09,255 - pyskl - INFO - +mean_acc 0.1963 +2024-07-23 03:27:09,270 - pyskl - INFO - Epoch(val) [31][309] top1_acc: 0.1963, top5_acc: 0.4347, mean_class_accuracy: 0.1963 +2024-07-23 03:31:01,523 - pyskl - INFO - Epoch [32][100/3746] lr: 8.981e-02, eta: 3 days, 19:06:20, time: 2.322, data_time: 1.331, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5172, loss_cls: 4.2063, loss: 4.2063 +2024-07-23 03:32:25,209 - pyskl - INFO - Epoch [32][200/3746] lr: 8.979e-02, eta: 3 days, 19:05:45, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5316, loss_cls: 4.1751, loss: 4.1751 +2024-07-23 03:33:48,072 - pyskl - INFO - Epoch [32][300/3746] lr: 8.978e-02, eta: 3 days, 19:05:07, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5356, loss_cls: 4.1735, loss: 4.1735 +2024-07-23 03:35:10,615 - pyskl - INFO - Epoch [32][400/3746] lr: 8.976e-02, eta: 3 days, 19:04:28, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5188, loss_cls: 4.2054, loss: 4.2054 +2024-07-23 03:36:33,896 - pyskl - INFO - Epoch [32][500/3746] lr: 8.974e-02, eta: 3 days, 19:03:51, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5267, loss_cls: 4.1807, loss: 4.1807 +2024-07-23 03:37:57,254 - pyskl - INFO - Epoch [32][600/3746] lr: 8.973e-02, eta: 3 days, 19:03:14, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5264, loss_cls: 4.1715, loss: 4.1715 +2024-07-23 03:39:20,290 - pyskl - INFO - Epoch [32][700/3746] lr: 8.971e-02, eta: 3 days, 19:02:37, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5228, loss_cls: 4.1986, loss: 4.1986 +2024-07-23 03:40:43,687 - pyskl - INFO - Epoch [32][800/3746] lr: 8.969e-02, eta: 3 days, 19:02:00, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5236, loss_cls: 4.2026, loss: 4.2026 +2024-07-23 03:42:06,760 - pyskl - INFO - Epoch [32][900/3746] lr: 8.967e-02, eta: 3 days, 19:01:22, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5308, loss_cls: 4.1678, loss: 4.1678 +2024-07-23 03:43:29,284 - pyskl - INFO - Epoch [32][1000/3746] lr: 8.966e-02, eta: 3 days, 19:00:42, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5211, loss_cls: 4.1967, loss: 4.1967 +2024-07-23 03:44:51,930 - pyskl - INFO - Epoch [32][1100/3746] lr: 8.964e-02, eta: 3 days, 19:00:03, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5231, loss_cls: 4.1947, loss: 4.1947 +2024-07-23 03:46:13,675 - pyskl - INFO - Epoch [32][1200/3746] lr: 8.962e-02, eta: 3 days, 18:59:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5262, loss_cls: 4.2056, loss: 4.2056 +2024-07-23 03:47:35,308 - pyskl - INFO - Epoch [32][1300/3746] lr: 8.961e-02, eta: 3 days, 18:58:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5181, loss_cls: 4.2086, loss: 4.2086 +2024-07-23 03:48:56,865 - pyskl - INFO - Epoch [32][1400/3746] lr: 8.959e-02, eta: 3 days, 18:57:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5164, loss_cls: 4.2443, loss: 4.2443 +2024-07-23 03:50:18,610 - pyskl - INFO - Epoch [32][1500/3746] lr: 8.957e-02, eta: 3 days, 18:57:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5252, loss_cls: 4.1755, loss: 4.1755 +2024-07-23 03:51:40,150 - pyskl - INFO - Epoch [32][1600/3746] lr: 8.955e-02, eta: 3 days, 18:56:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5233, loss_cls: 4.1743, loss: 4.1743 +2024-07-23 03:53:01,849 - pyskl - INFO - Epoch [32][1700/3746] lr: 8.954e-02, eta: 3 days, 18:55:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5261, loss_cls: 4.2318, loss: 4.2318 +2024-07-23 03:54:24,072 - pyskl - INFO - Epoch [32][1800/3746] lr: 8.952e-02, eta: 3 days, 18:54:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5214, loss_cls: 4.2180, loss: 4.2180 +2024-07-23 03:55:45,782 - pyskl - INFO - Epoch [32][1900/3746] lr: 8.950e-02, eta: 3 days, 18:54:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5148, loss_cls: 4.2539, loss: 4.2539 +2024-07-23 03:57:07,676 - pyskl - INFO - Epoch [32][2000/3746] lr: 8.949e-02, eta: 3 days, 18:53:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5216, loss_cls: 4.2253, loss: 4.2253 +2024-07-23 03:58:29,451 - pyskl - INFO - Epoch [32][2100/3746] lr: 8.947e-02, eta: 3 days, 18:52:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5217, loss_cls: 4.2416, loss: 4.2416 +2024-07-23 03:59:51,520 - pyskl - INFO - Epoch [32][2200/3746] lr: 8.945e-02, eta: 3 days, 18:52:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5259, loss_cls: 4.1985, loss: 4.1985 +2024-07-23 04:01:13,179 - pyskl - INFO - Epoch [32][2300/3746] lr: 8.943e-02, eta: 3 days, 18:51:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5241, loss_cls: 4.1743, loss: 4.1743 +2024-07-23 04:02:34,859 - pyskl - INFO - Epoch [32][2400/3746] lr: 8.942e-02, eta: 3 days, 18:50:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5378, loss_cls: 4.1394, loss: 4.1394 +2024-07-23 04:03:56,616 - pyskl - INFO - Epoch [32][2500/3746] lr: 8.940e-02, eta: 3 days, 18:49:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5222, loss_cls: 4.2106, loss: 4.2106 +2024-07-23 04:05:18,324 - pyskl - INFO - Epoch [32][2600/3746] lr: 8.938e-02, eta: 3 days, 18:49:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5273, loss_cls: 4.1807, loss: 4.1807 +2024-07-23 04:06:39,693 - pyskl - INFO - Epoch [32][2700/3746] lr: 8.937e-02, eta: 3 days, 18:48:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5169, loss_cls: 4.2405, loss: 4.2405 +2024-07-23 04:08:01,207 - pyskl - INFO - Epoch [32][2800/3746] lr: 8.935e-02, eta: 3 days, 18:47:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5223, loss_cls: 4.2224, loss: 4.2224 +2024-07-23 04:09:22,880 - pyskl - INFO - Epoch [32][2900/3746] lr: 8.933e-02, eta: 3 days, 18:46:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5227, loss_cls: 4.2256, loss: 4.2256 +2024-07-23 04:10:44,470 - pyskl - INFO - Epoch [32][3000/3746] lr: 8.931e-02, eta: 3 days, 18:46:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5161, loss_cls: 4.2320, loss: 4.2320 +2024-07-23 04:12:06,126 - pyskl - INFO - Epoch [32][3100/3746] lr: 8.930e-02, eta: 3 days, 18:45:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5170, loss_cls: 4.2228, loss: 4.2228 +2024-07-23 04:13:28,360 - pyskl - INFO - Epoch [32][3200/3746] lr: 8.928e-02, eta: 3 days, 18:44:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5208, loss_cls: 4.2107, loss: 4.2107 +2024-07-23 04:14:50,199 - pyskl - INFO - Epoch [32][3300/3746] lr: 8.926e-02, eta: 3 days, 18:43:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5211, loss_cls: 4.2342, loss: 4.2342 +2024-07-23 04:16:11,665 - pyskl - INFO - Epoch [32][3400/3746] lr: 8.924e-02, eta: 3 days, 18:43:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5170, loss_cls: 4.2226, loss: 4.2226 +2024-07-23 04:17:34,233 - pyskl - INFO - Epoch [32][3500/3746] lr: 8.923e-02, eta: 3 days, 18:42:32, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5234, loss_cls: 4.2034, loss: 4.2034 +2024-07-23 04:18:56,613 - pyskl - INFO - Epoch [32][3600/3746] lr: 8.921e-02, eta: 3 days, 18:41:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5217, loss_cls: 4.2181, loss: 4.2181 +2024-07-23 04:20:18,721 - pyskl - INFO - Epoch [32][3700/3746] lr: 8.919e-02, eta: 3 days, 18:41:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5200, loss_cls: 4.1988, loss: 4.1988 +2024-07-23 04:20:58,006 - pyskl - INFO - Saving checkpoint at 32 epochs +2024-07-23 04:22:51,496 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 04:22:52,245 - pyskl - INFO - +top1_acc 0.1809 +top5_acc 0.4129 +2024-07-23 04:22:52,246 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 04:22:52,289 - pyskl - INFO - +mean_acc 0.1807 +2024-07-23 04:22:52,303 - pyskl - INFO - Epoch(val) [32][309] top1_acc: 0.1809, top5_acc: 0.4129, mean_class_accuracy: 0.1807 +2024-07-23 04:26:41,535 - pyskl - INFO - Epoch [33][100/3746] lr: 8.917e-02, eta: 3 days, 18:46:46, time: 2.292, data_time: 1.310, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5308, loss_cls: 4.1844, loss: 4.1844 +2024-07-23 04:28:05,068 - pyskl - INFO - Epoch [33][200/3746] lr: 8.915e-02, eta: 3 days, 18:46:07, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5322, loss_cls: 4.1398, loss: 4.1398 +2024-07-23 04:29:27,014 - pyskl - INFO - Epoch [33][300/3746] lr: 8.913e-02, eta: 3 days, 18:45:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5284, loss_cls: 4.1481, loss: 4.1481 +2024-07-23 04:30:49,761 - pyskl - INFO - Epoch [33][400/3746] lr: 8.912e-02, eta: 3 days, 18:44:41, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5295, loss_cls: 4.1385, loss: 4.1385 +2024-07-23 04:32:12,970 - pyskl - INFO - Epoch [33][500/3746] lr: 8.910e-02, eta: 3 days, 18:44:01, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5291, loss_cls: 4.1587, loss: 4.1587 +2024-07-23 04:33:36,101 - pyskl - INFO - Epoch [33][600/3746] lr: 8.908e-02, eta: 3 days, 18:43:21, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5212, loss_cls: 4.2029, loss: 4.2029 +2024-07-23 04:34:59,188 - pyskl - INFO - Epoch [33][700/3746] lr: 8.906e-02, eta: 3 days, 18:42:40, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5306, loss_cls: 4.1771, loss: 4.1771 +2024-07-23 04:36:22,224 - pyskl - INFO - Epoch [33][800/3746] lr: 8.905e-02, eta: 3 days, 18:41:59, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5211, loss_cls: 4.1941, loss: 4.1941 +2024-07-23 04:37:45,121 - pyskl - INFO - Epoch [33][900/3746] lr: 8.903e-02, eta: 3 days, 18:41:17, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5284, loss_cls: 4.1861, loss: 4.1861 +2024-07-23 04:39:07,817 - pyskl - INFO - Epoch [33][1000/3746] lr: 8.901e-02, eta: 3 days, 18:40:35, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5244, loss_cls: 4.2013, loss: 4.2013 +2024-07-23 04:40:30,784 - pyskl - INFO - Epoch [33][1100/3746] lr: 8.899e-02, eta: 3 days, 18:39:54, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5248, loss_cls: 4.1902, loss: 4.1902 +2024-07-23 04:41:53,219 - pyskl - INFO - Epoch [33][1200/3746] lr: 8.898e-02, eta: 3 days, 18:39:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5336, loss_cls: 4.1368, loss: 4.1368 +2024-07-23 04:43:15,787 - pyskl - INFO - Epoch [33][1300/3746] lr: 8.896e-02, eta: 3 days, 18:38:27, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5173, loss_cls: 4.2008, loss: 4.2008 +2024-07-23 04:44:37,811 - pyskl - INFO - Epoch [33][1400/3746] lr: 8.894e-02, eta: 3 days, 18:37:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5212, loss_cls: 4.2076, loss: 4.2076 +2024-07-23 04:45:59,624 - pyskl - INFO - Epoch [33][1500/3746] lr: 8.892e-02, eta: 3 days, 18:36:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5247, loss_cls: 4.1815, loss: 4.1815 +2024-07-23 04:47:21,244 - pyskl - INFO - Epoch [33][1600/3746] lr: 8.891e-02, eta: 3 days, 18:36:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5164, loss_cls: 4.2251, loss: 4.2251 +2024-07-23 04:48:43,225 - pyskl - INFO - Epoch [33][1700/3746] lr: 8.889e-02, eta: 3 days, 18:35:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5134, loss_cls: 4.2587, loss: 4.2587 +2024-07-23 04:50:05,107 - pyskl - INFO - Epoch [33][1800/3746] lr: 8.887e-02, eta: 3 days, 18:34:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5158, loss_cls: 4.2285, loss: 4.2285 +2024-07-23 04:51:26,823 - pyskl - INFO - Epoch [33][1900/3746] lr: 8.885e-02, eta: 3 days, 18:33:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5273, loss_cls: 4.2033, loss: 4.2033 +2024-07-23 04:52:48,727 - pyskl - INFO - Epoch [33][2000/3746] lr: 8.884e-02, eta: 3 days, 18:33:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5239, loss_cls: 4.1794, loss: 4.1794 +2024-07-23 04:54:10,861 - pyskl - INFO - Epoch [33][2100/3746] lr: 8.882e-02, eta: 3 days, 18:32:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5156, loss_cls: 4.2206, loss: 4.2206 +2024-07-23 04:55:33,018 - pyskl - INFO - Epoch [33][2200/3746] lr: 8.880e-02, eta: 3 days, 18:31:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5267, loss_cls: 4.2203, loss: 4.2203 +2024-07-23 04:56:55,542 - pyskl - INFO - Epoch [33][2300/3746] lr: 8.878e-02, eta: 3 days, 18:30:53, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5103, loss_cls: 4.2489, loss: 4.2489 +2024-07-23 04:58:17,393 - pyskl - INFO - Epoch [33][2400/3746] lr: 8.876e-02, eta: 3 days, 18:30:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5177, loss_cls: 4.2102, loss: 4.2102 +2024-07-23 04:59:38,728 - pyskl - INFO - Epoch [33][2500/3746] lr: 8.875e-02, eta: 3 days, 18:29:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5192, loss_cls: 4.2268, loss: 4.2268 +2024-07-23 05:01:00,324 - pyskl - INFO - Epoch [33][2600/3746] lr: 8.873e-02, eta: 3 days, 18:28:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5181, loss_cls: 4.2197, loss: 4.2197 +2024-07-23 05:02:21,744 - pyskl - INFO - Epoch [33][2700/3746] lr: 8.871e-02, eta: 3 days, 18:27:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5106, loss_cls: 4.2343, loss: 4.2343 +2024-07-23 05:03:43,235 - pyskl - INFO - Epoch [33][2800/3746] lr: 8.869e-02, eta: 3 days, 18:26:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5242, loss_cls: 4.1949, loss: 4.1949 +2024-07-23 05:05:04,531 - pyskl - INFO - Epoch [33][2900/3746] lr: 8.868e-02, eta: 3 days, 18:26:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5311, loss_cls: 4.1779, loss: 4.1779 +2024-07-23 05:06:25,786 - pyskl - INFO - Epoch [33][3000/3746] lr: 8.866e-02, eta: 3 days, 18:25:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5328, loss_cls: 4.1782, loss: 4.1782 +2024-07-23 05:07:47,371 - pyskl - INFO - Epoch [33][3100/3746] lr: 8.864e-02, eta: 3 days, 18:24:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5225, loss_cls: 4.2303, loss: 4.2303 +2024-07-23 05:09:08,523 - pyskl - INFO - Epoch [33][3200/3746] lr: 8.862e-02, eta: 3 days, 18:23:41, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5178, loss_cls: 4.2187, loss: 4.2187 +2024-07-23 05:10:30,179 - pyskl - INFO - Epoch [33][3300/3746] lr: 8.861e-02, eta: 3 days, 18:22:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5303, loss_cls: 4.1715, loss: 4.1715 +2024-07-23 05:11:52,036 - pyskl - INFO - Epoch [33][3400/3746] lr: 8.859e-02, eta: 3 days, 18:22:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5169, loss_cls: 4.1935, loss: 4.1935 +2024-07-23 05:13:13,854 - pyskl - INFO - Epoch [33][3500/3746] lr: 8.857e-02, eta: 3 days, 18:21:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5197, loss_cls: 4.2515, loss: 4.2515 +2024-07-23 05:14:35,656 - pyskl - INFO - Epoch [33][3600/3746] lr: 8.855e-02, eta: 3 days, 18:20:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5164, loss_cls: 4.2101, loss: 4.2101 +2024-07-23 05:15:56,990 - pyskl - INFO - Epoch [33][3700/3746] lr: 8.853e-02, eta: 3 days, 18:19:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5172, loss_cls: 4.1935, loss: 4.1935 +2024-07-23 05:16:36,450 - pyskl - INFO - Saving checkpoint at 33 epochs +2024-07-23 05:18:29,704 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 05:18:30,369 - pyskl - INFO - +top1_acc 0.2197 +top5_acc 0.4547 +2024-07-23 05:18:30,369 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 05:18:30,411 - pyskl - INFO - +mean_acc 0.2193 +2024-07-23 05:18:30,416 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_28.pth was removed +2024-07-23 05:18:30,661 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_33.pth. +2024-07-23 05:18:30,661 - pyskl - INFO - Best top1_acc is 0.2197 at 33 epoch. +2024-07-23 05:18:30,675 - pyskl - INFO - Epoch(val) [33][309] top1_acc: 0.2197, top5_acc: 0.4547, mean_class_accuracy: 0.2193 +2024-07-23 05:22:23,970 - pyskl - INFO - Epoch [34][100/3746] lr: 8.851e-02, eta: 3 days, 18:25:18, time: 2.333, data_time: 1.342, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5228, loss_cls: 4.1661, loss: 4.1661 +2024-07-23 05:23:47,736 - pyskl - INFO - Epoch [34][200/3746] lr: 8.849e-02, eta: 3 days, 18:24:37, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5275, loss_cls: 4.1764, loss: 4.1764 +2024-07-23 05:25:10,358 - pyskl - INFO - Epoch [34][300/3746] lr: 8.847e-02, eta: 3 days, 18:23:53, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5288, loss_cls: 4.1374, loss: 4.1374 +2024-07-23 05:26:33,855 - pyskl - INFO - Epoch [34][400/3746] lr: 8.845e-02, eta: 3 days, 18:23:11, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5156, loss_cls: 4.2337, loss: 4.2337 +2024-07-23 05:27:57,438 - pyskl - INFO - Epoch [34][500/3746] lr: 8.844e-02, eta: 3 days, 18:22:29, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5294, loss_cls: 4.1603, loss: 4.1603 +2024-07-23 05:29:20,604 - pyskl - INFO - Epoch [34][600/3746] lr: 8.842e-02, eta: 3 days, 18:21:46, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5386, loss_cls: 4.1536, loss: 4.1536 +2024-07-23 05:30:43,738 - pyskl - INFO - Epoch [34][700/3746] lr: 8.840e-02, eta: 3 days, 18:21:03, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5253, loss_cls: 4.1919, loss: 4.1919 +2024-07-23 05:32:06,837 - pyskl - INFO - Epoch [34][800/3746] lr: 8.838e-02, eta: 3 days, 18:20:19, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5373, loss_cls: 4.1278, loss: 4.1278 +2024-07-23 05:33:28,955 - pyskl - INFO - Epoch [34][900/3746] lr: 8.836e-02, eta: 3 days, 18:19:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5205, loss_cls: 4.1968, loss: 4.1968 +2024-07-23 05:34:52,018 - pyskl - INFO - Epoch [34][1000/3746] lr: 8.835e-02, eta: 3 days, 18:18:48, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5202, loss_cls: 4.1979, loss: 4.1979 +2024-07-23 05:36:14,380 - pyskl - INFO - Epoch [34][1100/3746] lr: 8.833e-02, eta: 3 days, 18:18:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5228, loss_cls: 4.1752, loss: 4.1752 +2024-07-23 05:37:36,660 - pyskl - INFO - Epoch [34][1200/3746] lr: 8.831e-02, eta: 3 days, 18:17:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5369, loss_cls: 4.1520, loss: 4.1520 +2024-07-23 05:38:59,113 - pyskl - INFO - Epoch [34][1300/3746] lr: 8.829e-02, eta: 3 days, 18:16:29, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5231, loss_cls: 4.2031, loss: 4.2031 +2024-07-23 05:40:21,672 - pyskl - INFO - Epoch [34][1400/3746] lr: 8.828e-02, eta: 3 days, 18:15:44, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5256, loss_cls: 4.1978, loss: 4.1978 +2024-07-23 05:41:44,634 - pyskl - INFO - Epoch [34][1500/3746] lr: 8.826e-02, eta: 3 days, 18:14:59, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5206, loss_cls: 4.1974, loss: 4.1974 +2024-07-23 05:43:06,596 - pyskl - INFO - Epoch [34][1600/3746] lr: 8.824e-02, eta: 3 days, 18:14:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5302, loss_cls: 4.1606, loss: 4.1606 +2024-07-23 05:44:29,230 - pyskl - INFO - Epoch [34][1700/3746] lr: 8.822e-02, eta: 3 days, 18:13:26, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5205, loss_cls: 4.2072, loss: 4.2072 +2024-07-23 05:45:52,267 - pyskl - INFO - Epoch [34][1800/3746] lr: 8.820e-02, eta: 3 days, 18:12:41, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5242, loss_cls: 4.2210, loss: 4.2210 +2024-07-23 05:47:14,734 - pyskl - INFO - Epoch [34][1900/3746] lr: 8.819e-02, eta: 3 days, 18:11:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5231, loss_cls: 4.1936, loss: 4.1936 +2024-07-23 05:48:37,213 - pyskl - INFO - Epoch [34][2000/3746] lr: 8.817e-02, eta: 3 days, 18:11:09, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5267, loss_cls: 4.1954, loss: 4.1954 +2024-07-23 05:49:59,314 - pyskl - INFO - Epoch [34][2100/3746] lr: 8.815e-02, eta: 3 days, 18:10:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5155, loss_cls: 4.2303, loss: 4.2303 +2024-07-23 05:51:21,787 - pyskl - INFO - Epoch [34][2200/3746] lr: 8.813e-02, eta: 3 days, 18:09:34, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5300, loss_cls: 4.1905, loss: 4.1905 +2024-07-23 05:52:44,700 - pyskl - INFO - Epoch [34][2300/3746] lr: 8.811e-02, eta: 3 days, 18:08:49, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5284, loss_cls: 4.1932, loss: 4.1932 +2024-07-23 05:54:07,646 - pyskl - INFO - Epoch [34][2400/3746] lr: 8.809e-02, eta: 3 days, 18:08:04, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5155, loss_cls: 4.2311, loss: 4.2311 +2024-07-23 05:55:30,463 - pyskl - INFO - Epoch [34][2500/3746] lr: 8.808e-02, eta: 3 days, 18:07:19, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5248, loss_cls: 4.1946, loss: 4.1946 +2024-07-23 05:56:53,628 - pyskl - INFO - Epoch [34][2600/3746] lr: 8.806e-02, eta: 3 days, 18:06:34, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5172, loss_cls: 4.2167, loss: 4.2167 +2024-07-23 05:58:16,667 - pyskl - INFO - Epoch [34][2700/3746] lr: 8.804e-02, eta: 3 days, 18:05:49, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5230, loss_cls: 4.1735, loss: 4.1735 +2024-07-23 05:59:39,646 - pyskl - INFO - Epoch [34][2800/3746] lr: 8.802e-02, eta: 3 days, 18:05:04, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5262, loss_cls: 4.1846, loss: 4.1846 +2024-07-23 06:01:02,356 - pyskl - INFO - Epoch [34][2900/3746] lr: 8.800e-02, eta: 3 days, 18:04:18, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5189, loss_cls: 4.2232, loss: 4.2232 +2024-07-23 06:02:25,256 - pyskl - INFO - Epoch [34][3000/3746] lr: 8.799e-02, eta: 3 days, 18:03:33, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5150, loss_cls: 4.2456, loss: 4.2456 +2024-07-23 06:03:48,899 - pyskl - INFO - Epoch [34][3100/3746] lr: 8.797e-02, eta: 3 days, 18:02:50, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5233, loss_cls: 4.2162, loss: 4.2162 +2024-07-23 06:05:10,957 - pyskl - INFO - Epoch [34][3200/3746] lr: 8.795e-02, eta: 3 days, 18:02:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5288, loss_cls: 4.1985, loss: 4.1985 +2024-07-23 06:06:32,957 - pyskl - INFO - Epoch [34][3300/3746] lr: 8.793e-02, eta: 3 days, 18:01:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5325, loss_cls: 4.1863, loss: 4.1863 +2024-07-23 06:07:55,466 - pyskl - INFO - Epoch [34][3400/3746] lr: 8.791e-02, eta: 3 days, 18:00:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5267, loss_cls: 4.1906, loss: 4.1906 +2024-07-23 06:09:17,460 - pyskl - INFO - Epoch [34][3500/3746] lr: 8.789e-02, eta: 3 days, 17:59:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5212, loss_cls: 4.1728, loss: 4.1728 +2024-07-23 06:10:39,794 - pyskl - INFO - Epoch [34][3600/3746] lr: 8.788e-02, eta: 3 days, 17:58:48, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5194, loss_cls: 4.1976, loss: 4.1976 +2024-07-23 06:12:01,379 - pyskl - INFO - Epoch [34][3700/3746] lr: 8.786e-02, eta: 3 days, 17:57:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5323, loss_cls: 4.1604, loss: 4.1604 +2024-07-23 06:12:40,949 - pyskl - INFO - Saving checkpoint at 34 epochs +2024-07-23 06:14:34,975 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 06:14:35,752 - pyskl - INFO - +top1_acc 0.2121 +top5_acc 0.4498 +2024-07-23 06:14:35,753 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 06:14:35,802 - pyskl - INFO - +mean_acc 0.2120 +2024-07-23 06:14:35,814 - pyskl - INFO - Epoch(val) [34][309] top1_acc: 0.2121, top5_acc: 0.4498, mean_class_accuracy: 0.2120 +2024-07-23 06:18:25,168 - pyskl - INFO - Epoch [35][100/3746] lr: 8.783e-02, eta: 3 days, 18:03:00, time: 2.293, data_time: 1.299, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5447, loss_cls: 4.1069, loss: 4.1069 +2024-07-23 06:19:47,457 - pyskl - INFO - Epoch [35][200/3746] lr: 8.781e-02, eta: 3 days, 18:02:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5428, loss_cls: 4.1043, loss: 4.1043 +2024-07-23 06:21:10,038 - pyskl - INFO - Epoch [35][300/3746] lr: 8.780e-02, eta: 3 days, 18:01:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5308, loss_cls: 4.1616, loss: 4.1616 +2024-07-23 06:22:33,718 - pyskl - INFO - Epoch [35][400/3746] lr: 8.778e-02, eta: 3 days, 18:00:40, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5177, loss_cls: 4.2213, loss: 4.2213 +2024-07-23 06:23:57,496 - pyskl - INFO - Epoch [35][500/3746] lr: 8.776e-02, eta: 3 days, 17:59:56, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5312, loss_cls: 4.1679, loss: 4.1679 +2024-07-23 06:25:21,175 - pyskl - INFO - Epoch [35][600/3746] lr: 8.774e-02, eta: 3 days, 17:59:12, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5284, loss_cls: 4.1817, loss: 4.1817 +2024-07-23 06:26:44,479 - pyskl - INFO - Epoch [35][700/3746] lr: 8.772e-02, eta: 3 days, 17:58:27, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5230, loss_cls: 4.2028, loss: 4.2028 +2024-07-23 06:28:08,025 - pyskl - INFO - Epoch [35][800/3746] lr: 8.770e-02, eta: 3 days, 17:57:42, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5323, loss_cls: 4.1654, loss: 4.1654 +2024-07-23 06:29:31,532 - pyskl - INFO - Epoch [35][900/3746] lr: 8.769e-02, eta: 3 days, 17:56:58, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5275, loss_cls: 4.1672, loss: 4.1672 +2024-07-23 06:30:55,272 - pyskl - INFO - Epoch [35][1000/3746] lr: 8.767e-02, eta: 3 days, 17:56:14, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5156, loss_cls: 4.1947, loss: 4.1947 +2024-07-23 06:32:18,232 - pyskl - INFO - Epoch [35][1100/3746] lr: 8.765e-02, eta: 3 days, 17:55:27, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5244, loss_cls: 4.1871, loss: 4.1871 +2024-07-23 06:33:42,441 - pyskl - INFO - Epoch [35][1200/3746] lr: 8.763e-02, eta: 3 days, 17:54:44, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5236, loss_cls: 4.1843, loss: 4.1843 +2024-07-23 06:35:06,147 - pyskl - INFO - Epoch [35][1300/3746] lr: 8.761e-02, eta: 3 days, 17:54:00, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5288, loss_cls: 4.1662, loss: 4.1662 +2024-07-23 06:36:29,423 - pyskl - INFO - Epoch [35][1400/3746] lr: 8.759e-02, eta: 3 days, 17:53:14, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5284, loss_cls: 4.1739, loss: 4.1739 +2024-07-23 06:37:52,692 - pyskl - INFO - Epoch [35][1500/3746] lr: 8.757e-02, eta: 3 days, 17:52:28, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5166, loss_cls: 4.2421, loss: 4.2421 +2024-07-23 06:39:16,327 - pyskl - INFO - Epoch [35][1600/3746] lr: 8.756e-02, eta: 3 days, 17:51:43, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5164, loss_cls: 4.2221, loss: 4.2221 +2024-07-23 06:40:39,676 - pyskl - INFO - Epoch [35][1700/3746] lr: 8.754e-02, eta: 3 days, 17:50:58, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5133, loss_cls: 4.2365, loss: 4.2365 +2024-07-23 06:42:03,292 - pyskl - INFO - Epoch [35][1800/3746] lr: 8.752e-02, eta: 3 days, 17:50:13, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5238, loss_cls: 4.1917, loss: 4.1917 +2024-07-23 06:43:27,088 - pyskl - INFO - Epoch [35][1900/3746] lr: 8.750e-02, eta: 3 days, 17:49:28, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5220, loss_cls: 4.2267, loss: 4.2267 +2024-07-23 06:44:50,491 - pyskl - INFO - Epoch [35][2000/3746] lr: 8.748e-02, eta: 3 days, 17:48:42, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5150, loss_cls: 4.2247, loss: 4.2247 +2024-07-23 06:46:14,262 - pyskl - INFO - Epoch [35][2100/3746] lr: 8.746e-02, eta: 3 days, 17:47:58, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5303, loss_cls: 4.1744, loss: 4.1744 +2024-07-23 06:47:37,970 - pyskl - INFO - Epoch [35][2200/3746] lr: 8.745e-02, eta: 3 days, 17:47:13, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5248, loss_cls: 4.1999, loss: 4.1999 +2024-07-23 06:49:02,033 - pyskl - INFO - Epoch [35][2300/3746] lr: 8.743e-02, eta: 3 days, 17:46:29, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5272, loss_cls: 4.1956, loss: 4.1956 +2024-07-23 06:50:24,855 - pyskl - INFO - Epoch [35][2400/3746] lr: 8.741e-02, eta: 3 days, 17:45:41, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5327, loss_cls: 4.1455, loss: 4.1455 +2024-07-23 06:51:48,538 - pyskl - INFO - Epoch [35][2500/3746] lr: 8.739e-02, eta: 3 days, 17:44:56, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5358, loss_cls: 4.1377, loss: 4.1377 +2024-07-23 06:53:11,806 - pyskl - INFO - Epoch [35][2600/3746] lr: 8.737e-02, eta: 3 days, 17:44:09, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5230, loss_cls: 4.2079, loss: 4.2079 +2024-07-23 06:54:35,385 - pyskl - INFO - Epoch [35][2700/3746] lr: 8.735e-02, eta: 3 days, 17:43:24, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5227, loss_cls: 4.1880, loss: 4.1880 +2024-07-23 06:55:58,865 - pyskl - INFO - Epoch [35][2800/3746] lr: 8.733e-02, eta: 3 days, 17:42:38, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5219, loss_cls: 4.1834, loss: 4.1834 +2024-07-23 06:57:22,639 - pyskl - INFO - Epoch [35][2900/3746] lr: 8.732e-02, eta: 3 days, 17:41:53, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5216, loss_cls: 4.1969, loss: 4.1969 +2024-07-23 06:58:46,365 - pyskl - INFO - Epoch [35][3000/3746] lr: 8.730e-02, eta: 3 days, 17:41:07, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5372, loss_cls: 4.1536, loss: 4.1536 +2024-07-23 07:00:09,645 - pyskl - INFO - Epoch [35][3100/3746] lr: 8.728e-02, eta: 3 days, 17:40:20, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5227, loss_cls: 4.1892, loss: 4.1892 +2024-07-23 07:01:33,371 - pyskl - INFO - Epoch [35][3200/3746] lr: 8.726e-02, eta: 3 days, 17:39:35, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5141, loss_cls: 4.2302, loss: 4.2302 +2024-07-23 07:02:55,532 - pyskl - INFO - Epoch [35][3300/3746] lr: 8.724e-02, eta: 3 days, 17:38:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5130, loss_cls: 4.2369, loss: 4.2369 +2024-07-23 07:04:18,568 - pyskl - INFO - Epoch [35][3400/3746] lr: 8.722e-02, eta: 3 days, 17:37:57, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5317, loss_cls: 4.1727, loss: 4.1727 +2024-07-23 07:05:41,586 - pyskl - INFO - Epoch [35][3500/3746] lr: 8.720e-02, eta: 3 days, 17:37:09, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5222, loss_cls: 4.1758, loss: 4.1758 +2024-07-23 07:07:05,148 - pyskl - INFO - Epoch [35][3600/3746] lr: 8.718e-02, eta: 3 days, 17:36:22, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5127, loss_cls: 4.2534, loss: 4.2534 +2024-07-23 07:08:27,108 - pyskl - INFO - Epoch [35][3700/3746] lr: 8.717e-02, eta: 3 days, 17:35:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5108, loss_cls: 4.2474, loss: 4.2474 +2024-07-23 07:09:06,778 - pyskl - INFO - Saving checkpoint at 35 epochs +2024-07-23 07:10:58,138 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 07:10:58,867 - pyskl - INFO - +top1_acc 0.1903 +top5_acc 0.4186 +2024-07-23 07:10:58,867 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 07:10:58,907 - pyskl - INFO - +mean_acc 0.1903 +2024-07-23 07:10:58,917 - pyskl - INFO - Epoch(val) [35][309] top1_acc: 0.1903, top5_acc: 0.4186, mean_class_accuracy: 0.1903 +2024-07-23 07:14:44,050 - pyskl - INFO - Epoch [36][100/3746] lr: 8.714e-02, eta: 3 days, 17:40:02, time: 2.251, data_time: 1.262, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5336, loss_cls: 4.1671, loss: 4.1671 +2024-07-23 07:16:06,925 - pyskl - INFO - Epoch [36][200/3746] lr: 8.712e-02, eta: 3 days, 17:39:13, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5342, loss_cls: 4.1235, loss: 4.1235 +2024-07-23 07:17:30,607 - pyskl - INFO - Epoch [36][300/3746] lr: 8.710e-02, eta: 3 days, 17:38:27, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5223, loss_cls: 4.1550, loss: 4.1550 +2024-07-23 07:18:54,012 - pyskl - INFO - Epoch [36][400/3746] lr: 8.708e-02, eta: 3 days, 17:37:39, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5366, loss_cls: 4.1369, loss: 4.1369 +2024-07-23 07:20:17,763 - pyskl - INFO - Epoch [36][500/3746] lr: 8.706e-02, eta: 3 days, 17:36:53, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5214, loss_cls: 4.2160, loss: 4.2160 +2024-07-23 07:21:41,737 - pyskl - INFO - Epoch [36][600/3746] lr: 8.704e-02, eta: 3 days, 17:36:07, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5361, loss_cls: 4.1248, loss: 4.1248 +2024-07-23 07:23:05,001 - pyskl - INFO - Epoch [36][700/3746] lr: 8.703e-02, eta: 3 days, 17:35:19, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5217, loss_cls: 4.1818, loss: 4.1818 +2024-07-23 07:24:28,440 - pyskl - INFO - Epoch [36][800/3746] lr: 8.701e-02, eta: 3 days, 17:34:32, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5247, loss_cls: 4.2039, loss: 4.2039 +2024-07-23 07:25:51,861 - pyskl - INFO - Epoch [36][900/3746] lr: 8.699e-02, eta: 3 days, 17:33:44, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5298, loss_cls: 4.1578, loss: 4.1578 +2024-07-23 07:27:15,403 - pyskl - INFO - Epoch [36][1000/3746] lr: 8.697e-02, eta: 3 days, 17:32:57, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5334, loss_cls: 4.1574, loss: 4.1574 +2024-07-23 07:28:38,605 - pyskl - INFO - Epoch [36][1100/3746] lr: 8.695e-02, eta: 3 days, 17:32:09, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5331, loss_cls: 4.1861, loss: 4.1861 +2024-07-23 07:30:02,060 - pyskl - INFO - Epoch [36][1200/3746] lr: 8.693e-02, eta: 3 days, 17:31:21, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5247, loss_cls: 4.1811, loss: 4.1811 +2024-07-23 07:31:25,539 - pyskl - INFO - Epoch [36][1300/3746] lr: 8.691e-02, eta: 3 days, 17:30:34, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5344, loss_cls: 4.1677, loss: 4.1677 +2024-07-23 07:32:49,453 - pyskl - INFO - Epoch [36][1400/3746] lr: 8.689e-02, eta: 3 days, 17:29:47, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5283, loss_cls: 4.1853, loss: 4.1853 +2024-07-23 07:34:13,500 - pyskl - INFO - Epoch [36][1500/3746] lr: 8.688e-02, eta: 3 days, 17:29:01, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5291, loss_cls: 4.1752, loss: 4.1752 +2024-07-23 07:35:37,162 - pyskl - INFO - Epoch [36][1600/3746] lr: 8.686e-02, eta: 3 days, 17:28:14, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5319, loss_cls: 4.1278, loss: 4.1278 +2024-07-23 07:37:00,686 - pyskl - INFO - Epoch [36][1700/3746] lr: 8.684e-02, eta: 3 days, 17:27:27, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5203, loss_cls: 4.2178, loss: 4.2178 +2024-07-23 07:38:24,405 - pyskl - INFO - Epoch [36][1800/3746] lr: 8.682e-02, eta: 3 days, 17:26:40, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5281, loss_cls: 4.1674, loss: 4.1674 +2024-07-23 07:39:47,328 - pyskl - INFO - Epoch [36][1900/3746] lr: 8.680e-02, eta: 3 days, 17:25:50, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5291, loss_cls: 4.1628, loss: 4.1628 +2024-07-23 07:41:10,684 - pyskl - INFO - Epoch [36][2000/3746] lr: 8.678e-02, eta: 3 days, 17:25:02, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5280, loss_cls: 4.1649, loss: 4.1649 +2024-07-23 07:42:34,446 - pyskl - INFO - Epoch [36][2100/3746] lr: 8.676e-02, eta: 3 days, 17:24:14, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5253, loss_cls: 4.1899, loss: 4.1899 +2024-07-23 07:43:58,356 - pyskl - INFO - Epoch [36][2200/3746] lr: 8.674e-02, eta: 3 days, 17:23:28, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5131, loss_cls: 4.2338, loss: 4.2338 +2024-07-23 07:45:21,981 - pyskl - INFO - Epoch [36][2300/3746] lr: 8.672e-02, eta: 3 days, 17:22:40, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5188, loss_cls: 4.2114, loss: 4.2114 +2024-07-23 07:46:45,688 - pyskl - INFO - Epoch [36][2400/3746] lr: 8.671e-02, eta: 3 days, 17:21:53, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5208, loss_cls: 4.2145, loss: 4.2145 +2024-07-23 07:48:09,141 - pyskl - INFO - Epoch [36][2500/3746] lr: 8.669e-02, eta: 3 days, 17:21:04, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5206, loss_cls: 4.1892, loss: 4.1892 +2024-07-23 07:49:32,193 - pyskl - INFO - Epoch [36][2600/3746] lr: 8.667e-02, eta: 3 days, 17:20:15, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5170, loss_cls: 4.2324, loss: 4.2324 +2024-07-23 07:50:55,788 - pyskl - INFO - Epoch [36][2700/3746] lr: 8.665e-02, eta: 3 days, 17:19:27, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5272, loss_cls: 4.1544, loss: 4.1544 +2024-07-23 07:52:19,449 - pyskl - INFO - Epoch [36][2800/3746] lr: 8.663e-02, eta: 3 days, 17:18:39, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5306, loss_cls: 4.1436, loss: 4.1436 +2024-07-23 07:53:42,732 - pyskl - INFO - Epoch [36][2900/3746] lr: 8.661e-02, eta: 3 days, 17:17:50, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5206, loss_cls: 4.2258, loss: 4.2258 +2024-07-23 07:55:06,393 - pyskl - INFO - Epoch [36][3000/3746] lr: 8.659e-02, eta: 3 days, 17:17:02, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5155, loss_cls: 4.2349, loss: 4.2349 +2024-07-23 07:56:29,909 - pyskl - INFO - Epoch [36][3100/3746] lr: 8.657e-02, eta: 3 days, 17:16:13, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5178, loss_cls: 4.2025, loss: 4.2025 +2024-07-23 07:57:53,772 - pyskl - INFO - Epoch [36][3200/3746] lr: 8.655e-02, eta: 3 days, 17:15:26, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5122, loss_cls: 4.2475, loss: 4.2475 +2024-07-23 07:59:16,598 - pyskl - INFO - Epoch [36][3300/3746] lr: 8.653e-02, eta: 3 days, 17:14:35, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5241, loss_cls: 4.1835, loss: 4.1835 +2024-07-23 08:00:39,988 - pyskl - INFO - Epoch [36][3400/3746] lr: 8.651e-02, eta: 3 days, 17:13:46, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5170, loss_cls: 4.2319, loss: 4.2319 +2024-07-23 08:02:03,810 - pyskl - INFO - Epoch [36][3500/3746] lr: 8.650e-02, eta: 3 days, 17:12:59, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5208, loss_cls: 4.2010, loss: 4.2010 +2024-07-23 08:03:26,868 - pyskl - INFO - Epoch [36][3600/3746] lr: 8.648e-02, eta: 3 days, 17:12:09, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5294, loss_cls: 4.1909, loss: 4.1909 +2024-07-23 08:04:49,599 - pyskl - INFO - Epoch [36][3700/3746] lr: 8.646e-02, eta: 3 days, 17:11:17, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5169, loss_cls: 4.2141, loss: 4.2141 +2024-07-23 08:05:29,212 - pyskl - INFO - Saving checkpoint at 36 epochs +2024-07-23 08:07:20,592 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 08:07:21,357 - pyskl - INFO - +top1_acc 0.2101 +top5_acc 0.4467 +2024-07-23 08:07:21,357 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 08:07:21,435 - pyskl - INFO - +mean_acc 0.2100 +2024-07-23 08:07:21,459 - pyskl - INFO - Epoch(val) [36][309] top1_acc: 0.2101, top5_acc: 0.4467, mean_class_accuracy: 0.2100 +2024-07-23 08:11:07,080 - pyskl - INFO - Epoch [37][100/3746] lr: 8.643e-02, eta: 3 days, 17:15:34, time: 2.256, data_time: 1.275, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5225, loss_cls: 4.2039, loss: 4.2039 +2024-07-23 08:12:29,736 - pyskl - INFO - Epoch [37][200/3746] lr: 8.641e-02, eta: 3 days, 17:14:42, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5195, loss_cls: 4.1810, loss: 4.1810 +2024-07-23 08:13:53,426 - pyskl - INFO - Epoch [37][300/3746] lr: 8.639e-02, eta: 3 days, 17:13:53, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5311, loss_cls: 4.1292, loss: 4.1292 +2024-07-23 08:15:16,802 - pyskl - INFO - Epoch [37][400/3746] lr: 8.637e-02, eta: 3 days, 17:13:04, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5284, loss_cls: 4.1404, loss: 4.1404 +2024-07-23 08:16:39,850 - pyskl - INFO - Epoch [37][500/3746] lr: 8.635e-02, eta: 3 days, 17:12:13, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5278, loss_cls: 4.1856, loss: 4.1856 +2024-07-23 08:18:03,204 - pyskl - INFO - Epoch [37][600/3746] lr: 8.633e-02, eta: 3 days, 17:11:23, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5323, loss_cls: 4.1576, loss: 4.1576 +2024-07-23 08:19:26,342 - pyskl - INFO - Epoch [37][700/3746] lr: 8.631e-02, eta: 3 days, 17:10:32, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5191, loss_cls: 4.2036, loss: 4.2036 +2024-07-23 08:20:49,859 - pyskl - INFO - Epoch [37][800/3746] lr: 8.630e-02, eta: 3 days, 17:09:43, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5392, loss_cls: 4.1341, loss: 4.1341 +2024-07-23 08:22:13,247 - pyskl - INFO - Epoch [37][900/3746] lr: 8.628e-02, eta: 3 days, 17:08:53, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5286, loss_cls: 4.1715, loss: 4.1715 +2024-07-23 08:23:36,603 - pyskl - INFO - Epoch [37][1000/3746] lr: 8.626e-02, eta: 3 days, 17:08:03, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5384, loss_cls: 4.1533, loss: 4.1533 +2024-07-23 08:25:00,101 - pyskl - INFO - Epoch [37][1100/3746] lr: 8.624e-02, eta: 3 days, 17:07:13, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5316, loss_cls: 4.1753, loss: 4.1753 +2024-07-23 08:26:23,703 - pyskl - INFO - Epoch [37][1200/3746] lr: 8.622e-02, eta: 3 days, 17:06:24, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5244, loss_cls: 4.1823, loss: 4.1823 +2024-07-23 08:27:47,171 - pyskl - INFO - Epoch [37][1300/3746] lr: 8.620e-02, eta: 3 days, 17:05:34, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5314, loss_cls: 4.1519, loss: 4.1519 +2024-07-23 08:29:10,258 - pyskl - INFO - Epoch [37][1400/3746] lr: 8.618e-02, eta: 3 days, 17:04:43, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5302, loss_cls: 4.1692, loss: 4.1692 +2024-07-23 08:30:33,764 - pyskl - INFO - Epoch [37][1500/3746] lr: 8.616e-02, eta: 3 days, 17:03:53, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5177, loss_cls: 4.2069, loss: 4.2069 +2024-07-23 08:31:57,541 - pyskl - INFO - Epoch [37][1600/3746] lr: 8.614e-02, eta: 3 days, 17:03:04, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5291, loss_cls: 4.1883, loss: 4.1883 +2024-07-23 08:33:21,256 - pyskl - INFO - Epoch [37][1700/3746] lr: 8.612e-02, eta: 3 days, 17:02:15, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5181, loss_cls: 4.2136, loss: 4.2136 +2024-07-23 08:34:44,311 - pyskl - INFO - Epoch [37][1800/3746] lr: 8.610e-02, eta: 3 days, 17:01:23, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5312, loss_cls: 4.1870, loss: 4.1870 +2024-07-23 08:36:07,702 - pyskl - INFO - Epoch [37][1900/3746] lr: 8.608e-02, eta: 3 days, 17:00:33, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5266, loss_cls: 4.1829, loss: 4.1829 +2024-07-23 08:37:31,256 - pyskl - INFO - Epoch [37][2000/3746] lr: 8.606e-02, eta: 3 days, 16:59:43, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5352, loss_cls: 4.1422, loss: 4.1422 +2024-07-23 08:38:54,660 - pyskl - INFO - Epoch [37][2100/3746] lr: 8.604e-02, eta: 3 days, 16:58:52, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5244, loss_cls: 4.1990, loss: 4.1990 +2024-07-23 08:40:18,185 - pyskl - INFO - Epoch [37][2200/3746] lr: 8.602e-02, eta: 3 days, 16:58:02, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5316, loss_cls: 4.1199, loss: 4.1199 +2024-07-23 08:41:41,344 - pyskl - INFO - Epoch [37][2300/3746] lr: 8.601e-02, eta: 3 days, 16:57:11, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5270, loss_cls: 4.1414, loss: 4.1414 +2024-07-23 08:43:04,738 - pyskl - INFO - Epoch [37][2400/3746] lr: 8.599e-02, eta: 3 days, 16:56:20, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5289, loss_cls: 4.1886, loss: 4.1886 +2024-07-23 08:44:28,540 - pyskl - INFO - Epoch [37][2500/3746] lr: 8.597e-02, eta: 3 days, 16:55:31, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5284, loss_cls: 4.1967, loss: 4.1967 +2024-07-23 08:45:52,105 - pyskl - INFO - Epoch [37][2600/3746] lr: 8.595e-02, eta: 3 days, 16:54:40, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5206, loss_cls: 4.1769, loss: 4.1769 +2024-07-23 08:47:15,789 - pyskl - INFO - Epoch [37][2700/3746] lr: 8.593e-02, eta: 3 days, 16:53:51, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5281, loss_cls: 4.1600, loss: 4.1600 +2024-07-23 08:48:39,484 - pyskl - INFO - Epoch [37][2800/3746] lr: 8.591e-02, eta: 3 days, 16:53:01, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5262, loss_cls: 4.1571, loss: 4.1571 +2024-07-23 08:50:02,867 - pyskl - INFO - Epoch [37][2900/3746] lr: 8.589e-02, eta: 3 days, 16:52:10, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5198, loss_cls: 4.2376, loss: 4.2376 +2024-07-23 08:51:26,456 - pyskl - INFO - Epoch [37][3000/3746] lr: 8.587e-02, eta: 3 days, 16:51:19, time: 0.836, data_time: 0.001, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5242, loss_cls: 4.2334, loss: 4.2334 +2024-07-23 08:52:50,389 - pyskl - INFO - Epoch [37][3100/3746] lr: 8.585e-02, eta: 3 days, 16:50:30, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5177, loss_cls: 4.1993, loss: 4.1993 +2024-07-23 08:54:12,877 - pyskl - INFO - Epoch [37][3200/3746] lr: 8.583e-02, eta: 3 days, 16:49:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5259, loss_cls: 4.1843, loss: 4.1843 +2024-07-23 08:55:35,324 - pyskl - INFO - Epoch [37][3300/3746] lr: 8.581e-02, eta: 3 days, 16:48:42, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5236, loss_cls: 4.1942, loss: 4.1942 +2024-07-23 08:56:58,933 - pyskl - INFO - Epoch [37][3400/3746] lr: 8.579e-02, eta: 3 days, 16:47:52, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5244, loss_cls: 4.1873, loss: 4.1873 +2024-07-23 08:58:22,388 - pyskl - INFO - Epoch [37][3500/3746] lr: 8.577e-02, eta: 3 days, 16:47:01, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5292, loss_cls: 4.1782, loss: 4.1782 +2024-07-23 08:59:44,471 - pyskl - INFO - Epoch [37][3600/3746] lr: 8.575e-02, eta: 3 days, 16:46:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5273, loss_cls: 4.1843, loss: 4.1843 +2024-07-23 09:01:07,846 - pyskl - INFO - Epoch [37][3700/3746] lr: 8.573e-02, eta: 3 days, 16:45:14, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5181, loss_cls: 4.2129, loss: 4.2129 +2024-07-23 09:01:47,231 - pyskl - INFO - Saving checkpoint at 37 epochs +2024-07-23 09:03:38,896 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 09:03:39,660 - pyskl - INFO - +top1_acc 0.1849 +top5_acc 0.4234 +2024-07-23 09:03:39,660 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 09:03:39,699 - pyskl - INFO - +mean_acc 0.1847 +2024-07-23 09:03:39,709 - pyskl - INFO - Epoch(val) [37][309] top1_acc: 0.1849, top5_acc: 0.4234, mean_class_accuracy: 0.1847 +2024-07-23 09:07:26,717 - pyskl - INFO - Epoch [38][100/3746] lr: 8.570e-02, eta: 3 days, 16:49:21, time: 2.270, data_time: 1.287, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5425, loss_cls: 4.0951, loss: 4.0951 +2024-07-23 09:08:50,370 - pyskl - INFO - Epoch [38][200/3746] lr: 8.568e-02, eta: 3 days, 16:48:30, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5414, loss_cls: 4.1147, loss: 4.1147 +2024-07-23 09:10:13,690 - pyskl - INFO - Epoch [38][300/3746] lr: 8.567e-02, eta: 3 days, 16:47:38, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5186, loss_cls: 4.1832, loss: 4.1832 +2024-07-23 09:11:36,824 - pyskl - INFO - Epoch [38][400/3746] lr: 8.565e-02, eta: 3 days, 16:46:45, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5331, loss_cls: 4.1126, loss: 4.1126 +2024-07-23 09:13:00,005 - pyskl - INFO - Epoch [38][500/3746] lr: 8.563e-02, eta: 3 days, 16:45:53, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5309, loss_cls: 4.1591, loss: 4.1591 +2024-07-23 09:14:23,125 - pyskl - INFO - Epoch [38][600/3746] lr: 8.561e-02, eta: 3 days, 16:45:00, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5311, loss_cls: 4.1717, loss: 4.1717 +2024-07-23 09:15:46,333 - pyskl - INFO - Epoch [38][700/3746] lr: 8.559e-02, eta: 3 days, 16:44:08, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5280, loss_cls: 4.1855, loss: 4.1855 +2024-07-23 09:17:09,909 - pyskl - INFO - Epoch [38][800/3746] lr: 8.557e-02, eta: 3 days, 16:43:16, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5323, loss_cls: 4.1593, loss: 4.1593 +2024-07-23 09:18:33,248 - pyskl - INFO - Epoch [38][900/3746] lr: 8.555e-02, eta: 3 days, 16:42:24, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5252, loss_cls: 4.1821, loss: 4.1821 +2024-07-23 09:19:56,431 - pyskl - INFO - Epoch [38][1000/3746] lr: 8.553e-02, eta: 3 days, 16:41:31, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5275, loss_cls: 4.1518, loss: 4.1518 +2024-07-23 09:21:19,976 - pyskl - INFO - Epoch [38][1100/3746] lr: 8.551e-02, eta: 3 days, 16:40:40, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5364, loss_cls: 4.1453, loss: 4.1453 +2024-07-23 09:22:43,239 - pyskl - INFO - Epoch [38][1200/3746] lr: 8.549e-02, eta: 3 days, 16:39:47, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5259, loss_cls: 4.1746, loss: 4.1746 +2024-07-23 09:24:06,609 - pyskl - INFO - Epoch [38][1300/3746] lr: 8.547e-02, eta: 3 days, 16:38:55, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5323, loss_cls: 4.1460, loss: 4.1460 +2024-07-23 09:25:29,417 - pyskl - INFO - Epoch [38][1400/3746] lr: 8.545e-02, eta: 3 days, 16:38:01, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5320, loss_cls: 4.1602, loss: 4.1602 +2024-07-23 09:26:52,715 - pyskl - INFO - Epoch [38][1500/3746] lr: 8.543e-02, eta: 3 days, 16:37:08, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5297, loss_cls: 4.1760, loss: 4.1760 +2024-07-23 09:28:16,249 - pyskl - INFO - Epoch [38][1600/3746] lr: 8.541e-02, eta: 3 days, 16:36:17, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5430, loss_cls: 4.1296, loss: 4.1296 +2024-07-23 09:29:39,824 - pyskl - INFO - Epoch [38][1700/3746] lr: 8.539e-02, eta: 3 days, 16:35:25, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5400, loss_cls: 4.1036, loss: 4.1036 +2024-07-23 09:31:03,176 - pyskl - INFO - Epoch [38][1800/3746] lr: 8.537e-02, eta: 3 days, 16:34:32, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5363, loss_cls: 4.1337, loss: 4.1337 +2024-07-23 09:32:26,282 - pyskl - INFO - Epoch [38][1900/3746] lr: 8.535e-02, eta: 3 days, 16:33:39, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5156, loss_cls: 4.2129, loss: 4.2129 +2024-07-23 09:33:49,631 - pyskl - INFO - Epoch [38][2000/3746] lr: 8.533e-02, eta: 3 days, 16:32:46, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5308, loss_cls: 4.1521, loss: 4.1521 +2024-07-23 09:35:13,060 - pyskl - INFO - Epoch [38][2100/3746] lr: 8.531e-02, eta: 3 days, 16:31:54, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5267, loss_cls: 4.1727, loss: 4.1727 +2024-07-23 09:36:36,372 - pyskl - INFO - Epoch [38][2200/3746] lr: 8.529e-02, eta: 3 days, 16:31:01, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5167, loss_cls: 4.2120, loss: 4.2120 +2024-07-23 09:38:00,007 - pyskl - INFO - Epoch [38][2300/3746] lr: 8.527e-02, eta: 3 days, 16:30:09, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5233, loss_cls: 4.1882, loss: 4.1882 +2024-07-23 09:39:23,272 - pyskl - INFO - Epoch [38][2400/3746] lr: 8.525e-02, eta: 3 days, 16:29:16, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5225, loss_cls: 4.2060, loss: 4.2060 +2024-07-23 09:40:46,687 - pyskl - INFO - Epoch [38][2500/3746] lr: 8.523e-02, eta: 3 days, 16:28:23, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5225, loss_cls: 4.2120, loss: 4.2120 +2024-07-23 09:42:10,167 - pyskl - INFO - Epoch [38][2600/3746] lr: 8.521e-02, eta: 3 days, 16:27:31, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5245, loss_cls: 4.1938, loss: 4.1938 +2024-07-23 09:43:33,590 - pyskl - INFO - Epoch [38][2700/3746] lr: 8.519e-02, eta: 3 days, 16:26:38, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5242, loss_cls: 4.1878, loss: 4.1878 +2024-07-23 09:44:57,258 - pyskl - INFO - Epoch [38][2800/3746] lr: 8.517e-02, eta: 3 days, 16:25:46, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5245, loss_cls: 4.1597, loss: 4.1597 +2024-07-23 09:46:20,607 - pyskl - INFO - Epoch [38][2900/3746] lr: 8.515e-02, eta: 3 days, 16:24:53, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5205, loss_cls: 4.2020, loss: 4.2020 +2024-07-23 09:47:44,054 - pyskl - INFO - Epoch [38][3000/3746] lr: 8.513e-02, eta: 3 days, 16:24:01, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5262, loss_cls: 4.1396, loss: 4.1396 +2024-07-23 09:49:07,246 - pyskl - INFO - Epoch [38][3100/3746] lr: 8.511e-02, eta: 3 days, 16:23:07, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5289, loss_cls: 4.1586, loss: 4.1586 +2024-07-23 09:50:29,815 - pyskl - INFO - Epoch [38][3200/3746] lr: 8.509e-02, eta: 3 days, 16:22:12, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5209, loss_cls: 4.2065, loss: 4.2065 +2024-07-23 09:51:52,526 - pyskl - INFO - Epoch [38][3300/3746] lr: 8.507e-02, eta: 3 days, 16:21:16, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5220, loss_cls: 4.2004, loss: 4.2004 +2024-07-23 09:53:16,161 - pyskl - INFO - Epoch [38][3400/3746] lr: 8.505e-02, eta: 3 days, 16:20:24, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5227, loss_cls: 4.2102, loss: 4.2102 +2024-07-23 09:54:38,655 - pyskl - INFO - Epoch [38][3500/3746] lr: 8.503e-02, eta: 3 days, 16:19:28, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5331, loss_cls: 4.1364, loss: 4.1364 +2024-07-23 09:56:01,645 - pyskl - INFO - Epoch [38][3600/3746] lr: 8.501e-02, eta: 3 days, 16:18:34, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5220, loss_cls: 4.2007, loss: 4.2007 +2024-07-23 09:57:24,708 - pyskl - INFO - Epoch [38][3700/3746] lr: 8.499e-02, eta: 3 days, 16:17:40, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5292, loss_cls: 4.1992, loss: 4.1992 +2024-07-23 09:58:04,684 - pyskl - INFO - Saving checkpoint at 38 epochs +2024-07-23 09:59:58,125 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 09:59:58,925 - pyskl - INFO - +top1_acc 0.1996 +top5_acc 0.4377 +2024-07-23 09:59:58,925 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 09:59:58,964 - pyskl - INFO - +mean_acc 0.1993 +2024-07-23 09:59:58,975 - pyskl - INFO - Epoch(val) [38][309] top1_acc: 0.1996, top5_acc: 0.4377, mean_class_accuracy: 0.1993 +2024-07-23 10:03:51,760 - pyskl - INFO - Epoch [39][100/3746] lr: 8.496e-02, eta: 3 days, 16:21:49, time: 2.328, data_time: 1.325, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5333, loss_cls: 4.1320, loss: 4.1320 +2024-07-23 10:05:15,517 - pyskl - INFO - Epoch [39][200/3746] lr: 8.494e-02, eta: 3 days, 16:20:56, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5273, loss_cls: 4.1356, loss: 4.1356 +2024-07-23 10:06:39,317 - pyskl - INFO - Epoch [39][300/3746] lr: 8.492e-02, eta: 3 days, 16:20:04, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5186, loss_cls: 4.2087, loss: 4.2087 +2024-07-23 10:08:03,115 - pyskl - INFO - Epoch [39][400/3746] lr: 8.490e-02, eta: 3 days, 16:19:11, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5358, loss_cls: 4.1287, loss: 4.1287 +2024-07-23 10:09:26,903 - pyskl - INFO - Epoch [39][500/3746] lr: 8.488e-02, eta: 3 days, 16:18:19, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5261, loss_cls: 4.1272, loss: 4.1272 +2024-07-23 10:10:50,736 - pyskl - INFO - Epoch [39][600/3746] lr: 8.486e-02, eta: 3 days, 16:17:26, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5209, loss_cls: 4.1810, loss: 4.1810 +2024-07-23 10:12:14,331 - pyskl - INFO - Epoch [39][700/3746] lr: 8.484e-02, eta: 3 days, 16:16:33, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5394, loss_cls: 4.1411, loss: 4.1411 +2024-07-23 10:13:38,161 - pyskl - INFO - Epoch [39][800/3746] lr: 8.482e-02, eta: 3 days, 16:15:40, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5336, loss_cls: 4.1366, loss: 4.1366 +2024-07-23 10:15:02,193 - pyskl - INFO - Epoch [39][900/3746] lr: 8.480e-02, eta: 3 days, 16:14:48, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5297, loss_cls: 4.1329, loss: 4.1329 +2024-07-23 10:16:26,224 - pyskl - INFO - Epoch [39][1000/3746] lr: 8.478e-02, eta: 3 days, 16:13:56, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5281, loss_cls: 4.1670, loss: 4.1670 +2024-07-23 10:17:49,870 - pyskl - INFO - Epoch [39][1100/3746] lr: 8.476e-02, eta: 3 days, 16:13:03, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5347, loss_cls: 4.1688, loss: 4.1688 +2024-07-23 10:19:13,216 - pyskl - INFO - Epoch [39][1200/3746] lr: 8.474e-02, eta: 3 days, 16:12:09, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5259, loss_cls: 4.1689, loss: 4.1689 +2024-07-23 10:20:36,929 - pyskl - INFO - Epoch [39][1300/3746] lr: 8.472e-02, eta: 3 days, 16:11:16, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5241, loss_cls: 4.1602, loss: 4.1602 +2024-07-23 10:22:00,623 - pyskl - INFO - Epoch [39][1400/3746] lr: 8.470e-02, eta: 3 days, 16:10:22, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5413, loss_cls: 4.1362, loss: 4.1362 +2024-07-23 10:23:24,699 - pyskl - INFO - Epoch [39][1500/3746] lr: 8.468e-02, eta: 3 days, 16:09:30, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5220, loss_cls: 4.1823, loss: 4.1823 +2024-07-23 10:24:48,354 - pyskl - INFO - Epoch [39][1600/3746] lr: 8.466e-02, eta: 3 days, 16:08:37, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5275, loss_cls: 4.1535, loss: 4.1535 +2024-07-23 10:26:12,292 - pyskl - INFO - Epoch [39][1700/3746] lr: 8.464e-02, eta: 3 days, 16:07:44, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5275, loss_cls: 4.1540, loss: 4.1540 +2024-07-23 10:27:35,897 - pyskl - INFO - Epoch [39][1800/3746] lr: 8.462e-02, eta: 3 days, 16:06:50, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5425, loss_cls: 4.1460, loss: 4.1460 +2024-07-23 10:28:59,880 - pyskl - INFO - Epoch [39][1900/3746] lr: 8.460e-02, eta: 3 days, 16:05:58, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5297, loss_cls: 4.1717, loss: 4.1717 +2024-07-23 10:30:23,827 - pyskl - INFO - Epoch [39][2000/3746] lr: 8.458e-02, eta: 3 days, 16:05:05, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5341, loss_cls: 4.1293, loss: 4.1293 +2024-07-23 10:31:47,543 - pyskl - INFO - Epoch [39][2100/3746] lr: 8.456e-02, eta: 3 days, 16:04:12, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5284, loss_cls: 4.1539, loss: 4.1539 +2024-07-23 10:33:11,158 - pyskl - INFO - Epoch [39][2200/3746] lr: 8.454e-02, eta: 3 days, 16:03:18, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5395, loss_cls: 4.1493, loss: 4.1493 +2024-07-23 10:34:34,753 - pyskl - INFO - Epoch [39][2300/3746] lr: 8.452e-02, eta: 3 days, 16:02:24, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5231, loss_cls: 4.1885, loss: 4.1885 +2024-07-23 10:35:58,260 - pyskl - INFO - Epoch [39][2400/3746] lr: 8.450e-02, eta: 3 days, 16:01:30, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5387, loss_cls: 4.1149, loss: 4.1149 +2024-07-23 10:37:21,822 - pyskl - INFO - Epoch [39][2500/3746] lr: 8.448e-02, eta: 3 days, 16:00:36, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5289, loss_cls: 4.1785, loss: 4.1785 +2024-07-23 10:38:45,431 - pyskl - INFO - Epoch [39][2600/3746] lr: 8.446e-02, eta: 3 days, 15:59:42, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5205, loss_cls: 4.2092, loss: 4.2092 +2024-07-23 10:40:09,537 - pyskl - INFO - Epoch [39][2700/3746] lr: 8.444e-02, eta: 3 days, 15:58:49, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5239, loss_cls: 4.2000, loss: 4.2000 +2024-07-23 10:41:32,795 - pyskl - INFO - Epoch [39][2800/3746] lr: 8.442e-02, eta: 3 days, 15:57:54, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5386, loss_cls: 4.1441, loss: 4.1441 +2024-07-23 10:42:56,441 - pyskl - INFO - Epoch [39][2900/3746] lr: 8.440e-02, eta: 3 days, 15:57:00, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5288, loss_cls: 4.1345, loss: 4.1345 +2024-07-23 10:44:20,022 - pyskl - INFO - Epoch [39][3000/3746] lr: 8.438e-02, eta: 3 days, 15:56:06, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5327, loss_cls: 4.1543, loss: 4.1543 +2024-07-23 10:45:42,459 - pyskl - INFO - Epoch [39][3100/3746] lr: 8.436e-02, eta: 3 days, 15:55:08, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5227, loss_cls: 4.1956, loss: 4.1956 +2024-07-23 10:47:05,294 - pyskl - INFO - Epoch [39][3200/3746] lr: 8.434e-02, eta: 3 days, 15:54:12, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5353, loss_cls: 4.1416, loss: 4.1416 +2024-07-23 10:48:29,139 - pyskl - INFO - Epoch [39][3300/3746] lr: 8.432e-02, eta: 3 days, 15:53:18, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5211, loss_cls: 4.1935, loss: 4.1935 +2024-07-23 10:49:51,782 - pyskl - INFO - Epoch [39][3400/3746] lr: 8.430e-02, eta: 3 days, 15:52:21, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5314, loss_cls: 4.1624, loss: 4.1624 +2024-07-23 10:51:14,321 - pyskl - INFO - Epoch [39][3500/3746] lr: 8.428e-02, eta: 3 days, 15:51:23, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5322, loss_cls: 4.1960, loss: 4.1960 +2024-07-23 10:52:37,505 - pyskl - INFO - Epoch [39][3600/3746] lr: 8.426e-02, eta: 3 days, 15:50:28, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5252, loss_cls: 4.1611, loss: 4.1611 +2024-07-23 10:54:00,918 - pyskl - INFO - Epoch [39][3700/3746] lr: 8.424e-02, eta: 3 days, 15:49:33, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5220, loss_cls: 4.1812, loss: 4.1812 +2024-07-23 10:54:40,742 - pyskl - INFO - Saving checkpoint at 39 epochs +2024-07-23 10:56:34,186 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 10:56:34,868 - pyskl - INFO - +top1_acc 0.2230 +top5_acc 0.4535 +2024-07-23 10:56:34,868 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 10:56:34,915 - pyskl - INFO - +mean_acc 0.2226 +2024-07-23 10:56:34,920 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_33.pth was removed +2024-07-23 10:56:35,253 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_39.pth. +2024-07-23 10:56:35,254 - pyskl - INFO - Best top1_acc is 0.2230 at 39 epoch. +2024-07-23 10:56:35,268 - pyskl - INFO - Epoch(val) [39][309] top1_acc: 0.2230, top5_acc: 0.4535, mean_class_accuracy: 0.2226 +2024-07-23 11:00:23,508 - pyskl - INFO - Epoch [40][100/3746] lr: 8.421e-02, eta: 3 days, 15:53:15, time: 2.282, data_time: 1.285, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5470, loss_cls: 4.0652, loss: 4.0652 +2024-07-23 11:01:47,232 - pyskl - INFO - Epoch [40][200/3746] lr: 8.419e-02, eta: 3 days, 15:52:21, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5273, loss_cls: 4.1537, loss: 4.1537 +2024-07-23 11:03:11,083 - pyskl - INFO - Epoch [40][300/3746] lr: 8.417e-02, eta: 3 days, 15:51:27, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5370, loss_cls: 4.1221, loss: 4.1221 +2024-07-23 11:04:35,181 - pyskl - INFO - Epoch [40][400/3746] lr: 8.415e-02, eta: 3 days, 15:50:33, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5242, loss_cls: 4.1782, loss: 4.1782 +2024-07-23 11:05:58,898 - pyskl - INFO - Epoch [40][500/3746] lr: 8.413e-02, eta: 3 days, 15:49:38, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5198, loss_cls: 4.1911, loss: 4.1911 +2024-07-23 11:07:22,434 - pyskl - INFO - Epoch [40][600/3746] lr: 8.411e-02, eta: 3 days, 15:48:43, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5298, loss_cls: 4.1672, loss: 4.1672 +2024-07-23 11:08:46,199 - pyskl - INFO - Epoch [40][700/3746] lr: 8.408e-02, eta: 3 days, 15:47:49, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5416, loss_cls: 4.1104, loss: 4.1104 +2024-07-23 11:10:10,179 - pyskl - INFO - Epoch [40][800/3746] lr: 8.406e-02, eta: 3 days, 15:46:55, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5459, loss_cls: 4.0846, loss: 4.0846 +2024-07-23 11:11:34,028 - pyskl - INFO - Epoch [40][900/3746] lr: 8.404e-02, eta: 3 days, 15:46:00, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5320, loss_cls: 4.1789, loss: 4.1789 +2024-07-23 11:12:57,590 - pyskl - INFO - Epoch [40][1000/3746] lr: 8.402e-02, eta: 3 days, 15:45:05, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5325, loss_cls: 4.1169, loss: 4.1169 +2024-07-23 11:14:21,439 - pyskl - INFO - Epoch [40][1100/3746] lr: 8.400e-02, eta: 3 days, 15:44:11, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5272, loss_cls: 4.1888, loss: 4.1888 +2024-07-23 11:15:45,027 - pyskl - INFO - Epoch [40][1200/3746] lr: 8.398e-02, eta: 3 days, 15:43:15, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5302, loss_cls: 4.1572, loss: 4.1572 +2024-07-23 11:17:08,971 - pyskl - INFO - Epoch [40][1300/3746] lr: 8.396e-02, eta: 3 days, 15:42:21, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5334, loss_cls: 4.1668, loss: 4.1668 +2024-07-23 11:18:32,850 - pyskl - INFO - Epoch [40][1400/3746] lr: 8.394e-02, eta: 3 days, 15:41:26, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5352, loss_cls: 4.1346, loss: 4.1346 +2024-07-23 11:19:56,514 - pyskl - INFO - Epoch [40][1500/3746] lr: 8.392e-02, eta: 3 days, 15:40:31, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5336, loss_cls: 4.1308, loss: 4.1308 +2024-07-23 11:21:19,852 - pyskl - INFO - Epoch [40][1600/3746] lr: 8.390e-02, eta: 3 days, 15:39:35, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5280, loss_cls: 4.1643, loss: 4.1643 +2024-07-23 11:22:43,388 - pyskl - INFO - Epoch [40][1700/3746] lr: 8.388e-02, eta: 3 days, 15:38:40, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5311, loss_cls: 4.1475, loss: 4.1475 +2024-07-23 11:24:07,089 - pyskl - INFO - Epoch [40][1800/3746] lr: 8.386e-02, eta: 3 days, 15:37:44, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5177, loss_cls: 4.2033, loss: 4.2033 +2024-07-23 11:25:31,079 - pyskl - INFO - Epoch [40][1900/3746] lr: 8.384e-02, eta: 3 days, 15:36:50, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5331, loss_cls: 4.1560, loss: 4.1560 +2024-07-23 11:26:54,364 - pyskl - INFO - Epoch [40][2000/3746] lr: 8.382e-02, eta: 3 days, 15:35:54, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5306, loss_cls: 4.1387, loss: 4.1387 +2024-07-23 11:28:18,436 - pyskl - INFO - Epoch [40][2100/3746] lr: 8.380e-02, eta: 3 days, 15:34:59, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5334, loss_cls: 4.1529, loss: 4.1529 +2024-07-23 11:29:41,796 - pyskl - INFO - Epoch [40][2200/3746] lr: 8.378e-02, eta: 3 days, 15:34:03, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5284, loss_cls: 4.1339, loss: 4.1339 +2024-07-23 11:31:05,486 - pyskl - INFO - Epoch [40][2300/3746] lr: 8.376e-02, eta: 3 days, 15:33:08, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5248, loss_cls: 4.1903, loss: 4.1903 +2024-07-23 11:32:29,412 - pyskl - INFO - Epoch [40][2400/3746] lr: 8.374e-02, eta: 3 days, 15:32:13, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5317, loss_cls: 4.1355, loss: 4.1355 +2024-07-23 11:33:53,615 - pyskl - INFO - Epoch [40][2500/3746] lr: 8.371e-02, eta: 3 days, 15:31:19, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5312, loss_cls: 4.1836, loss: 4.1836 +2024-07-23 11:35:17,273 - pyskl - INFO - Epoch [40][2600/3746] lr: 8.369e-02, eta: 3 days, 15:30:23, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5344, loss_cls: 4.1328, loss: 4.1328 +2024-07-23 11:36:41,451 - pyskl - INFO - Epoch [40][2700/3746] lr: 8.367e-02, eta: 3 days, 15:29:29, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5275, loss_cls: 4.1734, loss: 4.1734 +2024-07-23 11:38:05,129 - pyskl - INFO - Epoch [40][2800/3746] lr: 8.365e-02, eta: 3 days, 15:28:33, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5344, loss_cls: 4.1705, loss: 4.1705 +2024-07-23 11:39:29,298 - pyskl - INFO - Epoch [40][2900/3746] lr: 8.363e-02, eta: 3 days, 15:27:39, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5219, loss_cls: 4.1652, loss: 4.1652 +2024-07-23 11:40:52,959 - pyskl - INFO - Epoch [40][3000/3746] lr: 8.361e-02, eta: 3 days, 15:26:43, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5302, loss_cls: 4.1534, loss: 4.1534 +2024-07-23 11:42:16,468 - pyskl - INFO - Epoch [40][3100/3746] lr: 8.359e-02, eta: 3 days, 15:25:47, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5220, loss_cls: 4.1815, loss: 4.1815 +2024-07-23 11:43:40,547 - pyskl - INFO - Epoch [40][3200/3746] lr: 8.357e-02, eta: 3 days, 15:24:52, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5238, loss_cls: 4.1731, loss: 4.1731 +2024-07-23 11:45:04,259 - pyskl - INFO - Epoch [40][3300/3746] lr: 8.355e-02, eta: 3 days, 15:23:57, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5387, loss_cls: 4.1279, loss: 4.1279 +2024-07-23 11:46:27,423 - pyskl - INFO - Epoch [40][3400/3746] lr: 8.353e-02, eta: 3 days, 15:22:59, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5384, loss_cls: 4.1337, loss: 4.1337 +2024-07-23 11:47:51,073 - pyskl - INFO - Epoch [40][3500/3746] lr: 8.351e-02, eta: 3 days, 15:22:03, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5242, loss_cls: 4.1965, loss: 4.1965 +2024-07-23 11:49:14,631 - pyskl - INFO - Epoch [40][3600/3746] lr: 8.349e-02, eta: 3 days, 15:21:07, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5295, loss_cls: 4.1792, loss: 4.1792 +2024-07-23 11:50:38,832 - pyskl - INFO - Epoch [40][3700/3746] lr: 8.347e-02, eta: 3 days, 15:20:13, time: 0.842, data_time: 0.001, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5280, loss_cls: 4.1824, loss: 4.1824 +2024-07-23 11:51:19,262 - pyskl - INFO - Saving checkpoint at 40 epochs +2024-07-23 11:53:12,411 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 11:53:13,143 - pyskl - INFO - +top1_acc 0.1358 +top5_acc 0.3388 +2024-07-23 11:53:13,144 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 11:53:13,182 - pyskl - INFO - +mean_acc 0.1354 +2024-07-23 11:53:13,192 - pyskl - INFO - Epoch(val) [40][309] top1_acc: 0.1358, top5_acc: 0.3388, mean_class_accuracy: 0.1354 +2024-07-23 11:57:02,924 - pyskl - INFO - Epoch [41][100/3746] lr: 8.344e-02, eta: 3 days, 15:23:46, time: 2.297, data_time: 1.304, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5441, loss_cls: 4.0580, loss: 4.0580 +2024-07-23 11:58:26,559 - pyskl - INFO - Epoch [41][200/3746] lr: 8.342e-02, eta: 3 days, 15:22:50, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5314, loss_cls: 4.1392, loss: 4.1392 +2024-07-23 11:59:49,904 - pyskl - INFO - Epoch [41][300/3746] lr: 8.339e-02, eta: 3 days, 15:21:53, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5513, loss_cls: 4.1025, loss: 4.1025 +2024-07-23 12:01:13,459 - pyskl - INFO - Epoch [41][400/3746] lr: 8.337e-02, eta: 3 days, 15:20:56, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5261, loss_cls: 4.1575, loss: 4.1575 +2024-07-23 12:02:37,128 - pyskl - INFO - Epoch [41][500/3746] lr: 8.335e-02, eta: 3 days, 15:19:59, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5395, loss_cls: 4.1270, loss: 4.1270 +2024-07-23 12:04:00,897 - pyskl - INFO - Epoch [41][600/3746] lr: 8.333e-02, eta: 3 days, 15:19:03, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5453, loss_cls: 4.0809, loss: 4.0809 +2024-07-23 12:05:24,637 - pyskl - INFO - Epoch [41][700/3746] lr: 8.331e-02, eta: 3 days, 15:18:07, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5425, loss_cls: 4.1092, loss: 4.1092 +2024-07-23 12:06:48,085 - pyskl - INFO - Epoch [41][800/3746] lr: 8.329e-02, eta: 3 days, 15:17:10, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5216, loss_cls: 4.1747, loss: 4.1747 +2024-07-23 12:08:11,479 - pyskl - INFO - Epoch [41][900/3746] lr: 8.327e-02, eta: 3 days, 15:16:12, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5173, loss_cls: 4.1627, loss: 4.1627 +2024-07-23 12:09:35,170 - pyskl - INFO - Epoch [41][1000/3746] lr: 8.325e-02, eta: 3 days, 15:15:16, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5327, loss_cls: 4.1329, loss: 4.1329 +2024-07-23 12:10:58,653 - pyskl - INFO - Epoch [41][1100/3746] lr: 8.323e-02, eta: 3 days, 15:14:19, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5347, loss_cls: 4.1585, loss: 4.1585 +2024-07-23 12:12:22,463 - pyskl - INFO - Epoch [41][1200/3746] lr: 8.321e-02, eta: 3 days, 15:13:22, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5319, loss_cls: 4.1694, loss: 4.1694 +2024-07-23 12:13:45,723 - pyskl - INFO - Epoch [41][1300/3746] lr: 8.319e-02, eta: 3 days, 15:12:24, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5397, loss_cls: 4.0994, loss: 4.0994 +2024-07-23 12:15:08,846 - pyskl - INFO - Epoch [41][1400/3746] lr: 8.316e-02, eta: 3 days, 15:11:26, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5303, loss_cls: 4.1671, loss: 4.1671 +2024-07-23 12:16:32,152 - pyskl - INFO - Epoch [41][1500/3746] lr: 8.314e-02, eta: 3 days, 15:10:28, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5386, loss_cls: 4.1285, loss: 4.1285 +2024-07-23 12:17:55,849 - pyskl - INFO - Epoch [41][1600/3746] lr: 8.312e-02, eta: 3 days, 15:09:32, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5309, loss_cls: 4.1654, loss: 4.1654 +2024-07-23 12:19:19,322 - pyskl - INFO - Epoch [41][1700/3746] lr: 8.310e-02, eta: 3 days, 15:08:34, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5316, loss_cls: 4.1512, loss: 4.1512 +2024-07-23 12:20:42,828 - pyskl - INFO - Epoch [41][1800/3746] lr: 8.308e-02, eta: 3 days, 15:07:37, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5286, loss_cls: 4.1553, loss: 4.1553 +2024-07-23 12:22:06,234 - pyskl - INFO - Epoch [41][1900/3746] lr: 8.306e-02, eta: 3 days, 15:06:39, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5294, loss_cls: 4.1446, loss: 4.1446 +2024-07-23 12:23:30,017 - pyskl - INFO - Epoch [41][2000/3746] lr: 8.304e-02, eta: 3 days, 15:05:43, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5244, loss_cls: 4.1763, loss: 4.1763 +2024-07-23 12:24:53,428 - pyskl - INFO - Epoch [41][2100/3746] lr: 8.302e-02, eta: 3 days, 15:04:45, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5275, loss_cls: 4.1814, loss: 4.1814 +2024-07-23 12:26:16,855 - pyskl - INFO - Epoch [41][2200/3746] lr: 8.300e-02, eta: 3 days, 15:03:47, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5297, loss_cls: 4.1348, loss: 4.1348 +2024-07-23 12:27:40,501 - pyskl - INFO - Epoch [41][2300/3746] lr: 8.298e-02, eta: 3 days, 15:02:50, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5320, loss_cls: 4.1263, loss: 4.1263 +2024-07-23 12:29:04,608 - pyskl - INFO - Epoch [41][2400/3746] lr: 8.296e-02, eta: 3 days, 15:01:54, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5297, loss_cls: 4.1392, loss: 4.1392 +2024-07-23 12:30:28,208 - pyskl - INFO - Epoch [41][2500/3746] lr: 8.293e-02, eta: 3 days, 15:00:57, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5381, loss_cls: 4.1449, loss: 4.1449 +2024-07-23 12:31:51,452 - pyskl - INFO - Epoch [41][2600/3746] lr: 8.291e-02, eta: 3 days, 14:59:58, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5252, loss_cls: 4.2017, loss: 4.2017 +2024-07-23 12:33:14,213 - pyskl - INFO - Epoch [41][2700/3746] lr: 8.289e-02, eta: 3 days, 14:58:59, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5450, loss_cls: 4.1092, loss: 4.1092 +2024-07-23 12:34:38,349 - pyskl - INFO - Epoch [41][2800/3746] lr: 8.287e-02, eta: 3 days, 14:58:03, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5295, loss_cls: 4.1431, loss: 4.1431 +2024-07-23 12:36:00,543 - pyskl - INFO - Epoch [41][2900/3746] lr: 8.285e-02, eta: 3 days, 14:57:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5281, loss_cls: 4.1434, loss: 4.1434 +2024-07-23 12:37:22,995 - pyskl - INFO - Epoch [41][3000/3746] lr: 8.283e-02, eta: 3 days, 14:56:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5366, loss_cls: 4.1173, loss: 4.1173 +2024-07-23 12:38:46,441 - pyskl - INFO - Epoch [41][3100/3746] lr: 8.281e-02, eta: 3 days, 14:55:03, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5244, loss_cls: 4.1694, loss: 4.1694 +2024-07-23 12:40:09,908 - pyskl - INFO - Epoch [41][3200/3746] lr: 8.279e-02, eta: 3 days, 14:54:05, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5244, loss_cls: 4.1385, loss: 4.1385 +2024-07-23 12:41:32,532 - pyskl - INFO - Epoch [41][3300/3746] lr: 8.277e-02, eta: 3 days, 14:53:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5339, loss_cls: 4.1505, loss: 4.1505 +2024-07-23 12:42:55,958 - pyskl - INFO - Epoch [41][3400/3746] lr: 8.274e-02, eta: 3 days, 14:52:07, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5323, loss_cls: 4.1498, loss: 4.1498 +2024-07-23 12:44:18,666 - pyskl - INFO - Epoch [41][3500/3746] lr: 8.272e-02, eta: 3 days, 14:51:07, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5281, loss_cls: 4.1749, loss: 4.1749 +2024-07-23 12:45:42,284 - pyskl - INFO - Epoch [41][3600/3746] lr: 8.270e-02, eta: 3 days, 14:50:09, time: 0.836, data_time: 0.001, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5277, loss_cls: 4.1609, loss: 4.1609 +2024-07-23 12:47:06,026 - pyskl - INFO - Epoch [41][3700/3746] lr: 8.268e-02, eta: 3 days, 14:49:12, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5316, loss_cls: 4.1790, loss: 4.1790 +2024-07-23 12:47:45,517 - pyskl - INFO - Saving checkpoint at 41 epochs +2024-07-23 12:49:38,232 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 12:49:38,927 - pyskl - INFO - +top1_acc 0.1914 +top5_acc 0.4131 +2024-07-23 12:49:38,927 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 12:49:38,965 - pyskl - INFO - +mean_acc 0.1912 +2024-07-23 12:49:38,976 - pyskl - INFO - Epoch(val) [41][309] top1_acc: 0.1914, top5_acc: 0.4131, mean_class_accuracy: 0.1912 +2024-07-23 12:53:23,954 - pyskl - INFO - Epoch [42][100/3746] lr: 8.265e-02, eta: 3 days, 14:52:21, time: 2.250, data_time: 1.263, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5559, loss_cls: 4.0385, loss: 4.0385 +2024-07-23 12:54:46,979 - pyskl - INFO - Epoch [42][200/3746] lr: 8.263e-02, eta: 3 days, 14:51:21, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5319, loss_cls: 4.1192, loss: 4.1192 +2024-07-23 12:56:10,056 - pyskl - INFO - Epoch [42][300/3746] lr: 8.261e-02, eta: 3 days, 14:50:21, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5439, loss_cls: 4.0839, loss: 4.0839 +2024-07-23 12:57:33,522 - pyskl - INFO - Epoch [42][400/3746] lr: 8.259e-02, eta: 3 days, 14:49:23, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5331, loss_cls: 4.1505, loss: 4.1505 +2024-07-23 12:58:56,739 - pyskl - INFO - Epoch [42][500/3746] lr: 8.257e-02, eta: 3 days, 14:48:24, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5139, loss_cls: 4.2069, loss: 4.2069 +2024-07-23 13:00:19,573 - pyskl - INFO - Epoch [42][600/3746] lr: 8.254e-02, eta: 3 days, 14:47:24, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5352, loss_cls: 4.1499, loss: 4.1499 +2024-07-23 13:01:42,287 - pyskl - INFO - Epoch [42][700/3746] lr: 8.252e-02, eta: 3 days, 14:46:23, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5228, loss_cls: 4.1587, loss: 4.1587 +2024-07-23 13:03:05,689 - pyskl - INFO - Epoch [42][800/3746] lr: 8.250e-02, eta: 3 days, 14:45:24, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5305, loss_cls: 4.1371, loss: 4.1371 +2024-07-23 13:04:28,899 - pyskl - INFO - Epoch [42][900/3746] lr: 8.248e-02, eta: 3 days, 14:44:25, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5234, loss_cls: 4.1981, loss: 4.1981 +2024-07-23 13:05:52,141 - pyskl - INFO - Epoch [42][1000/3746] lr: 8.246e-02, eta: 3 days, 14:43:26, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5300, loss_cls: 4.1549, loss: 4.1549 +2024-07-23 13:07:15,507 - pyskl - INFO - Epoch [42][1100/3746] lr: 8.244e-02, eta: 3 days, 14:42:27, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5363, loss_cls: 4.1239, loss: 4.1239 +2024-07-23 13:08:39,151 - pyskl - INFO - Epoch [42][1200/3746] lr: 8.242e-02, eta: 3 days, 14:41:28, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5344, loss_cls: 4.1452, loss: 4.1452 +2024-07-23 13:10:02,761 - pyskl - INFO - Epoch [42][1300/3746] lr: 8.240e-02, eta: 3 days, 14:40:30, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5419, loss_cls: 4.1157, loss: 4.1157 +2024-07-23 13:11:26,079 - pyskl - INFO - Epoch [42][1400/3746] lr: 8.237e-02, eta: 3 days, 14:39:31, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5364, loss_cls: 4.1163, loss: 4.1163 +2024-07-23 13:12:49,648 - pyskl - INFO - Epoch [42][1500/3746] lr: 8.235e-02, eta: 3 days, 14:38:32, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5336, loss_cls: 4.1273, loss: 4.1273 +2024-07-23 13:14:13,639 - pyskl - INFO - Epoch [42][1600/3746] lr: 8.233e-02, eta: 3 days, 14:37:35, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5156, loss_cls: 4.2115, loss: 4.2115 +2024-07-23 13:15:37,391 - pyskl - INFO - Epoch [42][1700/3746] lr: 8.231e-02, eta: 3 days, 14:36:37, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5337, loss_cls: 4.1212, loss: 4.1212 +2024-07-23 13:17:00,715 - pyskl - INFO - Epoch [42][1800/3746] lr: 8.229e-02, eta: 3 days, 14:35:37, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5358, loss_cls: 4.1177, loss: 4.1177 +2024-07-23 13:18:24,248 - pyskl - INFO - Epoch [42][1900/3746] lr: 8.227e-02, eta: 3 days, 14:34:38, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5300, loss_cls: 4.1723, loss: 4.1723 +2024-07-23 13:19:47,785 - pyskl - INFO - Epoch [42][2000/3746] lr: 8.225e-02, eta: 3 days, 14:33:40, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5334, loss_cls: 4.1348, loss: 4.1348 +2024-07-23 13:21:11,202 - pyskl - INFO - Epoch [42][2100/3746] lr: 8.222e-02, eta: 3 days, 14:32:40, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5298, loss_cls: 4.1697, loss: 4.1697 +2024-07-23 13:22:34,641 - pyskl - INFO - Epoch [42][2200/3746] lr: 8.220e-02, eta: 3 days, 14:31:41, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5297, loss_cls: 4.1256, loss: 4.1256 +2024-07-23 13:23:57,603 - pyskl - INFO - Epoch [42][2300/3746] lr: 8.218e-02, eta: 3 days, 14:30:41, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5364, loss_cls: 4.1076, loss: 4.1076 +2024-07-23 13:25:21,177 - pyskl - INFO - Epoch [42][2400/3746] lr: 8.216e-02, eta: 3 days, 14:29:42, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5403, loss_cls: 4.1329, loss: 4.1329 +2024-07-23 13:26:44,370 - pyskl - INFO - Epoch [42][2500/3746] lr: 8.214e-02, eta: 3 days, 14:28:42, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5445, loss_cls: 4.0635, loss: 4.0635 +2024-07-23 13:28:07,338 - pyskl - INFO - Epoch [42][2600/3746] lr: 8.212e-02, eta: 3 days, 14:27:42, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5228, loss_cls: 4.1498, loss: 4.1498 +2024-07-23 13:29:30,609 - pyskl - INFO - Epoch [42][2700/3746] lr: 8.210e-02, eta: 3 days, 14:26:42, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5325, loss_cls: 4.1492, loss: 4.1492 +2024-07-23 13:30:53,913 - pyskl - INFO - Epoch [42][2800/3746] lr: 8.207e-02, eta: 3 days, 14:25:42, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5288, loss_cls: 4.1750, loss: 4.1750 +2024-07-23 13:32:16,220 - pyskl - INFO - Epoch [42][2900/3746] lr: 8.205e-02, eta: 3 days, 14:24:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5345, loss_cls: 4.1612, loss: 4.1612 +2024-07-23 13:33:39,582 - pyskl - INFO - Epoch [42][3000/3746] lr: 8.203e-02, eta: 3 days, 14:23:41, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5350, loss_cls: 4.1267, loss: 4.1267 +2024-07-23 13:35:03,321 - pyskl - INFO - Epoch [42][3100/3746] lr: 8.201e-02, eta: 3 days, 14:22:42, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5361, loss_cls: 4.1445, loss: 4.1445 +2024-07-23 13:36:25,971 - pyskl - INFO - Epoch [42][3200/3746] lr: 8.199e-02, eta: 3 days, 14:21:40, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5283, loss_cls: 4.1684, loss: 4.1684 +2024-07-23 13:37:48,668 - pyskl - INFO - Epoch [42][3300/3746] lr: 8.197e-02, eta: 3 days, 14:20:39, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5359, loss_cls: 4.1145, loss: 4.1145 +2024-07-23 13:39:11,860 - pyskl - INFO - Epoch [42][3400/3746] lr: 8.195e-02, eta: 3 days, 14:19:39, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5291, loss_cls: 4.1888, loss: 4.1888 +2024-07-23 13:40:35,045 - pyskl - INFO - Epoch [42][3500/3746] lr: 8.192e-02, eta: 3 days, 14:18:39, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5323, loss_cls: 4.1706, loss: 4.1706 +2024-07-23 13:41:58,903 - pyskl - INFO - Epoch [42][3600/3746] lr: 8.190e-02, eta: 3 days, 14:17:40, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5373, loss_cls: 4.1232, loss: 4.1232 +2024-07-23 13:43:20,560 - pyskl - INFO - Epoch [42][3700/3746] lr: 8.188e-02, eta: 3 days, 14:16:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5341, loss_cls: 4.1175, loss: 4.1175 +2024-07-23 13:44:00,248 - pyskl - INFO - Saving checkpoint at 42 epochs +2024-07-23 13:45:52,640 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 13:45:53,373 - pyskl - INFO - +top1_acc 0.2274 +top5_acc 0.4725 +2024-07-23 13:45:53,373 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 13:45:53,416 - pyskl - INFO - +mean_acc 0.2274 +2024-07-23 13:45:53,421 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_39.pth was removed +2024-07-23 13:45:53,690 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_42.pth. +2024-07-23 13:45:53,691 - pyskl - INFO - Best top1_acc is 0.2274 at 42 epoch. +2024-07-23 13:45:53,701 - pyskl - INFO - Epoch(val) [42][309] top1_acc: 0.2274, top5_acc: 0.4725, mean_class_accuracy: 0.2274 +2024-07-23 13:49:38,064 - pyskl - INFO - Epoch [43][100/3746] lr: 8.185e-02, eta: 3 days, 14:19:32, time: 2.244, data_time: 1.256, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5442, loss_cls: 4.0875, loss: 4.0875 +2024-07-23 13:51:01,466 - pyskl - INFO - Epoch [43][200/3746] lr: 8.183e-02, eta: 3 days, 14:18:33, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5320, loss_cls: 4.1210, loss: 4.1210 +2024-07-23 13:52:24,634 - pyskl - INFO - Epoch [43][300/3746] lr: 8.181e-02, eta: 3 days, 14:17:32, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5342, loss_cls: 4.1430, loss: 4.1430 +2024-07-23 13:53:47,807 - pyskl - INFO - Epoch [43][400/3746] lr: 8.179e-02, eta: 3 days, 14:16:31, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5411, loss_cls: 4.1071, loss: 4.1071 +2024-07-23 13:55:10,714 - pyskl - INFO - Epoch [43][500/3746] lr: 8.176e-02, eta: 3 days, 14:15:30, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5422, loss_cls: 4.0977, loss: 4.0977 +2024-07-23 13:56:34,026 - pyskl - INFO - Epoch [43][600/3746] lr: 8.174e-02, eta: 3 days, 14:14:30, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5398, loss_cls: 4.1351, loss: 4.1351 +2024-07-23 13:57:56,990 - pyskl - INFO - Epoch [43][700/3746] lr: 8.172e-02, eta: 3 days, 14:13:28, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5452, loss_cls: 4.0851, loss: 4.0851 +2024-07-23 13:59:20,708 - pyskl - INFO - Epoch [43][800/3746] lr: 8.170e-02, eta: 3 days, 14:12:29, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5316, loss_cls: 4.1465, loss: 4.1465 +2024-07-23 14:00:44,266 - pyskl - INFO - Epoch [43][900/3746] lr: 8.168e-02, eta: 3 days, 14:11:29, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5286, loss_cls: 4.1604, loss: 4.1604 +2024-07-23 14:02:07,801 - pyskl - INFO - Epoch [43][1000/3746] lr: 8.166e-02, eta: 3 days, 14:10:29, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5261, loss_cls: 4.1704, loss: 4.1704 +2024-07-23 14:03:30,725 - pyskl - INFO - Epoch [43][1100/3746] lr: 8.163e-02, eta: 3 days, 14:09:28, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5312, loss_cls: 4.1294, loss: 4.1294 +2024-07-23 14:04:54,190 - pyskl - INFO - Epoch [43][1200/3746] lr: 8.161e-02, eta: 3 days, 14:08:28, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5352, loss_cls: 4.1303, loss: 4.1303 +2024-07-23 14:06:17,338 - pyskl - INFO - Epoch [43][1300/3746] lr: 8.159e-02, eta: 3 days, 14:07:27, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5302, loss_cls: 4.1594, loss: 4.1594 +2024-07-23 14:07:40,101 - pyskl - INFO - Epoch [43][1400/3746] lr: 8.157e-02, eta: 3 days, 14:06:25, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5344, loss_cls: 4.1395, loss: 4.1395 +2024-07-23 14:09:02,928 - pyskl - INFO - Epoch [43][1500/3746] lr: 8.155e-02, eta: 3 days, 14:05:23, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5342, loss_cls: 4.1520, loss: 4.1520 +2024-07-23 14:10:25,623 - pyskl - INFO - Epoch [43][1600/3746] lr: 8.153e-02, eta: 3 days, 14:04:21, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5359, loss_cls: 4.1326, loss: 4.1326 +2024-07-23 14:11:49,051 - pyskl - INFO - Epoch [43][1700/3746] lr: 8.150e-02, eta: 3 days, 14:03:21, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5358, loss_cls: 4.1126, loss: 4.1126 +2024-07-23 14:13:12,659 - pyskl - INFO - Epoch [43][1800/3746] lr: 8.148e-02, eta: 3 days, 14:02:21, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5280, loss_cls: 4.1552, loss: 4.1552 +2024-07-23 14:14:35,876 - pyskl - INFO - Epoch [43][1900/3746] lr: 8.146e-02, eta: 3 days, 14:01:20, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5409, loss_cls: 4.1270, loss: 4.1270 +2024-07-23 14:15:58,657 - pyskl - INFO - Epoch [43][2000/3746] lr: 8.144e-02, eta: 3 days, 14:00:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5347, loss_cls: 4.1103, loss: 4.1103 +2024-07-23 14:17:22,367 - pyskl - INFO - Epoch [43][2100/3746] lr: 8.142e-02, eta: 3 days, 13:59:18, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5281, loss_cls: 4.1492, loss: 4.1492 +2024-07-23 14:18:46,226 - pyskl - INFO - Epoch [43][2200/3746] lr: 8.140e-02, eta: 3 days, 13:58:18, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5458, loss_cls: 4.0918, loss: 4.0918 +2024-07-23 14:20:09,706 - pyskl - INFO - Epoch [43][2300/3746] lr: 8.137e-02, eta: 3 days, 13:57:18, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5353, loss_cls: 4.1128, loss: 4.1128 +2024-07-23 14:21:33,194 - pyskl - INFO - Epoch [43][2400/3746] lr: 8.135e-02, eta: 3 days, 13:56:18, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5334, loss_cls: 4.1458, loss: 4.1458 +2024-07-23 14:22:56,545 - pyskl - INFO - Epoch [43][2500/3746] lr: 8.133e-02, eta: 3 days, 13:55:17, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5309, loss_cls: 4.1377, loss: 4.1377 +2024-07-23 14:24:19,482 - pyskl - INFO - Epoch [43][2600/3746] lr: 8.131e-02, eta: 3 days, 13:54:15, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5275, loss_cls: 4.1526, loss: 4.1526 +2024-07-23 14:25:43,312 - pyskl - INFO - Epoch [43][2700/3746] lr: 8.129e-02, eta: 3 days, 13:53:15, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5250, loss_cls: 4.1686, loss: 4.1686 +2024-07-23 14:27:06,097 - pyskl - INFO - Epoch [43][2800/3746] lr: 8.126e-02, eta: 3 days, 13:52:13, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5292, loss_cls: 4.1597, loss: 4.1597 +2024-07-23 14:28:29,062 - pyskl - INFO - Epoch [43][2900/3746] lr: 8.124e-02, eta: 3 days, 13:51:11, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5298, loss_cls: 4.1576, loss: 4.1576 +2024-07-23 14:29:53,157 - pyskl - INFO - Epoch [43][3000/3746] lr: 8.122e-02, eta: 3 days, 13:50:12, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5469, loss_cls: 4.0859, loss: 4.0859 +2024-07-23 14:31:15,872 - pyskl - INFO - Epoch [43][3100/3746] lr: 8.120e-02, eta: 3 days, 13:49:10, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5341, loss_cls: 4.1514, loss: 4.1514 +2024-07-23 14:32:38,305 - pyskl - INFO - Epoch [43][3200/3746] lr: 8.118e-02, eta: 3 days, 13:48:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5278, loss_cls: 4.1533, loss: 4.1533 +2024-07-23 14:34:01,460 - pyskl - INFO - Epoch [43][3300/3746] lr: 8.116e-02, eta: 3 days, 13:47:05, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5405, loss_cls: 4.1261, loss: 4.1261 +2024-07-23 14:35:24,775 - pyskl - INFO - Epoch [43][3400/3746] lr: 8.113e-02, eta: 3 days, 13:46:04, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5366, loss_cls: 4.1182, loss: 4.1182 +2024-07-23 14:36:47,166 - pyskl - INFO - Epoch [43][3500/3746] lr: 8.111e-02, eta: 3 days, 13:45:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5269, loss_cls: 4.1954, loss: 4.1954 +2024-07-23 14:38:09,066 - pyskl - INFO - Epoch [43][3600/3746] lr: 8.109e-02, eta: 3 days, 13:43:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5337, loss_cls: 4.1612, loss: 4.1612 +2024-07-23 14:39:31,097 - pyskl - INFO - Epoch [43][3700/3746] lr: 8.107e-02, eta: 3 days, 13:42:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5366, loss_cls: 4.1261, loss: 4.1261 +2024-07-23 14:40:10,538 - pyskl - INFO - Saving checkpoint at 43 epochs +2024-07-23 14:42:03,868 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 14:42:04,581 - pyskl - INFO - +top1_acc 0.1972 +top5_acc 0.4191 +2024-07-23 14:42:04,581 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 14:42:04,628 - pyskl - INFO - +mean_acc 0.1969 +2024-07-23 14:42:04,642 - pyskl - INFO - Epoch(val) [43][309] top1_acc: 0.1972, top5_acc: 0.4191, mean_class_accuracy: 0.1969 +2024-07-23 14:45:48,206 - pyskl - INFO - Epoch [44][100/3746] lr: 8.104e-02, eta: 3 days, 13:45:35, time: 2.236, data_time: 1.243, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5358, loss_cls: 4.1159, loss: 4.1159 +2024-07-23 14:47:11,668 - pyskl - INFO - Epoch [44][200/3746] lr: 8.101e-02, eta: 3 days, 13:44:34, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5503, loss_cls: 4.0692, loss: 4.0692 +2024-07-23 14:48:34,974 - pyskl - INFO - Epoch [44][300/3746] lr: 8.099e-02, eta: 3 days, 13:43:33, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5417, loss_cls: 4.0749, loss: 4.0749 +2024-07-23 14:49:58,281 - pyskl - INFO - Epoch [44][400/3746] lr: 8.097e-02, eta: 3 days, 13:42:31, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5370, loss_cls: 4.1243, loss: 4.1243 +2024-07-23 14:51:21,343 - pyskl - INFO - Epoch [44][500/3746] lr: 8.095e-02, eta: 3 days, 13:41:29, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5344, loss_cls: 4.0863, loss: 4.0863 +2024-07-23 14:52:44,905 - pyskl - INFO - Epoch [44][600/3746] lr: 8.093e-02, eta: 3 days, 13:40:28, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5309, loss_cls: 4.1512, loss: 4.1512 +2024-07-23 14:54:08,375 - pyskl - INFO - Epoch [44][700/3746] lr: 8.090e-02, eta: 3 days, 13:39:27, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5442, loss_cls: 4.1207, loss: 4.1207 +2024-07-23 14:55:32,027 - pyskl - INFO - Epoch [44][800/3746] lr: 8.088e-02, eta: 3 days, 13:38:26, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5347, loss_cls: 4.1123, loss: 4.1123 +2024-07-23 14:56:55,403 - pyskl - INFO - Epoch [44][900/3746] lr: 8.086e-02, eta: 3 days, 13:37:25, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5348, loss_cls: 4.1289, loss: 4.1289 +2024-07-23 14:58:18,722 - pyskl - INFO - Epoch [44][1000/3746] lr: 8.084e-02, eta: 3 days, 13:36:23, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5302, loss_cls: 4.1452, loss: 4.1452 +2024-07-23 14:59:42,150 - pyskl - INFO - Epoch [44][1100/3746] lr: 8.082e-02, eta: 3 days, 13:35:21, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5342, loss_cls: 4.1273, loss: 4.1273 +2024-07-23 15:01:05,091 - pyskl - INFO - Epoch [44][1200/3746] lr: 8.079e-02, eta: 3 days, 13:34:19, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5262, loss_cls: 4.1751, loss: 4.1751 +2024-07-23 15:02:28,032 - pyskl - INFO - Epoch [44][1300/3746] lr: 8.077e-02, eta: 3 days, 13:33:16, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5402, loss_cls: 4.1166, loss: 4.1166 +2024-07-23 15:03:51,389 - pyskl - INFO - Epoch [44][1400/3746] lr: 8.075e-02, eta: 3 days, 13:32:14, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5363, loss_cls: 4.1342, loss: 4.1342 +2024-07-23 15:05:15,018 - pyskl - INFO - Epoch [44][1500/3746] lr: 8.073e-02, eta: 3 days, 13:31:13, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5373, loss_cls: 4.1178, loss: 4.1178 +2024-07-23 15:06:38,606 - pyskl - INFO - Epoch [44][1600/3746] lr: 8.071e-02, eta: 3 days, 13:30:12, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5389, loss_cls: 4.1095, loss: 4.1095 +2024-07-23 15:08:02,261 - pyskl - INFO - Epoch [44][1700/3746] lr: 8.068e-02, eta: 3 days, 13:29:11, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5377, loss_cls: 4.1257, loss: 4.1257 +2024-07-23 15:09:25,554 - pyskl - INFO - Epoch [44][1800/3746] lr: 8.066e-02, eta: 3 days, 13:28:09, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5331, loss_cls: 4.1462, loss: 4.1462 +2024-07-23 15:10:49,072 - pyskl - INFO - Epoch [44][1900/3746] lr: 8.064e-02, eta: 3 days, 13:27:08, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5395, loss_cls: 4.1040, loss: 4.1040 +2024-07-23 15:12:11,944 - pyskl - INFO - Epoch [44][2000/3746] lr: 8.062e-02, eta: 3 days, 13:26:05, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5364, loss_cls: 4.1101, loss: 4.1101 +2024-07-23 15:13:34,577 - pyskl - INFO - Epoch [44][2100/3746] lr: 8.060e-02, eta: 3 days, 13:25:01, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5328, loss_cls: 4.1258, loss: 4.1258 +2024-07-23 15:14:57,816 - pyskl - INFO - Epoch [44][2200/3746] lr: 8.057e-02, eta: 3 days, 13:23:59, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5375, loss_cls: 4.1078, loss: 4.1078 +2024-07-23 15:16:20,977 - pyskl - INFO - Epoch [44][2300/3746] lr: 8.055e-02, eta: 3 days, 13:22:56, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5372, loss_cls: 4.0918, loss: 4.0918 +2024-07-23 15:17:44,498 - pyskl - INFO - Epoch [44][2400/3746] lr: 8.053e-02, eta: 3 days, 13:21:55, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5413, loss_cls: 4.0910, loss: 4.0910 +2024-07-23 15:19:07,868 - pyskl - INFO - Epoch [44][2500/3746] lr: 8.051e-02, eta: 3 days, 13:20:53, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5336, loss_cls: 4.1587, loss: 4.1587 +2024-07-23 15:20:31,579 - pyskl - INFO - Epoch [44][2600/3746] lr: 8.048e-02, eta: 3 days, 13:19:52, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5281, loss_cls: 4.1474, loss: 4.1474 +2024-07-23 15:21:54,565 - pyskl - INFO - Epoch [44][2700/3746] lr: 8.046e-02, eta: 3 days, 13:18:49, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5416, loss_cls: 4.1020, loss: 4.1020 +2024-07-23 15:23:17,254 - pyskl - INFO - Epoch [44][2800/3746] lr: 8.044e-02, eta: 3 days, 13:17:45, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5344, loss_cls: 4.1375, loss: 4.1375 +2024-07-23 15:24:40,867 - pyskl - INFO - Epoch [44][2900/3746] lr: 8.042e-02, eta: 3 days, 13:16:44, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5345, loss_cls: 4.1247, loss: 4.1247 +2024-07-23 15:26:03,492 - pyskl - INFO - Epoch [44][3000/3746] lr: 8.040e-02, eta: 3 days, 13:15:40, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5286, loss_cls: 4.1716, loss: 4.1716 +2024-07-23 15:27:26,378 - pyskl - INFO - Epoch [44][3100/3746] lr: 8.037e-02, eta: 3 days, 13:14:37, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5366, loss_cls: 4.1278, loss: 4.1278 +2024-07-23 15:28:48,991 - pyskl - INFO - Epoch [44][3200/3746] lr: 8.035e-02, eta: 3 days, 13:13:33, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5322, loss_cls: 4.1554, loss: 4.1554 +2024-07-23 15:30:12,314 - pyskl - INFO - Epoch [44][3300/3746] lr: 8.033e-02, eta: 3 days, 13:12:30, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5288, loss_cls: 4.1764, loss: 4.1764 +2024-07-23 15:31:35,464 - pyskl - INFO - Epoch [44][3400/3746] lr: 8.031e-02, eta: 3 days, 13:11:28, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5283, loss_cls: 4.1387, loss: 4.1387 +2024-07-23 15:32:58,978 - pyskl - INFO - Epoch [44][3500/3746] lr: 8.028e-02, eta: 3 days, 13:10:26, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5352, loss_cls: 4.1478, loss: 4.1478 +2024-07-23 15:34:20,694 - pyskl - INFO - Epoch [44][3600/3746] lr: 8.026e-02, eta: 3 days, 13:09:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5297, loss_cls: 4.1740, loss: 4.1740 +2024-07-23 15:35:42,717 - pyskl - INFO - Epoch [44][3700/3746] lr: 8.024e-02, eta: 3 days, 13:08:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5341, loss_cls: 4.1354, loss: 4.1354 +2024-07-23 15:36:22,386 - pyskl - INFO - Saving checkpoint at 44 epochs +2024-07-23 15:38:14,842 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 15:38:15,540 - pyskl - INFO - +top1_acc 0.2000 +top5_acc 0.4327 +2024-07-23 15:38:15,540 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 15:38:15,582 - pyskl - INFO - +mean_acc 0.1999 +2024-07-23 15:38:15,596 - pyskl - INFO - Epoch(val) [44][309] top1_acc: 0.2000, top5_acc: 0.4327, mean_class_accuracy: 0.1999 +2024-07-23 15:42:00,714 - pyskl - INFO - Epoch [45][100/3746] lr: 8.021e-02, eta: 3 days, 13:10:52, time: 2.251, data_time: 1.258, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5386, loss_cls: 4.0891, loss: 4.0891 +2024-07-23 15:43:24,121 - pyskl - INFO - Epoch [45][200/3746] lr: 8.019e-02, eta: 3 days, 13:09:50, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5477, loss_cls: 4.0667, loss: 4.0667 +2024-07-23 15:44:47,437 - pyskl - INFO - Epoch [45][300/3746] lr: 8.016e-02, eta: 3 days, 13:08:47, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5331, loss_cls: 4.1304, loss: 4.1304 +2024-07-23 15:46:10,746 - pyskl - INFO - Epoch [45][400/3746] lr: 8.014e-02, eta: 3 days, 13:07:44, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5366, loss_cls: 4.1187, loss: 4.1187 +2024-07-23 15:47:34,523 - pyskl - INFO - Epoch [45][500/3746] lr: 8.012e-02, eta: 3 days, 13:06:43, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5389, loss_cls: 4.0996, loss: 4.0996 +2024-07-23 15:48:57,978 - pyskl - INFO - Epoch [45][600/3746] lr: 8.010e-02, eta: 3 days, 13:05:40, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5452, loss_cls: 4.0767, loss: 4.0767 +2024-07-23 15:50:21,330 - pyskl - INFO - Epoch [45][700/3746] lr: 8.007e-02, eta: 3 days, 13:04:37, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5392, loss_cls: 4.0883, loss: 4.0883 +2024-07-23 15:51:44,923 - pyskl - INFO - Epoch [45][800/3746] lr: 8.005e-02, eta: 3 days, 13:03:35, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5413, loss_cls: 4.1073, loss: 4.1073 +2024-07-23 15:53:08,596 - pyskl - INFO - Epoch [45][900/3746] lr: 8.003e-02, eta: 3 days, 13:02:33, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5311, loss_cls: 4.1327, loss: 4.1327 +2024-07-23 15:54:31,856 - pyskl - INFO - Epoch [45][1000/3746] lr: 8.001e-02, eta: 3 days, 13:01:30, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5403, loss_cls: 4.1144, loss: 4.1144 +2024-07-23 15:55:55,465 - pyskl - INFO - Epoch [45][1100/3746] lr: 7.998e-02, eta: 3 days, 13:00:28, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5389, loss_cls: 4.1129, loss: 4.1129 +2024-07-23 15:57:18,309 - pyskl - INFO - Epoch [45][1200/3746] lr: 7.996e-02, eta: 3 days, 12:59:24, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5408, loss_cls: 4.1325, loss: 4.1325 +2024-07-23 15:58:41,740 - pyskl - INFO - Epoch [45][1300/3746] lr: 7.994e-02, eta: 3 days, 12:58:21, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5197, loss_cls: 4.1945, loss: 4.1945 +2024-07-23 16:00:04,834 - pyskl - INFO - Epoch [45][1400/3746] lr: 7.992e-02, eta: 3 days, 12:57:18, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5367, loss_cls: 4.1254, loss: 4.1254 +2024-07-23 16:01:27,871 - pyskl - INFO - Epoch [45][1500/3746] lr: 7.990e-02, eta: 3 days, 12:56:14, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5377, loss_cls: 4.1249, loss: 4.1249 +2024-07-23 16:02:50,793 - pyskl - INFO - Epoch [45][1600/3746] lr: 7.987e-02, eta: 3 days, 12:55:10, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5314, loss_cls: 4.1314, loss: 4.1314 +2024-07-23 16:04:14,167 - pyskl - INFO - Epoch [45][1700/3746] lr: 7.985e-02, eta: 3 days, 12:54:07, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5430, loss_cls: 4.1036, loss: 4.1036 +2024-07-23 16:05:37,838 - pyskl - INFO - Epoch [45][1800/3746] lr: 7.983e-02, eta: 3 days, 12:53:05, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5258, loss_cls: 4.1462, loss: 4.1462 +2024-07-23 16:07:01,409 - pyskl - INFO - Epoch [45][1900/3746] lr: 7.981e-02, eta: 3 days, 12:52:03, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5436, loss_cls: 4.0769, loss: 4.0769 +2024-07-23 16:08:24,901 - pyskl - INFO - Epoch [45][2000/3746] lr: 7.978e-02, eta: 3 days, 12:51:00, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5358, loss_cls: 4.1183, loss: 4.1183 +2024-07-23 16:09:48,508 - pyskl - INFO - Epoch [45][2100/3746] lr: 7.976e-02, eta: 3 days, 12:49:58, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5306, loss_cls: 4.1419, loss: 4.1419 +2024-07-23 16:11:12,012 - pyskl - INFO - Epoch [45][2200/3746] lr: 7.974e-02, eta: 3 days, 12:48:55, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5314, loss_cls: 4.1497, loss: 4.1497 +2024-07-23 16:12:35,378 - pyskl - INFO - Epoch [45][2300/3746] lr: 7.972e-02, eta: 3 days, 12:47:52, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5350, loss_cls: 4.1272, loss: 4.1272 +2024-07-23 16:13:58,689 - pyskl - INFO - Epoch [45][2400/3746] lr: 7.969e-02, eta: 3 days, 12:46:49, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5377, loss_cls: 4.1086, loss: 4.1086 +2024-07-23 16:15:21,491 - pyskl - INFO - Epoch [45][2500/3746] lr: 7.967e-02, eta: 3 days, 12:45:44, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5308, loss_cls: 4.1486, loss: 4.1486 +2024-07-23 16:16:45,320 - pyskl - INFO - Epoch [45][2600/3746] lr: 7.965e-02, eta: 3 days, 12:44:42, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5358, loss_cls: 4.1269, loss: 4.1269 +2024-07-23 16:18:07,340 - pyskl - INFO - Epoch [45][2700/3746] lr: 7.963e-02, eta: 3 days, 12:43:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5291, loss_cls: 4.1395, loss: 4.1395 +2024-07-23 16:19:30,456 - pyskl - INFO - Epoch [45][2800/3746] lr: 7.960e-02, eta: 3 days, 12:42:32, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5464, loss_cls: 4.1157, loss: 4.1157 +2024-07-23 16:20:54,064 - pyskl - INFO - Epoch [45][2900/3746] lr: 7.958e-02, eta: 3 days, 12:41:30, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5383, loss_cls: 4.1047, loss: 4.1047 +2024-07-23 16:22:16,956 - pyskl - INFO - Epoch [45][3000/3746] lr: 7.956e-02, eta: 3 days, 12:40:25, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5302, loss_cls: 4.1394, loss: 4.1394 +2024-07-23 16:23:39,816 - pyskl - INFO - Epoch [45][3100/3746] lr: 7.954e-02, eta: 3 days, 12:39:21, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5428, loss_cls: 4.0846, loss: 4.0846 +2024-07-23 16:25:03,310 - pyskl - INFO - Epoch [45][3200/3746] lr: 7.951e-02, eta: 3 days, 12:38:18, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5305, loss_cls: 4.1904, loss: 4.1904 +2024-07-23 16:26:26,551 - pyskl - INFO - Epoch [45][3300/3746] lr: 7.949e-02, eta: 3 days, 12:37:14, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5473, loss_cls: 4.0766, loss: 4.0766 +2024-07-23 16:27:49,475 - pyskl - INFO - Epoch [45][3400/3746] lr: 7.947e-02, eta: 3 days, 12:36:10, time: 0.829, data_time: 0.001, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5261, loss_cls: 4.1719, loss: 4.1719 +2024-07-23 16:29:12,566 - pyskl - INFO - Epoch [45][3500/3746] lr: 7.945e-02, eta: 3 days, 12:35:06, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5455, loss_cls: 4.1078, loss: 4.1078 +2024-07-23 16:30:35,349 - pyskl - INFO - Epoch [45][3600/3746] lr: 7.942e-02, eta: 3 days, 12:34:01, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5458, loss_cls: 4.0870, loss: 4.0870 +2024-07-23 16:31:58,185 - pyskl - INFO - Epoch [45][3700/3746] lr: 7.940e-02, eta: 3 days, 12:32:57, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5233, loss_cls: 4.1667, loss: 4.1667 +2024-07-23 16:32:38,483 - pyskl - INFO - Saving checkpoint at 45 epochs +2024-07-23 16:34:30,903 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 16:34:31,594 - pyskl - INFO - +top1_acc 0.2004 +top5_acc 0.4352 +2024-07-23 16:34:31,594 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 16:34:31,632 - pyskl - INFO - +mean_acc 0.2002 +2024-07-23 16:34:31,643 - pyskl - INFO - Epoch(val) [45][309] top1_acc: 0.2004, top5_acc: 0.4352, mean_class_accuracy: 0.2002 +2024-07-23 16:38:15,595 - pyskl - INFO - Epoch [46][100/3746] lr: 7.937e-02, eta: 3 days, 12:35:22, time: 2.239, data_time: 1.249, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5556, loss_cls: 4.0364, loss: 4.0364 +2024-07-23 16:39:38,833 - pyskl - INFO - Epoch [46][200/3746] lr: 7.934e-02, eta: 3 days, 12:34:18, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5452, loss_cls: 4.0935, loss: 4.0935 +2024-07-23 16:41:01,799 - pyskl - INFO - Epoch [46][300/3746] lr: 7.932e-02, eta: 3 days, 12:33:14, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5427, loss_cls: 4.0661, loss: 4.0661 +2024-07-23 16:42:24,954 - pyskl - INFO - Epoch [46][400/3746] lr: 7.930e-02, eta: 3 days, 12:32:10, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5453, loss_cls: 4.0886, loss: 4.0886 +2024-07-23 16:43:48,012 - pyskl - INFO - Epoch [46][500/3746] lr: 7.928e-02, eta: 3 days, 12:31:05, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5372, loss_cls: 4.1161, loss: 4.1161 +2024-07-23 16:45:11,420 - pyskl - INFO - Epoch [46][600/3746] lr: 7.925e-02, eta: 3 days, 12:30:02, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5306, loss_cls: 4.1256, loss: 4.1256 +2024-07-23 16:46:34,846 - pyskl - INFO - Epoch [46][700/3746] lr: 7.923e-02, eta: 3 days, 12:28:58, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5394, loss_cls: 4.1114, loss: 4.1114 +2024-07-23 16:47:57,877 - pyskl - INFO - Epoch [46][800/3746] lr: 7.921e-02, eta: 3 days, 12:27:53, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5342, loss_cls: 4.1285, loss: 4.1285 +2024-07-23 16:49:20,750 - pyskl - INFO - Epoch [46][900/3746] lr: 7.919e-02, eta: 3 days, 12:26:48, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5266, loss_cls: 4.1484, loss: 4.1484 +2024-07-23 16:50:43,879 - pyskl - INFO - Epoch [46][1000/3746] lr: 7.916e-02, eta: 3 days, 12:25:44, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5475, loss_cls: 4.0646, loss: 4.0646 +2024-07-23 16:52:06,628 - pyskl - INFO - Epoch [46][1100/3746] lr: 7.914e-02, eta: 3 days, 12:24:39, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5369, loss_cls: 4.0849, loss: 4.0849 +2024-07-23 16:53:29,915 - pyskl - INFO - Epoch [46][1200/3746] lr: 7.912e-02, eta: 3 days, 12:23:35, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5434, loss_cls: 4.0909, loss: 4.0909 +2024-07-23 16:54:53,461 - pyskl - INFO - Epoch [46][1300/3746] lr: 7.909e-02, eta: 3 days, 12:22:31, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5312, loss_cls: 4.1260, loss: 4.1260 +2024-07-23 16:56:16,845 - pyskl - INFO - Epoch [46][1400/3746] lr: 7.907e-02, eta: 3 days, 12:21:27, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5383, loss_cls: 4.1076, loss: 4.1076 +2024-07-23 16:57:40,333 - pyskl - INFO - Epoch [46][1500/3746] lr: 7.905e-02, eta: 3 days, 12:20:24, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5419, loss_cls: 4.1007, loss: 4.1007 +2024-07-23 16:59:03,983 - pyskl - INFO - Epoch [46][1600/3746] lr: 7.903e-02, eta: 3 days, 12:19:20, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5325, loss_cls: 4.1707, loss: 4.1707 +2024-07-23 17:00:27,338 - pyskl - INFO - Epoch [46][1700/3746] lr: 7.900e-02, eta: 3 days, 12:18:16, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5333, loss_cls: 4.1380, loss: 4.1380 +2024-07-23 17:01:49,489 - pyskl - INFO - Epoch [46][1800/3746] lr: 7.898e-02, eta: 3 days, 12:17:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5375, loss_cls: 4.1109, loss: 4.1109 +2024-07-23 17:03:11,581 - pyskl - INFO - Epoch [46][1900/3746] lr: 7.896e-02, eta: 3 days, 12:16:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5456, loss_cls: 4.0998, loss: 4.0998 +2024-07-23 17:04:33,603 - pyskl - INFO - Epoch [46][2000/3746] lr: 7.894e-02, eta: 3 days, 12:14:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5417, loss_cls: 4.0956, loss: 4.0956 +2024-07-23 17:05:55,067 - pyskl - INFO - Epoch [46][2100/3746] lr: 7.891e-02, eta: 3 days, 12:13:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5430, loss_cls: 4.1024, loss: 4.1024 +2024-07-23 17:07:17,935 - pyskl - INFO - Epoch [46][2200/3746] lr: 7.889e-02, eta: 3 days, 12:12:42, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5442, loss_cls: 4.0896, loss: 4.0896 +2024-07-23 17:08:39,624 - pyskl - INFO - Epoch [46][2300/3746] lr: 7.887e-02, eta: 3 days, 12:11:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5394, loss_cls: 4.1142, loss: 4.1142 +2024-07-23 17:10:01,052 - pyskl - INFO - Epoch [46][2400/3746] lr: 7.884e-02, eta: 3 days, 12:10:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5431, loss_cls: 4.1091, loss: 4.1091 +2024-07-23 17:11:23,477 - pyskl - INFO - Epoch [46][2500/3746] lr: 7.882e-02, eta: 3 days, 12:09:19, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5386, loss_cls: 4.1060, loss: 4.1060 +2024-07-23 17:12:45,075 - pyskl - INFO - Epoch [46][2600/3746] lr: 7.880e-02, eta: 3 days, 12:08:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5281, loss_cls: 4.1579, loss: 4.1579 +2024-07-23 17:14:07,575 - pyskl - INFO - Epoch [46][2700/3746] lr: 7.878e-02, eta: 3 days, 12:07:05, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5347, loss_cls: 4.1155, loss: 4.1155 +2024-07-23 17:15:29,662 - pyskl - INFO - Epoch [46][2800/3746] lr: 7.875e-02, eta: 3 days, 12:05:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5384, loss_cls: 4.1179, loss: 4.1179 +2024-07-23 17:16:51,260 - pyskl - INFO - Epoch [46][2900/3746] lr: 7.873e-02, eta: 3 days, 12:04:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5419, loss_cls: 4.1145, loss: 4.1145 +2024-07-23 17:18:13,306 - pyskl - INFO - Epoch [46][3000/3746] lr: 7.871e-02, eta: 3 days, 12:03:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5252, loss_cls: 4.1701, loss: 4.1701 +2024-07-23 17:19:35,778 - pyskl - INFO - Epoch [46][3100/3746] lr: 7.868e-02, eta: 3 days, 12:02:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5405, loss_cls: 4.0728, loss: 4.0728 +2024-07-23 17:20:58,047 - pyskl - INFO - Epoch [46][3200/3746] lr: 7.866e-02, eta: 3 days, 12:01:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5455, loss_cls: 4.1057, loss: 4.1057 +2024-07-23 17:22:20,228 - pyskl - INFO - Epoch [46][3300/3746] lr: 7.864e-02, eta: 3 days, 12:00:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5345, loss_cls: 4.1260, loss: 4.1260 +2024-07-23 17:23:41,706 - pyskl - INFO - Epoch [46][3400/3746] lr: 7.862e-02, eta: 3 days, 11:59:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5348, loss_cls: 4.1598, loss: 4.1598 +2024-07-23 17:25:03,415 - pyskl - INFO - Epoch [46][3500/3746] lr: 7.859e-02, eta: 3 days, 11:58:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5320, loss_cls: 4.1602, loss: 4.1602 +2024-07-23 17:26:25,579 - pyskl - INFO - Epoch [46][3600/3746] lr: 7.857e-02, eta: 3 days, 11:56:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5341, loss_cls: 4.1444, loss: 4.1444 +2024-07-23 17:27:47,959 - pyskl - INFO - Epoch [46][3700/3746] lr: 7.855e-02, eta: 3 days, 11:55:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5306, loss_cls: 4.1159, loss: 4.1159 +2024-07-23 17:28:27,546 - pyskl - INFO - Saving checkpoint at 46 epochs +2024-07-23 17:30:20,929 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 17:30:21,604 - pyskl - INFO - +top1_acc 0.2059 +top5_acc 0.4425 +2024-07-23 17:30:21,604 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 17:30:21,647 - pyskl - INFO - +mean_acc 0.2057 +2024-07-23 17:30:21,662 - pyskl - INFO - Epoch(val) [46][309] top1_acc: 0.2059, top5_acc: 0.4425, mean_class_accuracy: 0.2057 +2024-07-23 17:34:11,651 - pyskl - INFO - Epoch [47][100/3746] lr: 7.851e-02, eta: 3 days, 11:58:22, time: 2.300, data_time: 1.304, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5594, loss_cls: 4.0207, loss: 4.0207 +2024-07-23 17:35:33,698 - pyskl - INFO - Epoch [47][200/3746] lr: 7.849e-02, eta: 3 days, 11:57:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5413, loss_cls: 4.0804, loss: 4.0804 +2024-07-23 17:36:55,651 - pyskl - INFO - Epoch [47][300/3746] lr: 7.847e-02, eta: 3 days, 11:56:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5494, loss_cls: 4.0415, loss: 4.0415 +2024-07-23 17:38:17,605 - pyskl - INFO - Epoch [47][400/3746] lr: 7.844e-02, eta: 3 days, 11:54:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5477, loss_cls: 4.0685, loss: 4.0685 +2024-07-23 17:39:39,174 - pyskl - INFO - Epoch [47][500/3746] lr: 7.842e-02, eta: 3 days, 11:53:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5366, loss_cls: 4.1095, loss: 4.1095 +2024-07-23 17:41:01,310 - pyskl - INFO - Epoch [47][600/3746] lr: 7.840e-02, eta: 3 days, 11:52:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5423, loss_cls: 4.0835, loss: 4.0835 +2024-07-23 17:42:23,146 - pyskl - INFO - Epoch [47][700/3746] lr: 7.838e-02, eta: 3 days, 11:51:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5377, loss_cls: 4.0941, loss: 4.0941 +2024-07-23 17:43:44,854 - pyskl - INFO - Epoch [47][800/3746] lr: 7.835e-02, eta: 3 days, 11:50:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5447, loss_cls: 4.0922, loss: 4.0922 +2024-07-23 17:45:06,393 - pyskl - INFO - Epoch [47][900/3746] lr: 7.833e-02, eta: 3 days, 11:49:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5336, loss_cls: 4.1435, loss: 4.1435 +2024-07-23 17:46:28,006 - pyskl - INFO - Epoch [47][1000/3746] lr: 7.831e-02, eta: 3 days, 11:48:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5389, loss_cls: 4.1152, loss: 4.1152 +2024-07-23 17:47:49,420 - pyskl - INFO - Epoch [47][1100/3746] lr: 7.828e-02, eta: 3 days, 11:46:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5278, loss_cls: 4.1657, loss: 4.1657 +2024-07-23 17:49:10,753 - pyskl - INFO - Epoch [47][1200/3746] lr: 7.826e-02, eta: 3 days, 11:45:50, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5366, loss_cls: 4.1098, loss: 4.1098 +2024-07-23 17:50:32,137 - pyskl - INFO - Epoch [47][1300/3746] lr: 7.824e-02, eta: 3 days, 11:44:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5470, loss_cls: 4.0750, loss: 4.0750 +2024-07-23 17:51:53,760 - pyskl - INFO - Epoch [47][1400/3746] lr: 7.821e-02, eta: 3 days, 11:43:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5392, loss_cls: 4.0956, loss: 4.0956 +2024-07-23 17:53:15,658 - pyskl - INFO - Epoch [47][1500/3746] lr: 7.819e-02, eta: 3 days, 11:42:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5491, loss_cls: 4.0701, loss: 4.0701 +2024-07-23 17:54:37,148 - pyskl - INFO - Epoch [47][1600/3746] lr: 7.817e-02, eta: 3 days, 11:41:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5303, loss_cls: 4.1242, loss: 4.1242 +2024-07-23 17:55:58,492 - pyskl - INFO - Epoch [47][1700/3746] lr: 7.814e-02, eta: 3 days, 11:40:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5392, loss_cls: 4.1392, loss: 4.1392 +2024-07-23 17:57:19,992 - pyskl - INFO - Epoch [47][1800/3746] lr: 7.812e-02, eta: 3 days, 11:38:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5341, loss_cls: 4.1185, loss: 4.1185 +2024-07-23 17:58:41,727 - pyskl - INFO - Epoch [47][1900/3746] lr: 7.810e-02, eta: 3 days, 11:37:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5391, loss_cls: 4.1111, loss: 4.1111 +2024-07-23 18:00:03,196 - pyskl - INFO - Epoch [47][2000/3746] lr: 7.808e-02, eta: 3 days, 11:36:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5298, loss_cls: 4.1570, loss: 4.1570 +2024-07-23 18:01:24,407 - pyskl - INFO - Epoch [47][2100/3746] lr: 7.805e-02, eta: 3 days, 11:35:28, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5345, loss_cls: 4.1360, loss: 4.1360 +2024-07-23 18:02:46,355 - pyskl - INFO - Epoch [47][2200/3746] lr: 7.803e-02, eta: 3 days, 11:34:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5387, loss_cls: 4.1300, loss: 4.1300 +2024-07-23 18:04:07,548 - pyskl - INFO - Epoch [47][2300/3746] lr: 7.801e-02, eta: 3 days, 11:33:10, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5353, loss_cls: 4.1294, loss: 4.1294 +2024-07-23 18:05:29,594 - pyskl - INFO - Epoch [47][2400/3746] lr: 7.798e-02, eta: 3 days, 11:32:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5327, loss_cls: 4.1253, loss: 4.1253 +2024-07-23 18:06:51,539 - pyskl - INFO - Epoch [47][2500/3746] lr: 7.796e-02, eta: 3 days, 11:30:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5317, loss_cls: 4.1425, loss: 4.1425 +2024-07-23 18:08:13,684 - pyskl - INFO - Epoch [47][2600/3746] lr: 7.794e-02, eta: 3 days, 11:29:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5416, loss_cls: 4.0866, loss: 4.0866 +2024-07-23 18:09:35,784 - pyskl - INFO - Epoch [47][2700/3746] lr: 7.791e-02, eta: 3 days, 11:28:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5222, loss_cls: 4.1341, loss: 4.1341 +2024-07-23 18:10:57,823 - pyskl - INFO - Epoch [47][2800/3746] lr: 7.789e-02, eta: 3 days, 11:27:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5416, loss_cls: 4.1026, loss: 4.1026 +2024-07-23 18:12:19,572 - pyskl - INFO - Epoch [47][2900/3746] lr: 7.787e-02, eta: 3 days, 11:26:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5387, loss_cls: 4.0961, loss: 4.0961 +2024-07-23 18:13:41,939 - pyskl - INFO - Epoch [47][3000/3746] lr: 7.784e-02, eta: 3 days, 11:25:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5398, loss_cls: 4.0980, loss: 4.0980 +2024-07-23 18:15:04,521 - pyskl - INFO - Epoch [47][3100/3746] lr: 7.782e-02, eta: 3 days, 11:24:08, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5548, loss_cls: 4.0667, loss: 4.0667 +2024-07-23 18:16:26,397 - pyskl - INFO - Epoch [47][3200/3746] lr: 7.780e-02, eta: 3 days, 11:22:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5323, loss_cls: 4.1379, loss: 4.1379 +2024-07-23 18:17:48,819 - pyskl - INFO - Epoch [47][3300/3746] lr: 7.777e-02, eta: 3 days, 11:21:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5431, loss_cls: 4.0814, loss: 4.0814 +2024-07-23 18:19:10,345 - pyskl - INFO - Epoch [47][3400/3746] lr: 7.775e-02, eta: 3 days, 11:20:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5267, loss_cls: 4.1994, loss: 4.1994 +2024-07-23 18:20:32,042 - pyskl - INFO - Epoch [47][3500/3746] lr: 7.773e-02, eta: 3 days, 11:19:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5394, loss_cls: 4.1283, loss: 4.1283 +2024-07-23 18:21:53,968 - pyskl - INFO - Epoch [47][3600/3746] lr: 7.770e-02, eta: 3 days, 11:18:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5403, loss_cls: 4.0812, loss: 4.0812 +2024-07-23 18:23:16,311 - pyskl - INFO - Epoch [47][3700/3746] lr: 7.768e-02, eta: 3 days, 11:17:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5442, loss_cls: 4.0726, loss: 4.0726 +2024-07-23 18:23:55,470 - pyskl - INFO - Saving checkpoint at 47 epochs +2024-07-23 18:25:48,828 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 18:25:49,499 - pyskl - INFO - +top1_acc 0.1976 +top5_acc 0.4300 +2024-07-23 18:25:49,499 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 18:25:49,543 - pyskl - INFO - +mean_acc 0.1974 +2024-07-23 18:25:49,556 - pyskl - INFO - Epoch(val) [47][309] top1_acc: 0.1976, top5_acc: 0.4300, mean_class_accuracy: 0.1974 +2024-07-23 18:29:45,492 - pyskl - INFO - Epoch [48][100/3746] lr: 7.765e-02, eta: 3 days, 11:19:52, time: 2.359, data_time: 1.367, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5417, loss_cls: 4.0927, loss: 4.0927 +2024-07-23 18:31:08,728 - pyskl - INFO - Epoch [48][200/3746] lr: 7.762e-02, eta: 3 days, 11:18:47, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5464, loss_cls: 4.0732, loss: 4.0732 +2024-07-23 18:32:31,972 - pyskl - INFO - Epoch [48][300/3746] lr: 7.760e-02, eta: 3 days, 11:17:41, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5427, loss_cls: 4.1144, loss: 4.1144 +2024-07-23 18:33:55,750 - pyskl - INFO - Epoch [48][400/3746] lr: 7.758e-02, eta: 3 days, 11:16:36, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5461, loss_cls: 4.0614, loss: 4.0614 +2024-07-23 18:35:18,462 - pyskl - INFO - Epoch [48][500/3746] lr: 7.755e-02, eta: 3 days, 11:15:29, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5548, loss_cls: 4.0309, loss: 4.0309 +2024-07-23 18:36:42,459 - pyskl - INFO - Epoch [48][600/3746] lr: 7.753e-02, eta: 3 days, 11:14:25, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5469, loss_cls: 4.0498, loss: 4.0498 +2024-07-23 18:38:05,506 - pyskl - INFO - Epoch [48][700/3746] lr: 7.751e-02, eta: 3 days, 11:13:19, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5381, loss_cls: 4.1313, loss: 4.1313 +2024-07-23 18:39:28,976 - pyskl - INFO - Epoch [48][800/3746] lr: 7.748e-02, eta: 3 days, 11:12:13, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5366, loss_cls: 4.1049, loss: 4.1049 +2024-07-23 18:40:51,797 - pyskl - INFO - Epoch [48][900/3746] lr: 7.746e-02, eta: 3 days, 11:11:06, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5378, loss_cls: 4.0874, loss: 4.0874 +2024-07-23 18:42:13,710 - pyskl - INFO - Epoch [48][1000/3746] lr: 7.744e-02, eta: 3 days, 11:09:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5473, loss_cls: 4.0610, loss: 4.0610 +2024-07-23 18:43:35,693 - pyskl - INFO - Epoch [48][1100/3746] lr: 7.741e-02, eta: 3 days, 11:08:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5467, loss_cls: 4.0461, loss: 4.0461 +2024-07-23 18:44:58,158 - pyskl - INFO - Epoch [48][1200/3746] lr: 7.739e-02, eta: 3 days, 11:07:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5291, loss_cls: 4.1410, loss: 4.1410 +2024-07-23 18:46:19,957 - pyskl - INFO - Epoch [48][1300/3746] lr: 7.737e-02, eta: 3 days, 11:06:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5450, loss_cls: 4.0807, loss: 4.0807 +2024-07-23 18:47:41,260 - pyskl - INFO - Epoch [48][1400/3746] lr: 7.734e-02, eta: 3 days, 11:05:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5389, loss_cls: 4.1104, loss: 4.1104 +2024-07-23 18:49:03,406 - pyskl - INFO - Epoch [48][1500/3746] lr: 7.732e-02, eta: 3 days, 11:04:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5411, loss_cls: 4.1037, loss: 4.1037 +2024-07-23 18:50:24,922 - pyskl - INFO - Epoch [48][1600/3746] lr: 7.730e-02, eta: 3 days, 11:03:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5378, loss_cls: 4.1041, loss: 4.1041 +2024-07-23 18:51:46,865 - pyskl - INFO - Epoch [48][1700/3746] lr: 7.727e-02, eta: 3 days, 11:01:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5408, loss_cls: 4.1275, loss: 4.1275 +2024-07-23 18:53:08,265 - pyskl - INFO - Epoch [48][1800/3746] lr: 7.725e-02, eta: 3 days, 11:00:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5391, loss_cls: 4.1247, loss: 4.1247 +2024-07-23 18:54:30,095 - pyskl - INFO - Epoch [48][1900/3746] lr: 7.723e-02, eta: 3 days, 10:59:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5314, loss_cls: 4.1391, loss: 4.1391 +2024-07-23 18:55:51,761 - pyskl - INFO - Epoch [48][2000/3746] lr: 7.720e-02, eta: 3 days, 10:58:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5413, loss_cls: 4.1266, loss: 4.1266 +2024-07-23 18:57:13,289 - pyskl - INFO - Epoch [48][2100/3746] lr: 7.718e-02, eta: 3 days, 10:57:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5342, loss_cls: 4.1321, loss: 4.1321 +2024-07-23 18:58:35,015 - pyskl - INFO - Epoch [48][2200/3746] lr: 7.716e-02, eta: 3 days, 10:56:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5353, loss_cls: 4.1240, loss: 4.1240 +2024-07-23 18:59:56,434 - pyskl - INFO - Epoch [48][2300/3746] lr: 7.713e-02, eta: 3 days, 10:54:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5392, loss_cls: 4.0898, loss: 4.0898 +2024-07-23 19:01:18,600 - pyskl - INFO - Epoch [48][2400/3746] lr: 7.711e-02, eta: 3 days, 10:53:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5439, loss_cls: 4.0797, loss: 4.0797 +2024-07-23 19:02:40,890 - pyskl - INFO - Epoch [48][2500/3746] lr: 7.709e-02, eta: 3 days, 10:52:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5402, loss_cls: 4.1003, loss: 4.1003 +2024-07-23 19:04:02,638 - pyskl - INFO - Epoch [48][2600/3746] lr: 7.706e-02, eta: 3 days, 10:51:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5370, loss_cls: 4.1265, loss: 4.1265 +2024-07-23 19:05:24,683 - pyskl - INFO - Epoch [48][2700/3746] lr: 7.704e-02, eta: 3 days, 10:50:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5423, loss_cls: 4.1311, loss: 4.1311 +2024-07-23 19:06:46,665 - pyskl - INFO - Epoch [48][2800/3746] lr: 7.701e-02, eta: 3 days, 10:49:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5441, loss_cls: 4.0727, loss: 4.0727 +2024-07-23 19:08:08,792 - pyskl - INFO - Epoch [48][2900/3746] lr: 7.699e-02, eta: 3 days, 10:48:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5448, loss_cls: 4.1019, loss: 4.1019 +2024-07-23 19:09:30,268 - pyskl - INFO - Epoch [48][3000/3746] lr: 7.697e-02, eta: 3 days, 10:46:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5394, loss_cls: 4.1376, loss: 4.1376 +2024-07-23 19:10:51,992 - pyskl - INFO - Epoch [48][3100/3746] lr: 7.694e-02, eta: 3 days, 10:45:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5486, loss_cls: 4.0660, loss: 4.0660 +2024-07-23 19:12:13,405 - pyskl - INFO - Epoch [48][3200/3746] lr: 7.692e-02, eta: 3 days, 10:44:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5431, loss_cls: 4.0861, loss: 4.0861 +2024-07-23 19:13:35,173 - pyskl - INFO - Epoch [48][3300/3746] lr: 7.690e-02, eta: 3 days, 10:43:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5361, loss_cls: 4.1914, loss: 4.1914 +2024-07-23 19:14:56,690 - pyskl - INFO - Epoch [48][3400/3746] lr: 7.687e-02, eta: 3 days, 10:42:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5409, loss_cls: 4.1090, loss: 4.1090 +2024-07-23 19:16:18,523 - pyskl - INFO - Epoch [48][3500/3746] lr: 7.685e-02, eta: 3 days, 10:41:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5420, loss_cls: 4.0995, loss: 4.0995 +2024-07-23 19:17:41,396 - pyskl - INFO - Epoch [48][3600/3746] lr: 7.683e-02, eta: 3 days, 10:39:58, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5414, loss_cls: 4.0898, loss: 4.0898 +2024-07-23 19:19:03,212 - pyskl - INFO - Epoch [48][3700/3746] lr: 7.680e-02, eta: 3 days, 10:38:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5327, loss_cls: 4.1395, loss: 4.1395 +2024-07-23 19:19:42,662 - pyskl - INFO - Saving checkpoint at 48 epochs +2024-07-23 19:21:36,410 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 19:21:37,094 - pyskl - INFO - +top1_acc 0.2082 +top5_acc 0.4460 +2024-07-23 19:21:37,094 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 19:21:37,136 - pyskl - INFO - +mean_acc 0.2082 +2024-07-23 19:21:37,149 - pyskl - INFO - Epoch(val) [48][309] top1_acc: 0.2082, top5_acc: 0.4460, mean_class_accuracy: 0.2082 +2024-07-23 19:25:33,207 - pyskl - INFO - Epoch [49][100/3746] lr: 7.677e-02, eta: 3 days, 10:41:15, time: 2.360, data_time: 1.357, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5459, loss_cls: 4.0723, loss: 4.0723 +2024-07-23 19:26:55,917 - pyskl - INFO - Epoch [49][200/3746] lr: 7.674e-02, eta: 3 days, 10:40:07, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5487, loss_cls: 4.0287, loss: 4.0287 +2024-07-23 19:28:18,118 - pyskl - INFO - Epoch [49][300/3746] lr: 7.672e-02, eta: 3 days, 10:38:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5522, loss_cls: 4.0554, loss: 4.0554 +2024-07-23 19:29:39,954 - pyskl - INFO - Epoch [49][400/3746] lr: 7.670e-02, eta: 3 days, 10:37:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5459, loss_cls: 4.0716, loss: 4.0716 +2024-07-23 19:31:02,043 - pyskl - INFO - Epoch [49][500/3746] lr: 7.667e-02, eta: 3 days, 10:36:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5377, loss_cls: 4.1070, loss: 4.1070 +2024-07-23 19:32:24,015 - pyskl - INFO - Epoch [49][600/3746] lr: 7.665e-02, eta: 3 days, 10:35:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5508, loss_cls: 4.0510, loss: 4.0510 +2024-07-23 19:33:45,513 - pyskl - INFO - Epoch [49][700/3746] lr: 7.663e-02, eta: 3 days, 10:34:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5377, loss_cls: 4.1412, loss: 4.1412 +2024-07-23 19:35:07,155 - pyskl - INFO - Epoch [49][800/3746] lr: 7.660e-02, eta: 3 days, 10:33:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5459, loss_cls: 4.0875, loss: 4.0875 +2024-07-23 19:36:28,387 - pyskl - INFO - Epoch [49][900/3746] lr: 7.658e-02, eta: 3 days, 10:31:58, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5302, loss_cls: 4.1594, loss: 4.1594 +2024-07-23 19:37:49,651 - pyskl - INFO - Epoch [49][1000/3746] lr: 7.656e-02, eta: 3 days, 10:30:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5436, loss_cls: 4.0790, loss: 4.0790 +2024-07-23 19:39:11,232 - pyskl - INFO - Epoch [49][1100/3746] lr: 7.653e-02, eta: 3 days, 10:29:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5345, loss_cls: 4.1095, loss: 4.1095 +2024-07-23 19:40:33,245 - pyskl - INFO - Epoch [49][1200/3746] lr: 7.651e-02, eta: 3 days, 10:28:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5253, loss_cls: 4.1423, loss: 4.1423 +2024-07-23 19:41:54,459 - pyskl - INFO - Epoch [49][1300/3746] lr: 7.648e-02, eta: 3 days, 10:27:16, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5408, loss_cls: 4.0839, loss: 4.0839 +2024-07-23 19:43:16,106 - pyskl - INFO - Epoch [49][1400/3746] lr: 7.646e-02, eta: 3 days, 10:26:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5423, loss_cls: 4.0793, loss: 4.0793 +2024-07-23 19:44:37,502 - pyskl - INFO - Epoch [49][1500/3746] lr: 7.644e-02, eta: 3 days, 10:24:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5441, loss_cls: 4.0838, loss: 4.0838 +2024-07-23 19:45:58,999 - pyskl - INFO - Epoch [49][1600/3746] lr: 7.641e-02, eta: 3 days, 10:23:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5464, loss_cls: 4.1068, loss: 4.1068 +2024-07-23 19:47:21,355 - pyskl - INFO - Epoch [49][1700/3746] lr: 7.639e-02, eta: 3 days, 10:22:36, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5392, loss_cls: 4.0968, loss: 4.0968 +2024-07-23 19:48:43,339 - pyskl - INFO - Epoch [49][1800/3746] lr: 7.637e-02, eta: 3 days, 10:21:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5373, loss_cls: 4.1173, loss: 4.1173 +2024-07-23 19:50:04,790 - pyskl - INFO - Epoch [49][1900/3746] lr: 7.634e-02, eta: 3 days, 10:20:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5433, loss_cls: 4.1078, loss: 4.1078 +2024-07-23 19:51:26,484 - pyskl - INFO - Epoch [49][2000/3746] lr: 7.632e-02, eta: 3 days, 10:19:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5431, loss_cls: 4.1136, loss: 4.1136 +2024-07-23 19:52:48,610 - pyskl - INFO - Epoch [49][2100/3746] lr: 7.629e-02, eta: 3 days, 10:17:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5339, loss_cls: 4.1216, loss: 4.1216 +2024-07-23 19:54:10,588 - pyskl - INFO - Epoch [49][2200/3746] lr: 7.627e-02, eta: 3 days, 10:16:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5427, loss_cls: 4.0796, loss: 4.0796 +2024-07-23 19:55:32,576 - pyskl - INFO - Epoch [49][2300/3746] lr: 7.625e-02, eta: 3 days, 10:15:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5464, loss_cls: 4.0761, loss: 4.0761 +2024-07-23 19:56:54,111 - pyskl - INFO - Epoch [49][2400/3746] lr: 7.622e-02, eta: 3 days, 10:14:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5425, loss_cls: 4.0854, loss: 4.0854 +2024-07-23 19:58:16,015 - pyskl - INFO - Epoch [49][2500/3746] lr: 7.620e-02, eta: 3 days, 10:13:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5369, loss_cls: 4.1082, loss: 4.1082 +2024-07-23 19:59:37,960 - pyskl - INFO - Epoch [49][2600/3746] lr: 7.618e-02, eta: 3 days, 10:12:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5380, loss_cls: 4.1286, loss: 4.1286 +2024-07-23 20:01:00,516 - pyskl - INFO - Epoch [49][2700/3746] lr: 7.615e-02, eta: 3 days, 10:10:59, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5422, loss_cls: 4.0757, loss: 4.0757 +2024-07-23 20:02:22,273 - pyskl - INFO - Epoch [49][2800/3746] lr: 7.613e-02, eta: 3 days, 10:09:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5386, loss_cls: 4.1384, loss: 4.1384 +2024-07-23 20:03:44,232 - pyskl - INFO - Epoch [49][2900/3746] lr: 7.610e-02, eta: 3 days, 10:08:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5480, loss_cls: 4.0523, loss: 4.0523 +2024-07-23 20:05:05,875 - pyskl - INFO - Epoch [49][3000/3746] lr: 7.608e-02, eta: 3 days, 10:07:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5483, loss_cls: 4.1157, loss: 4.1157 +2024-07-23 20:06:27,264 - pyskl - INFO - Epoch [49][3100/3746] lr: 7.606e-02, eta: 3 days, 10:06:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5505, loss_cls: 4.0697, loss: 4.0697 +2024-07-23 20:07:48,658 - pyskl - INFO - Epoch [49][3200/3746] lr: 7.603e-02, eta: 3 days, 10:05:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5384, loss_cls: 4.0938, loss: 4.0938 +2024-07-23 20:09:10,782 - pyskl - INFO - Epoch [49][3300/3746] lr: 7.601e-02, eta: 3 days, 10:03:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5339, loss_cls: 4.1068, loss: 4.1068 +2024-07-23 20:10:32,234 - pyskl - INFO - Epoch [49][3400/3746] lr: 7.598e-02, eta: 3 days, 10:02:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5397, loss_cls: 4.1316, loss: 4.1316 +2024-07-23 20:11:53,658 - pyskl - INFO - Epoch [49][3500/3746] lr: 7.596e-02, eta: 3 days, 10:01:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5436, loss_cls: 4.1057, loss: 4.1057 +2024-07-23 20:13:15,999 - pyskl - INFO - Epoch [49][3600/3746] lr: 7.594e-02, eta: 3 days, 10:00:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5364, loss_cls: 4.1287, loss: 4.1287 +2024-07-23 20:14:37,645 - pyskl - INFO - Epoch [49][3700/3746] lr: 7.591e-02, eta: 3 days, 9:59:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5397, loss_cls: 4.0674, loss: 4.0674 +2024-07-23 20:15:17,140 - pyskl - INFO - Saving checkpoint at 49 epochs +2024-07-23 20:17:10,425 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 20:17:11,116 - pyskl - INFO - +top1_acc 0.1599 +top5_acc 0.3647 +2024-07-23 20:17:11,116 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 20:17:11,167 - pyskl - INFO - +mean_acc 0.1595 +2024-07-23 20:17:11,181 - pyskl - INFO - Epoch(val) [49][309] top1_acc: 0.1599, top5_acc: 0.3647, mean_class_accuracy: 0.1595 +2024-07-23 20:21:01,276 - pyskl - INFO - Epoch [50][100/3746] lr: 7.588e-02, eta: 3 days, 10:01:21, time: 2.301, data_time: 1.310, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5566, loss_cls: 4.0107, loss: 4.0107 +2024-07-23 20:22:24,662 - pyskl - INFO - Epoch [50][200/3746] lr: 7.585e-02, eta: 3 days, 10:00:14, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5463, loss_cls: 4.0307, loss: 4.0307 +2024-07-23 20:23:47,427 - pyskl - INFO - Epoch [50][300/3746] lr: 7.583e-02, eta: 3 days, 9:59:06, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5536, loss_cls: 4.0719, loss: 4.0719 +2024-07-23 20:25:10,044 - pyskl - INFO - Epoch [50][400/3746] lr: 7.581e-02, eta: 3 days, 9:57:57, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5595, loss_cls: 4.0310, loss: 4.0310 +2024-07-23 20:26:31,744 - pyskl - INFO - Epoch [50][500/3746] lr: 7.578e-02, eta: 3 days, 9:56:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5441, loss_cls: 4.0680, loss: 4.0680 +2024-07-23 20:27:53,869 - pyskl - INFO - Epoch [50][600/3746] lr: 7.576e-02, eta: 3 days, 9:55:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5514, loss_cls: 4.0771, loss: 4.0771 +2024-07-23 20:29:15,941 - pyskl - INFO - Epoch [50][700/3746] lr: 7.573e-02, eta: 3 days, 9:54:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5383, loss_cls: 4.1113, loss: 4.1113 +2024-07-23 20:30:37,449 - pyskl - INFO - Epoch [50][800/3746] lr: 7.571e-02, eta: 3 days, 9:53:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5405, loss_cls: 4.0819, loss: 4.0819 +2024-07-23 20:31:59,318 - pyskl - INFO - Epoch [50][900/3746] lr: 7.569e-02, eta: 3 days, 9:52:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5494, loss_cls: 4.0680, loss: 4.0680 +2024-07-23 20:33:20,666 - pyskl - INFO - Epoch [50][1000/3746] lr: 7.566e-02, eta: 3 days, 9:50:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5419, loss_cls: 4.0930, loss: 4.0930 +2024-07-23 20:34:42,206 - pyskl - INFO - Epoch [50][1100/3746] lr: 7.564e-02, eta: 3 days, 9:49:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5477, loss_cls: 4.0782, loss: 4.0782 +2024-07-23 20:36:03,887 - pyskl - INFO - Epoch [50][1200/3746] lr: 7.561e-02, eta: 3 days, 9:48:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5434, loss_cls: 4.0939, loss: 4.0939 +2024-07-23 20:37:25,587 - pyskl - INFO - Epoch [50][1300/3746] lr: 7.559e-02, eta: 3 days, 9:47:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5478, loss_cls: 4.1010, loss: 4.1010 +2024-07-23 20:38:47,227 - pyskl - INFO - Epoch [50][1400/3746] lr: 7.557e-02, eta: 3 days, 9:46:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5367, loss_cls: 4.1184, loss: 4.1184 +2024-07-23 20:40:08,690 - pyskl - INFO - Epoch [50][1500/3746] lr: 7.554e-02, eta: 3 days, 9:44:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5472, loss_cls: 4.0726, loss: 4.0726 +2024-07-23 20:41:30,449 - pyskl - INFO - Epoch [50][1600/3746] lr: 7.552e-02, eta: 3 days, 9:43:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5305, loss_cls: 4.1161, loss: 4.1161 +2024-07-23 20:42:52,309 - pyskl - INFO - Epoch [50][1700/3746] lr: 7.549e-02, eta: 3 days, 9:42:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5336, loss_cls: 4.1274, loss: 4.1274 +2024-07-23 20:44:13,890 - pyskl - INFO - Epoch [50][1800/3746] lr: 7.547e-02, eta: 3 days, 9:41:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5545, loss_cls: 4.0402, loss: 4.0402 +2024-07-23 20:45:36,029 - pyskl - INFO - Epoch [50][1900/3746] lr: 7.545e-02, eta: 3 days, 9:40:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5312, loss_cls: 4.1417, loss: 4.1417 +2024-07-23 20:46:57,761 - pyskl - INFO - Epoch [50][2000/3746] lr: 7.542e-02, eta: 3 days, 9:39:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5394, loss_cls: 4.0696, loss: 4.0696 +2024-07-23 20:48:19,166 - pyskl - INFO - Epoch [50][2100/3746] lr: 7.540e-02, eta: 3 days, 9:37:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5467, loss_cls: 4.0966, loss: 4.0966 +2024-07-23 20:49:41,205 - pyskl - INFO - Epoch [50][2200/3746] lr: 7.537e-02, eta: 3 days, 9:36:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5477, loss_cls: 4.0573, loss: 4.0573 +2024-07-23 20:51:02,867 - pyskl - INFO - Epoch [50][2300/3746] lr: 7.535e-02, eta: 3 days, 9:35:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5402, loss_cls: 4.1292, loss: 4.1292 +2024-07-23 20:52:24,918 - pyskl - INFO - Epoch [50][2400/3746] lr: 7.533e-02, eta: 3 days, 9:34:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5416, loss_cls: 4.0435, loss: 4.0435 +2024-07-23 20:53:46,609 - pyskl - INFO - Epoch [50][2500/3746] lr: 7.530e-02, eta: 3 days, 9:33:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5448, loss_cls: 4.0836, loss: 4.0836 +2024-07-23 20:55:08,655 - pyskl - INFO - Epoch [50][2600/3746] lr: 7.528e-02, eta: 3 days, 9:32:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5380, loss_cls: 4.1248, loss: 4.1248 +2024-07-23 20:56:31,184 - pyskl - INFO - Epoch [50][2700/3746] lr: 7.525e-02, eta: 3 days, 9:30:54, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5422, loss_cls: 4.0975, loss: 4.0975 +2024-07-23 20:57:52,529 - pyskl - INFO - Epoch [50][2800/3746] lr: 7.523e-02, eta: 3 days, 9:29:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5375, loss_cls: 4.1197, loss: 4.1197 +2024-07-23 20:59:14,762 - pyskl - INFO - Epoch [50][2900/3746] lr: 7.520e-02, eta: 3 days, 9:28:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5444, loss_cls: 4.0890, loss: 4.0890 +2024-07-23 21:00:37,186 - pyskl - INFO - Epoch [50][3000/3746] lr: 7.518e-02, eta: 3 days, 9:27:23, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5413, loss_cls: 4.0731, loss: 4.0731 +2024-07-23 21:01:58,568 - pyskl - INFO - Epoch [50][3100/3746] lr: 7.516e-02, eta: 3 days, 9:26:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5341, loss_cls: 4.1244, loss: 4.1244 +2024-07-23 21:03:19,937 - pyskl - INFO - Epoch [50][3200/3746] lr: 7.513e-02, eta: 3 days, 9:24:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5509, loss_cls: 4.0336, loss: 4.0336 +2024-07-23 21:04:42,344 - pyskl - INFO - Epoch [50][3300/3746] lr: 7.511e-02, eta: 3 days, 9:23:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5430, loss_cls: 4.0811, loss: 4.0811 +2024-07-23 21:06:03,739 - pyskl - INFO - Epoch [50][3400/3746] lr: 7.508e-02, eta: 3 days, 9:22:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5463, loss_cls: 4.0910, loss: 4.0910 +2024-07-23 21:07:25,954 - pyskl - INFO - Epoch [50][3500/3746] lr: 7.506e-02, eta: 3 days, 9:21:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5370, loss_cls: 4.1060, loss: 4.1060 +2024-07-23 21:08:47,981 - pyskl - INFO - Epoch [50][3600/3746] lr: 7.504e-02, eta: 3 days, 9:20:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5336, loss_cls: 4.1248, loss: 4.1248 +2024-07-23 21:10:10,130 - pyskl - INFO - Epoch [50][3700/3746] lr: 7.501e-02, eta: 3 days, 9:19:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5370, loss_cls: 4.1084, loss: 4.1084 +2024-07-23 21:10:49,807 - pyskl - INFO - Saving checkpoint at 50 epochs +2024-07-23 21:12:43,498 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 21:12:44,237 - pyskl - INFO - +top1_acc 0.2252 +top5_acc 0.4592 +2024-07-23 21:12:44,237 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 21:12:44,289 - pyskl - INFO - +mean_acc 0.2251 +2024-07-23 21:12:44,308 - pyskl - INFO - Epoch(val) [50][309] top1_acc: 0.2252, top5_acc: 0.4592, mean_class_accuracy: 0.2251 +2024-07-23 21:16:36,430 - pyskl - INFO - Epoch [51][100/3746] lr: 7.498e-02, eta: 3 days, 9:21:10, time: 2.321, data_time: 1.334, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5530, loss_cls: 4.0175, loss: 4.0175 +2024-07-23 21:17:59,315 - pyskl - INFO - Epoch [51][200/3746] lr: 7.495e-02, eta: 3 days, 9:20:01, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5509, loss_cls: 4.0578, loss: 4.0578 +2024-07-23 21:19:21,250 - pyskl - INFO - Epoch [51][300/3746] lr: 7.493e-02, eta: 3 days, 9:18:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5561, loss_cls: 4.0482, loss: 4.0482 +2024-07-23 21:20:43,495 - pyskl - INFO - Epoch [51][400/3746] lr: 7.490e-02, eta: 3 days, 9:17:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5505, loss_cls: 4.0531, loss: 4.0531 +2024-07-23 21:22:05,000 - pyskl - INFO - Epoch [51][500/3746] lr: 7.488e-02, eta: 3 days, 9:16:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5406, loss_cls: 4.0761, loss: 4.0761 +2024-07-23 21:23:26,597 - pyskl - INFO - Epoch [51][600/3746] lr: 7.485e-02, eta: 3 days, 9:15:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5514, loss_cls: 4.0423, loss: 4.0423 +2024-07-23 21:24:48,366 - pyskl - INFO - Epoch [51][700/3746] lr: 7.483e-02, eta: 3 days, 9:14:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5461, loss_cls: 4.0716, loss: 4.0716 +2024-07-23 21:26:10,080 - pyskl - INFO - Epoch [51][800/3746] lr: 7.481e-02, eta: 3 days, 9:12:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5536, loss_cls: 4.0222, loss: 4.0222 +2024-07-23 21:27:31,814 - pyskl - INFO - Epoch [51][900/3746] lr: 7.478e-02, eta: 3 days, 9:11:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5383, loss_cls: 4.1129, loss: 4.1129 +2024-07-23 21:28:53,446 - pyskl - INFO - Epoch [51][1000/3746] lr: 7.476e-02, eta: 3 days, 9:10:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5484, loss_cls: 4.0574, loss: 4.0574 +2024-07-23 21:30:15,329 - pyskl - INFO - Epoch [51][1100/3746] lr: 7.473e-02, eta: 3 days, 9:09:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5341, loss_cls: 4.0854, loss: 4.0854 +2024-07-23 21:31:37,519 - pyskl - INFO - Epoch [51][1200/3746] lr: 7.471e-02, eta: 3 days, 9:08:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5467, loss_cls: 4.0722, loss: 4.0722 +2024-07-23 21:32:58,921 - pyskl - INFO - Epoch [51][1300/3746] lr: 7.468e-02, eta: 3 days, 9:06:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5381, loss_cls: 4.1148, loss: 4.1148 +2024-07-23 21:34:20,539 - pyskl - INFO - Epoch [51][1400/3746] lr: 7.466e-02, eta: 3 days, 9:05:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5466, loss_cls: 4.0506, loss: 4.0506 +2024-07-23 21:35:42,328 - pyskl - INFO - Epoch [51][1500/3746] lr: 7.464e-02, eta: 3 days, 9:04:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5336, loss_cls: 4.0912, loss: 4.0912 +2024-07-23 21:37:03,863 - pyskl - INFO - Epoch [51][1600/3746] lr: 7.461e-02, eta: 3 days, 9:03:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5298, loss_cls: 4.1252, loss: 4.1252 +2024-07-23 21:38:25,134 - pyskl - INFO - Epoch [51][1700/3746] lr: 7.459e-02, eta: 3 days, 9:02:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5463, loss_cls: 4.0812, loss: 4.0812 +2024-07-23 21:39:46,664 - pyskl - INFO - Epoch [51][1800/3746] lr: 7.456e-02, eta: 3 days, 9:01:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5441, loss_cls: 4.0868, loss: 4.0868 +2024-07-23 21:41:08,271 - pyskl - INFO - Epoch [51][1900/3746] lr: 7.454e-02, eta: 3 days, 8:59:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5352, loss_cls: 4.1102, loss: 4.1102 +2024-07-23 21:42:29,983 - pyskl - INFO - Epoch [51][2000/3746] lr: 7.451e-02, eta: 3 days, 8:58:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5389, loss_cls: 4.1148, loss: 4.1148 +2024-07-23 21:43:51,961 - pyskl - INFO - Epoch [51][2100/3746] lr: 7.449e-02, eta: 3 days, 8:57:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5380, loss_cls: 4.1322, loss: 4.1322 +2024-07-23 21:45:13,717 - pyskl - INFO - Epoch [51][2200/3746] lr: 7.447e-02, eta: 3 days, 8:56:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5366, loss_cls: 4.1105, loss: 4.1105 +2024-07-23 21:46:35,251 - pyskl - INFO - Epoch [51][2300/3746] lr: 7.444e-02, eta: 3 days, 8:55:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5458, loss_cls: 4.0713, loss: 4.0713 +2024-07-23 21:47:57,274 - pyskl - INFO - Epoch [51][2400/3746] lr: 7.442e-02, eta: 3 days, 8:53:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5427, loss_cls: 4.0907, loss: 4.0907 +2024-07-23 21:49:18,916 - pyskl - INFO - Epoch [51][2500/3746] lr: 7.439e-02, eta: 3 days, 8:52:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5487, loss_cls: 4.0564, loss: 4.0564 +2024-07-23 21:50:40,733 - pyskl - INFO - Epoch [51][2600/3746] lr: 7.437e-02, eta: 3 days, 8:51:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5520, loss_cls: 4.0476, loss: 4.0476 +2024-07-23 21:52:03,408 - pyskl - INFO - Epoch [51][2700/3746] lr: 7.434e-02, eta: 3 days, 8:50:20, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5380, loss_cls: 4.1181, loss: 4.1181 +2024-07-23 21:53:24,880 - pyskl - INFO - Epoch [51][2800/3746] lr: 7.432e-02, eta: 3 days, 8:49:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5452, loss_cls: 4.0975, loss: 4.0975 +2024-07-23 21:54:47,534 - pyskl - INFO - Epoch [51][2900/3746] lr: 7.429e-02, eta: 3 days, 8:47:58, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5405, loss_cls: 4.0993, loss: 4.0993 +2024-07-23 21:56:09,777 - pyskl - INFO - Epoch [51][3000/3746] lr: 7.427e-02, eta: 3 days, 8:46:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5373, loss_cls: 4.1142, loss: 4.1142 +2024-07-23 21:57:31,893 - pyskl - INFO - Epoch [51][3100/3746] lr: 7.425e-02, eta: 3 days, 8:45:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5616, loss_cls: 3.9865, loss: 3.9865 +2024-07-23 21:58:53,803 - pyskl - INFO - Epoch [51][3200/3746] lr: 7.422e-02, eta: 3 days, 8:44:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5419, loss_cls: 4.0879, loss: 4.0879 +2024-07-23 22:00:16,251 - pyskl - INFO - Epoch [51][3300/3746] lr: 7.420e-02, eta: 3 days, 8:43:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5411, loss_cls: 4.1117, loss: 4.1117 +2024-07-23 22:01:38,443 - pyskl - INFO - Epoch [51][3400/3746] lr: 7.417e-02, eta: 3 days, 8:42:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5402, loss_cls: 4.0889, loss: 4.0889 +2024-07-23 22:03:00,004 - pyskl - INFO - Epoch [51][3500/3746] lr: 7.415e-02, eta: 3 days, 8:40:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5392, loss_cls: 4.0825, loss: 4.0825 +2024-07-23 22:04:22,031 - pyskl - INFO - Epoch [51][3600/3746] lr: 7.412e-02, eta: 3 days, 8:39:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5378, loss_cls: 4.1233, loss: 4.1233 +2024-07-23 22:05:44,299 - pyskl - INFO - Epoch [51][3700/3746] lr: 7.410e-02, eta: 3 days, 8:38:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5516, loss_cls: 4.0463, loss: 4.0463 +2024-07-23 22:06:23,901 - pyskl - INFO - Saving checkpoint at 51 epochs +2024-07-23 22:08:18,059 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 22:08:18,738 - pyskl - INFO - +top1_acc 0.2202 +top5_acc 0.4715 +2024-07-23 22:08:18,738 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 22:08:18,783 - pyskl - INFO - +mean_acc 0.2200 +2024-07-23 22:08:18,796 - pyskl - INFO - Epoch(val) [51][309] top1_acc: 0.2202, top5_acc: 0.4715, mean_class_accuracy: 0.2200 +2024-07-23 22:12:13,575 - pyskl - INFO - Epoch [52][100/3746] lr: 7.406e-02, eta: 3 days, 8:40:31, time: 2.348, data_time: 1.358, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5492, loss_cls: 4.0417, loss: 4.0417 +2024-07-23 22:13:36,435 - pyskl - INFO - Epoch [52][200/3746] lr: 7.404e-02, eta: 3 days, 8:39:22, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5491, loss_cls: 4.0444, loss: 4.0444 +2024-07-23 22:14:59,584 - pyskl - INFO - Epoch [52][300/3746] lr: 7.401e-02, eta: 3 days, 8:38:13, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5427, loss_cls: 4.0701, loss: 4.0701 +2024-07-23 22:16:22,369 - pyskl - INFO - Epoch [52][400/3746] lr: 7.399e-02, eta: 3 days, 8:37:03, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5480, loss_cls: 4.0466, loss: 4.0466 +2024-07-23 22:17:45,142 - pyskl - INFO - Epoch [52][500/3746] lr: 7.397e-02, eta: 3 days, 8:35:53, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5525, loss_cls: 4.0496, loss: 4.0496 +2024-07-23 22:19:07,517 - pyskl - INFO - Epoch [52][600/3746] lr: 7.394e-02, eta: 3 days, 8:34:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5539, loss_cls: 4.0005, loss: 4.0005 +2024-07-23 22:20:29,360 - pyskl - INFO - Epoch [52][700/3746] lr: 7.392e-02, eta: 3 days, 8:33:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5473, loss_cls: 4.0748, loss: 4.0748 +2024-07-23 22:21:50,957 - pyskl - INFO - Epoch [52][800/3746] lr: 7.389e-02, eta: 3 days, 8:32:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5534, loss_cls: 4.0460, loss: 4.0460 +2024-07-23 22:23:13,100 - pyskl - INFO - Epoch [52][900/3746] lr: 7.387e-02, eta: 3 days, 8:31:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5423, loss_cls: 4.0843, loss: 4.0843 +2024-07-23 22:24:36,082 - pyskl - INFO - Epoch [52][1000/3746] lr: 7.384e-02, eta: 3 days, 8:29:58, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5344, loss_cls: 4.1349, loss: 4.1349 +2024-07-23 22:25:57,668 - pyskl - INFO - Epoch [52][1100/3746] lr: 7.382e-02, eta: 3 days, 8:28:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5395, loss_cls: 4.0795, loss: 4.0795 +2024-07-23 22:27:19,652 - pyskl - INFO - Epoch [52][1200/3746] lr: 7.379e-02, eta: 3 days, 8:27:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5373, loss_cls: 4.0711, loss: 4.0711 +2024-07-23 22:28:41,087 - pyskl - INFO - Epoch [52][1300/3746] lr: 7.377e-02, eta: 3 days, 8:26:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5425, loss_cls: 4.0558, loss: 4.0558 +2024-07-23 22:30:02,628 - pyskl - INFO - Epoch [52][1400/3746] lr: 7.374e-02, eta: 3 days, 8:25:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5541, loss_cls: 4.0524, loss: 4.0524 +2024-07-23 22:31:24,186 - pyskl - INFO - Epoch [52][1500/3746] lr: 7.372e-02, eta: 3 days, 8:23:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5442, loss_cls: 4.1047, loss: 4.1047 +2024-07-23 22:32:46,082 - pyskl - INFO - Epoch [52][1600/3746] lr: 7.370e-02, eta: 3 days, 8:22:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5478, loss_cls: 4.0584, loss: 4.0584 +2024-07-23 22:34:08,412 - pyskl - INFO - Epoch [52][1700/3746] lr: 7.367e-02, eta: 3 days, 8:21:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5478, loss_cls: 4.0615, loss: 4.0615 +2024-07-23 22:35:30,014 - pyskl - INFO - Epoch [52][1800/3746] lr: 7.365e-02, eta: 3 days, 8:20:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5364, loss_cls: 4.1019, loss: 4.1019 +2024-07-23 22:36:51,759 - pyskl - INFO - Epoch [52][1900/3746] lr: 7.362e-02, eta: 3 days, 8:19:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5427, loss_cls: 4.0736, loss: 4.0736 +2024-07-23 22:38:13,688 - pyskl - INFO - Epoch [52][2000/3746] lr: 7.360e-02, eta: 3 days, 8:18:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5405, loss_cls: 4.0618, loss: 4.0618 +2024-07-23 22:39:35,281 - pyskl - INFO - Epoch [52][2100/3746] lr: 7.357e-02, eta: 3 days, 8:16:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5450, loss_cls: 4.0851, loss: 4.0851 +2024-07-23 22:40:57,079 - pyskl - INFO - Epoch [52][2200/3746] lr: 7.355e-02, eta: 3 days, 8:15:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5447, loss_cls: 4.0553, loss: 4.0553 +2024-07-23 22:42:18,943 - pyskl - INFO - Epoch [52][2300/3746] lr: 7.352e-02, eta: 3 days, 8:14:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5517, loss_cls: 4.0241, loss: 4.0241 +2024-07-23 22:43:41,422 - pyskl - INFO - Epoch [52][2400/3746] lr: 7.350e-02, eta: 3 days, 8:13:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5495, loss_cls: 4.0414, loss: 4.0414 +2024-07-23 22:45:03,084 - pyskl - INFO - Epoch [52][2500/3746] lr: 7.347e-02, eta: 3 days, 8:12:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5406, loss_cls: 4.0871, loss: 4.0871 +2024-07-23 22:46:25,042 - pyskl - INFO - Epoch [52][2600/3746] lr: 7.345e-02, eta: 3 days, 8:10:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5477, loss_cls: 4.0591, loss: 4.0591 +2024-07-23 22:47:47,225 - pyskl - INFO - Epoch [52][2700/3746] lr: 7.342e-02, eta: 3 days, 8:09:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5380, loss_cls: 4.1270, loss: 4.1270 +2024-07-23 22:49:09,216 - pyskl - INFO - Epoch [52][2800/3746] lr: 7.340e-02, eta: 3 days, 8:08:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5498, loss_cls: 4.0765, loss: 4.0765 +2024-07-23 22:50:30,837 - pyskl - INFO - Epoch [52][2900/3746] lr: 7.337e-02, eta: 3 days, 8:07:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5397, loss_cls: 4.1041, loss: 4.1041 +2024-07-23 22:51:52,669 - pyskl - INFO - Epoch [52][3000/3746] lr: 7.335e-02, eta: 3 days, 8:06:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5445, loss_cls: 4.0794, loss: 4.0794 +2024-07-23 22:53:14,829 - pyskl - INFO - Epoch [52][3100/3746] lr: 7.332e-02, eta: 3 days, 8:04:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5373, loss_cls: 4.0937, loss: 4.0937 +2024-07-23 22:54:36,324 - pyskl - INFO - Epoch [52][3200/3746] lr: 7.330e-02, eta: 3 days, 8:03:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5500, loss_cls: 4.0388, loss: 4.0388 +2024-07-23 22:55:58,827 - pyskl - INFO - Epoch [52][3300/3746] lr: 7.328e-02, eta: 3 days, 8:02:29, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5312, loss_cls: 4.1014, loss: 4.1014 +2024-07-23 22:57:20,297 - pyskl - INFO - Epoch [52][3400/3746] lr: 7.325e-02, eta: 3 days, 8:01:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5517, loss_cls: 4.1008, loss: 4.1008 +2024-07-23 22:58:41,760 - pyskl - INFO - Epoch [52][3500/3746] lr: 7.323e-02, eta: 3 days, 8:00:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5372, loss_cls: 4.0857, loss: 4.0857 +2024-07-23 23:00:03,824 - pyskl - INFO - Epoch [52][3600/3746] lr: 7.320e-02, eta: 3 days, 7:58:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5434, loss_cls: 4.0811, loss: 4.0811 +2024-07-23 23:01:25,841 - pyskl - INFO - Epoch [52][3700/3746] lr: 7.318e-02, eta: 3 days, 7:57:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5439, loss_cls: 4.0770, loss: 4.0770 +2024-07-23 23:02:05,290 - pyskl - INFO - Saving checkpoint at 52 epochs +2024-07-23 23:03:58,473 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 23:03:59,140 - pyskl - INFO - +top1_acc 0.2200 +top5_acc 0.4666 +2024-07-23 23:03:59,140 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 23:03:59,183 - pyskl - INFO - +mean_acc 0.2200 +2024-07-23 23:03:59,197 - pyskl - INFO - Epoch(val) [52][309] top1_acc: 0.2200, top5_acc: 0.4666, mean_class_accuracy: 0.2200 +2024-07-23 23:07:49,943 - pyskl - INFO - Epoch [53][100/3746] lr: 7.314e-02, eta: 3 days, 7:59:25, time: 2.307, data_time: 1.321, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5564, loss_cls: 4.0100, loss: 4.0100 +2024-07-23 23:09:13,263 - pyskl - INFO - Epoch [53][200/3746] lr: 7.312e-02, eta: 3 days, 7:58:15, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5483, loss_cls: 4.0515, loss: 4.0515 +2024-07-23 23:10:36,246 - pyskl - INFO - Epoch [53][300/3746] lr: 7.309e-02, eta: 3 days, 7:57:05, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5491, loss_cls: 4.0549, loss: 4.0549 +2024-07-23 23:11:59,817 - pyskl - INFO - Epoch [53][400/3746] lr: 7.307e-02, eta: 3 days, 7:55:57, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5403, loss_cls: 4.0911, loss: 4.0911 +2024-07-23 23:13:23,028 - pyskl - INFO - Epoch [53][500/3746] lr: 7.304e-02, eta: 3 days, 7:54:47, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5542, loss_cls: 4.0122, loss: 4.0122 +2024-07-23 23:14:45,838 - pyskl - INFO - Epoch [53][600/3746] lr: 7.302e-02, eta: 3 days, 7:53:37, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5548, loss_cls: 3.9986, loss: 3.9986 +2024-07-23 23:16:08,989 - pyskl - INFO - Epoch [53][700/3746] lr: 7.299e-02, eta: 3 days, 7:52:27, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5405, loss_cls: 4.0596, loss: 4.0596 +2024-07-23 23:17:32,716 - pyskl - INFO - Epoch [53][800/3746] lr: 7.297e-02, eta: 3 days, 7:51:18, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5392, loss_cls: 4.1001, loss: 4.1001 +2024-07-23 23:18:55,488 - pyskl - INFO - Epoch [53][900/3746] lr: 7.294e-02, eta: 3 days, 7:50:08, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5602, loss_cls: 4.0112, loss: 4.0112 +2024-07-23 23:20:18,269 - pyskl - INFO - Epoch [53][1000/3746] lr: 7.292e-02, eta: 3 days, 7:48:57, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5606, loss_cls: 4.0220, loss: 4.0220 +2024-07-23 23:21:41,239 - pyskl - INFO - Epoch [53][1100/3746] lr: 7.289e-02, eta: 3 days, 7:47:47, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5513, loss_cls: 4.0213, loss: 4.0213 +2024-07-23 23:23:04,354 - pyskl - INFO - Epoch [53][1200/3746] lr: 7.287e-02, eta: 3 days, 7:46:37, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5430, loss_cls: 4.0897, loss: 4.0897 +2024-07-23 23:24:26,949 - pyskl - INFO - Epoch [53][1300/3746] lr: 7.284e-02, eta: 3 days, 7:45:27, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5552, loss_cls: 4.0375, loss: 4.0375 +2024-07-23 23:25:49,112 - pyskl - INFO - Epoch [53][1400/3746] lr: 7.282e-02, eta: 3 days, 7:44:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5522, loss_cls: 4.0552, loss: 4.0552 +2024-07-23 23:27:11,549 - pyskl - INFO - Epoch [53][1500/3746] lr: 7.279e-02, eta: 3 days, 7:43:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5448, loss_cls: 4.0910, loss: 4.0910 +2024-07-23 23:28:33,104 - pyskl - INFO - Epoch [53][1600/3746] lr: 7.277e-02, eta: 3 days, 7:41:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5487, loss_cls: 4.0729, loss: 4.0729 +2024-07-23 23:29:54,980 - pyskl - INFO - Epoch [53][1700/3746] lr: 7.274e-02, eta: 3 days, 7:40:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5448, loss_cls: 4.0626, loss: 4.0626 +2024-07-23 23:31:16,856 - pyskl - INFO - Epoch [53][1800/3746] lr: 7.272e-02, eta: 3 days, 7:39:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5564, loss_cls: 4.0293, loss: 4.0293 +2024-07-23 23:32:38,257 - pyskl - INFO - Epoch [53][1900/3746] lr: 7.269e-02, eta: 3 days, 7:38:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5481, loss_cls: 4.0639, loss: 4.0639 +2024-07-23 23:33:59,914 - pyskl - INFO - Epoch [53][2000/3746] lr: 7.267e-02, eta: 3 days, 7:37:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5489, loss_cls: 4.0479, loss: 4.0479 +2024-07-23 23:35:21,408 - pyskl - INFO - Epoch [53][2100/3746] lr: 7.264e-02, eta: 3 days, 7:35:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5419, loss_cls: 4.1117, loss: 4.1117 +2024-07-23 23:36:43,445 - pyskl - INFO - Epoch [53][2200/3746] lr: 7.262e-02, eta: 3 days, 7:34:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5502, loss_cls: 4.0899, loss: 4.0899 +2024-07-23 23:38:04,992 - pyskl - INFO - Epoch [53][2300/3746] lr: 7.259e-02, eta: 3 days, 7:33:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5494, loss_cls: 4.0425, loss: 4.0425 +2024-07-23 23:39:27,394 - pyskl - INFO - Epoch [53][2400/3746] lr: 7.257e-02, eta: 3 days, 7:32:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5484, loss_cls: 4.0609, loss: 4.0609 +2024-07-23 23:40:49,430 - pyskl - INFO - Epoch [53][2500/3746] lr: 7.254e-02, eta: 3 days, 7:31:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5389, loss_cls: 4.0940, loss: 4.0940 +2024-07-23 23:42:11,735 - pyskl - INFO - Epoch [53][2600/3746] lr: 7.252e-02, eta: 3 days, 7:29:48, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5502, loss_cls: 4.0779, loss: 4.0779 +2024-07-23 23:43:33,284 - pyskl - INFO - Epoch [53][2700/3746] lr: 7.249e-02, eta: 3 days, 7:28:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5378, loss_cls: 4.1061, loss: 4.1061 +2024-07-23 23:44:54,680 - pyskl - INFO - Epoch [53][2800/3746] lr: 7.247e-02, eta: 3 days, 7:27:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5330, loss_cls: 4.1096, loss: 4.1096 +2024-07-23 23:46:16,839 - pyskl - INFO - Epoch [53][2900/3746] lr: 7.244e-02, eta: 3 days, 7:26:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5475, loss_cls: 4.0625, loss: 4.0625 +2024-07-23 23:47:38,809 - pyskl - INFO - Epoch [53][3000/3746] lr: 7.242e-02, eta: 3 days, 7:24:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5441, loss_cls: 4.0816, loss: 4.0816 +2024-07-23 23:49:00,694 - pyskl - INFO - Epoch [53][3100/3746] lr: 7.239e-02, eta: 3 days, 7:23:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5430, loss_cls: 4.0691, loss: 4.0691 +2024-07-23 23:50:22,555 - pyskl - INFO - Epoch [53][3200/3746] lr: 7.237e-02, eta: 3 days, 7:22:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5398, loss_cls: 4.0896, loss: 4.0896 +2024-07-23 23:51:44,663 - pyskl - INFO - Epoch [53][3300/3746] lr: 7.234e-02, eta: 3 days, 7:21:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5511, loss_cls: 4.0287, loss: 4.0287 +2024-07-23 23:53:06,234 - pyskl - INFO - Epoch [53][3400/3746] lr: 7.232e-02, eta: 3 days, 7:20:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5566, loss_cls: 4.0261, loss: 4.0261 +2024-07-23 23:54:28,205 - pyskl - INFO - Epoch [53][3500/3746] lr: 7.229e-02, eta: 3 days, 7:18:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5334, loss_cls: 4.1171, loss: 4.1171 +2024-07-23 23:55:50,379 - pyskl - INFO - Epoch [53][3600/3746] lr: 7.227e-02, eta: 3 days, 7:17:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5453, loss_cls: 4.0784, loss: 4.0784 +2024-07-23 23:57:12,084 - pyskl - INFO - Epoch [53][3700/3746] lr: 7.224e-02, eta: 3 days, 7:16:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5369, loss_cls: 4.0993, loss: 4.0993 +2024-07-23 23:57:51,520 - pyskl - INFO - Saving checkpoint at 53 epochs +2024-07-23 23:59:44,833 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 23:59:45,545 - pyskl - INFO - +top1_acc 0.2213 +top5_acc 0.4629 +2024-07-23 23:59:45,545 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 23:59:45,599 - pyskl - INFO - +mean_acc 0.2211 +2024-07-23 23:59:45,615 - pyskl - INFO - Epoch(val) [53][309] top1_acc: 0.2213, top5_acc: 0.4629, mean_class_accuracy: 0.2211 +2024-07-24 00:03:33,044 - pyskl - INFO - Epoch [54][100/3746] lr: 7.221e-02, eta: 3 days, 7:18:03, time: 2.274, data_time: 1.282, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5547, loss_cls: 4.0226, loss: 4.0226 +2024-07-24 00:04:55,898 - pyskl - INFO - Epoch [54][200/3746] lr: 7.218e-02, eta: 3 days, 7:16:52, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5544, loss_cls: 4.0256, loss: 4.0256 +2024-07-24 00:06:18,758 - pyskl - INFO - Epoch [54][300/3746] lr: 7.216e-02, eta: 3 days, 7:15:42, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5531, loss_cls: 4.0382, loss: 4.0382 +2024-07-24 00:07:41,043 - pyskl - INFO - Epoch [54][400/3746] lr: 7.213e-02, eta: 3 days, 7:14:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5467, loss_cls: 4.0528, loss: 4.0528 +2024-07-24 00:09:02,832 - pyskl - INFO - Epoch [54][500/3746] lr: 7.211e-02, eta: 3 days, 7:13:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5587, loss_cls: 4.0088, loss: 4.0088 +2024-07-24 00:10:24,839 - pyskl - INFO - Epoch [54][600/3746] lr: 7.208e-02, eta: 3 days, 7:12:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5441, loss_cls: 4.0540, loss: 4.0540 +2024-07-24 00:11:46,370 - pyskl - INFO - Epoch [54][700/3746] lr: 7.206e-02, eta: 3 days, 7:10:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5522, loss_cls: 4.0295, loss: 4.0295 +2024-07-24 00:13:08,141 - pyskl - INFO - Epoch [54][800/3746] lr: 7.203e-02, eta: 3 days, 7:09:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5550, loss_cls: 4.0309, loss: 4.0309 +2024-07-24 00:14:29,736 - pyskl - INFO - Epoch [54][900/3746] lr: 7.201e-02, eta: 3 days, 7:08:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5372, loss_cls: 4.0919, loss: 4.0919 +2024-07-24 00:15:51,172 - pyskl - INFO - Epoch [54][1000/3746] lr: 7.198e-02, eta: 3 days, 7:07:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5419, loss_cls: 4.0756, loss: 4.0756 +2024-07-24 00:17:13,187 - pyskl - INFO - Epoch [54][1100/3746] lr: 7.196e-02, eta: 3 days, 7:06:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5519, loss_cls: 4.0336, loss: 4.0336 +2024-07-24 00:18:34,535 - pyskl - INFO - Epoch [54][1200/3746] lr: 7.193e-02, eta: 3 days, 7:04:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5497, loss_cls: 4.0442, loss: 4.0442 +2024-07-24 00:19:56,335 - pyskl - INFO - Epoch [54][1300/3746] lr: 7.191e-02, eta: 3 days, 7:03:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5617, loss_cls: 4.0014, loss: 4.0014 +2024-07-24 00:21:17,723 - pyskl - INFO - Epoch [54][1400/3746] lr: 7.188e-02, eta: 3 days, 7:02:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5455, loss_cls: 4.0452, loss: 4.0452 +2024-07-24 00:22:39,291 - pyskl - INFO - Epoch [54][1500/3746] lr: 7.186e-02, eta: 3 days, 7:01:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5566, loss_cls: 4.0458, loss: 4.0458 +2024-07-24 00:24:00,910 - pyskl - INFO - Epoch [54][1600/3746] lr: 7.183e-02, eta: 3 days, 6:59:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5420, loss_cls: 4.0670, loss: 4.0670 +2024-07-24 00:25:22,654 - pyskl - INFO - Epoch [54][1700/3746] lr: 7.181e-02, eta: 3 days, 6:58:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5391, loss_cls: 4.1177, loss: 4.1177 +2024-07-24 00:26:44,227 - pyskl - INFO - Epoch [54][1800/3746] lr: 7.178e-02, eta: 3 days, 6:57:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5544, loss_cls: 4.0113, loss: 4.0113 +2024-07-24 00:28:06,015 - pyskl - INFO - Epoch [54][1900/3746] lr: 7.176e-02, eta: 3 days, 6:56:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5431, loss_cls: 4.0576, loss: 4.0576 +2024-07-24 00:29:27,375 - pyskl - INFO - Epoch [54][2000/3746] lr: 7.173e-02, eta: 3 days, 6:55:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5441, loss_cls: 4.0581, loss: 4.0581 +2024-07-24 00:30:48,978 - pyskl - INFO - Epoch [54][2100/3746] lr: 7.170e-02, eta: 3 days, 6:53:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5452, loss_cls: 4.0842, loss: 4.0842 +2024-07-24 00:32:10,382 - pyskl - INFO - Epoch [54][2200/3746] lr: 7.168e-02, eta: 3 days, 6:52:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5528, loss_cls: 4.0228, loss: 4.0228 +2024-07-24 00:33:31,640 - pyskl - INFO - Epoch [54][2300/3746] lr: 7.165e-02, eta: 3 days, 6:51:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5500, loss_cls: 4.0627, loss: 4.0627 +2024-07-24 00:34:53,689 - pyskl - INFO - Epoch [54][2400/3746] lr: 7.163e-02, eta: 3 days, 6:50:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5316, loss_cls: 4.1334, loss: 4.1334 +2024-07-24 00:36:16,193 - pyskl - INFO - Epoch [54][2500/3746] lr: 7.160e-02, eta: 3 days, 6:48:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5500, loss_cls: 4.0353, loss: 4.0353 +2024-07-24 00:37:38,702 - pyskl - INFO - Epoch [54][2600/3746] lr: 7.158e-02, eta: 3 days, 6:47:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5483, loss_cls: 4.0680, loss: 4.0680 +2024-07-24 00:39:00,374 - pyskl - INFO - Epoch [54][2700/3746] lr: 7.155e-02, eta: 3 days, 6:46:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5450, loss_cls: 4.0763, loss: 4.0763 +2024-07-24 00:40:22,502 - pyskl - INFO - Epoch [54][2800/3746] lr: 7.153e-02, eta: 3 days, 6:45:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5467, loss_cls: 4.0468, loss: 4.0468 +2024-07-24 00:41:44,791 - pyskl - INFO - Epoch [54][2900/3746] lr: 7.150e-02, eta: 3 days, 6:44:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5511, loss_cls: 4.0615, loss: 4.0615 +2024-07-24 00:43:07,036 - pyskl - INFO - Epoch [54][3000/3746] lr: 7.148e-02, eta: 3 days, 6:42:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5498, loss_cls: 4.0371, loss: 4.0371 +2024-07-24 00:44:29,046 - pyskl - INFO - Epoch [54][3100/3746] lr: 7.145e-02, eta: 3 days, 6:41:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5350, loss_cls: 4.1409, loss: 4.1409 +2024-07-24 00:45:50,687 - pyskl - INFO - Epoch [54][3200/3746] lr: 7.143e-02, eta: 3 days, 6:40:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5480, loss_cls: 4.0777, loss: 4.0777 +2024-07-24 00:47:12,059 - pyskl - INFO - Epoch [54][3300/3746] lr: 7.140e-02, eta: 3 days, 6:39:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5420, loss_cls: 4.0765, loss: 4.0765 +2024-07-24 00:48:34,301 - pyskl - INFO - Epoch [54][3400/3746] lr: 7.138e-02, eta: 3 days, 6:38:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5494, loss_cls: 4.0642, loss: 4.0642 +2024-07-24 00:49:55,771 - pyskl - INFO - Epoch [54][3500/3746] lr: 7.135e-02, eta: 3 days, 6:36:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5523, loss_cls: 4.0104, loss: 4.0104 +2024-07-24 00:51:18,207 - pyskl - INFO - Epoch [54][3600/3746] lr: 7.133e-02, eta: 3 days, 6:35:36, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5464, loss_cls: 4.0593, loss: 4.0593 +2024-07-24 00:52:40,441 - pyskl - INFO - Epoch [54][3700/3746] lr: 7.130e-02, eta: 3 days, 6:34:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5491, loss_cls: 4.0427, loss: 4.0427 +2024-07-24 00:53:19,948 - pyskl - INFO - Saving checkpoint at 54 epochs +2024-07-24 00:55:13,396 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 00:55:14,102 - pyskl - INFO - +top1_acc 0.2162 +top5_acc 0.4578 +2024-07-24 00:55:14,103 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 00:55:14,147 - pyskl - INFO - +mean_acc 0.2158 +2024-07-24 00:55:14,158 - pyskl - INFO - Epoch(val) [54][309] top1_acc: 0.2162, top5_acc: 0.4578, mean_class_accuracy: 0.2158 +2024-07-24 00:59:03,122 - pyskl - INFO - Epoch [55][100/3746] lr: 7.126e-02, eta: 3 days, 6:35:52, time: 2.290, data_time: 1.303, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5577, loss_cls: 4.0029, loss: 4.0029 +2024-07-24 01:00:25,628 - pyskl - INFO - Epoch [55][200/3746] lr: 7.124e-02, eta: 3 days, 6:34:40, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5645, loss_cls: 3.9787, loss: 3.9787 +2024-07-24 01:01:47,228 - pyskl - INFO - Epoch [55][300/3746] lr: 7.121e-02, eta: 3 days, 6:33:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5414, loss_cls: 4.0794, loss: 4.0794 +2024-07-24 01:03:09,037 - pyskl - INFO - Epoch [55][400/3746] lr: 7.119e-02, eta: 3 days, 6:32:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5478, loss_cls: 4.0477, loss: 4.0477 +2024-07-24 01:04:30,633 - pyskl - INFO - Epoch [55][500/3746] lr: 7.116e-02, eta: 3 days, 6:31:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5397, loss_cls: 4.1012, loss: 4.1012 +2024-07-24 01:05:52,816 - pyskl - INFO - Epoch [55][600/3746] lr: 7.114e-02, eta: 3 days, 6:29:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5572, loss_cls: 3.9916, loss: 3.9916 +2024-07-24 01:07:15,004 - pyskl - INFO - Epoch [55][700/3746] lr: 7.111e-02, eta: 3 days, 6:28:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5478, loss_cls: 4.0445, loss: 4.0445 +2024-07-24 01:08:37,016 - pyskl - INFO - Epoch [55][800/3746] lr: 7.109e-02, eta: 3 days, 6:27:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5497, loss_cls: 4.0303, loss: 4.0303 +2024-07-24 01:09:59,044 - pyskl - INFO - Epoch [55][900/3746] lr: 7.106e-02, eta: 3 days, 6:26:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5534, loss_cls: 4.0279, loss: 4.0279 +2024-07-24 01:11:21,411 - pyskl - INFO - Epoch [55][1000/3746] lr: 7.104e-02, eta: 3 days, 6:24:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5577, loss_cls: 4.0336, loss: 4.0336 +2024-07-24 01:12:43,456 - pyskl - INFO - Epoch [55][1100/3746] lr: 7.101e-02, eta: 3 days, 6:23:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5594, loss_cls: 3.9856, loss: 3.9856 +2024-07-24 01:14:05,321 - pyskl - INFO - Epoch [55][1200/3746] lr: 7.099e-02, eta: 3 days, 6:22:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5559, loss_cls: 4.0113, loss: 4.0113 +2024-07-24 01:15:27,260 - pyskl - INFO - Epoch [55][1300/3746] lr: 7.096e-02, eta: 3 days, 6:21:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5641, loss_cls: 4.0118, loss: 4.0118 +2024-07-24 01:16:49,032 - pyskl - INFO - Epoch [55][1400/3746] lr: 7.093e-02, eta: 3 days, 6:20:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5514, loss_cls: 4.0563, loss: 4.0563 +2024-07-24 01:18:11,072 - pyskl - INFO - Epoch [55][1500/3746] lr: 7.091e-02, eta: 3 days, 6:18:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5505, loss_cls: 4.0308, loss: 4.0308 +2024-07-24 01:19:32,713 - pyskl - INFO - Epoch [55][1600/3746] lr: 7.088e-02, eta: 3 days, 6:17:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5422, loss_cls: 4.0952, loss: 4.0952 +2024-07-24 01:20:54,806 - pyskl - INFO - Epoch [55][1700/3746] lr: 7.086e-02, eta: 3 days, 6:16:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5461, loss_cls: 4.0748, loss: 4.0748 +2024-07-24 01:22:16,235 - pyskl - INFO - Epoch [55][1800/3746] lr: 7.083e-02, eta: 3 days, 6:15:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5517, loss_cls: 4.0257, loss: 4.0257 +2024-07-24 01:23:37,465 - pyskl - INFO - Epoch [55][1900/3746] lr: 7.081e-02, eta: 3 days, 6:13:56, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5427, loss_cls: 4.0764, loss: 4.0764 +2024-07-24 01:24:59,466 - pyskl - INFO - Epoch [55][2000/3746] lr: 7.078e-02, eta: 3 days, 6:12:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5647, loss_cls: 3.9952, loss: 3.9952 +2024-07-24 01:26:21,222 - pyskl - INFO - Epoch [55][2100/3746] lr: 7.076e-02, eta: 3 days, 6:11:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5481, loss_cls: 4.0358, loss: 4.0358 +2024-07-24 01:27:43,535 - pyskl - INFO - Epoch [55][2200/3746] lr: 7.073e-02, eta: 3 days, 6:10:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5555, loss_cls: 4.0122, loss: 4.0122 +2024-07-24 01:29:05,160 - pyskl - INFO - Epoch [55][2300/3746] lr: 7.071e-02, eta: 3 days, 6:09:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5458, loss_cls: 4.0614, loss: 4.0614 +2024-07-24 01:30:28,178 - pyskl - INFO - Epoch [55][2400/3746] lr: 7.068e-02, eta: 3 days, 6:07:53, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5530, loss_cls: 4.0674, loss: 4.0674 +2024-07-24 01:31:49,986 - pyskl - INFO - Epoch [55][2500/3746] lr: 7.065e-02, eta: 3 days, 6:06:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5497, loss_cls: 4.0746, loss: 4.0746 +2024-07-24 01:33:12,242 - pyskl - INFO - Epoch [55][2600/3746] lr: 7.063e-02, eta: 3 days, 6:05:27, time: 0.823, data_time: 0.001, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5444, loss_cls: 4.0741, loss: 4.0741 +2024-07-24 01:34:35,847 - pyskl - INFO - Epoch [55][2700/3746] lr: 7.060e-02, eta: 3 days, 6:04:16, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5505, loss_cls: 4.0768, loss: 4.0768 +2024-07-24 01:35:57,294 - pyskl - INFO - Epoch [55][2800/3746] lr: 7.058e-02, eta: 3 days, 6:03:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5522, loss_cls: 4.0645, loss: 4.0645 +2024-07-24 01:37:19,712 - pyskl - INFO - Epoch [55][2900/3746] lr: 7.055e-02, eta: 3 days, 6:01:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5345, loss_cls: 4.1102, loss: 4.1102 +2024-07-24 01:38:42,399 - pyskl - INFO - Epoch [55][3000/3746] lr: 7.053e-02, eta: 3 days, 6:00:38, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5569, loss_cls: 4.0223, loss: 4.0223 +2024-07-24 01:40:05,237 - pyskl - INFO - Epoch [55][3100/3746] lr: 7.050e-02, eta: 3 days, 5:59:26, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5487, loss_cls: 4.0734, loss: 4.0734 +2024-07-24 01:41:27,641 - pyskl - INFO - Epoch [55][3200/3746] lr: 7.048e-02, eta: 3 days, 5:58:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5534, loss_cls: 4.0537, loss: 4.0537 +2024-07-24 01:42:50,667 - pyskl - INFO - Epoch [55][3300/3746] lr: 7.045e-02, eta: 3 days, 5:57:03, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5439, loss_cls: 4.0538, loss: 4.0538 +2024-07-24 01:44:12,605 - pyskl - INFO - Epoch [55][3400/3746] lr: 7.043e-02, eta: 3 days, 5:55:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5467, loss_cls: 4.0374, loss: 4.0374 +2024-07-24 01:45:34,235 - pyskl - INFO - Epoch [55][3500/3746] lr: 7.040e-02, eta: 3 days, 5:54:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5461, loss_cls: 4.0561, loss: 4.0561 +2024-07-24 01:46:56,891 - pyskl - INFO - Epoch [55][3600/3746] lr: 7.037e-02, eta: 3 days, 5:53:23, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5495, loss_cls: 4.0723, loss: 4.0723 +2024-07-24 01:48:20,293 - pyskl - INFO - Epoch [55][3700/3746] lr: 7.035e-02, eta: 3 days, 5:52:13, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5297, loss_cls: 4.1171, loss: 4.1171 +2024-07-24 01:48:59,514 - pyskl - INFO - Saving checkpoint at 55 epochs +2024-07-24 01:50:51,307 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 01:50:52,039 - pyskl - INFO - +top1_acc 0.2149 +top5_acc 0.4464 +2024-07-24 01:50:52,040 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 01:50:52,085 - pyskl - INFO - +mean_acc 0.2146 +2024-07-24 01:50:52,098 - pyskl - INFO - Epoch(val) [55][309] top1_acc: 0.2149, top5_acc: 0.4464, mean_class_accuracy: 0.2146 +2024-07-24 01:54:36,409 - pyskl - INFO - Epoch [56][100/3746] lr: 7.031e-02, eta: 3 days, 5:53:26, time: 2.243, data_time: 1.258, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5681, loss_cls: 3.9576, loss: 3.9576 +2024-07-24 01:55:59,638 - pyskl - INFO - Epoch [56][200/3746] lr: 7.029e-02, eta: 3 days, 5:52:15, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5558, loss_cls: 4.0080, loss: 4.0080 +2024-07-24 01:57:22,085 - pyskl - INFO - Epoch [56][300/3746] lr: 7.026e-02, eta: 3 days, 5:51:03, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5533, loss_cls: 4.0081, loss: 4.0081 +2024-07-24 01:58:44,367 - pyskl - INFO - Epoch [56][400/3746] lr: 7.023e-02, eta: 3 days, 5:49:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5587, loss_cls: 3.9751, loss: 3.9751 +2024-07-24 02:00:06,478 - pyskl - INFO - Epoch [56][500/3746] lr: 7.021e-02, eta: 3 days, 5:48:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5608, loss_cls: 4.0193, loss: 4.0193 +2024-07-24 02:01:28,786 - pyskl - INFO - Epoch [56][600/3746] lr: 7.018e-02, eta: 3 days, 5:47:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5433, loss_cls: 4.0450, loss: 4.0450 +2024-07-24 02:02:51,643 - pyskl - INFO - Epoch [56][700/3746] lr: 7.016e-02, eta: 3 days, 5:46:12, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5545, loss_cls: 3.9917, loss: 3.9917 +2024-07-24 02:04:13,325 - pyskl - INFO - Epoch [56][800/3746] lr: 7.013e-02, eta: 3 days, 5:44:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5586, loss_cls: 3.9981, loss: 3.9981 +2024-07-24 02:05:35,963 - pyskl - INFO - Epoch [56][900/3746] lr: 7.011e-02, eta: 3 days, 5:43:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5613, loss_cls: 4.0030, loss: 4.0030 +2024-07-24 02:06:58,029 - pyskl - INFO - Epoch [56][1000/3746] lr: 7.008e-02, eta: 3 days, 5:42:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5520, loss_cls: 4.0098, loss: 4.0098 +2024-07-24 02:08:20,016 - pyskl - INFO - Epoch [56][1100/3746] lr: 7.006e-02, eta: 3 days, 5:41:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5514, loss_cls: 4.0652, loss: 4.0652 +2024-07-24 02:09:42,157 - pyskl - INFO - Epoch [56][1200/3746] lr: 7.003e-02, eta: 3 days, 5:40:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5467, loss_cls: 4.0429, loss: 4.0429 +2024-07-24 02:11:04,303 - pyskl - INFO - Epoch [56][1300/3746] lr: 7.000e-02, eta: 3 days, 5:38:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5431, loss_cls: 4.0778, loss: 4.0778 +2024-07-24 02:12:26,322 - pyskl - INFO - Epoch [56][1400/3746] lr: 6.998e-02, eta: 3 days, 5:37:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5441, loss_cls: 4.0738, loss: 4.0738 +2024-07-24 02:13:48,844 - pyskl - INFO - Epoch [56][1500/3746] lr: 6.995e-02, eta: 3 days, 5:36:27, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5547, loss_cls: 4.0178, loss: 4.0178 +2024-07-24 02:15:10,634 - pyskl - INFO - Epoch [56][1600/3746] lr: 6.993e-02, eta: 3 days, 5:35:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5519, loss_cls: 4.0514, loss: 4.0514 +2024-07-24 02:16:33,089 - pyskl - INFO - Epoch [56][1700/3746] lr: 6.990e-02, eta: 3 days, 5:34:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5527, loss_cls: 4.0270, loss: 4.0270 +2024-07-24 02:17:55,225 - pyskl - INFO - Epoch [56][1800/3746] lr: 6.988e-02, eta: 3 days, 5:32:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5545, loss_cls: 4.0398, loss: 4.0398 +2024-07-24 02:19:17,318 - pyskl - INFO - Epoch [56][1900/3746] lr: 6.985e-02, eta: 3 days, 5:31:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5572, loss_cls: 3.9994, loss: 3.9994 +2024-07-24 02:20:39,462 - pyskl - INFO - Epoch [56][2000/3746] lr: 6.983e-02, eta: 3 days, 5:30:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5573, loss_cls: 4.0202, loss: 4.0202 +2024-07-24 02:22:01,139 - pyskl - INFO - Epoch [56][2100/3746] lr: 6.980e-02, eta: 3 days, 5:29:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5514, loss_cls: 4.0553, loss: 4.0553 +2024-07-24 02:23:22,963 - pyskl - INFO - Epoch [56][2200/3746] lr: 6.977e-02, eta: 3 days, 5:27:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5483, loss_cls: 4.0628, loss: 4.0628 +2024-07-24 02:24:44,495 - pyskl - INFO - Epoch [56][2300/3746] lr: 6.975e-02, eta: 3 days, 5:26:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5483, loss_cls: 4.0509, loss: 4.0509 +2024-07-24 02:26:07,909 - pyskl - INFO - Epoch [56][2400/3746] lr: 6.972e-02, eta: 3 days, 5:25:27, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5600, loss_cls: 4.0259, loss: 4.0259 +2024-07-24 02:27:29,178 - pyskl - INFO - Epoch [56][2500/3746] lr: 6.970e-02, eta: 3 days, 5:24:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5550, loss_cls: 3.9992, loss: 3.9992 +2024-07-24 02:28:51,380 - pyskl - INFO - Epoch [56][2600/3746] lr: 6.967e-02, eta: 3 days, 5:22:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5441, loss_cls: 4.0650, loss: 4.0650 +2024-07-24 02:30:14,629 - pyskl - INFO - Epoch [56][2700/3746] lr: 6.965e-02, eta: 3 days, 5:21:48, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5495, loss_cls: 4.0788, loss: 4.0788 +2024-07-24 02:31:36,671 - pyskl - INFO - Epoch [56][2800/3746] lr: 6.962e-02, eta: 3 days, 5:20:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5406, loss_cls: 4.0536, loss: 4.0536 +2024-07-24 02:32:58,559 - pyskl - INFO - Epoch [56][2900/3746] lr: 6.959e-02, eta: 3 days, 5:19:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5547, loss_cls: 4.0553, loss: 4.0553 +2024-07-24 02:34:20,685 - pyskl - INFO - Epoch [56][3000/3746] lr: 6.957e-02, eta: 3 days, 5:18:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5559, loss_cls: 4.0025, loss: 4.0025 +2024-07-24 02:35:42,819 - pyskl - INFO - Epoch [56][3100/3746] lr: 6.954e-02, eta: 3 days, 5:16:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5511, loss_cls: 4.0523, loss: 4.0523 +2024-07-24 02:37:04,744 - pyskl - INFO - Epoch [56][3200/3746] lr: 6.952e-02, eta: 3 days, 5:15:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5573, loss_cls: 4.0451, loss: 4.0451 +2024-07-24 02:38:27,776 - pyskl - INFO - Epoch [56][3300/3746] lr: 6.949e-02, eta: 3 days, 5:14:28, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5473, loss_cls: 4.0755, loss: 4.0755 +2024-07-24 02:39:49,584 - pyskl - INFO - Epoch [56][3400/3746] lr: 6.947e-02, eta: 3 days, 5:13:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5448, loss_cls: 4.0806, loss: 4.0806 +2024-07-24 02:41:11,734 - pyskl - INFO - Epoch [56][3500/3746] lr: 6.944e-02, eta: 3 days, 5:12:01, time: 0.821, data_time: 0.001, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5533, loss_cls: 4.0442, loss: 4.0442 +2024-07-24 02:42:34,687 - pyskl - INFO - Epoch [56][3600/3746] lr: 6.941e-02, eta: 3 days, 5:10:49, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5406, loss_cls: 4.0901, loss: 4.0901 +2024-07-24 02:43:58,277 - pyskl - INFO - Epoch [56][3700/3746] lr: 6.939e-02, eta: 3 days, 5:09:38, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5534, loss_cls: 3.9984, loss: 3.9984 +2024-07-24 02:44:37,562 - pyskl - INFO - Saving checkpoint at 56 epochs +2024-07-24 02:46:29,042 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 02:46:29,773 - pyskl - INFO - +top1_acc 0.2112 +top5_acc 0.4545 +2024-07-24 02:46:29,774 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 02:46:29,812 - pyskl - INFO - +mean_acc 0.2109 +2024-07-24 02:46:29,822 - pyskl - INFO - Epoch(val) [56][309] top1_acc: 0.2112, top5_acc: 0.4545, mean_class_accuracy: 0.2109 +2024-07-24 02:50:12,220 - pyskl - INFO - Epoch [57][100/3746] lr: 6.935e-02, eta: 3 days, 5:10:43, time: 2.224, data_time: 1.240, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5530, loss_cls: 4.0302, loss: 4.0302 +2024-07-24 02:51:34,890 - pyskl - INFO - Epoch [57][200/3746] lr: 6.932e-02, eta: 3 days, 5:09:30, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5495, loss_cls: 4.0199, loss: 4.0199 +2024-07-24 02:52:57,045 - pyskl - INFO - Epoch [57][300/3746] lr: 6.930e-02, eta: 3 days, 5:08:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5437, loss_cls: 4.0558, loss: 4.0558 +2024-07-24 02:54:19,162 - pyskl - INFO - Epoch [57][400/3746] lr: 6.927e-02, eta: 3 days, 5:07:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5605, loss_cls: 3.9978, loss: 3.9978 +2024-07-24 02:55:40,849 - pyskl - INFO - Epoch [57][500/3746] lr: 6.925e-02, eta: 3 days, 5:05:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5616, loss_cls: 3.9875, loss: 3.9875 +2024-07-24 02:57:02,919 - pyskl - INFO - Epoch [57][600/3746] lr: 6.922e-02, eta: 3 days, 5:04:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5536, loss_cls: 4.0138, loss: 4.0138 +2024-07-24 02:58:24,940 - pyskl - INFO - Epoch [57][700/3746] lr: 6.920e-02, eta: 3 days, 5:03:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5466, loss_cls: 4.0565, loss: 4.0565 +2024-07-24 02:59:46,736 - pyskl - INFO - Epoch [57][800/3746] lr: 6.917e-02, eta: 3 days, 5:02:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5478, loss_cls: 4.0432, loss: 4.0432 +2024-07-24 03:01:08,927 - pyskl - INFO - Epoch [57][900/3746] lr: 6.914e-02, eta: 3 days, 5:00:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5509, loss_cls: 4.0204, loss: 4.0204 +2024-07-24 03:02:31,296 - pyskl - INFO - Epoch [57][1000/3746] lr: 6.912e-02, eta: 3 days, 4:59:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5600, loss_cls: 4.0018, loss: 4.0018 +2024-07-24 03:03:53,784 - pyskl - INFO - Epoch [57][1100/3746] lr: 6.909e-02, eta: 3 days, 4:58:27, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5569, loss_cls: 4.0045, loss: 4.0045 +2024-07-24 03:05:16,265 - pyskl - INFO - Epoch [57][1200/3746] lr: 6.907e-02, eta: 3 days, 4:57:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5383, loss_cls: 4.1107, loss: 4.1107 +2024-07-24 03:06:38,605 - pyskl - INFO - Epoch [57][1300/3746] lr: 6.904e-02, eta: 3 days, 4:56:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5613, loss_cls: 4.0344, loss: 4.0344 +2024-07-24 03:08:00,425 - pyskl - INFO - Epoch [57][1400/3746] lr: 6.901e-02, eta: 3 days, 4:54:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5514, loss_cls: 4.0209, loss: 4.0209 +2024-07-24 03:09:22,037 - pyskl - INFO - Epoch [57][1500/3746] lr: 6.899e-02, eta: 3 days, 4:53:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5495, loss_cls: 4.0471, loss: 4.0471 +2024-07-24 03:10:44,021 - pyskl - INFO - Epoch [57][1600/3746] lr: 6.896e-02, eta: 3 days, 4:52:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5570, loss_cls: 4.0056, loss: 4.0056 +2024-07-24 03:12:05,759 - pyskl - INFO - Epoch [57][1700/3746] lr: 6.894e-02, eta: 3 days, 4:51:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5544, loss_cls: 4.0065, loss: 4.0065 +2024-07-24 03:13:27,724 - pyskl - INFO - Epoch [57][1800/3746] lr: 6.891e-02, eta: 3 days, 4:49:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5656, loss_cls: 3.9776, loss: 3.9776 +2024-07-24 03:14:49,954 - pyskl - INFO - Epoch [57][1900/3746] lr: 6.889e-02, eta: 3 days, 4:48:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5617, loss_cls: 3.9930, loss: 3.9930 +2024-07-24 03:16:11,983 - pyskl - INFO - Epoch [57][2000/3746] lr: 6.886e-02, eta: 3 days, 4:47:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5420, loss_cls: 4.0690, loss: 4.0690 +2024-07-24 03:17:34,049 - pyskl - INFO - Epoch [57][2100/3746] lr: 6.883e-02, eta: 3 days, 4:46:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5472, loss_cls: 4.0601, loss: 4.0601 +2024-07-24 03:18:55,980 - pyskl - INFO - Epoch [57][2200/3746] lr: 6.881e-02, eta: 3 days, 4:44:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5552, loss_cls: 4.0270, loss: 4.0270 +2024-07-24 03:20:17,891 - pyskl - INFO - Epoch [57][2300/3746] lr: 6.878e-02, eta: 3 days, 4:43:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5470, loss_cls: 4.0376, loss: 4.0376 +2024-07-24 03:21:40,639 - pyskl - INFO - Epoch [57][2400/3746] lr: 6.876e-02, eta: 3 days, 4:42:28, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5597, loss_cls: 3.9933, loss: 3.9933 +2024-07-24 03:23:02,394 - pyskl - INFO - Epoch [57][2500/3746] lr: 6.873e-02, eta: 3 days, 4:41:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5506, loss_cls: 4.0217, loss: 4.0217 +2024-07-24 03:24:24,995 - pyskl - INFO - Epoch [57][2600/3746] lr: 6.870e-02, eta: 3 days, 4:40:00, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5572, loss_cls: 4.0226, loss: 4.0226 +2024-07-24 03:25:47,883 - pyskl - INFO - Epoch [57][2700/3746] lr: 6.868e-02, eta: 3 days, 4:38:48, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5494, loss_cls: 4.0265, loss: 4.0265 +2024-07-24 03:27:09,672 - pyskl - INFO - Epoch [57][2800/3746] lr: 6.865e-02, eta: 3 days, 4:37:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5398, loss_cls: 4.0700, loss: 4.0700 +2024-07-24 03:28:32,276 - pyskl - INFO - Epoch [57][2900/3746] lr: 6.863e-02, eta: 3 days, 4:36:21, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5492, loss_cls: 4.0357, loss: 4.0357 +2024-07-24 03:29:54,926 - pyskl - INFO - Epoch [57][3000/3746] lr: 6.860e-02, eta: 3 days, 4:35:08, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5445, loss_cls: 4.0627, loss: 4.0627 +2024-07-24 03:31:17,729 - pyskl - INFO - Epoch [57][3100/3746] lr: 6.857e-02, eta: 3 days, 4:33:55, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5463, loss_cls: 4.0721, loss: 4.0721 +2024-07-24 03:32:40,307 - pyskl - INFO - Epoch [57][3200/3746] lr: 6.855e-02, eta: 3 days, 4:32:42, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5536, loss_cls: 4.0122, loss: 4.0122 +2024-07-24 03:34:02,951 - pyskl - INFO - Epoch [57][3300/3746] lr: 6.852e-02, eta: 3 days, 4:31:29, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5653, loss_cls: 3.9508, loss: 3.9508 +2024-07-24 03:35:24,765 - pyskl - INFO - Epoch [57][3400/3746] lr: 6.850e-02, eta: 3 days, 4:30:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5539, loss_cls: 4.0463, loss: 4.0463 +2024-07-24 03:36:46,890 - pyskl - INFO - Epoch [57][3500/3746] lr: 6.847e-02, eta: 3 days, 4:29:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5528, loss_cls: 4.0290, loss: 4.0290 +2024-07-24 03:38:09,838 - pyskl - INFO - Epoch [57][3600/3746] lr: 6.844e-02, eta: 3 days, 4:27:48, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5447, loss_cls: 4.0599, loss: 4.0599 +2024-07-24 03:39:33,376 - pyskl - INFO - Epoch [57][3700/3746] lr: 6.842e-02, eta: 3 days, 4:26:37, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5497, loss_cls: 4.0797, loss: 4.0797 +2024-07-24 03:40:12,795 - pyskl - INFO - Saving checkpoint at 57 epochs +2024-07-24 03:42:04,383 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 03:42:05,208 - pyskl - INFO - +top1_acc 0.1851 +top5_acc 0.4171 +2024-07-24 03:42:05,208 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 03:42:05,249 - pyskl - INFO - +mean_acc 0.1850 +2024-07-24 03:42:05,262 - pyskl - INFO - Epoch(val) [57][309] top1_acc: 0.1851, top5_acc: 0.4171, mean_class_accuracy: 0.1850 +2024-07-24 03:45:52,215 - pyskl - INFO - Epoch [58][100/3746] lr: 6.838e-02, eta: 3 days, 4:27:43, time: 2.269, data_time: 1.270, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5664, loss_cls: 3.9550, loss: 3.9550 +2024-07-24 03:47:16,081 - pyskl - INFO - Epoch [58][200/3746] lr: 6.835e-02, eta: 3 days, 4:26:32, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5652, loss_cls: 3.9490, loss: 3.9490 +2024-07-24 03:48:39,514 - pyskl - INFO - Epoch [58][300/3746] lr: 6.833e-02, eta: 3 days, 4:25:20, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5614, loss_cls: 4.0179, loss: 4.0179 +2024-07-24 03:50:02,676 - pyskl - INFO - Epoch [58][400/3746] lr: 6.830e-02, eta: 3 days, 4:24:08, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5458, loss_cls: 4.0560, loss: 4.0560 +2024-07-24 03:51:25,952 - pyskl - INFO - Epoch [58][500/3746] lr: 6.828e-02, eta: 3 days, 4:22:56, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5477, loss_cls: 4.0540, loss: 4.0540 +2024-07-24 03:52:49,538 - pyskl - INFO - Epoch [58][600/3746] lr: 6.825e-02, eta: 3 days, 4:21:44, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5642, loss_cls: 4.0058, loss: 4.0058 +2024-07-24 03:54:12,884 - pyskl - INFO - Epoch [58][700/3746] lr: 6.822e-02, eta: 3 days, 4:20:32, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5558, loss_cls: 4.0321, loss: 4.0321 +2024-07-24 03:55:36,358 - pyskl - INFO - Epoch [58][800/3746] lr: 6.820e-02, eta: 3 days, 4:19:20, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5622, loss_cls: 3.9977, loss: 3.9977 +2024-07-24 03:57:00,455 - pyskl - INFO - Epoch [58][900/3746] lr: 6.817e-02, eta: 3 days, 4:18:09, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5544, loss_cls: 4.0400, loss: 4.0400 +2024-07-24 03:58:24,220 - pyskl - INFO - Epoch [58][1000/3746] lr: 6.815e-02, eta: 3 days, 4:16:58, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5566, loss_cls: 4.0172, loss: 4.0172 +2024-07-24 03:59:47,028 - pyskl - INFO - Epoch [58][1100/3746] lr: 6.812e-02, eta: 3 days, 4:15:45, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5494, loss_cls: 4.0487, loss: 4.0487 +2024-07-24 04:01:10,359 - pyskl - INFO - Epoch [58][1200/3746] lr: 6.809e-02, eta: 3 days, 4:14:33, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5522, loss_cls: 4.0207, loss: 4.0207 +2024-07-24 04:02:33,783 - pyskl - INFO - Epoch [58][1300/3746] lr: 6.807e-02, eta: 3 days, 4:13:21, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5563, loss_cls: 4.0149, loss: 4.0149 +2024-07-24 04:03:57,508 - pyskl - INFO - Epoch [58][1400/3746] lr: 6.804e-02, eta: 3 days, 4:12:09, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5453, loss_cls: 4.0533, loss: 4.0533 +2024-07-24 04:05:21,352 - pyskl - INFO - Epoch [58][1500/3746] lr: 6.802e-02, eta: 3 days, 4:10:58, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5545, loss_cls: 4.0448, loss: 4.0448 +2024-07-24 04:06:44,511 - pyskl - INFO - Epoch [58][1600/3746] lr: 6.799e-02, eta: 3 days, 4:09:45, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5580, loss_cls: 4.0213, loss: 4.0213 +2024-07-24 04:08:08,213 - pyskl - INFO - Epoch [58][1700/3746] lr: 6.796e-02, eta: 3 days, 4:08:34, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5567, loss_cls: 3.9857, loss: 3.9857 +2024-07-24 04:09:32,252 - pyskl - INFO - Epoch [58][1800/3746] lr: 6.794e-02, eta: 3 days, 4:07:23, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5470, loss_cls: 4.0679, loss: 4.0679 +2024-07-24 04:10:55,433 - pyskl - INFO - Epoch [58][1900/3746] lr: 6.791e-02, eta: 3 days, 4:06:10, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5464, loss_cls: 4.0282, loss: 4.0282 +2024-07-24 04:12:18,479 - pyskl - INFO - Epoch [58][2000/3746] lr: 6.789e-02, eta: 3 days, 4:04:58, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5553, loss_cls: 3.9860, loss: 3.9860 +2024-07-24 04:13:41,667 - pyskl - INFO - Epoch [58][2100/3746] lr: 6.786e-02, eta: 3 days, 4:03:45, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5553, loss_cls: 4.0445, loss: 4.0445 +2024-07-24 04:15:04,930 - pyskl - INFO - Epoch [58][2200/3746] lr: 6.783e-02, eta: 3 days, 4:02:33, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5587, loss_cls: 4.0291, loss: 4.0291 +2024-07-24 04:16:28,687 - pyskl - INFO - Epoch [58][2300/3746] lr: 6.781e-02, eta: 3 days, 4:01:21, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5530, loss_cls: 4.0033, loss: 4.0033 +2024-07-24 04:17:51,868 - pyskl - INFO - Epoch [58][2400/3746] lr: 6.778e-02, eta: 3 days, 4:00:09, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5587, loss_cls: 3.9912, loss: 3.9912 +2024-07-24 04:19:14,237 - pyskl - INFO - Epoch [58][2500/3746] lr: 6.775e-02, eta: 3 days, 3:58:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5664, loss_cls: 3.9850, loss: 3.9850 +2024-07-24 04:20:38,036 - pyskl - INFO - Epoch [58][2600/3746] lr: 6.773e-02, eta: 3 days, 3:57:43, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5581, loss_cls: 4.0056, loss: 4.0056 +2024-07-24 04:22:00,862 - pyskl - INFO - Epoch [58][2700/3746] lr: 6.770e-02, eta: 3 days, 3:56:30, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5598, loss_cls: 4.0035, loss: 4.0035 +2024-07-24 04:23:23,710 - pyskl - INFO - Epoch [58][2800/3746] lr: 6.768e-02, eta: 3 days, 3:55:17, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5567, loss_cls: 4.0133, loss: 4.0133 +2024-07-24 04:24:47,063 - pyskl - INFO - Epoch [58][2900/3746] lr: 6.765e-02, eta: 3 days, 3:54:05, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5545, loss_cls: 4.0237, loss: 4.0237 +2024-07-24 04:26:09,852 - pyskl - INFO - Epoch [58][3000/3746] lr: 6.762e-02, eta: 3 days, 3:52:52, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5494, loss_cls: 4.0347, loss: 4.0347 +2024-07-24 04:27:32,096 - pyskl - INFO - Epoch [58][3100/3746] lr: 6.760e-02, eta: 3 days, 3:51:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5530, loss_cls: 4.0362, loss: 4.0362 +2024-07-24 04:28:54,785 - pyskl - INFO - Epoch [58][3200/3746] lr: 6.757e-02, eta: 3 days, 3:50:24, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5533, loss_cls: 4.0452, loss: 4.0452 +2024-07-24 04:30:16,712 - pyskl - INFO - Epoch [58][3300/3746] lr: 6.755e-02, eta: 3 days, 3:49:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5634, loss_cls: 3.9984, loss: 3.9984 +2024-07-24 04:31:38,880 - pyskl - INFO - Epoch [58][3400/3746] lr: 6.752e-02, eta: 3 days, 3:47:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5541, loss_cls: 4.0383, loss: 4.0383 +2024-07-24 04:33:01,800 - pyskl - INFO - Epoch [58][3500/3746] lr: 6.749e-02, eta: 3 days, 3:46:42, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5364, loss_cls: 4.0958, loss: 4.0958 +2024-07-24 04:34:24,883 - pyskl - INFO - Epoch [58][3600/3746] lr: 6.747e-02, eta: 3 days, 3:45:30, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5591, loss_cls: 3.9829, loss: 3.9829 +2024-07-24 04:35:47,672 - pyskl - INFO - Epoch [58][3700/3746] lr: 6.744e-02, eta: 3 days, 3:44:16, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5539, loss_cls: 4.0206, loss: 4.0206 +2024-07-24 04:36:27,139 - pyskl - INFO - Saving checkpoint at 58 epochs +2024-07-24 04:38:19,699 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 04:38:20,373 - pyskl - INFO - +top1_acc 0.2132 +top5_acc 0.4486 +2024-07-24 04:38:20,373 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 04:38:20,423 - pyskl - INFO - +mean_acc 0.2129 +2024-07-24 04:38:20,437 - pyskl - INFO - Epoch(val) [58][309] top1_acc: 0.2132, top5_acc: 0.4486, mean_class_accuracy: 0.2129 +2024-07-24 04:42:09,917 - pyskl - INFO - Epoch [59][100/3746] lr: 6.740e-02, eta: 3 days, 3:45:21, time: 2.295, data_time: 1.303, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5636, loss_cls: 3.9787, loss: 3.9787 +2024-07-24 04:43:33,416 - pyskl - INFO - Epoch [59][200/3746] lr: 6.738e-02, eta: 3 days, 3:44:09, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5623, loss_cls: 3.9855, loss: 3.9855 +2024-07-24 04:44:56,245 - pyskl - INFO - Epoch [59][300/3746] lr: 6.735e-02, eta: 3 days, 3:42:56, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5530, loss_cls: 3.9817, loss: 3.9817 +2024-07-24 04:46:18,827 - pyskl - INFO - Epoch [59][400/3746] lr: 6.732e-02, eta: 3 days, 3:41:42, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5589, loss_cls: 3.9940, loss: 3.9940 +2024-07-24 04:47:41,449 - pyskl - INFO - Epoch [59][500/3746] lr: 6.730e-02, eta: 3 days, 3:40:28, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5541, loss_cls: 4.0293, loss: 4.0293 +2024-07-24 04:49:04,003 - pyskl - INFO - Epoch [59][600/3746] lr: 6.727e-02, eta: 3 days, 3:39:15, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5628, loss_cls: 3.9888, loss: 3.9888 +2024-07-24 04:50:25,766 - pyskl - INFO - Epoch [59][700/3746] lr: 6.725e-02, eta: 3 days, 3:37:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5580, loss_cls: 3.9753, loss: 3.9753 +2024-07-24 04:51:48,824 - pyskl - INFO - Epoch [59][800/3746] lr: 6.722e-02, eta: 3 days, 3:36:46, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5572, loss_cls: 4.0051, loss: 4.0051 +2024-07-24 04:53:11,466 - pyskl - INFO - Epoch [59][900/3746] lr: 6.719e-02, eta: 3 days, 3:35:33, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5577, loss_cls: 3.9930, loss: 3.9930 +2024-07-24 04:54:34,813 - pyskl - INFO - Epoch [59][1000/3746] lr: 6.717e-02, eta: 3 days, 3:34:20, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5536, loss_cls: 4.0177, loss: 4.0177 +2024-07-24 04:55:57,860 - pyskl - INFO - Epoch [59][1100/3746] lr: 6.714e-02, eta: 3 days, 3:33:07, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5505, loss_cls: 4.0333, loss: 4.0333 +2024-07-24 04:57:21,062 - pyskl - INFO - Epoch [59][1200/3746] lr: 6.711e-02, eta: 3 days, 3:31:54, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5531, loss_cls: 4.0258, loss: 4.0258 +2024-07-24 04:58:43,867 - pyskl - INFO - Epoch [59][1300/3746] lr: 6.709e-02, eta: 3 days, 3:30:41, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5617, loss_cls: 4.0162, loss: 4.0162 +2024-07-24 05:00:06,835 - pyskl - INFO - Epoch [59][1400/3746] lr: 6.706e-02, eta: 3 days, 3:29:28, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5531, loss_cls: 4.0379, loss: 4.0379 +2024-07-24 05:01:29,560 - pyskl - INFO - Epoch [59][1500/3746] lr: 6.704e-02, eta: 3 days, 3:28:14, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5583, loss_cls: 3.9918, loss: 3.9918 +2024-07-24 05:02:51,680 - pyskl - INFO - Epoch [59][1600/3746] lr: 6.701e-02, eta: 3 days, 3:26:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5509, loss_cls: 4.0361, loss: 4.0361 +2024-07-24 05:04:15,215 - pyskl - INFO - Epoch [59][1700/3746] lr: 6.698e-02, eta: 3 days, 3:25:47, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5569, loss_cls: 3.9895, loss: 3.9895 +2024-07-24 05:05:37,649 - pyskl - INFO - Epoch [59][1800/3746] lr: 6.696e-02, eta: 3 days, 3:24:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5616, loss_cls: 3.9826, loss: 3.9826 +2024-07-24 05:07:00,015 - pyskl - INFO - Epoch [59][1900/3746] lr: 6.693e-02, eta: 3 days, 3:23:19, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5409, loss_cls: 4.1054, loss: 4.1054 +2024-07-24 05:08:21,440 - pyskl - INFO - Epoch [59][2000/3746] lr: 6.690e-02, eta: 3 days, 3:22:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5600, loss_cls: 4.0112, loss: 4.0112 +2024-07-24 05:09:43,758 - pyskl - INFO - Epoch [59][2100/3746] lr: 6.688e-02, eta: 3 days, 3:20:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5664, loss_cls: 3.9703, loss: 3.9703 +2024-07-24 05:11:05,925 - pyskl - INFO - Epoch [59][2200/3746] lr: 6.685e-02, eta: 3 days, 3:19:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5664, loss_cls: 3.9746, loss: 3.9746 +2024-07-24 05:12:28,815 - pyskl - INFO - Epoch [59][2300/3746] lr: 6.682e-02, eta: 3 days, 3:18:21, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5513, loss_cls: 4.0510, loss: 4.0510 +2024-07-24 05:13:50,484 - pyskl - INFO - Epoch [59][2400/3746] lr: 6.680e-02, eta: 3 days, 3:17:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5505, loss_cls: 3.9937, loss: 3.9937 +2024-07-24 05:15:12,549 - pyskl - INFO - Epoch [59][2500/3746] lr: 6.677e-02, eta: 3 days, 3:15:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5539, loss_cls: 4.0071, loss: 4.0071 +2024-07-24 05:16:35,737 - pyskl - INFO - Epoch [59][2600/3746] lr: 6.675e-02, eta: 3 days, 3:14:38, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5508, loss_cls: 4.0448, loss: 4.0448 +2024-07-24 05:17:57,704 - pyskl - INFO - Epoch [59][2700/3746] lr: 6.672e-02, eta: 3 days, 3:13:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5636, loss_cls: 3.9715, loss: 3.9715 +2024-07-24 05:19:20,427 - pyskl - INFO - Epoch [59][2800/3746] lr: 6.669e-02, eta: 3 days, 3:12:09, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5461, loss_cls: 4.0919, loss: 4.0919 +2024-07-24 05:20:43,241 - pyskl - INFO - Epoch [59][2900/3746] lr: 6.667e-02, eta: 3 days, 3:10:55, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5652, loss_cls: 3.9720, loss: 3.9720 +2024-07-24 05:22:05,677 - pyskl - INFO - Epoch [59][3000/3746] lr: 6.664e-02, eta: 3 days, 3:09:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5561, loss_cls: 4.0032, loss: 4.0032 +2024-07-24 05:23:27,860 - pyskl - INFO - Epoch [59][3100/3746] lr: 6.661e-02, eta: 3 days, 3:08:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5478, loss_cls: 4.0446, loss: 4.0446 +2024-07-24 05:24:50,777 - pyskl - INFO - Epoch [59][3200/3746] lr: 6.659e-02, eta: 3 days, 3:07:13, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5609, loss_cls: 3.9808, loss: 3.9808 +2024-07-24 05:26:12,548 - pyskl - INFO - Epoch [59][3300/3746] lr: 6.656e-02, eta: 3 days, 3:05:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5613, loss_cls: 3.9773, loss: 3.9773 +2024-07-24 05:27:34,819 - pyskl - INFO - Epoch [59][3400/3746] lr: 6.653e-02, eta: 3 days, 3:04:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5537, loss_cls: 4.0133, loss: 4.0133 +2024-07-24 05:28:58,364 - pyskl - INFO - Epoch [59][3500/3746] lr: 6.651e-02, eta: 3 days, 3:03:31, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5575, loss_cls: 4.0116, loss: 4.0116 +2024-07-24 05:30:21,339 - pyskl - INFO - Epoch [59][3600/3746] lr: 6.648e-02, eta: 3 days, 3:02:18, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5602, loss_cls: 4.0071, loss: 4.0071 +2024-07-24 05:31:44,373 - pyskl - INFO - Epoch [59][3700/3746] lr: 6.646e-02, eta: 3 days, 3:01:04, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5681, loss_cls: 3.9400, loss: 3.9400 +2024-07-24 05:32:24,197 - pyskl - INFO - Saving checkpoint at 59 epochs +2024-07-24 05:34:16,840 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 05:34:17,662 - pyskl - INFO - +top1_acc 0.2381 +top5_acc 0.4761 +2024-07-24 05:34:17,662 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 05:34:17,701 - pyskl - INFO - +mean_acc 0.2377 +2024-07-24 05:34:17,706 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_42.pth was removed +2024-07-24 05:34:17,976 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_59.pth. +2024-07-24 05:34:17,976 - pyskl - INFO - Best top1_acc is 0.2381 at 59 epoch. +2024-07-24 05:34:17,992 - pyskl - INFO - Epoch(val) [59][309] top1_acc: 0.2381, top5_acc: 0.4761, mean_class_accuracy: 0.2377 +2024-07-24 05:38:02,605 - pyskl - INFO - Epoch [60][100/3746] lr: 6.642e-02, eta: 3 days, 3:01:57, time: 2.246, data_time: 1.257, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5681, loss_cls: 3.9683, loss: 3.9683 +2024-07-24 05:39:24,771 - pyskl - INFO - Epoch [60][200/3746] lr: 6.639e-02, eta: 3 days, 3:00:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5516, loss_cls: 4.0255, loss: 4.0255 +2024-07-24 05:40:46,917 - pyskl - INFO - Epoch [60][300/3746] lr: 6.636e-02, eta: 3 days, 2:59:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5634, loss_cls: 3.9618, loss: 3.9618 +2024-07-24 05:42:09,713 - pyskl - INFO - Epoch [60][400/3746] lr: 6.634e-02, eta: 3 days, 2:58:13, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5583, loss_cls: 3.9677, loss: 3.9677 +2024-07-24 05:43:31,808 - pyskl - INFO - Epoch [60][500/3746] lr: 6.631e-02, eta: 3 days, 2:56:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5631, loss_cls: 3.9785, loss: 3.9785 +2024-07-24 05:44:54,144 - pyskl - INFO - Epoch [60][600/3746] lr: 6.629e-02, eta: 3 days, 2:55:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5566, loss_cls: 3.9721, loss: 3.9721 +2024-07-24 05:46:16,036 - pyskl - INFO - Epoch [60][700/3746] lr: 6.626e-02, eta: 3 days, 2:54:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5619, loss_cls: 3.9521, loss: 3.9521 +2024-07-24 05:47:37,828 - pyskl - INFO - Epoch [60][800/3746] lr: 6.623e-02, eta: 3 days, 2:53:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5594, loss_cls: 4.0000, loss: 4.0000 +2024-07-24 05:48:59,692 - pyskl - INFO - Epoch [60][900/3746] lr: 6.621e-02, eta: 3 days, 2:51:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5578, loss_cls: 3.9762, loss: 3.9762 +2024-07-24 05:50:21,750 - pyskl - INFO - Epoch [60][1000/3746] lr: 6.618e-02, eta: 3 days, 2:50:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5581, loss_cls: 3.9953, loss: 3.9953 +2024-07-24 05:51:43,576 - pyskl - INFO - Epoch [60][1100/3746] lr: 6.615e-02, eta: 3 days, 2:49:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5561, loss_cls: 4.0084, loss: 4.0084 +2024-07-24 05:53:06,101 - pyskl - INFO - Epoch [60][1200/3746] lr: 6.613e-02, eta: 3 days, 2:48:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5486, loss_cls: 4.0539, loss: 4.0539 +2024-07-24 05:54:28,440 - pyskl - INFO - Epoch [60][1300/3746] lr: 6.610e-02, eta: 3 days, 2:46:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5611, loss_cls: 3.9836, loss: 3.9836 +2024-07-24 05:55:50,487 - pyskl - INFO - Epoch [60][1400/3746] lr: 6.607e-02, eta: 3 days, 2:45:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5495, loss_cls: 4.0027, loss: 4.0027 +2024-07-24 05:57:13,120 - pyskl - INFO - Epoch [60][1500/3746] lr: 6.605e-02, eta: 3 days, 2:44:29, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5608, loss_cls: 3.9465, loss: 3.9465 +2024-07-24 05:58:35,556 - pyskl - INFO - Epoch [60][1600/3746] lr: 6.602e-02, eta: 3 days, 2:43:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5544, loss_cls: 4.0386, loss: 4.0386 +2024-07-24 05:59:58,263 - pyskl - INFO - Epoch [60][1700/3746] lr: 6.599e-02, eta: 3 days, 2:42:01, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5592, loss_cls: 3.9958, loss: 3.9958 +2024-07-24 06:01:20,432 - pyskl - INFO - Epoch [60][1800/3746] lr: 6.597e-02, eta: 3 days, 2:40:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5658, loss_cls: 3.9654, loss: 3.9654 +2024-07-24 06:02:42,960 - pyskl - INFO - Epoch [60][1900/3746] lr: 6.594e-02, eta: 3 days, 2:39:31, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5492, loss_cls: 4.0560, loss: 4.0560 +2024-07-24 06:04:04,847 - pyskl - INFO - Epoch [60][2000/3746] lr: 6.591e-02, eta: 3 days, 2:38:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5439, loss_cls: 4.0580, loss: 4.0580 +2024-07-24 06:05:26,827 - pyskl - INFO - Epoch [60][2100/3746] lr: 6.589e-02, eta: 3 days, 2:37:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5577, loss_cls: 4.0116, loss: 4.0116 +2024-07-24 06:06:48,741 - pyskl - INFO - Epoch [60][2200/3746] lr: 6.586e-02, eta: 3 days, 2:35:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5450, loss_cls: 4.0614, loss: 4.0614 +2024-07-24 06:08:12,146 - pyskl - INFO - Epoch [60][2300/3746] lr: 6.584e-02, eta: 3 days, 2:34:32, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5505, loss_cls: 4.0183, loss: 4.0183 +2024-07-24 06:09:33,487 - pyskl - INFO - Epoch [60][2400/3746] lr: 6.581e-02, eta: 3 days, 2:33:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5497, loss_cls: 4.0187, loss: 4.0187 +2024-07-24 06:10:56,433 - pyskl - INFO - Epoch [60][2500/3746] lr: 6.578e-02, eta: 3 days, 2:32:02, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5500, loss_cls: 4.0557, loss: 4.0557 +2024-07-24 06:12:19,345 - pyskl - INFO - Epoch [60][2600/3746] lr: 6.576e-02, eta: 3 days, 2:30:49, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5652, loss_cls: 3.9414, loss: 3.9414 +2024-07-24 06:13:41,875 - pyskl - INFO - Epoch [60][2700/3746] lr: 6.573e-02, eta: 3 days, 2:29:34, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5581, loss_cls: 3.9902, loss: 3.9902 +2024-07-24 06:15:04,876 - pyskl - INFO - Epoch [60][2800/3746] lr: 6.570e-02, eta: 3 days, 2:28:21, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5647, loss_cls: 3.9597, loss: 3.9597 +2024-07-24 06:16:27,919 - pyskl - INFO - Epoch [60][2900/3746] lr: 6.568e-02, eta: 3 days, 2:27:07, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5555, loss_cls: 4.0086, loss: 4.0086 +2024-07-24 06:17:51,298 - pyskl - INFO - Epoch [60][3000/3746] lr: 6.565e-02, eta: 3 days, 2:25:54, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5592, loss_cls: 4.0137, loss: 4.0137 +2024-07-24 06:19:14,131 - pyskl - INFO - Epoch [60][3100/3746] lr: 6.562e-02, eta: 3 days, 2:24:40, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5578, loss_cls: 3.9981, loss: 3.9981 +2024-07-24 06:20:36,708 - pyskl - INFO - Epoch [60][3200/3746] lr: 6.560e-02, eta: 3 days, 2:23:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5528, loss_cls: 4.0109, loss: 4.0109 +2024-07-24 06:21:58,479 - pyskl - INFO - Epoch [60][3300/3746] lr: 6.557e-02, eta: 3 days, 2:22:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5472, loss_cls: 4.0076, loss: 4.0076 +2024-07-24 06:23:21,076 - pyskl - INFO - Epoch [60][3400/3746] lr: 6.554e-02, eta: 3 days, 2:20:55, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5669, loss_cls: 3.9642, loss: 3.9642 +2024-07-24 06:24:44,370 - pyskl - INFO - Epoch [60][3500/3746] lr: 6.552e-02, eta: 3 days, 2:19:42, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5550, loss_cls: 4.0056, loss: 4.0056 +2024-07-24 06:26:07,188 - pyskl - INFO - Epoch [60][3600/3746] lr: 6.549e-02, eta: 3 days, 2:18:28, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5573, loss_cls: 4.0064, loss: 4.0064 +2024-07-24 06:27:30,230 - pyskl - INFO - Epoch [60][3700/3746] lr: 6.546e-02, eta: 3 days, 2:17:14, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5563, loss_cls: 3.9961, loss: 3.9961 +2024-07-24 06:28:09,747 - pyskl - INFO - Saving checkpoint at 60 epochs +2024-07-24 06:30:01,764 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 06:30:02,427 - pyskl - INFO - +top1_acc 0.2108 +top5_acc 0.4429 +2024-07-24 06:30:02,427 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 06:30:02,467 - pyskl - INFO - +mean_acc 0.2106 +2024-07-24 06:30:02,478 - pyskl - INFO - Epoch(val) [60][309] top1_acc: 0.2108, top5_acc: 0.4429, mean_class_accuracy: 0.2106 +2024-07-24 06:33:47,098 - pyskl - INFO - Epoch [61][100/3746] lr: 6.542e-02, eta: 3 days, 2:18:02, time: 2.246, data_time: 1.255, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5630, loss_cls: 3.9835, loss: 3.9835 +2024-07-24 06:35:10,112 - pyskl - INFO - Epoch [61][200/3746] lr: 6.540e-02, eta: 3 days, 2:16:48, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5700, loss_cls: 3.9393, loss: 3.9393 +2024-07-24 06:36:33,982 - pyskl - INFO - Epoch [61][300/3746] lr: 6.537e-02, eta: 3 days, 2:15:35, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5678, loss_cls: 3.9429, loss: 3.9429 +2024-07-24 06:37:57,133 - pyskl - INFO - Epoch [61][400/3746] lr: 6.534e-02, eta: 3 days, 2:14:21, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5733, loss_cls: 3.9387, loss: 3.9387 +2024-07-24 06:39:20,597 - pyskl - INFO - Epoch [61][500/3746] lr: 6.532e-02, eta: 3 days, 2:13:08, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5602, loss_cls: 3.9942, loss: 3.9942 +2024-07-24 06:40:43,842 - pyskl - INFO - Epoch [61][600/3746] lr: 6.529e-02, eta: 3 days, 2:11:55, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5570, loss_cls: 3.9944, loss: 3.9944 +2024-07-24 06:42:07,104 - pyskl - INFO - Epoch [61][700/3746] lr: 6.526e-02, eta: 3 days, 2:10:41, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5575, loss_cls: 3.9660, loss: 3.9660 +2024-07-24 06:43:30,331 - pyskl - INFO - Epoch [61][800/3746] lr: 6.524e-02, eta: 3 days, 2:09:27, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5606, loss_cls: 3.9768, loss: 3.9768 +2024-07-24 06:44:53,520 - pyskl - INFO - Epoch [61][900/3746] lr: 6.521e-02, eta: 3 days, 2:08:14, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5653, loss_cls: 3.9555, loss: 3.9555 +2024-07-24 06:46:16,790 - pyskl - INFO - Epoch [61][1000/3746] lr: 6.519e-02, eta: 3 days, 2:07:00, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5547, loss_cls: 3.9851, loss: 3.9851 +2024-07-24 06:47:39,524 - pyskl - INFO - Epoch [61][1100/3746] lr: 6.516e-02, eta: 3 days, 2:05:46, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5561, loss_cls: 4.0192, loss: 4.0192 +2024-07-24 06:49:02,772 - pyskl - INFO - Epoch [61][1200/3746] lr: 6.513e-02, eta: 3 days, 2:04:32, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5567, loss_cls: 3.9954, loss: 3.9954 +2024-07-24 06:50:25,939 - pyskl - INFO - Epoch [61][1300/3746] lr: 6.511e-02, eta: 3 days, 2:03:18, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5616, loss_cls: 3.9778, loss: 3.9778 +2024-07-24 06:51:49,317 - pyskl - INFO - Epoch [61][1400/3746] lr: 6.508e-02, eta: 3 days, 2:02:05, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5611, loss_cls: 4.0014, loss: 4.0014 +2024-07-24 06:53:12,182 - pyskl - INFO - Epoch [61][1500/3746] lr: 6.505e-02, eta: 3 days, 2:00:51, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5595, loss_cls: 3.9920, loss: 3.9920 +2024-07-24 06:54:35,736 - pyskl - INFO - Epoch [61][1600/3746] lr: 6.503e-02, eta: 3 days, 1:59:37, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5648, loss_cls: 3.9805, loss: 3.9805 +2024-07-24 06:55:59,056 - pyskl - INFO - Epoch [61][1700/3746] lr: 6.500e-02, eta: 3 days, 1:58:24, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5623, loss_cls: 3.9703, loss: 3.9703 +2024-07-24 06:57:22,258 - pyskl - INFO - Epoch [61][1800/3746] lr: 6.497e-02, eta: 3 days, 1:57:10, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5548, loss_cls: 4.0177, loss: 4.0177 +2024-07-24 06:58:45,111 - pyskl - INFO - Epoch [61][1900/3746] lr: 6.495e-02, eta: 3 days, 1:55:56, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5606, loss_cls: 3.9573, loss: 3.9573 +2024-07-24 07:00:08,265 - pyskl - INFO - Epoch [61][2000/3746] lr: 6.492e-02, eta: 3 days, 1:54:42, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5453, loss_cls: 4.0206, loss: 4.0206 +2024-07-24 07:01:30,683 - pyskl - INFO - Epoch [61][2100/3746] lr: 6.489e-02, eta: 3 days, 1:53:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5687, loss_cls: 3.9812, loss: 3.9812 +2024-07-24 07:02:54,038 - pyskl - INFO - Epoch [61][2200/3746] lr: 6.487e-02, eta: 3 days, 1:52:14, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5577, loss_cls: 3.9869, loss: 3.9869 +2024-07-24 07:04:17,127 - pyskl - INFO - Epoch [61][2300/3746] lr: 6.484e-02, eta: 3 days, 1:51:00, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5469, loss_cls: 4.0771, loss: 4.0771 +2024-07-24 07:05:39,586 - pyskl - INFO - Epoch [61][2400/3746] lr: 6.481e-02, eta: 3 days, 1:49:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5656, loss_cls: 3.9667, loss: 3.9667 +2024-07-24 07:07:03,374 - pyskl - INFO - Epoch [61][2500/3746] lr: 6.478e-02, eta: 3 days, 1:48:32, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5456, loss_cls: 4.0444, loss: 4.0444 +2024-07-24 07:08:26,367 - pyskl - INFO - Epoch [61][2600/3746] lr: 6.476e-02, eta: 3 days, 1:47:18, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5503, loss_cls: 4.0352, loss: 4.0352 +2024-07-24 07:09:49,149 - pyskl - INFO - Epoch [61][2700/3746] lr: 6.473e-02, eta: 3 days, 1:46:03, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5616, loss_cls: 3.9861, loss: 3.9861 +2024-07-24 07:11:12,295 - pyskl - INFO - Epoch [61][2800/3746] lr: 6.470e-02, eta: 3 days, 1:44:49, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5539, loss_cls: 3.9563, loss: 3.9563 +2024-07-24 07:12:34,627 - pyskl - INFO - Epoch [61][2900/3746] lr: 6.468e-02, eta: 3 days, 1:43:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5514, loss_cls: 4.0117, loss: 4.0117 +2024-07-24 07:13:56,853 - pyskl - INFO - Epoch [61][3000/3746] lr: 6.465e-02, eta: 3 days, 1:42:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5477, loss_cls: 4.0614, loss: 4.0614 +2024-07-24 07:15:20,038 - pyskl - INFO - Epoch [61][3100/3746] lr: 6.462e-02, eta: 3 days, 1:41:05, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5527, loss_cls: 4.0095, loss: 4.0095 +2024-07-24 07:16:42,843 - pyskl - INFO - Epoch [61][3200/3746] lr: 6.460e-02, eta: 3 days, 1:39:51, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5555, loss_cls: 4.0207, loss: 4.0207 +2024-07-24 07:18:05,641 - pyskl - INFO - Epoch [61][3300/3746] lr: 6.457e-02, eta: 3 days, 1:38:36, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5639, loss_cls: 3.9792, loss: 3.9792 +2024-07-24 07:19:28,568 - pyskl - INFO - Epoch [61][3400/3746] lr: 6.454e-02, eta: 3 days, 1:37:22, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5431, loss_cls: 4.0796, loss: 4.0796 +2024-07-24 07:20:51,540 - pyskl - INFO - Epoch [61][3500/3746] lr: 6.452e-02, eta: 3 days, 1:36:08, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5561, loss_cls: 4.0166, loss: 4.0166 +2024-07-24 07:22:15,451 - pyskl - INFO - Epoch [61][3600/3746] lr: 6.449e-02, eta: 3 days, 1:34:55, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5591, loss_cls: 3.9866, loss: 3.9866 +2024-07-24 07:23:37,870 - pyskl - INFO - Epoch [61][3700/3746] lr: 6.446e-02, eta: 3 days, 1:33:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5595, loss_cls: 3.9620, loss: 3.9620 +2024-07-24 07:24:17,583 - pyskl - INFO - Saving checkpoint at 61 epochs +2024-07-24 07:26:11,092 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 07:26:11,877 - pyskl - INFO - +top1_acc 0.2273 +top5_acc 0.4671 +2024-07-24 07:26:11,878 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 07:26:11,918 - pyskl - INFO - +mean_acc 0.2272 +2024-07-24 07:26:11,929 - pyskl - INFO - Epoch(val) [61][309] top1_acc: 0.2273, top5_acc: 0.4671, mean_class_accuracy: 0.2272 +2024-07-24 07:29:56,676 - pyskl - INFO - Epoch [62][100/3746] lr: 6.443e-02, eta: 3 days, 1:34:22, time: 2.247, data_time: 1.255, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5619, loss_cls: 3.9666, loss: 3.9666 +2024-07-24 07:31:19,735 - pyskl - INFO - Epoch [62][200/3746] lr: 6.440e-02, eta: 3 days, 1:33:08, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5584, loss_cls: 3.9993, loss: 3.9993 +2024-07-24 07:32:42,490 - pyskl - INFO - Epoch [62][300/3746] lr: 6.437e-02, eta: 3 days, 1:31:53, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5648, loss_cls: 3.9565, loss: 3.9565 +2024-07-24 07:34:05,071 - pyskl - INFO - Epoch [62][400/3746] lr: 6.434e-02, eta: 3 days, 1:30:38, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5677, loss_cls: 3.9535, loss: 3.9535 +2024-07-24 07:35:26,980 - pyskl - INFO - Epoch [62][500/3746] lr: 6.432e-02, eta: 3 days, 1:29:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5575, loss_cls: 3.9795, loss: 3.9795 +2024-07-24 07:36:48,739 - pyskl - INFO - Epoch [62][600/3746] lr: 6.429e-02, eta: 3 days, 1:28:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5592, loss_cls: 3.9619, loss: 3.9619 +2024-07-24 07:38:10,833 - pyskl - INFO - Epoch [62][700/3746] lr: 6.426e-02, eta: 3 days, 1:26:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5502, loss_cls: 4.0008, loss: 4.0008 +2024-07-24 07:39:32,691 - pyskl - INFO - Epoch [62][800/3746] lr: 6.424e-02, eta: 3 days, 1:25:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5487, loss_cls: 4.0000, loss: 4.0000 +2024-07-24 07:40:54,928 - pyskl - INFO - Epoch [62][900/3746] lr: 6.421e-02, eta: 3 days, 1:24:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5627, loss_cls: 3.9690, loss: 3.9690 +2024-07-24 07:42:17,813 - pyskl - INFO - Epoch [62][1000/3746] lr: 6.418e-02, eta: 3 days, 1:23:05, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5605, loss_cls: 3.9573, loss: 3.9573 +2024-07-24 07:43:40,265 - pyskl - INFO - Epoch [62][1100/3746] lr: 6.416e-02, eta: 3 days, 1:21:49, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5619, loss_cls: 3.9808, loss: 3.9808 +2024-07-24 07:45:02,478 - pyskl - INFO - Epoch [62][1200/3746] lr: 6.413e-02, eta: 3 days, 1:20:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5591, loss_cls: 4.0231, loss: 4.0231 +2024-07-24 07:46:24,553 - pyskl - INFO - Epoch [62][1300/3746] lr: 6.410e-02, eta: 3 days, 1:19:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5633, loss_cls: 3.9945, loss: 3.9945 +2024-07-24 07:47:45,900 - pyskl - INFO - Epoch [62][1400/3746] lr: 6.408e-02, eta: 3 days, 1:18:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5570, loss_cls: 3.9976, loss: 3.9976 +2024-07-24 07:49:07,927 - pyskl - INFO - Epoch [62][1500/3746] lr: 6.405e-02, eta: 3 days, 1:16:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5684, loss_cls: 3.9337, loss: 3.9337 +2024-07-24 07:50:29,812 - pyskl - INFO - Epoch [62][1600/3746] lr: 6.402e-02, eta: 3 days, 1:15:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5563, loss_cls: 4.0259, loss: 4.0259 +2024-07-24 07:51:51,536 - pyskl - INFO - Epoch [62][1700/3746] lr: 6.400e-02, eta: 3 days, 1:14:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5467, loss_cls: 4.0504, loss: 4.0504 +2024-07-24 07:53:13,291 - pyskl - INFO - Epoch [62][1800/3746] lr: 6.397e-02, eta: 3 days, 1:12:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5580, loss_cls: 4.0005, loss: 4.0005 +2024-07-24 07:54:35,132 - pyskl - INFO - Epoch [62][1900/3746] lr: 6.394e-02, eta: 3 days, 1:11:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5625, loss_cls: 3.9796, loss: 3.9796 +2024-07-24 07:55:56,609 - pyskl - INFO - Epoch [62][2000/3746] lr: 6.392e-02, eta: 3 days, 1:10:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5558, loss_cls: 3.9956, loss: 3.9956 +2024-07-24 07:57:18,888 - pyskl - INFO - Epoch [62][2100/3746] lr: 6.389e-02, eta: 3 days, 1:09:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5725, loss_cls: 3.9426, loss: 3.9426 +2024-07-24 07:58:42,373 - pyskl - INFO - Epoch [62][2200/3746] lr: 6.386e-02, eta: 3 days, 1:07:55, time: 0.835, data_time: 0.001, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5595, loss_cls: 3.9720, loss: 3.9720 +2024-07-24 08:00:04,090 - pyskl - INFO - Epoch [62][2300/3746] lr: 6.384e-02, eta: 3 days, 1:06:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5559, loss_cls: 4.0276, loss: 4.0276 +2024-07-24 08:01:26,566 - pyskl - INFO - Epoch [62][2400/3746] lr: 6.381e-02, eta: 3 days, 1:05:24, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5591, loss_cls: 3.9742, loss: 3.9742 +2024-07-24 08:02:49,645 - pyskl - INFO - Epoch [62][2500/3746] lr: 6.378e-02, eta: 3 days, 1:04:09, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5442, loss_cls: 4.0750, loss: 4.0750 +2024-07-24 08:04:11,774 - pyskl - INFO - Epoch [62][2600/3746] lr: 6.375e-02, eta: 3 days, 1:02:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5548, loss_cls: 3.9937, loss: 3.9937 +2024-07-24 08:05:34,074 - pyskl - INFO - Epoch [62][2700/3746] lr: 6.373e-02, eta: 3 days, 1:01:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5656, loss_cls: 3.9749, loss: 3.9749 +2024-07-24 08:06:56,282 - pyskl - INFO - Epoch [62][2800/3746] lr: 6.370e-02, eta: 3 days, 1:00:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5653, loss_cls: 3.9444, loss: 3.9444 +2024-07-24 08:08:18,049 - pyskl - INFO - Epoch [62][2900/3746] lr: 6.367e-02, eta: 3 days, 0:59:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5613, loss_cls: 4.0131, loss: 4.0131 +2024-07-24 08:09:40,501 - pyskl - INFO - Epoch [62][3000/3746] lr: 6.365e-02, eta: 3 days, 0:57:51, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5608, loss_cls: 3.9676, loss: 3.9676 +2024-07-24 08:11:03,489 - pyskl - INFO - Epoch [62][3100/3746] lr: 6.362e-02, eta: 3 days, 0:56:36, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5666, loss_cls: 3.9750, loss: 3.9750 +2024-07-24 08:12:24,958 - pyskl - INFO - Epoch [62][3200/3746] lr: 6.359e-02, eta: 3 days, 0:55:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5725, loss_cls: 3.9304, loss: 3.9304 +2024-07-24 08:13:47,062 - pyskl - INFO - Epoch [62][3300/3746] lr: 6.357e-02, eta: 3 days, 0:54:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5439, loss_cls: 4.0618, loss: 4.0618 +2024-07-24 08:15:10,078 - pyskl - INFO - Epoch [62][3400/3746] lr: 6.354e-02, eta: 3 days, 0:52:49, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5603, loss_cls: 4.0023, loss: 4.0023 +2024-07-24 08:16:32,411 - pyskl - INFO - Epoch [62][3500/3746] lr: 6.351e-02, eta: 3 days, 0:51:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5552, loss_cls: 4.0477, loss: 4.0477 +2024-07-24 08:17:54,068 - pyskl - INFO - Epoch [62][3600/3746] lr: 6.349e-02, eta: 3 days, 0:50:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5525, loss_cls: 4.0007, loss: 4.0007 +2024-07-24 08:19:15,808 - pyskl - INFO - Epoch [62][3700/3746] lr: 6.346e-02, eta: 3 days, 0:49:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5472, loss_cls: 4.0214, loss: 4.0214 +2024-07-24 08:19:55,864 - pyskl - INFO - Saving checkpoint at 62 epochs +2024-07-24 08:21:49,253 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 08:21:49,945 - pyskl - INFO - +top1_acc 0.2156 +top5_acc 0.4507 +2024-07-24 08:21:49,945 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 08:21:49,984 - pyskl - INFO - +mean_acc 0.2154 +2024-07-24 08:21:49,994 - pyskl - INFO - Epoch(val) [62][309] top1_acc: 0.2156, top5_acc: 0.4507, mean_class_accuracy: 0.2154 +2024-07-24 08:25:35,465 - pyskl - INFO - Epoch [63][100/3746] lr: 6.342e-02, eta: 3 days, 0:49:40, time: 2.255, data_time: 1.267, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5681, loss_cls: 3.9418, loss: 3.9418 +2024-07-24 08:26:58,674 - pyskl - INFO - Epoch [63][200/3746] lr: 6.339e-02, eta: 3 days, 0:48:26, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5717, loss_cls: 3.9330, loss: 3.9330 +2024-07-24 08:28:21,620 - pyskl - INFO - Epoch [63][300/3746] lr: 6.337e-02, eta: 3 days, 0:47:11, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5708, loss_cls: 3.9338, loss: 3.9338 +2024-07-24 08:29:45,010 - pyskl - INFO - Epoch [63][400/3746] lr: 6.334e-02, eta: 3 days, 0:45:57, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5600, loss_cls: 3.9817, loss: 3.9817 +2024-07-24 08:31:08,561 - pyskl - INFO - Epoch [63][500/3746] lr: 6.331e-02, eta: 3 days, 0:44:43, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5616, loss_cls: 3.9891, loss: 3.9891 +2024-07-24 08:32:31,260 - pyskl - INFO - Epoch [63][600/3746] lr: 6.328e-02, eta: 3 days, 0:43:28, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5581, loss_cls: 3.9936, loss: 3.9936 +2024-07-24 08:33:53,966 - pyskl - INFO - Epoch [63][700/3746] lr: 6.326e-02, eta: 3 days, 0:42:13, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5553, loss_cls: 4.0195, loss: 4.0195 +2024-07-24 08:35:17,362 - pyskl - INFO - Epoch [63][800/3746] lr: 6.323e-02, eta: 3 days, 0:40:58, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5791, loss_cls: 3.8868, loss: 3.8868 +2024-07-24 08:36:40,444 - pyskl - INFO - Epoch [63][900/3746] lr: 6.320e-02, eta: 3 days, 0:39:44, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5492, loss_cls: 4.0123, loss: 4.0123 +2024-07-24 08:38:03,508 - pyskl - INFO - Epoch [63][1000/3746] lr: 6.318e-02, eta: 3 days, 0:38:29, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5498, loss_cls: 4.0173, loss: 4.0173 +2024-07-24 08:39:26,258 - pyskl - INFO - Epoch [63][1100/3746] lr: 6.315e-02, eta: 3 days, 0:37:14, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5591, loss_cls: 3.9943, loss: 3.9943 +2024-07-24 08:40:49,041 - pyskl - INFO - Epoch [63][1200/3746] lr: 6.312e-02, eta: 3 days, 0:35:59, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5605, loss_cls: 3.9884, loss: 3.9884 +2024-07-24 08:42:12,031 - pyskl - INFO - Epoch [63][1300/3746] lr: 6.310e-02, eta: 3 days, 0:34:44, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5563, loss_cls: 4.0208, loss: 4.0208 +2024-07-24 08:43:35,288 - pyskl - INFO - Epoch [63][1400/3746] lr: 6.307e-02, eta: 3 days, 0:33:30, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5703, loss_cls: 3.9395, loss: 3.9395 +2024-07-24 08:44:57,990 - pyskl - INFO - Epoch [63][1500/3746] lr: 6.304e-02, eta: 3 days, 0:32:15, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5547, loss_cls: 3.9932, loss: 3.9932 +2024-07-24 08:46:21,267 - pyskl - INFO - Epoch [63][1600/3746] lr: 6.301e-02, eta: 3 days, 0:31:00, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5673, loss_cls: 3.9457, loss: 3.9457 +2024-07-24 08:47:43,995 - pyskl - INFO - Epoch [63][1700/3746] lr: 6.299e-02, eta: 3 days, 0:29:45, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5667, loss_cls: 3.9688, loss: 3.9688 +2024-07-24 08:49:06,951 - pyskl - INFO - Epoch [63][1800/3746] lr: 6.296e-02, eta: 3 days, 0:28:30, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5592, loss_cls: 3.9958, loss: 3.9958 +2024-07-24 08:50:29,988 - pyskl - INFO - Epoch [63][1900/3746] lr: 6.293e-02, eta: 3 days, 0:27:15, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5655, loss_cls: 3.9832, loss: 3.9832 +2024-07-24 08:51:52,649 - pyskl - INFO - Epoch [63][2000/3746] lr: 6.291e-02, eta: 3 days, 0:26:00, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5694, loss_cls: 3.9804, loss: 3.9804 +2024-07-24 08:53:15,954 - pyskl - INFO - Epoch [63][2100/3746] lr: 6.288e-02, eta: 3 days, 0:24:46, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5555, loss_cls: 3.9794, loss: 3.9794 +2024-07-24 08:54:38,830 - pyskl - INFO - Epoch [63][2200/3746] lr: 6.285e-02, eta: 3 days, 0:23:31, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5578, loss_cls: 4.0143, loss: 4.0143 +2024-07-24 08:56:00,914 - pyskl - INFO - Epoch [63][2300/3746] lr: 6.283e-02, eta: 3 days, 0:22:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5642, loss_cls: 3.9992, loss: 3.9992 +2024-07-24 08:57:23,917 - pyskl - INFO - Epoch [63][2400/3746] lr: 6.280e-02, eta: 3 days, 0:21:00, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5505, loss_cls: 4.0149, loss: 4.0149 +2024-07-24 08:58:47,768 - pyskl - INFO - Epoch [63][2500/3746] lr: 6.277e-02, eta: 3 days, 0:19:46, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5653, loss_cls: 3.9436, loss: 3.9436 +2024-07-24 09:00:10,245 - pyskl - INFO - Epoch [63][2600/3746] lr: 6.274e-02, eta: 3 days, 0:18:31, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5475, loss_cls: 4.0291, loss: 4.0291 +2024-07-24 09:01:33,369 - pyskl - INFO - Epoch [63][2700/3746] lr: 6.272e-02, eta: 3 days, 0:17:16, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5614, loss_cls: 3.9487, loss: 3.9487 +2024-07-24 09:02:56,217 - pyskl - INFO - Epoch [63][2800/3746] lr: 6.269e-02, eta: 3 days, 0:16:01, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5658, loss_cls: 3.9696, loss: 3.9696 +2024-07-24 09:04:18,472 - pyskl - INFO - Epoch [63][2900/3746] lr: 6.266e-02, eta: 3 days, 0:14:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5709, loss_cls: 3.9609, loss: 3.9609 +2024-07-24 09:05:40,375 - pyskl - INFO - Epoch [63][3000/3746] lr: 6.264e-02, eta: 3 days, 0:13:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5737, loss_cls: 3.8996, loss: 3.8996 +2024-07-24 09:07:03,205 - pyskl - INFO - Epoch [63][3100/3746] lr: 6.261e-02, eta: 3 days, 0:12:13, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5478, loss_cls: 4.0353, loss: 4.0353 +2024-07-24 09:08:25,414 - pyskl - INFO - Epoch [63][3200/3746] lr: 6.258e-02, eta: 3 days, 0:10:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5608, loss_cls: 3.9784, loss: 3.9784 +2024-07-24 09:09:48,569 - pyskl - INFO - Epoch [63][3300/3746] lr: 6.256e-02, eta: 3 days, 0:09:43, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5541, loss_cls: 4.0197, loss: 4.0197 +2024-07-24 09:11:11,110 - pyskl - INFO - Epoch [63][3400/3746] lr: 6.253e-02, eta: 3 days, 0:08:27, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5558, loss_cls: 4.0026, loss: 4.0026 +2024-07-24 09:12:34,913 - pyskl - INFO - Epoch [63][3500/3746] lr: 6.250e-02, eta: 3 days, 0:07:13, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5598, loss_cls: 3.9597, loss: 3.9597 +2024-07-24 09:13:57,397 - pyskl - INFO - Epoch [63][3600/3746] lr: 6.247e-02, eta: 3 days, 0:05:58, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5697, loss_cls: 3.9477, loss: 3.9477 +2024-07-24 09:15:20,003 - pyskl - INFO - Epoch [63][3700/3746] lr: 6.245e-02, eta: 3 days, 0:04:42, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5595, loss_cls: 3.9985, loss: 3.9985 +2024-07-24 09:16:00,278 - pyskl - INFO - Saving checkpoint at 63 epochs +2024-07-24 09:17:53,225 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 09:17:53,925 - pyskl - INFO - +top1_acc 0.2331 +top5_acc 0.4829 +2024-07-24 09:17:53,925 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 09:17:53,966 - pyskl - INFO - +mean_acc 0.2329 +2024-07-24 09:17:53,978 - pyskl - INFO - Epoch(val) [63][309] top1_acc: 0.2331, top5_acc: 0.4829, mean_class_accuracy: 0.2329 +2024-07-24 09:21:41,456 - pyskl - INFO - Epoch [64][100/3746] lr: 6.241e-02, eta: 3 days, 0:05:20, time: 2.275, data_time: 1.283, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5608, loss_cls: 3.9665, loss: 3.9665 +2024-07-24 09:23:04,893 - pyskl - INFO - Epoch [64][200/3746] lr: 6.238e-02, eta: 3 days, 0:04:05, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5737, loss_cls: 3.9167, loss: 3.9167 +2024-07-24 09:24:28,097 - pyskl - INFO - Epoch [64][300/3746] lr: 6.235e-02, eta: 3 days, 0:02:50, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5648, loss_cls: 3.9428, loss: 3.9428 +2024-07-24 09:25:50,721 - pyskl - INFO - Epoch [64][400/3746] lr: 6.233e-02, eta: 3 days, 0:01:35, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5655, loss_cls: 3.9411, loss: 3.9411 +2024-07-24 09:27:13,699 - pyskl - INFO - Epoch [64][500/3746] lr: 6.230e-02, eta: 3 days, 0:00:20, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5736, loss_cls: 3.8978, loss: 3.8978 +2024-07-24 09:28:36,936 - pyskl - INFO - Epoch [64][600/3746] lr: 6.227e-02, eta: 2 days, 23:59:05, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5578, loss_cls: 4.0005, loss: 4.0005 +2024-07-24 09:30:00,014 - pyskl - INFO - Epoch [64][700/3746] lr: 6.225e-02, eta: 2 days, 23:57:50, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5581, loss_cls: 3.9797, loss: 3.9797 +2024-07-24 09:31:22,932 - pyskl - INFO - Epoch [64][800/3746] lr: 6.222e-02, eta: 2 days, 23:56:35, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5605, loss_cls: 3.9552, loss: 3.9552 +2024-07-24 09:32:46,082 - pyskl - INFO - Epoch [64][900/3746] lr: 6.219e-02, eta: 2 days, 23:55:20, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5577, loss_cls: 3.9715, loss: 3.9715 +2024-07-24 09:34:09,451 - pyskl - INFO - Epoch [64][1000/3746] lr: 6.216e-02, eta: 2 days, 23:54:05, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5581, loss_cls: 3.9877, loss: 3.9877 +2024-07-24 09:35:32,256 - pyskl - INFO - Epoch [64][1100/3746] lr: 6.214e-02, eta: 2 days, 23:52:50, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5580, loss_cls: 3.9883, loss: 3.9883 +2024-07-24 09:36:55,590 - pyskl - INFO - Epoch [64][1200/3746] lr: 6.211e-02, eta: 2 days, 23:51:35, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5716, loss_cls: 3.9251, loss: 3.9251 +2024-07-24 09:38:19,088 - pyskl - INFO - Epoch [64][1300/3746] lr: 6.208e-02, eta: 2 days, 23:50:21, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5659, loss_cls: 3.9539, loss: 3.9539 +2024-07-24 09:39:42,138 - pyskl - INFO - Epoch [64][1400/3746] lr: 6.206e-02, eta: 2 days, 23:49:06, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5694, loss_cls: 3.9354, loss: 3.9354 +2024-07-24 09:41:05,220 - pyskl - INFO - Epoch [64][1500/3746] lr: 6.203e-02, eta: 2 days, 23:47:51, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5689, loss_cls: 3.9538, loss: 3.9538 +2024-07-24 09:42:28,254 - pyskl - INFO - Epoch [64][1600/3746] lr: 6.200e-02, eta: 2 days, 23:46:36, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5617, loss_cls: 3.9866, loss: 3.9866 +2024-07-24 09:43:51,816 - pyskl - INFO - Epoch [64][1700/3746] lr: 6.197e-02, eta: 2 days, 23:45:21, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5556, loss_cls: 3.9958, loss: 3.9958 +2024-07-24 09:45:15,318 - pyskl - INFO - Epoch [64][1800/3746] lr: 6.195e-02, eta: 2 days, 23:44:07, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5609, loss_cls: 3.9651, loss: 3.9651 +2024-07-24 09:46:38,816 - pyskl - INFO - Epoch [64][1900/3746] lr: 6.192e-02, eta: 2 days, 23:42:52, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5617, loss_cls: 3.9637, loss: 3.9637 +2024-07-24 09:48:01,800 - pyskl - INFO - Epoch [64][2000/3746] lr: 6.189e-02, eta: 2 days, 23:41:37, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5666, loss_cls: 3.9614, loss: 3.9614 +2024-07-24 09:49:24,978 - pyskl - INFO - Epoch [64][2100/3746] lr: 6.187e-02, eta: 2 days, 23:40:22, time: 0.832, data_time: 0.001, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5658, loss_cls: 3.9538, loss: 3.9538 +2024-07-24 09:50:47,326 - pyskl - INFO - Epoch [64][2200/3746] lr: 6.184e-02, eta: 2 days, 23:39:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5572, loss_cls: 3.9867, loss: 3.9867 +2024-07-24 09:52:09,971 - pyskl - INFO - Epoch [64][2300/3746] lr: 6.181e-02, eta: 2 days, 23:37:50, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5667, loss_cls: 3.9801, loss: 3.9801 +2024-07-24 09:53:32,943 - pyskl - INFO - Epoch [64][2400/3746] lr: 6.178e-02, eta: 2 days, 23:36:35, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5573, loss_cls: 3.9929, loss: 3.9929 +2024-07-24 09:54:55,646 - pyskl - INFO - Epoch [64][2500/3746] lr: 6.176e-02, eta: 2 days, 23:35:20, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5763, loss_cls: 3.9072, loss: 3.9072 +2024-07-24 09:56:18,001 - pyskl - INFO - Epoch [64][2600/3746] lr: 6.173e-02, eta: 2 days, 23:34:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5558, loss_cls: 3.9949, loss: 3.9949 +2024-07-24 09:57:41,678 - pyskl - INFO - Epoch [64][2700/3746] lr: 6.170e-02, eta: 2 days, 23:32:49, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5697, loss_cls: 3.9523, loss: 3.9523 +2024-07-24 09:59:04,218 - pyskl - INFO - Epoch [64][2800/3746] lr: 6.168e-02, eta: 2 days, 23:31:33, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5697, loss_cls: 3.9492, loss: 3.9492 +2024-07-24 10:00:27,279 - pyskl - INFO - Epoch [64][2900/3746] lr: 6.165e-02, eta: 2 days, 23:30:18, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5648, loss_cls: 3.9477, loss: 3.9477 +2024-07-24 10:01:50,585 - pyskl - INFO - Epoch [64][3000/3746] lr: 6.162e-02, eta: 2 days, 23:29:03, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5595, loss_cls: 3.9973, loss: 3.9973 +2024-07-24 10:03:12,830 - pyskl - INFO - Epoch [64][3100/3746] lr: 6.159e-02, eta: 2 days, 23:27:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5578, loss_cls: 3.9837, loss: 3.9837 +2024-07-24 10:04:35,200 - pyskl - INFO - Epoch [64][3200/3746] lr: 6.157e-02, eta: 2 days, 23:26:31, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5639, loss_cls: 3.9506, loss: 3.9506 +2024-07-24 10:05:58,706 - pyskl - INFO - Epoch [64][3300/3746] lr: 6.154e-02, eta: 2 days, 23:25:17, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5513, loss_cls: 4.0217, loss: 4.0217 +2024-07-24 10:07:21,622 - pyskl - INFO - Epoch [64][3400/3746] lr: 6.151e-02, eta: 2 days, 23:24:01, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5559, loss_cls: 4.0052, loss: 4.0052 +2024-07-24 10:08:44,800 - pyskl - INFO - Epoch [64][3500/3746] lr: 6.148e-02, eta: 2 days, 23:22:46, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5656, loss_cls: 3.9776, loss: 3.9776 +2024-07-24 10:10:07,677 - pyskl - INFO - Epoch [64][3600/3746] lr: 6.146e-02, eta: 2 days, 23:21:31, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5645, loss_cls: 3.9501, loss: 3.9501 +2024-07-24 10:11:31,366 - pyskl - INFO - Epoch [64][3700/3746] lr: 6.143e-02, eta: 2 days, 23:20:16, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5630, loss_cls: 3.9101, loss: 3.9101 +2024-07-24 10:12:11,066 - pyskl - INFO - Saving checkpoint at 64 epochs +2024-07-24 10:14:03,577 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 10:14:04,280 - pyskl - INFO - +top1_acc 0.2253 +top5_acc 0.4620 +2024-07-24 10:14:04,280 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 10:14:04,328 - pyskl - INFO - +mean_acc 0.2252 +2024-07-24 10:14:04,343 - pyskl - INFO - Epoch(val) [64][309] top1_acc: 0.2253, top5_acc: 0.4620, mean_class_accuracy: 0.2252 +2024-07-24 10:17:50,974 - pyskl - INFO - Epoch [65][100/3746] lr: 6.139e-02, eta: 2 days, 23:20:48, time: 2.266, data_time: 1.274, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5805, loss_cls: 3.8939, loss: 3.8939 +2024-07-24 10:19:13,550 - pyskl - INFO - Epoch [65][200/3746] lr: 6.136e-02, eta: 2 days, 23:19:32, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5748, loss_cls: 3.9288, loss: 3.9288 +2024-07-24 10:20:36,214 - pyskl - INFO - Epoch [65][300/3746] lr: 6.134e-02, eta: 2 days, 23:18:16, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5694, loss_cls: 3.9116, loss: 3.9116 +2024-07-24 10:21:58,413 - pyskl - INFO - Epoch [65][400/3746] lr: 6.131e-02, eta: 2 days, 23:17:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5772, loss_cls: 3.9056, loss: 3.9056 +2024-07-24 10:23:20,934 - pyskl - INFO - Epoch [65][500/3746] lr: 6.128e-02, eta: 2 days, 23:15:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5713, loss_cls: 3.9460, loss: 3.9460 +2024-07-24 10:24:43,167 - pyskl - INFO - Epoch [65][600/3746] lr: 6.125e-02, eta: 2 days, 23:14:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5577, loss_cls: 3.9936, loss: 3.9936 +2024-07-24 10:26:05,190 - pyskl - INFO - Epoch [65][700/3746] lr: 6.123e-02, eta: 2 days, 23:13:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5716, loss_cls: 3.9298, loss: 3.9298 +2024-07-24 10:27:27,320 - pyskl - INFO - Epoch [65][800/3746] lr: 6.120e-02, eta: 2 days, 23:11:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5642, loss_cls: 3.9620, loss: 3.9620 +2024-07-24 10:28:49,135 - pyskl - INFO - Epoch [65][900/3746] lr: 6.117e-02, eta: 2 days, 23:10:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5556, loss_cls: 3.9881, loss: 3.9881 +2024-07-24 10:30:11,189 - pyskl - INFO - Epoch [65][1000/3746] lr: 6.115e-02, eta: 2 days, 23:09:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5683, loss_cls: 3.9452, loss: 3.9452 +2024-07-24 10:31:32,865 - pyskl - INFO - Epoch [65][1100/3746] lr: 6.112e-02, eta: 2 days, 23:08:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5691, loss_cls: 3.9215, loss: 3.9215 +2024-07-24 10:32:54,465 - pyskl - INFO - Epoch [65][1200/3746] lr: 6.109e-02, eta: 2 days, 23:06:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5636, loss_cls: 3.9724, loss: 3.9724 +2024-07-24 10:34:16,164 - pyskl - INFO - Epoch [65][1300/3746] lr: 6.106e-02, eta: 2 days, 23:05:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5689, loss_cls: 3.9264, loss: 3.9264 +2024-07-24 10:35:38,485 - pyskl - INFO - Epoch [65][1400/3746] lr: 6.104e-02, eta: 2 days, 23:04:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5622, loss_cls: 3.9474, loss: 3.9474 +2024-07-24 10:37:00,042 - pyskl - INFO - Epoch [65][1500/3746] lr: 6.101e-02, eta: 2 days, 23:02:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5722, loss_cls: 3.9321, loss: 3.9321 +2024-07-24 10:38:21,824 - pyskl - INFO - Epoch [65][1600/3746] lr: 6.098e-02, eta: 2 days, 23:01:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5658, loss_cls: 3.9357, loss: 3.9357 +2024-07-24 10:39:43,562 - pyskl - INFO - Epoch [65][1700/3746] lr: 6.095e-02, eta: 2 days, 23:00:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5666, loss_cls: 3.9628, loss: 3.9628 +2024-07-24 10:41:05,847 - pyskl - INFO - Epoch [65][1800/3746] lr: 6.093e-02, eta: 2 days, 22:59:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5634, loss_cls: 3.9706, loss: 3.9706 +2024-07-24 10:42:27,818 - pyskl - INFO - Epoch [65][1900/3746] lr: 6.090e-02, eta: 2 days, 22:57:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5687, loss_cls: 3.9758, loss: 3.9758 +2024-07-24 10:43:49,546 - pyskl - INFO - Epoch [65][2000/3746] lr: 6.087e-02, eta: 2 days, 22:56:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5641, loss_cls: 3.9698, loss: 3.9698 +2024-07-24 10:45:12,413 - pyskl - INFO - Epoch [65][2100/3746] lr: 6.085e-02, eta: 2 days, 22:55:14, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5603, loss_cls: 3.9516, loss: 3.9516 +2024-07-24 10:46:34,096 - pyskl - INFO - Epoch [65][2200/3746] lr: 6.082e-02, eta: 2 days, 22:53:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5705, loss_cls: 3.9150, loss: 3.9150 +2024-07-24 10:47:56,425 - pyskl - INFO - Epoch [65][2300/3746] lr: 6.079e-02, eta: 2 days, 22:52:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5703, loss_cls: 3.9179, loss: 3.9179 +2024-07-24 10:49:19,296 - pyskl - INFO - Epoch [65][2400/3746] lr: 6.076e-02, eta: 2 days, 22:51:25, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5675, loss_cls: 3.9411, loss: 3.9411 +2024-07-24 10:50:41,699 - pyskl - INFO - Epoch [65][2500/3746] lr: 6.074e-02, eta: 2 days, 22:50:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5709, loss_cls: 3.9634, loss: 3.9634 +2024-07-24 10:52:04,070 - pyskl - INFO - Epoch [65][2600/3746] lr: 6.071e-02, eta: 2 days, 22:48:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5606, loss_cls: 3.9840, loss: 3.9840 +2024-07-24 10:53:26,788 - pyskl - INFO - Epoch [65][2700/3746] lr: 6.068e-02, eta: 2 days, 22:47:36, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5623, loss_cls: 3.9400, loss: 3.9400 +2024-07-24 10:54:48,327 - pyskl - INFO - Epoch [65][2800/3746] lr: 6.065e-02, eta: 2 days, 22:46:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5603, loss_cls: 3.9778, loss: 3.9778 +2024-07-24 10:56:10,475 - pyskl - INFO - Epoch [65][2900/3746] lr: 6.063e-02, eta: 2 days, 22:45:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5606, loss_cls: 3.9904, loss: 3.9904 +2024-07-24 10:57:32,704 - pyskl - INFO - Epoch [65][3000/3746] lr: 6.060e-02, eta: 2 days, 22:43:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5608, loss_cls: 3.9836, loss: 3.9836 +2024-07-24 10:58:54,474 - pyskl - INFO - Epoch [65][3100/3746] lr: 6.057e-02, eta: 2 days, 22:42:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5616, loss_cls: 3.9879, loss: 3.9879 +2024-07-24 11:00:16,333 - pyskl - INFO - Epoch [65][3200/3746] lr: 6.055e-02, eta: 2 days, 22:41:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5600, loss_cls: 3.9814, loss: 3.9814 +2024-07-24 11:01:39,600 - pyskl - INFO - Epoch [65][3300/3746] lr: 6.052e-02, eta: 2 days, 22:39:56, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5520, loss_cls: 4.0241, loss: 4.0241 +2024-07-24 11:03:02,171 - pyskl - INFO - Epoch [65][3400/3746] lr: 6.049e-02, eta: 2 days, 22:38:40, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5609, loss_cls: 3.9891, loss: 3.9891 +2024-07-24 11:04:24,207 - pyskl - INFO - Epoch [65][3500/3746] lr: 6.046e-02, eta: 2 days, 22:37:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5692, loss_cls: 3.9344, loss: 3.9344 +2024-07-24 11:05:46,616 - pyskl - INFO - Epoch [65][3600/3746] lr: 6.044e-02, eta: 2 days, 22:36:07, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5583, loss_cls: 3.9581, loss: 3.9581 +2024-07-24 11:07:09,665 - pyskl - INFO - Epoch [65][3700/3746] lr: 6.041e-02, eta: 2 days, 22:34:51, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5633, loss_cls: 3.9389, loss: 3.9389 +2024-07-24 11:07:49,004 - pyskl - INFO - Saving checkpoint at 65 epochs +2024-07-24 11:09:41,635 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 11:09:42,304 - pyskl - INFO - +top1_acc 0.2384 +top5_acc 0.4886 +2024-07-24 11:09:42,305 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 11:09:42,344 - pyskl - INFO - +mean_acc 0.2381 +2024-07-24 11:09:42,348 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_59.pth was removed +2024-07-24 11:09:42,618 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_65.pth. +2024-07-24 11:09:42,619 - pyskl - INFO - Best top1_acc is 0.2384 at 65 epoch. +2024-07-24 11:09:42,630 - pyskl - INFO - Epoch(val) [65][309] top1_acc: 0.2384, top5_acc: 0.4886, mean_class_accuracy: 0.2381 +2024-07-24 11:13:25,539 - pyskl - INFO - Epoch [66][100/3746] lr: 6.037e-02, eta: 2 days, 22:35:14, time: 2.229, data_time: 1.243, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5694, loss_cls: 3.9241, loss: 3.9241 +2024-07-24 11:14:47,989 - pyskl - INFO - Epoch [66][200/3746] lr: 6.034e-02, eta: 2 days, 22:33:58, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5698, loss_cls: 3.9280, loss: 3.9280 +2024-07-24 11:16:10,345 - pyskl - INFO - Epoch [66][300/3746] lr: 6.031e-02, eta: 2 days, 22:32:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5664, loss_cls: 3.9214, loss: 3.9214 +2024-07-24 11:17:32,629 - pyskl - INFO - Epoch [66][400/3746] lr: 6.029e-02, eta: 2 days, 22:31:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5717, loss_cls: 3.9190, loss: 3.9190 +2024-07-24 11:18:54,795 - pyskl - INFO - Epoch [66][500/3746] lr: 6.026e-02, eta: 2 days, 22:30:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5733, loss_cls: 3.9361, loss: 3.9361 +2024-07-24 11:20:17,092 - pyskl - INFO - Epoch [66][600/3746] lr: 6.023e-02, eta: 2 days, 22:28:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5711, loss_cls: 3.9341, loss: 3.9341 +2024-07-24 11:21:38,967 - pyskl - INFO - Epoch [66][700/3746] lr: 6.020e-02, eta: 2 days, 22:27:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5614, loss_cls: 3.9937, loss: 3.9937 +2024-07-24 11:23:00,995 - pyskl - INFO - Epoch [66][800/3746] lr: 6.018e-02, eta: 2 days, 22:26:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5730, loss_cls: 3.9052, loss: 3.9052 +2024-07-24 11:24:23,013 - pyskl - INFO - Epoch [66][900/3746] lr: 6.015e-02, eta: 2 days, 22:25:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5702, loss_cls: 3.9436, loss: 3.9436 +2024-07-24 11:25:45,159 - pyskl - INFO - Epoch [66][1000/3746] lr: 6.012e-02, eta: 2 days, 22:23:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5637, loss_cls: 3.9792, loss: 3.9792 +2024-07-24 11:27:07,397 - pyskl - INFO - Epoch [66][1100/3746] lr: 6.009e-02, eta: 2 days, 22:22:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5670, loss_cls: 3.9652, loss: 3.9652 +2024-07-24 11:28:29,375 - pyskl - INFO - Epoch [66][1200/3746] lr: 6.007e-02, eta: 2 days, 22:21:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5719, loss_cls: 3.9284, loss: 3.9284 +2024-07-24 11:29:51,051 - pyskl - INFO - Epoch [66][1300/3746] lr: 6.004e-02, eta: 2 days, 22:19:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5748, loss_cls: 3.9006, loss: 3.9006 +2024-07-24 11:31:13,134 - pyskl - INFO - Epoch [66][1400/3746] lr: 6.001e-02, eta: 2 days, 22:18:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5763, loss_cls: 3.9196, loss: 3.9196 +2024-07-24 11:32:35,431 - pyskl - INFO - Epoch [66][1500/3746] lr: 5.999e-02, eta: 2 days, 22:17:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5602, loss_cls: 4.0175, loss: 4.0175 +2024-07-24 11:33:58,037 - pyskl - INFO - Epoch [66][1600/3746] lr: 5.996e-02, eta: 2 days, 22:16:02, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5569, loss_cls: 3.9717, loss: 3.9717 +2024-07-24 11:35:19,552 - pyskl - INFO - Epoch [66][1700/3746] lr: 5.993e-02, eta: 2 days, 22:14:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5753, loss_cls: 3.9266, loss: 3.9266 +2024-07-24 11:36:42,095 - pyskl - INFO - Epoch [66][1800/3746] lr: 5.990e-02, eta: 2 days, 22:13:28, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5819, loss_cls: 3.8958, loss: 3.8958 +2024-07-24 11:38:03,925 - pyskl - INFO - Epoch [66][1900/3746] lr: 5.988e-02, eta: 2 days, 22:12:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5719, loss_cls: 3.9112, loss: 3.9112 +2024-07-24 11:39:26,371 - pyskl - INFO - Epoch [66][2000/3746] lr: 5.985e-02, eta: 2 days, 22:10:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5630, loss_cls: 3.9446, loss: 3.9446 +2024-07-24 11:40:49,574 - pyskl - INFO - Epoch [66][2100/3746] lr: 5.982e-02, eta: 2 days, 22:09:38, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5725, loss_cls: 3.9256, loss: 3.9256 +2024-07-24 11:42:11,882 - pyskl - INFO - Epoch [66][2200/3746] lr: 5.979e-02, eta: 2 days, 22:08:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5680, loss_cls: 3.9319, loss: 3.9319 +2024-07-24 11:43:35,149 - pyskl - INFO - Epoch [66][2300/3746] lr: 5.977e-02, eta: 2 days, 22:07:06, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5675, loss_cls: 3.9408, loss: 3.9408 +2024-07-24 11:44:57,876 - pyskl - INFO - Epoch [66][2400/3746] lr: 5.974e-02, eta: 2 days, 22:05:50, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5656, loss_cls: 3.9561, loss: 3.9561 +2024-07-24 11:46:19,751 - pyskl - INFO - Epoch [66][2500/3746] lr: 5.971e-02, eta: 2 days, 22:04:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5687, loss_cls: 3.9485, loss: 3.9485 +2024-07-24 11:47:42,014 - pyskl - INFO - Epoch [66][2600/3746] lr: 5.968e-02, eta: 2 days, 22:03:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5636, loss_cls: 3.9526, loss: 3.9526 +2024-07-24 11:49:04,483 - pyskl - INFO - Epoch [66][2700/3746] lr: 5.966e-02, eta: 2 days, 22:02:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5719, loss_cls: 3.9110, loss: 3.9110 +2024-07-24 11:50:27,316 - pyskl - INFO - Epoch [66][2800/3746] lr: 5.963e-02, eta: 2 days, 22:00:43, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5669, loss_cls: 3.9644, loss: 3.9644 +2024-07-24 11:51:49,921 - pyskl - INFO - Epoch [66][2900/3746] lr: 5.960e-02, eta: 2 days, 21:59:27, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5500, loss_cls: 4.0092, loss: 4.0092 +2024-07-24 11:53:12,655 - pyskl - INFO - Epoch [66][3000/3746] lr: 5.957e-02, eta: 2 days, 21:58:11, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5663, loss_cls: 3.9630, loss: 3.9630 +2024-07-24 11:54:34,350 - pyskl - INFO - Epoch [66][3100/3746] lr: 5.955e-02, eta: 2 days, 21:56:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5741, loss_cls: 3.9164, loss: 3.9164 +2024-07-24 11:55:56,914 - pyskl - INFO - Epoch [66][3200/3746] lr: 5.952e-02, eta: 2 days, 21:55:37, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5722, loss_cls: 3.9225, loss: 3.9225 +2024-07-24 11:57:19,766 - pyskl - INFO - Epoch [66][3300/3746] lr: 5.949e-02, eta: 2 days, 21:54:21, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5603, loss_cls: 3.9968, loss: 3.9968 +2024-07-24 11:58:42,425 - pyskl - INFO - Epoch [66][3400/3746] lr: 5.946e-02, eta: 2 days, 21:53:05, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5608, loss_cls: 4.0168, loss: 4.0168 +2024-07-24 12:00:04,703 - pyskl - INFO - Epoch [66][3500/3746] lr: 5.944e-02, eta: 2 days, 21:51:48, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5602, loss_cls: 3.9737, loss: 3.9737 +2024-07-24 12:01:27,061 - pyskl - INFO - Epoch [66][3600/3746] lr: 5.941e-02, eta: 2 days, 21:50:31, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5580, loss_cls: 3.9794, loss: 3.9794 +2024-07-24 12:02:49,892 - pyskl - INFO - Epoch [66][3700/3746] lr: 5.938e-02, eta: 2 days, 21:49:15, time: 0.828, data_time: 0.001, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5684, loss_cls: 3.9348, loss: 3.9348 +2024-07-24 12:03:29,399 - pyskl - INFO - Saving checkpoint at 66 epochs +2024-07-24 12:05:21,928 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 12:05:22,613 - pyskl - INFO - +top1_acc 0.2264 +top5_acc 0.4531 +2024-07-24 12:05:22,613 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 12:05:22,651 - pyskl - INFO - +mean_acc 0.2260 +2024-07-24 12:05:22,662 - pyskl - INFO - Epoch(val) [66][309] top1_acc: 0.2264, top5_acc: 0.4531, mean_class_accuracy: 0.2260 +2024-07-24 12:09:06,270 - pyskl - INFO - Epoch [67][100/3746] lr: 5.934e-02, eta: 2 days, 21:49:35, time: 2.236, data_time: 1.246, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5748, loss_cls: 3.9152, loss: 3.9152 +2024-07-24 12:10:28,997 - pyskl - INFO - Epoch [67][200/3746] lr: 5.931e-02, eta: 2 days, 21:48:18, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5698, loss_cls: 3.9190, loss: 3.9190 +2024-07-24 12:11:50,724 - pyskl - INFO - Epoch [67][300/3746] lr: 5.929e-02, eta: 2 days, 21:47:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5766, loss_cls: 3.8983, loss: 3.8983 +2024-07-24 12:13:12,675 - pyskl - INFO - Epoch [67][400/3746] lr: 5.926e-02, eta: 2 days, 21:45:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5587, loss_cls: 3.9381, loss: 3.9381 +2024-07-24 12:14:35,352 - pyskl - INFO - Epoch [67][500/3746] lr: 5.923e-02, eta: 2 days, 21:44:27, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5733, loss_cls: 3.8762, loss: 3.8762 +2024-07-24 12:15:57,886 - pyskl - INFO - Epoch [67][600/3746] lr: 5.920e-02, eta: 2 days, 21:43:10, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5706, loss_cls: 3.9168, loss: 3.9168 +2024-07-24 12:17:19,795 - pyskl - INFO - Epoch [67][700/3746] lr: 5.918e-02, eta: 2 days, 21:41:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5655, loss_cls: 3.9635, loss: 3.9635 +2024-07-24 12:18:41,514 - pyskl - INFO - Epoch [67][800/3746] lr: 5.915e-02, eta: 2 days, 21:40:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5678, loss_cls: 3.9156, loss: 3.9156 +2024-07-24 12:20:03,681 - pyskl - INFO - Epoch [67][900/3746] lr: 5.912e-02, eta: 2 days, 21:39:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5678, loss_cls: 3.9359, loss: 3.9359 +2024-07-24 12:21:26,178 - pyskl - INFO - Epoch [67][1000/3746] lr: 5.909e-02, eta: 2 days, 21:38:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5737, loss_cls: 3.9240, loss: 3.9240 +2024-07-24 12:22:47,914 - pyskl - INFO - Epoch [67][1100/3746] lr: 5.907e-02, eta: 2 days, 21:36:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5744, loss_cls: 3.9109, loss: 3.9109 +2024-07-24 12:24:09,665 - pyskl - INFO - Epoch [67][1200/3746] lr: 5.904e-02, eta: 2 days, 21:35:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5687, loss_cls: 3.9294, loss: 3.9294 +2024-07-24 12:25:31,602 - pyskl - INFO - Epoch [67][1300/3746] lr: 5.901e-02, eta: 2 days, 21:34:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5817, loss_cls: 3.8552, loss: 3.8552 +2024-07-24 12:26:53,690 - pyskl - INFO - Epoch [67][1400/3746] lr: 5.898e-02, eta: 2 days, 21:32:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5611, loss_cls: 3.9678, loss: 3.9678 +2024-07-24 12:28:14,984 - pyskl - INFO - Epoch [67][1500/3746] lr: 5.896e-02, eta: 2 days, 21:31:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5709, loss_cls: 3.9142, loss: 3.9142 +2024-07-24 12:29:36,513 - pyskl - INFO - Epoch [67][1600/3746] lr: 5.893e-02, eta: 2 days, 21:30:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5647, loss_cls: 3.9559, loss: 3.9559 +2024-07-24 12:30:58,329 - pyskl - INFO - Epoch [67][1700/3746] lr: 5.890e-02, eta: 2 days, 21:28:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5759, loss_cls: 3.9129, loss: 3.9129 +2024-07-24 12:32:19,940 - pyskl - INFO - Epoch [67][1800/3746] lr: 5.887e-02, eta: 2 days, 21:27:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5628, loss_cls: 3.9379, loss: 3.9379 +2024-07-24 12:33:41,636 - pyskl - INFO - Epoch [67][1900/3746] lr: 5.885e-02, eta: 2 days, 21:26:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5689, loss_cls: 3.9284, loss: 3.9284 +2024-07-24 12:35:03,652 - pyskl - INFO - Epoch [67][2000/3746] lr: 5.882e-02, eta: 2 days, 21:25:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5628, loss_cls: 3.9799, loss: 3.9799 +2024-07-24 12:36:26,757 - pyskl - INFO - Epoch [67][2100/3746] lr: 5.879e-02, eta: 2 days, 21:23:49, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5636, loss_cls: 3.9609, loss: 3.9609 +2024-07-24 12:37:48,263 - pyskl - INFO - Epoch [67][2200/3746] lr: 5.876e-02, eta: 2 days, 21:22:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5680, loss_cls: 3.9205, loss: 3.9205 +2024-07-24 12:39:11,427 - pyskl - INFO - Epoch [67][2300/3746] lr: 5.874e-02, eta: 2 days, 21:21:15, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5730, loss_cls: 3.9322, loss: 3.9322 +2024-07-24 12:40:34,236 - pyskl - INFO - Epoch [67][2400/3746] lr: 5.871e-02, eta: 2 days, 21:19:59, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5637, loss_cls: 3.9664, loss: 3.9664 +2024-07-24 12:41:56,418 - pyskl - INFO - Epoch [67][2500/3746] lr: 5.868e-02, eta: 2 days, 21:18:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5639, loss_cls: 3.9715, loss: 3.9715 +2024-07-24 12:43:18,894 - pyskl - INFO - Epoch [67][2600/3746] lr: 5.865e-02, eta: 2 days, 21:17:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5628, loss_cls: 3.9728, loss: 3.9728 +2024-07-24 12:44:41,428 - pyskl - INFO - Epoch [67][2700/3746] lr: 5.863e-02, eta: 2 days, 21:16:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5637, loss_cls: 3.9270, loss: 3.9270 +2024-07-24 12:46:03,640 - pyskl - INFO - Epoch [67][2800/3746] lr: 5.860e-02, eta: 2 days, 21:14:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5678, loss_cls: 3.9602, loss: 3.9602 +2024-07-24 12:47:25,381 - pyskl - INFO - Epoch [67][2900/3746] lr: 5.857e-02, eta: 2 days, 21:13:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5741, loss_cls: 3.9170, loss: 3.9170 +2024-07-24 12:48:47,989 - pyskl - INFO - Epoch [67][3000/3746] lr: 5.854e-02, eta: 2 days, 21:12:17, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5595, loss_cls: 3.9862, loss: 3.9862 +2024-07-24 12:50:09,452 - pyskl - INFO - Epoch [67][3100/3746] lr: 5.852e-02, eta: 2 days, 21:10:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5672, loss_cls: 3.9059, loss: 3.9059 +2024-07-24 12:51:31,504 - pyskl - INFO - Epoch [67][3200/3746] lr: 5.849e-02, eta: 2 days, 21:09:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5589, loss_cls: 3.9650, loss: 3.9650 +2024-07-24 12:52:54,276 - pyskl - INFO - Epoch [67][3300/3746] lr: 5.846e-02, eta: 2 days, 21:08:25, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5578, loss_cls: 3.9555, loss: 3.9555 +2024-07-24 12:54:16,972 - pyskl - INFO - Epoch [67][3400/3746] lr: 5.843e-02, eta: 2 days, 21:07:09, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5723, loss_cls: 3.9232, loss: 3.9232 +2024-07-24 12:55:39,322 - pyskl - INFO - Epoch [67][3500/3746] lr: 5.841e-02, eta: 2 days, 21:05:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5711, loss_cls: 3.9109, loss: 3.9109 +2024-07-24 12:57:01,910 - pyskl - INFO - Epoch [67][3600/3746] lr: 5.838e-02, eta: 2 days, 21:04:35, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5630, loss_cls: 3.9513, loss: 3.9513 +2024-07-24 12:58:24,549 - pyskl - INFO - Epoch [67][3700/3746] lr: 5.835e-02, eta: 2 days, 21:03:19, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5672, loss_cls: 3.9425, loss: 3.9425 +2024-07-24 12:59:03,614 - pyskl - INFO - Saving checkpoint at 67 epochs +2024-07-24 13:00:56,759 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 13:00:57,523 - pyskl - INFO - +top1_acc 0.2173 +top5_acc 0.4544 +2024-07-24 13:00:57,523 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 13:00:57,568 - pyskl - INFO - +mean_acc 0.2172 +2024-07-24 13:00:57,581 - pyskl - INFO - Epoch(val) [67][309] top1_acc: 0.2173, top5_acc: 0.4544, mean_class_accuracy: 0.2172 +2024-07-24 13:04:47,570 - pyskl - INFO - Epoch [68][100/3746] lr: 5.831e-02, eta: 2 days, 21:03:42, time: 2.300, data_time: 1.304, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5761, loss_cls: 3.8766, loss: 3.8766 +2024-07-24 13:06:11,179 - pyskl - INFO - Epoch [68][200/3746] lr: 5.828e-02, eta: 2 days, 21:02:27, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5809, loss_cls: 3.8710, loss: 3.8710 +2024-07-24 13:07:33,954 - pyskl - INFO - Epoch [68][300/3746] lr: 5.826e-02, eta: 2 days, 21:01:10, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5672, loss_cls: 3.9589, loss: 3.9589 +2024-07-24 13:08:57,450 - pyskl - INFO - Epoch [68][400/3746] lr: 5.823e-02, eta: 2 days, 20:59:54, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5778, loss_cls: 3.8490, loss: 3.8490 +2024-07-24 13:10:20,429 - pyskl - INFO - Epoch [68][500/3746] lr: 5.820e-02, eta: 2 days, 20:58:38, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5752, loss_cls: 3.9044, loss: 3.9044 +2024-07-24 13:11:43,293 - pyskl - INFO - Epoch [68][600/3746] lr: 5.817e-02, eta: 2 days, 20:57:22, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5755, loss_cls: 3.9160, loss: 3.9160 +2024-07-24 13:13:06,174 - pyskl - INFO - Epoch [68][700/3746] lr: 5.815e-02, eta: 2 days, 20:56:05, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5697, loss_cls: 3.9290, loss: 3.9290 +2024-07-24 13:14:29,246 - pyskl - INFO - Epoch [68][800/3746] lr: 5.812e-02, eta: 2 days, 20:54:49, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5719, loss_cls: 3.9194, loss: 3.9194 +2024-07-24 13:15:51,840 - pyskl - INFO - Epoch [68][900/3746] lr: 5.809e-02, eta: 2 days, 20:53:32, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5703, loss_cls: 3.9134, loss: 3.9134 +2024-07-24 13:17:14,795 - pyskl - INFO - Epoch [68][1000/3746] lr: 5.806e-02, eta: 2 days, 20:52:16, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5667, loss_cls: 3.9315, loss: 3.9315 +2024-07-24 13:18:37,673 - pyskl - INFO - Epoch [68][1100/3746] lr: 5.804e-02, eta: 2 days, 20:50:59, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5681, loss_cls: 3.9591, loss: 3.9591 +2024-07-24 13:20:00,850 - pyskl - INFO - Epoch [68][1200/3746] lr: 5.801e-02, eta: 2 days, 20:49:43, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5734, loss_cls: 3.9040, loss: 3.9040 +2024-07-24 13:21:23,886 - pyskl - INFO - Epoch [68][1300/3746] lr: 5.798e-02, eta: 2 days, 20:48:27, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5706, loss_cls: 3.9200, loss: 3.9200 +2024-07-24 13:22:46,655 - pyskl - INFO - Epoch [68][1400/3746] lr: 5.795e-02, eta: 2 days, 20:47:10, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5725, loss_cls: 3.9322, loss: 3.9322 +2024-07-24 13:24:09,534 - pyskl - INFO - Epoch [68][1500/3746] lr: 5.792e-02, eta: 2 days, 20:45:54, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5725, loss_cls: 3.9023, loss: 3.9023 +2024-07-24 13:25:32,360 - pyskl - INFO - Epoch [68][1600/3746] lr: 5.790e-02, eta: 2 days, 20:44:37, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5748, loss_cls: 3.8983, loss: 3.8983 +2024-07-24 13:26:55,342 - pyskl - INFO - Epoch [68][1700/3746] lr: 5.787e-02, eta: 2 days, 20:43:21, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5702, loss_cls: 3.9205, loss: 3.9205 +2024-07-24 13:28:17,557 - pyskl - INFO - Epoch [68][1800/3746] lr: 5.784e-02, eta: 2 days, 20:42:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5731, loss_cls: 3.9105, loss: 3.9105 +2024-07-24 13:29:40,264 - pyskl - INFO - Epoch [68][1900/3746] lr: 5.781e-02, eta: 2 days, 20:40:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5683, loss_cls: 3.9282, loss: 3.9282 +2024-07-24 13:31:03,825 - pyskl - INFO - Epoch [68][2000/3746] lr: 5.779e-02, eta: 2 days, 20:39:31, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5717, loss_cls: 3.9603, loss: 3.9603 +2024-07-24 13:32:27,367 - pyskl - INFO - Epoch [68][2100/3746] lr: 5.776e-02, eta: 2 days, 20:38:15, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5658, loss_cls: 3.9391, loss: 3.9391 +2024-07-24 13:33:50,443 - pyskl - INFO - Epoch [68][2200/3746] lr: 5.773e-02, eta: 2 days, 20:36:59, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5550, loss_cls: 3.9829, loss: 3.9829 +2024-07-24 13:35:13,572 - pyskl - INFO - Epoch [68][2300/3746] lr: 5.770e-02, eta: 2 days, 20:35:43, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5741, loss_cls: 3.8871, loss: 3.8871 +2024-07-24 13:36:36,424 - pyskl - INFO - Epoch [68][2400/3746] lr: 5.768e-02, eta: 2 days, 20:34:26, time: 0.829, data_time: 0.001, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5816, loss_cls: 3.9026, loss: 3.9026 +2024-07-24 13:37:59,434 - pyskl - INFO - Epoch [68][2500/3746] lr: 5.765e-02, eta: 2 days, 20:33:10, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5791, loss_cls: 3.9036, loss: 3.9036 +2024-07-24 13:39:22,804 - pyskl - INFO - Epoch [68][2600/3746] lr: 5.762e-02, eta: 2 days, 20:31:54, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5664, loss_cls: 3.9522, loss: 3.9522 +2024-07-24 13:40:46,196 - pyskl - INFO - Epoch [68][2700/3746] lr: 5.759e-02, eta: 2 days, 20:30:38, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5661, loss_cls: 3.9277, loss: 3.9277 +2024-07-24 13:42:08,603 - pyskl - INFO - Epoch [68][2800/3746] lr: 5.757e-02, eta: 2 days, 20:29:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5731, loss_cls: 3.9190, loss: 3.9190 +2024-07-24 13:43:30,708 - pyskl - INFO - Epoch [68][2900/3746] lr: 5.754e-02, eta: 2 days, 20:28:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5708, loss_cls: 3.9368, loss: 3.9368 +2024-07-24 13:44:53,797 - pyskl - INFO - Epoch [68][3000/3746] lr: 5.751e-02, eta: 2 days, 20:26:47, time: 0.831, data_time: 0.001, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5664, loss_cls: 3.9549, loss: 3.9549 +2024-07-24 13:46:15,645 - pyskl - INFO - Epoch [68][3100/3746] lr: 5.748e-02, eta: 2 days, 20:25:29, time: 0.818, data_time: 0.001, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5794, loss_cls: 3.8993, loss: 3.8993 +2024-07-24 13:47:38,094 - pyskl - INFO - Epoch [68][3200/3746] lr: 5.746e-02, eta: 2 days, 20:24:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5811, loss_cls: 3.8862, loss: 3.8862 +2024-07-24 13:49:00,616 - pyskl - INFO - Epoch [68][3300/3746] lr: 5.743e-02, eta: 2 days, 20:22:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5700, loss_cls: 3.9179, loss: 3.9179 +2024-07-24 13:50:23,109 - pyskl - INFO - Epoch [68][3400/3746] lr: 5.740e-02, eta: 2 days, 20:21:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5659, loss_cls: 3.9419, loss: 3.9419 +2024-07-24 13:51:45,898 - pyskl - INFO - Epoch [68][3500/3746] lr: 5.737e-02, eta: 2 days, 20:20:22, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5834, loss_cls: 3.8940, loss: 3.8940 +2024-07-24 13:53:08,487 - pyskl - INFO - Epoch [68][3600/3746] lr: 5.734e-02, eta: 2 days, 20:19:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5702, loss_cls: 3.8975, loss: 3.8975 +2024-07-24 13:54:31,154 - pyskl - INFO - Epoch [68][3700/3746] lr: 5.732e-02, eta: 2 days, 20:17:48, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5620, loss_cls: 3.9313, loss: 3.9313 +2024-07-24 13:55:11,052 - pyskl - INFO - Saving checkpoint at 68 epochs +2024-07-24 13:57:04,912 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 13:57:05,671 - pyskl - INFO - +top1_acc 0.2442 +top5_acc 0.4877 +2024-07-24 13:57:05,671 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 13:57:05,719 - pyskl - INFO - +mean_acc 0.2441 +2024-07-24 13:57:05,724 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_65.pth was removed +2024-07-24 13:57:05,991 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_68.pth. +2024-07-24 13:57:05,992 - pyskl - INFO - Best top1_acc is 0.2442 at 68 epoch. +2024-07-24 13:57:06,007 - pyskl - INFO - Epoch(val) [68][309] top1_acc: 0.2442, top5_acc: 0.4877, mean_class_accuracy: 0.2441 +2024-07-24 14:00:54,153 - pyskl - INFO - Epoch [69][100/3746] lr: 5.728e-02, eta: 2 days, 20:18:05, time: 2.281, data_time: 1.283, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5863, loss_cls: 3.8533, loss: 3.8533 +2024-07-24 14:02:17,287 - pyskl - INFO - Epoch [69][200/3746] lr: 5.725e-02, eta: 2 days, 20:16:49, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5777, loss_cls: 3.8920, loss: 3.8920 +2024-07-24 14:03:39,918 - pyskl - INFO - Epoch [69][300/3746] lr: 5.722e-02, eta: 2 days, 20:15:32, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5808, loss_cls: 3.8565, loss: 3.8565 +2024-07-24 14:05:02,395 - pyskl - INFO - Epoch [69][400/3746] lr: 5.719e-02, eta: 2 days, 20:14:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5741, loss_cls: 3.8886, loss: 3.8886 +2024-07-24 14:06:24,405 - pyskl - INFO - Epoch [69][500/3746] lr: 5.717e-02, eta: 2 days, 20:12:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5769, loss_cls: 3.9102, loss: 3.9102 +2024-07-24 14:07:47,076 - pyskl - INFO - Epoch [69][600/3746] lr: 5.714e-02, eta: 2 days, 20:11:40, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5792, loss_cls: 3.9081, loss: 3.9081 +2024-07-24 14:09:09,267 - pyskl - INFO - Epoch [69][700/3746] lr: 5.711e-02, eta: 2 days, 20:10:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5728, loss_cls: 3.8909, loss: 3.8909 +2024-07-24 14:10:31,678 - pyskl - INFO - Epoch [69][800/3746] lr: 5.708e-02, eta: 2 days, 20:09:05, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5791, loss_cls: 3.8612, loss: 3.8612 +2024-07-24 14:11:54,228 - pyskl - INFO - Epoch [69][900/3746] lr: 5.706e-02, eta: 2 days, 20:07:48, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5700, loss_cls: 3.9062, loss: 3.9062 +2024-07-24 14:13:16,914 - pyskl - INFO - Epoch [69][1000/3746] lr: 5.703e-02, eta: 2 days, 20:06:31, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5816, loss_cls: 3.8875, loss: 3.8875 +2024-07-24 14:14:38,757 - pyskl - INFO - Epoch [69][1100/3746] lr: 5.700e-02, eta: 2 days, 20:05:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5711, loss_cls: 3.9230, loss: 3.9230 +2024-07-24 14:16:00,862 - pyskl - INFO - Epoch [69][1200/3746] lr: 5.697e-02, eta: 2 days, 20:03:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5669, loss_cls: 3.9284, loss: 3.9284 +2024-07-24 14:17:22,671 - pyskl - INFO - Epoch [69][1300/3746] lr: 5.694e-02, eta: 2 days, 20:02:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5669, loss_cls: 3.9272, loss: 3.9272 +2024-07-24 14:18:45,190 - pyskl - INFO - Epoch [69][1400/3746] lr: 5.692e-02, eta: 2 days, 20:01:20, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5725, loss_cls: 3.9458, loss: 3.9458 +2024-07-24 14:20:07,251 - pyskl - INFO - Epoch [69][1500/3746] lr: 5.689e-02, eta: 2 days, 20:00:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5737, loss_cls: 3.9109, loss: 3.9109 +2024-07-24 14:21:29,249 - pyskl - INFO - Epoch [69][1600/3746] lr: 5.686e-02, eta: 2 days, 19:58:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5689, loss_cls: 3.9308, loss: 3.9308 +2024-07-24 14:22:51,248 - pyskl - INFO - Epoch [69][1700/3746] lr: 5.683e-02, eta: 2 days, 19:57:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5645, loss_cls: 3.9535, loss: 3.9535 +2024-07-24 14:24:13,388 - pyskl - INFO - Epoch [69][1800/3746] lr: 5.681e-02, eta: 2 days, 19:56:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5902, loss_cls: 3.8599, loss: 3.8599 +2024-07-24 14:25:34,945 - pyskl - INFO - Epoch [69][1900/3746] lr: 5.678e-02, eta: 2 days, 19:54:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5747, loss_cls: 3.8987, loss: 3.8987 +2024-07-24 14:26:58,287 - pyskl - INFO - Epoch [69][2000/3746] lr: 5.675e-02, eta: 2 days, 19:53:34, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5736, loss_cls: 3.9144, loss: 3.9144 +2024-07-24 14:28:19,988 - pyskl - INFO - Epoch [69][2100/3746] lr: 5.672e-02, eta: 2 days, 19:52:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5745, loss_cls: 3.9005, loss: 3.9005 +2024-07-24 14:29:43,207 - pyskl - INFO - Epoch [69][2200/3746] lr: 5.670e-02, eta: 2 days, 19:51:00, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5628, loss_cls: 3.9584, loss: 3.9584 +2024-07-24 14:31:06,140 - pyskl - INFO - Epoch [69][2300/3746] lr: 5.667e-02, eta: 2 days, 19:49:43, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5705, loss_cls: 3.9317, loss: 3.9317 +2024-07-24 14:32:28,519 - pyskl - INFO - Epoch [69][2400/3746] lr: 5.664e-02, eta: 2 days, 19:48:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5836, loss_cls: 3.8913, loss: 3.8913 +2024-07-24 14:33:51,376 - pyskl - INFO - Epoch [69][2500/3746] lr: 5.661e-02, eta: 2 days, 19:47:09, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5822, loss_cls: 3.8597, loss: 3.8597 +2024-07-24 14:35:14,433 - pyskl - INFO - Epoch [69][2600/3746] lr: 5.658e-02, eta: 2 days, 19:45:52, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5775, loss_cls: 3.9096, loss: 3.9096 +2024-07-24 14:36:37,899 - pyskl - INFO - Epoch [69][2700/3746] lr: 5.656e-02, eta: 2 days, 19:44:36, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5680, loss_cls: 3.9015, loss: 3.9015 +2024-07-24 14:38:00,451 - pyskl - INFO - Epoch [69][2800/3746] lr: 5.653e-02, eta: 2 days, 19:43:19, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5692, loss_cls: 3.9018, loss: 3.9018 +2024-07-24 14:39:23,289 - pyskl - INFO - Epoch [69][2900/3746] lr: 5.650e-02, eta: 2 days, 19:42:02, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5669, loss_cls: 3.9442, loss: 3.9442 +2024-07-24 14:40:45,966 - pyskl - INFO - Epoch [69][3000/3746] lr: 5.647e-02, eta: 2 days, 19:40:45, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5777, loss_cls: 3.8882, loss: 3.8882 +2024-07-24 14:42:08,269 - pyskl - INFO - Epoch [69][3100/3746] lr: 5.645e-02, eta: 2 days, 19:39:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5716, loss_cls: 3.9191, loss: 3.9191 +2024-07-24 14:43:30,790 - pyskl - INFO - Epoch [69][3200/3746] lr: 5.642e-02, eta: 2 days, 19:38:10, time: 0.825, data_time: 0.001, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5778, loss_cls: 3.9213, loss: 3.9213 +2024-07-24 14:44:53,261 - pyskl - INFO - Epoch [69][3300/3746] lr: 5.639e-02, eta: 2 days, 19:36:53, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5686, loss_cls: 3.9425, loss: 3.9425 +2024-07-24 14:46:15,757 - pyskl - INFO - Epoch [69][3400/3746] lr: 5.636e-02, eta: 2 days, 19:35:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5661, loss_cls: 3.9500, loss: 3.9500 +2024-07-24 14:47:37,594 - pyskl - INFO - Epoch [69][3500/3746] lr: 5.634e-02, eta: 2 days, 19:34:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5789, loss_cls: 3.9031, loss: 3.9031 +2024-07-24 14:49:00,410 - pyskl - INFO - Epoch [69][3600/3746] lr: 5.631e-02, eta: 2 days, 19:33:01, time: 0.828, data_time: 0.001, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5655, loss_cls: 3.9498, loss: 3.9498 +2024-07-24 14:50:22,373 - pyskl - INFO - Epoch [69][3700/3746] lr: 5.628e-02, eta: 2 days, 19:31:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5650, loss_cls: 3.9181, loss: 3.9181 +2024-07-24 14:51:01,742 - pyskl - INFO - Saving checkpoint at 69 epochs +2024-07-24 14:52:54,369 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 14:52:55,197 - pyskl - INFO - +top1_acc 0.2348 +top5_acc 0.4803 +2024-07-24 14:52:55,197 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 14:52:55,241 - pyskl - INFO - +mean_acc 0.2346 +2024-07-24 14:52:55,254 - pyskl - INFO - Epoch(val) [69][309] top1_acc: 0.2348, top5_acc: 0.4803, mean_class_accuracy: 0.2346 +2024-07-24 14:56:45,610 - pyskl - INFO - Epoch [70][100/3746] lr: 5.624e-02, eta: 2 days, 19:31:59, time: 2.303, data_time: 1.307, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5906, loss_cls: 3.8270, loss: 3.8270 +2024-07-24 14:58:09,111 - pyskl - INFO - Epoch [70][200/3746] lr: 5.621e-02, eta: 2 days, 19:30:43, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5792, loss_cls: 3.8453, loss: 3.8453 +2024-07-24 14:59:32,303 - pyskl - INFO - Epoch [70][300/3746] lr: 5.618e-02, eta: 2 days, 19:29:26, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5880, loss_cls: 3.8507, loss: 3.8507 +2024-07-24 15:00:54,621 - pyskl - INFO - Epoch [70][400/3746] lr: 5.616e-02, eta: 2 days, 19:28:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5823, loss_cls: 3.8460, loss: 3.8460 +2024-07-24 15:02:17,073 - pyskl - INFO - Epoch [70][500/3746] lr: 5.613e-02, eta: 2 days, 19:26:51, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5777, loss_cls: 3.8851, loss: 3.8851 +2024-07-24 15:03:39,206 - pyskl - INFO - Epoch [70][600/3746] lr: 5.610e-02, eta: 2 days, 19:25:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5836, loss_cls: 3.8492, loss: 3.8492 +2024-07-24 15:05:01,765 - pyskl - INFO - Epoch [70][700/3746] lr: 5.607e-02, eta: 2 days, 19:24:16, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5830, loss_cls: 3.8651, loss: 3.8651 +2024-07-24 15:06:24,506 - pyskl - INFO - Epoch [70][800/3746] lr: 5.605e-02, eta: 2 days, 19:22:59, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5858, loss_cls: 3.8610, loss: 3.8610 +2024-07-24 15:07:47,084 - pyskl - INFO - Epoch [70][900/3746] lr: 5.602e-02, eta: 2 days, 19:21:41, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5672, loss_cls: 3.9203, loss: 3.9203 +2024-07-24 15:09:09,473 - pyskl - INFO - Epoch [70][1000/3746] lr: 5.599e-02, eta: 2 days, 19:20:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5853, loss_cls: 3.8376, loss: 3.8376 +2024-07-24 15:10:32,578 - pyskl - INFO - Epoch [70][1100/3746] lr: 5.596e-02, eta: 2 days, 19:19:07, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5794, loss_cls: 3.9082, loss: 3.9082 +2024-07-24 15:11:54,664 - pyskl - INFO - Epoch [70][1200/3746] lr: 5.593e-02, eta: 2 days, 19:17:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5833, loss_cls: 3.8557, loss: 3.8557 +2024-07-24 15:13:16,575 - pyskl - INFO - Epoch [70][1300/3746] lr: 5.591e-02, eta: 2 days, 19:16:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5792, loss_cls: 3.8748, loss: 3.8748 +2024-07-24 15:14:39,247 - pyskl - INFO - Epoch [70][1400/3746] lr: 5.588e-02, eta: 2 days, 19:15:14, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5756, loss_cls: 3.8899, loss: 3.8899 +2024-07-24 15:16:01,389 - pyskl - INFO - Epoch [70][1500/3746] lr: 5.585e-02, eta: 2 days, 19:13:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5666, loss_cls: 3.9376, loss: 3.9376 +2024-07-24 15:17:23,846 - pyskl - INFO - Epoch [70][1600/3746] lr: 5.582e-02, eta: 2 days, 19:12:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5752, loss_cls: 3.9055, loss: 3.9055 +2024-07-24 15:18:45,396 - pyskl - INFO - Epoch [70][1700/3746] lr: 5.580e-02, eta: 2 days, 19:11:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5622, loss_cls: 3.9223, loss: 3.9223 +2024-07-24 15:20:07,648 - pyskl - INFO - Epoch [70][1800/3746] lr: 5.577e-02, eta: 2 days, 19:10:02, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5680, loss_cls: 3.9173, loss: 3.9173 +2024-07-24 15:21:29,973 - pyskl - INFO - Epoch [70][1900/3746] lr: 5.574e-02, eta: 2 days, 19:08:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5742, loss_cls: 3.9070, loss: 3.9070 +2024-07-24 15:22:52,954 - pyskl - INFO - Epoch [70][2000/3746] lr: 5.571e-02, eta: 2 days, 19:07:27, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5695, loss_cls: 3.9063, loss: 3.9063 +2024-07-24 15:24:14,897 - pyskl - INFO - Epoch [70][2100/3746] lr: 5.568e-02, eta: 2 days, 19:06:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5777, loss_cls: 3.9056, loss: 3.9056 +2024-07-24 15:25:38,439 - pyskl - INFO - Epoch [70][2200/3746] lr: 5.566e-02, eta: 2 days, 19:04:53, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5736, loss_cls: 3.8646, loss: 3.8646 +2024-07-24 15:27:00,574 - pyskl - INFO - Epoch [70][2300/3746] lr: 5.563e-02, eta: 2 days, 19:03:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5637, loss_cls: 3.9309, loss: 3.9309 +2024-07-24 15:28:23,392 - pyskl - INFO - Epoch [70][2400/3746] lr: 5.560e-02, eta: 2 days, 19:02:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5672, loss_cls: 3.9075, loss: 3.9075 +2024-07-24 15:29:46,624 - pyskl - INFO - Epoch [70][2500/3746] lr: 5.557e-02, eta: 2 days, 19:01:01, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5728, loss_cls: 3.9041, loss: 3.9041 +2024-07-24 15:31:08,486 - pyskl - INFO - Epoch [70][2600/3746] lr: 5.555e-02, eta: 2 days, 18:59:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5633, loss_cls: 3.9682, loss: 3.9682 +2024-07-24 15:32:30,697 - pyskl - INFO - Epoch [70][2700/3746] lr: 5.552e-02, eta: 2 days, 18:58:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5714, loss_cls: 3.9188, loss: 3.9188 +2024-07-24 15:33:52,486 - pyskl - INFO - Epoch [70][2800/3746] lr: 5.549e-02, eta: 2 days, 18:57:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5694, loss_cls: 3.9333, loss: 3.9333 +2024-07-24 15:35:14,440 - pyskl - INFO - Epoch [70][2900/3746] lr: 5.546e-02, eta: 2 days, 18:55:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5683, loss_cls: 3.9155, loss: 3.9155 +2024-07-24 15:36:37,136 - pyskl - INFO - Epoch [70][3000/3746] lr: 5.543e-02, eta: 2 days, 18:54:32, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5789, loss_cls: 3.8829, loss: 3.8829 +2024-07-24 15:37:58,507 - pyskl - INFO - Epoch [70][3100/3746] lr: 5.541e-02, eta: 2 days, 18:53:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5666, loss_cls: 3.9564, loss: 3.9564 +2024-07-24 15:39:20,147 - pyskl - INFO - Epoch [70][3200/3746] lr: 5.538e-02, eta: 2 days, 18:51:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5664, loss_cls: 3.9352, loss: 3.9352 +2024-07-24 15:40:43,185 - pyskl - INFO - Epoch [70][3300/3746] lr: 5.535e-02, eta: 2 days, 18:50:37, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5770, loss_cls: 3.9014, loss: 3.9014 +2024-07-24 15:42:04,571 - pyskl - INFO - Epoch [70][3400/3746] lr: 5.532e-02, eta: 2 days, 18:49:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5722, loss_cls: 3.9222, loss: 3.9222 +2024-07-24 15:43:26,906 - pyskl - INFO - Epoch [70][3500/3746] lr: 5.530e-02, eta: 2 days, 18:48:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5748, loss_cls: 3.8757, loss: 3.8757 +2024-07-24 15:44:49,189 - pyskl - INFO - Epoch [70][3600/3746] lr: 5.527e-02, eta: 2 days, 18:46:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5822, loss_cls: 3.8749, loss: 3.8749 +2024-07-24 15:46:10,783 - pyskl - INFO - Epoch [70][3700/3746] lr: 5.524e-02, eta: 2 days, 18:45:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5805, loss_cls: 3.9040, loss: 3.9040 +2024-07-24 15:46:50,866 - pyskl - INFO - Saving checkpoint at 70 epochs +2024-07-24 15:48:43,297 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 15:48:43,967 - pyskl - INFO - +top1_acc 0.2310 +top5_acc 0.4720 +2024-07-24 15:48:43,967 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 15:48:44,010 - pyskl - INFO - +mean_acc 0.2309 +2024-07-24 15:48:44,024 - pyskl - INFO - Epoch(val) [70][309] top1_acc: 0.2310, top5_acc: 0.4720, mean_class_accuracy: 0.2309 +2024-07-24 15:52:27,333 - pyskl - INFO - Epoch [71][100/3746] lr: 5.520e-02, eta: 2 days, 18:45:29, time: 2.233, data_time: 1.244, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5852, loss_cls: 3.8284, loss: 3.8284 +2024-07-24 15:53:48,857 - pyskl - INFO - Epoch [71][200/3746] lr: 5.517e-02, eta: 2 days, 18:44:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5805, loss_cls: 3.8599, loss: 3.8599 +2024-07-24 15:55:10,541 - pyskl - INFO - Epoch [71][300/3746] lr: 5.514e-02, eta: 2 days, 18:42:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5739, loss_cls: 3.9122, loss: 3.9122 +2024-07-24 15:56:32,848 - pyskl - INFO - Epoch [71][400/3746] lr: 5.512e-02, eta: 2 days, 18:41:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5675, loss_cls: 3.9135, loss: 3.9135 +2024-07-24 15:57:55,080 - pyskl - INFO - Epoch [71][500/3746] lr: 5.509e-02, eta: 2 days, 18:40:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5836, loss_cls: 3.8758, loss: 3.8758 +2024-07-24 15:59:16,949 - pyskl - INFO - Epoch [71][600/3746] lr: 5.506e-02, eta: 2 days, 18:38:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5822, loss_cls: 3.8478, loss: 3.8478 +2024-07-24 16:00:38,342 - pyskl - INFO - Epoch [71][700/3746] lr: 5.503e-02, eta: 2 days, 18:37:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5789, loss_cls: 3.8910, loss: 3.8910 +2024-07-24 16:02:00,289 - pyskl - INFO - Epoch [71][800/3746] lr: 5.500e-02, eta: 2 days, 18:36:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5823, loss_cls: 3.8611, loss: 3.8611 +2024-07-24 16:03:22,517 - pyskl - INFO - Epoch [71][900/3746] lr: 5.498e-02, eta: 2 days, 18:35:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5736, loss_cls: 3.8953, loss: 3.8953 +2024-07-24 16:04:44,402 - pyskl - INFO - Epoch [71][1000/3746] lr: 5.495e-02, eta: 2 days, 18:33:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5702, loss_cls: 3.9156, loss: 3.9156 +2024-07-24 16:06:06,718 - pyskl - INFO - Epoch [71][1100/3746] lr: 5.492e-02, eta: 2 days, 18:32:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5819, loss_cls: 3.8808, loss: 3.8808 +2024-07-24 16:07:28,717 - pyskl - INFO - Epoch [71][1200/3746] lr: 5.489e-02, eta: 2 days, 18:31:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5845, loss_cls: 3.8429, loss: 3.8429 +2024-07-24 16:08:50,820 - pyskl - INFO - Epoch [71][1300/3746] lr: 5.487e-02, eta: 2 days, 18:29:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5797, loss_cls: 3.8627, loss: 3.8627 +2024-07-24 16:10:12,385 - pyskl - INFO - Epoch [71][1400/3746] lr: 5.484e-02, eta: 2 days, 18:28:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5827, loss_cls: 3.8261, loss: 3.8261 +2024-07-24 16:11:34,094 - pyskl - INFO - Epoch [71][1500/3746] lr: 5.481e-02, eta: 2 days, 18:27:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5734, loss_cls: 3.9164, loss: 3.9164 +2024-07-24 16:12:55,951 - pyskl - INFO - Epoch [71][1600/3746] lr: 5.478e-02, eta: 2 days, 18:25:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5775, loss_cls: 3.8612, loss: 3.8612 +2024-07-24 16:14:17,591 - pyskl - INFO - Epoch [71][1700/3746] lr: 5.475e-02, eta: 2 days, 18:24:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5803, loss_cls: 3.8863, loss: 3.8863 +2024-07-24 16:15:40,041 - pyskl - INFO - Epoch [71][1800/3746] lr: 5.473e-02, eta: 2 days, 18:23:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5741, loss_cls: 3.8850, loss: 3.8850 +2024-07-24 16:17:01,990 - pyskl - INFO - Epoch [71][1900/3746] lr: 5.470e-02, eta: 2 days, 18:21:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5689, loss_cls: 3.9138, loss: 3.9138 +2024-07-24 16:18:25,239 - pyskl - INFO - Epoch [71][2000/3746] lr: 5.467e-02, eta: 2 days, 18:20:42, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5855, loss_cls: 3.8704, loss: 3.8704 +2024-07-24 16:19:47,098 - pyskl - INFO - Epoch [71][2100/3746] lr: 5.464e-02, eta: 2 days, 18:19:24, time: 0.819, data_time: 0.001, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5791, loss_cls: 3.9111, loss: 3.9111 +2024-07-24 16:21:10,640 - pyskl - INFO - Epoch [71][2200/3746] lr: 5.461e-02, eta: 2 days, 18:18:08, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5703, loss_cls: 3.9277, loss: 3.9277 +2024-07-24 16:22:34,063 - pyskl - INFO - Epoch [71][2300/3746] lr: 5.459e-02, eta: 2 days, 18:16:51, time: 0.834, data_time: 0.001, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5742, loss_cls: 3.8927, loss: 3.8927 +2024-07-24 16:23:56,872 - pyskl - INFO - Epoch [71][2400/3746] lr: 5.456e-02, eta: 2 days, 18:15:34, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5623, loss_cls: 3.9501, loss: 3.9501 +2024-07-24 16:25:19,769 - pyskl - INFO - Epoch [71][2500/3746] lr: 5.453e-02, eta: 2 days, 18:14:16, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5713, loss_cls: 3.8949, loss: 3.8949 +2024-07-24 16:26:42,990 - pyskl - INFO - Epoch [71][2600/3746] lr: 5.450e-02, eta: 2 days, 18:12:59, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5820, loss_cls: 3.8769, loss: 3.8769 +2024-07-24 16:28:06,201 - pyskl - INFO - Epoch [71][2700/3746] lr: 5.448e-02, eta: 2 days, 18:11:43, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5805, loss_cls: 3.8600, loss: 3.8600 +2024-07-24 16:29:29,422 - pyskl - INFO - Epoch [71][2800/3746] lr: 5.445e-02, eta: 2 days, 18:10:26, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5641, loss_cls: 3.9277, loss: 3.9277 +2024-07-24 16:30:51,979 - pyskl - INFO - Epoch [71][2900/3746] lr: 5.442e-02, eta: 2 days, 18:09:08, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5795, loss_cls: 3.9132, loss: 3.9132 +2024-07-24 16:32:14,626 - pyskl - INFO - Epoch [71][3000/3746] lr: 5.439e-02, eta: 2 days, 18:07:50, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5803, loss_cls: 3.8885, loss: 3.8885 +2024-07-24 16:33:36,281 - pyskl - INFO - Epoch [71][3100/3746] lr: 5.436e-02, eta: 2 days, 18:06:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5842, loss_cls: 3.8325, loss: 3.8325 +2024-07-24 16:34:58,053 - pyskl - INFO - Epoch [71][3200/3746] lr: 5.434e-02, eta: 2 days, 18:05:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5752, loss_cls: 3.8963, loss: 3.8963 +2024-07-24 16:36:21,251 - pyskl - INFO - Epoch [71][3300/3746] lr: 5.431e-02, eta: 2 days, 18:03:56, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5695, loss_cls: 3.9012, loss: 3.9012 +2024-07-24 16:37:43,882 - pyskl - INFO - Epoch [71][3400/3746] lr: 5.428e-02, eta: 2 days, 18:02:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5677, loss_cls: 3.9385, loss: 3.9385 +2024-07-24 16:39:06,375 - pyskl - INFO - Epoch [71][3500/3746] lr: 5.425e-02, eta: 2 days, 18:01:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5800, loss_cls: 3.9066, loss: 3.9066 +2024-07-24 16:40:29,392 - pyskl - INFO - Epoch [71][3600/3746] lr: 5.422e-02, eta: 2 days, 18:00:04, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5728, loss_cls: 3.9028, loss: 3.9028 +2024-07-24 16:41:51,268 - pyskl - INFO - Epoch [71][3700/3746] lr: 5.420e-02, eta: 2 days, 17:58:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5783, loss_cls: 3.9118, loss: 3.9118 +2024-07-24 16:42:31,278 - pyskl - INFO - Saving checkpoint at 71 epochs +2024-07-24 16:44:24,795 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 16:44:25,473 - pyskl - INFO - +top1_acc 0.2253 +top5_acc 0.4657 +2024-07-24 16:44:25,473 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 16:44:25,512 - pyskl - INFO - +mean_acc 0.2250 +2024-07-24 16:44:25,524 - pyskl - INFO - Epoch(val) [71][309] top1_acc: 0.2253, top5_acc: 0.4657, mean_class_accuracy: 0.2250 +2024-07-24 16:48:11,465 - pyskl - INFO - Epoch [72][100/3746] lr: 5.416e-02, eta: 2 days, 17:58:49, time: 2.259, data_time: 1.265, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5919, loss_cls: 3.7948, loss: 3.7948 +2024-07-24 16:49:34,693 - pyskl - INFO - Epoch [72][200/3746] lr: 5.413e-02, eta: 2 days, 17:57:32, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5773, loss_cls: 3.8528, loss: 3.8528 +2024-07-24 16:50:57,056 - pyskl - INFO - Epoch [72][300/3746] lr: 5.410e-02, eta: 2 days, 17:56:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5842, loss_cls: 3.8418, loss: 3.8418 +2024-07-24 16:52:20,142 - pyskl - INFO - Epoch [72][400/3746] lr: 5.407e-02, eta: 2 days, 17:54:57, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5798, loss_cls: 3.8856, loss: 3.8856 +2024-07-24 16:53:43,000 - pyskl - INFO - Epoch [72][500/3746] lr: 5.404e-02, eta: 2 days, 17:53:39, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5945, loss_cls: 3.8147, loss: 3.8147 +2024-07-24 16:55:05,963 - pyskl - INFO - Epoch [72][600/3746] lr: 5.402e-02, eta: 2 days, 17:52:22, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5736, loss_cls: 3.9031, loss: 3.9031 +2024-07-24 16:56:29,416 - pyskl - INFO - Epoch [72][700/3746] lr: 5.399e-02, eta: 2 days, 17:51:05, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5877, loss_cls: 3.8627, loss: 3.8627 +2024-07-24 16:57:52,600 - pyskl - INFO - Epoch [72][800/3746] lr: 5.396e-02, eta: 2 days, 17:49:48, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5806, loss_cls: 3.8534, loss: 3.8534 +2024-07-24 16:59:16,264 - pyskl - INFO - Epoch [72][900/3746] lr: 5.393e-02, eta: 2 days, 17:48:32, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5702, loss_cls: 3.8768, loss: 3.8768 +2024-07-24 17:00:39,408 - pyskl - INFO - Epoch [72][1000/3746] lr: 5.391e-02, eta: 2 days, 17:47:14, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5691, loss_cls: 3.9302, loss: 3.9302 +2024-07-24 17:02:02,852 - pyskl - INFO - Epoch [72][1100/3746] lr: 5.388e-02, eta: 2 days, 17:45:58, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5711, loss_cls: 3.9167, loss: 3.9167 +2024-07-24 17:03:25,672 - pyskl - INFO - Epoch [72][1200/3746] lr: 5.385e-02, eta: 2 days, 17:44:40, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5817, loss_cls: 3.8539, loss: 3.8539 +2024-07-24 17:04:49,183 - pyskl - INFO - Epoch [72][1300/3746] lr: 5.382e-02, eta: 2 days, 17:43:23, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5828, loss_cls: 3.8309, loss: 3.8309 +2024-07-24 17:06:11,769 - pyskl - INFO - Epoch [72][1400/3746] lr: 5.379e-02, eta: 2 days, 17:42:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5753, loss_cls: 3.8924, loss: 3.8924 +2024-07-24 17:07:34,180 - pyskl - INFO - Epoch [72][1500/3746] lr: 5.377e-02, eta: 2 days, 17:40:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5753, loss_cls: 3.8901, loss: 3.8901 +2024-07-24 17:08:55,918 - pyskl - INFO - Epoch [72][1600/3746] lr: 5.374e-02, eta: 2 days, 17:39:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5777, loss_cls: 3.9016, loss: 3.9016 +2024-07-24 17:10:17,250 - pyskl - INFO - Epoch [72][1700/3746] lr: 5.371e-02, eta: 2 days, 17:38:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5748, loss_cls: 3.8796, loss: 3.8796 +2024-07-24 17:11:39,290 - pyskl - INFO - Epoch [72][1800/3746] lr: 5.368e-02, eta: 2 days, 17:36:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5736, loss_cls: 3.9059, loss: 3.9059 +2024-07-24 17:13:01,947 - pyskl - INFO - Epoch [72][1900/3746] lr: 5.365e-02, eta: 2 days, 17:35:33, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5745, loss_cls: 3.8866, loss: 3.8866 +2024-07-24 17:14:23,891 - pyskl - INFO - Epoch [72][2000/3746] lr: 5.363e-02, eta: 2 days, 17:34:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5817, loss_cls: 3.8868, loss: 3.8868 +2024-07-24 17:15:46,045 - pyskl - INFO - Epoch [72][2100/3746] lr: 5.360e-02, eta: 2 days, 17:32:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5742, loss_cls: 3.8689, loss: 3.8689 +2024-07-24 17:17:07,817 - pyskl - INFO - Epoch [72][2200/3746] lr: 5.357e-02, eta: 2 days, 17:31:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5814, loss_cls: 3.8895, loss: 3.8895 +2024-07-24 17:18:29,523 - pyskl - INFO - Epoch [72][2300/3746] lr: 5.354e-02, eta: 2 days, 17:30:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5805, loss_cls: 3.8842, loss: 3.8842 +2024-07-24 17:19:51,322 - pyskl - INFO - Epoch [72][2400/3746] lr: 5.352e-02, eta: 2 days, 17:29:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5850, loss_cls: 3.8460, loss: 3.8460 +2024-07-24 17:21:13,445 - pyskl - INFO - Epoch [72][2500/3746] lr: 5.349e-02, eta: 2 days, 17:27:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5789, loss_cls: 3.8774, loss: 3.8774 +2024-07-24 17:22:35,355 - pyskl - INFO - Epoch [72][2600/3746] lr: 5.346e-02, eta: 2 days, 17:26:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5734, loss_cls: 3.9025, loss: 3.9025 +2024-07-24 17:23:57,460 - pyskl - INFO - Epoch [72][2700/3746] lr: 5.343e-02, eta: 2 days, 17:25:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5803, loss_cls: 3.8629, loss: 3.8629 +2024-07-24 17:25:19,132 - pyskl - INFO - Epoch [72][2800/3746] lr: 5.340e-02, eta: 2 days, 17:23:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5795, loss_cls: 3.8576, loss: 3.8576 +2024-07-24 17:26:40,939 - pyskl - INFO - Epoch [72][2900/3746] lr: 5.338e-02, eta: 2 days, 17:22:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5781, loss_cls: 3.8733, loss: 3.8733 +2024-07-24 17:28:03,020 - pyskl - INFO - Epoch [72][3000/3746] lr: 5.335e-02, eta: 2 days, 17:21:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5822, loss_cls: 3.8624, loss: 3.8624 +2024-07-24 17:29:24,855 - pyskl - INFO - Epoch [72][3100/3746] lr: 5.332e-02, eta: 2 days, 17:19:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5784, loss_cls: 3.8802, loss: 3.8802 +2024-07-24 17:30:46,769 - pyskl - INFO - Epoch [72][3200/3746] lr: 5.329e-02, eta: 2 days, 17:18:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5761, loss_cls: 3.9073, loss: 3.9073 +2024-07-24 17:32:08,436 - pyskl - INFO - Epoch [72][3300/3746] lr: 5.326e-02, eta: 2 days, 17:17:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5737, loss_cls: 3.8987, loss: 3.8987 +2024-07-24 17:33:30,369 - pyskl - INFO - Epoch [72][3400/3746] lr: 5.324e-02, eta: 2 days, 17:15:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5741, loss_cls: 3.8845, loss: 3.8845 +2024-07-24 17:34:52,308 - pyskl - INFO - Epoch [72][3500/3746] lr: 5.321e-02, eta: 2 days, 17:14:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5834, loss_cls: 3.8782, loss: 3.8782 +2024-07-24 17:36:14,005 - pyskl - INFO - Epoch [72][3600/3746] lr: 5.318e-02, eta: 2 days, 17:13:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5764, loss_cls: 3.8790, loss: 3.8790 +2024-07-24 17:37:35,858 - pyskl - INFO - Epoch [72][3700/3746] lr: 5.315e-02, eta: 2 days, 17:11:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5684, loss_cls: 3.9191, loss: 3.9191 +2024-07-24 17:38:15,482 - pyskl - INFO - Saving checkpoint at 72 epochs +2024-07-24 17:40:08,826 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 17:40:09,490 - pyskl - INFO - +top1_acc 0.2486 +top5_acc 0.4983 +2024-07-24 17:40:09,490 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 17:40:09,533 - pyskl - INFO - +mean_acc 0.2484 +2024-07-24 17:40:09,540 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_68.pth was removed +2024-07-24 17:40:09,802 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_72.pth. +2024-07-24 17:40:09,803 - pyskl - INFO - Best top1_acc is 0.2486 at 72 epoch. +2024-07-24 17:40:09,816 - pyskl - INFO - Epoch(val) [72][309] top1_acc: 0.2486, top5_acc: 0.4983, mean_class_accuracy: 0.2484 +2024-07-24 17:44:06,773 - pyskl - INFO - Epoch [73][100/3746] lr: 5.311e-02, eta: 2 days, 17:12:11, time: 2.369, data_time: 1.377, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5933, loss_cls: 3.7979, loss: 3.7979 +2024-07-24 17:45:29,224 - pyskl - INFO - Epoch [73][200/3746] lr: 5.308e-02, eta: 2 days, 17:10:53, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5839, loss_cls: 3.8263, loss: 3.8263 +2024-07-24 17:46:51,719 - pyskl - INFO - Epoch [73][300/3746] lr: 5.306e-02, eta: 2 days, 17:09:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5900, loss_cls: 3.8293, loss: 3.8293 +2024-07-24 17:48:13,777 - pyskl - INFO - Epoch [73][400/3746] lr: 5.303e-02, eta: 2 days, 17:08:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5895, loss_cls: 3.8409, loss: 3.8409 +2024-07-24 17:49:35,473 - pyskl - INFO - Epoch [73][500/3746] lr: 5.300e-02, eta: 2 days, 17:06:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5947, loss_cls: 3.8077, loss: 3.8077 +2024-07-24 17:50:57,031 - pyskl - INFO - Epoch [73][600/3746] lr: 5.297e-02, eta: 2 days, 17:05:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5887, loss_cls: 3.8326, loss: 3.8326 +2024-07-24 17:52:18,582 - pyskl - INFO - Epoch [73][700/3746] lr: 5.294e-02, eta: 2 days, 17:04:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5816, loss_cls: 3.8827, loss: 3.8827 +2024-07-24 17:53:40,079 - pyskl - INFO - Epoch [73][800/3746] lr: 5.292e-02, eta: 2 days, 17:03:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5877, loss_cls: 3.8136, loss: 3.8136 +2024-07-24 17:55:01,464 - pyskl - INFO - Epoch [73][900/3746] lr: 5.289e-02, eta: 2 days, 17:01:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5786, loss_cls: 3.8856, loss: 3.8856 +2024-07-24 17:56:23,141 - pyskl - INFO - Epoch [73][1000/3746] lr: 5.286e-02, eta: 2 days, 17:00:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5792, loss_cls: 3.8718, loss: 3.8718 +2024-07-24 17:57:44,660 - pyskl - INFO - Epoch [73][1100/3746] lr: 5.283e-02, eta: 2 days, 16:59:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5770, loss_cls: 3.8916, loss: 3.8916 +2024-07-24 17:59:06,193 - pyskl - INFO - Epoch [73][1200/3746] lr: 5.280e-02, eta: 2 days, 16:57:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5772, loss_cls: 3.8811, loss: 3.8811 +2024-07-24 18:00:27,702 - pyskl - INFO - Epoch [73][1300/3746] lr: 5.278e-02, eta: 2 days, 16:56:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5758, loss_cls: 3.8832, loss: 3.8832 +2024-07-24 18:01:49,574 - pyskl - INFO - Epoch [73][1400/3746] lr: 5.275e-02, eta: 2 days, 16:55:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5845, loss_cls: 3.8732, loss: 3.8732 +2024-07-24 18:03:11,793 - pyskl - INFO - Epoch [73][1500/3746] lr: 5.272e-02, eta: 2 days, 16:53:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5863, loss_cls: 3.8297, loss: 3.8297 +2024-07-24 18:04:33,114 - pyskl - INFO - Epoch [73][1600/3746] lr: 5.269e-02, eta: 2 days, 16:52:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5836, loss_cls: 3.8455, loss: 3.8455 +2024-07-24 18:05:54,585 - pyskl - INFO - Epoch [73][1700/3746] lr: 5.267e-02, eta: 2 days, 16:51:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5839, loss_cls: 3.8560, loss: 3.8560 +2024-07-24 18:07:16,072 - pyskl - INFO - Epoch [73][1800/3746] lr: 5.264e-02, eta: 2 days, 16:49:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5836, loss_cls: 3.8296, loss: 3.8296 +2024-07-24 18:08:38,435 - pyskl - INFO - Epoch [73][1900/3746] lr: 5.261e-02, eta: 2 days, 16:48:31, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5784, loss_cls: 3.8626, loss: 3.8626 +2024-07-24 18:10:00,526 - pyskl - INFO - Epoch [73][2000/3746] lr: 5.258e-02, eta: 2 days, 16:47:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5823, loss_cls: 3.8117, loss: 3.8117 +2024-07-24 18:11:22,590 - pyskl - INFO - Epoch [73][2100/3746] lr: 5.255e-02, eta: 2 days, 16:45:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5823, loss_cls: 3.8456, loss: 3.8456 +2024-07-24 18:12:44,659 - pyskl - INFO - Epoch [73][2200/3746] lr: 5.253e-02, eta: 2 days, 16:44:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5798, loss_cls: 3.8598, loss: 3.8598 +2024-07-24 18:14:06,316 - pyskl - INFO - Epoch [73][2300/3746] lr: 5.250e-02, eta: 2 days, 16:43:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5767, loss_cls: 3.8227, loss: 3.8227 +2024-07-24 18:15:27,756 - pyskl - INFO - Epoch [73][2400/3746] lr: 5.247e-02, eta: 2 days, 16:41:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5827, loss_cls: 3.8726, loss: 3.8726 +2024-07-24 18:16:49,425 - pyskl - INFO - Epoch [73][2500/3746] lr: 5.244e-02, eta: 2 days, 16:40:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5673, loss_cls: 3.8997, loss: 3.8997 +2024-07-24 18:18:10,764 - pyskl - INFO - Epoch [73][2600/3746] lr: 5.241e-02, eta: 2 days, 16:39:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5848, loss_cls: 3.8526, loss: 3.8526 +2024-07-24 18:19:32,979 - pyskl - INFO - Epoch [73][2700/3746] lr: 5.239e-02, eta: 2 days, 16:38:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5713, loss_cls: 3.9180, loss: 3.9180 +2024-07-24 18:20:54,741 - pyskl - INFO - Epoch [73][2800/3746] lr: 5.236e-02, eta: 2 days, 16:36:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5686, loss_cls: 3.9341, loss: 3.9341 +2024-07-24 18:22:16,816 - pyskl - INFO - Epoch [73][2900/3746] lr: 5.233e-02, eta: 2 days, 16:35:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5742, loss_cls: 3.8822, loss: 3.8822 +2024-07-24 18:23:38,330 - pyskl - INFO - Epoch [73][3000/3746] lr: 5.230e-02, eta: 2 days, 16:34:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5819, loss_cls: 3.8607, loss: 3.8607 +2024-07-24 18:25:00,583 - pyskl - INFO - Epoch [73][3100/3746] lr: 5.227e-02, eta: 2 days, 16:32:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5689, loss_cls: 3.9070, loss: 3.9070 +2024-07-24 18:26:22,609 - pyskl - INFO - Epoch [73][3200/3746] lr: 5.225e-02, eta: 2 days, 16:31:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5602, loss_cls: 3.9470, loss: 3.9470 +2024-07-24 18:27:45,062 - pyskl - INFO - Epoch [73][3300/3746] lr: 5.222e-02, eta: 2 days, 16:30:09, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5777, loss_cls: 3.8904, loss: 3.8904 +2024-07-24 18:29:06,499 - pyskl - INFO - Epoch [73][3400/3746] lr: 5.219e-02, eta: 2 days, 16:28:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5755, loss_cls: 3.8683, loss: 3.8683 +2024-07-24 18:30:28,499 - pyskl - INFO - Epoch [73][3500/3746] lr: 5.216e-02, eta: 2 days, 16:27:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5795, loss_cls: 3.8961, loss: 3.8961 +2024-07-24 18:31:50,243 - pyskl - INFO - Epoch [73][3600/3746] lr: 5.213e-02, eta: 2 days, 16:26:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5734, loss_cls: 3.8826, loss: 3.8826 +2024-07-24 18:33:12,714 - pyskl - INFO - Epoch [73][3700/3746] lr: 5.211e-02, eta: 2 days, 16:24:54, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5797, loss_cls: 3.8576, loss: 3.8576 +2024-07-24 18:33:52,211 - pyskl - INFO - Saving checkpoint at 73 epochs +2024-07-24 18:35:44,688 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 18:35:45,355 - pyskl - INFO - +top1_acc 0.2467 +top5_acc 0.4984 +2024-07-24 18:35:45,355 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 18:35:45,401 - pyskl - INFO - +mean_acc 0.2464 +2024-07-24 18:35:45,414 - pyskl - INFO - Epoch(val) [73][309] top1_acc: 0.2467, top5_acc: 0.4984, mean_class_accuracy: 0.2464 +2024-07-24 18:39:37,388 - pyskl - INFO - Epoch [74][100/3746] lr: 5.207e-02, eta: 2 days, 16:24:57, time: 2.320, data_time: 1.337, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5780, loss_cls: 3.8679, loss: 3.8679 +2024-07-24 18:40:59,266 - pyskl - INFO - Epoch [74][200/3746] lr: 5.204e-02, eta: 2 days, 16:23:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5853, loss_cls: 3.8277, loss: 3.8277 +2024-07-24 18:42:21,590 - pyskl - INFO - Epoch [74][300/3746] lr: 5.201e-02, eta: 2 days, 16:22:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5953, loss_cls: 3.8123, loss: 3.8123 +2024-07-24 18:43:43,407 - pyskl - INFO - Epoch [74][400/3746] lr: 5.198e-02, eta: 2 days, 16:21:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5878, loss_cls: 3.8453, loss: 3.8453 +2024-07-24 18:45:05,295 - pyskl - INFO - Epoch [74][500/3746] lr: 5.195e-02, eta: 2 days, 16:19:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5794, loss_cls: 3.8490, loss: 3.8490 +2024-07-24 18:46:27,143 - pyskl - INFO - Epoch [74][600/3746] lr: 5.193e-02, eta: 2 days, 16:18:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.6006, loss_cls: 3.7732, loss: 3.7732 +2024-07-24 18:47:48,716 - pyskl - INFO - Epoch [74][700/3746] lr: 5.190e-02, eta: 2 days, 16:17:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5813, loss_cls: 3.8525, loss: 3.8525 +2024-07-24 18:49:10,567 - pyskl - INFO - Epoch [74][800/3746] lr: 5.187e-02, eta: 2 days, 16:15:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5730, loss_cls: 3.9017, loss: 3.9017 +2024-07-24 18:50:32,498 - pyskl - INFO - Epoch [74][900/3746] lr: 5.184e-02, eta: 2 days, 16:14:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5878, loss_cls: 3.8359, loss: 3.8359 +2024-07-24 18:51:54,264 - pyskl - INFO - Epoch [74][1000/3746] lr: 5.181e-02, eta: 2 days, 16:13:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5745, loss_cls: 3.8925, loss: 3.8925 +2024-07-24 18:53:15,700 - pyskl - INFO - Epoch [74][1100/3746] lr: 5.179e-02, eta: 2 days, 16:11:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.6034, loss_cls: 3.7559, loss: 3.7559 +2024-07-24 18:54:37,019 - pyskl - INFO - Epoch [74][1200/3746] lr: 5.176e-02, eta: 2 days, 16:10:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5872, loss_cls: 3.8317, loss: 3.8317 +2024-07-24 18:55:58,437 - pyskl - INFO - Epoch [74][1300/3746] lr: 5.173e-02, eta: 2 days, 16:09:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5863, loss_cls: 3.8457, loss: 3.8457 +2024-07-24 18:57:20,585 - pyskl - INFO - Epoch [74][1400/3746] lr: 5.170e-02, eta: 2 days, 16:07:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5850, loss_cls: 3.8608, loss: 3.8608 +2024-07-24 18:58:42,095 - pyskl - INFO - Epoch [74][1500/3746] lr: 5.168e-02, eta: 2 days, 16:06:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5852, loss_cls: 3.8386, loss: 3.8386 +2024-07-24 19:00:02,945 - pyskl - INFO - Epoch [74][1600/3746] lr: 5.165e-02, eta: 2 days, 16:05:11, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5744, loss_cls: 3.8701, loss: 3.8701 +2024-07-24 19:01:24,225 - pyskl - INFO - Epoch [74][1700/3746] lr: 5.162e-02, eta: 2 days, 16:03:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5763, loss_cls: 3.8606, loss: 3.8606 +2024-07-24 19:02:45,655 - pyskl - INFO - Epoch [74][1800/3746] lr: 5.159e-02, eta: 2 days, 16:02:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5723, loss_cls: 3.9168, loss: 3.9168 +2024-07-24 19:04:07,776 - pyskl - INFO - Epoch [74][1900/3746] lr: 5.156e-02, eta: 2 days, 16:01:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5850, loss_cls: 3.8787, loss: 3.8787 +2024-07-24 19:05:29,004 - pyskl - INFO - Epoch [74][2000/3746] lr: 5.154e-02, eta: 2 days, 15:59:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5833, loss_cls: 3.8450, loss: 3.8450 +2024-07-24 19:06:52,191 - pyskl - INFO - Epoch [74][2100/3746] lr: 5.151e-02, eta: 2 days, 15:58:36, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5786, loss_cls: 3.8303, loss: 3.8303 +2024-07-24 19:08:14,573 - pyskl - INFO - Epoch [74][2200/3746] lr: 5.148e-02, eta: 2 days, 15:57:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5736, loss_cls: 3.8820, loss: 3.8820 +2024-07-24 19:09:37,128 - pyskl - INFO - Epoch [74][2300/3746] lr: 5.145e-02, eta: 2 days, 15:55:59, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5797, loss_cls: 3.8753, loss: 3.8753 +2024-07-24 19:10:58,906 - pyskl - INFO - Epoch [74][2400/3746] lr: 5.142e-02, eta: 2 days, 15:54:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5830, loss_cls: 3.8501, loss: 3.8501 +2024-07-24 19:12:20,647 - pyskl - INFO - Epoch [74][2500/3746] lr: 5.140e-02, eta: 2 days, 15:53:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5836, loss_cls: 3.8490, loss: 3.8490 +2024-07-24 19:13:41,580 - pyskl - INFO - Epoch [74][2600/3746] lr: 5.137e-02, eta: 2 days, 15:52:01, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5777, loss_cls: 3.8747, loss: 3.8747 +2024-07-24 19:15:03,019 - pyskl - INFO - Epoch [74][2700/3746] lr: 5.134e-02, eta: 2 days, 15:50:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5859, loss_cls: 3.8390, loss: 3.8390 +2024-07-24 19:16:24,287 - pyskl - INFO - Epoch [74][2800/3746] lr: 5.131e-02, eta: 2 days, 15:49:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5864, loss_cls: 3.8163, loss: 3.8163 +2024-07-24 19:17:46,022 - pyskl - INFO - Epoch [74][2900/3746] lr: 5.128e-02, eta: 2 days, 15:48:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5859, loss_cls: 3.8232, loss: 3.8232 +2024-07-24 19:19:07,195 - pyskl - INFO - Epoch [74][3000/3746] lr: 5.126e-02, eta: 2 days, 15:46:43, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5825, loss_cls: 3.8536, loss: 3.8536 +2024-07-24 19:20:28,768 - pyskl - INFO - Epoch [74][3100/3746] lr: 5.123e-02, eta: 2 days, 15:45:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5756, loss_cls: 3.9196, loss: 3.9196 +2024-07-24 19:21:50,095 - pyskl - INFO - Epoch [74][3200/3746] lr: 5.120e-02, eta: 2 days, 15:44:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5769, loss_cls: 3.8913, loss: 3.8913 +2024-07-24 19:23:12,635 - pyskl - INFO - Epoch [74][3300/3746] lr: 5.117e-02, eta: 2 days, 15:42:46, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5833, loss_cls: 3.8392, loss: 3.8392 +2024-07-24 19:24:34,173 - pyskl - INFO - Epoch [74][3400/3746] lr: 5.114e-02, eta: 2 days, 15:41:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5834, loss_cls: 3.8559, loss: 3.8559 +2024-07-24 19:25:55,258 - pyskl - INFO - Epoch [74][3500/3746] lr: 5.112e-02, eta: 2 days, 15:40:07, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5845, loss_cls: 3.8777, loss: 3.8777 +2024-07-24 19:27:16,588 - pyskl - INFO - Epoch [74][3600/3746] lr: 5.109e-02, eta: 2 days, 15:38:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5728, loss_cls: 3.9267, loss: 3.9267 +2024-07-24 19:28:38,449 - pyskl - INFO - Epoch [74][3700/3746] lr: 5.106e-02, eta: 2 days, 15:37:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5814, loss_cls: 3.8388, loss: 3.8388 +2024-07-24 19:29:18,455 - pyskl - INFO - Saving checkpoint at 74 epochs +2024-07-24 19:31:10,512 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 19:31:11,181 - pyskl - INFO - +top1_acc 0.2324 +top5_acc 0.4631 +2024-07-24 19:31:11,181 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 19:31:11,223 - pyskl - INFO - +mean_acc 0.2323 +2024-07-24 19:31:11,236 - pyskl - INFO - Epoch(val) [74][309] top1_acc: 0.2324, top5_acc: 0.4631, mean_class_accuracy: 0.2323 +2024-07-24 19:35:04,260 - pyskl - INFO - Epoch [75][100/3746] lr: 5.102e-02, eta: 2 days, 15:37:30, time: 2.330, data_time: 1.349, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5964, loss_cls: 3.8148, loss: 3.8148 +2024-07-24 19:36:25,865 - pyskl - INFO - Epoch [75][200/3746] lr: 5.099e-02, eta: 2 days, 15:36:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5952, loss_cls: 3.8112, loss: 3.8112 +2024-07-24 19:37:47,808 - pyskl - INFO - Epoch [75][300/3746] lr: 5.096e-02, eta: 2 days, 15:34:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5903, loss_cls: 3.7999, loss: 3.7999 +2024-07-24 19:39:09,601 - pyskl - INFO - Epoch [75][400/3746] lr: 5.094e-02, eta: 2 days, 15:33:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5839, loss_cls: 3.8236, loss: 3.8236 +2024-07-24 19:40:30,817 - pyskl - INFO - Epoch [75][500/3746] lr: 5.091e-02, eta: 2 days, 15:32:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5784, loss_cls: 3.8700, loss: 3.8700 +2024-07-24 19:41:52,604 - pyskl - INFO - Epoch [75][600/3746] lr: 5.088e-02, eta: 2 days, 15:30:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5872, loss_cls: 3.8371, loss: 3.8371 +2024-07-24 19:43:13,914 - pyskl - INFO - Epoch [75][700/3746] lr: 5.085e-02, eta: 2 days, 15:29:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5877, loss_cls: 3.8083, loss: 3.8083 +2024-07-24 19:44:35,213 - pyskl - INFO - Epoch [75][800/3746] lr: 5.082e-02, eta: 2 days, 15:28:14, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5875, loss_cls: 3.8198, loss: 3.8198 +2024-07-24 19:45:56,454 - pyskl - INFO - Epoch [75][900/3746] lr: 5.080e-02, eta: 2 days, 15:26:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5816, loss_cls: 3.8446, loss: 3.8446 +2024-07-24 19:47:17,951 - pyskl - INFO - Epoch [75][1000/3746] lr: 5.077e-02, eta: 2 days, 15:25:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5916, loss_cls: 3.8000, loss: 3.8000 +2024-07-24 19:48:39,612 - pyskl - INFO - Epoch [75][1100/3746] lr: 5.074e-02, eta: 2 days, 15:24:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5817, loss_cls: 3.8428, loss: 3.8428 +2024-07-24 19:50:01,443 - pyskl - INFO - Epoch [75][1200/3746] lr: 5.071e-02, eta: 2 days, 15:22:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5858, loss_cls: 3.8378, loss: 3.8378 +2024-07-24 19:51:22,677 - pyskl - INFO - Epoch [75][1300/3746] lr: 5.068e-02, eta: 2 days, 15:21:37, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5889, loss_cls: 3.8231, loss: 3.8231 +2024-07-24 19:52:44,981 - pyskl - INFO - Epoch [75][1400/3746] lr: 5.066e-02, eta: 2 days, 15:20:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5839, loss_cls: 3.8609, loss: 3.8609 +2024-07-24 19:54:06,781 - pyskl - INFO - Epoch [75][1500/3746] lr: 5.063e-02, eta: 2 days, 15:18:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5913, loss_cls: 3.7825, loss: 3.7825 +2024-07-24 19:55:28,504 - pyskl - INFO - Epoch [75][1600/3746] lr: 5.060e-02, eta: 2 days, 15:17:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5811, loss_cls: 3.8711, loss: 3.8711 +2024-07-24 19:56:49,868 - pyskl - INFO - Epoch [75][1700/3746] lr: 5.057e-02, eta: 2 days, 15:16:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5794, loss_cls: 3.8565, loss: 3.8565 +2024-07-24 19:58:11,498 - pyskl - INFO - Epoch [75][1800/3746] lr: 5.054e-02, eta: 2 days, 15:15:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5898, loss_cls: 3.8203, loss: 3.8203 +2024-07-24 19:59:33,771 - pyskl - INFO - Epoch [75][1900/3746] lr: 5.052e-02, eta: 2 days, 15:13:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5780, loss_cls: 3.8980, loss: 3.8980 +2024-07-24 20:00:55,646 - pyskl - INFO - Epoch [75][2000/3746] lr: 5.049e-02, eta: 2 days, 15:12:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5761, loss_cls: 3.8592, loss: 3.8592 +2024-07-24 20:02:17,767 - pyskl - INFO - Epoch [75][2100/3746] lr: 5.046e-02, eta: 2 days, 15:11:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5872, loss_cls: 3.8028, loss: 3.8028 +2024-07-24 20:03:40,156 - pyskl - INFO - Epoch [75][2200/3746] lr: 5.043e-02, eta: 2 days, 15:09:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5831, loss_cls: 3.8493, loss: 3.8493 +2024-07-24 20:05:01,770 - pyskl - INFO - Epoch [75][2300/3746] lr: 5.040e-02, eta: 2 days, 15:08:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5895, loss_cls: 3.8206, loss: 3.8206 +2024-07-24 20:06:23,891 - pyskl - INFO - Epoch [75][2400/3746] lr: 5.038e-02, eta: 2 days, 15:07:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5787, loss_cls: 3.8535, loss: 3.8535 +2024-07-24 20:07:45,615 - pyskl - INFO - Epoch [75][2500/3746] lr: 5.035e-02, eta: 2 days, 15:05:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5806, loss_cls: 3.8454, loss: 3.8454 +2024-07-24 20:09:07,489 - pyskl - INFO - Epoch [75][2600/3746] lr: 5.032e-02, eta: 2 days, 15:04:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5828, loss_cls: 3.8442, loss: 3.8442 +2024-07-24 20:10:28,881 - pyskl - INFO - Epoch [75][2700/3746] lr: 5.029e-02, eta: 2 days, 15:03:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5789, loss_cls: 3.8762, loss: 3.8762 +2024-07-24 20:11:50,280 - pyskl - INFO - Epoch [75][2800/3746] lr: 5.026e-02, eta: 2 days, 15:01:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5903, loss_cls: 3.8128, loss: 3.8128 +2024-07-24 20:13:11,901 - pyskl - INFO - Epoch [75][2900/3746] lr: 5.024e-02, eta: 2 days, 15:00:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5753, loss_cls: 3.8717, loss: 3.8717 +2024-07-24 20:14:33,786 - pyskl - INFO - Epoch [75][3000/3746] lr: 5.021e-02, eta: 2 days, 14:59:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5758, loss_cls: 3.8710, loss: 3.8710 +2024-07-24 20:15:55,206 - pyskl - INFO - Epoch [75][3100/3746] lr: 5.018e-02, eta: 2 days, 14:57:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5852, loss_cls: 3.8612, loss: 3.8612 +2024-07-24 20:17:16,797 - pyskl - INFO - Epoch [75][3200/3746] lr: 5.015e-02, eta: 2 days, 14:56:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5820, loss_cls: 3.8830, loss: 3.8830 +2024-07-24 20:18:38,167 - pyskl - INFO - Epoch [75][3300/3746] lr: 5.012e-02, eta: 2 days, 14:55:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5869, loss_cls: 3.8470, loss: 3.8470 +2024-07-24 20:19:59,580 - pyskl - INFO - Epoch [75][3400/3746] lr: 5.010e-02, eta: 2 days, 14:53:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5816, loss_cls: 3.8350, loss: 3.8350 +2024-07-24 20:21:21,317 - pyskl - INFO - Epoch [75][3500/3746] lr: 5.007e-02, eta: 2 days, 14:52:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5781, loss_cls: 3.8502, loss: 3.8502 +2024-07-24 20:22:44,143 - pyskl - INFO - Epoch [75][3600/3746] lr: 5.004e-02, eta: 2 days, 14:51:16, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5877, loss_cls: 3.8501, loss: 3.8501 +2024-07-24 20:24:05,447 - pyskl - INFO - Epoch [75][3700/3746] lr: 5.001e-02, eta: 2 days, 14:49:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5891, loss_cls: 3.8508, loss: 3.8508 +2024-07-24 20:24:45,493 - pyskl - INFO - Saving checkpoint at 75 epochs +2024-07-24 20:26:37,608 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 20:26:38,276 - pyskl - INFO - +top1_acc 0.2482 +top5_acc 0.4923 +2024-07-24 20:26:38,276 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 20:26:38,319 - pyskl - INFO - +mean_acc 0.2481 +2024-07-24 20:26:38,332 - pyskl - INFO - Epoch(val) [75][309] top1_acc: 0.2482, top5_acc: 0.4923, mean_class_accuracy: 0.2481 +2024-07-24 20:30:33,423 - pyskl - INFO - Epoch [76][100/3746] lr: 4.997e-02, eta: 2 days, 14:49:56, time: 2.351, data_time: 1.363, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5905, loss_cls: 3.8192, loss: 3.8192 +2024-07-24 20:31:56,935 - pyskl - INFO - Epoch [76][200/3746] lr: 4.994e-02, eta: 2 days, 14:48:39, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5875, loss_cls: 3.8174, loss: 3.8174 +2024-07-24 20:33:19,646 - pyskl - INFO - Epoch [76][300/3746] lr: 4.992e-02, eta: 2 days, 14:47:20, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.6055, loss_cls: 3.7298, loss: 3.7298 +2024-07-24 20:34:42,027 - pyskl - INFO - Epoch [76][400/3746] lr: 4.989e-02, eta: 2 days, 14:46:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5775, loss_cls: 3.8401, loss: 3.8401 +2024-07-24 20:36:04,575 - pyskl - INFO - Epoch [76][500/3746] lr: 4.986e-02, eta: 2 days, 14:44:43, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5916, loss_cls: 3.8192, loss: 3.8192 +2024-07-24 20:37:27,058 - pyskl - INFO - Epoch [76][600/3746] lr: 4.983e-02, eta: 2 days, 14:43:24, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5819, loss_cls: 3.8472, loss: 3.8472 +2024-07-24 20:38:50,068 - pyskl - INFO - Epoch [76][700/3746] lr: 4.980e-02, eta: 2 days, 14:42:06, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5831, loss_cls: 3.8533, loss: 3.8533 +2024-07-24 20:40:12,640 - pyskl - INFO - Epoch [76][800/3746] lr: 4.978e-02, eta: 2 days, 14:40:48, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5816, loss_cls: 3.8413, loss: 3.8413 +2024-07-24 20:41:34,139 - pyskl - INFO - Epoch [76][900/3746] lr: 4.975e-02, eta: 2 days, 14:39:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5875, loss_cls: 3.7990, loss: 3.7990 +2024-07-24 20:42:55,972 - pyskl - INFO - Epoch [76][1000/3746] lr: 4.972e-02, eta: 2 days, 14:38:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5875, loss_cls: 3.8235, loss: 3.8235 +2024-07-24 20:44:17,765 - pyskl - INFO - Epoch [76][1100/3746] lr: 4.969e-02, eta: 2 days, 14:36:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5922, loss_cls: 3.8168, loss: 3.8168 +2024-07-24 20:45:39,551 - pyskl - INFO - Epoch [76][1200/3746] lr: 4.966e-02, eta: 2 days, 14:35:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5784, loss_cls: 3.8543, loss: 3.8543 +2024-07-24 20:47:00,923 - pyskl - INFO - Epoch [76][1300/3746] lr: 4.964e-02, eta: 2 days, 14:34:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5773, loss_cls: 3.8518, loss: 3.8518 +2024-07-24 20:48:22,689 - pyskl - INFO - Epoch [76][1400/3746] lr: 4.961e-02, eta: 2 days, 14:32:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5808, loss_cls: 3.8263, loss: 3.8263 +2024-07-24 20:49:44,262 - pyskl - INFO - Epoch [76][1500/3746] lr: 4.958e-02, eta: 2 days, 14:31:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5836, loss_cls: 3.8272, loss: 3.8272 +2024-07-24 20:51:05,585 - pyskl - INFO - Epoch [76][1600/3746] lr: 4.955e-02, eta: 2 days, 14:30:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5931, loss_cls: 3.7818, loss: 3.7818 +2024-07-24 20:52:27,173 - pyskl - INFO - Epoch [76][1700/3746] lr: 4.953e-02, eta: 2 days, 14:28:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5830, loss_cls: 3.8039, loss: 3.8039 +2024-07-24 20:53:48,812 - pyskl - INFO - Epoch [76][1800/3746] lr: 4.950e-02, eta: 2 days, 14:27:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5877, loss_cls: 3.8562, loss: 3.8562 +2024-07-24 20:55:10,706 - pyskl - INFO - Epoch [76][1900/3746] lr: 4.947e-02, eta: 2 days, 14:26:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5839, loss_cls: 3.8345, loss: 3.8345 +2024-07-24 20:56:32,708 - pyskl - INFO - Epoch [76][2000/3746] lr: 4.944e-02, eta: 2 days, 14:24:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5866, loss_cls: 3.8551, loss: 3.8551 +2024-07-24 20:57:55,661 - pyskl - INFO - Epoch [76][2100/3746] lr: 4.941e-02, eta: 2 days, 14:23:36, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5878, loss_cls: 3.8106, loss: 3.8106 +2024-07-24 20:59:17,740 - pyskl - INFO - Epoch [76][2200/3746] lr: 4.939e-02, eta: 2 days, 14:22:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5898, loss_cls: 3.8177, loss: 3.8177 +2024-07-24 21:00:39,857 - pyskl - INFO - Epoch [76][2300/3746] lr: 4.936e-02, eta: 2 days, 14:20:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5819, loss_cls: 3.8196, loss: 3.8196 +2024-07-24 21:02:02,244 - pyskl - INFO - Epoch [76][2400/3746] lr: 4.933e-02, eta: 2 days, 14:19:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5833, loss_cls: 3.8793, loss: 3.8793 +2024-07-24 21:03:24,141 - pyskl - INFO - Epoch [76][2500/3746] lr: 4.930e-02, eta: 2 days, 14:18:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5866, loss_cls: 3.8099, loss: 3.8099 +2024-07-24 21:04:45,475 - pyskl - INFO - Epoch [76][2600/3746] lr: 4.927e-02, eta: 2 days, 14:17:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5898, loss_cls: 3.8317, loss: 3.8317 +2024-07-24 21:06:07,248 - pyskl - INFO - Epoch [76][2700/3746] lr: 4.925e-02, eta: 2 days, 14:15:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5803, loss_cls: 3.8898, loss: 3.8898 +2024-07-24 21:07:28,811 - pyskl - INFO - Epoch [76][2800/3746] lr: 4.922e-02, eta: 2 days, 14:14:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5841, loss_cls: 3.8380, loss: 3.8380 +2024-07-24 21:08:50,521 - pyskl - INFO - Epoch [76][2900/3746] lr: 4.919e-02, eta: 2 days, 14:13:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5911, loss_cls: 3.7810, loss: 3.7810 +2024-07-24 21:10:12,017 - pyskl - INFO - Epoch [76][3000/3746] lr: 4.916e-02, eta: 2 days, 14:11:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5842, loss_cls: 3.8312, loss: 3.8312 +2024-07-24 21:11:34,452 - pyskl - INFO - Epoch [76][3100/3746] lr: 4.913e-02, eta: 2 days, 14:10:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5841, loss_cls: 3.8458, loss: 3.8458 +2024-07-24 21:12:56,116 - pyskl - INFO - Epoch [76][3200/3746] lr: 4.911e-02, eta: 2 days, 14:09:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5917, loss_cls: 3.7677, loss: 3.7677 +2024-07-24 21:14:17,895 - pyskl - INFO - Epoch [76][3300/3746] lr: 4.908e-02, eta: 2 days, 14:07:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5772, loss_cls: 3.8829, loss: 3.8829 +2024-07-24 21:15:39,583 - pyskl - INFO - Epoch [76][3400/3746] lr: 4.905e-02, eta: 2 days, 14:06:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5869, loss_cls: 3.8568, loss: 3.8568 +2024-07-24 21:17:01,058 - pyskl - INFO - Epoch [76][3500/3746] lr: 4.902e-02, eta: 2 days, 14:05:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5834, loss_cls: 3.8366, loss: 3.8366 +2024-07-24 21:18:23,888 - pyskl - INFO - Epoch [76][3600/3746] lr: 4.899e-02, eta: 2 days, 14:03:48, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5858, loss_cls: 3.8401, loss: 3.8401 +2024-07-24 21:19:45,199 - pyskl - INFO - Epoch [76][3700/3746] lr: 4.897e-02, eta: 2 days, 14:02:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5844, loss_cls: 3.8479, loss: 3.8479 +2024-07-24 21:20:25,288 - pyskl - INFO - Saving checkpoint at 76 epochs +2024-07-24 21:22:17,557 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 21:22:18,224 - pyskl - INFO - +top1_acc 0.2484 +top5_acc 0.5022 +2024-07-24 21:22:18,224 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 21:22:18,266 - pyskl - INFO - +mean_acc 0.2483 +2024-07-24 21:22:18,280 - pyskl - INFO - Epoch(val) [76][309] top1_acc: 0.2484, top5_acc: 0.5022, mean_class_accuracy: 0.2483 +2024-07-24 21:26:15,938 - pyskl - INFO - Epoch [77][100/3746] lr: 4.893e-02, eta: 2 days, 14:02:27, time: 2.376, data_time: 1.380, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5913, loss_cls: 3.8034, loss: 3.8034 +2024-07-24 21:27:38,684 - pyskl - INFO - Epoch [77][200/3746] lr: 4.890e-02, eta: 2 days, 14:01:08, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5973, loss_cls: 3.7604, loss: 3.7604 +2024-07-24 21:29:02,222 - pyskl - INFO - Epoch [77][300/3746] lr: 4.887e-02, eta: 2 days, 13:59:51, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5908, loss_cls: 3.8279, loss: 3.8279 +2024-07-24 21:30:25,971 - pyskl - INFO - Epoch [77][400/3746] lr: 4.884e-02, eta: 2 days, 13:58:33, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.5894, loss_cls: 3.7456, loss: 3.7456 +2024-07-24 21:31:49,134 - pyskl - INFO - Epoch [77][500/3746] lr: 4.881e-02, eta: 2 days, 13:57:15, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5900, loss_cls: 3.8070, loss: 3.8070 +2024-07-24 21:33:12,887 - pyskl - INFO - Epoch [77][600/3746] lr: 4.879e-02, eta: 2 days, 13:55:57, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5870, loss_cls: 3.8208, loss: 3.8208 +2024-07-24 21:34:36,432 - pyskl - INFO - Epoch [77][700/3746] lr: 4.876e-02, eta: 2 days, 13:54:40, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5900, loss_cls: 3.8084, loss: 3.8084 +2024-07-24 21:35:59,990 - pyskl - INFO - Epoch [77][800/3746] lr: 4.873e-02, eta: 2 days, 13:53:22, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5911, loss_cls: 3.8144, loss: 3.8144 +2024-07-24 21:37:23,747 - pyskl - INFO - Epoch [77][900/3746] lr: 4.870e-02, eta: 2 days, 13:52:04, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5853, loss_cls: 3.8173, loss: 3.8173 +2024-07-24 21:38:47,401 - pyskl - INFO - Epoch [77][1000/3746] lr: 4.867e-02, eta: 2 days, 13:50:47, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5816, loss_cls: 3.8482, loss: 3.8482 +2024-07-24 21:40:10,767 - pyskl - INFO - Epoch [77][1100/3746] lr: 4.865e-02, eta: 2 days, 13:49:29, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5833, loss_cls: 3.8550, loss: 3.8550 +2024-07-24 21:41:34,347 - pyskl - INFO - Epoch [77][1200/3746] lr: 4.862e-02, eta: 2 days, 13:48:11, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5789, loss_cls: 3.8576, loss: 3.8576 +2024-07-24 21:42:58,196 - pyskl - INFO - Epoch [77][1300/3746] lr: 4.859e-02, eta: 2 days, 13:46:54, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5922, loss_cls: 3.8092, loss: 3.8092 +2024-07-24 21:44:21,523 - pyskl - INFO - Epoch [77][1400/3746] lr: 4.856e-02, eta: 2 days, 13:45:36, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5833, loss_cls: 3.8694, loss: 3.8694 +2024-07-24 21:45:45,425 - pyskl - INFO - Epoch [77][1500/3746] lr: 4.853e-02, eta: 2 days, 13:44:18, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5953, loss_cls: 3.7938, loss: 3.7938 +2024-07-24 21:47:09,203 - pyskl - INFO - Epoch [77][1600/3746] lr: 4.851e-02, eta: 2 days, 13:43:00, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5873, loss_cls: 3.8286, loss: 3.8286 +2024-07-24 21:48:32,792 - pyskl - INFO - Epoch [77][1700/3746] lr: 4.848e-02, eta: 2 days, 13:41:43, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5844, loss_cls: 3.8301, loss: 3.8301 +2024-07-24 21:49:56,616 - pyskl - INFO - Epoch [77][1800/3746] lr: 4.845e-02, eta: 2 days, 13:40:25, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5850, loss_cls: 3.8524, loss: 3.8524 +2024-07-24 21:51:20,457 - pyskl - INFO - Epoch [77][1900/3746] lr: 4.842e-02, eta: 2 days, 13:39:08, time: 0.838, data_time: 0.001, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5861, loss_cls: 3.8245, loss: 3.8245 +2024-07-24 21:52:43,058 - pyskl - INFO - Epoch [77][2000/3746] lr: 4.839e-02, eta: 2 days, 13:37:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5947, loss_cls: 3.8000, loss: 3.8000 +2024-07-24 21:54:06,629 - pyskl - INFO - Epoch [77][2100/3746] lr: 4.837e-02, eta: 2 days, 13:36:31, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5844, loss_cls: 3.8112, loss: 3.8112 +2024-07-24 21:55:30,215 - pyskl - INFO - Epoch [77][2200/3746] lr: 4.834e-02, eta: 2 days, 13:35:13, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5869, loss_cls: 3.8318, loss: 3.8318 +2024-07-24 21:56:53,942 - pyskl - INFO - Epoch [77][2300/3746] lr: 4.831e-02, eta: 2 days, 13:33:56, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5942, loss_cls: 3.7950, loss: 3.7950 +2024-07-24 21:58:16,938 - pyskl - INFO - Epoch [77][2400/3746] lr: 4.828e-02, eta: 2 days, 13:32:37, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5806, loss_cls: 3.8451, loss: 3.8451 +2024-07-24 21:59:40,190 - pyskl - INFO - Epoch [77][2500/3746] lr: 4.825e-02, eta: 2 days, 13:31:19, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5952, loss_cls: 3.7807, loss: 3.7807 +2024-07-24 22:01:03,043 - pyskl - INFO - Epoch [77][2600/3746] lr: 4.823e-02, eta: 2 days, 13:30:01, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5848, loss_cls: 3.8186, loss: 3.8186 +2024-07-24 22:02:25,943 - pyskl - INFO - Epoch [77][2700/3746] lr: 4.820e-02, eta: 2 days, 13:28:42, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5852, loss_cls: 3.8487, loss: 3.8487 +2024-07-24 22:03:47,762 - pyskl - INFO - Epoch [77][2800/3746] lr: 4.817e-02, eta: 2 days, 13:27:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5897, loss_cls: 3.8008, loss: 3.8008 +2024-07-24 22:05:10,858 - pyskl - INFO - Epoch [77][2900/3746] lr: 4.814e-02, eta: 2 days, 13:26:05, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5863, loss_cls: 3.8513, loss: 3.8513 +2024-07-24 22:06:33,181 - pyskl - INFO - Epoch [77][3000/3746] lr: 4.811e-02, eta: 2 days, 13:24:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5934, loss_cls: 3.7955, loss: 3.7955 +2024-07-24 22:07:55,579 - pyskl - INFO - Epoch [77][3100/3746] lr: 4.809e-02, eta: 2 days, 13:23:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5750, loss_cls: 3.8538, loss: 3.8538 +2024-07-24 22:09:19,389 - pyskl - INFO - Epoch [77][3200/3746] lr: 4.806e-02, eta: 2 days, 13:22:09, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5842, loss_cls: 3.8280, loss: 3.8280 +2024-07-24 22:10:42,898 - pyskl - INFO - Epoch [77][3300/3746] lr: 4.803e-02, eta: 2 days, 13:20:51, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5830, loss_cls: 3.8410, loss: 3.8410 +2024-07-24 22:12:05,748 - pyskl - INFO - Epoch [77][3400/3746] lr: 4.800e-02, eta: 2 days, 13:19:33, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5913, loss_cls: 3.8163, loss: 3.8163 +2024-07-24 22:13:28,363 - pyskl - INFO - Epoch [77][3500/3746] lr: 4.798e-02, eta: 2 days, 13:18:14, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5905, loss_cls: 3.8385, loss: 3.8385 +2024-07-24 22:14:51,070 - pyskl - INFO - Epoch [77][3600/3746] lr: 4.795e-02, eta: 2 days, 13:16:55, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5839, loss_cls: 3.8293, loss: 3.8293 +2024-07-24 22:16:14,216 - pyskl - INFO - Epoch [77][3700/3746] lr: 4.792e-02, eta: 2 days, 13:15:37, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5947, loss_cls: 3.8078, loss: 3.8078 +2024-07-24 22:16:54,159 - pyskl - INFO - Saving checkpoint at 77 epochs +2024-07-24 22:18:47,073 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 22:18:47,734 - pyskl - INFO - +top1_acc 0.2517 +top5_acc 0.5029 +2024-07-24 22:18:47,734 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 22:18:47,773 - pyskl - INFO - +mean_acc 0.2514 +2024-07-24 22:18:47,778 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_72.pth was removed +2024-07-24 22:18:48,044 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_77.pth. +2024-07-24 22:18:48,044 - pyskl - INFO - Best top1_acc is 0.2517 at 77 epoch. +2024-07-24 22:18:48,055 - pyskl - INFO - Epoch(val) [77][309] top1_acc: 0.2517, top5_acc: 0.5029, mean_class_accuracy: 0.2514 +2024-07-24 22:22:40,251 - pyskl - INFO - Epoch [78][100/3746] lr: 4.788e-02, eta: 2 days, 13:15:28, time: 2.322, data_time: 1.323, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5978, loss_cls: 3.7676, loss: 3.7676 +2024-07-24 22:24:04,033 - pyskl - INFO - Epoch [78][200/3746] lr: 4.785e-02, eta: 2 days, 13:14:10, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5991, loss_cls: 3.7664, loss: 3.7664 +2024-07-24 22:25:27,550 - pyskl - INFO - Epoch [78][300/3746] lr: 4.782e-02, eta: 2 days, 13:12:52, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.6047, loss_cls: 3.7148, loss: 3.7148 +2024-07-24 22:26:50,434 - pyskl - INFO - Epoch [78][400/3746] lr: 4.779e-02, eta: 2 days, 13:11:33, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.6002, loss_cls: 3.7749, loss: 3.7749 +2024-07-24 22:28:13,173 - pyskl - INFO - Epoch [78][500/3746] lr: 4.777e-02, eta: 2 days, 13:10:15, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5902, loss_cls: 3.7891, loss: 3.7891 +2024-07-24 22:29:34,798 - pyskl - INFO - Epoch [78][600/3746] lr: 4.774e-02, eta: 2 days, 13:08:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5923, loss_cls: 3.7838, loss: 3.7838 +2024-07-24 22:30:56,864 - pyskl - INFO - Epoch [78][700/3746] lr: 4.771e-02, eta: 2 days, 13:07:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5984, loss_cls: 3.7784, loss: 3.7784 +2024-07-24 22:32:18,940 - pyskl - INFO - Epoch [78][800/3746] lr: 4.768e-02, eta: 2 days, 13:06:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5842, loss_cls: 3.8395, loss: 3.8395 +2024-07-24 22:33:41,195 - pyskl - INFO - Epoch [78][900/3746] lr: 4.766e-02, eta: 2 days, 13:04:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5906, loss_cls: 3.8022, loss: 3.8022 +2024-07-24 22:35:02,879 - pyskl - INFO - Epoch [78][1000/3746] lr: 4.763e-02, eta: 2 days, 13:03:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5916, loss_cls: 3.8552, loss: 3.8552 +2024-07-24 22:36:24,837 - pyskl - INFO - Epoch [78][1100/3746] lr: 4.760e-02, eta: 2 days, 13:02:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5883, loss_cls: 3.8231, loss: 3.8231 +2024-07-24 22:37:46,575 - pyskl - INFO - Epoch [78][1200/3746] lr: 4.757e-02, eta: 2 days, 13:00:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5958, loss_cls: 3.7860, loss: 3.7860 +2024-07-24 22:39:08,301 - pyskl - INFO - Epoch [78][1300/3746] lr: 4.754e-02, eta: 2 days, 12:59:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5817, loss_cls: 3.8469, loss: 3.8469 +2024-07-24 22:40:30,090 - pyskl - INFO - Epoch [78][1400/3746] lr: 4.752e-02, eta: 2 days, 12:58:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5891, loss_cls: 3.8090, loss: 3.8090 +2024-07-24 22:41:51,731 - pyskl - INFO - Epoch [78][1500/3746] lr: 4.749e-02, eta: 2 days, 12:56:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.6027, loss_cls: 3.7422, loss: 3.7422 +2024-07-24 22:43:13,351 - pyskl - INFO - Epoch [78][1600/3746] lr: 4.746e-02, eta: 2 days, 12:55:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.6033, loss_cls: 3.7629, loss: 3.7629 +2024-07-24 22:44:34,755 - pyskl - INFO - Epoch [78][1700/3746] lr: 4.743e-02, eta: 2 days, 12:54:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5913, loss_cls: 3.8025, loss: 3.8025 +2024-07-24 22:45:57,143 - pyskl - INFO - Epoch [78][1800/3746] lr: 4.740e-02, eta: 2 days, 12:53:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5830, loss_cls: 3.8210, loss: 3.8210 +2024-07-24 22:47:19,105 - pyskl - INFO - Epoch [78][1900/3746] lr: 4.738e-02, eta: 2 days, 12:51:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5836, loss_cls: 3.8318, loss: 3.8318 +2024-07-24 22:48:41,192 - pyskl - INFO - Epoch [78][2000/3746] lr: 4.735e-02, eta: 2 days, 12:50:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5864, loss_cls: 3.8124, loss: 3.8124 +2024-07-24 22:50:03,109 - pyskl - INFO - Epoch [78][2100/3746] lr: 4.732e-02, eta: 2 days, 12:49:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5827, loss_cls: 3.8255, loss: 3.8255 +2024-07-24 22:51:24,828 - pyskl - INFO - Epoch [78][2200/3746] lr: 4.729e-02, eta: 2 days, 12:47:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5927, loss_cls: 3.8163, loss: 3.8163 +2024-07-24 22:52:46,402 - pyskl - INFO - Epoch [78][2300/3746] lr: 4.726e-02, eta: 2 days, 12:46:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5845, loss_cls: 3.8831, loss: 3.8831 +2024-07-24 22:54:07,487 - pyskl - INFO - Epoch [78][2400/3746] lr: 4.724e-02, eta: 2 days, 12:45:02, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5933, loss_cls: 3.7941, loss: 3.7941 +2024-07-24 22:55:28,925 - pyskl - INFO - Epoch [78][2500/3746] lr: 4.721e-02, eta: 2 days, 12:43:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5936, loss_cls: 3.7820, loss: 3.7820 +2024-07-24 22:56:50,607 - pyskl - INFO - Epoch [78][2600/3746] lr: 4.718e-02, eta: 2 days, 12:42:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5892, loss_cls: 3.8030, loss: 3.8030 +2024-07-24 22:58:12,695 - pyskl - INFO - Epoch [78][2700/3746] lr: 4.715e-02, eta: 2 days, 12:41:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5877, loss_cls: 3.8218, loss: 3.8218 +2024-07-24 22:59:34,755 - pyskl - INFO - Epoch [78][2800/3746] lr: 4.712e-02, eta: 2 days, 12:39:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5789, loss_cls: 3.8685, loss: 3.8685 +2024-07-24 23:00:56,315 - pyskl - INFO - Epoch [78][2900/3746] lr: 4.710e-02, eta: 2 days, 12:38:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5872, loss_cls: 3.8156, loss: 3.8156 +2024-07-24 23:02:18,640 - pyskl - INFO - Epoch [78][3000/3746] lr: 4.707e-02, eta: 2 days, 12:37:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5800, loss_cls: 3.8857, loss: 3.8857 +2024-07-24 23:03:41,184 - pyskl - INFO - Epoch [78][3100/3746] lr: 4.704e-02, eta: 2 days, 12:35:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5923, loss_cls: 3.7953, loss: 3.7953 +2024-07-24 23:05:03,098 - pyskl - INFO - Epoch [78][3200/3746] lr: 4.701e-02, eta: 2 days, 12:34:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5955, loss_cls: 3.7733, loss: 3.7733 +2024-07-24 23:06:24,795 - pyskl - INFO - Epoch [78][3300/3746] lr: 4.699e-02, eta: 2 days, 12:33:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5753, loss_cls: 3.8667, loss: 3.8667 +2024-07-24 23:07:46,181 - pyskl - INFO - Epoch [78][3400/3746] lr: 4.696e-02, eta: 2 days, 12:31:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5820, loss_cls: 3.8061, loss: 3.8061 +2024-07-24 23:09:07,577 - pyskl - INFO - Epoch [78][3500/3746] lr: 4.693e-02, eta: 2 days, 12:30:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6006, loss_cls: 3.7385, loss: 3.7385 +2024-07-24 23:10:29,714 - pyskl - INFO - Epoch [78][3600/3746] lr: 4.690e-02, eta: 2 days, 12:29:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5958, loss_cls: 3.8142, loss: 3.8142 +2024-07-24 23:11:52,429 - pyskl - INFO - Epoch [78][3700/3746] lr: 4.687e-02, eta: 2 days, 12:27:48, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5781, loss_cls: 3.8600, loss: 3.8600 +2024-07-24 23:12:32,612 - pyskl - INFO - Saving checkpoint at 78 epochs +2024-07-24 23:14:25,584 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 23:14:26,271 - pyskl - INFO - +top1_acc 0.2620 +top5_acc 0.5121 +2024-07-24 23:14:26,271 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 23:14:26,323 - pyskl - INFO - +mean_acc 0.2619 +2024-07-24 23:14:26,328 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_77.pth was removed +2024-07-24 23:14:26,674 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_78.pth. +2024-07-24 23:14:26,675 - pyskl - INFO - Best top1_acc is 0.2620 at 78 epoch. +2024-07-24 23:14:26,687 - pyskl - INFO - Epoch(val) [78][309] top1_acc: 0.2620, top5_acc: 0.5121, mean_class_accuracy: 0.2619 +2024-07-24 23:18:20,063 - pyskl - INFO - Epoch [79][100/3746] lr: 4.683e-02, eta: 2 days, 12:27:36, time: 2.334, data_time: 1.344, memory: 15990, top1_acc: 0.3416, top5_acc: 0.6019, loss_cls: 3.7262, loss: 3.7262 +2024-07-24 23:19:42,370 - pyskl - INFO - Epoch [79][200/3746] lr: 4.680e-02, eta: 2 days, 12:26:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.6017, loss_cls: 3.7447, loss: 3.7447 +2024-07-24 23:21:04,498 - pyskl - INFO - Epoch [79][300/3746] lr: 4.678e-02, eta: 2 days, 12:24:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.6006, loss_cls: 3.7541, loss: 3.7541 +2024-07-24 23:22:26,436 - pyskl - INFO - Epoch [79][400/3746] lr: 4.675e-02, eta: 2 days, 12:23:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.6066, loss_cls: 3.7589, loss: 3.7589 +2024-07-24 23:23:48,374 - pyskl - INFO - Epoch [79][500/3746] lr: 4.672e-02, eta: 2 days, 12:22:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5967, loss_cls: 3.7644, loss: 3.7644 +2024-07-24 23:25:10,330 - pyskl - INFO - Epoch [79][600/3746] lr: 4.669e-02, eta: 2 days, 12:20:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5922, loss_cls: 3.7762, loss: 3.7762 +2024-07-24 23:26:31,931 - pyskl - INFO - Epoch [79][700/3746] lr: 4.667e-02, eta: 2 days, 12:19:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5897, loss_cls: 3.8022, loss: 3.8022 +2024-07-24 23:27:53,547 - pyskl - INFO - Epoch [79][800/3746] lr: 4.664e-02, eta: 2 days, 12:18:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.5947, loss_cls: 3.7635, loss: 3.7635 +2024-07-24 23:29:15,010 - pyskl - INFO - Epoch [79][900/3746] lr: 4.661e-02, eta: 2 days, 12:16:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5877, loss_cls: 3.8285, loss: 3.8285 +2024-07-24 23:30:36,676 - pyskl - INFO - Epoch [79][1000/3746] lr: 4.658e-02, eta: 2 days, 12:15:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5919, loss_cls: 3.8004, loss: 3.8004 +2024-07-24 23:31:58,038 - pyskl - INFO - Epoch [79][1100/3746] lr: 4.655e-02, eta: 2 days, 12:14:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5847, loss_cls: 3.8244, loss: 3.8244 +2024-07-24 23:33:19,434 - pyskl - INFO - Epoch [79][1200/3746] lr: 4.653e-02, eta: 2 days, 12:12:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5958, loss_cls: 3.7586, loss: 3.7586 +2024-07-24 23:34:40,958 - pyskl - INFO - Epoch [79][1300/3746] lr: 4.650e-02, eta: 2 days, 12:11:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.6011, loss_cls: 3.7540, loss: 3.7540 +2024-07-24 23:36:03,103 - pyskl - INFO - Epoch [79][1400/3746] lr: 4.647e-02, eta: 2 days, 12:10:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5938, loss_cls: 3.8139, loss: 3.8139 +2024-07-24 23:37:24,953 - pyskl - INFO - Epoch [79][1500/3746] lr: 4.644e-02, eta: 2 days, 12:09:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5873, loss_cls: 3.7991, loss: 3.7991 +2024-07-24 23:38:47,250 - pyskl - INFO - Epoch [79][1600/3746] lr: 4.641e-02, eta: 2 days, 12:07:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5875, loss_cls: 3.7843, loss: 3.7843 +2024-07-24 23:40:09,079 - pyskl - INFO - Epoch [79][1700/3746] lr: 4.639e-02, eta: 2 days, 12:06:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5855, loss_cls: 3.8587, loss: 3.8587 +2024-07-24 23:41:32,337 - pyskl - INFO - Epoch [79][1800/3746] lr: 4.636e-02, eta: 2 days, 12:05:02, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5959, loss_cls: 3.7752, loss: 3.7752 +2024-07-24 23:42:54,503 - pyskl - INFO - Epoch [79][1900/3746] lr: 4.633e-02, eta: 2 days, 12:03:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5870, loss_cls: 3.8322, loss: 3.8322 +2024-07-24 23:44:16,844 - pyskl - INFO - Epoch [79][2000/3746] lr: 4.630e-02, eta: 2 days, 12:02:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5925, loss_cls: 3.7978, loss: 3.7978 +2024-07-24 23:45:39,084 - pyskl - INFO - Epoch [79][2100/3746] lr: 4.628e-02, eta: 2 days, 12:01:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5895, loss_cls: 3.8175, loss: 3.8175 +2024-07-24 23:47:01,763 - pyskl - INFO - Epoch [79][2200/3746] lr: 4.625e-02, eta: 2 days, 11:59:45, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5864, loss_cls: 3.8159, loss: 3.8159 +2024-07-24 23:48:23,819 - pyskl - INFO - Epoch [79][2300/3746] lr: 4.622e-02, eta: 2 days, 11:58:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5894, loss_cls: 3.8127, loss: 3.8127 +2024-07-24 23:49:45,080 - pyskl - INFO - Epoch [79][2400/3746] lr: 4.619e-02, eta: 2 days, 11:57:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5952, loss_cls: 3.7846, loss: 3.7846 +2024-07-24 23:51:06,404 - pyskl - INFO - Epoch [79][2500/3746] lr: 4.616e-02, eta: 2 days, 11:55:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5939, loss_cls: 3.8223, loss: 3.8223 +2024-07-24 23:52:27,848 - pyskl - INFO - Epoch [79][2600/3746] lr: 4.614e-02, eta: 2 days, 11:54:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5891, loss_cls: 3.8518, loss: 3.8518 +2024-07-24 23:53:49,381 - pyskl - INFO - Epoch [79][2700/3746] lr: 4.611e-02, eta: 2 days, 11:53:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.6002, loss_cls: 3.7587, loss: 3.7587 +2024-07-24 23:55:11,736 - pyskl - INFO - Epoch [79][2800/3746] lr: 4.608e-02, eta: 2 days, 11:51:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5877, loss_cls: 3.8311, loss: 3.8311 +2024-07-24 23:56:33,272 - pyskl - INFO - Epoch [79][2900/3746] lr: 4.605e-02, eta: 2 days, 11:50:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.6003, loss_cls: 3.7490, loss: 3.7490 +2024-07-24 23:57:55,053 - pyskl - INFO - Epoch [79][3000/3746] lr: 4.602e-02, eta: 2 days, 11:49:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5945, loss_cls: 3.7828, loss: 3.7828 +2024-07-24 23:59:16,584 - pyskl - INFO - Epoch [79][3100/3746] lr: 4.600e-02, eta: 2 days, 11:47:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5889, loss_cls: 3.8227, loss: 3.8227 +2024-07-25 00:00:38,078 - pyskl - INFO - Epoch [79][3200/3746] lr: 4.597e-02, eta: 2 days, 11:46:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5994, loss_cls: 3.7646, loss: 3.7646 +2024-07-25 00:01:59,907 - pyskl - INFO - Epoch [79][3300/3746] lr: 4.594e-02, eta: 2 days, 11:45:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5927, loss_cls: 3.7641, loss: 3.7641 +2024-07-25 00:03:21,597 - pyskl - INFO - Epoch [79][3400/3746] lr: 4.591e-02, eta: 2 days, 11:43:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5919, loss_cls: 3.7937, loss: 3.7937 +2024-07-25 00:04:43,138 - pyskl - INFO - Epoch [79][3500/3746] lr: 4.588e-02, eta: 2 days, 11:42:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5864, loss_cls: 3.8003, loss: 3.8003 +2024-07-25 00:06:04,987 - pyskl - INFO - Epoch [79][3600/3746] lr: 4.586e-02, eta: 2 days, 11:41:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5934, loss_cls: 3.8231, loss: 3.8231 +2024-07-25 00:07:26,788 - pyskl - INFO - Epoch [79][3700/3746] lr: 4.583e-02, eta: 2 days, 11:39:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5969, loss_cls: 3.7776, loss: 3.7776 +2024-07-25 00:08:06,513 - pyskl - INFO - Saving checkpoint at 79 epochs +2024-07-25 00:10:00,467 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 00:10:01,198 - pyskl - INFO - +top1_acc 0.2798 +top5_acc 0.5333 +2024-07-25 00:10:01,199 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 00:10:01,253 - pyskl - INFO - +mean_acc 0.2795 +2024-07-25 00:10:01,260 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_78.pth was removed +2024-07-25 00:10:01,532 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_79.pth. +2024-07-25 00:10:01,533 - pyskl - INFO - Best top1_acc is 0.2798 at 79 epoch. +2024-07-25 00:10:01,544 - pyskl - INFO - Epoch(val) [79][309] top1_acc: 0.2798, top5_acc: 0.5333, mean_class_accuracy: 0.2795 +2024-07-25 00:13:52,093 - pyskl - INFO - Epoch [80][100/3746] lr: 4.579e-02, eta: 2 days, 11:39:30, time: 2.305, data_time: 1.313, memory: 15990, top1_acc: 0.3341, top5_acc: 0.6077, loss_cls: 3.7108, loss: 3.7108 +2024-07-25 00:15:14,509 - pyskl - INFO - Epoch [80][200/3746] lr: 4.576e-02, eta: 2 days, 11:38:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6031, loss_cls: 3.7317, loss: 3.7317 +2024-07-25 00:16:36,804 - pyskl - INFO - Epoch [80][300/3746] lr: 4.573e-02, eta: 2 days, 11:36:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5970, loss_cls: 3.7706, loss: 3.7706 +2024-07-25 00:17:58,779 - pyskl - INFO - Epoch [80][400/3746] lr: 4.570e-02, eta: 2 days, 11:35:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.6006, loss_cls: 3.7742, loss: 3.7742 +2024-07-25 00:19:20,508 - pyskl - INFO - Epoch [80][500/3746] lr: 4.568e-02, eta: 2 days, 11:34:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6058, loss_cls: 3.7418, loss: 3.7418 +2024-07-25 00:20:41,553 - pyskl - INFO - Epoch [80][600/3746] lr: 4.565e-02, eta: 2 days, 11:32:51, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5811, loss_cls: 3.8436, loss: 3.8436 +2024-07-25 00:22:03,049 - pyskl - INFO - Epoch [80][700/3746] lr: 4.562e-02, eta: 2 days, 11:31:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5894, loss_cls: 3.8217, loss: 3.8217 +2024-07-25 00:23:24,595 - pyskl - INFO - Epoch [80][800/3746] lr: 4.559e-02, eta: 2 days, 11:30:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5998, loss_cls: 3.7442, loss: 3.7442 +2024-07-25 00:24:46,176 - pyskl - INFO - Epoch [80][900/3746] lr: 4.557e-02, eta: 2 days, 11:28:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5970, loss_cls: 3.7592, loss: 3.7592 +2024-07-25 00:26:07,745 - pyskl - INFO - Epoch [80][1000/3746] lr: 4.554e-02, eta: 2 days, 11:27:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5934, loss_cls: 3.7817, loss: 3.7817 +2024-07-25 00:27:29,141 - pyskl - INFO - Epoch [80][1100/3746] lr: 4.551e-02, eta: 2 days, 11:26:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.5970, loss_cls: 3.7549, loss: 3.7549 +2024-07-25 00:28:51,054 - pyskl - INFO - Epoch [80][1200/3746] lr: 4.548e-02, eta: 2 days, 11:24:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.6009, loss_cls: 3.7332, loss: 3.7332 +2024-07-25 00:30:12,713 - pyskl - INFO - Epoch [80][1300/3746] lr: 4.545e-02, eta: 2 days, 11:23:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.6034, loss_cls: 3.7588, loss: 3.7588 +2024-07-25 00:31:34,373 - pyskl - INFO - Epoch [80][1400/3746] lr: 4.543e-02, eta: 2 days, 11:22:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6138, loss_cls: 3.6750, loss: 3.6750 +2024-07-25 00:32:55,846 - pyskl - INFO - Epoch [80][1500/3746] lr: 4.540e-02, eta: 2 days, 11:20:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5855, loss_cls: 3.7882, loss: 3.7882 +2024-07-25 00:34:17,862 - pyskl - INFO - Epoch [80][1600/3746] lr: 4.537e-02, eta: 2 days, 11:19:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.5997, loss_cls: 3.7587, loss: 3.7587 +2024-07-25 00:35:39,031 - pyskl - INFO - Epoch [80][1700/3746] lr: 4.534e-02, eta: 2 days, 11:18:10, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.6017, loss_cls: 3.7516, loss: 3.7516 +2024-07-25 00:37:01,283 - pyskl - INFO - Epoch [80][1800/3746] lr: 4.532e-02, eta: 2 days, 11:16:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5914, loss_cls: 3.8157, loss: 3.8157 +2024-07-25 00:38:23,259 - pyskl - INFO - Epoch [80][1900/3746] lr: 4.529e-02, eta: 2 days, 11:15:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.5992, loss_cls: 3.7553, loss: 3.7553 +2024-07-25 00:39:45,652 - pyskl - INFO - Epoch [80][2000/3746] lr: 4.526e-02, eta: 2 days, 11:14:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5827, loss_cls: 3.8030, loss: 3.8030 +2024-07-25 00:41:07,878 - pyskl - INFO - Epoch [80][2100/3746] lr: 4.523e-02, eta: 2 days, 11:12:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5914, loss_cls: 3.7839, loss: 3.7839 +2024-07-25 00:42:29,853 - pyskl - INFO - Epoch [80][2200/3746] lr: 4.520e-02, eta: 2 days, 11:11:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5877, loss_cls: 3.8097, loss: 3.8097 +2024-07-25 00:43:51,575 - pyskl - INFO - Epoch [80][2300/3746] lr: 4.518e-02, eta: 2 days, 11:10:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5867, loss_cls: 3.8459, loss: 3.8459 +2024-07-25 00:45:13,151 - pyskl - INFO - Epoch [80][2400/3746] lr: 4.515e-02, eta: 2 days, 11:08:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5944, loss_cls: 3.7739, loss: 3.7739 +2024-07-25 00:46:34,585 - pyskl - INFO - Epoch [80][2500/3746] lr: 4.512e-02, eta: 2 days, 11:07:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5847, loss_cls: 3.8098, loss: 3.8098 +2024-07-25 00:47:56,276 - pyskl - INFO - Epoch [80][2600/3746] lr: 4.509e-02, eta: 2 days, 11:06:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5928, loss_cls: 3.7685, loss: 3.7685 +2024-07-25 00:49:17,983 - pyskl - INFO - Epoch [80][2700/3746] lr: 4.506e-02, eta: 2 days, 11:04:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5878, loss_cls: 3.8316, loss: 3.8316 +2024-07-25 00:50:40,293 - pyskl - INFO - Epoch [80][2800/3746] lr: 4.504e-02, eta: 2 days, 11:03:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5964, loss_cls: 3.7530, loss: 3.7530 +2024-07-25 00:52:01,972 - pyskl - INFO - Epoch [80][2900/3746] lr: 4.501e-02, eta: 2 days, 11:02:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5961, loss_cls: 3.7949, loss: 3.7949 +2024-07-25 00:53:23,834 - pyskl - INFO - Epoch [80][3000/3746] lr: 4.498e-02, eta: 2 days, 11:00:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5839, loss_cls: 3.7891, loss: 3.7891 +2024-07-25 00:54:45,097 - pyskl - INFO - Epoch [80][3100/3746] lr: 4.495e-02, eta: 2 days, 10:59:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5950, loss_cls: 3.7665, loss: 3.7665 +2024-07-25 00:56:06,596 - pyskl - INFO - Epoch [80][3200/3746] lr: 4.493e-02, eta: 2 days, 10:58:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5927, loss_cls: 3.7986, loss: 3.7986 +2024-07-25 00:57:28,371 - pyskl - INFO - Epoch [80][3300/3746] lr: 4.490e-02, eta: 2 days, 10:56:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5873, loss_cls: 3.8144, loss: 3.8144 +2024-07-25 00:58:49,884 - pyskl - INFO - Epoch [80][3400/3746] lr: 4.487e-02, eta: 2 days, 10:55:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.6036, loss_cls: 3.7661, loss: 3.7661 +2024-07-25 01:00:11,288 - pyskl - INFO - Epoch [80][3500/3746] lr: 4.484e-02, eta: 2 days, 10:54:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5842, loss_cls: 3.8163, loss: 3.8163 +2024-07-25 01:01:33,203 - pyskl - INFO - Epoch [80][3600/3746] lr: 4.481e-02, eta: 2 days, 10:52:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5805, loss_cls: 3.8392, loss: 3.8392 +2024-07-25 01:02:55,567 - pyskl - INFO - Epoch [80][3700/3746] lr: 4.479e-02, eta: 2 days, 10:51:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5952, loss_cls: 3.7966, loss: 3.7966 +2024-07-25 01:03:35,176 - pyskl - INFO - Saving checkpoint at 80 epochs +2024-07-25 01:05:29,379 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 01:05:30,086 - pyskl - INFO - +top1_acc 0.2721 +top5_acc 0.5309 +2024-07-25 01:05:30,086 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 01:05:30,147 - pyskl - INFO - +mean_acc 0.2722 +2024-07-25 01:05:30,164 - pyskl - INFO - Epoch(val) [80][309] top1_acc: 0.2721, top5_acc: 0.5309, mean_class_accuracy: 0.2722 +2024-07-25 01:09:16,645 - pyskl - INFO - Epoch [81][100/3746] lr: 4.475e-02, eta: 2 days, 10:51:09, time: 2.265, data_time: 1.276, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6045, loss_cls: 3.7204, loss: 3.7204 +2024-07-25 01:10:39,944 - pyskl - INFO - Epoch [81][200/3746] lr: 4.472e-02, eta: 2 days, 10:49:51, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6161, loss_cls: 3.6793, loss: 3.6793 +2024-07-25 01:12:03,122 - pyskl - INFO - Epoch [81][300/3746] lr: 4.469e-02, eta: 2 days, 10:48:32, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.6066, loss_cls: 3.7191, loss: 3.7191 +2024-07-25 01:13:25,720 - pyskl - INFO - Epoch [81][400/3746] lr: 4.466e-02, eta: 2 days, 10:47:13, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6045, loss_cls: 3.7185, loss: 3.7185 +2024-07-25 01:14:47,665 - pyskl - INFO - Epoch [81][500/3746] lr: 4.463e-02, eta: 2 days, 10:45:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5939, loss_cls: 3.7631, loss: 3.7631 +2024-07-25 01:16:09,401 - pyskl - INFO - Epoch [81][600/3746] lr: 4.461e-02, eta: 2 days, 10:44:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5962, loss_cls: 3.7863, loss: 3.7863 +2024-07-25 01:17:31,474 - pyskl - INFO - Epoch [81][700/3746] lr: 4.458e-02, eta: 2 days, 10:43:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5981, loss_cls: 3.7703, loss: 3.7703 +2024-07-25 01:18:53,121 - pyskl - INFO - Epoch [81][800/3746] lr: 4.455e-02, eta: 2 days, 10:41:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5925, loss_cls: 3.7838, loss: 3.7838 +2024-07-25 01:20:15,112 - pyskl - INFO - Epoch [81][900/3746] lr: 4.452e-02, eta: 2 days, 10:40:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.5945, loss_cls: 3.7583, loss: 3.7583 +2024-07-25 01:21:37,073 - pyskl - INFO - Epoch [81][1000/3746] lr: 4.450e-02, eta: 2 days, 10:39:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6062, loss_cls: 3.7314, loss: 3.7314 +2024-07-25 01:22:58,485 - pyskl - INFO - Epoch [81][1100/3746] lr: 4.447e-02, eta: 2 days, 10:37:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5997, loss_cls: 3.7419, loss: 3.7419 +2024-07-25 01:24:20,574 - pyskl - INFO - Epoch [81][1200/3746] lr: 4.444e-02, eta: 2 days, 10:36:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5938, loss_cls: 3.7787, loss: 3.7787 +2024-07-25 01:25:42,295 - pyskl - INFO - Epoch [81][1300/3746] lr: 4.441e-02, eta: 2 days, 10:35:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5866, loss_cls: 3.8147, loss: 3.8147 +2024-07-25 01:27:03,648 - pyskl - INFO - Epoch [81][1400/3746] lr: 4.438e-02, eta: 2 days, 10:33:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.6039, loss_cls: 3.7462, loss: 3.7462 +2024-07-25 01:28:25,259 - pyskl - INFO - Epoch [81][1500/3746] lr: 4.436e-02, eta: 2 days, 10:32:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5988, loss_cls: 3.7795, loss: 3.7795 +2024-07-25 01:29:46,863 - pyskl - INFO - Epoch [81][1600/3746] lr: 4.433e-02, eta: 2 days, 10:31:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5859, loss_cls: 3.8053, loss: 3.8053 +2024-07-25 01:31:08,268 - pyskl - INFO - Epoch [81][1700/3746] lr: 4.430e-02, eta: 2 days, 10:29:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5983, loss_cls: 3.7793, loss: 3.7793 +2024-07-25 01:32:30,245 - pyskl - INFO - Epoch [81][1800/3746] lr: 4.427e-02, eta: 2 days, 10:28:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5903, loss_cls: 3.7898, loss: 3.7898 +2024-07-25 01:33:51,859 - pyskl - INFO - Epoch [81][1900/3746] lr: 4.425e-02, eta: 2 days, 10:27:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5939, loss_cls: 3.7833, loss: 3.7833 +2024-07-25 01:35:14,854 - pyskl - INFO - Epoch [81][2000/3746] lr: 4.422e-02, eta: 2 days, 10:25:53, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5913, loss_cls: 3.7751, loss: 3.7751 +2024-07-25 01:36:36,692 - pyskl - INFO - Epoch [81][2100/3746] lr: 4.419e-02, eta: 2 days, 10:24:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.6008, loss_cls: 3.7524, loss: 3.7524 +2024-07-25 01:37:58,225 - pyskl - INFO - Epoch [81][2200/3746] lr: 4.416e-02, eta: 2 days, 10:23:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5997, loss_cls: 3.7732, loss: 3.7732 +2024-07-25 01:39:20,587 - pyskl - INFO - Epoch [81][2300/3746] lr: 4.413e-02, eta: 2 days, 10:21:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5939, loss_cls: 3.7692, loss: 3.7692 +2024-07-25 01:40:42,379 - pyskl - INFO - Epoch [81][2400/3746] lr: 4.411e-02, eta: 2 days, 10:20:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6109, loss_cls: 3.7067, loss: 3.7067 +2024-07-25 01:42:04,992 - pyskl - INFO - Epoch [81][2500/3746] lr: 4.408e-02, eta: 2 days, 10:19:14, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.6002, loss_cls: 3.7323, loss: 3.7323 +2024-07-25 01:43:27,273 - pyskl - INFO - Epoch [81][2600/3746] lr: 4.405e-02, eta: 2 days, 10:17:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5962, loss_cls: 3.7667, loss: 3.7667 +2024-07-25 01:44:48,649 - pyskl - INFO - Epoch [81][2700/3746] lr: 4.402e-02, eta: 2 days, 10:16:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5920, loss_cls: 3.7826, loss: 3.7826 +2024-07-25 01:46:10,847 - pyskl - INFO - Epoch [81][2800/3746] lr: 4.400e-02, eta: 2 days, 10:15:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5855, loss_cls: 3.8401, loss: 3.8401 +2024-07-25 01:47:32,147 - pyskl - INFO - Epoch [81][2900/3746] lr: 4.397e-02, eta: 2 days, 10:13:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5970, loss_cls: 3.7560, loss: 3.7560 +2024-07-25 01:48:53,607 - pyskl - INFO - Epoch [81][3000/3746] lr: 4.394e-02, eta: 2 days, 10:12:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5919, loss_cls: 3.7726, loss: 3.7726 +2024-07-25 01:50:16,123 - pyskl - INFO - Epoch [81][3100/3746] lr: 4.391e-02, eta: 2 days, 10:11:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5911, loss_cls: 3.7801, loss: 3.7801 +2024-07-25 01:51:38,185 - pyskl - INFO - Epoch [81][3200/3746] lr: 4.389e-02, eta: 2 days, 10:09:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5989, loss_cls: 3.7482, loss: 3.7482 +2024-07-25 01:53:00,165 - pyskl - INFO - Epoch [81][3300/3746] lr: 4.386e-02, eta: 2 days, 10:08:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5855, loss_cls: 3.8185, loss: 3.8185 +2024-07-25 01:54:22,190 - pyskl - INFO - Epoch [81][3400/3746] lr: 4.383e-02, eta: 2 days, 10:07:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6081, loss_cls: 3.7172, loss: 3.7172 +2024-07-25 01:55:43,589 - pyskl - INFO - Epoch [81][3500/3746] lr: 4.380e-02, eta: 2 days, 10:05:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5995, loss_cls: 3.7878, loss: 3.7878 +2024-07-25 01:57:05,800 - pyskl - INFO - Epoch [81][3600/3746] lr: 4.377e-02, eta: 2 days, 10:04:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5944, loss_cls: 3.7641, loss: 3.7641 +2024-07-25 01:58:27,741 - pyskl - INFO - Epoch [81][3700/3746] lr: 4.375e-02, eta: 2 days, 10:03:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5850, loss_cls: 3.8251, loss: 3.8251 +2024-07-25 01:59:07,666 - pyskl - INFO - Saving checkpoint at 81 epochs +2024-07-25 02:01:00,216 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 02:01:00,913 - pyskl - INFO - +top1_acc 0.2640 +top5_acc 0.5166 +2024-07-25 02:01:00,913 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 02:01:00,962 - pyskl - INFO - +mean_acc 0.2639 +2024-07-25 02:01:00,974 - pyskl - INFO - Epoch(val) [81][309] top1_acc: 0.2640, top5_acc: 0.5166, mean_class_accuracy: 0.2639 +2024-07-25 02:04:50,452 - pyskl - INFO - Epoch [82][100/3746] lr: 4.371e-02, eta: 2 days, 10:02:52, time: 2.295, data_time: 1.309, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6052, loss_cls: 3.7028, loss: 3.7028 +2024-07-25 02:06:13,082 - pyskl - INFO - Epoch [82][200/3746] lr: 4.368e-02, eta: 2 days, 10:01:32, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6033, loss_cls: 3.7256, loss: 3.7256 +2024-07-25 02:07:35,347 - pyskl - INFO - Epoch [82][300/3746] lr: 4.365e-02, eta: 2 days, 10:00:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5983, loss_cls: 3.7260, loss: 3.7260 +2024-07-25 02:08:57,607 - pyskl - INFO - Epoch [82][400/3746] lr: 4.362e-02, eta: 2 days, 9:58:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6112, loss_cls: 3.6926, loss: 3.6926 +2024-07-25 02:10:19,478 - pyskl - INFO - Epoch [82][500/3746] lr: 4.359e-02, eta: 2 days, 9:57:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6066, loss_cls: 3.7144, loss: 3.7144 +2024-07-25 02:11:41,405 - pyskl - INFO - Epoch [82][600/3746] lr: 4.357e-02, eta: 2 days, 9:56:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.5981, loss_cls: 3.7552, loss: 3.7552 +2024-07-25 02:13:02,650 - pyskl - INFO - Epoch [82][700/3746] lr: 4.354e-02, eta: 2 days, 9:54:52, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6094, loss_cls: 3.6961, loss: 3.6961 +2024-07-25 02:14:24,838 - pyskl - INFO - Epoch [82][800/3746] lr: 4.351e-02, eta: 2 days, 9:53:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.5983, loss_cls: 3.7423, loss: 3.7423 +2024-07-25 02:15:46,330 - pyskl - INFO - Epoch [82][900/3746] lr: 4.348e-02, eta: 2 days, 9:52:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5944, loss_cls: 3.7960, loss: 3.7960 +2024-07-25 02:17:08,204 - pyskl - INFO - Epoch [82][1000/3746] lr: 4.346e-02, eta: 2 days, 9:50:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5973, loss_cls: 3.7359, loss: 3.7359 +2024-07-25 02:18:30,484 - pyskl - INFO - Epoch [82][1100/3746] lr: 4.343e-02, eta: 2 days, 9:49:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5878, loss_cls: 3.7794, loss: 3.7794 +2024-07-25 02:19:52,666 - pyskl - INFO - Epoch [82][1200/3746] lr: 4.340e-02, eta: 2 days, 9:48:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.6000, loss_cls: 3.7573, loss: 3.7573 +2024-07-25 02:21:14,680 - pyskl - INFO - Epoch [82][1300/3746] lr: 4.337e-02, eta: 2 days, 9:46:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6036, loss_cls: 3.7365, loss: 3.7365 +2024-07-25 02:22:37,030 - pyskl - INFO - Epoch [82][1400/3746] lr: 4.335e-02, eta: 2 days, 9:45:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.6005, loss_cls: 3.7283, loss: 3.7283 +2024-07-25 02:23:59,433 - pyskl - INFO - Epoch [82][1500/3746] lr: 4.332e-02, eta: 2 days, 9:44:13, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.5878, loss_cls: 3.7741, loss: 3.7741 +2024-07-25 02:25:22,421 - pyskl - INFO - Epoch [82][1600/3746] lr: 4.329e-02, eta: 2 days, 9:42:54, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5902, loss_cls: 3.7842, loss: 3.7842 +2024-07-25 02:26:44,452 - pyskl - INFO - Epoch [82][1700/3746] lr: 4.326e-02, eta: 2 days, 9:41:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.5923, loss_cls: 3.7555, loss: 3.7555 +2024-07-25 02:28:06,614 - pyskl - INFO - Epoch [82][1800/3746] lr: 4.323e-02, eta: 2 days, 9:40:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.6000, loss_cls: 3.7488, loss: 3.7488 +2024-07-25 02:29:29,422 - pyskl - INFO - Epoch [82][1900/3746] lr: 4.321e-02, eta: 2 days, 9:38:55, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5967, loss_cls: 3.7632, loss: 3.7632 +2024-07-25 02:30:52,590 - pyskl - INFO - Epoch [82][2000/3746] lr: 4.318e-02, eta: 2 days, 9:37:36, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5991, loss_cls: 3.7839, loss: 3.7839 +2024-07-25 02:32:15,806 - pyskl - INFO - Epoch [82][2100/3746] lr: 4.315e-02, eta: 2 days, 9:36:17, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.6012, loss_cls: 3.7402, loss: 3.7402 +2024-07-25 02:33:38,886 - pyskl - INFO - Epoch [82][2200/3746] lr: 4.312e-02, eta: 2 days, 9:34:58, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6097, loss_cls: 3.6906, loss: 3.6906 +2024-07-25 02:35:02,057 - pyskl - INFO - Epoch [82][2300/3746] lr: 4.310e-02, eta: 2 days, 9:33:39, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.5989, loss_cls: 3.7452, loss: 3.7452 +2024-07-25 02:36:25,381 - pyskl - INFO - Epoch [82][2400/3746] lr: 4.307e-02, eta: 2 days, 9:32:20, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5900, loss_cls: 3.8071, loss: 3.8071 +2024-07-25 02:37:48,734 - pyskl - INFO - Epoch [82][2500/3746] lr: 4.304e-02, eta: 2 days, 9:31:02, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6005, loss_cls: 3.7243, loss: 3.7243 +2024-07-25 02:39:12,458 - pyskl - INFO - Epoch [82][2600/3746] lr: 4.301e-02, eta: 2 days, 9:29:43, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5927, loss_cls: 3.7716, loss: 3.7716 +2024-07-25 02:40:35,370 - pyskl - INFO - Epoch [82][2700/3746] lr: 4.299e-02, eta: 2 days, 9:28:24, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5923, loss_cls: 3.7725, loss: 3.7725 +2024-07-25 02:41:58,383 - pyskl - INFO - Epoch [82][2800/3746] lr: 4.296e-02, eta: 2 days, 9:27:05, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5906, loss_cls: 3.7971, loss: 3.7971 +2024-07-25 02:43:20,882 - pyskl - INFO - Epoch [82][2900/3746] lr: 4.293e-02, eta: 2 days, 9:25:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.6017, loss_cls: 3.7510, loss: 3.7510 +2024-07-25 02:44:42,819 - pyskl - INFO - Epoch [82][3000/3746] lr: 4.290e-02, eta: 2 days, 9:24:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6070, loss_cls: 3.7269, loss: 3.7269 +2024-07-25 02:46:05,280 - pyskl - INFO - Epoch [82][3100/3746] lr: 4.287e-02, eta: 2 days, 9:23:05, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5991, loss_cls: 3.7721, loss: 3.7721 +2024-07-25 02:47:28,408 - pyskl - INFO - Epoch [82][3200/3746] lr: 4.285e-02, eta: 2 days, 9:21:46, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5956, loss_cls: 3.8024, loss: 3.8024 +2024-07-25 02:48:51,707 - pyskl - INFO - Epoch [82][3300/3746] lr: 4.282e-02, eta: 2 days, 9:20:28, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5819, loss_cls: 3.8332, loss: 3.8332 +2024-07-25 02:50:15,053 - pyskl - INFO - Epoch [82][3400/3746] lr: 4.279e-02, eta: 2 days, 9:19:09, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6122, loss_cls: 3.7027, loss: 3.7027 +2024-07-25 02:51:38,549 - pyskl - INFO - Epoch [82][3500/3746] lr: 4.276e-02, eta: 2 days, 9:17:50, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5986, loss_cls: 3.7772, loss: 3.7772 +2024-07-25 02:53:01,784 - pyskl - INFO - Epoch [82][3600/3746] lr: 4.274e-02, eta: 2 days, 9:16:31, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.6022, loss_cls: 3.7421, loss: 3.7421 +2024-07-25 02:54:24,857 - pyskl - INFO - Epoch [82][3700/3746] lr: 4.271e-02, eta: 2 days, 9:15:12, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.6044, loss_cls: 3.7618, loss: 3.7618 +2024-07-25 02:55:04,735 - pyskl - INFO - Saving checkpoint at 82 epochs +2024-07-25 02:56:57,600 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 02:56:58,281 - pyskl - INFO - +top1_acc 0.2904 +top5_acc 0.5376 +2024-07-25 02:56:58,281 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 02:56:58,319 - pyskl - INFO - +mean_acc 0.2903 +2024-07-25 02:56:58,324 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_79.pth was removed +2024-07-25 02:56:58,580 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_82.pth. +2024-07-25 02:56:58,580 - pyskl - INFO - Best top1_acc is 0.2904 at 82 epoch. +2024-07-25 02:56:58,590 - pyskl - INFO - Epoch(val) [82][309] top1_acc: 0.2904, top5_acc: 0.5376, mean_class_accuracy: 0.2903 +2024-07-25 03:00:45,185 - pyskl - INFO - Epoch [83][100/3746] lr: 4.267e-02, eta: 2 days, 9:14:44, time: 2.266, data_time: 1.275, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6123, loss_cls: 3.6849, loss: 3.6849 +2024-07-25 03:02:08,451 - pyskl - INFO - Epoch [83][200/3746] lr: 4.264e-02, eta: 2 days, 9:13:25, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5998, loss_cls: 3.7519, loss: 3.7519 +2024-07-25 03:03:31,347 - pyskl - INFO - Epoch [83][300/3746] lr: 4.261e-02, eta: 2 days, 9:12:05, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6219, loss_cls: 3.6270, loss: 3.6270 +2024-07-25 03:04:53,414 - pyskl - INFO - Epoch [83][400/3746] lr: 4.259e-02, eta: 2 days, 9:10:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6012, loss_cls: 3.7277, loss: 3.7277 +2024-07-25 03:06:15,874 - pyskl - INFO - Epoch [83][500/3746] lr: 4.256e-02, eta: 2 days, 9:09:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6023, loss_cls: 3.7086, loss: 3.7086 +2024-07-25 03:07:37,855 - pyskl - INFO - Epoch [83][600/3746] lr: 4.253e-02, eta: 2 days, 9:08:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6059, loss_cls: 3.7299, loss: 3.7299 +2024-07-25 03:08:59,984 - pyskl - INFO - Epoch [83][700/3746] lr: 4.250e-02, eta: 2 days, 9:06:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5981, loss_cls: 3.7499, loss: 3.7499 +2024-07-25 03:10:22,125 - pyskl - INFO - Epoch [83][800/3746] lr: 4.247e-02, eta: 2 days, 9:05:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.6016, loss_cls: 3.7222, loss: 3.7222 +2024-07-25 03:11:44,933 - pyskl - INFO - Epoch [83][900/3746] lr: 4.245e-02, eta: 2 days, 9:04:06, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.6092, loss_cls: 3.7277, loss: 3.7277 +2024-07-25 03:13:06,724 - pyskl - INFO - Epoch [83][1000/3746] lr: 4.242e-02, eta: 2 days, 9:02:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.6052, loss_cls: 3.7170, loss: 3.7170 +2024-07-25 03:14:28,852 - pyskl - INFO - Epoch [83][1100/3746] lr: 4.239e-02, eta: 2 days, 9:01:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.6052, loss_cls: 3.6862, loss: 3.6862 +2024-07-25 03:15:51,155 - pyskl - INFO - Epoch [83][1200/3746] lr: 4.236e-02, eta: 2 days, 9:00:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5877, loss_cls: 3.7968, loss: 3.7968 +2024-07-25 03:17:13,189 - pyskl - INFO - Epoch [83][1300/3746] lr: 4.234e-02, eta: 2 days, 8:58:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6081, loss_cls: 3.7087, loss: 3.7087 +2024-07-25 03:18:35,458 - pyskl - INFO - Epoch [83][1400/3746] lr: 4.231e-02, eta: 2 days, 8:57:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.6025, loss_cls: 3.7404, loss: 3.7404 +2024-07-25 03:19:57,367 - pyskl - INFO - Epoch [83][1500/3746] lr: 4.228e-02, eta: 2 days, 8:56:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5930, loss_cls: 3.7561, loss: 3.7561 +2024-07-25 03:21:19,504 - pyskl - INFO - Epoch [83][1600/3746] lr: 4.225e-02, eta: 2 days, 8:54:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.5956, loss_cls: 3.7573, loss: 3.7573 +2024-07-25 03:22:42,031 - pyskl - INFO - Epoch [83][1700/3746] lr: 4.223e-02, eta: 2 days, 8:53:27, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.6005, loss_cls: 3.7528, loss: 3.7528 +2024-07-25 03:24:04,312 - pyskl - INFO - Epoch [83][1800/3746] lr: 4.220e-02, eta: 2 days, 8:52:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6042, loss_cls: 3.7417, loss: 3.7417 +2024-07-25 03:25:27,030 - pyskl - INFO - Epoch [83][1900/3746] lr: 4.217e-02, eta: 2 days, 8:50:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5928, loss_cls: 3.7804, loss: 3.7804 +2024-07-25 03:26:49,604 - pyskl - INFO - Epoch [83][2000/3746] lr: 4.214e-02, eta: 2 days, 8:49:28, time: 0.826, data_time: 0.001, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6033, loss_cls: 3.7310, loss: 3.7310 +2024-07-25 03:28:12,106 - pyskl - INFO - Epoch [83][2100/3746] lr: 4.212e-02, eta: 2 days, 8:48:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.5975, loss_cls: 3.7246, loss: 3.7246 +2024-07-25 03:29:34,419 - pyskl - INFO - Epoch [83][2200/3746] lr: 4.209e-02, eta: 2 days, 8:46:48, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.6009, loss_cls: 3.7212, loss: 3.7212 +2024-07-25 03:30:56,660 - pyskl - INFO - Epoch [83][2300/3746] lr: 4.206e-02, eta: 2 days, 8:45:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5875, loss_cls: 3.8017, loss: 3.8017 +2024-07-25 03:32:18,588 - pyskl - INFO - Epoch [83][2400/3746] lr: 4.203e-02, eta: 2 days, 8:44:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.6008, loss_cls: 3.7633, loss: 3.7633 +2024-07-25 03:33:39,948 - pyskl - INFO - Epoch [83][2500/3746] lr: 4.201e-02, eta: 2 days, 8:42:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.6028, loss_cls: 3.7568, loss: 3.7568 +2024-07-25 03:35:02,103 - pyskl - INFO - Epoch [83][2600/3746] lr: 4.198e-02, eta: 2 days, 8:41:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6044, loss_cls: 3.7132, loss: 3.7132 +2024-07-25 03:36:23,743 - pyskl - INFO - Epoch [83][2700/3746] lr: 4.195e-02, eta: 2 days, 8:40:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5980, loss_cls: 3.7543, loss: 3.7543 +2024-07-25 03:37:46,634 - pyskl - INFO - Epoch [83][2800/3746] lr: 4.192e-02, eta: 2 days, 8:38:48, time: 0.829, data_time: 0.001, memory: 15990, top1_acc: 0.3344, top5_acc: 0.6042, loss_cls: 3.7428, loss: 3.7428 +2024-07-25 03:39:07,990 - pyskl - INFO - Epoch [83][2900/3746] lr: 4.190e-02, eta: 2 days, 8:37:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.6002, loss_cls: 3.7637, loss: 3.7637 +2024-07-25 03:40:29,855 - pyskl - INFO - Epoch [83][3000/3746] lr: 4.187e-02, eta: 2 days, 8:36:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6050, loss_cls: 3.7690, loss: 3.7690 +2024-07-25 03:41:52,514 - pyskl - INFO - Epoch [83][3100/3746] lr: 4.184e-02, eta: 2 days, 8:34:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5897, loss_cls: 3.7506, loss: 3.7506 +2024-07-25 03:43:14,646 - pyskl - INFO - Epoch [83][3200/3746] lr: 4.181e-02, eta: 2 days, 8:33:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5883, loss_cls: 3.8007, loss: 3.8007 +2024-07-25 03:44:36,683 - pyskl - INFO - Epoch [83][3300/3746] lr: 4.178e-02, eta: 2 days, 8:32:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.6053, loss_cls: 3.7264, loss: 3.7264 +2024-07-25 03:45:58,939 - pyskl - INFO - Epoch [83][3400/3746] lr: 4.176e-02, eta: 2 days, 8:30:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5992, loss_cls: 3.7686, loss: 3.7686 +2024-07-25 03:47:20,576 - pyskl - INFO - Epoch [83][3500/3746] lr: 4.173e-02, eta: 2 days, 8:29:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5997, loss_cls: 3.7542, loss: 3.7542 +2024-07-25 03:48:43,956 - pyskl - INFO - Epoch [83][3600/3746] lr: 4.170e-02, eta: 2 days, 8:28:08, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6008, loss_cls: 3.7598, loss: 3.7598 +2024-07-25 03:50:06,515 - pyskl - INFO - Epoch [83][3700/3746] lr: 4.167e-02, eta: 2 days, 8:26:48, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5970, loss_cls: 3.7611, loss: 3.7611 +2024-07-25 03:50:45,588 - pyskl - INFO - Saving checkpoint at 83 epochs +2024-07-25 03:52:37,353 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 03:52:38,018 - pyskl - INFO - +top1_acc 0.2844 +top5_acc 0.5424 +2024-07-25 03:52:38,018 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 03:52:38,057 - pyskl - INFO - +mean_acc 0.2841 +2024-07-25 03:52:38,068 - pyskl - INFO - Epoch(val) [83][309] top1_acc: 0.2844, top5_acc: 0.5424, mean_class_accuracy: 0.2841 +2024-07-25 03:56:21,183 - pyskl - INFO - Epoch [84][100/3746] lr: 4.163e-02, eta: 2 days, 8:26:15, time: 2.231, data_time: 1.243, memory: 15990, top1_acc: 0.3397, top5_acc: 0.6097, loss_cls: 3.6936, loss: 3.6936 +2024-07-25 03:57:43,410 - pyskl - INFO - Epoch [84][200/3746] lr: 4.161e-02, eta: 2 days, 8:24:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6016, loss_cls: 3.7258, loss: 3.7258 +2024-07-25 03:59:05,932 - pyskl - INFO - Epoch [84][300/3746] lr: 4.158e-02, eta: 2 days, 8:23:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6067, loss_cls: 3.7025, loss: 3.7025 +2024-07-25 04:00:28,412 - pyskl - INFO - Epoch [84][400/3746] lr: 4.155e-02, eta: 2 days, 8:22:15, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.5992, loss_cls: 3.7038, loss: 3.7038 +2024-07-25 04:01:50,017 - pyskl - INFO - Epoch [84][500/3746] lr: 4.152e-02, eta: 2 days, 8:20:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6161, loss_cls: 3.6517, loss: 3.6517 +2024-07-25 04:03:12,460 - pyskl - INFO - Epoch [84][600/3746] lr: 4.150e-02, eta: 2 days, 8:19:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6105, loss_cls: 3.6735, loss: 3.6735 +2024-07-25 04:04:34,841 - pyskl - INFO - Epoch [84][700/3746] lr: 4.147e-02, eta: 2 days, 8:18:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.6045, loss_cls: 3.7247, loss: 3.7247 +2024-07-25 04:05:56,999 - pyskl - INFO - Epoch [84][800/3746] lr: 4.144e-02, eta: 2 days, 8:16:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6028, loss_cls: 3.6923, loss: 3.6923 +2024-07-25 04:07:18,886 - pyskl - INFO - Epoch [84][900/3746] lr: 4.141e-02, eta: 2 days, 8:15:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.6088, loss_cls: 3.7108, loss: 3.7108 +2024-07-25 04:08:40,966 - pyskl - INFO - Epoch [84][1000/3746] lr: 4.139e-02, eta: 2 days, 8:14:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6086, loss_cls: 3.6931, loss: 3.6931 +2024-07-25 04:10:03,132 - pyskl - INFO - Epoch [84][1100/3746] lr: 4.136e-02, eta: 2 days, 8:12:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6050, loss_cls: 3.7136, loss: 3.7136 +2024-07-25 04:11:25,021 - pyskl - INFO - Epoch [84][1200/3746] lr: 4.133e-02, eta: 2 days, 8:11:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6062, loss_cls: 3.6980, loss: 3.6980 +2024-07-25 04:12:47,055 - pyskl - INFO - Epoch [84][1300/3746] lr: 4.130e-02, eta: 2 days, 8:10:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6061, loss_cls: 3.7010, loss: 3.7010 +2024-07-25 04:14:09,417 - pyskl - INFO - Epoch [84][1400/3746] lr: 4.128e-02, eta: 2 days, 8:08:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.6005, loss_cls: 3.7557, loss: 3.7557 +2024-07-25 04:15:31,727 - pyskl - INFO - Epoch [84][1500/3746] lr: 4.125e-02, eta: 2 days, 8:07:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.6020, loss_cls: 3.7587, loss: 3.7587 +2024-07-25 04:16:53,457 - pyskl - INFO - Epoch [84][1600/3746] lr: 4.122e-02, eta: 2 days, 8:06:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5997, loss_cls: 3.7227, loss: 3.7227 +2024-07-25 04:18:15,345 - pyskl - INFO - Epoch [84][1700/3746] lr: 4.119e-02, eta: 2 days, 8:04:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6072, loss_cls: 3.6901, loss: 3.6901 +2024-07-25 04:19:37,512 - pyskl - INFO - Epoch [84][1800/3746] lr: 4.117e-02, eta: 2 days, 8:03:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5883, loss_cls: 3.7744, loss: 3.7744 +2024-07-25 04:21:01,034 - pyskl - INFO - Epoch [84][1900/3746] lr: 4.114e-02, eta: 2 days, 8:02:15, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.6084, loss_cls: 3.7286, loss: 3.7286 +2024-07-25 04:22:24,108 - pyskl - INFO - Epoch [84][2000/3746] lr: 4.111e-02, eta: 2 days, 8:00:55, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5970, loss_cls: 3.7496, loss: 3.7496 +2024-07-25 04:23:46,557 - pyskl - INFO - Epoch [84][2100/3746] lr: 4.108e-02, eta: 2 days, 7:59:36, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5994, loss_cls: 3.7462, loss: 3.7462 +2024-07-25 04:25:09,961 - pyskl - INFO - Epoch [84][2200/3746] lr: 4.106e-02, eta: 2 days, 7:58:17, time: 0.834, data_time: 0.001, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5992, loss_cls: 3.7746, loss: 3.7746 +2024-07-25 04:26:33,487 - pyskl - INFO - Epoch [84][2300/3746] lr: 4.103e-02, eta: 2 days, 7:56:58, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6056, loss_cls: 3.7003, loss: 3.7003 +2024-07-25 04:27:56,886 - pyskl - INFO - Epoch [84][2400/3746] lr: 4.100e-02, eta: 2 days, 7:55:38, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5875, loss_cls: 3.8064, loss: 3.8064 +2024-07-25 04:29:20,095 - pyskl - INFO - Epoch [84][2500/3746] lr: 4.097e-02, eta: 2 days, 7:54:19, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.6005, loss_cls: 3.7753, loss: 3.7753 +2024-07-25 04:30:42,686 - pyskl - INFO - Epoch [84][2600/3746] lr: 4.095e-02, eta: 2 days, 7:52:59, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6075, loss_cls: 3.7321, loss: 3.7321 +2024-07-25 04:32:05,810 - pyskl - INFO - Epoch [84][2700/3746] lr: 4.092e-02, eta: 2 days, 7:51:40, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.6003, loss_cls: 3.7375, loss: 3.7375 +2024-07-25 04:33:29,078 - pyskl - INFO - Epoch [84][2800/3746] lr: 4.089e-02, eta: 2 days, 7:50:21, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.6033, loss_cls: 3.7612, loss: 3.7612 +2024-07-25 04:34:50,466 - pyskl - INFO - Epoch [84][2900/3746] lr: 4.086e-02, eta: 2 days, 7:49:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.5977, loss_cls: 3.7674, loss: 3.7674 +2024-07-25 04:36:12,367 - pyskl - INFO - Epoch [84][3000/3746] lr: 4.084e-02, eta: 2 days, 7:47:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.6055, loss_cls: 3.7174, loss: 3.7174 +2024-07-25 04:37:36,143 - pyskl - INFO - Epoch [84][3100/3746] lr: 4.081e-02, eta: 2 days, 7:46:21, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6055, loss_cls: 3.7140, loss: 3.7140 +2024-07-25 04:38:58,988 - pyskl - INFO - Epoch [84][3200/3746] lr: 4.078e-02, eta: 2 days, 7:45:02, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.6012, loss_cls: 3.7700, loss: 3.7700 +2024-07-25 04:40:21,425 - pyskl - INFO - Epoch [84][3300/3746] lr: 4.075e-02, eta: 2 days, 7:43:42, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.6012, loss_cls: 3.7628, loss: 3.7628 +2024-07-25 04:41:43,403 - pyskl - INFO - Epoch [84][3400/3746] lr: 4.073e-02, eta: 2 days, 7:42:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.6034, loss_cls: 3.7231, loss: 3.7231 +2024-07-25 04:43:05,854 - pyskl - INFO - Epoch [84][3500/3746] lr: 4.070e-02, eta: 2 days, 7:41:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5953, loss_cls: 3.7807, loss: 3.7807 +2024-07-25 04:44:29,117 - pyskl - INFO - Epoch [84][3600/3746] lr: 4.067e-02, eta: 2 days, 7:39:42, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6014, loss_cls: 3.7076, loss: 3.7076 +2024-07-25 04:45:51,086 - pyskl - INFO - Epoch [84][3700/3746] lr: 4.064e-02, eta: 2 days, 7:38:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5970, loss_cls: 3.7674, loss: 3.7674 +2024-07-25 04:46:30,208 - pyskl - INFO - Saving checkpoint at 84 epochs +2024-07-25 04:48:22,084 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 04:48:22,844 - pyskl - INFO - +top1_acc 0.2637 +top5_acc 0.5133 +2024-07-25 04:48:22,844 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 04:48:22,886 - pyskl - INFO - +mean_acc 0.2635 +2024-07-25 04:48:22,898 - pyskl - INFO - Epoch(val) [84][309] top1_acc: 0.2637, top5_acc: 0.5133, mean_class_accuracy: 0.2635 +2024-07-25 04:52:13,958 - pyskl - INFO - Epoch [85][100/3746] lr: 4.060e-02, eta: 2 days, 7:37:53, time: 2.311, data_time: 1.326, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6097, loss_cls: 3.6750, loss: 3.6750 +2024-07-25 04:53:35,978 - pyskl - INFO - Epoch [85][200/3746] lr: 4.058e-02, eta: 2 days, 7:36:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6166, loss_cls: 3.6431, loss: 3.6431 +2024-07-25 04:54:57,659 - pyskl - INFO - Epoch [85][300/3746] lr: 4.055e-02, eta: 2 days, 7:35:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6048, loss_cls: 3.7265, loss: 3.7265 +2024-07-25 04:56:19,651 - pyskl - INFO - Epoch [85][400/3746] lr: 4.052e-02, eta: 2 days, 7:33:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.6098, loss_cls: 3.6767, loss: 3.6767 +2024-07-25 04:57:41,432 - pyskl - INFO - Epoch [85][500/3746] lr: 4.049e-02, eta: 2 days, 7:32:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.6052, loss_cls: 3.7057, loss: 3.7057 +2024-07-25 04:59:02,795 - pyskl - INFO - Epoch [85][600/3746] lr: 4.047e-02, eta: 2 days, 7:31:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6150, loss_cls: 3.6871, loss: 3.6871 +2024-07-25 05:00:24,578 - pyskl - INFO - Epoch [85][700/3746] lr: 4.044e-02, eta: 2 days, 7:29:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.6069, loss_cls: 3.7096, loss: 3.7096 +2024-07-25 05:01:46,590 - pyskl - INFO - Epoch [85][800/3746] lr: 4.041e-02, eta: 2 days, 7:28:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5992, loss_cls: 3.7430, loss: 3.7430 +2024-07-25 05:03:08,075 - pyskl - INFO - Epoch [85][900/3746] lr: 4.038e-02, eta: 2 days, 7:27:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6114, loss_cls: 3.6742, loss: 3.6742 +2024-07-25 05:04:29,852 - pyskl - INFO - Epoch [85][1000/3746] lr: 4.036e-02, eta: 2 days, 7:25:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.6034, loss_cls: 3.7293, loss: 3.7293 +2024-07-25 05:05:51,214 - pyskl - INFO - Epoch [85][1100/3746] lr: 4.033e-02, eta: 2 days, 7:24:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.6017, loss_cls: 3.7209, loss: 3.7209 +2024-07-25 05:07:12,890 - pyskl - INFO - Epoch [85][1200/3746] lr: 4.030e-02, eta: 2 days, 7:23:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6164, loss_cls: 3.6529, loss: 3.6529 +2024-07-25 05:08:34,559 - pyskl - INFO - Epoch [85][1300/3746] lr: 4.027e-02, eta: 2 days, 7:21:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5955, loss_cls: 3.7507, loss: 3.7507 +2024-07-25 05:09:55,901 - pyskl - INFO - Epoch [85][1400/3746] lr: 4.025e-02, eta: 2 days, 7:20:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.6070, loss_cls: 3.7114, loss: 3.7114 +2024-07-25 05:11:17,315 - pyskl - INFO - Epoch [85][1500/3746] lr: 4.022e-02, eta: 2 days, 7:19:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6052, loss_cls: 3.7122, loss: 3.7122 +2024-07-25 05:12:39,022 - pyskl - INFO - Epoch [85][1600/3746] lr: 4.019e-02, eta: 2 days, 7:17:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5945, loss_cls: 3.7510, loss: 3.7510 +2024-07-25 05:14:00,419 - pyskl - INFO - Epoch [85][1700/3746] lr: 4.016e-02, eta: 2 days, 7:16:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.6061, loss_cls: 3.7204, loss: 3.7204 +2024-07-25 05:15:23,124 - pyskl - INFO - Epoch [85][1800/3746] lr: 4.014e-02, eta: 2 days, 7:15:04, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6003, loss_cls: 3.7452, loss: 3.7452 +2024-07-25 05:16:45,529 - pyskl - INFO - Epoch [85][1900/3746] lr: 4.011e-02, eta: 2 days, 7:13:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6052, loss_cls: 3.7274, loss: 3.7274 +2024-07-25 05:18:07,581 - pyskl - INFO - Epoch [85][2000/3746] lr: 4.008e-02, eta: 2 days, 7:12:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6155, loss_cls: 3.6666, loss: 3.6666 +2024-07-25 05:19:29,008 - pyskl - INFO - Epoch [85][2100/3746] lr: 4.006e-02, eta: 2 days, 7:11:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6092, loss_cls: 3.7029, loss: 3.7029 +2024-07-25 05:20:50,698 - pyskl - INFO - Epoch [85][2200/3746] lr: 4.003e-02, eta: 2 days, 7:09:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6102, loss_cls: 3.6806, loss: 3.6806 +2024-07-25 05:22:12,292 - pyskl - INFO - Epoch [85][2300/3746] lr: 4.000e-02, eta: 2 days, 7:08:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.5934, loss_cls: 3.7478, loss: 3.7478 +2024-07-25 05:23:34,030 - pyskl - INFO - Epoch [85][2400/3746] lr: 3.997e-02, eta: 2 days, 7:07:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.6052, loss_cls: 3.7278, loss: 3.7278 +2024-07-25 05:24:56,164 - pyskl - INFO - Epoch [85][2500/3746] lr: 3.995e-02, eta: 2 days, 7:05:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3452, top5_acc: 0.6008, loss_cls: 3.7289, loss: 3.7289 +2024-07-25 05:26:17,734 - pyskl - INFO - Epoch [85][2600/3746] lr: 3.992e-02, eta: 2 days, 7:04:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6117, loss_cls: 3.7030, loss: 3.7030 +2024-07-25 05:27:39,900 - pyskl - INFO - Epoch [85][2700/3746] lr: 3.989e-02, eta: 2 days, 7:03:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.5972, loss_cls: 3.7507, loss: 3.7507 +2024-07-25 05:29:01,964 - pyskl - INFO - Epoch [85][2800/3746] lr: 3.986e-02, eta: 2 days, 7:01:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6116, loss_cls: 3.6913, loss: 3.6913 +2024-07-25 05:30:23,359 - pyskl - INFO - Epoch [85][2900/3746] lr: 3.984e-02, eta: 2 days, 7:00:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6136, loss_cls: 3.6808, loss: 3.6808 +2024-07-25 05:31:45,053 - pyskl - INFO - Epoch [85][3000/3746] lr: 3.981e-02, eta: 2 days, 6:58:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.5975, loss_cls: 3.7247, loss: 3.7247 +2024-07-25 05:33:07,539 - pyskl - INFO - Epoch [85][3100/3746] lr: 3.978e-02, eta: 2 days, 6:57:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6031, loss_cls: 3.7132, loss: 3.7132 +2024-07-25 05:34:29,253 - pyskl - INFO - Epoch [85][3200/3746] lr: 3.975e-02, eta: 2 days, 6:56:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.6120, loss_cls: 3.7217, loss: 3.7217 +2024-07-25 05:35:50,416 - pyskl - INFO - Epoch [85][3300/3746] lr: 3.973e-02, eta: 2 days, 6:54:57, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6056, loss_cls: 3.7224, loss: 3.7224 +2024-07-25 05:37:11,882 - pyskl - INFO - Epoch [85][3400/3746] lr: 3.970e-02, eta: 2 days, 6:53:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3425, top5_acc: 0.6053, loss_cls: 3.7219, loss: 3.7219 +2024-07-25 05:38:33,041 - pyskl - INFO - Epoch [85][3500/3746] lr: 3.967e-02, eta: 2 days, 6:52:15, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.5972, loss_cls: 3.7486, loss: 3.7486 +2024-07-25 05:39:54,917 - pyskl - INFO - Epoch [85][3600/3746] lr: 3.964e-02, eta: 2 days, 6:50:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5970, loss_cls: 3.7412, loss: 3.7412 +2024-07-25 05:41:16,492 - pyskl - INFO - Epoch [85][3700/3746] lr: 3.962e-02, eta: 2 days, 6:49:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6030, loss_cls: 3.7186, loss: 3.7186 +2024-07-25 05:41:55,630 - pyskl - INFO - Saving checkpoint at 85 epochs +2024-07-25 05:43:48,499 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 05:43:49,187 - pyskl - INFO - +top1_acc 0.2778 +top5_acc 0.5255 +2024-07-25 05:43:49,187 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 05:43:49,231 - pyskl - INFO - +mean_acc 0.2776 +2024-07-25 05:43:49,245 - pyskl - INFO - Epoch(val) [85][309] top1_acc: 0.2778, top5_acc: 0.5255, mean_class_accuracy: 0.2776 +2024-07-25 05:47:41,563 - pyskl - INFO - Epoch [86][100/3746] lr: 3.958e-02, eta: 2 days, 6:49:03, time: 2.323, data_time: 1.338, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6247, loss_cls: 3.6034, loss: 3.6034 +2024-07-25 05:49:03,316 - pyskl - INFO - Epoch [86][200/3746] lr: 3.955e-02, eta: 2 days, 6:47:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6162, loss_cls: 3.6722, loss: 3.6722 +2024-07-25 05:50:25,358 - pyskl - INFO - Epoch [86][300/3746] lr: 3.952e-02, eta: 2 days, 6:46:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.6073, loss_cls: 3.7047, loss: 3.7047 +2024-07-25 05:51:47,117 - pyskl - INFO - Epoch [86][400/3746] lr: 3.950e-02, eta: 2 days, 6:45:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6161, loss_cls: 3.6668, loss: 3.6668 +2024-07-25 05:53:08,364 - pyskl - INFO - Epoch [86][500/3746] lr: 3.947e-02, eta: 2 days, 6:43:41, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6145, loss_cls: 3.6689, loss: 3.6689 +2024-07-25 05:54:29,842 - pyskl - INFO - Epoch [86][600/3746] lr: 3.944e-02, eta: 2 days, 6:42:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6131, loss_cls: 3.6520, loss: 3.6520 +2024-07-25 05:55:51,413 - pyskl - INFO - Epoch [86][700/3746] lr: 3.941e-02, eta: 2 days, 6:40:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.6000, loss_cls: 3.7429, loss: 3.7429 +2024-07-25 05:57:12,853 - pyskl - INFO - Epoch [86][800/3746] lr: 3.939e-02, eta: 2 days, 6:39:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6236, loss_cls: 3.6391, loss: 3.6391 +2024-07-25 05:58:34,284 - pyskl - INFO - Epoch [86][900/3746] lr: 3.936e-02, eta: 2 days, 6:38:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6084, loss_cls: 3.6657, loss: 3.6657 +2024-07-25 05:59:55,825 - pyskl - INFO - Epoch [86][1000/3746] lr: 3.933e-02, eta: 2 days, 6:36:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6075, loss_cls: 3.7043, loss: 3.7043 +2024-07-25 06:01:17,352 - pyskl - INFO - Epoch [86][1100/3746] lr: 3.930e-02, eta: 2 days, 6:35:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6078, loss_cls: 3.6729, loss: 3.6729 +2024-07-25 06:02:39,196 - pyskl - INFO - Epoch [86][1200/3746] lr: 3.928e-02, eta: 2 days, 6:34:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6147, loss_cls: 3.6685, loss: 3.6685 +2024-07-25 06:04:00,420 - pyskl - INFO - Epoch [86][1300/3746] lr: 3.925e-02, eta: 2 days, 6:32:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3425, top5_acc: 0.6055, loss_cls: 3.7274, loss: 3.7274 +2024-07-25 06:05:21,944 - pyskl - INFO - Epoch [86][1400/3746] lr: 3.922e-02, eta: 2 days, 6:31:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6097, loss_cls: 3.6802, loss: 3.6802 +2024-07-25 06:06:43,423 - pyskl - INFO - Epoch [86][1500/3746] lr: 3.919e-02, eta: 2 days, 6:30:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6080, loss_cls: 3.7073, loss: 3.7073 +2024-07-25 06:08:04,832 - pyskl - INFO - Epoch [86][1600/3746] lr: 3.917e-02, eta: 2 days, 6:28:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.6064, loss_cls: 3.6944, loss: 3.6944 +2024-07-25 06:09:26,426 - pyskl - INFO - Epoch [86][1700/3746] lr: 3.914e-02, eta: 2 days, 6:27:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6098, loss_cls: 3.7063, loss: 3.7063 +2024-07-25 06:10:48,343 - pyskl - INFO - Epoch [86][1800/3746] lr: 3.911e-02, eta: 2 days, 6:26:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6020, loss_cls: 3.7070, loss: 3.7070 +2024-07-25 06:12:10,675 - pyskl - INFO - Epoch [86][1900/3746] lr: 3.909e-02, eta: 2 days, 6:24:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.6061, loss_cls: 3.6987, loss: 3.6987 +2024-07-25 06:13:33,138 - pyskl - INFO - Epoch [86][2000/3746] lr: 3.906e-02, eta: 2 days, 6:23:31, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6011, loss_cls: 3.7278, loss: 3.7278 +2024-07-25 06:14:55,798 - pyskl - INFO - Epoch [86][2100/3746] lr: 3.903e-02, eta: 2 days, 6:22:11, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5955, loss_cls: 3.7442, loss: 3.7442 +2024-07-25 06:16:17,561 - pyskl - INFO - Epoch [86][2200/3746] lr: 3.900e-02, eta: 2 days, 6:20:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.6014, loss_cls: 3.7465, loss: 3.7465 +2024-07-25 06:17:39,095 - pyskl - INFO - Epoch [86][2300/3746] lr: 3.898e-02, eta: 2 days, 6:19:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6009, loss_cls: 3.7204, loss: 3.7204 +2024-07-25 06:19:00,501 - pyskl - INFO - Epoch [86][2400/3746] lr: 3.895e-02, eta: 2 days, 6:18:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.6011, loss_cls: 3.7371, loss: 3.7371 +2024-07-25 06:20:22,131 - pyskl - INFO - Epoch [86][2500/3746] lr: 3.892e-02, eta: 2 days, 6:16:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5942, loss_cls: 3.7623, loss: 3.7623 +2024-07-25 06:21:43,579 - pyskl - INFO - Epoch [86][2600/3746] lr: 3.889e-02, eta: 2 days, 6:15:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6030, loss_cls: 3.7361, loss: 3.7361 +2024-07-25 06:23:05,372 - pyskl - INFO - Epoch [86][2700/3746] lr: 3.887e-02, eta: 2 days, 6:14:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6047, loss_cls: 3.7128, loss: 3.7128 +2024-07-25 06:24:27,133 - pyskl - INFO - Epoch [86][2800/3746] lr: 3.884e-02, eta: 2 days, 6:12:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6073, loss_cls: 3.6865, loss: 3.6865 +2024-07-25 06:25:49,787 - pyskl - INFO - Epoch [86][2900/3746] lr: 3.881e-02, eta: 2 days, 6:11:26, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6155, loss_cls: 3.6605, loss: 3.6605 +2024-07-25 06:27:11,301 - pyskl - INFO - Epoch [86][3000/3746] lr: 3.879e-02, eta: 2 days, 6:10:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5972, loss_cls: 3.7304, loss: 3.7304 +2024-07-25 06:28:33,230 - pyskl - INFO - Epoch [86][3100/3746] lr: 3.876e-02, eta: 2 days, 6:08:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5989, loss_cls: 3.7358, loss: 3.7358 +2024-07-25 06:29:55,273 - pyskl - INFO - Epoch [86][3200/3746] lr: 3.873e-02, eta: 2 days, 6:07:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.6108, loss_cls: 3.7156, loss: 3.7156 +2024-07-25 06:31:17,134 - pyskl - INFO - Epoch [86][3300/3746] lr: 3.870e-02, eta: 2 days, 6:06:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5973, loss_cls: 3.7485, loss: 3.7485 +2024-07-25 06:32:38,644 - pyskl - INFO - Epoch [86][3400/3746] lr: 3.868e-02, eta: 2 days, 6:04:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6155, loss_cls: 3.6570, loss: 3.6570 +2024-07-25 06:34:00,651 - pyskl - INFO - Epoch [86][3500/3746] lr: 3.865e-02, eta: 2 days, 6:03:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.6019, loss_cls: 3.7310, loss: 3.7310 +2024-07-25 06:35:22,443 - pyskl - INFO - Epoch [86][3600/3746] lr: 3.862e-02, eta: 2 days, 6:02:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6089, loss_cls: 3.6897, loss: 3.6897 +2024-07-25 06:36:43,959 - pyskl - INFO - Epoch [86][3700/3746] lr: 3.860e-02, eta: 2 days, 6:00:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6048, loss_cls: 3.6909, loss: 3.6909 +2024-07-25 06:37:24,079 - pyskl - INFO - Saving checkpoint at 86 epochs +2024-07-25 06:39:18,114 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 06:39:18,792 - pyskl - INFO - +top1_acc 0.2845 +top5_acc 0.5347 +2024-07-25 06:39:18,792 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 06:39:18,837 - pyskl - INFO - +mean_acc 0.2843 +2024-07-25 06:39:18,850 - pyskl - INFO - Epoch(val) [86][309] top1_acc: 0.2845, top5_acc: 0.5347, mean_class_accuracy: 0.2843 +2024-07-25 06:43:11,274 - pyskl - INFO - Epoch [87][100/3746] lr: 3.856e-02, eta: 2 days, 6:00:08, time: 2.324, data_time: 1.334, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6181, loss_cls: 3.6504, loss: 3.6504 +2024-07-25 06:44:32,989 - pyskl - INFO - Epoch [87][200/3746] lr: 3.853e-02, eta: 2 days, 5:58:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6073, loss_cls: 3.6578, loss: 3.6578 +2024-07-25 06:45:54,725 - pyskl - INFO - Epoch [87][300/3746] lr: 3.850e-02, eta: 2 days, 5:57:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6231, loss_cls: 3.6427, loss: 3.6427 +2024-07-25 06:47:16,069 - pyskl - INFO - Epoch [87][400/3746] lr: 3.847e-02, eta: 2 days, 5:56:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6120, loss_cls: 3.6719, loss: 3.6719 +2024-07-25 06:48:38,069 - pyskl - INFO - Epoch [87][500/3746] lr: 3.845e-02, eta: 2 days, 5:54:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6084, loss_cls: 3.6795, loss: 3.6795 +2024-07-25 06:49:59,549 - pyskl - INFO - Epoch [87][600/3746] lr: 3.842e-02, eta: 2 days, 5:53:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6055, loss_cls: 3.6913, loss: 3.6913 +2024-07-25 06:51:21,175 - pyskl - INFO - Epoch [87][700/3746] lr: 3.839e-02, eta: 2 days, 5:52:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6172, loss_cls: 3.6656, loss: 3.6656 +2024-07-25 06:52:42,749 - pyskl - INFO - Epoch [87][800/3746] lr: 3.837e-02, eta: 2 days, 5:50:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.6053, loss_cls: 3.6919, loss: 3.6919 +2024-07-25 06:54:05,026 - pyskl - INFO - Epoch [87][900/3746] lr: 3.834e-02, eta: 2 days, 5:49:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6083, loss_cls: 3.7031, loss: 3.7031 +2024-07-25 06:55:27,074 - pyskl - INFO - Epoch [87][1000/3746] lr: 3.831e-02, eta: 2 days, 5:48:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6238, loss_cls: 3.6404, loss: 3.6404 +2024-07-25 06:56:48,709 - pyskl - INFO - Epoch [87][1100/3746] lr: 3.828e-02, eta: 2 days, 5:46:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6166, loss_cls: 3.6463, loss: 3.6463 +2024-07-25 06:58:10,230 - pyskl - INFO - Epoch [87][1200/3746] lr: 3.826e-02, eta: 2 days, 5:45:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6111, loss_cls: 3.6740, loss: 3.6740 +2024-07-25 06:59:31,807 - pyskl - INFO - Epoch [87][1300/3746] lr: 3.823e-02, eta: 2 days, 5:44:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6089, loss_cls: 3.7059, loss: 3.7059 +2024-07-25 07:00:53,637 - pyskl - INFO - Epoch [87][1400/3746] lr: 3.820e-02, eta: 2 days, 5:42:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6202, loss_cls: 3.6117, loss: 3.6117 +2024-07-25 07:02:14,931 - pyskl - INFO - Epoch [87][1500/3746] lr: 3.817e-02, eta: 2 days, 5:41:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6044, loss_cls: 3.6716, loss: 3.6716 +2024-07-25 07:03:36,318 - pyskl - INFO - Epoch [87][1600/3746] lr: 3.815e-02, eta: 2 days, 5:39:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.6109, loss_cls: 3.7221, loss: 3.7221 +2024-07-25 07:04:57,538 - pyskl - INFO - Epoch [87][1700/3746] lr: 3.812e-02, eta: 2 days, 5:38:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6070, loss_cls: 3.7205, loss: 3.7205 +2024-07-25 07:06:20,474 - pyskl - INFO - Epoch [87][1800/3746] lr: 3.809e-02, eta: 2 days, 5:37:16, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6055, loss_cls: 3.7230, loss: 3.7230 +2024-07-25 07:07:42,428 - pyskl - INFO - Epoch [87][1900/3746] lr: 3.807e-02, eta: 2 days, 5:35:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6109, loss_cls: 3.6785, loss: 3.6785 +2024-07-25 07:09:04,911 - pyskl - INFO - Epoch [87][2000/3746] lr: 3.804e-02, eta: 2 days, 5:34:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6109, loss_cls: 3.6620, loss: 3.6620 +2024-07-25 07:10:26,643 - pyskl - INFO - Epoch [87][2100/3746] lr: 3.801e-02, eta: 2 days, 5:33:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.5978, loss_cls: 3.7318, loss: 3.7318 +2024-07-25 07:11:48,028 - pyskl - INFO - Epoch [87][2200/3746] lr: 3.798e-02, eta: 2 days, 5:31:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6148, loss_cls: 3.6635, loss: 3.6635 +2024-07-25 07:13:09,665 - pyskl - INFO - Epoch [87][2300/3746] lr: 3.796e-02, eta: 2 days, 5:30:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6083, loss_cls: 3.7114, loss: 3.7114 +2024-07-25 07:14:31,151 - pyskl - INFO - Epoch [87][2400/3746] lr: 3.793e-02, eta: 2 days, 5:29:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6084, loss_cls: 3.6770, loss: 3.6770 +2024-07-25 07:15:52,820 - pyskl - INFO - Epoch [87][2500/3746] lr: 3.790e-02, eta: 2 days, 5:27:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6122, loss_cls: 3.6905, loss: 3.6905 +2024-07-25 07:17:14,652 - pyskl - INFO - Epoch [87][2600/3746] lr: 3.788e-02, eta: 2 days, 5:26:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.6053, loss_cls: 3.7296, loss: 3.7296 +2024-07-25 07:18:36,741 - pyskl - INFO - Epoch [87][2700/3746] lr: 3.785e-02, eta: 2 days, 5:25:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6173, loss_cls: 3.6814, loss: 3.6814 +2024-07-25 07:19:58,438 - pyskl - INFO - Epoch [87][2800/3746] lr: 3.782e-02, eta: 2 days, 5:23:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6100, loss_cls: 3.6599, loss: 3.6599 +2024-07-25 07:21:20,134 - pyskl - INFO - Epoch [87][2900/3746] lr: 3.779e-02, eta: 2 days, 5:22:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6080, loss_cls: 3.6810, loss: 3.6810 +2024-07-25 07:22:41,901 - pyskl - INFO - Epoch [87][3000/3746] lr: 3.777e-02, eta: 2 days, 5:21:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6056, loss_cls: 3.7216, loss: 3.7216 +2024-07-25 07:24:03,331 - pyskl - INFO - Epoch [87][3100/3746] lr: 3.774e-02, eta: 2 days, 5:19:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.6045, loss_cls: 3.7245, loss: 3.7245 +2024-07-25 07:25:25,029 - pyskl - INFO - Epoch [87][3200/3746] lr: 3.771e-02, eta: 2 days, 5:18:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6155, loss_cls: 3.6655, loss: 3.6655 +2024-07-25 07:26:46,721 - pyskl - INFO - Epoch [87][3300/3746] lr: 3.769e-02, eta: 2 days, 5:17:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6039, loss_cls: 3.6927, loss: 3.6927 +2024-07-25 07:28:09,147 - pyskl - INFO - Epoch [87][3400/3746] lr: 3.766e-02, eta: 2 days, 5:15:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.6080, loss_cls: 3.7174, loss: 3.7174 +2024-07-25 07:29:30,635 - pyskl - INFO - Epoch [87][3500/3746] lr: 3.763e-02, eta: 2 days, 5:14:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.6042, loss_cls: 3.7409, loss: 3.7409 +2024-07-25 07:30:52,012 - pyskl - INFO - Epoch [87][3600/3746] lr: 3.761e-02, eta: 2 days, 5:13:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6072, loss_cls: 3.7065, loss: 3.7065 +2024-07-25 07:32:13,909 - pyskl - INFO - Epoch [87][3700/3746] lr: 3.758e-02, eta: 2 days, 5:11:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5978, loss_cls: 3.7164, loss: 3.7164 +2024-07-25 07:32:54,057 - pyskl - INFO - Saving checkpoint at 87 epochs +2024-07-25 07:34:46,859 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 07:34:47,527 - pyskl - INFO - +top1_acc 0.2705 +top5_acc 0.5284 +2024-07-25 07:34:47,527 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 07:34:47,569 - pyskl - INFO - +mean_acc 0.2705 +2024-07-25 07:34:47,583 - pyskl - INFO - Epoch(val) [87][309] top1_acc: 0.2705, top5_acc: 0.5284, mean_class_accuracy: 0.2705 +2024-07-25 07:38:40,907 - pyskl - INFO - Epoch [88][100/3746] lr: 3.754e-02, eta: 2 days, 5:11:08, time: 2.333, data_time: 1.352, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6189, loss_cls: 3.6299, loss: 3.6299 +2024-07-25 07:40:03,399 - pyskl - INFO - Epoch [88][200/3746] lr: 3.751e-02, eta: 2 days, 5:09:48, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6050, loss_cls: 3.6902, loss: 3.6902 +2024-07-25 07:41:25,180 - pyskl - INFO - Epoch [88][300/3746] lr: 3.748e-02, eta: 2 days, 5:08:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6162, loss_cls: 3.6805, loss: 3.6805 +2024-07-25 07:42:46,352 - pyskl - INFO - Epoch [88][400/3746] lr: 3.746e-02, eta: 2 days, 5:07:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6225, loss_cls: 3.6230, loss: 3.6230 +2024-07-25 07:44:07,842 - pyskl - INFO - Epoch [88][500/3746] lr: 3.743e-02, eta: 2 days, 5:05:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6112, loss_cls: 3.6665, loss: 3.6665 +2024-07-25 07:45:29,927 - pyskl - INFO - Epoch [88][600/3746] lr: 3.740e-02, eta: 2 days, 5:04:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6167, loss_cls: 3.6606, loss: 3.6606 +2024-07-25 07:46:51,237 - pyskl - INFO - Epoch [88][700/3746] lr: 3.738e-02, eta: 2 days, 5:03:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6058, loss_cls: 3.6938, loss: 3.6938 +2024-07-25 07:48:12,863 - pyskl - INFO - Epoch [88][800/3746] lr: 3.735e-02, eta: 2 days, 5:01:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6150, loss_cls: 3.6509, loss: 3.6509 +2024-07-25 07:49:34,424 - pyskl - INFO - Epoch [88][900/3746] lr: 3.732e-02, eta: 2 days, 5:00:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6027, loss_cls: 3.7314, loss: 3.7314 +2024-07-25 07:50:56,264 - pyskl - INFO - Epoch [88][1000/3746] lr: 3.730e-02, eta: 2 days, 4:59:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6139, loss_cls: 3.6611, loss: 3.6611 +2024-07-25 07:52:17,808 - pyskl - INFO - Epoch [88][1100/3746] lr: 3.727e-02, eta: 2 days, 4:57:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6233, loss_cls: 3.6203, loss: 3.6203 +2024-07-25 07:53:39,785 - pyskl - INFO - Epoch [88][1200/3746] lr: 3.724e-02, eta: 2 days, 4:56:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6186, loss_cls: 3.6542, loss: 3.6542 +2024-07-25 07:55:01,478 - pyskl - INFO - Epoch [88][1300/3746] lr: 3.721e-02, eta: 2 days, 4:54:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6105, loss_cls: 3.6712, loss: 3.6712 +2024-07-25 07:56:22,852 - pyskl - INFO - Epoch [88][1400/3746] lr: 3.719e-02, eta: 2 days, 4:53:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6167, loss_cls: 3.6760, loss: 3.6760 +2024-07-25 07:57:44,604 - pyskl - INFO - Epoch [88][1500/3746] lr: 3.716e-02, eta: 2 days, 4:52:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6192, loss_cls: 3.6413, loss: 3.6413 +2024-07-25 07:59:07,251 - pyskl - INFO - Epoch [88][1600/3746] lr: 3.713e-02, eta: 2 days, 4:50:57, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6169, loss_cls: 3.6625, loss: 3.6625 +2024-07-25 08:00:28,889 - pyskl - INFO - Epoch [88][1700/3746] lr: 3.711e-02, eta: 2 days, 4:49:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6133, loss_cls: 3.6722, loss: 3.6722 +2024-07-25 08:01:51,073 - pyskl - INFO - Epoch [88][1800/3746] lr: 3.708e-02, eta: 2 days, 4:48:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6150, loss_cls: 3.6678, loss: 3.6678 +2024-07-25 08:03:13,430 - pyskl - INFO - Epoch [88][1900/3746] lr: 3.705e-02, eta: 2 days, 4:46:56, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6153, loss_cls: 3.6777, loss: 3.6777 +2024-07-25 08:04:36,440 - pyskl - INFO - Epoch [88][2000/3746] lr: 3.703e-02, eta: 2 days, 4:45:36, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6109, loss_cls: 3.6563, loss: 3.6563 +2024-07-25 08:05:58,990 - pyskl - INFO - Epoch [88][2100/3746] lr: 3.700e-02, eta: 2 days, 4:44:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6211, loss_cls: 3.6285, loss: 3.6285 +2024-07-25 08:07:21,091 - pyskl - INFO - Epoch [88][2200/3746] lr: 3.697e-02, eta: 2 days, 4:42:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6073, loss_cls: 3.7079, loss: 3.7079 +2024-07-25 08:08:42,858 - pyskl - INFO - Epoch [88][2300/3746] lr: 3.694e-02, eta: 2 days, 4:41:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6070, loss_cls: 3.6960, loss: 3.6960 +2024-07-25 08:10:04,289 - pyskl - INFO - Epoch [88][2400/3746] lr: 3.692e-02, eta: 2 days, 4:40:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.6125, loss_cls: 3.6753, loss: 3.6753 +2024-07-25 08:11:26,510 - pyskl - INFO - Epoch [88][2500/3746] lr: 3.689e-02, eta: 2 days, 4:38:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6027, loss_cls: 3.7201, loss: 3.7201 +2024-07-25 08:12:48,056 - pyskl - INFO - Epoch [88][2600/3746] lr: 3.686e-02, eta: 2 days, 4:37:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6094, loss_cls: 3.7085, loss: 3.7085 +2024-07-25 08:14:09,796 - pyskl - INFO - Epoch [88][2700/3746] lr: 3.684e-02, eta: 2 days, 4:36:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6117, loss_cls: 3.6835, loss: 3.6835 +2024-07-25 08:15:31,669 - pyskl - INFO - Epoch [88][2800/3746] lr: 3.681e-02, eta: 2 days, 4:34:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6133, loss_cls: 3.6464, loss: 3.6464 +2024-07-25 08:16:53,422 - pyskl - INFO - Epoch [88][2900/3746] lr: 3.678e-02, eta: 2 days, 4:33:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.5986, loss_cls: 3.6992, loss: 3.6992 +2024-07-25 08:18:15,152 - pyskl - INFO - Epoch [88][3000/3746] lr: 3.676e-02, eta: 2 days, 4:32:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6108, loss_cls: 3.6705, loss: 3.6705 +2024-07-25 08:19:36,786 - pyskl - INFO - Epoch [88][3100/3746] lr: 3.673e-02, eta: 2 days, 4:30:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6080, loss_cls: 3.6719, loss: 3.6719 +2024-07-25 08:20:58,306 - pyskl - INFO - Epoch [88][3200/3746] lr: 3.670e-02, eta: 2 days, 4:29:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6172, loss_cls: 3.6258, loss: 3.6258 +2024-07-25 08:22:19,324 - pyskl - INFO - Epoch [88][3300/3746] lr: 3.667e-02, eta: 2 days, 4:28:06, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.5988, loss_cls: 3.7401, loss: 3.7401 +2024-07-25 08:23:41,017 - pyskl - INFO - Epoch [88][3400/3746] lr: 3.665e-02, eta: 2 days, 4:26:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6098, loss_cls: 3.6957, loss: 3.6957 +2024-07-25 08:25:02,567 - pyskl - INFO - Epoch [88][3500/3746] lr: 3.662e-02, eta: 2 days, 4:25:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6192, loss_cls: 3.6608, loss: 3.6608 +2024-07-25 08:26:24,097 - pyskl - INFO - Epoch [88][3600/3746] lr: 3.659e-02, eta: 2 days, 4:24:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6108, loss_cls: 3.6771, loss: 3.6771 +2024-07-25 08:27:46,889 - pyskl - INFO - Epoch [88][3700/3746] lr: 3.657e-02, eta: 2 days, 4:22:43, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6209, loss_cls: 3.6408, loss: 3.6408 +2024-07-25 08:28:26,602 - pyskl - INFO - Saving checkpoint at 88 epochs +2024-07-25 08:30:18,795 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 08:30:19,462 - pyskl - INFO - +top1_acc 0.2731 +top5_acc 0.5260 +2024-07-25 08:30:19,462 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 08:30:19,508 - pyskl - INFO - +mean_acc 0.2729 +2024-07-25 08:30:19,522 - pyskl - INFO - Epoch(val) [88][309] top1_acc: 0.2731, top5_acc: 0.5260, mean_class_accuracy: 0.2729 +2024-07-25 08:34:13,335 - pyskl - INFO - Epoch [89][100/3746] lr: 3.653e-02, eta: 2 days, 4:22:06, time: 2.338, data_time: 1.355, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6245, loss_cls: 3.5754, loss: 3.5754 +2024-07-25 08:35:35,105 - pyskl - INFO - Epoch [89][200/3746] lr: 3.650e-02, eta: 2 days, 4:20:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6202, loss_cls: 3.6078, loss: 3.6078 +2024-07-25 08:36:57,202 - pyskl - INFO - Epoch [89][300/3746] lr: 3.647e-02, eta: 2 days, 4:19:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6214, loss_cls: 3.6435, loss: 3.6435 +2024-07-25 08:38:19,089 - pyskl - INFO - Epoch [89][400/3746] lr: 3.645e-02, eta: 2 days, 4:18:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6158, loss_cls: 3.6314, loss: 3.6314 +2024-07-25 08:39:40,956 - pyskl - INFO - Epoch [89][500/3746] lr: 3.642e-02, eta: 2 days, 4:16:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6234, loss_cls: 3.6290, loss: 3.6290 +2024-07-25 08:41:02,698 - pyskl - INFO - Epoch [89][600/3746] lr: 3.639e-02, eta: 2 days, 4:15:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6180, loss_cls: 3.6491, loss: 3.6491 +2024-07-25 08:42:24,518 - pyskl - INFO - Epoch [89][700/3746] lr: 3.637e-02, eta: 2 days, 4:14:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6195, loss_cls: 3.6400, loss: 3.6400 +2024-07-25 08:43:46,927 - pyskl - INFO - Epoch [89][800/3746] lr: 3.634e-02, eta: 2 days, 4:12:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6225, loss_cls: 3.6377, loss: 3.6377 +2024-07-25 08:45:09,084 - pyskl - INFO - Epoch [89][900/3746] lr: 3.631e-02, eta: 2 days, 4:11:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6145, loss_cls: 3.6370, loss: 3.6370 +2024-07-25 08:46:30,810 - pyskl - INFO - Epoch [89][1000/3746] lr: 3.629e-02, eta: 2 days, 4:10:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6195, loss_cls: 3.6152, loss: 3.6152 +2024-07-25 08:47:52,611 - pyskl - INFO - Epoch [89][1100/3746] lr: 3.626e-02, eta: 2 days, 4:08:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6152, loss_cls: 3.6626, loss: 3.6626 +2024-07-25 08:49:14,348 - pyskl - INFO - Epoch [89][1200/3746] lr: 3.623e-02, eta: 2 days, 4:07:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6134, loss_cls: 3.6839, loss: 3.6839 +2024-07-25 08:50:35,979 - pyskl - INFO - Epoch [89][1300/3746] lr: 3.620e-02, eta: 2 days, 4:05:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6144, loss_cls: 3.6380, loss: 3.6380 +2024-07-25 08:51:57,371 - pyskl - INFO - Epoch [89][1400/3746] lr: 3.618e-02, eta: 2 days, 4:04:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6245, loss_cls: 3.6489, loss: 3.6489 +2024-07-25 08:53:18,839 - pyskl - INFO - Epoch [89][1500/3746] lr: 3.615e-02, eta: 2 days, 4:03:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6102, loss_cls: 3.6765, loss: 3.6765 +2024-07-25 08:54:40,635 - pyskl - INFO - Epoch [89][1600/3746] lr: 3.612e-02, eta: 2 days, 4:01:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6161, loss_cls: 3.6530, loss: 3.6530 +2024-07-25 08:56:02,210 - pyskl - INFO - Epoch [89][1700/3746] lr: 3.610e-02, eta: 2 days, 4:00:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.6059, loss_cls: 3.6875, loss: 3.6875 +2024-07-25 08:57:24,727 - pyskl - INFO - Epoch [89][1800/3746] lr: 3.607e-02, eta: 2 days, 3:59:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6083, loss_cls: 3.6744, loss: 3.6744 +2024-07-25 08:58:46,789 - pyskl - INFO - Epoch [89][1900/3746] lr: 3.604e-02, eta: 2 days, 3:57:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6203, loss_cls: 3.6477, loss: 3.6477 +2024-07-25 09:00:09,546 - pyskl - INFO - Epoch [89][2000/3746] lr: 3.602e-02, eta: 2 days, 3:56:32, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6203, loss_cls: 3.6460, loss: 3.6460 +2024-07-25 09:01:31,421 - pyskl - INFO - Epoch [89][2100/3746] lr: 3.599e-02, eta: 2 days, 3:55:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6223, loss_cls: 3.6558, loss: 3.6558 +2024-07-25 09:02:53,508 - pyskl - INFO - Epoch [89][2200/3746] lr: 3.596e-02, eta: 2 days, 3:53:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6145, loss_cls: 3.6658, loss: 3.6658 +2024-07-25 09:04:15,800 - pyskl - INFO - Epoch [89][2300/3746] lr: 3.594e-02, eta: 2 days, 3:52:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6150, loss_cls: 3.6714, loss: 3.6714 +2024-07-25 09:05:37,150 - pyskl - INFO - Epoch [89][2400/3746] lr: 3.591e-02, eta: 2 days, 3:51:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6084, loss_cls: 3.6584, loss: 3.6584 +2024-07-25 09:06:58,532 - pyskl - INFO - Epoch [89][2500/3746] lr: 3.588e-02, eta: 2 days, 3:49:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.6014, loss_cls: 3.7249, loss: 3.7249 +2024-07-25 09:08:19,898 - pyskl - INFO - Epoch [89][2600/3746] lr: 3.586e-02, eta: 2 days, 3:48:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6173, loss_cls: 3.6615, loss: 3.6615 +2024-07-25 09:09:41,639 - pyskl - INFO - Epoch [89][2700/3746] lr: 3.583e-02, eta: 2 days, 3:47:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6078, loss_cls: 3.6640, loss: 3.6640 +2024-07-25 09:11:03,201 - pyskl - INFO - Epoch [89][2800/3746] lr: 3.580e-02, eta: 2 days, 3:45:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5988, loss_cls: 3.7472, loss: 3.7472 +2024-07-25 09:12:24,838 - pyskl - INFO - Epoch [89][2900/3746] lr: 3.578e-02, eta: 2 days, 3:44:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6123, loss_cls: 3.6903, loss: 3.6903 +2024-07-25 09:13:46,475 - pyskl - INFO - Epoch [89][3000/3746] lr: 3.575e-02, eta: 2 days, 3:43:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6148, loss_cls: 3.6622, loss: 3.6622 +2024-07-25 09:15:07,969 - pyskl - INFO - Epoch [89][3100/3746] lr: 3.572e-02, eta: 2 days, 3:41:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6098, loss_cls: 3.6740, loss: 3.6740 +2024-07-25 09:16:29,384 - pyskl - INFO - Epoch [89][3200/3746] lr: 3.569e-02, eta: 2 days, 3:40:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6183, loss_cls: 3.6608, loss: 3.6608 +2024-07-25 09:17:50,970 - pyskl - INFO - Epoch [89][3300/3746] lr: 3.567e-02, eta: 2 days, 3:39:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6188, loss_cls: 3.6006, loss: 3.6006 +2024-07-25 09:19:13,378 - pyskl - INFO - Epoch [89][3400/3746] lr: 3.564e-02, eta: 2 days, 3:37:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6106, loss_cls: 3.6716, loss: 3.6716 +2024-07-25 09:20:35,545 - pyskl - INFO - Epoch [89][3500/3746] lr: 3.561e-02, eta: 2 days, 3:36:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3452, top5_acc: 0.6052, loss_cls: 3.6958, loss: 3.6958 +2024-07-25 09:21:57,105 - pyskl - INFO - Epoch [89][3600/3746] lr: 3.559e-02, eta: 2 days, 3:34:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6091, loss_cls: 3.6777, loss: 3.6777 +2024-07-25 09:23:19,281 - pyskl - INFO - Epoch [89][3700/3746] lr: 3.556e-02, eta: 2 days, 3:33:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6161, loss_cls: 3.6552, loss: 3.6552 +2024-07-25 09:23:59,247 - pyskl - INFO - Saving checkpoint at 89 epochs +2024-07-25 09:25:51,073 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 09:25:51,742 - pyskl - INFO - +top1_acc 0.2806 +top5_acc 0.5432 +2024-07-25 09:25:51,742 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 09:25:51,786 - pyskl - INFO - +mean_acc 0.2805 +2024-07-25 09:25:51,799 - pyskl - INFO - Epoch(val) [89][309] top1_acc: 0.2806, top5_acc: 0.5432, mean_class_accuracy: 0.2805 +2024-07-25 09:29:49,128 - pyskl - INFO - Epoch [90][100/3746] lr: 3.552e-02, eta: 2 days, 3:33:01, time: 2.373, data_time: 1.394, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6159, loss_cls: 3.5931, loss: 3.5931 +2024-07-25 09:31:11,523 - pyskl - INFO - Epoch [90][200/3746] lr: 3.550e-02, eta: 2 days, 3:31:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6248, loss_cls: 3.6076, loss: 3.6076 +2024-07-25 09:32:33,333 - pyskl - INFO - Epoch [90][300/3746] lr: 3.547e-02, eta: 2 days, 3:30:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3622, top5_acc: 0.6255, loss_cls: 3.5818, loss: 3.5818 +2024-07-25 09:33:55,875 - pyskl - INFO - Epoch [90][400/3746] lr: 3.544e-02, eta: 2 days, 3:28:59, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6252, loss_cls: 3.5887, loss: 3.5887 +2024-07-25 09:35:17,477 - pyskl - INFO - Epoch [90][500/3746] lr: 3.541e-02, eta: 2 days, 3:27:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6238, loss_cls: 3.6155, loss: 3.6155 +2024-07-25 09:36:38,908 - pyskl - INFO - Epoch [90][600/3746] lr: 3.539e-02, eta: 2 days, 3:26:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6228, loss_cls: 3.6379, loss: 3.6379 +2024-07-25 09:38:00,879 - pyskl - INFO - Epoch [90][700/3746] lr: 3.536e-02, eta: 2 days, 3:24:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6305, loss_cls: 3.6152, loss: 3.6152 +2024-07-25 09:39:22,542 - pyskl - INFO - Epoch [90][800/3746] lr: 3.533e-02, eta: 2 days, 3:23:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6175, loss_cls: 3.6262, loss: 3.6262 +2024-07-25 09:40:44,388 - pyskl - INFO - Epoch [90][900/3746] lr: 3.531e-02, eta: 2 days, 3:22:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6156, loss_cls: 3.6257, loss: 3.6257 +2024-07-25 09:42:05,952 - pyskl - INFO - Epoch [90][1000/3746] lr: 3.528e-02, eta: 2 days, 3:20:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.6153, loss_cls: 3.6786, loss: 3.6786 +2024-07-25 09:43:27,764 - pyskl - INFO - Epoch [90][1100/3746] lr: 3.525e-02, eta: 2 days, 3:19:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6081, loss_cls: 3.6754, loss: 3.6754 +2024-07-25 09:44:49,356 - pyskl - INFO - Epoch [90][1200/3746] lr: 3.523e-02, eta: 2 days, 3:18:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6202, loss_cls: 3.5912, loss: 3.5912 +2024-07-25 09:46:11,026 - pyskl - INFO - Epoch [90][1300/3746] lr: 3.520e-02, eta: 2 days, 3:16:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6080, loss_cls: 3.6672, loss: 3.6672 +2024-07-25 09:47:32,880 - pyskl - INFO - Epoch [90][1400/3746] lr: 3.517e-02, eta: 2 days, 3:15:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6100, loss_cls: 3.7032, loss: 3.7032 +2024-07-25 09:48:54,528 - pyskl - INFO - Epoch [90][1500/3746] lr: 3.515e-02, eta: 2 days, 3:14:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6284, loss_cls: 3.5592, loss: 3.5592 +2024-07-25 09:50:15,862 - pyskl - INFO - Epoch [90][1600/3746] lr: 3.512e-02, eta: 2 days, 3:12:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6072, loss_cls: 3.6990, loss: 3.6990 +2024-07-25 09:51:37,386 - pyskl - INFO - Epoch [90][1700/3746] lr: 3.509e-02, eta: 2 days, 3:11:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6162, loss_cls: 3.6829, loss: 3.6829 +2024-07-25 09:52:59,857 - pyskl - INFO - Epoch [90][1800/3746] lr: 3.507e-02, eta: 2 days, 3:10:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6061, loss_cls: 3.6811, loss: 3.6811 +2024-07-25 09:54:22,458 - pyskl - INFO - Epoch [90][1900/3746] lr: 3.504e-02, eta: 2 days, 3:08:45, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6209, loss_cls: 3.6121, loss: 3.6121 +2024-07-25 09:55:44,192 - pyskl - INFO - Epoch [90][2000/3746] lr: 3.501e-02, eta: 2 days, 3:07:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6173, loss_cls: 3.6581, loss: 3.6581 +2024-07-25 09:57:06,022 - pyskl - INFO - Epoch [90][2100/3746] lr: 3.499e-02, eta: 2 days, 3:06:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6122, loss_cls: 3.6940, loss: 3.6940 +2024-07-25 09:58:28,254 - pyskl - INFO - Epoch [90][2200/3746] lr: 3.496e-02, eta: 2 days, 3:04:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6184, loss_cls: 3.6297, loss: 3.6297 +2024-07-25 09:59:49,893 - pyskl - INFO - Epoch [90][2300/3746] lr: 3.493e-02, eta: 2 days, 3:03:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6073, loss_cls: 3.6760, loss: 3.6760 +2024-07-25 10:01:11,025 - pyskl - INFO - Epoch [90][2400/3746] lr: 3.491e-02, eta: 2 days, 3:02:01, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6180, loss_cls: 3.6786, loss: 3.6786 +2024-07-25 10:02:32,642 - pyskl - INFO - Epoch [90][2500/3746] lr: 3.488e-02, eta: 2 days, 3:00:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6153, loss_cls: 3.6446, loss: 3.6446 +2024-07-25 10:03:54,139 - pyskl - INFO - Epoch [90][2600/3746] lr: 3.485e-02, eta: 2 days, 2:59:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6188, loss_cls: 3.6053, loss: 3.6053 +2024-07-25 10:05:16,207 - pyskl - INFO - Epoch [90][2700/3746] lr: 3.483e-02, eta: 2 days, 2:57:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6111, loss_cls: 3.6583, loss: 3.6583 +2024-07-25 10:06:37,929 - pyskl - INFO - Epoch [90][2800/3746] lr: 3.480e-02, eta: 2 days, 2:56:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3594, top5_acc: 0.6253, loss_cls: 3.6348, loss: 3.6348 +2024-07-25 10:07:59,674 - pyskl - INFO - Epoch [90][2900/3746] lr: 3.477e-02, eta: 2 days, 2:55:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6119, loss_cls: 3.6715, loss: 3.6715 +2024-07-25 10:09:21,126 - pyskl - INFO - Epoch [90][3000/3746] lr: 3.475e-02, eta: 2 days, 2:53:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6236, loss_cls: 3.6062, loss: 3.6062 +2024-07-25 10:10:42,897 - pyskl - INFO - Epoch [90][3100/3746] lr: 3.472e-02, eta: 2 days, 2:52:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6217, loss_cls: 3.6350, loss: 3.6350 +2024-07-25 10:12:04,239 - pyskl - INFO - Epoch [90][3200/3746] lr: 3.469e-02, eta: 2 days, 2:51:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6098, loss_cls: 3.6564, loss: 3.6564 +2024-07-25 10:13:25,488 - pyskl - INFO - Epoch [90][3300/3746] lr: 3.467e-02, eta: 2 days, 2:49:51, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6238, loss_cls: 3.6024, loss: 3.6024 +2024-07-25 10:14:47,318 - pyskl - INFO - Epoch [90][3400/3746] lr: 3.464e-02, eta: 2 days, 2:48:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6125, loss_cls: 3.6646, loss: 3.6646 +2024-07-25 10:16:09,055 - pyskl - INFO - Epoch [90][3500/3746] lr: 3.461e-02, eta: 2 days, 2:47:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6139, loss_cls: 3.6945, loss: 3.6945 +2024-07-25 10:17:30,517 - pyskl - INFO - Epoch [90][3600/3746] lr: 3.459e-02, eta: 2 days, 2:45:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6244, loss_cls: 3.6121, loss: 3.6121 +2024-07-25 10:18:52,423 - pyskl - INFO - Epoch [90][3700/3746] lr: 3.456e-02, eta: 2 days, 2:44:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6098, loss_cls: 3.6839, loss: 3.6839 +2024-07-25 10:19:32,641 - pyskl - INFO - Saving checkpoint at 90 epochs +2024-07-25 10:21:25,356 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 10:21:26,027 - pyskl - INFO - +top1_acc 0.2774 +top5_acc 0.5277 +2024-07-25 10:21:26,027 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 10:21:26,076 - pyskl - INFO - +mean_acc 0.2771 +2024-07-25 10:21:26,090 - pyskl - INFO - Epoch(val) [90][309] top1_acc: 0.2774, top5_acc: 0.5277, mean_class_accuracy: 0.2771 +2024-07-25 10:25:24,298 - pyskl - INFO - Epoch [91][100/3746] lr: 3.452e-02, eta: 2 days, 2:43:49, time: 2.382, data_time: 1.395, memory: 15990, top1_acc: 0.3656, top5_acc: 0.6297, loss_cls: 3.5848, loss: 3.5848 +2024-07-25 10:26:45,853 - pyskl - INFO - Epoch [91][200/3746] lr: 3.450e-02, eta: 2 days, 2:42:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6283, loss_cls: 3.6087, loss: 3.6087 +2024-07-25 10:28:07,619 - pyskl - INFO - Epoch [91][300/3746] lr: 3.447e-02, eta: 2 days, 2:41:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6347, loss_cls: 3.5684, loss: 3.5684 +2024-07-25 10:29:29,966 - pyskl - INFO - Epoch [91][400/3746] lr: 3.444e-02, eta: 2 days, 2:39:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6153, loss_cls: 3.6367, loss: 3.6367 +2024-07-25 10:30:51,683 - pyskl - INFO - Epoch [91][500/3746] lr: 3.442e-02, eta: 2 days, 2:38:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6227, loss_cls: 3.6376, loss: 3.6376 +2024-07-25 10:32:13,450 - pyskl - INFO - Epoch [91][600/3746] lr: 3.439e-02, eta: 2 days, 2:37:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6281, loss_cls: 3.5534, loss: 3.5534 +2024-07-25 10:33:35,468 - pyskl - INFO - Epoch [91][700/3746] lr: 3.436e-02, eta: 2 days, 2:35:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6138, loss_cls: 3.6514, loss: 3.6514 +2024-07-25 10:34:57,016 - pyskl - INFO - Epoch [91][800/3746] lr: 3.434e-02, eta: 2 days, 2:34:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6208, loss_cls: 3.5966, loss: 3.5966 +2024-07-25 10:36:18,596 - pyskl - INFO - Epoch [91][900/3746] lr: 3.431e-02, eta: 2 days, 2:33:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6362, loss_cls: 3.5502, loss: 3.5502 +2024-07-25 10:37:39,936 - pyskl - INFO - Epoch [91][1000/3746] lr: 3.428e-02, eta: 2 days, 2:31:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6111, loss_cls: 3.6642, loss: 3.6642 +2024-07-25 10:39:01,396 - pyskl - INFO - Epoch [91][1100/3746] lr: 3.426e-02, eta: 2 days, 2:30:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6253, loss_cls: 3.5994, loss: 3.5994 +2024-07-25 10:40:22,648 - pyskl - INFO - Epoch [91][1200/3746] lr: 3.423e-02, eta: 2 days, 2:28:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6188, loss_cls: 3.6479, loss: 3.6479 +2024-07-25 10:41:44,776 - pyskl - INFO - Epoch [91][1300/3746] lr: 3.420e-02, eta: 2 days, 2:27:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6177, loss_cls: 3.6352, loss: 3.6352 +2024-07-25 10:43:06,854 - pyskl - INFO - Epoch [91][1400/3746] lr: 3.418e-02, eta: 2 days, 2:26:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6233, loss_cls: 3.6314, loss: 3.6314 +2024-07-25 10:44:28,112 - pyskl - INFO - Epoch [91][1500/3746] lr: 3.415e-02, eta: 2 days, 2:24:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6262, loss_cls: 3.5917, loss: 3.5917 +2024-07-25 10:45:49,526 - pyskl - INFO - Epoch [91][1600/3746] lr: 3.412e-02, eta: 2 days, 2:23:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6228, loss_cls: 3.6202, loss: 3.6202 +2024-07-25 10:47:10,953 - pyskl - INFO - Epoch [91][1700/3746] lr: 3.410e-02, eta: 2 days, 2:22:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6223, loss_cls: 3.6019, loss: 3.6019 +2024-07-25 10:48:33,365 - pyskl - INFO - Epoch [91][1800/3746] lr: 3.407e-02, eta: 2 days, 2:20:51, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6155, loss_cls: 3.6397, loss: 3.6397 +2024-07-25 10:49:55,626 - pyskl - INFO - Epoch [91][1900/3746] lr: 3.405e-02, eta: 2 days, 2:19:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6139, loss_cls: 3.6350, loss: 3.6350 +2024-07-25 10:51:17,957 - pyskl - INFO - Epoch [91][2000/3746] lr: 3.402e-02, eta: 2 days, 2:18:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6145, loss_cls: 3.6519, loss: 3.6519 +2024-07-25 10:52:40,221 - pyskl - INFO - Epoch [91][2100/3746] lr: 3.399e-02, eta: 2 days, 2:16:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6194, loss_cls: 3.6016, loss: 3.6016 +2024-07-25 10:54:01,891 - pyskl - INFO - Epoch [91][2200/3746] lr: 3.397e-02, eta: 2 days, 2:15:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6208, loss_cls: 3.6236, loss: 3.6236 +2024-07-25 10:55:23,209 - pyskl - INFO - Epoch [91][2300/3746] lr: 3.394e-02, eta: 2 days, 2:14:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6166, loss_cls: 3.6667, loss: 3.6667 +2024-07-25 10:56:44,702 - pyskl - INFO - Epoch [91][2400/3746] lr: 3.391e-02, eta: 2 days, 2:12:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6078, loss_cls: 3.6674, loss: 3.6674 +2024-07-25 10:58:06,083 - pyskl - INFO - Epoch [91][2500/3746] lr: 3.389e-02, eta: 2 days, 2:11:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6198, loss_cls: 3.6163, loss: 3.6163 +2024-07-25 10:59:27,661 - pyskl - INFO - Epoch [91][2600/3746] lr: 3.386e-02, eta: 2 days, 2:10:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6070, loss_cls: 3.6833, loss: 3.6833 +2024-07-25 11:00:49,248 - pyskl - INFO - Epoch [91][2700/3746] lr: 3.383e-02, eta: 2 days, 2:08:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6230, loss_cls: 3.5933, loss: 3.5933 +2024-07-25 11:02:11,295 - pyskl - INFO - Epoch [91][2800/3746] lr: 3.381e-02, eta: 2 days, 2:07:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6061, loss_cls: 3.6751, loss: 3.6751 +2024-07-25 11:03:32,660 - pyskl - INFO - Epoch [91][2900/3746] lr: 3.378e-02, eta: 2 days, 2:06:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6167, loss_cls: 3.6576, loss: 3.6576 +2024-07-25 11:04:54,010 - pyskl - INFO - Epoch [91][3000/3746] lr: 3.375e-02, eta: 2 days, 2:04:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6092, loss_cls: 3.6460, loss: 3.6460 +2024-07-25 11:06:15,884 - pyskl - INFO - Epoch [91][3100/3746] lr: 3.373e-02, eta: 2 days, 2:03:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6191, loss_cls: 3.6065, loss: 3.6065 +2024-07-25 11:07:37,694 - pyskl - INFO - Epoch [91][3200/3746] lr: 3.370e-02, eta: 2 days, 2:01:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6267, loss_cls: 3.6106, loss: 3.6106 +2024-07-25 11:08:58,897 - pyskl - INFO - Epoch [91][3300/3746] lr: 3.367e-02, eta: 2 days, 2:00:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6202, loss_cls: 3.6368, loss: 3.6368 +2024-07-25 11:10:20,705 - pyskl - INFO - Epoch [91][3400/3746] lr: 3.365e-02, eta: 2 days, 1:59:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6200, loss_cls: 3.6464, loss: 3.6464 +2024-07-25 11:11:41,876 - pyskl - INFO - Epoch [91][3500/3746] lr: 3.362e-02, eta: 2 days, 1:57:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6145, loss_cls: 3.6661, loss: 3.6661 +2024-07-25 11:13:03,548 - pyskl - INFO - Epoch [91][3600/3746] lr: 3.360e-02, eta: 2 days, 1:56:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6186, loss_cls: 3.5779, loss: 3.5779 +2024-07-25 11:14:25,267 - pyskl - INFO - Epoch [91][3700/3746] lr: 3.357e-02, eta: 2 days, 1:55:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6170, loss_cls: 3.6304, loss: 3.6304 +2024-07-25 11:15:05,372 - pyskl - INFO - Saving checkpoint at 91 epochs +2024-07-25 11:16:58,676 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 11:16:59,359 - pyskl - INFO - +top1_acc 0.2934 +top5_acc 0.5541 +2024-07-25 11:16:59,359 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 11:16:59,405 - pyskl - INFO - +mean_acc 0.2932 +2024-07-25 11:16:59,410 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_82.pth was removed +2024-07-25 11:16:59,685 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_91.pth. +2024-07-25 11:16:59,686 - pyskl - INFO - Best top1_acc is 0.2934 at 91 epoch. +2024-07-25 11:16:59,701 - pyskl - INFO - Epoch(val) [91][309] top1_acc: 0.2934, top5_acc: 0.5541, mean_class_accuracy: 0.2932 +2024-07-25 11:20:56,122 - pyskl - INFO - Epoch [92][100/3746] lr: 3.353e-02, eta: 2 days, 1:54:29, time: 2.364, data_time: 1.374, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6261, loss_cls: 3.5715, loss: 3.5715 +2024-07-25 11:22:18,209 - pyskl - INFO - Epoch [92][200/3746] lr: 3.350e-02, eta: 2 days, 1:53:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6339, loss_cls: 3.5652, loss: 3.5652 +2024-07-25 11:23:40,328 - pyskl - INFO - Epoch [92][300/3746] lr: 3.348e-02, eta: 2 days, 1:51:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3819, top5_acc: 0.6483, loss_cls: 3.4912, loss: 3.4912 +2024-07-25 11:25:02,347 - pyskl - INFO - Epoch [92][400/3746] lr: 3.345e-02, eta: 2 days, 1:50:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6173, loss_cls: 3.6242, loss: 3.6242 +2024-07-25 11:26:23,446 - pyskl - INFO - Epoch [92][500/3746] lr: 3.342e-02, eta: 2 days, 1:49:05, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6330, loss_cls: 3.5628, loss: 3.5628 +2024-07-25 11:27:45,001 - pyskl - INFO - Epoch [92][600/3746] lr: 3.340e-02, eta: 2 days, 1:47:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6177, loss_cls: 3.6453, loss: 3.6453 +2024-07-25 11:29:06,262 - pyskl - INFO - Epoch [92][700/3746] lr: 3.337e-02, eta: 2 days, 1:46:23, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6259, loss_cls: 3.6050, loss: 3.6050 +2024-07-25 11:30:28,124 - pyskl - INFO - Epoch [92][800/3746] lr: 3.335e-02, eta: 2 days, 1:45:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6245, loss_cls: 3.5649, loss: 3.5649 +2024-07-25 11:31:49,871 - pyskl - INFO - Epoch [92][900/3746] lr: 3.332e-02, eta: 2 days, 1:43:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6189, loss_cls: 3.6034, loss: 3.6034 +2024-07-25 11:33:11,138 - pyskl - INFO - Epoch [92][1000/3746] lr: 3.329e-02, eta: 2 days, 1:42:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6177, loss_cls: 3.6422, loss: 3.6422 +2024-07-25 11:34:32,644 - pyskl - INFO - Epoch [92][1100/3746] lr: 3.327e-02, eta: 2 days, 1:40:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6144, loss_cls: 3.6399, loss: 3.6399 +2024-07-25 11:35:54,636 - pyskl - INFO - Epoch [92][1200/3746] lr: 3.324e-02, eta: 2 days, 1:39:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6155, loss_cls: 3.6433, loss: 3.6433 +2024-07-25 11:37:16,090 - pyskl - INFO - Epoch [92][1300/3746] lr: 3.321e-02, eta: 2 days, 1:38:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6316, loss_cls: 3.5449, loss: 3.5449 +2024-07-25 11:38:37,483 - pyskl - INFO - Epoch [92][1400/3746] lr: 3.319e-02, eta: 2 days, 1:36:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6283, loss_cls: 3.5683, loss: 3.5683 +2024-07-25 11:39:59,429 - pyskl - INFO - Epoch [92][1500/3746] lr: 3.316e-02, eta: 2 days, 1:35:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6138, loss_cls: 3.6240, loss: 3.6240 +2024-07-25 11:41:21,087 - pyskl - INFO - Epoch [92][1600/3746] lr: 3.314e-02, eta: 2 days, 1:34:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6245, loss_cls: 3.5837, loss: 3.5837 +2024-07-25 11:42:42,715 - pyskl - INFO - Epoch [92][1700/3746] lr: 3.311e-02, eta: 2 days, 1:32:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6258, loss_cls: 3.6146, loss: 3.6146 +2024-07-25 11:44:05,312 - pyskl - INFO - Epoch [92][1800/3746] lr: 3.308e-02, eta: 2 days, 1:31:31, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6222, loss_cls: 3.6230, loss: 3.6230 +2024-07-25 11:45:27,376 - pyskl - INFO - Epoch [92][1900/3746] lr: 3.306e-02, eta: 2 days, 1:30:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6184, loss_cls: 3.6245, loss: 3.6245 +2024-07-25 11:46:49,952 - pyskl - INFO - Epoch [92][2000/3746] lr: 3.303e-02, eta: 2 days, 1:28:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6164, loss_cls: 3.6442, loss: 3.6442 +2024-07-25 11:48:11,660 - pyskl - INFO - Epoch [92][2100/3746] lr: 3.300e-02, eta: 2 days, 1:27:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6277, loss_cls: 3.6157, loss: 3.6157 +2024-07-25 11:49:34,153 - pyskl - INFO - Epoch [92][2200/3746] lr: 3.298e-02, eta: 2 days, 1:26:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6161, loss_cls: 3.6528, loss: 3.6528 +2024-07-25 11:50:55,802 - pyskl - INFO - Epoch [92][2300/3746] lr: 3.295e-02, eta: 2 days, 1:24:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6152, loss_cls: 3.6350, loss: 3.6350 +2024-07-25 11:52:17,701 - pyskl - INFO - Epoch [92][2400/3746] lr: 3.292e-02, eta: 2 days, 1:23:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6255, loss_cls: 3.6061, loss: 3.6061 +2024-07-25 11:53:39,352 - pyskl - INFO - Epoch [92][2500/3746] lr: 3.290e-02, eta: 2 days, 1:22:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6320, loss_cls: 3.5758, loss: 3.5758 +2024-07-25 11:55:01,074 - pyskl - INFO - Epoch [92][2600/3746] lr: 3.287e-02, eta: 2 days, 1:20:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6133, loss_cls: 3.6630, loss: 3.6630 +2024-07-25 11:56:23,181 - pyskl - INFO - Epoch [92][2700/3746] lr: 3.285e-02, eta: 2 days, 1:19:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6216, loss_cls: 3.6235, loss: 3.6235 +2024-07-25 11:57:44,301 - pyskl - INFO - Epoch [92][2800/3746] lr: 3.282e-02, eta: 2 days, 1:18:01, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6202, loss_cls: 3.6443, loss: 3.6443 +2024-07-25 11:59:06,477 - pyskl - INFO - Epoch [92][2900/3746] lr: 3.279e-02, eta: 2 days, 1:16:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6178, loss_cls: 3.6208, loss: 3.6208 +2024-07-25 12:00:28,624 - pyskl - INFO - Epoch [92][3000/3746] lr: 3.277e-02, eta: 2 days, 1:15:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6203, loss_cls: 3.6117, loss: 3.6117 +2024-07-25 12:01:50,210 - pyskl - INFO - Epoch [92][3100/3746] lr: 3.274e-02, eta: 2 days, 1:13:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6302, loss_cls: 3.6117, loss: 3.6117 +2024-07-25 12:03:12,223 - pyskl - INFO - Epoch [92][3200/3746] lr: 3.271e-02, eta: 2 days, 1:12:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3594, top5_acc: 0.6212, loss_cls: 3.6054, loss: 3.6054 +2024-07-25 12:04:33,787 - pyskl - INFO - Epoch [92][3300/3746] lr: 3.269e-02, eta: 2 days, 1:11:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6175, loss_cls: 3.6427, loss: 3.6427 +2024-07-25 12:05:55,505 - pyskl - INFO - Epoch [92][3400/3746] lr: 3.266e-02, eta: 2 days, 1:09:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6164, loss_cls: 3.6252, loss: 3.6252 +2024-07-25 12:07:17,201 - pyskl - INFO - Epoch [92][3500/3746] lr: 3.264e-02, eta: 2 days, 1:08:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6275, loss_cls: 3.6031, loss: 3.6031 +2024-07-25 12:08:38,763 - pyskl - INFO - Epoch [92][3600/3746] lr: 3.261e-02, eta: 2 days, 1:07:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6144, loss_cls: 3.6575, loss: 3.6575 +2024-07-25 12:10:00,623 - pyskl - INFO - Epoch [92][3700/3746] lr: 3.258e-02, eta: 2 days, 1:05:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6248, loss_cls: 3.6162, loss: 3.6162 +2024-07-25 12:10:40,646 - pyskl - INFO - Saving checkpoint at 92 epochs +2024-07-25 12:12:33,018 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 12:12:33,682 - pyskl - INFO - +top1_acc 0.2913 +top5_acc 0.5490 +2024-07-25 12:12:33,682 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 12:12:33,724 - pyskl - INFO - +mean_acc 0.2911 +2024-07-25 12:12:33,737 - pyskl - INFO - Epoch(val) [92][309] top1_acc: 0.2913, top5_acc: 0.5490, mean_class_accuracy: 0.2911 +2024-07-25 12:16:29,874 - pyskl - INFO - Epoch [93][100/3746] lr: 3.255e-02, eta: 2 days, 1:05:07, time: 2.361, data_time: 1.366, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6334, loss_cls: 3.5373, loss: 3.5373 +2024-07-25 12:17:52,178 - pyskl - INFO - Epoch [93][200/3746] lr: 3.252e-02, eta: 2 days, 1:03:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6177, loss_cls: 3.5933, loss: 3.5933 +2024-07-25 12:19:15,033 - pyskl - INFO - Epoch [93][300/3746] lr: 3.249e-02, eta: 2 days, 1:02:26, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6292, loss_cls: 3.5884, loss: 3.5884 +2024-07-25 12:20:36,597 - pyskl - INFO - Epoch [93][400/3746] lr: 3.247e-02, eta: 2 days, 1:01:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6378, loss_cls: 3.5350, loss: 3.5350 +2024-07-25 12:21:58,109 - pyskl - INFO - Epoch [93][500/3746] lr: 3.244e-02, eta: 2 days, 0:59:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6222, loss_cls: 3.6134, loss: 3.6134 +2024-07-25 12:23:19,581 - pyskl - INFO - Epoch [93][600/3746] lr: 3.241e-02, eta: 2 days, 0:58:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6219, loss_cls: 3.6135, loss: 3.6135 +2024-07-25 12:24:41,465 - pyskl - INFO - Epoch [93][700/3746] lr: 3.239e-02, eta: 2 days, 0:57:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6341, loss_cls: 3.5740, loss: 3.5740 +2024-07-25 12:26:03,268 - pyskl - INFO - Epoch [93][800/3746] lr: 3.236e-02, eta: 2 days, 0:55:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6244, loss_cls: 3.5936, loss: 3.5936 +2024-07-25 12:27:24,391 - pyskl - INFO - Epoch [93][900/3746] lr: 3.234e-02, eta: 2 days, 0:54:18, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6358, loss_cls: 3.5598, loss: 3.5598 +2024-07-25 12:28:46,171 - pyskl - INFO - Epoch [93][1000/3746] lr: 3.231e-02, eta: 2 days, 0:52:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6152, loss_cls: 3.6112, loss: 3.6112 +2024-07-25 12:30:07,645 - pyskl - INFO - Epoch [93][1100/3746] lr: 3.228e-02, eta: 2 days, 0:51:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6353, loss_cls: 3.5407, loss: 3.5407 +2024-07-25 12:31:29,399 - pyskl - INFO - Epoch [93][1200/3746] lr: 3.226e-02, eta: 2 days, 0:50:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6378, loss_cls: 3.5326, loss: 3.5326 +2024-07-25 12:32:50,863 - pyskl - INFO - Epoch [93][1300/3746] lr: 3.223e-02, eta: 2 days, 0:48:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6211, loss_cls: 3.6180, loss: 3.6180 +2024-07-25 12:34:12,330 - pyskl - INFO - Epoch [93][1400/3746] lr: 3.221e-02, eta: 2 days, 0:47:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6312, loss_cls: 3.6045, loss: 3.6045 +2024-07-25 12:35:33,774 - pyskl - INFO - Epoch [93][1500/3746] lr: 3.218e-02, eta: 2 days, 0:46:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6220, loss_cls: 3.6161, loss: 3.6161 +2024-07-25 12:36:55,179 - pyskl - INFO - Epoch [93][1600/3746] lr: 3.215e-02, eta: 2 days, 0:44:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6289, loss_cls: 3.6028, loss: 3.6028 +2024-07-25 12:38:17,388 - pyskl - INFO - Epoch [93][1700/3746] lr: 3.213e-02, eta: 2 days, 0:43:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6177, loss_cls: 3.6351, loss: 3.6351 +2024-07-25 12:39:39,224 - pyskl - INFO - Epoch [93][1800/3746] lr: 3.210e-02, eta: 2 days, 0:42:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6323, loss_cls: 3.5760, loss: 3.5760 +2024-07-25 12:41:00,638 - pyskl - INFO - Epoch [93][1900/3746] lr: 3.207e-02, eta: 2 days, 0:40:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6266, loss_cls: 3.5882, loss: 3.5882 +2024-07-25 12:42:23,093 - pyskl - INFO - Epoch [93][2000/3746] lr: 3.205e-02, eta: 2 days, 0:39:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6238, loss_cls: 3.5770, loss: 3.5770 +2024-07-25 12:43:44,833 - pyskl - INFO - Epoch [93][2100/3746] lr: 3.202e-02, eta: 2 days, 0:38:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6180, loss_cls: 3.6256, loss: 3.6256 +2024-07-25 12:45:06,286 - pyskl - INFO - Epoch [93][2200/3746] lr: 3.200e-02, eta: 2 days, 0:36:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6245, loss_cls: 3.6012, loss: 3.6012 +2024-07-25 12:46:28,366 - pyskl - INFO - Epoch [93][2300/3746] lr: 3.197e-02, eta: 2 days, 0:35:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6362, loss_cls: 3.5547, loss: 3.5547 +2024-07-25 12:47:50,239 - pyskl - INFO - Epoch [93][2400/3746] lr: 3.194e-02, eta: 2 days, 0:34:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6239, loss_cls: 3.5866, loss: 3.5866 +2024-07-25 12:49:11,912 - pyskl - INFO - Epoch [93][2500/3746] lr: 3.192e-02, eta: 2 days, 0:32:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6172, loss_cls: 3.6208, loss: 3.6208 +2024-07-25 12:50:33,601 - pyskl - INFO - Epoch [93][2600/3746] lr: 3.189e-02, eta: 2 days, 0:31:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6227, loss_cls: 3.5987, loss: 3.5987 +2024-07-25 12:51:55,863 - pyskl - INFO - Epoch [93][2700/3746] lr: 3.187e-02, eta: 2 days, 0:29:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6266, loss_cls: 3.6125, loss: 3.6125 +2024-07-25 12:53:17,295 - pyskl - INFO - Epoch [93][2800/3746] lr: 3.184e-02, eta: 2 days, 0:28:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6239, loss_cls: 3.6398, loss: 3.6398 +2024-07-25 12:54:39,001 - pyskl - INFO - Epoch [93][2900/3746] lr: 3.181e-02, eta: 2 days, 0:27:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6395, loss_cls: 3.5281, loss: 3.5281 +2024-07-25 12:56:00,545 - pyskl - INFO - Epoch [93][3000/3746] lr: 3.179e-02, eta: 2 days, 0:25:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3739, top5_acc: 0.6319, loss_cls: 3.5331, loss: 3.5331 +2024-07-25 12:57:21,835 - pyskl - INFO - Epoch [93][3100/3746] lr: 3.176e-02, eta: 2 days, 0:24:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6081, loss_cls: 3.6562, loss: 3.6562 +2024-07-25 12:58:43,559 - pyskl - INFO - Epoch [93][3200/3746] lr: 3.174e-02, eta: 2 days, 0:23:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6275, loss_cls: 3.6106, loss: 3.6106 +2024-07-25 13:00:05,242 - pyskl - INFO - Epoch [93][3300/3746] lr: 3.171e-02, eta: 2 days, 0:21:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6156, loss_cls: 3.6463, loss: 3.6463 +2024-07-25 13:01:27,250 - pyskl - INFO - Epoch [93][3400/3746] lr: 3.168e-02, eta: 2 days, 0:20:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6111, loss_cls: 3.6547, loss: 3.6547 +2024-07-25 13:02:48,666 - pyskl - INFO - Epoch [93][3500/3746] lr: 3.166e-02, eta: 2 days, 0:19:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6377, loss_cls: 3.5437, loss: 3.5437 +2024-07-25 13:04:09,985 - pyskl - INFO - Epoch [93][3600/3746] lr: 3.163e-02, eta: 2 days, 0:17:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6211, loss_cls: 3.5985, loss: 3.5985 +2024-07-25 13:05:31,647 - pyskl - INFO - Epoch [93][3700/3746] lr: 3.161e-02, eta: 2 days, 0:16:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6173, loss_cls: 3.6501, loss: 3.6501 +2024-07-25 13:06:11,541 - pyskl - INFO - Saving checkpoint at 93 epochs +2024-07-25 13:08:04,293 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 13:08:04,960 - pyskl - INFO - +top1_acc 0.2944 +top5_acc 0.5498 +2024-07-25 13:08:04,960 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 13:08:05,004 - pyskl - INFO - +mean_acc 0.2942 +2024-07-25 13:08:05,009 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_91.pth was removed +2024-07-25 13:08:05,281 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_93.pth. +2024-07-25 13:08:05,282 - pyskl - INFO - Best top1_acc is 0.2944 at 93 epoch. +2024-07-25 13:08:05,296 - pyskl - INFO - Epoch(val) [93][309] top1_acc: 0.2944, top5_acc: 0.5498, mean_class_accuracy: 0.2942 +2024-07-25 13:12:04,007 - pyskl - INFO - Epoch [94][100/3746] lr: 3.157e-02, eta: 2 days, 0:15:40, time: 2.387, data_time: 1.389, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6398, loss_cls: 3.4889, loss: 3.4889 +2024-07-25 13:13:26,545 - pyskl - INFO - Epoch [94][200/3746] lr: 3.154e-02, eta: 2 days, 0:14:19, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6381, loss_cls: 3.5305, loss: 3.5305 +2024-07-25 13:14:48,838 - pyskl - INFO - Epoch [94][300/3746] lr: 3.152e-02, eta: 2 days, 0:12:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6423, loss_cls: 3.5259, loss: 3.5259 +2024-07-25 13:16:10,782 - pyskl - INFO - Epoch [94][400/3746] lr: 3.149e-02, eta: 2 days, 0:11:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6289, loss_cls: 3.5966, loss: 3.5966 +2024-07-25 13:17:32,699 - pyskl - INFO - Epoch [94][500/3746] lr: 3.146e-02, eta: 2 days, 0:10:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6269, loss_cls: 3.5813, loss: 3.5813 +2024-07-25 13:18:54,688 - pyskl - INFO - Epoch [94][600/3746] lr: 3.144e-02, eta: 2 days, 0:08:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6269, loss_cls: 3.5432, loss: 3.5432 +2024-07-25 13:20:16,870 - pyskl - INFO - Epoch [94][700/3746] lr: 3.141e-02, eta: 2 days, 0:07:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6288, loss_cls: 3.5664, loss: 3.5664 +2024-07-25 13:21:38,327 - pyskl - INFO - Epoch [94][800/3746] lr: 3.139e-02, eta: 2 days, 0:06:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6275, loss_cls: 3.5741, loss: 3.5741 +2024-07-25 13:23:00,319 - pyskl - INFO - Epoch [94][900/3746] lr: 3.136e-02, eta: 2 days, 0:04:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6241, loss_cls: 3.5878, loss: 3.5878 +2024-07-25 13:24:22,135 - pyskl - INFO - Epoch [94][1000/3746] lr: 3.133e-02, eta: 2 days, 0:03:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6320, loss_cls: 3.5476, loss: 3.5476 +2024-07-25 13:25:43,473 - pyskl - INFO - Epoch [94][1100/3746] lr: 3.131e-02, eta: 2 days, 0:02:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6236, loss_cls: 3.6122, loss: 3.6122 +2024-07-25 13:27:05,422 - pyskl - INFO - Epoch [94][1200/3746] lr: 3.128e-02, eta: 2 days, 0:00:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6389, loss_cls: 3.5398, loss: 3.5398 +2024-07-25 13:28:27,344 - pyskl - INFO - Epoch [94][1300/3746] lr: 3.126e-02, eta: 1 day, 23:59:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6230, loss_cls: 3.5848, loss: 3.5848 +2024-07-25 13:29:48,835 - pyskl - INFO - Epoch [94][1400/3746] lr: 3.123e-02, eta: 1 day, 23:58:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6212, loss_cls: 3.5927, loss: 3.5927 +2024-07-25 13:31:10,607 - pyskl - INFO - Epoch [94][1500/3746] lr: 3.120e-02, eta: 1 day, 23:56:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6288, loss_cls: 3.5612, loss: 3.5612 +2024-07-25 13:32:32,274 - pyskl - INFO - Epoch [94][1600/3746] lr: 3.118e-02, eta: 1 day, 23:55:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6370, loss_cls: 3.5664, loss: 3.5664 +2024-07-25 13:33:53,887 - pyskl - INFO - Epoch [94][1700/3746] lr: 3.115e-02, eta: 1 day, 23:54:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6200, loss_cls: 3.5960, loss: 3.5960 +2024-07-25 13:35:16,326 - pyskl - INFO - Epoch [94][1800/3746] lr: 3.113e-02, eta: 1 day, 23:52:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6322, loss_cls: 3.5795, loss: 3.5795 +2024-07-25 13:36:38,219 - pyskl - INFO - Epoch [94][1900/3746] lr: 3.110e-02, eta: 1 day, 23:51:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3748, top5_acc: 0.6370, loss_cls: 3.5089, loss: 3.5089 +2024-07-25 13:38:00,926 - pyskl - INFO - Epoch [94][2000/3746] lr: 3.108e-02, eta: 1 day, 23:49:59, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6278, loss_cls: 3.5970, loss: 3.5970 +2024-07-25 13:39:22,586 - pyskl - INFO - Epoch [94][2100/3746] lr: 3.105e-02, eta: 1 day, 23:48:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6239, loss_cls: 3.5762, loss: 3.5762 +2024-07-25 13:40:44,539 - pyskl - INFO - Epoch [94][2200/3746] lr: 3.102e-02, eta: 1 day, 23:47:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6214, loss_cls: 3.5888, loss: 3.5888 +2024-07-25 13:42:06,647 - pyskl - INFO - Epoch [94][2300/3746] lr: 3.100e-02, eta: 1 day, 23:45:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6369, loss_cls: 3.5517, loss: 3.5517 +2024-07-25 13:43:28,504 - pyskl - INFO - Epoch [94][2400/3746] lr: 3.097e-02, eta: 1 day, 23:44:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6234, loss_cls: 3.5547, loss: 3.5547 +2024-07-25 13:44:50,456 - pyskl - INFO - Epoch [94][2500/3746] lr: 3.095e-02, eta: 1 day, 23:43:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6250, loss_cls: 3.6261, loss: 3.6261 +2024-07-25 13:46:12,429 - pyskl - INFO - Epoch [94][2600/3746] lr: 3.092e-02, eta: 1 day, 23:41:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6294, loss_cls: 3.5952, loss: 3.5952 +2024-07-25 13:47:34,752 - pyskl - INFO - Epoch [94][2700/3746] lr: 3.089e-02, eta: 1 day, 23:40:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6264, loss_cls: 3.5681, loss: 3.5681 +2024-07-25 13:48:56,868 - pyskl - INFO - Epoch [94][2800/3746] lr: 3.087e-02, eta: 1 day, 23:39:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6347, loss_cls: 3.5837, loss: 3.5837 +2024-07-25 13:50:19,294 - pyskl - INFO - Epoch [94][2900/3746] lr: 3.084e-02, eta: 1 day, 23:37:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6219, loss_cls: 3.6183, loss: 3.6183 +2024-07-25 13:51:41,197 - pyskl - INFO - Epoch [94][3000/3746] lr: 3.082e-02, eta: 1 day, 23:36:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6298, loss_cls: 3.6019, loss: 3.6019 +2024-07-25 13:53:03,017 - pyskl - INFO - Epoch [94][3100/3746] lr: 3.079e-02, eta: 1 day, 23:35:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6236, loss_cls: 3.5820, loss: 3.5820 +2024-07-25 13:54:24,228 - pyskl - INFO - Epoch [94][3200/3746] lr: 3.077e-02, eta: 1 day, 23:33:46, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6280, loss_cls: 3.5758, loss: 3.5758 +2024-07-25 13:55:46,127 - pyskl - INFO - Epoch [94][3300/3746] lr: 3.074e-02, eta: 1 day, 23:32:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6180, loss_cls: 3.6183, loss: 3.6183 +2024-07-25 13:57:07,874 - pyskl - INFO - Epoch [94][3400/3746] lr: 3.071e-02, eta: 1 day, 23:31:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6195, loss_cls: 3.6473, loss: 3.6473 +2024-07-25 13:58:29,511 - pyskl - INFO - Epoch [94][3500/3746] lr: 3.069e-02, eta: 1 day, 23:29:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6175, loss_cls: 3.6069, loss: 3.6069 +2024-07-25 13:59:50,935 - pyskl - INFO - Epoch [94][3600/3746] lr: 3.066e-02, eta: 1 day, 23:28:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6244, loss_cls: 3.5472, loss: 3.5472 +2024-07-25 14:01:12,658 - pyskl - INFO - Epoch [94][3700/3746] lr: 3.064e-02, eta: 1 day, 23:27:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6233, loss_cls: 3.5894, loss: 3.5894 +2024-07-25 14:01:52,646 - pyskl - INFO - Saving checkpoint at 94 epochs +2024-07-25 14:03:45,704 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 14:03:46,399 - pyskl - INFO - +top1_acc 0.2938 +top5_acc 0.5419 +2024-07-25 14:03:46,399 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 14:03:46,452 - pyskl - INFO - +mean_acc 0.2935 +2024-07-25 14:03:46,465 - pyskl - INFO - Epoch(val) [94][309] top1_acc: 0.2938, top5_acc: 0.5419, mean_class_accuracy: 0.2935 +2024-07-25 14:07:40,963 - pyskl - INFO - Epoch [95][100/3746] lr: 3.060e-02, eta: 1 day, 23:26:10, time: 2.345, data_time: 1.349, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6391, loss_cls: 3.5134, loss: 3.5134 +2024-07-25 14:09:03,584 - pyskl - INFO - Epoch [95][200/3746] lr: 3.057e-02, eta: 1 day, 23:24:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6369, loss_cls: 3.5203, loss: 3.5203 +2024-07-25 14:10:26,296 - pyskl - INFO - Epoch [95][300/3746] lr: 3.055e-02, eta: 1 day, 23:23:29, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6281, loss_cls: 3.5427, loss: 3.5427 +2024-07-25 14:11:48,990 - pyskl - INFO - Epoch [95][400/3746] lr: 3.052e-02, eta: 1 day, 23:22:08, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6314, loss_cls: 3.5545, loss: 3.5545 +2024-07-25 14:13:11,232 - pyskl - INFO - Epoch [95][500/3746] lr: 3.050e-02, eta: 1 day, 23:20:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6227, loss_cls: 3.6034, loss: 3.6034 +2024-07-25 14:14:33,630 - pyskl - INFO - Epoch [95][600/3746] lr: 3.047e-02, eta: 1 day, 23:19:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6336, loss_cls: 3.5568, loss: 3.5568 +2024-07-25 14:15:55,551 - pyskl - INFO - Epoch [95][700/3746] lr: 3.044e-02, eta: 1 day, 23:18:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6333, loss_cls: 3.5286, loss: 3.5286 +2024-07-25 14:17:17,878 - pyskl - INFO - Epoch [95][800/3746] lr: 3.042e-02, eta: 1 day, 23:16:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6355, loss_cls: 3.5294, loss: 3.5294 +2024-07-25 14:18:39,483 - pyskl - INFO - Epoch [95][900/3746] lr: 3.039e-02, eta: 1 day, 23:15:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6223, loss_cls: 3.5919, loss: 3.5919 +2024-07-25 14:20:00,443 - pyskl - INFO - Epoch [95][1000/3746] lr: 3.037e-02, eta: 1 day, 23:14:01, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6380, loss_cls: 3.5447, loss: 3.5447 +2024-07-25 14:21:22,004 - pyskl - INFO - Epoch [95][1100/3746] lr: 3.034e-02, eta: 1 day, 23:12:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6316, loss_cls: 3.5547, loss: 3.5547 +2024-07-25 14:22:43,655 - pyskl - INFO - Epoch [95][1200/3746] lr: 3.032e-02, eta: 1 day, 23:11:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6398, loss_cls: 3.5108, loss: 3.5108 +2024-07-25 14:24:05,099 - pyskl - INFO - Epoch [95][1300/3746] lr: 3.029e-02, eta: 1 day, 23:09:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6328, loss_cls: 3.5364, loss: 3.5364 +2024-07-25 14:25:26,735 - pyskl - INFO - Epoch [95][1400/3746] lr: 3.026e-02, eta: 1 day, 23:08:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6334, loss_cls: 3.5564, loss: 3.5564 +2024-07-25 14:26:48,308 - pyskl - INFO - Epoch [95][1500/3746] lr: 3.024e-02, eta: 1 day, 23:07:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6308, loss_cls: 3.5593, loss: 3.5593 +2024-07-25 14:28:10,045 - pyskl - INFO - Epoch [95][1600/3746] lr: 3.021e-02, eta: 1 day, 23:05:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6288, loss_cls: 3.5265, loss: 3.5265 +2024-07-25 14:29:31,745 - pyskl - INFO - Epoch [95][1700/3746] lr: 3.019e-02, eta: 1 day, 23:04:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6317, loss_cls: 3.5499, loss: 3.5499 +2024-07-25 14:30:54,175 - pyskl - INFO - Epoch [95][1800/3746] lr: 3.016e-02, eta: 1 day, 23:03:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6316, loss_cls: 3.5489, loss: 3.5489 +2024-07-25 14:32:16,091 - pyskl - INFO - Epoch [95][1900/3746] lr: 3.014e-02, eta: 1 day, 23:01:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6214, loss_cls: 3.6035, loss: 3.6035 +2024-07-25 14:33:38,071 - pyskl - INFO - Epoch [95][2000/3746] lr: 3.011e-02, eta: 1 day, 23:00:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6297, loss_cls: 3.5894, loss: 3.5894 +2024-07-25 14:34:59,951 - pyskl - INFO - Epoch [95][2100/3746] lr: 3.008e-02, eta: 1 day, 22:59:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6191, loss_cls: 3.6311, loss: 3.6311 +2024-07-25 14:36:21,554 - pyskl - INFO - Epoch [95][2200/3746] lr: 3.006e-02, eta: 1 day, 22:57:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6352, loss_cls: 3.5251, loss: 3.5251 +2024-07-25 14:37:43,623 - pyskl - INFO - Epoch [95][2300/3746] lr: 3.003e-02, eta: 1 day, 22:56:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6211, loss_cls: 3.5769, loss: 3.5769 +2024-07-25 14:39:04,863 - pyskl - INFO - Epoch [95][2400/3746] lr: 3.001e-02, eta: 1 day, 22:55:03, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6225, loss_cls: 3.5799, loss: 3.5799 +2024-07-25 14:40:26,298 - pyskl - INFO - Epoch [95][2500/3746] lr: 2.998e-02, eta: 1 day, 22:53:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6173, loss_cls: 3.6172, loss: 3.6172 +2024-07-25 14:41:49,168 - pyskl - INFO - Epoch [95][2600/3746] lr: 2.996e-02, eta: 1 day, 22:52:21, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6250, loss_cls: 3.5642, loss: 3.5642 +2024-07-25 14:43:10,927 - pyskl - INFO - Epoch [95][2700/3746] lr: 2.993e-02, eta: 1 day, 22:51:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6377, loss_cls: 3.5394, loss: 3.5394 +2024-07-25 14:44:32,652 - pyskl - INFO - Epoch [95][2800/3746] lr: 2.991e-02, eta: 1 day, 22:49:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6267, loss_cls: 3.5975, loss: 3.5975 +2024-07-25 14:45:54,251 - pyskl - INFO - Epoch [95][2900/3746] lr: 2.988e-02, eta: 1 day, 22:48:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6127, loss_cls: 3.6381, loss: 3.6381 +2024-07-25 14:47:15,433 - pyskl - INFO - Epoch [95][3000/3746] lr: 2.985e-02, eta: 1 day, 22:46:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6238, loss_cls: 3.5780, loss: 3.5780 +2024-07-25 14:48:37,464 - pyskl - INFO - Epoch [95][3100/3746] lr: 2.983e-02, eta: 1 day, 22:45:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6295, loss_cls: 3.5417, loss: 3.5417 +2024-07-25 14:49:59,228 - pyskl - INFO - Epoch [95][3200/3746] lr: 2.980e-02, eta: 1 day, 22:44:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6233, loss_cls: 3.6373, loss: 3.6373 +2024-07-25 14:51:21,088 - pyskl - INFO - Epoch [95][3300/3746] lr: 2.978e-02, eta: 1 day, 22:42:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6269, loss_cls: 3.5906, loss: 3.5906 +2024-07-25 14:52:42,730 - pyskl - INFO - Epoch [95][3400/3746] lr: 2.975e-02, eta: 1 day, 22:41:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6288, loss_cls: 3.5814, loss: 3.5814 +2024-07-25 14:54:04,061 - pyskl - INFO - Epoch [95][3500/3746] lr: 2.973e-02, eta: 1 day, 22:40:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6336, loss_cls: 3.5356, loss: 3.5356 +2024-07-25 14:55:26,212 - pyskl - INFO - Epoch [95][3600/3746] lr: 2.970e-02, eta: 1 day, 22:38:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6180, loss_cls: 3.5970, loss: 3.5970 +2024-07-25 14:56:47,713 - pyskl - INFO - Epoch [95][3700/3746] lr: 2.968e-02, eta: 1 day, 22:37:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6352, loss_cls: 3.5562, loss: 3.5562 +2024-07-25 14:57:27,236 - pyskl - INFO - Saving checkpoint at 95 epochs +2024-07-25 14:59:19,911 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 14:59:20,582 - pyskl - INFO - +top1_acc 0.3047 +top5_acc 0.5641 +2024-07-25 14:59:20,582 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 14:59:20,630 - pyskl - INFO - +mean_acc 0.3047 +2024-07-25 14:59:20,635 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_93.pth was removed +2024-07-25 14:59:20,951 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_95.pth. +2024-07-25 14:59:20,952 - pyskl - INFO - Best top1_acc is 0.3047 at 95 epoch. +2024-07-25 14:59:20,966 - pyskl - INFO - Epoch(val) [95][309] top1_acc: 0.3047, top5_acc: 0.5641, mean_class_accuracy: 0.3047 +2024-07-25 15:03:19,185 - pyskl - INFO - Epoch [96][100/3746] lr: 2.964e-02, eta: 1 day, 22:36:37, time: 2.382, data_time: 1.379, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6331, loss_cls: 3.5191, loss: 3.5191 +2024-07-25 15:04:42,375 - pyskl - INFO - Epoch [96][200/3746] lr: 2.961e-02, eta: 1 day, 22:35:16, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6386, loss_cls: 3.5074, loss: 3.5074 +2024-07-25 15:06:06,353 - pyskl - INFO - Epoch [96][300/3746] lr: 2.959e-02, eta: 1 day, 22:33:56, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6367, loss_cls: 3.5191, loss: 3.5191 +2024-07-25 15:07:29,710 - pyskl - INFO - Epoch [96][400/3746] lr: 2.956e-02, eta: 1 day, 22:32:36, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6317, loss_cls: 3.5552, loss: 3.5552 +2024-07-25 15:08:52,320 - pyskl - INFO - Epoch [96][500/3746] lr: 2.954e-02, eta: 1 day, 22:31:15, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6286, loss_cls: 3.5670, loss: 3.5670 +2024-07-25 15:10:15,669 - pyskl - INFO - Epoch [96][600/3746] lr: 2.951e-02, eta: 1 day, 22:29:54, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3731, top5_acc: 0.6312, loss_cls: 3.5543, loss: 3.5543 +2024-07-25 15:11:38,471 - pyskl - INFO - Epoch [96][700/3746] lr: 2.948e-02, eta: 1 day, 22:28:34, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3739, top5_acc: 0.6341, loss_cls: 3.5451, loss: 3.5451 +2024-07-25 15:13:02,166 - pyskl - INFO - Epoch [96][800/3746] lr: 2.946e-02, eta: 1 day, 22:27:13, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6466, loss_cls: 3.5030, loss: 3.5030 +2024-07-25 15:14:25,909 - pyskl - INFO - Epoch [96][900/3746] lr: 2.943e-02, eta: 1 day, 22:25:53, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6255, loss_cls: 3.5843, loss: 3.5843 +2024-07-25 15:15:49,268 - pyskl - INFO - Epoch [96][1000/3746] lr: 2.941e-02, eta: 1 day, 22:24:33, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6497, loss_cls: 3.4389, loss: 3.4389 +2024-07-25 15:17:12,882 - pyskl - INFO - Epoch [96][1100/3746] lr: 2.938e-02, eta: 1 day, 22:23:13, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6272, loss_cls: 3.5706, loss: 3.5706 +2024-07-25 15:18:36,623 - pyskl - INFO - Epoch [96][1200/3746] lr: 2.936e-02, eta: 1 day, 22:21:52, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6386, loss_cls: 3.5202, loss: 3.5202 +2024-07-25 15:20:00,243 - pyskl - INFO - Epoch [96][1300/3746] lr: 2.933e-02, eta: 1 day, 22:20:32, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6297, loss_cls: 3.5756, loss: 3.5756 +2024-07-25 15:21:23,615 - pyskl - INFO - Epoch [96][1400/3746] lr: 2.931e-02, eta: 1 day, 22:19:12, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6292, loss_cls: 3.6052, loss: 3.6052 +2024-07-25 15:22:47,388 - pyskl - INFO - Epoch [96][1500/3746] lr: 2.928e-02, eta: 1 day, 22:17:51, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6430, loss_cls: 3.5420, loss: 3.5420 +2024-07-25 15:24:11,316 - pyskl - INFO - Epoch [96][1600/3746] lr: 2.926e-02, eta: 1 day, 22:16:31, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6303, loss_cls: 3.5571, loss: 3.5571 +2024-07-25 15:25:35,343 - pyskl - INFO - Epoch [96][1700/3746] lr: 2.923e-02, eta: 1 day, 22:15:11, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3723, top5_acc: 0.6275, loss_cls: 3.5490, loss: 3.5490 +2024-07-25 15:26:58,904 - pyskl - INFO - Epoch [96][1800/3746] lr: 2.920e-02, eta: 1 day, 22:13:51, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6370, loss_cls: 3.5418, loss: 3.5418 +2024-07-25 15:28:21,257 - pyskl - INFO - Epoch [96][1900/3746] lr: 2.918e-02, eta: 1 day, 22:12:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6306, loss_cls: 3.5385, loss: 3.5385 +2024-07-25 15:29:44,065 - pyskl - INFO - Epoch [96][2000/3746] lr: 2.915e-02, eta: 1 day, 22:11:09, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6291, loss_cls: 3.5620, loss: 3.5620 +2024-07-25 15:31:07,861 - pyskl - INFO - Epoch [96][2100/3746] lr: 2.913e-02, eta: 1 day, 22:09:49, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6369, loss_cls: 3.5446, loss: 3.5446 +2024-07-25 15:32:31,709 - pyskl - INFO - Epoch [96][2200/3746] lr: 2.910e-02, eta: 1 day, 22:08:29, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6241, loss_cls: 3.5894, loss: 3.5894 +2024-07-25 15:33:55,402 - pyskl - INFO - Epoch [96][2300/3746] lr: 2.908e-02, eta: 1 day, 22:07:09, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6314, loss_cls: 3.5667, loss: 3.5667 +2024-07-25 15:35:18,984 - pyskl - INFO - Epoch [96][2400/3746] lr: 2.905e-02, eta: 1 day, 22:05:48, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6344, loss_cls: 3.5413, loss: 3.5413 +2024-07-25 15:36:42,576 - pyskl - INFO - Epoch [96][2500/3746] lr: 2.903e-02, eta: 1 day, 22:04:28, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6289, loss_cls: 3.5517, loss: 3.5517 +2024-07-25 15:38:05,946 - pyskl - INFO - Epoch [96][2600/3746] lr: 2.900e-02, eta: 1 day, 22:03:08, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6327, loss_cls: 3.5380, loss: 3.5380 +2024-07-25 15:39:29,517 - pyskl - INFO - Epoch [96][2700/3746] lr: 2.898e-02, eta: 1 day, 22:01:47, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6434, loss_cls: 3.4916, loss: 3.4916 +2024-07-25 15:40:51,296 - pyskl - INFO - Epoch [96][2800/3746] lr: 2.895e-02, eta: 1 day, 22:00:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6219, loss_cls: 3.5593, loss: 3.5593 +2024-07-25 15:42:14,455 - pyskl - INFO - Epoch [96][2900/3746] lr: 2.893e-02, eta: 1 day, 21:59:05, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3622, top5_acc: 0.6341, loss_cls: 3.5784, loss: 3.5784 +2024-07-25 15:43:38,051 - pyskl - INFO - Epoch [96][3000/3746] lr: 2.890e-02, eta: 1 day, 21:57:45, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6362, loss_cls: 3.5453, loss: 3.5453 +2024-07-25 15:45:01,279 - pyskl - INFO - Epoch [96][3100/3746] lr: 2.887e-02, eta: 1 day, 21:56:25, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6336, loss_cls: 3.5530, loss: 3.5530 +2024-07-25 15:46:25,305 - pyskl - INFO - Epoch [96][3200/3746] lr: 2.885e-02, eta: 1 day, 21:55:05, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6264, loss_cls: 3.5624, loss: 3.5624 +2024-07-25 15:47:48,636 - pyskl - INFO - Epoch [96][3300/3746] lr: 2.882e-02, eta: 1 day, 21:53:44, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6283, loss_cls: 3.5678, loss: 3.5678 +2024-07-25 15:49:11,728 - pyskl - INFO - Epoch [96][3400/3746] lr: 2.880e-02, eta: 1 day, 21:52:23, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6308, loss_cls: 3.5477, loss: 3.5477 +2024-07-25 15:50:34,723 - pyskl - INFO - Epoch [96][3500/3746] lr: 2.877e-02, eta: 1 day, 21:51:03, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3622, top5_acc: 0.6236, loss_cls: 3.5816, loss: 3.5816 +2024-07-25 15:51:58,117 - pyskl - INFO - Epoch [96][3600/3746] lr: 2.875e-02, eta: 1 day, 21:49:42, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6381, loss_cls: 3.5030, loss: 3.5030 +2024-07-25 15:53:21,223 - pyskl - INFO - Epoch [96][3700/3746] lr: 2.872e-02, eta: 1 day, 21:48:22, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6334, loss_cls: 3.5361, loss: 3.5361 +2024-07-25 15:54:01,420 - pyskl - INFO - Saving checkpoint at 96 epochs +2024-07-25 15:55:54,581 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 15:55:55,368 - pyskl - INFO - +top1_acc 0.3210 +top5_acc 0.5795 +2024-07-25 15:55:55,369 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 15:55:55,410 - pyskl - INFO - +mean_acc 0.3207 +2024-07-25 15:55:55,415 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_95.pth was removed +2024-07-25 15:55:55,675 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_96.pth. +2024-07-25 15:55:55,676 - pyskl - INFO - Best top1_acc is 0.3210 at 96 epoch. +2024-07-25 15:55:55,688 - pyskl - INFO - Epoch(val) [96][309] top1_acc: 0.3210, top5_acc: 0.5795, mean_class_accuracy: 0.3207 +2024-07-25 15:59:48,072 - pyskl - INFO - Epoch [97][100/3746] lr: 2.869e-02, eta: 1 day, 21:47:27, time: 2.324, data_time: 1.344, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6412, loss_cls: 3.4843, loss: 3.4843 +2024-07-25 16:01:10,311 - pyskl - INFO - Epoch [97][200/3746] lr: 2.866e-02, eta: 1 day, 21:46:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6445, loss_cls: 3.4551, loss: 3.4551 +2024-07-25 16:02:31,924 - pyskl - INFO - Epoch [97][300/3746] lr: 2.864e-02, eta: 1 day, 21:44:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6592, loss_cls: 3.4281, loss: 3.4281 +2024-07-25 16:03:53,151 - pyskl - INFO - Epoch [97][400/3746] lr: 2.861e-02, eta: 1 day, 21:43:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6406, loss_cls: 3.5153, loss: 3.5153 +2024-07-25 16:05:14,772 - pyskl - INFO - Epoch [97][500/3746] lr: 2.858e-02, eta: 1 day, 21:42:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6398, loss_cls: 3.5279, loss: 3.5279 +2024-07-25 16:06:36,496 - pyskl - INFO - Epoch [97][600/3746] lr: 2.856e-02, eta: 1 day, 21:40:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6411, loss_cls: 3.5046, loss: 3.5046 +2024-07-25 16:07:58,245 - pyskl - INFO - Epoch [97][700/3746] lr: 2.853e-02, eta: 1 day, 21:39:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6289, loss_cls: 3.5680, loss: 3.5680 +2024-07-25 16:09:19,933 - pyskl - INFO - Epoch [97][800/3746] lr: 2.851e-02, eta: 1 day, 21:37:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3741, top5_acc: 0.6380, loss_cls: 3.4875, loss: 3.4875 +2024-07-25 16:10:41,682 - pyskl - INFO - Epoch [97][900/3746] lr: 2.848e-02, eta: 1 day, 21:36:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6364, loss_cls: 3.5079, loss: 3.5079 +2024-07-25 16:12:03,219 - pyskl - INFO - Epoch [97][1000/3746] lr: 2.846e-02, eta: 1 day, 21:35:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6395, loss_cls: 3.5028, loss: 3.5028 +2024-07-25 16:13:24,720 - pyskl - INFO - Epoch [97][1100/3746] lr: 2.843e-02, eta: 1 day, 21:33:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6420, loss_cls: 3.5037, loss: 3.5037 +2024-07-25 16:14:46,431 - pyskl - INFO - Epoch [97][1200/3746] lr: 2.841e-02, eta: 1 day, 21:32:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6375, loss_cls: 3.5370, loss: 3.5370 +2024-07-25 16:16:08,981 - pyskl - INFO - Epoch [97][1300/3746] lr: 2.838e-02, eta: 1 day, 21:31:10, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6366, loss_cls: 3.5179, loss: 3.5179 +2024-07-25 16:17:30,670 - pyskl - INFO - Epoch [97][1400/3746] lr: 2.836e-02, eta: 1 day, 21:29:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6416, loss_cls: 3.5115, loss: 3.5115 +2024-07-25 16:18:52,233 - pyskl - INFO - Epoch [97][1500/3746] lr: 2.833e-02, eta: 1 day, 21:28:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3723, top5_acc: 0.6333, loss_cls: 3.5516, loss: 3.5516 +2024-07-25 16:20:14,056 - pyskl - INFO - Epoch [97][1600/3746] lr: 2.831e-02, eta: 1 day, 21:27:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6333, loss_cls: 3.5145, loss: 3.5145 +2024-07-25 16:21:36,342 - pyskl - INFO - Epoch [97][1700/3746] lr: 2.828e-02, eta: 1 day, 21:25:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6303, loss_cls: 3.5352, loss: 3.5352 +2024-07-25 16:22:58,037 - pyskl - INFO - Epoch [97][1800/3746] lr: 2.826e-02, eta: 1 day, 21:24:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6383, loss_cls: 3.5112, loss: 3.5112 +2024-07-25 16:24:20,498 - pyskl - INFO - Epoch [97][1900/3746] lr: 2.823e-02, eta: 1 day, 21:23:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3762, top5_acc: 0.6431, loss_cls: 3.4896, loss: 3.4896 +2024-07-25 16:25:42,910 - pyskl - INFO - Epoch [97][2000/3746] lr: 2.821e-02, eta: 1 day, 21:21:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6392, loss_cls: 3.5193, loss: 3.5193 +2024-07-25 16:27:04,381 - pyskl - INFO - Epoch [97][2100/3746] lr: 2.818e-02, eta: 1 day, 21:20:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6372, loss_cls: 3.5393, loss: 3.5393 +2024-07-25 16:28:25,862 - pyskl - INFO - Epoch [97][2200/3746] lr: 2.816e-02, eta: 1 day, 21:18:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6408, loss_cls: 3.5089, loss: 3.5089 +2024-07-25 16:29:47,823 - pyskl - INFO - Epoch [97][2300/3746] lr: 2.813e-02, eta: 1 day, 21:17:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6234, loss_cls: 3.5655, loss: 3.5655 +2024-07-25 16:31:09,651 - pyskl - INFO - Epoch [97][2400/3746] lr: 2.811e-02, eta: 1 day, 21:16:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6475, loss_cls: 3.4903, loss: 3.4903 +2024-07-25 16:32:31,078 - pyskl - INFO - Epoch [97][2500/3746] lr: 2.808e-02, eta: 1 day, 21:14:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6256, loss_cls: 3.5937, loss: 3.5937 +2024-07-25 16:33:53,287 - pyskl - INFO - Epoch [97][2600/3746] lr: 2.806e-02, eta: 1 day, 21:13:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6230, loss_cls: 3.6363, loss: 3.6363 +2024-07-25 16:35:14,931 - pyskl - INFO - Epoch [97][2700/3746] lr: 2.803e-02, eta: 1 day, 21:12:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6339, loss_cls: 3.5532, loss: 3.5532 +2024-07-25 16:36:36,879 - pyskl - INFO - Epoch [97][2800/3746] lr: 2.801e-02, eta: 1 day, 21:10:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6373, loss_cls: 3.5350, loss: 3.5350 +2024-07-25 16:37:58,419 - pyskl - INFO - Epoch [97][2900/3746] lr: 2.798e-02, eta: 1 day, 21:09:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6334, loss_cls: 3.5482, loss: 3.5482 +2024-07-25 16:39:20,598 - pyskl - INFO - Epoch [97][3000/3746] lr: 2.796e-02, eta: 1 day, 21:08:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6203, loss_cls: 3.6009, loss: 3.6009 +2024-07-25 16:40:42,556 - pyskl - INFO - Epoch [97][3100/3746] lr: 2.793e-02, eta: 1 day, 21:06:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6312, loss_cls: 3.5164, loss: 3.5164 +2024-07-25 16:42:04,267 - pyskl - INFO - Epoch [97][3200/3746] lr: 2.791e-02, eta: 1 day, 21:05:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6280, loss_cls: 3.5606, loss: 3.5606 +2024-07-25 16:43:26,212 - pyskl - INFO - Epoch [97][3300/3746] lr: 2.788e-02, eta: 1 day, 21:04:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6269, loss_cls: 3.5700, loss: 3.5700 +2024-07-25 16:44:48,125 - pyskl - INFO - Epoch [97][3400/3746] lr: 2.786e-02, eta: 1 day, 21:02:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6314, loss_cls: 3.5727, loss: 3.5727 +2024-07-25 16:46:10,270 - pyskl - INFO - Epoch [97][3500/3746] lr: 2.783e-02, eta: 1 day, 21:01:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6300, loss_cls: 3.6063, loss: 3.6063 +2024-07-25 16:47:31,517 - pyskl - INFO - Epoch [97][3600/3746] lr: 2.781e-02, eta: 1 day, 20:59:58, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6284, loss_cls: 3.5856, loss: 3.5856 +2024-07-25 16:48:53,493 - pyskl - INFO - Epoch [97][3700/3746] lr: 2.778e-02, eta: 1 day, 20:58:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6317, loss_cls: 3.5600, loss: 3.5600 +2024-07-25 16:49:32,988 - pyskl - INFO - Saving checkpoint at 97 epochs +2024-07-25 16:51:25,903 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 16:51:26,590 - pyskl - INFO - +top1_acc 0.3016 +top5_acc 0.5618 +2024-07-25 16:51:26,590 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 16:51:26,642 - pyskl - INFO - +mean_acc 0.3014 +2024-07-25 16:51:26,654 - pyskl - INFO - Epoch(val) [97][309] top1_acc: 0.3016, top5_acc: 0.5618, mean_class_accuracy: 0.3014 +2024-07-25 16:55:20,697 - pyskl - INFO - Epoch [98][100/3746] lr: 2.774e-02, eta: 1 day, 20:57:41, time: 2.340, data_time: 1.341, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6480, loss_cls: 3.4519, loss: 3.4519 +2024-07-25 16:56:42,417 - pyskl - INFO - Epoch [98][200/3746] lr: 2.772e-02, eta: 1 day, 20:56:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6505, loss_cls: 3.4976, loss: 3.4976 +2024-07-25 16:58:04,423 - pyskl - INFO - Epoch [98][300/3746] lr: 2.769e-02, eta: 1 day, 20:54:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6497, loss_cls: 3.4478, loss: 3.4478 +2024-07-25 16:59:25,743 - pyskl - INFO - Epoch [98][400/3746] lr: 2.767e-02, eta: 1 day, 20:53:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3723, top5_acc: 0.6370, loss_cls: 3.4918, loss: 3.4918 +2024-07-25 17:00:47,895 - pyskl - INFO - Epoch [98][500/3746] lr: 2.764e-02, eta: 1 day, 20:52:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6397, loss_cls: 3.4986, loss: 3.4986 +2024-07-25 17:02:10,143 - pyskl - INFO - Epoch [98][600/3746] lr: 2.762e-02, eta: 1 day, 20:50:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6458, loss_cls: 3.4404, loss: 3.4404 +2024-07-25 17:03:32,115 - pyskl - INFO - Epoch [98][700/3746] lr: 2.759e-02, eta: 1 day, 20:49:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6312, loss_cls: 3.5495, loss: 3.5495 +2024-07-25 17:04:53,530 - pyskl - INFO - Epoch [98][800/3746] lr: 2.757e-02, eta: 1 day, 20:48:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6384, loss_cls: 3.5225, loss: 3.5225 +2024-07-25 17:06:15,266 - pyskl - INFO - Epoch [98][900/3746] lr: 2.754e-02, eta: 1 day, 20:46:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6359, loss_cls: 3.5277, loss: 3.5277 +2024-07-25 17:07:36,817 - pyskl - INFO - Epoch [98][1000/3746] lr: 2.752e-02, eta: 1 day, 20:45:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6442, loss_cls: 3.4778, loss: 3.4778 +2024-07-25 17:08:58,593 - pyskl - INFO - Epoch [98][1100/3746] lr: 2.749e-02, eta: 1 day, 20:44:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6345, loss_cls: 3.5175, loss: 3.5175 +2024-07-25 17:10:20,390 - pyskl - INFO - Epoch [98][1200/3746] lr: 2.747e-02, eta: 1 day, 20:42:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6319, loss_cls: 3.5405, loss: 3.5405 +2024-07-25 17:11:41,753 - pyskl - INFO - Epoch [98][1300/3746] lr: 2.744e-02, eta: 1 day, 20:41:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6306, loss_cls: 3.5514, loss: 3.5514 +2024-07-25 17:13:03,409 - pyskl - INFO - Epoch [98][1400/3746] lr: 2.742e-02, eta: 1 day, 20:40:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6427, loss_cls: 3.4528, loss: 3.4528 +2024-07-25 17:14:24,789 - pyskl - INFO - Epoch [98][1500/3746] lr: 2.739e-02, eta: 1 day, 20:38:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6358, loss_cls: 3.5271, loss: 3.5271 +2024-07-25 17:15:46,904 - pyskl - INFO - Epoch [98][1600/3746] lr: 2.737e-02, eta: 1 day, 20:37:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6369, loss_cls: 3.5183, loss: 3.5183 +2024-07-25 17:17:08,445 - pyskl - INFO - Epoch [98][1700/3746] lr: 2.734e-02, eta: 1 day, 20:35:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6361, loss_cls: 3.5388, loss: 3.5388 +2024-07-25 17:18:30,145 - pyskl - INFO - Epoch [98][1800/3746] lr: 2.732e-02, eta: 1 day, 20:34:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6388, loss_cls: 3.5011, loss: 3.5011 +2024-07-25 17:19:53,366 - pyskl - INFO - Epoch [98][1900/3746] lr: 2.729e-02, eta: 1 day, 20:33:15, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6408, loss_cls: 3.5074, loss: 3.5074 +2024-07-25 17:21:15,334 - pyskl - INFO - Epoch [98][2000/3746] lr: 2.727e-02, eta: 1 day, 20:31:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6353, loss_cls: 3.5235, loss: 3.5235 +2024-07-25 17:22:37,583 - pyskl - INFO - Epoch [98][2100/3746] lr: 2.724e-02, eta: 1 day, 20:30:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6547, loss_cls: 3.4651, loss: 3.4651 +2024-07-25 17:23:59,058 - pyskl - INFO - Epoch [98][2200/3746] lr: 2.722e-02, eta: 1 day, 20:29:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6281, loss_cls: 3.5749, loss: 3.5749 +2024-07-25 17:25:20,342 - pyskl - INFO - Epoch [98][2300/3746] lr: 2.719e-02, eta: 1 day, 20:27:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6392, loss_cls: 3.5142, loss: 3.5142 +2024-07-25 17:26:42,033 - pyskl - INFO - Epoch [98][2400/3746] lr: 2.717e-02, eta: 1 day, 20:26:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3812, top5_acc: 0.6509, loss_cls: 3.4551, loss: 3.4551 +2024-07-25 17:28:03,668 - pyskl - INFO - Epoch [98][2500/3746] lr: 2.714e-02, eta: 1 day, 20:25:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6280, loss_cls: 3.5785, loss: 3.5785 +2024-07-25 17:29:25,640 - pyskl - INFO - Epoch [98][2600/3746] lr: 2.712e-02, eta: 1 day, 20:23:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6378, loss_cls: 3.5601, loss: 3.5601 +2024-07-25 17:30:47,556 - pyskl - INFO - Epoch [98][2700/3746] lr: 2.709e-02, eta: 1 day, 20:22:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6381, loss_cls: 3.4998, loss: 3.4998 +2024-07-25 17:32:09,550 - pyskl - INFO - Epoch [98][2800/3746] lr: 2.707e-02, eta: 1 day, 20:21:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6305, loss_cls: 3.5756, loss: 3.5756 +2024-07-25 17:33:31,714 - pyskl - INFO - Epoch [98][2900/3746] lr: 2.705e-02, eta: 1 day, 20:19:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6252, loss_cls: 3.5857, loss: 3.5857 +2024-07-25 17:34:53,339 - pyskl - INFO - Epoch [98][3000/3746] lr: 2.702e-02, eta: 1 day, 20:18:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6450, loss_cls: 3.4780, loss: 3.4780 +2024-07-25 17:36:14,714 - pyskl - INFO - Epoch [98][3100/3746] lr: 2.700e-02, eta: 1 day, 20:16:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6384, loss_cls: 3.5359, loss: 3.5359 +2024-07-25 17:37:36,310 - pyskl - INFO - Epoch [98][3200/3746] lr: 2.697e-02, eta: 1 day, 20:15:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6420, loss_cls: 3.5171, loss: 3.5171 +2024-07-25 17:38:57,534 - pyskl - INFO - Epoch [98][3300/3746] lr: 2.695e-02, eta: 1 day, 20:14:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6372, loss_cls: 3.5478, loss: 3.5478 +2024-07-25 17:40:19,327 - pyskl - INFO - Epoch [98][3400/3746] lr: 2.692e-02, eta: 1 day, 20:12:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6283, loss_cls: 3.5960, loss: 3.5960 +2024-07-25 17:41:40,930 - pyskl - INFO - Epoch [98][3500/3746] lr: 2.690e-02, eta: 1 day, 20:11:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3812, top5_acc: 0.6466, loss_cls: 3.4687, loss: 3.4687 +2024-07-25 17:43:02,775 - pyskl - INFO - Epoch [98][3600/3746] lr: 2.687e-02, eta: 1 day, 20:10:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3656, top5_acc: 0.6372, loss_cls: 3.5288, loss: 3.5288 +2024-07-25 17:44:24,647 - pyskl - INFO - Epoch [98][3700/3746] lr: 2.685e-02, eta: 1 day, 20:08:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6366, loss_cls: 3.5277, loss: 3.5277 +2024-07-25 17:45:04,709 - pyskl - INFO - Saving checkpoint at 98 epochs +2024-07-25 17:46:58,177 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 17:46:58,852 - pyskl - INFO - +top1_acc 0.3150 +top5_acc 0.5703 +2024-07-25 17:46:58,852 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 17:46:58,901 - pyskl - INFO - +mean_acc 0.3147 +2024-07-25 17:46:58,914 - pyskl - INFO - Epoch(val) [98][309] top1_acc: 0.3150, top5_acc: 0.5703, mean_class_accuracy: 0.3147 +2024-07-25 17:50:51,864 - pyskl - INFO - Epoch [99][100/3746] lr: 2.681e-02, eta: 1 day, 20:07:49, time: 2.329, data_time: 1.346, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6647, loss_cls: 3.3922, loss: 3.3922 +2024-07-25 17:52:14,831 - pyskl - INFO - Epoch [99][200/3746] lr: 2.679e-02, eta: 1 day, 20:06:29, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6484, loss_cls: 3.4427, loss: 3.4427 +2024-07-25 17:53:38,503 - pyskl - INFO - Epoch [99][300/3746] lr: 2.676e-02, eta: 1 day, 20:05:08, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6453, loss_cls: 3.4943, loss: 3.4943 +2024-07-25 17:55:01,948 - pyskl - INFO - Epoch [99][400/3746] lr: 2.674e-02, eta: 1 day, 20:03:47, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3858, top5_acc: 0.6445, loss_cls: 3.4761, loss: 3.4761 +2024-07-25 17:56:25,020 - pyskl - INFO - Epoch [99][500/3746] lr: 2.671e-02, eta: 1 day, 20:02:27, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6539, loss_cls: 3.4554, loss: 3.4554 +2024-07-25 17:57:48,833 - pyskl - INFO - Epoch [99][600/3746] lr: 2.669e-02, eta: 1 day, 20:01:06, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6461, loss_cls: 3.4814, loss: 3.4814 +2024-07-25 17:59:12,319 - pyskl - INFO - Epoch [99][700/3746] lr: 2.666e-02, eta: 1 day, 19:59:46, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6364, loss_cls: 3.5163, loss: 3.5163 +2024-07-25 18:00:35,232 - pyskl - INFO - Epoch [99][800/3746] lr: 2.664e-02, eta: 1 day, 19:58:25, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6388, loss_cls: 3.5220, loss: 3.5220 +2024-07-25 18:01:58,745 - pyskl - INFO - Epoch [99][900/3746] lr: 2.661e-02, eta: 1 day, 19:57:04, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6480, loss_cls: 3.4635, loss: 3.4635 +2024-07-25 18:03:21,811 - pyskl - INFO - Epoch [99][1000/3746] lr: 2.659e-02, eta: 1 day, 19:55:43, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3723, top5_acc: 0.6408, loss_cls: 3.5173, loss: 3.5173 +2024-07-25 18:04:44,886 - pyskl - INFO - Epoch [99][1100/3746] lr: 2.656e-02, eta: 1 day, 19:54:22, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6308, loss_cls: 3.5175, loss: 3.5175 +2024-07-25 18:06:08,215 - pyskl - INFO - Epoch [99][1200/3746] lr: 2.654e-02, eta: 1 day, 19:53:02, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6439, loss_cls: 3.4653, loss: 3.4653 +2024-07-25 18:07:31,485 - pyskl - INFO - Epoch [99][1300/3746] lr: 2.651e-02, eta: 1 day, 19:51:41, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6381, loss_cls: 3.5094, loss: 3.5094 +2024-07-25 18:08:54,577 - pyskl - INFO - Epoch [99][1400/3746] lr: 2.649e-02, eta: 1 day, 19:50:20, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6531, loss_cls: 3.4842, loss: 3.4842 +2024-07-25 18:10:17,728 - pyskl - INFO - Epoch [99][1500/3746] lr: 2.646e-02, eta: 1 day, 19:48:59, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6352, loss_cls: 3.5300, loss: 3.5300 +2024-07-25 18:11:40,651 - pyskl - INFO - Epoch [99][1600/3746] lr: 2.644e-02, eta: 1 day, 19:47:38, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6369, loss_cls: 3.5086, loss: 3.5086 +2024-07-25 18:13:03,877 - pyskl - INFO - Epoch [99][1700/3746] lr: 2.642e-02, eta: 1 day, 19:46:18, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6486, loss_cls: 3.4792, loss: 3.4792 +2024-07-25 18:14:26,012 - pyskl - INFO - Epoch [99][1800/3746] lr: 2.639e-02, eta: 1 day, 19:44:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6361, loss_cls: 3.5312, loss: 3.5312 +2024-07-25 18:15:49,239 - pyskl - INFO - Epoch [99][1900/3746] lr: 2.637e-02, eta: 1 day, 19:43:36, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6458, loss_cls: 3.4875, loss: 3.4875 +2024-07-25 18:17:12,701 - pyskl - INFO - Epoch [99][2000/3746] lr: 2.634e-02, eta: 1 day, 19:42:15, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6434, loss_cls: 3.4923, loss: 3.4923 +2024-07-25 18:18:35,824 - pyskl - INFO - Epoch [99][2100/3746] lr: 2.632e-02, eta: 1 day, 19:40:54, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6397, loss_cls: 3.4949, loss: 3.4949 +2024-07-25 18:19:58,633 - pyskl - INFO - Epoch [99][2200/3746] lr: 2.629e-02, eta: 1 day, 19:39:33, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6422, loss_cls: 3.5084, loss: 3.5084 +2024-07-25 18:21:21,798 - pyskl - INFO - Epoch [99][2300/3746] lr: 2.627e-02, eta: 1 day, 19:38:12, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6417, loss_cls: 3.4802, loss: 3.4802 +2024-07-25 18:22:44,162 - pyskl - INFO - Epoch [99][2400/3746] lr: 2.624e-02, eta: 1 day, 19:36:51, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6389, loss_cls: 3.5311, loss: 3.5311 +2024-07-25 18:24:06,282 - pyskl - INFO - Epoch [99][2500/3746] lr: 2.622e-02, eta: 1 day, 19:35:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6347, loss_cls: 3.4927, loss: 3.4927 +2024-07-25 18:25:29,188 - pyskl - INFO - Epoch [99][2600/3746] lr: 2.619e-02, eta: 1 day, 19:34:09, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6375, loss_cls: 3.5247, loss: 3.5247 +2024-07-25 18:26:51,275 - pyskl - INFO - Epoch [99][2700/3746] lr: 2.617e-02, eta: 1 day, 19:32:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6280, loss_cls: 3.5753, loss: 3.5753 +2024-07-25 18:28:12,977 - pyskl - INFO - Epoch [99][2800/3746] lr: 2.614e-02, eta: 1 day, 19:31:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6455, loss_cls: 3.4779, loss: 3.4779 +2024-07-25 18:29:36,338 - pyskl - INFO - Epoch [99][2900/3746] lr: 2.612e-02, eta: 1 day, 19:30:05, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6422, loss_cls: 3.4775, loss: 3.4775 +2024-07-25 18:30:59,105 - pyskl - INFO - Epoch [99][3000/3746] lr: 2.610e-02, eta: 1 day, 19:28:44, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6408, loss_cls: 3.5195, loss: 3.5195 +2024-07-25 18:32:21,399 - pyskl - INFO - Epoch [99][3100/3746] lr: 2.607e-02, eta: 1 day, 19:27:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3831, top5_acc: 0.6444, loss_cls: 3.4743, loss: 3.4743 +2024-07-25 18:33:43,847 - pyskl - INFO - Epoch [99][3200/3746] lr: 2.605e-02, eta: 1 day, 19:26:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3837, top5_acc: 0.6462, loss_cls: 3.5093, loss: 3.5093 +2024-07-25 18:35:05,544 - pyskl - INFO - Epoch [99][3300/3746] lr: 2.602e-02, eta: 1 day, 19:24:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6361, loss_cls: 3.5188, loss: 3.5188 +2024-07-25 18:36:27,765 - pyskl - INFO - Epoch [99][3400/3746] lr: 2.600e-02, eta: 1 day, 19:23:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6430, loss_cls: 3.5016, loss: 3.5016 +2024-07-25 18:37:50,456 - pyskl - INFO - Epoch [99][3500/3746] lr: 2.597e-02, eta: 1 day, 19:21:58, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6350, loss_cls: 3.5537, loss: 3.5537 +2024-07-25 18:39:11,869 - pyskl - INFO - Epoch [99][3600/3746] lr: 2.595e-02, eta: 1 day, 19:20:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6453, loss_cls: 3.4871, loss: 3.4871 +2024-07-25 18:40:33,397 - pyskl - INFO - Epoch [99][3700/3746] lr: 2.592e-02, eta: 1 day, 19:19:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6352, loss_cls: 3.5226, loss: 3.5226 +2024-07-25 18:41:13,100 - pyskl - INFO - Saving checkpoint at 99 epochs +2024-07-25 18:43:05,102 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 18:43:05,822 - pyskl - INFO - +top1_acc 0.3173 +top5_acc 0.5775 +2024-07-25 18:43:05,822 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 18:43:05,876 - pyskl - INFO - +mean_acc 0.3172 +2024-07-25 18:43:05,893 - pyskl - INFO - Epoch(val) [99][309] top1_acc: 0.3173, top5_acc: 0.5775, mean_class_accuracy: 0.3172 +2024-07-25 18:46:59,437 - pyskl - INFO - Epoch [100][100/3746] lr: 2.589e-02, eta: 1 day, 19:18:14, time: 2.335, data_time: 1.359, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6667, loss_cls: 3.3414, loss: 3.3414 +2024-07-25 18:48:21,278 - pyskl - INFO - Epoch [100][200/3746] lr: 2.586e-02, eta: 1 day, 19:16:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6366, loss_cls: 3.4616, loss: 3.4616 +2024-07-25 18:49:42,999 - pyskl - INFO - Epoch [100][300/3746] lr: 2.584e-02, eta: 1 day, 19:15:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6506, loss_cls: 3.4527, loss: 3.4527 +2024-07-25 18:51:04,615 - pyskl - INFO - Epoch [100][400/3746] lr: 2.581e-02, eta: 1 day, 19:14:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6530, loss_cls: 3.4373, loss: 3.4373 +2024-07-25 18:52:26,143 - pyskl - INFO - Epoch [100][500/3746] lr: 2.579e-02, eta: 1 day, 19:12:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6462, loss_cls: 3.4319, loss: 3.4319 +2024-07-25 18:53:47,816 - pyskl - INFO - Epoch [100][600/3746] lr: 2.577e-02, eta: 1 day, 19:11:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6366, loss_cls: 3.5001, loss: 3.5001 +2024-07-25 18:55:09,482 - pyskl - INFO - Epoch [100][700/3746] lr: 2.574e-02, eta: 1 day, 19:10:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3822, top5_acc: 0.6473, loss_cls: 3.4744, loss: 3.4744 +2024-07-25 18:56:31,271 - pyskl - INFO - Epoch [100][800/3746] lr: 2.572e-02, eta: 1 day, 19:08:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3819, top5_acc: 0.6447, loss_cls: 3.4773, loss: 3.4773 +2024-07-25 18:57:53,257 - pyskl - INFO - Epoch [100][900/3746] lr: 2.569e-02, eta: 1 day, 19:07:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6466, loss_cls: 3.4677, loss: 3.4677 +2024-07-25 18:59:15,529 - pyskl - INFO - Epoch [100][1000/3746] lr: 2.567e-02, eta: 1 day, 19:06:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6483, loss_cls: 3.4657, loss: 3.4657 +2024-07-25 19:00:37,260 - pyskl - INFO - Epoch [100][1100/3746] lr: 2.564e-02, eta: 1 day, 19:04:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6470, loss_cls: 3.4853, loss: 3.4853 +2024-07-25 19:01:59,117 - pyskl - INFO - Epoch [100][1200/3746] lr: 2.562e-02, eta: 1 day, 19:03:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6453, loss_cls: 3.4791, loss: 3.4791 +2024-07-25 19:03:20,334 - pyskl - INFO - Epoch [100][1300/3746] lr: 2.559e-02, eta: 1 day, 19:01:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3819, top5_acc: 0.6534, loss_cls: 3.4277, loss: 3.4277 +2024-07-25 19:04:41,668 - pyskl - INFO - Epoch [100][1400/3746] lr: 2.557e-02, eta: 1 day, 19:00:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6564, loss_cls: 3.4304, loss: 3.4304 +2024-07-25 19:06:03,729 - pyskl - INFO - Epoch [100][1500/3746] lr: 2.555e-02, eta: 1 day, 18:59:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6473, loss_cls: 3.4710, loss: 3.4710 +2024-07-25 19:07:25,704 - pyskl - INFO - Epoch [100][1600/3746] lr: 2.552e-02, eta: 1 day, 18:57:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6398, loss_cls: 3.4675, loss: 3.4675 +2024-07-25 19:08:47,190 - pyskl - INFO - Epoch [100][1700/3746] lr: 2.550e-02, eta: 1 day, 18:56:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6477, loss_cls: 3.4728, loss: 3.4728 +2024-07-25 19:10:09,488 - pyskl - INFO - Epoch [100][1800/3746] lr: 2.547e-02, eta: 1 day, 18:55:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6584, loss_cls: 3.4228, loss: 3.4228 +2024-07-25 19:11:31,482 - pyskl - INFO - Epoch [100][1900/3746] lr: 2.545e-02, eta: 1 day, 18:53:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6428, loss_cls: 3.4851, loss: 3.4851 +2024-07-25 19:12:52,967 - pyskl - INFO - Epoch [100][2000/3746] lr: 2.542e-02, eta: 1 day, 18:52:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6367, loss_cls: 3.4920, loss: 3.4920 +2024-07-25 19:14:14,436 - pyskl - INFO - Epoch [100][2100/3746] lr: 2.540e-02, eta: 1 day, 18:51:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6556, loss_cls: 3.4367, loss: 3.4367 +2024-07-25 19:15:35,966 - pyskl - INFO - Epoch [100][2200/3746] lr: 2.538e-02, eta: 1 day, 18:49:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6425, loss_cls: 3.4746, loss: 3.4746 +2024-07-25 19:16:57,482 - pyskl - INFO - Epoch [100][2300/3746] lr: 2.535e-02, eta: 1 day, 18:48:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6336, loss_cls: 3.5365, loss: 3.5365 +2024-07-25 19:18:19,250 - pyskl - INFO - Epoch [100][2400/3746] lr: 2.533e-02, eta: 1 day, 18:46:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6419, loss_cls: 3.5188, loss: 3.5188 +2024-07-25 19:19:41,087 - pyskl - INFO - Epoch [100][2500/3746] lr: 2.530e-02, eta: 1 day, 18:45:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6383, loss_cls: 3.5110, loss: 3.5110 +2024-07-25 19:21:02,948 - pyskl - INFO - Epoch [100][2600/3746] lr: 2.528e-02, eta: 1 day, 18:44:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6458, loss_cls: 3.4944, loss: 3.4944 +2024-07-25 19:22:24,368 - pyskl - INFO - Epoch [100][2700/3746] lr: 2.525e-02, eta: 1 day, 18:42:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6386, loss_cls: 3.5071, loss: 3.5071 +2024-07-25 19:23:47,474 - pyskl - INFO - Epoch [100][2800/3746] lr: 2.523e-02, eta: 1 day, 18:41:32, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6364, loss_cls: 3.5154, loss: 3.5154 +2024-07-25 19:25:08,703 - pyskl - INFO - Epoch [100][2900/3746] lr: 2.521e-02, eta: 1 day, 18:40:10, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6422, loss_cls: 3.4813, loss: 3.4813 +2024-07-25 19:26:30,075 - pyskl - INFO - Epoch [100][3000/3746] lr: 2.518e-02, eta: 1 day, 18:38:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6341, loss_cls: 3.5403, loss: 3.5403 +2024-07-25 19:27:51,625 - pyskl - INFO - Epoch [100][3100/3746] lr: 2.516e-02, eta: 1 day, 18:37:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6381, loss_cls: 3.5102, loss: 3.5102 +2024-07-25 19:29:13,177 - pyskl - INFO - Epoch [100][3200/3746] lr: 2.513e-02, eta: 1 day, 18:36:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6456, loss_cls: 3.4521, loss: 3.4521 +2024-07-25 19:30:34,997 - pyskl - INFO - Epoch [100][3300/3746] lr: 2.511e-02, eta: 1 day, 18:34:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6448, loss_cls: 3.5107, loss: 3.5107 +2024-07-25 19:31:56,556 - pyskl - INFO - Epoch [100][3400/3746] lr: 2.508e-02, eta: 1 day, 18:33:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6347, loss_cls: 3.5095, loss: 3.5095 +2024-07-25 19:33:18,348 - pyskl - INFO - Epoch [100][3500/3746] lr: 2.506e-02, eta: 1 day, 18:32:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6373, loss_cls: 3.5409, loss: 3.5409 +2024-07-25 19:34:39,395 - pyskl - INFO - Epoch [100][3600/3746] lr: 2.504e-02, eta: 1 day, 18:30:38, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6353, loss_cls: 3.5183, loss: 3.5183 +2024-07-25 19:36:01,280 - pyskl - INFO - Epoch [100][3700/3746] lr: 2.501e-02, eta: 1 day, 18:29:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6453, loss_cls: 3.4892, loss: 3.4892 +2024-07-25 19:36:40,854 - pyskl - INFO - Saving checkpoint at 100 epochs +2024-07-25 19:38:34,501 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 19:38:35,169 - pyskl - INFO - +top1_acc 0.2961 +top5_acc 0.5563 +2024-07-25 19:38:35,169 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 19:38:35,212 - pyskl - INFO - +mean_acc 0.2960 +2024-07-25 19:38:35,225 - pyskl - INFO - Epoch(val) [100][309] top1_acc: 0.2961, top5_acc: 0.5563, mean_class_accuracy: 0.2960 +2024-07-25 19:42:29,288 - pyskl - INFO - Epoch [101][100/3746] lr: 2.498e-02, eta: 1 day, 18:28:14, time: 2.341, data_time: 1.358, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6495, loss_cls: 3.4449, loss: 3.4449 +2024-07-25 19:43:52,559 - pyskl - INFO - Epoch [101][200/3746] lr: 2.495e-02, eta: 1 day, 18:26:54, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6516, loss_cls: 3.4311, loss: 3.4311 +2024-07-25 19:45:16,241 - pyskl - INFO - Epoch [101][300/3746] lr: 2.493e-02, eta: 1 day, 18:25:33, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3947, top5_acc: 0.6581, loss_cls: 3.4057, loss: 3.4057 +2024-07-25 19:46:40,006 - pyskl - INFO - Epoch [101][400/3746] lr: 2.490e-02, eta: 1 day, 18:24:12, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6392, loss_cls: 3.4592, loss: 3.4592 +2024-07-25 19:48:03,676 - pyskl - INFO - Epoch [101][500/3746] lr: 2.488e-02, eta: 1 day, 18:22:52, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6470, loss_cls: 3.4379, loss: 3.4379 +2024-07-25 19:49:27,398 - pyskl - INFO - Epoch [101][600/3746] lr: 2.486e-02, eta: 1 day, 18:21:31, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6597, loss_cls: 3.4197, loss: 3.4197 +2024-07-25 19:50:50,683 - pyskl - INFO - Epoch [101][700/3746] lr: 2.483e-02, eta: 1 day, 18:20:10, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3748, top5_acc: 0.6397, loss_cls: 3.4855, loss: 3.4855 +2024-07-25 19:52:13,851 - pyskl - INFO - Epoch [101][800/3746] lr: 2.481e-02, eta: 1 day, 18:18:49, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6366, loss_cls: 3.4949, loss: 3.4949 +2024-07-25 19:53:36,882 - pyskl - INFO - Epoch [101][900/3746] lr: 2.478e-02, eta: 1 day, 18:17:28, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6339, loss_cls: 3.4882, loss: 3.4882 +2024-07-25 19:55:00,810 - pyskl - INFO - Epoch [101][1000/3746] lr: 2.476e-02, eta: 1 day, 18:16:08, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6505, loss_cls: 3.4075, loss: 3.4075 +2024-07-25 19:56:24,590 - pyskl - INFO - Epoch [101][1100/3746] lr: 2.473e-02, eta: 1 day, 18:14:47, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3859, top5_acc: 0.6511, loss_cls: 3.4485, loss: 3.4485 +2024-07-25 19:57:47,919 - pyskl - INFO - Epoch [101][1200/3746] lr: 2.471e-02, eta: 1 day, 18:13:26, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6519, loss_cls: 3.4298, loss: 3.4298 +2024-07-25 19:59:11,439 - pyskl - INFO - Epoch [101][1300/3746] lr: 2.469e-02, eta: 1 day, 18:12:05, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6438, loss_cls: 3.4698, loss: 3.4698 +2024-07-25 20:00:35,006 - pyskl - INFO - Epoch [101][1400/3746] lr: 2.466e-02, eta: 1 day, 18:10:45, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6439, loss_cls: 3.4561, loss: 3.4561 +2024-07-25 20:01:58,917 - pyskl - INFO - Epoch [101][1500/3746] lr: 2.464e-02, eta: 1 day, 18:09:24, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6508, loss_cls: 3.4438, loss: 3.4438 +2024-07-25 20:03:22,347 - pyskl - INFO - Epoch [101][1600/3746] lr: 2.461e-02, eta: 1 day, 18:08:03, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6503, loss_cls: 3.4604, loss: 3.4604 +2024-07-25 20:04:45,253 - pyskl - INFO - Epoch [101][1700/3746] lr: 2.459e-02, eta: 1 day, 18:06:42, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6503, loss_cls: 3.4390, loss: 3.4390 +2024-07-25 20:06:08,067 - pyskl - INFO - Epoch [101][1800/3746] lr: 2.457e-02, eta: 1 day, 18:05:21, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6411, loss_cls: 3.4936, loss: 3.4936 +2024-07-25 20:07:31,607 - pyskl - INFO - Epoch [101][1900/3746] lr: 2.454e-02, eta: 1 day, 18:04:00, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6500, loss_cls: 3.4388, loss: 3.4388 +2024-07-25 20:08:55,211 - pyskl - INFO - Epoch [101][2000/3746] lr: 2.452e-02, eta: 1 day, 18:02:39, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3762, top5_acc: 0.6377, loss_cls: 3.4898, loss: 3.4898 +2024-07-25 20:10:18,589 - pyskl - INFO - Epoch [101][2100/3746] lr: 2.449e-02, eta: 1 day, 18:01:19, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6347, loss_cls: 3.5194, loss: 3.5194 +2024-07-25 20:11:41,658 - pyskl - INFO - Epoch [101][2200/3746] lr: 2.447e-02, eta: 1 day, 17:59:58, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6475, loss_cls: 3.4698, loss: 3.4698 +2024-07-25 20:13:04,120 - pyskl - INFO - Epoch [101][2300/3746] lr: 2.445e-02, eta: 1 day, 17:58:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6414, loss_cls: 3.4919, loss: 3.4919 +2024-07-25 20:14:26,649 - pyskl - INFO - Epoch [101][2400/3746] lr: 2.442e-02, eta: 1 day, 17:57:15, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6512, loss_cls: 3.4062, loss: 3.4062 +2024-07-25 20:15:49,299 - pyskl - INFO - Epoch [101][2500/3746] lr: 2.440e-02, eta: 1 day, 17:55:54, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6523, loss_cls: 3.4305, loss: 3.4305 +2024-07-25 20:17:11,612 - pyskl - INFO - Epoch [101][2600/3746] lr: 2.437e-02, eta: 1 day, 17:54:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6475, loss_cls: 3.4361, loss: 3.4361 +2024-07-25 20:18:34,436 - pyskl - INFO - Epoch [101][2700/3746] lr: 2.435e-02, eta: 1 day, 17:53:11, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6461, loss_cls: 3.4658, loss: 3.4658 +2024-07-25 20:19:57,804 - pyskl - INFO - Epoch [101][2800/3746] lr: 2.433e-02, eta: 1 day, 17:51:50, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6473, loss_cls: 3.4709, loss: 3.4709 +2024-07-25 20:21:21,255 - pyskl - INFO - Epoch [101][2900/3746] lr: 2.430e-02, eta: 1 day, 17:50:30, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6352, loss_cls: 3.5273, loss: 3.5273 +2024-07-25 20:22:44,633 - pyskl - INFO - Epoch [101][3000/3746] lr: 2.428e-02, eta: 1 day, 17:49:09, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6439, loss_cls: 3.5028, loss: 3.5028 +2024-07-25 20:24:07,787 - pyskl - INFO - Epoch [101][3100/3746] lr: 2.425e-02, eta: 1 day, 17:47:48, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6452, loss_cls: 3.4867, loss: 3.4867 +2024-07-25 20:25:30,859 - pyskl - INFO - Epoch [101][3200/3746] lr: 2.423e-02, eta: 1 day, 17:46:27, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3819, top5_acc: 0.6444, loss_cls: 3.4801, loss: 3.4801 +2024-07-25 20:26:53,484 - pyskl - INFO - Epoch [101][3300/3746] lr: 2.421e-02, eta: 1 day, 17:45:06, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6477, loss_cls: 3.4922, loss: 3.4922 +2024-07-25 20:28:16,581 - pyskl - INFO - Epoch [101][3400/3746] lr: 2.418e-02, eta: 1 day, 17:43:45, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6470, loss_cls: 3.4382, loss: 3.4382 +2024-07-25 20:29:39,240 - pyskl - INFO - Epoch [101][3500/3746] lr: 2.416e-02, eta: 1 day, 17:42:23, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6506, loss_cls: 3.4871, loss: 3.4871 +2024-07-25 20:31:01,876 - pyskl - INFO - Epoch [101][3600/3746] lr: 2.413e-02, eta: 1 day, 17:41:02, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6436, loss_cls: 3.4996, loss: 3.4996 +2024-07-25 20:32:25,478 - pyskl - INFO - Epoch [101][3700/3746] lr: 2.411e-02, eta: 1 day, 17:39:41, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6562, loss_cls: 3.4375, loss: 3.4375 +2024-07-25 20:33:05,748 - pyskl - INFO - Saving checkpoint at 101 epochs +2024-07-25 20:34:58,681 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 20:34:59,380 - pyskl - INFO - +top1_acc 0.2990 +top5_acc 0.5593 +2024-07-25 20:34:59,380 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 20:34:59,422 - pyskl - INFO - +mean_acc 0.2989 +2024-07-25 20:34:59,434 - pyskl - INFO - Epoch(val) [101][309] top1_acc: 0.2990, top5_acc: 0.5593, mean_class_accuracy: 0.2989 +2024-07-25 20:38:50,870 - pyskl - INFO - Epoch [102][100/3746] lr: 2.407e-02, eta: 1 day, 17:38:36, time: 2.314, data_time: 1.322, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6625, loss_cls: 3.3980, loss: 3.3980 +2024-07-25 20:40:14,445 - pyskl - INFO - Epoch [102][200/3746] lr: 2.405e-02, eta: 1 day, 17:37:16, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6575, loss_cls: 3.4140, loss: 3.4140 +2024-07-25 20:41:37,332 - pyskl - INFO - Epoch [102][300/3746] lr: 2.403e-02, eta: 1 day, 17:35:54, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3941, top5_acc: 0.6589, loss_cls: 3.4001, loss: 3.4001 +2024-07-25 20:43:00,064 - pyskl - INFO - Epoch [102][400/3746] lr: 2.400e-02, eta: 1 day, 17:34:33, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6569, loss_cls: 3.4146, loss: 3.4146 +2024-07-25 20:44:22,094 - pyskl - INFO - Epoch [102][500/3746] lr: 2.398e-02, eta: 1 day, 17:33:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3909, top5_acc: 0.6548, loss_cls: 3.4112, loss: 3.4112 +2024-07-25 20:45:45,188 - pyskl - INFO - Epoch [102][600/3746] lr: 2.396e-02, eta: 1 day, 17:31:51, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6561, loss_cls: 3.4090, loss: 3.4090 +2024-07-25 20:47:07,557 - pyskl - INFO - Epoch [102][700/3746] lr: 2.393e-02, eta: 1 day, 17:30:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6555, loss_cls: 3.3982, loss: 3.3982 +2024-07-25 20:48:29,567 - pyskl - INFO - Epoch [102][800/3746] lr: 2.391e-02, eta: 1 day, 17:29:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6550, loss_cls: 3.4041, loss: 3.4041 +2024-07-25 20:49:51,337 - pyskl - INFO - Epoch [102][900/3746] lr: 2.388e-02, eta: 1 day, 17:27:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3966, top5_acc: 0.6594, loss_cls: 3.4263, loss: 3.4263 +2024-07-25 20:51:13,565 - pyskl - INFO - Epoch [102][1000/3746] lr: 2.386e-02, eta: 1 day, 17:26:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6405, loss_cls: 3.4764, loss: 3.4764 +2024-07-25 20:52:36,050 - pyskl - INFO - Epoch [102][1100/3746] lr: 2.384e-02, eta: 1 day, 17:25:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6581, loss_cls: 3.4170, loss: 3.4170 +2024-07-25 20:53:58,679 - pyskl - INFO - Epoch [102][1200/3746] lr: 2.381e-02, eta: 1 day, 17:23:42, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6523, loss_cls: 3.4262, loss: 3.4262 +2024-07-25 20:55:20,946 - pyskl - INFO - Epoch [102][1300/3746] lr: 2.379e-02, eta: 1 day, 17:22:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6455, loss_cls: 3.4517, loss: 3.4517 +2024-07-25 20:56:44,160 - pyskl - INFO - Epoch [102][1400/3746] lr: 2.376e-02, eta: 1 day, 17:20:59, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6508, loss_cls: 3.4834, loss: 3.4834 +2024-07-25 20:58:06,861 - pyskl - INFO - Epoch [102][1500/3746] lr: 2.374e-02, eta: 1 day, 17:19:38, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6470, loss_cls: 3.4442, loss: 3.4442 +2024-07-25 20:59:29,503 - pyskl - INFO - Epoch [102][1600/3746] lr: 2.372e-02, eta: 1 day, 17:18:17, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6616, loss_cls: 3.4036, loss: 3.4036 +2024-07-25 21:00:51,484 - pyskl - INFO - Epoch [102][1700/3746] lr: 2.369e-02, eta: 1 day, 17:16:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6473, loss_cls: 3.4591, loss: 3.4591 +2024-07-25 21:02:14,568 - pyskl - INFO - Epoch [102][1800/3746] lr: 2.367e-02, eta: 1 day, 17:15:34, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6578, loss_cls: 3.4049, loss: 3.4049 +2024-07-25 21:03:38,175 - pyskl - INFO - Epoch [102][1900/3746] lr: 2.365e-02, eta: 1 day, 17:14:13, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6598, loss_cls: 3.4099, loss: 3.4099 +2024-07-25 21:05:01,177 - pyskl - INFO - Epoch [102][2000/3746] lr: 2.362e-02, eta: 1 day, 17:12:52, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6459, loss_cls: 3.4541, loss: 3.4541 +2024-07-25 21:06:24,867 - pyskl - INFO - Epoch [102][2100/3746] lr: 2.360e-02, eta: 1 day, 17:11:31, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3731, top5_acc: 0.6353, loss_cls: 3.5071, loss: 3.5071 +2024-07-25 21:07:48,066 - pyskl - INFO - Epoch [102][2200/3746] lr: 2.357e-02, eta: 1 day, 17:10:10, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6508, loss_cls: 3.4111, loss: 3.4111 +2024-07-25 21:09:10,459 - pyskl - INFO - Epoch [102][2300/3746] lr: 2.355e-02, eta: 1 day, 17:08:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6497, loss_cls: 3.4497, loss: 3.4497 +2024-07-25 21:10:32,505 - pyskl - INFO - Epoch [102][2400/3746] lr: 2.353e-02, eta: 1 day, 17:07:27, time: 0.820, data_time: 0.001, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6414, loss_cls: 3.5159, loss: 3.5159 +2024-07-25 21:11:54,702 - pyskl - INFO - Epoch [102][2500/3746] lr: 2.350e-02, eta: 1 day, 17:06:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3914, top5_acc: 0.6489, loss_cls: 3.4124, loss: 3.4124 +2024-07-25 21:13:16,249 - pyskl - INFO - Epoch [102][2600/3746] lr: 2.348e-02, eta: 1 day, 17:04:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6656, loss_cls: 3.4026, loss: 3.4026 +2024-07-25 21:14:38,707 - pyskl - INFO - Epoch [102][2700/3746] lr: 2.346e-02, eta: 1 day, 17:03:23, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6478, loss_cls: 3.4355, loss: 3.4355 +2024-07-25 21:16:02,156 - pyskl - INFO - Epoch [102][2800/3746] lr: 2.343e-02, eta: 1 day, 17:02:02, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6373, loss_cls: 3.4868, loss: 3.4868 +2024-07-25 21:17:24,781 - pyskl - INFO - Epoch [102][2900/3746] lr: 2.341e-02, eta: 1 day, 17:00:40, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6480, loss_cls: 3.4692, loss: 3.4692 +2024-07-25 21:18:46,623 - pyskl - INFO - Epoch [102][3000/3746] lr: 2.339e-02, eta: 1 day, 16:59:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3822, top5_acc: 0.6478, loss_cls: 3.4506, loss: 3.4506 +2024-07-25 21:20:08,945 - pyskl - INFO - Epoch [102][3100/3746] lr: 2.336e-02, eta: 1 day, 16:57:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6509, loss_cls: 3.4464, loss: 3.4464 +2024-07-25 21:21:30,984 - pyskl - INFO - Epoch [102][3200/3746] lr: 2.334e-02, eta: 1 day, 16:56:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6522, loss_cls: 3.4484, loss: 3.4484 +2024-07-25 21:22:53,291 - pyskl - INFO - Epoch [102][3300/3746] lr: 2.331e-02, eta: 1 day, 16:55:14, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3872, top5_acc: 0.6500, loss_cls: 3.4476, loss: 3.4476 +2024-07-25 21:24:15,629 - pyskl - INFO - Epoch [102][3400/3746] lr: 2.329e-02, eta: 1 day, 16:53:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6373, loss_cls: 3.4958, loss: 3.4958 +2024-07-25 21:25:37,713 - pyskl - INFO - Epoch [102][3500/3746] lr: 2.327e-02, eta: 1 day, 16:52:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6486, loss_cls: 3.4470, loss: 3.4470 +2024-07-25 21:27:00,143 - pyskl - INFO - Epoch [102][3600/3746] lr: 2.324e-02, eta: 1 day, 16:51:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6430, loss_cls: 3.4756, loss: 3.4756 +2024-07-25 21:28:21,527 - pyskl - INFO - Epoch [102][3700/3746] lr: 2.322e-02, eta: 1 day, 16:49:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6452, loss_cls: 3.4235, loss: 3.4235 +2024-07-25 21:29:01,227 - pyskl - INFO - Saving checkpoint at 102 epochs +2024-07-25 21:30:53,823 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 21:30:54,498 - pyskl - INFO - +top1_acc 0.3267 +top5_acc 0.5885 +2024-07-25 21:30:54,499 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 21:30:54,544 - pyskl - INFO - +mean_acc 0.3264 +2024-07-25 21:30:54,549 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_96.pth was removed +2024-07-25 21:30:54,864 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_102.pth. +2024-07-25 21:30:54,864 - pyskl - INFO - Best top1_acc is 0.3267 at 102 epoch. +2024-07-25 21:30:54,880 - pyskl - INFO - Epoch(val) [102][309] top1_acc: 0.3267, top5_acc: 0.5885, mean_class_accuracy: 0.3264 +2024-07-25 21:34:45,355 - pyskl - INFO - Epoch [103][100/3746] lr: 2.319e-02, eta: 1 day, 16:48:41, time: 2.305, data_time: 1.310, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6623, loss_cls: 3.3782, loss: 3.3782 +2024-07-25 21:36:08,817 - pyskl - INFO - Epoch [103][200/3746] lr: 2.316e-02, eta: 1 day, 16:47:20, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3989, top5_acc: 0.6709, loss_cls: 3.3653, loss: 3.3653 +2024-07-25 21:37:32,171 - pyskl - INFO - Epoch [103][300/3746] lr: 2.314e-02, eta: 1 day, 16:45:59, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6683, loss_cls: 3.3264, loss: 3.3264 +2024-07-25 21:38:54,466 - pyskl - INFO - Epoch [103][400/3746] lr: 2.311e-02, eta: 1 day, 16:44:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6645, loss_cls: 3.3501, loss: 3.3501 +2024-07-25 21:40:16,883 - pyskl - INFO - Epoch [103][500/3746] lr: 2.309e-02, eta: 1 day, 16:43:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6491, loss_cls: 3.4069, loss: 3.4069 +2024-07-25 21:41:38,953 - pyskl - INFO - Epoch [103][600/3746] lr: 2.307e-02, eta: 1 day, 16:41:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6506, loss_cls: 3.4584, loss: 3.4584 +2024-07-25 21:43:01,048 - pyskl - INFO - Epoch [103][700/3746] lr: 2.304e-02, eta: 1 day, 16:40:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6545, loss_cls: 3.4365, loss: 3.4365 +2024-07-25 21:44:23,531 - pyskl - INFO - Epoch [103][800/3746] lr: 2.302e-02, eta: 1 day, 16:39:11, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6484, loss_cls: 3.4413, loss: 3.4413 +2024-07-25 21:45:46,083 - pyskl - INFO - Epoch [103][900/3746] lr: 2.300e-02, eta: 1 day, 16:37:50, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6625, loss_cls: 3.3660, loss: 3.3660 +2024-07-25 21:47:07,639 - pyskl - INFO - Epoch [103][1000/3746] lr: 2.297e-02, eta: 1 day, 16:36:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6525, loss_cls: 3.3845, loss: 3.3845 +2024-07-25 21:48:30,185 - pyskl - INFO - Epoch [103][1100/3746] lr: 2.295e-02, eta: 1 day, 16:35:07, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6505, loss_cls: 3.4511, loss: 3.4511 +2024-07-25 21:49:52,510 - pyskl - INFO - Epoch [103][1200/3746] lr: 2.293e-02, eta: 1 day, 16:33:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6502, loss_cls: 3.4324, loss: 3.4324 +2024-07-25 21:51:15,654 - pyskl - INFO - Epoch [103][1300/3746] lr: 2.290e-02, eta: 1 day, 16:32:24, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6553, loss_cls: 3.4357, loss: 3.4357 +2024-07-25 21:52:37,554 - pyskl - INFO - Epoch [103][1400/3746] lr: 2.288e-02, eta: 1 day, 16:31:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6584, loss_cls: 3.4050, loss: 3.4050 +2024-07-25 21:54:00,391 - pyskl - INFO - Epoch [103][1500/3746] lr: 2.286e-02, eta: 1 day, 16:29:41, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3908, top5_acc: 0.6581, loss_cls: 3.4235, loss: 3.4235 +2024-07-25 21:55:22,823 - pyskl - INFO - Epoch [103][1600/3746] lr: 2.283e-02, eta: 1 day, 16:28:20, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6428, loss_cls: 3.4461, loss: 3.4461 +2024-07-25 21:56:45,856 - pyskl - INFO - Epoch [103][1700/3746] lr: 2.281e-02, eta: 1 day, 16:26:59, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6567, loss_cls: 3.4000, loss: 3.4000 +2024-07-25 21:58:09,028 - pyskl - INFO - Epoch [103][1800/3746] lr: 2.279e-02, eta: 1 day, 16:25:38, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6469, loss_cls: 3.4129, loss: 3.4129 +2024-07-25 21:59:31,747 - pyskl - INFO - Epoch [103][1900/3746] lr: 2.276e-02, eta: 1 day, 16:24:16, time: 0.827, data_time: 0.001, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6489, loss_cls: 3.4275, loss: 3.4275 +2024-07-25 22:00:54,659 - pyskl - INFO - Epoch [103][2000/3746] lr: 2.274e-02, eta: 1 day, 16:22:55, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6509, loss_cls: 3.4356, loss: 3.4356 +2024-07-25 22:02:18,112 - pyskl - INFO - Epoch [103][2100/3746] lr: 2.272e-02, eta: 1 day, 16:21:34, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6498, loss_cls: 3.4297, loss: 3.4297 +2024-07-25 22:03:40,393 - pyskl - INFO - Epoch [103][2200/3746] lr: 2.269e-02, eta: 1 day, 16:20:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3992, top5_acc: 0.6573, loss_cls: 3.3772, loss: 3.3772 +2024-07-25 22:05:03,051 - pyskl - INFO - Epoch [103][2300/3746] lr: 2.267e-02, eta: 1 day, 16:18:51, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3977, top5_acc: 0.6559, loss_cls: 3.4273, loss: 3.4273 +2024-07-25 22:06:24,925 - pyskl - INFO - Epoch [103][2400/3746] lr: 2.264e-02, eta: 1 day, 16:17:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6555, loss_cls: 3.4264, loss: 3.4264 +2024-07-25 22:07:47,834 - pyskl - INFO - Epoch [103][2500/3746] lr: 2.262e-02, eta: 1 day, 16:16:08, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6456, loss_cls: 3.4476, loss: 3.4476 +2024-07-25 22:09:10,064 - pyskl - INFO - Epoch [103][2600/3746] lr: 2.260e-02, eta: 1 day, 16:14:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6489, loss_cls: 3.4462, loss: 3.4462 +2024-07-25 22:10:32,576 - pyskl - INFO - Epoch [103][2700/3746] lr: 2.257e-02, eta: 1 day, 16:13:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6534, loss_cls: 3.4610, loss: 3.4610 +2024-07-25 22:11:55,754 - pyskl - INFO - Epoch [103][2800/3746] lr: 2.255e-02, eta: 1 day, 16:12:04, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6470, loss_cls: 3.4380, loss: 3.4380 +2024-07-25 22:13:18,190 - pyskl - INFO - Epoch [103][2900/3746] lr: 2.253e-02, eta: 1 day, 16:10:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6561, loss_cls: 3.4161, loss: 3.4161 +2024-07-25 22:14:39,981 - pyskl - INFO - Epoch [103][3000/3746] lr: 2.250e-02, eta: 1 day, 16:09:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6520, loss_cls: 3.4120, loss: 3.4120 +2024-07-25 22:16:01,657 - pyskl - INFO - Epoch [103][3100/3746] lr: 2.248e-02, eta: 1 day, 16:07:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6541, loss_cls: 3.4013, loss: 3.4013 +2024-07-25 22:17:23,579 - pyskl - INFO - Epoch [103][3200/3746] lr: 2.246e-02, eta: 1 day, 16:06:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3872, top5_acc: 0.6455, loss_cls: 3.4437, loss: 3.4437 +2024-07-25 22:18:45,607 - pyskl - INFO - Epoch [103][3300/3746] lr: 2.243e-02, eta: 1 day, 16:05:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6525, loss_cls: 3.4341, loss: 3.4341 +2024-07-25 22:20:07,769 - pyskl - INFO - Epoch [103][3400/3746] lr: 2.241e-02, eta: 1 day, 16:03:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6478, loss_cls: 3.4312, loss: 3.4312 +2024-07-25 22:21:30,148 - pyskl - INFO - Epoch [103][3500/3746] lr: 2.239e-02, eta: 1 day, 16:02:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6469, loss_cls: 3.4513, loss: 3.4513 +2024-07-25 22:22:52,379 - pyskl - INFO - Epoch [103][3600/3746] lr: 2.236e-02, eta: 1 day, 16:01:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6527, loss_cls: 3.4662, loss: 3.4662 +2024-07-25 22:24:14,748 - pyskl - INFO - Epoch [103][3700/3746] lr: 2.234e-02, eta: 1 day, 15:59:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3739, top5_acc: 0.6427, loss_cls: 3.4857, loss: 3.4857 +2024-07-25 22:24:54,614 - pyskl - INFO - Saving checkpoint at 103 epochs +2024-07-25 22:26:48,646 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 22:26:49,382 - pyskl - INFO - +top1_acc 0.3156 +top5_acc 0.5748 +2024-07-25 22:26:49,382 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 22:26:49,428 - pyskl - INFO - +mean_acc 0.3153 +2024-07-25 22:26:49,443 - pyskl - INFO - Epoch(val) [103][309] top1_acc: 0.3156, top5_acc: 0.5748, mean_class_accuracy: 0.3153 +2024-07-25 22:30:37,324 - pyskl - INFO - Epoch [104][100/3746] lr: 2.231e-02, eta: 1 day, 15:58:40, time: 2.279, data_time: 1.290, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6614, loss_cls: 3.3430, loss: 3.3430 +2024-07-25 22:32:00,671 - pyskl - INFO - Epoch [104][200/3746] lr: 2.228e-02, eta: 1 day, 15:57:19, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6620, loss_cls: 3.3512, loss: 3.3512 +2024-07-25 22:33:23,380 - pyskl - INFO - Epoch [104][300/3746] lr: 2.226e-02, eta: 1 day, 15:55:57, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6586, loss_cls: 3.3547, loss: 3.3547 +2024-07-25 22:34:45,541 - pyskl - INFO - Epoch [104][400/3746] lr: 2.224e-02, eta: 1 day, 15:54:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6692, loss_cls: 3.3342, loss: 3.3342 +2024-07-25 22:36:08,367 - pyskl - INFO - Epoch [104][500/3746] lr: 2.221e-02, eta: 1 day, 15:53:14, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6572, loss_cls: 3.3741, loss: 3.3741 +2024-07-25 22:37:31,109 - pyskl - INFO - Epoch [104][600/3746] lr: 2.219e-02, eta: 1 day, 15:51:53, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6647, loss_cls: 3.3625, loss: 3.3625 +2024-07-25 22:38:53,742 - pyskl - INFO - Epoch [104][700/3746] lr: 2.217e-02, eta: 1 day, 15:50:32, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6687, loss_cls: 3.3412, loss: 3.3412 +2024-07-25 22:40:16,613 - pyskl - INFO - Epoch [104][800/3746] lr: 2.214e-02, eta: 1 day, 15:49:10, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6477, loss_cls: 3.4354, loss: 3.4354 +2024-07-25 22:41:39,260 - pyskl - INFO - Epoch [104][900/3746] lr: 2.212e-02, eta: 1 day, 15:47:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6589, loss_cls: 3.3909, loss: 3.3909 +2024-07-25 22:43:02,609 - pyskl - INFO - Epoch [104][1000/3746] lr: 2.210e-02, eta: 1 day, 15:46:28, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6520, loss_cls: 3.4471, loss: 3.4471 +2024-07-25 22:44:25,422 - pyskl - INFO - Epoch [104][1100/3746] lr: 2.208e-02, eta: 1 day, 15:45:07, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3909, top5_acc: 0.6570, loss_cls: 3.3986, loss: 3.3986 +2024-07-25 22:45:48,794 - pyskl - INFO - Epoch [104][1200/3746] lr: 2.205e-02, eta: 1 day, 15:43:45, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6558, loss_cls: 3.4013, loss: 3.4013 +2024-07-25 22:47:11,519 - pyskl - INFO - Epoch [104][1300/3746] lr: 2.203e-02, eta: 1 day, 15:42:24, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6558, loss_cls: 3.4109, loss: 3.4109 +2024-07-25 22:48:34,416 - pyskl - INFO - Epoch [104][1400/3746] lr: 2.201e-02, eta: 1 day, 15:41:03, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6566, loss_cls: 3.3957, loss: 3.3957 +2024-07-25 22:49:57,136 - pyskl - INFO - Epoch [104][1500/3746] lr: 2.198e-02, eta: 1 day, 15:39:41, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4072, top5_acc: 0.6584, loss_cls: 3.3948, loss: 3.3948 +2024-07-25 22:51:19,907 - pyskl - INFO - Epoch [104][1600/3746] lr: 2.196e-02, eta: 1 day, 15:38:20, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3945, top5_acc: 0.6561, loss_cls: 3.4034, loss: 3.4034 +2024-07-25 22:52:43,909 - pyskl - INFO - Epoch [104][1700/3746] lr: 2.194e-02, eta: 1 day, 15:36:59, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6598, loss_cls: 3.3653, loss: 3.3653 +2024-07-25 22:54:07,518 - pyskl - INFO - Epoch [104][1800/3746] lr: 2.191e-02, eta: 1 day, 15:35:38, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3933, top5_acc: 0.6623, loss_cls: 3.4058, loss: 3.4058 +2024-07-25 22:55:30,268 - pyskl - INFO - Epoch [104][1900/3746] lr: 2.189e-02, eta: 1 day, 15:34:17, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6473, loss_cls: 3.4491, loss: 3.4491 +2024-07-25 22:56:53,838 - pyskl - INFO - Epoch [104][2000/3746] lr: 2.187e-02, eta: 1 day, 15:32:56, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6586, loss_cls: 3.3935, loss: 3.3935 +2024-07-25 22:58:16,440 - pyskl - INFO - Epoch [104][2100/3746] lr: 2.184e-02, eta: 1 day, 15:31:34, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6587, loss_cls: 3.4006, loss: 3.4006 +2024-07-25 22:59:39,063 - pyskl - INFO - Epoch [104][2200/3746] lr: 2.182e-02, eta: 1 day, 15:30:13, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6458, loss_cls: 3.4245, loss: 3.4245 +2024-07-25 23:01:01,186 - pyskl - INFO - Epoch [104][2300/3746] lr: 2.180e-02, eta: 1 day, 15:28:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6453, loss_cls: 3.4338, loss: 3.4338 +2024-07-25 23:02:24,421 - pyskl - INFO - Epoch [104][2400/3746] lr: 2.177e-02, eta: 1 day, 15:27:30, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6595, loss_cls: 3.3863, loss: 3.3863 +2024-07-25 23:03:46,344 - pyskl - INFO - Epoch [104][2500/3746] lr: 2.175e-02, eta: 1 day, 15:26:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6509, loss_cls: 3.4466, loss: 3.4466 +2024-07-25 23:05:08,951 - pyskl - INFO - Epoch [104][2600/3746] lr: 2.173e-02, eta: 1 day, 15:24:47, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6534, loss_cls: 3.4259, loss: 3.4259 +2024-07-25 23:06:32,421 - pyskl - INFO - Epoch [104][2700/3746] lr: 2.171e-02, eta: 1 day, 15:23:26, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6502, loss_cls: 3.4730, loss: 3.4730 +2024-07-25 23:07:55,230 - pyskl - INFO - Epoch [104][2800/3746] lr: 2.168e-02, eta: 1 day, 15:22:05, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6558, loss_cls: 3.3881, loss: 3.3881 +2024-07-25 23:09:18,020 - pyskl - INFO - Epoch [104][2900/3746] lr: 2.166e-02, eta: 1 day, 15:20:43, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6438, loss_cls: 3.4616, loss: 3.4616 +2024-07-25 23:10:40,924 - pyskl - INFO - Epoch [104][3000/3746] lr: 2.164e-02, eta: 1 day, 15:19:22, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4006, top5_acc: 0.6500, loss_cls: 3.4115, loss: 3.4115 +2024-07-25 23:12:02,922 - pyskl - INFO - Epoch [104][3100/3746] lr: 2.161e-02, eta: 1 day, 15:18:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6531, loss_cls: 3.4268, loss: 3.4268 +2024-07-25 23:13:25,480 - pyskl - INFO - Epoch [104][3200/3746] lr: 2.159e-02, eta: 1 day, 15:16:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3903, top5_acc: 0.6531, loss_cls: 3.4233, loss: 3.4233 +2024-07-25 23:14:48,201 - pyskl - INFO - Epoch [104][3300/3746] lr: 2.157e-02, eta: 1 day, 15:15:17, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6584, loss_cls: 3.3863, loss: 3.3863 +2024-07-25 23:16:11,174 - pyskl - INFO - Epoch [104][3400/3746] lr: 2.154e-02, eta: 1 day, 15:13:56, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6547, loss_cls: 3.4210, loss: 3.4210 +2024-07-25 23:17:33,802 - pyskl - INFO - Epoch [104][3500/3746] lr: 2.152e-02, eta: 1 day, 15:12:35, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6564, loss_cls: 3.3840, loss: 3.3840 +2024-07-25 23:18:56,554 - pyskl - INFO - Epoch [104][3600/3746] lr: 2.150e-02, eta: 1 day, 15:11:13, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6600, loss_cls: 3.4076, loss: 3.4076 +2024-07-25 23:20:18,824 - pyskl - INFO - Epoch [104][3700/3746] lr: 2.148e-02, eta: 1 day, 15:09:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3941, top5_acc: 0.6514, loss_cls: 3.4169, loss: 3.4169 +2024-07-25 23:20:58,380 - pyskl - INFO - Saving checkpoint at 104 epochs +2024-07-25 23:22:51,845 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 23:22:52,561 - pyskl - INFO - +top1_acc 0.3296 +top5_acc 0.5902 +2024-07-25 23:22:52,562 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 23:22:52,603 - pyskl - INFO - +mean_acc 0.3295 +2024-07-25 23:22:52,608 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_102.pth was removed +2024-07-25 23:22:52,867 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_104.pth. +2024-07-25 23:22:52,867 - pyskl - INFO - Best top1_acc is 0.3296 at 104 epoch. +2024-07-25 23:22:52,879 - pyskl - INFO - Epoch(val) [104][309] top1_acc: 0.3296, top5_acc: 0.5902, mean_class_accuracy: 0.3295 +2024-07-25 23:26:40,719 - pyskl - INFO - Epoch [105][100/3746] lr: 2.144e-02, eta: 1 day, 15:08:40, time: 2.278, data_time: 1.283, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6803, loss_cls: 3.2756, loss: 3.2756 +2024-07-25 23:28:03,578 - pyskl - INFO - Epoch [105][200/3746] lr: 2.142e-02, eta: 1 day, 15:07:19, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6667, loss_cls: 3.3309, loss: 3.3309 +2024-07-25 23:29:25,955 - pyskl - INFO - Epoch [105][300/3746] lr: 2.140e-02, eta: 1 day, 15:05:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6656, loss_cls: 3.3436, loss: 3.3436 +2024-07-25 23:30:48,101 - pyskl - INFO - Epoch [105][400/3746] lr: 2.137e-02, eta: 1 day, 15:04:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6659, loss_cls: 3.3460, loss: 3.3460 +2024-07-25 23:32:10,200 - pyskl - INFO - Epoch [105][500/3746] lr: 2.135e-02, eta: 1 day, 15:03:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4009, top5_acc: 0.6634, loss_cls: 3.3755, loss: 3.3755 +2024-07-25 23:33:32,539 - pyskl - INFO - Epoch [105][600/3746] lr: 2.133e-02, eta: 1 day, 15:01:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4141, top5_acc: 0.6659, loss_cls: 3.3234, loss: 3.3234 +2024-07-25 23:34:54,852 - pyskl - INFO - Epoch [105][700/3746] lr: 2.130e-02, eta: 1 day, 15:00:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6661, loss_cls: 3.3486, loss: 3.3486 +2024-07-25 23:36:17,047 - pyskl - INFO - Epoch [105][800/3746] lr: 2.128e-02, eta: 1 day, 14:59:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6577, loss_cls: 3.3808, loss: 3.3808 +2024-07-25 23:37:38,811 - pyskl - INFO - Epoch [105][900/3746] lr: 2.126e-02, eta: 1 day, 14:57:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6575, loss_cls: 3.4043, loss: 3.4043 +2024-07-25 23:39:01,072 - pyskl - INFO - Epoch [105][1000/3746] lr: 2.124e-02, eta: 1 day, 14:56:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4005, top5_acc: 0.6614, loss_cls: 3.3758, loss: 3.3758 +2024-07-25 23:40:22,574 - pyskl - INFO - Epoch [105][1100/3746] lr: 2.121e-02, eta: 1 day, 14:55:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6512, loss_cls: 3.4201, loss: 3.4201 +2024-07-25 23:41:44,470 - pyskl - INFO - Epoch [105][1200/3746] lr: 2.119e-02, eta: 1 day, 14:53:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3986, top5_acc: 0.6572, loss_cls: 3.3968, loss: 3.3968 +2024-07-25 23:43:05,756 - pyskl - INFO - Epoch [105][1300/3746] lr: 2.117e-02, eta: 1 day, 14:52:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6639, loss_cls: 3.4028, loss: 3.4028 +2024-07-25 23:44:27,623 - pyskl - INFO - Epoch [105][1400/3746] lr: 2.114e-02, eta: 1 day, 14:50:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3903, top5_acc: 0.6586, loss_cls: 3.4131, loss: 3.4131 +2024-07-25 23:45:49,910 - pyskl - INFO - Epoch [105][1500/3746] lr: 2.112e-02, eta: 1 day, 14:49:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6581, loss_cls: 3.3963, loss: 3.3963 +2024-07-25 23:47:12,189 - pyskl - INFO - Epoch [105][1600/3746] lr: 2.110e-02, eta: 1 day, 14:48:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6497, loss_cls: 3.4060, loss: 3.4060 +2024-07-25 23:48:35,268 - pyskl - INFO - Epoch [105][1700/3746] lr: 2.108e-02, eta: 1 day, 14:46:53, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6603, loss_cls: 3.3713, loss: 3.3713 +2024-07-25 23:49:58,364 - pyskl - INFO - Epoch [105][1800/3746] lr: 2.105e-02, eta: 1 day, 14:45:32, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6541, loss_cls: 3.4065, loss: 3.4065 +2024-07-25 23:51:21,137 - pyskl - INFO - Epoch [105][1900/3746] lr: 2.103e-02, eta: 1 day, 14:44:11, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6575, loss_cls: 3.4052, loss: 3.4052 +2024-07-25 23:52:44,008 - pyskl - INFO - Epoch [105][2000/3746] lr: 2.101e-02, eta: 1 day, 14:42:49, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3970, top5_acc: 0.6528, loss_cls: 3.4093, loss: 3.4093 +2024-07-25 23:54:05,970 - pyskl - INFO - Epoch [105][2100/3746] lr: 2.098e-02, eta: 1 day, 14:41:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6656, loss_cls: 3.3549, loss: 3.3549 +2024-07-25 23:55:27,880 - pyskl - INFO - Epoch [105][2200/3746] lr: 2.096e-02, eta: 1 day, 14:40:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3947, top5_acc: 0.6636, loss_cls: 3.3803, loss: 3.3803 +2024-07-25 23:56:49,922 - pyskl - INFO - Epoch [105][2300/3746] lr: 2.094e-02, eta: 1 day, 14:38:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6584, loss_cls: 3.4079, loss: 3.4079 +2024-07-25 23:58:12,409 - pyskl - INFO - Epoch [105][2400/3746] lr: 2.092e-02, eta: 1 day, 14:37:22, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6578, loss_cls: 3.3908, loss: 3.3908 +2024-07-25 23:59:34,273 - pyskl - INFO - Epoch [105][2500/3746] lr: 2.089e-02, eta: 1 day, 14:36:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4016, top5_acc: 0.6634, loss_cls: 3.3583, loss: 3.3583 +2024-07-26 00:00:56,787 - pyskl - INFO - Epoch [105][2600/3746] lr: 2.087e-02, eta: 1 day, 14:34:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3997, top5_acc: 0.6644, loss_cls: 3.3429, loss: 3.3429 +2024-07-26 00:02:19,547 - pyskl - INFO - Epoch [105][2700/3746] lr: 2.085e-02, eta: 1 day, 14:33:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6559, loss_cls: 3.3924, loss: 3.3924 +2024-07-26 00:03:41,547 - pyskl - INFO - Epoch [105][2800/3746] lr: 2.083e-02, eta: 1 day, 14:31:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3844, top5_acc: 0.6431, loss_cls: 3.4621, loss: 3.4621 +2024-07-26 00:05:03,473 - pyskl - INFO - Epoch [105][2900/3746] lr: 2.080e-02, eta: 1 day, 14:30:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6506, loss_cls: 3.4075, loss: 3.4075 +2024-07-26 00:06:25,733 - pyskl - INFO - Epoch [105][3000/3746] lr: 2.078e-02, eta: 1 day, 14:29:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6587, loss_cls: 3.4038, loss: 3.4038 +2024-07-26 00:07:47,514 - pyskl - INFO - Epoch [105][3100/3746] lr: 2.076e-02, eta: 1 day, 14:27:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6466, loss_cls: 3.4179, loss: 3.4179 +2024-07-26 00:09:09,540 - pyskl - INFO - Epoch [105][3200/3746] lr: 2.073e-02, eta: 1 day, 14:26:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6708, loss_cls: 3.3460, loss: 3.3460 +2024-07-26 00:10:31,883 - pyskl - INFO - Epoch [105][3300/3746] lr: 2.071e-02, eta: 1 day, 14:25:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6675, loss_cls: 3.3834, loss: 3.3834 +2024-07-26 00:11:54,623 - pyskl - INFO - Epoch [105][3400/3746] lr: 2.069e-02, eta: 1 day, 14:23:46, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6523, loss_cls: 3.4164, loss: 3.4164 +2024-07-26 00:13:16,447 - pyskl - INFO - Epoch [105][3500/3746] lr: 2.067e-02, eta: 1 day, 14:22:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6469, loss_cls: 3.4376, loss: 3.4376 +2024-07-26 00:14:38,123 - pyskl - INFO - Epoch [105][3600/3746] lr: 2.064e-02, eta: 1 day, 14:21:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6452, loss_cls: 3.4521, loss: 3.4521 +2024-07-26 00:16:00,257 - pyskl - INFO - Epoch [105][3700/3746] lr: 2.062e-02, eta: 1 day, 14:19:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6577, loss_cls: 3.3810, loss: 3.3810 +2024-07-26 00:16:40,292 - pyskl - INFO - Saving checkpoint at 105 epochs +2024-07-26 00:18:32,792 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 00:18:33,616 - pyskl - INFO - +top1_acc 0.3310 +top5_acc 0.5908 +2024-07-26 00:18:33,616 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 00:18:33,656 - pyskl - INFO - +mean_acc 0.3306 +2024-07-26 00:18:33,661 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_104.pth was removed +2024-07-26 00:18:33,908 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_105.pth. +2024-07-26 00:18:33,909 - pyskl - INFO - Best top1_acc is 0.3310 at 105 epoch. +2024-07-26 00:18:33,920 - pyskl - INFO - Epoch(val) [105][309] top1_acc: 0.3310, top5_acc: 0.5908, mean_class_accuracy: 0.3306 +2024-07-26 00:22:19,516 - pyskl - INFO - Epoch [106][100/3746] lr: 2.059e-02, eta: 1 day, 14:18:26, time: 2.256, data_time: 1.265, memory: 15990, top1_acc: 0.4130, top5_acc: 0.6692, loss_cls: 3.2957, loss: 3.2957 +2024-07-26 00:23:42,079 - pyskl - INFO - Epoch [106][200/3746] lr: 2.057e-02, eta: 1 day, 14:17:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4086, top5_acc: 0.6634, loss_cls: 3.3309, loss: 3.3309 +2024-07-26 00:25:04,206 - pyskl - INFO - Epoch [106][300/3746] lr: 2.054e-02, eta: 1 day, 14:15:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4167, top5_acc: 0.6741, loss_cls: 3.2779, loss: 3.2779 +2024-07-26 00:26:26,000 - pyskl - INFO - Epoch [106][400/3746] lr: 2.052e-02, eta: 1 day, 14:14:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6619, loss_cls: 3.3356, loss: 3.3356 +2024-07-26 00:27:48,448 - pyskl - INFO - Epoch [106][500/3746] lr: 2.050e-02, eta: 1 day, 14:12:59, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6666, loss_cls: 3.3345, loss: 3.3345 +2024-07-26 00:29:10,332 - pyskl - INFO - Epoch [106][600/3746] lr: 2.048e-02, eta: 1 day, 14:11:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6683, loss_cls: 3.3389, loss: 3.3389 +2024-07-26 00:30:32,352 - pyskl - INFO - Epoch [106][700/3746] lr: 2.045e-02, eta: 1 day, 14:10:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4155, top5_acc: 0.6641, loss_cls: 3.3109, loss: 3.3109 +2024-07-26 00:31:54,454 - pyskl - INFO - Epoch [106][800/3746] lr: 2.043e-02, eta: 1 day, 14:08:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6613, loss_cls: 3.3677, loss: 3.3677 +2024-07-26 00:33:16,199 - pyskl - INFO - Epoch [106][900/3746] lr: 2.041e-02, eta: 1 day, 14:07:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6622, loss_cls: 3.3430, loss: 3.3430 +2024-07-26 00:34:38,282 - pyskl - INFO - Epoch [106][1000/3746] lr: 2.039e-02, eta: 1 day, 14:06:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3970, top5_acc: 0.6573, loss_cls: 3.3489, loss: 3.3489 +2024-07-26 00:36:00,735 - pyskl - INFO - Epoch [106][1100/3746] lr: 2.036e-02, eta: 1 day, 14:04:49, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6586, loss_cls: 3.3636, loss: 3.3636 +2024-07-26 00:37:22,669 - pyskl - INFO - Epoch [106][1200/3746] lr: 2.034e-02, eta: 1 day, 14:03:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6581, loss_cls: 3.3962, loss: 3.3962 +2024-07-26 00:38:44,562 - pyskl - INFO - Epoch [106][1300/3746] lr: 2.032e-02, eta: 1 day, 14:02:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3970, top5_acc: 0.6636, loss_cls: 3.3683, loss: 3.3683 +2024-07-26 00:40:07,441 - pyskl - INFO - Epoch [106][1400/3746] lr: 2.030e-02, eta: 1 day, 14:00:44, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6689, loss_cls: 3.3223, loss: 3.3223 +2024-07-26 00:41:30,206 - pyskl - INFO - Epoch [106][1500/3746] lr: 2.027e-02, eta: 1 day, 13:59:22, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6542, loss_cls: 3.4184, loss: 3.4184 +2024-07-26 00:42:52,928 - pyskl - INFO - Epoch [106][1600/3746] lr: 2.025e-02, eta: 1 day, 13:58:01, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6622, loss_cls: 3.3730, loss: 3.3730 +2024-07-26 00:44:16,259 - pyskl - INFO - Epoch [106][1700/3746] lr: 2.023e-02, eta: 1 day, 13:56:39, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6523, loss_cls: 3.4143, loss: 3.4143 +2024-07-26 00:45:39,516 - pyskl - INFO - Epoch [106][1800/3746] lr: 2.021e-02, eta: 1 day, 13:55:18, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6614, loss_cls: 3.3625, loss: 3.3625 +2024-07-26 00:47:01,988 - pyskl - INFO - Epoch [106][1900/3746] lr: 2.018e-02, eta: 1 day, 13:53:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6620, loss_cls: 3.3650, loss: 3.3650 +2024-07-26 00:48:24,517 - pyskl - INFO - Epoch [106][2000/3746] lr: 2.016e-02, eta: 1 day, 13:52:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6637, loss_cls: 3.3506, loss: 3.3506 +2024-07-26 00:49:46,526 - pyskl - INFO - Epoch [106][2100/3746] lr: 2.014e-02, eta: 1 day, 13:51:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6613, loss_cls: 3.3789, loss: 3.3789 +2024-07-26 00:51:08,729 - pyskl - INFO - Epoch [106][2200/3746] lr: 2.012e-02, eta: 1 day, 13:49:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6591, loss_cls: 3.3935, loss: 3.3935 +2024-07-26 00:52:31,999 - pyskl - INFO - Epoch [106][2300/3746] lr: 2.009e-02, eta: 1 day, 13:48:30, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4086, top5_acc: 0.6702, loss_cls: 3.3164, loss: 3.3164 +2024-07-26 00:53:54,196 - pyskl - INFO - Epoch [106][2400/3746] lr: 2.007e-02, eta: 1 day, 13:47:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6586, loss_cls: 3.3983, loss: 3.3983 +2024-07-26 00:55:16,356 - pyskl - INFO - Epoch [106][2500/3746] lr: 2.005e-02, eta: 1 day, 13:45:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6641, loss_cls: 3.3573, loss: 3.3573 +2024-07-26 00:56:39,202 - pyskl - INFO - Epoch [106][2600/3746] lr: 2.003e-02, eta: 1 day, 13:44:25, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6656, loss_cls: 3.3625, loss: 3.3625 +2024-07-26 00:58:02,103 - pyskl - INFO - Epoch [106][2700/3746] lr: 2.000e-02, eta: 1 day, 13:43:04, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6528, loss_cls: 3.3995, loss: 3.3995 +2024-07-26 00:59:24,223 - pyskl - INFO - Epoch [106][2800/3746] lr: 1.998e-02, eta: 1 day, 13:41:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3933, top5_acc: 0.6534, loss_cls: 3.4291, loss: 3.4291 +2024-07-26 01:00:46,429 - pyskl - INFO - Epoch [106][2900/3746] lr: 1.996e-02, eta: 1 day, 13:40:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4016, top5_acc: 0.6587, loss_cls: 3.3869, loss: 3.3869 +2024-07-26 01:02:08,952 - pyskl - INFO - Epoch [106][3000/3746] lr: 1.994e-02, eta: 1 day, 13:38:59, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6619, loss_cls: 3.3840, loss: 3.3840 +2024-07-26 01:03:31,559 - pyskl - INFO - Epoch [106][3100/3746] lr: 1.991e-02, eta: 1 day, 13:37:37, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4062, top5_acc: 0.6666, loss_cls: 3.3451, loss: 3.3451 +2024-07-26 01:04:53,929 - pyskl - INFO - Epoch [106][3200/3746] lr: 1.989e-02, eta: 1 day, 13:36:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6658, loss_cls: 3.3718, loss: 3.3718 +2024-07-26 01:06:16,214 - pyskl - INFO - Epoch [106][3300/3746] lr: 1.987e-02, eta: 1 day, 13:34:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3972, top5_acc: 0.6684, loss_cls: 3.3607, loss: 3.3607 +2024-07-26 01:07:38,649 - pyskl - INFO - Epoch [106][3400/3746] lr: 1.985e-02, eta: 1 day, 13:33:32, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6716, loss_cls: 3.3525, loss: 3.3525 +2024-07-26 01:09:01,704 - pyskl - INFO - Epoch [106][3500/3746] lr: 1.983e-02, eta: 1 day, 13:32:11, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6575, loss_cls: 3.3798, loss: 3.3798 +2024-07-26 01:10:23,909 - pyskl - INFO - Epoch [106][3600/3746] lr: 1.980e-02, eta: 1 day, 13:30:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3989, top5_acc: 0.6647, loss_cls: 3.3735, loss: 3.3735 +2024-07-26 01:11:46,500 - pyskl - INFO - Epoch [106][3700/3746] lr: 1.978e-02, eta: 1 day, 13:29:28, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6552, loss_cls: 3.4073, loss: 3.4073 +2024-07-26 01:12:26,684 - pyskl - INFO - Saving checkpoint at 106 epochs +2024-07-26 01:14:19,089 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 01:14:19,984 - pyskl - INFO - +top1_acc 0.3311 +top5_acc 0.5940 +2024-07-26 01:14:19,984 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 01:14:20,026 - pyskl - INFO - +mean_acc 0.3306 +2024-07-26 01:14:20,031 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_105.pth was removed +2024-07-26 01:14:20,294 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_106.pth. +2024-07-26 01:14:20,295 - pyskl - INFO - Best top1_acc is 0.3311 at 106 epoch. +2024-07-26 01:14:20,306 - pyskl - INFO - Epoch(val) [106][309] top1_acc: 0.3311, top5_acc: 0.5940, mean_class_accuracy: 0.3306 +2024-07-26 01:18:05,823 - pyskl - INFO - Epoch [107][100/3746] lr: 1.975e-02, eta: 1 day, 13:28:12, time: 2.255, data_time: 1.265, memory: 15990, top1_acc: 0.4139, top5_acc: 0.6736, loss_cls: 3.2870, loss: 3.2870 +2024-07-26 01:19:28,577 - pyskl - INFO - Epoch [107][200/3746] lr: 1.973e-02, eta: 1 day, 13:26:51, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6675, loss_cls: 3.3463, loss: 3.3463 +2024-07-26 01:20:50,856 - pyskl - INFO - Epoch [107][300/3746] lr: 1.970e-02, eta: 1 day, 13:25:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4062, top5_acc: 0.6711, loss_cls: 3.3326, loss: 3.3326 +2024-07-26 01:22:12,378 - pyskl - INFO - Epoch [107][400/3746] lr: 1.968e-02, eta: 1 day, 13:24:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6680, loss_cls: 3.3168, loss: 3.3168 +2024-07-26 01:23:33,697 - pyskl - INFO - Epoch [107][500/3746] lr: 1.966e-02, eta: 1 day, 13:22:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6739, loss_cls: 3.2917, loss: 3.2917 +2024-07-26 01:24:55,397 - pyskl - INFO - Epoch [107][600/3746] lr: 1.964e-02, eta: 1 day, 13:21:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6689, loss_cls: 3.2917, loss: 3.2917 +2024-07-26 01:26:17,664 - pyskl - INFO - Epoch [107][700/3746] lr: 1.961e-02, eta: 1 day, 13:20:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4033, top5_acc: 0.6705, loss_cls: 3.3261, loss: 3.3261 +2024-07-26 01:27:39,309 - pyskl - INFO - Epoch [107][800/3746] lr: 1.959e-02, eta: 1 day, 13:18:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6663, loss_cls: 3.3199, loss: 3.3199 +2024-07-26 01:29:00,834 - pyskl - INFO - Epoch [107][900/3746] lr: 1.957e-02, eta: 1 day, 13:17:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6723, loss_cls: 3.3409, loss: 3.3409 +2024-07-26 01:30:22,793 - pyskl - INFO - Epoch [107][1000/3746] lr: 1.955e-02, eta: 1 day, 13:15:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6613, loss_cls: 3.3585, loss: 3.3585 +2024-07-26 01:31:44,980 - pyskl - INFO - Epoch [107][1100/3746] lr: 1.953e-02, eta: 1 day, 13:14:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4016, top5_acc: 0.6722, loss_cls: 3.3332, loss: 3.3332 +2024-07-26 01:33:06,931 - pyskl - INFO - Epoch [107][1200/3746] lr: 1.950e-02, eta: 1 day, 13:13:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6597, loss_cls: 3.3582, loss: 3.3582 +2024-07-26 01:34:29,136 - pyskl - INFO - Epoch [107][1300/3746] lr: 1.948e-02, eta: 1 day, 13:11:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6642, loss_cls: 3.3537, loss: 3.3537 +2024-07-26 01:35:51,820 - pyskl - INFO - Epoch [107][1400/3746] lr: 1.946e-02, eta: 1 day, 13:10:28, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6684, loss_cls: 3.2992, loss: 3.2992 +2024-07-26 01:37:14,129 - pyskl - INFO - Epoch [107][1500/3746] lr: 1.944e-02, eta: 1 day, 13:09:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4008, top5_acc: 0.6597, loss_cls: 3.3955, loss: 3.3955 +2024-07-26 01:38:36,651 - pyskl - INFO - Epoch [107][1600/3746] lr: 1.942e-02, eta: 1 day, 13:07:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6653, loss_cls: 3.3491, loss: 3.3491 +2024-07-26 01:40:00,325 - pyskl - INFO - Epoch [107][1700/3746] lr: 1.939e-02, eta: 1 day, 13:06:24, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3933, top5_acc: 0.6603, loss_cls: 3.3707, loss: 3.3707 +2024-07-26 01:41:23,385 - pyskl - INFO - Epoch [107][1800/3746] lr: 1.937e-02, eta: 1 day, 13:05:02, time: 0.831, data_time: 0.001, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6575, loss_cls: 3.3468, loss: 3.3468 +2024-07-26 01:42:46,259 - pyskl - INFO - Epoch [107][1900/3746] lr: 1.935e-02, eta: 1 day, 13:03:41, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6648, loss_cls: 3.3536, loss: 3.3536 +2024-07-26 01:44:08,851 - pyskl - INFO - Epoch [107][2000/3746] lr: 1.933e-02, eta: 1 day, 13:02:19, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6600, loss_cls: 3.3642, loss: 3.3642 +2024-07-26 01:45:31,743 - pyskl - INFO - Epoch [107][2100/3746] lr: 1.930e-02, eta: 1 day, 13:00:58, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6636, loss_cls: 3.3414, loss: 3.3414 +2024-07-26 01:46:53,782 - pyskl - INFO - Epoch [107][2200/3746] lr: 1.928e-02, eta: 1 day, 12:59:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6622, loss_cls: 3.3491, loss: 3.3491 +2024-07-26 01:48:16,680 - pyskl - INFO - Epoch [107][2300/3746] lr: 1.926e-02, eta: 1 day, 12:58:14, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6764, loss_cls: 3.2763, loss: 3.2763 +2024-07-26 01:49:38,628 - pyskl - INFO - Epoch [107][2400/3746] lr: 1.924e-02, eta: 1 day, 12:56:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6637, loss_cls: 3.3632, loss: 3.3632 +2024-07-26 01:51:00,364 - pyskl - INFO - Epoch [107][2500/3746] lr: 1.922e-02, eta: 1 day, 12:55:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6631, loss_cls: 3.3507, loss: 3.3507 +2024-07-26 01:52:23,272 - pyskl - INFO - Epoch [107][2600/3746] lr: 1.919e-02, eta: 1 day, 12:54:09, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6558, loss_cls: 3.3949, loss: 3.3949 +2024-07-26 01:53:45,799 - pyskl - INFO - Epoch [107][2700/3746] lr: 1.917e-02, eta: 1 day, 12:52:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6592, loss_cls: 3.3851, loss: 3.3851 +2024-07-26 01:55:07,738 - pyskl - INFO - Epoch [107][2800/3746] lr: 1.915e-02, eta: 1 day, 12:51:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6586, loss_cls: 3.3740, loss: 3.3740 +2024-07-26 01:56:29,488 - pyskl - INFO - Epoch [107][2900/3746] lr: 1.913e-02, eta: 1 day, 12:50:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6558, loss_cls: 3.3651, loss: 3.3651 +2024-07-26 01:57:51,720 - pyskl - INFO - Epoch [107][3000/3746] lr: 1.911e-02, eta: 1 day, 12:48:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6528, loss_cls: 3.3728, loss: 3.3728 +2024-07-26 01:59:13,794 - pyskl - INFO - Epoch [107][3100/3746] lr: 1.908e-02, eta: 1 day, 12:47:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6562, loss_cls: 3.4033, loss: 3.4033 +2024-07-26 02:00:35,636 - pyskl - INFO - Epoch [107][3200/3746] lr: 1.906e-02, eta: 1 day, 12:45:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6719, loss_cls: 3.3390, loss: 3.3390 +2024-07-26 02:01:57,186 - pyskl - INFO - Epoch [107][3300/3746] lr: 1.904e-02, eta: 1 day, 12:44:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6616, loss_cls: 3.3737, loss: 3.3737 +2024-07-26 02:03:19,670 - pyskl - INFO - Epoch [107][3400/3746] lr: 1.902e-02, eta: 1 day, 12:43:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3986, top5_acc: 0.6689, loss_cls: 3.3143, loss: 3.3143 +2024-07-26 02:04:41,373 - pyskl - INFO - Epoch [107][3500/3746] lr: 1.900e-02, eta: 1 day, 12:41:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6680, loss_cls: 3.3387, loss: 3.3387 +2024-07-26 02:06:02,806 - pyskl - INFO - Epoch [107][3600/3746] lr: 1.897e-02, eta: 1 day, 12:40:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6653, loss_cls: 3.3311, loss: 3.3311 +2024-07-26 02:07:24,368 - pyskl - INFO - Epoch [107][3700/3746] lr: 1.895e-02, eta: 1 day, 12:39:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4062, top5_acc: 0.6686, loss_cls: 3.3457, loss: 3.3457 +2024-07-26 02:08:04,014 - pyskl - INFO - Saving checkpoint at 107 epochs +2024-07-26 02:09:56,143 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 02:09:57,061 - pyskl - INFO - +top1_acc 0.3240 +top5_acc 0.5852 +2024-07-26 02:09:57,062 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 02:09:57,110 - pyskl - INFO - +mean_acc 0.3236 +2024-07-26 02:09:57,126 - pyskl - INFO - Epoch(val) [107][309] top1_acc: 0.3240, top5_acc: 0.5852, mean_class_accuracy: 0.3236 +2024-07-26 02:13:42,675 - pyskl - INFO - Epoch [108][100/3746] lr: 1.892e-02, eta: 1 day, 12:37:51, time: 2.255, data_time: 1.267, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6778, loss_cls: 3.2429, loss: 3.2429 +2024-07-26 02:15:05,258 - pyskl - INFO - Epoch [108][200/3746] lr: 1.890e-02, eta: 1 day, 12:36:30, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6719, loss_cls: 3.3171, loss: 3.3171 +2024-07-26 02:16:27,737 - pyskl - INFO - Epoch [108][300/3746] lr: 1.888e-02, eta: 1 day, 12:35:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6756, loss_cls: 3.3015, loss: 3.3015 +2024-07-26 02:17:50,573 - pyskl - INFO - Epoch [108][400/3746] lr: 1.886e-02, eta: 1 day, 12:33:46, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4166, top5_acc: 0.6789, loss_cls: 3.2741, loss: 3.2741 +2024-07-26 02:19:12,321 - pyskl - INFO - Epoch [108][500/3746] lr: 1.883e-02, eta: 1 day, 12:32:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6734, loss_cls: 3.3145, loss: 3.3145 +2024-07-26 02:20:35,090 - pyskl - INFO - Epoch [108][600/3746] lr: 1.881e-02, eta: 1 day, 12:31:03, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6852, loss_cls: 3.2379, loss: 3.2379 +2024-07-26 02:21:56,931 - pyskl - INFO - Epoch [108][700/3746] lr: 1.879e-02, eta: 1 day, 12:29:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4155, top5_acc: 0.6755, loss_cls: 3.2811, loss: 3.2811 +2024-07-26 02:23:18,977 - pyskl - INFO - Epoch [108][800/3746] lr: 1.877e-02, eta: 1 day, 12:28:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6752, loss_cls: 3.3149, loss: 3.3149 +2024-07-26 02:24:40,896 - pyskl - INFO - Epoch [108][900/3746] lr: 1.875e-02, eta: 1 day, 12:26:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3992, top5_acc: 0.6628, loss_cls: 3.3663, loss: 3.3663 +2024-07-26 02:26:03,006 - pyskl - INFO - Epoch [108][1000/3746] lr: 1.872e-02, eta: 1 day, 12:25:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4072, top5_acc: 0.6780, loss_cls: 3.2836, loss: 3.2836 +2024-07-26 02:27:25,159 - pyskl - INFO - Epoch [108][1100/3746] lr: 1.870e-02, eta: 1 day, 12:24:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6723, loss_cls: 3.3112, loss: 3.3112 +2024-07-26 02:28:47,398 - pyskl - INFO - Epoch [108][1200/3746] lr: 1.868e-02, eta: 1 day, 12:22:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6744, loss_cls: 3.2874, loss: 3.2874 +2024-07-26 02:30:09,362 - pyskl - INFO - Epoch [108][1300/3746] lr: 1.866e-02, eta: 1 day, 12:21:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6658, loss_cls: 3.3056, loss: 3.3056 +2024-07-26 02:31:32,193 - pyskl - INFO - Epoch [108][1400/3746] lr: 1.864e-02, eta: 1 day, 12:20:08, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4102, top5_acc: 0.6723, loss_cls: 3.3007, loss: 3.3007 +2024-07-26 02:32:53,552 - pyskl - INFO - Epoch [108][1500/3746] lr: 1.862e-02, eta: 1 day, 12:18:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6694, loss_cls: 3.3155, loss: 3.3155 +2024-07-26 02:34:16,576 - pyskl - INFO - Epoch [108][1600/3746] lr: 1.859e-02, eta: 1 day, 12:17:25, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6734, loss_cls: 3.3414, loss: 3.3414 +2024-07-26 02:35:39,753 - pyskl - INFO - Epoch [108][1700/3746] lr: 1.857e-02, eta: 1 day, 12:16:03, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4188, top5_acc: 0.6814, loss_cls: 3.2619, loss: 3.2619 +2024-07-26 02:37:01,593 - pyskl - INFO - Epoch [108][1800/3746] lr: 1.855e-02, eta: 1 day, 12:14:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6666, loss_cls: 3.3205, loss: 3.3205 +2024-07-26 02:38:24,244 - pyskl - INFO - Epoch [108][1900/3746] lr: 1.853e-02, eta: 1 day, 12:13:20, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4155, top5_acc: 0.6783, loss_cls: 3.2870, loss: 3.2870 +2024-07-26 02:39:46,495 - pyskl - INFO - Epoch [108][2000/3746] lr: 1.851e-02, eta: 1 day, 12:11:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3994, top5_acc: 0.6630, loss_cls: 3.3497, loss: 3.3497 +2024-07-26 02:41:08,176 - pyskl - INFO - Epoch [108][2100/3746] lr: 1.848e-02, eta: 1 day, 12:10:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6748, loss_cls: 3.3146, loss: 3.3146 +2024-07-26 02:42:29,591 - pyskl - INFO - Epoch [108][2200/3746] lr: 1.846e-02, eta: 1 day, 12:09:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4017, top5_acc: 0.6602, loss_cls: 3.3339, loss: 3.3339 +2024-07-26 02:43:52,015 - pyskl - INFO - Epoch [108][2300/3746] lr: 1.844e-02, eta: 1 day, 12:07:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6653, loss_cls: 3.3199, loss: 3.3199 +2024-07-26 02:45:13,547 - pyskl - INFO - Epoch [108][2400/3746] lr: 1.842e-02, eta: 1 day, 12:06:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6663, loss_cls: 3.3525, loss: 3.3525 +2024-07-26 02:46:35,487 - pyskl - INFO - Epoch [108][2500/3746] lr: 1.840e-02, eta: 1 day, 12:05:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6589, loss_cls: 3.4099, loss: 3.4099 +2024-07-26 02:47:58,214 - pyskl - INFO - Epoch [108][2600/3746] lr: 1.838e-02, eta: 1 day, 12:03:46, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6659, loss_cls: 3.3166, loss: 3.3166 +2024-07-26 02:49:21,290 - pyskl - INFO - Epoch [108][2700/3746] lr: 1.835e-02, eta: 1 day, 12:02:25, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6664, loss_cls: 3.3288, loss: 3.3288 +2024-07-26 02:50:44,319 - pyskl - INFO - Epoch [108][2800/3746] lr: 1.833e-02, eta: 1 day, 12:01:03, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6591, loss_cls: 3.3769, loss: 3.3769 +2024-07-26 02:52:06,760 - pyskl - INFO - Epoch [108][2900/3746] lr: 1.831e-02, eta: 1 day, 11:59:42, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6663, loss_cls: 3.3435, loss: 3.3435 +2024-07-26 02:53:29,163 - pyskl - INFO - Epoch [108][3000/3746] lr: 1.829e-02, eta: 1 day, 11:58:20, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6595, loss_cls: 3.3479, loss: 3.3479 +2024-07-26 02:54:51,237 - pyskl - INFO - Epoch [108][3100/3746] lr: 1.827e-02, eta: 1 day, 11:56:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6741, loss_cls: 3.3126, loss: 3.3126 +2024-07-26 02:56:13,136 - pyskl - INFO - Epoch [108][3200/3746] lr: 1.825e-02, eta: 1 day, 11:55:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6644, loss_cls: 3.3430, loss: 3.3430 +2024-07-26 02:57:35,308 - pyskl - INFO - Epoch [108][3300/3746] lr: 1.823e-02, eta: 1 day, 11:54:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6658, loss_cls: 3.3684, loss: 3.3684 +2024-07-26 02:58:57,244 - pyskl - INFO - Epoch [108][3400/3746] lr: 1.820e-02, eta: 1 day, 11:52:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6634, loss_cls: 3.3198, loss: 3.3198 +2024-07-26 03:00:19,471 - pyskl - INFO - Epoch [108][3500/3746] lr: 1.818e-02, eta: 1 day, 11:51:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6619, loss_cls: 3.3569, loss: 3.3569 +2024-07-26 03:01:41,082 - pyskl - INFO - Epoch [108][3600/3746] lr: 1.816e-02, eta: 1 day, 11:50:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6650, loss_cls: 3.3467, loss: 3.3467 +2024-07-26 03:03:03,249 - pyskl - INFO - Epoch [108][3700/3746] lr: 1.814e-02, eta: 1 day, 11:48:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6548, loss_cls: 3.3609, loss: 3.3609 +2024-07-26 03:03:43,400 - pyskl - INFO - Saving checkpoint at 108 epochs +2024-07-26 03:05:36,084 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 03:05:36,815 - pyskl - INFO - +top1_acc 0.3337 +top5_acc 0.5939 +2024-07-26 03:05:36,815 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 03:05:36,880 - pyskl - INFO - +mean_acc 0.3334 +2024-07-26 03:05:36,886 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_106.pth was removed +2024-07-26 03:05:37,300 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_108.pth. +2024-07-26 03:05:37,301 - pyskl - INFO - Best top1_acc is 0.3337 at 108 epoch. +2024-07-26 03:05:37,341 - pyskl - INFO - Epoch(val) [108][309] top1_acc: 0.3337, top5_acc: 0.5939, mean_class_accuracy: 0.3334 +2024-07-26 03:09:23,477 - pyskl - INFO - Epoch [109][100/3746] lr: 1.811e-02, eta: 1 day, 11:47:28, time: 2.261, data_time: 1.270, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6852, loss_cls: 3.2412, loss: 3.2412 +2024-07-26 03:10:47,087 - pyskl - INFO - Epoch [109][200/3746] lr: 1.809e-02, eta: 1 day, 11:46:07, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6745, loss_cls: 3.2705, loss: 3.2705 +2024-07-26 03:12:09,612 - pyskl - INFO - Epoch [109][300/3746] lr: 1.806e-02, eta: 1 day, 11:44:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6841, loss_cls: 3.2219, loss: 3.2219 +2024-07-26 03:13:32,164 - pyskl - INFO - Epoch [109][400/3746] lr: 1.804e-02, eta: 1 day, 11:43:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6802, loss_cls: 3.2538, loss: 3.2538 +2024-07-26 03:14:54,164 - pyskl - INFO - Epoch [109][500/3746] lr: 1.802e-02, eta: 1 day, 11:42:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6802, loss_cls: 3.3077, loss: 3.3077 +2024-07-26 03:16:16,640 - pyskl - INFO - Epoch [109][600/3746] lr: 1.800e-02, eta: 1 day, 11:40:40, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6652, loss_cls: 3.3194, loss: 3.3194 +2024-07-26 03:17:38,417 - pyskl - INFO - Epoch [109][700/3746] lr: 1.798e-02, eta: 1 day, 11:39:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6795, loss_cls: 3.2597, loss: 3.2597 +2024-07-26 03:19:00,268 - pyskl - INFO - Epoch [109][800/3746] lr: 1.796e-02, eta: 1 day, 11:37:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6700, loss_cls: 3.2744, loss: 3.2744 +2024-07-26 03:20:22,079 - pyskl - INFO - Epoch [109][900/3746] lr: 1.794e-02, eta: 1 day, 11:36:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6744, loss_cls: 3.2798, loss: 3.2798 +2024-07-26 03:21:43,229 - pyskl - INFO - Epoch [109][1000/3746] lr: 1.791e-02, eta: 1 day, 11:35:12, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4106, top5_acc: 0.6691, loss_cls: 3.3031, loss: 3.3031 +2024-07-26 03:23:05,118 - pyskl - INFO - Epoch [109][1100/3746] lr: 1.789e-02, eta: 1 day, 11:33:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6769, loss_cls: 3.3485, loss: 3.3485 +2024-07-26 03:24:26,802 - pyskl - INFO - Epoch [109][1200/3746] lr: 1.787e-02, eta: 1 day, 11:32:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6830, loss_cls: 3.2780, loss: 3.2780 +2024-07-26 03:25:48,527 - pyskl - INFO - Epoch [109][1300/3746] lr: 1.785e-02, eta: 1 day, 11:31:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6678, loss_cls: 3.3147, loss: 3.3147 +2024-07-26 03:27:10,874 - pyskl - INFO - Epoch [109][1400/3746] lr: 1.783e-02, eta: 1 day, 11:29:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4114, top5_acc: 0.6742, loss_cls: 3.3150, loss: 3.3150 +2024-07-26 03:28:32,767 - pyskl - INFO - Epoch [109][1500/3746] lr: 1.781e-02, eta: 1 day, 11:28:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6764, loss_cls: 3.3033, loss: 3.3033 +2024-07-26 03:29:55,934 - pyskl - INFO - Epoch [109][1600/3746] lr: 1.779e-02, eta: 1 day, 11:27:00, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4117, top5_acc: 0.6700, loss_cls: 3.2897, loss: 3.2897 +2024-07-26 03:31:18,819 - pyskl - INFO - Epoch [109][1700/3746] lr: 1.776e-02, eta: 1 day, 11:25:39, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6687, loss_cls: 3.3167, loss: 3.3167 +2024-07-26 03:32:41,446 - pyskl - INFO - Epoch [109][1800/3746] lr: 1.774e-02, eta: 1 day, 11:24:17, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6787, loss_cls: 3.2644, loss: 3.2644 +2024-07-26 03:34:04,131 - pyskl - INFO - Epoch [109][1900/3746] lr: 1.772e-02, eta: 1 day, 11:22:55, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6584, loss_cls: 3.3312, loss: 3.3312 +2024-07-26 03:35:26,166 - pyskl - INFO - Epoch [109][2000/3746] lr: 1.770e-02, eta: 1 day, 11:21:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4113, top5_acc: 0.6691, loss_cls: 3.2950, loss: 3.2950 +2024-07-26 03:36:48,073 - pyskl - INFO - Epoch [109][2100/3746] lr: 1.768e-02, eta: 1 day, 11:20:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4016, top5_acc: 0.6664, loss_cls: 3.3562, loss: 3.3562 +2024-07-26 03:38:09,265 - pyskl - INFO - Epoch [109][2200/3746] lr: 1.766e-02, eta: 1 day, 11:18:49, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4044, top5_acc: 0.6697, loss_cls: 3.3325, loss: 3.3325 +2024-07-26 03:39:31,581 - pyskl - INFO - Epoch [109][2300/3746] lr: 1.764e-02, eta: 1 day, 11:17:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6678, loss_cls: 3.3496, loss: 3.3496 +2024-07-26 03:40:53,436 - pyskl - INFO - Epoch [109][2400/3746] lr: 1.761e-02, eta: 1 day, 11:16:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6672, loss_cls: 3.3377, loss: 3.3377 +2024-07-26 03:42:15,135 - pyskl - INFO - Epoch [109][2500/3746] lr: 1.759e-02, eta: 1 day, 11:14:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6709, loss_cls: 3.3055, loss: 3.3055 +2024-07-26 03:43:38,036 - pyskl - INFO - Epoch [109][2600/3746] lr: 1.757e-02, eta: 1 day, 11:13:22, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4163, top5_acc: 0.6687, loss_cls: 3.2730, loss: 3.2730 +2024-07-26 03:45:00,728 - pyskl - INFO - Epoch [109][2700/3746] lr: 1.755e-02, eta: 1 day, 11:12:00, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4088, top5_acc: 0.6764, loss_cls: 3.2802, loss: 3.2802 +2024-07-26 03:46:22,869 - pyskl - INFO - Epoch [109][2800/3746] lr: 1.753e-02, eta: 1 day, 11:10:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4059, top5_acc: 0.6730, loss_cls: 3.3084, loss: 3.3084 +2024-07-26 03:47:44,681 - pyskl - INFO - Epoch [109][2900/3746] lr: 1.751e-02, eta: 1 day, 11:09:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6731, loss_cls: 3.2888, loss: 3.2888 +2024-07-26 03:49:05,939 - pyskl - INFO - Epoch [109][3000/3746] lr: 1.749e-02, eta: 1 day, 11:07:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4147, top5_acc: 0.6620, loss_cls: 3.3464, loss: 3.3464 +2024-07-26 03:50:27,862 - pyskl - INFO - Epoch [109][3100/3746] lr: 1.747e-02, eta: 1 day, 11:06:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6683, loss_cls: 3.3368, loss: 3.3368 +2024-07-26 03:51:49,758 - pyskl - INFO - Epoch [109][3200/3746] lr: 1.744e-02, eta: 1 day, 11:05:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6620, loss_cls: 3.3872, loss: 3.3872 +2024-07-26 03:53:11,813 - pyskl - INFO - Epoch [109][3300/3746] lr: 1.742e-02, eta: 1 day, 11:03:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4170, top5_acc: 0.6686, loss_cls: 3.3185, loss: 3.3185 +2024-07-26 03:54:33,979 - pyskl - INFO - Epoch [109][3400/3746] lr: 1.740e-02, eta: 1 day, 11:02:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4072, top5_acc: 0.6775, loss_cls: 3.2754, loss: 3.2754 +2024-07-26 03:55:55,453 - pyskl - INFO - Epoch [109][3500/3746] lr: 1.738e-02, eta: 1 day, 11:01:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6689, loss_cls: 3.3205, loss: 3.3205 +2024-07-26 03:57:17,705 - pyskl - INFO - Epoch [109][3600/3746] lr: 1.736e-02, eta: 1 day, 10:59:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4164, top5_acc: 0.6711, loss_cls: 3.3035, loss: 3.3035 +2024-07-26 03:58:40,103 - pyskl - INFO - Epoch [109][3700/3746] lr: 1.734e-02, eta: 1 day, 10:58:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6672, loss_cls: 3.3440, loss: 3.3440 +2024-07-26 03:59:19,941 - pyskl - INFO - Saving checkpoint at 109 epochs +2024-07-26 04:01:13,009 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 04:01:13,761 - pyskl - INFO - +top1_acc 0.3406 +top5_acc 0.5996 +2024-07-26 04:01:13,761 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 04:01:13,805 - pyskl - INFO - +mean_acc 0.3405 +2024-07-26 04:01:13,811 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_108.pth was removed +2024-07-26 04:01:14,109 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_109.pth. +2024-07-26 04:01:14,109 - pyskl - INFO - Best top1_acc is 0.3406 at 109 epoch. +2024-07-26 04:01:14,125 - pyskl - INFO - Epoch(val) [109][309] top1_acc: 0.3406, top5_acc: 0.5996, mean_class_accuracy: 0.3405 +2024-07-26 04:05:01,276 - pyskl - INFO - Epoch [110][100/3746] lr: 1.731e-02, eta: 1 day, 10:57:01, time: 2.271, data_time: 1.279, memory: 15990, top1_acc: 0.4213, top5_acc: 0.6839, loss_cls: 3.2423, loss: 3.2423 +2024-07-26 04:06:24,532 - pyskl - INFO - Epoch [110][200/3746] lr: 1.729e-02, eta: 1 day, 10:55:40, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6841, loss_cls: 3.2239, loss: 3.2239 +2024-07-26 04:07:47,363 - pyskl - INFO - Epoch [110][300/3746] lr: 1.727e-02, eta: 1 day, 10:54:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4247, top5_acc: 0.6848, loss_cls: 3.2579, loss: 3.2579 +2024-07-26 04:09:09,630 - pyskl - INFO - Epoch [110][400/3746] lr: 1.724e-02, eta: 1 day, 10:52:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6730, loss_cls: 3.2665, loss: 3.2665 +2024-07-26 04:10:32,125 - pyskl - INFO - Epoch [110][500/3746] lr: 1.722e-02, eta: 1 day, 10:51:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6802, loss_cls: 3.2524, loss: 3.2524 +2024-07-26 04:11:54,898 - pyskl - INFO - Epoch [110][600/3746] lr: 1.720e-02, eta: 1 day, 10:50:13, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6784, loss_cls: 3.2744, loss: 3.2744 +2024-07-26 04:13:16,969 - pyskl - INFO - Epoch [110][700/3746] lr: 1.718e-02, eta: 1 day, 10:48:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4138, top5_acc: 0.6830, loss_cls: 3.2524, loss: 3.2524 +2024-07-26 04:14:39,321 - pyskl - INFO - Epoch [110][800/3746] lr: 1.716e-02, eta: 1 day, 10:47:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6906, loss_cls: 3.2237, loss: 3.2237 +2024-07-26 04:16:01,532 - pyskl - INFO - Epoch [110][900/3746] lr: 1.714e-02, eta: 1 day, 10:46:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4091, top5_acc: 0.6725, loss_cls: 3.2859, loss: 3.2859 +2024-07-26 04:17:23,728 - pyskl - INFO - Epoch [110][1000/3746] lr: 1.712e-02, eta: 1 day, 10:44:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6817, loss_cls: 3.2587, loss: 3.2587 +2024-07-26 04:18:45,693 - pyskl - INFO - Epoch [110][1100/3746] lr: 1.710e-02, eta: 1 day, 10:43:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6750, loss_cls: 3.2987, loss: 3.2987 +2024-07-26 04:20:07,726 - pyskl - INFO - Epoch [110][1200/3746] lr: 1.708e-02, eta: 1 day, 10:42:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6717, loss_cls: 3.3394, loss: 3.3394 +2024-07-26 04:21:29,473 - pyskl - INFO - Epoch [110][1300/3746] lr: 1.705e-02, eta: 1 day, 10:40:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4152, top5_acc: 0.6872, loss_cls: 3.2368, loss: 3.2368 +2024-07-26 04:22:52,565 - pyskl - INFO - Epoch [110][1400/3746] lr: 1.703e-02, eta: 1 day, 10:39:18, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6900, loss_cls: 3.2150, loss: 3.2150 +2024-07-26 04:24:14,109 - pyskl - INFO - Epoch [110][1500/3746] lr: 1.701e-02, eta: 1 day, 10:37:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4164, top5_acc: 0.6787, loss_cls: 3.2639, loss: 3.2639 +2024-07-26 04:25:37,649 - pyskl - INFO - Epoch [110][1600/3746] lr: 1.699e-02, eta: 1 day, 10:36:34, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6634, loss_cls: 3.3280, loss: 3.3280 +2024-07-26 04:26:59,973 - pyskl - INFO - Epoch [110][1700/3746] lr: 1.697e-02, eta: 1 day, 10:35:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4153, top5_acc: 0.6795, loss_cls: 3.2835, loss: 3.2835 +2024-07-26 04:28:22,526 - pyskl - INFO - Epoch [110][1800/3746] lr: 1.695e-02, eta: 1 day, 10:33:51, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4188, top5_acc: 0.6756, loss_cls: 3.2672, loss: 3.2672 +2024-07-26 04:29:45,046 - pyskl - INFO - Epoch [110][1900/3746] lr: 1.693e-02, eta: 1 day, 10:32:29, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6739, loss_cls: 3.2915, loss: 3.2915 +2024-07-26 04:31:06,705 - pyskl - INFO - Epoch [110][2000/3746] lr: 1.691e-02, eta: 1 day, 10:31:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4197, top5_acc: 0.6727, loss_cls: 3.2647, loss: 3.2647 +2024-07-26 04:32:28,417 - pyskl - INFO - Epoch [110][2100/3746] lr: 1.689e-02, eta: 1 day, 10:29:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4130, top5_acc: 0.6692, loss_cls: 3.2891, loss: 3.2891 +2024-07-26 04:33:50,585 - pyskl - INFO - Epoch [110][2200/3746] lr: 1.687e-02, eta: 1 day, 10:28:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4178, top5_acc: 0.6830, loss_cls: 3.2313, loss: 3.2313 +2024-07-26 04:35:13,335 - pyskl - INFO - Epoch [110][2300/3746] lr: 1.685e-02, eta: 1 day, 10:27:01, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4042, top5_acc: 0.6695, loss_cls: 3.3064, loss: 3.3064 +2024-07-26 04:36:36,288 - pyskl - INFO - Epoch [110][2400/3746] lr: 1.682e-02, eta: 1 day, 10:25:40, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6619, loss_cls: 3.3384, loss: 3.3384 +2024-07-26 04:37:58,819 - pyskl - INFO - Epoch [110][2500/3746] lr: 1.680e-02, eta: 1 day, 10:24:18, time: 0.825, data_time: 0.001, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6678, loss_cls: 3.3421, loss: 3.3421 +2024-07-26 04:39:21,365 - pyskl - INFO - Epoch [110][2600/3746] lr: 1.678e-02, eta: 1 day, 10:22:56, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4163, top5_acc: 0.6734, loss_cls: 3.2854, loss: 3.2854 +2024-07-26 04:40:44,377 - pyskl - INFO - Epoch [110][2700/3746] lr: 1.676e-02, eta: 1 day, 10:21:34, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4158, top5_acc: 0.6744, loss_cls: 3.2818, loss: 3.2818 +2024-07-26 04:42:06,402 - pyskl - INFO - Epoch [110][2800/3746] lr: 1.674e-02, eta: 1 day, 10:20:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4136, top5_acc: 0.6611, loss_cls: 3.3214, loss: 3.3214 +2024-07-26 04:43:28,328 - pyskl - INFO - Epoch [110][2900/3746] lr: 1.672e-02, eta: 1 day, 10:18:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4152, top5_acc: 0.6753, loss_cls: 3.2733, loss: 3.2733 +2024-07-26 04:44:49,884 - pyskl - INFO - Epoch [110][3000/3746] lr: 1.670e-02, eta: 1 day, 10:17:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4088, top5_acc: 0.6669, loss_cls: 3.3179, loss: 3.3179 +2024-07-26 04:46:11,766 - pyskl - INFO - Epoch [110][3100/3746] lr: 1.668e-02, eta: 1 day, 10:16:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4066, top5_acc: 0.6764, loss_cls: 3.2895, loss: 3.2895 +2024-07-26 04:47:33,092 - pyskl - INFO - Epoch [110][3200/3746] lr: 1.666e-02, eta: 1 day, 10:14:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4134, top5_acc: 0.6755, loss_cls: 3.2656, loss: 3.2656 +2024-07-26 04:48:55,401 - pyskl - INFO - Epoch [110][3300/3746] lr: 1.664e-02, eta: 1 day, 10:13:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4183, top5_acc: 0.6803, loss_cls: 3.2722, loss: 3.2722 +2024-07-26 04:50:17,699 - pyskl - INFO - Epoch [110][3400/3746] lr: 1.662e-02, eta: 1 day, 10:12:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6670, loss_cls: 3.3368, loss: 3.3368 +2024-07-26 04:51:39,971 - pyskl - INFO - Epoch [110][3500/3746] lr: 1.659e-02, eta: 1 day, 10:10:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4098, top5_acc: 0.6758, loss_cls: 3.2887, loss: 3.2887 +2024-07-26 04:53:01,835 - pyskl - INFO - Epoch [110][3600/3746] lr: 1.657e-02, eta: 1 day, 10:09:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6722, loss_cls: 3.2640, loss: 3.2640 +2024-07-26 04:54:24,835 - pyskl - INFO - Epoch [110][3700/3746] lr: 1.655e-02, eta: 1 day, 10:07:55, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6731, loss_cls: 3.2882, loss: 3.2882 +2024-07-26 04:55:04,478 - pyskl - INFO - Saving checkpoint at 110 epochs +2024-07-26 04:56:57,393 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 04:56:58,080 - pyskl - INFO - +top1_acc 0.3421 +top5_acc 0.6012 +2024-07-26 04:56:58,080 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 04:56:58,121 - pyskl - INFO - +mean_acc 0.3419 +2024-07-26 04:56:58,125 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_109.pth was removed +2024-07-26 04:56:58,388 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_110.pth. +2024-07-26 04:56:58,389 - pyskl - INFO - Best top1_acc is 0.3421 at 110 epoch. +2024-07-26 04:56:58,400 - pyskl - INFO - Epoch(val) [110][309] top1_acc: 0.3421, top5_acc: 0.6012, mean_class_accuracy: 0.3419 +2024-07-26 05:00:41,233 - pyskl - INFO - Epoch [111][100/3746] lr: 1.652e-02, eta: 1 day, 10:06:33, time: 2.228, data_time: 1.243, memory: 15990, top1_acc: 0.4213, top5_acc: 0.6902, loss_cls: 3.2334, loss: 3.2334 +2024-07-26 05:02:03,467 - pyskl - INFO - Epoch [111][200/3746] lr: 1.650e-02, eta: 1 day, 10:05:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4256, top5_acc: 0.6961, loss_cls: 3.1778, loss: 3.1778 +2024-07-26 05:03:26,047 - pyskl - INFO - Epoch [111][300/3746] lr: 1.648e-02, eta: 1 day, 10:03:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6895, loss_cls: 3.2048, loss: 3.2048 +2024-07-26 05:04:48,724 - pyskl - INFO - Epoch [111][400/3746] lr: 1.646e-02, eta: 1 day, 10:02:27, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6869, loss_cls: 3.2099, loss: 3.2099 +2024-07-26 05:06:10,630 - pyskl - INFO - Epoch [111][500/3746] lr: 1.644e-02, eta: 1 day, 10:01:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4269, top5_acc: 0.6866, loss_cls: 3.2081, loss: 3.2081 +2024-07-26 05:07:32,107 - pyskl - INFO - Epoch [111][600/3746] lr: 1.642e-02, eta: 1 day, 9:59:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4147, top5_acc: 0.6767, loss_cls: 3.2491, loss: 3.2491 +2024-07-26 05:08:54,666 - pyskl - INFO - Epoch [111][700/3746] lr: 1.640e-02, eta: 1 day, 9:58:21, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4164, top5_acc: 0.6837, loss_cls: 3.2304, loss: 3.2304 +2024-07-26 05:10:16,549 - pyskl - INFO - Epoch [111][800/3746] lr: 1.638e-02, eta: 1 day, 9:56:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4167, top5_acc: 0.6820, loss_cls: 3.2379, loss: 3.2379 +2024-07-26 05:11:37,946 - pyskl - INFO - Epoch [111][900/3746] lr: 1.636e-02, eta: 1 day, 9:55:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4283, top5_acc: 0.6817, loss_cls: 3.2152, loss: 3.2152 +2024-07-26 05:12:59,980 - pyskl - INFO - Epoch [111][1000/3746] lr: 1.634e-02, eta: 1 day, 9:54:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4316, top5_acc: 0.6872, loss_cls: 3.1780, loss: 3.1780 +2024-07-26 05:14:21,527 - pyskl - INFO - Epoch [111][1100/3746] lr: 1.632e-02, eta: 1 day, 9:52:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4119, top5_acc: 0.6734, loss_cls: 3.2737, loss: 3.2737 +2024-07-26 05:15:43,505 - pyskl - INFO - Epoch [111][1200/3746] lr: 1.630e-02, eta: 1 day, 9:51:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6716, loss_cls: 3.2895, loss: 3.2895 +2024-07-26 05:17:06,143 - pyskl - INFO - Epoch [111][1300/3746] lr: 1.627e-02, eta: 1 day, 9:50:09, time: 0.826, data_time: 0.001, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6687, loss_cls: 3.3274, loss: 3.3274 +2024-07-26 05:18:29,120 - pyskl - INFO - Epoch [111][1400/3746] lr: 1.625e-02, eta: 1 day, 9:48:47, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6745, loss_cls: 3.2922, loss: 3.2922 +2024-07-26 05:19:51,674 - pyskl - INFO - Epoch [111][1500/3746] lr: 1.623e-02, eta: 1 day, 9:47:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6794, loss_cls: 3.2191, loss: 3.2191 +2024-07-26 05:21:14,860 - pyskl - INFO - Epoch [111][1600/3746] lr: 1.621e-02, eta: 1 day, 9:46:04, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4202, top5_acc: 0.6836, loss_cls: 3.2260, loss: 3.2260 +2024-07-26 05:22:37,729 - pyskl - INFO - Epoch [111][1700/3746] lr: 1.619e-02, eta: 1 day, 9:44:42, time: 0.829, data_time: 0.001, memory: 15990, top1_acc: 0.4166, top5_acc: 0.6822, loss_cls: 3.2736, loss: 3.2736 +2024-07-26 05:24:00,235 - pyskl - INFO - Epoch [111][1800/3746] lr: 1.617e-02, eta: 1 day, 9:43:20, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4181, top5_acc: 0.6786, loss_cls: 3.2694, loss: 3.2694 +2024-07-26 05:25:23,103 - pyskl - INFO - Epoch [111][1900/3746] lr: 1.615e-02, eta: 1 day, 9:41:59, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6859, loss_cls: 3.2188, loss: 3.2188 +2024-07-26 05:26:44,889 - pyskl - INFO - Epoch [111][2000/3746] lr: 1.613e-02, eta: 1 day, 9:40:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4177, top5_acc: 0.6792, loss_cls: 3.2489, loss: 3.2489 +2024-07-26 05:28:06,976 - pyskl - INFO - Epoch [111][2100/3746] lr: 1.611e-02, eta: 1 day, 9:39:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4098, top5_acc: 0.6717, loss_cls: 3.2990, loss: 3.2990 +2024-07-26 05:29:28,593 - pyskl - INFO - Epoch [111][2200/3746] lr: 1.609e-02, eta: 1 day, 9:37:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6769, loss_cls: 3.2688, loss: 3.2688 +2024-07-26 05:30:51,397 - pyskl - INFO - Epoch [111][2300/3746] lr: 1.607e-02, eta: 1 day, 9:36:31, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6781, loss_cls: 3.2686, loss: 3.2686 +2024-07-26 05:32:13,474 - pyskl - INFO - Epoch [111][2400/3746] lr: 1.605e-02, eta: 1 day, 9:35:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6809, loss_cls: 3.2660, loss: 3.2660 +2024-07-26 05:33:35,113 - pyskl - INFO - Epoch [111][2500/3746] lr: 1.603e-02, eta: 1 day, 9:33:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4113, top5_acc: 0.6764, loss_cls: 3.2746, loss: 3.2746 +2024-07-26 05:34:57,198 - pyskl - INFO - Epoch [111][2600/3746] lr: 1.601e-02, eta: 1 day, 9:32:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6787, loss_cls: 3.2696, loss: 3.2696 +2024-07-26 05:36:19,384 - pyskl - INFO - Epoch [111][2700/3746] lr: 1.599e-02, eta: 1 day, 9:31:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6767, loss_cls: 3.2584, loss: 3.2584 +2024-07-26 05:37:41,014 - pyskl - INFO - Epoch [111][2800/3746] lr: 1.597e-02, eta: 1 day, 9:29:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4059, top5_acc: 0.6687, loss_cls: 3.3202, loss: 3.3202 +2024-07-26 05:39:02,837 - pyskl - INFO - Epoch [111][2900/3746] lr: 1.595e-02, eta: 1 day, 9:28:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4173, top5_acc: 0.6713, loss_cls: 3.2764, loss: 3.2764 +2024-07-26 05:40:24,362 - pyskl - INFO - Epoch [111][3000/3746] lr: 1.593e-02, eta: 1 day, 9:26:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6831, loss_cls: 3.2551, loss: 3.2551 +2024-07-26 05:41:46,119 - pyskl - INFO - Epoch [111][3100/3746] lr: 1.590e-02, eta: 1 day, 9:25:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4114, top5_acc: 0.6770, loss_cls: 3.2877, loss: 3.2877 +2024-07-26 05:43:07,690 - pyskl - INFO - Epoch [111][3200/3746] lr: 1.588e-02, eta: 1 day, 9:24:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6773, loss_cls: 3.2714, loss: 3.2714 +2024-07-26 05:44:28,938 - pyskl - INFO - Epoch [111][3300/3746] lr: 1.586e-02, eta: 1 day, 9:22:50, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4088, top5_acc: 0.6764, loss_cls: 3.2831, loss: 3.2831 +2024-07-26 05:45:51,148 - pyskl - INFO - Epoch [111][3400/3746] lr: 1.584e-02, eta: 1 day, 9:21:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4238, top5_acc: 0.6786, loss_cls: 3.2585, loss: 3.2585 +2024-07-26 05:47:12,669 - pyskl - INFO - Epoch [111][3500/3746] lr: 1.582e-02, eta: 1 day, 9:20:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4227, top5_acc: 0.6711, loss_cls: 3.2734, loss: 3.2734 +2024-07-26 05:48:34,917 - pyskl - INFO - Epoch [111][3600/3746] lr: 1.580e-02, eta: 1 day, 9:18:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4098, top5_acc: 0.6761, loss_cls: 3.2988, loss: 3.2988 +2024-07-26 05:49:57,249 - pyskl - INFO - Epoch [111][3700/3746] lr: 1.578e-02, eta: 1 day, 9:17:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6813, loss_cls: 3.2383, loss: 3.2383 +2024-07-26 05:50:37,326 - pyskl - INFO - Saving checkpoint at 111 epochs +2024-07-26 05:52:29,645 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 05:52:30,308 - pyskl - INFO - +top1_acc 0.3486 +top5_acc 0.6131 +2024-07-26 05:52:30,309 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 05:52:30,349 - pyskl - INFO - +mean_acc 0.3481 +2024-07-26 05:52:30,353 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_110.pth was removed +2024-07-26 05:52:30,609 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_111.pth. +2024-07-26 05:52:30,609 - pyskl - INFO - Best top1_acc is 0.3486 at 111 epoch. +2024-07-26 05:52:30,621 - pyskl - INFO - Epoch(val) [111][309] top1_acc: 0.3486, top5_acc: 0.6131, mean_class_accuracy: 0.3481 +2024-07-26 05:56:15,716 - pyskl - INFO - Epoch [112][100/3746] lr: 1.575e-02, eta: 1 day, 9:15:59, time: 2.251, data_time: 1.258, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6947, loss_cls: 3.1333, loss: 3.1333 +2024-07-26 05:57:38,803 - pyskl - INFO - Epoch [112][200/3746] lr: 1.573e-02, eta: 1 day, 9:14:37, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4322, top5_acc: 0.6972, loss_cls: 3.1738, loss: 3.1738 +2024-07-26 05:59:00,973 - pyskl - INFO - Epoch [112][300/3746] lr: 1.571e-02, eta: 1 day, 9:13:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4248, top5_acc: 0.6864, loss_cls: 3.2086, loss: 3.2086 +2024-07-26 06:00:22,682 - pyskl - INFO - Epoch [112][400/3746] lr: 1.569e-02, eta: 1 day, 9:11:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6911, loss_cls: 3.1779, loss: 3.1779 +2024-07-26 06:01:44,951 - pyskl - INFO - Epoch [112][500/3746] lr: 1.567e-02, eta: 1 day, 9:10:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4188, top5_acc: 0.6764, loss_cls: 3.2474, loss: 3.2474 +2024-07-26 06:03:06,672 - pyskl - INFO - Epoch [112][600/3746] lr: 1.565e-02, eta: 1 day, 9:09:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4258, top5_acc: 0.6867, loss_cls: 3.1964, loss: 3.1964 +2024-07-26 06:04:28,922 - pyskl - INFO - Epoch [112][700/3746] lr: 1.563e-02, eta: 1 day, 9:07:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6802, loss_cls: 3.2500, loss: 3.2500 +2024-07-26 06:05:51,061 - pyskl - INFO - Epoch [112][800/3746] lr: 1.561e-02, eta: 1 day, 9:06:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6895, loss_cls: 3.1970, loss: 3.1970 +2024-07-26 06:07:13,404 - pyskl - INFO - Epoch [112][900/3746] lr: 1.559e-02, eta: 1 day, 9:05:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6900, loss_cls: 3.1886, loss: 3.1886 +2024-07-26 06:08:35,526 - pyskl - INFO - Epoch [112][1000/3746] lr: 1.557e-02, eta: 1 day, 9:03:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6813, loss_cls: 3.2242, loss: 3.2242 +2024-07-26 06:09:57,266 - pyskl - INFO - Epoch [112][1100/3746] lr: 1.555e-02, eta: 1 day, 9:02:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4072, top5_acc: 0.6759, loss_cls: 3.2598, loss: 3.2598 +2024-07-26 06:11:18,849 - pyskl - INFO - Epoch [112][1200/3746] lr: 1.553e-02, eta: 1 day, 9:00:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6887, loss_cls: 3.1911, loss: 3.1911 +2024-07-26 06:12:40,863 - pyskl - INFO - Epoch [112][1300/3746] lr: 1.551e-02, eta: 1 day, 8:59:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6844, loss_cls: 3.2278, loss: 3.2278 +2024-07-26 06:14:03,585 - pyskl - INFO - Epoch [112][1400/3746] lr: 1.549e-02, eta: 1 day, 8:58:13, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4139, top5_acc: 0.6708, loss_cls: 3.2955, loss: 3.2955 +2024-07-26 06:15:25,743 - pyskl - INFO - Epoch [112][1500/3746] lr: 1.547e-02, eta: 1 day, 8:56:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6822, loss_cls: 3.2593, loss: 3.2593 +2024-07-26 06:16:49,717 - pyskl - INFO - Epoch [112][1600/3746] lr: 1.545e-02, eta: 1 day, 8:55:30, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.4316, top5_acc: 0.6895, loss_cls: 3.1868, loss: 3.1868 +2024-07-26 06:18:13,174 - pyskl - INFO - Epoch [112][1700/3746] lr: 1.543e-02, eta: 1 day, 8:54:08, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6933, loss_cls: 3.2010, loss: 3.2010 +2024-07-26 06:19:36,781 - pyskl - INFO - Epoch [112][1800/3746] lr: 1.541e-02, eta: 1 day, 8:52:47, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4286, top5_acc: 0.6817, loss_cls: 3.2033, loss: 3.2033 +2024-07-26 06:21:00,010 - pyskl - INFO - Epoch [112][1900/3746] lr: 1.539e-02, eta: 1 day, 8:51:25, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4086, top5_acc: 0.6816, loss_cls: 3.2563, loss: 3.2563 +2024-07-26 06:22:23,343 - pyskl - INFO - Epoch [112][2000/3746] lr: 1.537e-02, eta: 1 day, 8:50:04, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6772, loss_cls: 3.2966, loss: 3.2966 +2024-07-26 06:23:45,942 - pyskl - INFO - Epoch [112][2100/3746] lr: 1.535e-02, eta: 1 day, 8:48:42, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6786, loss_cls: 3.2653, loss: 3.2653 +2024-07-26 06:25:08,183 - pyskl - INFO - Epoch [112][2200/3746] lr: 1.533e-02, eta: 1 day, 8:47:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4183, top5_acc: 0.6798, loss_cls: 3.2236, loss: 3.2236 +2024-07-26 06:26:31,466 - pyskl - INFO - Epoch [112][2300/3746] lr: 1.531e-02, eta: 1 day, 8:45:58, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4125, top5_acc: 0.6800, loss_cls: 3.2506, loss: 3.2506 +2024-07-26 06:27:53,382 - pyskl - INFO - Epoch [112][2400/3746] lr: 1.529e-02, eta: 1 day, 8:44:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4227, top5_acc: 0.6794, loss_cls: 3.2224, loss: 3.2224 +2024-07-26 06:29:15,244 - pyskl - INFO - Epoch [112][2500/3746] lr: 1.527e-02, eta: 1 day, 8:43:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4238, top5_acc: 0.6836, loss_cls: 3.2273, loss: 3.2273 +2024-07-26 06:30:38,482 - pyskl - INFO - Epoch [112][2600/3746] lr: 1.525e-02, eta: 1 day, 8:41:52, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6848, loss_cls: 3.2340, loss: 3.2340 +2024-07-26 06:32:01,517 - pyskl - INFO - Epoch [112][2700/3746] lr: 1.523e-02, eta: 1 day, 8:40:31, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4259, top5_acc: 0.6873, loss_cls: 3.2074, loss: 3.2074 +2024-07-26 06:33:24,256 - pyskl - INFO - Epoch [112][2800/3746] lr: 1.521e-02, eta: 1 day, 8:39:09, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4264, top5_acc: 0.6883, loss_cls: 3.2160, loss: 3.2160 +2024-07-26 06:34:46,370 - pyskl - INFO - Epoch [112][2900/3746] lr: 1.519e-02, eta: 1 day, 8:37:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4289, top5_acc: 0.6808, loss_cls: 3.2070, loss: 3.2070 +2024-07-26 06:36:08,716 - pyskl - INFO - Epoch [112][3000/3746] lr: 1.517e-02, eta: 1 day, 8:36:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6784, loss_cls: 3.2786, loss: 3.2786 +2024-07-26 06:37:31,469 - pyskl - INFO - Epoch [112][3100/3746] lr: 1.515e-02, eta: 1 day, 8:35:03, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6737, loss_cls: 3.3048, loss: 3.3048 +2024-07-26 06:38:54,084 - pyskl - INFO - Epoch [112][3200/3746] lr: 1.513e-02, eta: 1 day, 8:33:41, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6777, loss_cls: 3.2596, loss: 3.2596 +2024-07-26 06:40:16,336 - pyskl - INFO - Epoch [112][3300/3746] lr: 1.511e-02, eta: 1 day, 8:32:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4114, top5_acc: 0.6744, loss_cls: 3.2800, loss: 3.2800 +2024-07-26 06:41:38,618 - pyskl - INFO - Epoch [112][3400/3746] lr: 1.509e-02, eta: 1 day, 8:30:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6828, loss_cls: 3.2542, loss: 3.2542 +2024-07-26 06:43:00,227 - pyskl - INFO - Epoch [112][3500/3746] lr: 1.507e-02, eta: 1 day, 8:29:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4073, top5_acc: 0.6717, loss_cls: 3.3006, loss: 3.3006 +2024-07-26 06:44:21,799 - pyskl - INFO - Epoch [112][3600/3746] lr: 1.505e-02, eta: 1 day, 8:28:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4113, top5_acc: 0.6717, loss_cls: 3.2869, loss: 3.2869 +2024-07-26 06:45:45,270 - pyskl - INFO - Epoch [112][3700/3746] lr: 1.503e-02, eta: 1 day, 8:26:52, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6683, loss_cls: 3.3051, loss: 3.3051 +2024-07-26 06:46:24,698 - pyskl - INFO - Saving checkpoint at 112 epochs +2024-07-26 06:48:18,459 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 06:48:19,116 - pyskl - INFO - +top1_acc 0.3551 +top5_acc 0.6211 +2024-07-26 06:48:19,116 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 06:48:19,156 - pyskl - INFO - +mean_acc 0.3548 +2024-07-26 06:48:19,161 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_111.pth was removed +2024-07-26 06:48:19,425 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_112.pth. +2024-07-26 06:48:19,425 - pyskl - INFO - Best top1_acc is 0.3551 at 112 epoch. +2024-07-26 06:48:19,436 - pyskl - INFO - Epoch(val) [112][309] top1_acc: 0.3551, top5_acc: 0.6211, mean_class_accuracy: 0.3548 +2024-07-26 06:52:08,309 - pyskl - INFO - Epoch [113][100/3746] lr: 1.500e-02, eta: 1 day, 8:25:29, time: 2.289, data_time: 1.299, memory: 15990, top1_acc: 0.4373, top5_acc: 0.6942, loss_cls: 3.1373, loss: 3.1373 +2024-07-26 06:53:31,192 - pyskl - INFO - Epoch [113][200/3746] lr: 1.498e-02, eta: 1 day, 8:24:07, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4450, top5_acc: 0.7069, loss_cls: 3.0996, loss: 3.0996 +2024-07-26 06:54:53,223 - pyskl - INFO - Epoch [113][300/3746] lr: 1.496e-02, eta: 1 day, 8:22:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6898, loss_cls: 3.1776, loss: 3.1776 +2024-07-26 06:56:14,735 - pyskl - INFO - Epoch [113][400/3746] lr: 1.494e-02, eta: 1 day, 8:21:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4355, top5_acc: 0.7005, loss_cls: 3.1420, loss: 3.1420 +2024-07-26 06:57:37,067 - pyskl - INFO - Epoch [113][500/3746] lr: 1.492e-02, eta: 1 day, 8:20:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6831, loss_cls: 3.2288, loss: 3.2288 +2024-07-26 06:58:59,369 - pyskl - INFO - Epoch [113][600/3746] lr: 1.490e-02, eta: 1 day, 8:18:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4256, top5_acc: 0.6931, loss_cls: 3.1803, loss: 3.1803 +2024-07-26 07:00:21,443 - pyskl - INFO - Epoch [113][700/3746] lr: 1.488e-02, eta: 1 day, 8:17:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6966, loss_cls: 3.1908, loss: 3.1908 +2024-07-26 07:01:43,396 - pyskl - INFO - Epoch [113][800/3746] lr: 1.486e-02, eta: 1 day, 8:15:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6933, loss_cls: 3.1641, loss: 3.1641 +2024-07-26 07:03:05,394 - pyskl - INFO - Epoch [113][900/3746] lr: 1.484e-02, eta: 1 day, 8:14:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4183, top5_acc: 0.6791, loss_cls: 3.2530, loss: 3.2530 +2024-07-26 07:04:28,081 - pyskl - INFO - Epoch [113][1000/3746] lr: 1.482e-02, eta: 1 day, 8:13:11, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4183, top5_acc: 0.6822, loss_cls: 3.2181, loss: 3.2181 +2024-07-26 07:05:50,582 - pyskl - INFO - Epoch [113][1100/3746] lr: 1.480e-02, eta: 1 day, 8:11:49, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6919, loss_cls: 3.1666, loss: 3.1666 +2024-07-26 07:07:12,425 - pyskl - INFO - Epoch [113][1200/3746] lr: 1.478e-02, eta: 1 day, 8:10:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4158, top5_acc: 0.6783, loss_cls: 3.2289, loss: 3.2289 +2024-07-26 07:08:35,075 - pyskl - INFO - Epoch [113][1300/3746] lr: 1.476e-02, eta: 1 day, 8:09:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4153, top5_acc: 0.6817, loss_cls: 3.2186, loss: 3.2186 +2024-07-26 07:09:56,810 - pyskl - INFO - Epoch [113][1400/3746] lr: 1.474e-02, eta: 1 day, 8:07:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4125, top5_acc: 0.6794, loss_cls: 3.2525, loss: 3.2525 +2024-07-26 07:11:20,128 - pyskl - INFO - Epoch [113][1500/3746] lr: 1.472e-02, eta: 1 day, 8:06:21, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4306, top5_acc: 0.6897, loss_cls: 3.1748, loss: 3.1748 +2024-07-26 07:12:42,889 - pyskl - INFO - Epoch [113][1600/3746] lr: 1.470e-02, eta: 1 day, 8:04:59, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4238, top5_acc: 0.6800, loss_cls: 3.2192, loss: 3.2192 +2024-07-26 07:14:06,035 - pyskl - INFO - Epoch [113][1700/3746] lr: 1.468e-02, eta: 1 day, 8:03:37, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6797, loss_cls: 3.1992, loss: 3.1992 +2024-07-26 07:15:29,286 - pyskl - INFO - Epoch [113][1800/3746] lr: 1.466e-02, eta: 1 day, 8:02:16, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6867, loss_cls: 3.1624, loss: 3.1624 +2024-07-26 07:16:51,701 - pyskl - INFO - Epoch [113][1900/3746] lr: 1.464e-02, eta: 1 day, 8:00:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6763, loss_cls: 3.2716, loss: 3.2716 +2024-07-26 07:18:14,089 - pyskl - INFO - Epoch [113][2000/3746] lr: 1.462e-02, eta: 1 day, 7:59:32, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6864, loss_cls: 3.2129, loss: 3.2129 +2024-07-26 07:19:36,447 - pyskl - INFO - Epoch [113][2100/3746] lr: 1.460e-02, eta: 1 day, 7:58:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4214, top5_acc: 0.6895, loss_cls: 3.1935, loss: 3.1935 +2024-07-26 07:20:59,464 - pyskl - INFO - Epoch [113][2200/3746] lr: 1.458e-02, eta: 1 day, 7:56:48, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6900, loss_cls: 3.2061, loss: 3.2061 +2024-07-26 07:22:22,734 - pyskl - INFO - Epoch [113][2300/3746] lr: 1.456e-02, eta: 1 day, 7:55:26, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6830, loss_cls: 3.2518, loss: 3.2518 +2024-07-26 07:23:45,412 - pyskl - INFO - Epoch [113][2400/3746] lr: 1.454e-02, eta: 1 day, 7:54:05, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6823, loss_cls: 3.2505, loss: 3.2505 +2024-07-26 07:25:08,755 - pyskl - INFO - Epoch [113][2500/3746] lr: 1.452e-02, eta: 1 day, 7:52:43, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6791, loss_cls: 3.2557, loss: 3.2557 +2024-07-26 07:26:32,317 - pyskl - INFO - Epoch [113][2600/3746] lr: 1.450e-02, eta: 1 day, 7:51:21, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4167, top5_acc: 0.6805, loss_cls: 3.2537, loss: 3.2537 +2024-07-26 07:27:55,595 - pyskl - INFO - Epoch [113][2700/3746] lr: 1.448e-02, eta: 1 day, 7:50:00, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6895, loss_cls: 3.1908, loss: 3.1908 +2024-07-26 07:29:18,369 - pyskl - INFO - Epoch [113][2800/3746] lr: 1.446e-02, eta: 1 day, 7:48:38, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4228, top5_acc: 0.6819, loss_cls: 3.2472, loss: 3.2472 +2024-07-26 07:30:40,625 - pyskl - INFO - Epoch [113][2900/3746] lr: 1.444e-02, eta: 1 day, 7:47:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6809, loss_cls: 3.2474, loss: 3.2474 +2024-07-26 07:32:03,041 - pyskl - INFO - Epoch [113][3000/3746] lr: 1.442e-02, eta: 1 day, 7:45:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4323, top5_acc: 0.6852, loss_cls: 3.1957, loss: 3.1957 +2024-07-26 07:33:26,066 - pyskl - INFO - Epoch [113][3100/3746] lr: 1.440e-02, eta: 1 day, 7:44:32, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6825, loss_cls: 3.2316, loss: 3.2316 +2024-07-26 07:34:48,785 - pyskl - INFO - Epoch [113][3200/3746] lr: 1.438e-02, eta: 1 day, 7:43:10, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4238, top5_acc: 0.6819, loss_cls: 3.2186, loss: 3.2186 +2024-07-26 07:36:11,600 - pyskl - INFO - Epoch [113][3300/3746] lr: 1.436e-02, eta: 1 day, 7:41:49, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4245, top5_acc: 0.6892, loss_cls: 3.1932, loss: 3.1932 +2024-07-26 07:37:34,240 - pyskl - INFO - Epoch [113][3400/3746] lr: 1.434e-02, eta: 1 day, 7:40:27, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6742, loss_cls: 3.2510, loss: 3.2510 +2024-07-26 07:38:57,075 - pyskl - INFO - Epoch [113][3500/3746] lr: 1.432e-02, eta: 1 day, 7:39:05, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4219, top5_acc: 0.6819, loss_cls: 3.2485, loss: 3.2485 +2024-07-26 07:40:19,698 - pyskl - INFO - Epoch [113][3600/3746] lr: 1.431e-02, eta: 1 day, 7:37:43, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6761, loss_cls: 3.2516, loss: 3.2516 +2024-07-26 07:41:43,339 - pyskl - INFO - Epoch [113][3700/3746] lr: 1.429e-02, eta: 1 day, 7:36:21, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6772, loss_cls: 3.2705, loss: 3.2705 +2024-07-26 07:42:22,491 - pyskl - INFO - Saving checkpoint at 113 epochs +2024-07-26 07:44:16,450 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 07:44:17,202 - pyskl - INFO - +top1_acc 0.3624 +top5_acc 0.6227 +2024-07-26 07:44:17,203 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 07:44:17,251 - pyskl - INFO - +mean_acc 0.3621 +2024-07-26 07:44:17,256 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_112.pth was removed +2024-07-26 07:44:17,527 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_113.pth. +2024-07-26 07:44:17,528 - pyskl - INFO - Best top1_acc is 0.3624 at 113 epoch. +2024-07-26 07:44:17,539 - pyskl - INFO - Epoch(val) [113][309] top1_acc: 0.3624, top5_acc: 0.6227, mean_class_accuracy: 0.3621 +2024-07-26 07:48:03,281 - pyskl - INFO - Epoch [114][100/3746] lr: 1.426e-02, eta: 1 day, 7:34:56, time: 2.257, data_time: 1.262, memory: 15990, top1_acc: 0.4253, top5_acc: 0.7008, loss_cls: 3.1417, loss: 3.1417 +2024-07-26 07:49:26,161 - pyskl - INFO - Epoch [114][200/3746] lr: 1.424e-02, eta: 1 day, 7:33:34, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4439, top5_acc: 0.7106, loss_cls: 3.0930, loss: 3.0930 +2024-07-26 07:50:48,416 - pyskl - INFO - Epoch [114][300/3746] lr: 1.422e-02, eta: 1 day, 7:32:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4400, top5_acc: 0.7027, loss_cls: 3.1440, loss: 3.1440 +2024-07-26 07:52:11,330 - pyskl - INFO - Epoch [114][400/3746] lr: 1.420e-02, eta: 1 day, 7:30:51, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.6853, loss_cls: 3.1768, loss: 3.1768 +2024-07-26 07:53:33,550 - pyskl - INFO - Epoch [114][500/3746] lr: 1.418e-02, eta: 1 day, 7:29:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4433, top5_acc: 0.7013, loss_cls: 3.1214, loss: 3.1214 +2024-07-26 07:54:55,852 - pyskl - INFO - Epoch [114][600/3746] lr: 1.416e-02, eta: 1 day, 7:28:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6883, loss_cls: 3.2121, loss: 3.2121 +2024-07-26 07:56:18,862 - pyskl - INFO - Epoch [114][700/3746] lr: 1.414e-02, eta: 1 day, 7:26:45, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4334, top5_acc: 0.6933, loss_cls: 3.1821, loss: 3.1821 +2024-07-26 07:57:41,198 - pyskl - INFO - Epoch [114][800/3746] lr: 1.412e-02, eta: 1 day, 7:25:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6970, loss_cls: 3.1901, loss: 3.1901 +2024-07-26 07:59:04,314 - pyskl - INFO - Epoch [114][900/3746] lr: 1.410e-02, eta: 1 day, 7:24:01, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4228, top5_acc: 0.6920, loss_cls: 3.2040, loss: 3.2040 +2024-07-26 08:00:27,445 - pyskl - INFO - Epoch [114][1000/3746] lr: 1.408e-02, eta: 1 day, 7:22:39, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.6980, loss_cls: 3.1333, loss: 3.1333 +2024-07-26 08:01:50,465 - pyskl - INFO - Epoch [114][1100/3746] lr: 1.406e-02, eta: 1 day, 7:21:17, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.7003, loss_cls: 3.1730, loss: 3.1730 +2024-07-26 08:03:13,656 - pyskl - INFO - Epoch [114][1200/3746] lr: 1.404e-02, eta: 1 day, 7:19:56, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4205, top5_acc: 0.6842, loss_cls: 3.2035, loss: 3.2035 +2024-07-26 08:04:36,966 - pyskl - INFO - Epoch [114][1300/3746] lr: 1.402e-02, eta: 1 day, 7:18:34, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4369, top5_acc: 0.6981, loss_cls: 3.1233, loss: 3.1233 +2024-07-26 08:05:59,881 - pyskl - INFO - Epoch [114][1400/3746] lr: 1.400e-02, eta: 1 day, 7:17:12, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6923, loss_cls: 3.1814, loss: 3.1814 +2024-07-26 08:07:23,045 - pyskl - INFO - Epoch [114][1500/3746] lr: 1.398e-02, eta: 1 day, 7:15:50, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6823, loss_cls: 3.2076, loss: 3.2076 +2024-07-26 08:08:46,412 - pyskl - INFO - Epoch [114][1600/3746] lr: 1.397e-02, eta: 1 day, 7:14:29, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4258, top5_acc: 0.6750, loss_cls: 3.2544, loss: 3.2544 +2024-07-26 08:10:09,561 - pyskl - INFO - Epoch [114][1700/3746] lr: 1.395e-02, eta: 1 day, 7:13:07, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6931, loss_cls: 3.1703, loss: 3.1703 +2024-07-26 08:11:32,449 - pyskl - INFO - Epoch [114][1800/3746] lr: 1.393e-02, eta: 1 day, 7:11:45, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4327, top5_acc: 0.6917, loss_cls: 3.1734, loss: 3.1734 +2024-07-26 08:12:55,296 - pyskl - INFO - Epoch [114][1900/3746] lr: 1.391e-02, eta: 1 day, 7:10:23, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4320, top5_acc: 0.6887, loss_cls: 3.1883, loss: 3.1883 +2024-07-26 08:14:17,854 - pyskl - INFO - Epoch [114][2000/3746] lr: 1.389e-02, eta: 1 day, 7:09:01, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4308, top5_acc: 0.6956, loss_cls: 3.1790, loss: 3.1790 +2024-07-26 08:15:40,451 - pyskl - INFO - Epoch [114][2100/3746] lr: 1.387e-02, eta: 1 day, 7:07:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4392, top5_acc: 0.6917, loss_cls: 3.1631, loss: 3.1631 +2024-07-26 08:17:04,224 - pyskl - INFO - Epoch [114][2200/3746] lr: 1.385e-02, eta: 1 day, 7:06:18, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4256, top5_acc: 0.6809, loss_cls: 3.2145, loss: 3.2145 +2024-07-26 08:18:27,016 - pyskl - INFO - Epoch [114][2300/3746] lr: 1.383e-02, eta: 1 day, 7:04:56, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4286, top5_acc: 0.6855, loss_cls: 3.2108, loss: 3.2108 +2024-07-26 08:19:49,352 - pyskl - INFO - Epoch [114][2400/3746] lr: 1.381e-02, eta: 1 day, 7:03:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6820, loss_cls: 3.2383, loss: 3.2383 +2024-07-26 08:21:13,207 - pyskl - INFO - Epoch [114][2500/3746] lr: 1.379e-02, eta: 1 day, 7:02:12, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4263, top5_acc: 0.6775, loss_cls: 3.2565, loss: 3.2565 +2024-07-26 08:22:36,283 - pyskl - INFO - Epoch [114][2600/3746] lr: 1.377e-02, eta: 1 day, 7:00:51, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4298, top5_acc: 0.6819, loss_cls: 3.2004, loss: 3.2004 +2024-07-26 08:23:58,621 - pyskl - INFO - Epoch [114][2700/3746] lr: 1.375e-02, eta: 1 day, 6:59:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6881, loss_cls: 3.2065, loss: 3.2065 +2024-07-26 08:25:21,100 - pyskl - INFO - Epoch [114][2800/3746] lr: 1.373e-02, eta: 1 day, 6:58:07, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4314, top5_acc: 0.6827, loss_cls: 3.1879, loss: 3.1879 +2024-07-26 08:26:43,022 - pyskl - INFO - Epoch [114][2900/3746] lr: 1.371e-02, eta: 1 day, 6:56:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6836, loss_cls: 3.2290, loss: 3.2290 +2024-07-26 08:28:05,755 - pyskl - INFO - Epoch [114][3000/3746] lr: 1.369e-02, eta: 1 day, 6:55:23, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4334, top5_acc: 0.6945, loss_cls: 3.1538, loss: 3.1538 +2024-07-26 08:29:27,712 - pyskl - INFO - Epoch [114][3100/3746] lr: 1.368e-02, eta: 1 day, 6:54:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4266, top5_acc: 0.6869, loss_cls: 3.1889, loss: 3.1889 +2024-07-26 08:30:49,688 - pyskl - INFO - Epoch [114][3200/3746] lr: 1.366e-02, eta: 1 day, 6:52:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6770, loss_cls: 3.2564, loss: 3.2564 +2024-07-26 08:32:11,495 - pyskl - INFO - Epoch [114][3300/3746] lr: 1.364e-02, eta: 1 day, 6:51:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6858, loss_cls: 3.2244, loss: 3.2244 +2024-07-26 08:33:33,973 - pyskl - INFO - Epoch [114][3400/3746] lr: 1.362e-02, eta: 1 day, 6:49:54, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4317, top5_acc: 0.6917, loss_cls: 3.1625, loss: 3.1625 +2024-07-26 08:34:56,088 - pyskl - INFO - Epoch [114][3500/3746] lr: 1.360e-02, eta: 1 day, 6:48:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6822, loss_cls: 3.2229, loss: 3.2229 +2024-07-26 08:36:18,291 - pyskl - INFO - Epoch [114][3600/3746] lr: 1.358e-02, eta: 1 day, 6:47:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4153, top5_acc: 0.6837, loss_cls: 3.2414, loss: 3.2414 +2024-07-26 08:37:41,553 - pyskl - INFO - Epoch [114][3700/3746] lr: 1.356e-02, eta: 1 day, 6:45:48, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4241, top5_acc: 0.6833, loss_cls: 3.2104, loss: 3.2104 +2024-07-26 08:38:20,712 - pyskl - INFO - Saving checkpoint at 114 epochs +2024-07-26 08:40:13,925 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 08:40:14,731 - pyskl - INFO - +top1_acc 0.3677 +top5_acc 0.6256 +2024-07-26 08:40:14,731 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 08:40:14,772 - pyskl - INFO - +mean_acc 0.3675 +2024-07-26 08:40:14,776 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_113.pth was removed +2024-07-26 08:40:15,044 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_114.pth. +2024-07-26 08:40:15,045 - pyskl - INFO - Best top1_acc is 0.3677 at 114 epoch. +2024-07-26 08:40:15,056 - pyskl - INFO - Epoch(val) [114][309] top1_acc: 0.3677, top5_acc: 0.6256, mean_class_accuracy: 0.3675 +2024-07-26 08:43:58,517 - pyskl - INFO - Epoch [115][100/3746] lr: 1.353e-02, eta: 1 day, 6:44:21, time: 2.235, data_time: 1.245, memory: 15990, top1_acc: 0.4414, top5_acc: 0.7042, loss_cls: 3.1046, loss: 3.1046 +2024-07-26 08:45:21,035 - pyskl - INFO - Epoch [115][200/3746] lr: 1.351e-02, eta: 1 day, 6:42:59, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4448, top5_acc: 0.7036, loss_cls: 3.1039, loss: 3.1039 +2024-07-26 08:46:43,403 - pyskl - INFO - Epoch [115][300/3746] lr: 1.349e-02, eta: 1 day, 6:41:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4402, top5_acc: 0.7013, loss_cls: 3.1205, loss: 3.1205 +2024-07-26 08:48:05,232 - pyskl - INFO - Epoch [115][400/3746] lr: 1.348e-02, eta: 1 day, 6:40:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4436, top5_acc: 0.7073, loss_cls: 3.0910, loss: 3.0910 +2024-07-26 08:49:27,092 - pyskl - INFO - Epoch [115][500/3746] lr: 1.346e-02, eta: 1 day, 6:38:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4373, top5_acc: 0.6956, loss_cls: 3.1116, loss: 3.1116 +2024-07-26 08:50:49,027 - pyskl - INFO - Epoch [115][600/3746] lr: 1.344e-02, eta: 1 day, 6:37:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4478, top5_acc: 0.7066, loss_cls: 3.0759, loss: 3.0759 +2024-07-26 08:52:10,545 - pyskl - INFO - Epoch [115][700/3746] lr: 1.342e-02, eta: 1 day, 6:36:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4320, top5_acc: 0.6886, loss_cls: 3.1781, loss: 3.1781 +2024-07-26 08:53:32,128 - pyskl - INFO - Epoch [115][800/3746] lr: 1.340e-02, eta: 1 day, 6:34:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4300, top5_acc: 0.6970, loss_cls: 3.1464, loss: 3.1464 +2024-07-26 08:54:53,795 - pyskl - INFO - Epoch [115][900/3746] lr: 1.338e-02, eta: 1 day, 6:33:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4292, top5_acc: 0.6820, loss_cls: 3.2085, loss: 3.2085 +2024-07-26 08:56:15,280 - pyskl - INFO - Epoch [115][1000/3746] lr: 1.336e-02, eta: 1 day, 6:32:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.6981, loss_cls: 3.1149, loss: 3.1149 +2024-07-26 08:57:36,576 - pyskl - INFO - Epoch [115][1100/3746] lr: 1.334e-02, eta: 1 day, 6:30:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4330, top5_acc: 0.6933, loss_cls: 3.1694, loss: 3.1694 +2024-07-26 08:58:58,579 - pyskl - INFO - Epoch [115][1200/3746] lr: 1.332e-02, eta: 1 day, 6:29:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4370, top5_acc: 0.6952, loss_cls: 3.1394, loss: 3.1394 +2024-07-26 09:00:20,252 - pyskl - INFO - Epoch [115][1300/3746] lr: 1.330e-02, eta: 1 day, 6:27:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4238, top5_acc: 0.6859, loss_cls: 3.1824, loss: 3.1824 +2024-07-26 09:01:42,811 - pyskl - INFO - Epoch [115][1400/3746] lr: 1.328e-02, eta: 1 day, 6:26:32, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.6958, loss_cls: 3.1758, loss: 3.1758 +2024-07-26 09:03:04,593 - pyskl - INFO - Epoch [115][1500/3746] lr: 1.327e-02, eta: 1 day, 6:25:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4372, top5_acc: 0.6987, loss_cls: 3.1156, loss: 3.1156 +2024-07-26 09:04:26,099 - pyskl - INFO - Epoch [115][1600/3746] lr: 1.325e-02, eta: 1 day, 6:23:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6887, loss_cls: 3.2099, loss: 3.2099 +2024-07-26 09:05:47,580 - pyskl - INFO - Epoch [115][1700/3746] lr: 1.323e-02, eta: 1 day, 6:22:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4302, top5_acc: 0.6998, loss_cls: 3.1431, loss: 3.1431 +2024-07-26 09:07:09,090 - pyskl - INFO - Epoch [115][1800/3746] lr: 1.321e-02, eta: 1 day, 6:21:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4341, top5_acc: 0.6958, loss_cls: 3.1600, loss: 3.1600 +2024-07-26 09:08:30,961 - pyskl - INFO - Epoch [115][1900/3746] lr: 1.319e-02, eta: 1 day, 6:19:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6878, loss_cls: 3.1877, loss: 3.1877 +2024-07-26 09:09:52,752 - pyskl - INFO - Epoch [115][2000/3746] lr: 1.317e-02, eta: 1 day, 6:18:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4281, top5_acc: 0.6889, loss_cls: 3.1751, loss: 3.1751 +2024-07-26 09:11:14,453 - pyskl - INFO - Epoch [115][2100/3746] lr: 1.315e-02, eta: 1 day, 6:16:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6872, loss_cls: 3.1989, loss: 3.1989 +2024-07-26 09:12:36,078 - pyskl - INFO - Epoch [115][2200/3746] lr: 1.313e-02, eta: 1 day, 6:15:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6861, loss_cls: 3.1950, loss: 3.1950 +2024-07-26 09:13:57,397 - pyskl - INFO - Epoch [115][2300/3746] lr: 1.311e-02, eta: 1 day, 6:14:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4195, top5_acc: 0.6783, loss_cls: 3.2246, loss: 3.2246 +2024-07-26 09:15:19,090 - pyskl - INFO - Epoch [115][2400/3746] lr: 1.310e-02, eta: 1 day, 6:12:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4372, top5_acc: 0.6930, loss_cls: 3.1561, loss: 3.1561 +2024-07-26 09:16:41,087 - pyskl - INFO - Epoch [115][2500/3746] lr: 1.308e-02, eta: 1 day, 6:11:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4256, top5_acc: 0.6934, loss_cls: 3.1675, loss: 3.1675 +2024-07-26 09:18:02,923 - pyskl - INFO - Epoch [115][2600/3746] lr: 1.306e-02, eta: 1 day, 6:10:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6803, loss_cls: 3.2425, loss: 3.2425 +2024-07-26 09:19:24,451 - pyskl - INFO - Epoch [115][2700/3746] lr: 1.304e-02, eta: 1 day, 6:08:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.6964, loss_cls: 3.1915, loss: 3.1915 +2024-07-26 09:20:45,939 - pyskl - INFO - Epoch [115][2800/3746] lr: 1.302e-02, eta: 1 day, 6:07:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4303, top5_acc: 0.6877, loss_cls: 3.1957, loss: 3.1957 +2024-07-26 09:22:07,483 - pyskl - INFO - Epoch [115][2900/3746] lr: 1.300e-02, eta: 1 day, 6:05:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4394, top5_acc: 0.6944, loss_cls: 3.1606, loss: 3.1606 +2024-07-26 09:23:29,051 - pyskl - INFO - Epoch [115][3000/3746] lr: 1.298e-02, eta: 1 day, 6:04:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4447, top5_acc: 0.7037, loss_cls: 3.1144, loss: 3.1144 +2024-07-26 09:24:50,591 - pyskl - INFO - Epoch [115][3100/3746] lr: 1.296e-02, eta: 1 day, 6:03:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4320, top5_acc: 0.7002, loss_cls: 3.1516, loss: 3.1516 +2024-07-26 09:26:12,500 - pyskl - INFO - Epoch [115][3200/3746] lr: 1.295e-02, eta: 1 day, 6:01:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6919, loss_cls: 3.1947, loss: 3.1947 +2024-07-26 09:27:34,225 - pyskl - INFO - Epoch [115][3300/3746] lr: 1.293e-02, eta: 1 day, 6:00:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6894, loss_cls: 3.2108, loss: 3.2108 +2024-07-26 09:28:56,567 - pyskl - INFO - Epoch [115][3400/3746] lr: 1.291e-02, eta: 1 day, 5:59:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4202, top5_acc: 0.6858, loss_cls: 3.2105, loss: 3.2105 +2024-07-26 09:30:18,240 - pyskl - INFO - Epoch [115][3500/3746] lr: 1.289e-02, eta: 1 day, 5:57:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6914, loss_cls: 3.2095, loss: 3.2095 +2024-07-26 09:31:41,249 - pyskl - INFO - Epoch [115][3600/3746] lr: 1.287e-02, eta: 1 day, 5:56:23, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4412, top5_acc: 0.6994, loss_cls: 3.1396, loss: 3.1396 +2024-07-26 09:33:03,107 - pyskl - INFO - Epoch [115][3700/3746] lr: 1.285e-02, eta: 1 day, 5:55:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6905, loss_cls: 3.1892, loss: 3.1892 +2024-07-26 09:33:42,548 - pyskl - INFO - Saving checkpoint at 115 epochs +2024-07-26 09:35:36,193 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 09:35:36,880 - pyskl - INFO - +top1_acc 0.3585 +top5_acc 0.6182 +2024-07-26 09:35:36,880 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 09:35:36,926 - pyskl - INFO - +mean_acc 0.3582 +2024-07-26 09:35:36,940 - pyskl - INFO - Epoch(val) [115][309] top1_acc: 0.3585, top5_acc: 0.6182, mean_class_accuracy: 0.3582 +2024-07-26 09:39:35,947 - pyskl - INFO - Epoch [116][100/3746] lr: 1.282e-02, eta: 1 day, 5:53:37, time: 2.390, data_time: 1.403, memory: 15990, top1_acc: 0.4536, top5_acc: 0.7106, loss_cls: 3.0439, loss: 3.0439 +2024-07-26 09:40:58,347 - pyskl - INFO - Epoch [116][200/3746] lr: 1.281e-02, eta: 1 day, 5:52:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4458, top5_acc: 0.7086, loss_cls: 3.0934, loss: 3.0934 +2024-07-26 09:42:20,134 - pyskl - INFO - Epoch [116][300/3746] lr: 1.279e-02, eta: 1 day, 5:50:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.7009, loss_cls: 3.1133, loss: 3.1133 +2024-07-26 09:43:41,478 - pyskl - INFO - Epoch [116][400/3746] lr: 1.277e-02, eta: 1 day, 5:49:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4494, top5_acc: 0.7056, loss_cls: 3.0584, loss: 3.0584 +2024-07-26 09:45:03,187 - pyskl - INFO - Epoch [116][500/3746] lr: 1.275e-02, eta: 1 day, 5:48:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.7030, loss_cls: 3.1241, loss: 3.1241 +2024-07-26 09:46:24,631 - pyskl - INFO - Epoch [116][600/3746] lr: 1.273e-02, eta: 1 day, 5:46:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4386, top5_acc: 0.7020, loss_cls: 3.1521, loss: 3.1521 +2024-07-26 09:47:46,148 - pyskl - INFO - Epoch [116][700/3746] lr: 1.271e-02, eta: 1 day, 5:45:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4470, top5_acc: 0.7114, loss_cls: 3.0546, loss: 3.0546 +2024-07-26 09:49:07,964 - pyskl - INFO - Epoch [116][800/3746] lr: 1.269e-02, eta: 1 day, 5:44:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4480, top5_acc: 0.7102, loss_cls: 3.0673, loss: 3.0673 +2024-07-26 09:50:29,373 - pyskl - INFO - Epoch [116][900/3746] lr: 1.268e-02, eta: 1 day, 5:42:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4425, top5_acc: 0.7034, loss_cls: 3.1032, loss: 3.1032 +2024-07-26 09:51:51,054 - pyskl - INFO - Epoch [116][1000/3746] lr: 1.266e-02, eta: 1 day, 5:41:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.6986, loss_cls: 3.1266, loss: 3.1266 +2024-07-26 09:53:12,685 - pyskl - INFO - Epoch [116][1100/3746] lr: 1.264e-02, eta: 1 day, 5:39:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6970, loss_cls: 3.1606, loss: 3.1606 +2024-07-26 09:54:34,707 - pyskl - INFO - Epoch [116][1200/3746] lr: 1.262e-02, eta: 1 day, 5:38:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6944, loss_cls: 3.1818, loss: 3.1818 +2024-07-26 09:55:56,393 - pyskl - INFO - Epoch [116][1300/3746] lr: 1.260e-02, eta: 1 day, 5:37:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6931, loss_cls: 3.1696, loss: 3.1696 +2024-07-26 09:57:18,625 - pyskl - INFO - Epoch [116][1400/3746] lr: 1.258e-02, eta: 1 day, 5:35:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4302, top5_acc: 0.6992, loss_cls: 3.1496, loss: 3.1496 +2024-07-26 09:58:39,890 - pyskl - INFO - Epoch [116][1500/3746] lr: 1.256e-02, eta: 1 day, 5:34:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4306, top5_acc: 0.6925, loss_cls: 3.1519, loss: 3.1519 +2024-07-26 10:00:01,745 - pyskl - INFO - Epoch [116][1600/3746] lr: 1.255e-02, eta: 1 day, 5:33:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4445, top5_acc: 0.6955, loss_cls: 3.1539, loss: 3.1539 +2024-07-26 10:01:23,226 - pyskl - INFO - Epoch [116][1700/3746] lr: 1.253e-02, eta: 1 day, 5:31:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4366, top5_acc: 0.6989, loss_cls: 3.0989, loss: 3.0989 +2024-07-26 10:02:44,852 - pyskl - INFO - Epoch [116][1800/3746] lr: 1.251e-02, eta: 1 day, 5:30:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4286, top5_acc: 0.6928, loss_cls: 3.1634, loss: 3.1634 +2024-07-26 10:04:06,010 - pyskl - INFO - Epoch [116][1900/3746] lr: 1.249e-02, eta: 1 day, 5:28:56, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6983, loss_cls: 3.1324, loss: 3.1324 +2024-07-26 10:05:27,694 - pyskl - INFO - Epoch [116][2000/3746] lr: 1.247e-02, eta: 1 day, 5:27:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4358, top5_acc: 0.6997, loss_cls: 3.1344, loss: 3.1344 +2024-07-26 10:06:49,849 - pyskl - INFO - Epoch [116][2100/3746] lr: 1.245e-02, eta: 1 day, 5:26:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4256, top5_acc: 0.6945, loss_cls: 3.1877, loss: 3.1877 +2024-07-26 10:08:11,746 - pyskl - INFO - Epoch [116][2200/3746] lr: 1.243e-02, eta: 1 day, 5:24:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4412, top5_acc: 0.6967, loss_cls: 3.1300, loss: 3.1300 +2024-07-26 10:09:33,405 - pyskl - INFO - Epoch [116][2300/3746] lr: 1.242e-02, eta: 1 day, 5:23:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4412, top5_acc: 0.7089, loss_cls: 3.1001, loss: 3.1001 +2024-07-26 10:10:55,785 - pyskl - INFO - Epoch [116][2400/3746] lr: 1.240e-02, eta: 1 day, 5:22:05, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4211, top5_acc: 0.6883, loss_cls: 3.2083, loss: 3.2083 +2024-07-26 10:12:17,790 - pyskl - INFO - Epoch [116][2500/3746] lr: 1.238e-02, eta: 1 day, 5:20:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4384, top5_acc: 0.6900, loss_cls: 3.1721, loss: 3.1721 +2024-07-26 10:13:39,965 - pyskl - INFO - Epoch [116][2600/3746] lr: 1.236e-02, eta: 1 day, 5:19:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4269, top5_acc: 0.6931, loss_cls: 3.1577, loss: 3.1577 +2024-07-26 10:15:01,193 - pyskl - INFO - Epoch [116][2700/3746] lr: 1.234e-02, eta: 1 day, 5:17:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6872, loss_cls: 3.2086, loss: 3.2086 +2024-07-26 10:16:23,675 - pyskl - INFO - Epoch [116][2800/3746] lr: 1.232e-02, eta: 1 day, 5:16:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4387, top5_acc: 0.6914, loss_cls: 3.1494, loss: 3.1494 +2024-07-26 10:17:45,578 - pyskl - INFO - Epoch [116][2900/3746] lr: 1.231e-02, eta: 1 day, 5:15:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4306, top5_acc: 0.6883, loss_cls: 3.1940, loss: 3.1940 +2024-07-26 10:19:07,076 - pyskl - INFO - Epoch [116][3000/3746] lr: 1.229e-02, eta: 1 day, 5:13:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4348, top5_acc: 0.6895, loss_cls: 3.1840, loss: 3.1840 +2024-07-26 10:20:28,282 - pyskl - INFO - Epoch [116][3100/3746] lr: 1.227e-02, eta: 1 day, 5:12:30, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4341, top5_acc: 0.7041, loss_cls: 3.1111, loss: 3.1111 +2024-07-26 10:21:49,955 - pyskl - INFO - Epoch [116][3200/3746] lr: 1.225e-02, eta: 1 day, 5:11:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4475, top5_acc: 0.7058, loss_cls: 3.0923, loss: 3.0923 +2024-07-26 10:23:12,125 - pyskl - INFO - Epoch [116][3300/3746] lr: 1.223e-02, eta: 1 day, 5:09:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4295, top5_acc: 0.6881, loss_cls: 3.1997, loss: 3.1997 +2024-07-26 10:24:33,814 - pyskl - INFO - Epoch [116][3400/3746] lr: 1.221e-02, eta: 1 day, 5:08:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4402, top5_acc: 0.6905, loss_cls: 3.1457, loss: 3.1457 +2024-07-26 10:25:55,317 - pyskl - INFO - Epoch [116][3500/3746] lr: 1.220e-02, eta: 1 day, 5:07:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4280, top5_acc: 0.6830, loss_cls: 3.1807, loss: 3.1807 +2024-07-26 10:27:16,597 - pyskl - INFO - Epoch [116][3600/3746] lr: 1.218e-02, eta: 1 day, 5:05:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6905, loss_cls: 3.1782, loss: 3.1782 +2024-07-26 10:28:39,148 - pyskl - INFO - Epoch [116][3700/3746] lr: 1.216e-02, eta: 1 day, 5:04:16, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4327, top5_acc: 0.7019, loss_cls: 3.1427, loss: 3.1427 +2024-07-26 10:29:18,585 - pyskl - INFO - Saving checkpoint at 116 epochs +2024-07-26 10:31:11,913 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 10:31:12,672 - pyskl - INFO - +top1_acc 0.3579 +top5_acc 0.6245 +2024-07-26 10:31:12,672 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 10:31:12,726 - pyskl - INFO - +mean_acc 0.3576 +2024-07-26 10:31:12,743 - pyskl - INFO - Epoch(val) [116][309] top1_acc: 0.3579, top5_acc: 0.6245, mean_class_accuracy: 0.3576 +2024-07-26 10:35:11,067 - pyskl - INFO - Epoch [117][100/3746] lr: 1.213e-02, eta: 1 day, 5:02:51, time: 2.383, data_time: 1.394, memory: 15990, top1_acc: 0.4544, top5_acc: 0.7161, loss_cls: 3.0205, loss: 3.0205 +2024-07-26 10:36:33,006 - pyskl - INFO - Epoch [117][200/3746] lr: 1.211e-02, eta: 1 day, 5:01:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4597, top5_acc: 0.7177, loss_cls: 3.0170, loss: 3.0170 +2024-07-26 10:37:54,814 - pyskl - INFO - Epoch [117][300/3746] lr: 1.210e-02, eta: 1 day, 5:00:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.7044, loss_cls: 3.1052, loss: 3.1052 +2024-07-26 10:39:16,442 - pyskl - INFO - Epoch [117][400/3746] lr: 1.208e-02, eta: 1 day, 4:58:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4583, top5_acc: 0.7175, loss_cls: 3.0089, loss: 3.0089 +2024-07-26 10:40:38,447 - pyskl - INFO - Epoch [117][500/3746] lr: 1.206e-02, eta: 1 day, 4:57:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4431, top5_acc: 0.7048, loss_cls: 3.1110, loss: 3.1110 +2024-07-26 10:42:00,008 - pyskl - INFO - Epoch [117][600/3746] lr: 1.204e-02, eta: 1 day, 4:55:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4327, top5_acc: 0.7033, loss_cls: 3.1108, loss: 3.1108 +2024-07-26 10:43:21,766 - pyskl - INFO - Epoch [117][700/3746] lr: 1.202e-02, eta: 1 day, 4:54:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4355, top5_acc: 0.7114, loss_cls: 3.0732, loss: 3.0732 +2024-07-26 10:44:43,530 - pyskl - INFO - Epoch [117][800/3746] lr: 1.200e-02, eta: 1 day, 4:53:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4347, top5_acc: 0.7064, loss_cls: 3.1126, loss: 3.1126 +2024-07-26 10:46:05,101 - pyskl - INFO - Epoch [117][900/3746] lr: 1.199e-02, eta: 1 day, 4:51:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.6970, loss_cls: 3.1291, loss: 3.1291 +2024-07-26 10:47:26,702 - pyskl - INFO - Epoch [117][1000/3746] lr: 1.197e-02, eta: 1 day, 4:50:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.7081, loss_cls: 3.0888, loss: 3.0888 +2024-07-26 10:48:48,653 - pyskl - INFO - Epoch [117][1100/3746] lr: 1.195e-02, eta: 1 day, 4:49:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.6991, loss_cls: 3.1281, loss: 3.1281 +2024-07-26 10:50:11,158 - pyskl - INFO - Epoch [117][1200/3746] lr: 1.193e-02, eta: 1 day, 4:47:46, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.6970, loss_cls: 3.1207, loss: 3.1207 +2024-07-26 10:51:32,585 - pyskl - INFO - Epoch [117][1300/3746] lr: 1.191e-02, eta: 1 day, 4:46:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4417, top5_acc: 0.7058, loss_cls: 3.0925, loss: 3.0925 +2024-07-26 10:52:55,123 - pyskl - INFO - Epoch [117][1400/3746] lr: 1.190e-02, eta: 1 day, 4:45:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4556, top5_acc: 0.7166, loss_cls: 3.0394, loss: 3.0394 +2024-07-26 10:54:16,834 - pyskl - INFO - Epoch [117][1500/3746] lr: 1.188e-02, eta: 1 day, 4:43:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.6977, loss_cls: 3.1472, loss: 3.1472 +2024-07-26 10:55:38,375 - pyskl - INFO - Epoch [117][1600/3746] lr: 1.186e-02, eta: 1 day, 4:42:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4328, top5_acc: 0.6953, loss_cls: 3.1681, loss: 3.1681 +2024-07-26 10:57:00,389 - pyskl - INFO - Epoch [117][1700/3746] lr: 1.184e-02, eta: 1 day, 4:40:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4452, top5_acc: 0.7052, loss_cls: 3.1068, loss: 3.1068 +2024-07-26 10:58:22,315 - pyskl - INFO - Epoch [117][1800/3746] lr: 1.182e-02, eta: 1 day, 4:39:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4370, top5_acc: 0.7008, loss_cls: 3.1360, loss: 3.1360 +2024-07-26 10:59:43,900 - pyskl - INFO - Epoch [117][1900/3746] lr: 1.181e-02, eta: 1 day, 4:38:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4377, top5_acc: 0.6981, loss_cls: 3.1532, loss: 3.1532 +2024-07-26 11:01:05,654 - pyskl - INFO - Epoch [117][2000/3746] lr: 1.179e-02, eta: 1 day, 4:36:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.6917, loss_cls: 3.1410, loss: 3.1410 +2024-07-26 11:02:27,541 - pyskl - INFO - Epoch [117][2100/3746] lr: 1.177e-02, eta: 1 day, 4:35:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4525, top5_acc: 0.7008, loss_cls: 3.1089, loss: 3.1089 +2024-07-26 11:03:49,157 - pyskl - INFO - Epoch [117][2200/3746] lr: 1.175e-02, eta: 1 day, 4:34:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4338, top5_acc: 0.6936, loss_cls: 3.1518, loss: 3.1518 +2024-07-26 11:05:11,070 - pyskl - INFO - Epoch [117][2300/3746] lr: 1.173e-02, eta: 1 day, 4:32:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4417, top5_acc: 0.6959, loss_cls: 3.1501, loss: 3.1501 +2024-07-26 11:06:32,723 - pyskl - INFO - Epoch [117][2400/3746] lr: 1.172e-02, eta: 1 day, 4:31:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4383, top5_acc: 0.6970, loss_cls: 3.1400, loss: 3.1400 +2024-07-26 11:07:53,960 - pyskl - INFO - Epoch [117][2500/3746] lr: 1.170e-02, eta: 1 day, 4:29:56, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4330, top5_acc: 0.6886, loss_cls: 3.1803, loss: 3.1803 +2024-07-26 11:09:15,851 - pyskl - INFO - Epoch [117][2600/3746] lr: 1.168e-02, eta: 1 day, 4:28:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.7008, loss_cls: 3.1446, loss: 3.1446 +2024-07-26 11:10:37,327 - pyskl - INFO - Epoch [117][2700/3746] lr: 1.166e-02, eta: 1 day, 4:27:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4469, top5_acc: 0.7014, loss_cls: 3.0876, loss: 3.0876 +2024-07-26 11:11:58,805 - pyskl - INFO - Epoch [117][2800/3746] lr: 1.164e-02, eta: 1 day, 4:25:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4338, top5_acc: 0.7013, loss_cls: 3.1343, loss: 3.1343 +2024-07-26 11:13:20,223 - pyskl - INFO - Epoch [117][2900/3746] lr: 1.163e-02, eta: 1 day, 4:24:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.6991, loss_cls: 3.1368, loss: 3.1368 +2024-07-26 11:14:41,724 - pyskl - INFO - Epoch [117][3000/3746] lr: 1.161e-02, eta: 1 day, 4:23:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4394, top5_acc: 0.6983, loss_cls: 3.1355, loss: 3.1355 +2024-07-26 11:16:03,156 - pyskl - INFO - Epoch [117][3100/3746] lr: 1.159e-02, eta: 1 day, 4:21:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.6986, loss_cls: 3.1530, loss: 3.1530 +2024-07-26 11:17:24,550 - pyskl - INFO - Epoch [117][3200/3746] lr: 1.157e-02, eta: 1 day, 4:20:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6945, loss_cls: 3.1569, loss: 3.1569 +2024-07-26 11:18:46,201 - pyskl - INFO - Epoch [117][3300/3746] lr: 1.155e-02, eta: 1 day, 4:18:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4338, top5_acc: 0.6917, loss_cls: 3.1689, loss: 3.1689 +2024-07-26 11:20:07,582 - pyskl - INFO - Epoch [117][3400/3746] lr: 1.154e-02, eta: 1 day, 4:17:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4433, top5_acc: 0.7034, loss_cls: 3.0874, loss: 3.0874 +2024-07-26 11:21:29,425 - pyskl - INFO - Epoch [117][3500/3746] lr: 1.152e-02, eta: 1 day, 4:16:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4291, top5_acc: 0.6959, loss_cls: 3.1716, loss: 3.1716 +2024-07-26 11:22:51,054 - pyskl - INFO - Epoch [117][3600/3746] lr: 1.150e-02, eta: 1 day, 4:14:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4338, top5_acc: 0.6969, loss_cls: 3.1188, loss: 3.1188 +2024-07-26 11:24:12,614 - pyskl - INFO - Epoch [117][3700/3746] lr: 1.148e-02, eta: 1 day, 4:13:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4464, top5_acc: 0.7050, loss_cls: 3.1037, loss: 3.1037 +2024-07-26 11:24:51,884 - pyskl - INFO - Saving checkpoint at 117 epochs +2024-07-26 11:26:45,717 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 11:26:46,380 - pyskl - INFO - +top1_acc 0.3603 +top5_acc 0.6193 +2024-07-26 11:26:46,380 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 11:26:46,422 - pyskl - INFO - +mean_acc 0.3600 +2024-07-26 11:26:46,436 - pyskl - INFO - Epoch(val) [117][309] top1_acc: 0.3603, top5_acc: 0.6193, mean_class_accuracy: 0.3600 +2024-07-26 11:30:40,622 - pyskl - INFO - Epoch [118][100/3746] lr: 1.146e-02, eta: 1 day, 4:12:00, time: 2.342, data_time: 1.363, memory: 15990, top1_acc: 0.4561, top5_acc: 0.7159, loss_cls: 3.0288, loss: 3.0288 +2024-07-26 11:32:02,248 - pyskl - INFO - Epoch [118][200/3746] lr: 1.144e-02, eta: 1 day, 4:10:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4494, top5_acc: 0.7120, loss_cls: 3.0537, loss: 3.0537 +2024-07-26 11:33:23,795 - pyskl - INFO - Epoch [118][300/3746] lr: 1.142e-02, eta: 1 day, 4:09:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4530, top5_acc: 0.7192, loss_cls: 3.0440, loss: 3.0440 +2024-07-26 11:34:45,691 - pyskl - INFO - Epoch [118][400/3746] lr: 1.140e-02, eta: 1 day, 4:07:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4425, top5_acc: 0.7097, loss_cls: 3.0892, loss: 3.0892 +2024-07-26 11:36:07,521 - pyskl - INFO - Epoch [118][500/3746] lr: 1.139e-02, eta: 1 day, 4:06:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4572, top5_acc: 0.7120, loss_cls: 3.0239, loss: 3.0239 +2024-07-26 11:37:29,020 - pyskl - INFO - Epoch [118][600/3746] lr: 1.137e-02, eta: 1 day, 4:05:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4530, top5_acc: 0.7130, loss_cls: 3.0466, loss: 3.0466 +2024-07-26 11:38:51,099 - pyskl - INFO - Epoch [118][700/3746] lr: 1.135e-02, eta: 1 day, 4:03:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4453, top5_acc: 0.7067, loss_cls: 3.0675, loss: 3.0675 +2024-07-26 11:40:13,302 - pyskl - INFO - Epoch [118][800/3746] lr: 1.133e-02, eta: 1 day, 4:02:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7127, loss_cls: 3.0427, loss: 3.0427 +2024-07-26 11:41:35,081 - pyskl - INFO - Epoch [118][900/3746] lr: 1.131e-02, eta: 1 day, 4:01:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4462, top5_acc: 0.7113, loss_cls: 3.0624, loss: 3.0624 +2024-07-26 11:42:56,397 - pyskl - INFO - Epoch [118][1000/3746] lr: 1.130e-02, eta: 1 day, 3:59:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4539, top5_acc: 0.7198, loss_cls: 3.0298, loss: 3.0298 +2024-07-26 11:44:18,140 - pyskl - INFO - Epoch [118][1100/3746] lr: 1.128e-02, eta: 1 day, 3:58:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4400, top5_acc: 0.6995, loss_cls: 3.1043, loss: 3.1043 +2024-07-26 11:45:40,459 - pyskl - INFO - Epoch [118][1200/3746] lr: 1.126e-02, eta: 1 day, 3:56:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4308, top5_acc: 0.6931, loss_cls: 3.1541, loss: 3.1541 +2024-07-26 11:47:02,388 - pyskl - INFO - Epoch [118][1300/3746] lr: 1.124e-02, eta: 1 day, 3:55:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4537, top5_acc: 0.7075, loss_cls: 3.0737, loss: 3.0737 +2024-07-26 11:48:24,606 - pyskl - INFO - Epoch [118][1400/3746] lr: 1.123e-02, eta: 1 day, 3:54:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4433, top5_acc: 0.7080, loss_cls: 3.1101, loss: 3.1101 +2024-07-26 11:49:46,289 - pyskl - INFO - Epoch [118][1500/3746] lr: 1.121e-02, eta: 1 day, 3:52:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4369, top5_acc: 0.7042, loss_cls: 3.0899, loss: 3.0899 +2024-07-26 11:51:08,948 - pyskl - INFO - Epoch [118][1600/3746] lr: 1.119e-02, eta: 1 day, 3:51:26, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.7078, loss_cls: 3.0906, loss: 3.0906 +2024-07-26 11:52:30,942 - pyskl - INFO - Epoch [118][1700/3746] lr: 1.117e-02, eta: 1 day, 3:50:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4356, top5_acc: 0.7005, loss_cls: 3.1273, loss: 3.1273 +2024-07-26 11:53:52,714 - pyskl - INFO - Epoch [118][1800/3746] lr: 1.116e-02, eta: 1 day, 3:48:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4489, top5_acc: 0.7066, loss_cls: 3.0697, loss: 3.0697 +2024-07-26 11:55:14,119 - pyskl - INFO - Epoch [118][1900/3746] lr: 1.114e-02, eta: 1 day, 3:47:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4506, top5_acc: 0.7137, loss_cls: 3.0530, loss: 3.0530 +2024-07-26 11:56:35,781 - pyskl - INFO - Epoch [118][2000/3746] lr: 1.112e-02, eta: 1 day, 3:45:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.7013, loss_cls: 3.1043, loss: 3.1043 +2024-07-26 11:57:57,385 - pyskl - INFO - Epoch [118][2100/3746] lr: 1.110e-02, eta: 1 day, 3:44:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4464, top5_acc: 0.7123, loss_cls: 3.0944, loss: 3.0944 +2024-07-26 11:59:18,931 - pyskl - INFO - Epoch [118][2200/3746] lr: 1.109e-02, eta: 1 day, 3:43:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4462, top5_acc: 0.6969, loss_cls: 3.0963, loss: 3.0963 +2024-07-26 12:00:40,979 - pyskl - INFO - Epoch [118][2300/3746] lr: 1.107e-02, eta: 1 day, 3:41:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4417, top5_acc: 0.7058, loss_cls: 3.0841, loss: 3.0841 +2024-07-26 12:02:02,850 - pyskl - INFO - Epoch [118][2400/3746] lr: 1.105e-02, eta: 1 day, 3:40:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4455, top5_acc: 0.7105, loss_cls: 3.0414, loss: 3.0414 +2024-07-26 12:03:24,476 - pyskl - INFO - Epoch [118][2500/3746] lr: 1.103e-02, eta: 1 day, 3:39:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4298, top5_acc: 0.6994, loss_cls: 3.1576, loss: 3.1576 +2024-07-26 12:04:45,671 - pyskl - INFO - Epoch [118][2600/3746] lr: 1.102e-02, eta: 1 day, 3:37:43, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4467, top5_acc: 0.7078, loss_cls: 3.0824, loss: 3.0824 +2024-07-26 12:06:07,484 - pyskl - INFO - Epoch [118][2700/3746] lr: 1.100e-02, eta: 1 day, 3:36:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4439, top5_acc: 0.7037, loss_cls: 3.1086, loss: 3.1086 +2024-07-26 12:07:29,254 - pyskl - INFO - Epoch [118][2800/3746] lr: 1.098e-02, eta: 1 day, 3:34:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.7022, loss_cls: 3.0970, loss: 3.0970 +2024-07-26 12:08:50,371 - pyskl - INFO - Epoch [118][2900/3746] lr: 1.096e-02, eta: 1 day, 3:33:36, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4358, top5_acc: 0.7025, loss_cls: 3.1195, loss: 3.1195 +2024-07-26 12:10:12,091 - pyskl - INFO - Epoch [118][3000/3746] lr: 1.095e-02, eta: 1 day, 3:32:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4381, top5_acc: 0.7087, loss_cls: 3.1015, loss: 3.1015 +2024-07-26 12:11:33,439 - pyskl - INFO - Epoch [118][3100/3746] lr: 1.093e-02, eta: 1 day, 3:30:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4458, top5_acc: 0.7108, loss_cls: 3.1118, loss: 3.1118 +2024-07-26 12:12:55,209 - pyskl - INFO - Epoch [118][3200/3746] lr: 1.091e-02, eta: 1 day, 3:29:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.7030, loss_cls: 3.0669, loss: 3.0669 +2024-07-26 12:14:16,705 - pyskl - INFO - Epoch [118][3300/3746] lr: 1.089e-02, eta: 1 day, 3:28:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4295, top5_acc: 0.6900, loss_cls: 3.1853, loss: 3.1853 +2024-07-26 12:15:37,941 - pyskl - INFO - Epoch [118][3400/3746] lr: 1.088e-02, eta: 1 day, 3:26:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6908, loss_cls: 3.1707, loss: 3.1707 +2024-07-26 12:16:59,696 - pyskl - INFO - Epoch [118][3500/3746] lr: 1.086e-02, eta: 1 day, 3:25:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.7053, loss_cls: 3.1042, loss: 3.1042 +2024-07-26 12:18:21,408 - pyskl - INFO - Epoch [118][3600/3746] lr: 1.084e-02, eta: 1 day, 3:23:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4422, top5_acc: 0.6980, loss_cls: 3.1056, loss: 3.1056 +2024-07-26 12:19:43,855 - pyskl - INFO - Epoch [118][3700/3746] lr: 1.082e-02, eta: 1 day, 3:22:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.7086, loss_cls: 3.0670, loss: 3.0670 +2024-07-26 12:20:23,150 - pyskl - INFO - Saving checkpoint at 118 epochs +2024-07-26 12:22:16,980 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 12:22:17,652 - pyskl - INFO - +top1_acc 0.3808 +top5_acc 0.6408 +2024-07-26 12:22:17,652 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 12:22:17,699 - pyskl - INFO - +mean_acc 0.3807 +2024-07-26 12:22:17,704 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_114.pth was removed +2024-07-26 12:22:17,968 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2024-07-26 12:22:17,969 - pyskl - INFO - Best top1_acc is 0.3808 at 118 epoch. +2024-07-26 12:22:17,984 - pyskl - INFO - Epoch(val) [118][309] top1_acc: 0.3808, top5_acc: 0.6408, mean_class_accuracy: 0.3807 +2024-07-26 12:26:09,868 - pyskl - INFO - Epoch [119][100/3746] lr: 1.080e-02, eta: 1 day, 3:21:08, time: 2.319, data_time: 1.326, memory: 15990, top1_acc: 0.4672, top5_acc: 0.7267, loss_cls: 2.9626, loss: 2.9626 +2024-07-26 12:27:33,484 - pyskl - INFO - Epoch [119][200/3746] lr: 1.078e-02, eta: 1 day, 3:19:46, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7141, loss_cls: 3.0400, loss: 3.0400 +2024-07-26 12:28:57,053 - pyskl - INFO - Epoch [119][300/3746] lr: 1.076e-02, eta: 1 day, 3:18:24, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4537, top5_acc: 0.7141, loss_cls: 3.0367, loss: 3.0367 +2024-07-26 12:30:19,953 - pyskl - INFO - Epoch [119][400/3746] lr: 1.075e-02, eta: 1 day, 3:17:02, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4586, top5_acc: 0.7075, loss_cls: 3.0273, loss: 3.0273 +2024-07-26 12:31:42,812 - pyskl - INFO - Epoch [119][500/3746] lr: 1.073e-02, eta: 1 day, 3:15:40, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4641, top5_acc: 0.7137, loss_cls: 3.0174, loss: 3.0174 +2024-07-26 12:33:05,477 - pyskl - INFO - Epoch [119][600/3746] lr: 1.071e-02, eta: 1 day, 3:14:18, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4570, top5_acc: 0.7042, loss_cls: 3.0552, loss: 3.0552 +2024-07-26 12:34:27,976 - pyskl - INFO - Epoch [119][700/3746] lr: 1.069e-02, eta: 1 day, 3:12:56, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4466, top5_acc: 0.7083, loss_cls: 3.0563, loss: 3.0563 +2024-07-26 12:35:50,070 - pyskl - INFO - Epoch [119][800/3746] lr: 1.068e-02, eta: 1 day, 3:11:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4564, top5_acc: 0.7200, loss_cls: 3.0157, loss: 3.0157 +2024-07-26 12:37:13,294 - pyskl - INFO - Epoch [119][900/3746] lr: 1.066e-02, eta: 1 day, 3:10:11, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4462, top5_acc: 0.7098, loss_cls: 3.0648, loss: 3.0648 +2024-07-26 12:38:36,384 - pyskl - INFO - Epoch [119][1000/3746] lr: 1.064e-02, eta: 1 day, 3:08:49, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4567, top5_acc: 0.7208, loss_cls: 3.0161, loss: 3.0161 +2024-07-26 12:39:58,742 - pyskl - INFO - Epoch [119][1100/3746] lr: 1.063e-02, eta: 1 day, 3:07:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4500, top5_acc: 0.7122, loss_cls: 3.0636, loss: 3.0636 +2024-07-26 12:41:20,925 - pyskl - INFO - Epoch [119][1200/3746] lr: 1.061e-02, eta: 1 day, 3:06:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4537, top5_acc: 0.7109, loss_cls: 3.0318, loss: 3.0318 +2024-07-26 12:42:42,858 - pyskl - INFO - Epoch [119][1300/3746] lr: 1.059e-02, eta: 1 day, 3:04:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4592, top5_acc: 0.7197, loss_cls: 3.0200, loss: 3.0200 +2024-07-26 12:44:05,155 - pyskl - INFO - Epoch [119][1400/3746] lr: 1.057e-02, eta: 1 day, 3:03:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4402, top5_acc: 0.7022, loss_cls: 3.0831, loss: 3.0831 +2024-07-26 12:45:27,306 - pyskl - INFO - Epoch [119][1500/3746] lr: 1.056e-02, eta: 1 day, 3:01:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4486, top5_acc: 0.7067, loss_cls: 3.0972, loss: 3.0972 +2024-07-26 12:46:48,991 - pyskl - INFO - Epoch [119][1600/3746] lr: 1.054e-02, eta: 1 day, 3:00:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4489, top5_acc: 0.7123, loss_cls: 3.0715, loss: 3.0715 +2024-07-26 12:48:10,242 - pyskl - INFO - Epoch [119][1700/3746] lr: 1.052e-02, eta: 1 day, 2:59:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4580, top5_acc: 0.7147, loss_cls: 3.0340, loss: 3.0340 +2024-07-26 12:49:32,346 - pyskl - INFO - Epoch [119][1800/3746] lr: 1.050e-02, eta: 1 day, 2:57:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.7133, loss_cls: 3.0729, loss: 3.0729 +2024-07-26 12:50:54,480 - pyskl - INFO - Epoch [119][1900/3746] lr: 1.049e-02, eta: 1 day, 2:56:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.7072, loss_cls: 3.0630, loss: 3.0630 +2024-07-26 12:52:16,306 - pyskl - INFO - Epoch [119][2000/3746] lr: 1.047e-02, eta: 1 day, 2:55:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4564, top5_acc: 0.7191, loss_cls: 3.0161, loss: 3.0161 +2024-07-26 12:53:38,537 - pyskl - INFO - Epoch [119][2100/3746] lr: 1.045e-02, eta: 1 day, 2:53:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4559, top5_acc: 0.7036, loss_cls: 3.0686, loss: 3.0686 +2024-07-26 12:55:00,498 - pyskl - INFO - Epoch [119][2200/3746] lr: 1.044e-02, eta: 1 day, 2:52:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.7020, loss_cls: 3.1032, loss: 3.1032 +2024-07-26 12:56:22,250 - pyskl - INFO - Epoch [119][2300/3746] lr: 1.042e-02, eta: 1 day, 2:51:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7133, loss_cls: 3.0410, loss: 3.0410 +2024-07-26 12:57:43,743 - pyskl - INFO - Epoch [119][2400/3746] lr: 1.040e-02, eta: 1 day, 2:49:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4539, top5_acc: 0.7125, loss_cls: 3.0561, loss: 3.0561 +2024-07-26 12:59:05,856 - pyskl - INFO - Epoch [119][2500/3746] lr: 1.039e-02, eta: 1 day, 2:48:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4525, top5_acc: 0.7020, loss_cls: 3.0688, loss: 3.0688 +2024-07-26 13:00:27,713 - pyskl - INFO - Epoch [119][2600/3746] lr: 1.037e-02, eta: 1 day, 2:46:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4598, top5_acc: 0.7100, loss_cls: 3.0456, loss: 3.0456 +2024-07-26 13:01:49,766 - pyskl - INFO - Epoch [119][2700/3746] lr: 1.035e-02, eta: 1 day, 2:45:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4366, top5_acc: 0.7003, loss_cls: 3.1211, loss: 3.1211 +2024-07-26 13:03:11,775 - pyskl - INFO - Epoch [119][2800/3746] lr: 1.033e-02, eta: 1 day, 2:44:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4394, top5_acc: 0.7095, loss_cls: 3.0878, loss: 3.0878 +2024-07-26 13:04:33,558 - pyskl - INFO - Epoch [119][2900/3746] lr: 1.032e-02, eta: 1 day, 2:42:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4452, top5_acc: 0.6997, loss_cls: 3.1203, loss: 3.1203 +2024-07-26 13:05:55,421 - pyskl - INFO - Epoch [119][3000/3746] lr: 1.030e-02, eta: 1 day, 2:41:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4633, top5_acc: 0.7111, loss_cls: 3.0198, loss: 3.0198 +2024-07-26 13:07:16,978 - pyskl - INFO - Epoch [119][3100/3746] lr: 1.028e-02, eta: 1 day, 2:40:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4447, top5_acc: 0.7111, loss_cls: 3.0723, loss: 3.0723 +2024-07-26 13:08:38,469 - pyskl - INFO - Epoch [119][3200/3746] lr: 1.027e-02, eta: 1 day, 2:38:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4437, top5_acc: 0.7052, loss_cls: 3.0763, loss: 3.0763 +2024-07-26 13:09:59,753 - pyskl - INFO - Epoch [119][3300/3746] lr: 1.025e-02, eta: 1 day, 2:37:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.6978, loss_cls: 3.1378, loss: 3.1378 +2024-07-26 13:11:21,507 - pyskl - INFO - Epoch [119][3400/3746] lr: 1.023e-02, eta: 1 day, 2:35:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4477, top5_acc: 0.7047, loss_cls: 3.0916, loss: 3.0916 +2024-07-26 13:12:43,115 - pyskl - INFO - Epoch [119][3500/3746] lr: 1.022e-02, eta: 1 day, 2:34:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4439, top5_acc: 0.7022, loss_cls: 3.0836, loss: 3.0836 +2024-07-26 13:14:04,504 - pyskl - INFO - Epoch [119][3600/3746] lr: 1.020e-02, eta: 1 day, 2:33:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.7061, loss_cls: 3.0888, loss: 3.0888 +2024-07-26 13:15:26,852 - pyskl - INFO - Epoch [119][3700/3746] lr: 1.018e-02, eta: 1 day, 2:31:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4494, top5_acc: 0.7081, loss_cls: 3.0593, loss: 3.0593 +2024-07-26 13:16:06,528 - pyskl - INFO - Saving checkpoint at 119 epochs +2024-07-26 13:18:00,177 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 13:18:00,858 - pyskl - INFO - +top1_acc 0.3834 +top5_acc 0.6403 +2024-07-26 13:18:00,858 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 13:18:00,903 - pyskl - INFO - +mean_acc 0.3832 +2024-07-26 13:18:00,907 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_118.pth was removed +2024-07-26 13:18:01,170 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_119.pth. +2024-07-26 13:18:01,171 - pyskl - INFO - Best top1_acc is 0.3834 at 119 epoch. +2024-07-26 13:18:01,185 - pyskl - INFO - Epoch(val) [119][309] top1_acc: 0.3834, top5_acc: 0.6403, mean_class_accuracy: 0.3832 +2024-07-26 13:21:57,463 - pyskl - INFO - Epoch [120][100/3746] lr: 1.016e-02, eta: 1 day, 2:30:17, time: 2.363, data_time: 1.363, memory: 15990, top1_acc: 0.4739, top5_acc: 0.7292, loss_cls: 2.9280, loss: 2.9280 +2024-07-26 13:23:20,694 - pyskl - INFO - Epoch [120][200/3746] lr: 1.014e-02, eta: 1 day, 2:28:55, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4708, top5_acc: 0.7297, loss_cls: 2.9343, loss: 2.9343 +2024-07-26 13:24:44,052 - pyskl - INFO - Epoch [120][300/3746] lr: 1.012e-02, eta: 1 day, 2:27:33, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4677, top5_acc: 0.7284, loss_cls: 2.9474, loss: 2.9474 +2024-07-26 13:26:06,800 - pyskl - INFO - Epoch [120][400/3746] lr: 1.011e-02, eta: 1 day, 2:26:11, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4547, top5_acc: 0.7159, loss_cls: 3.0246, loss: 3.0246 +2024-07-26 13:27:30,348 - pyskl - INFO - Epoch [120][500/3746] lr: 1.009e-02, eta: 1 day, 2:24:49, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4541, top5_acc: 0.7128, loss_cls: 3.0138, loss: 3.0138 +2024-07-26 13:28:53,227 - pyskl - INFO - Epoch [120][600/3746] lr: 1.007e-02, eta: 1 day, 2:23:27, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4627, top5_acc: 0.7191, loss_cls: 2.9865, loss: 2.9865 +2024-07-26 13:30:15,462 - pyskl - INFO - Epoch [120][700/3746] lr: 1.006e-02, eta: 1 day, 2:22:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4602, top5_acc: 0.7137, loss_cls: 3.0111, loss: 3.0111 +2024-07-26 13:31:37,618 - pyskl - INFO - Epoch [120][800/3746] lr: 1.004e-02, eta: 1 day, 2:20:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4620, top5_acc: 0.7241, loss_cls: 2.9987, loss: 2.9987 +2024-07-26 13:33:00,203 - pyskl - INFO - Epoch [120][900/3746] lr: 1.002e-02, eta: 1 day, 2:19:21, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4683, top5_acc: 0.7159, loss_cls: 2.9989, loss: 2.9989 +2024-07-26 13:34:22,721 - pyskl - INFO - Epoch [120][1000/3746] lr: 1.001e-02, eta: 1 day, 2:17:59, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4567, top5_acc: 0.7175, loss_cls: 3.0056, loss: 3.0056 +2024-07-26 13:35:45,173 - pyskl - INFO - Epoch [120][1100/3746] lr: 9.989e-03, eta: 1 day, 2:16:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4428, top5_acc: 0.7081, loss_cls: 3.0848, loss: 3.0848 +2024-07-26 13:37:08,340 - pyskl - INFO - Epoch [120][1200/3746] lr: 9.972e-03, eta: 1 day, 2:15:14, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4491, top5_acc: 0.7087, loss_cls: 3.0657, loss: 3.0657 +2024-07-26 13:38:31,352 - pyskl - INFO - Epoch [120][1300/3746] lr: 9.955e-03, eta: 1 day, 2:13:52, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4470, top5_acc: 0.7083, loss_cls: 3.0733, loss: 3.0733 +2024-07-26 13:39:54,820 - pyskl - INFO - Epoch [120][1400/3746] lr: 9.938e-03, eta: 1 day, 2:12:30, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4394, top5_acc: 0.6977, loss_cls: 3.1068, loss: 3.1068 +2024-07-26 13:41:17,982 - pyskl - INFO - Epoch [120][1500/3746] lr: 9.922e-03, eta: 1 day, 2:11:08, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4553, top5_acc: 0.7102, loss_cls: 3.0246, loss: 3.0246 +2024-07-26 13:42:41,447 - pyskl - INFO - Epoch [120][1600/3746] lr: 9.905e-03, eta: 1 day, 2:09:46, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4492, top5_acc: 0.7089, loss_cls: 3.0427, loss: 3.0427 +2024-07-26 13:44:04,177 - pyskl - INFO - Epoch [120][1700/3746] lr: 9.888e-03, eta: 1 day, 2:08:24, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4580, top5_acc: 0.7139, loss_cls: 3.0191, loss: 3.0191 +2024-07-26 13:45:26,326 - pyskl - INFO - Epoch [120][1800/3746] lr: 9.871e-03, eta: 1 day, 2:07:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.7056, loss_cls: 3.0899, loss: 3.0899 +2024-07-26 13:46:48,013 - pyskl - INFO - Epoch [120][1900/3746] lr: 9.855e-03, eta: 1 day, 2:05:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4628, top5_acc: 0.7134, loss_cls: 3.0188, loss: 3.0188 +2024-07-26 13:48:10,171 - pyskl - INFO - Epoch [120][2000/3746] lr: 9.838e-03, eta: 1 day, 2:04:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4606, top5_acc: 0.7155, loss_cls: 3.0230, loss: 3.0230 +2024-07-26 13:49:33,120 - pyskl - INFO - Epoch [120][2100/3746] lr: 9.821e-03, eta: 1 day, 2:02:55, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4533, top5_acc: 0.7128, loss_cls: 3.0573, loss: 3.0573 +2024-07-26 13:50:54,671 - pyskl - INFO - Epoch [120][2200/3746] lr: 9.805e-03, eta: 1 day, 2:01:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4509, top5_acc: 0.7144, loss_cls: 3.0372, loss: 3.0372 +2024-07-26 13:52:16,545 - pyskl - INFO - Epoch [120][2300/3746] lr: 9.788e-03, eta: 1 day, 2:00:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4489, top5_acc: 0.7142, loss_cls: 3.0323, loss: 3.0323 +2024-07-26 13:53:40,047 - pyskl - INFO - Epoch [120][2400/3746] lr: 9.772e-03, eta: 1 day, 1:58:49, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4505, top5_acc: 0.7098, loss_cls: 3.0656, loss: 3.0656 +2024-07-26 13:55:03,628 - pyskl - INFO - Epoch [120][2500/3746] lr: 9.755e-03, eta: 1 day, 1:57:27, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4519, top5_acc: 0.7098, loss_cls: 3.0903, loss: 3.0903 +2024-07-26 13:56:26,186 - pyskl - INFO - Epoch [120][2600/3746] lr: 9.738e-03, eta: 1 day, 1:56:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4548, top5_acc: 0.7150, loss_cls: 3.0193, loss: 3.0193 +2024-07-26 13:57:48,146 - pyskl - INFO - Epoch [120][2700/3746] lr: 9.722e-03, eta: 1 day, 1:54:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4511, top5_acc: 0.7130, loss_cls: 3.0522, loss: 3.0522 +2024-07-26 13:59:09,898 - pyskl - INFO - Epoch [120][2800/3746] lr: 9.705e-03, eta: 1 day, 1:53:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.6958, loss_cls: 3.1115, loss: 3.1115 +2024-07-26 14:00:31,905 - pyskl - INFO - Epoch [120][2900/3746] lr: 9.689e-03, eta: 1 day, 1:51:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4502, top5_acc: 0.7047, loss_cls: 3.0625, loss: 3.0625 +2024-07-26 14:01:54,025 - pyskl - INFO - Epoch [120][3000/3746] lr: 9.672e-03, eta: 1 day, 1:50:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4398, top5_acc: 0.7034, loss_cls: 3.1112, loss: 3.1112 +2024-07-26 14:03:16,710 - pyskl - INFO - Epoch [120][3100/3746] lr: 9.656e-03, eta: 1 day, 1:49:13, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7216, loss_cls: 3.0113, loss: 3.0113 +2024-07-26 14:04:39,414 - pyskl - INFO - Epoch [120][3200/3746] lr: 9.639e-03, eta: 1 day, 1:47:51, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4512, top5_acc: 0.7116, loss_cls: 3.0600, loss: 3.0600 +2024-07-26 14:06:01,591 - pyskl - INFO - Epoch [120][3300/3746] lr: 9.623e-03, eta: 1 day, 1:46:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4491, top5_acc: 0.7087, loss_cls: 3.0487, loss: 3.0487 +2024-07-26 14:07:24,260 - pyskl - INFO - Epoch [120][3400/3746] lr: 9.606e-03, eta: 1 day, 1:45:07, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.7045, loss_cls: 3.0768, loss: 3.0768 +2024-07-26 14:08:46,089 - pyskl - INFO - Epoch [120][3500/3746] lr: 9.590e-03, eta: 1 day, 1:43:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4502, top5_acc: 0.7109, loss_cls: 3.0548, loss: 3.0548 +2024-07-26 14:10:07,596 - pyskl - INFO - Epoch [120][3600/3746] lr: 9.573e-03, eta: 1 day, 1:42:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4541, top5_acc: 0.7116, loss_cls: 3.0221, loss: 3.0221 +2024-07-26 14:11:30,327 - pyskl - INFO - Epoch [120][3700/3746] lr: 9.557e-03, eta: 1 day, 1:41:00, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4537, top5_acc: 0.7108, loss_cls: 3.0340, loss: 3.0340 +2024-07-26 14:12:09,524 - pyskl - INFO - Saving checkpoint at 120 epochs +2024-07-26 14:14:01,919 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 14:14:02,603 - pyskl - INFO - +top1_acc 0.3724 +top5_acc 0.6291 +2024-07-26 14:14:02,603 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 14:14:02,642 - pyskl - INFO - +mean_acc 0.3721 +2024-07-26 14:14:02,653 - pyskl - INFO - Epoch(val) [120][309] top1_acc: 0.3724, top5_acc: 0.6291, mean_class_accuracy: 0.3721 +2024-07-26 14:17:54,272 - pyskl - INFO - Epoch [121][100/3746] lr: 9.533e-03, eta: 1 day, 1:39:28, time: 2.316, data_time: 1.319, memory: 15990, top1_acc: 0.4648, top5_acc: 0.7188, loss_cls: 2.9863, loss: 2.9863 +2024-07-26 14:19:17,222 - pyskl - INFO - Epoch [121][200/3746] lr: 9.516e-03, eta: 1 day, 1:38:06, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4606, top5_acc: 0.7216, loss_cls: 2.9665, loss: 2.9665 +2024-07-26 14:20:39,962 - pyskl - INFO - Epoch [121][300/3746] lr: 9.500e-03, eta: 1 day, 1:36:43, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4647, top5_acc: 0.7211, loss_cls: 2.9650, loss: 2.9650 +2024-07-26 14:22:02,687 - pyskl - INFO - Epoch [121][400/3746] lr: 9.484e-03, eta: 1 day, 1:35:21, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4669, top5_acc: 0.7178, loss_cls: 2.9786, loss: 2.9786 +2024-07-26 14:23:24,556 - pyskl - INFO - Epoch [121][500/3746] lr: 9.467e-03, eta: 1 day, 1:33:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4625, top5_acc: 0.7208, loss_cls: 2.9678, loss: 2.9678 +2024-07-26 14:24:46,266 - pyskl - INFO - Epoch [121][600/3746] lr: 9.451e-03, eta: 1 day, 1:32:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4573, top5_acc: 0.7238, loss_cls: 2.9887, loss: 2.9887 +2024-07-26 14:26:08,131 - pyskl - INFO - Epoch [121][700/3746] lr: 9.435e-03, eta: 1 day, 1:31:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4633, top5_acc: 0.7223, loss_cls: 2.9750, loss: 2.9750 +2024-07-26 14:27:29,495 - pyskl - INFO - Epoch [121][800/3746] lr: 9.418e-03, eta: 1 day, 1:29:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4633, top5_acc: 0.7209, loss_cls: 2.9845, loss: 2.9845 +2024-07-26 14:28:51,697 - pyskl - INFO - Epoch [121][900/3746] lr: 9.402e-03, eta: 1 day, 1:28:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4594, top5_acc: 0.7180, loss_cls: 2.9947, loss: 2.9947 +2024-07-26 14:30:13,429 - pyskl - INFO - Epoch [121][1000/3746] lr: 9.386e-03, eta: 1 day, 1:27:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4586, top5_acc: 0.7159, loss_cls: 3.0148, loss: 3.0148 +2024-07-26 14:31:35,368 - pyskl - INFO - Epoch [121][1100/3746] lr: 9.369e-03, eta: 1 day, 1:25:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4634, top5_acc: 0.7323, loss_cls: 2.9268, loss: 2.9268 +2024-07-26 14:32:57,294 - pyskl - INFO - Epoch [121][1200/3746] lr: 9.353e-03, eta: 1 day, 1:24:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7159, loss_cls: 3.0250, loss: 3.0250 +2024-07-26 14:34:19,348 - pyskl - INFO - Epoch [121][1300/3746] lr: 9.337e-03, eta: 1 day, 1:23:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4502, top5_acc: 0.7081, loss_cls: 3.0441, loss: 3.0441 +2024-07-26 14:35:41,617 - pyskl - INFO - Epoch [121][1400/3746] lr: 9.321e-03, eta: 1 day, 1:21:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4542, top5_acc: 0.7134, loss_cls: 3.0406, loss: 3.0406 +2024-07-26 14:37:03,183 - pyskl - INFO - Epoch [121][1500/3746] lr: 9.304e-03, eta: 1 day, 1:20:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4587, top5_acc: 0.7247, loss_cls: 2.9952, loss: 2.9952 +2024-07-26 14:38:24,501 - pyskl - INFO - Epoch [121][1600/3746] lr: 9.288e-03, eta: 1 day, 1:18:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4481, top5_acc: 0.7214, loss_cls: 3.0164, loss: 3.0164 +2024-07-26 14:39:46,065 - pyskl - INFO - Epoch [121][1700/3746] lr: 9.272e-03, eta: 1 day, 1:17:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4508, top5_acc: 0.7225, loss_cls: 3.0187, loss: 3.0187 +2024-07-26 14:41:07,961 - pyskl - INFO - Epoch [121][1800/3746] lr: 9.256e-03, eta: 1 day, 1:16:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4545, top5_acc: 0.7159, loss_cls: 3.0289, loss: 3.0289 +2024-07-26 14:42:29,733 - pyskl - INFO - Epoch [121][1900/3746] lr: 9.239e-03, eta: 1 day, 1:14:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7219, loss_cls: 3.0050, loss: 3.0050 +2024-07-26 14:43:51,960 - pyskl - INFO - Epoch [121][2000/3746] lr: 9.223e-03, eta: 1 day, 1:13:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4587, top5_acc: 0.7194, loss_cls: 3.0205, loss: 3.0205 +2024-07-26 14:45:13,225 - pyskl - INFO - Epoch [121][2100/3746] lr: 9.207e-03, eta: 1 day, 1:12:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4505, top5_acc: 0.7075, loss_cls: 3.0502, loss: 3.0502 +2024-07-26 14:46:35,164 - pyskl - INFO - Epoch [121][2200/3746] lr: 9.191e-03, eta: 1 day, 1:10:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4608, top5_acc: 0.7225, loss_cls: 2.9938, loss: 2.9938 +2024-07-26 14:47:56,999 - pyskl - INFO - Epoch [121][2300/3746] lr: 9.175e-03, eta: 1 day, 1:09:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4670, top5_acc: 0.7209, loss_cls: 2.9928, loss: 2.9928 +2024-07-26 14:49:18,942 - pyskl - INFO - Epoch [121][2400/3746] lr: 9.159e-03, eta: 1 day, 1:07:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4514, top5_acc: 0.7167, loss_cls: 3.0464, loss: 3.0464 +2024-07-26 14:50:40,460 - pyskl - INFO - Epoch [121][2500/3746] lr: 9.142e-03, eta: 1 day, 1:06:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4603, top5_acc: 0.7175, loss_cls: 3.0329, loss: 3.0329 +2024-07-26 14:52:01,759 - pyskl - INFO - Epoch [121][2600/3746] lr: 9.126e-03, eta: 1 day, 1:05:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4552, top5_acc: 0.7163, loss_cls: 3.0029, loss: 3.0029 +2024-07-26 14:53:23,708 - pyskl - INFO - Epoch [121][2700/3746] lr: 9.110e-03, eta: 1 day, 1:03:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4595, top5_acc: 0.7189, loss_cls: 3.0113, loss: 3.0113 +2024-07-26 14:54:45,164 - pyskl - INFO - Epoch [121][2800/3746] lr: 9.094e-03, eta: 1 day, 1:02:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4623, top5_acc: 0.7267, loss_cls: 2.9783, loss: 2.9783 +2024-07-26 14:56:06,755 - pyskl - INFO - Epoch [121][2900/3746] lr: 9.078e-03, eta: 1 day, 1:01:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4608, top5_acc: 0.7147, loss_cls: 3.0060, loss: 3.0060 +2024-07-26 14:57:28,148 - pyskl - INFO - Epoch [121][3000/3746] lr: 9.062e-03, eta: 1 day, 0:59:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4500, top5_acc: 0.7202, loss_cls: 3.0327, loss: 3.0327 +2024-07-26 14:58:49,260 - pyskl - INFO - Epoch [121][3100/3746] lr: 9.046e-03, eta: 1 day, 0:58:17, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4616, top5_acc: 0.7169, loss_cls: 2.9947, loss: 2.9947 +2024-07-26 15:00:10,552 - pyskl - INFO - Epoch [121][3200/3746] lr: 9.030e-03, eta: 1 day, 0:56:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4495, top5_acc: 0.7113, loss_cls: 3.0283, loss: 3.0283 +2024-07-26 15:01:32,296 - pyskl - INFO - Epoch [121][3300/3746] lr: 9.014e-03, eta: 1 day, 0:55:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4661, top5_acc: 0.7145, loss_cls: 2.9940, loss: 2.9940 +2024-07-26 15:02:54,063 - pyskl - INFO - Epoch [121][3400/3746] lr: 8.998e-03, eta: 1 day, 0:54:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4544, top5_acc: 0.7142, loss_cls: 3.0244, loss: 3.0244 +2024-07-26 15:04:16,073 - pyskl - INFO - Epoch [121][3500/3746] lr: 8.982e-03, eta: 1 day, 0:52:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4548, top5_acc: 0.7164, loss_cls: 3.0315, loss: 3.0315 +2024-07-26 15:05:37,988 - pyskl - INFO - Epoch [121][3600/3746] lr: 8.966e-03, eta: 1 day, 0:51:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4555, top5_acc: 0.7158, loss_cls: 3.0149, loss: 3.0149 +2024-07-26 15:06:59,380 - pyskl - INFO - Epoch [121][3700/3746] lr: 8.950e-03, eta: 1 day, 0:50:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7206, loss_cls: 2.9974, loss: 2.9974 +2024-07-26 15:07:38,934 - pyskl - INFO - Saving checkpoint at 121 epochs +2024-07-26 15:09:33,386 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 15:09:34,108 - pyskl - INFO - +top1_acc 0.3849 +top5_acc 0.6505 +2024-07-26 15:09:34,108 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 15:09:34,155 - pyskl - INFO - +mean_acc 0.3847 +2024-07-26 15:09:34,160 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_119.pth was removed +2024-07-26 15:09:34,443 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_121.pth. +2024-07-26 15:09:34,444 - pyskl - INFO - Best top1_acc is 0.3849 at 121 epoch. +2024-07-26 15:09:34,458 - pyskl - INFO - Epoch(val) [121][309] top1_acc: 0.3849, top5_acc: 0.6505, mean_class_accuracy: 0.3847 +2024-07-26 15:13:28,556 - pyskl - INFO - Epoch [122][100/3746] lr: 8.927e-03, eta: 1 day, 0:48:29, time: 2.341, data_time: 1.342, memory: 15990, top1_acc: 0.4669, top5_acc: 0.7225, loss_cls: 2.9599, loss: 2.9599 +2024-07-26 15:14:52,073 - pyskl - INFO - Epoch [122][200/3746] lr: 8.911e-03, eta: 1 day, 0:47:07, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4587, top5_acc: 0.7202, loss_cls: 2.9757, loss: 2.9757 +2024-07-26 15:16:15,894 - pyskl - INFO - Epoch [122][300/3746] lr: 8.895e-03, eta: 1 day, 0:45:45, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4641, top5_acc: 0.7258, loss_cls: 2.9638, loss: 2.9638 +2024-07-26 15:17:38,459 - pyskl - INFO - Epoch [122][400/3746] lr: 8.879e-03, eta: 1 day, 0:44:23, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4692, top5_acc: 0.7252, loss_cls: 2.9701, loss: 2.9701 +2024-07-26 15:19:01,286 - pyskl - INFO - Epoch [122][500/3746] lr: 8.863e-03, eta: 1 day, 0:43:01, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4677, top5_acc: 0.7239, loss_cls: 2.9356, loss: 2.9356 +2024-07-26 15:20:24,043 - pyskl - INFO - Epoch [122][600/3746] lr: 8.847e-03, eta: 1 day, 0:41:39, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4647, top5_acc: 0.7227, loss_cls: 2.9625, loss: 2.9625 +2024-07-26 15:21:46,231 - pyskl - INFO - Epoch [122][700/3746] lr: 8.831e-03, eta: 1 day, 0:40:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4728, top5_acc: 0.7334, loss_cls: 2.9101, loss: 2.9101 +2024-07-26 15:23:09,233 - pyskl - INFO - Epoch [122][800/3746] lr: 8.815e-03, eta: 1 day, 0:38:54, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7273, loss_cls: 2.9753, loss: 2.9753 +2024-07-26 15:24:31,249 - pyskl - INFO - Epoch [122][900/3746] lr: 8.800e-03, eta: 1 day, 0:37:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4670, top5_acc: 0.7302, loss_cls: 2.9417, loss: 2.9417 +2024-07-26 15:25:54,043 - pyskl - INFO - Epoch [122][1000/3746] lr: 8.784e-03, eta: 1 day, 0:36:10, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4650, top5_acc: 0.7158, loss_cls: 3.0044, loss: 3.0044 +2024-07-26 15:27:16,523 - pyskl - INFO - Epoch [122][1100/3746] lr: 8.768e-03, eta: 1 day, 0:34:48, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4763, top5_acc: 0.7294, loss_cls: 2.9310, loss: 2.9310 +2024-07-26 15:28:39,980 - pyskl - INFO - Epoch [122][1200/3746] lr: 8.752e-03, eta: 1 day, 0:33:26, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4580, top5_acc: 0.7256, loss_cls: 2.9769, loss: 2.9769 +2024-07-26 15:30:02,587 - pyskl - INFO - Epoch [122][1300/3746] lr: 8.736e-03, eta: 1 day, 0:32:03, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4598, top5_acc: 0.7195, loss_cls: 2.9912, loss: 2.9912 +2024-07-26 15:31:25,357 - pyskl - INFO - Epoch [122][1400/3746] lr: 8.721e-03, eta: 1 day, 0:30:41, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4756, top5_acc: 0.7280, loss_cls: 2.9326, loss: 2.9326 +2024-07-26 15:32:48,687 - pyskl - INFO - Epoch [122][1500/3746] lr: 8.705e-03, eta: 1 day, 0:29:19, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4694, top5_acc: 0.7302, loss_cls: 2.9699, loss: 2.9699 +2024-07-26 15:34:10,788 - pyskl - INFO - Epoch [122][1600/3746] lr: 8.689e-03, eta: 1 day, 0:27:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4598, top5_acc: 0.7236, loss_cls: 2.9569, loss: 2.9569 +2024-07-26 15:35:33,508 - pyskl - INFO - Epoch [122][1700/3746] lr: 8.673e-03, eta: 1 day, 0:26:35, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4612, top5_acc: 0.7244, loss_cls: 2.9664, loss: 2.9664 +2024-07-26 15:36:55,603 - pyskl - INFO - Epoch [122][1800/3746] lr: 8.658e-03, eta: 1 day, 0:25:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7217, loss_cls: 2.9432, loss: 2.9432 +2024-07-26 15:38:17,844 - pyskl - INFO - Epoch [122][1900/3746] lr: 8.642e-03, eta: 1 day, 0:23:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4661, top5_acc: 0.7144, loss_cls: 2.9793, loss: 2.9793 +2024-07-26 15:39:40,685 - pyskl - INFO - Epoch [122][2000/3746] lr: 8.626e-03, eta: 1 day, 0:22:28, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4616, top5_acc: 0.7208, loss_cls: 2.9931, loss: 2.9931 +2024-07-26 15:41:03,450 - pyskl - INFO - Epoch [122][2100/3746] lr: 8.610e-03, eta: 1 day, 0:21:06, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4653, top5_acc: 0.7231, loss_cls: 2.9701, loss: 2.9701 +2024-07-26 15:42:25,023 - pyskl - INFO - Epoch [122][2200/3746] lr: 8.595e-03, eta: 1 day, 0:19:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4686, top5_acc: 0.7217, loss_cls: 2.9582, loss: 2.9582 +2024-07-26 15:43:47,454 - pyskl - INFO - Epoch [122][2300/3746] lr: 8.579e-03, eta: 1 day, 0:18:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4577, top5_acc: 0.7145, loss_cls: 3.0285, loss: 3.0285 +2024-07-26 15:45:10,432 - pyskl - INFO - Epoch [122][2400/3746] lr: 8.563e-03, eta: 1 day, 0:16:59, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4539, top5_acc: 0.7131, loss_cls: 3.0193, loss: 3.0193 +2024-07-26 15:46:33,083 - pyskl - INFO - Epoch [122][2500/3746] lr: 8.548e-03, eta: 1 day, 0:15:37, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4558, top5_acc: 0.7202, loss_cls: 3.0025, loss: 3.0025 +2024-07-26 15:47:55,168 - pyskl - INFO - Epoch [122][2600/3746] lr: 8.532e-03, eta: 1 day, 0:14:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4570, top5_acc: 0.7156, loss_cls: 3.0029, loss: 3.0029 +2024-07-26 15:49:16,942 - pyskl - INFO - Epoch [122][2700/3746] lr: 8.517e-03, eta: 1 day, 0:12:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4675, top5_acc: 0.7200, loss_cls: 2.9605, loss: 2.9605 +2024-07-26 15:50:38,966 - pyskl - INFO - Epoch [122][2800/3746] lr: 8.501e-03, eta: 1 day, 0:11:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4631, top5_acc: 0.7200, loss_cls: 2.9815, loss: 2.9815 +2024-07-26 15:52:00,984 - pyskl - INFO - Epoch [122][2900/3746] lr: 8.485e-03, eta: 1 day, 0:10:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4645, top5_acc: 0.7155, loss_cls: 3.0115, loss: 3.0115 +2024-07-26 15:53:22,632 - pyskl - INFO - Epoch [122][3000/3746] lr: 8.470e-03, eta: 1 day, 0:08:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4580, top5_acc: 0.7206, loss_cls: 3.0180, loss: 3.0180 +2024-07-26 15:54:44,428 - pyskl - INFO - Epoch [122][3100/3746] lr: 8.454e-03, eta: 1 day, 0:07:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4661, top5_acc: 0.7267, loss_cls: 2.9876, loss: 2.9876 +2024-07-26 15:56:06,180 - pyskl - INFO - Epoch [122][3200/3746] lr: 8.439e-03, eta: 1 day, 0:06:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4558, top5_acc: 0.7184, loss_cls: 3.0270, loss: 3.0270 +2024-07-26 15:57:27,991 - pyskl - INFO - Epoch [122][3300/3746] lr: 8.423e-03, eta: 1 day, 0:04:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4545, top5_acc: 0.7142, loss_cls: 3.0167, loss: 3.0167 +2024-07-26 15:58:49,712 - pyskl - INFO - Epoch [122][3400/3746] lr: 8.408e-03, eta: 1 day, 0:03:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4622, top5_acc: 0.7253, loss_cls: 2.9685, loss: 2.9685 +2024-07-26 16:00:11,915 - pyskl - INFO - Epoch [122][3500/3746] lr: 8.392e-03, eta: 1 day, 0:01:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4523, top5_acc: 0.7163, loss_cls: 3.0193, loss: 3.0193 +2024-07-26 16:01:35,076 - pyskl - INFO - Epoch [122][3600/3746] lr: 8.377e-03, eta: 1 day, 0:00:31, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4681, top5_acc: 0.7231, loss_cls: 2.9474, loss: 2.9474 +2024-07-26 16:02:57,161 - pyskl - INFO - Epoch [122][3700/3746] lr: 8.361e-03, eta: 23:59:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4656, top5_acc: 0.7163, loss_cls: 3.0097, loss: 3.0097 +2024-07-26 16:03:36,778 - pyskl - INFO - Saving checkpoint at 122 epochs +2024-07-26 16:05:29,320 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 16:05:29,984 - pyskl - INFO - +top1_acc 0.3928 +top5_acc 0.6480 +2024-07-26 16:05:29,984 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 16:05:30,025 - pyskl - INFO - +mean_acc 0.3926 +2024-07-26 16:05:30,030 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_121.pth was removed +2024-07-26 16:05:30,297 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_122.pth. +2024-07-26 16:05:30,298 - pyskl - INFO - Best top1_acc is 0.3928 at 122 epoch. +2024-07-26 16:05:30,309 - pyskl - INFO - Epoch(val) [122][309] top1_acc: 0.3928, top5_acc: 0.6480, mean_class_accuracy: 0.3926 +2024-07-26 16:09:17,280 - pyskl - INFO - Epoch [123][100/3746] lr: 8.339e-03, eta: 23:57:33, time: 2.270, data_time: 1.283, memory: 15990, top1_acc: 0.4773, top5_acc: 0.7273, loss_cls: 2.9030, loss: 2.9030 +2024-07-26 16:10:40,120 - pyskl - INFO - Epoch [123][200/3746] lr: 8.323e-03, eta: 23:56:11, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4747, top5_acc: 0.7356, loss_cls: 2.8945, loss: 2.8945 +2024-07-26 16:12:02,314 - pyskl - INFO - Epoch [123][300/3746] lr: 8.308e-03, eta: 23:54:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4705, top5_acc: 0.7334, loss_cls: 2.9274, loss: 2.9274 +2024-07-26 16:13:24,034 - pyskl - INFO - Epoch [123][400/3746] lr: 8.292e-03, eta: 23:53:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4792, top5_acc: 0.7430, loss_cls: 2.8797, loss: 2.8797 +2024-07-26 16:14:44,909 - pyskl - INFO - Epoch [123][500/3746] lr: 8.277e-03, eta: 23:52:03, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4673, top5_acc: 0.7308, loss_cls: 2.9331, loss: 2.9331 +2024-07-26 16:16:06,832 - pyskl - INFO - Epoch [123][600/3746] lr: 8.262e-03, eta: 23:50:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4641, top5_acc: 0.7331, loss_cls: 2.9132, loss: 2.9132 +2024-07-26 16:17:28,940 - pyskl - INFO - Epoch [123][700/3746] lr: 8.246e-03, eta: 23:49:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7322, loss_cls: 2.8980, loss: 2.8980 +2024-07-26 16:18:50,429 - pyskl - INFO - Epoch [123][800/3746] lr: 8.231e-03, eta: 23:47:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4780, top5_acc: 0.7312, loss_cls: 2.9279, loss: 2.9279 +2024-07-26 16:20:12,466 - pyskl - INFO - Epoch [123][900/3746] lr: 8.215e-03, eta: 23:46:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4741, top5_acc: 0.7334, loss_cls: 2.9021, loss: 2.9021 +2024-07-26 16:21:33,934 - pyskl - INFO - Epoch [123][1000/3746] lr: 8.200e-03, eta: 23:45:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4695, top5_acc: 0.7333, loss_cls: 2.8984, loss: 2.8984 +2024-07-26 16:22:55,662 - pyskl - INFO - Epoch [123][1100/3746] lr: 8.185e-03, eta: 23:43:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4813, top5_acc: 0.7320, loss_cls: 2.9190, loss: 2.9190 +2024-07-26 16:24:18,377 - pyskl - INFO - Epoch [123][1200/3746] lr: 8.169e-03, eta: 23:42:26, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7319, loss_cls: 2.9237, loss: 2.9237 +2024-07-26 16:25:40,156 - pyskl - INFO - Epoch [123][1300/3746] lr: 8.154e-03, eta: 23:41:04, time: 0.818, data_time: 0.001, memory: 15990, top1_acc: 0.4733, top5_acc: 0.7298, loss_cls: 2.9381, loss: 2.9381 +2024-07-26 16:27:02,656 - pyskl - INFO - Epoch [123][1400/3746] lr: 8.139e-03, eta: 23:39:42, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4736, top5_acc: 0.7278, loss_cls: 2.9343, loss: 2.9343 +2024-07-26 16:28:25,404 - pyskl - INFO - Epoch [123][1500/3746] lr: 8.124e-03, eta: 23:38:19, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4694, top5_acc: 0.7359, loss_cls: 2.9043, loss: 2.9043 +2024-07-26 16:29:47,372 - pyskl - INFO - Epoch [123][1600/3746] lr: 8.108e-03, eta: 23:36:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4758, top5_acc: 0.7275, loss_cls: 2.9241, loss: 2.9241 +2024-07-26 16:31:10,440 - pyskl - INFO - Epoch [123][1700/3746] lr: 8.093e-03, eta: 23:35:35, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4669, top5_acc: 0.7297, loss_cls: 2.9489, loss: 2.9489 +2024-07-26 16:32:32,334 - pyskl - INFO - Epoch [123][1800/3746] lr: 8.078e-03, eta: 23:34:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4669, top5_acc: 0.7197, loss_cls: 2.9664, loss: 2.9664 +2024-07-26 16:33:53,899 - pyskl - INFO - Epoch [123][1900/3746] lr: 8.063e-03, eta: 23:32:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7256, loss_cls: 2.9285, loss: 2.9285 +2024-07-26 16:35:15,992 - pyskl - INFO - Epoch [123][2000/3746] lr: 8.047e-03, eta: 23:31:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4620, top5_acc: 0.7195, loss_cls: 2.9928, loss: 2.9928 +2024-07-26 16:36:38,371 - pyskl - INFO - Epoch [123][2100/3746] lr: 8.032e-03, eta: 23:30:05, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7284, loss_cls: 2.9448, loss: 2.9448 +2024-07-26 16:38:00,042 - pyskl - INFO - Epoch [123][2200/3746] lr: 8.017e-03, eta: 23:28:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4619, top5_acc: 0.7258, loss_cls: 2.9703, loss: 2.9703 +2024-07-26 16:39:22,391 - pyskl - INFO - Epoch [123][2300/3746] lr: 8.002e-03, eta: 23:27:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4620, top5_acc: 0.7200, loss_cls: 2.9795, loss: 2.9795 +2024-07-26 16:40:45,065 - pyskl - INFO - Epoch [123][2400/3746] lr: 7.987e-03, eta: 23:25:58, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4553, top5_acc: 0.7161, loss_cls: 3.0075, loss: 3.0075 +2024-07-26 16:42:06,863 - pyskl - INFO - Epoch [123][2500/3746] lr: 7.971e-03, eta: 23:24:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7144, loss_cls: 3.0134, loss: 3.0134 +2024-07-26 16:43:28,344 - pyskl - INFO - Epoch [123][2600/3746] lr: 7.956e-03, eta: 23:23:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4645, top5_acc: 0.7219, loss_cls: 2.9744, loss: 2.9744 +2024-07-26 16:44:50,233 - pyskl - INFO - Epoch [123][2700/3746] lr: 7.941e-03, eta: 23:21:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4655, top5_acc: 0.7219, loss_cls: 2.9739, loss: 2.9739 +2024-07-26 16:46:11,777 - pyskl - INFO - Epoch [123][2800/3746] lr: 7.926e-03, eta: 23:20:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4669, top5_acc: 0.7236, loss_cls: 2.9485, loss: 2.9485 +2024-07-26 16:47:33,209 - pyskl - INFO - Epoch [123][2900/3746] lr: 7.911e-03, eta: 23:19:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4692, top5_acc: 0.7317, loss_cls: 2.9102, loss: 2.9102 +2024-07-26 16:48:54,920 - pyskl - INFO - Epoch [123][3000/3746] lr: 7.896e-03, eta: 23:17:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4666, top5_acc: 0.7163, loss_cls: 2.9956, loss: 2.9956 +2024-07-26 16:50:16,805 - pyskl - INFO - Epoch [123][3100/3746] lr: 7.881e-03, eta: 23:16:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7159, loss_cls: 3.0053, loss: 3.0053 +2024-07-26 16:51:38,147 - pyskl - INFO - Epoch [123][3200/3746] lr: 7.866e-03, eta: 23:14:59, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4797, top5_acc: 0.7327, loss_cls: 2.8927, loss: 2.8927 +2024-07-26 16:52:59,734 - pyskl - INFO - Epoch [123][3300/3746] lr: 7.851e-03, eta: 23:13:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4573, top5_acc: 0.7161, loss_cls: 2.9990, loss: 2.9990 +2024-07-26 16:54:21,183 - pyskl - INFO - Epoch [123][3400/3746] lr: 7.836e-03, eta: 23:12:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4586, top5_acc: 0.7183, loss_cls: 2.9807, loss: 2.9807 +2024-07-26 16:55:42,430 - pyskl - INFO - Epoch [123][3500/3746] lr: 7.821e-03, eta: 23:10:51, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4591, top5_acc: 0.7180, loss_cls: 3.0199, loss: 3.0199 +2024-07-26 16:57:04,930 - pyskl - INFO - Epoch [123][3600/3746] lr: 7.806e-03, eta: 23:09:29, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4606, top5_acc: 0.7137, loss_cls: 2.9942, loss: 2.9942 +2024-07-26 16:58:26,996 - pyskl - INFO - Epoch [123][3700/3746] lr: 7.791e-03, eta: 23:08:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4681, top5_acc: 0.7148, loss_cls: 3.0165, loss: 3.0165 +2024-07-26 16:59:06,296 - pyskl - INFO - Saving checkpoint at 123 epochs +2024-07-26 17:00:59,071 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 17:00:59,732 - pyskl - INFO - +top1_acc 0.3847 +top5_acc 0.6410 +2024-07-26 17:00:59,732 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 17:00:59,772 - pyskl - INFO - +mean_acc 0.3845 +2024-07-26 17:00:59,783 - pyskl - INFO - Epoch(val) [123][309] top1_acc: 0.3847, top5_acc: 0.6410, mean_class_accuracy: 0.3845 +2024-07-26 17:04:46,043 - pyskl - INFO - Epoch [124][100/3746] lr: 7.769e-03, eta: 23:06:30, time: 2.263, data_time: 1.276, memory: 15990, top1_acc: 0.4798, top5_acc: 0.7419, loss_cls: 2.8576, loss: 2.8576 +2024-07-26 17:06:08,392 - pyskl - INFO - Epoch [124][200/3746] lr: 7.754e-03, eta: 23:05:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4764, top5_acc: 0.7405, loss_cls: 2.8727, loss: 2.8727 +2024-07-26 17:07:30,798 - pyskl - INFO - Epoch [124][300/3746] lr: 7.739e-03, eta: 23:03:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4820, top5_acc: 0.7395, loss_cls: 2.8721, loss: 2.8721 +2024-07-26 17:08:53,705 - pyskl - INFO - Epoch [124][400/3746] lr: 7.724e-03, eta: 23:02:23, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4773, top5_acc: 0.7311, loss_cls: 2.9098, loss: 2.9098 +2024-07-26 17:10:15,900 - pyskl - INFO - Epoch [124][500/3746] lr: 7.709e-03, eta: 23:01:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4823, top5_acc: 0.7348, loss_cls: 2.8967, loss: 2.8967 +2024-07-26 17:11:38,518 - pyskl - INFO - Epoch [124][600/3746] lr: 7.694e-03, eta: 22:59:38, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4761, top5_acc: 0.7411, loss_cls: 2.8825, loss: 2.8825 +2024-07-26 17:13:00,985 - pyskl - INFO - Epoch [124][700/3746] lr: 7.679e-03, eta: 22:58:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4795, top5_acc: 0.7355, loss_cls: 2.9014, loss: 2.9014 +2024-07-26 17:14:23,791 - pyskl - INFO - Epoch [124][800/3746] lr: 7.664e-03, eta: 22:56:54, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4744, top5_acc: 0.7291, loss_cls: 2.9160, loss: 2.9160 +2024-07-26 17:15:46,662 - pyskl - INFO - Epoch [124][900/3746] lr: 7.649e-03, eta: 22:55:31, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4728, top5_acc: 0.7447, loss_cls: 2.8744, loss: 2.8744 +2024-07-26 17:17:09,250 - pyskl - INFO - Epoch [124][1000/3746] lr: 7.635e-03, eta: 22:54:09, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4708, top5_acc: 0.7236, loss_cls: 2.9269, loss: 2.9269 +2024-07-26 17:18:31,545 - pyskl - INFO - Epoch [124][1100/3746] lr: 7.620e-03, eta: 22:52:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4720, top5_acc: 0.7298, loss_cls: 2.9040, loss: 2.9040 +2024-07-26 17:19:55,000 - pyskl - INFO - Epoch [124][1200/3746] lr: 7.605e-03, eta: 22:51:25, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4695, top5_acc: 0.7289, loss_cls: 2.9425, loss: 2.9425 +2024-07-26 17:21:16,591 - pyskl - INFO - Epoch [124][1300/3746] lr: 7.590e-03, eta: 22:50:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4806, top5_acc: 0.7364, loss_cls: 2.8843, loss: 2.8843 +2024-07-26 17:22:39,633 - pyskl - INFO - Epoch [124][1400/3746] lr: 7.575e-03, eta: 22:48:40, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4708, top5_acc: 0.7330, loss_cls: 2.9151, loss: 2.9151 +2024-07-26 17:24:02,329 - pyskl - INFO - Epoch [124][1500/3746] lr: 7.561e-03, eta: 22:47:18, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4669, top5_acc: 0.7217, loss_cls: 2.9729, loss: 2.9729 +2024-07-26 17:25:24,806 - pyskl - INFO - Epoch [124][1600/3746] lr: 7.546e-03, eta: 22:45:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4673, top5_acc: 0.7367, loss_cls: 2.9209, loss: 2.9209 +2024-07-26 17:26:47,796 - pyskl - INFO - Epoch [124][1700/3746] lr: 7.531e-03, eta: 22:44:33, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4705, top5_acc: 0.7284, loss_cls: 2.9395, loss: 2.9395 +2024-07-26 17:28:10,079 - pyskl - INFO - Epoch [124][1800/3746] lr: 7.516e-03, eta: 22:43:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4670, top5_acc: 0.7270, loss_cls: 2.9331, loss: 2.9331 +2024-07-26 17:29:31,904 - pyskl - INFO - Epoch [124][1900/3746] lr: 7.502e-03, eta: 22:41:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4750, top5_acc: 0.7298, loss_cls: 2.9200, loss: 2.9200 +2024-07-26 17:30:55,041 - pyskl - INFO - Epoch [124][2000/3746] lr: 7.487e-03, eta: 22:40:26, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4777, top5_acc: 0.7273, loss_cls: 2.9270, loss: 2.9270 +2024-07-26 17:32:17,081 - pyskl - INFO - Epoch [124][2100/3746] lr: 7.472e-03, eta: 22:39:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4677, top5_acc: 0.7278, loss_cls: 2.9241, loss: 2.9241 +2024-07-26 17:33:39,011 - pyskl - INFO - Epoch [124][2200/3746] lr: 7.457e-03, eta: 22:37:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4683, top5_acc: 0.7247, loss_cls: 2.9338, loss: 2.9338 +2024-07-26 17:35:01,583 - pyskl - INFO - Epoch [124][2300/3746] lr: 7.443e-03, eta: 22:36:19, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4772, top5_acc: 0.7350, loss_cls: 2.9208, loss: 2.9208 +2024-07-26 17:36:24,447 - pyskl - INFO - Epoch [124][2400/3746] lr: 7.428e-03, eta: 22:34:57, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4564, top5_acc: 0.7202, loss_cls: 2.9898, loss: 2.9898 +2024-07-26 17:37:46,624 - pyskl - INFO - Epoch [124][2500/3746] lr: 7.413e-03, eta: 22:33:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4709, top5_acc: 0.7303, loss_cls: 2.9301, loss: 2.9301 +2024-07-26 17:39:08,332 - pyskl - INFO - Epoch [124][2600/3746] lr: 7.399e-03, eta: 22:32:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4781, top5_acc: 0.7273, loss_cls: 2.9378, loss: 2.9378 +2024-07-26 17:40:30,119 - pyskl - INFO - Epoch [124][2700/3746] lr: 7.384e-03, eta: 22:30:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4714, top5_acc: 0.7317, loss_cls: 2.9198, loss: 2.9198 +2024-07-26 17:41:52,369 - pyskl - INFO - Epoch [124][2800/3746] lr: 7.370e-03, eta: 22:29:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7292, loss_cls: 2.8965, loss: 2.8965 +2024-07-26 17:43:13,874 - pyskl - INFO - Epoch [124][2900/3746] lr: 7.355e-03, eta: 22:28:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4673, top5_acc: 0.7258, loss_cls: 2.9611, loss: 2.9611 +2024-07-26 17:44:35,896 - pyskl - INFO - Epoch [124][3000/3746] lr: 7.340e-03, eta: 22:26:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4650, top5_acc: 0.7186, loss_cls: 2.9856, loss: 2.9856 +2024-07-26 17:45:57,340 - pyskl - INFO - Epoch [124][3100/3746] lr: 7.326e-03, eta: 22:25:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4714, top5_acc: 0.7331, loss_cls: 2.9324, loss: 2.9324 +2024-07-26 17:47:19,401 - pyskl - INFO - Epoch [124][3200/3746] lr: 7.311e-03, eta: 22:23:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4766, top5_acc: 0.7338, loss_cls: 2.8963, loss: 2.8963 +2024-07-26 17:48:41,216 - pyskl - INFO - Epoch [124][3300/3746] lr: 7.297e-03, eta: 22:22:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4761, top5_acc: 0.7327, loss_cls: 2.9176, loss: 2.9176 +2024-07-26 17:50:02,992 - pyskl - INFO - Epoch [124][3400/3746] lr: 7.282e-03, eta: 22:21:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4769, top5_acc: 0.7328, loss_cls: 2.8984, loss: 2.8984 +2024-07-26 17:51:24,896 - pyskl - INFO - Epoch [124][3500/3746] lr: 7.268e-03, eta: 22:19:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4794, top5_acc: 0.7294, loss_cls: 2.9087, loss: 2.9087 +2024-07-26 17:52:47,754 - pyskl - INFO - Epoch [124][3600/3746] lr: 7.253e-03, eta: 22:18:28, time: 0.829, data_time: 0.001, memory: 15990, top1_acc: 0.4672, top5_acc: 0.7208, loss_cls: 2.9561, loss: 2.9561 +2024-07-26 17:54:10,196 - pyskl - INFO - Epoch [124][3700/3746] lr: 7.239e-03, eta: 22:17:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4680, top5_acc: 0.7223, loss_cls: 2.9380, loss: 2.9380 +2024-07-26 17:54:49,763 - pyskl - INFO - Saving checkpoint at 124 epochs +2024-07-26 17:56:43,124 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 17:56:43,789 - pyskl - INFO - +top1_acc 0.3990 +top5_acc 0.6564 +2024-07-26 17:56:43,789 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 17:56:43,830 - pyskl - INFO - +mean_acc 0.3988 +2024-07-26 17:56:43,834 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_122.pth was removed +2024-07-26 17:56:44,105 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_124.pth. +2024-07-26 17:56:44,106 - pyskl - INFO - Best top1_acc is 0.3990 at 124 epoch. +2024-07-26 17:56:44,117 - pyskl - INFO - Epoch(val) [124][309] top1_acc: 0.3990, top5_acc: 0.6564, mean_class_accuracy: 0.3988 +2024-07-26 18:00:35,678 - pyskl - INFO - Epoch [125][100/3746] lr: 7.217e-03, eta: 22:15:29, time: 2.316, data_time: 1.323, memory: 15990, top1_acc: 0.4973, top5_acc: 0.7495, loss_cls: 2.8137, loss: 2.8137 +2024-07-26 18:01:58,290 - pyskl - INFO - Epoch [125][200/3746] lr: 7.203e-03, eta: 22:14:06, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4909, top5_acc: 0.7483, loss_cls: 2.7777, loss: 2.7777 +2024-07-26 18:03:20,947 - pyskl - INFO - Epoch [125][300/3746] lr: 7.189e-03, eta: 22:12:44, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4755, top5_acc: 0.7378, loss_cls: 2.8707, loss: 2.8707 +2024-07-26 18:04:43,951 - pyskl - INFO - Epoch [125][400/3746] lr: 7.174e-03, eta: 22:11:22, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4800, top5_acc: 0.7406, loss_cls: 2.8459, loss: 2.8459 +2024-07-26 18:06:06,483 - pyskl - INFO - Epoch [125][500/3746] lr: 7.160e-03, eta: 22:10:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4863, top5_acc: 0.7358, loss_cls: 2.8613, loss: 2.8613 +2024-07-26 18:07:29,335 - pyskl - INFO - Epoch [125][600/3746] lr: 7.145e-03, eta: 22:08:37, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4831, top5_acc: 0.7444, loss_cls: 2.8534, loss: 2.8534 +2024-07-26 18:08:51,671 - pyskl - INFO - Epoch [125][700/3746] lr: 7.131e-03, eta: 22:07:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4816, top5_acc: 0.7442, loss_cls: 2.8440, loss: 2.8440 +2024-07-26 18:10:14,034 - pyskl - INFO - Epoch [125][800/3746] lr: 7.117e-03, eta: 22:05:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4781, top5_acc: 0.7442, loss_cls: 2.8636, loss: 2.8636 +2024-07-26 18:11:35,608 - pyskl - INFO - Epoch [125][900/3746] lr: 7.102e-03, eta: 22:04:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4795, top5_acc: 0.7423, loss_cls: 2.8888, loss: 2.8888 +2024-07-26 18:12:56,976 - pyskl - INFO - Epoch [125][1000/3746] lr: 7.088e-03, eta: 22:03:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4789, top5_acc: 0.7430, loss_cls: 2.8595, loss: 2.8595 +2024-07-26 18:14:19,172 - pyskl - INFO - Epoch [125][1100/3746] lr: 7.073e-03, eta: 22:01:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4917, top5_acc: 0.7467, loss_cls: 2.8208, loss: 2.8208 +2024-07-26 18:15:40,730 - pyskl - INFO - Epoch [125][1200/3746] lr: 7.059e-03, eta: 22:00:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4877, top5_acc: 0.7427, loss_cls: 2.8359, loss: 2.8359 +2024-07-26 18:17:02,728 - pyskl - INFO - Epoch [125][1300/3746] lr: 7.045e-03, eta: 21:59:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4830, top5_acc: 0.7383, loss_cls: 2.8521, loss: 2.8521 +2024-07-26 18:18:25,132 - pyskl - INFO - Epoch [125][1400/3746] lr: 7.031e-03, eta: 21:57:38, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4730, top5_acc: 0.7262, loss_cls: 2.9201, loss: 2.9201 +2024-07-26 18:19:47,395 - pyskl - INFO - Epoch [125][1500/3746] lr: 7.016e-03, eta: 21:56:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4780, top5_acc: 0.7320, loss_cls: 2.9086, loss: 2.9086 +2024-07-26 18:21:09,703 - pyskl - INFO - Epoch [125][1600/3746] lr: 7.002e-03, eta: 21:54:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4698, top5_acc: 0.7364, loss_cls: 2.8762, loss: 2.8762 +2024-07-26 18:22:31,082 - pyskl - INFO - Epoch [125][1700/3746] lr: 6.988e-03, eta: 21:53:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4709, top5_acc: 0.7294, loss_cls: 2.9202, loss: 2.9202 +2024-07-26 18:23:52,782 - pyskl - INFO - Epoch [125][1800/3746] lr: 6.973e-03, eta: 21:52:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4755, top5_acc: 0.7356, loss_cls: 2.8627, loss: 2.8627 +2024-07-26 18:25:14,770 - pyskl - INFO - Epoch [125][1900/3746] lr: 6.959e-03, eta: 21:50:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4811, top5_acc: 0.7391, loss_cls: 2.8906, loss: 2.8906 +2024-07-26 18:26:37,440 - pyskl - INFO - Epoch [125][2000/3746] lr: 6.945e-03, eta: 21:49:23, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4748, top5_acc: 0.7306, loss_cls: 2.9016, loss: 2.9016 +2024-07-26 18:27:59,140 - pyskl - INFO - Epoch [125][2100/3746] lr: 6.931e-03, eta: 21:48:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4813, top5_acc: 0.7280, loss_cls: 2.9104, loss: 2.9104 +2024-07-26 18:29:21,398 - pyskl - INFO - Epoch [125][2200/3746] lr: 6.917e-03, eta: 21:46:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7292, loss_cls: 2.9104, loss: 2.9104 +2024-07-26 18:30:43,350 - pyskl - INFO - Epoch [125][2300/3746] lr: 6.902e-03, eta: 21:45:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4720, top5_acc: 0.7342, loss_cls: 2.9087, loss: 2.9087 +2024-07-26 18:32:04,869 - pyskl - INFO - Epoch [125][2400/3746] lr: 6.888e-03, eta: 21:43:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4769, top5_acc: 0.7173, loss_cls: 2.9416, loss: 2.9416 +2024-07-26 18:33:26,143 - pyskl - INFO - Epoch [125][2500/3746] lr: 6.874e-03, eta: 21:42:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4666, top5_acc: 0.7297, loss_cls: 2.9246, loss: 2.9246 +2024-07-26 18:34:47,895 - pyskl - INFO - Epoch [125][2600/3746] lr: 6.860e-03, eta: 21:41:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4859, top5_acc: 0.7392, loss_cls: 2.8875, loss: 2.8875 +2024-07-26 18:36:10,210 - pyskl - INFO - Epoch [125][2700/3746] lr: 6.846e-03, eta: 21:39:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7325, loss_cls: 2.8881, loss: 2.8881 +2024-07-26 18:37:32,020 - pyskl - INFO - Epoch [125][2800/3746] lr: 6.832e-03, eta: 21:38:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4705, top5_acc: 0.7303, loss_cls: 2.9337, loss: 2.9337 +2024-07-26 18:38:53,588 - pyskl - INFO - Epoch [125][2900/3746] lr: 6.818e-03, eta: 21:37:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4716, top5_acc: 0.7255, loss_cls: 2.9506, loss: 2.9506 +2024-07-26 18:40:14,973 - pyskl - INFO - Epoch [125][3000/3746] lr: 6.804e-03, eta: 21:35:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4833, top5_acc: 0.7341, loss_cls: 2.8938, loss: 2.8938 +2024-07-26 18:41:36,509 - pyskl - INFO - Epoch [125][3100/3746] lr: 6.789e-03, eta: 21:34:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4714, top5_acc: 0.7334, loss_cls: 2.9134, loss: 2.9134 +2024-07-26 18:42:58,344 - pyskl - INFO - Epoch [125][3200/3746] lr: 6.775e-03, eta: 21:32:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4742, top5_acc: 0.7345, loss_cls: 2.8925, loss: 2.8925 +2024-07-26 18:44:19,880 - pyskl - INFO - Epoch [125][3300/3746] lr: 6.761e-03, eta: 21:31:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4741, top5_acc: 0.7311, loss_cls: 2.8928, loss: 2.8928 +2024-07-26 18:45:41,465 - pyskl - INFO - Epoch [125][3400/3746] lr: 6.747e-03, eta: 21:30:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4830, top5_acc: 0.7302, loss_cls: 2.9002, loss: 2.9002 +2024-07-26 18:47:03,293 - pyskl - INFO - Epoch [125][3500/3746] lr: 6.733e-03, eta: 21:28:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4752, top5_acc: 0.7392, loss_cls: 2.8891, loss: 2.8891 +2024-07-26 18:48:25,866 - pyskl - INFO - Epoch [125][3600/3746] lr: 6.719e-03, eta: 21:27:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4716, top5_acc: 0.7369, loss_cls: 2.9189, loss: 2.9189 +2024-07-26 18:49:47,716 - pyskl - INFO - Epoch [125][3700/3746] lr: 6.705e-03, eta: 21:26:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4753, top5_acc: 0.7309, loss_cls: 2.8944, loss: 2.8944 +2024-07-26 18:50:27,224 - pyskl - INFO - Saving checkpoint at 125 epochs +2024-07-26 18:52:21,094 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 18:52:21,778 - pyskl - INFO - +top1_acc 0.3941 +top5_acc 0.6443 +2024-07-26 18:52:21,779 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 18:52:21,823 - pyskl - INFO - +mean_acc 0.3939 +2024-07-26 18:52:21,835 - pyskl - INFO - Epoch(val) [125][309] top1_acc: 0.3941, top5_acc: 0.6443, mean_class_accuracy: 0.3939 +2024-07-26 18:56:19,952 - pyskl - INFO - Epoch [126][100/3746] lr: 6.685e-03, eta: 21:24:25, time: 2.381, data_time: 1.391, memory: 15990, top1_acc: 0.5050, top5_acc: 0.7569, loss_cls: 2.7632, loss: 2.7632 +2024-07-26 18:57:43,435 - pyskl - INFO - Epoch [126][200/3746] lr: 6.671e-03, eta: 21:23:03, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4933, top5_acc: 0.7512, loss_cls: 2.8000, loss: 2.8000 +2024-07-26 18:59:06,070 - pyskl - INFO - Epoch [126][300/3746] lr: 6.657e-03, eta: 21:21:40, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4923, top5_acc: 0.7439, loss_cls: 2.8129, loss: 2.8129 +2024-07-26 19:00:28,511 - pyskl - INFO - Epoch [126][400/3746] lr: 6.643e-03, eta: 21:20:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4836, top5_acc: 0.7442, loss_cls: 2.8341, loss: 2.8341 +2024-07-26 19:01:51,391 - pyskl - INFO - Epoch [126][500/3746] lr: 6.629e-03, eta: 21:18:56, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4930, top5_acc: 0.7420, loss_cls: 2.8220, loss: 2.8220 +2024-07-26 19:03:14,605 - pyskl - INFO - Epoch [126][600/3746] lr: 6.615e-03, eta: 21:17:33, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5023, top5_acc: 0.7516, loss_cls: 2.7808, loss: 2.7808 +2024-07-26 19:04:37,492 - pyskl - INFO - Epoch [126][700/3746] lr: 6.601e-03, eta: 21:16:11, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7530, loss_cls: 2.7894, loss: 2.7894 +2024-07-26 19:06:00,533 - pyskl - INFO - Epoch [126][800/3746] lr: 6.587e-03, eta: 21:14:49, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4936, top5_acc: 0.7420, loss_cls: 2.8093, loss: 2.8093 +2024-07-26 19:07:22,626 - pyskl - INFO - Epoch [126][900/3746] lr: 6.574e-03, eta: 21:13:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7427, loss_cls: 2.8258, loss: 2.8258 +2024-07-26 19:08:44,365 - pyskl - INFO - Epoch [126][1000/3746] lr: 6.560e-03, eta: 21:12:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4905, top5_acc: 0.7433, loss_cls: 2.8178, loss: 2.8178 +2024-07-26 19:10:06,291 - pyskl - INFO - Epoch [126][1100/3746] lr: 6.546e-03, eta: 21:10:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4794, top5_acc: 0.7406, loss_cls: 2.8450, loss: 2.8450 +2024-07-26 19:11:27,939 - pyskl - INFO - Epoch [126][1200/3746] lr: 6.532e-03, eta: 21:09:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4869, top5_acc: 0.7406, loss_cls: 2.8560, loss: 2.8560 +2024-07-26 19:12:50,596 - pyskl - INFO - Epoch [126][1300/3746] lr: 6.518e-03, eta: 21:07:57, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7402, loss_cls: 2.8539, loss: 2.8539 +2024-07-26 19:14:12,746 - pyskl - INFO - Epoch [126][1400/3746] lr: 6.505e-03, eta: 21:06:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4748, top5_acc: 0.7406, loss_cls: 2.8739, loss: 2.8739 +2024-07-26 19:15:34,679 - pyskl - INFO - Epoch [126][1500/3746] lr: 6.491e-03, eta: 21:05:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4939, top5_acc: 0.7372, loss_cls: 2.8508, loss: 2.8508 +2024-07-26 19:16:56,483 - pyskl - INFO - Epoch [126][1600/3746] lr: 6.477e-03, eta: 21:03:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4778, top5_acc: 0.7378, loss_cls: 2.8681, loss: 2.8681 +2024-07-26 19:18:18,142 - pyskl - INFO - Epoch [126][1700/3746] lr: 6.463e-03, eta: 21:02:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4950, top5_acc: 0.7488, loss_cls: 2.8138, loss: 2.8138 +2024-07-26 19:19:39,813 - pyskl - INFO - Epoch [126][1800/3746] lr: 6.449e-03, eta: 21:01:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4913, top5_acc: 0.7456, loss_cls: 2.8312, loss: 2.8312 +2024-07-26 19:21:02,078 - pyskl - INFO - Epoch [126][1900/3746] lr: 6.436e-03, eta: 20:59:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4852, top5_acc: 0.7342, loss_cls: 2.8776, loss: 2.8776 +2024-07-26 19:22:24,067 - pyskl - INFO - Epoch [126][2000/3746] lr: 6.422e-03, eta: 20:58:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7434, loss_cls: 2.8487, loss: 2.8487 +2024-07-26 19:23:45,734 - pyskl - INFO - Epoch [126][2100/3746] lr: 6.408e-03, eta: 20:56:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4922, top5_acc: 0.7309, loss_cls: 2.8457, loss: 2.8457 +2024-07-26 19:25:07,728 - pyskl - INFO - Epoch [126][2200/3746] lr: 6.395e-03, eta: 20:55:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4809, top5_acc: 0.7336, loss_cls: 2.8700, loss: 2.8700 +2024-07-26 19:26:28,944 - pyskl - INFO - Epoch [126][2300/3746] lr: 6.381e-03, eta: 20:54:12, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4880, top5_acc: 0.7414, loss_cls: 2.8358, loss: 2.8358 +2024-07-26 19:27:51,282 - pyskl - INFO - Epoch [126][2400/3746] lr: 6.367e-03, eta: 20:52:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4739, top5_acc: 0.7278, loss_cls: 2.9062, loss: 2.9062 +2024-07-26 19:29:12,550 - pyskl - INFO - Epoch [126][2500/3746] lr: 6.354e-03, eta: 20:51:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4706, top5_acc: 0.7334, loss_cls: 2.9167, loss: 2.9167 +2024-07-26 19:30:34,155 - pyskl - INFO - Epoch [126][2600/3746] lr: 6.340e-03, eta: 20:50:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4733, top5_acc: 0.7355, loss_cls: 2.8825, loss: 2.8825 +2024-07-26 19:31:55,187 - pyskl - INFO - Epoch [126][2700/3746] lr: 6.326e-03, eta: 20:48:42, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4734, top5_acc: 0.7347, loss_cls: 2.8839, loss: 2.8839 +2024-07-26 19:33:16,674 - pyskl - INFO - Epoch [126][2800/3746] lr: 6.313e-03, eta: 20:47:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4706, top5_acc: 0.7336, loss_cls: 2.9041, loss: 2.9041 +2024-07-26 19:34:38,049 - pyskl - INFO - Epoch [126][2900/3746] lr: 6.299e-03, eta: 20:45:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4773, top5_acc: 0.7358, loss_cls: 2.8849, loss: 2.8849 +2024-07-26 19:35:58,880 - pyskl - INFO - Epoch [126][3000/3746] lr: 6.286e-03, eta: 20:44:34, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4788, top5_acc: 0.7352, loss_cls: 2.8818, loss: 2.8818 +2024-07-26 19:37:20,045 - pyskl - INFO - Epoch [126][3100/3746] lr: 6.272e-03, eta: 20:43:11, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4770, top5_acc: 0.7275, loss_cls: 2.9084, loss: 2.9084 +2024-07-26 19:38:41,297 - pyskl - INFO - Epoch [126][3200/3746] lr: 6.259e-03, eta: 20:41:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4916, top5_acc: 0.7456, loss_cls: 2.8323, loss: 2.8323 +2024-07-26 19:40:02,819 - pyskl - INFO - Epoch [126][3300/3746] lr: 6.245e-03, eta: 20:40:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4820, top5_acc: 0.7372, loss_cls: 2.8761, loss: 2.8761 +2024-07-26 19:41:24,428 - pyskl - INFO - Epoch [126][3400/3746] lr: 6.231e-03, eta: 20:39:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4730, top5_acc: 0.7333, loss_cls: 2.9172, loss: 2.9172 +2024-07-26 19:42:45,768 - pyskl - INFO - Epoch [126][3500/3746] lr: 6.218e-03, eta: 20:37:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7358, loss_cls: 2.8536, loss: 2.8536 +2024-07-26 19:44:07,418 - pyskl - INFO - Epoch [126][3600/3746] lr: 6.204e-03, eta: 20:36:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4798, top5_acc: 0.7322, loss_cls: 2.8912, loss: 2.8912 +2024-07-26 19:45:30,074 - pyskl - INFO - Epoch [126][3700/3746] lr: 6.191e-03, eta: 20:34:56, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4813, top5_acc: 0.7405, loss_cls: 2.8576, loss: 2.8576 +2024-07-26 19:46:09,638 - pyskl - INFO - Saving checkpoint at 126 epochs +2024-07-26 19:48:02,258 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 19:48:02,952 - pyskl - INFO - +top1_acc 0.4018 +top5_acc 0.6641 +2024-07-26 19:48:02,953 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 19:48:03,002 - pyskl - INFO - +mean_acc 0.4015 +2024-07-26 19:48:03,007 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_124.pth was removed +2024-07-26 19:48:03,307 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_126.pth. +2024-07-26 19:48:03,308 - pyskl - INFO - Best top1_acc is 0.4018 at 126 epoch. +2024-07-26 19:48:03,323 - pyskl - INFO - Epoch(val) [126][309] top1_acc: 0.4018, top5_acc: 0.6641, mean_class_accuracy: 0.4015 +2024-07-26 19:51:58,074 - pyskl - INFO - Epoch [127][100/3746] lr: 6.171e-03, eta: 20:33:18, time: 2.347, data_time: 1.359, memory: 15990, top1_acc: 0.5059, top5_acc: 0.7523, loss_cls: 2.7258, loss: 2.7258 +2024-07-26 19:53:19,405 - pyskl - INFO - Epoch [127][200/3746] lr: 6.158e-03, eta: 20:31:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5033, top5_acc: 0.7542, loss_cls: 2.7526, loss: 2.7526 +2024-07-26 19:54:41,088 - pyskl - INFO - Epoch [127][300/3746] lr: 6.144e-03, eta: 20:30:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4981, top5_acc: 0.7458, loss_cls: 2.7890, loss: 2.7890 +2024-07-26 19:56:02,674 - pyskl - INFO - Epoch [127][400/3746] lr: 6.131e-03, eta: 20:29:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7530, loss_cls: 2.7924, loss: 2.7924 +2024-07-26 19:57:24,070 - pyskl - INFO - Epoch [127][500/3746] lr: 6.118e-03, eta: 20:27:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5000, top5_acc: 0.7522, loss_cls: 2.7519, loss: 2.7519 +2024-07-26 19:58:45,231 - pyskl - INFO - Epoch [127][600/3746] lr: 6.104e-03, eta: 20:26:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4991, top5_acc: 0.7453, loss_cls: 2.8018, loss: 2.8018 +2024-07-26 20:00:06,429 - pyskl - INFO - Epoch [127][700/3746] lr: 6.091e-03, eta: 20:25:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4977, top5_acc: 0.7577, loss_cls: 2.7673, loss: 2.7673 +2024-07-26 20:01:28,154 - pyskl - INFO - Epoch [127][800/3746] lr: 6.077e-03, eta: 20:23:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7562, loss_cls: 2.7681, loss: 2.7681 +2024-07-26 20:02:49,466 - pyskl - INFO - Epoch [127][900/3746] lr: 6.064e-03, eta: 20:22:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4931, top5_acc: 0.7438, loss_cls: 2.7938, loss: 2.7938 +2024-07-26 20:04:10,635 - pyskl - INFO - Epoch [127][1000/3746] lr: 6.051e-03, eta: 20:20:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4908, top5_acc: 0.7503, loss_cls: 2.8039, loss: 2.8039 +2024-07-26 20:05:32,429 - pyskl - INFO - Epoch [127][1100/3746] lr: 6.037e-03, eta: 20:19:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4841, top5_acc: 0.7448, loss_cls: 2.8282, loss: 2.8282 +2024-07-26 20:06:54,004 - pyskl - INFO - Epoch [127][1200/3746] lr: 6.024e-03, eta: 20:18:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4914, top5_acc: 0.7488, loss_cls: 2.7916, loss: 2.7916 +2024-07-26 20:08:16,615 - pyskl - INFO - Epoch [127][1300/3746] lr: 6.011e-03, eta: 20:16:47, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4842, top5_acc: 0.7458, loss_cls: 2.8440, loss: 2.8440 +2024-07-26 20:09:38,212 - pyskl - INFO - Epoch [127][1400/3746] lr: 5.998e-03, eta: 20:15:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4894, top5_acc: 0.7472, loss_cls: 2.8267, loss: 2.8267 +2024-07-26 20:10:59,466 - pyskl - INFO - Epoch [127][1500/3746] lr: 5.984e-03, eta: 20:14:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4867, top5_acc: 0.7442, loss_cls: 2.8407, loss: 2.8407 +2024-07-26 20:12:21,052 - pyskl - INFO - Epoch [127][1600/3746] lr: 5.971e-03, eta: 20:12:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4961, top5_acc: 0.7572, loss_cls: 2.7904, loss: 2.7904 +2024-07-26 20:13:42,620 - pyskl - INFO - Epoch [127][1700/3746] lr: 5.958e-03, eta: 20:11:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4953, top5_acc: 0.7409, loss_cls: 2.8209, loss: 2.8209 +2024-07-26 20:15:04,090 - pyskl - INFO - Epoch [127][1800/3746] lr: 5.945e-03, eta: 20:09:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7392, loss_cls: 2.8484, loss: 2.8484 +2024-07-26 20:16:25,862 - pyskl - INFO - Epoch [127][1900/3746] lr: 5.931e-03, eta: 20:08:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4978, top5_acc: 0.7484, loss_cls: 2.7988, loss: 2.7988 +2024-07-26 20:17:47,415 - pyskl - INFO - Epoch [127][2000/3746] lr: 5.918e-03, eta: 20:07:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4908, top5_acc: 0.7433, loss_cls: 2.8162, loss: 2.8162 +2024-07-26 20:19:09,181 - pyskl - INFO - Epoch [127][2100/3746] lr: 5.905e-03, eta: 20:05:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4889, top5_acc: 0.7364, loss_cls: 2.8610, loss: 2.8610 +2024-07-26 20:20:31,015 - pyskl - INFO - Epoch [127][2200/3746] lr: 5.892e-03, eta: 20:04:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4963, top5_acc: 0.7508, loss_cls: 2.7951, loss: 2.7951 +2024-07-26 20:21:52,662 - pyskl - INFO - Epoch [127][2300/3746] lr: 5.879e-03, eta: 20:03:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4753, top5_acc: 0.7317, loss_cls: 2.8904, loss: 2.8904 +2024-07-26 20:23:14,291 - pyskl - INFO - Epoch [127][2400/3746] lr: 5.866e-03, eta: 20:01:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4869, top5_acc: 0.7391, loss_cls: 2.8225, loss: 2.8225 +2024-07-26 20:24:35,933 - pyskl - INFO - Epoch [127][2500/3746] lr: 5.852e-03, eta: 20:00:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4858, top5_acc: 0.7467, loss_cls: 2.8300, loss: 2.8300 +2024-07-26 20:25:57,409 - pyskl - INFO - Epoch [127][2600/3746] lr: 5.839e-03, eta: 19:58:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4844, top5_acc: 0.7381, loss_cls: 2.8377, loss: 2.8377 +2024-07-26 20:27:19,404 - pyskl - INFO - Epoch [127][2700/3746] lr: 5.826e-03, eta: 19:57:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4884, top5_acc: 0.7372, loss_cls: 2.8523, loss: 2.8523 +2024-07-26 20:28:40,635 - pyskl - INFO - Epoch [127][2800/3746] lr: 5.813e-03, eta: 19:56:09, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4888, top5_acc: 0.7414, loss_cls: 2.8316, loss: 2.8316 +2024-07-26 20:30:02,665 - pyskl - INFO - Epoch [127][2900/3746] lr: 5.800e-03, eta: 19:54:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4831, top5_acc: 0.7438, loss_cls: 2.8373, loss: 2.8373 +2024-07-26 20:31:24,171 - pyskl - INFO - Epoch [127][3000/3746] lr: 5.787e-03, eta: 19:53:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4916, top5_acc: 0.7409, loss_cls: 2.8332, loss: 2.8332 +2024-07-26 20:32:46,022 - pyskl - INFO - Epoch [127][3100/3746] lr: 5.774e-03, eta: 19:52:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4819, top5_acc: 0.7442, loss_cls: 2.8146, loss: 2.8146 +2024-07-26 20:34:07,677 - pyskl - INFO - Epoch [127][3200/3746] lr: 5.761e-03, eta: 19:50:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4836, top5_acc: 0.7355, loss_cls: 2.8499, loss: 2.8499 +2024-07-26 20:35:29,114 - pyskl - INFO - Epoch [127][3300/3746] lr: 5.748e-03, eta: 19:49:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4938, top5_acc: 0.7559, loss_cls: 2.7802, loss: 2.7802 +2024-07-26 20:36:50,478 - pyskl - INFO - Epoch [127][3400/3746] lr: 5.735e-03, eta: 19:47:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4844, top5_acc: 0.7416, loss_cls: 2.8426, loss: 2.8426 +2024-07-26 20:38:12,187 - pyskl - INFO - Epoch [127][3500/3746] lr: 5.722e-03, eta: 19:46:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4884, top5_acc: 0.7428, loss_cls: 2.8340, loss: 2.8340 +2024-07-26 20:39:33,371 - pyskl - INFO - Epoch [127][3600/3746] lr: 5.709e-03, eta: 19:45:08, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4872, top5_acc: 0.7425, loss_cls: 2.8481, loss: 2.8481 +2024-07-26 20:40:54,962 - pyskl - INFO - Epoch [127][3700/3746] lr: 5.696e-03, eta: 19:43:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4864, top5_acc: 0.7417, loss_cls: 2.8537, loss: 2.8537 +2024-07-26 20:41:35,260 - pyskl - INFO - Saving checkpoint at 127 epochs +2024-07-26 20:43:27,446 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 20:43:28,127 - pyskl - INFO - +top1_acc 0.4159 +top5_acc 0.6687 +2024-07-26 20:43:28,127 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 20:43:28,170 - pyskl - INFO - +mean_acc 0.4157 +2024-07-26 20:43:28,175 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_126.pth was removed +2024-07-26 20:43:28,435 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_127.pth. +2024-07-26 20:43:28,436 - pyskl - INFO - Best top1_acc is 0.4159 at 127 epoch. +2024-07-26 20:43:28,449 - pyskl - INFO - Epoch(val) [127][309] top1_acc: 0.4159, top5_acc: 0.6687, mean_class_accuracy: 0.4157 +2024-07-26 20:47:23,210 - pyskl - INFO - Epoch [128][100/3746] lr: 5.677e-03, eta: 19:42:06, time: 2.348, data_time: 1.351, memory: 15990, top1_acc: 0.5033, top5_acc: 0.7681, loss_cls: 2.7182, loss: 2.7182 +2024-07-26 20:48:46,414 - pyskl - INFO - Epoch [128][200/3746] lr: 5.664e-03, eta: 19:40:44, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5155, top5_acc: 0.7656, loss_cls: 2.7132, loss: 2.7132 +2024-07-26 20:50:09,255 - pyskl - INFO - Epoch [128][300/3746] lr: 5.651e-03, eta: 19:39:22, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5095, top5_acc: 0.7555, loss_cls: 2.7311, loss: 2.7311 +2024-07-26 20:51:32,027 - pyskl - INFO - Epoch [128][400/3746] lr: 5.638e-03, eta: 19:37:59, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4959, top5_acc: 0.7589, loss_cls: 2.7679, loss: 2.7679 +2024-07-26 20:52:54,744 - pyskl - INFO - Epoch [128][500/3746] lr: 5.625e-03, eta: 19:36:37, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4889, top5_acc: 0.7469, loss_cls: 2.8087, loss: 2.8087 +2024-07-26 20:54:17,556 - pyskl - INFO - Epoch [128][600/3746] lr: 5.612e-03, eta: 19:35:15, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7541, loss_cls: 2.7401, loss: 2.7401 +2024-07-26 20:55:39,913 - pyskl - INFO - Epoch [128][700/3746] lr: 5.600e-03, eta: 19:33:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5086, top5_acc: 0.7612, loss_cls: 2.7107, loss: 2.7107 +2024-07-26 20:57:03,182 - pyskl - INFO - Epoch [128][800/3746] lr: 5.587e-03, eta: 19:32:30, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4972, top5_acc: 0.7542, loss_cls: 2.7841, loss: 2.7841 +2024-07-26 20:58:26,051 - pyskl - INFO - Epoch [128][900/3746] lr: 5.574e-03, eta: 19:31:08, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5141, top5_acc: 0.7580, loss_cls: 2.7249, loss: 2.7249 +2024-07-26 20:59:47,262 - pyskl - INFO - Epoch [128][1000/3746] lr: 5.561e-03, eta: 19:29:45, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5002, top5_acc: 0.7456, loss_cls: 2.7941, loss: 2.7941 +2024-07-26 21:01:09,551 - pyskl - INFO - Epoch [128][1100/3746] lr: 5.548e-03, eta: 19:28:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4967, top5_acc: 0.7511, loss_cls: 2.7846, loss: 2.7846 +2024-07-26 21:02:31,030 - pyskl - INFO - Epoch [128][1200/3746] lr: 5.536e-03, eta: 19:27:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4891, top5_acc: 0.7505, loss_cls: 2.8004, loss: 2.8004 +2024-07-26 21:03:53,675 - pyskl - INFO - Epoch [128][1300/3746] lr: 5.523e-03, eta: 19:25:38, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5012, top5_acc: 0.7472, loss_cls: 2.7578, loss: 2.7578 +2024-07-26 21:05:15,490 - pyskl - INFO - Epoch [128][1400/3746] lr: 5.510e-03, eta: 19:24:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4847, top5_acc: 0.7455, loss_cls: 2.7992, loss: 2.7992 +2024-07-26 21:06:37,375 - pyskl - INFO - Epoch [128][1500/3746] lr: 5.497e-03, eta: 19:22:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4897, top5_acc: 0.7419, loss_cls: 2.8242, loss: 2.8242 +2024-07-26 21:07:59,420 - pyskl - INFO - Epoch [128][1600/3746] lr: 5.485e-03, eta: 19:21:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4950, top5_acc: 0.7514, loss_cls: 2.7791, loss: 2.7791 +2024-07-26 21:09:21,083 - pyskl - INFO - Epoch [128][1700/3746] lr: 5.472e-03, eta: 19:20:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5041, top5_acc: 0.7588, loss_cls: 2.7452, loss: 2.7452 +2024-07-26 21:10:42,832 - pyskl - INFO - Epoch [128][1800/3746] lr: 5.459e-03, eta: 19:18:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4964, top5_acc: 0.7505, loss_cls: 2.7945, loss: 2.7945 +2024-07-26 21:12:04,913 - pyskl - INFO - Epoch [128][1900/3746] lr: 5.446e-03, eta: 19:17:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4881, top5_acc: 0.7361, loss_cls: 2.8406, loss: 2.8406 +2024-07-26 21:13:26,696 - pyskl - INFO - Epoch [128][2000/3746] lr: 5.434e-03, eta: 19:16:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5009, top5_acc: 0.7484, loss_cls: 2.7760, loss: 2.7760 +2024-07-26 21:14:49,236 - pyskl - INFO - Epoch [128][2100/3746] lr: 5.421e-03, eta: 19:14:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4953, top5_acc: 0.7516, loss_cls: 2.7728, loss: 2.7728 +2024-07-26 21:16:10,985 - pyskl - INFO - Epoch [128][2200/3746] lr: 5.408e-03, eta: 19:13:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4903, top5_acc: 0.7459, loss_cls: 2.8153, loss: 2.8153 +2024-07-26 21:17:32,514 - pyskl - INFO - Epoch [128][2300/3746] lr: 5.396e-03, eta: 19:11:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4981, top5_acc: 0.7577, loss_cls: 2.7689, loss: 2.7689 +2024-07-26 21:18:54,050 - pyskl - INFO - Epoch [128][2400/3746] lr: 5.383e-03, eta: 19:10:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4905, top5_acc: 0.7514, loss_cls: 2.8026, loss: 2.8026 +2024-07-26 21:20:15,507 - pyskl - INFO - Epoch [128][2500/3746] lr: 5.370e-03, eta: 19:09:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4945, top5_acc: 0.7455, loss_cls: 2.8262, loss: 2.8262 +2024-07-26 21:21:37,047 - pyskl - INFO - Epoch [128][2600/3746] lr: 5.358e-03, eta: 19:07:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4947, top5_acc: 0.7464, loss_cls: 2.7929, loss: 2.7929 +2024-07-26 21:22:58,775 - pyskl - INFO - Epoch [128][2700/3746] lr: 5.345e-03, eta: 19:06:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5042, top5_acc: 0.7491, loss_cls: 2.7812, loss: 2.7812 +2024-07-26 21:24:20,339 - pyskl - INFO - Epoch [128][2800/3746] lr: 5.333e-03, eta: 19:05:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4948, top5_acc: 0.7489, loss_cls: 2.7845, loss: 2.7845 +2024-07-26 21:25:42,094 - pyskl - INFO - Epoch [128][2900/3746] lr: 5.320e-03, eta: 19:03:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4995, top5_acc: 0.7503, loss_cls: 2.7841, loss: 2.7841 +2024-07-26 21:27:03,302 - pyskl - INFO - Epoch [128][3000/3746] lr: 5.308e-03, eta: 19:02:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4869, top5_acc: 0.7420, loss_cls: 2.8124, loss: 2.8124 +2024-07-26 21:28:25,074 - pyskl - INFO - Epoch [128][3100/3746] lr: 5.295e-03, eta: 19:00:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4930, top5_acc: 0.7502, loss_cls: 2.7914, loss: 2.7914 +2024-07-26 21:29:46,541 - pyskl - INFO - Epoch [128][3200/3746] lr: 5.283e-03, eta: 18:59:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4833, top5_acc: 0.7384, loss_cls: 2.8372, loss: 2.8372 +2024-07-26 21:31:08,011 - pyskl - INFO - Epoch [128][3300/3746] lr: 5.270e-03, eta: 18:58:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4945, top5_acc: 0.7447, loss_cls: 2.7820, loss: 2.7820 +2024-07-26 21:32:30,031 - pyskl - INFO - Epoch [128][3400/3746] lr: 5.258e-03, eta: 18:56:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7459, loss_cls: 2.7982, loss: 2.7982 +2024-07-26 21:33:51,611 - pyskl - INFO - Epoch [128][3500/3746] lr: 5.245e-03, eta: 18:55:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4928, top5_acc: 0.7475, loss_cls: 2.8105, loss: 2.8105 +2024-07-26 21:35:13,019 - pyskl - INFO - Epoch [128][3600/3746] lr: 5.233e-03, eta: 18:53:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4925, top5_acc: 0.7450, loss_cls: 2.8115, loss: 2.8115 +2024-07-26 21:36:35,061 - pyskl - INFO - Epoch [128][3700/3746] lr: 5.220e-03, eta: 18:52:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7475, loss_cls: 2.7847, loss: 2.7847 +2024-07-26 21:37:15,396 - pyskl - INFO - Saving checkpoint at 128 epochs +2024-07-26 21:39:07,903 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 21:39:08,591 - pyskl - INFO - +top1_acc 0.4179 +top5_acc 0.6715 +2024-07-26 21:39:08,591 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 21:39:08,635 - pyskl - INFO - +mean_acc 0.4176 +2024-07-26 21:39:08,640 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_127.pth was removed +2024-07-26 21:39:08,887 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_128.pth. +2024-07-26 21:39:08,887 - pyskl - INFO - Best top1_acc is 0.4179 at 128 epoch. +2024-07-26 21:39:08,901 - pyskl - INFO - Epoch(val) [128][309] top1_acc: 0.4179, top5_acc: 0.6715, mean_class_accuracy: 0.4176 +2024-07-26 21:43:00,687 - pyskl - INFO - Epoch [129][100/3746] lr: 5.202e-03, eta: 18:50:55, time: 2.318, data_time: 1.319, memory: 15990, top1_acc: 0.5102, top5_acc: 0.7555, loss_cls: 2.7186, loss: 2.7186 +2024-07-26 21:44:24,260 - pyskl - INFO - Epoch [129][200/3746] lr: 5.190e-03, eta: 18:49:33, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5088, top5_acc: 0.7605, loss_cls: 2.6924, loss: 2.6924 +2024-07-26 21:45:47,792 - pyskl - INFO - Epoch [129][300/3746] lr: 5.177e-03, eta: 18:48:11, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5136, top5_acc: 0.7659, loss_cls: 2.6888, loss: 2.6888 +2024-07-26 21:47:11,013 - pyskl - INFO - Epoch [129][400/3746] lr: 5.165e-03, eta: 18:46:49, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5102, top5_acc: 0.7550, loss_cls: 2.7646, loss: 2.7646 +2024-07-26 21:48:34,805 - pyskl - INFO - Epoch [129][500/3746] lr: 5.153e-03, eta: 18:45:26, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5070, top5_acc: 0.7628, loss_cls: 2.7040, loss: 2.7040 +2024-07-26 21:49:58,583 - pyskl - INFO - Epoch [129][600/3746] lr: 5.140e-03, eta: 18:44:04, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5114, top5_acc: 0.7630, loss_cls: 2.7059, loss: 2.7059 +2024-07-26 21:51:22,073 - pyskl - INFO - Epoch [129][700/3746] lr: 5.128e-03, eta: 18:42:42, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5173, top5_acc: 0.7650, loss_cls: 2.6719, loss: 2.6719 +2024-07-26 21:52:45,003 - pyskl - INFO - Epoch [129][800/3746] lr: 5.116e-03, eta: 18:41:20, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5120, top5_acc: 0.7577, loss_cls: 2.6962, loss: 2.6962 +2024-07-26 21:54:07,285 - pyskl - INFO - Epoch [129][900/3746] lr: 5.103e-03, eta: 18:39:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5053, top5_acc: 0.7562, loss_cls: 2.7430, loss: 2.7430 +2024-07-26 21:55:28,942 - pyskl - INFO - Epoch [129][1000/3746] lr: 5.091e-03, eta: 18:38:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5109, top5_acc: 0.7564, loss_cls: 2.7285, loss: 2.7285 +2024-07-26 21:56:50,621 - pyskl - INFO - Epoch [129][1100/3746] lr: 5.079e-03, eta: 18:37:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5108, top5_acc: 0.7594, loss_cls: 2.7232, loss: 2.7232 +2024-07-26 21:58:12,309 - pyskl - INFO - Epoch [129][1200/3746] lr: 5.066e-03, eta: 18:35:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5052, top5_acc: 0.7595, loss_cls: 2.7305, loss: 2.7305 +2024-07-26 21:59:35,077 - pyskl - INFO - Epoch [129][1300/3746] lr: 5.054e-03, eta: 18:34:27, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5006, top5_acc: 0.7552, loss_cls: 2.7392, loss: 2.7392 +2024-07-26 22:00:56,921 - pyskl - INFO - Epoch [129][1400/3746] lr: 5.042e-03, eta: 18:33:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5014, top5_acc: 0.7495, loss_cls: 2.7550, loss: 2.7550 +2024-07-26 22:02:19,202 - pyskl - INFO - Epoch [129][1500/3746] lr: 5.030e-03, eta: 18:31:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4931, top5_acc: 0.7461, loss_cls: 2.7897, loss: 2.7897 +2024-07-26 22:03:41,257 - pyskl - INFO - Epoch [129][1600/3746] lr: 5.017e-03, eta: 18:30:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5081, top5_acc: 0.7606, loss_cls: 2.7221, loss: 2.7221 +2024-07-26 22:05:03,500 - pyskl - INFO - Epoch [129][1700/3746] lr: 5.005e-03, eta: 18:28:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5027, top5_acc: 0.7520, loss_cls: 2.7333, loss: 2.7333 +2024-07-26 22:06:25,221 - pyskl - INFO - Epoch [129][1800/3746] lr: 4.993e-03, eta: 18:27:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5011, top5_acc: 0.7553, loss_cls: 2.7575, loss: 2.7575 +2024-07-26 22:07:47,399 - pyskl - INFO - Epoch [129][1900/3746] lr: 4.981e-03, eta: 18:26:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5075, top5_acc: 0.7577, loss_cls: 2.7388, loss: 2.7388 +2024-07-26 22:09:09,218 - pyskl - INFO - Epoch [129][2000/3746] lr: 4.969e-03, eta: 18:24:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5019, top5_acc: 0.7603, loss_cls: 2.7717, loss: 2.7717 +2024-07-26 22:10:31,132 - pyskl - INFO - Epoch [129][2100/3746] lr: 4.957e-03, eta: 18:23:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4941, top5_acc: 0.7461, loss_cls: 2.8080, loss: 2.8080 +2024-07-26 22:11:53,112 - pyskl - INFO - Epoch [129][2200/3746] lr: 4.944e-03, eta: 18:22:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5017, top5_acc: 0.7591, loss_cls: 2.7600, loss: 2.7600 +2024-07-26 22:13:14,758 - pyskl - INFO - Epoch [129][2300/3746] lr: 4.932e-03, eta: 18:20:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4869, top5_acc: 0.7480, loss_cls: 2.7870, loss: 2.7870 +2024-07-26 22:14:36,592 - pyskl - INFO - Epoch [129][2400/3746] lr: 4.920e-03, eta: 18:19:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5006, top5_acc: 0.7528, loss_cls: 2.7772, loss: 2.7772 +2024-07-26 22:15:58,207 - pyskl - INFO - Epoch [129][2500/3746] lr: 4.908e-03, eta: 18:17:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4948, top5_acc: 0.7577, loss_cls: 2.7317, loss: 2.7317 +2024-07-26 22:17:19,419 - pyskl - INFO - Epoch [129][2600/3746] lr: 4.896e-03, eta: 18:16:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4936, top5_acc: 0.7498, loss_cls: 2.7975, loss: 2.7975 +2024-07-26 22:18:40,555 - pyskl - INFO - Epoch [129][2700/3746] lr: 4.884e-03, eta: 18:15:11, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7477, loss_cls: 2.7838, loss: 2.7838 +2024-07-26 22:20:01,967 - pyskl - INFO - Epoch [129][2800/3746] lr: 4.872e-03, eta: 18:13:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4947, top5_acc: 0.7539, loss_cls: 2.7884, loss: 2.7884 +2024-07-26 22:21:23,346 - pyskl - INFO - Epoch [129][2900/3746] lr: 4.860e-03, eta: 18:12:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5012, top5_acc: 0.7512, loss_cls: 2.7544, loss: 2.7544 +2024-07-26 22:22:45,086 - pyskl - INFO - Epoch [129][3000/3746] lr: 4.848e-03, eta: 18:11:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5005, top5_acc: 0.7530, loss_cls: 2.7721, loss: 2.7721 +2024-07-26 22:24:06,568 - pyskl - INFO - Epoch [129][3100/3746] lr: 4.836e-03, eta: 18:09:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5002, top5_acc: 0.7486, loss_cls: 2.7936, loss: 2.7936 +2024-07-26 22:25:29,066 - pyskl - INFO - Epoch [129][3200/3746] lr: 4.824e-03, eta: 18:08:19, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5056, top5_acc: 0.7580, loss_cls: 2.7641, loss: 2.7641 +2024-07-26 22:26:50,336 - pyskl - INFO - Epoch [129][3300/3746] lr: 4.812e-03, eta: 18:06:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4995, top5_acc: 0.7472, loss_cls: 2.7856, loss: 2.7856 +2024-07-26 22:28:11,792 - pyskl - INFO - Epoch [129][3400/3746] lr: 4.800e-03, eta: 18:05:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5027, top5_acc: 0.7511, loss_cls: 2.7754, loss: 2.7754 +2024-07-26 22:29:33,847 - pyskl - INFO - Epoch [129][3500/3746] lr: 4.788e-03, eta: 18:04:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4963, top5_acc: 0.7542, loss_cls: 2.7575, loss: 2.7575 +2024-07-26 22:30:55,322 - pyskl - INFO - Epoch [129][3600/3746] lr: 4.776e-03, eta: 18:02:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5044, top5_acc: 0.7584, loss_cls: 2.7355, loss: 2.7355 +2024-07-26 22:32:17,400 - pyskl - INFO - Epoch [129][3700/3746] lr: 4.764e-03, eta: 18:01:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5028, top5_acc: 0.7569, loss_cls: 2.7624, loss: 2.7624 +2024-07-26 22:32:56,917 - pyskl - INFO - Saving checkpoint at 129 epochs +2024-07-26 22:34:50,283 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 22:34:50,976 - pyskl - INFO - +top1_acc 0.4218 +top5_acc 0.6771 +2024-07-26 22:34:50,977 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 22:34:51,020 - pyskl - INFO - +mean_acc 0.4216 +2024-07-26 22:34:51,024 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_128.pth was removed +2024-07-26 22:34:51,291 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_129.pth. +2024-07-26 22:34:51,292 - pyskl - INFO - Best top1_acc is 0.4218 at 129 epoch. +2024-07-26 22:34:51,306 - pyskl - INFO - Epoch(val) [129][309] top1_acc: 0.4218, top5_acc: 0.6771, mean_class_accuracy: 0.4216 +2024-07-26 22:38:44,774 - pyskl - INFO - Epoch [130][100/3746] lr: 4.747e-03, eta: 17:59:44, time: 2.335, data_time: 1.343, memory: 15990, top1_acc: 0.5173, top5_acc: 0.7791, loss_cls: 2.6323, loss: 2.6323 +2024-07-26 22:40:07,856 - pyskl - INFO - Epoch [130][200/3746] lr: 4.735e-03, eta: 17:58:22, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5239, top5_acc: 0.7741, loss_cls: 2.6320, loss: 2.6320 +2024-07-26 22:41:30,672 - pyskl - INFO - Epoch [130][300/3746] lr: 4.723e-03, eta: 17:56:59, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5212, top5_acc: 0.7667, loss_cls: 2.6592, loss: 2.6592 +2024-07-26 22:42:53,182 - pyskl - INFO - Epoch [130][400/3746] lr: 4.711e-03, eta: 17:55:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5052, top5_acc: 0.7630, loss_cls: 2.6965, loss: 2.6965 +2024-07-26 22:44:16,053 - pyskl - INFO - Epoch [130][500/3746] lr: 4.699e-03, eta: 17:54:14, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5130, top5_acc: 0.7612, loss_cls: 2.6963, loss: 2.6963 +2024-07-26 22:45:39,172 - pyskl - INFO - Epoch [130][600/3746] lr: 4.688e-03, eta: 17:52:52, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5125, top5_acc: 0.7667, loss_cls: 2.6957, loss: 2.6957 +2024-07-26 22:47:01,571 - pyskl - INFO - Epoch [130][700/3746] lr: 4.676e-03, eta: 17:51:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5080, top5_acc: 0.7542, loss_cls: 2.7012, loss: 2.7012 +2024-07-26 22:48:24,152 - pyskl - INFO - Epoch [130][800/3746] lr: 4.664e-03, eta: 17:50:07, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5170, top5_acc: 0.7702, loss_cls: 2.6690, loss: 2.6690 +2024-07-26 22:49:46,552 - pyskl - INFO - Epoch [130][900/3746] lr: 4.652e-03, eta: 17:48:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5098, top5_acc: 0.7641, loss_cls: 2.6934, loss: 2.6934 +2024-07-26 22:51:09,006 - pyskl - INFO - Epoch [130][1000/3746] lr: 4.640e-03, eta: 17:47:22, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5239, top5_acc: 0.7569, loss_cls: 2.6808, loss: 2.6808 +2024-07-26 22:52:31,476 - pyskl - INFO - Epoch [130][1100/3746] lr: 4.629e-03, eta: 17:46:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5223, top5_acc: 0.7684, loss_cls: 2.6791, loss: 2.6791 +2024-07-26 22:53:53,924 - pyskl - INFO - Epoch [130][1200/3746] lr: 4.617e-03, eta: 17:44:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5061, top5_acc: 0.7558, loss_cls: 2.7296, loss: 2.7296 +2024-07-26 22:55:17,122 - pyskl - INFO - Epoch [130][1300/3746] lr: 4.605e-03, eta: 17:43:15, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5094, top5_acc: 0.7616, loss_cls: 2.6976, loss: 2.6976 +2024-07-26 22:56:40,932 - pyskl - INFO - Epoch [130][1400/3746] lr: 4.594e-03, eta: 17:41:53, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5145, top5_acc: 0.7666, loss_cls: 2.6705, loss: 2.6705 +2024-07-26 22:58:03,918 - pyskl - INFO - Epoch [130][1500/3746] lr: 4.582e-03, eta: 17:40:30, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5123, top5_acc: 0.7623, loss_cls: 2.7128, loss: 2.7128 +2024-07-26 22:59:27,224 - pyskl - INFO - Epoch [130][1600/3746] lr: 4.570e-03, eta: 17:39:08, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5094, top5_acc: 0.7627, loss_cls: 2.6905, loss: 2.6905 +2024-07-26 23:00:50,692 - pyskl - INFO - Epoch [130][1700/3746] lr: 4.558e-03, eta: 17:37:46, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5081, top5_acc: 0.7609, loss_cls: 2.7416, loss: 2.7416 +2024-07-26 23:02:13,521 - pyskl - INFO - Epoch [130][1800/3746] lr: 4.547e-03, eta: 17:36:23, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5106, top5_acc: 0.7627, loss_cls: 2.7006, loss: 2.7006 +2024-07-26 23:03:37,021 - pyskl - INFO - Epoch [130][1900/3746] lr: 4.535e-03, eta: 17:35:01, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5159, top5_acc: 0.7684, loss_cls: 2.6906, loss: 2.6906 +2024-07-26 23:04:59,795 - pyskl - INFO - Epoch [130][2000/3746] lr: 4.524e-03, eta: 17:33:39, time: 0.828, data_time: 0.001, memory: 15990, top1_acc: 0.5094, top5_acc: 0.7664, loss_cls: 2.7074, loss: 2.7074 +2024-07-26 23:06:21,738 - pyskl - INFO - Epoch [130][2100/3746] lr: 4.512e-03, eta: 17:32:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5075, top5_acc: 0.7598, loss_cls: 2.7354, loss: 2.7354 +2024-07-26 23:07:44,730 - pyskl - INFO - Epoch [130][2200/3746] lr: 4.500e-03, eta: 17:30:54, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5094, top5_acc: 0.7577, loss_cls: 2.7196, loss: 2.7196 +2024-07-26 23:09:08,342 - pyskl - INFO - Epoch [130][2300/3746] lr: 4.489e-03, eta: 17:29:31, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5084, top5_acc: 0.7569, loss_cls: 2.7494, loss: 2.7494 +2024-07-26 23:10:31,300 - pyskl - INFO - Epoch [130][2400/3746] lr: 4.477e-03, eta: 17:28:09, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5134, top5_acc: 0.7586, loss_cls: 2.6969, loss: 2.6969 +2024-07-26 23:11:53,561 - pyskl - INFO - Epoch [130][2500/3746] lr: 4.466e-03, eta: 17:26:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4972, top5_acc: 0.7578, loss_cls: 2.7580, loss: 2.7580 +2024-07-26 23:13:15,463 - pyskl - INFO - Epoch [130][2600/3746] lr: 4.454e-03, eta: 17:25:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5080, top5_acc: 0.7531, loss_cls: 2.7337, loss: 2.7337 +2024-07-26 23:14:37,489 - pyskl - INFO - Epoch [130][2700/3746] lr: 4.443e-03, eta: 17:24:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5069, top5_acc: 0.7602, loss_cls: 2.7414, loss: 2.7414 +2024-07-26 23:15:59,819 - pyskl - INFO - Epoch [130][2800/3746] lr: 4.431e-03, eta: 17:22:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5202, top5_acc: 0.7642, loss_cls: 2.6875, loss: 2.6875 +2024-07-26 23:17:22,086 - pyskl - INFO - Epoch [130][2900/3746] lr: 4.420e-03, eta: 17:21:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5111, top5_acc: 0.7603, loss_cls: 2.6786, loss: 2.6786 +2024-07-26 23:18:44,343 - pyskl - INFO - Epoch [130][3000/3746] lr: 4.408e-03, eta: 17:19:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4995, top5_acc: 0.7550, loss_cls: 2.7488, loss: 2.7488 +2024-07-26 23:20:06,289 - pyskl - INFO - Epoch [130][3100/3746] lr: 4.397e-03, eta: 17:18:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5036, top5_acc: 0.7573, loss_cls: 2.7199, loss: 2.7199 +2024-07-26 23:21:28,509 - pyskl - INFO - Epoch [130][3200/3746] lr: 4.385e-03, eta: 17:17:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5044, top5_acc: 0.7595, loss_cls: 2.7082, loss: 2.7082 +2024-07-26 23:22:51,191 - pyskl - INFO - Epoch [130][3300/3746] lr: 4.374e-03, eta: 17:15:46, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4902, top5_acc: 0.7544, loss_cls: 2.7682, loss: 2.7682 +2024-07-26 23:24:13,478 - pyskl - INFO - Epoch [130][3400/3746] lr: 4.362e-03, eta: 17:14:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4994, top5_acc: 0.7528, loss_cls: 2.7766, loss: 2.7766 +2024-07-26 23:25:35,128 - pyskl - INFO - Epoch [130][3500/3746] lr: 4.351e-03, eta: 17:13:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5036, top5_acc: 0.7566, loss_cls: 2.7380, loss: 2.7380 +2024-07-26 23:26:57,714 - pyskl - INFO - Epoch [130][3600/3746] lr: 4.339e-03, eta: 17:11:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5048, top5_acc: 0.7514, loss_cls: 2.7762, loss: 2.7762 +2024-07-26 23:28:20,660 - pyskl - INFO - Epoch [130][3700/3746] lr: 4.328e-03, eta: 17:10:17, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5061, top5_acc: 0.7583, loss_cls: 2.7322, loss: 2.7322 +2024-07-26 23:29:00,234 - pyskl - INFO - Saving checkpoint at 130 epochs +2024-07-26 23:30:53,782 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 23:30:54,459 - pyskl - INFO - +top1_acc 0.4165 +top5_acc 0.6752 +2024-07-26 23:30:54,459 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 23:30:54,504 - pyskl - INFO - +mean_acc 0.4162 +2024-07-26 23:30:54,517 - pyskl - INFO - Epoch(val) [130][309] top1_acc: 0.4165, top5_acc: 0.6752, mean_class_accuracy: 0.4162 +2024-07-26 23:34:44,750 - pyskl - INFO - Epoch [131][100/3746] lr: 4.311e-03, eta: 17:08:33, time: 2.302, data_time: 1.314, memory: 15990, top1_acc: 0.5292, top5_acc: 0.7717, loss_cls: 2.6110, loss: 2.6110 +2024-07-26 23:36:07,503 - pyskl - INFO - Epoch [131][200/3746] lr: 4.300e-03, eta: 17:07:11, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5173, top5_acc: 0.7759, loss_cls: 2.6340, loss: 2.6340 +2024-07-26 23:37:30,571 - pyskl - INFO - Epoch [131][300/3746] lr: 4.289e-03, eta: 17:05:48, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5184, top5_acc: 0.7631, loss_cls: 2.6764, loss: 2.6764 +2024-07-26 23:38:53,663 - pyskl - INFO - Epoch [131][400/3746] lr: 4.277e-03, eta: 17:04:26, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5247, top5_acc: 0.7773, loss_cls: 2.6216, loss: 2.6216 +2024-07-26 23:40:16,321 - pyskl - INFO - Epoch [131][500/3746] lr: 4.266e-03, eta: 17:03:03, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5294, top5_acc: 0.7741, loss_cls: 2.6152, loss: 2.6152 +2024-07-26 23:41:38,933 - pyskl - INFO - Epoch [131][600/3746] lr: 4.255e-03, eta: 17:01:41, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5206, top5_acc: 0.7698, loss_cls: 2.6716, loss: 2.6716 +2024-07-26 23:43:00,750 - pyskl - INFO - Epoch [131][700/3746] lr: 4.244e-03, eta: 17:00:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5294, top5_acc: 0.7741, loss_cls: 2.6230, loss: 2.6230 +2024-07-26 23:44:23,552 - pyskl - INFO - Epoch [131][800/3746] lr: 4.232e-03, eta: 16:58:56, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5131, top5_acc: 0.7673, loss_cls: 2.6594, loss: 2.6594 +2024-07-26 23:45:45,302 - pyskl - INFO - Epoch [131][900/3746] lr: 4.221e-03, eta: 16:57:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5153, top5_acc: 0.7672, loss_cls: 2.6698, loss: 2.6698 +2024-07-26 23:47:07,371 - pyskl - INFO - Epoch [131][1000/3746] lr: 4.210e-03, eta: 16:56:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5214, top5_acc: 0.7677, loss_cls: 2.6495, loss: 2.6495 +2024-07-26 23:48:29,380 - pyskl - INFO - Epoch [131][1100/3746] lr: 4.199e-03, eta: 16:54:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5145, top5_acc: 0.7677, loss_cls: 2.6577, loss: 2.6577 +2024-07-26 23:49:51,825 - pyskl - INFO - Epoch [131][1200/3746] lr: 4.187e-03, eta: 16:53:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5164, top5_acc: 0.7673, loss_cls: 2.6559, loss: 2.6559 +2024-07-26 23:51:13,955 - pyskl - INFO - Epoch [131][1300/3746] lr: 4.176e-03, eta: 16:52:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5156, top5_acc: 0.7628, loss_cls: 2.7038, loss: 2.7038 +2024-07-26 23:52:36,366 - pyskl - INFO - Epoch [131][1400/3746] lr: 4.165e-03, eta: 16:50:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5200, top5_acc: 0.7681, loss_cls: 2.6675, loss: 2.6675 +2024-07-26 23:53:58,844 - pyskl - INFO - Epoch [131][1500/3746] lr: 4.154e-03, eta: 16:49:18, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5091, top5_acc: 0.7661, loss_cls: 2.6850, loss: 2.6850 +2024-07-26 23:55:20,484 - pyskl - INFO - Epoch [131][1600/3746] lr: 4.143e-03, eta: 16:47:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5133, top5_acc: 0.7583, loss_cls: 2.6973, loss: 2.6973 +2024-07-26 23:56:42,455 - pyskl - INFO - Epoch [131][1700/3746] lr: 4.132e-03, eta: 16:46:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5134, top5_acc: 0.7647, loss_cls: 2.6738, loss: 2.6738 +2024-07-26 23:58:04,485 - pyskl - INFO - Epoch [131][1800/3746] lr: 4.120e-03, eta: 16:45:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5155, top5_acc: 0.7723, loss_cls: 2.6332, loss: 2.6332 +2024-07-26 23:59:25,983 - pyskl - INFO - Epoch [131][1900/3746] lr: 4.109e-03, eta: 16:43:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5139, top5_acc: 0.7611, loss_cls: 2.7049, loss: 2.7049 +2024-07-27 00:00:47,815 - pyskl - INFO - Epoch [131][2000/3746] lr: 4.098e-03, eta: 16:42:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5112, top5_acc: 0.7645, loss_cls: 2.7176, loss: 2.7176 +2024-07-27 00:02:09,077 - pyskl - INFO - Epoch [131][2100/3746] lr: 4.087e-03, eta: 16:41:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5120, top5_acc: 0.7631, loss_cls: 2.7063, loss: 2.7063 +2024-07-27 00:03:30,982 - pyskl - INFO - Epoch [131][2200/3746] lr: 4.076e-03, eta: 16:39:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5105, top5_acc: 0.7634, loss_cls: 2.7006, loss: 2.7006 +2024-07-27 00:04:52,755 - pyskl - INFO - Epoch [131][2300/3746] lr: 4.065e-03, eta: 16:38:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5125, top5_acc: 0.7692, loss_cls: 2.6644, loss: 2.6644 +2024-07-27 00:06:14,419 - pyskl - INFO - Epoch [131][2400/3746] lr: 4.054e-03, eta: 16:36:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5075, top5_acc: 0.7631, loss_cls: 2.6873, loss: 2.6873 +2024-07-27 00:07:36,149 - pyskl - INFO - Epoch [131][2500/3746] lr: 4.043e-03, eta: 16:35:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5130, top5_acc: 0.7720, loss_cls: 2.6579, loss: 2.6579 +2024-07-27 00:08:58,386 - pyskl - INFO - Epoch [131][2600/3746] lr: 4.032e-03, eta: 16:34:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5142, top5_acc: 0.7666, loss_cls: 2.6756, loss: 2.6756 +2024-07-27 00:10:19,661 - pyskl - INFO - Epoch [131][2700/3746] lr: 4.021e-03, eta: 16:32:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5097, top5_acc: 0.7636, loss_cls: 2.6802, loss: 2.6802 +2024-07-27 00:11:41,761 - pyskl - INFO - Epoch [131][2800/3746] lr: 4.010e-03, eta: 16:31:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5173, top5_acc: 0.7616, loss_cls: 2.6906, loss: 2.6906 +2024-07-27 00:13:02,912 - pyskl - INFO - Epoch [131][2900/3746] lr: 3.999e-03, eta: 16:30:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5078, top5_acc: 0.7714, loss_cls: 2.6694, loss: 2.6694 +2024-07-27 00:14:24,706 - pyskl - INFO - Epoch [131][3000/3746] lr: 3.988e-03, eta: 16:28:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5028, top5_acc: 0.7655, loss_cls: 2.6826, loss: 2.6826 +2024-07-27 00:15:46,012 - pyskl - INFO - Epoch [131][3100/3746] lr: 3.977e-03, eta: 16:27:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5102, top5_acc: 0.7620, loss_cls: 2.6960, loss: 2.6960 +2024-07-27 00:17:07,743 - pyskl - INFO - Epoch [131][3200/3746] lr: 3.966e-03, eta: 16:25:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5122, top5_acc: 0.7709, loss_cls: 2.6753, loss: 2.6753 +2024-07-27 00:18:28,927 - pyskl - INFO - Epoch [131][3300/3746] lr: 3.955e-03, eta: 16:24:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5161, top5_acc: 0.7648, loss_cls: 2.6903, loss: 2.6903 +2024-07-27 00:19:51,039 - pyskl - INFO - Epoch [131][3400/3746] lr: 3.945e-03, eta: 16:23:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5106, top5_acc: 0.7609, loss_cls: 2.7132, loss: 2.7132 +2024-07-27 00:21:12,397 - pyskl - INFO - Epoch [131][3500/3746] lr: 3.934e-03, eta: 16:21:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5098, top5_acc: 0.7553, loss_cls: 2.7324, loss: 2.7324 +2024-07-27 00:22:33,623 - pyskl - INFO - Epoch [131][3600/3746] lr: 3.923e-03, eta: 16:20:24, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5183, top5_acc: 0.7669, loss_cls: 2.6783, loss: 2.6783 +2024-07-27 00:23:55,481 - pyskl - INFO - Epoch [131][3700/3746] lr: 3.912e-03, eta: 16:19:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5053, top5_acc: 0.7606, loss_cls: 2.7178, loss: 2.7178 +2024-07-27 00:24:34,605 - pyskl - INFO - Saving checkpoint at 131 epochs +2024-07-27 00:26:28,762 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 00:26:29,428 - pyskl - INFO - +top1_acc 0.4250 +top5_acc 0.6851 +2024-07-27 00:26:29,428 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 00:26:29,469 - pyskl - INFO - +mean_acc 0.4246 +2024-07-27 00:26:29,474 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_129.pth was removed +2024-07-27 00:26:29,746 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_131.pth. +2024-07-27 00:26:29,747 - pyskl - INFO - Best top1_acc is 0.4250 at 131 epoch. +2024-07-27 00:26:29,759 - pyskl - INFO - Epoch(val) [131][309] top1_acc: 0.4250, top5_acc: 0.6851, mean_class_accuracy: 0.4246 +2024-07-27 00:30:21,124 - pyskl - INFO - Epoch [132][100/3746] lr: 3.896e-03, eta: 16:17:17, time: 2.314, data_time: 1.323, memory: 15990, top1_acc: 0.5353, top5_acc: 0.7859, loss_cls: 2.5483, loss: 2.5483 +2024-07-27 00:31:44,257 - pyskl - INFO - Epoch [132][200/3746] lr: 3.885e-03, eta: 16:15:54, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5392, top5_acc: 0.7891, loss_cls: 2.5360, loss: 2.5360 +2024-07-27 00:33:06,711 - pyskl - INFO - Epoch [132][300/3746] lr: 3.875e-03, eta: 16:14:32, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5408, top5_acc: 0.7828, loss_cls: 2.5512, loss: 2.5512 +2024-07-27 00:34:28,775 - pyskl - INFO - Epoch [132][400/3746] lr: 3.864e-03, eta: 16:13:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5339, top5_acc: 0.7802, loss_cls: 2.5829, loss: 2.5829 +2024-07-27 00:35:51,116 - pyskl - INFO - Epoch [132][500/3746] lr: 3.853e-03, eta: 16:11:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5405, top5_acc: 0.7791, loss_cls: 2.5556, loss: 2.5556 +2024-07-27 00:37:13,256 - pyskl - INFO - Epoch [132][600/3746] lr: 3.842e-03, eta: 16:10:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5272, top5_acc: 0.7775, loss_cls: 2.5841, loss: 2.5841 +2024-07-27 00:38:35,321 - pyskl - INFO - Epoch [132][700/3746] lr: 3.831e-03, eta: 16:09:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5288, top5_acc: 0.7736, loss_cls: 2.6185, loss: 2.6185 +2024-07-27 00:39:58,131 - pyskl - INFO - Epoch [132][800/3746] lr: 3.821e-03, eta: 16:07:39, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5267, top5_acc: 0.7716, loss_cls: 2.6096, loss: 2.6096 +2024-07-27 00:41:20,662 - pyskl - INFO - Epoch [132][900/3746] lr: 3.810e-03, eta: 16:06:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5245, top5_acc: 0.7717, loss_cls: 2.6299, loss: 2.6299 +2024-07-27 00:42:43,725 - pyskl - INFO - Epoch [132][1000/3746] lr: 3.799e-03, eta: 16:04:54, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5173, top5_acc: 0.7661, loss_cls: 2.6538, loss: 2.6538 +2024-07-27 00:44:06,784 - pyskl - INFO - Epoch [132][1100/3746] lr: 3.789e-03, eta: 16:03:32, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5242, top5_acc: 0.7727, loss_cls: 2.6372, loss: 2.6372 +2024-07-27 00:45:29,374 - pyskl - INFO - Epoch [132][1200/3746] lr: 3.778e-03, eta: 16:02:09, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5239, top5_acc: 0.7695, loss_cls: 2.6406, loss: 2.6406 +2024-07-27 00:46:52,370 - pyskl - INFO - Epoch [132][1300/3746] lr: 3.767e-03, eta: 16:00:47, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5273, top5_acc: 0.7777, loss_cls: 2.5955, loss: 2.5955 +2024-07-27 00:48:14,987 - pyskl - INFO - Epoch [132][1400/3746] lr: 3.757e-03, eta: 15:59:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5270, top5_acc: 0.7748, loss_cls: 2.6185, loss: 2.6185 +2024-07-27 00:49:36,957 - pyskl - INFO - Epoch [132][1500/3746] lr: 3.746e-03, eta: 15:58:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5155, top5_acc: 0.7628, loss_cls: 2.6836, loss: 2.6836 +2024-07-27 00:51:00,382 - pyskl - INFO - Epoch [132][1600/3746] lr: 3.735e-03, eta: 15:56:39, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5133, top5_acc: 0.7697, loss_cls: 2.6663, loss: 2.6663 +2024-07-27 00:52:23,828 - pyskl - INFO - Epoch [132][1700/3746] lr: 3.725e-03, eta: 15:55:17, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5309, top5_acc: 0.7758, loss_cls: 2.6036, loss: 2.6036 +2024-07-27 00:53:46,102 - pyskl - INFO - Epoch [132][1800/3746] lr: 3.714e-03, eta: 15:53:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7769, loss_cls: 2.6032, loss: 2.6032 +2024-07-27 00:55:09,234 - pyskl - INFO - Epoch [132][1900/3746] lr: 3.704e-03, eta: 15:52:32, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5264, top5_acc: 0.7647, loss_cls: 2.6335, loss: 2.6335 +2024-07-27 00:56:31,626 - pyskl - INFO - Epoch [132][2000/3746] lr: 3.693e-03, eta: 15:51:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5175, top5_acc: 0.7633, loss_cls: 2.6616, loss: 2.6616 +2024-07-27 00:57:54,381 - pyskl - INFO - Epoch [132][2100/3746] lr: 3.683e-03, eta: 15:49:47, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5214, top5_acc: 0.7708, loss_cls: 2.6441, loss: 2.6441 +2024-07-27 00:59:17,743 - pyskl - INFO - Epoch [132][2200/3746] lr: 3.672e-03, eta: 15:48:25, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5223, top5_acc: 0.7662, loss_cls: 2.6536, loss: 2.6536 +2024-07-27 01:00:41,384 - pyskl - INFO - Epoch [132][2300/3746] lr: 3.662e-03, eta: 15:47:02, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5311, top5_acc: 0.7783, loss_cls: 2.6021, loss: 2.6021 +2024-07-27 01:02:04,684 - pyskl - INFO - Epoch [132][2400/3746] lr: 3.651e-03, eta: 15:45:40, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5139, top5_acc: 0.7631, loss_cls: 2.6513, loss: 2.6513 +2024-07-27 01:03:27,537 - pyskl - INFO - Epoch [132][2500/3746] lr: 3.641e-03, eta: 15:44:17, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5205, top5_acc: 0.7731, loss_cls: 2.6355, loss: 2.6355 +2024-07-27 01:04:49,944 - pyskl - INFO - Epoch [132][2600/3746] lr: 3.630e-03, eta: 15:42:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5189, top5_acc: 0.7692, loss_cls: 2.6663, loss: 2.6663 +2024-07-27 01:06:12,969 - pyskl - INFO - Epoch [132][2700/3746] lr: 3.620e-03, eta: 15:41:32, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5216, top5_acc: 0.7727, loss_cls: 2.6384, loss: 2.6384 +2024-07-27 01:07:35,590 - pyskl - INFO - Epoch [132][2800/3746] lr: 3.609e-03, eta: 15:40:10, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5172, top5_acc: 0.7695, loss_cls: 2.6583, loss: 2.6583 +2024-07-27 01:08:58,232 - pyskl - INFO - Epoch [132][2900/3746] lr: 3.599e-03, eta: 15:38:48, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5234, top5_acc: 0.7727, loss_cls: 2.6389, loss: 2.6389 +2024-07-27 01:10:19,463 - pyskl - INFO - Epoch [132][3000/3746] lr: 3.588e-03, eta: 15:37:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5203, top5_acc: 0.7683, loss_cls: 2.6543, loss: 2.6543 +2024-07-27 01:11:40,856 - pyskl - INFO - Epoch [132][3100/3746] lr: 3.578e-03, eta: 15:36:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5156, top5_acc: 0.7711, loss_cls: 2.6722, loss: 2.6722 +2024-07-27 01:13:02,559 - pyskl - INFO - Epoch [132][3200/3746] lr: 3.568e-03, eta: 15:34:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5155, top5_acc: 0.7700, loss_cls: 2.6795, loss: 2.6795 +2024-07-27 01:14:24,028 - pyskl - INFO - Epoch [132][3300/3746] lr: 3.557e-03, eta: 15:33:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5148, top5_acc: 0.7627, loss_cls: 2.6704, loss: 2.6704 +2024-07-27 01:15:45,671 - pyskl - INFO - Epoch [132][3400/3746] lr: 3.547e-03, eta: 15:31:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5105, top5_acc: 0.7619, loss_cls: 2.6735, loss: 2.6735 +2024-07-27 01:17:07,330 - pyskl - INFO - Epoch [132][3500/3746] lr: 3.537e-03, eta: 15:30:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5269, top5_acc: 0.7750, loss_cls: 2.6276, loss: 2.6276 +2024-07-27 01:18:29,616 - pyskl - INFO - Epoch [132][3600/3746] lr: 3.526e-03, eta: 15:29:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5238, top5_acc: 0.7727, loss_cls: 2.6244, loss: 2.6244 +2024-07-27 01:19:51,733 - pyskl - INFO - Epoch [132][3700/3746] lr: 3.516e-03, eta: 15:27:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5194, top5_acc: 0.7725, loss_cls: 2.6235, loss: 2.6235 +2024-07-27 01:20:31,515 - pyskl - INFO - Saving checkpoint at 132 epochs +2024-07-27 01:22:24,453 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 01:22:25,192 - pyskl - INFO - +top1_acc 0.4349 +top5_acc 0.6899 +2024-07-27 01:22:25,193 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 01:22:25,241 - pyskl - INFO - +mean_acc 0.4346 +2024-07-27 01:22:25,246 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_131.pth was removed +2024-07-27 01:22:25,554 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_132.pth. +2024-07-27 01:22:25,555 - pyskl - INFO - Best top1_acc is 0.4349 at 132 epoch. +2024-07-27 01:22:25,570 - pyskl - INFO - Epoch(val) [132][309] top1_acc: 0.4349, top5_acc: 0.6899, mean_class_accuracy: 0.4346 +2024-07-27 01:26:15,848 - pyskl - INFO - Epoch [133][100/3746] lr: 3.501e-03, eta: 15:26:01, time: 2.303, data_time: 1.307, memory: 15990, top1_acc: 0.5442, top5_acc: 0.7887, loss_cls: 2.4960, loss: 2.4960 +2024-07-27 01:27:38,903 - pyskl - INFO - Epoch [133][200/3746] lr: 3.491e-03, eta: 15:24:39, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5461, top5_acc: 0.7903, loss_cls: 2.5211, loss: 2.5211 +2024-07-27 01:29:02,488 - pyskl - INFO - Epoch [133][300/3746] lr: 3.480e-03, eta: 15:23:16, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5373, top5_acc: 0.7922, loss_cls: 2.5266, loss: 2.5266 +2024-07-27 01:30:25,318 - pyskl - INFO - Epoch [133][400/3746] lr: 3.470e-03, eta: 15:21:54, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5319, top5_acc: 0.7867, loss_cls: 2.5560, loss: 2.5560 +2024-07-27 01:31:48,249 - pyskl - INFO - Epoch [133][500/3746] lr: 3.460e-03, eta: 15:20:31, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5394, top5_acc: 0.7855, loss_cls: 2.5397, loss: 2.5397 +2024-07-27 01:33:11,309 - pyskl - INFO - Epoch [133][600/3746] lr: 3.450e-03, eta: 15:19:09, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5386, top5_acc: 0.7869, loss_cls: 2.5396, loss: 2.5396 +2024-07-27 01:34:34,470 - pyskl - INFO - Epoch [133][700/3746] lr: 3.440e-03, eta: 15:17:46, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5363, top5_acc: 0.7859, loss_cls: 2.5633, loss: 2.5633 +2024-07-27 01:35:56,488 - pyskl - INFO - Epoch [133][800/3746] lr: 3.429e-03, eta: 15:16:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5230, top5_acc: 0.7778, loss_cls: 2.5831, loss: 2.5831 +2024-07-27 01:37:19,191 - pyskl - INFO - Epoch [133][900/3746] lr: 3.419e-03, eta: 15:15:01, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5331, top5_acc: 0.7747, loss_cls: 2.6063, loss: 2.6063 +2024-07-27 01:38:43,027 - pyskl - INFO - Epoch [133][1000/3746] lr: 3.409e-03, eta: 15:13:39, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5348, top5_acc: 0.7837, loss_cls: 2.5372, loss: 2.5372 +2024-07-27 01:40:06,053 - pyskl - INFO - Epoch [133][1100/3746] lr: 3.399e-03, eta: 15:12:17, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5270, top5_acc: 0.7772, loss_cls: 2.5943, loss: 2.5943 +2024-07-27 01:41:29,183 - pyskl - INFO - Epoch [133][1200/3746] lr: 3.389e-03, eta: 15:10:54, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5308, top5_acc: 0.7811, loss_cls: 2.5353, loss: 2.5353 +2024-07-27 01:42:52,767 - pyskl - INFO - Epoch [133][1300/3746] lr: 3.379e-03, eta: 15:09:32, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5363, top5_acc: 0.7798, loss_cls: 2.5739, loss: 2.5739 +2024-07-27 01:44:15,724 - pyskl - INFO - Epoch [133][1400/3746] lr: 3.369e-03, eta: 15:08:09, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5289, top5_acc: 0.7806, loss_cls: 2.5877, loss: 2.5877 +2024-07-27 01:45:38,973 - pyskl - INFO - Epoch [133][1500/3746] lr: 3.359e-03, eta: 15:06:47, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5302, top5_acc: 0.7819, loss_cls: 2.5761, loss: 2.5761 +2024-07-27 01:47:02,668 - pyskl - INFO - Epoch [133][1600/3746] lr: 3.348e-03, eta: 15:05:24, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5281, top5_acc: 0.7842, loss_cls: 2.5759, loss: 2.5759 +2024-07-27 01:48:25,063 - pyskl - INFO - Epoch [133][1700/3746] lr: 3.338e-03, eta: 15:04:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5227, top5_acc: 0.7730, loss_cls: 2.6276, loss: 2.6276 +2024-07-27 01:49:47,127 - pyskl - INFO - Epoch [133][1800/3746] lr: 3.328e-03, eta: 15:02:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5300, top5_acc: 0.7775, loss_cls: 2.5954, loss: 2.5954 +2024-07-27 01:51:09,857 - pyskl - INFO - Epoch [133][1900/3746] lr: 3.318e-03, eta: 15:01:17, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5164, top5_acc: 0.7711, loss_cls: 2.6246, loss: 2.6246 +2024-07-27 01:52:32,866 - pyskl - INFO - Epoch [133][2000/3746] lr: 3.308e-03, eta: 14:59:54, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5231, top5_acc: 0.7712, loss_cls: 2.6259, loss: 2.6259 +2024-07-27 01:53:56,171 - pyskl - INFO - Epoch [133][2100/3746] lr: 3.298e-03, eta: 14:58:32, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5327, top5_acc: 0.7783, loss_cls: 2.5906, loss: 2.5906 +2024-07-27 01:55:19,054 - pyskl - INFO - Epoch [133][2200/3746] lr: 3.288e-03, eta: 14:57:09, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5247, top5_acc: 0.7755, loss_cls: 2.5971, loss: 2.5971 +2024-07-27 01:56:41,745 - pyskl - INFO - Epoch [133][2300/3746] lr: 3.278e-03, eta: 14:55:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5355, top5_acc: 0.7789, loss_cls: 2.5921, loss: 2.5921 +2024-07-27 01:58:04,268 - pyskl - INFO - Epoch [133][2400/3746] lr: 3.268e-03, eta: 14:54:24, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5250, top5_acc: 0.7797, loss_cls: 2.5949, loss: 2.5949 +2024-07-27 01:59:26,563 - pyskl - INFO - Epoch [133][2500/3746] lr: 3.259e-03, eta: 14:53:02, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5269, top5_acc: 0.7722, loss_cls: 2.6355, loss: 2.6355 +2024-07-27 02:00:49,147 - pyskl - INFO - Epoch [133][2600/3746] lr: 3.249e-03, eta: 14:51:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5280, top5_acc: 0.7734, loss_cls: 2.6032, loss: 2.6032 +2024-07-27 02:02:11,977 - pyskl - INFO - Epoch [133][2700/3746] lr: 3.239e-03, eta: 14:50:17, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5262, top5_acc: 0.7784, loss_cls: 2.6089, loss: 2.6089 +2024-07-27 02:03:34,007 - pyskl - INFO - Epoch [133][2800/3746] lr: 3.229e-03, eta: 14:48:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5400, top5_acc: 0.7789, loss_cls: 2.5712, loss: 2.5712 +2024-07-27 02:04:56,436 - pyskl - INFO - Epoch [133][2900/3746] lr: 3.219e-03, eta: 14:47:32, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5250, top5_acc: 0.7703, loss_cls: 2.6400, loss: 2.6400 +2024-07-27 02:06:19,059 - pyskl - INFO - Epoch [133][3000/3746] lr: 3.209e-03, eta: 14:46:09, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5230, top5_acc: 0.7727, loss_cls: 2.6406, loss: 2.6406 +2024-07-27 02:07:41,279 - pyskl - INFO - Epoch [133][3100/3746] lr: 3.199e-03, eta: 14:44:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5300, top5_acc: 0.7714, loss_cls: 2.6341, loss: 2.6341 +2024-07-27 02:09:03,618 - pyskl - INFO - Epoch [133][3200/3746] lr: 3.189e-03, eta: 14:43:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5130, top5_acc: 0.7733, loss_cls: 2.6589, loss: 2.6589 +2024-07-27 02:10:26,576 - pyskl - INFO - Epoch [133][3300/3746] lr: 3.180e-03, eta: 14:42:02, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5361, top5_acc: 0.7814, loss_cls: 2.5742, loss: 2.5742 +2024-07-27 02:11:49,312 - pyskl - INFO - Epoch [133][3400/3746] lr: 3.170e-03, eta: 14:40:39, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5323, top5_acc: 0.7669, loss_cls: 2.6328, loss: 2.6328 +2024-07-27 02:13:12,035 - pyskl - INFO - Epoch [133][3500/3746] lr: 3.160e-03, eta: 14:39:17, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5333, top5_acc: 0.7756, loss_cls: 2.6182, loss: 2.6182 +2024-07-27 02:14:35,751 - pyskl - INFO - Epoch [133][3600/3746] lr: 3.150e-03, eta: 14:37:54, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5270, top5_acc: 0.7728, loss_cls: 2.6113, loss: 2.6113 +2024-07-27 02:15:58,137 - pyskl - INFO - Epoch [133][3700/3746] lr: 3.140e-03, eta: 14:36:32, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5380, top5_acc: 0.7864, loss_cls: 2.5618, loss: 2.5618 +2024-07-27 02:16:37,306 - pyskl - INFO - Saving checkpoint at 133 epochs +2024-07-27 02:18:30,367 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 02:18:31,030 - pyskl - INFO - +top1_acc 0.4354 +top5_acc 0.6897 +2024-07-27 02:18:31,030 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 02:18:31,071 - pyskl - INFO - +mean_acc 0.4352 +2024-07-27 02:18:31,076 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_132.pth was removed +2024-07-27 02:18:31,327 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_133.pth. +2024-07-27 02:18:31,328 - pyskl - INFO - Best top1_acc is 0.4354 at 133 epoch. +2024-07-27 02:18:31,339 - pyskl - INFO - Epoch(val) [133][309] top1_acc: 0.4354, top5_acc: 0.6897, mean_class_accuracy: 0.4352 +2024-07-27 02:22:16,318 - pyskl - INFO - Epoch [134][100/3746] lr: 3.126e-03, eta: 14:34:45, time: 2.250, data_time: 1.260, memory: 15990, top1_acc: 0.5536, top5_acc: 0.7967, loss_cls: 2.4756, loss: 2.4756 +2024-07-27 02:23:39,074 - pyskl - INFO - Epoch [134][200/3746] lr: 3.117e-03, eta: 14:33:22, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5414, top5_acc: 0.7903, loss_cls: 2.5061, loss: 2.5061 +2024-07-27 02:25:01,846 - pyskl - INFO - Epoch [134][300/3746] lr: 3.107e-03, eta: 14:32:00, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5459, top5_acc: 0.7906, loss_cls: 2.4911, loss: 2.4911 +2024-07-27 02:26:24,801 - pyskl - INFO - Epoch [134][400/3746] lr: 3.097e-03, eta: 14:30:37, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5456, top5_acc: 0.7947, loss_cls: 2.4860, loss: 2.4860 +2024-07-27 02:27:47,553 - pyskl - INFO - Epoch [134][500/3746] lr: 3.087e-03, eta: 14:29:14, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5500, top5_acc: 0.7863, loss_cls: 2.5277, loss: 2.5277 +2024-07-27 02:29:10,346 - pyskl - INFO - Epoch [134][600/3746] lr: 3.078e-03, eta: 14:27:52, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5486, top5_acc: 0.8005, loss_cls: 2.4652, loss: 2.4652 +2024-07-27 02:30:33,293 - pyskl - INFO - Epoch [134][700/3746] lr: 3.068e-03, eta: 14:26:29, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5422, top5_acc: 0.7933, loss_cls: 2.4968, loss: 2.4968 +2024-07-27 02:31:56,015 - pyskl - INFO - Epoch [134][800/3746] lr: 3.059e-03, eta: 14:25:07, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5347, top5_acc: 0.7772, loss_cls: 2.5563, loss: 2.5563 +2024-07-27 02:33:19,057 - pyskl - INFO - Epoch [134][900/3746] lr: 3.049e-03, eta: 14:23:44, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5409, top5_acc: 0.7902, loss_cls: 2.5150, loss: 2.5150 +2024-07-27 02:34:42,039 - pyskl - INFO - Epoch [134][1000/3746] lr: 3.039e-03, eta: 14:22:22, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5509, top5_acc: 0.7828, loss_cls: 2.5355, loss: 2.5355 +2024-07-27 02:36:05,073 - pyskl - INFO - Epoch [134][1100/3746] lr: 3.030e-03, eta: 14:20:59, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5437, top5_acc: 0.7917, loss_cls: 2.5031, loss: 2.5031 +2024-07-27 02:37:29,083 - pyskl - INFO - Epoch [134][1200/3746] lr: 3.020e-03, eta: 14:19:37, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5392, top5_acc: 0.7866, loss_cls: 2.5350, loss: 2.5350 +2024-07-27 02:38:52,349 - pyskl - INFO - Epoch [134][1300/3746] lr: 3.011e-03, eta: 14:18:15, time: 0.833, data_time: 0.001, memory: 15990, top1_acc: 0.5420, top5_acc: 0.7833, loss_cls: 2.5375, loss: 2.5375 +2024-07-27 02:40:15,849 - pyskl - INFO - Epoch [134][1400/3746] lr: 3.001e-03, eta: 14:16:52, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5430, top5_acc: 0.7911, loss_cls: 2.5360, loss: 2.5360 +2024-07-27 02:41:39,359 - pyskl - INFO - Epoch [134][1500/3746] lr: 2.991e-03, eta: 14:15:30, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5531, top5_acc: 0.7939, loss_cls: 2.5019, loss: 2.5019 +2024-07-27 02:43:02,819 - pyskl - INFO - Epoch [134][1600/3746] lr: 2.982e-03, eta: 14:14:07, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5370, top5_acc: 0.7831, loss_cls: 2.5605, loss: 2.5605 +2024-07-27 02:44:25,159 - pyskl - INFO - Epoch [134][1700/3746] lr: 2.972e-03, eta: 14:12:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5300, top5_acc: 0.7841, loss_cls: 2.5630, loss: 2.5630 +2024-07-27 02:45:48,148 - pyskl - INFO - Epoch [134][1800/3746] lr: 2.963e-03, eta: 14:11:22, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5381, top5_acc: 0.7780, loss_cls: 2.5562, loss: 2.5562 +2024-07-27 02:47:10,542 - pyskl - INFO - Epoch [134][1900/3746] lr: 2.953e-03, eta: 14:10:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5411, top5_acc: 0.7878, loss_cls: 2.5205, loss: 2.5205 +2024-07-27 02:48:32,730 - pyskl - INFO - Epoch [134][2000/3746] lr: 2.944e-03, eta: 14:08:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5245, top5_acc: 0.7770, loss_cls: 2.5947, loss: 2.5947 +2024-07-27 02:49:56,155 - pyskl - INFO - Epoch [134][2100/3746] lr: 2.935e-03, eta: 14:07:15, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5372, top5_acc: 0.7805, loss_cls: 2.5669, loss: 2.5669 +2024-07-27 02:51:19,401 - pyskl - INFO - Epoch [134][2200/3746] lr: 2.925e-03, eta: 14:05:52, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5295, top5_acc: 0.7856, loss_cls: 2.5698, loss: 2.5698 +2024-07-27 02:52:42,497 - pyskl - INFO - Epoch [134][2300/3746] lr: 2.916e-03, eta: 14:04:30, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5408, top5_acc: 0.7842, loss_cls: 2.5470, loss: 2.5470 +2024-07-27 02:54:05,212 - pyskl - INFO - Epoch [134][2400/3746] lr: 2.906e-03, eta: 14:03:07, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5428, top5_acc: 0.7847, loss_cls: 2.5384, loss: 2.5384 +2024-07-27 02:55:27,511 - pyskl - INFO - Epoch [134][2500/3746] lr: 2.897e-03, eta: 14:01:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5448, top5_acc: 0.7836, loss_cls: 2.5351, loss: 2.5351 +2024-07-27 02:56:49,625 - pyskl - INFO - Epoch [134][2600/3746] lr: 2.888e-03, eta: 14:00:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5378, top5_acc: 0.7844, loss_cls: 2.5548, loss: 2.5548 +2024-07-27 02:58:12,298 - pyskl - INFO - Epoch [134][2700/3746] lr: 2.878e-03, eta: 13:59:00, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5430, top5_acc: 0.7812, loss_cls: 2.5165, loss: 2.5165 +2024-07-27 02:59:34,479 - pyskl - INFO - Epoch [134][2800/3746] lr: 2.869e-03, eta: 13:57:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5397, top5_acc: 0.7833, loss_cls: 2.5409, loss: 2.5409 +2024-07-27 03:00:56,997 - pyskl - INFO - Epoch [134][2900/3746] lr: 2.860e-03, eta: 13:56:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5275, top5_acc: 0.7884, loss_cls: 2.5853, loss: 2.5853 +2024-07-27 03:02:19,954 - pyskl - INFO - Epoch [134][3000/3746] lr: 2.850e-03, eta: 13:54:52, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5355, top5_acc: 0.7738, loss_cls: 2.6014, loss: 2.6014 +2024-07-27 03:03:42,671 - pyskl - INFO - Epoch [134][3100/3746] lr: 2.841e-03, eta: 13:53:29, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5175, top5_acc: 0.7761, loss_cls: 2.6166, loss: 2.6166 +2024-07-27 03:05:05,416 - pyskl - INFO - Epoch [134][3200/3746] lr: 2.832e-03, eta: 13:52:07, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5278, top5_acc: 0.7786, loss_cls: 2.5998, loss: 2.5998 +2024-07-27 03:06:28,272 - pyskl - INFO - Epoch [134][3300/3746] lr: 2.822e-03, eta: 13:50:44, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5322, top5_acc: 0.7802, loss_cls: 2.5816, loss: 2.5816 +2024-07-27 03:07:50,721 - pyskl - INFO - Epoch [134][3400/3746] lr: 2.813e-03, eta: 13:49:22, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5256, top5_acc: 0.7761, loss_cls: 2.5889, loss: 2.5889 +2024-07-27 03:09:13,811 - pyskl - INFO - Epoch [134][3500/3746] lr: 2.804e-03, eta: 13:47:59, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5355, top5_acc: 0.7839, loss_cls: 2.5563, loss: 2.5563 +2024-07-27 03:10:36,352 - pyskl - INFO - Epoch [134][3600/3746] lr: 2.795e-03, eta: 13:46:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5411, top5_acc: 0.7875, loss_cls: 2.5516, loss: 2.5516 +2024-07-27 03:11:59,254 - pyskl - INFO - Epoch [134][3700/3746] lr: 2.786e-03, eta: 13:45:14, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5292, top5_acc: 0.7794, loss_cls: 2.5977, loss: 2.5977 +2024-07-27 03:12:38,670 - pyskl - INFO - Saving checkpoint at 134 epochs +2024-07-27 03:14:32,180 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 03:14:32,857 - pyskl - INFO - +top1_acc 0.4388 +top5_acc 0.6901 +2024-07-27 03:14:32,857 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 03:14:32,897 - pyskl - INFO - +mean_acc 0.4384 +2024-07-27 03:14:32,901 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_133.pth was removed +2024-07-27 03:14:33,168 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_134.pth. +2024-07-27 03:14:33,169 - pyskl - INFO - Best top1_acc is 0.4388 at 134 epoch. +2024-07-27 03:14:33,179 - pyskl - INFO - Epoch(val) [134][309] top1_acc: 0.4388, top5_acc: 0.6901, mean_class_accuracy: 0.4384 +2024-07-27 03:18:18,972 - pyskl - INFO - Epoch [135][100/3746] lr: 2.772e-03, eta: 13:43:26, time: 2.258, data_time: 1.267, memory: 15990, top1_acc: 0.5542, top5_acc: 0.8052, loss_cls: 2.4446, loss: 2.4446 +2024-07-27 03:19:41,541 - pyskl - INFO - Epoch [135][200/3746] lr: 2.763e-03, eta: 13:42:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5625, top5_acc: 0.8058, loss_cls: 2.4126, loss: 2.4126 +2024-07-27 03:21:04,020 - pyskl - INFO - Epoch [135][300/3746] lr: 2.754e-03, eta: 13:40:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5508, top5_acc: 0.7939, loss_cls: 2.4676, loss: 2.4676 +2024-07-27 03:22:25,986 - pyskl - INFO - Epoch [135][400/3746] lr: 2.745e-03, eta: 13:39:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5545, top5_acc: 0.7959, loss_cls: 2.4801, loss: 2.4801 +2024-07-27 03:23:48,243 - pyskl - INFO - Epoch [135][500/3746] lr: 2.735e-03, eta: 13:37:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5511, top5_acc: 0.7897, loss_cls: 2.5172, loss: 2.5172 +2024-07-27 03:25:10,156 - pyskl - INFO - Epoch [135][600/3746] lr: 2.726e-03, eta: 13:36:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5609, top5_acc: 0.8000, loss_cls: 2.4486, loss: 2.4486 +2024-07-27 03:26:32,522 - pyskl - INFO - Epoch [135][700/3746] lr: 2.717e-03, eta: 13:35:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5523, top5_acc: 0.7884, loss_cls: 2.4739, loss: 2.4739 +2024-07-27 03:27:54,155 - pyskl - INFO - Epoch [135][800/3746] lr: 2.708e-03, eta: 13:33:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5475, top5_acc: 0.7898, loss_cls: 2.4921, loss: 2.4921 +2024-07-27 03:29:17,294 - pyskl - INFO - Epoch [135][900/3746] lr: 2.699e-03, eta: 13:32:25, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5428, top5_acc: 0.7914, loss_cls: 2.4920, loss: 2.4920 +2024-07-27 03:30:39,760 - pyskl - INFO - Epoch [135][1000/3746] lr: 2.690e-03, eta: 13:31:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5431, top5_acc: 0.7919, loss_cls: 2.5089, loss: 2.5089 +2024-07-27 03:32:02,641 - pyskl - INFO - Epoch [135][1100/3746] lr: 2.681e-03, eta: 13:29:40, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5403, top5_acc: 0.7970, loss_cls: 2.4880, loss: 2.4880 +2024-07-27 03:33:26,198 - pyskl - INFO - Epoch [135][1200/3746] lr: 2.672e-03, eta: 13:28:18, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5531, top5_acc: 0.7956, loss_cls: 2.4674, loss: 2.4674 +2024-07-27 03:34:48,413 - pyskl - INFO - Epoch [135][1300/3746] lr: 2.663e-03, eta: 13:26:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5483, top5_acc: 0.7937, loss_cls: 2.4891, loss: 2.4891 +2024-07-27 03:36:11,511 - pyskl - INFO - Epoch [135][1400/3746] lr: 2.654e-03, eta: 13:25:33, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5537, top5_acc: 0.7944, loss_cls: 2.4743, loss: 2.4743 +2024-07-27 03:37:34,970 - pyskl - INFO - Epoch [135][1500/3746] lr: 2.645e-03, eta: 13:24:10, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5531, top5_acc: 0.7991, loss_cls: 2.4670, loss: 2.4670 +2024-07-27 03:38:58,150 - pyskl - INFO - Epoch [135][1600/3746] lr: 2.636e-03, eta: 13:22:48, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5536, top5_acc: 0.7942, loss_cls: 2.4938, loss: 2.4938 +2024-07-27 03:40:20,426 - pyskl - INFO - Epoch [135][1700/3746] lr: 2.627e-03, eta: 13:21:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5391, top5_acc: 0.7911, loss_cls: 2.5153, loss: 2.5153 +2024-07-27 03:41:43,812 - pyskl - INFO - Epoch [135][1800/3746] lr: 2.618e-03, eta: 13:20:03, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5561, top5_acc: 0.7958, loss_cls: 2.4595, loss: 2.4595 +2024-07-27 03:43:06,471 - pyskl - INFO - Epoch [135][1900/3746] lr: 2.609e-03, eta: 13:18:40, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5386, top5_acc: 0.7825, loss_cls: 2.5328, loss: 2.5328 +2024-07-27 03:44:29,679 - pyskl - INFO - Epoch [135][2000/3746] lr: 2.600e-03, eta: 13:17:18, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5564, top5_acc: 0.7911, loss_cls: 2.4810, loss: 2.4810 +2024-07-27 03:45:53,582 - pyskl - INFO - Epoch [135][2100/3746] lr: 2.591e-03, eta: 13:15:55, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5414, top5_acc: 0.7806, loss_cls: 2.5486, loss: 2.5486 +2024-07-27 03:47:16,458 - pyskl - INFO - Epoch [135][2200/3746] lr: 2.583e-03, eta: 13:14:33, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5383, top5_acc: 0.7934, loss_cls: 2.5155, loss: 2.5155 +2024-07-27 03:48:39,340 - pyskl - INFO - Epoch [135][2300/3746] lr: 2.574e-03, eta: 13:13:10, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5381, top5_acc: 0.7858, loss_cls: 2.5273, loss: 2.5273 +2024-07-27 03:50:01,731 - pyskl - INFO - Epoch [135][2400/3746] lr: 2.565e-03, eta: 13:11:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5436, top5_acc: 0.7842, loss_cls: 2.5225, loss: 2.5225 +2024-07-27 03:51:23,956 - pyskl - INFO - Epoch [135][2500/3746] lr: 2.556e-03, eta: 13:10:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5450, top5_acc: 0.7937, loss_cls: 2.4946, loss: 2.4946 +2024-07-27 03:52:46,305 - pyskl - INFO - Epoch [135][2600/3746] lr: 2.547e-03, eta: 13:09:02, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5456, top5_acc: 0.7900, loss_cls: 2.5261, loss: 2.5261 +2024-07-27 03:54:09,002 - pyskl - INFO - Epoch [135][2700/3746] lr: 2.538e-03, eta: 13:07:40, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5425, top5_acc: 0.7884, loss_cls: 2.5256, loss: 2.5256 +2024-07-27 03:55:31,874 - pyskl - INFO - Epoch [135][2800/3746] lr: 2.530e-03, eta: 13:06:17, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5436, top5_acc: 0.7942, loss_cls: 2.5115, loss: 2.5115 +2024-07-27 03:56:54,617 - pyskl - INFO - Epoch [135][2900/3746] lr: 2.521e-03, eta: 13:04:55, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5359, top5_acc: 0.7842, loss_cls: 2.5733, loss: 2.5733 +2024-07-27 03:58:17,360 - pyskl - INFO - Epoch [135][3000/3746] lr: 2.512e-03, eta: 13:03:32, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5498, top5_acc: 0.7958, loss_cls: 2.4883, loss: 2.4883 +2024-07-27 03:59:39,881 - pyskl - INFO - Epoch [135][3100/3746] lr: 2.503e-03, eta: 13:02:10, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5441, top5_acc: 0.7994, loss_cls: 2.4682, loss: 2.4682 +2024-07-27 04:01:02,239 - pyskl - INFO - Epoch [135][3200/3746] lr: 2.495e-03, eta: 13:00:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5361, top5_acc: 0.7850, loss_cls: 2.5583, loss: 2.5583 +2024-07-27 04:02:25,712 - pyskl - INFO - Epoch [135][3300/3746] lr: 2.486e-03, eta: 12:59:24, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5406, top5_acc: 0.7841, loss_cls: 2.5407, loss: 2.5407 +2024-07-27 04:03:47,804 - pyskl - INFO - Epoch [135][3400/3746] lr: 2.477e-03, eta: 12:58:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5463, top5_acc: 0.7845, loss_cls: 2.5185, loss: 2.5185 +2024-07-27 04:05:10,941 - pyskl - INFO - Epoch [135][3500/3746] lr: 2.469e-03, eta: 12:56:39, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5464, top5_acc: 0.7894, loss_cls: 2.4904, loss: 2.4904 +2024-07-27 04:06:33,645 - pyskl - INFO - Epoch [135][3600/3746] lr: 2.460e-03, eta: 12:55:17, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5577, top5_acc: 0.7977, loss_cls: 2.4640, loss: 2.4640 +2024-07-27 04:07:56,162 - pyskl - INFO - Epoch [135][3700/3746] lr: 2.451e-03, eta: 12:53:54, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5295, top5_acc: 0.7778, loss_cls: 2.5760, loss: 2.5760 +2024-07-27 04:08:36,003 - pyskl - INFO - Saving checkpoint at 135 epochs +2024-07-27 04:10:28,670 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 04:10:29,327 - pyskl - INFO - +top1_acc 0.4393 +top5_acc 0.6944 +2024-07-27 04:10:29,328 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 04:10:29,367 - pyskl - INFO - +mean_acc 0.4390 +2024-07-27 04:10:29,372 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_134.pth was removed +2024-07-27 04:10:29,656 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_135.pth. +2024-07-27 04:10:29,657 - pyskl - INFO - Best top1_acc is 0.4393 at 135 epoch. +2024-07-27 04:10:29,670 - pyskl - INFO - Epoch(val) [135][309] top1_acc: 0.4393, top5_acc: 0.6944, mean_class_accuracy: 0.4390 +2024-07-27 04:14:16,397 - pyskl - INFO - Epoch [136][100/3746] lr: 2.439e-03, eta: 12:52:05, time: 2.267, data_time: 1.271, memory: 15990, top1_acc: 0.5758, top5_acc: 0.8094, loss_cls: 2.3785, loss: 2.3785 +2024-07-27 04:15:39,697 - pyskl - INFO - Epoch [136][200/3746] lr: 2.430e-03, eta: 12:50:43, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5702, top5_acc: 0.8016, loss_cls: 2.4074, loss: 2.4074 +2024-07-27 04:17:02,063 - pyskl - INFO - Epoch [136][300/3746] lr: 2.421e-03, eta: 12:49:20, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5697, top5_acc: 0.8089, loss_cls: 2.3752, loss: 2.3752 +2024-07-27 04:18:24,468 - pyskl - INFO - Epoch [136][400/3746] lr: 2.413e-03, eta: 12:47:58, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5573, top5_acc: 0.8053, loss_cls: 2.4293, loss: 2.4293 +2024-07-27 04:19:46,037 - pyskl - INFO - Epoch [136][500/3746] lr: 2.404e-03, eta: 12:46:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5620, top5_acc: 0.8087, loss_cls: 2.3962, loss: 2.3962 +2024-07-27 04:21:08,155 - pyskl - INFO - Epoch [136][600/3746] lr: 2.396e-03, eta: 12:45:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5533, top5_acc: 0.8030, loss_cls: 2.4454, loss: 2.4454 +2024-07-27 04:22:30,235 - pyskl - INFO - Epoch [136][700/3746] lr: 2.387e-03, eta: 12:43:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5698, top5_acc: 0.8052, loss_cls: 2.3945, loss: 2.3945 +2024-07-27 04:23:52,970 - pyskl - INFO - Epoch [136][800/3746] lr: 2.379e-03, eta: 12:42:27, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5711, top5_acc: 0.8095, loss_cls: 2.3745, loss: 2.3745 +2024-07-27 04:25:14,654 - pyskl - INFO - Epoch [136][900/3746] lr: 2.370e-03, eta: 12:41:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5661, top5_acc: 0.8092, loss_cls: 2.3997, loss: 2.3997 +2024-07-27 04:26:36,550 - pyskl - INFO - Epoch [136][1000/3746] lr: 2.362e-03, eta: 12:39:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5556, top5_acc: 0.8030, loss_cls: 2.4211, loss: 2.4211 +2024-07-27 04:27:58,823 - pyskl - INFO - Epoch [136][1100/3746] lr: 2.353e-03, eta: 12:38:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5555, top5_acc: 0.8045, loss_cls: 2.4292, loss: 2.4292 +2024-07-27 04:29:21,009 - pyskl - INFO - Epoch [136][1200/3746] lr: 2.345e-03, eta: 12:36:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5459, top5_acc: 0.7908, loss_cls: 2.4887, loss: 2.4887 +2024-07-27 04:30:42,913 - pyskl - INFO - Epoch [136][1300/3746] lr: 2.336e-03, eta: 12:35:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5537, top5_acc: 0.7958, loss_cls: 2.4328, loss: 2.4328 +2024-07-27 04:32:04,769 - pyskl - INFO - Epoch [136][1400/3746] lr: 2.328e-03, eta: 12:34:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5606, top5_acc: 0.7983, loss_cls: 2.4328, loss: 2.4328 +2024-07-27 04:33:26,215 - pyskl - INFO - Epoch [136][1500/3746] lr: 2.319e-03, eta: 12:32:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5597, top5_acc: 0.7966, loss_cls: 2.4639, loss: 2.4639 +2024-07-27 04:34:47,186 - pyskl - INFO - Epoch [136][1600/3746] lr: 2.311e-03, eta: 12:31:26, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5539, top5_acc: 0.8009, loss_cls: 2.4462, loss: 2.4462 +2024-07-27 04:36:09,064 - pyskl - INFO - Epoch [136][1700/3746] lr: 2.303e-03, eta: 12:30:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5564, top5_acc: 0.7948, loss_cls: 2.4428, loss: 2.4428 +2024-07-27 04:37:30,203 - pyskl - INFO - Epoch [136][1800/3746] lr: 2.294e-03, eta: 12:28:40, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5473, top5_acc: 0.7947, loss_cls: 2.4743, loss: 2.4743 +2024-07-27 04:38:52,463 - pyskl - INFO - Epoch [136][1900/3746] lr: 2.286e-03, eta: 12:27:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5511, top5_acc: 0.7970, loss_cls: 2.4697, loss: 2.4697 +2024-07-27 04:40:14,343 - pyskl - INFO - Epoch [136][2000/3746] lr: 2.277e-03, eta: 12:25:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5584, top5_acc: 0.8000, loss_cls: 2.4314, loss: 2.4314 +2024-07-27 04:41:36,230 - pyskl - INFO - Epoch [136][2100/3746] lr: 2.269e-03, eta: 12:24:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5541, top5_acc: 0.8028, loss_cls: 2.4357, loss: 2.4357 +2024-07-27 04:42:57,883 - pyskl - INFO - Epoch [136][2200/3746] lr: 2.261e-03, eta: 12:23:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5564, top5_acc: 0.7952, loss_cls: 2.4375, loss: 2.4375 +2024-07-27 04:44:19,756 - pyskl - INFO - Epoch [136][2300/3746] lr: 2.253e-03, eta: 12:21:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5614, top5_acc: 0.8009, loss_cls: 2.4483, loss: 2.4483 +2024-07-27 04:45:41,172 - pyskl - INFO - Epoch [136][2400/3746] lr: 2.244e-03, eta: 12:20:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5519, top5_acc: 0.7922, loss_cls: 2.5018, loss: 2.5018 +2024-07-27 04:47:03,009 - pyskl - INFO - Epoch [136][2500/3746] lr: 2.236e-03, eta: 12:19:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5487, top5_acc: 0.7953, loss_cls: 2.4521, loss: 2.4521 +2024-07-27 04:48:24,286 - pyskl - INFO - Epoch [136][2600/3746] lr: 2.228e-03, eta: 12:17:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5552, top5_acc: 0.7923, loss_cls: 2.4693, loss: 2.4693 +2024-07-27 04:49:46,016 - pyskl - INFO - Epoch [136][2700/3746] lr: 2.219e-03, eta: 12:16:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5445, top5_acc: 0.7866, loss_cls: 2.5042, loss: 2.5042 +2024-07-27 04:51:08,240 - pyskl - INFO - Epoch [136][2800/3746] lr: 2.211e-03, eta: 12:14:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5502, top5_acc: 0.7916, loss_cls: 2.4727, loss: 2.4727 +2024-07-27 04:52:29,881 - pyskl - INFO - Epoch [136][2900/3746] lr: 2.203e-03, eta: 12:13:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5455, top5_acc: 0.7905, loss_cls: 2.5034, loss: 2.5034 +2024-07-27 04:53:51,342 - pyskl - INFO - Epoch [136][3000/3746] lr: 2.195e-03, eta: 12:12:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5498, top5_acc: 0.7925, loss_cls: 2.4820, loss: 2.4820 +2024-07-27 04:55:12,355 - pyskl - INFO - Epoch [136][3100/3746] lr: 2.187e-03, eta: 12:10:45, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5513, top5_acc: 0.7894, loss_cls: 2.4820, loss: 2.4820 +2024-07-27 04:56:34,568 - pyskl - INFO - Epoch [136][3200/3746] lr: 2.178e-03, eta: 12:09:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5502, top5_acc: 0.7903, loss_cls: 2.4704, loss: 2.4704 +2024-07-27 04:57:55,958 - pyskl - INFO - Epoch [136][3300/3746] lr: 2.170e-03, eta: 12:08:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5489, top5_acc: 0.7928, loss_cls: 2.4971, loss: 2.4971 +2024-07-27 04:59:17,851 - pyskl - INFO - Epoch [136][3400/3746] lr: 2.162e-03, eta: 12:06:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5447, top5_acc: 0.7894, loss_cls: 2.5092, loss: 2.5092 +2024-07-27 05:00:40,892 - pyskl - INFO - Epoch [136][3500/3746] lr: 2.154e-03, eta: 12:05:15, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5566, top5_acc: 0.7991, loss_cls: 2.4463, loss: 2.4463 +2024-07-27 05:02:03,045 - pyskl - INFO - Epoch [136][3600/3746] lr: 2.146e-03, eta: 12:03:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5536, top5_acc: 0.7906, loss_cls: 2.4854, loss: 2.4854 +2024-07-27 05:03:25,799 - pyskl - INFO - Epoch [136][3700/3746] lr: 2.138e-03, eta: 12:02:30, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5580, top5_acc: 0.7917, loss_cls: 2.4786, loss: 2.4786 +2024-07-27 05:04:04,730 - pyskl - INFO - Saving checkpoint at 136 epochs +2024-07-27 05:05:58,868 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 05:05:59,553 - pyskl - INFO - +top1_acc 0.4420 +top5_acc 0.6942 +2024-07-27 05:05:59,553 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 05:05:59,600 - pyskl - INFO - +mean_acc 0.4417 +2024-07-27 05:05:59,605 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_135.pth was removed +2024-07-27 05:05:59,861 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_136.pth. +2024-07-27 05:05:59,862 - pyskl - INFO - Best top1_acc is 0.4420 at 136 epoch. +2024-07-27 05:05:59,878 - pyskl - INFO - Epoch(val) [136][309] top1_acc: 0.4420, top5_acc: 0.6942, mean_class_accuracy: 0.4417 +2024-07-27 05:09:51,788 - pyskl - INFO - Epoch [137][100/3746] lr: 2.126e-03, eta: 12:00:40, time: 2.319, data_time: 1.323, memory: 15990, top1_acc: 0.5806, top5_acc: 0.8181, loss_cls: 2.3175, loss: 2.3175 +2024-07-27 05:11:15,132 - pyskl - INFO - Epoch [137][200/3746] lr: 2.118e-03, eta: 11:59:18, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5825, top5_acc: 0.8156, loss_cls: 2.3155, loss: 2.3155 +2024-07-27 05:12:37,954 - pyskl - INFO - Epoch [137][300/3746] lr: 2.110e-03, eta: 11:57:55, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5648, top5_acc: 0.8098, loss_cls: 2.3749, loss: 2.3749 +2024-07-27 05:14:00,940 - pyskl - INFO - Epoch [137][400/3746] lr: 2.102e-03, eta: 11:56:33, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5647, top5_acc: 0.8070, loss_cls: 2.3895, loss: 2.3895 +2024-07-27 05:15:23,317 - pyskl - INFO - Epoch [137][500/3746] lr: 2.094e-03, eta: 11:55:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5639, top5_acc: 0.8075, loss_cls: 2.3994, loss: 2.3994 +2024-07-27 05:16:46,003 - pyskl - INFO - Epoch [137][600/3746] lr: 2.086e-03, eta: 11:53:48, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5559, top5_acc: 0.8027, loss_cls: 2.4336, loss: 2.4336 +2024-07-27 05:18:08,587 - pyskl - INFO - Epoch [137][700/3746] lr: 2.078e-03, eta: 11:52:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5705, top5_acc: 0.8006, loss_cls: 2.4060, loss: 2.4060 +2024-07-27 05:19:31,510 - pyskl - INFO - Epoch [137][800/3746] lr: 2.070e-03, eta: 11:51:02, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5627, top5_acc: 0.8053, loss_cls: 2.3653, loss: 2.3653 +2024-07-27 05:20:53,763 - pyskl - INFO - Epoch [137][900/3746] lr: 2.062e-03, eta: 11:49:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5600, top5_acc: 0.8064, loss_cls: 2.3882, loss: 2.3882 +2024-07-27 05:22:16,062 - pyskl - INFO - Epoch [137][1000/3746] lr: 2.054e-03, eta: 11:48:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5739, top5_acc: 0.8145, loss_cls: 2.3343, loss: 2.3343 +2024-07-27 05:23:39,911 - pyskl - INFO - Epoch [137][1100/3746] lr: 2.046e-03, eta: 11:46:55, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5741, top5_acc: 0.8094, loss_cls: 2.3689, loss: 2.3689 +2024-07-27 05:25:02,770 - pyskl - INFO - Epoch [137][1200/3746] lr: 2.038e-03, eta: 11:45:32, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5727, top5_acc: 0.8072, loss_cls: 2.3745, loss: 2.3745 +2024-07-27 05:26:25,854 - pyskl - INFO - Epoch [137][1300/3746] lr: 2.030e-03, eta: 11:44:09, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5713, top5_acc: 0.8070, loss_cls: 2.3958, loss: 2.3958 +2024-07-27 05:27:49,443 - pyskl - INFO - Epoch [137][1400/3746] lr: 2.022e-03, eta: 11:42:47, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5550, top5_acc: 0.8030, loss_cls: 2.4563, loss: 2.4563 +2024-07-27 05:29:12,846 - pyskl - INFO - Epoch [137][1500/3746] lr: 2.015e-03, eta: 11:41:24, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5609, top5_acc: 0.8022, loss_cls: 2.4234, loss: 2.4234 +2024-07-27 05:30:35,764 - pyskl - INFO - Epoch [137][1600/3746] lr: 2.007e-03, eta: 11:40:02, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5675, top5_acc: 0.8072, loss_cls: 2.4019, loss: 2.4019 +2024-07-27 05:31:58,062 - pyskl - INFO - Epoch [137][1700/3746] lr: 1.999e-03, eta: 11:38:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5611, top5_acc: 0.8048, loss_cls: 2.4166, loss: 2.4166 +2024-07-27 05:33:20,186 - pyskl - INFO - Epoch [137][1800/3746] lr: 1.991e-03, eta: 11:37:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5683, top5_acc: 0.8019, loss_cls: 2.4015, loss: 2.4015 +2024-07-27 05:34:43,074 - pyskl - INFO - Epoch [137][1900/3746] lr: 1.983e-03, eta: 11:35:54, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5586, top5_acc: 0.8056, loss_cls: 2.4161, loss: 2.4161 +2024-07-27 05:36:06,353 - pyskl - INFO - Epoch [137][2000/3746] lr: 1.976e-03, eta: 11:34:31, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5652, top5_acc: 0.8056, loss_cls: 2.3765, loss: 2.3765 +2024-07-27 05:37:29,705 - pyskl - INFO - Epoch [137][2100/3746] lr: 1.968e-03, eta: 11:33:09, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5536, top5_acc: 0.8042, loss_cls: 2.4082, loss: 2.4082 +2024-07-27 05:38:52,435 - pyskl - INFO - Epoch [137][2200/3746] lr: 1.960e-03, eta: 11:31:46, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5656, top5_acc: 0.8006, loss_cls: 2.4170, loss: 2.4170 +2024-07-27 05:40:15,144 - pyskl - INFO - Epoch [137][2300/3746] lr: 1.952e-03, eta: 11:30:24, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5545, top5_acc: 0.8031, loss_cls: 2.4274, loss: 2.4274 +2024-07-27 05:41:37,454 - pyskl - INFO - Epoch [137][2400/3746] lr: 1.944e-03, eta: 11:29:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5609, top5_acc: 0.8055, loss_cls: 2.4058, loss: 2.4058 +2024-07-27 05:42:59,620 - pyskl - INFO - Epoch [137][2500/3746] lr: 1.937e-03, eta: 11:27:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5517, top5_acc: 0.7931, loss_cls: 2.4680, loss: 2.4680 +2024-07-27 05:44:21,622 - pyskl - INFO - Epoch [137][2600/3746] lr: 1.929e-03, eta: 11:26:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5491, top5_acc: 0.7987, loss_cls: 2.4571, loss: 2.4571 +2024-07-27 05:45:44,250 - pyskl - INFO - Epoch [137][2700/3746] lr: 1.921e-03, eta: 11:24:53, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5670, top5_acc: 0.8059, loss_cls: 2.4093, loss: 2.4093 +2024-07-27 05:47:06,722 - pyskl - INFO - Epoch [137][2800/3746] lr: 1.914e-03, eta: 11:23:30, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5666, top5_acc: 0.8025, loss_cls: 2.4043, loss: 2.4043 +2024-07-27 05:48:29,745 - pyskl - INFO - Epoch [137][2900/3746] lr: 1.906e-03, eta: 11:22:08, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5645, top5_acc: 0.8055, loss_cls: 2.3932, loss: 2.3932 +2024-07-27 05:49:52,170 - pyskl - INFO - Epoch [137][3000/3746] lr: 1.898e-03, eta: 11:20:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5663, top5_acc: 0.8070, loss_cls: 2.3792, loss: 2.3792 +2024-07-27 05:51:14,763 - pyskl - INFO - Epoch [137][3100/3746] lr: 1.891e-03, eta: 11:19:23, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5550, top5_acc: 0.8006, loss_cls: 2.4495, loss: 2.4495 +2024-07-27 05:52:37,168 - pyskl - INFO - Epoch [137][3200/3746] lr: 1.883e-03, eta: 11:18:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5642, top5_acc: 0.7986, loss_cls: 2.4437, loss: 2.4437 +2024-07-27 05:53:59,675 - pyskl - INFO - Epoch [137][3300/3746] lr: 1.876e-03, eta: 11:16:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5555, top5_acc: 0.8044, loss_cls: 2.4363, loss: 2.4363 +2024-07-27 05:55:22,757 - pyskl - INFO - Epoch [137][3400/3746] lr: 1.868e-03, eta: 11:15:15, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5589, top5_acc: 0.7963, loss_cls: 2.4363, loss: 2.4363 +2024-07-27 05:56:45,598 - pyskl - INFO - Epoch [137][3500/3746] lr: 1.860e-03, eta: 11:13:52, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5581, top5_acc: 0.7955, loss_cls: 2.4369, loss: 2.4369 +2024-07-27 05:58:08,260 - pyskl - INFO - Epoch [137][3600/3746] lr: 1.853e-03, eta: 11:12:30, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5616, top5_acc: 0.8008, loss_cls: 2.4350, loss: 2.4350 +2024-07-27 05:59:31,113 - pyskl - INFO - Epoch [137][3700/3746] lr: 1.845e-03, eta: 11:11:07, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5513, top5_acc: 0.7975, loss_cls: 2.4621, loss: 2.4621 +2024-07-27 06:00:10,966 - pyskl - INFO - Saving checkpoint at 137 epochs +2024-07-27 06:02:04,325 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 06:02:04,992 - pyskl - INFO - +top1_acc 0.4453 +top5_acc 0.6990 +2024-07-27 06:02:04,992 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 06:02:05,036 - pyskl - INFO - +mean_acc 0.4451 +2024-07-27 06:02:05,041 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_136.pth was removed +2024-07-27 06:02:05,311 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_137.pth. +2024-07-27 06:02:05,311 - pyskl - INFO - Best top1_acc is 0.4453 at 137 epoch. +2024-07-27 06:02:05,323 - pyskl - INFO - Epoch(val) [137][309] top1_acc: 0.4453, top5_acc: 0.6990, mean_class_accuracy: 0.4451 +2024-07-27 06:05:53,292 - pyskl - INFO - Epoch [138][100/3746] lr: 1.834e-03, eta: 11:09:17, time: 2.280, data_time: 1.296, memory: 15990, top1_acc: 0.5711, top5_acc: 0.8142, loss_cls: 2.3379, loss: 2.3379 +2024-07-27 06:07:16,447 - pyskl - INFO - Epoch [138][200/3746] lr: 1.827e-03, eta: 11:07:54, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5841, top5_acc: 0.8223, loss_cls: 2.2836, loss: 2.2836 +2024-07-27 06:08:39,286 - pyskl - INFO - Epoch [138][300/3746] lr: 1.819e-03, eta: 11:06:31, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5648, top5_acc: 0.8141, loss_cls: 2.3710, loss: 2.3710 +2024-07-27 06:10:02,492 - pyskl - INFO - Epoch [138][400/3746] lr: 1.812e-03, eta: 11:05:09, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5844, top5_acc: 0.8197, loss_cls: 2.2843, loss: 2.2843 +2024-07-27 06:11:25,789 - pyskl - INFO - Epoch [138][500/3746] lr: 1.805e-03, eta: 11:03:46, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5767, top5_acc: 0.8220, loss_cls: 2.3362, loss: 2.3362 +2024-07-27 06:12:48,682 - pyskl - INFO - Epoch [138][600/3746] lr: 1.797e-03, eta: 11:02:24, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5825, top5_acc: 0.8231, loss_cls: 2.2691, loss: 2.2691 +2024-07-27 06:14:11,156 - pyskl - INFO - Epoch [138][700/3746] lr: 1.790e-03, eta: 11:01:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5822, top5_acc: 0.8211, loss_cls: 2.3225, loss: 2.3225 +2024-07-27 06:15:35,041 - pyskl - INFO - Epoch [138][800/3746] lr: 1.782e-03, eta: 10:59:39, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5763, top5_acc: 0.8116, loss_cls: 2.3563, loss: 2.3563 +2024-07-27 06:16:57,779 - pyskl - INFO - Epoch [138][900/3746] lr: 1.775e-03, eta: 10:58:16, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5650, top5_acc: 0.8086, loss_cls: 2.3684, loss: 2.3684 +2024-07-27 06:18:21,333 - pyskl - INFO - Epoch [138][1000/3746] lr: 1.768e-03, eta: 10:56:53, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5733, top5_acc: 0.8209, loss_cls: 2.3332, loss: 2.3332 +2024-07-27 06:19:44,930 - pyskl - INFO - Epoch [138][1100/3746] lr: 1.760e-03, eta: 10:55:31, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5739, top5_acc: 0.8139, loss_cls: 2.3237, loss: 2.3237 +2024-07-27 06:21:07,476 - pyskl - INFO - Epoch [138][1200/3746] lr: 1.753e-03, eta: 10:54:08, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5737, top5_acc: 0.8173, loss_cls: 2.3347, loss: 2.3347 +2024-07-27 06:22:30,667 - pyskl - INFO - Epoch [138][1300/3746] lr: 1.745e-03, eta: 10:52:46, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5727, top5_acc: 0.8105, loss_cls: 2.3482, loss: 2.3482 +2024-07-27 06:23:54,035 - pyskl - INFO - Epoch [138][1400/3746] lr: 1.738e-03, eta: 10:51:23, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5742, top5_acc: 0.8117, loss_cls: 2.3489, loss: 2.3489 +2024-07-27 06:25:16,653 - pyskl - INFO - Epoch [138][1500/3746] lr: 1.731e-03, eta: 10:50:00, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5708, top5_acc: 0.8125, loss_cls: 2.3511, loss: 2.3511 +2024-07-27 06:26:39,611 - pyskl - INFO - Epoch [138][1600/3746] lr: 1.724e-03, eta: 10:48:38, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5842, top5_acc: 0.8170, loss_cls: 2.3241, loss: 2.3241 +2024-07-27 06:28:01,555 - pyskl - INFO - Epoch [138][1700/3746] lr: 1.716e-03, eta: 10:47:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5764, top5_acc: 0.8117, loss_cls: 2.3547, loss: 2.3547 +2024-07-27 06:29:23,835 - pyskl - INFO - Epoch [138][1800/3746] lr: 1.709e-03, eta: 10:45:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5766, top5_acc: 0.8084, loss_cls: 2.3519, loss: 2.3519 +2024-07-27 06:30:46,865 - pyskl - INFO - Epoch [138][1900/3746] lr: 1.702e-03, eta: 10:44:30, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5672, top5_acc: 0.8050, loss_cls: 2.3930, loss: 2.3930 +2024-07-27 06:32:10,576 - pyskl - INFO - Epoch [138][2000/3746] lr: 1.695e-03, eta: 10:43:07, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5687, top5_acc: 0.8058, loss_cls: 2.3791, loss: 2.3791 +2024-07-27 06:33:33,754 - pyskl - INFO - Epoch [138][2100/3746] lr: 1.687e-03, eta: 10:41:45, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5798, top5_acc: 0.8195, loss_cls: 2.3428, loss: 2.3428 +2024-07-27 06:34:56,315 - pyskl - INFO - Epoch [138][2200/3746] lr: 1.680e-03, eta: 10:40:22, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5663, top5_acc: 0.8095, loss_cls: 2.3835, loss: 2.3835 +2024-07-27 06:36:19,092 - pyskl - INFO - Epoch [138][2300/3746] lr: 1.673e-03, eta: 10:38:59, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5711, top5_acc: 0.8087, loss_cls: 2.3679, loss: 2.3679 +2024-07-27 06:37:42,221 - pyskl - INFO - Epoch [138][2400/3746] lr: 1.666e-03, eta: 10:37:37, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5800, top5_acc: 0.8091, loss_cls: 2.3559, loss: 2.3559 +2024-07-27 06:39:04,173 - pyskl - INFO - Epoch [138][2500/3746] lr: 1.659e-03, eta: 10:36:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5689, top5_acc: 0.8008, loss_cls: 2.4041, loss: 2.4041 +2024-07-27 06:40:26,718 - pyskl - INFO - Epoch [138][2600/3746] lr: 1.652e-03, eta: 10:34:52, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5780, top5_acc: 0.8167, loss_cls: 2.3439, loss: 2.3439 +2024-07-27 06:41:49,965 - pyskl - INFO - Epoch [138][2700/3746] lr: 1.644e-03, eta: 10:33:29, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5698, top5_acc: 0.8075, loss_cls: 2.3598, loss: 2.3598 +2024-07-27 06:43:12,743 - pyskl - INFO - Epoch [138][2800/3746] lr: 1.637e-03, eta: 10:32:06, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5627, top5_acc: 0.8064, loss_cls: 2.3844, loss: 2.3844 +2024-07-27 06:44:35,279 - pyskl - INFO - Epoch [138][2900/3746] lr: 1.630e-03, eta: 10:30:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5672, top5_acc: 0.8069, loss_cls: 2.3739, loss: 2.3739 +2024-07-27 06:45:58,392 - pyskl - INFO - Epoch [138][3000/3746] lr: 1.623e-03, eta: 10:29:21, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5678, top5_acc: 0.7997, loss_cls: 2.3975, loss: 2.3975 +2024-07-27 06:47:21,244 - pyskl - INFO - Epoch [138][3100/3746] lr: 1.616e-03, eta: 10:27:58, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5664, top5_acc: 0.8087, loss_cls: 2.3665, loss: 2.3665 +2024-07-27 06:48:43,422 - pyskl - INFO - Epoch [138][3200/3746] lr: 1.609e-03, eta: 10:26:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5678, top5_acc: 0.7998, loss_cls: 2.3804, loss: 2.3804 +2024-07-27 06:50:06,054 - pyskl - INFO - Epoch [138][3300/3746] lr: 1.602e-03, eta: 10:25:13, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5822, top5_acc: 0.8136, loss_cls: 2.3084, loss: 2.3084 +2024-07-27 06:51:29,532 - pyskl - INFO - Epoch [138][3400/3746] lr: 1.595e-03, eta: 10:23:51, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5633, top5_acc: 0.8094, loss_cls: 2.3732, loss: 2.3732 +2024-07-27 06:52:52,576 - pyskl - INFO - Epoch [138][3500/3746] lr: 1.588e-03, eta: 10:22:28, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5644, top5_acc: 0.8037, loss_cls: 2.4016, loss: 2.4016 +2024-07-27 06:54:15,272 - pyskl - INFO - Epoch [138][3600/3746] lr: 1.581e-03, eta: 10:21:05, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5680, top5_acc: 0.8016, loss_cls: 2.3938, loss: 2.3938 +2024-07-27 06:55:38,718 - pyskl - INFO - Epoch [138][3700/3746] lr: 1.574e-03, eta: 10:19:43, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5723, top5_acc: 0.8048, loss_cls: 2.3720, loss: 2.3720 +2024-07-27 06:56:18,189 - pyskl - INFO - Saving checkpoint at 138 epochs +2024-07-27 06:58:11,123 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 06:58:11,797 - pyskl - INFO - +top1_acc 0.4533 +top5_acc 0.7101 +2024-07-27 06:58:11,797 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 06:58:11,841 - pyskl - INFO - +mean_acc 0.4530 +2024-07-27 06:58:11,846 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_137.pth was removed +2024-07-27 06:58:12,110 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_138.pth. +2024-07-27 06:58:12,110 - pyskl - INFO - Best top1_acc is 0.4533 at 138 epoch. +2024-07-27 06:58:12,123 - pyskl - INFO - Epoch(val) [138][309] top1_acc: 0.4533, top5_acc: 0.7101, mean_class_accuracy: 0.4530 +2024-07-27 07:01:58,768 - pyskl - INFO - Epoch [139][100/3746] lr: 1.564e-03, eta: 10:17:51, time: 2.266, data_time: 1.281, memory: 15990, top1_acc: 0.6003, top5_acc: 0.8239, loss_cls: 2.2466, loss: 2.2466 +2024-07-27 07:03:20,992 - pyskl - INFO - Epoch [139][200/3746] lr: 1.557e-03, eta: 10:16:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5878, top5_acc: 0.8198, loss_cls: 2.2681, loss: 2.2681 +2024-07-27 07:04:43,463 - pyskl - INFO - Epoch [139][300/3746] lr: 1.550e-03, eta: 10:15:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5947, top5_acc: 0.8244, loss_cls: 2.2334, loss: 2.2334 +2024-07-27 07:06:05,322 - pyskl - INFO - Epoch [139][400/3746] lr: 1.543e-03, eta: 10:13:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5844, top5_acc: 0.8208, loss_cls: 2.3101, loss: 2.3101 +2024-07-27 07:07:27,564 - pyskl - INFO - Epoch [139][500/3746] lr: 1.536e-03, eta: 10:12:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5866, top5_acc: 0.8205, loss_cls: 2.2610, loss: 2.2610 +2024-07-27 07:08:48,987 - pyskl - INFO - Epoch [139][600/3746] lr: 1.529e-03, eta: 10:10:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5892, top5_acc: 0.8266, loss_cls: 2.2664, loss: 2.2664 +2024-07-27 07:10:11,783 - pyskl - INFO - Epoch [139][700/3746] lr: 1.523e-03, eta: 10:09:35, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5998, top5_acc: 0.8289, loss_cls: 2.2433, loss: 2.2433 +2024-07-27 07:11:34,769 - pyskl - INFO - Epoch [139][800/3746] lr: 1.516e-03, eta: 10:08:13, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5822, top5_acc: 0.8252, loss_cls: 2.2742, loss: 2.2742 +2024-07-27 07:12:56,946 - pyskl - INFO - Epoch [139][900/3746] lr: 1.509e-03, eta: 10:06:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5852, top5_acc: 0.8173, loss_cls: 2.3197, loss: 2.3197 +2024-07-27 07:14:20,259 - pyskl - INFO - Epoch [139][1000/3746] lr: 1.502e-03, eta: 10:05:27, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5933, top5_acc: 0.8302, loss_cls: 2.2449, loss: 2.2449 +2024-07-27 07:15:42,950 - pyskl - INFO - Epoch [139][1100/3746] lr: 1.495e-03, eta: 10:04:05, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5831, top5_acc: 0.8211, loss_cls: 2.2799, loss: 2.2799 +2024-07-27 07:17:05,426 - pyskl - INFO - Epoch [139][1200/3746] lr: 1.489e-03, eta: 10:02:42, time: 0.825, data_time: 0.001, memory: 15990, top1_acc: 0.5839, top5_acc: 0.8209, loss_cls: 2.2804, loss: 2.2804 +2024-07-27 07:18:28,768 - pyskl - INFO - Epoch [139][1300/3746] lr: 1.482e-03, eta: 10:01:19, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5783, top5_acc: 0.8098, loss_cls: 2.3307, loss: 2.3307 +2024-07-27 07:19:52,038 - pyskl - INFO - Epoch [139][1400/3746] lr: 1.475e-03, eta: 9:59:57, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5863, top5_acc: 0.8214, loss_cls: 2.2725, loss: 2.2725 +2024-07-27 07:21:14,463 - pyskl - INFO - Epoch [139][1500/3746] lr: 1.468e-03, eta: 9:58:34, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5837, top5_acc: 0.8114, loss_cls: 2.3161, loss: 2.3161 +2024-07-27 07:22:37,408 - pyskl - INFO - Epoch [139][1600/3746] lr: 1.462e-03, eta: 9:57:11, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5802, top5_acc: 0.8137, loss_cls: 2.3403, loss: 2.3403 +2024-07-27 07:23:59,742 - pyskl - INFO - Epoch [139][1700/3746] lr: 1.455e-03, eta: 9:55:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5861, top5_acc: 0.8234, loss_cls: 2.2592, loss: 2.2592 +2024-07-27 07:25:22,893 - pyskl - INFO - Epoch [139][1800/3746] lr: 1.448e-03, eta: 9:54:26, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5783, top5_acc: 0.8133, loss_cls: 2.3305, loss: 2.3305 +2024-07-27 07:26:46,628 - pyskl - INFO - Epoch [139][1900/3746] lr: 1.442e-03, eta: 9:53:04, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5902, top5_acc: 0.8125, loss_cls: 2.3001, loss: 2.3001 +2024-07-27 07:28:09,794 - pyskl - INFO - Epoch [139][2000/3746] lr: 1.435e-03, eta: 9:51:41, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5867, top5_acc: 0.8175, loss_cls: 2.3030, loss: 2.3030 +2024-07-27 07:29:32,399 - pyskl - INFO - Epoch [139][2100/3746] lr: 1.428e-03, eta: 9:50:18, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5858, top5_acc: 0.8167, loss_cls: 2.3127, loss: 2.3127 +2024-07-27 07:30:54,695 - pyskl - INFO - Epoch [139][2200/3746] lr: 1.422e-03, eta: 9:48:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5808, top5_acc: 0.8222, loss_cls: 2.2791, loss: 2.2791 +2024-07-27 07:32:17,472 - pyskl - INFO - Epoch [139][2300/3746] lr: 1.415e-03, eta: 9:47:33, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5822, top5_acc: 0.8106, loss_cls: 2.3211, loss: 2.3211 +2024-07-27 07:33:40,105 - pyskl - INFO - Epoch [139][2400/3746] lr: 1.408e-03, eta: 9:46:10, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5909, top5_acc: 0.8225, loss_cls: 2.2525, loss: 2.2525 +2024-07-27 07:35:02,859 - pyskl - INFO - Epoch [139][2500/3746] lr: 1.402e-03, eta: 9:44:48, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5766, top5_acc: 0.8139, loss_cls: 2.3320, loss: 2.3320 +2024-07-27 07:36:25,770 - pyskl - INFO - Epoch [139][2600/3746] lr: 1.395e-03, eta: 9:43:25, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5825, top5_acc: 0.8169, loss_cls: 2.2973, loss: 2.2973 +2024-07-27 07:37:49,007 - pyskl - INFO - Epoch [139][2700/3746] lr: 1.389e-03, eta: 9:42:02, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5798, top5_acc: 0.8137, loss_cls: 2.3091, loss: 2.3091 +2024-07-27 07:39:11,633 - pyskl - INFO - Epoch [139][2800/3746] lr: 1.382e-03, eta: 9:40:40, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5866, top5_acc: 0.8186, loss_cls: 2.2978, loss: 2.2978 +2024-07-27 07:40:34,068 - pyskl - INFO - Epoch [139][2900/3746] lr: 1.376e-03, eta: 9:39:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5808, top5_acc: 0.8208, loss_cls: 2.3174, loss: 2.3174 +2024-07-27 07:41:57,130 - pyskl - INFO - Epoch [139][3000/3746] lr: 1.369e-03, eta: 9:37:55, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5828, top5_acc: 0.8137, loss_cls: 2.3159, loss: 2.3159 +2024-07-27 07:43:19,000 - pyskl - INFO - Epoch [139][3100/3746] lr: 1.363e-03, eta: 9:36:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5744, top5_acc: 0.8080, loss_cls: 2.3602, loss: 2.3602 +2024-07-27 07:44:40,739 - pyskl - INFO - Epoch [139][3200/3746] lr: 1.356e-03, eta: 9:35:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5733, top5_acc: 0.8194, loss_cls: 2.3422, loss: 2.3422 +2024-07-27 07:46:02,939 - pyskl - INFO - Epoch [139][3300/3746] lr: 1.350e-03, eta: 9:33:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5953, top5_acc: 0.8223, loss_cls: 2.2440, loss: 2.2440 +2024-07-27 07:47:24,191 - pyskl - INFO - Epoch [139][3400/3746] lr: 1.343e-03, eta: 9:32:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5750, top5_acc: 0.8139, loss_cls: 2.3418, loss: 2.3418 +2024-07-27 07:48:46,233 - pyskl - INFO - Epoch [139][3500/3746] lr: 1.337e-03, eta: 9:31:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5791, top5_acc: 0.8158, loss_cls: 2.3034, loss: 2.3034 +2024-07-27 07:50:08,324 - pyskl - INFO - Epoch [139][3600/3746] lr: 1.330e-03, eta: 9:29:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5731, top5_acc: 0.8103, loss_cls: 2.3518, loss: 2.3518 +2024-07-27 07:51:29,708 - pyskl - INFO - Epoch [139][3700/3746] lr: 1.324e-03, eta: 9:28:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5806, top5_acc: 0.8187, loss_cls: 2.3044, loss: 2.3044 +2024-07-27 07:52:09,312 - pyskl - INFO - Saving checkpoint at 139 epochs +2024-07-27 07:54:02,060 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 07:54:02,749 - pyskl - INFO - +top1_acc 0.4554 +top5_acc 0.7083 +2024-07-27 07:54:02,749 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 07:54:02,802 - pyskl - INFO - +mean_acc 0.4551 +2024-07-27 07:54:02,809 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_138.pth was removed +2024-07-27 07:54:03,083 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_139.pth. +2024-07-27 07:54:03,083 - pyskl - INFO - Best top1_acc is 0.4554 at 139 epoch. +2024-07-27 07:54:03,102 - pyskl - INFO - Epoch(val) [139][309] top1_acc: 0.4554, top5_acc: 0.7083, mean_class_accuracy: 0.4551 +2024-07-27 07:57:53,189 - pyskl - INFO - Epoch [140][100/3746] lr: 1.315e-03, eta: 9:26:23, time: 2.301, data_time: 1.310, memory: 15990, top1_acc: 0.6170, top5_acc: 0.8355, loss_cls: 2.1723, loss: 2.1723 +2024-07-27 07:59:15,797 - pyskl - INFO - Epoch [140][200/3746] lr: 1.308e-03, eta: 9:25:01, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5992, top5_acc: 0.8355, loss_cls: 2.1872, loss: 2.1872 +2024-07-27 08:00:38,082 - pyskl - INFO - Epoch [140][300/3746] lr: 1.302e-03, eta: 9:23:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5889, top5_acc: 0.8300, loss_cls: 2.2396, loss: 2.2396 +2024-07-27 08:02:00,435 - pyskl - INFO - Epoch [140][400/3746] lr: 1.296e-03, eta: 9:22:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6067, top5_acc: 0.8383, loss_cls: 2.1898, loss: 2.1898 +2024-07-27 08:03:22,975 - pyskl - INFO - Epoch [140][500/3746] lr: 1.289e-03, eta: 9:20:53, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6008, top5_acc: 0.8314, loss_cls: 2.2057, loss: 2.2057 +2024-07-27 08:04:45,068 - pyskl - INFO - Epoch [140][600/3746] lr: 1.283e-03, eta: 9:19:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5927, top5_acc: 0.8269, loss_cls: 2.2495, loss: 2.2495 +2024-07-27 08:06:08,338 - pyskl - INFO - Epoch [140][700/3746] lr: 1.277e-03, eta: 9:18:07, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5953, top5_acc: 0.8223, loss_cls: 2.2537, loss: 2.2537 +2024-07-27 08:07:30,996 - pyskl - INFO - Epoch [140][800/3746] lr: 1.271e-03, eta: 9:16:45, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5967, top5_acc: 0.8347, loss_cls: 2.1948, loss: 2.1948 +2024-07-27 08:08:53,833 - pyskl - INFO - Epoch [140][900/3746] lr: 1.264e-03, eta: 9:15:22, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6064, top5_acc: 0.8259, loss_cls: 2.2262, loss: 2.2262 +2024-07-27 08:10:17,779 - pyskl - INFO - Epoch [140][1000/3746] lr: 1.258e-03, eta: 9:13:59, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.6047, top5_acc: 0.8302, loss_cls: 2.1980, loss: 2.1980 +2024-07-27 08:11:40,474 - pyskl - INFO - Epoch [140][1100/3746] lr: 1.252e-03, eta: 9:12:37, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6075, top5_acc: 0.8297, loss_cls: 2.2017, loss: 2.2017 +2024-07-27 08:13:03,700 - pyskl - INFO - Epoch [140][1200/3746] lr: 1.246e-03, eta: 9:11:14, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5981, top5_acc: 0.8194, loss_cls: 2.2487, loss: 2.2487 +2024-07-27 08:14:26,866 - pyskl - INFO - Epoch [140][1300/3746] lr: 1.239e-03, eta: 9:09:51, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5823, top5_acc: 0.8189, loss_cls: 2.2851, loss: 2.2851 +2024-07-27 08:15:49,628 - pyskl - INFO - Epoch [140][1400/3746] lr: 1.233e-03, eta: 9:08:29, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5919, top5_acc: 0.8211, loss_cls: 2.2312, loss: 2.2312 +2024-07-27 08:17:13,151 - pyskl - INFO - Epoch [140][1500/3746] lr: 1.227e-03, eta: 9:07:06, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5909, top5_acc: 0.8223, loss_cls: 2.2636, loss: 2.2636 +2024-07-27 08:18:36,378 - pyskl - INFO - Epoch [140][1600/3746] lr: 1.221e-03, eta: 9:05:44, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5994, top5_acc: 0.8283, loss_cls: 2.2147, loss: 2.2147 +2024-07-27 08:19:59,157 - pyskl - INFO - Epoch [140][1700/3746] lr: 1.215e-03, eta: 9:04:21, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5931, top5_acc: 0.8247, loss_cls: 2.2382, loss: 2.2382 +2024-07-27 08:21:22,580 - pyskl - INFO - Epoch [140][1800/3746] lr: 1.209e-03, eta: 9:02:58, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5925, top5_acc: 0.8217, loss_cls: 2.2509, loss: 2.2509 +2024-07-27 08:22:45,202 - pyskl - INFO - Epoch [140][1900/3746] lr: 1.203e-03, eta: 9:01:36, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6000, top5_acc: 0.8239, loss_cls: 2.2150, loss: 2.2150 +2024-07-27 08:24:08,351 - pyskl - INFO - Epoch [140][2000/3746] lr: 1.196e-03, eta: 9:00:13, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5905, top5_acc: 0.8256, loss_cls: 2.2529, loss: 2.2529 +2024-07-27 08:25:31,401 - pyskl - INFO - Epoch [140][2100/3746] lr: 1.190e-03, eta: 8:58:50, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5973, top5_acc: 0.8233, loss_cls: 2.2465, loss: 2.2465 +2024-07-27 08:26:54,238 - pyskl - INFO - Epoch [140][2200/3746] lr: 1.184e-03, eta: 8:57:28, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5950, top5_acc: 0.8253, loss_cls: 2.2470, loss: 2.2470 +2024-07-27 08:28:17,021 - pyskl - INFO - Epoch [140][2300/3746] lr: 1.178e-03, eta: 8:56:05, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5833, top5_acc: 0.8266, loss_cls: 2.2763, loss: 2.2763 +2024-07-27 08:29:39,381 - pyskl - INFO - Epoch [140][2400/3746] lr: 1.172e-03, eta: 8:54:42, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5884, top5_acc: 0.8222, loss_cls: 2.2559, loss: 2.2559 +2024-07-27 08:31:01,239 - pyskl - INFO - Epoch [140][2500/3746] lr: 1.166e-03, eta: 8:53:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6083, top5_acc: 0.8363, loss_cls: 2.1890, loss: 2.1890 +2024-07-27 08:32:23,103 - pyskl - INFO - Epoch [140][2600/3746] lr: 1.160e-03, eta: 8:51:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5825, top5_acc: 0.8181, loss_cls: 2.3056, loss: 2.3056 +2024-07-27 08:33:45,114 - pyskl - INFO - Epoch [140][2700/3746] lr: 1.154e-03, eta: 8:50:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5898, top5_acc: 0.8244, loss_cls: 2.2680, loss: 2.2680 +2024-07-27 08:35:07,369 - pyskl - INFO - Epoch [140][2800/3746] lr: 1.148e-03, eta: 8:49:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5813, top5_acc: 0.8213, loss_cls: 2.2995, loss: 2.2995 +2024-07-27 08:36:30,289 - pyskl - INFO - Epoch [140][2900/3746] lr: 1.142e-03, eta: 8:47:49, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5906, top5_acc: 0.8248, loss_cls: 2.2614, loss: 2.2614 +2024-07-27 08:37:52,389 - pyskl - INFO - Epoch [140][3000/3746] lr: 1.136e-03, eta: 8:46:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5805, top5_acc: 0.8200, loss_cls: 2.3077, loss: 2.3077 +2024-07-27 08:39:14,388 - pyskl - INFO - Epoch [140][3100/3746] lr: 1.131e-03, eta: 8:45:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5939, top5_acc: 0.8305, loss_cls: 2.2360, loss: 2.2360 +2024-07-27 08:40:36,054 - pyskl - INFO - Epoch [140][3200/3746] lr: 1.125e-03, eta: 8:43:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5856, top5_acc: 0.8195, loss_cls: 2.2797, loss: 2.2797 +2024-07-27 08:41:59,375 - pyskl - INFO - Epoch [140][3300/3746] lr: 1.119e-03, eta: 8:42:18, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5880, top5_acc: 0.8169, loss_cls: 2.2678, loss: 2.2678 +2024-07-27 08:43:21,734 - pyskl - INFO - Epoch [140][3400/3746] lr: 1.113e-03, eta: 8:40:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5892, top5_acc: 0.8252, loss_cls: 2.2475, loss: 2.2475 +2024-07-27 08:44:44,040 - pyskl - INFO - Epoch [140][3500/3746] lr: 1.107e-03, eta: 8:39:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5883, top5_acc: 0.8248, loss_cls: 2.2529, loss: 2.2529 +2024-07-27 08:46:07,127 - pyskl - INFO - Epoch [140][3600/3746] lr: 1.101e-03, eta: 8:38:10, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5897, top5_acc: 0.8223, loss_cls: 2.2605, loss: 2.2605 +2024-07-27 08:47:30,076 - pyskl - INFO - Epoch [140][3700/3746] lr: 1.095e-03, eta: 8:36:47, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5837, top5_acc: 0.8170, loss_cls: 2.3071, loss: 2.3071 +2024-07-27 08:48:09,721 - pyskl - INFO - Saving checkpoint at 140 epochs +2024-07-27 08:50:02,905 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 08:50:03,570 - pyskl - INFO - +top1_acc 0.4586 +top5_acc 0.7094 +2024-07-27 08:50:03,571 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 08:50:03,612 - pyskl - INFO - +mean_acc 0.4583 +2024-07-27 08:50:03,617 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_139.pth was removed +2024-07-27 08:50:03,880 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_140.pth. +2024-07-27 08:50:03,881 - pyskl - INFO - Best top1_acc is 0.4586 at 140 epoch. +2024-07-27 08:50:03,893 - pyskl - INFO - Epoch(val) [140][309] top1_acc: 0.4586, top5_acc: 0.7094, mean_class_accuracy: 0.4583 +2024-07-27 08:53:49,410 - pyskl - INFO - Epoch [141][100/3746] lr: 1.087e-03, eta: 8:34:54, time: 2.255, data_time: 1.265, memory: 15990, top1_acc: 0.6102, top5_acc: 0.8352, loss_cls: 2.1708, loss: 2.1708 +2024-07-27 08:55:11,032 - pyskl - INFO - Epoch [141][200/3746] lr: 1.081e-03, eta: 8:33:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6064, top5_acc: 0.8380, loss_cls: 2.1748, loss: 2.1748 +2024-07-27 08:56:33,862 - pyskl - INFO - Epoch [141][300/3746] lr: 1.075e-03, eta: 8:32:09, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6084, top5_acc: 0.8342, loss_cls: 2.1848, loss: 2.1848 +2024-07-27 08:57:56,132 - pyskl - INFO - Epoch [141][400/3746] lr: 1.070e-03, eta: 8:30:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6044, top5_acc: 0.8403, loss_cls: 2.1574, loss: 2.1574 +2024-07-27 08:59:18,196 - pyskl - INFO - Epoch [141][500/3746] lr: 1.064e-03, eta: 8:29:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6191, top5_acc: 0.8370, loss_cls: 2.1563, loss: 2.1563 +2024-07-27 09:00:40,465 - pyskl - INFO - Epoch [141][600/3746] lr: 1.058e-03, eta: 8:28:00, time: 0.823, data_time: 0.001, memory: 15990, top1_acc: 0.5989, top5_acc: 0.8409, loss_cls: 2.1765, loss: 2.1765 +2024-07-27 09:02:02,957 - pyskl - INFO - Epoch [141][700/3746] lr: 1.052e-03, eta: 8:26:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6153, top5_acc: 0.8353, loss_cls: 2.1530, loss: 2.1530 +2024-07-27 09:03:25,379 - pyskl - INFO - Epoch [141][800/3746] lr: 1.047e-03, eta: 8:25:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6053, top5_acc: 0.8397, loss_cls: 2.1700, loss: 2.1700 +2024-07-27 09:04:49,113 - pyskl - INFO - Epoch [141][900/3746] lr: 1.041e-03, eta: 8:23:52, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.6028, top5_acc: 0.8416, loss_cls: 2.1838, loss: 2.1838 +2024-07-27 09:06:11,541 - pyskl - INFO - Epoch [141][1000/3746] lr: 1.035e-03, eta: 8:22:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6062, top5_acc: 0.8336, loss_cls: 2.1978, loss: 2.1978 +2024-07-27 09:07:33,606 - pyskl - INFO - Epoch [141][1100/3746] lr: 1.030e-03, eta: 8:21:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6150, top5_acc: 0.8408, loss_cls: 2.1259, loss: 2.1259 +2024-07-27 09:08:56,706 - pyskl - INFO - Epoch [141][1200/3746] lr: 1.024e-03, eta: 8:19:44, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6186, top5_acc: 0.8431, loss_cls: 2.1489, loss: 2.1489 +2024-07-27 09:10:19,194 - pyskl - INFO - Epoch [141][1300/3746] lr: 1.018e-03, eta: 8:18:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6102, top5_acc: 0.8363, loss_cls: 2.1640, loss: 2.1640 +2024-07-27 09:11:41,171 - pyskl - INFO - Epoch [141][1400/3746] lr: 1.013e-03, eta: 8:16:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6094, top5_acc: 0.8345, loss_cls: 2.2031, loss: 2.2031 +2024-07-27 09:13:03,450 - pyskl - INFO - Epoch [141][1500/3746] lr: 1.007e-03, eta: 8:15:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6045, top5_acc: 0.8381, loss_cls: 2.1871, loss: 2.1871 +2024-07-27 09:14:25,291 - pyskl - INFO - Epoch [141][1600/3746] lr: 1.002e-03, eta: 8:14:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6138, top5_acc: 0.8359, loss_cls: 2.1601, loss: 2.1601 +2024-07-27 09:15:47,182 - pyskl - INFO - Epoch [141][1700/3746] lr: 9.961e-04, eta: 8:12:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6139, top5_acc: 0.8298, loss_cls: 2.1634, loss: 2.1634 +2024-07-27 09:17:09,713 - pyskl - INFO - Epoch [141][1800/3746] lr: 9.905e-04, eta: 8:11:28, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6045, top5_acc: 0.8320, loss_cls: 2.1955, loss: 2.1955 +2024-07-27 09:18:32,035 - pyskl - INFO - Epoch [141][1900/3746] lr: 9.850e-04, eta: 8:10:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5978, top5_acc: 0.8352, loss_cls: 2.2071, loss: 2.2071 +2024-07-27 09:19:53,901 - pyskl - INFO - Epoch [141][2000/3746] lr: 9.795e-04, eta: 8:08:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6086, top5_acc: 0.8311, loss_cls: 2.1970, loss: 2.1970 +2024-07-27 09:21:16,133 - pyskl - INFO - Epoch [141][2100/3746] lr: 9.740e-04, eta: 8:07:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6055, top5_acc: 0.8381, loss_cls: 2.1706, loss: 2.1706 +2024-07-27 09:22:38,159 - pyskl - INFO - Epoch [141][2200/3746] lr: 9.685e-04, eta: 8:05:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6000, top5_acc: 0.8298, loss_cls: 2.2066, loss: 2.2066 +2024-07-27 09:24:00,107 - pyskl - INFO - Epoch [141][2300/3746] lr: 9.630e-04, eta: 8:04:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6005, top5_acc: 0.8377, loss_cls: 2.1752, loss: 2.1752 +2024-07-27 09:25:22,158 - pyskl - INFO - Epoch [141][2400/3746] lr: 9.576e-04, eta: 8:03:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6145, top5_acc: 0.8387, loss_cls: 2.1484, loss: 2.1484 +2024-07-27 09:26:44,376 - pyskl - INFO - Epoch [141][2500/3746] lr: 9.522e-04, eta: 8:01:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6020, top5_acc: 0.8375, loss_cls: 2.1948, loss: 2.1948 +2024-07-27 09:28:06,256 - pyskl - INFO - Epoch [141][2600/3746] lr: 9.467e-04, eta: 8:00:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6009, top5_acc: 0.8284, loss_cls: 2.2183, loss: 2.2183 +2024-07-27 09:29:27,872 - pyskl - INFO - Epoch [141][2700/3746] lr: 9.413e-04, eta: 7:59:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5988, top5_acc: 0.8352, loss_cls: 2.1981, loss: 2.1981 +2024-07-27 09:30:49,709 - pyskl - INFO - Epoch [141][2800/3746] lr: 9.359e-04, eta: 7:57:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6012, top5_acc: 0.8306, loss_cls: 2.1950, loss: 2.1950 +2024-07-27 09:32:11,159 - pyskl - INFO - Epoch [141][2900/3746] lr: 9.306e-04, eta: 7:56:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5997, top5_acc: 0.8241, loss_cls: 2.2304, loss: 2.2304 +2024-07-27 09:33:32,556 - pyskl - INFO - Epoch [141][3000/3746] lr: 9.252e-04, eta: 7:54:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6044, top5_acc: 0.8355, loss_cls: 2.1885, loss: 2.1885 +2024-07-27 09:34:54,485 - pyskl - INFO - Epoch [141][3100/3746] lr: 9.199e-04, eta: 7:53:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5956, top5_acc: 0.8270, loss_cls: 2.2201, loss: 2.2201 +2024-07-27 09:36:16,666 - pyskl - INFO - Epoch [141][3200/3746] lr: 9.145e-04, eta: 7:52:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5897, top5_acc: 0.8186, loss_cls: 2.2594, loss: 2.2594 +2024-07-27 09:37:39,480 - pyskl - INFO - Epoch [141][3300/3746] lr: 9.092e-04, eta: 7:50:47, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5927, top5_acc: 0.8322, loss_cls: 2.2127, loss: 2.2127 +2024-07-27 09:39:01,913 - pyskl - INFO - Epoch [141][3400/3746] lr: 9.039e-04, eta: 7:49:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5973, top5_acc: 0.8306, loss_cls: 2.2147, loss: 2.2147 +2024-07-27 09:40:24,015 - pyskl - INFO - Epoch [141][3500/3746] lr: 8.986e-04, eta: 7:48:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5989, top5_acc: 0.8281, loss_cls: 2.2253, loss: 2.2253 +2024-07-27 09:41:46,629 - pyskl - INFO - Epoch [141][3600/3746] lr: 8.934e-04, eta: 7:46:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6027, top5_acc: 0.8284, loss_cls: 2.2061, loss: 2.2061 +2024-07-27 09:43:08,896 - pyskl - INFO - Epoch [141][3700/3746] lr: 8.881e-04, eta: 7:45:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5903, top5_acc: 0.8237, loss_cls: 2.2317, loss: 2.2317 +2024-07-27 09:43:48,272 - pyskl - INFO - Saving checkpoint at 141 epochs +2024-07-27 09:45:40,984 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 09:45:41,654 - pyskl - INFO - +top1_acc 0.4615 +top5_acc 0.7127 +2024-07-27 09:45:41,654 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 09:45:41,696 - pyskl - INFO - +mean_acc 0.4613 +2024-07-27 09:45:41,701 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_140.pth was removed +2024-07-27 09:45:41,975 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_141.pth. +2024-07-27 09:45:41,976 - pyskl - INFO - Best top1_acc is 0.4615 at 141 epoch. +2024-07-27 09:45:41,993 - pyskl - INFO - Epoch(val) [141][309] top1_acc: 0.4615, top5_acc: 0.7127, mean_class_accuracy: 0.4613 +2024-07-27 09:49:26,683 - pyskl - INFO - Epoch [142][100/3746] lr: 8.805e-04, eta: 7:43:22, time: 2.247, data_time: 1.258, memory: 15990, top1_acc: 0.6270, top5_acc: 0.8473, loss_cls: 2.0695, loss: 2.0695 +2024-07-27 09:50:49,641 - pyskl - INFO - Epoch [142][200/3746] lr: 8.752e-04, eta: 7:41:59, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.6292, top5_acc: 0.8481, loss_cls: 2.0865, loss: 2.0865 +2024-07-27 09:52:12,281 - pyskl - INFO - Epoch [142][300/3746] lr: 8.700e-04, eta: 7:40:36, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6247, top5_acc: 0.8406, loss_cls: 2.1013, loss: 2.1013 +2024-07-27 09:53:34,645 - pyskl - INFO - Epoch [142][400/3746] lr: 8.649e-04, eta: 7:39:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6105, top5_acc: 0.8403, loss_cls: 2.1346, loss: 2.1346 +2024-07-27 09:54:56,980 - pyskl - INFO - Epoch [142][500/3746] lr: 8.597e-04, eta: 7:37:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6230, top5_acc: 0.8473, loss_cls: 2.0765, loss: 2.0765 +2024-07-27 09:56:20,128 - pyskl - INFO - Epoch [142][600/3746] lr: 8.545e-04, eta: 7:36:28, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6180, top5_acc: 0.8450, loss_cls: 2.1171, loss: 2.1171 +2024-07-27 09:57:43,232 - pyskl - INFO - Epoch [142][700/3746] lr: 8.494e-04, eta: 7:35:06, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6244, top5_acc: 0.8498, loss_cls: 2.0932, loss: 2.0932 +2024-07-27 09:59:05,090 - pyskl - INFO - Epoch [142][800/3746] lr: 8.443e-04, eta: 7:33:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6288, top5_acc: 0.8441, loss_cls: 2.1008, loss: 2.1008 +2024-07-27 10:00:28,822 - pyskl - INFO - Epoch [142][900/3746] lr: 8.392e-04, eta: 7:32:20, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.6223, top5_acc: 0.8444, loss_cls: 2.1099, loss: 2.1099 +2024-07-27 10:01:52,199 - pyskl - INFO - Epoch [142][1000/3746] lr: 8.341e-04, eta: 7:30:57, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6122, top5_acc: 0.8431, loss_cls: 2.1385, loss: 2.1385 +2024-07-27 10:03:15,330 - pyskl - INFO - Epoch [142][1100/3746] lr: 8.290e-04, eta: 7:29:35, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6248, top5_acc: 0.8422, loss_cls: 2.0931, loss: 2.0931 +2024-07-27 10:04:38,286 - pyskl - INFO - Epoch [142][1200/3746] lr: 8.239e-04, eta: 7:28:12, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.6062, top5_acc: 0.8391, loss_cls: 2.1649, loss: 2.1649 +2024-07-27 10:06:01,637 - pyskl - INFO - Epoch [142][1300/3746] lr: 8.189e-04, eta: 7:26:49, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6208, top5_acc: 0.8453, loss_cls: 2.1224, loss: 2.1224 +2024-07-27 10:07:23,973 - pyskl - INFO - Epoch [142][1400/3746] lr: 8.139e-04, eta: 7:25:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6198, top5_acc: 0.8442, loss_cls: 2.1065, loss: 2.1065 +2024-07-27 10:08:46,827 - pyskl - INFO - Epoch [142][1500/3746] lr: 8.088e-04, eta: 7:24:04, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6133, top5_acc: 0.8416, loss_cls: 2.1433, loss: 2.1433 +2024-07-27 10:10:09,222 - pyskl - INFO - Epoch [142][1600/3746] lr: 8.038e-04, eta: 7:22:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6195, top5_acc: 0.8369, loss_cls: 2.1174, loss: 2.1174 +2024-07-27 10:11:32,589 - pyskl - INFO - Epoch [142][1700/3746] lr: 7.989e-04, eta: 7:21:19, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6188, top5_acc: 0.8453, loss_cls: 2.1284, loss: 2.1284 +2024-07-27 10:12:55,674 - pyskl - INFO - Epoch [142][1800/3746] lr: 7.939e-04, eta: 7:19:56, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6100, top5_acc: 0.8350, loss_cls: 2.1532, loss: 2.1532 +2024-07-27 10:14:19,098 - pyskl - INFO - Epoch [142][1900/3746] lr: 7.889e-04, eta: 7:18:33, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6048, top5_acc: 0.8325, loss_cls: 2.1682, loss: 2.1682 +2024-07-27 10:15:41,114 - pyskl - INFO - Epoch [142][2000/3746] lr: 7.840e-04, eta: 7:17:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6153, top5_acc: 0.8417, loss_cls: 2.1358, loss: 2.1358 +2024-07-27 10:17:03,763 - pyskl - INFO - Epoch [142][2100/3746] lr: 7.791e-04, eta: 7:15:48, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6142, top5_acc: 0.8419, loss_cls: 2.1177, loss: 2.1177 +2024-07-27 10:18:26,362 - pyskl - INFO - Epoch [142][2200/3746] lr: 7.742e-04, eta: 7:14:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5998, top5_acc: 0.8336, loss_cls: 2.2003, loss: 2.2003 +2024-07-27 10:19:48,379 - pyskl - INFO - Epoch [142][2300/3746] lr: 7.693e-04, eta: 7:13:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6173, top5_acc: 0.8444, loss_cls: 2.1097, loss: 2.1097 +2024-07-27 10:21:10,609 - pyskl - INFO - Epoch [142][2400/3746] lr: 7.644e-04, eta: 7:11:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6158, top5_acc: 0.8387, loss_cls: 2.1522, loss: 2.1522 +2024-07-27 10:22:32,316 - pyskl - INFO - Epoch [142][2500/3746] lr: 7.595e-04, eta: 7:10:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6061, top5_acc: 0.8380, loss_cls: 2.1528, loss: 2.1528 +2024-07-27 10:23:54,635 - pyskl - INFO - Epoch [142][2600/3746] lr: 7.547e-04, eta: 7:08:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6156, top5_acc: 0.8408, loss_cls: 2.1294, loss: 2.1294 +2024-07-27 10:25:16,637 - pyskl - INFO - Epoch [142][2700/3746] lr: 7.499e-04, eta: 7:07:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6050, top5_acc: 0.8330, loss_cls: 2.1879, loss: 2.1879 +2024-07-27 10:26:39,265 - pyskl - INFO - Epoch [142][2800/3746] lr: 7.450e-04, eta: 7:06:09, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6095, top5_acc: 0.8333, loss_cls: 2.1682, loss: 2.1682 +2024-07-27 10:28:01,718 - pyskl - INFO - Epoch [142][2900/3746] lr: 7.402e-04, eta: 7:04:46, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6067, top5_acc: 0.8394, loss_cls: 2.1536, loss: 2.1536 +2024-07-27 10:29:23,186 - pyskl - INFO - Epoch [142][3000/3746] lr: 7.355e-04, eta: 7:03:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6177, top5_acc: 0.8370, loss_cls: 2.1424, loss: 2.1424 +2024-07-27 10:30:44,818 - pyskl - INFO - Epoch [142][3100/3746] lr: 7.307e-04, eta: 7:02:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6084, top5_acc: 0.8369, loss_cls: 2.1519, loss: 2.1519 +2024-07-27 10:32:07,248 - pyskl - INFO - Epoch [142][3200/3746] lr: 7.259e-04, eta: 7:00:38, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6152, top5_acc: 0.8459, loss_cls: 2.1168, loss: 2.1168 +2024-07-27 10:33:30,305 - pyskl - INFO - Epoch [142][3300/3746] lr: 7.212e-04, eta: 6:59:15, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6214, top5_acc: 0.8391, loss_cls: 2.1579, loss: 2.1579 +2024-07-27 10:34:53,081 - pyskl - INFO - Epoch [142][3400/3746] lr: 7.165e-04, eta: 6:57:52, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6122, top5_acc: 0.8408, loss_cls: 2.1386, loss: 2.1386 +2024-07-27 10:36:15,695 - pyskl - INFO - Epoch [142][3500/3746] lr: 7.118e-04, eta: 6:56:29, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6128, top5_acc: 0.8248, loss_cls: 2.1777, loss: 2.1777 +2024-07-27 10:37:38,914 - pyskl - INFO - Epoch [142][3600/3746] lr: 7.071e-04, eta: 6:55:07, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.6188, top5_acc: 0.8439, loss_cls: 2.1140, loss: 2.1140 +2024-07-27 10:39:01,804 - pyskl - INFO - Epoch [142][3700/3746] lr: 7.024e-04, eta: 6:53:44, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6077, top5_acc: 0.8341, loss_cls: 2.1551, loss: 2.1551 +2024-07-27 10:39:41,042 - pyskl - INFO - Saving checkpoint at 142 epochs +2024-07-27 10:41:34,077 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 10:41:34,746 - pyskl - INFO - +top1_acc 0.4634 +top5_acc 0.7153 +2024-07-27 10:41:34,746 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 10:41:34,787 - pyskl - INFO - +mean_acc 0.4632 +2024-07-27 10:41:34,792 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_141.pth was removed +2024-07-27 10:41:35,064 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_142.pth. +2024-07-27 10:41:35,065 - pyskl - INFO - Best top1_acc is 0.4634 at 142 epoch. +2024-07-27 10:41:35,076 - pyskl - INFO - Epoch(val) [142][309] top1_acc: 0.4634, top5_acc: 0.7153, mean_class_accuracy: 0.4632 +2024-07-27 10:45:20,347 - pyskl - INFO - Epoch [143][100/3746] lr: 6.956e-04, eta: 6:51:49, time: 2.253, data_time: 1.272, memory: 15990, top1_acc: 0.6344, top5_acc: 0.8561, loss_cls: 2.0093, loss: 2.0093 +2024-07-27 10:46:42,991 - pyskl - INFO - Epoch [143][200/3746] lr: 6.910e-04, eta: 6:50:26, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6303, top5_acc: 0.8498, loss_cls: 2.0675, loss: 2.0675 +2024-07-27 10:48:05,456 - pyskl - INFO - Epoch [143][300/3746] lr: 6.863e-04, eta: 6:49:04, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6323, top5_acc: 0.8480, loss_cls: 2.0661, loss: 2.0661 +2024-07-27 10:49:27,602 - pyskl - INFO - Epoch [143][400/3746] lr: 6.817e-04, eta: 6:47:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6259, top5_acc: 0.8497, loss_cls: 2.0843, loss: 2.0843 +2024-07-27 10:50:49,896 - pyskl - INFO - Epoch [143][500/3746] lr: 6.771e-04, eta: 6:46:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6389, top5_acc: 0.8509, loss_cls: 2.0378, loss: 2.0378 +2024-07-27 10:52:13,035 - pyskl - INFO - Epoch [143][600/3746] lr: 6.725e-04, eta: 6:44:55, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6308, top5_acc: 0.8597, loss_cls: 2.0167, loss: 2.0167 +2024-07-27 10:53:35,029 - pyskl - INFO - Epoch [143][700/3746] lr: 6.680e-04, eta: 6:43:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6197, top5_acc: 0.8408, loss_cls: 2.1155, loss: 2.1155 +2024-07-27 10:54:57,520 - pyskl - INFO - Epoch [143][800/3746] lr: 6.634e-04, eta: 6:42:10, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6253, top5_acc: 0.8420, loss_cls: 2.1072, loss: 2.1072 +2024-07-27 10:56:20,922 - pyskl - INFO - Epoch [143][900/3746] lr: 6.589e-04, eta: 6:40:47, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6305, top5_acc: 0.8589, loss_cls: 2.0188, loss: 2.0188 +2024-07-27 10:57:43,567 - pyskl - INFO - Epoch [143][1000/3746] lr: 6.544e-04, eta: 6:39:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6383, top5_acc: 0.8494, loss_cls: 2.0629, loss: 2.0629 +2024-07-27 10:59:06,962 - pyskl - INFO - Epoch [143][1100/3746] lr: 6.499e-04, eta: 6:38:02, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6275, top5_acc: 0.8486, loss_cls: 2.0888, loss: 2.0888 +2024-07-27 11:00:29,640 - pyskl - INFO - Epoch [143][1200/3746] lr: 6.454e-04, eta: 6:36:39, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6142, top5_acc: 0.8458, loss_cls: 2.1323, loss: 2.1323 +2024-07-27 11:01:52,105 - pyskl - INFO - Epoch [143][1300/3746] lr: 6.409e-04, eta: 6:35:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6294, top5_acc: 0.8483, loss_cls: 2.0779, loss: 2.0779 +2024-07-27 11:03:14,766 - pyskl - INFO - Epoch [143][1400/3746] lr: 6.365e-04, eta: 6:33:54, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6145, top5_acc: 0.8423, loss_cls: 2.1207, loss: 2.1207 +2024-07-27 11:04:36,366 - pyskl - INFO - Epoch [143][1500/3746] lr: 6.320e-04, eta: 6:32:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6352, top5_acc: 0.8516, loss_cls: 2.0491, loss: 2.0491 +2024-07-27 11:05:58,528 - pyskl - INFO - Epoch [143][1600/3746] lr: 6.276e-04, eta: 6:31:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6258, top5_acc: 0.8492, loss_cls: 2.0626, loss: 2.0626 +2024-07-27 11:07:21,710 - pyskl - INFO - Epoch [143][1700/3746] lr: 6.232e-04, eta: 6:29:45, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.6245, top5_acc: 0.8514, loss_cls: 2.0747, loss: 2.0747 +2024-07-27 11:08:45,025 - pyskl - INFO - Epoch [143][1800/3746] lr: 6.188e-04, eta: 6:28:23, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.6159, top5_acc: 0.8492, loss_cls: 2.0921, loss: 2.0921 +2024-07-27 11:10:07,149 - pyskl - INFO - Epoch [143][1900/3746] lr: 6.144e-04, eta: 6:27:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6298, top5_acc: 0.8519, loss_cls: 2.0739, loss: 2.0739 +2024-07-27 11:11:29,200 - pyskl - INFO - Epoch [143][2000/3746] lr: 6.101e-04, eta: 6:25:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6277, top5_acc: 0.8512, loss_cls: 2.0528, loss: 2.0528 +2024-07-27 11:12:51,513 - pyskl - INFO - Epoch [143][2100/3746] lr: 6.057e-04, eta: 6:24:14, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6342, top5_acc: 0.8530, loss_cls: 2.0477, loss: 2.0477 +2024-07-27 11:14:13,980 - pyskl - INFO - Epoch [143][2200/3746] lr: 6.014e-04, eta: 6:22:52, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6228, top5_acc: 0.8455, loss_cls: 2.0846, loss: 2.0846 +2024-07-27 11:15:36,189 - pyskl - INFO - Epoch [143][2300/3746] lr: 5.971e-04, eta: 6:21:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6369, top5_acc: 0.8466, loss_cls: 2.0602, loss: 2.0602 +2024-07-27 11:16:57,932 - pyskl - INFO - Epoch [143][2400/3746] lr: 5.928e-04, eta: 6:20:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6353, top5_acc: 0.8469, loss_cls: 2.0686, loss: 2.0686 +2024-07-27 11:18:20,747 - pyskl - INFO - Epoch [143][2500/3746] lr: 5.885e-04, eta: 6:18:43, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6161, top5_acc: 0.8428, loss_cls: 2.1106, loss: 2.1106 +2024-07-27 11:19:42,882 - pyskl - INFO - Epoch [143][2600/3746] lr: 5.842e-04, eta: 6:17:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6147, top5_acc: 0.8442, loss_cls: 2.1055, loss: 2.1055 +2024-07-27 11:21:06,001 - pyskl - INFO - Epoch [143][2700/3746] lr: 5.800e-04, eta: 6:15:58, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6225, top5_acc: 0.8488, loss_cls: 2.0886, loss: 2.0886 +2024-07-27 11:22:28,397 - pyskl - INFO - Epoch [143][2800/3746] lr: 5.757e-04, eta: 6:14:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6172, top5_acc: 0.8483, loss_cls: 2.0874, loss: 2.0874 +2024-07-27 11:23:50,899 - pyskl - INFO - Epoch [143][2900/3746] lr: 5.715e-04, eta: 6:13:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6259, top5_acc: 0.8467, loss_cls: 2.0680, loss: 2.0680 +2024-07-27 11:25:13,831 - pyskl - INFO - Epoch [143][3000/3746] lr: 5.673e-04, eta: 6:11:50, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6295, top5_acc: 0.8436, loss_cls: 2.0899, loss: 2.0899 +2024-07-27 11:26:36,120 - pyskl - INFO - Epoch [143][3100/3746] lr: 5.631e-04, eta: 6:10:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6300, top5_acc: 0.8498, loss_cls: 2.0725, loss: 2.0725 +2024-07-27 11:27:59,180 - pyskl - INFO - Epoch [143][3200/3746] lr: 5.590e-04, eta: 6:09:04, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6258, top5_acc: 0.8489, loss_cls: 2.0898, loss: 2.0898 +2024-07-27 11:29:21,461 - pyskl - INFO - Epoch [143][3300/3746] lr: 5.548e-04, eta: 6:07:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6217, top5_acc: 0.8411, loss_cls: 2.1155, loss: 2.1155 +2024-07-27 11:30:44,349 - pyskl - INFO - Epoch [143][3400/3746] lr: 5.506e-04, eta: 6:06:19, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6186, top5_acc: 0.8411, loss_cls: 2.1189, loss: 2.1189 +2024-07-27 11:32:07,765 - pyskl - INFO - Epoch [143][3500/3746] lr: 5.465e-04, eta: 6:04:56, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6220, top5_acc: 0.8436, loss_cls: 2.0905, loss: 2.0905 +2024-07-27 11:33:31,455 - pyskl - INFO - Epoch [143][3600/3746] lr: 5.424e-04, eta: 6:03:33, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.6158, top5_acc: 0.8428, loss_cls: 2.1265, loss: 2.1265 +2024-07-27 11:34:54,355 - pyskl - INFO - Epoch [143][3700/3746] lr: 5.383e-04, eta: 6:02:11, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6173, top5_acc: 0.8494, loss_cls: 2.0653, loss: 2.0653 +2024-07-27 11:35:33,725 - pyskl - INFO - Saving checkpoint at 143 epochs +2024-07-27 11:37:26,232 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 11:37:26,904 - pyskl - INFO - +top1_acc 0.4661 +top5_acc 0.7180 +2024-07-27 11:37:26,905 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 11:37:26,950 - pyskl - INFO - +mean_acc 0.4659 +2024-07-27 11:37:26,955 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_142.pth was removed +2024-07-27 11:37:27,210 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_143.pth. +2024-07-27 11:37:27,210 - pyskl - INFO - Best top1_acc is 0.4661 at 143 epoch. +2024-07-27 11:37:27,224 - pyskl - INFO - Epoch(val) [143][309] top1_acc: 0.4661, top5_acc: 0.7180, mean_class_accuracy: 0.4659 +2024-07-27 11:41:11,509 - pyskl - INFO - Epoch [144][100/3746] lr: 5.323e-04, eta: 6:00:15, time: 2.243, data_time: 1.267, memory: 15990, top1_acc: 0.6364, top5_acc: 0.8481, loss_cls: 2.0438, loss: 2.0438 +2024-07-27 11:42:33,996 - pyskl - INFO - Epoch [144][200/3746] lr: 5.283e-04, eta: 5:58:52, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6523, top5_acc: 0.8673, loss_cls: 1.9623, loss: 1.9623 +2024-07-27 11:43:55,932 - pyskl - INFO - Epoch [144][300/3746] lr: 5.242e-04, eta: 5:57:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6472, top5_acc: 0.8606, loss_cls: 1.9802, loss: 1.9802 +2024-07-27 11:45:18,253 - pyskl - INFO - Epoch [144][400/3746] lr: 5.202e-04, eta: 5:56:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6338, top5_acc: 0.8552, loss_cls: 2.0384, loss: 2.0384 +2024-07-27 11:46:40,218 - pyskl - INFO - Epoch [144][500/3746] lr: 5.162e-04, eta: 5:54:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6377, top5_acc: 0.8547, loss_cls: 2.0340, loss: 2.0340 +2024-07-27 11:48:03,029 - pyskl - INFO - Epoch [144][600/3746] lr: 5.122e-04, eta: 5:53:21, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6328, top5_acc: 0.8550, loss_cls: 2.0214, loss: 2.0214 +2024-07-27 11:49:25,036 - pyskl - INFO - Epoch [144][700/3746] lr: 5.082e-04, eta: 5:51:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6470, top5_acc: 0.8623, loss_cls: 1.9810, loss: 1.9810 +2024-07-27 11:50:48,357 - pyskl - INFO - Epoch [144][800/3746] lr: 5.042e-04, eta: 5:50:36, time: 0.833, data_time: 0.001, memory: 15990, top1_acc: 0.6391, top5_acc: 0.8520, loss_cls: 2.0276, loss: 2.0276 +2024-07-27 11:52:11,156 - pyskl - INFO - Epoch [144][900/3746] lr: 5.003e-04, eta: 5:49:13, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6355, top5_acc: 0.8538, loss_cls: 2.0419, loss: 2.0419 +2024-07-27 11:53:33,580 - pyskl - INFO - Epoch [144][1000/3746] lr: 4.964e-04, eta: 5:47:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6445, top5_acc: 0.8598, loss_cls: 1.9932, loss: 1.9932 +2024-07-27 11:54:56,842 - pyskl - INFO - Epoch [144][1100/3746] lr: 4.924e-04, eta: 5:46:27, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.6352, top5_acc: 0.8558, loss_cls: 2.0308, loss: 2.0308 +2024-07-27 11:56:19,749 - pyskl - INFO - Epoch [144][1200/3746] lr: 4.885e-04, eta: 5:45:05, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6372, top5_acc: 0.8559, loss_cls: 2.0447, loss: 2.0447 +2024-07-27 11:57:42,642 - pyskl - INFO - Epoch [144][1300/3746] lr: 4.846e-04, eta: 5:43:42, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6297, top5_acc: 0.8489, loss_cls: 2.0599, loss: 2.0599 +2024-07-27 11:59:05,923 - pyskl - INFO - Epoch [144][1400/3746] lr: 4.808e-04, eta: 5:42:19, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.6402, top5_acc: 0.8573, loss_cls: 2.0169, loss: 2.0169 +2024-07-27 12:00:28,130 - pyskl - INFO - Epoch [144][1500/3746] lr: 4.769e-04, eta: 5:40:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6331, top5_acc: 0.8502, loss_cls: 2.0421, loss: 2.0421 +2024-07-27 12:01:50,718 - pyskl - INFO - Epoch [144][1600/3746] lr: 4.731e-04, eta: 5:39:34, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6330, top5_acc: 0.8555, loss_cls: 2.0317, loss: 2.0317 +2024-07-27 12:03:13,910 - pyskl - INFO - Epoch [144][1700/3746] lr: 4.692e-04, eta: 5:38:11, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.6380, top5_acc: 0.8555, loss_cls: 2.0095, loss: 2.0095 +2024-07-27 12:04:37,338 - pyskl - INFO - Epoch [144][1800/3746] lr: 4.654e-04, eta: 5:36:48, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6397, top5_acc: 0.8603, loss_cls: 2.0074, loss: 2.0074 +2024-07-27 12:06:00,249 - pyskl - INFO - Epoch [144][1900/3746] lr: 4.616e-04, eta: 5:35:25, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6344, top5_acc: 0.8623, loss_cls: 1.9928, loss: 1.9928 +2024-07-27 12:07:22,442 - pyskl - INFO - Epoch [144][2000/3746] lr: 4.578e-04, eta: 5:34:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6441, top5_acc: 0.8595, loss_cls: 1.9846, loss: 1.9846 +2024-07-27 12:08:44,111 - pyskl - INFO - Epoch [144][2100/3746] lr: 4.541e-04, eta: 5:32:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6495, top5_acc: 0.8603, loss_cls: 1.9535, loss: 1.9535 +2024-07-27 12:10:06,580 - pyskl - INFO - Epoch [144][2200/3746] lr: 4.503e-04, eta: 5:31:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6283, top5_acc: 0.8534, loss_cls: 2.0386, loss: 2.0386 +2024-07-27 12:11:29,348 - pyskl - INFO - Epoch [144][2300/3746] lr: 4.466e-04, eta: 5:29:54, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6328, top5_acc: 0.8548, loss_cls: 2.0484, loss: 2.0484 +2024-07-27 12:12:52,150 - pyskl - INFO - Epoch [144][2400/3746] lr: 4.429e-04, eta: 5:28:32, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6223, top5_acc: 0.8523, loss_cls: 2.0435, loss: 2.0435 +2024-07-27 12:14:15,047 - pyskl - INFO - Epoch [144][2500/3746] lr: 4.392e-04, eta: 5:27:09, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6269, top5_acc: 0.8423, loss_cls: 2.0777, loss: 2.0777 +2024-07-27 12:15:37,876 - pyskl - INFO - Epoch [144][2600/3746] lr: 4.355e-04, eta: 5:25:46, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6322, top5_acc: 0.8548, loss_cls: 2.0391, loss: 2.0391 +2024-07-27 12:17:00,044 - pyskl - INFO - Epoch [144][2700/3746] lr: 4.318e-04, eta: 5:24:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6370, top5_acc: 0.8538, loss_cls: 2.0305, loss: 2.0305 +2024-07-27 12:18:21,781 - pyskl - INFO - Epoch [144][2800/3746] lr: 4.281e-04, eta: 5:23:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6377, top5_acc: 0.8566, loss_cls: 2.0245, loss: 2.0245 +2024-07-27 12:19:43,889 - pyskl - INFO - Epoch [144][2900/3746] lr: 4.245e-04, eta: 5:21:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6419, top5_acc: 0.8625, loss_cls: 1.9799, loss: 1.9799 +2024-07-27 12:21:06,552 - pyskl - INFO - Epoch [144][3000/3746] lr: 4.209e-04, eta: 5:20:15, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6441, top5_acc: 0.8538, loss_cls: 2.0050, loss: 2.0050 +2024-07-27 12:22:28,379 - pyskl - INFO - Epoch [144][3100/3746] lr: 4.173e-04, eta: 5:18:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6281, top5_acc: 0.8509, loss_cls: 2.0648, loss: 2.0648 +2024-07-27 12:23:51,736 - pyskl - INFO - Epoch [144][3200/3746] lr: 4.137e-04, eta: 5:17:30, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6402, top5_acc: 0.8556, loss_cls: 2.0139, loss: 2.0139 +2024-07-27 12:25:13,767 - pyskl - INFO - Epoch [144][3300/3746] lr: 4.101e-04, eta: 5:16:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6239, top5_acc: 0.8489, loss_cls: 2.0764, loss: 2.0764 +2024-07-27 12:26:36,242 - pyskl - INFO - Epoch [144][3400/3746] lr: 4.065e-04, eta: 5:14:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6386, top5_acc: 0.8617, loss_cls: 2.0056, loss: 2.0056 +2024-07-27 12:27:58,580 - pyskl - INFO - Epoch [144][3500/3746] lr: 4.030e-04, eta: 5:13:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6434, top5_acc: 0.8598, loss_cls: 1.9991, loss: 1.9991 +2024-07-27 12:29:21,483 - pyskl - INFO - Epoch [144][3600/3746] lr: 3.994e-04, eta: 5:11:59, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6319, top5_acc: 0.8575, loss_cls: 2.0204, loss: 2.0204 +2024-07-27 12:30:43,674 - pyskl - INFO - Epoch [144][3700/3746] lr: 3.959e-04, eta: 5:10:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6288, top5_acc: 0.8486, loss_cls: 2.0654, loss: 2.0654 +2024-07-27 12:31:22,373 - pyskl - INFO - Saving checkpoint at 144 epochs +2024-07-27 12:33:15,552 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 12:33:16,249 - pyskl - INFO - +top1_acc 0.4662 +top5_acc 0.7162 +2024-07-27 12:33:16,249 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 12:33:16,299 - pyskl - INFO - +mean_acc 0.4659 +2024-07-27 12:33:16,305 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_143.pth was removed +2024-07-27 12:33:16,582 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_144.pth. +2024-07-27 12:33:16,583 - pyskl - INFO - Best top1_acc is 0.4662 at 144 epoch. +2024-07-27 12:33:16,598 - pyskl - INFO - Epoch(val) [144][309] top1_acc: 0.4662, top5_acc: 0.7162, mean_class_accuracy: 0.4659 +2024-07-27 12:37:05,276 - pyskl - INFO - Epoch [145][100/3746] lr: 3.908e-04, eta: 5:08:39, time: 2.287, data_time: 1.301, memory: 15990, top1_acc: 0.6511, top5_acc: 0.8606, loss_cls: 1.9653, loss: 1.9653 +2024-07-27 12:38:28,556 - pyskl - INFO - Epoch [145][200/3746] lr: 3.873e-04, eta: 5:07:17, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.6555, top5_acc: 0.8630, loss_cls: 1.9397, loss: 1.9397 +2024-07-27 12:39:51,908 - pyskl - INFO - Epoch [145][300/3746] lr: 3.839e-04, eta: 5:05:54, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6445, top5_acc: 0.8631, loss_cls: 1.9541, loss: 1.9541 +2024-07-27 12:41:14,291 - pyskl - INFO - Epoch [145][400/3746] lr: 3.804e-04, eta: 5:04:31, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6492, top5_acc: 0.8606, loss_cls: 1.9718, loss: 1.9718 +2024-07-27 12:42:38,042 - pyskl - INFO - Epoch [145][500/3746] lr: 3.770e-04, eta: 5:03:08, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.6503, top5_acc: 0.8631, loss_cls: 1.9569, loss: 1.9569 +2024-07-27 12:44:00,431 - pyskl - INFO - Epoch [145][600/3746] lr: 3.736e-04, eta: 5:01:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6533, top5_acc: 0.8666, loss_cls: 1.9604, loss: 1.9604 +2024-07-27 12:45:23,840 - pyskl - INFO - Epoch [145][700/3746] lr: 3.702e-04, eta: 5:00:23, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6497, top5_acc: 0.8648, loss_cls: 1.9586, loss: 1.9586 +2024-07-27 12:46:47,718 - pyskl - INFO - Epoch [145][800/3746] lr: 3.668e-04, eta: 4:59:00, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.6520, top5_acc: 0.8655, loss_cls: 1.9535, loss: 1.9535 +2024-07-27 12:48:10,664 - pyskl - INFO - Epoch [145][900/3746] lr: 3.634e-04, eta: 4:57:37, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6503, top5_acc: 0.8642, loss_cls: 1.9571, loss: 1.9571 +2024-07-27 12:49:33,872 - pyskl - INFO - Epoch [145][1000/3746] lr: 3.600e-04, eta: 4:56:15, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.6622, top5_acc: 0.8681, loss_cls: 1.9293, loss: 1.9293 +2024-07-27 12:50:57,545 - pyskl - INFO - Epoch [145][1100/3746] lr: 3.567e-04, eta: 4:54:52, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.6569, top5_acc: 0.8659, loss_cls: 1.9445, loss: 1.9445 +2024-07-27 12:52:20,693 - pyskl - INFO - Epoch [145][1200/3746] lr: 3.534e-04, eta: 4:53:29, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6428, top5_acc: 0.8619, loss_cls: 1.9884, loss: 1.9884 +2024-07-27 12:53:44,349 - pyskl - INFO - Epoch [145][1300/3746] lr: 3.501e-04, eta: 4:52:06, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.6416, top5_acc: 0.8577, loss_cls: 1.9915, loss: 1.9915 +2024-07-27 12:55:07,175 - pyskl - INFO - Epoch [145][1400/3746] lr: 3.468e-04, eta: 4:50:44, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6462, top5_acc: 0.8625, loss_cls: 1.9766, loss: 1.9766 +2024-07-27 12:56:30,311 - pyskl - INFO - Epoch [145][1500/3746] lr: 3.435e-04, eta: 4:49:21, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6569, top5_acc: 0.8695, loss_cls: 1.9157, loss: 1.9157 +2024-07-27 12:57:53,708 - pyskl - INFO - Epoch [145][1600/3746] lr: 3.402e-04, eta: 4:47:58, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6475, top5_acc: 0.8611, loss_cls: 1.9909, loss: 1.9909 +2024-07-27 12:59:16,924 - pyskl - INFO - Epoch [145][1700/3746] lr: 3.370e-04, eta: 4:46:35, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.6397, top5_acc: 0.8594, loss_cls: 1.9962, loss: 1.9962 +2024-07-27 13:00:40,602 - pyskl - INFO - Epoch [145][1800/3746] lr: 3.337e-04, eta: 4:45:13, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.6414, top5_acc: 0.8584, loss_cls: 1.9941, loss: 1.9941 +2024-07-27 13:02:02,516 - pyskl - INFO - Epoch [145][1900/3746] lr: 3.305e-04, eta: 4:43:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6442, top5_acc: 0.8633, loss_cls: 2.0024, loss: 2.0024 +2024-07-27 13:03:25,024 - pyskl - INFO - Epoch [145][2000/3746] lr: 3.273e-04, eta: 4:42:27, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6456, top5_acc: 0.8581, loss_cls: 1.9992, loss: 1.9992 +2024-07-27 13:04:47,733 - pyskl - INFO - Epoch [145][2100/3746] lr: 3.241e-04, eta: 4:41:04, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6395, top5_acc: 0.8572, loss_cls: 2.0170, loss: 2.0170 +2024-07-27 13:06:10,679 - pyskl - INFO - Epoch [145][2200/3746] lr: 3.210e-04, eta: 4:39:42, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6420, top5_acc: 0.8584, loss_cls: 2.0083, loss: 2.0083 +2024-07-27 13:07:33,152 - pyskl - INFO - Epoch [145][2300/3746] lr: 3.178e-04, eta: 4:38:19, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6472, top5_acc: 0.8591, loss_cls: 1.9878, loss: 1.9878 +2024-07-27 13:08:55,865 - pyskl - INFO - Epoch [145][2400/3746] lr: 3.147e-04, eta: 4:36:56, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6498, top5_acc: 0.8642, loss_cls: 1.9577, loss: 1.9577 +2024-07-27 13:10:18,481 - pyskl - INFO - Epoch [145][2500/3746] lr: 3.116e-04, eta: 4:35:33, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6483, top5_acc: 0.8653, loss_cls: 1.9715, loss: 1.9715 +2024-07-27 13:11:40,531 - pyskl - INFO - Epoch [145][2600/3746] lr: 3.084e-04, eta: 4:34:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6431, top5_acc: 0.8580, loss_cls: 1.9846, loss: 1.9846 +2024-07-27 13:13:02,101 - pyskl - INFO - Epoch [145][2700/3746] lr: 3.054e-04, eta: 4:32:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6436, top5_acc: 0.8575, loss_cls: 2.0073, loss: 2.0073 +2024-07-27 13:14:23,618 - pyskl - INFO - Epoch [145][2800/3746] lr: 3.023e-04, eta: 4:31:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6458, top5_acc: 0.8545, loss_cls: 1.9716, loss: 1.9716 +2024-07-27 13:15:45,199 - pyskl - INFO - Epoch [145][2900/3746] lr: 2.992e-04, eta: 4:30:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6439, top5_acc: 0.8584, loss_cls: 1.9724, loss: 1.9724 +2024-07-27 13:17:07,084 - pyskl - INFO - Epoch [145][3000/3746] lr: 2.962e-04, eta: 4:28:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6431, top5_acc: 0.8633, loss_cls: 1.9814, loss: 1.9814 +2024-07-27 13:18:29,486 - pyskl - INFO - Epoch [145][3100/3746] lr: 2.931e-04, eta: 4:27:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6386, top5_acc: 0.8552, loss_cls: 2.0153, loss: 2.0153 +2024-07-27 13:19:50,982 - pyskl - INFO - Epoch [145][3200/3746] lr: 2.901e-04, eta: 4:25:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6369, top5_acc: 0.8553, loss_cls: 2.0306, loss: 2.0306 +2024-07-27 13:21:13,034 - pyskl - INFO - Epoch [145][3300/3746] lr: 2.871e-04, eta: 4:24:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6519, top5_acc: 0.8614, loss_cls: 1.9624, loss: 1.9624 +2024-07-27 13:22:34,817 - pyskl - INFO - Epoch [145][3400/3746] lr: 2.841e-04, eta: 4:23:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6383, top5_acc: 0.8561, loss_cls: 2.0139, loss: 2.0139 +2024-07-27 13:23:56,477 - pyskl - INFO - Epoch [145][3500/3746] lr: 2.812e-04, eta: 4:21:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6580, top5_acc: 0.8633, loss_cls: 1.9549, loss: 1.9549 +2024-07-27 13:25:17,687 - pyskl - INFO - Epoch [145][3600/3746] lr: 2.782e-04, eta: 4:20:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6428, top5_acc: 0.8589, loss_cls: 1.9750, loss: 1.9750 +2024-07-27 13:26:39,786 - pyskl - INFO - Epoch [145][3700/3746] lr: 2.753e-04, eta: 4:19:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6452, top5_acc: 0.8583, loss_cls: 1.9992, loss: 1.9992 +2024-07-27 13:27:18,808 - pyskl - INFO - Saving checkpoint at 145 epochs +2024-07-27 13:29:13,053 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 13:29:13,721 - pyskl - INFO - +top1_acc 0.4673 +top5_acc 0.7175 +2024-07-27 13:29:13,721 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 13:29:13,763 - pyskl - INFO - +mean_acc 0.4670 +2024-07-27 13:29:13,768 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_144.pth was removed +2024-07-27 13:29:14,027 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_145.pth. +2024-07-27 13:29:14,027 - pyskl - INFO - Best top1_acc is 0.4673 at 145 epoch. +2024-07-27 13:29:14,040 - pyskl - INFO - Epoch(val) [145][309] top1_acc: 0.4673, top5_acc: 0.7175, mean_class_accuracy: 0.4670 +2024-07-27 13:33:09,124 - pyskl - INFO - Epoch [146][100/3746] lr: 2.710e-04, eta: 4:17:03, time: 2.351, data_time: 1.370, memory: 15990, top1_acc: 0.6702, top5_acc: 0.8755, loss_cls: 1.9053, loss: 1.9053 +2024-07-27 13:34:30,748 - pyskl - INFO - Epoch [146][200/3746] lr: 2.681e-04, eta: 4:15:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6567, top5_acc: 0.8652, loss_cls: 1.9203, loss: 1.9203 +2024-07-27 13:35:52,865 - pyskl - INFO - Epoch [146][300/3746] lr: 2.652e-04, eta: 4:14:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6502, top5_acc: 0.8708, loss_cls: 1.9228, loss: 1.9228 +2024-07-27 13:37:14,873 - pyskl - INFO - Epoch [146][400/3746] lr: 2.624e-04, eta: 4:12:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6711, top5_acc: 0.8745, loss_cls: 1.8723, loss: 1.8723 +2024-07-27 13:38:36,416 - pyskl - INFO - Epoch [146][500/3746] lr: 2.595e-04, eta: 4:11:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6503, top5_acc: 0.8686, loss_cls: 1.9337, loss: 1.9337 +2024-07-27 13:39:58,212 - pyskl - INFO - Epoch [146][600/3746] lr: 2.567e-04, eta: 4:10:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6606, top5_acc: 0.8694, loss_cls: 1.9326, loss: 1.9326 +2024-07-27 13:41:20,609 - pyskl - INFO - Epoch [146][700/3746] lr: 2.539e-04, eta: 4:08:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6592, top5_acc: 0.8656, loss_cls: 1.9317, loss: 1.9317 +2024-07-27 13:42:42,572 - pyskl - INFO - Epoch [146][800/3746] lr: 2.511e-04, eta: 4:07:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6553, top5_acc: 0.8648, loss_cls: 1.9359, loss: 1.9359 +2024-07-27 13:44:04,122 - pyskl - INFO - Epoch [146][900/3746] lr: 2.483e-04, eta: 4:06:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6467, top5_acc: 0.8592, loss_cls: 1.9817, loss: 1.9817 +2024-07-27 13:45:25,564 - pyskl - INFO - Epoch [146][1000/3746] lr: 2.455e-04, eta: 4:04:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6652, top5_acc: 0.8719, loss_cls: 1.8991, loss: 1.8991 +2024-07-27 13:46:46,769 - pyskl - INFO - Epoch [146][1100/3746] lr: 2.427e-04, eta: 4:03:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6545, top5_acc: 0.8645, loss_cls: 1.9418, loss: 1.9418 +2024-07-27 13:48:08,212 - pyskl - INFO - Epoch [146][1200/3746] lr: 2.400e-04, eta: 4:01:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6472, top5_acc: 0.8683, loss_cls: 1.9446, loss: 1.9446 +2024-07-27 13:49:29,313 - pyskl - INFO - Epoch [146][1300/3746] lr: 2.373e-04, eta: 4:00:29, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6572, top5_acc: 0.8641, loss_cls: 1.9538, loss: 1.9538 +2024-07-27 13:50:51,025 - pyskl - INFO - Epoch [146][1400/3746] lr: 2.345e-04, eta: 3:59:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6647, top5_acc: 0.8727, loss_cls: 1.9197, loss: 1.9197 +2024-07-27 13:52:12,780 - pyskl - INFO - Epoch [146][1500/3746] lr: 2.318e-04, eta: 3:57:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6630, top5_acc: 0.8681, loss_cls: 1.9099, loss: 1.9099 +2024-07-27 13:53:34,255 - pyskl - INFO - Epoch [146][1600/3746] lr: 2.292e-04, eta: 3:56:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6570, top5_acc: 0.8648, loss_cls: 1.9220, loss: 1.9220 +2024-07-27 13:54:55,540 - pyskl - INFO - Epoch [146][1700/3746] lr: 2.265e-04, eta: 3:54:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6519, top5_acc: 0.8680, loss_cls: 1.9247, loss: 1.9247 +2024-07-27 13:56:17,282 - pyskl - INFO - Epoch [146][1800/3746] lr: 2.239e-04, eta: 3:53:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6530, top5_acc: 0.8680, loss_cls: 1.9231, loss: 1.9231 +2024-07-27 13:57:38,870 - pyskl - INFO - Epoch [146][1900/3746] lr: 2.212e-04, eta: 3:52:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6545, top5_acc: 0.8694, loss_cls: 1.9533, loss: 1.9533 +2024-07-27 13:59:00,502 - pyskl - INFO - Epoch [146][2000/3746] lr: 2.186e-04, eta: 3:50:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6556, top5_acc: 0.8712, loss_cls: 1.9139, loss: 1.9139 +2024-07-27 14:00:21,368 - pyskl - INFO - Epoch [146][2100/3746] lr: 2.160e-04, eta: 3:49:26, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6553, top5_acc: 0.8658, loss_cls: 1.9310, loss: 1.9310 +2024-07-27 14:01:43,628 - pyskl - INFO - Epoch [146][2200/3746] lr: 2.134e-04, eta: 3:48:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6611, top5_acc: 0.8705, loss_cls: 1.9172, loss: 1.9172 +2024-07-27 14:03:05,153 - pyskl - INFO - Epoch [146][2300/3746] lr: 2.108e-04, eta: 3:46:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6519, top5_acc: 0.8630, loss_cls: 1.9355, loss: 1.9355 +2024-07-27 14:04:26,305 - pyskl - INFO - Epoch [146][2400/3746] lr: 2.083e-04, eta: 3:45:18, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6567, top5_acc: 0.8748, loss_cls: 1.8929, loss: 1.8929 +2024-07-27 14:05:48,299 - pyskl - INFO - Epoch [146][2500/3746] lr: 2.057e-04, eta: 3:43:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6522, top5_acc: 0.8655, loss_cls: 1.9371, loss: 1.9371 +2024-07-27 14:07:09,767 - pyskl - INFO - Epoch [146][2600/3746] lr: 2.032e-04, eta: 3:42:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6498, top5_acc: 0.8672, loss_cls: 1.9468, loss: 1.9468 +2024-07-27 14:08:31,619 - pyskl - INFO - Epoch [146][2700/3746] lr: 2.007e-04, eta: 3:41:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6525, top5_acc: 0.8631, loss_cls: 1.9541, loss: 1.9541 +2024-07-27 14:09:53,082 - pyskl - INFO - Epoch [146][2800/3746] lr: 1.982e-04, eta: 3:39:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6622, top5_acc: 0.8673, loss_cls: 1.9012, loss: 1.9012 +2024-07-27 14:11:14,610 - pyskl - INFO - Epoch [146][2900/3746] lr: 1.957e-04, eta: 3:38:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6478, top5_acc: 0.8617, loss_cls: 1.9731, loss: 1.9731 +2024-07-27 14:12:35,628 - pyskl - INFO - Epoch [146][3000/3746] lr: 1.933e-04, eta: 3:37:01, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6622, top5_acc: 0.8662, loss_cls: 1.9135, loss: 1.9135 +2024-07-27 14:13:57,327 - pyskl - INFO - Epoch [146][3100/3746] lr: 1.908e-04, eta: 3:35:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6587, top5_acc: 0.8608, loss_cls: 1.9468, loss: 1.9468 +2024-07-27 14:15:18,980 - pyskl - INFO - Epoch [146][3200/3746] lr: 1.884e-04, eta: 3:34:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6472, top5_acc: 0.8645, loss_cls: 1.9627, loss: 1.9627 +2024-07-27 14:16:40,971 - pyskl - INFO - Epoch [146][3300/3746] lr: 1.860e-04, eta: 3:32:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6597, top5_acc: 0.8619, loss_cls: 1.9473, loss: 1.9473 +2024-07-27 14:18:02,673 - pyskl - INFO - Epoch [146][3400/3746] lr: 1.836e-04, eta: 3:31:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6525, top5_acc: 0.8619, loss_cls: 1.9579, loss: 1.9579 +2024-07-27 14:19:24,203 - pyskl - INFO - Epoch [146][3500/3746] lr: 1.812e-04, eta: 3:30:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6559, top5_acc: 0.8675, loss_cls: 1.9338, loss: 1.9338 +2024-07-27 14:20:45,612 - pyskl - INFO - Epoch [146][3600/3746] lr: 1.788e-04, eta: 3:28:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6613, top5_acc: 0.8745, loss_cls: 1.8974, loss: 1.8974 +2024-07-27 14:22:07,169 - pyskl - INFO - Epoch [146][3700/3746] lr: 1.765e-04, eta: 3:27:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6681, top5_acc: 0.8702, loss_cls: 1.9157, loss: 1.9157 +2024-07-27 14:22:46,746 - pyskl - INFO - Saving checkpoint at 146 epochs +2024-07-27 14:24:40,453 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 14:24:41,119 - pyskl - INFO - +top1_acc 0.4685 +top5_acc 0.7188 +2024-07-27 14:24:41,119 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 14:24:41,161 - pyskl - INFO - +mean_acc 0.4682 +2024-07-27 14:24:41,166 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_145.pth was removed +2024-07-27 14:24:41,419 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_146.pth. +2024-07-27 14:24:41,419 - pyskl - INFO - Best top1_acc is 0.4685 at 146 epoch. +2024-07-27 14:24:41,432 - pyskl - INFO - Epoch(val) [146][309] top1_acc: 0.4685, top5_acc: 0.7188, mean_class_accuracy: 0.4682 +2024-07-27 14:28:36,231 - pyskl - INFO - Epoch [147][100/3746] lr: 1.730e-04, eta: 3:25:23, time: 2.348, data_time: 1.363, memory: 15990, top1_acc: 0.6703, top5_acc: 0.8761, loss_cls: 1.8674, loss: 1.8674 +2024-07-27 14:29:58,684 - pyskl - INFO - Epoch [147][200/3746] lr: 1.707e-04, eta: 3:24:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6700, top5_acc: 0.8702, loss_cls: 1.8970, loss: 1.8970 +2024-07-27 14:31:21,783 - pyskl - INFO - Epoch [147][300/3746] lr: 1.684e-04, eta: 3:22:38, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6780, top5_acc: 0.8789, loss_cls: 1.8276, loss: 1.8276 +2024-07-27 14:32:44,269 - pyskl - INFO - Epoch [147][400/3746] lr: 1.661e-04, eta: 3:21:15, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6634, top5_acc: 0.8697, loss_cls: 1.9034, loss: 1.9034 +2024-07-27 14:34:06,689 - pyskl - INFO - Epoch [147][500/3746] lr: 1.639e-04, eta: 3:19:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6608, top5_acc: 0.8731, loss_cls: 1.9052, loss: 1.9052 +2024-07-27 14:35:29,534 - pyskl - INFO - Epoch [147][600/3746] lr: 1.616e-04, eta: 3:18:29, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6703, top5_acc: 0.8708, loss_cls: 1.8749, loss: 1.8749 +2024-07-27 14:36:53,458 - pyskl - INFO - Epoch [147][700/3746] lr: 1.594e-04, eta: 3:17:07, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.6645, top5_acc: 0.8736, loss_cls: 1.8953, loss: 1.8953 +2024-07-27 14:38:16,755 - pyskl - INFO - Epoch [147][800/3746] lr: 1.572e-04, eta: 3:15:44, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.6708, top5_acc: 0.8748, loss_cls: 1.8995, loss: 1.8995 +2024-07-27 14:39:39,436 - pyskl - INFO - Epoch [147][900/3746] lr: 1.550e-04, eta: 3:14:21, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6706, top5_acc: 0.8786, loss_cls: 1.8538, loss: 1.8538 +2024-07-27 14:41:02,548 - pyskl - INFO - Epoch [147][1000/3746] lr: 1.528e-04, eta: 3:12:58, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6617, top5_acc: 0.8738, loss_cls: 1.8953, loss: 1.8953 +2024-07-27 14:42:25,958 - pyskl - INFO - Epoch [147][1100/3746] lr: 1.506e-04, eta: 3:11:35, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6642, top5_acc: 0.8753, loss_cls: 1.8876, loss: 1.8876 +2024-07-27 14:43:49,082 - pyskl - INFO - Epoch [147][1200/3746] lr: 1.484e-04, eta: 3:10:13, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6714, top5_acc: 0.8723, loss_cls: 1.8733, loss: 1.8733 +2024-07-27 14:45:12,402 - pyskl - INFO - Epoch [147][1300/3746] lr: 1.463e-04, eta: 3:08:50, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.6637, top5_acc: 0.8766, loss_cls: 1.8699, loss: 1.8699 +2024-07-27 14:46:35,005 - pyskl - INFO - Epoch [147][1400/3746] lr: 1.442e-04, eta: 3:07:27, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6584, top5_acc: 0.8633, loss_cls: 1.9262, loss: 1.9262 +2024-07-27 14:47:58,631 - pyskl - INFO - Epoch [147][1500/3746] lr: 1.420e-04, eta: 3:06:04, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.6634, top5_acc: 0.8762, loss_cls: 1.8903, loss: 1.8903 +2024-07-27 14:49:22,293 - pyskl - INFO - Epoch [147][1600/3746] lr: 1.399e-04, eta: 3:04:42, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.6637, top5_acc: 0.8745, loss_cls: 1.8824, loss: 1.8824 +2024-07-27 14:50:45,436 - pyskl - INFO - Epoch [147][1700/3746] lr: 1.379e-04, eta: 3:03:19, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6633, top5_acc: 0.8716, loss_cls: 1.8862, loss: 1.8862 +2024-07-27 14:52:08,061 - pyskl - INFO - Epoch [147][1800/3746] lr: 1.358e-04, eta: 3:01:56, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6703, top5_acc: 0.8661, loss_cls: 1.9070, loss: 1.9070 +2024-07-27 14:53:30,487 - pyskl - INFO - Epoch [147][1900/3746] lr: 1.337e-04, eta: 3:00:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6619, top5_acc: 0.8739, loss_cls: 1.9054, loss: 1.9054 +2024-07-27 14:54:52,991 - pyskl - INFO - Epoch [147][2000/3746] lr: 1.317e-04, eta: 2:59:10, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6680, top5_acc: 0.8755, loss_cls: 1.8783, loss: 1.8783 +2024-07-27 14:56:15,810 - pyskl - INFO - Epoch [147][2100/3746] lr: 1.297e-04, eta: 2:57:48, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6652, top5_acc: 0.8717, loss_cls: 1.9087, loss: 1.9087 +2024-07-27 14:57:37,846 - pyskl - INFO - Epoch [147][2200/3746] lr: 1.277e-04, eta: 2:56:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6609, top5_acc: 0.8698, loss_cls: 1.9169, loss: 1.9169 +2024-07-27 14:59:00,409 - pyskl - INFO - Epoch [147][2300/3746] lr: 1.257e-04, eta: 2:55:02, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6559, top5_acc: 0.8658, loss_cls: 1.9157, loss: 1.9157 +2024-07-27 15:00:22,873 - pyskl - INFO - Epoch [147][2400/3746] lr: 1.237e-04, eta: 2:53:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6602, top5_acc: 0.8692, loss_cls: 1.9077, loss: 1.9077 +2024-07-27 15:01:45,997 - pyskl - INFO - Epoch [147][2500/3746] lr: 1.218e-04, eta: 2:52:16, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6602, top5_acc: 0.8675, loss_cls: 1.9290, loss: 1.9290 +2024-07-27 15:03:08,457 - pyskl - INFO - Epoch [147][2600/3746] lr: 1.198e-04, eta: 2:50:53, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6664, top5_acc: 0.8778, loss_cls: 1.8666, loss: 1.8666 +2024-07-27 15:04:31,978 - pyskl - INFO - Epoch [147][2700/3746] lr: 1.179e-04, eta: 2:49:31, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.6592, top5_acc: 0.8700, loss_cls: 1.9039, loss: 1.9039 +2024-07-27 15:05:54,410 - pyskl - INFO - Epoch [147][2800/3746] lr: 1.160e-04, eta: 2:48:08, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6617, top5_acc: 0.8752, loss_cls: 1.8970, loss: 1.8970 +2024-07-27 15:07:15,494 - pyskl - INFO - Epoch [147][2900/3746] lr: 1.141e-04, eta: 2:46:45, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6670, top5_acc: 0.8688, loss_cls: 1.8970, loss: 1.8970 +2024-07-27 15:08:36,971 - pyskl - INFO - Epoch [147][3000/3746] lr: 1.122e-04, eta: 2:45:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6727, top5_acc: 0.8753, loss_cls: 1.8733, loss: 1.8733 +2024-07-27 15:09:58,881 - pyskl - INFO - Epoch [147][3100/3746] lr: 1.103e-04, eta: 2:43:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6494, top5_acc: 0.8659, loss_cls: 1.9637, loss: 1.9637 +2024-07-27 15:11:21,186 - pyskl - INFO - Epoch [147][3200/3746] lr: 1.085e-04, eta: 2:42:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6641, top5_acc: 0.8719, loss_cls: 1.9143, loss: 1.9143 +2024-07-27 15:12:43,292 - pyskl - INFO - Epoch [147][3300/3746] lr: 1.067e-04, eta: 2:41:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6672, top5_acc: 0.8672, loss_cls: 1.8884, loss: 1.8884 +2024-07-27 15:14:05,251 - pyskl - INFO - Epoch [147][3400/3746] lr: 1.048e-04, eta: 2:39:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6663, top5_acc: 0.8717, loss_cls: 1.8885, loss: 1.8885 +2024-07-27 15:15:26,389 - pyskl - INFO - Epoch [147][3500/3746] lr: 1.030e-04, eta: 2:38:28, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6689, top5_acc: 0.8780, loss_cls: 1.8693, loss: 1.8693 +2024-07-27 15:16:48,262 - pyskl - INFO - Epoch [147][3600/3746] lr: 1.013e-04, eta: 2:37:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6670, top5_acc: 0.8708, loss_cls: 1.8944, loss: 1.8944 +2024-07-27 15:18:09,629 - pyskl - INFO - Epoch [147][3700/3746] lr: 9.949e-05, eta: 2:35:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6652, top5_acc: 0.8677, loss_cls: 1.8926, loss: 1.8926 +2024-07-27 15:18:49,345 - pyskl - INFO - Saving checkpoint at 147 epochs +2024-07-27 15:20:42,831 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 15:20:43,518 - pyskl - INFO - +top1_acc 0.4710 +top5_acc 0.7205 +2024-07-27 15:20:43,519 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 15:20:43,563 - pyskl - INFO - +mean_acc 0.4708 +2024-07-27 15:20:43,568 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_146.pth was removed +2024-07-27 15:20:43,816 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_147.pth. +2024-07-27 15:20:43,816 - pyskl - INFO - Best top1_acc is 0.4710 at 147 epoch. +2024-07-27 15:20:43,835 - pyskl - INFO - Epoch(val) [147][309] top1_acc: 0.4710, top5_acc: 0.7205, mean_class_accuracy: 0.4708 +2024-07-27 15:24:37,599 - pyskl - INFO - Epoch [148][100/3746] lr: 9.693e-05, eta: 2:33:44, time: 2.338, data_time: 1.337, memory: 15990, top1_acc: 0.6677, top5_acc: 0.8734, loss_cls: 1.8686, loss: 1.8686 +2024-07-27 15:26:00,703 - pyskl - INFO - Epoch [148][200/3746] lr: 9.520e-05, eta: 2:32:21, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6770, top5_acc: 0.8744, loss_cls: 1.8726, loss: 1.8726 +2024-07-27 15:27:23,048 - pyskl - INFO - Epoch [148][300/3746] lr: 9.348e-05, eta: 2:30:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6655, top5_acc: 0.8762, loss_cls: 1.8661, loss: 1.8661 +2024-07-27 15:28:46,148 - pyskl - INFO - Epoch [148][400/3746] lr: 9.178e-05, eta: 2:29:35, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6633, top5_acc: 0.8739, loss_cls: 1.8786, loss: 1.8786 +2024-07-27 15:30:08,479 - pyskl - INFO - Epoch [148][500/3746] lr: 9.010e-05, eta: 2:28:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6764, top5_acc: 0.8794, loss_cls: 1.8477, loss: 1.8477 +2024-07-27 15:31:31,345 - pyskl - INFO - Epoch [148][600/3746] lr: 8.843e-05, eta: 2:26:50, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6641, top5_acc: 0.8708, loss_cls: 1.9071, loss: 1.9071 +2024-07-27 15:32:55,083 - pyskl - INFO - Epoch [148][700/3746] lr: 8.678e-05, eta: 2:25:27, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.6727, top5_acc: 0.8694, loss_cls: 1.8812, loss: 1.8812 +2024-07-27 15:34:18,292 - pyskl - INFO - Epoch [148][800/3746] lr: 8.514e-05, eta: 2:24:04, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.6670, top5_acc: 0.8791, loss_cls: 1.8731, loss: 1.8731 +2024-07-27 15:35:41,363 - pyskl - INFO - Epoch [148][900/3746] lr: 8.351e-05, eta: 2:22:41, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6813, top5_acc: 0.8766, loss_cls: 1.8267, loss: 1.8267 +2024-07-27 15:37:04,980 - pyskl - INFO - Epoch [148][1000/3746] lr: 8.191e-05, eta: 2:21:19, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.6778, top5_acc: 0.8823, loss_cls: 1.8492, loss: 1.8492 +2024-07-27 15:38:28,087 - pyskl - INFO - Epoch [148][1100/3746] lr: 8.031e-05, eta: 2:19:56, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6634, top5_acc: 0.8727, loss_cls: 1.8952, loss: 1.8952 +2024-07-27 15:39:51,024 - pyskl - INFO - Epoch [148][1200/3746] lr: 7.874e-05, eta: 2:18:33, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6778, top5_acc: 0.8744, loss_cls: 1.8481, loss: 1.8481 +2024-07-27 15:41:13,379 - pyskl - INFO - Epoch [148][1300/3746] lr: 7.718e-05, eta: 2:17:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6730, top5_acc: 0.8725, loss_cls: 1.8859, loss: 1.8859 +2024-07-27 15:42:35,655 - pyskl - INFO - Epoch [148][1400/3746] lr: 7.563e-05, eta: 2:15:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6698, top5_acc: 0.8802, loss_cls: 1.8662, loss: 1.8662 +2024-07-27 15:43:59,011 - pyskl - INFO - Epoch [148][1500/3746] lr: 7.410e-05, eta: 2:14:24, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6748, top5_acc: 0.8777, loss_cls: 1.8515, loss: 1.8515 +2024-07-27 15:45:21,911 - pyskl - INFO - Epoch [148][1600/3746] lr: 7.259e-05, eta: 2:13:02, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6642, top5_acc: 0.8672, loss_cls: 1.8884, loss: 1.8884 +2024-07-27 15:46:44,652 - pyskl - INFO - Epoch [148][1700/3746] lr: 7.109e-05, eta: 2:11:39, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6813, top5_acc: 0.8816, loss_cls: 1.8073, loss: 1.8073 +2024-07-27 15:48:06,673 - pyskl - INFO - Epoch [148][1800/3746] lr: 6.961e-05, eta: 2:10:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6709, top5_acc: 0.8752, loss_cls: 1.8604, loss: 1.8604 +2024-07-27 15:49:28,928 - pyskl - INFO - Epoch [148][1900/3746] lr: 6.814e-05, eta: 2:08:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6658, top5_acc: 0.8694, loss_cls: 1.8994, loss: 1.8994 +2024-07-27 15:50:51,867 - pyskl - INFO - Epoch [148][2000/3746] lr: 6.669e-05, eta: 2:07:30, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6667, top5_acc: 0.8681, loss_cls: 1.8996, loss: 1.8996 +2024-07-27 15:52:13,633 - pyskl - INFO - Epoch [148][2100/3746] lr: 6.526e-05, eta: 2:06:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6727, top5_acc: 0.8792, loss_cls: 1.8514, loss: 1.8514 +2024-07-27 15:53:35,859 - pyskl - INFO - Epoch [148][2200/3746] lr: 6.384e-05, eta: 2:04:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6698, top5_acc: 0.8748, loss_cls: 1.8780, loss: 1.8780 +2024-07-27 15:54:57,836 - pyskl - INFO - Epoch [148][2300/3746] lr: 6.243e-05, eta: 2:03:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6731, top5_acc: 0.8773, loss_cls: 1.8515, loss: 1.8515 +2024-07-27 15:56:20,005 - pyskl - INFO - Epoch [148][2400/3746] lr: 6.104e-05, eta: 2:01:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6742, top5_acc: 0.8780, loss_cls: 1.8265, loss: 1.8265 +2024-07-27 15:57:42,111 - pyskl - INFO - Epoch [148][2500/3746] lr: 5.967e-05, eta: 2:00:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6753, top5_acc: 0.8767, loss_cls: 1.8513, loss: 1.8513 +2024-07-27 15:59:03,600 - pyskl - INFO - Epoch [148][2600/3746] lr: 5.831e-05, eta: 1:59:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6786, top5_acc: 0.8881, loss_cls: 1.8244, loss: 1.8244 +2024-07-27 16:00:25,179 - pyskl - INFO - Epoch [148][2700/3746] lr: 5.697e-05, eta: 1:57:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6766, top5_acc: 0.8800, loss_cls: 1.8396, loss: 1.8396 +2024-07-27 16:01:47,017 - pyskl - INFO - Epoch [148][2800/3746] lr: 5.564e-05, eta: 1:56:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6648, top5_acc: 0.8691, loss_cls: 1.8730, loss: 1.8730 +2024-07-27 16:03:09,281 - pyskl - INFO - Epoch [148][2900/3746] lr: 5.433e-05, eta: 1:55:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6737, top5_acc: 0.8816, loss_cls: 1.8325, loss: 1.8325 +2024-07-27 16:04:31,217 - pyskl - INFO - Epoch [148][3000/3746] lr: 5.304e-05, eta: 1:53:42, time: 0.819, data_time: 0.001, memory: 15990, top1_acc: 0.6602, top5_acc: 0.8684, loss_cls: 1.8939, loss: 1.8939 +2024-07-27 16:05:54,409 - pyskl - INFO - Epoch [148][3100/3746] lr: 5.176e-05, eta: 1:52:19, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.6733, top5_acc: 0.8800, loss_cls: 1.8521, loss: 1.8521 +2024-07-27 16:07:16,150 - pyskl - INFO - Epoch [148][3200/3746] lr: 5.050e-05, eta: 1:50:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6628, top5_acc: 0.8728, loss_cls: 1.8648, loss: 1.8648 +2024-07-27 16:08:39,503 - pyskl - INFO - Epoch [148][3300/3746] lr: 4.925e-05, eta: 1:49:34, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6734, top5_acc: 0.8762, loss_cls: 1.8598, loss: 1.8598 +2024-07-27 16:10:02,905 - pyskl - INFO - Epoch [148][3400/3746] lr: 4.801e-05, eta: 1:48:11, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6736, top5_acc: 0.8767, loss_cls: 1.8334, loss: 1.8334 +2024-07-27 16:11:26,094 - pyskl - INFO - Epoch [148][3500/3746] lr: 4.680e-05, eta: 1:46:48, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.6709, top5_acc: 0.8708, loss_cls: 1.8862, loss: 1.8862 +2024-07-27 16:12:48,555 - pyskl - INFO - Epoch [148][3600/3746] lr: 4.560e-05, eta: 1:45:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6648, top5_acc: 0.8730, loss_cls: 1.8746, loss: 1.8746 +2024-07-27 16:14:10,359 - pyskl - INFO - Epoch [148][3700/3746] lr: 4.441e-05, eta: 1:44:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6652, top5_acc: 0.8659, loss_cls: 1.8940, loss: 1.8940 +2024-07-27 16:14:49,531 - pyskl - INFO - Saving checkpoint at 148 epochs +2024-07-27 16:16:42,639 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 16:16:43,302 - pyskl - INFO - +top1_acc 0.4701 +top5_acc 0.7216 +2024-07-27 16:16:43,303 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 16:16:43,343 - pyskl - INFO - +mean_acc 0.4698 +2024-07-27 16:16:43,354 - pyskl - INFO - Epoch(val) [148][309] top1_acc: 0.4701, top5_acc: 0.7216, mean_class_accuracy: 0.4698 +2024-07-27 16:20:31,193 - pyskl - INFO - Epoch [149][100/3746] lr: 4.271e-05, eta: 1:42:03, time: 2.278, data_time: 1.291, memory: 15990, top1_acc: 0.6695, top5_acc: 0.8753, loss_cls: 1.8638, loss: 1.8638 +2024-07-27 16:21:54,300 - pyskl - INFO - Epoch [149][200/3746] lr: 4.156e-05, eta: 1:40:40, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6723, top5_acc: 0.8766, loss_cls: 1.8421, loss: 1.8421 +2024-07-27 16:23:17,472 - pyskl - INFO - Epoch [149][300/3746] lr: 4.043e-05, eta: 1:39:17, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.6795, top5_acc: 0.8719, loss_cls: 1.8500, loss: 1.8500 +2024-07-27 16:24:40,618 - pyskl - INFO - Epoch [149][400/3746] lr: 3.931e-05, eta: 1:37:54, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6731, top5_acc: 0.8738, loss_cls: 1.8518, loss: 1.8518 +2024-07-27 16:26:02,778 - pyskl - INFO - Epoch [149][500/3746] lr: 3.821e-05, eta: 1:36:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6722, top5_acc: 0.8767, loss_cls: 1.8484, loss: 1.8484 +2024-07-27 16:27:26,165 - pyskl - INFO - Epoch [149][600/3746] lr: 3.713e-05, eta: 1:35:09, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6644, top5_acc: 0.8705, loss_cls: 1.8856, loss: 1.8856 +2024-07-27 16:28:49,574 - pyskl - INFO - Epoch [149][700/3746] lr: 3.606e-05, eta: 1:33:46, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6775, top5_acc: 0.8797, loss_cls: 1.8484, loss: 1.8484 +2024-07-27 16:30:12,215 - pyskl - INFO - Epoch [149][800/3746] lr: 3.500e-05, eta: 1:32:23, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6833, top5_acc: 0.8903, loss_cls: 1.7806, loss: 1.7806 +2024-07-27 16:31:35,817 - pyskl - INFO - Epoch [149][900/3746] lr: 3.397e-05, eta: 1:31:00, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.6789, top5_acc: 0.8825, loss_cls: 1.8277, loss: 1.8277 +2024-07-27 16:32:59,067 - pyskl - INFO - Epoch [149][1000/3746] lr: 3.294e-05, eta: 1:29:37, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.6709, top5_acc: 0.8756, loss_cls: 1.8530, loss: 1.8530 +2024-07-27 16:34:22,113 - pyskl - INFO - Epoch [149][1100/3746] lr: 3.194e-05, eta: 1:28:14, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.6700, top5_acc: 0.8752, loss_cls: 1.8799, loss: 1.8799 +2024-07-27 16:35:44,977 - pyskl - INFO - Epoch [149][1200/3746] lr: 3.095e-05, eta: 1:26:52, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6642, top5_acc: 0.8756, loss_cls: 1.8778, loss: 1.8778 +2024-07-27 16:37:06,972 - pyskl - INFO - Epoch [149][1300/3746] lr: 2.997e-05, eta: 1:25:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6725, top5_acc: 0.8784, loss_cls: 1.8446, loss: 1.8446 +2024-07-27 16:38:29,525 - pyskl - INFO - Epoch [149][1400/3746] lr: 2.901e-05, eta: 1:24:06, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6763, top5_acc: 0.8770, loss_cls: 1.8341, loss: 1.8341 +2024-07-27 16:39:52,091 - pyskl - INFO - Epoch [149][1500/3746] lr: 2.807e-05, eta: 1:22:43, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6725, top5_acc: 0.8825, loss_cls: 1.8442, loss: 1.8442 +2024-07-27 16:41:15,102 - pyskl - INFO - Epoch [149][1600/3746] lr: 2.714e-05, eta: 1:21:20, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.6803, top5_acc: 0.8792, loss_cls: 1.8279, loss: 1.8279 +2024-07-27 16:42:37,478 - pyskl - INFO - Epoch [149][1700/3746] lr: 2.622e-05, eta: 1:19:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6745, top5_acc: 0.8756, loss_cls: 1.8478, loss: 1.8478 +2024-07-27 16:43:59,803 - pyskl - INFO - Epoch [149][1800/3746] lr: 2.533e-05, eta: 1:18:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6672, top5_acc: 0.8700, loss_cls: 1.8818, loss: 1.8818 +2024-07-27 16:45:21,561 - pyskl - INFO - Epoch [149][1900/3746] lr: 2.444e-05, eta: 1:17:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6711, top5_acc: 0.8795, loss_cls: 1.8349, loss: 1.8349 +2024-07-27 16:46:43,534 - pyskl - INFO - Epoch [149][2000/3746] lr: 2.358e-05, eta: 1:15:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6747, top5_acc: 0.8762, loss_cls: 1.8450, loss: 1.8450 +2024-07-27 16:48:05,423 - pyskl - INFO - Epoch [149][2100/3746] lr: 2.273e-05, eta: 1:14:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6734, top5_acc: 0.8775, loss_cls: 1.8270, loss: 1.8270 +2024-07-27 16:49:27,103 - pyskl - INFO - Epoch [149][2200/3746] lr: 2.189e-05, eta: 1:13:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6744, top5_acc: 0.8805, loss_cls: 1.8480, loss: 1.8480 +2024-07-27 16:50:49,254 - pyskl - INFO - Epoch [149][2300/3746] lr: 2.107e-05, eta: 1:11:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6750, top5_acc: 0.8778, loss_cls: 1.8448, loss: 1.8448 +2024-07-27 16:52:11,026 - pyskl - INFO - Epoch [149][2400/3746] lr: 2.027e-05, eta: 1:10:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6744, top5_acc: 0.8752, loss_cls: 1.8602, loss: 1.8602 +2024-07-27 16:53:33,476 - pyskl - INFO - Epoch [149][2500/3746] lr: 1.948e-05, eta: 1:08:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6737, top5_acc: 0.8823, loss_cls: 1.8500, loss: 1.8500 +2024-07-27 16:54:55,436 - pyskl - INFO - Epoch [149][2600/3746] lr: 1.871e-05, eta: 1:07:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6773, top5_acc: 0.8823, loss_cls: 1.8420, loss: 1.8420 +2024-07-27 16:56:17,328 - pyskl - INFO - Epoch [149][2700/3746] lr: 1.795e-05, eta: 1:06:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6627, top5_acc: 0.8719, loss_cls: 1.8839, loss: 1.8839 +2024-07-27 16:57:40,226 - pyskl - INFO - Epoch [149][2800/3746] lr: 1.721e-05, eta: 1:04:46, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6786, top5_acc: 0.8803, loss_cls: 1.8273, loss: 1.8273 +2024-07-27 16:59:01,898 - pyskl - INFO - Epoch [149][2900/3746] lr: 1.649e-05, eta: 1:03:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6833, top5_acc: 0.8881, loss_cls: 1.8115, loss: 1.8115 +2024-07-27 17:00:25,046 - pyskl - INFO - Epoch [149][3000/3746] lr: 1.578e-05, eta: 1:02:00, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6767, top5_acc: 0.8792, loss_cls: 1.8567, loss: 1.8567 +2024-07-27 17:01:47,321 - pyskl - INFO - Epoch [149][3100/3746] lr: 1.508e-05, eta: 1:00:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6727, top5_acc: 0.8802, loss_cls: 1.8480, loss: 1.8480 +2024-07-27 17:03:09,804 - pyskl - INFO - Epoch [149][3200/3746] lr: 1.440e-05, eta: 0:59:15, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6830, top5_acc: 0.8775, loss_cls: 1.8274, loss: 1.8274 +2024-07-27 17:04:32,251 - pyskl - INFO - Epoch [149][3300/3746] lr: 1.374e-05, eta: 0:57:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6791, top5_acc: 0.8822, loss_cls: 1.8356, loss: 1.8356 +2024-07-27 17:05:55,467 - pyskl - INFO - Epoch [149][3400/3746] lr: 1.309e-05, eta: 0:56:29, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.6722, top5_acc: 0.8842, loss_cls: 1.8379, loss: 1.8379 +2024-07-27 17:07:18,010 - pyskl - INFO - Epoch [149][3500/3746] lr: 1.246e-05, eta: 0:55:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6730, top5_acc: 0.8766, loss_cls: 1.8310, loss: 1.8310 +2024-07-27 17:08:40,435 - pyskl - INFO - Epoch [149][3600/3746] lr: 1.184e-05, eta: 0:53:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6713, top5_acc: 0.8786, loss_cls: 1.8441, loss: 1.8441 +2024-07-27 17:10:02,213 - pyskl - INFO - Epoch [149][3700/3746] lr: 1.124e-05, eta: 0:52:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6750, top5_acc: 0.8747, loss_cls: 1.8509, loss: 1.8509 +2024-07-27 17:10:41,344 - pyskl - INFO - Saving checkpoint at 149 epochs +2024-07-27 17:12:34,547 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 17:12:35,225 - pyskl - INFO - +top1_acc 0.4723 +top5_acc 0.7198 +2024-07-27 17:12:35,225 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 17:12:35,270 - pyskl - INFO - +mean_acc 0.4721 +2024-07-27 17:12:35,275 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_147.pth was removed +2024-07-27 17:12:35,552 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_149.pth. +2024-07-27 17:12:35,553 - pyskl - INFO - Best top1_acc is 0.4723 at 149 epoch. +2024-07-27 17:12:35,565 - pyskl - INFO - Epoch(val) [149][309] top1_acc: 0.4723, top5_acc: 0.7198, mean_class_accuracy: 0.4721 +2024-07-27 17:16:24,302 - pyskl - INFO - Epoch [150][100/3746] lr: 1.039e-05, eta: 0:50:20, time: 2.287, data_time: 1.298, memory: 15990, top1_acc: 0.6841, top5_acc: 0.8786, loss_cls: 1.8274, loss: 1.8274 +2024-07-27 17:17:47,024 - pyskl - INFO - Epoch [150][200/3746] lr: 9.832e-06, eta: 0:48:58, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6805, top5_acc: 0.8834, loss_cls: 1.8133, loss: 1.8133 +2024-07-27 17:19:10,898 - pyskl - INFO - Epoch [150][300/3746] lr: 9.285e-06, eta: 0:47:35, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.6734, top5_acc: 0.8764, loss_cls: 1.8505, loss: 1.8505 +2024-07-27 17:20:33,748 - pyskl - INFO - Epoch [150][400/3746] lr: 8.754e-06, eta: 0:46:12, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6703, top5_acc: 0.8784, loss_cls: 1.8479, loss: 1.8479 +2024-07-27 17:21:56,931 - pyskl - INFO - Epoch [150][500/3746] lr: 8.239e-06, eta: 0:44:49, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.6631, top5_acc: 0.8700, loss_cls: 1.8936, loss: 1.8936 +2024-07-27 17:23:20,559 - pyskl - INFO - Epoch [150][600/3746] lr: 7.739e-06, eta: 0:43:26, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.6748, top5_acc: 0.8800, loss_cls: 1.8496, loss: 1.8496 +2024-07-27 17:24:43,579 - pyskl - INFO - Epoch [150][700/3746] lr: 7.255e-06, eta: 0:42:03, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.6819, top5_acc: 0.8841, loss_cls: 1.8008, loss: 1.8008 +2024-07-27 17:26:05,842 - pyskl - INFO - Epoch [150][800/3746] lr: 6.787e-06, eta: 0:40:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6789, top5_acc: 0.8853, loss_cls: 1.8212, loss: 1.8212 +2024-07-27 17:27:28,939 - pyskl - INFO - Epoch [150][900/3746] lr: 6.334e-06, eta: 0:39:18, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6803, top5_acc: 0.8816, loss_cls: 1.8421, loss: 1.8421 +2024-07-27 17:28:51,473 - pyskl - INFO - Epoch [150][1000/3746] lr: 5.897e-06, eta: 0:37:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6786, top5_acc: 0.8828, loss_cls: 1.8187, loss: 1.8187 +2024-07-27 17:30:14,681 - pyskl - INFO - Epoch [150][1100/3746] lr: 5.475e-06, eta: 0:36:32, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.6833, top5_acc: 0.8811, loss_cls: 1.8345, loss: 1.8345 +2024-07-27 17:31:36,943 - pyskl - INFO - Epoch [150][1200/3746] lr: 5.070e-06, eta: 0:35:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6706, top5_acc: 0.8744, loss_cls: 1.8773, loss: 1.8773 +2024-07-27 17:32:59,233 - pyskl - INFO - Epoch [150][1300/3746] lr: 4.679e-06, eta: 0:33:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6748, top5_acc: 0.8817, loss_cls: 1.8274, loss: 1.8274 +2024-07-27 17:34:21,442 - pyskl - INFO - Epoch [150][1400/3746] lr: 4.305e-06, eta: 0:32:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6817, top5_acc: 0.8772, loss_cls: 1.8312, loss: 1.8312 +2024-07-27 17:35:43,079 - pyskl - INFO - Epoch [150][1500/3746] lr: 3.946e-06, eta: 0:31:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6833, top5_acc: 0.8823, loss_cls: 1.7838, loss: 1.7838 +2024-07-27 17:37:04,442 - pyskl - INFO - Epoch [150][1600/3746] lr: 3.602e-06, eta: 0:29:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6753, top5_acc: 0.8756, loss_cls: 1.8559, loss: 1.8559 +2024-07-27 17:38:25,888 - pyskl - INFO - Epoch [150][1700/3746] lr: 3.275e-06, eta: 0:28:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6714, top5_acc: 0.8747, loss_cls: 1.8354, loss: 1.8354 +2024-07-27 17:39:47,570 - pyskl - INFO - Epoch [150][1800/3746] lr: 2.962e-06, eta: 0:26:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6698, top5_acc: 0.8766, loss_cls: 1.8656, loss: 1.8656 +2024-07-27 17:41:09,010 - pyskl - INFO - Epoch [150][1900/3746] lr: 2.666e-06, eta: 0:25:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6716, top5_acc: 0.8792, loss_cls: 1.8434, loss: 1.8434 +2024-07-27 17:42:30,609 - pyskl - INFO - Epoch [150][2000/3746] lr: 2.385e-06, eta: 0:24:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6627, top5_acc: 0.8770, loss_cls: 1.8807, loss: 1.8807 +2024-07-27 17:43:52,191 - pyskl - INFO - Epoch [150][2100/3746] lr: 2.120e-06, eta: 0:22:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6719, top5_acc: 0.8822, loss_cls: 1.8452, loss: 1.8452 +2024-07-27 17:45:14,247 - pyskl - INFO - Epoch [150][2200/3746] lr: 1.870e-06, eta: 0:21:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6816, top5_acc: 0.8770, loss_cls: 1.8234, loss: 1.8234 +2024-07-27 17:46:35,695 - pyskl - INFO - Epoch [150][2300/3746] lr: 1.636e-06, eta: 0:19:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6820, top5_acc: 0.8856, loss_cls: 1.7878, loss: 1.7878 +2024-07-27 17:47:57,481 - pyskl - INFO - Epoch [150][2400/3746] lr: 1.418e-06, eta: 0:18:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6714, top5_acc: 0.8761, loss_cls: 1.8479, loss: 1.8479 +2024-07-27 17:49:18,924 - pyskl - INFO - Epoch [150][2500/3746] lr: 1.215e-06, eta: 0:17:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6822, top5_acc: 0.8834, loss_cls: 1.8124, loss: 1.8124 +2024-07-27 17:50:39,647 - pyskl - INFO - Epoch [150][2600/3746] lr: 1.028e-06, eta: 0:15:49, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.6789, top5_acc: 0.8795, loss_cls: 1.8450, loss: 1.8450 +2024-07-27 17:52:00,932 - pyskl - INFO - Epoch [150][2700/3746] lr: 8.567e-07, eta: 0:14:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6745, top5_acc: 0.8794, loss_cls: 1.8637, loss: 1.8637 +2024-07-27 17:53:22,202 - pyskl - INFO - Epoch [150][2800/3746] lr: 7.008e-07, eta: 0:13:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6608, top5_acc: 0.8741, loss_cls: 1.8865, loss: 1.8865 +2024-07-27 17:54:43,662 - pyskl - INFO - Epoch [150][2900/3746] lr: 5.606e-07, eta: 0:11:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6711, top5_acc: 0.8761, loss_cls: 1.8697, loss: 1.8697 +2024-07-27 17:56:05,894 - pyskl - INFO - Epoch [150][3000/3746] lr: 4.361e-07, eta: 0:10:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6722, top5_acc: 0.8822, loss_cls: 1.8395, loss: 1.8395 +2024-07-27 17:57:27,465 - pyskl - INFO - Epoch [150][3100/3746] lr: 3.271e-07, eta: 0:08:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6825, top5_acc: 0.8848, loss_cls: 1.8142, loss: 1.8142 +2024-07-27 17:58:49,537 - pyskl - INFO - Epoch [150][3200/3746] lr: 2.338e-07, eta: 0:07:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6892, top5_acc: 0.8825, loss_cls: 1.7851, loss: 1.7851 +2024-07-27 18:00:10,703 - pyskl - INFO - Epoch [150][3300/3746] lr: 1.561e-07, eta: 0:06:09, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6722, top5_acc: 0.8753, loss_cls: 1.8545, loss: 1.8545 +2024-07-27 18:01:31,547 - pyskl - INFO - Epoch [150][3400/3746] lr: 9.410e-08, eta: 0:04:46, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.6900, top5_acc: 0.8850, loss_cls: 1.7888, loss: 1.7888 +2024-07-27 18:02:52,722 - pyskl - INFO - Epoch [150][3500/3746] lr: 4.768e-08, eta: 0:03:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6672, top5_acc: 0.8714, loss_cls: 1.8736, loss: 1.8736 +2024-07-27 18:04:13,772 - pyskl - INFO - Epoch [150][3600/3746] lr: 1.689e-08, eta: 0:02:00, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6780, top5_acc: 0.8814, loss_cls: 1.8378, loss: 1.8378 +2024-07-27 18:05:34,790 - pyskl - INFO - Epoch [150][3700/3746] lr: 1.726e-09, eta: 0:00:38, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6763, top5_acc: 0.8817, loss_cls: 1.8221, loss: 1.8221 +2024-07-27 18:06:13,508 - pyskl - INFO - Saving checkpoint at 150 epochs +2024-07-27 18:08:03,538 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 18:08:04,161 - pyskl - INFO - +top1_acc 0.4730 +top5_acc 0.7193 +2024-07-27 18:08:04,161 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 18:08:04,201 - pyskl - INFO - +mean_acc 0.4727 +2024-07-27 18:08:04,206 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_149.pth was removed +2024-07-27 18:08:04,455 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_150.pth. +2024-07-27 18:08:04,456 - pyskl - INFO - Best top1_acc is 0.4730 at 150 epoch. +2024-07-27 18:08:04,468 - pyskl - INFO - Epoch(val) [150][309] top1_acc: 0.4730, top5_acc: 0.7193, mean_class_accuracy: 0.4727 +2024-07-27 18:08:18,148 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-07-27 18:20:22,688 - pyskl - INFO - Testing results of the last checkpoint +2024-07-27 18:20:22,688 - pyskl - INFO - top1_acc: 0.4817 +2024-07-27 18:20:22,688 - pyskl - INFO - top5_acc: 0.7286 +2024-07-27 18:20:22,688 - pyskl - INFO - mean_class_accuracy: 0.4815 +2024-07-27 18:20:22,689 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/k400/j_2/best_top1_acc_epoch_150.pth +2024-07-27 18:32:17,978 - pyskl - INFO - Testing results of the best checkpoint +2024-07-27 18:32:17,979 - pyskl - INFO - top1_acc: 0.4817 +2024-07-27 18:32:17,979 - pyskl - INFO - top5_acc: 0.7286 +2024-07-27 18:32:17,979 - pyskl - INFO - mean_class_accuracy: 0.4815 diff --git a/k400/j_2/20240722_022822.log.json b/k400/j_2/20240722_022822.log.json new file mode 100644 index 0000000000000000000000000000000000000000..b17e8b4c08be0f0313424df345bff423780ecbf4 --- /dev/null +++ b/k400/j_2/20240722_022822.log.json @@ -0,0 +1,5701 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 1837183614, "config_name": "j_2.py", "work_dir": "j_2", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.1, "memory": 15990, "data_time": 1.25948, "top1_acc": 0.005, "top5_acc": 0.02562, "loss_cls": 6.49925, "loss": 6.49925, "time": 1.97685} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.1, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.01, "top5_acc": 0.04531, "loss_cls": 6.45744, "loss": 6.45744, "time": 0.71385} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.1, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.01312, "top5_acc": 0.06219, "loss_cls": 6.27905, "loss": 6.27905, "time": 0.71321} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.1, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.01844, "top5_acc": 0.07109, "loss_cls": 6.12153, "loss": 6.12153, "time": 0.70924} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.1, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.01953, "top5_acc": 0.08531, "loss_cls": 6.02021, "loss": 6.02021, "time": 0.70616} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.02641, "top5_acc": 0.09938, "loss_cls": 5.92982, "loss": 5.92982, "time": 0.7051} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.025, "top5_acc": 0.10562, "loss_cls": 5.86754, "loss": 5.86754, "time": 0.70412} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.02656, "top5_acc": 0.10766, "loss_cls": 5.84804, "loss": 5.84804, "time": 0.70621} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.1, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.03375, "top5_acc": 0.11828, "loss_cls": 5.80587, "loss": 5.80587, "time": 0.70507} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.1, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.03938, "top5_acc": 0.135, "loss_cls": 5.76776, "loss": 5.76776, "time": 0.70421} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.035, "top5_acc": 0.1325, "loss_cls": 5.7369, "loss": 5.7369, "time": 0.70704} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.03953, "top5_acc": 0.14297, "loss_cls": 5.6848, "loss": 5.6848, "time": 0.70353} +{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.04797, "top5_acc": 0.15203, "loss_cls": 5.63938, "loss": 5.63938, "time": 0.70158} +{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.1, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.05172, "top5_acc": 0.16484, "loss_cls": 5.61714, "loss": 5.61714, "time": 0.70326} +{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.04625, "top5_acc": 0.16344, "loss_cls": 5.60786, "loss": 5.60786, "time": 0.70207} +{"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.1, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.04828, "top5_acc": 0.16469, "loss_cls": 5.56168, "loss": 5.56168, "time": 0.70098} +{"mode": "train", "epoch": 1, "iter": 1700, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.05016, "top5_acc": 0.17062, "loss_cls": 5.54589, "loss": 5.54589, "time": 0.70803} +{"mode": "train", "epoch": 1, "iter": 1800, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.05766, "top5_acc": 0.18219, "loss_cls": 5.52832, "loss": 5.52832, "time": 0.70537} +{"mode": "train", "epoch": 1, "iter": 1900, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.05719, "top5_acc": 0.1875, "loss_cls": 5.46381, "loss": 5.46381, "time": 0.70454} +{"mode": "train", "epoch": 1, "iter": 2000, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.05906, "top5_acc": 0.19344, "loss_cls": 5.47708, "loss": 5.47708, "time": 0.71788} +{"mode": "train", "epoch": 1, "iter": 2100, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.07281, "top5_acc": 0.20922, "loss_cls": 5.41172, "loss": 5.41172, "time": 0.73105} +{"mode": "train", "epoch": 1, "iter": 2200, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.0725, "top5_acc": 0.21156, "loss_cls": 5.39956, "loss": 5.39956, "time": 0.71233} +{"mode": "train", "epoch": 1, "iter": 2300, "lr": 0.1, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.06969, "top5_acc": 0.21891, "loss_cls": 5.40073, "loss": 5.40073, "time": 0.70349} +{"mode": "train", "epoch": 1, "iter": 2400, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.07344, "top5_acc": 0.22484, "loss_cls": 5.34573, "loss": 5.34573, "time": 0.706} +{"mode": "train", "epoch": 1, "iter": 2500, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.08609, "top5_acc": 0.24172, "loss_cls": 5.28226, "loss": 5.28226, "time": 0.70674} +{"mode": "train", "epoch": 1, "iter": 2600, "lr": 0.09999, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.08203, "top5_acc": 0.24172, "loss_cls": 5.28536, "loss": 5.28536, "time": 0.70521} +{"mode": "train", "epoch": 1, "iter": 2700, "lr": 0.09999, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.08734, "top5_acc": 0.25219, "loss_cls": 5.23966, "loss": 5.23966, "time": 0.70847} +{"mode": "train", "epoch": 1, "iter": 2800, "lr": 0.09999, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.09031, "top5_acc": 0.24484, "loss_cls": 5.24359, "loss": 5.24359, "time": 0.70208} +{"mode": "train", "epoch": 1, "iter": 2900, "lr": 0.09999, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.09547, "top5_acc": 0.25828, "loss_cls": 5.21673, "loss": 5.21673, "time": 0.70302} +{"mode": "train", "epoch": 1, "iter": 3000, "lr": 0.09999, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.09391, "top5_acc": 0.26922, "loss_cls": 5.17972, "loss": 5.17972, "time": 0.70157} +{"mode": "train", "epoch": 1, "iter": 3100, "lr": 0.09999, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.09844, "top5_acc": 0.26594, "loss_cls": 5.18974, "loss": 5.18974, "time": 0.69964} +{"mode": "train", "epoch": 1, "iter": 3200, "lr": 0.09999, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.09984, "top5_acc": 0.28141, "loss_cls": 5.12044, "loss": 5.12044, "time": 0.69987} +{"mode": "train", "epoch": 1, "iter": 3300, "lr": 0.09999, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.10188, "top5_acc": 0.27453, "loss_cls": 5.12069, "loss": 5.12069, "time": 0.69819} +{"mode": "train", "epoch": 1, "iter": 3400, "lr": 0.09999, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.10938, "top5_acc": 0.28172, "loss_cls": 5.09316, "loss": 5.09316, "time": 0.70242} +{"mode": "train", "epoch": 1, "iter": 3500, "lr": 0.09999, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.1, "top5_acc": 0.29109, "loss_cls": 5.05737, "loss": 5.05737, "time": 0.69969} +{"mode": "train", "epoch": 1, "iter": 3600, "lr": 0.09999, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.10562, "top5_acc": 0.28766, "loss_cls": 5.07686, "loss": 5.07686, "time": 0.70083} +{"mode": "train", "epoch": 1, "iter": 3700, "lr": 0.09999, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.10562, "top5_acc": 0.28813, "loss_cls": 5.04038, "loss": 5.04038, "time": 0.70269} +{"mode": "val", "epoch": 1, "iter": 309, "lr": 0.09999, "top1_acc": 0.06215, "top5_acc": 0.19151, "mean_class_accuracy": 0.06193} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.09999, "memory": 15990, "data_time": 1.35774, "top1_acc": 0.11547, "top5_acc": 0.30594, "loss_cls": 5.00844, "loss": 5.00844, "time": 2.07123} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.09999, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.11875, "top5_acc": 0.30578, "loss_cls": 5.0076, "loss": 5.0076, "time": 0.71229} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.09999, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.11516, "top5_acc": 0.30141, "loss_cls": 5.02845, "loss": 5.02845, "time": 0.70555} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.09999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.12312, "top5_acc": 0.31531, "loss_cls": 4.96674, "loss": 4.96674, "time": 0.7031} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.09999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.12109, "top5_acc": 0.31594, "loss_cls": 4.96353, "loss": 4.96353, "time": 0.70232} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.09999, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.12828, "top5_acc": 0.3225, "loss_cls": 4.95386, "loss": 4.95386, "time": 0.70052} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.09998, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.12516, "top5_acc": 0.32641, "loss_cls": 4.91786, "loss": 4.91786, "time": 0.70481} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.09998, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.12, "top5_acc": 0.32328, "loss_cls": 4.92575, "loss": 4.92575, "time": 0.70228} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.09998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.11906, "top5_acc": 0.31594, "loss_cls": 4.96233, "loss": 4.96233, "time": 0.7043} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.09998, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.13047, "top5_acc": 0.33156, "loss_cls": 4.90427, "loss": 4.90427, "time": 0.70367} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.09998, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.12922, "top5_acc": 0.33359, "loss_cls": 4.87111, "loss": 4.87111, "time": 0.70258} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.09998, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.13438, "top5_acc": 0.34609, "loss_cls": 4.88491, "loss": 4.88491, "time": 0.702} +{"mode": "train", "epoch": 2, "iter": 1300, "lr": 0.09998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.12937, "top5_acc": 0.32078, "loss_cls": 4.93503, "loss": 4.93503, "time": 0.69986} +{"mode": "train", "epoch": 2, "iter": 1400, "lr": 0.09998, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.13172, "top5_acc": 0.33375, "loss_cls": 4.89782, "loss": 4.89782, "time": 0.69722} +{"mode": "train", "epoch": 2, "iter": 1500, "lr": 0.09998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.13141, "top5_acc": 0.34, "loss_cls": 4.86744, "loss": 4.86744, "time": 0.69883} +{"mode": "train", "epoch": 2, "iter": 1600, "lr": 0.09998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.13484, "top5_acc": 0.33203, "loss_cls": 4.88441, "loss": 4.88441, "time": 0.69883} +{"mode": "train", "epoch": 2, "iter": 1700, "lr": 0.09998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.13516, "top5_acc": 0.34406, "loss_cls": 4.85317, "loss": 4.85317, "time": 0.69702} +{"mode": "train", "epoch": 2, "iter": 1800, "lr": 0.09998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.13328, "top5_acc": 0.34938, "loss_cls": 4.82063, "loss": 4.82063, "time": 0.69962} +{"mode": "train", "epoch": 2, "iter": 1900, "lr": 0.09998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.14188, "top5_acc": 0.34938, "loss_cls": 4.83286, "loss": 4.83286, "time": 0.69664} +{"mode": "train", "epoch": 2, "iter": 2000, "lr": 0.09997, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.14938, "top5_acc": 0.35391, "loss_cls": 4.78074, "loss": 4.78074, "time": 0.69921} +{"mode": "train", "epoch": 2, "iter": 2100, "lr": 0.09997, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.14562, "top5_acc": 0.35672, "loss_cls": 4.7945, "loss": 4.7945, "time": 0.70096} +{"mode": "train", "epoch": 2, "iter": 2200, "lr": 0.09997, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.15219, "top5_acc": 0.35297, "loss_cls": 4.80582, "loss": 4.80582, "time": 0.69639} +{"mode": "train", "epoch": 2, "iter": 2300, "lr": 0.09997, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.14312, "top5_acc": 0.34094, "loss_cls": 4.84207, "loss": 4.84207, "time": 0.69724} +{"mode": "train", "epoch": 2, "iter": 2400, "lr": 0.09997, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.14625, "top5_acc": 0.36344, "loss_cls": 4.76589, "loss": 4.76589, "time": 0.69753} +{"mode": "train", "epoch": 2, "iter": 2500, "lr": 0.09997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15, "top5_acc": 0.36391, "loss_cls": 4.77644, "loss": 4.77644, "time": 0.69847} +{"mode": "train", "epoch": 2, "iter": 2600, "lr": 0.09997, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.15641, "top5_acc": 0.37109, "loss_cls": 4.74133, "loss": 4.74133, "time": 0.69892} +{"mode": "train", "epoch": 2, "iter": 2700, "lr": 0.09997, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.15672, "top5_acc": 0.36359, "loss_cls": 4.74091, "loss": 4.74091, "time": 0.69688} +{"mode": "train", "epoch": 2, "iter": 2800, "lr": 0.09997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.14562, "top5_acc": 0.36, "loss_cls": 4.77305, "loss": 4.77305, "time": 0.6992} +{"mode": "train", "epoch": 2, "iter": 2900, "lr": 0.09997, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.155, "top5_acc": 0.36875, "loss_cls": 4.72629, "loss": 4.72629, "time": 0.69997} +{"mode": "train", "epoch": 2, "iter": 3000, "lr": 0.09996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15516, "top5_acc": 0.36359, "loss_cls": 4.73639, "loss": 4.73639, "time": 0.69728} +{"mode": "train", "epoch": 2, "iter": 3100, "lr": 0.09996, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15734, "top5_acc": 0.37016, "loss_cls": 4.75694, "loss": 4.75694, "time": 0.69792} +{"mode": "train", "epoch": 2, "iter": 3200, "lr": 0.09996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15453, "top5_acc": 0.37016, "loss_cls": 4.71949, "loss": 4.71949, "time": 0.69867} +{"mode": "train", "epoch": 2, "iter": 3300, "lr": 0.09996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.15375, "top5_acc": 0.36594, "loss_cls": 4.73775, "loss": 4.73775, "time": 0.69746} +{"mode": "train", "epoch": 2, "iter": 3400, "lr": 0.09996, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.14797, "top5_acc": 0.35734, "loss_cls": 4.78695, "loss": 4.78695, "time": 0.70537} +{"mode": "train", "epoch": 2, "iter": 3500, "lr": 0.09996, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.16031, "top5_acc": 0.37875, "loss_cls": 4.68352, "loss": 4.68352, "time": 0.7013} +{"mode": "train", "epoch": 2, "iter": 3600, "lr": 0.09996, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.16297, "top5_acc": 0.37688, "loss_cls": 4.70312, "loss": 4.70312, "time": 0.70291} +{"mode": "train", "epoch": 2, "iter": 3700, "lr": 0.09996, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.15625, "top5_acc": 0.37297, "loss_cls": 4.68633, "loss": 4.68633, "time": 0.7038} +{"mode": "val", "epoch": 2, "iter": 309, "lr": 0.09996, "top1_acc": 0.09836, "top5_acc": 0.27402, "mean_class_accuracy": 0.0983} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.09995, "memory": 15990, "data_time": 1.32337, "top1_acc": 0.16062, "top5_acc": 0.37875, "loss_cls": 4.69287, "loss": 4.69287, "time": 2.03564} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.09995, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.16734, "top5_acc": 0.38922, "loss_cls": 4.62811, "loss": 4.62811, "time": 0.71457} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.09995, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.16, "top5_acc": 0.37391, "loss_cls": 4.67023, "loss": 4.67023, "time": 0.70726} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.09995, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.16766, "top5_acc": 0.38312, "loss_cls": 4.67375, "loss": 4.67375, "time": 0.70584} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.09995, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.16469, "top5_acc": 0.37922, "loss_cls": 4.63602, "loss": 4.63602, "time": 0.70353} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.09995, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.16625, "top5_acc": 0.38609, "loss_cls": 4.6536, "loss": 4.6536, "time": 0.70314} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.09995, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.16156, "top5_acc": 0.38797, "loss_cls": 4.66515, "loss": 4.66515, "time": 0.70097} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.09995, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.16734, "top5_acc": 0.39094, "loss_cls": 4.62994, "loss": 4.62994, "time": 0.70039} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.09994, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.16641, "top5_acc": 0.38453, "loss_cls": 4.63633, "loss": 4.63633, "time": 0.70014} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.09994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17594, "top5_acc": 0.395, "loss_cls": 4.6321, "loss": 4.6321, "time": 0.70039} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.09994, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.16516, "top5_acc": 0.39797, "loss_cls": 4.628, "loss": 4.628, "time": 0.70343} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.09994, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.17641, "top5_acc": 0.39969, "loss_cls": 4.58078, "loss": 4.58078, "time": 0.70143} +{"mode": "train", "epoch": 3, "iter": 1300, "lr": 0.09994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17188, "top5_acc": 0.39766, "loss_cls": 4.60901, "loss": 4.60901, "time": 0.69644} +{"mode": "train", "epoch": 3, "iter": 1400, "lr": 0.09994, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18188, "top5_acc": 0.40969, "loss_cls": 4.55007, "loss": 4.55007, "time": 0.69843} +{"mode": "train", "epoch": 3, "iter": 1500, "lr": 0.09994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16844, "top5_acc": 0.38922, "loss_cls": 4.60325, "loss": 4.60325, "time": 0.69947} +{"mode": "train", "epoch": 3, "iter": 1600, "lr": 0.09994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18188, "top5_acc": 0.39906, "loss_cls": 4.59339, "loss": 4.59339, "time": 0.69749} +{"mode": "train", "epoch": 3, "iter": 1700, "lr": 0.09993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17391, "top5_acc": 0.39078, "loss_cls": 4.63229, "loss": 4.63229, "time": 0.69849} +{"mode": "train", "epoch": 3, "iter": 1800, "lr": 0.09993, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16734, "top5_acc": 0.39547, "loss_cls": 4.61528, "loss": 4.61528, "time": 0.69841} +{"mode": "train", "epoch": 3, "iter": 1900, "lr": 0.09993, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17281, "top5_acc": 0.39391, "loss_cls": 4.59593, "loss": 4.59593, "time": 0.69804} +{"mode": "train", "epoch": 3, "iter": 2000, "lr": 0.09993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18047, "top5_acc": 0.40344, "loss_cls": 4.58947, "loss": 4.58947, "time": 0.69819} +{"mode": "train", "epoch": 3, "iter": 2100, "lr": 0.09993, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.16969, "top5_acc": 0.39484, "loss_cls": 4.60143, "loss": 4.60143, "time": 0.69929} +{"mode": "train", "epoch": 3, "iter": 2200, "lr": 0.09993, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17531, "top5_acc": 0.39875, "loss_cls": 4.5738, "loss": 4.5738, "time": 0.69837} +{"mode": "train", "epoch": 3, "iter": 2300, "lr": 0.09993, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17891, "top5_acc": 0.40078, "loss_cls": 4.54998, "loss": 4.54998, "time": 0.69535} +{"mode": "train", "epoch": 3, "iter": 2400, "lr": 0.09992, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18859, "top5_acc": 0.40953, "loss_cls": 4.52848, "loss": 4.52848, "time": 0.69721} +{"mode": "train", "epoch": 3, "iter": 2500, "lr": 0.09992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17406, "top5_acc": 0.40891, "loss_cls": 4.57194, "loss": 4.57194, "time": 0.69703} +{"mode": "train", "epoch": 3, "iter": 2600, "lr": 0.09992, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.18844, "top5_acc": 0.42141, "loss_cls": 4.49209, "loss": 4.49209, "time": 0.69992} +{"mode": "train", "epoch": 3, "iter": 2700, "lr": 0.09992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18125, "top5_acc": 0.41234, "loss_cls": 4.53272, "loss": 4.53272, "time": 0.69565} +{"mode": "train", "epoch": 3, "iter": 2800, "lr": 0.09992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19203, "top5_acc": 0.42578, "loss_cls": 4.50039, "loss": 4.50039, "time": 0.69675} +{"mode": "train", "epoch": 3, "iter": 2900, "lr": 0.09992, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.17906, "top5_acc": 0.4075, "loss_cls": 4.59076, "loss": 4.59076, "time": 0.69651} +{"mode": "train", "epoch": 3, "iter": 3000, "lr": 0.09991, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19109, "top5_acc": 0.42688, "loss_cls": 4.50256, "loss": 4.50256, "time": 0.69809} +{"mode": "train", "epoch": 3, "iter": 3100, "lr": 0.09991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17625, "top5_acc": 0.40328, "loss_cls": 4.54127, "loss": 4.54127, "time": 0.69677} +{"mode": "train", "epoch": 3, "iter": 3200, "lr": 0.09991, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.18188, "top5_acc": 0.41, "loss_cls": 4.53508, "loss": 4.53508, "time": 0.69685} +{"mode": "train", "epoch": 3, "iter": 3300, "lr": 0.09991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19469, "top5_acc": 0.41766, "loss_cls": 4.51146, "loss": 4.51146, "time": 0.69694} +{"mode": "train", "epoch": 3, "iter": 3400, "lr": 0.09991, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18016, "top5_acc": 0.40797, "loss_cls": 4.54536, "loss": 4.54536, "time": 0.70079} +{"mode": "train", "epoch": 3, "iter": 3500, "lr": 0.09991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18484, "top5_acc": 0.41906, "loss_cls": 4.497, "loss": 4.497, "time": 0.69731} +{"mode": "train", "epoch": 3, "iter": 3600, "lr": 0.0999, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.17969, "top5_acc": 0.41625, "loss_cls": 4.523, "loss": 4.523, "time": 0.69973} +{"mode": "train", "epoch": 3, "iter": 3700, "lr": 0.0999, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.18, "top5_acc": 0.42, "loss_cls": 4.50574, "loss": 4.50574, "time": 0.70204} +{"mode": "val", "epoch": 3, "iter": 309, "lr": 0.0999, "top1_acc": 0.13002, "top5_acc": 0.31773, "mean_class_accuracy": 0.12981} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.0999, "memory": 15990, "data_time": 1.35938, "top1_acc": 0.19453, "top5_acc": 0.41859, "loss_cls": 4.44969, "loss": 4.44969, "time": 2.06278} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.0999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19031, "top5_acc": 0.41672, "loss_cls": 4.47364, "loss": 4.47364, "time": 0.7005} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.0999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20453, "top5_acc": 0.42812, "loss_cls": 4.41253, "loss": 4.41253, "time": 0.70131} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.09989, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19609, "top5_acc": 0.41641, "loss_cls": 4.5013, "loss": 4.5013, "time": 0.70068} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.09989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18422, "top5_acc": 0.42453, "loss_cls": 4.49724, "loss": 4.49724, "time": 0.69991} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.09989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19984, "top5_acc": 0.44031, "loss_cls": 4.41656, "loss": 4.41656, "time": 0.69826} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.09989, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18469, "top5_acc": 0.42578, "loss_cls": 4.46636, "loss": 4.46636, "time": 0.69921} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.09989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19266, "top5_acc": 0.43156, "loss_cls": 4.44647, "loss": 4.44647, "time": 0.69939} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.09988, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18969, "top5_acc": 0.42547, "loss_cls": 4.46406, "loss": 4.46406, "time": 0.69943} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.09988, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19484, "top5_acc": 0.43125, "loss_cls": 4.45555, "loss": 4.45555, "time": 0.70012} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.09988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20953, "top5_acc": 0.44375, "loss_cls": 4.39199, "loss": 4.39199, "time": 0.70183} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.09988, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18953, "top5_acc": 0.42297, "loss_cls": 4.4849, "loss": 4.4849, "time": 0.69871} +{"mode": "train", "epoch": 4, "iter": 1300, "lr": 0.09988, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.19938, "top5_acc": 0.44, "loss_cls": 4.4052, "loss": 4.4052, "time": 0.69958} +{"mode": "train", "epoch": 4, "iter": 1400, "lr": 0.09988, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19844, "top5_acc": 0.42781, "loss_cls": 4.47179, "loss": 4.47179, "time": 0.703} +{"mode": "train", "epoch": 4, "iter": 1500, "lr": 0.09987, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19406, "top5_acc": 0.42125, "loss_cls": 4.47666, "loss": 4.47666, "time": 0.69764} +{"mode": "train", "epoch": 4, "iter": 1600, "lr": 0.09987, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19453, "top5_acc": 0.43141, "loss_cls": 4.44568, "loss": 4.44568, "time": 0.69754} +{"mode": "train", "epoch": 4, "iter": 1700, "lr": 0.09987, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20141, "top5_acc": 0.44078, "loss_cls": 4.41193, "loss": 4.41193, "time": 0.69834} +{"mode": "train", "epoch": 4, "iter": 1800, "lr": 0.09987, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21359, "top5_acc": 0.45078, "loss_cls": 4.36281, "loss": 4.36281, "time": 0.70039} +{"mode": "train", "epoch": 4, "iter": 1900, "lr": 0.09987, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20281, "top5_acc": 0.43719, "loss_cls": 4.43349, "loss": 4.43349, "time": 0.70083} +{"mode": "train", "epoch": 4, "iter": 2000, "lr": 0.09986, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20719, "top5_acc": 0.43734, "loss_cls": 4.4329, "loss": 4.4329, "time": 0.69761} +{"mode": "train", "epoch": 4, "iter": 2100, "lr": 0.09986, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.205, "top5_acc": 0.43688, "loss_cls": 4.42033, "loss": 4.42033, "time": 0.70159} +{"mode": "train", "epoch": 4, "iter": 2200, "lr": 0.09986, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20141, "top5_acc": 0.43266, "loss_cls": 4.43744, "loss": 4.43744, "time": 0.69969} +{"mode": "train", "epoch": 4, "iter": 2300, "lr": 0.09986, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19781, "top5_acc": 0.43812, "loss_cls": 4.4112, "loss": 4.4112, "time": 0.70013} +{"mode": "train", "epoch": 4, "iter": 2400, "lr": 0.09985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19609, "top5_acc": 0.43516, "loss_cls": 4.43315, "loss": 4.43315, "time": 0.70072} +{"mode": "train", "epoch": 4, "iter": 2500, "lr": 0.09985, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20531, "top5_acc": 0.43938, "loss_cls": 4.37729, "loss": 4.37729, "time": 0.70064} +{"mode": "train", "epoch": 4, "iter": 2600, "lr": 0.09985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20391, "top5_acc": 0.445, "loss_cls": 4.41612, "loss": 4.41612, "time": 0.69835} +{"mode": "train", "epoch": 4, "iter": 2700, "lr": 0.09985, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2025, "top5_acc": 0.42906, "loss_cls": 4.43835, "loss": 4.43835, "time": 0.698} +{"mode": "train", "epoch": 4, "iter": 2800, "lr": 0.09985, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19703, "top5_acc": 0.43, "loss_cls": 4.42665, "loss": 4.42665, "time": 0.69792} +{"mode": "train", "epoch": 4, "iter": 2900, "lr": 0.09984, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21016, "top5_acc": 0.43844, "loss_cls": 4.37975, "loss": 4.37975, "time": 0.69839} +{"mode": "train", "epoch": 4, "iter": 3000, "lr": 0.09984, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20219, "top5_acc": 0.43734, "loss_cls": 4.41733, "loss": 4.41733, "time": 0.69732} +{"mode": "train", "epoch": 4, "iter": 3100, "lr": 0.09984, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20828, "top5_acc": 0.43406, "loss_cls": 4.41096, "loss": 4.41096, "time": 0.70168} +{"mode": "train", "epoch": 4, "iter": 3200, "lr": 0.09984, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20516, "top5_acc": 0.44062, "loss_cls": 4.37797, "loss": 4.37797, "time": 0.69865} +{"mode": "train", "epoch": 4, "iter": 3300, "lr": 0.09983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21844, "top5_acc": 0.45188, "loss_cls": 4.3562, "loss": 4.3562, "time": 0.69614} +{"mode": "train", "epoch": 4, "iter": 3400, "lr": 0.09983, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19125, "top5_acc": 0.43281, "loss_cls": 4.43023, "loss": 4.43023, "time": 0.70776} +{"mode": "train", "epoch": 4, "iter": 3500, "lr": 0.09983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20859, "top5_acc": 0.45031, "loss_cls": 4.35093, "loss": 4.35093, "time": 0.69831} +{"mode": "train", "epoch": 4, "iter": 3600, "lr": 0.09983, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.19859, "top5_acc": 0.43109, "loss_cls": 4.39453, "loss": 4.39453, "time": 0.70361} +{"mode": "train", "epoch": 4, "iter": 3700, "lr": 0.09983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20656, "top5_acc": 0.4525, "loss_cls": 4.35145, "loss": 4.35145, "time": 0.70291} +{"mode": "val", "epoch": 4, "iter": 309, "lr": 0.09982, "top1_acc": 0.14289, "top5_acc": 0.35684, "mean_class_accuracy": 0.14273} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.09982, "memory": 15990, "data_time": 1.28933, "top1_acc": 0.22125, "top5_acc": 0.45688, "loss_cls": 4.30541, "loss": 4.30541, "time": 1.99381} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.09982, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20438, "top5_acc": 0.43547, "loss_cls": 4.39203, "loss": 4.39203, "time": 0.702} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.09982, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20219, "top5_acc": 0.45125, "loss_cls": 4.37485, "loss": 4.37485, "time": 0.69851} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.09982, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20891, "top5_acc": 0.44969, "loss_cls": 4.34587, "loss": 4.34587, "time": 0.69989} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.09981, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21188, "top5_acc": 0.44438, "loss_cls": 4.35597, "loss": 4.35597, "time": 0.70163} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.09981, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21281, "top5_acc": 0.44969, "loss_cls": 4.31888, "loss": 4.31888, "time": 0.70121} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.09981, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21156, "top5_acc": 0.45375, "loss_cls": 4.33865, "loss": 4.33865, "time": 0.70117} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.09981, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21453, "top5_acc": 0.45578, "loss_cls": 4.33127, "loss": 4.33127, "time": 0.69819} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.0998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22016, "top5_acc": 0.45422, "loss_cls": 4.35038, "loss": 4.35038, "time": 0.69896} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.0998, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21969, "top5_acc": 0.45516, "loss_cls": 4.33961, "loss": 4.33961, "time": 0.70301} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.0998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21016, "top5_acc": 0.45016, "loss_cls": 4.32777, "loss": 4.32777, "time": 0.70257} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.0998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21484, "top5_acc": 0.44781, "loss_cls": 4.36356, "loss": 4.36356, "time": 0.702} +{"mode": "train", "epoch": 5, "iter": 1300, "lr": 0.09979, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21688, "top5_acc": 0.46047, "loss_cls": 4.31251, "loss": 4.31251, "time": 0.69717} +{"mode": "train", "epoch": 5, "iter": 1400, "lr": 0.09979, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21656, "top5_acc": 0.46219, "loss_cls": 4.30716, "loss": 4.30716, "time": 0.69815} +{"mode": "train", "epoch": 5, "iter": 1500, "lr": 0.09979, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21828, "top5_acc": 0.45484, "loss_cls": 4.32847, "loss": 4.32847, "time": 0.69945} +{"mode": "train", "epoch": 5, "iter": 1600, "lr": 0.09979, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22266, "top5_acc": 0.46375, "loss_cls": 4.303, "loss": 4.303, "time": 0.69989} +{"mode": "train", "epoch": 5, "iter": 1700, "lr": 0.09978, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21531, "top5_acc": 0.44922, "loss_cls": 4.35627, "loss": 4.35627, "time": 0.69884} +{"mode": "train", "epoch": 5, "iter": 1800, "lr": 0.09978, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21219, "top5_acc": 0.45438, "loss_cls": 4.32993, "loss": 4.32993, "time": 0.70215} +{"mode": "train", "epoch": 5, "iter": 1900, "lr": 0.09978, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21281, "top5_acc": 0.45156, "loss_cls": 4.34304, "loss": 4.34304, "time": 0.70118} +{"mode": "train", "epoch": 5, "iter": 2000, "lr": 0.09977, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2175, "top5_acc": 0.45797, "loss_cls": 4.33889, "loss": 4.33889, "time": 0.70046} +{"mode": "train", "epoch": 5, "iter": 2100, "lr": 0.09977, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2175, "top5_acc": 0.45734, "loss_cls": 4.32173, "loss": 4.32173, "time": 0.70022} +{"mode": "train", "epoch": 5, "iter": 2200, "lr": 0.09977, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21219, "top5_acc": 0.45234, "loss_cls": 4.34928, "loss": 4.34928, "time": 0.70154} +{"mode": "train", "epoch": 5, "iter": 2300, "lr": 0.09977, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21203, "top5_acc": 0.45297, "loss_cls": 4.34975, "loss": 4.34975, "time": 0.69902} +{"mode": "train", "epoch": 5, "iter": 2400, "lr": 0.09976, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21922, "top5_acc": 0.46094, "loss_cls": 4.30111, "loss": 4.30111, "time": 0.69665} +{"mode": "train", "epoch": 5, "iter": 2500, "lr": 0.09976, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21234, "top5_acc": 0.45547, "loss_cls": 4.36448, "loss": 4.36448, "time": 0.69955} +{"mode": "train", "epoch": 5, "iter": 2600, "lr": 0.09976, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21219, "top5_acc": 0.44625, "loss_cls": 4.35426, "loss": 4.35426, "time": 0.70074} +{"mode": "train", "epoch": 5, "iter": 2700, "lr": 0.09976, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21234, "top5_acc": 0.45422, "loss_cls": 4.32931, "loss": 4.32931, "time": 0.70555} +{"mode": "train", "epoch": 5, "iter": 2800, "lr": 0.09975, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21672, "top5_acc": 0.46, "loss_cls": 4.31648, "loss": 4.31648, "time": 0.70009} +{"mode": "train", "epoch": 5, "iter": 2900, "lr": 0.09975, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21828, "top5_acc": 0.45719, "loss_cls": 4.34055, "loss": 4.34055, "time": 0.69631} +{"mode": "train", "epoch": 5, "iter": 3000, "lr": 0.09975, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21328, "top5_acc": 0.46016, "loss_cls": 4.30592, "loss": 4.30592, "time": 0.69871} +{"mode": "train", "epoch": 5, "iter": 3100, "lr": 0.09974, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22047, "top5_acc": 0.46453, "loss_cls": 4.28558, "loss": 4.28558, "time": 0.70188} +{"mode": "train", "epoch": 5, "iter": 3200, "lr": 0.09974, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21406, "top5_acc": 0.4575, "loss_cls": 4.30117, "loss": 4.30117, "time": 0.69587} +{"mode": "train", "epoch": 5, "iter": 3300, "lr": 0.09974, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22766, "top5_acc": 0.46859, "loss_cls": 4.2774, "loss": 4.2774, "time": 0.69715} +{"mode": "train", "epoch": 5, "iter": 3400, "lr": 0.09974, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22438, "top5_acc": 0.4675, "loss_cls": 4.27533, "loss": 4.27533, "time": 0.7039} +{"mode": "train", "epoch": 5, "iter": 3500, "lr": 0.09973, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21938, "top5_acc": 0.45578, "loss_cls": 4.3099, "loss": 4.3099, "time": 0.7023} +{"mode": "train", "epoch": 5, "iter": 3600, "lr": 0.09973, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21734, "top5_acc": 0.46469, "loss_cls": 4.30739, "loss": 4.30739, "time": 0.69965} +{"mode": "train", "epoch": 5, "iter": 3700, "lr": 0.09973, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22688, "top5_acc": 0.46188, "loss_cls": 4.28713, "loss": 4.28713, "time": 0.7014} +{"mode": "val", "epoch": 5, "iter": 309, "lr": 0.09973, "top1_acc": 0.12111, "top5_acc": 0.31429, "mean_class_accuracy": 0.12103} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.09972, "memory": 15990, "data_time": 1.30435, "top1_acc": 0.22703, "top5_acc": 0.46828, "loss_cls": 4.2553, "loss": 4.2553, "time": 2.00678} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.09972, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2225, "top5_acc": 0.4675, "loss_cls": 4.27283, "loss": 4.27283, "time": 0.70009} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.09972, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22203, "top5_acc": 0.46391, "loss_cls": 4.27949, "loss": 4.27949, "time": 0.69924} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.09971, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22641, "top5_acc": 0.45969, "loss_cls": 4.27139, "loss": 4.27139, "time": 0.69847} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.09971, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22453, "top5_acc": 0.46484, "loss_cls": 4.29988, "loss": 4.29988, "time": 0.69897} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.09971, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22141, "top5_acc": 0.47703, "loss_cls": 4.26267, "loss": 4.26267, "time": 0.69925} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.09971, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22234, "top5_acc": 0.46688, "loss_cls": 4.28325, "loss": 4.28325, "time": 0.69632} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.0997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22062, "top5_acc": 0.46984, "loss_cls": 4.28293, "loss": 4.28293, "time": 0.70282} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.0997, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21453, "top5_acc": 0.46469, "loss_cls": 4.2687, "loss": 4.2687, "time": 0.70199} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.0997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22141, "top5_acc": 0.46797, "loss_cls": 4.26685, "loss": 4.26685, "time": 0.69747} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.09969, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21719, "top5_acc": 0.46453, "loss_cls": 4.28672, "loss": 4.28672, "time": 0.69837} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.09969, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21422, "top5_acc": 0.46703, "loss_cls": 4.29571, "loss": 4.29571, "time": 0.6992} +{"mode": "train", "epoch": 6, "iter": 1300, "lr": 0.09969, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22641, "top5_acc": 0.45984, "loss_cls": 4.29525, "loss": 4.29525, "time": 0.70161} +{"mode": "train", "epoch": 6, "iter": 1400, "lr": 0.09968, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22203, "top5_acc": 0.47594, "loss_cls": 4.25583, "loss": 4.25583, "time": 0.70281} +{"mode": "train", "epoch": 6, "iter": 1500, "lr": 0.09968, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23766, "top5_acc": 0.47109, "loss_cls": 4.24903, "loss": 4.24903, "time": 0.69603} +{"mode": "train", "epoch": 6, "iter": 1600, "lr": 0.09968, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22281, "top5_acc": 0.46922, "loss_cls": 4.27782, "loss": 4.27782, "time": 0.69727} +{"mode": "train", "epoch": 6, "iter": 1700, "lr": 0.09967, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22016, "top5_acc": 0.47062, "loss_cls": 4.24897, "loss": 4.24897, "time": 0.70029} +{"mode": "train", "epoch": 6, "iter": 1800, "lr": 0.09967, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21891, "top5_acc": 0.45453, "loss_cls": 4.32676, "loss": 4.32676, "time": 0.69716} +{"mode": "train", "epoch": 6, "iter": 1900, "lr": 0.09967, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21859, "top5_acc": 0.46906, "loss_cls": 4.25566, "loss": 4.25566, "time": 0.69635} +{"mode": "train", "epoch": 6, "iter": 2000, "lr": 0.09966, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22281, "top5_acc": 0.465, "loss_cls": 4.28271, "loss": 4.28271, "time": 0.69895} +{"mode": "train", "epoch": 6, "iter": 2100, "lr": 0.09966, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21281, "top5_acc": 0.47094, "loss_cls": 4.29509, "loss": 4.29509, "time": 0.69917} +{"mode": "train", "epoch": 6, "iter": 2200, "lr": 0.09966, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22844, "top5_acc": 0.46656, "loss_cls": 4.26144, "loss": 4.26144, "time": 0.69824} +{"mode": "train", "epoch": 6, "iter": 2300, "lr": 0.09965, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22438, "top5_acc": 0.46562, "loss_cls": 4.28099, "loss": 4.28099, "time": 0.69925} +{"mode": "train", "epoch": 6, "iter": 2400, "lr": 0.09965, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22734, "top5_acc": 0.46141, "loss_cls": 4.29457, "loss": 4.29457, "time": 0.69682} +{"mode": "train", "epoch": 6, "iter": 2500, "lr": 0.09965, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23141, "top5_acc": 0.475, "loss_cls": 4.24506, "loss": 4.24506, "time": 0.70106} +{"mode": "train", "epoch": 6, "iter": 2600, "lr": 0.09964, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22953, "top5_acc": 0.46984, "loss_cls": 4.2275, "loss": 4.2275, "time": 0.69866} +{"mode": "train", "epoch": 6, "iter": 2700, "lr": 0.09964, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21922, "top5_acc": 0.46016, "loss_cls": 4.30174, "loss": 4.30174, "time": 0.69888} +{"mode": "train", "epoch": 6, "iter": 2800, "lr": 0.09964, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22453, "top5_acc": 0.47328, "loss_cls": 4.27813, "loss": 4.27813, "time": 0.70011} +{"mode": "train", "epoch": 6, "iter": 2900, "lr": 0.09963, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21891, "top5_acc": 0.46609, "loss_cls": 4.28321, "loss": 4.28321, "time": 0.69748} +{"mode": "train", "epoch": 6, "iter": 3000, "lr": 0.09963, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21859, "top5_acc": 0.45953, "loss_cls": 4.3275, "loss": 4.3275, "time": 0.69826} +{"mode": "train", "epoch": 6, "iter": 3100, "lr": 0.09963, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22156, "top5_acc": 0.46172, "loss_cls": 4.28178, "loss": 4.28178, "time": 0.70013} +{"mode": "train", "epoch": 6, "iter": 3200, "lr": 0.09962, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22719, "top5_acc": 0.47859, "loss_cls": 4.20189, "loss": 4.20189, "time": 0.69926} +{"mode": "train", "epoch": 6, "iter": 3300, "lr": 0.09962, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22062, "top5_acc": 0.45938, "loss_cls": 4.27467, "loss": 4.27467, "time": 0.69912} +{"mode": "train", "epoch": 6, "iter": 3400, "lr": 0.09962, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2275, "top5_acc": 0.46609, "loss_cls": 4.26063, "loss": 4.26063, "time": 0.70307} +{"mode": "train", "epoch": 6, "iter": 3500, "lr": 0.09961, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23344, "top5_acc": 0.49047, "loss_cls": 4.18994, "loss": 4.18994, "time": 0.70124} +{"mode": "train", "epoch": 6, "iter": 3600, "lr": 0.09961, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22297, "top5_acc": 0.47031, "loss_cls": 4.26272, "loss": 4.26272, "time": 0.70387} +{"mode": "train", "epoch": 6, "iter": 3700, "lr": 0.09961, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22219, "top5_acc": 0.45953, "loss_cls": 4.30529, "loss": 4.30529, "time": 0.70017} +{"mode": "val", "epoch": 6, "iter": 309, "lr": 0.09961, "top1_acc": 0.14248, "top5_acc": 0.34286, "mean_class_accuracy": 0.14239} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0996, "memory": 15990, "data_time": 1.34754, "top1_acc": 0.22766, "top5_acc": 0.47234, "loss_cls": 4.26196, "loss": 4.26196, "time": 2.05439} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0996, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23328, "top5_acc": 0.48156, "loss_cls": 4.19888, "loss": 4.19888, "time": 0.70474} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.0996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23234, "top5_acc": 0.48172, "loss_cls": 4.19608, "loss": 4.19608, "time": 0.70214} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.09959, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23609, "top5_acc": 0.48672, "loss_cls": 4.17758, "loss": 4.17758, "time": 0.70716} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.09959, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22891, "top5_acc": 0.47547, "loss_cls": 4.24317, "loss": 4.24317, "time": 0.69897} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.09958, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22875, "top5_acc": 0.47547, "loss_cls": 4.22989, "loss": 4.22989, "time": 0.70289} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.09958, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22609, "top5_acc": 0.46438, "loss_cls": 4.22648, "loss": 4.22648, "time": 0.70219} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.09958, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23766, "top5_acc": 0.47828, "loss_cls": 4.2194, "loss": 4.2194, "time": 0.69887} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.09957, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21922, "top5_acc": 0.46516, "loss_cls": 4.25838, "loss": 4.25838, "time": 0.70302} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.09957, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22859, "top5_acc": 0.46781, "loss_cls": 4.24748, "loss": 4.24748, "time": 0.70102} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.09957, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23219, "top5_acc": 0.47797, "loss_cls": 4.20784, "loss": 4.20784, "time": 0.69648} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.09956, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22719, "top5_acc": 0.47031, "loss_cls": 4.2416, "loss": 4.2416, "time": 0.70102} +{"mode": "train", "epoch": 7, "iter": 1300, "lr": 0.09956, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22531, "top5_acc": 0.47188, "loss_cls": 4.23287, "loss": 4.23287, "time": 0.7006} +{"mode": "train", "epoch": 7, "iter": 1400, "lr": 0.09956, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22562, "top5_acc": 0.46688, "loss_cls": 4.26345, "loss": 4.26345, "time": 0.6997} +{"mode": "train", "epoch": 7, "iter": 1500, "lr": 0.09955, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23406, "top5_acc": 0.48328, "loss_cls": 4.21938, "loss": 4.21938, "time": 0.70005} +{"mode": "train", "epoch": 7, "iter": 1600, "lr": 0.09955, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23094, "top5_acc": 0.47797, "loss_cls": 4.24353, "loss": 4.24353, "time": 0.70345} +{"mode": "train", "epoch": 7, "iter": 1700, "lr": 0.09954, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22953, "top5_acc": 0.47797, "loss_cls": 4.20665, "loss": 4.20665, "time": 0.69771} +{"mode": "train", "epoch": 7, "iter": 1800, "lr": 0.09954, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23016, "top5_acc": 0.47484, "loss_cls": 4.24311, "loss": 4.24311, "time": 0.69963} +{"mode": "train", "epoch": 7, "iter": 1900, "lr": 0.09954, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23312, "top5_acc": 0.47438, "loss_cls": 4.23788, "loss": 4.23788, "time": 0.70194} +{"mode": "train", "epoch": 7, "iter": 2000, "lr": 0.09953, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22078, "top5_acc": 0.46812, "loss_cls": 4.26124, "loss": 4.26124, "time": 0.70231} +{"mode": "train", "epoch": 7, "iter": 2100, "lr": 0.09953, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24344, "top5_acc": 0.48078, "loss_cls": 4.18498, "loss": 4.18498, "time": 0.7011} +{"mode": "train", "epoch": 7, "iter": 2200, "lr": 0.09952, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22344, "top5_acc": 0.47422, "loss_cls": 4.24848, "loss": 4.24848, "time": 0.70024} +{"mode": "train", "epoch": 7, "iter": 2300, "lr": 0.09952, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23281, "top5_acc": 0.47109, "loss_cls": 4.2449, "loss": 4.2449, "time": 0.70032} +{"mode": "train", "epoch": 7, "iter": 2400, "lr": 0.09952, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.235, "top5_acc": 0.47688, "loss_cls": 4.21916, "loss": 4.21916, "time": 0.6977} +{"mode": "train", "epoch": 7, "iter": 2500, "lr": 0.09951, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23469, "top5_acc": 0.48453, "loss_cls": 4.19215, "loss": 4.19215, "time": 0.69719} +{"mode": "train", "epoch": 7, "iter": 2600, "lr": 0.09951, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2275, "top5_acc": 0.47391, "loss_cls": 4.24895, "loss": 4.24895, "time": 0.69823} +{"mode": "train", "epoch": 7, "iter": 2700, "lr": 0.09951, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21906, "top5_acc": 0.47375, "loss_cls": 4.27249, "loss": 4.27249, "time": 0.69845} +{"mode": "train", "epoch": 7, "iter": 2800, "lr": 0.0995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22828, "top5_acc": 0.46938, "loss_cls": 4.23605, "loss": 4.23605, "time": 0.70076} +{"mode": "train", "epoch": 7, "iter": 2900, "lr": 0.0995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23, "top5_acc": 0.47328, "loss_cls": 4.23989, "loss": 4.23989, "time": 0.69967} +{"mode": "train", "epoch": 7, "iter": 3000, "lr": 0.09949, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23328, "top5_acc": 0.47344, "loss_cls": 4.20716, "loss": 4.20716, "time": 0.69929} +{"mode": "train", "epoch": 7, "iter": 3100, "lr": 0.09949, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22047, "top5_acc": 0.47266, "loss_cls": 4.2392, "loss": 4.2392, "time": 0.70055} +{"mode": "train", "epoch": 7, "iter": 3200, "lr": 0.09949, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23422, "top5_acc": 0.47188, "loss_cls": 4.24339, "loss": 4.24339, "time": 0.70278} +{"mode": "train", "epoch": 7, "iter": 3300, "lr": 0.09948, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23641, "top5_acc": 0.4875, "loss_cls": 4.22158, "loss": 4.22158, "time": 0.70002} +{"mode": "train", "epoch": 7, "iter": 3400, "lr": 0.09948, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21969, "top5_acc": 0.46281, "loss_cls": 4.26812, "loss": 4.26812, "time": 0.70884} +{"mode": "train", "epoch": 7, "iter": 3500, "lr": 0.09947, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22219, "top5_acc": 0.46844, "loss_cls": 4.26429, "loss": 4.26429, "time": 0.7009} +{"mode": "train", "epoch": 7, "iter": 3600, "lr": 0.09947, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22578, "top5_acc": 0.46328, "loss_cls": 4.24548, "loss": 4.24548, "time": 0.70064} +{"mode": "train", "epoch": 7, "iter": 3700, "lr": 0.09947, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24016, "top5_acc": 0.49766, "loss_cls": 4.14661, "loss": 4.14661, "time": 0.70197} +{"mode": "val", "epoch": 7, "iter": 309, "lr": 0.09946, "top1_acc": 0.1478, "top5_acc": 0.36342, "mean_class_accuracy": 0.14767} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.09946, "memory": 15990, "data_time": 1.30589, "top1_acc": 0.22891, "top5_acc": 0.48281, "loss_cls": 4.19223, "loss": 4.19223, "time": 2.00674} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.09946, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23125, "top5_acc": 0.48156, "loss_cls": 4.20347, "loss": 4.20347, "time": 0.7019} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.09945, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24, "top5_acc": 0.48906, "loss_cls": 4.16392, "loss": 4.16392, "time": 0.69889} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.09945, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23219, "top5_acc": 0.47812, "loss_cls": 4.22183, "loss": 4.22183, "time": 0.69877} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.09944, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23703, "top5_acc": 0.475, "loss_cls": 4.19719, "loss": 4.19719, "time": 0.69646} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.09944, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22547, "top5_acc": 0.47109, "loss_cls": 4.22094, "loss": 4.22094, "time": 0.7015} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.09943, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22672, "top5_acc": 0.47422, "loss_cls": 4.2068, "loss": 4.2068, "time": 0.70138} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.09943, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23219, "top5_acc": 0.48641, "loss_cls": 4.16351, "loss": 4.16351, "time": 0.69743} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.09943, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23188, "top5_acc": 0.47797, "loss_cls": 4.20289, "loss": 4.20289, "time": 0.69858} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.09942, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23531, "top5_acc": 0.48406, "loss_cls": 4.18856, "loss": 4.18856, "time": 0.70295} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.09942, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24234, "top5_acc": 0.48859, "loss_cls": 4.16958, "loss": 4.16958, "time": 0.69886} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.09941, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23953, "top5_acc": 0.48391, "loss_cls": 4.17895, "loss": 4.17895, "time": 0.69816} +{"mode": "train", "epoch": 8, "iter": 1300, "lr": 0.09941, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22953, "top5_acc": 0.47969, "loss_cls": 4.21867, "loss": 4.21867, "time": 0.70056} +{"mode": "train", "epoch": 8, "iter": 1400, "lr": 0.0994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23906, "top5_acc": 0.48875, "loss_cls": 4.19369, "loss": 4.19369, "time": 0.69931} +{"mode": "train", "epoch": 8, "iter": 1500, "lr": 0.0994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22672, "top5_acc": 0.48062, "loss_cls": 4.20229, "loss": 4.20229, "time": 0.69766} +{"mode": "train", "epoch": 8, "iter": 1600, "lr": 0.0994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23484, "top5_acc": 0.48266, "loss_cls": 4.18991, "loss": 4.18991, "time": 0.69993} +{"mode": "train", "epoch": 8, "iter": 1700, "lr": 0.09939, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24062, "top5_acc": 0.47906, "loss_cls": 4.20648, "loss": 4.20648, "time": 0.69964} +{"mode": "train", "epoch": 8, "iter": 1800, "lr": 0.09939, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23453, "top5_acc": 0.48297, "loss_cls": 4.18962, "loss": 4.18962, "time": 0.70072} +{"mode": "train", "epoch": 8, "iter": 1900, "lr": 0.09938, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24078, "top5_acc": 0.48625, "loss_cls": 4.16388, "loss": 4.16388, "time": 0.69863} +{"mode": "train", "epoch": 8, "iter": 2000, "lr": 0.09938, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23328, "top5_acc": 0.48125, "loss_cls": 4.19773, "loss": 4.19773, "time": 0.70111} +{"mode": "train", "epoch": 8, "iter": 2100, "lr": 0.09937, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23797, "top5_acc": 0.48312, "loss_cls": 4.19815, "loss": 4.19815, "time": 0.69938} +{"mode": "train", "epoch": 8, "iter": 2200, "lr": 0.09937, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24609, "top5_acc": 0.49312, "loss_cls": 4.13746, "loss": 4.13746, "time": 0.69704} +{"mode": "train", "epoch": 8, "iter": 2300, "lr": 0.09937, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24094, "top5_acc": 0.48078, "loss_cls": 4.1936, "loss": 4.1936, "time": 0.69875} +{"mode": "train", "epoch": 8, "iter": 2400, "lr": 0.09936, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23109, "top5_acc": 0.47469, "loss_cls": 4.23063, "loss": 4.23063, "time": 0.70045} +{"mode": "train", "epoch": 8, "iter": 2500, "lr": 0.09936, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23688, "top5_acc": 0.47859, "loss_cls": 4.21105, "loss": 4.21105, "time": 0.70291} +{"mode": "train", "epoch": 8, "iter": 2600, "lr": 0.09935, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23594, "top5_acc": 0.48703, "loss_cls": 4.1883, "loss": 4.1883, "time": 0.69843} +{"mode": "train", "epoch": 8, "iter": 2700, "lr": 0.09935, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23734, "top5_acc": 0.48812, "loss_cls": 4.17851, "loss": 4.17851, "time": 0.69891} +{"mode": "train", "epoch": 8, "iter": 2800, "lr": 0.09934, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23641, "top5_acc": 0.47609, "loss_cls": 4.22077, "loss": 4.22077, "time": 0.70021} +{"mode": "train", "epoch": 8, "iter": 2900, "lr": 0.09934, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24578, "top5_acc": 0.49047, "loss_cls": 4.16323, "loss": 4.16323, "time": 0.70041} +{"mode": "train", "epoch": 8, "iter": 3000, "lr": 0.09933, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23719, "top5_acc": 0.47188, "loss_cls": 4.23885, "loss": 4.23885, "time": 0.69847} +{"mode": "train", "epoch": 8, "iter": 3100, "lr": 0.09933, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.225, "top5_acc": 0.47547, "loss_cls": 4.2491, "loss": 4.2491, "time": 0.69768} +{"mode": "train", "epoch": 8, "iter": 3200, "lr": 0.09933, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22703, "top5_acc": 0.47625, "loss_cls": 4.22018, "loss": 4.22018, "time": 0.70165} +{"mode": "train", "epoch": 8, "iter": 3300, "lr": 0.09932, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23953, "top5_acc": 0.48703, "loss_cls": 4.19483, "loss": 4.19483, "time": 0.69836} +{"mode": "train", "epoch": 8, "iter": 3400, "lr": 0.09932, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23484, "top5_acc": 0.48141, "loss_cls": 4.2019, "loss": 4.2019, "time": 0.70358} +{"mode": "train", "epoch": 8, "iter": 3500, "lr": 0.09931, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23625, "top5_acc": 0.48453, "loss_cls": 4.18169, "loss": 4.18169, "time": 0.6999} +{"mode": "train", "epoch": 8, "iter": 3600, "lr": 0.09931, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23594, "top5_acc": 0.48516, "loss_cls": 4.1788, "loss": 4.1788, "time": 0.70083} +{"mode": "train", "epoch": 8, "iter": 3700, "lr": 0.0993, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22906, "top5_acc": 0.47391, "loss_cls": 4.23401, "loss": 4.23401, "time": 0.7032} +{"mode": "val", "epoch": 8, "iter": 309, "lr": 0.0993, "top1_acc": 0.16887, "top5_acc": 0.38935, "mean_class_accuracy": 0.16878} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.0993, "memory": 15990, "data_time": 1.36095, "top1_acc": 0.24719, "top5_acc": 0.48266, "loss_cls": 4.15517, "loss": 4.15517, "time": 2.06586} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.09929, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23656, "top5_acc": 0.47734, "loss_cls": 4.20448, "loss": 4.20448, "time": 0.70541} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.09929, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24016, "top5_acc": 0.48141, "loss_cls": 4.17123, "loss": 4.17123, "time": 0.70293} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.09928, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24172, "top5_acc": 0.49594, "loss_cls": 4.15998, "loss": 4.15998, "time": 0.69962} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.09928, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2425, "top5_acc": 0.49219, "loss_cls": 4.13131, "loss": 4.13131, "time": 0.70262} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.09927, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23656, "top5_acc": 0.48062, "loss_cls": 4.19915, "loss": 4.19915, "time": 0.70248} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.09927, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24188, "top5_acc": 0.48734, "loss_cls": 4.18266, "loss": 4.18266, "time": 0.70188} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.09926, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2475, "top5_acc": 0.49625, "loss_cls": 4.14163, "loss": 4.14163, "time": 0.69972} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.09926, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23953, "top5_acc": 0.48625, "loss_cls": 4.18976, "loss": 4.18976, "time": 0.69787} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.09925, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23125, "top5_acc": 0.48422, "loss_cls": 4.18496, "loss": 4.18496, "time": 0.69716} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.09925, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24188, "top5_acc": 0.49109, "loss_cls": 4.17191, "loss": 4.17191, "time": 0.70231} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.09924, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23031, "top5_acc": 0.47531, "loss_cls": 4.23126, "loss": 4.23126, "time": 0.70145} +{"mode": "train", "epoch": 9, "iter": 1300, "lr": 0.09924, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24219, "top5_acc": 0.4975, "loss_cls": 4.13704, "loss": 4.13704, "time": 0.6992} +{"mode": "train", "epoch": 9, "iter": 1400, "lr": 0.09923, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23562, "top5_acc": 0.48281, "loss_cls": 4.18652, "loss": 4.18652, "time": 0.69968} +{"mode": "train", "epoch": 9, "iter": 1500, "lr": 0.09923, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24828, "top5_acc": 0.49422, "loss_cls": 4.11785, "loss": 4.11785, "time": 0.69734} +{"mode": "train", "epoch": 9, "iter": 1600, "lr": 0.09922, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24047, "top5_acc": 0.49281, "loss_cls": 4.17083, "loss": 4.17083, "time": 0.70069} +{"mode": "train", "epoch": 9, "iter": 1700, "lr": 0.09922, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24688, "top5_acc": 0.48906, "loss_cls": 4.16034, "loss": 4.16034, "time": 0.70244} +{"mode": "train", "epoch": 9, "iter": 1800, "lr": 0.09921, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23859, "top5_acc": 0.48141, "loss_cls": 4.1971, "loss": 4.1971, "time": 0.70146} +{"mode": "train", "epoch": 9, "iter": 1900, "lr": 0.09921, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23875, "top5_acc": 0.47922, "loss_cls": 4.19628, "loss": 4.19628, "time": 0.70311} +{"mode": "train", "epoch": 9, "iter": 2000, "lr": 0.0992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23688, "top5_acc": 0.48578, "loss_cls": 4.18813, "loss": 4.18813, "time": 0.70103} +{"mode": "train", "epoch": 9, "iter": 2100, "lr": 0.0992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23438, "top5_acc": 0.48516, "loss_cls": 4.20699, "loss": 4.20699, "time": 0.70297} +{"mode": "train", "epoch": 9, "iter": 2200, "lr": 0.09919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23844, "top5_acc": 0.49328, "loss_cls": 4.14149, "loss": 4.14149, "time": 0.69925} +{"mode": "train", "epoch": 9, "iter": 2300, "lr": 0.09919, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24344, "top5_acc": 0.49359, "loss_cls": 4.15775, "loss": 4.15775, "time": 0.70135} +{"mode": "train", "epoch": 9, "iter": 2400, "lr": 0.09918, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24203, "top5_acc": 0.48906, "loss_cls": 4.16235, "loss": 4.16235, "time": 0.69769} +{"mode": "train", "epoch": 9, "iter": 2500, "lr": 0.09918, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23516, "top5_acc": 0.47438, "loss_cls": 4.19287, "loss": 4.19287, "time": 0.69937} +{"mode": "train", "epoch": 9, "iter": 2600, "lr": 0.09917, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23141, "top5_acc": 0.48766, "loss_cls": 4.18845, "loss": 4.18845, "time": 0.69911} +{"mode": "train", "epoch": 9, "iter": 2700, "lr": 0.09917, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24266, "top5_acc": 0.48625, "loss_cls": 4.17819, "loss": 4.17819, "time": 0.69966} +{"mode": "train", "epoch": 9, "iter": 2800, "lr": 0.09916, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25281, "top5_acc": 0.505, "loss_cls": 4.10338, "loss": 4.10338, "time": 0.69722} +{"mode": "train", "epoch": 9, "iter": 2900, "lr": 0.09916, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23391, "top5_acc": 0.48, "loss_cls": 4.19243, "loss": 4.19243, "time": 0.69982} +{"mode": "train", "epoch": 9, "iter": 3000, "lr": 0.09915, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23969, "top5_acc": 0.48625, "loss_cls": 4.15522, "loss": 4.15522, "time": 0.69928} +{"mode": "train", "epoch": 9, "iter": 3100, "lr": 0.09915, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23984, "top5_acc": 0.48125, "loss_cls": 4.19128, "loss": 4.19128, "time": 0.69974} +{"mode": "train", "epoch": 9, "iter": 3200, "lr": 0.09914, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23391, "top5_acc": 0.48188, "loss_cls": 4.1874, "loss": 4.1874, "time": 0.69769} +{"mode": "train", "epoch": 9, "iter": 3300, "lr": 0.09914, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25, "top5_acc": 0.49266, "loss_cls": 4.14403, "loss": 4.14403, "time": 0.69933} +{"mode": "train", "epoch": 9, "iter": 3400, "lr": 0.09913, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23891, "top5_acc": 0.49312, "loss_cls": 4.15324, "loss": 4.15324, "time": 0.70405} +{"mode": "train", "epoch": 9, "iter": 3500, "lr": 0.09913, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24, "top5_acc": 0.47984, "loss_cls": 4.20571, "loss": 4.20571, "time": 0.7025} +{"mode": "train", "epoch": 9, "iter": 3600, "lr": 0.09912, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23906, "top5_acc": 0.49719, "loss_cls": 4.13837, "loss": 4.13837, "time": 0.70514} +{"mode": "train", "epoch": 9, "iter": 3700, "lr": 0.09912, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.245, "top5_acc": 0.48672, "loss_cls": 4.14712, "loss": 4.14712, "time": 0.70574} +{"mode": "val", "epoch": 9, "iter": 309, "lr": 0.09911, "top1_acc": 0.15418, "top5_acc": 0.37279, "mean_class_accuracy": 0.15406} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.09911, "memory": 15990, "data_time": 1.30001, "top1_acc": 0.24859, "top5_acc": 0.50094, "loss_cls": 4.11694, "loss": 4.11694, "time": 2.00984} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.0991, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24391, "top5_acc": 0.49828, "loss_cls": 4.11822, "loss": 4.11822, "time": 0.70283} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.0991, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24406, "top5_acc": 0.49703, "loss_cls": 4.12781, "loss": 4.12781, "time": 0.70001} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.09909, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24375, "top5_acc": 0.49188, "loss_cls": 4.16043, "loss": 4.16043, "time": 0.70337} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.09909, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24234, "top5_acc": 0.49, "loss_cls": 4.12888, "loss": 4.12888, "time": 0.70095} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.09908, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24703, "top5_acc": 0.49922, "loss_cls": 4.11572, "loss": 4.11572, "time": 0.70245} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.09908, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24438, "top5_acc": 0.48875, "loss_cls": 4.1374, "loss": 4.1374, "time": 0.70037} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.09907, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23297, "top5_acc": 0.48375, "loss_cls": 4.17378, "loss": 4.17378, "time": 0.70006} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.09907, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24031, "top5_acc": 0.49469, "loss_cls": 4.13227, "loss": 4.13227, "time": 0.70122} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.09906, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24016, "top5_acc": 0.49812, "loss_cls": 4.1388, "loss": 4.1388, "time": 0.69928} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.09906, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24094, "top5_acc": 0.49688, "loss_cls": 4.1317, "loss": 4.1317, "time": 0.69903} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.09905, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24719, "top5_acc": 0.48953, "loss_cls": 4.12378, "loss": 4.12378, "time": 0.70027} +{"mode": "train", "epoch": 10, "iter": 1300, "lr": 0.09905, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23328, "top5_acc": 0.48266, "loss_cls": 4.19184, "loss": 4.19184, "time": 0.69757} +{"mode": "train", "epoch": 10, "iter": 1400, "lr": 0.09904, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23453, "top5_acc": 0.48828, "loss_cls": 4.15635, "loss": 4.15635, "time": 0.70412} +{"mode": "train", "epoch": 10, "iter": 1500, "lr": 0.09903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2525, "top5_acc": 0.49531, "loss_cls": 4.13493, "loss": 4.13493, "time": 0.69791} +{"mode": "train", "epoch": 10, "iter": 1600, "lr": 0.09903, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23625, "top5_acc": 0.48703, "loss_cls": 4.18159, "loss": 4.18159, "time": 0.70211} +{"mode": "train", "epoch": 10, "iter": 1700, "lr": 0.09902, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24172, "top5_acc": 0.49297, "loss_cls": 4.15873, "loss": 4.15873, "time": 0.69872} +{"mode": "train", "epoch": 10, "iter": 1800, "lr": 0.09902, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24359, "top5_acc": 0.48734, "loss_cls": 4.16111, "loss": 4.16111, "time": 0.69815} +{"mode": "train", "epoch": 10, "iter": 1900, "lr": 0.09901, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25469, "top5_acc": 0.49969, "loss_cls": 4.09852, "loss": 4.09852, "time": 0.70433} +{"mode": "train", "epoch": 10, "iter": 2000, "lr": 0.09901, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23766, "top5_acc": 0.4925, "loss_cls": 4.13319, "loss": 4.13319, "time": 0.70493} +{"mode": "train", "epoch": 10, "iter": 2100, "lr": 0.099, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24156, "top5_acc": 0.48797, "loss_cls": 4.15247, "loss": 4.15247, "time": 0.70032} +{"mode": "train", "epoch": 10, "iter": 2200, "lr": 0.099, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23859, "top5_acc": 0.48688, "loss_cls": 4.16391, "loss": 4.16391, "time": 0.69841} +{"mode": "train", "epoch": 10, "iter": 2300, "lr": 0.09899, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24406, "top5_acc": 0.48672, "loss_cls": 4.18809, "loss": 4.18809, "time": 0.70356} +{"mode": "train", "epoch": 10, "iter": 2400, "lr": 0.09898, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22672, "top5_acc": 0.48328, "loss_cls": 4.21118, "loss": 4.21118, "time": 0.70059} +{"mode": "train", "epoch": 10, "iter": 2500, "lr": 0.09898, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24469, "top5_acc": 0.48906, "loss_cls": 4.1323, "loss": 4.1323, "time": 0.70316} +{"mode": "train", "epoch": 10, "iter": 2600, "lr": 0.09897, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24172, "top5_acc": 0.49578, "loss_cls": 4.15303, "loss": 4.15303, "time": 0.69871} +{"mode": "train", "epoch": 10, "iter": 2700, "lr": 0.09897, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24125, "top5_acc": 0.48781, "loss_cls": 4.16111, "loss": 4.16111, "time": 0.69936} +{"mode": "train", "epoch": 10, "iter": 2800, "lr": 0.09896, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23875, "top5_acc": 0.49609, "loss_cls": 4.1437, "loss": 4.1437, "time": 0.69962} +{"mode": "train", "epoch": 10, "iter": 2900, "lr": 0.09896, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23812, "top5_acc": 0.48844, "loss_cls": 4.16582, "loss": 4.16582, "time": 0.70369} +{"mode": "train", "epoch": 10, "iter": 3000, "lr": 0.09895, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23281, "top5_acc": 0.48953, "loss_cls": 4.15228, "loss": 4.15228, "time": 0.69951} +{"mode": "train", "epoch": 10, "iter": 3100, "lr": 0.09894, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24219, "top5_acc": 0.49094, "loss_cls": 4.1493, "loss": 4.1493, "time": 0.69637} +{"mode": "train", "epoch": 10, "iter": 3200, "lr": 0.09894, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24312, "top5_acc": 0.4875, "loss_cls": 4.16218, "loss": 4.16218, "time": 0.69742} +{"mode": "train", "epoch": 10, "iter": 3300, "lr": 0.09893, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24172, "top5_acc": 0.49234, "loss_cls": 4.13251, "loss": 4.13251, "time": 0.70237} +{"mode": "train", "epoch": 10, "iter": 3400, "lr": 0.09893, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24453, "top5_acc": 0.48891, "loss_cls": 4.15902, "loss": 4.15902, "time": 0.70456} +{"mode": "train", "epoch": 10, "iter": 3500, "lr": 0.09892, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24312, "top5_acc": 0.48766, "loss_cls": 4.1415, "loss": 4.1415, "time": 0.70025} +{"mode": "train", "epoch": 10, "iter": 3600, "lr": 0.09892, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22703, "top5_acc": 0.47844, "loss_cls": 4.19664, "loss": 4.19664, "time": 0.70174} +{"mode": "train", "epoch": 10, "iter": 3700, "lr": 0.09891, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24891, "top5_acc": 0.49266, "loss_cls": 4.09967, "loss": 4.09967, "time": 0.70712} +{"mode": "val", "epoch": 10, "iter": 309, "lr": 0.09891, "top1_acc": 0.13483, "top5_acc": 0.33906, "mean_class_accuracy": 0.13476} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.0989, "memory": 15990, "data_time": 1.36323, "top1_acc": 0.24703, "top5_acc": 0.50219, "loss_cls": 4.09852, "loss": 4.09852, "time": 2.07699} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.0989, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25156, "top5_acc": 0.49688, "loss_cls": 4.12308, "loss": 4.12308, "time": 0.7074} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.09889, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24391, "top5_acc": 0.48859, "loss_cls": 4.1416, "loss": 4.1416, "time": 0.6986} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.09888, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25203, "top5_acc": 0.50016, "loss_cls": 4.10305, "loss": 4.10305, "time": 0.7025} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.09888, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25531, "top5_acc": 0.50438, "loss_cls": 4.07902, "loss": 4.07902, "time": 0.7017} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.09887, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23906, "top5_acc": 0.49828, "loss_cls": 4.12836, "loss": 4.12836, "time": 0.6989} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.09887, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25453, "top5_acc": 0.50781, "loss_cls": 4.0513, "loss": 4.0513, "time": 0.70331} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.09886, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23906, "top5_acc": 0.48, "loss_cls": 4.16503, "loss": 4.16503, "time": 0.70036} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.09885, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24828, "top5_acc": 0.49125, "loss_cls": 4.13729, "loss": 4.13729, "time": 0.69897} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.09885, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24812, "top5_acc": 0.50938, "loss_cls": 4.07155, "loss": 4.07155, "time": 0.70142} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.09884, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25297, "top5_acc": 0.49109, "loss_cls": 4.14181, "loss": 4.14181, "time": 0.7007} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.09884, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24047, "top5_acc": 0.49812, "loss_cls": 4.15156, "loss": 4.15156, "time": 0.69632} +{"mode": "train", "epoch": 11, "iter": 1300, "lr": 0.09883, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23797, "top5_acc": 0.48578, "loss_cls": 4.1671, "loss": 4.1671, "time": 0.702} +{"mode": "train", "epoch": 11, "iter": 1400, "lr": 0.09882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23797, "top5_acc": 0.49156, "loss_cls": 4.14876, "loss": 4.14876, "time": 0.70095} +{"mode": "train", "epoch": 11, "iter": 1500, "lr": 0.09882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24359, "top5_acc": 0.48531, "loss_cls": 4.14629, "loss": 4.14629, "time": 0.69817} +{"mode": "train", "epoch": 11, "iter": 1600, "lr": 0.09881, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24281, "top5_acc": 0.48719, "loss_cls": 4.12755, "loss": 4.12755, "time": 0.69847} +{"mode": "train", "epoch": 11, "iter": 1700, "lr": 0.09881, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24047, "top5_acc": 0.49047, "loss_cls": 4.16647, "loss": 4.16647, "time": 0.69908} +{"mode": "train", "epoch": 11, "iter": 1800, "lr": 0.0988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24594, "top5_acc": 0.49312, "loss_cls": 4.13986, "loss": 4.13986, "time": 0.69983} +{"mode": "train", "epoch": 11, "iter": 1900, "lr": 0.09879, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2475, "top5_acc": 0.49891, "loss_cls": 4.13207, "loss": 4.13207, "time": 0.69933} +{"mode": "train", "epoch": 11, "iter": 2000, "lr": 0.09879, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24578, "top5_acc": 0.48688, "loss_cls": 4.14249, "loss": 4.14249, "time": 0.69849} +{"mode": "train", "epoch": 11, "iter": 2100, "lr": 0.09878, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23641, "top5_acc": 0.48656, "loss_cls": 4.16955, "loss": 4.16955, "time": 0.7018} +{"mode": "train", "epoch": 11, "iter": 2200, "lr": 0.09878, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24297, "top5_acc": 0.49156, "loss_cls": 4.13927, "loss": 4.13927, "time": 0.70072} +{"mode": "train", "epoch": 11, "iter": 2300, "lr": 0.09877, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22656, "top5_acc": 0.48562, "loss_cls": 4.1805, "loss": 4.1805, "time": 0.70027} +{"mode": "train", "epoch": 11, "iter": 2400, "lr": 0.09876, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23094, "top5_acc": 0.47906, "loss_cls": 4.19601, "loss": 4.19601, "time": 0.69928} +{"mode": "train", "epoch": 11, "iter": 2500, "lr": 0.09876, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23906, "top5_acc": 0.49141, "loss_cls": 4.15797, "loss": 4.15797, "time": 0.70021} +{"mode": "train", "epoch": 11, "iter": 2600, "lr": 0.09875, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24812, "top5_acc": 0.49484, "loss_cls": 4.1448, "loss": 4.1448, "time": 0.69882} +{"mode": "train", "epoch": 11, "iter": 2700, "lr": 0.09874, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24594, "top5_acc": 0.495, "loss_cls": 4.13231, "loss": 4.13231, "time": 0.69785} +{"mode": "train", "epoch": 11, "iter": 2800, "lr": 0.09874, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23953, "top5_acc": 0.49828, "loss_cls": 4.12878, "loss": 4.12878, "time": 0.70426} +{"mode": "train", "epoch": 11, "iter": 2900, "lr": 0.09873, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24484, "top5_acc": 0.49031, "loss_cls": 4.15302, "loss": 4.15302, "time": 0.69656} +{"mode": "train", "epoch": 11, "iter": 3000, "lr": 0.09873, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24172, "top5_acc": 0.49594, "loss_cls": 4.13511, "loss": 4.13511, "time": 0.69942} +{"mode": "train", "epoch": 11, "iter": 3100, "lr": 0.09872, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24641, "top5_acc": 0.4975, "loss_cls": 4.13909, "loss": 4.13909, "time": 0.70294} +{"mode": "train", "epoch": 11, "iter": 3200, "lr": 0.09871, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23406, "top5_acc": 0.47875, "loss_cls": 4.1893, "loss": 4.1893, "time": 0.6956} +{"mode": "train", "epoch": 11, "iter": 3300, "lr": 0.09871, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25297, "top5_acc": 0.49812, "loss_cls": 4.11697, "loss": 4.11697, "time": 0.69918} +{"mode": "train", "epoch": 11, "iter": 3400, "lr": 0.0987, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25031, "top5_acc": 0.495, "loss_cls": 4.12823, "loss": 4.12823, "time": 0.70596} +{"mode": "train", "epoch": 11, "iter": 3500, "lr": 0.09869, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23844, "top5_acc": 0.47984, "loss_cls": 4.19745, "loss": 4.19745, "time": 0.69787} +{"mode": "train", "epoch": 11, "iter": 3600, "lr": 0.09869, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24781, "top5_acc": 0.49562, "loss_cls": 4.11947, "loss": 4.11947, "time": 0.69951} +{"mode": "train", "epoch": 11, "iter": 3700, "lr": 0.09868, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25641, "top5_acc": 0.4975, "loss_cls": 4.12365, "loss": 4.12365, "time": 0.70178} +{"mode": "val", "epoch": 11, "iter": 309, "lr": 0.09868, "top1_acc": 0.17049, "top5_acc": 0.39194, "mean_class_accuracy": 0.17032} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.09867, "memory": 15990, "data_time": 1.31337, "top1_acc": 0.24172, "top5_acc": 0.49359, "loss_cls": 4.12044, "loss": 4.12044, "time": 2.02086} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.09867, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24516, "top5_acc": 0.50406, "loss_cls": 4.08982, "loss": 4.08982, "time": 0.70392} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.09866, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23781, "top5_acc": 0.48875, "loss_cls": 4.12172, "loss": 4.12172, "time": 0.7019} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.09865, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25062, "top5_acc": 0.49328, "loss_cls": 4.11712, "loss": 4.11712, "time": 0.70392} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.09865, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25641, "top5_acc": 0.50719, "loss_cls": 4.04965, "loss": 4.04965, "time": 0.70466} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.09864, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24703, "top5_acc": 0.49469, "loss_cls": 4.1096, "loss": 4.1096, "time": 0.69862} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.09863, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24953, "top5_acc": 0.50578, "loss_cls": 4.06466, "loss": 4.06466, "time": 0.69942} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.09863, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25516, "top5_acc": 0.50016, "loss_cls": 4.09775, "loss": 4.09775, "time": 0.70025} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.09862, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25172, "top5_acc": 0.50016, "loss_cls": 4.08648, "loss": 4.08648, "time": 0.70299} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.09861, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24484, "top5_acc": 0.49531, "loss_cls": 4.11945, "loss": 4.11945, "time": 0.69953} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.09861, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25219, "top5_acc": 0.49562, "loss_cls": 4.1293, "loss": 4.1293, "time": 0.70088} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.0986, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23281, "top5_acc": 0.50156, "loss_cls": 4.13674, "loss": 4.13674, "time": 0.70257} +{"mode": "train", "epoch": 12, "iter": 1300, "lr": 0.09859, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24969, "top5_acc": 0.48609, "loss_cls": 4.11717, "loss": 4.11717, "time": 0.70086} +{"mode": "train", "epoch": 12, "iter": 1400, "lr": 0.09859, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24344, "top5_acc": 0.49672, "loss_cls": 4.1687, "loss": 4.1687, "time": 0.70489} +{"mode": "train", "epoch": 12, "iter": 1500, "lr": 0.09858, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.245, "top5_acc": 0.49766, "loss_cls": 4.1447, "loss": 4.1447, "time": 0.69994} +{"mode": "train", "epoch": 12, "iter": 1600, "lr": 0.09857, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24375, "top5_acc": 0.49484, "loss_cls": 4.14865, "loss": 4.14865, "time": 0.69939} +{"mode": "train", "epoch": 12, "iter": 1700, "lr": 0.09857, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25266, "top5_acc": 0.50203, "loss_cls": 4.09802, "loss": 4.09802, "time": 0.70084} +{"mode": "train", "epoch": 12, "iter": 1800, "lr": 0.09856, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24312, "top5_acc": 0.48859, "loss_cls": 4.16479, "loss": 4.16479, "time": 0.69818} +{"mode": "train", "epoch": 12, "iter": 1900, "lr": 0.09855, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26, "top5_acc": 0.50766, "loss_cls": 4.09532, "loss": 4.09532, "time": 0.69919} +{"mode": "train", "epoch": 12, "iter": 2000, "lr": 0.09855, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24172, "top5_acc": 0.49281, "loss_cls": 4.12553, "loss": 4.12553, "time": 0.69894} +{"mode": "train", "epoch": 12, "iter": 2100, "lr": 0.09854, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23922, "top5_acc": 0.48109, "loss_cls": 4.18871, "loss": 4.18871, "time": 0.70041} +{"mode": "train", "epoch": 12, "iter": 2200, "lr": 0.09853, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24859, "top5_acc": 0.50281, "loss_cls": 4.11704, "loss": 4.11704, "time": 0.70132} +{"mode": "train", "epoch": 12, "iter": 2300, "lr": 0.09853, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24484, "top5_acc": 0.49625, "loss_cls": 4.12344, "loss": 4.12344, "time": 0.70125} +{"mode": "train", "epoch": 12, "iter": 2400, "lr": 0.09852, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24516, "top5_acc": 0.49406, "loss_cls": 4.13071, "loss": 4.13071, "time": 0.69872} +{"mode": "train", "epoch": 12, "iter": 2500, "lr": 0.09851, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2375, "top5_acc": 0.48578, "loss_cls": 4.17405, "loss": 4.17405, "time": 0.69782} +{"mode": "train", "epoch": 12, "iter": 2600, "lr": 0.09851, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23812, "top5_acc": 0.48672, "loss_cls": 4.16875, "loss": 4.16875, "time": 0.6983} +{"mode": "train", "epoch": 12, "iter": 2700, "lr": 0.0985, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24625, "top5_acc": 0.49016, "loss_cls": 4.15122, "loss": 4.15122, "time": 0.7015} +{"mode": "train", "epoch": 12, "iter": 2800, "lr": 0.09849, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23891, "top5_acc": 0.48406, "loss_cls": 4.17995, "loss": 4.17995, "time": 0.69914} +{"mode": "train", "epoch": 12, "iter": 2900, "lr": 0.09849, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23812, "top5_acc": 0.49188, "loss_cls": 4.16925, "loss": 4.16925, "time": 0.69932} +{"mode": "train", "epoch": 12, "iter": 3000, "lr": 0.09848, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24078, "top5_acc": 0.47656, "loss_cls": 4.18728, "loss": 4.18728, "time": 0.69798} +{"mode": "train", "epoch": 12, "iter": 3100, "lr": 0.09847, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24344, "top5_acc": 0.49281, "loss_cls": 4.12433, "loss": 4.12433, "time": 0.70085} +{"mode": "train", "epoch": 12, "iter": 3200, "lr": 0.09847, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24906, "top5_acc": 0.50313, "loss_cls": 4.10421, "loss": 4.10421, "time": 0.69896} +{"mode": "train", "epoch": 12, "iter": 3300, "lr": 0.09846, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24, "top5_acc": 0.48609, "loss_cls": 4.17636, "loss": 4.17636, "time": 0.69901} +{"mode": "train", "epoch": 12, "iter": 3400, "lr": 0.09845, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24656, "top5_acc": 0.50109, "loss_cls": 4.11703, "loss": 4.11703, "time": 0.70469} +{"mode": "train", "epoch": 12, "iter": 3500, "lr": 0.09845, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2475, "top5_acc": 0.50266, "loss_cls": 4.10555, "loss": 4.10555, "time": 0.70374} +{"mode": "train", "epoch": 12, "iter": 3600, "lr": 0.09844, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25344, "top5_acc": 0.49734, "loss_cls": 4.1185, "loss": 4.1185, "time": 0.70153} +{"mode": "train", "epoch": 12, "iter": 3700, "lr": 0.09843, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24469, "top5_acc": 0.49641, "loss_cls": 4.14275, "loss": 4.14275, "time": 0.69983} +{"mode": "val", "epoch": 12, "iter": 309, "lr": 0.09843, "top1_acc": 0.16082, "top5_acc": 0.38358, "mean_class_accuracy": 0.16077} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.09842, "memory": 15990, "data_time": 1.29825, "top1_acc": 0.25484, "top5_acc": 0.50062, "loss_cls": 4.06831, "loss": 4.06831, "time": 2.00675} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.09842, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24797, "top5_acc": 0.49766, "loss_cls": 4.09883, "loss": 4.09883, "time": 0.70655} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.09841, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25031, "top5_acc": 0.50156, "loss_cls": 4.09971, "loss": 4.09971, "time": 0.70788} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.0984, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24234, "top5_acc": 0.49953, "loss_cls": 4.11389, "loss": 4.11389, "time": 0.70074} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.09839, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25578, "top5_acc": 0.49766, "loss_cls": 4.09653, "loss": 4.09653, "time": 0.70062} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.09839, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23875, "top5_acc": 0.48734, "loss_cls": 4.14329, "loss": 4.14329, "time": 0.70381} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.09838, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2525, "top5_acc": 0.50281, "loss_cls": 4.07759, "loss": 4.07759, "time": 0.70182} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.09837, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24453, "top5_acc": 0.49875, "loss_cls": 4.12238, "loss": 4.12238, "time": 0.7001} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.09837, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25219, "top5_acc": 0.50813, "loss_cls": 4.06663, "loss": 4.06663, "time": 0.69781} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.09836, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23797, "top5_acc": 0.49922, "loss_cls": 4.13627, "loss": 4.13627, "time": 0.70077} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.09835, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24797, "top5_acc": 0.50688, "loss_cls": 4.10091, "loss": 4.10091, "time": 0.70219} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.09834, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24828, "top5_acc": 0.50094, "loss_cls": 4.10032, "loss": 4.10032, "time": 0.70022} +{"mode": "train", "epoch": 13, "iter": 1300, "lr": 0.09834, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24562, "top5_acc": 0.49703, "loss_cls": 4.11222, "loss": 4.11222, "time": 0.70153} +{"mode": "train", "epoch": 13, "iter": 1400, "lr": 0.09833, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25109, "top5_acc": 0.49953, "loss_cls": 4.0827, "loss": 4.0827, "time": 0.70073} +{"mode": "train", "epoch": 13, "iter": 1500, "lr": 0.09832, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24688, "top5_acc": 0.48984, "loss_cls": 4.14013, "loss": 4.14013, "time": 0.7008} +{"mode": "train", "epoch": 13, "iter": 1600, "lr": 0.09832, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25172, "top5_acc": 0.49688, "loss_cls": 4.11293, "loss": 4.11293, "time": 0.70001} +{"mode": "train", "epoch": 13, "iter": 1700, "lr": 0.09831, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24188, "top5_acc": 0.48594, "loss_cls": 4.15978, "loss": 4.15978, "time": 0.69783} +{"mode": "train", "epoch": 13, "iter": 1800, "lr": 0.0983, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23906, "top5_acc": 0.48859, "loss_cls": 4.14775, "loss": 4.14775, "time": 0.69932} +{"mode": "train", "epoch": 13, "iter": 1900, "lr": 0.09829, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24484, "top5_acc": 0.49422, "loss_cls": 4.13497, "loss": 4.13497, "time": 0.69958} +{"mode": "train", "epoch": 13, "iter": 2000, "lr": 0.09829, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25734, "top5_acc": 0.50344, "loss_cls": 4.11102, "loss": 4.11102, "time": 0.70085} +{"mode": "train", "epoch": 13, "iter": 2100, "lr": 0.09828, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25141, "top5_acc": 0.49656, "loss_cls": 4.10697, "loss": 4.10697, "time": 0.70209} +{"mode": "train", "epoch": 13, "iter": 2200, "lr": 0.09827, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25109, "top5_acc": 0.50594, "loss_cls": 4.09801, "loss": 4.09801, "time": 0.70245} +{"mode": "train", "epoch": 13, "iter": 2300, "lr": 0.09827, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24906, "top5_acc": 0.49031, "loss_cls": 4.13269, "loss": 4.13269, "time": 0.70194} +{"mode": "train", "epoch": 13, "iter": 2400, "lr": 0.09826, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24328, "top5_acc": 0.48609, "loss_cls": 4.16722, "loss": 4.16722, "time": 0.6999} +{"mode": "train", "epoch": 13, "iter": 2500, "lr": 0.09825, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25047, "top5_acc": 0.4975, "loss_cls": 4.11426, "loss": 4.11426, "time": 0.69762} +{"mode": "train", "epoch": 13, "iter": 2600, "lr": 0.09824, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24047, "top5_acc": 0.49359, "loss_cls": 4.15326, "loss": 4.15326, "time": 0.69909} +{"mode": "train", "epoch": 13, "iter": 2700, "lr": 0.09824, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25406, "top5_acc": 0.50375, "loss_cls": 4.08771, "loss": 4.08771, "time": 0.69782} +{"mode": "train", "epoch": 13, "iter": 2800, "lr": 0.09823, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24656, "top5_acc": 0.49234, "loss_cls": 4.14743, "loss": 4.14743, "time": 0.69977} +{"mode": "train", "epoch": 13, "iter": 2900, "lr": 0.09822, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24281, "top5_acc": 0.48641, "loss_cls": 4.13338, "loss": 4.13338, "time": 0.69957} +{"mode": "train", "epoch": 13, "iter": 3000, "lr": 0.09821, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24562, "top5_acc": 0.49812, "loss_cls": 4.12312, "loss": 4.12312, "time": 0.70056} +{"mode": "train", "epoch": 13, "iter": 3100, "lr": 0.09821, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25578, "top5_acc": 0.49922, "loss_cls": 4.10032, "loss": 4.10032, "time": 0.69893} +{"mode": "train", "epoch": 13, "iter": 3200, "lr": 0.0982, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25375, "top5_acc": 0.50406, "loss_cls": 4.07521, "loss": 4.07521, "time": 0.69869} +{"mode": "train", "epoch": 13, "iter": 3300, "lr": 0.09819, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24734, "top5_acc": 0.49734, "loss_cls": 4.13172, "loss": 4.13172, "time": 0.70004} +{"mode": "train", "epoch": 13, "iter": 3400, "lr": 0.09818, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23953, "top5_acc": 0.49781, "loss_cls": 4.14328, "loss": 4.14328, "time": 0.70682} +{"mode": "train", "epoch": 13, "iter": 3500, "lr": 0.09818, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24875, "top5_acc": 0.50359, "loss_cls": 4.09399, "loss": 4.09399, "time": 0.70293} +{"mode": "train", "epoch": 13, "iter": 3600, "lr": 0.09817, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25156, "top5_acc": 0.49484, "loss_cls": 4.13969, "loss": 4.13969, "time": 0.70194} +{"mode": "train", "epoch": 13, "iter": 3700, "lr": 0.09816, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24625, "top5_acc": 0.49438, "loss_cls": 4.11292, "loss": 4.11292, "time": 0.70118} +{"mode": "val", "epoch": 13, "iter": 309, "lr": 0.09816, "top1_acc": 0.16776, "top5_acc": 0.3932, "mean_class_accuracy": 0.16774} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.09815, "memory": 15990, "data_time": 1.31528, "top1_acc": 0.25141, "top5_acc": 0.50656, "loss_cls": 4.08046, "loss": 4.08046, "time": 2.0191} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.09814, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25469, "top5_acc": 0.50297, "loss_cls": 4.08547, "loss": 4.08547, "time": 0.70841} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.09814, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24766, "top5_acc": 0.50625, "loss_cls": 4.07275, "loss": 4.07275, "time": 0.70134} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.09813, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25625, "top5_acc": 0.50906, "loss_cls": 4.0771, "loss": 4.0771, "time": 0.69986} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.09812, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25016, "top5_acc": 0.50984, "loss_cls": 4.06043, "loss": 4.06043, "time": 0.70117} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.09811, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24469, "top5_acc": 0.50375, "loss_cls": 4.09205, "loss": 4.09205, "time": 0.70102} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.09811, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25641, "top5_acc": 0.5025, "loss_cls": 4.06179, "loss": 4.06179, "time": 0.69899} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.0981, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24766, "top5_acc": 0.49797, "loss_cls": 4.09506, "loss": 4.09506, "time": 0.70228} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.09809, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24078, "top5_acc": 0.49125, "loss_cls": 4.15355, "loss": 4.15355, "time": 0.69998} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.09808, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24047, "top5_acc": 0.48641, "loss_cls": 4.1359, "loss": 4.1359, "time": 0.69983} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.09807, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24297, "top5_acc": 0.4975, "loss_cls": 4.13472, "loss": 4.13472, "time": 0.69967} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.09807, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25188, "top5_acc": 0.50859, "loss_cls": 4.0766, "loss": 4.0766, "time": 0.69829} +{"mode": "train", "epoch": 14, "iter": 1300, "lr": 0.09806, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25078, "top5_acc": 0.505, "loss_cls": 4.08573, "loss": 4.08573, "time": 0.70034} +{"mode": "train", "epoch": 14, "iter": 1400, "lr": 0.09805, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24078, "top5_acc": 0.49375, "loss_cls": 4.12927, "loss": 4.12927, "time": 0.70413} +{"mode": "train", "epoch": 14, "iter": 1500, "lr": 0.09804, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25406, "top5_acc": 0.49734, "loss_cls": 4.08816, "loss": 4.08816, "time": 0.70507} +{"mode": "train", "epoch": 14, "iter": 1600, "lr": 0.09804, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25062, "top5_acc": 0.50078, "loss_cls": 4.1036, "loss": 4.1036, "time": 0.70141} +{"mode": "train", "epoch": 14, "iter": 1700, "lr": 0.09803, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24922, "top5_acc": 0.50172, "loss_cls": 4.09818, "loss": 4.09818, "time": 0.70032} +{"mode": "train", "epoch": 14, "iter": 1800, "lr": 0.09802, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24766, "top5_acc": 0.4975, "loss_cls": 4.11087, "loss": 4.11087, "time": 0.70132} +{"mode": "train", "epoch": 14, "iter": 1900, "lr": 0.09801, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23938, "top5_acc": 0.49547, "loss_cls": 4.13773, "loss": 4.13773, "time": 0.70003} +{"mode": "train", "epoch": 14, "iter": 2000, "lr": 0.098, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24094, "top5_acc": 0.49328, "loss_cls": 4.13434, "loss": 4.13434, "time": 0.70155} +{"mode": "train", "epoch": 14, "iter": 2100, "lr": 0.098, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24344, "top5_acc": 0.49328, "loss_cls": 4.12455, "loss": 4.12455, "time": 0.70224} +{"mode": "train", "epoch": 14, "iter": 2200, "lr": 0.09799, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25641, "top5_acc": 0.51562, "loss_cls": 4.0472, "loss": 4.0472, "time": 0.69957} +{"mode": "train", "epoch": 14, "iter": 2300, "lr": 0.09798, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23656, "top5_acc": 0.48844, "loss_cls": 4.1577, "loss": 4.1577, "time": 0.7005} +{"mode": "train", "epoch": 14, "iter": 2400, "lr": 0.09797, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24875, "top5_acc": 0.50609, "loss_cls": 4.08499, "loss": 4.08499, "time": 0.70242} +{"mode": "train", "epoch": 14, "iter": 2500, "lr": 0.09797, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25375, "top5_acc": 0.49094, "loss_cls": 4.12563, "loss": 4.12563, "time": 0.70251} +{"mode": "train", "epoch": 14, "iter": 2600, "lr": 0.09796, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25297, "top5_acc": 0.49234, "loss_cls": 4.134, "loss": 4.134, "time": 0.7007} +{"mode": "train", "epoch": 14, "iter": 2700, "lr": 0.09795, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25031, "top5_acc": 0.49703, "loss_cls": 4.11747, "loss": 4.11747, "time": 0.70222} +{"mode": "train", "epoch": 14, "iter": 2800, "lr": 0.09794, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26125, "top5_acc": 0.51, "loss_cls": 4.06069, "loss": 4.06069, "time": 0.69986} +{"mode": "train", "epoch": 14, "iter": 2900, "lr": 0.09793, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24828, "top5_acc": 0.50203, "loss_cls": 4.10383, "loss": 4.10383, "time": 0.70126} +{"mode": "train", "epoch": 14, "iter": 3000, "lr": 0.09793, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25266, "top5_acc": 0.49875, "loss_cls": 4.07284, "loss": 4.07284, "time": 0.70587} +{"mode": "train", "epoch": 14, "iter": 3100, "lr": 0.09792, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25578, "top5_acc": 0.51109, "loss_cls": 4.06935, "loss": 4.06935, "time": 0.70195} +{"mode": "train", "epoch": 14, "iter": 3200, "lr": 0.09791, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25844, "top5_acc": 0.50516, "loss_cls": 4.11375, "loss": 4.11375, "time": 0.69786} +{"mode": "train", "epoch": 14, "iter": 3300, "lr": 0.0979, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25203, "top5_acc": 0.49922, "loss_cls": 4.10399, "loss": 4.10399, "time": 0.70369} +{"mode": "train", "epoch": 14, "iter": 3400, "lr": 0.09789, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25734, "top5_acc": 0.50406, "loss_cls": 4.08499, "loss": 4.08499, "time": 0.70038} +{"mode": "train", "epoch": 14, "iter": 3500, "lr": 0.09789, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25094, "top5_acc": 0.50078, "loss_cls": 4.09267, "loss": 4.09267, "time": 0.70379} +{"mode": "train", "epoch": 14, "iter": 3600, "lr": 0.09788, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25797, "top5_acc": 0.50656, "loss_cls": 4.07779, "loss": 4.07779, "time": 0.70117} +{"mode": "train", "epoch": 14, "iter": 3700, "lr": 0.09787, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24562, "top5_acc": 0.48906, "loss_cls": 4.14062, "loss": 4.14062, "time": 0.70024} +{"mode": "val", "epoch": 14, "iter": 309, "lr": 0.09787, "top1_acc": 0.16046, "top5_acc": 0.37178, "mean_class_accuracy": 0.16044} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.09786, "memory": 15990, "data_time": 1.2965, "top1_acc": 0.25125, "top5_acc": 0.50094, "loss_cls": 4.09423, "loss": 4.09423, "time": 1.99918} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.09785, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24969, "top5_acc": 0.50281, "loss_cls": 4.09097, "loss": 4.09097, "time": 0.7123} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.09784, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24781, "top5_acc": 0.49906, "loss_cls": 4.09964, "loss": 4.09964, "time": 0.70028} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.09783, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26016, "top5_acc": 0.50891, "loss_cls": 4.03321, "loss": 4.03321, "time": 0.70078} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.09783, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25266, "top5_acc": 0.50547, "loss_cls": 4.05411, "loss": 4.05411, "time": 0.70207} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.09782, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25938, "top5_acc": 0.51281, "loss_cls": 4.0466, "loss": 4.0466, "time": 0.69892} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.09781, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24547, "top5_acc": 0.50344, "loss_cls": 4.09174, "loss": 4.09174, "time": 0.69896} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.0978, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25438, "top5_acc": 0.50578, "loss_cls": 4.08134, "loss": 4.08134, "time": 0.69865} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.09779, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24859, "top5_acc": 0.50953, "loss_cls": 4.05696, "loss": 4.05696, "time": 0.69686} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.09778, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25578, "top5_acc": 0.50719, "loss_cls": 4.06635, "loss": 4.06635, "time": 0.69718} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.09778, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25828, "top5_acc": 0.51344, "loss_cls": 4.04393, "loss": 4.04393, "time": 0.6999} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.09777, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25328, "top5_acc": 0.50172, "loss_cls": 4.05925, "loss": 4.05925, "time": 0.69806} +{"mode": "train", "epoch": 15, "iter": 1300, "lr": 0.09776, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24719, "top5_acc": 0.50875, "loss_cls": 4.08297, "loss": 4.08297, "time": 0.6983} +{"mode": "train", "epoch": 15, "iter": 1400, "lr": 0.09775, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24547, "top5_acc": 0.48266, "loss_cls": 4.173, "loss": 4.173, "time": 0.6973} +{"mode": "train", "epoch": 15, "iter": 1500, "lr": 0.09774, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24781, "top5_acc": 0.50531, "loss_cls": 4.08176, "loss": 4.08176, "time": 0.69723} +{"mode": "train", "epoch": 15, "iter": 1600, "lr": 0.09773, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24547, "top5_acc": 0.49984, "loss_cls": 4.12399, "loss": 4.12399, "time": 0.69802} +{"mode": "train", "epoch": 15, "iter": 1700, "lr": 0.09773, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25203, "top5_acc": 0.50141, "loss_cls": 4.07787, "loss": 4.07787, "time": 0.69744} +{"mode": "train", "epoch": 15, "iter": 1800, "lr": 0.09772, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25188, "top5_acc": 0.50313, "loss_cls": 4.10128, "loss": 4.10128, "time": 0.70259} +{"mode": "train", "epoch": 15, "iter": 1900, "lr": 0.09771, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25531, "top5_acc": 0.50547, "loss_cls": 4.05956, "loss": 4.05956, "time": 0.70129} +{"mode": "train", "epoch": 15, "iter": 2000, "lr": 0.0977, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24062, "top5_acc": 0.49188, "loss_cls": 4.12836, "loss": 4.12836, "time": 0.69673} +{"mode": "train", "epoch": 15, "iter": 2100, "lr": 0.09769, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24469, "top5_acc": 0.49578, "loss_cls": 4.12862, "loss": 4.12862, "time": 0.69851} +{"mode": "train", "epoch": 15, "iter": 2200, "lr": 0.09768, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2475, "top5_acc": 0.50891, "loss_cls": 4.0845, "loss": 4.0845, "time": 0.69568} +{"mode": "train", "epoch": 15, "iter": 2300, "lr": 0.09768, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.245, "top5_acc": 0.49891, "loss_cls": 4.1026, "loss": 4.1026, "time": 0.69814} +{"mode": "train", "epoch": 15, "iter": 2400, "lr": 0.09767, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2475, "top5_acc": 0.49922, "loss_cls": 4.11096, "loss": 4.11096, "time": 0.69613} +{"mode": "train", "epoch": 15, "iter": 2500, "lr": 0.09766, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25422, "top5_acc": 0.50875, "loss_cls": 4.07637, "loss": 4.07637, "time": 0.70152} +{"mode": "train", "epoch": 15, "iter": 2600, "lr": 0.09765, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24469, "top5_acc": 0.50047, "loss_cls": 4.11613, "loss": 4.11613, "time": 0.69687} +{"mode": "train", "epoch": 15, "iter": 2700, "lr": 0.09764, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25797, "top5_acc": 0.50734, "loss_cls": 4.07708, "loss": 4.07708, "time": 0.69903} +{"mode": "train", "epoch": 15, "iter": 2800, "lr": 0.09763, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25688, "top5_acc": 0.50578, "loss_cls": 4.06774, "loss": 4.06774, "time": 0.69852} +{"mode": "train", "epoch": 15, "iter": 2900, "lr": 0.09763, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25219, "top5_acc": 0.50984, "loss_cls": 4.06776, "loss": 4.06776, "time": 0.70214} +{"mode": "train", "epoch": 15, "iter": 3000, "lr": 0.09762, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24797, "top5_acc": 0.49516, "loss_cls": 4.13348, "loss": 4.13348, "time": 0.69826} +{"mode": "train", "epoch": 15, "iter": 3100, "lr": 0.09761, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24906, "top5_acc": 0.49234, "loss_cls": 4.10628, "loss": 4.10628, "time": 0.6974} +{"mode": "train", "epoch": 15, "iter": 3200, "lr": 0.0976, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25312, "top5_acc": 0.50203, "loss_cls": 4.08366, "loss": 4.08366, "time": 0.69896} +{"mode": "train", "epoch": 15, "iter": 3300, "lr": 0.09759, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25906, "top5_acc": 0.50594, "loss_cls": 4.07878, "loss": 4.07878, "time": 0.69726} +{"mode": "train", "epoch": 15, "iter": 3400, "lr": 0.09758, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24688, "top5_acc": 0.49828, "loss_cls": 4.11529, "loss": 4.11529, "time": 0.70253} +{"mode": "train", "epoch": 15, "iter": 3500, "lr": 0.09757, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24891, "top5_acc": 0.50781, "loss_cls": 4.0731, "loss": 4.0731, "time": 0.69889} +{"mode": "train", "epoch": 15, "iter": 3600, "lr": 0.09757, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.245, "top5_acc": 0.49578, "loss_cls": 4.11271, "loss": 4.11271, "time": 0.70283} +{"mode": "train", "epoch": 15, "iter": 3700, "lr": 0.09756, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25, "top5_acc": 0.50578, "loss_cls": 4.09886, "loss": 4.09886, "time": 0.70031} +{"mode": "val", "epoch": 15, "iter": 309, "lr": 0.09755, "top1_acc": 0.17672, "top5_acc": 0.39969, "mean_class_accuracy": 0.17676} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.09754, "memory": 15990, "data_time": 1.30056, "top1_acc": 0.25234, "top5_acc": 0.50813, "loss_cls": 4.07471, "loss": 4.07471, "time": 2.00227} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.09754, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25109, "top5_acc": 0.50938, "loss_cls": 4.04526, "loss": 4.04526, "time": 0.70914} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.09753, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26188, "top5_acc": 0.51391, "loss_cls": 4.00661, "loss": 4.00661, "time": 0.70648} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.09752, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25562, "top5_acc": 0.50578, "loss_cls": 4.06311, "loss": 4.06311, "time": 0.70601} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.09751, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26953, "top5_acc": 0.50578, "loss_cls": 4.04207, "loss": 4.04207, "time": 0.70057} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.0975, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25125, "top5_acc": 0.50344, "loss_cls": 4.07323, "loss": 4.07323, "time": 0.70062} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.09749, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25531, "top5_acc": 0.51125, "loss_cls": 4.07859, "loss": 4.07859, "time": 0.69595} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.09748, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23906, "top5_acc": 0.48859, "loss_cls": 4.12217, "loss": 4.12217, "time": 0.69928} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.09747, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26188, "top5_acc": 0.51313, "loss_cls": 4.04348, "loss": 4.04348, "time": 0.69598} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.09747, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25828, "top5_acc": 0.50062, "loss_cls": 4.0976, "loss": 4.0976, "time": 0.69625} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.09746, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25, "top5_acc": 0.49859, "loss_cls": 4.08347, "loss": 4.08347, "time": 0.69864} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.09745, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25375, "top5_acc": 0.50484, "loss_cls": 4.06445, "loss": 4.06445, "time": 0.69597} +{"mode": "train", "epoch": 16, "iter": 1300, "lr": 0.09744, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2625, "top5_acc": 0.51266, "loss_cls": 4.05348, "loss": 4.05348, "time": 0.69766} +{"mode": "train", "epoch": 16, "iter": 1400, "lr": 0.09743, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24797, "top5_acc": 0.49281, "loss_cls": 4.10538, "loss": 4.10538, "time": 0.69756} +{"mode": "train", "epoch": 16, "iter": 1500, "lr": 0.09742, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24203, "top5_acc": 0.48922, "loss_cls": 4.1401, "loss": 4.1401, "time": 0.69961} +{"mode": "train", "epoch": 16, "iter": 1600, "lr": 0.09741, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25203, "top5_acc": 0.49984, "loss_cls": 4.09247, "loss": 4.09247, "time": 0.70037} +{"mode": "train", "epoch": 16, "iter": 1700, "lr": 0.0974, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25141, "top5_acc": 0.50797, "loss_cls": 4.08877, "loss": 4.08877, "time": 0.69746} +{"mode": "train", "epoch": 16, "iter": 1800, "lr": 0.0974, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24281, "top5_acc": 0.49062, "loss_cls": 4.14859, "loss": 4.14859, "time": 0.69724} +{"mode": "train", "epoch": 16, "iter": 1900, "lr": 0.09739, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24891, "top5_acc": 0.51328, "loss_cls": 4.05683, "loss": 4.05683, "time": 0.69822} +{"mode": "train", "epoch": 16, "iter": 2000, "lr": 0.09738, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25797, "top5_acc": 0.50531, "loss_cls": 4.06984, "loss": 4.06984, "time": 0.70048} +{"mode": "train", "epoch": 16, "iter": 2100, "lr": 0.09737, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25219, "top5_acc": 0.50375, "loss_cls": 4.06665, "loss": 4.06665, "time": 0.69834} +{"mode": "train", "epoch": 16, "iter": 2200, "lr": 0.09736, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24625, "top5_acc": 0.49016, "loss_cls": 4.13707, "loss": 4.13707, "time": 0.69791} +{"mode": "train", "epoch": 16, "iter": 2300, "lr": 0.09735, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24797, "top5_acc": 0.51125, "loss_cls": 4.06487, "loss": 4.06487, "time": 0.69464} +{"mode": "train", "epoch": 16, "iter": 2400, "lr": 0.09734, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24375, "top5_acc": 0.50359, "loss_cls": 4.09866, "loss": 4.09866, "time": 0.69772} +{"mode": "train", "epoch": 16, "iter": 2500, "lr": 0.09733, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25797, "top5_acc": 0.51109, "loss_cls": 4.02274, "loss": 4.02274, "time": 0.69821} +{"mode": "train", "epoch": 16, "iter": 2600, "lr": 0.09732, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25391, "top5_acc": 0.49359, "loss_cls": 4.09718, "loss": 4.09718, "time": 0.69695} +{"mode": "train", "epoch": 16, "iter": 2700, "lr": 0.09731, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25312, "top5_acc": 0.505, "loss_cls": 4.09167, "loss": 4.09167, "time": 0.69907} +{"mode": "train", "epoch": 16, "iter": 2800, "lr": 0.09731, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24969, "top5_acc": 0.50016, "loss_cls": 4.10614, "loss": 4.10614, "time": 0.69502} +{"mode": "train", "epoch": 16, "iter": 2900, "lr": 0.0973, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25547, "top5_acc": 0.50688, "loss_cls": 4.08015, "loss": 4.08015, "time": 0.69832} +{"mode": "train", "epoch": 16, "iter": 3000, "lr": 0.09729, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24594, "top5_acc": 0.49875, "loss_cls": 4.1119, "loss": 4.1119, "time": 0.69985} +{"mode": "train", "epoch": 16, "iter": 3100, "lr": 0.09728, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25141, "top5_acc": 0.4975, "loss_cls": 4.10279, "loss": 4.10279, "time": 0.69767} +{"mode": "train", "epoch": 16, "iter": 3200, "lr": 0.09727, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26047, "top5_acc": 0.50781, "loss_cls": 4.0595, "loss": 4.0595, "time": 0.6987} +{"mode": "train", "epoch": 16, "iter": 3300, "lr": 0.09726, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24906, "top5_acc": 0.50094, "loss_cls": 4.11494, "loss": 4.11494, "time": 0.70228} +{"mode": "train", "epoch": 16, "iter": 3400, "lr": 0.09725, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25797, "top5_acc": 0.50844, "loss_cls": 4.05468, "loss": 4.05468, "time": 0.70678} +{"mode": "train", "epoch": 16, "iter": 3500, "lr": 0.09724, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25797, "top5_acc": 0.50484, "loss_cls": 4.05434, "loss": 4.05434, "time": 0.69899} +{"mode": "train", "epoch": 16, "iter": 3600, "lr": 0.09723, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25234, "top5_acc": 0.50266, "loss_cls": 4.09433, "loss": 4.09433, "time": 0.7027} +{"mode": "train", "epoch": 16, "iter": 3700, "lr": 0.09722, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24766, "top5_acc": 0.51125, "loss_cls": 4.07679, "loss": 4.07679, "time": 0.70124} +{"mode": "val", "epoch": 16, "iter": 309, "lr": 0.09722, "top1_acc": 0.18548, "top5_acc": 0.40637, "mean_class_accuracy": 0.18554} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.09721, "memory": 15990, "data_time": 1.30695, "top1_acc": 0.26156, "top5_acc": 0.51656, "loss_cls": 4.01071, "loss": 4.01071, "time": 2.00933} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.0972, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25219, "top5_acc": 0.51031, "loss_cls": 4.05185, "loss": 4.05185, "time": 0.71058} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.09719, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26234, "top5_acc": 0.51781, "loss_cls": 3.98888, "loss": 3.98888, "time": 0.69915} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.09718, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2525, "top5_acc": 0.5025, "loss_cls": 4.05184, "loss": 4.05184, "time": 0.70273} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.09717, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26125, "top5_acc": 0.51047, "loss_cls": 4.04263, "loss": 4.04263, "time": 0.70033} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.09716, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24781, "top5_acc": 0.49938, "loss_cls": 4.09418, "loss": 4.09418, "time": 0.69821} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.09715, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25891, "top5_acc": 0.51078, "loss_cls": 4.04965, "loss": 4.04965, "time": 0.6998} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.09714, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26531, "top5_acc": 0.51094, "loss_cls": 4.04918, "loss": 4.04918, "time": 0.6963} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.09714, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26391, "top5_acc": 0.51313, "loss_cls": 4.02624, "loss": 4.02624, "time": 0.70119} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.09713, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25453, "top5_acc": 0.50703, "loss_cls": 4.05683, "loss": 4.05683, "time": 0.6978} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.09712, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24891, "top5_acc": 0.50766, "loss_cls": 4.06962, "loss": 4.06962, "time": 0.69707} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.09711, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25125, "top5_acc": 0.51125, "loss_cls": 4.05724, "loss": 4.05724, "time": 0.69746} +{"mode": "train", "epoch": 17, "iter": 1300, "lr": 0.0971, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24688, "top5_acc": 0.50438, "loss_cls": 4.10076, "loss": 4.10076, "time": 0.69665} +{"mode": "train", "epoch": 17, "iter": 1400, "lr": 0.09709, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25156, "top5_acc": 0.50453, "loss_cls": 4.08426, "loss": 4.08426, "time": 0.69748} +{"mode": "train", "epoch": 17, "iter": 1500, "lr": 0.09708, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25859, "top5_acc": 0.50406, "loss_cls": 4.05392, "loss": 4.05392, "time": 0.69967} +{"mode": "train", "epoch": 17, "iter": 1600, "lr": 0.09707, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24953, "top5_acc": 0.505, "loss_cls": 4.08834, "loss": 4.08834, "time": 0.69722} +{"mode": "train", "epoch": 17, "iter": 1700, "lr": 0.09706, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25328, "top5_acc": 0.50813, "loss_cls": 4.06707, "loss": 4.06707, "time": 0.7006} +{"mode": "train", "epoch": 17, "iter": 1800, "lr": 0.09705, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25062, "top5_acc": 0.50438, "loss_cls": 4.09412, "loss": 4.09412, "time": 0.69781} +{"mode": "train", "epoch": 17, "iter": 1900, "lr": 0.09704, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25578, "top5_acc": 0.49703, "loss_cls": 4.09538, "loss": 4.09538, "time": 0.69662} +{"mode": "train", "epoch": 17, "iter": 2000, "lr": 0.09703, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25031, "top5_acc": 0.50813, "loss_cls": 4.09071, "loss": 4.09071, "time": 0.6977} +{"mode": "train", "epoch": 17, "iter": 2100, "lr": 0.09702, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24766, "top5_acc": 0.49219, "loss_cls": 4.10731, "loss": 4.10731, "time": 0.69612} +{"mode": "train", "epoch": 17, "iter": 2200, "lr": 0.09701, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24703, "top5_acc": 0.505, "loss_cls": 4.07465, "loss": 4.07465, "time": 0.69892} +{"mode": "train", "epoch": 17, "iter": 2300, "lr": 0.097, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25172, "top5_acc": 0.50125, "loss_cls": 4.0654, "loss": 4.0654, "time": 0.69928} +{"mode": "train", "epoch": 17, "iter": 2400, "lr": 0.09699, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24922, "top5_acc": 0.49922, "loss_cls": 4.11083, "loss": 4.11083, "time": 0.69605} +{"mode": "train", "epoch": 17, "iter": 2500, "lr": 0.09698, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25562, "top5_acc": 0.51844, "loss_cls": 4.05998, "loss": 4.05998, "time": 0.69942} +{"mode": "train", "epoch": 17, "iter": 2600, "lr": 0.09697, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24953, "top5_acc": 0.50406, "loss_cls": 4.09672, "loss": 4.09672, "time": 0.6991} +{"mode": "train", "epoch": 17, "iter": 2700, "lr": 0.09697, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.245, "top5_acc": 0.49922, "loss_cls": 4.11687, "loss": 4.11687, "time": 0.7021} +{"mode": "train", "epoch": 17, "iter": 2800, "lr": 0.09696, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26203, "top5_acc": 0.51422, "loss_cls": 4.04369, "loss": 4.04369, "time": 0.69929} +{"mode": "train", "epoch": 17, "iter": 2900, "lr": 0.09695, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26438, "top5_acc": 0.51016, "loss_cls": 4.03152, "loss": 4.03152, "time": 0.69939} +{"mode": "train", "epoch": 17, "iter": 3000, "lr": 0.09694, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24562, "top5_acc": 0.48469, "loss_cls": 4.14196, "loss": 4.14196, "time": 0.69856} +{"mode": "train", "epoch": 17, "iter": 3100, "lr": 0.09693, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24875, "top5_acc": 0.50062, "loss_cls": 4.08067, "loss": 4.08067, "time": 0.7032} +{"mode": "train", "epoch": 17, "iter": 3200, "lr": 0.09692, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25062, "top5_acc": 0.50562, "loss_cls": 4.07528, "loss": 4.07528, "time": 0.69863} +{"mode": "train", "epoch": 17, "iter": 3300, "lr": 0.09691, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25141, "top5_acc": 0.50344, "loss_cls": 4.06609, "loss": 4.06609, "time": 0.69759} +{"mode": "train", "epoch": 17, "iter": 3400, "lr": 0.0969, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25781, "top5_acc": 0.50328, "loss_cls": 4.07037, "loss": 4.07037, "time": 0.70415} +{"mode": "train", "epoch": 17, "iter": 3500, "lr": 0.09689, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25609, "top5_acc": 0.50078, "loss_cls": 4.05538, "loss": 4.05538, "time": 0.70294} +{"mode": "train", "epoch": 17, "iter": 3600, "lr": 0.09688, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25734, "top5_acc": 0.51141, "loss_cls": 4.05768, "loss": 4.05768, "time": 0.69992} +{"mode": "train", "epoch": 17, "iter": 3700, "lr": 0.09687, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25359, "top5_acc": 0.50297, "loss_cls": 4.07008, "loss": 4.07008, "time": 0.69803} +{"mode": "val", "epoch": 17, "iter": 309, "lr": 0.09686, "top1_acc": 0.18675, "top5_acc": 0.41083, "mean_class_accuracy": 0.18652} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.09685, "memory": 15990, "data_time": 1.36932, "top1_acc": 0.24547, "top5_acc": 0.51422, "loss_cls": 4.05104, "loss": 4.05104, "time": 2.07297} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.09684, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26844, "top5_acc": 0.51531, "loss_cls": 4.02945, "loss": 4.02945, "time": 0.71037} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.09683, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26062, "top5_acc": 0.50359, "loss_cls": 4.06542, "loss": 4.06542, "time": 0.70399} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.09683, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26484, "top5_acc": 0.51156, "loss_cls": 4.02916, "loss": 4.02916, "time": 0.71337} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.09682, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24922, "top5_acc": 0.50328, "loss_cls": 4.07965, "loss": 4.07965, "time": 0.70341} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.09681, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25422, "top5_acc": 0.5075, "loss_cls": 4.07443, "loss": 4.07443, "time": 0.69942} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.0968, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25281, "top5_acc": 0.5, "loss_cls": 4.06355, "loss": 4.06355, "time": 0.69822} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.09679, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25391, "top5_acc": 0.50828, "loss_cls": 4.10055, "loss": 4.10055, "time": 0.69737} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.09678, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24953, "top5_acc": 0.49781, "loss_cls": 4.09865, "loss": 4.09865, "time": 0.69687} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.09677, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25781, "top5_acc": 0.50516, "loss_cls": 4.06655, "loss": 4.06655, "time": 0.6992} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.09676, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25453, "top5_acc": 0.50187, "loss_cls": 4.06926, "loss": 4.06926, "time": 0.70064} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.09675, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25656, "top5_acc": 0.50578, "loss_cls": 4.04855, "loss": 4.04855, "time": 0.69948} +{"mode": "train", "epoch": 18, "iter": 1300, "lr": 0.09674, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25281, "top5_acc": 0.51297, "loss_cls": 4.04101, "loss": 4.04101, "time": 0.70094} +{"mode": "train", "epoch": 18, "iter": 1400, "lr": 0.09673, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25766, "top5_acc": 0.51703, "loss_cls": 4.02879, "loss": 4.02879, "time": 0.69925} +{"mode": "train", "epoch": 18, "iter": 1500, "lr": 0.09672, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25859, "top5_acc": 0.50703, "loss_cls": 4.0483, "loss": 4.0483, "time": 0.69685} +{"mode": "train", "epoch": 18, "iter": 1600, "lr": 0.09671, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24188, "top5_acc": 0.50578, "loss_cls": 4.08708, "loss": 4.08708, "time": 0.69915} +{"mode": "train", "epoch": 18, "iter": 1700, "lr": 0.0967, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25109, "top5_acc": 0.50484, "loss_cls": 4.07783, "loss": 4.07783, "time": 0.69798} +{"mode": "train", "epoch": 18, "iter": 1800, "lr": 0.09669, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25312, "top5_acc": 0.50453, "loss_cls": 4.07926, "loss": 4.07926, "time": 0.69724} +{"mode": "train", "epoch": 18, "iter": 1900, "lr": 0.09668, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26109, "top5_acc": 0.50766, "loss_cls": 4.05701, "loss": 4.05701, "time": 0.69783} +{"mode": "train", "epoch": 18, "iter": 2000, "lr": 0.09667, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24906, "top5_acc": 0.49906, "loss_cls": 4.11016, "loss": 4.11016, "time": 0.69811} +{"mode": "train", "epoch": 18, "iter": 2100, "lr": 0.09666, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25047, "top5_acc": 0.49969, "loss_cls": 4.10632, "loss": 4.10632, "time": 0.69848} +{"mode": "train", "epoch": 18, "iter": 2200, "lr": 0.09665, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25844, "top5_acc": 0.50953, "loss_cls": 4.07183, "loss": 4.07183, "time": 0.69748} +{"mode": "train", "epoch": 18, "iter": 2300, "lr": 0.09664, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24781, "top5_acc": 0.50328, "loss_cls": 4.09408, "loss": 4.09408, "time": 0.69947} +{"mode": "train", "epoch": 18, "iter": 2400, "lr": 0.09663, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25391, "top5_acc": 0.51047, "loss_cls": 4.06539, "loss": 4.06539, "time": 0.69776} +{"mode": "train", "epoch": 18, "iter": 2500, "lr": 0.09662, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25766, "top5_acc": 0.5025, "loss_cls": 4.08393, "loss": 4.08393, "time": 0.6975} +{"mode": "train", "epoch": 18, "iter": 2600, "lr": 0.09661, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25281, "top5_acc": 0.51125, "loss_cls": 4.06105, "loss": 4.06105, "time": 0.69742} +{"mode": "train", "epoch": 18, "iter": 2700, "lr": 0.0966, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25188, "top5_acc": 0.5075, "loss_cls": 4.07075, "loss": 4.07075, "time": 0.69909} +{"mode": "train", "epoch": 18, "iter": 2800, "lr": 0.09659, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25484, "top5_acc": 0.50047, "loss_cls": 4.07142, "loss": 4.07142, "time": 0.69715} +{"mode": "train", "epoch": 18, "iter": 2900, "lr": 0.09658, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25156, "top5_acc": 0.50062, "loss_cls": 4.09742, "loss": 4.09742, "time": 0.69862} +{"mode": "train", "epoch": 18, "iter": 3000, "lr": 0.09657, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26687, "top5_acc": 0.51094, "loss_cls": 4.05154, "loss": 4.05154, "time": 0.69681} +{"mode": "train", "epoch": 18, "iter": 3100, "lr": 0.09656, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26234, "top5_acc": 0.51016, "loss_cls": 4.03618, "loss": 4.03618, "time": 0.69874} +{"mode": "train", "epoch": 18, "iter": 3200, "lr": 0.09654, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25438, "top5_acc": 0.50234, "loss_cls": 4.07185, "loss": 4.07185, "time": 0.69556} +{"mode": "train", "epoch": 18, "iter": 3300, "lr": 0.09653, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25625, "top5_acc": 0.50906, "loss_cls": 4.05697, "loss": 4.05697, "time": 0.69965} +{"mode": "train", "epoch": 18, "iter": 3400, "lr": 0.09652, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25922, "top5_acc": 0.51313, "loss_cls": 4.04338, "loss": 4.04338, "time": 0.70423} +{"mode": "train", "epoch": 18, "iter": 3500, "lr": 0.09651, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26062, "top5_acc": 0.50969, "loss_cls": 4.05305, "loss": 4.05305, "time": 0.69803} +{"mode": "train", "epoch": 18, "iter": 3600, "lr": 0.0965, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25391, "top5_acc": 0.49922, "loss_cls": 4.06452, "loss": 4.06452, "time": 0.70009} +{"mode": "train", "epoch": 18, "iter": 3700, "lr": 0.09649, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25312, "top5_acc": 0.50594, "loss_cls": 4.04599, "loss": 4.04599, "time": 0.70399} +{"mode": "val", "epoch": 18, "iter": 309, "lr": 0.09649, "top1_acc": 0.16487, "top5_acc": 0.37998, "mean_class_accuracy": 0.16474} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.09648, "memory": 15990, "data_time": 1.37784, "top1_acc": 0.26109, "top5_acc": 0.51406, "loss_cls": 4.03787, "loss": 4.03787, "time": 2.08541} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.09647, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.25625, "top5_acc": 0.5, "loss_cls": 4.06668, "loss": 4.06668, "time": 0.7111} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.09646, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25312, "top5_acc": 0.5075, "loss_cls": 4.06595, "loss": 4.06595, "time": 0.70187} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.09645, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25562, "top5_acc": 0.50422, "loss_cls": 4.05835, "loss": 4.05835, "time": 0.69938} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.09644, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25531, "top5_acc": 0.51109, "loss_cls": 4.0135, "loss": 4.0135, "time": 0.70832} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.09643, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25516, "top5_acc": 0.51594, "loss_cls": 4.02813, "loss": 4.02813, "time": 0.70192} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.09642, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26016, "top5_acc": 0.50875, "loss_cls": 4.08167, "loss": 4.08167, "time": 0.70099} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.09641, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26594, "top5_acc": 0.51172, "loss_cls": 4.03812, "loss": 4.03812, "time": 0.70071} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.0964, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25531, "top5_acc": 0.51297, "loss_cls": 4.06478, "loss": 4.06478, "time": 0.6967} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.09639, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2475, "top5_acc": 0.50125, "loss_cls": 4.09792, "loss": 4.09792, "time": 0.70322} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.09637, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26797, "top5_acc": 0.51828, "loss_cls": 4.01404, "loss": 4.01404, "time": 0.69833} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.09636, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24906, "top5_acc": 0.50516, "loss_cls": 4.06725, "loss": 4.06725, "time": 0.69965} +{"mode": "train", "epoch": 19, "iter": 1300, "lr": 0.09635, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25953, "top5_acc": 0.51094, "loss_cls": 4.05042, "loss": 4.05042, "time": 0.69839} +{"mode": "train", "epoch": 19, "iter": 1400, "lr": 0.09634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26578, "top5_acc": 0.50984, "loss_cls": 4.001, "loss": 4.001, "time": 0.69967} +{"mode": "train", "epoch": 19, "iter": 1500, "lr": 0.09633, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25656, "top5_acc": 0.50781, "loss_cls": 4.06692, "loss": 4.06692, "time": 0.69876} +{"mode": "train", "epoch": 19, "iter": 1600, "lr": 0.09632, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25406, "top5_acc": 0.50406, "loss_cls": 4.06569, "loss": 4.06569, "time": 0.70138} +{"mode": "train", "epoch": 19, "iter": 1700, "lr": 0.09631, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25578, "top5_acc": 0.51125, "loss_cls": 4.06139, "loss": 4.06139, "time": 0.69825} +{"mode": "train", "epoch": 19, "iter": 1800, "lr": 0.0963, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26844, "top5_acc": 0.51703, "loss_cls": 4.0003, "loss": 4.0003, "time": 0.69959} +{"mode": "train", "epoch": 19, "iter": 1900, "lr": 0.09629, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25609, "top5_acc": 0.49922, "loss_cls": 4.07932, "loss": 4.07932, "time": 0.69616} +{"mode": "train", "epoch": 19, "iter": 2000, "lr": 0.09628, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26797, "top5_acc": 0.51109, "loss_cls": 4.07178, "loss": 4.07178, "time": 0.69927} +{"mode": "train", "epoch": 19, "iter": 2100, "lr": 0.09627, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25281, "top5_acc": 0.50953, "loss_cls": 4.0928, "loss": 4.0928, "time": 0.69881} +{"mode": "train", "epoch": 19, "iter": 2200, "lr": 0.09626, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25969, "top5_acc": 0.51359, "loss_cls": 4.04306, "loss": 4.04306, "time": 0.69613} +{"mode": "train", "epoch": 19, "iter": 2300, "lr": 0.09625, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26422, "top5_acc": 0.51781, "loss_cls": 4.02918, "loss": 4.02918, "time": 0.69722} +{"mode": "train", "epoch": 19, "iter": 2400, "lr": 0.09624, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25172, "top5_acc": 0.50516, "loss_cls": 4.0503, "loss": 4.0503, "time": 0.69807} +{"mode": "train", "epoch": 19, "iter": 2500, "lr": 0.09623, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26391, "top5_acc": 0.51516, "loss_cls": 4.04894, "loss": 4.04894, "time": 0.69717} +{"mode": "train", "epoch": 19, "iter": 2600, "lr": 0.09622, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25281, "top5_acc": 0.50641, "loss_cls": 4.06714, "loss": 4.06714, "time": 0.69761} +{"mode": "train", "epoch": 19, "iter": 2700, "lr": 0.09621, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24875, "top5_acc": 0.50281, "loss_cls": 4.08434, "loss": 4.08434, "time": 0.69722} +{"mode": "train", "epoch": 19, "iter": 2800, "lr": 0.0962, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26141, "top5_acc": 0.51391, "loss_cls": 4.02025, "loss": 4.02025, "time": 0.697} +{"mode": "train", "epoch": 19, "iter": 2900, "lr": 0.09618, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25938, "top5_acc": 0.51125, "loss_cls": 4.0334, "loss": 4.0334, "time": 0.69574} +{"mode": "train", "epoch": 19, "iter": 3000, "lr": 0.09617, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26516, "top5_acc": 0.51047, "loss_cls": 4.05723, "loss": 4.05723, "time": 0.70134} +{"mode": "train", "epoch": 19, "iter": 3100, "lr": 0.09616, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25422, "top5_acc": 0.50922, "loss_cls": 4.0495, "loss": 4.0495, "time": 0.69763} +{"mode": "train", "epoch": 19, "iter": 3200, "lr": 0.09615, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24844, "top5_acc": 0.50375, "loss_cls": 4.0936, "loss": 4.0936, "time": 0.70096} +{"mode": "train", "epoch": 19, "iter": 3300, "lr": 0.09614, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25891, "top5_acc": 0.50469, "loss_cls": 4.04583, "loss": 4.04583, "time": 0.70063} +{"mode": "train", "epoch": 19, "iter": 3400, "lr": 0.09613, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25422, "top5_acc": 0.50547, "loss_cls": 4.06419, "loss": 4.06419, "time": 0.70542} +{"mode": "train", "epoch": 19, "iter": 3500, "lr": 0.09612, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25766, "top5_acc": 0.49719, "loss_cls": 4.05728, "loss": 4.05728, "time": 0.70115} +{"mode": "train", "epoch": 19, "iter": 3600, "lr": 0.09611, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24938, "top5_acc": 0.49703, "loss_cls": 4.09496, "loss": 4.09496, "time": 0.69953} +{"mode": "train", "epoch": 19, "iter": 3700, "lr": 0.0961, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24672, "top5_acc": 0.50438, "loss_cls": 4.06577, "loss": 4.06577, "time": 0.70112} +{"mode": "val", "epoch": 19, "iter": 309, "lr": 0.09609, "top1_acc": 0.16862, "top5_acc": 0.38642, "mean_class_accuracy": 0.16855} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.09608, "memory": 15990, "data_time": 1.3542, "top1_acc": 0.26844, "top5_acc": 0.51781, "loss_cls": 3.9745, "loss": 3.9745, "time": 2.0618} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.09607, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.25812, "top5_acc": 0.50703, "loss_cls": 4.05007, "loss": 4.05007, "time": 0.70486} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.09606, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26641, "top5_acc": 0.51188, "loss_cls": 4.0307, "loss": 4.0307, "time": 0.70071} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.09605, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24844, "top5_acc": 0.49469, "loss_cls": 4.10706, "loss": 4.10706, "time": 0.70319} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.09604, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26937, "top5_acc": 0.51781, "loss_cls": 4.00345, "loss": 4.00345, "time": 0.70547} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.09603, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25547, "top5_acc": 0.52109, "loss_cls": 4.0366, "loss": 4.0366, "time": 0.70124} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.09602, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25781, "top5_acc": 0.50813, "loss_cls": 4.04313, "loss": 4.04313, "time": 0.70051} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.09601, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26078, "top5_acc": 0.51281, "loss_cls": 4.02575, "loss": 4.02575, "time": 0.69857} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.096, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25766, "top5_acc": 0.50828, "loss_cls": 4.03627, "loss": 4.03627, "time": 0.69898} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.09598, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24812, "top5_acc": 0.50469, "loss_cls": 4.0965, "loss": 4.0965, "time": 0.69882} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.09597, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24891, "top5_acc": 0.50781, "loss_cls": 4.08079, "loss": 4.08079, "time": 0.69624} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.09596, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25781, "top5_acc": 0.51156, "loss_cls": 4.04748, "loss": 4.04748, "time": 0.69906} +{"mode": "train", "epoch": 20, "iter": 1300, "lr": 0.09595, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26516, "top5_acc": 0.51859, "loss_cls": 4.01501, "loss": 4.01501, "time": 0.69905} +{"mode": "train", "epoch": 20, "iter": 1400, "lr": 0.09594, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26297, "top5_acc": 0.51594, "loss_cls": 4.00825, "loss": 4.00825, "time": 0.69741} +{"mode": "train", "epoch": 20, "iter": 1500, "lr": 0.09593, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25797, "top5_acc": 0.5125, "loss_cls": 4.05094, "loss": 4.05094, "time": 0.69657} +{"mode": "train", "epoch": 20, "iter": 1600, "lr": 0.09592, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24969, "top5_acc": 0.50578, "loss_cls": 4.06119, "loss": 4.06119, "time": 0.69775} +{"mode": "train", "epoch": 20, "iter": 1700, "lr": 0.09591, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25953, "top5_acc": 0.50578, "loss_cls": 4.06324, "loss": 4.06324, "time": 0.69973} +{"mode": "train", "epoch": 20, "iter": 1800, "lr": 0.0959, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25688, "top5_acc": 0.50656, "loss_cls": 4.06716, "loss": 4.06716, "time": 0.69618} +{"mode": "train", "epoch": 20, "iter": 1900, "lr": 0.09588, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25078, "top5_acc": 0.50703, "loss_cls": 4.0909, "loss": 4.0909, "time": 0.69704} +{"mode": "train", "epoch": 20, "iter": 2000, "lr": 0.09587, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25859, "top5_acc": 0.51078, "loss_cls": 4.0385, "loss": 4.0385, "time": 0.69851} +{"mode": "train", "epoch": 20, "iter": 2100, "lr": 0.09586, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25531, "top5_acc": 0.50125, "loss_cls": 4.05861, "loss": 4.05861, "time": 0.69883} +{"mode": "train", "epoch": 20, "iter": 2200, "lr": 0.09585, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25406, "top5_acc": 0.51047, "loss_cls": 4.05114, "loss": 4.05114, "time": 0.69954} +{"mode": "train", "epoch": 20, "iter": 2300, "lr": 0.09584, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25875, "top5_acc": 0.50531, "loss_cls": 4.05866, "loss": 4.05866, "time": 0.70132} +{"mode": "train", "epoch": 20, "iter": 2400, "lr": 0.09583, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25969, "top5_acc": 0.50922, "loss_cls": 4.02756, "loss": 4.02756, "time": 0.69666} +{"mode": "train", "epoch": 20, "iter": 2500, "lr": 0.09582, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26906, "top5_acc": 0.51469, "loss_cls": 3.99744, "loss": 3.99744, "time": 0.69585} +{"mode": "train", "epoch": 20, "iter": 2600, "lr": 0.09581, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27141, "top5_acc": 0.51047, "loss_cls": 4.00584, "loss": 4.00584, "time": 0.70068} +{"mode": "train", "epoch": 20, "iter": 2700, "lr": 0.0958, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25609, "top5_acc": 0.51641, "loss_cls": 4.04408, "loss": 4.04408, "time": 0.69802} +{"mode": "train", "epoch": 20, "iter": 2800, "lr": 0.09578, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25062, "top5_acc": 0.50547, "loss_cls": 4.08292, "loss": 4.08292, "time": 0.70247} +{"mode": "train", "epoch": 20, "iter": 2900, "lr": 0.09577, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26078, "top5_acc": 0.50609, "loss_cls": 4.02977, "loss": 4.02977, "time": 0.69871} +{"mode": "train", "epoch": 20, "iter": 3000, "lr": 0.09576, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26469, "top5_acc": 0.50969, "loss_cls": 4.03009, "loss": 4.03009, "time": 0.70011} +{"mode": "train", "epoch": 20, "iter": 3100, "lr": 0.09575, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26047, "top5_acc": 0.50609, "loss_cls": 4.05795, "loss": 4.05795, "time": 0.69694} +{"mode": "train", "epoch": 20, "iter": 3200, "lr": 0.09574, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25625, "top5_acc": 0.50172, "loss_cls": 4.10875, "loss": 4.10875, "time": 0.69983} +{"mode": "train", "epoch": 20, "iter": 3300, "lr": 0.09573, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25047, "top5_acc": 0.50516, "loss_cls": 4.08691, "loss": 4.08691, "time": 0.69991} +{"mode": "train", "epoch": 20, "iter": 3400, "lr": 0.09572, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26453, "top5_acc": 0.51781, "loss_cls": 4.04185, "loss": 4.04185, "time": 0.70007} +{"mode": "train", "epoch": 20, "iter": 3500, "lr": 0.09571, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26328, "top5_acc": 0.505, "loss_cls": 4.06643, "loss": 4.06643, "time": 0.70425} +{"mode": "train", "epoch": 20, "iter": 3600, "lr": 0.09569, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26328, "top5_acc": 0.51875, "loss_cls": 4.01943, "loss": 4.01943, "time": 0.6984} +{"mode": "train", "epoch": 20, "iter": 3700, "lr": 0.09568, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26047, "top5_acc": 0.51156, "loss_cls": 4.04538, "loss": 4.04538, "time": 0.70245} +{"mode": "val", "epoch": 20, "iter": 309, "lr": 0.09568, "top1_acc": 0.19009, "top5_acc": 0.41929, "mean_class_accuracy": 0.19002} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.09567, "memory": 15990, "data_time": 1.32037, "top1_acc": 0.2625, "top5_acc": 0.51562, "loss_cls": 4.03366, "loss": 4.03366, "time": 2.02203} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.09565, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28094, "top5_acc": 0.53156, "loss_cls": 3.95812, "loss": 3.95812, "time": 0.70282} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.09564, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26156, "top5_acc": 0.51828, "loss_cls": 4.00492, "loss": 4.00492, "time": 0.7031} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.09563, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27172, "top5_acc": 0.53078, "loss_cls": 3.96751, "loss": 3.96751, "time": 0.70095} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.09562, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2625, "top5_acc": 0.51609, "loss_cls": 4.03394, "loss": 4.03394, "time": 0.69844} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.09561, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26141, "top5_acc": 0.51234, "loss_cls": 4.02333, "loss": 4.02333, "time": 0.70178} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.0956, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25391, "top5_acc": 0.50906, "loss_cls": 4.05277, "loss": 4.05277, "time": 0.69962} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.09559, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24922, "top5_acc": 0.50813, "loss_cls": 4.08777, "loss": 4.08777, "time": 0.70075} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.09557, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26703, "top5_acc": 0.51141, "loss_cls": 4.043, "loss": 4.043, "time": 0.70034} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.09556, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25734, "top5_acc": 0.50797, "loss_cls": 4.03451, "loss": 4.03451, "time": 0.69714} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.09555, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24641, "top5_acc": 0.49953, "loss_cls": 4.08589, "loss": 4.08589, "time": 0.69867} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.09554, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24656, "top5_acc": 0.50297, "loss_cls": 4.09168, "loss": 4.09168, "time": 0.69856} +{"mode": "train", "epoch": 21, "iter": 1300, "lr": 0.09553, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25656, "top5_acc": 0.49859, "loss_cls": 4.07545, "loss": 4.07545, "time": 0.7016} +{"mode": "train", "epoch": 21, "iter": 1400, "lr": 0.09552, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25766, "top5_acc": 0.51422, "loss_cls": 4.04428, "loss": 4.04428, "time": 0.69943} +{"mode": "train", "epoch": 21, "iter": 1500, "lr": 0.09551, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25797, "top5_acc": 0.51453, "loss_cls": 4.04036, "loss": 4.04036, "time": 0.6993} +{"mode": "train", "epoch": 21, "iter": 1600, "lr": 0.09549, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25516, "top5_acc": 0.50984, "loss_cls": 4.02925, "loss": 4.02925, "time": 0.69802} +{"mode": "train", "epoch": 21, "iter": 1700, "lr": 0.09548, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26219, "top5_acc": 0.5175, "loss_cls": 4.04388, "loss": 4.04388, "time": 0.69926} +{"mode": "train", "epoch": 21, "iter": 1800, "lr": 0.09547, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25438, "top5_acc": 0.5025, "loss_cls": 4.0767, "loss": 4.0767, "time": 0.70097} +{"mode": "train", "epoch": 21, "iter": 1900, "lr": 0.09546, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26234, "top5_acc": 0.51922, "loss_cls": 4.02558, "loss": 4.02558, "time": 0.69706} +{"mode": "train", "epoch": 21, "iter": 2000, "lr": 0.09545, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.255, "top5_acc": 0.51109, "loss_cls": 4.07313, "loss": 4.07313, "time": 0.69875} +{"mode": "train", "epoch": 21, "iter": 2100, "lr": 0.09544, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25484, "top5_acc": 0.50438, "loss_cls": 4.0693, "loss": 4.0693, "time": 0.69721} +{"mode": "train", "epoch": 21, "iter": 2200, "lr": 0.09542, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26594, "top5_acc": 0.51031, "loss_cls": 4.01087, "loss": 4.01087, "time": 0.69809} +{"mode": "train", "epoch": 21, "iter": 2300, "lr": 0.09541, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25953, "top5_acc": 0.50906, "loss_cls": 4.03779, "loss": 4.03779, "time": 0.69676} +{"mode": "train", "epoch": 21, "iter": 2400, "lr": 0.0954, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25062, "top5_acc": 0.51219, "loss_cls": 4.07024, "loss": 4.07024, "time": 0.69665} +{"mode": "train", "epoch": 21, "iter": 2500, "lr": 0.09539, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24906, "top5_acc": 0.51078, "loss_cls": 4.08692, "loss": 4.08692, "time": 0.6993} +{"mode": "train", "epoch": 21, "iter": 2600, "lr": 0.09538, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25, "top5_acc": 0.51125, "loss_cls": 4.04779, "loss": 4.04779, "time": 0.70089} +{"mode": "train", "epoch": 21, "iter": 2700, "lr": 0.09537, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26, "top5_acc": 0.51547, "loss_cls": 4.02325, "loss": 4.02325, "time": 0.69887} +{"mode": "train", "epoch": 21, "iter": 2800, "lr": 0.09535, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26359, "top5_acc": 0.51469, "loss_cls": 4.0217, "loss": 4.0217, "time": 0.70033} +{"mode": "train", "epoch": 21, "iter": 2900, "lr": 0.09534, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26609, "top5_acc": 0.51062, "loss_cls": 4.01722, "loss": 4.01722, "time": 0.69836} +{"mode": "train", "epoch": 21, "iter": 3000, "lr": 0.09533, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26438, "top5_acc": 0.50938, "loss_cls": 4.0222, "loss": 4.0222, "time": 0.69726} +{"mode": "train", "epoch": 21, "iter": 3100, "lr": 0.09532, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26125, "top5_acc": 0.50906, "loss_cls": 4.03416, "loss": 4.03416, "time": 0.69786} +{"mode": "train", "epoch": 21, "iter": 3200, "lr": 0.09531, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26031, "top5_acc": 0.50891, "loss_cls": 4.03761, "loss": 4.03761, "time": 0.6988} +{"mode": "train", "epoch": 21, "iter": 3300, "lr": 0.09529, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25188, "top5_acc": 0.50703, "loss_cls": 4.05389, "loss": 4.05389, "time": 0.69934} +{"mode": "train", "epoch": 21, "iter": 3400, "lr": 0.09528, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24547, "top5_acc": 0.49578, "loss_cls": 4.10907, "loss": 4.10907, "time": 0.70654} +{"mode": "train", "epoch": 21, "iter": 3500, "lr": 0.09527, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25922, "top5_acc": 0.51203, "loss_cls": 4.04787, "loss": 4.04787, "time": 0.70206} +{"mode": "train", "epoch": 21, "iter": 3600, "lr": 0.09526, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25359, "top5_acc": 0.50266, "loss_cls": 4.07618, "loss": 4.07618, "time": 0.7017} +{"mode": "train", "epoch": 21, "iter": 3700, "lr": 0.09525, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.255, "top5_acc": 0.50531, "loss_cls": 4.05809, "loss": 4.05809, "time": 0.70243} +{"mode": "val", "epoch": 21, "iter": 309, "lr": 0.09524, "top1_acc": 0.17262, "top5_acc": 0.3971, "mean_class_accuracy": 0.17249} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.09523, "memory": 15990, "data_time": 1.37871, "top1_acc": 0.25953, "top5_acc": 0.51688, "loss_cls": 4.0307, "loss": 4.0307, "time": 2.085} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.09522, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25625, "top5_acc": 0.51313, "loss_cls": 4.0289, "loss": 4.0289, "time": 0.7041} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.09521, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26328, "top5_acc": 0.51313, "loss_cls": 4.01473, "loss": 4.01473, "time": 0.70873} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.09519, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26484, "top5_acc": 0.52062, "loss_cls": 3.99483, "loss": 3.99483, "time": 0.70765} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.09518, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25875, "top5_acc": 0.51203, "loss_cls": 4.03212, "loss": 4.03212, "time": 0.70433} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.09517, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.265, "top5_acc": 0.50766, "loss_cls": 4.055, "loss": 4.055, "time": 0.70205} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.09516, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26422, "top5_acc": 0.51359, "loss_cls": 4.03521, "loss": 4.03521, "time": 0.70264} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.09515, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26922, "top5_acc": 0.51391, "loss_cls": 4.04122, "loss": 4.04122, "time": 0.69833} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.09513, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26484, "top5_acc": 0.51875, "loss_cls": 4.02237, "loss": 4.02237, "time": 0.69837} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.09512, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26016, "top5_acc": 0.51266, "loss_cls": 4.0489, "loss": 4.0489, "time": 0.69797} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.09511, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25547, "top5_acc": 0.51859, "loss_cls": 4.03145, "loss": 4.03145, "time": 0.69677} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0951, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25609, "top5_acc": 0.50578, "loss_cls": 4.06844, "loss": 4.06844, "time": 0.69776} +{"mode": "train", "epoch": 22, "iter": 1300, "lr": 0.09509, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25312, "top5_acc": 0.51313, "loss_cls": 4.08411, "loss": 4.08411, "time": 0.69847} +{"mode": "train", "epoch": 22, "iter": 1400, "lr": 0.09507, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26672, "top5_acc": 0.51281, "loss_cls": 3.99194, "loss": 3.99194, "time": 0.69806} +{"mode": "train", "epoch": 22, "iter": 1500, "lr": 0.09506, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26188, "top5_acc": 0.50484, "loss_cls": 4.05741, "loss": 4.05741, "time": 0.69657} +{"mode": "train", "epoch": 22, "iter": 1600, "lr": 0.09505, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26078, "top5_acc": 0.50891, "loss_cls": 4.05789, "loss": 4.05789, "time": 0.70073} +{"mode": "train", "epoch": 22, "iter": 1700, "lr": 0.09504, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26547, "top5_acc": 0.52406, "loss_cls": 4.00099, "loss": 4.00099, "time": 0.69846} +{"mode": "train", "epoch": 22, "iter": 1800, "lr": 0.09502, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25688, "top5_acc": 0.50531, "loss_cls": 4.08711, "loss": 4.08711, "time": 0.69979} +{"mode": "train", "epoch": 22, "iter": 1900, "lr": 0.09501, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25344, "top5_acc": 0.51641, "loss_cls": 4.03452, "loss": 4.03452, "time": 0.70067} +{"mode": "train", "epoch": 22, "iter": 2000, "lr": 0.095, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24766, "top5_acc": 0.49906, "loss_cls": 4.10453, "loss": 4.10453, "time": 0.69996} +{"mode": "train", "epoch": 22, "iter": 2100, "lr": 0.09499, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25188, "top5_acc": 0.51078, "loss_cls": 4.04848, "loss": 4.04848, "time": 0.69906} +{"mode": "train", "epoch": 22, "iter": 2200, "lr": 0.09498, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25031, "top5_acc": 0.50281, "loss_cls": 4.07316, "loss": 4.07316, "time": 0.70004} +{"mode": "train", "epoch": 22, "iter": 2300, "lr": 0.09496, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26234, "top5_acc": 0.50781, "loss_cls": 4.05258, "loss": 4.05258, "time": 0.6959} +{"mode": "train", "epoch": 22, "iter": 2400, "lr": 0.09495, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26422, "top5_acc": 0.51688, "loss_cls": 4.02363, "loss": 4.02363, "time": 0.6974} +{"mode": "train", "epoch": 22, "iter": 2500, "lr": 0.09494, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26094, "top5_acc": 0.51031, "loss_cls": 4.00831, "loss": 4.00831, "time": 0.70072} +{"mode": "train", "epoch": 22, "iter": 2600, "lr": 0.09493, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26922, "top5_acc": 0.50062, "loss_cls": 4.04277, "loss": 4.04277, "time": 0.6983} +{"mode": "train", "epoch": 22, "iter": 2700, "lr": 0.09491, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26125, "top5_acc": 0.50969, "loss_cls": 4.03004, "loss": 4.03004, "time": 0.69955} +{"mode": "train", "epoch": 22, "iter": 2800, "lr": 0.0949, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25375, "top5_acc": 0.50578, "loss_cls": 4.06781, "loss": 4.06781, "time": 0.69752} +{"mode": "train", "epoch": 22, "iter": 2900, "lr": 0.09489, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25469, "top5_acc": 0.51422, "loss_cls": 4.06227, "loss": 4.06227, "time": 0.70181} +{"mode": "train", "epoch": 22, "iter": 3000, "lr": 0.09488, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26078, "top5_acc": 0.51688, "loss_cls": 4.04055, "loss": 4.04055, "time": 0.69966} +{"mode": "train", "epoch": 22, "iter": 3100, "lr": 0.09487, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26141, "top5_acc": 0.51359, "loss_cls": 4.02246, "loss": 4.02246, "time": 0.69707} +{"mode": "train", "epoch": 22, "iter": 3200, "lr": 0.09485, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26375, "top5_acc": 0.51672, "loss_cls": 4.02427, "loss": 4.02427, "time": 0.69795} +{"mode": "train", "epoch": 22, "iter": 3300, "lr": 0.09484, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26609, "top5_acc": 0.50828, "loss_cls": 4.02642, "loss": 4.02642, "time": 0.69937} +{"mode": "train", "epoch": 22, "iter": 3400, "lr": 0.09483, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.255, "top5_acc": 0.50672, "loss_cls": 4.05997, "loss": 4.05997, "time": 0.70058} +{"mode": "train", "epoch": 22, "iter": 3500, "lr": 0.09482, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25547, "top5_acc": 0.51203, "loss_cls": 4.05053, "loss": 4.05053, "time": 0.70021} +{"mode": "train", "epoch": 22, "iter": 3600, "lr": 0.0948, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26406, "top5_acc": 0.52078, "loss_cls": 4.02234, "loss": 4.02234, "time": 0.70843} +{"mode": "train", "epoch": 22, "iter": 3700, "lr": 0.09479, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25562, "top5_acc": 0.50828, "loss_cls": 4.03806, "loss": 4.03806, "time": 0.70345} +{"mode": "val", "epoch": 22, "iter": 309, "lr": 0.09479, "top1_acc": 0.18579, "top5_acc": 0.41387, "mean_class_accuracy": 0.18551} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.09477, "memory": 15990, "data_time": 1.34218, "top1_acc": 0.265, "top5_acc": 0.5275, "loss_cls": 3.95414, "loss": 3.95414, "time": 2.04354} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.09476, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26953, "top5_acc": 0.52516, "loss_cls": 3.95288, "loss": 3.95288, "time": 0.70566} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.09475, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26828, "top5_acc": 0.51656, "loss_cls": 4.01368, "loss": 4.01368, "time": 0.70116} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.09474, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26, "top5_acc": 0.51172, "loss_cls": 4.0227, "loss": 4.0227, "time": 0.70755} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.09472, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25203, "top5_acc": 0.50234, "loss_cls": 4.09283, "loss": 4.09283, "time": 0.70163} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.09471, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26891, "top5_acc": 0.52172, "loss_cls": 3.9719, "loss": 3.9719, "time": 0.7009} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.0947, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26484, "top5_acc": 0.51719, "loss_cls": 4.00328, "loss": 4.00328, "time": 0.70005} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.09469, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25688, "top5_acc": 0.49812, "loss_cls": 4.06612, "loss": 4.06612, "time": 0.69875} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.09467, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26859, "top5_acc": 0.51641, "loss_cls": 4.00387, "loss": 4.00387, "time": 0.69964} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.09466, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25562, "top5_acc": 0.50969, "loss_cls": 4.06049, "loss": 4.06049, "time": 0.69775} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.09465, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26281, "top5_acc": 0.51375, "loss_cls": 4.00789, "loss": 4.00789, "time": 0.69777} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.09464, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26031, "top5_acc": 0.50813, "loss_cls": 4.0557, "loss": 4.0557, "time": 0.69559} +{"mode": "train", "epoch": 23, "iter": 1300, "lr": 0.09462, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26547, "top5_acc": 0.51594, "loss_cls": 4.00623, "loss": 4.00623, "time": 0.69919} +{"mode": "train", "epoch": 23, "iter": 1400, "lr": 0.09461, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25672, "top5_acc": 0.51344, "loss_cls": 4.03746, "loss": 4.03746, "time": 0.69726} +{"mode": "train", "epoch": 23, "iter": 1500, "lr": 0.0946, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25906, "top5_acc": 0.50609, "loss_cls": 4.05941, "loss": 4.05941, "time": 0.69671} +{"mode": "train", "epoch": 23, "iter": 1600, "lr": 0.09459, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25984, "top5_acc": 0.50859, "loss_cls": 4.04906, "loss": 4.04906, "time": 0.69838} +{"mode": "train", "epoch": 23, "iter": 1700, "lr": 0.09457, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25938, "top5_acc": 0.50609, "loss_cls": 4.04879, "loss": 4.04879, "time": 0.70023} +{"mode": "train", "epoch": 23, "iter": 1800, "lr": 0.09456, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26344, "top5_acc": 0.52266, "loss_cls": 4.04059, "loss": 4.04059, "time": 0.69919} +{"mode": "train", "epoch": 23, "iter": 1900, "lr": 0.09455, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2575, "top5_acc": 0.51375, "loss_cls": 4.05916, "loss": 4.05916, "time": 0.69582} +{"mode": "train", "epoch": 23, "iter": 2000, "lr": 0.09453, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25578, "top5_acc": 0.51031, "loss_cls": 4.05296, "loss": 4.05296, "time": 0.69835} +{"mode": "train", "epoch": 23, "iter": 2100, "lr": 0.09452, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26438, "top5_acc": 0.50828, "loss_cls": 4.06363, "loss": 4.06363, "time": 0.69645} +{"mode": "train", "epoch": 23, "iter": 2200, "lr": 0.09451, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26078, "top5_acc": 0.51609, "loss_cls": 4.03299, "loss": 4.03299, "time": 0.70077} +{"mode": "train", "epoch": 23, "iter": 2300, "lr": 0.0945, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25922, "top5_acc": 0.50828, "loss_cls": 4.04723, "loss": 4.04723, "time": 0.69699} +{"mode": "train", "epoch": 23, "iter": 2400, "lr": 0.09448, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26047, "top5_acc": 0.5025, "loss_cls": 4.05214, "loss": 4.05214, "time": 0.69884} +{"mode": "train", "epoch": 23, "iter": 2500, "lr": 0.09447, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26297, "top5_acc": 0.51172, "loss_cls": 4.05954, "loss": 4.05954, "time": 0.69682} +{"mode": "train", "epoch": 23, "iter": 2600, "lr": 0.09446, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27062, "top5_acc": 0.51672, "loss_cls": 3.98582, "loss": 3.98582, "time": 0.69953} +{"mode": "train", "epoch": 23, "iter": 2700, "lr": 0.09445, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25719, "top5_acc": 0.51031, "loss_cls": 4.07312, "loss": 4.07312, "time": 0.69699} +{"mode": "train", "epoch": 23, "iter": 2800, "lr": 0.09443, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24891, "top5_acc": 0.51188, "loss_cls": 4.04423, "loss": 4.04423, "time": 0.69789} +{"mode": "train", "epoch": 23, "iter": 2900, "lr": 0.09442, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26281, "top5_acc": 0.51578, "loss_cls": 4.03205, "loss": 4.03205, "time": 0.69767} +{"mode": "train", "epoch": 23, "iter": 3000, "lr": 0.09441, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26062, "top5_acc": 0.52141, "loss_cls": 4.01361, "loss": 4.01361, "time": 0.69724} +{"mode": "train", "epoch": 23, "iter": 3100, "lr": 0.09439, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25391, "top5_acc": 0.50688, "loss_cls": 4.06439, "loss": 4.06439, "time": 0.69609} +{"mode": "train", "epoch": 23, "iter": 3200, "lr": 0.09438, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.255, "top5_acc": 0.50875, "loss_cls": 4.06336, "loss": 4.06336, "time": 0.6972} +{"mode": "train", "epoch": 23, "iter": 3300, "lr": 0.09437, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25594, "top5_acc": 0.50453, "loss_cls": 4.08205, "loss": 4.08205, "time": 0.70215} +{"mode": "train", "epoch": 23, "iter": 3400, "lr": 0.09436, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2575, "top5_acc": 0.51547, "loss_cls": 4.03895, "loss": 4.03895, "time": 0.70172} +{"mode": "train", "epoch": 23, "iter": 3500, "lr": 0.09434, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26016, "top5_acc": 0.51359, "loss_cls": 4.05344, "loss": 4.05344, "time": 0.70143} +{"mode": "train", "epoch": 23, "iter": 3600, "lr": 0.09433, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25406, "top5_acc": 0.51484, "loss_cls": 4.02919, "loss": 4.02919, "time": 0.6977} +{"mode": "train", "epoch": 23, "iter": 3700, "lr": 0.09432, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25031, "top5_acc": 0.50141, "loss_cls": 4.06928, "loss": 4.06928, "time": 0.70219} +{"mode": "val", "epoch": 23, "iter": 309, "lr": 0.09431, "top1_acc": 0.18037, "top5_acc": 0.40323, "mean_class_accuracy": 0.18035} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.0943, "memory": 15990, "data_time": 1.29423, "top1_acc": 0.27703, "top5_acc": 0.53375, "loss_cls": 3.93767, "loss": 3.93767, "time": 1.99699} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.09428, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27109, "top5_acc": 0.52219, "loss_cls": 3.96914, "loss": 3.96914, "time": 0.71084} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.09427, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26, "top5_acc": 0.51375, "loss_cls": 4.06088, "loss": 4.06088, "time": 0.70907} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.09426, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26281, "top5_acc": 0.52312, "loss_cls": 3.97528, "loss": 3.97528, "time": 0.70193} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.09425, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26016, "top5_acc": 0.51094, "loss_cls": 4.00723, "loss": 4.00723, "time": 0.70446} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.09423, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25984, "top5_acc": 0.51375, "loss_cls": 4.01361, "loss": 4.01361, "time": 0.70036} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.09422, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27609, "top5_acc": 0.52469, "loss_cls": 3.98909, "loss": 3.98909, "time": 0.70275} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.09421, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26891, "top5_acc": 0.51156, "loss_cls": 4.03172, "loss": 4.03172, "time": 0.698} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.09419, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25453, "top5_acc": 0.51, "loss_cls": 4.07251, "loss": 4.07251, "time": 0.69859} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.09418, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25719, "top5_acc": 0.51234, "loss_cls": 4.02412, "loss": 4.02412, "time": 0.69689} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.09417, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24891, "top5_acc": 0.50313, "loss_cls": 4.09737, "loss": 4.09737, "time": 0.69847} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.09415, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26094, "top5_acc": 0.51188, "loss_cls": 4.02671, "loss": 4.02671, "time": 0.69844} +{"mode": "train", "epoch": 24, "iter": 1300, "lr": 0.09414, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26203, "top5_acc": 0.51062, "loss_cls": 4.03608, "loss": 4.03608, "time": 0.69797} +{"mode": "train", "epoch": 24, "iter": 1400, "lr": 0.09413, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26469, "top5_acc": 0.52031, "loss_cls": 4.0093, "loss": 4.0093, "time": 0.69638} +{"mode": "train", "epoch": 24, "iter": 1500, "lr": 0.09411, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26312, "top5_acc": 0.52172, "loss_cls": 4.0202, "loss": 4.0202, "time": 0.69972} +{"mode": "train", "epoch": 24, "iter": 1600, "lr": 0.0941, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25797, "top5_acc": 0.51234, "loss_cls": 4.0532, "loss": 4.0532, "time": 0.69875} +{"mode": "train", "epoch": 24, "iter": 1700, "lr": 0.09409, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26438, "top5_acc": 0.5225, "loss_cls": 3.99789, "loss": 3.99789, "time": 0.69842} +{"mode": "train", "epoch": 24, "iter": 1800, "lr": 0.09407, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26516, "top5_acc": 0.51609, "loss_cls": 4.00211, "loss": 4.00211, "time": 0.6977} +{"mode": "train", "epoch": 24, "iter": 1900, "lr": 0.09406, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26141, "top5_acc": 0.51812, "loss_cls": 4.02286, "loss": 4.02286, "time": 0.69801} +{"mode": "train", "epoch": 24, "iter": 2000, "lr": 0.09405, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26797, "top5_acc": 0.51953, "loss_cls": 4.00482, "loss": 4.00482, "time": 0.69568} +{"mode": "train", "epoch": 24, "iter": 2100, "lr": 0.09404, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26188, "top5_acc": 0.52266, "loss_cls": 4.02844, "loss": 4.02844, "time": 0.69647} +{"mode": "train", "epoch": 24, "iter": 2200, "lr": 0.09402, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26453, "top5_acc": 0.51469, "loss_cls": 4.03668, "loss": 4.03668, "time": 0.69774} +{"mode": "train", "epoch": 24, "iter": 2300, "lr": 0.09401, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26047, "top5_acc": 0.51375, "loss_cls": 4.04209, "loss": 4.04209, "time": 0.6974} +{"mode": "train", "epoch": 24, "iter": 2400, "lr": 0.094, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25031, "top5_acc": 0.50406, "loss_cls": 4.05045, "loss": 4.05045, "time": 0.69663} +{"mode": "train", "epoch": 24, "iter": 2500, "lr": 0.09398, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25797, "top5_acc": 0.51469, "loss_cls": 4.02909, "loss": 4.02909, "time": 0.69668} +{"mode": "train", "epoch": 24, "iter": 2600, "lr": 0.09397, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25531, "top5_acc": 0.50547, "loss_cls": 4.04266, "loss": 4.04266, "time": 0.69664} +{"mode": "train", "epoch": 24, "iter": 2700, "lr": 0.09396, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26312, "top5_acc": 0.51906, "loss_cls": 4.01868, "loss": 4.01868, "time": 0.69715} +{"mode": "train", "epoch": 24, "iter": 2800, "lr": 0.09394, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26078, "top5_acc": 0.5175, "loss_cls": 4.02746, "loss": 4.02746, "time": 0.69704} +{"mode": "train", "epoch": 24, "iter": 2900, "lr": 0.09393, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25469, "top5_acc": 0.50078, "loss_cls": 4.05997, "loss": 4.05997, "time": 0.6973} +{"mode": "train", "epoch": 24, "iter": 3000, "lr": 0.09392, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25719, "top5_acc": 0.50922, "loss_cls": 4.02941, "loss": 4.02941, "time": 0.7} +{"mode": "train", "epoch": 24, "iter": 3100, "lr": 0.0939, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26547, "top5_acc": 0.52656, "loss_cls": 3.97998, "loss": 3.97998, "time": 0.698} +{"mode": "train", "epoch": 24, "iter": 3200, "lr": 0.09389, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2625, "top5_acc": 0.52094, "loss_cls": 4.00772, "loss": 4.00772, "time": 0.6967} +{"mode": "train", "epoch": 24, "iter": 3300, "lr": 0.09388, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26438, "top5_acc": 0.51078, "loss_cls": 4.04465, "loss": 4.04465, "time": 0.7036} +{"mode": "train", "epoch": 24, "iter": 3400, "lr": 0.09386, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26109, "top5_acc": 0.51641, "loss_cls": 4.0327, "loss": 4.0327, "time": 0.70147} +{"mode": "train", "epoch": 24, "iter": 3500, "lr": 0.09385, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26391, "top5_acc": 0.50734, "loss_cls": 4.03642, "loss": 4.03642, "time": 0.70281} +{"mode": "train", "epoch": 24, "iter": 3600, "lr": 0.09384, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25297, "top5_acc": 0.50469, "loss_cls": 4.09177, "loss": 4.09177, "time": 0.69982} +{"mode": "train", "epoch": 24, "iter": 3700, "lr": 0.09382, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26141, "top5_acc": 0.50844, "loss_cls": 4.02401, "loss": 4.02401, "time": 0.70271} +{"mode": "val", "epoch": 24, "iter": 309, "lr": 0.09382, "top1_acc": 0.19475, "top5_acc": 0.42116, "mean_class_accuracy": 0.19472} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.0938, "memory": 15990, "data_time": 1.31638, "top1_acc": 0.26812, "top5_acc": 0.52391, "loss_cls": 3.97004, "loss": 3.97004, "time": 2.01913} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.09379, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25984, "top5_acc": 0.51172, "loss_cls": 4.02583, "loss": 4.02583, "time": 0.70888} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.09378, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27234, "top5_acc": 0.52438, "loss_cls": 3.96243, "loss": 3.96243, "time": 0.70547} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.09376, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24797, "top5_acc": 0.50953, "loss_cls": 4.03895, "loss": 4.03895, "time": 0.70141} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.09375, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26344, "top5_acc": 0.51406, "loss_cls": 4.03667, "loss": 4.03667, "time": 0.70234} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.09373, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25375, "top5_acc": 0.51703, "loss_cls": 4.04083, "loss": 4.04083, "time": 0.69969} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.09372, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26203, "top5_acc": 0.52438, "loss_cls": 3.97151, "loss": 3.97151, "time": 0.69922} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.09371, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26, "top5_acc": 0.52188, "loss_cls": 4.01743, "loss": 4.01743, "time": 0.7003} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.09369, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26656, "top5_acc": 0.52203, "loss_cls": 3.99988, "loss": 3.99988, "time": 0.69775} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.09368, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27016, "top5_acc": 0.52188, "loss_cls": 3.98781, "loss": 3.98781, "time": 0.69852} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.09367, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26016, "top5_acc": 0.51172, "loss_cls": 4.02952, "loss": 4.02952, "time": 0.69919} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.09365, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2725, "top5_acc": 0.52219, "loss_cls": 3.96301, "loss": 3.96301, "time": 0.69869} +{"mode": "train", "epoch": 25, "iter": 1300, "lr": 0.09364, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27047, "top5_acc": 0.52328, "loss_cls": 4.00672, "loss": 4.00672, "time": 0.69935} +{"mode": "train", "epoch": 25, "iter": 1400, "lr": 0.09363, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26734, "top5_acc": 0.51188, "loss_cls": 4.04099, "loss": 4.04099, "time": 0.69707} +{"mode": "train", "epoch": 25, "iter": 1500, "lr": 0.09361, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26234, "top5_acc": 0.51828, "loss_cls": 4.02192, "loss": 4.02192, "time": 0.69881} +{"mode": "train", "epoch": 25, "iter": 1600, "lr": 0.0936, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26094, "top5_acc": 0.52406, "loss_cls": 4.0106, "loss": 4.0106, "time": 0.69833} +{"mode": "train", "epoch": 25, "iter": 1700, "lr": 0.09358, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25375, "top5_acc": 0.51125, "loss_cls": 4.05908, "loss": 4.05908, "time": 0.69848} +{"mode": "train", "epoch": 25, "iter": 1800, "lr": 0.09357, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26375, "top5_acc": 0.50891, "loss_cls": 4.01056, "loss": 4.01056, "time": 0.69594} +{"mode": "train", "epoch": 25, "iter": 1900, "lr": 0.09356, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27688, "top5_acc": 0.52656, "loss_cls": 3.97259, "loss": 3.97259, "time": 0.69724} +{"mode": "train", "epoch": 25, "iter": 2000, "lr": 0.09354, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26281, "top5_acc": 0.51313, "loss_cls": 4.027, "loss": 4.027, "time": 0.69976} +{"mode": "train", "epoch": 25, "iter": 2100, "lr": 0.09353, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25734, "top5_acc": 0.51328, "loss_cls": 4.03518, "loss": 4.03518, "time": 0.69913} +{"mode": "train", "epoch": 25, "iter": 2200, "lr": 0.09352, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26047, "top5_acc": 0.51875, "loss_cls": 4.02298, "loss": 4.02298, "time": 0.69711} +{"mode": "train", "epoch": 25, "iter": 2300, "lr": 0.0935, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25844, "top5_acc": 0.50953, "loss_cls": 4.05583, "loss": 4.05583, "time": 0.69625} +{"mode": "train", "epoch": 25, "iter": 2400, "lr": 0.09349, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25906, "top5_acc": 0.51578, "loss_cls": 4.02086, "loss": 4.02086, "time": 0.70019} +{"mode": "train", "epoch": 25, "iter": 2500, "lr": 0.09347, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25562, "top5_acc": 0.51219, "loss_cls": 4.06298, "loss": 4.06298, "time": 0.69647} +{"mode": "train", "epoch": 25, "iter": 2600, "lr": 0.09346, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26297, "top5_acc": 0.51391, "loss_cls": 4.02451, "loss": 4.02451, "time": 0.69772} +{"mode": "train", "epoch": 25, "iter": 2700, "lr": 0.09345, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25953, "top5_acc": 0.51, "loss_cls": 4.04214, "loss": 4.04214, "time": 0.6978} +{"mode": "train", "epoch": 25, "iter": 2800, "lr": 0.09343, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25859, "top5_acc": 0.49984, "loss_cls": 4.06477, "loss": 4.06477, "time": 0.69791} +{"mode": "train", "epoch": 25, "iter": 2900, "lr": 0.09342, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25391, "top5_acc": 0.50828, "loss_cls": 4.0663, "loss": 4.0663, "time": 0.70002} +{"mode": "train", "epoch": 25, "iter": 3000, "lr": 0.09341, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25719, "top5_acc": 0.51125, "loss_cls": 4.02508, "loss": 4.02508, "time": 0.69805} +{"mode": "train", "epoch": 25, "iter": 3100, "lr": 0.09339, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26141, "top5_acc": 0.52016, "loss_cls": 4.01353, "loss": 4.01353, "time": 0.69814} +{"mode": "train", "epoch": 25, "iter": 3200, "lr": 0.09338, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26703, "top5_acc": 0.51969, "loss_cls": 4.01824, "loss": 4.01824, "time": 0.69809} +{"mode": "train", "epoch": 25, "iter": 3300, "lr": 0.09336, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26016, "top5_acc": 0.5175, "loss_cls": 3.9943, "loss": 3.9943, "time": 0.69964} +{"mode": "train", "epoch": 25, "iter": 3400, "lr": 0.09335, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25422, "top5_acc": 0.51, "loss_cls": 4.07146, "loss": 4.07146, "time": 0.70269} +{"mode": "train", "epoch": 25, "iter": 3500, "lr": 0.09334, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25297, "top5_acc": 0.51328, "loss_cls": 4.03204, "loss": 4.03204, "time": 0.70155} +{"mode": "train", "epoch": 25, "iter": 3600, "lr": 0.09332, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26, "top5_acc": 0.51094, "loss_cls": 4.03337, "loss": 4.03337, "time": 0.70047} +{"mode": "train", "epoch": 25, "iter": 3700, "lr": 0.09331, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26687, "top5_acc": 0.51609, "loss_cls": 4.01904, "loss": 4.01904, "time": 0.69852} +{"mode": "val", "epoch": 25, "iter": 309, "lr": 0.0933, "top1_acc": 0.17834, "top5_acc": 0.39508, "mean_class_accuracy": 0.17816} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.09329, "memory": 15990, "data_time": 1.32151, "top1_acc": 0.25531, "top5_acc": 0.50813, "loss_cls": 4.02966, "loss": 4.02966, "time": 2.02445} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.09327, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27266, "top5_acc": 0.52703, "loss_cls": 3.97018, "loss": 3.97018, "time": 0.70464} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.09326, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25844, "top5_acc": 0.51703, "loss_cls": 4.00611, "loss": 4.00611, "time": 0.70383} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.09325, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26, "top5_acc": 0.52016, "loss_cls": 4.02307, "loss": 4.02307, "time": 0.70604} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.09323, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26734, "top5_acc": 0.51625, "loss_cls": 3.99874, "loss": 3.99874, "time": 0.70028} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.09322, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26891, "top5_acc": 0.515, "loss_cls": 4.01902, "loss": 4.01902, "time": 0.70306} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.0932, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26844, "top5_acc": 0.51422, "loss_cls": 3.99318, "loss": 3.99318, "time": 0.70002} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.09319, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25922, "top5_acc": 0.50797, "loss_cls": 4.04205, "loss": 4.04205, "time": 0.69797} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.09318, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26687, "top5_acc": 0.50938, "loss_cls": 4.01001, "loss": 4.01001, "time": 0.69896} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.09316, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25953, "top5_acc": 0.52125, "loss_cls": 4.03394, "loss": 4.03394, "time": 0.70002} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.09315, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26297, "top5_acc": 0.50859, "loss_cls": 4.02448, "loss": 4.02448, "time": 0.69899} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.09313, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25656, "top5_acc": 0.52094, "loss_cls": 4.02247, "loss": 4.02247, "time": 0.69794} +{"mode": "train", "epoch": 26, "iter": 1300, "lr": 0.09312, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25812, "top5_acc": 0.51906, "loss_cls": 4.01639, "loss": 4.01639, "time": 0.69916} +{"mode": "train", "epoch": 26, "iter": 1400, "lr": 0.0931, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26141, "top5_acc": 0.52672, "loss_cls": 4.01557, "loss": 4.01557, "time": 0.69933} +{"mode": "train", "epoch": 26, "iter": 1500, "lr": 0.09309, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25984, "top5_acc": 0.50453, "loss_cls": 4.06717, "loss": 4.06717, "time": 0.69617} +{"mode": "train", "epoch": 26, "iter": 1600, "lr": 0.09308, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25547, "top5_acc": 0.51234, "loss_cls": 4.03185, "loss": 4.03185, "time": 0.69853} +{"mode": "train", "epoch": 26, "iter": 1700, "lr": 0.09306, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26375, "top5_acc": 0.51984, "loss_cls": 4.0195, "loss": 4.0195, "time": 0.698} +{"mode": "train", "epoch": 26, "iter": 1800, "lr": 0.09305, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25922, "top5_acc": 0.51438, "loss_cls": 4.02561, "loss": 4.02561, "time": 0.6982} +{"mode": "train", "epoch": 26, "iter": 1900, "lr": 0.09303, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26391, "top5_acc": 0.52203, "loss_cls": 3.99448, "loss": 3.99448, "time": 0.70138} +{"mode": "train", "epoch": 26, "iter": 2000, "lr": 0.09302, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26328, "top5_acc": 0.52172, "loss_cls": 3.98659, "loss": 3.98659, "time": 0.69812} +{"mode": "train", "epoch": 26, "iter": 2100, "lr": 0.093, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25625, "top5_acc": 0.50688, "loss_cls": 4.06301, "loss": 4.06301, "time": 0.70002} +{"mode": "train", "epoch": 26, "iter": 2200, "lr": 0.09299, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26812, "top5_acc": 0.51609, "loss_cls": 3.99727, "loss": 3.99727, "time": 0.70149} +{"mode": "train", "epoch": 26, "iter": 2300, "lr": 0.09298, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25203, "top5_acc": 0.50797, "loss_cls": 4.05964, "loss": 4.05964, "time": 0.69961} +{"mode": "train", "epoch": 26, "iter": 2400, "lr": 0.09296, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25672, "top5_acc": 0.50672, "loss_cls": 4.05467, "loss": 4.05467, "time": 0.7032} +{"mode": "train", "epoch": 26, "iter": 2500, "lr": 0.09295, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25953, "top5_acc": 0.51359, "loss_cls": 4.05024, "loss": 4.05024, "time": 0.69776} +{"mode": "train", "epoch": 26, "iter": 2600, "lr": 0.09293, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26219, "top5_acc": 0.51266, "loss_cls": 4.03931, "loss": 4.03931, "time": 0.6979} +{"mode": "train", "epoch": 26, "iter": 2700, "lr": 0.09292, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26609, "top5_acc": 0.51688, "loss_cls": 4.03706, "loss": 4.03706, "time": 0.69783} +{"mode": "train", "epoch": 26, "iter": 2800, "lr": 0.0929, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26781, "top5_acc": 0.525, "loss_cls": 4.00118, "loss": 4.00118, "time": 0.69804} +{"mode": "train", "epoch": 26, "iter": 2900, "lr": 0.09289, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27141, "top5_acc": 0.52578, "loss_cls": 3.97394, "loss": 3.97394, "time": 0.69862} +{"mode": "train", "epoch": 26, "iter": 3000, "lr": 0.09288, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26781, "top5_acc": 0.51719, "loss_cls": 3.98719, "loss": 3.98719, "time": 0.69933} +{"mode": "train", "epoch": 26, "iter": 3100, "lr": 0.09286, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25656, "top5_acc": 0.51391, "loss_cls": 4.02148, "loss": 4.02148, "time": 0.69738} +{"mode": "train", "epoch": 26, "iter": 3200, "lr": 0.09285, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26641, "top5_acc": 0.51062, "loss_cls": 4.01535, "loss": 4.01535, "time": 0.70144} +{"mode": "train", "epoch": 26, "iter": 3300, "lr": 0.09283, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27656, "top5_acc": 0.53109, "loss_cls": 3.97006, "loss": 3.97006, "time": 0.70113} +{"mode": "train", "epoch": 26, "iter": 3400, "lr": 0.09282, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26703, "top5_acc": 0.52281, "loss_cls": 4.00067, "loss": 4.00067, "time": 0.70206} +{"mode": "train", "epoch": 26, "iter": 3500, "lr": 0.0928, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26656, "top5_acc": 0.51281, "loss_cls": 4.01498, "loss": 4.01498, "time": 0.70562} +{"mode": "train", "epoch": 26, "iter": 3600, "lr": 0.09279, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27187, "top5_acc": 0.52547, "loss_cls": 3.97377, "loss": 3.97377, "time": 0.70097} +{"mode": "train", "epoch": 26, "iter": 3700, "lr": 0.09278, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26297, "top5_acc": 0.51734, "loss_cls": 4.00976, "loss": 4.00976, "time": 0.69974} +{"mode": "val", "epoch": 26, "iter": 309, "lr": 0.09277, "top1_acc": 0.18629, "top5_acc": 0.40946, "mean_class_accuracy": 0.18623} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.09275, "memory": 15990, "data_time": 1.31495, "top1_acc": 0.27, "top5_acc": 0.5225, "loss_cls": 3.99363, "loss": 3.99363, "time": 2.02188} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.09274, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26078, "top5_acc": 0.51656, "loss_cls": 4.01863, "loss": 4.01863, "time": 0.70247} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.09272, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26312, "top5_acc": 0.51297, "loss_cls": 4.02901, "loss": 4.02901, "time": 0.70697} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.09271, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27062, "top5_acc": 0.515, "loss_cls": 4.00207, "loss": 4.00207, "time": 0.70109} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.0927, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25984, "top5_acc": 0.51031, "loss_cls": 4.03353, "loss": 4.03353, "time": 0.70816} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.09268, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26406, "top5_acc": 0.51359, "loss_cls": 4.04194, "loss": 4.04194, "time": 0.70275} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.09267, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26953, "top5_acc": 0.51297, "loss_cls": 3.99427, "loss": 3.99427, "time": 0.70029} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.09265, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26484, "top5_acc": 0.51969, "loss_cls": 4.00139, "loss": 4.00139, "time": 0.69856} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.09264, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26641, "top5_acc": 0.52281, "loss_cls": 3.99852, "loss": 3.99852, "time": 0.69747} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.09262, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26734, "top5_acc": 0.52062, "loss_cls": 4.01902, "loss": 4.01902, "time": 0.69687} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.09261, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26109, "top5_acc": 0.51906, "loss_cls": 4.02077, "loss": 4.02077, "time": 0.69694} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.09259, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27609, "top5_acc": 0.52703, "loss_cls": 3.97451, "loss": 3.97451, "time": 0.69708} +{"mode": "train", "epoch": 27, "iter": 1300, "lr": 0.09258, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26281, "top5_acc": 0.51672, "loss_cls": 4.0486, "loss": 4.0486, "time": 0.69661} +{"mode": "train", "epoch": 27, "iter": 1400, "lr": 0.09256, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26406, "top5_acc": 0.51234, "loss_cls": 4.04025, "loss": 4.04025, "time": 0.70023} +{"mode": "train", "epoch": 27, "iter": 1500, "lr": 0.09255, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26391, "top5_acc": 0.52047, "loss_cls": 4.0018, "loss": 4.0018, "time": 0.69732} +{"mode": "train", "epoch": 27, "iter": 1600, "lr": 0.09253, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26703, "top5_acc": 0.51562, "loss_cls": 4.00795, "loss": 4.00795, "time": 0.69723} +{"mode": "train", "epoch": 27, "iter": 1700, "lr": 0.09252, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25766, "top5_acc": 0.51672, "loss_cls": 4.01287, "loss": 4.01287, "time": 0.69541} +{"mode": "train", "epoch": 27, "iter": 1800, "lr": 0.09251, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25359, "top5_acc": 0.50969, "loss_cls": 4.06855, "loss": 4.06855, "time": 0.6966} +{"mode": "train", "epoch": 27, "iter": 1900, "lr": 0.09249, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27219, "top5_acc": 0.5175, "loss_cls": 3.99875, "loss": 3.99875, "time": 0.6986} +{"mode": "train", "epoch": 27, "iter": 2000, "lr": 0.09248, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27641, "top5_acc": 0.52797, "loss_cls": 3.96339, "loss": 3.96339, "time": 0.69825} +{"mode": "train", "epoch": 27, "iter": 2100, "lr": 0.09246, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27203, "top5_acc": 0.51859, "loss_cls": 3.96551, "loss": 3.96551, "time": 0.6979} +{"mode": "train", "epoch": 27, "iter": 2200, "lr": 0.09245, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25547, "top5_acc": 0.51438, "loss_cls": 4.02206, "loss": 4.02206, "time": 0.69739} +{"mode": "train", "epoch": 27, "iter": 2300, "lr": 0.09243, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26531, "top5_acc": 0.51719, "loss_cls": 4.03832, "loss": 4.03832, "time": 0.69773} +{"mode": "train", "epoch": 27, "iter": 2400, "lr": 0.09242, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26672, "top5_acc": 0.52234, "loss_cls": 4.00906, "loss": 4.00906, "time": 0.69602} +{"mode": "train", "epoch": 27, "iter": 2500, "lr": 0.0924, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.265, "top5_acc": 0.52094, "loss_cls": 3.97442, "loss": 3.97442, "time": 0.69848} +{"mode": "train", "epoch": 27, "iter": 2600, "lr": 0.09239, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26578, "top5_acc": 0.53016, "loss_cls": 3.99673, "loss": 3.99673, "time": 0.69693} +{"mode": "train", "epoch": 27, "iter": 2700, "lr": 0.09237, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26812, "top5_acc": 0.51672, "loss_cls": 3.99636, "loss": 3.99636, "time": 0.6977} +{"mode": "train", "epoch": 27, "iter": 2800, "lr": 0.09236, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26734, "top5_acc": 0.52312, "loss_cls": 4.00507, "loss": 4.00507, "time": 0.69768} +{"mode": "train", "epoch": 27, "iter": 2900, "lr": 0.09234, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26828, "top5_acc": 0.52016, "loss_cls": 3.99652, "loss": 3.99652, "time": 0.69769} +{"mode": "train", "epoch": 27, "iter": 3000, "lr": 0.09233, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26312, "top5_acc": 0.51844, "loss_cls": 4.03946, "loss": 4.03946, "time": 0.69704} +{"mode": "train", "epoch": 27, "iter": 3100, "lr": 0.09231, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25641, "top5_acc": 0.51234, "loss_cls": 4.05726, "loss": 4.05726, "time": 0.69831} +{"mode": "train", "epoch": 27, "iter": 3200, "lr": 0.0923, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26297, "top5_acc": 0.51641, "loss_cls": 4.02086, "loss": 4.02086, "time": 0.69506} +{"mode": "train", "epoch": 27, "iter": 3300, "lr": 0.09228, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27187, "top5_acc": 0.53266, "loss_cls": 3.9278, "loss": 3.9278, "time": 0.70078} +{"mode": "train", "epoch": 27, "iter": 3400, "lr": 0.09227, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25734, "top5_acc": 0.51531, "loss_cls": 4.03401, "loss": 4.03401, "time": 0.70186} +{"mode": "train", "epoch": 27, "iter": 3500, "lr": 0.09225, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26453, "top5_acc": 0.51656, "loss_cls": 3.99309, "loss": 3.99309, "time": 0.70125} +{"mode": "train", "epoch": 27, "iter": 3600, "lr": 0.09224, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25766, "top5_acc": 0.51688, "loss_cls": 4.00885, "loss": 4.00885, "time": 0.69925} +{"mode": "train", "epoch": 27, "iter": 3700, "lr": 0.09222, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25703, "top5_acc": 0.51641, "loss_cls": 4.02694, "loss": 4.02694, "time": 0.70097} +{"mode": "val", "epoch": 27, "iter": 309, "lr": 0.09222, "top1_acc": 0.18386, "top5_acc": 0.40799, "mean_class_accuracy": 0.18341} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.0922, "memory": 15990, "data_time": 1.32024, "top1_acc": 0.26891, "top5_acc": 0.52641, "loss_cls": 3.94511, "loss": 3.94511, "time": 2.02036} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.09219, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27469, "top5_acc": 0.51547, "loss_cls": 3.97977, "loss": 3.97977, "time": 0.70088} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.09217, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26812, "top5_acc": 0.52641, "loss_cls": 3.97157, "loss": 3.97157, "time": 0.70528} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.09216, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26125, "top5_acc": 0.51422, "loss_cls": 3.99989, "loss": 3.99989, "time": 0.70028} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.09214, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27328, "top5_acc": 0.52781, "loss_cls": 3.96584, "loss": 3.96584, "time": 0.70253} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.09213, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26062, "top5_acc": 0.52094, "loss_cls": 4.00217, "loss": 4.00217, "time": 0.70207} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.09211, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2675, "top5_acc": 0.52375, "loss_cls": 3.99482, "loss": 3.99482, "time": 0.69979} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.0921, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27266, "top5_acc": 0.51969, "loss_cls": 3.99433, "loss": 3.99433, "time": 0.69853} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.09208, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25766, "top5_acc": 0.51641, "loss_cls": 4.01844, "loss": 4.01844, "time": 0.69836} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.09207, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26047, "top5_acc": 0.52094, "loss_cls": 3.97778, "loss": 3.97778, "time": 0.69759} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.09205, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2675, "top5_acc": 0.52266, "loss_cls": 3.98475, "loss": 3.98475, "time": 0.69784} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.09204, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25953, "top5_acc": 0.51719, "loss_cls": 4.00311, "loss": 4.00311, "time": 0.69738} +{"mode": "train", "epoch": 28, "iter": 1300, "lr": 0.09202, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26188, "top5_acc": 0.51562, "loss_cls": 4.02181, "loss": 4.02181, "time": 0.69651} +{"mode": "train", "epoch": 28, "iter": 1400, "lr": 0.09201, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2625, "top5_acc": 0.51391, "loss_cls": 4.0006, "loss": 4.0006, "time": 0.69992} +{"mode": "train", "epoch": 28, "iter": 1500, "lr": 0.09199, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26625, "top5_acc": 0.51625, "loss_cls": 4.02607, "loss": 4.02607, "time": 0.69703} +{"mode": "train", "epoch": 28, "iter": 1600, "lr": 0.09198, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27141, "top5_acc": 0.52688, "loss_cls": 3.95194, "loss": 3.95194, "time": 0.70028} +{"mode": "train", "epoch": 28, "iter": 1700, "lr": 0.09196, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26094, "top5_acc": 0.50734, "loss_cls": 4.03587, "loss": 4.03587, "time": 0.69825} +{"mode": "train", "epoch": 28, "iter": 1800, "lr": 0.09194, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26156, "top5_acc": 0.5075, "loss_cls": 4.04686, "loss": 4.04686, "time": 0.69568} +{"mode": "train", "epoch": 28, "iter": 1900, "lr": 0.09193, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26234, "top5_acc": 0.51453, "loss_cls": 4.00678, "loss": 4.00678, "time": 0.69625} +{"mode": "train", "epoch": 28, "iter": 2000, "lr": 0.09191, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26344, "top5_acc": 0.51188, "loss_cls": 4.0174, "loss": 4.0174, "time": 0.6993} +{"mode": "train", "epoch": 28, "iter": 2100, "lr": 0.0919, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26734, "top5_acc": 0.50922, "loss_cls": 4.03583, "loss": 4.03583, "time": 0.70014} +{"mode": "train", "epoch": 28, "iter": 2200, "lr": 0.09188, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27422, "top5_acc": 0.52672, "loss_cls": 3.98536, "loss": 3.98536, "time": 0.69822} +{"mode": "train", "epoch": 28, "iter": 2300, "lr": 0.09187, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26516, "top5_acc": 0.51891, "loss_cls": 4.00175, "loss": 4.00175, "time": 0.69733} +{"mode": "train", "epoch": 28, "iter": 2400, "lr": 0.09185, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25969, "top5_acc": 0.50875, "loss_cls": 4.04355, "loss": 4.04355, "time": 0.70089} +{"mode": "train", "epoch": 28, "iter": 2500, "lr": 0.09184, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26656, "top5_acc": 0.51734, "loss_cls": 3.99298, "loss": 3.99298, "time": 0.69814} +{"mode": "train", "epoch": 28, "iter": 2600, "lr": 0.09182, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26359, "top5_acc": 0.50828, "loss_cls": 4.02671, "loss": 4.02671, "time": 0.69619} +{"mode": "train", "epoch": 28, "iter": 2700, "lr": 0.09181, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26844, "top5_acc": 0.51859, "loss_cls": 3.99691, "loss": 3.99691, "time": 0.69781} +{"mode": "train", "epoch": 28, "iter": 2800, "lr": 0.09179, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26937, "top5_acc": 0.51625, "loss_cls": 4.01566, "loss": 4.01566, "time": 0.69989} +{"mode": "train", "epoch": 28, "iter": 2900, "lr": 0.09178, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2625, "top5_acc": 0.51391, "loss_cls": 4.01486, "loss": 4.01486, "time": 0.70016} +{"mode": "train", "epoch": 28, "iter": 3000, "lr": 0.09176, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26422, "top5_acc": 0.52031, "loss_cls": 3.99002, "loss": 3.99002, "time": 0.70017} +{"mode": "train", "epoch": 28, "iter": 3100, "lr": 0.09175, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26656, "top5_acc": 0.51938, "loss_cls": 4.01693, "loss": 4.01693, "time": 0.7007} +{"mode": "train", "epoch": 28, "iter": 3200, "lr": 0.09173, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27203, "top5_acc": 0.52312, "loss_cls": 3.9705, "loss": 3.9705, "time": 0.70215} +{"mode": "train", "epoch": 28, "iter": 3300, "lr": 0.09172, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26547, "top5_acc": 0.51344, "loss_cls": 4.04274, "loss": 4.04274, "time": 0.70241} +{"mode": "train", "epoch": 28, "iter": 3400, "lr": 0.0917, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.53062, "loss_cls": 3.97189, "loss": 3.97189, "time": 0.69933} +{"mode": "train", "epoch": 28, "iter": 3500, "lr": 0.09168, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25562, "top5_acc": 0.5125, "loss_cls": 4.04879, "loss": 4.04879, "time": 0.70084} +{"mode": "train", "epoch": 28, "iter": 3600, "lr": 0.09167, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27281, "top5_acc": 0.52219, "loss_cls": 3.99489, "loss": 3.99489, "time": 0.70484} +{"mode": "train", "epoch": 28, "iter": 3700, "lr": 0.09165, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26656, "top5_acc": 0.52359, "loss_cls": 3.99589, "loss": 3.99589, "time": 0.70023} +{"mode": "val", "epoch": 28, "iter": 309, "lr": 0.09165, "top1_acc": 0.2027, "top5_acc": 0.44294, "mean_class_accuracy": 0.20255} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.09163, "memory": 15990, "data_time": 1.3054, "top1_acc": 0.27562, "top5_acc": 0.53359, "loss_cls": 3.94924, "loss": 3.94924, "time": 2.0061} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.09162, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26469, "top5_acc": 0.52219, "loss_cls": 3.99316, "loss": 3.99316, "time": 0.70352} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.0916, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26875, "top5_acc": 0.52344, "loss_cls": 3.96744, "loss": 3.96744, "time": 0.70651} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.09158, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26156, "top5_acc": 0.51672, "loss_cls": 4.00215, "loss": 4.00215, "time": 0.70347} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.09157, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27875, "top5_acc": 0.52812, "loss_cls": 3.95714, "loss": 3.95714, "time": 0.70434} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.09155, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25766, "top5_acc": 0.52188, "loss_cls": 4.00428, "loss": 4.00428, "time": 0.7025} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.09154, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27875, "top5_acc": 0.52969, "loss_cls": 3.96572, "loss": 3.96572, "time": 0.70034} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.09152, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26125, "top5_acc": 0.52375, "loss_cls": 3.97916, "loss": 3.97916, "time": 0.69936} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.09151, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26422, "top5_acc": 0.51453, "loss_cls": 4.02847, "loss": 4.02847, "time": 0.69799} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.09149, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25875, "top5_acc": 0.51484, "loss_cls": 4.01961, "loss": 4.01961, "time": 0.69873} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.09148, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27219, "top5_acc": 0.52438, "loss_cls": 3.99173, "loss": 3.99173, "time": 0.69904} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.09146, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25938, "top5_acc": 0.51234, "loss_cls": 4.02144, "loss": 4.02144, "time": 0.70091} +{"mode": "train", "epoch": 29, "iter": 1300, "lr": 0.09144, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25422, "top5_acc": 0.51609, "loss_cls": 4.02716, "loss": 4.02716, "time": 0.69896} +{"mode": "train", "epoch": 29, "iter": 1400, "lr": 0.09143, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26578, "top5_acc": 0.52562, "loss_cls": 3.98199, "loss": 3.98199, "time": 0.6965} +{"mode": "train", "epoch": 29, "iter": 1500, "lr": 0.09141, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26953, "top5_acc": 0.52484, "loss_cls": 3.99598, "loss": 3.99598, "time": 0.69904} +{"mode": "train", "epoch": 29, "iter": 1600, "lr": 0.0914, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27187, "top5_acc": 0.52266, "loss_cls": 3.98775, "loss": 3.98775, "time": 0.69915} +{"mode": "train", "epoch": 29, "iter": 1700, "lr": 0.09138, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26828, "top5_acc": 0.515, "loss_cls": 4.01502, "loss": 4.01502, "time": 0.69859} +{"mode": "train", "epoch": 29, "iter": 1800, "lr": 0.09137, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27266, "top5_acc": 0.51469, "loss_cls": 4.02135, "loss": 4.02135, "time": 0.70261} +{"mode": "train", "epoch": 29, "iter": 1900, "lr": 0.09135, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27297, "top5_acc": 0.52438, "loss_cls": 3.97288, "loss": 3.97288, "time": 0.6975} +{"mode": "train", "epoch": 29, "iter": 2000, "lr": 0.09133, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25484, "top5_acc": 0.51672, "loss_cls": 4.00592, "loss": 4.00592, "time": 0.69751} +{"mode": "train", "epoch": 29, "iter": 2100, "lr": 0.09132, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26219, "top5_acc": 0.52609, "loss_cls": 3.99101, "loss": 3.99101, "time": 0.69871} +{"mode": "train", "epoch": 29, "iter": 2200, "lr": 0.0913, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26141, "top5_acc": 0.51766, "loss_cls": 4.00315, "loss": 4.00315, "time": 0.701} +{"mode": "train", "epoch": 29, "iter": 2300, "lr": 0.09129, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26359, "top5_acc": 0.5225, "loss_cls": 4.00759, "loss": 4.00759, "time": 0.70077} +{"mode": "train", "epoch": 29, "iter": 2400, "lr": 0.09127, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26359, "top5_acc": 0.51938, "loss_cls": 4.00925, "loss": 4.00925, "time": 0.69745} +{"mode": "train", "epoch": 29, "iter": 2500, "lr": 0.09126, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26031, "top5_acc": 0.51906, "loss_cls": 3.96775, "loss": 3.96775, "time": 0.70025} +{"mode": "train", "epoch": 29, "iter": 2600, "lr": 0.09124, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26703, "top5_acc": 0.52422, "loss_cls": 3.98983, "loss": 3.98983, "time": 0.69713} +{"mode": "train", "epoch": 29, "iter": 2700, "lr": 0.09122, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26469, "top5_acc": 0.50766, "loss_cls": 4.04791, "loss": 4.04791, "time": 0.69793} +{"mode": "train", "epoch": 29, "iter": 2800, "lr": 0.09121, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26609, "top5_acc": 0.51328, "loss_cls": 4.01701, "loss": 4.01701, "time": 0.69809} +{"mode": "train", "epoch": 29, "iter": 2900, "lr": 0.09119, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26547, "top5_acc": 0.51812, "loss_cls": 4.00199, "loss": 4.00199, "time": 0.70034} +{"mode": "train", "epoch": 29, "iter": 3000, "lr": 0.09118, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26656, "top5_acc": 0.51938, "loss_cls": 3.98948, "loss": 3.98948, "time": 0.69624} +{"mode": "train", "epoch": 29, "iter": 3100, "lr": 0.09116, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25703, "top5_acc": 0.51, "loss_cls": 4.03835, "loss": 4.03835, "time": 0.69741} +{"mode": "train", "epoch": 29, "iter": 3200, "lr": 0.09114, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26625, "top5_acc": 0.51875, "loss_cls": 3.98192, "loss": 3.98192, "time": 0.70044} +{"mode": "train", "epoch": 29, "iter": 3300, "lr": 0.09113, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26266, "top5_acc": 0.51812, "loss_cls": 4.01196, "loss": 4.01196, "time": 0.70051} +{"mode": "train", "epoch": 29, "iter": 3400, "lr": 0.09111, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26266, "top5_acc": 0.51594, "loss_cls": 4.03217, "loss": 4.03217, "time": 0.69875} +{"mode": "train", "epoch": 29, "iter": 3500, "lr": 0.0911, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26844, "top5_acc": 0.52469, "loss_cls": 3.98509, "loss": 3.98509, "time": 0.69985} +{"mode": "train", "epoch": 29, "iter": 3600, "lr": 0.09108, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2625, "top5_acc": 0.51141, "loss_cls": 4.03416, "loss": 4.03416, "time": 0.70193} +{"mode": "train", "epoch": 29, "iter": 3700, "lr": 0.09106, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26766, "top5_acc": 0.51672, "loss_cls": 4.01635, "loss": 4.01635, "time": 0.70159} +{"mode": "val", "epoch": 29, "iter": 309, "lr": 0.09106, "top1_acc": 0.18862, "top5_acc": 0.41594, "mean_class_accuracy": 0.18856} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.09104, "memory": 15990, "data_time": 1.34603, "top1_acc": 0.27328, "top5_acc": 0.52406, "loss_cls": 3.95727, "loss": 3.95727, "time": 2.16526} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.09103, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27016, "top5_acc": 0.52625, "loss_cls": 3.95369, "loss": 3.95369, "time": 0.81368} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.09101, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.28094, "top5_acc": 0.53641, "loss_cls": 3.9084, "loss": 3.9084, "time": 0.8198} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.09099, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27312, "top5_acc": 0.53266, "loss_cls": 3.94617, "loss": 3.94617, "time": 0.80948} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.09098, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25672, "top5_acc": 0.51141, "loss_cls": 4.02439, "loss": 4.02439, "time": 0.81255} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.09096, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2675, "top5_acc": 0.52125, "loss_cls": 3.96036, "loss": 3.96036, "time": 0.81849} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.09095, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26687, "top5_acc": 0.52766, "loss_cls": 3.97639, "loss": 3.97639, "time": 0.81948} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.09093, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26047, "top5_acc": 0.515, "loss_cls": 4.02802, "loss": 4.02802, "time": 0.817} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.09091, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26141, "top5_acc": 0.51281, "loss_cls": 4.00835, "loss": 4.00835, "time": 0.81172} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.0909, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26125, "top5_acc": 0.51719, "loss_cls": 3.99234, "loss": 3.99234, "time": 0.81368} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.09088, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27031, "top5_acc": 0.52188, "loss_cls": 3.96048, "loss": 3.96048, "time": 0.8112} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.09087, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26375, "top5_acc": 0.51703, "loss_cls": 4.02148, "loss": 4.02148, "time": 0.80553} +{"mode": "train", "epoch": 30, "iter": 1300, "lr": 0.09085, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26828, "top5_acc": 0.52031, "loss_cls": 4.00876, "loss": 4.00876, "time": 0.80516} +{"mode": "train", "epoch": 30, "iter": 1400, "lr": 0.09083, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26594, "top5_acc": 0.51859, "loss_cls": 3.98379, "loss": 3.98379, "time": 0.8027} +{"mode": "train", "epoch": 30, "iter": 1500, "lr": 0.09082, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27156, "top5_acc": 0.515, "loss_cls": 4.00302, "loss": 4.00302, "time": 0.79958} +{"mode": "train", "epoch": 30, "iter": 1600, "lr": 0.0908, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27969, "top5_acc": 0.52203, "loss_cls": 3.95436, "loss": 3.95436, "time": 0.80463} +{"mode": "train", "epoch": 30, "iter": 1700, "lr": 0.09078, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26672, "top5_acc": 0.51484, "loss_cls": 4.02857, "loss": 4.02857, "time": 0.80528} +{"mode": "train", "epoch": 30, "iter": 1800, "lr": 0.09077, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26937, "top5_acc": 0.52953, "loss_cls": 3.95761, "loss": 3.95761, "time": 0.7998} +{"mode": "train", "epoch": 30, "iter": 1900, "lr": 0.09075, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27234, "top5_acc": 0.52078, "loss_cls": 4.0134, "loss": 4.0134, "time": 0.80699} +{"mode": "train", "epoch": 30, "iter": 2000, "lr": 0.09074, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25719, "top5_acc": 0.51453, "loss_cls": 4.04957, "loss": 4.04957, "time": 0.80358} +{"mode": "train", "epoch": 30, "iter": 2100, "lr": 0.09072, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27578, "top5_acc": 0.5225, "loss_cls": 3.96923, "loss": 3.96923, "time": 0.80024} +{"mode": "train", "epoch": 30, "iter": 2200, "lr": 0.0907, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25172, "top5_acc": 0.51266, "loss_cls": 4.01913, "loss": 4.01913, "time": 0.79572} +{"mode": "train", "epoch": 30, "iter": 2300, "lr": 0.09069, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27406, "top5_acc": 0.52969, "loss_cls": 3.98266, "loss": 3.98266, "time": 0.80156} +{"mode": "train", "epoch": 30, "iter": 2400, "lr": 0.09067, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26125, "top5_acc": 0.51609, "loss_cls": 4.04125, "loss": 4.04125, "time": 0.79832} +{"mode": "train", "epoch": 30, "iter": 2500, "lr": 0.09065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26906, "top5_acc": 0.53047, "loss_cls": 3.97239, "loss": 3.97239, "time": 0.80844} +{"mode": "train", "epoch": 30, "iter": 2600, "lr": 0.09064, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27094, "top5_acc": 0.52594, "loss_cls": 3.95853, "loss": 3.95853, "time": 0.80423} +{"mode": "train", "epoch": 30, "iter": 2700, "lr": 0.09062, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26234, "top5_acc": 0.52594, "loss_cls": 3.99133, "loss": 3.99133, "time": 0.80171} +{"mode": "train", "epoch": 30, "iter": 2800, "lr": 0.09061, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26906, "top5_acc": 0.52516, "loss_cls": 3.96944, "loss": 3.96944, "time": 0.79714} +{"mode": "train", "epoch": 30, "iter": 2900, "lr": 0.09059, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2625, "top5_acc": 0.51297, "loss_cls": 4.05284, "loss": 4.05284, "time": 0.80265} +{"mode": "train", "epoch": 30, "iter": 3000, "lr": 0.09057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27344, "top5_acc": 0.51938, "loss_cls": 3.99708, "loss": 3.99708, "time": 0.79714} +{"mode": "train", "epoch": 30, "iter": 3100, "lr": 0.09056, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26937, "top5_acc": 0.52078, "loss_cls": 3.98203, "loss": 3.98203, "time": 0.8011} +{"mode": "train", "epoch": 30, "iter": 3200, "lr": 0.09054, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26469, "top5_acc": 0.52203, "loss_cls": 4.00874, "loss": 4.00874, "time": 0.79621} +{"mode": "train", "epoch": 30, "iter": 3300, "lr": 0.09052, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26719, "top5_acc": 0.52, "loss_cls": 3.98213, "loss": 3.98213, "time": 0.80109} +{"mode": "train", "epoch": 30, "iter": 3400, "lr": 0.09051, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26406, "top5_acc": 0.52344, "loss_cls": 4.00898, "loss": 4.00898, "time": 0.79645} +{"mode": "train", "epoch": 30, "iter": 3500, "lr": 0.09049, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26172, "top5_acc": 0.51734, "loss_cls": 3.99526, "loss": 3.99526, "time": 0.8043} +{"mode": "train", "epoch": 30, "iter": 3600, "lr": 0.09047, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26797, "top5_acc": 0.52328, "loss_cls": 4.00643, "loss": 4.00643, "time": 0.80852} +{"mode": "train", "epoch": 30, "iter": 3700, "lr": 0.09046, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26969, "top5_acc": 0.52016, "loss_cls": 3.98258, "loss": 3.98258, "time": 0.80611} +{"mode": "val", "epoch": 30, "iter": 309, "lr": 0.09045, "top1_acc": 0.20078, "top5_acc": 0.43357, "mean_class_accuracy": 0.20071} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.09043, "memory": 15990, "data_time": 1.35717, "top1_acc": 0.26984, "top5_acc": 0.53031, "loss_cls": 4.16323, "loss": 4.16323, "time": 2.3412} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.09042, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26656, "top5_acc": 0.52156, "loss_cls": 4.22063, "loss": 4.22063, "time": 0.83073} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.0904, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26094, "top5_acc": 0.53438, "loss_cls": 4.19959, "loss": 4.19959, "time": 0.82579} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.09039, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27453, "top5_acc": 0.52297, "loss_cls": 4.18565, "loss": 4.18565, "time": 0.82624} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.09037, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26359, "top5_acc": 0.51781, "loss_cls": 4.22803, "loss": 4.22803, "time": 0.83021} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.09035, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26344, "top5_acc": 0.52234, "loss_cls": 4.21078, "loss": 4.21078, "time": 0.8299} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.09034, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25922, "top5_acc": 0.50969, "loss_cls": 4.2528, "loss": 4.2528, "time": 0.83046} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.09032, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26594, "top5_acc": 0.52078, "loss_cls": 4.21215, "loss": 4.21215, "time": 0.82183} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0903, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27031, "top5_acc": 0.51422, "loss_cls": 4.20535, "loss": 4.20535, "time": 0.8268} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.09029, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26859, "top5_acc": 0.52344, "loss_cls": 4.20271, "loss": 4.20271, "time": 0.82321} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.09027, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26562, "top5_acc": 0.52641, "loss_cls": 4.20791, "loss": 4.20791, "time": 0.82506} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.09025, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27156, "top5_acc": 0.51625, "loss_cls": 4.21568, "loss": 4.21568, "time": 0.82082} +{"mode": "train", "epoch": 31, "iter": 1300, "lr": 0.09024, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27328, "top5_acc": 0.52047, "loss_cls": 4.18952, "loss": 4.18952, "time": 0.8253} +{"mode": "train", "epoch": 31, "iter": 1400, "lr": 0.09022, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27062, "top5_acc": 0.52734, "loss_cls": 4.17961, "loss": 4.17961, "time": 0.81489} +{"mode": "train", "epoch": 31, "iter": 1500, "lr": 0.0902, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26906, "top5_acc": 0.52469, "loss_cls": 4.2127, "loss": 4.2127, "time": 0.82282} +{"mode": "train", "epoch": 31, "iter": 1600, "lr": 0.09019, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26266, "top5_acc": 0.52109, "loss_cls": 4.22912, "loss": 4.22912, "time": 0.82415} +{"mode": "train", "epoch": 31, "iter": 1700, "lr": 0.09017, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26594, "top5_acc": 0.52703, "loss_cls": 4.20394, "loss": 4.20394, "time": 0.82651} +{"mode": "train", "epoch": 31, "iter": 1800, "lr": 0.09015, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26625, "top5_acc": 0.52109, "loss_cls": 4.2071, "loss": 4.2071, "time": 0.82829} +{"mode": "train", "epoch": 31, "iter": 1900, "lr": 0.09014, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.265, "top5_acc": 0.51875, "loss_cls": 4.22365, "loss": 4.22365, "time": 0.82637} +{"mode": "train", "epoch": 31, "iter": 2000, "lr": 0.09012, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27047, "top5_acc": 0.52922, "loss_cls": 4.18929, "loss": 4.18929, "time": 0.81939} +{"mode": "train", "epoch": 31, "iter": 2100, "lr": 0.0901, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27422, "top5_acc": 0.53344, "loss_cls": 4.17083, "loss": 4.17083, "time": 0.82756} +{"mode": "train", "epoch": 31, "iter": 2200, "lr": 0.09009, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26828, "top5_acc": 0.52688, "loss_cls": 4.20346, "loss": 4.20346, "time": 0.83451} +{"mode": "train", "epoch": 31, "iter": 2300, "lr": 0.09007, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26922, "top5_acc": 0.52141, "loss_cls": 4.20407, "loss": 4.20407, "time": 0.82397} +{"mode": "train", "epoch": 31, "iter": 2400, "lr": 0.09005, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26047, "top5_acc": 0.50688, "loss_cls": 4.27199, "loss": 4.27199, "time": 0.8219} +{"mode": "train", "epoch": 31, "iter": 2500, "lr": 0.09004, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26359, "top5_acc": 0.51141, "loss_cls": 4.23665, "loss": 4.23665, "time": 0.8234} +{"mode": "train", "epoch": 31, "iter": 2600, "lr": 0.09002, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26937, "top5_acc": 0.52453, "loss_cls": 4.20074, "loss": 4.20074, "time": 0.82148} +{"mode": "train", "epoch": 31, "iter": 2700, "lr": 0.09, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2675, "top5_acc": 0.53203, "loss_cls": 4.16345, "loss": 4.16345, "time": 0.82976} +{"mode": "train", "epoch": 31, "iter": 2800, "lr": 0.08999, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26328, "top5_acc": 0.52, "loss_cls": 4.22473, "loss": 4.22473, "time": 0.81884} +{"mode": "train", "epoch": 31, "iter": 2900, "lr": 0.08997, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26359, "top5_acc": 0.51906, "loss_cls": 4.2187, "loss": 4.2187, "time": 0.81306} +{"mode": "train", "epoch": 31, "iter": 3000, "lr": 0.08995, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26016, "top5_acc": 0.515, "loss_cls": 4.24701, "loss": 4.24701, "time": 0.81676} +{"mode": "train", "epoch": 31, "iter": 3100, "lr": 0.08994, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26484, "top5_acc": 0.51422, "loss_cls": 4.20941, "loss": 4.20941, "time": 0.82213} +{"mode": "train", "epoch": 31, "iter": 3200, "lr": 0.08992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26969, "top5_acc": 0.525, "loss_cls": 4.21555, "loss": 4.21555, "time": 0.81567} +{"mode": "train", "epoch": 31, "iter": 3300, "lr": 0.0899, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27062, "top5_acc": 0.52625, "loss_cls": 4.20282, "loss": 4.20282, "time": 0.81966} +{"mode": "train", "epoch": 31, "iter": 3400, "lr": 0.08989, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27125, "top5_acc": 0.52375, "loss_cls": 4.24091, "loss": 4.24091, "time": 0.82155} +{"mode": "train", "epoch": 31, "iter": 3500, "lr": 0.08987, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26234, "top5_acc": 0.51609, "loss_cls": 4.22734, "loss": 4.22734, "time": 0.82442} +{"mode": "train", "epoch": 31, "iter": 3600, "lr": 0.08985, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26937, "top5_acc": 0.51922, "loss_cls": 4.23227, "loss": 4.23227, "time": 0.81669} +{"mode": "train", "epoch": 31, "iter": 3700, "lr": 0.08983, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25781, "top5_acc": 0.51469, "loss_cls": 4.23695, "loss": 4.23695, "time": 0.82261} +{"mode": "val", "epoch": 31, "iter": 309, "lr": 0.08983, "top1_acc": 0.19632, "top5_acc": 0.43474, "mean_class_accuracy": 0.19629} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.08981, "memory": 15990, "data_time": 1.33069, "top1_acc": 0.26625, "top5_acc": 0.51719, "loss_cls": 4.20627, "loss": 4.20627, "time": 2.3224} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.08979, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27719, "top5_acc": 0.53156, "loss_cls": 4.17514, "loss": 4.17514, "time": 0.83686} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.08978, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27453, "top5_acc": 0.53562, "loss_cls": 4.17347, "loss": 4.17347, "time": 0.82861} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.08976, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26641, "top5_acc": 0.51875, "loss_cls": 4.20541, "loss": 4.20541, "time": 0.82543} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.08974, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27047, "top5_acc": 0.52672, "loss_cls": 4.18065, "loss": 4.18065, "time": 0.8328} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.08973, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28031, "top5_acc": 0.52641, "loss_cls": 4.17148, "loss": 4.17148, "time": 0.83357} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.08971, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27938, "top5_acc": 0.52281, "loss_cls": 4.19859, "loss": 4.19859, "time": 0.83034} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.08969, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26906, "top5_acc": 0.52359, "loss_cls": 4.20264, "loss": 4.20264, "time": 0.83396} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.08967, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28016, "top5_acc": 0.53078, "loss_cls": 4.16776, "loss": 4.16776, "time": 0.83072} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.08966, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26937, "top5_acc": 0.52109, "loss_cls": 4.19668, "loss": 4.19668, "time": 0.82523} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.08964, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27437, "top5_acc": 0.52312, "loss_cls": 4.19475, "loss": 4.19475, "time": 0.82644} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.08962, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27, "top5_acc": 0.52625, "loss_cls": 4.20562, "loss": 4.20562, "time": 0.81745} +{"mode": "train", "epoch": 32, "iter": 1300, "lr": 0.08961, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26484, "top5_acc": 0.51812, "loss_cls": 4.2086, "loss": 4.2086, "time": 0.81632} +{"mode": "train", "epoch": 32, "iter": 1400, "lr": 0.08959, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25828, "top5_acc": 0.51641, "loss_cls": 4.24429, "loss": 4.24429, "time": 0.81557} +{"mode": "train", "epoch": 32, "iter": 1500, "lr": 0.08957, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27609, "top5_acc": 0.52516, "loss_cls": 4.1755, "loss": 4.1755, "time": 0.81743} +{"mode": "train", "epoch": 32, "iter": 1600, "lr": 0.08955, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27297, "top5_acc": 0.52328, "loss_cls": 4.17426, "loss": 4.17426, "time": 0.81539} +{"mode": "train", "epoch": 32, "iter": 1700, "lr": 0.08954, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26, "top5_acc": 0.52609, "loss_cls": 4.23181, "loss": 4.23181, "time": 0.81698} +{"mode": "train", "epoch": 32, "iter": 1800, "lr": 0.08952, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26391, "top5_acc": 0.52141, "loss_cls": 4.21796, "loss": 4.21796, "time": 0.82223} +{"mode": "train", "epoch": 32, "iter": 1900, "lr": 0.0895, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25328, "top5_acc": 0.51484, "loss_cls": 4.25389, "loss": 4.25389, "time": 0.81709} +{"mode": "train", "epoch": 32, "iter": 2000, "lr": 0.08949, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26891, "top5_acc": 0.52156, "loss_cls": 4.22533, "loss": 4.22533, "time": 0.81893} +{"mode": "train", "epoch": 32, "iter": 2100, "lr": 0.08947, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26641, "top5_acc": 0.52172, "loss_cls": 4.24163, "loss": 4.24163, "time": 0.81774} +{"mode": "train", "epoch": 32, "iter": 2200, "lr": 0.08945, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27187, "top5_acc": 0.52594, "loss_cls": 4.19848, "loss": 4.19848, "time": 0.82069} +{"mode": "train", "epoch": 32, "iter": 2300, "lr": 0.08943, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27562, "top5_acc": 0.52406, "loss_cls": 4.17434, "loss": 4.17434, "time": 0.81658} +{"mode": "train", "epoch": 32, "iter": 2400, "lr": 0.08942, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27938, "top5_acc": 0.53781, "loss_cls": 4.13936, "loss": 4.13936, "time": 0.81678} +{"mode": "train", "epoch": 32, "iter": 2500, "lr": 0.0894, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.265, "top5_acc": 0.52219, "loss_cls": 4.21057, "loss": 4.21057, "time": 0.81757} +{"mode": "train", "epoch": 32, "iter": 2600, "lr": 0.08938, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27219, "top5_acc": 0.52734, "loss_cls": 4.18067, "loss": 4.18067, "time": 0.81707} +{"mode": "train", "epoch": 32, "iter": 2700, "lr": 0.08937, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25812, "top5_acc": 0.51688, "loss_cls": 4.24052, "loss": 4.24052, "time": 0.81368} +{"mode": "train", "epoch": 32, "iter": 2800, "lr": 0.08935, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26703, "top5_acc": 0.52234, "loss_cls": 4.2224, "loss": 4.2224, "time": 0.81513} +{"mode": "train", "epoch": 32, "iter": 2900, "lr": 0.08933, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25719, "top5_acc": 0.52266, "loss_cls": 4.22556, "loss": 4.22556, "time": 0.81672} +{"mode": "train", "epoch": 32, "iter": 3000, "lr": 0.08931, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26438, "top5_acc": 0.51609, "loss_cls": 4.23196, "loss": 4.23196, "time": 0.81589} +{"mode": "train", "epoch": 32, "iter": 3100, "lr": 0.0893, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26406, "top5_acc": 0.51703, "loss_cls": 4.22281, "loss": 4.22281, "time": 0.81656} +{"mode": "train", "epoch": 32, "iter": 3200, "lr": 0.08928, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26453, "top5_acc": 0.52078, "loss_cls": 4.21068, "loss": 4.21068, "time": 0.82233} +{"mode": "train", "epoch": 32, "iter": 3300, "lr": 0.08926, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26953, "top5_acc": 0.52109, "loss_cls": 4.23421, "loss": 4.23421, "time": 0.81838} +{"mode": "train", "epoch": 32, "iter": 3400, "lr": 0.08924, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26578, "top5_acc": 0.51703, "loss_cls": 4.22261, "loss": 4.22261, "time": 0.81465} +{"mode": "train", "epoch": 32, "iter": 3500, "lr": 0.08923, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26719, "top5_acc": 0.52344, "loss_cls": 4.2034, "loss": 4.2034, "time": 0.82567} +{"mode": "train", "epoch": 32, "iter": 3600, "lr": 0.08921, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26812, "top5_acc": 0.52172, "loss_cls": 4.21812, "loss": 4.21812, "time": 0.8238} +{"mode": "train", "epoch": 32, "iter": 3700, "lr": 0.08919, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26703, "top5_acc": 0.52, "loss_cls": 4.19878, "loss": 4.19878, "time": 0.82107} +{"mode": "val", "epoch": 32, "iter": 309, "lr": 0.08918, "top1_acc": 0.18092, "top5_acc": 0.41286, "mean_class_accuracy": 0.18074} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.08917, "memory": 15990, "data_time": 1.30986, "top1_acc": 0.27734, "top5_acc": 0.53078, "loss_cls": 4.18443, "loss": 4.18443, "time": 2.29221} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.08915, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28734, "top5_acc": 0.53219, "loss_cls": 4.13979, "loss": 4.13979, "time": 0.83532} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.08913, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28234, "top5_acc": 0.52844, "loss_cls": 4.1481, "loss": 4.1481, "time": 0.81944} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.08912, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28625, "top5_acc": 0.52953, "loss_cls": 4.13846, "loss": 4.13846, "time": 0.82747} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.0891, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27156, "top5_acc": 0.52906, "loss_cls": 4.15869, "loss": 4.15869, "time": 0.83208} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.08908, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26937, "top5_acc": 0.52125, "loss_cls": 4.20295, "loss": 4.20295, "time": 0.8313} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.08906, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27344, "top5_acc": 0.53062, "loss_cls": 4.17708, "loss": 4.17708, "time": 0.83085} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.08905, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27, "top5_acc": 0.52109, "loss_cls": 4.19411, "loss": 4.19411, "time": 0.83035} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.08903, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27656, "top5_acc": 0.52844, "loss_cls": 4.1861, "loss": 4.1861, "time": 0.82896} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.08901, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26578, "top5_acc": 0.52438, "loss_cls": 4.20129, "loss": 4.20129, "time": 0.82695} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.08899, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27297, "top5_acc": 0.52484, "loss_cls": 4.19021, "loss": 4.19021, "time": 0.82966} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.08898, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28, "top5_acc": 0.53359, "loss_cls": 4.13685, "loss": 4.13685, "time": 0.82433} +{"mode": "train", "epoch": 33, "iter": 1300, "lr": 0.08896, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26828, "top5_acc": 0.51734, "loss_cls": 4.20081, "loss": 4.20081, "time": 0.82567} +{"mode": "train", "epoch": 33, "iter": 1400, "lr": 0.08894, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26328, "top5_acc": 0.52125, "loss_cls": 4.20762, "loss": 4.20762, "time": 0.82022} +{"mode": "train", "epoch": 33, "iter": 1500, "lr": 0.08892, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27578, "top5_acc": 0.52469, "loss_cls": 4.18154, "loss": 4.18154, "time": 0.81812} +{"mode": "train", "epoch": 33, "iter": 1600, "lr": 0.08891, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25812, "top5_acc": 0.51641, "loss_cls": 4.22507, "loss": 4.22507, "time": 0.81621} +{"mode": "train", "epoch": 33, "iter": 1700, "lr": 0.08889, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26891, "top5_acc": 0.51344, "loss_cls": 4.25869, "loss": 4.25869, "time": 0.81979} +{"mode": "train", "epoch": 33, "iter": 1800, "lr": 0.08887, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26312, "top5_acc": 0.51578, "loss_cls": 4.22849, "loss": 4.22849, "time": 0.81882} +{"mode": "train", "epoch": 33, "iter": 1900, "lr": 0.08885, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27875, "top5_acc": 0.52734, "loss_cls": 4.20327, "loss": 4.20327, "time": 0.81715} +{"mode": "train", "epoch": 33, "iter": 2000, "lr": 0.08884, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27141, "top5_acc": 0.52391, "loss_cls": 4.17937, "loss": 4.17937, "time": 0.81903} +{"mode": "train", "epoch": 33, "iter": 2100, "lr": 0.08882, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26578, "top5_acc": 0.51562, "loss_cls": 4.22061, "loss": 4.22061, "time": 0.82133} +{"mode": "train", "epoch": 33, "iter": 2200, "lr": 0.0888, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26078, "top5_acc": 0.52672, "loss_cls": 4.22034, "loss": 4.22034, "time": 0.82155} +{"mode": "train", "epoch": 33, "iter": 2300, "lr": 0.08878, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26109, "top5_acc": 0.51031, "loss_cls": 4.24888, "loss": 4.24888, "time": 0.82524} +{"mode": "train", "epoch": 33, "iter": 2400, "lr": 0.08876, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26469, "top5_acc": 0.51766, "loss_cls": 4.21019, "loss": 4.21019, "time": 0.81849} +{"mode": "train", "epoch": 33, "iter": 2500, "lr": 0.08875, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26891, "top5_acc": 0.51922, "loss_cls": 4.22676, "loss": 4.22676, "time": 0.81335} +{"mode": "train", "epoch": 33, "iter": 2600, "lr": 0.08873, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26453, "top5_acc": 0.51812, "loss_cls": 4.21972, "loss": 4.21972, "time": 0.81595} +{"mode": "train", "epoch": 33, "iter": 2700, "lr": 0.08871, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26578, "top5_acc": 0.51062, "loss_cls": 4.23435, "loss": 4.23435, "time": 0.81419} +{"mode": "train", "epoch": 33, "iter": 2800, "lr": 0.08869, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26922, "top5_acc": 0.52422, "loss_cls": 4.19488, "loss": 4.19488, "time": 0.81489} +{"mode": "train", "epoch": 33, "iter": 2900, "lr": 0.08868, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27062, "top5_acc": 0.53109, "loss_cls": 4.17786, "loss": 4.17786, "time": 0.81296} +{"mode": "train", "epoch": 33, "iter": 3000, "lr": 0.08866, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27141, "top5_acc": 0.53281, "loss_cls": 4.17824, "loss": 4.17824, "time": 0.81254} +{"mode": "train", "epoch": 33, "iter": 3100, "lr": 0.08864, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25938, "top5_acc": 0.5225, "loss_cls": 4.23031, "loss": 4.23031, "time": 0.81584} +{"mode": "train", "epoch": 33, "iter": 3200, "lr": 0.08862, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27516, "top5_acc": 0.51781, "loss_cls": 4.21871, "loss": 4.21871, "time": 0.81152} +{"mode": "train", "epoch": 33, "iter": 3300, "lr": 0.08861, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26922, "top5_acc": 0.53031, "loss_cls": 4.17146, "loss": 4.17146, "time": 0.81655} +{"mode": "train", "epoch": 33, "iter": 3400, "lr": 0.08859, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27984, "top5_acc": 0.51688, "loss_cls": 4.19348, "loss": 4.19348, "time": 0.81856} +{"mode": "train", "epoch": 33, "iter": 3500, "lr": 0.08857, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26422, "top5_acc": 0.51969, "loss_cls": 4.25149, "loss": 4.25149, "time": 0.81817} +{"mode": "train", "epoch": 33, "iter": 3600, "lr": 0.08855, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2625, "top5_acc": 0.51641, "loss_cls": 4.21006, "loss": 4.21006, "time": 0.81801} +{"mode": "train", "epoch": 33, "iter": 3700, "lr": 0.08853, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26734, "top5_acc": 0.51719, "loss_cls": 4.1935, "loss": 4.1935, "time": 0.81334} +{"mode": "val", "epoch": 33, "iter": 309, "lr": 0.08853, "top1_acc": 0.21972, "top5_acc": 0.45469, "mean_class_accuracy": 0.21934} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.08851, "memory": 15990, "data_time": 1.34207, "top1_acc": 0.27453, "top5_acc": 0.52281, "loss_cls": 4.16607, "loss": 4.16607, "time": 2.33285} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.08849, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27656, "top5_acc": 0.5275, "loss_cls": 4.17638, "loss": 4.17638, "time": 0.83765} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.08847, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27688, "top5_acc": 0.52875, "loss_cls": 4.13736, "loss": 4.13736, "time": 0.82621} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.08845, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26172, "top5_acc": 0.51562, "loss_cls": 4.23365, "loss": 4.23365, "time": 0.83496} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.08844, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27234, "top5_acc": 0.52938, "loss_cls": 4.16029, "loss": 4.16029, "time": 0.83581} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.08842, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27172, "top5_acc": 0.53859, "loss_cls": 4.15358, "loss": 4.15358, "time": 0.83165} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.0884, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.275, "top5_acc": 0.52531, "loss_cls": 4.19192, "loss": 4.19192, "time": 0.83133} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.08838, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28031, "top5_acc": 0.53734, "loss_cls": 4.12781, "loss": 4.12781, "time": 0.83098} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.08836, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26781, "top5_acc": 0.52047, "loss_cls": 4.19682, "loss": 4.19682, "time": 0.82117} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.08835, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27016, "top5_acc": 0.52016, "loss_cls": 4.19786, "loss": 4.19786, "time": 0.83062} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.08833, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27844, "top5_acc": 0.52281, "loss_cls": 4.17521, "loss": 4.17521, "time": 0.82361} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.08831, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27406, "top5_acc": 0.53688, "loss_cls": 4.15202, "loss": 4.15202, "time": 0.82279} +{"mode": "train", "epoch": 34, "iter": 1300, "lr": 0.08829, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27078, "top5_acc": 0.52312, "loss_cls": 4.20309, "loss": 4.20309, "time": 0.82451} +{"mode": "train", "epoch": 34, "iter": 1400, "lr": 0.08828, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26797, "top5_acc": 0.52562, "loss_cls": 4.19779, "loss": 4.19779, "time": 0.82559} +{"mode": "train", "epoch": 34, "iter": 1500, "lr": 0.08826, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27203, "top5_acc": 0.52062, "loss_cls": 4.19735, "loss": 4.19735, "time": 0.82961} +{"mode": "train", "epoch": 34, "iter": 1600, "lr": 0.08824, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27797, "top5_acc": 0.53016, "loss_cls": 4.16062, "loss": 4.16062, "time": 0.8196} +{"mode": "train", "epoch": 34, "iter": 1700, "lr": 0.08822, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26203, "top5_acc": 0.52047, "loss_cls": 4.20723, "loss": 4.20723, "time": 0.82633} +{"mode": "train", "epoch": 34, "iter": 1800, "lr": 0.0882, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26234, "top5_acc": 0.52422, "loss_cls": 4.22097, "loss": 4.22097, "time": 0.83037} +{"mode": "train", "epoch": 34, "iter": 1900, "lr": 0.08819, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27187, "top5_acc": 0.52312, "loss_cls": 4.19362, "loss": 4.19362, "time": 0.82465} +{"mode": "train", "epoch": 34, "iter": 2000, "lr": 0.08817, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27141, "top5_acc": 0.52672, "loss_cls": 4.19541, "loss": 4.19541, "time": 0.82479} +{"mode": "train", "epoch": 34, "iter": 2100, "lr": 0.08815, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27281, "top5_acc": 0.51547, "loss_cls": 4.23028, "loss": 4.23028, "time": 0.821} +{"mode": "train", "epoch": 34, "iter": 2200, "lr": 0.08813, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27328, "top5_acc": 0.53, "loss_cls": 4.19049, "loss": 4.19049, "time": 0.82472} +{"mode": "train", "epoch": 34, "iter": 2300, "lr": 0.08811, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26609, "top5_acc": 0.52844, "loss_cls": 4.19318, "loss": 4.19318, "time": 0.82912} +{"mode": "train", "epoch": 34, "iter": 2400, "lr": 0.08809, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26562, "top5_acc": 0.51547, "loss_cls": 4.23106, "loss": 4.23106, "time": 0.82944} +{"mode": "train", "epoch": 34, "iter": 2500, "lr": 0.08808, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26266, "top5_acc": 0.52484, "loss_cls": 4.19458, "loss": 4.19458, "time": 0.82817} +{"mode": "train", "epoch": 34, "iter": 2600, "lr": 0.08806, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26984, "top5_acc": 0.51719, "loss_cls": 4.21667, "loss": 4.21667, "time": 0.83164} +{"mode": "train", "epoch": 34, "iter": 2700, "lr": 0.08804, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2725, "top5_acc": 0.52297, "loss_cls": 4.17346, "loss": 4.17346, "time": 0.83038} +{"mode": "train", "epoch": 34, "iter": 2800, "lr": 0.08802, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.275, "top5_acc": 0.52625, "loss_cls": 4.18461, "loss": 4.18461, "time": 0.82978} +{"mode": "train", "epoch": 34, "iter": 2900, "lr": 0.088, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26469, "top5_acc": 0.51891, "loss_cls": 4.22317, "loss": 4.22317, "time": 0.82709} +{"mode": "train", "epoch": 34, "iter": 3000, "lr": 0.08799, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26, "top5_acc": 0.515, "loss_cls": 4.24564, "loss": 4.24564, "time": 0.829} +{"mode": "train", "epoch": 34, "iter": 3100, "lr": 0.08797, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26312, "top5_acc": 0.52328, "loss_cls": 4.21617, "loss": 4.21617, "time": 0.83642} +{"mode": "train", "epoch": 34, "iter": 3200, "lr": 0.08795, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27453, "top5_acc": 0.52875, "loss_cls": 4.19849, "loss": 4.19849, "time": 0.82057} +{"mode": "train", "epoch": 34, "iter": 3300, "lr": 0.08793, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27391, "top5_acc": 0.5325, "loss_cls": 4.18629, "loss": 4.18629, "time": 0.81999} +{"mode": "train", "epoch": 34, "iter": 3400, "lr": 0.08791, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27484, "top5_acc": 0.52672, "loss_cls": 4.19063, "loss": 4.19063, "time": 0.82508} +{"mode": "train", "epoch": 34, "iter": 3500, "lr": 0.08789, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27859, "top5_acc": 0.52125, "loss_cls": 4.17281, "loss": 4.17281, "time": 0.81993} +{"mode": "train", "epoch": 34, "iter": 3600, "lr": 0.08788, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27422, "top5_acc": 0.51938, "loss_cls": 4.19757, "loss": 4.19757, "time": 0.82334} +{"mode": "train", "epoch": 34, "iter": 3700, "lr": 0.08786, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27812, "top5_acc": 0.53234, "loss_cls": 4.16038, "loss": 4.16038, "time": 0.81584} +{"mode": "val", "epoch": 34, "iter": 309, "lr": 0.08785, "top1_acc": 0.21213, "top5_acc": 0.44978, "mean_class_accuracy": 0.21197} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.08783, "memory": 15990, "data_time": 1.29873, "top1_acc": 0.27406, "top5_acc": 0.54469, "loss_cls": 4.10694, "loss": 4.10694, "time": 2.29339} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.08781, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27969, "top5_acc": 0.54281, "loss_cls": 4.10429, "loss": 4.10429, "time": 0.82288} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.0878, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27875, "top5_acc": 0.53078, "loss_cls": 4.1616, "loss": 4.1616, "time": 0.82579} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.08778, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26656, "top5_acc": 0.51766, "loss_cls": 4.22132, "loss": 4.22132, "time": 0.83679} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.08776, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27047, "top5_acc": 0.53125, "loss_cls": 4.1679, "loss": 4.1679, "time": 0.83777} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.08774, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26672, "top5_acc": 0.52844, "loss_cls": 4.18165, "loss": 4.18165, "time": 0.83677} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.08772, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26844, "top5_acc": 0.52297, "loss_cls": 4.20278, "loss": 4.20278, "time": 0.83304} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.0877, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27359, "top5_acc": 0.53234, "loss_cls": 4.16542, "loss": 4.16542, "time": 0.83544} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.08769, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27828, "top5_acc": 0.5275, "loss_cls": 4.16716, "loss": 4.16716, "time": 0.83506} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.08767, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27125, "top5_acc": 0.51562, "loss_cls": 4.19465, "loss": 4.19465, "time": 0.83739} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.08765, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27156, "top5_acc": 0.52438, "loss_cls": 4.18713, "loss": 4.18713, "time": 0.82959} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.08763, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27672, "top5_acc": 0.52359, "loss_cls": 4.18431, "loss": 4.18431, "time": 0.84207} +{"mode": "train", "epoch": 35, "iter": 1300, "lr": 0.08761, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27016, "top5_acc": 0.52875, "loss_cls": 4.16621, "loss": 4.16621, "time": 0.83705} +{"mode": "train", "epoch": 35, "iter": 1400, "lr": 0.08759, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27109, "top5_acc": 0.52844, "loss_cls": 4.17394, "loss": 4.17394, "time": 0.83275} +{"mode": "train", "epoch": 35, "iter": 1500, "lr": 0.08757, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26281, "top5_acc": 0.51656, "loss_cls": 4.24213, "loss": 4.24213, "time": 0.83268} +{"mode": "train", "epoch": 35, "iter": 1600, "lr": 0.08756, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25906, "top5_acc": 0.51641, "loss_cls": 4.2221, "loss": 4.2221, "time": 0.83635} +{"mode": "train", "epoch": 35, "iter": 1700, "lr": 0.08754, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26234, "top5_acc": 0.51328, "loss_cls": 4.23654, "loss": 4.23654, "time": 0.83347} +{"mode": "train", "epoch": 35, "iter": 1800, "lr": 0.08752, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26844, "top5_acc": 0.52375, "loss_cls": 4.19173, "loss": 4.19173, "time": 0.83615} +{"mode": "train", "epoch": 35, "iter": 1900, "lr": 0.0875, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26641, "top5_acc": 0.52203, "loss_cls": 4.22672, "loss": 4.22672, "time": 0.83795} +{"mode": "train", "epoch": 35, "iter": 2000, "lr": 0.08748, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26594, "top5_acc": 0.515, "loss_cls": 4.22473, "loss": 4.22473, "time": 0.83402} +{"mode": "train", "epoch": 35, "iter": 2100, "lr": 0.08746, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26937, "top5_acc": 0.53031, "loss_cls": 4.17443, "loss": 4.17443, "time": 0.8377} +{"mode": "train", "epoch": 35, "iter": 2200, "lr": 0.08745, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26844, "top5_acc": 0.52484, "loss_cls": 4.19987, "loss": 4.19987, "time": 0.83706} +{"mode": "train", "epoch": 35, "iter": 2300, "lr": 0.08743, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27453, "top5_acc": 0.52719, "loss_cls": 4.19559, "loss": 4.19559, "time": 0.84063} +{"mode": "train", "epoch": 35, "iter": 2400, "lr": 0.08741, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27672, "top5_acc": 0.53266, "loss_cls": 4.14549, "loss": 4.14549, "time": 0.82821} +{"mode": "train", "epoch": 35, "iter": 2500, "lr": 0.08739, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27812, "top5_acc": 0.53578, "loss_cls": 4.13766, "loss": 4.13766, "time": 0.83682} +{"mode": "train", "epoch": 35, "iter": 2600, "lr": 0.08737, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26016, "top5_acc": 0.52297, "loss_cls": 4.20788, "loss": 4.20788, "time": 0.83267} +{"mode": "train", "epoch": 35, "iter": 2700, "lr": 0.08735, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27094, "top5_acc": 0.52266, "loss_cls": 4.18804, "loss": 4.18804, "time": 0.83578} +{"mode": "train", "epoch": 35, "iter": 2800, "lr": 0.08733, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26812, "top5_acc": 0.52188, "loss_cls": 4.18338, "loss": 4.18338, "time": 0.83479} +{"mode": "train", "epoch": 35, "iter": 2900, "lr": 0.08732, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27156, "top5_acc": 0.52156, "loss_cls": 4.19693, "loss": 4.19693, "time": 0.83773} +{"mode": "train", "epoch": 35, "iter": 3000, "lr": 0.0873, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27797, "top5_acc": 0.53719, "loss_cls": 4.15363, "loss": 4.15363, "time": 0.83726} +{"mode": "train", "epoch": 35, "iter": 3100, "lr": 0.08728, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27406, "top5_acc": 0.52266, "loss_cls": 4.18925, "loss": 4.18925, "time": 0.83279} +{"mode": "train", "epoch": 35, "iter": 3200, "lr": 0.08726, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26828, "top5_acc": 0.51406, "loss_cls": 4.23024, "loss": 4.23024, "time": 0.83725} +{"mode": "train", "epoch": 35, "iter": 3300, "lr": 0.08724, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26672, "top5_acc": 0.51297, "loss_cls": 4.23695, "loss": 4.23695, "time": 0.82161} +{"mode": "train", "epoch": 35, "iter": 3400, "lr": 0.08722, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26891, "top5_acc": 0.53172, "loss_cls": 4.1727, "loss": 4.1727, "time": 0.83035} +{"mode": "train", "epoch": 35, "iter": 3500, "lr": 0.0872, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27125, "top5_acc": 0.52219, "loss_cls": 4.17581, "loss": 4.17581, "time": 0.83017} +{"mode": "train", "epoch": 35, "iter": 3600, "lr": 0.08718, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26234, "top5_acc": 0.51266, "loss_cls": 4.25342, "loss": 4.25342, "time": 0.83561} +{"mode": "train", "epoch": 35, "iter": 3700, "lr": 0.08717, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26687, "top5_acc": 0.51078, "loss_cls": 4.24736, "loss": 4.24736, "time": 0.81959} +{"mode": "val", "epoch": 35, "iter": 309, "lr": 0.08716, "top1_acc": 0.1903, "top5_acc": 0.41858, "mean_class_accuracy": 0.19026} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.08714, "memory": 15990, "data_time": 1.26191, "top1_acc": 0.26969, "top5_acc": 0.53359, "loss_cls": 4.16708, "loss": 4.16708, "time": 2.25125} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.08712, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27719, "top5_acc": 0.53422, "loss_cls": 4.12354, "loss": 4.12354, "time": 0.82873} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.0871, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27812, "top5_acc": 0.52234, "loss_cls": 4.15504, "loss": 4.15504, "time": 0.83681} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.08708, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27187, "top5_acc": 0.53656, "loss_cls": 4.1369, "loss": 4.1369, "time": 0.83404} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.08706, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26859, "top5_acc": 0.52141, "loss_cls": 4.21599, "loss": 4.21599, "time": 0.83749} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.08704, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27484, "top5_acc": 0.53609, "loss_cls": 4.1248, "loss": 4.1248, "time": 0.83973} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.08703, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26828, "top5_acc": 0.52172, "loss_cls": 4.18178, "loss": 4.18178, "time": 0.83263} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.08701, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26234, "top5_acc": 0.52469, "loss_cls": 4.20391, "loss": 4.20391, "time": 0.83438} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.08699, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27906, "top5_acc": 0.52984, "loss_cls": 4.15779, "loss": 4.15779, "time": 0.8342} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.08697, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27562, "top5_acc": 0.53344, "loss_cls": 4.15737, "loss": 4.15737, "time": 0.83541} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.08695, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27062, "top5_acc": 0.53312, "loss_cls": 4.18613, "loss": 4.18613, "time": 0.83201} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.08693, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27688, "top5_acc": 0.52469, "loss_cls": 4.18114, "loss": 4.18114, "time": 0.83453} +{"mode": "train", "epoch": 36, "iter": 1300, "lr": 0.08691, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27219, "top5_acc": 0.53438, "loss_cls": 4.16769, "loss": 4.16769, "time": 0.83479} +{"mode": "train", "epoch": 36, "iter": 1400, "lr": 0.08689, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27156, "top5_acc": 0.52828, "loss_cls": 4.18531, "loss": 4.18531, "time": 0.83912} +{"mode": "train", "epoch": 36, "iter": 1500, "lr": 0.08688, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26703, "top5_acc": 0.52906, "loss_cls": 4.17523, "loss": 4.17523, "time": 0.84046} +{"mode": "train", "epoch": 36, "iter": 1600, "lr": 0.08686, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28141, "top5_acc": 0.53188, "loss_cls": 4.12779, "loss": 4.12779, "time": 0.83662} +{"mode": "train", "epoch": 36, "iter": 1700, "lr": 0.08684, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26297, "top5_acc": 0.52031, "loss_cls": 4.21785, "loss": 4.21785, "time": 0.83523} +{"mode": "train", "epoch": 36, "iter": 1800, "lr": 0.08682, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27219, "top5_acc": 0.52812, "loss_cls": 4.16738, "loss": 4.16738, "time": 0.83718} +{"mode": "train", "epoch": 36, "iter": 1900, "lr": 0.0868, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27703, "top5_acc": 0.52906, "loss_cls": 4.16281, "loss": 4.16281, "time": 0.82923} +{"mode": "train", "epoch": 36, "iter": 2000, "lr": 0.08678, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27984, "top5_acc": 0.52797, "loss_cls": 4.16489, "loss": 4.16489, "time": 0.83355} +{"mode": "train", "epoch": 36, "iter": 2100, "lr": 0.08676, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27891, "top5_acc": 0.52531, "loss_cls": 4.18986, "loss": 4.18986, "time": 0.83761} +{"mode": "train", "epoch": 36, "iter": 2200, "lr": 0.08674, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26984, "top5_acc": 0.51313, "loss_cls": 4.23379, "loss": 4.23379, "time": 0.83909} +{"mode": "train", "epoch": 36, "iter": 2300, "lr": 0.08672, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26359, "top5_acc": 0.51875, "loss_cls": 4.21138, "loss": 4.21138, "time": 0.83625} +{"mode": "train", "epoch": 36, "iter": 2400, "lr": 0.08671, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27266, "top5_acc": 0.52078, "loss_cls": 4.21448, "loss": 4.21448, "time": 0.83706} +{"mode": "train", "epoch": 36, "iter": 2500, "lr": 0.08669, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26594, "top5_acc": 0.52062, "loss_cls": 4.18917, "loss": 4.18917, "time": 0.83451} +{"mode": "train", "epoch": 36, "iter": 2600, "lr": 0.08667, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26609, "top5_acc": 0.51703, "loss_cls": 4.23241, "loss": 4.23241, "time": 0.83051} +{"mode": "train", "epoch": 36, "iter": 2700, "lr": 0.08665, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27703, "top5_acc": 0.52719, "loss_cls": 4.15435, "loss": 4.15435, "time": 0.83594} +{"mode": "train", "epoch": 36, "iter": 2800, "lr": 0.08663, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27047, "top5_acc": 0.53062, "loss_cls": 4.14362, "loss": 4.14362, "time": 0.83661} +{"mode": "train", "epoch": 36, "iter": 2900, "lr": 0.08661, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26078, "top5_acc": 0.52062, "loss_cls": 4.22581, "loss": 4.22581, "time": 0.83282} +{"mode": "train", "epoch": 36, "iter": 3000, "lr": 0.08659, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.265, "top5_acc": 0.51547, "loss_cls": 4.23491, "loss": 4.23491, "time": 0.8366} +{"mode": "train", "epoch": 36, "iter": 3100, "lr": 0.08657, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26375, "top5_acc": 0.51781, "loss_cls": 4.20252, "loss": 4.20252, "time": 0.83516} +{"mode": "train", "epoch": 36, "iter": 3200, "lr": 0.08655, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26047, "top5_acc": 0.51219, "loss_cls": 4.24747, "loss": 4.24747, "time": 0.83862} +{"mode": "train", "epoch": 36, "iter": 3300, "lr": 0.08653, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27516, "top5_acc": 0.52406, "loss_cls": 4.1835, "loss": 4.1835, "time": 0.82825} +{"mode": "train", "epoch": 36, "iter": 3400, "lr": 0.08651, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26578, "top5_acc": 0.51703, "loss_cls": 4.23193, "loss": 4.23193, "time": 0.83389} +{"mode": "train", "epoch": 36, "iter": 3500, "lr": 0.0865, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26359, "top5_acc": 0.52078, "loss_cls": 4.20099, "loss": 4.20099, "time": 0.83821} +{"mode": "train", "epoch": 36, "iter": 3600, "lr": 0.08648, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26469, "top5_acc": 0.52938, "loss_cls": 4.19089, "loss": 4.19089, "time": 0.83058} +{"mode": "train", "epoch": 36, "iter": 3700, "lr": 0.08646, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27031, "top5_acc": 0.51688, "loss_cls": 4.21409, "loss": 4.21409, "time": 0.8273} +{"mode": "val", "epoch": 36, "iter": 309, "lr": 0.08645, "top1_acc": 0.2101, "top5_acc": 0.44669, "mean_class_accuracy": 0.20996} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.08643, "memory": 15990, "data_time": 1.27507, "top1_acc": 0.26375, "top5_acc": 0.5225, "loss_cls": 4.20387, "loss": 4.20387, "time": 2.25606} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.08641, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27078, "top5_acc": 0.51953, "loss_cls": 4.18101, "loss": 4.18101, "time": 0.82654} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.08639, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27984, "top5_acc": 0.53109, "loss_cls": 4.12922, "loss": 4.12922, "time": 0.83689} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.08637, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27609, "top5_acc": 0.52844, "loss_cls": 4.14038, "loss": 4.14038, "time": 0.83375} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.08635, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26797, "top5_acc": 0.52781, "loss_cls": 4.18559, "loss": 4.18559, "time": 0.83047} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.08633, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27797, "top5_acc": 0.53234, "loss_cls": 4.15764, "loss": 4.15764, "time": 0.83353} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.08631, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27, "top5_acc": 0.51906, "loss_cls": 4.20362, "loss": 4.20362, "time": 0.83137} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0863, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27406, "top5_acc": 0.53922, "loss_cls": 4.13406, "loss": 4.13406, "time": 0.83517} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.08628, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27109, "top5_acc": 0.52859, "loss_cls": 4.17146, "loss": 4.17146, "time": 0.83386} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.08626, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28469, "top5_acc": 0.53844, "loss_cls": 4.15329, "loss": 4.15329, "time": 0.83356} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.08624, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27812, "top5_acc": 0.53156, "loss_cls": 4.17527, "loss": 4.17527, "time": 0.83496} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.08622, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27187, "top5_acc": 0.52438, "loss_cls": 4.18234, "loss": 4.18234, "time": 0.83601} +{"mode": "train", "epoch": 37, "iter": 1300, "lr": 0.0862, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27187, "top5_acc": 0.53141, "loss_cls": 4.15193, "loss": 4.15193, "time": 0.83467} +{"mode": "train", "epoch": 37, "iter": 1400, "lr": 0.08618, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26672, "top5_acc": 0.53016, "loss_cls": 4.16918, "loss": 4.16918, "time": 0.83086} +{"mode": "train", "epoch": 37, "iter": 1500, "lr": 0.08616, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27062, "top5_acc": 0.51766, "loss_cls": 4.20687, "loss": 4.20687, "time": 0.83506} +{"mode": "train", "epoch": 37, "iter": 1600, "lr": 0.08614, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26219, "top5_acc": 0.52906, "loss_cls": 4.18829, "loss": 4.18829, "time": 0.83775} +{"mode": "train", "epoch": 37, "iter": 1700, "lr": 0.08612, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27141, "top5_acc": 0.51812, "loss_cls": 4.21355, "loss": 4.21355, "time": 0.83714} +{"mode": "train", "epoch": 37, "iter": 1800, "lr": 0.0861, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26922, "top5_acc": 0.53125, "loss_cls": 4.18698, "loss": 4.18698, "time": 0.83055} +{"mode": "train", "epoch": 37, "iter": 1900, "lr": 0.08608, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27141, "top5_acc": 0.52656, "loss_cls": 4.18293, "loss": 4.18293, "time": 0.8339} +{"mode": "train", "epoch": 37, "iter": 2000, "lr": 0.08606, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27766, "top5_acc": 0.53516, "loss_cls": 4.14223, "loss": 4.14223, "time": 0.83553} +{"mode": "train", "epoch": 37, "iter": 2100, "lr": 0.08604, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27281, "top5_acc": 0.52438, "loss_cls": 4.19901, "loss": 4.19901, "time": 0.83402} +{"mode": "train", "epoch": 37, "iter": 2200, "lr": 0.08602, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28391, "top5_acc": 0.53156, "loss_cls": 4.11988, "loss": 4.11988, "time": 0.83524} +{"mode": "train", "epoch": 37, "iter": 2300, "lr": 0.08601, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28453, "top5_acc": 0.52703, "loss_cls": 4.1414, "loss": 4.1414, "time": 0.83158} +{"mode": "train", "epoch": 37, "iter": 2400, "lr": 0.08599, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27344, "top5_acc": 0.52891, "loss_cls": 4.1886, "loss": 4.1886, "time": 0.83394} +{"mode": "train", "epoch": 37, "iter": 2500, "lr": 0.08597, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26453, "top5_acc": 0.52844, "loss_cls": 4.19666, "loss": 4.19666, "time": 0.83799} +{"mode": "train", "epoch": 37, "iter": 2600, "lr": 0.08595, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26875, "top5_acc": 0.52062, "loss_cls": 4.17689, "loss": 4.17689, "time": 0.83566} +{"mode": "train", "epoch": 37, "iter": 2700, "lr": 0.08593, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28625, "top5_acc": 0.52812, "loss_cls": 4.16002, "loss": 4.16002, "time": 0.83682} +{"mode": "train", "epoch": 37, "iter": 2800, "lr": 0.08591, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27922, "top5_acc": 0.52625, "loss_cls": 4.15712, "loss": 4.15712, "time": 0.83694} +{"mode": "train", "epoch": 37, "iter": 2900, "lr": 0.08589, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26, "top5_acc": 0.51984, "loss_cls": 4.23764, "loss": 4.23764, "time": 0.83382} +{"mode": "train", "epoch": 37, "iter": 3000, "lr": 0.08587, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.26875, "top5_acc": 0.52422, "loss_cls": 4.23345, "loss": 4.23345, "time": 0.83587} +{"mode": "train", "epoch": 37, "iter": 3100, "lr": 0.08585, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26719, "top5_acc": 0.51766, "loss_cls": 4.19927, "loss": 4.19927, "time": 0.83933} +{"mode": "train", "epoch": 37, "iter": 3200, "lr": 0.08583, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26391, "top5_acc": 0.52594, "loss_cls": 4.18434, "loss": 4.18434, "time": 0.82487} +{"mode": "train", "epoch": 37, "iter": 3300, "lr": 0.08581, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26641, "top5_acc": 0.52359, "loss_cls": 4.19415, "loss": 4.19415, "time": 0.82446} +{"mode": "train", "epoch": 37, "iter": 3400, "lr": 0.08579, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27172, "top5_acc": 0.52438, "loss_cls": 4.18734, "loss": 4.18734, "time": 0.83609} +{"mode": "train", "epoch": 37, "iter": 3500, "lr": 0.08577, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27594, "top5_acc": 0.52922, "loss_cls": 4.17825, "loss": 4.17825, "time": 0.83454} +{"mode": "train", "epoch": 37, "iter": 3600, "lr": 0.08575, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27047, "top5_acc": 0.52734, "loss_cls": 4.18432, "loss": 4.18432, "time": 0.82082} +{"mode": "train", "epoch": 37, "iter": 3700, "lr": 0.08573, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27359, "top5_acc": 0.51812, "loss_cls": 4.21288, "loss": 4.21288, "time": 0.83374} +{"mode": "val", "epoch": 37, "iter": 309, "lr": 0.08572, "top1_acc": 0.18488, "top5_acc": 0.42344, "mean_class_accuracy": 0.18475} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.0857, "memory": 15990, "data_time": 1.28734, "top1_acc": 0.27891, "top5_acc": 0.5425, "loss_cls": 4.09505, "loss": 4.09505, "time": 2.26999} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.08568, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28078, "top5_acc": 0.54141, "loss_cls": 4.11471, "loss": 4.11471, "time": 0.83652} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.08567, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26875, "top5_acc": 0.51859, "loss_cls": 4.1832, "loss": 4.1832, "time": 0.83319} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.08565, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28453, "top5_acc": 0.53312, "loss_cls": 4.1126, "loss": 4.1126, "time": 0.83133} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.08563, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27297, "top5_acc": 0.53094, "loss_cls": 4.15909, "loss": 4.15909, "time": 0.8318} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.08561, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26547, "top5_acc": 0.53109, "loss_cls": 4.1717, "loss": 4.1717, "time": 0.83119} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.08559, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26812, "top5_acc": 0.52797, "loss_cls": 4.18549, "loss": 4.18549, "time": 0.83206} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.08557, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27656, "top5_acc": 0.53234, "loss_cls": 4.1593, "loss": 4.1593, "time": 0.83576} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.08555, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26719, "top5_acc": 0.52516, "loss_cls": 4.18209, "loss": 4.18209, "time": 0.83338} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.08553, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27641, "top5_acc": 0.5275, "loss_cls": 4.15178, "loss": 4.15178, "time": 0.83183} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.08551, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28219, "top5_acc": 0.53641, "loss_cls": 4.14531, "loss": 4.14531, "time": 0.83544} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.08549, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27391, "top5_acc": 0.52594, "loss_cls": 4.1746, "loss": 4.1746, "time": 0.83262} +{"mode": "train", "epoch": 38, "iter": 1300, "lr": 0.08547, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27328, "top5_acc": 0.53234, "loss_cls": 4.14599, "loss": 4.14599, "time": 0.83369} +{"mode": "train", "epoch": 38, "iter": 1400, "lr": 0.08545, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27422, "top5_acc": 0.53203, "loss_cls": 4.16025, "loss": 4.16025, "time": 0.82808} +{"mode": "train", "epoch": 38, "iter": 1500, "lr": 0.08543, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27328, "top5_acc": 0.52969, "loss_cls": 4.176, "loss": 4.176, "time": 0.83297} +{"mode": "train", "epoch": 38, "iter": 1600, "lr": 0.08541, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28047, "top5_acc": 0.54297, "loss_cls": 4.12956, "loss": 4.12956, "time": 0.83533} +{"mode": "train", "epoch": 38, "iter": 1700, "lr": 0.08539, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29, "top5_acc": 0.54, "loss_cls": 4.10363, "loss": 4.10363, "time": 0.83574} +{"mode": "train", "epoch": 38, "iter": 1800, "lr": 0.08537, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27875, "top5_acc": 0.53625, "loss_cls": 4.13366, "loss": 4.13366, "time": 0.8335} +{"mode": "train", "epoch": 38, "iter": 1900, "lr": 0.08535, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26438, "top5_acc": 0.51562, "loss_cls": 4.21295, "loss": 4.21295, "time": 0.83105} +{"mode": "train", "epoch": 38, "iter": 2000, "lr": 0.08533, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27531, "top5_acc": 0.53078, "loss_cls": 4.15211, "loss": 4.15211, "time": 0.83347} +{"mode": "train", "epoch": 38, "iter": 2100, "lr": 0.08531, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28109, "top5_acc": 0.52672, "loss_cls": 4.1727, "loss": 4.1727, "time": 0.83429} +{"mode": "train", "epoch": 38, "iter": 2200, "lr": 0.08529, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26469, "top5_acc": 0.51672, "loss_cls": 4.212, "loss": 4.212, "time": 0.83311} +{"mode": "train", "epoch": 38, "iter": 2300, "lr": 0.08527, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2625, "top5_acc": 0.52328, "loss_cls": 4.18818, "loss": 4.18818, "time": 0.83634} +{"mode": "train", "epoch": 38, "iter": 2400, "lr": 0.08525, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26687, "top5_acc": 0.5225, "loss_cls": 4.20605, "loss": 4.20605, "time": 0.83263} +{"mode": "train", "epoch": 38, "iter": 2500, "lr": 0.08523, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26719, "top5_acc": 0.5225, "loss_cls": 4.21199, "loss": 4.21199, "time": 0.83415} +{"mode": "train", "epoch": 38, "iter": 2600, "lr": 0.08521, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26578, "top5_acc": 0.52453, "loss_cls": 4.19382, "loss": 4.19382, "time": 0.83479} +{"mode": "train", "epoch": 38, "iter": 2700, "lr": 0.08519, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27516, "top5_acc": 0.52422, "loss_cls": 4.18776, "loss": 4.18776, "time": 0.83422} +{"mode": "train", "epoch": 38, "iter": 2800, "lr": 0.08517, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28188, "top5_acc": 0.52453, "loss_cls": 4.15971, "loss": 4.15971, "time": 0.83667} +{"mode": "train", "epoch": 38, "iter": 2900, "lr": 0.08515, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26906, "top5_acc": 0.52047, "loss_cls": 4.20205, "loss": 4.20205, "time": 0.83348} +{"mode": "train", "epoch": 38, "iter": 3000, "lr": 0.08513, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27625, "top5_acc": 0.52625, "loss_cls": 4.13961, "loss": 4.13961, "time": 0.83446} +{"mode": "train", "epoch": 38, "iter": 3100, "lr": 0.08511, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27719, "top5_acc": 0.52891, "loss_cls": 4.15859, "loss": 4.15859, "time": 0.83191} +{"mode": "train", "epoch": 38, "iter": 3200, "lr": 0.08509, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26937, "top5_acc": 0.52094, "loss_cls": 4.20654, "loss": 4.20654, "time": 0.82568} +{"mode": "train", "epoch": 38, "iter": 3300, "lr": 0.08507, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27312, "top5_acc": 0.52203, "loss_cls": 4.20044, "loss": 4.20044, "time": 0.8271} +{"mode": "train", "epoch": 38, "iter": 3400, "lr": 0.08505, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26766, "top5_acc": 0.52266, "loss_cls": 4.21023, "loss": 4.21023, "time": 0.83635} +{"mode": "train", "epoch": 38, "iter": 3500, "lr": 0.08503, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27797, "top5_acc": 0.53312, "loss_cls": 4.13645, "loss": 4.13645, "time": 0.82493} +{"mode": "train", "epoch": 38, "iter": 3600, "lr": 0.08501, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26859, "top5_acc": 0.52203, "loss_cls": 4.20074, "loss": 4.20074, "time": 0.82989} +{"mode": "train", "epoch": 38, "iter": 3700, "lr": 0.08499, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27047, "top5_acc": 0.52922, "loss_cls": 4.19915, "loss": 4.19915, "time": 0.83062} +{"mode": "val", "epoch": 38, "iter": 309, "lr": 0.08498, "top1_acc": 0.19962, "top5_acc": 0.43772, "mean_class_accuracy": 0.19934} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.08496, "memory": 15990, "data_time": 1.32546, "top1_acc": 0.27562, "top5_acc": 0.53328, "loss_cls": 4.132, "loss": 4.132, "time": 2.32777} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.08494, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27781, "top5_acc": 0.52734, "loss_cls": 4.13563, "loss": 4.13563, "time": 0.83755} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.08492, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26234, "top5_acc": 0.51859, "loss_cls": 4.20865, "loss": 4.20865, "time": 0.83799} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.0849, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27844, "top5_acc": 0.53578, "loss_cls": 4.1287, "loss": 4.1287, "time": 0.83796} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.08488, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27781, "top5_acc": 0.52609, "loss_cls": 4.12718, "loss": 4.12718, "time": 0.83787} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.08486, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27453, "top5_acc": 0.52094, "loss_cls": 4.18097, "loss": 4.18097, "time": 0.83832} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.08484, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28156, "top5_acc": 0.53938, "loss_cls": 4.14115, "loss": 4.14115, "time": 0.83594} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.08482, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28094, "top5_acc": 0.53359, "loss_cls": 4.13661, "loss": 4.13661, "time": 0.83829} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.0848, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27672, "top5_acc": 0.52969, "loss_cls": 4.13285, "loss": 4.13285, "time": 0.84032} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.08478, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27516, "top5_acc": 0.52812, "loss_cls": 4.16705, "loss": 4.16705, "time": 0.84029} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.08476, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27125, "top5_acc": 0.53469, "loss_cls": 4.16882, "loss": 4.16882, "time": 0.83646} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.08474, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27453, "top5_acc": 0.52594, "loss_cls": 4.16893, "loss": 4.16893, "time": 0.83345} +{"mode": "train", "epoch": 39, "iter": 1300, "lr": 0.08472, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27219, "top5_acc": 0.52406, "loss_cls": 4.16021, "loss": 4.16021, "time": 0.83712} +{"mode": "train", "epoch": 39, "iter": 1400, "lr": 0.0847, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27859, "top5_acc": 0.54125, "loss_cls": 4.13621, "loss": 4.13621, "time": 0.83693} +{"mode": "train", "epoch": 39, "iter": 1500, "lr": 0.08468, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27875, "top5_acc": 0.52203, "loss_cls": 4.18231, "loss": 4.18231, "time": 0.84075} +{"mode": "train", "epoch": 39, "iter": 1600, "lr": 0.08466, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27828, "top5_acc": 0.5275, "loss_cls": 4.15348, "loss": 4.15348, "time": 0.83654} +{"mode": "train", "epoch": 39, "iter": 1700, "lr": 0.08464, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27156, "top5_acc": 0.5275, "loss_cls": 4.15399, "loss": 4.15399, "time": 0.83938} +{"mode": "train", "epoch": 39, "iter": 1800, "lr": 0.08462, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27672, "top5_acc": 0.5425, "loss_cls": 4.14597, "loss": 4.14597, "time": 0.83604} +{"mode": "train", "epoch": 39, "iter": 1900, "lr": 0.0846, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27516, "top5_acc": 0.52969, "loss_cls": 4.1717, "loss": 4.1717, "time": 0.83983} +{"mode": "train", "epoch": 39, "iter": 2000, "lr": 0.08458, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27922, "top5_acc": 0.53406, "loss_cls": 4.12935, "loss": 4.12935, "time": 0.83946} +{"mode": "train", "epoch": 39, "iter": 2100, "lr": 0.08456, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26641, "top5_acc": 0.52844, "loss_cls": 4.15385, "loss": 4.15385, "time": 0.83715} +{"mode": "train", "epoch": 39, "iter": 2200, "lr": 0.08454, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27266, "top5_acc": 0.53953, "loss_cls": 4.14935, "loss": 4.14935, "time": 0.83614} +{"mode": "train", "epoch": 39, "iter": 2300, "lr": 0.08452, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26422, "top5_acc": 0.52312, "loss_cls": 4.18849, "loss": 4.18849, "time": 0.83595} +{"mode": "train", "epoch": 39, "iter": 2400, "lr": 0.0845, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27688, "top5_acc": 0.53875, "loss_cls": 4.11491, "loss": 4.11491, "time": 0.83506} +{"mode": "train", "epoch": 39, "iter": 2500, "lr": 0.08448, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26578, "top5_acc": 0.52891, "loss_cls": 4.17846, "loss": 4.17846, "time": 0.83561} +{"mode": "train", "epoch": 39, "iter": 2600, "lr": 0.08446, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26828, "top5_acc": 0.52047, "loss_cls": 4.20922, "loss": 4.20922, "time": 0.83608} +{"mode": "train", "epoch": 39, "iter": 2700, "lr": 0.08444, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27156, "top5_acc": 0.52391, "loss_cls": 4.19996, "loss": 4.19996, "time": 0.84105} +{"mode": "train", "epoch": 39, "iter": 2800, "lr": 0.08442, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28312, "top5_acc": 0.53859, "loss_cls": 4.14408, "loss": 4.14408, "time": 0.83257} +{"mode": "train", "epoch": 39, "iter": 2900, "lr": 0.0844, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28125, "top5_acc": 0.52875, "loss_cls": 4.13446, "loss": 4.13446, "time": 0.83645} +{"mode": "train", "epoch": 39, "iter": 3000, "lr": 0.08438, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27953, "top5_acc": 0.53266, "loss_cls": 4.15435, "loss": 4.15435, "time": 0.8358} +{"mode": "train", "epoch": 39, "iter": 3100, "lr": 0.08436, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27172, "top5_acc": 0.52266, "loss_cls": 4.19564, "loss": 4.19564, "time": 0.82436} +{"mode": "train", "epoch": 39, "iter": 3200, "lr": 0.08434, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27828, "top5_acc": 0.53531, "loss_cls": 4.14159, "loss": 4.14159, "time": 0.82835} +{"mode": "train", "epoch": 39, "iter": 3300, "lr": 0.08432, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27328, "top5_acc": 0.52109, "loss_cls": 4.19351, "loss": 4.19351, "time": 0.83836} +{"mode": "train", "epoch": 39, "iter": 3400, "lr": 0.0843, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.27578, "top5_acc": 0.53141, "loss_cls": 4.16239, "loss": 4.16239, "time": 0.82651} +{"mode": "train", "epoch": 39, "iter": 3500, "lr": 0.08428, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26328, "top5_acc": 0.53219, "loss_cls": 4.19597, "loss": 4.19597, "time": 0.82538} +{"mode": "train", "epoch": 39, "iter": 3600, "lr": 0.08426, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27484, "top5_acc": 0.52516, "loss_cls": 4.16108, "loss": 4.16108, "time": 0.83182} +{"mode": "train", "epoch": 39, "iter": 3700, "lr": 0.08424, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27938, "top5_acc": 0.52203, "loss_cls": 4.18118, "loss": 4.18118, "time": 0.83413} +{"mode": "val", "epoch": 39, "iter": 309, "lr": 0.08423, "top1_acc": 0.22297, "top5_acc": 0.45348, "mean_class_accuracy": 0.22258} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.08421, "memory": 15990, "data_time": 1.28516, "top1_acc": 0.29594, "top5_acc": 0.54703, "loss_cls": 4.06517, "loss": 4.06517, "time": 2.2823} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.08419, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27562, "top5_acc": 0.52734, "loss_cls": 4.15367, "loss": 4.15367, "time": 0.83722} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.08417, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28, "top5_acc": 0.53703, "loss_cls": 4.1221, "loss": 4.1221, "time": 0.8385} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.08415, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27453, "top5_acc": 0.52422, "loss_cls": 4.17816, "loss": 4.17816, "time": 0.84097} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.08413, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27172, "top5_acc": 0.51984, "loss_cls": 4.19108, "loss": 4.19108, "time": 0.83716} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.08411, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26687, "top5_acc": 0.52984, "loss_cls": 4.16716, "loss": 4.16716, "time": 0.83535} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.08408, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28344, "top5_acc": 0.54156, "loss_cls": 4.11044, "loss": 4.11044, "time": 0.83764} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.08406, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28734, "top5_acc": 0.54594, "loss_cls": 4.08465, "loss": 4.08465, "time": 0.8398} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.08404, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26891, "top5_acc": 0.53203, "loss_cls": 4.17885, "loss": 4.17885, "time": 0.83847} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.08402, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27812, "top5_acc": 0.5325, "loss_cls": 4.1169, "loss": 4.1169, "time": 0.83562} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.084, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27281, "top5_acc": 0.52719, "loss_cls": 4.18883, "loss": 4.18883, "time": 0.83848} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.08398, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2825, "top5_acc": 0.53016, "loss_cls": 4.15723, "loss": 4.15723, "time": 0.83586} +{"mode": "train", "epoch": 40, "iter": 1300, "lr": 0.08396, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27312, "top5_acc": 0.53344, "loss_cls": 4.16678, "loss": 4.16678, "time": 0.83944} +{"mode": "train", "epoch": 40, "iter": 1400, "lr": 0.08394, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27703, "top5_acc": 0.53516, "loss_cls": 4.13465, "loss": 4.13465, "time": 0.83878} +{"mode": "train", "epoch": 40, "iter": 1500, "lr": 0.08392, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28188, "top5_acc": 0.53359, "loss_cls": 4.13076, "loss": 4.13076, "time": 0.83663} +{"mode": "train", "epoch": 40, "iter": 1600, "lr": 0.0839, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27828, "top5_acc": 0.52797, "loss_cls": 4.16434, "loss": 4.16434, "time": 0.83337} +{"mode": "train", "epoch": 40, "iter": 1700, "lr": 0.08388, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27172, "top5_acc": 0.53109, "loss_cls": 4.14754, "loss": 4.14754, "time": 0.83535} +{"mode": "train", "epoch": 40, "iter": 1800, "lr": 0.08386, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27109, "top5_acc": 0.51766, "loss_cls": 4.20326, "loss": 4.20326, "time": 0.83701} +{"mode": "train", "epoch": 40, "iter": 1900, "lr": 0.08384, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27922, "top5_acc": 0.53312, "loss_cls": 4.15595, "loss": 4.15595, "time": 0.83989} +{"mode": "train", "epoch": 40, "iter": 2000, "lr": 0.08382, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27, "top5_acc": 0.53062, "loss_cls": 4.13867, "loss": 4.13867, "time": 0.83284} +{"mode": "train", "epoch": 40, "iter": 2100, "lr": 0.0838, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27688, "top5_acc": 0.53344, "loss_cls": 4.15286, "loss": 4.15286, "time": 0.84071} +{"mode": "train", "epoch": 40, "iter": 2200, "lr": 0.08378, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28062, "top5_acc": 0.52844, "loss_cls": 4.13386, "loss": 4.13386, "time": 0.83358} +{"mode": "train", "epoch": 40, "iter": 2300, "lr": 0.08376, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26687, "top5_acc": 0.52484, "loss_cls": 4.19028, "loss": 4.19028, "time": 0.83689} +{"mode": "train", "epoch": 40, "iter": 2400, "lr": 0.08374, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28203, "top5_acc": 0.53172, "loss_cls": 4.13555, "loss": 4.13555, "time": 0.83926} +{"mode": "train", "epoch": 40, "iter": 2500, "lr": 0.08371, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27125, "top5_acc": 0.53125, "loss_cls": 4.18357, "loss": 4.18357, "time": 0.84202} +{"mode": "train", "epoch": 40, "iter": 2600, "lr": 0.08369, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27984, "top5_acc": 0.53438, "loss_cls": 4.13281, "loss": 4.13281, "time": 0.83657} +{"mode": "train", "epoch": 40, "iter": 2700, "lr": 0.08367, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26828, "top5_acc": 0.5275, "loss_cls": 4.17344, "loss": 4.17344, "time": 0.84177} +{"mode": "train", "epoch": 40, "iter": 2800, "lr": 0.08365, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27719, "top5_acc": 0.53438, "loss_cls": 4.17047, "loss": 4.17047, "time": 0.83676} +{"mode": "train", "epoch": 40, "iter": 2900, "lr": 0.08363, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26969, "top5_acc": 0.52188, "loss_cls": 4.16523, "loss": 4.16523, "time": 0.84168} +{"mode": "train", "epoch": 40, "iter": 3000, "lr": 0.08361, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26766, "top5_acc": 0.53016, "loss_cls": 4.15341, "loss": 4.15341, "time": 0.8366} +{"mode": "train", "epoch": 40, "iter": 3100, "lr": 0.08359, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27437, "top5_acc": 0.52203, "loss_cls": 4.18147, "loss": 4.18147, "time": 0.83508} +{"mode": "train", "epoch": 40, "iter": 3200, "lr": 0.08357, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2675, "top5_acc": 0.52375, "loss_cls": 4.1731, "loss": 4.1731, "time": 0.84078} +{"mode": "train", "epoch": 40, "iter": 3300, "lr": 0.08355, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27437, "top5_acc": 0.53875, "loss_cls": 4.12786, "loss": 4.12786, "time": 0.83711} +{"mode": "train", "epoch": 40, "iter": 3400, "lr": 0.08353, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28547, "top5_acc": 0.53844, "loss_cls": 4.13371, "loss": 4.13371, "time": 0.83163} +{"mode": "train", "epoch": 40, "iter": 3500, "lr": 0.08351, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27469, "top5_acc": 0.52422, "loss_cls": 4.19648, "loss": 4.19648, "time": 0.83649} +{"mode": "train", "epoch": 40, "iter": 3600, "lr": 0.08349, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27016, "top5_acc": 0.52953, "loss_cls": 4.17917, "loss": 4.17917, "time": 0.83557} +{"mode": "train", "epoch": 40, "iter": 3700, "lr": 0.08347, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.27047, "top5_acc": 0.52797, "loss_cls": 4.18238, "loss": 4.18238, "time": 0.842} +{"mode": "val", "epoch": 40, "iter": 309, "lr": 0.08346, "top1_acc": 0.13579, "top5_acc": 0.33875, "mean_class_accuracy": 0.13538} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.08344, "memory": 15990, "data_time": 1.30401, "top1_acc": 0.29031, "top5_acc": 0.54406, "loss_cls": 4.05802, "loss": 4.05802, "time": 2.29724} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.08342, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28031, "top5_acc": 0.53141, "loss_cls": 4.13921, "loss": 4.13921, "time": 0.83634} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.08339, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28781, "top5_acc": 0.55125, "loss_cls": 4.10253, "loss": 4.10253, "time": 0.83344} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.08337, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27609, "top5_acc": 0.52609, "loss_cls": 4.15748, "loss": 4.15748, "time": 0.83554} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.08335, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26953, "top5_acc": 0.53953, "loss_cls": 4.12698, "loss": 4.12698, "time": 0.83667} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.08333, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28031, "top5_acc": 0.54531, "loss_cls": 4.08088, "loss": 4.08088, "time": 0.83768} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.08331, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27516, "top5_acc": 0.5425, "loss_cls": 4.10919, "loss": 4.10919, "time": 0.8374} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.08329, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26781, "top5_acc": 0.52156, "loss_cls": 4.1747, "loss": 4.1747, "time": 0.83447} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.08327, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27469, "top5_acc": 0.51734, "loss_cls": 4.16269, "loss": 4.16269, "time": 0.83392} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.08325, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27375, "top5_acc": 0.53266, "loss_cls": 4.13294, "loss": 4.13294, "time": 0.8369} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.08323, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26828, "top5_acc": 0.53469, "loss_cls": 4.15848, "loss": 4.15848, "time": 0.83482} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.08321, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28156, "top5_acc": 0.53188, "loss_cls": 4.16938, "loss": 4.16938, "time": 0.83809} +{"mode": "train", "epoch": 41, "iter": 1300, "lr": 0.08319, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28422, "top5_acc": 0.53969, "loss_cls": 4.09941, "loss": 4.09941, "time": 0.83259} +{"mode": "train", "epoch": 41, "iter": 1400, "lr": 0.08316, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.265, "top5_acc": 0.53031, "loss_cls": 4.16711, "loss": 4.16711, "time": 0.83123} +{"mode": "train", "epoch": 41, "iter": 1500, "lr": 0.08314, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27562, "top5_acc": 0.53859, "loss_cls": 4.12853, "loss": 4.12853, "time": 0.83305} +{"mode": "train", "epoch": 41, "iter": 1600, "lr": 0.08312, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26687, "top5_acc": 0.53094, "loss_cls": 4.16536, "loss": 4.16536, "time": 0.83696} +{"mode": "train", "epoch": 41, "iter": 1700, "lr": 0.0831, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27891, "top5_acc": 0.53156, "loss_cls": 4.15125, "loss": 4.15125, "time": 0.83472} +{"mode": "train", "epoch": 41, "iter": 1800, "lr": 0.08308, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27047, "top5_acc": 0.52859, "loss_cls": 4.15534, "loss": 4.15534, "time": 0.83504} +{"mode": "train", "epoch": 41, "iter": 1900, "lr": 0.08306, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28156, "top5_acc": 0.52938, "loss_cls": 4.14461, "loss": 4.14461, "time": 0.83406} +{"mode": "train", "epoch": 41, "iter": 2000, "lr": 0.08304, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27312, "top5_acc": 0.52438, "loss_cls": 4.17632, "loss": 4.17632, "time": 0.83782} +{"mode": "train", "epoch": 41, "iter": 2100, "lr": 0.08302, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27453, "top5_acc": 0.5275, "loss_cls": 4.18139, "loss": 4.18139, "time": 0.8341} +{"mode": "train", "epoch": 41, "iter": 2200, "lr": 0.083, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27156, "top5_acc": 0.52969, "loss_cls": 4.1348, "loss": 4.1348, "time": 0.83426} +{"mode": "train", "epoch": 41, "iter": 2300, "lr": 0.08298, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28266, "top5_acc": 0.53203, "loss_cls": 4.12628, "loss": 4.12628, "time": 0.83645} +{"mode": "train", "epoch": 41, "iter": 2400, "lr": 0.08296, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27297, "top5_acc": 0.52969, "loss_cls": 4.13919, "loss": 4.13919, "time": 0.84106} +{"mode": "train", "epoch": 41, "iter": 2500, "lr": 0.08293, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27297, "top5_acc": 0.53812, "loss_cls": 4.1449, "loss": 4.1449, "time": 0.83599} +{"mode": "train", "epoch": 41, "iter": 2600, "lr": 0.08291, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27578, "top5_acc": 0.52516, "loss_cls": 4.2017, "loss": 4.2017, "time": 0.83243} +{"mode": "train", "epoch": 41, "iter": 2700, "lr": 0.08289, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28078, "top5_acc": 0.545, "loss_cls": 4.10919, "loss": 4.10919, "time": 0.8276} +{"mode": "train", "epoch": 41, "iter": 2800, "lr": 0.08287, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27156, "top5_acc": 0.52953, "loss_cls": 4.14311, "loss": 4.14311, "time": 0.84135} +{"mode": "train", "epoch": 41, "iter": 2900, "lr": 0.08285, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.2775, "top5_acc": 0.52812, "loss_cls": 4.14345, "loss": 4.14345, "time": 0.82193} +{"mode": "train", "epoch": 41, "iter": 3000, "lr": 0.08283, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28625, "top5_acc": 0.53656, "loss_cls": 4.11725, "loss": 4.11725, "time": 0.82451} +{"mode": "train", "epoch": 41, "iter": 3100, "lr": 0.08281, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27516, "top5_acc": 0.52438, "loss_cls": 4.16941, "loss": 4.16941, "time": 0.83445} +{"mode": "train", "epoch": 41, "iter": 3200, "lr": 0.08279, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27969, "top5_acc": 0.52438, "loss_cls": 4.13853, "loss": 4.13853, "time": 0.83466} +{"mode": "train", "epoch": 41, "iter": 3300, "lr": 0.08277, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27078, "top5_acc": 0.53391, "loss_cls": 4.15054, "loss": 4.15054, "time": 0.82623} +{"mode": "train", "epoch": 41, "iter": 3400, "lr": 0.08274, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27891, "top5_acc": 0.53234, "loss_cls": 4.14984, "loss": 4.14984, "time": 0.83425} +{"mode": "train", "epoch": 41, "iter": 3500, "lr": 0.08272, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27359, "top5_acc": 0.52812, "loss_cls": 4.17488, "loss": 4.17488, "time": 0.82708} +{"mode": "train", "epoch": 41, "iter": 3600, "lr": 0.0827, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.27906, "top5_acc": 0.52766, "loss_cls": 4.1609, "loss": 4.1609, "time": 0.83616} +{"mode": "train", "epoch": 41, "iter": 3700, "lr": 0.08268, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26969, "top5_acc": 0.53156, "loss_cls": 4.179, "loss": 4.179, "time": 0.83741} +{"mode": "val", "epoch": 41, "iter": 309, "lr": 0.08267, "top1_acc": 0.19136, "top5_acc": 0.41311, "mean_class_accuracy": 0.19122} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.08265, "memory": 15990, "data_time": 1.26315, "top1_acc": 0.28813, "top5_acc": 0.55594, "loss_cls": 4.03846, "loss": 4.03846, "time": 2.2497} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.08263, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29109, "top5_acc": 0.53188, "loss_cls": 4.11919, "loss": 4.11919, "time": 0.83024} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.08261, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29109, "top5_acc": 0.54391, "loss_cls": 4.0839, "loss": 4.0839, "time": 0.83075} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.08259, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26656, "top5_acc": 0.53312, "loss_cls": 4.1505, "loss": 4.1505, "time": 0.83465} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.08257, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27266, "top5_acc": 0.51391, "loss_cls": 4.20695, "loss": 4.20695, "time": 0.83216} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.08254, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27562, "top5_acc": 0.53516, "loss_cls": 4.14989, "loss": 4.14989, "time": 0.82834} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.08252, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27156, "top5_acc": 0.52281, "loss_cls": 4.15868, "loss": 4.15868, "time": 0.82713} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.0825, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27844, "top5_acc": 0.53047, "loss_cls": 4.13709, "loss": 4.13709, "time": 0.83402} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.08248, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26625, "top5_acc": 0.52344, "loss_cls": 4.19814, "loss": 4.19814, "time": 0.83209} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.08246, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27656, "top5_acc": 0.53, "loss_cls": 4.15486, "loss": 4.15486, "time": 0.83241} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.08244, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27625, "top5_acc": 0.53625, "loss_cls": 4.12389, "loss": 4.12389, "time": 0.83364} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.08242, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2775, "top5_acc": 0.53438, "loss_cls": 4.14521, "loss": 4.14521, "time": 0.83643} +{"mode": "train", "epoch": 42, "iter": 1300, "lr": 0.0824, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28453, "top5_acc": 0.54188, "loss_cls": 4.11566, "loss": 4.11566, "time": 0.83609} +{"mode": "train", "epoch": 42, "iter": 1400, "lr": 0.08237, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28406, "top5_acc": 0.53641, "loss_cls": 4.11634, "loss": 4.11634, "time": 0.83317} +{"mode": "train", "epoch": 42, "iter": 1500, "lr": 0.08235, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29, "top5_acc": 0.53359, "loss_cls": 4.12733, "loss": 4.12733, "time": 0.83569} +{"mode": "train", "epoch": 42, "iter": 1600, "lr": 0.08233, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27578, "top5_acc": 0.51562, "loss_cls": 4.21149, "loss": 4.21149, "time": 0.8399} +{"mode": "train", "epoch": 42, "iter": 1700, "lr": 0.08231, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28234, "top5_acc": 0.53375, "loss_cls": 4.12118, "loss": 4.12118, "time": 0.83751} +{"mode": "train", "epoch": 42, "iter": 1800, "lr": 0.08229, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28047, "top5_acc": 0.53578, "loss_cls": 4.1177, "loss": 4.1177, "time": 0.83324} +{"mode": "train", "epoch": 42, "iter": 1900, "lr": 0.08227, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26531, "top5_acc": 0.53, "loss_cls": 4.17232, "loss": 4.17232, "time": 0.83533} +{"mode": "train", "epoch": 42, "iter": 2000, "lr": 0.08225, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28172, "top5_acc": 0.53344, "loss_cls": 4.13478, "loss": 4.13478, "time": 0.83536} +{"mode": "train", "epoch": 42, "iter": 2100, "lr": 0.08222, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27, "top5_acc": 0.52984, "loss_cls": 4.16967, "loss": 4.16967, "time": 0.83415} +{"mode": "train", "epoch": 42, "iter": 2200, "lr": 0.0822, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27609, "top5_acc": 0.52969, "loss_cls": 4.12563, "loss": 4.12563, "time": 0.83438} +{"mode": "train", "epoch": 42, "iter": 2300, "lr": 0.08218, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29109, "top5_acc": 0.53641, "loss_cls": 4.10764, "loss": 4.10764, "time": 0.82962} +{"mode": "train", "epoch": 42, "iter": 2400, "lr": 0.08216, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28094, "top5_acc": 0.54031, "loss_cls": 4.13286, "loss": 4.13286, "time": 0.83572} +{"mode": "train", "epoch": 42, "iter": 2500, "lr": 0.08214, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29141, "top5_acc": 0.54453, "loss_cls": 4.06346, "loss": 4.06346, "time": 0.83193} +{"mode": "train", "epoch": 42, "iter": 2600, "lr": 0.08212, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27828, "top5_acc": 0.52281, "loss_cls": 4.14976, "loss": 4.14976, "time": 0.82967} +{"mode": "train", "epoch": 42, "iter": 2700, "lr": 0.0821, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2725, "top5_acc": 0.5325, "loss_cls": 4.1492, "loss": 4.1492, "time": 0.8327} +{"mode": "train", "epoch": 42, "iter": 2800, "lr": 0.08207, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27047, "top5_acc": 0.52875, "loss_cls": 4.17502, "loss": 4.17502, "time": 0.83303} +{"mode": "train", "epoch": 42, "iter": 2900, "lr": 0.08205, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27875, "top5_acc": 0.53453, "loss_cls": 4.16119, "loss": 4.16119, "time": 0.82306} +{"mode": "train", "epoch": 42, "iter": 3000, "lr": 0.08203, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28047, "top5_acc": 0.535, "loss_cls": 4.12672, "loss": 4.12672, "time": 0.83362} +{"mode": "train", "epoch": 42, "iter": 3100, "lr": 0.08201, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27594, "top5_acc": 0.53609, "loss_cls": 4.1445, "loss": 4.1445, "time": 0.83737} +{"mode": "train", "epoch": 42, "iter": 3200, "lr": 0.08199, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28031, "top5_acc": 0.52828, "loss_cls": 4.16842, "loss": 4.16842, "time": 0.8265} +{"mode": "train", "epoch": 42, "iter": 3300, "lr": 0.08197, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28562, "top5_acc": 0.53594, "loss_cls": 4.1145, "loss": 4.1145, "time": 0.82696} +{"mode": "train", "epoch": 42, "iter": 3400, "lr": 0.08195, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26875, "top5_acc": 0.52906, "loss_cls": 4.18885, "loss": 4.18885, "time": 0.83192} +{"mode": "train", "epoch": 42, "iter": 3500, "lr": 0.08192, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27406, "top5_acc": 0.53234, "loss_cls": 4.17064, "loss": 4.17064, "time": 0.83183} +{"mode": "train", "epoch": 42, "iter": 3600, "lr": 0.0819, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27812, "top5_acc": 0.53734, "loss_cls": 4.12318, "loss": 4.12318, "time": 0.83858} +{"mode": "train", "epoch": 42, "iter": 3700, "lr": 0.08188, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27344, "top5_acc": 0.53406, "loss_cls": 4.11749, "loss": 4.11749, "time": 0.81656} +{"mode": "val", "epoch": 42, "iter": 309, "lr": 0.08187, "top1_acc": 0.22737, "top5_acc": 0.47252, "mean_class_accuracy": 0.22736} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.08185, "memory": 15990, "data_time": 1.25583, "top1_acc": 0.28656, "top5_acc": 0.54422, "loss_cls": 4.08753, "loss": 4.08753, "time": 2.24355} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.08183, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27484, "top5_acc": 0.53203, "loss_cls": 4.12098, "loss": 4.12098, "time": 0.834} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.08181, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27547, "top5_acc": 0.53422, "loss_cls": 4.14302, "loss": 4.14302, "time": 0.83167} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.08179, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27891, "top5_acc": 0.54109, "loss_cls": 4.10705, "loss": 4.10705, "time": 0.83172} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.08176, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28391, "top5_acc": 0.54219, "loss_cls": 4.09772, "loss": 4.09772, "time": 0.82905} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.08174, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27703, "top5_acc": 0.53984, "loss_cls": 4.13506, "loss": 4.13506, "time": 0.83311} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.08172, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29078, "top5_acc": 0.54516, "loss_cls": 4.08513, "loss": 4.08513, "time": 0.82963} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.0817, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28297, "top5_acc": 0.53156, "loss_cls": 4.1465, "loss": 4.1465, "time": 0.83718} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.08168, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27625, "top5_acc": 0.52859, "loss_cls": 4.16039, "loss": 4.16039, "time": 0.83556} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.08166, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27516, "top5_acc": 0.52609, "loss_cls": 4.17042, "loss": 4.17042, "time": 0.83534} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.08163, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27594, "top5_acc": 0.53125, "loss_cls": 4.12938, "loss": 4.12938, "time": 0.82923} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.08161, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27984, "top5_acc": 0.53516, "loss_cls": 4.13029, "loss": 4.13029, "time": 0.83465} +{"mode": "train", "epoch": 43, "iter": 1300, "lr": 0.08159, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27406, "top5_acc": 0.53016, "loss_cls": 4.15936, "loss": 4.15936, "time": 0.83146} +{"mode": "train", "epoch": 43, "iter": 1400, "lr": 0.08157, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27656, "top5_acc": 0.53438, "loss_cls": 4.13954, "loss": 4.13954, "time": 0.82763} +{"mode": "train", "epoch": 43, "iter": 1500, "lr": 0.08155, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27797, "top5_acc": 0.53422, "loss_cls": 4.15196, "loss": 4.15196, "time": 0.82826} +{"mode": "train", "epoch": 43, "iter": 1600, "lr": 0.08153, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27562, "top5_acc": 0.53594, "loss_cls": 4.13262, "loss": 4.13262, "time": 0.82695} +{"mode": "train", "epoch": 43, "iter": 1700, "lr": 0.0815, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28609, "top5_acc": 0.53578, "loss_cls": 4.11263, "loss": 4.11263, "time": 0.83427} +{"mode": "train", "epoch": 43, "iter": 1800, "lr": 0.08148, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.275, "top5_acc": 0.52797, "loss_cls": 4.15517, "loss": 4.15517, "time": 0.83607} +{"mode": "train", "epoch": 43, "iter": 1900, "lr": 0.08146, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27297, "top5_acc": 0.54094, "loss_cls": 4.12703, "loss": 4.12703, "time": 0.83216} +{"mode": "train", "epoch": 43, "iter": 2000, "lr": 0.08144, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28203, "top5_acc": 0.53469, "loss_cls": 4.11029, "loss": 4.11029, "time": 0.82779} +{"mode": "train", "epoch": 43, "iter": 2100, "lr": 0.08142, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28188, "top5_acc": 0.52812, "loss_cls": 4.14916, "loss": 4.14916, "time": 0.83709} +{"mode": "train", "epoch": 43, "iter": 2200, "lr": 0.0814, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28406, "top5_acc": 0.54578, "loss_cls": 4.09182, "loss": 4.09182, "time": 0.83858} +{"mode": "train", "epoch": 43, "iter": 2300, "lr": 0.08137, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28656, "top5_acc": 0.53531, "loss_cls": 4.11278, "loss": 4.11278, "time": 0.83479} +{"mode": "train", "epoch": 43, "iter": 2400, "lr": 0.08135, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27406, "top5_acc": 0.53344, "loss_cls": 4.14584, "loss": 4.14584, "time": 0.83487} +{"mode": "train", "epoch": 43, "iter": 2500, "lr": 0.08133, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27719, "top5_acc": 0.53094, "loss_cls": 4.13774, "loss": 4.13774, "time": 0.8335} +{"mode": "train", "epoch": 43, "iter": 2600, "lr": 0.08131, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27, "top5_acc": 0.5275, "loss_cls": 4.15259, "loss": 4.15259, "time": 0.82936} +{"mode": "train", "epoch": 43, "iter": 2700, "lr": 0.08129, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27578, "top5_acc": 0.525, "loss_cls": 4.16858, "loss": 4.16858, "time": 0.83829} +{"mode": "train", "epoch": 43, "iter": 2800, "lr": 0.08126, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27641, "top5_acc": 0.52922, "loss_cls": 4.15971, "loss": 4.15971, "time": 0.82784} +{"mode": "train", "epoch": 43, "iter": 2900, "lr": 0.08124, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27266, "top5_acc": 0.52984, "loss_cls": 4.15755, "loss": 4.15755, "time": 0.82964} +{"mode": "train", "epoch": 43, "iter": 3000, "lr": 0.08122, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28359, "top5_acc": 0.54688, "loss_cls": 4.08593, "loss": 4.08593, "time": 0.84094} +{"mode": "train", "epoch": 43, "iter": 3100, "lr": 0.0812, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.2775, "top5_acc": 0.53406, "loss_cls": 4.15142, "loss": 4.15142, "time": 0.82714} +{"mode": "train", "epoch": 43, "iter": 3200, "lr": 0.08118, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27469, "top5_acc": 0.52781, "loss_cls": 4.15332, "loss": 4.15332, "time": 0.82433} +{"mode": "train", "epoch": 43, "iter": 3300, "lr": 0.08116, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28234, "top5_acc": 0.54047, "loss_cls": 4.12614, "loss": 4.12614, "time": 0.83154} +{"mode": "train", "epoch": 43, "iter": 3400, "lr": 0.08113, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28016, "top5_acc": 0.53656, "loss_cls": 4.11824, "loss": 4.11824, "time": 0.83314} +{"mode": "train", "epoch": 43, "iter": 3500, "lr": 0.08111, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.27047, "top5_acc": 0.52688, "loss_cls": 4.19536, "loss": 4.19536, "time": 0.8239} +{"mode": "train", "epoch": 43, "iter": 3600, "lr": 0.08109, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27922, "top5_acc": 0.53375, "loss_cls": 4.16122, "loss": 4.16122, "time": 0.819} +{"mode": "train", "epoch": 43, "iter": 3700, "lr": 0.08107, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.53656, "loss_cls": 4.12614, "loss": 4.12614, "time": 0.8203} +{"mode": "val", "epoch": 43, "iter": 309, "lr": 0.08106, "top1_acc": 0.19718, "top5_acc": 0.41909, "mean_class_accuracy": 0.19691} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.08104, "memory": 15990, "data_time": 1.24274, "top1_acc": 0.28703, "top5_acc": 0.53578, "loss_cls": 4.11593, "loss": 4.11593, "time": 2.23554} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.08101, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28766, "top5_acc": 0.55031, "loss_cls": 4.06922, "loss": 4.06922, "time": 0.83461} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.08099, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29063, "top5_acc": 0.54172, "loss_cls": 4.0749, "loss": 4.0749, "time": 0.83305} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.08097, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28, "top5_acc": 0.53703, "loss_cls": 4.1243, "loss": 4.1243, "time": 0.83306} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.08095, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27953, "top5_acc": 0.53438, "loss_cls": 4.08628, "loss": 4.08628, "time": 0.83061} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.08093, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27484, "top5_acc": 0.53094, "loss_cls": 4.15123, "loss": 4.15123, "time": 0.83561} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.0809, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28188, "top5_acc": 0.54422, "loss_cls": 4.12069, "loss": 4.12069, "time": 0.83469} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.08088, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28109, "top5_acc": 0.53469, "loss_cls": 4.11229, "loss": 4.11229, "time": 0.83651} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.08086, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27531, "top5_acc": 0.53484, "loss_cls": 4.12892, "loss": 4.12892, "time": 0.83375} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.08084, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27547, "top5_acc": 0.53016, "loss_cls": 4.14517, "loss": 4.14517, "time": 0.83318} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.08082, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27406, "top5_acc": 0.53422, "loss_cls": 4.12726, "loss": 4.12726, "time": 0.83427} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.08079, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27047, "top5_acc": 0.52625, "loss_cls": 4.17511, "loss": 4.17511, "time": 0.8294} +{"mode": "train", "epoch": 44, "iter": 1300, "lr": 0.08077, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27969, "top5_acc": 0.54016, "loss_cls": 4.11661, "loss": 4.11661, "time": 0.8294} +{"mode": "train", "epoch": 44, "iter": 1400, "lr": 0.08075, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27562, "top5_acc": 0.53625, "loss_cls": 4.13417, "loss": 4.13417, "time": 0.83356} +{"mode": "train", "epoch": 44, "iter": 1500, "lr": 0.08073, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28125, "top5_acc": 0.53734, "loss_cls": 4.11785, "loss": 4.11785, "time": 0.83628} +{"mode": "train", "epoch": 44, "iter": 1600, "lr": 0.08071, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28, "top5_acc": 0.53891, "loss_cls": 4.10947, "loss": 4.10947, "time": 0.83587} +{"mode": "train", "epoch": 44, "iter": 1700, "lr": 0.08068, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28813, "top5_acc": 0.53766, "loss_cls": 4.12571, "loss": 4.12571, "time": 0.83655} +{"mode": "train", "epoch": 44, "iter": 1800, "lr": 0.08066, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27906, "top5_acc": 0.53312, "loss_cls": 4.14624, "loss": 4.14624, "time": 0.83291} +{"mode": "train", "epoch": 44, "iter": 1900, "lr": 0.08064, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27922, "top5_acc": 0.53953, "loss_cls": 4.104, "loss": 4.104, "time": 0.83517} +{"mode": "train", "epoch": 44, "iter": 2000, "lr": 0.08062, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28516, "top5_acc": 0.53641, "loss_cls": 4.11005, "loss": 4.11005, "time": 0.82871} +{"mode": "train", "epoch": 44, "iter": 2100, "lr": 0.0806, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27656, "top5_acc": 0.53281, "loss_cls": 4.12583, "loss": 4.12583, "time": 0.82632} +{"mode": "train", "epoch": 44, "iter": 2200, "lr": 0.08057, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28688, "top5_acc": 0.5375, "loss_cls": 4.10777, "loss": 4.10777, "time": 0.83239} +{"mode": "train", "epoch": 44, "iter": 2300, "lr": 0.08055, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28562, "top5_acc": 0.53719, "loss_cls": 4.09178, "loss": 4.09178, "time": 0.8316} +{"mode": "train", "epoch": 44, "iter": 2400, "lr": 0.08053, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27844, "top5_acc": 0.54125, "loss_cls": 4.09103, "loss": 4.09103, "time": 0.8352} +{"mode": "train", "epoch": 44, "iter": 2500, "lr": 0.08051, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27875, "top5_acc": 0.53359, "loss_cls": 4.15869, "loss": 4.15869, "time": 0.83369} +{"mode": "train", "epoch": 44, "iter": 2600, "lr": 0.08048, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.26469, "top5_acc": 0.52812, "loss_cls": 4.1474, "loss": 4.1474, "time": 0.8371} +{"mode": "train", "epoch": 44, "iter": 2700, "lr": 0.08046, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28016, "top5_acc": 0.54156, "loss_cls": 4.10204, "loss": 4.10204, "time": 0.82986} +{"mode": "train", "epoch": 44, "iter": 2800, "lr": 0.08044, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27328, "top5_acc": 0.53438, "loss_cls": 4.13755, "loss": 4.13755, "time": 0.82688} +{"mode": "train", "epoch": 44, "iter": 2900, "lr": 0.08042, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27703, "top5_acc": 0.53453, "loss_cls": 4.12469, "loss": 4.12469, "time": 0.83612} +{"mode": "train", "epoch": 44, "iter": 3000, "lr": 0.0804, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27406, "top5_acc": 0.52859, "loss_cls": 4.17161, "loss": 4.17161, "time": 0.82624} +{"mode": "train", "epoch": 44, "iter": 3100, "lr": 0.08037, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27641, "top5_acc": 0.53656, "loss_cls": 4.12776, "loss": 4.12776, "time": 0.82885} +{"mode": "train", "epoch": 44, "iter": 3200, "lr": 0.08035, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27359, "top5_acc": 0.53219, "loss_cls": 4.15538, "loss": 4.15538, "time": 0.82613} +{"mode": "train", "epoch": 44, "iter": 3300, "lr": 0.08033, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27156, "top5_acc": 0.52875, "loss_cls": 4.17641, "loss": 4.17641, "time": 0.83322} +{"mode": "train", "epoch": 44, "iter": 3400, "lr": 0.08031, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27781, "top5_acc": 0.52828, "loss_cls": 4.13866, "loss": 4.13866, "time": 0.83148} +{"mode": "train", "epoch": 44, "iter": 3500, "lr": 0.08028, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28172, "top5_acc": 0.53516, "loss_cls": 4.1478, "loss": 4.1478, "time": 0.83514} +{"mode": "train", "epoch": 44, "iter": 3600, "lr": 0.08026, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27437, "top5_acc": 0.52969, "loss_cls": 4.174, "loss": 4.174, "time": 0.81714} +{"mode": "train", "epoch": 44, "iter": 3700, "lr": 0.08024, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28484, "top5_acc": 0.53406, "loss_cls": 4.13539, "loss": 4.13539, "time": 0.82023} +{"mode": "val", "epoch": 44, "iter": 309, "lr": 0.08023, "top1_acc": 0.20002, "top5_acc": 0.43271, "mean_class_accuracy": 0.19993} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.08021, "memory": 15990, "data_time": 1.25847, "top1_acc": 0.28156, "top5_acc": 0.53859, "loss_cls": 4.0891, "loss": 4.0891, "time": 2.25108} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.08019, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28781, "top5_acc": 0.54766, "loss_cls": 4.06669, "loss": 4.06669, "time": 0.83406} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.08016, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28547, "top5_acc": 0.53312, "loss_cls": 4.13041, "loss": 4.13041, "time": 0.83315} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.08014, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28312, "top5_acc": 0.53656, "loss_cls": 4.11871, "loss": 4.11871, "time": 0.83308} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.08012, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28625, "top5_acc": 0.53891, "loss_cls": 4.09962, "loss": 4.09962, "time": 0.83776} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.0801, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28422, "top5_acc": 0.54516, "loss_cls": 4.07667, "loss": 4.07667, "time": 0.83454} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.08007, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28078, "top5_acc": 0.53922, "loss_cls": 4.08829, "loss": 4.08829, "time": 0.83352} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.08005, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28266, "top5_acc": 0.54125, "loss_cls": 4.10732, "loss": 4.10732, "time": 0.83591} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.08003, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27453, "top5_acc": 0.53109, "loss_cls": 4.13267, "loss": 4.13267, "time": 0.83672} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.08001, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27609, "top5_acc": 0.54031, "loss_cls": 4.1144, "loss": 4.1144, "time": 0.83259} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.07998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27891, "top5_acc": 0.53891, "loss_cls": 4.11287, "loss": 4.11287, "time": 0.83607} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.07996, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27672, "top5_acc": 0.54078, "loss_cls": 4.1325, "loss": 4.1325, "time": 0.82844} +{"mode": "train", "epoch": 45, "iter": 1300, "lr": 0.07994, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27453, "top5_acc": 0.51969, "loss_cls": 4.19454, "loss": 4.19454, "time": 0.83429} +{"mode": "train", "epoch": 45, "iter": 1400, "lr": 0.07992, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28469, "top5_acc": 0.53672, "loss_cls": 4.12544, "loss": 4.12544, "time": 0.83094} +{"mode": "train", "epoch": 45, "iter": 1500, "lr": 0.0799, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28047, "top5_acc": 0.53766, "loss_cls": 4.1249, "loss": 4.1249, "time": 0.83036} +{"mode": "train", "epoch": 45, "iter": 1600, "lr": 0.07987, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28406, "top5_acc": 0.53141, "loss_cls": 4.13142, "loss": 4.13142, "time": 0.82921} +{"mode": "train", "epoch": 45, "iter": 1700, "lr": 0.07985, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28062, "top5_acc": 0.54297, "loss_cls": 4.1036, "loss": 4.1036, "time": 0.83372} +{"mode": "train", "epoch": 45, "iter": 1800, "lr": 0.07983, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28156, "top5_acc": 0.52578, "loss_cls": 4.14619, "loss": 4.14619, "time": 0.83671} +{"mode": "train", "epoch": 45, "iter": 1900, "lr": 0.07981, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28609, "top5_acc": 0.54359, "loss_cls": 4.07691, "loss": 4.07691, "time": 0.8357} +{"mode": "train", "epoch": 45, "iter": 2000, "lr": 0.07978, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27609, "top5_acc": 0.53578, "loss_cls": 4.11829, "loss": 4.11829, "time": 0.83491} +{"mode": "train", "epoch": 45, "iter": 2100, "lr": 0.07976, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28188, "top5_acc": 0.53062, "loss_cls": 4.14193, "loss": 4.14193, "time": 0.83606} +{"mode": "train", "epoch": 45, "iter": 2200, "lr": 0.07974, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27859, "top5_acc": 0.53141, "loss_cls": 4.14966, "loss": 4.14966, "time": 0.83504} +{"mode": "train", "epoch": 45, "iter": 2300, "lr": 0.07972, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27844, "top5_acc": 0.535, "loss_cls": 4.12721, "loss": 4.12721, "time": 0.83365} +{"mode": "train", "epoch": 45, "iter": 2400, "lr": 0.07969, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28375, "top5_acc": 0.53766, "loss_cls": 4.10855, "loss": 4.10855, "time": 0.8331} +{"mode": "train", "epoch": 45, "iter": 2500, "lr": 0.07967, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28344, "top5_acc": 0.53078, "loss_cls": 4.14863, "loss": 4.14863, "time": 0.82801} +{"mode": "train", "epoch": 45, "iter": 2600, "lr": 0.07965, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28328, "top5_acc": 0.53578, "loss_cls": 4.12689, "loss": 4.12689, "time": 0.83828} +{"mode": "train", "epoch": 45, "iter": 2700, "lr": 0.07963, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28078, "top5_acc": 0.52906, "loss_cls": 4.13955, "loss": 4.13955, "time": 0.8202} +{"mode": "train", "epoch": 45, "iter": 2800, "lr": 0.0796, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28125, "top5_acc": 0.54641, "loss_cls": 4.11567, "loss": 4.11567, "time": 0.83115} +{"mode": "train", "epoch": 45, "iter": 2900, "lr": 0.07958, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28328, "top5_acc": 0.53828, "loss_cls": 4.10472, "loss": 4.10472, "time": 0.83607} +{"mode": "train", "epoch": 45, "iter": 3000, "lr": 0.07956, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27562, "top5_acc": 0.53016, "loss_cls": 4.13941, "loss": 4.13941, "time": 0.82891} +{"mode": "train", "epoch": 45, "iter": 3100, "lr": 0.07954, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28438, "top5_acc": 0.54281, "loss_cls": 4.08465, "loss": 4.08465, "time": 0.8286} +{"mode": "train", "epoch": 45, "iter": 3200, "lr": 0.07951, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27406, "top5_acc": 0.53047, "loss_cls": 4.19041, "loss": 4.19041, "time": 0.83493} +{"mode": "train", "epoch": 45, "iter": 3300, "lr": 0.07949, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28156, "top5_acc": 0.54734, "loss_cls": 4.07659, "loss": 4.07659, "time": 0.8324} +{"mode": "train", "epoch": 45, "iter": 3400, "lr": 0.07947, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.27281, "top5_acc": 0.52609, "loss_cls": 4.17193, "loss": 4.17193, "time": 0.82923} +{"mode": "train", "epoch": 45, "iter": 3500, "lr": 0.07945, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27922, "top5_acc": 0.54547, "loss_cls": 4.10779, "loss": 4.10779, "time": 0.83089} +{"mode": "train", "epoch": 45, "iter": 3600, "lr": 0.07942, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29469, "top5_acc": 0.54578, "loss_cls": 4.08696, "loss": 4.08696, "time": 0.82783} +{"mode": "train", "epoch": 45, "iter": 3700, "lr": 0.0794, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27078, "top5_acc": 0.52328, "loss_cls": 4.16667, "loss": 4.16667, "time": 0.82835} +{"mode": "val", "epoch": 45, "iter": 309, "lr": 0.07939, "top1_acc": 0.20037, "top5_acc": 0.43519, "mean_class_accuracy": 0.20023} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.07937, "memory": 15990, "data_time": 1.24931, "top1_acc": 0.30109, "top5_acc": 0.55562, "loss_cls": 4.03638, "loss": 4.03638, "time": 2.23945} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.07934, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28062, "top5_acc": 0.54516, "loss_cls": 4.09352, "loss": 4.09352, "time": 0.83236} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.07932, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28391, "top5_acc": 0.54266, "loss_cls": 4.06612, "loss": 4.06612, "time": 0.82965} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.0793, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28875, "top5_acc": 0.54531, "loss_cls": 4.08857, "loss": 4.08857, "time": 0.83154} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.07928, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28156, "top5_acc": 0.53719, "loss_cls": 4.11608, "loss": 4.11608, "time": 0.83057} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.07925, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28078, "top5_acc": 0.53062, "loss_cls": 4.12564, "loss": 4.12564, "time": 0.83407} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.07923, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27953, "top5_acc": 0.53938, "loss_cls": 4.11142, "loss": 4.11142, "time": 0.83425} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.07921, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27562, "top5_acc": 0.53422, "loss_cls": 4.1285, "loss": 4.1285, "time": 0.83029} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.07919, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27641, "top5_acc": 0.52656, "loss_cls": 4.14837, "loss": 4.14837, "time": 0.82873} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.07916, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28781, "top5_acc": 0.5475, "loss_cls": 4.06455, "loss": 4.06455, "time": 0.83128} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.07914, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28578, "top5_acc": 0.53688, "loss_cls": 4.08486, "loss": 4.08486, "time": 0.82748} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.07912, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28328, "top5_acc": 0.54344, "loss_cls": 4.09085, "loss": 4.09085, "time": 0.83285} +{"mode": "train", "epoch": 46, "iter": 1300, "lr": 0.07909, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28531, "top5_acc": 0.53125, "loss_cls": 4.12602, "loss": 4.12602, "time": 0.83546} +{"mode": "train", "epoch": 46, "iter": 1400, "lr": 0.07907, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28156, "top5_acc": 0.53828, "loss_cls": 4.10758, "loss": 4.10758, "time": 0.83382} +{"mode": "train", "epoch": 46, "iter": 1500, "lr": 0.07905, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28172, "top5_acc": 0.54188, "loss_cls": 4.10072, "loss": 4.10072, "time": 0.83487} +{"mode": "train", "epoch": 46, "iter": 1600, "lr": 0.07903, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27359, "top5_acc": 0.5325, "loss_cls": 4.17067, "loss": 4.17067, "time": 0.83649} +{"mode": "train", "epoch": 46, "iter": 1700, "lr": 0.079, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28125, "top5_acc": 0.53328, "loss_cls": 4.13795, "loss": 4.13795, "time": 0.83354} +{"mode": "train", "epoch": 46, "iter": 1800, "lr": 0.07898, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27297, "top5_acc": 0.5375, "loss_cls": 4.11092, "loss": 4.11092, "time": 0.82149} +{"mode": "train", "epoch": 46, "iter": 1900, "lr": 0.07896, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29438, "top5_acc": 0.54562, "loss_cls": 4.09978, "loss": 4.09978, "time": 0.82092} +{"mode": "train", "epoch": 46, "iter": 2000, "lr": 0.07894, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29141, "top5_acc": 0.54172, "loss_cls": 4.0956, "loss": 4.0956, "time": 0.82021} +{"mode": "train", "epoch": 46, "iter": 2100, "lr": 0.07891, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28125, "top5_acc": 0.54297, "loss_cls": 4.10245, "loss": 4.10245, "time": 0.81464} +{"mode": "train", "epoch": 46, "iter": 2200, "lr": 0.07889, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28062, "top5_acc": 0.54422, "loss_cls": 4.08964, "loss": 4.08964, "time": 0.82867} +{"mode": "train", "epoch": 46, "iter": 2300, "lr": 0.07887, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28078, "top5_acc": 0.53938, "loss_cls": 4.11423, "loss": 4.11423, "time": 0.81688} +{"mode": "train", "epoch": 46, "iter": 2400, "lr": 0.07884, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28875, "top5_acc": 0.54312, "loss_cls": 4.10913, "loss": 4.10913, "time": 0.81427} +{"mode": "train", "epoch": 46, "iter": 2500, "lr": 0.07882, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28484, "top5_acc": 0.53859, "loss_cls": 4.10604, "loss": 4.10604, "time": 0.82424} +{"mode": "train", "epoch": 46, "iter": 2600, "lr": 0.0788, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2775, "top5_acc": 0.52812, "loss_cls": 4.15793, "loss": 4.15793, "time": 0.81597} +{"mode": "train", "epoch": 46, "iter": 2700, "lr": 0.07878, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28031, "top5_acc": 0.53469, "loss_cls": 4.11546, "loss": 4.11546, "time": 0.825} +{"mode": "train", "epoch": 46, "iter": 2800, "lr": 0.07875, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27969, "top5_acc": 0.53844, "loss_cls": 4.11794, "loss": 4.11794, "time": 0.82085} +{"mode": "train", "epoch": 46, "iter": 2900, "lr": 0.07873, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27734, "top5_acc": 0.54188, "loss_cls": 4.11451, "loss": 4.11451, "time": 0.81597} +{"mode": "train", "epoch": 46, "iter": 3000, "lr": 0.07871, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27094, "top5_acc": 0.52516, "loss_cls": 4.17006, "loss": 4.17006, "time": 0.82045} +{"mode": "train", "epoch": 46, "iter": 3100, "lr": 0.07868, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28969, "top5_acc": 0.54047, "loss_cls": 4.07277, "loss": 4.07277, "time": 0.82471} +{"mode": "train", "epoch": 46, "iter": 3200, "lr": 0.07866, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28266, "top5_acc": 0.54547, "loss_cls": 4.1057, "loss": 4.1057, "time": 0.82267} +{"mode": "train", "epoch": 46, "iter": 3300, "lr": 0.07864, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28016, "top5_acc": 0.53453, "loss_cls": 4.12603, "loss": 4.12603, "time": 0.8218} +{"mode": "train", "epoch": 46, "iter": 3400, "lr": 0.07862, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27406, "top5_acc": 0.53484, "loss_cls": 4.15976, "loss": 4.15976, "time": 0.81478} +{"mode": "train", "epoch": 46, "iter": 3500, "lr": 0.07859, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28562, "top5_acc": 0.53203, "loss_cls": 4.16018, "loss": 4.16018, "time": 0.81708} +{"mode": "train", "epoch": 46, "iter": 3600, "lr": 0.07857, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28484, "top5_acc": 0.53406, "loss_cls": 4.14439, "loss": 4.14439, "time": 0.82163} +{"mode": "train", "epoch": 46, "iter": 3700, "lr": 0.07855, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28438, "top5_acc": 0.53062, "loss_cls": 4.11587, "loss": 4.11587, "time": 0.82379} +{"mode": "val", "epoch": 46, "iter": 309, "lr": 0.07854, "top1_acc": 0.20595, "top5_acc": 0.44249, "mean_class_accuracy": 0.2057} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.07851, "memory": 15990, "data_time": 1.30411, "top1_acc": 0.29203, "top5_acc": 0.55937, "loss_cls": 4.0207, "loss": 4.0207, "time": 2.29978} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.07849, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28078, "top5_acc": 0.54125, "loss_cls": 4.08041, "loss": 4.08041, "time": 0.82046} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.07847, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28734, "top5_acc": 0.54937, "loss_cls": 4.04145, "loss": 4.04145, "time": 0.81952} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.07844, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29359, "top5_acc": 0.54766, "loss_cls": 4.06852, "loss": 4.06852, "time": 0.81952} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.07842, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28047, "top5_acc": 0.53656, "loss_cls": 4.1095, "loss": 4.1095, "time": 0.81569} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.0784, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28719, "top5_acc": 0.54234, "loss_cls": 4.08348, "loss": 4.08348, "time": 0.82134} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.07838, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28484, "top5_acc": 0.53766, "loss_cls": 4.09407, "loss": 4.09407, "time": 0.81835} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.07835, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29203, "top5_acc": 0.54469, "loss_cls": 4.09216, "loss": 4.09216, "time": 0.81708} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.07833, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27828, "top5_acc": 0.53359, "loss_cls": 4.14355, "loss": 4.14355, "time": 0.81538} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.07831, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28594, "top5_acc": 0.53891, "loss_cls": 4.11516, "loss": 4.11516, "time": 0.81611} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.07828, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27297, "top5_acc": 0.52781, "loss_cls": 4.16574, "loss": 4.16574, "time": 0.81413} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.07826, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28016, "top5_acc": 0.53656, "loss_cls": 4.10984, "loss": 4.10984, "time": 0.81332} +{"mode": "train", "epoch": 47, "iter": 1300, "lr": 0.07824, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28406, "top5_acc": 0.54703, "loss_cls": 4.07498, "loss": 4.07498, "time": 0.81383} +{"mode": "train", "epoch": 47, "iter": 1400, "lr": 0.07821, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28125, "top5_acc": 0.53922, "loss_cls": 4.09564, "loss": 4.09564, "time": 0.81623} +{"mode": "train", "epoch": 47, "iter": 1500, "lr": 0.07819, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28969, "top5_acc": 0.54906, "loss_cls": 4.07007, "loss": 4.07007, "time": 0.81897} +{"mode": "train", "epoch": 47, "iter": 1600, "lr": 0.07817, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28562, "top5_acc": 0.53031, "loss_cls": 4.12421, "loss": 4.12421, "time": 0.81489} +{"mode": "train", "epoch": 47, "iter": 1700, "lr": 0.07814, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27406, "top5_acc": 0.53922, "loss_cls": 4.13916, "loss": 4.13916, "time": 0.81343} +{"mode": "train", "epoch": 47, "iter": 1800, "lr": 0.07812, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28172, "top5_acc": 0.53406, "loss_cls": 4.11845, "loss": 4.11845, "time": 0.81499} +{"mode": "train", "epoch": 47, "iter": 1900, "lr": 0.0781, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28203, "top5_acc": 0.53906, "loss_cls": 4.11115, "loss": 4.11115, "time": 0.81734} +{"mode": "train", "epoch": 47, "iter": 2000, "lr": 0.07808, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28438, "top5_acc": 0.52984, "loss_cls": 4.15697, "loss": 4.15697, "time": 0.81468} +{"mode": "train", "epoch": 47, "iter": 2100, "lr": 0.07805, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28484, "top5_acc": 0.53453, "loss_cls": 4.13599, "loss": 4.13599, "time": 0.8121} +{"mode": "train", "epoch": 47, "iter": 2200, "lr": 0.07803, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28062, "top5_acc": 0.53875, "loss_cls": 4.13003, "loss": 4.13003, "time": 0.81948} +{"mode": "train", "epoch": 47, "iter": 2300, "lr": 0.07801, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2825, "top5_acc": 0.53531, "loss_cls": 4.1294, "loss": 4.1294, "time": 0.81192} +{"mode": "train", "epoch": 47, "iter": 2400, "lr": 0.07798, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28688, "top5_acc": 0.53266, "loss_cls": 4.12531, "loss": 4.12531, "time": 0.82044} +{"mode": "train", "epoch": 47, "iter": 2500, "lr": 0.07796, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27859, "top5_acc": 0.53172, "loss_cls": 4.1425, "loss": 4.1425, "time": 0.81944} +{"mode": "train", "epoch": 47, "iter": 2600, "lr": 0.07794, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28484, "top5_acc": 0.54156, "loss_cls": 4.08657, "loss": 4.08657, "time": 0.82144} +{"mode": "train", "epoch": 47, "iter": 2700, "lr": 0.07791, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2675, "top5_acc": 0.52219, "loss_cls": 4.1341, "loss": 4.1341, "time": 0.82099} +{"mode": "train", "epoch": 47, "iter": 2800, "lr": 0.07789, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28406, "top5_acc": 0.54156, "loss_cls": 4.10264, "loss": 4.10264, "time": 0.82039} +{"mode": "train", "epoch": 47, "iter": 2900, "lr": 0.07787, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28047, "top5_acc": 0.53875, "loss_cls": 4.09612, "loss": 4.09612, "time": 0.81747} +{"mode": "train", "epoch": 47, "iter": 3000, "lr": 0.07784, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28172, "top5_acc": 0.53984, "loss_cls": 4.098, "loss": 4.098, "time": 0.82366} +{"mode": "train", "epoch": 47, "iter": 3100, "lr": 0.07782, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29, "top5_acc": 0.55484, "loss_cls": 4.06674, "loss": 4.06674, "time": 0.82581} +{"mode": "train", "epoch": 47, "iter": 3200, "lr": 0.0778, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27641, "top5_acc": 0.53234, "loss_cls": 4.13785, "loss": 4.13785, "time": 0.81876} +{"mode": "train", "epoch": 47, "iter": 3300, "lr": 0.07777, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29328, "top5_acc": 0.54312, "loss_cls": 4.08137, "loss": 4.08137, "time": 0.82421} +{"mode": "train", "epoch": 47, "iter": 3400, "lr": 0.07775, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27297, "top5_acc": 0.52672, "loss_cls": 4.19942, "loss": 4.19942, "time": 0.81524} +{"mode": "train", "epoch": 47, "iter": 3500, "lr": 0.07773, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28344, "top5_acc": 0.53938, "loss_cls": 4.12834, "loss": 4.12834, "time": 0.81697} +{"mode": "train", "epoch": 47, "iter": 3600, "lr": 0.0777, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29266, "top5_acc": 0.54031, "loss_cls": 4.08117, "loss": 4.08117, "time": 0.81924} +{"mode": "train", "epoch": 47, "iter": 3700, "lr": 0.07768, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28688, "top5_acc": 0.54422, "loss_cls": 4.07262, "loss": 4.07262, "time": 0.82343} +{"mode": "val", "epoch": 47, "iter": 309, "lr": 0.07767, "top1_acc": 0.19759, "top5_acc": 0.43003, "mean_class_accuracy": 0.19735} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.07765, "memory": 15990, "data_time": 1.36734, "top1_acc": 0.28766, "top5_acc": 0.54172, "loss_cls": 4.09266, "loss": 4.09266, "time": 2.35926} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.07762, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28688, "top5_acc": 0.54641, "loss_cls": 4.07318, "loss": 4.07318, "time": 0.83235} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.0776, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28219, "top5_acc": 0.54266, "loss_cls": 4.11437, "loss": 4.11437, "time": 0.83242} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.07758, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28, "top5_acc": 0.54609, "loss_cls": 4.06135, "loss": 4.06135, "time": 0.83778} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.07755, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28922, "top5_acc": 0.55484, "loss_cls": 4.03089, "loss": 4.03089, "time": 0.82709} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.07753, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29609, "top5_acc": 0.54688, "loss_cls": 4.04978, "loss": 4.04978, "time": 0.83996} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.07751, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28188, "top5_acc": 0.53812, "loss_cls": 4.13131, "loss": 4.13131, "time": 0.83046} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.07748, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27969, "top5_acc": 0.53656, "loss_cls": 4.10488, "loss": 4.10488, "time": 0.83469} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.07746, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28672, "top5_acc": 0.53781, "loss_cls": 4.08735, "loss": 4.08735, "time": 0.8282} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.07744, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28531, "top5_acc": 0.54734, "loss_cls": 4.06103, "loss": 4.06103, "time": 0.81913} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.07741, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29516, "top5_acc": 0.54672, "loss_cls": 4.04608, "loss": 4.04608, "time": 0.81982} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.07739, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27766, "top5_acc": 0.52906, "loss_cls": 4.141, "loss": 4.141, "time": 0.82464} +{"mode": "train", "epoch": 48, "iter": 1300, "lr": 0.07737, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29016, "top5_acc": 0.545, "loss_cls": 4.08072, "loss": 4.08072, "time": 0.81797} +{"mode": "train", "epoch": 48, "iter": 1400, "lr": 0.07734, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28984, "top5_acc": 0.53891, "loss_cls": 4.11043, "loss": 4.11043, "time": 0.81302} +{"mode": "train", "epoch": 48, "iter": 1500, "lr": 0.07732, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27688, "top5_acc": 0.54109, "loss_cls": 4.10369, "loss": 4.10369, "time": 0.82145} +{"mode": "train", "epoch": 48, "iter": 1600, "lr": 0.0773, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28469, "top5_acc": 0.53781, "loss_cls": 4.10407, "loss": 4.10407, "time": 0.81515} +{"mode": "train", "epoch": 48, "iter": 1700, "lr": 0.07727, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28234, "top5_acc": 0.54078, "loss_cls": 4.12752, "loss": 4.12752, "time": 0.81942} +{"mode": "train", "epoch": 48, "iter": 1800, "lr": 0.07725, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28438, "top5_acc": 0.53906, "loss_cls": 4.12468, "loss": 4.12468, "time": 0.814} +{"mode": "train", "epoch": 48, "iter": 1900, "lr": 0.07723, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28062, "top5_acc": 0.53141, "loss_cls": 4.13908, "loss": 4.13908, "time": 0.81829} +{"mode": "train", "epoch": 48, "iter": 2000, "lr": 0.0772, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28578, "top5_acc": 0.54125, "loss_cls": 4.12659, "loss": 4.12659, "time": 0.81665} +{"mode": "train", "epoch": 48, "iter": 2100, "lr": 0.07718, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28297, "top5_acc": 0.53422, "loss_cls": 4.13207, "loss": 4.13207, "time": 0.81527} +{"mode": "train", "epoch": 48, "iter": 2200, "lr": 0.07716, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27844, "top5_acc": 0.53531, "loss_cls": 4.12396, "loss": 4.12396, "time": 0.81725} +{"mode": "train", "epoch": 48, "iter": 2300, "lr": 0.07713, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27531, "top5_acc": 0.53922, "loss_cls": 4.08981, "loss": 4.08981, "time": 0.81418} +{"mode": "train", "epoch": 48, "iter": 2400, "lr": 0.07711, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28078, "top5_acc": 0.54391, "loss_cls": 4.07972, "loss": 4.07972, "time": 0.82165} +{"mode": "train", "epoch": 48, "iter": 2500, "lr": 0.07709, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28359, "top5_acc": 0.54016, "loss_cls": 4.10031, "loss": 4.10031, "time": 0.8229} +{"mode": "train", "epoch": 48, "iter": 2600, "lr": 0.07706, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28312, "top5_acc": 0.53703, "loss_cls": 4.12653, "loss": 4.12653, "time": 0.81747} +{"mode": "train", "epoch": 48, "iter": 2700, "lr": 0.07704, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27984, "top5_acc": 0.54234, "loss_cls": 4.13109, "loss": 4.13109, "time": 0.82044} +{"mode": "train", "epoch": 48, "iter": 2800, "lr": 0.07701, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27953, "top5_acc": 0.54406, "loss_cls": 4.07272, "loss": 4.07272, "time": 0.81981} +{"mode": "train", "epoch": 48, "iter": 2900, "lr": 0.07699, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28328, "top5_acc": 0.54484, "loss_cls": 4.10191, "loss": 4.10191, "time": 0.82126} +{"mode": "train", "epoch": 48, "iter": 3000, "lr": 0.07697, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28312, "top5_acc": 0.53938, "loss_cls": 4.13759, "loss": 4.13759, "time": 0.81475} +{"mode": "train", "epoch": 48, "iter": 3100, "lr": 0.07694, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27812, "top5_acc": 0.54859, "loss_cls": 4.06602, "loss": 4.06602, "time": 0.81723} +{"mode": "train", "epoch": 48, "iter": 3200, "lr": 0.07692, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27938, "top5_acc": 0.54312, "loss_cls": 4.08608, "loss": 4.08608, "time": 0.81412} +{"mode": "train", "epoch": 48, "iter": 3300, "lr": 0.0769, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27031, "top5_acc": 0.53609, "loss_cls": 4.19141, "loss": 4.19141, "time": 0.81767} +{"mode": "train", "epoch": 48, "iter": 3400, "lr": 0.07687, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27656, "top5_acc": 0.54094, "loss_cls": 4.10904, "loss": 4.10904, "time": 0.81515} +{"mode": "train", "epoch": 48, "iter": 3500, "lr": 0.07685, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27984, "top5_acc": 0.54203, "loss_cls": 4.09946, "loss": 4.09946, "time": 0.81833} +{"mode": "train", "epoch": 48, "iter": 3600, "lr": 0.07683, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28141, "top5_acc": 0.54141, "loss_cls": 4.08979, "loss": 4.08979, "time": 0.82872} +{"mode": "train", "epoch": 48, "iter": 3700, "lr": 0.0768, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27812, "top5_acc": 0.53266, "loss_cls": 4.13953, "loss": 4.13953, "time": 0.81815} +{"mode": "val", "epoch": 48, "iter": 309, "lr": 0.07679, "top1_acc": 0.20823, "top5_acc": 0.44598, "mean_class_accuracy": 0.20823} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.07677, "memory": 15990, "data_time": 1.35733, "top1_acc": 0.27891, "top5_acc": 0.54594, "loss_cls": 4.07227, "loss": 4.07227, "time": 2.36048} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.07674, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29547, "top5_acc": 0.54875, "loss_cls": 4.02873, "loss": 4.02873, "time": 0.82709} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.07672, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29297, "top5_acc": 0.55219, "loss_cls": 4.05535, "loss": 4.05535, "time": 0.822} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.0767, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28453, "top5_acc": 0.54594, "loss_cls": 4.07163, "loss": 4.07163, "time": 0.81835} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.07667, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28969, "top5_acc": 0.53766, "loss_cls": 4.10705, "loss": 4.10705, "time": 0.82087} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.07665, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28703, "top5_acc": 0.55078, "loss_cls": 4.05103, "loss": 4.05103, "time": 0.81972} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.07663, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28016, "top5_acc": 0.53766, "loss_cls": 4.14117, "loss": 4.14117, "time": 0.81497} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.0766, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29047, "top5_acc": 0.54594, "loss_cls": 4.08747, "loss": 4.08747, "time": 0.81641} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.07658, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27453, "top5_acc": 0.53016, "loss_cls": 4.1594, "loss": 4.1594, "time": 0.81231} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.07656, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28438, "top5_acc": 0.54359, "loss_cls": 4.07903, "loss": 4.07903, "time": 0.81263} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.07653, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27828, "top5_acc": 0.53453, "loss_cls": 4.10952, "loss": 4.10952, "time": 0.8158} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.07651, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27531, "top5_acc": 0.52531, "loss_cls": 4.14229, "loss": 4.14229, "time": 0.82012} +{"mode": "train", "epoch": 49, "iter": 1300, "lr": 0.07648, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28375, "top5_acc": 0.54078, "loss_cls": 4.08392, "loss": 4.08392, "time": 0.81213} +{"mode": "train", "epoch": 49, "iter": 1400, "lr": 0.07646, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28875, "top5_acc": 0.54234, "loss_cls": 4.07929, "loss": 4.07929, "time": 0.81647} +{"mode": "train", "epoch": 49, "iter": 1500, "lr": 0.07644, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28938, "top5_acc": 0.54406, "loss_cls": 4.0838, "loss": 4.0838, "time": 0.81395} +{"mode": "train", "epoch": 49, "iter": 1600, "lr": 0.07641, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27219, "top5_acc": 0.54641, "loss_cls": 4.10677, "loss": 4.10677, "time": 0.81496} +{"mode": "train", "epoch": 49, "iter": 1700, "lr": 0.07639, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28266, "top5_acc": 0.53922, "loss_cls": 4.09682, "loss": 4.09682, "time": 0.82356} +{"mode": "train", "epoch": 49, "iter": 1800, "lr": 0.07637, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27828, "top5_acc": 0.53734, "loss_cls": 4.1173, "loss": 4.1173, "time": 0.81983} +{"mode": "train", "epoch": 49, "iter": 1900, "lr": 0.07634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29125, "top5_acc": 0.54328, "loss_cls": 4.10779, "loss": 4.10779, "time": 0.81449} +{"mode": "train", "epoch": 49, "iter": 2000, "lr": 0.07632, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28453, "top5_acc": 0.54312, "loss_cls": 4.11363, "loss": 4.11363, "time": 0.81693} +{"mode": "train", "epoch": 49, "iter": 2100, "lr": 0.07629, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27906, "top5_acc": 0.53391, "loss_cls": 4.12165, "loss": 4.12165, "time": 0.82125} +{"mode": "train", "epoch": 49, "iter": 2200, "lr": 0.07627, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28672, "top5_acc": 0.54266, "loss_cls": 4.07955, "loss": 4.07955, "time": 0.81978} +{"mode": "train", "epoch": 49, "iter": 2300, "lr": 0.07625, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29063, "top5_acc": 0.54641, "loss_cls": 4.07613, "loss": 4.07613, "time": 0.81987} +{"mode": "train", "epoch": 49, "iter": 2400, "lr": 0.07622, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28469, "top5_acc": 0.5425, "loss_cls": 4.08535, "loss": 4.08535, "time": 0.81534} +{"mode": "train", "epoch": 49, "iter": 2500, "lr": 0.0762, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28359, "top5_acc": 0.53688, "loss_cls": 4.10817, "loss": 4.10817, "time": 0.81903} +{"mode": "train", "epoch": 49, "iter": 2600, "lr": 0.07618, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27844, "top5_acc": 0.53797, "loss_cls": 4.12855, "loss": 4.12855, "time": 0.81943} +{"mode": "train", "epoch": 49, "iter": 2700, "lr": 0.07615, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29203, "top5_acc": 0.54219, "loss_cls": 4.07568, "loss": 4.07568, "time": 0.82555} +{"mode": "train", "epoch": 49, "iter": 2800, "lr": 0.07613, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27984, "top5_acc": 0.53859, "loss_cls": 4.1384, "loss": 4.1384, "time": 0.81756} +{"mode": "train", "epoch": 49, "iter": 2900, "lr": 0.0761, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28938, "top5_acc": 0.54797, "loss_cls": 4.05232, "loss": 4.05232, "time": 0.81958} +{"mode": "train", "epoch": 49, "iter": 3000, "lr": 0.07608, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28078, "top5_acc": 0.54828, "loss_cls": 4.11569, "loss": 4.11569, "time": 0.81642} +{"mode": "train", "epoch": 49, "iter": 3100, "lr": 0.07606, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2875, "top5_acc": 0.55047, "loss_cls": 4.06968, "loss": 4.06968, "time": 0.81388} +{"mode": "train", "epoch": 49, "iter": 3200, "lr": 0.07603, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28484, "top5_acc": 0.53844, "loss_cls": 4.09376, "loss": 4.09376, "time": 0.81393} +{"mode": "train", "epoch": 49, "iter": 3300, "lr": 0.07601, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28406, "top5_acc": 0.53391, "loss_cls": 4.10683, "loss": 4.10683, "time": 0.82123} +{"mode": "train", "epoch": 49, "iter": 3400, "lr": 0.07598, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28953, "top5_acc": 0.53969, "loss_cls": 4.13159, "loss": 4.13159, "time": 0.81451} +{"mode": "train", "epoch": 49, "iter": 3500, "lr": 0.07596, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28953, "top5_acc": 0.54359, "loss_cls": 4.10572, "loss": 4.10572, "time": 0.81423} +{"mode": "train", "epoch": 49, "iter": 3600, "lr": 0.07594, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28156, "top5_acc": 0.53641, "loss_cls": 4.12871, "loss": 4.12871, "time": 0.82341} +{"mode": "train", "epoch": 49, "iter": 3700, "lr": 0.07591, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28797, "top5_acc": 0.53969, "loss_cls": 4.06736, "loss": 4.06736, "time": 0.81645} +{"mode": "val", "epoch": 49, "iter": 309, "lr": 0.0759, "top1_acc": 0.15985, "top5_acc": 0.36469, "mean_class_accuracy": 0.15954} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.07588, "memory": 15990, "data_time": 1.30962, "top1_acc": 0.30234, "top5_acc": 0.55656, "loss_cls": 4.01065, "loss": 4.01065, "time": 2.30085} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.07585, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29516, "top5_acc": 0.54625, "loss_cls": 4.03075, "loss": 4.03075, "time": 0.83384} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.07583, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28766, "top5_acc": 0.55359, "loss_cls": 4.07193, "loss": 4.07193, "time": 0.82765} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.07581, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29219, "top5_acc": 0.55953, "loss_cls": 4.031, "loss": 4.031, "time": 0.82615} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.07578, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28312, "top5_acc": 0.54406, "loss_cls": 4.068, "loss": 4.068, "time": 0.81699} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.07576, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28078, "top5_acc": 0.55141, "loss_cls": 4.0771, "loss": 4.0771, "time": 0.82124} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.07573, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28656, "top5_acc": 0.53828, "loss_cls": 4.11126, "loss": 4.11126, "time": 0.82072} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.07571, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28562, "top5_acc": 0.54047, "loss_cls": 4.08187, "loss": 4.08187, "time": 0.81507} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.07569, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28484, "top5_acc": 0.54937, "loss_cls": 4.06796, "loss": 4.06796, "time": 0.81868} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.07566, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28375, "top5_acc": 0.54188, "loss_cls": 4.09304, "loss": 4.09304, "time": 0.81348} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.07564, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28453, "top5_acc": 0.54766, "loss_cls": 4.07822, "loss": 4.07822, "time": 0.81539} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.07561, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28031, "top5_acc": 0.54344, "loss_cls": 4.09386, "loss": 4.09386, "time": 0.8168} +{"mode": "train", "epoch": 50, "iter": 1300, "lr": 0.07559, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28203, "top5_acc": 0.54781, "loss_cls": 4.10105, "loss": 4.10105, "time": 0.81699} +{"mode": "train", "epoch": 50, "iter": 1400, "lr": 0.07557, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28203, "top5_acc": 0.53672, "loss_cls": 4.11845, "loss": 4.11845, "time": 0.81639} +{"mode": "train", "epoch": 50, "iter": 1500, "lr": 0.07554, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28516, "top5_acc": 0.54719, "loss_cls": 4.07264, "loss": 4.07264, "time": 0.81462} +{"mode": "train", "epoch": 50, "iter": 1600, "lr": 0.07552, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28234, "top5_acc": 0.53047, "loss_cls": 4.11615, "loss": 4.11615, "time": 0.81758} +{"mode": "train", "epoch": 50, "iter": 1700, "lr": 0.07549, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28281, "top5_acc": 0.53359, "loss_cls": 4.12737, "loss": 4.12737, "time": 0.81859} +{"mode": "train", "epoch": 50, "iter": 1800, "lr": 0.07547, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29422, "top5_acc": 0.55453, "loss_cls": 4.04018, "loss": 4.04018, "time": 0.8158} +{"mode": "train", "epoch": 50, "iter": 1900, "lr": 0.07545, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29094, "top5_acc": 0.53125, "loss_cls": 4.14171, "loss": 4.14171, "time": 0.82138} +{"mode": "train", "epoch": 50, "iter": 2000, "lr": 0.07542, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28813, "top5_acc": 0.53938, "loss_cls": 4.06956, "loss": 4.06956, "time": 0.81731} +{"mode": "train", "epoch": 50, "iter": 2100, "lr": 0.0754, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28531, "top5_acc": 0.54672, "loss_cls": 4.09655, "loss": 4.09655, "time": 0.81404} +{"mode": "train", "epoch": 50, "iter": 2200, "lr": 0.07537, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29812, "top5_acc": 0.54766, "loss_cls": 4.05732, "loss": 4.05732, "time": 0.82039} +{"mode": "train", "epoch": 50, "iter": 2300, "lr": 0.07535, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28219, "top5_acc": 0.54016, "loss_cls": 4.12923, "loss": 4.12923, "time": 0.81662} +{"mode": "train", "epoch": 50, "iter": 2400, "lr": 0.07533, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28797, "top5_acc": 0.54156, "loss_cls": 4.04347, "loss": 4.04347, "time": 0.82049} +{"mode": "train", "epoch": 50, "iter": 2500, "lr": 0.0753, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28922, "top5_acc": 0.54484, "loss_cls": 4.08359, "loss": 4.08359, "time": 0.8169} +{"mode": "train", "epoch": 50, "iter": 2600, "lr": 0.07528, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27891, "top5_acc": 0.53797, "loss_cls": 4.12482, "loss": 4.12482, "time": 0.82045} +{"mode": "train", "epoch": 50, "iter": 2700, "lr": 0.07525, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29047, "top5_acc": 0.54219, "loss_cls": 4.09754, "loss": 4.09754, "time": 0.82528} +{"mode": "train", "epoch": 50, "iter": 2800, "lr": 0.07523, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27594, "top5_acc": 0.5375, "loss_cls": 4.1197, "loss": 4.1197, "time": 0.81344} +{"mode": "train", "epoch": 50, "iter": 2900, "lr": 0.0752, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28844, "top5_acc": 0.54438, "loss_cls": 4.08904, "loss": 4.08904, "time": 0.82232} +{"mode": "train", "epoch": 50, "iter": 3000, "lr": 0.07518, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28859, "top5_acc": 0.54125, "loss_cls": 4.07308, "loss": 4.07308, "time": 0.82423} +{"mode": "train", "epoch": 50, "iter": 3100, "lr": 0.07516, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27344, "top5_acc": 0.53406, "loss_cls": 4.12435, "loss": 4.12435, "time": 0.81381} +{"mode": "train", "epoch": 50, "iter": 3200, "lr": 0.07513, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29469, "top5_acc": 0.55094, "loss_cls": 4.0336, "loss": 4.0336, "time": 0.81369} +{"mode": "train", "epoch": 50, "iter": 3300, "lr": 0.07511, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28609, "top5_acc": 0.54297, "loss_cls": 4.08113, "loss": 4.08113, "time": 0.82406} +{"mode": "train", "epoch": 50, "iter": 3400, "lr": 0.07508, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28531, "top5_acc": 0.54625, "loss_cls": 4.09105, "loss": 4.09105, "time": 0.81393} +{"mode": "train", "epoch": 50, "iter": 3500, "lr": 0.07506, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27812, "top5_acc": 0.53703, "loss_cls": 4.10603, "loss": 4.10603, "time": 0.82215} +{"mode": "train", "epoch": 50, "iter": 3600, "lr": 0.07504, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28031, "top5_acc": 0.53359, "loss_cls": 4.12482, "loss": 4.12482, "time": 0.82026} +{"mode": "train", "epoch": 50, "iter": 3700, "lr": 0.07501, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28359, "top5_acc": 0.53703, "loss_cls": 4.10837, "loss": 4.10837, "time": 0.82149} +{"mode": "val", "epoch": 50, "iter": 309, "lr": 0.075, "top1_acc": 0.22524, "top5_acc": 0.4592, "mean_class_accuracy": 0.22506} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.07498, "memory": 15990, "data_time": 1.33373, "top1_acc": 0.29766, "top5_acc": 0.55297, "loss_cls": 4.01745, "loss": 4.01745, "time": 2.3211} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.07495, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28453, "top5_acc": 0.55094, "loss_cls": 4.0578, "loss": 4.0578, "time": 0.82884} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.07493, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28891, "top5_acc": 0.55609, "loss_cls": 4.0482, "loss": 4.0482, "time": 0.81934} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.0749, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29188, "top5_acc": 0.55047, "loss_cls": 4.0531, "loss": 4.0531, "time": 0.82244} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.07488, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28781, "top5_acc": 0.54062, "loss_cls": 4.07612, "loss": 4.07612, "time": 0.81504} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.07485, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29656, "top5_acc": 0.55141, "loss_cls": 4.04229, "loss": 4.04229, "time": 0.81596} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.07483, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28969, "top5_acc": 0.54609, "loss_cls": 4.07161, "loss": 4.07161, "time": 0.81768} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.07481, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29156, "top5_acc": 0.55359, "loss_cls": 4.02219, "loss": 4.02219, "time": 0.81713} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.07478, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28625, "top5_acc": 0.53828, "loss_cls": 4.11286, "loss": 4.11286, "time": 0.81732} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.07476, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29313, "top5_acc": 0.54844, "loss_cls": 4.05738, "loss": 4.05738, "time": 0.81631} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.07473, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29266, "top5_acc": 0.53406, "loss_cls": 4.08541, "loss": 4.08541, "time": 0.81882} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.07471, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2925, "top5_acc": 0.54672, "loss_cls": 4.07218, "loss": 4.07218, "time": 0.82189} +{"mode": "train", "epoch": 51, "iter": 1300, "lr": 0.07468, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27625, "top5_acc": 0.53812, "loss_cls": 4.11478, "loss": 4.11478, "time": 0.814} +{"mode": "train", "epoch": 51, "iter": 1400, "lr": 0.07466, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29531, "top5_acc": 0.54656, "loss_cls": 4.0506, "loss": 4.0506, "time": 0.81617} +{"mode": "train", "epoch": 51, "iter": 1500, "lr": 0.07464, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28641, "top5_acc": 0.53359, "loss_cls": 4.09119, "loss": 4.09119, "time": 0.81788} +{"mode": "train", "epoch": 51, "iter": 1600, "lr": 0.07461, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27766, "top5_acc": 0.52984, "loss_cls": 4.12517, "loss": 4.12517, "time": 0.81535} +{"mode": "train", "epoch": 51, "iter": 1700, "lr": 0.07459, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2875, "top5_acc": 0.54625, "loss_cls": 4.08116, "loss": 4.08116, "time": 0.8127} +{"mode": "train", "epoch": 51, "iter": 1800, "lr": 0.07456, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28641, "top5_acc": 0.54406, "loss_cls": 4.08678, "loss": 4.08678, "time": 0.81529} +{"mode": "train", "epoch": 51, "iter": 1900, "lr": 0.07454, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28234, "top5_acc": 0.53516, "loss_cls": 4.11017, "loss": 4.11017, "time": 0.81607} +{"mode": "train", "epoch": 51, "iter": 2000, "lr": 0.07451, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28344, "top5_acc": 0.53891, "loss_cls": 4.11476, "loss": 4.11476, "time": 0.8171} +{"mode": "train", "epoch": 51, "iter": 2100, "lr": 0.07449, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28078, "top5_acc": 0.53797, "loss_cls": 4.13217, "loss": 4.13217, "time": 0.81978} +{"mode": "train", "epoch": 51, "iter": 2200, "lr": 0.07447, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2775, "top5_acc": 0.53656, "loss_cls": 4.11053, "loss": 4.11053, "time": 0.81755} +{"mode": "train", "epoch": 51, "iter": 2300, "lr": 0.07444, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28516, "top5_acc": 0.54578, "loss_cls": 4.0713, "loss": 4.0713, "time": 0.81533} +{"mode": "train", "epoch": 51, "iter": 2400, "lr": 0.07442, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28438, "top5_acc": 0.54266, "loss_cls": 4.09065, "loss": 4.09065, "time": 0.82022} +{"mode": "train", "epoch": 51, "iter": 2500, "lr": 0.07439, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29078, "top5_acc": 0.54875, "loss_cls": 4.05642, "loss": 4.05642, "time": 0.81642} +{"mode": "train", "epoch": 51, "iter": 2600, "lr": 0.07437, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29563, "top5_acc": 0.55203, "loss_cls": 4.04763, "loss": 4.04763, "time": 0.81815} +{"mode": "train", "epoch": 51, "iter": 2700, "lr": 0.07434, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27109, "top5_acc": 0.53797, "loss_cls": 4.11808, "loss": 4.11808, "time": 0.82674} +{"mode": "train", "epoch": 51, "iter": 2800, "lr": 0.07432, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27984, "top5_acc": 0.54516, "loss_cls": 4.09747, "loss": 4.09747, "time": 0.81472} +{"mode": "train", "epoch": 51, "iter": 2900, "lr": 0.07429, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28891, "top5_acc": 0.54047, "loss_cls": 4.09928, "loss": 4.09928, "time": 0.82652} +{"mode": "train", "epoch": 51, "iter": 3000, "lr": 0.07427, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28656, "top5_acc": 0.53734, "loss_cls": 4.11419, "loss": 4.11419, "time": 0.82242} +{"mode": "train", "epoch": 51, "iter": 3100, "lr": 0.07425, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3025, "top5_acc": 0.56156, "loss_cls": 3.98649, "loss": 3.98649, "time": 0.82116} +{"mode": "train", "epoch": 51, "iter": 3200, "lr": 0.07422, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28328, "top5_acc": 0.54188, "loss_cls": 4.08793, "loss": 4.08793, "time": 0.81909} +{"mode": "train", "epoch": 51, "iter": 3300, "lr": 0.0742, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28766, "top5_acc": 0.54109, "loss_cls": 4.11165, "loss": 4.11165, "time": 0.82447} +{"mode": "train", "epoch": 51, "iter": 3400, "lr": 0.07417, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29641, "top5_acc": 0.54016, "loss_cls": 4.08893, "loss": 4.08893, "time": 0.82191} +{"mode": "train", "epoch": 51, "iter": 3500, "lr": 0.07415, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29125, "top5_acc": 0.53922, "loss_cls": 4.08251, "loss": 4.08251, "time": 0.81559} +{"mode": "train", "epoch": 51, "iter": 3600, "lr": 0.07412, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27938, "top5_acc": 0.53781, "loss_cls": 4.12333, "loss": 4.12333, "time": 0.82027} +{"mode": "train", "epoch": 51, "iter": 3700, "lr": 0.0741, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29781, "top5_acc": 0.55156, "loss_cls": 4.04634, "loss": 4.04634, "time": 0.82267} +{"mode": "val", "epoch": 51, "iter": 309, "lr": 0.07409, "top1_acc": 0.22018, "top5_acc": 0.47151, "mean_class_accuracy": 0.22004} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.07406, "memory": 15990, "data_time": 1.35843, "top1_acc": 0.28391, "top5_acc": 0.54922, "loss_cls": 4.04172, "loss": 4.04172, "time": 2.34768} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.07404, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29688, "top5_acc": 0.54906, "loss_cls": 4.04443, "loss": 4.04443, "time": 0.82858} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.07401, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29375, "top5_acc": 0.54266, "loss_cls": 4.07005, "loss": 4.07005, "time": 0.83148} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.07399, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29031, "top5_acc": 0.54797, "loss_cls": 4.0466, "loss": 4.0466, "time": 0.82784} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.07397, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28141, "top5_acc": 0.5525, "loss_cls": 4.04956, "loss": 4.04956, "time": 0.82772} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.07394, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29109, "top5_acc": 0.55391, "loss_cls": 4.00046, "loss": 4.00046, "time": 0.82374} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.07392, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28109, "top5_acc": 0.54734, "loss_cls": 4.07484, "loss": 4.07484, "time": 0.81842} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.07389, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29703, "top5_acc": 0.55344, "loss_cls": 4.04601, "loss": 4.04601, "time": 0.81596} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.07387, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28109, "top5_acc": 0.54234, "loss_cls": 4.08432, "loss": 4.08432, "time": 0.82142} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.07384, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27891, "top5_acc": 0.53438, "loss_cls": 4.13486, "loss": 4.13486, "time": 0.82981} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.07382, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29547, "top5_acc": 0.53953, "loss_cls": 4.07953, "loss": 4.07953, "time": 0.81586} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.07379, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28688, "top5_acc": 0.53734, "loss_cls": 4.07106, "loss": 4.07106, "time": 0.81983} +{"mode": "train", "epoch": 52, "iter": 1300, "lr": 0.07377, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28328, "top5_acc": 0.5425, "loss_cls": 4.05576, "loss": 4.05576, "time": 0.81434} +{"mode": "train", "epoch": 52, "iter": 1400, "lr": 0.07374, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29125, "top5_acc": 0.55406, "loss_cls": 4.0524, "loss": 4.0524, "time": 0.8154} +{"mode": "train", "epoch": 52, "iter": 1500, "lr": 0.07372, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28375, "top5_acc": 0.54422, "loss_cls": 4.10469, "loss": 4.10469, "time": 0.81557} +{"mode": "train", "epoch": 52, "iter": 1600, "lr": 0.0737, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29281, "top5_acc": 0.54781, "loss_cls": 4.05836, "loss": 4.05836, "time": 0.81895} +{"mode": "train", "epoch": 52, "iter": 1700, "lr": 0.07367, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29219, "top5_acc": 0.54781, "loss_cls": 4.06145, "loss": 4.06145, "time": 0.82329} +{"mode": "train", "epoch": 52, "iter": 1800, "lr": 0.07365, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28609, "top5_acc": 0.53641, "loss_cls": 4.10195, "loss": 4.10195, "time": 0.81601} +{"mode": "train", "epoch": 52, "iter": 1900, "lr": 0.07362, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28859, "top5_acc": 0.54266, "loss_cls": 4.07364, "loss": 4.07364, "time": 0.81744} +{"mode": "train", "epoch": 52, "iter": 2000, "lr": 0.0736, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29047, "top5_acc": 0.54047, "loss_cls": 4.06185, "loss": 4.06185, "time": 0.81928} +{"mode": "train", "epoch": 52, "iter": 2100, "lr": 0.07357, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2825, "top5_acc": 0.545, "loss_cls": 4.08506, "loss": 4.08506, "time": 0.81592} +{"mode": "train", "epoch": 52, "iter": 2200, "lr": 0.07355, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29047, "top5_acc": 0.54469, "loss_cls": 4.05535, "loss": 4.05535, "time": 0.81797} +{"mode": "train", "epoch": 52, "iter": 2300, "lr": 0.07352, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29656, "top5_acc": 0.55172, "loss_cls": 4.02414, "loss": 4.02414, "time": 0.81863} +{"mode": "train", "epoch": 52, "iter": 2400, "lr": 0.0735, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2925, "top5_acc": 0.54953, "loss_cls": 4.04138, "loss": 4.04138, "time": 0.82478} +{"mode": "train", "epoch": 52, "iter": 2500, "lr": 0.07347, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29047, "top5_acc": 0.54062, "loss_cls": 4.08707, "loss": 4.08707, "time": 0.81661} +{"mode": "train", "epoch": 52, "iter": 2600, "lr": 0.07345, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28484, "top5_acc": 0.54766, "loss_cls": 4.05912, "loss": 4.05912, "time": 0.81958} +{"mode": "train", "epoch": 52, "iter": 2700, "lr": 0.07342, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28047, "top5_acc": 0.53797, "loss_cls": 4.127, "loss": 4.127, "time": 0.82182} +{"mode": "train", "epoch": 52, "iter": 2800, "lr": 0.0734, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28172, "top5_acc": 0.54984, "loss_cls": 4.07654, "loss": 4.07654, "time": 0.8199} +{"mode": "train", "epoch": 52, "iter": 2900, "lr": 0.07337, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28781, "top5_acc": 0.53969, "loss_cls": 4.1041, "loss": 4.1041, "time": 0.81621} +{"mode": "train", "epoch": 52, "iter": 3000, "lr": 0.07335, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.54453, "loss_cls": 4.07942, "loss": 4.07942, "time": 0.8183} +{"mode": "train", "epoch": 52, "iter": 3100, "lr": 0.07332, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2875, "top5_acc": 0.53734, "loss_cls": 4.09373, "loss": 4.09373, "time": 0.8216} +{"mode": "train", "epoch": 52, "iter": 3200, "lr": 0.0733, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28703, "top5_acc": 0.55, "loss_cls": 4.03879, "loss": 4.03879, "time": 0.81494} +{"mode": "train", "epoch": 52, "iter": 3300, "lr": 0.07328, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28859, "top5_acc": 0.53125, "loss_cls": 4.10137, "loss": 4.10137, "time": 0.82502} +{"mode": "train", "epoch": 52, "iter": 3400, "lr": 0.07325, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27578, "top5_acc": 0.55172, "loss_cls": 4.1008, "loss": 4.1008, "time": 0.81469} +{"mode": "train", "epoch": 52, "iter": 3500, "lr": 0.07323, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29313, "top5_acc": 0.53719, "loss_cls": 4.08568, "loss": 4.08568, "time": 0.81463} +{"mode": "train", "epoch": 52, "iter": 3600, "lr": 0.0732, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28766, "top5_acc": 0.54344, "loss_cls": 4.08115, "loss": 4.08115, "time": 0.82063} +{"mode": "train", "epoch": 52, "iter": 3700, "lr": 0.07318, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28062, "top5_acc": 0.54391, "loss_cls": 4.07703, "loss": 4.07703, "time": 0.82016} +{"mode": "val", "epoch": 52, "iter": 309, "lr": 0.07317, "top1_acc": 0.22003, "top5_acc": 0.4666, "mean_class_accuracy": 0.21998} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.07314, "memory": 15990, "data_time": 1.32134, "top1_acc": 0.29891, "top5_acc": 0.55641, "loss_cls": 4.01004, "loss": 4.01004, "time": 2.30736} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.07312, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27984, "top5_acc": 0.54828, "loss_cls": 4.05148, "loss": 4.05148, "time": 0.83319} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.07309, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28547, "top5_acc": 0.54906, "loss_cls": 4.05489, "loss": 4.05489, "time": 0.82982} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.07307, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27359, "top5_acc": 0.54031, "loss_cls": 4.09112, "loss": 4.09112, "time": 0.83571} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.07304, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30109, "top5_acc": 0.55422, "loss_cls": 4.01224, "loss": 4.01224, "time": 0.83209} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.07302, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30094, "top5_acc": 0.55484, "loss_cls": 3.99858, "loss": 3.99858, "time": 0.82809} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.07299, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29781, "top5_acc": 0.54047, "loss_cls": 4.05959, "loss": 4.05959, "time": 0.8315} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.07297, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28188, "top5_acc": 0.53922, "loss_cls": 4.10014, "loss": 4.10014, "time": 0.83726} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.07294, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29484, "top5_acc": 0.56016, "loss_cls": 4.01118, "loss": 4.01118, "time": 0.8277} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.07292, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29609, "top5_acc": 0.56063, "loss_cls": 4.02201, "loss": 4.02201, "time": 0.82781} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.07289, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29938, "top5_acc": 0.55125, "loss_cls": 4.02129, "loss": 4.02129, "time": 0.82969} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.07287, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28094, "top5_acc": 0.54297, "loss_cls": 4.08974, "loss": 4.08974, "time": 0.83114} +{"mode": "train", "epoch": 53, "iter": 1300, "lr": 0.07284, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29688, "top5_acc": 0.55516, "loss_cls": 4.03753, "loss": 4.03753, "time": 0.82594} +{"mode": "train", "epoch": 53, "iter": 1400, "lr": 0.07282, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29547, "top5_acc": 0.55219, "loss_cls": 4.05518, "loss": 4.05518, "time": 0.82162} +{"mode": "train", "epoch": 53, "iter": 1500, "lr": 0.07279, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28781, "top5_acc": 0.54484, "loss_cls": 4.09098, "loss": 4.09098, "time": 0.82436} +{"mode": "train", "epoch": 53, "iter": 1600, "lr": 0.07277, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29516, "top5_acc": 0.54875, "loss_cls": 4.07291, "loss": 4.07291, "time": 0.81555} +{"mode": "train", "epoch": 53, "iter": 1700, "lr": 0.07274, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28562, "top5_acc": 0.54484, "loss_cls": 4.06258, "loss": 4.06258, "time": 0.81876} +{"mode": "train", "epoch": 53, "iter": 1800, "lr": 0.07272, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29859, "top5_acc": 0.55641, "loss_cls": 4.02929, "loss": 4.02929, "time": 0.81874} +{"mode": "train", "epoch": 53, "iter": 1900, "lr": 0.07269, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28766, "top5_acc": 0.54812, "loss_cls": 4.06389, "loss": 4.06389, "time": 0.81401} +{"mode": "train", "epoch": 53, "iter": 2000, "lr": 0.07267, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29063, "top5_acc": 0.54891, "loss_cls": 4.0479, "loss": 4.0479, "time": 0.81656} +{"mode": "train", "epoch": 53, "iter": 2100, "lr": 0.07264, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27797, "top5_acc": 0.54188, "loss_cls": 4.11173, "loss": 4.11173, "time": 0.81493} +{"mode": "train", "epoch": 53, "iter": 2200, "lr": 0.07262, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29344, "top5_acc": 0.55016, "loss_cls": 4.08986, "loss": 4.08986, "time": 0.82037} +{"mode": "train", "epoch": 53, "iter": 2300, "lr": 0.07259, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29734, "top5_acc": 0.54937, "loss_cls": 4.0425, "loss": 4.0425, "time": 0.81546} +{"mode": "train", "epoch": 53, "iter": 2400, "lr": 0.07257, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28516, "top5_acc": 0.54844, "loss_cls": 4.06094, "loss": 4.06094, "time": 0.82401} +{"mode": "train", "epoch": 53, "iter": 2500, "lr": 0.07254, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28547, "top5_acc": 0.53891, "loss_cls": 4.09402, "loss": 4.09402, "time": 0.82035} +{"mode": "train", "epoch": 53, "iter": 2600, "lr": 0.07252, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28156, "top5_acc": 0.55016, "loss_cls": 4.07795, "loss": 4.07795, "time": 0.82304} +{"mode": "train", "epoch": 53, "iter": 2700, "lr": 0.07249, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27703, "top5_acc": 0.53781, "loss_cls": 4.10608, "loss": 4.10608, "time": 0.81548} +{"mode": "train", "epoch": 53, "iter": 2800, "lr": 0.07247, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29188, "top5_acc": 0.53297, "loss_cls": 4.10964, "loss": 4.10964, "time": 0.81395} +{"mode": "train", "epoch": 53, "iter": 2900, "lr": 0.07244, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2875, "top5_acc": 0.5475, "loss_cls": 4.06252, "loss": 4.06252, "time": 0.82158} +{"mode": "train", "epoch": 53, "iter": 3000, "lr": 0.07242, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28891, "top5_acc": 0.54406, "loss_cls": 4.08162, "loss": 4.08162, "time": 0.81969} +{"mode": "train", "epoch": 53, "iter": 3100, "lr": 0.07239, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28672, "top5_acc": 0.54297, "loss_cls": 4.06914, "loss": 4.06914, "time": 0.81884} +{"mode": "train", "epoch": 53, "iter": 3200, "lr": 0.07237, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27906, "top5_acc": 0.53984, "loss_cls": 4.08958, "loss": 4.08958, "time": 0.8186} +{"mode": "train", "epoch": 53, "iter": 3300, "lr": 0.07234, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29016, "top5_acc": 0.55109, "loss_cls": 4.02873, "loss": 4.02873, "time": 0.82108} +{"mode": "train", "epoch": 53, "iter": 3400, "lr": 0.07232, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29219, "top5_acc": 0.55656, "loss_cls": 4.02613, "loss": 4.02613, "time": 0.8157} +{"mode": "train", "epoch": 53, "iter": 3500, "lr": 0.07229, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28266, "top5_acc": 0.53344, "loss_cls": 4.11711, "loss": 4.11711, "time": 0.8197} +{"mode": "train", "epoch": 53, "iter": 3600, "lr": 0.07227, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29313, "top5_acc": 0.54531, "loss_cls": 4.0784, "loss": 4.0784, "time": 0.82174} +{"mode": "train", "epoch": 53, "iter": 3700, "lr": 0.07224, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28234, "top5_acc": 0.53688, "loss_cls": 4.09934, "loss": 4.09934, "time": 0.81704} +{"mode": "val", "epoch": 53, "iter": 309, "lr": 0.07223, "top1_acc": 0.22129, "top5_acc": 0.46295, "mean_class_accuracy": 0.22106} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.07221, "memory": 15990, "data_time": 1.2824, "top1_acc": 0.29281, "top5_acc": 0.55469, "loss_cls": 4.02257, "loss": 4.02257, "time": 2.27418} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.07218, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29109, "top5_acc": 0.55437, "loss_cls": 4.02563, "loss": 4.02563, "time": 0.82853} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.07216, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28844, "top5_acc": 0.55312, "loss_cls": 4.0382, "loss": 4.0382, "time": 0.82859} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.07213, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28922, "top5_acc": 0.54672, "loss_cls": 4.05277, "loss": 4.05277, "time": 0.82283} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.07211, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29469, "top5_acc": 0.55875, "loss_cls": 4.00882, "loss": 4.00882, "time": 0.81789} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.07208, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29109, "top5_acc": 0.54406, "loss_cls": 4.05405, "loss": 4.05405, "time": 0.82006} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.07206, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29781, "top5_acc": 0.55219, "loss_cls": 4.02954, "loss": 4.02954, "time": 0.8153} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.07203, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29359, "top5_acc": 0.555, "loss_cls": 4.03088, "loss": 4.03088, "time": 0.81769} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.07201, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27781, "top5_acc": 0.53719, "loss_cls": 4.09193, "loss": 4.09193, "time": 0.81594} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.07198, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29656, "top5_acc": 0.54188, "loss_cls": 4.07562, "loss": 4.07562, "time": 0.81435} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.07196, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29469, "top5_acc": 0.55188, "loss_cls": 4.03362, "loss": 4.03362, "time": 0.82014} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.07193, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29156, "top5_acc": 0.54969, "loss_cls": 4.04424, "loss": 4.04424, "time": 0.81347} +{"mode": "train", "epoch": 54, "iter": 1300, "lr": 0.07191, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30391, "top5_acc": 0.56172, "loss_cls": 4.00141, "loss": 4.00141, "time": 0.81799} +{"mode": "train", "epoch": 54, "iter": 1400, "lr": 0.07188, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29547, "top5_acc": 0.54547, "loss_cls": 4.04525, "loss": 4.04525, "time": 0.81386} +{"mode": "train", "epoch": 54, "iter": 1500, "lr": 0.07186, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29547, "top5_acc": 0.55656, "loss_cls": 4.04582, "loss": 4.04582, "time": 0.81567} +{"mode": "train", "epoch": 54, "iter": 1600, "lr": 0.07183, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29156, "top5_acc": 0.54203, "loss_cls": 4.06705, "loss": 4.06705, "time": 0.81618} +{"mode": "train", "epoch": 54, "iter": 1700, "lr": 0.07181, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29047, "top5_acc": 0.53906, "loss_cls": 4.11767, "loss": 4.11767, "time": 0.81744} +{"mode": "train", "epoch": 54, "iter": 1800, "lr": 0.07178, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29563, "top5_acc": 0.55437, "loss_cls": 4.01129, "loss": 4.01129, "time": 0.81571} +{"mode": "train", "epoch": 54, "iter": 1900, "lr": 0.07176, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28953, "top5_acc": 0.54312, "loss_cls": 4.05761, "loss": 4.05761, "time": 0.81787} +{"mode": "train", "epoch": 54, "iter": 2000, "lr": 0.07173, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28641, "top5_acc": 0.54406, "loss_cls": 4.05809, "loss": 4.05809, "time": 0.81358} +{"mode": "train", "epoch": 54, "iter": 2100, "lr": 0.0717, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28031, "top5_acc": 0.54516, "loss_cls": 4.0842, "loss": 4.0842, "time": 0.81602} +{"mode": "train", "epoch": 54, "iter": 2200, "lr": 0.07168, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29141, "top5_acc": 0.55281, "loss_cls": 4.02275, "loss": 4.02275, "time": 0.81403} +{"mode": "train", "epoch": 54, "iter": 2300, "lr": 0.07165, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28891, "top5_acc": 0.55, "loss_cls": 4.0627, "loss": 4.0627, "time": 0.81257} +{"mode": "train", "epoch": 54, "iter": 2400, "lr": 0.07163, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28219, "top5_acc": 0.53156, "loss_cls": 4.13345, "loss": 4.13345, "time": 0.82048} +{"mode": "train", "epoch": 54, "iter": 2500, "lr": 0.0716, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29578, "top5_acc": 0.55, "loss_cls": 4.03532, "loss": 4.03532, "time": 0.82503} +{"mode": "train", "epoch": 54, "iter": 2600, "lr": 0.07158, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28531, "top5_acc": 0.54828, "loss_cls": 4.068, "loss": 4.068, "time": 0.82508} +{"mode": "train", "epoch": 54, "iter": 2700, "lr": 0.07155, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28406, "top5_acc": 0.545, "loss_cls": 4.07633, "loss": 4.07633, "time": 0.81671} +{"mode": "train", "epoch": 54, "iter": 2800, "lr": 0.07153, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29094, "top5_acc": 0.54672, "loss_cls": 4.04678, "loss": 4.04678, "time": 0.82126} +{"mode": "train", "epoch": 54, "iter": 2900, "lr": 0.0715, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28953, "top5_acc": 0.55109, "loss_cls": 4.06154, "loss": 4.06154, "time": 0.82289} +{"mode": "train", "epoch": 54, "iter": 3000, "lr": 0.07148, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29469, "top5_acc": 0.54984, "loss_cls": 4.03711, "loss": 4.03711, "time": 0.82244} +{"mode": "train", "epoch": 54, "iter": 3100, "lr": 0.07145, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27797, "top5_acc": 0.535, "loss_cls": 4.1409, "loss": 4.1409, "time": 0.82009} +{"mode": "train", "epoch": 54, "iter": 3200, "lr": 0.07143, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28859, "top5_acc": 0.54797, "loss_cls": 4.07771, "loss": 4.07771, "time": 0.8164} +{"mode": "train", "epoch": 54, "iter": 3300, "lr": 0.0714, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29328, "top5_acc": 0.54203, "loss_cls": 4.0765, "loss": 4.0765, "time": 0.81371} +{"mode": "train", "epoch": 54, "iter": 3400, "lr": 0.07138, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28688, "top5_acc": 0.54937, "loss_cls": 4.06422, "loss": 4.06422, "time": 0.82241} +{"mode": "train", "epoch": 54, "iter": 3500, "lr": 0.07135, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29172, "top5_acc": 0.55234, "loss_cls": 4.01044, "loss": 4.01044, "time": 0.8147} +{"mode": "train", "epoch": 54, "iter": 3600, "lr": 0.07133, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29156, "top5_acc": 0.54641, "loss_cls": 4.05927, "loss": 4.05927, "time": 0.82435} +{"mode": "train", "epoch": 54, "iter": 3700, "lr": 0.0713, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29719, "top5_acc": 0.54906, "loss_cls": 4.0427, "loss": 4.0427, "time": 0.82233} +{"mode": "val", "epoch": 54, "iter": 309, "lr": 0.07129, "top1_acc": 0.21618, "top5_acc": 0.45783, "mean_class_accuracy": 0.21583} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.07126, "memory": 15990, "data_time": 1.3033, "top1_acc": 0.29938, "top5_acc": 0.55766, "loss_cls": 4.00293, "loss": 4.00293, "time": 2.28954} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.07124, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31156, "top5_acc": 0.56453, "loss_cls": 3.97868, "loss": 3.97868, "time": 0.82504} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.07121, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29328, "top5_acc": 0.54141, "loss_cls": 4.07939, "loss": 4.07939, "time": 0.81599} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.07119, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28609, "top5_acc": 0.54781, "loss_cls": 4.04766, "loss": 4.04766, "time": 0.81809} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.07116, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.53969, "loss_cls": 4.10116, "loss": 4.10116, "time": 0.81594} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.07114, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30453, "top5_acc": 0.55719, "loss_cls": 3.99162, "loss": 3.99162, "time": 0.82182} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.07111, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29328, "top5_acc": 0.54781, "loss_cls": 4.0445, "loss": 4.0445, "time": 0.82187} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.07109, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29063, "top5_acc": 0.54969, "loss_cls": 4.03033, "loss": 4.03033, "time": 0.82011} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.07106, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29719, "top5_acc": 0.55344, "loss_cls": 4.02793, "loss": 4.02793, "time": 0.82028} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.07104, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29766, "top5_acc": 0.55766, "loss_cls": 4.03362, "loss": 4.03362, "time": 0.82366} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.07101, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29906, "top5_acc": 0.55937, "loss_cls": 3.98555, "loss": 3.98555, "time": 0.82044} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.07099, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29375, "top5_acc": 0.55594, "loss_cls": 4.01135, "loss": 4.01135, "time": 0.81864} +{"mode": "train", "epoch": 55, "iter": 1300, "lr": 0.07096, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29906, "top5_acc": 0.56406, "loss_cls": 4.01176, "loss": 4.01176, "time": 0.81937} +{"mode": "train", "epoch": 55, "iter": 1400, "lr": 0.07093, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28547, "top5_acc": 0.55141, "loss_cls": 4.05633, "loss": 4.05633, "time": 0.81771} +{"mode": "train", "epoch": 55, "iter": 1500, "lr": 0.07091, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30141, "top5_acc": 0.55047, "loss_cls": 4.03082, "loss": 4.03082, "time": 0.8204} +{"mode": "train", "epoch": 55, "iter": 1600, "lr": 0.07088, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28484, "top5_acc": 0.54219, "loss_cls": 4.09517, "loss": 4.09517, "time": 0.8164} +{"mode": "train", "epoch": 55, "iter": 1700, "lr": 0.07086, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28453, "top5_acc": 0.54609, "loss_cls": 4.0748, "loss": 4.0748, "time": 0.82093} +{"mode": "train", "epoch": 55, "iter": 1800, "lr": 0.07083, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29094, "top5_acc": 0.55172, "loss_cls": 4.02572, "loss": 4.02572, "time": 0.81428} +{"mode": "train", "epoch": 55, "iter": 1900, "lr": 0.07081, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2925, "top5_acc": 0.54266, "loss_cls": 4.07635, "loss": 4.07635, "time": 0.81229} +{"mode": "train", "epoch": 55, "iter": 2000, "lr": 0.07078, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29766, "top5_acc": 0.56469, "loss_cls": 3.99524, "loss": 3.99524, "time": 0.82} +{"mode": "train", "epoch": 55, "iter": 2100, "lr": 0.07076, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29828, "top5_acc": 0.54812, "loss_cls": 4.03585, "loss": 4.03585, "time": 0.81754} +{"mode": "train", "epoch": 55, "iter": 2200, "lr": 0.07073, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29828, "top5_acc": 0.55547, "loss_cls": 4.01215, "loss": 4.01215, "time": 0.82313} +{"mode": "train", "epoch": 55, "iter": 2300, "lr": 0.07071, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28781, "top5_acc": 0.54578, "loss_cls": 4.06141, "loss": 4.06141, "time": 0.81624} +{"mode": "train", "epoch": 55, "iter": 2400, "lr": 0.07068, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29469, "top5_acc": 0.55297, "loss_cls": 4.06737, "loss": 4.06737, "time": 0.83015} +{"mode": "train", "epoch": 55, "iter": 2500, "lr": 0.07065, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28406, "top5_acc": 0.54969, "loss_cls": 4.07458, "loss": 4.07458, "time": 0.81809} +{"mode": "train", "epoch": 55, "iter": 2600, "lr": 0.07063, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.28141, "top5_acc": 0.54438, "loss_cls": 4.07407, "loss": 4.07407, "time": 0.82254} +{"mode": "train", "epoch": 55, "iter": 2700, "lr": 0.0706, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28875, "top5_acc": 0.55047, "loss_cls": 4.0768, "loss": 4.0768, "time": 0.83604} +{"mode": "train", "epoch": 55, "iter": 2800, "lr": 0.07058, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28938, "top5_acc": 0.55219, "loss_cls": 4.0645, "loss": 4.0645, "time": 0.81446} +{"mode": "train", "epoch": 55, "iter": 2900, "lr": 0.07055, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28828, "top5_acc": 0.53453, "loss_cls": 4.11022, "loss": 4.11022, "time": 0.82418} +{"mode": "train", "epoch": 55, "iter": 3000, "lr": 0.07053, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29719, "top5_acc": 0.55688, "loss_cls": 4.0223, "loss": 4.0223, "time": 0.82686} +{"mode": "train", "epoch": 55, "iter": 3100, "lr": 0.0705, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28234, "top5_acc": 0.54875, "loss_cls": 4.07339, "loss": 4.07339, "time": 0.82837} +{"mode": "train", "epoch": 55, "iter": 3200, "lr": 0.07048, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29109, "top5_acc": 0.55344, "loss_cls": 4.05365, "loss": 4.05365, "time": 0.82403} +{"mode": "train", "epoch": 55, "iter": 3300, "lr": 0.07045, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29672, "top5_acc": 0.54391, "loss_cls": 4.05376, "loss": 4.05376, "time": 0.83025} +{"mode": "train", "epoch": 55, "iter": 3400, "lr": 0.07043, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.295, "top5_acc": 0.54672, "loss_cls": 4.03743, "loss": 4.03743, "time": 0.81937} +{"mode": "train", "epoch": 55, "iter": 3500, "lr": 0.0704, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28078, "top5_acc": 0.54609, "loss_cls": 4.05611, "loss": 4.05611, "time": 0.8163} +{"mode": "train", "epoch": 55, "iter": 3600, "lr": 0.07037, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28234, "top5_acc": 0.54953, "loss_cls": 4.07227, "loss": 4.07227, "time": 0.82655} +{"mode": "train", "epoch": 55, "iter": 3700, "lr": 0.07035, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27656, "top5_acc": 0.52969, "loss_cls": 4.11714, "loss": 4.11714, "time": 0.83401} +{"mode": "val", "epoch": 55, "iter": 309, "lr": 0.07034, "top1_acc": 0.21491, "top5_acc": 0.44639, "mean_class_accuracy": 0.21459} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.07031, "memory": 15990, "data_time": 1.25833, "top1_acc": 0.30453, "top5_acc": 0.56812, "loss_cls": 3.95757, "loss": 3.95757, "time": 2.243} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.07029, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29688, "top5_acc": 0.55578, "loss_cls": 4.00804, "loss": 4.00804, "time": 0.83228} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.07026, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30094, "top5_acc": 0.55328, "loss_cls": 4.00808, "loss": 4.00808, "time": 0.82446} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.07023, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3, "top5_acc": 0.55875, "loss_cls": 3.97514, "loss": 3.97514, "time": 0.8228} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.07021, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29688, "top5_acc": 0.56078, "loss_cls": 4.01928, "loss": 4.01928, "time": 0.82111} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.07018, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28625, "top5_acc": 0.54328, "loss_cls": 4.04505, "loss": 4.04505, "time": 0.82306} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.07016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30875, "top5_acc": 0.55453, "loss_cls": 3.99174, "loss": 3.99174, "time": 0.82856} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.07013, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31172, "top5_acc": 0.55859, "loss_cls": 3.99811, "loss": 3.99811, "time": 0.81681} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.07011, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28813, "top5_acc": 0.56125, "loss_cls": 4.00305, "loss": 4.00305, "time": 0.82638} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.07008, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29469, "top5_acc": 0.55203, "loss_cls": 4.00982, "loss": 4.00982, "time": 0.82065} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.07006, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28703, "top5_acc": 0.55141, "loss_cls": 4.06522, "loss": 4.06522, "time": 0.81986} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.07003, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28859, "top5_acc": 0.54672, "loss_cls": 4.04293, "loss": 4.04293, "time": 0.8214} +{"mode": "train", "epoch": 56, "iter": 1300, "lr": 0.07, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28844, "top5_acc": 0.54312, "loss_cls": 4.07781, "loss": 4.07781, "time": 0.82145} +{"mode": "train", "epoch": 56, "iter": 1400, "lr": 0.06998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2875, "top5_acc": 0.54406, "loss_cls": 4.07385, "loss": 4.07385, "time": 0.82018} +{"mode": "train", "epoch": 56, "iter": 1500, "lr": 0.06995, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29203, "top5_acc": 0.55469, "loss_cls": 4.0178, "loss": 4.0178, "time": 0.82521} +{"mode": "train", "epoch": 56, "iter": 1600, "lr": 0.06993, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28672, "top5_acc": 0.55188, "loss_cls": 4.05139, "loss": 4.05139, "time": 0.8179} +{"mode": "train", "epoch": 56, "iter": 1700, "lr": 0.0699, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28906, "top5_acc": 0.55266, "loss_cls": 4.02703, "loss": 4.02703, "time": 0.82454} +{"mode": "train", "epoch": 56, "iter": 1800, "lr": 0.06988, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29, "top5_acc": 0.55453, "loss_cls": 4.0398, "loss": 4.0398, "time": 0.82135} +{"mode": "train", "epoch": 56, "iter": 1900, "lr": 0.06985, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29172, "top5_acc": 0.55719, "loss_cls": 3.99938, "loss": 3.99938, "time": 0.82091} +{"mode": "train", "epoch": 56, "iter": 2000, "lr": 0.06983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29219, "top5_acc": 0.55734, "loss_cls": 4.02017, "loss": 4.02017, "time": 0.82143} +{"mode": "train", "epoch": 56, "iter": 2100, "lr": 0.0698, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29625, "top5_acc": 0.55141, "loss_cls": 4.05528, "loss": 4.05528, "time": 0.81676} +{"mode": "train", "epoch": 56, "iter": 2200, "lr": 0.06977, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28453, "top5_acc": 0.54828, "loss_cls": 4.06285, "loss": 4.06285, "time": 0.81824} +{"mode": "train", "epoch": 56, "iter": 2300, "lr": 0.06975, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.29109, "top5_acc": 0.54828, "loss_cls": 4.05088, "loss": 4.05088, "time": 0.81531} +{"mode": "train", "epoch": 56, "iter": 2400, "lr": 0.06972, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28969, "top5_acc": 0.56, "loss_cls": 4.02585, "loss": 4.02585, "time": 0.83413} +{"mode": "train", "epoch": 56, "iter": 2500, "lr": 0.0697, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30047, "top5_acc": 0.555, "loss_cls": 3.9992, "loss": 3.9992, "time": 0.81268} +{"mode": "train", "epoch": 56, "iter": 2600, "lr": 0.06967, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28328, "top5_acc": 0.54406, "loss_cls": 4.06505, "loss": 4.06505, "time": 0.82202} +{"mode": "train", "epoch": 56, "iter": 2700, "lr": 0.06965, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29078, "top5_acc": 0.54953, "loss_cls": 4.07876, "loss": 4.07876, "time": 0.83247} +{"mode": "train", "epoch": 56, "iter": 2800, "lr": 0.06962, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29844, "top5_acc": 0.54062, "loss_cls": 4.05361, "loss": 4.05361, "time": 0.82041} +{"mode": "train", "epoch": 56, "iter": 2900, "lr": 0.06959, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28594, "top5_acc": 0.55469, "loss_cls": 4.05533, "loss": 4.05533, "time": 0.81888} +{"mode": "train", "epoch": 56, "iter": 3000, "lr": 0.06957, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29984, "top5_acc": 0.55594, "loss_cls": 4.00248, "loss": 4.00248, "time": 0.82125} +{"mode": "train", "epoch": 56, "iter": 3100, "lr": 0.06954, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29484, "top5_acc": 0.55109, "loss_cls": 4.05225, "loss": 4.05225, "time": 0.82133} +{"mode": "train", "epoch": 56, "iter": 3200, "lr": 0.06952, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.29797, "top5_acc": 0.55734, "loss_cls": 4.04509, "loss": 4.04509, "time": 0.81925} +{"mode": "train", "epoch": 56, "iter": 3300, "lr": 0.06949, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28672, "top5_acc": 0.54734, "loss_cls": 4.07546, "loss": 4.07546, "time": 0.83031} +{"mode": "train", "epoch": 56, "iter": 3400, "lr": 0.06947, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28438, "top5_acc": 0.54484, "loss_cls": 4.08063, "loss": 4.08063, "time": 0.81807} +{"mode": "train", "epoch": 56, "iter": 3500, "lr": 0.06944, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.29563, "top5_acc": 0.55328, "loss_cls": 4.04421, "loss": 4.04421, "time": 0.82149} +{"mode": "train", "epoch": 56, "iter": 3600, "lr": 0.06941, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28875, "top5_acc": 0.54062, "loss_cls": 4.09014, "loss": 4.09014, "time": 0.82951} +{"mode": "train", "epoch": 56, "iter": 3700, "lr": 0.06939, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30484, "top5_acc": 0.55344, "loss_cls": 3.99836, "loss": 3.99836, "time": 0.83589} +{"mode": "val", "epoch": 56, "iter": 309, "lr": 0.06938, "top1_acc": 0.21116, "top5_acc": 0.45454, "mean_class_accuracy": 0.21093} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.06935, "memory": 15990, "data_time": 1.24031, "top1_acc": 0.28859, "top5_acc": 0.55297, "loss_cls": 4.03019, "loss": 4.03019, "time": 2.22389} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.06932, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29578, "top5_acc": 0.54953, "loss_cls": 4.01991, "loss": 4.01991, "time": 0.82669} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.0693, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29391, "top5_acc": 0.54375, "loss_cls": 4.0558, "loss": 4.0558, "time": 0.82154} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.06927, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29844, "top5_acc": 0.56047, "loss_cls": 3.99778, "loss": 3.99778, "time": 0.82116} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.06925, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29844, "top5_acc": 0.56156, "loss_cls": 3.98748, "loss": 3.98748, "time": 0.81686} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.06922, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30219, "top5_acc": 0.55359, "loss_cls": 4.01378, "loss": 4.01378, "time": 0.82069} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.0692, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29109, "top5_acc": 0.54656, "loss_cls": 4.05646, "loss": 4.05646, "time": 0.8202} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.06917, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28703, "top5_acc": 0.54781, "loss_cls": 4.04323, "loss": 4.04323, "time": 0.81795} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.06914, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28938, "top5_acc": 0.55094, "loss_cls": 4.02041, "loss": 4.02041, "time": 0.8219} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.06912, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29281, "top5_acc": 0.56, "loss_cls": 4.00185, "loss": 4.00185, "time": 0.82368} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.06909, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29875, "top5_acc": 0.55688, "loss_cls": 4.00455, "loss": 4.00455, "time": 0.82487} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.06907, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27938, "top5_acc": 0.53828, "loss_cls": 4.11067, "loss": 4.11067, "time": 0.82479} +{"mode": "train", "epoch": 57, "iter": 1300, "lr": 0.06904, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2925, "top5_acc": 0.56125, "loss_cls": 4.03436, "loss": 4.03436, "time": 0.8234} +{"mode": "train", "epoch": 57, "iter": 1400, "lr": 0.06901, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28719, "top5_acc": 0.55141, "loss_cls": 4.02091, "loss": 4.02091, "time": 0.81819} +{"mode": "train", "epoch": 57, "iter": 1500, "lr": 0.06899, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28922, "top5_acc": 0.54953, "loss_cls": 4.0471, "loss": 4.0471, "time": 0.81611} +{"mode": "train", "epoch": 57, "iter": 1600, "lr": 0.06896, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29984, "top5_acc": 0.55703, "loss_cls": 4.00557, "loss": 4.00557, "time": 0.81983} +{"mode": "train", "epoch": 57, "iter": 1700, "lr": 0.06894, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30203, "top5_acc": 0.55437, "loss_cls": 4.00647, "loss": 4.00647, "time": 0.81737} +{"mode": "train", "epoch": 57, "iter": 1800, "lr": 0.06891, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30234, "top5_acc": 0.56563, "loss_cls": 3.97756, "loss": 3.97756, "time": 0.81965} +{"mode": "train", "epoch": 57, "iter": 1900, "lr": 0.06889, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30125, "top5_acc": 0.56172, "loss_cls": 3.993, "loss": 3.993, "time": 0.82228} +{"mode": "train", "epoch": 57, "iter": 2000, "lr": 0.06886, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29344, "top5_acc": 0.54203, "loss_cls": 4.069, "loss": 4.069, "time": 0.82028} +{"mode": "train", "epoch": 57, "iter": 2100, "lr": 0.06883, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29188, "top5_acc": 0.54719, "loss_cls": 4.06012, "loss": 4.06012, "time": 0.82065} +{"mode": "train", "epoch": 57, "iter": 2200, "lr": 0.06881, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29172, "top5_acc": 0.55516, "loss_cls": 4.02696, "loss": 4.02696, "time": 0.8193} +{"mode": "train", "epoch": 57, "iter": 2300, "lr": 0.06878, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29781, "top5_acc": 0.54703, "loss_cls": 4.03764, "loss": 4.03764, "time": 0.81911} +{"mode": "train", "epoch": 57, "iter": 2400, "lr": 0.06876, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29938, "top5_acc": 0.55969, "loss_cls": 3.99329, "loss": 3.99329, "time": 0.82746} +{"mode": "train", "epoch": 57, "iter": 2500, "lr": 0.06873, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29047, "top5_acc": 0.55062, "loss_cls": 4.02166, "loss": 4.02166, "time": 0.81754} +{"mode": "train", "epoch": 57, "iter": 2600, "lr": 0.0687, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29328, "top5_acc": 0.55719, "loss_cls": 4.02256, "loss": 4.02256, "time": 0.826} +{"mode": "train", "epoch": 57, "iter": 2700, "lr": 0.06868, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30547, "top5_acc": 0.54937, "loss_cls": 4.02645, "loss": 4.02645, "time": 0.82888} +{"mode": "train", "epoch": 57, "iter": 2800, "lr": 0.06865, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29234, "top5_acc": 0.53984, "loss_cls": 4.07001, "loss": 4.07001, "time": 0.81788} +{"mode": "train", "epoch": 57, "iter": 2900, "lr": 0.06863, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29219, "top5_acc": 0.54922, "loss_cls": 4.03572, "loss": 4.03572, "time": 0.82604} +{"mode": "train", "epoch": 57, "iter": 3000, "lr": 0.0686, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28734, "top5_acc": 0.54453, "loss_cls": 4.06266, "loss": 4.06266, "time": 0.82648} +{"mode": "train", "epoch": 57, "iter": 3100, "lr": 0.06857, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29219, "top5_acc": 0.54625, "loss_cls": 4.07209, "loss": 4.07209, "time": 0.82802} +{"mode": "train", "epoch": 57, "iter": 3200, "lr": 0.06855, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30438, "top5_acc": 0.55359, "loss_cls": 4.01219, "loss": 4.01219, "time": 0.82577} +{"mode": "train", "epoch": 57, "iter": 3300, "lr": 0.06852, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30609, "top5_acc": 0.56531, "loss_cls": 3.95084, "loss": 3.95084, "time": 0.82644} +{"mode": "train", "epoch": 57, "iter": 3400, "lr": 0.0685, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29, "top5_acc": 0.55391, "loss_cls": 4.04632, "loss": 4.04632, "time": 0.81813} +{"mode": "train", "epoch": 57, "iter": 3500, "lr": 0.06847, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30125, "top5_acc": 0.55281, "loss_cls": 4.02896, "loss": 4.02896, "time": 0.82124} +{"mode": "train", "epoch": 57, "iter": 3600, "lr": 0.06844, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27922, "top5_acc": 0.54469, "loss_cls": 4.05988, "loss": 4.05988, "time": 0.82948} +{"mode": "train", "epoch": 57, "iter": 3700, "lr": 0.06842, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28844, "top5_acc": 0.54969, "loss_cls": 4.07966, "loss": 4.07966, "time": 0.83536} +{"mode": "val", "epoch": 57, "iter": 309, "lr": 0.06841, "top1_acc": 0.18508, "top5_acc": 0.41706, "mean_class_accuracy": 0.18502} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.06838, "memory": 15990, "data_time": 1.26989, "top1_acc": 0.30328, "top5_acc": 0.56641, "loss_cls": 3.95501, "loss": 3.95501, "time": 2.26945} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.06835, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30281, "top5_acc": 0.56516, "loss_cls": 3.94901, "loss": 3.94901, "time": 0.83864} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.06833, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28766, "top5_acc": 0.56141, "loss_cls": 4.01793, "loss": 4.01793, "time": 0.83431} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.0683, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28797, "top5_acc": 0.54578, "loss_cls": 4.05596, "loss": 4.05596, "time": 0.83161} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.06828, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28844, "top5_acc": 0.54766, "loss_cls": 4.05399, "loss": 4.05399, "time": 0.83275} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.06825, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30422, "top5_acc": 0.56422, "loss_cls": 4.00582, "loss": 4.00582, "time": 0.83585} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.06822, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30234, "top5_acc": 0.55578, "loss_cls": 4.03209, "loss": 4.03209, "time": 0.83345} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.0682, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29594, "top5_acc": 0.56219, "loss_cls": 3.99769, "loss": 3.99769, "time": 0.83473} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.06817, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29172, "top5_acc": 0.55437, "loss_cls": 4.04003, "loss": 4.04003, "time": 0.84096} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.06815, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29563, "top5_acc": 0.55656, "loss_cls": 4.0172, "loss": 4.0172, "time": 0.83763} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.06812, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29016, "top5_acc": 0.54937, "loss_cls": 4.04872, "loss": 4.04872, "time": 0.82808} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.06809, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29656, "top5_acc": 0.55219, "loss_cls": 4.02073, "loss": 4.02073, "time": 0.8333} +{"mode": "train", "epoch": 58, "iter": 1300, "lr": 0.06807, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28656, "top5_acc": 0.55625, "loss_cls": 4.01488, "loss": 4.01488, "time": 0.83423} +{"mode": "train", "epoch": 58, "iter": 1400, "lr": 0.06804, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29391, "top5_acc": 0.54531, "loss_cls": 4.05327, "loss": 4.05327, "time": 0.83724} +{"mode": "train", "epoch": 58, "iter": 1500, "lr": 0.06802, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29266, "top5_acc": 0.55453, "loss_cls": 4.04476, "loss": 4.04476, "time": 0.83843} +{"mode": "train", "epoch": 58, "iter": 1600, "lr": 0.06799, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2975, "top5_acc": 0.55797, "loss_cls": 4.02129, "loss": 4.02129, "time": 0.83158} +{"mode": "train", "epoch": 58, "iter": 1700, "lr": 0.06796, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30625, "top5_acc": 0.55672, "loss_cls": 3.98567, "loss": 3.98567, "time": 0.83701} +{"mode": "train", "epoch": 58, "iter": 1800, "lr": 0.06794, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28688, "top5_acc": 0.54703, "loss_cls": 4.06792, "loss": 4.06792, "time": 0.84038} +{"mode": "train", "epoch": 58, "iter": 1900, "lr": 0.06791, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29594, "top5_acc": 0.54641, "loss_cls": 4.02816, "loss": 4.02816, "time": 0.8318} +{"mode": "train", "epoch": 58, "iter": 2000, "lr": 0.06789, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30234, "top5_acc": 0.55531, "loss_cls": 3.98599, "loss": 3.98599, "time": 0.83044} +{"mode": "train", "epoch": 58, "iter": 2100, "lr": 0.06786, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29469, "top5_acc": 0.55531, "loss_cls": 4.04446, "loss": 4.04446, "time": 0.83187} +{"mode": "train", "epoch": 58, "iter": 2200, "lr": 0.06783, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29547, "top5_acc": 0.55875, "loss_cls": 4.0291, "loss": 4.0291, "time": 0.83262} +{"mode": "train", "epoch": 58, "iter": 2300, "lr": 0.06781, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30125, "top5_acc": 0.55297, "loss_cls": 4.00331, "loss": 4.00331, "time": 0.83756} +{"mode": "train", "epoch": 58, "iter": 2400, "lr": 0.06778, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31344, "top5_acc": 0.55875, "loss_cls": 3.9912, "loss": 3.9912, "time": 0.8318} +{"mode": "train", "epoch": 58, "iter": 2500, "lr": 0.06775, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30125, "top5_acc": 0.56641, "loss_cls": 3.98497, "loss": 3.98497, "time": 0.82368} +{"mode": "train", "epoch": 58, "iter": 2600, "lr": 0.06773, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29672, "top5_acc": 0.55812, "loss_cls": 4.00564, "loss": 4.00564, "time": 0.83797} +{"mode": "train", "epoch": 58, "iter": 2700, "lr": 0.0677, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30031, "top5_acc": 0.55984, "loss_cls": 4.00348, "loss": 4.00348, "time": 0.82826} +{"mode": "train", "epoch": 58, "iter": 2800, "lr": 0.06768, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29391, "top5_acc": 0.55672, "loss_cls": 4.01332, "loss": 4.01332, "time": 0.82848} +{"mode": "train", "epoch": 58, "iter": 2900, "lr": 0.06765, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29328, "top5_acc": 0.55453, "loss_cls": 4.02371, "loss": 4.02371, "time": 0.83352} +{"mode": "train", "epoch": 58, "iter": 3000, "lr": 0.06762, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29141, "top5_acc": 0.54937, "loss_cls": 4.03471, "loss": 4.03471, "time": 0.82787} +{"mode": "train", "epoch": 58, "iter": 3100, "lr": 0.0676, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29125, "top5_acc": 0.55297, "loss_cls": 4.03625, "loss": 4.03625, "time": 0.82243} +{"mode": "train", "epoch": 58, "iter": 3200, "lr": 0.06757, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28781, "top5_acc": 0.55328, "loss_cls": 4.04524, "loss": 4.04524, "time": 0.82688} +{"mode": "train", "epoch": 58, "iter": 3300, "lr": 0.06755, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29547, "top5_acc": 0.56344, "loss_cls": 3.99836, "loss": 3.99836, "time": 0.81926} +{"mode": "train", "epoch": 58, "iter": 3400, "lr": 0.06752, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28828, "top5_acc": 0.55406, "loss_cls": 4.03833, "loss": 4.03833, "time": 0.82166} +{"mode": "train", "epoch": 58, "iter": 3500, "lr": 0.06749, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28312, "top5_acc": 0.53641, "loss_cls": 4.09585, "loss": 4.09585, "time": 0.82919} +{"mode": "train", "epoch": 58, "iter": 3600, "lr": 0.06747, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30375, "top5_acc": 0.55906, "loss_cls": 3.98289, "loss": 3.98289, "time": 0.83083} +{"mode": "train", "epoch": 58, "iter": 3700, "lr": 0.06744, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30156, "top5_acc": 0.55391, "loss_cls": 4.02063, "loss": 4.02063, "time": 0.82788} +{"mode": "val", "epoch": 58, "iter": 309, "lr": 0.06743, "top1_acc": 0.21319, "top5_acc": 0.44856, "mean_class_accuracy": 0.21293} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.0674, "memory": 15990, "data_time": 1.30343, "top1_acc": 0.30203, "top5_acc": 0.56359, "loss_cls": 3.97873, "loss": 3.97873, "time": 2.29468} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.06738, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29922, "top5_acc": 0.56234, "loss_cls": 3.98551, "loss": 3.98551, "time": 0.83498} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.06735, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30422, "top5_acc": 0.55297, "loss_cls": 3.98174, "loss": 3.98174, "time": 0.82828} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.06732, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29891, "top5_acc": 0.55891, "loss_cls": 3.99398, "loss": 3.99398, "time": 0.8258} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.0673, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29906, "top5_acc": 0.55406, "loss_cls": 4.02927, "loss": 4.02927, "time": 0.82621} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.06727, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30125, "top5_acc": 0.56281, "loss_cls": 3.98878, "loss": 3.98878, "time": 0.82553} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.06725, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30641, "top5_acc": 0.55797, "loss_cls": 3.97526, "loss": 3.97526, "time": 0.81761} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.06722, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29828, "top5_acc": 0.55719, "loss_cls": 4.00514, "loss": 4.00514, "time": 0.83057} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.06719, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30047, "top5_acc": 0.55766, "loss_cls": 3.99303, "loss": 3.99303, "time": 0.82642} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.06717, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29797, "top5_acc": 0.55359, "loss_cls": 4.01775, "loss": 4.01775, "time": 0.83345} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.06714, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29234, "top5_acc": 0.55047, "loss_cls": 4.03328, "loss": 4.03328, "time": 0.83046} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.06711, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29484, "top5_acc": 0.55312, "loss_cls": 4.02576, "loss": 4.02576, "time": 0.83201} +{"mode": "train", "epoch": 59, "iter": 1300, "lr": 0.06709, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29672, "top5_acc": 0.56172, "loss_cls": 4.01622, "loss": 4.01622, "time": 0.82804} +{"mode": "train", "epoch": 59, "iter": 1400, "lr": 0.06706, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29063, "top5_acc": 0.55312, "loss_cls": 4.0379, "loss": 4.0379, "time": 0.82968} +{"mode": "train", "epoch": 59, "iter": 1500, "lr": 0.06704, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29922, "top5_acc": 0.55828, "loss_cls": 3.99181, "loss": 3.99181, "time": 0.82724} +{"mode": "train", "epoch": 59, "iter": 1600, "lr": 0.06701, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29422, "top5_acc": 0.55094, "loss_cls": 4.03613, "loss": 4.03613, "time": 0.82119} +{"mode": "train", "epoch": 59, "iter": 1700, "lr": 0.06698, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29828, "top5_acc": 0.55688, "loss_cls": 3.98946, "loss": 3.98946, "time": 0.83534} +{"mode": "train", "epoch": 59, "iter": 1800, "lr": 0.06696, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29672, "top5_acc": 0.56156, "loss_cls": 3.98263, "loss": 3.98263, "time": 0.82432} +{"mode": "train", "epoch": 59, "iter": 1900, "lr": 0.06693, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28672, "top5_acc": 0.54094, "loss_cls": 4.10538, "loss": 4.10538, "time": 0.82365} +{"mode": "train", "epoch": 59, "iter": 2000, "lr": 0.0669, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30266, "top5_acc": 0.56, "loss_cls": 4.01116, "loss": 4.01116, "time": 0.81425} +{"mode": "train", "epoch": 59, "iter": 2100, "lr": 0.06688, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30219, "top5_acc": 0.56641, "loss_cls": 3.97033, "loss": 3.97033, "time": 0.82316} +{"mode": "train", "epoch": 59, "iter": 2200, "lr": 0.06685, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29797, "top5_acc": 0.56641, "loss_cls": 3.97459, "loss": 3.97459, "time": 0.82166} +{"mode": "train", "epoch": 59, "iter": 2300, "lr": 0.06682, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2925, "top5_acc": 0.55125, "loss_cls": 4.05104, "loss": 4.05104, "time": 0.8289} +{"mode": "train", "epoch": 59, "iter": 2400, "lr": 0.0668, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29828, "top5_acc": 0.55047, "loss_cls": 3.99374, "loss": 3.99374, "time": 0.81668} +{"mode": "train", "epoch": 59, "iter": 2500, "lr": 0.06677, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29531, "top5_acc": 0.55391, "loss_cls": 4.00706, "loss": 4.00706, "time": 0.82064} +{"mode": "train", "epoch": 59, "iter": 2600, "lr": 0.06675, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28703, "top5_acc": 0.55078, "loss_cls": 4.04475, "loss": 4.04475, "time": 0.83187} +{"mode": "train", "epoch": 59, "iter": 2700, "lr": 0.06672, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30672, "top5_acc": 0.56359, "loss_cls": 3.97149, "loss": 3.97149, "time": 0.81967} +{"mode": "train", "epoch": 59, "iter": 2800, "lr": 0.06669, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28344, "top5_acc": 0.54609, "loss_cls": 4.09187, "loss": 4.09187, "time": 0.82721} +{"mode": "train", "epoch": 59, "iter": 2900, "lr": 0.06667, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30094, "top5_acc": 0.56516, "loss_cls": 3.972, "loss": 3.972, "time": 0.82814} +{"mode": "train", "epoch": 59, "iter": 3000, "lr": 0.06664, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30156, "top5_acc": 0.55609, "loss_cls": 4.00324, "loss": 4.00324, "time": 0.82435} +{"mode": "train", "epoch": 59, "iter": 3100, "lr": 0.06661, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28813, "top5_acc": 0.54781, "loss_cls": 4.04464, "loss": 4.04464, "time": 0.82182} +{"mode": "train", "epoch": 59, "iter": 3200, "lr": 0.06659, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29625, "top5_acc": 0.56094, "loss_cls": 3.98081, "loss": 3.98081, "time": 0.82916} +{"mode": "train", "epoch": 59, "iter": 3300, "lr": 0.06656, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30969, "top5_acc": 0.56125, "loss_cls": 3.97726, "loss": 3.97726, "time": 0.8177} +{"mode": "train", "epoch": 59, "iter": 3400, "lr": 0.06653, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.29781, "top5_acc": 0.55375, "loss_cls": 4.01326, "loss": 4.01326, "time": 0.8227} +{"mode": "train", "epoch": 59, "iter": 3500, "lr": 0.06651, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.305, "top5_acc": 0.5575, "loss_cls": 4.01161, "loss": 4.01161, "time": 0.83544} +{"mode": "train", "epoch": 59, "iter": 3600, "lr": 0.06648, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30453, "top5_acc": 0.56016, "loss_cls": 4.00715, "loss": 4.00715, "time": 0.82974} +{"mode": "train", "epoch": 59, "iter": 3700, "lr": 0.06646, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30188, "top5_acc": 0.56812, "loss_cls": 3.93995, "loss": 3.93995, "time": 0.83033} +{"mode": "val", "epoch": 59, "iter": 309, "lr": 0.06644, "top1_acc": 0.23811, "top5_acc": 0.47607, "mean_class_accuracy": 0.23775} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.06642, "memory": 15990, "data_time": 1.25702, "top1_acc": 0.30406, "top5_acc": 0.56812, "loss_cls": 3.96826, "loss": 3.96826, "time": 2.24605} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.06639, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28562, "top5_acc": 0.55156, "loss_cls": 4.02547, "loss": 4.02547, "time": 0.82165} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.06636, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30719, "top5_acc": 0.56344, "loss_cls": 3.96181, "loss": 3.96181, "time": 0.82144} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.06634, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30094, "top5_acc": 0.55828, "loss_cls": 3.96767, "loss": 3.96767, "time": 0.82795} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.06631, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30234, "top5_acc": 0.56312, "loss_cls": 3.97854, "loss": 3.97854, "time": 0.82094} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.06629, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30625, "top5_acc": 0.55656, "loss_cls": 3.9721, "loss": 3.9721, "time": 0.82335} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.06626, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30812, "top5_acc": 0.56188, "loss_cls": 3.95215, "loss": 3.95215, "time": 0.81891} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.06623, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29859, "top5_acc": 0.55937, "loss_cls": 4.00003, "loss": 4.00003, "time": 0.81792} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.06621, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29922, "top5_acc": 0.55781, "loss_cls": 3.97621, "loss": 3.97621, "time": 0.81862} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.06618, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30062, "top5_acc": 0.55812, "loss_cls": 3.99534, "loss": 3.99534, "time": 0.82057} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.06615, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29234, "top5_acc": 0.55609, "loss_cls": 4.00838, "loss": 4.00838, "time": 0.81826} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.06613, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28703, "top5_acc": 0.54859, "loss_cls": 4.05393, "loss": 4.05393, "time": 0.82524} +{"mode": "train", "epoch": 60, "iter": 1300, "lr": 0.0661, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30891, "top5_acc": 0.56109, "loss_cls": 3.98363, "loss": 3.98363, "time": 0.82338} +{"mode": "train", "epoch": 60, "iter": 1400, "lr": 0.06607, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29734, "top5_acc": 0.54953, "loss_cls": 4.00275, "loss": 4.00275, "time": 0.82046} +{"mode": "train", "epoch": 60, "iter": 1500, "lr": 0.06605, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30219, "top5_acc": 0.56078, "loss_cls": 3.94651, "loss": 3.94651, "time": 0.82632} +{"mode": "train", "epoch": 60, "iter": 1600, "lr": 0.06602, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29297, "top5_acc": 0.55437, "loss_cls": 4.03857, "loss": 4.03857, "time": 0.82436} +{"mode": "train", "epoch": 60, "iter": 1700, "lr": 0.06599, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30547, "top5_acc": 0.55922, "loss_cls": 3.99577, "loss": 3.99577, "time": 0.82706} +{"mode": "train", "epoch": 60, "iter": 1800, "lr": 0.06597, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3075, "top5_acc": 0.56578, "loss_cls": 3.96536, "loss": 3.96536, "time": 0.82168} +{"mode": "train", "epoch": 60, "iter": 1900, "lr": 0.06594, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29812, "top5_acc": 0.54922, "loss_cls": 4.05596, "loss": 4.05596, "time": 0.82527} +{"mode": "train", "epoch": 60, "iter": 2000, "lr": 0.06591, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29016, "top5_acc": 0.54391, "loss_cls": 4.05801, "loss": 4.05801, "time": 0.81887} +{"mode": "train", "epoch": 60, "iter": 2100, "lr": 0.06589, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29516, "top5_acc": 0.55766, "loss_cls": 4.01156, "loss": 4.01156, "time": 0.81979} +{"mode": "train", "epoch": 60, "iter": 2200, "lr": 0.06586, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29563, "top5_acc": 0.545, "loss_cls": 4.06135, "loss": 4.06135, "time": 0.81914} +{"mode": "train", "epoch": 60, "iter": 2300, "lr": 0.06584, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30188, "top5_acc": 0.55047, "loss_cls": 4.01835, "loss": 4.01835, "time": 0.83404} +{"mode": "train", "epoch": 60, "iter": 2400, "lr": 0.06581, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29938, "top5_acc": 0.54969, "loss_cls": 4.01874, "loss": 4.01874, "time": 0.81341} +{"mode": "train", "epoch": 60, "iter": 2500, "lr": 0.06578, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2975, "top5_acc": 0.55, "loss_cls": 4.05571, "loss": 4.05571, "time": 0.82945} +{"mode": "train", "epoch": 60, "iter": 2600, "lr": 0.06576, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30656, "top5_acc": 0.56516, "loss_cls": 3.94138, "loss": 3.94138, "time": 0.82912} +{"mode": "train", "epoch": 60, "iter": 2700, "lr": 0.06573, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29516, "top5_acc": 0.55812, "loss_cls": 3.99016, "loss": 3.99016, "time": 0.82529} +{"mode": "train", "epoch": 60, "iter": 2800, "lr": 0.0657, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30812, "top5_acc": 0.56469, "loss_cls": 3.9597, "loss": 3.9597, "time": 0.83001} +{"mode": "train", "epoch": 60, "iter": 2900, "lr": 0.06568, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29844, "top5_acc": 0.55547, "loss_cls": 4.00862, "loss": 4.00862, "time": 0.83042} +{"mode": "train", "epoch": 60, "iter": 3000, "lr": 0.06565, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29063, "top5_acc": 0.55922, "loss_cls": 4.0137, "loss": 4.0137, "time": 0.83379} +{"mode": "train", "epoch": 60, "iter": 3100, "lr": 0.06562, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30656, "top5_acc": 0.55781, "loss_cls": 3.99809, "loss": 3.99809, "time": 0.82832} +{"mode": "train", "epoch": 60, "iter": 3200, "lr": 0.0656, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29797, "top5_acc": 0.55281, "loss_cls": 4.01094, "loss": 4.01094, "time": 0.82576} +{"mode": "train", "epoch": 60, "iter": 3300, "lr": 0.06557, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29891, "top5_acc": 0.54719, "loss_cls": 4.00755, "loss": 4.00755, "time": 0.81771} +{"mode": "train", "epoch": 60, "iter": 3400, "lr": 0.06554, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29953, "top5_acc": 0.56688, "loss_cls": 3.96419, "loss": 3.96419, "time": 0.82595} +{"mode": "train", "epoch": 60, "iter": 3500, "lr": 0.06552, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28984, "top5_acc": 0.555, "loss_cls": 4.00557, "loss": 4.00557, "time": 0.83294} +{"mode": "train", "epoch": 60, "iter": 3600, "lr": 0.06549, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29578, "top5_acc": 0.55734, "loss_cls": 4.00644, "loss": 4.00644, "time": 0.82816} +{"mode": "train", "epoch": 60, "iter": 3700, "lr": 0.06546, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30234, "top5_acc": 0.55625, "loss_cls": 3.99611, "loss": 3.99611, "time": 0.83041} +{"mode": "val", "epoch": 60, "iter": 309, "lr": 0.06545, "top1_acc": 0.21076, "top5_acc": 0.44294, "mean_class_accuracy": 0.21064} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.06542, "memory": 15990, "data_time": 1.25453, "top1_acc": 0.30359, "top5_acc": 0.56297, "loss_cls": 3.9835, "loss": 3.9835, "time": 2.2461} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.0654, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30781, "top5_acc": 0.57, "loss_cls": 3.93928, "loss": 3.93928, "time": 0.83013} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.06537, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.305, "top5_acc": 0.56781, "loss_cls": 3.94286, "loss": 3.94286, "time": 0.83869} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.06534, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31078, "top5_acc": 0.57328, "loss_cls": 3.93868, "loss": 3.93868, "time": 0.8315} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.06532, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29656, "top5_acc": 0.56016, "loss_cls": 3.99417, "loss": 3.99417, "time": 0.83462} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.06529, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29938, "top5_acc": 0.55703, "loss_cls": 3.99437, "loss": 3.99437, "time": 0.83245} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.06526, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31484, "top5_acc": 0.5575, "loss_cls": 3.96596, "loss": 3.96596, "time": 0.83261} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.06524, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.295, "top5_acc": 0.56063, "loss_cls": 3.97681, "loss": 3.97681, "time": 0.83225} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.06521, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30703, "top5_acc": 0.56531, "loss_cls": 3.9555, "loss": 3.9555, "time": 0.83188} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.06519, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3, "top5_acc": 0.55469, "loss_cls": 3.98511, "loss": 3.98511, "time": 0.83269} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.06516, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29172, "top5_acc": 0.55609, "loss_cls": 4.01917, "loss": 4.01917, "time": 0.82733} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.06513, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29969, "top5_acc": 0.55672, "loss_cls": 3.99536, "loss": 3.99536, "time": 0.83247} +{"mode": "train", "epoch": 61, "iter": 1300, "lr": 0.06511, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30016, "top5_acc": 0.56156, "loss_cls": 3.97776, "loss": 3.97776, "time": 0.83166} +{"mode": "train", "epoch": 61, "iter": 1400, "lr": 0.06508, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29438, "top5_acc": 0.56109, "loss_cls": 4.00141, "loss": 4.00141, "time": 0.83377} +{"mode": "train", "epoch": 61, "iter": 1500, "lr": 0.06505, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29953, "top5_acc": 0.55953, "loss_cls": 3.99203, "loss": 3.99203, "time": 0.82864} +{"mode": "train", "epoch": 61, "iter": 1600, "lr": 0.06503, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30125, "top5_acc": 0.56484, "loss_cls": 3.98053, "loss": 3.98053, "time": 0.83553} +{"mode": "train", "epoch": 61, "iter": 1700, "lr": 0.065, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30953, "top5_acc": 0.56234, "loss_cls": 3.97031, "loss": 3.97031, "time": 0.83318} +{"mode": "train", "epoch": 61, "iter": 1800, "lr": 0.06497, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30094, "top5_acc": 0.55484, "loss_cls": 4.01775, "loss": 4.01775, "time": 0.83201} +{"mode": "train", "epoch": 61, "iter": 1900, "lr": 0.06495, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30797, "top5_acc": 0.56063, "loss_cls": 3.9573, "loss": 3.9573, "time": 0.82853} +{"mode": "train", "epoch": 61, "iter": 2000, "lr": 0.06492, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29609, "top5_acc": 0.54531, "loss_cls": 4.02065, "loss": 4.02065, "time": 0.83153} +{"mode": "train", "epoch": 61, "iter": 2100, "lr": 0.06489, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30547, "top5_acc": 0.56875, "loss_cls": 3.98124, "loss": 3.98124, "time": 0.82417} +{"mode": "train", "epoch": 61, "iter": 2200, "lr": 0.06487, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29938, "top5_acc": 0.55766, "loss_cls": 3.98693, "loss": 3.98693, "time": 0.83354} +{"mode": "train", "epoch": 61, "iter": 2300, "lr": 0.06484, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.285, "top5_acc": 0.54688, "loss_cls": 4.07713, "loss": 4.07713, "time": 0.83088} +{"mode": "train", "epoch": 61, "iter": 2400, "lr": 0.06481, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.305, "top5_acc": 0.56563, "loss_cls": 3.96665, "loss": 3.96665, "time": 0.82457} +{"mode": "train", "epoch": 61, "iter": 2500, "lr": 0.06478, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29188, "top5_acc": 0.54562, "loss_cls": 4.04444, "loss": 4.04444, "time": 0.83787} +{"mode": "train", "epoch": 61, "iter": 2600, "lr": 0.06476, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29281, "top5_acc": 0.55031, "loss_cls": 4.03517, "loss": 4.03517, "time": 0.82992} +{"mode": "train", "epoch": 61, "iter": 2700, "lr": 0.06473, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29391, "top5_acc": 0.56156, "loss_cls": 3.98609, "loss": 3.98609, "time": 0.82781} +{"mode": "train", "epoch": 61, "iter": 2800, "lr": 0.0647, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29719, "top5_acc": 0.55391, "loss_cls": 3.95628, "loss": 3.95628, "time": 0.83145} +{"mode": "train", "epoch": 61, "iter": 2900, "lr": 0.06468, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29469, "top5_acc": 0.55141, "loss_cls": 4.01174, "loss": 4.01174, "time": 0.82331} +{"mode": "train", "epoch": 61, "iter": 3000, "lr": 0.06465, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29641, "top5_acc": 0.54766, "loss_cls": 4.06136, "loss": 4.06136, "time": 0.82224} +{"mode": "train", "epoch": 61, "iter": 3100, "lr": 0.06462, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29016, "top5_acc": 0.55266, "loss_cls": 4.00947, "loss": 4.00947, "time": 0.83184} +{"mode": "train", "epoch": 61, "iter": 3200, "lr": 0.0646, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29953, "top5_acc": 0.55547, "loss_cls": 4.02074, "loss": 4.02074, "time": 0.82804} +{"mode": "train", "epoch": 61, "iter": 3300, "lr": 0.06457, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30641, "top5_acc": 0.56391, "loss_cls": 3.97918, "loss": 3.97918, "time": 0.82798} +{"mode": "train", "epoch": 61, "iter": 3400, "lr": 0.06454, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28312, "top5_acc": 0.54312, "loss_cls": 4.07957, "loss": 4.07957, "time": 0.82926} +{"mode": "train", "epoch": 61, "iter": 3500, "lr": 0.06452, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30125, "top5_acc": 0.55609, "loss_cls": 4.01664, "loss": 4.01664, "time": 0.82971} +{"mode": "train", "epoch": 61, "iter": 3600, "lr": 0.06449, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30219, "top5_acc": 0.55906, "loss_cls": 3.98662, "loss": 3.98662, "time": 0.83911} +{"mode": "train", "epoch": 61, "iter": 3700, "lr": 0.06446, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30484, "top5_acc": 0.55953, "loss_cls": 3.96204, "loss": 3.96204, "time": 0.82418} +{"mode": "val", "epoch": 61, "iter": 309, "lr": 0.06445, "top1_acc": 0.22727, "top5_acc": 0.46705, "mean_class_accuracy": 0.22723} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.06443, "memory": 15990, "data_time": 1.25466, "top1_acc": 0.30516, "top5_acc": 0.56188, "loss_cls": 3.9666, "loss": 3.9666, "time": 2.24737} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.0644, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29922, "top5_acc": 0.55844, "loss_cls": 3.99935, "loss": 3.99935, "time": 0.83058} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.06437, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30734, "top5_acc": 0.56484, "loss_cls": 3.95647, "loss": 3.95647, "time": 0.8275} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.06434, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31078, "top5_acc": 0.56766, "loss_cls": 3.9535, "loss": 3.9535, "time": 0.82584} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.06432, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29719, "top5_acc": 0.5575, "loss_cls": 3.97954, "loss": 3.97954, "time": 0.81909} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.06429, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30469, "top5_acc": 0.55922, "loss_cls": 3.96191, "loss": 3.96191, "time": 0.81758} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.06426, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29531, "top5_acc": 0.55016, "loss_cls": 4.00084, "loss": 4.00084, "time": 0.82093} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.06424, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29516, "top5_acc": 0.54875, "loss_cls": 3.99999, "loss": 3.99999, "time": 0.81858} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.06421, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30359, "top5_acc": 0.56266, "loss_cls": 3.96898, "loss": 3.96898, "time": 0.82235} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.06418, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30594, "top5_acc": 0.56047, "loss_cls": 3.95733, "loss": 3.95733, "time": 0.82884} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.06416, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30531, "top5_acc": 0.56188, "loss_cls": 3.98082, "loss": 3.98082, "time": 0.82452} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.06413, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28922, "top5_acc": 0.55906, "loss_cls": 4.02307, "loss": 4.02307, "time": 0.82212} +{"mode": "train", "epoch": 62, "iter": 1300, "lr": 0.0641, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29844, "top5_acc": 0.56328, "loss_cls": 3.99454, "loss": 3.99454, "time": 0.82074} +{"mode": "train", "epoch": 62, "iter": 1400, "lr": 0.06408, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29672, "top5_acc": 0.55703, "loss_cls": 3.99758, "loss": 3.99758, "time": 0.81346} +{"mode": "train", "epoch": 62, "iter": 1500, "lr": 0.06405, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30406, "top5_acc": 0.56844, "loss_cls": 3.93368, "loss": 3.93368, "time": 0.82026} +{"mode": "train", "epoch": 62, "iter": 1600, "lr": 0.06402, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29953, "top5_acc": 0.55625, "loss_cls": 4.02593, "loss": 4.02593, "time": 0.81885} +{"mode": "train", "epoch": 62, "iter": 1700, "lr": 0.064, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29391, "top5_acc": 0.54672, "loss_cls": 4.05035, "loss": 4.05035, "time": 0.81722} +{"mode": "train", "epoch": 62, "iter": 1800, "lr": 0.06397, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3, "top5_acc": 0.55797, "loss_cls": 4.00053, "loss": 4.00053, "time": 0.81754} +{"mode": "train", "epoch": 62, "iter": 1900, "lr": 0.06394, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30062, "top5_acc": 0.5625, "loss_cls": 3.9796, "loss": 3.9796, "time": 0.81841} +{"mode": "train", "epoch": 62, "iter": 2000, "lr": 0.06392, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30016, "top5_acc": 0.55578, "loss_cls": 3.9956, "loss": 3.9956, "time": 0.81476} +{"mode": "train", "epoch": 62, "iter": 2100, "lr": 0.06389, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30125, "top5_acc": 0.5725, "loss_cls": 3.94264, "loss": 3.94264, "time": 0.82277} +{"mode": "train", "epoch": 62, "iter": 2200, "lr": 0.06386, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.30562, "top5_acc": 0.55953, "loss_cls": 3.97201, "loss": 3.97201, "time": 0.83485} +{"mode": "train", "epoch": 62, "iter": 2300, "lr": 0.06384, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29125, "top5_acc": 0.55594, "loss_cls": 4.02761, "loss": 4.02761, "time": 0.81716} +{"mode": "train", "epoch": 62, "iter": 2400, "lr": 0.06381, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30703, "top5_acc": 0.55906, "loss_cls": 3.97424, "loss": 3.97424, "time": 0.82475} +{"mode": "train", "epoch": 62, "iter": 2500, "lr": 0.06378, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28781, "top5_acc": 0.54422, "loss_cls": 4.07502, "loss": 4.07502, "time": 0.83079} +{"mode": "train", "epoch": 62, "iter": 2600, "lr": 0.06375, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.29766, "top5_acc": 0.55484, "loss_cls": 3.99367, "loss": 3.99367, "time": 0.82129} +{"mode": "train", "epoch": 62, "iter": 2700, "lr": 0.06373, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29375, "top5_acc": 0.56563, "loss_cls": 3.9749, "loss": 3.9749, "time": 0.82299} +{"mode": "train", "epoch": 62, "iter": 2800, "lr": 0.0637, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31547, "top5_acc": 0.56531, "loss_cls": 3.94443, "loss": 3.94443, "time": 0.82207} +{"mode": "train", "epoch": 62, "iter": 2900, "lr": 0.06367, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29766, "top5_acc": 0.56125, "loss_cls": 4.01311, "loss": 4.01311, "time": 0.81766} +{"mode": "train", "epoch": 62, "iter": 3000, "lr": 0.06365, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29656, "top5_acc": 0.56078, "loss_cls": 3.96758, "loss": 3.96758, "time": 0.82451} +{"mode": "train", "epoch": 62, "iter": 3100, "lr": 0.06362, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30922, "top5_acc": 0.56656, "loss_cls": 3.97501, "loss": 3.97501, "time": 0.82987} +{"mode": "train", "epoch": 62, "iter": 3200, "lr": 0.06359, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32156, "top5_acc": 0.5725, "loss_cls": 3.93039, "loss": 3.93039, "time": 0.81469} +{"mode": "train", "epoch": 62, "iter": 3300, "lr": 0.06357, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28938, "top5_acc": 0.54391, "loss_cls": 4.0618, "loss": 4.0618, "time": 0.82103} +{"mode": "train", "epoch": 62, "iter": 3400, "lr": 0.06354, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29422, "top5_acc": 0.56031, "loss_cls": 4.00235, "loss": 4.00235, "time": 0.83015} +{"mode": "train", "epoch": 62, "iter": 3500, "lr": 0.06351, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29344, "top5_acc": 0.55516, "loss_cls": 4.04772, "loss": 4.04772, "time": 0.82333} +{"mode": "train", "epoch": 62, "iter": 3600, "lr": 0.06349, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29969, "top5_acc": 0.5525, "loss_cls": 4.00069, "loss": 4.00069, "time": 0.81656} +{"mode": "train", "epoch": 62, "iter": 3700, "lr": 0.06346, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29047, "top5_acc": 0.54719, "loss_cls": 4.02139, "loss": 4.02139, "time": 0.81739} +{"mode": "val", "epoch": 62, "iter": 309, "lr": 0.06345, "top1_acc": 0.21562, "top5_acc": 0.45069, "mean_class_accuracy": 0.2154} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.06342, "memory": 15990, "data_time": 1.26728, "top1_acc": 0.30172, "top5_acc": 0.56812, "loss_cls": 3.94177, "loss": 3.94177, "time": 2.25461} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.06339, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31297, "top5_acc": 0.57172, "loss_cls": 3.93299, "loss": 3.93299, "time": 0.83208} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.06337, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30375, "top5_acc": 0.57078, "loss_cls": 3.93383, "loss": 3.93383, "time": 0.82945} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.06334, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30406, "top5_acc": 0.56, "loss_cls": 3.98173, "loss": 3.98173, "time": 0.83389} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.06331, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30234, "top5_acc": 0.56156, "loss_cls": 3.98913, "loss": 3.98913, "time": 0.83551} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.06328, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30547, "top5_acc": 0.55812, "loss_cls": 3.99362, "loss": 3.99362, "time": 0.82697} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.06326, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29516, "top5_acc": 0.55531, "loss_cls": 4.01953, "loss": 4.01953, "time": 0.82706} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.06323, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31469, "top5_acc": 0.57906, "loss_cls": 3.88679, "loss": 3.88679, "time": 0.83394} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.0632, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29766, "top5_acc": 0.54922, "loss_cls": 4.01227, "loss": 4.01227, "time": 0.83081} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.06318, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29844, "top5_acc": 0.54984, "loss_cls": 4.01726, "loss": 4.01726, "time": 0.83063} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.06315, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30078, "top5_acc": 0.55906, "loss_cls": 3.99431, "loss": 3.99431, "time": 0.82749} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.06312, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29734, "top5_acc": 0.56047, "loss_cls": 3.98838, "loss": 3.98838, "time": 0.82782} +{"mode": "train", "epoch": 63, "iter": 1300, "lr": 0.0631, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30125, "top5_acc": 0.55625, "loss_cls": 4.02079, "loss": 4.02079, "time": 0.82989} +{"mode": "train", "epoch": 63, "iter": 1400, "lr": 0.06307, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30781, "top5_acc": 0.57031, "loss_cls": 3.93946, "loss": 3.93946, "time": 0.83257} +{"mode": "train", "epoch": 63, "iter": 1500, "lr": 0.06304, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29953, "top5_acc": 0.55469, "loss_cls": 3.99324, "loss": 3.99324, "time": 0.82701} +{"mode": "train", "epoch": 63, "iter": 1600, "lr": 0.06301, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30906, "top5_acc": 0.56734, "loss_cls": 3.94574, "loss": 3.94574, "time": 0.83276} +{"mode": "train", "epoch": 63, "iter": 1700, "lr": 0.06299, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29078, "top5_acc": 0.56672, "loss_cls": 3.96879, "loss": 3.96879, "time": 0.82728} +{"mode": "train", "epoch": 63, "iter": 1800, "lr": 0.06296, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31047, "top5_acc": 0.55922, "loss_cls": 3.99579, "loss": 3.99579, "time": 0.82955} +{"mode": "train", "epoch": 63, "iter": 1900, "lr": 0.06293, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30609, "top5_acc": 0.56547, "loss_cls": 3.98316, "loss": 3.98316, "time": 0.83037} +{"mode": "train", "epoch": 63, "iter": 2000, "lr": 0.06291, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30172, "top5_acc": 0.56938, "loss_cls": 3.9804, "loss": 3.9804, "time": 0.8266} +{"mode": "train", "epoch": 63, "iter": 2100, "lr": 0.06288, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29938, "top5_acc": 0.55547, "loss_cls": 3.97943, "loss": 3.97943, "time": 0.83303} +{"mode": "train", "epoch": 63, "iter": 2200, "lr": 0.06285, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.2925, "top5_acc": 0.55781, "loss_cls": 4.01431, "loss": 4.01431, "time": 0.82876} +{"mode": "train", "epoch": 63, "iter": 2300, "lr": 0.06283, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30125, "top5_acc": 0.56422, "loss_cls": 3.99924, "loss": 3.99924, "time": 0.82083} +{"mode": "train", "epoch": 63, "iter": 2400, "lr": 0.0628, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28813, "top5_acc": 0.55047, "loss_cls": 4.01493, "loss": 4.01493, "time": 0.83002} +{"mode": "train", "epoch": 63, "iter": 2500, "lr": 0.06277, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30078, "top5_acc": 0.56531, "loss_cls": 3.9436, "loss": 3.9436, "time": 0.8385} +{"mode": "train", "epoch": 63, "iter": 2600, "lr": 0.06274, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29609, "top5_acc": 0.5475, "loss_cls": 4.02906, "loss": 4.02906, "time": 0.82476} +{"mode": "train", "epoch": 63, "iter": 2700, "lr": 0.06272, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30859, "top5_acc": 0.56141, "loss_cls": 3.94868, "loss": 3.94868, "time": 0.83124} +{"mode": "train", "epoch": 63, "iter": 2800, "lr": 0.06269, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30078, "top5_acc": 0.56578, "loss_cls": 3.96959, "loss": 3.96959, "time": 0.82847} +{"mode": "train", "epoch": 63, "iter": 2900, "lr": 0.06266, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30531, "top5_acc": 0.57094, "loss_cls": 3.96092, "loss": 3.96092, "time": 0.82254} +{"mode": "train", "epoch": 63, "iter": 3000, "lr": 0.06264, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31641, "top5_acc": 0.57375, "loss_cls": 3.89965, "loss": 3.89965, "time": 0.81902} +{"mode": "train", "epoch": 63, "iter": 3100, "lr": 0.06261, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29859, "top5_acc": 0.54781, "loss_cls": 4.03531, "loss": 4.03531, "time": 0.82829} +{"mode": "train", "epoch": 63, "iter": 3200, "lr": 0.06258, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29984, "top5_acc": 0.56078, "loss_cls": 3.97839, "loss": 3.97839, "time": 0.82209} +{"mode": "train", "epoch": 63, "iter": 3300, "lr": 0.06256, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29656, "top5_acc": 0.55406, "loss_cls": 4.01972, "loss": 4.01972, "time": 0.83153} +{"mode": "train", "epoch": 63, "iter": 3400, "lr": 0.06253, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29859, "top5_acc": 0.55578, "loss_cls": 4.00259, "loss": 4.00259, "time": 0.8254} +{"mode": "train", "epoch": 63, "iter": 3500, "lr": 0.0625, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30594, "top5_acc": 0.55984, "loss_cls": 3.95972, "loss": 3.95972, "time": 0.83803} +{"mode": "train", "epoch": 63, "iter": 3600, "lr": 0.06247, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30219, "top5_acc": 0.56969, "loss_cls": 3.94771, "loss": 3.94771, "time": 0.82483} +{"mode": "train", "epoch": 63, "iter": 3700, "lr": 0.06245, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30422, "top5_acc": 0.55953, "loss_cls": 3.99848, "loss": 3.99848, "time": 0.82605} +{"mode": "val", "epoch": 63, "iter": 309, "lr": 0.06243, "top1_acc": 0.23315, "top5_acc": 0.48285, "mean_class_accuracy": 0.23289} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.06241, "memory": 15990, "data_time": 1.28297, "top1_acc": 0.3025, "top5_acc": 0.56078, "loss_cls": 3.96646, "loss": 3.96646, "time": 2.27469} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.06238, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31266, "top5_acc": 0.57375, "loss_cls": 3.91672, "loss": 3.91672, "time": 0.83436} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.06235, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30922, "top5_acc": 0.56484, "loss_cls": 3.94279, "loss": 3.94279, "time": 0.83202} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.06233, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30594, "top5_acc": 0.56547, "loss_cls": 3.94113, "loss": 3.94113, "time": 0.82623} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.0623, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31672, "top5_acc": 0.57359, "loss_cls": 3.89777, "loss": 3.89777, "time": 0.82978} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.06227, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29828, "top5_acc": 0.55781, "loss_cls": 4.00049, "loss": 4.00049, "time": 0.83236} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.06225, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30484, "top5_acc": 0.55812, "loss_cls": 3.97967, "loss": 3.97967, "time": 0.83077} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.06222, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30328, "top5_acc": 0.56047, "loss_cls": 3.95523, "loss": 3.95523, "time": 0.82917} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.06219, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30531, "top5_acc": 0.55766, "loss_cls": 3.9715, "loss": 3.9715, "time": 0.83149} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.06216, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29922, "top5_acc": 0.55812, "loss_cls": 3.98768, "loss": 3.98768, "time": 0.83368} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.06214, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30016, "top5_acc": 0.55797, "loss_cls": 3.98832, "loss": 3.98832, "time": 0.82804} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.06211, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30781, "top5_acc": 0.57156, "loss_cls": 3.92508, "loss": 3.92508, "time": 0.83333} +{"mode": "train", "epoch": 64, "iter": 1300, "lr": 0.06208, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3, "top5_acc": 0.56594, "loss_cls": 3.95392, "loss": 3.95392, "time": 0.83497} +{"mode": "train", "epoch": 64, "iter": 1400, "lr": 0.06206, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30875, "top5_acc": 0.56938, "loss_cls": 3.93536, "loss": 3.93536, "time": 0.83049} +{"mode": "train", "epoch": 64, "iter": 1500, "lr": 0.06203, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30578, "top5_acc": 0.56891, "loss_cls": 3.95381, "loss": 3.95381, "time": 0.83081} +{"mode": "train", "epoch": 64, "iter": 1600, "lr": 0.062, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30375, "top5_acc": 0.56172, "loss_cls": 3.98664, "loss": 3.98664, "time": 0.83033} +{"mode": "train", "epoch": 64, "iter": 1700, "lr": 0.06197, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29844, "top5_acc": 0.55562, "loss_cls": 3.99584, "loss": 3.99584, "time": 0.83561} +{"mode": "train", "epoch": 64, "iter": 1800, "lr": 0.06195, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30438, "top5_acc": 0.56094, "loss_cls": 3.96511, "loss": 3.96511, "time": 0.83501} +{"mode": "train", "epoch": 64, "iter": 1900, "lr": 0.06192, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.305, "top5_acc": 0.56172, "loss_cls": 3.9637, "loss": 3.9637, "time": 0.83497} +{"mode": "train", "epoch": 64, "iter": 2000, "lr": 0.06189, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.305, "top5_acc": 0.56656, "loss_cls": 3.96136, "loss": 3.96136, "time": 0.82984} +{"mode": "train", "epoch": 64, "iter": 2100, "lr": 0.06187, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.30797, "top5_acc": 0.56578, "loss_cls": 3.95379, "loss": 3.95379, "time": 0.83177} +{"mode": "train", "epoch": 64, "iter": 2200, "lr": 0.06184, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30125, "top5_acc": 0.55719, "loss_cls": 3.9867, "loss": 3.9867, "time": 0.82348} +{"mode": "train", "epoch": 64, "iter": 2300, "lr": 0.06181, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30062, "top5_acc": 0.56672, "loss_cls": 3.98011, "loss": 3.98011, "time": 0.82644} +{"mode": "train", "epoch": 64, "iter": 2400, "lr": 0.06178, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30344, "top5_acc": 0.55734, "loss_cls": 3.99287, "loss": 3.99287, "time": 0.82971} +{"mode": "train", "epoch": 64, "iter": 2500, "lr": 0.06176, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31375, "top5_acc": 0.57625, "loss_cls": 3.90718, "loss": 3.90718, "time": 0.82702} +{"mode": "train", "epoch": 64, "iter": 2600, "lr": 0.06173, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29594, "top5_acc": 0.55578, "loss_cls": 3.99495, "loss": 3.99495, "time": 0.82354} +{"mode": "train", "epoch": 64, "iter": 2700, "lr": 0.0617, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31219, "top5_acc": 0.56969, "loss_cls": 3.95228, "loss": 3.95228, "time": 0.83676} +{"mode": "train", "epoch": 64, "iter": 2800, "lr": 0.06168, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30391, "top5_acc": 0.56969, "loss_cls": 3.94918, "loss": 3.94918, "time": 0.82539} +{"mode": "train", "epoch": 64, "iter": 2900, "lr": 0.06165, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31688, "top5_acc": 0.56484, "loss_cls": 3.94766, "loss": 3.94766, "time": 0.8306} +{"mode": "train", "epoch": 64, "iter": 3000, "lr": 0.06162, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30375, "top5_acc": 0.55953, "loss_cls": 3.99727, "loss": 3.99727, "time": 0.83305} +{"mode": "train", "epoch": 64, "iter": 3100, "lr": 0.06159, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29828, "top5_acc": 0.55781, "loss_cls": 3.98365, "loss": 3.98365, "time": 0.82244} +{"mode": "train", "epoch": 64, "iter": 3200, "lr": 0.06157, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30797, "top5_acc": 0.56391, "loss_cls": 3.95055, "loss": 3.95055, "time": 0.82369} +{"mode": "train", "epoch": 64, "iter": 3300, "lr": 0.06154, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29766, "top5_acc": 0.55125, "loss_cls": 4.02174, "loss": 4.02174, "time": 0.83506} +{"mode": "train", "epoch": 64, "iter": 3400, "lr": 0.06151, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29703, "top5_acc": 0.55594, "loss_cls": 4.00523, "loss": 4.00523, "time": 0.82915} +{"mode": "train", "epoch": 64, "iter": 3500, "lr": 0.06148, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29953, "top5_acc": 0.56563, "loss_cls": 3.97762, "loss": 3.97762, "time": 0.83177} +{"mode": "train", "epoch": 64, "iter": 3600, "lr": 0.06146, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31297, "top5_acc": 0.56453, "loss_cls": 3.95011, "loss": 3.95011, "time": 0.82875} +{"mode": "train", "epoch": 64, "iter": 3700, "lr": 0.06143, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3175, "top5_acc": 0.56297, "loss_cls": 3.91014, "loss": 3.91014, "time": 0.83688} +{"mode": "val", "epoch": 64, "iter": 309, "lr": 0.06142, "top1_acc": 0.2253, "top5_acc": 0.46199, "mean_class_accuracy": 0.22518} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.06139, "memory": 15990, "data_time": 1.27397, "top1_acc": 0.31656, "top5_acc": 0.58047, "loss_cls": 3.89389, "loss": 3.89389, "time": 2.26621} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.06136, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31375, "top5_acc": 0.57484, "loss_cls": 3.92879, "loss": 3.92879, "time": 0.82575} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.06134, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31891, "top5_acc": 0.56938, "loss_cls": 3.91161, "loss": 3.91161, "time": 0.82663} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.06131, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32156, "top5_acc": 0.57719, "loss_cls": 3.90557, "loss": 3.90557, "time": 0.82198} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.06128, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30297, "top5_acc": 0.57125, "loss_cls": 3.946, "loss": 3.946, "time": 0.8252} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.06125, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29922, "top5_acc": 0.55766, "loss_cls": 3.99363, "loss": 3.99363, "time": 0.82233} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.06123, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30828, "top5_acc": 0.57156, "loss_cls": 3.92984, "loss": 3.92984, "time": 0.82021} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0612, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30812, "top5_acc": 0.56422, "loss_cls": 3.96201, "loss": 3.96201, "time": 0.82129} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.06117, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29078, "top5_acc": 0.55562, "loss_cls": 3.98814, "loss": 3.98814, "time": 0.81814} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.06115, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30391, "top5_acc": 0.56828, "loss_cls": 3.9452, "loss": 3.9452, "time": 0.82053} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.06112, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30906, "top5_acc": 0.56906, "loss_cls": 3.92151, "loss": 3.92151, "time": 0.81675} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.06109, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30688, "top5_acc": 0.56359, "loss_cls": 3.97241, "loss": 3.97241, "time": 0.816} +{"mode": "train", "epoch": 65, "iter": 1300, "lr": 0.06106, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31031, "top5_acc": 0.56891, "loss_cls": 3.92644, "loss": 3.92644, "time": 0.81697} +{"mode": "train", "epoch": 65, "iter": 1400, "lr": 0.06104, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31312, "top5_acc": 0.56219, "loss_cls": 3.9474, "loss": 3.9474, "time": 0.82321} +{"mode": "train", "epoch": 65, "iter": 1500, "lr": 0.06101, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30703, "top5_acc": 0.57219, "loss_cls": 3.93213, "loss": 3.93213, "time": 0.81556} +{"mode": "train", "epoch": 65, "iter": 1600, "lr": 0.06098, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31484, "top5_acc": 0.56578, "loss_cls": 3.93572, "loss": 3.93572, "time": 0.81781} +{"mode": "train", "epoch": 65, "iter": 1700, "lr": 0.06095, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30219, "top5_acc": 0.56656, "loss_cls": 3.96285, "loss": 3.96285, "time": 0.81737} +{"mode": "train", "epoch": 65, "iter": 1800, "lr": 0.06093, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30594, "top5_acc": 0.56344, "loss_cls": 3.97056, "loss": 3.97056, "time": 0.82284} +{"mode": "train", "epoch": 65, "iter": 1900, "lr": 0.0609, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30859, "top5_acc": 0.56875, "loss_cls": 3.97578, "loss": 3.97578, "time": 0.8197} +{"mode": "train", "epoch": 65, "iter": 2000, "lr": 0.06087, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30031, "top5_acc": 0.56406, "loss_cls": 3.96982, "loss": 3.96982, "time": 0.81727} +{"mode": "train", "epoch": 65, "iter": 2100, "lr": 0.06085, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30484, "top5_acc": 0.56031, "loss_cls": 3.9516, "loss": 3.9516, "time": 0.82866} +{"mode": "train", "epoch": 65, "iter": 2200, "lr": 0.06082, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30922, "top5_acc": 0.57047, "loss_cls": 3.91504, "loss": 3.91504, "time": 0.81683} +{"mode": "train", "epoch": 65, "iter": 2300, "lr": 0.06079, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30922, "top5_acc": 0.57031, "loss_cls": 3.91789, "loss": 3.91789, "time": 0.82328} +{"mode": "train", "epoch": 65, "iter": 2400, "lr": 0.06076, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30484, "top5_acc": 0.5675, "loss_cls": 3.94108, "loss": 3.94108, "time": 0.8287} +{"mode": "train", "epoch": 65, "iter": 2500, "lr": 0.06074, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30156, "top5_acc": 0.57094, "loss_cls": 3.96337, "loss": 3.96337, "time": 0.82402} +{"mode": "train", "epoch": 65, "iter": 2600, "lr": 0.06071, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30219, "top5_acc": 0.56063, "loss_cls": 3.984, "loss": 3.984, "time": 0.82371} +{"mode": "train", "epoch": 65, "iter": 2700, "lr": 0.06068, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30922, "top5_acc": 0.56234, "loss_cls": 3.93999, "loss": 3.93999, "time": 0.82717} +{"mode": "train", "epoch": 65, "iter": 2800, "lr": 0.06065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30047, "top5_acc": 0.56031, "loss_cls": 3.97785, "loss": 3.97785, "time": 0.81539} +{"mode": "train", "epoch": 65, "iter": 2900, "lr": 0.06063, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.305, "top5_acc": 0.56063, "loss_cls": 3.99035, "loss": 3.99035, "time": 0.82147} +{"mode": "train", "epoch": 65, "iter": 3000, "lr": 0.0606, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30094, "top5_acc": 0.56078, "loss_cls": 3.98363, "loss": 3.98363, "time": 0.82228} +{"mode": "train", "epoch": 65, "iter": 3100, "lr": 0.06057, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30359, "top5_acc": 0.56156, "loss_cls": 3.98795, "loss": 3.98795, "time": 0.8177} +{"mode": "train", "epoch": 65, "iter": 3200, "lr": 0.06055, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3025, "top5_acc": 0.56, "loss_cls": 3.98138, "loss": 3.98138, "time": 0.81858} +{"mode": "train", "epoch": 65, "iter": 3300, "lr": 0.06052, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29016, "top5_acc": 0.55203, "loss_cls": 4.02408, "loss": 4.02408, "time": 0.83266} +{"mode": "train", "epoch": 65, "iter": 3400, "lr": 0.06049, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.295, "top5_acc": 0.56094, "loss_cls": 3.98911, "loss": 3.98911, "time": 0.8257} +{"mode": "train", "epoch": 65, "iter": 3500, "lr": 0.06046, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3, "top5_acc": 0.56922, "loss_cls": 3.93439, "loss": 3.93439, "time": 0.82036} +{"mode": "train", "epoch": 65, "iter": 3600, "lr": 0.06044, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30609, "top5_acc": 0.55828, "loss_cls": 3.95812, "loss": 3.95812, "time": 0.82408} +{"mode": "train", "epoch": 65, "iter": 3700, "lr": 0.06041, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30953, "top5_acc": 0.56328, "loss_cls": 3.93893, "loss": 3.93893, "time": 0.83048} +{"mode": "val", "epoch": 65, "iter": 309, "lr": 0.0604, "top1_acc": 0.23841, "top5_acc": 0.48863, "mean_class_accuracy": 0.23807} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.06037, "memory": 15990, "data_time": 1.24286, "top1_acc": 0.31828, "top5_acc": 0.56938, "loss_cls": 3.92406, "loss": 3.92406, "time": 2.229} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.06034, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30203, "top5_acc": 0.56984, "loss_cls": 3.92802, "loss": 3.92802, "time": 0.82449} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.06031, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31078, "top5_acc": 0.56641, "loss_cls": 3.92143, "loss": 3.92143, "time": 0.82355} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.06029, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30531, "top5_acc": 0.57172, "loss_cls": 3.91899, "loss": 3.91899, "time": 0.82284} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.06026, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31359, "top5_acc": 0.57328, "loss_cls": 3.93609, "loss": 3.93609, "time": 0.82164} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.06023, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31266, "top5_acc": 0.57109, "loss_cls": 3.93405, "loss": 3.93405, "time": 0.82296} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.0602, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30078, "top5_acc": 0.56141, "loss_cls": 3.99375, "loss": 3.99375, "time": 0.81874} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.06018, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31297, "top5_acc": 0.57297, "loss_cls": 3.90516, "loss": 3.90516, "time": 0.82028} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.06015, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30828, "top5_acc": 0.57016, "loss_cls": 3.94362, "loss": 3.94362, "time": 0.82016} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.06012, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30484, "top5_acc": 0.56375, "loss_cls": 3.97924, "loss": 3.97924, "time": 0.82146} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.06009, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3025, "top5_acc": 0.56703, "loss_cls": 3.9652, "loss": 3.9652, "time": 0.82237} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.06007, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31109, "top5_acc": 0.57188, "loss_cls": 3.92835, "loss": 3.92835, "time": 0.81977} +{"mode": "train", "epoch": 66, "iter": 1300, "lr": 0.06004, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31344, "top5_acc": 0.57484, "loss_cls": 3.90056, "loss": 3.90056, "time": 0.81675} +{"mode": "train", "epoch": 66, "iter": 1400, "lr": 0.06001, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31297, "top5_acc": 0.57625, "loss_cls": 3.91957, "loss": 3.91957, "time": 0.82082} +{"mode": "train", "epoch": 66, "iter": 1500, "lr": 0.05999, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29156, "top5_acc": 0.56016, "loss_cls": 4.01751, "loss": 4.01751, "time": 0.82296} +{"mode": "train", "epoch": 66, "iter": 1600, "lr": 0.05996, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3025, "top5_acc": 0.55688, "loss_cls": 3.97166, "loss": 3.97166, "time": 0.82605} +{"mode": "train", "epoch": 66, "iter": 1700, "lr": 0.05993, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30766, "top5_acc": 0.57531, "loss_cls": 3.92661, "loss": 3.92661, "time": 0.81514} +{"mode": "train", "epoch": 66, "iter": 1800, "lr": 0.0599, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31453, "top5_acc": 0.58188, "loss_cls": 3.89575, "loss": 3.89575, "time": 0.82543} +{"mode": "train", "epoch": 66, "iter": 1900, "lr": 0.05988, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30734, "top5_acc": 0.57188, "loss_cls": 3.9112, "loss": 3.9112, "time": 0.81829} +{"mode": "train", "epoch": 66, "iter": 2000, "lr": 0.05985, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31016, "top5_acc": 0.56297, "loss_cls": 3.94465, "loss": 3.94465, "time": 0.82445} +{"mode": "train", "epoch": 66, "iter": 2100, "lr": 0.05982, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.31203, "top5_acc": 0.5725, "loss_cls": 3.92558, "loss": 3.92558, "time": 0.83203} +{"mode": "train", "epoch": 66, "iter": 2200, "lr": 0.05979, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30547, "top5_acc": 0.56797, "loss_cls": 3.93187, "loss": 3.93187, "time": 0.82307} +{"mode": "train", "epoch": 66, "iter": 2300, "lr": 0.05977, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.30625, "top5_acc": 0.5675, "loss_cls": 3.94083, "loss": 3.94083, "time": 0.83265} +{"mode": "train", "epoch": 66, "iter": 2400, "lr": 0.05974, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29922, "top5_acc": 0.56563, "loss_cls": 3.95612, "loss": 3.95612, "time": 0.82727} +{"mode": "train", "epoch": 66, "iter": 2500, "lr": 0.05971, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30172, "top5_acc": 0.56875, "loss_cls": 3.94851, "loss": 3.94851, "time": 0.81874} +{"mode": "train", "epoch": 66, "iter": 2600, "lr": 0.05968, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30547, "top5_acc": 0.56359, "loss_cls": 3.95258, "loss": 3.95258, "time": 0.82262} +{"mode": "train", "epoch": 66, "iter": 2700, "lr": 0.05966, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31422, "top5_acc": 0.57188, "loss_cls": 3.911, "loss": 3.911, "time": 0.82468} +{"mode": "train", "epoch": 66, "iter": 2800, "lr": 0.05963, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30266, "top5_acc": 0.56688, "loss_cls": 3.96437, "loss": 3.96437, "time": 0.82832} +{"mode": "train", "epoch": 66, "iter": 2900, "lr": 0.0596, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.30328, "top5_acc": 0.55, "loss_cls": 4.00919, "loss": 4.00919, "time": 0.82604} +{"mode": "train", "epoch": 66, "iter": 3000, "lr": 0.05957, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29875, "top5_acc": 0.56625, "loss_cls": 3.96304, "loss": 3.96304, "time": 0.82733} +{"mode": "train", "epoch": 66, "iter": 3100, "lr": 0.05955, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.31203, "top5_acc": 0.57406, "loss_cls": 3.91635, "loss": 3.91635, "time": 0.81694} +{"mode": "train", "epoch": 66, "iter": 3200, "lr": 0.05952, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.31688, "top5_acc": 0.57219, "loss_cls": 3.92248, "loss": 3.92248, "time": 0.82563} +{"mode": "train", "epoch": 66, "iter": 3300, "lr": 0.05949, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29938, "top5_acc": 0.56031, "loss_cls": 3.99681, "loss": 3.99681, "time": 0.82852} +{"mode": "train", "epoch": 66, "iter": 3400, "lr": 0.05946, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30047, "top5_acc": 0.56078, "loss_cls": 4.01679, "loss": 4.01679, "time": 0.82658} +{"mode": "train", "epoch": 66, "iter": 3500, "lr": 0.05944, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29438, "top5_acc": 0.56016, "loss_cls": 3.97369, "loss": 3.97369, "time": 0.82277} +{"mode": "train", "epoch": 66, "iter": 3600, "lr": 0.05941, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29969, "top5_acc": 0.55797, "loss_cls": 3.97936, "loss": 3.97936, "time": 0.82357} +{"mode": "train", "epoch": 66, "iter": 3700, "lr": 0.05938, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.30594, "top5_acc": 0.56844, "loss_cls": 3.93476, "loss": 3.93476, "time": 0.82829} +{"mode": "val", "epoch": 66, "iter": 309, "lr": 0.05937, "top1_acc": 0.22641, "top5_acc": 0.45312, "mean_class_accuracy": 0.22603} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.05934, "memory": 15990, "data_time": 1.24626, "top1_acc": 0.32047, "top5_acc": 0.57484, "loss_cls": 3.91518, "loss": 3.91518, "time": 2.236} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.05931, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31359, "top5_acc": 0.56984, "loss_cls": 3.91901, "loss": 3.91901, "time": 0.82725} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.05929, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31047, "top5_acc": 0.57656, "loss_cls": 3.8983, "loss": 3.8983, "time": 0.81727} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.05926, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30578, "top5_acc": 0.55875, "loss_cls": 3.93814, "loss": 3.93814, "time": 0.81949} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.05923, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31812, "top5_acc": 0.57328, "loss_cls": 3.87615, "loss": 3.87615, "time": 0.82675} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.0592, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31594, "top5_acc": 0.57063, "loss_cls": 3.9168, "loss": 3.9168, "time": 0.82533} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.05918, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30094, "top5_acc": 0.56547, "loss_cls": 3.96346, "loss": 3.96346, "time": 0.81908} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.05915, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30672, "top5_acc": 0.56781, "loss_cls": 3.91557, "loss": 3.91557, "time": 0.81718} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.05912, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30812, "top5_acc": 0.56781, "loss_cls": 3.93592, "loss": 3.93592, "time": 0.82167} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.05909, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30516, "top5_acc": 0.57375, "loss_cls": 3.92402, "loss": 3.92402, "time": 0.82496} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.05907, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30641, "top5_acc": 0.57438, "loss_cls": 3.91086, "loss": 3.91086, "time": 0.81735} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.05904, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3075, "top5_acc": 0.56875, "loss_cls": 3.92936, "loss": 3.92936, "time": 0.81751} +{"mode": "train", "epoch": 67, "iter": 1300, "lr": 0.05901, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32016, "top5_acc": 0.58172, "loss_cls": 3.85521, "loss": 3.85521, "time": 0.81936} +{"mode": "train", "epoch": 67, "iter": 1400, "lr": 0.05898, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30531, "top5_acc": 0.56109, "loss_cls": 3.96777, "loss": 3.96777, "time": 0.82087} +{"mode": "train", "epoch": 67, "iter": 1500, "lr": 0.05896, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30984, "top5_acc": 0.57094, "loss_cls": 3.9142, "loss": 3.9142, "time": 0.81293} +{"mode": "train", "epoch": 67, "iter": 1600, "lr": 0.05893, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30344, "top5_acc": 0.56469, "loss_cls": 3.9559, "loss": 3.9559, "time": 0.81528} +{"mode": "train", "epoch": 67, "iter": 1700, "lr": 0.0589, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31141, "top5_acc": 0.57594, "loss_cls": 3.91292, "loss": 3.91292, "time": 0.81816} +{"mode": "train", "epoch": 67, "iter": 1800, "lr": 0.05887, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30531, "top5_acc": 0.56281, "loss_cls": 3.93785, "loss": 3.93785, "time": 0.8161} +{"mode": "train", "epoch": 67, "iter": 1900, "lr": 0.05885, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31125, "top5_acc": 0.56891, "loss_cls": 3.92835, "loss": 3.92835, "time": 0.81695} +{"mode": "train", "epoch": 67, "iter": 2000, "lr": 0.05882, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30875, "top5_acc": 0.56281, "loss_cls": 3.97986, "loss": 3.97986, "time": 0.82016} +{"mode": "train", "epoch": 67, "iter": 2100, "lr": 0.05879, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30641, "top5_acc": 0.56359, "loss_cls": 3.96087, "loss": 3.96087, "time": 0.83103} +{"mode": "train", "epoch": 67, "iter": 2200, "lr": 0.05876, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30609, "top5_acc": 0.56797, "loss_cls": 3.92054, "loss": 3.92054, "time": 0.81506} +{"mode": "train", "epoch": 67, "iter": 2300, "lr": 0.05874, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30719, "top5_acc": 0.57297, "loss_cls": 3.93219, "loss": 3.93219, "time": 0.83162} +{"mode": "train", "epoch": 67, "iter": 2400, "lr": 0.05871, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29906, "top5_acc": 0.56375, "loss_cls": 3.96639, "loss": 3.96639, "time": 0.82809} +{"mode": "train", "epoch": 67, "iter": 2500, "lr": 0.05868, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29953, "top5_acc": 0.56391, "loss_cls": 3.97146, "loss": 3.97146, "time": 0.82181} +{"mode": "train", "epoch": 67, "iter": 2600, "lr": 0.05865, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29719, "top5_acc": 0.56281, "loss_cls": 3.97276, "loss": 3.97276, "time": 0.82474} +{"mode": "train", "epoch": 67, "iter": 2700, "lr": 0.05863, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31797, "top5_acc": 0.56375, "loss_cls": 3.92699, "loss": 3.92699, "time": 0.82533} +{"mode": "train", "epoch": 67, "iter": 2800, "lr": 0.0586, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30438, "top5_acc": 0.56781, "loss_cls": 3.96017, "loss": 3.96017, "time": 0.82211} +{"mode": "train", "epoch": 67, "iter": 2900, "lr": 0.05857, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30844, "top5_acc": 0.57406, "loss_cls": 3.91699, "loss": 3.91699, "time": 0.8174} +{"mode": "train", "epoch": 67, "iter": 3000, "lr": 0.05854, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29531, "top5_acc": 0.55953, "loss_cls": 3.98621, "loss": 3.98621, "time": 0.82607} +{"mode": "train", "epoch": 67, "iter": 3100, "lr": 0.05852, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31, "top5_acc": 0.56719, "loss_cls": 3.90593, "loss": 3.90593, "time": 0.81463} +{"mode": "train", "epoch": 67, "iter": 3200, "lr": 0.05849, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30531, "top5_acc": 0.55891, "loss_cls": 3.96503, "loss": 3.96503, "time": 0.8205} +{"mode": "train", "epoch": 67, "iter": 3300, "lr": 0.05846, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30281, "top5_acc": 0.55781, "loss_cls": 3.95554, "loss": 3.95554, "time": 0.82772} +{"mode": "train", "epoch": 67, "iter": 3400, "lr": 0.05843, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31719, "top5_acc": 0.57234, "loss_cls": 3.92321, "loss": 3.92321, "time": 0.82695} +{"mode": "train", "epoch": 67, "iter": 3500, "lr": 0.05841, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31297, "top5_acc": 0.57109, "loss_cls": 3.91088, "loss": 3.91088, "time": 0.82349} +{"mode": "train", "epoch": 67, "iter": 3600, "lr": 0.05838, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30797, "top5_acc": 0.56297, "loss_cls": 3.95126, "loss": 3.95126, "time": 0.82588} +{"mode": "train", "epoch": 67, "iter": 3700, "lr": 0.05835, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30234, "top5_acc": 0.56719, "loss_cls": 3.94248, "loss": 3.94248, "time": 0.82638} +{"mode": "val", "epoch": 67, "iter": 309, "lr": 0.05834, "top1_acc": 0.21729, "top5_acc": 0.45444, "mean_class_accuracy": 0.21723} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.05831, "memory": 15990, "data_time": 1.30379, "top1_acc": 0.30516, "top5_acc": 0.57609, "loss_cls": 3.87659, "loss": 3.87659, "time": 2.29974} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.05828, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31688, "top5_acc": 0.58094, "loss_cls": 3.87102, "loss": 3.87102, "time": 0.83608} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.05826, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30109, "top5_acc": 0.56719, "loss_cls": 3.9589, "loss": 3.9589, "time": 0.82773} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.05823, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31812, "top5_acc": 0.57781, "loss_cls": 3.84904, "loss": 3.84904, "time": 0.83496} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.0582, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30875, "top5_acc": 0.57516, "loss_cls": 3.90438, "loss": 3.90438, "time": 0.82977} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.05817, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30641, "top5_acc": 0.57547, "loss_cls": 3.91605, "loss": 3.91605, "time": 0.82862} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.05815, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30719, "top5_acc": 0.56969, "loss_cls": 3.92898, "loss": 3.92898, "time": 0.82881} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.05812, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30453, "top5_acc": 0.57188, "loss_cls": 3.91941, "loss": 3.91941, "time": 0.83071} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.05809, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30953, "top5_acc": 0.57031, "loss_cls": 3.9134, "loss": 3.9134, "time": 0.82593} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.05806, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31234, "top5_acc": 0.56672, "loss_cls": 3.93145, "loss": 3.93145, "time": 0.82954} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.05804, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30172, "top5_acc": 0.56812, "loss_cls": 3.95905, "loss": 3.95905, "time": 0.82877} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.05801, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31578, "top5_acc": 0.57344, "loss_cls": 3.90395, "loss": 3.90395, "time": 0.83176} +{"mode": "train", "epoch": 68, "iter": 1300, "lr": 0.05798, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30969, "top5_acc": 0.57063, "loss_cls": 3.91996, "loss": 3.91996, "time": 0.83035} +{"mode": "train", "epoch": 68, "iter": 1400, "lr": 0.05795, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30156, "top5_acc": 0.5725, "loss_cls": 3.93216, "loss": 3.93216, "time": 0.82768} +{"mode": "train", "epoch": 68, "iter": 1500, "lr": 0.05792, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31344, "top5_acc": 0.5725, "loss_cls": 3.90231, "loss": 3.90231, "time": 0.82879} +{"mode": "train", "epoch": 68, "iter": 1600, "lr": 0.0579, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32016, "top5_acc": 0.57484, "loss_cls": 3.89831, "loss": 3.89831, "time": 0.82825} +{"mode": "train", "epoch": 68, "iter": 1700, "lr": 0.05787, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3125, "top5_acc": 0.57016, "loss_cls": 3.92054, "loss": 3.92054, "time": 0.82981} +{"mode": "train", "epoch": 68, "iter": 1800, "lr": 0.05784, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30844, "top5_acc": 0.57312, "loss_cls": 3.91047, "loss": 3.91047, "time": 0.82214} +{"mode": "train", "epoch": 68, "iter": 1900, "lr": 0.05781, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31109, "top5_acc": 0.56828, "loss_cls": 3.92821, "loss": 3.92821, "time": 0.82706} +{"mode": "train", "epoch": 68, "iter": 2000, "lr": 0.05779, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30328, "top5_acc": 0.57172, "loss_cls": 3.9603, "loss": 3.9603, "time": 0.8356} +{"mode": "train", "epoch": 68, "iter": 2100, "lr": 0.05776, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30859, "top5_acc": 0.56578, "loss_cls": 3.93912, "loss": 3.93912, "time": 0.83541} +{"mode": "train", "epoch": 68, "iter": 2200, "lr": 0.05773, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.30391, "top5_acc": 0.555, "loss_cls": 3.98288, "loss": 3.98288, "time": 0.83075} +{"mode": "train", "epoch": 68, "iter": 2300, "lr": 0.0577, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30984, "top5_acc": 0.57406, "loss_cls": 3.8871, "loss": 3.8871, "time": 0.83129} +{"mode": "train", "epoch": 68, "iter": 2400, "lr": 0.05768, "memory": 15990, "data_time": 0.0008, "top1_acc": 0.32031, "top5_acc": 0.58156, "loss_cls": 3.90261, "loss": 3.90261, "time": 0.82851} +{"mode": "train", "epoch": 68, "iter": 2500, "lr": 0.05765, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32125, "top5_acc": 0.57906, "loss_cls": 3.90359, "loss": 3.90359, "time": 0.83009} +{"mode": "train", "epoch": 68, "iter": 2600, "lr": 0.05762, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30578, "top5_acc": 0.56641, "loss_cls": 3.95224, "loss": 3.95224, "time": 0.8337} +{"mode": "train", "epoch": 68, "iter": 2700, "lr": 0.05759, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30922, "top5_acc": 0.56609, "loss_cls": 3.92769, "loss": 3.92769, "time": 0.83391} +{"mode": "train", "epoch": 68, "iter": 2800, "lr": 0.05757, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30703, "top5_acc": 0.57312, "loss_cls": 3.91901, "loss": 3.91901, "time": 0.82406} +{"mode": "train", "epoch": 68, "iter": 2900, "lr": 0.05754, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.31078, "top5_acc": 0.57078, "loss_cls": 3.93678, "loss": 3.93678, "time": 0.82104} +{"mode": "train", "epoch": 68, "iter": 3000, "lr": 0.05751, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.3, "top5_acc": 0.56641, "loss_cls": 3.95486, "loss": 3.95486, "time": 0.83088} +{"mode": "train", "epoch": 68, "iter": 3100, "lr": 0.05748, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.31578, "top5_acc": 0.57938, "loss_cls": 3.89925, "loss": 3.89925, "time": 0.81847} +{"mode": "train", "epoch": 68, "iter": 3200, "lr": 0.05746, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32125, "top5_acc": 0.58109, "loss_cls": 3.88616, "loss": 3.88616, "time": 0.82449} +{"mode": "train", "epoch": 68, "iter": 3300, "lr": 0.05743, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31438, "top5_acc": 0.57, "loss_cls": 3.91787, "loss": 3.91787, "time": 0.82521} +{"mode": "train", "epoch": 68, "iter": 3400, "lr": 0.0574, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31094, "top5_acc": 0.56594, "loss_cls": 3.94186, "loss": 3.94186, "time": 0.82492} +{"mode": "train", "epoch": 68, "iter": 3500, "lr": 0.05737, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31484, "top5_acc": 0.58344, "loss_cls": 3.89399, "loss": 3.89399, "time": 0.82789} +{"mode": "train", "epoch": 68, "iter": 3600, "lr": 0.05734, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31266, "top5_acc": 0.57016, "loss_cls": 3.89752, "loss": 3.89752, "time": 0.82588} +{"mode": "train", "epoch": 68, "iter": 3700, "lr": 0.05732, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30328, "top5_acc": 0.56203, "loss_cls": 3.93129, "loss": 3.93129, "time": 0.82666} +{"mode": "val", "epoch": 68, "iter": 309, "lr": 0.0573, "top1_acc": 0.24419, "top5_acc": 0.48767, "mean_class_accuracy": 0.24408} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.05728, "memory": 15990, "data_time": 1.28318, "top1_acc": 0.31688, "top5_acc": 0.58625, "loss_cls": 3.85335, "loss": 3.85335, "time": 2.28137} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.05725, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31938, "top5_acc": 0.57766, "loss_cls": 3.89196, "loss": 3.89196, "time": 0.83133} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.05722, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30969, "top5_acc": 0.58078, "loss_cls": 3.85648, "loss": 3.85648, "time": 0.8263} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.05719, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31547, "top5_acc": 0.57406, "loss_cls": 3.88861, "loss": 3.88861, "time": 0.82476} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.05717, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30828, "top5_acc": 0.57688, "loss_cls": 3.91016, "loss": 3.91016, "time": 0.82009} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.05714, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31469, "top5_acc": 0.57922, "loss_cls": 3.90805, "loss": 3.90805, "time": 0.8267} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.05711, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30625, "top5_acc": 0.57281, "loss_cls": 3.89087, "loss": 3.89087, "time": 0.8219} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.05708, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31906, "top5_acc": 0.57906, "loss_cls": 3.86125, "loss": 3.86125, "time": 0.8241} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.05706, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31422, "top5_acc": 0.57, "loss_cls": 3.90618, "loss": 3.90618, "time": 0.82547} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.05703, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32328, "top5_acc": 0.58156, "loss_cls": 3.88748, "loss": 3.88748, "time": 0.82687} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.057, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31, "top5_acc": 0.57109, "loss_cls": 3.92299, "loss": 3.92299, "time": 0.81842} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.05697, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31234, "top5_acc": 0.56688, "loss_cls": 3.92843, "loss": 3.92843, "time": 0.82104} +{"mode": "train", "epoch": 69, "iter": 1300, "lr": 0.05694, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30781, "top5_acc": 0.56688, "loss_cls": 3.9272, "loss": 3.9272, "time": 0.81808} +{"mode": "train", "epoch": 69, "iter": 1400, "lr": 0.05692, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29734, "top5_acc": 0.5725, "loss_cls": 3.94582, "loss": 3.94582, "time": 0.82519} +{"mode": "train", "epoch": 69, "iter": 1500, "lr": 0.05689, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30812, "top5_acc": 0.57375, "loss_cls": 3.91091, "loss": 3.91091, "time": 0.8206} +{"mode": "train", "epoch": 69, "iter": 1600, "lr": 0.05686, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30312, "top5_acc": 0.56891, "loss_cls": 3.9308, "loss": 3.9308, "time": 0.81996} +{"mode": "train", "epoch": 69, "iter": 1700, "lr": 0.05683, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30656, "top5_acc": 0.56453, "loss_cls": 3.95347, "loss": 3.95347, "time": 0.81998} +{"mode": "train", "epoch": 69, "iter": 1800, "lr": 0.05681, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32484, "top5_acc": 0.59016, "loss_cls": 3.85993, "loss": 3.85993, "time": 0.8214} +{"mode": "train", "epoch": 69, "iter": 1900, "lr": 0.05678, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31453, "top5_acc": 0.57469, "loss_cls": 3.89867, "loss": 3.89867, "time": 0.81556} +{"mode": "train", "epoch": 69, "iter": 2000, "lr": 0.05675, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31594, "top5_acc": 0.57359, "loss_cls": 3.91441, "loss": 3.91441, "time": 0.83341} +{"mode": "train", "epoch": 69, "iter": 2100, "lr": 0.05672, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31625, "top5_acc": 0.57453, "loss_cls": 3.90045, "loss": 3.90045, "time": 0.817} +{"mode": "train", "epoch": 69, "iter": 2200, "lr": 0.0567, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.2975, "top5_acc": 0.56281, "loss_cls": 3.95839, "loss": 3.95839, "time": 0.83218} +{"mode": "train", "epoch": 69, "iter": 2300, "lr": 0.05667, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31266, "top5_acc": 0.57047, "loss_cls": 3.93174, "loss": 3.93174, "time": 0.82933} +{"mode": "train", "epoch": 69, "iter": 2400, "lr": 0.05664, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31578, "top5_acc": 0.58359, "loss_cls": 3.89135, "loss": 3.89135, "time": 0.82378} +{"mode": "train", "epoch": 69, "iter": 2500, "lr": 0.05661, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32531, "top5_acc": 0.58219, "loss_cls": 3.85966, "loss": 3.85966, "time": 0.82856} +{"mode": "train", "epoch": 69, "iter": 2600, "lr": 0.05658, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31219, "top5_acc": 0.5775, "loss_cls": 3.90958, "loss": 3.90958, "time": 0.83056} +{"mode": "train", "epoch": 69, "iter": 2700, "lr": 0.05656, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31516, "top5_acc": 0.56797, "loss_cls": 3.90147, "loss": 3.90147, "time": 0.83464} +{"mode": "train", "epoch": 69, "iter": 2800, "lr": 0.05653, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30938, "top5_acc": 0.56922, "loss_cls": 3.90183, "loss": 3.90183, "time": 0.82551} +{"mode": "train", "epoch": 69, "iter": 2900, "lr": 0.0565, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31125, "top5_acc": 0.56688, "loss_cls": 3.94418, "loss": 3.94418, "time": 0.82836} +{"mode": "train", "epoch": 69, "iter": 3000, "lr": 0.05647, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31578, "top5_acc": 0.57766, "loss_cls": 3.88818, "loss": 3.88818, "time": 0.82677} +{"mode": "train", "epoch": 69, "iter": 3100, "lr": 0.05645, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31469, "top5_acc": 0.57156, "loss_cls": 3.91915, "loss": 3.91915, "time": 0.82302} +{"mode": "train", "epoch": 69, "iter": 3200, "lr": 0.05642, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.30984, "top5_acc": 0.57781, "loss_cls": 3.92131, "loss": 3.92131, "time": 0.8252} +{"mode": "train", "epoch": 69, "iter": 3300, "lr": 0.05639, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31438, "top5_acc": 0.56859, "loss_cls": 3.94255, "loss": 3.94255, "time": 0.8247} +{"mode": "train", "epoch": 69, "iter": 3400, "lr": 0.05636, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30109, "top5_acc": 0.56609, "loss_cls": 3.94998, "loss": 3.94998, "time": 0.82495} +{"mode": "train", "epoch": 69, "iter": 3500, "lr": 0.05634, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30422, "top5_acc": 0.57891, "loss_cls": 3.90314, "loss": 3.90314, "time": 0.81836} +{"mode": "train", "epoch": 69, "iter": 3600, "lr": 0.05631, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.30438, "top5_acc": 0.56547, "loss_cls": 3.9498, "loss": 3.9498, "time": 0.82815} +{"mode": "train", "epoch": 69, "iter": 3700, "lr": 0.05628, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30984, "top5_acc": 0.565, "loss_cls": 3.91812, "loss": 3.91812, "time": 0.81962} +{"mode": "val", "epoch": 69, "iter": 309, "lr": 0.05627, "top1_acc": 0.23482, "top5_acc": 0.48032, "mean_class_accuracy": 0.23461} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.05624, "memory": 15990, "data_time": 1.30748, "top1_acc": 0.32562, "top5_acc": 0.59062, "loss_cls": 3.82701, "loss": 3.82701, "time": 2.30347} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.05621, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31688, "top5_acc": 0.57922, "loss_cls": 3.84525, "loss": 3.84525, "time": 0.83499} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.05618, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31812, "top5_acc": 0.58797, "loss_cls": 3.85072, "loss": 3.85072, "time": 0.83191} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.05616, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32906, "top5_acc": 0.58234, "loss_cls": 3.84599, "loss": 3.84599, "time": 0.82316} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.05613, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32703, "top5_acc": 0.57766, "loss_cls": 3.88507, "loss": 3.88507, "time": 0.82452} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.0561, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31469, "top5_acc": 0.58359, "loss_cls": 3.84917, "loss": 3.84917, "time": 0.82132} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.05607, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32031, "top5_acc": 0.58297, "loss_cls": 3.86514, "loss": 3.86514, "time": 0.82557} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.05605, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31453, "top5_acc": 0.58578, "loss_cls": 3.86104, "loss": 3.86104, "time": 0.8274} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.05602, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31672, "top5_acc": 0.56719, "loss_cls": 3.92032, "loss": 3.92032, "time": 0.82578} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.05599, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32469, "top5_acc": 0.58531, "loss_cls": 3.83756, "loss": 3.83756, "time": 0.82388} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.05596, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31281, "top5_acc": 0.57938, "loss_cls": 3.90824, "loss": 3.90824, "time": 0.83104} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.05593, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32031, "top5_acc": 0.58328, "loss_cls": 3.85573, "loss": 3.85573, "time": 0.82086} +{"mode": "train", "epoch": 70, "iter": 1300, "lr": 0.05591, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31734, "top5_acc": 0.57922, "loss_cls": 3.87482, "loss": 3.87482, "time": 0.81909} +{"mode": "train", "epoch": 70, "iter": 1400, "lr": 0.05588, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31031, "top5_acc": 0.57563, "loss_cls": 3.8899, "loss": 3.8899, "time": 0.82671} +{"mode": "train", "epoch": 70, "iter": 1500, "lr": 0.05585, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31031, "top5_acc": 0.56656, "loss_cls": 3.93756, "loss": 3.93756, "time": 0.82141} +{"mode": "train", "epoch": 70, "iter": 1600, "lr": 0.05582, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31062, "top5_acc": 0.57516, "loss_cls": 3.90555, "loss": 3.90555, "time": 0.82456} +{"mode": "train", "epoch": 70, "iter": 1700, "lr": 0.0558, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30859, "top5_acc": 0.56219, "loss_cls": 3.92228, "loss": 3.92228, "time": 0.81549} +{"mode": "train", "epoch": 70, "iter": 1800, "lr": 0.05577, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30641, "top5_acc": 0.56797, "loss_cls": 3.91729, "loss": 3.91729, "time": 0.82251} +{"mode": "train", "epoch": 70, "iter": 1900, "lr": 0.05574, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30797, "top5_acc": 0.57422, "loss_cls": 3.90699, "loss": 3.90699, "time": 0.82324} +{"mode": "train", "epoch": 70, "iter": 2000, "lr": 0.05571, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31609, "top5_acc": 0.56953, "loss_cls": 3.90625, "loss": 3.90625, "time": 0.8298} +{"mode": "train", "epoch": 70, "iter": 2100, "lr": 0.05568, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32219, "top5_acc": 0.57766, "loss_cls": 3.90557, "loss": 3.90557, "time": 0.81942} +{"mode": "train", "epoch": 70, "iter": 2200, "lr": 0.05566, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31781, "top5_acc": 0.57359, "loss_cls": 3.86455, "loss": 3.86455, "time": 0.83541} +{"mode": "train", "epoch": 70, "iter": 2300, "lr": 0.05563, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31125, "top5_acc": 0.56375, "loss_cls": 3.93095, "loss": 3.93095, "time": 0.82134} +{"mode": "train", "epoch": 70, "iter": 2400, "lr": 0.0556, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31016, "top5_acc": 0.56719, "loss_cls": 3.90745, "loss": 3.90745, "time": 0.82818} +{"mode": "train", "epoch": 70, "iter": 2500, "lr": 0.05557, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31438, "top5_acc": 0.57281, "loss_cls": 3.90413, "loss": 3.90413, "time": 0.8323} +{"mode": "train", "epoch": 70, "iter": 2600, "lr": 0.05555, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30625, "top5_acc": 0.56328, "loss_cls": 3.96825, "loss": 3.96825, "time": 0.81861} +{"mode": "train", "epoch": 70, "iter": 2700, "lr": 0.05552, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31, "top5_acc": 0.57141, "loss_cls": 3.9188, "loss": 3.9188, "time": 0.82211} +{"mode": "train", "epoch": 70, "iter": 2800, "lr": 0.05549, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31078, "top5_acc": 0.56938, "loss_cls": 3.93333, "loss": 3.93333, "time": 0.81788} +{"mode": "train", "epoch": 70, "iter": 2900, "lr": 0.05546, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31453, "top5_acc": 0.56828, "loss_cls": 3.91551, "loss": 3.91551, "time": 0.81953} +{"mode": "train", "epoch": 70, "iter": 3000, "lr": 0.05543, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31797, "top5_acc": 0.57891, "loss_cls": 3.88293, "loss": 3.88293, "time": 0.82695} +{"mode": "train", "epoch": 70, "iter": 3100, "lr": 0.05541, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30844, "top5_acc": 0.56656, "loss_cls": 3.95636, "loss": 3.95636, "time": 0.8137} +{"mode": "train", "epoch": 70, "iter": 3200, "lr": 0.05538, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31359, "top5_acc": 0.56641, "loss_cls": 3.93515, "loss": 3.93515, "time": 0.81639} +{"mode": "train", "epoch": 70, "iter": 3300, "lr": 0.05535, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31188, "top5_acc": 0.57703, "loss_cls": 3.9014, "loss": 3.9014, "time": 0.83037} +{"mode": "train", "epoch": 70, "iter": 3400, "lr": 0.05532, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30094, "top5_acc": 0.57219, "loss_cls": 3.92225, "loss": 3.92225, "time": 0.81386} +{"mode": "train", "epoch": 70, "iter": 3500, "lr": 0.0553, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31172, "top5_acc": 0.57484, "loss_cls": 3.87566, "loss": 3.87566, "time": 0.82334} +{"mode": "train", "epoch": 70, "iter": 3600, "lr": 0.05527, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31547, "top5_acc": 0.58219, "loss_cls": 3.87492, "loss": 3.87492, "time": 0.82282} +{"mode": "train", "epoch": 70, "iter": 3700, "lr": 0.05524, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31375, "top5_acc": 0.58047, "loss_cls": 3.90398, "loss": 3.90398, "time": 0.81592} +{"mode": "val", "epoch": 70, "iter": 309, "lr": 0.05523, "top1_acc": 0.23097, "top5_acc": 0.47196, "mean_class_accuracy": 0.23094} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.0552, "memory": 15990, "data_time": 1.24419, "top1_acc": 0.32719, "top5_acc": 0.58516, "loss_cls": 3.82836, "loss": 3.82836, "time": 2.23297} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.05517, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31719, "top5_acc": 0.58047, "loss_cls": 3.8599, "loss": 3.8599, "time": 0.81522} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.05514, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31, "top5_acc": 0.57391, "loss_cls": 3.91224, "loss": 3.91224, "time": 0.81683} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.05512, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31797, "top5_acc": 0.5675, "loss_cls": 3.91349, "loss": 3.91349, "time": 0.82306} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.05509, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31812, "top5_acc": 0.58359, "loss_cls": 3.8758, "loss": 3.8758, "time": 0.82231} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.05506, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32266, "top5_acc": 0.58219, "loss_cls": 3.84777, "loss": 3.84777, "time": 0.81868} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.05503, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31672, "top5_acc": 0.57891, "loss_cls": 3.89099, "loss": 3.89099, "time": 0.81392} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.055, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32938, "top5_acc": 0.58234, "loss_cls": 3.86109, "loss": 3.86109, "time": 0.81946} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.05498, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31875, "top5_acc": 0.57359, "loss_cls": 3.89531, "loss": 3.89531, "time": 0.82228} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.05495, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31312, "top5_acc": 0.57016, "loss_cls": 3.91557, "loss": 3.91557, "time": 0.81883} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.05492, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31438, "top5_acc": 0.58188, "loss_cls": 3.88078, "loss": 3.88078, "time": 0.82315} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.05489, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3175, "top5_acc": 0.58453, "loss_cls": 3.84289, "loss": 3.84289, "time": 0.81998} +{"mode": "train", "epoch": 71, "iter": 1300, "lr": 0.05487, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31719, "top5_acc": 0.57969, "loss_cls": 3.86272, "loss": 3.86272, "time": 0.82102} +{"mode": "train", "epoch": 71, "iter": 1400, "lr": 0.05484, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33078, "top5_acc": 0.58266, "loss_cls": 3.82611, "loss": 3.82611, "time": 0.81564} +{"mode": "train", "epoch": 71, "iter": 1500, "lr": 0.05481, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30594, "top5_acc": 0.57344, "loss_cls": 3.91645, "loss": 3.91645, "time": 0.81708} +{"mode": "train", "epoch": 71, "iter": 1600, "lr": 0.05478, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31766, "top5_acc": 0.5775, "loss_cls": 3.86125, "loss": 3.86125, "time": 0.81856} +{"mode": "train", "epoch": 71, "iter": 1700, "lr": 0.05475, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31281, "top5_acc": 0.58031, "loss_cls": 3.88632, "loss": 3.88632, "time": 0.81639} +{"mode": "train", "epoch": 71, "iter": 1800, "lr": 0.05473, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31625, "top5_acc": 0.57406, "loss_cls": 3.88501, "loss": 3.88501, "time": 0.82449} +{"mode": "train", "epoch": 71, "iter": 1900, "lr": 0.0547, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31094, "top5_acc": 0.56891, "loss_cls": 3.91378, "loss": 3.91378, "time": 0.81948} +{"mode": "train", "epoch": 71, "iter": 2000, "lr": 0.05467, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33047, "top5_acc": 0.58547, "loss_cls": 3.87042, "loss": 3.87042, "time": 0.83248} +{"mode": "train", "epoch": 71, "iter": 2100, "lr": 0.05464, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.31609, "top5_acc": 0.57906, "loss_cls": 3.91111, "loss": 3.91111, "time": 0.81858} +{"mode": "train", "epoch": 71, "iter": 2200, "lr": 0.05461, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.3125, "top5_acc": 0.57031, "loss_cls": 3.92772, "loss": 3.92772, "time": 0.83541} +{"mode": "train", "epoch": 71, "iter": 2300, "lr": 0.05459, "memory": 15990, "data_time": 0.00068, "top1_acc": 0.31562, "top5_acc": 0.57422, "loss_cls": 3.89271, "loss": 3.89271, "time": 0.83422} +{"mode": "train", "epoch": 71, "iter": 2400, "lr": 0.05456, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30391, "top5_acc": 0.56234, "loss_cls": 3.95012, "loss": 3.95012, "time": 0.82808} +{"mode": "train", "epoch": 71, "iter": 2500, "lr": 0.05453, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31188, "top5_acc": 0.57125, "loss_cls": 3.89487, "loss": 3.89487, "time": 0.82896} +{"mode": "train", "epoch": 71, "iter": 2600, "lr": 0.0545, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32406, "top5_acc": 0.58203, "loss_cls": 3.87686, "loss": 3.87686, "time": 0.83221} +{"mode": "train", "epoch": 71, "iter": 2700, "lr": 0.05448, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32797, "top5_acc": 0.58047, "loss_cls": 3.85997, "loss": 3.85997, "time": 0.8321} +{"mode": "train", "epoch": 71, "iter": 2800, "lr": 0.05445, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31125, "top5_acc": 0.56406, "loss_cls": 3.9277, "loss": 3.9277, "time": 0.83219} +{"mode": "train", "epoch": 71, "iter": 2900, "lr": 0.05442, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30797, "top5_acc": 0.57953, "loss_cls": 3.91324, "loss": 3.91324, "time": 0.82557} +{"mode": "train", "epoch": 71, "iter": 3000, "lr": 0.05439, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31891, "top5_acc": 0.58031, "loss_cls": 3.88851, "loss": 3.88851, "time": 0.82646} +{"mode": "train", "epoch": 71, "iter": 3100, "lr": 0.05436, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32125, "top5_acc": 0.58422, "loss_cls": 3.83251, "loss": 3.83251, "time": 0.81654} +{"mode": "train", "epoch": 71, "iter": 3200, "lr": 0.05434, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31125, "top5_acc": 0.57516, "loss_cls": 3.89626, "loss": 3.89626, "time": 0.81771} +{"mode": "train", "epoch": 71, "iter": 3300, "lr": 0.05431, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30766, "top5_acc": 0.56953, "loss_cls": 3.90122, "loss": 3.90122, "time": 0.83197} +{"mode": "train", "epoch": 71, "iter": 3400, "lr": 0.05428, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30438, "top5_acc": 0.56766, "loss_cls": 3.93854, "loss": 3.93854, "time": 0.82631} +{"mode": "train", "epoch": 71, "iter": 3500, "lr": 0.05425, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.30312, "top5_acc": 0.58, "loss_cls": 3.90661, "loss": 3.90661, "time": 0.82492} +{"mode": "train", "epoch": 71, "iter": 3600, "lr": 0.05422, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3175, "top5_acc": 0.57281, "loss_cls": 3.9028, "loss": 3.9028, "time": 0.83016} +{"mode": "train", "epoch": 71, "iter": 3700, "lr": 0.0542, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.31016, "top5_acc": 0.57828, "loss_cls": 3.91185, "loss": 3.91185, "time": 0.81875} +{"mode": "val", "epoch": 71, "iter": 309, "lr": 0.05418, "top1_acc": 0.2253, "top5_acc": 0.46573, "mean_class_accuracy": 0.22502} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.05416, "memory": 15990, "data_time": 1.26485, "top1_acc": 0.3325, "top5_acc": 0.59188, "loss_cls": 3.79484, "loss": 3.79484, "time": 2.25932} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.05413, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32125, "top5_acc": 0.57734, "loss_cls": 3.85275, "loss": 3.85275, "time": 0.83226} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.0541, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31547, "top5_acc": 0.58422, "loss_cls": 3.84182, "loss": 3.84182, "time": 0.82362} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.05407, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30438, "top5_acc": 0.57984, "loss_cls": 3.88562, "loss": 3.88562, "time": 0.83085} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.05404, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32406, "top5_acc": 0.59453, "loss_cls": 3.81474, "loss": 3.81474, "time": 0.82857} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.05402, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31375, "top5_acc": 0.57359, "loss_cls": 3.90311, "loss": 3.90311, "time": 0.82961} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.05399, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31219, "top5_acc": 0.58766, "loss_cls": 3.8627, "loss": 3.8627, "time": 0.83451} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.05396, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32609, "top5_acc": 0.58062, "loss_cls": 3.85337, "loss": 3.85337, "time": 0.83184} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.05393, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30672, "top5_acc": 0.57016, "loss_cls": 3.87681, "loss": 3.87681, "time": 0.83663} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.05391, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30797, "top5_acc": 0.56906, "loss_cls": 3.93024, "loss": 3.93024, "time": 0.83143} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.05388, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32156, "top5_acc": 0.57109, "loss_cls": 3.91675, "loss": 3.91675, "time": 0.83442} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.05385, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32094, "top5_acc": 0.58172, "loss_cls": 3.85387, "loss": 3.85387, "time": 0.8282} +{"mode": "train", "epoch": 72, "iter": 1300, "lr": 0.05382, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32875, "top5_acc": 0.58281, "loss_cls": 3.83094, "loss": 3.83094, "time": 0.8351} +{"mode": "train", "epoch": 72, "iter": 1400, "lr": 0.05379, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31703, "top5_acc": 0.57531, "loss_cls": 3.89244, "loss": 3.89244, "time": 0.82585} +{"mode": "train", "epoch": 72, "iter": 1500, "lr": 0.05377, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32094, "top5_acc": 0.57531, "loss_cls": 3.89014, "loss": 3.89014, "time": 0.82409} +{"mode": "train", "epoch": 72, "iter": 1600, "lr": 0.05374, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30984, "top5_acc": 0.57766, "loss_cls": 3.9016, "loss": 3.9016, "time": 0.81737} +{"mode": "train", "epoch": 72, "iter": 1700, "lr": 0.05371, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31453, "top5_acc": 0.57484, "loss_cls": 3.87964, "loss": 3.87964, "time": 0.81332} +{"mode": "train", "epoch": 72, "iter": 1800, "lr": 0.05368, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31172, "top5_acc": 0.57359, "loss_cls": 3.90592, "loss": 3.90592, "time": 0.82039} +{"mode": "train", "epoch": 72, "iter": 1900, "lr": 0.05365, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31547, "top5_acc": 0.57453, "loss_cls": 3.88658, "loss": 3.88658, "time": 0.82656} +{"mode": "train", "epoch": 72, "iter": 2000, "lr": 0.05363, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31344, "top5_acc": 0.58172, "loss_cls": 3.8868, "loss": 3.8868, "time": 0.81943} +{"mode": "train", "epoch": 72, "iter": 2100, "lr": 0.0536, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32, "top5_acc": 0.57422, "loss_cls": 3.86893, "loss": 3.86893, "time": 0.82153} +{"mode": "train", "epoch": 72, "iter": 2200, "lr": 0.05357, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32078, "top5_acc": 0.58141, "loss_cls": 3.88952, "loss": 3.88952, "time": 0.81771} +{"mode": "train", "epoch": 72, "iter": 2300, "lr": 0.05354, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32031, "top5_acc": 0.58047, "loss_cls": 3.88418, "loss": 3.88418, "time": 0.81706} +{"mode": "train", "epoch": 72, "iter": 2400, "lr": 0.05352, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31906, "top5_acc": 0.585, "loss_cls": 3.84596, "loss": 3.84596, "time": 0.81797} +{"mode": "train", "epoch": 72, "iter": 2500, "lr": 0.05349, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32438, "top5_acc": 0.57891, "loss_cls": 3.87738, "loss": 3.87738, "time": 0.82123} +{"mode": "train", "epoch": 72, "iter": 2600, "lr": 0.05346, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31328, "top5_acc": 0.57344, "loss_cls": 3.9025, "loss": 3.9025, "time": 0.81909} +{"mode": "train", "epoch": 72, "iter": 2700, "lr": 0.05343, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32234, "top5_acc": 0.58031, "loss_cls": 3.86295, "loss": 3.86295, "time": 0.82104} +{"mode": "train", "epoch": 72, "iter": 2800, "lr": 0.0534, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32266, "top5_acc": 0.57953, "loss_cls": 3.85757, "loss": 3.85757, "time": 0.81671} +{"mode": "train", "epoch": 72, "iter": 2900, "lr": 0.05338, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31828, "top5_acc": 0.57812, "loss_cls": 3.87332, "loss": 3.87332, "time": 0.81807} +{"mode": "train", "epoch": 72, "iter": 3000, "lr": 0.05335, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32438, "top5_acc": 0.58219, "loss_cls": 3.86238, "loss": 3.86238, "time": 0.8208} +{"mode": "train", "epoch": 72, "iter": 3100, "lr": 0.05332, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31594, "top5_acc": 0.57844, "loss_cls": 3.88019, "loss": 3.88019, "time": 0.81834} +{"mode": "train", "epoch": 72, "iter": 3200, "lr": 0.05329, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31047, "top5_acc": 0.57609, "loss_cls": 3.90734, "loss": 3.90734, "time": 0.81913} +{"mode": "train", "epoch": 72, "iter": 3300, "lr": 0.05326, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31734, "top5_acc": 0.57375, "loss_cls": 3.89866, "loss": 3.89866, "time": 0.81666} +{"mode": "train", "epoch": 72, "iter": 3400, "lr": 0.05324, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32031, "top5_acc": 0.57406, "loss_cls": 3.88448, "loss": 3.88448, "time": 0.81932} +{"mode": "train", "epoch": 72, "iter": 3500, "lr": 0.05321, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30781, "top5_acc": 0.58344, "loss_cls": 3.87823, "loss": 3.87823, "time": 0.81938} +{"mode": "train", "epoch": 72, "iter": 3600, "lr": 0.05318, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31266, "top5_acc": 0.57641, "loss_cls": 3.87903, "loss": 3.87903, "time": 0.81697} +{"mode": "train", "epoch": 72, "iter": 3700, "lr": 0.05315, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30984, "top5_acc": 0.56844, "loss_cls": 3.9191, "loss": 3.9191, "time": 0.81852} +{"mode": "val", "epoch": 72, "iter": 309, "lr": 0.05314, "top1_acc": 0.24865, "top5_acc": 0.4983, "mean_class_accuracy": 0.24844} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.05311, "memory": 15990, "data_time": 1.37705, "top1_acc": 0.32547, "top5_acc": 0.59328, "loss_cls": 3.79794, "loss": 3.79794, "time": 2.36947} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.05308, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32531, "top5_acc": 0.58391, "loss_cls": 3.82631, "loss": 3.82631, "time": 0.8245} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.05306, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32609, "top5_acc": 0.59, "loss_cls": 3.82935, "loss": 3.82935, "time": 0.82493} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.05303, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32469, "top5_acc": 0.58953, "loss_cls": 3.84088, "loss": 3.84088, "time": 0.82056} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.053, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32047, "top5_acc": 0.59469, "loss_cls": 3.80765, "loss": 3.80765, "time": 0.81695} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.05297, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32047, "top5_acc": 0.58875, "loss_cls": 3.83256, "loss": 3.83256, "time": 0.81558} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.05294, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32078, "top5_acc": 0.58156, "loss_cls": 3.88269, "loss": 3.88269, "time": 0.8155} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.05292, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32797, "top5_acc": 0.58766, "loss_cls": 3.81357, "loss": 3.81357, "time": 0.81495} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.05289, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31453, "top5_acc": 0.57859, "loss_cls": 3.88562, "loss": 3.88562, "time": 0.81384} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.05286, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31453, "top5_acc": 0.57922, "loss_cls": 3.87175, "loss": 3.87175, "time": 0.81676} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.05283, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31547, "top5_acc": 0.57703, "loss_cls": 3.89155, "loss": 3.89155, "time": 0.81518} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.0528, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31812, "top5_acc": 0.57719, "loss_cls": 3.88108, "loss": 3.88108, "time": 0.81533} +{"mode": "train", "epoch": 73, "iter": 1300, "lr": 0.05278, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31812, "top5_acc": 0.57578, "loss_cls": 3.88322, "loss": 3.88322, "time": 0.81507} +{"mode": "train", "epoch": 73, "iter": 1400, "lr": 0.05275, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31562, "top5_acc": 0.58453, "loss_cls": 3.87323, "loss": 3.87323, "time": 0.81871} +{"mode": "train", "epoch": 73, "iter": 1500, "lr": 0.05272, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3225, "top5_acc": 0.58625, "loss_cls": 3.82966, "loss": 3.82966, "time": 0.82218} +{"mode": "train", "epoch": 73, "iter": 1600, "lr": 0.05269, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32016, "top5_acc": 0.58359, "loss_cls": 3.84548, "loss": 3.84548, "time": 0.81321} +{"mode": "train", "epoch": 73, "iter": 1700, "lr": 0.05267, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32219, "top5_acc": 0.58391, "loss_cls": 3.85603, "loss": 3.85603, "time": 0.8147} +{"mode": "train", "epoch": 73, "iter": 1800, "lr": 0.05264, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32047, "top5_acc": 0.58359, "loss_cls": 3.82958, "loss": 3.82958, "time": 0.81485} +{"mode": "train", "epoch": 73, "iter": 1900, "lr": 0.05261, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32156, "top5_acc": 0.57844, "loss_cls": 3.86262, "loss": 3.86262, "time": 0.82362} +{"mode": "train", "epoch": 73, "iter": 2000, "lr": 0.05258, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.325, "top5_acc": 0.58234, "loss_cls": 3.8117, "loss": 3.8117, "time": 0.82091} +{"mode": "train", "epoch": 73, "iter": 2100, "lr": 0.05255, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32281, "top5_acc": 0.58234, "loss_cls": 3.84564, "loss": 3.84564, "time": 0.82062} +{"mode": "train", "epoch": 73, "iter": 2200, "lr": 0.05253, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32766, "top5_acc": 0.57984, "loss_cls": 3.8598, "loss": 3.8598, "time": 0.82069} +{"mode": "train", "epoch": 73, "iter": 2300, "lr": 0.0525, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32719, "top5_acc": 0.57672, "loss_cls": 3.82274, "loss": 3.82274, "time": 0.81656} +{"mode": "train", "epoch": 73, "iter": 2400, "lr": 0.05247, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31969, "top5_acc": 0.58266, "loss_cls": 3.87264, "loss": 3.87264, "time": 0.81439} +{"mode": "train", "epoch": 73, "iter": 2500, "lr": 0.05244, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31141, "top5_acc": 0.56734, "loss_cls": 3.89971, "loss": 3.89971, "time": 0.81668} +{"mode": "train", "epoch": 73, "iter": 2600, "lr": 0.05241, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32031, "top5_acc": 0.58484, "loss_cls": 3.85257, "loss": 3.85257, "time": 0.81338} +{"mode": "train", "epoch": 73, "iter": 2700, "lr": 0.05239, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31656, "top5_acc": 0.57125, "loss_cls": 3.91796, "loss": 3.91796, "time": 0.82214} +{"mode": "train", "epoch": 73, "iter": 2800, "lr": 0.05236, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30922, "top5_acc": 0.56859, "loss_cls": 3.93414, "loss": 3.93414, "time": 0.81761} +{"mode": "train", "epoch": 73, "iter": 2900, "lr": 0.05233, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31078, "top5_acc": 0.57422, "loss_cls": 3.88224, "loss": 3.88224, "time": 0.82075} +{"mode": "train", "epoch": 73, "iter": 3000, "lr": 0.0523, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31125, "top5_acc": 0.58188, "loss_cls": 3.86074, "loss": 3.86074, "time": 0.81513} +{"mode": "train", "epoch": 73, "iter": 3100, "lr": 0.05227, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30766, "top5_acc": 0.56891, "loss_cls": 3.90704, "loss": 3.90704, "time": 0.82253} +{"mode": "train", "epoch": 73, "iter": 3200, "lr": 0.05225, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30141, "top5_acc": 0.56016, "loss_cls": 3.94697, "loss": 3.94697, "time": 0.82024} +{"mode": "train", "epoch": 73, "iter": 3300, "lr": 0.05222, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30953, "top5_acc": 0.57766, "loss_cls": 3.89041, "loss": 3.89041, "time": 0.82453} +{"mode": "train", "epoch": 73, "iter": 3400, "lr": 0.05219, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32141, "top5_acc": 0.57547, "loss_cls": 3.86826, "loss": 3.86826, "time": 0.81436} +{"mode": "train", "epoch": 73, "iter": 3500, "lr": 0.05216, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31625, "top5_acc": 0.57953, "loss_cls": 3.8961, "loss": 3.8961, "time": 0.81998} +{"mode": "train", "epoch": 73, "iter": 3600, "lr": 0.05213, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31453, "top5_acc": 0.57344, "loss_cls": 3.88261, "loss": 3.88261, "time": 0.81743} +{"mode": "train", "epoch": 73, "iter": 3700, "lr": 0.05211, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31875, "top5_acc": 0.57969, "loss_cls": 3.85763, "loss": 3.85763, "time": 0.82471} +{"mode": "val", "epoch": 73, "iter": 309, "lr": 0.05209, "top1_acc": 0.24672, "top5_acc": 0.49835, "mean_class_accuracy": 0.24644} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.05207, "memory": 15990, "data_time": 1.33727, "top1_acc": 0.32109, "top5_acc": 0.57797, "loss_cls": 3.86788, "loss": 3.86788, "time": 2.31964} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.05204, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32391, "top5_acc": 0.58531, "loss_cls": 3.82771, "loss": 3.82771, "time": 0.81877} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.05201, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32422, "top5_acc": 0.59531, "loss_cls": 3.81234, "loss": 3.81234, "time": 0.82323} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.05198, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31984, "top5_acc": 0.58781, "loss_cls": 3.84529, "loss": 3.84529, "time": 0.81816} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.05195, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31859, "top5_acc": 0.57938, "loss_cls": 3.84903, "loss": 3.84903, "time": 0.81888} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.05193, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33719, "top5_acc": 0.60062, "loss_cls": 3.7732, "loss": 3.7732, "time": 0.81847} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.0519, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.315, "top5_acc": 0.58125, "loss_cls": 3.85251, "loss": 3.85251, "time": 0.81572} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.05187, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.315, "top5_acc": 0.57297, "loss_cls": 3.90166, "loss": 3.90166, "time": 0.81851} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.05184, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31594, "top5_acc": 0.58781, "loss_cls": 3.83591, "loss": 3.83591, "time": 0.8193} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.05181, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31203, "top5_acc": 0.57453, "loss_cls": 3.89249, "loss": 3.89249, "time": 0.81765} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.05179, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34031, "top5_acc": 0.60344, "loss_cls": 3.7559, "loss": 3.7559, "time": 0.81436} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.05176, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32375, "top5_acc": 0.58719, "loss_cls": 3.83174, "loss": 3.83174, "time": 0.81318} +{"mode": "train", "epoch": 74, "iter": 1300, "lr": 0.05173, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31766, "top5_acc": 0.58625, "loss_cls": 3.84568, "loss": 3.84568, "time": 0.81417} +{"mode": "train", "epoch": 74, "iter": 1400, "lr": 0.0517, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31719, "top5_acc": 0.585, "loss_cls": 3.86075, "loss": 3.86075, "time": 0.82147} +{"mode": "train", "epoch": 74, "iter": 1500, "lr": 0.05168, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32609, "top5_acc": 0.58516, "loss_cls": 3.83864, "loss": 3.83864, "time": 0.81509} +{"mode": "train", "epoch": 74, "iter": 1600, "lr": 0.05165, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30891, "top5_acc": 0.57438, "loss_cls": 3.87014, "loss": 3.87014, "time": 0.80849} +{"mode": "train", "epoch": 74, "iter": 1700, "lr": 0.05162, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31891, "top5_acc": 0.57625, "loss_cls": 3.8606, "loss": 3.8606, "time": 0.81279} +{"mode": "train", "epoch": 74, "iter": 1800, "lr": 0.05159, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30891, "top5_acc": 0.57234, "loss_cls": 3.91679, "loss": 3.91679, "time": 0.81429} +{"mode": "train", "epoch": 74, "iter": 1900, "lr": 0.05156, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31719, "top5_acc": 0.585, "loss_cls": 3.87866, "loss": 3.87866, "time": 0.8212} +{"mode": "train", "epoch": 74, "iter": 2000, "lr": 0.05154, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32219, "top5_acc": 0.58328, "loss_cls": 3.84504, "loss": 3.84504, "time": 0.81227} +{"mode": "train", "epoch": 74, "iter": 2100, "lr": 0.05151, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32562, "top5_acc": 0.57859, "loss_cls": 3.83031, "loss": 3.83031, "time": 0.83186} +{"mode": "train", "epoch": 74, "iter": 2200, "lr": 0.05148, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31797, "top5_acc": 0.57359, "loss_cls": 3.88201, "loss": 3.88201, "time": 0.82381} +{"mode": "train", "epoch": 74, "iter": 2300, "lr": 0.05145, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31938, "top5_acc": 0.57969, "loss_cls": 3.87531, "loss": 3.87531, "time": 0.82554} +{"mode": "train", "epoch": 74, "iter": 2400, "lr": 0.05142, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31891, "top5_acc": 0.58297, "loss_cls": 3.85007, "loss": 3.85007, "time": 0.81777} +{"mode": "train", "epoch": 74, "iter": 2500, "lr": 0.0514, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32, "top5_acc": 0.58359, "loss_cls": 3.84898, "loss": 3.84898, "time": 0.8174} +{"mode": "train", "epoch": 74, "iter": 2600, "lr": 0.05137, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31641, "top5_acc": 0.57766, "loss_cls": 3.87468, "loss": 3.87468, "time": 0.80933} +{"mode": "train", "epoch": 74, "iter": 2700, "lr": 0.05134, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32219, "top5_acc": 0.58594, "loss_cls": 3.83903, "loss": 3.83903, "time": 0.81437} +{"mode": "train", "epoch": 74, "iter": 2800, "lr": 0.05131, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33141, "top5_acc": 0.58641, "loss_cls": 3.81625, "loss": 3.81625, "time": 0.81268} +{"mode": "train", "epoch": 74, "iter": 2900, "lr": 0.05128, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.325, "top5_acc": 0.58594, "loss_cls": 3.8232, "loss": 3.8232, "time": 0.81734} +{"mode": "train", "epoch": 74, "iter": 3000, "lr": 0.05126, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31547, "top5_acc": 0.5825, "loss_cls": 3.85358, "loss": 3.85358, "time": 0.81172} +{"mode": "train", "epoch": 74, "iter": 3100, "lr": 0.05123, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30547, "top5_acc": 0.57563, "loss_cls": 3.91956, "loss": 3.91956, "time": 0.81572} +{"mode": "train", "epoch": 74, "iter": 3200, "lr": 0.0512, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31359, "top5_acc": 0.57688, "loss_cls": 3.89127, "loss": 3.89127, "time": 0.81327} +{"mode": "train", "epoch": 74, "iter": 3300, "lr": 0.05117, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3225, "top5_acc": 0.58328, "loss_cls": 3.83918, "loss": 3.83918, "time": 0.8254} +{"mode": "train", "epoch": 74, "iter": 3400, "lr": 0.05114, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31609, "top5_acc": 0.58344, "loss_cls": 3.85595, "loss": 3.85595, "time": 0.81537} +{"mode": "train", "epoch": 74, "iter": 3500, "lr": 0.05112, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32953, "top5_acc": 0.58453, "loss_cls": 3.87766, "loss": 3.87766, "time": 0.81085} +{"mode": "train", "epoch": 74, "iter": 3600, "lr": 0.05109, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31406, "top5_acc": 0.57281, "loss_cls": 3.92669, "loss": 3.92669, "time": 0.81329} +{"mode": "train", "epoch": 74, "iter": 3700, "lr": 0.05106, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32078, "top5_acc": 0.58141, "loss_cls": 3.83878, "loss": 3.83878, "time": 0.8186} +{"mode": "val", "epoch": 74, "iter": 309, "lr": 0.05105, "top1_acc": 0.23244, "top5_acc": 0.4631, "mean_class_accuracy": 0.23228} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.05102, "memory": 15990, "data_time": 1.34945, "top1_acc": 0.32281, "top5_acc": 0.59641, "loss_cls": 3.81476, "loss": 3.81476, "time": 2.33015} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.05099, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33141, "top5_acc": 0.59516, "loss_cls": 3.81124, "loss": 3.81124, "time": 0.81604} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.05096, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33641, "top5_acc": 0.59031, "loss_cls": 3.79991, "loss": 3.79991, "time": 0.81942} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.05094, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32062, "top5_acc": 0.58391, "loss_cls": 3.82355, "loss": 3.82355, "time": 0.81792} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.05091, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30766, "top5_acc": 0.57844, "loss_cls": 3.86997, "loss": 3.86997, "time": 0.81214} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.05088, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32172, "top5_acc": 0.58719, "loss_cls": 3.83712, "loss": 3.83712, "time": 0.81786} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.05085, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32844, "top5_acc": 0.58766, "loss_cls": 3.80825, "loss": 3.80825, "time": 0.8131} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.05082, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32547, "top5_acc": 0.5875, "loss_cls": 3.81978, "loss": 3.81978, "time": 0.81298} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.0508, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32359, "top5_acc": 0.58156, "loss_cls": 3.84462, "loss": 3.84462, "time": 0.8124} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.05077, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32969, "top5_acc": 0.59156, "loss_cls": 3.79998, "loss": 3.79998, "time": 0.81497} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.05074, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32453, "top5_acc": 0.58172, "loss_cls": 3.84284, "loss": 3.84284, "time": 0.8166} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.05071, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31688, "top5_acc": 0.58578, "loss_cls": 3.83777, "loss": 3.83777, "time": 0.8183} +{"mode": "train", "epoch": 75, "iter": 1300, "lr": 0.05068, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31594, "top5_acc": 0.58891, "loss_cls": 3.82307, "loss": 3.82307, "time": 0.81232} +{"mode": "train", "epoch": 75, "iter": 1400, "lr": 0.05066, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31859, "top5_acc": 0.58391, "loss_cls": 3.8609, "loss": 3.8609, "time": 0.82304} +{"mode": "train", "epoch": 75, "iter": 1500, "lr": 0.05063, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33609, "top5_acc": 0.59125, "loss_cls": 3.78248, "loss": 3.78248, "time": 0.81799} +{"mode": "train", "epoch": 75, "iter": 1600, "lr": 0.0506, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31219, "top5_acc": 0.58109, "loss_cls": 3.87113, "loss": 3.87113, "time": 0.81722} +{"mode": "train", "epoch": 75, "iter": 1700, "lr": 0.05057, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31828, "top5_acc": 0.57938, "loss_cls": 3.85649, "loss": 3.85649, "time": 0.81363} +{"mode": "train", "epoch": 75, "iter": 1800, "lr": 0.05054, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32469, "top5_acc": 0.58984, "loss_cls": 3.8203, "loss": 3.8203, "time": 0.81629} +{"mode": "train", "epoch": 75, "iter": 1900, "lr": 0.05052, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31453, "top5_acc": 0.57797, "loss_cls": 3.898, "loss": 3.898, "time": 0.82272} +{"mode": "train", "epoch": 75, "iter": 2000, "lr": 0.05049, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32297, "top5_acc": 0.57609, "loss_cls": 3.85919, "loss": 3.85919, "time": 0.81874} +{"mode": "train", "epoch": 75, "iter": 2100, "lr": 0.05046, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33469, "top5_acc": 0.58719, "loss_cls": 3.80276, "loss": 3.80276, "time": 0.8212} +{"mode": "train", "epoch": 75, "iter": 2200, "lr": 0.05043, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33062, "top5_acc": 0.58312, "loss_cls": 3.84925, "loss": 3.84925, "time": 0.82388} +{"mode": "train", "epoch": 75, "iter": 2300, "lr": 0.0504, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31984, "top5_acc": 0.58953, "loss_cls": 3.82064, "loss": 3.82064, "time": 0.81612} +{"mode": "train", "epoch": 75, "iter": 2400, "lr": 0.05038, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32047, "top5_acc": 0.57875, "loss_cls": 3.85352, "loss": 3.85352, "time": 0.8212} +{"mode": "train", "epoch": 75, "iter": 2500, "lr": 0.05035, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31984, "top5_acc": 0.58062, "loss_cls": 3.84539, "loss": 3.84539, "time": 0.81724} +{"mode": "train", "epoch": 75, "iter": 2600, "lr": 0.05032, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32484, "top5_acc": 0.58281, "loss_cls": 3.84415, "loss": 3.84415, "time": 0.81873} +{"mode": "train", "epoch": 75, "iter": 2700, "lr": 0.05029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31797, "top5_acc": 0.57891, "loss_cls": 3.87624, "loss": 3.87624, "time": 0.81391} +{"mode": "train", "epoch": 75, "iter": 2800, "lr": 0.05026, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32859, "top5_acc": 0.59031, "loss_cls": 3.81283, "loss": 3.81283, "time": 0.81398} +{"mode": "train", "epoch": 75, "iter": 2900, "lr": 0.05024, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32, "top5_acc": 0.57531, "loss_cls": 3.87167, "loss": 3.87167, "time": 0.81621} +{"mode": "train", "epoch": 75, "iter": 3000, "lr": 0.05021, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31391, "top5_acc": 0.57578, "loss_cls": 3.87103, "loss": 3.87103, "time": 0.81884} +{"mode": "train", "epoch": 75, "iter": 3100, "lr": 0.05018, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32391, "top5_acc": 0.58516, "loss_cls": 3.86125, "loss": 3.86125, "time": 0.8142} +{"mode": "train", "epoch": 75, "iter": 3200, "lr": 0.05015, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31219, "top5_acc": 0.58203, "loss_cls": 3.88299, "loss": 3.88299, "time": 0.81591} +{"mode": "train", "epoch": 75, "iter": 3300, "lr": 0.05012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32797, "top5_acc": 0.58688, "loss_cls": 3.84698, "loss": 3.84698, "time": 0.81369} +{"mode": "train", "epoch": 75, "iter": 3400, "lr": 0.0501, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.315, "top5_acc": 0.58156, "loss_cls": 3.83505, "loss": 3.83505, "time": 0.81412} +{"mode": "train", "epoch": 75, "iter": 3500, "lr": 0.05007, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32391, "top5_acc": 0.57812, "loss_cls": 3.85017, "loss": 3.85017, "time": 0.81735} +{"mode": "train", "epoch": 75, "iter": 3600, "lr": 0.05004, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32531, "top5_acc": 0.58766, "loss_cls": 3.85007, "loss": 3.85007, "time": 0.82826} +{"mode": "train", "epoch": 75, "iter": 3700, "lr": 0.05001, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32297, "top5_acc": 0.58906, "loss_cls": 3.85083, "loss": 3.85083, "time": 0.81304} +{"mode": "val", "epoch": 75, "iter": 309, "lr": 0.05, "top1_acc": 0.24819, "top5_acc": 0.49233, "mean_class_accuracy": 0.24806} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.04997, "memory": 15990, "data_time": 1.36335, "top1_acc": 0.32906, "top5_acc": 0.59047, "loss_cls": 3.81917, "loss": 3.81917, "time": 2.35072} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.04994, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.3275, "top5_acc": 0.5875, "loss_cls": 3.81737, "loss": 3.81737, "time": 0.83511} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.04992, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33516, "top5_acc": 0.60547, "loss_cls": 3.72983, "loss": 3.72983, "time": 0.82709} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.04989, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32453, "top5_acc": 0.5775, "loss_cls": 3.84008, "loss": 3.84008, "time": 0.8238} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.04986, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32219, "top5_acc": 0.59156, "loss_cls": 3.8192, "loss": 3.8192, "time": 0.82547} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.04983, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33234, "top5_acc": 0.58188, "loss_cls": 3.84724, "loss": 3.84724, "time": 0.82482} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.0498, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31719, "top5_acc": 0.58312, "loss_cls": 3.8533, "loss": 3.8533, "time": 0.83008} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.04978, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32609, "top5_acc": 0.58156, "loss_cls": 3.84125, "loss": 3.84125, "time": 0.82572} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.04975, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32828, "top5_acc": 0.5875, "loss_cls": 3.79897, "loss": 3.79897, "time": 0.81497} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.04972, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33406, "top5_acc": 0.5875, "loss_cls": 3.82349, "loss": 3.82349, "time": 0.81833} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.04969, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32703, "top5_acc": 0.59219, "loss_cls": 3.81677, "loss": 3.81677, "time": 0.81792} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.04966, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31797, "top5_acc": 0.57844, "loss_cls": 3.85433, "loss": 3.85433, "time": 0.81785} +{"mode": "train", "epoch": 76, "iter": 1300, "lr": 0.04964, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31969, "top5_acc": 0.57734, "loss_cls": 3.85179, "loss": 3.85179, "time": 0.81371} +{"mode": "train", "epoch": 76, "iter": 1400, "lr": 0.04961, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32453, "top5_acc": 0.58078, "loss_cls": 3.82632, "loss": 3.82632, "time": 0.81765} +{"mode": "train", "epoch": 76, "iter": 1500, "lr": 0.04958, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32422, "top5_acc": 0.58359, "loss_cls": 3.82715, "loss": 3.82715, "time": 0.81573} +{"mode": "train", "epoch": 76, "iter": 1600, "lr": 0.04955, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32641, "top5_acc": 0.59312, "loss_cls": 3.78178, "loss": 3.78178, "time": 0.81322} +{"mode": "train", "epoch": 76, "iter": 1700, "lr": 0.04953, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32953, "top5_acc": 0.58297, "loss_cls": 3.8039, "loss": 3.8039, "time": 0.81587} +{"mode": "train", "epoch": 76, "iter": 1800, "lr": 0.0495, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32562, "top5_acc": 0.58766, "loss_cls": 3.85617, "loss": 3.85617, "time": 0.81638} +{"mode": "train", "epoch": 76, "iter": 1900, "lr": 0.04947, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32234, "top5_acc": 0.58391, "loss_cls": 3.83452, "loss": 3.83452, "time": 0.81893} +{"mode": "train", "epoch": 76, "iter": 2000, "lr": 0.04944, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32906, "top5_acc": 0.58656, "loss_cls": 3.85514, "loss": 3.85514, "time": 0.82001} +{"mode": "train", "epoch": 76, "iter": 2100, "lr": 0.04941, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32984, "top5_acc": 0.58781, "loss_cls": 3.81063, "loss": 3.81063, "time": 0.82951} +{"mode": "train", "epoch": 76, "iter": 2200, "lr": 0.04939, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.3275, "top5_acc": 0.58984, "loss_cls": 3.81765, "loss": 3.81765, "time": 0.82078} +{"mode": "train", "epoch": 76, "iter": 2300, "lr": 0.04936, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32562, "top5_acc": 0.58188, "loss_cls": 3.81963, "loss": 3.81963, "time": 0.82116} +{"mode": "train", "epoch": 76, "iter": 2400, "lr": 0.04933, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32031, "top5_acc": 0.58328, "loss_cls": 3.87929, "loss": 3.87929, "time": 0.82387} +{"mode": "train", "epoch": 76, "iter": 2500, "lr": 0.0493, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32875, "top5_acc": 0.58656, "loss_cls": 3.80995, "loss": 3.80995, "time": 0.81897} +{"mode": "train", "epoch": 76, "iter": 2600, "lr": 0.04927, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33141, "top5_acc": 0.58984, "loss_cls": 3.83166, "loss": 3.83166, "time": 0.81333} +{"mode": "train", "epoch": 76, "iter": 2700, "lr": 0.04925, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31141, "top5_acc": 0.58031, "loss_cls": 3.88982, "loss": 3.88982, "time": 0.81772} +{"mode": "train", "epoch": 76, "iter": 2800, "lr": 0.04922, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32578, "top5_acc": 0.58406, "loss_cls": 3.83795, "loss": 3.83795, "time": 0.81562} +{"mode": "train", "epoch": 76, "iter": 2900, "lr": 0.04919, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33297, "top5_acc": 0.59109, "loss_cls": 3.78101, "loss": 3.78101, "time": 0.81709} +{"mode": "train", "epoch": 76, "iter": 3000, "lr": 0.04916, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.325, "top5_acc": 0.58422, "loss_cls": 3.83121, "loss": 3.83121, "time": 0.81495} +{"mode": "train", "epoch": 76, "iter": 3100, "lr": 0.04913, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32406, "top5_acc": 0.58406, "loss_cls": 3.84576, "loss": 3.84576, "time": 0.82434} +{"mode": "train", "epoch": 76, "iter": 3200, "lr": 0.04911, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33578, "top5_acc": 0.59172, "loss_cls": 3.76768, "loss": 3.76768, "time": 0.81664} +{"mode": "train", "epoch": 76, "iter": 3300, "lr": 0.04908, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31641, "top5_acc": 0.57719, "loss_cls": 3.8829, "loss": 3.8829, "time": 0.81778} +{"mode": "train", "epoch": 76, "iter": 3400, "lr": 0.04905, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31828, "top5_acc": 0.58688, "loss_cls": 3.85681, "loss": 3.85681, "time": 0.81687} +{"mode": "train", "epoch": 76, "iter": 3500, "lr": 0.04902, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32219, "top5_acc": 0.58344, "loss_cls": 3.83664, "loss": 3.83664, "time": 0.81475} +{"mode": "train", "epoch": 76, "iter": 3600, "lr": 0.04899, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32312, "top5_acc": 0.58578, "loss_cls": 3.84006, "loss": 3.84006, "time": 0.82829} +{"mode": "train", "epoch": 76, "iter": 3700, "lr": 0.04897, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32266, "top5_acc": 0.58438, "loss_cls": 3.84793, "loss": 3.84793, "time": 0.8131} +{"mode": "val", "epoch": 76, "iter": 309, "lr": 0.04895, "top1_acc": 0.24844, "top5_acc": 0.50215, "mean_class_accuracy": 0.24829} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.04893, "memory": 15990, "data_time": 1.37953, "top1_acc": 0.32391, "top5_acc": 0.59125, "loss_cls": 3.80338, "loss": 3.80338, "time": 2.37648} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0489, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33828, "top5_acc": 0.59734, "loss_cls": 3.76041, "loss": 3.76041, "time": 0.82745} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.04887, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32438, "top5_acc": 0.59078, "loss_cls": 3.82787, "loss": 3.82787, "time": 0.83537} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.04884, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34328, "top5_acc": 0.58938, "loss_cls": 3.74561, "loss": 3.74561, "time": 0.83748} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.04881, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32922, "top5_acc": 0.59, "loss_cls": 3.80696, "loss": 3.80696, "time": 0.83162} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.04879, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33047, "top5_acc": 0.58703, "loss_cls": 3.82076, "loss": 3.82076, "time": 0.83752} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.04876, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32906, "top5_acc": 0.59, "loss_cls": 3.80837, "loss": 3.80837, "time": 0.83544} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.04873, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32609, "top5_acc": 0.59109, "loss_cls": 3.81443, "loss": 3.81443, "time": 0.83557} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.0487, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32375, "top5_acc": 0.58531, "loss_cls": 3.81732, "loss": 3.81732, "time": 0.83755} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.04867, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32656, "top5_acc": 0.58156, "loss_cls": 3.84816, "loss": 3.84816, "time": 0.83654} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.04865, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31891, "top5_acc": 0.58328, "loss_cls": 3.85503, "loss": 3.85503, "time": 0.83365} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.04862, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31453, "top5_acc": 0.57891, "loss_cls": 3.85756, "loss": 3.85756, "time": 0.8358} +{"mode": "train", "epoch": 77, "iter": 1300, "lr": 0.04859, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32641, "top5_acc": 0.59219, "loss_cls": 3.80923, "loss": 3.80923, "time": 0.83847} +{"mode": "train", "epoch": 77, "iter": 1400, "lr": 0.04856, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31531, "top5_acc": 0.58328, "loss_cls": 3.86936, "loss": 3.86936, "time": 0.83326} +{"mode": "train", "epoch": 77, "iter": 1500, "lr": 0.04853, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32531, "top5_acc": 0.59531, "loss_cls": 3.79381, "loss": 3.79381, "time": 0.83901} +{"mode": "train", "epoch": 77, "iter": 1600, "lr": 0.04851, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32484, "top5_acc": 0.58734, "loss_cls": 3.82857, "loss": 3.82857, "time": 0.83778} +{"mode": "train", "epoch": 77, "iter": 1700, "lr": 0.04848, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32266, "top5_acc": 0.58438, "loss_cls": 3.83014, "loss": 3.83014, "time": 0.83587} +{"mode": "train", "epoch": 77, "iter": 1800, "lr": 0.04845, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32688, "top5_acc": 0.585, "loss_cls": 3.85243, "loss": 3.85243, "time": 0.83823} +{"mode": "train", "epoch": 77, "iter": 1900, "lr": 0.04842, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.32797, "top5_acc": 0.58609, "loss_cls": 3.82449, "loss": 3.82449, "time": 0.8384} +{"mode": "train", "epoch": 77, "iter": 2000, "lr": 0.04839, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32469, "top5_acc": 0.59469, "loss_cls": 3.79997, "loss": 3.79997, "time": 0.826} +{"mode": "train", "epoch": 77, "iter": 2100, "lr": 0.04837, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32688, "top5_acc": 0.58438, "loss_cls": 3.81118, "loss": 3.81118, "time": 0.8357} +{"mode": "train", "epoch": 77, "iter": 2200, "lr": 0.04834, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32328, "top5_acc": 0.58688, "loss_cls": 3.83177, "loss": 3.83177, "time": 0.83585} +{"mode": "train", "epoch": 77, "iter": 2300, "lr": 0.04831, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32188, "top5_acc": 0.59422, "loss_cls": 3.79496, "loss": 3.79496, "time": 0.83726} +{"mode": "train", "epoch": 77, "iter": 2400, "lr": 0.04828, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.3225, "top5_acc": 0.58062, "loss_cls": 3.84509, "loss": 3.84509, "time": 0.82995} +{"mode": "train", "epoch": 77, "iter": 2500, "lr": 0.04825, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32984, "top5_acc": 0.59516, "loss_cls": 3.78074, "loss": 3.78074, "time": 0.83251} +{"mode": "train", "epoch": 77, "iter": 2600, "lr": 0.04823, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33031, "top5_acc": 0.58484, "loss_cls": 3.81856, "loss": 3.81856, "time": 0.82852} +{"mode": "train", "epoch": 77, "iter": 2700, "lr": 0.0482, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32359, "top5_acc": 0.58516, "loss_cls": 3.84866, "loss": 3.84866, "time": 0.82899} +{"mode": "train", "epoch": 77, "iter": 2800, "lr": 0.04817, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32672, "top5_acc": 0.58969, "loss_cls": 3.80085, "loss": 3.80085, "time": 0.81818} +{"mode": "train", "epoch": 77, "iter": 2900, "lr": 0.04814, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32203, "top5_acc": 0.58625, "loss_cls": 3.85134, "loss": 3.85134, "time": 0.83095} +{"mode": "train", "epoch": 77, "iter": 3000, "lr": 0.04811, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33141, "top5_acc": 0.59344, "loss_cls": 3.79555, "loss": 3.79555, "time": 0.82322} +{"mode": "train", "epoch": 77, "iter": 3100, "lr": 0.04809, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32484, "top5_acc": 0.575, "loss_cls": 3.85376, "loss": 3.85376, "time": 0.82397} +{"mode": "train", "epoch": 77, "iter": 3200, "lr": 0.04806, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32328, "top5_acc": 0.58422, "loss_cls": 3.82799, "loss": 3.82799, "time": 0.83809} +{"mode": "train", "epoch": 77, "iter": 3300, "lr": 0.04803, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31797, "top5_acc": 0.58297, "loss_cls": 3.84102, "loss": 3.84102, "time": 0.83508} +{"mode": "train", "epoch": 77, "iter": 3400, "lr": 0.048, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33562, "top5_acc": 0.59125, "loss_cls": 3.81627, "loss": 3.81627, "time": 0.82849} +{"mode": "train", "epoch": 77, "iter": 3500, "lr": 0.04798, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32562, "top5_acc": 0.59047, "loss_cls": 3.83846, "loss": 3.83846, "time": 0.82615} +{"mode": "train", "epoch": 77, "iter": 3600, "lr": 0.04795, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33, "top5_acc": 0.58391, "loss_cls": 3.82931, "loss": 3.82931, "time": 0.82705} +{"mode": "train", "epoch": 77, "iter": 3700, "lr": 0.04792, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32844, "top5_acc": 0.59469, "loss_cls": 3.80781, "loss": 3.80781, "time": 0.83146} +{"mode": "val", "epoch": 77, "iter": 309, "lr": 0.04791, "top1_acc": 0.25168, "top5_acc": 0.50286, "mean_class_accuracy": 0.25139} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.04788, "memory": 15990, "data_time": 1.32255, "top1_acc": 0.33094, "top5_acc": 0.59781, "loss_cls": 3.76758, "loss": 3.76758, "time": 2.32188} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.04785, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34047, "top5_acc": 0.59906, "loss_cls": 3.76639, "loss": 3.76639, "time": 0.8378} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.04782, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33953, "top5_acc": 0.60469, "loss_cls": 3.7148, "loss": 3.7148, "time": 0.83516} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.04779, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32516, "top5_acc": 0.60016, "loss_cls": 3.77492, "loss": 3.77492, "time": 0.82883} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.04777, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33375, "top5_acc": 0.59016, "loss_cls": 3.78915, "loss": 3.78915, "time": 0.82738} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.04774, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32875, "top5_acc": 0.59234, "loss_cls": 3.78377, "loss": 3.78377, "time": 0.81624} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.04771, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33422, "top5_acc": 0.59844, "loss_cls": 3.77844, "loss": 3.77844, "time": 0.82065} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.04768, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31906, "top5_acc": 0.58422, "loss_cls": 3.83954, "loss": 3.83954, "time": 0.82075} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.04766, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32453, "top5_acc": 0.59062, "loss_cls": 3.80219, "loss": 3.80219, "time": 0.82255} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.04763, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31484, "top5_acc": 0.59156, "loss_cls": 3.85516, "loss": 3.85516, "time": 0.81682} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.0476, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32203, "top5_acc": 0.58828, "loss_cls": 3.82313, "loss": 3.82313, "time": 0.81958} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.04757, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32891, "top5_acc": 0.59578, "loss_cls": 3.78599, "loss": 3.78599, "time": 0.81736} +{"mode": "train", "epoch": 78, "iter": 1300, "lr": 0.04754, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32312, "top5_acc": 0.58172, "loss_cls": 3.84686, "loss": 3.84686, "time": 0.81725} +{"mode": "train", "epoch": 78, "iter": 1400, "lr": 0.04752, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33078, "top5_acc": 0.58906, "loss_cls": 3.80897, "loss": 3.80897, "time": 0.81788} +{"mode": "train", "epoch": 78, "iter": 1500, "lr": 0.04749, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33234, "top5_acc": 0.60266, "loss_cls": 3.74216, "loss": 3.74216, "time": 0.8164} +{"mode": "train", "epoch": 78, "iter": 1600, "lr": 0.04746, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32984, "top5_acc": 0.60328, "loss_cls": 3.76288, "loss": 3.76288, "time": 0.81619} +{"mode": "train", "epoch": 78, "iter": 1700, "lr": 0.04743, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33203, "top5_acc": 0.59125, "loss_cls": 3.80254, "loss": 3.80254, "time": 0.81403} +{"mode": "train", "epoch": 78, "iter": 1800, "lr": 0.0474, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32359, "top5_acc": 0.58297, "loss_cls": 3.82095, "loss": 3.82095, "time": 0.82386} +{"mode": "train", "epoch": 78, "iter": 1900, "lr": 0.04738, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32422, "top5_acc": 0.58359, "loss_cls": 3.83179, "loss": 3.83179, "time": 0.81961} +{"mode": "train", "epoch": 78, "iter": 2000, "lr": 0.04735, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32609, "top5_acc": 0.58641, "loss_cls": 3.81243, "loss": 3.81243, "time": 0.82086} +{"mode": "train", "epoch": 78, "iter": 2100, "lr": 0.04732, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32516, "top5_acc": 0.58266, "loss_cls": 3.82552, "loss": 3.82552, "time": 0.81917} +{"mode": "train", "epoch": 78, "iter": 2200, "lr": 0.04729, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32875, "top5_acc": 0.59266, "loss_cls": 3.8163, "loss": 3.8163, "time": 0.81718} +{"mode": "train", "epoch": 78, "iter": 2300, "lr": 0.04726, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32375, "top5_acc": 0.58453, "loss_cls": 3.88309, "loss": 3.88309, "time": 0.81574} +{"mode": "train", "epoch": 78, "iter": 2400, "lr": 0.04724, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33469, "top5_acc": 0.59328, "loss_cls": 3.79414, "loss": 3.79414, "time": 0.81084} +{"mode": "train", "epoch": 78, "iter": 2500, "lr": 0.04721, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32734, "top5_acc": 0.59359, "loss_cls": 3.78198, "loss": 3.78198, "time": 0.81438} +{"mode": "train", "epoch": 78, "iter": 2600, "lr": 0.04718, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33594, "top5_acc": 0.58922, "loss_cls": 3.80298, "loss": 3.80298, "time": 0.81681} +{"mode": "train", "epoch": 78, "iter": 2700, "lr": 0.04715, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3275, "top5_acc": 0.58766, "loss_cls": 3.82175, "loss": 3.82175, "time": 0.82087} +{"mode": "train", "epoch": 78, "iter": 2800, "lr": 0.04712, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31266, "top5_acc": 0.57891, "loss_cls": 3.86847, "loss": 3.86847, "time": 0.82059} +{"mode": "train", "epoch": 78, "iter": 2900, "lr": 0.0471, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32438, "top5_acc": 0.58719, "loss_cls": 3.81555, "loss": 3.81555, "time": 0.81559} +{"mode": "train", "epoch": 78, "iter": 3000, "lr": 0.04707, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3125, "top5_acc": 0.58, "loss_cls": 3.88571, "loss": 3.88571, "time": 0.82324} +{"mode": "train", "epoch": 78, "iter": 3100, "lr": 0.04704, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32891, "top5_acc": 0.59234, "loss_cls": 3.79525, "loss": 3.79525, "time": 0.82544} +{"mode": "train", "epoch": 78, "iter": 3200, "lr": 0.04701, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33734, "top5_acc": 0.59547, "loss_cls": 3.77331, "loss": 3.77331, "time": 0.81913} +{"mode": "train", "epoch": 78, "iter": 3300, "lr": 0.04699, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31641, "top5_acc": 0.57531, "loss_cls": 3.86675, "loss": 3.86675, "time": 0.81697} +{"mode": "train", "epoch": 78, "iter": 3400, "lr": 0.04696, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32594, "top5_acc": 0.58203, "loss_cls": 3.80611, "loss": 3.80611, "time": 0.81385} +{"mode": "train", "epoch": 78, "iter": 3500, "lr": 0.04693, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33906, "top5_acc": 0.60062, "loss_cls": 3.73847, "loss": 3.73847, "time": 0.81395} +{"mode": "train", "epoch": 78, "iter": 3600, "lr": 0.0469, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32281, "top5_acc": 0.59578, "loss_cls": 3.81417, "loss": 3.81417, "time": 0.82136} +{"mode": "train", "epoch": 78, "iter": 3700, "lr": 0.04687, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32406, "top5_acc": 0.57812, "loss_cls": 3.85996, "loss": 3.85996, "time": 0.82714} +{"mode": "val", "epoch": 78, "iter": 309, "lr": 0.04686, "top1_acc": 0.26202, "top5_acc": 0.51213, "mean_class_accuracy": 0.26187} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.04683, "memory": 15990, "data_time": 1.34364, "top1_acc": 0.34156, "top5_acc": 0.60188, "loss_cls": 3.72623, "loss": 3.72623, "time": 2.33366} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.0468, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33859, "top5_acc": 0.60172, "loss_cls": 3.74473, "loss": 3.74473, "time": 0.82305} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.04678, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33328, "top5_acc": 0.60062, "loss_cls": 3.75407, "loss": 3.75407, "time": 0.82128} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.04675, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32875, "top5_acc": 0.60656, "loss_cls": 3.75889, "loss": 3.75889, "time": 0.81936} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.04672, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3375, "top5_acc": 0.59672, "loss_cls": 3.76435, "loss": 3.76435, "time": 0.81938} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.04669, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33641, "top5_acc": 0.59219, "loss_cls": 3.77623, "loss": 3.77623, "time": 0.81955} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.04667, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33641, "top5_acc": 0.58969, "loss_cls": 3.80224, "loss": 3.80224, "time": 0.816} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.04664, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34406, "top5_acc": 0.59469, "loss_cls": 3.76346, "loss": 3.76346, "time": 0.81615} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.04661, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32719, "top5_acc": 0.58766, "loss_cls": 3.82846, "loss": 3.82846, "time": 0.81462} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.04658, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33234, "top5_acc": 0.59188, "loss_cls": 3.8004, "loss": 3.8004, "time": 0.81665} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.04655, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.315, "top5_acc": 0.58469, "loss_cls": 3.82437, "loss": 3.82437, "time": 0.81361} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.04653, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34188, "top5_acc": 0.59578, "loss_cls": 3.75858, "loss": 3.75858, "time": 0.81396} +{"mode": "train", "epoch": 79, "iter": 1300, "lr": 0.0465, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32828, "top5_acc": 0.60109, "loss_cls": 3.75398, "loss": 3.75398, "time": 0.81522} +{"mode": "train", "epoch": 79, "iter": 1400, "lr": 0.04647, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32266, "top5_acc": 0.59375, "loss_cls": 3.81389, "loss": 3.81389, "time": 0.82145} +{"mode": "train", "epoch": 79, "iter": 1500, "lr": 0.04644, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32625, "top5_acc": 0.58734, "loss_cls": 3.79915, "loss": 3.79915, "time": 0.81849} +{"mode": "train", "epoch": 79, "iter": 1600, "lr": 0.04641, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32875, "top5_acc": 0.5875, "loss_cls": 3.78425, "loss": 3.78425, "time": 0.82296} +{"mode": "train", "epoch": 79, "iter": 1700, "lr": 0.04639, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32203, "top5_acc": 0.58547, "loss_cls": 3.85875, "loss": 3.85875, "time": 0.81828} +{"mode": "train", "epoch": 79, "iter": 1800, "lr": 0.04636, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32688, "top5_acc": 0.59594, "loss_cls": 3.77522, "loss": 3.77522, "time": 0.83257} +{"mode": "train", "epoch": 79, "iter": 1900, "lr": 0.04633, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32156, "top5_acc": 0.58703, "loss_cls": 3.83223, "loss": 3.83223, "time": 0.82165} +{"mode": "train", "epoch": 79, "iter": 2000, "lr": 0.0463, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32906, "top5_acc": 0.5925, "loss_cls": 3.7978, "loss": 3.7978, "time": 0.8234} +{"mode": "train", "epoch": 79, "iter": 2100, "lr": 0.04628, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32266, "top5_acc": 0.58953, "loss_cls": 3.81746, "loss": 3.81746, "time": 0.82238} +{"mode": "train", "epoch": 79, "iter": 2200, "lr": 0.04625, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.3275, "top5_acc": 0.58641, "loss_cls": 3.81591, "loss": 3.81591, "time": 0.82678} +{"mode": "train", "epoch": 79, "iter": 2300, "lr": 0.04622, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32359, "top5_acc": 0.58938, "loss_cls": 3.81272, "loss": 3.81272, "time": 0.82056} +{"mode": "train", "epoch": 79, "iter": 2400, "lr": 0.04619, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32953, "top5_acc": 0.59516, "loss_cls": 3.78458, "loss": 3.78458, "time": 0.81259} +{"mode": "train", "epoch": 79, "iter": 2500, "lr": 0.04616, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32328, "top5_acc": 0.59391, "loss_cls": 3.82232, "loss": 3.82232, "time": 0.81323} +{"mode": "train", "epoch": 79, "iter": 2600, "lr": 0.04614, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32156, "top5_acc": 0.58906, "loss_cls": 3.85182, "loss": 3.85182, "time": 0.81443} +{"mode": "train", "epoch": 79, "iter": 2700, "lr": 0.04611, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33359, "top5_acc": 0.60016, "loss_cls": 3.75865, "loss": 3.75865, "time": 0.81533} +{"mode": "train", "epoch": 79, "iter": 2800, "lr": 0.04608, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32938, "top5_acc": 0.58766, "loss_cls": 3.83112, "loss": 3.83112, "time": 0.82353} +{"mode": "train", "epoch": 79, "iter": 2900, "lr": 0.04605, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33656, "top5_acc": 0.60031, "loss_cls": 3.74897, "loss": 3.74897, "time": 0.81535} +{"mode": "train", "epoch": 79, "iter": 3000, "lr": 0.04602, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32547, "top5_acc": 0.59453, "loss_cls": 3.78282, "loss": 3.78282, "time": 0.8178} +{"mode": "train", "epoch": 79, "iter": 3100, "lr": 0.046, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33125, "top5_acc": 0.58891, "loss_cls": 3.82273, "loss": 3.82273, "time": 0.8153} +{"mode": "train", "epoch": 79, "iter": 3200, "lr": 0.04597, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33047, "top5_acc": 0.59938, "loss_cls": 3.76464, "loss": 3.76464, "time": 0.81494} +{"mode": "train", "epoch": 79, "iter": 3300, "lr": 0.04594, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33641, "top5_acc": 0.59266, "loss_cls": 3.76408, "loss": 3.76408, "time": 0.81828} +{"mode": "train", "epoch": 79, "iter": 3400, "lr": 0.04591, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32734, "top5_acc": 0.59188, "loss_cls": 3.79367, "loss": 3.79367, "time": 0.81689} +{"mode": "train", "epoch": 79, "iter": 3500, "lr": 0.04588, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32672, "top5_acc": 0.58641, "loss_cls": 3.80035, "loss": 3.80035, "time": 0.8154} +{"mode": "train", "epoch": 79, "iter": 3600, "lr": 0.04586, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32141, "top5_acc": 0.59344, "loss_cls": 3.82314, "loss": 3.82314, "time": 0.81849} +{"mode": "train", "epoch": 79, "iter": 3700, "lr": 0.04583, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32719, "top5_acc": 0.59688, "loss_cls": 3.77758, "loss": 3.77758, "time": 0.818} +{"mode": "val", "epoch": 79, "iter": 309, "lr": 0.04582, "top1_acc": 0.27985, "top5_acc": 0.5333, "mean_class_accuracy": 0.27954} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.04579, "memory": 15990, "data_time": 1.31325, "top1_acc": 0.33406, "top5_acc": 0.60766, "loss_cls": 3.71082, "loss": 3.71082, "time": 2.30539} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.04576, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34188, "top5_acc": 0.60312, "loss_cls": 3.73171, "loss": 3.73171, "time": 0.82415} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.04573, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33016, "top5_acc": 0.59703, "loss_cls": 3.77057, "loss": 3.77057, "time": 0.82293} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.0457, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33047, "top5_acc": 0.60062, "loss_cls": 3.77422, "loss": 3.77422, "time": 0.81974} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.04568, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34016, "top5_acc": 0.60578, "loss_cls": 3.74181, "loss": 3.74181, "time": 0.81728} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.04565, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32453, "top5_acc": 0.58109, "loss_cls": 3.84356, "loss": 3.84356, "time": 0.81044} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.04562, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32094, "top5_acc": 0.58938, "loss_cls": 3.82174, "loss": 3.82174, "time": 0.81496} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.04559, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33672, "top5_acc": 0.59984, "loss_cls": 3.74418, "loss": 3.74418, "time": 0.81545} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.04557, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34047, "top5_acc": 0.59703, "loss_cls": 3.75915, "loss": 3.75915, "time": 0.81579} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.04554, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32422, "top5_acc": 0.59344, "loss_cls": 3.78169, "loss": 3.78169, "time": 0.81568} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.04551, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33953, "top5_acc": 0.59703, "loss_cls": 3.75489, "loss": 3.75489, "time": 0.81395} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.04548, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33922, "top5_acc": 0.60094, "loss_cls": 3.73321, "loss": 3.73321, "time": 0.81912} +{"mode": "train", "epoch": 80, "iter": 1300, "lr": 0.04545, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33203, "top5_acc": 0.60344, "loss_cls": 3.75882, "loss": 3.75882, "time": 0.81658} +{"mode": "train", "epoch": 80, "iter": 1400, "lr": 0.04543, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34609, "top5_acc": 0.61375, "loss_cls": 3.67501, "loss": 3.67501, "time": 0.8166} +{"mode": "train", "epoch": 80, "iter": 1500, "lr": 0.0454, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32891, "top5_acc": 0.58547, "loss_cls": 3.7882, "loss": 3.7882, "time": 0.81472} +{"mode": "train", "epoch": 80, "iter": 1600, "lr": 0.04537, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34375, "top5_acc": 0.59969, "loss_cls": 3.75871, "loss": 3.75871, "time": 0.82015} +{"mode": "train", "epoch": 80, "iter": 1700, "lr": 0.04534, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33875, "top5_acc": 0.60172, "loss_cls": 3.75156, "loss": 3.75156, "time": 0.81168} +{"mode": "train", "epoch": 80, "iter": 1800, "lr": 0.04532, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32953, "top5_acc": 0.59141, "loss_cls": 3.81573, "loss": 3.81573, "time": 0.82251} +{"mode": "train", "epoch": 80, "iter": 1900, "lr": 0.04529, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33953, "top5_acc": 0.59922, "loss_cls": 3.7553, "loss": 3.7553, "time": 0.81975} +{"mode": "train", "epoch": 80, "iter": 2000, "lr": 0.04526, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32344, "top5_acc": 0.58266, "loss_cls": 3.80302, "loss": 3.80302, "time": 0.82392} +{"mode": "train", "epoch": 80, "iter": 2100, "lr": 0.04523, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33641, "top5_acc": 0.59141, "loss_cls": 3.78392, "loss": 3.78392, "time": 0.82225} +{"mode": "train", "epoch": 80, "iter": 2200, "lr": 0.0452, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33078, "top5_acc": 0.58766, "loss_cls": 3.80966, "loss": 3.80966, "time": 0.81974} +{"mode": "train", "epoch": 80, "iter": 2300, "lr": 0.04518, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32141, "top5_acc": 0.58672, "loss_cls": 3.84593, "loss": 3.84593, "time": 0.81721} +{"mode": "train", "epoch": 80, "iter": 2400, "lr": 0.04515, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34031, "top5_acc": 0.59438, "loss_cls": 3.77392, "loss": 3.77392, "time": 0.81575} +{"mode": "train", "epoch": 80, "iter": 2500, "lr": 0.04512, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33188, "top5_acc": 0.58469, "loss_cls": 3.80975, "loss": 3.80975, "time": 0.81433} +{"mode": "train", "epoch": 80, "iter": 2600, "lr": 0.04509, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33297, "top5_acc": 0.59281, "loss_cls": 3.76851, "loss": 3.76851, "time": 0.8169} +{"mode": "train", "epoch": 80, "iter": 2700, "lr": 0.04506, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32172, "top5_acc": 0.58781, "loss_cls": 3.83157, "loss": 3.83157, "time": 0.81706} +{"mode": "train", "epoch": 80, "iter": 2800, "lr": 0.04504, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32609, "top5_acc": 0.59641, "loss_cls": 3.75301, "loss": 3.75301, "time": 0.82309} +{"mode": "train", "epoch": 80, "iter": 2900, "lr": 0.04501, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33047, "top5_acc": 0.59609, "loss_cls": 3.79489, "loss": 3.79489, "time": 0.81678} +{"mode": "train", "epoch": 80, "iter": 3000, "lr": 0.04498, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33188, "top5_acc": 0.58391, "loss_cls": 3.78908, "loss": 3.78908, "time": 0.81861} +{"mode": "train", "epoch": 80, "iter": 3100, "lr": 0.04495, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33594, "top5_acc": 0.595, "loss_cls": 3.76645, "loss": 3.76645, "time": 0.81262} +{"mode": "train", "epoch": 80, "iter": 3200, "lr": 0.04493, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32547, "top5_acc": 0.59266, "loss_cls": 3.79858, "loss": 3.79858, "time": 0.81498} +{"mode": "train", "epoch": 80, "iter": 3300, "lr": 0.0449, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33281, "top5_acc": 0.58734, "loss_cls": 3.81444, "loss": 3.81444, "time": 0.81775} +{"mode": "train", "epoch": 80, "iter": 3400, "lr": 0.04487, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33047, "top5_acc": 0.60359, "loss_cls": 3.76607, "loss": 3.76607, "time": 0.81512} +{"mode": "train", "epoch": 80, "iter": 3500, "lr": 0.04484, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32469, "top5_acc": 0.58422, "loss_cls": 3.81626, "loss": 3.81626, "time": 0.81403} +{"mode": "train", "epoch": 80, "iter": 3600, "lr": 0.04481, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32219, "top5_acc": 0.58047, "loss_cls": 3.83921, "loss": 3.83921, "time": 0.81914} +{"mode": "train", "epoch": 80, "iter": 3700, "lr": 0.04479, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32875, "top5_acc": 0.59516, "loss_cls": 3.79664, "loss": 3.79664, "time": 0.82364} +{"mode": "val", "epoch": 80, "iter": 309, "lr": 0.04477, "top1_acc": 0.27215, "top5_acc": 0.53087, "mean_class_accuracy": 0.27219} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.04475, "memory": 15990, "data_time": 1.27616, "top1_acc": 0.35391, "top5_acc": 0.60453, "loss_cls": 3.72044, "loss": 3.72044, "time": 2.26471} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.04472, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34188, "top5_acc": 0.61609, "loss_cls": 3.67935, "loss": 3.67935, "time": 0.83298} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.04469, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33625, "top5_acc": 0.60656, "loss_cls": 3.71913, "loss": 3.71913, "time": 0.83177} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.04466, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34797, "top5_acc": 0.60453, "loss_cls": 3.71848, "loss": 3.71848, "time": 0.82597} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.04463, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33812, "top5_acc": 0.59391, "loss_cls": 3.76309, "loss": 3.76309, "time": 0.81943} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.04461, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33016, "top5_acc": 0.59625, "loss_cls": 3.78631, "loss": 3.78631, "time": 0.81736} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.04458, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33188, "top5_acc": 0.59812, "loss_cls": 3.77025, "loss": 3.77025, "time": 0.82071} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.04455, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33141, "top5_acc": 0.5925, "loss_cls": 3.78381, "loss": 3.78381, "time": 0.81647} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.04452, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33656, "top5_acc": 0.59453, "loss_cls": 3.75831, "loss": 3.75831, "time": 0.8199} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.0445, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34078, "top5_acc": 0.60625, "loss_cls": 3.73136, "loss": 3.73136, "time": 0.8196} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.04447, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33719, "top5_acc": 0.59969, "loss_cls": 3.74194, "loss": 3.74194, "time": 0.81411} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.04444, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32734, "top5_acc": 0.59375, "loss_cls": 3.77872, "loss": 3.77872, "time": 0.82088} +{"mode": "train", "epoch": 81, "iter": 1300, "lr": 0.04441, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32391, "top5_acc": 0.58656, "loss_cls": 3.81468, "loss": 3.81468, "time": 0.8172} +{"mode": "train", "epoch": 81, "iter": 1400, "lr": 0.04438, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33359, "top5_acc": 0.60391, "loss_cls": 3.74624, "loss": 3.74624, "time": 0.81352} +{"mode": "train", "epoch": 81, "iter": 1500, "lr": 0.04436, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33234, "top5_acc": 0.59875, "loss_cls": 3.77947, "loss": 3.77947, "time": 0.8161} +{"mode": "train", "epoch": 81, "iter": 1600, "lr": 0.04433, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32688, "top5_acc": 0.58594, "loss_cls": 3.8053, "loss": 3.8053, "time": 0.81602} +{"mode": "train", "epoch": 81, "iter": 1700, "lr": 0.0443, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32812, "top5_acc": 0.59828, "loss_cls": 3.77928, "loss": 3.77928, "time": 0.81405} +{"mode": "train", "epoch": 81, "iter": 1800, "lr": 0.04427, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33109, "top5_acc": 0.59031, "loss_cls": 3.78984, "loss": 3.78984, "time": 0.81976} +{"mode": "train", "epoch": 81, "iter": 1900, "lr": 0.04425, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32844, "top5_acc": 0.59391, "loss_cls": 3.78329, "loss": 3.78329, "time": 0.81613} +{"mode": "train", "epoch": 81, "iter": 2000, "lr": 0.04422, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33094, "top5_acc": 0.59125, "loss_cls": 3.77509, "loss": 3.77509, "time": 0.82993} +{"mode": "train", "epoch": 81, "iter": 2100, "lr": 0.04419, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33391, "top5_acc": 0.60078, "loss_cls": 3.7524, "loss": 3.7524, "time": 0.81838} +{"mode": "train", "epoch": 81, "iter": 2200, "lr": 0.04416, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32797, "top5_acc": 0.59969, "loss_cls": 3.77316, "loss": 3.77316, "time": 0.81532} +{"mode": "train", "epoch": 81, "iter": 2300, "lr": 0.04413, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33234, "top5_acc": 0.59391, "loss_cls": 3.76922, "loss": 3.76922, "time": 0.82361} +{"mode": "train", "epoch": 81, "iter": 2400, "lr": 0.04411, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34266, "top5_acc": 0.61094, "loss_cls": 3.70667, "loss": 3.70667, "time": 0.81791} +{"mode": "train", "epoch": 81, "iter": 2500, "lr": 0.04408, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33844, "top5_acc": 0.60016, "loss_cls": 3.73228, "loss": 3.73228, "time": 0.82612} +{"mode": "train", "epoch": 81, "iter": 2600, "lr": 0.04405, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33531, "top5_acc": 0.59625, "loss_cls": 3.76667, "loss": 3.76667, "time": 0.8228} +{"mode": "train", "epoch": 81, "iter": 2700, "lr": 0.04402, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33469, "top5_acc": 0.59203, "loss_cls": 3.78256, "loss": 3.78256, "time": 0.81375} +{"mode": "train", "epoch": 81, "iter": 2800, "lr": 0.044, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32375, "top5_acc": 0.58547, "loss_cls": 3.84006, "loss": 3.84006, "time": 0.82197} +{"mode": "train", "epoch": 81, "iter": 2900, "lr": 0.04397, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33203, "top5_acc": 0.59703, "loss_cls": 3.75601, "loss": 3.75601, "time": 0.81299} +{"mode": "train", "epoch": 81, "iter": 3000, "lr": 0.04394, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3325, "top5_acc": 0.59188, "loss_cls": 3.77259, "loss": 3.77259, "time": 0.81459} +{"mode": "train", "epoch": 81, "iter": 3100, "lr": 0.04391, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33438, "top5_acc": 0.59109, "loss_cls": 3.78011, "loss": 3.78011, "time": 0.82515} +{"mode": "train", "epoch": 81, "iter": 3200, "lr": 0.04389, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33719, "top5_acc": 0.59891, "loss_cls": 3.74823, "loss": 3.74823, "time": 0.8206} +{"mode": "train", "epoch": 81, "iter": 3300, "lr": 0.04386, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32406, "top5_acc": 0.58547, "loss_cls": 3.81854, "loss": 3.81854, "time": 0.81979} +{"mode": "train", "epoch": 81, "iter": 3400, "lr": 0.04383, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34203, "top5_acc": 0.60812, "loss_cls": 3.71721, "loss": 3.71721, "time": 0.82024} +{"mode": "train", "epoch": 81, "iter": 3500, "lr": 0.0438, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33578, "top5_acc": 0.59953, "loss_cls": 3.78784, "loss": 3.78784, "time": 0.81398} +{"mode": "train", "epoch": 81, "iter": 3600, "lr": 0.04377, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33578, "top5_acc": 0.59438, "loss_cls": 3.76407, "loss": 3.76407, "time": 0.8221} +{"mode": "train", "epoch": 81, "iter": 3700, "lr": 0.04375, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31531, "top5_acc": 0.585, "loss_cls": 3.82505, "loss": 3.82505, "time": 0.81941} +{"mode": "val", "epoch": 81, "iter": 309, "lr": 0.04373, "top1_acc": 0.26399, "top5_acc": 0.51664, "mean_class_accuracy": 0.26392} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.04371, "memory": 15990, "data_time": 1.30856, "top1_acc": 0.34938, "top5_acc": 0.60516, "loss_cls": 3.70285, "loss": 3.70285, "time": 2.29468} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.04368, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33906, "top5_acc": 0.60328, "loss_cls": 3.7256, "loss": 3.7256, "time": 0.82629} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.04365, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33875, "top5_acc": 0.59828, "loss_cls": 3.72599, "loss": 3.72599, "time": 0.82264} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.04362, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34547, "top5_acc": 0.61125, "loss_cls": 3.69256, "loss": 3.69256, "time": 0.82259} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.04359, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33891, "top5_acc": 0.60656, "loss_cls": 3.71437, "loss": 3.71437, "time": 0.8187} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.04357, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33781, "top5_acc": 0.59812, "loss_cls": 3.75522, "loss": 3.75522, "time": 0.81926} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.04354, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34625, "top5_acc": 0.60938, "loss_cls": 3.69614, "loss": 3.69614, "time": 0.81244} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.04351, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34219, "top5_acc": 0.59828, "loss_cls": 3.74227, "loss": 3.74227, "time": 0.82188} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.04348, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32797, "top5_acc": 0.59438, "loss_cls": 3.79603, "loss": 3.79603, "time": 0.81491} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.04346, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33812, "top5_acc": 0.59734, "loss_cls": 3.7359, "loss": 3.7359, "time": 0.81872} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.04343, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33875, "top5_acc": 0.58781, "loss_cls": 3.77937, "loss": 3.77937, "time": 0.82279} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.0434, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33281, "top5_acc": 0.6, "loss_cls": 3.75725, "loss": 3.75725, "time": 0.82181} +{"mode": "train", "epoch": 82, "iter": 1300, "lr": 0.04337, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34047, "top5_acc": 0.60359, "loss_cls": 3.73647, "loss": 3.73647, "time": 0.82014} +{"mode": "train", "epoch": 82, "iter": 1400, "lr": 0.04335, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33594, "top5_acc": 0.60047, "loss_cls": 3.7283, "loss": 3.7283, "time": 0.82349} +{"mode": "train", "epoch": 82, "iter": 1500, "lr": 0.04332, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33656, "top5_acc": 0.58781, "loss_cls": 3.77413, "loss": 3.77413, "time": 0.82402} +{"mode": "train", "epoch": 82, "iter": 1600, "lr": 0.04329, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32594, "top5_acc": 0.59016, "loss_cls": 3.78422, "loss": 3.78422, "time": 0.82988} +{"mode": "train", "epoch": 82, "iter": 1700, "lr": 0.04326, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34203, "top5_acc": 0.59234, "loss_cls": 3.75555, "loss": 3.75555, "time": 0.8203} +{"mode": "train", "epoch": 82, "iter": 1800, "lr": 0.04323, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34031, "top5_acc": 0.6, "loss_cls": 3.74877, "loss": 3.74877, "time": 0.82161} +{"mode": "train", "epoch": 82, "iter": 1900, "lr": 0.04321, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33281, "top5_acc": 0.59672, "loss_cls": 3.76316, "loss": 3.76316, "time": 0.82807} +{"mode": "train", "epoch": 82, "iter": 2000, "lr": 0.04318, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3325, "top5_acc": 0.59906, "loss_cls": 3.78388, "loss": 3.78388, "time": 0.83167} +{"mode": "train", "epoch": 82, "iter": 2100, "lr": 0.04315, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33562, "top5_acc": 0.60125, "loss_cls": 3.74018, "loss": 3.74018, "time": 0.83215} +{"mode": "train", "epoch": 82, "iter": 2200, "lr": 0.04312, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35109, "top5_acc": 0.60969, "loss_cls": 3.69055, "loss": 3.69055, "time": 0.83079} +{"mode": "train", "epoch": 82, "iter": 2300, "lr": 0.0431, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33844, "top5_acc": 0.59891, "loss_cls": 3.74518, "loss": 3.74518, "time": 0.83171} +{"mode": "train", "epoch": 82, "iter": 2400, "lr": 0.04307, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.3225, "top5_acc": 0.59, "loss_cls": 3.80706, "loss": 3.80706, "time": 0.83323} +{"mode": "train", "epoch": 82, "iter": 2500, "lr": 0.04304, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34438, "top5_acc": 0.60047, "loss_cls": 3.72433, "loss": 3.72433, "time": 0.83352} +{"mode": "train", "epoch": 82, "iter": 2600, "lr": 0.04301, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33531, "top5_acc": 0.59266, "loss_cls": 3.77156, "loss": 3.77156, "time": 0.83723} +{"mode": "train", "epoch": 82, "iter": 2700, "lr": 0.04299, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32328, "top5_acc": 0.59234, "loss_cls": 3.77251, "loss": 3.77251, "time": 0.82911} +{"mode": "train", "epoch": 82, "iter": 2800, "lr": 0.04296, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33281, "top5_acc": 0.59062, "loss_cls": 3.79706, "loss": 3.79706, "time": 0.83012} +{"mode": "train", "epoch": 82, "iter": 2900, "lr": 0.04293, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33094, "top5_acc": 0.60172, "loss_cls": 3.75099, "loss": 3.75099, "time": 0.82498} +{"mode": "train", "epoch": 82, "iter": 3000, "lr": 0.0429, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34297, "top5_acc": 0.60703, "loss_cls": 3.72689, "loss": 3.72689, "time": 0.81936} +{"mode": "train", "epoch": 82, "iter": 3100, "lr": 0.04287, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33734, "top5_acc": 0.59906, "loss_cls": 3.77207, "loss": 3.77207, "time": 0.82461} +{"mode": "train", "epoch": 82, "iter": 3200, "lr": 0.04285, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32688, "top5_acc": 0.59562, "loss_cls": 3.80242, "loss": 3.80242, "time": 0.83127} +{"mode": "train", "epoch": 82, "iter": 3300, "lr": 0.04282, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32172, "top5_acc": 0.58188, "loss_cls": 3.83322, "loss": 3.83322, "time": 0.83299} +{"mode": "train", "epoch": 82, "iter": 3400, "lr": 0.04279, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3475, "top5_acc": 0.61219, "loss_cls": 3.70265, "loss": 3.70265, "time": 0.83345} +{"mode": "train", "epoch": 82, "iter": 3500, "lr": 0.04276, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32484, "top5_acc": 0.59859, "loss_cls": 3.7772, "loss": 3.7772, "time": 0.83495} +{"mode": "train", "epoch": 82, "iter": 3600, "lr": 0.04274, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33844, "top5_acc": 0.60219, "loss_cls": 3.74211, "loss": 3.74211, "time": 0.83235} +{"mode": "train", "epoch": 82, "iter": 3700, "lr": 0.04271, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33875, "top5_acc": 0.60438, "loss_cls": 3.76179, "loss": 3.76179, "time": 0.83072} +{"mode": "val", "epoch": 82, "iter": 309, "lr": 0.0427, "top1_acc": 0.29043, "top5_acc": 0.53756, "mean_class_accuracy": 0.29027} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.04267, "memory": 15990, "data_time": 1.27476, "top1_acc": 0.34812, "top5_acc": 0.61234, "loss_cls": 3.68493, "loss": 3.68493, "time": 2.26586} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.04264, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34109, "top5_acc": 0.59984, "loss_cls": 3.75194, "loss": 3.75194, "time": 0.83264} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.04261, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35562, "top5_acc": 0.62187, "loss_cls": 3.62702, "loss": 3.62702, "time": 0.82896} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.04259, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34047, "top5_acc": 0.60125, "loss_cls": 3.72765, "loss": 3.72765, "time": 0.82065} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.04256, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34, "top5_acc": 0.60234, "loss_cls": 3.70862, "loss": 3.70862, "time": 0.82458} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.04253, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34344, "top5_acc": 0.60594, "loss_cls": 3.72987, "loss": 3.72987, "time": 0.81981} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.0425, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33234, "top5_acc": 0.59812, "loss_cls": 3.74994, "loss": 3.74994, "time": 0.82128} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.04247, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33188, "top5_acc": 0.60156, "loss_cls": 3.72221, "loss": 3.72221, "time": 0.8214} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.04245, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33203, "top5_acc": 0.60922, "loss_cls": 3.72773, "loss": 3.72773, "time": 0.82807} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.04242, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33719, "top5_acc": 0.60516, "loss_cls": 3.71701, "loss": 3.71701, "time": 0.8179} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.04239, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34234, "top5_acc": 0.60516, "loss_cls": 3.68618, "loss": 3.68618, "time": 0.82127} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.04236, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32734, "top5_acc": 0.58766, "loss_cls": 3.79675, "loss": 3.79675, "time": 0.82302} +{"mode": "train", "epoch": 83, "iter": 1300, "lr": 0.04234, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34625, "top5_acc": 0.60812, "loss_cls": 3.70875, "loss": 3.70875, "time": 0.82033} +{"mode": "train", "epoch": 83, "iter": 1400, "lr": 0.04231, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33766, "top5_acc": 0.6025, "loss_cls": 3.74042, "loss": 3.74042, "time": 0.82268} +{"mode": "train", "epoch": 83, "iter": 1500, "lr": 0.04228, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33094, "top5_acc": 0.59297, "loss_cls": 3.75614, "loss": 3.75614, "time": 0.81909} +{"mode": "train", "epoch": 83, "iter": 1600, "lr": 0.04225, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34375, "top5_acc": 0.59562, "loss_cls": 3.7573, "loss": 3.7573, "time": 0.82136} +{"mode": "train", "epoch": 83, "iter": 1700, "lr": 0.04223, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33734, "top5_acc": 0.60047, "loss_cls": 3.75277, "loss": 3.75277, "time": 0.82526} +{"mode": "train", "epoch": 83, "iter": 1800, "lr": 0.0422, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33906, "top5_acc": 0.60422, "loss_cls": 3.74174, "loss": 3.74174, "time": 0.8228} +{"mode": "train", "epoch": 83, "iter": 1900, "lr": 0.04217, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33734, "top5_acc": 0.59281, "loss_cls": 3.78042, "loss": 3.78042, "time": 0.82717} +{"mode": "train", "epoch": 83, "iter": 2000, "lr": 0.04214, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.34547, "top5_acc": 0.60328, "loss_cls": 3.73105, "loss": 3.73105, "time": 0.82574} +{"mode": "train", "epoch": 83, "iter": 2100, "lr": 0.04212, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34531, "top5_acc": 0.5975, "loss_cls": 3.72464, "loss": 3.72464, "time": 0.82501} +{"mode": "train", "epoch": 83, "iter": 2200, "lr": 0.04209, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34141, "top5_acc": 0.60094, "loss_cls": 3.72121, "loss": 3.72121, "time": 0.82312} +{"mode": "train", "epoch": 83, "iter": 2300, "lr": 0.04206, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32719, "top5_acc": 0.5875, "loss_cls": 3.80173, "loss": 3.80173, "time": 0.8224} +{"mode": "train", "epoch": 83, "iter": 2400, "lr": 0.04203, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34156, "top5_acc": 0.60078, "loss_cls": 3.76334, "loss": 3.76334, "time": 0.81927} +{"mode": "train", "epoch": 83, "iter": 2500, "lr": 0.04201, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33141, "top5_acc": 0.60281, "loss_cls": 3.75677, "loss": 3.75677, "time": 0.81359} +{"mode": "train", "epoch": 83, "iter": 2600, "lr": 0.04198, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33906, "top5_acc": 0.60438, "loss_cls": 3.71324, "loss": 3.71324, "time": 0.82154} +{"mode": "train", "epoch": 83, "iter": 2700, "lr": 0.04195, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33375, "top5_acc": 0.59797, "loss_cls": 3.75429, "loss": 3.75429, "time": 0.81639} +{"mode": "train", "epoch": 83, "iter": 2800, "lr": 0.04192, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.33438, "top5_acc": 0.60422, "loss_cls": 3.74281, "loss": 3.74281, "time": 0.8289} +{"mode": "train", "epoch": 83, "iter": 2900, "lr": 0.0419, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33516, "top5_acc": 0.60016, "loss_cls": 3.76368, "loss": 3.76368, "time": 0.81355} +{"mode": "train", "epoch": 83, "iter": 3000, "lr": 0.04187, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34203, "top5_acc": 0.605, "loss_cls": 3.76898, "loss": 3.76898, "time": 0.81864} +{"mode": "train", "epoch": 83, "iter": 3100, "lr": 0.04184, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33438, "top5_acc": 0.58969, "loss_cls": 3.75059, "loss": 3.75059, "time": 0.82658} +{"mode": "train", "epoch": 83, "iter": 3200, "lr": 0.04181, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33828, "top5_acc": 0.58828, "loss_cls": 3.8007, "loss": 3.8007, "time": 0.82131} +{"mode": "train", "epoch": 83, "iter": 3300, "lr": 0.04178, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34141, "top5_acc": 0.60531, "loss_cls": 3.7264, "loss": 3.7264, "time": 0.82035} +{"mode": "train", "epoch": 83, "iter": 3400, "lr": 0.04176, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33297, "top5_acc": 0.59922, "loss_cls": 3.76862, "loss": 3.76862, "time": 0.82256} +{"mode": "train", "epoch": 83, "iter": 3500, "lr": 0.04173, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33625, "top5_acc": 0.59969, "loss_cls": 3.75416, "loss": 3.75416, "time": 0.81636} +{"mode": "train", "epoch": 83, "iter": 3600, "lr": 0.0417, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34078, "top5_acc": 0.60078, "loss_cls": 3.75979, "loss": 3.75979, "time": 0.83379} +{"mode": "train", "epoch": 83, "iter": 3700, "lr": 0.04167, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33062, "top5_acc": 0.59703, "loss_cls": 3.76114, "loss": 3.76114, "time": 0.82559} +{"mode": "val", "epoch": 83, "iter": 309, "lr": 0.04166, "top1_acc": 0.28435, "top5_acc": 0.54242, "mean_class_accuracy": 0.28414} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.04163, "memory": 15990, "data_time": 1.24333, "top1_acc": 0.33969, "top5_acc": 0.60969, "loss_cls": 3.69357, "loss": 3.69357, "time": 2.23105} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.04161, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34172, "top5_acc": 0.60156, "loss_cls": 3.72575, "loss": 3.72575, "time": 0.82226} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.04158, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34562, "top5_acc": 0.60672, "loss_cls": 3.70252, "loss": 3.70252, "time": 0.82521} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.04155, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34594, "top5_acc": 0.59922, "loss_cls": 3.70385, "loss": 3.70385, "time": 0.82479} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.04152, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35219, "top5_acc": 0.61609, "loss_cls": 3.65169, "loss": 3.65169, "time": 0.81604} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.0415, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34484, "top5_acc": 0.61047, "loss_cls": 3.67349, "loss": 3.67349, "time": 0.82442} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.04147, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33844, "top5_acc": 0.60453, "loss_cls": 3.72472, "loss": 3.72472, "time": 0.8238} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.04144, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34594, "top5_acc": 0.60281, "loss_cls": 3.69229, "loss": 3.69229, "time": 0.82157} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.04141, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33672, "top5_acc": 0.60875, "loss_cls": 3.71081, "loss": 3.71081, "time": 0.81886} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.04139, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35078, "top5_acc": 0.60859, "loss_cls": 3.69308, "loss": 3.69308, "time": 0.82079} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.04136, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34688, "top5_acc": 0.605, "loss_cls": 3.71356, "loss": 3.71356, "time": 0.82165} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.04133, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34359, "top5_acc": 0.60625, "loss_cls": 3.69804, "loss": 3.69804, "time": 0.81888} +{"mode": "train", "epoch": 84, "iter": 1300, "lr": 0.0413, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35016, "top5_acc": 0.60609, "loss_cls": 3.70099, "loss": 3.70099, "time": 0.82034} +{"mode": "train", "epoch": 84, "iter": 1400, "lr": 0.04128, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33609, "top5_acc": 0.60047, "loss_cls": 3.75566, "loss": 3.75566, "time": 0.82361} +{"mode": "train", "epoch": 84, "iter": 1500, "lr": 0.04125, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33328, "top5_acc": 0.60203, "loss_cls": 3.75866, "loss": 3.75866, "time": 0.82309} +{"mode": "train", "epoch": 84, "iter": 1600, "lr": 0.04122, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33438, "top5_acc": 0.59969, "loss_cls": 3.72274, "loss": 3.72274, "time": 0.81729} +{"mode": "train", "epoch": 84, "iter": 1700, "lr": 0.04119, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34375, "top5_acc": 0.60719, "loss_cls": 3.69007, "loss": 3.69007, "time": 0.81887} +{"mode": "train", "epoch": 84, "iter": 1800, "lr": 0.04117, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34047, "top5_acc": 0.58828, "loss_cls": 3.7744, "loss": 3.7744, "time": 0.82167} +{"mode": "train", "epoch": 84, "iter": 1900, "lr": 0.04114, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33594, "top5_acc": 0.60844, "loss_cls": 3.72862, "loss": 3.72862, "time": 0.83521} +{"mode": "train", "epoch": 84, "iter": 2000, "lr": 0.04111, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32562, "top5_acc": 0.59703, "loss_cls": 3.74962, "loss": 3.74962, "time": 0.83073} +{"mode": "train", "epoch": 84, "iter": 2100, "lr": 0.04108, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33891, "top5_acc": 0.59938, "loss_cls": 3.74615, "loss": 3.74615, "time": 0.82448} +{"mode": "train", "epoch": 84, "iter": 2200, "lr": 0.04106, "memory": 15990, "data_time": 0.00078, "top1_acc": 0.33391, "top5_acc": 0.59922, "loss_cls": 3.77463, "loss": 3.77463, "time": 0.83403} +{"mode": "train", "epoch": 84, "iter": 2300, "lr": 0.04103, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35234, "top5_acc": 0.60562, "loss_cls": 3.70032, "loss": 3.70032, "time": 0.83526} +{"mode": "train", "epoch": 84, "iter": 2400, "lr": 0.041, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32547, "top5_acc": 0.5875, "loss_cls": 3.80637, "loss": 3.80637, "time": 0.83397} +{"mode": "train", "epoch": 84, "iter": 2500, "lr": 0.04097, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33703, "top5_acc": 0.60047, "loss_cls": 3.77529, "loss": 3.77529, "time": 0.83208} +{"mode": "train", "epoch": 84, "iter": 2600, "lr": 0.04095, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34938, "top5_acc": 0.6075, "loss_cls": 3.73209, "loss": 3.73209, "time": 0.82591} +{"mode": "train", "epoch": 84, "iter": 2700, "lr": 0.04092, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33375, "top5_acc": 0.60031, "loss_cls": 3.73753, "loss": 3.73753, "time": 0.83123} +{"mode": "train", "epoch": 84, "iter": 2800, "lr": 0.04089, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.33562, "top5_acc": 0.60328, "loss_cls": 3.76121, "loss": 3.76121, "time": 0.83268} +{"mode": "train", "epoch": 84, "iter": 2900, "lr": 0.04086, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33781, "top5_acc": 0.59766, "loss_cls": 3.76745, "loss": 3.76745, "time": 0.81387} +{"mode": "train", "epoch": 84, "iter": 3000, "lr": 0.04084, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33844, "top5_acc": 0.60547, "loss_cls": 3.71735, "loss": 3.71735, "time": 0.819} +{"mode": "train", "epoch": 84, "iter": 3100, "lr": 0.04081, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34281, "top5_acc": 0.60547, "loss_cls": 3.71399, "loss": 3.71399, "time": 0.83775} +{"mode": "train", "epoch": 84, "iter": 3200, "lr": 0.04078, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33312, "top5_acc": 0.60125, "loss_cls": 3.76999, "loss": 3.76999, "time": 0.82844} +{"mode": "train", "epoch": 84, "iter": 3300, "lr": 0.04075, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33734, "top5_acc": 0.60125, "loss_cls": 3.76282, "loss": 3.76282, "time": 0.82437} +{"mode": "train", "epoch": 84, "iter": 3400, "lr": 0.04073, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33969, "top5_acc": 0.60344, "loss_cls": 3.72305, "loss": 3.72305, "time": 0.81977} +{"mode": "train", "epoch": 84, "iter": 3500, "lr": 0.0407, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33188, "top5_acc": 0.59531, "loss_cls": 3.78066, "loss": 3.78066, "time": 0.8245} +{"mode": "train", "epoch": 84, "iter": 3600, "lr": 0.04067, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34734, "top5_acc": 0.60141, "loss_cls": 3.70765, "loss": 3.70765, "time": 0.83262} +{"mode": "train", "epoch": 84, "iter": 3700, "lr": 0.04064, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33609, "top5_acc": 0.59703, "loss_cls": 3.76737, "loss": 3.76737, "time": 0.81969} +{"mode": "val", "epoch": 84, "iter": 309, "lr": 0.04063, "top1_acc": 0.26369, "top5_acc": 0.5133, "mean_class_accuracy": 0.2635} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.0406, "memory": 15990, "data_time": 1.32624, "top1_acc": 0.34, "top5_acc": 0.60969, "loss_cls": 3.675, "loss": 3.675, "time": 2.31051} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.04058, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35141, "top5_acc": 0.61656, "loss_cls": 3.6431, "loss": 3.6431, "time": 0.82018} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.04055, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34312, "top5_acc": 0.60484, "loss_cls": 3.72647, "loss": 3.72647, "time": 0.8168} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.04052, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33797, "top5_acc": 0.60984, "loss_cls": 3.67668, "loss": 3.67668, "time": 0.81992} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.04049, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33797, "top5_acc": 0.60516, "loss_cls": 3.70569, "loss": 3.70569, "time": 0.8178} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.04047, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34578, "top5_acc": 0.615, "loss_cls": 3.68706, "loss": 3.68706, "time": 0.81362} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.04044, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34141, "top5_acc": 0.60688, "loss_cls": 3.70959, "loss": 3.70959, "time": 0.81782} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.04041, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33484, "top5_acc": 0.59922, "loss_cls": 3.74297, "loss": 3.74297, "time": 0.82011} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.04038, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34312, "top5_acc": 0.61141, "loss_cls": 3.67415, "loss": 3.67415, "time": 0.81484} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.04036, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34062, "top5_acc": 0.60344, "loss_cls": 3.72935, "loss": 3.72935, "time": 0.81777} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.04033, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33719, "top5_acc": 0.60172, "loss_cls": 3.72095, "loss": 3.72095, "time": 0.8136} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.0403, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35062, "top5_acc": 0.61641, "loss_cls": 3.65286, "loss": 3.65286, "time": 0.81676} +{"mode": "train", "epoch": 85, "iter": 1300, "lr": 0.04027, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33906, "top5_acc": 0.59547, "loss_cls": 3.75072, "loss": 3.75072, "time": 0.81668} +{"mode": "train", "epoch": 85, "iter": 1400, "lr": 0.04025, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33984, "top5_acc": 0.60703, "loss_cls": 3.71141, "loss": 3.71141, "time": 0.81341} +{"mode": "train", "epoch": 85, "iter": 1500, "lr": 0.04022, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34391, "top5_acc": 0.60516, "loss_cls": 3.71218, "loss": 3.71218, "time": 0.81412} +{"mode": "train", "epoch": 85, "iter": 1600, "lr": 0.04019, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33641, "top5_acc": 0.59453, "loss_cls": 3.75103, "loss": 3.75103, "time": 0.81707} +{"mode": "train", "epoch": 85, "iter": 1700, "lr": 0.04016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3375, "top5_acc": 0.60609, "loss_cls": 3.72044, "loss": 3.72044, "time": 0.81396} +{"mode": "train", "epoch": 85, "iter": 1800, "lr": 0.04014, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34312, "top5_acc": 0.60031, "loss_cls": 3.74525, "loss": 3.74525, "time": 0.82704} +{"mode": "train", "epoch": 85, "iter": 1900, "lr": 0.04011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34047, "top5_acc": 0.60516, "loss_cls": 3.72738, "loss": 3.72738, "time": 0.82404} +{"mode": "train", "epoch": 85, "iter": 2000, "lr": 0.04008, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34969, "top5_acc": 0.61547, "loss_cls": 3.66656, "loss": 3.66656, "time": 0.82052} +{"mode": "train", "epoch": 85, "iter": 2100, "lr": 0.04006, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34984, "top5_acc": 0.60922, "loss_cls": 3.70291, "loss": 3.70291, "time": 0.81426} +{"mode": "train", "epoch": 85, "iter": 2200, "lr": 0.04003, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34391, "top5_acc": 0.61016, "loss_cls": 3.68065, "loss": 3.68065, "time": 0.81689} +{"mode": "train", "epoch": 85, "iter": 2300, "lr": 0.04, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33953, "top5_acc": 0.59344, "loss_cls": 3.74784, "loss": 3.74784, "time": 0.81593} +{"mode": "train", "epoch": 85, "iter": 2400, "lr": 0.03997, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34219, "top5_acc": 0.60516, "loss_cls": 3.72781, "loss": 3.72781, "time": 0.81737} +{"mode": "train", "epoch": 85, "iter": 2500, "lr": 0.03995, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34516, "top5_acc": 0.60078, "loss_cls": 3.72891, "loss": 3.72891, "time": 0.82134} +{"mode": "train", "epoch": 85, "iter": 2600, "lr": 0.03992, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34922, "top5_acc": 0.61172, "loss_cls": 3.70296, "loss": 3.70296, "time": 0.81569} +{"mode": "train", "epoch": 85, "iter": 2700, "lr": 0.03989, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33938, "top5_acc": 0.59719, "loss_cls": 3.7507, "loss": 3.7507, "time": 0.82165} +{"mode": "train", "epoch": 85, "iter": 2800, "lr": 0.03986, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34734, "top5_acc": 0.61156, "loss_cls": 3.69133, "loss": 3.69133, "time": 0.82063} +{"mode": "train", "epoch": 85, "iter": 2900, "lr": 0.03984, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34172, "top5_acc": 0.61359, "loss_cls": 3.6808, "loss": 3.6808, "time": 0.81394} +{"mode": "train", "epoch": 85, "iter": 3000, "lr": 0.03981, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34125, "top5_acc": 0.5975, "loss_cls": 3.7247, "loss": 3.7247, "time": 0.81693} +{"mode": "train", "epoch": 85, "iter": 3100, "lr": 0.03978, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34391, "top5_acc": 0.60312, "loss_cls": 3.71315, "loss": 3.71315, "time": 0.82485} +{"mode": "train", "epoch": 85, "iter": 3200, "lr": 0.03975, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33422, "top5_acc": 0.61203, "loss_cls": 3.72173, "loss": 3.72173, "time": 0.81712} +{"mode": "train", "epoch": 85, "iter": 3300, "lr": 0.03973, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34172, "top5_acc": 0.60562, "loss_cls": 3.72239, "loss": 3.72239, "time": 0.81163} +{"mode": "train", "epoch": 85, "iter": 3400, "lr": 0.0397, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3425, "top5_acc": 0.60531, "loss_cls": 3.72194, "loss": 3.72194, "time": 0.81465} +{"mode": "train", "epoch": 85, "iter": 3500, "lr": 0.03967, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34172, "top5_acc": 0.59719, "loss_cls": 3.74855, "loss": 3.74855, "time": 0.81158} +{"mode": "train", "epoch": 85, "iter": 3600, "lr": 0.03964, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3375, "top5_acc": 0.59703, "loss_cls": 3.74116, "loss": 3.74116, "time": 0.81875} +{"mode": "train", "epoch": 85, "iter": 3700, "lr": 0.03962, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34344, "top5_acc": 0.60297, "loss_cls": 3.71859, "loss": 3.71859, "time": 0.81574} +{"mode": "val", "epoch": 85, "iter": 309, "lr": 0.0396, "top1_acc": 0.27782, "top5_acc": 0.5255, "mean_class_accuracy": 0.2776} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.03958, "memory": 15990, "data_time": 1.33793, "top1_acc": 0.36297, "top5_acc": 0.62469, "loss_cls": 3.60342, "loss": 3.60342, "time": 2.32307} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.03955, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34547, "top5_acc": 0.61625, "loss_cls": 3.67225, "loss": 3.67225, "time": 0.81752} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.03952, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33094, "top5_acc": 0.60734, "loss_cls": 3.70472, "loss": 3.70472, "time": 0.82041} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.0395, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34406, "top5_acc": 0.61609, "loss_cls": 3.66676, "loss": 3.66676, "time": 0.81758} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.03947, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34781, "top5_acc": 0.61453, "loss_cls": 3.66891, "loss": 3.66891, "time": 0.81246} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.03944, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35297, "top5_acc": 0.61312, "loss_cls": 3.65201, "loss": 3.65201, "time": 0.81477} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.03941, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33453, "top5_acc": 0.6, "loss_cls": 3.74291, "loss": 3.74291, "time": 0.8157} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.03939, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35297, "top5_acc": 0.62359, "loss_cls": 3.63912, "loss": 3.63912, "time": 0.81439} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.03936, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34891, "top5_acc": 0.60844, "loss_cls": 3.66568, "loss": 3.66568, "time": 0.8143} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.03933, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34531, "top5_acc": 0.6075, "loss_cls": 3.70434, "loss": 3.70434, "time": 0.8154} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.0393, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34344, "top5_acc": 0.60781, "loss_cls": 3.67289, "loss": 3.67289, "time": 0.81526} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.03928, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35062, "top5_acc": 0.61469, "loss_cls": 3.66849, "loss": 3.66849, "time": 0.81844} +{"mode": "train", "epoch": 86, "iter": 1300, "lr": 0.03925, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3425, "top5_acc": 0.60547, "loss_cls": 3.72736, "loss": 3.72736, "time": 0.81223} +{"mode": "train", "epoch": 86, "iter": 1400, "lr": 0.03922, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34359, "top5_acc": 0.60969, "loss_cls": 3.68021, "loss": 3.68021, "time": 0.81523} +{"mode": "train", "epoch": 86, "iter": 1500, "lr": 0.03919, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34703, "top5_acc": 0.60797, "loss_cls": 3.70734, "loss": 3.70734, "time": 0.81478} +{"mode": "train", "epoch": 86, "iter": 1600, "lr": 0.03917, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34125, "top5_acc": 0.60641, "loss_cls": 3.6944, "loss": 3.6944, "time": 0.81408} +{"mode": "train", "epoch": 86, "iter": 1700, "lr": 0.03914, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34328, "top5_acc": 0.60984, "loss_cls": 3.70628, "loss": 3.70628, "time": 0.81594} +{"mode": "train", "epoch": 86, "iter": 1800, "lr": 0.03911, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34609, "top5_acc": 0.60203, "loss_cls": 3.70696, "loss": 3.70696, "time": 0.81916} +{"mode": "train", "epoch": 86, "iter": 1900, "lr": 0.03909, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34062, "top5_acc": 0.60609, "loss_cls": 3.69869, "loss": 3.69869, "time": 0.8233} +{"mode": "train", "epoch": 86, "iter": 2000, "lr": 0.03906, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34266, "top5_acc": 0.60109, "loss_cls": 3.72781, "loss": 3.72781, "time": 0.82463} +{"mode": "train", "epoch": 86, "iter": 2100, "lr": 0.03903, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33891, "top5_acc": 0.59547, "loss_cls": 3.74423, "loss": 3.74423, "time": 0.82659} +{"mode": "train", "epoch": 86, "iter": 2200, "lr": 0.039, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33578, "top5_acc": 0.60141, "loss_cls": 3.74647, "loss": 3.74647, "time": 0.81762} +{"mode": "train", "epoch": 86, "iter": 2300, "lr": 0.03898, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34375, "top5_acc": 0.60094, "loss_cls": 3.7204, "loss": 3.7204, "time": 0.81534} +{"mode": "train", "epoch": 86, "iter": 2400, "lr": 0.03895, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33734, "top5_acc": 0.60109, "loss_cls": 3.73712, "loss": 3.73712, "time": 0.81405} +{"mode": "train", "epoch": 86, "iter": 2500, "lr": 0.03892, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33125, "top5_acc": 0.59422, "loss_cls": 3.76233, "loss": 3.76233, "time": 0.81629} +{"mode": "train", "epoch": 86, "iter": 2600, "lr": 0.03889, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34359, "top5_acc": 0.60297, "loss_cls": 3.73609, "loss": 3.73609, "time": 0.81447} +{"mode": "train", "epoch": 86, "iter": 2700, "lr": 0.03887, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34266, "top5_acc": 0.60469, "loss_cls": 3.71279, "loss": 3.71279, "time": 0.81792} +{"mode": "train", "epoch": 86, "iter": 2800, "lr": 0.03884, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34344, "top5_acc": 0.60734, "loss_cls": 3.68653, "loss": 3.68653, "time": 0.8176} +{"mode": "train", "epoch": 86, "iter": 2900, "lr": 0.03881, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35266, "top5_acc": 0.61547, "loss_cls": 3.66048, "loss": 3.66048, "time": 0.82653} +{"mode": "train", "epoch": 86, "iter": 3000, "lr": 0.03879, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33797, "top5_acc": 0.59719, "loss_cls": 3.73038, "loss": 3.73038, "time": 0.81513} +{"mode": "train", "epoch": 86, "iter": 3100, "lr": 0.03876, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34047, "top5_acc": 0.59891, "loss_cls": 3.73584, "loss": 3.73584, "time": 0.81929} +{"mode": "train", "epoch": 86, "iter": 3200, "lr": 0.03873, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34234, "top5_acc": 0.61078, "loss_cls": 3.71558, "loss": 3.71558, "time": 0.82042} +{"mode": "train", "epoch": 86, "iter": 3300, "lr": 0.0387, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33375, "top5_acc": 0.59734, "loss_cls": 3.74849, "loss": 3.74849, "time": 0.81861} +{"mode": "train", "epoch": 86, "iter": 3400, "lr": 0.03868, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34984, "top5_acc": 0.61547, "loss_cls": 3.657, "loss": 3.657, "time": 0.81509} +{"mode": "train", "epoch": 86, "iter": 3500, "lr": 0.03865, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32953, "top5_acc": 0.60188, "loss_cls": 3.73097, "loss": 3.73097, "time": 0.82006} +{"mode": "train", "epoch": 86, "iter": 3600, "lr": 0.03862, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35031, "top5_acc": 0.60891, "loss_cls": 3.68968, "loss": 3.68968, "time": 0.81791} +{"mode": "train", "epoch": 86, "iter": 3700, "lr": 0.0386, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35359, "top5_acc": 0.60484, "loss_cls": 3.69093, "loss": 3.69093, "time": 0.81515} +{"mode": "val", "epoch": 86, "iter": 309, "lr": 0.03858, "top1_acc": 0.28451, "top5_acc": 0.53472, "mean_class_accuracy": 0.28435} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.03856, "memory": 15990, "data_time": 1.3339, "top1_acc": 0.35203, "top5_acc": 0.61812, "loss_cls": 3.65038, "loss": 3.65038, "time": 2.32414} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.03853, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35078, "top5_acc": 0.60734, "loss_cls": 3.65779, "loss": 3.65779, "time": 0.81714} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.0385, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35188, "top5_acc": 0.62313, "loss_cls": 3.64266, "loss": 3.64266, "time": 0.81735} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.03847, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35312, "top5_acc": 0.61203, "loss_cls": 3.6719, "loss": 3.6719, "time": 0.81343} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.03845, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34672, "top5_acc": 0.60844, "loss_cls": 3.6795, "loss": 3.6795, "time": 0.81999} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.03842, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34969, "top5_acc": 0.60547, "loss_cls": 3.69134, "loss": 3.69134, "time": 0.81478} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.03839, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35156, "top5_acc": 0.61719, "loss_cls": 3.66562, "loss": 3.66562, "time": 0.81625} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.03837, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33812, "top5_acc": 0.60531, "loss_cls": 3.69193, "loss": 3.69193, "time": 0.81574} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.03834, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34406, "top5_acc": 0.60828, "loss_cls": 3.70306, "loss": 3.70306, "time": 0.82275} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.03831, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35, "top5_acc": 0.62375, "loss_cls": 3.64044, "loss": 3.64044, "time": 0.82048} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.03828, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35438, "top5_acc": 0.61656, "loss_cls": 3.64626, "loss": 3.64626, "time": 0.81633} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.03826, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34984, "top5_acc": 0.61109, "loss_cls": 3.67401, "loss": 3.67401, "time": 0.8152} +{"mode": "train", "epoch": 87, "iter": 1300, "lr": 0.03823, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34547, "top5_acc": 0.60891, "loss_cls": 3.70588, "loss": 3.70588, "time": 0.81576} +{"mode": "train", "epoch": 87, "iter": 1400, "lr": 0.0382, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36, "top5_acc": 0.62016, "loss_cls": 3.61165, "loss": 3.61165, "time": 0.81829} +{"mode": "train", "epoch": 87, "iter": 1500, "lr": 0.03817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35422, "top5_acc": 0.60438, "loss_cls": 3.67155, "loss": 3.67155, "time": 0.81293} +{"mode": "train", "epoch": 87, "iter": 1600, "lr": 0.03815, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34031, "top5_acc": 0.61094, "loss_cls": 3.72212, "loss": 3.72212, "time": 0.81386} +{"mode": "train", "epoch": 87, "iter": 1700, "lr": 0.03812, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34344, "top5_acc": 0.60703, "loss_cls": 3.72046, "loss": 3.72046, "time": 0.8122} +{"mode": "train", "epoch": 87, "iter": 1800, "lr": 0.03809, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34469, "top5_acc": 0.60547, "loss_cls": 3.72303, "loss": 3.72303, "time": 0.82934} +{"mode": "train", "epoch": 87, "iter": 1900, "lr": 0.03807, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34531, "top5_acc": 0.61094, "loss_cls": 3.67848, "loss": 3.67848, "time": 0.81954} +{"mode": "train", "epoch": 87, "iter": 2000, "lr": 0.03804, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34875, "top5_acc": 0.61094, "loss_cls": 3.66204, "loss": 3.66204, "time": 0.82482} +{"mode": "train", "epoch": 87, "iter": 2100, "lr": 0.03801, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34438, "top5_acc": 0.59781, "loss_cls": 3.73176, "loss": 3.73176, "time": 0.81731} +{"mode": "train", "epoch": 87, "iter": 2200, "lr": 0.03798, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35438, "top5_acc": 0.61484, "loss_cls": 3.66349, "loss": 3.66349, "time": 0.81384} +{"mode": "train", "epoch": 87, "iter": 2300, "lr": 0.03796, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34484, "top5_acc": 0.60828, "loss_cls": 3.71139, "loss": 3.71139, "time": 0.81636} +{"mode": "train", "epoch": 87, "iter": 2400, "lr": 0.03793, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34969, "top5_acc": 0.60844, "loss_cls": 3.677, "loss": 3.677, "time": 0.81486} +{"mode": "train", "epoch": 87, "iter": 2500, "lr": 0.0379, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.345, "top5_acc": 0.61219, "loss_cls": 3.69053, "loss": 3.69053, "time": 0.81668} +{"mode": "train", "epoch": 87, "iter": 2600, "lr": 0.03788, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33844, "top5_acc": 0.60531, "loss_cls": 3.72959, "loss": 3.72959, "time": 0.81831} +{"mode": "train", "epoch": 87, "iter": 2700, "lr": 0.03785, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35219, "top5_acc": 0.61734, "loss_cls": 3.68138, "loss": 3.68138, "time": 0.82088} +{"mode": "train", "epoch": 87, "iter": 2800, "lr": 0.03782, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34688, "top5_acc": 0.61, "loss_cls": 3.65993, "loss": 3.65993, "time": 0.81697} +{"mode": "train", "epoch": 87, "iter": 2900, "lr": 0.03779, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34547, "top5_acc": 0.60797, "loss_cls": 3.68098, "loss": 3.68098, "time": 0.81695} +{"mode": "train", "epoch": 87, "iter": 3000, "lr": 0.03777, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34281, "top5_acc": 0.60562, "loss_cls": 3.72161, "loss": 3.72161, "time": 0.81766} +{"mode": "train", "epoch": 87, "iter": 3100, "lr": 0.03774, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33781, "top5_acc": 0.60453, "loss_cls": 3.72449, "loss": 3.72449, "time": 0.81429} +{"mode": "train", "epoch": 87, "iter": 3200, "lr": 0.03771, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34844, "top5_acc": 0.61547, "loss_cls": 3.66549, "loss": 3.66549, "time": 0.81698} +{"mode": "train", "epoch": 87, "iter": 3300, "lr": 0.03769, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34719, "top5_acc": 0.60391, "loss_cls": 3.69267, "loss": 3.69267, "time": 0.81691} +{"mode": "train", "epoch": 87, "iter": 3400, "lr": 0.03766, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33, "top5_acc": 0.60797, "loss_cls": 3.71742, "loss": 3.71742, "time": 0.82425} +{"mode": "train", "epoch": 87, "iter": 3500, "lr": 0.03763, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33203, "top5_acc": 0.60422, "loss_cls": 3.74089, "loss": 3.74089, "time": 0.81488} +{"mode": "train", "epoch": 87, "iter": 3600, "lr": 0.03761, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34547, "top5_acc": 0.60719, "loss_cls": 3.70653, "loss": 3.70653, "time": 0.81376} +{"mode": "train", "epoch": 87, "iter": 3700, "lr": 0.03758, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34188, "top5_acc": 0.59781, "loss_cls": 3.71638, "loss": 3.71638, "time": 0.81895} +{"mode": "val", "epoch": 87, "iter": 309, "lr": 0.03757, "top1_acc": 0.27053, "top5_acc": 0.52844, "mean_class_accuracy": 0.27049} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.03754, "memory": 15990, "data_time": 1.3521, "top1_acc": 0.35609, "top5_acc": 0.61891, "loss_cls": 3.62992, "loss": 3.62992, "time": 2.33314} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.03751, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34906, "top5_acc": 0.605, "loss_cls": 3.69015, "loss": 3.69015, "time": 0.82492} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.03748, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35031, "top5_acc": 0.61625, "loss_cls": 3.68047, "loss": 3.68047, "time": 0.81779} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.03746, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35844, "top5_acc": 0.6225, "loss_cls": 3.62301, "loss": 3.62301, "time": 0.81172} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.03743, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34734, "top5_acc": 0.61125, "loss_cls": 3.66647, "loss": 3.66647, "time": 0.81489} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.0374, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35062, "top5_acc": 0.61672, "loss_cls": 3.66058, "loss": 3.66058, "time": 0.82084} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.03738, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34531, "top5_acc": 0.60578, "loss_cls": 3.69378, "loss": 3.69378, "time": 0.81309} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.03735, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35266, "top5_acc": 0.615, "loss_cls": 3.65086, "loss": 3.65086, "time": 0.81625} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.03732, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34047, "top5_acc": 0.60266, "loss_cls": 3.73144, "loss": 3.73144, "time": 0.8156} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.0373, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34922, "top5_acc": 0.61391, "loss_cls": 3.66108, "loss": 3.66108, "time": 0.8184} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.03727, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35906, "top5_acc": 0.62328, "loss_cls": 3.62031, "loss": 3.62031, "time": 0.81543} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.03724, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35391, "top5_acc": 0.61859, "loss_cls": 3.65421, "loss": 3.65421, "time": 0.81977} +{"mode": "train", "epoch": 88, "iter": 1300, "lr": 0.03721, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34828, "top5_acc": 0.61047, "loss_cls": 3.67118, "loss": 3.67118, "time": 0.81692} +{"mode": "train", "epoch": 88, "iter": 1400, "lr": 0.03719, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34594, "top5_acc": 0.61672, "loss_cls": 3.67599, "loss": 3.67599, "time": 0.81373} +{"mode": "train", "epoch": 88, "iter": 1500, "lr": 0.03716, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34828, "top5_acc": 0.61922, "loss_cls": 3.64131, "loss": 3.64131, "time": 0.81751} +{"mode": "train", "epoch": 88, "iter": 1600, "lr": 0.03713, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35703, "top5_acc": 0.61687, "loss_cls": 3.66246, "loss": 3.66246, "time": 0.82647} +{"mode": "train", "epoch": 88, "iter": 1700, "lr": 0.03711, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34812, "top5_acc": 0.61328, "loss_cls": 3.67223, "loss": 3.67223, "time": 0.81637} +{"mode": "train", "epoch": 88, "iter": 1800, "lr": 0.03708, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35297, "top5_acc": 0.615, "loss_cls": 3.66785, "loss": 3.66785, "time": 0.82182} +{"mode": "train", "epoch": 88, "iter": 1900, "lr": 0.03705, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36141, "top5_acc": 0.61531, "loss_cls": 3.67771, "loss": 3.67771, "time": 0.82356} +{"mode": "train", "epoch": 88, "iter": 2000, "lr": 0.03703, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34891, "top5_acc": 0.61094, "loss_cls": 3.65626, "loss": 3.65626, "time": 0.8301} +{"mode": "train", "epoch": 88, "iter": 2100, "lr": 0.037, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36062, "top5_acc": 0.62109, "loss_cls": 3.6285, "loss": 3.6285, "time": 0.82549} +{"mode": "train", "epoch": 88, "iter": 2200, "lr": 0.03697, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34656, "top5_acc": 0.60734, "loss_cls": 3.70786, "loss": 3.70786, "time": 0.821} +{"mode": "train", "epoch": 88, "iter": 2300, "lr": 0.03694, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34406, "top5_acc": 0.60703, "loss_cls": 3.69602, "loss": 3.69602, "time": 0.81767} +{"mode": "train", "epoch": 88, "iter": 2400, "lr": 0.03692, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33781, "top5_acc": 0.6125, "loss_cls": 3.67534, "loss": 3.67534, "time": 0.8143} +{"mode": "train", "epoch": 88, "iter": 2500, "lr": 0.03689, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34594, "top5_acc": 0.60266, "loss_cls": 3.72012, "loss": 3.72012, "time": 0.8222} +{"mode": "train", "epoch": 88, "iter": 2600, "lr": 0.03686, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34203, "top5_acc": 0.60938, "loss_cls": 3.70846, "loss": 3.70846, "time": 0.81546} +{"mode": "train", "epoch": 88, "iter": 2700, "lr": 0.03684, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35531, "top5_acc": 0.61172, "loss_cls": 3.68348, "loss": 3.68348, "time": 0.81738} +{"mode": "train", "epoch": 88, "iter": 2800, "lr": 0.03681, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35078, "top5_acc": 0.61328, "loss_cls": 3.64637, "loss": 3.64637, "time": 0.81872} +{"mode": "train", "epoch": 88, "iter": 2900, "lr": 0.03678, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34656, "top5_acc": 0.59859, "loss_cls": 3.69921, "loss": 3.69921, "time": 0.81752} +{"mode": "train", "epoch": 88, "iter": 3000, "lr": 0.03676, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34859, "top5_acc": 0.61078, "loss_cls": 3.67049, "loss": 3.67049, "time": 0.81729} +{"mode": "train", "epoch": 88, "iter": 3100, "lr": 0.03673, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34656, "top5_acc": 0.60797, "loss_cls": 3.67193, "loss": 3.67193, "time": 0.81633} +{"mode": "train", "epoch": 88, "iter": 3200, "lr": 0.0367, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35844, "top5_acc": 0.61719, "loss_cls": 3.62583, "loss": 3.62583, "time": 0.8152} +{"mode": "train", "epoch": 88, "iter": 3300, "lr": 0.03667, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34125, "top5_acc": 0.59875, "loss_cls": 3.74013, "loss": 3.74013, "time": 0.81017} +{"mode": "train", "epoch": 88, "iter": 3400, "lr": 0.03665, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34531, "top5_acc": 0.60984, "loss_cls": 3.69568, "loss": 3.69568, "time": 0.81692} +{"mode": "train", "epoch": 88, "iter": 3500, "lr": 0.03662, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34984, "top5_acc": 0.61922, "loss_cls": 3.66078, "loss": 3.66078, "time": 0.81549} +{"mode": "train", "epoch": 88, "iter": 3600, "lr": 0.03659, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34109, "top5_acc": 0.61078, "loss_cls": 3.67713, "loss": 3.67713, "time": 0.8153} +{"mode": "train", "epoch": 88, "iter": 3700, "lr": 0.03657, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35547, "top5_acc": 0.62094, "loss_cls": 3.64084, "loss": 3.64084, "time": 0.8279} +{"mode": "val", "epoch": 88, "iter": 309, "lr": 0.03655, "top1_acc": 0.27306, "top5_acc": 0.52601, "mean_class_accuracy": 0.27293} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.03653, "memory": 15990, "data_time": 1.35466, "top1_acc": 0.34922, "top5_acc": 0.62453, "loss_cls": 3.57536, "loss": 3.57536, "time": 2.33804} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0365, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35797, "top5_acc": 0.62016, "loss_cls": 3.60784, "loss": 3.60784, "time": 0.81768} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.03647, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35297, "top5_acc": 0.62141, "loss_cls": 3.64354, "loss": 3.64354, "time": 0.82096} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.03645, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35516, "top5_acc": 0.61578, "loss_cls": 3.63137, "loss": 3.63137, "time": 0.81886} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.03642, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35531, "top5_acc": 0.62344, "loss_cls": 3.62905, "loss": 3.62905, "time": 0.81866} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.03639, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35234, "top5_acc": 0.61797, "loss_cls": 3.64906, "loss": 3.64906, "time": 0.81741} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.03637, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35188, "top5_acc": 0.61953, "loss_cls": 3.63997, "loss": 3.63997, "time": 0.81819} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.03634, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34719, "top5_acc": 0.6225, "loss_cls": 3.63773, "loss": 3.63773, "time": 0.82409} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.03631, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35359, "top5_acc": 0.61453, "loss_cls": 3.63703, "loss": 3.63703, "time": 0.82156} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.03629, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36469, "top5_acc": 0.61953, "loss_cls": 3.61517, "loss": 3.61517, "time": 0.81725} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.03626, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34484, "top5_acc": 0.61516, "loss_cls": 3.66262, "loss": 3.66262, "time": 0.818} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.03623, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34875, "top5_acc": 0.61344, "loss_cls": 3.68387, "loss": 3.68387, "time": 0.81736} +{"mode": "train", "epoch": 89, "iter": 1300, "lr": 0.0362, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35219, "top5_acc": 0.61438, "loss_cls": 3.63796, "loss": 3.63796, "time": 0.81631} +{"mode": "train", "epoch": 89, "iter": 1400, "lr": 0.03618, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3525, "top5_acc": 0.62453, "loss_cls": 3.64889, "loss": 3.64889, "time": 0.81391} +{"mode": "train", "epoch": 89, "iter": 1500, "lr": 0.03615, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34406, "top5_acc": 0.61016, "loss_cls": 3.67646, "loss": 3.67646, "time": 0.81467} +{"mode": "train", "epoch": 89, "iter": 1600, "lr": 0.03612, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35078, "top5_acc": 0.61609, "loss_cls": 3.65295, "loss": 3.65295, "time": 0.81795} +{"mode": "train", "epoch": 89, "iter": 1700, "lr": 0.0361, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34094, "top5_acc": 0.60594, "loss_cls": 3.68751, "loss": 3.68751, "time": 0.81574} +{"mode": "train", "epoch": 89, "iter": 1800, "lr": 0.03607, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35156, "top5_acc": 0.60828, "loss_cls": 3.67444, "loss": 3.67444, "time": 0.82516} +{"mode": "train", "epoch": 89, "iter": 1900, "lr": 0.03604, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35562, "top5_acc": 0.62031, "loss_cls": 3.64773, "loss": 3.64773, "time": 0.82062} +{"mode": "train", "epoch": 89, "iter": 2000, "lr": 0.03602, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3575, "top5_acc": 0.62031, "loss_cls": 3.64599, "loss": 3.64599, "time": 0.82756} +{"mode": "train", "epoch": 89, "iter": 2100, "lr": 0.03599, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34969, "top5_acc": 0.62234, "loss_cls": 3.65579, "loss": 3.65579, "time": 0.81873} +{"mode": "train", "epoch": 89, "iter": 2200, "lr": 0.03596, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34844, "top5_acc": 0.61453, "loss_cls": 3.66583, "loss": 3.66583, "time": 0.82086} +{"mode": "train", "epoch": 89, "iter": 2300, "lr": 0.03594, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3525, "top5_acc": 0.615, "loss_cls": 3.67145, "loss": 3.67145, "time": 0.82291} +{"mode": "train", "epoch": 89, "iter": 2400, "lr": 0.03591, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35531, "top5_acc": 0.60844, "loss_cls": 3.65839, "loss": 3.65839, "time": 0.81349} +{"mode": "train", "epoch": 89, "iter": 2500, "lr": 0.03588, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3375, "top5_acc": 0.60141, "loss_cls": 3.72485, "loss": 3.72485, "time": 0.81382} +{"mode": "train", "epoch": 89, "iter": 2600, "lr": 0.03586, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34766, "top5_acc": 0.61734, "loss_cls": 3.66155, "loss": 3.66155, "time": 0.81365} +{"mode": "train", "epoch": 89, "iter": 2700, "lr": 0.03583, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35219, "top5_acc": 0.60781, "loss_cls": 3.66396, "loss": 3.66396, "time": 0.81739} +{"mode": "train", "epoch": 89, "iter": 2800, "lr": 0.0358, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33406, "top5_acc": 0.59875, "loss_cls": 3.7472, "loss": 3.7472, "time": 0.81562} +{"mode": "train", "epoch": 89, "iter": 2900, "lr": 0.03578, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34375, "top5_acc": 0.61234, "loss_cls": 3.6903, "loss": 3.6903, "time": 0.81636} +{"mode": "train", "epoch": 89, "iter": 3000, "lr": 0.03575, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35391, "top5_acc": 0.61484, "loss_cls": 3.66222, "loss": 3.66222, "time": 0.81636} +{"mode": "train", "epoch": 89, "iter": 3100, "lr": 0.03572, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34672, "top5_acc": 0.60984, "loss_cls": 3.67405, "loss": 3.67405, "time": 0.81493} +{"mode": "train", "epoch": 89, "iter": 3200, "lr": 0.03569, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35109, "top5_acc": 0.61828, "loss_cls": 3.66084, "loss": 3.66084, "time": 0.81415} +{"mode": "train", "epoch": 89, "iter": 3300, "lr": 0.03567, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35484, "top5_acc": 0.61875, "loss_cls": 3.60056, "loss": 3.60056, "time": 0.81585} +{"mode": "train", "epoch": 89, "iter": 3400, "lr": 0.03564, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35141, "top5_acc": 0.61062, "loss_cls": 3.67161, "loss": 3.67161, "time": 0.82408} +{"mode": "train", "epoch": 89, "iter": 3500, "lr": 0.03561, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34516, "top5_acc": 0.60516, "loss_cls": 3.69583, "loss": 3.69583, "time": 0.82166} +{"mode": "train", "epoch": 89, "iter": 3600, "lr": 0.03559, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35516, "top5_acc": 0.60906, "loss_cls": 3.67772, "loss": 3.67772, "time": 0.81559} +{"mode": "train", "epoch": 89, "iter": 3700, "lr": 0.03556, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34844, "top5_acc": 0.61609, "loss_cls": 3.65515, "loss": 3.65515, "time": 0.82176} +{"mode": "val", "epoch": 89, "iter": 309, "lr": 0.03555, "top1_acc": 0.28061, "top5_acc": 0.54323, "mean_class_accuracy": 0.28048} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.03552, "memory": 15990, "data_time": 1.39374, "top1_acc": 0.36844, "top5_acc": 0.61594, "loss_cls": 3.59309, "loss": 3.59309, "time": 2.37319} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.0355, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35188, "top5_acc": 0.62484, "loss_cls": 3.60759, "loss": 3.60759, "time": 0.82395} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.03547, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36219, "top5_acc": 0.62547, "loss_cls": 3.5818, "loss": 3.5818, "time": 0.81808} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.03544, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36312, "top5_acc": 0.62516, "loss_cls": 3.5887, "loss": 3.5887, "time": 0.82541} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.03541, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35562, "top5_acc": 0.62375, "loss_cls": 3.61551, "loss": 3.61551, "time": 0.816} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.03539, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35438, "top5_acc": 0.62281, "loss_cls": 3.63786, "loss": 3.63786, "time": 0.8143} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.03536, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35906, "top5_acc": 0.63047, "loss_cls": 3.61515, "loss": 3.61515, "time": 0.81971} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.03533, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35828, "top5_acc": 0.6175, "loss_cls": 3.62615, "loss": 3.62615, "time": 0.81662} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.03531, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35609, "top5_acc": 0.61562, "loss_cls": 3.62568, "loss": 3.62568, "time": 0.81845} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.03528, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34125, "top5_acc": 0.61531, "loss_cls": 3.67862, "loss": 3.67862, "time": 0.81563} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.03525, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35219, "top5_acc": 0.60812, "loss_cls": 3.67543, "loss": 3.67543, "time": 0.81811} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.03523, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35906, "top5_acc": 0.62016, "loss_cls": 3.59119, "loss": 3.59119, "time": 0.81591} +{"mode": "train", "epoch": 90, "iter": 1300, "lr": 0.0352, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35172, "top5_acc": 0.60797, "loss_cls": 3.66722, "loss": 3.66722, "time": 0.81669} +{"mode": "train", "epoch": 90, "iter": 1400, "lr": 0.03517, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35156, "top5_acc": 0.61, "loss_cls": 3.70315, "loss": 3.70315, "time": 0.81853} +{"mode": "train", "epoch": 90, "iter": 1500, "lr": 0.03515, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36703, "top5_acc": 0.62844, "loss_cls": 3.55917, "loss": 3.55917, "time": 0.81647} +{"mode": "train", "epoch": 90, "iter": 1600, "lr": 0.03512, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35047, "top5_acc": 0.60719, "loss_cls": 3.69901, "loss": 3.69901, "time": 0.81334} +{"mode": "train", "epoch": 90, "iter": 1700, "lr": 0.03509, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.355, "top5_acc": 0.61625, "loss_cls": 3.68291, "loss": 3.68291, "time": 0.81523} +{"mode": "train", "epoch": 90, "iter": 1800, "lr": 0.03507, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34344, "top5_acc": 0.60609, "loss_cls": 3.68109, "loss": 3.68109, "time": 0.8247} +{"mode": "train", "epoch": 90, "iter": 1900, "lr": 0.03504, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36328, "top5_acc": 0.62094, "loss_cls": 3.61214, "loss": 3.61214, "time": 0.82599} +{"mode": "train", "epoch": 90, "iter": 2000, "lr": 0.03501, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34766, "top5_acc": 0.61734, "loss_cls": 3.65809, "loss": 3.65809, "time": 0.81734} +{"mode": "train", "epoch": 90, "iter": 2100, "lr": 0.03499, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3475, "top5_acc": 0.61219, "loss_cls": 3.694, "loss": 3.694, "time": 0.81828} +{"mode": "train", "epoch": 90, "iter": 2200, "lr": 0.03496, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35844, "top5_acc": 0.61844, "loss_cls": 3.62974, "loss": 3.62974, "time": 0.82231} +{"mode": "train", "epoch": 90, "iter": 2300, "lr": 0.03493, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35172, "top5_acc": 0.60734, "loss_cls": 3.67601, "loss": 3.67601, "time": 0.81639} +{"mode": "train", "epoch": 90, "iter": 2400, "lr": 0.03491, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34312, "top5_acc": 0.61797, "loss_cls": 3.67857, "loss": 3.67857, "time": 0.8113} +{"mode": "train", "epoch": 90, "iter": 2500, "lr": 0.03488, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35828, "top5_acc": 0.61531, "loss_cls": 3.64456, "loss": 3.64456, "time": 0.81616} +{"mode": "train", "epoch": 90, "iter": 2600, "lr": 0.03485, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35359, "top5_acc": 0.61875, "loss_cls": 3.60529, "loss": 3.60529, "time": 0.81497} +{"mode": "train", "epoch": 90, "iter": 2700, "lr": 0.03483, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35203, "top5_acc": 0.61109, "loss_cls": 3.65825, "loss": 3.65825, "time": 0.82067} +{"mode": "train", "epoch": 90, "iter": 2800, "lr": 0.0348, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35938, "top5_acc": 0.62531, "loss_cls": 3.63482, "loss": 3.63482, "time": 0.81721} +{"mode": "train", "epoch": 90, "iter": 2900, "lr": 0.03477, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35312, "top5_acc": 0.61187, "loss_cls": 3.67151, "loss": 3.67151, "time": 0.81744} +{"mode": "train", "epoch": 90, "iter": 3000, "lr": 0.03475, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.355, "top5_acc": 0.62359, "loss_cls": 3.60624, "loss": 3.60624, "time": 0.81451} +{"mode": "train", "epoch": 90, "iter": 3100, "lr": 0.03472, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.355, "top5_acc": 0.62172, "loss_cls": 3.63498, "loss": 3.63498, "time": 0.8177} +{"mode": "train", "epoch": 90, "iter": 3200, "lr": 0.03469, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34984, "top5_acc": 0.60984, "loss_cls": 3.65643, "loss": 3.65643, "time": 0.81341} +{"mode": "train", "epoch": 90, "iter": 3300, "lr": 0.03467, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35953, "top5_acc": 0.62375, "loss_cls": 3.60236, "loss": 3.60236, "time": 0.81249} +{"mode": "train", "epoch": 90, "iter": 3400, "lr": 0.03464, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3475, "top5_acc": 0.6125, "loss_cls": 3.66463, "loss": 3.66463, "time": 0.81828} +{"mode": "train", "epoch": 90, "iter": 3500, "lr": 0.03461, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34391, "top5_acc": 0.61391, "loss_cls": 3.69455, "loss": 3.69455, "time": 0.81736} +{"mode": "train", "epoch": 90, "iter": 3600, "lr": 0.03459, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35609, "top5_acc": 0.62438, "loss_cls": 3.61209, "loss": 3.61209, "time": 0.81462} +{"mode": "train", "epoch": 90, "iter": 3700, "lr": 0.03456, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34844, "top5_acc": 0.60984, "loss_cls": 3.68388, "loss": 3.68388, "time": 0.81905} +{"mode": "val", "epoch": 90, "iter": 309, "lr": 0.03455, "top1_acc": 0.27741, "top5_acc": 0.52773, "mean_class_accuracy": 0.27714} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.03452, "memory": 15990, "data_time": 1.395, "top1_acc": 0.36562, "top5_acc": 0.62969, "loss_cls": 3.58483, "loss": 3.58483, "time": 2.38198} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0345, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34906, "top5_acc": 0.62828, "loss_cls": 3.60869, "loss": 3.60869, "time": 0.81555} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.03447, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36297, "top5_acc": 0.63469, "loss_cls": 3.56839, "loss": 3.56839, "time": 0.81765} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.03444, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35125, "top5_acc": 0.61531, "loss_cls": 3.63668, "loss": 3.63668, "time": 0.82346} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.03442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34641, "top5_acc": 0.62266, "loss_cls": 3.63761, "loss": 3.63761, "time": 0.81716} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.03439, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36812, "top5_acc": 0.62813, "loss_cls": 3.55338, "loss": 3.55338, "time": 0.81766} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.03436, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35062, "top5_acc": 0.61375, "loss_cls": 3.65142, "loss": 3.65142, "time": 0.82017} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.03434, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36656, "top5_acc": 0.62078, "loss_cls": 3.59664, "loss": 3.59664, "time": 0.81547} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.03431, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37, "top5_acc": 0.63625, "loss_cls": 3.55019, "loss": 3.55019, "time": 0.81579} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.03428, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35359, "top5_acc": 0.61109, "loss_cls": 3.6642, "loss": 3.6642, "time": 0.8134} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.03426, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36922, "top5_acc": 0.62531, "loss_cls": 3.59942, "loss": 3.59942, "time": 0.81459} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.03423, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35375, "top5_acc": 0.61875, "loss_cls": 3.64788, "loss": 3.64788, "time": 0.81251} +{"mode": "train", "epoch": 91, "iter": 1300, "lr": 0.0342, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36297, "top5_acc": 0.61766, "loss_cls": 3.63517, "loss": 3.63517, "time": 0.82126} +{"mode": "train", "epoch": 91, "iter": 1400, "lr": 0.03418, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36234, "top5_acc": 0.62328, "loss_cls": 3.63145, "loss": 3.63145, "time": 0.82078} +{"mode": "train", "epoch": 91, "iter": 1500, "lr": 0.03415, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35609, "top5_acc": 0.62625, "loss_cls": 3.59172, "loss": 3.59172, "time": 0.81257} +{"mode": "train", "epoch": 91, "iter": 1600, "lr": 0.03412, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35188, "top5_acc": 0.62281, "loss_cls": 3.62021, "loss": 3.62021, "time": 0.81413} +{"mode": "train", "epoch": 91, "iter": 1700, "lr": 0.0341, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35891, "top5_acc": 0.62234, "loss_cls": 3.60191, "loss": 3.60191, "time": 0.81427} +{"mode": "train", "epoch": 91, "iter": 1800, "lr": 0.03407, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35312, "top5_acc": 0.61547, "loss_cls": 3.63969, "loss": 3.63969, "time": 0.82411} +{"mode": "train", "epoch": 91, "iter": 1900, "lr": 0.03405, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35516, "top5_acc": 0.61391, "loss_cls": 3.63496, "loss": 3.63496, "time": 0.82259} +{"mode": "train", "epoch": 91, "iter": 2000, "lr": 0.03402, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35344, "top5_acc": 0.61453, "loss_cls": 3.65189, "loss": 3.65189, "time": 0.8233} +{"mode": "train", "epoch": 91, "iter": 2100, "lr": 0.03399, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35969, "top5_acc": 0.61938, "loss_cls": 3.60165, "loss": 3.60165, "time": 0.82263} +{"mode": "train", "epoch": 91, "iter": 2200, "lr": 0.03397, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35266, "top5_acc": 0.62078, "loss_cls": 3.62359, "loss": 3.62359, "time": 0.81669} +{"mode": "train", "epoch": 91, "iter": 2300, "lr": 0.03394, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34859, "top5_acc": 0.61656, "loss_cls": 3.66668, "loss": 3.66668, "time": 0.81318} +{"mode": "train", "epoch": 91, "iter": 2400, "lr": 0.03391, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34984, "top5_acc": 0.60781, "loss_cls": 3.66745, "loss": 3.66745, "time": 0.81492} +{"mode": "train", "epoch": 91, "iter": 2500, "lr": 0.03389, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35578, "top5_acc": 0.61984, "loss_cls": 3.61634, "loss": 3.61634, "time": 0.8138} +{"mode": "train", "epoch": 91, "iter": 2600, "lr": 0.03386, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34766, "top5_acc": 0.60703, "loss_cls": 3.68332, "loss": 3.68332, "time": 0.81578} +{"mode": "train", "epoch": 91, "iter": 2700, "lr": 0.03383, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36062, "top5_acc": 0.62297, "loss_cls": 3.59327, "loss": 3.59327, "time": 0.81585} +{"mode": "train", "epoch": 91, "iter": 2800, "lr": 0.03381, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35453, "top5_acc": 0.60609, "loss_cls": 3.67513, "loss": 3.67513, "time": 0.82046} +{"mode": "train", "epoch": 91, "iter": 2900, "lr": 0.03378, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35984, "top5_acc": 0.61672, "loss_cls": 3.65762, "loss": 3.65762, "time": 0.81364} +{"mode": "train", "epoch": 91, "iter": 3000, "lr": 0.03375, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34938, "top5_acc": 0.60922, "loss_cls": 3.64605, "loss": 3.64605, "time": 0.81349} +{"mode": "train", "epoch": 91, "iter": 3100, "lr": 0.03373, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36469, "top5_acc": 0.61906, "loss_cls": 3.60649, "loss": 3.60649, "time": 0.81873} +{"mode": "train", "epoch": 91, "iter": 3200, "lr": 0.0337, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36234, "top5_acc": 0.62672, "loss_cls": 3.61063, "loss": 3.61063, "time": 0.8181} +{"mode": "train", "epoch": 91, "iter": 3300, "lr": 0.03367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35688, "top5_acc": 0.62016, "loss_cls": 3.63681, "loss": 3.63681, "time": 0.81202} +{"mode": "train", "epoch": 91, "iter": 3400, "lr": 0.03365, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35078, "top5_acc": 0.62, "loss_cls": 3.64636, "loss": 3.64636, "time": 0.81807} +{"mode": "train", "epoch": 91, "iter": 3500, "lr": 0.03362, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34953, "top5_acc": 0.61453, "loss_cls": 3.66607, "loss": 3.66607, "time": 0.8117} +{"mode": "train", "epoch": 91, "iter": 3600, "lr": 0.0336, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36281, "top5_acc": 0.61859, "loss_cls": 3.57793, "loss": 3.57793, "time": 0.81671} +{"mode": "train", "epoch": 91, "iter": 3700, "lr": 0.03357, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35625, "top5_acc": 0.61703, "loss_cls": 3.63044, "loss": 3.63044, "time": 0.81718} +{"mode": "val", "epoch": 91, "iter": 309, "lr": 0.03356, "top1_acc": 0.29337, "top5_acc": 0.55412, "mean_class_accuracy": 0.29317} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.03353, "memory": 15990, "data_time": 1.37424, "top1_acc": 0.37156, "top5_acc": 0.62609, "loss_cls": 3.5715, "loss": 3.5715, "time": 2.36412} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.0335, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36203, "top5_acc": 0.63391, "loss_cls": 3.56518, "loss": 3.56518, "time": 0.82086} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.03348, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38188, "top5_acc": 0.64828, "loss_cls": 3.49122, "loss": 3.49122, "time": 0.82118} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.03345, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35125, "top5_acc": 0.61734, "loss_cls": 3.62415, "loss": 3.62415, "time": 0.82018} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.03342, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3675, "top5_acc": 0.63297, "loss_cls": 3.56279, "loss": 3.56279, "time": 0.81098} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.0334, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36531, "top5_acc": 0.61766, "loss_cls": 3.64531, "loss": 3.64531, "time": 0.81555} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.03337, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35328, "top5_acc": 0.62594, "loss_cls": 3.605, "loss": 3.605, "time": 0.8126} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.03335, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36438, "top5_acc": 0.62453, "loss_cls": 3.56487, "loss": 3.56487, "time": 0.81861} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.03332, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34969, "top5_acc": 0.61891, "loss_cls": 3.60343, "loss": 3.60343, "time": 0.81746} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.03329, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35125, "top5_acc": 0.61766, "loss_cls": 3.64221, "loss": 3.64221, "time": 0.81265} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.03327, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34984, "top5_acc": 0.61438, "loss_cls": 3.63991, "loss": 3.63991, "time": 0.81506} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.03324, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35672, "top5_acc": 0.61547, "loss_cls": 3.64333, "loss": 3.64333, "time": 0.8199} +{"mode": "train", "epoch": 92, "iter": 1300, "lr": 0.03321, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3675, "top5_acc": 0.63156, "loss_cls": 3.54492, "loss": 3.54492, "time": 0.81454} +{"mode": "train", "epoch": 92, "iter": 1400, "lr": 0.03319, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36156, "top5_acc": 0.62828, "loss_cls": 3.56829, "loss": 3.56829, "time": 0.81392} +{"mode": "train", "epoch": 92, "iter": 1500, "lr": 0.03316, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35328, "top5_acc": 0.61375, "loss_cls": 3.62401, "loss": 3.62401, "time": 0.81945} +{"mode": "train", "epoch": 92, "iter": 1600, "lr": 0.03314, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37531, "top5_acc": 0.62453, "loss_cls": 3.58374, "loss": 3.58374, "time": 0.81657} +{"mode": "train", "epoch": 92, "iter": 1700, "lr": 0.03311, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35125, "top5_acc": 0.62578, "loss_cls": 3.61463, "loss": 3.61463, "time": 0.81627} +{"mode": "train", "epoch": 92, "iter": 1800, "lr": 0.03308, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34688, "top5_acc": 0.62219, "loss_cls": 3.62301, "loss": 3.62301, "time": 0.82597} +{"mode": "train", "epoch": 92, "iter": 1900, "lr": 0.03306, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35953, "top5_acc": 0.61844, "loss_cls": 3.62452, "loss": 3.62452, "time": 0.82062} +{"mode": "train", "epoch": 92, "iter": 2000, "lr": 0.03303, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35531, "top5_acc": 0.61641, "loss_cls": 3.64419, "loss": 3.64419, "time": 0.82576} +{"mode": "train", "epoch": 92, "iter": 2100, "lr": 0.033, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35891, "top5_acc": 0.62766, "loss_cls": 3.61574, "loss": 3.61574, "time": 0.81706} +{"mode": "train", "epoch": 92, "iter": 2200, "lr": 0.03298, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34719, "top5_acc": 0.61609, "loss_cls": 3.6528, "loss": 3.6528, "time": 0.82493} +{"mode": "train", "epoch": 92, "iter": 2300, "lr": 0.03295, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35859, "top5_acc": 0.61516, "loss_cls": 3.63495, "loss": 3.63495, "time": 0.81647} +{"mode": "train", "epoch": 92, "iter": 2400, "lr": 0.03292, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35703, "top5_acc": 0.62547, "loss_cls": 3.60611, "loss": 3.60611, "time": 0.81898} +{"mode": "train", "epoch": 92, "iter": 2500, "lr": 0.0329, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36172, "top5_acc": 0.63203, "loss_cls": 3.57584, "loss": 3.57584, "time": 0.81651} +{"mode": "train", "epoch": 92, "iter": 2600, "lr": 0.03287, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35859, "top5_acc": 0.61328, "loss_cls": 3.66302, "loss": 3.66302, "time": 0.81721} +{"mode": "train", "epoch": 92, "iter": 2700, "lr": 0.03285, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35578, "top5_acc": 0.62156, "loss_cls": 3.6235, "loss": 3.6235, "time": 0.82106} +{"mode": "train", "epoch": 92, "iter": 2800, "lr": 0.03282, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35422, "top5_acc": 0.62016, "loss_cls": 3.6443, "loss": 3.6443, "time": 0.81119} +{"mode": "train", "epoch": 92, "iter": 2900, "lr": 0.03279, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35672, "top5_acc": 0.61781, "loss_cls": 3.62079, "loss": 3.62079, "time": 0.82176} +{"mode": "train", "epoch": 92, "iter": 3000, "lr": 0.03277, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35547, "top5_acc": 0.62031, "loss_cls": 3.61175, "loss": 3.61175, "time": 0.82145} +{"mode": "train", "epoch": 92, "iter": 3100, "lr": 0.03274, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35094, "top5_acc": 0.63016, "loss_cls": 3.61167, "loss": 3.61167, "time": 0.81585} +{"mode": "train", "epoch": 92, "iter": 3200, "lr": 0.03271, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35938, "top5_acc": 0.62125, "loss_cls": 3.6054, "loss": 3.6054, "time": 0.82013} +{"mode": "train", "epoch": 92, "iter": 3300, "lr": 0.03269, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35453, "top5_acc": 0.6175, "loss_cls": 3.64273, "loss": 3.64273, "time": 0.81563} +{"mode": "train", "epoch": 92, "iter": 3400, "lr": 0.03266, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36031, "top5_acc": 0.61641, "loss_cls": 3.62523, "loss": 3.62523, "time": 0.81718} +{"mode": "train", "epoch": 92, "iter": 3500, "lr": 0.03264, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35609, "top5_acc": 0.6275, "loss_cls": 3.60308, "loss": 3.60308, "time": 0.81695} +{"mode": "train", "epoch": 92, "iter": 3600, "lr": 0.03261, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34969, "top5_acc": 0.61438, "loss_cls": 3.65752, "loss": 3.65752, "time": 0.81562} +{"mode": "train", "epoch": 92, "iter": 3700, "lr": 0.03258, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35781, "top5_acc": 0.62484, "loss_cls": 3.61616, "loss": 3.61616, "time": 0.81859} +{"mode": "val", "epoch": 92, "iter": 309, "lr": 0.03257, "top1_acc": 0.29129, "top5_acc": 0.549, "mean_class_accuracy": 0.2911} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.03255, "memory": 15990, "data_time": 1.36589, "top1_acc": 0.38156, "top5_acc": 0.63344, "loss_cls": 3.53729, "loss": 3.53729, "time": 2.36127} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.03252, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36375, "top5_acc": 0.61766, "loss_cls": 3.59331, "loss": 3.59331, "time": 0.82302} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.03249, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36016, "top5_acc": 0.62922, "loss_cls": 3.58842, "loss": 3.58842, "time": 0.82855} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.03247, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36938, "top5_acc": 0.63781, "loss_cls": 3.53505, "loss": 3.53505, "time": 0.81563} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.03244, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3575, "top5_acc": 0.62219, "loss_cls": 3.61345, "loss": 3.61345, "time": 0.81511} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.03241, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35953, "top5_acc": 0.62187, "loss_cls": 3.61349, "loss": 3.61349, "time": 0.81471} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.03239, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36672, "top5_acc": 0.63406, "loss_cls": 3.57401, "loss": 3.57401, "time": 0.81883} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.03236, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36547, "top5_acc": 0.62438, "loss_cls": 3.59358, "loss": 3.59358, "time": 0.81802} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.03234, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36672, "top5_acc": 0.63578, "loss_cls": 3.55978, "loss": 3.55978, "time": 0.81122} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.03231, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36016, "top5_acc": 0.61516, "loss_cls": 3.61121, "loss": 3.61121, "time": 0.81779} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.03228, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36516, "top5_acc": 0.63531, "loss_cls": 3.54073, "loss": 3.54073, "time": 0.81473} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.03226, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37188, "top5_acc": 0.63781, "loss_cls": 3.53261, "loss": 3.53261, "time": 0.81753} +{"mode": "train", "epoch": 93, "iter": 1300, "lr": 0.03223, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35578, "top5_acc": 0.62109, "loss_cls": 3.61802, "loss": 3.61802, "time": 0.81463} +{"mode": "train", "epoch": 93, "iter": 1400, "lr": 0.03221, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36125, "top5_acc": 0.63125, "loss_cls": 3.60447, "loss": 3.60447, "time": 0.81467} +{"mode": "train", "epoch": 93, "iter": 1500, "lr": 0.03218, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35281, "top5_acc": 0.62203, "loss_cls": 3.61605, "loss": 3.61605, "time": 0.81443} +{"mode": "train", "epoch": 93, "iter": 1600, "lr": 0.03215, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35422, "top5_acc": 0.62891, "loss_cls": 3.60277, "loss": 3.60277, "time": 0.81404} +{"mode": "train", "epoch": 93, "iter": 1700, "lr": 0.03213, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35891, "top5_acc": 0.61766, "loss_cls": 3.63506, "loss": 3.63506, "time": 0.82208} +{"mode": "train", "epoch": 93, "iter": 1800, "lr": 0.0321, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35891, "top5_acc": 0.63234, "loss_cls": 3.57602, "loss": 3.57602, "time": 0.81835} +{"mode": "train", "epoch": 93, "iter": 1900, "lr": 0.03207, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3675, "top5_acc": 0.62656, "loss_cls": 3.58824, "loss": 3.58824, "time": 0.81414} +{"mode": "train", "epoch": 93, "iter": 2000, "lr": 0.03205, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36453, "top5_acc": 0.62375, "loss_cls": 3.57704, "loss": 3.57704, "time": 0.82454} +{"mode": "train", "epoch": 93, "iter": 2100, "lr": 0.03202, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34984, "top5_acc": 0.61797, "loss_cls": 3.62556, "loss": 3.62556, "time": 0.81739} +{"mode": "train", "epoch": 93, "iter": 2200, "lr": 0.032, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35656, "top5_acc": 0.62453, "loss_cls": 3.60119, "loss": 3.60119, "time": 0.81452} +{"mode": "train", "epoch": 93, "iter": 2300, "lr": 0.03197, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37219, "top5_acc": 0.63625, "loss_cls": 3.55469, "loss": 3.55469, "time": 0.82075} +{"mode": "train", "epoch": 93, "iter": 2400, "lr": 0.03194, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36719, "top5_acc": 0.62391, "loss_cls": 3.58662, "loss": 3.58662, "time": 0.81872} +{"mode": "train", "epoch": 93, "iter": 2500, "lr": 0.03192, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35922, "top5_acc": 0.61719, "loss_cls": 3.6208, "loss": 3.6208, "time": 0.81671} +{"mode": "train", "epoch": 93, "iter": 2600, "lr": 0.03189, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36594, "top5_acc": 0.62266, "loss_cls": 3.59869, "loss": 3.59869, "time": 0.81689} +{"mode": "train", "epoch": 93, "iter": 2700, "lr": 0.03187, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35547, "top5_acc": 0.62656, "loss_cls": 3.6125, "loss": 3.6125, "time": 0.82261} +{"mode": "train", "epoch": 93, "iter": 2800, "lr": 0.03184, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35531, "top5_acc": 0.62391, "loss_cls": 3.63983, "loss": 3.63983, "time": 0.81431} +{"mode": "train", "epoch": 93, "iter": 2900, "lr": 0.03181, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37125, "top5_acc": 0.63953, "loss_cls": 3.52815, "loss": 3.52815, "time": 0.81705} +{"mode": "train", "epoch": 93, "iter": 3000, "lr": 0.03179, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37391, "top5_acc": 0.63188, "loss_cls": 3.53314, "loss": 3.53314, "time": 0.81544} +{"mode": "train", "epoch": 93, "iter": 3100, "lr": 0.03176, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35266, "top5_acc": 0.60812, "loss_cls": 3.65618, "loss": 3.65618, "time": 0.81289} +{"mode": "train", "epoch": 93, "iter": 3200, "lr": 0.03174, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36094, "top5_acc": 0.6275, "loss_cls": 3.61063, "loss": 3.61063, "time": 0.81724} +{"mode": "train", "epoch": 93, "iter": 3300, "lr": 0.03171, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34844, "top5_acc": 0.61562, "loss_cls": 3.64628, "loss": 3.64628, "time": 0.81682} +{"mode": "train", "epoch": 93, "iter": 3400, "lr": 0.03168, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35109, "top5_acc": 0.61109, "loss_cls": 3.65472, "loss": 3.65472, "time": 0.82006} +{"mode": "train", "epoch": 93, "iter": 3500, "lr": 0.03166, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37531, "top5_acc": 0.63766, "loss_cls": 3.54374, "loss": 3.54374, "time": 0.81416} +{"mode": "train", "epoch": 93, "iter": 3600, "lr": 0.03163, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37188, "top5_acc": 0.62109, "loss_cls": 3.59847, "loss": 3.59847, "time": 0.81318} +{"mode": "train", "epoch": 93, "iter": 3700, "lr": 0.03161, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34656, "top5_acc": 0.61734, "loss_cls": 3.65012, "loss": 3.65012, "time": 0.81662} +{"mode": "val", "epoch": 93, "iter": 309, "lr": 0.03159, "top1_acc": 0.29438, "top5_acc": 0.54982, "mean_class_accuracy": 0.29424} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.03157, "memory": 15990, "data_time": 1.38887, "top1_acc": 0.37531, "top5_acc": 0.63984, "loss_cls": 3.48893, "loss": 3.48893, "time": 2.38702} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.03154, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36594, "top5_acc": 0.63813, "loss_cls": 3.5305, "loss": 3.5305, "time": 0.82537} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.03152, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36656, "top5_acc": 0.64234, "loss_cls": 3.52591, "loss": 3.52591, "time": 0.82293} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.03149, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36422, "top5_acc": 0.62891, "loss_cls": 3.59656, "loss": 3.59656, "time": 0.81943} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.03146, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36141, "top5_acc": 0.62687, "loss_cls": 3.58134, "loss": 3.58134, "time": 0.81915} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.03144, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37609, "top5_acc": 0.62687, "loss_cls": 3.54324, "loss": 3.54324, "time": 0.81988} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.03141, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36656, "top5_acc": 0.62875, "loss_cls": 3.56642, "loss": 3.56642, "time": 0.82182} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.03139, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36406, "top5_acc": 0.6275, "loss_cls": 3.57405, "loss": 3.57405, "time": 0.81456} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.03136, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36656, "top5_acc": 0.62406, "loss_cls": 3.5878, "loss": 3.5878, "time": 0.81991} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.03133, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.375, "top5_acc": 0.63203, "loss_cls": 3.54759, "loss": 3.54759, "time": 0.81815} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.03131, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35844, "top5_acc": 0.62359, "loss_cls": 3.61216, "loss": 3.61216, "time": 0.81338} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.03128, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36641, "top5_acc": 0.63891, "loss_cls": 3.5398, "loss": 3.5398, "time": 0.81948} +{"mode": "train", "epoch": 94, "iter": 1300, "lr": 0.03126, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36641, "top5_acc": 0.62297, "loss_cls": 3.58476, "loss": 3.58476, "time": 0.81922} +{"mode": "train", "epoch": 94, "iter": 1400, "lr": 0.03123, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36062, "top5_acc": 0.62125, "loss_cls": 3.59269, "loss": 3.59269, "time": 0.8149} +{"mode": "train", "epoch": 94, "iter": 1500, "lr": 0.0312, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37547, "top5_acc": 0.62875, "loss_cls": 3.56119, "loss": 3.56119, "time": 0.81771} +{"mode": "train", "epoch": 94, "iter": 1600, "lr": 0.03118, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37156, "top5_acc": 0.63703, "loss_cls": 3.56641, "loss": 3.56641, "time": 0.81666} +{"mode": "train", "epoch": 94, "iter": 1700, "lr": 0.03115, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35016, "top5_acc": 0.62, "loss_cls": 3.59603, "loss": 3.59603, "time": 0.81613} +{"mode": "train", "epoch": 94, "iter": 1800, "lr": 0.03113, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36516, "top5_acc": 0.63219, "loss_cls": 3.57945, "loss": 3.57945, "time": 0.82438} +{"mode": "train", "epoch": 94, "iter": 1900, "lr": 0.0311, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37484, "top5_acc": 0.63703, "loss_cls": 3.50886, "loss": 3.50886, "time": 0.81892} +{"mode": "train", "epoch": 94, "iter": 2000, "lr": 0.03108, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35344, "top5_acc": 0.62781, "loss_cls": 3.59705, "loss": 3.59705, "time": 0.82706} +{"mode": "train", "epoch": 94, "iter": 2100, "lr": 0.03105, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36969, "top5_acc": 0.62391, "loss_cls": 3.5762, "loss": 3.5762, "time": 0.81659} +{"mode": "train", "epoch": 94, "iter": 2200, "lr": 0.03102, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36547, "top5_acc": 0.62141, "loss_cls": 3.58878, "loss": 3.58878, "time": 0.81953} +{"mode": "train", "epoch": 94, "iter": 2300, "lr": 0.031, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37188, "top5_acc": 0.63688, "loss_cls": 3.55166, "loss": 3.55166, "time": 0.82108} +{"mode": "train", "epoch": 94, "iter": 2400, "lr": 0.03097, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36406, "top5_acc": 0.62344, "loss_cls": 3.55473, "loss": 3.55473, "time": 0.81856} +{"mode": "train", "epoch": 94, "iter": 2500, "lr": 0.03095, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35203, "top5_acc": 0.625, "loss_cls": 3.62613, "loss": 3.62613, "time": 0.81952} +{"mode": "train", "epoch": 94, "iter": 2600, "lr": 0.03092, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36297, "top5_acc": 0.62938, "loss_cls": 3.59519, "loss": 3.59519, "time": 0.81972} +{"mode": "train", "epoch": 94, "iter": 2700, "lr": 0.03089, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35844, "top5_acc": 0.62641, "loss_cls": 3.56806, "loss": 3.56806, "time": 0.82322} +{"mode": "train", "epoch": 94, "iter": 2800, "lr": 0.03087, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35562, "top5_acc": 0.63469, "loss_cls": 3.58369, "loss": 3.58369, "time": 0.82115} +{"mode": "train", "epoch": 94, "iter": 2900, "lr": 0.03084, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36391, "top5_acc": 0.62187, "loss_cls": 3.61833, "loss": 3.61833, "time": 0.82426} +{"mode": "train", "epoch": 94, "iter": 3000, "lr": 0.03082, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36172, "top5_acc": 0.62984, "loss_cls": 3.60188, "loss": 3.60188, "time": 0.81901} +{"mode": "train", "epoch": 94, "iter": 3100, "lr": 0.03079, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36672, "top5_acc": 0.62359, "loss_cls": 3.58203, "loss": 3.58203, "time": 0.8182} +{"mode": "train", "epoch": 94, "iter": 3200, "lr": 0.03077, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35703, "top5_acc": 0.62797, "loss_cls": 3.57579, "loss": 3.57579, "time": 0.8121} +{"mode": "train", "epoch": 94, "iter": 3300, "lr": 0.03074, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36, "top5_acc": 0.61797, "loss_cls": 3.61827, "loss": 3.61827, "time": 0.81899} +{"mode": "train", "epoch": 94, "iter": 3400, "lr": 0.03071, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35953, "top5_acc": 0.61953, "loss_cls": 3.64726, "loss": 3.64726, "time": 0.81746} +{"mode": "train", "epoch": 94, "iter": 3500, "lr": 0.03069, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36797, "top5_acc": 0.6175, "loss_cls": 3.6069, "loss": 3.6069, "time": 0.81636} +{"mode": "train", "epoch": 94, "iter": 3600, "lr": 0.03066, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36875, "top5_acc": 0.62438, "loss_cls": 3.54718, "loss": 3.54718, "time": 0.81423} +{"mode": "train", "epoch": 94, "iter": 3700, "lr": 0.03064, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36672, "top5_acc": 0.62328, "loss_cls": 3.58937, "loss": 3.58937, "time": 0.81722} +{"mode": "val", "epoch": 94, "iter": 309, "lr": 0.03062, "top1_acc": 0.29378, "top5_acc": 0.54186, "mean_class_accuracy": 0.29349} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.0306, "memory": 15990, "data_time": 1.34911, "top1_acc": 0.37297, "top5_acc": 0.63906, "loss_cls": 3.51335, "loss": 3.51335, "time": 2.34488} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.03057, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37328, "top5_acc": 0.63688, "loss_cls": 3.52028, "loss": 3.52028, "time": 0.82619} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.03055, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36812, "top5_acc": 0.62813, "loss_cls": 3.54268, "loss": 3.54268, "time": 0.8271} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.03052, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37094, "top5_acc": 0.63141, "loss_cls": 3.5545, "loss": 3.5545, "time": 0.82692} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.0305, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36078, "top5_acc": 0.62266, "loss_cls": 3.60335, "loss": 3.60335, "time": 0.82241} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.03047, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36531, "top5_acc": 0.63359, "loss_cls": 3.55675, "loss": 3.55675, "time": 0.82397} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.03044, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36719, "top5_acc": 0.63328, "loss_cls": 3.52863, "loss": 3.52863, "time": 0.8192} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.03042, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37562, "top5_acc": 0.63547, "loss_cls": 3.52942, "loss": 3.52942, "time": 0.82327} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.03039, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35594, "top5_acc": 0.62234, "loss_cls": 3.59185, "loss": 3.59185, "time": 0.81604} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.03037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36828, "top5_acc": 0.63797, "loss_cls": 3.54465, "loss": 3.54465, "time": 0.80959} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.03034, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37125, "top5_acc": 0.63156, "loss_cls": 3.55474, "loss": 3.55474, "time": 0.81559} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.03032, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37531, "top5_acc": 0.63984, "loss_cls": 3.51084, "loss": 3.51084, "time": 0.8165} +{"mode": "train", "epoch": 95, "iter": 1300, "lr": 0.03029, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36672, "top5_acc": 0.63281, "loss_cls": 3.53637, "loss": 3.53637, "time": 0.81443} +{"mode": "train", "epoch": 95, "iter": 1400, "lr": 0.03026, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36844, "top5_acc": 0.63344, "loss_cls": 3.55642, "loss": 3.55642, "time": 0.81635} +{"mode": "train", "epoch": 95, "iter": 1500, "lr": 0.03024, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36375, "top5_acc": 0.63078, "loss_cls": 3.55929, "loss": 3.55929, "time": 0.81572} +{"mode": "train", "epoch": 95, "iter": 1600, "lr": 0.03021, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36531, "top5_acc": 0.62875, "loss_cls": 3.52655, "loss": 3.52655, "time": 0.81736} +{"mode": "train", "epoch": 95, "iter": 1700, "lr": 0.03019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36328, "top5_acc": 0.63172, "loss_cls": 3.54985, "loss": 3.54985, "time": 0.81699} +{"mode": "train", "epoch": 95, "iter": 1800, "lr": 0.03016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37047, "top5_acc": 0.63156, "loss_cls": 3.54895, "loss": 3.54895, "time": 0.8243} +{"mode": "train", "epoch": 95, "iter": 1900, "lr": 0.03014, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36969, "top5_acc": 0.62141, "loss_cls": 3.60353, "loss": 3.60353, "time": 0.81915} +{"mode": "train", "epoch": 95, "iter": 2000, "lr": 0.03011, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36172, "top5_acc": 0.62969, "loss_cls": 3.58939, "loss": 3.58939, "time": 0.81979} +{"mode": "train", "epoch": 95, "iter": 2100, "lr": 0.03008, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36375, "top5_acc": 0.61906, "loss_cls": 3.63114, "loss": 3.63114, "time": 0.81879} +{"mode": "train", "epoch": 95, "iter": 2200, "lr": 0.03006, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36875, "top5_acc": 0.63516, "loss_cls": 3.52506, "loss": 3.52506, "time": 0.81602} +{"mode": "train", "epoch": 95, "iter": 2300, "lr": 0.03003, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36125, "top5_acc": 0.62109, "loss_cls": 3.57686, "loss": 3.57686, "time": 0.82068} +{"mode": "train", "epoch": 95, "iter": 2400, "lr": 0.03001, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36469, "top5_acc": 0.6225, "loss_cls": 3.57994, "loss": 3.57994, "time": 0.81239} +{"mode": "train", "epoch": 95, "iter": 2500, "lr": 0.02998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35719, "top5_acc": 0.61734, "loss_cls": 3.61717, "loss": 3.61717, "time": 0.81435} +{"mode": "train", "epoch": 95, "iter": 2600, "lr": 0.02996, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36812, "top5_acc": 0.625, "loss_cls": 3.56415, "loss": 3.56415, "time": 0.82869} +{"mode": "train", "epoch": 95, "iter": 2700, "lr": 0.02993, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35984, "top5_acc": 0.63766, "loss_cls": 3.53941, "loss": 3.53941, "time": 0.81758} +{"mode": "train", "epoch": 95, "iter": 2800, "lr": 0.02991, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36312, "top5_acc": 0.62672, "loss_cls": 3.59747, "loss": 3.59747, "time": 0.81724} +{"mode": "train", "epoch": 95, "iter": 2900, "lr": 0.02988, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35234, "top5_acc": 0.61266, "loss_cls": 3.63813, "loss": 3.63813, "time": 0.81598} +{"mode": "train", "epoch": 95, "iter": 3000, "lr": 0.02985, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36703, "top5_acc": 0.62375, "loss_cls": 3.57801, "loss": 3.57801, "time": 0.8118} +{"mode": "train", "epoch": 95, "iter": 3100, "lr": 0.02983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37141, "top5_acc": 0.62953, "loss_cls": 3.54173, "loss": 3.54173, "time": 0.8203} +{"mode": "train", "epoch": 95, "iter": 3200, "lr": 0.0298, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35438, "top5_acc": 0.62328, "loss_cls": 3.63727, "loss": 3.63727, "time": 0.81764} +{"mode": "train", "epoch": 95, "iter": 3300, "lr": 0.02978, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35703, "top5_acc": 0.62687, "loss_cls": 3.59058, "loss": 3.59058, "time": 0.81859} +{"mode": "train", "epoch": 95, "iter": 3400, "lr": 0.02975, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36531, "top5_acc": 0.62875, "loss_cls": 3.58141, "loss": 3.58141, "time": 0.81642} +{"mode": "train", "epoch": 95, "iter": 3500, "lr": 0.02973, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36812, "top5_acc": 0.63359, "loss_cls": 3.53561, "loss": 3.53561, "time": 0.8133} +{"mode": "train", "epoch": 95, "iter": 3600, "lr": 0.0297, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35734, "top5_acc": 0.61797, "loss_cls": 3.59705, "loss": 3.59705, "time": 0.82151} +{"mode": "train", "epoch": 95, "iter": 3700, "lr": 0.02968, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37, "top5_acc": 0.63516, "loss_cls": 3.55622, "loss": 3.55622, "time": 0.815} +{"mode": "val", "epoch": 95, "iter": 309, "lr": 0.02966, "top1_acc": 0.30472, "top5_acc": 0.56415, "mean_class_accuracy": 0.30465} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.02964, "memory": 15990, "data_time": 1.37875, "top1_acc": 0.37438, "top5_acc": 0.63313, "loss_cls": 3.51908, "loss": 3.51908, "time": 2.38208} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.02961, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37438, "top5_acc": 0.63859, "loss_cls": 3.50738, "loss": 3.50738, "time": 0.83189} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.02959, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3775, "top5_acc": 0.63672, "loss_cls": 3.51907, "loss": 3.51907, "time": 0.83977} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.02956, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36422, "top5_acc": 0.63172, "loss_cls": 3.55517, "loss": 3.55517, "time": 0.83356} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.02954, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36359, "top5_acc": 0.62859, "loss_cls": 3.56701, "loss": 3.56701, "time": 0.82608} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.02951, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37312, "top5_acc": 0.63125, "loss_cls": 3.55428, "loss": 3.55428, "time": 0.83348} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.02948, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37391, "top5_acc": 0.63406, "loss_cls": 3.54514, "loss": 3.54514, "time": 0.82802} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.02946, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36891, "top5_acc": 0.64656, "loss_cls": 3.50297, "loss": 3.50297, "time": 0.83694} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.02943, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37125, "top5_acc": 0.62547, "loss_cls": 3.58431, "loss": 3.58431, "time": 0.83742} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.02941, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38281, "top5_acc": 0.64969, "loss_cls": 3.43887, "loss": 3.43887, "time": 0.83358} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.02938, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36469, "top5_acc": 0.62719, "loss_cls": 3.57061, "loss": 3.57061, "time": 0.83613} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.02936, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37016, "top5_acc": 0.63859, "loss_cls": 3.52019, "loss": 3.52019, "time": 0.8374} +{"mode": "train", "epoch": 96, "iter": 1300, "lr": 0.02933, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36797, "top5_acc": 0.62969, "loss_cls": 3.5756, "loss": 3.5756, "time": 0.83619} +{"mode": "train", "epoch": 96, "iter": 1400, "lr": 0.02931, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3575, "top5_acc": 0.62922, "loss_cls": 3.6052, "loss": 3.6052, "time": 0.83371} +{"mode": "train", "epoch": 96, "iter": 1500, "lr": 0.02928, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36938, "top5_acc": 0.64297, "loss_cls": 3.54196, "loss": 3.54196, "time": 0.83772} +{"mode": "train", "epoch": 96, "iter": 1600, "lr": 0.02926, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36969, "top5_acc": 0.63031, "loss_cls": 3.55709, "loss": 3.55709, "time": 0.83927} +{"mode": "train", "epoch": 96, "iter": 1700, "lr": 0.02923, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37234, "top5_acc": 0.6275, "loss_cls": 3.54899, "loss": 3.54899, "time": 0.84025} +{"mode": "train", "epoch": 96, "iter": 1800, "lr": 0.0292, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37125, "top5_acc": 0.63703, "loss_cls": 3.5418, "loss": 3.5418, "time": 0.8356} +{"mode": "train", "epoch": 96, "iter": 1900, "lr": 0.02918, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36266, "top5_acc": 0.63062, "loss_cls": 3.53852, "loss": 3.53852, "time": 0.82352} +{"mode": "train", "epoch": 96, "iter": 2000, "lr": 0.02915, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37531, "top5_acc": 0.62906, "loss_cls": 3.56201, "loss": 3.56201, "time": 0.82807} +{"mode": "train", "epoch": 96, "iter": 2100, "lr": 0.02913, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36922, "top5_acc": 0.63688, "loss_cls": 3.54457, "loss": 3.54457, "time": 0.83795} +{"mode": "train", "epoch": 96, "iter": 2200, "lr": 0.0291, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35562, "top5_acc": 0.62406, "loss_cls": 3.58936, "loss": 3.58936, "time": 0.83847} +{"mode": "train", "epoch": 96, "iter": 2300, "lr": 0.02908, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36484, "top5_acc": 0.63141, "loss_cls": 3.56675, "loss": 3.56675, "time": 0.83691} +{"mode": "train", "epoch": 96, "iter": 2400, "lr": 0.02905, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37, "top5_acc": 0.63438, "loss_cls": 3.54128, "loss": 3.54128, "time": 0.83581} +{"mode": "train", "epoch": 96, "iter": 2500, "lr": 0.02903, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35969, "top5_acc": 0.62891, "loss_cls": 3.55175, "loss": 3.55175, "time": 0.83591} +{"mode": "train", "epoch": 96, "iter": 2600, "lr": 0.029, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36797, "top5_acc": 0.63266, "loss_cls": 3.53802, "loss": 3.53802, "time": 0.83369} +{"mode": "train", "epoch": 96, "iter": 2700, "lr": 0.02898, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36703, "top5_acc": 0.64344, "loss_cls": 3.49158, "loss": 3.49158, "time": 0.8357} +{"mode": "train", "epoch": 96, "iter": 2800, "lr": 0.02895, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37266, "top5_acc": 0.62187, "loss_cls": 3.55935, "loss": 3.55935, "time": 0.81778} +{"mode": "train", "epoch": 96, "iter": 2900, "lr": 0.02893, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36219, "top5_acc": 0.63406, "loss_cls": 3.57837, "loss": 3.57837, "time": 0.83158} +{"mode": "train", "epoch": 96, "iter": 3000, "lr": 0.0289, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37094, "top5_acc": 0.63625, "loss_cls": 3.54533, "loss": 3.54533, "time": 0.83595} +{"mode": "train", "epoch": 96, "iter": 3100, "lr": 0.02887, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36578, "top5_acc": 0.63359, "loss_cls": 3.55297, "loss": 3.55297, "time": 0.83228} +{"mode": "train", "epoch": 96, "iter": 3200, "lr": 0.02885, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36438, "top5_acc": 0.62641, "loss_cls": 3.56244, "loss": 3.56244, "time": 0.84025} +{"mode": "train", "epoch": 96, "iter": 3300, "lr": 0.02882, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36922, "top5_acc": 0.62828, "loss_cls": 3.56778, "loss": 3.56778, "time": 0.8333} +{"mode": "train", "epoch": 96, "iter": 3400, "lr": 0.0288, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37141, "top5_acc": 0.63078, "loss_cls": 3.54767, "loss": 3.54767, "time": 0.8309} +{"mode": "train", "epoch": 96, "iter": 3500, "lr": 0.02877, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36219, "top5_acc": 0.62359, "loss_cls": 3.58163, "loss": 3.58163, "time": 0.82995} +{"mode": "train", "epoch": 96, "iter": 3600, "lr": 0.02875, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38078, "top5_acc": 0.63813, "loss_cls": 3.50301, "loss": 3.50301, "time": 0.83392} +{"mode": "train", "epoch": 96, "iter": 3700, "lr": 0.02872, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36844, "top5_acc": 0.63344, "loss_cls": 3.53611, "loss": 3.53611, "time": 0.83106} +{"mode": "val", "epoch": 96, "iter": 309, "lr": 0.02871, "top1_acc": 0.32103, "top5_acc": 0.5795, "mean_class_accuracy": 0.3207} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.02869, "memory": 15990, "data_time": 1.3441, "top1_acc": 0.37891, "top5_acc": 0.64125, "loss_cls": 3.48425, "loss": 3.48425, "time": 2.32375} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.02866, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38234, "top5_acc": 0.64453, "loss_cls": 3.45505, "loss": 3.45505, "time": 0.82237} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.02864, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3925, "top5_acc": 0.65922, "loss_cls": 3.42807, "loss": 3.42807, "time": 0.81613} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.02861, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37156, "top5_acc": 0.64062, "loss_cls": 3.51534, "loss": 3.51534, "time": 0.81226} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.02858, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36406, "top5_acc": 0.63984, "loss_cls": 3.52792, "loss": 3.52792, "time": 0.8162} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.02856, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37031, "top5_acc": 0.64109, "loss_cls": 3.50457, "loss": 3.50457, "time": 0.81723} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.02853, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36391, "top5_acc": 0.62891, "loss_cls": 3.56802, "loss": 3.56802, "time": 0.81748} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.02851, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37406, "top5_acc": 0.63797, "loss_cls": 3.48749, "loss": 3.48749, "time": 0.81687} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.02848, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37641, "top5_acc": 0.63641, "loss_cls": 3.50789, "loss": 3.50789, "time": 0.81749} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.02846, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38281, "top5_acc": 0.63953, "loss_cls": 3.50282, "loss": 3.50282, "time": 0.81536} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.02843, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38, "top5_acc": 0.64203, "loss_cls": 3.5037, "loss": 3.5037, "time": 0.815} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.02841, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.365, "top5_acc": 0.6375, "loss_cls": 3.53702, "loss": 3.53702, "time": 0.8171} +{"mode": "train", "epoch": 97, "iter": 1300, "lr": 0.02838, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37344, "top5_acc": 0.63656, "loss_cls": 3.51787, "loss": 3.51787, "time": 0.82549} +{"mode": "train", "epoch": 97, "iter": 1400, "lr": 0.02836, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37219, "top5_acc": 0.64156, "loss_cls": 3.51155, "loss": 3.51155, "time": 0.81688} +{"mode": "train", "epoch": 97, "iter": 1500, "lr": 0.02833, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37234, "top5_acc": 0.63328, "loss_cls": 3.55158, "loss": 3.55158, "time": 0.81562} +{"mode": "train", "epoch": 97, "iter": 1600, "lr": 0.02831, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37797, "top5_acc": 0.63328, "loss_cls": 3.51447, "loss": 3.51447, "time": 0.81822} +{"mode": "train", "epoch": 97, "iter": 1700, "lr": 0.02828, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37297, "top5_acc": 0.63031, "loss_cls": 3.53517, "loss": 3.53517, "time": 0.82285} +{"mode": "train", "epoch": 97, "iter": 1800, "lr": 0.02826, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37266, "top5_acc": 0.63828, "loss_cls": 3.51123, "loss": 3.51123, "time": 0.81694} +{"mode": "train", "epoch": 97, "iter": 1900, "lr": 0.02823, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37625, "top5_acc": 0.64312, "loss_cls": 3.48957, "loss": 3.48957, "time": 0.8246} +{"mode": "train", "epoch": 97, "iter": 2000, "lr": 0.02821, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38016, "top5_acc": 0.63922, "loss_cls": 3.51926, "loss": 3.51926, "time": 0.82412} +{"mode": "train", "epoch": 97, "iter": 2100, "lr": 0.02818, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36688, "top5_acc": 0.63719, "loss_cls": 3.53928, "loss": 3.53928, "time": 0.8147} +{"mode": "train", "epoch": 97, "iter": 2200, "lr": 0.02816, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37531, "top5_acc": 0.64078, "loss_cls": 3.50894, "loss": 3.50894, "time": 0.8148} +{"mode": "train", "epoch": 97, "iter": 2300, "lr": 0.02813, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36062, "top5_acc": 0.62344, "loss_cls": 3.56554, "loss": 3.56554, "time": 0.81961} +{"mode": "train", "epoch": 97, "iter": 2400, "lr": 0.02811, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38234, "top5_acc": 0.6475, "loss_cls": 3.4903, "loss": 3.4903, "time": 0.81827} +{"mode": "train", "epoch": 97, "iter": 2500, "lr": 0.02808, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35766, "top5_acc": 0.62562, "loss_cls": 3.59371, "loss": 3.59371, "time": 0.81425} +{"mode": "train", "epoch": 97, "iter": 2600, "lr": 0.02806, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.355, "top5_acc": 0.62297, "loss_cls": 3.63628, "loss": 3.63628, "time": 0.82209} +{"mode": "train", "epoch": 97, "iter": 2700, "lr": 0.02803, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36578, "top5_acc": 0.63391, "loss_cls": 3.55324, "loss": 3.55324, "time": 0.81643} +{"mode": "train", "epoch": 97, "iter": 2800, "lr": 0.02801, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36922, "top5_acc": 0.63734, "loss_cls": 3.53503, "loss": 3.53503, "time": 0.81947} +{"mode": "train", "epoch": 97, "iter": 2900, "lr": 0.02798, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37172, "top5_acc": 0.63344, "loss_cls": 3.54819, "loss": 3.54819, "time": 0.8154} +{"mode": "train", "epoch": 97, "iter": 3000, "lr": 0.02796, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36391, "top5_acc": 0.62031, "loss_cls": 3.60089, "loss": 3.60089, "time": 0.82178} +{"mode": "train", "epoch": 97, "iter": 3100, "lr": 0.02793, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37344, "top5_acc": 0.63125, "loss_cls": 3.51638, "loss": 3.51638, "time": 0.81957} +{"mode": "train", "epoch": 97, "iter": 3200, "lr": 0.02791, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37141, "top5_acc": 0.62797, "loss_cls": 3.56063, "loss": 3.56063, "time": 0.8171} +{"mode": "train", "epoch": 97, "iter": 3300, "lr": 0.02788, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36453, "top5_acc": 0.62687, "loss_cls": 3.57001, "loss": 3.57001, "time": 0.81944} +{"mode": "train", "epoch": 97, "iter": 3400, "lr": 0.02786, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36844, "top5_acc": 0.63141, "loss_cls": 3.57271, "loss": 3.57271, "time": 0.81912} +{"mode": "train", "epoch": 97, "iter": 3500, "lr": 0.02783, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36656, "top5_acc": 0.63, "loss_cls": 3.60632, "loss": 3.60632, "time": 0.82144} +{"mode": "train", "epoch": 97, "iter": 3600, "lr": 0.02781, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36594, "top5_acc": 0.62844, "loss_cls": 3.58563, "loss": 3.58563, "time": 0.81246} +{"mode": "train", "epoch": 97, "iter": 3700, "lr": 0.02778, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36344, "top5_acc": 0.63172, "loss_cls": 3.56001, "loss": 3.56001, "time": 0.81975} +{"mode": "val", "epoch": 97, "iter": 309, "lr": 0.02777, "top1_acc": 0.30163, "top5_acc": 0.56177, "mean_class_accuracy": 0.30136} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.02774, "memory": 15990, "data_time": 1.34108, "top1_acc": 0.38781, "top5_acc": 0.64797, "loss_cls": 3.45193, "loss": 3.45193, "time": 2.34033} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.02772, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37875, "top5_acc": 0.65047, "loss_cls": 3.49759, "loss": 3.49759, "time": 0.81719} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.02769, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38531, "top5_acc": 0.64969, "loss_cls": 3.44782, "loss": 3.44782, "time": 0.82005} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.02767, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37234, "top5_acc": 0.63703, "loss_cls": 3.4918, "loss": 3.4918, "time": 0.81319} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.02764, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37328, "top5_acc": 0.63969, "loss_cls": 3.49865, "loss": 3.49865, "time": 0.82152} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.02762, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38406, "top5_acc": 0.64578, "loss_cls": 3.44044, "loss": 3.44044, "time": 0.82247} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.02759, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36969, "top5_acc": 0.63125, "loss_cls": 3.54949, "loss": 3.54949, "time": 0.81972} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.02757, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36875, "top5_acc": 0.63844, "loss_cls": 3.52252, "loss": 3.52252, "time": 0.81414} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.02754, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36844, "top5_acc": 0.63594, "loss_cls": 3.52768, "loss": 3.52768, "time": 0.81735} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.02752, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38344, "top5_acc": 0.64422, "loss_cls": 3.47781, "loss": 3.47781, "time": 0.8155} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.02749, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38078, "top5_acc": 0.63453, "loss_cls": 3.5175, "loss": 3.5175, "time": 0.81775} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.02747, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37156, "top5_acc": 0.63188, "loss_cls": 3.54045, "loss": 3.54045, "time": 0.81797} +{"mode": "train", "epoch": 98, "iter": 1300, "lr": 0.02744, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36344, "top5_acc": 0.63062, "loss_cls": 3.55135, "loss": 3.55135, "time": 0.81362} +{"mode": "train", "epoch": 98, "iter": 1400, "lr": 0.02742, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38156, "top5_acc": 0.64266, "loss_cls": 3.45279, "loss": 3.45279, "time": 0.81655} +{"mode": "train", "epoch": 98, "iter": 1500, "lr": 0.02739, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36875, "top5_acc": 0.63578, "loss_cls": 3.52712, "loss": 3.52712, "time": 0.81379} +{"mode": "train", "epoch": 98, "iter": 1600, "lr": 0.02737, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36938, "top5_acc": 0.63688, "loss_cls": 3.51827, "loss": 3.51827, "time": 0.82114} +{"mode": "train", "epoch": 98, "iter": 1700, "lr": 0.02734, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36625, "top5_acc": 0.63609, "loss_cls": 3.53876, "loss": 3.53876, "time": 0.81541} +{"mode": "train", "epoch": 98, "iter": 1800, "lr": 0.02732, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37594, "top5_acc": 0.63875, "loss_cls": 3.50109, "loss": 3.50109, "time": 0.817} +{"mode": "train", "epoch": 98, "iter": 1900, "lr": 0.02729, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37, "top5_acc": 0.64078, "loss_cls": 3.50744, "loss": 3.50744, "time": 0.83219} +{"mode": "train", "epoch": 98, "iter": 2000, "lr": 0.02727, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37375, "top5_acc": 0.63531, "loss_cls": 3.52354, "loss": 3.52354, "time": 0.81968} +{"mode": "train", "epoch": 98, "iter": 2100, "lr": 0.02724, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.3925, "top5_acc": 0.65469, "loss_cls": 3.46508, "loss": 3.46508, "time": 0.82248} +{"mode": "train", "epoch": 98, "iter": 2200, "lr": 0.02722, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36578, "top5_acc": 0.62813, "loss_cls": 3.57489, "loss": 3.57489, "time": 0.81474} +{"mode": "train", "epoch": 98, "iter": 2300, "lr": 0.02719, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37781, "top5_acc": 0.63922, "loss_cls": 3.51418, "loss": 3.51418, "time": 0.81283} +{"mode": "train", "epoch": 98, "iter": 2400, "lr": 0.02717, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38125, "top5_acc": 0.65094, "loss_cls": 3.4551, "loss": 3.4551, "time": 0.81691} +{"mode": "train", "epoch": 98, "iter": 2500, "lr": 0.02714, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36766, "top5_acc": 0.62797, "loss_cls": 3.57848, "loss": 3.57848, "time": 0.81634} +{"mode": "train", "epoch": 98, "iter": 2600, "lr": 0.02712, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36672, "top5_acc": 0.63781, "loss_cls": 3.56007, "loss": 3.56007, "time": 0.81971} +{"mode": "train", "epoch": 98, "iter": 2700, "lr": 0.02709, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37609, "top5_acc": 0.63813, "loss_cls": 3.49976, "loss": 3.49976, "time": 0.81915} +{"mode": "train", "epoch": 98, "iter": 2800, "lr": 0.02707, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37266, "top5_acc": 0.63047, "loss_cls": 3.57555, "loss": 3.57555, "time": 0.81993} +{"mode": "train", "epoch": 98, "iter": 2900, "lr": 0.02705, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36547, "top5_acc": 0.62516, "loss_cls": 3.5857, "loss": 3.5857, "time": 0.82163} +{"mode": "train", "epoch": 98, "iter": 3000, "lr": 0.02702, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37953, "top5_acc": 0.645, "loss_cls": 3.47796, "loss": 3.47796, "time": 0.81624} +{"mode": "train", "epoch": 98, "iter": 3100, "lr": 0.027, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37375, "top5_acc": 0.63844, "loss_cls": 3.53594, "loss": 3.53594, "time": 0.81374} +{"mode": "train", "epoch": 98, "iter": 3200, "lr": 0.02697, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37438, "top5_acc": 0.64203, "loss_cls": 3.51706, "loss": 3.51706, "time": 0.81595} +{"mode": "train", "epoch": 98, "iter": 3300, "lr": 0.02695, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37281, "top5_acc": 0.63719, "loss_cls": 3.54778, "loss": 3.54778, "time": 0.81224} +{"mode": "train", "epoch": 98, "iter": 3400, "lr": 0.02692, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36594, "top5_acc": 0.62828, "loss_cls": 3.596, "loss": 3.596, "time": 0.81792} +{"mode": "train", "epoch": 98, "iter": 3500, "lr": 0.0269, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38125, "top5_acc": 0.64656, "loss_cls": 3.46871, "loss": 3.46871, "time": 0.81602} +{"mode": "train", "epoch": 98, "iter": 3600, "lr": 0.02687, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36562, "top5_acc": 0.63719, "loss_cls": 3.52883, "loss": 3.52883, "time": 0.81844} +{"mode": "train", "epoch": 98, "iter": 3700, "lr": 0.02685, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37703, "top5_acc": 0.63656, "loss_cls": 3.52768, "loss": 3.52768, "time": 0.81871} +{"mode": "val", "epoch": 98, "iter": 309, "lr": 0.02684, "top1_acc": 0.315, "top5_acc": 0.57033, "mean_class_accuracy": 0.31474} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.02681, "memory": 15990, "data_time": 1.34649, "top1_acc": 0.39297, "top5_acc": 0.66469, "loss_cls": 3.3922, "loss": 3.3922, "time": 2.32939} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.02679, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37906, "top5_acc": 0.64844, "loss_cls": 3.4427, "loss": 3.4427, "time": 0.82965} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.02676, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37172, "top5_acc": 0.64531, "loss_cls": 3.49429, "loss": 3.49429, "time": 0.83671} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.02674, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38578, "top5_acc": 0.64453, "loss_cls": 3.47606, "loss": 3.47606, "time": 0.83444} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.02671, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38422, "top5_acc": 0.65391, "loss_cls": 3.4554, "loss": 3.4554, "time": 0.83071} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.02669, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38016, "top5_acc": 0.64609, "loss_cls": 3.48143, "loss": 3.48143, "time": 0.83811} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.02666, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37438, "top5_acc": 0.63641, "loss_cls": 3.51634, "loss": 3.51634, "time": 0.83486} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.02664, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37766, "top5_acc": 0.63875, "loss_cls": 3.52196, "loss": 3.52196, "time": 0.82912} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.02661, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38891, "top5_acc": 0.64797, "loss_cls": 3.46352, "loss": 3.46352, "time": 0.83512} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.02659, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37234, "top5_acc": 0.64078, "loss_cls": 3.51731, "loss": 3.51731, "time": 0.83065} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.02656, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38016, "top5_acc": 0.63078, "loss_cls": 3.51752, "loss": 3.51752, "time": 0.83074} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.02654, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38516, "top5_acc": 0.64391, "loss_cls": 3.46531, "loss": 3.46531, "time": 0.83328} +{"mode": "train", "epoch": 99, "iter": 1300, "lr": 0.02651, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38047, "top5_acc": 0.63813, "loss_cls": 3.50938, "loss": 3.50938, "time": 0.83268} +{"mode": "train", "epoch": 99, "iter": 1400, "lr": 0.02649, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37547, "top5_acc": 0.65312, "loss_cls": 3.48416, "loss": 3.48416, "time": 0.83092} +{"mode": "train", "epoch": 99, "iter": 1500, "lr": 0.02646, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37297, "top5_acc": 0.63516, "loss_cls": 3.53003, "loss": 3.53003, "time": 0.83149} +{"mode": "train", "epoch": 99, "iter": 1600, "lr": 0.02644, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38078, "top5_acc": 0.63688, "loss_cls": 3.50858, "loss": 3.50858, "time": 0.82922} +{"mode": "train", "epoch": 99, "iter": 1700, "lr": 0.02642, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37812, "top5_acc": 0.64859, "loss_cls": 3.47917, "loss": 3.47917, "time": 0.83225} +{"mode": "train", "epoch": 99, "iter": 1800, "lr": 0.02639, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.375, "top5_acc": 0.63609, "loss_cls": 3.5312, "loss": 3.5312, "time": 0.82134} +{"mode": "train", "epoch": 99, "iter": 1900, "lr": 0.02637, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37719, "top5_acc": 0.64578, "loss_cls": 3.48749, "loss": 3.48749, "time": 0.83226} +{"mode": "train", "epoch": 99, "iter": 2000, "lr": 0.02634, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37859, "top5_acc": 0.64344, "loss_cls": 3.49228, "loss": 3.49228, "time": 0.83461} +{"mode": "train", "epoch": 99, "iter": 2100, "lr": 0.02632, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37922, "top5_acc": 0.63969, "loss_cls": 3.49487, "loss": 3.49487, "time": 0.83122} +{"mode": "train", "epoch": 99, "iter": 2200, "lr": 0.02629, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37781, "top5_acc": 0.64219, "loss_cls": 3.50837, "loss": 3.50837, "time": 0.82809} +{"mode": "train", "epoch": 99, "iter": 2300, "lr": 0.02627, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38656, "top5_acc": 0.64172, "loss_cls": 3.48023, "loss": 3.48023, "time": 0.83164} +{"mode": "train", "epoch": 99, "iter": 2400, "lr": 0.02624, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37359, "top5_acc": 0.63891, "loss_cls": 3.53111, "loss": 3.53111, "time": 0.82363} +{"mode": "train", "epoch": 99, "iter": 2500, "lr": 0.02622, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37812, "top5_acc": 0.63469, "loss_cls": 3.49267, "loss": 3.49267, "time": 0.82119} +{"mode": "train", "epoch": 99, "iter": 2600, "lr": 0.02619, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36734, "top5_acc": 0.6375, "loss_cls": 3.52471, "loss": 3.52471, "time": 0.82905} +{"mode": "train", "epoch": 99, "iter": 2700, "lr": 0.02617, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36797, "top5_acc": 0.62797, "loss_cls": 3.57533, "loss": 3.57533, "time": 0.82086} +{"mode": "train", "epoch": 99, "iter": 2800, "lr": 0.02614, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.36953, "top5_acc": 0.64547, "loss_cls": 3.47793, "loss": 3.47793, "time": 0.81701} +{"mode": "train", "epoch": 99, "iter": 2900, "lr": 0.02612, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37547, "top5_acc": 0.64219, "loss_cls": 3.47746, "loss": 3.47746, "time": 0.8336} +{"mode": "train", "epoch": 99, "iter": 3000, "lr": 0.0261, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37516, "top5_acc": 0.64078, "loss_cls": 3.51946, "loss": 3.51946, "time": 0.82766} +{"mode": "train", "epoch": 99, "iter": 3100, "lr": 0.02607, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38312, "top5_acc": 0.64438, "loss_cls": 3.47435, "loss": 3.47435, "time": 0.82294} +{"mode": "train", "epoch": 99, "iter": 3200, "lr": 0.02605, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38375, "top5_acc": 0.64625, "loss_cls": 3.50926, "loss": 3.50926, "time": 0.82447} +{"mode": "train", "epoch": 99, "iter": 3300, "lr": 0.02602, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37609, "top5_acc": 0.63609, "loss_cls": 3.51879, "loss": 3.51879, "time": 0.81696} +{"mode": "train", "epoch": 99, "iter": 3400, "lr": 0.026, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37422, "top5_acc": 0.64297, "loss_cls": 3.50158, "loss": 3.50158, "time": 0.82221} +{"mode": "train", "epoch": 99, "iter": 3500, "lr": 0.02597, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36719, "top5_acc": 0.635, "loss_cls": 3.55371, "loss": 3.55371, "time": 0.8269} +{"mode": "train", "epoch": 99, "iter": 3600, "lr": 0.02595, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37719, "top5_acc": 0.64531, "loss_cls": 3.48706, "loss": 3.48706, "time": 0.81413} +{"mode": "train", "epoch": 99, "iter": 3700, "lr": 0.02592, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37297, "top5_acc": 0.63516, "loss_cls": 3.52256, "loss": 3.52256, "time": 0.81527} +{"mode": "val", "epoch": 99, "iter": 309, "lr": 0.02591, "top1_acc": 0.31733, "top5_acc": 0.57747, "mean_class_accuracy": 0.3172} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.02589, "memory": 15990, "data_time": 1.35882, "top1_acc": 0.3975, "top5_acc": 0.66672, "loss_cls": 3.34139, "loss": 3.34139, "time": 2.3353} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.02586, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37531, "top5_acc": 0.63656, "loss_cls": 3.46164, "loss": 3.46164, "time": 0.8184} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.02584, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38469, "top5_acc": 0.65062, "loss_cls": 3.4527, "loss": 3.4527, "time": 0.8172} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.02581, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.385, "top5_acc": 0.65297, "loss_cls": 3.43728, "loss": 3.43728, "time": 0.81616} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.02579, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38688, "top5_acc": 0.64625, "loss_cls": 3.43185, "loss": 3.43185, "time": 0.81526} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.02577, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37281, "top5_acc": 0.63656, "loss_cls": 3.50011, "loss": 3.50011, "time": 0.81672} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.02574, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38219, "top5_acc": 0.64734, "loss_cls": 3.47436, "loss": 3.47436, "time": 0.81665} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.02572, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38188, "top5_acc": 0.64469, "loss_cls": 3.47726, "loss": 3.47726, "time": 0.81787} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.02569, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37469, "top5_acc": 0.64656, "loss_cls": 3.46772, "loss": 3.46772, "time": 0.81985} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.02567, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38359, "top5_acc": 0.64828, "loss_cls": 3.46569, "loss": 3.46569, "time": 0.8227} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.02564, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38562, "top5_acc": 0.64703, "loss_cls": 3.48532, "loss": 3.48532, "time": 0.81731} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.02562, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38703, "top5_acc": 0.64531, "loss_cls": 3.47912, "loss": 3.47912, "time": 0.81856} +{"mode": "train", "epoch": 100, "iter": 1300, "lr": 0.02559, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38188, "top5_acc": 0.65344, "loss_cls": 3.42767, "loss": 3.42767, "time": 0.81216} +{"mode": "train", "epoch": 100, "iter": 1400, "lr": 0.02557, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38938, "top5_acc": 0.65641, "loss_cls": 3.43036, "loss": 3.43036, "time": 0.81332} +{"mode": "train", "epoch": 100, "iter": 1500, "lr": 0.02555, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38141, "top5_acc": 0.64734, "loss_cls": 3.47095, "loss": 3.47095, "time": 0.82061} +{"mode": "train", "epoch": 100, "iter": 1600, "lr": 0.02552, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38812, "top5_acc": 0.63984, "loss_cls": 3.46746, "loss": 3.46746, "time": 0.81974} +{"mode": "train", "epoch": 100, "iter": 1700, "lr": 0.0255, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37578, "top5_acc": 0.64766, "loss_cls": 3.47276, "loss": 3.47276, "time": 0.81484} +{"mode": "train", "epoch": 100, "iter": 1800, "lr": 0.02547, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38359, "top5_acc": 0.65844, "loss_cls": 3.42282, "loss": 3.42282, "time": 0.82298} +{"mode": "train", "epoch": 100, "iter": 1900, "lr": 0.02545, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37594, "top5_acc": 0.64281, "loss_cls": 3.48511, "loss": 3.48511, "time": 0.81992} +{"mode": "train", "epoch": 100, "iter": 2000, "lr": 0.02542, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37703, "top5_acc": 0.63672, "loss_cls": 3.49198, "loss": 3.49198, "time": 0.81484} +{"mode": "train", "epoch": 100, "iter": 2100, "lr": 0.0254, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38703, "top5_acc": 0.65562, "loss_cls": 3.43669, "loss": 3.43669, "time": 0.81468} +{"mode": "train", "epoch": 100, "iter": 2200, "lr": 0.02538, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37547, "top5_acc": 0.6425, "loss_cls": 3.47464, "loss": 3.47464, "time": 0.81529} +{"mode": "train", "epoch": 100, "iter": 2300, "lr": 0.02535, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3725, "top5_acc": 0.63359, "loss_cls": 3.53652, "loss": 3.53652, "time": 0.81515} +{"mode": "train", "epoch": 100, "iter": 2400, "lr": 0.02533, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36812, "top5_acc": 0.64188, "loss_cls": 3.5188, "loss": 3.5188, "time": 0.81767} +{"mode": "train", "epoch": 100, "iter": 2500, "lr": 0.0253, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38203, "top5_acc": 0.63828, "loss_cls": 3.51104, "loss": 3.51104, "time": 0.81836} +{"mode": "train", "epoch": 100, "iter": 2600, "lr": 0.02528, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38016, "top5_acc": 0.64578, "loss_cls": 3.49442, "loss": 3.49442, "time": 0.81861} +{"mode": "train", "epoch": 100, "iter": 2700, "lr": 0.02525, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37578, "top5_acc": 0.63859, "loss_cls": 3.50714, "loss": 3.50714, "time": 0.81418} +{"mode": "train", "epoch": 100, "iter": 2800, "lr": 0.02523, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37062, "top5_acc": 0.63641, "loss_cls": 3.51541, "loss": 3.51541, "time": 0.83105} +{"mode": "train", "epoch": 100, "iter": 2900, "lr": 0.02521, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37891, "top5_acc": 0.64219, "loss_cls": 3.48134, "loss": 3.48134, "time": 0.81229} +{"mode": "train", "epoch": 100, "iter": 3000, "lr": 0.02518, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3725, "top5_acc": 0.63406, "loss_cls": 3.54032, "loss": 3.54032, "time": 0.8137} +{"mode": "train", "epoch": 100, "iter": 3100, "lr": 0.02516, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37438, "top5_acc": 0.63813, "loss_cls": 3.51019, "loss": 3.51019, "time": 0.8155} +{"mode": "train", "epoch": 100, "iter": 3200, "lr": 0.02513, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.385, "top5_acc": 0.64562, "loss_cls": 3.45212, "loss": 3.45212, "time": 0.81551} +{"mode": "train", "epoch": 100, "iter": 3300, "lr": 0.02511, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37531, "top5_acc": 0.64484, "loss_cls": 3.51072, "loss": 3.51072, "time": 0.8182} +{"mode": "train", "epoch": 100, "iter": 3400, "lr": 0.02508, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38422, "top5_acc": 0.63469, "loss_cls": 3.50948, "loss": 3.50948, "time": 0.81558} +{"mode": "train", "epoch": 100, "iter": 3500, "lr": 0.02506, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37438, "top5_acc": 0.63734, "loss_cls": 3.54091, "loss": 3.54091, "time": 0.8179} +{"mode": "train", "epoch": 100, "iter": 3600, "lr": 0.02504, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37266, "top5_acc": 0.63531, "loss_cls": 3.51831, "loss": 3.51831, "time": 0.81047} +{"mode": "train", "epoch": 100, "iter": 3700, "lr": 0.02501, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38094, "top5_acc": 0.64531, "loss_cls": 3.48919, "loss": 3.48919, "time": 0.81885} +{"mode": "val", "epoch": 100, "iter": 309, "lr": 0.025, "top1_acc": 0.2961, "top5_acc": 0.5563, "mean_class_accuracy": 0.29605} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.02498, "memory": 15990, "data_time": 1.3578, "top1_acc": 0.38156, "top5_acc": 0.64953, "loss_cls": 3.44495, "loss": 3.44495, "time": 2.34052} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.02495, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38766, "top5_acc": 0.65156, "loss_cls": 3.43114, "loss": 3.43114, "time": 0.8327} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.02493, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39469, "top5_acc": 0.65812, "loss_cls": 3.40568, "loss": 3.40568, "time": 0.83681} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.0249, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38359, "top5_acc": 0.63922, "loss_cls": 3.45919, "loss": 3.45919, "time": 0.83764} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.02488, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38562, "top5_acc": 0.64703, "loss_cls": 3.43789, "loss": 3.43789, "time": 0.83668} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.02486, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38938, "top5_acc": 0.65969, "loss_cls": 3.41972, "loss": 3.41972, "time": 0.83721} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.02483, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37484, "top5_acc": 0.63969, "loss_cls": 3.48551, "loss": 3.48551, "time": 0.83284} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.02481, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37797, "top5_acc": 0.63656, "loss_cls": 3.49489, "loss": 3.49489, "time": 0.83167} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.02478, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36734, "top5_acc": 0.63391, "loss_cls": 3.48817, "loss": 3.48817, "time": 0.83031} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.02476, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38797, "top5_acc": 0.65047, "loss_cls": 3.40746, "loss": 3.40746, "time": 0.83927} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.02473, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38594, "top5_acc": 0.65109, "loss_cls": 3.44847, "loss": 3.44847, "time": 0.83779} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.02471, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38938, "top5_acc": 0.65188, "loss_cls": 3.42977, "loss": 3.42977, "time": 0.83328} +{"mode": "train", "epoch": 101, "iter": 1300, "lr": 0.02469, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38625, "top5_acc": 0.64375, "loss_cls": 3.46981, "loss": 3.46981, "time": 0.8352} +{"mode": "train", "epoch": 101, "iter": 1400, "lr": 0.02466, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38266, "top5_acc": 0.64391, "loss_cls": 3.45609, "loss": 3.45609, "time": 0.83566} +{"mode": "train", "epoch": 101, "iter": 1500, "lr": 0.02464, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38688, "top5_acc": 0.65078, "loss_cls": 3.4438, "loss": 3.4438, "time": 0.8391} +{"mode": "train", "epoch": 101, "iter": 1600, "lr": 0.02461, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38422, "top5_acc": 0.65031, "loss_cls": 3.46037, "loss": 3.46037, "time": 0.83428} +{"mode": "train", "epoch": 101, "iter": 1700, "lr": 0.02459, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37547, "top5_acc": 0.65031, "loss_cls": 3.43897, "loss": 3.43897, "time": 0.82905} +{"mode": "train", "epoch": 101, "iter": 1800, "lr": 0.02457, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37734, "top5_acc": 0.64109, "loss_cls": 3.49357, "loss": 3.49357, "time": 0.82813} +{"mode": "train", "epoch": 101, "iter": 1900, "lr": 0.02454, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38234, "top5_acc": 0.65, "loss_cls": 3.43884, "loss": 3.43884, "time": 0.83538} +{"mode": "train", "epoch": 101, "iter": 2000, "lr": 0.02452, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37625, "top5_acc": 0.63766, "loss_cls": 3.48976, "loss": 3.48976, "time": 0.83603} +{"mode": "train", "epoch": 101, "iter": 2100, "lr": 0.02449, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36938, "top5_acc": 0.63469, "loss_cls": 3.51936, "loss": 3.51936, "time": 0.83377} +{"mode": "train", "epoch": 101, "iter": 2200, "lr": 0.02447, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38391, "top5_acc": 0.6475, "loss_cls": 3.46977, "loss": 3.46977, "time": 0.83068} +{"mode": "train", "epoch": 101, "iter": 2300, "lr": 0.02445, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37859, "top5_acc": 0.64141, "loss_cls": 3.49187, "loss": 3.49187, "time": 0.82461} +{"mode": "train", "epoch": 101, "iter": 2400, "lr": 0.02442, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39047, "top5_acc": 0.65125, "loss_cls": 3.40617, "loss": 3.40617, "time": 0.82528} +{"mode": "train", "epoch": 101, "iter": 2500, "lr": 0.0244, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38859, "top5_acc": 0.65234, "loss_cls": 3.43051, "loss": 3.43051, "time": 0.82649} +{"mode": "train", "epoch": 101, "iter": 2600, "lr": 0.02437, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38922, "top5_acc": 0.6475, "loss_cls": 3.43608, "loss": 3.43608, "time": 0.82311} +{"mode": "train", "epoch": 101, "iter": 2700, "lr": 0.02435, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37844, "top5_acc": 0.64609, "loss_cls": 3.46584, "loss": 3.46584, "time": 0.82823} +{"mode": "train", "epoch": 101, "iter": 2800, "lr": 0.02433, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38547, "top5_acc": 0.64734, "loss_cls": 3.47088, "loss": 3.47088, "time": 0.83367} +{"mode": "train", "epoch": 101, "iter": 2900, "lr": 0.0243, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36594, "top5_acc": 0.63516, "loss_cls": 3.52725, "loss": 3.52725, "time": 0.8345} +{"mode": "train", "epoch": 101, "iter": 3000, "lr": 0.02428, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37844, "top5_acc": 0.64391, "loss_cls": 3.50283, "loss": 3.50283, "time": 0.83377} +{"mode": "train", "epoch": 101, "iter": 3100, "lr": 0.02425, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37703, "top5_acc": 0.64516, "loss_cls": 3.48673, "loss": 3.48673, "time": 0.83153} +{"mode": "train", "epoch": 101, "iter": 3200, "lr": 0.02423, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38188, "top5_acc": 0.64438, "loss_cls": 3.4801, "loss": 3.4801, "time": 0.83071} +{"mode": "train", "epoch": 101, "iter": 3300, "lr": 0.02421, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38625, "top5_acc": 0.64766, "loss_cls": 3.49217, "loss": 3.49217, "time": 0.82624} +{"mode": "train", "epoch": 101, "iter": 3400, "lr": 0.02418, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39016, "top5_acc": 0.64703, "loss_cls": 3.43821, "loss": 3.43821, "time": 0.83095} +{"mode": "train", "epoch": 101, "iter": 3500, "lr": 0.02416, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38281, "top5_acc": 0.65062, "loss_cls": 3.48713, "loss": 3.48713, "time": 0.82658} +{"mode": "train", "epoch": 101, "iter": 3600, "lr": 0.02413, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37281, "top5_acc": 0.64359, "loss_cls": 3.49956, "loss": 3.49956, "time": 0.82635} +{"mode": "train", "epoch": 101, "iter": 3700, "lr": 0.02411, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3875, "top5_acc": 0.65625, "loss_cls": 3.43749, "loss": 3.43749, "time": 0.83602} +{"mode": "val", "epoch": 101, "iter": 309, "lr": 0.0241, "top1_acc": 0.29899, "top5_acc": 0.55934, "mean_class_accuracy": 0.29892} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.02407, "memory": 15990, "data_time": 1.32213, "top1_acc": 0.39734, "top5_acc": 0.6625, "loss_cls": 3.39803, "loss": 3.39803, "time": 2.31426} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.02405, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38844, "top5_acc": 0.6575, "loss_cls": 3.41404, "loss": 3.41404, "time": 0.83575} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.02403, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39406, "top5_acc": 0.65891, "loss_cls": 3.40013, "loss": 3.40013, "time": 0.82885} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.024, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3925, "top5_acc": 0.65688, "loss_cls": 3.41463, "loss": 3.41463, "time": 0.82731} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.02398, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39094, "top5_acc": 0.65484, "loss_cls": 3.41123, "loss": 3.41123, "time": 0.82029} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.02396, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39, "top5_acc": 0.65609, "loss_cls": 3.40904, "loss": 3.40904, "time": 0.83092} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.02393, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39562, "top5_acc": 0.65547, "loss_cls": 3.39819, "loss": 3.39819, "time": 0.82368} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.02391, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39531, "top5_acc": 0.655, "loss_cls": 3.40411, "loss": 3.40411, "time": 0.82009} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.02388, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39656, "top5_acc": 0.65938, "loss_cls": 3.42627, "loss": 3.42627, "time": 0.81769} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.02386, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37781, "top5_acc": 0.64047, "loss_cls": 3.47637, "loss": 3.47637, "time": 0.82227} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.02384, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39016, "top5_acc": 0.65812, "loss_cls": 3.417, "loss": 3.417, "time": 0.82484} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.02381, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39, "top5_acc": 0.65234, "loss_cls": 3.42621, "loss": 3.42621, "time": 0.82628} +{"mode": "train", "epoch": 102, "iter": 1300, "lr": 0.02379, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37859, "top5_acc": 0.64547, "loss_cls": 3.45173, "loss": 3.45173, "time": 0.82266} +{"mode": "train", "epoch": 102, "iter": 1400, "lr": 0.02376, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38469, "top5_acc": 0.65078, "loss_cls": 3.48336, "loss": 3.48336, "time": 0.83213} +{"mode": "train", "epoch": 102, "iter": 1500, "lr": 0.02374, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38422, "top5_acc": 0.64703, "loss_cls": 3.44416, "loss": 3.44416, "time": 0.827} +{"mode": "train", "epoch": 102, "iter": 1600, "lr": 0.02372, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.3875, "top5_acc": 0.66156, "loss_cls": 3.40362, "loss": 3.40362, "time": 0.82641} +{"mode": "train", "epoch": 102, "iter": 1700, "lr": 0.02369, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38141, "top5_acc": 0.64734, "loss_cls": 3.45907, "loss": 3.45907, "time": 0.8198} +{"mode": "train", "epoch": 102, "iter": 1800, "lr": 0.02367, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39391, "top5_acc": 0.65781, "loss_cls": 3.40495, "loss": 3.40495, "time": 0.83084} +{"mode": "train", "epoch": 102, "iter": 1900, "lr": 0.02365, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39109, "top5_acc": 0.65984, "loss_cls": 3.40992, "loss": 3.40992, "time": 0.83605} +{"mode": "train", "epoch": 102, "iter": 2000, "lr": 0.02362, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37969, "top5_acc": 0.64594, "loss_cls": 3.45414, "loss": 3.45414, "time": 0.83002} +{"mode": "train", "epoch": 102, "iter": 2100, "lr": 0.0236, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37312, "top5_acc": 0.63531, "loss_cls": 3.5071, "loss": 3.5071, "time": 0.83689} +{"mode": "train", "epoch": 102, "iter": 2200, "lr": 0.02357, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38969, "top5_acc": 0.65078, "loss_cls": 3.41105, "loss": 3.41105, "time": 0.83198} +{"mode": "train", "epoch": 102, "iter": 2300, "lr": 0.02355, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38672, "top5_acc": 0.64969, "loss_cls": 3.44972, "loss": 3.44972, "time": 0.82392} +{"mode": "train", "epoch": 102, "iter": 2400, "lr": 0.02353, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.37359, "top5_acc": 0.64141, "loss_cls": 3.51595, "loss": 3.51595, "time": 0.82045} +{"mode": "train", "epoch": 102, "iter": 2500, "lr": 0.0235, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39141, "top5_acc": 0.64891, "loss_cls": 3.41242, "loss": 3.41242, "time": 0.82196} +{"mode": "train", "epoch": 102, "iter": 2600, "lr": 0.02348, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38609, "top5_acc": 0.66562, "loss_cls": 3.40257, "loss": 3.40257, "time": 0.81546} +{"mode": "train", "epoch": 102, "iter": 2700, "lr": 0.02346, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38297, "top5_acc": 0.64781, "loss_cls": 3.43552, "loss": 3.43552, "time": 0.82458} +{"mode": "train", "epoch": 102, "iter": 2800, "lr": 0.02343, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38359, "top5_acc": 0.63734, "loss_cls": 3.48684, "loss": 3.48684, "time": 0.83448} +{"mode": "train", "epoch": 102, "iter": 2900, "lr": 0.02341, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38016, "top5_acc": 0.64797, "loss_cls": 3.46916, "loss": 3.46916, "time": 0.82624} +{"mode": "train", "epoch": 102, "iter": 3000, "lr": 0.02339, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38219, "top5_acc": 0.64781, "loss_cls": 3.45061, "loss": 3.45061, "time": 0.81842} +{"mode": "train", "epoch": 102, "iter": 3100, "lr": 0.02336, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.385, "top5_acc": 0.65094, "loss_cls": 3.44638, "loss": 3.44638, "time": 0.8232} +{"mode": "train", "epoch": 102, "iter": 3200, "lr": 0.02334, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38969, "top5_acc": 0.65219, "loss_cls": 3.44844, "loss": 3.44844, "time": 0.82038} +{"mode": "train", "epoch": 102, "iter": 3300, "lr": 0.02331, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38719, "top5_acc": 0.65, "loss_cls": 3.4476, "loss": 3.4476, "time": 0.82307} +{"mode": "train", "epoch": 102, "iter": 3400, "lr": 0.02329, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3775, "top5_acc": 0.63734, "loss_cls": 3.49581, "loss": 3.49581, "time": 0.82337} +{"mode": "train", "epoch": 102, "iter": 3500, "lr": 0.02327, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38547, "top5_acc": 0.64859, "loss_cls": 3.447, "loss": 3.447, "time": 0.82084} +{"mode": "train", "epoch": 102, "iter": 3600, "lr": 0.02324, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37812, "top5_acc": 0.64297, "loss_cls": 3.4756, "loss": 3.4756, "time": 0.82429} +{"mode": "train", "epoch": 102, "iter": 3700, "lr": 0.02322, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39641, "top5_acc": 0.64516, "loss_cls": 3.42348, "loss": 3.42348, "time": 0.81383} +{"mode": "val", "epoch": 102, "iter": 309, "lr": 0.02321, "top1_acc": 0.3267, "top5_acc": 0.58851, "mean_class_accuracy": 0.3264} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.02319, "memory": 15990, "data_time": 1.31005, "top1_acc": 0.3975, "top5_acc": 0.66234, "loss_cls": 3.37818, "loss": 3.37818, "time": 2.30465} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.02316, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39891, "top5_acc": 0.67094, "loss_cls": 3.36527, "loss": 3.36527, "time": 0.83461} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.02314, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40812, "top5_acc": 0.66828, "loss_cls": 3.32643, "loss": 3.32643, "time": 0.83352} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.02311, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40281, "top5_acc": 0.66453, "loss_cls": 3.35008, "loss": 3.35008, "time": 0.82295} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.02309, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3825, "top5_acc": 0.64906, "loss_cls": 3.40695, "loss": 3.40695, "time": 0.82416} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.02307, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38812, "top5_acc": 0.65062, "loss_cls": 3.45836, "loss": 3.45836, "time": 0.82069} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.02304, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37922, "top5_acc": 0.65453, "loss_cls": 3.43655, "loss": 3.43655, "time": 0.82094} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.02302, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38422, "top5_acc": 0.64844, "loss_cls": 3.4413, "loss": 3.4413, "time": 0.82481} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.023, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39609, "top5_acc": 0.6625, "loss_cls": 3.36604, "loss": 3.36604, "time": 0.8255} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.02297, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39047, "top5_acc": 0.6525, "loss_cls": 3.38448, "loss": 3.38448, "time": 0.81557} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.02295, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37969, "top5_acc": 0.65047, "loss_cls": 3.45109, "loss": 3.45109, "time": 0.82545} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.02293, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39109, "top5_acc": 0.65016, "loss_cls": 3.43238, "loss": 3.43238, "time": 0.82324} +{"mode": "train", "epoch": 103, "iter": 1300, "lr": 0.0229, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38969, "top5_acc": 0.65531, "loss_cls": 3.43568, "loss": 3.43568, "time": 0.83143} +{"mode": "train", "epoch": 103, "iter": 1400, "lr": 0.02288, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38906, "top5_acc": 0.65844, "loss_cls": 3.40499, "loss": 3.40499, "time": 0.819} +{"mode": "train", "epoch": 103, "iter": 1500, "lr": 0.02286, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39078, "top5_acc": 0.65812, "loss_cls": 3.42349, "loss": 3.42349, "time": 0.82836} +{"mode": "train", "epoch": 103, "iter": 1600, "lr": 0.02283, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.3875, "top5_acc": 0.64281, "loss_cls": 3.44608, "loss": 3.44608, "time": 0.82431} +{"mode": "train", "epoch": 103, "iter": 1700, "lr": 0.02281, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38453, "top5_acc": 0.65672, "loss_cls": 3.39999, "loss": 3.39999, "time": 0.83032} +{"mode": "train", "epoch": 103, "iter": 1800, "lr": 0.02279, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39203, "top5_acc": 0.64688, "loss_cls": 3.41288, "loss": 3.41288, "time": 0.8317} +{"mode": "train", "epoch": 103, "iter": 1900, "lr": 0.02276, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.39172, "top5_acc": 0.64891, "loss_cls": 3.42746, "loss": 3.42746, "time": 0.82718} +{"mode": "train", "epoch": 103, "iter": 2000, "lr": 0.02274, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.385, "top5_acc": 0.65094, "loss_cls": 3.43563, "loss": 3.43563, "time": 0.82911} +{"mode": "train", "epoch": 103, "iter": 2100, "lr": 0.02272, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39109, "top5_acc": 0.64984, "loss_cls": 3.42969, "loss": 3.42969, "time": 0.83452} +{"mode": "train", "epoch": 103, "iter": 2200, "lr": 0.02269, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39922, "top5_acc": 0.65734, "loss_cls": 3.37719, "loss": 3.37719, "time": 0.82281} +{"mode": "train", "epoch": 103, "iter": 2300, "lr": 0.02267, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39766, "top5_acc": 0.65594, "loss_cls": 3.42727, "loss": 3.42727, "time": 0.82657} +{"mode": "train", "epoch": 103, "iter": 2400, "lr": 0.02264, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39281, "top5_acc": 0.65547, "loss_cls": 3.42637, "loss": 3.42637, "time": 0.81874} +{"mode": "train", "epoch": 103, "iter": 2500, "lr": 0.02262, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38969, "top5_acc": 0.64562, "loss_cls": 3.44764, "loss": 3.44764, "time": 0.82908} +{"mode": "train", "epoch": 103, "iter": 2600, "lr": 0.0226, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38938, "top5_acc": 0.64891, "loss_cls": 3.44623, "loss": 3.44623, "time": 0.82229} +{"mode": "train", "epoch": 103, "iter": 2700, "lr": 0.02257, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38406, "top5_acc": 0.65344, "loss_cls": 3.46104, "loss": 3.46104, "time": 0.82511} +{"mode": "train", "epoch": 103, "iter": 2800, "lr": 0.02255, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39109, "top5_acc": 0.64703, "loss_cls": 3.43803, "loss": 3.43803, "time": 0.83178} +{"mode": "train", "epoch": 103, "iter": 2900, "lr": 0.02253, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39297, "top5_acc": 0.65609, "loss_cls": 3.41614, "loss": 3.41614, "time": 0.82435} +{"mode": "train", "epoch": 103, "iter": 3000, "lr": 0.0225, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3925, "top5_acc": 0.65203, "loss_cls": 3.412, "loss": 3.412, "time": 0.8179} +{"mode": "train", "epoch": 103, "iter": 3100, "lr": 0.02248, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39609, "top5_acc": 0.65406, "loss_cls": 3.40131, "loss": 3.40131, "time": 0.81675} +{"mode": "train", "epoch": 103, "iter": 3200, "lr": 0.02246, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38719, "top5_acc": 0.64547, "loss_cls": 3.44367, "loss": 3.44367, "time": 0.81921} +{"mode": "train", "epoch": 103, "iter": 3300, "lr": 0.02243, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38812, "top5_acc": 0.6525, "loss_cls": 3.43406, "loss": 3.43406, "time": 0.82027} +{"mode": "train", "epoch": 103, "iter": 3400, "lr": 0.02241, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38297, "top5_acc": 0.64781, "loss_cls": 3.43121, "loss": 3.43121, "time": 0.82161} +{"mode": "train", "epoch": 103, "iter": 3500, "lr": 0.02239, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38656, "top5_acc": 0.64688, "loss_cls": 3.45127, "loss": 3.45127, "time": 0.82378} +{"mode": "train", "epoch": 103, "iter": 3600, "lr": 0.02236, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38859, "top5_acc": 0.65266, "loss_cls": 3.46621, "loss": 3.46621, "time": 0.8223} +{"mode": "train", "epoch": 103, "iter": 3700, "lr": 0.02234, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37391, "top5_acc": 0.64266, "loss_cls": 3.48568, "loss": 3.48568, "time": 0.82369} +{"mode": "val", "epoch": 103, "iter": 309, "lr": 0.02233, "top1_acc": 0.31555, "top5_acc": 0.57484, "mean_class_accuracy": 0.31534} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.02231, "memory": 15990, "data_time": 1.29024, "top1_acc": 0.39734, "top5_acc": 0.66141, "loss_cls": 3.34298, "loss": 3.34298, "time": 2.27872} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.02228, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.4075, "top5_acc": 0.66203, "loss_cls": 3.35121, "loss": 3.35121, "time": 0.83346} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.02226, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40219, "top5_acc": 0.65859, "loss_cls": 3.35468, "loss": 3.35468, "time": 0.82708} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.02224, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3975, "top5_acc": 0.66922, "loss_cls": 3.33422, "loss": 3.33422, "time": 0.8216} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.02221, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3975, "top5_acc": 0.65719, "loss_cls": 3.3741, "loss": 3.3741, "time": 0.82824} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.02219, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39875, "top5_acc": 0.66469, "loss_cls": 3.36246, "loss": 3.36246, "time": 0.82741} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.02217, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39812, "top5_acc": 0.66875, "loss_cls": 3.34116, "loss": 3.34116, "time": 0.82633} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.02214, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38141, "top5_acc": 0.64766, "loss_cls": 3.43539, "loss": 3.43539, "time": 0.8287} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.02212, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38953, "top5_acc": 0.65891, "loss_cls": 3.39088, "loss": 3.39088, "time": 0.82645} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.0221, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37906, "top5_acc": 0.65203, "loss_cls": 3.44709, "loss": 3.44709, "time": 0.83348} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.02208, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39094, "top5_acc": 0.65703, "loss_cls": 3.39857, "loss": 3.39857, "time": 0.82812} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.02205, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39156, "top5_acc": 0.65578, "loss_cls": 3.4013, "loss": 3.4013, "time": 0.83372} +{"mode": "train", "epoch": 104, "iter": 1300, "lr": 0.02203, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38344, "top5_acc": 0.65578, "loss_cls": 3.41088, "loss": 3.41088, "time": 0.82725} +{"mode": "train", "epoch": 104, "iter": 1400, "lr": 0.02201, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39344, "top5_acc": 0.65656, "loss_cls": 3.39569, "loss": 3.39569, "time": 0.82895} +{"mode": "train", "epoch": 104, "iter": 1500, "lr": 0.02198, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40719, "top5_acc": 0.65844, "loss_cls": 3.39482, "loss": 3.39482, "time": 0.82719} +{"mode": "train", "epoch": 104, "iter": 1600, "lr": 0.02196, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39453, "top5_acc": 0.65609, "loss_cls": 3.4034, "loss": 3.4034, "time": 0.8277} +{"mode": "train", "epoch": 104, "iter": 1700, "lr": 0.02194, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40031, "top5_acc": 0.65984, "loss_cls": 3.36532, "loss": 3.36532, "time": 0.84001} +{"mode": "train", "epoch": 104, "iter": 1800, "lr": 0.02191, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39328, "top5_acc": 0.66234, "loss_cls": 3.40576, "loss": 3.40576, "time": 0.83608} +{"mode": "train", "epoch": 104, "iter": 1900, "lr": 0.02189, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38266, "top5_acc": 0.64734, "loss_cls": 3.44914, "loss": 3.44914, "time": 0.82749} +{"mode": "train", "epoch": 104, "iter": 2000, "lr": 0.02187, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39125, "top5_acc": 0.65859, "loss_cls": 3.39353, "loss": 3.39353, "time": 0.83569} +{"mode": "train", "epoch": 104, "iter": 2100, "lr": 0.02184, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40234, "top5_acc": 0.65875, "loss_cls": 3.40061, "loss": 3.40061, "time": 0.82601} +{"mode": "train", "epoch": 104, "iter": 2200, "lr": 0.02182, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39203, "top5_acc": 0.64578, "loss_cls": 3.42446, "loss": 3.42446, "time": 0.82623} +{"mode": "train", "epoch": 104, "iter": 2300, "lr": 0.0218, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38766, "top5_acc": 0.64531, "loss_cls": 3.4338, "loss": 3.4338, "time": 0.82123} +{"mode": "train", "epoch": 104, "iter": 2400, "lr": 0.02177, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3975, "top5_acc": 0.65953, "loss_cls": 3.38627, "loss": 3.38627, "time": 0.83233} +{"mode": "train", "epoch": 104, "iter": 2500, "lr": 0.02175, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38812, "top5_acc": 0.65094, "loss_cls": 3.44663, "loss": 3.44663, "time": 0.81922} +{"mode": "train", "epoch": 104, "iter": 2600, "lr": 0.02173, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39188, "top5_acc": 0.65344, "loss_cls": 3.42595, "loss": 3.42595, "time": 0.82606} +{"mode": "train", "epoch": 104, "iter": 2700, "lr": 0.02171, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37375, "top5_acc": 0.65016, "loss_cls": 3.47301, "loss": 3.47301, "time": 0.83469} +{"mode": "train", "epoch": 104, "iter": 2800, "lr": 0.02168, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39109, "top5_acc": 0.65578, "loss_cls": 3.38809, "loss": 3.38809, "time": 0.82808} +{"mode": "train", "epoch": 104, "iter": 2900, "lr": 0.02166, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38156, "top5_acc": 0.64375, "loss_cls": 3.4616, "loss": 3.4616, "time": 0.82789} +{"mode": "train", "epoch": 104, "iter": 3000, "lr": 0.02164, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40062, "top5_acc": 0.65, "loss_cls": 3.41147, "loss": 3.41147, "time": 0.82903} +{"mode": "train", "epoch": 104, "iter": 3100, "lr": 0.02161, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38844, "top5_acc": 0.65312, "loss_cls": 3.42684, "loss": 3.42684, "time": 0.81997} +{"mode": "train", "epoch": 104, "iter": 3200, "lr": 0.02159, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39031, "top5_acc": 0.65312, "loss_cls": 3.42327, "loss": 3.42327, "time": 0.82557} +{"mode": "train", "epoch": 104, "iter": 3300, "lr": 0.02157, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38922, "top5_acc": 0.65844, "loss_cls": 3.38632, "loss": 3.38632, "time": 0.8272} +{"mode": "train", "epoch": 104, "iter": 3400, "lr": 0.02154, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38766, "top5_acc": 0.65469, "loss_cls": 3.42104, "loss": 3.42104, "time": 0.82972} +{"mode": "train", "epoch": 104, "iter": 3500, "lr": 0.02152, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39672, "top5_acc": 0.65641, "loss_cls": 3.38401, "loss": 3.38401, "time": 0.82627} +{"mode": "train", "epoch": 104, "iter": 3600, "lr": 0.0215, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39203, "top5_acc": 0.66, "loss_cls": 3.40756, "loss": 3.40756, "time": 0.82751} +{"mode": "train", "epoch": 104, "iter": 3700, "lr": 0.02148, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39406, "top5_acc": 0.65141, "loss_cls": 3.41689, "loss": 3.41689, "time": 0.8227} +{"mode": "val", "epoch": 104, "iter": 309, "lr": 0.02146, "top1_acc": 0.32964, "top5_acc": 0.59018, "mean_class_accuracy": 0.32948} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.02144, "memory": 15990, "data_time": 1.28344, "top1_acc": 0.41797, "top5_acc": 0.68031, "loss_cls": 3.27561, "loss": 3.27561, "time": 2.27831} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.02142, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40609, "top5_acc": 0.66672, "loss_cls": 3.33091, "loss": 3.33091, "time": 0.82858} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.0214, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40469, "top5_acc": 0.66562, "loss_cls": 3.34363, "loss": 3.34363, "time": 0.82376} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.02137, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40406, "top5_acc": 0.66594, "loss_cls": 3.34602, "loss": 3.34602, "time": 0.82145} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.02135, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40094, "top5_acc": 0.66344, "loss_cls": 3.37551, "loss": 3.37551, "time": 0.82098} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.02133, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41406, "top5_acc": 0.66594, "loss_cls": 3.32341, "loss": 3.32341, "time": 0.82337} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.0213, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39547, "top5_acc": 0.66609, "loss_cls": 3.34863, "loss": 3.34863, "time": 0.82313} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.02128, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40031, "top5_acc": 0.65766, "loss_cls": 3.38075, "loss": 3.38075, "time": 0.82194} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.02126, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38938, "top5_acc": 0.6575, "loss_cls": 3.40427, "loss": 3.40427, "time": 0.81763} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.02124, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40047, "top5_acc": 0.66141, "loss_cls": 3.37585, "loss": 3.37585, "time": 0.82261} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.02121, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39047, "top5_acc": 0.65125, "loss_cls": 3.42008, "loss": 3.42008, "time": 0.81501} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.02119, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39859, "top5_acc": 0.65719, "loss_cls": 3.39675, "loss": 3.39675, "time": 0.81895} +{"mode": "train", "epoch": 105, "iter": 1300, "lr": 0.02117, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39438, "top5_acc": 0.66391, "loss_cls": 3.40281, "loss": 3.40281, "time": 0.81286} +{"mode": "train", "epoch": 105, "iter": 1400, "lr": 0.02114, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39031, "top5_acc": 0.65859, "loss_cls": 3.41315, "loss": 3.41315, "time": 0.81866} +{"mode": "train", "epoch": 105, "iter": 1500, "lr": 0.02112, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39672, "top5_acc": 0.65812, "loss_cls": 3.39628, "loss": 3.39628, "time": 0.82286} +{"mode": "train", "epoch": 105, "iter": 1600, "lr": 0.0211, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39344, "top5_acc": 0.64969, "loss_cls": 3.40602, "loss": 3.40602, "time": 0.82278} +{"mode": "train", "epoch": 105, "iter": 1700, "lr": 0.02108, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40453, "top5_acc": 0.66031, "loss_cls": 3.37135, "loss": 3.37135, "time": 0.83078} +{"mode": "train", "epoch": 105, "iter": 1800, "lr": 0.02105, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39516, "top5_acc": 0.65406, "loss_cls": 3.40648, "loss": 3.40648, "time": 0.83095} +{"mode": "train", "epoch": 105, "iter": 1900, "lr": 0.02103, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39109, "top5_acc": 0.6575, "loss_cls": 3.40517, "loss": 3.40517, "time": 0.82772} +{"mode": "train", "epoch": 105, "iter": 2000, "lr": 0.02101, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39703, "top5_acc": 0.65281, "loss_cls": 3.40933, "loss": 3.40933, "time": 0.8287} +{"mode": "train", "epoch": 105, "iter": 2100, "lr": 0.02098, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39609, "top5_acc": 0.66562, "loss_cls": 3.35494, "loss": 3.35494, "time": 0.81961} +{"mode": "train", "epoch": 105, "iter": 2200, "lr": 0.02096, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39469, "top5_acc": 0.66359, "loss_cls": 3.38029, "loss": 3.38029, "time": 0.81909} +{"mode": "train", "epoch": 105, "iter": 2300, "lr": 0.02094, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39219, "top5_acc": 0.65844, "loss_cls": 3.40785, "loss": 3.40785, "time": 0.82041} +{"mode": "train", "epoch": 105, "iter": 2400, "lr": 0.02092, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39641, "top5_acc": 0.65781, "loss_cls": 3.39082, "loss": 3.39082, "time": 0.82486} +{"mode": "train", "epoch": 105, "iter": 2500, "lr": 0.02089, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40156, "top5_acc": 0.66344, "loss_cls": 3.35832, "loss": 3.35832, "time": 0.81863} +{"mode": "train", "epoch": 105, "iter": 2600, "lr": 0.02087, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39969, "top5_acc": 0.66438, "loss_cls": 3.3429, "loss": 3.3429, "time": 0.82514} +{"mode": "train", "epoch": 105, "iter": 2700, "lr": 0.02085, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39562, "top5_acc": 0.65594, "loss_cls": 3.39239, "loss": 3.39239, "time": 0.82758} +{"mode": "train", "epoch": 105, "iter": 2800, "lr": 0.02083, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38438, "top5_acc": 0.64312, "loss_cls": 3.46205, "loss": 3.46205, "time": 0.81999} +{"mode": "train", "epoch": 105, "iter": 2900, "lr": 0.0208, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39391, "top5_acc": 0.65062, "loss_cls": 3.40746, "loss": 3.40746, "time": 0.81926} +{"mode": "train", "epoch": 105, "iter": 3000, "lr": 0.02078, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39344, "top5_acc": 0.65875, "loss_cls": 3.40382, "loss": 3.40382, "time": 0.82259} +{"mode": "train", "epoch": 105, "iter": 3100, "lr": 0.02076, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38906, "top5_acc": 0.64656, "loss_cls": 3.41788, "loss": 3.41788, "time": 0.8178} +{"mode": "train", "epoch": 105, "iter": 3200, "lr": 0.02073, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39438, "top5_acc": 0.67078, "loss_cls": 3.34596, "loss": 3.34596, "time": 0.82025} +{"mode": "train", "epoch": 105, "iter": 3300, "lr": 0.02071, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39062, "top5_acc": 0.6675, "loss_cls": 3.38338, "loss": 3.38338, "time": 0.82342} +{"mode": "train", "epoch": 105, "iter": 3400, "lr": 0.02069, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39297, "top5_acc": 0.65234, "loss_cls": 3.41636, "loss": 3.41636, "time": 0.82739} +{"mode": "train", "epoch": 105, "iter": 3500, "lr": 0.02067, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38531, "top5_acc": 0.64688, "loss_cls": 3.43761, "loss": 3.43761, "time": 0.81824} +{"mode": "train", "epoch": 105, "iter": 3600, "lr": 0.02064, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38766, "top5_acc": 0.64516, "loss_cls": 3.4521, "loss": 3.4521, "time": 0.81675} +{"mode": "train", "epoch": 105, "iter": 3700, "lr": 0.02062, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39438, "top5_acc": 0.65766, "loss_cls": 3.38101, "loss": 3.38101, "time": 0.82134} +{"mode": "val", "epoch": 105, "iter": 309, "lr": 0.02061, "top1_acc": 0.33095, "top5_acc": 0.59084, "mean_class_accuracy": 0.33061} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.02059, "memory": 15990, "data_time": 1.26483, "top1_acc": 0.41297, "top5_acc": 0.66922, "loss_cls": 3.29567, "loss": 3.29567, "time": 2.25587} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.02057, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40859, "top5_acc": 0.66344, "loss_cls": 3.3309, "loss": 3.3309, "time": 0.82562} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.02054, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41672, "top5_acc": 0.67406, "loss_cls": 3.27786, "loss": 3.27786, "time": 0.82125} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.02052, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40312, "top5_acc": 0.66188, "loss_cls": 3.33565, "loss": 3.33565, "time": 0.81794} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.0205, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.395, "top5_acc": 0.66656, "loss_cls": 3.33446, "loss": 3.33446, "time": 0.82447} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.02048, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40141, "top5_acc": 0.66828, "loss_cls": 3.33892, "loss": 3.33892, "time": 0.81883} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.02045, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41547, "top5_acc": 0.66406, "loss_cls": 3.31089, "loss": 3.31089, "time": 0.82019} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.02043, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38922, "top5_acc": 0.66125, "loss_cls": 3.36768, "loss": 3.36768, "time": 0.82101} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.02041, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39984, "top5_acc": 0.66219, "loss_cls": 3.343, "loss": 3.343, "time": 0.81744} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.02039, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39703, "top5_acc": 0.65734, "loss_cls": 3.34893, "loss": 3.34893, "time": 0.82082} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.02036, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.395, "top5_acc": 0.65859, "loss_cls": 3.36362, "loss": 3.36362, "time": 0.82452} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.02034, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38703, "top5_acc": 0.65812, "loss_cls": 3.3962, "loss": 3.3962, "time": 0.81933} +{"mode": "train", "epoch": 106, "iter": 1300, "lr": 0.02032, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39703, "top5_acc": 0.66359, "loss_cls": 3.36831, "loss": 3.36831, "time": 0.81892} +{"mode": "train", "epoch": 106, "iter": 1400, "lr": 0.0203, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40125, "top5_acc": 0.66891, "loss_cls": 3.32229, "loss": 3.32229, "time": 0.82878} +{"mode": "train", "epoch": 106, "iter": 1500, "lr": 0.02027, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38875, "top5_acc": 0.65422, "loss_cls": 3.41837, "loss": 3.41837, "time": 0.82764} +{"mode": "train", "epoch": 106, "iter": 1600, "lr": 0.02025, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39812, "top5_acc": 0.66219, "loss_cls": 3.37298, "loss": 3.37298, "time": 0.82721} +{"mode": "train", "epoch": 106, "iter": 1700, "lr": 0.02023, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.39734, "top5_acc": 0.65234, "loss_cls": 3.41429, "loss": 3.41429, "time": 0.8333} +{"mode": "train", "epoch": 106, "iter": 1800, "lr": 0.02021, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.405, "top5_acc": 0.66141, "loss_cls": 3.36249, "loss": 3.36249, "time": 0.83256} +{"mode": "train", "epoch": 106, "iter": 1900, "lr": 0.02018, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39547, "top5_acc": 0.66203, "loss_cls": 3.36499, "loss": 3.36499, "time": 0.82471} +{"mode": "train", "epoch": 106, "iter": 2000, "lr": 0.02016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.395, "top5_acc": 0.66375, "loss_cls": 3.35056, "loss": 3.35056, "time": 0.82528} +{"mode": "train", "epoch": 106, "iter": 2100, "lr": 0.02014, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38953, "top5_acc": 0.66125, "loss_cls": 3.3789, "loss": 3.3789, "time": 0.82009} +{"mode": "train", "epoch": 106, "iter": 2200, "lr": 0.02012, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39438, "top5_acc": 0.65906, "loss_cls": 3.39345, "loss": 3.39345, "time": 0.82202} +{"mode": "train", "epoch": 106, "iter": 2300, "lr": 0.02009, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40859, "top5_acc": 0.67016, "loss_cls": 3.31644, "loss": 3.31644, "time": 0.83269} +{"mode": "train", "epoch": 106, "iter": 2400, "lr": 0.02007, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.39312, "top5_acc": 0.65859, "loss_cls": 3.39827, "loss": 3.39827, "time": 0.82196} +{"mode": "train", "epoch": 106, "iter": 2500, "lr": 0.02005, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4, "top5_acc": 0.66406, "loss_cls": 3.35729, "loss": 3.35729, "time": 0.8216} +{"mode": "train", "epoch": 106, "iter": 2600, "lr": 0.02003, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39875, "top5_acc": 0.66562, "loss_cls": 3.36252, "loss": 3.36252, "time": 0.82845} +{"mode": "train", "epoch": 106, "iter": 2700, "lr": 0.02, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39344, "top5_acc": 0.65281, "loss_cls": 3.39952, "loss": 3.39952, "time": 0.82899} +{"mode": "train", "epoch": 106, "iter": 2800, "lr": 0.01998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39328, "top5_acc": 0.65344, "loss_cls": 3.42912, "loss": 3.42912, "time": 0.8212} +{"mode": "train", "epoch": 106, "iter": 2900, "lr": 0.01996, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40156, "top5_acc": 0.65875, "loss_cls": 3.38693, "loss": 3.38693, "time": 0.82205} +{"mode": "train", "epoch": 106, "iter": 3000, "lr": 0.01994, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.395, "top5_acc": 0.66188, "loss_cls": 3.384, "loss": 3.384, "time": 0.82522} +{"mode": "train", "epoch": 106, "iter": 3100, "lr": 0.01991, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40625, "top5_acc": 0.66656, "loss_cls": 3.34515, "loss": 3.34515, "time": 0.82606} +{"mode": "train", "epoch": 106, "iter": 3200, "lr": 0.01989, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39812, "top5_acc": 0.66578, "loss_cls": 3.37181, "loss": 3.37181, "time": 0.82369} +{"mode": "train", "epoch": 106, "iter": 3300, "lr": 0.01987, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39719, "top5_acc": 0.66844, "loss_cls": 3.36069, "loss": 3.36069, "time": 0.82284} +{"mode": "train", "epoch": 106, "iter": 3400, "lr": 0.01985, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39844, "top5_acc": 0.67156, "loss_cls": 3.35253, "loss": 3.35253, "time": 0.82434} +{"mode": "train", "epoch": 106, "iter": 3500, "lr": 0.01983, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39906, "top5_acc": 0.6575, "loss_cls": 3.37983, "loss": 3.37983, "time": 0.83054} +{"mode": "train", "epoch": 106, "iter": 3600, "lr": 0.0198, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39891, "top5_acc": 0.66469, "loss_cls": 3.37346, "loss": 3.37346, "time": 0.82204} +{"mode": "train", "epoch": 106, "iter": 3700, "lr": 0.01978, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39375, "top5_acc": 0.65516, "loss_cls": 3.4073, "loss": 3.4073, "time": 0.8259} +{"mode": "val", "epoch": 106, "iter": 309, "lr": 0.01977, "top1_acc": 0.33105, "top5_acc": 0.59403, "mean_class_accuracy": 0.3306} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.01975, "memory": 15990, "data_time": 1.26451, "top1_acc": 0.41391, "top5_acc": 0.67359, "loss_cls": 3.28701, "loss": 3.28701, "time": 2.25508} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.01973, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40188, "top5_acc": 0.6675, "loss_cls": 3.34625, "loss": 3.34625, "time": 0.82753} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.0197, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40625, "top5_acc": 0.67109, "loss_cls": 3.33262, "loss": 3.33262, "time": 0.82278} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.01968, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40406, "top5_acc": 0.66797, "loss_cls": 3.3168, "loss": 3.3168, "time": 0.81521} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.01966, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40344, "top5_acc": 0.67391, "loss_cls": 3.29167, "loss": 3.29167, "time": 0.81318} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.01964, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41, "top5_acc": 0.66891, "loss_cls": 3.29166, "loss": 3.29166, "time": 0.81698} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.01961, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40328, "top5_acc": 0.67047, "loss_cls": 3.32606, "loss": 3.32606, "time": 0.82266} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.01959, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40453, "top5_acc": 0.66625, "loss_cls": 3.31986, "loss": 3.31986, "time": 0.81644} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.01957, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39953, "top5_acc": 0.67234, "loss_cls": 3.34089, "loss": 3.34089, "time": 0.81524} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.01955, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40141, "top5_acc": 0.66125, "loss_cls": 3.35846, "loss": 3.35846, "time": 0.81958} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.01953, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40156, "top5_acc": 0.67219, "loss_cls": 3.3332, "loss": 3.3332, "time": 0.82186} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.0195, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40344, "top5_acc": 0.65969, "loss_cls": 3.35821, "loss": 3.35821, "time": 0.8195} +{"mode": "train", "epoch": 107, "iter": 1300, "lr": 0.01948, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39578, "top5_acc": 0.66422, "loss_cls": 3.35375, "loss": 3.35375, "time": 0.82203} +{"mode": "train", "epoch": 107, "iter": 1400, "lr": 0.01946, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40688, "top5_acc": 0.66844, "loss_cls": 3.2992, "loss": 3.2992, "time": 0.82683} +{"mode": "train", "epoch": 107, "iter": 1500, "lr": 0.01944, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40078, "top5_acc": 0.65969, "loss_cls": 3.39547, "loss": 3.39547, "time": 0.82309} +{"mode": "train", "epoch": 107, "iter": 1600, "lr": 0.01942, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.4, "top5_acc": 0.66531, "loss_cls": 3.34908, "loss": 3.34908, "time": 0.82521} +{"mode": "train", "epoch": 107, "iter": 1700, "lr": 0.01939, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39328, "top5_acc": 0.66031, "loss_cls": 3.37073, "loss": 3.37073, "time": 0.83672} +{"mode": "train", "epoch": 107, "iter": 1800, "lr": 0.01937, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.40266, "top5_acc": 0.6575, "loss_cls": 3.34683, "loss": 3.34683, "time": 0.8306} +{"mode": "train", "epoch": 107, "iter": 1900, "lr": 0.01935, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39906, "top5_acc": 0.66484, "loss_cls": 3.35361, "loss": 3.35361, "time": 0.82873} +{"mode": "train", "epoch": 107, "iter": 2000, "lr": 0.01933, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40312, "top5_acc": 0.66, "loss_cls": 3.36416, "loss": 3.36416, "time": 0.82592} +{"mode": "train", "epoch": 107, "iter": 2100, "lr": 0.0193, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40891, "top5_acc": 0.66359, "loss_cls": 3.34141, "loss": 3.34141, "time": 0.82891} +{"mode": "train", "epoch": 107, "iter": 2200, "lr": 0.01928, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40297, "top5_acc": 0.66219, "loss_cls": 3.34913, "loss": 3.34913, "time": 0.82038} +{"mode": "train", "epoch": 107, "iter": 2300, "lr": 0.01926, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40812, "top5_acc": 0.67641, "loss_cls": 3.27628, "loss": 3.27628, "time": 0.82898} +{"mode": "train", "epoch": 107, "iter": 2400, "lr": 0.01924, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40391, "top5_acc": 0.66375, "loss_cls": 3.36318, "loss": 3.36318, "time": 0.81947} +{"mode": "train", "epoch": 107, "iter": 2500, "lr": 0.01922, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40234, "top5_acc": 0.66312, "loss_cls": 3.35066, "loss": 3.35066, "time": 0.81735} +{"mode": "train", "epoch": 107, "iter": 2600, "lr": 0.01919, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39641, "top5_acc": 0.65578, "loss_cls": 3.39492, "loss": 3.39492, "time": 0.82907} +{"mode": "train", "epoch": 107, "iter": 2700, "lr": 0.01917, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39609, "top5_acc": 0.65922, "loss_cls": 3.38513, "loss": 3.38513, "time": 0.82526} +{"mode": "train", "epoch": 107, "iter": 2800, "lr": 0.01915, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39672, "top5_acc": 0.65859, "loss_cls": 3.37397, "loss": 3.37397, "time": 0.81939} +{"mode": "train", "epoch": 107, "iter": 2900, "lr": 0.01913, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39875, "top5_acc": 0.65578, "loss_cls": 3.36513, "loss": 3.36513, "time": 0.81749} +{"mode": "train", "epoch": 107, "iter": 3000, "lr": 0.01911, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39297, "top5_acc": 0.65281, "loss_cls": 3.37283, "loss": 3.37283, "time": 0.82232} +{"mode": "train", "epoch": 107, "iter": 3100, "lr": 0.01908, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38844, "top5_acc": 0.65625, "loss_cls": 3.40332, "loss": 3.40332, "time": 0.82073} +{"mode": "train", "epoch": 107, "iter": 3200, "lr": 0.01906, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40531, "top5_acc": 0.67188, "loss_cls": 3.33896, "loss": 3.33896, "time": 0.81841} +{"mode": "train", "epoch": 107, "iter": 3300, "lr": 0.01904, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39875, "top5_acc": 0.66156, "loss_cls": 3.37365, "loss": 3.37365, "time": 0.81549} +{"mode": "train", "epoch": 107, "iter": 3400, "lr": 0.01902, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39859, "top5_acc": 0.66891, "loss_cls": 3.31431, "loss": 3.31431, "time": 0.82483} +{"mode": "train", "epoch": 107, "iter": 3500, "lr": 0.019, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40406, "top5_acc": 0.66797, "loss_cls": 3.33872, "loss": 3.33872, "time": 0.81702} +{"mode": "train", "epoch": 107, "iter": 3600, "lr": 0.01897, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40344, "top5_acc": 0.66531, "loss_cls": 3.33112, "loss": 3.33112, "time": 0.81431} +{"mode": "train", "epoch": 107, "iter": 3700, "lr": 0.01895, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40625, "top5_acc": 0.66859, "loss_cls": 3.34574, "loss": 3.34574, "time": 0.81562} +{"mode": "val", "epoch": 107, "iter": 309, "lr": 0.01894, "top1_acc": 0.32396, "top5_acc": 0.58522, "mean_class_accuracy": 0.32363} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.01892, "memory": 15990, "data_time": 1.26738, "top1_acc": 0.42156, "top5_acc": 0.67781, "loss_cls": 3.24293, "loss": 3.24293, "time": 2.2554} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0189, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40922, "top5_acc": 0.67188, "loss_cls": 3.31706, "loss": 3.31706, "time": 0.82581} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.01888, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41312, "top5_acc": 0.67562, "loss_cls": 3.30148, "loss": 3.30148, "time": 0.82478} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.01886, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41656, "top5_acc": 0.67891, "loss_cls": 3.27412, "loss": 3.27412, "time": 0.82835} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.01883, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40922, "top5_acc": 0.67344, "loss_cls": 3.31446, "loss": 3.31446, "time": 0.81747} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.01881, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41984, "top5_acc": 0.68516, "loss_cls": 3.23793, "loss": 3.23793, "time": 0.82768} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.01879, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41547, "top5_acc": 0.67547, "loss_cls": 3.2811, "loss": 3.2811, "time": 0.8184} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.01877, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40672, "top5_acc": 0.67516, "loss_cls": 3.31487, "loss": 3.31487, "time": 0.82045} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.01875, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39922, "top5_acc": 0.66281, "loss_cls": 3.36632, "loss": 3.36632, "time": 0.81918} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.01872, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40719, "top5_acc": 0.67797, "loss_cls": 3.28358, "loss": 3.28358, "time": 0.82109} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.0187, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41109, "top5_acc": 0.67234, "loss_cls": 3.31115, "loss": 3.31115, "time": 0.82152} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.01868, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41219, "top5_acc": 0.67438, "loss_cls": 3.28738, "loss": 3.28738, "time": 0.82238} +{"mode": "train", "epoch": 108, "iter": 1300, "lr": 0.01866, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41203, "top5_acc": 0.66578, "loss_cls": 3.30563, "loss": 3.30563, "time": 0.81964} +{"mode": "train", "epoch": 108, "iter": 1400, "lr": 0.01864, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41016, "top5_acc": 0.67234, "loss_cls": 3.30068, "loss": 3.30068, "time": 0.8283} +{"mode": "train", "epoch": 108, "iter": 1500, "lr": 0.01862, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40766, "top5_acc": 0.66938, "loss_cls": 3.31552, "loss": 3.31552, "time": 0.81358} +{"mode": "train", "epoch": 108, "iter": 1600, "lr": 0.01859, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.40344, "top5_acc": 0.67344, "loss_cls": 3.34143, "loss": 3.34143, "time": 0.83023} +{"mode": "train", "epoch": 108, "iter": 1700, "lr": 0.01857, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41875, "top5_acc": 0.68141, "loss_cls": 3.26188, "loss": 3.26188, "time": 0.83176} +{"mode": "train", "epoch": 108, "iter": 1800, "lr": 0.01855, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40844, "top5_acc": 0.66656, "loss_cls": 3.32049, "loss": 3.32049, "time": 0.81839} +{"mode": "train", "epoch": 108, "iter": 1900, "lr": 0.01853, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41547, "top5_acc": 0.67828, "loss_cls": 3.28698, "loss": 3.28698, "time": 0.8265} +{"mode": "train", "epoch": 108, "iter": 2000, "lr": 0.01851, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39938, "top5_acc": 0.66297, "loss_cls": 3.34973, "loss": 3.34973, "time": 0.8225} +{"mode": "train", "epoch": 108, "iter": 2100, "lr": 0.01848, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40891, "top5_acc": 0.67484, "loss_cls": 3.31457, "loss": 3.31457, "time": 0.8168} +{"mode": "train", "epoch": 108, "iter": 2200, "lr": 0.01846, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40172, "top5_acc": 0.66016, "loss_cls": 3.33387, "loss": 3.33387, "time": 0.81414} +{"mode": "train", "epoch": 108, "iter": 2300, "lr": 0.01844, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40797, "top5_acc": 0.66531, "loss_cls": 3.31986, "loss": 3.31986, "time": 0.82424} +{"mode": "train", "epoch": 108, "iter": 2400, "lr": 0.01842, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40188, "top5_acc": 0.66625, "loss_cls": 3.35253, "loss": 3.35253, "time": 0.81531} +{"mode": "train", "epoch": 108, "iter": 2500, "lr": 0.0184, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38812, "top5_acc": 0.65891, "loss_cls": 3.40993, "loss": 3.40993, "time": 0.81938} +{"mode": "train", "epoch": 108, "iter": 2600, "lr": 0.01838, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40484, "top5_acc": 0.66594, "loss_cls": 3.3166, "loss": 3.3166, "time": 0.82727} +{"mode": "train", "epoch": 108, "iter": 2700, "lr": 0.01835, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40188, "top5_acc": 0.66641, "loss_cls": 3.32885, "loss": 3.32885, "time": 0.83075} +{"mode": "train", "epoch": 108, "iter": 2800, "lr": 0.01833, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39734, "top5_acc": 0.65906, "loss_cls": 3.37686, "loss": 3.37686, "time": 0.83028} +{"mode": "train", "epoch": 108, "iter": 2900, "lr": 0.01831, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40219, "top5_acc": 0.66625, "loss_cls": 3.34345, "loss": 3.34345, "time": 0.8244} +{"mode": "train", "epoch": 108, "iter": 3000, "lr": 0.01829, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40516, "top5_acc": 0.65953, "loss_cls": 3.34793, "loss": 3.34793, "time": 0.82402} +{"mode": "train", "epoch": 108, "iter": 3100, "lr": 0.01827, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.4075, "top5_acc": 0.67406, "loss_cls": 3.3126, "loss": 3.3126, "time": 0.82073} +{"mode": "train", "epoch": 108, "iter": 3200, "lr": 0.01825, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40031, "top5_acc": 0.66438, "loss_cls": 3.343, "loss": 3.343, "time": 0.81898} +{"mode": "train", "epoch": 108, "iter": 3300, "lr": 0.01823, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39625, "top5_acc": 0.66578, "loss_cls": 3.36842, "loss": 3.36842, "time": 0.82171} +{"mode": "train", "epoch": 108, "iter": 3400, "lr": 0.0182, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39953, "top5_acc": 0.66344, "loss_cls": 3.31976, "loss": 3.31976, "time": 0.81935} +{"mode": "train", "epoch": 108, "iter": 3500, "lr": 0.01818, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39578, "top5_acc": 0.66188, "loss_cls": 3.35687, "loss": 3.35687, "time": 0.82227} +{"mode": "train", "epoch": 108, "iter": 3600, "lr": 0.01816, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39906, "top5_acc": 0.665, "loss_cls": 3.34667, "loss": 3.34667, "time": 0.8161} +{"mode": "train", "epoch": 108, "iter": 3700, "lr": 0.01814, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4, "top5_acc": 0.65484, "loss_cls": 3.36095, "loss": 3.36095, "time": 0.82167} +{"mode": "val", "epoch": 108, "iter": 309, "lr": 0.01813, "top1_acc": 0.33369, "top5_acc": 0.59393, "mean_class_accuracy": 0.33338} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.01811, "memory": 15990, "data_time": 1.26964, "top1_acc": 0.42219, "top5_acc": 0.68516, "loss_cls": 3.24116, "loss": 3.24116, "time": 2.26123} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.01809, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41328, "top5_acc": 0.67453, "loss_cls": 3.27054, "loss": 3.27054, "time": 0.8361} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.01806, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.4175, "top5_acc": 0.68406, "loss_cls": 3.22191, "loss": 3.22191, "time": 0.82524} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.01804, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41109, "top5_acc": 0.68016, "loss_cls": 3.25382, "loss": 3.25382, "time": 0.82552} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.01802, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40688, "top5_acc": 0.68016, "loss_cls": 3.30772, "loss": 3.30772, "time": 0.81998} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.018, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40266, "top5_acc": 0.66516, "loss_cls": 3.31941, "loss": 3.31941, "time": 0.82475} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.01798, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41, "top5_acc": 0.67953, "loss_cls": 3.25969, "loss": 3.25969, "time": 0.81777} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.01796, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41438, "top5_acc": 0.67, "loss_cls": 3.27442, "loss": 3.27442, "time": 0.8185} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.01794, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40562, "top5_acc": 0.67438, "loss_cls": 3.27977, "loss": 3.27977, "time": 0.8181} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.01791, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41062, "top5_acc": 0.66906, "loss_cls": 3.30307, "loss": 3.30307, "time": 0.81149} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.01789, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40281, "top5_acc": 0.67688, "loss_cls": 3.34853, "loss": 3.34853, "time": 0.81889} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.01787, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41906, "top5_acc": 0.68297, "loss_cls": 3.27797, "loss": 3.27797, "time": 0.81683} +{"mode": "train", "epoch": 109, "iter": 1300, "lr": 0.01785, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40938, "top5_acc": 0.66781, "loss_cls": 3.3147, "loss": 3.3147, "time": 0.81724} +{"mode": "train", "epoch": 109, "iter": 1400, "lr": 0.01783, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41141, "top5_acc": 0.67422, "loss_cls": 3.31496, "loss": 3.31496, "time": 0.82346} +{"mode": "train", "epoch": 109, "iter": 1500, "lr": 0.01781, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40828, "top5_acc": 0.67641, "loss_cls": 3.30325, "loss": 3.30325, "time": 0.81892} +{"mode": "train", "epoch": 109, "iter": 1600, "lr": 0.01779, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41172, "top5_acc": 0.67, "loss_cls": 3.28972, "loss": 3.28972, "time": 0.83166} +{"mode": "train", "epoch": 109, "iter": 1700, "lr": 0.01776, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40547, "top5_acc": 0.66875, "loss_cls": 3.31667, "loss": 3.31667, "time": 0.82884} +{"mode": "train", "epoch": 109, "iter": 1800, "lr": 0.01774, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41797, "top5_acc": 0.67875, "loss_cls": 3.26437, "loss": 3.26437, "time": 0.82626} +{"mode": "train", "epoch": 109, "iter": 1900, "lr": 0.01772, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40688, "top5_acc": 0.65844, "loss_cls": 3.33124, "loss": 3.33124, "time": 0.82685} +{"mode": "train", "epoch": 109, "iter": 2000, "lr": 0.0177, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41125, "top5_acc": 0.66906, "loss_cls": 3.29502, "loss": 3.29502, "time": 0.82034} +{"mode": "train", "epoch": 109, "iter": 2100, "lr": 0.01768, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40156, "top5_acc": 0.66641, "loss_cls": 3.35619, "loss": 3.35619, "time": 0.81906} +{"mode": "train", "epoch": 109, "iter": 2200, "lr": 0.01766, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40438, "top5_acc": 0.66969, "loss_cls": 3.33249, "loss": 3.33249, "time": 0.81191} +{"mode": "train", "epoch": 109, "iter": 2300, "lr": 0.01764, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.40578, "top5_acc": 0.66781, "loss_cls": 3.34964, "loss": 3.34964, "time": 0.82315} +{"mode": "train", "epoch": 109, "iter": 2400, "lr": 0.01761, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39953, "top5_acc": 0.66719, "loss_cls": 3.33775, "loss": 3.33775, "time": 0.81854} +{"mode": "train", "epoch": 109, "iter": 2500, "lr": 0.01759, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40922, "top5_acc": 0.67094, "loss_cls": 3.30554, "loss": 3.30554, "time": 0.81699} +{"mode": "train", "epoch": 109, "iter": 2600, "lr": 0.01757, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41625, "top5_acc": 0.66875, "loss_cls": 3.27296, "loss": 3.27296, "time": 0.82899} +{"mode": "train", "epoch": 109, "iter": 2700, "lr": 0.01755, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40875, "top5_acc": 0.67641, "loss_cls": 3.28019, "loss": 3.28019, "time": 0.82692} +{"mode": "train", "epoch": 109, "iter": 2800, "lr": 0.01753, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40594, "top5_acc": 0.67297, "loss_cls": 3.30836, "loss": 3.30836, "time": 0.8214} +{"mode": "train", "epoch": 109, "iter": 2900, "lr": 0.01751, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41047, "top5_acc": 0.67312, "loss_cls": 3.2888, "loss": 3.2888, "time": 0.81811} +{"mode": "train", "epoch": 109, "iter": 3000, "lr": 0.01749, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41469, "top5_acc": 0.66203, "loss_cls": 3.34644, "loss": 3.34644, "time": 0.81258} +{"mode": "train", "epoch": 109, "iter": 3100, "lr": 0.01747, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40375, "top5_acc": 0.66828, "loss_cls": 3.33685, "loss": 3.33685, "time": 0.81922} +{"mode": "train", "epoch": 109, "iter": 3200, "lr": 0.01744, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39344, "top5_acc": 0.66203, "loss_cls": 3.38716, "loss": 3.38716, "time": 0.81894} +{"mode": "train", "epoch": 109, "iter": 3300, "lr": 0.01742, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41703, "top5_acc": 0.66859, "loss_cls": 3.31848, "loss": 3.31848, "time": 0.82055} +{"mode": "train", "epoch": 109, "iter": 3400, "lr": 0.0174, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40719, "top5_acc": 0.6775, "loss_cls": 3.27545, "loss": 3.27545, "time": 0.82165} +{"mode": "train", "epoch": 109, "iter": 3500, "lr": 0.01738, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41312, "top5_acc": 0.66891, "loss_cls": 3.32055, "loss": 3.32055, "time": 0.81474} +{"mode": "train", "epoch": 109, "iter": 3600, "lr": 0.01736, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41641, "top5_acc": 0.67109, "loss_cls": 3.30355, "loss": 3.30355, "time": 0.82251} +{"mode": "train", "epoch": 109, "iter": 3700, "lr": 0.01734, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40016, "top5_acc": 0.66719, "loss_cls": 3.34402, "loss": 3.34402, "time": 0.82397} +{"mode": "val", "epoch": 109, "iter": 309, "lr": 0.01733, "top1_acc": 0.34063, "top5_acc": 0.59955, "mean_class_accuracy": 0.34049} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.01731, "memory": 15990, "data_time": 1.27901, "top1_acc": 0.42125, "top5_acc": 0.68391, "loss_cls": 3.24234, "loss": 3.24234, "time": 2.27142} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.01729, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42094, "top5_acc": 0.68406, "loss_cls": 3.22386, "loss": 3.22386, "time": 0.83255} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.01727, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42469, "top5_acc": 0.68484, "loss_cls": 3.25787, "loss": 3.25787, "time": 0.8283} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.01724, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41594, "top5_acc": 0.67297, "loss_cls": 3.26651, "loss": 3.26651, "time": 0.82266} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.01722, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41719, "top5_acc": 0.68016, "loss_cls": 3.25235, "loss": 3.25235, "time": 0.82494} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.0172, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40688, "top5_acc": 0.67844, "loss_cls": 3.27436, "loss": 3.27436, "time": 0.82771} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.01718, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41375, "top5_acc": 0.68297, "loss_cls": 3.25235, "loss": 3.25235, "time": 0.8207} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.01716, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41984, "top5_acc": 0.69062, "loss_cls": 3.22369, "loss": 3.22369, "time": 0.82352} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.01714, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40906, "top5_acc": 0.6725, "loss_cls": 3.28587, "loss": 3.28587, "time": 0.8221} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.01712, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41438, "top5_acc": 0.68172, "loss_cls": 3.25866, "loss": 3.25866, "time": 0.82195} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.0171, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39781, "top5_acc": 0.675, "loss_cls": 3.29872, "loss": 3.29872, "time": 0.81965} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.01708, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40016, "top5_acc": 0.67172, "loss_cls": 3.33938, "loss": 3.33938, "time": 0.82032} +{"mode": "train", "epoch": 110, "iter": 1300, "lr": 0.01705, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41516, "top5_acc": 0.68719, "loss_cls": 3.23677, "loss": 3.23677, "time": 0.81746} +{"mode": "train", "epoch": 110, "iter": 1400, "lr": 0.01703, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41594, "top5_acc": 0.69, "loss_cls": 3.21503, "loss": 3.21503, "time": 0.83092} +{"mode": "train", "epoch": 110, "iter": 1500, "lr": 0.01701, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41641, "top5_acc": 0.67875, "loss_cls": 3.26395, "loss": 3.26395, "time": 0.81543} +{"mode": "train", "epoch": 110, "iter": 1600, "lr": 0.01699, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41328, "top5_acc": 0.66344, "loss_cls": 3.32799, "loss": 3.32799, "time": 0.83539} +{"mode": "train", "epoch": 110, "iter": 1700, "lr": 0.01697, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41531, "top5_acc": 0.67953, "loss_cls": 3.28349, "loss": 3.28349, "time": 0.82323} +{"mode": "train", "epoch": 110, "iter": 1800, "lr": 0.01695, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41875, "top5_acc": 0.67562, "loss_cls": 3.26721, "loss": 3.26721, "time": 0.82552} +{"mode": "train", "epoch": 110, "iter": 1900, "lr": 0.01693, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41219, "top5_acc": 0.67391, "loss_cls": 3.29154, "loss": 3.29154, "time": 0.82519} +{"mode": "train", "epoch": 110, "iter": 2000, "lr": 0.01691, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41969, "top5_acc": 0.67266, "loss_cls": 3.26469, "loss": 3.26469, "time": 0.81658} +{"mode": "train", "epoch": 110, "iter": 2100, "lr": 0.01689, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41297, "top5_acc": 0.66922, "loss_cls": 3.28912, "loss": 3.28912, "time": 0.81711} +{"mode": "train", "epoch": 110, "iter": 2200, "lr": 0.01687, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41781, "top5_acc": 0.68297, "loss_cls": 3.2313, "loss": 3.2313, "time": 0.82168} +{"mode": "train", "epoch": 110, "iter": 2300, "lr": 0.01685, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40422, "top5_acc": 0.66953, "loss_cls": 3.30643, "loss": 3.30643, "time": 0.82749} +{"mode": "train", "epoch": 110, "iter": 2400, "lr": 0.01682, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40406, "top5_acc": 0.66188, "loss_cls": 3.33835, "loss": 3.33835, "time": 0.82951} +{"mode": "train", "epoch": 110, "iter": 2500, "lr": 0.0168, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.40141, "top5_acc": 0.66781, "loss_cls": 3.34208, "loss": 3.34208, "time": 0.8253} +{"mode": "train", "epoch": 110, "iter": 2600, "lr": 0.01678, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41625, "top5_acc": 0.67344, "loss_cls": 3.28536, "loss": 3.28536, "time": 0.82545} +{"mode": "train", "epoch": 110, "iter": 2700, "lr": 0.01676, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41578, "top5_acc": 0.67438, "loss_cls": 3.28177, "loss": 3.28177, "time": 0.83011} +{"mode": "train", "epoch": 110, "iter": 2800, "lr": 0.01674, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41359, "top5_acc": 0.66109, "loss_cls": 3.32138, "loss": 3.32138, "time": 0.82024} +{"mode": "train", "epoch": 110, "iter": 2900, "lr": 0.01672, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41516, "top5_acc": 0.67531, "loss_cls": 3.27335, "loss": 3.27335, "time": 0.81926} +{"mode": "train", "epoch": 110, "iter": 3000, "lr": 0.0167, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40875, "top5_acc": 0.66688, "loss_cls": 3.31787, "loss": 3.31787, "time": 0.81555} +{"mode": "train", "epoch": 110, "iter": 3100, "lr": 0.01668, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40656, "top5_acc": 0.67641, "loss_cls": 3.28954, "loss": 3.28954, "time": 0.81881} +{"mode": "train", "epoch": 110, "iter": 3200, "lr": 0.01666, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41344, "top5_acc": 0.67547, "loss_cls": 3.26555, "loss": 3.26555, "time": 0.81325} +{"mode": "train", "epoch": 110, "iter": 3300, "lr": 0.01664, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41828, "top5_acc": 0.68031, "loss_cls": 3.27224, "loss": 3.27224, "time": 0.82309} +{"mode": "train", "epoch": 110, "iter": 3400, "lr": 0.01662, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40766, "top5_acc": 0.66703, "loss_cls": 3.33682, "loss": 3.33682, "time": 0.82296} +{"mode": "train", "epoch": 110, "iter": 3500, "lr": 0.01659, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40984, "top5_acc": 0.67578, "loss_cls": 3.28868, "loss": 3.28868, "time": 0.82272} +{"mode": "train", "epoch": 110, "iter": 3600, "lr": 0.01657, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41719, "top5_acc": 0.67219, "loss_cls": 3.26402, "loss": 3.26402, "time": 0.81864} +{"mode": "train", "epoch": 110, "iter": 3700, "lr": 0.01655, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41047, "top5_acc": 0.67312, "loss_cls": 3.28819, "loss": 3.28819, "time": 0.82998} +{"mode": "val", "epoch": 110, "iter": 309, "lr": 0.01654, "top1_acc": 0.3421, "top5_acc": 0.60123, "mean_class_accuracy": 0.34195} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.01652, "memory": 15990, "data_time": 1.24259, "top1_acc": 0.42125, "top5_acc": 0.69016, "loss_cls": 3.23336, "loss": 3.23336, "time": 2.22824} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.0165, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42562, "top5_acc": 0.69609, "loss_cls": 3.17776, "loss": 3.17776, "time": 0.82233} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.01648, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42531, "top5_acc": 0.68953, "loss_cls": 3.20483, "loss": 3.20483, "time": 0.82579} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.01646, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42156, "top5_acc": 0.68688, "loss_cls": 3.20993, "loss": 3.20993, "time": 0.82676} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.01644, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42688, "top5_acc": 0.68656, "loss_cls": 3.20806, "loss": 3.20806, "time": 0.81905} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.01642, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41469, "top5_acc": 0.67672, "loss_cls": 3.24908, "loss": 3.24908, "time": 0.81476} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.0164, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41641, "top5_acc": 0.68375, "loss_cls": 3.23042, "loss": 3.23042, "time": 0.82559} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.01638, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41672, "top5_acc": 0.68203, "loss_cls": 3.23786, "loss": 3.23786, "time": 0.81882} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.01636, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42828, "top5_acc": 0.68172, "loss_cls": 3.21525, "loss": 3.21525, "time": 0.81396} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.01634, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43156, "top5_acc": 0.68719, "loss_cls": 3.17801, "loss": 3.17801, "time": 0.82033} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.01632, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41188, "top5_acc": 0.67344, "loss_cls": 3.27375, "loss": 3.27375, "time": 0.81547} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.0163, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40766, "top5_acc": 0.67156, "loss_cls": 3.28954, "loss": 3.28954, "time": 0.81977} +{"mode": "train", "epoch": 111, "iter": 1300, "lr": 0.01627, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.40531, "top5_acc": 0.66875, "loss_cls": 3.32738, "loss": 3.32738, "time": 0.82637} +{"mode": "train", "epoch": 111, "iter": 1400, "lr": 0.01625, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40531, "top5_acc": 0.67453, "loss_cls": 3.29224, "loss": 3.29224, "time": 0.82976} +{"mode": "train", "epoch": 111, "iter": 1500, "lr": 0.01623, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42094, "top5_acc": 0.67938, "loss_cls": 3.21908, "loss": 3.21908, "time": 0.82553} +{"mode": "train", "epoch": 111, "iter": 1600, "lr": 0.01621, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42016, "top5_acc": 0.68359, "loss_cls": 3.226, "loss": 3.226, "time": 0.83185} +{"mode": "train", "epoch": 111, "iter": 1700, "lr": 0.01619, "memory": 15990, "data_time": 0.00074, "top1_acc": 0.41656, "top5_acc": 0.68219, "loss_cls": 3.27365, "loss": 3.27365, "time": 0.82868} +{"mode": "train", "epoch": 111, "iter": 1800, "lr": 0.01617, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41812, "top5_acc": 0.67859, "loss_cls": 3.26943, "loss": 3.26943, "time": 0.82506} +{"mode": "train", "epoch": 111, "iter": 1900, "lr": 0.01615, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41844, "top5_acc": 0.68594, "loss_cls": 3.21878, "loss": 3.21878, "time": 0.82867} +{"mode": "train", "epoch": 111, "iter": 2000, "lr": 0.01613, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41766, "top5_acc": 0.67922, "loss_cls": 3.24895, "loss": 3.24895, "time": 0.81786} +{"mode": "train", "epoch": 111, "iter": 2100, "lr": 0.01611, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40984, "top5_acc": 0.67172, "loss_cls": 3.29895, "loss": 3.29895, "time": 0.82086} +{"mode": "train", "epoch": 111, "iter": 2200, "lr": 0.01609, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40688, "top5_acc": 0.67688, "loss_cls": 3.26885, "loss": 3.26885, "time": 0.81615} +{"mode": "train", "epoch": 111, "iter": 2300, "lr": 0.01607, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.42359, "top5_acc": 0.67812, "loss_cls": 3.26857, "loss": 3.26857, "time": 0.82804} +{"mode": "train", "epoch": 111, "iter": 2400, "lr": 0.01605, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41031, "top5_acc": 0.68094, "loss_cls": 3.26598, "loss": 3.26598, "time": 0.82076} +{"mode": "train", "epoch": 111, "iter": 2500, "lr": 0.01603, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41125, "top5_acc": 0.67641, "loss_cls": 3.27456, "loss": 3.27456, "time": 0.81638} +{"mode": "train", "epoch": 111, "iter": 2600, "lr": 0.01601, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41922, "top5_acc": 0.67875, "loss_cls": 3.26961, "loss": 3.26961, "time": 0.82084} +{"mode": "train", "epoch": 111, "iter": 2700, "lr": 0.01599, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41797, "top5_acc": 0.67672, "loss_cls": 3.25839, "loss": 3.25839, "time": 0.82185} +{"mode": "train", "epoch": 111, "iter": 2800, "lr": 0.01597, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40594, "top5_acc": 0.66875, "loss_cls": 3.3202, "loss": 3.3202, "time": 0.81629} +{"mode": "train", "epoch": 111, "iter": 2900, "lr": 0.01595, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41734, "top5_acc": 0.67125, "loss_cls": 3.27636, "loss": 3.27636, "time": 0.81823} +{"mode": "train", "epoch": 111, "iter": 3000, "lr": 0.01593, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42219, "top5_acc": 0.68312, "loss_cls": 3.25511, "loss": 3.25511, "time": 0.81524} +{"mode": "train", "epoch": 111, "iter": 3100, "lr": 0.0159, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41141, "top5_acc": 0.67703, "loss_cls": 3.28767, "loss": 3.28767, "time": 0.81757} +{"mode": "train", "epoch": 111, "iter": 3200, "lr": 0.01588, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41797, "top5_acc": 0.67734, "loss_cls": 3.27137, "loss": 3.27137, "time": 0.8157} +{"mode": "train", "epoch": 111, "iter": 3300, "lr": 0.01586, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40875, "top5_acc": 0.67641, "loss_cls": 3.2831, "loss": 3.2831, "time": 0.81247} +{"mode": "train", "epoch": 111, "iter": 3400, "lr": 0.01584, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42375, "top5_acc": 0.67859, "loss_cls": 3.25852, "loss": 3.25852, "time": 0.82209} +{"mode": "train", "epoch": 111, "iter": 3500, "lr": 0.01582, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42266, "top5_acc": 0.67109, "loss_cls": 3.27341, "loss": 3.27341, "time": 0.81521} +{"mode": "train", "epoch": 111, "iter": 3600, "lr": 0.0158, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40984, "top5_acc": 0.67609, "loss_cls": 3.29877, "loss": 3.29877, "time": 0.82247} +{"mode": "train", "epoch": 111, "iter": 3700, "lr": 0.01578, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41859, "top5_acc": 0.68125, "loss_cls": 3.23829, "loss": 3.23829, "time": 0.82331} +{"mode": "val", "epoch": 111, "iter": 309, "lr": 0.01577, "top1_acc": 0.34858, "top5_acc": 0.61308, "mean_class_accuracy": 0.3481} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.01575, "memory": 15990, "data_time": 1.25824, "top1_acc": 0.43188, "top5_acc": 0.69469, "loss_cls": 3.1333, "loss": 3.1333, "time": 2.25087} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.01573, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43219, "top5_acc": 0.69719, "loss_cls": 3.17381, "loss": 3.17381, "time": 0.83085} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.01571, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42484, "top5_acc": 0.68641, "loss_cls": 3.2086, "loss": 3.2086, "time": 0.82169} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.01569, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42781, "top5_acc": 0.69109, "loss_cls": 3.17792, "loss": 3.17792, "time": 0.81708} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.01567, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41875, "top5_acc": 0.67641, "loss_cls": 3.24738, "loss": 3.24738, "time": 0.82268} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.01565, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42578, "top5_acc": 0.68672, "loss_cls": 3.19644, "loss": 3.19644, "time": 0.81719} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.01563, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42547, "top5_acc": 0.68016, "loss_cls": 3.25004, "loss": 3.25004, "time": 0.82249} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.01561, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41906, "top5_acc": 0.68953, "loss_cls": 3.19697, "loss": 3.19697, "time": 0.82138} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.01559, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42547, "top5_acc": 0.69, "loss_cls": 3.18862, "loss": 3.18862, "time": 0.82342} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.01557, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41922, "top5_acc": 0.68125, "loss_cls": 3.22421, "loss": 3.22421, "time": 0.82121} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.01555, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40719, "top5_acc": 0.67594, "loss_cls": 3.25979, "loss": 3.25979, "time": 0.81739} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.01553, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42547, "top5_acc": 0.68875, "loss_cls": 3.19106, "loss": 3.19106, "time": 0.81583} +{"mode": "train", "epoch": 112, "iter": 1300, "lr": 0.01551, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41719, "top5_acc": 0.68438, "loss_cls": 3.2278, "loss": 3.2278, "time": 0.82013} +{"mode": "train", "epoch": 112, "iter": 1400, "lr": 0.01549, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41391, "top5_acc": 0.67078, "loss_cls": 3.29554, "loss": 3.29554, "time": 0.82721} +{"mode": "train", "epoch": 112, "iter": 1500, "lr": 0.01547, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41797, "top5_acc": 0.68219, "loss_cls": 3.25933, "loss": 3.25933, "time": 0.82158} +{"mode": "train", "epoch": 112, "iter": 1600, "lr": 0.01545, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43156, "top5_acc": 0.68953, "loss_cls": 3.1868, "loss": 3.1868, "time": 0.83972} +{"mode": "train", "epoch": 112, "iter": 1700, "lr": 0.01543, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41922, "top5_acc": 0.69328, "loss_cls": 3.20105, "loss": 3.20105, "time": 0.83456} +{"mode": "train", "epoch": 112, "iter": 1800, "lr": 0.01541, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42859, "top5_acc": 0.68172, "loss_cls": 3.20334, "loss": 3.20334, "time": 0.83606} +{"mode": "train", "epoch": 112, "iter": 1900, "lr": 0.01539, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40859, "top5_acc": 0.68156, "loss_cls": 3.25627, "loss": 3.25627, "time": 0.83228} +{"mode": "train", "epoch": 112, "iter": 2000, "lr": 0.01537, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41047, "top5_acc": 0.67719, "loss_cls": 3.29659, "loss": 3.29659, "time": 0.83333} +{"mode": "train", "epoch": 112, "iter": 2100, "lr": 0.01535, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41484, "top5_acc": 0.67859, "loss_cls": 3.26526, "loss": 3.26526, "time": 0.82598} +{"mode": "train", "epoch": 112, "iter": 2200, "lr": 0.01533, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41828, "top5_acc": 0.67984, "loss_cls": 3.22364, "loss": 3.22364, "time": 0.8224} +{"mode": "train", "epoch": 112, "iter": 2300, "lr": 0.01531, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.4125, "top5_acc": 0.68, "loss_cls": 3.25055, "loss": 3.25055, "time": 0.83282} +{"mode": "train", "epoch": 112, "iter": 2400, "lr": 0.01529, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42266, "top5_acc": 0.67938, "loss_cls": 3.22239, "loss": 3.22239, "time": 0.81915} +{"mode": "train", "epoch": 112, "iter": 2500, "lr": 0.01527, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42375, "top5_acc": 0.68359, "loss_cls": 3.22729, "loss": 3.22729, "time": 0.81861} +{"mode": "train", "epoch": 112, "iter": 2600, "lr": 0.01525, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41984, "top5_acc": 0.68484, "loss_cls": 3.23396, "loss": 3.23396, "time": 0.83237} +{"mode": "train", "epoch": 112, "iter": 2700, "lr": 0.01523, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42594, "top5_acc": 0.68734, "loss_cls": 3.20737, "loss": 3.20737, "time": 0.83033} +{"mode": "train", "epoch": 112, "iter": 2800, "lr": 0.01521, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42641, "top5_acc": 0.68828, "loss_cls": 3.21596, "loss": 3.21596, "time": 0.82738} +{"mode": "train", "epoch": 112, "iter": 2900, "lr": 0.01519, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42891, "top5_acc": 0.68078, "loss_cls": 3.207, "loss": 3.207, "time": 0.82114} +{"mode": "train", "epoch": 112, "iter": 3000, "lr": 0.01517, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41266, "top5_acc": 0.67844, "loss_cls": 3.27862, "loss": 3.27862, "time": 0.82345} +{"mode": "train", "epoch": 112, "iter": 3100, "lr": 0.01515, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40828, "top5_acc": 0.67375, "loss_cls": 3.3048, "loss": 3.3048, "time": 0.82752} +{"mode": "train", "epoch": 112, "iter": 3200, "lr": 0.01513, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42078, "top5_acc": 0.67766, "loss_cls": 3.25965, "loss": 3.25965, "time": 0.82614} +{"mode": "train", "epoch": 112, "iter": 3300, "lr": 0.01511, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41141, "top5_acc": 0.67438, "loss_cls": 3.27998, "loss": 3.27998, "time": 0.82251} +{"mode": "train", "epoch": 112, "iter": 3400, "lr": 0.01509, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40828, "top5_acc": 0.68281, "loss_cls": 3.25424, "loss": 3.25424, "time": 0.82281} +{"mode": "train", "epoch": 112, "iter": 3500, "lr": 0.01507, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40734, "top5_acc": 0.67172, "loss_cls": 3.30058, "loss": 3.30058, "time": 0.81608} +{"mode": "train", "epoch": 112, "iter": 3600, "lr": 0.01505, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41125, "top5_acc": 0.67172, "loss_cls": 3.28693, "loss": 3.28693, "time": 0.81571} +{"mode": "train", "epoch": 112, "iter": 3700, "lr": 0.01503, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40812, "top5_acc": 0.66828, "loss_cls": 3.30505, "loss": 3.30505, "time": 0.8347} +{"mode": "val", "epoch": 112, "iter": 309, "lr": 0.01502, "top1_acc": 0.35511, "top5_acc": 0.62113, "mean_class_accuracy": 0.35483} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.015, "memory": 15990, "data_time": 1.29927, "top1_acc": 0.43734, "top5_acc": 0.69422, "loss_cls": 3.1373, "loss": 3.1373, "time": 2.28864} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.01498, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.445, "top5_acc": 0.70688, "loss_cls": 3.09958, "loss": 3.09958, "time": 0.82881} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.01496, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42844, "top5_acc": 0.68984, "loss_cls": 3.17765, "loss": 3.17765, "time": 0.8203} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.01494, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43547, "top5_acc": 0.70047, "loss_cls": 3.14198, "loss": 3.14198, "time": 0.81511} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.01492, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42062, "top5_acc": 0.68312, "loss_cls": 3.2288, "loss": 3.2288, "time": 0.82332} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.0149, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42562, "top5_acc": 0.69312, "loss_cls": 3.18034, "loss": 3.18034, "time": 0.823} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.01488, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42547, "top5_acc": 0.69656, "loss_cls": 3.19085, "loss": 3.19085, "time": 0.82072} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.01486, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42875, "top5_acc": 0.69328, "loss_cls": 3.16406, "loss": 3.16406, "time": 0.81953} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.01484, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41828, "top5_acc": 0.67906, "loss_cls": 3.25301, "loss": 3.25301, "time": 0.81997} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.01482, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41828, "top5_acc": 0.68219, "loss_cls": 3.21808, "loss": 3.21808, "time": 0.82686} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0148, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42781, "top5_acc": 0.69188, "loss_cls": 3.16663, "loss": 3.16663, "time": 0.825} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.01478, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41578, "top5_acc": 0.67828, "loss_cls": 3.22894, "loss": 3.22894, "time": 0.81842} +{"mode": "train", "epoch": 113, "iter": 1300, "lr": 0.01476, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41531, "top5_acc": 0.68172, "loss_cls": 3.21861, "loss": 3.21861, "time": 0.82649} +{"mode": "train", "epoch": 113, "iter": 1400, "lr": 0.01474, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4125, "top5_acc": 0.67938, "loss_cls": 3.25247, "loss": 3.25247, "time": 0.81735} +{"mode": "train", "epoch": 113, "iter": 1500, "lr": 0.01472, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43062, "top5_acc": 0.68969, "loss_cls": 3.1748, "loss": 3.1748, "time": 0.83317} +{"mode": "train", "epoch": 113, "iter": 1600, "lr": 0.0147, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42375, "top5_acc": 0.68, "loss_cls": 3.21922, "loss": 3.21922, "time": 0.8276} +{"mode": "train", "epoch": 113, "iter": 1700, "lr": 0.01468, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42547, "top5_acc": 0.67969, "loss_cls": 3.19916, "loss": 3.19916, "time": 0.83145} +{"mode": "train", "epoch": 113, "iter": 1800, "lr": 0.01466, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42422, "top5_acc": 0.68672, "loss_cls": 3.16241, "loss": 3.16241, "time": 0.8325} +{"mode": "train", "epoch": 113, "iter": 1900, "lr": 0.01464, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41984, "top5_acc": 0.67625, "loss_cls": 3.27157, "loss": 3.27157, "time": 0.82414} +{"mode": "train", "epoch": 113, "iter": 2000, "lr": 0.01462, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42094, "top5_acc": 0.68641, "loss_cls": 3.21285, "loss": 3.21285, "time": 0.82387} +{"mode": "train", "epoch": 113, "iter": 2100, "lr": 0.0146, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42141, "top5_acc": 0.68953, "loss_cls": 3.19355, "loss": 3.19355, "time": 0.82357} +{"mode": "train", "epoch": 113, "iter": 2200, "lr": 0.01458, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42344, "top5_acc": 0.69, "loss_cls": 3.20611, "loss": 3.20611, "time": 0.83016} +{"mode": "train", "epoch": 113, "iter": 2300, "lr": 0.01456, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41797, "top5_acc": 0.68297, "loss_cls": 3.25175, "loss": 3.25175, "time": 0.83269} +{"mode": "train", "epoch": 113, "iter": 2400, "lr": 0.01454, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41328, "top5_acc": 0.68234, "loss_cls": 3.25051, "loss": 3.25051, "time": 0.82677} +{"mode": "train", "epoch": 113, "iter": 2500, "lr": 0.01452, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42078, "top5_acc": 0.67906, "loss_cls": 3.25573, "loss": 3.25573, "time": 0.83343} +{"mode": "train", "epoch": 113, "iter": 2600, "lr": 0.0145, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41672, "top5_acc": 0.68047, "loss_cls": 3.25368, "loss": 3.25368, "time": 0.83561} +{"mode": "train", "epoch": 113, "iter": 2700, "lr": 0.01448, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43359, "top5_acc": 0.68953, "loss_cls": 3.19085, "loss": 3.19085, "time": 0.83277} +{"mode": "train", "epoch": 113, "iter": 2800, "lr": 0.01446, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42281, "top5_acc": 0.68188, "loss_cls": 3.24725, "loss": 3.24725, "time": 0.82773} +{"mode": "train", "epoch": 113, "iter": 2900, "lr": 0.01444, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42062, "top5_acc": 0.68094, "loss_cls": 3.24739, "loss": 3.24739, "time": 0.82255} +{"mode": "train", "epoch": 113, "iter": 3000, "lr": 0.01442, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43234, "top5_acc": 0.68516, "loss_cls": 3.1957, "loss": 3.1957, "time": 0.82414} +{"mode": "train", "epoch": 113, "iter": 3100, "lr": 0.0144, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42438, "top5_acc": 0.6825, "loss_cls": 3.23157, "loss": 3.23157, "time": 0.83024} +{"mode": "train", "epoch": 113, "iter": 3200, "lr": 0.01438, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42375, "top5_acc": 0.68188, "loss_cls": 3.21857, "loss": 3.21857, "time": 0.82718} +{"mode": "train", "epoch": 113, "iter": 3300, "lr": 0.01436, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42453, "top5_acc": 0.68922, "loss_cls": 3.19319, "loss": 3.19319, "time": 0.82814} +{"mode": "train", "epoch": 113, "iter": 3400, "lr": 0.01434, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.4175, "top5_acc": 0.67422, "loss_cls": 3.25096, "loss": 3.25096, "time": 0.82639} +{"mode": "train", "epoch": 113, "iter": 3500, "lr": 0.01432, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42188, "top5_acc": 0.68188, "loss_cls": 3.24848, "loss": 3.24848, "time": 0.82834} +{"mode": "train", "epoch": 113, "iter": 3600, "lr": 0.01431, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42172, "top5_acc": 0.67609, "loss_cls": 3.25159, "loss": 3.25159, "time": 0.82623} +{"mode": "train", "epoch": 113, "iter": 3700, "lr": 0.01429, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.4175, "top5_acc": 0.67719, "loss_cls": 3.27048, "loss": 3.27048, "time": 0.8364} +{"mode": "val", "epoch": 113, "iter": 309, "lr": 0.01428, "top1_acc": 0.36236, "top5_acc": 0.6227, "mean_class_accuracy": 0.36213} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.01426, "memory": 15990, "data_time": 1.26162, "top1_acc": 0.42531, "top5_acc": 0.70078, "loss_cls": 3.14174, "loss": 3.14174, "time": 2.25734} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.01424, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44391, "top5_acc": 0.71062, "loss_cls": 3.093, "loss": 3.093, "time": 0.82878} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.01422, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44, "top5_acc": 0.70266, "loss_cls": 3.14403, "loss": 3.14403, "time": 0.82253} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.0142, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43531, "top5_acc": 0.68531, "loss_cls": 3.17679, "loss": 3.17679, "time": 0.82913} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.01418, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44328, "top5_acc": 0.70125, "loss_cls": 3.12141, "loss": 3.12141, "time": 0.82219} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.01416, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42531, "top5_acc": 0.68828, "loss_cls": 3.21214, "loss": 3.21214, "time": 0.82301} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.01414, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43344, "top5_acc": 0.69328, "loss_cls": 3.18208, "loss": 3.18208, "time": 0.83008} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.01412, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42875, "top5_acc": 0.69703, "loss_cls": 3.19014, "loss": 3.19014, "time": 0.82335} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.0141, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42281, "top5_acc": 0.69203, "loss_cls": 3.20396, "loss": 3.20396, "time": 0.83115} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.01408, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43531, "top5_acc": 0.69797, "loss_cls": 3.13326, "loss": 3.13326, "time": 0.83129} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.01406, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42422, "top5_acc": 0.70031, "loss_cls": 3.17304, "loss": 3.17304, "time": 0.8302} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.01404, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42047, "top5_acc": 0.68422, "loss_cls": 3.20354, "loss": 3.20354, "time": 0.8319} +{"mode": "train", "epoch": 114, "iter": 1300, "lr": 0.01402, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43688, "top5_acc": 0.69812, "loss_cls": 3.12332, "loss": 3.12332, "time": 0.83309} +{"mode": "train", "epoch": 114, "iter": 1400, "lr": 0.014, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43328, "top5_acc": 0.69234, "loss_cls": 3.1814, "loss": 3.1814, "time": 0.82914} +{"mode": "train", "epoch": 114, "iter": 1500, "lr": 0.01398, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.42328, "top5_acc": 0.68234, "loss_cls": 3.20757, "loss": 3.20757, "time": 0.83163} +{"mode": "train", "epoch": 114, "iter": 1600, "lr": 0.01397, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42578, "top5_acc": 0.675, "loss_cls": 3.25436, "loss": 3.25436, "time": 0.83366} +{"mode": "train", "epoch": 114, "iter": 1700, "lr": 0.01395, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.4325, "top5_acc": 0.69312, "loss_cls": 3.17031, "loss": 3.17031, "time": 0.83148} +{"mode": "train", "epoch": 114, "iter": 1800, "lr": 0.01393, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43266, "top5_acc": 0.69172, "loss_cls": 3.17336, "loss": 3.17336, "time": 0.82887} +{"mode": "train", "epoch": 114, "iter": 1900, "lr": 0.01391, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43203, "top5_acc": 0.68875, "loss_cls": 3.1883, "loss": 3.1883, "time": 0.82846} +{"mode": "train", "epoch": 114, "iter": 2000, "lr": 0.01389, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43078, "top5_acc": 0.69562, "loss_cls": 3.17897, "loss": 3.17897, "time": 0.82557} +{"mode": "train", "epoch": 114, "iter": 2100, "lr": 0.01387, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43922, "top5_acc": 0.69172, "loss_cls": 3.1631, "loss": 3.1631, "time": 0.82596} +{"mode": "train", "epoch": 114, "iter": 2200, "lr": 0.01385, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42562, "top5_acc": 0.68094, "loss_cls": 3.21452, "loss": 3.21452, "time": 0.83771} +{"mode": "train", "epoch": 114, "iter": 2300, "lr": 0.01383, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42859, "top5_acc": 0.68547, "loss_cls": 3.21083, "loss": 3.21083, "time": 0.82791} +{"mode": "train", "epoch": 114, "iter": 2400, "lr": 0.01381, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42172, "top5_acc": 0.68203, "loss_cls": 3.23827, "loss": 3.23827, "time": 0.82335} +{"mode": "train", "epoch": 114, "iter": 2500, "lr": 0.01379, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42625, "top5_acc": 0.6775, "loss_cls": 3.25651, "loss": 3.25651, "time": 0.83854} +{"mode": "train", "epoch": 114, "iter": 2600, "lr": 0.01377, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42984, "top5_acc": 0.68188, "loss_cls": 3.20036, "loss": 3.20036, "time": 0.83075} +{"mode": "train", "epoch": 114, "iter": 2700, "lr": 0.01375, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42422, "top5_acc": 0.68812, "loss_cls": 3.20655, "loss": 3.20655, "time": 0.82336} +{"mode": "train", "epoch": 114, "iter": 2800, "lr": 0.01373, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43141, "top5_acc": 0.68266, "loss_cls": 3.18786, "loss": 3.18786, "time": 0.82479} +{"mode": "train", "epoch": 114, "iter": 2900, "lr": 0.01371, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42422, "top5_acc": 0.68359, "loss_cls": 3.22903, "loss": 3.22903, "time": 0.81921} +{"mode": "train", "epoch": 114, "iter": 3000, "lr": 0.01369, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43344, "top5_acc": 0.69453, "loss_cls": 3.15382, "loss": 3.15382, "time": 0.82732} +{"mode": "train", "epoch": 114, "iter": 3100, "lr": 0.01368, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42656, "top5_acc": 0.68688, "loss_cls": 3.18891, "loss": 3.18891, "time": 0.81957} +{"mode": "train", "epoch": 114, "iter": 3200, "lr": 0.01366, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41984, "top5_acc": 0.67703, "loss_cls": 3.25638, "loss": 3.25638, "time": 0.81975} +{"mode": "train", "epoch": 114, "iter": 3300, "lr": 0.01364, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4175, "top5_acc": 0.68578, "loss_cls": 3.22438, "loss": 3.22438, "time": 0.81806} +{"mode": "train", "epoch": 114, "iter": 3400, "lr": 0.01362, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43172, "top5_acc": 0.69172, "loss_cls": 3.16248, "loss": 3.16248, "time": 0.82476} +{"mode": "train", "epoch": 114, "iter": 3500, "lr": 0.0136, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42391, "top5_acc": 0.68219, "loss_cls": 3.22293, "loss": 3.22293, "time": 0.82114} +{"mode": "train", "epoch": 114, "iter": 3600, "lr": 0.01358, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.41531, "top5_acc": 0.68375, "loss_cls": 3.24144, "loss": 3.24144, "time": 0.82203} +{"mode": "train", "epoch": 114, "iter": 3700, "lr": 0.01356, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42406, "top5_acc": 0.68328, "loss_cls": 3.21036, "loss": 3.21036, "time": 0.83262} +{"mode": "val", "epoch": 114, "iter": 309, "lr": 0.01355, "top1_acc": 0.36773, "top5_acc": 0.62564, "mean_class_accuracy": 0.36746} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.01353, "memory": 15990, "data_time": 1.24455, "top1_acc": 0.44141, "top5_acc": 0.70422, "loss_cls": 3.10464, "loss": 3.10464, "time": 2.23451} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.01351, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44484, "top5_acc": 0.70359, "loss_cls": 3.10388, "loss": 3.10388, "time": 0.82517} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.01349, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44016, "top5_acc": 0.70125, "loss_cls": 3.12051, "loss": 3.12051, "time": 0.82367} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.01348, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44359, "top5_acc": 0.70734, "loss_cls": 3.09103, "loss": 3.09103, "time": 0.81828} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.01346, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43734, "top5_acc": 0.69562, "loss_cls": 3.11159, "loss": 3.11159, "time": 0.81859} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.01344, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44781, "top5_acc": 0.70656, "loss_cls": 3.07589, "loss": 3.07589, "time": 0.81935} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.01342, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43203, "top5_acc": 0.68859, "loss_cls": 3.17811, "loss": 3.17811, "time": 0.81517} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.0134, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43, "top5_acc": 0.69703, "loss_cls": 3.1464, "loss": 3.1464, "time": 0.81582} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.01338, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42922, "top5_acc": 0.68203, "loss_cls": 3.20851, "loss": 3.20851, "time": 0.81666} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.01336, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44297, "top5_acc": 0.69812, "loss_cls": 3.11491, "loss": 3.11491, "time": 0.81484} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.01334, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43297, "top5_acc": 0.69328, "loss_cls": 3.16942, "loss": 3.16942, "time": 0.81295} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.01332, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43703, "top5_acc": 0.69516, "loss_cls": 3.13936, "loss": 3.13936, "time": 0.82002} +{"mode": "train", "epoch": 115, "iter": 1300, "lr": 0.0133, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42375, "top5_acc": 0.68594, "loss_cls": 3.18239, "loss": 3.18239, "time": 0.81672} +{"mode": "train", "epoch": 115, "iter": 1400, "lr": 0.01328, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.43047, "top5_acc": 0.69578, "loss_cls": 3.17579, "loss": 3.17579, "time": 0.82558} +{"mode": "train", "epoch": 115, "iter": 1500, "lr": 0.01327, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43719, "top5_acc": 0.69875, "loss_cls": 3.11558, "loss": 3.11558, "time": 0.81781} +{"mode": "train", "epoch": 115, "iter": 1600, "lr": 0.01325, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42344, "top5_acc": 0.68875, "loss_cls": 3.20993, "loss": 3.20993, "time": 0.81506} +{"mode": "train", "epoch": 115, "iter": 1700, "lr": 0.01323, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43016, "top5_acc": 0.69984, "loss_cls": 3.14308, "loss": 3.14308, "time": 0.8148} +{"mode": "train", "epoch": 115, "iter": 1800, "lr": 0.01321, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43406, "top5_acc": 0.69578, "loss_cls": 3.15999, "loss": 3.15999, "time": 0.8151} +{"mode": "train", "epoch": 115, "iter": 1900, "lr": 0.01319, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42344, "top5_acc": 0.68781, "loss_cls": 3.18767, "loss": 3.18767, "time": 0.81869} +{"mode": "train", "epoch": 115, "iter": 2000, "lr": 0.01317, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42812, "top5_acc": 0.68891, "loss_cls": 3.17515, "loss": 3.17515, "time": 0.81791} +{"mode": "train", "epoch": 115, "iter": 2100, "lr": 0.01315, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42875, "top5_acc": 0.68719, "loss_cls": 3.19886, "loss": 3.19886, "time": 0.817} +{"mode": "train", "epoch": 115, "iter": 2200, "lr": 0.01313, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42234, "top5_acc": 0.68609, "loss_cls": 3.19505, "loss": 3.19505, "time": 0.81625} +{"mode": "train", "epoch": 115, "iter": 2300, "lr": 0.01311, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41953, "top5_acc": 0.67828, "loss_cls": 3.22455, "loss": 3.22455, "time": 0.81317} +{"mode": "train", "epoch": 115, "iter": 2400, "lr": 0.0131, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43719, "top5_acc": 0.69297, "loss_cls": 3.15608, "loss": 3.15608, "time": 0.81692} +{"mode": "train", "epoch": 115, "iter": 2500, "lr": 0.01308, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42562, "top5_acc": 0.69344, "loss_cls": 3.1675, "loss": 3.1675, "time": 0.81997} +{"mode": "train", "epoch": 115, "iter": 2600, "lr": 0.01306, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41984, "top5_acc": 0.68031, "loss_cls": 3.24248, "loss": 3.24248, "time": 0.81835} +{"mode": "train", "epoch": 115, "iter": 2700, "lr": 0.01304, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43047, "top5_acc": 0.69641, "loss_cls": 3.19152, "loss": 3.19152, "time": 0.81527} +{"mode": "train", "epoch": 115, "iter": 2800, "lr": 0.01302, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43031, "top5_acc": 0.68766, "loss_cls": 3.19574, "loss": 3.19574, "time": 0.81487} +{"mode": "train", "epoch": 115, "iter": 2900, "lr": 0.013, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43938, "top5_acc": 0.69438, "loss_cls": 3.16062, "loss": 3.16062, "time": 0.81543} +{"mode": "train", "epoch": 115, "iter": 3000, "lr": 0.01298, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44469, "top5_acc": 0.70375, "loss_cls": 3.11436, "loss": 3.11436, "time": 0.81567} +{"mode": "train", "epoch": 115, "iter": 3100, "lr": 0.01296, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43203, "top5_acc": 0.70016, "loss_cls": 3.15164, "loss": 3.15164, "time": 0.81539} +{"mode": "train", "epoch": 115, "iter": 3200, "lr": 0.01295, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42938, "top5_acc": 0.69188, "loss_cls": 3.19474, "loss": 3.19474, "time": 0.81908} +{"mode": "train", "epoch": 115, "iter": 3300, "lr": 0.01293, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43328, "top5_acc": 0.68938, "loss_cls": 3.21083, "loss": 3.21083, "time": 0.81724} +{"mode": "train", "epoch": 115, "iter": 3400, "lr": 0.01291, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42016, "top5_acc": 0.68578, "loss_cls": 3.21051, "loss": 3.21051, "time": 0.8234} +{"mode": "train", "epoch": 115, "iter": 3500, "lr": 0.01289, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42, "top5_acc": 0.69141, "loss_cls": 3.20953, "loss": 3.20953, "time": 0.81672} +{"mode": "train", "epoch": 115, "iter": 3600, "lr": 0.01287, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44125, "top5_acc": 0.69938, "loss_cls": 3.13958, "loss": 3.13958, "time": 0.83009} +{"mode": "train", "epoch": 115, "iter": 3700, "lr": 0.01285, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.4225, "top5_acc": 0.69047, "loss_cls": 3.18917, "loss": 3.18917, "time": 0.81857} +{"mode": "val", "epoch": 115, "iter": 309, "lr": 0.01284, "top1_acc": 0.35846, "top5_acc": 0.61824, "mean_class_accuracy": 0.35821} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.01282, "memory": 15990, "data_time": 1.40322, "top1_acc": 0.45359, "top5_acc": 0.71062, "loss_cls": 3.04387, "loss": 3.04387, "time": 2.38996} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.01281, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44578, "top5_acc": 0.70859, "loss_cls": 3.09335, "loss": 3.09335, "time": 0.82398} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.01279, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43969, "top5_acc": 0.70094, "loss_cls": 3.11331, "loss": 3.11331, "time": 0.81786} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.01277, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44938, "top5_acc": 0.70562, "loss_cls": 3.05844, "loss": 3.05844, "time": 0.81342} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.01275, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43641, "top5_acc": 0.70297, "loss_cls": 3.1241, "loss": 3.1241, "time": 0.81708} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.01273, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43859, "top5_acc": 0.70203, "loss_cls": 3.15211, "loss": 3.15211, "time": 0.81443} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.01271, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44703, "top5_acc": 0.71141, "loss_cls": 3.05465, "loss": 3.05465, "time": 0.81516} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.01269, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44797, "top5_acc": 0.71016, "loss_cls": 3.06727, "loss": 3.06727, "time": 0.81816} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.01268, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4425, "top5_acc": 0.70344, "loss_cls": 3.10317, "loss": 3.10317, "time": 0.81408} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.01266, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44094, "top5_acc": 0.69859, "loss_cls": 3.12665, "loss": 3.12665, "time": 0.8168} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.01264, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42938, "top5_acc": 0.69703, "loss_cls": 3.16063, "loss": 3.16063, "time": 0.8163} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.01262, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42844, "top5_acc": 0.69438, "loss_cls": 3.18182, "loss": 3.18182, "time": 0.8202} +{"mode": "train", "epoch": 116, "iter": 1300, "lr": 0.0126, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42547, "top5_acc": 0.69312, "loss_cls": 3.16957, "loss": 3.16957, "time": 0.81686} +{"mode": "train", "epoch": 116, "iter": 1400, "lr": 0.01258, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.43016, "top5_acc": 0.69922, "loss_cls": 3.14964, "loss": 3.14964, "time": 0.82231} +{"mode": "train", "epoch": 116, "iter": 1500, "lr": 0.01256, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43062, "top5_acc": 0.6925, "loss_cls": 3.15186, "loss": 3.15186, "time": 0.81263} +{"mode": "train", "epoch": 116, "iter": 1600, "lr": 0.01255, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44453, "top5_acc": 0.69547, "loss_cls": 3.15391, "loss": 3.15391, "time": 0.81854} +{"mode": "train", "epoch": 116, "iter": 1700, "lr": 0.01253, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43656, "top5_acc": 0.69891, "loss_cls": 3.09887, "loss": 3.09887, "time": 0.81481} +{"mode": "train", "epoch": 116, "iter": 1800, "lr": 0.01251, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42859, "top5_acc": 0.69281, "loss_cls": 3.16336, "loss": 3.16336, "time": 0.81625} +{"mode": "train", "epoch": 116, "iter": 1900, "lr": 0.01249, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43328, "top5_acc": 0.69828, "loss_cls": 3.1324, "loss": 3.1324, "time": 0.81157} +{"mode": "train", "epoch": 116, "iter": 2000, "lr": 0.01247, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43578, "top5_acc": 0.69969, "loss_cls": 3.13444, "loss": 3.13444, "time": 0.81684} +{"mode": "train", "epoch": 116, "iter": 2100, "lr": 0.01245, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42562, "top5_acc": 0.69453, "loss_cls": 3.18771, "loss": 3.18771, "time": 0.82154} +{"mode": "train", "epoch": 116, "iter": 2200, "lr": 0.01243, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44125, "top5_acc": 0.69672, "loss_cls": 3.13004, "loss": 3.13004, "time": 0.81896} +{"mode": "train", "epoch": 116, "iter": 2300, "lr": 0.01242, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44125, "top5_acc": 0.70891, "loss_cls": 3.10015, "loss": 3.10015, "time": 0.81658} +{"mode": "train", "epoch": 116, "iter": 2400, "lr": 0.0124, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42109, "top5_acc": 0.68828, "loss_cls": 3.20827, "loss": 3.20827, "time": 0.82379} +{"mode": "train", "epoch": 116, "iter": 2500, "lr": 0.01238, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43844, "top5_acc": 0.69, "loss_cls": 3.17212, "loss": 3.17212, "time": 0.82004} +{"mode": "train", "epoch": 116, "iter": 2600, "lr": 0.01236, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42688, "top5_acc": 0.69312, "loss_cls": 3.15774, "loss": 3.15774, "time": 0.82174} +{"mode": "train", "epoch": 116, "iter": 2700, "lr": 0.01234, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43359, "top5_acc": 0.68719, "loss_cls": 3.20865, "loss": 3.20865, "time": 0.81227} +{"mode": "train", "epoch": 116, "iter": 2800, "lr": 0.01232, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43875, "top5_acc": 0.69141, "loss_cls": 3.1494, "loss": 3.1494, "time": 0.82481} +{"mode": "train", "epoch": 116, "iter": 2900, "lr": 0.01231, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43062, "top5_acc": 0.68828, "loss_cls": 3.19399, "loss": 3.19399, "time": 0.81902} +{"mode": "train", "epoch": 116, "iter": 3000, "lr": 0.01229, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43484, "top5_acc": 0.68953, "loss_cls": 3.18403, "loss": 3.18403, "time": 0.81497} +{"mode": "train", "epoch": 116, "iter": 3100, "lr": 0.01227, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43406, "top5_acc": 0.70406, "loss_cls": 3.1111, "loss": 3.1111, "time": 0.81206} +{"mode": "train", "epoch": 116, "iter": 3200, "lr": 0.01225, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4475, "top5_acc": 0.70578, "loss_cls": 3.09226, "loss": 3.09226, "time": 0.81672} +{"mode": "train", "epoch": 116, "iter": 3300, "lr": 0.01223, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42953, "top5_acc": 0.68812, "loss_cls": 3.19968, "loss": 3.19968, "time": 0.82169} +{"mode": "train", "epoch": 116, "iter": 3400, "lr": 0.01221, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44016, "top5_acc": 0.69047, "loss_cls": 3.14567, "loss": 3.14567, "time": 0.81687} +{"mode": "train", "epoch": 116, "iter": 3500, "lr": 0.0122, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42797, "top5_acc": 0.68297, "loss_cls": 3.18073, "loss": 3.18073, "time": 0.81503} +{"mode": "train", "epoch": 116, "iter": 3600, "lr": 0.01218, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42875, "top5_acc": 0.69047, "loss_cls": 3.17821, "loss": 3.17821, "time": 0.81279} +{"mode": "train", "epoch": 116, "iter": 3700, "lr": 0.01216, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43266, "top5_acc": 0.70188, "loss_cls": 3.14269, "loss": 3.14269, "time": 0.8255} +{"mode": "val", "epoch": 116, "iter": 309, "lr": 0.01215, "top1_acc": 0.35795, "top5_acc": 0.62447, "mean_class_accuracy": 0.35761} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.01213, "memory": 15990, "data_time": 1.39378, "top1_acc": 0.45438, "top5_acc": 0.71609, "loss_cls": 3.02048, "loss": 3.02048, "time": 2.3831} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.01211, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45969, "top5_acc": 0.71766, "loss_cls": 3.01702, "loss": 3.01702, "time": 0.81938} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.0121, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44109, "top5_acc": 0.70438, "loss_cls": 3.10524, "loss": 3.10524, "time": 0.81807} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.01208, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45828, "top5_acc": 0.7175, "loss_cls": 3.00895, "loss": 3.00895, "time": 0.81627} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.01206, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44312, "top5_acc": 0.70484, "loss_cls": 3.11096, "loss": 3.11096, "time": 0.82003} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.01204, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43266, "top5_acc": 0.70328, "loss_cls": 3.11079, "loss": 3.11079, "time": 0.8156} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.01202, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43547, "top5_acc": 0.71141, "loss_cls": 3.07318, "loss": 3.07318, "time": 0.81758} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.012, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43469, "top5_acc": 0.70641, "loss_cls": 3.11265, "loss": 3.11265, "time": 0.81762} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.01199, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43531, "top5_acc": 0.69703, "loss_cls": 3.12907, "loss": 3.12907, "time": 0.81571} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.01197, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44234, "top5_acc": 0.70812, "loss_cls": 3.08884, "loss": 3.08884, "time": 0.816} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.01195, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44109, "top5_acc": 0.69906, "loss_cls": 3.12806, "loss": 3.12806, "time": 0.81949} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.01193, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44719, "top5_acc": 0.69703, "loss_cls": 3.12073, "loss": 3.12073, "time": 0.82504} +{"mode": "train", "epoch": 117, "iter": 1300, "lr": 0.01191, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44172, "top5_acc": 0.70578, "loss_cls": 3.09247, "loss": 3.09247, "time": 0.81426} +{"mode": "train", "epoch": 117, "iter": 1400, "lr": 0.0119, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45562, "top5_acc": 0.71656, "loss_cls": 3.03939, "loss": 3.03939, "time": 0.82536} +{"mode": "train", "epoch": 117, "iter": 1500, "lr": 0.01188, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43969, "top5_acc": 0.69766, "loss_cls": 3.14719, "loss": 3.14719, "time": 0.81711} +{"mode": "train", "epoch": 117, "iter": 1600, "lr": 0.01186, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43281, "top5_acc": 0.69531, "loss_cls": 3.16805, "loss": 3.16805, "time": 0.81539} +{"mode": "train", "epoch": 117, "iter": 1700, "lr": 0.01184, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44516, "top5_acc": 0.70516, "loss_cls": 3.10679, "loss": 3.10679, "time": 0.82013} +{"mode": "train", "epoch": 117, "iter": 1800, "lr": 0.01182, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43703, "top5_acc": 0.70078, "loss_cls": 3.13604, "loss": 3.13604, "time": 0.81925} +{"mode": "train", "epoch": 117, "iter": 1900, "lr": 0.01181, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43766, "top5_acc": 0.69812, "loss_cls": 3.15323, "loss": 3.15323, "time": 0.81584} +{"mode": "train", "epoch": 117, "iter": 2000, "lr": 0.01179, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44109, "top5_acc": 0.69172, "loss_cls": 3.14097, "loss": 3.14097, "time": 0.81753} +{"mode": "train", "epoch": 117, "iter": 2100, "lr": 0.01177, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4525, "top5_acc": 0.70078, "loss_cls": 3.10891, "loss": 3.10891, "time": 0.81886} +{"mode": "train", "epoch": 117, "iter": 2200, "lr": 0.01175, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43375, "top5_acc": 0.69359, "loss_cls": 3.15183, "loss": 3.15183, "time": 0.81615} +{"mode": "train", "epoch": 117, "iter": 2300, "lr": 0.01173, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44172, "top5_acc": 0.69594, "loss_cls": 3.15012, "loss": 3.15012, "time": 0.81912} +{"mode": "train", "epoch": 117, "iter": 2400, "lr": 0.01172, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43828, "top5_acc": 0.69703, "loss_cls": 3.14004, "loss": 3.14004, "time": 0.81652} +{"mode": "train", "epoch": 117, "iter": 2500, "lr": 0.0117, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43297, "top5_acc": 0.68859, "loss_cls": 3.18029, "loss": 3.18029, "time": 0.81236} +{"mode": "train", "epoch": 117, "iter": 2600, "lr": 0.01168, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43531, "top5_acc": 0.70078, "loss_cls": 3.14462, "loss": 3.14462, "time": 0.81891} +{"mode": "train", "epoch": 117, "iter": 2700, "lr": 0.01166, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44688, "top5_acc": 0.70141, "loss_cls": 3.08757, "loss": 3.08757, "time": 0.81475} +{"mode": "train", "epoch": 117, "iter": 2800, "lr": 0.01164, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43375, "top5_acc": 0.70125, "loss_cls": 3.13425, "loss": 3.13425, "time": 0.81477} +{"mode": "train", "epoch": 117, "iter": 2900, "lr": 0.01163, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43672, "top5_acc": 0.69906, "loss_cls": 3.13685, "loss": 3.13685, "time": 0.81417} +{"mode": "train", "epoch": 117, "iter": 3000, "lr": 0.01161, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43938, "top5_acc": 0.69828, "loss_cls": 3.13548, "loss": 3.13548, "time": 0.815} +{"mode": "train", "epoch": 117, "iter": 3100, "lr": 0.01159, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43531, "top5_acc": 0.69859, "loss_cls": 3.15298, "loss": 3.15298, "time": 0.81431} +{"mode": "train", "epoch": 117, "iter": 3200, "lr": 0.01157, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4325, "top5_acc": 0.69453, "loss_cls": 3.15687, "loss": 3.15687, "time": 0.81393} +{"mode": "train", "epoch": 117, "iter": 3300, "lr": 0.01155, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43375, "top5_acc": 0.69172, "loss_cls": 3.16887, "loss": 3.16887, "time": 0.8165} +{"mode": "train", "epoch": 117, "iter": 3400, "lr": 0.01154, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44328, "top5_acc": 0.70344, "loss_cls": 3.0874, "loss": 3.0874, "time": 0.8138} +{"mode": "train", "epoch": 117, "iter": 3500, "lr": 0.01152, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42906, "top5_acc": 0.69594, "loss_cls": 3.17156, "loss": 3.17156, "time": 0.81843} +{"mode": "train", "epoch": 117, "iter": 3600, "lr": 0.0115, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43375, "top5_acc": 0.69688, "loss_cls": 3.11877, "loss": 3.11877, "time": 0.81628} +{"mode": "train", "epoch": 117, "iter": 3700, "lr": 0.01148, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44641, "top5_acc": 0.705, "loss_cls": 3.10371, "loss": 3.10371, "time": 0.81559} +{"mode": "val", "epoch": 117, "iter": 309, "lr": 0.01147, "top1_acc": 0.36028, "top5_acc": 0.61926, "mean_class_accuracy": 0.36001} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.01146, "memory": 15990, "data_time": 1.36252, "top1_acc": 0.45609, "top5_acc": 0.71594, "loss_cls": 3.02878, "loss": 3.02878, "time": 2.34177} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.01144, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44938, "top5_acc": 0.71203, "loss_cls": 3.05373, "loss": 3.05373, "time": 0.81624} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.01142, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45297, "top5_acc": 0.71922, "loss_cls": 3.04399, "loss": 3.04399, "time": 0.81546} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.0114, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4425, "top5_acc": 0.70969, "loss_cls": 3.08924, "loss": 3.08924, "time": 0.81895} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.01139, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45719, "top5_acc": 0.71203, "loss_cls": 3.02391, "loss": 3.02391, "time": 0.8183} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.01137, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45297, "top5_acc": 0.71297, "loss_cls": 3.04664, "loss": 3.04664, "time": 0.81498} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.01135, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44531, "top5_acc": 0.70672, "loss_cls": 3.0675, "loss": 3.0675, "time": 0.82078} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.01133, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45312, "top5_acc": 0.71266, "loss_cls": 3.04269, "loss": 3.04269, "time": 0.82202} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.01131, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44625, "top5_acc": 0.71125, "loss_cls": 3.06243, "loss": 3.06243, "time": 0.81778} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.0113, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45391, "top5_acc": 0.71984, "loss_cls": 3.02985, "loss": 3.02985, "time": 0.81315} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.01128, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44, "top5_acc": 0.69953, "loss_cls": 3.10431, "loss": 3.10431, "time": 0.81741} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.01126, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43078, "top5_acc": 0.69312, "loss_cls": 3.15408, "loss": 3.15408, "time": 0.82318} +{"mode": "train", "epoch": 118, "iter": 1300, "lr": 0.01124, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45375, "top5_acc": 0.7075, "loss_cls": 3.07365, "loss": 3.07365, "time": 0.81928} +{"mode": "train", "epoch": 118, "iter": 1400, "lr": 0.01123, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44328, "top5_acc": 0.70797, "loss_cls": 3.11008, "loss": 3.11008, "time": 0.82217} +{"mode": "train", "epoch": 118, "iter": 1500, "lr": 0.01121, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43688, "top5_acc": 0.70422, "loss_cls": 3.08993, "loss": 3.08993, "time": 0.81683} +{"mode": "train", "epoch": 118, "iter": 1600, "lr": 0.01119, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44109, "top5_acc": 0.70781, "loss_cls": 3.0906, "loss": 3.0906, "time": 0.82658} +{"mode": "train", "epoch": 118, "iter": 1700, "lr": 0.01117, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43562, "top5_acc": 0.70047, "loss_cls": 3.12732, "loss": 3.12732, "time": 0.81994} +{"mode": "train", "epoch": 118, "iter": 1800, "lr": 0.01116, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44891, "top5_acc": 0.70656, "loss_cls": 3.0697, "loss": 3.0697, "time": 0.8177} +{"mode": "train", "epoch": 118, "iter": 1900, "lr": 0.01114, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45062, "top5_acc": 0.71375, "loss_cls": 3.05297, "loss": 3.05297, "time": 0.81405} +{"mode": "train", "epoch": 118, "iter": 2000, "lr": 0.01112, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43531, "top5_acc": 0.70125, "loss_cls": 3.10434, "loss": 3.10434, "time": 0.8166} +{"mode": "train", "epoch": 118, "iter": 2100, "lr": 0.0111, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44641, "top5_acc": 0.71234, "loss_cls": 3.09435, "loss": 3.09435, "time": 0.81603} +{"mode": "train", "epoch": 118, "iter": 2200, "lr": 0.01109, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44625, "top5_acc": 0.69688, "loss_cls": 3.09634, "loss": 3.09634, "time": 0.81546} +{"mode": "train", "epoch": 118, "iter": 2300, "lr": 0.01107, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44172, "top5_acc": 0.70578, "loss_cls": 3.08413, "loss": 3.08413, "time": 0.82047} +{"mode": "train", "epoch": 118, "iter": 2400, "lr": 0.01105, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44547, "top5_acc": 0.71047, "loss_cls": 3.04142, "loss": 3.04142, "time": 0.8187} +{"mode": "train", "epoch": 118, "iter": 2500, "lr": 0.01103, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42984, "top5_acc": 0.69938, "loss_cls": 3.15763, "loss": 3.15763, "time": 0.81625} +{"mode": "train", "epoch": 118, "iter": 2600, "lr": 0.01102, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44672, "top5_acc": 0.70781, "loss_cls": 3.08243, "loss": 3.08243, "time": 0.81194} +{"mode": "train", "epoch": 118, "iter": 2700, "lr": 0.011, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44391, "top5_acc": 0.70375, "loss_cls": 3.10862, "loss": 3.10862, "time": 0.81812} +{"mode": "train", "epoch": 118, "iter": 2800, "lr": 0.01098, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43969, "top5_acc": 0.70219, "loss_cls": 3.09702, "loss": 3.09702, "time": 0.81769} +{"mode": "train", "epoch": 118, "iter": 2900, "lr": 0.01096, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43578, "top5_acc": 0.7025, "loss_cls": 3.11952, "loss": 3.11952, "time": 0.81117} +{"mode": "train", "epoch": 118, "iter": 3000, "lr": 0.01095, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43812, "top5_acc": 0.70875, "loss_cls": 3.10146, "loss": 3.10146, "time": 0.81718} +{"mode": "train", "epoch": 118, "iter": 3100, "lr": 0.01093, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44578, "top5_acc": 0.71078, "loss_cls": 3.11183, "loss": 3.11183, "time": 0.81347} +{"mode": "train", "epoch": 118, "iter": 3200, "lr": 0.01091, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44875, "top5_acc": 0.70297, "loss_cls": 3.06688, "loss": 3.06688, "time": 0.81769} +{"mode": "train", "epoch": 118, "iter": 3300, "lr": 0.01089, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42953, "top5_acc": 0.69, "loss_cls": 3.18534, "loss": 3.18534, "time": 0.81495} +{"mode": "train", "epoch": 118, "iter": 3400, "lr": 0.01088, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42875, "top5_acc": 0.69078, "loss_cls": 3.17074, "loss": 3.17074, "time": 0.81236} +{"mode": "train", "epoch": 118, "iter": 3500, "lr": 0.01086, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43969, "top5_acc": 0.70531, "loss_cls": 3.10424, "loss": 3.10424, "time": 0.81754} +{"mode": "train", "epoch": 118, "iter": 3600, "lr": 0.01084, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44219, "top5_acc": 0.69797, "loss_cls": 3.10557, "loss": 3.10557, "time": 0.81711} +{"mode": "train", "epoch": 118, "iter": 3700, "lr": 0.01082, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44875, "top5_acc": 0.70859, "loss_cls": 3.06701, "loss": 3.06701, "time": 0.82446} +{"mode": "val", "epoch": 118, "iter": 309, "lr": 0.01082, "top1_acc": 0.38079, "top5_acc": 0.64083, "mean_class_accuracy": 0.38067} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.0108, "memory": 15990, "data_time": 1.32619, "top1_acc": 0.46719, "top5_acc": 0.72672, "loss_cls": 2.96258, "loss": 2.96258, "time": 2.31873} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.01078, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45156, "top5_acc": 0.71406, "loss_cls": 3.03999, "loss": 3.03999, "time": 0.83615} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.01076, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45375, "top5_acc": 0.71406, "loss_cls": 3.0367, "loss": 3.0367, "time": 0.83567} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.01075, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45859, "top5_acc": 0.7075, "loss_cls": 3.02731, "loss": 3.02731, "time": 0.829} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.01073, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46406, "top5_acc": 0.71375, "loss_cls": 3.01741, "loss": 3.01741, "time": 0.82858} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.01071, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45703, "top5_acc": 0.70422, "loss_cls": 3.05521, "loss": 3.05521, "time": 0.82663} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.01069, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44656, "top5_acc": 0.70828, "loss_cls": 3.05633, "loss": 3.05633, "time": 0.82498} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.01068, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45641, "top5_acc": 0.72, "loss_cls": 3.01569, "loss": 3.01569, "time": 0.82092} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.01066, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44625, "top5_acc": 0.70984, "loss_cls": 3.06478, "loss": 3.06478, "time": 0.83223} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.01064, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45672, "top5_acc": 0.72078, "loss_cls": 3.01609, "loss": 3.01609, "time": 0.83089} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.01063, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45, "top5_acc": 0.71219, "loss_cls": 3.06363, "loss": 3.06363, "time": 0.82357} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.01061, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45375, "top5_acc": 0.71094, "loss_cls": 3.03184, "loss": 3.03184, "time": 0.82181} +{"mode": "train", "epoch": 119, "iter": 1300, "lr": 0.01059, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.45922, "top5_acc": 0.71969, "loss_cls": 3.02004, "loss": 3.02004, "time": 0.81933} +{"mode": "train", "epoch": 119, "iter": 1400, "lr": 0.01057, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44016, "top5_acc": 0.70219, "loss_cls": 3.08312, "loss": 3.08312, "time": 0.82296} +{"mode": "train", "epoch": 119, "iter": 1500, "lr": 0.01056, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.44859, "top5_acc": 0.70672, "loss_cls": 3.09718, "loss": 3.09718, "time": 0.82149} +{"mode": "train", "epoch": 119, "iter": 1600, "lr": 0.01054, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44891, "top5_acc": 0.71234, "loss_cls": 3.07146, "loss": 3.07146, "time": 0.81684} +{"mode": "train", "epoch": 119, "iter": 1700, "lr": 0.01052, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45797, "top5_acc": 0.71469, "loss_cls": 3.03395, "loss": 3.03395, "time": 0.8125} +{"mode": "train", "epoch": 119, "iter": 1800, "lr": 0.0105, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44297, "top5_acc": 0.71328, "loss_cls": 3.07287, "loss": 3.07287, "time": 0.82103} +{"mode": "train", "epoch": 119, "iter": 1900, "lr": 0.01049, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44719, "top5_acc": 0.70719, "loss_cls": 3.063, "loss": 3.063, "time": 0.82133} +{"mode": "train", "epoch": 119, "iter": 2000, "lr": 0.01047, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45641, "top5_acc": 0.71906, "loss_cls": 3.01605, "loss": 3.01605, "time": 0.81825} +{"mode": "train", "epoch": 119, "iter": 2100, "lr": 0.01045, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45594, "top5_acc": 0.70359, "loss_cls": 3.06861, "loss": 3.06861, "time": 0.82229} +{"mode": "train", "epoch": 119, "iter": 2200, "lr": 0.01044, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43641, "top5_acc": 0.70203, "loss_cls": 3.10316, "loss": 3.10316, "time": 0.8196} +{"mode": "train", "epoch": 119, "iter": 2300, "lr": 0.01042, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45312, "top5_acc": 0.71328, "loss_cls": 3.04104, "loss": 3.04104, "time": 0.81751} +{"mode": "train", "epoch": 119, "iter": 2400, "lr": 0.0104, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45391, "top5_acc": 0.7125, "loss_cls": 3.05614, "loss": 3.05614, "time": 0.81493} +{"mode": "train", "epoch": 119, "iter": 2500, "lr": 0.01039, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4525, "top5_acc": 0.70203, "loss_cls": 3.06878, "loss": 3.06878, "time": 0.82112} +{"mode": "train", "epoch": 119, "iter": 2600, "lr": 0.01037, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45984, "top5_acc": 0.71, "loss_cls": 3.04561, "loss": 3.04561, "time": 0.81856} +{"mode": "train", "epoch": 119, "iter": 2700, "lr": 0.01035, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43656, "top5_acc": 0.70031, "loss_cls": 3.12109, "loss": 3.12109, "time": 0.82053} +{"mode": "train", "epoch": 119, "iter": 2800, "lr": 0.01033, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43938, "top5_acc": 0.70953, "loss_cls": 3.08782, "loss": 3.08782, "time": 0.82008} +{"mode": "train", "epoch": 119, "iter": 2900, "lr": 0.01032, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44516, "top5_acc": 0.69969, "loss_cls": 3.12031, "loss": 3.12031, "time": 0.81782} +{"mode": "train", "epoch": 119, "iter": 3000, "lr": 0.0103, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46328, "top5_acc": 0.71109, "loss_cls": 3.01981, "loss": 3.01981, "time": 0.81862} +{"mode": "train", "epoch": 119, "iter": 3100, "lr": 0.01028, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44469, "top5_acc": 0.71109, "loss_cls": 3.07231, "loss": 3.07231, "time": 0.81556} +{"mode": "train", "epoch": 119, "iter": 3200, "lr": 0.01027, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44375, "top5_acc": 0.70516, "loss_cls": 3.07628, "loss": 3.07628, "time": 0.8149} +{"mode": "train", "epoch": 119, "iter": 3300, "lr": 0.01025, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44234, "top5_acc": 0.69781, "loss_cls": 3.13784, "loss": 3.13784, "time": 0.81284} +{"mode": "train", "epoch": 119, "iter": 3400, "lr": 0.01023, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44766, "top5_acc": 0.70469, "loss_cls": 3.09162, "loss": 3.09162, "time": 0.81753} +{"mode": "train", "epoch": 119, "iter": 3500, "lr": 0.01022, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44391, "top5_acc": 0.70219, "loss_cls": 3.08356, "loss": 3.08356, "time": 0.81607} +{"mode": "train", "epoch": 119, "iter": 3600, "lr": 0.0102, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44094, "top5_acc": 0.70609, "loss_cls": 3.08875, "loss": 3.08875, "time": 0.81388} +{"mode": "train", "epoch": 119, "iter": 3700, "lr": 0.01018, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44938, "top5_acc": 0.70812, "loss_cls": 3.05931, "loss": 3.05931, "time": 0.82347} +{"mode": "val", "epoch": 119, "iter": 309, "lr": 0.01017, "top1_acc": 0.38338, "top5_acc": 0.64028, "mean_class_accuracy": 0.3832} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.01016, "memory": 15990, "data_time": 1.36311, "top1_acc": 0.47391, "top5_acc": 0.72922, "loss_cls": 2.92795, "loss": 2.92795, "time": 2.36267} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.01014, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47078, "top5_acc": 0.72969, "loss_cls": 2.93435, "loss": 2.93435, "time": 0.83229} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.01012, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46766, "top5_acc": 0.72844, "loss_cls": 2.94741, "loss": 2.94741, "time": 0.83358} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.01011, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45469, "top5_acc": 0.71594, "loss_cls": 3.02456, "loss": 3.02456, "time": 0.82747} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.01009, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45406, "top5_acc": 0.71281, "loss_cls": 3.01381, "loss": 3.01381, "time": 0.83548} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.01007, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46266, "top5_acc": 0.71906, "loss_cls": 2.98651, "loss": 2.98651, "time": 0.82878} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.01006, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46016, "top5_acc": 0.71375, "loss_cls": 3.01112, "loss": 3.01112, "time": 0.82234} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.01004, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46203, "top5_acc": 0.72406, "loss_cls": 2.9987, "loss": 2.9987, "time": 0.82155} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.01002, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46828, "top5_acc": 0.71594, "loss_cls": 2.99889, "loss": 2.99889, "time": 0.82584} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.01001, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45672, "top5_acc": 0.7175, "loss_cls": 3.0056, "loss": 3.0056, "time": 0.82517} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00999, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44281, "top5_acc": 0.70812, "loss_cls": 3.08482, "loss": 3.08482, "time": 0.82451} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.00997, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44906, "top5_acc": 0.70875, "loss_cls": 3.06567, "loss": 3.06567, "time": 0.83166} +{"mode": "train", "epoch": 120, "iter": 1300, "lr": 0.00996, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44703, "top5_acc": 0.70828, "loss_cls": 3.07328, "loss": 3.07328, "time": 0.83011} +{"mode": "train", "epoch": 120, "iter": 1400, "lr": 0.00994, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43938, "top5_acc": 0.69766, "loss_cls": 3.10682, "loss": 3.10682, "time": 0.83467} +{"mode": "train", "epoch": 120, "iter": 1500, "lr": 0.00992, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45531, "top5_acc": 0.71016, "loss_cls": 3.02461, "loss": 3.02461, "time": 0.83161} +{"mode": "train", "epoch": 120, "iter": 1600, "lr": 0.0099, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44922, "top5_acc": 0.70891, "loss_cls": 3.04273, "loss": 3.04273, "time": 0.83465} +{"mode": "train", "epoch": 120, "iter": 1700, "lr": 0.00989, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45797, "top5_acc": 0.71391, "loss_cls": 3.01908, "loss": 3.01908, "time": 0.82728} +{"mode": "train", "epoch": 120, "iter": 1800, "lr": 0.00987, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44719, "top5_acc": 0.70562, "loss_cls": 3.08991, "loss": 3.08991, "time": 0.82148} +{"mode": "train", "epoch": 120, "iter": 1900, "lr": 0.00985, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46281, "top5_acc": 0.71344, "loss_cls": 3.01877, "loss": 3.01877, "time": 0.81686} +{"mode": "train", "epoch": 120, "iter": 2000, "lr": 0.00984, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46062, "top5_acc": 0.71547, "loss_cls": 3.02301, "loss": 3.02301, "time": 0.82157} +{"mode": "train", "epoch": 120, "iter": 2100, "lr": 0.00982, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45328, "top5_acc": 0.71281, "loss_cls": 3.05726, "loss": 3.05726, "time": 0.82949} +{"mode": "train", "epoch": 120, "iter": 2200, "lr": 0.0098, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45094, "top5_acc": 0.71438, "loss_cls": 3.03721, "loss": 3.03721, "time": 0.81549} +{"mode": "train", "epoch": 120, "iter": 2300, "lr": 0.00979, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44891, "top5_acc": 0.71422, "loss_cls": 3.03229, "loss": 3.03229, "time": 0.81874} +{"mode": "train", "epoch": 120, "iter": 2400, "lr": 0.00977, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45047, "top5_acc": 0.70984, "loss_cls": 3.06563, "loss": 3.06563, "time": 0.83501} +{"mode": "train", "epoch": 120, "iter": 2500, "lr": 0.00976, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45188, "top5_acc": 0.70984, "loss_cls": 3.09026, "loss": 3.09026, "time": 0.8358} +{"mode": "train", "epoch": 120, "iter": 2600, "lr": 0.00974, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45484, "top5_acc": 0.715, "loss_cls": 3.01932, "loss": 3.01932, "time": 0.82557} +{"mode": "train", "epoch": 120, "iter": 2700, "lr": 0.00972, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45109, "top5_acc": 0.71297, "loss_cls": 3.05222, "loss": 3.05222, "time": 0.81959} +{"mode": "train", "epoch": 120, "iter": 2800, "lr": 0.00971, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44203, "top5_acc": 0.69578, "loss_cls": 3.11152, "loss": 3.11152, "time": 0.81751} +{"mode": "train", "epoch": 120, "iter": 2900, "lr": 0.00969, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45016, "top5_acc": 0.70469, "loss_cls": 3.06248, "loss": 3.06248, "time": 0.82006} +{"mode": "train", "epoch": 120, "iter": 3000, "lr": 0.00967, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43984, "top5_acc": 0.70344, "loss_cls": 3.11117, "loss": 3.11117, "time": 0.82119} +{"mode": "train", "epoch": 120, "iter": 3100, "lr": 0.00966, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46422, "top5_acc": 0.72156, "loss_cls": 3.01132, "loss": 3.01132, "time": 0.82684} +{"mode": "train", "epoch": 120, "iter": 3200, "lr": 0.00964, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45125, "top5_acc": 0.71156, "loss_cls": 3.05996, "loss": 3.05996, "time": 0.82703} +{"mode": "train", "epoch": 120, "iter": 3300, "lr": 0.00962, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44906, "top5_acc": 0.70875, "loss_cls": 3.04867, "loss": 3.04867, "time": 0.82176} +{"mode": "train", "epoch": 120, "iter": 3400, "lr": 0.00961, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44297, "top5_acc": 0.70453, "loss_cls": 3.07683, "loss": 3.07683, "time": 0.82667} +{"mode": "train", "epoch": 120, "iter": 3500, "lr": 0.00959, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45016, "top5_acc": 0.71094, "loss_cls": 3.05477, "loss": 3.05477, "time": 0.81829} +{"mode": "train", "epoch": 120, "iter": 3600, "lr": 0.00957, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45406, "top5_acc": 0.71156, "loss_cls": 3.02208, "loss": 3.02208, "time": 0.81506} +{"mode": "train", "epoch": 120, "iter": 3700, "lr": 0.00956, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45375, "top5_acc": 0.71078, "loss_cls": 3.03403, "loss": 3.03403, "time": 0.8273} +{"mode": "val", "epoch": 120, "iter": 309, "lr": 0.00955, "top1_acc": 0.37239, "top5_acc": 0.62908, "mean_class_accuracy": 0.37214} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00953, "memory": 15990, "data_time": 1.31904, "top1_acc": 0.46484, "top5_acc": 0.71875, "loss_cls": 2.98628, "loss": 2.98628, "time": 2.31611} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00952, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46062, "top5_acc": 0.72156, "loss_cls": 2.96647, "loss": 2.96647, "time": 0.82948} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.0095, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46469, "top5_acc": 0.72109, "loss_cls": 2.96504, "loss": 2.96504, "time": 0.8274} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00948, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46688, "top5_acc": 0.71781, "loss_cls": 2.97859, "loss": 2.97859, "time": 0.82724} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00947, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.4625, "top5_acc": 0.72078, "loss_cls": 2.96784, "loss": 2.96784, "time": 0.81867} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00945, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45734, "top5_acc": 0.72375, "loss_cls": 2.98871, "loss": 2.98871, "time": 0.8171} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.00943, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46328, "top5_acc": 0.72234, "loss_cls": 2.97503, "loss": 2.97503, "time": 0.81863} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00942, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46328, "top5_acc": 0.72094, "loss_cls": 2.98445, "loss": 2.98445, "time": 0.81363} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.0094, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45938, "top5_acc": 0.71797, "loss_cls": 2.99468, "loss": 2.99468, "time": 0.82201} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00939, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45859, "top5_acc": 0.71594, "loss_cls": 3.01484, "loss": 3.01484, "time": 0.81731} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00937, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46344, "top5_acc": 0.73234, "loss_cls": 2.92678, "loss": 2.92678, "time": 0.81939} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00935, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45219, "top5_acc": 0.71594, "loss_cls": 3.02496, "loss": 3.02496, "time": 0.81925} +{"mode": "train", "epoch": 121, "iter": 1300, "lr": 0.00934, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45016, "top5_acc": 0.70812, "loss_cls": 3.0441, "loss": 3.0441, "time": 0.82053} +{"mode": "train", "epoch": 121, "iter": 1400, "lr": 0.00932, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45422, "top5_acc": 0.71344, "loss_cls": 3.04059, "loss": 3.04059, "time": 0.82268} +{"mode": "train", "epoch": 121, "iter": 1500, "lr": 0.0093, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45875, "top5_acc": 0.72469, "loss_cls": 2.99522, "loss": 2.99522, "time": 0.81565} +{"mode": "train", "epoch": 121, "iter": 1600, "lr": 0.00929, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44812, "top5_acc": 0.72141, "loss_cls": 3.01635, "loss": 3.01635, "time": 0.81317} +{"mode": "train", "epoch": 121, "iter": 1700, "lr": 0.00927, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45078, "top5_acc": 0.7225, "loss_cls": 3.01869, "loss": 3.01869, "time": 0.81563} +{"mode": "train", "epoch": 121, "iter": 1800, "lr": 0.00926, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45453, "top5_acc": 0.71594, "loss_cls": 3.02886, "loss": 3.02886, "time": 0.81895} +{"mode": "train", "epoch": 121, "iter": 1900, "lr": 0.00924, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45656, "top5_acc": 0.72188, "loss_cls": 3.00503, "loss": 3.00503, "time": 0.81772} +{"mode": "train", "epoch": 121, "iter": 2000, "lr": 0.00922, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45875, "top5_acc": 0.71938, "loss_cls": 3.02048, "loss": 3.02048, "time": 0.82226} +{"mode": "train", "epoch": 121, "iter": 2100, "lr": 0.00921, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45047, "top5_acc": 0.7075, "loss_cls": 3.05019, "loss": 3.05019, "time": 0.81264} +{"mode": "train", "epoch": 121, "iter": 2200, "lr": 0.00919, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46078, "top5_acc": 0.7225, "loss_cls": 2.99381, "loss": 2.99381, "time": 0.81939} +{"mode": "train", "epoch": 121, "iter": 2300, "lr": 0.00917, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46703, "top5_acc": 0.72094, "loss_cls": 2.99278, "loss": 2.99278, "time": 0.81834} +{"mode": "train", "epoch": 121, "iter": 2400, "lr": 0.00916, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45141, "top5_acc": 0.71672, "loss_cls": 3.04641, "loss": 3.04641, "time": 0.81941} +{"mode": "train", "epoch": 121, "iter": 2500, "lr": 0.00914, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46031, "top5_acc": 0.7175, "loss_cls": 3.03294, "loss": 3.03294, "time": 0.81517} +{"mode": "train", "epoch": 121, "iter": 2600, "lr": 0.00913, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45516, "top5_acc": 0.71625, "loss_cls": 3.00289, "loss": 3.00289, "time": 0.81298} +{"mode": "train", "epoch": 121, "iter": 2700, "lr": 0.00911, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45953, "top5_acc": 0.71891, "loss_cls": 3.01133, "loss": 3.01133, "time": 0.81948} +{"mode": "train", "epoch": 121, "iter": 2800, "lr": 0.00909, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46234, "top5_acc": 0.72672, "loss_cls": 2.97832, "loss": 2.97832, "time": 0.81455} +{"mode": "train", "epoch": 121, "iter": 2900, "lr": 0.00908, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46078, "top5_acc": 0.71469, "loss_cls": 3.00595, "loss": 3.00595, "time": 0.8159} +{"mode": "train", "epoch": 121, "iter": 3000, "lr": 0.00906, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45, "top5_acc": 0.72016, "loss_cls": 3.03269, "loss": 3.03269, "time": 0.81393} +{"mode": "train", "epoch": 121, "iter": 3100, "lr": 0.00905, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46156, "top5_acc": 0.71688, "loss_cls": 2.99471, "loss": 2.99471, "time": 0.81111} +{"mode": "train", "epoch": 121, "iter": 3200, "lr": 0.00903, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44953, "top5_acc": 0.71125, "loss_cls": 3.02827, "loss": 3.02827, "time": 0.81292} +{"mode": "train", "epoch": 121, "iter": 3300, "lr": 0.00901, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46609, "top5_acc": 0.71453, "loss_cls": 2.994, "loss": 2.994, "time": 0.81742} +{"mode": "train", "epoch": 121, "iter": 3400, "lr": 0.009, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45438, "top5_acc": 0.71422, "loss_cls": 3.02444, "loss": 3.02444, "time": 0.81766} +{"mode": "train", "epoch": 121, "iter": 3500, "lr": 0.00898, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45484, "top5_acc": 0.71641, "loss_cls": 3.03147, "loss": 3.03147, "time": 0.82009} +{"mode": "train", "epoch": 121, "iter": 3600, "lr": 0.00897, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45547, "top5_acc": 0.71578, "loss_cls": 3.01494, "loss": 3.01494, "time": 0.81914} +{"mode": "train", "epoch": 121, "iter": 3700, "lr": 0.00895, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46109, "top5_acc": 0.72062, "loss_cls": 2.9974, "loss": 2.9974, "time": 0.81392} +{"mode": "val", "epoch": 121, "iter": 309, "lr": 0.00894, "top1_acc": 0.38495, "top5_acc": 0.65051, "mean_class_accuracy": 0.38466} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00893, "memory": 15990, "data_time": 1.34192, "top1_acc": 0.46688, "top5_acc": 0.7225, "loss_cls": 2.9599, "loss": 2.9599, "time": 2.34088} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00891, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45875, "top5_acc": 0.72016, "loss_cls": 2.97571, "loss": 2.97571, "time": 0.83516} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.00889, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46406, "top5_acc": 0.72578, "loss_cls": 2.96379, "loss": 2.96379, "time": 0.8382} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00888, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46922, "top5_acc": 0.72516, "loss_cls": 2.97013, "loss": 2.97013, "time": 0.82563} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00886, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46766, "top5_acc": 0.72391, "loss_cls": 2.93563, "loss": 2.93563, "time": 0.82826} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00885, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46469, "top5_acc": 0.72266, "loss_cls": 2.96248, "loss": 2.96248, "time": 0.82756} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00883, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47281, "top5_acc": 0.73344, "loss_cls": 2.91009, "loss": 2.91009, "time": 0.82186} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00882, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45812, "top5_acc": 0.72734, "loss_cls": 2.9753, "loss": 2.9753, "time": 0.83001} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.0088, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46703, "top5_acc": 0.73016, "loss_cls": 2.94175, "loss": 2.94175, "time": 0.82014} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00878, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.465, "top5_acc": 0.71578, "loss_cls": 3.00436, "loss": 3.00436, "time": 0.82794} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00877, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47625, "top5_acc": 0.72938, "loss_cls": 2.93101, "loss": 2.93101, "time": 0.82479} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.00875, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45797, "top5_acc": 0.72562, "loss_cls": 2.97687, "loss": 2.97687, "time": 0.83456} +{"mode": "train", "epoch": 122, "iter": 1300, "lr": 0.00874, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45984, "top5_acc": 0.71953, "loss_cls": 2.99125, "loss": 2.99125, "time": 0.82607} +{"mode": "train", "epoch": 122, "iter": 1400, "lr": 0.00872, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47562, "top5_acc": 0.72797, "loss_cls": 2.93259, "loss": 2.93259, "time": 0.82768} +{"mode": "train", "epoch": 122, "iter": 1500, "lr": 0.0087, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46938, "top5_acc": 0.73016, "loss_cls": 2.96991, "loss": 2.96991, "time": 0.8333} +{"mode": "train", "epoch": 122, "iter": 1600, "lr": 0.00869, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45984, "top5_acc": 0.72359, "loss_cls": 2.95688, "loss": 2.95688, "time": 0.821} +{"mode": "train", "epoch": 122, "iter": 1700, "lr": 0.00867, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46125, "top5_acc": 0.72438, "loss_cls": 2.96641, "loss": 2.96641, "time": 0.82719} +{"mode": "train", "epoch": 122, "iter": 1800, "lr": 0.00866, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46, "top5_acc": 0.72172, "loss_cls": 2.94318, "loss": 2.94318, "time": 0.82094} +{"mode": "train", "epoch": 122, "iter": 1900, "lr": 0.00864, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46609, "top5_acc": 0.71438, "loss_cls": 2.97927, "loss": 2.97927, "time": 0.8224} +{"mode": "train", "epoch": 122, "iter": 2000, "lr": 0.00863, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46156, "top5_acc": 0.72078, "loss_cls": 2.99308, "loss": 2.99308, "time": 0.8284} +{"mode": "train", "epoch": 122, "iter": 2100, "lr": 0.00861, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46531, "top5_acc": 0.72312, "loss_cls": 2.97006, "loss": 2.97006, "time": 0.82764} +{"mode": "train", "epoch": 122, "iter": 2200, "lr": 0.00859, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46859, "top5_acc": 0.72172, "loss_cls": 2.95823, "loss": 2.95823, "time": 0.81572} +{"mode": "train", "epoch": 122, "iter": 2300, "lr": 0.00858, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45766, "top5_acc": 0.71453, "loss_cls": 3.02847, "loss": 3.02847, "time": 0.82431} +{"mode": "train", "epoch": 122, "iter": 2400, "lr": 0.00856, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45391, "top5_acc": 0.71312, "loss_cls": 3.0193, "loss": 3.0193, "time": 0.82977} +{"mode": "train", "epoch": 122, "iter": 2500, "lr": 0.00855, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45578, "top5_acc": 0.72016, "loss_cls": 3.00252, "loss": 3.00252, "time": 0.8265} +{"mode": "train", "epoch": 122, "iter": 2600, "lr": 0.00853, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45703, "top5_acc": 0.71562, "loss_cls": 3.00292, "loss": 3.00292, "time": 0.82084} +{"mode": "train", "epoch": 122, "iter": 2700, "lr": 0.00852, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4675, "top5_acc": 0.72, "loss_cls": 2.96052, "loss": 2.96052, "time": 0.81774} +{"mode": "train", "epoch": 122, "iter": 2800, "lr": 0.0085, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46312, "top5_acc": 0.72, "loss_cls": 2.98152, "loss": 2.98152, "time": 0.82022} +{"mode": "train", "epoch": 122, "iter": 2900, "lr": 0.00849, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46453, "top5_acc": 0.71547, "loss_cls": 3.01149, "loss": 3.01149, "time": 0.82018} +{"mode": "train", "epoch": 122, "iter": 3000, "lr": 0.00847, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45797, "top5_acc": 0.72062, "loss_cls": 3.018, "loss": 3.018, "time": 0.81647} +{"mode": "train", "epoch": 122, "iter": 3100, "lr": 0.00845, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46609, "top5_acc": 0.72672, "loss_cls": 2.98763, "loss": 2.98763, "time": 0.81795} +{"mode": "train", "epoch": 122, "iter": 3200, "lr": 0.00844, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45578, "top5_acc": 0.71844, "loss_cls": 3.02703, "loss": 3.02703, "time": 0.81751} +{"mode": "train", "epoch": 122, "iter": 3300, "lr": 0.00842, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45453, "top5_acc": 0.71422, "loss_cls": 3.01667, "loss": 3.01667, "time": 0.81811} +{"mode": "train", "epoch": 122, "iter": 3400, "lr": 0.00841, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46219, "top5_acc": 0.72531, "loss_cls": 2.96854, "loss": 2.96854, "time": 0.8172} +{"mode": "train", "epoch": 122, "iter": 3500, "lr": 0.00839, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45234, "top5_acc": 0.71625, "loss_cls": 3.01932, "loss": 3.01932, "time": 0.82202} +{"mode": "train", "epoch": 122, "iter": 3600, "lr": 0.00838, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46812, "top5_acc": 0.72312, "loss_cls": 2.94739, "loss": 2.94739, "time": 0.8316} +{"mode": "train", "epoch": 122, "iter": 3700, "lr": 0.00836, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46562, "top5_acc": 0.71625, "loss_cls": 3.00969, "loss": 3.00969, "time": 0.82085} +{"mode": "val", "epoch": 122, "iter": 309, "lr": 0.00835, "top1_acc": 0.39285, "top5_acc": 0.64803, "mean_class_accuracy": 0.39261} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00834, "memory": 15990, "data_time": 1.28329, "top1_acc": 0.47734, "top5_acc": 0.72734, "loss_cls": 2.90303, "loss": 2.90303, "time": 2.26962} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00832, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47469, "top5_acc": 0.73562, "loss_cls": 2.89452, "loss": 2.89452, "time": 0.82839} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00831, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47047, "top5_acc": 0.73344, "loss_cls": 2.92741, "loss": 2.92741, "time": 0.82193} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00829, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47922, "top5_acc": 0.74297, "loss_cls": 2.87971, "loss": 2.87971, "time": 0.81718} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00828, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46734, "top5_acc": 0.73078, "loss_cls": 2.93306, "loss": 2.93306, "time": 0.80875} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00826, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46406, "top5_acc": 0.73312, "loss_cls": 2.91323, "loss": 2.91323, "time": 0.81922} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00825, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47219, "top5_acc": 0.73219, "loss_cls": 2.89805, "loss": 2.89805, "time": 0.82107} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.00823, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47797, "top5_acc": 0.73125, "loss_cls": 2.9279, "loss": 2.9279, "time": 0.81487} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00822, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47406, "top5_acc": 0.73344, "loss_cls": 2.90209, "loss": 2.90209, "time": 0.82037} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.0082, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46953, "top5_acc": 0.73328, "loss_cls": 2.89844, "loss": 2.89844, "time": 0.81467} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00818, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48125, "top5_acc": 0.73203, "loss_cls": 2.91899, "loss": 2.91899, "time": 0.81727} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00817, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46672, "top5_acc": 0.73188, "loss_cls": 2.92369, "loss": 2.92369, "time": 0.82714} +{"mode": "train", "epoch": 123, "iter": 1300, "lr": 0.00815, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.47328, "top5_acc": 0.72984, "loss_cls": 2.93812, "loss": 2.93812, "time": 0.81779} +{"mode": "train", "epoch": 123, "iter": 1400, "lr": 0.00814, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47359, "top5_acc": 0.72781, "loss_cls": 2.93429, "loss": 2.93429, "time": 0.82499} +{"mode": "train", "epoch": 123, "iter": 1500, "lr": 0.00812, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46938, "top5_acc": 0.73594, "loss_cls": 2.90425, "loss": 2.90425, "time": 0.82748} +{"mode": "train", "epoch": 123, "iter": 1600, "lr": 0.00811, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.47578, "top5_acc": 0.7275, "loss_cls": 2.92408, "loss": 2.92408, "time": 0.81966} +{"mode": "train", "epoch": 123, "iter": 1700, "lr": 0.00809, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46688, "top5_acc": 0.72969, "loss_cls": 2.94886, "loss": 2.94886, "time": 0.83068} +{"mode": "train", "epoch": 123, "iter": 1800, "lr": 0.00808, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46688, "top5_acc": 0.71969, "loss_cls": 2.96636, "loss": 2.96636, "time": 0.81892} +{"mode": "train", "epoch": 123, "iter": 1900, "lr": 0.00806, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46672, "top5_acc": 0.72562, "loss_cls": 2.92848, "loss": 2.92848, "time": 0.81565} +{"mode": "train", "epoch": 123, "iter": 2000, "lr": 0.00805, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46203, "top5_acc": 0.71953, "loss_cls": 2.99283, "loss": 2.99283, "time": 0.82092} +{"mode": "train", "epoch": 123, "iter": 2100, "lr": 0.00803, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46672, "top5_acc": 0.72844, "loss_cls": 2.94482, "loss": 2.94482, "time": 0.82378} +{"mode": "train", "epoch": 123, "iter": 2200, "lr": 0.00802, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46188, "top5_acc": 0.72578, "loss_cls": 2.97027, "loss": 2.97027, "time": 0.8167} +{"mode": "train", "epoch": 123, "iter": 2300, "lr": 0.008, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46203, "top5_acc": 0.72, "loss_cls": 2.97951, "loss": 2.97951, "time": 0.82348} +{"mode": "train", "epoch": 123, "iter": 2400, "lr": 0.00799, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45531, "top5_acc": 0.71609, "loss_cls": 3.00745, "loss": 3.00745, "time": 0.82673} +{"mode": "train", "epoch": 123, "iter": 2500, "lr": 0.00797, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45656, "top5_acc": 0.71438, "loss_cls": 3.01339, "loss": 3.01339, "time": 0.81797} +{"mode": "train", "epoch": 123, "iter": 2600, "lr": 0.00796, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46453, "top5_acc": 0.72188, "loss_cls": 2.97444, "loss": 2.97444, "time": 0.81481} +{"mode": "train", "epoch": 123, "iter": 2700, "lr": 0.00794, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46547, "top5_acc": 0.72188, "loss_cls": 2.97391, "loss": 2.97391, "time": 0.81887} +{"mode": "train", "epoch": 123, "iter": 2800, "lr": 0.00793, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46688, "top5_acc": 0.72359, "loss_cls": 2.94845, "loss": 2.94845, "time": 0.81543} +{"mode": "train", "epoch": 123, "iter": 2900, "lr": 0.00791, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46922, "top5_acc": 0.73172, "loss_cls": 2.91016, "loss": 2.91016, "time": 0.81431} +{"mode": "train", "epoch": 123, "iter": 3000, "lr": 0.0079, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46656, "top5_acc": 0.71625, "loss_cls": 2.99559, "loss": 2.99559, "time": 0.8171} +{"mode": "train", "epoch": 123, "iter": 3100, "lr": 0.00788, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45812, "top5_acc": 0.71594, "loss_cls": 3.00527, "loss": 3.00527, "time": 0.81884} +{"mode": "train", "epoch": 123, "iter": 3200, "lr": 0.00787, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47969, "top5_acc": 0.73266, "loss_cls": 2.89266, "loss": 2.89266, "time": 0.81342} +{"mode": "train", "epoch": 123, "iter": 3300, "lr": 0.00785, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45734, "top5_acc": 0.71609, "loss_cls": 2.999, "loss": 2.999, "time": 0.81586} +{"mode": "train", "epoch": 123, "iter": 3400, "lr": 0.00784, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45859, "top5_acc": 0.71828, "loss_cls": 2.98072, "loss": 2.98072, "time": 0.81448} +{"mode": "train", "epoch": 123, "iter": 3500, "lr": 0.00782, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45906, "top5_acc": 0.71797, "loss_cls": 3.01995, "loss": 3.01995, "time": 0.81247} +{"mode": "train", "epoch": 123, "iter": 3600, "lr": 0.00781, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.46062, "top5_acc": 0.71375, "loss_cls": 2.99416, "loss": 2.99416, "time": 0.82499} +{"mode": "train", "epoch": 123, "iter": 3700, "lr": 0.00779, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46812, "top5_acc": 0.71484, "loss_cls": 3.01646, "loss": 3.01646, "time": 0.82065} +{"mode": "val", "epoch": 123, "iter": 309, "lr": 0.00778, "top1_acc": 0.38469, "top5_acc": 0.64104, "mean_class_accuracy": 0.38448} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00777, "memory": 15990, "data_time": 1.27645, "top1_acc": 0.47984, "top5_acc": 0.74188, "loss_cls": 2.85757, "loss": 2.85757, "time": 2.2625} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00775, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47641, "top5_acc": 0.74047, "loss_cls": 2.87267, "loss": 2.87267, "time": 0.82349} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00774, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48203, "top5_acc": 0.73953, "loss_cls": 2.87209, "loss": 2.87209, "time": 0.82405} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.00772, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47734, "top5_acc": 0.73109, "loss_cls": 2.90983, "loss": 2.90983, "time": 0.82905} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00771, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48234, "top5_acc": 0.73484, "loss_cls": 2.89672, "loss": 2.89672, "time": 0.82194} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00769, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47609, "top5_acc": 0.74109, "loss_cls": 2.88254, "loss": 2.88254, "time": 0.82617} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00768, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47953, "top5_acc": 0.73547, "loss_cls": 2.90139, "loss": 2.90139, "time": 0.82466} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00766, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47438, "top5_acc": 0.72906, "loss_cls": 2.91595, "loss": 2.91595, "time": 0.82805} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00765, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47281, "top5_acc": 0.74469, "loss_cls": 2.87445, "loss": 2.87445, "time": 0.8287} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00763, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47078, "top5_acc": 0.72359, "loss_cls": 2.92691, "loss": 2.92691, "time": 0.82587} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00762, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.47203, "top5_acc": 0.72984, "loss_cls": 2.90404, "loss": 2.90404, "time": 0.82294} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.0076, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46953, "top5_acc": 0.72891, "loss_cls": 2.94247, "loss": 2.94247, "time": 0.83455} +{"mode": "train", "epoch": 124, "iter": 1300, "lr": 0.00759, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48062, "top5_acc": 0.73641, "loss_cls": 2.88431, "loss": 2.88431, "time": 0.81589} +{"mode": "train", "epoch": 124, "iter": 1400, "lr": 0.00758, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47078, "top5_acc": 0.73297, "loss_cls": 2.91507, "loss": 2.91507, "time": 0.83042} +{"mode": "train", "epoch": 124, "iter": 1500, "lr": 0.00756, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46688, "top5_acc": 0.72172, "loss_cls": 2.97295, "loss": 2.97295, "time": 0.82695} +{"mode": "train", "epoch": 124, "iter": 1600, "lr": 0.00755, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.46734, "top5_acc": 0.73672, "loss_cls": 2.92092, "loss": 2.92092, "time": 0.82476} +{"mode": "train", "epoch": 124, "iter": 1700, "lr": 0.00753, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47047, "top5_acc": 0.72844, "loss_cls": 2.9395, "loss": 2.9395, "time": 0.82989} +{"mode": "train", "epoch": 124, "iter": 1800, "lr": 0.00752, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46703, "top5_acc": 0.72703, "loss_cls": 2.9331, "loss": 2.9331, "time": 0.82283} +{"mode": "train", "epoch": 124, "iter": 1900, "lr": 0.0075, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.475, "top5_acc": 0.72984, "loss_cls": 2.91996, "loss": 2.91996, "time": 0.81824} +{"mode": "train", "epoch": 124, "iter": 2000, "lr": 0.00749, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47766, "top5_acc": 0.72734, "loss_cls": 2.92698, "loss": 2.92698, "time": 0.83137} +{"mode": "train", "epoch": 124, "iter": 2100, "lr": 0.00747, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46766, "top5_acc": 0.72781, "loss_cls": 2.92414, "loss": 2.92414, "time": 0.82039} +{"mode": "train", "epoch": 124, "iter": 2200, "lr": 0.00746, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46828, "top5_acc": 0.72469, "loss_cls": 2.93376, "loss": 2.93376, "time": 0.81929} +{"mode": "train", "epoch": 124, "iter": 2300, "lr": 0.00744, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47719, "top5_acc": 0.735, "loss_cls": 2.92079, "loss": 2.92079, "time": 0.82571} +{"mode": "train", "epoch": 124, "iter": 2400, "lr": 0.00743, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45641, "top5_acc": 0.72016, "loss_cls": 2.98984, "loss": 2.98984, "time": 0.82863} +{"mode": "train", "epoch": 124, "iter": 2500, "lr": 0.00741, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47094, "top5_acc": 0.73031, "loss_cls": 2.93009, "loss": 2.93009, "time": 0.82176} +{"mode": "train", "epoch": 124, "iter": 2600, "lr": 0.0074, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47812, "top5_acc": 0.72734, "loss_cls": 2.93779, "loss": 2.93779, "time": 0.81707} +{"mode": "train", "epoch": 124, "iter": 2700, "lr": 0.00738, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47141, "top5_acc": 0.73172, "loss_cls": 2.91981, "loss": 2.91981, "time": 0.81787} +{"mode": "train", "epoch": 124, "iter": 2800, "lr": 0.00737, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47, "top5_acc": 0.72922, "loss_cls": 2.89646, "loss": 2.89646, "time": 0.82249} +{"mode": "train", "epoch": 124, "iter": 2900, "lr": 0.00735, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46734, "top5_acc": 0.72578, "loss_cls": 2.96107, "loss": 2.96107, "time": 0.81504} +{"mode": "train", "epoch": 124, "iter": 3000, "lr": 0.00734, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.465, "top5_acc": 0.71859, "loss_cls": 2.98559, "loss": 2.98559, "time": 0.82021} +{"mode": "train", "epoch": 124, "iter": 3100, "lr": 0.00733, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47141, "top5_acc": 0.73312, "loss_cls": 2.93245, "loss": 2.93245, "time": 0.81443} +{"mode": "train", "epoch": 124, "iter": 3200, "lr": 0.00731, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47656, "top5_acc": 0.73375, "loss_cls": 2.89625, "loss": 2.89625, "time": 0.8206} +{"mode": "train", "epoch": 124, "iter": 3300, "lr": 0.0073, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47609, "top5_acc": 0.73266, "loss_cls": 2.91756, "loss": 2.91756, "time": 0.81814} +{"mode": "train", "epoch": 124, "iter": 3400, "lr": 0.00728, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47688, "top5_acc": 0.73281, "loss_cls": 2.89836, "loss": 2.89836, "time": 0.81776} +{"mode": "train", "epoch": 124, "iter": 3500, "lr": 0.00727, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47938, "top5_acc": 0.72938, "loss_cls": 2.90868, "loss": 2.90868, "time": 0.81903} +{"mode": "train", "epoch": 124, "iter": 3600, "lr": 0.00725, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.46719, "top5_acc": 0.72078, "loss_cls": 2.95611, "loss": 2.95611, "time": 0.82856} +{"mode": "train", "epoch": 124, "iter": 3700, "lr": 0.00724, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46797, "top5_acc": 0.72234, "loss_cls": 2.93797, "loss": 2.93797, "time": 0.82442} +{"mode": "val", "epoch": 124, "iter": 309, "lr": 0.00723, "top1_acc": 0.39898, "top5_acc": 0.65644, "mean_class_accuracy": 0.39878} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.00722, "memory": 15990, "data_time": 1.3231, "top1_acc": 0.49734, "top5_acc": 0.74953, "loss_cls": 2.81373, "loss": 2.81373, "time": 2.31552} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.0072, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.49094, "top5_acc": 0.74828, "loss_cls": 2.77772, "loss": 2.77772, "time": 0.82611} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00719, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47547, "top5_acc": 0.73781, "loss_cls": 2.87074, "loss": 2.87074, "time": 0.82656} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00717, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48, "top5_acc": 0.74062, "loss_cls": 2.84591, "loss": 2.84591, "time": 0.83002} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00716, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48625, "top5_acc": 0.73578, "loss_cls": 2.86129, "loss": 2.86129, "time": 0.82531} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00715, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48312, "top5_acc": 0.74438, "loss_cls": 2.85342, "loss": 2.85342, "time": 0.82852} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00713, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.48156, "top5_acc": 0.74422, "loss_cls": 2.844, "loss": 2.844, "time": 0.82334} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00712, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47812, "top5_acc": 0.74422, "loss_cls": 2.86364, "loss": 2.86364, "time": 0.82362} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.0071, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47953, "top5_acc": 0.74234, "loss_cls": 2.88882, "loss": 2.88882, "time": 0.81574} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.00709, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47891, "top5_acc": 0.74297, "loss_cls": 2.85946, "loss": 2.85946, "time": 0.81367} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00707, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49172, "top5_acc": 0.74672, "loss_cls": 2.82084, "loss": 2.82084, "time": 0.82195} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00706, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48766, "top5_acc": 0.74266, "loss_cls": 2.83594, "loss": 2.83594, "time": 0.81556} +{"mode": "train", "epoch": 125, "iter": 1300, "lr": 0.00704, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48297, "top5_acc": 0.73828, "loss_cls": 2.85212, "loss": 2.85212, "time": 0.81998} +{"mode": "train", "epoch": 125, "iter": 1400, "lr": 0.00703, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47297, "top5_acc": 0.72625, "loss_cls": 2.92009, "loss": 2.92009, "time": 0.82403} +{"mode": "train", "epoch": 125, "iter": 1500, "lr": 0.00702, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47797, "top5_acc": 0.73203, "loss_cls": 2.90859, "loss": 2.90859, "time": 0.82262} +{"mode": "train", "epoch": 125, "iter": 1600, "lr": 0.007, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46984, "top5_acc": 0.73641, "loss_cls": 2.87616, "loss": 2.87616, "time": 0.82307} +{"mode": "train", "epoch": 125, "iter": 1700, "lr": 0.00699, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47094, "top5_acc": 0.72938, "loss_cls": 2.92017, "loss": 2.92017, "time": 0.81378} +{"mode": "train", "epoch": 125, "iter": 1800, "lr": 0.00697, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47547, "top5_acc": 0.73562, "loss_cls": 2.86266, "loss": 2.86266, "time": 0.81699} +{"mode": "train", "epoch": 125, "iter": 1900, "lr": 0.00696, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48109, "top5_acc": 0.73906, "loss_cls": 2.89062, "loss": 2.89062, "time": 0.81988} +{"mode": "train", "epoch": 125, "iter": 2000, "lr": 0.00694, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47484, "top5_acc": 0.73062, "loss_cls": 2.90159, "loss": 2.90159, "time": 0.82669} +{"mode": "train", "epoch": 125, "iter": 2100, "lr": 0.00693, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48125, "top5_acc": 0.72797, "loss_cls": 2.91043, "loss": 2.91043, "time": 0.81699} +{"mode": "train", "epoch": 125, "iter": 2200, "lr": 0.00692, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48078, "top5_acc": 0.72922, "loss_cls": 2.91039, "loss": 2.91039, "time": 0.82257} +{"mode": "train", "epoch": 125, "iter": 2300, "lr": 0.0069, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47203, "top5_acc": 0.73422, "loss_cls": 2.90874, "loss": 2.90874, "time": 0.81952} +{"mode": "train", "epoch": 125, "iter": 2400, "lr": 0.00689, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47688, "top5_acc": 0.71734, "loss_cls": 2.94161, "loss": 2.94161, "time": 0.81518} +{"mode": "train", "epoch": 125, "iter": 2500, "lr": 0.00687, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46656, "top5_acc": 0.72969, "loss_cls": 2.92458, "loss": 2.92458, "time": 0.81273} +{"mode": "train", "epoch": 125, "iter": 2600, "lr": 0.00686, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48594, "top5_acc": 0.73922, "loss_cls": 2.88749, "loss": 2.88749, "time": 0.81751} +{"mode": "train", "epoch": 125, "iter": 2700, "lr": 0.00685, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47594, "top5_acc": 0.7325, "loss_cls": 2.8881, "loss": 2.8881, "time": 0.82314} +{"mode": "train", "epoch": 125, "iter": 2800, "lr": 0.00683, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47047, "top5_acc": 0.73031, "loss_cls": 2.9337, "loss": 2.9337, "time": 0.8181} +{"mode": "train", "epoch": 125, "iter": 2900, "lr": 0.00682, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47156, "top5_acc": 0.72547, "loss_cls": 2.95061, "loss": 2.95061, "time": 0.81567} +{"mode": "train", "epoch": 125, "iter": 3000, "lr": 0.0068, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48328, "top5_acc": 0.73406, "loss_cls": 2.89379, "loss": 2.89379, "time": 0.81384} +{"mode": "train", "epoch": 125, "iter": 3100, "lr": 0.00679, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47141, "top5_acc": 0.73344, "loss_cls": 2.9134, "loss": 2.9134, "time": 0.81535} +{"mode": "train", "epoch": 125, "iter": 3200, "lr": 0.00678, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47422, "top5_acc": 0.73453, "loss_cls": 2.89249, "loss": 2.89249, "time": 0.81834} +{"mode": "train", "epoch": 125, "iter": 3300, "lr": 0.00676, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47406, "top5_acc": 0.73109, "loss_cls": 2.89282, "loss": 2.89282, "time": 0.81535} +{"mode": "train", "epoch": 125, "iter": 3400, "lr": 0.00675, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48297, "top5_acc": 0.73016, "loss_cls": 2.90024, "loss": 2.90024, "time": 0.81584} +{"mode": "train", "epoch": 125, "iter": 3500, "lr": 0.00673, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47516, "top5_acc": 0.73922, "loss_cls": 2.88909, "loss": 2.88909, "time": 0.81828} +{"mode": "train", "epoch": 125, "iter": 3600, "lr": 0.00672, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47156, "top5_acc": 0.73688, "loss_cls": 2.91894, "loss": 2.91894, "time": 0.82571} +{"mode": "train", "epoch": 125, "iter": 3700, "lr": 0.00671, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47531, "top5_acc": 0.73094, "loss_cls": 2.89443, "loss": 2.89443, "time": 0.8185} +{"mode": "val", "epoch": 125, "iter": 309, "lr": 0.0067, "top1_acc": 0.39411, "top5_acc": 0.64433, "mean_class_accuracy": 0.39392} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00668, "memory": 15990, "data_time": 1.39105, "top1_acc": 0.505, "top5_acc": 0.75688, "loss_cls": 2.76319, "loss": 2.76319, "time": 2.38107} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00667, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49328, "top5_acc": 0.75125, "loss_cls": 2.8, "loss": 2.8, "time": 0.83481} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00666, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49234, "top5_acc": 0.74391, "loss_cls": 2.81295, "loss": 2.81295, "time": 0.82634} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00664, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48359, "top5_acc": 0.74422, "loss_cls": 2.83413, "loss": 2.83413, "time": 0.8244} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00663, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49297, "top5_acc": 0.74203, "loss_cls": 2.82196, "loss": 2.82196, "time": 0.82879} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00662, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.50234, "top5_acc": 0.75156, "loss_cls": 2.7808, "loss": 2.7808, "time": 0.83213} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0066, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49688, "top5_acc": 0.75297, "loss_cls": 2.78943, "loss": 2.78943, "time": 0.82886} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00659, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49359, "top5_acc": 0.74203, "loss_cls": 2.8093, "loss": 2.8093, "time": 0.8304} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00657, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48562, "top5_acc": 0.74266, "loss_cls": 2.82579, "loss": 2.82579, "time": 0.82092} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00656, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49047, "top5_acc": 0.74328, "loss_cls": 2.81776, "loss": 2.81776, "time": 0.81738} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00655, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.47938, "top5_acc": 0.74062, "loss_cls": 2.84498, "loss": 2.84498, "time": 0.81925} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00653, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48688, "top5_acc": 0.74062, "loss_cls": 2.85602, "loss": 2.85602, "time": 0.81648} +{"mode": "train", "epoch": 126, "iter": 1300, "lr": 0.00652, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47594, "top5_acc": 0.74016, "loss_cls": 2.85393, "loss": 2.85393, "time": 0.82655} +{"mode": "train", "epoch": 126, "iter": 1400, "lr": 0.0065, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47484, "top5_acc": 0.74062, "loss_cls": 2.8739, "loss": 2.8739, "time": 0.8215} +{"mode": "train", "epoch": 126, "iter": 1500, "lr": 0.00649, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49391, "top5_acc": 0.73719, "loss_cls": 2.85084, "loss": 2.85084, "time": 0.81931} +{"mode": "train", "epoch": 126, "iter": 1600, "lr": 0.00648, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47781, "top5_acc": 0.73781, "loss_cls": 2.86815, "loss": 2.86815, "time": 0.81804} +{"mode": "train", "epoch": 126, "iter": 1700, "lr": 0.00646, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.495, "top5_acc": 0.74875, "loss_cls": 2.81381, "loss": 2.81381, "time": 0.81658} +{"mode": "train", "epoch": 126, "iter": 1800, "lr": 0.00645, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49125, "top5_acc": 0.74562, "loss_cls": 2.83118, "loss": 2.83118, "time": 0.81669} +{"mode": "train", "epoch": 126, "iter": 1900, "lr": 0.00644, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48516, "top5_acc": 0.73422, "loss_cls": 2.87765, "loss": 2.87765, "time": 0.82265} +{"mode": "train", "epoch": 126, "iter": 2000, "lr": 0.00642, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48078, "top5_acc": 0.74344, "loss_cls": 2.84871, "loss": 2.84871, "time": 0.81987} +{"mode": "train", "epoch": 126, "iter": 2100, "lr": 0.00641, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49219, "top5_acc": 0.73094, "loss_cls": 2.84571, "loss": 2.84571, "time": 0.81666} +{"mode": "train", "epoch": 126, "iter": 2200, "lr": 0.00639, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.48094, "top5_acc": 0.73359, "loss_cls": 2.87005, "loss": 2.87005, "time": 0.81994} +{"mode": "train", "epoch": 126, "iter": 2300, "lr": 0.00638, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48797, "top5_acc": 0.74141, "loss_cls": 2.83585, "loss": 2.83585, "time": 0.81215} +{"mode": "train", "epoch": 126, "iter": 2400, "lr": 0.00637, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47391, "top5_acc": 0.72781, "loss_cls": 2.90618, "loss": 2.90618, "time": 0.82337} +{"mode": "train", "epoch": 126, "iter": 2500, "lr": 0.00635, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47062, "top5_acc": 0.73344, "loss_cls": 2.91675, "loss": 2.91675, "time": 0.81267} +{"mode": "train", "epoch": 126, "iter": 2600, "lr": 0.00634, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47328, "top5_acc": 0.73547, "loss_cls": 2.88253, "loss": 2.88253, "time": 0.81605} +{"mode": "train", "epoch": 126, "iter": 2700, "lr": 0.00633, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47344, "top5_acc": 0.73469, "loss_cls": 2.88386, "loss": 2.88386, "time": 0.81031} +{"mode": "train", "epoch": 126, "iter": 2800, "lr": 0.00631, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47062, "top5_acc": 0.73359, "loss_cls": 2.90413, "loss": 2.90413, "time": 0.81486} +{"mode": "train", "epoch": 126, "iter": 2900, "lr": 0.0063, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47734, "top5_acc": 0.73578, "loss_cls": 2.88489, "loss": 2.88489, "time": 0.81375} +{"mode": "train", "epoch": 126, "iter": 3000, "lr": 0.00629, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47875, "top5_acc": 0.73516, "loss_cls": 2.8818, "loss": 2.8818, "time": 0.8083} +{"mode": "train", "epoch": 126, "iter": 3100, "lr": 0.00627, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47703, "top5_acc": 0.7275, "loss_cls": 2.90843, "loss": 2.90843, "time": 0.81165} +{"mode": "train", "epoch": 126, "iter": 3200, "lr": 0.00626, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49156, "top5_acc": 0.74562, "loss_cls": 2.83233, "loss": 2.83233, "time": 0.8125} +{"mode": "train", "epoch": 126, "iter": 3300, "lr": 0.00625, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48203, "top5_acc": 0.73719, "loss_cls": 2.87609, "loss": 2.87609, "time": 0.81521} +{"mode": "train", "epoch": 126, "iter": 3400, "lr": 0.00623, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47297, "top5_acc": 0.73328, "loss_cls": 2.91723, "loss": 2.91723, "time": 0.81609} +{"mode": "train", "epoch": 126, "iter": 3500, "lr": 0.00622, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49109, "top5_acc": 0.73578, "loss_cls": 2.85355, "loss": 2.85355, "time": 0.81339} +{"mode": "train", "epoch": 126, "iter": 3600, "lr": 0.0062, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47984, "top5_acc": 0.73219, "loss_cls": 2.89121, "loss": 2.89121, "time": 0.81649} +{"mode": "train", "epoch": 126, "iter": 3700, "lr": 0.00619, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.48125, "top5_acc": 0.74047, "loss_cls": 2.85764, "loss": 2.85764, "time": 0.82655} +{"mode": "val", "epoch": 126, "iter": 309, "lr": 0.00618, "top1_acc": 0.40176, "top5_acc": 0.66413, "mean_class_accuracy": 0.40152} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00617, "memory": 15990, "data_time": 1.35921, "top1_acc": 0.50594, "top5_acc": 0.75234, "loss_cls": 2.72584, "loss": 2.72584, "time": 2.34742} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00616, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50328, "top5_acc": 0.75422, "loss_cls": 2.75258, "loss": 2.75258, "time": 0.8133} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00614, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49812, "top5_acc": 0.74578, "loss_cls": 2.78903, "loss": 2.78903, "time": 0.81682} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00613, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49578, "top5_acc": 0.75297, "loss_cls": 2.79241, "loss": 2.79241, "time": 0.81584} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.00612, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5, "top5_acc": 0.75219, "loss_cls": 2.75186, "loss": 2.75186, "time": 0.81396} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.0061, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49906, "top5_acc": 0.74531, "loss_cls": 2.80181, "loss": 2.80181, "time": 0.8116} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00609, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49766, "top5_acc": 0.75766, "loss_cls": 2.76732, "loss": 2.76732, "time": 0.81198} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00608, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49688, "top5_acc": 0.75625, "loss_cls": 2.76814, "loss": 2.76814, "time": 0.81723} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00606, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49312, "top5_acc": 0.74375, "loss_cls": 2.79376, "loss": 2.79376, "time": 0.81312} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00605, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49078, "top5_acc": 0.75031, "loss_cls": 2.80386, "loss": 2.80386, "time": 0.81168} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00604, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48406, "top5_acc": 0.74484, "loss_cls": 2.82815, "loss": 2.82815, "time": 0.81793} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00602, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49141, "top5_acc": 0.74875, "loss_cls": 2.79162, "loss": 2.79162, "time": 0.81575} +{"mode": "train", "epoch": 127, "iter": 1300, "lr": 0.00601, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48422, "top5_acc": 0.74578, "loss_cls": 2.84399, "loss": 2.84399, "time": 0.8261} +{"mode": "train", "epoch": 127, "iter": 1400, "lr": 0.006, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48938, "top5_acc": 0.74719, "loss_cls": 2.8267, "loss": 2.8267, "time": 0.81596} +{"mode": "train", "epoch": 127, "iter": 1500, "lr": 0.00598, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48672, "top5_acc": 0.74422, "loss_cls": 2.84075, "loss": 2.84075, "time": 0.81253} +{"mode": "train", "epoch": 127, "iter": 1600, "lr": 0.00597, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49609, "top5_acc": 0.75719, "loss_cls": 2.79038, "loss": 2.79038, "time": 0.81586} +{"mode": "train", "epoch": 127, "iter": 1700, "lr": 0.00596, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49531, "top5_acc": 0.74094, "loss_cls": 2.82094, "loss": 2.82094, "time": 0.81566} +{"mode": "train", "epoch": 127, "iter": 1800, "lr": 0.00594, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49578, "top5_acc": 0.73922, "loss_cls": 2.84843, "loss": 2.84843, "time": 0.81469} +{"mode": "train", "epoch": 127, "iter": 1900, "lr": 0.00593, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49781, "top5_acc": 0.74844, "loss_cls": 2.79884, "loss": 2.79884, "time": 0.81771} +{"mode": "train", "epoch": 127, "iter": 2000, "lr": 0.00592, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49078, "top5_acc": 0.74328, "loss_cls": 2.81625, "loss": 2.81625, "time": 0.81553} +{"mode": "train", "epoch": 127, "iter": 2100, "lr": 0.00591, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48891, "top5_acc": 0.73641, "loss_cls": 2.861, "loss": 2.861, "time": 0.81765} +{"mode": "train", "epoch": 127, "iter": 2200, "lr": 0.00589, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49625, "top5_acc": 0.75078, "loss_cls": 2.79505, "loss": 2.79505, "time": 0.81833} +{"mode": "train", "epoch": 127, "iter": 2300, "lr": 0.00588, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47531, "top5_acc": 0.73172, "loss_cls": 2.89037, "loss": 2.89037, "time": 0.81647} +{"mode": "train", "epoch": 127, "iter": 2400, "lr": 0.00587, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48688, "top5_acc": 0.73906, "loss_cls": 2.82252, "loss": 2.82252, "time": 0.81628} +{"mode": "train", "epoch": 127, "iter": 2500, "lr": 0.00585, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48578, "top5_acc": 0.74672, "loss_cls": 2.82998, "loss": 2.82998, "time": 0.81641} +{"mode": "train", "epoch": 127, "iter": 2600, "lr": 0.00584, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48438, "top5_acc": 0.73812, "loss_cls": 2.83767, "loss": 2.83767, "time": 0.81475} +{"mode": "train", "epoch": 127, "iter": 2700, "lr": 0.00583, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48844, "top5_acc": 0.73719, "loss_cls": 2.85228, "loss": 2.85228, "time": 0.81995} +{"mode": "train", "epoch": 127, "iter": 2800, "lr": 0.00581, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48875, "top5_acc": 0.74141, "loss_cls": 2.83157, "loss": 2.83157, "time": 0.8123} +{"mode": "train", "epoch": 127, "iter": 2900, "lr": 0.0058, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48312, "top5_acc": 0.74375, "loss_cls": 2.83729, "loss": 2.83729, "time": 0.8203} +{"mode": "train", "epoch": 127, "iter": 3000, "lr": 0.00579, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49156, "top5_acc": 0.74094, "loss_cls": 2.83321, "loss": 2.83321, "time": 0.81504} +{"mode": "train", "epoch": 127, "iter": 3100, "lr": 0.00577, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48188, "top5_acc": 0.74422, "loss_cls": 2.81458, "loss": 2.81458, "time": 0.81851} +{"mode": "train", "epoch": 127, "iter": 3200, "lr": 0.00576, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48359, "top5_acc": 0.73547, "loss_cls": 2.84994, "loss": 2.84994, "time": 0.81654} +{"mode": "train", "epoch": 127, "iter": 3300, "lr": 0.00575, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49375, "top5_acc": 0.75594, "loss_cls": 2.78018, "loss": 2.78018, "time": 0.81436} +{"mode": "train", "epoch": 127, "iter": 3400, "lr": 0.00573, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48438, "top5_acc": 0.74156, "loss_cls": 2.84257, "loss": 2.84257, "time": 0.81363} +{"mode": "train", "epoch": 127, "iter": 3500, "lr": 0.00572, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48844, "top5_acc": 0.74281, "loss_cls": 2.83403, "loss": 2.83403, "time": 0.81708} +{"mode": "train", "epoch": 127, "iter": 3600, "lr": 0.00571, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48719, "top5_acc": 0.7425, "loss_cls": 2.84808, "loss": 2.84808, "time": 0.81184} +{"mode": "train", "epoch": 127, "iter": 3700, "lr": 0.0057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48641, "top5_acc": 0.74172, "loss_cls": 2.85368, "loss": 2.85368, "time": 0.8159} +{"mode": "val", "epoch": 127, "iter": 309, "lr": 0.00569, "top1_acc": 0.41589, "top5_acc": 0.66869, "mean_class_accuracy": 0.41568} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00568, "memory": 15990, "data_time": 1.35055, "top1_acc": 0.50328, "top5_acc": 0.76812, "loss_cls": 2.71823, "loss": 2.71823, "time": 2.34751} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.00566, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.51547, "top5_acc": 0.76562, "loss_cls": 2.71318, "loss": 2.71318, "time": 0.83203} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00565, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.50953, "top5_acc": 0.75547, "loss_cls": 2.73114, "loss": 2.73114, "time": 0.8284} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00564, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49594, "top5_acc": 0.75891, "loss_cls": 2.76791, "loss": 2.76791, "time": 0.8277} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00563, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48891, "top5_acc": 0.74688, "loss_cls": 2.80871, "loss": 2.80871, "time": 0.82716} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00561, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.49578, "top5_acc": 0.75406, "loss_cls": 2.74011, "loss": 2.74011, "time": 0.82811} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.0056, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.50859, "top5_acc": 0.76125, "loss_cls": 2.71069, "loss": 2.71069, "time": 0.82356} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00559, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49719, "top5_acc": 0.75422, "loss_cls": 2.78407, "loss": 2.78407, "time": 0.83267} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00557, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.51406, "top5_acc": 0.75797, "loss_cls": 2.72493, "loss": 2.72493, "time": 0.82868} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00556, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50016, "top5_acc": 0.74562, "loss_cls": 2.79411, "loss": 2.79411, "time": 0.8121} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00555, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49672, "top5_acc": 0.75109, "loss_cls": 2.78462, "loss": 2.78462, "time": 0.82288} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00554, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48906, "top5_acc": 0.75047, "loss_cls": 2.80036, "loss": 2.80036, "time": 0.81478} +{"mode": "train", "epoch": 128, "iter": 1300, "lr": 0.00552, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50125, "top5_acc": 0.74719, "loss_cls": 2.75775, "loss": 2.75775, "time": 0.82644} +{"mode": "train", "epoch": 128, "iter": 1400, "lr": 0.00551, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48469, "top5_acc": 0.74547, "loss_cls": 2.79924, "loss": 2.79924, "time": 0.81814} +{"mode": "train", "epoch": 128, "iter": 1500, "lr": 0.0055, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48969, "top5_acc": 0.74188, "loss_cls": 2.82419, "loss": 2.82419, "time": 0.81884} +{"mode": "train", "epoch": 128, "iter": 1600, "lr": 0.00548, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.495, "top5_acc": 0.75141, "loss_cls": 2.77914, "loss": 2.77914, "time": 0.82044} +{"mode": "train", "epoch": 128, "iter": 1700, "lr": 0.00547, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50406, "top5_acc": 0.75875, "loss_cls": 2.7452, "loss": 2.7452, "time": 0.8166} +{"mode": "train", "epoch": 128, "iter": 1800, "lr": 0.00546, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49641, "top5_acc": 0.75047, "loss_cls": 2.79452, "loss": 2.79452, "time": 0.81749} +{"mode": "train", "epoch": 128, "iter": 1900, "lr": 0.00545, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48812, "top5_acc": 0.73609, "loss_cls": 2.84062, "loss": 2.84062, "time": 0.82081} +{"mode": "train", "epoch": 128, "iter": 2000, "lr": 0.00543, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50094, "top5_acc": 0.74844, "loss_cls": 2.77604, "loss": 2.77604, "time": 0.81782} +{"mode": "train", "epoch": 128, "iter": 2100, "lr": 0.00542, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49531, "top5_acc": 0.75156, "loss_cls": 2.77281, "loss": 2.77281, "time": 0.82533} +{"mode": "train", "epoch": 128, "iter": 2200, "lr": 0.00541, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.49031, "top5_acc": 0.74594, "loss_cls": 2.81531, "loss": 2.81531, "time": 0.81753} +{"mode": "train", "epoch": 128, "iter": 2300, "lr": 0.0054, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49812, "top5_acc": 0.75766, "loss_cls": 2.76892, "loss": 2.76892, "time": 0.81528} +{"mode": "train", "epoch": 128, "iter": 2400, "lr": 0.00538, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49047, "top5_acc": 0.75141, "loss_cls": 2.80255, "loss": 2.80255, "time": 0.81535} +{"mode": "train", "epoch": 128, "iter": 2500, "lr": 0.00537, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49453, "top5_acc": 0.74547, "loss_cls": 2.82618, "loss": 2.82618, "time": 0.81456} +{"mode": "train", "epoch": 128, "iter": 2600, "lr": 0.00536, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49469, "top5_acc": 0.74641, "loss_cls": 2.79288, "loss": 2.79288, "time": 0.81539} +{"mode": "train", "epoch": 128, "iter": 2700, "lr": 0.00535, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50422, "top5_acc": 0.74906, "loss_cls": 2.78119, "loss": 2.78119, "time": 0.81726} +{"mode": "train", "epoch": 128, "iter": 2800, "lr": 0.00533, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49484, "top5_acc": 0.74891, "loss_cls": 2.78451, "loss": 2.78451, "time": 0.81564} +{"mode": "train", "epoch": 128, "iter": 2900, "lr": 0.00532, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49953, "top5_acc": 0.75031, "loss_cls": 2.78408, "loss": 2.78408, "time": 0.81754} +{"mode": "train", "epoch": 128, "iter": 3000, "lr": 0.00531, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48688, "top5_acc": 0.74203, "loss_cls": 2.81241, "loss": 2.81241, "time": 0.81207} +{"mode": "train", "epoch": 128, "iter": 3100, "lr": 0.0053, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49297, "top5_acc": 0.75016, "loss_cls": 2.79141, "loss": 2.79141, "time": 0.81771} +{"mode": "train", "epoch": 128, "iter": 3200, "lr": 0.00528, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48328, "top5_acc": 0.73844, "loss_cls": 2.83721, "loss": 2.83721, "time": 0.81467} +{"mode": "train", "epoch": 128, "iter": 3300, "lr": 0.00527, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49453, "top5_acc": 0.74469, "loss_cls": 2.78198, "loss": 2.78198, "time": 0.81469} +{"mode": "train", "epoch": 128, "iter": 3400, "lr": 0.00526, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49688, "top5_acc": 0.74594, "loss_cls": 2.79819, "loss": 2.79819, "time": 0.82019} +{"mode": "train", "epoch": 128, "iter": 3500, "lr": 0.00525, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49281, "top5_acc": 0.7475, "loss_cls": 2.81051, "loss": 2.81051, "time": 0.81579} +{"mode": "train", "epoch": 128, "iter": 3600, "lr": 0.00523, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4925, "top5_acc": 0.745, "loss_cls": 2.81151, "loss": 2.81151, "time": 0.81407} +{"mode": "train", "epoch": 128, "iter": 3700, "lr": 0.00522, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49688, "top5_acc": 0.7475, "loss_cls": 2.7847, "loss": 2.7847, "time": 0.82041} +{"mode": "val", "epoch": 128, "iter": 309, "lr": 0.00521, "top1_acc": 0.41787, "top5_acc": 0.67148, "mean_class_accuracy": 0.4176} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.0052, "memory": 15990, "data_time": 1.31873, "top1_acc": 0.51016, "top5_acc": 0.75547, "loss_cls": 2.7186, "loss": 2.7186, "time": 2.31777} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00519, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.50875, "top5_acc": 0.76047, "loss_cls": 2.69241, "loss": 2.69241, "time": 0.83572} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00518, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.51359, "top5_acc": 0.76594, "loss_cls": 2.68882, "loss": 2.68882, "time": 0.8353} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00516, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.51016, "top5_acc": 0.755, "loss_cls": 2.76462, "loss": 2.76462, "time": 0.8322} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00515, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.50703, "top5_acc": 0.76281, "loss_cls": 2.70397, "loss": 2.70397, "time": 0.83791} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00514, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.51141, "top5_acc": 0.76297, "loss_cls": 2.70589, "loss": 2.70589, "time": 0.83777} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00513, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.51734, "top5_acc": 0.765, "loss_cls": 2.67186, "loss": 2.67186, "time": 0.83488} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00512, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.51203, "top5_acc": 0.75766, "loss_cls": 2.6962, "loss": 2.6962, "time": 0.82928} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.0051, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.50531, "top5_acc": 0.75625, "loss_cls": 2.74296, "loss": 2.74296, "time": 0.82282} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00509, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51094, "top5_acc": 0.75641, "loss_cls": 2.7285, "loss": 2.7285, "time": 0.81655} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00508, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51078, "top5_acc": 0.75938, "loss_cls": 2.72316, "loss": 2.72316, "time": 0.81678} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.00507, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50516, "top5_acc": 0.75953, "loss_cls": 2.73048, "loss": 2.73048, "time": 0.81687} +{"mode": "train", "epoch": 129, "iter": 1300, "lr": 0.00505, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.50062, "top5_acc": 0.75516, "loss_cls": 2.7392, "loss": 2.7392, "time": 0.82767} +{"mode": "train", "epoch": 129, "iter": 1400, "lr": 0.00504, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50141, "top5_acc": 0.74953, "loss_cls": 2.75497, "loss": 2.75497, "time": 0.81844} +{"mode": "train", "epoch": 129, "iter": 1500, "lr": 0.00503, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.49312, "top5_acc": 0.74609, "loss_cls": 2.7897, "loss": 2.7897, "time": 0.8228} +{"mode": "train", "epoch": 129, "iter": 1600, "lr": 0.00502, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.50813, "top5_acc": 0.76062, "loss_cls": 2.72211, "loss": 2.72211, "time": 0.82054} +{"mode": "train", "epoch": 129, "iter": 1700, "lr": 0.00501, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50266, "top5_acc": 0.75203, "loss_cls": 2.73332, "loss": 2.73332, "time": 0.82242} +{"mode": "train", "epoch": 129, "iter": 1800, "lr": 0.00499, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50109, "top5_acc": 0.75531, "loss_cls": 2.75748, "loss": 2.75748, "time": 0.8172} +{"mode": "train", "epoch": 129, "iter": 1900, "lr": 0.00498, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.5075, "top5_acc": 0.75766, "loss_cls": 2.73882, "loss": 2.73882, "time": 0.82176} +{"mode": "train", "epoch": 129, "iter": 2000, "lr": 0.00497, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50187, "top5_acc": 0.76031, "loss_cls": 2.77173, "loss": 2.77173, "time": 0.81819} +{"mode": "train", "epoch": 129, "iter": 2100, "lr": 0.00496, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49406, "top5_acc": 0.74609, "loss_cls": 2.80802, "loss": 2.80802, "time": 0.81912} +{"mode": "train", "epoch": 129, "iter": 2200, "lr": 0.00494, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50172, "top5_acc": 0.75906, "loss_cls": 2.76001, "loss": 2.76001, "time": 0.81981} +{"mode": "train", "epoch": 129, "iter": 2300, "lr": 0.00493, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48688, "top5_acc": 0.74797, "loss_cls": 2.78701, "loss": 2.78701, "time": 0.81644} +{"mode": "train", "epoch": 129, "iter": 2400, "lr": 0.00492, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50062, "top5_acc": 0.75281, "loss_cls": 2.77716, "loss": 2.77716, "time": 0.81833} +{"mode": "train", "epoch": 129, "iter": 2500, "lr": 0.00491, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49484, "top5_acc": 0.75766, "loss_cls": 2.73174, "loss": 2.73174, "time": 0.81615} +{"mode": "train", "epoch": 129, "iter": 2600, "lr": 0.0049, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49359, "top5_acc": 0.74984, "loss_cls": 2.79747, "loss": 2.79747, "time": 0.81211} +{"mode": "train", "epoch": 129, "iter": 2700, "lr": 0.00488, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49688, "top5_acc": 0.74766, "loss_cls": 2.78383, "loss": 2.78383, "time": 0.81135} +{"mode": "train", "epoch": 129, "iter": 2800, "lr": 0.00487, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49469, "top5_acc": 0.75391, "loss_cls": 2.7884, "loss": 2.7884, "time": 0.81411} +{"mode": "train", "epoch": 129, "iter": 2900, "lr": 0.00486, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50125, "top5_acc": 0.75125, "loss_cls": 2.75444, "loss": 2.75444, "time": 0.81378} +{"mode": "train", "epoch": 129, "iter": 3000, "lr": 0.00485, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50047, "top5_acc": 0.75297, "loss_cls": 2.77214, "loss": 2.77214, "time": 0.8174} +{"mode": "train", "epoch": 129, "iter": 3100, "lr": 0.00484, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50016, "top5_acc": 0.74859, "loss_cls": 2.7936, "loss": 2.7936, "time": 0.81481} +{"mode": "train", "epoch": 129, "iter": 3200, "lr": 0.00482, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50562, "top5_acc": 0.75797, "loss_cls": 2.76413, "loss": 2.76413, "time": 0.82497} +{"mode": "train", "epoch": 129, "iter": 3300, "lr": 0.00481, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49953, "top5_acc": 0.74719, "loss_cls": 2.78557, "loss": 2.78557, "time": 0.81269} +{"mode": "train", "epoch": 129, "iter": 3400, "lr": 0.0048, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50266, "top5_acc": 0.75109, "loss_cls": 2.77542, "loss": 2.77542, "time": 0.81455} +{"mode": "train", "epoch": 129, "iter": 3500, "lr": 0.00479, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49625, "top5_acc": 0.75422, "loss_cls": 2.75751, "loss": 2.75751, "time": 0.82054} +{"mode": "train", "epoch": 129, "iter": 3600, "lr": 0.00478, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50438, "top5_acc": 0.75844, "loss_cls": 2.73549, "loss": 2.73549, "time": 0.81475} +{"mode": "train", "epoch": 129, "iter": 3700, "lr": 0.00476, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.50281, "top5_acc": 0.75688, "loss_cls": 2.76244, "loss": 2.76244, "time": 0.82077} +{"mode": "val", "epoch": 129, "iter": 309, "lr": 0.00476, "top1_acc": 0.42177, "top5_acc": 0.6771, "mean_class_accuracy": 0.42157} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00475, "memory": 15990, "data_time": 1.34338, "top1_acc": 0.51734, "top5_acc": 0.77906, "loss_cls": 2.63227, "loss": 2.63227, "time": 2.33459} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00473, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52391, "top5_acc": 0.77406, "loss_cls": 2.63202, "loss": 2.63202, "time": 0.8308} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00472, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52125, "top5_acc": 0.76672, "loss_cls": 2.6592, "loss": 2.6592, "time": 0.82815} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00471, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.50516, "top5_acc": 0.76297, "loss_cls": 2.69647, "loss": 2.69647, "time": 0.82509} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.0047, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.51297, "top5_acc": 0.76125, "loss_cls": 2.69627, "loss": 2.69627, "time": 0.8287} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00469, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.5125, "top5_acc": 0.76672, "loss_cls": 2.69571, "loss": 2.69571, "time": 0.83117} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00468, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.50797, "top5_acc": 0.75422, "loss_cls": 2.70115, "loss": 2.70115, "time": 0.82398} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00466, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.51703, "top5_acc": 0.77016, "loss_cls": 2.66902, "loss": 2.66902, "time": 0.8258} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00465, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.50984, "top5_acc": 0.76406, "loss_cls": 2.69338, "loss": 2.69338, "time": 0.82399} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.00464, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52391, "top5_acc": 0.75688, "loss_cls": 2.68082, "loss": 2.68082, "time": 0.82454} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.00463, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52234, "top5_acc": 0.76844, "loss_cls": 2.6791, "loss": 2.6791, "time": 0.82469} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00462, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.50609, "top5_acc": 0.75578, "loss_cls": 2.72962, "loss": 2.72962, "time": 0.82448} +{"mode": "train", "epoch": 130, "iter": 1300, "lr": 0.00461, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.50938, "top5_acc": 0.76156, "loss_cls": 2.69763, "loss": 2.69763, "time": 0.83196} +{"mode": "train", "epoch": 130, "iter": 1400, "lr": 0.00459, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.51453, "top5_acc": 0.76656, "loss_cls": 2.67049, "loss": 2.67049, "time": 0.83809} +{"mode": "train", "epoch": 130, "iter": 1500, "lr": 0.00458, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.51234, "top5_acc": 0.76234, "loss_cls": 2.71277, "loss": 2.71277, "time": 0.82985} +{"mode": "train", "epoch": 130, "iter": 1600, "lr": 0.00457, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.50938, "top5_acc": 0.76266, "loss_cls": 2.69047, "loss": 2.69047, "time": 0.83305} +{"mode": "train", "epoch": 130, "iter": 1700, "lr": 0.00456, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50813, "top5_acc": 0.76094, "loss_cls": 2.74163, "loss": 2.74163, "time": 0.83467} +{"mode": "train", "epoch": 130, "iter": 1800, "lr": 0.00455, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.51062, "top5_acc": 0.76266, "loss_cls": 2.70058, "loss": 2.70058, "time": 0.82828} +{"mode": "train", "epoch": 130, "iter": 1900, "lr": 0.00454, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51594, "top5_acc": 0.76844, "loss_cls": 2.69064, "loss": 2.69064, "time": 0.83499} +{"mode": "train", "epoch": 130, "iter": 2000, "lr": 0.00452, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.50938, "top5_acc": 0.76641, "loss_cls": 2.70739, "loss": 2.70739, "time": 0.82773} +{"mode": "train", "epoch": 130, "iter": 2100, "lr": 0.00451, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.5075, "top5_acc": 0.75984, "loss_cls": 2.73538, "loss": 2.73538, "time": 0.81942} +{"mode": "train", "epoch": 130, "iter": 2200, "lr": 0.0045, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50938, "top5_acc": 0.75766, "loss_cls": 2.71958, "loss": 2.71958, "time": 0.82992} +{"mode": "train", "epoch": 130, "iter": 2300, "lr": 0.00449, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50844, "top5_acc": 0.75688, "loss_cls": 2.74939, "loss": 2.74939, "time": 0.83611} +{"mode": "train", "epoch": 130, "iter": 2400, "lr": 0.00448, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.51344, "top5_acc": 0.75859, "loss_cls": 2.69691, "loss": 2.69691, "time": 0.82958} +{"mode": "train", "epoch": 130, "iter": 2500, "lr": 0.00447, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49719, "top5_acc": 0.75781, "loss_cls": 2.75805, "loss": 2.75805, "time": 0.8226} +{"mode": "train", "epoch": 130, "iter": 2600, "lr": 0.00445, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.50797, "top5_acc": 0.75312, "loss_cls": 2.73368, "loss": 2.73368, "time": 0.81901} +{"mode": "train", "epoch": 130, "iter": 2700, "lr": 0.00444, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50688, "top5_acc": 0.76016, "loss_cls": 2.74136, "loss": 2.74136, "time": 0.82025} +{"mode": "train", "epoch": 130, "iter": 2800, "lr": 0.00443, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52016, "top5_acc": 0.76422, "loss_cls": 2.68751, "loss": 2.68751, "time": 0.82329} +{"mode": "train", "epoch": 130, "iter": 2900, "lr": 0.00442, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51109, "top5_acc": 0.76031, "loss_cls": 2.67861, "loss": 2.67861, "time": 0.82266} +{"mode": "train", "epoch": 130, "iter": 3000, "lr": 0.00441, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49953, "top5_acc": 0.755, "loss_cls": 2.74884, "loss": 2.74884, "time": 0.82256} +{"mode": "train", "epoch": 130, "iter": 3100, "lr": 0.0044, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50359, "top5_acc": 0.75734, "loss_cls": 2.71994, "loss": 2.71994, "time": 0.81945} +{"mode": "train", "epoch": 130, "iter": 3200, "lr": 0.00439, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50438, "top5_acc": 0.75953, "loss_cls": 2.70817, "loss": 2.70817, "time": 0.82219} +{"mode": "train", "epoch": 130, "iter": 3300, "lr": 0.00437, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49016, "top5_acc": 0.75438, "loss_cls": 2.76819, "loss": 2.76819, "time": 0.82682} +{"mode": "train", "epoch": 130, "iter": 3400, "lr": 0.00436, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49938, "top5_acc": 0.75281, "loss_cls": 2.77664, "loss": 2.77664, "time": 0.82286} +{"mode": "train", "epoch": 130, "iter": 3500, "lr": 0.00435, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50359, "top5_acc": 0.75656, "loss_cls": 2.73802, "loss": 2.73802, "time": 0.81649} +{"mode": "train", "epoch": 130, "iter": 3600, "lr": 0.00434, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50484, "top5_acc": 0.75141, "loss_cls": 2.77615, "loss": 2.77615, "time": 0.82585} +{"mode": "train", "epoch": 130, "iter": 3700, "lr": 0.00433, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.50609, "top5_acc": 0.75828, "loss_cls": 2.73216, "loss": 2.73216, "time": 0.82945} +{"mode": "val", "epoch": 130, "iter": 309, "lr": 0.00432, "top1_acc": 0.4165, "top5_acc": 0.67518, "mean_class_accuracy": 0.41622} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00431, "memory": 15990, "data_time": 1.31355, "top1_acc": 0.52922, "top5_acc": 0.77172, "loss_cls": 2.61096, "loss": 2.61096, "time": 2.30223} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.0043, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.51734, "top5_acc": 0.77594, "loss_cls": 2.634, "loss": 2.634, "time": 0.82752} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00429, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.51844, "top5_acc": 0.76312, "loss_cls": 2.67645, "loss": 2.67645, "time": 0.83067} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00428, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52469, "top5_acc": 0.77734, "loss_cls": 2.62158, "loss": 2.62158, "time": 0.83091} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00427, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52938, "top5_acc": 0.77406, "loss_cls": 2.61517, "loss": 2.61517, "time": 0.82656} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00425, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52062, "top5_acc": 0.76984, "loss_cls": 2.67156, "loss": 2.67156, "time": 0.82611} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00424, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52938, "top5_acc": 0.77406, "loss_cls": 2.62295, "loss": 2.62295, "time": 0.81817} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00423, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51313, "top5_acc": 0.76734, "loss_cls": 2.65943, "loss": 2.65943, "time": 0.82801} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00422, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51531, "top5_acc": 0.76719, "loss_cls": 2.66984, "loss": 2.66984, "time": 0.81749} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.00421, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.52141, "top5_acc": 0.76766, "loss_cls": 2.64947, "loss": 2.64947, "time": 0.82067} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.0042, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51453, "top5_acc": 0.76766, "loss_cls": 2.65768, "loss": 2.65768, "time": 0.82009} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00419, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.51641, "top5_acc": 0.76734, "loss_cls": 2.65594, "loss": 2.65594, "time": 0.82444} +{"mode": "train", "epoch": 131, "iter": 1300, "lr": 0.00418, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51562, "top5_acc": 0.76281, "loss_cls": 2.70385, "loss": 2.70385, "time": 0.82129} +{"mode": "train", "epoch": 131, "iter": 1400, "lr": 0.00417, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.52, "top5_acc": 0.76812, "loss_cls": 2.66755, "loss": 2.66755, "time": 0.82408} +{"mode": "train", "epoch": 131, "iter": 1500, "lr": 0.00415, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.50906, "top5_acc": 0.76609, "loss_cls": 2.685, "loss": 2.685, "time": 0.82479} +{"mode": "train", "epoch": 131, "iter": 1600, "lr": 0.00414, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51328, "top5_acc": 0.75828, "loss_cls": 2.69728, "loss": 2.69728, "time": 0.81639} +{"mode": "train", "epoch": 131, "iter": 1700, "lr": 0.00413, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51344, "top5_acc": 0.76469, "loss_cls": 2.67383, "loss": 2.67383, "time": 0.8197} +{"mode": "train", "epoch": 131, "iter": 1800, "lr": 0.00412, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51547, "top5_acc": 0.77234, "loss_cls": 2.6332, "loss": 2.6332, "time": 0.82029} +{"mode": "train", "epoch": 131, "iter": 1900, "lr": 0.00411, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51391, "top5_acc": 0.76109, "loss_cls": 2.70492, "loss": 2.70492, "time": 0.81497} +{"mode": "train", "epoch": 131, "iter": 2000, "lr": 0.0041, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51125, "top5_acc": 0.76453, "loss_cls": 2.71755, "loss": 2.71755, "time": 0.81831} +{"mode": "train", "epoch": 131, "iter": 2100, "lr": 0.00409, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51203, "top5_acc": 0.76312, "loss_cls": 2.70627, "loss": 2.70627, "time": 0.81261} +{"mode": "train", "epoch": 131, "iter": 2200, "lr": 0.00408, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51047, "top5_acc": 0.76344, "loss_cls": 2.70059, "loss": 2.70059, "time": 0.81904} +{"mode": "train", "epoch": 131, "iter": 2300, "lr": 0.00407, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.5125, "top5_acc": 0.76922, "loss_cls": 2.66437, "loss": 2.66437, "time": 0.81772} +{"mode": "train", "epoch": 131, "iter": 2400, "lr": 0.00405, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5075, "top5_acc": 0.76312, "loss_cls": 2.68727, "loss": 2.68727, "time": 0.81663} +{"mode": "train", "epoch": 131, "iter": 2500, "lr": 0.00404, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51297, "top5_acc": 0.77203, "loss_cls": 2.65786, "loss": 2.65786, "time": 0.81729} +{"mode": "train", "epoch": 131, "iter": 2600, "lr": 0.00403, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51422, "top5_acc": 0.76656, "loss_cls": 2.67558, "loss": 2.67558, "time": 0.82236} +{"mode": "train", "epoch": 131, "iter": 2700, "lr": 0.00402, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50969, "top5_acc": 0.76359, "loss_cls": 2.68016, "loss": 2.68016, "time": 0.81274} +{"mode": "train", "epoch": 131, "iter": 2800, "lr": 0.00401, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51734, "top5_acc": 0.76156, "loss_cls": 2.69061, "loss": 2.69061, "time": 0.821} +{"mode": "train", "epoch": 131, "iter": 2900, "lr": 0.004, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.50781, "top5_acc": 0.77141, "loss_cls": 2.66937, "loss": 2.66937, "time": 0.8115} +{"mode": "train", "epoch": 131, "iter": 3000, "lr": 0.00399, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50281, "top5_acc": 0.76547, "loss_cls": 2.68263, "loss": 2.68263, "time": 0.81793} +{"mode": "train", "epoch": 131, "iter": 3100, "lr": 0.00398, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51016, "top5_acc": 0.76203, "loss_cls": 2.69602, "loss": 2.69602, "time": 0.81305} +{"mode": "train", "epoch": 131, "iter": 3200, "lr": 0.00397, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51219, "top5_acc": 0.77094, "loss_cls": 2.67532, "loss": 2.67532, "time": 0.8173} +{"mode": "train", "epoch": 131, "iter": 3300, "lr": 0.00396, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51609, "top5_acc": 0.76484, "loss_cls": 2.69035, "loss": 2.69035, "time": 0.81183} +{"mode": "train", "epoch": 131, "iter": 3400, "lr": 0.00394, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51062, "top5_acc": 0.76094, "loss_cls": 2.71321, "loss": 2.71321, "time": 0.82111} +{"mode": "train", "epoch": 131, "iter": 3500, "lr": 0.00393, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.50984, "top5_acc": 0.75531, "loss_cls": 2.73242, "loss": 2.73242, "time": 0.81358} +{"mode": "train", "epoch": 131, "iter": 3600, "lr": 0.00392, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51828, "top5_acc": 0.76688, "loss_cls": 2.67827, "loss": 2.67827, "time": 0.81225} +{"mode": "train", "epoch": 131, "iter": 3700, "lr": 0.00391, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50531, "top5_acc": 0.76062, "loss_cls": 2.7178, "loss": 2.7178, "time": 0.81857} +{"mode": "val", "epoch": 131, "iter": 309, "lr": 0.00391, "top1_acc": 0.42496, "top5_acc": 0.68505, "mean_class_accuracy": 0.42464} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.0039, "memory": 15990, "data_time": 1.32292, "top1_acc": 0.53531, "top5_acc": 0.78594, "loss_cls": 2.54833, "loss": 2.54833, "time": 2.31355} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00389, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53922, "top5_acc": 0.78906, "loss_cls": 2.536, "loss": 2.536, "time": 0.83133} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00387, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54078, "top5_acc": 0.78281, "loss_cls": 2.5512, "loss": 2.5512, "time": 0.82452} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00386, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53391, "top5_acc": 0.78016, "loss_cls": 2.58295, "loss": 2.58295, "time": 0.82062} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00385, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54047, "top5_acc": 0.77906, "loss_cls": 2.55564, "loss": 2.55564, "time": 0.8234} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00384, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52719, "top5_acc": 0.7775, "loss_cls": 2.58407, "loss": 2.58407, "time": 0.8214} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00383, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52875, "top5_acc": 0.77359, "loss_cls": 2.61853, "loss": 2.61853, "time": 0.82064} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00382, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.52672, "top5_acc": 0.77156, "loss_cls": 2.60959, "loss": 2.60959, "time": 0.82809} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00381, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.52453, "top5_acc": 0.77172, "loss_cls": 2.62991, "loss": 2.62991, "time": 0.82529} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0038, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.51734, "top5_acc": 0.76609, "loss_cls": 2.65384, "loss": 2.65384, "time": 0.83063} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00379, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52422, "top5_acc": 0.77266, "loss_cls": 2.6372, "loss": 2.6372, "time": 0.83057} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00378, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.52391, "top5_acc": 0.76953, "loss_cls": 2.64061, "loss": 2.64061, "time": 0.8259} +{"mode": "train", "epoch": 132, "iter": 1300, "lr": 0.00377, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.52734, "top5_acc": 0.77766, "loss_cls": 2.59553, "loss": 2.59553, "time": 0.82994} +{"mode": "train", "epoch": 132, "iter": 1400, "lr": 0.00376, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52703, "top5_acc": 0.77484, "loss_cls": 2.61848, "loss": 2.61848, "time": 0.82616} +{"mode": "train", "epoch": 132, "iter": 1500, "lr": 0.00375, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51547, "top5_acc": 0.76281, "loss_cls": 2.68363, "loss": 2.68363, "time": 0.81969} +{"mode": "train", "epoch": 132, "iter": 1600, "lr": 0.00374, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51328, "top5_acc": 0.76969, "loss_cls": 2.66626, "loss": 2.66626, "time": 0.83424} +{"mode": "train", "epoch": 132, "iter": 1700, "lr": 0.00372, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53094, "top5_acc": 0.77578, "loss_cls": 2.60362, "loss": 2.60362, "time": 0.83445} +{"mode": "train", "epoch": 132, "iter": 1800, "lr": 0.00371, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52281, "top5_acc": 0.77688, "loss_cls": 2.60322, "loss": 2.60322, "time": 0.82273} +{"mode": "train", "epoch": 132, "iter": 1900, "lr": 0.0037, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.52641, "top5_acc": 0.76469, "loss_cls": 2.63354, "loss": 2.63354, "time": 0.8313} +{"mode": "train", "epoch": 132, "iter": 2000, "lr": 0.00369, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.5175, "top5_acc": 0.76328, "loss_cls": 2.66156, "loss": 2.66156, "time": 0.82392} +{"mode": "train", "epoch": 132, "iter": 2100, "lr": 0.00368, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.52141, "top5_acc": 0.77078, "loss_cls": 2.6441, "loss": 2.6441, "time": 0.82754} +{"mode": "train", "epoch": 132, "iter": 2200, "lr": 0.00367, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52234, "top5_acc": 0.76625, "loss_cls": 2.65363, "loss": 2.65363, "time": 0.83361} +{"mode": "train", "epoch": 132, "iter": 2300, "lr": 0.00366, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53109, "top5_acc": 0.77828, "loss_cls": 2.60207, "loss": 2.60207, "time": 0.83641} +{"mode": "train", "epoch": 132, "iter": 2400, "lr": 0.00365, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.51391, "top5_acc": 0.76312, "loss_cls": 2.65128, "loss": 2.65128, "time": 0.83299} +{"mode": "train", "epoch": 132, "iter": 2500, "lr": 0.00364, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52047, "top5_acc": 0.77312, "loss_cls": 2.63548, "loss": 2.63548, "time": 0.82852} +{"mode": "train", "epoch": 132, "iter": 2600, "lr": 0.00363, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.51891, "top5_acc": 0.76922, "loss_cls": 2.66627, "loss": 2.66627, "time": 0.82405} +{"mode": "train", "epoch": 132, "iter": 2700, "lr": 0.00362, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.52156, "top5_acc": 0.77266, "loss_cls": 2.63844, "loss": 2.63844, "time": 0.83024} +{"mode": "train", "epoch": 132, "iter": 2800, "lr": 0.00361, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.51719, "top5_acc": 0.76953, "loss_cls": 2.65825, "loss": 2.65825, "time": 0.8262} +{"mode": "train", "epoch": 132, "iter": 2900, "lr": 0.0036, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.52344, "top5_acc": 0.77266, "loss_cls": 2.63889, "loss": 2.63889, "time": 0.82641} +{"mode": "train", "epoch": 132, "iter": 3000, "lr": 0.00359, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52031, "top5_acc": 0.76828, "loss_cls": 2.65426, "loss": 2.65426, "time": 0.8123} +{"mode": "train", "epoch": 132, "iter": 3100, "lr": 0.00358, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51562, "top5_acc": 0.77109, "loss_cls": 2.67225, "loss": 2.67225, "time": 0.81393} +{"mode": "train", "epoch": 132, "iter": 3200, "lr": 0.00357, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51547, "top5_acc": 0.77, "loss_cls": 2.67949, "loss": 2.67949, "time": 0.81702} +{"mode": "train", "epoch": 132, "iter": 3300, "lr": 0.00356, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51484, "top5_acc": 0.76266, "loss_cls": 2.6704, "loss": 2.6704, "time": 0.81469} +{"mode": "train", "epoch": 132, "iter": 3400, "lr": 0.00355, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51047, "top5_acc": 0.76188, "loss_cls": 2.67348, "loss": 2.67348, "time": 0.81641} +{"mode": "train", "epoch": 132, "iter": 3500, "lr": 0.00354, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52688, "top5_acc": 0.775, "loss_cls": 2.62763, "loss": 2.62763, "time": 0.81658} +{"mode": "train", "epoch": 132, "iter": 3600, "lr": 0.00353, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.52375, "top5_acc": 0.77266, "loss_cls": 2.62444, "loss": 2.62444, "time": 0.82286} +{"mode": "train", "epoch": 132, "iter": 3700, "lr": 0.00352, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51938, "top5_acc": 0.7725, "loss_cls": 2.62349, "loss": 2.62349, "time": 0.82117} +{"mode": "val", "epoch": 132, "iter": 309, "lr": 0.00351, "top1_acc": 0.43494, "top5_acc": 0.68986, "mean_class_accuracy": 0.43459} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.0035, "memory": 15990, "data_time": 1.30664, "top1_acc": 0.54422, "top5_acc": 0.78875, "loss_cls": 2.49601, "loss": 2.49601, "time": 2.30268} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00349, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54609, "top5_acc": 0.79031, "loss_cls": 2.5211, "loss": 2.5211, "time": 0.83054} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00348, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53734, "top5_acc": 0.79219, "loss_cls": 2.52661, "loss": 2.52661, "time": 0.83584} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00347, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.53188, "top5_acc": 0.78672, "loss_cls": 2.556, "loss": 2.556, "time": 0.82829} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00346, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53938, "top5_acc": 0.78547, "loss_cls": 2.53971, "loss": 2.53971, "time": 0.82929} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00345, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.53859, "top5_acc": 0.78688, "loss_cls": 2.53959, "loss": 2.53959, "time": 0.83059} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00344, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.53625, "top5_acc": 0.78594, "loss_cls": 2.56335, "loss": 2.56335, "time": 0.83161} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00343, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52297, "top5_acc": 0.77781, "loss_cls": 2.58312, "loss": 2.58312, "time": 0.82017} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00342, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.53312, "top5_acc": 0.77469, "loss_cls": 2.60626, "loss": 2.60626, "time": 0.82702} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.00341, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.53484, "top5_acc": 0.78375, "loss_cls": 2.53724, "loss": 2.53724, "time": 0.83835} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0034, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.52703, "top5_acc": 0.77719, "loss_cls": 2.5943, "loss": 2.5943, "time": 0.83025} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00339, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.53078, "top5_acc": 0.78109, "loss_cls": 2.53534, "loss": 2.53534, "time": 0.83129} +{"mode": "train", "epoch": 133, "iter": 1300, "lr": 0.00338, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.53625, "top5_acc": 0.77984, "loss_cls": 2.57387, "loss": 2.57387, "time": 0.83583} +{"mode": "train", "epoch": 133, "iter": 1400, "lr": 0.00337, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.52891, "top5_acc": 0.78062, "loss_cls": 2.58766, "loss": 2.58766, "time": 0.82956} +{"mode": "train", "epoch": 133, "iter": 1500, "lr": 0.00336, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53016, "top5_acc": 0.78188, "loss_cls": 2.57609, "loss": 2.57609, "time": 0.83248} +{"mode": "train", "epoch": 133, "iter": 1600, "lr": 0.00335, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.52812, "top5_acc": 0.78422, "loss_cls": 2.57587, "loss": 2.57587, "time": 0.83693} +{"mode": "train", "epoch": 133, "iter": 1700, "lr": 0.00334, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52266, "top5_acc": 0.77297, "loss_cls": 2.6276, "loss": 2.6276, "time": 0.82394} +{"mode": "train", "epoch": 133, "iter": 1800, "lr": 0.00333, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53, "top5_acc": 0.7775, "loss_cls": 2.59541, "loss": 2.59541, "time": 0.82063} +{"mode": "train", "epoch": 133, "iter": 1900, "lr": 0.00332, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51641, "top5_acc": 0.77109, "loss_cls": 2.62458, "loss": 2.62458, "time": 0.8273} +{"mode": "train", "epoch": 133, "iter": 2000, "lr": 0.00331, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52312, "top5_acc": 0.77125, "loss_cls": 2.62593, "loss": 2.62593, "time": 0.83008} +{"mode": "train", "epoch": 133, "iter": 2100, "lr": 0.0033, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.53266, "top5_acc": 0.77828, "loss_cls": 2.5906, "loss": 2.5906, "time": 0.83303} +{"mode": "train", "epoch": 133, "iter": 2200, "lr": 0.00329, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52469, "top5_acc": 0.77547, "loss_cls": 2.59714, "loss": 2.59714, "time": 0.82883} +{"mode": "train", "epoch": 133, "iter": 2300, "lr": 0.00328, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53547, "top5_acc": 0.77891, "loss_cls": 2.59206, "loss": 2.59206, "time": 0.8269} +{"mode": "train", "epoch": 133, "iter": 2400, "lr": 0.00327, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.525, "top5_acc": 0.77969, "loss_cls": 2.59486, "loss": 2.59486, "time": 0.82523} +{"mode": "train", "epoch": 133, "iter": 2500, "lr": 0.00326, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52688, "top5_acc": 0.77219, "loss_cls": 2.6355, "loss": 2.6355, "time": 0.82294} +{"mode": "train", "epoch": 133, "iter": 2600, "lr": 0.00325, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.52797, "top5_acc": 0.77344, "loss_cls": 2.60322, "loss": 2.60322, "time": 0.82583} +{"mode": "train", "epoch": 133, "iter": 2700, "lr": 0.00324, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.52625, "top5_acc": 0.77844, "loss_cls": 2.60894, "loss": 2.60894, "time": 0.82828} +{"mode": "train", "epoch": 133, "iter": 2800, "lr": 0.00323, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54, "top5_acc": 0.77891, "loss_cls": 2.57119, "loss": 2.57119, "time": 0.8203} +{"mode": "train", "epoch": 133, "iter": 2900, "lr": 0.00322, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.525, "top5_acc": 0.77031, "loss_cls": 2.64004, "loss": 2.64004, "time": 0.82428} +{"mode": "train", "epoch": 133, "iter": 3000, "lr": 0.00321, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.52297, "top5_acc": 0.77266, "loss_cls": 2.64056, "loss": 2.64056, "time": 0.82622} +{"mode": "train", "epoch": 133, "iter": 3100, "lr": 0.0032, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.53, "top5_acc": 0.77141, "loss_cls": 2.63408, "loss": 2.63408, "time": 0.82219} +{"mode": "train", "epoch": 133, "iter": 3200, "lr": 0.00319, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.51297, "top5_acc": 0.77328, "loss_cls": 2.65886, "loss": 2.65886, "time": 0.82339} +{"mode": "train", "epoch": 133, "iter": 3300, "lr": 0.00318, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53609, "top5_acc": 0.78141, "loss_cls": 2.57417, "loss": 2.57417, "time": 0.82956} +{"mode": "train", "epoch": 133, "iter": 3400, "lr": 0.00317, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.53234, "top5_acc": 0.76688, "loss_cls": 2.63285, "loss": 2.63285, "time": 0.82735} +{"mode": "train", "epoch": 133, "iter": 3500, "lr": 0.00316, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53328, "top5_acc": 0.77562, "loss_cls": 2.61817, "loss": 2.61817, "time": 0.82722} +{"mode": "train", "epoch": 133, "iter": 3600, "lr": 0.00315, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52703, "top5_acc": 0.77281, "loss_cls": 2.61134, "loss": 2.61134, "time": 0.83716} +{"mode": "train", "epoch": 133, "iter": 3700, "lr": 0.00314, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53797, "top5_acc": 0.78641, "loss_cls": 2.56183, "loss": 2.56183, "time": 0.82385} +{"mode": "val", "epoch": 133, "iter": 309, "lr": 0.00314, "top1_acc": 0.43545, "top5_acc": 0.68966, "mean_class_accuracy": 0.4352} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00313, "memory": 15990, "data_time": 1.25996, "top1_acc": 0.55359, "top5_acc": 0.79672, "loss_cls": 2.47559, "loss": 2.47559, "time": 2.2497} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00312, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54141, "top5_acc": 0.79031, "loss_cls": 2.50615, "loss": 2.50615, "time": 0.82755} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00311, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.54594, "top5_acc": 0.79062, "loss_cls": 2.49115, "loss": 2.49115, "time": 0.82771} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.0031, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54562, "top5_acc": 0.79469, "loss_cls": 2.48601, "loss": 2.48601, "time": 0.82954} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00309, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.55, "top5_acc": 0.78625, "loss_cls": 2.52772, "loss": 2.52772, "time": 0.8275} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00308, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54859, "top5_acc": 0.80047, "loss_cls": 2.46518, "loss": 2.46518, "time": 0.82793} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00307, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54219, "top5_acc": 0.79328, "loss_cls": 2.4968, "loss": 2.4968, "time": 0.82945} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00306, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53469, "top5_acc": 0.77719, "loss_cls": 2.55629, "loss": 2.55629, "time": 0.82721} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00305, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.54094, "top5_acc": 0.79016, "loss_cls": 2.51495, "loss": 2.51495, "time": 0.8304} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00304, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55094, "top5_acc": 0.78281, "loss_cls": 2.53552, "loss": 2.53552, "time": 0.82981} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00303, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54375, "top5_acc": 0.79172, "loss_cls": 2.50305, "loss": 2.50305, "time": 0.83033} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.00302, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53922, "top5_acc": 0.78656, "loss_cls": 2.535, "loss": 2.535, "time": 0.84009} +{"mode": "train", "epoch": 134, "iter": 1300, "lr": 0.00301, "memory": 15990, "data_time": 0.00067, "top1_acc": 0.54203, "top5_acc": 0.78328, "loss_cls": 2.53746, "loss": 2.53746, "time": 0.83265} +{"mode": "train", "epoch": 134, "iter": 1400, "lr": 0.003, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54297, "top5_acc": 0.79109, "loss_cls": 2.53595, "loss": 2.53595, "time": 0.83499} +{"mode": "train", "epoch": 134, "iter": 1500, "lr": 0.00299, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.55312, "top5_acc": 0.79391, "loss_cls": 2.50195, "loss": 2.50195, "time": 0.8351} +{"mode": "train", "epoch": 134, "iter": 1600, "lr": 0.00298, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53703, "top5_acc": 0.78312, "loss_cls": 2.56048, "loss": 2.56048, "time": 0.83459} +{"mode": "train", "epoch": 134, "iter": 1700, "lr": 0.00297, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53, "top5_acc": 0.78406, "loss_cls": 2.56297, "loss": 2.56297, "time": 0.82339} +{"mode": "train", "epoch": 134, "iter": 1800, "lr": 0.00296, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53812, "top5_acc": 0.77797, "loss_cls": 2.55615, "loss": 2.55615, "time": 0.82988} +{"mode": "train", "epoch": 134, "iter": 1900, "lr": 0.00295, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54109, "top5_acc": 0.78781, "loss_cls": 2.52045, "loss": 2.52045, "time": 0.82394} +{"mode": "train", "epoch": 134, "iter": 2000, "lr": 0.00294, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.52453, "top5_acc": 0.77703, "loss_cls": 2.59472, "loss": 2.59472, "time": 0.82187} +{"mode": "train", "epoch": 134, "iter": 2100, "lr": 0.00293, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53719, "top5_acc": 0.78047, "loss_cls": 2.56695, "loss": 2.56695, "time": 0.83424} +{"mode": "train", "epoch": 134, "iter": 2200, "lr": 0.00293, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52953, "top5_acc": 0.78562, "loss_cls": 2.56984, "loss": 2.56984, "time": 0.83245} +{"mode": "train", "epoch": 134, "iter": 2300, "lr": 0.00292, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54078, "top5_acc": 0.78422, "loss_cls": 2.54704, "loss": 2.54704, "time": 0.83096} +{"mode": "train", "epoch": 134, "iter": 2400, "lr": 0.00291, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54281, "top5_acc": 0.78469, "loss_cls": 2.53843, "loss": 2.53843, "time": 0.82713} +{"mode": "train", "epoch": 134, "iter": 2500, "lr": 0.0029, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54484, "top5_acc": 0.78359, "loss_cls": 2.53515, "loss": 2.53515, "time": 0.82299} +{"mode": "train", "epoch": 134, "iter": 2600, "lr": 0.00289, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.53781, "top5_acc": 0.78438, "loss_cls": 2.55477, "loss": 2.55477, "time": 0.82112} +{"mode": "train", "epoch": 134, "iter": 2700, "lr": 0.00288, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54297, "top5_acc": 0.78125, "loss_cls": 2.51652, "loss": 2.51652, "time": 0.82672} +{"mode": "train", "epoch": 134, "iter": 2800, "lr": 0.00287, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53969, "top5_acc": 0.78328, "loss_cls": 2.54094, "loss": 2.54094, "time": 0.82181} +{"mode": "train", "epoch": 134, "iter": 2900, "lr": 0.00286, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.5275, "top5_acc": 0.78844, "loss_cls": 2.5853, "loss": 2.5853, "time": 0.82516} +{"mode": "train", "epoch": 134, "iter": 3000, "lr": 0.00285, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53547, "top5_acc": 0.77375, "loss_cls": 2.60144, "loss": 2.60144, "time": 0.82956} +{"mode": "train", "epoch": 134, "iter": 3100, "lr": 0.00284, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.5175, "top5_acc": 0.77609, "loss_cls": 2.61655, "loss": 2.61655, "time": 0.82717} +{"mode": "train", "epoch": 134, "iter": 3200, "lr": 0.00283, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.52781, "top5_acc": 0.77859, "loss_cls": 2.59977, "loss": 2.59977, "time": 0.82744} +{"mode": "train", "epoch": 134, "iter": 3300, "lr": 0.00282, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53219, "top5_acc": 0.78016, "loss_cls": 2.58164, "loss": 2.58164, "time": 0.82855} +{"mode": "train", "epoch": 134, "iter": 3400, "lr": 0.00281, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52562, "top5_acc": 0.77609, "loss_cls": 2.58888, "loss": 2.58888, "time": 0.82447} +{"mode": "train", "epoch": 134, "iter": 3500, "lr": 0.0028, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53547, "top5_acc": 0.78391, "loss_cls": 2.55631, "loss": 2.55631, "time": 0.83089} +{"mode": "train", "epoch": 134, "iter": 3600, "lr": 0.00279, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.54109, "top5_acc": 0.7875, "loss_cls": 2.5516, "loss": 2.5516, "time": 0.82541} +{"mode": "train", "epoch": 134, "iter": 3700, "lr": 0.00279, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.52922, "top5_acc": 0.77938, "loss_cls": 2.59774, "loss": 2.59774, "time": 0.82901} +{"mode": "val", "epoch": 134, "iter": 309, "lr": 0.00278, "top1_acc": 0.43879, "top5_acc": 0.69012, "mean_class_accuracy": 0.43844} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00277, "memory": 15990, "data_time": 1.26693, "top1_acc": 0.55422, "top5_acc": 0.80516, "loss_cls": 2.44462, "loss": 2.44462, "time": 2.25784} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00276, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.5625, "top5_acc": 0.80578, "loss_cls": 2.41263, "loss": 2.41263, "time": 0.82567} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00275, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.55078, "top5_acc": 0.79391, "loss_cls": 2.46758, "loss": 2.46758, "time": 0.82478} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00274, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55453, "top5_acc": 0.79594, "loss_cls": 2.48008, "loss": 2.48008, "time": 0.81965} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00274, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55109, "top5_acc": 0.78969, "loss_cls": 2.51721, "loss": 2.51721, "time": 0.82256} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00273, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56094, "top5_acc": 0.8, "loss_cls": 2.4486, "loss": 2.4486, "time": 0.81913} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00272, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55234, "top5_acc": 0.78844, "loss_cls": 2.47395, "loss": 2.47395, "time": 0.82364} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00271, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.5475, "top5_acc": 0.78984, "loss_cls": 2.4921, "loss": 2.4921, "time": 0.81632} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.0027, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54281, "top5_acc": 0.79141, "loss_cls": 2.49198, "loss": 2.49198, "time": 0.83139} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00269, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54312, "top5_acc": 0.79188, "loss_cls": 2.50889, "loss": 2.50889, "time": 0.82464} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00268, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54031, "top5_acc": 0.79703, "loss_cls": 2.48797, "loss": 2.48797, "time": 0.82881} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00267, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.55312, "top5_acc": 0.79562, "loss_cls": 2.46739, "loss": 2.46739, "time": 0.83556} +{"mode": "train", "epoch": 135, "iter": 1300, "lr": 0.00266, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.54828, "top5_acc": 0.79375, "loss_cls": 2.48908, "loss": 2.48908, "time": 0.82214} +{"mode": "train", "epoch": 135, "iter": 1400, "lr": 0.00265, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55375, "top5_acc": 0.79438, "loss_cls": 2.47434, "loss": 2.47434, "time": 0.83097} +{"mode": "train", "epoch": 135, "iter": 1500, "lr": 0.00265, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.55312, "top5_acc": 0.79906, "loss_cls": 2.46701, "loss": 2.46701, "time": 0.83458} +{"mode": "train", "epoch": 135, "iter": 1600, "lr": 0.00264, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.55359, "top5_acc": 0.79422, "loss_cls": 2.4938, "loss": 2.4938, "time": 0.83179} +{"mode": "train", "epoch": 135, "iter": 1700, "lr": 0.00263, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53906, "top5_acc": 0.79109, "loss_cls": 2.51525, "loss": 2.51525, "time": 0.82275} +{"mode": "train", "epoch": 135, "iter": 1800, "lr": 0.00262, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55609, "top5_acc": 0.79578, "loss_cls": 2.45953, "loss": 2.45953, "time": 0.83385} +{"mode": "train", "epoch": 135, "iter": 1900, "lr": 0.00261, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.53859, "top5_acc": 0.7825, "loss_cls": 2.53284, "loss": 2.53284, "time": 0.82657} +{"mode": "train", "epoch": 135, "iter": 2000, "lr": 0.0026, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.55641, "top5_acc": 0.79109, "loss_cls": 2.48095, "loss": 2.48095, "time": 0.83207} +{"mode": "train", "epoch": 135, "iter": 2100, "lr": 0.00259, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54141, "top5_acc": 0.78062, "loss_cls": 2.54863, "loss": 2.54863, "time": 0.83902} +{"mode": "train", "epoch": 135, "iter": 2200, "lr": 0.00258, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53828, "top5_acc": 0.79344, "loss_cls": 2.51546, "loss": 2.51546, "time": 0.82875} +{"mode": "train", "epoch": 135, "iter": 2300, "lr": 0.00257, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53812, "top5_acc": 0.78578, "loss_cls": 2.52727, "loss": 2.52727, "time": 0.82882} +{"mode": "train", "epoch": 135, "iter": 2400, "lr": 0.00256, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54359, "top5_acc": 0.78422, "loss_cls": 2.52251, "loss": 2.52251, "time": 0.8239} +{"mode": "train", "epoch": 135, "iter": 2500, "lr": 0.00256, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.545, "top5_acc": 0.79375, "loss_cls": 2.4946, "loss": 2.4946, "time": 0.82223} +{"mode": "train", "epoch": 135, "iter": 2600, "lr": 0.00255, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54562, "top5_acc": 0.79, "loss_cls": 2.52608, "loss": 2.52608, "time": 0.82349} +{"mode": "train", "epoch": 135, "iter": 2700, "lr": 0.00254, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.5425, "top5_acc": 0.78844, "loss_cls": 2.52559, "loss": 2.52559, "time": 0.82695} +{"mode": "train", "epoch": 135, "iter": 2800, "lr": 0.00253, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54359, "top5_acc": 0.79422, "loss_cls": 2.51152, "loss": 2.51152, "time": 0.8287} +{"mode": "train", "epoch": 135, "iter": 2900, "lr": 0.00252, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.53594, "top5_acc": 0.78422, "loss_cls": 2.57328, "loss": 2.57328, "time": 0.82743} +{"mode": "train", "epoch": 135, "iter": 3000, "lr": 0.00251, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.54984, "top5_acc": 0.79578, "loss_cls": 2.4883, "loss": 2.4883, "time": 0.82742} +{"mode": "train", "epoch": 135, "iter": 3100, "lr": 0.0025, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54406, "top5_acc": 0.79938, "loss_cls": 2.46822, "loss": 2.46822, "time": 0.82519} +{"mode": "train", "epoch": 135, "iter": 3200, "lr": 0.00249, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53609, "top5_acc": 0.785, "loss_cls": 2.55834, "loss": 2.55834, "time": 0.82358} +{"mode": "train", "epoch": 135, "iter": 3300, "lr": 0.00249, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54062, "top5_acc": 0.78406, "loss_cls": 2.54072, "loss": 2.54072, "time": 0.83472} +{"mode": "train", "epoch": 135, "iter": 3400, "lr": 0.00248, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54625, "top5_acc": 0.78453, "loss_cls": 2.51849, "loss": 2.51849, "time": 0.82091} +{"mode": "train", "epoch": 135, "iter": 3500, "lr": 0.00247, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.54641, "top5_acc": 0.78938, "loss_cls": 2.49041, "loss": 2.49041, "time": 0.83136} +{"mode": "train", "epoch": 135, "iter": 3600, "lr": 0.00246, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55766, "top5_acc": 0.79766, "loss_cls": 2.46401, "loss": 2.46401, "time": 0.82702} +{"mode": "train", "epoch": 135, "iter": 3700, "lr": 0.00245, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.52953, "top5_acc": 0.77781, "loss_cls": 2.57597, "loss": 2.57597, "time": 0.82518} +{"mode": "val", "epoch": 135, "iter": 309, "lr": 0.00245, "top1_acc": 0.43929, "top5_acc": 0.69437, "mean_class_accuracy": 0.43897} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00244, "memory": 15990, "data_time": 1.27146, "top1_acc": 0.57578, "top5_acc": 0.80938, "loss_cls": 2.37851, "loss": 2.37851, "time": 2.26717} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.00243, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.57016, "top5_acc": 0.80156, "loss_cls": 2.40737, "loss": 2.40737, "time": 0.83299} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00242, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.56969, "top5_acc": 0.80891, "loss_cls": 2.37522, "loss": 2.37522, "time": 0.82365} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00241, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.55734, "top5_acc": 0.80531, "loss_cls": 2.42934, "loss": 2.42934, "time": 0.82404} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.0024, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56203, "top5_acc": 0.80875, "loss_cls": 2.39624, "loss": 2.39624, "time": 0.81568} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.0024, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.55328, "top5_acc": 0.80297, "loss_cls": 2.44544, "loss": 2.44544, "time": 0.82117} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00239, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56984, "top5_acc": 0.80516, "loss_cls": 2.39447, "loss": 2.39447, "time": 0.82079} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00238, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57109, "top5_acc": 0.80953, "loss_cls": 2.37445, "loss": 2.37445, "time": 0.82733} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00237, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.56609, "top5_acc": 0.80922, "loss_cls": 2.39968, "loss": 2.39968, "time": 0.81683} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00236, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55562, "top5_acc": 0.80297, "loss_cls": 2.42109, "loss": 2.42109, "time": 0.81895} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00235, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55547, "top5_acc": 0.80453, "loss_cls": 2.42923, "loss": 2.42923, "time": 0.82272} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00234, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54594, "top5_acc": 0.79078, "loss_cls": 2.48872, "loss": 2.48872, "time": 0.82185} +{"mode": "train", "epoch": 136, "iter": 1300, "lr": 0.00234, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55375, "top5_acc": 0.79578, "loss_cls": 2.43278, "loss": 2.43278, "time": 0.81903} +{"mode": "train", "epoch": 136, "iter": 1400, "lr": 0.00233, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56063, "top5_acc": 0.79828, "loss_cls": 2.43282, "loss": 2.43282, "time": 0.81856} +{"mode": "train", "epoch": 136, "iter": 1500, "lr": 0.00232, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55969, "top5_acc": 0.79656, "loss_cls": 2.46393, "loss": 2.46393, "time": 0.81445} +{"mode": "train", "epoch": 136, "iter": 1600, "lr": 0.00231, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55391, "top5_acc": 0.80094, "loss_cls": 2.44621, "loss": 2.44621, "time": 0.8097} +{"mode": "train", "epoch": 136, "iter": 1700, "lr": 0.0023, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55641, "top5_acc": 0.79484, "loss_cls": 2.44278, "loss": 2.44278, "time": 0.81876} +{"mode": "train", "epoch": 136, "iter": 1800, "lr": 0.00229, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54734, "top5_acc": 0.79469, "loss_cls": 2.47426, "loss": 2.47426, "time": 0.81138} +{"mode": "train", "epoch": 136, "iter": 1900, "lr": 0.00229, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.55109, "top5_acc": 0.79703, "loss_cls": 2.46972, "loss": 2.46972, "time": 0.82259} +{"mode": "train", "epoch": 136, "iter": 2000, "lr": 0.00228, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55844, "top5_acc": 0.8, "loss_cls": 2.43139, "loss": 2.43139, "time": 0.81878} +{"mode": "train", "epoch": 136, "iter": 2100, "lr": 0.00227, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55406, "top5_acc": 0.80281, "loss_cls": 2.43566, "loss": 2.43566, "time": 0.81887} +{"mode": "train", "epoch": 136, "iter": 2200, "lr": 0.00226, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55641, "top5_acc": 0.79516, "loss_cls": 2.43753, "loss": 2.43753, "time": 0.81652} +{"mode": "train", "epoch": 136, "iter": 2300, "lr": 0.00225, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56141, "top5_acc": 0.80094, "loss_cls": 2.44828, "loss": 2.44828, "time": 0.81871} +{"mode": "train", "epoch": 136, "iter": 2400, "lr": 0.00224, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55188, "top5_acc": 0.79219, "loss_cls": 2.50179, "loss": 2.50179, "time": 0.81416} +{"mode": "train", "epoch": 136, "iter": 2500, "lr": 0.00224, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54875, "top5_acc": 0.79531, "loss_cls": 2.45212, "loss": 2.45212, "time": 0.81835} +{"mode": "train", "epoch": 136, "iter": 2600, "lr": 0.00223, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55516, "top5_acc": 0.79234, "loss_cls": 2.46929, "loss": 2.46929, "time": 0.81276} +{"mode": "train", "epoch": 136, "iter": 2700, "lr": 0.00222, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54453, "top5_acc": 0.78656, "loss_cls": 2.50418, "loss": 2.50418, "time": 0.81729} +{"mode": "train", "epoch": 136, "iter": 2800, "lr": 0.00221, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55016, "top5_acc": 0.79156, "loss_cls": 2.47275, "loss": 2.47275, "time": 0.82223} +{"mode": "train", "epoch": 136, "iter": 2900, "lr": 0.0022, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54547, "top5_acc": 0.79047, "loss_cls": 2.50345, "loss": 2.50345, "time": 0.81641} +{"mode": "train", "epoch": 136, "iter": 3000, "lr": 0.00219, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54984, "top5_acc": 0.7925, "loss_cls": 2.48202, "loss": 2.48202, "time": 0.8146} +{"mode": "train", "epoch": 136, "iter": 3100, "lr": 0.00219, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55125, "top5_acc": 0.78938, "loss_cls": 2.482, "loss": 2.482, "time": 0.81012} +{"mode": "train", "epoch": 136, "iter": 3200, "lr": 0.00218, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55016, "top5_acc": 0.79031, "loss_cls": 2.47036, "loss": 2.47036, "time": 0.82211} +{"mode": "train", "epoch": 136, "iter": 3300, "lr": 0.00217, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54891, "top5_acc": 0.79281, "loss_cls": 2.4971, "loss": 2.4971, "time": 0.81389} +{"mode": "train", "epoch": 136, "iter": 3400, "lr": 0.00216, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54469, "top5_acc": 0.78938, "loss_cls": 2.50923, "loss": 2.50923, "time": 0.81893} +{"mode": "train", "epoch": 136, "iter": 3500, "lr": 0.00215, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55656, "top5_acc": 0.79906, "loss_cls": 2.44629, "loss": 2.44629, "time": 0.8304} +{"mode": "train", "epoch": 136, "iter": 3600, "lr": 0.00215, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55359, "top5_acc": 0.79062, "loss_cls": 2.48539, "loss": 2.48539, "time": 0.82152} +{"mode": "train", "epoch": 136, "iter": 3700, "lr": 0.00214, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55797, "top5_acc": 0.79172, "loss_cls": 2.47856, "loss": 2.47856, "time": 0.82753} +{"mode": "val", "epoch": 136, "iter": 309, "lr": 0.00213, "top1_acc": 0.44203, "top5_acc": 0.69422, "mean_class_accuracy": 0.44172} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00213, "memory": 15990, "data_time": 1.32307, "top1_acc": 0.58062, "top5_acc": 0.81812, "loss_cls": 2.31752, "loss": 2.31752, "time": 2.319} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00212, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.5825, "top5_acc": 0.81562, "loss_cls": 2.31548, "loss": 2.31548, "time": 0.83343} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00211, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.56484, "top5_acc": 0.80984, "loss_cls": 2.37486, "loss": 2.37486, "time": 0.82821} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.0021, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56469, "top5_acc": 0.80703, "loss_cls": 2.3895, "loss": 2.3895, "time": 0.82985} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.00209, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.56391, "top5_acc": 0.8075, "loss_cls": 2.39937, "loss": 2.39937, "time": 0.82376} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.00209, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55594, "top5_acc": 0.80266, "loss_cls": 2.43364, "loss": 2.43364, "time": 0.82684} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00208, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.57047, "top5_acc": 0.80062, "loss_cls": 2.40603, "loss": 2.40603, "time": 0.82583} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00207, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.56266, "top5_acc": 0.80531, "loss_cls": 2.36525, "loss": 2.36525, "time": 0.82922} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00206, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.56, "top5_acc": 0.80641, "loss_cls": 2.38819, "loss": 2.38819, "time": 0.82253} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00205, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.57391, "top5_acc": 0.81453, "loss_cls": 2.33434, "loss": 2.33434, "time": 0.82298} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00205, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57406, "top5_acc": 0.80938, "loss_cls": 2.36891, "loss": 2.36891, "time": 0.83848} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00204, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.57266, "top5_acc": 0.80719, "loss_cls": 2.37447, "loss": 2.37447, "time": 0.82859} +{"mode": "train", "epoch": 137, "iter": 1300, "lr": 0.00203, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.57125, "top5_acc": 0.80703, "loss_cls": 2.39578, "loss": 2.39578, "time": 0.83083} +{"mode": "train", "epoch": 137, "iter": 1400, "lr": 0.00202, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.555, "top5_acc": 0.80297, "loss_cls": 2.45627, "loss": 2.45627, "time": 0.83588} +{"mode": "train", "epoch": 137, "iter": 1500, "lr": 0.00201, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.56094, "top5_acc": 0.80219, "loss_cls": 2.42338, "loss": 2.42338, "time": 0.83402} +{"mode": "train", "epoch": 137, "iter": 1600, "lr": 0.00201, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.5675, "top5_acc": 0.80719, "loss_cls": 2.40192, "loss": 2.40192, "time": 0.82917} +{"mode": "train", "epoch": 137, "iter": 1700, "lr": 0.002, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56109, "top5_acc": 0.80484, "loss_cls": 2.41656, "loss": 2.41656, "time": 0.82297} +{"mode": "train", "epoch": 137, "iter": 1800, "lr": 0.00199, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.56828, "top5_acc": 0.80188, "loss_cls": 2.40148, "loss": 2.40148, "time": 0.82123} +{"mode": "train", "epoch": 137, "iter": 1900, "lr": 0.00198, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55859, "top5_acc": 0.80562, "loss_cls": 2.4161, "loss": 2.4161, "time": 0.82887} +{"mode": "train", "epoch": 137, "iter": 2000, "lr": 0.00198, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56516, "top5_acc": 0.80562, "loss_cls": 2.37646, "loss": 2.37646, "time": 0.83278} +{"mode": "train", "epoch": 137, "iter": 2100, "lr": 0.00197, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55359, "top5_acc": 0.80422, "loss_cls": 2.4082, "loss": 2.4082, "time": 0.83352} +{"mode": "train", "epoch": 137, "iter": 2200, "lr": 0.00196, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.56563, "top5_acc": 0.80062, "loss_cls": 2.41703, "loss": 2.41703, "time": 0.82729} +{"mode": "train", "epoch": 137, "iter": 2300, "lr": 0.00195, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55453, "top5_acc": 0.80312, "loss_cls": 2.42744, "loss": 2.42744, "time": 0.82708} +{"mode": "train", "epoch": 137, "iter": 2400, "lr": 0.00194, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56094, "top5_acc": 0.80547, "loss_cls": 2.40582, "loss": 2.40582, "time": 0.82309} +{"mode": "train", "epoch": 137, "iter": 2500, "lr": 0.00194, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55172, "top5_acc": 0.79312, "loss_cls": 2.46797, "loss": 2.46797, "time": 0.82165} +{"mode": "train", "epoch": 137, "iter": 2600, "lr": 0.00193, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54906, "top5_acc": 0.79875, "loss_cls": 2.45715, "loss": 2.45715, "time": 0.82001} +{"mode": "train", "epoch": 137, "iter": 2700, "lr": 0.00192, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.56703, "top5_acc": 0.80594, "loss_cls": 2.40933, "loss": 2.40933, "time": 0.82627} +{"mode": "train", "epoch": 137, "iter": 2800, "lr": 0.00191, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.56656, "top5_acc": 0.8025, "loss_cls": 2.40432, "loss": 2.40432, "time": 0.8247} +{"mode": "train", "epoch": 137, "iter": 2900, "lr": 0.00191, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.56453, "top5_acc": 0.80547, "loss_cls": 2.39323, "loss": 2.39323, "time": 0.83023} +{"mode": "train", "epoch": 137, "iter": 3000, "lr": 0.0019, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56625, "top5_acc": 0.80703, "loss_cls": 2.37918, "loss": 2.37918, "time": 0.82424} +{"mode": "train", "epoch": 137, "iter": 3100, "lr": 0.00189, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.555, "top5_acc": 0.80062, "loss_cls": 2.44948, "loss": 2.44948, "time": 0.82592} +{"mode": "train", "epoch": 137, "iter": 3200, "lr": 0.00188, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.56422, "top5_acc": 0.79859, "loss_cls": 2.4437, "loss": 2.4437, "time": 0.82404} +{"mode": "train", "epoch": 137, "iter": 3300, "lr": 0.00188, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55547, "top5_acc": 0.80438, "loss_cls": 2.4363, "loss": 2.4363, "time": 0.82507} +{"mode": "train", "epoch": 137, "iter": 3400, "lr": 0.00187, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.55891, "top5_acc": 0.79625, "loss_cls": 2.43632, "loss": 2.43632, "time": 0.83081} +{"mode": "train", "epoch": 137, "iter": 3500, "lr": 0.00186, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55812, "top5_acc": 0.79547, "loss_cls": 2.43695, "loss": 2.43695, "time": 0.8284} +{"mode": "train", "epoch": 137, "iter": 3600, "lr": 0.00185, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.56156, "top5_acc": 0.80078, "loss_cls": 2.43502, "loss": 2.43502, "time": 0.82661} +{"mode": "train", "epoch": 137, "iter": 3700, "lr": 0.00185, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55125, "top5_acc": 0.7975, "loss_cls": 2.46211, "loss": 2.46211, "time": 0.82853} +{"mode": "val", "epoch": 137, "iter": 309, "lr": 0.00184, "top1_acc": 0.44532, "top5_acc": 0.69898, "mean_class_accuracy": 0.44505} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00183, "memory": 15990, "data_time": 1.29604, "top1_acc": 0.57109, "top5_acc": 0.81422, "loss_cls": 2.33791, "loss": 2.33791, "time": 2.2796} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00183, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.58406, "top5_acc": 0.82234, "loss_cls": 2.28361, "loss": 2.28361, "time": 0.83152} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00182, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.56484, "top5_acc": 0.81406, "loss_cls": 2.37104, "loss": 2.37104, "time": 0.82838} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00181, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.58438, "top5_acc": 0.81969, "loss_cls": 2.28428, "loss": 2.28428, "time": 0.83206} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.0018, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57672, "top5_acc": 0.82203, "loss_cls": 2.33618, "loss": 2.33618, "time": 0.83296} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.0018, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.5825, "top5_acc": 0.82312, "loss_cls": 2.26912, "loss": 2.26912, "time": 0.82891} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00179, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.58219, "top5_acc": 0.82109, "loss_cls": 2.32252, "loss": 2.32252, "time": 0.82473} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00178, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.57625, "top5_acc": 0.81156, "loss_cls": 2.35634, "loss": 2.35634, "time": 0.83884} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00177, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.565, "top5_acc": 0.80859, "loss_cls": 2.36842, "loss": 2.36842, "time": 0.82737} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00177, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57328, "top5_acc": 0.82094, "loss_cls": 2.33323, "loss": 2.33323, "time": 0.83554} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.00176, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.57391, "top5_acc": 0.81391, "loss_cls": 2.32373, "loss": 2.32373, "time": 0.83591} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.00175, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57375, "top5_acc": 0.81734, "loss_cls": 2.33466, "loss": 2.33466, "time": 0.8255} +{"mode": "train", "epoch": 138, "iter": 1300, "lr": 0.00175, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.57266, "top5_acc": 0.81047, "loss_cls": 2.34823, "loss": 2.34823, "time": 0.83189} +{"mode": "train", "epoch": 138, "iter": 1400, "lr": 0.00174, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.57422, "top5_acc": 0.81172, "loss_cls": 2.34894, "loss": 2.34894, "time": 0.83368} +{"mode": "train", "epoch": 138, "iter": 1500, "lr": 0.00173, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57078, "top5_acc": 0.8125, "loss_cls": 2.35114, "loss": 2.35114, "time": 0.82616} +{"mode": "train", "epoch": 138, "iter": 1600, "lr": 0.00172, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58422, "top5_acc": 0.81703, "loss_cls": 2.32409, "loss": 2.32409, "time": 0.82957} +{"mode": "train", "epoch": 138, "iter": 1700, "lr": 0.00172, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57641, "top5_acc": 0.81172, "loss_cls": 2.35468, "loss": 2.35468, "time": 0.81943} +{"mode": "train", "epoch": 138, "iter": 1800, "lr": 0.00171, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57656, "top5_acc": 0.80844, "loss_cls": 2.3519, "loss": 2.3519, "time": 0.82279} +{"mode": "train", "epoch": 138, "iter": 1900, "lr": 0.0017, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56719, "top5_acc": 0.805, "loss_cls": 2.39298, "loss": 2.39298, "time": 0.8303} +{"mode": "train", "epoch": 138, "iter": 2000, "lr": 0.00169, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56875, "top5_acc": 0.80578, "loss_cls": 2.37906, "loss": 2.37906, "time": 0.8371} +{"mode": "train", "epoch": 138, "iter": 2100, "lr": 0.00169, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.57984, "top5_acc": 0.81953, "loss_cls": 2.34281, "loss": 2.34281, "time": 0.83177} +{"mode": "train", "epoch": 138, "iter": 2200, "lr": 0.00168, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56625, "top5_acc": 0.80953, "loss_cls": 2.38348, "loss": 2.38348, "time": 0.8256} +{"mode": "train", "epoch": 138, "iter": 2300, "lr": 0.00167, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.57109, "top5_acc": 0.80875, "loss_cls": 2.36787, "loss": 2.36787, "time": 0.82776} +{"mode": "train", "epoch": 138, "iter": 2400, "lr": 0.00167, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.58, "top5_acc": 0.80906, "loss_cls": 2.35593, "loss": 2.35593, "time": 0.83128} +{"mode": "train", "epoch": 138, "iter": 2500, "lr": 0.00166, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56891, "top5_acc": 0.80078, "loss_cls": 2.40406, "loss": 2.40406, "time": 0.8195} +{"mode": "train", "epoch": 138, "iter": 2600, "lr": 0.00165, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57797, "top5_acc": 0.81672, "loss_cls": 2.34395, "loss": 2.34395, "time": 0.82545} +{"mode": "train", "epoch": 138, "iter": 2700, "lr": 0.00164, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.56984, "top5_acc": 0.8075, "loss_cls": 2.35979, "loss": 2.35979, "time": 0.83246} +{"mode": "train", "epoch": 138, "iter": 2800, "lr": 0.00164, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56266, "top5_acc": 0.80641, "loss_cls": 2.38443, "loss": 2.38443, "time": 0.82777} +{"mode": "train", "epoch": 138, "iter": 2900, "lr": 0.00163, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.56719, "top5_acc": 0.80688, "loss_cls": 2.37389, "loss": 2.37389, "time": 0.82535} +{"mode": "train", "epoch": 138, "iter": 3000, "lr": 0.00162, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56781, "top5_acc": 0.79969, "loss_cls": 2.39747, "loss": 2.39747, "time": 0.83112} +{"mode": "train", "epoch": 138, "iter": 3100, "lr": 0.00162, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.56641, "top5_acc": 0.80875, "loss_cls": 2.36653, "loss": 2.36653, "time": 0.82852} +{"mode": "train", "epoch": 138, "iter": 3200, "lr": 0.00161, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56781, "top5_acc": 0.79984, "loss_cls": 2.38039, "loss": 2.38039, "time": 0.82177} +{"mode": "train", "epoch": 138, "iter": 3300, "lr": 0.0016, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.58219, "top5_acc": 0.81359, "loss_cls": 2.30836, "loss": 2.30836, "time": 0.82631} +{"mode": "train", "epoch": 138, "iter": 3400, "lr": 0.0016, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.56328, "top5_acc": 0.80938, "loss_cls": 2.37322, "loss": 2.37322, "time": 0.83477} +{"mode": "train", "epoch": 138, "iter": 3500, "lr": 0.00159, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.56437, "top5_acc": 0.80375, "loss_cls": 2.40164, "loss": 2.40164, "time": 0.83043} +{"mode": "train", "epoch": 138, "iter": 3600, "lr": 0.00158, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56797, "top5_acc": 0.80156, "loss_cls": 2.39383, "loss": 2.39383, "time": 0.82695} +{"mode": "train", "epoch": 138, "iter": 3700, "lr": 0.00157, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.57234, "top5_acc": 0.80484, "loss_cls": 2.37202, "loss": 2.37202, "time": 0.83445} +{"mode": "val", "epoch": 138, "iter": 309, "lr": 0.00157, "top1_acc": 0.45327, "top5_acc": 0.71013, "mean_class_accuracy": 0.45303} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00156, "memory": 15990, "data_time": 1.28122, "top1_acc": 0.60031, "top5_acc": 0.82391, "loss_cls": 2.24656, "loss": 2.24656, "time": 2.26636} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00156, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.58781, "top5_acc": 0.81984, "loss_cls": 2.26805, "loss": 2.26805, "time": 0.82223} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00155, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.59469, "top5_acc": 0.82438, "loss_cls": 2.23338, "loss": 2.23338, "time": 0.82469} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00154, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58438, "top5_acc": 0.82078, "loss_cls": 2.31006, "loss": 2.31006, "time": 0.81858} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00154, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58656, "top5_acc": 0.82047, "loss_cls": 2.26097, "loss": 2.26097, "time": 0.82241} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00153, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.58922, "top5_acc": 0.82656, "loss_cls": 2.2664, "loss": 2.2664, "time": 0.81422} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00152, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.59984, "top5_acc": 0.82891, "loss_cls": 2.24333, "loss": 2.24333, "time": 0.82795} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00152, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58219, "top5_acc": 0.82516, "loss_cls": 2.27416, "loss": 2.27416, "time": 0.82985} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00151, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58516, "top5_acc": 0.81734, "loss_cls": 2.31968, "loss": 2.31968, "time": 0.82176} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.0015, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.59328, "top5_acc": 0.83016, "loss_cls": 2.24492, "loss": 2.24492, "time": 0.83313} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.0015, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.58312, "top5_acc": 0.82109, "loss_cls": 2.27994, "loss": 2.27994, "time": 0.8269} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00149, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.58391, "top5_acc": 0.82094, "loss_cls": 2.28045, "loss": 2.28045, "time": 0.82475} +{"mode": "train", "epoch": 139, "iter": 1300, "lr": 0.00148, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.57828, "top5_acc": 0.80984, "loss_cls": 2.33066, "loss": 2.33066, "time": 0.83342} +{"mode": "train", "epoch": 139, "iter": 1400, "lr": 0.00148, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58625, "top5_acc": 0.82141, "loss_cls": 2.27252, "loss": 2.27252, "time": 0.83268} +{"mode": "train", "epoch": 139, "iter": 1500, "lr": 0.00147, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58375, "top5_acc": 0.81141, "loss_cls": 2.31608, "loss": 2.31608, "time": 0.82424} +{"mode": "train", "epoch": 139, "iter": 1600, "lr": 0.00146, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58016, "top5_acc": 0.81375, "loss_cls": 2.34026, "loss": 2.34026, "time": 0.82945} +{"mode": "train", "epoch": 139, "iter": 1700, "lr": 0.00145, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.58609, "top5_acc": 0.82344, "loss_cls": 2.25921, "loss": 2.25921, "time": 0.82332} +{"mode": "train", "epoch": 139, "iter": 1800, "lr": 0.00145, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.57828, "top5_acc": 0.81328, "loss_cls": 2.33049, "loss": 2.33049, "time": 0.83151} +{"mode": "train", "epoch": 139, "iter": 1900, "lr": 0.00144, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.59016, "top5_acc": 0.8125, "loss_cls": 2.30006, "loss": 2.30006, "time": 0.83734} +{"mode": "train", "epoch": 139, "iter": 2000, "lr": 0.00143, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58672, "top5_acc": 0.8175, "loss_cls": 2.30305, "loss": 2.30305, "time": 0.83165} +{"mode": "train", "epoch": 139, "iter": 2100, "lr": 0.00143, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58578, "top5_acc": 0.81672, "loss_cls": 2.31271, "loss": 2.31271, "time": 0.82604} +{"mode": "train", "epoch": 139, "iter": 2200, "lr": 0.00142, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.58078, "top5_acc": 0.82219, "loss_cls": 2.27913, "loss": 2.27913, "time": 0.82295} +{"mode": "train", "epoch": 139, "iter": 2300, "lr": 0.00142, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.58219, "top5_acc": 0.81062, "loss_cls": 2.32108, "loss": 2.32108, "time": 0.82776} +{"mode": "train", "epoch": 139, "iter": 2400, "lr": 0.00141, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.59094, "top5_acc": 0.8225, "loss_cls": 2.25252, "loss": 2.25252, "time": 0.82632} +{"mode": "train", "epoch": 139, "iter": 2500, "lr": 0.0014, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57656, "top5_acc": 0.81391, "loss_cls": 2.33203, "loss": 2.33203, "time": 0.82753} +{"mode": "train", "epoch": 139, "iter": 2600, "lr": 0.0014, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.5825, "top5_acc": 0.81688, "loss_cls": 2.29728, "loss": 2.29728, "time": 0.8291} +{"mode": "train", "epoch": 139, "iter": 2700, "lr": 0.00139, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57984, "top5_acc": 0.81375, "loss_cls": 2.30907, "loss": 2.30907, "time": 0.83235} +{"mode": "train", "epoch": 139, "iter": 2800, "lr": 0.00138, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.58656, "top5_acc": 0.81859, "loss_cls": 2.29784, "loss": 2.29784, "time": 0.82625} +{"mode": "train", "epoch": 139, "iter": 2900, "lr": 0.00138, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.58078, "top5_acc": 0.82078, "loss_cls": 2.31736, "loss": 2.31736, "time": 0.82434} +{"mode": "train", "epoch": 139, "iter": 3000, "lr": 0.00137, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.58281, "top5_acc": 0.81375, "loss_cls": 2.31595, "loss": 2.31595, "time": 0.83061} +{"mode": "train", "epoch": 139, "iter": 3100, "lr": 0.00136, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.57438, "top5_acc": 0.80797, "loss_cls": 2.36021, "loss": 2.36021, "time": 0.8187} +{"mode": "train", "epoch": 139, "iter": 3200, "lr": 0.00136, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.57328, "top5_acc": 0.81938, "loss_cls": 2.34222, "loss": 2.34222, "time": 0.81737} +{"mode": "train", "epoch": 139, "iter": 3300, "lr": 0.00135, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.59531, "top5_acc": 0.82234, "loss_cls": 2.24397, "loss": 2.24397, "time": 0.822} +{"mode": "train", "epoch": 139, "iter": 3400, "lr": 0.00134, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.575, "top5_acc": 0.81391, "loss_cls": 2.34182, "loss": 2.34182, "time": 0.81251} +{"mode": "train", "epoch": 139, "iter": 3500, "lr": 0.00134, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.57906, "top5_acc": 0.81578, "loss_cls": 2.30336, "loss": 2.30336, "time": 0.82041} +{"mode": "train", "epoch": 139, "iter": 3600, "lr": 0.00133, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.57312, "top5_acc": 0.81031, "loss_cls": 2.35184, "loss": 2.35184, "time": 0.8209} +{"mode": "train", "epoch": 139, "iter": 3700, "lr": 0.00132, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58062, "top5_acc": 0.81875, "loss_cls": 2.30443, "loss": 2.30443, "time": 0.81383} +{"mode": "val", "epoch": 139, "iter": 309, "lr": 0.00132, "top1_acc": 0.4554, "top5_acc": 0.7083, "mean_class_accuracy": 0.45514} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00131, "memory": 15990, "data_time": 1.30953, "top1_acc": 0.61703, "top5_acc": 0.83547, "loss_cls": 2.1723, "loss": 2.1723, "time": 2.30076} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00131, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.59922, "top5_acc": 0.83547, "loss_cls": 2.1872, "loss": 2.1872, "time": 0.82607} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.0013, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58891, "top5_acc": 0.83, "loss_cls": 2.23955, "loss": 2.23955, "time": 0.82284} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.0013, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.60672, "top5_acc": 0.83828, "loss_cls": 2.18984, "loss": 2.18984, "time": 0.82352} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00129, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.60078, "top5_acc": 0.83141, "loss_cls": 2.20574, "loss": 2.20574, "time": 0.82539} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.00128, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59266, "top5_acc": 0.82688, "loss_cls": 2.24946, "loss": 2.24946, "time": 0.82092} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.00128, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.59531, "top5_acc": 0.82234, "loss_cls": 2.25367, "loss": 2.25367, "time": 0.83268} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00127, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.59672, "top5_acc": 0.83469, "loss_cls": 2.19483, "loss": 2.19483, "time": 0.82657} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00126, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60641, "top5_acc": 0.82594, "loss_cls": 2.22617, "loss": 2.22617, "time": 0.82836} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00126, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.60469, "top5_acc": 0.83016, "loss_cls": 2.19798, "loss": 2.19798, "time": 0.83946} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00125, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.6075, "top5_acc": 0.82969, "loss_cls": 2.20168, "loss": 2.20168, "time": 0.82694} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00125, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59812, "top5_acc": 0.81938, "loss_cls": 2.24871, "loss": 2.24871, "time": 0.83225} +{"mode": "train", "epoch": 140, "iter": 1300, "lr": 0.00124, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58234, "top5_acc": 0.81891, "loss_cls": 2.28506, "loss": 2.28506, "time": 0.83165} +{"mode": "train", "epoch": 140, "iter": 1400, "lr": 0.00123, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.59188, "top5_acc": 0.82109, "loss_cls": 2.23122, "loss": 2.23122, "time": 0.82761} +{"mode": "train", "epoch": 140, "iter": 1500, "lr": 0.00123, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.59094, "top5_acc": 0.82234, "loss_cls": 2.26358, "loss": 2.26358, "time": 0.83522} +{"mode": "train", "epoch": 140, "iter": 1600, "lr": 0.00122, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.59938, "top5_acc": 0.82828, "loss_cls": 2.21467, "loss": 2.21467, "time": 0.83226} +{"mode": "train", "epoch": 140, "iter": 1700, "lr": 0.00121, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.59312, "top5_acc": 0.82469, "loss_cls": 2.2382, "loss": 2.2382, "time": 0.82778} +{"mode": "train", "epoch": 140, "iter": 1800, "lr": 0.00121, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.5925, "top5_acc": 0.82172, "loss_cls": 2.25086, "loss": 2.25086, "time": 0.83422} +{"mode": "train", "epoch": 140, "iter": 1900, "lr": 0.0012, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.6, "top5_acc": 0.82391, "loss_cls": 2.21504, "loss": 2.21504, "time": 0.82621} +{"mode": "train", "epoch": 140, "iter": 2000, "lr": 0.0012, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.59047, "top5_acc": 0.82562, "loss_cls": 2.25288, "loss": 2.25288, "time": 0.83149} +{"mode": "train", "epoch": 140, "iter": 2100, "lr": 0.00119, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59734, "top5_acc": 0.82328, "loss_cls": 2.24649, "loss": 2.24649, "time": 0.83048} +{"mode": "train", "epoch": 140, "iter": 2200, "lr": 0.00118, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.595, "top5_acc": 0.82531, "loss_cls": 2.247, "loss": 2.247, "time": 0.82836} +{"mode": "train", "epoch": 140, "iter": 2300, "lr": 0.00118, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.58328, "top5_acc": 0.82656, "loss_cls": 2.27626, "loss": 2.27626, "time": 0.82783} +{"mode": "train", "epoch": 140, "iter": 2400, "lr": 0.00117, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58844, "top5_acc": 0.82219, "loss_cls": 2.25592, "loss": 2.25592, "time": 0.82358} +{"mode": "train", "epoch": 140, "iter": 2500, "lr": 0.00117, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.60828, "top5_acc": 0.83625, "loss_cls": 2.18896, "loss": 2.18896, "time": 0.81858} +{"mode": "train", "epoch": 140, "iter": 2600, "lr": 0.00116, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.5825, "top5_acc": 0.81812, "loss_cls": 2.30556, "loss": 2.30556, "time": 0.81864} +{"mode": "train", "epoch": 140, "iter": 2700, "lr": 0.00115, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58984, "top5_acc": 0.82438, "loss_cls": 2.26805, "loss": 2.26805, "time": 0.8201} +{"mode": "train", "epoch": 140, "iter": 2800, "lr": 0.00115, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58125, "top5_acc": 0.82125, "loss_cls": 2.29948, "loss": 2.29948, "time": 0.82254} +{"mode": "train", "epoch": 140, "iter": 2900, "lr": 0.00114, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.59062, "top5_acc": 0.82484, "loss_cls": 2.26143, "loss": 2.26143, "time": 0.82919} +{"mode": "train", "epoch": 140, "iter": 3000, "lr": 0.00114, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58047, "top5_acc": 0.82, "loss_cls": 2.30773, "loss": 2.30773, "time": 0.82099} +{"mode": "train", "epoch": 140, "iter": 3100, "lr": 0.00113, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59391, "top5_acc": 0.83047, "loss_cls": 2.23598, "loss": 2.23598, "time": 0.81998} +{"mode": "train", "epoch": 140, "iter": 3200, "lr": 0.00112, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58562, "top5_acc": 0.81953, "loss_cls": 2.27968, "loss": 2.27968, "time": 0.81665} +{"mode": "train", "epoch": 140, "iter": 3300, "lr": 0.00112, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58797, "top5_acc": 0.81688, "loss_cls": 2.26784, "loss": 2.26784, "time": 0.8332} +{"mode": "train", "epoch": 140, "iter": 3400, "lr": 0.00111, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58922, "top5_acc": 0.82516, "loss_cls": 2.2475, "loss": 2.2475, "time": 0.82357} +{"mode": "train", "epoch": 140, "iter": 3500, "lr": 0.00111, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.58828, "top5_acc": 0.82484, "loss_cls": 2.25292, "loss": 2.25292, "time": 0.82305} +{"mode": "train", "epoch": 140, "iter": 3600, "lr": 0.0011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58969, "top5_acc": 0.82234, "loss_cls": 2.26055, "loss": 2.26055, "time": 0.83086} +{"mode": "train", "epoch": 140, "iter": 3700, "lr": 0.0011, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58375, "top5_acc": 0.81703, "loss_cls": 2.30706, "loss": 2.30706, "time": 0.82948} +{"mode": "val", "epoch": 140, "iter": 309, "lr": 0.00109, "top1_acc": 0.45859, "top5_acc": 0.70937, "mean_class_accuracy": 0.45828} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00109, "memory": 15990, "data_time": 1.2649, "top1_acc": 0.61016, "top5_acc": 0.83516, "loss_cls": 2.1708, "loss": 2.1708, "time": 2.25508} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00108, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.60641, "top5_acc": 0.83797, "loss_cls": 2.1748, "loss": 2.1748, "time": 0.8162} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00108, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.60844, "top5_acc": 0.83422, "loss_cls": 2.18476, "loss": 2.18476, "time": 0.82829} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00107, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.60438, "top5_acc": 0.84031, "loss_cls": 2.15742, "loss": 2.15742, "time": 0.82269} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00106, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61906, "top5_acc": 0.83703, "loss_cls": 2.15633, "loss": 2.15633, "time": 0.82063} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00106, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.59891, "top5_acc": 0.84094, "loss_cls": 2.17646, "loss": 2.17646, "time": 0.82267} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00105, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.61531, "top5_acc": 0.83531, "loss_cls": 2.15297, "loss": 2.15297, "time": 0.82491} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00105, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60531, "top5_acc": 0.83969, "loss_cls": 2.16997, "loss": 2.16997, "time": 0.82421} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00104, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.60281, "top5_acc": 0.84156, "loss_cls": 2.18383, "loss": 2.18383, "time": 0.83733} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00104, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60625, "top5_acc": 0.83359, "loss_cls": 2.19784, "loss": 2.19784, "time": 0.82427} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00103, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.615, "top5_acc": 0.84078, "loss_cls": 2.12587, "loss": 2.12587, "time": 0.82064} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00102, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61859, "top5_acc": 0.84312, "loss_cls": 2.14887, "loss": 2.14887, "time": 0.83099} +{"mode": "train", "epoch": 141, "iter": 1300, "lr": 0.00102, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61016, "top5_acc": 0.83625, "loss_cls": 2.16398, "loss": 2.16398, "time": 0.82487} +{"mode": "train", "epoch": 141, "iter": 1400, "lr": 0.00101, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.60938, "top5_acc": 0.83453, "loss_cls": 2.20309, "loss": 2.20309, "time": 0.81976} +{"mode": "train", "epoch": 141, "iter": 1500, "lr": 0.00101, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60453, "top5_acc": 0.83812, "loss_cls": 2.18714, "loss": 2.18714, "time": 0.82278} +{"mode": "train", "epoch": 141, "iter": 1600, "lr": 0.001, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61375, "top5_acc": 0.83594, "loss_cls": 2.16012, "loss": 2.16012, "time": 0.81841} +{"mode": "train", "epoch": 141, "iter": 1700, "lr": 0.001, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61391, "top5_acc": 0.82984, "loss_cls": 2.16343, "loss": 2.16343, "time": 0.8189} +{"mode": "train", "epoch": 141, "iter": 1800, "lr": 0.00099, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60453, "top5_acc": 0.83203, "loss_cls": 2.19552, "loss": 2.19552, "time": 0.8253} +{"mode": "train", "epoch": 141, "iter": 1900, "lr": 0.00099, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59781, "top5_acc": 0.83516, "loss_cls": 2.20711, "loss": 2.20711, "time": 0.82322} +{"mode": "train", "epoch": 141, "iter": 2000, "lr": 0.00098, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60859, "top5_acc": 0.83109, "loss_cls": 2.19699, "loss": 2.19699, "time": 0.81865} +{"mode": "train", "epoch": 141, "iter": 2100, "lr": 0.00097, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60547, "top5_acc": 0.83812, "loss_cls": 2.1706, "loss": 2.1706, "time": 0.82231} +{"mode": "train", "epoch": 141, "iter": 2200, "lr": 0.00097, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.6, "top5_acc": 0.82984, "loss_cls": 2.20658, "loss": 2.20658, "time": 0.82025} +{"mode": "train", "epoch": 141, "iter": 2300, "lr": 0.00096, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60047, "top5_acc": 0.83766, "loss_cls": 2.17522, "loss": 2.17522, "time": 0.81947} +{"mode": "train", "epoch": 141, "iter": 2400, "lr": 0.00096, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61453, "top5_acc": 0.83875, "loss_cls": 2.1484, "loss": 2.1484, "time": 0.82049} +{"mode": "train", "epoch": 141, "iter": 2500, "lr": 0.00095, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60203, "top5_acc": 0.8375, "loss_cls": 2.19481, "loss": 2.19481, "time": 0.82218} +{"mode": "train", "epoch": 141, "iter": 2600, "lr": 0.00095, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60094, "top5_acc": 0.82844, "loss_cls": 2.21833, "loss": 2.21833, "time": 0.81879} +{"mode": "train", "epoch": 141, "iter": 2700, "lr": 0.00094, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59875, "top5_acc": 0.83516, "loss_cls": 2.19814, "loss": 2.19814, "time": 0.81615} +{"mode": "train", "epoch": 141, "iter": 2800, "lr": 0.00094, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60125, "top5_acc": 0.83062, "loss_cls": 2.19498, "loss": 2.19498, "time": 0.81837} +{"mode": "train", "epoch": 141, "iter": 2900, "lr": 0.00093, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59969, "top5_acc": 0.82406, "loss_cls": 2.23042, "loss": 2.23042, "time": 0.81449} +{"mode": "train", "epoch": 141, "iter": 3000, "lr": 0.00093, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60438, "top5_acc": 0.83547, "loss_cls": 2.18853, "loss": 2.18853, "time": 0.81396} +{"mode": "train", "epoch": 141, "iter": 3100, "lr": 0.00092, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59562, "top5_acc": 0.82703, "loss_cls": 2.2201, "loss": 2.2201, "time": 0.81928} +{"mode": "train", "epoch": 141, "iter": 3200, "lr": 0.00091, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.58969, "top5_acc": 0.81859, "loss_cls": 2.25936, "loss": 2.25936, "time": 0.8218} +{"mode": "train", "epoch": 141, "iter": 3300, "lr": 0.00091, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59266, "top5_acc": 0.83219, "loss_cls": 2.21269, "loss": 2.21269, "time": 0.82813} +{"mode": "train", "epoch": 141, "iter": 3400, "lr": 0.0009, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59734, "top5_acc": 0.83062, "loss_cls": 2.2147, "loss": 2.2147, "time": 0.82432} +{"mode": "train", "epoch": 141, "iter": 3500, "lr": 0.0009, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.59891, "top5_acc": 0.82812, "loss_cls": 2.22528, "loss": 2.22528, "time": 0.82101} +{"mode": "train", "epoch": 141, "iter": 3600, "lr": 0.00089, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60266, "top5_acc": 0.82844, "loss_cls": 2.20614, "loss": 2.20614, "time": 0.82614} +{"mode": "train", "epoch": 141, "iter": 3700, "lr": 0.00089, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59031, "top5_acc": 0.82375, "loss_cls": 2.23171, "loss": 2.23171, "time": 0.82265} +{"mode": "val", "epoch": 141, "iter": 309, "lr": 0.00089, "top1_acc": 0.46153, "top5_acc": 0.71266, "mean_class_accuracy": 0.46131} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00088, "memory": 15990, "data_time": 1.25801, "top1_acc": 0.62703, "top5_acc": 0.84734, "loss_cls": 2.06953, "loss": 2.06953, "time": 2.24681} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00088, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.62922, "top5_acc": 0.84812, "loss_cls": 2.08647, "loss": 2.08647, "time": 0.82957} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00087, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.62469, "top5_acc": 0.84062, "loss_cls": 2.1013, "loss": 2.1013, "time": 0.82639} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00086, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.61047, "top5_acc": 0.84031, "loss_cls": 2.1346, "loss": 2.1346, "time": 0.82363} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.00086, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.62297, "top5_acc": 0.84734, "loss_cls": 2.07654, "loss": 2.07654, "time": 0.82334} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.00085, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.61797, "top5_acc": 0.845, "loss_cls": 2.1171, "loss": 2.1171, "time": 0.83147} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.00085, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.62438, "top5_acc": 0.84984, "loss_cls": 2.09319, "loss": 2.09319, "time": 0.83103} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00084, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62875, "top5_acc": 0.84406, "loss_cls": 2.10077, "loss": 2.10077, "time": 0.81858} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00084, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.62234, "top5_acc": 0.84438, "loss_cls": 2.10993, "loss": 2.10993, "time": 0.83731} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00083, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.61219, "top5_acc": 0.84312, "loss_cls": 2.13849, "loss": 2.13849, "time": 0.83376} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00083, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.62484, "top5_acc": 0.84219, "loss_cls": 2.09306, "loss": 2.09306, "time": 0.8313} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00082, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60625, "top5_acc": 0.83906, "loss_cls": 2.16486, "loss": 2.16486, "time": 0.82955} +{"mode": "train", "epoch": 142, "iter": 1300, "lr": 0.00082, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.62078, "top5_acc": 0.84531, "loss_cls": 2.12239, "loss": 2.12239, "time": 0.8335} +{"mode": "train", "epoch": 142, "iter": 1400, "lr": 0.00081, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.61984, "top5_acc": 0.84422, "loss_cls": 2.10652, "loss": 2.10652, "time": 0.82335} +{"mode": "train", "epoch": 142, "iter": 1500, "lr": 0.00081, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.61328, "top5_acc": 0.84156, "loss_cls": 2.14334, "loss": 2.14334, "time": 0.82853} +{"mode": "train", "epoch": 142, "iter": 1600, "lr": 0.0008, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.61953, "top5_acc": 0.83688, "loss_cls": 2.11742, "loss": 2.11742, "time": 0.82394} +{"mode": "train", "epoch": 142, "iter": 1700, "lr": 0.0008, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.61875, "top5_acc": 0.84531, "loss_cls": 2.12837, "loss": 2.12837, "time": 0.83366} +{"mode": "train", "epoch": 142, "iter": 1800, "lr": 0.00079, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.61, "top5_acc": 0.835, "loss_cls": 2.15322, "loss": 2.15322, "time": 0.83085} +{"mode": "train", "epoch": 142, "iter": 1900, "lr": 0.00079, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.60484, "top5_acc": 0.8325, "loss_cls": 2.16824, "loss": 2.16824, "time": 0.83422} +{"mode": "train", "epoch": 142, "iter": 2000, "lr": 0.00078, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61531, "top5_acc": 0.84172, "loss_cls": 2.13581, "loss": 2.13581, "time": 0.82015} +{"mode": "train", "epoch": 142, "iter": 2100, "lr": 0.00078, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.61422, "top5_acc": 0.84188, "loss_cls": 2.11775, "loss": 2.11775, "time": 0.82648} +{"mode": "train", "epoch": 142, "iter": 2200, "lr": 0.00077, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.59984, "top5_acc": 0.83359, "loss_cls": 2.20033, "loss": 2.20033, "time": 0.82597} +{"mode": "train", "epoch": 142, "iter": 2300, "lr": 0.00077, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.61734, "top5_acc": 0.84438, "loss_cls": 2.10971, "loss": 2.10971, "time": 0.82017} +{"mode": "train", "epoch": 142, "iter": 2400, "lr": 0.00076, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.61578, "top5_acc": 0.83875, "loss_cls": 2.1522, "loss": 2.1522, "time": 0.82229} +{"mode": "train", "epoch": 142, "iter": 2500, "lr": 0.00076, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.60609, "top5_acc": 0.83797, "loss_cls": 2.15282, "loss": 2.15282, "time": 0.81705} +{"mode": "train", "epoch": 142, "iter": 2600, "lr": 0.00075, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.61562, "top5_acc": 0.84078, "loss_cls": 2.1294, "loss": 2.1294, "time": 0.82319} +{"mode": "train", "epoch": 142, "iter": 2700, "lr": 0.00075, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.605, "top5_acc": 0.83297, "loss_cls": 2.18791, "loss": 2.18791, "time": 0.82001} +{"mode": "train", "epoch": 142, "iter": 2800, "lr": 0.00075, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60953, "top5_acc": 0.83328, "loss_cls": 2.16818, "loss": 2.16818, "time": 0.82627} +{"mode": "train", "epoch": 142, "iter": 2900, "lr": 0.00074, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60672, "top5_acc": 0.83938, "loss_cls": 2.15364, "loss": 2.15364, "time": 0.82453} +{"mode": "train", "epoch": 142, "iter": 3000, "lr": 0.00074, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61766, "top5_acc": 0.83703, "loss_cls": 2.14237, "loss": 2.14237, "time": 0.81467} +{"mode": "train", "epoch": 142, "iter": 3100, "lr": 0.00073, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60844, "top5_acc": 0.83688, "loss_cls": 2.15194, "loss": 2.15194, "time": 0.81631} +{"mode": "train", "epoch": 142, "iter": 3200, "lr": 0.00073, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.61516, "top5_acc": 0.84594, "loss_cls": 2.11683, "loss": 2.11683, "time": 0.82428} +{"mode": "train", "epoch": 142, "iter": 3300, "lr": 0.00072, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.62141, "top5_acc": 0.83906, "loss_cls": 2.15791, "loss": 2.15791, "time": 0.83057} +{"mode": "train", "epoch": 142, "iter": 3400, "lr": 0.00072, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.61219, "top5_acc": 0.84078, "loss_cls": 2.13858, "loss": 2.13858, "time": 0.82774} +{"mode": "train", "epoch": 142, "iter": 3500, "lr": 0.00071, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61281, "top5_acc": 0.82484, "loss_cls": 2.17771, "loss": 2.17771, "time": 0.82613} +{"mode": "train", "epoch": 142, "iter": 3600, "lr": 0.00071, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.61875, "top5_acc": 0.84391, "loss_cls": 2.11402, "loss": 2.11402, "time": 0.83219} +{"mode": "train", "epoch": 142, "iter": 3700, "lr": 0.0007, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60766, "top5_acc": 0.83406, "loss_cls": 2.15513, "loss": 2.15513, "time": 0.82889} +{"mode": "val", "epoch": 142, "iter": 309, "lr": 0.0007, "top1_acc": 0.4634, "top5_acc": 0.71534, "mean_class_accuracy": 0.46315} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.0007, "memory": 15990, "data_time": 1.27191, "top1_acc": 0.63438, "top5_acc": 0.85609, "loss_cls": 2.0093, "loss": 2.0093, "time": 2.25261} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00069, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.63031, "top5_acc": 0.84984, "loss_cls": 2.06753, "loss": 2.06753, "time": 0.82643} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00069, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.63234, "top5_acc": 0.84797, "loss_cls": 2.06614, "loss": 2.06614, "time": 0.82464} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00068, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.62594, "top5_acc": 0.84969, "loss_cls": 2.08433, "loss": 2.08433, "time": 0.82145} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00068, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63891, "top5_acc": 0.85094, "loss_cls": 2.03776, "loss": 2.03776, "time": 0.82293} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00067, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.63078, "top5_acc": 0.85969, "loss_cls": 2.01667, "loss": 2.01667, "time": 0.83137} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00067, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.61969, "top5_acc": 0.84078, "loss_cls": 2.1155, "loss": 2.1155, "time": 0.81994} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00066, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62531, "top5_acc": 0.84203, "loss_cls": 2.10716, "loss": 2.10716, "time": 0.82489} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00066, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63047, "top5_acc": 0.85891, "loss_cls": 2.01882, "loss": 2.01882, "time": 0.83401} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00065, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63828, "top5_acc": 0.84938, "loss_cls": 2.06287, "loss": 2.06287, "time": 0.82644} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00065, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.6275, "top5_acc": 0.84859, "loss_cls": 2.08875, "loss": 2.08875, "time": 0.83394} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00065, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.61422, "top5_acc": 0.84578, "loss_cls": 2.13232, "loss": 2.13232, "time": 0.82677} +{"mode": "train", "epoch": 143, "iter": 1300, "lr": 0.00064, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.62938, "top5_acc": 0.84828, "loss_cls": 2.07788, "loss": 2.07788, "time": 0.82464} +{"mode": "train", "epoch": 143, "iter": 1400, "lr": 0.00064, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.61453, "top5_acc": 0.84234, "loss_cls": 2.12067, "loss": 2.12067, "time": 0.8266} +{"mode": "train", "epoch": 143, "iter": 1500, "lr": 0.00063, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63516, "top5_acc": 0.85156, "loss_cls": 2.04905, "loss": 2.04905, "time": 0.81599} +{"mode": "train", "epoch": 143, "iter": 1600, "lr": 0.00063, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.62578, "top5_acc": 0.84922, "loss_cls": 2.06257, "loss": 2.06257, "time": 0.82162} +{"mode": "train", "epoch": 143, "iter": 1700, "lr": 0.00062, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.62453, "top5_acc": 0.85141, "loss_cls": 2.07473, "loss": 2.07473, "time": 0.8318} +{"mode": "train", "epoch": 143, "iter": 1800, "lr": 0.00062, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.61594, "top5_acc": 0.84922, "loss_cls": 2.09215, "loss": 2.09215, "time": 0.83314} +{"mode": "train", "epoch": 143, "iter": 1900, "lr": 0.00061, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.62984, "top5_acc": 0.85188, "loss_cls": 2.07391, "loss": 2.07391, "time": 0.82122} +{"mode": "train", "epoch": 143, "iter": 2000, "lr": 0.00061, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.62766, "top5_acc": 0.85125, "loss_cls": 2.05275, "loss": 2.05275, "time": 0.82052} +{"mode": "train", "epoch": 143, "iter": 2100, "lr": 0.00061, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.63422, "top5_acc": 0.85297, "loss_cls": 2.04768, "loss": 2.04768, "time": 0.82312} +{"mode": "train", "epoch": 143, "iter": 2200, "lr": 0.0006, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.62281, "top5_acc": 0.84547, "loss_cls": 2.08455, "loss": 2.08455, "time": 0.82466} +{"mode": "train", "epoch": 143, "iter": 2300, "lr": 0.0006, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.63688, "top5_acc": 0.84656, "loss_cls": 2.06016, "loss": 2.06016, "time": 0.82208} +{"mode": "train", "epoch": 143, "iter": 2400, "lr": 0.00059, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.63531, "top5_acc": 0.84688, "loss_cls": 2.06856, "loss": 2.06856, "time": 0.81742} +{"mode": "train", "epoch": 143, "iter": 2500, "lr": 0.00059, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.61609, "top5_acc": 0.84281, "loss_cls": 2.11063, "loss": 2.11063, "time": 0.82814} +{"mode": "train", "epoch": 143, "iter": 2600, "lr": 0.00058, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.61469, "top5_acc": 0.84422, "loss_cls": 2.10554, "loss": 2.10554, "time": 0.82134} +{"mode": "train", "epoch": 143, "iter": 2700, "lr": 0.00058, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.6225, "top5_acc": 0.84875, "loss_cls": 2.08863, "loss": 2.08863, "time": 0.83118} +{"mode": "train", "epoch": 143, "iter": 2800, "lr": 0.00058, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.61719, "top5_acc": 0.84828, "loss_cls": 2.0874, "loss": 2.0874, "time": 0.82394} +{"mode": "train", "epoch": 143, "iter": 2900, "lr": 0.00057, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.62594, "top5_acc": 0.84672, "loss_cls": 2.06796, "loss": 2.06796, "time": 0.82501} +{"mode": "train", "epoch": 143, "iter": 3000, "lr": 0.00057, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.62953, "top5_acc": 0.84359, "loss_cls": 2.08991, "loss": 2.08991, "time": 0.8293} +{"mode": "train", "epoch": 143, "iter": 3100, "lr": 0.00056, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.63, "top5_acc": 0.84984, "loss_cls": 2.07255, "loss": 2.07255, "time": 0.82289} +{"mode": "train", "epoch": 143, "iter": 3200, "lr": 0.00056, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.62578, "top5_acc": 0.84891, "loss_cls": 2.08984, "loss": 2.08984, "time": 0.83059} +{"mode": "train", "epoch": 143, "iter": 3300, "lr": 0.00055, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.62172, "top5_acc": 0.84109, "loss_cls": 2.11553, "loss": 2.11553, "time": 0.8228} +{"mode": "train", "epoch": 143, "iter": 3400, "lr": 0.00055, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.61859, "top5_acc": 0.84109, "loss_cls": 2.11892, "loss": 2.11892, "time": 0.82886} +{"mode": "train", "epoch": 143, "iter": 3500, "lr": 0.00055, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.62203, "top5_acc": 0.84359, "loss_cls": 2.09046, "loss": 2.09046, "time": 0.83416} +{"mode": "train", "epoch": 143, "iter": 3600, "lr": 0.00054, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.61578, "top5_acc": 0.84281, "loss_cls": 2.1265, "loss": 2.1265, "time": 0.8369} +{"mode": "train", "epoch": 143, "iter": 3700, "lr": 0.00054, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.61734, "top5_acc": 0.84938, "loss_cls": 2.06527, "loss": 2.06527, "time": 0.82899} +{"mode": "val", "epoch": 143, "iter": 309, "lr": 0.00054, "top1_acc": 0.46614, "top5_acc": 0.71798, "mean_class_accuracy": 0.46587} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00053, "memory": 15990, "data_time": 1.26688, "top1_acc": 0.63641, "top5_acc": 0.84812, "loss_cls": 2.04384, "loss": 2.04384, "time": 2.24276} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00053, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.65234, "top5_acc": 0.86734, "loss_cls": 1.96228, "loss": 1.96228, "time": 0.82486} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00052, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64719, "top5_acc": 0.86062, "loss_cls": 1.98022, "loss": 1.98022, "time": 0.81935} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00052, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63375, "top5_acc": 0.85516, "loss_cls": 2.03835, "loss": 2.03835, "time": 0.8232} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00052, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63766, "top5_acc": 0.85469, "loss_cls": 2.03397, "loss": 2.03397, "time": 0.81964} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00051, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.63281, "top5_acc": 0.855, "loss_cls": 2.02141, "loss": 2.02141, "time": 0.8281} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00051, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64703, "top5_acc": 0.86234, "loss_cls": 1.981, "loss": 1.981, "time": 0.82006} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.0005, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.63906, "top5_acc": 0.85203, "loss_cls": 2.02763, "loss": 2.02763, "time": 0.83321} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.0005, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.63547, "top5_acc": 0.85375, "loss_cls": 2.04187, "loss": 2.04187, "time": 0.82798} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.0005, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.64453, "top5_acc": 0.85984, "loss_cls": 1.99323, "loss": 1.99323, "time": 0.82423} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.00049, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63516, "top5_acc": 0.85578, "loss_cls": 2.03078, "loss": 2.03078, "time": 0.83261} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.00049, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.63719, "top5_acc": 0.85594, "loss_cls": 2.04474, "loss": 2.04474, "time": 0.82906} +{"mode": "train", "epoch": 144, "iter": 1300, "lr": 0.00048, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.62969, "top5_acc": 0.84891, "loss_cls": 2.05991, "loss": 2.05991, "time": 0.82892} +{"mode": "train", "epoch": 144, "iter": 1400, "lr": 0.00048, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.64016, "top5_acc": 0.85734, "loss_cls": 2.01692, "loss": 2.01692, "time": 0.8328} +{"mode": "train", "epoch": 144, "iter": 1500, "lr": 0.00048, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63313, "top5_acc": 0.85016, "loss_cls": 2.04206, "loss": 2.04206, "time": 0.82206} +{"mode": "train", "epoch": 144, "iter": 1600, "lr": 0.00047, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.63297, "top5_acc": 0.85547, "loss_cls": 2.03169, "loss": 2.03169, "time": 0.82587} +{"mode": "train", "epoch": 144, "iter": 1700, "lr": 0.00047, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.63797, "top5_acc": 0.85547, "loss_cls": 2.00946, "loss": 2.00946, "time": 0.83191} +{"mode": "train", "epoch": 144, "iter": 1800, "lr": 0.00047, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.63969, "top5_acc": 0.86031, "loss_cls": 2.00741, "loss": 2.00741, "time": 0.83427} +{"mode": "train", "epoch": 144, "iter": 1900, "lr": 0.00046, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.63438, "top5_acc": 0.86234, "loss_cls": 1.99279, "loss": 1.99279, "time": 0.8291} +{"mode": "train", "epoch": 144, "iter": 2000, "lr": 0.00046, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.64406, "top5_acc": 0.85953, "loss_cls": 1.98462, "loss": 1.98462, "time": 0.82193} +{"mode": "train", "epoch": 144, "iter": 2100, "lr": 0.00045, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.64953, "top5_acc": 0.86031, "loss_cls": 1.95351, "loss": 1.95351, "time": 0.81668} +{"mode": "train", "epoch": 144, "iter": 2200, "lr": 0.00045, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62828, "top5_acc": 0.85344, "loss_cls": 2.03862, "loss": 2.03862, "time": 0.82468} +{"mode": "train", "epoch": 144, "iter": 2300, "lr": 0.00045, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63281, "top5_acc": 0.85484, "loss_cls": 2.04835, "loss": 2.04835, "time": 0.82767} +{"mode": "train", "epoch": 144, "iter": 2400, "lr": 0.00044, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.62234, "top5_acc": 0.85234, "loss_cls": 2.04352, "loss": 2.04352, "time": 0.82801} +{"mode": "train", "epoch": 144, "iter": 2500, "lr": 0.00044, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.62687, "top5_acc": 0.84234, "loss_cls": 2.07766, "loss": 2.07766, "time": 0.82896} +{"mode": "train", "epoch": 144, "iter": 2600, "lr": 0.00044, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.63219, "top5_acc": 0.85484, "loss_cls": 2.03912, "loss": 2.03912, "time": 0.82828} +{"mode": "train", "epoch": 144, "iter": 2700, "lr": 0.00043, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63703, "top5_acc": 0.85375, "loss_cls": 2.03049, "loss": 2.03049, "time": 0.82168} +{"mode": "train", "epoch": 144, "iter": 2800, "lr": 0.00043, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63766, "top5_acc": 0.85656, "loss_cls": 2.02449, "loss": 2.02449, "time": 0.81736} +{"mode": "train", "epoch": 144, "iter": 2900, "lr": 0.00042, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.64188, "top5_acc": 0.8625, "loss_cls": 1.97986, "loss": 1.97986, "time": 0.82107} +{"mode": "train", "epoch": 144, "iter": 3000, "lr": 0.00042, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64406, "top5_acc": 0.85375, "loss_cls": 2.00502, "loss": 2.00502, "time": 0.82662} +{"mode": "train", "epoch": 144, "iter": 3100, "lr": 0.00042, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.62813, "top5_acc": 0.85094, "loss_cls": 2.06483, "loss": 2.06483, "time": 0.81826} +{"mode": "train", "epoch": 144, "iter": 3200, "lr": 0.00041, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.64016, "top5_acc": 0.85562, "loss_cls": 2.01393, "loss": 2.01393, "time": 0.83355} +{"mode": "train", "epoch": 144, "iter": 3300, "lr": 0.00041, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.62391, "top5_acc": 0.84891, "loss_cls": 2.07641, "loss": 2.07641, "time": 0.82031} +{"mode": "train", "epoch": 144, "iter": 3400, "lr": 0.00041, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.63859, "top5_acc": 0.86172, "loss_cls": 2.00564, "loss": 2.00564, "time": 0.82474} +{"mode": "train", "epoch": 144, "iter": 3500, "lr": 0.0004, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64344, "top5_acc": 0.85984, "loss_cls": 1.99905, "loss": 1.99905, "time": 0.82337} +{"mode": "train", "epoch": 144, "iter": 3600, "lr": 0.0004, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63188, "top5_acc": 0.8575, "loss_cls": 2.02043, "loss": 2.02043, "time": 0.82902} +{"mode": "train", "epoch": 144, "iter": 3700, "lr": 0.0004, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.62875, "top5_acc": 0.84859, "loss_cls": 2.06543, "loss": 2.06543, "time": 0.8219} +{"mode": "val", "epoch": 144, "iter": 309, "lr": 0.00039, "top1_acc": 0.46619, "top5_acc": 0.71615, "mean_class_accuracy": 0.46594} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.00039, "memory": 15990, "data_time": 1.30135, "top1_acc": 0.65109, "top5_acc": 0.86062, "loss_cls": 1.96528, "loss": 1.96528, "time": 2.28666} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 0.00039, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.65547, "top5_acc": 0.86297, "loss_cls": 1.93967, "loss": 1.93967, "time": 0.83278} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 0.00038, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64453, "top5_acc": 0.86312, "loss_cls": 1.95405, "loss": 1.95405, "time": 0.83351} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 0.00038, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.64922, "top5_acc": 0.86062, "loss_cls": 1.9718, "loss": 1.9718, "time": 0.82382} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 0.00038, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.65031, "top5_acc": 0.86312, "loss_cls": 1.95691, "loss": 1.95691, "time": 0.8375} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 0.00037, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.65328, "top5_acc": 0.86656, "loss_cls": 1.96039, "loss": 1.96039, "time": 0.82388} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 0.00037, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.64969, "top5_acc": 0.86484, "loss_cls": 1.95864, "loss": 1.95864, "time": 0.83408} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 0.00037, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.65203, "top5_acc": 0.86547, "loss_cls": 1.95347, "loss": 1.95347, "time": 0.83876} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 0.00036, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.65031, "top5_acc": 0.86422, "loss_cls": 1.95712, "loss": 1.95712, "time": 0.82945} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 0.00036, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.66219, "top5_acc": 0.86812, "loss_cls": 1.92926, "loss": 1.92926, "time": 0.83208} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 0.00036, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.65688, "top5_acc": 0.86594, "loss_cls": 1.94446, "loss": 1.94446, "time": 0.83672} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 0.00035, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.64281, "top5_acc": 0.86188, "loss_cls": 1.98839, "loss": 1.98839, "time": 0.83147} +{"mode": "train", "epoch": 145, "iter": 1300, "lr": 0.00035, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.64156, "top5_acc": 0.85766, "loss_cls": 1.9915, "loss": 1.9915, "time": 0.83655} +{"mode": "train", "epoch": 145, "iter": 1400, "lr": 0.00035, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.64625, "top5_acc": 0.8625, "loss_cls": 1.97661, "loss": 1.97661, "time": 0.82825} +{"mode": "train", "epoch": 145, "iter": 1500, "lr": 0.00034, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.65688, "top5_acc": 0.86953, "loss_cls": 1.91572, "loss": 1.91572, "time": 0.83135} +{"mode": "train", "epoch": 145, "iter": 1600, "lr": 0.00034, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.6475, "top5_acc": 0.86109, "loss_cls": 1.99088, "loss": 1.99088, "time": 0.83396} +{"mode": "train", "epoch": 145, "iter": 1700, "lr": 0.00034, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.63969, "top5_acc": 0.85938, "loss_cls": 1.99615, "loss": 1.99615, "time": 0.83215} +{"mode": "train", "epoch": 145, "iter": 1800, "lr": 0.00033, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.64141, "top5_acc": 0.85844, "loss_cls": 1.99406, "loss": 1.99406, "time": 0.83677} +{"mode": "train", "epoch": 145, "iter": 1900, "lr": 0.00033, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.64422, "top5_acc": 0.86328, "loss_cls": 2.00244, "loss": 2.00244, "time": 0.81913} +{"mode": "train", "epoch": 145, "iter": 2000, "lr": 0.00033, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.64562, "top5_acc": 0.85812, "loss_cls": 1.9992, "loss": 1.9992, "time": 0.82507} +{"mode": "train", "epoch": 145, "iter": 2100, "lr": 0.00032, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.63953, "top5_acc": 0.85719, "loss_cls": 2.01701, "loss": 2.01701, "time": 0.82708} +{"mode": "train", "epoch": 145, "iter": 2200, "lr": 0.00032, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.64203, "top5_acc": 0.85844, "loss_cls": 2.00826, "loss": 2.00826, "time": 0.82944} +{"mode": "train", "epoch": 145, "iter": 2300, "lr": 0.00032, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.64719, "top5_acc": 0.85906, "loss_cls": 1.98782, "loss": 1.98782, "time": 0.82473} +{"mode": "train", "epoch": 145, "iter": 2400, "lr": 0.00031, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.64984, "top5_acc": 0.86422, "loss_cls": 1.95769, "loss": 1.95769, "time": 0.82712} +{"mode": "train", "epoch": 145, "iter": 2500, "lr": 0.00031, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.64828, "top5_acc": 0.86531, "loss_cls": 1.97147, "loss": 1.97147, "time": 0.82615} +{"mode": "train", "epoch": 145, "iter": 2600, "lr": 0.00031, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.64312, "top5_acc": 0.85797, "loss_cls": 1.98463, "loss": 1.98463, "time": 0.82048} +{"mode": "train", "epoch": 145, "iter": 2700, "lr": 0.00031, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.64359, "top5_acc": 0.8575, "loss_cls": 2.00727, "loss": 2.00727, "time": 0.8157} +{"mode": "train", "epoch": 145, "iter": 2800, "lr": 0.0003, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.64578, "top5_acc": 0.85453, "loss_cls": 1.97164, "loss": 1.97164, "time": 0.81516} +{"mode": "train", "epoch": 145, "iter": 2900, "lr": 0.0003, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64391, "top5_acc": 0.85844, "loss_cls": 1.97236, "loss": 1.97236, "time": 0.8158} +{"mode": "train", "epoch": 145, "iter": 3000, "lr": 0.0003, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.64312, "top5_acc": 0.86328, "loss_cls": 1.98141, "loss": 1.98141, "time": 0.81884} +{"mode": "train", "epoch": 145, "iter": 3100, "lr": 0.00029, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.63859, "top5_acc": 0.85516, "loss_cls": 2.01531, "loss": 2.01531, "time": 0.82402} +{"mode": "train", "epoch": 145, "iter": 3200, "lr": 0.00029, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.63688, "top5_acc": 0.85531, "loss_cls": 2.03064, "loss": 2.03064, "time": 0.81495} +{"mode": "train", "epoch": 145, "iter": 3300, "lr": 0.00029, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.65188, "top5_acc": 0.86141, "loss_cls": 1.96236, "loss": 1.96236, "time": 0.82051} +{"mode": "train", "epoch": 145, "iter": 3400, "lr": 0.00028, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.63828, "top5_acc": 0.85609, "loss_cls": 2.01386, "loss": 2.01386, "time": 0.81782} +{"mode": "train", "epoch": 145, "iter": 3500, "lr": 0.00028, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.65797, "top5_acc": 0.86328, "loss_cls": 1.95492, "loss": 1.95492, "time": 0.8166} +{"mode": "train", "epoch": 145, "iter": 3600, "lr": 0.00028, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.64281, "top5_acc": 0.85891, "loss_cls": 1.97497, "loss": 1.97497, "time": 0.81209} +{"mode": "train", "epoch": 145, "iter": 3700, "lr": 0.00028, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.64516, "top5_acc": 0.85828, "loss_cls": 1.99924, "loss": 1.99924, "time": 0.82098} +{"mode": "val", "epoch": 145, "iter": 309, "lr": 0.00027, "top1_acc": 0.46725, "top5_acc": 0.71747, "mean_class_accuracy": 0.46697} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 0.00027, "memory": 15990, "data_time": 1.3698, "top1_acc": 0.67016, "top5_acc": 0.87547, "loss_cls": 1.90529, "loss": 1.90529, "time": 2.35073} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 0.00027, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.65672, "top5_acc": 0.86516, "loss_cls": 1.92034, "loss": 1.92034, "time": 0.81623} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 0.00027, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65016, "top5_acc": 0.87078, "loss_cls": 1.92277, "loss": 1.92277, "time": 0.82116} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 0.00026, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67109, "top5_acc": 0.87453, "loss_cls": 1.87231, "loss": 1.87231, "time": 0.82007} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 0.00026, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65031, "top5_acc": 0.86859, "loss_cls": 1.93373, "loss": 1.93373, "time": 0.81542} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 0.00026, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66062, "top5_acc": 0.86938, "loss_cls": 1.93264, "loss": 1.93264, "time": 0.81795} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 0.00025, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.65922, "top5_acc": 0.86562, "loss_cls": 1.93169, "loss": 1.93169, "time": 0.82396} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 0.00025, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65531, "top5_acc": 0.86484, "loss_cls": 1.93585, "loss": 1.93585, "time": 0.81963} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 0.00025, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64672, "top5_acc": 0.85922, "loss_cls": 1.98172, "loss": 1.98172, "time": 0.81549} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 0.00025, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66516, "top5_acc": 0.87188, "loss_cls": 1.8991, "loss": 1.8991, "time": 0.8144} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 0.00024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65453, "top5_acc": 0.86453, "loss_cls": 1.94176, "loss": 1.94176, "time": 0.81205} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 0.00024, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64719, "top5_acc": 0.86828, "loss_cls": 1.94461, "loss": 1.94461, "time": 0.81442} +{"mode": "train", "epoch": 146, "iter": 1300, "lr": 0.00024, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.65719, "top5_acc": 0.86406, "loss_cls": 1.95375, "loss": 1.95375, "time": 0.811} +{"mode": "train", "epoch": 146, "iter": 1400, "lr": 0.00023, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66469, "top5_acc": 0.87266, "loss_cls": 1.91966, "loss": 1.91966, "time": 0.81711} +{"mode": "train", "epoch": 146, "iter": 1500, "lr": 0.00023, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.66297, "top5_acc": 0.86812, "loss_cls": 1.90986, "loss": 1.90986, "time": 0.81754} +{"mode": "train", "epoch": 146, "iter": 1600, "lr": 0.00023, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65703, "top5_acc": 0.86484, "loss_cls": 1.92204, "loss": 1.92204, "time": 0.81474} +{"mode": "train", "epoch": 146, "iter": 1700, "lr": 0.00023, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65188, "top5_acc": 0.86797, "loss_cls": 1.92472, "loss": 1.92472, "time": 0.81285} +{"mode": "train", "epoch": 146, "iter": 1800, "lr": 0.00022, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.65297, "top5_acc": 0.86797, "loss_cls": 1.92308, "loss": 1.92308, "time": 0.81741} +{"mode": "train", "epoch": 146, "iter": 1900, "lr": 0.00022, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65453, "top5_acc": 0.86938, "loss_cls": 1.95326, "loss": 1.95326, "time": 0.81587} +{"mode": "train", "epoch": 146, "iter": 2000, "lr": 0.00022, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65562, "top5_acc": 0.87125, "loss_cls": 1.91386, "loss": 1.91386, "time": 0.8163} +{"mode": "train", "epoch": 146, "iter": 2100, "lr": 0.00022, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65531, "top5_acc": 0.86578, "loss_cls": 1.93101, "loss": 1.93101, "time": 0.80866} +{"mode": "train", "epoch": 146, "iter": 2200, "lr": 0.00021, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66109, "top5_acc": 0.87047, "loss_cls": 1.91715, "loss": 1.91715, "time": 0.82259} +{"mode": "train", "epoch": 146, "iter": 2300, "lr": 0.00021, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65188, "top5_acc": 0.86297, "loss_cls": 1.93554, "loss": 1.93554, "time": 0.81525} +{"mode": "train", "epoch": 146, "iter": 2400, "lr": 0.00021, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65672, "top5_acc": 0.87484, "loss_cls": 1.89293, "loss": 1.89293, "time": 0.8115} +{"mode": "train", "epoch": 146, "iter": 2500, "lr": 0.00021, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65219, "top5_acc": 0.86547, "loss_cls": 1.93712, "loss": 1.93712, "time": 0.81994} +{"mode": "train", "epoch": 146, "iter": 2600, "lr": 0.0002, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64984, "top5_acc": 0.86719, "loss_cls": 1.94676, "loss": 1.94676, "time": 0.81467} +{"mode": "train", "epoch": 146, "iter": 2700, "lr": 0.0002, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.6525, "top5_acc": 0.86312, "loss_cls": 1.95411, "loss": 1.95411, "time": 0.81851} +{"mode": "train", "epoch": 146, "iter": 2800, "lr": 0.0002, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66219, "top5_acc": 0.86734, "loss_cls": 1.90122, "loss": 1.90122, "time": 0.81462} +{"mode": "train", "epoch": 146, "iter": 2900, "lr": 0.0002, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64781, "top5_acc": 0.86172, "loss_cls": 1.97309, "loss": 1.97309, "time": 0.81528} +{"mode": "train", "epoch": 146, "iter": 3000, "lr": 0.00019, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66219, "top5_acc": 0.86625, "loss_cls": 1.91347, "loss": 1.91347, "time": 0.81017} +{"mode": "train", "epoch": 146, "iter": 3100, "lr": 0.00019, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65875, "top5_acc": 0.86078, "loss_cls": 1.94682, "loss": 1.94682, "time": 0.81698} +{"mode": "train", "epoch": 146, "iter": 3200, "lr": 0.00019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64719, "top5_acc": 0.86453, "loss_cls": 1.96269, "loss": 1.96269, "time": 0.81652} +{"mode": "train", "epoch": 146, "iter": 3300, "lr": 0.00019, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.65969, "top5_acc": 0.86188, "loss_cls": 1.94731, "loss": 1.94731, "time": 0.81991} +{"mode": "train", "epoch": 146, "iter": 3400, "lr": 0.00018, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.6525, "top5_acc": 0.86188, "loss_cls": 1.95786, "loss": 1.95786, "time": 0.817} +{"mode": "train", "epoch": 146, "iter": 3500, "lr": 0.00018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65594, "top5_acc": 0.8675, "loss_cls": 1.93383, "loss": 1.93383, "time": 0.8153} +{"mode": "train", "epoch": 146, "iter": 3600, "lr": 0.00018, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66125, "top5_acc": 0.87453, "loss_cls": 1.89739, "loss": 1.89739, "time": 0.81408} +{"mode": "train", "epoch": 146, "iter": 3700, "lr": 0.00018, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.66812, "top5_acc": 0.87016, "loss_cls": 1.91572, "loss": 1.91572, "time": 0.81556} +{"mode": "val", "epoch": 146, "iter": 309, "lr": 0.00018, "top1_acc": 0.46852, "top5_acc": 0.71879, "mean_class_accuracy": 0.46824} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 0.00017, "memory": 15990, "data_time": 1.36311, "top1_acc": 0.67031, "top5_acc": 0.87609, "loss_cls": 1.86744, "loss": 1.86744, "time": 2.3479} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 0.00017, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67, "top5_acc": 0.87016, "loss_cls": 1.89696, "loss": 1.89696, "time": 0.82451} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 0.00017, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.67797, "top5_acc": 0.87891, "loss_cls": 1.82758, "loss": 1.82758, "time": 0.83098} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 0.00017, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.66344, "top5_acc": 0.86969, "loss_cls": 1.90343, "loss": 1.90343, "time": 0.82485} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 0.00016, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.66078, "top5_acc": 0.87312, "loss_cls": 1.90518, "loss": 1.90518, "time": 0.82419} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 0.00016, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67031, "top5_acc": 0.87078, "loss_cls": 1.87485, "loss": 1.87485, "time": 0.82844} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 0.00016, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66453, "top5_acc": 0.87359, "loss_cls": 1.89526, "loss": 1.89526, "time": 0.83923} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 0.00016, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.67078, "top5_acc": 0.87484, "loss_cls": 1.89952, "loss": 1.89952, "time": 0.83296} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 0.00015, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.67062, "top5_acc": 0.87859, "loss_cls": 1.85379, "loss": 1.85379, "time": 0.82681} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 0.00015, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.66172, "top5_acc": 0.87375, "loss_cls": 1.89532, "loss": 1.89532, "time": 0.83111} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 0.00015, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.66422, "top5_acc": 0.87531, "loss_cls": 1.88764, "loss": 1.88764, "time": 0.83409} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 0.00015, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67141, "top5_acc": 0.87234, "loss_cls": 1.87327, "loss": 1.87327, "time": 0.83123} +{"mode": "train", "epoch": 147, "iter": 1300, "lr": 0.00015, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.66375, "top5_acc": 0.87656, "loss_cls": 1.86985, "loss": 1.86985, "time": 0.83308} +{"mode": "train", "epoch": 147, "iter": 1400, "lr": 0.00014, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.65844, "top5_acc": 0.86328, "loss_cls": 1.92617, "loss": 1.92617, "time": 0.82613} +{"mode": "train", "epoch": 147, "iter": 1500, "lr": 0.00014, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.66344, "top5_acc": 0.87625, "loss_cls": 1.89029, "loss": 1.89029, "time": 0.83624} +{"mode": "train", "epoch": 147, "iter": 1600, "lr": 0.00014, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.66375, "top5_acc": 0.87453, "loss_cls": 1.88244, "loss": 1.88244, "time": 0.83661} +{"mode": "train", "epoch": 147, "iter": 1700, "lr": 0.00014, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.66328, "top5_acc": 0.87156, "loss_cls": 1.88616, "loss": 1.88616, "time": 0.83142} +{"mode": "train", "epoch": 147, "iter": 1800, "lr": 0.00014, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.67031, "top5_acc": 0.86609, "loss_cls": 1.90702, "loss": 1.90702, "time": 0.82624} +{"mode": "train", "epoch": 147, "iter": 1900, "lr": 0.00013, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.66188, "top5_acc": 0.87391, "loss_cls": 1.90537, "loss": 1.90537, "time": 0.82424} +{"mode": "train", "epoch": 147, "iter": 2000, "lr": 0.00013, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.66797, "top5_acc": 0.87547, "loss_cls": 1.87829, "loss": 1.87829, "time": 0.82504} +{"mode": "train", "epoch": 147, "iter": 2100, "lr": 0.00013, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.66516, "top5_acc": 0.87172, "loss_cls": 1.90873, "loss": 1.90873, "time": 0.82818} +{"mode": "train", "epoch": 147, "iter": 2200, "lr": 0.00013, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.66094, "top5_acc": 0.86984, "loss_cls": 1.91691, "loss": 1.91691, "time": 0.82035} +{"mode": "train", "epoch": 147, "iter": 2300, "lr": 0.00013, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.65594, "top5_acc": 0.86578, "loss_cls": 1.91565, "loss": 1.91565, "time": 0.82562} +{"mode": "train", "epoch": 147, "iter": 2400, "lr": 0.00012, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.66016, "top5_acc": 0.86922, "loss_cls": 1.90774, "loss": 1.90774, "time": 0.82462} +{"mode": "train", "epoch": 147, "iter": 2500, "lr": 0.00012, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66016, "top5_acc": 0.8675, "loss_cls": 1.929, "loss": 1.929, "time": 0.83124} +{"mode": "train", "epoch": 147, "iter": 2600, "lr": 0.00012, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.66641, "top5_acc": 0.87781, "loss_cls": 1.8666, "loss": 1.8666, "time": 0.82458} +{"mode": "train", "epoch": 147, "iter": 2700, "lr": 0.00012, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.65922, "top5_acc": 0.87, "loss_cls": 1.90391, "loss": 1.90391, "time": 0.83521} +{"mode": "train", "epoch": 147, "iter": 2800, "lr": 0.00012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66172, "top5_acc": 0.87516, "loss_cls": 1.89699, "loss": 1.89699, "time": 0.82431} +{"mode": "train", "epoch": 147, "iter": 2900, "lr": 0.00011, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66703, "top5_acc": 0.86875, "loss_cls": 1.897, "loss": 1.897, "time": 0.81083} +{"mode": "train", "epoch": 147, "iter": 3000, "lr": 0.00011, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67266, "top5_acc": 0.87531, "loss_cls": 1.87327, "loss": 1.87327, "time": 0.81476} +{"mode": "train", "epoch": 147, "iter": 3100, "lr": 0.00011, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64938, "top5_acc": 0.86594, "loss_cls": 1.9637, "loss": 1.9637, "time": 0.81909} +{"mode": "train", "epoch": 147, "iter": 3200, "lr": 0.00011, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66406, "top5_acc": 0.87188, "loss_cls": 1.91431, "loss": 1.91431, "time": 0.82304} +{"mode": "train", "epoch": 147, "iter": 3300, "lr": 0.00011, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.66719, "top5_acc": 0.86719, "loss_cls": 1.88841, "loss": 1.88841, "time": 0.82106} +{"mode": "train", "epoch": 147, "iter": 3400, "lr": 0.0001, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66625, "top5_acc": 0.87172, "loss_cls": 1.88848, "loss": 1.88848, "time": 0.81958} +{"mode": "train", "epoch": 147, "iter": 3500, "lr": 0.0001, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66891, "top5_acc": 0.87797, "loss_cls": 1.86934, "loss": 1.86934, "time": 0.81138} +{"mode": "train", "epoch": 147, "iter": 3600, "lr": 0.0001, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66703, "top5_acc": 0.87078, "loss_cls": 1.89442, "loss": 1.89442, "time": 0.81872} +{"mode": "train", "epoch": 147, "iter": 3700, "lr": 0.0001, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66516, "top5_acc": 0.86766, "loss_cls": 1.89261, "loss": 1.89261, "time": 0.81366} +{"mode": "val", "epoch": 147, "iter": 309, "lr": 0.0001, "top1_acc": 0.471, "top5_acc": 0.72051, "mean_class_accuracy": 0.47075} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 0.0001, "memory": 15990, "data_time": 1.33742, "top1_acc": 0.66766, "top5_acc": 0.87344, "loss_cls": 1.86862, "loss": 1.86862, "time": 2.33754} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 0.0001, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.67703, "top5_acc": 0.87438, "loss_cls": 1.87258, "loss": 1.87258, "time": 0.83103} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 9e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.66547, "top5_acc": 0.87625, "loss_cls": 1.86608, "loss": 1.86608, "time": 0.82344} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 9e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66328, "top5_acc": 0.87391, "loss_cls": 1.87862, "loss": 1.87862, "time": 0.83099} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 9e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67641, "top5_acc": 0.87938, "loss_cls": 1.84771, "loss": 1.84771, "time": 0.8233} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 9e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66406, "top5_acc": 0.87078, "loss_cls": 1.90706, "loss": 1.90706, "time": 0.82864} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 9e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67266, "top5_acc": 0.86938, "loss_cls": 1.88121, "loss": 1.88121, "time": 0.83738} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 9e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.66703, "top5_acc": 0.87906, "loss_cls": 1.87312, "loss": 1.87312, "time": 0.83208} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 8e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.68125, "top5_acc": 0.87656, "loss_cls": 1.8267, "loss": 1.8267, "time": 0.83069} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 8e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67781, "top5_acc": 0.88234, "loss_cls": 1.84915, "loss": 1.84915, "time": 0.83616} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 8e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.66344, "top5_acc": 0.87266, "loss_cls": 1.89516, "loss": 1.89516, "time": 0.83106} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 8e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.67781, "top5_acc": 0.87438, "loss_cls": 1.84805, "loss": 1.84805, "time": 0.82936} +{"mode": "train", "epoch": 148, "iter": 1300, "lr": 8e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67297, "top5_acc": 0.8725, "loss_cls": 1.8859, "loss": 1.8859, "time": 0.82354} +{"mode": "train", "epoch": 148, "iter": 1400, "lr": 8e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.66984, "top5_acc": 0.88016, "loss_cls": 1.86622, "loss": 1.86622, "time": 0.82275} +{"mode": "train", "epoch": 148, "iter": 1500, "lr": 7e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67484, "top5_acc": 0.87766, "loss_cls": 1.85147, "loss": 1.85147, "time": 0.83355} +{"mode": "train", "epoch": 148, "iter": 1600, "lr": 7e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66422, "top5_acc": 0.86719, "loss_cls": 1.88842, "loss": 1.88842, "time": 0.829} +{"mode": "train", "epoch": 148, "iter": 1700, "lr": 7e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.68125, "top5_acc": 0.88156, "loss_cls": 1.80731, "loss": 1.80731, "time": 0.82739} +{"mode": "train", "epoch": 148, "iter": 1800, "lr": 7e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67094, "top5_acc": 0.87516, "loss_cls": 1.8604, "loss": 1.8604, "time": 0.82021} +{"mode": "train", "epoch": 148, "iter": 1900, "lr": 7e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.66578, "top5_acc": 0.86938, "loss_cls": 1.89936, "loss": 1.89936, "time": 0.82253} +{"mode": "train", "epoch": 148, "iter": 2000, "lr": 7e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66672, "top5_acc": 0.86812, "loss_cls": 1.89964, "loss": 1.89964, "time": 0.82938} +{"mode": "train", "epoch": 148, "iter": 2100, "lr": 7e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67266, "top5_acc": 0.87922, "loss_cls": 1.85136, "loss": 1.85136, "time": 0.81765} +{"mode": "train", "epoch": 148, "iter": 2200, "lr": 6e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66984, "top5_acc": 0.87484, "loss_cls": 1.878, "loss": 1.878, "time": 0.82225} +{"mode": "train", "epoch": 148, "iter": 2300, "lr": 6e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67312, "top5_acc": 0.87734, "loss_cls": 1.85154, "loss": 1.85154, "time": 0.81976} +{"mode": "train", "epoch": 148, "iter": 2400, "lr": 6e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67422, "top5_acc": 0.87797, "loss_cls": 1.82654, "loss": 1.82654, "time": 0.82168} +{"mode": "train", "epoch": 148, "iter": 2500, "lr": 6e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67531, "top5_acc": 0.87672, "loss_cls": 1.85125, "loss": 1.85125, "time": 0.82105} +{"mode": "train", "epoch": 148, "iter": 2600, "lr": 6e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67859, "top5_acc": 0.88812, "loss_cls": 1.82444, "loss": 1.82444, "time": 0.81488} +{"mode": "train", "epoch": 148, "iter": 2700, "lr": 6e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67656, "top5_acc": 0.88, "loss_cls": 1.83964, "loss": 1.83964, "time": 0.81577} +{"mode": "train", "epoch": 148, "iter": 2800, "lr": 6e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66484, "top5_acc": 0.86906, "loss_cls": 1.873, "loss": 1.873, "time": 0.81838} +{"mode": "train", "epoch": 148, "iter": 2900, "lr": 5e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67375, "top5_acc": 0.88156, "loss_cls": 1.83247, "loss": 1.83247, "time": 0.82263} +{"mode": "train", "epoch": 148, "iter": 3000, "lr": 5e-05, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.66016, "top5_acc": 0.86844, "loss_cls": 1.89391, "loss": 1.89391, "time": 0.81936} +{"mode": "train", "epoch": 148, "iter": 3100, "lr": 5e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67328, "top5_acc": 0.88, "loss_cls": 1.85208, "loss": 1.85208, "time": 0.8319} +{"mode": "train", "epoch": 148, "iter": 3200, "lr": 5e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66281, "top5_acc": 0.87281, "loss_cls": 1.86475, "loss": 1.86475, "time": 0.81741} +{"mode": "train", "epoch": 148, "iter": 3300, "lr": 5e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67344, "top5_acc": 0.87625, "loss_cls": 1.85978, "loss": 1.85978, "time": 0.83352} +{"mode": "train", "epoch": 148, "iter": 3400, "lr": 5e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67359, "top5_acc": 0.87672, "loss_cls": 1.83339, "loss": 1.83339, "time": 0.83401} +{"mode": "train", "epoch": 148, "iter": 3500, "lr": 5e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67094, "top5_acc": 0.87078, "loss_cls": 1.88618, "loss": 1.88618, "time": 0.83188} +{"mode": "train", "epoch": 148, "iter": 3600, "lr": 5e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66484, "top5_acc": 0.87297, "loss_cls": 1.87464, "loss": 1.87464, "time": 0.8246} +{"mode": "train", "epoch": 148, "iter": 3700, "lr": 4e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66516, "top5_acc": 0.86594, "loss_cls": 1.89396, "loss": 1.89396, "time": 0.81804} +{"mode": "val", "epoch": 148, "iter": 309, "lr": 4e-05, "top1_acc": 0.47009, "top5_acc": 0.72157, "mean_class_accuracy": 0.46983} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 4e-05, "memory": 15990, "data_time": 1.29125, "top1_acc": 0.66953, "top5_acc": 0.87531, "loss_cls": 1.86383, "loss": 1.86383, "time": 2.27829} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 4e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67234, "top5_acc": 0.87656, "loss_cls": 1.84211, "loss": 1.84211, "time": 0.83106} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 4e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67953, "top5_acc": 0.87188, "loss_cls": 1.85003, "loss": 1.85003, "time": 0.8317} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 4e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67312, "top5_acc": 0.87375, "loss_cls": 1.85179, "loss": 1.85179, "time": 0.83146} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 4e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67219, "top5_acc": 0.87672, "loss_cls": 1.84835, "loss": 1.84835, "time": 0.82159} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 4e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66438, "top5_acc": 0.87047, "loss_cls": 1.8856, "loss": 1.8856, "time": 0.83386} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 4e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.6775, "top5_acc": 0.87969, "loss_cls": 1.8484, "loss": 1.8484, "time": 0.83409} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 4e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.68328, "top5_acc": 0.89031, "loss_cls": 1.78062, "loss": 1.78062, "time": 0.82639} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 3e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67891, "top5_acc": 0.8825, "loss_cls": 1.8277, "loss": 1.8277, "time": 0.83602} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 3e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67094, "top5_acc": 0.87562, "loss_cls": 1.85304, "loss": 1.85304, "time": 0.83249} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 3e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67, "top5_acc": 0.87516, "loss_cls": 1.87994, "loss": 1.87994, "time": 0.83045} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 3e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66422, "top5_acc": 0.87562, "loss_cls": 1.87782, "loss": 1.87782, "time": 0.82863} +{"mode": "train", "epoch": 149, "iter": 1300, "lr": 3e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.6725, "top5_acc": 0.87844, "loss_cls": 1.84459, "loss": 1.84459, "time": 0.81994} +{"mode": "train", "epoch": 149, "iter": 1400, "lr": 3e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67625, "top5_acc": 0.87703, "loss_cls": 1.83406, "loss": 1.83406, "time": 0.82552} +{"mode": "train", "epoch": 149, "iter": 1500, "lr": 3e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.6725, "top5_acc": 0.8825, "loss_cls": 1.84423, "loss": 1.84423, "time": 0.82565} +{"mode": "train", "epoch": 149, "iter": 1600, "lr": 3e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.68031, "top5_acc": 0.87922, "loss_cls": 1.82787, "loss": 1.82787, "time": 0.8301} +{"mode": "train", "epoch": 149, "iter": 1700, "lr": 3e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67453, "top5_acc": 0.87562, "loss_cls": 1.84775, "loss": 1.84775, "time": 0.82375} +{"mode": "train", "epoch": 149, "iter": 1800, "lr": 3e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.66719, "top5_acc": 0.87, "loss_cls": 1.88181, "loss": 1.88181, "time": 0.82324} +{"mode": "train", "epoch": 149, "iter": 1900, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67109, "top5_acc": 0.87953, "loss_cls": 1.83491, "loss": 1.83491, "time": 0.81757} +{"mode": "train", "epoch": 149, "iter": 2000, "lr": 2e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67469, "top5_acc": 0.87625, "loss_cls": 1.84502, "loss": 1.84502, "time": 0.81972} +{"mode": "train", "epoch": 149, "iter": 2100, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67344, "top5_acc": 0.8775, "loss_cls": 1.82704, "loss": 1.82704, "time": 0.81888} +{"mode": "train", "epoch": 149, "iter": 2200, "lr": 2e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67438, "top5_acc": 0.88047, "loss_cls": 1.84804, "loss": 1.84804, "time": 0.81678} +{"mode": "train", "epoch": 149, "iter": 2300, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.675, "top5_acc": 0.87781, "loss_cls": 1.84483, "loss": 1.84483, "time": 0.8215} +{"mode": "train", "epoch": 149, "iter": 2400, "lr": 2e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67438, "top5_acc": 0.87516, "loss_cls": 1.86023, "loss": 1.86023, "time": 0.81771} +{"mode": "train", "epoch": 149, "iter": 2500, "lr": 2e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67375, "top5_acc": 0.88234, "loss_cls": 1.84997, "loss": 1.84997, "time": 0.8245} +{"mode": "train", "epoch": 149, "iter": 2600, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67734, "top5_acc": 0.88234, "loss_cls": 1.84203, "loss": 1.84203, "time": 0.81958} +{"mode": "train", "epoch": 149, "iter": 2700, "lr": 2e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.66266, "top5_acc": 0.87188, "loss_cls": 1.88388, "loss": 1.88388, "time": 0.81892} +{"mode": "train", "epoch": 149, "iter": 2800, "lr": 2e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67859, "top5_acc": 0.88031, "loss_cls": 1.82727, "loss": 1.82727, "time": 0.82897} +{"mode": "train", "epoch": 149, "iter": 2900, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.68328, "top5_acc": 0.88812, "loss_cls": 1.81147, "loss": 1.81147, "time": 0.81671} +{"mode": "train", "epoch": 149, "iter": 3000, "lr": 2e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.67672, "top5_acc": 0.87922, "loss_cls": 1.85666, "loss": 1.85666, "time": 0.83147} +{"mode": "train", "epoch": 149, "iter": 3100, "lr": 2e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.67266, "top5_acc": 0.88016, "loss_cls": 1.84798, "loss": 1.84798, "time": 0.82275} +{"mode": "train", "epoch": 149, "iter": 3200, "lr": 1e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.68297, "top5_acc": 0.8775, "loss_cls": 1.82737, "loss": 1.82737, "time": 0.82482} +{"mode": "train", "epoch": 149, "iter": 3300, "lr": 1e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67906, "top5_acc": 0.88219, "loss_cls": 1.83555, "loss": 1.83555, "time": 0.82446} +{"mode": "train", "epoch": 149, "iter": 3400, "lr": 1e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67219, "top5_acc": 0.88422, "loss_cls": 1.83794, "loss": 1.83794, "time": 0.83215} +{"mode": "train", "epoch": 149, "iter": 3500, "lr": 1e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67297, "top5_acc": 0.87656, "loss_cls": 1.83105, "loss": 1.83105, "time": 0.82542} +{"mode": "train", "epoch": 149, "iter": 3600, "lr": 1e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67125, "top5_acc": 0.87859, "loss_cls": 1.84408, "loss": 1.84408, "time": 0.82425} +{"mode": "train", "epoch": 149, "iter": 3700, "lr": 1e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.675, "top5_acc": 0.87469, "loss_cls": 1.85088, "loss": 1.85088, "time": 0.81777} +{"mode": "val", "epoch": 149, "iter": 309, "lr": 1e-05, "top1_acc": 0.47232, "top5_acc": 0.7198, "mean_class_accuracy": 0.47205} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 1e-05, "memory": 15990, "data_time": 1.298, "top1_acc": 0.68406, "top5_acc": 0.87859, "loss_cls": 1.82736, "loss": 1.82736, "time": 2.28727} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 1e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.68047, "top5_acc": 0.88344, "loss_cls": 1.81328, "loss": 1.81328, "time": 0.8272} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 1e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67344, "top5_acc": 0.87641, "loss_cls": 1.85046, "loss": 1.85046, "time": 0.83873} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 1e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67031, "top5_acc": 0.87844, "loss_cls": 1.84791, "loss": 1.84791, "time": 0.82849} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 1e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66312, "top5_acc": 0.87, "loss_cls": 1.89362, "loss": 1.89362, "time": 0.83182} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 1e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67484, "top5_acc": 0.88, "loss_cls": 1.84961, "loss": 1.84961, "time": 0.83627} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 1e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.68188, "top5_acc": 0.88406, "loss_cls": 1.80082, "loss": 1.80082, "time": 0.83019} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 1e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67891, "top5_acc": 0.88531, "loss_cls": 1.82122, "loss": 1.82122, "time": 0.82262} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 1e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.68031, "top5_acc": 0.88156, "loss_cls": 1.84206, "loss": 1.84206, "time": 0.83096} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 1e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.67859, "top5_acc": 0.88281, "loss_cls": 1.81873, "loss": 1.81873, "time": 0.82534} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 1e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.68328, "top5_acc": 0.88109, "loss_cls": 1.83453, "loss": 1.83453, "time": 0.83207} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 1e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67062, "top5_acc": 0.87438, "loss_cls": 1.87733, "loss": 1.87733, "time": 0.82261} +{"mode": "train", "epoch": 150, "iter": 1300, "lr": 0.0, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67484, "top5_acc": 0.88172, "loss_cls": 1.82742, "loss": 1.82742, "time": 0.82289} +{"mode": "train", "epoch": 150, "iter": 1400, "lr": 0.0, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.68172, "top5_acc": 0.87719, "loss_cls": 1.83122, "loss": 1.83122, "time": 0.82208} +{"mode": "train", "epoch": 150, "iter": 1500, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.68328, "top5_acc": 0.88234, "loss_cls": 1.78383, "loss": 1.78383, "time": 0.81636} +{"mode": "train", "epoch": 150, "iter": 1600, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67531, "top5_acc": 0.87562, "loss_cls": 1.85595, "loss": 1.85595, "time": 0.81362} +{"mode": "train", "epoch": 150, "iter": 1700, "lr": 0.0, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67141, "top5_acc": 0.87469, "loss_cls": 1.83539, "loss": 1.83539, "time": 0.81445} +{"mode": "train", "epoch": 150, "iter": 1800, "lr": 0.0, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66984, "top5_acc": 0.87656, "loss_cls": 1.8656, "loss": 1.8656, "time": 0.81681} +{"mode": "train", "epoch": 150, "iter": 1900, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67156, "top5_acc": 0.87922, "loss_cls": 1.84345, "loss": 1.84345, "time": 0.8144} +{"mode": "train", "epoch": 150, "iter": 2000, "lr": 0.0, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66266, "top5_acc": 0.87703, "loss_cls": 1.88074, "loss": 1.88074, "time": 0.81598} +{"mode": "train", "epoch": 150, "iter": 2100, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67188, "top5_acc": 0.88219, "loss_cls": 1.84525, "loss": 1.84525, "time": 0.81581} +{"mode": "train", "epoch": 150, "iter": 2200, "lr": 0.0, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.68156, "top5_acc": 0.87703, "loss_cls": 1.8234, "loss": 1.8234, "time": 0.82055} +{"mode": "train", "epoch": 150, "iter": 2300, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.68203, "top5_acc": 0.88562, "loss_cls": 1.78775, "loss": 1.78775, "time": 0.81448} +{"mode": "train", "epoch": 150, "iter": 2400, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67141, "top5_acc": 0.87609, "loss_cls": 1.84785, "loss": 1.84785, "time": 0.81785} +{"mode": "train", "epoch": 150, "iter": 2500, "lr": 0.0, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.68219, "top5_acc": 0.88344, "loss_cls": 1.81244, "loss": 1.81244, "time": 0.81442} +{"mode": "train", "epoch": 150, "iter": 2600, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67891, "top5_acc": 0.87953, "loss_cls": 1.84495, "loss": 1.84495, "time": 0.80722} +{"mode": "train", "epoch": 150, "iter": 2700, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67453, "top5_acc": 0.87938, "loss_cls": 1.86371, "loss": 1.86371, "time": 0.81284} +{"mode": "train", "epoch": 150, "iter": 2800, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66078, "top5_acc": 0.87406, "loss_cls": 1.88648, "loss": 1.88648, "time": 0.81269} +{"mode": "train", "epoch": 150, "iter": 2900, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67109, "top5_acc": 0.87609, "loss_cls": 1.86974, "loss": 1.86974, "time": 0.8146} +{"mode": "train", "epoch": 150, "iter": 3000, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67219, "top5_acc": 0.88219, "loss_cls": 1.83948, "loss": 1.83948, "time": 0.82231} +{"mode": "train", "epoch": 150, "iter": 3100, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.6825, "top5_acc": 0.88484, "loss_cls": 1.8142, "loss": 1.8142, "time": 0.8157} +{"mode": "train", "epoch": 150, "iter": 3200, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.68922, "top5_acc": 0.8825, "loss_cls": 1.78508, "loss": 1.78508, "time": 0.82072} +{"mode": "train", "epoch": 150, "iter": 3300, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67219, "top5_acc": 0.87531, "loss_cls": 1.85448, "loss": 1.85448, "time": 0.81165} +{"mode": "train", "epoch": 150, "iter": 3400, "lr": 0.0, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.69, "top5_acc": 0.885, "loss_cls": 1.78882, "loss": 1.78882, "time": 0.80843} +{"mode": "train", "epoch": 150, "iter": 3500, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66719, "top5_acc": 0.87141, "loss_cls": 1.87363, "loss": 1.87363, "time": 0.81174} +{"mode": "train", "epoch": 150, "iter": 3600, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67797, "top5_acc": 0.88141, "loss_cls": 1.8378, "loss": 1.8378, "time": 0.8105} +{"mode": "train", "epoch": 150, "iter": 3700, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67625, "top5_acc": 0.88172, "loss_cls": 1.82208, "loss": 1.82208, "time": 0.81017} +{"mode": "val", "epoch": 150, "iter": 309, "lr": 0.0, "top1_acc": 0.47298, "top5_acc": 0.71929, "mean_class_accuracy": 0.47271} diff --git a/k400/j_2/best_pred.pkl b/k400/j_2/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..98ee5d4eb511a680fea575f6f1e36f71656af2f8 --- /dev/null +++ b/k400/j_2/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f0927807974d18c34933caa9c7ce569ccccb6e6fd7162880306d75e3eedf909 +size 44886568 diff --git a/k400/j_2/best_top1_acc_epoch_150.pth b/k400/j_2/best_top1_acc_epoch_150.pth new file mode 100644 index 0000000000000000000000000000000000000000..bdae2ad404bcae42ebb9954e3524476cc5778a82 --- /dev/null +++ b/k400/j_2/best_top1_acc_epoch_150.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0173dfda3198bb047e4a96273ad429655e116a4e721ca6772e820bcbbf0d2a4c +size 32926705 diff --git a/k400/j_2/j_2.py b/k400/j_2/j_2.py new file mode 100644 index 0000000000000000000000000000000000000000..0f1239feb03ef8bf6b09c182903a70d0cb413e9c --- /dev/null +++ b/k400/j_2/j_2.py @@ -0,0 +1,133 @@ +modality = 'j' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/j_2' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/k400/j_3/20240716_064615.log b/k400/j_3/20240716_064615.log new file mode 100644 index 0000000000000000000000000000000000000000..8839acdb6af5510a39caab137b2e372b180faeea --- /dev/null +++ b/k400/j_3/20240716_064615.log @@ -0,0 +1,7325 @@ +2024-07-16 06:46:15,256 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2024-07-16 06:46:15,577 - pyskl - INFO - Config: modality = 'j' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/j_3' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2024-07-16 06:46:15,577 - pyskl - INFO - Set random seed to 1362284316, deterministic: False +2024-07-16 06:46:25,831 - pyskl - INFO - 239737 videos remain after valid thresholding +2024-07-16 06:46:40,117 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-07-16 06:46:40,119 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3 +2024-07-16 06:46:40,126 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2024-07-16 06:46:40,148 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2024-07-16 06:46:40,152 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3 by HardDiskBackend. +2024-07-16 06:49:48,288 - pyskl - INFO - Epoch [1][100/3746] lr: 1.000e-01, eta: 12 days, 5:35:09, time: 1.881, data_time: 1.174, memory: 15990, top1_acc: 0.0053, top5_acc: 0.0266, loss_cls: 6.4777, loss: 6.4777 +2024-07-16 06:50:58,410 - pyskl - INFO - Epoch [1][200/3746] lr: 1.000e-01, eta: 8 days, 9:28:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0088, top5_acc: 0.0447, loss_cls: 6.4506, loss: 6.4506 +2024-07-16 06:52:08,374 - pyskl - INFO - Epoch [1][300/3746] lr: 1.000e-01, eta: 7 days, 2:40:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0163, top5_acc: 0.0678, loss_cls: 6.2620, loss: 6.2620 +2024-07-16 06:53:18,330 - pyskl - INFO - Epoch [1][400/3746] lr: 1.000e-01, eta: 6 days, 11:15:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0191, top5_acc: 0.0792, loss_cls: 6.1127, loss: 6.1127 +2024-07-16 06:54:28,531 - pyskl - INFO - Epoch [1][500/3746] lr: 1.000e-01, eta: 6 days, 2:04:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0252, top5_acc: 0.1003, loss_cls: 6.0058, loss: 6.0058 +2024-07-16 06:55:38,614 - pyskl - INFO - Epoch [1][600/3746] lr: 1.000e-01, eta: 5 days, 19:55:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0269, top5_acc: 0.1086, loss_cls: 5.8808, loss: 5.8808 +2024-07-16 06:56:48,867 - pyskl - INFO - Epoch [1][700/3746] lr: 1.000e-01, eta: 5 days, 15:33:25, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0311, top5_acc: 0.1158, loss_cls: 5.8533, loss: 5.8533 +2024-07-16 06:57:59,214 - pyskl - INFO - Epoch [1][800/3746] lr: 1.000e-01, eta: 5 days, 12:17:47, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0342, top5_acc: 0.1203, loss_cls: 5.8104, loss: 5.8104 +2024-07-16 06:59:09,328 - pyskl - INFO - Epoch [1][900/3746] lr: 1.000e-01, eta: 5 days, 9:42:57, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0361, top5_acc: 0.1330, loss_cls: 5.7639, loss: 5.7639 +2024-07-16 07:00:19,481 - pyskl - INFO - Epoch [1][1000/3746] lr: 1.000e-01, eta: 5 days, 7:39:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0389, top5_acc: 0.1436, loss_cls: 5.7138, loss: 5.7138 +2024-07-16 07:01:29,521 - pyskl - INFO - Epoch [1][1100/3746] lr: 1.000e-01, eta: 5 days, 5:56:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0403, top5_acc: 0.1452, loss_cls: 5.7213, loss: 5.7213 +2024-07-16 07:02:39,330 - pyskl - INFO - Epoch [1][1200/3746] lr: 1.000e-01, eta: 5 days, 4:29:27, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.0448, top5_acc: 0.1625, loss_cls: 5.6356, loss: 5.6356 +2024-07-16 07:03:49,202 - pyskl - INFO - Epoch [1][1300/3746] lr: 1.000e-01, eta: 5 days, 3:15:50, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0469, top5_acc: 0.1580, loss_cls: 5.6328, loss: 5.6328 +2024-07-16 07:04:59,321 - pyskl - INFO - Epoch [1][1400/3746] lr: 1.000e-01, eta: 5 days, 2:14:12, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0477, top5_acc: 0.1663, loss_cls: 5.5779, loss: 5.5779 +2024-07-16 07:06:09,285 - pyskl - INFO - Epoch [1][1500/3746] lr: 1.000e-01, eta: 5 days, 1:19:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0492, top5_acc: 0.1716, loss_cls: 5.5491, loss: 5.5491 +2024-07-16 07:07:19,078 - pyskl - INFO - Epoch [1][1600/3746] lr: 1.000e-01, eta: 5 days, 0:30:49, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.0502, top5_acc: 0.1697, loss_cls: 5.5792, loss: 5.5792 +2024-07-16 07:08:28,992 - pyskl - INFO - Epoch [1][1700/3746] lr: 1.000e-01, eta: 4 days, 23:48:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0566, top5_acc: 0.1786, loss_cls: 5.5474, loss: 5.5474 +2024-07-16 07:09:39,514 - pyskl - INFO - Epoch [1][1800/3746] lr: 1.000e-01, eta: 4 days, 23:13:24, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0612, top5_acc: 0.1923, loss_cls: 5.4733, loss: 5.4733 +2024-07-16 07:10:49,895 - pyskl - INFO - Epoch [1][1900/3746] lr: 1.000e-01, eta: 4 days, 22:41:25, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0575, top5_acc: 0.1833, loss_cls: 5.5017, loss: 5.5017 +2024-07-16 07:12:00,431 - pyskl - INFO - Epoch [1][2000/3746] lr: 1.000e-01, eta: 4 days, 22:13:15, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0655, top5_acc: 0.2031, loss_cls: 5.4467, loss: 5.4467 +2024-07-16 07:13:10,720 - pyskl - INFO - Epoch [1][2100/3746] lr: 1.000e-01, eta: 4 days, 21:46:33, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0661, top5_acc: 0.1986, loss_cls: 5.4360, loss: 5.4360 +2024-07-16 07:14:21,086 - pyskl - INFO - Epoch [1][2200/3746] lr: 1.000e-01, eta: 4 days, 21:22:30, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0672, top5_acc: 0.2148, loss_cls: 5.3981, loss: 5.3981 +2024-07-16 07:15:31,525 - pyskl - INFO - Epoch [1][2300/3746] lr: 1.000e-01, eta: 4 days, 21:00:43, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0700, top5_acc: 0.2105, loss_cls: 5.3866, loss: 5.3866 +2024-07-16 07:16:42,523 - pyskl - INFO - Epoch [1][2400/3746] lr: 1.000e-01, eta: 4 days, 20:42:51, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.0723, top5_acc: 0.2181, loss_cls: 5.3961, loss: 5.3961 +2024-07-16 07:17:53,006 - pyskl - INFO - Epoch [1][2500/3746] lr: 1.000e-01, eta: 4 days, 20:24:23, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0798, top5_acc: 0.2361, loss_cls: 5.3062, loss: 5.3062 +2024-07-16 07:19:03,404 - pyskl - INFO - Epoch [1][2600/3746] lr: 9.999e-02, eta: 4 days, 20:06:56, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0802, top5_acc: 0.2302, loss_cls: 5.3080, loss: 5.3080 +2024-07-16 07:20:14,658 - pyskl - INFO - Epoch [1][2700/3746] lr: 9.999e-02, eta: 4 days, 19:53:40, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.0856, top5_acc: 0.2450, loss_cls: 5.2866, loss: 5.2866 +2024-07-16 07:21:25,210 - pyskl - INFO - Epoch [1][2800/3746] lr: 9.999e-02, eta: 4 days, 19:38:55, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.0881, top5_acc: 0.2498, loss_cls: 5.2508, loss: 5.2508 +2024-07-16 07:22:35,908 - pyskl - INFO - Epoch [1][2900/3746] lr: 9.999e-02, eta: 4 days, 19:25:34, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.0903, top5_acc: 0.2517, loss_cls: 5.2214, loss: 5.2214 +2024-07-16 07:23:46,992 - pyskl - INFO - Epoch [1][3000/3746] lr: 9.999e-02, eta: 4 days, 19:14:14, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.0967, top5_acc: 0.2605, loss_cls: 5.1817, loss: 5.1817 +2024-07-16 07:24:57,645 - pyskl - INFO - Epoch [1][3100/3746] lr: 9.999e-02, eta: 4 days, 19:02:15, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.0895, top5_acc: 0.2691, loss_cls: 5.1932, loss: 5.1932 +2024-07-16 07:26:08,337 - pyskl - INFO - Epoch [1][3200/3746] lr: 9.999e-02, eta: 4 days, 18:51:04, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.0984, top5_acc: 0.2777, loss_cls: 5.1194, loss: 5.1194 +2024-07-16 07:27:18,888 - pyskl - INFO - Epoch [1][3300/3746] lr: 9.999e-02, eta: 4 days, 18:40:05, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0992, top5_acc: 0.2781, loss_cls: 5.1242, loss: 5.1242 +2024-07-16 07:28:29,633 - pyskl - INFO - Epoch [1][3400/3746] lr: 9.999e-02, eta: 4 days, 18:30:13, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1009, top5_acc: 0.2702, loss_cls: 5.1242, loss: 5.1242 +2024-07-16 07:29:40,272 - pyskl - INFO - Epoch [1][3500/3746] lr: 9.999e-02, eta: 4 days, 18:20:33, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1172, top5_acc: 0.2994, loss_cls: 5.0453, loss: 5.0453 +2024-07-16 07:30:50,386 - pyskl - INFO - Epoch [1][3600/3746] lr: 9.999e-02, eta: 4 days, 18:10:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1037, top5_acc: 0.2933, loss_cls: 5.0892, loss: 5.0892 +2024-07-16 07:32:00,795 - pyskl - INFO - Epoch [1][3700/3746] lr: 9.999e-02, eta: 4 days, 18:00:43, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1212, top5_acc: 0.3083, loss_cls: 5.0145, loss: 5.0145 +2024-07-16 07:32:35,067 - pyskl - INFO - Saving checkpoint at 1 epochs +2024-07-16 07:34:24,629 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 07:34:25,352 - pyskl - INFO - +top1_acc 0.0804 +top5_acc 0.2351 +2024-07-16 07:34:25,352 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 07:34:25,405 - pyskl - INFO - +mean_acc 0.0804 +2024-07-16 07:34:25,682 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2024-07-16 07:34:25,682 - pyskl - INFO - Best top1_acc is 0.0804 at 1 epoch. +2024-07-16 07:34:25,696 - pyskl - INFO - Epoch(val) [1][309] top1_acc: 0.0804, top5_acc: 0.2351, mean_class_accuracy: 0.0804 +2024-07-16 07:37:44,904 - pyskl - INFO - Epoch [2][100/3746] lr: 9.999e-02, eta: 4 days, 21:41:03, time: 1.992, data_time: 1.279, memory: 15990, top1_acc: 0.1141, top5_acc: 0.3005, loss_cls: 4.9951, loss: 4.9951 +2024-07-16 07:38:56,149 - pyskl - INFO - Epoch [2][200/3746] lr: 9.999e-02, eta: 4 days, 21:28:46, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1244, top5_acc: 0.3122, loss_cls: 4.9427, loss: 4.9427 +2024-07-16 07:40:06,700 - pyskl - INFO - Epoch [2][300/3746] lr: 9.999e-02, eta: 4 days, 21:15:26, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1184, top5_acc: 0.3141, loss_cls: 4.9627, loss: 4.9627 +2024-07-16 07:41:17,175 - pyskl - INFO - Epoch [2][400/3746] lr: 9.999e-02, eta: 4 days, 21:02:32, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1233, top5_acc: 0.3200, loss_cls: 4.9674, loss: 4.9674 +2024-07-16 07:42:27,196 - pyskl - INFO - Epoch [2][500/3746] lr: 9.999e-02, eta: 4 days, 20:49:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1252, top5_acc: 0.3203, loss_cls: 4.9415, loss: 4.9415 +2024-07-16 07:43:37,476 - pyskl - INFO - Epoch [2][600/3746] lr: 9.999e-02, eta: 4 days, 20:36:56, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1222, top5_acc: 0.3219, loss_cls: 4.9422, loss: 4.9422 +2024-07-16 07:44:47,614 - pyskl - INFO - Epoch [2][700/3746] lr: 9.998e-02, eta: 4 days, 20:24:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1281, top5_acc: 0.3328, loss_cls: 4.8801, loss: 4.8801 +2024-07-16 07:45:57,878 - pyskl - INFO - Epoch [2][800/3746] lr: 9.998e-02, eta: 4 days, 20:13:36, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1316, top5_acc: 0.3272, loss_cls: 4.9070, loss: 4.9070 +2024-07-16 07:47:08,409 - pyskl - INFO - Epoch [2][900/3746] lr: 9.998e-02, eta: 4 days, 20:03:16, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1342, top5_acc: 0.3316, loss_cls: 4.8856, loss: 4.8856 +2024-07-16 07:48:18,863 - pyskl - INFO - Epoch [2][1000/3746] lr: 9.998e-02, eta: 4 days, 19:53:10, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1364, top5_acc: 0.3452, loss_cls: 4.8492, loss: 4.8492 +2024-07-16 07:49:29,248 - pyskl - INFO - Epoch [2][1100/3746] lr: 9.998e-02, eta: 4 days, 19:43:19, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1420, top5_acc: 0.3552, loss_cls: 4.8174, loss: 4.8174 +2024-07-16 07:50:39,689 - pyskl - INFO - Epoch [2][1200/3746] lr: 9.998e-02, eta: 4 days, 19:33:55, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1431, top5_acc: 0.3414, loss_cls: 4.8303, loss: 4.8303 +2024-07-16 07:51:49,846 - pyskl - INFO - Epoch [2][1300/3746] lr: 9.998e-02, eta: 4 days, 19:24:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1398, top5_acc: 0.3506, loss_cls: 4.8199, loss: 4.8199 +2024-07-16 07:53:00,067 - pyskl - INFO - Epoch [2][1400/3746] lr: 9.998e-02, eta: 4 days, 19:15:09, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1416, top5_acc: 0.3467, loss_cls: 4.8357, loss: 4.8357 +2024-07-16 07:54:10,100 - pyskl - INFO - Epoch [2][1500/3746] lr: 9.998e-02, eta: 4 days, 19:05:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1394, top5_acc: 0.3403, loss_cls: 4.8507, loss: 4.8507 +2024-07-16 07:55:20,410 - pyskl - INFO - Epoch [2][1600/3746] lr: 9.998e-02, eta: 4 days, 18:57:34, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1437, top5_acc: 0.3513, loss_cls: 4.7956, loss: 4.7956 +2024-07-16 07:56:30,415 - pyskl - INFO - Epoch [2][1700/3746] lr: 9.998e-02, eta: 4 days, 18:48:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1508, top5_acc: 0.3619, loss_cls: 4.7811, loss: 4.7811 +2024-07-16 07:57:40,422 - pyskl - INFO - Epoch [2][1800/3746] lr: 9.998e-02, eta: 4 days, 18:40:32, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1550, top5_acc: 0.3684, loss_cls: 4.7450, loss: 4.7450 +2024-07-16 07:58:50,857 - pyskl - INFO - Epoch [2][1900/3746] lr: 9.998e-02, eta: 4 days, 18:33:06, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1519, top5_acc: 0.3644, loss_cls: 4.7501, loss: 4.7501 +2024-07-16 08:00:01,278 - pyskl - INFO - Epoch [2][2000/3746] lr: 9.997e-02, eta: 4 days, 18:25:52, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1497, top5_acc: 0.3672, loss_cls: 4.7349, loss: 4.7349 +2024-07-16 08:01:12,205 - pyskl - INFO - Epoch [2][2100/3746] lr: 9.997e-02, eta: 4 days, 18:19:39, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1555, top5_acc: 0.3767, loss_cls: 4.7161, loss: 4.7161 +2024-07-16 08:02:22,383 - pyskl - INFO - Epoch [2][2200/3746] lr: 9.997e-02, eta: 4 days, 18:12:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1577, top5_acc: 0.3669, loss_cls: 4.7283, loss: 4.7283 +2024-07-16 08:03:32,829 - pyskl - INFO - Epoch [2][2300/3746] lr: 9.997e-02, eta: 4 days, 18:05:49, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1562, top5_acc: 0.3741, loss_cls: 4.7232, loss: 4.7232 +2024-07-16 08:04:44,145 - pyskl - INFO - Epoch [2][2400/3746] lr: 9.997e-02, eta: 4 days, 18:00:42, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1517, top5_acc: 0.3719, loss_cls: 4.7082, loss: 4.7082 +2024-07-16 08:05:55,411 - pyskl - INFO - Epoch [2][2500/3746] lr: 9.997e-02, eta: 4 days, 17:55:38, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1588, top5_acc: 0.3733, loss_cls: 4.7403, loss: 4.7403 +2024-07-16 08:07:06,516 - pyskl - INFO - Epoch [2][2600/3746] lr: 9.997e-02, eta: 4 days, 17:50:27, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1555, top5_acc: 0.3653, loss_cls: 4.7131, loss: 4.7131 +2024-07-16 08:08:17,673 - pyskl - INFO - Epoch [2][2700/3746] lr: 9.997e-02, eta: 4 days, 17:45:28, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1400, top5_acc: 0.3577, loss_cls: 4.7855, loss: 4.7855 +2024-07-16 08:09:28,973 - pyskl - INFO - Epoch [2][2800/3746] lr: 9.997e-02, eta: 4 days, 17:40:48, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1539, top5_acc: 0.3694, loss_cls: 4.7219, loss: 4.7219 +2024-07-16 08:10:39,987 - pyskl - INFO - Epoch [2][2900/3746] lr: 9.997e-02, eta: 4 days, 17:35:51, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1578, top5_acc: 0.3791, loss_cls: 4.6805, loss: 4.6805 +2024-07-16 08:11:50,748 - pyskl - INFO - Epoch [2][3000/3746] lr: 9.996e-02, eta: 4 days, 17:30:39, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1652, top5_acc: 0.3827, loss_cls: 4.6510, loss: 4.6510 +2024-07-16 08:13:01,747 - pyskl - INFO - Epoch [2][3100/3746] lr: 9.996e-02, eta: 4 days, 17:25:54, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1655, top5_acc: 0.3827, loss_cls: 4.6505, loss: 4.6505 +2024-07-16 08:14:12,485 - pyskl - INFO - Epoch [2][3200/3746] lr: 9.996e-02, eta: 4 days, 17:20:54, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1648, top5_acc: 0.3862, loss_cls: 4.6709, loss: 4.6709 +2024-07-16 08:15:23,419 - pyskl - INFO - Epoch [2][3300/3746] lr: 9.996e-02, eta: 4 days, 17:16:16, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1695, top5_acc: 0.3931, loss_cls: 4.6452, loss: 4.6452 +2024-07-16 08:16:34,345 - pyskl - INFO - Epoch [2][3400/3746] lr: 9.996e-02, eta: 4 days, 17:11:43, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1717, top5_acc: 0.4153, loss_cls: 4.5758, loss: 4.5758 +2024-07-16 08:17:45,411 - pyskl - INFO - Epoch [2][3500/3746] lr: 9.996e-02, eta: 4 days, 17:07:27, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1650, top5_acc: 0.3852, loss_cls: 4.6415, loss: 4.6415 +2024-07-16 08:18:56,654 - pyskl - INFO - Epoch [2][3600/3746] lr: 9.996e-02, eta: 4 days, 17:03:29, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1636, top5_acc: 0.3758, loss_cls: 4.7179, loss: 4.7179 +2024-07-16 08:20:07,754 - pyskl - INFO - Epoch [2][3700/3746] lr: 9.996e-02, eta: 4 days, 16:59:25, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1784, top5_acc: 0.4041, loss_cls: 4.5916, loss: 4.5916 +2024-07-16 08:20:42,006 - pyskl - INFO - Saving checkpoint at 2 epochs +2024-07-16 08:22:32,477 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 08:22:33,191 - pyskl - INFO - +top1_acc 0.0997 +top5_acc 0.2611 +2024-07-16 08:22:33,192 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 08:22:33,242 - pyskl - INFO - +mean_acc 0.0995 +2024-07-16 08:22:33,251 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_1.pth was removed +2024-07-16 08:22:33,489 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2024-07-16 08:22:33,490 - pyskl - INFO - Best top1_acc is 0.0997 at 2 epoch. +2024-07-16 08:22:33,510 - pyskl - INFO - Epoch(val) [2][309] top1_acc: 0.0997, top5_acc: 0.2611, mean_class_accuracy: 0.0995 +2024-07-16 08:25:53,014 - pyskl - INFO - Epoch [3][100/3746] lr: 9.995e-02, eta: 4 days, 18:50:03, time: 1.995, data_time: 1.284, memory: 15990, top1_acc: 0.1666, top5_acc: 0.3964, loss_cls: 4.5869, loss: 4.5869 +2024-07-16 08:27:04,498 - pyskl - INFO - Epoch [3][200/3746] lr: 9.995e-02, eta: 4 days, 18:45:05, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1856, top5_acc: 0.4039, loss_cls: 4.5494, loss: 4.5494 +2024-07-16 08:28:15,464 - pyskl - INFO - Epoch [3][300/3746] lr: 9.995e-02, eta: 4 days, 18:39:36, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1777, top5_acc: 0.4088, loss_cls: 4.5681, loss: 4.5681 +2024-07-16 08:29:25,825 - pyskl - INFO - Epoch [3][400/3746] lr: 9.995e-02, eta: 4 days, 18:33:32, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1875, top5_acc: 0.4016, loss_cls: 4.5543, loss: 4.5543 +2024-07-16 08:30:36,089 - pyskl - INFO - Epoch [3][500/3746] lr: 9.995e-02, eta: 4 days, 18:27:28, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1775, top5_acc: 0.4041, loss_cls: 4.5920, loss: 4.5920 +2024-07-16 08:31:46,477 - pyskl - INFO - Epoch [3][600/3746] lr: 9.995e-02, eta: 4 days, 18:21:39, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1733, top5_acc: 0.3970, loss_cls: 4.5925, loss: 4.5925 +2024-07-16 08:32:56,817 - pyskl - INFO - Epoch [3][700/3746] lr: 9.995e-02, eta: 4 days, 18:15:54, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1786, top5_acc: 0.4100, loss_cls: 4.5378, loss: 4.5378 +2024-07-16 08:34:07,036 - pyskl - INFO - Epoch [3][800/3746] lr: 9.995e-02, eta: 4 days, 18:10:08, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1805, top5_acc: 0.4006, loss_cls: 4.5773, loss: 4.5773 +2024-07-16 08:35:17,463 - pyskl - INFO - Epoch [3][900/3746] lr: 9.994e-02, eta: 4 days, 18:04:42, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1822, top5_acc: 0.4114, loss_cls: 4.5382, loss: 4.5382 +2024-07-16 08:36:27,653 - pyskl - INFO - Epoch [3][1000/3746] lr: 9.994e-02, eta: 4 days, 17:59:07, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1747, top5_acc: 0.4098, loss_cls: 4.5469, loss: 4.5469 +2024-07-16 08:37:38,036 - pyskl - INFO - Epoch [3][1100/3746] lr: 9.994e-02, eta: 4 days, 17:53:50, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1759, top5_acc: 0.4153, loss_cls: 4.5386, loss: 4.5386 +2024-07-16 08:38:48,427 - pyskl - INFO - Epoch [3][1200/3746] lr: 9.994e-02, eta: 4 days, 17:48:40, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1794, top5_acc: 0.4197, loss_cls: 4.5193, loss: 4.5193 +2024-07-16 08:39:58,805 - pyskl - INFO - Epoch [3][1300/3746] lr: 9.994e-02, eta: 4 days, 17:43:34, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1853, top5_acc: 0.4138, loss_cls: 4.5234, loss: 4.5234 +2024-07-16 08:41:09,183 - pyskl - INFO - Epoch [3][1400/3746] lr: 9.994e-02, eta: 4 days, 17:38:33, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1855, top5_acc: 0.4127, loss_cls: 4.5440, loss: 4.5440 +2024-07-16 08:42:19,181 - pyskl - INFO - Epoch [3][1500/3746] lr: 9.994e-02, eta: 4 days, 17:33:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1881, top5_acc: 0.4177, loss_cls: 4.5175, loss: 4.5175 +2024-07-16 08:43:29,484 - pyskl - INFO - Epoch [3][1600/3746] lr: 9.994e-02, eta: 4 days, 17:28:19, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1797, top5_acc: 0.4125, loss_cls: 4.5271, loss: 4.5271 +2024-07-16 08:44:40,169 - pyskl - INFO - Epoch [3][1700/3746] lr: 9.993e-02, eta: 4 days, 17:23:52, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1870, top5_acc: 0.4228, loss_cls: 4.5159, loss: 4.5159 +2024-07-16 08:45:50,778 - pyskl - INFO - Epoch [3][1800/3746] lr: 9.993e-02, eta: 4 days, 17:19:25, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1894, top5_acc: 0.4109, loss_cls: 4.4907, loss: 4.4907 +2024-07-16 08:47:01,179 - pyskl - INFO - Epoch [3][1900/3746] lr: 9.993e-02, eta: 4 days, 17:14:50, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1864, top5_acc: 0.4263, loss_cls: 4.4841, loss: 4.4841 +2024-07-16 08:48:11,906 - pyskl - INFO - Epoch [3][2000/3746] lr: 9.993e-02, eta: 4 days, 17:10:38, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1852, top5_acc: 0.4103, loss_cls: 4.4896, loss: 4.4896 +2024-07-16 08:49:22,658 - pyskl - INFO - Epoch [3][2100/3746] lr: 9.993e-02, eta: 4 days, 17:06:31, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1800, top5_acc: 0.4156, loss_cls: 4.4883, loss: 4.4883 +2024-07-16 08:50:33,371 - pyskl - INFO - Epoch [3][2200/3746] lr: 9.993e-02, eta: 4 days, 17:02:26, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1961, top5_acc: 0.4263, loss_cls: 4.4304, loss: 4.4304 +2024-07-16 08:51:43,680 - pyskl - INFO - Epoch [3][2300/3746] lr: 9.993e-02, eta: 4 days, 16:58:01, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1925, top5_acc: 0.4261, loss_cls: 4.4769, loss: 4.4769 +2024-07-16 08:52:55,049 - pyskl - INFO - Epoch [3][2400/3746] lr: 9.992e-02, eta: 4 days, 16:54:39, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2033, top5_acc: 0.4338, loss_cls: 4.4346, loss: 4.4346 +2024-07-16 08:54:06,090 - pyskl - INFO - Epoch [3][2500/3746] lr: 9.992e-02, eta: 4 days, 16:51:02, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1884, top5_acc: 0.4245, loss_cls: 4.4710, loss: 4.4710 +2024-07-16 08:55:17,006 - pyskl - INFO - Epoch [3][2600/3746] lr: 9.992e-02, eta: 4 days, 16:47:21, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1923, top5_acc: 0.4198, loss_cls: 4.5027, loss: 4.5027 +2024-07-16 08:56:27,514 - pyskl - INFO - Epoch [3][2700/3746] lr: 9.992e-02, eta: 4 days, 16:43:21, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1970, top5_acc: 0.4264, loss_cls: 4.4840, loss: 4.4840 +2024-07-16 08:57:38,146 - pyskl - INFO - Epoch [3][2800/3746] lr: 9.992e-02, eta: 4 days, 16:39:31, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1852, top5_acc: 0.4158, loss_cls: 4.5154, loss: 4.5154 +2024-07-16 08:58:48,908 - pyskl - INFO - Epoch [3][2900/3746] lr: 9.992e-02, eta: 4 days, 16:35:51, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4402, loss_cls: 4.4096, loss: 4.4096 +2024-07-16 08:59:59,803 - pyskl - INFO - Epoch [3][3000/3746] lr: 9.991e-02, eta: 4 days, 16:32:20, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4397, loss_cls: 4.4111, loss: 4.4111 +2024-07-16 09:01:10,964 - pyskl - INFO - Epoch [3][3100/3746] lr: 9.991e-02, eta: 4 days, 16:29:07, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1927, top5_acc: 0.4233, loss_cls: 4.4823, loss: 4.4823 +2024-07-16 09:02:21,755 - pyskl - INFO - Epoch [3][3200/3746] lr: 9.991e-02, eta: 4 days, 16:25:36, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4266, loss_cls: 4.4401, loss: 4.4401 +2024-07-16 09:03:32,290 - pyskl - INFO - Epoch [3][3300/3746] lr: 9.991e-02, eta: 4 days, 16:21:55, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4400, loss_cls: 4.4075, loss: 4.4075 +2024-07-16 09:04:43,394 - pyskl - INFO - Epoch [3][3400/3746] lr: 9.991e-02, eta: 4 days, 16:18:45, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1975, top5_acc: 0.4320, loss_cls: 4.4409, loss: 4.4409 +2024-07-16 09:05:54,361 - pyskl - INFO - Epoch [3][3500/3746] lr: 9.991e-02, eta: 4 days, 16:15:31, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4517, loss_cls: 4.3845, loss: 4.3845 +2024-07-16 09:07:04,924 - pyskl - INFO - Epoch [3][3600/3746] lr: 9.990e-02, eta: 4 days, 16:11:59, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4484, loss_cls: 4.3779, loss: 4.3779 +2024-07-16 09:08:15,694 - pyskl - INFO - Epoch [3][3700/3746] lr: 9.990e-02, eta: 4 days, 16:08:39, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4386, loss_cls: 4.4040, loss: 4.4040 +2024-07-16 09:08:49,542 - pyskl - INFO - Saving checkpoint at 3 epochs +2024-07-16 09:10:40,894 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 09:10:41,577 - pyskl - INFO - +top1_acc 0.1102 +top5_acc 0.2990 +2024-07-16 09:10:41,578 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 09:10:41,617 - pyskl - INFO - +mean_acc 0.1100 +2024-07-16 09:10:41,622 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_2.pth was removed +2024-07-16 09:10:41,863 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2024-07-16 09:10:41,863 - pyskl - INFO - Best top1_acc is 0.1102 at 3 epoch. +2024-07-16 09:10:41,874 - pyskl - INFO - Epoch(val) [3][309] top1_acc: 0.1102, top5_acc: 0.2990, mean_class_accuracy: 0.1100 +2024-07-16 09:14:02,321 - pyskl - INFO - Epoch [4][100/3746] lr: 9.990e-02, eta: 4 days, 17:22:28, time: 2.004, data_time: 1.294, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4511, loss_cls: 4.3748, loss: 4.3748 +2024-07-16 09:15:13,622 - pyskl - INFO - Epoch [4][200/3746] lr: 9.990e-02, eta: 4 days, 17:18:57, time: 0.713, data_time: 0.001, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4356, loss_cls: 4.3836, loss: 4.3836 +2024-07-16 09:16:24,515 - pyskl - INFO - Epoch [4][300/3746] lr: 9.990e-02, eta: 4 days, 17:15:10, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4617, loss_cls: 4.3235, loss: 4.3235 +2024-07-16 09:17:35,428 - pyskl - INFO - Epoch [4][400/3746] lr: 9.989e-02, eta: 4 days, 17:11:26, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4475, loss_cls: 4.3831, loss: 4.3831 +2024-07-16 09:18:45,804 - pyskl - INFO - Epoch [4][500/3746] lr: 9.989e-02, eta: 4 days, 17:07:19, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1989, top5_acc: 0.4363, loss_cls: 4.4210, loss: 4.4210 +2024-07-16 09:19:56,063 - pyskl - INFO - Epoch [4][600/3746] lr: 9.989e-02, eta: 4 days, 17:03:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1995, top5_acc: 0.4458, loss_cls: 4.3682, loss: 4.3682 +2024-07-16 09:21:06,264 - pyskl - INFO - Epoch [4][700/3746] lr: 9.989e-02, eta: 4 days, 16:59:02, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4417, loss_cls: 4.4020, loss: 4.4020 +2024-07-16 09:22:16,444 - pyskl - INFO - Epoch [4][800/3746] lr: 9.989e-02, eta: 4 days, 16:54:55, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4420, loss_cls: 4.3905, loss: 4.3905 +2024-07-16 09:23:26,446 - pyskl - INFO - Epoch [4][900/3746] lr: 9.988e-02, eta: 4 days, 16:50:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4506, loss_cls: 4.3667, loss: 4.3667 +2024-07-16 09:24:36,385 - pyskl - INFO - Epoch [4][1000/3746] lr: 9.988e-02, eta: 4 days, 16:46:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4383, loss_cls: 4.3792, loss: 4.3792 +2024-07-16 09:25:46,480 - pyskl - INFO - Epoch [4][1100/3746] lr: 9.988e-02, eta: 4 days, 16:42:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1989, top5_acc: 0.4383, loss_cls: 4.4354, loss: 4.4354 +2024-07-16 09:26:56,513 - pyskl - INFO - Epoch [4][1200/3746] lr: 9.988e-02, eta: 4 days, 16:38:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4464, loss_cls: 4.3874, loss: 4.3874 +2024-07-16 09:28:06,856 - pyskl - INFO - Epoch [4][1300/3746] lr: 9.988e-02, eta: 4 days, 16:34:43, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4517, loss_cls: 4.3341, loss: 4.3341 +2024-07-16 09:29:16,915 - pyskl - INFO - Epoch [4][1400/3746] lr: 9.988e-02, eta: 4 days, 16:30:48, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4417, loss_cls: 4.4028, loss: 4.4028 +2024-07-16 09:30:26,912 - pyskl - INFO - Epoch [4][1500/3746] lr: 9.987e-02, eta: 4 days, 16:26:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4412, loss_cls: 4.3926, loss: 4.3926 +2024-07-16 09:31:37,311 - pyskl - INFO - Epoch [4][1600/3746] lr: 9.987e-02, eta: 4 days, 16:23:17, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2108, top5_acc: 0.4495, loss_cls: 4.3690, loss: 4.3690 +2024-07-16 09:32:47,357 - pyskl - INFO - Epoch [4][1700/3746] lr: 9.987e-02, eta: 4 days, 16:19:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4527, loss_cls: 4.3652, loss: 4.3652 +2024-07-16 09:33:57,454 - pyskl - INFO - Epoch [4][1800/3746] lr: 9.987e-02, eta: 4 days, 16:15:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4477, loss_cls: 4.3480, loss: 4.3480 +2024-07-16 09:35:07,613 - pyskl - INFO - Epoch [4][1900/3746] lr: 9.987e-02, eta: 4 days, 16:12:06, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2059, top5_acc: 0.4486, loss_cls: 4.3766, loss: 4.3766 +2024-07-16 09:36:17,543 - pyskl - INFO - Epoch [4][2000/3746] lr: 9.986e-02, eta: 4 days, 16:08:20, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4584, loss_cls: 4.3308, loss: 4.3308 +2024-07-16 09:37:28,191 - pyskl - INFO - Epoch [4][2100/3746] lr: 9.986e-02, eta: 4 days, 16:05:06, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4573, loss_cls: 4.3264, loss: 4.3264 +2024-07-16 09:38:38,057 - pyskl - INFO - Epoch [4][2200/3746] lr: 9.986e-02, eta: 4 days, 16:01:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4628, loss_cls: 4.3059, loss: 4.3059 +2024-07-16 09:39:48,523 - pyskl - INFO - Epoch [4][2300/3746] lr: 9.986e-02, eta: 4 days, 15:58:04, time: 0.705, data_time: 0.001, memory: 15990, top1_acc: 0.2045, top5_acc: 0.4511, loss_cls: 4.3448, loss: 4.3448 +2024-07-16 09:40:59,861 - pyskl - INFO - Epoch [4][2400/3746] lr: 9.985e-02, eta: 4 days, 15:55:24, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4495, loss_cls: 4.3784, loss: 4.3784 +2024-07-16 09:42:10,634 - pyskl - INFO - Epoch [4][2500/3746] lr: 9.985e-02, eta: 4 days, 15:52:22, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4467, loss_cls: 4.3796, loss: 4.3796 +2024-07-16 09:43:21,587 - pyskl - INFO - Epoch [4][2600/3746] lr: 9.985e-02, eta: 4 days, 15:49:28, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4531, loss_cls: 4.3620, loss: 4.3620 +2024-07-16 09:44:32,000 - pyskl - INFO - Epoch [4][2700/3746] lr: 9.985e-02, eta: 4 days, 15:46:15, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4548, loss_cls: 4.3403, loss: 4.3403 +2024-07-16 09:45:42,388 - pyskl - INFO - Epoch [4][2800/3746] lr: 9.985e-02, eta: 4 days, 15:43:03, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4525, loss_cls: 4.3487, loss: 4.3487 +2024-07-16 09:46:52,944 - pyskl - INFO - Epoch [4][2900/3746] lr: 9.984e-02, eta: 4 days, 15:39:59, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4686, loss_cls: 4.3081, loss: 4.3081 +2024-07-16 09:48:03,102 - pyskl - INFO - Epoch [4][3000/3746] lr: 9.984e-02, eta: 4 days, 15:36:41, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4545, loss_cls: 4.3280, loss: 4.3280 +2024-07-16 09:49:13,429 - pyskl - INFO - Epoch [4][3100/3746] lr: 9.984e-02, eta: 4 days, 15:33:32, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4539, loss_cls: 4.3519, loss: 4.3519 +2024-07-16 09:50:23,967 - pyskl - INFO - Epoch [4][3200/3746] lr: 9.984e-02, eta: 4 days, 15:30:32, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4462, loss_cls: 4.3484, loss: 4.3484 +2024-07-16 09:51:34,894 - pyskl - INFO - Epoch [4][3300/3746] lr: 9.983e-02, eta: 4 days, 15:27:48, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2056, top5_acc: 0.4544, loss_cls: 4.3442, loss: 4.3442 +2024-07-16 09:52:45,515 - pyskl - INFO - Epoch [4][3400/3746] lr: 9.983e-02, eta: 4 days, 15:24:54, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4688, loss_cls: 4.2978, loss: 4.2978 +2024-07-16 09:53:56,143 - pyskl - INFO - Epoch [4][3500/3746] lr: 9.983e-02, eta: 4 days, 15:22:02, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4528, loss_cls: 4.2968, loss: 4.2968 +2024-07-16 09:55:06,972 - pyskl - INFO - Epoch [4][3600/3746] lr: 9.983e-02, eta: 4 days, 15:19:19, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4611, loss_cls: 4.3111, loss: 4.3111 +2024-07-16 09:56:17,472 - pyskl - INFO - Epoch [4][3700/3746] lr: 9.983e-02, eta: 4 days, 15:16:25, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4611, loss_cls: 4.2949, loss: 4.2949 +2024-07-16 09:56:51,582 - pyskl - INFO - Saving checkpoint at 4 epochs +2024-07-16 09:58:42,389 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 09:58:43,061 - pyskl - INFO - +top1_acc 0.1475 +top5_acc 0.3557 +2024-07-16 09:58:43,061 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 09:58:43,097 - pyskl - INFO - +mean_acc 0.1474 +2024-07-16 09:58:43,102 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_3.pth was removed +2024-07-16 09:58:43,504 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2024-07-16 09:58:43,505 - pyskl - INFO - Best top1_acc is 0.1475 at 4 epoch. +2024-07-16 09:58:43,514 - pyskl - INFO - Epoch(val) [4][309] top1_acc: 0.1475, top5_acc: 0.3557, mean_class_accuracy: 0.1474 +2024-07-16 10:01:58,787 - pyskl - INFO - Epoch [5][100/3746] lr: 9.982e-02, eta: 4 days, 16:08:00, time: 1.953, data_time: 1.237, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4608, loss_cls: 4.2828, loss: 4.2828 +2024-07-16 10:03:10,232 - pyskl - INFO - Epoch [5][200/3746] lr: 9.982e-02, eta: 4 days, 16:05:20, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4589, loss_cls: 4.2915, loss: 4.2915 +2024-07-16 10:04:20,708 - pyskl - INFO - Epoch [5][300/3746] lr: 9.982e-02, eta: 4 days, 16:02:07, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4641, loss_cls: 4.2755, loss: 4.2755 +2024-07-16 10:05:31,183 - pyskl - INFO - Epoch [5][400/3746] lr: 9.982e-02, eta: 4 days, 15:58:56, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4630, loss_cls: 4.2845, loss: 4.2845 +2024-07-16 10:06:42,103 - pyskl - INFO - Epoch [5][500/3746] lr: 9.981e-02, eta: 4 days, 15:56:02, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4614, loss_cls: 4.2985, loss: 4.2985 +2024-07-16 10:07:52,564 - pyskl - INFO - Epoch [5][600/3746] lr: 9.981e-02, eta: 4 days, 15:52:53, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4577, loss_cls: 4.3187, loss: 4.3187 +2024-07-16 10:09:03,164 - pyskl - INFO - Epoch [5][700/3746] lr: 9.981e-02, eta: 4 days, 15:49:50, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4673, loss_cls: 4.2959, loss: 4.2959 +2024-07-16 10:10:13,520 - pyskl - INFO - Epoch [5][800/3746] lr: 9.981e-02, eta: 4 days, 15:46:41, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4645, loss_cls: 4.2714, loss: 4.2714 +2024-07-16 10:11:23,988 - pyskl - INFO - Epoch [5][900/3746] lr: 9.980e-02, eta: 4 days, 15:43:36, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4525, loss_cls: 4.3375, loss: 4.3375 +2024-07-16 10:12:34,612 - pyskl - INFO - Epoch [5][1000/3746] lr: 9.980e-02, eta: 4 days, 15:40:39, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4623, loss_cls: 4.3071, loss: 4.3071 +2024-07-16 10:13:45,132 - pyskl - INFO - Epoch [5][1100/3746] lr: 9.980e-02, eta: 4 days, 15:37:39, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4637, loss_cls: 4.2998, loss: 4.2998 +2024-07-16 10:14:55,651 - pyskl - INFO - Epoch [5][1200/3746] lr: 9.980e-02, eta: 4 days, 15:34:41, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4684, loss_cls: 4.2671, loss: 4.2671 +2024-07-16 10:16:06,292 - pyskl - INFO - Epoch [5][1300/3746] lr: 9.979e-02, eta: 4 days, 15:31:48, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4673, loss_cls: 4.2748, loss: 4.2748 +2024-07-16 10:17:16,489 - pyskl - INFO - Epoch [5][1400/3746] lr: 9.979e-02, eta: 4 days, 15:28:41, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4569, loss_cls: 4.3324, loss: 4.3324 +2024-07-16 10:18:26,828 - pyskl - INFO - Epoch [5][1500/3746] lr: 9.979e-02, eta: 4 days, 15:25:41, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4719, loss_cls: 4.2694, loss: 4.2694 +2024-07-16 10:19:37,171 - pyskl - INFO - Epoch [5][1600/3746] lr: 9.979e-02, eta: 4 days, 15:22:42, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4686, loss_cls: 4.2667, loss: 4.2667 +2024-07-16 10:20:47,312 - pyskl - INFO - Epoch [5][1700/3746] lr: 9.978e-02, eta: 4 days, 15:19:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4789, loss_cls: 4.2517, loss: 4.2517 +2024-07-16 10:21:58,143 - pyskl - INFO - Epoch [5][1800/3746] lr: 9.978e-02, eta: 4 days, 15:16:57, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4741, loss_cls: 4.2800, loss: 4.2800 +2024-07-16 10:23:08,703 - pyskl - INFO - Epoch [5][1900/3746] lr: 9.978e-02, eta: 4 days, 15:14:09, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4625, loss_cls: 4.2700, loss: 4.2700 +2024-07-16 10:24:19,060 - pyskl - INFO - Epoch [5][2000/3746] lr: 9.977e-02, eta: 4 days, 15:11:15, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4706, loss_cls: 4.2525, loss: 4.2525 +2024-07-16 10:25:29,882 - pyskl - INFO - Epoch [5][2100/3746] lr: 9.977e-02, eta: 4 days, 15:08:38, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4744, loss_cls: 4.2463, loss: 4.2463 +2024-07-16 10:26:40,410 - pyskl - INFO - Epoch [5][2200/3746] lr: 9.977e-02, eta: 4 days, 15:05:52, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4622, loss_cls: 4.3026, loss: 4.3026 +2024-07-16 10:27:50,957 - pyskl - INFO - Epoch [5][2300/3746] lr: 9.977e-02, eta: 4 days, 15:03:08, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4720, loss_cls: 4.2656, loss: 4.2656 +2024-07-16 10:29:02,243 - pyskl - INFO - Epoch [5][2400/3746] lr: 9.976e-02, eta: 4 days, 15:00:48, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4859, loss_cls: 4.2257, loss: 4.2257 +2024-07-16 10:30:12,471 - pyskl - INFO - Epoch [5][2500/3746] lr: 9.976e-02, eta: 4 days, 14:57:56, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4514, loss_cls: 4.3185, loss: 4.3185 +2024-07-16 10:31:23,492 - pyskl - INFO - Epoch [5][2600/3746] lr: 9.976e-02, eta: 4 days, 14:55:30, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4670, loss_cls: 4.2804, loss: 4.2804 +2024-07-16 10:32:34,726 - pyskl - INFO - Epoch [5][2700/3746] lr: 9.976e-02, eta: 4 days, 14:53:11, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4703, loss_cls: 4.2681, loss: 4.2681 +2024-07-16 10:33:45,359 - pyskl - INFO - Epoch [5][2800/3746] lr: 9.975e-02, eta: 4 days, 14:50:34, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4600, loss_cls: 4.2812, loss: 4.2812 +2024-07-16 10:34:56,173 - pyskl - INFO - Epoch [5][2900/3746] lr: 9.975e-02, eta: 4 days, 14:48:04, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4623, loss_cls: 4.2983, loss: 4.2983 +2024-07-16 10:36:07,222 - pyskl - INFO - Epoch [5][3000/3746] lr: 9.975e-02, eta: 4 days, 14:45:42, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4612, loss_cls: 4.2887, loss: 4.2887 +2024-07-16 10:37:18,053 - pyskl - INFO - Epoch [5][3100/3746] lr: 9.974e-02, eta: 4 days, 14:43:14, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4684, loss_cls: 4.2760, loss: 4.2760 +2024-07-16 10:38:28,345 - pyskl - INFO - Epoch [5][3200/3746] lr: 9.974e-02, eta: 4 days, 14:40:31, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4722, loss_cls: 4.2437, loss: 4.2437 +2024-07-16 10:39:38,783 - pyskl - INFO - Epoch [5][3300/3746] lr: 9.974e-02, eta: 4 days, 14:37:53, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4597, loss_cls: 4.3162, loss: 4.3162 +2024-07-16 10:40:49,625 - pyskl - INFO - Epoch [5][3400/3746] lr: 9.974e-02, eta: 4 days, 14:35:28, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4695, loss_cls: 4.2579, loss: 4.2579 +2024-07-16 10:42:00,785 - pyskl - INFO - Epoch [5][3500/3746] lr: 9.973e-02, eta: 4 days, 14:33:14, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4659, loss_cls: 4.2706, loss: 4.2706 +2024-07-16 10:43:11,438 - pyskl - INFO - Epoch [5][3600/3746] lr: 9.973e-02, eta: 4 days, 14:30:45, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4741, loss_cls: 4.2224, loss: 4.2224 +2024-07-16 10:44:21,624 - pyskl - INFO - Epoch [5][3700/3746] lr: 9.973e-02, eta: 4 days, 14:28:03, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4692, loss_cls: 4.2584, loss: 4.2584 +2024-07-16 10:44:55,746 - pyskl - INFO - Saving checkpoint at 5 epochs +2024-07-16 10:46:45,687 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 10:46:46,704 - pyskl - INFO - +top1_acc 0.1676 +top5_acc 0.3828 +2024-07-16 10:46:46,704 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 10:46:46,744 - pyskl - INFO - +mean_acc 0.1675 +2024-07-16 10:46:46,749 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_4.pth was removed +2024-07-16 10:46:47,017 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2024-07-16 10:46:47,018 - pyskl - INFO - Best top1_acc is 0.1676 at 5 epoch. +2024-07-16 10:46:47,029 - pyskl - INFO - Epoch(val) [5][309] top1_acc: 0.1676, top5_acc: 0.3828, mean_class_accuracy: 0.1675 +2024-07-16 10:50:07,110 - pyskl - INFO - Epoch [6][100/3746] lr: 9.972e-02, eta: 4 days, 15:11:04, time: 2.001, data_time: 1.288, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4819, loss_cls: 4.2315, loss: 4.2315 +2024-07-16 10:51:18,565 - pyskl - INFO - Epoch [6][200/3746] lr: 9.972e-02, eta: 4 days, 15:08:45, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4750, loss_cls: 4.2147, loss: 4.2147 +2024-07-16 10:52:29,032 - pyskl - INFO - Epoch [6][300/3746] lr: 9.972e-02, eta: 4 days, 15:06:00, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4694, loss_cls: 4.2563, loss: 4.2563 +2024-07-16 10:53:39,365 - pyskl - INFO - Epoch [6][400/3746] lr: 9.971e-02, eta: 4 days, 15:03:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4838, loss_cls: 4.1806, loss: 4.1806 +2024-07-16 10:54:49,738 - pyskl - INFO - Epoch [6][500/3746] lr: 9.971e-02, eta: 4 days, 15:00:25, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4672, loss_cls: 4.2384, loss: 4.2384 +2024-07-16 10:56:00,084 - pyskl - INFO - Epoch [6][600/3746] lr: 9.971e-02, eta: 4 days, 14:57:39, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4727, loss_cls: 4.2799, loss: 4.2799 +2024-07-16 10:57:10,107 - pyskl - INFO - Epoch [6][700/3746] lr: 9.971e-02, eta: 4 days, 14:54:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4678, loss_cls: 4.2427, loss: 4.2427 +2024-07-16 10:58:20,040 - pyskl - INFO - Epoch [6][800/3746] lr: 9.970e-02, eta: 4 days, 14:51:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4748, loss_cls: 4.2240, loss: 4.2240 +2024-07-16 10:59:30,178 - pyskl - INFO - Epoch [6][900/3746] lr: 9.970e-02, eta: 4 days, 14:49:00, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4792, loss_cls: 4.2102, loss: 4.2102 +2024-07-16 11:00:40,667 - pyskl - INFO - Epoch [6][1000/3746] lr: 9.970e-02, eta: 4 days, 14:46:22, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4822, loss_cls: 4.1869, loss: 4.1869 +2024-07-16 11:01:51,031 - pyskl - INFO - Epoch [6][1100/3746] lr: 9.969e-02, eta: 4 days, 14:43:41, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4655, loss_cls: 4.2529, loss: 4.2529 +2024-07-16 11:03:01,244 - pyskl - INFO - Epoch [6][1200/3746] lr: 9.969e-02, eta: 4 days, 14:40:57, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4733, loss_cls: 4.2206, loss: 4.2206 +2024-07-16 11:04:11,246 - pyskl - INFO - Epoch [6][1300/3746] lr: 9.969e-02, eta: 4 days, 14:38:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4831, loss_cls: 4.2267, loss: 4.2267 +2024-07-16 11:05:21,308 - pyskl - INFO - Epoch [6][1400/3746] lr: 9.968e-02, eta: 4 days, 14:35:22, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4753, loss_cls: 4.2306, loss: 4.2306 +2024-07-16 11:06:31,247 - pyskl - INFO - Epoch [6][1500/3746] lr: 9.968e-02, eta: 4 days, 14:32:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4656, loss_cls: 4.2891, loss: 4.2891 +2024-07-16 11:07:41,205 - pyskl - INFO - Epoch [6][1600/3746] lr: 9.968e-02, eta: 4 days, 14:29:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4720, loss_cls: 4.2528, loss: 4.2528 +2024-07-16 11:08:51,293 - pyskl - INFO - Epoch [6][1700/3746] lr: 9.967e-02, eta: 4 days, 14:27:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4870, loss_cls: 4.2211, loss: 4.2211 +2024-07-16 11:10:01,507 - pyskl - INFO - Epoch [6][1800/3746] lr: 9.967e-02, eta: 4 days, 14:24:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4838, loss_cls: 4.2208, loss: 4.2208 +2024-07-16 11:11:11,541 - pyskl - INFO - Epoch [6][1900/3746] lr: 9.967e-02, eta: 4 days, 14:21:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4786, loss_cls: 4.1967, loss: 4.1967 +2024-07-16 11:12:22,305 - pyskl - INFO - Epoch [6][2000/3746] lr: 9.966e-02, eta: 4 days, 14:19:21, time: 0.708, data_time: 0.001, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4902, loss_cls: 4.1881, loss: 4.1881 +2024-07-16 11:13:32,381 - pyskl - INFO - Epoch [6][2100/3746] lr: 9.966e-02, eta: 4 days, 14:16:42, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4722, loss_cls: 4.2289, loss: 4.2289 +2024-07-16 11:14:42,561 - pyskl - INFO - Epoch [6][2200/3746] lr: 9.966e-02, eta: 4 days, 14:14:06, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4681, loss_cls: 4.2873, loss: 4.2873 +2024-07-16 11:15:53,128 - pyskl - INFO - Epoch [6][2300/3746] lr: 9.965e-02, eta: 4 days, 14:11:41, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4814, loss_cls: 4.2238, loss: 4.2238 +2024-07-16 11:17:04,522 - pyskl - INFO - Epoch [6][2400/3746] lr: 9.965e-02, eta: 4 days, 14:09:37, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4720, loss_cls: 4.2340, loss: 4.2340 +2024-07-16 11:18:15,418 - pyskl - INFO - Epoch [6][2500/3746] lr: 9.965e-02, eta: 4 days, 14:07:22, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4839, loss_cls: 4.1880, loss: 4.1880 +2024-07-16 11:19:26,234 - pyskl - INFO - Epoch [6][2600/3746] lr: 9.964e-02, eta: 4 days, 14:05:05, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4763, loss_cls: 4.2122, loss: 4.2122 +2024-07-16 11:20:37,274 - pyskl - INFO - Epoch [6][2700/3746] lr: 9.964e-02, eta: 4 days, 14:02:54, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4752, loss_cls: 4.2293, loss: 4.2293 +2024-07-16 11:21:48,023 - pyskl - INFO - Epoch [6][2800/3746] lr: 9.964e-02, eta: 4 days, 14:00:37, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4702, loss_cls: 4.2306, loss: 4.2306 +2024-07-16 11:22:58,531 - pyskl - INFO - Epoch [6][2900/3746] lr: 9.963e-02, eta: 4 days, 13:58:14, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4816, loss_cls: 4.1847, loss: 4.1847 +2024-07-16 11:24:09,209 - pyskl - INFO - Epoch [6][3000/3746] lr: 9.963e-02, eta: 4 days, 13:55:56, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4695, loss_cls: 4.2304, loss: 4.2304 +2024-07-16 11:25:19,745 - pyskl - INFO - Epoch [6][3100/3746] lr: 9.963e-02, eta: 4 days, 13:53:35, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4847, loss_cls: 4.2264, loss: 4.2264 +2024-07-16 11:26:30,692 - pyskl - INFO - Epoch [6][3200/3746] lr: 9.962e-02, eta: 4 days, 13:51:25, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4819, loss_cls: 4.2022, loss: 4.2022 +2024-07-16 11:27:41,362 - pyskl - INFO - Epoch [6][3300/3746] lr: 9.962e-02, eta: 4 days, 13:49:09, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4763, loss_cls: 4.2185, loss: 4.2185 +2024-07-16 11:28:52,140 - pyskl - INFO - Epoch [6][3400/3746] lr: 9.962e-02, eta: 4 days, 13:46:56, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4741, loss_cls: 4.2169, loss: 4.2169 +2024-07-16 11:30:03,038 - pyskl - INFO - Epoch [6][3500/3746] lr: 9.961e-02, eta: 4 days, 13:44:46, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4814, loss_cls: 4.2201, loss: 4.2201 +2024-07-16 11:31:13,607 - pyskl - INFO - Epoch [6][3600/3746] lr: 9.961e-02, eta: 4 days, 13:42:29, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4834, loss_cls: 4.1928, loss: 4.1928 +2024-07-16 11:32:24,098 - pyskl - INFO - Epoch [6][3700/3746] lr: 9.961e-02, eta: 4 days, 13:40:11, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4864, loss_cls: 4.1955, loss: 4.1955 +2024-07-16 11:32:58,644 - pyskl - INFO - Saving checkpoint at 6 epochs +2024-07-16 11:34:49,774 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 11:34:50,436 - pyskl - INFO - +top1_acc 0.1711 +top5_acc 0.3893 +2024-07-16 11:34:50,436 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 11:34:50,473 - pyskl - INFO - +mean_acc 0.1710 +2024-07-16 11:34:50,478 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_5.pth was removed +2024-07-16 11:34:50,721 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_6.pth. +2024-07-16 11:34:50,721 - pyskl - INFO - Best top1_acc is 0.1711 at 6 epoch. +2024-07-16 11:34:50,731 - pyskl - INFO - Epoch(val) [6][309] top1_acc: 0.1711, top5_acc: 0.3893, mean_class_accuracy: 0.1710 +2024-07-16 11:38:08,218 - pyskl - INFO - Epoch [7][100/3746] lr: 9.960e-02, eta: 4 days, 14:14:29, time: 1.975, data_time: 1.267, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4908, loss_cls: 4.1642, loss: 4.1642 +2024-07-16 11:39:18,884 - pyskl - INFO - Epoch [7][200/3746] lr: 9.960e-02, eta: 4 days, 14:12:06, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4814, loss_cls: 4.2034, loss: 4.2034 +2024-07-16 11:40:29,639 - pyskl - INFO - Epoch [7][300/3746] lr: 9.960e-02, eta: 4 days, 14:09:46, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4942, loss_cls: 4.1580, loss: 4.1580 +2024-07-16 11:41:39,910 - pyskl - INFO - Epoch [7][400/3746] lr: 9.959e-02, eta: 4 days, 14:07:15, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4761, loss_cls: 4.2137, loss: 4.2137 +2024-07-16 11:42:49,990 - pyskl - INFO - Epoch [7][500/3746] lr: 9.959e-02, eta: 4 days, 14:04:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4844, loss_cls: 4.1419, loss: 4.1419 +2024-07-16 11:44:00,288 - pyskl - INFO - Epoch [7][600/3746] lr: 9.958e-02, eta: 4 days, 14:02:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4823, loss_cls: 4.1939, loss: 4.1939 +2024-07-16 11:45:10,315 - pyskl - INFO - Epoch [7][700/3746] lr: 9.958e-02, eta: 4 days, 13:59:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4813, loss_cls: 4.1915, loss: 4.1915 +2024-07-16 11:46:20,504 - pyskl - INFO - Epoch [7][800/3746] lr: 9.958e-02, eta: 4 days, 13:57:06, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4873, loss_cls: 4.1876, loss: 4.1876 +2024-07-16 11:47:30,567 - pyskl - INFO - Epoch [7][900/3746] lr: 9.957e-02, eta: 4 days, 13:54:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4850, loss_cls: 4.2100, loss: 4.2100 +2024-07-16 11:48:40,631 - pyskl - INFO - Epoch [7][1000/3746] lr: 9.957e-02, eta: 4 days, 13:52:02, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4891, loss_cls: 4.1765, loss: 4.1765 +2024-07-16 11:49:50,821 - pyskl - INFO - Epoch [7][1100/3746] lr: 9.957e-02, eta: 4 days, 13:49:34, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4709, loss_cls: 4.2116, loss: 4.2116 +2024-07-16 11:51:01,066 - pyskl - INFO - Epoch [7][1200/3746] lr: 9.956e-02, eta: 4 days, 13:47:07, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4777, loss_cls: 4.2198, loss: 4.2198 +2024-07-16 11:52:10,867 - pyskl - INFO - Epoch [7][1300/3746] lr: 9.956e-02, eta: 4 days, 13:44:32, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4798, loss_cls: 4.2001, loss: 4.2001 +2024-07-16 11:53:20,841 - pyskl - INFO - Epoch [7][1400/3746] lr: 9.956e-02, eta: 4 days, 13:42:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4852, loss_cls: 4.1793, loss: 4.1793 +2024-07-16 11:54:30,589 - pyskl - INFO - Epoch [7][1500/3746] lr: 9.955e-02, eta: 4 days, 13:39:25, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4891, loss_cls: 4.1799, loss: 4.1799 +2024-07-16 11:55:40,553 - pyskl - INFO - Epoch [7][1600/3746] lr: 9.955e-02, eta: 4 days, 13:36:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4786, loss_cls: 4.2223, loss: 4.2223 +2024-07-16 11:56:50,174 - pyskl - INFO - Epoch [7][1700/3746] lr: 9.954e-02, eta: 4 days, 13:34:19, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4792, loss_cls: 4.2195, loss: 4.2195 +2024-07-16 11:58:00,034 - pyskl - INFO - Epoch [7][1800/3746] lr: 9.954e-02, eta: 4 days, 13:31:48, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4800, loss_cls: 4.2096, loss: 4.2096 +2024-07-16 11:59:10,131 - pyskl - INFO - Epoch [7][1900/3746] lr: 9.954e-02, eta: 4 days, 13:29:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4828, loss_cls: 4.1844, loss: 4.1844 +2024-07-16 12:00:20,448 - pyskl - INFO - Epoch [7][2000/3746] lr: 9.953e-02, eta: 4 days, 13:27:03, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4742, loss_cls: 4.2047, loss: 4.2047 +2024-07-16 12:01:30,403 - pyskl - INFO - Epoch [7][2100/3746] lr: 9.953e-02, eta: 4 days, 13:24:37, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4878, loss_cls: 4.1762, loss: 4.1762 +2024-07-16 12:02:40,418 - pyskl - INFO - Epoch [7][2200/3746] lr: 9.952e-02, eta: 4 days, 13:22:12, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4798, loss_cls: 4.1816, loss: 4.1816 +2024-07-16 12:03:50,742 - pyskl - INFO - Epoch [7][2300/3746] lr: 9.952e-02, eta: 4 days, 13:19:54, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4770, loss_cls: 4.1969, loss: 4.1969 +2024-07-16 12:05:00,611 - pyskl - INFO - Epoch [7][2400/3746] lr: 9.952e-02, eta: 4 days, 13:17:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4759, loss_cls: 4.2045, loss: 4.2045 +2024-07-16 12:06:10,468 - pyskl - INFO - Epoch [7][2500/3746] lr: 9.951e-02, eta: 4 days, 13:15:01, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4717, loss_cls: 4.2337, loss: 4.2337 +2024-07-16 12:07:20,240 - pyskl - INFO - Epoch [7][2600/3746] lr: 9.951e-02, eta: 4 days, 13:12:33, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4817, loss_cls: 4.2189, loss: 4.2189 +2024-07-16 12:08:29,949 - pyskl - INFO - Epoch [7][2700/3746] lr: 9.951e-02, eta: 4 days, 13:10:04, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4814, loss_cls: 4.1934, loss: 4.1934 +2024-07-16 12:09:39,958 - pyskl - INFO - Epoch [7][2800/3746] lr: 9.950e-02, eta: 4 days, 13:07:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4816, loss_cls: 4.1779, loss: 4.1779 +2024-07-16 12:10:49,631 - pyskl - INFO - Epoch [7][2900/3746] lr: 9.950e-02, eta: 4 days, 13:05:15, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4875, loss_cls: 4.1787, loss: 4.1787 +2024-07-16 12:11:59,900 - pyskl - INFO - Epoch [7][3000/3746] lr: 9.949e-02, eta: 4 days, 13:03:00, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4869, loss_cls: 4.1677, loss: 4.1677 +2024-07-16 12:13:10,145 - pyskl - INFO - Epoch [7][3100/3746] lr: 9.949e-02, eta: 4 days, 13:00:45, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4875, loss_cls: 4.2002, loss: 4.2002 +2024-07-16 12:14:20,270 - pyskl - INFO - Epoch [7][3200/3746] lr: 9.949e-02, eta: 4 days, 12:58:28, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4873, loss_cls: 4.1950, loss: 4.1950 +2024-07-16 12:15:30,167 - pyskl - INFO - Epoch [7][3300/3746] lr: 9.948e-02, eta: 4 days, 12:56:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4881, loss_cls: 4.1907, loss: 4.1907 +2024-07-16 12:16:39,955 - pyskl - INFO - Epoch [7][3400/3746] lr: 9.948e-02, eta: 4 days, 12:53:44, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4777, loss_cls: 4.2085, loss: 4.2085 +2024-07-16 12:17:49,626 - pyskl - INFO - Epoch [7][3500/3746] lr: 9.947e-02, eta: 4 days, 12:51:20, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4966, loss_cls: 4.1726, loss: 4.1726 +2024-07-16 12:18:59,245 - pyskl - INFO - Epoch [7][3600/3746] lr: 9.947e-02, eta: 4 days, 12:48:55, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4878, loss_cls: 4.1752, loss: 4.1752 +2024-07-16 12:20:09,200 - pyskl - INFO - Epoch [7][3700/3746] lr: 9.947e-02, eta: 4 days, 12:46:37, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4813, loss_cls: 4.2183, loss: 4.2183 +2024-07-16 12:20:43,651 - pyskl - INFO - Saving checkpoint at 7 epochs +2024-07-16 12:22:35,475 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 12:22:36,159 - pyskl - INFO - +top1_acc 0.1712 +top5_acc 0.3942 +2024-07-16 12:22:36,159 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 12:22:36,200 - pyskl - INFO - +mean_acc 0.1710 +2024-07-16 12:22:36,205 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_6.pth was removed +2024-07-16 12:22:36,483 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2024-07-16 12:22:36,483 - pyskl - INFO - Best top1_acc is 0.1712 at 7 epoch. +2024-07-16 12:22:36,495 - pyskl - INFO - Epoch(val) [7][309] top1_acc: 0.1712, top5_acc: 0.3942, mean_class_accuracy: 0.1710 +2024-07-16 12:25:57,178 - pyskl - INFO - Epoch [8][100/3746] lr: 9.946e-02, eta: 4 days, 13:16:42, time: 2.007, data_time: 1.300, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4967, loss_cls: 4.1199, loss: 4.1199 +2024-07-16 12:27:07,605 - pyskl - INFO - Epoch [8][200/3746] lr: 9.946e-02, eta: 4 days, 13:14:27, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4792, loss_cls: 4.2076, loss: 4.2076 +2024-07-16 12:28:17,870 - pyskl - INFO - Epoch [8][300/3746] lr: 9.945e-02, eta: 4 days, 13:12:10, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4878, loss_cls: 4.1628, loss: 4.1628 +2024-07-16 12:29:28,344 - pyskl - INFO - Epoch [8][400/3746] lr: 9.945e-02, eta: 4 days, 13:09:57, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4856, loss_cls: 4.1955, loss: 4.1955 +2024-07-16 12:30:38,330 - pyskl - INFO - Epoch [8][500/3746] lr: 9.944e-02, eta: 4 days, 13:07:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4830, loss_cls: 4.1866, loss: 4.1866 +2024-07-16 12:31:48,093 - pyskl - INFO - Epoch [8][600/3746] lr: 9.944e-02, eta: 4 days, 13:05:08, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4816, loss_cls: 4.2065, loss: 4.2065 +2024-07-16 12:32:57,986 - pyskl - INFO - Epoch [8][700/3746] lr: 9.943e-02, eta: 4 days, 13:02:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4872, loss_cls: 4.1588, loss: 4.1588 +2024-07-16 12:34:07,413 - pyskl - INFO - Epoch [8][800/3746] lr: 9.943e-02, eta: 4 days, 13:00:14, time: 0.694, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4911, loss_cls: 4.1322, loss: 4.1322 +2024-07-16 12:35:17,309 - pyskl - INFO - Epoch [8][900/3746] lr: 9.943e-02, eta: 4 days, 12:57:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4867, loss_cls: 4.1786, loss: 4.1786 +2024-07-16 12:36:27,015 - pyskl - INFO - Epoch [8][1000/3746] lr: 9.942e-02, eta: 4 days, 12:55:27, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4844, loss_cls: 4.1956, loss: 4.1956 +2024-07-16 12:37:36,758 - pyskl - INFO - Epoch [8][1100/3746] lr: 9.942e-02, eta: 4 days, 12:53:03, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4955, loss_cls: 4.1717, loss: 4.1717 +2024-07-16 12:38:46,495 - pyskl - INFO - Epoch [8][1200/3746] lr: 9.941e-02, eta: 4 days, 12:50:40, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4933, loss_cls: 4.1338, loss: 4.1338 +2024-07-16 12:39:56,354 - pyskl - INFO - Epoch [8][1300/3746] lr: 9.941e-02, eta: 4 days, 12:48:20, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4933, loss_cls: 4.1706, loss: 4.1706 +2024-07-16 12:41:06,308 - pyskl - INFO - Epoch [8][1400/3746] lr: 9.940e-02, eta: 4 days, 12:46:02, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4927, loss_cls: 4.1558, loss: 4.1558 +2024-07-16 12:42:16,014 - pyskl - INFO - Epoch [8][1500/3746] lr: 9.940e-02, eta: 4 days, 12:43:39, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4942, loss_cls: 4.1596, loss: 4.1596 +2024-07-16 12:43:25,847 - pyskl - INFO - Epoch [8][1600/3746] lr: 9.940e-02, eta: 4 days, 12:41:20, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4852, loss_cls: 4.1789, loss: 4.1789 +2024-07-16 12:44:35,540 - pyskl - INFO - Epoch [8][1700/3746] lr: 9.939e-02, eta: 4 days, 12:38:58, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4833, loss_cls: 4.1970, loss: 4.1970 +2024-07-16 12:45:45,441 - pyskl - INFO - Epoch [8][1800/3746] lr: 9.939e-02, eta: 4 days, 12:36:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4916, loss_cls: 4.1517, loss: 4.1517 +2024-07-16 12:46:55,147 - pyskl - INFO - Epoch [8][1900/3746] lr: 9.938e-02, eta: 4 days, 12:34:21, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4895, loss_cls: 4.1428, loss: 4.1428 +2024-07-16 12:48:05,133 - pyskl - INFO - Epoch [8][2000/3746] lr: 9.938e-02, eta: 4 days, 12:32:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4855, loss_cls: 4.1704, loss: 4.1704 +2024-07-16 12:49:15,072 - pyskl - INFO - Epoch [8][2100/3746] lr: 9.937e-02, eta: 4 days, 12:29:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4848, loss_cls: 4.1575, loss: 4.1575 +2024-07-16 12:50:25,236 - pyskl - INFO - Epoch [8][2200/3746] lr: 9.937e-02, eta: 4 days, 12:27:41, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4830, loss_cls: 4.1715, loss: 4.1715 +2024-07-16 12:51:35,066 - pyskl - INFO - Epoch [8][2300/3746] lr: 9.937e-02, eta: 4 days, 12:25:25, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4969, loss_cls: 4.1255, loss: 4.1255 +2024-07-16 12:52:45,082 - pyskl - INFO - Epoch [8][2400/3746] lr: 9.936e-02, eta: 4 days, 12:23:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4889, loss_cls: 4.1551, loss: 4.1551 +2024-07-16 12:53:54,940 - pyskl - INFO - Epoch [8][2500/3746] lr: 9.936e-02, eta: 4 days, 12:20:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4953, loss_cls: 4.1730, loss: 4.1730 +2024-07-16 12:55:04,710 - pyskl - INFO - Epoch [8][2600/3746] lr: 9.935e-02, eta: 4 days, 12:18:43, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4902, loss_cls: 4.1567, loss: 4.1567 +2024-07-16 12:56:14,387 - pyskl - INFO - Epoch [8][2700/3746] lr: 9.935e-02, eta: 4 days, 12:16:26, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4764, loss_cls: 4.2110, loss: 4.2110 +2024-07-16 12:57:24,195 - pyskl - INFO - Epoch [8][2800/3746] lr: 9.934e-02, eta: 4 days, 12:14:11, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4741, loss_cls: 4.2226, loss: 4.2226 +2024-07-16 12:58:34,093 - pyskl - INFO - Epoch [8][2900/3746] lr: 9.934e-02, eta: 4 days, 12:11:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4903, loss_cls: 4.1700, loss: 4.1700 +2024-07-16 12:59:43,914 - pyskl - INFO - Epoch [8][3000/3746] lr: 9.933e-02, eta: 4 days, 12:09:46, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4964, loss_cls: 4.1151, loss: 4.1151 +2024-07-16 13:00:54,108 - pyskl - INFO - Epoch [8][3100/3746] lr: 9.933e-02, eta: 4 days, 12:07:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4831, loss_cls: 4.1545, loss: 4.1545 +2024-07-16 13:02:04,230 - pyskl - INFO - Epoch [8][3200/3746] lr: 9.933e-02, eta: 4 days, 12:05:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4894, loss_cls: 4.1767, loss: 4.1767 +2024-07-16 13:03:14,145 - pyskl - INFO - Epoch [8][3300/3746] lr: 9.932e-02, eta: 4 days, 12:03:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4850, loss_cls: 4.1996, loss: 4.1996 +2024-07-16 13:04:24,251 - pyskl - INFO - Epoch [8][3400/3746] lr: 9.932e-02, eta: 4 days, 12:01:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4803, loss_cls: 4.1896, loss: 4.1896 +2024-07-16 13:05:34,392 - pyskl - INFO - Epoch [8][3500/3746] lr: 9.931e-02, eta: 4 days, 11:59:12, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4914, loss_cls: 4.1525, loss: 4.1525 +2024-07-16 13:06:44,373 - pyskl - INFO - Epoch [8][3600/3746] lr: 9.931e-02, eta: 4 days, 11:57:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4867, loss_cls: 4.1680, loss: 4.1680 +2024-07-16 13:07:54,246 - pyskl - INFO - Epoch [8][3700/3746] lr: 9.930e-02, eta: 4 days, 11:54:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.5020, loss_cls: 4.1183, loss: 4.1183 +2024-07-16 13:08:29,206 - pyskl - INFO - Saving checkpoint at 8 epochs +2024-07-16 13:10:21,190 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 13:10:21,848 - pyskl - INFO - +top1_acc 0.1624 +top5_acc 0.3764 +2024-07-16 13:10:21,848 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 13:10:21,887 - pyskl - INFO - +mean_acc 0.1623 +2024-07-16 13:10:21,898 - pyskl - INFO - Epoch(val) [8][309] top1_acc: 0.1624, top5_acc: 0.3764, mean_class_accuracy: 0.1623 +2024-07-16 13:13:40,365 - pyskl - INFO - Epoch [9][100/3746] lr: 9.930e-02, eta: 4 days, 12:20:12, time: 1.985, data_time: 1.282, memory: 15990, top1_acc: 0.2420, top5_acc: 0.5056, loss_cls: 4.1086, loss: 4.1086 +2024-07-16 13:14:50,932 - pyskl - INFO - Epoch [9][200/3746] lr: 9.929e-02, eta: 4 days, 12:18:10, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4909, loss_cls: 4.1255, loss: 4.1255 +2024-07-16 13:16:01,076 - pyskl - INFO - Epoch [9][300/3746] lr: 9.929e-02, eta: 4 days, 12:16:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4861, loss_cls: 4.1760, loss: 4.1760 +2024-07-16 13:17:11,209 - pyskl - INFO - Epoch [9][400/3746] lr: 9.928e-02, eta: 4 days, 12:13:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4884, loss_cls: 4.1742, loss: 4.1742 +2024-07-16 13:18:21,063 - pyskl - INFO - Epoch [9][500/3746] lr: 9.928e-02, eta: 4 days, 12:11:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4997, loss_cls: 4.1171, loss: 4.1171 +2024-07-16 13:19:31,197 - pyskl - INFO - Epoch [9][600/3746] lr: 9.927e-02, eta: 4 days, 12:09:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4872, loss_cls: 4.1458, loss: 4.1458 +2024-07-16 13:20:41,075 - pyskl - INFO - Epoch [9][700/3746] lr: 9.927e-02, eta: 4 days, 12:07:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4930, loss_cls: 4.1611, loss: 4.1611 +2024-07-16 13:21:51,080 - pyskl - INFO - Epoch [9][800/3746] lr: 9.926e-02, eta: 4 days, 12:05:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4872, loss_cls: 4.1339, loss: 4.1339 +2024-07-16 13:23:01,461 - pyskl - INFO - Epoch [9][900/3746] lr: 9.926e-02, eta: 4 days, 12:03:05, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4889, loss_cls: 4.1344, loss: 4.1344 +2024-07-16 13:24:11,941 - pyskl - INFO - Epoch [9][1000/3746] lr: 9.925e-02, eta: 4 days, 12:01:05, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4878, loss_cls: 4.1352, loss: 4.1352 +2024-07-16 13:25:22,020 - pyskl - INFO - Epoch [9][1100/3746] lr: 9.925e-02, eta: 4 days, 11:58:57, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4970, loss_cls: 4.1309, loss: 4.1309 +2024-07-16 13:26:32,184 - pyskl - INFO - Epoch [9][1200/3746] lr: 9.924e-02, eta: 4 days, 11:56:52, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4973, loss_cls: 4.1316, loss: 4.1316 +2024-07-16 13:27:42,525 - pyskl - INFO - Epoch [9][1300/3746] lr: 9.924e-02, eta: 4 days, 11:54:50, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4961, loss_cls: 4.1285, loss: 4.1285 +2024-07-16 13:28:52,701 - pyskl - INFO - Epoch [9][1400/3746] lr: 9.923e-02, eta: 4 days, 11:52:45, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4769, loss_cls: 4.1974, loss: 4.1974 +2024-07-16 13:30:03,144 - pyskl - INFO - Epoch [9][1500/3746] lr: 9.923e-02, eta: 4 days, 11:50:45, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4898, loss_cls: 4.1308, loss: 4.1308 +2024-07-16 13:31:13,279 - pyskl - INFO - Epoch [9][1600/3746] lr: 9.922e-02, eta: 4 days, 11:48:41, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4852, loss_cls: 4.1604, loss: 4.1604 +2024-07-16 13:32:22,953 - pyskl - INFO - Epoch [9][1700/3746] lr: 9.922e-02, eta: 4 days, 11:46:29, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4909, loss_cls: 4.1639, loss: 4.1639 +2024-07-16 13:33:32,882 - pyskl - INFO - Epoch [9][1800/3746] lr: 9.921e-02, eta: 4 days, 11:44:21, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.5009, loss_cls: 4.1262, loss: 4.1262 +2024-07-16 13:34:43,172 - pyskl - INFO - Epoch [9][1900/3746] lr: 9.921e-02, eta: 4 days, 11:42:20, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4967, loss_cls: 4.1537, loss: 4.1537 +2024-07-16 13:35:53,570 - pyskl - INFO - Epoch [9][2000/3746] lr: 9.920e-02, eta: 4 days, 11:40:22, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4930, loss_cls: 4.1757, loss: 4.1757 +2024-07-16 13:37:03,595 - pyskl - INFO - Epoch [9][2100/3746] lr: 9.920e-02, eta: 4 days, 11:38:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4952, loss_cls: 4.1373, loss: 4.1373 +2024-07-16 13:38:13,841 - pyskl - INFO - Epoch [9][2200/3746] lr: 9.919e-02, eta: 4 days, 11:36:16, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4936, loss_cls: 4.1416, loss: 4.1416 +2024-07-16 13:39:23,785 - pyskl - INFO - Epoch [9][2300/3746] lr: 9.919e-02, eta: 4 days, 11:34:11, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4923, loss_cls: 4.1226, loss: 4.1226 +2024-07-16 13:40:33,954 - pyskl - INFO - Epoch [9][2400/3746] lr: 9.918e-02, eta: 4 days, 11:32:09, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4848, loss_cls: 4.1796, loss: 4.1796 +2024-07-16 13:41:43,731 - pyskl - INFO - Epoch [9][2500/3746] lr: 9.918e-02, eta: 4 days, 11:30:02, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4980, loss_cls: 4.1278, loss: 4.1278 +2024-07-16 13:42:53,755 - pyskl - INFO - Epoch [9][2600/3746] lr: 9.917e-02, eta: 4 days, 11:27:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.5002, loss_cls: 4.1449, loss: 4.1449 +2024-07-16 13:44:03,632 - pyskl - INFO - Epoch [9][2700/3746] lr: 9.917e-02, eta: 4 days, 11:25:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4789, loss_cls: 4.1769, loss: 4.1769 +2024-07-16 13:45:13,714 - pyskl - INFO - Epoch [9][2800/3746] lr: 9.916e-02, eta: 4 days, 11:23:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4925, loss_cls: 4.1216, loss: 4.1216 +2024-07-16 13:46:23,505 - pyskl - INFO - Epoch [9][2900/3746] lr: 9.916e-02, eta: 4 days, 11:21:46, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4933, loss_cls: 4.1379, loss: 4.1379 +2024-07-16 13:47:33,282 - pyskl - INFO - Epoch [9][3000/3746] lr: 9.915e-02, eta: 4 days, 11:19:41, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4934, loss_cls: 4.1289, loss: 4.1289 +2024-07-16 13:48:43,473 - pyskl - INFO - Epoch [9][3100/3746] lr: 9.915e-02, eta: 4 days, 11:17:42, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4909, loss_cls: 4.1389, loss: 4.1389 +2024-07-16 13:49:53,325 - pyskl - INFO - Epoch [9][3200/3746] lr: 9.914e-02, eta: 4 days, 11:15:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4914, loss_cls: 4.1833, loss: 4.1833 +2024-07-16 13:51:03,164 - pyskl - INFO - Epoch [9][3300/3746] lr: 9.914e-02, eta: 4 days, 11:13:34, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4872, loss_cls: 4.1645, loss: 4.1645 +2024-07-16 13:52:13,442 - pyskl - INFO - Epoch [9][3400/3746] lr: 9.913e-02, eta: 4 days, 11:11:38, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.5003, loss_cls: 4.1326, loss: 4.1326 +2024-07-16 13:53:23,194 - pyskl - INFO - Epoch [9][3500/3746] lr: 9.913e-02, eta: 4 days, 11:09:33, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4967, loss_cls: 4.1328, loss: 4.1328 +2024-07-16 13:54:33,068 - pyskl - INFO - Epoch [9][3600/3746] lr: 9.912e-02, eta: 4 days, 11:07:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4830, loss_cls: 4.1772, loss: 4.1772 +2024-07-16 13:55:43,211 - pyskl - INFO - Epoch [9][3700/3746] lr: 9.912e-02, eta: 4 days, 11:05:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4952, loss_cls: 4.1198, loss: 4.1198 +2024-07-16 13:56:18,416 - pyskl - INFO - Saving checkpoint at 9 epochs +2024-07-16 13:58:08,982 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 13:58:09,666 - pyskl - INFO - +top1_acc 0.1608 +top5_acc 0.3772 +2024-07-16 13:58:09,666 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 13:58:09,705 - pyskl - INFO - +mean_acc 0.1605 +2024-07-16 13:58:09,716 - pyskl - INFO - Epoch(val) [9][309] top1_acc: 0.1608, top5_acc: 0.3772, mean_class_accuracy: 0.1605 +2024-07-16 14:01:28,502 - pyskl - INFO - Epoch [10][100/3746] lr: 9.911e-02, eta: 4 days, 11:27:47, time: 1.988, data_time: 1.287, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5075, loss_cls: 4.0756, loss: 4.0756 +2024-07-16 14:02:38,888 - pyskl - INFO - Epoch [10][200/3746] lr: 9.910e-02, eta: 4 days, 11:25:49, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5020, loss_cls: 4.0878, loss: 4.0878 +2024-07-16 14:03:49,386 - pyskl - INFO - Epoch [10][300/3746] lr: 9.910e-02, eta: 4 days, 11:23:53, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5059, loss_cls: 4.0456, loss: 4.0456 +2024-07-16 14:04:59,328 - pyskl - INFO - Epoch [10][400/3746] lr: 9.909e-02, eta: 4 days, 11:21:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4969, loss_cls: 4.1097, loss: 4.1097 +2024-07-16 14:06:09,194 - pyskl - INFO - Epoch [10][500/3746] lr: 9.909e-02, eta: 4 days, 11:19:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4975, loss_cls: 4.1554, loss: 4.1554 +2024-07-16 14:07:19,078 - pyskl - INFO - Epoch [10][600/3746] lr: 9.908e-02, eta: 4 days, 11:17:39, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4923, loss_cls: 4.1544, loss: 4.1544 +2024-07-16 14:08:28,695 - pyskl - INFO - Epoch [10][700/3746] lr: 9.908e-02, eta: 4 days, 11:15:31, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4933, loss_cls: 4.1365, loss: 4.1365 +2024-07-16 14:09:38,310 - pyskl - INFO - Epoch [10][800/3746] lr: 9.907e-02, eta: 4 days, 11:13:23, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4944, loss_cls: 4.1046, loss: 4.1046 +2024-07-16 14:10:48,117 - pyskl - INFO - Epoch [10][900/3746] lr: 9.907e-02, eta: 4 days, 11:11:18, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4889, loss_cls: 4.1516, loss: 4.1516 +2024-07-16 14:11:58,071 - pyskl - INFO - Epoch [10][1000/3746] lr: 9.906e-02, eta: 4 days, 11:09:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4952, loss_cls: 4.1539, loss: 4.1539 +2024-07-16 14:13:07,752 - pyskl - INFO - Epoch [10][1100/3746] lr: 9.906e-02, eta: 4 days, 11:07:10, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4873, loss_cls: 4.1638, loss: 4.1638 +2024-07-16 14:14:17,626 - pyskl - INFO - Epoch [10][1200/3746] lr: 9.905e-02, eta: 4 days, 11:05:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4878, loss_cls: 4.1522, loss: 4.1522 +2024-07-16 14:15:27,725 - pyskl - INFO - Epoch [10][1300/3746] lr: 9.905e-02, eta: 4 days, 11:03:08, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5052, loss_cls: 4.0910, loss: 4.0910 +2024-07-16 14:16:37,699 - pyskl - INFO - Epoch [10][1400/3746] lr: 9.904e-02, eta: 4 days, 11:01:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4953, loss_cls: 4.1495, loss: 4.1495 +2024-07-16 14:17:48,150 - pyskl - INFO - Epoch [10][1500/3746] lr: 9.903e-02, eta: 4 days, 10:59:14, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4897, loss_cls: 4.1505, loss: 4.1505 +2024-07-16 14:18:57,714 - pyskl - INFO - Epoch [10][1600/3746] lr: 9.903e-02, eta: 4 days, 10:57:07, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4844, loss_cls: 4.1808, loss: 4.1808 +2024-07-16 14:20:07,858 - pyskl - INFO - Epoch [10][1700/3746] lr: 9.902e-02, eta: 4 days, 10:55:10, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4972, loss_cls: 4.0966, loss: 4.0966 +2024-07-16 14:21:17,687 - pyskl - INFO - Epoch [10][1800/3746] lr: 9.902e-02, eta: 4 days, 10:53:08, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4863, loss_cls: 4.1180, loss: 4.1180 +2024-07-16 14:22:27,379 - pyskl - INFO - Epoch [10][1900/3746] lr: 9.901e-02, eta: 4 days, 10:51:05, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4891, loss_cls: 4.1271, loss: 4.1271 +2024-07-16 14:23:37,747 - pyskl - INFO - Epoch [10][2000/3746] lr: 9.901e-02, eta: 4 days, 10:49:12, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4892, loss_cls: 4.1153, loss: 4.1153 +2024-07-16 14:24:47,757 - pyskl - INFO - Epoch [10][2100/3746] lr: 9.900e-02, eta: 4 days, 10:47:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4995, loss_cls: 4.1155, loss: 4.1155 +2024-07-16 14:25:58,288 - pyskl - INFO - Epoch [10][2200/3746] lr: 9.900e-02, eta: 4 days, 10:45:23, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4920, loss_cls: 4.1378, loss: 4.1378 +2024-07-16 14:27:08,095 - pyskl - INFO - Epoch [10][2300/3746] lr: 9.899e-02, eta: 4 days, 10:43:22, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5006, loss_cls: 4.0972, loss: 4.0972 +2024-07-16 14:28:17,758 - pyskl - INFO - Epoch [10][2400/3746] lr: 9.898e-02, eta: 4 days, 10:41:20, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4889, loss_cls: 4.1649, loss: 4.1649 +2024-07-16 14:29:27,390 - pyskl - INFO - Epoch [10][2500/3746] lr: 9.898e-02, eta: 4 days, 10:39:17, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.5027, loss_cls: 4.1024, loss: 4.1024 +2024-07-16 14:30:37,282 - pyskl - INFO - Epoch [10][2600/3746] lr: 9.897e-02, eta: 4 days, 10:37:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4856, loss_cls: 4.1572, loss: 4.1572 +2024-07-16 14:31:47,108 - pyskl - INFO - Epoch [10][2700/3746] lr: 9.897e-02, eta: 4 days, 10:35:19, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4902, loss_cls: 4.1436, loss: 4.1436 +2024-07-16 14:32:57,131 - pyskl - INFO - Epoch [10][2800/3746] lr: 9.896e-02, eta: 4 days, 10:33:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.5023, loss_cls: 4.1052, loss: 4.1052 +2024-07-16 14:34:06,947 - pyskl - INFO - Epoch [10][2900/3746] lr: 9.896e-02, eta: 4 days, 10:31:24, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4931, loss_cls: 4.1333, loss: 4.1333 +2024-07-16 14:35:16,713 - pyskl - INFO - Epoch [10][3000/3746] lr: 9.895e-02, eta: 4 days, 10:29:25, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4881, loss_cls: 4.1605, loss: 4.1605 +2024-07-16 14:36:26,843 - pyskl - INFO - Epoch [10][3100/3746] lr: 9.894e-02, eta: 4 days, 10:27:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4933, loss_cls: 4.1097, loss: 4.1097 +2024-07-16 14:37:36,535 - pyskl - INFO - Epoch [10][3200/3746] lr: 9.894e-02, eta: 4 days, 10:25:31, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4884, loss_cls: 4.1747, loss: 4.1747 +2024-07-16 14:38:46,525 - pyskl - INFO - Epoch [10][3300/3746] lr: 9.893e-02, eta: 4 days, 10:23:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4950, loss_cls: 4.0996, loss: 4.0996 +2024-07-16 14:39:56,314 - pyskl - INFO - Epoch [10][3400/3746] lr: 9.893e-02, eta: 4 days, 10:21:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4988, loss_cls: 4.1123, loss: 4.1123 +2024-07-16 14:41:05,992 - pyskl - INFO - Epoch [10][3500/3746] lr: 9.892e-02, eta: 4 days, 10:19:38, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4978, loss_cls: 4.1065, loss: 4.1065 +2024-07-16 14:42:15,734 - pyskl - INFO - Epoch [10][3600/3746] lr: 9.892e-02, eta: 4 days, 10:17:40, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4889, loss_cls: 4.1525, loss: 4.1525 +2024-07-16 14:43:26,226 - pyskl - INFO - Epoch [10][3700/3746] lr: 9.891e-02, eta: 4 days, 10:15:53, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4981, loss_cls: 4.1299, loss: 4.1299 +2024-07-16 14:44:00,670 - pyskl - INFO - Saving checkpoint at 10 epochs +2024-07-16 14:45:51,073 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 14:45:51,742 - pyskl - INFO - +top1_acc 0.1669 +top5_acc 0.3886 +2024-07-16 14:45:51,742 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 14:45:51,782 - pyskl - INFO - +mean_acc 0.1667 +2024-07-16 14:45:51,794 - pyskl - INFO - Epoch(val) [10][309] top1_acc: 0.1669, top5_acc: 0.3886, mean_class_accuracy: 0.1667 +2024-07-16 14:49:12,246 - pyskl - INFO - Epoch [11][100/3746] lr: 9.890e-02, eta: 4 days, 10:35:58, time: 2.004, data_time: 1.294, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4969, loss_cls: 4.1160, loss: 4.1160 +2024-07-16 14:50:22,793 - pyskl - INFO - Epoch [11][200/3746] lr: 9.890e-02, eta: 4 days, 10:34:08, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5002, loss_cls: 4.1018, loss: 4.1018 +2024-07-16 14:51:33,281 - pyskl - INFO - Epoch [11][300/3746] lr: 9.889e-02, eta: 4 days, 10:32:17, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4961, loss_cls: 4.1115, loss: 4.1115 +2024-07-16 14:52:43,109 - pyskl - INFO - Epoch [11][400/3746] lr: 9.888e-02, eta: 4 days, 10:30:18, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4903, loss_cls: 4.1488, loss: 4.1488 +2024-07-16 14:53:53,152 - pyskl - INFO - Epoch [11][500/3746] lr: 9.888e-02, eta: 4 days, 10:28:22, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.5059, loss_cls: 4.0997, loss: 4.0997 +2024-07-16 14:55:02,997 - pyskl - INFO - Epoch [11][600/3746] lr: 9.887e-02, eta: 4 days, 10:26:23, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4942, loss_cls: 4.1367, loss: 4.1367 +2024-07-16 14:56:12,619 - pyskl - INFO - Epoch [11][700/3746] lr: 9.887e-02, eta: 4 days, 10:24:21, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4994, loss_cls: 4.0862, loss: 4.0862 +2024-07-16 14:57:22,592 - pyskl - INFO - Epoch [11][800/3746] lr: 9.886e-02, eta: 4 days, 10:22:25, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4861, loss_cls: 4.1508, loss: 4.1508 +2024-07-16 14:58:32,417 - pyskl - INFO - Epoch [11][900/3746] lr: 9.885e-02, eta: 4 days, 10:20:27, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4905, loss_cls: 4.1671, loss: 4.1671 +2024-07-16 14:59:42,388 - pyskl - INFO - Epoch [11][1000/3746] lr: 9.885e-02, eta: 4 days, 10:18:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5061, loss_cls: 4.0886, loss: 4.0886 +2024-07-16 15:00:52,230 - pyskl - INFO - Epoch [11][1100/3746] lr: 9.884e-02, eta: 4 days, 10:16:33, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4984, loss_cls: 4.1200, loss: 4.1200 +2024-07-16 15:02:02,247 - pyskl - INFO - Epoch [11][1200/3746] lr: 9.884e-02, eta: 4 days, 10:14:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5042, loss_cls: 4.0635, loss: 4.0635 +2024-07-16 15:03:12,228 - pyskl - INFO - Epoch [11][1300/3746] lr: 9.883e-02, eta: 4 days, 10:12:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4969, loss_cls: 4.1260, loss: 4.1260 +2024-07-16 15:04:22,034 - pyskl - INFO - Epoch [11][1400/3746] lr: 9.882e-02, eta: 4 days, 10:10:46, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4994, loss_cls: 4.1084, loss: 4.1084 +2024-07-16 15:05:32,006 - pyskl - INFO - Epoch [11][1500/3746] lr: 9.882e-02, eta: 4 days, 10:08:51, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4927, loss_cls: 4.1441, loss: 4.1441 +2024-07-16 15:06:41,734 - pyskl - INFO - Epoch [11][1600/3746] lr: 9.881e-02, eta: 4 days, 10:06:53, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.5000, loss_cls: 4.1482, loss: 4.1482 +2024-07-16 15:07:51,605 - pyskl - INFO - Epoch [11][1700/3746] lr: 9.881e-02, eta: 4 days, 10:04:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4980, loss_cls: 4.1195, loss: 4.1195 +2024-07-16 15:09:01,336 - pyskl - INFO - Epoch [11][1800/3746] lr: 9.880e-02, eta: 4 days, 10:03:00, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5022, loss_cls: 4.0828, loss: 4.0828 +2024-07-16 15:10:11,382 - pyskl - INFO - Epoch [11][1900/3746] lr: 9.879e-02, eta: 4 days, 10:01:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4991, loss_cls: 4.1064, loss: 4.1064 +2024-07-16 15:11:21,670 - pyskl - INFO - Epoch [11][2000/3746] lr: 9.879e-02, eta: 4 days, 9:59:17, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4994, loss_cls: 4.1287, loss: 4.1287 +2024-07-16 15:12:31,609 - pyskl - INFO - Epoch [11][2100/3746] lr: 9.878e-02, eta: 4 days, 9:57:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4959, loss_cls: 4.1234, loss: 4.1234 +2024-07-16 15:13:41,812 - pyskl - INFO - Epoch [11][2200/3746] lr: 9.878e-02, eta: 4 days, 9:55:33, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4925, loss_cls: 4.1215, loss: 4.1215 +2024-07-16 15:14:51,926 - pyskl - INFO - Epoch [11][2300/3746] lr: 9.877e-02, eta: 4 days, 9:53:42, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4981, loss_cls: 4.0889, loss: 4.0889 +2024-07-16 15:16:01,881 - pyskl - INFO - Epoch [11][2400/3746] lr: 9.876e-02, eta: 4 days, 9:51:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4934, loss_cls: 4.1393, loss: 4.1393 +2024-07-16 15:17:11,473 - pyskl - INFO - Epoch [11][2500/3746] lr: 9.876e-02, eta: 4 days, 9:49:51, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4967, loss_cls: 4.1229, loss: 4.1229 +2024-07-16 15:18:21,186 - pyskl - INFO - Epoch [11][2600/3746] lr: 9.875e-02, eta: 4 days, 9:47:56, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5064, loss_cls: 4.0910, loss: 4.0910 +2024-07-16 15:19:31,068 - pyskl - INFO - Epoch [11][2700/3746] lr: 9.874e-02, eta: 4 days, 9:46:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.4942, loss_cls: 4.1174, loss: 4.1174 +2024-07-16 15:20:41,344 - pyskl - INFO - Epoch [11][2800/3746] lr: 9.874e-02, eta: 4 days, 9:44:14, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5014, loss_cls: 4.0870, loss: 4.0870 +2024-07-16 15:21:51,391 - pyskl - INFO - Epoch [11][2900/3746] lr: 9.873e-02, eta: 4 days, 9:42:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5047, loss_cls: 4.1277, loss: 4.1277 +2024-07-16 15:23:01,259 - pyskl - INFO - Epoch [11][3000/3746] lr: 9.873e-02, eta: 4 days, 9:40:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4933, loss_cls: 4.1249, loss: 4.1249 +2024-07-16 15:24:11,181 - pyskl - INFO - Epoch [11][3100/3746] lr: 9.872e-02, eta: 4 days, 9:38:39, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5000, loss_cls: 4.0869, loss: 4.0869 +2024-07-16 15:25:21,133 - pyskl - INFO - Epoch [11][3200/3746] lr: 9.871e-02, eta: 4 days, 9:36:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4880, loss_cls: 4.1488, loss: 4.1488 +2024-07-16 15:26:30,941 - pyskl - INFO - Epoch [11][3300/3746] lr: 9.871e-02, eta: 4 days, 9:34:54, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4928, loss_cls: 4.1383, loss: 4.1383 +2024-07-16 15:27:40,999 - pyskl - INFO - Epoch [11][3400/3746] lr: 9.870e-02, eta: 4 days, 9:33:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4923, loss_cls: 4.1147, loss: 4.1147 +2024-07-16 15:28:50,853 - pyskl - INFO - Epoch [11][3500/3746] lr: 9.869e-02, eta: 4 days, 9:31:13, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4953, loss_cls: 4.1122, loss: 4.1122 +2024-07-16 15:30:00,433 - pyskl - INFO - Epoch [11][3600/3746] lr: 9.869e-02, eta: 4 days, 9:29:17, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4995, loss_cls: 4.1024, loss: 4.1024 +2024-07-16 15:31:10,328 - pyskl - INFO - Epoch [11][3700/3746] lr: 9.868e-02, eta: 4 days, 9:27:26, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5031, loss_cls: 4.0722, loss: 4.0722 +2024-07-16 15:31:44,810 - pyskl - INFO - Saving checkpoint at 11 epochs +2024-07-16 15:33:35,325 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 15:33:36,002 - pyskl - INFO - +top1_acc 0.1769 +top5_acc 0.4084 +2024-07-16 15:33:36,003 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 15:33:36,042 - pyskl - INFO - +mean_acc 0.1767 +2024-07-16 15:33:36,047 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_7.pth was removed +2024-07-16 15:33:36,302 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_11.pth. +2024-07-16 15:33:36,302 - pyskl - INFO - Best top1_acc is 0.1769 at 11 epoch. +2024-07-16 15:33:36,313 - pyskl - INFO - Epoch(val) [11][309] top1_acc: 0.1769, top5_acc: 0.4084, mean_class_accuracy: 0.1767 +2024-07-16 15:36:54,560 - pyskl - INFO - Epoch [12][100/3746] lr: 9.867e-02, eta: 4 days, 9:44:57, time: 1.982, data_time: 1.275, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4997, loss_cls: 4.1003, loss: 4.1003 +2024-07-16 15:38:05,315 - pyskl - INFO - Epoch [12][200/3746] lr: 9.867e-02, eta: 4 days, 9:43:14, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.5091, loss_cls: 4.0796, loss: 4.0796 +2024-07-16 15:39:15,452 - pyskl - INFO - Epoch [12][300/3746] lr: 9.866e-02, eta: 4 days, 9:41:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5117, loss_cls: 4.0542, loss: 4.0542 +2024-07-16 15:40:25,519 - pyskl - INFO - Epoch [12][400/3746] lr: 9.865e-02, eta: 4 days, 9:39:32, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5019, loss_cls: 4.0600, loss: 4.0600 +2024-07-16 15:41:35,568 - pyskl - INFO - Epoch [12][500/3746] lr: 9.865e-02, eta: 4 days, 9:37:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4919, loss_cls: 4.1122, loss: 4.1122 +2024-07-16 15:42:45,405 - pyskl - INFO - Epoch [12][600/3746] lr: 9.864e-02, eta: 4 days, 9:35:47, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4994, loss_cls: 4.1056, loss: 4.1056 +2024-07-16 15:43:55,508 - pyskl - INFO - Epoch [12][700/3746] lr: 9.863e-02, eta: 4 days, 9:33:57, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4975, loss_cls: 4.0768, loss: 4.0768 +2024-07-16 15:45:05,465 - pyskl - INFO - Epoch [12][800/3746] lr: 9.863e-02, eta: 4 days, 9:32:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4839, loss_cls: 4.1373, loss: 4.1373 +2024-07-16 15:46:15,441 - pyskl - INFO - Epoch [12][900/3746] lr: 9.862e-02, eta: 4 days, 9:30:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4920, loss_cls: 4.1198, loss: 4.1198 +2024-07-16 15:47:25,455 - pyskl - INFO - Epoch [12][1000/3746] lr: 9.861e-02, eta: 4 days, 9:28:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4977, loss_cls: 4.1364, loss: 4.1364 +2024-07-16 15:48:35,380 - pyskl - INFO - Epoch [12][1100/3746] lr: 9.861e-02, eta: 4 days, 9:26:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4994, loss_cls: 4.0967, loss: 4.0967 +2024-07-16 15:49:45,202 - pyskl - INFO - Epoch [12][1200/3746] lr: 9.860e-02, eta: 4 days, 9:24:39, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4897, loss_cls: 4.1300, loss: 4.1300 +2024-07-16 15:50:55,196 - pyskl - INFO - Epoch [12][1300/3746] lr: 9.859e-02, eta: 4 days, 9:22:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5030, loss_cls: 4.0949, loss: 4.0949 +2024-07-16 15:52:05,064 - pyskl - INFO - Epoch [12][1400/3746] lr: 9.859e-02, eta: 4 days, 9:20:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5030, loss_cls: 4.0975, loss: 4.0975 +2024-07-16 15:53:15,077 - pyskl - INFO - Epoch [12][1500/3746] lr: 9.858e-02, eta: 4 days, 9:19:07, time: 0.700, data_time: 0.001, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4920, loss_cls: 4.1344, loss: 4.1344 +2024-07-16 15:54:25,046 - pyskl - INFO - Epoch [12][1600/3746] lr: 9.857e-02, eta: 4 days, 9:17:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.4972, loss_cls: 4.1077, loss: 4.1077 +2024-07-16 15:55:34,956 - pyskl - INFO - Epoch [12][1700/3746] lr: 9.857e-02, eta: 4 days, 9:15:26, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4863, loss_cls: 4.1607, loss: 4.1607 +2024-07-16 15:56:44,863 - pyskl - INFO - Epoch [12][1800/3746] lr: 9.856e-02, eta: 4 days, 9:13:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5105, loss_cls: 4.0646, loss: 4.0646 +2024-07-16 15:57:54,712 - pyskl - INFO - Epoch [12][1900/3746] lr: 9.855e-02, eta: 4 days, 9:11:44, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5145, loss_cls: 4.0691, loss: 4.0691 +2024-07-16 15:59:05,112 - pyskl - INFO - Epoch [12][2000/3746] lr: 9.855e-02, eta: 4 days, 9:10:00, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.5066, loss_cls: 4.0886, loss: 4.0886 +2024-07-16 16:00:15,159 - pyskl - INFO - Epoch [12][2100/3746] lr: 9.854e-02, eta: 4 days, 9:08:12, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5094, loss_cls: 4.0747, loss: 4.0747 +2024-07-16 16:01:25,067 - pyskl - INFO - Epoch [12][2200/3746] lr: 9.853e-02, eta: 4 days, 9:06:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4906, loss_cls: 4.1427, loss: 4.1427 +2024-07-16 16:02:34,989 - pyskl - INFO - Epoch [12][2300/3746] lr: 9.853e-02, eta: 4 days, 9:04:33, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4903, loss_cls: 4.1355, loss: 4.1355 +2024-07-16 16:03:44,887 - pyskl - INFO - Epoch [12][2400/3746] lr: 9.852e-02, eta: 4 days, 9:02:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4975, loss_cls: 4.1099, loss: 4.1099 +2024-07-16 16:04:54,464 - pyskl - INFO - Epoch [12][2500/3746] lr: 9.851e-02, eta: 4 days, 9:00:50, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4944, loss_cls: 4.1122, loss: 4.1122 +2024-07-16 16:06:04,362 - pyskl - INFO - Epoch [12][2600/3746] lr: 9.851e-02, eta: 4 days, 8:59:01, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.5002, loss_cls: 4.0896, loss: 4.0896 +2024-07-16 16:07:14,349 - pyskl - INFO - Epoch [12][2700/3746] lr: 9.850e-02, eta: 4 days, 8:57:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.5019, loss_cls: 4.1283, loss: 4.1283 +2024-07-16 16:08:24,238 - pyskl - INFO - Epoch [12][2800/3746] lr: 9.849e-02, eta: 4 days, 8:55:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4822, loss_cls: 4.1523, loss: 4.1523 +2024-07-16 16:09:33,918 - pyskl - INFO - Epoch [12][2900/3746] lr: 9.849e-02, eta: 4 days, 8:53:33, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5042, loss_cls: 4.1004, loss: 4.1004 +2024-07-16 16:10:43,752 - pyskl - INFO - Epoch [12][3000/3746] lr: 9.848e-02, eta: 4 days, 8:51:44, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4867, loss_cls: 4.1454, loss: 4.1454 +2024-07-16 16:11:53,749 - pyskl - INFO - Epoch [12][3100/3746] lr: 9.847e-02, eta: 4 days, 8:49:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4988, loss_cls: 4.1028, loss: 4.1028 +2024-07-16 16:13:03,377 - pyskl - INFO - Epoch [12][3200/3746] lr: 9.847e-02, eta: 4 days, 8:48:06, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4873, loss_cls: 4.1544, loss: 4.1544 +2024-07-16 16:14:13,103 - pyskl - INFO - Epoch [12][3300/3746] lr: 9.846e-02, eta: 4 days, 8:46:16, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5084, loss_cls: 4.0620, loss: 4.0620 +2024-07-16 16:15:22,883 - pyskl - INFO - Epoch [12][3400/3746] lr: 9.845e-02, eta: 4 days, 8:44:27, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4995, loss_cls: 4.1227, loss: 4.1227 +2024-07-16 16:16:32,650 - pyskl - INFO - Epoch [12][3500/3746] lr: 9.845e-02, eta: 4 days, 8:42:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5016, loss_cls: 4.0900, loss: 4.0900 +2024-07-16 16:17:42,545 - pyskl - INFO - Epoch [12][3600/3746] lr: 9.844e-02, eta: 4 days, 8:40:50, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4978, loss_cls: 4.1037, loss: 4.1037 +2024-07-16 16:18:52,409 - pyskl - INFO - Epoch [12][3700/3746] lr: 9.843e-02, eta: 4 days, 8:39:03, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.5014, loss_cls: 4.1090, loss: 4.1090 +2024-07-16 16:19:27,148 - pyskl - INFO - Saving checkpoint at 12 epochs +2024-07-16 16:21:17,111 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 16:21:17,774 - pyskl - INFO - +top1_acc 0.1773 +top5_acc 0.4044 +2024-07-16 16:21:17,774 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 16:21:17,813 - pyskl - INFO - +mean_acc 0.1771 +2024-07-16 16:21:17,817 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_11.pth was removed +2024-07-16 16:21:18,228 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_12.pth. +2024-07-16 16:21:18,229 - pyskl - INFO - Best top1_acc is 0.1773 at 12 epoch. +2024-07-16 16:21:18,244 - pyskl - INFO - Epoch(val) [12][309] top1_acc: 0.1773, top5_acc: 0.4044, mean_class_accuracy: 0.1771 +2024-07-16 16:24:37,931 - pyskl - INFO - Epoch [13][100/3746] lr: 9.842e-02, eta: 4 days, 8:55:07, time: 1.997, data_time: 1.292, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5019, loss_cls: 4.0693, loss: 4.0693 +2024-07-16 16:25:48,565 - pyskl - INFO - Epoch [13][200/3746] lr: 9.842e-02, eta: 4 days, 8:53:25, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5022, loss_cls: 4.0760, loss: 4.0760 +2024-07-16 16:26:58,402 - pyskl - INFO - Epoch [13][300/3746] lr: 9.841e-02, eta: 4 days, 8:51:35, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5123, loss_cls: 4.0337, loss: 4.0337 +2024-07-16 16:28:08,297 - pyskl - INFO - Epoch [13][400/3746] lr: 9.840e-02, eta: 4 days, 8:49:46, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4973, loss_cls: 4.1092, loss: 4.1092 +2024-07-16 16:29:19,023 - pyskl - INFO - Epoch [13][500/3746] lr: 9.839e-02, eta: 4 days, 8:48:07, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5134, loss_cls: 4.0549, loss: 4.0549 +2024-07-16 16:30:28,857 - pyskl - INFO - Epoch [13][600/3746] lr: 9.839e-02, eta: 4 days, 8:46:17, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4973, loss_cls: 4.1201, loss: 4.1201 +2024-07-16 16:31:38,859 - pyskl - INFO - Epoch [13][700/3746] lr: 9.838e-02, eta: 4 days, 8:44:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5008, loss_cls: 4.0705, loss: 4.0705 +2024-07-16 16:32:49,134 - pyskl - INFO - Epoch [13][800/3746] lr: 9.837e-02, eta: 4 days, 8:42:45, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4980, loss_cls: 4.1292, loss: 4.1292 +2024-07-16 16:33:59,078 - pyskl - INFO - Epoch [13][900/3746] lr: 9.837e-02, eta: 4 days, 8:40:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.5031, loss_cls: 4.0946, loss: 4.0946 +2024-07-16 16:35:08,972 - pyskl - INFO - Epoch [13][1000/3746] lr: 9.836e-02, eta: 4 days, 8:39:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4994, loss_cls: 4.1058, loss: 4.1058 +2024-07-16 16:36:18,756 - pyskl - INFO - Epoch [13][1100/3746] lr: 9.835e-02, eta: 4 days, 8:37:20, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4972, loss_cls: 4.1136, loss: 4.1136 +2024-07-16 16:37:28,359 - pyskl - INFO - Epoch [13][1200/3746] lr: 9.834e-02, eta: 4 days, 8:35:29, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4988, loss_cls: 4.1296, loss: 4.1296 +2024-07-16 16:38:38,488 - pyskl - INFO - Epoch [13][1300/3746] lr: 9.834e-02, eta: 4 days, 8:33:44, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.5066, loss_cls: 4.0759, loss: 4.0759 +2024-07-16 16:39:48,316 - pyskl - INFO - Epoch [13][1400/3746] lr: 9.833e-02, eta: 4 days, 8:31:55, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.4978, loss_cls: 4.0666, loss: 4.0666 +2024-07-16 16:40:58,144 - pyskl - INFO - Epoch [13][1500/3746] lr: 9.832e-02, eta: 4 days, 8:30:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5030, loss_cls: 4.0844, loss: 4.0844 +2024-07-16 16:42:08,076 - pyskl - INFO - Epoch [13][1600/3746] lr: 9.832e-02, eta: 4 days, 8:28:20, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5055, loss_cls: 4.0685, loss: 4.0685 +2024-07-16 16:43:17,896 - pyskl - INFO - Epoch [13][1700/3746] lr: 9.831e-02, eta: 4 days, 8:26:33, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5059, loss_cls: 4.0823, loss: 4.0823 +2024-07-16 16:44:27,851 - pyskl - INFO - Epoch [13][1800/3746] lr: 9.830e-02, eta: 4 days, 8:24:46, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4925, loss_cls: 4.1265, loss: 4.1265 +2024-07-16 16:45:37,555 - pyskl - INFO - Epoch [13][1900/3746] lr: 9.829e-02, eta: 4 days, 8:22:58, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5002, loss_cls: 4.0812, loss: 4.0812 +2024-07-16 16:46:47,537 - pyskl - INFO - Epoch [13][2000/3746] lr: 9.829e-02, eta: 4 days, 8:21:12, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4889, loss_cls: 4.1335, loss: 4.1335 +2024-07-16 16:47:57,154 - pyskl - INFO - Epoch [13][2100/3746] lr: 9.828e-02, eta: 4 days, 8:19:22, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5073, loss_cls: 4.0708, loss: 4.0708 +2024-07-16 16:49:07,299 - pyskl - INFO - Epoch [13][2200/3746] lr: 9.827e-02, eta: 4 days, 8:17:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5003, loss_cls: 4.0705, loss: 4.0705 +2024-07-16 16:50:17,292 - pyskl - INFO - Epoch [13][2300/3746] lr: 9.827e-02, eta: 4 days, 8:15:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5019, loss_cls: 4.0882, loss: 4.0882 +2024-07-16 16:51:27,256 - pyskl - INFO - Epoch [13][2400/3746] lr: 9.826e-02, eta: 4 days, 8:14:09, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4916, loss_cls: 4.1400, loss: 4.1400 +2024-07-16 16:52:37,025 - pyskl - INFO - Epoch [13][2500/3746] lr: 9.825e-02, eta: 4 days, 8:12:21, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5058, loss_cls: 4.1039, loss: 4.1039 +2024-07-16 16:53:47,055 - pyskl - INFO - Epoch [13][2600/3746] lr: 9.824e-02, eta: 4 days, 8:10:37, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4953, loss_cls: 4.1114, loss: 4.1114 +2024-07-16 16:54:56,775 - pyskl - INFO - Epoch [13][2700/3746] lr: 9.824e-02, eta: 4 days, 8:08:50, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4952, loss_cls: 4.1412, loss: 4.1412 +2024-07-16 16:56:06,620 - pyskl - INFO - Epoch [13][2800/3746] lr: 9.823e-02, eta: 4 days, 8:07:04, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.5009, loss_cls: 4.1147, loss: 4.1147 +2024-07-16 16:57:16,876 - pyskl - INFO - Epoch [13][2900/3746] lr: 9.822e-02, eta: 4 days, 8:05:23, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4963, loss_cls: 4.0885, loss: 4.0885 +2024-07-16 16:58:26,597 - pyskl - INFO - Epoch [13][3000/3746] lr: 9.821e-02, eta: 4 days, 8:03:36, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4839, loss_cls: 4.1560, loss: 4.1560 +2024-07-16 16:59:36,626 - pyskl - INFO - Epoch [13][3100/3746] lr: 9.821e-02, eta: 4 days, 8:01:52, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5105, loss_cls: 4.0700, loss: 4.0700 +2024-07-16 17:00:46,393 - pyskl - INFO - Epoch [13][3200/3746] lr: 9.820e-02, eta: 4 days, 8:00:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5000, loss_cls: 4.1228, loss: 4.1228 +2024-07-16 17:01:56,165 - pyskl - INFO - Epoch [13][3300/3746] lr: 9.819e-02, eta: 4 days, 7:58:20, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4975, loss_cls: 4.1388, loss: 4.1388 +2024-07-16 17:03:06,058 - pyskl - INFO - Epoch [13][3400/3746] lr: 9.818e-02, eta: 4 days, 7:56:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5056, loss_cls: 4.0834, loss: 4.0834 +2024-07-16 17:04:16,081 - pyskl - INFO - Epoch [13][3500/3746] lr: 9.818e-02, eta: 4 days, 7:54:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.5014, loss_cls: 4.1229, loss: 4.1229 +2024-07-16 17:05:26,238 - pyskl - INFO - Epoch [13][3600/3746] lr: 9.817e-02, eta: 4 days, 7:53:11, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5027, loss_cls: 4.0799, loss: 4.0799 +2024-07-16 17:06:36,537 - pyskl - INFO - Epoch [13][3700/3746] lr: 9.816e-02, eta: 4 days, 7:51:31, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4977, loss_cls: 4.1312, loss: 4.1312 +2024-07-16 17:07:11,401 - pyskl - INFO - Saving checkpoint at 13 epochs +2024-07-16 17:09:02,630 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 17:09:03,302 - pyskl - INFO - +top1_acc 0.1718 +top5_acc 0.3942 +2024-07-16 17:09:03,302 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 17:09:03,344 - pyskl - INFO - +mean_acc 0.1715 +2024-07-16 17:09:03,359 - pyskl - INFO - Epoch(val) [13][309] top1_acc: 0.1718, top5_acc: 0.3942, mean_class_accuracy: 0.1715 +2024-07-16 17:12:21,515 - pyskl - INFO - Epoch [14][100/3746] lr: 9.815e-02, eta: 4 days, 8:05:50, time: 1.981, data_time: 1.277, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5092, loss_cls: 4.0577, loss: 4.0577 +2024-07-16 17:13:32,018 - pyskl - INFO - Epoch [14][200/3746] lr: 9.814e-02, eta: 4 days, 8:04:10, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5089, loss_cls: 4.0673, loss: 4.0673 +2024-07-16 17:14:42,193 - pyskl - INFO - Epoch [14][300/3746] lr: 9.814e-02, eta: 4 days, 8:02:28, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5077, loss_cls: 4.0676, loss: 4.0676 +2024-07-16 17:15:51,884 - pyskl - INFO - Epoch [14][400/3746] lr: 9.813e-02, eta: 4 days, 8:00:40, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4931, loss_cls: 4.1025, loss: 4.1025 +2024-07-16 17:17:02,157 - pyskl - INFO - Epoch [14][500/3746] lr: 9.812e-02, eta: 4 days, 7:58:58, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4995, loss_cls: 4.0954, loss: 4.0954 +2024-07-16 17:18:11,768 - pyskl - INFO - Epoch [14][600/3746] lr: 9.811e-02, eta: 4 days, 7:57:10, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.5008, loss_cls: 4.1109, loss: 4.1109 +2024-07-16 17:19:21,611 - pyskl - INFO - Epoch [14][700/3746] lr: 9.811e-02, eta: 4 days, 7:55:24, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5006, loss_cls: 4.1031, loss: 4.1031 +2024-07-16 17:20:31,479 - pyskl - INFO - Epoch [14][800/3746] lr: 9.810e-02, eta: 4 days, 7:53:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.4933, loss_cls: 4.0798, loss: 4.0798 +2024-07-16 17:21:41,401 - pyskl - INFO - Epoch [14][900/3746] lr: 9.809e-02, eta: 4 days, 7:51:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5023, loss_cls: 4.1013, loss: 4.1013 +2024-07-16 17:22:51,336 - pyskl - INFO - Epoch [14][1000/3746] lr: 9.808e-02, eta: 4 days, 7:50:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5031, loss_cls: 4.0715, loss: 4.0715 +2024-07-16 17:24:01,057 - pyskl - INFO - Epoch [14][1100/3746] lr: 9.807e-02, eta: 4 days, 7:48:23, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4944, loss_cls: 4.1283, loss: 4.1283 +2024-07-16 17:25:10,867 - pyskl - INFO - Epoch [14][1200/3746] lr: 9.807e-02, eta: 4 days, 7:46:37, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5109, loss_cls: 4.0847, loss: 4.0847 +2024-07-16 17:26:20,608 - pyskl - INFO - Epoch [14][1300/3746] lr: 9.806e-02, eta: 4 days, 7:44:51, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5034, loss_cls: 4.0521, loss: 4.0521 +2024-07-16 17:27:30,400 - pyskl - INFO - Epoch [14][1400/3746] lr: 9.805e-02, eta: 4 days, 7:43:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5077, loss_cls: 4.0687, loss: 4.0687 +2024-07-16 17:28:40,278 - pyskl - INFO - Epoch [14][1500/3746] lr: 9.804e-02, eta: 4 days, 7:41:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4964, loss_cls: 4.1140, loss: 4.1140 +2024-07-16 17:29:50,039 - pyskl - INFO - Epoch [14][1600/3746] lr: 9.804e-02, eta: 4 days, 7:39:36, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5005, loss_cls: 4.0915, loss: 4.0915 +2024-07-16 17:30:59,655 - pyskl - INFO - Epoch [14][1700/3746] lr: 9.803e-02, eta: 4 days, 7:37:49, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5062, loss_cls: 4.0858, loss: 4.0858 +2024-07-16 17:32:09,358 - pyskl - INFO - Epoch [14][1800/3746] lr: 9.802e-02, eta: 4 days, 7:36:04, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.5086, loss_cls: 4.0832, loss: 4.0832 +2024-07-16 17:33:19,466 - pyskl - INFO - Epoch [14][1900/3746] lr: 9.801e-02, eta: 4 days, 7:34:22, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.5006, loss_cls: 4.1057, loss: 4.1057 +2024-07-16 17:34:29,671 - pyskl - INFO - Epoch [14][2000/3746] lr: 9.800e-02, eta: 4 days, 7:32:42, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5164, loss_cls: 4.0327, loss: 4.0327 +2024-07-16 17:35:39,637 - pyskl - INFO - Epoch [14][2100/3746] lr: 9.800e-02, eta: 4 days, 7:30:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4947, loss_cls: 4.0990, loss: 4.0990 +2024-07-16 17:36:49,720 - pyskl - INFO - Epoch [14][2200/3746] lr: 9.799e-02, eta: 4 days, 7:29:18, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.4994, loss_cls: 4.0847, loss: 4.0847 +2024-07-16 17:37:59,590 - pyskl - INFO - Epoch [14][2300/3746] lr: 9.798e-02, eta: 4 days, 7:27:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5005, loss_cls: 4.1103, loss: 4.1103 +2024-07-16 17:39:09,597 - pyskl - INFO - Epoch [14][2400/3746] lr: 9.797e-02, eta: 4 days, 7:25:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5066, loss_cls: 4.0471, loss: 4.0471 +2024-07-16 17:40:19,501 - pyskl - INFO - Epoch [14][2500/3746] lr: 9.797e-02, eta: 4 days, 7:24:10, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5041, loss_cls: 4.0862, loss: 4.0862 +2024-07-16 17:41:29,200 - pyskl - INFO - Epoch [14][2600/3746] lr: 9.796e-02, eta: 4 days, 7:22:25, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5086, loss_cls: 4.0697, loss: 4.0697 +2024-07-16 17:42:39,089 - pyskl - INFO - Epoch [14][2700/3746] lr: 9.795e-02, eta: 4 days, 7:20:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5031, loss_cls: 4.0991, loss: 4.0991 +2024-07-16 17:43:49,078 - pyskl - INFO - Epoch [14][2800/3746] lr: 9.794e-02, eta: 4 days, 7:19:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5100, loss_cls: 4.0625, loss: 4.0625 +2024-07-16 17:44:58,963 - pyskl - INFO - Epoch [14][2900/3746] lr: 9.793e-02, eta: 4 days, 7:17:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4978, loss_cls: 4.0949, loss: 4.0949 +2024-07-16 17:46:08,953 - pyskl - INFO - Epoch [14][3000/3746] lr: 9.793e-02, eta: 4 days, 7:15:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4961, loss_cls: 4.1380, loss: 4.1380 +2024-07-16 17:47:18,870 - pyskl - INFO - Epoch [14][3100/3746] lr: 9.792e-02, eta: 4 days, 7:13:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4945, loss_cls: 4.1193, loss: 4.1193 +2024-07-16 17:48:28,661 - pyskl - INFO - Epoch [14][3200/3746] lr: 9.791e-02, eta: 4 days, 7:12:13, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4900, loss_cls: 4.1046, loss: 4.1046 +2024-07-16 17:49:38,582 - pyskl - INFO - Epoch [14][3300/3746] lr: 9.790e-02, eta: 4 days, 7:10:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4967, loss_cls: 4.1156, loss: 4.1156 +2024-07-16 17:50:48,417 - pyskl - INFO - Epoch [14][3400/3746] lr: 9.789e-02, eta: 4 days, 7:08:49, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5011, loss_cls: 4.0802, loss: 4.0802 +2024-07-16 17:51:58,035 - pyskl - INFO - Epoch [14][3500/3746] lr: 9.789e-02, eta: 4 days, 7:07:05, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.5066, loss_cls: 4.1163, loss: 4.1163 +2024-07-16 17:53:07,715 - pyskl - INFO - Epoch [14][3600/3746] lr: 9.788e-02, eta: 4 days, 7:05:21, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5094, loss_cls: 4.0860, loss: 4.0860 +2024-07-16 17:54:17,352 - pyskl - INFO - Epoch [14][3700/3746] lr: 9.787e-02, eta: 4 days, 7:03:37, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5005, loss_cls: 4.0967, loss: 4.0967 +2024-07-16 17:54:52,205 - pyskl - INFO - Saving checkpoint at 14 epochs +2024-07-16 17:56:42,121 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 17:56:42,788 - pyskl - INFO - +top1_acc 0.1643 +top5_acc 0.3899 +2024-07-16 17:56:42,789 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 17:56:42,828 - pyskl - INFO - +mean_acc 0.1640 +2024-07-16 17:56:42,839 - pyskl - INFO - Epoch(val) [14][309] top1_acc: 0.1643, top5_acc: 0.3899, mean_class_accuracy: 0.1640 +2024-07-16 18:00:04,107 - pyskl - INFO - Epoch [15][100/3746] lr: 9.786e-02, eta: 4 days, 7:17:11, time: 2.013, data_time: 1.308, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5097, loss_cls: 4.0822, loss: 4.0822 +2024-07-16 18:01:14,390 - pyskl - INFO - Epoch [15][200/3746] lr: 9.785e-02, eta: 4 days, 7:15:32, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5077, loss_cls: 4.0476, loss: 4.0476 +2024-07-16 18:02:24,736 - pyskl - INFO - Epoch [15][300/3746] lr: 9.784e-02, eta: 4 days, 7:13:53, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5056, loss_cls: 4.0659, loss: 4.0659 +2024-07-16 18:03:35,045 - pyskl - INFO - Epoch [15][400/3746] lr: 9.783e-02, eta: 4 days, 7:12:14, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5120, loss_cls: 4.0071, loss: 4.0071 +2024-07-16 18:04:45,020 - pyskl - INFO - Epoch [15][500/3746] lr: 9.783e-02, eta: 4 days, 7:10:33, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5097, loss_cls: 4.0580, loss: 4.0580 +2024-07-16 18:05:54,726 - pyskl - INFO - Epoch [15][600/3746] lr: 9.782e-02, eta: 4 days, 7:08:48, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5073, loss_cls: 4.0691, loss: 4.0691 +2024-07-16 18:07:04,861 - pyskl - INFO - Epoch [15][700/3746] lr: 9.781e-02, eta: 4 days, 7:07:08, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5086, loss_cls: 4.0462, loss: 4.0462 +2024-07-16 18:08:14,988 - pyskl - INFO - Epoch [15][800/3746] lr: 9.780e-02, eta: 4 days, 7:05:28, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5169, loss_cls: 4.0153, loss: 4.0153 +2024-07-16 18:09:25,062 - pyskl - INFO - Epoch [15][900/3746] lr: 9.779e-02, eta: 4 days, 7:03:47, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5027, loss_cls: 4.0802, loss: 4.0802 +2024-07-16 18:10:34,812 - pyskl - INFO - Epoch [15][1000/3746] lr: 9.778e-02, eta: 4 days, 7:02:04, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4995, loss_cls: 4.1147, loss: 4.1147 +2024-07-16 18:11:44,496 - pyskl - INFO - Epoch [15][1100/3746] lr: 9.778e-02, eta: 4 days, 7:00:20, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.5006, loss_cls: 4.0928, loss: 4.0928 +2024-07-16 18:12:54,477 - pyskl - INFO - Epoch [15][1200/3746] lr: 9.777e-02, eta: 4 days, 6:58:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.4973, loss_cls: 4.1123, loss: 4.1123 +2024-07-16 18:14:04,312 - pyskl - INFO - Epoch [15][1300/3746] lr: 9.776e-02, eta: 4 days, 6:56:57, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.4980, loss_cls: 4.0925, loss: 4.0925 +2024-07-16 18:15:14,256 - pyskl - INFO - Epoch [15][1400/3746] lr: 9.775e-02, eta: 4 days, 6:55:15, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5012, loss_cls: 4.0879, loss: 4.0879 +2024-07-16 18:16:24,111 - pyskl - INFO - Epoch [15][1500/3746] lr: 9.774e-02, eta: 4 days, 6:53:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5008, loss_cls: 4.0882, loss: 4.0882 +2024-07-16 18:17:33,957 - pyskl - INFO - Epoch [15][1600/3746] lr: 9.773e-02, eta: 4 days, 6:51:52, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5095, loss_cls: 4.0706, loss: 4.0706 +2024-07-16 18:18:43,734 - pyskl - INFO - Epoch [15][1700/3746] lr: 9.773e-02, eta: 4 days, 6:50:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5025, loss_cls: 4.0671, loss: 4.0671 +2024-07-16 18:19:54,056 - pyskl - INFO - Epoch [15][1800/3746] lr: 9.772e-02, eta: 4 days, 6:48:32, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.5033, loss_cls: 4.0763, loss: 4.0763 +2024-07-16 18:21:04,091 - pyskl - INFO - Epoch [15][1900/3746] lr: 9.771e-02, eta: 4 days, 6:46:52, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5070, loss_cls: 4.0971, loss: 4.0971 +2024-07-16 18:22:14,354 - pyskl - INFO - Epoch [15][2000/3746] lr: 9.770e-02, eta: 4 days, 6:45:15, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5094, loss_cls: 4.0467, loss: 4.0467 +2024-07-16 18:23:24,663 - pyskl - INFO - Epoch [15][2100/3746] lr: 9.769e-02, eta: 4 days, 6:43:38, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5041, loss_cls: 4.0743, loss: 4.0743 +2024-07-16 18:24:34,563 - pyskl - INFO - Epoch [15][2200/3746] lr: 9.768e-02, eta: 4 days, 6:41:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4955, loss_cls: 4.1333, loss: 4.1333 +2024-07-16 18:25:44,786 - pyskl - INFO - Epoch [15][2300/3746] lr: 9.768e-02, eta: 4 days, 6:40:20, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5086, loss_cls: 4.0453, loss: 4.0453 +2024-07-16 18:26:54,707 - pyskl - INFO - Epoch [15][2400/3746] lr: 9.767e-02, eta: 4 days, 6:38:40, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4953, loss_cls: 4.1441, loss: 4.1441 +2024-07-16 18:28:04,635 - pyskl - INFO - Epoch [15][2500/3746] lr: 9.766e-02, eta: 4 days, 6:36:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5133, loss_cls: 4.0493, loss: 4.0493 +2024-07-16 18:29:14,677 - pyskl - INFO - Epoch [15][2600/3746] lr: 9.765e-02, eta: 4 days, 6:35:21, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5144, loss_cls: 4.0646, loss: 4.0646 +2024-07-16 18:30:24,625 - pyskl - INFO - Epoch [15][2700/3746] lr: 9.764e-02, eta: 4 days, 6:33:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5089, loss_cls: 4.0798, loss: 4.0798 +2024-07-16 18:31:34,404 - pyskl - INFO - Epoch [15][2800/3746] lr: 9.763e-02, eta: 4 days, 6:32:00, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5020, loss_cls: 4.0865, loss: 4.0865 +2024-07-16 18:32:43,999 - pyskl - INFO - Epoch [15][2900/3746] lr: 9.763e-02, eta: 4 days, 6:30:17, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5044, loss_cls: 4.0747, loss: 4.0747 +2024-07-16 18:33:53,873 - pyskl - INFO - Epoch [15][3000/3746] lr: 9.762e-02, eta: 4 days, 6:28:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4956, loss_cls: 4.1388, loss: 4.1388 +2024-07-16 18:35:03,446 - pyskl - INFO - Epoch [15][3100/3746] lr: 9.761e-02, eta: 4 days, 6:26:54, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5003, loss_cls: 4.0836, loss: 4.0836 +2024-07-16 18:36:13,236 - pyskl - INFO - Epoch [15][3200/3746] lr: 9.760e-02, eta: 4 days, 6:25:14, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5044, loss_cls: 4.0625, loss: 4.0625 +2024-07-16 18:37:23,172 - pyskl - INFO - Epoch [15][3300/3746] lr: 9.759e-02, eta: 4 days, 6:23:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4955, loss_cls: 4.1184, loss: 4.1184 +2024-07-16 18:38:32,927 - pyskl - INFO - Epoch [15][3400/3746] lr: 9.758e-02, eta: 4 days, 6:21:54, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5003, loss_cls: 4.1421, loss: 4.1421 +2024-07-16 18:39:42,676 - pyskl - INFO - Epoch [15][3500/3746] lr: 9.757e-02, eta: 4 days, 6:20:13, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.5012, loss_cls: 4.1117, loss: 4.1117 +2024-07-16 18:40:52,627 - pyskl - INFO - Epoch [15][3600/3746] lr: 9.757e-02, eta: 4 days, 6:18:35, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5072, loss_cls: 4.0844, loss: 4.0844 +2024-07-16 18:42:02,844 - pyskl - INFO - Epoch [15][3700/3746] lr: 9.756e-02, eta: 4 days, 6:16:59, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4972, loss_cls: 4.1141, loss: 4.1141 +2024-07-16 18:42:37,577 - pyskl - INFO - Saving checkpoint at 15 epochs +2024-07-16 18:44:26,514 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 18:44:27,171 - pyskl - INFO - +top1_acc 0.1799 +top5_acc 0.4006 +2024-07-16 18:44:27,171 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 18:44:27,210 - pyskl - INFO - +mean_acc 0.1798 +2024-07-16 18:44:27,214 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_12.pth was removed +2024-07-16 18:44:27,458 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_15.pth. +2024-07-16 18:44:27,459 - pyskl - INFO - Best top1_acc is 0.1799 at 15 epoch. +2024-07-16 18:44:27,471 - pyskl - INFO - Epoch(val) [15][309] top1_acc: 0.1799, top5_acc: 0.4006, mean_class_accuracy: 0.1798 +2024-07-16 18:47:47,807 - pyskl - INFO - Epoch [16][100/3746] lr: 9.754e-02, eta: 4 days, 6:29:17, time: 2.003, data_time: 1.295, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5150, loss_cls: 4.0084, loss: 4.0084 +2024-07-16 18:48:58,443 - pyskl - INFO - Epoch [16][200/3746] lr: 9.754e-02, eta: 4 days, 6:27:43, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5122, loss_cls: 4.0356, loss: 4.0356 +2024-07-16 18:50:08,854 - pyskl - INFO - Epoch [16][300/3746] lr: 9.753e-02, eta: 4 days, 6:26:07, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5131, loss_cls: 4.0193, loss: 4.0193 +2024-07-16 18:51:18,775 - pyskl - INFO - Epoch [16][400/3746] lr: 9.752e-02, eta: 4 days, 6:24:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5111, loss_cls: 4.0533, loss: 4.0533 +2024-07-16 18:52:29,147 - pyskl - INFO - Epoch [16][500/3746] lr: 9.751e-02, eta: 4 days, 6:22:51, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5025, loss_cls: 4.1014, loss: 4.1014 +2024-07-16 18:53:39,125 - pyskl - INFO - Epoch [16][600/3746] lr: 9.750e-02, eta: 4 days, 6:21:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4975, loss_cls: 4.0694, loss: 4.0694 +2024-07-16 18:54:49,239 - pyskl - INFO - Epoch [16][700/3746] lr: 9.749e-02, eta: 4 days, 6:19:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4903, loss_cls: 4.1432, loss: 4.1432 +2024-07-16 18:55:59,453 - pyskl - INFO - Epoch [16][800/3746] lr: 9.748e-02, eta: 4 days, 6:17:56, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.5095, loss_cls: 4.0571, loss: 4.0571 +2024-07-16 18:57:09,247 - pyskl - INFO - Epoch [16][900/3746] lr: 9.747e-02, eta: 4 days, 6:16:15, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5045, loss_cls: 4.0742, loss: 4.0742 +2024-07-16 18:58:19,425 - pyskl - INFO - Epoch [16][1000/3746] lr: 9.747e-02, eta: 4 days, 6:14:38, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4986, loss_cls: 4.1030, loss: 4.1030 +2024-07-16 18:59:29,555 - pyskl - INFO - Epoch [16][1100/3746] lr: 9.746e-02, eta: 4 days, 6:13:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5166, loss_cls: 4.0322, loss: 4.0322 +2024-07-16 19:00:39,373 - pyskl - INFO - Epoch [16][1200/3746] lr: 9.745e-02, eta: 4 days, 6:11:20, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5098, loss_cls: 4.0787, loss: 4.0787 +2024-07-16 19:01:49,472 - pyskl - INFO - Epoch [16][1300/3746] lr: 9.744e-02, eta: 4 days, 6:09:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.5038, loss_cls: 4.0891, loss: 4.0891 +2024-07-16 19:02:59,438 - pyskl - INFO - Epoch [16][1400/3746] lr: 9.743e-02, eta: 4 days, 6:08:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.5073, loss_cls: 4.0702, loss: 4.0702 +2024-07-16 19:04:09,478 - pyskl - INFO - Epoch [16][1500/3746] lr: 9.742e-02, eta: 4 days, 6:06:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5131, loss_cls: 4.0306, loss: 4.0306 +2024-07-16 19:05:19,299 - pyskl - INFO - Epoch [16][1600/3746] lr: 9.741e-02, eta: 4 days, 6:04:46, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4983, loss_cls: 4.1220, loss: 4.1220 +2024-07-16 19:06:29,075 - pyskl - INFO - Epoch [16][1700/3746] lr: 9.740e-02, eta: 4 days, 6:03:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.5011, loss_cls: 4.1049, loss: 4.1049 +2024-07-16 19:07:39,311 - pyskl - INFO - Epoch [16][1800/3746] lr: 9.740e-02, eta: 4 days, 6:01:30, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5092, loss_cls: 4.0534, loss: 4.0534 +2024-07-16 19:08:49,615 - pyskl - INFO - Epoch [16][1900/3746] lr: 9.739e-02, eta: 4 days, 5:59:55, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.5009, loss_cls: 4.1325, loss: 4.1325 +2024-07-16 19:09:59,564 - pyskl - INFO - Epoch [16][2000/3746] lr: 9.738e-02, eta: 4 days, 5:58:16, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.5102, loss_cls: 4.0531, loss: 4.0531 +2024-07-16 19:11:09,387 - pyskl - INFO - Epoch [16][2100/3746] lr: 9.737e-02, eta: 4 days, 5:56:37, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5041, loss_cls: 4.0978, loss: 4.0978 +2024-07-16 19:12:19,373 - pyskl - INFO - Epoch [16][2200/3746] lr: 9.736e-02, eta: 4 days, 5:54:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5059, loss_cls: 4.0626, loss: 4.0626 +2024-07-16 19:13:29,585 - pyskl - INFO - Epoch [16][2300/3746] lr: 9.735e-02, eta: 4 days, 5:53:24, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5075, loss_cls: 4.0852, loss: 4.0852 +2024-07-16 19:14:39,378 - pyskl - INFO - Epoch [16][2400/3746] lr: 9.734e-02, eta: 4 days, 5:51:44, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5153, loss_cls: 4.0789, loss: 4.0789 +2024-07-16 19:15:49,199 - pyskl - INFO - Epoch [16][2500/3746] lr: 9.733e-02, eta: 4 days, 5:50:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5047, loss_cls: 4.0561, loss: 4.0561 +2024-07-16 19:16:59,430 - pyskl - INFO - Epoch [16][2600/3746] lr: 9.732e-02, eta: 4 days, 5:48:30, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5100, loss_cls: 4.0485, loss: 4.0485 +2024-07-16 19:18:09,517 - pyskl - INFO - Epoch [16][2700/3746] lr: 9.731e-02, eta: 4 days, 5:46:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4869, loss_cls: 4.1280, loss: 4.1280 +2024-07-16 19:19:19,669 - pyskl - INFO - Epoch [16][2800/3746] lr: 9.731e-02, eta: 4 days, 5:45:18, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4844, loss_cls: 4.1492, loss: 4.1492 +2024-07-16 19:20:29,441 - pyskl - INFO - Epoch [16][2900/3746] lr: 9.730e-02, eta: 4 days, 5:43:39, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5061, loss_cls: 4.0719, loss: 4.0719 +2024-07-16 19:21:39,723 - pyskl - INFO - Epoch [16][3000/3746] lr: 9.729e-02, eta: 4 days, 5:42:04, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5042, loss_cls: 4.0675, loss: 4.0675 +2024-07-16 19:22:49,661 - pyskl - INFO - Epoch [16][3100/3746] lr: 9.728e-02, eta: 4 days, 5:40:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4991, loss_cls: 4.0886, loss: 4.0886 +2024-07-16 19:23:59,696 - pyskl - INFO - Epoch [16][3200/3746] lr: 9.727e-02, eta: 4 days, 5:38:51, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5073, loss_cls: 4.0632, loss: 4.0632 +2024-07-16 19:25:09,565 - pyskl - INFO - Epoch [16][3300/3746] lr: 9.726e-02, eta: 4 days, 5:37:13, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5047, loss_cls: 4.0795, loss: 4.0795 +2024-07-16 19:26:19,302 - pyskl - INFO - Epoch [16][3400/3746] lr: 9.725e-02, eta: 4 days, 5:35:34, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5028, loss_cls: 4.0987, loss: 4.0987 +2024-07-16 19:27:29,107 - pyskl - INFO - Epoch [16][3500/3746] lr: 9.724e-02, eta: 4 days, 5:33:56, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5067, loss_cls: 4.0784, loss: 4.0784 +2024-07-16 19:28:39,123 - pyskl - INFO - Epoch [16][3600/3746] lr: 9.723e-02, eta: 4 days, 5:32:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5042, loss_cls: 4.0726, loss: 4.0726 +2024-07-16 19:29:49,571 - pyskl - INFO - Epoch [16][3700/3746] lr: 9.722e-02, eta: 4 days, 5:30:47, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5114, loss_cls: 4.0498, loss: 4.0498 +2024-07-16 19:30:24,457 - pyskl - INFO - Saving checkpoint at 16 epochs +2024-07-16 19:32:14,217 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 19:32:14,887 - pyskl - INFO - +top1_acc 0.1913 +top5_acc 0.4217 +2024-07-16 19:32:14,887 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 19:32:14,929 - pyskl - INFO - +mean_acc 0.1912 +2024-07-16 19:32:14,934 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_15.pth was removed +2024-07-16 19:32:15,205 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_16.pth. +2024-07-16 19:32:15,206 - pyskl - INFO - Best top1_acc is 0.1913 at 16 epoch. +2024-07-16 19:32:15,218 - pyskl - INFO - Epoch(val) [16][309] top1_acc: 0.1913, top5_acc: 0.4217, mean_class_accuracy: 0.1912 +2024-07-16 19:35:36,452 - pyskl - INFO - Epoch [17][100/3746] lr: 9.721e-02, eta: 4 days, 5:42:15, time: 2.012, data_time: 1.306, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5170, loss_cls: 3.9946, loss: 3.9946 +2024-07-16 19:36:46,968 - pyskl - INFO - Epoch [17][200/3746] lr: 9.720e-02, eta: 4 days, 5:40:41, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5142, loss_cls: 4.0283, loss: 4.0283 +2024-07-16 19:37:57,601 - pyskl - INFO - Epoch [17][300/3746] lr: 9.719e-02, eta: 4 days, 5:39:09, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5155, loss_cls: 4.0246, loss: 4.0246 +2024-07-16 19:39:07,671 - pyskl - INFO - Epoch [17][400/3746] lr: 9.718e-02, eta: 4 days, 5:37:32, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5067, loss_cls: 4.0455, loss: 4.0455 +2024-07-16 19:40:17,893 - pyskl - INFO - Epoch [17][500/3746] lr: 9.717e-02, eta: 4 days, 5:35:57, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5180, loss_cls: 4.0381, loss: 4.0381 +2024-07-16 19:41:27,987 - pyskl - INFO - Epoch [17][600/3746] lr: 9.716e-02, eta: 4 days, 5:34:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5123, loss_cls: 4.0662, loss: 4.0662 +2024-07-16 19:42:37,865 - pyskl - INFO - Epoch [17][700/3746] lr: 9.715e-02, eta: 4 days, 5:32:42, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.5056, loss_cls: 4.0907, loss: 4.0907 +2024-07-16 19:43:47,566 - pyskl - INFO - Epoch [17][800/3746] lr: 9.714e-02, eta: 4 days, 5:31:02, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.5006, loss_cls: 4.0819, loss: 4.0819 +2024-07-16 19:44:57,734 - pyskl - INFO - Epoch [17][900/3746] lr: 9.714e-02, eta: 4 days, 5:29:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5064, loss_cls: 4.0765, loss: 4.0765 +2024-07-16 19:46:07,547 - pyskl - INFO - Epoch [17][1000/3746] lr: 9.713e-02, eta: 4 days, 5:27:48, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5045, loss_cls: 4.0708, loss: 4.0708 +2024-07-16 19:47:17,582 - pyskl - INFO - Epoch [17][1100/3746] lr: 9.712e-02, eta: 4 days, 5:26:12, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.5042, loss_cls: 4.0594, loss: 4.0594 +2024-07-16 19:48:27,542 - pyskl - INFO - Epoch [17][1200/3746] lr: 9.711e-02, eta: 4 days, 5:24:35, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5072, loss_cls: 4.0826, loss: 4.0826 +2024-07-16 19:49:37,195 - pyskl - INFO - Epoch [17][1300/3746] lr: 9.710e-02, eta: 4 days, 5:22:55, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5012, loss_cls: 4.0984, loss: 4.0984 +2024-07-16 19:50:47,073 - pyskl - INFO - Epoch [17][1400/3746] lr: 9.709e-02, eta: 4 days, 5:21:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.5042, loss_cls: 4.0900, loss: 4.0900 +2024-07-16 19:51:56,792 - pyskl - INFO - Epoch [17][1500/3746] lr: 9.708e-02, eta: 4 days, 5:19:39, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4952, loss_cls: 4.1057, loss: 4.1057 +2024-07-16 19:53:06,436 - pyskl - INFO - Epoch [17][1600/3746] lr: 9.707e-02, eta: 4 days, 5:18:00, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4970, loss_cls: 4.1050, loss: 4.1050 +2024-07-16 19:54:16,529 - pyskl - INFO - Epoch [17][1700/3746] lr: 9.706e-02, eta: 4 days, 5:16:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4889, loss_cls: 4.1401, loss: 4.1401 +2024-07-16 19:55:26,211 - pyskl - INFO - Epoch [17][1800/3746] lr: 9.705e-02, eta: 4 days, 5:14:45, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5012, loss_cls: 4.0844, loss: 4.0844 +2024-07-16 19:56:36,186 - pyskl - INFO - Epoch [17][1900/3746] lr: 9.704e-02, eta: 4 days, 5:13:09, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5128, loss_cls: 4.0404, loss: 4.0404 +2024-07-16 19:57:46,442 - pyskl - INFO - Epoch [17][2000/3746] lr: 9.703e-02, eta: 4 days, 5:11:35, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.4992, loss_cls: 4.0850, loss: 4.0850 +2024-07-16 19:58:56,717 - pyskl - INFO - Epoch [17][2100/3746] lr: 9.702e-02, eta: 4 days, 5:10:01, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.4977, loss_cls: 4.0715, loss: 4.0715 +2024-07-16 20:00:06,669 - pyskl - INFO - Epoch [17][2200/3746] lr: 9.701e-02, eta: 4 days, 5:08:25, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5244, loss_cls: 3.9977, loss: 3.9977 +2024-07-16 20:01:16,705 - pyskl - INFO - Epoch [17][2300/3746] lr: 9.700e-02, eta: 4 days, 5:06:50, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5091, loss_cls: 4.0632, loss: 4.0632 +2024-07-16 20:02:26,726 - pyskl - INFO - Epoch [17][2400/3746] lr: 9.699e-02, eta: 4 days, 5:05:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4977, loss_cls: 4.0946, loss: 4.0946 +2024-07-16 20:03:36,556 - pyskl - INFO - Epoch [17][2500/3746] lr: 9.698e-02, eta: 4 days, 5:03:37, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.5083, loss_cls: 4.0973, loss: 4.0973 +2024-07-16 20:04:46,267 - pyskl - INFO - Epoch [17][2600/3746] lr: 9.697e-02, eta: 4 days, 5:01:59, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5033, loss_cls: 4.0977, loss: 4.0977 +2024-07-16 20:05:56,122 - pyskl - INFO - Epoch [17][2700/3746] lr: 9.697e-02, eta: 4 days, 5:00:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5019, loss_cls: 4.0981, loss: 4.0981 +2024-07-16 20:07:05,863 - pyskl - INFO - Epoch [17][2800/3746] lr: 9.696e-02, eta: 4 days, 4:58:45, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5162, loss_cls: 4.0520, loss: 4.0520 +2024-07-16 20:08:15,531 - pyskl - INFO - Epoch [17][2900/3746] lr: 9.695e-02, eta: 4 days, 4:57:07, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5053, loss_cls: 4.0947, loss: 4.0947 +2024-07-16 20:09:25,652 - pyskl - INFO - Epoch [17][3000/3746] lr: 9.694e-02, eta: 4 days, 4:55:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5028, loss_cls: 4.0972, loss: 4.0972 +2024-07-16 20:10:35,545 - pyskl - INFO - Epoch [17][3100/3746] lr: 9.693e-02, eta: 4 days, 4:53:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5017, loss_cls: 4.0928, loss: 4.0928 +2024-07-16 20:11:45,328 - pyskl - INFO - Epoch [17][3200/3746] lr: 9.692e-02, eta: 4 days, 4:52:20, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.5008, loss_cls: 4.0970, loss: 4.0970 +2024-07-16 20:12:55,034 - pyskl - INFO - Epoch [17][3300/3746] lr: 9.691e-02, eta: 4 days, 4:50:43, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5002, loss_cls: 4.0619, loss: 4.0619 +2024-07-16 20:14:04,938 - pyskl - INFO - Epoch [17][3400/3746] lr: 9.690e-02, eta: 4 days, 4:49:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4956, loss_cls: 4.1345, loss: 4.1345 +2024-07-16 20:15:14,733 - pyskl - INFO - Epoch [17][3500/3746] lr: 9.689e-02, eta: 4 days, 4:47:31, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5066, loss_cls: 4.0715, loss: 4.0715 +2024-07-16 20:16:24,826 - pyskl - INFO - Epoch [17][3600/3746] lr: 9.688e-02, eta: 4 days, 4:45:57, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5008, loss_cls: 4.0710, loss: 4.0710 +2024-07-16 20:17:35,348 - pyskl - INFO - Epoch [17][3700/3746] lr: 9.687e-02, eta: 4 days, 4:44:26, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5158, loss_cls: 4.0642, loss: 4.0642 +2024-07-16 20:18:10,173 - pyskl - INFO - Saving checkpoint at 17 epochs +2024-07-16 20:20:00,291 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 20:20:00,956 - pyskl - INFO - +top1_acc 0.0946 +top5_acc 0.2464 +2024-07-16 20:20:00,956 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 20:20:00,995 - pyskl - INFO - +mean_acc 0.0947 +2024-07-16 20:20:01,007 - pyskl - INFO - Epoch(val) [17][309] top1_acc: 0.0946, top5_acc: 0.2464, mean_class_accuracy: 0.0947 +2024-07-16 20:23:20,321 - pyskl - INFO - Epoch [18][100/3746] lr: 9.685e-02, eta: 4 days, 4:54:47, time: 1.993, data_time: 1.286, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5186, loss_cls: 4.0125, loss: 4.0125 +2024-07-16 20:24:30,484 - pyskl - INFO - Epoch [18][200/3746] lr: 9.684e-02, eta: 4 days, 4:53:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5000, loss_cls: 4.0533, loss: 4.0533 +2024-07-16 20:25:40,801 - pyskl - INFO - Epoch [18][300/3746] lr: 9.683e-02, eta: 4 days, 4:51:39, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5080, loss_cls: 4.0392, loss: 4.0392 +2024-07-16 20:26:51,194 - pyskl - INFO - Epoch [18][400/3746] lr: 9.683e-02, eta: 4 days, 4:50:06, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5117, loss_cls: 4.0445, loss: 4.0445 +2024-07-16 20:28:01,409 - pyskl - INFO - Epoch [18][500/3746] lr: 9.682e-02, eta: 4 days, 4:48:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5059, loss_cls: 4.0690, loss: 4.0690 +2024-07-16 20:29:11,364 - pyskl - INFO - Epoch [18][600/3746] lr: 9.681e-02, eta: 4 days, 4:46:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5039, loss_cls: 4.0672, loss: 4.0672 +2024-07-16 20:30:21,468 - pyskl - INFO - Epoch [18][700/3746] lr: 9.680e-02, eta: 4 days, 4:45:22, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4989, loss_cls: 4.0910, loss: 4.0910 +2024-07-16 20:31:31,303 - pyskl - INFO - Epoch [18][800/3746] lr: 9.679e-02, eta: 4 days, 4:43:45, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5120, loss_cls: 4.0386, loss: 4.0386 +2024-07-16 20:32:41,280 - pyskl - INFO - Epoch [18][900/3746] lr: 9.678e-02, eta: 4 days, 4:42:10, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5028, loss_cls: 4.1067, loss: 4.1067 +2024-07-16 20:33:51,239 - pyskl - INFO - Epoch [18][1000/3746] lr: 9.677e-02, eta: 4 days, 4:40:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4973, loss_cls: 4.1148, loss: 4.1148 +2024-07-16 20:35:01,148 - pyskl - INFO - Epoch [18][1100/3746] lr: 9.676e-02, eta: 4 days, 4:38:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5048, loss_cls: 4.0973, loss: 4.0973 +2024-07-16 20:36:11,099 - pyskl - INFO - Epoch [18][1200/3746] lr: 9.675e-02, eta: 4 days, 4:37:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.5011, loss_cls: 4.0827, loss: 4.0827 +2024-07-16 20:37:20,866 - pyskl - INFO - Epoch [18][1300/3746] lr: 9.674e-02, eta: 4 days, 4:35:47, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5072, loss_cls: 4.0920, loss: 4.0920 +2024-07-16 20:38:30,740 - pyskl - INFO - Epoch [18][1400/3746] lr: 9.673e-02, eta: 4 days, 4:34:11, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4953, loss_cls: 4.1176, loss: 4.1176 +2024-07-16 20:39:40,734 - pyskl - INFO - Epoch [18][1500/3746] lr: 9.672e-02, eta: 4 days, 4:32:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5038, loss_cls: 4.0506, loss: 4.0506 +2024-07-16 20:40:50,581 - pyskl - INFO - Epoch [18][1600/3746] lr: 9.671e-02, eta: 4 days, 4:31:00, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5044, loss_cls: 4.0889, loss: 4.0889 +2024-07-16 20:42:00,454 - pyskl - INFO - Epoch [18][1700/3746] lr: 9.670e-02, eta: 4 days, 4:29:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5136, loss_cls: 4.0528, loss: 4.0528 +2024-07-16 20:43:10,147 - pyskl - INFO - Epoch [18][1800/3746] lr: 9.669e-02, eta: 4 days, 4:27:48, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5017, loss_cls: 4.0913, loss: 4.0913 +2024-07-16 20:44:20,552 - pyskl - INFO - Epoch [18][1900/3746] lr: 9.668e-02, eta: 4 days, 4:26:16, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5016, loss_cls: 4.0714, loss: 4.0714 +2024-07-16 20:45:31,261 - pyskl - INFO - Epoch [18][2000/3746] lr: 9.667e-02, eta: 4 days, 4:24:47, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5173, loss_cls: 4.0028, loss: 4.0028 +2024-07-16 20:46:41,120 - pyskl - INFO - Epoch [18][2100/3746] lr: 9.666e-02, eta: 4 days, 4:23:12, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5097, loss_cls: 4.0446, loss: 4.0446 +2024-07-16 20:47:51,406 - pyskl - INFO - Epoch [18][2200/3746] lr: 9.665e-02, eta: 4 days, 4:21:40, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5066, loss_cls: 4.0691, loss: 4.0691 +2024-07-16 20:49:01,364 - pyskl - INFO - Epoch [18][2300/3746] lr: 9.664e-02, eta: 4 days, 4:20:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5184, loss_cls: 4.0171, loss: 4.0171 +2024-07-16 20:50:11,471 - pyskl - INFO - Epoch [18][2400/3746] lr: 9.663e-02, eta: 4 days, 4:18:32, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4988, loss_cls: 4.1032, loss: 4.1032 +2024-07-16 20:51:21,321 - pyskl - INFO - Epoch [18][2500/3746] lr: 9.662e-02, eta: 4 days, 4:16:57, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5144, loss_cls: 4.0057, loss: 4.0057 +2024-07-16 20:52:31,059 - pyskl - INFO - Epoch [18][2600/3746] lr: 9.661e-02, eta: 4 days, 4:15:21, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5225, loss_cls: 4.0179, loss: 4.0179 +2024-07-16 20:53:40,891 - pyskl - INFO - Epoch [18][2700/3746] lr: 9.660e-02, eta: 4 days, 4:13:46, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5125, loss_cls: 4.0381, loss: 4.0381 +2024-07-16 20:54:50,738 - pyskl - INFO - Epoch [18][2800/3746] lr: 9.659e-02, eta: 4 days, 4:12:11, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5181, loss_cls: 4.0283, loss: 4.0283 +2024-07-16 20:56:00,548 - pyskl - INFO - Epoch [18][2900/3746] lr: 9.658e-02, eta: 4 days, 4:10:35, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.5005, loss_cls: 4.1003, loss: 4.1003 +2024-07-16 20:57:10,408 - pyskl - INFO - Epoch [18][3000/3746] lr: 9.657e-02, eta: 4 days, 4:09:01, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5116, loss_cls: 4.0326, loss: 4.0326 +2024-07-16 20:58:20,235 - pyskl - INFO - Epoch [18][3100/3746] lr: 9.656e-02, eta: 4 days, 4:07:26, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5162, loss_cls: 4.0362, loss: 4.0362 +2024-07-16 20:59:30,470 - pyskl - INFO - Epoch [18][3200/3746] lr: 9.654e-02, eta: 4 days, 4:05:54, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.5105, loss_cls: 4.0775, loss: 4.0775 +2024-07-16 21:00:40,474 - pyskl - INFO - Epoch [18][3300/3746] lr: 9.653e-02, eta: 4 days, 4:04:21, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5048, loss_cls: 4.0795, loss: 4.0795 +2024-07-16 21:01:50,199 - pyskl - INFO - Epoch [18][3400/3746] lr: 9.652e-02, eta: 4 days, 4:02:45, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4989, loss_cls: 4.1122, loss: 4.1122 +2024-07-16 21:02:59,989 - pyskl - INFO - Epoch [18][3500/3746] lr: 9.651e-02, eta: 4 days, 4:01:10, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4984, loss_cls: 4.1099, loss: 4.1099 +2024-07-16 21:04:09,829 - pyskl - INFO - Epoch [18][3600/3746] lr: 9.650e-02, eta: 4 days, 3:59:36, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5066, loss_cls: 4.0178, loss: 4.0178 +2024-07-16 21:05:19,972 - pyskl - INFO - Epoch [18][3700/3746] lr: 9.649e-02, eta: 4 days, 3:58:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4995, loss_cls: 4.0982, loss: 4.0982 +2024-07-16 21:05:54,737 - pyskl - INFO - Saving checkpoint at 18 epochs +2024-07-16 21:07:44,792 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 21:07:45,474 - pyskl - INFO - +top1_acc 0.1802 +top5_acc 0.4112 +2024-07-16 21:07:45,474 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 21:07:45,517 - pyskl - INFO - +mean_acc 0.1799 +2024-07-16 21:07:45,529 - pyskl - INFO - Epoch(val) [18][309] top1_acc: 0.1802, top5_acc: 0.4112, mean_class_accuracy: 0.1799 +2024-07-16 21:11:05,249 - pyskl - INFO - Epoch [19][100/3746] lr: 9.648e-02, eta: 4 days, 4:07:42, time: 1.997, data_time: 1.288, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5173, loss_cls: 4.0179, loss: 4.0179 +2024-07-16 21:12:15,751 - pyskl - INFO - Epoch [19][200/3746] lr: 9.647e-02, eta: 4 days, 4:06:11, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5131, loss_cls: 4.0392, loss: 4.0392 +2024-07-16 21:13:26,299 - pyskl - INFO - Epoch [19][300/3746] lr: 9.646e-02, eta: 4 days, 4:04:41, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5167, loss_cls: 4.0258, loss: 4.0258 +2024-07-16 21:14:36,617 - pyskl - INFO - Epoch [19][400/3746] lr: 9.645e-02, eta: 4 days, 4:03:09, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5061, loss_cls: 4.0639, loss: 4.0639 +2024-07-16 21:15:46,668 - pyskl - INFO - Epoch [19][500/3746] lr: 9.644e-02, eta: 4 days, 4:01:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5081, loss_cls: 4.0602, loss: 4.0602 +2024-07-16 21:16:56,672 - pyskl - INFO - Epoch [19][600/3746] lr: 9.643e-02, eta: 4 days, 4:00:02, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5080, loss_cls: 4.0459, loss: 4.0459 +2024-07-16 21:18:06,403 - pyskl - INFO - Epoch [19][700/3746] lr: 9.642e-02, eta: 4 days, 3:58:26, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5156, loss_cls: 4.0424, loss: 4.0424 +2024-07-16 21:19:15,959 - pyskl - INFO - Epoch [19][800/3746] lr: 9.641e-02, eta: 4 days, 3:56:49, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5203, loss_cls: 4.0189, loss: 4.0189 +2024-07-16 21:20:25,899 - pyskl - INFO - Epoch [19][900/3746] lr: 9.640e-02, eta: 4 days, 3:55:15, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5134, loss_cls: 4.0478, loss: 4.0478 +2024-07-16 21:21:35,818 - pyskl - INFO - Epoch [19][1000/3746] lr: 9.639e-02, eta: 4 days, 3:53:40, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5120, loss_cls: 4.0367, loss: 4.0367 +2024-07-16 21:22:45,500 - pyskl - INFO - Epoch [19][1100/3746] lr: 9.637e-02, eta: 4 days, 3:52:05, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5031, loss_cls: 4.0662, loss: 4.0662 +2024-07-16 21:23:55,299 - pyskl - INFO - Epoch [19][1200/3746] lr: 9.636e-02, eta: 4 days, 3:50:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5147, loss_cls: 4.0556, loss: 4.0556 +2024-07-16 21:25:05,493 - pyskl - INFO - Epoch [19][1300/3746] lr: 9.635e-02, eta: 4 days, 3:48:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5078, loss_cls: 4.0640, loss: 4.0640 +2024-07-16 21:26:15,478 - pyskl - INFO - Epoch [19][1400/3746] lr: 9.634e-02, eta: 4 days, 3:47:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5066, loss_cls: 4.0741, loss: 4.0741 +2024-07-16 21:27:25,579 - pyskl - INFO - Epoch [19][1500/3746] lr: 9.633e-02, eta: 4 days, 3:45:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5047, loss_cls: 4.0805, loss: 4.0805 +2024-07-16 21:28:35,347 - pyskl - INFO - Epoch [19][1600/3746] lr: 9.632e-02, eta: 4 days, 3:44:17, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5092, loss_cls: 4.0370, loss: 4.0370 +2024-07-16 21:29:45,469 - pyskl - INFO - Epoch [19][1700/3746] lr: 9.631e-02, eta: 4 days, 3:42:44, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5034, loss_cls: 4.1016, loss: 4.1016 +2024-07-16 21:30:55,446 - pyskl - INFO - Epoch [19][1800/3746] lr: 9.630e-02, eta: 4 days, 3:41:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5081, loss_cls: 4.0638, loss: 4.0638 +2024-07-16 21:32:05,484 - pyskl - INFO - Epoch [19][1900/3746] lr: 9.629e-02, eta: 4 days, 3:39:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5070, loss_cls: 4.0429, loss: 4.0429 +2024-07-16 21:33:15,623 - pyskl - INFO - Epoch [19][2000/3746] lr: 9.628e-02, eta: 4 days, 3:38:06, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4972, loss_cls: 4.0774, loss: 4.0774 +2024-07-16 21:34:25,825 - pyskl - INFO - Epoch [19][2100/3746] lr: 9.627e-02, eta: 4 days, 3:36:35, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5153, loss_cls: 4.0305, loss: 4.0305 +2024-07-16 21:35:35,659 - pyskl - INFO - Epoch [19][2200/3746] lr: 9.626e-02, eta: 4 days, 3:35:01, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5052, loss_cls: 4.0743, loss: 4.0743 +2024-07-16 21:36:45,581 - pyskl - INFO - Epoch [19][2300/3746] lr: 9.625e-02, eta: 4 days, 3:33:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5055, loss_cls: 4.0623, loss: 4.0623 +2024-07-16 21:37:55,626 - pyskl - INFO - Epoch [19][2400/3746] lr: 9.624e-02, eta: 4 days, 3:31:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5134, loss_cls: 4.0426, loss: 4.0426 +2024-07-16 21:39:05,607 - pyskl - INFO - Epoch [19][2500/3746] lr: 9.623e-02, eta: 4 days, 3:30:22, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4916, loss_cls: 4.1269, loss: 4.1269 +2024-07-16 21:40:15,695 - pyskl - INFO - Epoch [19][2600/3746] lr: 9.622e-02, eta: 4 days, 3:28:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.5000, loss_cls: 4.0775, loss: 4.0775 +2024-07-16 21:41:25,814 - pyskl - INFO - Epoch [19][2700/3746] lr: 9.621e-02, eta: 4 days, 3:27:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5059, loss_cls: 4.0656, loss: 4.0656 +2024-07-16 21:42:35,431 - pyskl - INFO - Epoch [19][2800/3746] lr: 9.620e-02, eta: 4 days, 3:25:44, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5100, loss_cls: 4.0885, loss: 4.0885 +2024-07-16 21:43:45,475 - pyskl - INFO - Epoch [19][2900/3746] lr: 9.618e-02, eta: 4 days, 3:24:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5152, loss_cls: 4.0547, loss: 4.0547 +2024-07-16 21:44:55,294 - pyskl - INFO - Epoch [19][3000/3746] lr: 9.617e-02, eta: 4 days, 3:22:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5011, loss_cls: 4.0781, loss: 4.0781 +2024-07-16 21:46:05,106 - pyskl - INFO - Epoch [19][3100/3746] lr: 9.616e-02, eta: 4 days, 3:21:04, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5188, loss_cls: 3.9830, loss: 3.9830 +2024-07-16 21:47:15,010 - pyskl - INFO - Epoch [19][3200/3746] lr: 9.615e-02, eta: 4 days, 3:19:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4995, loss_cls: 4.1165, loss: 4.1165 +2024-07-16 21:48:24,665 - pyskl - INFO - Epoch [19][3300/3746] lr: 9.614e-02, eta: 4 days, 3:17:57, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5038, loss_cls: 4.0705, loss: 4.0705 +2024-07-16 21:49:34,400 - pyskl - INFO - Epoch [19][3400/3746] lr: 9.613e-02, eta: 4 days, 3:16:23, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5028, loss_cls: 4.0673, loss: 4.0673 +2024-07-16 21:50:44,386 - pyskl - INFO - Epoch [19][3500/3746] lr: 9.612e-02, eta: 4 days, 3:14:51, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5036, loss_cls: 4.0624, loss: 4.0624 +2024-07-16 21:51:54,330 - pyskl - INFO - Epoch [19][3600/3746] lr: 9.611e-02, eta: 4 days, 3:13:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5058, loss_cls: 4.0684, loss: 4.0684 +2024-07-16 21:53:04,472 - pyskl - INFO - Epoch [19][3700/3746] lr: 9.610e-02, eta: 4 days, 3:11:48, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5120, loss_cls: 4.0660, loss: 4.0660 +2024-07-16 21:53:39,206 - pyskl - INFO - Saving checkpoint at 19 epochs +2024-07-16 21:55:29,952 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 21:55:30,620 - pyskl - INFO - +top1_acc 0.1772 +top5_acc 0.4025 +2024-07-16 21:55:30,620 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 21:55:30,660 - pyskl - INFO - +mean_acc 0.1771 +2024-07-16 21:55:30,671 - pyskl - INFO - Epoch(val) [19][309] top1_acc: 0.1772, top5_acc: 0.4025, mean_class_accuracy: 0.1771 +2024-07-16 21:58:49,786 - pyskl - INFO - Epoch [20][100/3746] lr: 9.608e-02, eta: 4 days, 3:20:41, time: 1.991, data_time: 1.276, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5145, loss_cls: 4.0359, loss: 4.0359 +2024-07-16 22:00:00,208 - pyskl - INFO - Epoch [20][200/3746] lr: 9.607e-02, eta: 4 days, 3:19:11, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5117, loss_cls: 4.0440, loss: 4.0440 +2024-07-16 22:01:10,534 - pyskl - INFO - Epoch [20][300/3746] lr: 9.606e-02, eta: 4 days, 3:17:40, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5061, loss_cls: 4.0896, loss: 4.0896 +2024-07-16 22:02:20,529 - pyskl - INFO - Epoch [20][400/3746] lr: 9.605e-02, eta: 4 days, 3:16:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5164, loss_cls: 4.0165, loss: 4.0165 +2024-07-16 22:03:30,708 - pyskl - INFO - Epoch [20][500/3746] lr: 9.604e-02, eta: 4 days, 3:14:36, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5192, loss_cls: 4.0346, loss: 4.0346 +2024-07-16 22:04:40,886 - pyskl - INFO - Epoch [20][600/3746] lr: 9.603e-02, eta: 4 days, 3:13:05, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5197, loss_cls: 3.9921, loss: 3.9921 +2024-07-16 22:05:50,802 - pyskl - INFO - Epoch [20][700/3746] lr: 9.602e-02, eta: 4 days, 3:11:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5155, loss_cls: 4.0512, loss: 4.0512 +2024-07-16 22:07:00,599 - pyskl - INFO - Epoch [20][800/3746] lr: 9.601e-02, eta: 4 days, 3:09:58, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5050, loss_cls: 4.0721, loss: 4.0721 +2024-07-16 22:08:10,643 - pyskl - INFO - Epoch [20][900/3746] lr: 9.600e-02, eta: 4 days, 3:08:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5184, loss_cls: 4.0237, loss: 4.0237 +2024-07-16 22:09:20,495 - pyskl - INFO - Epoch [20][1000/3746] lr: 9.598e-02, eta: 4 days, 3:06:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5092, loss_cls: 4.0357, loss: 4.0357 +2024-07-16 22:10:30,486 - pyskl - INFO - Epoch [20][1100/3746] lr: 9.597e-02, eta: 4 days, 3:05:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5106, loss_cls: 4.0723, loss: 4.0723 +2024-07-16 22:11:40,234 - pyskl - INFO - Epoch [20][1200/3746] lr: 9.596e-02, eta: 4 days, 3:03:46, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5092, loss_cls: 4.0683, loss: 4.0683 +2024-07-16 22:12:50,389 - pyskl - INFO - Epoch [20][1300/3746] lr: 9.595e-02, eta: 4 days, 3:02:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5072, loss_cls: 4.0671, loss: 4.0671 +2024-07-16 22:14:00,359 - pyskl - INFO - Epoch [20][1400/3746] lr: 9.594e-02, eta: 4 days, 3:00:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5108, loss_cls: 4.0509, loss: 4.0509 +2024-07-16 22:15:10,391 - pyskl - INFO - Epoch [20][1500/3746] lr: 9.593e-02, eta: 4 days, 2:59:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5128, loss_cls: 4.0192, loss: 4.0192 +2024-07-16 22:16:20,152 - pyskl - INFO - Epoch [20][1600/3746] lr: 9.592e-02, eta: 4 days, 2:57:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5108, loss_cls: 4.0345, loss: 4.0345 +2024-07-16 22:17:29,976 - pyskl - INFO - Epoch [20][1700/3746] lr: 9.591e-02, eta: 4 days, 2:56:05, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5130, loss_cls: 4.0456, loss: 4.0456 +2024-07-16 22:18:39,759 - pyskl - INFO - Epoch [20][1800/3746] lr: 9.590e-02, eta: 4 days, 2:54:31, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5100, loss_cls: 4.0364, loss: 4.0364 +2024-07-16 22:19:49,605 - pyskl - INFO - Epoch [20][1900/3746] lr: 9.588e-02, eta: 4 days, 2:52:59, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5048, loss_cls: 4.0692, loss: 4.0692 +2024-07-16 22:20:59,995 - pyskl - INFO - Epoch [20][2000/3746] lr: 9.587e-02, eta: 4 days, 2:51:30, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5036, loss_cls: 4.0942, loss: 4.0942 +2024-07-16 22:22:09,827 - pyskl - INFO - Epoch [20][2100/3746] lr: 9.586e-02, eta: 4 days, 2:49:57, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5016, loss_cls: 4.1147, loss: 4.1147 +2024-07-16 22:23:20,272 - pyskl - INFO - Epoch [20][2200/3746] lr: 9.585e-02, eta: 4 days, 2:48:28, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5070, loss_cls: 4.0552, loss: 4.0552 +2024-07-16 22:24:30,077 - pyskl - INFO - Epoch [20][2300/3746] lr: 9.584e-02, eta: 4 days, 2:46:55, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.5086, loss_cls: 4.0669, loss: 4.0669 +2024-07-16 22:25:39,994 - pyskl - INFO - Epoch [20][2400/3746] lr: 9.583e-02, eta: 4 days, 2:45:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4994, loss_cls: 4.0899, loss: 4.0899 +2024-07-16 22:26:49,797 - pyskl - INFO - Epoch [20][2500/3746] lr: 9.582e-02, eta: 4 days, 2:43:51, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4920, loss_cls: 4.1352, loss: 4.1352 +2024-07-16 22:27:59,462 - pyskl - INFO - Epoch [20][2600/3746] lr: 9.581e-02, eta: 4 days, 2:42:17, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5000, loss_cls: 4.0999, loss: 4.0999 +2024-07-16 22:29:09,844 - pyskl - INFO - Epoch [20][2700/3746] lr: 9.580e-02, eta: 4 days, 2:40:48, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5017, loss_cls: 4.0785, loss: 4.0785 +2024-07-16 22:30:19,595 - pyskl - INFO - Epoch [20][2800/3746] lr: 9.578e-02, eta: 4 days, 2:39:16, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5156, loss_cls: 4.0403, loss: 4.0403 +2024-07-16 22:31:29,418 - pyskl - INFO - Epoch [20][2900/3746] lr: 9.577e-02, eta: 4 days, 2:37:43, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5144, loss_cls: 3.9959, loss: 3.9959 +2024-07-16 22:32:39,194 - pyskl - INFO - Epoch [20][3000/3746] lr: 9.576e-02, eta: 4 days, 2:36:11, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5108, loss_cls: 4.0343, loss: 4.0343 +2024-07-16 22:33:49,149 - pyskl - INFO - Epoch [20][3100/3746] lr: 9.575e-02, eta: 4 days, 2:34:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4967, loss_cls: 4.1161, loss: 4.1161 +2024-07-16 22:34:58,950 - pyskl - INFO - Epoch [20][3200/3746] lr: 9.574e-02, eta: 4 days, 2:33:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4963, loss_cls: 4.1255, loss: 4.1255 +2024-07-16 22:36:08,819 - pyskl - INFO - Epoch [20][3300/3746] lr: 9.573e-02, eta: 4 days, 2:31:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5070, loss_cls: 4.0720, loss: 4.0720 +2024-07-16 22:37:18,442 - pyskl - INFO - Epoch [20][3400/3746] lr: 9.572e-02, eta: 4 days, 2:30:02, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5041, loss_cls: 4.0725, loss: 4.0725 +2024-07-16 22:38:28,744 - pyskl - INFO - Epoch [20][3500/3746] lr: 9.571e-02, eta: 4 days, 2:28:33, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5077, loss_cls: 4.0261, loss: 4.0261 +2024-07-16 22:39:38,663 - pyskl - INFO - Epoch [20][3600/3746] lr: 9.569e-02, eta: 4 days, 2:27:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5159, loss_cls: 4.0477, loss: 4.0477 +2024-07-16 22:40:48,629 - pyskl - INFO - Epoch [20][3700/3746] lr: 9.568e-02, eta: 4 days, 2:25:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5102, loss_cls: 4.0378, loss: 4.0378 +2024-07-16 22:41:23,264 - pyskl - INFO - Saving checkpoint at 20 epochs +2024-07-16 22:43:14,062 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 22:43:14,725 - pyskl - INFO - +top1_acc 0.1909 +top5_acc 0.4078 +2024-07-16 22:43:14,726 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 22:43:14,765 - pyskl - INFO - +mean_acc 0.1909 +2024-07-16 22:43:14,776 - pyskl - INFO - Epoch(val) [20][309] top1_acc: 0.1909, top5_acc: 0.4078, mean_class_accuracy: 0.1909 +2024-07-16 22:46:33,365 - pyskl - INFO - Epoch [21][100/3746] lr: 9.567e-02, eta: 4 days, 2:33:44, time: 1.986, data_time: 1.277, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5122, loss_cls: 4.0083, loss: 4.0083 +2024-07-16 22:47:44,188 - pyskl - INFO - Epoch [21][200/3746] lr: 9.565e-02, eta: 4 days, 2:32:18, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5150, loss_cls: 4.0171, loss: 4.0171 +2024-07-16 22:48:54,785 - pyskl - INFO - Epoch [21][300/3746] lr: 9.564e-02, eta: 4 days, 2:30:50, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5109, loss_cls: 4.0467, loss: 4.0467 +2024-07-16 22:50:04,956 - pyskl - INFO - Epoch [21][400/3746] lr: 9.563e-02, eta: 4 days, 2:29:20, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5030, loss_cls: 4.0633, loss: 4.0633 +2024-07-16 22:51:15,227 - pyskl - INFO - Epoch [21][500/3746] lr: 9.562e-02, eta: 4 days, 2:27:50, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5072, loss_cls: 4.0807, loss: 4.0807 +2024-07-16 22:52:25,006 - pyskl - INFO - Epoch [21][600/3746] lr: 9.561e-02, eta: 4 days, 2:26:18, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5036, loss_cls: 4.0651, loss: 4.0651 +2024-07-16 22:53:34,875 - pyskl - INFO - Epoch [21][700/3746] lr: 9.560e-02, eta: 4 days, 2:24:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5080, loss_cls: 4.0679, loss: 4.0679 +2024-07-16 22:54:44,558 - pyskl - INFO - Epoch [21][800/3746] lr: 9.559e-02, eta: 4 days, 2:23:12, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5152, loss_cls: 4.0129, loss: 4.0129 +2024-07-16 22:55:54,331 - pyskl - INFO - Epoch [21][900/3746] lr: 9.557e-02, eta: 4 days, 2:21:40, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5209, loss_cls: 4.0206, loss: 4.0206 +2024-07-16 22:57:03,893 - pyskl - INFO - Epoch [21][1000/3746] lr: 9.556e-02, eta: 4 days, 2:20:06, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5136, loss_cls: 4.0424, loss: 4.0424 +2024-07-16 22:58:13,609 - pyskl - INFO - Epoch [21][1100/3746] lr: 9.555e-02, eta: 4 days, 2:18:33, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5164, loss_cls: 4.0112, loss: 4.0112 +2024-07-16 22:59:23,491 - pyskl - INFO - Epoch [21][1200/3746] lr: 9.554e-02, eta: 4 days, 2:17:01, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5120, loss_cls: 4.0264, loss: 4.0264 +2024-07-16 23:00:33,566 - pyskl - INFO - Epoch [21][1300/3746] lr: 9.553e-02, eta: 4 days, 2:15:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5109, loss_cls: 4.0577, loss: 4.0577 +2024-07-16 23:01:43,591 - pyskl - INFO - Epoch [21][1400/3746] lr: 9.552e-02, eta: 4 days, 2:14:00, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5116, loss_cls: 4.0711, loss: 4.0711 +2024-07-16 23:02:53,736 - pyskl - INFO - Epoch [21][1500/3746] lr: 9.551e-02, eta: 4 days, 2:12:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5108, loss_cls: 4.0540, loss: 4.0540 +2024-07-16 23:04:03,616 - pyskl - INFO - Epoch [21][1600/3746] lr: 9.549e-02, eta: 4 days, 2:10:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5150, loss_cls: 4.0335, loss: 4.0335 +2024-07-16 23:05:13,565 - pyskl - INFO - Epoch [21][1700/3746] lr: 9.548e-02, eta: 4 days, 2:09:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5106, loss_cls: 4.0530, loss: 4.0530 +2024-07-16 23:06:23,545 - pyskl - INFO - Epoch [21][1800/3746] lr: 9.547e-02, eta: 4 days, 2:07:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5136, loss_cls: 4.0196, loss: 4.0196 +2024-07-16 23:07:33,546 - pyskl - INFO - Epoch [21][1900/3746] lr: 9.546e-02, eta: 4 days, 2:06:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5033, loss_cls: 4.0770, loss: 4.0770 +2024-07-16 23:08:43,843 - pyskl - INFO - Epoch [21][2000/3746] lr: 9.545e-02, eta: 4 days, 2:04:58, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5109, loss_cls: 4.0488, loss: 4.0488 +2024-07-16 23:09:53,562 - pyskl - INFO - Epoch [21][2100/3746] lr: 9.544e-02, eta: 4 days, 2:03:25, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5102, loss_cls: 4.0570, loss: 4.0570 +2024-07-16 23:11:03,603 - pyskl - INFO - Epoch [21][2200/3746] lr: 9.542e-02, eta: 4 days, 2:01:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5058, loss_cls: 4.0443, loss: 4.0443 +2024-07-16 23:12:13,411 - pyskl - INFO - Epoch [21][2300/3746] lr: 9.541e-02, eta: 4 days, 2:00:24, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5073, loss_cls: 4.0570, loss: 4.0570 +2024-07-16 23:13:23,197 - pyskl - INFO - Epoch [21][2400/3746] lr: 9.540e-02, eta: 4 days, 1:58:52, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5050, loss_cls: 4.0681, loss: 4.0681 +2024-07-16 23:14:32,837 - pyskl - INFO - Epoch [21][2500/3746] lr: 9.539e-02, eta: 4 days, 1:57:19, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5138, loss_cls: 4.0235, loss: 4.0235 +2024-07-16 23:15:43,020 - pyskl - INFO - Epoch [21][2600/3746] lr: 9.538e-02, eta: 4 days, 1:55:50, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.5056, loss_cls: 4.0621, loss: 4.0621 +2024-07-16 23:16:53,127 - pyskl - INFO - Epoch [21][2700/3746] lr: 9.537e-02, eta: 4 days, 1:54:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5008, loss_cls: 4.0823, loss: 4.0823 +2024-07-16 23:18:02,881 - pyskl - INFO - Epoch [21][2800/3746] lr: 9.535e-02, eta: 4 days, 1:52:49, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5073, loss_cls: 4.0760, loss: 4.0760 +2024-07-16 23:19:12,663 - pyskl - INFO - Epoch [21][2900/3746] lr: 9.534e-02, eta: 4 days, 1:51:18, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5069, loss_cls: 4.0772, loss: 4.0772 +2024-07-16 23:20:22,595 - pyskl - INFO - Epoch [21][3000/3746] lr: 9.533e-02, eta: 4 days, 1:49:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5122, loss_cls: 4.0537, loss: 4.0537 +2024-07-16 23:21:32,474 - pyskl - INFO - Epoch [21][3100/3746] lr: 9.532e-02, eta: 4 days, 1:48:17, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5027, loss_cls: 4.0816, loss: 4.0816 +2024-07-16 23:22:42,619 - pyskl - INFO - Epoch [21][3200/3746] lr: 9.531e-02, eta: 4 days, 1:46:48, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5231, loss_cls: 4.0170, loss: 4.0170 +2024-07-16 23:23:52,089 - pyskl - INFO - Epoch [21][3300/3746] lr: 9.529e-02, eta: 4 days, 1:45:15, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.5050, loss_cls: 4.1007, loss: 4.1007 +2024-07-16 23:25:01,966 - pyskl - INFO - Epoch [21][3400/3746] lr: 9.528e-02, eta: 4 days, 1:43:44, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5069, loss_cls: 4.0730, loss: 4.0730 +2024-07-16 23:26:11,867 - pyskl - INFO - Epoch [21][3500/3746] lr: 9.527e-02, eta: 4 days, 1:42:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5028, loss_cls: 4.0919, loss: 4.0919 +2024-07-16 23:27:21,586 - pyskl - INFO - Epoch [21][3600/3746] lr: 9.526e-02, eta: 4 days, 1:40:42, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5125, loss_cls: 4.0662, loss: 4.0662 +2024-07-16 23:28:31,440 - pyskl - INFO - Epoch [21][3700/3746] lr: 9.525e-02, eta: 4 days, 1:39:12, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5067, loss_cls: 4.0612, loss: 4.0612 +2024-07-16 23:29:06,022 - pyskl - INFO - Saving checkpoint at 21 epochs +2024-07-16 23:30:55,841 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 23:30:56,510 - pyskl - INFO - +top1_acc 0.1927 +top5_acc 0.4255 +2024-07-16 23:30:56,510 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 23:30:56,549 - pyskl - INFO - +mean_acc 0.1927 +2024-07-16 23:30:56,554 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_16.pth was removed +2024-07-16 23:30:56,814 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_21.pth. +2024-07-16 23:30:56,815 - pyskl - INFO - Best top1_acc is 0.1927 at 21 epoch. +2024-07-16 23:30:56,827 - pyskl - INFO - Epoch(val) [21][309] top1_acc: 0.1927, top5_acc: 0.4255, mean_class_accuracy: 0.1927 +2024-07-16 23:34:15,520 - pyskl - INFO - Epoch [22][100/3746] lr: 9.523e-02, eta: 4 days, 1:46:53, time: 1.987, data_time: 1.281, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5298, loss_cls: 3.9870, loss: 3.9870 +2024-07-16 23:35:26,682 - pyskl - INFO - Epoch [22][200/3746] lr: 9.522e-02, eta: 4 days, 1:45:30, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5241, loss_cls: 4.0211, loss: 4.0211 +2024-07-16 23:36:37,072 - pyskl - INFO - Epoch [22][300/3746] lr: 9.521e-02, eta: 4 days, 1:44:02, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5155, loss_cls: 4.0235, loss: 4.0235 +2024-07-16 23:37:47,286 - pyskl - INFO - Epoch [22][400/3746] lr: 9.519e-02, eta: 4 days, 1:42:33, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5162, loss_cls: 4.0040, loss: 4.0040 +2024-07-16 23:38:57,982 - pyskl - INFO - Epoch [22][500/3746] lr: 9.518e-02, eta: 4 days, 1:41:07, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5203, loss_cls: 3.9980, loss: 3.9980 +2024-07-16 23:40:07,963 - pyskl - INFO - Epoch [22][600/3746] lr: 9.517e-02, eta: 4 days, 1:39:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5117, loss_cls: 4.0530, loss: 4.0530 +2024-07-16 23:41:17,771 - pyskl - INFO - Epoch [22][700/3746] lr: 9.516e-02, eta: 4 days, 1:38:05, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5202, loss_cls: 4.0288, loss: 4.0288 +2024-07-16 23:42:27,877 - pyskl - INFO - Epoch [22][800/3746] lr: 9.515e-02, eta: 4 days, 1:36:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5159, loss_cls: 4.0281, loss: 4.0281 +2024-07-16 23:43:37,781 - pyskl - INFO - Epoch [22][900/3746] lr: 9.513e-02, eta: 4 days, 1:35:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5055, loss_cls: 4.0442, loss: 4.0442 +2024-07-16 23:44:47,635 - pyskl - INFO - Epoch [22][1000/3746] lr: 9.512e-02, eta: 4 days, 1:33:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5058, loss_cls: 4.0369, loss: 4.0369 +2024-07-16 23:45:57,327 - pyskl - INFO - Epoch [22][1100/3746] lr: 9.511e-02, eta: 4 days, 1:32:02, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5058, loss_cls: 4.0642, loss: 4.0642 +2024-07-16 23:47:07,158 - pyskl - INFO - Epoch [22][1200/3746] lr: 9.510e-02, eta: 4 days, 1:30:31, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5102, loss_cls: 4.0619, loss: 4.0619 +2024-07-16 23:48:16,959 - pyskl - INFO - Epoch [22][1300/3746] lr: 9.509e-02, eta: 4 days, 1:29:00, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5139, loss_cls: 4.0469, loss: 4.0469 +2024-07-16 23:49:26,704 - pyskl - INFO - Epoch [22][1400/3746] lr: 9.507e-02, eta: 4 days, 1:27:29, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5108, loss_cls: 4.0341, loss: 4.0341 +2024-07-16 23:50:36,245 - pyskl - INFO - Epoch [22][1500/3746] lr: 9.506e-02, eta: 4 days, 1:25:56, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5103, loss_cls: 4.0672, loss: 4.0672 +2024-07-16 23:51:45,845 - pyskl - INFO - Epoch [22][1600/3746] lr: 9.505e-02, eta: 4 days, 1:24:24, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5111, loss_cls: 4.0236, loss: 4.0236 +2024-07-16 23:52:55,624 - pyskl - INFO - Epoch [22][1700/3746] lr: 9.504e-02, eta: 4 days, 1:22:53, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5177, loss_cls: 4.0205, loss: 4.0205 +2024-07-16 23:54:05,703 - pyskl - INFO - Epoch [22][1800/3746] lr: 9.502e-02, eta: 4 days, 1:21:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5042, loss_cls: 4.0841, loss: 4.0841 +2024-07-16 23:55:15,519 - pyskl - INFO - Epoch [22][1900/3746] lr: 9.501e-02, eta: 4 days, 1:19:54, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5111, loss_cls: 4.0356, loss: 4.0356 +2024-07-16 23:56:25,747 - pyskl - INFO - Epoch [22][2000/3746] lr: 9.500e-02, eta: 4 days, 1:18:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5081, loss_cls: 4.0664, loss: 4.0664 +2024-07-16 23:57:35,596 - pyskl - INFO - Epoch [22][2100/3746] lr: 9.499e-02, eta: 4 days, 1:16:55, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5134, loss_cls: 4.0187, loss: 4.0187 +2024-07-16 23:58:45,541 - pyskl - INFO - Epoch [22][2200/3746] lr: 9.498e-02, eta: 4 days, 1:15:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5197, loss_cls: 4.0257, loss: 4.0257 +2024-07-16 23:59:55,635 - pyskl - INFO - Epoch [22][2300/3746] lr: 9.496e-02, eta: 4 days, 1:13:57, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5119, loss_cls: 4.0414, loss: 4.0414 +2024-07-17 00:01:05,752 - pyskl - INFO - Epoch [22][2400/3746] lr: 9.495e-02, eta: 4 days, 1:12:28, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5067, loss_cls: 4.0349, loss: 4.0349 +2024-07-17 00:02:15,550 - pyskl - INFO - Epoch [22][2500/3746] lr: 9.494e-02, eta: 4 days, 1:10:58, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5111, loss_cls: 4.0677, loss: 4.0677 +2024-07-17 00:03:25,447 - pyskl - INFO - Epoch [22][2600/3746] lr: 9.493e-02, eta: 4 days, 1:09:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5069, loss_cls: 4.0642, loss: 4.0642 +2024-07-17 00:04:35,385 - pyskl - INFO - Epoch [22][2700/3746] lr: 9.491e-02, eta: 4 days, 1:07:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5058, loss_cls: 4.0435, loss: 4.0435 +2024-07-17 00:05:45,417 - pyskl - INFO - Epoch [22][2800/3746] lr: 9.490e-02, eta: 4 days, 1:06:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4989, loss_cls: 4.0739, loss: 4.0739 +2024-07-17 00:06:55,049 - pyskl - INFO - Epoch [22][2900/3746] lr: 9.489e-02, eta: 4 days, 1:04:58, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5088, loss_cls: 4.0904, loss: 4.0904 +2024-07-17 00:08:04,858 - pyskl - INFO - Epoch [22][3000/3746] lr: 9.488e-02, eta: 4 days, 1:03:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5100, loss_cls: 4.0437, loss: 4.0437 +2024-07-17 00:09:14,925 - pyskl - INFO - Epoch [22][3100/3746] lr: 9.487e-02, eta: 4 days, 1:01:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5130, loss_cls: 4.0500, loss: 4.0500 +2024-07-17 00:10:24,514 - pyskl - INFO - Epoch [22][3200/3746] lr: 9.485e-02, eta: 4 days, 1:00:28, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5045, loss_cls: 4.0915, loss: 4.0915 +2024-07-17 00:11:34,481 - pyskl - INFO - Epoch [22][3300/3746] lr: 9.484e-02, eta: 4 days, 0:58:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5038, loss_cls: 4.0690, loss: 4.0690 +2024-07-17 00:12:44,307 - pyskl - INFO - Epoch [22][3400/3746] lr: 9.483e-02, eta: 4 days, 0:57:29, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5150, loss_cls: 4.0260, loss: 4.0260 +2024-07-17 00:13:54,170 - pyskl - INFO - Epoch [22][3500/3746] lr: 9.482e-02, eta: 4 days, 0:56:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4988, loss_cls: 4.0953, loss: 4.0953 +2024-07-17 00:15:04,023 - pyskl - INFO - Epoch [22][3600/3746] lr: 9.480e-02, eta: 4 days, 0:54:30, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5006, loss_cls: 4.0632, loss: 4.0632 +2024-07-17 00:16:14,453 - pyskl - INFO - Epoch [22][3700/3746] lr: 9.479e-02, eta: 4 days, 0:53:04, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.5077, loss_cls: 4.0799, loss: 4.0799 +2024-07-17 00:16:49,119 - pyskl - INFO - Saving checkpoint at 22 epochs +2024-07-17 00:18:38,520 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 00:18:39,184 - pyskl - INFO - +top1_acc 0.1714 +top5_acc 0.3932 +2024-07-17 00:18:39,184 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 00:18:39,225 - pyskl - INFO - +mean_acc 0.1712 +2024-07-17 00:18:39,237 - pyskl - INFO - Epoch(val) [22][309] top1_acc: 0.1714, top5_acc: 0.3932, mean_class_accuracy: 0.1712 +2024-07-17 00:21:56,576 - pyskl - INFO - Epoch [23][100/3746] lr: 9.477e-02, eta: 4 days, 1:00:07, time: 1.973, data_time: 1.270, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5158, loss_cls: 4.0201, loss: 4.0201 +2024-07-17 00:23:07,174 - pyskl - INFO - Epoch [23][200/3746] lr: 9.476e-02, eta: 4 days, 0:58:41, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5162, loss_cls: 4.0115, loss: 4.0115 +2024-07-17 00:24:17,554 - pyskl - INFO - Epoch [23][300/3746] lr: 9.475e-02, eta: 4 days, 0:57:14, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5059, loss_cls: 4.0755, loss: 4.0755 +2024-07-17 00:25:28,215 - pyskl - INFO - Epoch [23][400/3746] lr: 9.474e-02, eta: 4 days, 0:55:49, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5077, loss_cls: 4.0346, loss: 4.0346 +2024-07-17 00:26:38,533 - pyskl - INFO - Epoch [23][500/3746] lr: 9.472e-02, eta: 4 days, 0:54:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5238, loss_cls: 3.9833, loss: 3.9833 +2024-07-17 00:27:48,241 - pyskl - INFO - Epoch [23][600/3746] lr: 9.471e-02, eta: 4 days, 0:52:51, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5064, loss_cls: 4.0510, loss: 4.0510 +2024-07-17 00:28:58,332 - pyskl - INFO - Epoch [23][700/3746] lr: 9.470e-02, eta: 4 days, 0:51:22, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5236, loss_cls: 3.9970, loss: 3.9970 +2024-07-17 00:30:08,443 - pyskl - INFO - Epoch [23][800/3746] lr: 9.469e-02, eta: 4 days, 0:49:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5211, loss_cls: 4.0032, loss: 4.0032 +2024-07-17 00:31:17,952 - pyskl - INFO - Epoch [23][900/3746] lr: 9.467e-02, eta: 4 days, 0:48:22, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5016, loss_cls: 4.0480, loss: 4.0480 +2024-07-17 00:32:27,988 - pyskl - INFO - Epoch [23][1000/3746] lr: 9.466e-02, eta: 4 days, 0:46:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5123, loss_cls: 4.0410, loss: 4.0410 +2024-07-17 00:33:37,889 - pyskl - INFO - Epoch [23][1100/3746] lr: 9.465e-02, eta: 4 days, 0:45:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5100, loss_cls: 4.0452, loss: 4.0452 +2024-07-17 00:34:47,726 - pyskl - INFO - Epoch [23][1200/3746] lr: 9.464e-02, eta: 4 days, 0:43:53, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5128, loss_cls: 4.0299, loss: 4.0299 +2024-07-17 00:35:57,532 - pyskl - INFO - Epoch [23][1300/3746] lr: 9.462e-02, eta: 4 days, 0:42:23, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.5064, loss_cls: 4.0527, loss: 4.0527 +2024-07-17 00:37:07,449 - pyskl - INFO - Epoch [23][1400/3746] lr: 9.461e-02, eta: 4 days, 0:40:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5159, loss_cls: 4.0448, loss: 4.0448 +2024-07-17 00:38:17,469 - pyskl - INFO - Epoch [23][1500/3746] lr: 9.460e-02, eta: 4 days, 0:39:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5228, loss_cls: 3.9940, loss: 3.9940 +2024-07-17 00:39:27,181 - pyskl - INFO - Epoch [23][1600/3746] lr: 9.459e-02, eta: 4 days, 0:37:55, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5094, loss_cls: 4.0328, loss: 4.0328 +2024-07-17 00:40:37,203 - pyskl - INFO - Epoch [23][1700/3746] lr: 9.457e-02, eta: 4 days, 0:36:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5158, loss_cls: 4.0433, loss: 4.0433 +2024-07-17 00:41:47,168 - pyskl - INFO - Epoch [23][1800/3746] lr: 9.456e-02, eta: 4 days, 0:34:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5128, loss_cls: 4.0382, loss: 4.0382 +2024-07-17 00:42:56,917 - pyskl - INFO - Epoch [23][1900/3746] lr: 9.455e-02, eta: 4 days, 0:33:28, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5166, loss_cls: 4.0292, loss: 4.0292 +2024-07-17 00:44:06,832 - pyskl - INFO - Epoch [23][2000/3746] lr: 9.453e-02, eta: 4 days, 0:31:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5136, loss_cls: 4.0288, loss: 4.0288 +2024-07-17 00:45:16,651 - pyskl - INFO - Epoch [23][2100/3746] lr: 9.452e-02, eta: 4 days, 0:30:29, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5134, loss_cls: 4.0129, loss: 4.0129 +2024-07-17 00:46:26,534 - pyskl - INFO - Epoch [23][2200/3746] lr: 9.451e-02, eta: 4 days, 0:29:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5089, loss_cls: 4.0640, loss: 4.0640 +2024-07-17 00:47:36,490 - pyskl - INFO - Epoch [23][2300/3746] lr: 9.450e-02, eta: 4 days, 0:27:32, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5153, loss_cls: 4.0366, loss: 4.0366 +2024-07-17 00:48:46,549 - pyskl - INFO - Epoch [23][2400/3746] lr: 9.448e-02, eta: 4 days, 0:26:03, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5078, loss_cls: 4.0558, loss: 4.0558 +2024-07-17 00:49:56,547 - pyskl - INFO - Epoch [23][2500/3746] lr: 9.447e-02, eta: 4 days, 0:24:35, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5108, loss_cls: 4.0552, loss: 4.0552 +2024-07-17 00:51:06,327 - pyskl - INFO - Epoch [23][2600/3746] lr: 9.446e-02, eta: 4 days, 0:23:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.5059, loss_cls: 4.0759, loss: 4.0759 +2024-07-17 00:52:15,972 - pyskl - INFO - Epoch [23][2700/3746] lr: 9.445e-02, eta: 4 days, 0:21:35, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5167, loss_cls: 4.0249, loss: 4.0249 +2024-07-17 00:53:25,743 - pyskl - INFO - Epoch [23][2800/3746] lr: 9.443e-02, eta: 4 days, 0:20:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5122, loss_cls: 4.0505, loss: 4.0505 +2024-07-17 00:54:35,823 - pyskl - INFO - Epoch [23][2900/3746] lr: 9.442e-02, eta: 4 days, 0:18:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5112, loss_cls: 4.0463, loss: 4.0463 +2024-07-17 00:55:45,593 - pyskl - INFO - Epoch [23][3000/3746] lr: 9.441e-02, eta: 4 days, 0:17:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5120, loss_cls: 4.0406, loss: 4.0406 +2024-07-17 00:56:55,180 - pyskl - INFO - Epoch [23][3100/3746] lr: 9.439e-02, eta: 4 days, 0:15:39, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5097, loss_cls: 4.0572, loss: 4.0572 +2024-07-17 00:58:05,005 - pyskl - INFO - Epoch [23][3200/3746] lr: 9.438e-02, eta: 4 days, 0:14:10, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5045, loss_cls: 4.0656, loss: 4.0656 +2024-07-17 00:59:15,068 - pyskl - INFO - Epoch [23][3300/3746] lr: 9.437e-02, eta: 4 days, 0:12:42, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5073, loss_cls: 4.0556, loss: 4.0556 +2024-07-17 01:00:24,730 - pyskl - INFO - Epoch [23][3400/3746] lr: 9.436e-02, eta: 4 days, 0:11:12, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5030, loss_cls: 4.0839, loss: 4.0839 +2024-07-17 01:01:34,381 - pyskl - INFO - Epoch [23][3500/3746] lr: 9.434e-02, eta: 4 days, 0:09:42, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5050, loss_cls: 4.0850, loss: 4.0850 +2024-07-17 01:02:44,383 - pyskl - INFO - Epoch [23][3600/3746] lr: 9.433e-02, eta: 4 days, 0:08:15, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5006, loss_cls: 4.0804, loss: 4.0804 +2024-07-17 01:03:54,577 - pyskl - INFO - Epoch [23][3700/3746] lr: 9.432e-02, eta: 4 days, 0:06:48, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5041, loss_cls: 4.0657, loss: 4.0657 +2024-07-17 01:04:29,411 - pyskl - INFO - Saving checkpoint at 23 epochs +2024-07-17 01:06:18,639 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 01:06:19,300 - pyskl - INFO - +top1_acc 0.1995 +top5_acc 0.4336 +2024-07-17 01:06:19,300 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 01:06:19,339 - pyskl - INFO - +mean_acc 0.1993 +2024-07-17 01:06:19,343 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_21.pth was removed +2024-07-17 01:06:19,599 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_23.pth. +2024-07-17 01:06:19,600 - pyskl - INFO - Best top1_acc is 0.1995 at 23 epoch. +2024-07-17 01:06:19,611 - pyskl - INFO - Epoch(val) [23][309] top1_acc: 0.1995, top5_acc: 0.4336, mean_class_accuracy: 0.1993 +2024-07-17 01:09:39,939 - pyskl - INFO - Epoch [24][100/3746] lr: 9.430e-02, eta: 4 days, 0:13:41, time: 2.003, data_time: 1.297, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5181, loss_cls: 3.9999, loss: 3.9999 +2024-07-17 01:10:50,919 - pyskl - INFO - Epoch [24][200/3746] lr: 9.428e-02, eta: 4 days, 0:12:18, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5138, loss_cls: 4.0265, loss: 4.0265 +2024-07-17 01:12:01,049 - pyskl - INFO - Epoch [24][300/3746] lr: 9.427e-02, eta: 4 days, 0:10:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5108, loss_cls: 4.0546, loss: 4.0546 +2024-07-17 01:13:12,210 - pyskl - INFO - Epoch [24][400/3746] lr: 9.426e-02, eta: 4 days, 0:09:28, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5245, loss_cls: 3.9848, loss: 3.9848 +2024-07-17 01:14:22,158 - pyskl - INFO - Epoch [24][500/3746] lr: 9.425e-02, eta: 4 days, 0:07:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5117, loss_cls: 4.0596, loss: 4.0596 +2024-07-17 01:15:31,918 - pyskl - INFO - Epoch [24][600/3746] lr: 9.423e-02, eta: 4 days, 0:06:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5122, loss_cls: 4.0419, loss: 4.0419 +2024-07-17 01:16:41,728 - pyskl - INFO - Epoch [24][700/3746] lr: 9.422e-02, eta: 4 days, 0:05:01, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5095, loss_cls: 4.0679, loss: 4.0679 +2024-07-17 01:17:51,834 - pyskl - INFO - Epoch [24][800/3746] lr: 9.421e-02, eta: 4 days, 0:03:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5206, loss_cls: 4.0109, loss: 4.0109 +2024-07-17 01:19:01,742 - pyskl - INFO - Epoch [24][900/3746] lr: 9.419e-02, eta: 4 days, 0:02:04, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5114, loss_cls: 4.0074, loss: 4.0074 +2024-07-17 01:20:11,610 - pyskl - INFO - Epoch [24][1000/3746] lr: 9.418e-02, eta: 4 days, 0:00:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5205, loss_cls: 3.9957, loss: 3.9957 +2024-07-17 01:21:21,488 - pyskl - INFO - Epoch [24][1100/3746] lr: 9.417e-02, eta: 3 days, 23:59:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5033, loss_cls: 4.0650, loss: 4.0650 +2024-07-17 01:22:31,332 - pyskl - INFO - Epoch [24][1200/3746] lr: 9.415e-02, eta: 3 days, 23:57:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5177, loss_cls: 4.0166, loss: 4.0166 +2024-07-17 01:23:41,116 - pyskl - INFO - Epoch [24][1300/3746] lr: 9.414e-02, eta: 3 days, 23:56:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5062, loss_cls: 4.0857, loss: 4.0857 +2024-07-17 01:24:50,924 - pyskl - INFO - Epoch [24][1400/3746] lr: 9.413e-02, eta: 3 days, 23:54:40, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5234, loss_cls: 3.9914, loss: 3.9914 +2024-07-17 01:26:00,721 - pyskl - INFO - Epoch [24][1500/3746] lr: 9.411e-02, eta: 3 days, 23:53:11, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5256, loss_cls: 3.9829, loss: 3.9829 +2024-07-17 01:27:10,521 - pyskl - INFO - Epoch [24][1600/3746] lr: 9.410e-02, eta: 3 days, 23:51:42, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5027, loss_cls: 4.0594, loss: 4.0594 +2024-07-17 01:28:20,254 - pyskl - INFO - Epoch [24][1700/3746] lr: 9.409e-02, eta: 3 days, 23:50:13, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5216, loss_cls: 4.0180, loss: 4.0180 +2024-07-17 01:29:30,239 - pyskl - INFO - Epoch [24][1800/3746] lr: 9.407e-02, eta: 3 days, 23:48:45, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5145, loss_cls: 4.0249, loss: 4.0249 +2024-07-17 01:30:40,256 - pyskl - INFO - Epoch [24][1900/3746] lr: 9.406e-02, eta: 3 days, 23:47:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5100, loss_cls: 4.0470, loss: 4.0470 +2024-07-17 01:31:50,545 - pyskl - INFO - Epoch [24][2000/3746] lr: 9.405e-02, eta: 3 days, 23:45:51, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5167, loss_cls: 4.0151, loss: 4.0151 +2024-07-17 01:33:00,479 - pyskl - INFO - Epoch [24][2100/3746] lr: 9.404e-02, eta: 3 days, 23:44:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5164, loss_cls: 4.0168, loss: 4.0168 +2024-07-17 01:34:10,481 - pyskl - INFO - Epoch [24][2200/3746] lr: 9.402e-02, eta: 3 days, 23:42:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5211, loss_cls: 4.0499, loss: 4.0499 +2024-07-17 01:35:20,635 - pyskl - INFO - Epoch [24][2300/3746] lr: 9.401e-02, eta: 3 days, 23:41:29, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5012, loss_cls: 4.0673, loss: 4.0673 +2024-07-17 01:36:30,559 - pyskl - INFO - Epoch [24][2400/3746] lr: 9.400e-02, eta: 3 days, 23:40:01, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5144, loss_cls: 4.0323, loss: 4.0323 +2024-07-17 01:37:40,386 - pyskl - INFO - Epoch [24][2500/3746] lr: 9.398e-02, eta: 3 days, 23:38:32, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5138, loss_cls: 4.0413, loss: 4.0413 +2024-07-17 01:38:49,876 - pyskl - INFO - Epoch [24][2600/3746] lr: 9.397e-02, eta: 3 days, 23:37:02, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5172, loss_cls: 4.0410, loss: 4.0410 +2024-07-17 01:39:59,463 - pyskl - INFO - Epoch [24][2700/3746] lr: 9.396e-02, eta: 3 days, 23:35:32, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5125, loss_cls: 4.0273, loss: 4.0273 +2024-07-17 01:41:09,208 - pyskl - INFO - Epoch [24][2800/3746] lr: 9.394e-02, eta: 3 days, 23:34:04, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5120, loss_cls: 4.0295, loss: 4.0295 +2024-07-17 01:42:19,096 - pyskl - INFO - Epoch [24][2900/3746] lr: 9.393e-02, eta: 3 days, 23:32:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5134, loss_cls: 4.0313, loss: 4.0313 +2024-07-17 01:43:28,972 - pyskl - INFO - Epoch [24][3000/3746] lr: 9.392e-02, eta: 3 days, 23:31:08, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5095, loss_cls: 4.0539, loss: 4.0539 +2024-07-17 01:44:38,747 - pyskl - INFO - Epoch [24][3100/3746] lr: 9.390e-02, eta: 3 days, 23:29:39, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5083, loss_cls: 4.0500, loss: 4.0500 +2024-07-17 01:45:48,426 - pyskl - INFO - Epoch [24][3200/3746] lr: 9.389e-02, eta: 3 days, 23:28:10, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.5028, loss_cls: 4.1028, loss: 4.1028 +2024-07-17 01:46:58,167 - pyskl - INFO - Epoch [24][3300/3746] lr: 9.388e-02, eta: 3 days, 23:26:42, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5136, loss_cls: 4.0143, loss: 4.0143 +2024-07-17 01:48:07,810 - pyskl - INFO - Epoch [24][3400/3746] lr: 9.386e-02, eta: 3 days, 23:25:13, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5166, loss_cls: 4.0345, loss: 4.0345 +2024-07-17 01:49:17,711 - pyskl - INFO - Epoch [24][3500/3746] lr: 9.385e-02, eta: 3 days, 23:23:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5073, loss_cls: 4.0803, loss: 4.0803 +2024-07-17 01:50:27,613 - pyskl - INFO - Epoch [24][3600/3746] lr: 9.384e-02, eta: 3 days, 23:22:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5120, loss_cls: 4.0358, loss: 4.0358 +2024-07-17 01:51:37,691 - pyskl - INFO - Epoch [24][3700/3746] lr: 9.382e-02, eta: 3 days, 23:20:51, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.5030, loss_cls: 4.0874, loss: 4.0874 +2024-07-17 01:52:12,344 - pyskl - INFO - Saving checkpoint at 24 epochs +2024-07-17 01:54:01,587 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 01:54:02,249 - pyskl - INFO - +top1_acc 0.1774 +top5_acc 0.3995 +2024-07-17 01:54:02,249 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 01:54:02,289 - pyskl - INFO - +mean_acc 0.1772 +2024-07-17 01:54:02,302 - pyskl - INFO - Epoch(val) [24][309] top1_acc: 0.1774, top5_acc: 0.3995, mean_class_accuracy: 0.1772 +2024-07-17 01:57:20,602 - pyskl - INFO - Epoch [25][100/3746] lr: 9.380e-02, eta: 3 days, 23:27:08, time: 1.983, data_time: 1.280, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5236, loss_cls: 3.9892, loss: 3.9892 +2024-07-17 01:58:31,604 - pyskl - INFO - Epoch [25][200/3746] lr: 9.379e-02, eta: 3 days, 23:25:46, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5220, loss_cls: 3.9769, loss: 3.9769 +2024-07-17 01:59:42,061 - pyskl - INFO - Epoch [25][300/3746] lr: 9.378e-02, eta: 3 days, 23:24:21, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5086, loss_cls: 4.0669, loss: 4.0669 +2024-07-17 02:00:52,578 - pyskl - INFO - Epoch [25][400/3746] lr: 9.376e-02, eta: 3 days, 23:22:56, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5062, loss_cls: 4.0500, loss: 4.0500 +2024-07-17 02:02:03,036 - pyskl - INFO - Epoch [25][500/3746] lr: 9.375e-02, eta: 3 days, 23:21:31, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5264, loss_cls: 3.9829, loss: 3.9829 +2024-07-17 02:03:13,137 - pyskl - INFO - Epoch [25][600/3746] lr: 9.373e-02, eta: 3 days, 23:20:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5058, loss_cls: 4.0712, loss: 4.0712 +2024-07-17 02:04:22,937 - pyskl - INFO - Epoch [25][700/3746] lr: 9.372e-02, eta: 3 days, 23:18:35, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5011, loss_cls: 4.0544, loss: 4.0544 +2024-07-17 02:05:32,704 - pyskl - INFO - Epoch [25][800/3746] lr: 9.371e-02, eta: 3 days, 23:17:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5155, loss_cls: 4.0291, loss: 4.0291 +2024-07-17 02:06:42,482 - pyskl - INFO - Epoch [25][900/3746] lr: 9.369e-02, eta: 3 days, 23:15:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5092, loss_cls: 4.0761, loss: 4.0761 +2024-07-17 02:07:52,198 - pyskl - INFO - Epoch [25][1000/3746] lr: 9.368e-02, eta: 3 days, 23:14:09, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5214, loss_cls: 4.0112, loss: 4.0112 +2024-07-17 02:09:01,968 - pyskl - INFO - Epoch [25][1100/3746] lr: 9.367e-02, eta: 3 days, 23:12:41, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5145, loss_cls: 4.0411, loss: 4.0411 +2024-07-17 02:10:11,630 - pyskl - INFO - Epoch [25][1200/3746] lr: 9.365e-02, eta: 3 days, 23:11:12, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4998, loss_cls: 4.0792, loss: 4.0792 +2024-07-17 02:11:21,297 - pyskl - INFO - Epoch [25][1300/3746] lr: 9.364e-02, eta: 3 days, 23:09:43, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5177, loss_cls: 4.0534, loss: 4.0534 +2024-07-17 02:12:31,293 - pyskl - INFO - Epoch [25][1400/3746] lr: 9.363e-02, eta: 3 days, 23:08:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5062, loss_cls: 4.0536, loss: 4.0536 +2024-07-17 02:13:41,238 - pyskl - INFO - Epoch [25][1500/3746] lr: 9.361e-02, eta: 3 days, 23:06:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5219, loss_cls: 3.9999, loss: 3.9999 +2024-07-17 02:14:51,052 - pyskl - INFO - Epoch [25][1600/3746] lr: 9.360e-02, eta: 3 days, 23:05:21, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5069, loss_cls: 4.0447, loss: 4.0447 +2024-07-17 02:16:00,923 - pyskl - INFO - Epoch [25][1700/3746] lr: 9.358e-02, eta: 3 days, 23:03:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5164, loss_cls: 4.0345, loss: 4.0345 +2024-07-17 02:17:10,730 - pyskl - INFO - Epoch [25][1800/3746] lr: 9.357e-02, eta: 3 days, 23:02:25, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5077, loss_cls: 4.0535, loss: 4.0535 +2024-07-17 02:18:20,694 - pyskl - INFO - Epoch [25][1900/3746] lr: 9.356e-02, eta: 3 days, 23:00:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5220, loss_cls: 3.9829, loss: 3.9829 +2024-07-17 02:19:31,015 - pyskl - INFO - Epoch [25][2000/3746] lr: 9.354e-02, eta: 3 days, 22:59:33, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5248, loss_cls: 3.9779, loss: 3.9779 +2024-07-17 02:20:41,386 - pyskl - INFO - Epoch [25][2100/3746] lr: 9.353e-02, eta: 3 days, 22:58:08, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5189, loss_cls: 3.9782, loss: 3.9782 +2024-07-17 02:21:51,343 - pyskl - INFO - Epoch [25][2200/3746] lr: 9.352e-02, eta: 3 days, 22:56:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5116, loss_cls: 4.0216, loss: 4.0216 +2024-07-17 02:23:01,139 - pyskl - INFO - Epoch [25][2300/3746] lr: 9.350e-02, eta: 3 days, 22:55:13, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5098, loss_cls: 4.0578, loss: 4.0578 +2024-07-17 02:24:10,953 - pyskl - INFO - Epoch [25][2400/3746] lr: 9.349e-02, eta: 3 days, 22:53:45, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5102, loss_cls: 4.0660, loss: 4.0660 +2024-07-17 02:25:20,922 - pyskl - INFO - Epoch [25][2500/3746] lr: 9.347e-02, eta: 3 days, 22:52:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5236, loss_cls: 4.0167, loss: 4.0167 +2024-07-17 02:26:30,981 - pyskl - INFO - Epoch [25][2600/3746] lr: 9.346e-02, eta: 3 days, 22:50:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5223, loss_cls: 3.9976, loss: 3.9976 +2024-07-17 02:27:41,152 - pyskl - INFO - Epoch [25][2700/3746] lr: 9.345e-02, eta: 3 days, 22:49:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5156, loss_cls: 4.0126, loss: 4.0126 +2024-07-17 02:28:51,061 - pyskl - INFO - Epoch [25][2800/3746] lr: 9.343e-02, eta: 3 days, 22:47:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5039, loss_cls: 4.0494, loss: 4.0494 +2024-07-17 02:30:01,233 - pyskl - INFO - Epoch [25][2900/3746] lr: 9.342e-02, eta: 3 days, 22:46:33, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5161, loss_cls: 4.0211, loss: 4.0211 +2024-07-17 02:31:11,280 - pyskl - INFO - Epoch [25][3000/3746] lr: 9.341e-02, eta: 3 days, 22:45:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5105, loss_cls: 4.0338, loss: 4.0338 +2024-07-17 02:32:21,287 - pyskl - INFO - Epoch [25][3100/3746] lr: 9.339e-02, eta: 3 days, 22:43:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5177, loss_cls: 3.9986, loss: 3.9986 +2024-07-17 02:33:31,296 - pyskl - INFO - Epoch [25][3200/3746] lr: 9.338e-02, eta: 3 days, 22:42:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5197, loss_cls: 4.0406, loss: 4.0406 +2024-07-17 02:34:41,255 - pyskl - INFO - Epoch [25][3300/3746] lr: 9.336e-02, eta: 3 days, 22:40:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5181, loss_cls: 4.0344, loss: 4.0344 +2024-07-17 02:35:51,323 - pyskl - INFO - Epoch [25][3400/3746] lr: 9.335e-02, eta: 3 days, 22:39:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5162, loss_cls: 4.0407, loss: 4.0407 +2024-07-17 02:37:01,289 - pyskl - INFO - Epoch [25][3500/3746] lr: 9.334e-02, eta: 3 days, 22:37:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5106, loss_cls: 4.0420, loss: 4.0420 +2024-07-17 02:38:11,225 - pyskl - INFO - Epoch [25][3600/3746] lr: 9.332e-02, eta: 3 days, 22:36:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5034, loss_cls: 4.0856, loss: 4.0856 +2024-07-17 02:39:21,423 - pyskl - INFO - Epoch [25][3700/3746] lr: 9.331e-02, eta: 3 days, 22:35:03, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5084, loss_cls: 4.0450, loss: 4.0450 +2024-07-17 02:39:56,366 - pyskl - INFO - Saving checkpoint at 25 epochs +2024-07-17 02:41:47,720 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 02:41:48,386 - pyskl - INFO - +top1_acc 0.1674 +top5_acc 0.3742 +2024-07-17 02:41:48,386 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 02:41:48,427 - pyskl - INFO - +mean_acc 0.1672 +2024-07-17 02:41:48,438 - pyskl - INFO - Epoch(val) [25][309] top1_acc: 0.1674, top5_acc: 0.3742, mean_class_accuracy: 0.1672 +2024-07-17 02:45:11,708 - pyskl - INFO - Epoch [26][100/3746] lr: 9.329e-02, eta: 3 days, 22:41:22, time: 2.033, data_time: 1.329, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5259, loss_cls: 3.9828, loss: 3.9828 +2024-07-17 02:46:22,850 - pyskl - INFO - Epoch [26][200/3746] lr: 9.327e-02, eta: 3 days, 22:40:00, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5186, loss_cls: 3.9910, loss: 3.9910 +2024-07-17 02:47:33,230 - pyskl - INFO - Epoch [26][300/3746] lr: 9.326e-02, eta: 3 days, 22:38:35, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5173, loss_cls: 4.0207, loss: 4.0207 +2024-07-17 02:48:43,959 - pyskl - INFO - Epoch [26][400/3746] lr: 9.325e-02, eta: 3 days, 22:37:12, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5198, loss_cls: 4.0114, loss: 4.0114 +2024-07-17 02:49:54,596 - pyskl - INFO - Epoch [26][500/3746] lr: 9.323e-02, eta: 3 days, 22:35:49, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5139, loss_cls: 4.0108, loss: 4.0108 +2024-07-17 02:51:04,697 - pyskl - INFO - Epoch [26][600/3746] lr: 9.322e-02, eta: 3 days, 22:34:22, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5144, loss_cls: 4.0293, loss: 4.0293 +2024-07-17 02:52:14,686 - pyskl - INFO - Epoch [26][700/3746] lr: 9.320e-02, eta: 3 days, 22:32:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5189, loss_cls: 3.9945, loss: 3.9945 +2024-07-17 02:53:25,009 - pyskl - INFO - Epoch [26][800/3746] lr: 9.319e-02, eta: 3 days, 22:31:31, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5138, loss_cls: 4.0337, loss: 4.0337 +2024-07-17 02:54:34,854 - pyskl - INFO - Epoch [26][900/3746] lr: 9.318e-02, eta: 3 days, 22:30:03, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5131, loss_cls: 4.0415, loss: 4.0415 +2024-07-17 02:55:44,668 - pyskl - INFO - Epoch [26][1000/3746] lr: 9.316e-02, eta: 3 days, 22:28:36, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5181, loss_cls: 4.0373, loss: 4.0373 +2024-07-17 02:56:54,668 - pyskl - INFO - Epoch [26][1100/3746] lr: 9.315e-02, eta: 3 days, 22:27:09, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5133, loss_cls: 4.0397, loss: 4.0397 +2024-07-17 02:58:04,593 - pyskl - INFO - Epoch [26][1200/3746] lr: 9.313e-02, eta: 3 days, 22:25:42, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5220, loss_cls: 3.9585, loss: 3.9585 +2024-07-17 02:59:14,581 - pyskl - INFO - Epoch [26][1300/3746] lr: 9.312e-02, eta: 3 days, 22:24:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5128, loss_cls: 4.0366, loss: 4.0366 +2024-07-17 03:00:24,605 - pyskl - INFO - Epoch [26][1400/3746] lr: 9.310e-02, eta: 3 days, 22:22:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5025, loss_cls: 4.0735, loss: 4.0735 +2024-07-17 03:01:34,577 - pyskl - INFO - Epoch [26][1500/3746] lr: 9.309e-02, eta: 3 days, 22:21:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5264, loss_cls: 3.9897, loss: 3.9897 +2024-07-17 03:02:44,461 - pyskl - INFO - Epoch [26][1600/3746] lr: 9.308e-02, eta: 3 days, 22:19:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5241, loss_cls: 4.0029, loss: 4.0029 +2024-07-17 03:03:54,424 - pyskl - INFO - Epoch [26][1700/3746] lr: 9.306e-02, eta: 3 days, 22:18:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5192, loss_cls: 3.9956, loss: 3.9956 +2024-07-17 03:05:04,398 - pyskl - INFO - Epoch [26][1800/3746] lr: 9.305e-02, eta: 3 days, 22:17:03, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5053, loss_cls: 4.0482, loss: 4.0482 +2024-07-17 03:06:14,467 - pyskl - INFO - Epoch [26][1900/3746] lr: 9.303e-02, eta: 3 days, 22:15:37, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5166, loss_cls: 4.0537, loss: 4.0537 +2024-07-17 03:07:24,759 - pyskl - INFO - Epoch [26][2000/3746] lr: 9.302e-02, eta: 3 days, 22:14:12, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.5138, loss_cls: 4.0262, loss: 4.0262 +2024-07-17 03:08:34,840 - pyskl - INFO - Epoch [26][2100/3746] lr: 9.300e-02, eta: 3 days, 22:12:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5180, loss_cls: 4.0147, loss: 4.0147 +2024-07-17 03:09:44,824 - pyskl - INFO - Epoch [26][2200/3746] lr: 9.299e-02, eta: 3 days, 22:11:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5045, loss_cls: 4.0522, loss: 4.0522 +2024-07-17 03:10:54,958 - pyskl - INFO - Epoch [26][2300/3746] lr: 9.298e-02, eta: 3 days, 22:09:55, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5166, loss_cls: 4.0009, loss: 4.0009 +2024-07-17 03:12:05,519 - pyskl - INFO - Epoch [26][2400/3746] lr: 9.296e-02, eta: 3 days, 22:08:31, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5205, loss_cls: 4.0319, loss: 4.0319 +2024-07-17 03:13:15,856 - pyskl - INFO - Epoch [26][2500/3746] lr: 9.295e-02, eta: 3 days, 22:07:07, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5239, loss_cls: 4.0086, loss: 4.0086 +2024-07-17 03:14:26,269 - pyskl - INFO - Epoch [26][2600/3746] lr: 9.293e-02, eta: 3 days, 22:05:43, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5152, loss_cls: 4.0501, loss: 4.0501 +2024-07-17 03:15:36,167 - pyskl - INFO - Epoch [26][2700/3746] lr: 9.292e-02, eta: 3 days, 22:04:16, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5136, loss_cls: 4.0490, loss: 4.0490 +2024-07-17 03:16:46,236 - pyskl - INFO - Epoch [26][2800/3746] lr: 9.290e-02, eta: 3 days, 22:02:51, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5136, loss_cls: 4.0656, loss: 4.0656 +2024-07-17 03:17:55,957 - pyskl - INFO - Epoch [26][2900/3746] lr: 9.289e-02, eta: 3 days, 22:01:23, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5088, loss_cls: 4.0644, loss: 4.0644 +2024-07-17 03:19:05,975 - pyskl - INFO - Epoch [26][3000/3746] lr: 9.288e-02, eta: 3 days, 21:59:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5169, loss_cls: 4.0001, loss: 4.0001 +2024-07-17 03:20:15,931 - pyskl - INFO - Epoch [26][3100/3746] lr: 9.286e-02, eta: 3 days, 21:58:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5173, loss_cls: 4.0160, loss: 4.0160 +2024-07-17 03:21:25,957 - pyskl - INFO - Epoch [26][3200/3746] lr: 9.285e-02, eta: 3 days, 21:57:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4900, loss_cls: 4.1167, loss: 4.1167 +2024-07-17 03:22:36,170 - pyskl - INFO - Epoch [26][3300/3746] lr: 9.283e-02, eta: 3 days, 21:55:41, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5152, loss_cls: 4.0642, loss: 4.0642 +2024-07-17 03:23:46,046 - pyskl - INFO - Epoch [26][3400/3746] lr: 9.282e-02, eta: 3 days, 21:54:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5202, loss_cls: 3.9956, loss: 3.9956 +2024-07-17 03:24:55,871 - pyskl - INFO - Epoch [26][3500/3746] lr: 9.280e-02, eta: 3 days, 21:52:48, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5050, loss_cls: 4.0751, loss: 4.0751 +2024-07-17 03:26:06,240 - pyskl - INFO - Epoch [26][3600/3746] lr: 9.279e-02, eta: 3 days, 21:51:24, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5108, loss_cls: 4.0278, loss: 4.0278 +2024-07-17 03:27:16,496 - pyskl - INFO - Epoch [26][3700/3746] lr: 9.278e-02, eta: 3 days, 21:49:59, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5142, loss_cls: 4.0421, loss: 4.0421 +2024-07-17 03:27:51,070 - pyskl - INFO - Saving checkpoint at 26 epochs +2024-07-17 03:29:42,159 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 03:29:42,840 - pyskl - INFO - +top1_acc 0.1623 +top5_acc 0.3651 +2024-07-17 03:29:42,840 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 03:29:42,882 - pyskl - INFO - +mean_acc 0.1622 +2024-07-17 03:29:42,894 - pyskl - INFO - Epoch(val) [26][309] top1_acc: 0.1623, top5_acc: 0.3651, mean_class_accuracy: 0.1622 +2024-07-17 03:33:05,890 - pyskl - INFO - Epoch [27][100/3746] lr: 9.275e-02, eta: 3 days, 21:55:54, time: 2.030, data_time: 1.328, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5142, loss_cls: 4.0296, loss: 4.0296 +2024-07-17 03:34:17,107 - pyskl - INFO - Epoch [27][200/3746] lr: 9.274e-02, eta: 3 days, 21:54:34, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5183, loss_cls: 4.0213, loss: 4.0213 +2024-07-17 03:35:27,604 - pyskl - INFO - Epoch [27][300/3746] lr: 9.272e-02, eta: 3 days, 21:53:10, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5152, loss_cls: 4.0144, loss: 4.0144 +2024-07-17 03:36:37,868 - pyskl - INFO - Epoch [27][400/3746] lr: 9.271e-02, eta: 3 days, 21:51:45, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5175, loss_cls: 4.0025, loss: 4.0025 +2024-07-17 03:37:48,391 - pyskl - INFO - Epoch [27][500/3746] lr: 9.270e-02, eta: 3 days, 21:50:22, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5153, loss_cls: 4.0298, loss: 4.0298 +2024-07-17 03:38:58,485 - pyskl - INFO - Epoch [27][600/3746] lr: 9.268e-02, eta: 3 days, 21:48:56, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5158, loss_cls: 4.0235, loss: 4.0235 +2024-07-17 03:40:08,548 - pyskl - INFO - Epoch [27][700/3746] lr: 9.267e-02, eta: 3 days, 21:47:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5133, loss_cls: 4.0276, loss: 4.0276 +2024-07-17 03:41:18,606 - pyskl - INFO - Epoch [27][800/3746] lr: 9.265e-02, eta: 3 days, 21:46:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5286, loss_cls: 4.0062, loss: 4.0062 +2024-07-17 03:42:28,633 - pyskl - INFO - Epoch [27][900/3746] lr: 9.264e-02, eta: 3 days, 21:44:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5128, loss_cls: 4.0314, loss: 4.0314 +2024-07-17 03:43:38,449 - pyskl - INFO - Epoch [27][1000/3746] lr: 9.262e-02, eta: 3 days, 21:43:12, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5152, loss_cls: 4.0340, loss: 4.0340 +2024-07-17 03:44:48,689 - pyskl - INFO - Epoch [27][1100/3746] lr: 9.261e-02, eta: 3 days, 21:41:47, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5134, loss_cls: 3.9844, loss: 3.9844 +2024-07-17 03:45:58,494 - pyskl - INFO - Epoch [27][1200/3746] lr: 9.259e-02, eta: 3 days, 21:40:20, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5027, loss_cls: 4.0471, loss: 4.0471 +2024-07-17 03:47:08,382 - pyskl - INFO - Epoch [27][1300/3746] lr: 9.258e-02, eta: 3 days, 21:38:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5080, loss_cls: 4.0461, loss: 4.0461 +2024-07-17 03:48:18,306 - pyskl - INFO - Epoch [27][1400/3746] lr: 9.256e-02, eta: 3 days, 21:37:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5248, loss_cls: 3.9654, loss: 3.9654 +2024-07-17 03:49:28,309 - pyskl - INFO - Epoch [27][1500/3746] lr: 9.255e-02, eta: 3 days, 21:36:02, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.5119, loss_cls: 4.0536, loss: 4.0536 +2024-07-17 03:50:38,295 - pyskl - INFO - Epoch [27][1600/3746] lr: 9.253e-02, eta: 3 days, 21:34:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5125, loss_cls: 4.0459, loss: 4.0459 +2024-07-17 03:51:48,394 - pyskl - INFO - Epoch [27][1700/3746] lr: 9.252e-02, eta: 3 days, 21:33:11, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5100, loss_cls: 4.0349, loss: 4.0349 +2024-07-17 03:52:58,258 - pyskl - INFO - Epoch [27][1800/3746] lr: 9.251e-02, eta: 3 days, 21:31:44, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5064, loss_cls: 4.0496, loss: 4.0496 +2024-07-17 03:54:08,160 - pyskl - INFO - Epoch [27][1900/3746] lr: 9.249e-02, eta: 3 days, 21:30:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5077, loss_cls: 4.0715, loss: 4.0715 +2024-07-17 03:55:19,037 - pyskl - INFO - Epoch [27][2000/3746] lr: 9.248e-02, eta: 3 days, 21:28:57, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5120, loss_cls: 4.0096, loss: 4.0096 +2024-07-17 03:56:28,912 - pyskl - INFO - Epoch [27][2100/3746] lr: 9.246e-02, eta: 3 days, 21:27:30, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5159, loss_cls: 4.0158, loss: 4.0158 +2024-07-17 03:57:39,057 - pyskl - INFO - Epoch [27][2200/3746] lr: 9.245e-02, eta: 3 days, 21:26:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5058, loss_cls: 4.0963, loss: 4.0963 +2024-07-17 03:58:49,001 - pyskl - INFO - Epoch [27][2300/3746] lr: 9.243e-02, eta: 3 days, 21:24:40, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5197, loss_cls: 4.0393, loss: 4.0393 +2024-07-17 03:59:59,033 - pyskl - INFO - Epoch [27][2400/3746] lr: 9.242e-02, eta: 3 days, 21:23:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5169, loss_cls: 3.9943, loss: 3.9943 +2024-07-17 04:01:08,850 - pyskl - INFO - Epoch [27][2500/3746] lr: 9.240e-02, eta: 3 days, 21:21:48, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5194, loss_cls: 3.9926, loss: 3.9926 +2024-07-17 04:02:18,390 - pyskl - INFO - Epoch [27][2600/3746] lr: 9.239e-02, eta: 3 days, 21:20:20, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5162, loss_cls: 3.9848, loss: 3.9848 +2024-07-17 04:03:28,073 - pyskl - INFO - Epoch [27][2700/3746] lr: 9.237e-02, eta: 3 days, 21:18:54, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5147, loss_cls: 4.0390, loss: 4.0390 +2024-07-17 04:04:38,022 - pyskl - INFO - Epoch [27][2800/3746] lr: 9.236e-02, eta: 3 days, 21:17:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5103, loss_cls: 4.0087, loss: 4.0087 +2024-07-17 04:05:47,683 - pyskl - INFO - Epoch [27][2900/3746] lr: 9.234e-02, eta: 3 days, 21:16:01, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5192, loss_cls: 4.0099, loss: 4.0099 +2024-07-17 04:06:57,303 - pyskl - INFO - Epoch [27][3000/3746] lr: 9.233e-02, eta: 3 days, 21:14:34, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5119, loss_cls: 4.0176, loss: 4.0176 +2024-07-17 04:08:07,059 - pyskl - INFO - Epoch [27][3100/3746] lr: 9.231e-02, eta: 3 days, 21:13:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5173, loss_cls: 4.0039, loss: 4.0039 +2024-07-17 04:09:16,861 - pyskl - INFO - Epoch [27][3200/3746] lr: 9.230e-02, eta: 3 days, 21:11:41, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5203, loss_cls: 4.0084, loss: 4.0084 +2024-07-17 04:10:26,729 - pyskl - INFO - Epoch [27][3300/3746] lr: 9.228e-02, eta: 3 days, 21:10:15, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.4983, loss_cls: 4.0957, loss: 4.0957 +2024-07-17 04:11:36,554 - pyskl - INFO - Epoch [27][3400/3746] lr: 9.227e-02, eta: 3 days, 21:08:49, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5203, loss_cls: 4.0090, loss: 4.0090 +2024-07-17 04:12:46,242 - pyskl - INFO - Epoch [27][3500/3746] lr: 9.225e-02, eta: 3 days, 21:07:23, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5095, loss_cls: 4.0240, loss: 4.0240 +2024-07-17 04:13:55,905 - pyskl - INFO - Epoch [27][3600/3746] lr: 9.224e-02, eta: 3 days, 21:05:56, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5095, loss_cls: 4.0627, loss: 4.0627 +2024-07-17 04:15:05,745 - pyskl - INFO - Epoch [27][3700/3746] lr: 9.222e-02, eta: 3 days, 21:04:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5062, loss_cls: 4.0588, loss: 4.0588 +2024-07-17 04:15:40,499 - pyskl - INFO - Saving checkpoint at 27 epochs +2024-07-17 04:17:30,174 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 04:17:30,839 - pyskl - INFO - +top1_acc 0.2005 +top5_acc 0.4362 +2024-07-17 04:17:30,839 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 04:17:30,880 - pyskl - INFO - +mean_acc 0.2004 +2024-07-17 04:17:30,885 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_23.pth was removed +2024-07-17 04:17:31,141 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_27.pth. +2024-07-17 04:17:31,142 - pyskl - INFO - Best top1_acc is 0.2005 at 27 epoch. +2024-07-17 04:17:31,153 - pyskl - INFO - Epoch(val) [27][309] top1_acc: 0.2005, top5_acc: 0.4362, mean_class_accuracy: 0.2004 +2024-07-17 04:20:47,985 - pyskl - INFO - Epoch [28][100/3746] lr: 9.220e-02, eta: 3 days, 21:09:37, time: 1.968, data_time: 1.267, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5222, loss_cls: 3.9974, loss: 3.9974 +2024-07-17 04:21:58,516 - pyskl - INFO - Epoch [28][200/3746] lr: 9.219e-02, eta: 3 days, 21:08:14, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5319, loss_cls: 3.9152, loss: 3.9152 +2024-07-17 04:23:09,545 - pyskl - INFO - Epoch [28][300/3746] lr: 9.217e-02, eta: 3 days, 21:06:53, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5208, loss_cls: 4.0127, loss: 4.0127 +2024-07-17 04:24:19,856 - pyskl - INFO - Epoch [28][400/3746] lr: 9.216e-02, eta: 3 days, 21:05:29, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5205, loss_cls: 4.0108, loss: 4.0108 +2024-07-17 04:25:30,082 - pyskl - INFO - Epoch [28][500/3746] lr: 9.214e-02, eta: 3 days, 21:04:04, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5234, loss_cls: 3.9555, loss: 3.9555 +2024-07-17 04:26:40,022 - pyskl - INFO - Epoch [28][600/3746] lr: 9.213e-02, eta: 3 days, 21:02:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5145, loss_cls: 4.0439, loss: 4.0439 +2024-07-17 04:27:50,036 - pyskl - INFO - Epoch [28][700/3746] lr: 9.211e-02, eta: 3 days, 21:01:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5142, loss_cls: 4.0249, loss: 4.0249 +2024-07-17 04:28:59,982 - pyskl - INFO - Epoch [28][800/3746] lr: 9.210e-02, eta: 3 days, 20:59:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5181, loss_cls: 3.9979, loss: 3.9979 +2024-07-17 04:30:09,650 - pyskl - INFO - Epoch [28][900/3746] lr: 9.208e-02, eta: 3 days, 20:58:21, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5281, loss_cls: 3.9997, loss: 3.9997 +2024-07-17 04:31:19,262 - pyskl - INFO - Epoch [28][1000/3746] lr: 9.207e-02, eta: 3 days, 20:56:54, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5097, loss_cls: 4.0480, loss: 4.0480 +2024-07-17 04:32:29,012 - pyskl - INFO - Epoch [28][1100/3746] lr: 9.205e-02, eta: 3 days, 20:55:27, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5164, loss_cls: 4.0242, loss: 4.0242 +2024-07-17 04:33:38,658 - pyskl - INFO - Epoch [28][1200/3746] lr: 9.204e-02, eta: 3 days, 20:54:00, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5211, loss_cls: 3.9978, loss: 3.9978 +2024-07-17 04:34:48,354 - pyskl - INFO - Epoch [28][1300/3746] lr: 9.202e-02, eta: 3 days, 20:52:34, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5092, loss_cls: 4.0513, loss: 4.0513 +2024-07-17 04:35:58,050 - pyskl - INFO - Epoch [28][1400/3746] lr: 9.201e-02, eta: 3 days, 20:51:07, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5177, loss_cls: 4.0128, loss: 4.0128 +2024-07-17 04:37:07,965 - pyskl - INFO - Epoch [28][1500/3746] lr: 9.199e-02, eta: 3 days, 20:49:42, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5200, loss_cls: 4.0061, loss: 4.0061 +2024-07-17 04:38:17,598 - pyskl - INFO - Epoch [28][1600/3746] lr: 9.198e-02, eta: 3 days, 20:48:15, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5233, loss_cls: 4.0222, loss: 4.0222 +2024-07-17 04:39:27,354 - pyskl - INFO - Epoch [28][1700/3746] lr: 9.196e-02, eta: 3 days, 20:46:49, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5192, loss_cls: 4.0027, loss: 4.0027 +2024-07-17 04:40:37,121 - pyskl - INFO - Epoch [28][1800/3746] lr: 9.194e-02, eta: 3 days, 20:45:23, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5084, loss_cls: 4.0302, loss: 4.0302 +2024-07-17 04:41:47,179 - pyskl - INFO - Epoch [28][1900/3746] lr: 9.193e-02, eta: 3 days, 20:43:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5083, loss_cls: 4.0227, loss: 4.0227 +2024-07-17 04:42:57,268 - pyskl - INFO - Epoch [28][2000/3746] lr: 9.191e-02, eta: 3 days, 20:42:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5117, loss_cls: 4.0473, loss: 4.0473 +2024-07-17 04:44:07,432 - pyskl - INFO - Epoch [28][2100/3746] lr: 9.190e-02, eta: 3 days, 20:41:09, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5134, loss_cls: 4.0232, loss: 4.0232 +2024-07-17 04:45:17,463 - pyskl - INFO - Epoch [28][2200/3746] lr: 9.188e-02, eta: 3 days, 20:39:44, time: 0.700, data_time: 0.001, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5162, loss_cls: 4.0203, loss: 4.0203 +2024-07-17 04:46:27,225 - pyskl - INFO - Epoch [28][2300/3746] lr: 9.187e-02, eta: 3 days, 20:38:18, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5130, loss_cls: 4.0381, loss: 4.0381 +2024-07-17 04:47:37,235 - pyskl - INFO - Epoch [28][2400/3746] lr: 9.185e-02, eta: 3 days, 20:36:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5177, loss_cls: 4.0062, loss: 4.0062 +2024-07-17 04:48:47,102 - pyskl - INFO - Epoch [28][2500/3746] lr: 9.184e-02, eta: 3 days, 20:35:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5220, loss_cls: 3.9781, loss: 3.9781 +2024-07-17 04:49:57,112 - pyskl - INFO - Epoch [28][2600/3746] lr: 9.182e-02, eta: 3 days, 20:34:03, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5177, loss_cls: 4.0374, loss: 4.0374 +2024-07-17 04:51:07,008 - pyskl - INFO - Epoch [28][2700/3746] lr: 9.181e-02, eta: 3 days, 20:32:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5222, loss_cls: 3.9877, loss: 3.9877 +2024-07-17 04:52:16,717 - pyskl - INFO - Epoch [28][2800/3746] lr: 9.179e-02, eta: 3 days, 20:31:12, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5086, loss_cls: 4.0380, loss: 4.0380 +2024-07-17 04:53:26,326 - pyskl - INFO - Epoch [28][2900/3746] lr: 9.178e-02, eta: 3 days, 20:29:45, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5117, loss_cls: 4.0394, loss: 4.0394 +2024-07-17 04:54:36,031 - pyskl - INFO - Epoch [28][3000/3746] lr: 9.176e-02, eta: 3 days, 20:28:19, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5144, loss_cls: 4.0444, loss: 4.0444 +2024-07-17 04:55:45,700 - pyskl - INFO - Epoch [28][3100/3746] lr: 9.175e-02, eta: 3 days, 20:26:53, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5159, loss_cls: 4.0127, loss: 4.0127 +2024-07-17 04:56:55,538 - pyskl - INFO - Epoch [28][3200/3746] lr: 9.173e-02, eta: 3 days, 20:25:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5205, loss_cls: 4.0033, loss: 4.0033 +2024-07-17 04:58:05,390 - pyskl - INFO - Epoch [28][3300/3746] lr: 9.172e-02, eta: 3 days, 20:24:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5219, loss_cls: 3.9980, loss: 3.9980 +2024-07-17 04:59:15,006 - pyskl - INFO - Epoch [28][3400/3746] lr: 9.170e-02, eta: 3 days, 20:22:36, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5294, loss_cls: 3.9821, loss: 3.9821 +2024-07-17 05:00:24,620 - pyskl - INFO - Epoch [28][3500/3746] lr: 9.168e-02, eta: 3 days, 20:21:10, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5098, loss_cls: 4.0121, loss: 4.0121 +2024-07-17 05:01:34,203 - pyskl - INFO - Epoch [28][3600/3746] lr: 9.167e-02, eta: 3 days, 20:19:44, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5158, loss_cls: 4.0397, loss: 4.0397 +2024-07-17 05:02:44,211 - pyskl - INFO - Epoch [28][3700/3746] lr: 9.165e-02, eta: 3 days, 20:18:19, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.5125, loss_cls: 4.0427, loss: 4.0427 +2024-07-17 05:03:18,541 - pyskl - INFO - Saving checkpoint at 28 epochs +2024-07-17 05:05:08,376 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 05:05:09,040 - pyskl - INFO - +top1_acc 0.2076 +top5_acc 0.4381 +2024-07-17 05:05:09,040 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 05:05:09,080 - pyskl - INFO - +mean_acc 0.2074 +2024-07-17 05:05:09,084 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_27.pth was removed +2024-07-17 05:05:09,343 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_28.pth. +2024-07-17 05:05:09,343 - pyskl - INFO - Best top1_acc is 0.2076 at 28 epoch. +2024-07-17 05:05:09,355 - pyskl - INFO - Epoch(val) [28][309] top1_acc: 0.2076, top5_acc: 0.4381, mean_class_accuracy: 0.2074 +2024-07-17 05:08:26,928 - pyskl - INFO - Epoch [29][100/3746] lr: 9.163e-02, eta: 3 days, 20:23:11, time: 1.976, data_time: 1.275, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5295, loss_cls: 3.9687, loss: 3.9687 +2024-07-17 05:09:37,189 - pyskl - INFO - Epoch [29][200/3746] lr: 9.162e-02, eta: 3 days, 20:21:47, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5166, loss_cls: 4.0161, loss: 4.0161 +2024-07-17 05:10:47,886 - pyskl - INFO - Epoch [29][300/3746] lr: 9.160e-02, eta: 3 days, 20:20:25, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5298, loss_cls: 3.9698, loss: 3.9698 +2024-07-17 05:11:58,264 - pyskl - INFO - Epoch [29][400/3746] lr: 9.158e-02, eta: 3 days, 20:19:02, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5208, loss_cls: 3.9833, loss: 3.9833 +2024-07-17 05:13:09,069 - pyskl - INFO - Epoch [29][500/3746] lr: 9.157e-02, eta: 3 days, 20:17:41, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5241, loss_cls: 3.9786, loss: 3.9786 +2024-07-17 05:14:19,133 - pyskl - INFO - Epoch [29][600/3746] lr: 9.155e-02, eta: 3 days, 20:16:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5212, loss_cls: 3.9948, loss: 3.9948 +2024-07-17 05:15:28,979 - pyskl - INFO - Epoch [29][700/3746] lr: 9.154e-02, eta: 3 days, 20:14:51, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5141, loss_cls: 3.9980, loss: 3.9980 +2024-07-17 05:16:39,031 - pyskl - INFO - Epoch [29][800/3746] lr: 9.152e-02, eta: 3 days, 20:13:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5142, loss_cls: 4.0154, loss: 4.0154 +2024-07-17 05:17:48,630 - pyskl - INFO - Epoch [29][900/3746] lr: 9.151e-02, eta: 3 days, 20:12:00, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5095, loss_cls: 4.0616, loss: 4.0616 +2024-07-17 05:18:58,556 - pyskl - INFO - Epoch [29][1000/3746] lr: 9.149e-02, eta: 3 days, 20:10:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5273, loss_cls: 3.9798, loss: 3.9798 +2024-07-17 05:20:08,313 - pyskl - INFO - Epoch [29][1100/3746] lr: 9.148e-02, eta: 3 days, 20:09:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5186, loss_cls: 4.0183, loss: 4.0183 +2024-07-17 05:21:18,159 - pyskl - INFO - Epoch [29][1200/3746] lr: 9.146e-02, eta: 3 days, 20:07:43, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5061, loss_cls: 4.0326, loss: 4.0326 +2024-07-17 05:22:28,227 - pyskl - INFO - Epoch [29][1300/3746] lr: 9.144e-02, eta: 3 days, 20:06:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5262, loss_cls: 3.9895, loss: 3.9895 +2024-07-17 05:23:38,347 - pyskl - INFO - Epoch [29][1400/3746] lr: 9.143e-02, eta: 3 days, 20:04:55, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5145, loss_cls: 4.0054, loss: 4.0054 +2024-07-17 05:24:48,371 - pyskl - INFO - Epoch [29][1500/3746] lr: 9.141e-02, eta: 3 days, 20:03:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5247, loss_cls: 3.9719, loss: 3.9719 +2024-07-17 05:25:58,105 - pyskl - INFO - Epoch [29][1600/3746] lr: 9.140e-02, eta: 3 days, 20:02:05, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5244, loss_cls: 3.9890, loss: 3.9890 +2024-07-17 05:27:07,783 - pyskl - INFO - Epoch [29][1700/3746] lr: 9.138e-02, eta: 3 days, 20:00:39, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5109, loss_cls: 4.0401, loss: 4.0401 +2024-07-17 05:28:17,531 - pyskl - INFO - Epoch [29][1800/3746] lr: 9.137e-02, eta: 3 days, 19:59:13, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5095, loss_cls: 4.0342, loss: 4.0342 +2024-07-17 05:29:27,556 - pyskl - INFO - Epoch [29][1900/3746] lr: 9.135e-02, eta: 3 days, 19:57:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5083, loss_cls: 4.0493, loss: 4.0493 +2024-07-17 05:30:37,941 - pyskl - INFO - Epoch [29][2000/3746] lr: 9.133e-02, eta: 3 days, 19:56:26, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5042, loss_cls: 4.0598, loss: 4.0598 +2024-07-17 05:31:47,841 - pyskl - INFO - Epoch [29][2100/3746] lr: 9.132e-02, eta: 3 days, 19:55:01, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5191, loss_cls: 4.0166, loss: 4.0166 +2024-07-17 05:32:57,854 - pyskl - INFO - Epoch [29][2200/3746] lr: 9.130e-02, eta: 3 days, 19:53:37, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5172, loss_cls: 4.0306, loss: 4.0306 +2024-07-17 05:34:07,754 - pyskl - INFO - Epoch [29][2300/3746] lr: 9.129e-02, eta: 3 days, 19:52:12, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5178, loss_cls: 4.0110, loss: 4.0110 +2024-07-17 05:35:17,668 - pyskl - INFO - Epoch [29][2400/3746] lr: 9.127e-02, eta: 3 days, 19:50:48, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5108, loss_cls: 4.0560, loss: 4.0560 +2024-07-17 05:36:27,541 - pyskl - INFO - Epoch [29][2500/3746] lr: 9.126e-02, eta: 3 days, 19:49:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5166, loss_cls: 4.0204, loss: 4.0204 +2024-07-17 05:37:37,146 - pyskl - INFO - Epoch [29][2600/3746] lr: 9.124e-02, eta: 3 days, 19:47:57, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5070, loss_cls: 4.0597, loss: 4.0597 +2024-07-17 05:38:46,902 - pyskl - INFO - Epoch [29][2700/3746] lr: 9.122e-02, eta: 3 days, 19:46:32, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5336, loss_cls: 3.9634, loss: 3.9634 +2024-07-17 05:39:56,767 - pyskl - INFO - Epoch [29][2800/3746] lr: 9.121e-02, eta: 3 days, 19:45:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5111, loss_cls: 4.0169, loss: 4.0169 +2024-07-17 05:41:06,586 - pyskl - INFO - Epoch [29][2900/3746] lr: 9.119e-02, eta: 3 days, 19:43:42, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5195, loss_cls: 4.0084, loss: 4.0084 +2024-07-17 05:42:16,355 - pyskl - INFO - Epoch [29][3000/3746] lr: 9.118e-02, eta: 3 days, 19:42:17, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5252, loss_cls: 3.9735, loss: 3.9735 +2024-07-17 05:43:26,151 - pyskl - INFO - Epoch [29][3100/3746] lr: 9.116e-02, eta: 3 days, 19:40:52, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5184, loss_cls: 4.0291, loss: 4.0291 +2024-07-17 05:44:35,783 - pyskl - INFO - Epoch [29][3200/3746] lr: 9.114e-02, eta: 3 days, 19:39:26, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5205, loss_cls: 4.0058, loss: 4.0058 +2024-07-17 05:45:45,559 - pyskl - INFO - Epoch [29][3300/3746] lr: 9.113e-02, eta: 3 days, 19:38:01, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5047, loss_cls: 4.0343, loss: 4.0343 +2024-07-17 05:46:55,624 - pyskl - INFO - Epoch [29][3400/3746] lr: 9.111e-02, eta: 3 days, 19:36:37, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5161, loss_cls: 4.0060, loss: 4.0060 +2024-07-17 05:48:05,376 - pyskl - INFO - Epoch [29][3500/3746] lr: 9.110e-02, eta: 3 days, 19:35:12, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5295, loss_cls: 3.9971, loss: 3.9971 +2024-07-17 05:49:15,213 - pyskl - INFO - Epoch [29][3600/3746] lr: 9.108e-02, eta: 3 days, 19:33:48, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5077, loss_cls: 4.0567, loss: 4.0567 +2024-07-17 05:50:25,446 - pyskl - INFO - Epoch [29][3700/3746] lr: 9.106e-02, eta: 3 days, 19:32:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5248, loss_cls: 4.0227, loss: 4.0227 +2024-07-17 05:50:59,949 - pyskl - INFO - Saving checkpoint at 29 epochs +2024-07-17 05:52:50,039 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 05:52:50,700 - pyskl - INFO - +top1_acc 0.1640 +top5_acc 0.3822 +2024-07-17 05:52:50,700 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 05:52:50,740 - pyskl - INFO - +mean_acc 0.1641 +2024-07-17 05:52:50,751 - pyskl - INFO - Epoch(val) [29][309] top1_acc: 0.1640, top5_acc: 0.3822, mean_class_accuracy: 0.1641 +2024-07-17 05:56:19,641 - pyskl - INFO - Epoch [30][100/3746] lr: 9.104e-02, eta: 3 days, 19:37:47, time: 2.089, data_time: 1.283, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5275, loss_cls: 3.9620, loss: 3.9620 +2024-07-17 05:57:41,338 - pyskl - INFO - Epoch [30][200/3746] lr: 9.103e-02, eta: 3 days, 19:37:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5211, loss_cls: 3.9815, loss: 3.9815 +2024-07-17 05:59:02,606 - pyskl - INFO - Epoch [30][300/3746] lr: 9.101e-02, eta: 3 days, 19:36:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5361, loss_cls: 3.9453, loss: 3.9453 +2024-07-17 06:00:23,113 - pyskl - INFO - Epoch [30][400/3746] lr: 9.099e-02, eta: 3 days, 19:35:52, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5197, loss_cls: 4.0177, loss: 4.0177 +2024-07-17 06:01:43,671 - pyskl - INFO - Epoch [30][500/3746] lr: 9.098e-02, eta: 3 days, 19:35:12, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5147, loss_cls: 4.0207, loss: 4.0207 +2024-07-17 06:03:04,375 - pyskl - INFO - Epoch [30][600/3746] lr: 9.096e-02, eta: 3 days, 19:34:32, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5122, loss_cls: 3.9996, loss: 3.9996 +2024-07-17 06:04:24,831 - pyskl - INFO - Epoch [30][700/3746] lr: 9.095e-02, eta: 3 days, 19:33:50, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5236, loss_cls: 3.9894, loss: 3.9894 +2024-07-17 06:05:44,714 - pyskl - INFO - Epoch [30][800/3746] lr: 9.093e-02, eta: 3 days, 19:33:07, time: 0.799, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5167, loss_cls: 3.9984, loss: 3.9984 +2024-07-17 06:07:04,666 - pyskl - INFO - Epoch [30][900/3746] lr: 9.091e-02, eta: 3 days, 19:32:23, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5111, loss_cls: 4.0332, loss: 4.0332 +2024-07-17 06:08:25,154 - pyskl - INFO - Epoch [30][1000/3746] lr: 9.090e-02, eta: 3 days, 19:31:42, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5172, loss_cls: 3.9844, loss: 3.9844 +2024-07-17 06:09:45,417 - pyskl - INFO - Epoch [30][1100/3746] lr: 9.088e-02, eta: 3 days, 19:30:59, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5250, loss_cls: 4.0036, loss: 4.0036 +2024-07-17 06:11:05,447 - pyskl - INFO - Epoch [30][1200/3746] lr: 9.087e-02, eta: 3 days, 19:30:16, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5289, loss_cls: 3.9859, loss: 3.9859 +2024-07-17 06:12:26,250 - pyskl - INFO - Epoch [30][1300/3746] lr: 9.085e-02, eta: 3 days, 19:29:36, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5192, loss_cls: 4.0220, loss: 4.0220 +2024-07-17 06:13:46,976 - pyskl - INFO - Epoch [30][1400/3746] lr: 9.083e-02, eta: 3 days, 19:28:55, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5164, loss_cls: 3.9966, loss: 3.9966 +2024-07-17 06:15:07,069 - pyskl - INFO - Epoch [30][1500/3746] lr: 9.082e-02, eta: 3 days, 19:28:12, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5164, loss_cls: 3.9933, loss: 3.9933 +2024-07-17 06:16:27,688 - pyskl - INFO - Epoch [30][1600/3746] lr: 9.080e-02, eta: 3 days, 19:27:31, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5130, loss_cls: 4.0353, loss: 4.0353 +2024-07-17 06:17:47,974 - pyskl - INFO - Epoch [30][1700/3746] lr: 9.078e-02, eta: 3 days, 19:26:48, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5127, loss_cls: 4.0344, loss: 4.0344 +2024-07-17 06:19:08,173 - pyskl - INFO - Epoch [30][1800/3746] lr: 9.077e-02, eta: 3 days, 19:26:05, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5147, loss_cls: 4.0090, loss: 4.0090 +2024-07-17 06:20:28,354 - pyskl - INFO - Epoch [30][1900/3746] lr: 9.075e-02, eta: 3 days, 19:25:22, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5203, loss_cls: 4.0141, loss: 4.0141 +2024-07-17 06:21:49,093 - pyskl - INFO - Epoch [30][2000/3746] lr: 9.074e-02, eta: 3 days, 19:24:41, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5056, loss_cls: 4.0930, loss: 4.0930 +2024-07-17 06:23:09,556 - pyskl - INFO - Epoch [30][2100/3746] lr: 9.072e-02, eta: 3 days, 19:23:58, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5248, loss_cls: 3.9540, loss: 3.9540 +2024-07-17 06:24:29,834 - pyskl - INFO - Epoch [30][2200/3746] lr: 9.070e-02, eta: 3 days, 19:23:15, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5098, loss_cls: 4.0539, loss: 4.0539 +2024-07-17 06:25:50,011 - pyskl - INFO - Epoch [30][2300/3746] lr: 9.069e-02, eta: 3 days, 19:22:32, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5212, loss_cls: 4.0140, loss: 4.0140 +2024-07-17 06:27:10,460 - pyskl - INFO - Epoch [30][2400/3746] lr: 9.067e-02, eta: 3 days, 19:21:49, time: 0.804, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5206, loss_cls: 3.9944, loss: 3.9944 +2024-07-17 06:28:30,644 - pyskl - INFO - Epoch [30][2500/3746] lr: 9.065e-02, eta: 3 days, 19:21:06, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5128, loss_cls: 4.0205, loss: 4.0205 +2024-07-17 06:29:51,459 - pyskl - INFO - Epoch [30][2600/3746] lr: 9.064e-02, eta: 3 days, 19:20:25, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5130, loss_cls: 4.0441, loss: 4.0441 +2024-07-17 06:31:11,978 - pyskl - INFO - Epoch [30][2700/3746] lr: 9.062e-02, eta: 3 days, 19:19:42, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5188, loss_cls: 4.0348, loss: 4.0348 +2024-07-17 06:32:33,224 - pyskl - INFO - Epoch [30][2800/3746] lr: 9.061e-02, eta: 3 days, 19:19:03, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5128, loss_cls: 4.0409, loss: 4.0409 +2024-07-17 06:33:53,531 - pyskl - INFO - Epoch [30][2900/3746] lr: 9.059e-02, eta: 3 days, 19:18:19, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5100, loss_cls: 4.0140, loss: 4.0140 +2024-07-17 06:35:13,785 - pyskl - INFO - Epoch [30][3000/3746] lr: 9.057e-02, eta: 3 days, 19:17:36, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5278, loss_cls: 3.9540, loss: 3.9540 +2024-07-17 06:36:34,277 - pyskl - INFO - Epoch [30][3100/3746] lr: 9.056e-02, eta: 3 days, 19:16:53, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5186, loss_cls: 4.0120, loss: 4.0120 +2024-07-17 06:37:54,932 - pyskl - INFO - Epoch [30][3200/3746] lr: 9.054e-02, eta: 3 days, 19:16:11, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5170, loss_cls: 4.0078, loss: 4.0078 +2024-07-17 06:39:15,071 - pyskl - INFO - Epoch [30][3300/3746] lr: 9.052e-02, eta: 3 days, 19:15:26, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5120, loss_cls: 4.0082, loss: 4.0082 +2024-07-17 06:40:34,988 - pyskl - INFO - Epoch [30][3400/3746] lr: 9.051e-02, eta: 3 days, 19:14:41, time: 0.799, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5334, loss_cls: 3.9358, loss: 3.9358 +2024-07-17 06:41:55,713 - pyskl - INFO - Epoch [30][3500/3746] lr: 9.049e-02, eta: 3 days, 19:13:59, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5248, loss_cls: 3.9732, loss: 3.9732 +2024-07-17 06:43:15,964 - pyskl - INFO - Epoch [30][3600/3746] lr: 9.047e-02, eta: 3 days, 19:13:15, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5138, loss_cls: 4.0409, loss: 4.0409 +2024-07-17 06:44:36,465 - pyskl - INFO - Epoch [30][3700/3746] lr: 9.046e-02, eta: 3 days, 19:12:32, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5125, loss_cls: 4.0344, loss: 4.0344 +2024-07-17 06:45:15,378 - pyskl - INFO - Saving checkpoint at 30 epochs +2024-07-17 06:47:05,473 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 06:47:06,144 - pyskl - INFO - +top1_acc 0.1670 +top5_acc 0.3917 +2024-07-17 06:47:06,144 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 06:47:06,184 - pyskl - INFO - +mean_acc 0.1670 +2024-07-17 06:47:06,196 - pyskl - INFO - Epoch(val) [30][309] top1_acc: 0.1670, top5_acc: 0.3917, mean_class_accuracy: 0.1670 +2024-07-17 06:50:57,656 - pyskl - INFO - Epoch [31][100/3746] lr: 9.043e-02, eta: 3 days, 19:19:04, time: 2.314, data_time: 1.334, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5400, loss_cls: 4.1308, loss: 4.1308 +2024-07-17 06:52:20,322 - pyskl - INFO - Epoch [31][200/3746] lr: 9.042e-02, eta: 3 days, 19:18:29, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5148, loss_cls: 4.2189, loss: 4.2189 +2024-07-17 06:53:43,343 - pyskl - INFO - Epoch [31][300/3746] lr: 9.040e-02, eta: 3 days, 19:17:55, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5384, loss_cls: 4.1683, loss: 4.1683 +2024-07-17 06:55:05,208 - pyskl - INFO - Epoch [31][400/3746] lr: 9.039e-02, eta: 3 days, 19:17:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5147, loss_cls: 4.2258, loss: 4.2258 +2024-07-17 06:56:27,680 - pyskl - INFO - Epoch [31][500/3746] lr: 9.037e-02, eta: 3 days, 19:16:40, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5247, loss_cls: 4.2022, loss: 4.2022 +2024-07-17 06:57:50,591 - pyskl - INFO - Epoch [31][600/3746] lr: 9.035e-02, eta: 3 days, 19:16:06, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5181, loss_cls: 4.1990, loss: 4.1990 +2024-07-17 06:59:13,222 - pyskl - INFO - Epoch [31][700/3746] lr: 9.034e-02, eta: 3 days, 19:15:30, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5339, loss_cls: 4.1559, loss: 4.1559 +2024-07-17 07:00:35,952 - pyskl - INFO - Epoch [31][800/3746] lr: 9.032e-02, eta: 3 days, 19:14:55, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5161, loss_cls: 4.2399, loss: 4.2399 +2024-07-17 07:01:57,784 - pyskl - INFO - Epoch [31][900/3746] lr: 9.030e-02, eta: 3 days, 19:14:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5214, loss_cls: 4.2209, loss: 4.2209 +2024-07-17 07:03:20,060 - pyskl - INFO - Epoch [31][1000/3746] lr: 9.029e-02, eta: 3 days, 19:13:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5131, loss_cls: 4.2492, loss: 4.2492 +2024-07-17 07:04:41,740 - pyskl - INFO - Epoch [31][1100/3746] lr: 9.027e-02, eta: 3 days, 19:12:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5208, loss_cls: 4.2030, loss: 4.2030 +2024-07-17 07:06:03,374 - pyskl - INFO - Epoch [31][1200/3746] lr: 9.025e-02, eta: 3 days, 19:12:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5175, loss_cls: 4.2115, loss: 4.2115 +2024-07-17 07:07:25,092 - pyskl - INFO - Epoch [31][1300/3746] lr: 9.024e-02, eta: 3 days, 19:11:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5103, loss_cls: 4.2545, loss: 4.2545 +2024-07-17 07:08:47,756 - pyskl - INFO - Epoch [31][1400/3746] lr: 9.022e-02, eta: 3 days, 19:11:02, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5312, loss_cls: 4.1810, loss: 4.1810 +2024-07-17 07:10:09,857 - pyskl - INFO - Epoch [31][1500/3746] lr: 9.020e-02, eta: 3 days, 19:10:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5153, loss_cls: 4.2253, loss: 4.2253 +2024-07-17 07:11:32,832 - pyskl - INFO - Epoch [31][1600/3746] lr: 9.019e-02, eta: 3 days, 19:09:49, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5172, loss_cls: 4.2012, loss: 4.2012 +2024-07-17 07:12:55,106 - pyskl - INFO - Epoch [31][1700/3746] lr: 9.017e-02, eta: 3 days, 19:09:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5116, loss_cls: 4.2840, loss: 4.2840 +2024-07-17 07:14:18,039 - pyskl - INFO - Epoch [31][1800/3746] lr: 9.015e-02, eta: 3 days, 19:08:35, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5147, loss_cls: 4.2343, loss: 4.2343 +2024-07-17 07:15:40,572 - pyskl - INFO - Epoch [31][1900/3746] lr: 9.014e-02, eta: 3 days, 19:07:58, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5202, loss_cls: 4.2198, loss: 4.2198 +2024-07-17 07:17:03,696 - pyskl - INFO - Epoch [31][2000/3746] lr: 9.012e-02, eta: 3 days, 19:07:23, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5234, loss_cls: 4.1890, loss: 4.1890 +2024-07-17 07:18:26,084 - pyskl - INFO - Epoch [31][2100/3746] lr: 9.010e-02, eta: 3 days, 19:06:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5225, loss_cls: 4.2239, loss: 4.2239 +2024-07-17 07:19:47,893 - pyskl - INFO - Epoch [31][2200/3746] lr: 9.009e-02, eta: 3 days, 19:06:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5198, loss_cls: 4.1972, loss: 4.1972 +2024-07-17 07:21:11,041 - pyskl - INFO - Epoch [31][2300/3746] lr: 9.007e-02, eta: 3 days, 19:05:31, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5241, loss_cls: 4.2220, loss: 4.2220 +2024-07-17 07:22:34,139 - pyskl - INFO - Epoch [31][2400/3746] lr: 9.005e-02, eta: 3 days, 19:04:55, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5112, loss_cls: 4.2366, loss: 4.2366 +2024-07-17 07:23:57,174 - pyskl - INFO - Epoch [31][2500/3746] lr: 9.004e-02, eta: 3 days, 19:04:20, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5142, loss_cls: 4.2331, loss: 4.2331 +2024-07-17 07:25:20,651 - pyskl - INFO - Epoch [31][2600/3746] lr: 9.002e-02, eta: 3 days, 19:03:46, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5166, loss_cls: 4.1987, loss: 4.1987 +2024-07-17 07:26:44,167 - pyskl - INFO - Epoch [31][2700/3746] lr: 9.000e-02, eta: 3 days, 19:03:12, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5184, loss_cls: 4.2286, loss: 4.2286 +2024-07-17 07:28:06,450 - pyskl - INFO - Epoch [31][2800/3746] lr: 8.999e-02, eta: 3 days, 19:02:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5272, loss_cls: 4.2089, loss: 4.2089 +2024-07-17 07:29:29,460 - pyskl - INFO - Epoch [31][2900/3746] lr: 8.997e-02, eta: 3 days, 19:01:57, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5159, loss_cls: 4.2452, loss: 4.2452 +2024-07-17 07:30:52,163 - pyskl - INFO - Epoch [31][3000/3746] lr: 8.995e-02, eta: 3 days, 19:01:20, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5127, loss_cls: 4.2575, loss: 4.2575 +2024-07-17 07:32:15,030 - pyskl - INFO - Epoch [31][3100/3746] lr: 8.994e-02, eta: 3 days, 19:00:43, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5198, loss_cls: 4.2405, loss: 4.2405 +2024-07-17 07:33:37,868 - pyskl - INFO - Epoch [31][3200/3746] lr: 8.992e-02, eta: 3 days, 19:00:06, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5331, loss_cls: 4.1600, loss: 4.1600 +2024-07-17 07:35:00,256 - pyskl - INFO - Epoch [31][3300/3746] lr: 8.990e-02, eta: 3 days, 18:59:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5161, loss_cls: 4.2331, loss: 4.2331 +2024-07-17 07:36:23,544 - pyskl - INFO - Epoch [31][3400/3746] lr: 8.989e-02, eta: 3 days, 18:58:52, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5200, loss_cls: 4.2124, loss: 4.2124 +2024-07-17 07:37:45,910 - pyskl - INFO - Epoch [31][3500/3746] lr: 8.987e-02, eta: 3 days, 18:58:13, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5236, loss_cls: 4.2133, loss: 4.2133 +2024-07-17 07:39:08,408 - pyskl - INFO - Epoch [31][3600/3746] lr: 8.985e-02, eta: 3 days, 18:57:34, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5314, loss_cls: 4.1955, loss: 4.1955 +2024-07-17 07:40:32,194 - pyskl - INFO - Epoch [31][3700/3746] lr: 8.983e-02, eta: 3 days, 18:57:01, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5144, loss_cls: 4.2844, loss: 4.2844 +2024-07-17 07:41:11,528 - pyskl - INFO - Saving checkpoint at 31 epochs +2024-07-17 07:43:02,706 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 07:43:03,381 - pyskl - INFO - +top1_acc 0.2047 +top5_acc 0.4339 +2024-07-17 07:43:03,381 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 07:43:03,421 - pyskl - INFO - +mean_acc 0.2046 +2024-07-17 07:43:03,434 - pyskl - INFO - Epoch(val) [31][309] top1_acc: 0.2047, top5_acc: 0.4339, mean_class_accuracy: 0.2046 +2024-07-17 07:46:48,836 - pyskl - INFO - Epoch [32][100/3746] lr: 8.981e-02, eta: 3 days, 19:02:47, time: 2.254, data_time: 1.271, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5306, loss_cls: 4.1783, loss: 4.1783 +2024-07-17 07:48:11,634 - pyskl - INFO - Epoch [32][200/3746] lr: 8.979e-02, eta: 3 days, 19:02:09, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5345, loss_cls: 4.1666, loss: 4.1666 +2024-07-17 07:49:34,810 - pyskl - INFO - Epoch [32][300/3746] lr: 8.978e-02, eta: 3 days, 19:01:32, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5188, loss_cls: 4.2346, loss: 4.2346 +2024-07-17 07:50:57,296 - pyskl - INFO - Epoch [32][400/3746] lr: 8.976e-02, eta: 3 days, 19:00:52, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5194, loss_cls: 4.2011, loss: 4.2011 +2024-07-17 07:52:20,229 - pyskl - INFO - Epoch [32][500/3746] lr: 8.974e-02, eta: 3 days, 19:00:15, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5419, loss_cls: 4.1292, loss: 4.1292 +2024-07-17 07:53:43,252 - pyskl - INFO - Epoch [32][600/3746] lr: 8.973e-02, eta: 3 days, 18:59:37, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5277, loss_cls: 4.1798, loss: 4.1798 +2024-07-17 07:55:06,279 - pyskl - INFO - Epoch [32][700/3746] lr: 8.971e-02, eta: 3 days, 18:58:59, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5228, loss_cls: 4.1910, loss: 4.1910 +2024-07-17 07:56:28,215 - pyskl - INFO - Epoch [32][800/3746] lr: 8.969e-02, eta: 3 days, 18:58:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5269, loss_cls: 4.1884, loss: 4.1884 +2024-07-17 07:57:49,510 - pyskl - INFO - Epoch [32][900/3746] lr: 8.967e-02, eta: 3 days, 18:57:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5239, loss_cls: 4.1973, loss: 4.1973 +2024-07-17 07:59:11,065 - pyskl - INFO - Epoch [32][1000/3746] lr: 8.966e-02, eta: 3 days, 18:56:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5094, loss_cls: 4.2681, loss: 4.2681 +2024-07-17 08:00:32,847 - pyskl - INFO - Epoch [32][1100/3746] lr: 8.964e-02, eta: 3 days, 18:56:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5203, loss_cls: 4.2077, loss: 4.2077 +2024-07-17 08:01:54,214 - pyskl - INFO - Epoch [32][1200/3746] lr: 8.962e-02, eta: 3 days, 18:55:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5125, loss_cls: 4.2622, loss: 4.2622 +2024-07-17 08:03:15,738 - pyskl - INFO - Epoch [32][1300/3746] lr: 8.961e-02, eta: 3 days, 18:54:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5161, loss_cls: 4.2687, loss: 4.2687 +2024-07-17 08:04:36,735 - pyskl - INFO - Epoch [32][1400/3746] lr: 8.959e-02, eta: 3 days, 18:53:53, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5291, loss_cls: 4.1795, loss: 4.1795 +2024-07-17 08:05:58,069 - pyskl - INFO - Epoch [32][1500/3746] lr: 8.957e-02, eta: 3 days, 18:53:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5405, loss_cls: 4.1484, loss: 4.1484 +2024-07-17 08:07:19,724 - pyskl - INFO - Epoch [32][1600/3746] lr: 8.955e-02, eta: 3 days, 18:52:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5188, loss_cls: 4.2061, loss: 4.2061 +2024-07-17 08:08:41,744 - pyskl - INFO - Epoch [32][1700/3746] lr: 8.954e-02, eta: 3 days, 18:51:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5264, loss_cls: 4.2008, loss: 4.2008 +2024-07-17 08:10:03,783 - pyskl - INFO - Epoch [32][1800/3746] lr: 8.952e-02, eta: 3 days, 18:51:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5177, loss_cls: 4.2443, loss: 4.2443 +2024-07-17 08:11:24,653 - pyskl - INFO - Epoch [32][1900/3746] lr: 8.950e-02, eta: 3 days, 18:50:15, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5181, loss_cls: 4.2224, loss: 4.2224 +2024-07-17 08:12:45,753 - pyskl - INFO - Epoch [32][2000/3746] lr: 8.949e-02, eta: 3 days, 18:49:29, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5189, loss_cls: 4.2021, loss: 4.2021 +2024-07-17 08:14:07,417 - pyskl - INFO - Epoch [32][2100/3746] lr: 8.947e-02, eta: 3 days, 18:48:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5152, loss_cls: 4.2302, loss: 4.2302 +2024-07-17 08:15:29,676 - pyskl - INFO - Epoch [32][2200/3746] lr: 8.945e-02, eta: 3 days, 18:48:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5155, loss_cls: 4.2250, loss: 4.2250 +2024-07-17 08:16:51,758 - pyskl - INFO - Epoch [32][2300/3746] lr: 8.943e-02, eta: 3 days, 18:47:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5238, loss_cls: 4.2282, loss: 4.2282 +2024-07-17 08:18:13,065 - pyskl - INFO - Epoch [32][2400/3746] lr: 8.942e-02, eta: 3 days, 18:46:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5186, loss_cls: 4.2121, loss: 4.2121 +2024-07-17 08:19:34,849 - pyskl - INFO - Epoch [32][2500/3746] lr: 8.940e-02, eta: 3 days, 18:45:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5180, loss_cls: 4.2366, loss: 4.2366 +2024-07-17 08:20:56,688 - pyskl - INFO - Epoch [32][2600/3746] lr: 8.938e-02, eta: 3 days, 18:45:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5177, loss_cls: 4.2441, loss: 4.2441 +2024-07-17 08:22:18,264 - pyskl - INFO - Epoch [32][2700/3746] lr: 8.937e-02, eta: 3 days, 18:44:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5164, loss_cls: 4.2240, loss: 4.2240 +2024-07-17 08:23:40,183 - pyskl - INFO - Epoch [32][2800/3746] lr: 8.935e-02, eta: 3 days, 18:43:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5181, loss_cls: 4.2591, loss: 4.2591 +2024-07-17 08:25:01,761 - pyskl - INFO - Epoch [32][2900/3746] lr: 8.933e-02, eta: 3 days, 18:42:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5184, loss_cls: 4.2386, loss: 4.2386 +2024-07-17 08:26:23,501 - pyskl - INFO - Epoch [32][3000/3746] lr: 8.931e-02, eta: 3 days, 18:42:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5220, loss_cls: 4.1952, loss: 4.1952 +2024-07-17 08:27:45,292 - pyskl - INFO - Epoch [32][3100/3746] lr: 8.930e-02, eta: 3 days, 18:41:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5291, loss_cls: 4.1655, loss: 4.1655 +2024-07-17 08:29:06,920 - pyskl - INFO - Epoch [32][3200/3746] lr: 8.928e-02, eta: 3 days, 18:40:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5173, loss_cls: 4.2453, loss: 4.2453 +2024-07-17 08:30:27,973 - pyskl - INFO - Epoch [32][3300/3746] lr: 8.926e-02, eta: 3 days, 18:39:58, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5178, loss_cls: 4.2121, loss: 4.2121 +2024-07-17 08:31:49,257 - pyskl - INFO - Epoch [32][3400/3746] lr: 8.924e-02, eta: 3 days, 18:39:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5139, loss_cls: 4.2379, loss: 4.2379 +2024-07-17 08:33:11,187 - pyskl - INFO - Epoch [32][3500/3746] lr: 8.923e-02, eta: 3 days, 18:38:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5194, loss_cls: 4.2039, loss: 4.2039 +2024-07-17 08:34:33,027 - pyskl - INFO - Epoch [32][3600/3746] lr: 8.921e-02, eta: 3 days, 18:37:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5145, loss_cls: 4.2576, loss: 4.2576 +2024-07-17 08:35:54,862 - pyskl - INFO - Epoch [32][3700/3746] lr: 8.919e-02, eta: 3 days, 18:37:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5294, loss_cls: 4.1825, loss: 4.1825 +2024-07-17 08:36:34,204 - pyskl - INFO - Saving checkpoint at 32 epochs +2024-07-17 08:38:23,636 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 08:38:24,307 - pyskl - INFO - +top1_acc 0.1963 +top5_acc 0.4237 +2024-07-17 08:38:24,307 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 08:38:24,350 - pyskl - INFO - +mean_acc 0.1963 +2024-07-17 08:38:24,363 - pyskl - INFO - Epoch(val) [32][309] top1_acc: 0.1963, top5_acc: 0.4237, mean_class_accuracy: 0.1963 +2024-07-17 08:42:02,984 - pyskl - INFO - Epoch [33][100/3746] lr: 8.917e-02, eta: 3 days, 18:42:01, time: 2.186, data_time: 1.200, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5288, loss_cls: 4.1579, loss: 4.1579 +2024-07-17 08:43:25,353 - pyskl - INFO - Epoch [33][200/3746] lr: 8.915e-02, eta: 3 days, 18:41:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5233, loss_cls: 4.2169, loss: 4.2169 +2024-07-17 08:44:47,468 - pyskl - INFO - Epoch [33][300/3746] lr: 8.913e-02, eta: 3 days, 18:40:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5281, loss_cls: 4.1554, loss: 4.1554 +2024-07-17 08:46:09,676 - pyskl - INFO - Epoch [33][400/3746] lr: 8.912e-02, eta: 3 days, 18:39:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5309, loss_cls: 4.1618, loss: 4.1618 +2024-07-17 08:47:32,122 - pyskl - INFO - Epoch [33][500/3746] lr: 8.910e-02, eta: 3 days, 18:39:08, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5188, loss_cls: 4.2332, loss: 4.2332 +2024-07-17 08:48:54,681 - pyskl - INFO - Epoch [33][600/3746] lr: 8.908e-02, eta: 3 days, 18:38:26, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5364, loss_cls: 4.1422, loss: 4.1422 +2024-07-17 08:50:16,973 - pyskl - INFO - Epoch [33][700/3746] lr: 8.906e-02, eta: 3 days, 18:37:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5373, loss_cls: 4.1965, loss: 4.1965 +2024-07-17 08:51:38,987 - pyskl - INFO - Epoch [33][800/3746] lr: 8.905e-02, eta: 3 days, 18:36:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5205, loss_cls: 4.1853, loss: 4.1853 +2024-07-17 08:53:00,904 - pyskl - INFO - Epoch [33][900/3746] lr: 8.903e-02, eta: 3 days, 18:36:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5180, loss_cls: 4.2117, loss: 4.2117 +2024-07-17 08:54:22,212 - pyskl - INFO - Epoch [33][1000/3746] lr: 8.901e-02, eta: 3 days, 18:35:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5150, loss_cls: 4.2557, loss: 4.2557 +2024-07-17 08:55:43,521 - pyskl - INFO - Epoch [33][1100/3746] lr: 8.899e-02, eta: 3 days, 18:34:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5172, loss_cls: 4.2223, loss: 4.2223 +2024-07-17 08:57:05,244 - pyskl - INFO - Epoch [33][1200/3746] lr: 8.898e-02, eta: 3 days, 18:33:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5262, loss_cls: 4.1713, loss: 4.1713 +2024-07-17 08:58:26,754 - pyskl - INFO - Epoch [33][1300/3746] lr: 8.896e-02, eta: 3 days, 18:33:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5181, loss_cls: 4.2136, loss: 4.2136 +2024-07-17 08:59:48,366 - pyskl - INFO - Epoch [33][1400/3746] lr: 8.894e-02, eta: 3 days, 18:32:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5302, loss_cls: 4.1874, loss: 4.1874 +2024-07-17 09:01:09,763 - pyskl - INFO - Epoch [33][1500/3746] lr: 8.892e-02, eta: 3 days, 18:31:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5220, loss_cls: 4.2081, loss: 4.2081 +2024-07-17 09:02:31,559 - pyskl - INFO - Epoch [33][1600/3746] lr: 8.891e-02, eta: 3 days, 18:30:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5252, loss_cls: 4.1794, loss: 4.1794 +2024-07-17 09:03:53,229 - pyskl - INFO - Epoch [33][1700/3746] lr: 8.889e-02, eta: 3 days, 18:30:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5309, loss_cls: 4.1970, loss: 4.1970 +2024-07-17 09:05:14,483 - pyskl - INFO - Epoch [33][1800/3746] lr: 8.887e-02, eta: 3 days, 18:29:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5133, loss_cls: 4.2233, loss: 4.2233 +2024-07-17 09:06:36,135 - pyskl - INFO - Epoch [33][1900/3746] lr: 8.885e-02, eta: 3 days, 18:28:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5189, loss_cls: 4.2024, loss: 4.2024 +2024-07-17 09:07:57,643 - pyskl - INFO - Epoch [33][2000/3746] lr: 8.884e-02, eta: 3 days, 18:27:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5169, loss_cls: 4.2005, loss: 4.2005 +2024-07-17 09:09:19,020 - pyskl - INFO - Epoch [33][2100/3746] lr: 8.882e-02, eta: 3 days, 18:26:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5162, loss_cls: 4.2203, loss: 4.2203 +2024-07-17 09:10:40,830 - pyskl - INFO - Epoch [33][2200/3746] lr: 8.880e-02, eta: 3 days, 18:26:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5253, loss_cls: 4.1865, loss: 4.1865 +2024-07-17 09:12:02,459 - pyskl - INFO - Epoch [33][2300/3746] lr: 8.878e-02, eta: 3 days, 18:25:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5181, loss_cls: 4.2011, loss: 4.2011 +2024-07-17 09:13:23,969 - pyskl - INFO - Epoch [33][2400/3746] lr: 8.876e-02, eta: 3 days, 18:24:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5255, loss_cls: 4.1883, loss: 4.1883 +2024-07-17 09:14:45,389 - pyskl - INFO - Epoch [33][2500/3746] lr: 8.875e-02, eta: 3 days, 18:23:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5281, loss_cls: 4.1654, loss: 4.1654 +2024-07-17 09:16:07,168 - pyskl - INFO - Epoch [33][2600/3746] lr: 8.873e-02, eta: 3 days, 18:23:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5200, loss_cls: 4.2372, loss: 4.2372 +2024-07-17 09:17:29,352 - pyskl - INFO - Epoch [33][2700/3746] lr: 8.871e-02, eta: 3 days, 18:22:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5312, loss_cls: 4.1849, loss: 4.1849 +2024-07-17 09:18:50,806 - pyskl - INFO - Epoch [33][2800/3746] lr: 8.869e-02, eta: 3 days, 18:21:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5297, loss_cls: 4.1969, loss: 4.1969 +2024-07-17 09:20:12,274 - pyskl - INFO - Epoch [33][2900/3746] lr: 8.868e-02, eta: 3 days, 18:20:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5164, loss_cls: 4.2237, loss: 4.2237 +2024-07-17 09:21:34,062 - pyskl - INFO - Epoch [33][3000/3746] lr: 8.866e-02, eta: 3 days, 18:19:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5148, loss_cls: 4.2534, loss: 4.2534 +2024-07-17 09:22:55,363 - pyskl - INFO - Epoch [33][3100/3746] lr: 8.864e-02, eta: 3 days, 18:19:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5147, loss_cls: 4.2347, loss: 4.2347 +2024-07-17 09:24:17,147 - pyskl - INFO - Epoch [33][3200/3746] lr: 8.862e-02, eta: 3 days, 18:18:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5273, loss_cls: 4.1913, loss: 4.1913 +2024-07-17 09:25:38,727 - pyskl - INFO - Epoch [33][3300/3746] lr: 8.861e-02, eta: 3 days, 18:17:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5248, loss_cls: 4.1951, loss: 4.1951 +2024-07-17 09:26:59,811 - pyskl - INFO - Epoch [33][3400/3746] lr: 8.859e-02, eta: 3 days, 18:16:43, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5264, loss_cls: 4.1730, loss: 4.1730 +2024-07-17 09:28:21,536 - pyskl - INFO - Epoch [33][3500/3746] lr: 8.857e-02, eta: 3 days, 18:15:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5286, loss_cls: 4.2256, loss: 4.2256 +2024-07-17 09:29:42,847 - pyskl - INFO - Epoch [33][3600/3746] lr: 8.855e-02, eta: 3 days, 18:15:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5248, loss_cls: 4.1919, loss: 4.1919 +2024-07-17 09:31:05,148 - pyskl - INFO - Epoch [33][3700/3746] lr: 8.853e-02, eta: 3 days, 18:14:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5159, loss_cls: 4.2525, loss: 4.2525 +2024-07-17 09:31:44,598 - pyskl - INFO - Saving checkpoint at 33 epochs +2024-07-17 09:33:34,593 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 09:33:35,254 - pyskl - INFO - +top1_acc 0.1965 +top5_acc 0.4291 +2024-07-17 09:33:35,254 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 09:33:35,293 - pyskl - INFO - +mean_acc 0.1965 +2024-07-17 09:33:35,303 - pyskl - INFO - Epoch(val) [33][309] top1_acc: 0.1965, top5_acc: 0.4291, mean_class_accuracy: 0.1965 +2024-07-17 09:37:13,312 - pyskl - INFO - Epoch [34][100/3746] lr: 8.851e-02, eta: 3 days, 18:19:03, time: 2.180, data_time: 1.210, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5311, loss_cls: 4.1662, loss: 4.1662 +2024-07-17 09:38:35,018 - pyskl - INFO - Epoch [34][200/3746] lr: 8.849e-02, eta: 3 days, 18:18:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5206, loss_cls: 4.2012, loss: 4.2012 +2024-07-17 09:39:57,362 - pyskl - INFO - Epoch [34][300/3746] lr: 8.847e-02, eta: 3 days, 18:17:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5364, loss_cls: 4.1275, loss: 4.1275 +2024-07-17 09:41:19,781 - pyskl - INFO - Epoch [34][400/3746] lr: 8.845e-02, eta: 3 days, 18:16:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5112, loss_cls: 4.2354, loss: 4.2354 +2024-07-17 09:42:42,114 - pyskl - INFO - Epoch [34][500/3746] lr: 8.844e-02, eta: 3 days, 18:15:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5320, loss_cls: 4.1753, loss: 4.1753 +2024-07-17 09:44:05,010 - pyskl - INFO - Epoch [34][600/3746] lr: 8.842e-02, eta: 3 days, 18:15:15, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5186, loss_cls: 4.2190, loss: 4.2190 +2024-07-17 09:45:27,849 - pyskl - INFO - Epoch [34][700/3746] lr: 8.840e-02, eta: 3 days, 18:14:31, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5164, loss_cls: 4.2354, loss: 4.2354 +2024-07-17 09:46:49,871 - pyskl - INFO - Epoch [34][800/3746] lr: 8.838e-02, eta: 3 days, 18:13:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5345, loss_cls: 4.1545, loss: 4.1545 +2024-07-17 09:48:12,256 - pyskl - INFO - Epoch [34][900/3746] lr: 8.836e-02, eta: 3 days, 18:12:58, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5252, loss_cls: 4.1744, loss: 4.1744 +2024-07-17 09:49:34,099 - pyskl - INFO - Epoch [34][1000/3746] lr: 8.835e-02, eta: 3 days, 18:12:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5222, loss_cls: 4.1737, loss: 4.1737 +2024-07-17 09:50:55,971 - pyskl - INFO - Epoch [34][1100/3746] lr: 8.833e-02, eta: 3 days, 18:11:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5286, loss_cls: 4.1796, loss: 4.1796 +2024-07-17 09:52:17,839 - pyskl - INFO - Epoch [34][1200/3746] lr: 8.831e-02, eta: 3 days, 18:10:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5250, loss_cls: 4.1760, loss: 4.1760 +2024-07-17 09:53:39,324 - pyskl - INFO - Epoch [34][1300/3746] lr: 8.829e-02, eta: 3 days, 18:09:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5264, loss_cls: 4.2134, loss: 4.2134 +2024-07-17 09:55:00,506 - pyskl - INFO - Epoch [34][1400/3746] lr: 8.828e-02, eta: 3 days, 18:08:56, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5323, loss_cls: 4.1855, loss: 4.1855 +2024-07-17 09:56:22,561 - pyskl - INFO - Epoch [34][1500/3746] lr: 8.826e-02, eta: 3 days, 18:08:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5220, loss_cls: 4.2156, loss: 4.2156 +2024-07-17 09:57:44,203 - pyskl - INFO - Epoch [34][1600/3746] lr: 8.824e-02, eta: 3 days, 18:07:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5262, loss_cls: 4.1960, loss: 4.1960 +2024-07-17 09:59:05,348 - pyskl - INFO - Epoch [34][1700/3746] lr: 8.822e-02, eta: 3 days, 18:06:30, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5253, loss_cls: 4.2143, loss: 4.2143 +2024-07-17 10:00:27,182 - pyskl - INFO - Epoch [34][1800/3746] lr: 8.820e-02, eta: 3 days, 18:05:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5208, loss_cls: 4.1957, loss: 4.1957 +2024-07-17 10:01:48,780 - pyskl - INFO - Epoch [34][1900/3746] lr: 8.819e-02, eta: 3 days, 18:04:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5220, loss_cls: 4.1925, loss: 4.1925 +2024-07-17 10:03:10,557 - pyskl - INFO - Epoch [34][2000/3746] lr: 8.817e-02, eta: 3 days, 18:04:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5333, loss_cls: 4.1216, loss: 4.1216 +2024-07-17 10:04:32,080 - pyskl - INFO - Epoch [34][2100/3746] lr: 8.815e-02, eta: 3 days, 18:03:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5222, loss_cls: 4.2029, loss: 4.2029 +2024-07-17 10:05:53,627 - pyskl - INFO - Epoch [34][2200/3746] lr: 8.813e-02, eta: 3 days, 18:02:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5330, loss_cls: 4.1861, loss: 4.1861 +2024-07-17 10:07:15,130 - pyskl - INFO - Epoch [34][2300/3746] lr: 8.811e-02, eta: 3 days, 18:01:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5084, loss_cls: 4.2376, loss: 4.2376 +2024-07-17 10:08:36,533 - pyskl - INFO - Epoch [34][2400/3746] lr: 8.809e-02, eta: 3 days, 18:00:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5066, loss_cls: 4.2481, loss: 4.2481 +2024-07-17 10:09:58,792 - pyskl - INFO - Epoch [34][2500/3746] lr: 8.808e-02, eta: 3 days, 18:00:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5386, loss_cls: 4.1502, loss: 4.1502 +2024-07-17 10:11:20,392 - pyskl - INFO - Epoch [34][2600/3746] lr: 8.806e-02, eta: 3 days, 17:59:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5184, loss_cls: 4.1927, loss: 4.1927 +2024-07-17 10:12:42,324 - pyskl - INFO - Epoch [34][2700/3746] lr: 8.804e-02, eta: 3 days, 17:58:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5206, loss_cls: 4.2101, loss: 4.2101 +2024-07-17 10:14:03,595 - pyskl - INFO - Epoch [34][2800/3746] lr: 8.802e-02, eta: 3 days, 17:57:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5173, loss_cls: 4.2206, loss: 4.2206 +2024-07-17 10:15:25,109 - pyskl - INFO - Epoch [34][2900/3746] lr: 8.800e-02, eta: 3 days, 17:56:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5277, loss_cls: 4.1721, loss: 4.1721 +2024-07-17 10:16:46,734 - pyskl - INFO - Epoch [34][3000/3746] lr: 8.799e-02, eta: 3 days, 17:55:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5270, loss_cls: 4.1566, loss: 4.1566 +2024-07-17 10:18:08,730 - pyskl - INFO - Epoch [34][3100/3746] lr: 8.797e-02, eta: 3 days, 17:55:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5153, loss_cls: 4.2300, loss: 4.2300 +2024-07-17 10:19:30,068 - pyskl - INFO - Epoch [34][3200/3746] lr: 8.795e-02, eta: 3 days, 17:54:14, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5212, loss_cls: 4.2229, loss: 4.2229 +2024-07-17 10:20:51,922 - pyskl - INFO - Epoch [34][3300/3746] lr: 8.793e-02, eta: 3 days, 17:53:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5228, loss_cls: 4.2145, loss: 4.2145 +2024-07-17 10:22:13,415 - pyskl - INFO - Epoch [34][3400/3746] lr: 8.791e-02, eta: 3 days, 17:52:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5252, loss_cls: 4.2015, loss: 4.2015 +2024-07-17 10:23:35,260 - pyskl - INFO - Epoch [34][3500/3746] lr: 8.789e-02, eta: 3 days, 17:51:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5228, loss_cls: 4.2026, loss: 4.2026 +2024-07-17 10:24:57,585 - pyskl - INFO - Epoch [34][3600/3746] lr: 8.788e-02, eta: 3 days, 17:50:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5292, loss_cls: 4.1731, loss: 4.1731 +2024-07-17 10:26:20,187 - pyskl - INFO - Epoch [34][3700/3746] lr: 8.786e-02, eta: 3 days, 17:50:12, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5394, loss_cls: 4.1324, loss: 4.1324 +2024-07-17 10:26:59,589 - pyskl - INFO - Saving checkpoint at 34 epochs +2024-07-17 10:28:49,993 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 10:28:50,658 - pyskl - INFO - +top1_acc 0.1567 +top5_acc 0.3735 +2024-07-17 10:28:50,658 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 10:28:50,697 - pyskl - INFO - +mean_acc 0.1567 +2024-07-17 10:28:50,708 - pyskl - INFO - Epoch(val) [34][309] top1_acc: 0.1567, top5_acc: 0.3735, mean_class_accuracy: 0.1567 +2024-07-17 10:32:27,608 - pyskl - INFO - Epoch [35][100/3746] lr: 8.783e-02, eta: 3 days, 17:54:32, time: 2.169, data_time: 1.194, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5311, loss_cls: 4.1698, loss: 4.1698 +2024-07-17 10:33:49,626 - pyskl - INFO - Epoch [35][200/3746] lr: 8.781e-02, eta: 3 days, 17:53:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5248, loss_cls: 4.1901, loss: 4.1901 +2024-07-17 10:35:11,893 - pyskl - INFO - Epoch [35][300/3746] lr: 8.780e-02, eta: 3 days, 17:52:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5325, loss_cls: 4.1644, loss: 4.1644 +2024-07-17 10:36:34,672 - pyskl - INFO - Epoch [35][400/3746] lr: 8.778e-02, eta: 3 days, 17:52:09, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5320, loss_cls: 4.1484, loss: 4.1484 +2024-07-17 10:37:57,124 - pyskl - INFO - Epoch [35][500/3746] lr: 8.776e-02, eta: 3 days, 17:51:21, time: 0.825, data_time: 0.001, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5261, loss_cls: 4.1879, loss: 4.1879 +2024-07-17 10:39:19,408 - pyskl - INFO - Epoch [35][600/3746] lr: 8.774e-02, eta: 3 days, 17:50:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5375, loss_cls: 4.1581, loss: 4.1581 +2024-07-17 10:40:42,030 - pyskl - INFO - Epoch [35][700/3746] lr: 8.772e-02, eta: 3 days, 17:49:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5230, loss_cls: 4.1839, loss: 4.1839 +2024-07-17 10:42:04,126 - pyskl - INFO - Epoch [35][800/3746] lr: 8.770e-02, eta: 3 days, 17:48:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5216, loss_cls: 4.1914, loss: 4.1914 +2024-07-17 10:43:26,440 - pyskl - INFO - Epoch [35][900/3746] lr: 8.769e-02, eta: 3 days, 17:48:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5225, loss_cls: 4.1810, loss: 4.1810 +2024-07-17 10:44:48,790 - pyskl - INFO - Epoch [35][1000/3746] lr: 8.767e-02, eta: 3 days, 17:47:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5216, loss_cls: 4.1970, loss: 4.1970 +2024-07-17 10:46:10,458 - pyskl - INFO - Epoch [35][1100/3746] lr: 8.765e-02, eta: 3 days, 17:46:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5253, loss_cls: 4.1604, loss: 4.1604 +2024-07-17 10:47:32,420 - pyskl - INFO - Epoch [35][1200/3746] lr: 8.763e-02, eta: 3 days, 17:45:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5314, loss_cls: 4.1394, loss: 4.1394 +2024-07-17 10:48:54,043 - pyskl - INFO - Epoch [35][1300/3746] lr: 8.761e-02, eta: 3 days, 17:44:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5239, loss_cls: 4.1819, loss: 4.1819 +2024-07-17 10:50:16,059 - pyskl - INFO - Epoch [35][1400/3746] lr: 8.759e-02, eta: 3 days, 17:44:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5402, loss_cls: 4.1396, loss: 4.1396 +2024-07-17 10:51:37,524 - pyskl - INFO - Epoch [35][1500/3746] lr: 8.757e-02, eta: 3 days, 17:43:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5209, loss_cls: 4.1723, loss: 4.1723 +2024-07-17 10:52:58,778 - pyskl - INFO - Epoch [35][1600/3746] lr: 8.756e-02, eta: 3 days, 17:42:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5330, loss_cls: 4.1760, loss: 4.1760 +2024-07-17 10:54:19,554 - pyskl - INFO - Epoch [35][1700/3746] lr: 8.754e-02, eta: 3 days, 17:41:22, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5281, loss_cls: 4.1723, loss: 4.1723 +2024-07-17 10:55:40,476 - pyskl - INFO - Epoch [35][1800/3746] lr: 8.752e-02, eta: 3 days, 17:40:29, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5264, loss_cls: 4.2197, loss: 4.2197 +2024-07-17 10:57:01,576 - pyskl - INFO - Epoch [35][1900/3746] lr: 8.750e-02, eta: 3 days, 17:39:36, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5252, loss_cls: 4.1714, loss: 4.1714 +2024-07-17 10:58:23,285 - pyskl - INFO - Epoch [35][2000/3746] lr: 8.748e-02, eta: 3 days, 17:38:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5214, loss_cls: 4.2164, loss: 4.2164 +2024-07-17 10:59:44,760 - pyskl - INFO - Epoch [35][2100/3746] lr: 8.746e-02, eta: 3 days, 17:37:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5269, loss_cls: 4.1656, loss: 4.1656 +2024-07-17 11:01:06,279 - pyskl - INFO - Epoch [35][2200/3746] lr: 8.745e-02, eta: 3 days, 17:37:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5294, loss_cls: 4.1711, loss: 4.1711 +2024-07-17 11:02:28,117 - pyskl - INFO - Epoch [35][2300/3746] lr: 8.743e-02, eta: 3 days, 17:36:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5262, loss_cls: 4.1866, loss: 4.1866 +2024-07-17 11:03:48,944 - pyskl - INFO - Epoch [35][2400/3746] lr: 8.741e-02, eta: 3 days, 17:35:17, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5155, loss_cls: 4.2209, loss: 4.2209 +2024-07-17 11:05:10,474 - pyskl - INFO - Epoch [35][2500/3746] lr: 8.739e-02, eta: 3 days, 17:34:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5266, loss_cls: 4.2104, loss: 4.2104 +2024-07-17 11:06:32,738 - pyskl - INFO - Epoch [35][2600/3746] lr: 8.737e-02, eta: 3 days, 17:33:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5172, loss_cls: 4.2177, loss: 4.2177 +2024-07-17 11:07:54,480 - pyskl - INFO - Epoch [35][2700/3746] lr: 8.735e-02, eta: 3 days, 17:32:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5212, loss_cls: 4.2256, loss: 4.2256 +2024-07-17 11:09:16,050 - pyskl - INFO - Epoch [35][2800/3746] lr: 8.733e-02, eta: 3 days, 17:31:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5227, loss_cls: 4.1811, loss: 4.1811 +2024-07-17 11:10:37,742 - pyskl - INFO - Epoch [35][2900/3746] lr: 8.732e-02, eta: 3 days, 17:31:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5292, loss_cls: 4.1696, loss: 4.1696 +2024-07-17 11:11:59,292 - pyskl - INFO - Epoch [35][3000/3746] lr: 8.730e-02, eta: 3 days, 17:30:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5206, loss_cls: 4.1939, loss: 4.1939 +2024-07-17 11:13:20,621 - pyskl - INFO - Epoch [35][3100/3746] lr: 8.728e-02, eta: 3 days, 17:29:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5081, loss_cls: 4.2266, loss: 4.2266 +2024-07-17 11:14:42,425 - pyskl - INFO - Epoch [35][3200/3746] lr: 8.726e-02, eta: 3 days, 17:28:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5125, loss_cls: 4.2602, loss: 4.2602 +2024-07-17 11:16:03,934 - pyskl - INFO - Epoch [35][3300/3746] lr: 8.724e-02, eta: 3 days, 17:27:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5123, loss_cls: 4.2521, loss: 4.2521 +2024-07-17 11:17:25,847 - pyskl - INFO - Epoch [35][3400/3746] lr: 8.722e-02, eta: 3 days, 17:26:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5270, loss_cls: 4.1892, loss: 4.1892 +2024-07-17 11:18:47,362 - pyskl - INFO - Epoch [35][3500/3746] lr: 8.720e-02, eta: 3 days, 17:25:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5133, loss_cls: 4.2341, loss: 4.2341 +2024-07-17 11:20:09,377 - pyskl - INFO - Epoch [35][3600/3746] lr: 8.718e-02, eta: 3 days, 17:25:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5241, loss_cls: 4.1901, loss: 4.1901 +2024-07-17 11:21:31,159 - pyskl - INFO - Epoch [35][3700/3746] lr: 8.717e-02, eta: 3 days, 17:24:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5156, loss_cls: 4.2291, loss: 4.2291 +2024-07-17 11:22:10,261 - pyskl - INFO - Saving checkpoint at 35 epochs +2024-07-17 11:24:00,156 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 11:24:00,812 - pyskl - INFO - +top1_acc 0.1822 +top5_acc 0.3928 +2024-07-17 11:24:00,812 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 11:24:00,851 - pyskl - INFO - +mean_acc 0.1820 +2024-07-17 11:24:00,861 - pyskl - INFO - Epoch(val) [35][309] top1_acc: 0.1822, top5_acc: 0.3928, mean_class_accuracy: 0.1820 +2024-07-17 11:27:38,412 - pyskl - INFO - Epoch [36][100/3746] lr: 8.714e-02, eta: 3 days, 17:28:16, time: 2.175, data_time: 1.199, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5417, loss_cls: 4.1082, loss: 4.1082 +2024-07-17 11:29:00,749 - pyskl - INFO - Epoch [36][200/3746] lr: 8.712e-02, eta: 3 days, 17:27:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5312, loss_cls: 4.1643, loss: 4.1643 +2024-07-17 11:30:22,936 - pyskl - INFO - Epoch [36][300/3746] lr: 8.710e-02, eta: 3 days, 17:26:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5222, loss_cls: 4.1757, loss: 4.1757 +2024-07-17 11:31:44,567 - pyskl - INFO - Epoch [36][400/3746] lr: 8.708e-02, eta: 3 days, 17:25:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5373, loss_cls: 4.0825, loss: 4.0825 +2024-07-17 11:33:07,376 - pyskl - INFO - Epoch [36][500/3746] lr: 8.706e-02, eta: 3 days, 17:24:54, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5259, loss_cls: 4.1859, loss: 4.1859 +2024-07-17 11:34:29,733 - pyskl - INFO - Epoch [36][600/3746] lr: 8.704e-02, eta: 3 days, 17:24:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5288, loss_cls: 4.1484, loss: 4.1484 +2024-07-17 11:35:51,451 - pyskl - INFO - Epoch [36][700/3746] lr: 8.703e-02, eta: 3 days, 17:23:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5280, loss_cls: 4.1728, loss: 4.1728 +2024-07-17 11:37:13,764 - pyskl - INFO - Epoch [36][800/3746] lr: 8.701e-02, eta: 3 days, 17:22:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5241, loss_cls: 4.2005, loss: 4.2005 +2024-07-17 11:38:35,282 - pyskl - INFO - Epoch [36][900/3746] lr: 8.699e-02, eta: 3 days, 17:21:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5134, loss_cls: 4.2308, loss: 4.2308 +2024-07-17 11:39:57,035 - pyskl - INFO - Epoch [36][1000/3746] lr: 8.697e-02, eta: 3 days, 17:20:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5300, loss_cls: 4.1876, loss: 4.1876 +2024-07-17 11:41:19,197 - pyskl - INFO - Epoch [36][1100/3746] lr: 8.695e-02, eta: 3 days, 17:19:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5363, loss_cls: 4.1174, loss: 4.1174 +2024-07-17 11:42:41,073 - pyskl - INFO - Epoch [36][1200/3746] lr: 8.693e-02, eta: 3 days, 17:18:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5266, loss_cls: 4.2209, loss: 4.2209 +2024-07-17 11:44:03,051 - pyskl - INFO - Epoch [36][1300/3746] lr: 8.691e-02, eta: 3 days, 17:18:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5152, loss_cls: 4.1948, loss: 4.1948 +2024-07-17 11:45:25,976 - pyskl - INFO - Epoch [36][1400/3746] lr: 8.689e-02, eta: 3 days, 17:17:13, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5239, loss_cls: 4.1782, loss: 4.1782 +2024-07-17 11:46:47,913 - pyskl - INFO - Epoch [36][1500/3746] lr: 8.688e-02, eta: 3 days, 17:16:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5200, loss_cls: 4.1796, loss: 4.1796 +2024-07-17 11:48:09,600 - pyskl - INFO - Epoch [36][1600/3746] lr: 8.686e-02, eta: 3 days, 17:15:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5289, loss_cls: 4.1762, loss: 4.1762 +2024-07-17 11:49:31,714 - pyskl - INFO - Epoch [36][1700/3746] lr: 8.684e-02, eta: 3 days, 17:14:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5245, loss_cls: 4.2239, loss: 4.2239 +2024-07-17 11:50:52,765 - pyskl - INFO - Epoch [36][1800/3746] lr: 8.682e-02, eta: 3 days, 17:13:42, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5209, loss_cls: 4.2199, loss: 4.2199 +2024-07-17 11:52:14,204 - pyskl - INFO - Epoch [36][1900/3746] lr: 8.680e-02, eta: 3 days, 17:12:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5070, loss_cls: 4.2494, loss: 4.2494 +2024-07-17 11:53:35,940 - pyskl - INFO - Epoch [36][2000/3746] lr: 8.678e-02, eta: 3 days, 17:11:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5380, loss_cls: 4.1504, loss: 4.1504 +2024-07-17 11:54:57,246 - pyskl - INFO - Epoch [36][2100/3746] lr: 8.676e-02, eta: 3 days, 17:11:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5203, loss_cls: 4.2213, loss: 4.2213 +2024-07-17 11:56:19,040 - pyskl - INFO - Epoch [36][2200/3746] lr: 8.674e-02, eta: 3 days, 17:10:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5297, loss_cls: 4.1695, loss: 4.1695 +2024-07-17 11:57:41,388 - pyskl - INFO - Epoch [36][2300/3746] lr: 8.672e-02, eta: 3 days, 17:09:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5289, loss_cls: 4.1512, loss: 4.1512 +2024-07-17 11:59:02,833 - pyskl - INFO - Epoch [36][2400/3746] lr: 8.671e-02, eta: 3 days, 17:08:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5373, loss_cls: 4.1204, loss: 4.1204 +2024-07-17 12:00:24,789 - pyskl - INFO - Epoch [36][2500/3746] lr: 8.669e-02, eta: 3 days, 17:07:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5306, loss_cls: 4.1731, loss: 4.1731 +2024-07-17 12:01:46,902 - pyskl - INFO - Epoch [36][2600/3746] lr: 8.667e-02, eta: 3 days, 17:06:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5327, loss_cls: 4.1495, loss: 4.1495 +2024-07-17 12:03:08,833 - pyskl - INFO - Epoch [36][2700/3746] lr: 8.665e-02, eta: 3 days, 17:05:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5319, loss_cls: 4.1723, loss: 4.1723 +2024-07-17 12:04:29,952 - pyskl - INFO - Epoch [36][2800/3746] lr: 8.663e-02, eta: 3 days, 17:04:52, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5277, loss_cls: 4.1598, loss: 4.1598 +2024-07-17 12:05:51,188 - pyskl - INFO - Epoch [36][2900/3746] lr: 8.661e-02, eta: 3 days, 17:03:57, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5317, loss_cls: 4.1674, loss: 4.1674 +2024-07-17 12:07:13,388 - pyskl - INFO - Epoch [36][3000/3746] lr: 8.659e-02, eta: 3 days, 17:03:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5261, loss_cls: 4.1952, loss: 4.1952 +2024-07-17 12:08:34,542 - pyskl - INFO - Epoch [36][3100/3746] lr: 8.657e-02, eta: 3 days, 17:02:10, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5178, loss_cls: 4.1867, loss: 4.1867 +2024-07-17 12:09:56,215 - pyskl - INFO - Epoch [36][3200/3746] lr: 8.655e-02, eta: 3 days, 17:01:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5297, loss_cls: 4.1411, loss: 4.1411 +2024-07-17 12:11:17,609 - pyskl - INFO - Epoch [36][3300/3746] lr: 8.653e-02, eta: 3 days, 17:00:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5278, loss_cls: 4.2166, loss: 4.2166 +2024-07-17 12:12:39,280 - pyskl - INFO - Epoch [36][3400/3746] lr: 8.651e-02, eta: 3 days, 16:59:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5256, loss_cls: 4.1831, loss: 4.1831 +2024-07-17 12:14:00,495 - pyskl - INFO - Epoch [36][3500/3746] lr: 8.650e-02, eta: 3 days, 16:58:33, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5302, loss_cls: 4.1533, loss: 4.1533 +2024-07-17 12:15:22,117 - pyskl - INFO - Epoch [36][3600/3746] lr: 8.648e-02, eta: 3 days, 16:57:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5214, loss_cls: 4.2317, loss: 4.2317 +2024-07-17 12:16:44,074 - pyskl - INFO - Epoch [36][3700/3746] lr: 8.646e-02, eta: 3 days, 16:56:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5203, loss_cls: 4.2114, loss: 4.2114 +2024-07-17 12:17:23,187 - pyskl - INFO - Saving checkpoint at 36 epochs +2024-07-17 12:19:13,180 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 12:19:13,839 - pyskl - INFO - +top1_acc 0.1910 +top5_acc 0.4315 +2024-07-17 12:19:13,839 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 12:19:13,879 - pyskl - INFO - +mean_acc 0.1910 +2024-07-17 12:19:13,889 - pyskl - INFO - Epoch(val) [36][309] top1_acc: 0.1910, top5_acc: 0.4315, mean_class_accuracy: 0.1910 +2024-07-17 12:22:53,028 - pyskl - INFO - Epoch [37][100/3746] lr: 8.643e-02, eta: 3 days, 17:00:44, time: 2.191, data_time: 1.214, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5345, loss_cls: 4.1247, loss: 4.1247 +2024-07-17 12:24:15,182 - pyskl - INFO - Epoch [37][200/3746] lr: 8.641e-02, eta: 3 days, 16:59:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5284, loss_cls: 4.1447, loss: 4.1447 +2024-07-17 12:25:37,334 - pyskl - INFO - Epoch [37][300/3746] lr: 8.639e-02, eta: 3 days, 16:58:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5269, loss_cls: 4.1761, loss: 4.1761 +2024-07-17 12:26:59,236 - pyskl - INFO - Epoch [37][400/3746] lr: 8.637e-02, eta: 3 days, 16:58:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5297, loss_cls: 4.1584, loss: 4.1584 +2024-07-17 12:28:22,473 - pyskl - INFO - Epoch [37][500/3746] lr: 8.635e-02, eta: 3 days, 16:57:16, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5170, loss_cls: 4.2053, loss: 4.2053 +2024-07-17 12:29:44,383 - pyskl - INFO - Epoch [37][600/3746] lr: 8.633e-02, eta: 3 days, 16:56:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5223, loss_cls: 4.1774, loss: 4.1774 +2024-07-17 12:31:06,881 - pyskl - INFO - Epoch [37][700/3746] lr: 8.631e-02, eta: 3 days, 16:55:30, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5192, loss_cls: 4.1897, loss: 4.1897 +2024-07-17 12:32:29,911 - pyskl - INFO - Epoch [37][800/3746] lr: 8.630e-02, eta: 3 days, 16:54:40, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5269, loss_cls: 4.1831, loss: 4.1831 +2024-07-17 12:33:52,175 - pyskl - INFO - Epoch [37][900/3746] lr: 8.628e-02, eta: 3 days, 16:53:48, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5389, loss_cls: 4.1416, loss: 4.1416 +2024-07-17 12:35:14,900 - pyskl - INFO - Epoch [37][1000/3746] lr: 8.626e-02, eta: 3 days, 16:52:57, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5280, loss_cls: 4.1645, loss: 4.1645 +2024-07-17 12:36:36,611 - pyskl - INFO - Epoch [37][1100/3746] lr: 8.624e-02, eta: 3 days, 16:52:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5253, loss_cls: 4.1629, loss: 4.1629 +2024-07-17 12:37:58,413 - pyskl - INFO - Epoch [37][1200/3746] lr: 8.622e-02, eta: 3 days, 16:51:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5217, loss_cls: 4.2301, loss: 4.2301 +2024-07-17 12:39:19,901 - pyskl - INFO - Epoch [37][1300/3746] lr: 8.620e-02, eta: 3 days, 16:50:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5212, loss_cls: 4.1705, loss: 4.1705 +2024-07-17 12:40:41,635 - pyskl - INFO - Epoch [37][1400/3746] lr: 8.618e-02, eta: 3 days, 16:49:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5216, loss_cls: 4.1931, loss: 4.1931 +2024-07-17 12:42:03,438 - pyskl - INFO - Epoch [37][1500/3746] lr: 8.616e-02, eta: 3 days, 16:48:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5353, loss_cls: 4.1549, loss: 4.1549 +2024-07-17 12:43:25,205 - pyskl - INFO - Epoch [37][1600/3746] lr: 8.614e-02, eta: 3 days, 16:47:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5231, loss_cls: 4.1808, loss: 4.1808 +2024-07-17 12:44:46,230 - pyskl - INFO - Epoch [37][1700/3746] lr: 8.612e-02, eta: 3 days, 16:46:33, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5223, loss_cls: 4.1717, loss: 4.1717 +2024-07-17 12:46:08,163 - pyskl - INFO - Epoch [37][1800/3746] lr: 8.610e-02, eta: 3 days, 16:45:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5214, loss_cls: 4.1805, loss: 4.1805 +2024-07-17 12:47:29,919 - pyskl - INFO - Epoch [37][1900/3746] lr: 8.608e-02, eta: 3 days, 16:44:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5270, loss_cls: 4.1738, loss: 4.1738 +2024-07-17 12:48:51,885 - pyskl - INFO - Epoch [37][2000/3746] lr: 8.606e-02, eta: 3 days, 16:43:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5177, loss_cls: 4.2013, loss: 4.2013 +2024-07-17 12:50:13,293 - pyskl - INFO - Epoch [37][2100/3746] lr: 8.604e-02, eta: 3 days, 16:42:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5233, loss_cls: 4.1864, loss: 4.1864 +2024-07-17 12:51:35,387 - pyskl - INFO - Epoch [37][2200/3746] lr: 8.602e-02, eta: 3 days, 16:42:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5341, loss_cls: 4.1389, loss: 4.1389 +2024-07-17 12:52:57,416 - pyskl - INFO - Epoch [37][2300/3746] lr: 8.601e-02, eta: 3 days, 16:41:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5242, loss_cls: 4.2007, loss: 4.2007 +2024-07-17 12:54:18,412 - pyskl - INFO - Epoch [37][2400/3746] lr: 8.599e-02, eta: 3 days, 16:40:10, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5316, loss_cls: 4.1967, loss: 4.1967 +2024-07-17 12:55:40,293 - pyskl - INFO - Epoch [37][2500/3746] lr: 8.597e-02, eta: 3 days, 16:39:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5216, loss_cls: 4.1919, loss: 4.1919 +2024-07-17 12:57:02,057 - pyskl - INFO - Epoch [37][2600/3746] lr: 8.595e-02, eta: 3 days, 16:38:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5250, loss_cls: 4.2149, loss: 4.2149 +2024-07-17 12:58:23,988 - pyskl - INFO - Epoch [37][2700/3746] lr: 8.593e-02, eta: 3 days, 16:37:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5284, loss_cls: 4.1952, loss: 4.1952 +2024-07-17 12:59:45,387 - pyskl - INFO - Epoch [37][2800/3746] lr: 8.591e-02, eta: 3 days, 16:36:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5345, loss_cls: 4.1495, loss: 4.1495 +2024-07-17 13:01:07,096 - pyskl - INFO - Epoch [37][2900/3746] lr: 8.589e-02, eta: 3 days, 16:35:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5383, loss_cls: 4.1240, loss: 4.1240 +2024-07-17 13:02:28,755 - pyskl - INFO - Epoch [37][3000/3746] lr: 8.587e-02, eta: 3 days, 16:34:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5233, loss_cls: 4.1841, loss: 4.1841 +2024-07-17 13:03:50,725 - pyskl - INFO - Epoch [37][3100/3746] lr: 8.585e-02, eta: 3 days, 16:33:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5188, loss_cls: 4.2114, loss: 4.2114 +2024-07-17 13:05:13,085 - pyskl - INFO - Epoch [37][3200/3746] lr: 8.583e-02, eta: 3 days, 16:32:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5097, loss_cls: 4.2381, loss: 4.2381 +2024-07-17 13:06:34,972 - pyskl - INFO - Epoch [37][3300/3746] lr: 8.581e-02, eta: 3 days, 16:31:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5233, loss_cls: 4.1656, loss: 4.1656 +2024-07-17 13:07:56,664 - pyskl - INFO - Epoch [37][3400/3746] lr: 8.579e-02, eta: 3 days, 16:31:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5330, loss_cls: 4.1559, loss: 4.1559 +2024-07-17 13:09:18,221 - pyskl - INFO - Epoch [37][3500/3746] lr: 8.577e-02, eta: 3 days, 16:30:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5195, loss_cls: 4.2313, loss: 4.2313 +2024-07-17 13:10:40,685 - pyskl - INFO - Epoch [37][3600/3746] lr: 8.575e-02, eta: 3 days, 16:29:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5191, loss_cls: 4.2126, loss: 4.2126 +2024-07-17 13:12:02,864 - pyskl - INFO - Epoch [37][3700/3746] lr: 8.573e-02, eta: 3 days, 16:28:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5298, loss_cls: 4.1391, loss: 4.1391 +2024-07-17 13:12:42,148 - pyskl - INFO - Saving checkpoint at 37 epochs +2024-07-17 13:14:32,080 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 13:14:32,745 - pyskl - INFO - +top1_acc 0.1970 +top5_acc 0.4281 +2024-07-17 13:14:32,745 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 13:14:32,786 - pyskl - INFO - +mean_acc 0.1968 +2024-07-17 13:14:32,798 - pyskl - INFO - Epoch(val) [37][309] top1_acc: 0.1970, top5_acc: 0.4281, mean_class_accuracy: 0.1968 +2024-07-17 13:18:10,850 - pyskl - INFO - Epoch [38][100/3746] lr: 8.570e-02, eta: 3 days, 16:31:59, time: 2.180, data_time: 1.205, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5419, loss_cls: 4.1221, loss: 4.1221 +2024-07-17 13:19:31,982 - pyskl - INFO - Epoch [38][200/3746] lr: 8.568e-02, eta: 3 days, 16:31:02, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5239, loss_cls: 4.1677, loss: 4.1677 +2024-07-17 13:20:53,635 - pyskl - INFO - Epoch [38][300/3746] lr: 8.567e-02, eta: 3 days, 16:30:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5348, loss_cls: 4.1523, loss: 4.1523 +2024-07-17 13:22:16,669 - pyskl - INFO - Epoch [38][400/3746] lr: 8.565e-02, eta: 3 days, 16:29:14, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5361, loss_cls: 4.1322, loss: 4.1322 +2024-07-17 13:23:39,575 - pyskl - INFO - Epoch [38][500/3746] lr: 8.563e-02, eta: 3 days, 16:28:22, time: 0.829, data_time: 0.001, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5298, loss_cls: 4.1570, loss: 4.1570 +2024-07-17 13:25:01,844 - pyskl - INFO - Epoch [38][600/3746] lr: 8.561e-02, eta: 3 days, 16:27:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5337, loss_cls: 4.1202, loss: 4.1202 +2024-07-17 13:26:24,198 - pyskl - INFO - Epoch [38][700/3746] lr: 8.559e-02, eta: 3 days, 16:26:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5364, loss_cls: 4.1584, loss: 4.1584 +2024-07-17 13:27:46,275 - pyskl - INFO - Epoch [38][800/3746] lr: 8.557e-02, eta: 3 days, 16:25:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5238, loss_cls: 4.2158, loss: 4.2158 +2024-07-17 13:29:08,449 - pyskl - INFO - Epoch [38][900/3746] lr: 8.555e-02, eta: 3 days, 16:24:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5262, loss_cls: 4.1720, loss: 4.1720 +2024-07-17 13:30:29,879 - pyskl - INFO - Epoch [38][1000/3746] lr: 8.553e-02, eta: 3 days, 16:23:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5312, loss_cls: 4.1807, loss: 4.1807 +2024-07-17 13:31:51,289 - pyskl - INFO - Epoch [38][1100/3746] lr: 8.551e-02, eta: 3 days, 16:22:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5208, loss_cls: 4.1942, loss: 4.1942 +2024-07-17 13:33:13,280 - pyskl - INFO - Epoch [38][1200/3746] lr: 8.549e-02, eta: 3 days, 16:21:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5147, loss_cls: 4.1843, loss: 4.1843 +2024-07-17 13:34:34,350 - pyskl - INFO - Epoch [38][1300/3746] lr: 8.547e-02, eta: 3 days, 16:20:56, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5319, loss_cls: 4.1476, loss: 4.1476 +2024-07-17 13:35:56,077 - pyskl - INFO - Epoch [38][1400/3746] lr: 8.545e-02, eta: 3 days, 16:20:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5206, loss_cls: 4.2208, loss: 4.2208 +2024-07-17 13:37:18,118 - pyskl - INFO - Epoch [38][1500/3746] lr: 8.543e-02, eta: 3 days, 16:19:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5319, loss_cls: 4.1691, loss: 4.1691 +2024-07-17 13:38:40,343 - pyskl - INFO - Epoch [38][1600/3746] lr: 8.541e-02, eta: 3 days, 16:18:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5258, loss_cls: 4.1729, loss: 4.1729 +2024-07-17 13:40:01,696 - pyskl - INFO - Epoch [38][1700/3746] lr: 8.539e-02, eta: 3 days, 16:17:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5359, loss_cls: 4.1316, loss: 4.1316 +2024-07-17 13:41:23,228 - pyskl - INFO - Epoch [38][1800/3746] lr: 8.537e-02, eta: 3 days, 16:16:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5220, loss_cls: 4.2080, loss: 4.2080 +2024-07-17 13:42:44,748 - pyskl - INFO - Epoch [38][1900/3746] lr: 8.535e-02, eta: 3 days, 16:15:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5358, loss_cls: 4.1547, loss: 4.1547 +2024-07-17 13:44:05,884 - pyskl - INFO - Epoch [38][2000/3746] lr: 8.533e-02, eta: 3 days, 16:14:20, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5314, loss_cls: 4.1299, loss: 4.1299 +2024-07-17 13:45:27,519 - pyskl - INFO - Epoch [38][2100/3746] lr: 8.531e-02, eta: 3 days, 16:13:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5334, loss_cls: 4.1300, loss: 4.1300 +2024-07-17 13:46:48,703 - pyskl - INFO - Epoch [38][2200/3746] lr: 8.529e-02, eta: 3 days, 16:12:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5123, loss_cls: 4.2267, loss: 4.2267 +2024-07-17 13:48:10,707 - pyskl - INFO - Epoch [38][2300/3746] lr: 8.527e-02, eta: 3 days, 16:11:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5247, loss_cls: 4.2042, loss: 4.2042 +2024-07-17 13:49:32,401 - pyskl - INFO - Epoch [38][2400/3746] lr: 8.525e-02, eta: 3 days, 16:10:33, time: 0.817, data_time: 0.001, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5288, loss_cls: 4.1584, loss: 4.1584 +2024-07-17 13:50:53,583 - pyskl - INFO - Epoch [38][2500/3746] lr: 8.523e-02, eta: 3 days, 16:09:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5123, loss_cls: 4.2494, loss: 4.2494 +2024-07-17 13:52:15,615 - pyskl - INFO - Epoch [38][2600/3746] lr: 8.521e-02, eta: 3 days, 16:08:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5188, loss_cls: 4.2321, loss: 4.2321 +2024-07-17 13:53:37,320 - pyskl - INFO - Epoch [38][2700/3746] lr: 8.519e-02, eta: 3 days, 16:07:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5241, loss_cls: 4.1899, loss: 4.1899 +2024-07-17 13:54:59,307 - pyskl - INFO - Epoch [38][2800/3746] lr: 8.517e-02, eta: 3 days, 16:06:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5295, loss_cls: 4.1142, loss: 4.1142 +2024-07-17 13:56:21,009 - pyskl - INFO - Epoch [38][2900/3746] lr: 8.515e-02, eta: 3 days, 16:05:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5236, loss_cls: 4.1927, loss: 4.1927 +2024-07-17 13:57:42,790 - pyskl - INFO - Epoch [38][3000/3746] lr: 8.513e-02, eta: 3 days, 16:04:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5223, loss_cls: 4.2170, loss: 4.2170 +2024-07-17 13:59:04,928 - pyskl - INFO - Epoch [38][3100/3746] lr: 8.511e-02, eta: 3 days, 16:03:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5294, loss_cls: 4.2007, loss: 4.2007 +2024-07-17 14:00:26,948 - pyskl - INFO - Epoch [38][3200/3746] lr: 8.509e-02, eta: 3 days, 16:03:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5428, loss_cls: 4.1248, loss: 4.1248 +2024-07-17 14:01:48,868 - pyskl - INFO - Epoch [38][3300/3746] lr: 8.507e-02, eta: 3 days, 16:02:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5134, loss_cls: 4.2140, loss: 4.2140 +2024-07-17 14:03:10,766 - pyskl - INFO - Epoch [38][3400/3746] lr: 8.505e-02, eta: 3 days, 16:01:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5272, loss_cls: 4.1773, loss: 4.1773 +2024-07-17 14:04:32,213 - pyskl - INFO - Epoch [38][3500/3746] lr: 8.503e-02, eta: 3 days, 16:00:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5247, loss_cls: 4.1907, loss: 4.1907 +2024-07-17 14:05:54,696 - pyskl - INFO - Epoch [38][3600/3746] lr: 8.501e-02, eta: 3 days, 15:59:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5333, loss_cls: 4.1543, loss: 4.1543 +2024-07-17 14:07:16,522 - pyskl - INFO - Epoch [38][3700/3746] lr: 8.499e-02, eta: 3 days, 15:58:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5158, loss_cls: 4.1909, loss: 4.1909 +2024-07-17 14:07:56,014 - pyskl - INFO - Saving checkpoint at 38 epochs +2024-07-17 14:09:45,500 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 14:09:46,265 - pyskl - INFO - +top1_acc 0.1726 +top5_acc 0.3921 +2024-07-17 14:09:46,265 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 14:09:46,302 - pyskl - INFO - +mean_acc 0.1723 +2024-07-17 14:09:46,314 - pyskl - INFO - Epoch(val) [38][309] top1_acc: 0.1726, top5_acc: 0.3921, mean_class_accuracy: 0.1723 +2024-07-17 14:13:24,423 - pyskl - INFO - Epoch [39][100/3746] lr: 8.496e-02, eta: 3 days, 16:01:47, time: 2.181, data_time: 1.202, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5464, loss_cls: 4.1003, loss: 4.1003 +2024-07-17 14:14:46,071 - pyskl - INFO - Epoch [39][200/3746] lr: 8.494e-02, eta: 3 days, 16:00:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5403, loss_cls: 4.1292, loss: 4.1292 +2024-07-17 14:16:07,801 - pyskl - INFO - Epoch [39][300/3746] lr: 8.492e-02, eta: 3 days, 15:59:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5392, loss_cls: 4.1121, loss: 4.1121 +2024-07-17 14:17:30,947 - pyskl - INFO - Epoch [39][400/3746] lr: 8.490e-02, eta: 3 days, 15:58:58, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5381, loss_cls: 4.1263, loss: 4.1263 +2024-07-17 14:18:52,654 - pyskl - INFO - Epoch [39][500/3746] lr: 8.488e-02, eta: 3 days, 15:58:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5370, loss_cls: 4.0987, loss: 4.0987 +2024-07-17 14:20:15,152 - pyskl - INFO - Epoch [39][600/3746] lr: 8.486e-02, eta: 3 days, 15:57:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5288, loss_cls: 4.1691, loss: 4.1691 +2024-07-17 14:21:38,017 - pyskl - INFO - Epoch [39][700/3746] lr: 8.484e-02, eta: 3 days, 15:56:11, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5312, loss_cls: 4.1555, loss: 4.1555 +2024-07-17 14:23:00,637 - pyskl - INFO - Epoch [39][800/3746] lr: 8.482e-02, eta: 3 days, 15:55:16, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5308, loss_cls: 4.1565, loss: 4.1565 +2024-07-17 14:24:23,541 - pyskl - INFO - Epoch [39][900/3746] lr: 8.480e-02, eta: 3 days, 15:54:22, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5314, loss_cls: 4.1448, loss: 4.1448 +2024-07-17 14:25:45,619 - pyskl - INFO - Epoch [39][1000/3746] lr: 8.478e-02, eta: 3 days, 15:53:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5330, loss_cls: 4.1558, loss: 4.1558 +2024-07-17 14:27:08,256 - pyskl - INFO - Epoch [39][1100/3746] lr: 8.476e-02, eta: 3 days, 15:52:31, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5247, loss_cls: 4.1986, loss: 4.1986 +2024-07-17 14:28:30,147 - pyskl - INFO - Epoch [39][1200/3746] lr: 8.474e-02, eta: 3 days, 15:51:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5244, loss_cls: 4.1952, loss: 4.1952 +2024-07-17 14:29:52,504 - pyskl - INFO - Epoch [39][1300/3746] lr: 8.472e-02, eta: 3 days, 15:50:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5225, loss_cls: 4.1787, loss: 4.1787 +2024-07-17 14:31:13,731 - pyskl - INFO - Epoch [39][1400/3746] lr: 8.470e-02, eta: 3 days, 15:49:38, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5278, loss_cls: 4.1806, loss: 4.1806 +2024-07-17 14:32:35,679 - pyskl - INFO - Epoch [39][1500/3746] lr: 8.468e-02, eta: 3 days, 15:48:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5320, loss_cls: 4.1435, loss: 4.1435 +2024-07-17 14:33:57,017 - pyskl - INFO - Epoch [39][1600/3746] lr: 8.466e-02, eta: 3 days, 15:47:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5292, loss_cls: 4.1701, loss: 4.1701 +2024-07-17 14:35:19,190 - pyskl - INFO - Epoch [39][1700/3746] lr: 8.464e-02, eta: 3 days, 15:46:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5266, loss_cls: 4.1624, loss: 4.1624 +2024-07-17 14:36:41,144 - pyskl - INFO - Epoch [39][1800/3746] lr: 8.462e-02, eta: 3 days, 15:45:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5278, loss_cls: 4.1902, loss: 4.1902 +2024-07-17 14:38:02,281 - pyskl - INFO - Epoch [39][1900/3746] lr: 8.460e-02, eta: 3 days, 15:44:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5373, loss_cls: 4.1232, loss: 4.1232 +2024-07-17 14:39:24,157 - pyskl - INFO - Epoch [39][2000/3746] lr: 8.458e-02, eta: 3 days, 15:43:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5267, loss_cls: 4.1839, loss: 4.1839 +2024-07-17 14:40:44,986 - pyskl - INFO - Epoch [39][2100/3746] lr: 8.456e-02, eta: 3 days, 15:42:50, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5198, loss_cls: 4.1785, loss: 4.1785 +2024-07-17 14:42:06,401 - pyskl - INFO - Epoch [39][2200/3746] lr: 8.454e-02, eta: 3 days, 15:41:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5247, loss_cls: 4.2056, loss: 4.2056 +2024-07-17 14:43:28,559 - pyskl - INFO - Epoch [39][2300/3746] lr: 8.452e-02, eta: 3 days, 15:40:54, time: 0.822, data_time: 0.001, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5383, loss_cls: 4.1457, loss: 4.1457 +2024-07-17 14:44:49,978 - pyskl - INFO - Epoch [39][2400/3746] lr: 8.450e-02, eta: 3 days, 15:39:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5258, loss_cls: 4.1802, loss: 4.1802 +2024-07-17 14:46:11,901 - pyskl - INFO - Epoch [39][2500/3746] lr: 8.448e-02, eta: 3 days, 15:38:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5167, loss_cls: 4.1703, loss: 4.1703 +2024-07-17 14:47:33,331 - pyskl - INFO - Epoch [39][2600/3746] lr: 8.446e-02, eta: 3 days, 15:37:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5152, loss_cls: 4.2165, loss: 4.2165 +2024-07-17 14:48:55,229 - pyskl - INFO - Epoch [39][2700/3746] lr: 8.444e-02, eta: 3 days, 15:37:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5141, loss_cls: 4.2284, loss: 4.2284 +2024-07-17 14:50:17,609 - pyskl - INFO - Epoch [39][2800/3746] lr: 8.442e-02, eta: 3 days, 15:36:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5233, loss_cls: 4.1643, loss: 4.1643 +2024-07-17 14:51:39,016 - pyskl - INFO - Epoch [39][2900/3746] lr: 8.440e-02, eta: 3 days, 15:35:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5272, loss_cls: 4.1772, loss: 4.1772 +2024-07-17 14:53:01,123 - pyskl - INFO - Epoch [39][3000/3746] lr: 8.438e-02, eta: 3 days, 15:34:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5252, loss_cls: 4.1703, loss: 4.1703 +2024-07-17 14:54:22,926 - pyskl - INFO - Epoch [39][3100/3746] lr: 8.436e-02, eta: 3 days, 15:33:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5181, loss_cls: 4.1883, loss: 4.1883 +2024-07-17 14:55:44,819 - pyskl - INFO - Epoch [39][3200/3746] lr: 8.434e-02, eta: 3 days, 15:32:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5120, loss_cls: 4.2123, loss: 4.2123 +2024-07-17 14:57:05,774 - pyskl - INFO - Epoch [39][3300/3746] lr: 8.432e-02, eta: 3 days, 15:31:11, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5228, loss_cls: 4.2148, loss: 4.2148 +2024-07-17 14:58:26,652 - pyskl - INFO - Epoch [39][3400/3746] lr: 8.430e-02, eta: 3 days, 15:30:10, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5220, loss_cls: 4.1775, loss: 4.1775 +2024-07-17 14:59:48,125 - pyskl - INFO - Epoch [39][3500/3746] lr: 8.428e-02, eta: 3 days, 15:29:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5275, loss_cls: 4.1779, loss: 4.1779 +2024-07-17 15:01:10,693 - pyskl - INFO - Epoch [39][3600/3746] lr: 8.426e-02, eta: 3 days, 15:28:15, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5356, loss_cls: 4.1818, loss: 4.1818 +2024-07-17 15:02:32,466 - pyskl - INFO - Epoch [39][3700/3746] lr: 8.424e-02, eta: 3 days, 15:27:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5248, loss_cls: 4.1709, loss: 4.1709 +2024-07-17 15:03:11,986 - pyskl - INFO - Saving checkpoint at 39 epochs +2024-07-17 15:05:01,139 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 15:05:01,823 - pyskl - INFO - +top1_acc 0.1587 +top5_acc 0.3620 +2024-07-17 15:05:01,823 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 15:05:01,859 - pyskl - INFO - +mean_acc 0.1586 +2024-07-17 15:05:01,869 - pyskl - INFO - Epoch(val) [39][309] top1_acc: 0.1587, top5_acc: 0.3620, mean_class_accuracy: 0.1586 +2024-07-17 15:08:40,830 - pyskl - INFO - Epoch [40][100/3746] lr: 8.421e-02, eta: 3 days, 15:30:34, time: 2.190, data_time: 1.217, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5345, loss_cls: 4.1322, loss: 4.1322 +2024-07-17 15:10:03,081 - pyskl - INFO - Epoch [40][200/3746] lr: 8.419e-02, eta: 3 days, 15:29:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5364, loss_cls: 4.1492, loss: 4.1492 +2024-07-17 15:11:24,571 - pyskl - INFO - Epoch [40][300/3746] lr: 8.417e-02, eta: 3 days, 15:28:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5353, loss_cls: 4.1489, loss: 4.1489 +2024-07-17 15:12:47,103 - pyskl - INFO - Epoch [40][400/3746] lr: 8.415e-02, eta: 3 days, 15:27:40, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5366, loss_cls: 4.0864, loss: 4.0864 +2024-07-17 15:14:08,745 - pyskl - INFO - Epoch [40][500/3746] lr: 8.413e-02, eta: 3 days, 15:26:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5408, loss_cls: 4.1261, loss: 4.1261 +2024-07-17 15:15:31,597 - pyskl - INFO - Epoch [40][600/3746] lr: 8.411e-02, eta: 3 days, 15:25:45, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5327, loss_cls: 4.1506, loss: 4.1506 +2024-07-17 15:16:53,300 - pyskl - INFO - Epoch [40][700/3746] lr: 8.408e-02, eta: 3 days, 15:24:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5317, loss_cls: 4.1381, loss: 4.1381 +2024-07-17 15:18:15,374 - pyskl - INFO - Epoch [40][800/3746] lr: 8.406e-02, eta: 3 days, 15:23:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5266, loss_cls: 4.1451, loss: 4.1451 +2024-07-17 15:19:37,352 - pyskl - INFO - Epoch [40][900/3746] lr: 8.404e-02, eta: 3 days, 15:22:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5372, loss_cls: 4.1206, loss: 4.1206 +2024-07-17 15:20:59,193 - pyskl - INFO - Epoch [40][1000/3746] lr: 8.402e-02, eta: 3 days, 15:21:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5259, loss_cls: 4.1699, loss: 4.1699 +2024-07-17 15:22:20,820 - pyskl - INFO - Epoch [40][1100/3746] lr: 8.400e-02, eta: 3 days, 15:20:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5342, loss_cls: 4.1517, loss: 4.1517 +2024-07-17 15:23:42,223 - pyskl - INFO - Epoch [40][1200/3746] lr: 8.398e-02, eta: 3 days, 15:19:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5194, loss_cls: 4.1858, loss: 4.1858 +2024-07-17 15:25:04,030 - pyskl - INFO - Epoch [40][1300/3746] lr: 8.396e-02, eta: 3 days, 15:18:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5248, loss_cls: 4.1913, loss: 4.1913 +2024-07-17 15:26:26,307 - pyskl - INFO - Epoch [40][1400/3746] lr: 8.394e-02, eta: 3 days, 15:17:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5264, loss_cls: 4.1825, loss: 4.1825 +2024-07-17 15:27:48,550 - pyskl - INFO - Epoch [40][1500/3746] lr: 8.392e-02, eta: 3 days, 15:16:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5364, loss_cls: 4.1705, loss: 4.1705 +2024-07-17 15:29:10,486 - pyskl - INFO - Epoch [40][1600/3746] lr: 8.390e-02, eta: 3 days, 15:15:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5364, loss_cls: 4.1523, loss: 4.1523 +2024-07-17 15:30:32,247 - pyskl - INFO - Epoch [40][1700/3746] lr: 8.388e-02, eta: 3 days, 15:14:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5283, loss_cls: 4.1795, loss: 4.1795 +2024-07-17 15:31:53,608 - pyskl - INFO - Epoch [40][1800/3746] lr: 8.386e-02, eta: 3 days, 15:13:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5314, loss_cls: 4.1728, loss: 4.1728 +2024-07-17 15:33:15,378 - pyskl - INFO - Epoch [40][1900/3746] lr: 8.384e-02, eta: 3 days, 15:12:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5356, loss_cls: 4.1573, loss: 4.1573 +2024-07-17 15:34:37,113 - pyskl - INFO - Epoch [40][2000/3746] lr: 8.382e-02, eta: 3 days, 15:12:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5242, loss_cls: 4.1741, loss: 4.1741 +2024-07-17 15:35:58,298 - pyskl - INFO - Epoch [40][2100/3746] lr: 8.380e-02, eta: 3 days, 15:10:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5286, loss_cls: 4.1582, loss: 4.1582 +2024-07-17 15:37:19,358 - pyskl - INFO - Epoch [40][2200/3746] lr: 8.378e-02, eta: 3 days, 15:09:57, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5292, loss_cls: 4.1591, loss: 4.1591 +2024-07-17 15:38:41,616 - pyskl - INFO - Epoch [40][2300/3746] lr: 8.376e-02, eta: 3 days, 15:08:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5244, loss_cls: 4.1856, loss: 4.1856 +2024-07-17 15:40:03,261 - pyskl - INFO - Epoch [40][2400/3746] lr: 8.374e-02, eta: 3 days, 15:07:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5303, loss_cls: 4.1699, loss: 4.1699 +2024-07-17 15:41:24,917 - pyskl - INFO - Epoch [40][2500/3746] lr: 8.371e-02, eta: 3 days, 15:06:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5216, loss_cls: 4.1929, loss: 4.1929 +2024-07-17 15:42:46,809 - pyskl - INFO - Epoch [40][2600/3746] lr: 8.369e-02, eta: 3 days, 15:06:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5323, loss_cls: 4.1856, loss: 4.1856 +2024-07-17 15:44:08,324 - pyskl - INFO - Epoch [40][2700/3746] lr: 8.367e-02, eta: 3 days, 15:05:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5281, loss_cls: 4.1969, loss: 4.1969 +2024-07-17 15:45:30,594 - pyskl - INFO - Epoch [40][2800/3746] lr: 8.365e-02, eta: 3 days, 15:04:02, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5297, loss_cls: 4.1644, loss: 4.1644 +2024-07-17 15:46:52,478 - pyskl - INFO - Epoch [40][2900/3746] lr: 8.363e-02, eta: 3 days, 15:03:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5323, loss_cls: 4.1738, loss: 4.1738 +2024-07-17 15:48:13,793 - pyskl - INFO - Epoch [40][3000/3746] lr: 8.361e-02, eta: 3 days, 15:02:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5258, loss_cls: 4.1746, loss: 4.1746 +2024-07-17 15:49:35,043 - pyskl - INFO - Epoch [40][3100/3746] lr: 8.359e-02, eta: 3 days, 15:01:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5197, loss_cls: 4.1644, loss: 4.1644 +2024-07-17 15:50:56,106 - pyskl - INFO - Epoch [40][3200/3746] lr: 8.357e-02, eta: 3 days, 14:59:59, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5259, loss_cls: 4.1736, loss: 4.1736 +2024-07-17 15:52:17,154 - pyskl - INFO - Epoch [40][3300/3746] lr: 8.355e-02, eta: 3 days, 14:58:57, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5262, loss_cls: 4.1834, loss: 4.1834 +2024-07-17 15:53:38,926 - pyskl - INFO - Epoch [40][3400/3746] lr: 8.353e-02, eta: 3 days, 14:57:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5320, loss_cls: 4.1762, loss: 4.1762 +2024-07-17 15:54:59,997 - pyskl - INFO - Epoch [40][3500/3746] lr: 8.351e-02, eta: 3 days, 14:56:56, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5311, loss_cls: 4.1835, loss: 4.1835 +2024-07-17 15:56:22,680 - pyskl - INFO - Epoch [40][3600/3746] lr: 8.349e-02, eta: 3 days, 14:55:58, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5278, loss_cls: 4.1538, loss: 4.1538 +2024-07-17 15:57:44,064 - pyskl - INFO - Epoch [40][3700/3746] lr: 8.347e-02, eta: 3 days, 14:54:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5250, loss_cls: 4.1676, loss: 4.1676 +2024-07-17 15:58:23,070 - pyskl - INFO - Saving checkpoint at 40 epochs +2024-07-17 16:00:12,697 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 16:00:13,367 - pyskl - INFO - +top1_acc 0.1970 +top5_acc 0.4278 +2024-07-17 16:00:13,367 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 16:00:13,405 - pyskl - INFO - +mean_acc 0.1971 +2024-07-17 16:00:13,415 - pyskl - INFO - Epoch(val) [40][309] top1_acc: 0.1970, top5_acc: 0.4278, mean_class_accuracy: 0.1971 +2024-07-17 16:03:51,759 - pyskl - INFO - Epoch [41][100/3746] lr: 8.344e-02, eta: 3 days, 14:58:02, time: 2.183, data_time: 1.205, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5383, loss_cls: 4.1253, loss: 4.1253 +2024-07-17 16:05:13,482 - pyskl - INFO - Epoch [41][200/3746] lr: 8.342e-02, eta: 3 days, 14:57:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5281, loss_cls: 4.1418, loss: 4.1418 +2024-07-17 16:06:35,793 - pyskl - INFO - Epoch [41][300/3746] lr: 8.339e-02, eta: 3 days, 14:56:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5427, loss_cls: 4.1379, loss: 4.1379 +2024-07-17 16:07:58,216 - pyskl - INFO - Epoch [41][400/3746] lr: 8.337e-02, eta: 3 days, 14:55:05, time: 0.824, data_time: 0.001, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5219, loss_cls: 4.1877, loss: 4.1877 +2024-07-17 16:09:20,559 - pyskl - INFO - Epoch [41][500/3746] lr: 8.335e-02, eta: 3 days, 14:54:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5364, loss_cls: 4.1336, loss: 4.1336 +2024-07-17 16:10:43,269 - pyskl - INFO - Epoch [41][600/3746] lr: 8.333e-02, eta: 3 days, 14:53:08, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5358, loss_cls: 4.1390, loss: 4.1390 +2024-07-17 16:12:06,155 - pyskl - INFO - Epoch [41][700/3746] lr: 8.331e-02, eta: 3 days, 14:52:11, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5363, loss_cls: 4.1151, loss: 4.1151 +2024-07-17 16:13:29,083 - pyskl - INFO - Epoch [41][800/3746] lr: 8.329e-02, eta: 3 days, 14:51:14, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5211, loss_cls: 4.1830, loss: 4.1830 +2024-07-17 16:14:51,145 - pyskl - INFO - Epoch [41][900/3746] lr: 8.327e-02, eta: 3 days, 14:50:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5391, loss_cls: 4.1314, loss: 4.1314 +2024-07-17 16:16:12,762 - pyskl - INFO - Epoch [41][1000/3746] lr: 8.325e-02, eta: 3 days, 14:49:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5342, loss_cls: 4.1213, loss: 4.1213 +2024-07-17 16:17:33,829 - pyskl - INFO - Epoch [41][1100/3746] lr: 8.323e-02, eta: 3 days, 14:48:11, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5373, loss_cls: 4.1124, loss: 4.1124 +2024-07-17 16:18:54,976 - pyskl - INFO - Epoch [41][1200/3746] lr: 8.321e-02, eta: 3 days, 14:47:09, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5245, loss_cls: 4.1695, loss: 4.1695 +2024-07-17 16:20:16,360 - pyskl - INFO - Epoch [41][1300/3746] lr: 8.319e-02, eta: 3 days, 14:46:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5295, loss_cls: 4.1671, loss: 4.1671 +2024-07-17 16:21:38,066 - pyskl - INFO - Epoch [41][1400/3746] lr: 8.316e-02, eta: 3 days, 14:45:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5336, loss_cls: 4.1747, loss: 4.1747 +2024-07-17 16:22:59,122 - pyskl - INFO - Epoch [41][1500/3746] lr: 8.314e-02, eta: 3 days, 14:44:04, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5303, loss_cls: 4.1496, loss: 4.1496 +2024-07-17 16:24:20,423 - pyskl - INFO - Epoch [41][1600/3746] lr: 8.312e-02, eta: 3 days, 14:43:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5325, loss_cls: 4.1499, loss: 4.1499 +2024-07-17 16:25:42,432 - pyskl - INFO - Epoch [41][1700/3746] lr: 8.310e-02, eta: 3 days, 14:42:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5208, loss_cls: 4.2185, loss: 4.2185 +2024-07-17 16:27:04,210 - pyskl - INFO - Epoch [41][1800/3746] lr: 8.308e-02, eta: 3 days, 14:41:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5255, loss_cls: 4.1769, loss: 4.1769 +2024-07-17 16:28:26,329 - pyskl - INFO - Epoch [41][1900/3746] lr: 8.306e-02, eta: 3 days, 14:40:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5164, loss_cls: 4.1947, loss: 4.1947 +2024-07-17 16:29:48,204 - pyskl - INFO - Epoch [41][2000/3746] lr: 8.304e-02, eta: 3 days, 14:39:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5312, loss_cls: 4.1428, loss: 4.1428 +2024-07-17 16:31:10,176 - pyskl - INFO - Epoch [41][2100/3746] lr: 8.302e-02, eta: 3 days, 14:38:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5283, loss_cls: 4.1489, loss: 4.1489 +2024-07-17 16:32:31,471 - pyskl - INFO - Epoch [41][2200/3746] lr: 8.300e-02, eta: 3 days, 14:37:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5347, loss_cls: 4.1238, loss: 4.1238 +2024-07-17 16:33:53,691 - pyskl - INFO - Epoch [41][2300/3746] lr: 8.298e-02, eta: 3 days, 14:36:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5225, loss_cls: 4.1727, loss: 4.1727 +2024-07-17 16:35:14,734 - pyskl - INFO - Epoch [41][2400/3746] lr: 8.296e-02, eta: 3 days, 14:34:57, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5273, loss_cls: 4.1601, loss: 4.1601 +2024-07-17 16:36:36,308 - pyskl - INFO - Epoch [41][2500/3746] lr: 8.293e-02, eta: 3 days, 14:33:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5325, loss_cls: 4.1205, loss: 4.1205 +2024-07-17 16:37:58,226 - pyskl - INFO - Epoch [41][2600/3746] lr: 8.291e-02, eta: 3 days, 14:32:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5309, loss_cls: 4.1402, loss: 4.1402 +2024-07-17 16:39:20,440 - pyskl - INFO - Epoch [41][2700/3746] lr: 8.289e-02, eta: 3 days, 14:31:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5284, loss_cls: 4.1671, loss: 4.1671 +2024-07-17 16:40:42,357 - pyskl - INFO - Epoch [41][2800/3746] lr: 8.287e-02, eta: 3 days, 14:30:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5333, loss_cls: 4.1427, loss: 4.1427 +2024-07-17 16:42:03,589 - pyskl - INFO - Epoch [41][2900/3746] lr: 8.285e-02, eta: 3 days, 14:29:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5389, loss_cls: 4.1456, loss: 4.1456 +2024-07-17 16:43:24,941 - pyskl - INFO - Epoch [41][3000/3746] lr: 8.283e-02, eta: 3 days, 14:28:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5245, loss_cls: 4.1651, loss: 4.1651 +2024-07-17 16:44:46,970 - pyskl - INFO - Epoch [41][3100/3746] lr: 8.281e-02, eta: 3 days, 14:27:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5228, loss_cls: 4.2010, loss: 4.2010 +2024-07-17 16:46:08,445 - pyskl - INFO - Epoch [41][3200/3746] lr: 8.279e-02, eta: 3 days, 14:26:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5327, loss_cls: 4.1599, loss: 4.1599 +2024-07-17 16:47:30,326 - pyskl - INFO - Epoch [41][3300/3746] lr: 8.277e-02, eta: 3 days, 14:25:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5186, loss_cls: 4.2348, loss: 4.2348 +2024-07-17 16:48:52,369 - pyskl - INFO - Epoch [41][3400/3746] lr: 8.274e-02, eta: 3 days, 14:24:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5256, loss_cls: 4.1902, loss: 4.1902 +2024-07-17 16:50:14,377 - pyskl - INFO - Epoch [41][3500/3746] lr: 8.272e-02, eta: 3 days, 14:23:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5136, loss_cls: 4.2023, loss: 4.2023 +2024-07-17 16:51:37,503 - pyskl - INFO - Epoch [41][3600/3746] lr: 8.270e-02, eta: 3 days, 14:22:50, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5256, loss_cls: 4.1779, loss: 4.1779 +2024-07-17 16:52:58,736 - pyskl - INFO - Epoch [41][3700/3746] lr: 8.268e-02, eta: 3 days, 14:21:47, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5255, loss_cls: 4.2058, loss: 4.2058 +2024-07-17 16:53:38,233 - pyskl - INFO - Saving checkpoint at 41 epochs +2024-07-17 16:55:27,715 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 16:55:28,429 - pyskl - INFO - +top1_acc 0.1994 +top5_acc 0.4326 +2024-07-17 16:55:28,429 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 16:55:28,467 - pyskl - INFO - +mean_acc 0.1991 +2024-07-17 16:55:28,476 - pyskl - INFO - Epoch(val) [41][309] top1_acc: 0.1994, top5_acc: 0.4326, mean_class_accuracy: 0.1991 +2024-07-17 16:59:08,049 - pyskl - INFO - Epoch [42][100/3746] lr: 8.265e-02, eta: 3 days, 14:24:44, time: 2.196, data_time: 1.212, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5303, loss_cls: 4.1479, loss: 4.1479 +2024-07-17 17:00:30,089 - pyskl - INFO - Epoch [42][200/3746] lr: 8.263e-02, eta: 3 days, 14:23:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5306, loss_cls: 4.1527, loss: 4.1527 +2024-07-17 17:01:51,605 - pyskl - INFO - Epoch [42][300/3746] lr: 8.261e-02, eta: 3 days, 14:22:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5302, loss_cls: 4.1719, loss: 4.1719 +2024-07-17 17:03:13,484 - pyskl - INFO - Epoch [42][400/3746] lr: 8.259e-02, eta: 3 days, 14:21:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5327, loss_cls: 4.1399, loss: 4.1399 +2024-07-17 17:04:36,074 - pyskl - INFO - Epoch [42][500/3746] lr: 8.257e-02, eta: 3 days, 14:20:41, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5372, loss_cls: 4.1325, loss: 4.1325 +2024-07-17 17:05:58,141 - pyskl - INFO - Epoch [42][600/3746] lr: 8.254e-02, eta: 3 days, 14:19:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5361, loss_cls: 4.1336, loss: 4.1336 +2024-07-17 17:07:20,687 - pyskl - INFO - Epoch [42][700/3746] lr: 8.252e-02, eta: 3 days, 14:18:40, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5355, loss_cls: 4.1608, loss: 4.1608 +2024-07-17 17:08:42,504 - pyskl - INFO - Epoch [42][800/3746] lr: 8.250e-02, eta: 3 days, 14:17:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5323, loss_cls: 4.1269, loss: 4.1269 +2024-07-17 17:10:04,582 - pyskl - INFO - Epoch [42][900/3746] lr: 8.248e-02, eta: 3 days, 14:16:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5405, loss_cls: 4.1060, loss: 4.1060 +2024-07-17 17:11:26,590 - pyskl - INFO - Epoch [42][1000/3746] lr: 8.246e-02, eta: 3 days, 14:15:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5294, loss_cls: 4.1360, loss: 4.1360 +2024-07-17 17:12:47,826 - pyskl - INFO - Epoch [42][1100/3746] lr: 8.244e-02, eta: 3 days, 14:14:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5269, loss_cls: 4.1727, loss: 4.1727 +2024-07-17 17:14:09,548 - pyskl - INFO - Epoch [42][1200/3746] lr: 8.242e-02, eta: 3 days, 14:13:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5297, loss_cls: 4.1878, loss: 4.1878 +2024-07-17 17:15:31,177 - pyskl - INFO - Epoch [42][1300/3746] lr: 8.240e-02, eta: 3 days, 14:12:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5352, loss_cls: 4.1615, loss: 4.1615 +2024-07-17 17:16:53,208 - pyskl - INFO - Epoch [42][1400/3746] lr: 8.237e-02, eta: 3 days, 14:11:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5375, loss_cls: 4.1475, loss: 4.1475 +2024-07-17 17:18:14,719 - pyskl - INFO - Epoch [42][1500/3746] lr: 8.235e-02, eta: 3 days, 14:10:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5380, loss_cls: 4.1129, loss: 4.1129 +2024-07-17 17:19:36,354 - pyskl - INFO - Epoch [42][1600/3746] lr: 8.233e-02, eta: 3 days, 14:09:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5264, loss_cls: 4.1903, loss: 4.1903 +2024-07-17 17:20:58,187 - pyskl - INFO - Epoch [42][1700/3746] lr: 8.231e-02, eta: 3 days, 14:08:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5381, loss_cls: 4.1539, loss: 4.1539 +2024-07-17 17:22:19,889 - pyskl - INFO - Epoch [42][1800/3746] lr: 8.229e-02, eta: 3 days, 14:07:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5383, loss_cls: 4.1548, loss: 4.1548 +2024-07-17 17:23:41,474 - pyskl - INFO - Epoch [42][1900/3746] lr: 8.227e-02, eta: 3 days, 14:06:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5319, loss_cls: 4.1443, loss: 4.1443 +2024-07-17 17:25:03,179 - pyskl - INFO - Epoch [42][2000/3746] lr: 8.225e-02, eta: 3 days, 14:05:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5344, loss_cls: 4.1268, loss: 4.1268 +2024-07-17 17:26:24,440 - pyskl - INFO - Epoch [42][2100/3746] lr: 8.222e-02, eta: 3 days, 14:04:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5284, loss_cls: 4.1756, loss: 4.1756 +2024-07-17 17:27:45,991 - pyskl - INFO - Epoch [42][2200/3746] lr: 8.220e-02, eta: 3 days, 14:03:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5300, loss_cls: 4.1335, loss: 4.1335 +2024-07-17 17:29:08,129 - pyskl - INFO - Epoch [42][2300/3746] lr: 8.218e-02, eta: 3 days, 14:02:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5248, loss_cls: 4.1665, loss: 4.1665 +2024-07-17 17:30:29,595 - pyskl - INFO - Epoch [42][2400/3746] lr: 8.216e-02, eta: 3 days, 14:01:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5298, loss_cls: 4.1508, loss: 4.1508 +2024-07-17 17:31:51,191 - pyskl - INFO - Epoch [42][2500/3746] lr: 8.214e-02, eta: 3 days, 14:00:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5212, loss_cls: 4.1868, loss: 4.1868 +2024-07-17 17:33:13,124 - pyskl - INFO - Epoch [42][2600/3746] lr: 8.212e-02, eta: 3 days, 13:59:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5311, loss_cls: 4.1357, loss: 4.1357 +2024-07-17 17:34:35,214 - pyskl - INFO - Epoch [42][2700/3746] lr: 8.210e-02, eta: 3 days, 13:58:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5300, loss_cls: 4.1563, loss: 4.1563 +2024-07-17 17:35:56,317 - pyskl - INFO - Epoch [42][2800/3746] lr: 8.207e-02, eta: 3 days, 13:56:57, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5369, loss_cls: 4.1331, loss: 4.1331 +2024-07-17 17:37:17,529 - pyskl - INFO - Epoch [42][2900/3746] lr: 8.205e-02, eta: 3 days, 13:55:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5295, loss_cls: 4.1528, loss: 4.1528 +2024-07-17 17:38:39,138 - pyskl - INFO - Epoch [42][3000/3746] lr: 8.203e-02, eta: 3 days, 13:54:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5353, loss_cls: 4.1485, loss: 4.1485 +2024-07-17 17:40:00,811 - pyskl - INFO - Epoch [42][3100/3746] lr: 8.201e-02, eta: 3 days, 13:53:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5350, loss_cls: 4.1628, loss: 4.1628 +2024-07-17 17:41:21,894 - pyskl - INFO - Epoch [42][3200/3746] lr: 8.199e-02, eta: 3 days, 13:52:45, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5286, loss_cls: 4.1612, loss: 4.1612 +2024-07-17 17:42:43,767 - pyskl - INFO - Epoch [42][3300/3746] lr: 8.197e-02, eta: 3 days, 13:51:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5331, loss_cls: 4.1196, loss: 4.1196 +2024-07-17 17:44:05,115 - pyskl - INFO - Epoch [42][3400/3746] lr: 8.195e-02, eta: 3 days, 13:50:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5342, loss_cls: 4.1465, loss: 4.1465 +2024-07-17 17:45:26,674 - pyskl - INFO - Epoch [42][3500/3746] lr: 8.192e-02, eta: 3 days, 13:49:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5258, loss_cls: 4.1655, loss: 4.1655 +2024-07-17 17:46:49,172 - pyskl - INFO - Epoch [42][3600/3746] lr: 8.190e-02, eta: 3 days, 13:48:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5383, loss_cls: 4.1099, loss: 4.1099 +2024-07-17 17:48:10,297 - pyskl - INFO - Epoch [42][3700/3746] lr: 8.188e-02, eta: 3 days, 13:47:32, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5302, loss_cls: 4.1401, loss: 4.1401 +2024-07-17 17:48:49,577 - pyskl - INFO - Saving checkpoint at 42 epochs +2024-07-17 17:50:38,894 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 17:50:39,553 - pyskl - INFO - +top1_acc 0.2104 +top5_acc 0.4456 +2024-07-17 17:50:39,553 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 17:50:39,591 - pyskl - INFO - +mean_acc 0.2102 +2024-07-17 17:50:39,596 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_28.pth was removed +2024-07-17 17:50:39,855 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_42.pth. +2024-07-17 17:50:39,856 - pyskl - INFO - Best top1_acc is 0.2104 at 42 epoch. +2024-07-17 17:50:39,866 - pyskl - INFO - Epoch(val) [42][309] top1_acc: 0.2104, top5_acc: 0.4456, mean_class_accuracy: 0.2102 +2024-07-17 17:54:24,482 - pyskl - INFO - Epoch [43][100/3746] lr: 8.185e-02, eta: 3 days, 13:50:31, time: 2.246, data_time: 1.260, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5383, loss_cls: 4.1227, loss: 4.1227 +2024-07-17 17:55:46,879 - pyskl - INFO - Epoch [43][200/3746] lr: 8.183e-02, eta: 3 days, 13:49:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5450, loss_cls: 4.0601, loss: 4.0601 +2024-07-17 17:57:09,814 - pyskl - INFO - Epoch [43][300/3746] lr: 8.181e-02, eta: 3 days, 13:48:31, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5312, loss_cls: 4.1742, loss: 4.1742 +2024-07-17 17:58:32,083 - pyskl - INFO - Epoch [43][400/3746] lr: 8.179e-02, eta: 3 days, 13:47:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5328, loss_cls: 4.1276, loss: 4.1276 +2024-07-17 17:59:54,158 - pyskl - INFO - Epoch [43][500/3746] lr: 8.176e-02, eta: 3 days, 13:46:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5442, loss_cls: 4.0948, loss: 4.0948 +2024-07-17 18:01:15,280 - pyskl - INFO - Epoch [43][600/3746] lr: 8.174e-02, eta: 3 days, 13:45:23, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5328, loss_cls: 4.1406, loss: 4.1406 +2024-07-17 18:02:38,063 - pyskl - INFO - Epoch [43][700/3746] lr: 8.172e-02, eta: 3 days, 13:44:23, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5387, loss_cls: 4.1151, loss: 4.1151 +2024-07-17 18:04:00,511 - pyskl - INFO - Epoch [43][800/3746] lr: 8.170e-02, eta: 3 days, 13:43:22, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5408, loss_cls: 4.0697, loss: 4.0697 +2024-07-17 18:05:23,493 - pyskl - INFO - Epoch [43][900/3746] lr: 8.168e-02, eta: 3 days, 13:42:22, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5234, loss_cls: 4.1574, loss: 4.1574 +2024-07-17 18:06:46,226 - pyskl - INFO - Epoch [43][1000/3746] lr: 8.166e-02, eta: 3 days, 13:41:22, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5370, loss_cls: 4.1049, loss: 4.1049 +2024-07-17 18:08:08,637 - pyskl - INFO - Epoch [43][1100/3746] lr: 8.163e-02, eta: 3 days, 13:40:20, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5375, loss_cls: 4.1077, loss: 4.1077 +2024-07-17 18:09:30,891 - pyskl - INFO - Epoch [43][1200/3746] lr: 8.161e-02, eta: 3 days, 13:39:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5336, loss_cls: 4.1441, loss: 4.1441 +2024-07-17 18:10:52,538 - pyskl - INFO - Epoch [43][1300/3746] lr: 8.159e-02, eta: 3 days, 13:38:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5373, loss_cls: 4.1517, loss: 4.1517 +2024-07-17 18:12:14,392 - pyskl - INFO - Epoch [43][1400/3746] lr: 8.157e-02, eta: 3 days, 13:37:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5370, loss_cls: 4.1154, loss: 4.1154 +2024-07-17 18:13:35,818 - pyskl - INFO - Epoch [43][1500/3746] lr: 8.155e-02, eta: 3 days, 13:36:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5361, loss_cls: 4.1488, loss: 4.1488 +2024-07-17 18:14:56,971 - pyskl - INFO - Epoch [43][1600/3746] lr: 8.153e-02, eta: 3 days, 13:35:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5303, loss_cls: 4.1428, loss: 4.1428 +2024-07-17 18:16:18,521 - pyskl - INFO - Epoch [43][1700/3746] lr: 8.150e-02, eta: 3 days, 13:34:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5303, loss_cls: 4.1184, loss: 4.1184 +2024-07-17 18:17:39,821 - pyskl - INFO - Epoch [43][1800/3746] lr: 8.148e-02, eta: 3 days, 13:32:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5347, loss_cls: 4.1474, loss: 4.1474 +2024-07-17 18:19:01,538 - pyskl - INFO - Epoch [43][1900/3746] lr: 8.146e-02, eta: 3 days, 13:31:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5339, loss_cls: 4.1388, loss: 4.1388 +2024-07-17 18:20:22,725 - pyskl - INFO - Epoch [43][2000/3746] lr: 8.144e-02, eta: 3 days, 13:30:49, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5181, loss_cls: 4.1907, loss: 4.1907 +2024-07-17 18:21:44,079 - pyskl - INFO - Epoch [43][2100/3746] lr: 8.142e-02, eta: 3 days, 13:29:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5252, loss_cls: 4.1776, loss: 4.1776 +2024-07-17 18:23:05,245 - pyskl - INFO - Epoch [43][2200/3746] lr: 8.140e-02, eta: 3 days, 13:28:40, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5298, loss_cls: 4.1473, loss: 4.1473 +2024-07-17 18:24:26,825 - pyskl - INFO - Epoch [43][2300/3746] lr: 8.137e-02, eta: 3 days, 13:27:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5239, loss_cls: 4.1620, loss: 4.1620 +2024-07-17 18:25:48,736 - pyskl - INFO - Epoch [43][2400/3746] lr: 8.135e-02, eta: 3 days, 13:26:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5253, loss_cls: 4.1621, loss: 4.1621 +2024-07-17 18:27:10,194 - pyskl - INFO - Epoch [43][2500/3746] lr: 8.133e-02, eta: 3 days, 13:25:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5322, loss_cls: 4.1418, loss: 4.1418 +2024-07-17 18:28:32,286 - pyskl - INFO - Epoch [43][2600/3746] lr: 8.131e-02, eta: 3 days, 13:24:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5238, loss_cls: 4.1554, loss: 4.1554 +2024-07-17 18:29:54,247 - pyskl - INFO - Epoch [43][2700/3746] lr: 8.129e-02, eta: 3 days, 13:23:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5348, loss_cls: 4.1485, loss: 4.1485 +2024-07-17 18:31:16,232 - pyskl - INFO - Epoch [43][2800/3746] lr: 8.126e-02, eta: 3 days, 13:22:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5292, loss_cls: 4.1747, loss: 4.1747 +2024-07-17 18:32:38,180 - pyskl - INFO - Epoch [43][2900/3746] lr: 8.124e-02, eta: 3 days, 13:21:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5334, loss_cls: 4.1069, loss: 4.1069 +2024-07-17 18:34:00,015 - pyskl - INFO - Epoch [43][3000/3746] lr: 8.122e-02, eta: 3 days, 13:20:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5250, loss_cls: 4.1689, loss: 4.1689 +2024-07-17 18:35:22,032 - pyskl - INFO - Epoch [43][3100/3746] lr: 8.120e-02, eta: 3 days, 13:19:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5256, loss_cls: 4.1786, loss: 4.1786 +2024-07-17 18:36:43,080 - pyskl - INFO - Epoch [43][3200/3746] lr: 8.118e-02, eta: 3 days, 13:18:08, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5252, loss_cls: 4.1469, loss: 4.1469 +2024-07-17 18:38:04,195 - pyskl - INFO - Epoch [43][3300/3746] lr: 8.116e-02, eta: 3 days, 13:17:03, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5327, loss_cls: 4.1379, loss: 4.1379 +2024-07-17 18:39:25,787 - pyskl - INFO - Epoch [43][3400/3746] lr: 8.113e-02, eta: 3 days, 13:15:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5311, loss_cls: 4.1537, loss: 4.1537 +2024-07-17 18:40:47,508 - pyskl - INFO - Epoch [43][3500/3746] lr: 8.111e-02, eta: 3 days, 13:14:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5269, loss_cls: 4.1968, loss: 4.1968 +2024-07-17 18:42:09,970 - pyskl - INFO - Epoch [43][3600/3746] lr: 8.109e-02, eta: 3 days, 13:13:54, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5259, loss_cls: 4.1784, loss: 4.1784 +2024-07-17 18:43:31,851 - pyskl - INFO - Epoch [43][3700/3746] lr: 8.107e-02, eta: 3 days, 13:12:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5370, loss_cls: 4.1204, loss: 4.1204 +2024-07-17 18:44:10,685 - pyskl - INFO - Saving checkpoint at 43 epochs +2024-07-17 18:46:01,122 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 18:46:01,777 - pyskl - INFO - +top1_acc 0.2030 +top5_acc 0.4307 +2024-07-17 18:46:01,777 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 18:46:01,816 - pyskl - INFO - +mean_acc 0.2027 +2024-07-17 18:46:01,827 - pyskl - INFO - Epoch(val) [43][309] top1_acc: 0.2030, top5_acc: 0.4307, mean_class_accuracy: 0.2027 +2024-07-17 18:49:44,222 - pyskl - INFO - Epoch [44][100/3746] lr: 8.104e-02, eta: 3 days, 13:15:34, time: 2.224, data_time: 1.255, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5545, loss_cls: 4.0467, loss: 4.0467 +2024-07-17 18:51:06,197 - pyskl - INFO - Epoch [44][200/3746] lr: 8.101e-02, eta: 3 days, 13:14:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5414, loss_cls: 4.0717, loss: 4.0717 +2024-07-17 18:52:28,265 - pyskl - INFO - Epoch [44][300/3746] lr: 8.099e-02, eta: 3 days, 13:13:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5436, loss_cls: 4.0839, loss: 4.0839 +2024-07-17 18:53:50,336 - pyskl - INFO - Epoch [44][400/3746] lr: 8.097e-02, eta: 3 days, 13:12:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5444, loss_cls: 4.0614, loss: 4.0614 +2024-07-17 18:55:12,854 - pyskl - INFO - Epoch [44][500/3746] lr: 8.095e-02, eta: 3 days, 13:11:23, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5356, loss_cls: 4.1679, loss: 4.1679 +2024-07-17 18:56:34,238 - pyskl - INFO - Epoch [44][600/3746] lr: 8.093e-02, eta: 3 days, 13:10:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5364, loss_cls: 4.1342, loss: 4.1342 +2024-07-17 18:57:56,346 - pyskl - INFO - Epoch [44][700/3746] lr: 8.090e-02, eta: 3 days, 13:09:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5283, loss_cls: 4.1710, loss: 4.1710 +2024-07-17 18:59:19,150 - pyskl - INFO - Epoch [44][800/3746] lr: 8.088e-02, eta: 3 days, 13:08:14, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5417, loss_cls: 4.1249, loss: 4.1249 +2024-07-17 19:00:41,273 - pyskl - INFO - Epoch [44][900/3746] lr: 8.086e-02, eta: 3 days, 13:07:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5272, loss_cls: 4.1787, loss: 4.1787 +2024-07-17 19:02:03,071 - pyskl - INFO - Epoch [44][1000/3746] lr: 8.084e-02, eta: 3 days, 13:06:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5188, loss_cls: 4.2003, loss: 4.2003 +2024-07-17 19:03:24,706 - pyskl - INFO - Epoch [44][1100/3746] lr: 8.082e-02, eta: 3 days, 13:05:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5312, loss_cls: 4.1152, loss: 4.1152 +2024-07-17 19:04:46,250 - pyskl - INFO - Epoch [44][1200/3746] lr: 8.079e-02, eta: 3 days, 13:03:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5425, loss_cls: 4.1257, loss: 4.1257 +2024-07-17 19:06:08,622 - pyskl - INFO - Epoch [44][1300/3746] lr: 8.077e-02, eta: 3 days, 13:02:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5353, loss_cls: 4.1411, loss: 4.1411 +2024-07-17 19:07:29,710 - pyskl - INFO - Epoch [44][1400/3746] lr: 8.075e-02, eta: 3 days, 13:01:50, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5275, loss_cls: 4.1745, loss: 4.1745 +2024-07-17 19:08:51,026 - pyskl - INFO - Epoch [44][1500/3746] lr: 8.073e-02, eta: 3 days, 13:00:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5406, loss_cls: 4.0847, loss: 4.0847 +2024-07-17 19:10:12,323 - pyskl - INFO - Epoch [44][1600/3746] lr: 8.071e-02, eta: 3 days, 12:59:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5289, loss_cls: 4.1664, loss: 4.1664 +2024-07-17 19:11:33,708 - pyskl - INFO - Epoch [44][1700/3746] lr: 8.068e-02, eta: 3 days, 12:58:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5423, loss_cls: 4.1002, loss: 4.1002 +2024-07-17 19:12:55,354 - pyskl - INFO - Epoch [44][1800/3746] lr: 8.066e-02, eta: 3 days, 12:57:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5269, loss_cls: 4.1511, loss: 4.1511 +2024-07-17 19:14:17,131 - pyskl - INFO - Epoch [44][1900/3746] lr: 8.064e-02, eta: 3 days, 12:56:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5402, loss_cls: 4.1239, loss: 4.1239 +2024-07-17 19:15:39,229 - pyskl - INFO - Epoch [44][2000/3746] lr: 8.062e-02, eta: 3 days, 12:55:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5441, loss_cls: 4.0931, loss: 4.0931 +2024-07-17 19:17:00,695 - pyskl - INFO - Epoch [44][2100/3746] lr: 8.060e-02, eta: 3 days, 12:54:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5267, loss_cls: 4.1850, loss: 4.1850 +2024-07-17 19:18:22,186 - pyskl - INFO - Epoch [44][2200/3746] lr: 8.057e-02, eta: 3 days, 12:53:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5437, loss_cls: 4.1146, loss: 4.1146 +2024-07-17 19:19:44,080 - pyskl - INFO - Epoch [44][2300/3746] lr: 8.055e-02, eta: 3 days, 12:52:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5303, loss_cls: 4.1575, loss: 4.1575 +2024-07-17 19:21:05,401 - pyskl - INFO - Epoch [44][2400/3746] lr: 8.053e-02, eta: 3 days, 12:51:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5225, loss_cls: 4.1877, loss: 4.1877 +2024-07-17 19:22:26,391 - pyskl - INFO - Epoch [44][2500/3746] lr: 8.051e-02, eta: 3 days, 12:49:57, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5433, loss_cls: 4.0931, loss: 4.0931 +2024-07-17 19:23:48,245 - pyskl - INFO - Epoch [44][2600/3746] lr: 8.048e-02, eta: 3 days, 12:48:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5331, loss_cls: 4.1380, loss: 4.1380 +2024-07-17 19:25:09,770 - pyskl - INFO - Epoch [44][2700/3746] lr: 8.046e-02, eta: 3 days, 12:47:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5267, loss_cls: 4.1545, loss: 4.1545 +2024-07-17 19:26:30,957 - pyskl - INFO - Epoch [44][2800/3746] lr: 8.044e-02, eta: 3 days, 12:46:43, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5370, loss_cls: 4.1202, loss: 4.1202 +2024-07-17 19:27:52,706 - pyskl - INFO - Epoch [44][2900/3746] lr: 8.042e-02, eta: 3 days, 12:45:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5417, loss_cls: 4.0971, loss: 4.0971 +2024-07-17 19:29:14,029 - pyskl - INFO - Epoch [44][3000/3746] lr: 8.040e-02, eta: 3 days, 12:44:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5408, loss_cls: 4.1220, loss: 4.1220 +2024-07-17 19:30:35,372 - pyskl - INFO - Epoch [44][3100/3746] lr: 8.037e-02, eta: 3 days, 12:43:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5339, loss_cls: 4.1334, loss: 4.1334 +2024-07-17 19:31:56,760 - pyskl - INFO - Epoch [44][3200/3746] lr: 8.035e-02, eta: 3 days, 12:42:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5308, loss_cls: 4.1378, loss: 4.1378 +2024-07-17 19:33:18,231 - pyskl - INFO - Epoch [44][3300/3746] lr: 8.033e-02, eta: 3 days, 12:41:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5258, loss_cls: 4.1737, loss: 4.1737 +2024-07-17 19:34:40,140 - pyskl - INFO - Epoch [44][3400/3746] lr: 8.031e-02, eta: 3 days, 12:40:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5414, loss_cls: 4.1021, loss: 4.1021 +2024-07-17 19:36:01,436 - pyskl - INFO - Epoch [44][3500/3746] lr: 8.028e-02, eta: 3 days, 12:39:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5277, loss_cls: 4.1677, loss: 4.1677 +2024-07-17 19:37:24,158 - pyskl - INFO - Epoch [44][3600/3746] lr: 8.026e-02, eta: 3 days, 12:38:05, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5413, loss_cls: 4.1288, loss: 4.1288 +2024-07-17 19:38:46,565 - pyskl - INFO - Epoch [44][3700/3746] lr: 8.024e-02, eta: 3 days, 12:37:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5377, loss_cls: 4.1593, loss: 4.1593 +2024-07-17 19:39:25,739 - pyskl - INFO - Saving checkpoint at 44 epochs +2024-07-17 19:41:16,258 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 19:41:16,930 - pyskl - INFO - +top1_acc 0.2173 +top5_acc 0.4567 +2024-07-17 19:41:16,930 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 19:41:16,970 - pyskl - INFO - +mean_acc 0.2170 +2024-07-17 19:41:16,975 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_42.pth was removed +2024-07-17 19:41:17,299 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_44.pth. +2024-07-17 19:41:17,299 - pyskl - INFO - Best top1_acc is 0.2173 at 44 epoch. +2024-07-17 19:41:17,313 - pyskl - INFO - Epoch(val) [44][309] top1_acc: 0.2173, top5_acc: 0.4567, mean_class_accuracy: 0.2170 +2024-07-17 19:45:00,503 - pyskl - INFO - Epoch [45][100/3746] lr: 8.021e-02, eta: 3 days, 12:39:38, time: 2.232, data_time: 1.257, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5437, loss_cls: 4.0880, loss: 4.0880 +2024-07-17 19:46:22,956 - pyskl - INFO - Epoch [45][200/3746] lr: 8.019e-02, eta: 3 days, 12:38:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5467, loss_cls: 4.0735, loss: 4.0735 +2024-07-17 19:47:44,980 - pyskl - INFO - Epoch [45][300/3746] lr: 8.016e-02, eta: 3 days, 12:37:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5373, loss_cls: 4.1216, loss: 4.1216 +2024-07-17 19:49:06,527 - pyskl - INFO - Epoch [45][400/3746] lr: 8.014e-02, eta: 3 days, 12:36:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5448, loss_cls: 4.1136, loss: 4.1136 +2024-07-17 19:50:28,402 - pyskl - INFO - Epoch [45][500/3746] lr: 8.012e-02, eta: 3 days, 12:35:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5314, loss_cls: 4.1465, loss: 4.1465 +2024-07-17 19:51:50,025 - pyskl - INFO - Epoch [45][600/3746] lr: 8.010e-02, eta: 3 days, 12:34:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5477, loss_cls: 4.0588, loss: 4.0588 +2024-07-17 19:53:11,624 - pyskl - INFO - Epoch [45][700/3746] lr: 8.007e-02, eta: 3 days, 12:33:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5294, loss_cls: 4.1403, loss: 4.1403 +2024-07-17 19:54:34,502 - pyskl - INFO - Epoch [45][800/3746] lr: 8.005e-02, eta: 3 days, 12:32:08, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5425, loss_cls: 4.0980, loss: 4.0980 +2024-07-17 19:55:56,467 - pyskl - INFO - Epoch [45][900/3746] lr: 8.003e-02, eta: 3 days, 12:31:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5283, loss_cls: 4.1752, loss: 4.1752 +2024-07-17 19:57:18,990 - pyskl - INFO - Epoch [45][1000/3746] lr: 8.001e-02, eta: 3 days, 12:30:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5306, loss_cls: 4.1430, loss: 4.1430 +2024-07-17 19:58:41,447 - pyskl - INFO - Epoch [45][1100/3746] lr: 7.998e-02, eta: 3 days, 12:28:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5391, loss_cls: 4.1127, loss: 4.1127 +2024-07-17 20:00:02,570 - pyskl - INFO - Epoch [45][1200/3746] lr: 7.996e-02, eta: 3 days, 12:27:50, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5403, loss_cls: 4.1116, loss: 4.1116 +2024-07-17 20:01:23,558 - pyskl - INFO - Epoch [45][1300/3746] lr: 7.994e-02, eta: 3 days, 12:26:44, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5384, loss_cls: 4.1197, loss: 4.1197 +2024-07-17 20:02:44,930 - pyskl - INFO - Epoch [45][1400/3746] lr: 7.992e-02, eta: 3 days, 12:25:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5392, loss_cls: 4.1073, loss: 4.1073 +2024-07-17 20:04:06,142 - pyskl - INFO - Epoch [45][1500/3746] lr: 7.990e-02, eta: 3 days, 12:24:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5242, loss_cls: 4.1871, loss: 4.1871 +2024-07-17 20:05:27,477 - pyskl - INFO - Epoch [45][1600/3746] lr: 7.987e-02, eta: 3 days, 12:23:25, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5337, loss_cls: 4.1224, loss: 4.1224 +2024-07-17 20:06:48,891 - pyskl - INFO - Epoch [45][1700/3746] lr: 7.985e-02, eta: 3 days, 12:22:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5309, loss_cls: 4.1551, loss: 4.1551 +2024-07-17 20:08:10,759 - pyskl - INFO - Epoch [45][1800/3746] lr: 7.983e-02, eta: 3 days, 12:21:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5298, loss_cls: 4.1479, loss: 4.1479 +2024-07-17 20:09:32,271 - pyskl - INFO - Epoch [45][1900/3746] lr: 7.981e-02, eta: 3 days, 12:20:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5384, loss_cls: 4.1086, loss: 4.1086 +2024-07-17 20:10:53,643 - pyskl - INFO - Epoch [45][2000/3746] lr: 7.978e-02, eta: 3 days, 12:19:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5369, loss_cls: 4.1214, loss: 4.1214 +2024-07-17 20:12:15,238 - pyskl - INFO - Epoch [45][2100/3746] lr: 7.976e-02, eta: 3 days, 12:17:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5436, loss_cls: 4.0712, loss: 4.0712 +2024-07-17 20:13:36,418 - pyskl - INFO - Epoch [45][2200/3746] lr: 7.974e-02, eta: 3 days, 12:16:50, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5380, loss_cls: 4.1301, loss: 4.1301 +2024-07-17 20:14:58,025 - pyskl - INFO - Epoch [45][2300/3746] lr: 7.972e-02, eta: 3 days, 12:15:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5288, loss_cls: 4.1523, loss: 4.1523 +2024-07-17 20:16:20,512 - pyskl - INFO - Epoch [45][2400/3746] lr: 7.969e-02, eta: 3 days, 12:14:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5381, loss_cls: 4.1063, loss: 4.1063 +2024-07-17 20:17:41,839 - pyskl - INFO - Epoch [45][2500/3746] lr: 7.967e-02, eta: 3 days, 12:13:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5450, loss_cls: 4.0858, loss: 4.0858 +2024-07-17 20:19:04,081 - pyskl - INFO - Epoch [45][2600/3746] lr: 7.965e-02, eta: 3 days, 12:12:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5286, loss_cls: 4.1560, loss: 4.1560 +2024-07-17 20:20:26,059 - pyskl - INFO - Epoch [45][2700/3746] lr: 7.963e-02, eta: 3 days, 12:11:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5356, loss_cls: 4.1394, loss: 4.1394 +2024-07-17 20:21:47,236 - pyskl - INFO - Epoch [45][2800/3746] lr: 7.960e-02, eta: 3 days, 12:10:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5306, loss_cls: 4.1359, loss: 4.1359 +2024-07-17 20:23:08,702 - pyskl - INFO - Epoch [45][2900/3746] lr: 7.958e-02, eta: 3 days, 12:09:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5233, loss_cls: 4.1703, loss: 4.1703 +2024-07-17 20:24:29,770 - pyskl - INFO - Epoch [45][3000/3746] lr: 7.956e-02, eta: 3 days, 12:08:06, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5355, loss_cls: 4.1352, loss: 4.1352 +2024-07-17 20:25:51,210 - pyskl - INFO - Epoch [45][3100/3746] lr: 7.954e-02, eta: 3 days, 12:07:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5425, loss_cls: 4.1176, loss: 4.1176 +2024-07-17 20:27:12,451 - pyskl - INFO - Epoch [45][3200/3746] lr: 7.951e-02, eta: 3 days, 12:05:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5400, loss_cls: 4.1324, loss: 4.1324 +2024-07-17 20:28:34,058 - pyskl - INFO - Epoch [45][3300/3746] lr: 7.949e-02, eta: 3 days, 12:04:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5372, loss_cls: 4.1370, loss: 4.1370 +2024-07-17 20:29:55,614 - pyskl - INFO - Epoch [45][3400/3746] lr: 7.947e-02, eta: 3 days, 12:03:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5430, loss_cls: 4.0615, loss: 4.0615 +2024-07-17 20:31:17,672 - pyskl - INFO - Epoch [45][3500/3746] lr: 7.945e-02, eta: 3 days, 12:02:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5344, loss_cls: 4.1299, loss: 4.1299 +2024-07-17 20:32:40,047 - pyskl - INFO - Epoch [45][3600/3746] lr: 7.942e-02, eta: 3 days, 12:01:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5344, loss_cls: 4.1335, loss: 4.1335 +2024-07-17 20:34:01,799 - pyskl - INFO - Epoch [45][3700/3746] lr: 7.940e-02, eta: 3 days, 12:00:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5303, loss_cls: 4.1314, loss: 4.1314 +2024-07-17 20:34:41,099 - pyskl - INFO - Saving checkpoint at 45 epochs +2024-07-17 20:36:31,480 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 20:36:32,141 - pyskl - INFO - +top1_acc 0.1978 +top5_acc 0.4274 +2024-07-17 20:36:32,141 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 20:36:32,180 - pyskl - INFO - +mean_acc 0.1975 +2024-07-17 20:36:32,191 - pyskl - INFO - Epoch(val) [45][309] top1_acc: 0.1978, top5_acc: 0.4274, mean_class_accuracy: 0.1975 +2024-07-17 20:40:12,307 - pyskl - INFO - Epoch [46][100/3746] lr: 7.937e-02, eta: 3 days, 12:02:47, time: 2.201, data_time: 1.228, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5348, loss_cls: 4.1368, loss: 4.1368 +2024-07-17 20:41:34,346 - pyskl - INFO - Epoch [46][200/3746] lr: 7.934e-02, eta: 3 days, 12:01:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5480, loss_cls: 4.0587, loss: 4.0587 +2024-07-17 20:42:56,750 - pyskl - INFO - Epoch [46][300/3746] lr: 7.932e-02, eta: 3 days, 12:00:38, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5431, loss_cls: 4.0815, loss: 4.0815 +2024-07-17 20:44:18,179 - pyskl - INFO - Epoch [46][400/3746] lr: 7.930e-02, eta: 3 days, 11:59:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5389, loss_cls: 4.0939, loss: 4.0939 +2024-07-17 20:45:40,225 - pyskl - INFO - Epoch [46][500/3746] lr: 7.928e-02, eta: 3 days, 11:58:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5423, loss_cls: 4.0810, loss: 4.0810 +2024-07-17 20:47:02,563 - pyskl - INFO - Epoch [46][600/3746] lr: 7.925e-02, eta: 3 days, 11:57:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5289, loss_cls: 4.1432, loss: 4.1432 +2024-07-17 20:48:24,117 - pyskl - INFO - Epoch [46][700/3746] lr: 7.923e-02, eta: 3 days, 11:56:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5206, loss_cls: 4.1842, loss: 4.1842 +2024-07-17 20:49:46,539 - pyskl - INFO - Epoch [46][800/3746] lr: 7.921e-02, eta: 3 days, 11:55:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5375, loss_cls: 4.1139, loss: 4.1139 +2024-07-17 20:51:09,384 - pyskl - INFO - Epoch [46][900/3746] lr: 7.919e-02, eta: 3 days, 11:54:08, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5497, loss_cls: 4.0438, loss: 4.0438 +2024-07-17 20:52:30,647 - pyskl - INFO - Epoch [46][1000/3746] lr: 7.916e-02, eta: 3 days, 11:53:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5314, loss_cls: 4.1163, loss: 4.1163 +2024-07-17 20:53:52,403 - pyskl - INFO - Epoch [46][1100/3746] lr: 7.914e-02, eta: 3 days, 11:51:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5258, loss_cls: 4.1659, loss: 4.1659 +2024-07-17 20:55:14,390 - pyskl - INFO - Epoch [46][1200/3746] lr: 7.912e-02, eta: 3 days, 11:50:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5294, loss_cls: 4.1423, loss: 4.1423 +2024-07-17 20:56:36,483 - pyskl - INFO - Epoch [46][1300/3746] lr: 7.909e-02, eta: 3 days, 11:49:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5366, loss_cls: 4.1007, loss: 4.1007 +2024-07-17 20:57:58,036 - pyskl - INFO - Epoch [46][1400/3746] lr: 7.907e-02, eta: 3 days, 11:48:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5309, loss_cls: 4.1327, loss: 4.1327 +2024-07-17 20:59:19,714 - pyskl - INFO - Epoch [46][1500/3746] lr: 7.905e-02, eta: 3 days, 11:47:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5391, loss_cls: 4.1304, loss: 4.1304 +2024-07-17 21:00:41,735 - pyskl - INFO - Epoch [46][1600/3746] lr: 7.903e-02, eta: 3 days, 11:46:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5341, loss_cls: 4.1490, loss: 4.1490 +2024-07-17 21:02:03,101 - pyskl - INFO - Epoch [46][1700/3746] lr: 7.900e-02, eta: 3 days, 11:45:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5506, loss_cls: 4.0790, loss: 4.0790 +2024-07-17 21:03:24,892 - pyskl - INFO - Epoch [46][1800/3746] lr: 7.898e-02, eta: 3 days, 11:44:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5377, loss_cls: 4.0687, loss: 4.0687 +2024-07-17 21:04:46,530 - pyskl - INFO - Epoch [46][1900/3746] lr: 7.896e-02, eta: 3 days, 11:43:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5417, loss_cls: 4.0998, loss: 4.0998 +2024-07-17 21:06:08,636 - pyskl - INFO - Epoch [46][2000/3746] lr: 7.894e-02, eta: 3 days, 11:42:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5320, loss_cls: 4.1398, loss: 4.1398 +2024-07-17 21:07:29,992 - pyskl - INFO - Epoch [46][2100/3746] lr: 7.891e-02, eta: 3 days, 11:40:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5328, loss_cls: 4.1084, loss: 4.1084 +2024-07-17 21:08:51,851 - pyskl - INFO - Epoch [46][2200/3746] lr: 7.889e-02, eta: 3 days, 11:39:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5498, loss_cls: 4.0979, loss: 4.0979 +2024-07-17 21:10:13,378 - pyskl - INFO - Epoch [46][2300/3746] lr: 7.887e-02, eta: 3 days, 11:38:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5358, loss_cls: 4.1314, loss: 4.1314 +2024-07-17 21:11:35,331 - pyskl - INFO - Epoch [46][2400/3746] lr: 7.884e-02, eta: 3 days, 11:37:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5459, loss_cls: 4.0939, loss: 4.0939 +2024-07-17 21:12:56,897 - pyskl - INFO - Epoch [46][2500/3746] lr: 7.882e-02, eta: 3 days, 11:36:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5288, loss_cls: 4.1621, loss: 4.1621 +2024-07-17 21:14:18,525 - pyskl - INFO - Epoch [46][2600/3746] lr: 7.880e-02, eta: 3 days, 11:35:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5466, loss_cls: 4.1142, loss: 4.1142 +2024-07-17 21:15:40,752 - pyskl - INFO - Epoch [46][2700/3746] lr: 7.878e-02, eta: 3 days, 11:34:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5364, loss_cls: 4.0977, loss: 4.0977 +2024-07-17 21:17:02,174 - pyskl - INFO - Epoch [46][2800/3746] lr: 7.875e-02, eta: 3 days, 11:33:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5345, loss_cls: 4.1437, loss: 4.1437 +2024-07-17 21:18:23,265 - pyskl - INFO - Epoch [46][2900/3746] lr: 7.873e-02, eta: 3 days, 11:32:03, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5463, loss_cls: 4.1036, loss: 4.1036 +2024-07-17 21:19:44,872 - pyskl - INFO - Epoch [46][3000/3746] lr: 7.871e-02, eta: 3 days, 11:30:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5413, loss_cls: 4.1422, loss: 4.1422 +2024-07-17 21:21:06,224 - pyskl - INFO - Epoch [46][3100/3746] lr: 7.868e-02, eta: 3 days, 11:29:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5370, loss_cls: 4.1376, loss: 4.1376 +2024-07-17 21:22:28,627 - pyskl - INFO - Epoch [46][3200/3746] lr: 7.866e-02, eta: 3 days, 11:28:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5409, loss_cls: 4.1205, loss: 4.1205 +2024-07-17 21:23:50,066 - pyskl - INFO - Epoch [46][3300/3746] lr: 7.864e-02, eta: 3 days, 11:27:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5391, loss_cls: 4.1125, loss: 4.1125 +2024-07-17 21:25:11,639 - pyskl - INFO - Epoch [46][3400/3746] lr: 7.862e-02, eta: 3 days, 11:26:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5402, loss_cls: 4.0997, loss: 4.0997 +2024-07-17 21:26:33,174 - pyskl - INFO - Epoch [46][3500/3746] lr: 7.859e-02, eta: 3 days, 11:25:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5284, loss_cls: 4.1547, loss: 4.1547 +2024-07-17 21:27:56,141 - pyskl - INFO - Epoch [46][3600/3746] lr: 7.857e-02, eta: 3 days, 11:24:21, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5319, loss_cls: 4.1016, loss: 4.1016 +2024-07-17 21:29:18,140 - pyskl - INFO - Epoch [46][3700/3746] lr: 7.855e-02, eta: 3 days, 11:23:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5439, loss_cls: 4.1291, loss: 4.1291 +2024-07-17 21:29:57,432 - pyskl - INFO - Saving checkpoint at 46 epochs +2024-07-17 21:31:46,758 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 21:31:47,478 - pyskl - INFO - +top1_acc 0.2124 +top5_acc 0.4547 +2024-07-17 21:31:47,479 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 21:31:47,517 - pyskl - INFO - +mean_acc 0.2123 +2024-07-17 21:31:47,527 - pyskl - INFO - Epoch(val) [46][309] top1_acc: 0.2124, top5_acc: 0.4547, mean_class_accuracy: 0.2123 +2024-07-17 21:35:26,143 - pyskl - INFO - Epoch [47][100/3746] lr: 7.851e-02, eta: 3 days, 11:25:22, time: 2.186, data_time: 1.223, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5523, loss_cls: 4.0132, loss: 4.0132 +2024-07-17 21:36:47,825 - pyskl - INFO - Epoch [47][200/3746] lr: 7.849e-02, eta: 3 days, 11:24:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5281, loss_cls: 4.1608, loss: 4.1608 +2024-07-17 21:38:09,450 - pyskl - INFO - Epoch [47][300/3746] lr: 7.847e-02, eta: 3 days, 11:23:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5566, loss_cls: 4.0460, loss: 4.0460 +2024-07-17 21:39:31,018 - pyskl - INFO - Epoch [47][400/3746] lr: 7.844e-02, eta: 3 days, 11:22:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5402, loss_cls: 4.0768, loss: 4.0768 +2024-07-17 21:40:52,038 - pyskl - INFO - Epoch [47][500/3746] lr: 7.842e-02, eta: 3 days, 11:20:53, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5425, loss_cls: 4.1194, loss: 4.1194 +2024-07-17 21:42:14,465 - pyskl - INFO - Epoch [47][600/3746] lr: 7.840e-02, eta: 3 days, 11:19:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5341, loss_cls: 4.1407, loss: 4.1407 +2024-07-17 21:43:36,644 - pyskl - INFO - Epoch [47][700/3746] lr: 7.838e-02, eta: 3 days, 11:18:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5353, loss_cls: 4.0997, loss: 4.0997 +2024-07-17 21:44:58,304 - pyskl - INFO - Epoch [47][800/3746] lr: 7.835e-02, eta: 3 days, 11:17:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5470, loss_cls: 4.0825, loss: 4.0825 +2024-07-17 21:46:20,661 - pyskl - INFO - Epoch [47][900/3746] lr: 7.833e-02, eta: 3 days, 11:16:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5406, loss_cls: 4.1117, loss: 4.1117 +2024-07-17 21:47:43,628 - pyskl - INFO - Epoch [47][1000/3746] lr: 7.831e-02, eta: 3 days, 11:15:25, time: 0.830, data_time: 0.001, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5409, loss_cls: 4.0941, loss: 4.0941 +2024-07-17 21:49:05,292 - pyskl - INFO - Epoch [47][1100/3746] lr: 7.828e-02, eta: 3 days, 11:14:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5359, loss_cls: 4.1318, loss: 4.1318 +2024-07-17 21:50:27,700 - pyskl - INFO - Epoch [47][1200/3746] lr: 7.826e-02, eta: 3 days, 11:13:13, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5334, loss_cls: 4.1315, loss: 4.1315 +2024-07-17 21:51:49,328 - pyskl - INFO - Epoch [47][1300/3746] lr: 7.824e-02, eta: 3 days, 11:12:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5417, loss_cls: 4.0970, loss: 4.0970 +2024-07-17 21:53:10,927 - pyskl - INFO - Epoch [47][1400/3746] lr: 7.821e-02, eta: 3 days, 11:10:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5325, loss_cls: 4.1336, loss: 4.1336 +2024-07-17 21:54:32,135 - pyskl - INFO - Epoch [47][1500/3746] lr: 7.819e-02, eta: 3 days, 11:09:51, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5278, loss_cls: 4.1527, loss: 4.1527 +2024-07-17 21:55:53,397 - pyskl - INFO - Epoch [47][1600/3746] lr: 7.817e-02, eta: 3 days, 11:08:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5345, loss_cls: 4.1666, loss: 4.1666 +2024-07-17 21:57:14,913 - pyskl - INFO - Epoch [47][1700/3746] lr: 7.814e-02, eta: 3 days, 11:07:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5447, loss_cls: 4.0824, loss: 4.0824 +2024-07-17 21:58:36,497 - pyskl - INFO - Epoch [47][1800/3746] lr: 7.812e-02, eta: 3 days, 11:06:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5453, loss_cls: 4.0856, loss: 4.0856 +2024-07-17 21:59:58,057 - pyskl - INFO - Epoch [47][1900/3746] lr: 7.810e-02, eta: 3 days, 11:05:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5383, loss_cls: 4.1071, loss: 4.1071 +2024-07-17 22:01:19,439 - pyskl - INFO - Epoch [47][2000/3746] lr: 7.808e-02, eta: 3 days, 11:04:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5258, loss_cls: 4.1685, loss: 4.1685 +2024-07-17 22:02:40,836 - pyskl - INFO - Epoch [47][2100/3746] lr: 7.805e-02, eta: 3 days, 11:03:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5375, loss_cls: 4.1293, loss: 4.1293 +2024-07-17 22:04:02,274 - pyskl - INFO - Epoch [47][2200/3746] lr: 7.803e-02, eta: 3 days, 11:01:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5337, loss_cls: 4.1212, loss: 4.1212 +2024-07-17 22:05:23,811 - pyskl - INFO - Epoch [47][2300/3746] lr: 7.801e-02, eta: 3 days, 11:00:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5312, loss_cls: 4.1150, loss: 4.1150 +2024-07-17 22:06:46,396 - pyskl - INFO - Epoch [47][2400/3746] lr: 7.798e-02, eta: 3 days, 10:59:45, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5311, loss_cls: 4.1609, loss: 4.1609 +2024-07-17 22:08:07,751 - pyskl - INFO - Epoch [47][2500/3746] lr: 7.796e-02, eta: 3 days, 10:58:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5298, loss_cls: 4.1353, loss: 4.1353 +2024-07-17 22:09:29,032 - pyskl - INFO - Epoch [47][2600/3746] lr: 7.794e-02, eta: 3 days, 10:57:29, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5295, loss_cls: 4.1446, loss: 4.1446 +2024-07-17 22:10:51,144 - pyskl - INFO - Epoch [47][2700/3746] lr: 7.791e-02, eta: 3 days, 10:56:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5281, loss_cls: 4.1789, loss: 4.1789 +2024-07-17 22:12:12,945 - pyskl - INFO - Epoch [47][2800/3746] lr: 7.789e-02, eta: 3 days, 10:55:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5352, loss_cls: 4.1332, loss: 4.1332 +2024-07-17 22:13:34,265 - pyskl - INFO - Epoch [47][2900/3746] lr: 7.787e-02, eta: 3 days, 10:54:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5417, loss_cls: 4.0654, loss: 4.0654 +2024-07-17 22:14:56,005 - pyskl - INFO - Epoch [47][3000/3746] lr: 7.784e-02, eta: 3 days, 10:53:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5356, loss_cls: 4.1034, loss: 4.1034 +2024-07-17 22:16:17,108 - pyskl - INFO - Epoch [47][3100/3746] lr: 7.782e-02, eta: 3 days, 10:51:52, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5361, loss_cls: 4.0903, loss: 4.0903 +2024-07-17 22:17:38,163 - pyskl - INFO - Epoch [47][3200/3746] lr: 7.780e-02, eta: 3 days, 10:50:43, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5341, loss_cls: 4.1343, loss: 4.1343 +2024-07-17 22:19:00,082 - pyskl - INFO - Epoch [47][3300/3746] lr: 7.777e-02, eta: 3 days, 10:49:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5264, loss_cls: 4.1735, loss: 4.1735 +2024-07-17 22:20:22,000 - pyskl - INFO - Epoch [47][3400/3746] lr: 7.775e-02, eta: 3 days, 10:48:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5378, loss_cls: 4.1061, loss: 4.1061 +2024-07-17 22:21:43,482 - pyskl - INFO - Epoch [47][3500/3746] lr: 7.773e-02, eta: 3 days, 10:47:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5391, loss_cls: 4.1204, loss: 4.1204 +2024-07-17 22:23:06,094 - pyskl - INFO - Epoch [47][3600/3746] lr: 7.770e-02, eta: 3 days, 10:46:17, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5389, loss_cls: 4.1225, loss: 4.1225 +2024-07-17 22:24:28,140 - pyskl - INFO - Epoch [47][3700/3746] lr: 7.768e-02, eta: 3 days, 10:45:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5394, loss_cls: 4.1281, loss: 4.1281 +2024-07-17 22:25:07,515 - pyskl - INFO - Saving checkpoint at 47 epochs +2024-07-17 22:26:57,905 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 22:26:58,590 - pyskl - INFO - +top1_acc 0.2043 +top5_acc 0.4366 +2024-07-17 22:26:58,590 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 22:26:58,628 - pyskl - INFO - +mean_acc 0.2041 +2024-07-17 22:26:58,639 - pyskl - INFO - Epoch(val) [47][309] top1_acc: 0.2043, top5_acc: 0.4366, mean_class_accuracy: 0.2041 +2024-07-17 22:30:41,166 - pyskl - INFO - Epoch [48][100/3746] lr: 7.765e-02, eta: 3 days, 10:47:18, time: 2.225, data_time: 1.253, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5525, loss_cls: 4.0587, loss: 4.0587 +2024-07-17 22:32:03,962 - pyskl - INFO - Epoch [48][200/3746] lr: 7.762e-02, eta: 3 days, 10:46:13, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5498, loss_cls: 4.0201, loss: 4.0201 +2024-07-17 22:33:26,613 - pyskl - INFO - Epoch [48][300/3746] lr: 7.760e-02, eta: 3 days, 10:45:07, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5383, loss_cls: 4.1223, loss: 4.1223 +2024-07-17 22:34:48,764 - pyskl - INFO - Epoch [48][400/3746] lr: 7.758e-02, eta: 3 days, 10:44:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5387, loss_cls: 4.1039, loss: 4.1039 +2024-07-17 22:36:10,634 - pyskl - INFO - Epoch [48][500/3746] lr: 7.755e-02, eta: 3 days, 10:42:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5431, loss_cls: 4.0931, loss: 4.0931 +2024-07-17 22:37:33,445 - pyskl - INFO - Epoch [48][600/3746] lr: 7.753e-02, eta: 3 days, 10:41:48, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5403, loss_cls: 4.0905, loss: 4.0905 +2024-07-17 22:38:54,537 - pyskl - INFO - Epoch [48][700/3746] lr: 7.751e-02, eta: 3 days, 10:40:39, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5448, loss_cls: 4.1150, loss: 4.1150 +2024-07-17 22:40:16,362 - pyskl - INFO - Epoch [48][800/3746] lr: 7.748e-02, eta: 3 days, 10:39:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5427, loss_cls: 4.0574, loss: 4.0574 +2024-07-17 22:41:38,732 - pyskl - INFO - Epoch [48][900/3746] lr: 7.746e-02, eta: 3 days, 10:38:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5405, loss_cls: 4.1027, loss: 4.1027 +2024-07-17 22:43:00,988 - pyskl - INFO - Epoch [48][1000/3746] lr: 7.744e-02, eta: 3 days, 10:37:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5392, loss_cls: 4.0852, loss: 4.0852 +2024-07-17 22:44:23,486 - pyskl - INFO - Epoch [48][1100/3746] lr: 7.741e-02, eta: 3 days, 10:36:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5567, loss_cls: 4.0357, loss: 4.0357 +2024-07-17 22:45:45,514 - pyskl - INFO - Epoch [48][1200/3746] lr: 7.739e-02, eta: 3 days, 10:35:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5323, loss_cls: 4.1285, loss: 4.1285 +2024-07-17 22:47:07,779 - pyskl - INFO - Epoch [48][1300/3746] lr: 7.737e-02, eta: 3 days, 10:34:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5350, loss_cls: 4.1394, loss: 4.1394 +2024-07-17 22:48:29,525 - pyskl - INFO - Epoch [48][1400/3746] lr: 7.734e-02, eta: 3 days, 10:32:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5395, loss_cls: 4.1148, loss: 4.1148 +2024-07-17 22:49:50,942 - pyskl - INFO - Epoch [48][1500/3746] lr: 7.732e-02, eta: 3 days, 10:31:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5356, loss_cls: 4.1117, loss: 4.1117 +2024-07-17 22:51:12,725 - pyskl - INFO - Epoch [48][1600/3746] lr: 7.730e-02, eta: 3 days, 10:30:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5427, loss_cls: 4.0771, loss: 4.0771 +2024-07-17 22:52:34,444 - pyskl - INFO - Epoch [48][1700/3746] lr: 7.727e-02, eta: 3 days, 10:29:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5370, loss_cls: 4.1087, loss: 4.1087 +2024-07-17 22:53:56,485 - pyskl - INFO - Epoch [48][1800/3746] lr: 7.725e-02, eta: 3 days, 10:28:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5373, loss_cls: 4.1123, loss: 4.1123 +2024-07-17 22:55:18,207 - pyskl - INFO - Epoch [48][1900/3746] lr: 7.723e-02, eta: 3 days, 10:27:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5423, loss_cls: 4.0667, loss: 4.0667 +2024-07-17 22:56:39,848 - pyskl - INFO - Epoch [48][2000/3746] lr: 7.720e-02, eta: 3 days, 10:26:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5308, loss_cls: 4.1459, loss: 4.1459 +2024-07-17 22:58:01,421 - pyskl - INFO - Epoch [48][2100/3746] lr: 7.718e-02, eta: 3 days, 10:24:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5400, loss_cls: 4.1122, loss: 4.1122 +2024-07-17 22:59:23,186 - pyskl - INFO - Epoch [48][2200/3746] lr: 7.716e-02, eta: 3 days, 10:23:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5416, loss_cls: 4.1532, loss: 4.1532 +2024-07-17 23:00:44,679 - pyskl - INFO - Epoch [48][2300/3746] lr: 7.713e-02, eta: 3 days, 10:22:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5363, loss_cls: 4.1108, loss: 4.1108 +2024-07-17 23:02:06,712 - pyskl - INFO - Epoch [48][2400/3746] lr: 7.711e-02, eta: 3 days, 10:21:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5286, loss_cls: 4.1620, loss: 4.1620 +2024-07-17 23:03:27,868 - pyskl - INFO - Epoch [48][2500/3746] lr: 7.709e-02, eta: 3 days, 10:20:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5344, loss_cls: 4.1410, loss: 4.1410 +2024-07-17 23:04:49,327 - pyskl - INFO - Epoch [48][2600/3746] lr: 7.706e-02, eta: 3 days, 10:19:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5327, loss_cls: 4.1052, loss: 4.1052 +2024-07-17 23:06:11,388 - pyskl - INFO - Epoch [48][2700/3746] lr: 7.704e-02, eta: 3 days, 10:18:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5294, loss_cls: 4.1529, loss: 4.1529 +2024-07-17 23:07:33,051 - pyskl - INFO - Epoch [48][2800/3746] lr: 7.701e-02, eta: 3 days, 10:17:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5475, loss_cls: 4.0876, loss: 4.0876 +2024-07-17 23:08:54,803 - pyskl - INFO - Epoch [48][2900/3746] lr: 7.699e-02, eta: 3 days, 10:15:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5331, loss_cls: 4.1600, loss: 4.1600 +2024-07-17 23:10:17,194 - pyskl - INFO - Epoch [48][3000/3746] lr: 7.697e-02, eta: 3 days, 10:14:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5353, loss_cls: 4.1162, loss: 4.1162 +2024-07-17 23:11:38,782 - pyskl - INFO - Epoch [48][3100/3746] lr: 7.694e-02, eta: 3 days, 10:13:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5427, loss_cls: 4.1013, loss: 4.1013 +2024-07-17 23:13:00,659 - pyskl - INFO - Epoch [48][3200/3746] lr: 7.692e-02, eta: 3 days, 10:12:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5389, loss_cls: 4.1274, loss: 4.1274 +2024-07-17 23:14:22,585 - pyskl - INFO - Epoch [48][3300/3746] lr: 7.690e-02, eta: 3 days, 10:11:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5303, loss_cls: 4.1240, loss: 4.1240 +2024-07-17 23:15:44,394 - pyskl - INFO - Epoch [48][3400/3746] lr: 7.687e-02, eta: 3 days, 10:10:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5402, loss_cls: 4.1339, loss: 4.1339 +2024-07-17 23:17:05,593 - pyskl - INFO - Epoch [48][3500/3746] lr: 7.685e-02, eta: 3 days, 10:09:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5355, loss_cls: 4.1141, loss: 4.1141 +2024-07-17 23:18:28,060 - pyskl - INFO - Epoch [48][3600/3746] lr: 7.683e-02, eta: 3 days, 10:08:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5284, loss_cls: 4.1406, loss: 4.1406 +2024-07-17 23:19:49,128 - pyskl - INFO - Epoch [48][3700/3746] lr: 7.680e-02, eta: 3 days, 10:06:50, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5314, loss_cls: 4.1398, loss: 4.1398 +2024-07-17 23:20:28,613 - pyskl - INFO - Saving checkpoint at 48 epochs +2024-07-17 23:22:19,309 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 23:22:19,990 - pyskl - INFO - +top1_acc 0.1922 +top5_acc 0.4220 +2024-07-17 23:22:19,990 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 23:22:20,028 - pyskl - INFO - +mean_acc 0.1923 +2024-07-17 23:22:20,039 - pyskl - INFO - Epoch(val) [48][309] top1_acc: 0.1922, top5_acc: 0.4220, mean_class_accuracy: 0.1923 +2024-07-17 23:25:58,708 - pyskl - INFO - Epoch [49][100/3746] lr: 7.677e-02, eta: 3 days, 10:08:42, time: 2.187, data_time: 1.221, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5623, loss_cls: 4.0171, loss: 4.0171 +2024-07-17 23:27:21,867 - pyskl - INFO - Epoch [49][200/3746] lr: 7.674e-02, eta: 3 days, 10:07:36, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5384, loss_cls: 4.0900, loss: 4.0900 +2024-07-17 23:28:44,565 - pyskl - INFO - Epoch [49][300/3746] lr: 7.672e-02, eta: 3 days, 10:06:30, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5589, loss_cls: 4.0276, loss: 4.0276 +2024-07-17 23:30:06,227 - pyskl - INFO - Epoch [49][400/3746] lr: 7.670e-02, eta: 3 days, 10:05:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5494, loss_cls: 4.0830, loss: 4.0830 +2024-07-17 23:31:27,665 - pyskl - INFO - Epoch [49][500/3746] lr: 7.667e-02, eta: 3 days, 10:04:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5380, loss_cls: 4.1108, loss: 4.1108 +2024-07-17 23:32:50,538 - pyskl - INFO - Epoch [49][600/3746] lr: 7.665e-02, eta: 3 days, 10:03:07, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5545, loss_cls: 4.0320, loss: 4.0320 +2024-07-17 23:34:11,996 - pyskl - INFO - Epoch [49][700/3746] lr: 7.663e-02, eta: 3 days, 10:01:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5328, loss_cls: 4.1093, loss: 4.1093 +2024-07-17 23:35:33,917 - pyskl - INFO - Epoch [49][800/3746] lr: 7.660e-02, eta: 3 days, 10:00:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5453, loss_cls: 4.0786, loss: 4.0786 +2024-07-17 23:36:56,543 - pyskl - INFO - Epoch [49][900/3746] lr: 7.658e-02, eta: 3 days, 9:59:44, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5428, loss_cls: 4.0998, loss: 4.0998 +2024-07-17 23:38:18,216 - pyskl - INFO - Epoch [49][1000/3746] lr: 7.656e-02, eta: 3 days, 9:58:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5337, loss_cls: 4.1487, loss: 4.1487 +2024-07-17 23:39:41,081 - pyskl - INFO - Epoch [49][1100/3746] lr: 7.653e-02, eta: 3 days, 9:57:29, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5341, loss_cls: 4.1226, loss: 4.1226 +2024-07-17 23:41:03,269 - pyskl - INFO - Epoch [49][1200/3746] lr: 7.651e-02, eta: 3 days, 9:56:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5356, loss_cls: 4.1069, loss: 4.1069 +2024-07-17 23:42:25,071 - pyskl - INFO - Epoch [49][1300/3746] lr: 7.648e-02, eta: 3 days, 9:55:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5445, loss_cls: 4.0704, loss: 4.0704 +2024-07-17 23:43:47,105 - pyskl - INFO - Epoch [49][1400/3746] lr: 7.646e-02, eta: 3 days, 9:54:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5328, loss_cls: 4.1051, loss: 4.1051 +2024-07-17 23:45:08,953 - pyskl - INFO - Epoch [49][1500/3746] lr: 7.644e-02, eta: 3 days, 9:52:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5367, loss_cls: 4.1084, loss: 4.1084 +2024-07-17 23:46:30,852 - pyskl - INFO - Epoch [49][1600/3746] lr: 7.641e-02, eta: 3 days, 9:51:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5370, loss_cls: 4.1093, loss: 4.1093 +2024-07-17 23:47:52,036 - pyskl - INFO - Epoch [49][1700/3746] lr: 7.639e-02, eta: 3 days, 9:50:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5389, loss_cls: 4.1046, loss: 4.1046 +2024-07-17 23:49:13,656 - pyskl - INFO - Epoch [49][1800/3746] lr: 7.637e-02, eta: 3 days, 9:49:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5369, loss_cls: 4.1104, loss: 4.1104 +2024-07-17 23:50:35,113 - pyskl - INFO - Epoch [49][1900/3746] lr: 7.634e-02, eta: 3 days, 9:48:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5430, loss_cls: 4.1185, loss: 4.1185 +2024-07-17 23:51:57,291 - pyskl - INFO - Epoch [49][2000/3746] lr: 7.632e-02, eta: 3 days, 9:47:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5408, loss_cls: 4.1025, loss: 4.1025 +2024-07-17 23:53:18,938 - pyskl - INFO - Epoch [49][2100/3746] lr: 7.629e-02, eta: 3 days, 9:46:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5344, loss_cls: 4.1525, loss: 4.1525 +2024-07-17 23:54:40,283 - pyskl - INFO - Epoch [49][2200/3746] lr: 7.627e-02, eta: 3 days, 9:44:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5370, loss_cls: 4.1101, loss: 4.1101 +2024-07-17 23:56:01,579 - pyskl - INFO - Epoch [49][2300/3746] lr: 7.625e-02, eta: 3 days, 9:43:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5433, loss_cls: 4.0749, loss: 4.0749 +2024-07-17 23:57:23,821 - pyskl - INFO - Epoch [49][2400/3746] lr: 7.622e-02, eta: 3 days, 9:42:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5273, loss_cls: 4.1494, loss: 4.1494 +2024-07-17 23:58:44,845 - pyskl - INFO - Epoch [49][2500/3746] lr: 7.620e-02, eta: 3 days, 9:41:29, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5516, loss_cls: 4.0584, loss: 4.0584 +2024-07-18 00:00:06,050 - pyskl - INFO - Epoch [49][2600/3746] lr: 7.618e-02, eta: 3 days, 9:40:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5392, loss_cls: 4.1106, loss: 4.1106 +2024-07-18 00:01:28,355 - pyskl - INFO - Epoch [49][2700/3746] lr: 7.615e-02, eta: 3 days, 9:39:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5342, loss_cls: 4.1375, loss: 4.1375 +2024-07-18 00:02:50,134 - pyskl - INFO - Epoch [49][2800/3746] lr: 7.613e-02, eta: 3 days, 9:38:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5459, loss_cls: 4.0926, loss: 4.0926 +2024-07-18 00:04:11,830 - pyskl - INFO - Epoch [49][2900/3746] lr: 7.610e-02, eta: 3 days, 9:36:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5413, loss_cls: 4.1141, loss: 4.1141 +2024-07-18 00:05:33,432 - pyskl - INFO - Epoch [49][3000/3746] lr: 7.608e-02, eta: 3 days, 9:35:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5400, loss_cls: 4.0943, loss: 4.0943 +2024-07-18 00:06:54,987 - pyskl - INFO - Epoch [49][3100/3746] lr: 7.606e-02, eta: 3 days, 9:34:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5378, loss_cls: 4.1063, loss: 4.1063 +2024-07-18 00:08:16,792 - pyskl - INFO - Epoch [49][3200/3746] lr: 7.603e-02, eta: 3 days, 9:33:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5353, loss_cls: 4.1331, loss: 4.1331 +2024-07-18 00:09:38,047 - pyskl - INFO - Epoch [49][3300/3746] lr: 7.601e-02, eta: 3 days, 9:32:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5316, loss_cls: 4.1352, loss: 4.1352 +2024-07-18 00:10:58,993 - pyskl - INFO - Epoch [49][3400/3746] lr: 7.598e-02, eta: 3 days, 9:31:08, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5411, loss_cls: 4.0815, loss: 4.0815 +2024-07-18 00:12:20,523 - pyskl - INFO - Epoch [49][3500/3746] lr: 7.596e-02, eta: 3 days, 9:29:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5450, loss_cls: 4.1040, loss: 4.1040 +2024-07-18 00:13:43,344 - pyskl - INFO - Epoch [49][3600/3746] lr: 7.594e-02, eta: 3 days, 9:28:52, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5386, loss_cls: 4.1010, loss: 4.1010 +2024-07-18 00:15:05,243 - pyskl - INFO - Epoch [49][3700/3746] lr: 7.591e-02, eta: 3 days, 9:27:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5413, loss_cls: 4.0862, loss: 4.0862 +2024-07-18 00:15:44,292 - pyskl - INFO - Saving checkpoint at 49 epochs +2024-07-18 00:17:33,452 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 00:17:34,112 - pyskl - INFO - +top1_acc 0.2201 +top5_acc 0.4664 +2024-07-18 00:17:34,112 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 00:17:34,151 - pyskl - INFO - +mean_acc 0.2199 +2024-07-18 00:17:34,155 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_44.pth was removed +2024-07-18 00:17:34,425 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_49.pth. +2024-07-18 00:17:34,426 - pyskl - INFO - Best top1_acc is 0.2201 at 49 epoch. +2024-07-18 00:17:34,436 - pyskl - INFO - Epoch(val) [49][309] top1_acc: 0.2201, top5_acc: 0.4664, mean_class_accuracy: 0.2199 +2024-07-18 00:21:13,473 - pyskl - INFO - Epoch [50][100/3746] lr: 7.588e-02, eta: 3 days, 9:29:29, time: 2.190, data_time: 1.221, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5364, loss_cls: 4.0796, loss: 4.0796 +2024-07-18 00:22:36,232 - pyskl - INFO - Epoch [50][200/3746] lr: 7.585e-02, eta: 3 days, 9:28:22, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5434, loss_cls: 4.0754, loss: 4.0754 +2024-07-18 00:23:58,412 - pyskl - INFO - Epoch [50][300/3746] lr: 7.583e-02, eta: 3 days, 9:27:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5528, loss_cls: 4.0383, loss: 4.0383 +2024-07-18 00:25:20,353 - pyskl - INFO - Epoch [50][400/3746] lr: 7.581e-02, eta: 3 days, 9:26:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5586, loss_cls: 4.0221, loss: 4.0221 +2024-07-18 00:26:41,930 - pyskl - INFO - Epoch [50][500/3746] lr: 7.578e-02, eta: 3 days, 9:24:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5380, loss_cls: 4.1065, loss: 4.1065 +2024-07-18 00:28:04,066 - pyskl - INFO - Epoch [50][600/3746] lr: 7.576e-02, eta: 3 days, 9:23:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5470, loss_cls: 4.0576, loss: 4.0576 +2024-07-18 00:29:26,611 - pyskl - INFO - Epoch [50][700/3746] lr: 7.573e-02, eta: 3 days, 9:22:40, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5511, loss_cls: 4.0886, loss: 4.0886 +2024-07-18 00:30:47,617 - pyskl - INFO - Epoch [50][800/3746] lr: 7.571e-02, eta: 3 days, 9:21:30, time: 0.810, data_time: 0.001, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5375, loss_cls: 4.1316, loss: 4.1316 +2024-07-18 00:32:10,279 - pyskl - INFO - Epoch [50][900/3746] lr: 7.569e-02, eta: 3 days, 9:20:22, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5570, loss_cls: 4.0501, loss: 4.0501 +2024-07-18 00:33:32,289 - pyskl - INFO - Epoch [50][1000/3746] lr: 7.566e-02, eta: 3 days, 9:19:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5367, loss_cls: 4.1053, loss: 4.1053 +2024-07-18 00:34:54,105 - pyskl - INFO - Epoch [50][1100/3746] lr: 7.564e-02, eta: 3 days, 9:18:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5363, loss_cls: 4.1118, loss: 4.1118 +2024-07-18 00:36:16,459 - pyskl - INFO - Epoch [50][1200/3746] lr: 7.561e-02, eta: 3 days, 9:16:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5433, loss_cls: 4.0914, loss: 4.0914 +2024-07-18 00:37:38,551 - pyskl - INFO - Epoch [50][1300/3746] lr: 7.559e-02, eta: 3 days, 9:15:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5523, loss_cls: 4.0678, loss: 4.0678 +2024-07-18 00:39:00,030 - pyskl - INFO - Epoch [50][1400/3746] lr: 7.557e-02, eta: 3 days, 9:14:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5327, loss_cls: 4.1327, loss: 4.1327 +2024-07-18 00:40:21,445 - pyskl - INFO - Epoch [50][1500/3746] lr: 7.554e-02, eta: 3 days, 9:13:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5359, loss_cls: 4.0928, loss: 4.0928 +2024-07-18 00:41:42,738 - pyskl - INFO - Epoch [50][1600/3746] lr: 7.552e-02, eta: 3 days, 9:12:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5377, loss_cls: 4.0924, loss: 4.0924 +2024-07-18 00:43:04,396 - pyskl - INFO - Epoch [50][1700/3746] lr: 7.549e-02, eta: 3 days, 9:11:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5466, loss_cls: 4.0636, loss: 4.0636 +2024-07-18 00:44:25,346 - pyskl - INFO - Epoch [50][1800/3746] lr: 7.547e-02, eta: 3 days, 9:09:59, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5372, loss_cls: 4.1210, loss: 4.1210 +2024-07-18 00:45:46,517 - pyskl - INFO - Epoch [50][1900/3746] lr: 7.545e-02, eta: 3 days, 9:08:49, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5456, loss_cls: 4.0954, loss: 4.0954 +2024-07-18 00:47:07,964 - pyskl - INFO - Epoch [50][2000/3746] lr: 7.542e-02, eta: 3 days, 9:07:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5341, loss_cls: 4.1150, loss: 4.1150 +2024-07-18 00:48:29,569 - pyskl - INFO - Epoch [50][2100/3746] lr: 7.540e-02, eta: 3 days, 9:06:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5409, loss_cls: 4.1105, loss: 4.1105 +2024-07-18 00:49:50,887 - pyskl - INFO - Epoch [50][2200/3746] lr: 7.537e-02, eta: 3 days, 9:05:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5416, loss_cls: 4.1024, loss: 4.1024 +2024-07-18 00:51:12,398 - pyskl - INFO - Epoch [50][2300/3746] lr: 7.535e-02, eta: 3 days, 9:04:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5319, loss_cls: 4.1473, loss: 4.1473 +2024-07-18 00:52:34,750 - pyskl - INFO - Epoch [50][2400/3746] lr: 7.533e-02, eta: 3 days, 9:03:01, time: 0.824, data_time: 0.001, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5334, loss_cls: 4.1221, loss: 4.1221 +2024-07-18 00:53:56,450 - pyskl - INFO - Epoch [50][2500/3746] lr: 7.530e-02, eta: 3 days, 9:01:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5466, loss_cls: 4.1045, loss: 4.1045 +2024-07-18 00:55:18,266 - pyskl - INFO - Epoch [50][2600/3746] lr: 7.528e-02, eta: 3 days, 9:00:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5380, loss_cls: 4.1097, loss: 4.1097 +2024-07-18 00:56:40,604 - pyskl - INFO - Epoch [50][2700/3746] lr: 7.525e-02, eta: 3 days, 8:59:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5333, loss_cls: 4.1262, loss: 4.1262 +2024-07-18 00:58:02,067 - pyskl - INFO - Epoch [50][2800/3746] lr: 7.523e-02, eta: 3 days, 8:58:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5413, loss_cls: 4.0975, loss: 4.0975 +2024-07-18 00:59:23,953 - pyskl - INFO - Epoch [50][2900/3746] lr: 7.520e-02, eta: 3 days, 8:57:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5394, loss_cls: 4.1040, loss: 4.1040 +2024-07-18 01:00:44,789 - pyskl - INFO - Epoch [50][3000/3746] lr: 7.518e-02, eta: 3 days, 8:56:05, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5427, loss_cls: 4.1134, loss: 4.1134 +2024-07-18 01:02:05,985 - pyskl - INFO - Epoch [50][3100/3746] lr: 7.516e-02, eta: 3 days, 8:54:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5402, loss_cls: 4.0986, loss: 4.0986 +2024-07-18 01:03:27,556 - pyskl - INFO - Epoch [50][3200/3746] lr: 7.513e-02, eta: 3 days, 8:53:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5458, loss_cls: 4.0597, loss: 4.0597 +2024-07-18 01:04:49,231 - pyskl - INFO - Epoch [50][3300/3746] lr: 7.511e-02, eta: 3 days, 8:52:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5420, loss_cls: 4.1011, loss: 4.1011 +2024-07-18 01:06:10,483 - pyskl - INFO - Epoch [50][3400/3746] lr: 7.508e-02, eta: 3 days, 8:51:25, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5422, loss_cls: 4.1040, loss: 4.1040 +2024-07-18 01:07:32,120 - pyskl - INFO - Epoch [50][3500/3746] lr: 7.506e-02, eta: 3 days, 8:50:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5381, loss_cls: 4.0933, loss: 4.0933 +2024-07-18 01:08:54,262 - pyskl - INFO - Epoch [50][3600/3746] lr: 7.504e-02, eta: 3 days, 8:49:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5364, loss_cls: 4.1094, loss: 4.1094 +2024-07-18 01:10:15,770 - pyskl - INFO - Epoch [50][3700/3746] lr: 7.501e-02, eta: 3 days, 8:47:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5352, loss_cls: 4.1278, loss: 4.1278 +2024-07-18 01:10:55,483 - pyskl - INFO - Saving checkpoint at 50 epochs +2024-07-18 01:12:45,003 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 01:12:45,663 - pyskl - INFO - +top1_acc 0.2060 +top5_acc 0.4412 +2024-07-18 01:12:45,663 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 01:12:45,701 - pyskl - INFO - +mean_acc 0.2058 +2024-07-18 01:12:45,711 - pyskl - INFO - Epoch(val) [50][309] top1_acc: 0.2060, top5_acc: 0.4412, mean_class_accuracy: 0.2058 +2024-07-18 01:16:24,511 - pyskl - INFO - Epoch [51][100/3746] lr: 7.498e-02, eta: 3 days, 8:49:34, time: 2.188, data_time: 1.224, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5572, loss_cls: 4.0399, loss: 4.0399 +2024-07-18 01:17:46,836 - pyskl - INFO - Epoch [51][200/3746] lr: 7.495e-02, eta: 3 days, 8:48:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5541, loss_cls: 4.0630, loss: 4.0630 +2024-07-18 01:19:08,170 - pyskl - INFO - Epoch [51][300/3746] lr: 7.493e-02, eta: 3 days, 8:47:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5370, loss_cls: 4.1178, loss: 4.1178 +2024-07-18 01:20:29,700 - pyskl - INFO - Epoch [51][400/3746] lr: 7.490e-02, eta: 3 days, 8:46:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5469, loss_cls: 4.0994, loss: 4.0994 +2024-07-18 01:21:50,967 - pyskl - INFO - Epoch [51][500/3746] lr: 7.488e-02, eta: 3 days, 8:44:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5420, loss_cls: 4.0668, loss: 4.0668 +2024-07-18 01:23:12,171 - pyskl - INFO - Epoch [51][600/3746] lr: 7.485e-02, eta: 3 days, 8:43:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5552, loss_cls: 4.0511, loss: 4.0511 +2024-07-18 01:24:34,482 - pyskl - INFO - Epoch [51][700/3746] lr: 7.483e-02, eta: 3 days, 8:42:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5350, loss_cls: 4.1127, loss: 4.1127 +2024-07-18 01:25:56,146 - pyskl - INFO - Epoch [51][800/3746] lr: 7.481e-02, eta: 3 days, 8:41:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5378, loss_cls: 4.1053, loss: 4.1053 +2024-07-18 01:27:17,772 - pyskl - INFO - Epoch [51][900/3746] lr: 7.478e-02, eta: 3 days, 8:40:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5463, loss_cls: 4.0801, loss: 4.0801 +2024-07-18 01:28:40,213 - pyskl - INFO - Epoch [51][1000/3746] lr: 7.476e-02, eta: 3 days, 8:39:07, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5302, loss_cls: 4.1140, loss: 4.1140 +2024-07-18 01:30:01,957 - pyskl - INFO - Epoch [51][1100/3746] lr: 7.473e-02, eta: 3 days, 8:37:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5333, loss_cls: 4.1364, loss: 4.1364 +2024-07-18 01:31:24,223 - pyskl - INFO - Epoch [51][1200/3746] lr: 7.471e-02, eta: 3 days, 8:36:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5425, loss_cls: 4.0908, loss: 4.0908 +2024-07-18 01:32:45,811 - pyskl - INFO - Epoch [51][1300/3746] lr: 7.468e-02, eta: 3 days, 8:35:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5394, loss_cls: 4.1217, loss: 4.1217 +2024-07-18 01:34:07,598 - pyskl - INFO - Epoch [51][1400/3746] lr: 7.466e-02, eta: 3 days, 8:34:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5505, loss_cls: 4.0578, loss: 4.0578 +2024-07-18 01:35:29,107 - pyskl - INFO - Epoch [51][1500/3746] lr: 7.464e-02, eta: 3 days, 8:33:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5366, loss_cls: 4.1012, loss: 4.1012 +2024-07-18 01:36:50,047 - pyskl - INFO - Epoch [51][1600/3746] lr: 7.461e-02, eta: 3 days, 8:32:07, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5456, loss_cls: 4.0517, loss: 4.0517 +2024-07-18 01:38:10,817 - pyskl - INFO - Epoch [51][1700/3746] lr: 7.459e-02, eta: 3 days, 8:30:55, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5333, loss_cls: 4.1062, loss: 4.1062 +2024-07-18 01:39:32,595 - pyskl - INFO - Epoch [51][1800/3746] lr: 7.456e-02, eta: 3 days, 8:29:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5416, loss_cls: 4.1233, loss: 4.1233 +2024-07-18 01:40:55,097 - pyskl - INFO - Epoch [51][1900/3746] lr: 7.454e-02, eta: 3 days, 8:28:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5384, loss_cls: 4.0998, loss: 4.0998 +2024-07-18 01:42:16,713 - pyskl - INFO - Epoch [51][2000/3746] lr: 7.451e-02, eta: 3 days, 8:27:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5363, loss_cls: 4.1242, loss: 4.1242 +2024-07-18 01:43:38,219 - pyskl - INFO - Epoch [51][2100/3746] lr: 7.449e-02, eta: 3 days, 8:26:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5509, loss_cls: 4.0632, loss: 4.0632 +2024-07-18 01:44:59,386 - pyskl - INFO - Epoch [51][2200/3746] lr: 7.447e-02, eta: 3 days, 8:25:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5391, loss_cls: 4.0699, loss: 4.0699 +2024-07-18 01:46:20,617 - pyskl - INFO - Epoch [51][2300/3746] lr: 7.444e-02, eta: 3 days, 8:23:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5428, loss_cls: 4.0947, loss: 4.0947 +2024-07-18 01:47:42,029 - pyskl - INFO - Epoch [51][2400/3746] lr: 7.442e-02, eta: 3 days, 8:22:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5539, loss_cls: 4.0012, loss: 4.0012 +2024-07-18 01:49:04,103 - pyskl - INFO - Epoch [51][2500/3746] lr: 7.439e-02, eta: 3 days, 8:21:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5392, loss_cls: 4.1037, loss: 4.1037 +2024-07-18 01:50:25,815 - pyskl - INFO - Epoch [51][2600/3746] lr: 7.437e-02, eta: 3 days, 8:20:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5436, loss_cls: 4.0798, loss: 4.0798 +2024-07-18 01:51:47,751 - pyskl - INFO - Epoch [51][2700/3746] lr: 7.434e-02, eta: 3 days, 8:19:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5500, loss_cls: 4.0455, loss: 4.0455 +2024-07-18 01:53:10,410 - pyskl - INFO - Epoch [51][2800/3746] lr: 7.432e-02, eta: 3 days, 8:18:08, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5431, loss_cls: 4.0629, loss: 4.0629 +2024-07-18 01:54:32,452 - pyskl - INFO - Epoch [51][2900/3746] lr: 7.429e-02, eta: 3 days, 8:16:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5420, loss_cls: 4.0774, loss: 4.0774 +2024-07-18 01:55:54,155 - pyskl - INFO - Epoch [51][3000/3746] lr: 7.427e-02, eta: 3 days, 8:15:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5413, loss_cls: 4.1128, loss: 4.1128 +2024-07-18 01:57:15,311 - pyskl - INFO - Epoch [51][3100/3746] lr: 7.425e-02, eta: 3 days, 8:14:37, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5417, loss_cls: 4.0763, loss: 4.0763 +2024-07-18 01:58:36,743 - pyskl - INFO - Epoch [51][3200/3746] lr: 7.422e-02, eta: 3 days, 8:13:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5367, loss_cls: 4.0892, loss: 4.0892 +2024-07-18 01:59:58,082 - pyskl - INFO - Epoch [51][3300/3746] lr: 7.420e-02, eta: 3 days, 8:12:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5467, loss_cls: 4.0632, loss: 4.0632 +2024-07-18 02:01:19,737 - pyskl - INFO - Epoch [51][3400/3746] lr: 7.417e-02, eta: 3 days, 8:11:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5350, loss_cls: 4.1064, loss: 4.1064 +2024-07-18 02:02:41,438 - pyskl - INFO - Epoch [51][3500/3746] lr: 7.415e-02, eta: 3 days, 8:09:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5473, loss_cls: 4.0668, loss: 4.0668 +2024-07-18 02:04:04,113 - pyskl - INFO - Epoch [51][3600/3746] lr: 7.412e-02, eta: 3 days, 8:08:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5312, loss_cls: 4.1323, loss: 4.1323 +2024-07-18 02:05:25,314 - pyskl - INFO - Epoch [51][3700/3746] lr: 7.410e-02, eta: 3 days, 8:07:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5464, loss_cls: 4.0569, loss: 4.0569 +2024-07-18 02:06:04,405 - pyskl - INFO - Saving checkpoint at 51 epochs +2024-07-18 02:07:54,186 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 02:07:54,850 - pyskl - INFO - +top1_acc 0.1908 +top5_acc 0.4067 +2024-07-18 02:07:54,850 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 02:07:54,889 - pyskl - INFO - +mean_acc 0.1907 +2024-07-18 02:07:54,900 - pyskl - INFO - Epoch(val) [51][309] top1_acc: 0.1908, top5_acc: 0.4067, mean_class_accuracy: 0.1907 +2024-07-18 02:11:34,505 - pyskl - INFO - Epoch [52][100/3746] lr: 7.406e-02, eta: 3 days, 8:09:08, time: 2.196, data_time: 1.224, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5608, loss_cls: 4.0042, loss: 4.0042 +2024-07-18 02:12:56,432 - pyskl - INFO - Epoch [52][200/3746] lr: 7.404e-02, eta: 3 days, 8:07:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5572, loss_cls: 3.9984, loss: 3.9984 +2024-07-18 02:14:17,868 - pyskl - INFO - Epoch [52][300/3746] lr: 7.401e-02, eta: 3 days, 8:06:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5342, loss_cls: 4.0982, loss: 4.0982 +2024-07-18 02:15:38,837 - pyskl - INFO - Epoch [52][400/3746] lr: 7.399e-02, eta: 3 days, 8:05:36, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5366, loss_cls: 4.0926, loss: 4.0926 +2024-07-18 02:17:00,250 - pyskl - INFO - Epoch [52][500/3746] lr: 7.397e-02, eta: 3 days, 8:04:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5480, loss_cls: 4.0637, loss: 4.0637 +2024-07-18 02:18:22,127 - pyskl - INFO - Epoch [52][600/3746] lr: 7.394e-02, eta: 3 days, 8:03:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5389, loss_cls: 4.0935, loss: 4.0935 +2024-07-18 02:19:44,546 - pyskl - INFO - Epoch [52][700/3746] lr: 7.392e-02, eta: 3 days, 8:02:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5477, loss_cls: 4.0414, loss: 4.0414 +2024-07-18 02:21:06,077 - pyskl - INFO - Epoch [52][800/3746] lr: 7.389e-02, eta: 3 days, 8:00:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5402, loss_cls: 4.1228, loss: 4.1228 +2024-07-18 02:22:28,003 - pyskl - INFO - Epoch [52][900/3746] lr: 7.387e-02, eta: 3 days, 7:59:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5467, loss_cls: 4.0905, loss: 4.0905 +2024-07-18 02:23:51,215 - pyskl - INFO - Epoch [52][1000/3746] lr: 7.384e-02, eta: 3 days, 7:58:37, time: 0.832, data_time: 0.001, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5466, loss_cls: 4.0420, loss: 4.0420 +2024-07-18 02:25:13,247 - pyskl - INFO - Epoch [52][1100/3746] lr: 7.382e-02, eta: 3 days, 7:57:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5417, loss_cls: 4.0949, loss: 4.0949 +2024-07-18 02:26:35,026 - pyskl - INFO - Epoch [52][1200/3746] lr: 7.379e-02, eta: 3 days, 7:56:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5477, loss_cls: 4.0749, loss: 4.0749 +2024-07-18 02:27:57,174 - pyskl - INFO - Epoch [52][1300/3746] lr: 7.377e-02, eta: 3 days, 7:55:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5419, loss_cls: 4.0849, loss: 4.0849 +2024-07-18 02:29:18,774 - pyskl - INFO - Epoch [52][1400/3746] lr: 7.374e-02, eta: 3 days, 7:53:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5523, loss_cls: 4.0278, loss: 4.0278 +2024-07-18 02:30:40,262 - pyskl - INFO - Epoch [52][1500/3746] lr: 7.372e-02, eta: 3 days, 7:52:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5447, loss_cls: 4.1106, loss: 4.1106 +2024-07-18 02:32:01,767 - pyskl - INFO - Epoch [52][1600/3746] lr: 7.370e-02, eta: 3 days, 7:51:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5406, loss_cls: 4.0861, loss: 4.0861 +2024-07-18 02:33:23,051 - pyskl - INFO - Epoch [52][1700/3746] lr: 7.367e-02, eta: 3 days, 7:50:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5441, loss_cls: 4.0954, loss: 4.0954 +2024-07-18 02:34:44,005 - pyskl - INFO - Epoch [52][1800/3746] lr: 7.365e-02, eta: 3 days, 7:49:12, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5455, loss_cls: 4.0613, loss: 4.0613 +2024-07-18 02:36:05,393 - pyskl - INFO - Epoch [52][1900/3746] lr: 7.362e-02, eta: 3 days, 7:48:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5319, loss_cls: 4.1345, loss: 4.1345 +2024-07-18 02:37:26,918 - pyskl - INFO - Epoch [52][2000/3746] lr: 7.360e-02, eta: 3 days, 7:46:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5358, loss_cls: 4.0847, loss: 4.0847 +2024-07-18 02:38:48,408 - pyskl - INFO - Epoch [52][2100/3746] lr: 7.357e-02, eta: 3 days, 7:45:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5445, loss_cls: 4.0602, loss: 4.0602 +2024-07-18 02:40:09,929 - pyskl - INFO - Epoch [52][2200/3746] lr: 7.355e-02, eta: 3 days, 7:44:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5355, loss_cls: 4.1096, loss: 4.1096 +2024-07-18 02:41:31,606 - pyskl - INFO - Epoch [52][2300/3746] lr: 7.352e-02, eta: 3 days, 7:43:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5509, loss_cls: 4.0504, loss: 4.0504 +2024-07-18 02:42:53,101 - pyskl - INFO - Epoch [52][2400/3746] lr: 7.350e-02, eta: 3 days, 7:42:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5423, loss_cls: 4.0533, loss: 4.0533 +2024-07-18 02:44:14,750 - pyskl - INFO - Epoch [52][2500/3746] lr: 7.347e-02, eta: 3 days, 7:40:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5425, loss_cls: 4.0864, loss: 4.0864 +2024-07-18 02:45:35,967 - pyskl - INFO - Epoch [52][2600/3746] lr: 7.345e-02, eta: 3 days, 7:39:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5337, loss_cls: 4.1415, loss: 4.1415 +2024-07-18 02:46:57,555 - pyskl - INFO - Epoch [52][2700/3746] lr: 7.342e-02, eta: 3 days, 7:38:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5408, loss_cls: 4.1008, loss: 4.1008 +2024-07-18 02:48:20,060 - pyskl - INFO - Epoch [52][2800/3746] lr: 7.340e-02, eta: 3 days, 7:37:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5530, loss_cls: 4.0548, loss: 4.0548 +2024-07-18 02:49:41,681 - pyskl - INFO - Epoch [52][2900/3746] lr: 7.337e-02, eta: 3 days, 7:36:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5500, loss_cls: 4.0535, loss: 4.0535 +2024-07-18 02:51:03,172 - pyskl - INFO - Epoch [52][3000/3746] lr: 7.335e-02, eta: 3 days, 7:35:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5572, loss_cls: 4.0553, loss: 4.0553 +2024-07-18 02:52:24,231 - pyskl - INFO - Epoch [52][3100/3746] lr: 7.332e-02, eta: 3 days, 7:33:51, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5333, loss_cls: 4.1157, loss: 4.1157 +2024-07-18 02:53:45,561 - pyskl - INFO - Epoch [52][3200/3746] lr: 7.330e-02, eta: 3 days, 7:32:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5447, loss_cls: 4.0875, loss: 4.0875 +2024-07-18 02:55:06,778 - pyskl - INFO - Epoch [52][3300/3746] lr: 7.328e-02, eta: 3 days, 7:31:28, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5411, loss_cls: 4.1091, loss: 4.1091 +2024-07-18 02:56:28,153 - pyskl - INFO - Epoch [52][3400/3746] lr: 7.325e-02, eta: 3 days, 7:30:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5406, loss_cls: 4.0788, loss: 4.0788 +2024-07-18 02:57:49,638 - pyskl - INFO - Epoch [52][3500/3746] lr: 7.323e-02, eta: 3 days, 7:29:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5453, loss_cls: 4.0549, loss: 4.0549 +2024-07-18 02:59:11,722 - pyskl - INFO - Epoch [52][3600/3746] lr: 7.320e-02, eta: 3 days, 7:27:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5472, loss_cls: 4.0761, loss: 4.0761 +2024-07-18 03:00:33,566 - pyskl - INFO - Epoch [52][3700/3746] lr: 7.318e-02, eta: 3 days, 7:26:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5419, loss_cls: 4.0990, loss: 4.0990 +2024-07-18 03:01:12,816 - pyskl - INFO - Saving checkpoint at 52 epochs +2024-07-18 03:03:02,318 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 03:03:02,973 - pyskl - INFO - +top1_acc 0.2121 +top5_acc 0.4501 +2024-07-18 03:03:02,973 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 03:03:03,010 - pyskl - INFO - +mean_acc 0.2119 +2024-07-18 03:03:03,020 - pyskl - INFO - Epoch(val) [52][309] top1_acc: 0.2121, top5_acc: 0.4501, mean_class_accuracy: 0.2119 +2024-07-18 03:06:41,630 - pyskl - INFO - Epoch [53][100/3746] lr: 7.314e-02, eta: 3 days, 7:28:09, time: 2.186, data_time: 1.216, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5620, loss_cls: 3.9948, loss: 3.9948 +2024-07-18 03:08:04,213 - pyskl - INFO - Epoch [53][200/3746] lr: 7.312e-02, eta: 3 days, 7:27:00, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5573, loss_cls: 4.0205, loss: 4.0205 +2024-07-18 03:09:25,946 - pyskl - INFO - Epoch [53][300/3746] lr: 7.309e-02, eta: 3 days, 7:25:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5463, loss_cls: 4.0884, loss: 4.0884 +2024-07-18 03:10:48,050 - pyskl - INFO - Epoch [53][400/3746] lr: 7.307e-02, eta: 3 days, 7:24:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5461, loss_cls: 4.0306, loss: 4.0306 +2024-07-18 03:12:09,192 - pyskl - INFO - Epoch [53][500/3746] lr: 7.304e-02, eta: 3 days, 7:23:27, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5427, loss_cls: 4.0714, loss: 4.0714 +2024-07-18 03:13:30,671 - pyskl - INFO - Epoch [53][600/3746] lr: 7.302e-02, eta: 3 days, 7:22:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5514, loss_cls: 4.0421, loss: 4.0421 +2024-07-18 03:14:53,070 - pyskl - INFO - Epoch [53][700/3746] lr: 7.299e-02, eta: 3 days, 7:21:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5569, loss_cls: 4.0406, loss: 4.0406 +2024-07-18 03:16:15,817 - pyskl - INFO - Epoch [53][800/3746] lr: 7.297e-02, eta: 3 days, 7:19:57, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5563, loss_cls: 4.0104, loss: 4.0104 +2024-07-18 03:17:37,155 - pyskl - INFO - Epoch [53][900/3746] lr: 7.294e-02, eta: 3 days, 7:18:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5439, loss_cls: 4.0558, loss: 4.0558 +2024-07-18 03:18:59,253 - pyskl - INFO - Epoch [53][1000/3746] lr: 7.292e-02, eta: 3 days, 7:17:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5453, loss_cls: 4.0532, loss: 4.0532 +2024-07-18 03:20:21,406 - pyskl - INFO - Epoch [53][1100/3746] lr: 7.289e-02, eta: 3 days, 7:16:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5433, loss_cls: 4.0576, loss: 4.0576 +2024-07-18 03:21:42,854 - pyskl - INFO - Epoch [53][1200/3746] lr: 7.287e-02, eta: 3 days, 7:15:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5361, loss_cls: 4.0709, loss: 4.0709 +2024-07-18 03:23:04,593 - pyskl - INFO - Epoch [53][1300/3746] lr: 7.284e-02, eta: 3 days, 7:14:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5489, loss_cls: 4.0339, loss: 4.0339 +2024-07-18 03:24:26,147 - pyskl - INFO - Epoch [53][1400/3746] lr: 7.282e-02, eta: 3 days, 7:12:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5402, loss_cls: 4.0973, loss: 4.0973 +2024-07-18 03:25:47,605 - pyskl - INFO - Epoch [53][1500/3746] lr: 7.279e-02, eta: 3 days, 7:11:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5334, loss_cls: 4.1515, loss: 4.1515 +2024-07-18 03:27:09,580 - pyskl - INFO - Epoch [53][1600/3746] lr: 7.277e-02, eta: 3 days, 7:10:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5491, loss_cls: 4.0673, loss: 4.0673 +2024-07-18 03:28:30,797 - pyskl - INFO - Epoch [53][1700/3746] lr: 7.274e-02, eta: 3 days, 7:09:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5452, loss_cls: 4.0882, loss: 4.0882 +2024-07-18 03:29:51,772 - pyskl - INFO - Epoch [53][1800/3746] lr: 7.272e-02, eta: 3 days, 7:08:04, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5408, loss_cls: 4.0880, loss: 4.0880 +2024-07-18 03:31:13,378 - pyskl - INFO - Epoch [53][1900/3746] lr: 7.269e-02, eta: 3 days, 7:06:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5442, loss_cls: 4.1052, loss: 4.1052 +2024-07-18 03:32:34,543 - pyskl - INFO - Epoch [53][2000/3746] lr: 7.267e-02, eta: 3 days, 7:05:41, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5344, loss_cls: 4.1323, loss: 4.1323 +2024-07-18 03:33:55,813 - pyskl - INFO - Epoch [53][2100/3746] lr: 7.264e-02, eta: 3 days, 7:04:29, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5472, loss_cls: 4.0846, loss: 4.0846 +2024-07-18 03:35:16,668 - pyskl - INFO - Epoch [53][2200/3746] lr: 7.262e-02, eta: 3 days, 7:03:16, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5467, loss_cls: 4.0790, loss: 4.0790 +2024-07-18 03:36:37,555 - pyskl - INFO - Epoch [53][2300/3746] lr: 7.259e-02, eta: 3 days, 7:02:04, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5483, loss_cls: 4.0389, loss: 4.0389 +2024-07-18 03:37:58,078 - pyskl - INFO - Epoch [53][2400/3746] lr: 7.257e-02, eta: 3 days, 7:00:50, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5406, loss_cls: 4.0859, loss: 4.0859 +2024-07-18 03:39:19,919 - pyskl - INFO - Epoch [53][2500/3746] lr: 7.254e-02, eta: 3 days, 6:59:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5592, loss_cls: 4.0067, loss: 4.0067 +2024-07-18 03:40:41,531 - pyskl - INFO - Epoch [53][2600/3746] lr: 7.252e-02, eta: 3 days, 6:58:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5455, loss_cls: 4.0529, loss: 4.0529 +2024-07-18 03:42:03,351 - pyskl - INFO - Epoch [53][2700/3746] lr: 7.249e-02, eta: 3 days, 6:57:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5502, loss_cls: 4.0574, loss: 4.0574 +2024-07-18 03:43:26,050 - pyskl - INFO - Epoch [53][2800/3746] lr: 7.247e-02, eta: 3 days, 6:56:08, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5459, loss_cls: 4.0562, loss: 4.0562 +2024-07-18 03:44:47,956 - pyskl - INFO - Epoch [53][2900/3746] lr: 7.244e-02, eta: 3 days, 6:54:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5589, loss_cls: 4.0116, loss: 4.0116 +2024-07-18 03:46:09,159 - pyskl - INFO - Epoch [53][3000/3746] lr: 7.242e-02, eta: 3 days, 6:53:45, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5356, loss_cls: 4.1091, loss: 4.1091 +2024-07-18 03:47:30,901 - pyskl - INFO - Epoch [53][3100/3746] lr: 7.239e-02, eta: 3 days, 6:52:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5431, loss_cls: 4.0847, loss: 4.0847 +2024-07-18 03:48:51,876 - pyskl - INFO - Epoch [53][3200/3746] lr: 7.237e-02, eta: 3 days, 6:51:21, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5284, loss_cls: 4.1645, loss: 4.1645 +2024-07-18 03:50:13,570 - pyskl - INFO - Epoch [53][3300/3746] lr: 7.234e-02, eta: 3 days, 6:50:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5347, loss_cls: 4.1295, loss: 4.1295 +2024-07-18 03:51:35,214 - pyskl - INFO - Epoch [53][3400/3746] lr: 7.232e-02, eta: 3 days, 6:48:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5419, loss_cls: 4.0988, loss: 4.0988 +2024-07-18 03:52:56,778 - pyskl - INFO - Epoch [53][3500/3746] lr: 7.229e-02, eta: 3 days, 6:47:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5517, loss_cls: 4.0427, loss: 4.0427 +2024-07-18 03:54:18,960 - pyskl - INFO - Epoch [53][3600/3746] lr: 7.227e-02, eta: 3 days, 6:46:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5470, loss_cls: 4.0855, loss: 4.0855 +2024-07-18 03:55:40,521 - pyskl - INFO - Epoch [53][3700/3746] lr: 7.224e-02, eta: 3 days, 6:45:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5355, loss_cls: 4.1357, loss: 4.1357 +2024-07-18 03:56:19,761 - pyskl - INFO - Saving checkpoint at 53 epochs +2024-07-18 03:58:09,460 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 03:58:10,118 - pyskl - INFO - +top1_acc 0.2220 +top5_acc 0.4634 +2024-07-18 03:58:10,118 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 03:58:10,155 - pyskl - INFO - +mean_acc 0.2219 +2024-07-18 03:58:10,160 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_49.pth was removed +2024-07-18 03:58:10,409 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_53.pth. +2024-07-18 03:58:10,409 - pyskl - INFO - Best top1_acc is 0.2220 at 53 epoch. +2024-07-18 03:58:10,419 - pyskl - INFO - Epoch(val) [53][309] top1_acc: 0.2220, top5_acc: 0.4634, mean_class_accuracy: 0.2219 +2024-07-18 04:01:49,692 - pyskl - INFO - Epoch [54][100/3746] lr: 7.221e-02, eta: 3 days, 6:46:44, time: 2.193, data_time: 1.227, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5508, loss_cls: 4.0200, loss: 4.0200 +2024-07-18 04:03:12,457 - pyskl - INFO - Epoch [54][200/3746] lr: 7.218e-02, eta: 3 days, 6:45:35, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5375, loss_cls: 4.0957, loss: 4.0957 +2024-07-18 04:04:34,718 - pyskl - INFO - Epoch [54][300/3746] lr: 7.216e-02, eta: 3 days, 6:44:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5492, loss_cls: 4.0521, loss: 4.0521 +2024-07-18 04:05:56,395 - pyskl - INFO - Epoch [54][400/3746] lr: 7.213e-02, eta: 3 days, 6:43:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5366, loss_cls: 4.0906, loss: 4.0906 +2024-07-18 04:07:18,074 - pyskl - INFO - Epoch [54][500/3746] lr: 7.211e-02, eta: 3 days, 6:42:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5566, loss_cls: 4.0349, loss: 4.0349 +2024-07-18 04:08:39,575 - pyskl - INFO - Epoch [54][600/3746] lr: 7.208e-02, eta: 3 days, 6:40:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5506, loss_cls: 4.0371, loss: 4.0371 +2024-07-18 04:10:01,013 - pyskl - INFO - Epoch [54][700/3746] lr: 7.206e-02, eta: 3 days, 6:39:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5470, loss_cls: 4.0713, loss: 4.0713 +2024-07-18 04:11:23,389 - pyskl - INFO - Epoch [54][800/3746] lr: 7.203e-02, eta: 3 days, 6:38:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5392, loss_cls: 4.1113, loss: 4.1113 +2024-07-18 04:12:44,720 - pyskl - INFO - Epoch [54][900/3746] lr: 7.201e-02, eta: 3 days, 6:37:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5403, loss_cls: 4.0854, loss: 4.0854 +2024-07-18 04:14:06,110 - pyskl - INFO - Epoch [54][1000/3746] lr: 7.198e-02, eta: 3 days, 6:36:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5434, loss_cls: 4.0940, loss: 4.0940 +2024-07-18 04:15:28,939 - pyskl - INFO - Epoch [54][1100/3746] lr: 7.196e-02, eta: 3 days, 6:34:53, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5541, loss_cls: 4.0093, loss: 4.0093 +2024-07-18 04:16:50,716 - pyskl - INFO - Epoch [54][1200/3746] lr: 7.193e-02, eta: 3 days, 6:33:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5478, loss_cls: 4.0477, loss: 4.0477 +2024-07-18 04:18:13,047 - pyskl - INFO - Epoch [54][1300/3746] lr: 7.191e-02, eta: 3 days, 6:32:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5569, loss_cls: 4.0268, loss: 4.0268 +2024-07-18 04:19:34,495 - pyskl - INFO - Epoch [54][1400/3746] lr: 7.188e-02, eta: 3 days, 6:31:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5381, loss_cls: 4.0990, loss: 4.0990 +2024-07-18 04:20:56,532 - pyskl - INFO - Epoch [54][1500/3746] lr: 7.186e-02, eta: 3 days, 6:30:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5397, loss_cls: 4.0980, loss: 4.0980 +2024-07-18 04:22:18,117 - pyskl - INFO - Epoch [54][1600/3746] lr: 7.183e-02, eta: 3 days, 6:28:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5434, loss_cls: 4.0636, loss: 4.0636 +2024-07-18 04:23:39,346 - pyskl - INFO - Epoch [54][1700/3746] lr: 7.181e-02, eta: 3 days, 6:27:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5486, loss_cls: 4.0365, loss: 4.0365 +2024-07-18 04:25:01,349 - pyskl - INFO - Epoch [54][1800/3746] lr: 7.178e-02, eta: 3 days, 6:26:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5367, loss_cls: 4.1010, loss: 4.1010 +2024-07-18 04:26:22,739 - pyskl - INFO - Epoch [54][1900/3746] lr: 7.176e-02, eta: 3 days, 6:25:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5392, loss_cls: 4.0685, loss: 4.0685 +2024-07-18 04:27:44,726 - pyskl - INFO - Epoch [54][2000/3746] lr: 7.173e-02, eta: 3 days, 6:24:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5495, loss_cls: 4.0510, loss: 4.0510 +2024-07-18 04:29:06,797 - pyskl - INFO - Epoch [54][2100/3746] lr: 7.170e-02, eta: 3 days, 6:22:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5356, loss_cls: 4.0982, loss: 4.0982 +2024-07-18 04:30:28,513 - pyskl - INFO - Epoch [54][2200/3746] lr: 7.168e-02, eta: 3 days, 6:21:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5547, loss_cls: 4.0418, loss: 4.0418 +2024-07-18 04:31:50,022 - pyskl - INFO - Epoch [54][2300/3746] lr: 7.165e-02, eta: 3 days, 6:20:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5431, loss_cls: 4.0734, loss: 4.0734 +2024-07-18 04:33:11,729 - pyskl - INFO - Epoch [54][2400/3746] lr: 7.163e-02, eta: 3 days, 6:19:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5473, loss_cls: 4.0829, loss: 4.0829 +2024-07-18 04:34:34,667 - pyskl - INFO - Epoch [54][2500/3746] lr: 7.160e-02, eta: 3 days, 6:18:14, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5403, loss_cls: 4.0898, loss: 4.0898 +2024-07-18 04:35:55,975 - pyskl - INFO - Epoch [54][2600/3746] lr: 7.158e-02, eta: 3 days, 6:17:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5463, loss_cls: 4.0658, loss: 4.0658 +2024-07-18 04:37:17,267 - pyskl - INFO - Epoch [54][2700/3746] lr: 7.155e-02, eta: 3 days, 6:15:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5494, loss_cls: 4.0533, loss: 4.0533 +2024-07-18 04:38:39,569 - pyskl - INFO - Epoch [54][2800/3746] lr: 7.153e-02, eta: 3 days, 6:14:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5423, loss_cls: 4.0797, loss: 4.0797 +2024-07-18 04:40:00,743 - pyskl - INFO - Epoch [54][2900/3746] lr: 7.150e-02, eta: 3 days, 6:13:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5448, loss_cls: 4.0826, loss: 4.0826 +2024-07-18 04:41:21,877 - pyskl - INFO - Epoch [54][3000/3746] lr: 7.148e-02, eta: 3 days, 6:12:13, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5425, loss_cls: 4.0582, loss: 4.0582 +2024-07-18 04:42:43,578 - pyskl - INFO - Epoch [54][3100/3746] lr: 7.145e-02, eta: 3 days, 6:11:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5397, loss_cls: 4.1111, loss: 4.1111 +2024-07-18 04:44:05,027 - pyskl - INFO - Epoch [54][3200/3746] lr: 7.143e-02, eta: 3 days, 6:09:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5377, loss_cls: 4.0910, loss: 4.0910 +2024-07-18 04:45:26,754 - pyskl - INFO - Epoch [54][3300/3746] lr: 7.140e-02, eta: 3 days, 6:08:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5350, loss_cls: 4.1108, loss: 4.1108 +2024-07-18 04:46:48,120 - pyskl - INFO - Epoch [54][3400/3746] lr: 7.138e-02, eta: 3 days, 6:07:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5487, loss_cls: 4.0478, loss: 4.0478 +2024-07-18 04:48:09,594 - pyskl - INFO - Epoch [54][3500/3746] lr: 7.135e-02, eta: 3 days, 6:06:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5506, loss_cls: 4.0103, loss: 4.0103 +2024-07-18 04:49:32,613 - pyskl - INFO - Epoch [54][3600/3746] lr: 7.133e-02, eta: 3 days, 6:05:04, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5419, loss_cls: 4.0691, loss: 4.0691 +2024-07-18 04:50:54,687 - pyskl - INFO - Epoch [54][3700/3746] lr: 7.130e-02, eta: 3 days, 6:03:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5491, loss_cls: 4.0436, loss: 4.0436 +2024-07-18 04:51:34,100 - pyskl - INFO - Saving checkpoint at 54 epochs +2024-07-18 04:53:24,167 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 04:53:24,866 - pyskl - INFO - +top1_acc 0.2011 +top5_acc 0.4350 +2024-07-18 04:53:24,866 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 04:53:24,908 - pyskl - INFO - +mean_acc 0.2010 +2024-07-18 04:53:24,920 - pyskl - INFO - Epoch(val) [54][309] top1_acc: 0.2011, top5_acc: 0.4350, mean_class_accuracy: 0.2010 +2024-07-18 04:57:04,491 - pyskl - INFO - Epoch [55][100/3746] lr: 7.126e-02, eta: 3 days, 6:05:06, time: 2.196, data_time: 1.223, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5541, loss_cls: 4.0187, loss: 4.0187 +2024-07-18 04:58:26,566 - pyskl - INFO - Epoch [55][200/3746] lr: 7.124e-02, eta: 3 days, 6:03:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5489, loss_cls: 4.0501, loss: 4.0501 +2024-07-18 04:59:47,902 - pyskl - INFO - Epoch [55][300/3746] lr: 7.121e-02, eta: 3 days, 6:02:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5534, loss_cls: 3.9995, loss: 3.9995 +2024-07-18 05:01:10,007 - pyskl - INFO - Epoch [55][400/3746] lr: 7.119e-02, eta: 3 days, 6:01:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5519, loss_cls: 4.0397, loss: 4.0397 +2024-07-18 05:02:31,701 - pyskl - INFO - Epoch [55][500/3746] lr: 7.116e-02, eta: 3 days, 6:00:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5619, loss_cls: 3.9977, loss: 3.9977 +2024-07-18 05:03:53,414 - pyskl - INFO - Epoch [55][600/3746] lr: 7.114e-02, eta: 3 days, 5:59:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5536, loss_cls: 4.0398, loss: 4.0398 +2024-07-18 05:05:15,394 - pyskl - INFO - Epoch [55][700/3746] lr: 7.111e-02, eta: 3 days, 5:57:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5386, loss_cls: 4.1136, loss: 4.1136 +2024-07-18 05:06:37,352 - pyskl - INFO - Epoch [55][800/3746] lr: 7.109e-02, eta: 3 days, 5:56:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5548, loss_cls: 4.0112, loss: 4.0112 +2024-07-18 05:07:58,953 - pyskl - INFO - Epoch [55][900/3746] lr: 7.106e-02, eta: 3 days, 5:55:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5472, loss_cls: 4.0567, loss: 4.0567 +2024-07-18 05:09:20,207 - pyskl - INFO - Epoch [55][1000/3746] lr: 7.104e-02, eta: 3 days, 5:54:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5506, loss_cls: 4.0380, loss: 4.0380 +2024-07-18 05:10:42,811 - pyskl - INFO - Epoch [55][1100/3746] lr: 7.101e-02, eta: 3 days, 5:53:08, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5520, loss_cls: 4.0442, loss: 4.0442 +2024-07-18 05:12:04,265 - pyskl - INFO - Epoch [55][1200/3746] lr: 7.099e-02, eta: 3 days, 5:51:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5595, loss_cls: 4.0292, loss: 4.0292 +2024-07-18 05:13:25,786 - pyskl - INFO - Epoch [55][1300/3746] lr: 7.096e-02, eta: 3 days, 5:50:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5439, loss_cls: 4.0921, loss: 4.0921 +2024-07-18 05:14:48,438 - pyskl - INFO - Epoch [55][1400/3746] lr: 7.093e-02, eta: 3 days, 5:49:33, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5477, loss_cls: 4.0367, loss: 4.0367 +2024-07-18 05:16:09,675 - pyskl - INFO - Epoch [55][1500/3746] lr: 7.091e-02, eta: 3 days, 5:48:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5511, loss_cls: 4.0515, loss: 4.0515 +2024-07-18 05:17:31,201 - pyskl - INFO - Epoch [55][1600/3746] lr: 7.088e-02, eta: 3 days, 5:47:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5470, loss_cls: 4.0716, loss: 4.0716 +2024-07-18 05:18:53,092 - pyskl - INFO - Epoch [55][1700/3746] lr: 7.086e-02, eta: 3 days, 5:45:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5370, loss_cls: 4.1228, loss: 4.1228 +2024-07-18 05:20:15,119 - pyskl - INFO - Epoch [55][1800/3746] lr: 7.083e-02, eta: 3 days, 5:44:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5511, loss_cls: 4.0721, loss: 4.0721 +2024-07-18 05:21:36,825 - pyskl - INFO - Epoch [55][1900/3746] lr: 7.081e-02, eta: 3 days, 5:43:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5456, loss_cls: 4.0546, loss: 4.0546 +2024-07-18 05:22:58,263 - pyskl - INFO - Epoch [55][2000/3746] lr: 7.078e-02, eta: 3 days, 5:42:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5425, loss_cls: 4.0985, loss: 4.0985 +2024-07-18 05:24:19,537 - pyskl - INFO - Epoch [55][2100/3746] lr: 7.076e-02, eta: 3 days, 5:41:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5425, loss_cls: 4.0787, loss: 4.0787 +2024-07-18 05:25:40,993 - pyskl - INFO - Epoch [55][2200/3746] lr: 7.073e-02, eta: 3 days, 5:39:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5458, loss_cls: 4.0630, loss: 4.0630 +2024-07-18 05:27:02,115 - pyskl - INFO - Epoch [55][2300/3746] lr: 7.071e-02, eta: 3 days, 5:38:41, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5564, loss_cls: 4.0282, loss: 4.0282 +2024-07-18 05:28:23,440 - pyskl - INFO - Epoch [55][2400/3746] lr: 7.068e-02, eta: 3 days, 5:37:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5564, loss_cls: 4.0194, loss: 4.0194 +2024-07-18 05:29:45,806 - pyskl - INFO - Epoch [55][2500/3746] lr: 7.065e-02, eta: 3 days, 5:36:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5486, loss_cls: 4.0706, loss: 4.0706 +2024-07-18 05:31:07,600 - pyskl - INFO - Epoch [55][2600/3746] lr: 7.063e-02, eta: 3 days, 5:35:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5400, loss_cls: 4.0994, loss: 4.0994 +2024-07-18 05:32:29,505 - pyskl - INFO - Epoch [55][2700/3746] lr: 7.060e-02, eta: 3 days, 5:33:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5391, loss_cls: 4.1193, loss: 4.1193 +2024-07-18 05:33:51,603 - pyskl - INFO - Epoch [55][2800/3746] lr: 7.058e-02, eta: 3 days, 5:32:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5430, loss_cls: 4.0799, loss: 4.0799 +2024-07-18 05:35:13,314 - pyskl - INFO - Epoch [55][2900/3746] lr: 7.055e-02, eta: 3 days, 5:31:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5548, loss_cls: 4.0256, loss: 4.0256 +2024-07-18 05:36:35,216 - pyskl - INFO - Epoch [55][3000/3746] lr: 7.053e-02, eta: 3 days, 5:30:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5442, loss_cls: 4.0828, loss: 4.0828 +2024-07-18 05:37:56,930 - pyskl - INFO - Epoch [55][3100/3746] lr: 7.050e-02, eta: 3 days, 5:29:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5466, loss_cls: 4.0616, loss: 4.0616 +2024-07-18 05:39:18,396 - pyskl - INFO - Epoch [55][3200/3746] lr: 7.048e-02, eta: 3 days, 5:27:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5530, loss_cls: 4.0372, loss: 4.0372 +2024-07-18 05:40:39,934 - pyskl - INFO - Epoch [55][3300/3746] lr: 7.045e-02, eta: 3 days, 5:26:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5448, loss_cls: 4.0440, loss: 4.0440 +2024-07-18 05:42:01,430 - pyskl - INFO - Epoch [55][3400/3746] lr: 7.043e-02, eta: 3 days, 5:25:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5525, loss_cls: 4.0374, loss: 4.0374 +2024-07-18 05:43:22,481 - pyskl - INFO - Epoch [55][3500/3746] lr: 7.040e-02, eta: 3 days, 5:24:14, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5566, loss_cls: 4.0451, loss: 4.0451 +2024-07-18 05:44:44,528 - pyskl - INFO - Epoch [55][3600/3746] lr: 7.037e-02, eta: 3 days, 5:23:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5478, loss_cls: 4.0527, loss: 4.0527 +2024-07-18 05:46:05,731 - pyskl - INFO - Epoch [55][3700/3746] lr: 7.035e-02, eta: 3 days, 5:21:49, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5514, loss_cls: 4.0521, loss: 4.0521 +2024-07-18 05:46:44,930 - pyskl - INFO - Saving checkpoint at 55 epochs +2024-07-18 05:48:34,860 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 05:48:35,594 - pyskl - INFO - +top1_acc 0.2167 +top5_acc 0.4486 +2024-07-18 05:48:35,594 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 05:48:35,633 - pyskl - INFO - +mean_acc 0.2165 +2024-07-18 05:48:35,644 - pyskl - INFO - Epoch(val) [55][309] top1_acc: 0.2167, top5_acc: 0.4486, mean_class_accuracy: 0.2165 +2024-07-18 05:52:14,494 - pyskl - INFO - Epoch [56][100/3746] lr: 7.031e-02, eta: 3 days, 5:22:56, time: 2.188, data_time: 1.218, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5597, loss_cls: 3.9598, loss: 3.9598 +2024-07-18 05:53:36,815 - pyskl - INFO - Epoch [56][200/3746] lr: 7.029e-02, eta: 3 days, 5:21:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5522, loss_cls: 4.0001, loss: 4.0001 +2024-07-18 05:54:58,034 - pyskl - INFO - Epoch [56][300/3746] lr: 7.026e-02, eta: 3 days, 5:20:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5611, loss_cls: 3.9677, loss: 3.9677 +2024-07-18 05:56:19,499 - pyskl - INFO - Epoch [56][400/3746] lr: 7.023e-02, eta: 3 days, 5:19:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5534, loss_cls: 4.0267, loss: 4.0267 +2024-07-18 05:57:41,278 - pyskl - INFO - Epoch [56][500/3746] lr: 7.021e-02, eta: 3 days, 5:18:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5397, loss_cls: 4.0855, loss: 4.0855 +2024-07-18 05:59:02,434 - pyskl - INFO - Epoch [56][600/3746] lr: 7.018e-02, eta: 3 days, 5:16:52, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5473, loss_cls: 4.0587, loss: 4.0587 +2024-07-18 06:00:24,030 - pyskl - INFO - Epoch [56][700/3746] lr: 7.016e-02, eta: 3 days, 5:15:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5567, loss_cls: 4.0084, loss: 4.0084 +2024-07-18 06:01:45,537 - pyskl - INFO - Epoch [56][800/3746] lr: 7.013e-02, eta: 3 days, 5:14:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5528, loss_cls: 4.0214, loss: 4.0214 +2024-07-18 06:03:07,411 - pyskl - INFO - Epoch [56][900/3746] lr: 7.011e-02, eta: 3 days, 5:13:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5556, loss_cls: 4.0300, loss: 4.0300 +2024-07-18 06:04:29,033 - pyskl - INFO - Epoch [56][1000/3746] lr: 7.008e-02, eta: 3 days, 5:12:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5539, loss_cls: 4.0467, loss: 4.0467 +2024-07-18 06:05:51,442 - pyskl - INFO - Epoch [56][1100/3746] lr: 7.006e-02, eta: 3 days, 5:10:51, time: 0.824, data_time: 0.001, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5450, loss_cls: 4.0638, loss: 4.0638 +2024-07-18 06:07:13,948 - pyskl - INFO - Epoch [56][1200/3746] lr: 7.003e-02, eta: 3 days, 5:09:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5411, loss_cls: 4.0912, loss: 4.0912 +2024-07-18 06:08:35,596 - pyskl - INFO - Epoch [56][1300/3746] lr: 7.000e-02, eta: 3 days, 5:08:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5472, loss_cls: 4.0622, loss: 4.0622 +2024-07-18 06:09:57,516 - pyskl - INFO - Epoch [56][1400/3746] lr: 6.998e-02, eta: 3 days, 5:07:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5639, loss_cls: 4.0047, loss: 4.0047 +2024-07-18 06:11:19,739 - pyskl - INFO - Epoch [56][1500/3746] lr: 6.995e-02, eta: 3 days, 5:06:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5572, loss_cls: 4.0239, loss: 4.0239 +2024-07-18 06:12:41,564 - pyskl - INFO - Epoch [56][1600/3746] lr: 6.993e-02, eta: 3 days, 5:04:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5452, loss_cls: 4.0535, loss: 4.0535 +2024-07-18 06:14:03,603 - pyskl - INFO - Epoch [56][1700/3746] lr: 6.990e-02, eta: 3 days, 5:03:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5478, loss_cls: 4.0704, loss: 4.0704 +2024-07-18 06:15:25,226 - pyskl - INFO - Epoch [56][1800/3746] lr: 6.988e-02, eta: 3 days, 5:02:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5402, loss_cls: 4.0840, loss: 4.0840 +2024-07-18 06:16:46,304 - pyskl - INFO - Epoch [56][1900/3746] lr: 6.985e-02, eta: 3 days, 5:01:12, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5563, loss_cls: 4.0438, loss: 4.0438 +2024-07-18 06:18:08,039 - pyskl - INFO - Epoch [56][2000/3746] lr: 6.983e-02, eta: 3 days, 5:00:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5417, loss_cls: 4.0798, loss: 4.0798 +2024-07-18 06:19:29,451 - pyskl - INFO - Epoch [56][2100/3746] lr: 6.980e-02, eta: 3 days, 4:58:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5434, loss_cls: 4.0554, loss: 4.0554 +2024-07-18 06:20:50,981 - pyskl - INFO - Epoch [56][2200/3746] lr: 6.977e-02, eta: 3 days, 4:57:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5503, loss_cls: 4.0425, loss: 4.0425 +2024-07-18 06:22:12,234 - pyskl - INFO - Epoch [56][2300/3746] lr: 6.975e-02, eta: 3 days, 4:56:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5487, loss_cls: 4.0682, loss: 4.0682 +2024-07-18 06:23:33,483 - pyskl - INFO - Epoch [56][2400/3746] lr: 6.972e-02, eta: 3 days, 4:55:07, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5531, loss_cls: 4.0519, loss: 4.0519 +2024-07-18 06:24:55,747 - pyskl - INFO - Epoch [56][2500/3746] lr: 6.970e-02, eta: 3 days, 4:53:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5409, loss_cls: 4.0778, loss: 4.0778 +2024-07-18 06:26:17,180 - pyskl - INFO - Epoch [56][2600/3746] lr: 6.967e-02, eta: 3 days, 4:52:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5483, loss_cls: 4.0680, loss: 4.0680 +2024-07-18 06:27:38,685 - pyskl - INFO - Epoch [56][2700/3746] lr: 6.965e-02, eta: 3 days, 4:51:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5591, loss_cls: 4.0250, loss: 4.0250 +2024-07-18 06:29:01,023 - pyskl - INFO - Epoch [56][2800/3746] lr: 6.962e-02, eta: 3 days, 4:50:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5491, loss_cls: 4.0585, loss: 4.0585 +2024-07-18 06:30:23,521 - pyskl - INFO - Epoch [56][2900/3746] lr: 6.959e-02, eta: 3 days, 4:49:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5345, loss_cls: 4.1183, loss: 4.1183 +2024-07-18 06:31:44,648 - pyskl - INFO - Epoch [56][3000/3746] lr: 6.957e-02, eta: 3 days, 4:47:52, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5459, loss_cls: 4.0941, loss: 4.0941 +2024-07-18 06:33:06,053 - pyskl - INFO - Epoch [56][3100/3746] lr: 6.954e-02, eta: 3 days, 4:46:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5567, loss_cls: 4.0525, loss: 4.0525 +2024-07-18 06:34:26,922 - pyskl - INFO - Epoch [56][3200/3746] lr: 6.952e-02, eta: 3 days, 4:45:25, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5333, loss_cls: 4.1078, loss: 4.1078 +2024-07-18 06:35:48,318 - pyskl - INFO - Epoch [56][3300/3746] lr: 6.949e-02, eta: 3 days, 4:44:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5595, loss_cls: 3.9999, loss: 3.9999 +2024-07-18 06:37:09,824 - pyskl - INFO - Epoch [56][3400/3746] lr: 6.947e-02, eta: 3 days, 4:42:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5422, loss_cls: 4.0479, loss: 4.0479 +2024-07-18 06:38:31,142 - pyskl - INFO - Epoch [56][3500/3746] lr: 6.944e-02, eta: 3 days, 4:41:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5439, loss_cls: 4.0446, loss: 4.0446 +2024-07-18 06:39:53,976 - pyskl - INFO - Epoch [56][3600/3746] lr: 6.941e-02, eta: 3 days, 4:40:35, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5480, loss_cls: 4.0536, loss: 4.0536 +2024-07-18 06:41:15,694 - pyskl - INFO - Epoch [56][3700/3746] lr: 6.939e-02, eta: 3 days, 4:39:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5575, loss_cls: 4.0253, loss: 4.0253 +2024-07-18 06:41:54,525 - pyskl - INFO - Saving checkpoint at 56 epochs +2024-07-18 06:43:44,941 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 06:43:45,603 - pyskl - INFO - +top1_acc 0.2316 +top5_acc 0.4812 +2024-07-18 06:43:45,603 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 06:43:45,642 - pyskl - INFO - +mean_acc 0.2313 +2024-07-18 06:43:45,646 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_53.pth was removed +2024-07-18 06:43:45,904 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_56.pth. +2024-07-18 06:43:45,904 - pyskl - INFO - Best top1_acc is 0.2316 at 56 epoch. +2024-07-18 06:43:45,915 - pyskl - INFO - Epoch(val) [56][309] top1_acc: 0.2316, top5_acc: 0.4812, mean_class_accuracy: 0.2313 +2024-07-18 06:47:26,466 - pyskl - INFO - Epoch [57][100/3746] lr: 6.935e-02, eta: 3 days, 4:40:26, time: 2.205, data_time: 1.233, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5569, loss_cls: 4.0020, loss: 4.0020 +2024-07-18 06:48:48,817 - pyskl - INFO - Epoch [57][200/3746] lr: 6.932e-02, eta: 3 days, 4:39:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5634, loss_cls: 3.9515, loss: 3.9515 +2024-07-18 06:50:10,794 - pyskl - INFO - Epoch [57][300/3746] lr: 6.930e-02, eta: 3 days, 4:38:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5698, loss_cls: 3.9351, loss: 3.9351 +2024-07-18 06:51:32,438 - pyskl - INFO - Epoch [57][400/3746] lr: 6.927e-02, eta: 3 days, 4:36:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5527, loss_cls: 4.0528, loss: 4.0528 +2024-07-18 06:52:53,819 - pyskl - INFO - Epoch [57][500/3746] lr: 6.925e-02, eta: 3 days, 4:35:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5544, loss_cls: 4.0282, loss: 4.0282 +2024-07-18 06:54:15,300 - pyskl - INFO - Epoch [57][600/3746] lr: 6.922e-02, eta: 3 days, 4:34:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5616, loss_cls: 4.0082, loss: 4.0082 +2024-07-18 06:55:36,744 - pyskl - INFO - Epoch [57][700/3746] lr: 6.920e-02, eta: 3 days, 4:33:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5533, loss_cls: 4.0374, loss: 4.0374 +2024-07-18 06:56:57,899 - pyskl - INFO - Epoch [57][800/3746] lr: 6.917e-02, eta: 3 days, 4:31:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5544, loss_cls: 4.0444, loss: 4.0444 +2024-07-18 06:58:19,649 - pyskl - INFO - Epoch [57][900/3746] lr: 6.914e-02, eta: 3 days, 4:30:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5597, loss_cls: 4.0114, loss: 4.0114 +2024-07-18 06:59:40,998 - pyskl - INFO - Epoch [57][1000/3746] lr: 6.912e-02, eta: 3 days, 4:29:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5391, loss_cls: 4.1077, loss: 4.1077 +2024-07-18 07:01:03,023 - pyskl - INFO - Epoch [57][1100/3746] lr: 6.909e-02, eta: 3 days, 4:28:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5431, loss_cls: 4.0731, loss: 4.0731 +2024-07-18 07:02:25,299 - pyskl - INFO - Epoch [57][1200/3746] lr: 6.907e-02, eta: 3 days, 4:27:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5450, loss_cls: 4.0712, loss: 4.0712 +2024-07-18 07:03:47,093 - pyskl - INFO - Epoch [57][1300/3746] lr: 6.904e-02, eta: 3 days, 4:25:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5516, loss_cls: 4.0263, loss: 4.0263 +2024-07-18 07:05:08,756 - pyskl - INFO - Epoch [57][1400/3746] lr: 6.901e-02, eta: 3 days, 4:24:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5452, loss_cls: 4.0377, loss: 4.0377 +2024-07-18 07:06:30,889 - pyskl - INFO - Epoch [57][1500/3746] lr: 6.899e-02, eta: 3 days, 4:23:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5461, loss_cls: 4.0696, loss: 4.0696 +2024-07-18 07:07:52,345 - pyskl - INFO - Epoch [57][1600/3746] lr: 6.896e-02, eta: 3 days, 4:22:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5578, loss_cls: 4.0267, loss: 4.0267 +2024-07-18 07:09:13,786 - pyskl - INFO - Epoch [57][1700/3746] lr: 6.894e-02, eta: 3 days, 4:20:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5425, loss_cls: 4.0584, loss: 4.0584 +2024-07-18 07:10:35,329 - pyskl - INFO - Epoch [57][1800/3746] lr: 6.891e-02, eta: 3 days, 4:19:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5300, loss_cls: 4.1252, loss: 4.1252 +2024-07-18 07:11:57,168 - pyskl - INFO - Epoch [57][1900/3746] lr: 6.889e-02, eta: 3 days, 4:18:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5434, loss_cls: 4.0578, loss: 4.0578 +2024-07-18 07:13:19,093 - pyskl - INFO - Epoch [57][2000/3746] lr: 6.886e-02, eta: 3 days, 4:17:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5544, loss_cls: 4.0107, loss: 4.0107 +2024-07-18 07:14:41,278 - pyskl - INFO - Epoch [57][2100/3746] lr: 6.883e-02, eta: 3 days, 4:16:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5586, loss_cls: 4.0203, loss: 4.0203 +2024-07-18 07:16:02,313 - pyskl - INFO - Epoch [57][2200/3746] lr: 6.881e-02, eta: 3 days, 4:14:53, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5494, loss_cls: 4.0366, loss: 4.0366 +2024-07-18 07:17:24,059 - pyskl - INFO - Epoch [57][2300/3746] lr: 6.878e-02, eta: 3 days, 4:13:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5414, loss_cls: 4.1080, loss: 4.1080 +2024-07-18 07:18:44,992 - pyskl - INFO - Epoch [57][2400/3746] lr: 6.876e-02, eta: 3 days, 4:12:26, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5578, loss_cls: 4.0167, loss: 4.0167 +2024-07-18 07:20:06,576 - pyskl - INFO - Epoch [57][2500/3746] lr: 6.873e-02, eta: 3 days, 4:11:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5598, loss_cls: 3.9769, loss: 3.9769 +2024-07-18 07:21:28,385 - pyskl - INFO - Epoch [57][2600/3746] lr: 6.870e-02, eta: 3 days, 4:10:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5583, loss_cls: 4.0242, loss: 4.0242 +2024-07-18 07:22:49,270 - pyskl - INFO - Epoch [57][2700/3746] lr: 6.868e-02, eta: 3 days, 4:08:45, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5509, loss_cls: 4.0641, loss: 4.0641 +2024-07-18 07:24:11,145 - pyskl - INFO - Epoch [57][2800/3746] lr: 6.865e-02, eta: 3 days, 4:07:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5513, loss_cls: 4.0329, loss: 4.0329 +2024-07-18 07:25:33,111 - pyskl - INFO - Epoch [57][2900/3746] lr: 6.863e-02, eta: 3 days, 4:06:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5464, loss_cls: 4.0711, loss: 4.0711 +2024-07-18 07:26:54,662 - pyskl - INFO - Epoch [57][3000/3746] lr: 6.860e-02, eta: 3 days, 4:05:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5536, loss_cls: 4.0363, loss: 4.0363 +2024-07-18 07:28:16,301 - pyskl - INFO - Epoch [57][3100/3746] lr: 6.857e-02, eta: 3 days, 4:03:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5587, loss_cls: 4.0159, loss: 4.0159 +2024-07-18 07:29:37,518 - pyskl - INFO - Epoch [57][3200/3746] lr: 6.855e-02, eta: 3 days, 4:02:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5367, loss_cls: 4.0949, loss: 4.0949 +2024-07-18 07:30:58,910 - pyskl - INFO - Epoch [57][3300/3746] lr: 6.852e-02, eta: 3 days, 4:01:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5497, loss_cls: 4.0845, loss: 4.0845 +2024-07-18 07:32:20,421 - pyskl - INFO - Epoch [57][3400/3746] lr: 6.850e-02, eta: 3 days, 4:00:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5519, loss_cls: 4.0574, loss: 4.0574 +2024-07-18 07:33:41,567 - pyskl - INFO - Epoch [57][3500/3746] lr: 6.847e-02, eta: 3 days, 3:58:58, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5531, loss_cls: 4.0351, loss: 4.0351 +2024-07-18 07:35:03,957 - pyskl - INFO - Epoch [57][3600/3746] lr: 6.844e-02, eta: 3 days, 3:57:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5433, loss_cls: 4.0631, loss: 4.0631 +2024-07-18 07:36:25,799 - pyskl - INFO - Epoch [57][3700/3746] lr: 6.842e-02, eta: 3 days, 3:56:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5470, loss_cls: 4.0744, loss: 4.0744 +2024-07-18 07:37:05,066 - pyskl - INFO - Saving checkpoint at 57 epochs +2024-07-18 07:38:56,266 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 07:38:56,977 - pyskl - INFO - +top1_acc 0.2126 +top5_acc 0.4449 +2024-07-18 07:38:56,977 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 07:38:57,014 - pyskl - INFO - +mean_acc 0.2125 +2024-07-18 07:38:57,024 - pyskl - INFO - Epoch(val) [57][309] top1_acc: 0.2126, top5_acc: 0.4449, mean_class_accuracy: 0.2125 +2024-07-18 07:42:36,595 - pyskl - INFO - Epoch [58][100/3746] lr: 6.838e-02, eta: 3 days, 3:57:30, time: 2.196, data_time: 1.223, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5595, loss_cls: 3.9696, loss: 3.9696 +2024-07-18 07:43:59,316 - pyskl - INFO - Epoch [58][200/3746] lr: 6.835e-02, eta: 3 days, 3:56:18, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5580, loss_cls: 4.0146, loss: 4.0146 +2024-07-18 07:45:21,124 - pyskl - INFO - Epoch [58][300/3746] lr: 6.833e-02, eta: 3 days, 3:55:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5541, loss_cls: 3.9972, loss: 3.9972 +2024-07-18 07:46:42,436 - pyskl - INFO - Epoch [58][400/3746] lr: 6.830e-02, eta: 3 days, 3:53:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5587, loss_cls: 4.0008, loss: 4.0008 +2024-07-18 07:48:04,076 - pyskl - INFO - Epoch [58][500/3746] lr: 6.828e-02, eta: 3 days, 3:52:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5567, loss_cls: 4.0347, loss: 4.0347 +2024-07-18 07:49:25,666 - pyskl - INFO - Epoch [58][600/3746] lr: 6.825e-02, eta: 3 days, 3:51:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5548, loss_cls: 3.9988, loss: 3.9988 +2024-07-18 07:50:48,043 - pyskl - INFO - Epoch [58][700/3746] lr: 6.822e-02, eta: 3 days, 3:50:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5405, loss_cls: 4.0578, loss: 4.0578 +2024-07-18 07:52:09,350 - pyskl - INFO - Epoch [58][800/3746] lr: 6.820e-02, eta: 3 days, 3:48:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5478, loss_cls: 4.0439, loss: 4.0439 +2024-07-18 07:53:31,219 - pyskl - INFO - Epoch [58][900/3746] lr: 6.817e-02, eta: 3 days, 3:47:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5525, loss_cls: 4.0267, loss: 4.0267 +2024-07-18 07:54:53,305 - pyskl - INFO - Epoch [58][1000/3746] lr: 6.815e-02, eta: 3 days, 3:46:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5614, loss_cls: 3.9809, loss: 3.9809 +2024-07-18 07:56:14,694 - pyskl - INFO - Epoch [58][1100/3746] lr: 6.812e-02, eta: 3 days, 3:45:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5498, loss_cls: 4.0136, loss: 4.0136 +2024-07-18 07:57:37,645 - pyskl - INFO - Epoch [58][1200/3746] lr: 6.809e-02, eta: 3 days, 3:44:06, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5481, loss_cls: 4.0348, loss: 4.0348 +2024-07-18 07:59:00,346 - pyskl - INFO - Epoch [58][1300/3746] lr: 6.807e-02, eta: 3 days, 3:42:55, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5523, loss_cls: 4.0323, loss: 4.0323 +2024-07-18 08:00:22,509 - pyskl - INFO - Epoch [58][1400/3746] lr: 6.804e-02, eta: 3 days, 3:41:42, time: 0.822, data_time: 0.001, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5364, loss_cls: 4.0801, loss: 4.0801 +2024-07-18 08:01:44,716 - pyskl - INFO - Epoch [58][1500/3746] lr: 6.802e-02, eta: 3 days, 3:40:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5497, loss_cls: 3.9958, loss: 3.9958 +2024-07-18 08:03:06,628 - pyskl - INFO - Epoch [58][1600/3746] lr: 6.799e-02, eta: 3 days, 3:39:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5573, loss_cls: 3.9977, loss: 3.9977 +2024-07-18 08:04:28,505 - pyskl - INFO - Epoch [58][1700/3746] lr: 6.796e-02, eta: 3 days, 3:38:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5556, loss_cls: 4.0270, loss: 4.0270 +2024-07-18 08:05:50,387 - pyskl - INFO - Epoch [58][1800/3746] lr: 6.794e-02, eta: 3 days, 3:36:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5537, loss_cls: 4.0689, loss: 4.0689 +2024-07-18 08:07:12,435 - pyskl - INFO - Epoch [58][1900/3746] lr: 6.791e-02, eta: 3 days, 3:35:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5536, loss_cls: 4.0449, loss: 4.0449 +2024-07-18 08:08:34,046 - pyskl - INFO - Epoch [58][2000/3746] lr: 6.789e-02, eta: 3 days, 3:34:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5483, loss_cls: 4.0499, loss: 4.0499 +2024-07-18 08:09:55,631 - pyskl - INFO - Epoch [58][2100/3746] lr: 6.786e-02, eta: 3 days, 3:33:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5487, loss_cls: 4.0423, loss: 4.0423 +2024-07-18 08:11:17,380 - pyskl - INFO - Epoch [58][2200/3746] lr: 6.783e-02, eta: 3 days, 3:31:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5437, loss_cls: 4.0525, loss: 4.0525 +2024-07-18 08:12:39,162 - pyskl - INFO - Epoch [58][2300/3746] lr: 6.781e-02, eta: 3 days, 3:30:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5583, loss_cls: 3.9938, loss: 3.9938 +2024-07-18 08:14:00,566 - pyskl - INFO - Epoch [58][2400/3746] lr: 6.778e-02, eta: 3 days, 3:29:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5477, loss_cls: 4.0478, loss: 4.0478 +2024-07-18 08:15:22,464 - pyskl - INFO - Epoch [58][2500/3746] lr: 6.775e-02, eta: 3 days, 3:28:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5514, loss_cls: 4.0516, loss: 4.0516 +2024-07-18 08:16:44,412 - pyskl - INFO - Epoch [58][2600/3746] lr: 6.773e-02, eta: 3 days, 3:27:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5525, loss_cls: 4.0336, loss: 4.0336 +2024-07-18 08:18:05,427 - pyskl - INFO - Epoch [58][2700/3746] lr: 6.770e-02, eta: 3 days, 3:25:48, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5445, loss_cls: 4.0485, loss: 4.0485 +2024-07-18 08:19:27,113 - pyskl - INFO - Epoch [58][2800/3746] lr: 6.768e-02, eta: 3 days, 3:24:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5603, loss_cls: 4.0233, loss: 4.0233 +2024-07-18 08:20:49,034 - pyskl - INFO - Epoch [58][2900/3746] lr: 6.765e-02, eta: 3 days, 3:23:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5547, loss_cls: 4.0468, loss: 4.0468 +2024-07-18 08:22:10,720 - pyskl - INFO - Epoch [58][3000/3746] lr: 6.762e-02, eta: 3 days, 3:22:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5536, loss_cls: 4.0517, loss: 4.0517 +2024-07-18 08:23:32,387 - pyskl - INFO - Epoch [58][3100/3746] lr: 6.760e-02, eta: 3 days, 3:20:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5430, loss_cls: 4.1071, loss: 4.1071 +2024-07-18 08:24:53,612 - pyskl - INFO - Epoch [58][3200/3746] lr: 6.757e-02, eta: 3 days, 3:19:40, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5428, loss_cls: 4.0644, loss: 4.0644 +2024-07-18 08:26:15,155 - pyskl - INFO - Epoch [58][3300/3746] lr: 6.755e-02, eta: 3 days, 3:18:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5614, loss_cls: 4.0047, loss: 4.0047 +2024-07-18 08:27:36,813 - pyskl - INFO - Epoch [58][3400/3746] lr: 6.752e-02, eta: 3 days, 3:17:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5431, loss_cls: 4.0665, loss: 4.0665 +2024-07-18 08:28:58,010 - pyskl - INFO - Epoch [58][3500/3746] lr: 6.749e-02, eta: 3 days, 3:15:58, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5517, loss_cls: 4.0261, loss: 4.0261 +2024-07-18 08:30:20,638 - pyskl - INFO - Epoch [58][3600/3746] lr: 6.747e-02, eta: 3 days, 3:14:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5545, loss_cls: 4.0569, loss: 4.0569 +2024-07-18 08:31:42,281 - pyskl - INFO - Epoch [58][3700/3746] lr: 6.744e-02, eta: 3 days, 3:13:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5530, loss_cls: 4.0266, loss: 4.0266 +2024-07-18 08:32:21,547 - pyskl - INFO - Saving checkpoint at 58 epochs +2024-07-18 08:34:11,925 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 08:34:12,667 - pyskl - INFO - +top1_acc 0.1832 +top5_acc 0.4048 +2024-07-18 08:34:12,667 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 08:34:12,708 - pyskl - INFO - +mean_acc 0.1829 +2024-07-18 08:34:12,719 - pyskl - INFO - Epoch(val) [58][309] top1_acc: 0.1832, top5_acc: 0.4048, mean_class_accuracy: 0.1829 +2024-07-18 08:37:52,254 - pyskl - INFO - Epoch [59][100/3746] lr: 6.740e-02, eta: 3 days, 3:14:23, time: 2.195, data_time: 1.225, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5592, loss_cls: 3.9992, loss: 3.9992 +2024-07-18 08:39:14,717 - pyskl - INFO - Epoch [59][200/3746] lr: 6.738e-02, eta: 3 days, 3:13:11, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5655, loss_cls: 3.9856, loss: 3.9856 +2024-07-18 08:40:36,554 - pyskl - INFO - Epoch [59][300/3746] lr: 6.735e-02, eta: 3 days, 3:11:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5500, loss_cls: 4.0423, loss: 4.0423 +2024-07-18 08:41:58,045 - pyskl - INFO - Epoch [59][400/3746] lr: 6.732e-02, eta: 3 days, 3:10:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5464, loss_cls: 4.0340, loss: 4.0340 +2024-07-18 08:43:20,150 - pyskl - INFO - Epoch [59][500/3746] lr: 6.730e-02, eta: 3 days, 3:09:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5550, loss_cls: 3.9969, loss: 3.9969 +2024-07-18 08:44:42,005 - pyskl - INFO - Epoch [59][600/3746] lr: 6.727e-02, eta: 3 days, 3:08:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5614, loss_cls: 3.9691, loss: 3.9691 +2024-07-18 08:46:03,356 - pyskl - INFO - Epoch [59][700/3746] lr: 6.725e-02, eta: 3 days, 3:07:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5580, loss_cls: 4.0145, loss: 4.0145 +2024-07-18 08:47:24,444 - pyskl - INFO - Epoch [59][800/3746] lr: 6.722e-02, eta: 3 days, 3:05:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5459, loss_cls: 4.0426, loss: 4.0426 +2024-07-18 08:48:45,668 - pyskl - INFO - Epoch [59][900/3746] lr: 6.719e-02, eta: 3 days, 3:04:33, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5548, loss_cls: 4.0113, loss: 4.0113 +2024-07-18 08:50:07,861 - pyskl - INFO - Epoch [59][1000/3746] lr: 6.717e-02, eta: 3 days, 3:03:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5603, loss_cls: 3.9735, loss: 3.9735 +2024-07-18 08:51:28,674 - pyskl - INFO - Epoch [59][1100/3746] lr: 6.714e-02, eta: 3 days, 3:02:05, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5636, loss_cls: 3.9953, loss: 3.9953 +2024-07-18 08:52:50,641 - pyskl - INFO - Epoch [59][1200/3746] lr: 6.711e-02, eta: 3 days, 3:00:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5461, loss_cls: 4.0800, loss: 4.0800 +2024-07-18 08:54:13,218 - pyskl - INFO - Epoch [59][1300/3746] lr: 6.709e-02, eta: 3 days, 2:59:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5544, loss_cls: 4.0561, loss: 4.0561 +2024-07-18 08:55:35,344 - pyskl - INFO - Epoch [59][1400/3746] lr: 6.706e-02, eta: 3 days, 2:58:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5597, loss_cls: 4.0063, loss: 4.0063 +2024-07-18 08:56:57,164 - pyskl - INFO - Epoch [59][1500/3746] lr: 6.704e-02, eta: 3 days, 2:57:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5555, loss_cls: 4.0185, loss: 4.0185 +2024-07-18 08:58:18,810 - pyskl - INFO - Epoch [59][1600/3746] lr: 6.701e-02, eta: 3 days, 2:55:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5542, loss_cls: 4.0121, loss: 4.0121 +2024-07-18 08:59:40,748 - pyskl - INFO - Epoch [59][1700/3746] lr: 6.698e-02, eta: 3 days, 2:54:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5570, loss_cls: 3.9958, loss: 3.9958 +2024-07-18 09:01:02,066 - pyskl - INFO - Epoch [59][1800/3746] lr: 6.696e-02, eta: 3 days, 2:53:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5623, loss_cls: 3.9933, loss: 3.9933 +2024-07-18 09:02:23,744 - pyskl - INFO - Epoch [59][1900/3746] lr: 6.693e-02, eta: 3 days, 2:52:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5641, loss_cls: 3.9952, loss: 3.9952 +2024-07-18 09:03:45,283 - pyskl - INFO - Epoch [59][2000/3746] lr: 6.690e-02, eta: 3 days, 2:51:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5550, loss_cls: 4.0383, loss: 4.0383 +2024-07-18 09:05:06,764 - pyskl - INFO - Epoch [59][2100/3746] lr: 6.688e-02, eta: 3 days, 2:49:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5427, loss_cls: 4.0556, loss: 4.0556 +2024-07-18 09:06:28,325 - pyskl - INFO - Epoch [59][2200/3746] lr: 6.685e-02, eta: 3 days, 2:48:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5497, loss_cls: 4.0430, loss: 4.0430 +2024-07-18 09:07:49,807 - pyskl - INFO - Epoch [59][2300/3746] lr: 6.682e-02, eta: 3 days, 2:47:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5511, loss_cls: 4.0092, loss: 4.0092 +2024-07-18 09:09:11,462 - pyskl - INFO - Epoch [59][2400/3746] lr: 6.680e-02, eta: 3 days, 2:46:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5520, loss_cls: 4.0327, loss: 4.0327 +2024-07-18 09:10:32,833 - pyskl - INFO - Epoch [59][2500/3746] lr: 6.677e-02, eta: 3 days, 2:44:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5537, loss_cls: 4.0145, loss: 4.0145 +2024-07-18 09:11:54,769 - pyskl - INFO - Epoch [59][2600/3746] lr: 6.675e-02, eta: 3 days, 2:43:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5537, loss_cls: 4.0403, loss: 4.0403 +2024-07-18 09:13:15,969 - pyskl - INFO - Epoch [59][2700/3746] lr: 6.672e-02, eta: 3 days, 2:42:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5506, loss_cls: 4.0676, loss: 4.0676 +2024-07-18 09:14:38,231 - pyskl - INFO - Epoch [59][2800/3746] lr: 6.669e-02, eta: 3 days, 2:41:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5566, loss_cls: 4.0110, loss: 4.0110 +2024-07-18 09:16:00,510 - pyskl - INFO - Epoch [59][2900/3746] lr: 6.667e-02, eta: 3 days, 2:39:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5375, loss_cls: 4.0741, loss: 4.0741 +2024-07-18 09:17:22,300 - pyskl - INFO - Epoch [59][3000/3746] lr: 6.664e-02, eta: 3 days, 2:38:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5541, loss_cls: 4.0205, loss: 4.0205 +2024-07-18 09:18:44,201 - pyskl - INFO - Epoch [59][3100/3746] lr: 6.661e-02, eta: 3 days, 2:37:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5550, loss_cls: 4.0288, loss: 4.0288 +2024-07-18 09:20:05,662 - pyskl - INFO - Epoch [59][3200/3746] lr: 6.659e-02, eta: 3 days, 2:36:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5555, loss_cls: 3.9976, loss: 3.9976 +2024-07-18 09:21:27,541 - pyskl - INFO - Epoch [59][3300/3746] lr: 6.656e-02, eta: 3 days, 2:35:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5637, loss_cls: 3.9598, loss: 3.9598 +2024-07-18 09:22:49,084 - pyskl - INFO - Epoch [59][3400/3746] lr: 6.653e-02, eta: 3 days, 2:33:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5500, loss_cls: 4.0715, loss: 4.0715 +2024-07-18 09:24:10,909 - pyskl - INFO - Epoch [59][3500/3746] lr: 6.651e-02, eta: 3 days, 2:32:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5514, loss_cls: 4.0383, loss: 4.0383 +2024-07-18 09:25:32,551 - pyskl - INFO - Epoch [59][3600/3746] lr: 6.648e-02, eta: 3 days, 2:31:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5561, loss_cls: 4.0084, loss: 4.0084 +2024-07-18 09:26:54,376 - pyskl - INFO - Epoch [59][3700/3746] lr: 6.646e-02, eta: 3 days, 2:30:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5472, loss_cls: 4.0711, loss: 4.0711 +2024-07-18 09:27:33,731 - pyskl - INFO - Saving checkpoint at 59 epochs +2024-07-18 09:29:23,795 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 09:29:24,499 - pyskl - INFO - +top1_acc 0.2180 +top5_acc 0.4583 +2024-07-18 09:29:24,499 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 09:29:24,539 - pyskl - INFO - +mean_acc 0.2178 +2024-07-18 09:29:24,550 - pyskl - INFO - Epoch(val) [59][309] top1_acc: 0.2180, top5_acc: 0.4583, mean_class_accuracy: 0.2178 +2024-07-18 09:33:02,545 - pyskl - INFO - Epoch [60][100/3746] lr: 6.642e-02, eta: 3 days, 2:30:50, time: 2.180, data_time: 1.211, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5625, loss_cls: 3.9815, loss: 3.9815 +2024-07-18 09:34:24,868 - pyskl - INFO - Epoch [60][200/3746] lr: 6.639e-02, eta: 3 days, 2:29:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5611, loss_cls: 3.9802, loss: 3.9802 +2024-07-18 09:35:46,351 - pyskl - INFO - Epoch [60][300/3746] lr: 6.636e-02, eta: 3 days, 2:28:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5570, loss_cls: 4.0190, loss: 4.0190 +2024-07-18 09:37:07,969 - pyskl - INFO - Epoch [60][400/3746] lr: 6.634e-02, eta: 3 days, 2:27:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5480, loss_cls: 3.9875, loss: 3.9875 +2024-07-18 09:38:29,023 - pyskl - INFO - Epoch [60][500/3746] lr: 6.631e-02, eta: 3 days, 2:25:53, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5663, loss_cls: 3.9384, loss: 3.9384 +2024-07-18 09:39:50,759 - pyskl - INFO - Epoch [60][600/3746] lr: 6.629e-02, eta: 3 days, 2:24:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5622, loss_cls: 3.9989, loss: 3.9989 +2024-07-18 09:41:12,999 - pyskl - INFO - Epoch [60][700/3746] lr: 6.626e-02, eta: 3 days, 2:23:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5427, loss_cls: 4.0411, loss: 4.0411 +2024-07-18 09:42:34,209 - pyskl - INFO - Epoch [60][800/3746] lr: 6.623e-02, eta: 3 days, 2:22:11, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5559, loss_cls: 3.9932, loss: 3.9932 +2024-07-18 09:43:55,900 - pyskl - INFO - Epoch [60][900/3746] lr: 6.621e-02, eta: 3 days, 2:20:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5597, loss_cls: 4.0071, loss: 4.0071 +2024-07-18 09:45:18,644 - pyskl - INFO - Epoch [60][1000/3746] lr: 6.618e-02, eta: 3 days, 2:19:44, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5566, loss_cls: 3.9960, loss: 3.9960 +2024-07-18 09:46:40,057 - pyskl - INFO - Epoch [60][1100/3746] lr: 6.615e-02, eta: 3 days, 2:18:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5533, loss_cls: 4.0149, loss: 4.0149 +2024-07-18 09:48:01,642 - pyskl - INFO - Epoch [60][1200/3746] lr: 6.613e-02, eta: 3 days, 2:17:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5613, loss_cls: 4.0200, loss: 4.0200 +2024-07-18 09:49:24,063 - pyskl - INFO - Epoch [60][1300/3746] lr: 6.610e-02, eta: 3 days, 2:16:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5481, loss_cls: 4.0601, loss: 4.0601 +2024-07-18 09:50:45,479 - pyskl - INFO - Epoch [60][1400/3746] lr: 6.607e-02, eta: 3 days, 2:14:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5675, loss_cls: 3.9711, loss: 3.9711 +2024-07-18 09:52:06,804 - pyskl - INFO - Epoch [60][1500/3746] lr: 6.605e-02, eta: 3 days, 2:13:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5586, loss_cls: 4.0299, loss: 4.0299 +2024-07-18 09:53:29,419 - pyskl - INFO - Epoch [60][1600/3746] lr: 6.602e-02, eta: 3 days, 2:12:19, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5452, loss_cls: 4.0666, loss: 4.0666 +2024-07-18 09:54:50,903 - pyskl - INFO - Epoch [60][1700/3746] lr: 6.599e-02, eta: 3 days, 2:11:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5553, loss_cls: 4.0122, loss: 4.0122 +2024-07-18 09:56:12,373 - pyskl - INFO - Epoch [60][1800/3746] lr: 6.597e-02, eta: 3 days, 2:09:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5547, loss_cls: 4.0201, loss: 4.0201 +2024-07-18 09:57:33,789 - pyskl - INFO - Epoch [60][1900/3746] lr: 6.594e-02, eta: 3 days, 2:08:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5570, loss_cls: 4.0088, loss: 4.0088 +2024-07-18 09:58:55,418 - pyskl - INFO - Epoch [60][2000/3746] lr: 6.591e-02, eta: 3 days, 2:07:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5572, loss_cls: 4.0025, loss: 4.0025 +2024-07-18 10:00:16,942 - pyskl - INFO - Epoch [60][2100/3746] lr: 6.589e-02, eta: 3 days, 2:06:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5480, loss_cls: 4.0601, loss: 4.0601 +2024-07-18 10:01:38,402 - pyskl - INFO - Epoch [60][2200/3746] lr: 6.586e-02, eta: 3 days, 2:04:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5639, loss_cls: 3.9834, loss: 3.9834 +2024-07-18 10:02:59,541 - pyskl - INFO - Epoch [60][2300/3746] lr: 6.584e-02, eta: 3 days, 2:03:37, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5494, loss_cls: 4.0397, loss: 4.0397 +2024-07-18 10:04:20,858 - pyskl - INFO - Epoch [60][2400/3746] lr: 6.581e-02, eta: 3 days, 2:02:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5537, loss_cls: 4.0107, loss: 4.0107 +2024-07-18 10:05:41,915 - pyskl - INFO - Epoch [60][2500/3746] lr: 6.578e-02, eta: 3 days, 2:01:07, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5466, loss_cls: 4.0747, loss: 4.0747 +2024-07-18 10:07:04,195 - pyskl - INFO - Epoch [60][2600/3746] lr: 6.576e-02, eta: 3 days, 1:59:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5605, loss_cls: 4.0225, loss: 4.0225 +2024-07-18 10:08:25,536 - pyskl - INFO - Epoch [60][2700/3746] lr: 6.573e-02, eta: 3 days, 1:58:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5555, loss_cls: 4.0279, loss: 4.0279 +2024-07-18 10:09:47,219 - pyskl - INFO - Epoch [60][2800/3746] lr: 6.570e-02, eta: 3 days, 1:57:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5520, loss_cls: 4.0155, loss: 4.0155 +2024-07-18 10:11:09,891 - pyskl - INFO - Epoch [60][2900/3746] lr: 6.568e-02, eta: 3 days, 1:56:12, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5591, loss_cls: 4.0059, loss: 4.0059 +2024-07-18 10:12:31,479 - pyskl - INFO - Epoch [60][3000/3746] lr: 6.565e-02, eta: 3 days, 1:54:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5595, loss_cls: 3.9972, loss: 3.9972 +2024-07-18 10:13:52,899 - pyskl - INFO - Epoch [60][3100/3746] lr: 6.562e-02, eta: 3 days, 1:53:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5589, loss_cls: 3.9691, loss: 3.9691 +2024-07-18 10:15:14,647 - pyskl - INFO - Epoch [60][3200/3746] lr: 6.560e-02, eta: 3 days, 1:52:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5663, loss_cls: 3.9662, loss: 3.9662 +2024-07-18 10:16:36,405 - pyskl - INFO - Epoch [60][3300/3746] lr: 6.557e-02, eta: 3 days, 1:51:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5433, loss_cls: 4.0569, loss: 4.0569 +2024-07-18 10:17:57,473 - pyskl - INFO - Epoch [60][3400/3746] lr: 6.554e-02, eta: 3 days, 1:49:59, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5527, loss_cls: 4.0328, loss: 4.0328 +2024-07-18 10:19:18,835 - pyskl - INFO - Epoch [60][3500/3746] lr: 6.552e-02, eta: 3 days, 1:48:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5627, loss_cls: 3.9728, loss: 3.9728 +2024-07-18 10:20:41,100 - pyskl - INFO - Epoch [60][3600/3746] lr: 6.549e-02, eta: 3 days, 1:47:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5456, loss_cls: 4.0203, loss: 4.0203 +2024-07-18 10:22:03,173 - pyskl - INFO - Epoch [60][3700/3746] lr: 6.546e-02, eta: 3 days, 1:46:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5439, loss_cls: 4.0549, loss: 4.0549 +2024-07-18 10:22:42,452 - pyskl - INFO - Saving checkpoint at 60 epochs +2024-07-18 10:24:33,522 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 10:24:34,188 - pyskl - INFO - +top1_acc 0.2133 +top5_acc 0.4499 +2024-07-18 10:24:34,188 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 10:24:34,225 - pyskl - INFO - +mean_acc 0.2131 +2024-07-18 10:24:34,235 - pyskl - INFO - Epoch(val) [60][309] top1_acc: 0.2133, top5_acc: 0.4499, mean_class_accuracy: 0.2131 +2024-07-18 10:28:20,561 - pyskl - INFO - Epoch [61][100/3746] lr: 6.542e-02, eta: 3 days, 1:47:08, time: 2.263, data_time: 1.282, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5681, loss_cls: 3.9547, loss: 3.9547 +2024-07-18 10:29:43,621 - pyskl - INFO - Epoch [61][200/3746] lr: 6.540e-02, eta: 3 days, 1:45:56, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5694, loss_cls: 3.9600, loss: 3.9600 +2024-07-18 10:31:06,551 - pyskl - INFO - Epoch [61][300/3746] lr: 6.537e-02, eta: 3 days, 1:44:43, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5642, loss_cls: 3.9552, loss: 3.9552 +2024-07-18 10:32:29,523 - pyskl - INFO - Epoch [61][400/3746] lr: 6.534e-02, eta: 3 days, 1:43:31, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5653, loss_cls: 3.9814, loss: 3.9814 +2024-07-18 10:33:52,299 - pyskl - INFO - Epoch [61][500/3746] lr: 6.532e-02, eta: 3 days, 1:42:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5627, loss_cls: 4.0005, loss: 4.0005 +2024-07-18 10:35:15,680 - pyskl - INFO - Epoch [61][600/3746] lr: 6.529e-02, eta: 3 days, 1:41:06, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5642, loss_cls: 3.9874, loss: 3.9874 +2024-07-18 10:36:38,791 - pyskl - INFO - Epoch [61][700/3746] lr: 6.526e-02, eta: 3 days, 1:39:53, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5627, loss_cls: 3.9558, loss: 3.9558 +2024-07-18 10:38:02,181 - pyskl - INFO - Epoch [61][800/3746] lr: 6.524e-02, eta: 3 days, 1:38:41, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5566, loss_cls: 4.0247, loss: 4.0247 +2024-07-18 10:39:25,121 - pyskl - INFO - Epoch [61][900/3746] lr: 6.521e-02, eta: 3 days, 1:37:29, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5700, loss_cls: 3.9551, loss: 3.9551 +2024-07-18 10:40:48,261 - pyskl - INFO - Epoch [61][1000/3746] lr: 6.519e-02, eta: 3 days, 1:36:16, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5473, loss_cls: 4.0174, loss: 4.0174 +2024-07-18 10:42:11,283 - pyskl - INFO - Epoch [61][1100/3746] lr: 6.516e-02, eta: 3 days, 1:35:04, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5580, loss_cls: 4.0098, loss: 4.0098 +2024-07-18 10:43:34,063 - pyskl - INFO - Epoch [61][1200/3746] lr: 6.513e-02, eta: 3 days, 1:33:51, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5634, loss_cls: 3.9714, loss: 3.9714 +2024-07-18 10:44:57,122 - pyskl - INFO - Epoch [61][1300/3746] lr: 6.511e-02, eta: 3 days, 1:32:38, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5595, loss_cls: 4.0232, loss: 4.0232 +2024-07-18 10:46:19,185 - pyskl - INFO - Epoch [61][1400/3746] lr: 6.508e-02, eta: 3 days, 1:31:24, time: 0.821, data_time: 0.001, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5506, loss_cls: 4.0226, loss: 4.0226 +2024-07-18 10:47:41,597 - pyskl - INFO - Epoch [61][1500/3746] lr: 6.505e-02, eta: 3 days, 1:30:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5634, loss_cls: 3.9929, loss: 3.9929 +2024-07-18 10:49:04,758 - pyskl - INFO - Epoch [61][1600/3746] lr: 6.503e-02, eta: 3 days, 1:28:58, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5619, loss_cls: 4.0067, loss: 4.0067 +2024-07-18 10:50:26,934 - pyskl - INFO - Epoch [61][1700/3746] lr: 6.500e-02, eta: 3 days, 1:27:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5563, loss_cls: 4.0345, loss: 4.0345 +2024-07-18 10:51:49,322 - pyskl - INFO - Epoch [61][1800/3746] lr: 6.497e-02, eta: 3 days, 1:26:31, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5584, loss_cls: 3.9757, loss: 3.9757 +2024-07-18 10:53:11,171 - pyskl - INFO - Epoch [61][1900/3746] lr: 6.495e-02, eta: 3 days, 1:25:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5578, loss_cls: 3.9928, loss: 3.9928 +2024-07-18 10:54:32,529 - pyskl - INFO - Epoch [61][2000/3746] lr: 6.492e-02, eta: 3 days, 1:24:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5477, loss_cls: 4.0434, loss: 4.0434 +2024-07-18 10:55:54,090 - pyskl - INFO - Epoch [61][2100/3746] lr: 6.489e-02, eta: 3 days, 1:22:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5620, loss_cls: 4.0025, loss: 4.0025 +2024-07-18 10:57:15,936 - pyskl - INFO - Epoch [61][2200/3746] lr: 6.487e-02, eta: 3 days, 1:21:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5450, loss_cls: 4.0580, loss: 4.0580 +2024-07-18 10:58:37,410 - pyskl - INFO - Epoch [61][2300/3746] lr: 6.484e-02, eta: 3 days, 1:20:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5587, loss_cls: 4.0063, loss: 4.0063 +2024-07-18 10:59:59,121 - pyskl - INFO - Epoch [61][2400/3746] lr: 6.481e-02, eta: 3 days, 1:19:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5650, loss_cls: 3.9713, loss: 3.9713 +2024-07-18 11:01:20,868 - pyskl - INFO - Epoch [61][2500/3746] lr: 6.478e-02, eta: 3 days, 1:17:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5536, loss_cls: 4.0297, loss: 4.0297 +2024-07-18 11:02:43,247 - pyskl - INFO - Epoch [61][2600/3746] lr: 6.476e-02, eta: 3 days, 1:16:34, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5566, loss_cls: 4.0036, loss: 4.0036 +2024-07-18 11:04:04,755 - pyskl - INFO - Epoch [61][2700/3746] lr: 6.473e-02, eta: 3 days, 1:15:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5541, loss_cls: 4.0195, loss: 4.0195 +2024-07-18 11:05:26,261 - pyskl - INFO - Epoch [61][2800/3746] lr: 6.470e-02, eta: 3 days, 1:14:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5666, loss_cls: 3.9840, loss: 3.9840 +2024-07-18 11:06:48,660 - pyskl - INFO - Epoch [61][2900/3746] lr: 6.468e-02, eta: 3 days, 1:12:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5572, loss_cls: 4.0223, loss: 4.0223 +2024-07-18 11:08:11,179 - pyskl - INFO - Epoch [61][3000/3746] lr: 6.465e-02, eta: 3 days, 1:11:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5547, loss_cls: 4.0116, loss: 4.0116 +2024-07-18 11:09:33,451 - pyskl - INFO - Epoch [61][3100/3746] lr: 6.462e-02, eta: 3 days, 1:10:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5544, loss_cls: 3.9955, loss: 3.9955 +2024-07-18 11:10:55,115 - pyskl - INFO - Epoch [61][3200/3746] lr: 6.460e-02, eta: 3 days, 1:09:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5537, loss_cls: 4.0148, loss: 4.0148 +2024-07-18 11:12:16,796 - pyskl - INFO - Epoch [61][3300/3746] lr: 6.457e-02, eta: 3 days, 1:07:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5644, loss_cls: 3.9806, loss: 3.9806 +2024-07-18 11:13:38,570 - pyskl - INFO - Epoch [61][3400/3746] lr: 6.454e-02, eta: 3 days, 1:06:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5439, loss_cls: 4.0519, loss: 4.0519 +2024-07-18 11:15:00,592 - pyskl - INFO - Epoch [61][3500/3746] lr: 6.452e-02, eta: 3 days, 1:05:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5547, loss_cls: 4.0056, loss: 4.0056 +2024-07-18 11:16:21,976 - pyskl - INFO - Epoch [61][3600/3746] lr: 6.449e-02, eta: 3 days, 1:04:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5591, loss_cls: 3.9973, loss: 3.9973 +2024-07-18 11:17:44,326 - pyskl - INFO - Epoch [61][3700/3746] lr: 6.446e-02, eta: 3 days, 1:02:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5544, loss_cls: 4.0175, loss: 4.0175 +2024-07-18 11:18:23,493 - pyskl - INFO - Saving checkpoint at 61 epochs +2024-07-18 11:20:15,242 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 11:20:15,892 - pyskl - INFO - +top1_acc 0.2235 +top5_acc 0.4687 +2024-07-18 11:20:15,892 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 11:20:15,929 - pyskl - INFO - +mean_acc 0.2232 +2024-07-18 11:20:15,939 - pyskl - INFO - Epoch(val) [61][309] top1_acc: 0.2235, top5_acc: 0.4687, mean_class_accuracy: 0.2232 +2024-07-18 11:23:57,452 - pyskl - INFO - Epoch [62][100/3746] lr: 6.443e-02, eta: 3 days, 1:03:36, time: 2.215, data_time: 1.233, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5700, loss_cls: 3.9203, loss: 3.9203 +2024-07-18 11:25:19,118 - pyskl - INFO - Epoch [62][200/3746] lr: 6.440e-02, eta: 3 days, 1:02:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5591, loss_cls: 3.9708, loss: 3.9708 +2024-07-18 11:26:40,453 - pyskl - INFO - Epoch [62][300/3746] lr: 6.437e-02, eta: 3 days, 1:01:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5594, loss_cls: 3.9799, loss: 3.9799 +2024-07-18 11:28:01,753 - pyskl - INFO - Epoch [62][400/3746] lr: 6.434e-02, eta: 3 days, 0:59:50, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5561, loss_cls: 4.0117, loss: 4.0117 +2024-07-18 11:29:23,129 - pyskl - INFO - Epoch [62][500/3746] lr: 6.432e-02, eta: 3 days, 0:58:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5637, loss_cls: 3.9757, loss: 3.9757 +2024-07-18 11:30:44,410 - pyskl - INFO - Epoch [62][600/3746] lr: 6.429e-02, eta: 3 days, 0:57:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5636, loss_cls: 3.9716, loss: 3.9716 +2024-07-18 11:32:06,171 - pyskl - INFO - Epoch [62][700/3746] lr: 6.426e-02, eta: 3 days, 0:56:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5680, loss_cls: 3.9511, loss: 3.9511 +2024-07-18 11:33:27,748 - pyskl - INFO - Epoch [62][800/3746] lr: 6.424e-02, eta: 3 days, 0:54:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5555, loss_cls: 4.0186, loss: 4.0186 +2024-07-18 11:34:49,637 - pyskl - INFO - Epoch [62][900/3746] lr: 6.421e-02, eta: 3 days, 0:53:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5659, loss_cls: 3.9641, loss: 3.9641 +2024-07-18 11:36:11,862 - pyskl - INFO - Epoch [62][1000/3746] lr: 6.418e-02, eta: 3 days, 0:52:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5544, loss_cls: 3.9955, loss: 3.9955 +2024-07-18 11:37:33,742 - pyskl - INFO - Epoch [62][1100/3746] lr: 6.416e-02, eta: 3 days, 0:51:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5594, loss_cls: 3.9829, loss: 3.9829 +2024-07-18 11:38:55,470 - pyskl - INFO - Epoch [62][1200/3746] lr: 6.413e-02, eta: 3 days, 0:49:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5597, loss_cls: 3.9773, loss: 3.9773 +2024-07-18 11:40:17,905 - pyskl - INFO - Epoch [62][1300/3746] lr: 6.410e-02, eta: 3 days, 0:48:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5641, loss_cls: 3.9530, loss: 3.9530 +2024-07-18 11:41:40,062 - pyskl - INFO - Epoch [62][1400/3746] lr: 6.408e-02, eta: 3 days, 0:47:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5633, loss_cls: 3.9657, loss: 3.9657 +2024-07-18 11:43:01,734 - pyskl - INFO - Epoch [62][1500/3746] lr: 6.405e-02, eta: 3 days, 0:46:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5584, loss_cls: 3.9921, loss: 3.9921 +2024-07-18 11:44:24,233 - pyskl - INFO - Epoch [62][1600/3746] lr: 6.402e-02, eta: 3 days, 0:44:54, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5639, loss_cls: 3.9869, loss: 3.9869 +2024-07-18 11:45:46,321 - pyskl - INFO - Epoch [62][1700/3746] lr: 6.400e-02, eta: 3 days, 0:43:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5658, loss_cls: 3.9757, loss: 3.9757 +2024-07-18 11:47:08,511 - pyskl - INFO - Epoch [62][1800/3746] lr: 6.397e-02, eta: 3 days, 0:42:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5578, loss_cls: 3.9822, loss: 3.9822 +2024-07-18 11:48:29,726 - pyskl - INFO - Epoch [62][1900/3746] lr: 6.394e-02, eta: 3 days, 0:41:10, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5580, loss_cls: 3.9827, loss: 3.9827 +2024-07-18 11:49:51,447 - pyskl - INFO - Epoch [62][2000/3746] lr: 6.392e-02, eta: 3 days, 0:39:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5558, loss_cls: 4.0128, loss: 4.0128 +2024-07-18 11:51:13,712 - pyskl - INFO - Epoch [62][2100/3746] lr: 6.389e-02, eta: 3 days, 0:38:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5559, loss_cls: 3.9945, loss: 3.9945 +2024-07-18 11:52:35,310 - pyskl - INFO - Epoch [62][2200/3746] lr: 6.386e-02, eta: 3 days, 0:37:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5558, loss_cls: 4.0072, loss: 4.0072 +2024-07-18 11:53:56,656 - pyskl - INFO - Epoch [62][2300/3746] lr: 6.384e-02, eta: 3 days, 0:36:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5567, loss_cls: 4.0184, loss: 4.0184 +2024-07-18 11:55:17,792 - pyskl - INFO - Epoch [62][2400/3746] lr: 6.381e-02, eta: 3 days, 0:34:54, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5553, loss_cls: 4.0134, loss: 4.0134 +2024-07-18 11:56:39,425 - pyskl - INFO - Epoch [62][2500/3746] lr: 6.378e-02, eta: 3 days, 0:33:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5653, loss_cls: 3.9685, loss: 3.9685 +2024-07-18 11:58:02,318 - pyskl - INFO - Epoch [62][2600/3746] lr: 6.375e-02, eta: 3 days, 0:32:26, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5583, loss_cls: 4.0132, loss: 4.0132 +2024-07-18 11:59:24,530 - pyskl - INFO - Epoch [62][2700/3746] lr: 6.373e-02, eta: 3 days, 0:31:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5659, loss_cls: 3.9934, loss: 3.9934 +2024-07-18 12:00:46,216 - pyskl - INFO - Epoch [62][2800/3746] lr: 6.370e-02, eta: 3 days, 0:29:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5575, loss_cls: 3.9703, loss: 3.9703 +2024-07-18 12:02:08,642 - pyskl - INFO - Epoch [62][2900/3746] lr: 6.367e-02, eta: 3 days, 0:28:42, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5547, loss_cls: 4.0142, loss: 4.0142 +2024-07-18 12:03:30,169 - pyskl - INFO - Epoch [62][3000/3746] lr: 6.365e-02, eta: 3 days, 0:27:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5481, loss_cls: 4.0431, loss: 4.0431 +2024-07-18 12:04:51,946 - pyskl - INFO - Epoch [62][3100/3746] lr: 6.362e-02, eta: 3 days, 0:26:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5550, loss_cls: 4.0109, loss: 4.0109 +2024-07-18 12:06:14,158 - pyskl - INFO - Epoch [62][3200/3746] lr: 6.359e-02, eta: 3 days, 0:24:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5453, loss_cls: 4.0632, loss: 4.0632 +2024-07-18 12:07:36,040 - pyskl - INFO - Epoch [62][3300/3746] lr: 6.357e-02, eta: 3 days, 0:23:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5577, loss_cls: 4.0167, loss: 4.0167 +2024-07-18 12:08:57,774 - pyskl - INFO - Epoch [62][3400/3746] lr: 6.354e-02, eta: 3 days, 0:22:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5536, loss_cls: 4.0124, loss: 4.0124 +2024-07-18 12:10:19,421 - pyskl - INFO - Epoch [62][3500/3746] lr: 6.351e-02, eta: 3 days, 0:21:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5514, loss_cls: 4.0019, loss: 4.0019 +2024-07-18 12:11:40,677 - pyskl - INFO - Epoch [62][3600/3746] lr: 6.349e-02, eta: 3 days, 0:19:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5672, loss_cls: 3.9701, loss: 3.9701 +2024-07-18 12:13:03,613 - pyskl - INFO - Epoch [62][3700/3746] lr: 6.346e-02, eta: 3 days, 0:18:44, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5503, loss_cls: 4.0351, loss: 4.0351 +2024-07-18 12:13:43,203 - pyskl - INFO - Saving checkpoint at 62 epochs +2024-07-18 12:15:34,281 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 12:15:34,947 - pyskl - INFO - +top1_acc 0.2318 +top5_acc 0.4735 +2024-07-18 12:15:34,947 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 12:15:34,986 - pyskl - INFO - +mean_acc 0.2316 +2024-07-18 12:15:34,991 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_56.pth was removed +2024-07-18 12:15:35,234 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_62.pth. +2024-07-18 12:15:35,234 - pyskl - INFO - Best top1_acc is 0.2318 at 62 epoch. +2024-07-18 12:15:35,245 - pyskl - INFO - Epoch(val) [62][309] top1_acc: 0.2318, top5_acc: 0.4735, mean_class_accuracy: 0.2316 +2024-07-18 12:19:17,620 - pyskl - INFO - Epoch [63][100/3746] lr: 6.342e-02, eta: 3 days, 0:19:21, time: 2.224, data_time: 1.241, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5714, loss_cls: 3.9430, loss: 3.9430 +2024-07-18 12:20:39,872 - pyskl - INFO - Epoch [63][200/3746] lr: 6.339e-02, eta: 3 days, 0:18:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5672, loss_cls: 3.9686, loss: 3.9686 +2024-07-18 12:22:02,262 - pyskl - INFO - Epoch [63][300/3746] lr: 6.337e-02, eta: 3 days, 0:16:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5620, loss_cls: 3.9461, loss: 3.9461 +2024-07-18 12:23:24,365 - pyskl - INFO - Epoch [63][400/3746] lr: 6.334e-02, eta: 3 days, 0:15:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5700, loss_cls: 3.9366, loss: 3.9366 +2024-07-18 12:24:46,603 - pyskl - INFO - Epoch [63][500/3746] lr: 6.331e-02, eta: 3 days, 0:14:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5648, loss_cls: 3.9631, loss: 3.9631 +2024-07-18 12:26:08,999 - pyskl - INFO - Epoch [63][600/3746] lr: 6.328e-02, eta: 3 days, 0:13:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5530, loss_cls: 4.0009, loss: 4.0009 +2024-07-18 12:27:31,720 - pyskl - INFO - Epoch [63][700/3746] lr: 6.326e-02, eta: 3 days, 0:11:55, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5563, loss_cls: 3.9787, loss: 3.9787 +2024-07-18 12:28:53,837 - pyskl - INFO - Epoch [63][800/3746] lr: 6.323e-02, eta: 3 days, 0:10:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5564, loss_cls: 4.0317, loss: 4.0317 +2024-07-18 12:30:15,792 - pyskl - INFO - Epoch [63][900/3746] lr: 6.320e-02, eta: 3 days, 0:09:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5581, loss_cls: 3.9687, loss: 3.9687 +2024-07-18 12:31:38,095 - pyskl - INFO - Epoch [63][1000/3746] lr: 6.318e-02, eta: 3 days, 0:08:11, time: 0.823, data_time: 0.001, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5631, loss_cls: 3.9914, loss: 3.9914 +2024-07-18 12:32:59,794 - pyskl - INFO - Epoch [63][1100/3746] lr: 6.315e-02, eta: 3 days, 0:06:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5631, loss_cls: 3.9734, loss: 3.9734 +2024-07-18 12:34:21,243 - pyskl - INFO - Epoch [63][1200/3746] lr: 6.312e-02, eta: 3 days, 0:05:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5617, loss_cls: 3.9715, loss: 3.9715 +2024-07-18 12:35:43,938 - pyskl - INFO - Epoch [63][1300/3746] lr: 6.310e-02, eta: 3 days, 0:04:26, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5648, loss_cls: 3.9612, loss: 3.9612 +2024-07-18 12:37:05,737 - pyskl - INFO - Epoch [63][1400/3746] lr: 6.307e-02, eta: 3 days, 0:03:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5506, loss_cls: 4.0184, loss: 4.0184 +2024-07-18 12:38:27,084 - pyskl - INFO - Epoch [63][1500/3746] lr: 6.304e-02, eta: 3 days, 0:01:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5736, loss_cls: 3.9203, loss: 3.9203 +2024-07-18 12:39:49,607 - pyskl - INFO - Epoch [63][1600/3746] lr: 6.301e-02, eta: 3 days, 0:00:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5630, loss_cls: 3.9773, loss: 3.9773 +2024-07-18 12:41:11,440 - pyskl - INFO - Epoch [63][1700/3746] lr: 6.299e-02, eta: 2 days, 23:59:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5534, loss_cls: 4.0069, loss: 4.0069 +2024-07-18 12:42:32,979 - pyskl - INFO - Epoch [63][1800/3746] lr: 6.296e-02, eta: 2 days, 23:58:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5600, loss_cls: 3.9830, loss: 3.9830 +2024-07-18 12:43:54,688 - pyskl - INFO - Epoch [63][1900/3746] lr: 6.293e-02, eta: 2 days, 23:56:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5652, loss_cls: 3.9686, loss: 3.9686 +2024-07-18 12:45:16,131 - pyskl - INFO - Epoch [63][2000/3746] lr: 6.291e-02, eta: 2 days, 23:55:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5630, loss_cls: 3.9863, loss: 3.9863 +2024-07-18 12:46:37,600 - pyskl - INFO - Epoch [63][2100/3746] lr: 6.288e-02, eta: 2 days, 23:54:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5609, loss_cls: 3.9762, loss: 3.9762 +2024-07-18 12:47:58,784 - pyskl - INFO - Epoch [63][2200/3746] lr: 6.285e-02, eta: 2 days, 23:53:08, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5597, loss_cls: 4.0053, loss: 4.0053 +2024-07-18 12:49:20,100 - pyskl - INFO - Epoch [63][2300/3746] lr: 6.283e-02, eta: 2 days, 23:51:52, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5508, loss_cls: 4.0272, loss: 4.0272 +2024-07-18 12:50:41,252 - pyskl - INFO - Epoch [63][2400/3746] lr: 6.280e-02, eta: 2 days, 23:50:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5667, loss_cls: 3.9303, loss: 3.9303 +2024-07-18 12:52:02,439 - pyskl - INFO - Epoch [63][2500/3746] lr: 6.277e-02, eta: 2 days, 23:49:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5556, loss_cls: 4.0016, loss: 4.0016 +2024-07-18 12:53:25,126 - pyskl - INFO - Epoch [63][2600/3746] lr: 6.274e-02, eta: 2 days, 23:48:06, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5614, loss_cls: 3.9852, loss: 3.9852 +2024-07-18 12:54:46,428 - pyskl - INFO - Epoch [63][2700/3746] lr: 6.272e-02, eta: 2 days, 23:46:50, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5603, loss_cls: 3.9969, loss: 3.9969 +2024-07-18 12:56:07,938 - pyskl - INFO - Epoch [63][2800/3746] lr: 6.269e-02, eta: 2 days, 23:45:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5559, loss_cls: 3.9793, loss: 3.9793 +2024-07-18 12:57:30,149 - pyskl - INFO - Epoch [63][2900/3746] lr: 6.266e-02, eta: 2 days, 23:44:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5492, loss_cls: 4.0203, loss: 4.0203 +2024-07-18 12:58:51,194 - pyskl - INFO - Epoch [63][3000/3746] lr: 6.264e-02, eta: 2 days, 23:43:04, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5611, loss_cls: 4.0004, loss: 4.0004 +2024-07-18 13:00:12,474 - pyskl - INFO - Epoch [63][3100/3746] lr: 6.261e-02, eta: 2 days, 23:41:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5545, loss_cls: 4.0390, loss: 4.0390 +2024-07-18 13:01:33,877 - pyskl - INFO - Epoch [63][3200/3746] lr: 6.258e-02, eta: 2 days, 23:40:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5625, loss_cls: 3.9749, loss: 3.9749 +2024-07-18 13:02:55,650 - pyskl - INFO - Epoch [63][3300/3746] lr: 6.256e-02, eta: 2 days, 23:39:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5544, loss_cls: 3.9951, loss: 3.9951 +2024-07-18 13:04:16,832 - pyskl - INFO - Epoch [63][3400/3746] lr: 6.253e-02, eta: 2 days, 23:38:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5661, loss_cls: 3.9362, loss: 3.9362 +2024-07-18 13:05:38,566 - pyskl - INFO - Epoch [63][3500/3746] lr: 6.250e-02, eta: 2 days, 23:36:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5520, loss_cls: 4.0169, loss: 4.0169 +2024-07-18 13:06:59,907 - pyskl - INFO - Epoch [63][3600/3746] lr: 6.247e-02, eta: 2 days, 23:35:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5592, loss_cls: 3.9778, loss: 3.9778 +2024-07-18 13:08:22,236 - pyskl - INFO - Epoch [63][3700/3746] lr: 6.245e-02, eta: 2 days, 23:34:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5567, loss_cls: 4.0144, loss: 4.0144 +2024-07-18 13:09:01,671 - pyskl - INFO - Saving checkpoint at 63 epochs +2024-07-18 13:10:53,301 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 13:10:53,993 - pyskl - INFO - +top1_acc 0.2148 +top5_acc 0.4408 +2024-07-18 13:10:53,993 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 13:10:54,037 - pyskl - INFO - +mean_acc 0.2146 +2024-07-18 13:10:54,049 - pyskl - INFO - Epoch(val) [63][309] top1_acc: 0.2148, top5_acc: 0.4408, mean_class_accuracy: 0.2146 +2024-07-18 13:14:41,444 - pyskl - INFO - Epoch [64][100/3746] lr: 6.241e-02, eta: 2 days, 23:34:54, time: 2.274, data_time: 1.282, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5637, loss_cls: 3.9538, loss: 3.9538 +2024-07-18 13:16:03,464 - pyskl - INFO - Epoch [64][200/3746] lr: 6.238e-02, eta: 2 days, 23:33:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5622, loss_cls: 3.9459, loss: 3.9459 +2024-07-18 13:17:25,553 - pyskl - INFO - Epoch [64][300/3746] lr: 6.235e-02, eta: 2 days, 23:32:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5573, loss_cls: 3.9988, loss: 3.9988 +2024-07-18 13:18:47,513 - pyskl - INFO - Epoch [64][400/3746] lr: 6.233e-02, eta: 2 days, 23:31:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5747, loss_cls: 3.9480, loss: 3.9480 +2024-07-18 13:20:09,573 - pyskl - INFO - Epoch [64][500/3746] lr: 6.230e-02, eta: 2 days, 23:29:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5669, loss_cls: 3.9453, loss: 3.9453 +2024-07-18 13:21:31,329 - pyskl - INFO - Epoch [64][600/3746] lr: 6.227e-02, eta: 2 days, 23:28:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5756, loss_cls: 3.9265, loss: 3.9265 +2024-07-18 13:22:53,714 - pyskl - INFO - Epoch [64][700/3746] lr: 6.225e-02, eta: 2 days, 23:27:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5666, loss_cls: 3.9581, loss: 3.9581 +2024-07-18 13:24:15,779 - pyskl - INFO - Epoch [64][800/3746] lr: 6.222e-02, eta: 2 days, 23:26:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5563, loss_cls: 3.9884, loss: 3.9884 +2024-07-18 13:25:38,013 - pyskl - INFO - Epoch [64][900/3746] lr: 6.219e-02, eta: 2 days, 23:24:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5691, loss_cls: 3.9671, loss: 3.9671 +2024-07-18 13:27:00,236 - pyskl - INFO - Epoch [64][1000/3746] lr: 6.216e-02, eta: 2 days, 23:23:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5569, loss_cls: 3.9799, loss: 3.9799 +2024-07-18 13:28:22,181 - pyskl - INFO - Epoch [64][1100/3746] lr: 6.214e-02, eta: 2 days, 23:22:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5636, loss_cls: 3.9660, loss: 3.9660 +2024-07-18 13:29:44,546 - pyskl - INFO - Epoch [64][1200/3746] lr: 6.211e-02, eta: 2 days, 23:21:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5677, loss_cls: 3.9225, loss: 3.9225 +2024-07-18 13:31:07,315 - pyskl - INFO - Epoch [64][1300/3746] lr: 6.208e-02, eta: 2 days, 23:19:55, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5623, loss_cls: 3.9619, loss: 3.9619 +2024-07-18 13:32:29,527 - pyskl - INFO - Epoch [64][1400/3746] lr: 6.206e-02, eta: 2 days, 23:18:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5609, loss_cls: 4.0153, loss: 4.0153 +2024-07-18 13:33:51,301 - pyskl - INFO - Epoch [64][1500/3746] lr: 6.203e-02, eta: 2 days, 23:17:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5755, loss_cls: 3.9307, loss: 3.9307 +2024-07-18 13:35:12,969 - pyskl - INFO - Epoch [64][1600/3746] lr: 6.200e-02, eta: 2 days, 23:16:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5580, loss_cls: 3.9982, loss: 3.9982 +2024-07-18 13:36:34,235 - pyskl - INFO - Epoch [64][1700/3746] lr: 6.197e-02, eta: 2 days, 23:14:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5581, loss_cls: 3.9739, loss: 3.9739 +2024-07-18 13:37:55,925 - pyskl - INFO - Epoch [64][1800/3746] lr: 6.195e-02, eta: 2 days, 23:13:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5655, loss_cls: 3.9858, loss: 3.9858 +2024-07-18 13:39:18,242 - pyskl - INFO - Epoch [64][1900/3746] lr: 6.192e-02, eta: 2 days, 23:12:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5533, loss_cls: 4.0177, loss: 4.0177 +2024-07-18 13:40:40,000 - pyskl - INFO - Epoch [64][2000/3746] lr: 6.189e-02, eta: 2 days, 23:11:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5628, loss_cls: 3.9987, loss: 3.9987 +2024-07-18 13:42:02,125 - pyskl - INFO - Epoch [64][2100/3746] lr: 6.187e-02, eta: 2 days, 23:09:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5595, loss_cls: 3.9794, loss: 3.9794 +2024-07-18 13:43:24,409 - pyskl - INFO - Epoch [64][2200/3746] lr: 6.184e-02, eta: 2 days, 23:08:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5483, loss_cls: 4.0236, loss: 4.0236 +2024-07-18 13:44:46,208 - pyskl - INFO - Epoch [64][2300/3746] lr: 6.181e-02, eta: 2 days, 23:07:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5636, loss_cls: 3.9398, loss: 3.9398 +2024-07-18 13:46:07,869 - pyskl - INFO - Epoch [64][2400/3746] lr: 6.178e-02, eta: 2 days, 23:06:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5697, loss_cls: 3.9293, loss: 3.9293 +2024-07-18 13:47:29,558 - pyskl - INFO - Epoch [64][2500/3746] lr: 6.176e-02, eta: 2 days, 23:04:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5503, loss_cls: 4.0331, loss: 4.0331 +2024-07-18 13:48:51,392 - pyskl - INFO - Epoch [64][2600/3746] lr: 6.173e-02, eta: 2 days, 23:03:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5577, loss_cls: 3.9912, loss: 3.9912 +2024-07-18 13:50:13,067 - pyskl - INFO - Epoch [64][2700/3746] lr: 6.170e-02, eta: 2 days, 23:02:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5630, loss_cls: 3.9717, loss: 3.9717 +2024-07-18 13:51:35,232 - pyskl - INFO - Epoch [64][2800/3746] lr: 6.168e-02, eta: 2 days, 23:01:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5742, loss_cls: 3.8831, loss: 3.8831 +2024-07-18 13:52:57,132 - pyskl - INFO - Epoch [64][2900/3746] lr: 6.165e-02, eta: 2 days, 22:59:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5425, loss_cls: 4.0375, loss: 4.0375 +2024-07-18 13:54:19,356 - pyskl - INFO - Epoch [64][3000/3746] lr: 6.162e-02, eta: 2 days, 22:58:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5597, loss_cls: 3.9553, loss: 3.9553 +2024-07-18 13:55:41,293 - pyskl - INFO - Epoch [64][3100/3746] lr: 6.159e-02, eta: 2 days, 22:57:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5636, loss_cls: 3.9765, loss: 3.9765 +2024-07-18 13:57:02,921 - pyskl - INFO - Epoch [64][3200/3746] lr: 6.157e-02, eta: 2 days, 22:56:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5606, loss_cls: 3.9691, loss: 3.9691 +2024-07-18 13:58:24,951 - pyskl - INFO - Epoch [64][3300/3746] lr: 6.154e-02, eta: 2 days, 22:54:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5622, loss_cls: 3.9670, loss: 3.9670 +2024-07-18 13:59:46,869 - pyskl - INFO - Epoch [64][3400/3746] lr: 6.151e-02, eta: 2 days, 22:53:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5636, loss_cls: 3.9906, loss: 3.9906 +2024-07-18 14:01:08,816 - pyskl - INFO - Epoch [64][3500/3746] lr: 6.148e-02, eta: 2 days, 22:52:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5536, loss_cls: 4.0526, loss: 4.0526 +2024-07-18 14:02:31,258 - pyskl - INFO - Epoch [64][3600/3746] lr: 6.146e-02, eta: 2 days, 22:51:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5567, loss_cls: 4.0048, loss: 4.0048 +2024-07-18 14:03:53,234 - pyskl - INFO - Epoch [64][3700/3746] lr: 6.143e-02, eta: 2 days, 22:49:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5594, loss_cls: 3.9991, loss: 3.9991 +2024-07-18 14:04:33,074 - pyskl - INFO - Saving checkpoint at 64 epochs +2024-07-18 14:06:24,713 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 14:06:25,379 - pyskl - INFO - +top1_acc 0.2287 +top5_acc 0.4602 +2024-07-18 14:06:25,379 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 14:06:25,422 - pyskl - INFO - +mean_acc 0.2284 +2024-07-18 14:06:25,434 - pyskl - INFO - Epoch(val) [64][309] top1_acc: 0.2287, top5_acc: 0.4602, mean_class_accuracy: 0.2284 +2024-07-18 14:10:17,347 - pyskl - INFO - Epoch [65][100/3746] lr: 6.139e-02, eta: 2 days, 22:50:28, time: 2.319, data_time: 1.324, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5645, loss_cls: 3.9198, loss: 3.9198 +2024-07-18 14:11:40,629 - pyskl - INFO - Epoch [65][200/3746] lr: 6.136e-02, eta: 2 days, 22:49:14, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5783, loss_cls: 3.8735, loss: 3.8735 +2024-07-18 14:13:03,761 - pyskl - INFO - Epoch [65][300/3746] lr: 6.134e-02, eta: 2 days, 22:48:00, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5641, loss_cls: 3.9355, loss: 3.9355 +2024-07-18 14:14:26,831 - pyskl - INFO - Epoch [65][400/3746] lr: 6.131e-02, eta: 2 days, 22:46:46, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5663, loss_cls: 3.9388, loss: 3.9388 +2024-07-18 14:15:50,366 - pyskl - INFO - Epoch [65][500/3746] lr: 6.128e-02, eta: 2 days, 22:45:33, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5777, loss_cls: 3.9274, loss: 3.9274 +2024-07-18 14:17:13,252 - pyskl - INFO - Epoch [65][600/3746] lr: 6.125e-02, eta: 2 days, 22:44:19, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5670, loss_cls: 3.9467, loss: 3.9467 +2024-07-18 14:18:36,818 - pyskl - INFO - Epoch [65][700/3746] lr: 6.123e-02, eta: 2 days, 22:43:05, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5558, loss_cls: 3.9759, loss: 3.9759 +2024-07-18 14:19:59,197 - pyskl - INFO - Epoch [65][800/3746] lr: 6.120e-02, eta: 2 days, 22:41:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5672, loss_cls: 3.9242, loss: 3.9242 +2024-07-18 14:21:21,795 - pyskl - INFO - Epoch [65][900/3746] lr: 6.117e-02, eta: 2 days, 22:40:36, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5759, loss_cls: 3.9206, loss: 3.9206 +2024-07-18 14:22:44,340 - pyskl - INFO - Epoch [65][1000/3746] lr: 6.115e-02, eta: 2 days, 22:39:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5737, loss_cls: 3.9530, loss: 3.9530 +2024-07-18 14:24:07,582 - pyskl - INFO - Epoch [65][1100/3746] lr: 6.112e-02, eta: 2 days, 22:38:07, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5548, loss_cls: 3.9931, loss: 3.9931 +2024-07-18 14:25:30,145 - pyskl - INFO - Epoch [65][1200/3746] lr: 6.109e-02, eta: 2 days, 22:36:52, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5637, loss_cls: 3.9545, loss: 3.9545 +2024-07-18 14:26:52,773 - pyskl - INFO - Epoch [65][1300/3746] lr: 6.106e-02, eta: 2 days, 22:35:38, time: 0.826, data_time: 0.001, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5633, loss_cls: 3.9691, loss: 3.9691 +2024-07-18 14:28:15,524 - pyskl - INFO - Epoch [65][1400/3746] lr: 6.104e-02, eta: 2 days, 22:34:23, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5708, loss_cls: 3.9432, loss: 3.9432 +2024-07-18 14:29:38,068 - pyskl - INFO - Epoch [65][1500/3746] lr: 6.101e-02, eta: 2 days, 22:33:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5609, loss_cls: 3.9875, loss: 3.9875 +2024-07-18 14:31:00,769 - pyskl - INFO - Epoch [65][1600/3746] lr: 6.098e-02, eta: 2 days, 22:31:54, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5508, loss_cls: 4.0137, loss: 4.0137 +2024-07-18 14:32:23,985 - pyskl - INFO - Epoch [65][1700/3746] lr: 6.095e-02, eta: 2 days, 22:30:40, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5770, loss_cls: 3.9240, loss: 3.9240 +2024-07-18 14:33:47,254 - pyskl - INFO - Epoch [65][1800/3746] lr: 6.093e-02, eta: 2 days, 22:29:26, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5642, loss_cls: 3.9687, loss: 3.9687 +2024-07-18 14:35:10,018 - pyskl - INFO - Epoch [65][1900/3746] lr: 6.090e-02, eta: 2 days, 22:28:12, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5681, loss_cls: 3.9576, loss: 3.9576 +2024-07-18 14:36:32,379 - pyskl - INFO - Epoch [65][2000/3746] lr: 6.087e-02, eta: 2 days, 22:26:56, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5595, loss_cls: 3.9688, loss: 3.9688 +2024-07-18 14:37:55,131 - pyskl - INFO - Epoch [65][2100/3746] lr: 6.085e-02, eta: 2 days, 22:25:42, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5631, loss_cls: 3.9833, loss: 3.9833 +2024-07-18 14:39:17,902 - pyskl - INFO - Epoch [65][2200/3746] lr: 6.082e-02, eta: 2 days, 22:24:27, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5570, loss_cls: 3.9866, loss: 3.9866 +2024-07-18 14:40:40,891 - pyskl - INFO - Epoch [65][2300/3746] lr: 6.079e-02, eta: 2 days, 22:23:13, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5622, loss_cls: 3.9712, loss: 3.9712 +2024-07-18 14:42:03,273 - pyskl - INFO - Epoch [65][2400/3746] lr: 6.076e-02, eta: 2 days, 22:21:58, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5609, loss_cls: 3.9792, loss: 3.9792 +2024-07-18 14:43:26,586 - pyskl - INFO - Epoch [65][2500/3746] lr: 6.074e-02, eta: 2 days, 22:20:44, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5603, loss_cls: 3.9737, loss: 3.9737 +2024-07-18 14:44:49,186 - pyskl - INFO - Epoch [65][2600/3746] lr: 6.071e-02, eta: 2 days, 22:19:30, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5569, loss_cls: 3.9926, loss: 3.9926 +2024-07-18 14:46:11,448 - pyskl - INFO - Epoch [65][2700/3746] lr: 6.068e-02, eta: 2 days, 22:18:14, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5697, loss_cls: 3.9419, loss: 3.9419 +2024-07-18 14:47:33,649 - pyskl - INFO - Epoch [65][2800/3746] lr: 6.065e-02, eta: 2 days, 22:16:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5673, loss_cls: 3.9562, loss: 3.9562 +2024-07-18 14:48:56,793 - pyskl - INFO - Epoch [65][2900/3746] lr: 6.063e-02, eta: 2 days, 22:15:45, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5666, loss_cls: 3.9558, loss: 3.9558 +2024-07-18 14:50:19,391 - pyskl - INFO - Epoch [65][3000/3746] lr: 6.060e-02, eta: 2 days, 22:14:30, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5520, loss_cls: 4.0326, loss: 4.0326 +2024-07-18 14:51:41,728 - pyskl - INFO - Epoch [65][3100/3746] lr: 6.057e-02, eta: 2 days, 22:13:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5728, loss_cls: 3.9463, loss: 3.9463 +2024-07-18 14:53:04,142 - pyskl - INFO - Epoch [65][3200/3746] lr: 6.055e-02, eta: 2 days, 22:12:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5603, loss_cls: 3.9602, loss: 3.9602 +2024-07-18 14:54:26,626 - pyskl - INFO - Epoch [65][3300/3746] lr: 6.052e-02, eta: 2 days, 22:10:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5702, loss_cls: 3.9495, loss: 3.9495 +2024-07-18 14:55:49,180 - pyskl - INFO - Epoch [65][3400/3746] lr: 6.049e-02, eta: 2 days, 22:09:30, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5625, loss_cls: 3.9881, loss: 3.9881 +2024-07-18 14:57:11,324 - pyskl - INFO - Epoch [65][3500/3746] lr: 6.046e-02, eta: 2 days, 22:08:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5623, loss_cls: 3.9508, loss: 3.9508 +2024-07-18 14:58:34,820 - pyskl - INFO - Epoch [65][3600/3746] lr: 6.044e-02, eta: 2 days, 22:07:01, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5525, loss_cls: 4.0181, loss: 4.0181 +2024-07-18 14:59:57,795 - pyskl - INFO - Epoch [65][3700/3746] lr: 6.041e-02, eta: 2 days, 22:05:47, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5620, loss_cls: 3.9829, loss: 3.9829 +2024-07-18 15:00:38,048 - pyskl - INFO - Saving checkpoint at 65 epochs +2024-07-18 15:02:29,519 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 15:02:30,198 - pyskl - INFO - +top1_acc 0.2296 +top5_acc 0.4677 +2024-07-18 15:02:30,198 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 15:02:30,241 - pyskl - INFO - +mean_acc 0.2294 +2024-07-18 15:02:30,254 - pyskl - INFO - Epoch(val) [65][309] top1_acc: 0.2296, top5_acc: 0.4677, mean_class_accuracy: 0.2294 +2024-07-18 15:06:17,146 - pyskl - INFO - Epoch [66][100/3746] lr: 6.037e-02, eta: 2 days, 22:06:16, time: 2.269, data_time: 1.288, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5689, loss_cls: 3.9515, loss: 3.9515 +2024-07-18 15:07:39,548 - pyskl - INFO - Epoch [66][200/3746] lr: 6.034e-02, eta: 2 days, 22:05:01, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5747, loss_cls: 3.9010, loss: 3.9010 +2024-07-18 15:09:02,841 - pyskl - INFO - Epoch [66][300/3746] lr: 6.031e-02, eta: 2 days, 22:03:47, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5680, loss_cls: 3.9412, loss: 3.9412 +2024-07-18 15:10:25,604 - pyskl - INFO - Epoch [66][400/3746] lr: 6.029e-02, eta: 2 days, 22:02:32, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5709, loss_cls: 3.9204, loss: 3.9204 +2024-07-18 15:11:47,262 - pyskl - INFO - Epoch [66][500/3746] lr: 6.026e-02, eta: 2 days, 22:01:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5783, loss_cls: 3.8701, loss: 3.8701 +2024-07-18 15:13:09,075 - pyskl - INFO - Epoch [66][600/3746] lr: 6.023e-02, eta: 2 days, 22:00:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5573, loss_cls: 3.9823, loss: 3.9823 +2024-07-18 15:14:30,830 - pyskl - INFO - Epoch [66][700/3746] lr: 6.020e-02, eta: 2 days, 21:58:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5798, loss_cls: 3.8440, loss: 3.8440 +2024-07-18 15:15:52,740 - pyskl - INFO - Epoch [66][800/3746] lr: 6.018e-02, eta: 2 days, 21:57:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5691, loss_cls: 3.9587, loss: 3.9587 +2024-07-18 15:17:15,586 - pyskl - INFO - Epoch [66][900/3746] lr: 6.015e-02, eta: 2 days, 21:56:13, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5736, loss_cls: 3.9498, loss: 3.9498 +2024-07-18 15:18:37,639 - pyskl - INFO - Epoch [66][1000/3746] lr: 6.012e-02, eta: 2 days, 21:54:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5663, loss_cls: 3.9453, loss: 3.9453 +2024-07-18 15:20:00,107 - pyskl - INFO - Epoch [66][1100/3746] lr: 6.009e-02, eta: 2 days, 21:53:42, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5595, loss_cls: 3.9753, loss: 3.9753 +2024-07-18 15:21:22,185 - pyskl - INFO - Epoch [66][1200/3746] lr: 6.007e-02, eta: 2 days, 21:52:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5659, loss_cls: 3.9456, loss: 3.9456 +2024-07-18 15:22:45,103 - pyskl - INFO - Epoch [66][1300/3746] lr: 6.004e-02, eta: 2 days, 21:51:12, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5525, loss_cls: 3.9887, loss: 3.9887 +2024-07-18 15:24:07,163 - pyskl - INFO - Epoch [66][1400/3746] lr: 6.001e-02, eta: 2 days, 21:49:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5727, loss_cls: 3.9444, loss: 3.9444 +2024-07-18 15:25:29,806 - pyskl - INFO - Epoch [66][1500/3746] lr: 5.999e-02, eta: 2 days, 21:48:41, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5705, loss_cls: 3.9267, loss: 3.9267 +2024-07-18 15:26:52,451 - pyskl - INFO - Epoch [66][1600/3746] lr: 5.996e-02, eta: 2 days, 21:47:26, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5580, loss_cls: 3.9889, loss: 3.9889 +2024-07-18 15:28:15,208 - pyskl - INFO - Epoch [66][1700/3746] lr: 5.993e-02, eta: 2 days, 21:46:12, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5728, loss_cls: 3.9188, loss: 3.9188 +2024-07-18 15:29:36,986 - pyskl - INFO - Epoch [66][1800/3746] lr: 5.990e-02, eta: 2 days, 21:44:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5669, loss_cls: 3.9812, loss: 3.9812 +2024-07-18 15:30:59,197 - pyskl - INFO - Epoch [66][1900/3746] lr: 5.988e-02, eta: 2 days, 21:43:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5628, loss_cls: 3.9578, loss: 3.9578 +2024-07-18 15:32:20,900 - pyskl - INFO - Epoch [66][2000/3746] lr: 5.985e-02, eta: 2 days, 21:42:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5572, loss_cls: 3.9613, loss: 3.9613 +2024-07-18 15:33:42,398 - pyskl - INFO - Epoch [66][2100/3746] lr: 5.982e-02, eta: 2 days, 21:41:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5608, loss_cls: 3.9626, loss: 3.9626 +2024-07-18 15:35:04,572 - pyskl - INFO - Epoch [66][2200/3746] lr: 5.979e-02, eta: 2 days, 21:39:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5758, loss_cls: 3.8994, loss: 3.8994 +2024-07-18 15:36:26,534 - pyskl - INFO - Epoch [66][2300/3746] lr: 5.977e-02, eta: 2 days, 21:38:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5572, loss_cls: 4.0241, loss: 4.0241 +2024-07-18 15:37:48,618 - pyskl - INFO - Epoch [66][2400/3746] lr: 5.974e-02, eta: 2 days, 21:37:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5659, loss_cls: 3.9490, loss: 3.9490 +2024-07-18 15:39:10,841 - pyskl - INFO - Epoch [66][2500/3746] lr: 5.971e-02, eta: 2 days, 21:36:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5692, loss_cls: 3.9348, loss: 3.9348 +2024-07-18 15:40:32,875 - pyskl - INFO - Epoch [66][2600/3746] lr: 5.968e-02, eta: 2 days, 21:34:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5611, loss_cls: 4.0003, loss: 4.0003 +2024-07-18 15:41:55,195 - pyskl - INFO - Epoch [66][2700/3746] lr: 5.966e-02, eta: 2 days, 21:33:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5606, loss_cls: 3.9654, loss: 3.9654 +2024-07-18 15:43:16,880 - pyskl - INFO - Epoch [66][2800/3746] lr: 5.963e-02, eta: 2 days, 21:32:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5655, loss_cls: 3.9739, loss: 3.9739 +2024-07-18 15:44:38,855 - pyskl - INFO - Epoch [66][2900/3746] lr: 5.960e-02, eta: 2 days, 21:31:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5642, loss_cls: 3.9833, loss: 3.9833 +2024-07-18 15:46:00,230 - pyskl - INFO - Epoch [66][3000/3746] lr: 5.957e-02, eta: 2 days, 21:29:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5792, loss_cls: 3.8933, loss: 3.8933 +2024-07-18 15:47:21,828 - pyskl - INFO - Epoch [66][3100/3746] lr: 5.955e-02, eta: 2 days, 21:28:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5633, loss_cls: 3.9780, loss: 3.9780 +2024-07-18 15:48:44,449 - pyskl - INFO - Epoch [66][3200/3746] lr: 5.952e-02, eta: 2 days, 21:27:13, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5589, loss_cls: 3.9804, loss: 3.9804 +2024-07-18 15:50:07,037 - pyskl - INFO - Epoch [66][3300/3746] lr: 5.949e-02, eta: 2 days, 21:25:57, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5613, loss_cls: 3.9752, loss: 3.9752 +2024-07-18 15:51:29,002 - pyskl - INFO - Epoch [66][3400/3746] lr: 5.946e-02, eta: 2 days, 21:24:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5631, loss_cls: 3.9321, loss: 3.9321 +2024-07-18 15:52:51,746 - pyskl - INFO - Epoch [66][3500/3746] lr: 5.944e-02, eta: 2 days, 21:23:26, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5667, loss_cls: 3.9311, loss: 3.9311 +2024-07-18 15:54:14,251 - pyskl - INFO - Epoch [66][3600/3746] lr: 5.941e-02, eta: 2 days, 21:22:11, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5611, loss_cls: 3.9953, loss: 3.9953 +2024-07-18 15:55:36,785 - pyskl - INFO - Epoch [66][3700/3746] lr: 5.938e-02, eta: 2 days, 21:20:56, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5583, loss_cls: 3.9801, loss: 3.9801 +2024-07-18 15:56:17,103 - pyskl - INFO - Saving checkpoint at 66 epochs +2024-07-18 15:58:09,308 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 15:58:09,964 - pyskl - INFO - +top1_acc 0.2475 +top5_acc 0.4983 +2024-07-18 15:58:09,964 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 15:58:10,006 - pyskl - INFO - +mean_acc 0.2472 +2024-07-18 15:58:10,011 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_62.pth was removed +2024-07-18 15:58:10,274 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_66.pth. +2024-07-18 15:58:10,275 - pyskl - INFO - Best top1_acc is 0.2475 at 66 epoch. +2024-07-18 15:58:10,288 - pyskl - INFO - Epoch(val) [66][309] top1_acc: 0.2475, top5_acc: 0.4983, mean_class_accuracy: 0.2472 +2024-07-18 16:01:58,627 - pyskl - INFO - Epoch [67][100/3746] lr: 5.934e-02, eta: 2 days, 21:21:23, time: 2.283, data_time: 1.306, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5777, loss_cls: 3.8792, loss: 3.8792 +2024-07-18 16:03:20,260 - pyskl - INFO - Epoch [67][200/3746] lr: 5.931e-02, eta: 2 days, 21:20:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5803, loss_cls: 3.8578, loss: 3.8578 +2024-07-18 16:04:42,488 - pyskl - INFO - Epoch [67][300/3746] lr: 5.929e-02, eta: 2 days, 21:18:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5589, loss_cls: 3.9544, loss: 3.9544 +2024-07-18 16:06:04,727 - pyskl - INFO - Epoch [67][400/3746] lr: 5.926e-02, eta: 2 days, 21:17:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5644, loss_cls: 3.9463, loss: 3.9463 +2024-07-18 16:07:26,236 - pyskl - INFO - Epoch [67][500/3746] lr: 5.923e-02, eta: 2 days, 21:16:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5620, loss_cls: 3.9962, loss: 3.9962 +2024-07-18 16:08:48,054 - pyskl - INFO - Epoch [67][600/3746] lr: 5.920e-02, eta: 2 days, 21:15:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5700, loss_cls: 3.9212, loss: 3.9212 +2024-07-18 16:10:09,774 - pyskl - INFO - Epoch [67][700/3746] lr: 5.918e-02, eta: 2 days, 21:13:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5555, loss_cls: 3.9583, loss: 3.9583 +2024-07-18 16:11:31,612 - pyskl - INFO - Epoch [67][800/3746] lr: 5.915e-02, eta: 2 days, 21:12:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5637, loss_cls: 3.9883, loss: 3.9883 +2024-07-18 16:12:53,027 - pyskl - INFO - Epoch [67][900/3746] lr: 5.912e-02, eta: 2 days, 21:11:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5634, loss_cls: 3.9377, loss: 3.9377 +2024-07-18 16:14:14,946 - pyskl - INFO - Epoch [67][1000/3746] lr: 5.909e-02, eta: 2 days, 21:09:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5564, loss_cls: 4.0004, loss: 4.0004 +2024-07-18 16:15:36,438 - pyskl - INFO - Epoch [67][1100/3746] lr: 5.907e-02, eta: 2 days, 21:08:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5623, loss_cls: 3.9669, loss: 3.9669 +2024-07-18 16:16:58,262 - pyskl - INFO - Epoch [67][1200/3746] lr: 5.904e-02, eta: 2 days, 21:07:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5750, loss_cls: 3.9277, loss: 3.9277 +2024-07-18 16:18:21,229 - pyskl - INFO - Epoch [67][1300/3746] lr: 5.901e-02, eta: 2 days, 21:06:09, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5780, loss_cls: 3.8813, loss: 3.8813 +2024-07-18 16:19:43,412 - pyskl - INFO - Epoch [67][1400/3746] lr: 5.898e-02, eta: 2 days, 21:04:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5641, loss_cls: 3.9348, loss: 3.9348 +2024-07-18 16:21:05,525 - pyskl - INFO - Epoch [67][1500/3746] lr: 5.896e-02, eta: 2 days, 21:03:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5655, loss_cls: 3.9524, loss: 3.9524 +2024-07-18 16:22:27,403 - pyskl - INFO - Epoch [67][1600/3746] lr: 5.893e-02, eta: 2 days, 21:02:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5669, loss_cls: 3.9827, loss: 3.9827 +2024-07-18 16:23:48,981 - pyskl - INFO - Epoch [67][1700/3746] lr: 5.890e-02, eta: 2 days, 21:01:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5727, loss_cls: 3.9166, loss: 3.9166 +2024-07-18 16:25:10,654 - pyskl - INFO - Epoch [67][1800/3746] lr: 5.887e-02, eta: 2 days, 20:59:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5641, loss_cls: 3.9269, loss: 3.9269 +2024-07-18 16:26:32,434 - pyskl - INFO - Epoch [67][1900/3746] lr: 5.885e-02, eta: 2 days, 20:58:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5678, loss_cls: 3.9598, loss: 3.9598 +2024-07-18 16:27:54,147 - pyskl - INFO - Epoch [67][2000/3746] lr: 5.882e-02, eta: 2 days, 20:57:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5722, loss_cls: 3.9342, loss: 3.9342 +2024-07-18 16:29:15,647 - pyskl - INFO - Epoch [67][2100/3746] lr: 5.879e-02, eta: 2 days, 20:55:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5623, loss_cls: 3.9283, loss: 3.9283 +2024-07-18 16:30:37,697 - pyskl - INFO - Epoch [67][2200/3746] lr: 5.876e-02, eta: 2 days, 20:54:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5628, loss_cls: 3.9539, loss: 3.9539 +2024-07-18 16:31:59,484 - pyskl - INFO - Epoch [67][2300/3746] lr: 5.874e-02, eta: 2 days, 20:53:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5594, loss_cls: 3.9652, loss: 3.9652 +2024-07-18 16:33:21,050 - pyskl - INFO - Epoch [67][2400/3746] lr: 5.871e-02, eta: 2 days, 20:52:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5670, loss_cls: 3.9245, loss: 3.9245 +2024-07-18 16:34:43,381 - pyskl - INFO - Epoch [67][2500/3746] lr: 5.868e-02, eta: 2 days, 20:50:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5683, loss_cls: 3.9526, loss: 3.9526 +2024-07-18 16:36:05,488 - pyskl - INFO - Epoch [67][2600/3746] lr: 5.865e-02, eta: 2 days, 20:49:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5583, loss_cls: 3.9933, loss: 3.9933 +2024-07-18 16:37:27,244 - pyskl - INFO - Epoch [67][2700/3746] lr: 5.863e-02, eta: 2 days, 20:48:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5750, loss_cls: 3.8958, loss: 3.8958 +2024-07-18 16:38:49,209 - pyskl - INFO - Epoch [67][2800/3746] lr: 5.860e-02, eta: 2 days, 20:47:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5741, loss_cls: 3.9546, loss: 3.9546 +2024-07-18 16:40:11,215 - pyskl - INFO - Epoch [67][2900/3746] lr: 5.857e-02, eta: 2 days, 20:45:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5811, loss_cls: 3.9065, loss: 3.9065 +2024-07-18 16:41:32,986 - pyskl - INFO - Epoch [67][3000/3746] lr: 5.854e-02, eta: 2 days, 20:44:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5698, loss_cls: 3.9391, loss: 3.9391 +2024-07-18 16:42:54,619 - pyskl - INFO - Epoch [67][3100/3746] lr: 5.852e-02, eta: 2 days, 20:43:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5705, loss_cls: 3.9154, loss: 3.9154 +2024-07-18 16:44:16,263 - pyskl - INFO - Epoch [67][3200/3746] lr: 5.849e-02, eta: 2 days, 20:41:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5778, loss_cls: 3.9185, loss: 3.9185 +2024-07-18 16:45:37,822 - pyskl - INFO - Epoch [67][3300/3746] lr: 5.846e-02, eta: 2 days, 20:40:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5661, loss_cls: 3.9485, loss: 3.9485 +2024-07-18 16:46:59,437 - pyskl - INFO - Epoch [67][3400/3746] lr: 5.843e-02, eta: 2 days, 20:39:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5630, loss_cls: 3.9458, loss: 3.9458 +2024-07-18 16:48:22,211 - pyskl - INFO - Epoch [67][3500/3746] lr: 5.841e-02, eta: 2 days, 20:38:10, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5686, loss_cls: 3.9279, loss: 3.9279 +2024-07-18 16:49:44,728 - pyskl - INFO - Epoch [67][3600/3746] lr: 5.838e-02, eta: 2 days, 20:36:54, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5627, loss_cls: 3.9970, loss: 3.9970 +2024-07-18 16:51:07,346 - pyskl - INFO - Epoch [67][3700/3746] lr: 5.835e-02, eta: 2 days, 20:35:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5737, loss_cls: 3.9401, loss: 3.9401 +2024-07-18 16:51:47,026 - pyskl - INFO - Saving checkpoint at 67 epochs +2024-07-18 16:53:39,919 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 16:53:40,656 - pyskl - INFO - +top1_acc 0.2288 +top5_acc 0.4763 +2024-07-18 16:53:40,656 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 16:53:40,698 - pyskl - INFO - +mean_acc 0.2286 +2024-07-18 16:53:40,710 - pyskl - INFO - Epoch(val) [67][309] top1_acc: 0.2288, top5_acc: 0.4763, mean_class_accuracy: 0.2286 +2024-07-18 16:57:29,818 - pyskl - INFO - Epoch [68][100/3746] lr: 5.831e-02, eta: 2 days, 20:36:03, time: 2.291, data_time: 1.301, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5850, loss_cls: 3.8482, loss: 3.8482 +2024-07-18 16:58:52,033 - pyskl - INFO - Epoch [68][200/3746] lr: 5.828e-02, eta: 2 days, 20:34:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5753, loss_cls: 3.8818, loss: 3.8818 +2024-07-18 17:00:13,934 - pyskl - INFO - Epoch [68][300/3746] lr: 5.826e-02, eta: 2 days, 20:33:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5811, loss_cls: 3.8792, loss: 3.8792 +2024-07-18 17:01:35,619 - pyskl - INFO - Epoch [68][400/3746] lr: 5.823e-02, eta: 2 days, 20:32:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5725, loss_cls: 3.9137, loss: 3.9137 +2024-07-18 17:02:57,387 - pyskl - INFO - Epoch [68][500/3746] lr: 5.820e-02, eta: 2 days, 20:30:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5778, loss_cls: 3.9089, loss: 3.9089 +2024-07-18 17:04:19,121 - pyskl - INFO - Epoch [68][600/3746] lr: 5.817e-02, eta: 2 days, 20:29:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5669, loss_cls: 3.9522, loss: 3.9522 +2024-07-18 17:05:41,158 - pyskl - INFO - Epoch [68][700/3746] lr: 5.815e-02, eta: 2 days, 20:28:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5650, loss_cls: 3.9371, loss: 3.9371 +2024-07-18 17:07:02,789 - pyskl - INFO - Epoch [68][800/3746] lr: 5.812e-02, eta: 2 days, 20:27:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5781, loss_cls: 3.9150, loss: 3.9150 +2024-07-18 17:08:24,731 - pyskl - INFO - Epoch [68][900/3746] lr: 5.809e-02, eta: 2 days, 20:25:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5720, loss_cls: 3.9026, loss: 3.9026 +2024-07-18 17:09:46,413 - pyskl - INFO - Epoch [68][1000/3746] lr: 5.806e-02, eta: 2 days, 20:24:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5672, loss_cls: 3.9531, loss: 3.9531 +2024-07-18 17:11:08,325 - pyskl - INFO - Epoch [68][1100/3746] lr: 5.804e-02, eta: 2 days, 20:23:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5739, loss_cls: 3.9332, loss: 3.9332 +2024-07-18 17:12:30,194 - pyskl - INFO - Epoch [68][1200/3746] lr: 5.801e-02, eta: 2 days, 20:22:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5783, loss_cls: 3.9211, loss: 3.9211 +2024-07-18 17:13:52,751 - pyskl - INFO - Epoch [68][1300/3746] lr: 5.798e-02, eta: 2 days, 20:20:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5547, loss_cls: 3.9831, loss: 3.9831 +2024-07-18 17:15:15,034 - pyskl - INFO - Epoch [68][1400/3746] lr: 5.795e-02, eta: 2 days, 20:19:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5727, loss_cls: 3.9128, loss: 3.9128 +2024-07-18 17:16:36,959 - pyskl - INFO - Epoch [68][1500/3746] lr: 5.792e-02, eta: 2 days, 20:18:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5752, loss_cls: 3.8817, loss: 3.8817 +2024-07-18 17:17:59,035 - pyskl - INFO - Epoch [68][1600/3746] lr: 5.790e-02, eta: 2 days, 20:16:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5677, loss_cls: 3.9198, loss: 3.9198 +2024-07-18 17:19:20,523 - pyskl - INFO - Epoch [68][1700/3746] lr: 5.787e-02, eta: 2 days, 20:15:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5480, loss_cls: 3.9888, loss: 3.9888 +2024-07-18 17:20:42,062 - pyskl - INFO - Epoch [68][1800/3746] lr: 5.784e-02, eta: 2 days, 20:14:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5697, loss_cls: 3.9493, loss: 3.9493 +2024-07-18 17:22:04,228 - pyskl - INFO - Epoch [68][1900/3746] lr: 5.781e-02, eta: 2 days, 20:13:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5723, loss_cls: 3.9162, loss: 3.9162 +2024-07-18 17:23:26,067 - pyskl - INFO - Epoch [68][2000/3746] lr: 5.779e-02, eta: 2 days, 20:11:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5764, loss_cls: 3.9129, loss: 3.9129 +2024-07-18 17:24:47,777 - pyskl - INFO - Epoch [68][2100/3746] lr: 5.776e-02, eta: 2 days, 20:10:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5586, loss_cls: 3.9731, loss: 3.9731 +2024-07-18 17:26:09,419 - pyskl - INFO - Epoch [68][2200/3746] lr: 5.773e-02, eta: 2 days, 20:09:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5739, loss_cls: 3.9052, loss: 3.9052 +2024-07-18 17:27:31,076 - pyskl - INFO - Epoch [68][2300/3746] lr: 5.770e-02, eta: 2 days, 20:08:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5642, loss_cls: 3.9489, loss: 3.9489 +2024-07-18 17:28:52,796 - pyskl - INFO - Epoch [68][2400/3746] lr: 5.768e-02, eta: 2 days, 20:06:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5647, loss_cls: 3.9623, loss: 3.9623 +2024-07-18 17:30:14,911 - pyskl - INFO - Epoch [68][2500/3746] lr: 5.765e-02, eta: 2 days, 20:05:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5689, loss_cls: 3.9443, loss: 3.9443 +2024-07-18 17:31:36,430 - pyskl - INFO - Epoch [68][2600/3746] lr: 5.762e-02, eta: 2 days, 20:04:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5730, loss_cls: 3.9049, loss: 3.9049 +2024-07-18 17:32:58,650 - pyskl - INFO - Epoch [68][2700/3746] lr: 5.759e-02, eta: 2 days, 20:02:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5627, loss_cls: 3.9827, loss: 3.9827 +2024-07-18 17:34:20,512 - pyskl - INFO - Epoch [68][2800/3746] lr: 5.757e-02, eta: 2 days, 20:01:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5652, loss_cls: 3.9484, loss: 3.9484 +2024-07-18 17:35:42,922 - pyskl - INFO - Epoch [68][2900/3746] lr: 5.754e-02, eta: 2 days, 20:00:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5686, loss_cls: 3.9332, loss: 3.9332 +2024-07-18 17:37:04,434 - pyskl - INFO - Epoch [68][3000/3746] lr: 5.751e-02, eta: 2 days, 19:59:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5737, loss_cls: 3.9195, loss: 3.9195 +2024-07-18 17:38:26,312 - pyskl - INFO - Epoch [68][3100/3746] lr: 5.748e-02, eta: 2 days, 19:57:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5761, loss_cls: 3.9081, loss: 3.9081 +2024-07-18 17:39:47,827 - pyskl - INFO - Epoch [68][3200/3746] lr: 5.746e-02, eta: 2 days, 19:56:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5673, loss_cls: 3.9327, loss: 3.9327 +2024-07-18 17:41:09,254 - pyskl - INFO - Epoch [68][3300/3746] lr: 5.743e-02, eta: 2 days, 19:55:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5625, loss_cls: 3.9496, loss: 3.9496 +2024-07-18 17:42:31,204 - pyskl - INFO - Epoch [68][3400/3746] lr: 5.740e-02, eta: 2 days, 19:53:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5736, loss_cls: 3.9291, loss: 3.9291 +2024-07-18 17:43:53,746 - pyskl - INFO - Epoch [68][3500/3746] lr: 5.737e-02, eta: 2 days, 19:52:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5663, loss_cls: 3.9179, loss: 3.9179 +2024-07-18 17:45:15,927 - pyskl - INFO - Epoch [68][3600/3746] lr: 5.734e-02, eta: 2 days, 19:51:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5722, loss_cls: 3.9231, loss: 3.9231 +2024-07-18 17:46:38,606 - pyskl - INFO - Epoch [68][3700/3746] lr: 5.732e-02, eta: 2 days, 19:50:09, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5702, loss_cls: 3.9062, loss: 3.9062 +2024-07-18 17:47:18,206 - pyskl - INFO - Saving checkpoint at 68 epochs +2024-07-18 17:49:10,300 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 17:49:10,963 - pyskl - INFO - +top1_acc 0.2480 +top5_acc 0.4904 +2024-07-18 17:49:10,963 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 17:49:11,004 - pyskl - INFO - +mean_acc 0.2479 +2024-07-18 17:49:11,008 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_66.pth was removed +2024-07-18 17:49:11,264 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_68.pth. +2024-07-18 17:49:11,264 - pyskl - INFO - Best top1_acc is 0.2480 at 68 epoch. +2024-07-18 17:49:11,278 - pyskl - INFO - Epoch(val) [68][309] top1_acc: 0.2480, top5_acc: 0.4904, mean_class_accuracy: 0.2479 +2024-07-18 17:53:00,421 - pyskl - INFO - Epoch [69][100/3746] lr: 5.728e-02, eta: 2 days, 19:50:29, time: 2.291, data_time: 1.301, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5964, loss_cls: 3.8085, loss: 3.8085 +2024-07-18 17:54:22,671 - pyskl - INFO - Epoch [69][200/3746] lr: 5.725e-02, eta: 2 days, 19:49:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5923, loss_cls: 3.8351, loss: 3.8351 +2024-07-18 17:55:44,187 - pyskl - INFO - Epoch [69][300/3746] lr: 5.722e-02, eta: 2 days, 19:47:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5673, loss_cls: 3.9330, loss: 3.9330 +2024-07-18 17:57:05,923 - pyskl - INFO - Epoch [69][400/3746] lr: 5.719e-02, eta: 2 days, 19:46:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5775, loss_cls: 3.8853, loss: 3.8853 +2024-07-18 17:58:27,213 - pyskl - INFO - Epoch [69][500/3746] lr: 5.717e-02, eta: 2 days, 19:45:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5733, loss_cls: 3.9135, loss: 3.9135 +2024-07-18 17:59:49,356 - pyskl - INFO - Epoch [69][600/3746] lr: 5.714e-02, eta: 2 days, 19:44:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5834, loss_cls: 3.8963, loss: 3.8963 +2024-07-18 18:01:10,775 - pyskl - INFO - Epoch [69][700/3746] lr: 5.711e-02, eta: 2 days, 19:42:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5828, loss_cls: 3.8638, loss: 3.8638 +2024-07-18 18:02:33,504 - pyskl - INFO - Epoch [69][800/3746] lr: 5.708e-02, eta: 2 days, 19:41:32, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5741, loss_cls: 3.9100, loss: 3.9100 +2024-07-18 18:03:54,894 - pyskl - INFO - Epoch [69][900/3746] lr: 5.706e-02, eta: 2 days, 19:40:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5781, loss_cls: 3.8883, loss: 3.8883 +2024-07-18 18:05:16,698 - pyskl - INFO - Epoch [69][1000/3746] lr: 5.703e-02, eta: 2 days, 19:38:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5611, loss_cls: 3.9528, loss: 3.9528 +2024-07-18 18:06:38,586 - pyskl - INFO - Epoch [69][1100/3746] lr: 5.700e-02, eta: 2 days, 19:37:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5648, loss_cls: 3.9730, loss: 3.9730 +2024-07-18 18:08:00,741 - pyskl - INFO - Epoch [69][1200/3746] lr: 5.697e-02, eta: 2 days, 19:36:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5655, loss_cls: 3.9257, loss: 3.9257 +2024-07-18 18:09:23,446 - pyskl - INFO - Epoch [69][1300/3746] lr: 5.694e-02, eta: 2 days, 19:35:09, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5567, loss_cls: 3.9664, loss: 3.9664 +2024-07-18 18:10:45,851 - pyskl - INFO - Epoch [69][1400/3746] lr: 5.692e-02, eta: 2 days, 19:33:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5756, loss_cls: 3.9026, loss: 3.9026 +2024-07-18 18:12:08,205 - pyskl - INFO - Epoch [69][1500/3746] lr: 5.689e-02, eta: 2 days, 19:32:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5789, loss_cls: 3.9281, loss: 3.9281 +2024-07-18 18:13:30,456 - pyskl - INFO - Epoch [69][1600/3746] lr: 5.686e-02, eta: 2 days, 19:31:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5661, loss_cls: 3.9551, loss: 3.9551 +2024-07-18 18:14:52,628 - pyskl - INFO - Epoch [69][1700/3746] lr: 5.683e-02, eta: 2 days, 19:30:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5697, loss_cls: 3.9568, loss: 3.9568 +2024-07-18 18:16:14,398 - pyskl - INFO - Epoch [69][1800/3746] lr: 5.681e-02, eta: 2 days, 19:28:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5655, loss_cls: 3.9472, loss: 3.9472 +2024-07-18 18:17:36,239 - pyskl - INFO - Epoch [69][1900/3746] lr: 5.678e-02, eta: 2 days, 19:27:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5800, loss_cls: 3.8698, loss: 3.8698 +2024-07-18 18:18:57,791 - pyskl - INFO - Epoch [69][2000/3746] lr: 5.675e-02, eta: 2 days, 19:26:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5752, loss_cls: 3.9122, loss: 3.9122 +2024-07-18 18:20:19,942 - pyskl - INFO - Epoch [69][2100/3746] lr: 5.672e-02, eta: 2 days, 19:24:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5722, loss_cls: 3.9128, loss: 3.9128 +2024-07-18 18:21:41,895 - pyskl - INFO - Epoch [69][2200/3746] lr: 5.670e-02, eta: 2 days, 19:23:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5644, loss_cls: 3.9535, loss: 3.9535 +2024-07-18 18:23:03,678 - pyskl - INFO - Epoch [69][2300/3746] lr: 5.667e-02, eta: 2 days, 19:22:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5722, loss_cls: 3.8979, loss: 3.8979 +2024-07-18 18:24:25,461 - pyskl - INFO - Epoch [69][2400/3746] lr: 5.664e-02, eta: 2 days, 19:21:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5675, loss_cls: 3.9335, loss: 3.9335 +2024-07-18 18:25:47,611 - pyskl - INFO - Epoch [69][2500/3746] lr: 5.661e-02, eta: 2 days, 19:19:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5619, loss_cls: 3.9654, loss: 3.9654 +2024-07-18 18:27:09,068 - pyskl - INFO - Epoch [69][2600/3746] lr: 5.658e-02, eta: 2 days, 19:18:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5756, loss_cls: 3.9070, loss: 3.9070 +2024-07-18 18:28:31,026 - pyskl - INFO - Epoch [69][2700/3746] lr: 5.656e-02, eta: 2 days, 19:17:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5652, loss_cls: 3.9517, loss: 3.9517 +2024-07-18 18:29:53,410 - pyskl - INFO - Epoch [69][2800/3746] lr: 5.653e-02, eta: 2 days, 19:15:59, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5706, loss_cls: 3.9347, loss: 3.9347 +2024-07-18 18:31:15,075 - pyskl - INFO - Epoch [69][2900/3746] lr: 5.650e-02, eta: 2 days, 19:14:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5717, loss_cls: 3.9198, loss: 3.9198 +2024-07-18 18:32:37,443 - pyskl - INFO - Epoch [69][3000/3746] lr: 5.647e-02, eta: 2 days, 19:13:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5709, loss_cls: 3.9264, loss: 3.9264 +2024-07-18 18:33:59,135 - pyskl - INFO - Epoch [69][3100/3746] lr: 5.645e-02, eta: 2 days, 19:12:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5705, loss_cls: 3.9011, loss: 3.9011 +2024-07-18 18:35:21,263 - pyskl - INFO - Epoch [69][3200/3746] lr: 5.642e-02, eta: 2 days, 19:10:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5514, loss_cls: 4.0238, loss: 4.0238 +2024-07-18 18:36:42,645 - pyskl - INFO - Epoch [69][3300/3746] lr: 5.639e-02, eta: 2 days, 19:09:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5667, loss_cls: 3.9745, loss: 3.9745 +2024-07-18 18:38:04,356 - pyskl - INFO - Epoch [69][3400/3746] lr: 5.636e-02, eta: 2 days, 19:08:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5703, loss_cls: 3.9469, loss: 3.9469 +2024-07-18 18:39:26,824 - pyskl - INFO - Epoch [69][3500/3746] lr: 5.634e-02, eta: 2 days, 19:07:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5697, loss_cls: 3.9072, loss: 3.9072 +2024-07-18 18:40:49,466 - pyskl - INFO - Epoch [69][3600/3746] lr: 5.631e-02, eta: 2 days, 19:05:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5755, loss_cls: 3.9217, loss: 3.9217 +2024-07-18 18:42:11,807 - pyskl - INFO - Epoch [69][3700/3746] lr: 5.628e-02, eta: 2 days, 19:04:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5741, loss_cls: 3.9014, loss: 3.9014 +2024-07-18 18:42:51,351 - pyskl - INFO - Saving checkpoint at 69 epochs +2024-07-18 18:44:43,248 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 18:44:43,905 - pyskl - INFO - +top1_acc 0.2330 +top5_acc 0.4749 +2024-07-18 18:44:43,905 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 18:44:43,947 - pyskl - INFO - +mean_acc 0.2329 +2024-07-18 18:44:43,961 - pyskl - INFO - Epoch(val) [69][309] top1_acc: 0.2330, top5_acc: 0.4749, mean_class_accuracy: 0.2329 +2024-07-18 18:48:32,862 - pyskl - INFO - Epoch [70][100/3746] lr: 5.624e-02, eta: 2 days, 19:04:45, time: 2.289, data_time: 1.295, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5855, loss_cls: 3.8326, loss: 3.8326 +2024-07-18 18:49:54,654 - pyskl - INFO - Epoch [70][200/3746] lr: 5.621e-02, eta: 2 days, 19:03:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5730, loss_cls: 3.8996, loss: 3.8996 +2024-07-18 18:51:16,189 - pyskl - INFO - Epoch [70][300/3746] lr: 5.618e-02, eta: 2 days, 19:02:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5816, loss_cls: 3.8867, loss: 3.8867 +2024-07-18 18:52:37,844 - pyskl - INFO - Epoch [70][400/3746] lr: 5.616e-02, eta: 2 days, 19:00:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5675, loss_cls: 3.9359, loss: 3.9359 +2024-07-18 18:53:59,824 - pyskl - INFO - Epoch [70][500/3746] lr: 5.613e-02, eta: 2 days, 18:59:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5794, loss_cls: 3.8946, loss: 3.8946 +2024-07-18 18:55:21,654 - pyskl - INFO - Epoch [70][600/3746] lr: 5.610e-02, eta: 2 days, 18:58:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5830, loss_cls: 3.8482, loss: 3.8482 +2024-07-18 18:56:43,286 - pyskl - INFO - Epoch [70][700/3746] lr: 5.607e-02, eta: 2 days, 18:57:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5670, loss_cls: 3.9313, loss: 3.9313 +2024-07-18 18:58:05,546 - pyskl - INFO - Epoch [70][800/3746] lr: 5.605e-02, eta: 2 days, 18:55:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5673, loss_cls: 3.8986, loss: 3.8986 +2024-07-18 18:59:27,223 - pyskl - INFO - Epoch [70][900/3746] lr: 5.602e-02, eta: 2 days, 18:54:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5714, loss_cls: 3.9544, loss: 3.9544 +2024-07-18 19:00:49,138 - pyskl - INFO - Epoch [70][1000/3746] lr: 5.599e-02, eta: 2 days, 18:53:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5664, loss_cls: 3.9521, loss: 3.9521 +2024-07-18 19:02:11,247 - pyskl - INFO - Epoch [70][1100/3746] lr: 5.596e-02, eta: 2 days, 18:51:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5650, loss_cls: 3.9040, loss: 3.9040 +2024-07-18 19:03:33,224 - pyskl - INFO - Epoch [70][1200/3746] lr: 5.593e-02, eta: 2 days, 18:50:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5830, loss_cls: 3.8587, loss: 3.8587 +2024-07-18 19:04:55,706 - pyskl - INFO - Epoch [70][1300/3746] lr: 5.591e-02, eta: 2 days, 18:49:22, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5720, loss_cls: 3.9071, loss: 3.9071 +2024-07-18 19:06:18,166 - pyskl - INFO - Epoch [70][1400/3746] lr: 5.588e-02, eta: 2 days, 18:48:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5700, loss_cls: 3.9360, loss: 3.9360 +2024-07-18 19:07:40,673 - pyskl - INFO - Epoch [70][1500/3746] lr: 5.585e-02, eta: 2 days, 18:46:49, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5636, loss_cls: 3.9172, loss: 3.9172 +2024-07-18 19:09:02,532 - pyskl - INFO - Epoch [70][1600/3746] lr: 5.582e-02, eta: 2 days, 18:45:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5717, loss_cls: 3.9277, loss: 3.9277 +2024-07-18 19:10:24,130 - pyskl - INFO - Epoch [70][1700/3746] lr: 5.580e-02, eta: 2 days, 18:44:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5789, loss_cls: 3.9246, loss: 3.9246 +2024-07-18 19:11:45,712 - pyskl - INFO - Epoch [70][1800/3746] lr: 5.577e-02, eta: 2 days, 18:42:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5764, loss_cls: 3.9139, loss: 3.9139 +2024-07-18 19:13:07,975 - pyskl - INFO - Epoch [70][1900/3746] lr: 5.574e-02, eta: 2 days, 18:41:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5642, loss_cls: 3.9281, loss: 3.9281 +2024-07-18 19:14:29,260 - pyskl - INFO - Epoch [70][2000/3746] lr: 5.571e-02, eta: 2 days, 18:40:23, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5772, loss_cls: 3.9147, loss: 3.9147 +2024-07-18 19:15:50,868 - pyskl - INFO - Epoch [70][2100/3746] lr: 5.568e-02, eta: 2 days, 18:39:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5736, loss_cls: 3.9284, loss: 3.9284 +2024-07-18 19:17:12,766 - pyskl - INFO - Epoch [70][2200/3746] lr: 5.566e-02, eta: 2 days, 18:37:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5756, loss_cls: 3.9065, loss: 3.9065 +2024-07-18 19:18:34,459 - pyskl - INFO - Epoch [70][2300/3746] lr: 5.563e-02, eta: 2 days, 18:36:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5755, loss_cls: 3.8997, loss: 3.8997 +2024-07-18 19:19:56,179 - pyskl - INFO - Epoch [70][2400/3746] lr: 5.560e-02, eta: 2 days, 18:35:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5633, loss_cls: 3.9558, loss: 3.9558 +2024-07-18 19:21:17,938 - pyskl - INFO - Epoch [70][2500/3746] lr: 5.557e-02, eta: 2 days, 18:33:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5742, loss_cls: 3.9089, loss: 3.9089 +2024-07-18 19:22:39,791 - pyskl - INFO - Epoch [70][2600/3746] lr: 5.555e-02, eta: 2 days, 18:32:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5748, loss_cls: 3.8869, loss: 3.8869 +2024-07-18 19:24:02,039 - pyskl - INFO - Epoch [70][2700/3746] lr: 5.552e-02, eta: 2 days, 18:31:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5670, loss_cls: 3.9033, loss: 3.9033 +2024-07-18 19:25:23,643 - pyskl - INFO - Epoch [70][2800/3746] lr: 5.549e-02, eta: 2 days, 18:30:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5895, loss_cls: 3.8097, loss: 3.8097 +2024-07-18 19:26:45,568 - pyskl - INFO - Epoch [70][2900/3746] lr: 5.546e-02, eta: 2 days, 18:28:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5737, loss_cls: 3.8876, loss: 3.8876 +2024-07-18 19:28:07,097 - pyskl - INFO - Epoch [70][3000/3746] lr: 5.543e-02, eta: 2 days, 18:27:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5711, loss_cls: 3.9413, loss: 3.9413 +2024-07-18 19:29:29,362 - pyskl - INFO - Epoch [70][3100/3746] lr: 5.541e-02, eta: 2 days, 18:26:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5597, loss_cls: 3.9611, loss: 3.9611 +2024-07-18 19:30:50,860 - pyskl - INFO - Epoch [70][3200/3746] lr: 5.538e-02, eta: 2 days, 18:24:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5734, loss_cls: 3.9071, loss: 3.9071 +2024-07-18 19:32:12,855 - pyskl - INFO - Epoch [70][3300/3746] lr: 5.535e-02, eta: 2 days, 18:23:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5745, loss_cls: 3.9351, loss: 3.9351 +2024-07-18 19:33:35,089 - pyskl - INFO - Epoch [70][3400/3746] lr: 5.532e-02, eta: 2 days, 18:22:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5761, loss_cls: 3.9083, loss: 3.9083 +2024-07-18 19:34:57,860 - pyskl - INFO - Epoch [70][3500/3746] lr: 5.530e-02, eta: 2 days, 18:21:08, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5708, loss_cls: 3.9326, loss: 3.9326 +2024-07-18 19:36:19,931 - pyskl - INFO - Epoch [70][3600/3746] lr: 5.527e-02, eta: 2 days, 18:19:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5647, loss_cls: 3.9493, loss: 3.9493 +2024-07-18 19:37:42,675 - pyskl - INFO - Epoch [70][3700/3746] lr: 5.524e-02, eta: 2 days, 18:18:35, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5763, loss_cls: 3.9254, loss: 3.9254 +2024-07-18 19:38:22,484 - pyskl - INFO - Saving checkpoint at 70 epochs +2024-07-18 19:40:13,768 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 19:40:14,434 - pyskl - INFO - +top1_acc 0.1814 +top5_acc 0.4130 +2024-07-18 19:40:14,435 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 19:40:14,478 - pyskl - INFO - +mean_acc 0.1813 +2024-07-18 19:40:14,490 - pyskl - INFO - Epoch(val) [70][309] top1_acc: 0.1814, top5_acc: 0.4130, mean_class_accuracy: 0.1813 +2024-07-18 19:44:04,967 - pyskl - INFO - Epoch [71][100/3746] lr: 5.520e-02, eta: 2 days, 18:18:49, time: 2.305, data_time: 1.321, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5844, loss_cls: 3.8485, loss: 3.8485 +2024-07-18 19:45:27,298 - pyskl - INFO - Epoch [71][200/3746] lr: 5.517e-02, eta: 2 days, 18:17:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5780, loss_cls: 3.8818, loss: 3.8818 +2024-07-18 19:46:49,554 - pyskl - INFO - Epoch [71][300/3746] lr: 5.514e-02, eta: 2 days, 18:16:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5875, loss_cls: 3.8258, loss: 3.8258 +2024-07-18 19:48:11,230 - pyskl - INFO - Epoch [71][400/3746] lr: 5.512e-02, eta: 2 days, 18:14:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5808, loss_cls: 3.8576, loss: 3.8576 +2024-07-18 19:49:32,919 - pyskl - INFO - Epoch [71][500/3746] lr: 5.509e-02, eta: 2 days, 18:13:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5747, loss_cls: 3.8982, loss: 3.8982 +2024-07-18 19:50:54,876 - pyskl - INFO - Epoch [71][600/3746] lr: 5.506e-02, eta: 2 days, 18:12:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5706, loss_cls: 3.9125, loss: 3.9125 +2024-07-18 19:52:16,675 - pyskl - INFO - Epoch [71][700/3746] lr: 5.503e-02, eta: 2 days, 18:11:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5780, loss_cls: 3.8962, loss: 3.8962 +2024-07-18 19:53:38,714 - pyskl - INFO - Epoch [71][800/3746] lr: 5.500e-02, eta: 2 days, 18:09:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5811, loss_cls: 3.9129, loss: 3.9129 +2024-07-18 19:55:00,834 - pyskl - INFO - Epoch [71][900/3746] lr: 5.498e-02, eta: 2 days, 18:08:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5730, loss_cls: 3.9100, loss: 3.9100 +2024-07-18 19:56:22,794 - pyskl - INFO - Epoch [71][1000/3746] lr: 5.495e-02, eta: 2 days, 18:07:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5794, loss_cls: 3.8563, loss: 3.8563 +2024-07-18 19:57:44,525 - pyskl - INFO - Epoch [71][1100/3746] lr: 5.492e-02, eta: 2 days, 18:05:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5828, loss_cls: 3.8907, loss: 3.8907 +2024-07-18 19:59:07,237 - pyskl - INFO - Epoch [71][1200/3746] lr: 5.489e-02, eta: 2 days, 18:04:42, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5855, loss_cls: 3.8684, loss: 3.8684 +2024-07-18 20:00:29,587 - pyskl - INFO - Epoch [71][1300/3746] lr: 5.487e-02, eta: 2 days, 18:03:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5769, loss_cls: 3.8987, loss: 3.8987 +2024-07-18 20:01:51,664 - pyskl - INFO - Epoch [71][1400/3746] lr: 5.484e-02, eta: 2 days, 18:02:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5603, loss_cls: 3.9495, loss: 3.9495 +2024-07-18 20:03:14,042 - pyskl - INFO - Epoch [71][1500/3746] lr: 5.481e-02, eta: 2 days, 18:00:51, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5775, loss_cls: 3.8800, loss: 3.8800 +2024-07-18 20:04:36,141 - pyskl - INFO - Epoch [71][1600/3746] lr: 5.478e-02, eta: 2 days, 17:59:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5772, loss_cls: 3.9277, loss: 3.9277 +2024-07-18 20:05:57,768 - pyskl - INFO - Epoch [71][1700/3746] lr: 5.475e-02, eta: 2 days, 17:58:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5858, loss_cls: 3.8540, loss: 3.8540 +2024-07-18 20:07:19,525 - pyskl - INFO - Epoch [71][1800/3746] lr: 5.473e-02, eta: 2 days, 17:57:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5773, loss_cls: 3.8997, loss: 3.8997 +2024-07-18 20:08:41,520 - pyskl - INFO - Epoch [71][1900/3746] lr: 5.470e-02, eta: 2 days, 17:55:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5698, loss_cls: 3.9123, loss: 3.9123 +2024-07-18 20:10:03,007 - pyskl - INFO - Epoch [71][2000/3746] lr: 5.467e-02, eta: 2 days, 17:54:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5653, loss_cls: 3.9564, loss: 3.9564 +2024-07-18 20:11:24,663 - pyskl - INFO - Epoch [71][2100/3746] lr: 5.464e-02, eta: 2 days, 17:53:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5670, loss_cls: 3.9289, loss: 3.9289 +2024-07-18 20:12:46,413 - pyskl - INFO - Epoch [71][2200/3746] lr: 5.461e-02, eta: 2 days, 17:51:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5761, loss_cls: 3.9077, loss: 3.9077 +2024-07-18 20:14:08,038 - pyskl - INFO - Epoch [71][2300/3746] lr: 5.459e-02, eta: 2 days, 17:50:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5747, loss_cls: 3.9178, loss: 3.9178 +2024-07-18 20:15:29,762 - pyskl - INFO - Epoch [71][2400/3746] lr: 5.456e-02, eta: 2 days, 17:49:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5780, loss_cls: 3.9177, loss: 3.9177 +2024-07-18 20:16:51,912 - pyskl - INFO - Epoch [71][2500/3746] lr: 5.453e-02, eta: 2 days, 17:47:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5827, loss_cls: 3.8832, loss: 3.8832 +2024-07-18 20:18:13,900 - pyskl - INFO - Epoch [71][2600/3746] lr: 5.450e-02, eta: 2 days, 17:46:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5634, loss_cls: 3.9587, loss: 3.9587 +2024-07-18 20:19:35,597 - pyskl - INFO - Epoch [71][2700/3746] lr: 5.448e-02, eta: 2 days, 17:45:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5747, loss_cls: 3.8873, loss: 3.8873 +2024-07-18 20:20:57,537 - pyskl - INFO - Epoch [71][2800/3746] lr: 5.445e-02, eta: 2 days, 17:44:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5769, loss_cls: 3.8760, loss: 3.8760 +2024-07-18 20:22:18,921 - pyskl - INFO - Epoch [71][2900/3746] lr: 5.442e-02, eta: 2 days, 17:42:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5734, loss_cls: 3.9312, loss: 3.9312 +2024-07-18 20:23:40,576 - pyskl - INFO - Epoch [71][3000/3746] lr: 5.439e-02, eta: 2 days, 17:41:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5744, loss_cls: 3.9137, loss: 3.9137 +2024-07-18 20:25:02,110 - pyskl - INFO - Epoch [71][3100/3746] lr: 5.436e-02, eta: 2 days, 17:40:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5733, loss_cls: 3.9217, loss: 3.9217 +2024-07-18 20:26:23,753 - pyskl - INFO - Epoch [71][3200/3746] lr: 5.434e-02, eta: 2 days, 17:38:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5733, loss_cls: 3.8742, loss: 3.8742 +2024-07-18 20:27:45,132 - pyskl - INFO - Epoch [71][3300/3746] lr: 5.431e-02, eta: 2 days, 17:37:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5736, loss_cls: 3.9138, loss: 3.9138 +2024-07-18 20:29:07,588 - pyskl - INFO - Epoch [71][3400/3746] lr: 5.428e-02, eta: 2 days, 17:36:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5786, loss_cls: 3.8678, loss: 3.8678 +2024-07-18 20:30:29,322 - pyskl - INFO - Epoch [71][3500/3746] lr: 5.425e-02, eta: 2 days, 17:35:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5737, loss_cls: 3.8939, loss: 3.8939 +2024-07-18 20:31:51,484 - pyskl - INFO - Epoch [71][3600/3746] lr: 5.422e-02, eta: 2 days, 17:33:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5769, loss_cls: 3.9023, loss: 3.9023 +2024-07-18 20:33:13,448 - pyskl - INFO - Epoch [71][3700/3746] lr: 5.420e-02, eta: 2 days, 17:32:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5748, loss_cls: 3.9130, loss: 3.9130 +2024-07-18 20:33:53,331 - pyskl - INFO - Saving checkpoint at 71 epochs +2024-07-18 20:35:44,752 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 20:35:45,419 - pyskl - INFO - +top1_acc 0.2367 +top5_acc 0.4769 +2024-07-18 20:35:45,419 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 20:35:45,462 - pyskl - INFO - +mean_acc 0.2365 +2024-07-18 20:35:45,475 - pyskl - INFO - Epoch(val) [71][309] top1_acc: 0.2367, top5_acc: 0.4769, mean_class_accuracy: 0.2365 +2024-07-18 20:39:32,602 - pyskl - INFO - Epoch [72][100/3746] lr: 5.416e-02, eta: 2 days, 17:32:36, time: 2.271, data_time: 1.289, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5848, loss_cls: 3.8373, loss: 3.8373 +2024-07-18 20:40:54,640 - pyskl - INFO - Epoch [72][200/3746] lr: 5.413e-02, eta: 2 days, 17:31:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5777, loss_cls: 3.8763, loss: 3.8763 +2024-07-18 20:42:16,384 - pyskl - INFO - Epoch [72][300/3746] lr: 5.410e-02, eta: 2 days, 17:30:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5658, loss_cls: 3.9230, loss: 3.9230 +2024-07-18 20:43:37,707 - pyskl - INFO - Epoch [72][400/3746] lr: 5.407e-02, eta: 2 days, 17:28:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5828, loss_cls: 3.8615, loss: 3.8615 +2024-07-18 20:44:59,434 - pyskl - INFO - Epoch [72][500/3746] lr: 5.404e-02, eta: 2 days, 17:27:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5764, loss_cls: 3.8984, loss: 3.8984 +2024-07-18 20:46:21,389 - pyskl - INFO - Epoch [72][600/3746] lr: 5.402e-02, eta: 2 days, 17:26:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5848, loss_cls: 3.8365, loss: 3.8365 +2024-07-18 20:47:42,773 - pyskl - INFO - Epoch [72][700/3746] lr: 5.399e-02, eta: 2 days, 17:24:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5800, loss_cls: 3.8774, loss: 3.8774 +2024-07-18 20:49:04,741 - pyskl - INFO - Epoch [72][800/3746] lr: 5.396e-02, eta: 2 days, 17:23:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5769, loss_cls: 3.8735, loss: 3.8735 +2024-07-18 20:50:26,574 - pyskl - INFO - Epoch [72][900/3746] lr: 5.393e-02, eta: 2 days, 17:22:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5842, loss_cls: 3.8787, loss: 3.8787 +2024-07-18 20:51:48,115 - pyskl - INFO - Epoch [72][1000/3746] lr: 5.391e-02, eta: 2 days, 17:20:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5820, loss_cls: 3.8639, loss: 3.8639 +2024-07-18 20:53:09,860 - pyskl - INFO - Epoch [72][1100/3746] lr: 5.388e-02, eta: 2 days, 17:19:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5719, loss_cls: 3.9157, loss: 3.9157 +2024-07-18 20:54:31,509 - pyskl - INFO - Epoch [72][1200/3746] lr: 5.385e-02, eta: 2 days, 17:18:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5783, loss_cls: 3.9105, loss: 3.9105 +2024-07-18 20:55:53,993 - pyskl - INFO - Epoch [72][1300/3746] lr: 5.382e-02, eta: 2 days, 17:17:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5792, loss_cls: 3.8847, loss: 3.8847 +2024-07-18 20:57:16,676 - pyskl - INFO - Epoch [72][1400/3746] lr: 5.379e-02, eta: 2 days, 17:15:49, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5653, loss_cls: 3.9396, loss: 3.9396 +2024-07-18 20:58:39,700 - pyskl - INFO - Epoch [72][1500/3746] lr: 5.377e-02, eta: 2 days, 17:14:33, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5761, loss_cls: 3.8885, loss: 3.8885 +2024-07-18 21:00:01,672 - pyskl - INFO - Epoch [72][1600/3746] lr: 5.374e-02, eta: 2 days, 17:13:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5702, loss_cls: 3.9455, loss: 3.9455 +2024-07-18 21:01:23,375 - pyskl - INFO - Epoch [72][1700/3746] lr: 5.371e-02, eta: 2 days, 17:11:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5855, loss_cls: 3.8708, loss: 3.8708 +2024-07-18 21:02:45,076 - pyskl - INFO - Epoch [72][1800/3746] lr: 5.368e-02, eta: 2 days, 17:10:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5872, loss_cls: 3.8617, loss: 3.8617 +2024-07-18 21:04:06,702 - pyskl - INFO - Epoch [72][1900/3746] lr: 5.365e-02, eta: 2 days, 17:09:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5763, loss_cls: 3.8680, loss: 3.8680 +2024-07-18 21:05:28,061 - pyskl - INFO - Epoch [72][2000/3746] lr: 5.363e-02, eta: 2 days, 17:08:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5698, loss_cls: 3.9396, loss: 3.9396 +2024-07-18 21:06:49,847 - pyskl - INFO - Epoch [72][2100/3746] lr: 5.360e-02, eta: 2 days, 17:06:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5787, loss_cls: 3.8847, loss: 3.8847 +2024-07-18 21:08:12,122 - pyskl - INFO - Epoch [72][2200/3746] lr: 5.357e-02, eta: 2 days, 17:05:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5767, loss_cls: 3.9063, loss: 3.9063 +2024-07-18 21:09:34,164 - pyskl - INFO - Epoch [72][2300/3746] lr: 5.354e-02, eta: 2 days, 17:04:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5672, loss_cls: 3.9486, loss: 3.9486 +2024-07-18 21:10:56,329 - pyskl - INFO - Epoch [72][2400/3746] lr: 5.352e-02, eta: 2 days, 17:02:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5670, loss_cls: 3.9137, loss: 3.9137 +2024-07-18 21:12:18,152 - pyskl - INFO - Epoch [72][2500/3746] lr: 5.349e-02, eta: 2 days, 17:01:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5758, loss_cls: 3.8722, loss: 3.8722 +2024-07-18 21:13:40,060 - pyskl - INFO - Epoch [72][2600/3746] lr: 5.346e-02, eta: 2 days, 17:00:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5661, loss_cls: 3.9285, loss: 3.9285 +2024-07-18 21:15:01,564 - pyskl - INFO - Epoch [72][2700/3746] lr: 5.343e-02, eta: 2 days, 16:59:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5791, loss_cls: 3.8973, loss: 3.8973 +2024-07-18 21:16:22,776 - pyskl - INFO - Epoch [72][2800/3746] lr: 5.340e-02, eta: 2 days, 16:57:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5772, loss_cls: 3.8932, loss: 3.8932 +2024-07-18 21:17:43,951 - pyskl - INFO - Epoch [72][2900/3746] lr: 5.338e-02, eta: 2 days, 16:56:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5745, loss_cls: 3.8575, loss: 3.8575 +2024-07-18 21:19:05,552 - pyskl - INFO - Epoch [72][3000/3746] lr: 5.335e-02, eta: 2 days, 16:55:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5792, loss_cls: 3.8805, loss: 3.8805 +2024-07-18 21:20:27,026 - pyskl - INFO - Epoch [72][3100/3746] lr: 5.332e-02, eta: 2 days, 16:53:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5720, loss_cls: 3.8984, loss: 3.8984 +2024-07-18 21:21:48,821 - pyskl - INFO - Epoch [72][3200/3746] lr: 5.329e-02, eta: 2 days, 16:52:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5778, loss_cls: 3.8925, loss: 3.8925 +2024-07-18 21:23:10,330 - pyskl - INFO - Epoch [72][3300/3746] lr: 5.326e-02, eta: 2 days, 16:51:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5733, loss_cls: 3.9287, loss: 3.9287 +2024-07-18 21:24:32,840 - pyskl - INFO - Epoch [72][3400/3746] lr: 5.324e-02, eta: 2 days, 16:49:58, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5787, loss_cls: 3.8350, loss: 3.8350 +2024-07-18 21:25:54,704 - pyskl - INFO - Epoch [72][3500/3746] lr: 5.321e-02, eta: 2 days, 16:48:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5903, loss_cls: 3.8228, loss: 3.8228 +2024-07-18 21:27:17,294 - pyskl - INFO - Epoch [72][3600/3746] lr: 5.318e-02, eta: 2 days, 16:47:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5619, loss_cls: 3.9367, loss: 3.9367 +2024-07-18 21:28:38,746 - pyskl - INFO - Epoch [72][3700/3746] lr: 5.315e-02, eta: 2 days, 16:46:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5737, loss_cls: 3.8960, loss: 3.8960 +2024-07-18 21:29:19,360 - pyskl - INFO - Saving checkpoint at 72 epochs +2024-07-18 21:31:10,524 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 21:31:11,192 - pyskl - INFO - +top1_acc 0.2367 +top5_acc 0.4814 +2024-07-18 21:31:11,192 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 21:31:11,233 - pyskl - INFO - +mean_acc 0.2366 +2024-07-18 21:31:11,252 - pyskl - INFO - Epoch(val) [72][309] top1_acc: 0.2367, top5_acc: 0.4814, mean_class_accuracy: 0.2366 +2024-07-18 21:34:59,021 - pyskl - INFO - Epoch [73][100/3746] lr: 5.311e-02, eta: 2 days, 16:46:10, time: 2.278, data_time: 1.291, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5742, loss_cls: 3.8557, loss: 3.8557 +2024-07-18 21:36:21,385 - pyskl - INFO - Epoch [73][200/3746] lr: 5.308e-02, eta: 2 days, 16:44:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5820, loss_cls: 3.8789, loss: 3.8789 +2024-07-18 21:37:43,123 - pyskl - INFO - Epoch [73][300/3746] lr: 5.306e-02, eta: 2 days, 16:43:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5897, loss_cls: 3.7966, loss: 3.7966 +2024-07-18 21:39:04,895 - pyskl - INFO - Epoch [73][400/3746] lr: 5.303e-02, eta: 2 days, 16:42:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5809, loss_cls: 3.8799, loss: 3.8799 +2024-07-18 21:40:26,440 - pyskl - INFO - Epoch [73][500/3746] lr: 5.300e-02, eta: 2 days, 16:40:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5920, loss_cls: 3.8141, loss: 3.8141 +2024-07-18 21:41:48,195 - pyskl - INFO - Epoch [73][600/3746] lr: 5.297e-02, eta: 2 days, 16:39:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5750, loss_cls: 3.8870, loss: 3.8870 +2024-07-18 21:43:10,066 - pyskl - INFO - Epoch [73][700/3746] lr: 5.294e-02, eta: 2 days, 16:38:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5841, loss_cls: 3.8289, loss: 3.8289 +2024-07-18 21:44:32,191 - pyskl - INFO - Epoch [73][800/3746] lr: 5.292e-02, eta: 2 days, 16:37:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5792, loss_cls: 3.8665, loss: 3.8665 +2024-07-18 21:45:53,714 - pyskl - INFO - Epoch [73][900/3746] lr: 5.289e-02, eta: 2 days, 16:35:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5823, loss_cls: 3.8609, loss: 3.8609 +2024-07-18 21:47:15,185 - pyskl - INFO - Epoch [73][1000/3746] lr: 5.286e-02, eta: 2 days, 16:34:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5766, loss_cls: 3.8592, loss: 3.8592 +2024-07-18 21:48:37,044 - pyskl - INFO - Epoch [73][1100/3746] lr: 5.283e-02, eta: 2 days, 16:33:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5794, loss_cls: 3.8869, loss: 3.8869 +2024-07-18 21:49:58,826 - pyskl - INFO - Epoch [73][1200/3746] lr: 5.280e-02, eta: 2 days, 16:31:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5841, loss_cls: 3.8525, loss: 3.8525 +2024-07-18 21:51:21,079 - pyskl - INFO - Epoch [73][1300/3746] lr: 5.278e-02, eta: 2 days, 16:30:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5795, loss_cls: 3.8831, loss: 3.8831 +2024-07-18 21:52:44,342 - pyskl - INFO - Epoch [73][1400/3746] lr: 5.275e-02, eta: 2 days, 16:29:21, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5833, loss_cls: 3.8816, loss: 3.8816 +2024-07-18 21:54:06,730 - pyskl - INFO - Epoch [73][1500/3746] lr: 5.272e-02, eta: 2 days, 16:28:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5742, loss_cls: 3.9332, loss: 3.9332 +2024-07-18 21:55:29,362 - pyskl - INFO - Epoch [73][1600/3746] lr: 5.269e-02, eta: 2 days, 16:26:47, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5706, loss_cls: 3.9425, loss: 3.9425 +2024-07-18 21:56:51,209 - pyskl - INFO - Epoch [73][1700/3746] lr: 5.267e-02, eta: 2 days, 16:25:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5730, loss_cls: 3.9089, loss: 3.9089 +2024-07-18 21:58:12,968 - pyskl - INFO - Epoch [73][1800/3746] lr: 5.264e-02, eta: 2 days, 16:24:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5756, loss_cls: 3.8548, loss: 3.8548 +2024-07-18 21:59:34,837 - pyskl - INFO - Epoch [73][1900/3746] lr: 5.261e-02, eta: 2 days, 16:22:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5786, loss_cls: 3.8606, loss: 3.8606 +2024-07-18 22:00:56,662 - pyskl - INFO - Epoch [73][2000/3746] lr: 5.258e-02, eta: 2 days, 16:21:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5706, loss_cls: 3.9245, loss: 3.9245 +2024-07-18 22:02:18,635 - pyskl - INFO - Epoch [73][2100/3746] lr: 5.255e-02, eta: 2 days, 16:20:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5806, loss_cls: 3.8438, loss: 3.8438 +2024-07-18 22:03:41,014 - pyskl - INFO - Epoch [73][2200/3746] lr: 5.253e-02, eta: 2 days, 16:19:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5780, loss_cls: 3.8558, loss: 3.8558 +2024-07-18 22:05:03,214 - pyskl - INFO - Epoch [73][2300/3746] lr: 5.250e-02, eta: 2 days, 16:17:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5741, loss_cls: 3.8591, loss: 3.8591 +2024-07-18 22:06:25,601 - pyskl - INFO - Epoch [73][2400/3746] lr: 5.247e-02, eta: 2 days, 16:16:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5820, loss_cls: 3.8804, loss: 3.8804 +2024-07-18 22:07:47,816 - pyskl - INFO - Epoch [73][2500/3746] lr: 5.244e-02, eta: 2 days, 16:15:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5822, loss_cls: 3.8432, loss: 3.8432 +2024-07-18 22:09:09,904 - pyskl - INFO - Epoch [73][2600/3746] lr: 5.241e-02, eta: 2 days, 16:13:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5673, loss_cls: 3.9294, loss: 3.9294 +2024-07-18 22:10:31,691 - pyskl - INFO - Epoch [73][2700/3746] lr: 5.239e-02, eta: 2 days, 16:12:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5747, loss_cls: 3.8976, loss: 3.8976 +2024-07-18 22:11:53,706 - pyskl - INFO - Epoch [73][2800/3746] lr: 5.236e-02, eta: 2 days, 16:11:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5722, loss_cls: 3.9156, loss: 3.9156 +2024-07-18 22:13:15,459 - pyskl - INFO - Epoch [73][2900/3746] lr: 5.233e-02, eta: 2 days, 16:09:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5858, loss_cls: 3.8483, loss: 3.8483 +2024-07-18 22:14:37,042 - pyskl - INFO - Epoch [73][3000/3746] lr: 5.230e-02, eta: 2 days, 16:08:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5755, loss_cls: 3.8714, loss: 3.8714 +2024-07-18 22:15:58,771 - pyskl - INFO - Epoch [73][3100/3746] lr: 5.227e-02, eta: 2 days, 16:07:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5714, loss_cls: 3.9050, loss: 3.9050 +2024-07-18 22:17:20,558 - pyskl - INFO - Epoch [73][3200/3746] lr: 5.225e-02, eta: 2 days, 16:06:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5858, loss_cls: 3.8477, loss: 3.8477 +2024-07-18 22:18:42,065 - pyskl - INFO - Epoch [73][3300/3746] lr: 5.222e-02, eta: 2 days, 16:04:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5709, loss_cls: 3.9141, loss: 3.9141 +2024-07-18 22:20:04,600 - pyskl - INFO - Epoch [73][3400/3746] lr: 5.219e-02, eta: 2 days, 16:03:30, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5770, loss_cls: 3.8770, loss: 3.8770 +2024-07-18 22:21:26,495 - pyskl - INFO - Epoch [73][3500/3746] lr: 5.216e-02, eta: 2 days, 16:02:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5819, loss_cls: 3.8610, loss: 3.8610 +2024-07-18 22:22:48,581 - pyskl - INFO - Epoch [73][3600/3746] lr: 5.213e-02, eta: 2 days, 16:00:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5731, loss_cls: 3.8782, loss: 3.8782 +2024-07-18 22:24:10,994 - pyskl - INFO - Epoch [73][3700/3746] lr: 5.211e-02, eta: 2 days, 15:59:38, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5795, loss_cls: 3.8900, loss: 3.8900 +2024-07-18 22:24:51,252 - pyskl - INFO - Saving checkpoint at 73 epochs +2024-07-18 22:26:41,517 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 22:26:42,180 - pyskl - INFO - +top1_acc 0.2418 +top5_acc 0.4785 +2024-07-18 22:26:42,180 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 22:26:42,221 - pyskl - INFO - +mean_acc 0.2416 +2024-07-18 22:26:42,234 - pyskl - INFO - Epoch(val) [73][309] top1_acc: 0.2418, top5_acc: 0.4785, mean_class_accuracy: 0.2416 +2024-07-18 22:30:31,551 - pyskl - INFO - Epoch [74][100/3746] lr: 5.207e-02, eta: 2 days, 15:59:40, time: 2.293, data_time: 1.302, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5813, loss_cls: 3.8482, loss: 3.8482 +2024-07-18 22:31:53,764 - pyskl - INFO - Epoch [74][200/3746] lr: 5.204e-02, eta: 2 days, 15:58:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5836, loss_cls: 3.8397, loss: 3.8397 +2024-07-18 22:33:15,463 - pyskl - INFO - Epoch [74][300/3746] lr: 5.201e-02, eta: 2 days, 15:57:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5864, loss_cls: 3.8364, loss: 3.8364 +2024-07-18 22:34:37,145 - pyskl - INFO - Epoch [74][400/3746] lr: 5.198e-02, eta: 2 days, 15:55:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5816, loss_cls: 3.8319, loss: 3.8319 +2024-07-18 22:35:58,922 - pyskl - INFO - Epoch [74][500/3746] lr: 5.195e-02, eta: 2 days, 15:54:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5927, loss_cls: 3.8053, loss: 3.8053 +2024-07-18 22:37:20,606 - pyskl - INFO - Epoch [74][600/3746] lr: 5.193e-02, eta: 2 days, 15:53:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5727, loss_cls: 3.8803, loss: 3.8803 +2024-07-18 22:38:42,545 - pyskl - INFO - Epoch [74][700/3746] lr: 5.190e-02, eta: 2 days, 15:51:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5905, loss_cls: 3.8522, loss: 3.8522 +2024-07-18 22:40:04,216 - pyskl - INFO - Epoch [74][800/3746] lr: 5.187e-02, eta: 2 days, 15:50:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5836, loss_cls: 3.8403, loss: 3.8403 +2024-07-18 22:41:26,267 - pyskl - INFO - Epoch [74][900/3746] lr: 5.184e-02, eta: 2 days, 15:49:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5827, loss_cls: 3.8192, loss: 3.8192 +2024-07-18 22:42:47,709 - pyskl - INFO - Epoch [74][1000/3746] lr: 5.181e-02, eta: 2 days, 15:47:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5889, loss_cls: 3.8431, loss: 3.8431 +2024-07-18 22:44:09,255 - pyskl - INFO - Epoch [74][1100/3746] lr: 5.179e-02, eta: 2 days, 15:46:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5694, loss_cls: 3.9008, loss: 3.9008 +2024-07-18 22:45:31,054 - pyskl - INFO - Epoch [74][1200/3746] lr: 5.176e-02, eta: 2 days, 15:45:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5867, loss_cls: 3.8302, loss: 3.8302 +2024-07-18 22:46:52,765 - pyskl - INFO - Epoch [74][1300/3746] lr: 5.173e-02, eta: 2 days, 15:44:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5808, loss_cls: 3.8836, loss: 3.8836 +2024-07-18 22:48:15,337 - pyskl - INFO - Epoch [74][1400/3746] lr: 5.170e-02, eta: 2 days, 15:42:48, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5809, loss_cls: 3.8677, loss: 3.8677 +2024-07-18 22:49:37,570 - pyskl - INFO - Epoch [74][1500/3746] lr: 5.168e-02, eta: 2 days, 15:41:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5759, loss_cls: 3.8651, loss: 3.8651 +2024-07-18 22:50:59,914 - pyskl - INFO - Epoch [74][1600/3746] lr: 5.165e-02, eta: 2 days, 15:40:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5837, loss_cls: 3.8548, loss: 3.8548 +2024-07-18 22:52:21,969 - pyskl - INFO - Epoch [74][1700/3746] lr: 5.162e-02, eta: 2 days, 15:38:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5616, loss_cls: 3.9309, loss: 3.9309 +2024-07-18 22:53:43,473 - pyskl - INFO - Epoch [74][1800/3746] lr: 5.159e-02, eta: 2 days, 15:37:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5823, loss_cls: 3.8323, loss: 3.8323 +2024-07-18 22:55:05,280 - pyskl - INFO - Epoch [74][1900/3746] lr: 5.156e-02, eta: 2 days, 15:36:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5825, loss_cls: 3.8429, loss: 3.8429 +2024-07-18 22:56:27,525 - pyskl - INFO - Epoch [74][2000/3746] lr: 5.154e-02, eta: 2 days, 15:35:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5836, loss_cls: 3.8122, loss: 3.8122 +2024-07-18 22:57:50,221 - pyskl - INFO - Epoch [74][2100/3746] lr: 5.151e-02, eta: 2 days, 15:33:45, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5842, loss_cls: 3.8692, loss: 3.8692 +2024-07-18 22:59:11,396 - pyskl - INFO - Epoch [74][2200/3746] lr: 5.148e-02, eta: 2 days, 15:32:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5766, loss_cls: 3.8840, loss: 3.8840 +2024-07-18 23:00:33,456 - pyskl - INFO - Epoch [74][2300/3746] lr: 5.145e-02, eta: 2 days, 15:31:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5753, loss_cls: 3.9086, loss: 3.9086 +2024-07-18 23:01:56,025 - pyskl - INFO - Epoch [74][2400/3746] lr: 5.142e-02, eta: 2 days, 15:29:51, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5745, loss_cls: 3.9022, loss: 3.9022 +2024-07-18 23:03:18,135 - pyskl - INFO - Epoch [74][2500/3746] lr: 5.140e-02, eta: 2 days, 15:28:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5859, loss_cls: 3.8691, loss: 3.8691 +2024-07-18 23:04:40,307 - pyskl - INFO - Epoch [74][2600/3746] lr: 5.137e-02, eta: 2 days, 15:27:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5817, loss_cls: 3.8564, loss: 3.8564 +2024-07-18 23:06:02,048 - pyskl - INFO - Epoch [74][2700/3746] lr: 5.134e-02, eta: 2 days, 15:25:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5911, loss_cls: 3.8213, loss: 3.8213 +2024-07-18 23:07:23,719 - pyskl - INFO - Epoch [74][2800/3746] lr: 5.131e-02, eta: 2 days, 15:24:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5847, loss_cls: 3.8496, loss: 3.8496 +2024-07-18 23:08:45,865 - pyskl - INFO - Epoch [74][2900/3746] lr: 5.128e-02, eta: 2 days, 15:23:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5864, loss_cls: 3.8715, loss: 3.8715 +2024-07-18 23:10:07,608 - pyskl - INFO - Epoch [74][3000/3746] lr: 5.126e-02, eta: 2 days, 15:22:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5677, loss_cls: 3.8980, loss: 3.8980 +2024-07-18 23:11:29,291 - pyskl - INFO - Epoch [74][3100/3746] lr: 5.123e-02, eta: 2 days, 15:20:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5855, loss_cls: 3.8662, loss: 3.8662 +2024-07-18 23:12:51,132 - pyskl - INFO - Epoch [74][3200/3746] lr: 5.120e-02, eta: 2 days, 15:19:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5772, loss_cls: 3.8970, loss: 3.8970 +2024-07-18 23:14:13,703 - pyskl - INFO - Epoch [74][3300/3746] lr: 5.117e-02, eta: 2 days, 15:18:11, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5822, loss_cls: 3.8923, loss: 3.8923 +2024-07-18 23:15:35,677 - pyskl - INFO - Epoch [74][3400/3746] lr: 5.114e-02, eta: 2 days, 15:16:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5789, loss_cls: 3.8403, loss: 3.8403 +2024-07-18 23:16:57,793 - pyskl - INFO - Epoch [74][3500/3746] lr: 5.112e-02, eta: 2 days, 15:15:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5780, loss_cls: 3.9112, loss: 3.9112 +2024-07-18 23:18:19,620 - pyskl - INFO - Epoch [74][3600/3746] lr: 5.109e-02, eta: 2 days, 15:14:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5691, loss_cls: 3.8815, loss: 3.8815 +2024-07-18 23:19:41,861 - pyskl - INFO - Epoch [74][3700/3746] lr: 5.106e-02, eta: 2 days, 15:13:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5602, loss_cls: 3.8977, loss: 3.8977 +2024-07-18 23:20:21,459 - pyskl - INFO - Saving checkpoint at 74 epochs +2024-07-18 23:22:12,522 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 23:22:13,182 - pyskl - INFO - +top1_acc 0.2458 +top5_acc 0.4951 +2024-07-18 23:22:13,182 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 23:22:13,226 - pyskl - INFO - +mean_acc 0.2457 +2024-07-18 23:22:13,240 - pyskl - INFO - Epoch(val) [74][309] top1_acc: 0.2458, top5_acc: 0.4951, mean_class_accuracy: 0.2457 +2024-07-18 23:26:01,583 - pyskl - INFO - Epoch [75][100/3746] lr: 5.102e-02, eta: 2 days, 15:12:58, time: 2.283, data_time: 1.304, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5800, loss_cls: 3.8749, loss: 3.8749 +2024-07-18 23:27:23,926 - pyskl - INFO - Epoch [75][200/3746] lr: 5.099e-02, eta: 2 days, 15:11:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5922, loss_cls: 3.8132, loss: 3.8132 +2024-07-18 23:28:45,405 - pyskl - INFO - Epoch [75][300/3746] lr: 5.096e-02, eta: 2 days, 15:10:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5866, loss_cls: 3.8133, loss: 3.8133 +2024-07-18 23:30:07,663 - pyskl - INFO - Epoch [75][400/3746] lr: 5.094e-02, eta: 2 days, 15:09:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5878, loss_cls: 3.8072, loss: 3.8072 +2024-07-18 23:31:29,498 - pyskl - INFO - Epoch [75][500/3746] lr: 5.091e-02, eta: 2 days, 15:07:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5870, loss_cls: 3.8486, loss: 3.8486 +2024-07-18 23:32:51,369 - pyskl - INFO - Epoch [75][600/3746] lr: 5.088e-02, eta: 2 days, 15:06:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5791, loss_cls: 3.8673, loss: 3.8673 +2024-07-18 23:34:13,349 - pyskl - INFO - Epoch [75][700/3746] lr: 5.085e-02, eta: 2 days, 15:05:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5839, loss_cls: 3.8567, loss: 3.8567 +2024-07-18 23:35:35,290 - pyskl - INFO - Epoch [75][800/3746] lr: 5.082e-02, eta: 2 days, 15:03:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5873, loss_cls: 3.8375, loss: 3.8375 +2024-07-18 23:36:56,922 - pyskl - INFO - Epoch [75][900/3746] lr: 5.080e-02, eta: 2 days, 15:02:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5783, loss_cls: 3.8643, loss: 3.8643 +2024-07-18 23:38:18,863 - pyskl - INFO - Epoch [75][1000/3746] lr: 5.077e-02, eta: 2 days, 15:01:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5833, loss_cls: 3.8443, loss: 3.8443 +2024-07-18 23:39:41,139 - pyskl - INFO - Epoch [75][1100/3746] lr: 5.074e-02, eta: 2 days, 14:59:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5773, loss_cls: 3.8818, loss: 3.8818 +2024-07-18 23:41:02,726 - pyskl - INFO - Epoch [75][1200/3746] lr: 5.071e-02, eta: 2 days, 14:58:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5784, loss_cls: 3.8878, loss: 3.8878 +2024-07-18 23:42:24,511 - pyskl - INFO - Epoch [75][1300/3746] lr: 5.068e-02, eta: 2 days, 14:57:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5945, loss_cls: 3.7830, loss: 3.7830 +2024-07-18 23:43:47,130 - pyskl - INFO - Epoch [75][1400/3746] lr: 5.066e-02, eta: 2 days, 14:56:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5986, loss_cls: 3.7857, loss: 3.7857 +2024-07-18 23:45:09,730 - pyskl - INFO - Epoch [75][1500/3746] lr: 5.063e-02, eta: 2 days, 14:54:47, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5742, loss_cls: 3.8780, loss: 3.8780 +2024-07-18 23:46:31,802 - pyskl - INFO - Epoch [75][1600/3746] lr: 5.060e-02, eta: 2 days, 14:53:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5855, loss_cls: 3.8437, loss: 3.8437 +2024-07-18 23:47:53,879 - pyskl - INFO - Epoch [75][1700/3746] lr: 5.057e-02, eta: 2 days, 14:52:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5833, loss_cls: 3.8768, loss: 3.8768 +2024-07-18 23:49:15,691 - pyskl - INFO - Epoch [75][1800/3746] lr: 5.054e-02, eta: 2 days, 14:50:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5778, loss_cls: 3.9057, loss: 3.9057 +2024-07-18 23:50:37,607 - pyskl - INFO - Epoch [75][1900/3746] lr: 5.052e-02, eta: 2 days, 14:49:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5930, loss_cls: 3.8198, loss: 3.8198 +2024-07-18 23:51:58,935 - pyskl - INFO - Epoch [75][2000/3746] lr: 5.049e-02, eta: 2 days, 14:48:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5902, loss_cls: 3.8400, loss: 3.8400 +2024-07-18 23:53:20,957 - pyskl - INFO - Epoch [75][2100/3746] lr: 5.046e-02, eta: 2 days, 14:46:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5827, loss_cls: 3.8531, loss: 3.8531 +2024-07-18 23:54:42,523 - pyskl - INFO - Epoch [75][2200/3746] lr: 5.043e-02, eta: 2 days, 14:45:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5819, loss_cls: 3.8738, loss: 3.8738 +2024-07-18 23:56:04,320 - pyskl - INFO - Epoch [75][2300/3746] lr: 5.040e-02, eta: 2 days, 14:44:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5781, loss_cls: 3.8564, loss: 3.8564 +2024-07-18 23:57:26,412 - pyskl - INFO - Epoch [75][2400/3746] lr: 5.038e-02, eta: 2 days, 14:43:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5663, loss_cls: 3.9340, loss: 3.9340 +2024-07-18 23:58:47,803 - pyskl - INFO - Epoch [75][2500/3746] lr: 5.035e-02, eta: 2 days, 14:41:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5864, loss_cls: 3.8480, loss: 3.8480 +2024-07-19 00:00:10,115 - pyskl - INFO - Epoch [75][2600/3746] lr: 5.032e-02, eta: 2 days, 14:40:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5894, loss_cls: 3.8334, loss: 3.8334 +2024-07-19 00:01:31,930 - pyskl - INFO - Epoch [75][2700/3746] lr: 5.029e-02, eta: 2 days, 14:39:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5794, loss_cls: 3.8908, loss: 3.8908 +2024-07-19 00:02:54,896 - pyskl - INFO - Epoch [75][2800/3746] lr: 5.026e-02, eta: 2 days, 14:37:53, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5877, loss_cls: 3.8227, loss: 3.8227 +2024-07-19 00:04:17,106 - pyskl - INFO - Epoch [75][2900/3746] lr: 5.024e-02, eta: 2 days, 14:36:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5727, loss_cls: 3.8877, loss: 3.8877 +2024-07-19 00:05:38,982 - pyskl - INFO - Epoch [75][3000/3746] lr: 5.021e-02, eta: 2 days, 14:35:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5845, loss_cls: 3.8362, loss: 3.8362 +2024-07-19 00:07:00,941 - pyskl - INFO - Epoch [75][3100/3746] lr: 5.018e-02, eta: 2 days, 14:34:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5953, loss_cls: 3.8070, loss: 3.8070 +2024-07-19 00:08:22,882 - pyskl - INFO - Epoch [75][3200/3746] lr: 5.015e-02, eta: 2 days, 14:32:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5739, loss_cls: 3.9267, loss: 3.9267 +2024-07-19 00:09:45,329 - pyskl - INFO - Epoch [75][3300/3746] lr: 5.012e-02, eta: 2 days, 14:31:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5811, loss_cls: 3.8905, loss: 3.8905 +2024-07-19 00:11:07,299 - pyskl - INFO - Epoch [75][3400/3746] lr: 5.010e-02, eta: 2 days, 14:30:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5831, loss_cls: 3.8304, loss: 3.8304 +2024-07-19 00:12:29,660 - pyskl - INFO - Epoch [75][3500/3746] lr: 5.007e-02, eta: 2 days, 14:28:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5806, loss_cls: 3.8829, loss: 3.8829 +2024-07-19 00:13:51,450 - pyskl - INFO - Epoch [75][3600/3746] lr: 5.004e-02, eta: 2 days, 14:27:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5881, loss_cls: 3.7905, loss: 3.7905 +2024-07-19 00:15:13,120 - pyskl - INFO - Epoch [75][3700/3746] lr: 5.001e-02, eta: 2 days, 14:26:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5775, loss_cls: 3.8774, loss: 3.8774 +2024-07-19 00:15:53,174 - pyskl - INFO - Saving checkpoint at 75 epochs +2024-07-19 00:17:44,841 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 00:17:45,507 - pyskl - INFO - +top1_acc 0.2525 +top5_acc 0.5114 +2024-07-19 00:17:45,507 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 00:17:45,549 - pyskl - INFO - +mean_acc 0.2523 +2024-07-19 00:17:45,553 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_68.pth was removed +2024-07-19 00:17:45,806 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_75.pth. +2024-07-19 00:17:45,807 - pyskl - INFO - Best top1_acc is 0.2525 at 75 epoch. +2024-07-19 00:17:45,820 - pyskl - INFO - Epoch(val) [75][309] top1_acc: 0.2525, top5_acc: 0.5114, mean_class_accuracy: 0.2523 +2024-07-19 00:21:32,290 - pyskl - INFO - Epoch [76][100/3746] lr: 4.997e-02, eta: 2 days, 14:26:05, time: 2.265, data_time: 1.285, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5973, loss_cls: 3.7727, loss: 3.7727 +2024-07-19 00:22:54,465 - pyskl - INFO - Epoch [76][200/3746] lr: 4.994e-02, eta: 2 days, 14:24:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5894, loss_cls: 3.8002, loss: 3.8002 +2024-07-19 00:24:16,655 - pyskl - INFO - Epoch [76][300/3746] lr: 4.992e-02, eta: 2 days, 14:23:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5938, loss_cls: 3.7902, loss: 3.7902 +2024-07-19 00:25:38,472 - pyskl - INFO - Epoch [76][400/3746] lr: 4.989e-02, eta: 2 days, 14:22:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5881, loss_cls: 3.8082, loss: 3.8082 +2024-07-19 00:27:00,310 - pyskl - INFO - Epoch [76][500/3746] lr: 4.986e-02, eta: 2 days, 14:20:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5911, loss_cls: 3.8102, loss: 3.8102 +2024-07-19 00:28:21,884 - pyskl - INFO - Epoch [76][600/3746] lr: 4.983e-02, eta: 2 days, 14:19:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5803, loss_cls: 3.8521, loss: 3.8521 +2024-07-19 00:29:43,582 - pyskl - INFO - Epoch [76][700/3746] lr: 4.980e-02, eta: 2 days, 14:18:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5958, loss_cls: 3.7804, loss: 3.7804 +2024-07-19 00:31:05,835 - pyskl - INFO - Epoch [76][800/3746] lr: 4.978e-02, eta: 2 days, 14:16:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5842, loss_cls: 3.8491, loss: 3.8491 +2024-07-19 00:32:27,639 - pyskl - INFO - Epoch [76][900/3746] lr: 4.975e-02, eta: 2 days, 14:15:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5763, loss_cls: 3.8849, loss: 3.8849 +2024-07-19 00:33:49,620 - pyskl - INFO - Epoch [76][1000/3746] lr: 4.972e-02, eta: 2 days, 14:14:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5867, loss_cls: 3.8382, loss: 3.8382 +2024-07-19 00:35:11,195 - pyskl - INFO - Epoch [76][1100/3746] lr: 4.969e-02, eta: 2 days, 14:13:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5866, loss_cls: 3.8384, loss: 3.8384 +2024-07-19 00:36:32,704 - pyskl - INFO - Epoch [76][1200/3746] lr: 4.966e-02, eta: 2 days, 14:11:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5869, loss_cls: 3.8048, loss: 3.8048 +2024-07-19 00:37:54,884 - pyskl - INFO - Epoch [76][1300/3746] lr: 4.964e-02, eta: 2 days, 14:10:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5894, loss_cls: 3.8483, loss: 3.8483 +2024-07-19 00:39:17,337 - pyskl - INFO - Epoch [76][1400/3746] lr: 4.961e-02, eta: 2 days, 14:09:09, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5852, loss_cls: 3.8370, loss: 3.8370 +2024-07-19 00:40:39,580 - pyskl - INFO - Epoch [76][1500/3746] lr: 4.958e-02, eta: 2 days, 14:07:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5903, loss_cls: 3.8250, loss: 3.8250 +2024-07-19 00:42:01,412 - pyskl - INFO - Epoch [76][1600/3746] lr: 4.955e-02, eta: 2 days, 14:06:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5827, loss_cls: 3.8579, loss: 3.8579 +2024-07-19 00:43:23,263 - pyskl - INFO - Epoch [76][1700/3746] lr: 4.953e-02, eta: 2 days, 14:05:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5891, loss_cls: 3.8615, loss: 3.8615 +2024-07-19 00:44:45,233 - pyskl - INFO - Epoch [76][1800/3746] lr: 4.950e-02, eta: 2 days, 14:03:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5797, loss_cls: 3.8352, loss: 3.8352 +2024-07-19 00:46:06,831 - pyskl - INFO - Epoch [76][1900/3746] lr: 4.947e-02, eta: 2 days, 14:02:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5909, loss_cls: 3.8176, loss: 3.8176 +2024-07-19 00:47:28,758 - pyskl - INFO - Epoch [76][2000/3746] lr: 4.944e-02, eta: 2 days, 14:01:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5773, loss_cls: 3.8465, loss: 3.8465 +2024-07-19 00:48:50,819 - pyskl - INFO - Epoch [76][2100/3746] lr: 4.941e-02, eta: 2 days, 14:00:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5886, loss_cls: 3.8242, loss: 3.8242 +2024-07-19 00:50:12,324 - pyskl - INFO - Epoch [76][2200/3746] lr: 4.939e-02, eta: 2 days, 13:58:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5861, loss_cls: 3.8449, loss: 3.8449 +2024-07-19 00:51:33,650 - pyskl - INFO - Epoch [76][2300/3746] lr: 4.936e-02, eta: 2 days, 13:57:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5783, loss_cls: 3.8452, loss: 3.8452 +2024-07-19 00:52:55,937 - pyskl - INFO - Epoch [76][2400/3746] lr: 4.933e-02, eta: 2 days, 13:56:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5753, loss_cls: 3.8836, loss: 3.8836 +2024-07-19 00:54:17,331 - pyskl - INFO - Epoch [76][2500/3746] lr: 4.930e-02, eta: 2 days, 13:54:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5777, loss_cls: 3.8480, loss: 3.8480 +2024-07-19 00:55:39,126 - pyskl - INFO - Epoch [76][2600/3746] lr: 4.927e-02, eta: 2 days, 13:53:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5794, loss_cls: 3.8522, loss: 3.8522 +2024-07-19 00:57:00,877 - pyskl - INFO - Epoch [76][2700/3746] lr: 4.925e-02, eta: 2 days, 13:52:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5827, loss_cls: 3.8685, loss: 3.8685 +2024-07-19 00:58:22,648 - pyskl - INFO - Epoch [76][2800/3746] lr: 4.922e-02, eta: 2 days, 13:50:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5920, loss_cls: 3.8314, loss: 3.8314 +2024-07-19 00:59:44,106 - pyskl - INFO - Epoch [76][2900/3746] lr: 4.919e-02, eta: 2 days, 13:49:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5744, loss_cls: 3.8952, loss: 3.8952 +2024-07-19 01:01:05,679 - pyskl - INFO - Epoch [76][3000/3746] lr: 4.916e-02, eta: 2 days, 13:48:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5847, loss_cls: 3.8793, loss: 3.8793 +2024-07-19 01:02:27,404 - pyskl - INFO - Epoch [76][3100/3746] lr: 4.913e-02, eta: 2 days, 13:46:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5737, loss_cls: 3.8942, loss: 3.8942 +2024-07-19 01:03:49,207 - pyskl - INFO - Epoch [76][3200/3746] lr: 4.911e-02, eta: 2 days, 13:45:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5734, loss_cls: 3.9026, loss: 3.9026 +2024-07-19 01:05:11,357 - pyskl - INFO - Epoch [76][3300/3746] lr: 4.908e-02, eta: 2 days, 13:44:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5822, loss_cls: 3.8482, loss: 3.8482 +2024-07-19 01:06:33,209 - pyskl - INFO - Epoch [76][3400/3746] lr: 4.905e-02, eta: 2 days, 13:43:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5789, loss_cls: 3.8332, loss: 3.8332 +2024-07-19 01:07:55,925 - pyskl - INFO - Epoch [76][3500/3746] lr: 4.902e-02, eta: 2 days, 13:41:45, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5816, loss_cls: 3.8473, loss: 3.8473 +2024-07-19 01:09:18,120 - pyskl - INFO - Epoch [76][3600/3746] lr: 4.899e-02, eta: 2 days, 13:40:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5872, loss_cls: 3.8561, loss: 3.8561 +2024-07-19 01:10:39,755 - pyskl - INFO - Epoch [76][3700/3746] lr: 4.897e-02, eta: 2 days, 13:39:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5889, loss_cls: 3.7883, loss: 3.7883 +2024-07-19 01:11:19,982 - pyskl - INFO - Saving checkpoint at 76 epochs +2024-07-19 01:13:11,054 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 01:13:11,764 - pyskl - INFO - +top1_acc 0.2456 +top5_acc 0.5077 +2024-07-19 01:13:11,764 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 01:13:11,811 - pyskl - INFO - +mean_acc 0.2454 +2024-07-19 01:13:11,824 - pyskl - INFO - Epoch(val) [76][309] top1_acc: 0.2456, top5_acc: 0.5077, mean_class_accuracy: 0.2454 +2024-07-19 01:17:03,135 - pyskl - INFO - Epoch [77][100/3746] lr: 4.893e-02, eta: 2 days, 13:39:03, time: 2.313, data_time: 1.311, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6045, loss_cls: 3.7328, loss: 3.7328 +2024-07-19 01:18:25,707 - pyskl - INFO - Epoch [77][200/3746] lr: 4.890e-02, eta: 2 days, 13:37:45, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5967, loss_cls: 3.7550, loss: 3.7550 +2024-07-19 01:19:48,442 - pyskl - INFO - Epoch [77][300/3746] lr: 4.887e-02, eta: 2 days, 13:36:27, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5956, loss_cls: 3.7961, loss: 3.7961 +2024-07-19 01:21:10,338 - pyskl - INFO - Epoch [77][400/3746] lr: 4.884e-02, eta: 2 days, 13:35:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5881, loss_cls: 3.8240, loss: 3.8240 +2024-07-19 01:22:32,300 - pyskl - INFO - Epoch [77][500/3746] lr: 4.881e-02, eta: 2 days, 13:33:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5947, loss_cls: 3.7862, loss: 3.7862 +2024-07-19 01:23:54,584 - pyskl - INFO - Epoch [77][600/3746] lr: 4.879e-02, eta: 2 days, 13:32:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5967, loss_cls: 3.7937, loss: 3.7937 +2024-07-19 01:25:16,586 - pyskl - INFO - Epoch [77][700/3746] lr: 4.876e-02, eta: 2 days, 13:31:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5775, loss_cls: 3.8472, loss: 3.8472 +2024-07-19 01:26:38,226 - pyskl - INFO - Epoch [77][800/3746] lr: 4.873e-02, eta: 2 days, 13:29:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5897, loss_cls: 3.8101, loss: 3.8101 +2024-07-19 01:28:00,330 - pyskl - INFO - Epoch [77][900/3746] lr: 4.870e-02, eta: 2 days, 13:28:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5914, loss_cls: 3.8005, loss: 3.8005 +2024-07-19 01:29:22,114 - pyskl - INFO - Epoch [77][1000/3746] lr: 4.867e-02, eta: 2 days, 13:27:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5864, loss_cls: 3.8237, loss: 3.8237 +2024-07-19 01:30:44,101 - pyskl - INFO - Epoch [77][1100/3746] lr: 4.865e-02, eta: 2 days, 13:26:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5787, loss_cls: 3.8633, loss: 3.8633 +2024-07-19 01:32:05,946 - pyskl - INFO - Epoch [77][1200/3746] lr: 4.862e-02, eta: 2 days, 13:24:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5819, loss_cls: 3.8951, loss: 3.8951 +2024-07-19 01:33:27,721 - pyskl - INFO - Epoch [77][1300/3746] lr: 4.859e-02, eta: 2 days, 13:23:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5825, loss_cls: 3.8487, loss: 3.8487 +2024-07-19 01:34:50,089 - pyskl - INFO - Epoch [77][1400/3746] lr: 4.856e-02, eta: 2 days, 13:22:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5883, loss_cls: 3.8218, loss: 3.8218 +2024-07-19 01:36:11,809 - pyskl - INFO - Epoch [77][1500/3746] lr: 4.853e-02, eta: 2 days, 13:20:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5839, loss_cls: 3.8530, loss: 3.8530 +2024-07-19 01:37:34,329 - pyskl - INFO - Epoch [77][1600/3746] lr: 4.851e-02, eta: 2 days, 13:19:30, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5880, loss_cls: 3.8343, loss: 3.8343 +2024-07-19 01:38:56,204 - pyskl - INFO - Epoch [77][1700/3746] lr: 4.848e-02, eta: 2 days, 13:18:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5845, loss_cls: 3.8534, loss: 3.8534 +2024-07-19 01:40:18,561 - pyskl - INFO - Epoch [77][1800/3746] lr: 4.845e-02, eta: 2 days, 13:16:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5909, loss_cls: 3.8189, loss: 3.8189 +2024-07-19 01:41:40,691 - pyskl - INFO - Epoch [77][1900/3746] lr: 4.842e-02, eta: 2 days, 13:15:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5859, loss_cls: 3.8319, loss: 3.8319 +2024-07-19 01:43:02,610 - pyskl - INFO - Epoch [77][2000/3746] lr: 4.839e-02, eta: 2 days, 13:14:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.5817, loss_cls: 3.8077, loss: 3.8077 +2024-07-19 01:44:24,398 - pyskl - INFO - Epoch [77][2100/3746] lr: 4.837e-02, eta: 2 days, 13:12:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5889, loss_cls: 3.8172, loss: 3.8172 +2024-07-19 01:45:45,985 - pyskl - INFO - Epoch [77][2200/3746] lr: 4.834e-02, eta: 2 days, 13:11:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5859, loss_cls: 3.8217, loss: 3.8217 +2024-07-19 01:47:08,444 - pyskl - INFO - Epoch [77][2300/3746] lr: 4.831e-02, eta: 2 days, 13:10:22, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5761, loss_cls: 3.8845, loss: 3.8845 +2024-07-19 01:48:30,497 - pyskl - INFO - Epoch [77][2400/3746] lr: 4.828e-02, eta: 2 days, 13:09:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5730, loss_cls: 3.8662, loss: 3.8662 +2024-07-19 01:49:52,345 - pyskl - INFO - Epoch [77][2500/3746] lr: 4.825e-02, eta: 2 days, 13:07:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5909, loss_cls: 3.8132, loss: 3.8132 +2024-07-19 01:51:14,523 - pyskl - INFO - Epoch [77][2600/3746] lr: 4.823e-02, eta: 2 days, 13:06:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5802, loss_cls: 3.8697, loss: 3.8697 +2024-07-19 01:52:36,640 - pyskl - INFO - Epoch [77][2700/3746] lr: 4.820e-02, eta: 2 days, 13:05:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5855, loss_cls: 3.8287, loss: 3.8287 +2024-07-19 01:53:58,972 - pyskl - INFO - Epoch [77][2800/3746] lr: 4.817e-02, eta: 2 days, 13:03:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5864, loss_cls: 3.7993, loss: 3.7993 +2024-07-19 01:55:21,276 - pyskl - INFO - Epoch [77][2900/3746] lr: 4.814e-02, eta: 2 days, 13:02:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5866, loss_cls: 3.8338, loss: 3.8338 +2024-07-19 01:56:43,249 - pyskl - INFO - Epoch [77][3000/3746] lr: 4.811e-02, eta: 2 days, 13:01:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5903, loss_cls: 3.8158, loss: 3.8158 +2024-07-19 01:58:04,764 - pyskl - INFO - Epoch [77][3100/3746] lr: 4.809e-02, eta: 2 days, 12:59:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5825, loss_cls: 3.8454, loss: 3.8454 +2024-07-19 01:59:27,145 - pyskl - INFO - Epoch [77][3200/3746] lr: 4.806e-02, eta: 2 days, 12:58:38, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5938, loss_cls: 3.8037, loss: 3.8037 +2024-07-19 02:00:49,306 - pyskl - INFO - Epoch [77][3300/3746] lr: 4.803e-02, eta: 2 days, 12:57:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5669, loss_cls: 3.8867, loss: 3.8867 +2024-07-19 02:02:11,710 - pyskl - INFO - Epoch [77][3400/3746] lr: 4.800e-02, eta: 2 days, 12:56:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5797, loss_cls: 3.8557, loss: 3.8557 +2024-07-19 02:03:34,198 - pyskl - INFO - Epoch [77][3500/3746] lr: 4.798e-02, eta: 2 days, 12:54:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5878, loss_cls: 3.8316, loss: 3.8316 +2024-07-19 02:04:55,797 - pyskl - INFO - Epoch [77][3600/3746] lr: 4.795e-02, eta: 2 days, 12:53:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5866, loss_cls: 3.8597, loss: 3.8597 +2024-07-19 02:06:17,882 - pyskl - INFO - Epoch [77][3700/3746] lr: 4.792e-02, eta: 2 days, 12:52:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5956, loss_cls: 3.7808, loss: 3.7808 +2024-07-19 02:06:57,557 - pyskl - INFO - Saving checkpoint at 77 epochs +2024-07-19 02:08:48,492 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 02:08:49,169 - pyskl - INFO - +top1_acc 0.2600 +top5_acc 0.5052 +2024-07-19 02:08:49,169 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 02:08:49,212 - pyskl - INFO - +mean_acc 0.2599 +2024-07-19 02:08:49,216 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_75.pth was removed +2024-07-19 02:08:49,474 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_77.pth. +2024-07-19 02:08:49,474 - pyskl - INFO - Best top1_acc is 0.2600 at 77 epoch. +2024-07-19 02:08:49,488 - pyskl - INFO - Epoch(val) [77][309] top1_acc: 0.2600, top5_acc: 0.5052, mean_class_accuracy: 0.2599 +2024-07-19 02:12:42,280 - pyskl - INFO - Epoch [78][100/3746] lr: 4.788e-02, eta: 2 days, 12:52:00, time: 2.328, data_time: 1.342, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5938, loss_cls: 3.7932, loss: 3.7932 +2024-07-19 02:14:04,201 - pyskl - INFO - Epoch [78][200/3746] lr: 4.785e-02, eta: 2 days, 12:50:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5986, loss_cls: 3.7990, loss: 3.7990 +2024-07-19 02:15:26,274 - pyskl - INFO - Epoch [78][300/3746] lr: 4.782e-02, eta: 2 days, 12:49:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5997, loss_cls: 3.7719, loss: 3.7719 +2024-07-19 02:16:47,856 - pyskl - INFO - Epoch [78][400/3746] lr: 4.779e-02, eta: 2 days, 12:48:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5859, loss_cls: 3.8250, loss: 3.8250 +2024-07-19 02:18:09,977 - pyskl - INFO - Epoch [78][500/3746] lr: 4.777e-02, eta: 2 days, 12:46:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5961, loss_cls: 3.7629, loss: 3.7629 +2024-07-19 02:19:31,822 - pyskl - INFO - Epoch [78][600/3746] lr: 4.774e-02, eta: 2 days, 12:45:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5906, loss_cls: 3.7920, loss: 3.7920 +2024-07-19 02:20:54,039 - pyskl - INFO - Epoch [78][700/3746] lr: 4.771e-02, eta: 2 days, 12:44:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5877, loss_cls: 3.8311, loss: 3.8311 +2024-07-19 02:22:15,736 - pyskl - INFO - Epoch [78][800/3746] lr: 4.768e-02, eta: 2 days, 12:42:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5981, loss_cls: 3.7554, loss: 3.7554 +2024-07-19 02:23:37,741 - pyskl - INFO - Epoch [78][900/3746] lr: 4.766e-02, eta: 2 days, 12:41:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5823, loss_cls: 3.8089, loss: 3.8089 +2024-07-19 02:24:59,166 - pyskl - INFO - Epoch [78][1000/3746] lr: 4.763e-02, eta: 2 days, 12:40:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5863, loss_cls: 3.8252, loss: 3.8252 +2024-07-19 02:26:20,749 - pyskl - INFO - Epoch [78][1100/3746] lr: 4.760e-02, eta: 2 days, 12:38:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.5978, loss_cls: 3.7728, loss: 3.7728 +2024-07-19 02:27:43,769 - pyskl - INFO - Epoch [78][1200/3746] lr: 4.757e-02, eta: 2 days, 12:37:36, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5955, loss_cls: 3.8220, loss: 3.8220 +2024-07-19 02:29:05,508 - pyskl - INFO - Epoch [78][1300/3746] lr: 4.754e-02, eta: 2 days, 12:36:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5895, loss_cls: 3.8078, loss: 3.8078 +2024-07-19 02:30:27,364 - pyskl - INFO - Epoch [78][1400/3746] lr: 4.752e-02, eta: 2 days, 12:34:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5873, loss_cls: 3.8273, loss: 3.8273 +2024-07-19 02:31:49,182 - pyskl - INFO - Epoch [78][1500/3746] lr: 4.749e-02, eta: 2 days, 12:33:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5836, loss_cls: 3.8151, loss: 3.8151 +2024-07-19 02:33:12,288 - pyskl - INFO - Epoch [78][1600/3746] lr: 4.746e-02, eta: 2 days, 12:32:23, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5897, loss_cls: 3.8156, loss: 3.8156 +2024-07-19 02:34:33,887 - pyskl - INFO - Epoch [78][1700/3746] lr: 4.743e-02, eta: 2 days, 12:31:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5806, loss_cls: 3.8506, loss: 3.8506 +2024-07-19 02:35:55,935 - pyskl - INFO - Epoch [78][1800/3746] lr: 4.740e-02, eta: 2 days, 12:29:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5988, loss_cls: 3.8119, loss: 3.8119 +2024-07-19 02:37:17,636 - pyskl - INFO - Epoch [78][1900/3746] lr: 4.738e-02, eta: 2 days, 12:28:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5814, loss_cls: 3.8688, loss: 3.8688 +2024-07-19 02:38:39,627 - pyskl - INFO - Epoch [78][2000/3746] lr: 4.735e-02, eta: 2 days, 12:27:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5797, loss_cls: 3.8267, loss: 3.8267 +2024-07-19 02:40:01,510 - pyskl - INFO - Epoch [78][2100/3746] lr: 4.732e-02, eta: 2 days, 12:25:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5919, loss_cls: 3.7976, loss: 3.7976 +2024-07-19 02:41:23,332 - pyskl - INFO - Epoch [78][2200/3746] lr: 4.729e-02, eta: 2 days, 12:24:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5772, loss_cls: 3.8685, loss: 3.8685 +2024-07-19 02:42:45,839 - pyskl - INFO - Epoch [78][2300/3746] lr: 4.726e-02, eta: 2 days, 12:23:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5820, loss_cls: 3.8093, loss: 3.8093 +2024-07-19 02:44:08,664 - pyskl - INFO - Epoch [78][2400/3746] lr: 4.724e-02, eta: 2 days, 12:21:56, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5917, loss_cls: 3.8072, loss: 3.8072 +2024-07-19 02:45:30,742 - pyskl - INFO - Epoch [78][2500/3746] lr: 4.721e-02, eta: 2 days, 12:20:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5777, loss_cls: 3.8821, loss: 3.8821 +2024-07-19 02:46:52,651 - pyskl - INFO - Epoch [78][2600/3746] lr: 4.718e-02, eta: 2 days, 12:19:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5958, loss_cls: 3.7783, loss: 3.7783 +2024-07-19 02:48:14,161 - pyskl - INFO - Epoch [78][2700/3746] lr: 4.715e-02, eta: 2 days, 12:18:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5814, loss_cls: 3.8449, loss: 3.8449 +2024-07-19 02:49:36,084 - pyskl - INFO - Epoch [78][2800/3746] lr: 4.712e-02, eta: 2 days, 12:16:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5948, loss_cls: 3.7765, loss: 3.7765 +2024-07-19 02:50:58,029 - pyskl - INFO - Epoch [78][2900/3746] lr: 4.710e-02, eta: 2 days, 12:15:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5742, loss_cls: 3.8832, loss: 3.8832 +2024-07-19 02:52:19,445 - pyskl - INFO - Epoch [78][3000/3746] lr: 4.707e-02, eta: 2 days, 12:14:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5889, loss_cls: 3.8305, loss: 3.8305 +2024-07-19 02:53:41,133 - pyskl - INFO - Epoch [78][3100/3746] lr: 4.704e-02, eta: 2 days, 12:12:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5908, loss_cls: 3.8185, loss: 3.8185 +2024-07-19 02:55:03,434 - pyskl - INFO - Epoch [78][3200/3746] lr: 4.701e-02, eta: 2 days, 12:11:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5775, loss_cls: 3.8760, loss: 3.8760 +2024-07-19 02:56:25,129 - pyskl - INFO - Epoch [78][3300/3746] lr: 4.699e-02, eta: 2 days, 12:10:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5805, loss_cls: 3.8507, loss: 3.8507 +2024-07-19 02:57:47,491 - pyskl - INFO - Epoch [78][3400/3746] lr: 4.696e-02, eta: 2 days, 12:08:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5931, loss_cls: 3.7853, loss: 3.7853 +2024-07-19 02:59:09,208 - pyskl - INFO - Epoch [78][3500/3746] lr: 4.693e-02, eta: 2 days, 12:07:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5981, loss_cls: 3.7612, loss: 3.7612 +2024-07-19 03:00:30,862 - pyskl - INFO - Epoch [78][3600/3746] lr: 4.690e-02, eta: 2 days, 12:06:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5820, loss_cls: 3.8333, loss: 3.8333 +2024-07-19 03:01:52,225 - pyskl - INFO - Epoch [78][3700/3746] lr: 4.687e-02, eta: 2 days, 12:04:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5872, loss_cls: 3.8208, loss: 3.8208 +2024-07-19 03:02:32,015 - pyskl - INFO - Saving checkpoint at 78 epochs +2024-07-19 03:04:23,317 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 03:04:24,072 - pyskl - INFO - +top1_acc 0.2416 +top5_acc 0.4940 +2024-07-19 03:04:24,072 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 03:04:24,114 - pyskl - INFO - +mean_acc 0.2413 +2024-07-19 03:04:24,127 - pyskl - INFO - Epoch(val) [78][309] top1_acc: 0.2416, top5_acc: 0.4940, mean_class_accuracy: 0.2413 +2024-07-19 03:08:16,519 - pyskl - INFO - Epoch [79][100/3746] lr: 4.683e-02, eta: 2 days, 12:04:43, time: 2.324, data_time: 1.335, memory: 15990, top1_acc: 0.3352, top5_acc: 0.6011, loss_cls: 3.7474, loss: 3.7474 +2024-07-19 03:09:38,303 - pyskl - INFO - Epoch [79][200/3746] lr: 4.680e-02, eta: 2 days, 12:03:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5886, loss_cls: 3.7740, loss: 3.7740 +2024-07-19 03:11:00,533 - pyskl - INFO - Epoch [79][300/3746] lr: 4.678e-02, eta: 2 days, 12:02:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5992, loss_cls: 3.7978, loss: 3.7978 +2024-07-19 03:12:22,487 - pyskl - INFO - Epoch [79][400/3746] lr: 4.675e-02, eta: 2 days, 12:00:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5950, loss_cls: 3.7771, loss: 3.7771 +2024-07-19 03:13:44,139 - pyskl - INFO - Epoch [79][500/3746] lr: 4.672e-02, eta: 2 days, 11:59:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6005, loss_cls: 3.7443, loss: 3.7443 +2024-07-19 03:15:05,616 - pyskl - INFO - Epoch [79][600/3746] lr: 4.669e-02, eta: 2 days, 11:58:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6008, loss_cls: 3.7480, loss: 3.7480 +2024-07-19 03:16:27,446 - pyskl - INFO - Epoch [79][700/3746] lr: 4.667e-02, eta: 2 days, 11:56:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5859, loss_cls: 3.8305, loss: 3.8305 +2024-07-19 03:17:49,208 - pyskl - INFO - Epoch [79][800/3746] lr: 4.664e-02, eta: 2 days, 11:55:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5881, loss_cls: 3.8017, loss: 3.8017 +2024-07-19 03:19:11,039 - pyskl - INFO - Epoch [79][900/3746] lr: 4.661e-02, eta: 2 days, 11:54:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6009, loss_cls: 3.7577, loss: 3.7577 +2024-07-19 03:20:32,721 - pyskl - INFO - Epoch [79][1000/3746] lr: 4.658e-02, eta: 2 days, 11:52:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5930, loss_cls: 3.7861, loss: 3.7861 +2024-07-19 03:21:54,322 - pyskl - INFO - Epoch [79][1100/3746] lr: 4.655e-02, eta: 2 days, 11:51:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5887, loss_cls: 3.8187, loss: 3.8187 +2024-07-19 03:23:16,380 - pyskl - INFO - Epoch [79][1200/3746] lr: 4.653e-02, eta: 2 days, 11:50:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5961, loss_cls: 3.7846, loss: 3.7846 +2024-07-19 03:24:38,381 - pyskl - INFO - Epoch [79][1300/3746] lr: 4.650e-02, eta: 2 days, 11:48:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5995, loss_cls: 3.7820, loss: 3.7820 +2024-07-19 03:26:00,675 - pyskl - INFO - Epoch [79][1400/3746] lr: 4.647e-02, eta: 2 days, 11:47:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5972, loss_cls: 3.8332, loss: 3.8332 +2024-07-19 03:27:22,863 - pyskl - INFO - Epoch [79][1500/3746] lr: 4.644e-02, eta: 2 days, 11:46:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5847, loss_cls: 3.8424, loss: 3.8424 +2024-07-19 03:28:46,074 - pyskl - INFO - Epoch [79][1600/3746] lr: 4.641e-02, eta: 2 days, 11:45:03, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5978, loss_cls: 3.7823, loss: 3.7823 +2024-07-19 03:30:07,993 - pyskl - INFO - Epoch [79][1700/3746] lr: 4.639e-02, eta: 2 days, 11:43:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5678, loss_cls: 3.9044, loss: 3.9044 +2024-07-19 03:31:29,941 - pyskl - INFO - Epoch [79][1800/3746] lr: 4.636e-02, eta: 2 days, 11:42:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5709, loss_cls: 3.9018, loss: 3.9018 +2024-07-19 03:32:51,799 - pyskl - INFO - Epoch [79][1900/3746] lr: 4.633e-02, eta: 2 days, 11:41:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5833, loss_cls: 3.7970, loss: 3.7970 +2024-07-19 03:34:13,814 - pyskl - INFO - Epoch [79][2000/3746] lr: 4.630e-02, eta: 2 days, 11:39:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5880, loss_cls: 3.8025, loss: 3.8025 +2024-07-19 03:35:35,465 - pyskl - INFO - Epoch [79][2100/3746] lr: 4.628e-02, eta: 2 days, 11:38:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5894, loss_cls: 3.8068, loss: 3.8068 +2024-07-19 03:36:57,336 - pyskl - INFO - Epoch [79][2200/3746] lr: 4.625e-02, eta: 2 days, 11:37:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5944, loss_cls: 3.8079, loss: 3.8079 +2024-07-19 03:38:19,471 - pyskl - INFO - Epoch [79][2300/3746] lr: 4.622e-02, eta: 2 days, 11:35:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5903, loss_cls: 3.8244, loss: 3.8244 +2024-07-19 03:39:41,654 - pyskl - INFO - Epoch [79][2400/3746] lr: 4.619e-02, eta: 2 days, 11:34:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5923, loss_cls: 3.7893, loss: 3.7893 +2024-07-19 03:41:03,594 - pyskl - INFO - Epoch [79][2500/3746] lr: 4.616e-02, eta: 2 days, 11:33:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5855, loss_cls: 3.8417, loss: 3.8417 +2024-07-19 03:42:25,625 - pyskl - INFO - Epoch [79][2600/3746] lr: 4.614e-02, eta: 2 days, 11:31:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5981, loss_cls: 3.7730, loss: 3.7730 +2024-07-19 03:43:47,401 - pyskl - INFO - Epoch [79][2700/3746] lr: 4.611e-02, eta: 2 days, 11:30:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5855, loss_cls: 3.8057, loss: 3.8057 +2024-07-19 03:45:09,614 - pyskl - INFO - Epoch [79][2800/3746] lr: 4.608e-02, eta: 2 days, 11:29:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5978, loss_cls: 3.7717, loss: 3.7717 +2024-07-19 03:46:31,226 - pyskl - INFO - Epoch [79][2900/3746] lr: 4.605e-02, eta: 2 days, 11:28:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.6048, loss_cls: 3.7676, loss: 3.7676 +2024-07-19 03:47:53,214 - pyskl - INFO - Epoch [79][3000/3746] lr: 4.602e-02, eta: 2 days, 11:26:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5966, loss_cls: 3.7945, loss: 3.7945 +2024-07-19 03:49:14,748 - pyskl - INFO - Epoch [79][3100/3746] lr: 4.600e-02, eta: 2 days, 11:25:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5837, loss_cls: 3.8261, loss: 3.8261 +2024-07-19 03:50:37,351 - pyskl - INFO - Epoch [79][3200/3746] lr: 4.597e-02, eta: 2 days, 11:24:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5877, loss_cls: 3.8063, loss: 3.8063 +2024-07-19 03:51:59,036 - pyskl - INFO - Epoch [79][3300/3746] lr: 4.594e-02, eta: 2 days, 11:22:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.5952, loss_cls: 3.7712, loss: 3.7712 +2024-07-19 03:53:21,344 - pyskl - INFO - Epoch [79][3400/3746] lr: 4.591e-02, eta: 2 days, 11:21:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5952, loss_cls: 3.7961, loss: 3.7961 +2024-07-19 03:54:43,039 - pyskl - INFO - Epoch [79][3500/3746] lr: 4.588e-02, eta: 2 days, 11:20:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5916, loss_cls: 3.7888, loss: 3.7888 +2024-07-19 03:56:04,719 - pyskl - INFO - Epoch [79][3600/3746] lr: 4.586e-02, eta: 2 days, 11:18:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5725, loss_cls: 3.8881, loss: 3.8881 +2024-07-19 03:57:26,737 - pyskl - INFO - Epoch [79][3700/3746] lr: 4.583e-02, eta: 2 days, 11:17:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5808, loss_cls: 3.8588, loss: 3.8588 +2024-07-19 03:58:06,190 - pyskl - INFO - Saving checkpoint at 79 epochs +2024-07-19 03:59:55,915 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 03:59:56,645 - pyskl - INFO - +top1_acc 0.2656 +top5_acc 0.5160 +2024-07-19 03:59:56,645 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 03:59:56,690 - pyskl - INFO - +mean_acc 0.2655 +2024-07-19 03:59:56,695 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_77.pth was removed +2024-07-19 03:59:56,953 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_79.pth. +2024-07-19 03:59:56,954 - pyskl - INFO - Best top1_acc is 0.2656 at 79 epoch. +2024-07-19 03:59:56,967 - pyskl - INFO - Epoch(val) [79][309] top1_acc: 0.2656, top5_acc: 0.5160, mean_class_accuracy: 0.2655 +2024-07-19 04:03:51,767 - pyskl - INFO - Epoch [80][100/3746] lr: 4.579e-02, eta: 2 days, 11:17:19, time: 2.348, data_time: 1.357, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6058, loss_cls: 3.7053, loss: 3.7053 +2024-07-19 04:05:15,058 - pyskl - INFO - Epoch [80][200/3746] lr: 4.576e-02, eta: 2 days, 11:16:01, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.6031, loss_cls: 3.7281, loss: 3.7281 +2024-07-19 04:06:38,245 - pyskl - INFO - Epoch [80][300/3746] lr: 4.573e-02, eta: 2 days, 11:14:44, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.5969, loss_cls: 3.7625, loss: 3.7625 +2024-07-19 04:08:00,978 - pyskl - INFO - Epoch [80][400/3746] lr: 4.570e-02, eta: 2 days, 11:13:26, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5867, loss_cls: 3.8070, loss: 3.8070 +2024-07-19 04:09:24,347 - pyskl - INFO - Epoch [80][500/3746] lr: 4.568e-02, eta: 2 days, 11:12:08, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5948, loss_cls: 3.7904, loss: 3.7904 +2024-07-19 04:10:47,503 - pyskl - INFO - Epoch [80][600/3746] lr: 4.565e-02, eta: 2 days, 11:10:50, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5989, loss_cls: 3.7460, loss: 3.7460 +2024-07-19 04:12:09,818 - pyskl - INFO - Epoch [80][700/3746] lr: 4.562e-02, eta: 2 days, 11:09:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5997, loss_cls: 3.7676, loss: 3.7676 +2024-07-19 04:13:31,694 - pyskl - INFO - Epoch [80][800/3746] lr: 4.559e-02, eta: 2 days, 11:08:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.6022, loss_cls: 3.7513, loss: 3.7513 +2024-07-19 04:14:53,504 - pyskl - INFO - Epoch [80][900/3746] lr: 4.557e-02, eta: 2 days, 11:06:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.6017, loss_cls: 3.7726, loss: 3.7726 +2024-07-19 04:16:15,037 - pyskl - INFO - Epoch [80][1000/3746] lr: 4.554e-02, eta: 2 days, 11:05:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5883, loss_cls: 3.7858, loss: 3.7858 +2024-07-19 04:17:36,842 - pyskl - INFO - Epoch [80][1100/3746] lr: 4.551e-02, eta: 2 days, 11:04:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5933, loss_cls: 3.7748, loss: 3.7748 +2024-07-19 04:18:58,526 - pyskl - INFO - Epoch [80][1200/3746] lr: 4.548e-02, eta: 2 days, 11:02:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5909, loss_cls: 3.8044, loss: 3.8044 +2024-07-19 04:20:20,523 - pyskl - INFO - Epoch [80][1300/3746] lr: 4.545e-02, eta: 2 days, 11:01:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5972, loss_cls: 3.7737, loss: 3.7737 +2024-07-19 04:21:42,764 - pyskl - INFO - Epoch [80][1400/3746] lr: 4.543e-02, eta: 2 days, 11:00:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5978, loss_cls: 3.7836, loss: 3.7836 +2024-07-19 04:23:04,842 - pyskl - INFO - Epoch [80][1500/3746] lr: 4.540e-02, eta: 2 days, 10:59:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5934, loss_cls: 3.7998, loss: 3.7998 +2024-07-19 04:24:27,726 - pyskl - INFO - Epoch [80][1600/3746] lr: 4.537e-02, eta: 2 days, 10:57:43, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5783, loss_cls: 3.8173, loss: 3.8173 +2024-07-19 04:25:49,605 - pyskl - INFO - Epoch [80][1700/3746] lr: 4.534e-02, eta: 2 days, 10:56:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.6027, loss_cls: 3.7635, loss: 3.7635 +2024-07-19 04:27:11,783 - pyskl - INFO - Epoch [80][1800/3746] lr: 4.532e-02, eta: 2 days, 10:55:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5958, loss_cls: 3.7905, loss: 3.7905 +2024-07-19 04:28:33,703 - pyskl - INFO - Epoch [80][1900/3746] lr: 4.529e-02, eta: 2 days, 10:53:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5919, loss_cls: 3.8102, loss: 3.8102 +2024-07-19 04:29:55,235 - pyskl - INFO - Epoch [80][2000/3746] lr: 4.526e-02, eta: 2 days, 10:52:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5833, loss_cls: 3.8542, loss: 3.8542 +2024-07-19 04:31:17,332 - pyskl - INFO - Epoch [80][2100/3746] lr: 4.523e-02, eta: 2 days, 10:51:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5875, loss_cls: 3.7969, loss: 3.7969 +2024-07-19 04:32:39,284 - pyskl - INFO - Epoch [80][2200/3746] lr: 4.520e-02, eta: 2 days, 10:49:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5852, loss_cls: 3.7844, loss: 3.7844 +2024-07-19 04:34:01,759 - pyskl - INFO - Epoch [80][2300/3746] lr: 4.518e-02, eta: 2 days, 10:48:32, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5861, loss_cls: 3.8199, loss: 3.8199 +2024-07-19 04:35:23,872 - pyskl - INFO - Epoch [80][2400/3746] lr: 4.515e-02, eta: 2 days, 10:47:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.6017, loss_cls: 3.7526, loss: 3.7526 +2024-07-19 04:36:45,665 - pyskl - INFO - Epoch [80][2500/3746] lr: 4.512e-02, eta: 2 days, 10:45:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5816, loss_cls: 3.8436, loss: 3.8436 +2024-07-19 04:38:07,943 - pyskl - INFO - Epoch [80][2600/3746] lr: 4.509e-02, eta: 2 days, 10:44:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5989, loss_cls: 3.7761, loss: 3.7761 +2024-07-19 04:39:29,803 - pyskl - INFO - Epoch [80][2700/3746] lr: 4.506e-02, eta: 2 days, 10:43:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5964, loss_cls: 3.7992, loss: 3.7992 +2024-07-19 04:40:51,973 - pyskl - INFO - Epoch [80][2800/3746] lr: 4.504e-02, eta: 2 days, 10:41:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5913, loss_cls: 3.8238, loss: 3.8238 +2024-07-19 04:42:13,803 - pyskl - INFO - Epoch [80][2900/3746] lr: 4.501e-02, eta: 2 days, 10:40:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5902, loss_cls: 3.7959, loss: 3.7959 +2024-07-19 04:43:36,330 - pyskl - INFO - Epoch [80][3000/3746] lr: 4.498e-02, eta: 2 days, 10:39:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5914, loss_cls: 3.7720, loss: 3.7720 +2024-07-19 04:44:58,730 - pyskl - INFO - Epoch [80][3100/3746] lr: 4.495e-02, eta: 2 days, 10:38:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5941, loss_cls: 3.8220, loss: 3.8220 +2024-07-19 04:46:21,195 - pyskl - INFO - Epoch [80][3200/3746] lr: 4.493e-02, eta: 2 days, 10:36:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5858, loss_cls: 3.8050, loss: 3.8050 +2024-07-19 04:47:43,526 - pyskl - INFO - Epoch [80][3300/3746] lr: 4.490e-02, eta: 2 days, 10:35:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5809, loss_cls: 3.8550, loss: 3.8550 +2024-07-19 04:49:06,234 - pyskl - INFO - Epoch [80][3400/3746] lr: 4.487e-02, eta: 2 days, 10:34:07, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.6044, loss_cls: 3.7690, loss: 3.7690 +2024-07-19 04:50:27,985 - pyskl - INFO - Epoch [80][3500/3746] lr: 4.484e-02, eta: 2 days, 10:32:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5930, loss_cls: 3.7835, loss: 3.7835 +2024-07-19 04:51:49,960 - pyskl - INFO - Epoch [80][3600/3746] lr: 4.481e-02, eta: 2 days, 10:31:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5878, loss_cls: 3.8103, loss: 3.8103 +2024-07-19 04:53:12,006 - pyskl - INFO - Epoch [80][3700/3746] lr: 4.479e-02, eta: 2 days, 10:30:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5875, loss_cls: 3.8012, loss: 3.8012 +2024-07-19 04:53:51,483 - pyskl - INFO - Saving checkpoint at 80 epochs +2024-07-19 04:55:41,376 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 04:55:42,085 - pyskl - INFO - +top1_acc 0.2639 +top5_acc 0.5183 +2024-07-19 04:55:42,085 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 04:55:42,137 - pyskl - INFO - +mean_acc 0.2636 +2024-07-19 04:55:42,150 - pyskl - INFO - Epoch(val) [80][309] top1_acc: 0.2639, top5_acc: 0.5183, mean_class_accuracy: 0.2636 +2024-07-19 04:59:30,356 - pyskl - INFO - Epoch [81][100/3746] lr: 4.475e-02, eta: 2 days, 10:29:50, time: 2.282, data_time: 1.298, memory: 15990, top1_acc: 0.3347, top5_acc: 0.6031, loss_cls: 3.7445, loss: 3.7445 +2024-07-19 05:00:52,602 - pyskl - INFO - Epoch [81][200/3746] lr: 4.472e-02, eta: 2 days, 10:28:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.6031, loss_cls: 3.7546, loss: 3.7546 +2024-07-19 05:02:15,068 - pyskl - INFO - Epoch [81][300/3746] lr: 4.469e-02, eta: 2 days, 10:27:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5961, loss_cls: 3.7377, loss: 3.7377 +2024-07-19 05:03:37,716 - pyskl - INFO - Epoch [81][400/3746] lr: 4.466e-02, eta: 2 days, 10:25:55, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6097, loss_cls: 3.7079, loss: 3.7079 +2024-07-19 05:05:00,158 - pyskl - INFO - Epoch [81][500/3746] lr: 4.463e-02, eta: 2 days, 10:24:36, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5988, loss_cls: 3.7705, loss: 3.7705 +2024-07-19 05:06:22,682 - pyskl - INFO - Epoch [81][600/3746] lr: 4.461e-02, eta: 2 days, 10:23:18, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5856, loss_cls: 3.8084, loss: 3.8084 +2024-07-19 05:07:44,313 - pyskl - INFO - Epoch [81][700/3746] lr: 4.458e-02, eta: 2 days, 10:21:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5919, loss_cls: 3.8161, loss: 3.8161 +2024-07-19 05:09:06,085 - pyskl - INFO - Epoch [81][800/3746] lr: 4.455e-02, eta: 2 days, 10:20:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5970, loss_cls: 3.7839, loss: 3.7839 +2024-07-19 05:10:28,090 - pyskl - INFO - Epoch [81][900/3746] lr: 4.452e-02, eta: 2 days, 10:19:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5870, loss_cls: 3.8071, loss: 3.8071 +2024-07-19 05:11:49,836 - pyskl - INFO - Epoch [81][1000/3746] lr: 4.450e-02, eta: 2 days, 10:18:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5877, loss_cls: 3.8049, loss: 3.8049 +2024-07-19 05:13:12,311 - pyskl - INFO - Epoch [81][1100/3746] lr: 4.447e-02, eta: 2 days, 10:16:43, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5989, loss_cls: 3.7535, loss: 3.7535 +2024-07-19 05:14:34,014 - pyskl - INFO - Epoch [81][1200/3746] lr: 4.444e-02, eta: 2 days, 10:15:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5883, loss_cls: 3.8268, loss: 3.8268 +2024-07-19 05:15:55,663 - pyskl - INFO - Epoch [81][1300/3746] lr: 4.441e-02, eta: 2 days, 10:14:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.6014, loss_cls: 3.7599, loss: 3.7599 +2024-07-19 05:17:17,730 - pyskl - INFO - Epoch [81][1400/3746] lr: 4.438e-02, eta: 2 days, 10:12:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5975, loss_cls: 3.7786, loss: 3.7786 +2024-07-19 05:18:39,559 - pyskl - INFO - Epoch [81][1500/3746] lr: 4.436e-02, eta: 2 days, 10:11:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.6030, loss_cls: 3.7530, loss: 3.7530 +2024-07-19 05:20:01,552 - pyskl - INFO - Epoch [81][1600/3746] lr: 4.433e-02, eta: 2 days, 10:10:08, time: 0.820, data_time: 0.001, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5853, loss_cls: 3.8207, loss: 3.8207 +2024-07-19 05:21:23,279 - pyskl - INFO - Epoch [81][1700/3746] lr: 4.430e-02, eta: 2 days, 10:08:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.6005, loss_cls: 3.7514, loss: 3.7514 +2024-07-19 05:22:46,145 - pyskl - INFO - Epoch [81][1800/3746] lr: 4.427e-02, eta: 2 days, 10:07:30, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5933, loss_cls: 3.7644, loss: 3.7644 +2024-07-19 05:24:08,169 - pyskl - INFO - Epoch [81][1900/3746] lr: 4.425e-02, eta: 2 days, 10:06:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5973, loss_cls: 3.7825, loss: 3.7825 +2024-07-19 05:25:30,215 - pyskl - INFO - Epoch [81][2000/3746] lr: 4.422e-02, eta: 2 days, 10:04:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5898, loss_cls: 3.8048, loss: 3.8048 +2024-07-19 05:26:51,750 - pyskl - INFO - Epoch [81][2100/3746] lr: 4.419e-02, eta: 2 days, 10:03:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5978, loss_cls: 3.7630, loss: 3.7630 +2024-07-19 05:28:13,410 - pyskl - INFO - Epoch [81][2200/3746] lr: 4.416e-02, eta: 2 days, 10:02:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.5981, loss_cls: 3.7667, loss: 3.7667 +2024-07-19 05:29:35,211 - pyskl - INFO - Epoch [81][2300/3746] lr: 4.413e-02, eta: 2 days, 10:00:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.6031, loss_cls: 3.7519, loss: 3.7519 +2024-07-19 05:30:56,889 - pyskl - INFO - Epoch [81][2400/3746] lr: 4.411e-02, eta: 2 days, 9:59:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5898, loss_cls: 3.8177, loss: 3.8177 +2024-07-19 05:32:18,640 - pyskl - INFO - Epoch [81][2500/3746] lr: 4.408e-02, eta: 2 days, 9:58:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5933, loss_cls: 3.8173, loss: 3.8173 +2024-07-19 05:33:40,725 - pyskl - INFO - Epoch [81][2600/3746] lr: 4.405e-02, eta: 2 days, 9:56:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5822, loss_cls: 3.8201, loss: 3.8201 +2024-07-19 05:35:02,682 - pyskl - INFO - Epoch [81][2700/3746] lr: 4.402e-02, eta: 2 days, 9:55:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5972, loss_cls: 3.7875, loss: 3.7875 +2024-07-19 05:36:24,792 - pyskl - INFO - Epoch [81][2800/3746] lr: 4.400e-02, eta: 2 days, 9:54:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6050, loss_cls: 3.7274, loss: 3.7274 +2024-07-19 05:37:46,598 - pyskl - INFO - Epoch [81][2900/3746] lr: 4.397e-02, eta: 2 days, 9:53:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6091, loss_cls: 3.6996, loss: 3.6996 +2024-07-19 05:39:08,130 - pyskl - INFO - Epoch [81][3000/3746] lr: 4.394e-02, eta: 2 days, 9:51:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5939, loss_cls: 3.7889, loss: 3.7889 +2024-07-19 05:40:30,131 - pyskl - INFO - Epoch [81][3100/3746] lr: 4.391e-02, eta: 2 days, 9:50:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5927, loss_cls: 3.7837, loss: 3.7837 +2024-07-19 05:41:51,895 - pyskl - INFO - Epoch [81][3200/3746] lr: 4.389e-02, eta: 2 days, 9:49:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5961, loss_cls: 3.7801, loss: 3.7801 +2024-07-19 05:43:14,362 - pyskl - INFO - Epoch [81][3300/3746] lr: 4.386e-02, eta: 2 days, 9:47:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5845, loss_cls: 3.8268, loss: 3.8268 +2024-07-19 05:44:36,487 - pyskl - INFO - Epoch [81][3400/3746] lr: 4.383e-02, eta: 2 days, 9:46:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5852, loss_cls: 3.8605, loss: 3.8605 +2024-07-19 05:45:58,097 - pyskl - INFO - Epoch [81][3500/3746] lr: 4.380e-02, eta: 2 days, 9:45:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5956, loss_cls: 3.7762, loss: 3.7762 +2024-07-19 05:47:20,516 - pyskl - INFO - Epoch [81][3600/3746] lr: 4.377e-02, eta: 2 days, 9:43:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.6000, loss_cls: 3.7580, loss: 3.7580 +2024-07-19 05:48:42,369 - pyskl - INFO - Epoch [81][3700/3746] lr: 4.375e-02, eta: 2 days, 9:42:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.6020, loss_cls: 3.7679, loss: 3.7679 +2024-07-19 05:49:21,902 - pyskl - INFO - Saving checkpoint at 81 epochs +2024-07-19 05:51:12,428 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 05:51:13,150 - pyskl - INFO - +top1_acc 0.2723 +top5_acc 0.5228 +2024-07-19 05:51:13,150 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 05:51:13,192 - pyskl - INFO - +mean_acc 0.2722 +2024-07-19 05:51:13,197 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_79.pth was removed +2024-07-19 05:51:13,452 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_81.pth. +2024-07-19 05:51:13,453 - pyskl - INFO - Best top1_acc is 0.2723 at 81 epoch. +2024-07-19 05:51:13,465 - pyskl - INFO - Epoch(val) [81][309] top1_acc: 0.2723, top5_acc: 0.5228, mean_class_accuracy: 0.2722 +2024-07-19 05:55:02,609 - pyskl - INFO - Epoch [82][100/3746] lr: 4.371e-02, eta: 2 days, 9:42:07, time: 2.291, data_time: 1.310, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5845, loss_cls: 3.7863, loss: 3.7863 +2024-07-19 05:56:24,759 - pyskl - INFO - Epoch [82][200/3746] lr: 4.368e-02, eta: 2 days, 9:40:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.6078, loss_cls: 3.6959, loss: 3.6959 +2024-07-19 05:57:46,919 - pyskl - INFO - Epoch [82][300/3746] lr: 4.365e-02, eta: 2 days, 9:39:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.6056, loss_cls: 3.7619, loss: 3.7619 +2024-07-19 05:59:08,441 - pyskl - INFO - Epoch [82][400/3746] lr: 4.362e-02, eta: 2 days, 9:38:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6073, loss_cls: 3.7017, loss: 3.7017 +2024-07-19 06:00:31,058 - pyskl - INFO - Epoch [82][500/3746] lr: 4.359e-02, eta: 2 days, 9:36:51, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.6053, loss_cls: 3.7704, loss: 3.7704 +2024-07-19 06:01:53,118 - pyskl - INFO - Epoch [82][600/3746] lr: 4.357e-02, eta: 2 days, 9:35:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.6003, loss_cls: 3.7623, loss: 3.7623 +2024-07-19 06:03:14,914 - pyskl - INFO - Epoch [82][700/3746] lr: 4.354e-02, eta: 2 days, 9:34:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.5988, loss_cls: 3.7472, loss: 3.7472 +2024-07-19 06:04:36,439 - pyskl - INFO - Epoch [82][800/3746] lr: 4.351e-02, eta: 2 days, 9:32:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5902, loss_cls: 3.7860, loss: 3.7860 +2024-07-19 06:05:58,331 - pyskl - INFO - Epoch [82][900/3746] lr: 4.348e-02, eta: 2 days, 9:31:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6002, loss_cls: 3.7283, loss: 3.7283 +2024-07-19 06:07:20,062 - pyskl - INFO - Epoch [82][1000/3746] lr: 4.346e-02, eta: 2 days, 9:30:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.6005, loss_cls: 3.7595, loss: 3.7595 +2024-07-19 06:08:41,562 - pyskl - INFO - Epoch [82][1100/3746] lr: 4.343e-02, eta: 2 days, 9:28:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5988, loss_cls: 3.7211, loss: 3.7211 +2024-07-19 06:10:03,226 - pyskl - INFO - Epoch [82][1200/3746] lr: 4.340e-02, eta: 2 days, 9:27:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.5931, loss_cls: 3.7467, loss: 3.7467 +2024-07-19 06:11:24,957 - pyskl - INFO - Epoch [82][1300/3746] lr: 4.337e-02, eta: 2 days, 9:26:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.6031, loss_cls: 3.7644, loss: 3.7644 +2024-07-19 06:12:47,573 - pyskl - INFO - Epoch [82][1400/3746] lr: 4.335e-02, eta: 2 days, 9:24:58, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5948, loss_cls: 3.7879, loss: 3.7879 +2024-07-19 06:14:09,605 - pyskl - INFO - Epoch [82][1500/3746] lr: 4.332e-02, eta: 2 days, 9:23:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5875, loss_cls: 3.7901, loss: 3.7901 +2024-07-19 06:15:32,645 - pyskl - INFO - Epoch [82][1600/3746] lr: 4.329e-02, eta: 2 days, 9:22:21, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.6030, loss_cls: 3.7628, loss: 3.7628 +2024-07-19 06:16:54,825 - pyskl - INFO - Epoch [82][1700/3746] lr: 4.326e-02, eta: 2 days, 9:21:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6078, loss_cls: 3.7275, loss: 3.7275 +2024-07-19 06:18:16,810 - pyskl - INFO - Epoch [82][1800/3746] lr: 4.323e-02, eta: 2 days, 9:19:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5936, loss_cls: 3.7905, loss: 3.7905 +2024-07-19 06:19:38,804 - pyskl - INFO - Epoch [82][1900/3746] lr: 4.321e-02, eta: 2 days, 9:18:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.6019, loss_cls: 3.7291, loss: 3.7291 +2024-07-19 06:21:00,891 - pyskl - INFO - Epoch [82][2000/3746] lr: 4.318e-02, eta: 2 days, 9:17:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5852, loss_cls: 3.7943, loss: 3.7943 +2024-07-19 06:22:22,937 - pyskl - INFO - Epoch [82][2100/3746] lr: 4.315e-02, eta: 2 days, 9:15:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5891, loss_cls: 3.8144, loss: 3.8144 +2024-07-19 06:23:44,766 - pyskl - INFO - Epoch [82][2200/3746] lr: 4.312e-02, eta: 2 days, 9:14:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5880, loss_cls: 3.8045, loss: 3.8045 +2024-07-19 06:25:06,821 - pyskl - INFO - Epoch [82][2300/3746] lr: 4.310e-02, eta: 2 days, 9:13:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6031, loss_cls: 3.7157, loss: 3.7157 +2024-07-19 06:26:29,240 - pyskl - INFO - Epoch [82][2400/3746] lr: 4.307e-02, eta: 2 days, 9:11:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5914, loss_cls: 3.7953, loss: 3.7953 +2024-07-19 06:27:51,171 - pyskl - INFO - Epoch [82][2500/3746] lr: 4.304e-02, eta: 2 days, 9:10:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.6083, loss_cls: 3.7343, loss: 3.7343 +2024-07-19 06:29:13,511 - pyskl - INFO - Epoch [82][2600/3746] lr: 4.301e-02, eta: 2 days, 9:09:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5916, loss_cls: 3.8271, loss: 3.8271 +2024-07-19 06:30:35,190 - pyskl - INFO - Epoch [82][2700/3746] lr: 4.299e-02, eta: 2 days, 9:07:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.6031, loss_cls: 3.7481, loss: 3.7481 +2024-07-19 06:31:57,385 - pyskl - INFO - Epoch [82][2800/3746] lr: 4.296e-02, eta: 2 days, 9:06:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.6017, loss_cls: 3.7509, loss: 3.7509 +2024-07-19 06:33:19,108 - pyskl - INFO - Epoch [82][2900/3746] lr: 4.293e-02, eta: 2 days, 9:05:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5964, loss_cls: 3.7953, loss: 3.7953 +2024-07-19 06:34:40,679 - pyskl - INFO - Epoch [82][3000/3746] lr: 4.290e-02, eta: 2 days, 9:03:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5834, loss_cls: 3.8483, loss: 3.8483 +2024-07-19 06:36:03,618 - pyskl - INFO - Epoch [82][3100/3746] lr: 4.287e-02, eta: 2 days, 9:02:35, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5984, loss_cls: 3.7715, loss: 3.7715 +2024-07-19 06:37:26,180 - pyskl - INFO - Epoch [82][3200/3746] lr: 4.285e-02, eta: 2 days, 9:01:17, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5994, loss_cls: 3.7695, loss: 3.7695 +2024-07-19 06:38:48,598 - pyskl - INFO - Epoch [82][3300/3746] lr: 4.282e-02, eta: 2 days, 8:59:58, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6056, loss_cls: 3.7377, loss: 3.7377 +2024-07-19 06:40:11,059 - pyskl - INFO - Epoch [82][3400/3746] lr: 4.279e-02, eta: 2 days, 8:58:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5847, loss_cls: 3.8189, loss: 3.8189 +2024-07-19 06:41:32,637 - pyskl - INFO - Epoch [82][3500/3746] lr: 4.276e-02, eta: 2 days, 8:57:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.6030, loss_cls: 3.7604, loss: 3.7604 +2024-07-19 06:42:54,243 - pyskl - INFO - Epoch [82][3600/3746] lr: 4.274e-02, eta: 2 days, 8:56:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5977, loss_cls: 3.7675, loss: 3.7675 +2024-07-19 06:44:15,976 - pyskl - INFO - Epoch [82][3700/3746] lr: 4.271e-02, eta: 2 days, 8:54:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5863, loss_cls: 3.8255, loss: 3.8255 +2024-07-19 06:44:55,387 - pyskl - INFO - Saving checkpoint at 82 epochs +2024-07-19 06:46:45,921 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 06:46:46,585 - pyskl - INFO - +top1_acc 0.2775 +top5_acc 0.5294 +2024-07-19 06:46:46,585 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 06:46:46,628 - pyskl - INFO - +mean_acc 0.2773 +2024-07-19 06:46:46,632 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_81.pth was removed +2024-07-19 06:46:46,898 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_82.pth. +2024-07-19 06:46:46,899 - pyskl - INFO - Best top1_acc is 0.2775 at 82 epoch. +2024-07-19 06:46:46,913 - pyskl - INFO - Epoch(val) [82][309] top1_acc: 0.2775, top5_acc: 0.5294, mean_class_accuracy: 0.2773 +2024-07-19 06:50:38,472 - pyskl - INFO - Epoch [83][100/3746] lr: 4.267e-02, eta: 2 days, 8:54:18, time: 2.315, data_time: 1.334, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5931, loss_cls: 3.7375, loss: 3.7375 +2024-07-19 06:52:01,118 - pyskl - INFO - Epoch [83][200/3746] lr: 4.264e-02, eta: 2 days, 8:53:00, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.6020, loss_cls: 3.7466, loss: 3.7466 +2024-07-19 06:53:23,068 - pyskl - INFO - Epoch [83][300/3746] lr: 4.261e-02, eta: 2 days, 8:51:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6088, loss_cls: 3.7255, loss: 3.7255 +2024-07-19 06:54:45,405 - pyskl - INFO - Epoch [83][400/3746] lr: 4.259e-02, eta: 2 days, 8:50:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.6042, loss_cls: 3.7409, loss: 3.7409 +2024-07-19 06:56:07,113 - pyskl - INFO - Epoch [83][500/3746] lr: 4.256e-02, eta: 2 days, 8:49:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6066, loss_cls: 3.7092, loss: 3.7092 +2024-07-19 06:57:28,807 - pyskl - INFO - Epoch [83][600/3746] lr: 4.253e-02, eta: 2 days, 8:47:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5970, loss_cls: 3.7742, loss: 3.7742 +2024-07-19 06:58:50,695 - pyskl - INFO - Epoch [83][700/3746] lr: 4.250e-02, eta: 2 days, 8:46:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5956, loss_cls: 3.7792, loss: 3.7792 +2024-07-19 07:00:13,016 - pyskl - INFO - Epoch [83][800/3746] lr: 4.247e-02, eta: 2 days, 8:45:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6052, loss_cls: 3.7387, loss: 3.7387 +2024-07-19 07:01:34,957 - pyskl - INFO - Epoch [83][900/3746] lr: 4.245e-02, eta: 2 days, 8:43:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.5991, loss_cls: 3.7388, loss: 3.7388 +2024-07-19 07:02:57,068 - pyskl - INFO - Epoch [83][1000/3746] lr: 4.242e-02, eta: 2 days, 8:42:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.5988, loss_cls: 3.7513, loss: 3.7513 +2024-07-19 07:04:18,830 - pyskl - INFO - Epoch [83][1100/3746] lr: 4.239e-02, eta: 2 days, 8:41:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.6020, loss_cls: 3.7469, loss: 3.7469 +2024-07-19 07:05:40,331 - pyskl - INFO - Epoch [83][1200/3746] lr: 4.236e-02, eta: 2 days, 8:39:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5927, loss_cls: 3.7700, loss: 3.7700 +2024-07-19 07:07:02,390 - pyskl - INFO - Epoch [83][1300/3746] lr: 4.234e-02, eta: 2 days, 8:38:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.6077, loss_cls: 3.7219, loss: 3.7219 +2024-07-19 07:08:24,667 - pyskl - INFO - Epoch [83][1400/3746] lr: 4.231e-02, eta: 2 days, 8:37:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5958, loss_cls: 3.7885, loss: 3.7885 +2024-07-19 07:09:46,339 - pyskl - INFO - Epoch [83][1500/3746] lr: 4.228e-02, eta: 2 days, 8:35:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5925, loss_cls: 3.7818, loss: 3.7818 +2024-07-19 07:11:08,897 - pyskl - INFO - Epoch [83][1600/3746] lr: 4.225e-02, eta: 2 days, 8:34:31, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6003, loss_cls: 3.7321, loss: 3.7321 +2024-07-19 07:12:31,295 - pyskl - INFO - Epoch [83][1700/3746] lr: 4.223e-02, eta: 2 days, 8:33:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5972, loss_cls: 3.7762, loss: 3.7762 +2024-07-19 07:13:53,816 - pyskl - INFO - Epoch [83][1800/3746] lr: 4.220e-02, eta: 2 days, 8:31:53, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5894, loss_cls: 3.7746, loss: 3.7746 +2024-07-19 07:15:15,751 - pyskl - INFO - Epoch [83][1900/3746] lr: 4.217e-02, eta: 2 days, 8:30:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5975, loss_cls: 3.8001, loss: 3.8001 +2024-07-19 07:16:37,339 - pyskl - INFO - Epoch [83][2000/3746] lr: 4.214e-02, eta: 2 days, 8:29:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6069, loss_cls: 3.7245, loss: 3.7245 +2024-07-19 07:17:59,068 - pyskl - INFO - Epoch [83][2100/3746] lr: 4.212e-02, eta: 2 days, 8:27:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5961, loss_cls: 3.7568, loss: 3.7568 +2024-07-19 07:19:21,129 - pyskl - INFO - Epoch [83][2200/3746] lr: 4.209e-02, eta: 2 days, 8:26:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5942, loss_cls: 3.8231, loss: 3.8231 +2024-07-19 07:20:43,416 - pyskl - INFO - Epoch [83][2300/3746] lr: 4.206e-02, eta: 2 days, 8:25:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.6045, loss_cls: 3.7239, loss: 3.7239 +2024-07-19 07:22:05,554 - pyskl - INFO - Epoch [83][2400/3746] lr: 4.203e-02, eta: 2 days, 8:23:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5869, loss_cls: 3.7989, loss: 3.7989 +2024-07-19 07:23:27,931 - pyskl - INFO - Epoch [83][2500/3746] lr: 4.201e-02, eta: 2 days, 8:22:39, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5939, loss_cls: 3.7536, loss: 3.7536 +2024-07-19 07:24:49,906 - pyskl - INFO - Epoch [83][2600/3746] lr: 4.198e-02, eta: 2 days, 8:21:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6069, loss_cls: 3.7166, loss: 3.7166 +2024-07-19 07:26:11,630 - pyskl - INFO - Epoch [83][2700/3746] lr: 4.195e-02, eta: 2 days, 8:20:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.6067, loss_cls: 3.7415, loss: 3.7415 +2024-07-19 07:27:33,376 - pyskl - INFO - Epoch [83][2800/3746] lr: 4.192e-02, eta: 2 days, 8:18:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5942, loss_cls: 3.7537, loss: 3.7537 +2024-07-19 07:28:55,951 - pyskl - INFO - Epoch [83][2900/3746] lr: 4.190e-02, eta: 2 days, 8:17:22, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5886, loss_cls: 3.7856, loss: 3.7856 +2024-07-19 07:30:17,815 - pyskl - INFO - Epoch [83][3000/3746] lr: 4.187e-02, eta: 2 days, 8:16:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6059, loss_cls: 3.6663, loss: 3.6663 +2024-07-19 07:31:40,671 - pyskl - INFO - Epoch [83][3100/3746] lr: 4.184e-02, eta: 2 days, 8:14:44, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5984, loss_cls: 3.7686, loss: 3.7686 +2024-07-19 07:33:03,226 - pyskl - INFO - Epoch [83][3200/3746] lr: 4.181e-02, eta: 2 days, 8:13:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.6027, loss_cls: 3.7312, loss: 3.7312 +2024-07-19 07:34:26,044 - pyskl - INFO - Epoch [83][3300/3746] lr: 4.178e-02, eta: 2 days, 8:12:07, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.5995, loss_cls: 3.7504, loss: 3.7504 +2024-07-19 07:35:48,094 - pyskl - INFO - Epoch [83][3400/3746] lr: 4.176e-02, eta: 2 days, 8:10:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5953, loss_cls: 3.7788, loss: 3.7788 +2024-07-19 07:37:10,249 - pyskl - INFO - Epoch [83][3500/3746] lr: 4.173e-02, eta: 2 days, 8:09:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5942, loss_cls: 3.7637, loss: 3.7637 +2024-07-19 07:38:32,439 - pyskl - INFO - Epoch [83][3600/3746] lr: 4.170e-02, eta: 2 days, 8:08:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6097, loss_cls: 3.7142, loss: 3.7142 +2024-07-19 07:39:54,304 - pyskl - INFO - Epoch [83][3700/3746] lr: 4.167e-02, eta: 2 days, 8:06:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.5967, loss_cls: 3.7331, loss: 3.7331 +2024-07-19 07:40:33,585 - pyskl - INFO - Saving checkpoint at 83 epochs +2024-07-19 07:42:24,853 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 07:42:25,519 - pyskl - INFO - +top1_acc 0.2641 +top5_acc 0.5142 +2024-07-19 07:42:25,519 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 07:42:25,561 - pyskl - INFO - +mean_acc 0.2638 +2024-07-19 07:42:25,573 - pyskl - INFO - Epoch(val) [83][309] top1_acc: 0.2641, top5_acc: 0.5142, mean_class_accuracy: 0.2638 +2024-07-19 07:46:16,500 - pyskl - INFO - Epoch [84][100/3746] lr: 4.163e-02, eta: 2 days, 8:06:24, time: 2.309, data_time: 1.321, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6166, loss_cls: 3.6457, loss: 3.6457 +2024-07-19 07:47:38,867 - pyskl - INFO - Epoch [84][200/3746] lr: 4.161e-02, eta: 2 days, 8:05:05, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6047, loss_cls: 3.7164, loss: 3.7164 +2024-07-19 07:49:01,034 - pyskl - INFO - Epoch [84][300/3746] lr: 4.158e-02, eta: 2 days, 8:03:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.6008, loss_cls: 3.7638, loss: 3.7638 +2024-07-19 07:50:23,300 - pyskl - INFO - Epoch [84][400/3746] lr: 4.155e-02, eta: 2 days, 8:02:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.6000, loss_cls: 3.7407, loss: 3.7407 +2024-07-19 07:51:45,594 - pyskl - INFO - Epoch [84][500/3746] lr: 4.152e-02, eta: 2 days, 8:01:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5931, loss_cls: 3.7715, loss: 3.7715 +2024-07-19 07:53:08,428 - pyskl - INFO - Epoch [84][600/3746] lr: 4.150e-02, eta: 2 days, 7:59:49, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6172, loss_cls: 3.6696, loss: 3.6696 +2024-07-19 07:54:31,111 - pyskl - INFO - Epoch [84][700/3746] lr: 4.147e-02, eta: 2 days, 7:58:30, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.6070, loss_cls: 3.7020, loss: 3.7020 +2024-07-19 07:55:54,153 - pyskl - INFO - Epoch [84][800/3746] lr: 4.144e-02, eta: 2 days, 7:57:12, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.6000, loss_cls: 3.7553, loss: 3.7553 +2024-07-19 07:57:16,566 - pyskl - INFO - Epoch [84][900/3746] lr: 4.141e-02, eta: 2 days, 7:55:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5945, loss_cls: 3.7520, loss: 3.7520 +2024-07-19 07:58:38,776 - pyskl - INFO - Epoch [84][1000/3746] lr: 4.139e-02, eta: 2 days, 7:54:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5975, loss_cls: 3.7587, loss: 3.7587 +2024-07-19 08:00:01,409 - pyskl - INFO - Epoch [84][1100/3746] lr: 4.136e-02, eta: 2 days, 7:53:15, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5931, loss_cls: 3.7735, loss: 3.7735 +2024-07-19 08:01:23,497 - pyskl - INFO - Epoch [84][1200/3746] lr: 4.133e-02, eta: 2 days, 7:51:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.6000, loss_cls: 3.7583, loss: 3.7583 +2024-07-19 08:02:45,012 - pyskl - INFO - Epoch [84][1300/3746] lr: 4.130e-02, eta: 2 days, 7:50:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.5991, loss_cls: 3.7383, loss: 3.7383 +2024-07-19 08:04:07,444 - pyskl - INFO - Epoch [84][1400/3746] lr: 4.128e-02, eta: 2 days, 7:49:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.6066, loss_cls: 3.7173, loss: 3.7173 +2024-07-19 08:05:29,576 - pyskl - INFO - Epoch [84][1500/3746] lr: 4.125e-02, eta: 2 days, 7:47:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6022, loss_cls: 3.7264, loss: 3.7264 +2024-07-19 08:06:52,067 - pyskl - INFO - Epoch [84][1600/3746] lr: 4.122e-02, eta: 2 days, 7:46:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.6022, loss_cls: 3.7421, loss: 3.7421 +2024-07-19 08:08:14,237 - pyskl - INFO - Epoch [84][1700/3746] lr: 4.119e-02, eta: 2 days, 7:45:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5913, loss_cls: 3.7823, loss: 3.7823 +2024-07-19 08:09:36,779 - pyskl - INFO - Epoch [84][1800/3746] lr: 4.117e-02, eta: 2 days, 7:44:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.6012, loss_cls: 3.7544, loss: 3.7544 +2024-07-19 08:10:58,785 - pyskl - INFO - Epoch [84][1900/3746] lr: 4.114e-02, eta: 2 days, 7:42:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6030, loss_cls: 3.7058, loss: 3.7058 +2024-07-19 08:12:20,520 - pyskl - INFO - Epoch [84][2000/3746] lr: 4.111e-02, eta: 2 days, 7:41:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.5981, loss_cls: 3.7804, loss: 3.7804 +2024-07-19 08:13:42,055 - pyskl - INFO - Epoch [84][2100/3746] lr: 4.108e-02, eta: 2 days, 7:40:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6095, loss_cls: 3.6904, loss: 3.6904 +2024-07-19 08:15:03,628 - pyskl - INFO - Epoch [84][2200/3746] lr: 4.106e-02, eta: 2 days, 7:38:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6081, loss_cls: 3.7216, loss: 3.7216 +2024-07-19 08:16:25,696 - pyskl - INFO - Epoch [84][2300/3746] lr: 4.103e-02, eta: 2 days, 7:37:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.6094, loss_cls: 3.7190, loss: 3.7190 +2024-07-19 08:17:47,542 - pyskl - INFO - Epoch [84][2400/3746] lr: 4.100e-02, eta: 2 days, 7:36:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.5911, loss_cls: 3.7483, loss: 3.7483 +2024-07-19 08:19:09,342 - pyskl - INFO - Epoch [84][2500/3746] lr: 4.097e-02, eta: 2 days, 7:34:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.6002, loss_cls: 3.7274, loss: 3.7274 +2024-07-19 08:20:30,879 - pyskl - INFO - Epoch [84][2600/3746] lr: 4.095e-02, eta: 2 days, 7:33:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5958, loss_cls: 3.7586, loss: 3.7586 +2024-07-19 08:21:52,983 - pyskl - INFO - Epoch [84][2700/3746] lr: 4.092e-02, eta: 2 days, 7:32:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5994, loss_cls: 3.7461, loss: 3.7461 +2024-07-19 08:23:15,017 - pyskl - INFO - Epoch [84][2800/3746] lr: 4.089e-02, eta: 2 days, 7:30:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5933, loss_cls: 3.7558, loss: 3.7558 +2024-07-19 08:24:36,891 - pyskl - INFO - Epoch [84][2900/3746] lr: 4.086e-02, eta: 2 days, 7:29:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5934, loss_cls: 3.7932, loss: 3.7932 +2024-07-19 08:25:59,551 - pyskl - INFO - Epoch [84][3000/3746] lr: 4.084e-02, eta: 2 days, 7:28:07, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.6056, loss_cls: 3.7214, loss: 3.7214 +2024-07-19 08:27:21,427 - pyskl - INFO - Epoch [84][3100/3746] lr: 4.081e-02, eta: 2 days, 7:26:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5986, loss_cls: 3.7625, loss: 3.7625 +2024-07-19 08:28:43,716 - pyskl - INFO - Epoch [84][3200/3746] lr: 4.078e-02, eta: 2 days, 7:25:29, time: 0.823, data_time: 0.001, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6036, loss_cls: 3.7169, loss: 3.7169 +2024-07-19 08:30:05,512 - pyskl - INFO - Epoch [84][3300/3746] lr: 4.075e-02, eta: 2 days, 7:24:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.6000, loss_cls: 3.7608, loss: 3.7608 +2024-07-19 08:31:27,551 - pyskl - INFO - Epoch [84][3400/3746] lr: 4.073e-02, eta: 2 days, 7:22:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5972, loss_cls: 3.7804, loss: 3.7804 +2024-07-19 08:32:49,236 - pyskl - INFO - Epoch [84][3500/3746] lr: 4.070e-02, eta: 2 days, 7:21:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5970, loss_cls: 3.7552, loss: 3.7552 +2024-07-19 08:34:11,127 - pyskl - INFO - Epoch [84][3600/3746] lr: 4.067e-02, eta: 2 days, 7:20:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5945, loss_cls: 3.7839, loss: 3.7839 +2024-07-19 08:35:33,484 - pyskl - INFO - Epoch [84][3700/3746] lr: 4.064e-02, eta: 2 days, 7:18:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.6047, loss_cls: 3.7356, loss: 3.7356 +2024-07-19 08:36:13,093 - pyskl - INFO - Saving checkpoint at 84 epochs +2024-07-19 08:38:03,627 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 08:38:04,295 - pyskl - INFO - +top1_acc 0.2723 +top5_acc 0.5343 +2024-07-19 08:38:04,295 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 08:38:04,337 - pyskl - INFO - +mean_acc 0.2720 +2024-07-19 08:38:04,350 - pyskl - INFO - Epoch(val) [84][309] top1_acc: 0.2723, top5_acc: 0.5343, mean_class_accuracy: 0.2720 +2024-07-19 08:41:53,969 - pyskl - INFO - Epoch [85][100/3746] lr: 4.060e-02, eta: 2 days, 7:18:22, time: 2.296, data_time: 1.316, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6166, loss_cls: 3.6408, loss: 3.6408 +2024-07-19 08:43:16,634 - pyskl - INFO - Epoch [85][200/3746] lr: 4.058e-02, eta: 2 days, 7:17:03, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6217, loss_cls: 3.6434, loss: 3.6434 +2024-07-19 08:44:38,355 - pyskl - INFO - Epoch [85][300/3746] lr: 4.055e-02, eta: 2 days, 7:15:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6103, loss_cls: 3.7018, loss: 3.7018 +2024-07-19 08:46:00,579 - pyskl - INFO - Epoch [85][400/3746] lr: 4.052e-02, eta: 2 days, 7:14:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.6017, loss_cls: 3.7366, loss: 3.7366 +2024-07-19 08:47:22,520 - pyskl - INFO - Epoch [85][500/3746] lr: 4.049e-02, eta: 2 days, 7:13:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6034, loss_cls: 3.7477, loss: 3.7477 +2024-07-19 08:48:45,412 - pyskl - INFO - Epoch [85][600/3746] lr: 4.047e-02, eta: 2 days, 7:11:46, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6159, loss_cls: 3.6804, loss: 3.6804 +2024-07-19 08:50:07,172 - pyskl - INFO - Epoch [85][700/3746] lr: 4.044e-02, eta: 2 days, 7:10:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.6109, loss_cls: 3.6835, loss: 3.6835 +2024-07-19 08:51:28,686 - pyskl - INFO - Epoch [85][800/3746] lr: 4.041e-02, eta: 2 days, 7:09:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6038, loss_cls: 3.7313, loss: 3.7313 +2024-07-19 08:52:50,277 - pyskl - INFO - Epoch [85][900/3746] lr: 4.038e-02, eta: 2 days, 7:07:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6123, loss_cls: 3.6641, loss: 3.6641 +2024-07-19 08:54:12,137 - pyskl - INFO - Epoch [85][1000/3746] lr: 4.036e-02, eta: 2 days, 7:06:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6105, loss_cls: 3.7010, loss: 3.7010 +2024-07-19 08:55:33,682 - pyskl - INFO - Epoch [85][1100/3746] lr: 4.033e-02, eta: 2 days, 7:05:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6059, loss_cls: 3.7163, loss: 3.7163 +2024-07-19 08:56:55,367 - pyskl - INFO - Epoch [85][1200/3746] lr: 4.030e-02, eta: 2 days, 7:03:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6059, loss_cls: 3.7179, loss: 3.7179 +2024-07-19 08:58:17,730 - pyskl - INFO - Epoch [85][1300/3746] lr: 4.027e-02, eta: 2 days, 7:02:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.5978, loss_cls: 3.7422, loss: 3.7422 +2024-07-19 08:59:40,016 - pyskl - INFO - Epoch [85][1400/3746] lr: 4.025e-02, eta: 2 days, 7:01:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5942, loss_cls: 3.7626, loss: 3.7626 +2024-07-19 09:01:02,332 - pyskl - INFO - Epoch [85][1500/3746] lr: 4.022e-02, eta: 2 days, 6:59:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.6020, loss_cls: 3.7476, loss: 3.7476 +2024-07-19 09:02:25,091 - pyskl - INFO - Epoch [85][1600/3746] lr: 4.019e-02, eta: 2 days, 6:58:31, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5978, loss_cls: 3.7793, loss: 3.7793 +2024-07-19 09:03:47,886 - pyskl - INFO - Epoch [85][1700/3746] lr: 4.016e-02, eta: 2 days, 6:57:12, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.5970, loss_cls: 3.7291, loss: 3.7291 +2024-07-19 09:05:09,681 - pyskl - INFO - Epoch [85][1800/3746] lr: 4.014e-02, eta: 2 days, 6:55:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5981, loss_cls: 3.7743, loss: 3.7743 +2024-07-19 09:06:31,895 - pyskl - INFO - Epoch [85][1900/3746] lr: 4.011e-02, eta: 2 days, 6:54:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5953, loss_cls: 3.7626, loss: 3.7626 +2024-07-19 09:07:54,613 - pyskl - INFO - Epoch [85][2000/3746] lr: 4.008e-02, eta: 2 days, 6:53:15, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.6038, loss_cls: 3.7133, loss: 3.7133 +2024-07-19 09:09:18,086 - pyskl - INFO - Epoch [85][2100/3746] lr: 4.006e-02, eta: 2 days, 6:51:56, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6100, loss_cls: 3.6606, loss: 3.6606 +2024-07-19 09:10:40,810 - pyskl - INFO - Epoch [85][2200/3746] lr: 4.003e-02, eta: 2 days, 6:50:37, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6058, loss_cls: 3.7283, loss: 3.7283 +2024-07-19 09:12:02,603 - pyskl - INFO - Epoch [85][2300/3746] lr: 4.000e-02, eta: 2 days, 6:49:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6052, loss_cls: 3.7122, loss: 3.7122 +2024-07-19 09:13:24,685 - pyskl - INFO - Epoch [85][2400/3746] lr: 3.997e-02, eta: 2 days, 6:47:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6033, loss_cls: 3.7105, loss: 3.7105 +2024-07-19 09:14:45,732 - pyskl - INFO - Epoch [85][2500/3746] lr: 3.995e-02, eta: 2 days, 6:46:38, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6020, loss_cls: 3.7212, loss: 3.7212 +2024-07-19 09:16:07,638 - pyskl - INFO - Epoch [85][2600/3746] lr: 3.992e-02, eta: 2 days, 6:45:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6095, loss_cls: 3.6762, loss: 3.6762 +2024-07-19 09:17:29,969 - pyskl - INFO - Epoch [85][2700/3746] lr: 3.989e-02, eta: 2 days, 6:43:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5895, loss_cls: 3.7995, loss: 3.7995 +2024-07-19 09:18:52,614 - pyskl - INFO - Epoch [85][2800/3746] lr: 3.986e-02, eta: 2 days, 6:42:40, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.5988, loss_cls: 3.7527, loss: 3.7527 +2024-07-19 09:20:14,293 - pyskl - INFO - Epoch [85][2900/3746] lr: 3.984e-02, eta: 2 days, 6:41:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.5917, loss_cls: 3.7742, loss: 3.7742 +2024-07-19 09:21:36,572 - pyskl - INFO - Epoch [85][3000/3746] lr: 3.981e-02, eta: 2 days, 6:40:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5980, loss_cls: 3.7585, loss: 3.7585 +2024-07-19 09:22:58,570 - pyskl - INFO - Epoch [85][3100/3746] lr: 3.978e-02, eta: 2 days, 6:38:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.5998, loss_cls: 3.7364, loss: 3.7364 +2024-07-19 09:24:20,102 - pyskl - INFO - Epoch [85][3200/3746] lr: 3.975e-02, eta: 2 days, 6:37:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6112, loss_cls: 3.7228, loss: 3.7228 +2024-07-19 09:25:42,280 - pyskl - INFO - Epoch [85][3300/3746] lr: 3.973e-02, eta: 2 days, 6:36:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.6072, loss_cls: 3.7127, loss: 3.7127 +2024-07-19 09:27:04,064 - pyskl - INFO - Epoch [85][3400/3746] lr: 3.970e-02, eta: 2 days, 6:34:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6114, loss_cls: 3.6830, loss: 3.6830 +2024-07-19 09:28:25,521 - pyskl - INFO - Epoch [85][3500/3746] lr: 3.967e-02, eta: 2 days, 6:33:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.5978, loss_cls: 3.7567, loss: 3.7567 +2024-07-19 09:29:47,117 - pyskl - INFO - Epoch [85][3600/3746] lr: 3.964e-02, eta: 2 days, 6:32:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6081, loss_cls: 3.7247, loss: 3.7247 +2024-07-19 09:31:08,624 - pyskl - INFO - Epoch [85][3700/3746] lr: 3.962e-02, eta: 2 days, 6:30:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5909, loss_cls: 3.7799, loss: 3.7799 +2024-07-19 09:31:48,205 - pyskl - INFO - Saving checkpoint at 85 epochs +2024-07-19 09:33:39,942 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 09:33:40,711 - pyskl - INFO - +top1_acc 0.2573 +top5_acc 0.5180 +2024-07-19 09:33:40,712 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 09:33:40,755 - pyskl - INFO - +mean_acc 0.2568 +2024-07-19 09:33:40,767 - pyskl - INFO - Epoch(val) [85][309] top1_acc: 0.2573, top5_acc: 0.5180, mean_class_accuracy: 0.2568 +2024-07-19 09:37:26,317 - pyskl - INFO - Epoch [86][100/3746] lr: 3.958e-02, eta: 2 days, 6:30:08, time: 2.255, data_time: 1.287, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6141, loss_cls: 3.6676, loss: 3.6676 +2024-07-19 09:38:48,248 - pyskl - INFO - Epoch [86][200/3746] lr: 3.955e-02, eta: 2 days, 6:28:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6102, loss_cls: 3.6939, loss: 3.6939 +2024-07-19 09:40:10,518 - pyskl - INFO - Epoch [86][300/3746] lr: 3.952e-02, eta: 2 days, 6:27:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6120, loss_cls: 3.6797, loss: 3.6797 +2024-07-19 09:41:32,295 - pyskl - INFO - Epoch [86][400/3746] lr: 3.950e-02, eta: 2 days, 6:26:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6089, loss_cls: 3.6825, loss: 3.6825 +2024-07-19 09:42:54,470 - pyskl - INFO - Epoch [86][500/3746] lr: 3.947e-02, eta: 2 days, 6:24:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.6072, loss_cls: 3.7127, loss: 3.7127 +2024-07-19 09:44:16,255 - pyskl - INFO - Epoch [86][600/3746] lr: 3.944e-02, eta: 2 days, 6:23:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6183, loss_cls: 3.6394, loss: 3.6394 +2024-07-19 09:45:38,186 - pyskl - INFO - Epoch [86][700/3746] lr: 3.941e-02, eta: 2 days, 6:22:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6098, loss_cls: 3.7210, loss: 3.7210 +2024-07-19 09:46:59,727 - pyskl - INFO - Epoch [86][800/3746] lr: 3.939e-02, eta: 2 days, 6:20:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.6088, loss_cls: 3.7528, loss: 3.7528 +2024-07-19 09:48:21,461 - pyskl - INFO - Epoch [86][900/3746] lr: 3.936e-02, eta: 2 days, 6:19:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.5981, loss_cls: 3.7271, loss: 3.7271 +2024-07-19 09:49:43,316 - pyskl - INFO - Epoch [86][1000/3746] lr: 3.933e-02, eta: 2 days, 6:18:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6036, loss_cls: 3.7097, loss: 3.7097 +2024-07-19 09:51:05,257 - pyskl - INFO - Epoch [86][1100/3746] lr: 3.930e-02, eta: 2 days, 6:16:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6117, loss_cls: 3.6728, loss: 3.6728 +2024-07-19 09:52:26,431 - pyskl - INFO - Epoch [86][1200/3746] lr: 3.928e-02, eta: 2 days, 6:15:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6039, loss_cls: 3.7152, loss: 3.7152 +2024-07-19 09:53:48,102 - pyskl - INFO - Epoch [86][1300/3746] lr: 3.925e-02, eta: 2 days, 6:14:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6070, loss_cls: 3.7012, loss: 3.7012 +2024-07-19 09:55:10,246 - pyskl - INFO - Epoch [86][1400/3746] lr: 3.922e-02, eta: 2 days, 6:12:52, time: 0.821, data_time: 0.001, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6089, loss_cls: 3.6720, loss: 3.6720 +2024-07-19 09:56:32,644 - pyskl - INFO - Epoch [86][1500/3746] lr: 3.919e-02, eta: 2 days, 6:11:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6133, loss_cls: 3.7031, loss: 3.7031 +2024-07-19 09:57:54,179 - pyskl - INFO - Epoch [86][1600/3746] lr: 3.917e-02, eta: 2 days, 6:10:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.6047, loss_cls: 3.7256, loss: 3.7256 +2024-07-19 09:59:16,586 - pyskl - INFO - Epoch [86][1700/3746] lr: 3.914e-02, eta: 2 days, 6:08:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6127, loss_cls: 3.6934, loss: 3.6934 +2024-07-19 10:00:38,662 - pyskl - INFO - Epoch [86][1800/3746] lr: 3.911e-02, eta: 2 days, 6:07:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6014, loss_cls: 3.7762, loss: 3.7762 +2024-07-19 10:02:01,591 - pyskl - INFO - Epoch [86][1900/3746] lr: 3.909e-02, eta: 2 days, 6:06:15, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6145, loss_cls: 3.6792, loss: 3.6792 +2024-07-19 10:03:24,190 - pyskl - INFO - Epoch [86][2000/3746] lr: 3.906e-02, eta: 2 days, 6:04:56, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.6017, loss_cls: 3.7409, loss: 3.7409 +2024-07-19 10:04:46,462 - pyskl - INFO - Epoch [86][2100/3746] lr: 3.903e-02, eta: 2 days, 6:03:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6109, loss_cls: 3.7068, loss: 3.7068 +2024-07-19 10:06:08,361 - pyskl - INFO - Epoch [86][2200/3746] lr: 3.900e-02, eta: 2 days, 6:02:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6161, loss_cls: 3.6809, loss: 3.6809 +2024-07-19 10:07:30,731 - pyskl - INFO - Epoch [86][2300/3746] lr: 3.898e-02, eta: 2 days, 6:00:58, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6042, loss_cls: 3.6982, loss: 3.6982 +2024-07-19 10:08:53,067 - pyskl - INFO - Epoch [86][2400/3746] lr: 3.895e-02, eta: 2 days, 5:59:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3425, top5_acc: 0.6084, loss_cls: 3.6975, loss: 3.6975 +2024-07-19 10:10:14,537 - pyskl - INFO - Epoch [86][2500/3746] lr: 3.892e-02, eta: 2 days, 5:58:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6091, loss_cls: 3.6901, loss: 3.6901 +2024-07-19 10:11:36,200 - pyskl - INFO - Epoch [86][2600/3746] lr: 3.889e-02, eta: 2 days, 5:56:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6070, loss_cls: 3.7152, loss: 3.7152 +2024-07-19 10:12:58,774 - pyskl - INFO - Epoch [86][2700/3746] lr: 3.887e-02, eta: 2 days, 5:55:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6073, loss_cls: 3.7117, loss: 3.7117 +2024-07-19 10:14:21,381 - pyskl - INFO - Epoch [86][2800/3746] lr: 3.884e-02, eta: 2 days, 5:54:20, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5936, loss_cls: 3.7828, loss: 3.7828 +2024-07-19 10:15:43,624 - pyskl - INFO - Epoch [86][2900/3746] lr: 3.881e-02, eta: 2 days, 5:53:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6109, loss_cls: 3.6855, loss: 3.6855 +2024-07-19 10:17:06,052 - pyskl - INFO - Epoch [86][3000/3746] lr: 3.879e-02, eta: 2 days, 5:51:42, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6075, loss_cls: 3.6978, loss: 3.6978 +2024-07-19 10:18:27,734 - pyskl - INFO - Epoch [86][3100/3746] lr: 3.876e-02, eta: 2 days, 5:50:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.6034, loss_cls: 3.7172, loss: 3.7172 +2024-07-19 10:19:49,601 - pyskl - INFO - Epoch [86][3200/3746] lr: 3.873e-02, eta: 2 days, 5:49:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.5953, loss_cls: 3.7543, loss: 3.7543 +2024-07-19 10:21:12,175 - pyskl - INFO - Epoch [86][3300/3746] lr: 3.870e-02, eta: 2 days, 5:47:43, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.5988, loss_cls: 3.7118, loss: 3.7118 +2024-07-19 10:22:34,165 - pyskl - INFO - Epoch [86][3400/3746] lr: 3.868e-02, eta: 2 days, 5:46:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.6000, loss_cls: 3.7334, loss: 3.7334 +2024-07-19 10:23:56,428 - pyskl - INFO - Epoch [86][3500/3746] lr: 3.865e-02, eta: 2 days, 5:45:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.6019, loss_cls: 3.7399, loss: 3.7399 +2024-07-19 10:25:18,263 - pyskl - INFO - Epoch [86][3600/3746] lr: 3.862e-02, eta: 2 days, 5:43:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.6016, loss_cls: 3.7029, loss: 3.7029 +2024-07-19 10:26:39,532 - pyskl - INFO - Epoch [86][3700/3746] lr: 3.860e-02, eta: 2 days, 5:42:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6147, loss_cls: 3.7006, loss: 3.7006 +2024-07-19 10:27:18,416 - pyskl - INFO - Saving checkpoint at 86 epochs +2024-07-19 10:29:08,178 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 10:29:08,841 - pyskl - INFO - +top1_acc 0.2541 +top5_acc 0.5022 +2024-07-19 10:29:08,841 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 10:29:08,880 - pyskl - INFO - +mean_acc 0.2539 +2024-07-19 10:29:08,892 - pyskl - INFO - Epoch(val) [86][309] top1_acc: 0.2541, top5_acc: 0.5022, mean_class_accuracy: 0.2539 +2024-07-19 10:32:55,380 - pyskl - INFO - Epoch [87][100/3746] lr: 3.856e-02, eta: 2 days, 5:41:47, time: 2.265, data_time: 1.294, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6270, loss_cls: 3.5799, loss: 3.5799 +2024-07-19 10:34:17,568 - pyskl - INFO - Epoch [87][200/3746] lr: 3.853e-02, eta: 2 days, 5:40:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.6117, loss_cls: 3.6755, loss: 3.6755 +2024-07-19 10:35:39,680 - pyskl - INFO - Epoch [87][300/3746] lr: 3.850e-02, eta: 2 days, 5:39:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6231, loss_cls: 3.6226, loss: 3.6226 +2024-07-19 10:37:01,381 - pyskl - INFO - Epoch [87][400/3746] lr: 3.847e-02, eta: 2 days, 5:37:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6066, loss_cls: 3.7058, loss: 3.7058 +2024-07-19 10:38:22,804 - pyskl - INFO - Epoch [87][500/3746] lr: 3.845e-02, eta: 2 days, 5:36:28, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.6031, loss_cls: 3.7253, loss: 3.7253 +2024-07-19 10:39:44,197 - pyskl - INFO - Epoch [87][600/3746] lr: 3.842e-02, eta: 2 days, 5:35:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6109, loss_cls: 3.6607, loss: 3.6607 +2024-07-19 10:41:05,820 - pyskl - INFO - Epoch [87][700/3746] lr: 3.839e-02, eta: 2 days, 5:33:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6094, loss_cls: 3.7020, loss: 3.7020 +2024-07-19 10:42:27,925 - pyskl - INFO - Epoch [87][800/3746] lr: 3.837e-02, eta: 2 days, 5:32:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6131, loss_cls: 3.6879, loss: 3.6879 +2024-07-19 10:43:49,848 - pyskl - INFO - Epoch [87][900/3746] lr: 3.834e-02, eta: 2 days, 5:31:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5997, loss_cls: 3.7210, loss: 3.7210 +2024-07-19 10:45:11,588 - pyskl - INFO - Epoch [87][1000/3746] lr: 3.831e-02, eta: 2 days, 5:29:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.6142, loss_cls: 3.6682, loss: 3.6682 +2024-07-19 10:46:33,540 - pyskl - INFO - Epoch [87][1100/3746] lr: 3.828e-02, eta: 2 days, 5:28:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6139, loss_cls: 3.6641, loss: 3.6641 +2024-07-19 10:47:55,437 - pyskl - INFO - Epoch [87][1200/3746] lr: 3.826e-02, eta: 2 days, 5:27:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6056, loss_cls: 3.7094, loss: 3.7094 +2024-07-19 10:49:17,280 - pyskl - INFO - Epoch [87][1300/3746] lr: 3.823e-02, eta: 2 days, 5:25:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6192, loss_cls: 3.5961, loss: 3.5961 +2024-07-19 10:50:38,642 - pyskl - INFO - Epoch [87][1400/3746] lr: 3.820e-02, eta: 2 days, 5:24:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.6022, loss_cls: 3.7166, loss: 3.7166 +2024-07-19 10:52:01,255 - pyskl - INFO - Epoch [87][1500/3746] lr: 3.817e-02, eta: 2 days, 5:23:10, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.6036, loss_cls: 3.7292, loss: 3.7292 +2024-07-19 10:53:22,841 - pyskl - INFO - Epoch [87][1600/3746] lr: 3.815e-02, eta: 2 days, 5:21:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6116, loss_cls: 3.6850, loss: 3.6850 +2024-07-19 10:54:45,044 - pyskl - INFO - Epoch [87][1700/3746] lr: 3.812e-02, eta: 2 days, 5:20:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6078, loss_cls: 3.7068, loss: 3.7068 +2024-07-19 10:56:07,679 - pyskl - INFO - Epoch [87][1800/3746] lr: 3.809e-02, eta: 2 days, 5:19:11, time: 0.826, data_time: 0.001, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6091, loss_cls: 3.6787, loss: 3.6787 +2024-07-19 10:57:30,451 - pyskl - INFO - Epoch [87][1900/3746] lr: 3.807e-02, eta: 2 days, 5:17:52, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.5998, loss_cls: 3.7233, loss: 3.7233 +2024-07-19 10:58:53,235 - pyskl - INFO - Epoch [87][2000/3746] lr: 3.804e-02, eta: 2 days, 5:16:33, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5973, loss_cls: 3.7439, loss: 3.7439 +2024-07-19 11:00:15,449 - pyskl - INFO - Epoch [87][2100/3746] lr: 3.801e-02, eta: 2 days, 5:15:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.6066, loss_cls: 3.7104, loss: 3.7104 +2024-07-19 11:01:37,638 - pyskl - INFO - Epoch [87][2200/3746] lr: 3.798e-02, eta: 2 days, 5:13:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6088, loss_cls: 3.7035, loss: 3.7035 +2024-07-19 11:03:00,287 - pyskl - INFO - Epoch [87][2300/3746] lr: 3.796e-02, eta: 2 days, 5:12:35, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6081, loss_cls: 3.6973, loss: 3.6973 +2024-07-19 11:04:22,112 - pyskl - INFO - Epoch [87][2400/3746] lr: 3.793e-02, eta: 2 days, 5:11:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6103, loss_cls: 3.6715, loss: 3.6715 +2024-07-19 11:05:43,674 - pyskl - INFO - Epoch [87][2500/3746] lr: 3.790e-02, eta: 2 days, 5:09:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6083, loss_cls: 3.7061, loss: 3.7061 +2024-07-19 11:07:05,323 - pyskl - INFO - Epoch [87][2600/3746] lr: 3.788e-02, eta: 2 days, 5:08:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3425, top5_acc: 0.6066, loss_cls: 3.7076, loss: 3.7076 +2024-07-19 11:08:27,164 - pyskl - INFO - Epoch [87][2700/3746] lr: 3.785e-02, eta: 2 days, 5:07:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6162, loss_cls: 3.6456, loss: 3.6456 +2024-07-19 11:09:49,715 - pyskl - INFO - Epoch [87][2800/3746] lr: 3.782e-02, eta: 2 days, 5:05:56, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5850, loss_cls: 3.8123, loss: 3.8123 +2024-07-19 11:11:11,641 - pyskl - INFO - Epoch [87][2900/3746] lr: 3.779e-02, eta: 2 days, 5:04:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.6077, loss_cls: 3.7066, loss: 3.7066 +2024-07-19 11:12:34,168 - pyskl - INFO - Epoch [87][3000/3746] lr: 3.777e-02, eta: 2 days, 5:03:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.6011, loss_cls: 3.7222, loss: 3.7222 +2024-07-19 11:13:55,160 - pyskl - INFO - Epoch [87][3100/3746] lr: 3.774e-02, eta: 2 days, 5:01:56, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6077, loss_cls: 3.6910, loss: 3.6910 +2024-07-19 11:15:17,046 - pyskl - INFO - Epoch [87][3200/3746] lr: 3.771e-02, eta: 2 days, 5:00:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6189, loss_cls: 3.6290, loss: 3.6290 +2024-07-19 11:16:39,387 - pyskl - INFO - Epoch [87][3300/3746] lr: 3.769e-02, eta: 2 days, 4:59:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6050, loss_cls: 3.7219, loss: 3.7219 +2024-07-19 11:18:01,535 - pyskl - INFO - Epoch [87][3400/3746] lr: 3.766e-02, eta: 2 days, 4:57:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5980, loss_cls: 3.7571, loss: 3.7571 +2024-07-19 11:19:23,409 - pyskl - INFO - Epoch [87][3500/3746] lr: 3.763e-02, eta: 2 days, 4:56:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6064, loss_cls: 3.6988, loss: 3.6988 +2024-07-19 11:20:45,496 - pyskl - INFO - Epoch [87][3600/3746] lr: 3.761e-02, eta: 2 days, 4:55:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6052, loss_cls: 3.7275, loss: 3.7275 +2024-07-19 11:22:07,137 - pyskl - INFO - Epoch [87][3700/3746] lr: 3.758e-02, eta: 2 days, 4:53:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.6047, loss_cls: 3.7173, loss: 3.7173 +2024-07-19 11:22:46,301 - pyskl - INFO - Saving checkpoint at 87 epochs +2024-07-19 11:24:36,720 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 11:24:37,444 - pyskl - INFO - +top1_acc 0.2779 +top5_acc 0.5347 +2024-07-19 11:24:37,444 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 11:24:37,483 - pyskl - INFO - +mean_acc 0.2775 +2024-07-19 11:24:37,488 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_82.pth was removed +2024-07-19 11:24:37,807 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_87.pth. +2024-07-19 11:24:37,808 - pyskl - INFO - Best top1_acc is 0.2779 at 87 epoch. +2024-07-19 11:24:37,822 - pyskl - INFO - Epoch(val) [87][309] top1_acc: 0.2779, top5_acc: 0.5347, mean_class_accuracy: 0.2775 +2024-07-19 11:28:22,872 - pyskl - INFO - Epoch [88][100/3746] lr: 3.754e-02, eta: 2 days, 4:53:18, time: 2.250, data_time: 1.280, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6269, loss_cls: 3.6180, loss: 3.6180 +2024-07-19 11:29:45,623 - pyskl - INFO - Epoch [88][200/3746] lr: 3.751e-02, eta: 2 days, 4:51:58, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6175, loss_cls: 3.6149, loss: 3.6149 +2024-07-19 11:31:08,267 - pyskl - INFO - Epoch [88][300/3746] lr: 3.748e-02, eta: 2 days, 4:50:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6159, loss_cls: 3.6427, loss: 3.6427 +2024-07-19 11:32:30,081 - pyskl - INFO - Epoch [88][400/3746] lr: 3.746e-02, eta: 2 days, 4:49:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6072, loss_cls: 3.6608, loss: 3.6608 +2024-07-19 11:33:51,312 - pyskl - INFO - Epoch [88][500/3746] lr: 3.743e-02, eta: 2 days, 4:47:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.6117, loss_cls: 3.7090, loss: 3.7090 +2024-07-19 11:35:13,151 - pyskl - INFO - Epoch [88][600/3746] lr: 3.740e-02, eta: 2 days, 4:46:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6089, loss_cls: 3.7148, loss: 3.7148 +2024-07-19 11:36:35,155 - pyskl - INFO - Epoch [88][700/3746] lr: 3.738e-02, eta: 2 days, 4:45:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6177, loss_cls: 3.6346, loss: 3.6346 +2024-07-19 11:37:56,646 - pyskl - INFO - Epoch [88][800/3746] lr: 3.735e-02, eta: 2 days, 4:43:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.6025, loss_cls: 3.7310, loss: 3.7310 +2024-07-19 11:39:18,425 - pyskl - INFO - Epoch [88][900/3746] lr: 3.732e-02, eta: 2 days, 4:42:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6189, loss_cls: 3.6199, loss: 3.6199 +2024-07-19 11:40:40,212 - pyskl - INFO - Epoch [88][1000/3746] lr: 3.730e-02, eta: 2 days, 4:41:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6178, loss_cls: 3.6598, loss: 3.6598 +2024-07-19 11:42:02,340 - pyskl - INFO - Epoch [88][1100/3746] lr: 3.727e-02, eta: 2 days, 4:39:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6242, loss_cls: 3.6237, loss: 3.6237 +2024-07-19 11:43:24,248 - pyskl - INFO - Epoch [88][1200/3746] lr: 3.724e-02, eta: 2 days, 4:38:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6150, loss_cls: 3.6419, loss: 3.6419 +2024-07-19 11:44:46,080 - pyskl - INFO - Epoch [88][1300/3746] lr: 3.721e-02, eta: 2 days, 4:37:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6202, loss_cls: 3.6622, loss: 3.6622 +2024-07-19 11:46:07,647 - pyskl - INFO - Epoch [88][1400/3746] lr: 3.719e-02, eta: 2 days, 4:36:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.6214, loss_cls: 3.6587, loss: 3.6587 +2024-07-19 11:47:29,487 - pyskl - INFO - Epoch [88][1500/3746] lr: 3.716e-02, eta: 2 days, 4:34:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.6072, loss_cls: 3.7282, loss: 3.7282 +2024-07-19 11:48:50,767 - pyskl - INFO - Epoch [88][1600/3746] lr: 3.713e-02, eta: 2 days, 4:33:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6056, loss_cls: 3.7124, loss: 3.7124 +2024-07-19 11:50:13,281 - pyskl - INFO - Epoch [88][1700/3746] lr: 3.711e-02, eta: 2 days, 4:32:00, time: 0.825, data_time: 0.001, memory: 15990, top1_acc: 0.3383, top5_acc: 0.6039, loss_cls: 3.7315, loss: 3.7315 +2024-07-19 11:51:35,905 - pyskl - INFO - Epoch [88][1800/3746] lr: 3.708e-02, eta: 2 days, 4:30:40, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6198, loss_cls: 3.6494, loss: 3.6494 +2024-07-19 11:52:58,386 - pyskl - INFO - Epoch [88][1900/3746] lr: 3.705e-02, eta: 2 days, 4:29:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6117, loss_cls: 3.6974, loss: 3.6974 +2024-07-19 11:54:20,960 - pyskl - INFO - Epoch [88][2000/3746] lr: 3.703e-02, eta: 2 days, 4:28:02, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6056, loss_cls: 3.6906, loss: 3.6906 +2024-07-19 11:55:43,727 - pyskl - INFO - Epoch [88][2100/3746] lr: 3.700e-02, eta: 2 days, 4:26:42, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.5975, loss_cls: 3.7258, loss: 3.7258 +2024-07-19 11:57:06,434 - pyskl - INFO - Epoch [88][2200/3746] lr: 3.697e-02, eta: 2 days, 4:25:23, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6069, loss_cls: 3.6922, loss: 3.6922 +2024-07-19 11:58:28,634 - pyskl - INFO - Epoch [88][2300/3746] lr: 3.694e-02, eta: 2 days, 4:24:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.6050, loss_cls: 3.7226, loss: 3.7226 +2024-07-19 11:59:50,912 - pyskl - INFO - Epoch [88][2400/3746] lr: 3.692e-02, eta: 2 days, 4:22:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6092, loss_cls: 3.6918, loss: 3.6918 +2024-07-19 12:01:12,000 - pyskl - INFO - Epoch [88][2500/3746] lr: 3.689e-02, eta: 2 days, 4:21:23, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6078, loss_cls: 3.6725, loss: 3.6725 +2024-07-19 12:02:34,347 - pyskl - INFO - Epoch [88][2600/3746] lr: 3.686e-02, eta: 2 days, 4:20:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6147, loss_cls: 3.6514, loss: 3.6514 +2024-07-19 12:03:56,762 - pyskl - INFO - Epoch [88][2700/3746] lr: 3.684e-02, eta: 2 days, 4:18:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6066, loss_cls: 3.7023, loss: 3.7023 +2024-07-19 12:05:18,891 - pyskl - INFO - Epoch [88][2800/3746] lr: 3.681e-02, eta: 2 days, 4:17:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6000, loss_cls: 3.7339, loss: 3.7339 +2024-07-19 12:06:40,998 - pyskl - INFO - Epoch [88][2900/3746] lr: 3.678e-02, eta: 2 days, 4:16:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6078, loss_cls: 3.6814, loss: 3.6814 +2024-07-19 12:08:03,152 - pyskl - INFO - Epoch [88][3000/3746] lr: 3.676e-02, eta: 2 days, 4:14:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6089, loss_cls: 3.7148, loss: 3.7148 +2024-07-19 12:09:24,992 - pyskl - INFO - Epoch [88][3100/3746] lr: 3.673e-02, eta: 2 days, 4:13:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6205, loss_cls: 3.6153, loss: 3.6153 +2024-07-19 12:10:46,613 - pyskl - INFO - Epoch [88][3200/3746] lr: 3.670e-02, eta: 2 days, 4:12:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.6128, loss_cls: 3.6982, loss: 3.6982 +2024-07-19 12:12:09,326 - pyskl - INFO - Epoch [88][3300/3746] lr: 3.667e-02, eta: 2 days, 4:10:46, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.6111, loss_cls: 3.7058, loss: 3.7058 +2024-07-19 12:13:31,048 - pyskl - INFO - Epoch [88][3400/3746] lr: 3.665e-02, eta: 2 days, 4:09:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6122, loss_cls: 3.6717, loss: 3.6717 +2024-07-19 12:14:52,455 - pyskl - INFO - Epoch [88][3500/3746] lr: 3.662e-02, eta: 2 days, 4:08:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6122, loss_cls: 3.6852, loss: 3.6852 +2024-07-19 12:16:14,356 - pyskl - INFO - Epoch [88][3600/3746] lr: 3.659e-02, eta: 2 days, 4:06:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6103, loss_cls: 3.6947, loss: 3.6947 +2024-07-19 12:17:36,321 - pyskl - INFO - Epoch [88][3700/3746] lr: 3.657e-02, eta: 2 days, 4:05:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6011, loss_cls: 3.6975, loss: 3.6975 +2024-07-19 12:18:15,805 - pyskl - INFO - Saving checkpoint at 88 epochs +2024-07-19 12:20:05,495 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 12:20:06,179 - pyskl - INFO - +top1_acc 0.2766 +top5_acc 0.5315 +2024-07-19 12:20:06,179 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 12:20:06,224 - pyskl - INFO - +mean_acc 0.2764 +2024-07-19 12:20:06,236 - pyskl - INFO - Epoch(val) [88][309] top1_acc: 0.2766, top5_acc: 0.5315, mean_class_accuracy: 0.2764 +2024-07-19 12:23:52,440 - pyskl - INFO - Epoch [89][100/3746] lr: 3.653e-02, eta: 2 days, 4:04:44, time: 2.262, data_time: 1.295, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6217, loss_cls: 3.6188, loss: 3.6188 +2024-07-19 12:25:14,728 - pyskl - INFO - Epoch [89][200/3746] lr: 3.650e-02, eta: 2 days, 4:03:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6344, loss_cls: 3.5526, loss: 3.5526 +2024-07-19 12:26:36,878 - pyskl - INFO - Epoch [89][300/3746] lr: 3.647e-02, eta: 2 days, 4:02:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6230, loss_cls: 3.6311, loss: 3.6311 +2024-07-19 12:27:58,841 - pyskl - INFO - Epoch [89][400/3746] lr: 3.645e-02, eta: 2 days, 4:00:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6072, loss_cls: 3.6711, loss: 3.6711 +2024-07-19 12:29:20,697 - pyskl - INFO - Epoch [89][500/3746] lr: 3.642e-02, eta: 2 days, 3:59:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6252, loss_cls: 3.6284, loss: 3.6284 +2024-07-19 12:30:42,493 - pyskl - INFO - Epoch [89][600/3746] lr: 3.639e-02, eta: 2 days, 3:58:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.6094, loss_cls: 3.7141, loss: 3.7141 +2024-07-19 12:32:03,994 - pyskl - INFO - Epoch [89][700/3746] lr: 3.637e-02, eta: 2 days, 3:56:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6312, loss_cls: 3.5910, loss: 3.5910 +2024-07-19 12:33:25,215 - pyskl - INFO - Epoch [89][800/3746] lr: 3.634e-02, eta: 2 days, 3:55:24, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6206, loss_cls: 3.6443, loss: 3.6443 +2024-07-19 12:34:48,117 - pyskl - INFO - Epoch [89][900/3746] lr: 3.631e-02, eta: 2 days, 3:54:05, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6083, loss_cls: 3.6830, loss: 3.6830 +2024-07-19 12:36:10,372 - pyskl - INFO - Epoch [89][1000/3746] lr: 3.629e-02, eta: 2 days, 3:52:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6198, loss_cls: 3.6695, loss: 3.6695 +2024-07-19 12:37:31,939 - pyskl - INFO - Epoch [89][1100/3746] lr: 3.626e-02, eta: 2 days, 3:51:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6184, loss_cls: 3.6426, loss: 3.6426 +2024-07-19 12:38:53,752 - pyskl - INFO - Epoch [89][1200/3746] lr: 3.623e-02, eta: 2 days, 3:50:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6216, loss_cls: 3.6314, loss: 3.6314 +2024-07-19 12:40:15,924 - pyskl - INFO - Epoch [89][1300/3746] lr: 3.620e-02, eta: 2 days, 3:48:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6028, loss_cls: 3.7172, loss: 3.7172 +2024-07-19 12:41:37,582 - pyskl - INFO - Epoch [89][1400/3746] lr: 3.618e-02, eta: 2 days, 3:47:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6019, loss_cls: 3.7247, loss: 3.7247 +2024-07-19 12:43:00,055 - pyskl - INFO - Epoch [89][1500/3746] lr: 3.615e-02, eta: 2 days, 3:46:05, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6016, loss_cls: 3.6908, loss: 3.6908 +2024-07-19 12:44:21,962 - pyskl - INFO - Epoch [89][1600/3746] lr: 3.612e-02, eta: 2 days, 3:44:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6166, loss_cls: 3.6495, loss: 3.6495 +2024-07-19 12:45:45,491 - pyskl - INFO - Epoch [89][1700/3746] lr: 3.610e-02, eta: 2 days, 3:43:26, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6180, loss_cls: 3.6468, loss: 3.6468 +2024-07-19 12:47:08,081 - pyskl - INFO - Epoch [89][1800/3746] lr: 3.607e-02, eta: 2 days, 3:42:07, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6089, loss_cls: 3.7066, loss: 3.7066 +2024-07-19 12:48:30,653 - pyskl - INFO - Epoch [89][1900/3746] lr: 3.604e-02, eta: 2 days, 3:40:47, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6211, loss_cls: 3.6406, loss: 3.6406 +2024-07-19 12:49:53,258 - pyskl - INFO - Epoch [89][2000/3746] lr: 3.602e-02, eta: 2 days, 3:39:28, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6127, loss_cls: 3.6558, loss: 3.6558 +2024-07-19 12:51:15,352 - pyskl - INFO - Epoch [89][2100/3746] lr: 3.599e-02, eta: 2 days, 3:38:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.5995, loss_cls: 3.7289, loss: 3.7289 +2024-07-19 12:52:37,092 - pyskl - INFO - Epoch [89][2200/3746] lr: 3.596e-02, eta: 2 days, 3:36:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6005, loss_cls: 3.7178, loss: 3.7178 +2024-07-19 12:53:59,430 - pyskl - INFO - Epoch [89][2300/3746] lr: 3.594e-02, eta: 2 days, 3:35:28, time: 0.823, data_time: 0.001, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6155, loss_cls: 3.6476, loss: 3.6476 +2024-07-19 12:55:20,734 - pyskl - INFO - Epoch [89][2400/3746] lr: 3.591e-02, eta: 2 days, 3:34:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6081, loss_cls: 3.6898, loss: 3.6898 +2024-07-19 12:56:41,851 - pyskl - INFO - Epoch [89][2500/3746] lr: 3.588e-02, eta: 2 days, 3:32:47, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6173, loss_cls: 3.6680, loss: 3.6680 +2024-07-19 12:58:03,528 - pyskl - INFO - Epoch [89][2600/3746] lr: 3.586e-02, eta: 2 days, 3:31:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6117, loss_cls: 3.6712, loss: 3.6712 +2024-07-19 12:59:25,855 - pyskl - INFO - Epoch [89][2700/3746] lr: 3.583e-02, eta: 2 days, 3:30:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6178, loss_cls: 3.6314, loss: 3.6314 +2024-07-19 13:00:47,748 - pyskl - INFO - Epoch [89][2800/3746] lr: 3.580e-02, eta: 2 days, 3:28:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6097, loss_cls: 3.7080, loss: 3.7080 +2024-07-19 13:02:10,005 - pyskl - INFO - Epoch [89][2900/3746] lr: 3.578e-02, eta: 2 days, 3:27:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6120, loss_cls: 3.6769, loss: 3.6769 +2024-07-19 13:03:32,189 - pyskl - INFO - Epoch [89][3000/3746] lr: 3.575e-02, eta: 2 days, 3:26:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6064, loss_cls: 3.6998, loss: 3.6998 +2024-07-19 13:04:53,659 - pyskl - INFO - Epoch [89][3100/3746] lr: 3.572e-02, eta: 2 days, 3:24:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6156, loss_cls: 3.6421, loss: 3.6421 +2024-07-19 13:06:15,630 - pyskl - INFO - Epoch [89][3200/3746] lr: 3.569e-02, eta: 2 days, 3:23:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6153, loss_cls: 3.6545, loss: 3.6545 +2024-07-19 13:07:37,904 - pyskl - INFO - Epoch [89][3300/3746] lr: 3.567e-02, eta: 2 days, 3:22:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6069, loss_cls: 3.6871, loss: 3.6871 +2024-07-19 13:09:00,334 - pyskl - INFO - Epoch [89][3400/3746] lr: 3.564e-02, eta: 2 days, 3:20:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6194, loss_cls: 3.6737, loss: 3.6737 +2024-07-19 13:10:22,015 - pyskl - INFO - Epoch [89][3500/3746] lr: 3.561e-02, eta: 2 days, 3:19:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.6102, loss_cls: 3.6753, loss: 3.6753 +2024-07-19 13:11:43,671 - pyskl - INFO - Epoch [89][3600/3746] lr: 3.559e-02, eta: 2 days, 3:18:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6208, loss_cls: 3.6431, loss: 3.6431 +2024-07-19 13:13:04,924 - pyskl - INFO - Epoch [89][3700/3746] lr: 3.556e-02, eta: 2 days, 3:16:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6097, loss_cls: 3.6694, loss: 3.6694 +2024-07-19 13:13:44,054 - pyskl - INFO - Saving checkpoint at 89 epochs +2024-07-19 13:15:33,516 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 13:15:34,179 - pyskl - INFO - +top1_acc 0.2612 +top5_acc 0.5130 +2024-07-19 13:15:34,179 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 13:15:34,218 - pyskl - INFO - +mean_acc 0.2610 +2024-07-19 13:15:34,229 - pyskl - INFO - Epoch(val) [89][309] top1_acc: 0.2612, top5_acc: 0.5130, mean_class_accuracy: 0.2610 +2024-07-19 13:19:23,716 - pyskl - INFO - Epoch [90][100/3746] lr: 3.552e-02, eta: 2 days, 3:16:06, time: 2.295, data_time: 1.316, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6291, loss_cls: 3.5830, loss: 3.5830 +2024-07-19 13:20:46,014 - pyskl - INFO - Epoch [90][200/3746] lr: 3.550e-02, eta: 2 days, 3:14:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6309, loss_cls: 3.5750, loss: 3.5750 +2024-07-19 13:22:08,601 - pyskl - INFO - Epoch [90][300/3746] lr: 3.547e-02, eta: 2 days, 3:13:27, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6253, loss_cls: 3.5911, loss: 3.5911 +2024-07-19 13:23:30,105 - pyskl - INFO - Epoch [90][400/3746] lr: 3.544e-02, eta: 2 days, 3:12:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6216, loss_cls: 3.6408, loss: 3.6408 +2024-07-19 13:24:51,990 - pyskl - INFO - Epoch [90][500/3746] lr: 3.541e-02, eta: 2 days, 3:10:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6130, loss_cls: 3.6609, loss: 3.6609 +2024-07-19 13:26:13,484 - pyskl - INFO - Epoch [90][600/3746] lr: 3.539e-02, eta: 2 days, 3:09:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6244, loss_cls: 3.5970, loss: 3.5970 +2024-07-19 13:27:34,570 - pyskl - INFO - Epoch [90][700/3746] lr: 3.536e-02, eta: 2 days, 3:08:05, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6206, loss_cls: 3.6168, loss: 3.6168 +2024-07-19 13:28:56,000 - pyskl - INFO - Epoch [90][800/3746] lr: 3.533e-02, eta: 2 days, 3:06:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6150, loss_cls: 3.6604, loss: 3.6604 +2024-07-19 13:30:17,846 - pyskl - INFO - Epoch [90][900/3746] lr: 3.531e-02, eta: 2 days, 3:05:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6119, loss_cls: 3.6733, loss: 3.6733 +2024-07-19 13:31:40,305 - pyskl - INFO - Epoch [90][1000/3746] lr: 3.528e-02, eta: 2 days, 3:04:05, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6119, loss_cls: 3.6736, loss: 3.6736 +2024-07-19 13:33:02,512 - pyskl - INFO - Epoch [90][1100/3746] lr: 3.525e-02, eta: 2 days, 3:02:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6155, loss_cls: 3.6616, loss: 3.6616 +2024-07-19 13:34:24,729 - pyskl - INFO - Epoch [90][1200/3746] lr: 3.523e-02, eta: 2 days, 3:01:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6095, loss_cls: 3.6975, loss: 3.6975 +2024-07-19 13:35:46,760 - pyskl - INFO - Epoch [90][1300/3746] lr: 3.520e-02, eta: 2 days, 3:00:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6236, loss_cls: 3.6110, loss: 3.6110 +2024-07-19 13:37:08,219 - pyskl - INFO - Epoch [90][1400/3746] lr: 3.517e-02, eta: 2 days, 2:58:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6130, loss_cls: 3.6427, loss: 3.6427 +2024-07-19 13:38:31,035 - pyskl - INFO - Epoch [90][1500/3746] lr: 3.515e-02, eta: 2 days, 2:57:26, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6228, loss_cls: 3.6259, loss: 3.6259 +2024-07-19 13:39:52,797 - pyskl - INFO - Epoch [90][1600/3746] lr: 3.512e-02, eta: 2 days, 2:56:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6228, loss_cls: 3.6297, loss: 3.6297 +2024-07-19 13:41:15,179 - pyskl - INFO - Epoch [90][1700/3746] lr: 3.509e-02, eta: 2 days, 2:54:46, time: 0.824, data_time: 0.001, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6062, loss_cls: 3.6645, loss: 3.6645 +2024-07-19 13:42:38,595 - pyskl - INFO - Epoch [90][1800/3746] lr: 3.507e-02, eta: 2 days, 2:53:27, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6133, loss_cls: 3.6758, loss: 3.6758 +2024-07-19 13:44:01,299 - pyskl - INFO - Epoch [90][1900/3746] lr: 3.504e-02, eta: 2 days, 2:52:07, time: 0.827, data_time: 0.001, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6120, loss_cls: 3.7020, loss: 3.7020 +2024-07-19 13:45:23,302 - pyskl - INFO - Epoch [90][2000/3746] lr: 3.501e-02, eta: 2 days, 2:50:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6119, loss_cls: 3.6826, loss: 3.6826 +2024-07-19 13:46:45,649 - pyskl - INFO - Epoch [90][2100/3746] lr: 3.499e-02, eta: 2 days, 2:49:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6275, loss_cls: 3.5988, loss: 3.5988 +2024-07-19 13:48:07,560 - pyskl - INFO - Epoch [90][2200/3746] lr: 3.496e-02, eta: 2 days, 2:48:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6130, loss_cls: 3.6798, loss: 3.6798 +2024-07-19 13:49:29,633 - pyskl - INFO - Epoch [90][2300/3746] lr: 3.493e-02, eta: 2 days, 2:46:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6164, loss_cls: 3.6736, loss: 3.6736 +2024-07-19 13:50:51,668 - pyskl - INFO - Epoch [90][2400/3746] lr: 3.491e-02, eta: 2 days, 2:45:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6022, loss_cls: 3.6811, loss: 3.6811 +2024-07-19 13:52:13,161 - pyskl - INFO - Epoch [90][2500/3746] lr: 3.488e-02, eta: 2 days, 2:44:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6125, loss_cls: 3.6712, loss: 3.6712 +2024-07-19 13:53:35,974 - pyskl - INFO - Epoch [90][2600/3746] lr: 3.485e-02, eta: 2 days, 2:42:48, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6080, loss_cls: 3.6547, loss: 3.6547 +2024-07-19 13:54:58,099 - pyskl - INFO - Epoch [90][2700/3746] lr: 3.483e-02, eta: 2 days, 2:41:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6098, loss_cls: 3.6931, loss: 3.6931 +2024-07-19 13:56:20,215 - pyskl - INFO - Epoch [90][2800/3746] lr: 3.480e-02, eta: 2 days, 2:40:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6147, loss_cls: 3.6510, loss: 3.6510 +2024-07-19 13:57:42,300 - pyskl - INFO - Epoch [90][2900/3746] lr: 3.477e-02, eta: 2 days, 2:38:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6188, loss_cls: 3.6510, loss: 3.6510 +2024-07-19 13:59:04,528 - pyskl - INFO - Epoch [90][3000/3746] lr: 3.475e-02, eta: 2 days, 2:37:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6128, loss_cls: 3.6921, loss: 3.6921 +2024-07-19 14:00:26,144 - pyskl - INFO - Epoch [90][3100/3746] lr: 3.472e-02, eta: 2 days, 2:36:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6206, loss_cls: 3.6306, loss: 3.6306 +2024-07-19 14:01:48,355 - pyskl - INFO - Epoch [90][3200/3746] lr: 3.469e-02, eta: 2 days, 2:34:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6162, loss_cls: 3.6656, loss: 3.6656 +2024-07-19 14:03:10,191 - pyskl - INFO - Epoch [90][3300/3746] lr: 3.467e-02, eta: 2 days, 2:33:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6203, loss_cls: 3.6357, loss: 3.6357 +2024-07-19 14:04:31,918 - pyskl - INFO - Epoch [90][3400/3746] lr: 3.464e-02, eta: 2 days, 2:32:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6144, loss_cls: 3.6664, loss: 3.6664 +2024-07-19 14:05:53,162 - pyskl - INFO - Epoch [90][3500/3746] lr: 3.461e-02, eta: 2 days, 2:30:47, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6167, loss_cls: 3.6449, loss: 3.6449 +2024-07-19 14:07:15,026 - pyskl - INFO - Epoch [90][3600/3746] lr: 3.459e-02, eta: 2 days, 2:29:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6164, loss_cls: 3.6694, loss: 3.6694 +2024-07-19 14:08:37,007 - pyskl - INFO - Epoch [90][3700/3746] lr: 3.456e-02, eta: 2 days, 2:28:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6120, loss_cls: 3.6788, loss: 3.6788 +2024-07-19 14:09:16,580 - pyskl - INFO - Saving checkpoint at 90 epochs +2024-07-19 14:11:07,177 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 14:11:07,842 - pyskl - INFO - +top1_acc 0.2845 +top5_acc 0.5362 +2024-07-19 14:11:07,842 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 14:11:07,881 - pyskl - INFO - +mean_acc 0.2844 +2024-07-19 14:11:07,886 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_87.pth was removed +2024-07-19 14:11:08,135 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_90.pth. +2024-07-19 14:11:08,135 - pyskl - INFO - Best top1_acc is 0.2845 at 90 epoch. +2024-07-19 14:11:08,146 - pyskl - INFO - Epoch(val) [90][309] top1_acc: 0.2845, top5_acc: 0.5362, mean_class_accuracy: 0.2844 +2024-07-19 14:14:53,552 - pyskl - INFO - Epoch [91][100/3746] lr: 3.452e-02, eta: 2 days, 2:27:20, time: 2.254, data_time: 1.278, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6330, loss_cls: 3.5773, loss: 3.5773 +2024-07-19 14:16:16,594 - pyskl - INFO - Epoch [91][200/3746] lr: 3.450e-02, eta: 2 days, 2:26:01, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6308, loss_cls: 3.5775, loss: 3.5775 +2024-07-19 14:17:39,914 - pyskl - INFO - Epoch [91][300/3746] lr: 3.447e-02, eta: 2 days, 2:24:42, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6258, loss_cls: 3.6175, loss: 3.6175 +2024-07-19 14:19:02,779 - pyskl - INFO - Epoch [91][400/3746] lr: 3.444e-02, eta: 2 days, 2:23:22, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6288, loss_cls: 3.5738, loss: 3.5738 +2024-07-19 14:20:25,665 - pyskl - INFO - Epoch [91][500/3746] lr: 3.442e-02, eta: 2 days, 2:22:03, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6253, loss_cls: 3.5877, loss: 3.5877 +2024-07-19 14:21:48,900 - pyskl - INFO - Epoch [91][600/3746] lr: 3.439e-02, eta: 2 days, 2:20:43, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6341, loss_cls: 3.5594, loss: 3.5594 +2024-07-19 14:23:11,583 - pyskl - INFO - Epoch [91][700/3746] lr: 3.436e-02, eta: 2 days, 2:19:24, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6212, loss_cls: 3.6153, loss: 3.6153 +2024-07-19 14:24:33,944 - pyskl - INFO - Epoch [91][800/3746] lr: 3.434e-02, eta: 2 days, 2:18:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6295, loss_cls: 3.6020, loss: 3.6020 +2024-07-19 14:25:56,743 - pyskl - INFO - Epoch [91][900/3746] lr: 3.431e-02, eta: 2 days, 2:16:44, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6219, loss_cls: 3.6262, loss: 3.6262 +2024-07-19 14:27:19,651 - pyskl - INFO - Epoch [91][1000/3746] lr: 3.428e-02, eta: 2 days, 2:15:25, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6245, loss_cls: 3.5970, loss: 3.5970 +2024-07-19 14:28:42,521 - pyskl - INFO - Epoch [91][1100/3746] lr: 3.426e-02, eta: 2 days, 2:14:05, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6248, loss_cls: 3.5912, loss: 3.5912 +2024-07-19 14:30:05,641 - pyskl - INFO - Epoch [91][1200/3746] lr: 3.423e-02, eta: 2 days, 2:12:46, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6289, loss_cls: 3.5937, loss: 3.5937 +2024-07-19 14:31:28,326 - pyskl - INFO - Epoch [91][1300/3746] lr: 3.420e-02, eta: 2 days, 2:11:26, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6128, loss_cls: 3.6533, loss: 3.6533 +2024-07-19 14:32:50,444 - pyskl - INFO - Epoch [91][1400/3746] lr: 3.418e-02, eta: 2 days, 2:10:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6253, loss_cls: 3.6226, loss: 3.6226 +2024-07-19 14:34:13,352 - pyskl - INFO - Epoch [91][1500/3746] lr: 3.415e-02, eta: 2 days, 2:08:47, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6066, loss_cls: 3.6934, loss: 3.6934 +2024-07-19 14:35:35,332 - pyskl - INFO - Epoch [91][1600/3746] lr: 3.412e-02, eta: 2 days, 2:07:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6188, loss_cls: 3.5979, loss: 3.5979 +2024-07-19 14:36:57,956 - pyskl - INFO - Epoch [91][1700/3746] lr: 3.410e-02, eta: 2 days, 2:06:07, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6162, loss_cls: 3.6270, loss: 3.6270 +2024-07-19 14:38:20,658 - pyskl - INFO - Epoch [91][1800/3746] lr: 3.407e-02, eta: 2 days, 2:04:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6097, loss_cls: 3.6920, loss: 3.6920 +2024-07-19 14:39:43,862 - pyskl - INFO - Epoch [91][1900/3746] lr: 3.405e-02, eta: 2 days, 2:03:28, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6244, loss_cls: 3.6410, loss: 3.6410 +2024-07-19 14:41:06,764 - pyskl - INFO - Epoch [91][2000/3746] lr: 3.402e-02, eta: 2 days, 2:02:08, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.6106, loss_cls: 3.7182, loss: 3.7182 +2024-07-19 14:42:29,503 - pyskl - INFO - Epoch [91][2100/3746] lr: 3.399e-02, eta: 2 days, 2:00:49, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6131, loss_cls: 3.6503, loss: 3.6503 +2024-07-19 14:43:52,488 - pyskl - INFO - Epoch [91][2200/3746] lr: 3.397e-02, eta: 2 days, 1:59:29, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6127, loss_cls: 3.6620, loss: 3.6620 +2024-07-19 14:45:15,628 - pyskl - INFO - Epoch [91][2300/3746] lr: 3.394e-02, eta: 2 days, 1:58:10, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6028, loss_cls: 3.6681, loss: 3.6681 +2024-07-19 14:46:38,013 - pyskl - INFO - Epoch [91][2400/3746] lr: 3.391e-02, eta: 2 days, 1:56:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6216, loss_cls: 3.6236, loss: 3.6236 +2024-07-19 14:47:59,953 - pyskl - INFO - Epoch [91][2500/3746] lr: 3.389e-02, eta: 2 days, 1:55:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6161, loss_cls: 3.6560, loss: 3.6560 +2024-07-19 14:49:22,911 - pyskl - INFO - Epoch [91][2600/3746] lr: 3.386e-02, eta: 2 days, 1:54:10, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6191, loss_cls: 3.6289, loss: 3.6289 +2024-07-19 14:50:45,416 - pyskl - INFO - Epoch [91][2700/3746] lr: 3.383e-02, eta: 2 days, 1:52:51, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6039, loss_cls: 3.7011, loss: 3.7011 +2024-07-19 14:52:08,295 - pyskl - INFO - Epoch [91][2800/3746] lr: 3.381e-02, eta: 2 days, 1:51:31, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6214, loss_cls: 3.6524, loss: 3.6524 +2024-07-19 14:53:31,133 - pyskl - INFO - Epoch [91][2900/3746] lr: 3.378e-02, eta: 2 days, 1:50:11, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6223, loss_cls: 3.6289, loss: 3.6289 +2024-07-19 14:54:53,987 - pyskl - INFO - Epoch [91][3000/3746] lr: 3.375e-02, eta: 2 days, 1:48:52, time: 0.829, data_time: 0.001, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6089, loss_cls: 3.6749, loss: 3.6749 +2024-07-19 14:56:17,400 - pyskl - INFO - Epoch [91][3100/3746] lr: 3.373e-02, eta: 2 days, 1:47:33, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6097, loss_cls: 3.6949, loss: 3.6949 +2024-07-19 14:57:39,904 - pyskl - INFO - Epoch [91][3200/3746] lr: 3.370e-02, eta: 2 days, 1:46:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6144, loss_cls: 3.6282, loss: 3.6282 +2024-07-19 14:59:02,195 - pyskl - INFO - Epoch [91][3300/3746] lr: 3.367e-02, eta: 2 days, 1:44:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6227, loss_cls: 3.6104, loss: 3.6104 +2024-07-19 15:00:25,634 - pyskl - INFO - Epoch [91][3400/3746] lr: 3.365e-02, eta: 2 days, 1:43:34, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6097, loss_cls: 3.6695, loss: 3.6695 +2024-07-19 15:01:48,358 - pyskl - INFO - Epoch [91][3500/3746] lr: 3.362e-02, eta: 2 days, 1:42:14, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6072, loss_cls: 3.6819, loss: 3.6819 +2024-07-19 15:03:10,871 - pyskl - INFO - Epoch [91][3600/3746] lr: 3.360e-02, eta: 2 days, 1:40:54, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6106, loss_cls: 3.6933, loss: 3.6933 +2024-07-19 15:04:33,815 - pyskl - INFO - Epoch [91][3700/3746] lr: 3.357e-02, eta: 2 days, 1:39:35, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6198, loss_cls: 3.6473, loss: 3.6473 +2024-07-19 15:05:13,339 - pyskl - INFO - Saving checkpoint at 91 epochs +2024-07-19 15:07:05,831 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 15:07:06,566 - pyskl - INFO - +top1_acc 0.2756 +top5_acc 0.5346 +2024-07-19 15:07:06,567 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 15:07:06,610 - pyskl - INFO - +mean_acc 0.2752 +2024-07-19 15:07:06,621 - pyskl - INFO - Epoch(val) [91][309] top1_acc: 0.2756, top5_acc: 0.5346, mean_class_accuracy: 0.2752 +2024-07-19 15:11:01,524 - pyskl - INFO - Epoch [92][100/3746] lr: 3.353e-02, eta: 2 days, 1:38:52, time: 2.349, data_time: 1.347, memory: 15990, top1_acc: 0.3720, top5_acc: 0.6247, loss_cls: 3.5733, loss: 3.5733 +2024-07-19 15:12:25,236 - pyskl - INFO - Epoch [92][200/3746] lr: 3.350e-02, eta: 2 days, 1:37:33, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6230, loss_cls: 3.5953, loss: 3.5953 +2024-07-19 15:13:48,744 - pyskl - INFO - Epoch [92][300/3746] lr: 3.348e-02, eta: 2 days, 1:36:14, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6261, loss_cls: 3.5703, loss: 3.5703 +2024-07-19 15:15:11,580 - pyskl - INFO - Epoch [92][400/3746] lr: 3.345e-02, eta: 2 days, 1:34:54, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6322, loss_cls: 3.5972, loss: 3.5972 +2024-07-19 15:16:35,127 - pyskl - INFO - Epoch [92][500/3746] lr: 3.342e-02, eta: 2 days, 1:33:35, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6127, loss_cls: 3.6704, loss: 3.6704 +2024-07-19 15:17:58,552 - pyskl - INFO - Epoch [92][600/3746] lr: 3.340e-02, eta: 2 days, 1:32:16, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6328, loss_cls: 3.5588, loss: 3.5588 +2024-07-19 15:19:22,060 - pyskl - INFO - Epoch [92][700/3746] lr: 3.337e-02, eta: 2 days, 1:30:57, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6284, loss_cls: 3.5969, loss: 3.5969 +2024-07-19 15:20:45,538 - pyskl - INFO - Epoch [92][800/3746] lr: 3.335e-02, eta: 2 days, 1:29:37, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6128, loss_cls: 3.6543, loss: 3.6543 +2024-07-19 15:22:08,935 - pyskl - INFO - Epoch [92][900/3746] lr: 3.332e-02, eta: 2 days, 1:28:18, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6255, loss_cls: 3.6142, loss: 3.6142 +2024-07-19 15:23:32,653 - pyskl - INFO - Epoch [92][1000/3746] lr: 3.329e-02, eta: 2 days, 1:26:59, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6250, loss_cls: 3.5984, loss: 3.5984 +2024-07-19 15:24:55,899 - pyskl - INFO - Epoch [92][1100/3746] lr: 3.327e-02, eta: 2 days, 1:25:39, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6086, loss_cls: 3.6758, loss: 3.6758 +2024-07-19 15:26:18,792 - pyskl - INFO - Epoch [92][1200/3746] lr: 3.324e-02, eta: 2 days, 1:24:20, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6209, loss_cls: 3.6448, loss: 3.6448 +2024-07-19 15:27:41,404 - pyskl - INFO - Epoch [92][1300/3746] lr: 3.321e-02, eta: 2 days, 1:23:00, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6295, loss_cls: 3.5922, loss: 3.5922 +2024-07-19 15:29:03,735 - pyskl - INFO - Epoch [92][1400/3746] lr: 3.319e-02, eta: 2 days, 1:21:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6227, loss_cls: 3.6302, loss: 3.6302 +2024-07-19 15:30:26,431 - pyskl - INFO - Epoch [92][1500/3746] lr: 3.316e-02, eta: 2 days, 1:20:20, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6233, loss_cls: 3.6290, loss: 3.6290 +2024-07-19 15:31:48,357 - pyskl - INFO - Epoch [92][1600/3746] lr: 3.314e-02, eta: 2 days, 1:19:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6256, loss_cls: 3.6111, loss: 3.6111 +2024-07-19 15:33:10,401 - pyskl - INFO - Epoch [92][1700/3746] lr: 3.311e-02, eta: 2 days, 1:17:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6153, loss_cls: 3.6685, loss: 3.6685 +2024-07-19 15:34:32,652 - pyskl - INFO - Epoch [92][1800/3746] lr: 3.308e-02, eta: 2 days, 1:16:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6234, loss_cls: 3.5985, loss: 3.5985 +2024-07-19 15:35:55,294 - pyskl - INFO - Epoch [92][1900/3746] lr: 3.306e-02, eta: 2 days, 1:15:00, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6289, loss_cls: 3.5841, loss: 3.5841 +2024-07-19 15:37:17,247 - pyskl - INFO - Epoch [92][2000/3746] lr: 3.303e-02, eta: 2 days, 1:13:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6228, loss_cls: 3.6085, loss: 3.6085 +2024-07-19 15:38:38,935 - pyskl - INFO - Epoch [92][2100/3746] lr: 3.300e-02, eta: 2 days, 1:12:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6181, loss_cls: 3.6434, loss: 3.6434 +2024-07-19 15:40:01,540 - pyskl - INFO - Epoch [92][2200/3746] lr: 3.298e-02, eta: 2 days, 1:10:59, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6100, loss_cls: 3.6715, loss: 3.6715 +2024-07-19 15:41:23,143 - pyskl - INFO - Epoch [92][2300/3746] lr: 3.295e-02, eta: 2 days, 1:09:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6223, loss_cls: 3.6373, loss: 3.6373 +2024-07-19 15:42:44,695 - pyskl - INFO - Epoch [92][2400/3746] lr: 3.292e-02, eta: 2 days, 1:08:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6223, loss_cls: 3.6181, loss: 3.6181 +2024-07-19 15:44:06,481 - pyskl - INFO - Epoch [92][2500/3746] lr: 3.290e-02, eta: 2 days, 1:06:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6170, loss_cls: 3.6455, loss: 3.6455 +2024-07-19 15:45:28,319 - pyskl - INFO - Epoch [92][2600/3746] lr: 3.287e-02, eta: 2 days, 1:05:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6136, loss_cls: 3.6598, loss: 3.6598 +2024-07-19 15:46:50,701 - pyskl - INFO - Epoch [92][2700/3746] lr: 3.285e-02, eta: 2 days, 1:04:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6167, loss_cls: 3.6329, loss: 3.6329 +2024-07-19 15:48:12,941 - pyskl - INFO - Epoch [92][2800/3746] lr: 3.282e-02, eta: 2 days, 1:02:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6225, loss_cls: 3.6005, loss: 3.6005 +2024-07-19 15:49:35,418 - pyskl - INFO - Epoch [92][2900/3746] lr: 3.279e-02, eta: 2 days, 1:01:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6245, loss_cls: 3.6061, loss: 3.6061 +2024-07-19 15:50:57,838 - pyskl - INFO - Epoch [92][3000/3746] lr: 3.277e-02, eta: 2 days, 1:00:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6145, loss_cls: 3.6689, loss: 3.6689 +2024-07-19 15:52:20,053 - pyskl - INFO - Epoch [92][3100/3746] lr: 3.274e-02, eta: 2 days, 0:58:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6248, loss_cls: 3.6490, loss: 3.6490 +2024-07-19 15:53:41,893 - pyskl - INFO - Epoch [92][3200/3746] lr: 3.271e-02, eta: 2 days, 0:57:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6308, loss_cls: 3.5948, loss: 3.5948 +2024-07-19 15:55:03,633 - pyskl - INFO - Epoch [92][3300/3746] lr: 3.269e-02, eta: 2 days, 0:56:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6109, loss_cls: 3.6494, loss: 3.6494 +2024-07-19 15:56:25,442 - pyskl - INFO - Epoch [92][3400/3746] lr: 3.266e-02, eta: 2 days, 0:54:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6175, loss_cls: 3.6383, loss: 3.6383 +2024-07-19 15:57:47,221 - pyskl - INFO - Epoch [92][3500/3746] lr: 3.264e-02, eta: 2 days, 0:53:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6145, loss_cls: 3.6590, loss: 3.6590 +2024-07-19 15:59:09,124 - pyskl - INFO - Epoch [92][3600/3746] lr: 3.261e-02, eta: 2 days, 0:52:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6184, loss_cls: 3.5945, loss: 3.5945 +2024-07-19 16:00:31,330 - pyskl - INFO - Epoch [92][3700/3746] lr: 3.258e-02, eta: 2 days, 0:50:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6334, loss_cls: 3.5538, loss: 3.5538 +2024-07-19 16:01:11,069 - pyskl - INFO - Saving checkpoint at 92 epochs +2024-07-19 16:03:03,474 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 16:03:04,136 - pyskl - INFO - +top1_acc 0.2789 +top5_acc 0.5313 +2024-07-19 16:03:04,136 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 16:03:04,177 - pyskl - INFO - +mean_acc 0.2786 +2024-07-19 16:03:04,190 - pyskl - INFO - Epoch(val) [92][309] top1_acc: 0.2789, top5_acc: 0.5313, mean_class_accuracy: 0.2786 +2024-07-19 16:06:55,958 - pyskl - INFO - Epoch [93][100/3746] lr: 3.255e-02, eta: 2 days, 0:50:09, time: 2.318, data_time: 1.329, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6338, loss_cls: 3.5484, loss: 3.5484 +2024-07-19 16:08:18,333 - pyskl - INFO - Epoch [93][200/3746] lr: 3.252e-02, eta: 2 days, 0:48:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3656, top5_acc: 0.6366, loss_cls: 3.5632, loss: 3.5632 +2024-07-19 16:09:40,432 - pyskl - INFO - Epoch [93][300/3746] lr: 3.249e-02, eta: 2 days, 0:47:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3656, top5_acc: 0.6270, loss_cls: 3.5754, loss: 3.5754 +2024-07-19 16:11:02,401 - pyskl - INFO - Epoch [93][400/3746] lr: 3.247e-02, eta: 2 days, 0:46:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6247, loss_cls: 3.6119, loss: 3.6119 +2024-07-19 16:12:24,466 - pyskl - INFO - Epoch [93][500/3746] lr: 3.244e-02, eta: 2 days, 0:44:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6300, loss_cls: 3.5813, loss: 3.5813 +2024-07-19 16:13:46,155 - pyskl - INFO - Epoch [93][600/3746] lr: 3.241e-02, eta: 2 days, 0:43:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6231, loss_cls: 3.6009, loss: 3.6009 +2024-07-19 16:15:08,429 - pyskl - INFO - Epoch [93][700/3746] lr: 3.239e-02, eta: 2 days, 0:42:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6288, loss_cls: 3.5995, loss: 3.5995 +2024-07-19 16:16:30,100 - pyskl - INFO - Epoch [93][800/3746] lr: 3.236e-02, eta: 2 days, 0:40:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6327, loss_cls: 3.5947, loss: 3.5947 +2024-07-19 16:17:51,731 - pyskl - INFO - Epoch [93][900/3746] lr: 3.234e-02, eta: 2 days, 0:39:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3622, top5_acc: 0.6180, loss_cls: 3.6354, loss: 3.6354 +2024-07-19 16:19:13,439 - pyskl - INFO - Epoch [93][1000/3746] lr: 3.231e-02, eta: 2 days, 0:38:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6364, loss_cls: 3.5438, loss: 3.5438 +2024-07-19 16:20:35,109 - pyskl - INFO - Epoch [93][1100/3746] lr: 3.228e-02, eta: 2 days, 0:36:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6223, loss_cls: 3.6072, loss: 3.6072 +2024-07-19 16:21:56,718 - pyskl - INFO - Epoch [93][1200/3746] lr: 3.226e-02, eta: 2 days, 0:35:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6306, loss_cls: 3.5757, loss: 3.5757 +2024-07-19 16:23:18,724 - pyskl - INFO - Epoch [93][1300/3746] lr: 3.223e-02, eta: 2 days, 0:34:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6297, loss_cls: 3.5583, loss: 3.5583 +2024-07-19 16:24:40,995 - pyskl - INFO - Epoch [93][1400/3746] lr: 3.221e-02, eta: 2 days, 0:32:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6280, loss_cls: 3.5756, loss: 3.5756 +2024-07-19 16:26:02,975 - pyskl - INFO - Epoch [93][1500/3746] lr: 3.218e-02, eta: 2 days, 0:31:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6225, loss_cls: 3.6316, loss: 3.6316 +2024-07-19 16:27:25,767 - pyskl - INFO - Epoch [93][1600/3746] lr: 3.215e-02, eta: 2 days, 0:30:04, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6216, loss_cls: 3.6335, loss: 3.6335 +2024-07-19 16:28:47,985 - pyskl - INFO - Epoch [93][1700/3746] lr: 3.213e-02, eta: 2 days, 0:28:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6280, loss_cls: 3.5838, loss: 3.5838 +2024-07-19 16:30:09,925 - pyskl - INFO - Epoch [93][1800/3746] lr: 3.210e-02, eta: 2 days, 0:27:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6255, loss_cls: 3.5927, loss: 3.5927 +2024-07-19 16:31:31,741 - pyskl - INFO - Epoch [93][1900/3746] lr: 3.207e-02, eta: 2 days, 0:26:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6252, loss_cls: 3.5822, loss: 3.5822 +2024-07-19 16:32:53,721 - pyskl - INFO - Epoch [93][2000/3746] lr: 3.205e-02, eta: 2 days, 0:24:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6284, loss_cls: 3.6009, loss: 3.6009 +2024-07-19 16:34:15,841 - pyskl - INFO - Epoch [93][2100/3746] lr: 3.202e-02, eta: 2 days, 0:23:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6294, loss_cls: 3.5795, loss: 3.5795 +2024-07-19 16:35:37,864 - pyskl - INFO - Epoch [93][2200/3746] lr: 3.200e-02, eta: 2 days, 0:22:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6127, loss_cls: 3.6206, loss: 3.6206 +2024-07-19 16:36:59,481 - pyskl - INFO - Epoch [93][2300/3746] lr: 3.197e-02, eta: 2 days, 0:20:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6330, loss_cls: 3.5885, loss: 3.5885 +2024-07-19 16:38:21,481 - pyskl - INFO - Epoch [93][2400/3746] lr: 3.194e-02, eta: 2 days, 0:19:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6195, loss_cls: 3.6071, loss: 3.6071 +2024-07-19 16:39:43,332 - pyskl - INFO - Epoch [93][2500/3746] lr: 3.192e-02, eta: 2 days, 0:18:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3691, top5_acc: 0.6302, loss_cls: 3.5825, loss: 3.5825 +2024-07-19 16:41:05,061 - pyskl - INFO - Epoch [93][2600/3746] lr: 3.189e-02, eta: 2 days, 0:16:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3656, top5_acc: 0.6316, loss_cls: 3.5516, loss: 3.5516 +2024-07-19 16:42:27,312 - pyskl - INFO - Epoch [93][2700/3746] lr: 3.187e-02, eta: 2 days, 0:15:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6233, loss_cls: 3.5847, loss: 3.5847 +2024-07-19 16:43:49,255 - pyskl - INFO - Epoch [93][2800/3746] lr: 3.184e-02, eta: 2 days, 0:14:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6216, loss_cls: 3.6405, loss: 3.6405 +2024-07-19 16:45:11,256 - pyskl - INFO - Epoch [93][2900/3746] lr: 3.181e-02, eta: 2 days, 0:12:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6272, loss_cls: 3.6023, loss: 3.6023 +2024-07-19 16:46:33,457 - pyskl - INFO - Epoch [93][3000/3746] lr: 3.179e-02, eta: 2 days, 0:11:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6256, loss_cls: 3.5559, loss: 3.5559 +2024-07-19 16:47:55,142 - pyskl - INFO - Epoch [93][3100/3746] lr: 3.176e-02, eta: 2 days, 0:09:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6100, loss_cls: 3.6560, loss: 3.6560 +2024-07-19 16:49:16,969 - pyskl - INFO - Epoch [93][3200/3746] lr: 3.174e-02, eta: 2 days, 0:08:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6175, loss_cls: 3.6823, loss: 3.6823 +2024-07-19 16:50:38,610 - pyskl - INFO - Epoch [93][3300/3746] lr: 3.171e-02, eta: 2 days, 0:07:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6189, loss_cls: 3.6086, loss: 3.6086 +2024-07-19 16:52:00,294 - pyskl - INFO - Epoch [93][3400/3746] lr: 3.168e-02, eta: 2 days, 0:05:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6114, loss_cls: 3.6773, loss: 3.6773 +2024-07-19 16:53:22,578 - pyskl - INFO - Epoch [93][3500/3746] lr: 3.166e-02, eta: 2 days, 0:04:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6208, loss_cls: 3.6227, loss: 3.6227 +2024-07-19 16:54:44,135 - pyskl - INFO - Epoch [93][3600/3746] lr: 3.163e-02, eta: 2 days, 0:03:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6195, loss_cls: 3.6630, loss: 3.6630 +2024-07-19 16:56:05,782 - pyskl - INFO - Epoch [93][3700/3746] lr: 3.161e-02, eta: 2 days, 0:01:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6133, loss_cls: 3.6423, loss: 3.6423 +2024-07-19 16:56:45,495 - pyskl - INFO - Saving checkpoint at 93 epochs +2024-07-19 16:58:36,850 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 16:58:37,526 - pyskl - INFO - +top1_acc 0.2962 +top5_acc 0.5527 +2024-07-19 16:58:37,526 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 16:58:37,572 - pyskl - INFO - +mean_acc 0.2959 +2024-07-19 16:58:37,577 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_90.pth was removed +2024-07-19 16:58:37,827 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_93.pth. +2024-07-19 16:58:37,827 - pyskl - INFO - Best top1_acc is 0.2962 at 93 epoch. +2024-07-19 16:58:37,841 - pyskl - INFO - Epoch(val) [93][309] top1_acc: 0.2962, top5_acc: 0.5527, mean_class_accuracy: 0.2959 +2024-07-19 17:02:26,931 - pyskl - INFO - Epoch [94][100/3746] lr: 3.157e-02, eta: 2 days, 0:01:06, time: 2.291, data_time: 1.304, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6350, loss_cls: 3.5500, loss: 3.5500 +2024-07-19 17:03:49,530 - pyskl - INFO - Epoch [94][200/3746] lr: 3.154e-02, eta: 1 day, 23:59:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6277, loss_cls: 3.5705, loss: 3.5705 +2024-07-19 17:05:11,455 - pyskl - INFO - Epoch [94][300/3746] lr: 3.152e-02, eta: 1 day, 23:58:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6414, loss_cls: 3.5193, loss: 3.5193 +2024-07-19 17:06:33,559 - pyskl - INFO - Epoch [94][400/3746] lr: 3.149e-02, eta: 1 day, 23:57:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6281, loss_cls: 3.5515, loss: 3.5515 +2024-07-19 17:07:55,269 - pyskl - INFO - Epoch [94][500/3746] lr: 3.146e-02, eta: 1 day, 23:55:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6236, loss_cls: 3.6018, loss: 3.6018 +2024-07-19 17:09:17,120 - pyskl - INFO - Epoch [94][600/3746] lr: 3.144e-02, eta: 1 day, 23:54:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6377, loss_cls: 3.5158, loss: 3.5158 +2024-07-19 17:10:38,563 - pyskl - INFO - Epoch [94][700/3746] lr: 3.141e-02, eta: 1 day, 23:53:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6275, loss_cls: 3.5882, loss: 3.5882 +2024-07-19 17:12:00,144 - pyskl - INFO - Epoch [94][800/3746] lr: 3.139e-02, eta: 1 day, 23:51:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6302, loss_cls: 3.5752, loss: 3.5752 +2024-07-19 17:13:22,063 - pyskl - INFO - Epoch [94][900/3746] lr: 3.136e-02, eta: 1 day, 23:50:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6223, loss_cls: 3.6301, loss: 3.6301 +2024-07-19 17:14:43,686 - pyskl - INFO - Epoch [94][1000/3746] lr: 3.133e-02, eta: 1 day, 23:49:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6211, loss_cls: 3.5970, loss: 3.5970 +2024-07-19 17:16:05,383 - pyskl - INFO - Epoch [94][1100/3746] lr: 3.131e-02, eta: 1 day, 23:47:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6269, loss_cls: 3.5575, loss: 3.5575 +2024-07-19 17:17:27,246 - pyskl - INFO - Epoch [94][1200/3746] lr: 3.128e-02, eta: 1 day, 23:46:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6200, loss_cls: 3.6120, loss: 3.6120 +2024-07-19 17:18:49,109 - pyskl - INFO - Epoch [94][1300/3746] lr: 3.126e-02, eta: 1 day, 23:45:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6241, loss_cls: 3.6090, loss: 3.6090 +2024-07-19 17:20:11,180 - pyskl - INFO - Epoch [94][1400/3746] lr: 3.123e-02, eta: 1 day, 23:43:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6186, loss_cls: 3.6141, loss: 3.6141 +2024-07-19 17:21:33,321 - pyskl - INFO - Epoch [94][1500/3746] lr: 3.120e-02, eta: 1 day, 23:42:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6284, loss_cls: 3.5725, loss: 3.5725 +2024-07-19 17:22:55,272 - pyskl - INFO - Epoch [94][1600/3746] lr: 3.118e-02, eta: 1 day, 23:40:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6253, loss_cls: 3.6043, loss: 3.6043 +2024-07-19 17:24:17,841 - pyskl - INFO - Epoch [94][1700/3746] lr: 3.115e-02, eta: 1 day, 23:39:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6277, loss_cls: 3.6100, loss: 3.6100 +2024-07-19 17:25:40,017 - pyskl - INFO - Epoch [94][1800/3746] lr: 3.113e-02, eta: 1 day, 23:38:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6192, loss_cls: 3.6297, loss: 3.6297 +2024-07-19 17:27:01,878 - pyskl - INFO - Epoch [94][1900/3746] lr: 3.110e-02, eta: 1 day, 23:36:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6228, loss_cls: 3.5788, loss: 3.5788 +2024-07-19 17:28:24,055 - pyskl - INFO - Epoch [94][2000/3746] lr: 3.108e-02, eta: 1 day, 23:35:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6309, loss_cls: 3.5740, loss: 3.5740 +2024-07-19 17:29:45,780 - pyskl - INFO - Epoch [94][2100/3746] lr: 3.105e-02, eta: 1 day, 23:34:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6161, loss_cls: 3.6276, loss: 3.6276 +2024-07-19 17:31:07,482 - pyskl - INFO - Epoch [94][2200/3746] lr: 3.102e-02, eta: 1 day, 23:32:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6253, loss_cls: 3.6151, loss: 3.6151 +2024-07-19 17:32:29,072 - pyskl - INFO - Epoch [94][2300/3746] lr: 3.100e-02, eta: 1 day, 23:31:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6227, loss_cls: 3.6081, loss: 3.6081 +2024-07-19 17:33:50,940 - pyskl - INFO - Epoch [94][2400/3746] lr: 3.097e-02, eta: 1 day, 23:30:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6205, loss_cls: 3.6081, loss: 3.6081 +2024-07-19 17:35:12,650 - pyskl - INFO - Epoch [94][2500/3746] lr: 3.095e-02, eta: 1 day, 23:28:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6242, loss_cls: 3.6015, loss: 3.6015 +2024-07-19 17:36:34,884 - pyskl - INFO - Epoch [94][2600/3746] lr: 3.092e-02, eta: 1 day, 23:27:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6219, loss_cls: 3.6160, loss: 3.6160 +2024-07-19 17:37:56,865 - pyskl - INFO - Epoch [94][2700/3746] lr: 3.089e-02, eta: 1 day, 23:26:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6166, loss_cls: 3.6506, loss: 3.6506 +2024-07-19 17:39:19,107 - pyskl - INFO - Epoch [94][2800/3746] lr: 3.087e-02, eta: 1 day, 23:24:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6273, loss_cls: 3.6162, loss: 3.6162 +2024-07-19 17:40:41,189 - pyskl - INFO - Epoch [94][2900/3746] lr: 3.084e-02, eta: 1 day, 23:23:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6319, loss_cls: 3.5832, loss: 3.5832 +2024-07-19 17:42:02,953 - pyskl - INFO - Epoch [94][3000/3746] lr: 3.082e-02, eta: 1 day, 23:22:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6323, loss_cls: 3.5707, loss: 3.5707 +2024-07-19 17:43:24,667 - pyskl - INFO - Epoch [94][3100/3746] lr: 3.079e-02, eta: 1 day, 23:20:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6322, loss_cls: 3.5758, loss: 3.5758 +2024-07-19 17:44:46,287 - pyskl - INFO - Epoch [94][3200/3746] lr: 3.077e-02, eta: 1 day, 23:19:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6267, loss_cls: 3.5903, loss: 3.5903 +2024-07-19 17:46:08,078 - pyskl - INFO - Epoch [94][3300/3746] lr: 3.074e-02, eta: 1 day, 23:18:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6378, loss_cls: 3.5458, loss: 3.5458 +2024-07-19 17:47:30,683 - pyskl - INFO - Epoch [94][3400/3746] lr: 3.071e-02, eta: 1 day, 23:16:52, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6083, loss_cls: 3.6523, loss: 3.6523 +2024-07-19 17:48:52,248 - pyskl - INFO - Epoch [94][3500/3746] lr: 3.069e-02, eta: 1 day, 23:15:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6283, loss_cls: 3.5867, loss: 3.5867 +2024-07-19 17:50:14,291 - pyskl - INFO - Epoch [94][3600/3746] lr: 3.066e-02, eta: 1 day, 23:14:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6195, loss_cls: 3.5965, loss: 3.5965 +2024-07-19 17:51:36,016 - pyskl - INFO - Epoch [94][3700/3746] lr: 3.064e-02, eta: 1 day, 23:12:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6430, loss_cls: 3.5190, loss: 3.5190 +2024-07-19 17:52:15,758 - pyskl - INFO - Saving checkpoint at 94 epochs +2024-07-19 17:54:05,668 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 17:54:06,333 - pyskl - INFO - +top1_acc 0.2852 +top5_acc 0.5377 +2024-07-19 17:54:06,333 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 17:54:06,375 - pyskl - INFO - +mean_acc 0.2850 +2024-07-19 17:54:06,387 - pyskl - INFO - Epoch(val) [94][309] top1_acc: 0.2852, top5_acc: 0.5377, mean_class_accuracy: 0.2850 +2024-07-19 17:57:56,180 - pyskl - INFO - Epoch [95][100/3746] lr: 3.060e-02, eta: 1 day, 23:11:58, time: 2.298, data_time: 1.322, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6409, loss_cls: 3.4953, loss: 3.4953 +2024-07-19 17:59:18,284 - pyskl - INFO - Epoch [95][200/3746] lr: 3.057e-02, eta: 1 day, 23:10:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6266, loss_cls: 3.5775, loss: 3.5775 +2024-07-19 18:00:40,971 - pyskl - INFO - Epoch [95][300/3746] lr: 3.055e-02, eta: 1 day, 23:09:18, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6362, loss_cls: 3.5174, loss: 3.5174 +2024-07-19 18:02:02,886 - pyskl - INFO - Epoch [95][400/3746] lr: 3.052e-02, eta: 1 day, 23:07:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6433, loss_cls: 3.5361, loss: 3.5361 +2024-07-19 18:03:25,498 - pyskl - INFO - Epoch [95][500/3746] lr: 3.050e-02, eta: 1 day, 23:06:37, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6303, loss_cls: 3.5614, loss: 3.5614 +2024-07-19 18:04:47,281 - pyskl - INFO - Epoch [95][600/3746] lr: 3.047e-02, eta: 1 day, 23:05:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6253, loss_cls: 3.5788, loss: 3.5788 +2024-07-19 18:06:09,474 - pyskl - INFO - Epoch [95][700/3746] lr: 3.044e-02, eta: 1 day, 23:03:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6230, loss_cls: 3.5906, loss: 3.5906 +2024-07-19 18:07:31,241 - pyskl - INFO - Epoch [95][800/3746] lr: 3.042e-02, eta: 1 day, 23:02:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6317, loss_cls: 3.5521, loss: 3.5521 +2024-07-19 18:08:52,873 - pyskl - INFO - Epoch [95][900/3746] lr: 3.039e-02, eta: 1 day, 23:01:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6428, loss_cls: 3.5068, loss: 3.5068 +2024-07-19 18:10:15,015 - pyskl - INFO - Epoch [95][1000/3746] lr: 3.037e-02, eta: 1 day, 22:59:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6447, loss_cls: 3.5182, loss: 3.5182 +2024-07-19 18:11:37,204 - pyskl - INFO - Epoch [95][1100/3746] lr: 3.034e-02, eta: 1 day, 22:58:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6203, loss_cls: 3.5763, loss: 3.5763 +2024-07-19 18:12:58,691 - pyskl - INFO - Epoch [95][1200/3746] lr: 3.032e-02, eta: 1 day, 22:57:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6297, loss_cls: 3.5770, loss: 3.5770 +2024-07-19 18:14:20,507 - pyskl - INFO - Epoch [95][1300/3746] lr: 3.029e-02, eta: 1 day, 22:55:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3588, top5_acc: 0.6278, loss_cls: 3.5786, loss: 3.5786 +2024-07-19 18:15:42,524 - pyskl - INFO - Epoch [95][1400/3746] lr: 3.026e-02, eta: 1 day, 22:54:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6239, loss_cls: 3.5848, loss: 3.5848 +2024-07-19 18:17:04,527 - pyskl - INFO - Epoch [95][1500/3746] lr: 3.024e-02, eta: 1 day, 22:53:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6252, loss_cls: 3.5934, loss: 3.5934 +2024-07-19 18:18:27,111 - pyskl - INFO - Epoch [95][1600/3746] lr: 3.021e-02, eta: 1 day, 22:51:52, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6317, loss_cls: 3.5610, loss: 3.5610 +2024-07-19 18:19:49,445 - pyskl - INFO - Epoch [95][1700/3746] lr: 3.019e-02, eta: 1 day, 22:50:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6267, loss_cls: 3.5778, loss: 3.5778 +2024-07-19 18:21:11,503 - pyskl - INFO - Epoch [95][1800/3746] lr: 3.016e-02, eta: 1 day, 22:49:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6370, loss_cls: 3.5358, loss: 3.5358 +2024-07-19 18:22:33,986 - pyskl - INFO - Epoch [95][1900/3746] lr: 3.014e-02, eta: 1 day, 22:47:51, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6277, loss_cls: 3.5677, loss: 3.5677 +2024-07-19 18:23:56,098 - pyskl - INFO - Epoch [95][2000/3746] lr: 3.011e-02, eta: 1 day, 22:46:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6247, loss_cls: 3.5846, loss: 3.5846 +2024-07-19 18:25:17,767 - pyskl - INFO - Epoch [95][2100/3746] lr: 3.008e-02, eta: 1 day, 22:45:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6294, loss_cls: 3.5841, loss: 3.5841 +2024-07-19 18:26:40,324 - pyskl - INFO - Epoch [95][2200/3746] lr: 3.006e-02, eta: 1 day, 22:43:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3691, top5_acc: 0.6216, loss_cls: 3.5977, loss: 3.5977 +2024-07-19 18:28:01,985 - pyskl - INFO - Epoch [95][2300/3746] lr: 3.003e-02, eta: 1 day, 22:42:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6394, loss_cls: 3.5385, loss: 3.5385 +2024-07-19 18:29:23,914 - pyskl - INFO - Epoch [95][2400/3746] lr: 3.001e-02, eta: 1 day, 22:41:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6167, loss_cls: 3.6291, loss: 3.6291 +2024-07-19 18:30:45,832 - pyskl - INFO - Epoch [95][2500/3746] lr: 2.998e-02, eta: 1 day, 22:39:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6270, loss_cls: 3.6053, loss: 3.6053 +2024-07-19 18:32:07,428 - pyskl - INFO - Epoch [95][2600/3746] lr: 2.996e-02, eta: 1 day, 22:38:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6361, loss_cls: 3.5648, loss: 3.5648 +2024-07-19 18:33:30,127 - pyskl - INFO - Epoch [95][2700/3746] lr: 2.993e-02, eta: 1 day, 22:37:07, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6264, loss_cls: 3.6104, loss: 3.6104 +2024-07-19 18:34:52,240 - pyskl - INFO - Epoch [95][2800/3746] lr: 2.991e-02, eta: 1 day, 22:35:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6170, loss_cls: 3.5747, loss: 3.5747 +2024-07-19 18:36:14,431 - pyskl - INFO - Epoch [95][2900/3746] lr: 2.988e-02, eta: 1 day, 22:34:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6183, loss_cls: 3.6375, loss: 3.6375 +2024-07-19 18:37:36,546 - pyskl - INFO - Epoch [95][3000/3746] lr: 2.985e-02, eta: 1 day, 22:33:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6355, loss_cls: 3.5563, loss: 3.5563 +2024-07-19 18:38:58,057 - pyskl - INFO - Epoch [95][3100/3746] lr: 2.983e-02, eta: 1 day, 22:31:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6272, loss_cls: 3.5876, loss: 3.5876 +2024-07-19 18:40:19,734 - pyskl - INFO - Epoch [95][3200/3746] lr: 2.980e-02, eta: 1 day, 22:30:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3588, top5_acc: 0.6217, loss_cls: 3.6170, loss: 3.6170 +2024-07-19 18:41:41,230 - pyskl - INFO - Epoch [95][3300/3746] lr: 2.978e-02, eta: 1 day, 22:29:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6308, loss_cls: 3.5331, loss: 3.5331 +2024-07-19 18:43:02,855 - pyskl - INFO - Epoch [95][3400/3746] lr: 2.975e-02, eta: 1 day, 22:27:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6300, loss_cls: 3.5933, loss: 3.5933 +2024-07-19 18:44:24,353 - pyskl - INFO - Epoch [95][3500/3746] lr: 2.973e-02, eta: 1 day, 22:26:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6327, loss_cls: 3.5674, loss: 3.5674 +2024-07-19 18:45:46,472 - pyskl - INFO - Epoch [95][3600/3746] lr: 2.970e-02, eta: 1 day, 22:25:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6195, loss_cls: 3.6446, loss: 3.6446 +2024-07-19 18:47:07,971 - pyskl - INFO - Epoch [95][3700/3746] lr: 2.968e-02, eta: 1 day, 22:23:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6273, loss_cls: 3.5896, loss: 3.5896 +2024-07-19 18:47:47,866 - pyskl - INFO - Saving checkpoint at 95 epochs +2024-07-19 18:49:38,510 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 18:49:39,174 - pyskl - INFO - +top1_acc 0.2722 +top5_acc 0.5323 +2024-07-19 18:49:39,174 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 18:49:39,216 - pyskl - INFO - +mean_acc 0.2721 +2024-07-19 18:49:39,228 - pyskl - INFO - Epoch(val) [95][309] top1_acc: 0.2722, top5_acc: 0.5323, mean_class_accuracy: 0.2721 +2024-07-19 18:53:29,562 - pyskl - INFO - Epoch [96][100/3746] lr: 2.964e-02, eta: 1 day, 22:22:47, time: 2.303, data_time: 1.323, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6373, loss_cls: 3.5148, loss: 3.5148 +2024-07-19 18:54:51,756 - pyskl - INFO - Epoch [96][200/3746] lr: 2.961e-02, eta: 1 day, 22:21:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6384, loss_cls: 3.4800, loss: 3.4800 +2024-07-19 18:56:13,826 - pyskl - INFO - Epoch [96][300/3746] lr: 2.959e-02, eta: 1 day, 22:20:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3691, top5_acc: 0.6388, loss_cls: 3.5279, loss: 3.5279 +2024-07-19 18:57:35,563 - pyskl - INFO - Epoch [96][400/3746] lr: 2.956e-02, eta: 1 day, 22:18:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6300, loss_cls: 3.5712, loss: 3.5712 +2024-07-19 18:58:57,264 - pyskl - INFO - Epoch [96][500/3746] lr: 2.954e-02, eta: 1 day, 22:17:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6308, loss_cls: 3.5942, loss: 3.5942 +2024-07-19 19:00:19,023 - pyskl - INFO - Epoch [96][600/3746] lr: 2.951e-02, eta: 1 day, 22:16:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6333, loss_cls: 3.5565, loss: 3.5565 +2024-07-19 19:01:40,655 - pyskl - INFO - Epoch [96][700/3746] lr: 2.948e-02, eta: 1 day, 22:14:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6406, loss_cls: 3.5027, loss: 3.5027 +2024-07-19 19:03:02,311 - pyskl - INFO - Epoch [96][800/3746] lr: 2.946e-02, eta: 1 day, 22:13:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6336, loss_cls: 3.5534, loss: 3.5534 +2024-07-19 19:04:24,314 - pyskl - INFO - Epoch [96][900/3746] lr: 2.943e-02, eta: 1 day, 22:12:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6416, loss_cls: 3.5453, loss: 3.5453 +2024-07-19 19:05:46,332 - pyskl - INFO - Epoch [96][1000/3746] lr: 2.941e-02, eta: 1 day, 22:10:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6270, loss_cls: 3.5907, loss: 3.5907 +2024-07-19 19:07:07,684 - pyskl - INFO - Epoch [96][1100/3746] lr: 2.938e-02, eta: 1 day, 22:09:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6322, loss_cls: 3.5358, loss: 3.5358 +2024-07-19 19:08:29,307 - pyskl - INFO - Epoch [96][1200/3746] lr: 2.936e-02, eta: 1 day, 22:08:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6302, loss_cls: 3.5907, loss: 3.5907 +2024-07-19 19:09:51,457 - pyskl - INFO - Epoch [96][1300/3746] lr: 2.933e-02, eta: 1 day, 22:06:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3741, top5_acc: 0.6338, loss_cls: 3.5269, loss: 3.5269 +2024-07-19 19:11:13,598 - pyskl - INFO - Epoch [96][1400/3746] lr: 2.931e-02, eta: 1 day, 22:05:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6180, loss_cls: 3.6040, loss: 3.6040 +2024-07-19 19:12:36,473 - pyskl - INFO - Epoch [96][1500/3746] lr: 2.928e-02, eta: 1 day, 22:03:59, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6338, loss_cls: 3.5438, loss: 3.5438 +2024-07-19 19:13:58,282 - pyskl - INFO - Epoch [96][1600/3746] lr: 2.926e-02, eta: 1 day, 22:02:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6394, loss_cls: 3.5274, loss: 3.5274 +2024-07-19 19:15:20,695 - pyskl - INFO - Epoch [96][1700/3746] lr: 2.923e-02, eta: 1 day, 22:01:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3723, top5_acc: 0.6312, loss_cls: 3.5454, loss: 3.5454 +2024-07-19 19:16:42,865 - pyskl - INFO - Epoch [96][1800/3746] lr: 2.920e-02, eta: 1 day, 21:59:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6316, loss_cls: 3.5609, loss: 3.5609 +2024-07-19 19:18:05,381 - pyskl - INFO - Epoch [96][1900/3746] lr: 2.918e-02, eta: 1 day, 21:58:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6364, loss_cls: 3.5438, loss: 3.5438 +2024-07-19 19:19:27,734 - pyskl - INFO - Epoch [96][2000/3746] lr: 2.915e-02, eta: 1 day, 21:57:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6284, loss_cls: 3.5976, loss: 3.5976 +2024-07-19 19:20:49,703 - pyskl - INFO - Epoch [96][2100/3746] lr: 2.913e-02, eta: 1 day, 21:55:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6312, loss_cls: 3.5582, loss: 3.5582 +2024-07-19 19:22:12,409 - pyskl - INFO - Epoch [96][2200/3746] lr: 2.910e-02, eta: 1 day, 21:54:36, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6373, loss_cls: 3.5106, loss: 3.5106 +2024-07-19 19:23:33,854 - pyskl - INFO - Epoch [96][2300/3746] lr: 2.908e-02, eta: 1 day, 21:53:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6294, loss_cls: 3.5690, loss: 3.5690 +2024-07-19 19:24:56,094 - pyskl - INFO - Epoch [96][2400/3746] lr: 2.905e-02, eta: 1 day, 21:51:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6266, loss_cls: 3.6115, loss: 3.6115 +2024-07-19 19:26:17,981 - pyskl - INFO - Epoch [96][2500/3746] lr: 2.903e-02, eta: 1 day, 21:50:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6280, loss_cls: 3.5752, loss: 3.5752 +2024-07-19 19:27:39,853 - pyskl - INFO - Epoch [96][2600/3746] lr: 2.900e-02, eta: 1 day, 21:49:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6255, loss_cls: 3.5760, loss: 3.5760 +2024-07-19 19:29:02,098 - pyskl - INFO - Epoch [96][2700/3746] lr: 2.898e-02, eta: 1 day, 21:47:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6258, loss_cls: 3.6028, loss: 3.6028 +2024-07-19 19:30:23,785 - pyskl - INFO - Epoch [96][2800/3746] lr: 2.895e-02, eta: 1 day, 21:46:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6384, loss_cls: 3.5415, loss: 3.5415 +2024-07-19 19:31:46,531 - pyskl - INFO - Epoch [96][2900/3746] lr: 2.893e-02, eta: 1 day, 21:45:12, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6455, loss_cls: 3.5029, loss: 3.5029 +2024-07-19 19:33:08,585 - pyskl - INFO - Epoch [96][3000/3746] lr: 2.890e-02, eta: 1 day, 21:43:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6300, loss_cls: 3.5928, loss: 3.5928 +2024-07-19 19:34:30,366 - pyskl - INFO - Epoch [96][3100/3746] lr: 2.887e-02, eta: 1 day, 21:42:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6314, loss_cls: 3.5658, loss: 3.5658 +2024-07-19 19:35:52,373 - pyskl - INFO - Epoch [96][3200/3746] lr: 2.885e-02, eta: 1 day, 21:41:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6283, loss_cls: 3.5571, loss: 3.5571 +2024-07-19 19:37:14,430 - pyskl - INFO - Epoch [96][3300/3746] lr: 2.882e-02, eta: 1 day, 21:39:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6188, loss_cls: 3.6382, loss: 3.6382 +2024-07-19 19:38:36,774 - pyskl - INFO - Epoch [96][3400/3746] lr: 2.880e-02, eta: 1 day, 21:38:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6272, loss_cls: 3.5768, loss: 3.5768 +2024-07-19 19:39:58,587 - pyskl - INFO - Epoch [96][3500/3746] lr: 2.877e-02, eta: 1 day, 21:37:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3691, top5_acc: 0.6284, loss_cls: 3.5657, loss: 3.5657 +2024-07-19 19:41:20,408 - pyskl - INFO - Epoch [96][3600/3746] lr: 2.875e-02, eta: 1 day, 21:35:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6317, loss_cls: 3.5581, loss: 3.5581 +2024-07-19 19:42:42,023 - pyskl - INFO - Epoch [96][3700/3746] lr: 2.872e-02, eta: 1 day, 21:34:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6328, loss_cls: 3.5716, loss: 3.5716 +2024-07-19 19:43:21,336 - pyskl - INFO - Saving checkpoint at 96 epochs +2024-07-19 19:45:11,933 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 19:45:12,595 - pyskl - INFO - +top1_acc 0.2904 +top5_acc 0.5512 +2024-07-19 19:45:12,595 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 19:45:12,636 - pyskl - INFO - +mean_acc 0.2901 +2024-07-19 19:45:12,648 - pyskl - INFO - Epoch(val) [96][309] top1_acc: 0.2904, top5_acc: 0.5512, mean_class_accuracy: 0.2901 +2024-07-19 19:49:01,223 - pyskl - INFO - Epoch [97][100/3746] lr: 2.869e-02, eta: 1 day, 21:33:31, time: 2.286, data_time: 1.307, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6459, loss_cls: 3.4664, loss: 3.4664 +2024-07-19 19:50:23,754 - pyskl - INFO - Epoch [97][200/3746] lr: 2.866e-02, eta: 1 day, 21:32:10, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6481, loss_cls: 3.4564, loss: 3.4564 +2024-07-19 19:51:45,670 - pyskl - INFO - Epoch [97][300/3746] lr: 2.864e-02, eta: 1 day, 21:30:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6364, loss_cls: 3.5249, loss: 3.5249 +2024-07-19 19:53:08,394 - pyskl - INFO - Epoch [97][400/3746] lr: 2.861e-02, eta: 1 day, 21:29:29, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6305, loss_cls: 3.5200, loss: 3.5200 +2024-07-19 19:54:30,468 - pyskl - INFO - Epoch [97][500/3746] lr: 2.858e-02, eta: 1 day, 21:28:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6461, loss_cls: 3.5026, loss: 3.5026 +2024-07-19 19:55:53,199 - pyskl - INFO - Epoch [97][600/3746] lr: 2.856e-02, eta: 1 day, 21:26:49, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6377, loss_cls: 3.5460, loss: 3.5460 +2024-07-19 19:57:15,602 - pyskl - INFO - Epoch [97][700/3746] lr: 2.853e-02, eta: 1 day, 21:25:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6423, loss_cls: 3.4858, loss: 3.4858 +2024-07-19 19:58:37,949 - pyskl - INFO - Epoch [97][800/3746] lr: 2.851e-02, eta: 1 day, 21:24:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6362, loss_cls: 3.5509, loss: 3.5509 +2024-07-19 20:00:00,730 - pyskl - INFO - Epoch [97][900/3746] lr: 2.848e-02, eta: 1 day, 21:22:48, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6402, loss_cls: 3.4921, loss: 3.4921 +2024-07-19 20:01:22,773 - pyskl - INFO - Epoch [97][1000/3746] lr: 2.846e-02, eta: 1 day, 21:21:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6295, loss_cls: 3.5630, loss: 3.5630 +2024-07-19 20:02:44,927 - pyskl - INFO - Epoch [97][1100/3746] lr: 2.843e-02, eta: 1 day, 21:20:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6303, loss_cls: 3.5632, loss: 3.5632 +2024-07-19 20:04:07,455 - pyskl - INFO - Epoch [97][1200/3746] lr: 2.841e-02, eta: 1 day, 21:18:46, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6275, loss_cls: 3.5620, loss: 3.5620 +2024-07-19 20:05:30,148 - pyskl - INFO - Epoch [97][1300/3746] lr: 2.838e-02, eta: 1 day, 21:17:26, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6372, loss_cls: 3.5371, loss: 3.5371 +2024-07-19 20:06:52,408 - pyskl - INFO - Epoch [97][1400/3746] lr: 2.836e-02, eta: 1 day, 21:16:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6317, loss_cls: 3.5527, loss: 3.5527 +2024-07-19 20:08:15,750 - pyskl - INFO - Epoch [97][1500/3746] lr: 2.833e-02, eta: 1 day, 21:14:45, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6339, loss_cls: 3.5864, loss: 3.5864 +2024-07-19 20:09:38,163 - pyskl - INFO - Epoch [97][1600/3746] lr: 2.831e-02, eta: 1 day, 21:13:25, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3739, top5_acc: 0.6278, loss_cls: 3.5474, loss: 3.5474 +2024-07-19 20:11:00,456 - pyskl - INFO - Epoch [97][1700/3746] lr: 2.828e-02, eta: 1 day, 21:12:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6262, loss_cls: 3.5654, loss: 3.5654 +2024-07-19 20:12:22,935 - pyskl - INFO - Epoch [97][1800/3746] lr: 2.826e-02, eta: 1 day, 21:10:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6298, loss_cls: 3.5664, loss: 3.5664 +2024-07-19 20:13:46,196 - pyskl - INFO - Epoch [97][1900/3746] lr: 2.823e-02, eta: 1 day, 21:09:24, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6398, loss_cls: 3.4884, loss: 3.4884 +2024-07-19 20:15:08,363 - pyskl - INFO - Epoch [97][2000/3746] lr: 2.821e-02, eta: 1 day, 21:08:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6244, loss_cls: 3.5614, loss: 3.5614 +2024-07-19 20:16:30,552 - pyskl - INFO - Epoch [97][2100/3746] lr: 2.818e-02, eta: 1 day, 21:06:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6434, loss_cls: 3.4942, loss: 3.4942 +2024-07-19 20:17:53,400 - pyskl - INFO - Epoch [97][2200/3746] lr: 2.816e-02, eta: 1 day, 21:05:23, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6417, loss_cls: 3.4913, loss: 3.4913 +2024-07-19 20:19:15,855 - pyskl - INFO - Epoch [97][2300/3746] lr: 2.813e-02, eta: 1 day, 21:04:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6253, loss_cls: 3.5708, loss: 3.5708 +2024-07-19 20:20:37,940 - pyskl - INFO - Epoch [97][2400/3746] lr: 2.811e-02, eta: 1 day, 21:02:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6266, loss_cls: 3.5928, loss: 3.5928 +2024-07-19 20:22:00,970 - pyskl - INFO - Epoch [97][2500/3746] lr: 2.808e-02, eta: 1 day, 21:01:22, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6367, loss_cls: 3.5280, loss: 3.5280 +2024-07-19 20:23:23,689 - pyskl - INFO - Epoch [97][2600/3746] lr: 2.806e-02, eta: 1 day, 21:00:02, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3741, top5_acc: 0.6317, loss_cls: 3.5372, loss: 3.5372 +2024-07-19 20:24:46,348 - pyskl - INFO - Epoch [97][2700/3746] lr: 2.803e-02, eta: 1 day, 20:58:41, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3708, top5_acc: 0.6372, loss_cls: 3.5367, loss: 3.5367 +2024-07-19 20:26:08,203 - pyskl - INFO - Epoch [97][2800/3746] lr: 2.801e-02, eta: 1 day, 20:57:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6311, loss_cls: 3.5540, loss: 3.5540 +2024-07-19 20:27:31,597 - pyskl - INFO - Epoch [97][2900/3746] lr: 2.798e-02, eta: 1 day, 20:56:01, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6319, loss_cls: 3.5375, loss: 3.5375 +2024-07-19 20:28:54,358 - pyskl - INFO - Epoch [97][3000/3746] lr: 2.796e-02, eta: 1 day, 20:54:40, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6408, loss_cls: 3.5421, loss: 3.5421 +2024-07-19 20:30:16,489 - pyskl - INFO - Epoch [97][3100/3746] lr: 2.793e-02, eta: 1 day, 20:53:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6297, loss_cls: 3.5712, loss: 3.5712 +2024-07-19 20:31:38,985 - pyskl - INFO - Epoch [97][3200/3746] lr: 2.791e-02, eta: 1 day, 20:51:59, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6275, loss_cls: 3.5885, loss: 3.5885 +2024-07-19 20:33:01,116 - pyskl - INFO - Epoch [97][3300/3746] lr: 2.788e-02, eta: 1 day, 20:50:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6311, loss_cls: 3.5373, loss: 3.5373 +2024-07-19 20:34:23,487 - pyskl - INFO - Epoch [97][3400/3746] lr: 2.786e-02, eta: 1 day, 20:49:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6452, loss_cls: 3.5214, loss: 3.5214 +2024-07-19 20:35:45,937 - pyskl - INFO - Epoch [97][3500/3746] lr: 2.783e-02, eta: 1 day, 20:47:58, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3588, top5_acc: 0.6339, loss_cls: 3.5689, loss: 3.5689 +2024-07-19 20:37:08,594 - pyskl - INFO - Epoch [97][3600/3746] lr: 2.781e-02, eta: 1 day, 20:46:38, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6116, loss_cls: 3.6229, loss: 3.6229 +2024-07-19 20:38:31,035 - pyskl - INFO - Epoch [97][3700/3746] lr: 2.778e-02, eta: 1 day, 20:45:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6245, loss_cls: 3.5924, loss: 3.5924 +2024-07-19 20:39:10,908 - pyskl - INFO - Saving checkpoint at 97 epochs +2024-07-19 20:41:02,340 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 20:41:03,167 - pyskl - INFO - +top1_acc 0.3127 +top5_acc 0.5718 +2024-07-19 20:41:03,167 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 20:41:03,207 - pyskl - INFO - +mean_acc 0.3125 +2024-07-19 20:41:03,212 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_93.pth was removed +2024-07-19 20:41:03,476 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_97.pth. +2024-07-19 20:41:03,477 - pyskl - INFO - Best top1_acc is 0.3127 at 97 epoch. +2024-07-19 20:41:03,489 - pyskl - INFO - Epoch(val) [97][309] top1_acc: 0.3127, top5_acc: 0.5718, mean_class_accuracy: 0.3125 +2024-07-19 20:44:54,002 - pyskl - INFO - Epoch [98][100/3746] lr: 2.774e-02, eta: 1 day, 20:44:20, time: 2.305, data_time: 1.319, memory: 15990, top1_acc: 0.3822, top5_acc: 0.6509, loss_cls: 3.4425, loss: 3.4425 +2024-07-19 20:46:16,279 - pyskl - INFO - Epoch [98][200/3746] lr: 2.772e-02, eta: 1 day, 20:42:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6412, loss_cls: 3.4984, loss: 3.4984 +2024-07-19 20:47:38,549 - pyskl - INFO - Epoch [98][300/3746] lr: 2.769e-02, eta: 1 day, 20:41:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6391, loss_cls: 3.4907, loss: 3.4907 +2024-07-19 20:49:00,558 - pyskl - INFO - Epoch [98][400/3746] lr: 2.767e-02, eta: 1 day, 20:40:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6419, loss_cls: 3.4991, loss: 3.4991 +2024-07-19 20:50:22,713 - pyskl - INFO - Epoch [98][500/3746] lr: 2.764e-02, eta: 1 day, 20:38:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6417, loss_cls: 3.5000, loss: 3.5000 +2024-07-19 20:51:44,415 - pyskl - INFO - Epoch [98][600/3746] lr: 2.762e-02, eta: 1 day, 20:37:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6530, loss_cls: 3.4555, loss: 3.4555 +2024-07-19 20:53:06,329 - pyskl - INFO - Epoch [98][700/3746] lr: 2.759e-02, eta: 1 day, 20:36:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6458, loss_cls: 3.4459, loss: 3.4459 +2024-07-19 20:54:28,118 - pyskl - INFO - Epoch [98][800/3746] lr: 2.757e-02, eta: 1 day, 20:34:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3686, top5_acc: 0.6342, loss_cls: 3.5156, loss: 3.5156 +2024-07-19 20:55:50,018 - pyskl - INFO - Epoch [98][900/3746] lr: 2.754e-02, eta: 1 day, 20:33:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6362, loss_cls: 3.5321, loss: 3.5321 +2024-07-19 20:57:11,906 - pyskl - INFO - Epoch [98][1000/3746] lr: 2.752e-02, eta: 1 day, 20:32:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6245, loss_cls: 3.5430, loss: 3.5430 +2024-07-19 20:58:34,266 - pyskl - INFO - Epoch [98][1100/3746] lr: 2.749e-02, eta: 1 day, 20:30:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6430, loss_cls: 3.4792, loss: 3.4792 +2024-07-19 20:59:56,280 - pyskl - INFO - Epoch [98][1200/3746] lr: 2.747e-02, eta: 1 day, 20:29:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6294, loss_cls: 3.5712, loss: 3.5712 +2024-07-19 21:01:17,945 - pyskl - INFO - Epoch [98][1300/3746] lr: 2.744e-02, eta: 1 day, 20:28:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6378, loss_cls: 3.5074, loss: 3.5074 +2024-07-19 21:02:40,249 - pyskl - INFO - Epoch [98][1400/3746] lr: 2.742e-02, eta: 1 day, 20:26:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6409, loss_cls: 3.5199, loss: 3.5199 +2024-07-19 21:04:02,543 - pyskl - INFO - Epoch [98][1500/3746] lr: 2.739e-02, eta: 1 day, 20:25:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6370, loss_cls: 3.5122, loss: 3.5122 +2024-07-19 21:05:24,877 - pyskl - INFO - Epoch [98][1600/3746] lr: 2.737e-02, eta: 1 day, 20:24:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6345, loss_cls: 3.5402, loss: 3.5402 +2024-07-19 21:06:46,752 - pyskl - INFO - Epoch [98][1700/3746] lr: 2.734e-02, eta: 1 day, 20:22:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3762, top5_acc: 0.6434, loss_cls: 3.5022, loss: 3.5022 +2024-07-19 21:08:09,068 - pyskl - INFO - Epoch [98][1800/3746] lr: 2.732e-02, eta: 1 day, 20:21:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6400, loss_cls: 3.5456, loss: 3.5456 +2024-07-19 21:09:30,834 - pyskl - INFO - Epoch [98][1900/3746] lr: 2.729e-02, eta: 1 day, 20:20:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6278, loss_cls: 3.5595, loss: 3.5595 +2024-07-19 21:10:52,691 - pyskl - INFO - Epoch [98][2000/3746] lr: 2.727e-02, eta: 1 day, 20:18:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6358, loss_cls: 3.5447, loss: 3.5447 +2024-07-19 21:12:15,102 - pyskl - INFO - Epoch [98][2100/3746] lr: 2.724e-02, eta: 1 day, 20:17:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6253, loss_cls: 3.5747, loss: 3.5747 +2024-07-19 21:13:37,077 - pyskl - INFO - Epoch [98][2200/3746] lr: 2.722e-02, eta: 1 day, 20:16:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6320, loss_cls: 3.5289, loss: 3.5289 +2024-07-19 21:14:58,790 - pyskl - INFO - Epoch [98][2300/3746] lr: 2.719e-02, eta: 1 day, 20:14:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6381, loss_cls: 3.5226, loss: 3.5226 +2024-07-19 21:16:20,888 - pyskl - INFO - Epoch [98][2400/3746] lr: 2.717e-02, eta: 1 day, 20:13:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6383, loss_cls: 3.5079, loss: 3.5079 +2024-07-19 21:17:43,512 - pyskl - INFO - Epoch [98][2500/3746] lr: 2.714e-02, eta: 1 day, 20:12:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6289, loss_cls: 3.5680, loss: 3.5680 +2024-07-19 21:19:05,947 - pyskl - INFO - Epoch [98][2600/3746] lr: 2.712e-02, eta: 1 day, 20:10:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6389, loss_cls: 3.5537, loss: 3.5537 +2024-07-19 21:20:27,663 - pyskl - INFO - Epoch [98][2700/3746] lr: 2.709e-02, eta: 1 day, 20:09:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6305, loss_cls: 3.5638, loss: 3.5638 +2024-07-19 21:21:49,610 - pyskl - INFO - Epoch [98][2800/3746] lr: 2.707e-02, eta: 1 day, 20:08:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6436, loss_cls: 3.4916, loss: 3.4916 +2024-07-19 21:23:11,938 - pyskl - INFO - Epoch [98][2900/3746] lr: 2.705e-02, eta: 1 day, 20:06:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6402, loss_cls: 3.4913, loss: 3.4913 +2024-07-19 21:24:33,964 - pyskl - INFO - Epoch [98][3000/3746] lr: 2.702e-02, eta: 1 day, 20:05:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6381, loss_cls: 3.5166, loss: 3.5166 +2024-07-19 21:25:55,948 - pyskl - INFO - Epoch [98][3100/3746] lr: 2.700e-02, eta: 1 day, 20:03:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6341, loss_cls: 3.5441, loss: 3.5441 +2024-07-19 21:27:17,617 - pyskl - INFO - Epoch [98][3200/3746] lr: 2.697e-02, eta: 1 day, 20:02:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3686, top5_acc: 0.6450, loss_cls: 3.5159, loss: 3.5159 +2024-07-19 21:28:39,354 - pyskl - INFO - Epoch [98][3300/3746] lr: 2.695e-02, eta: 1 day, 20:01:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6291, loss_cls: 3.5586, loss: 3.5586 +2024-07-19 21:30:01,096 - pyskl - INFO - Epoch [98][3400/3746] lr: 2.692e-02, eta: 1 day, 19:59:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6427, loss_cls: 3.4943, loss: 3.4943 +2024-07-19 21:31:22,839 - pyskl - INFO - Epoch [98][3500/3746] lr: 2.690e-02, eta: 1 day, 19:58:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6402, loss_cls: 3.4894, loss: 3.4894 +2024-07-19 21:32:44,695 - pyskl - INFO - Epoch [98][3600/3746] lr: 2.687e-02, eta: 1 day, 19:57:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6286, loss_cls: 3.5629, loss: 3.5629 +2024-07-19 21:34:06,332 - pyskl - INFO - Epoch [98][3700/3746] lr: 2.685e-02, eta: 1 day, 19:55:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6361, loss_cls: 3.5331, loss: 3.5331 +2024-07-19 21:34:45,314 - pyskl - INFO - Saving checkpoint at 98 epochs +2024-07-19 21:36:35,087 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 21:36:35,761 - pyskl - INFO - +top1_acc 0.3102 +top5_acc 0.5587 +2024-07-19 21:36:35,762 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 21:36:35,805 - pyskl - INFO - +mean_acc 0.3100 +2024-07-19 21:36:35,818 - pyskl - INFO - Epoch(val) [98][309] top1_acc: 0.3102, top5_acc: 0.5587, mean_class_accuracy: 0.3100 +2024-07-19 21:40:25,611 - pyskl - INFO - Epoch [99][100/3746] lr: 2.681e-02, eta: 1 day, 19:54:55, time: 2.298, data_time: 1.318, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6445, loss_cls: 3.4201, loss: 3.4201 +2024-07-19 21:41:47,776 - pyskl - INFO - Epoch [99][200/3746] lr: 2.679e-02, eta: 1 day, 19:53:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3908, top5_acc: 0.6528, loss_cls: 3.4230, loss: 3.4230 +2024-07-19 21:43:10,454 - pyskl - INFO - Epoch [99][300/3746] lr: 2.676e-02, eta: 1 day, 19:52:14, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6417, loss_cls: 3.4948, loss: 3.4948 +2024-07-19 21:44:33,197 - pyskl - INFO - Epoch [99][400/3746] lr: 2.674e-02, eta: 1 day, 19:50:53, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6462, loss_cls: 3.4374, loss: 3.4374 +2024-07-19 21:45:55,706 - pyskl - INFO - Epoch [99][500/3746] lr: 2.671e-02, eta: 1 day, 19:49:33, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6406, loss_cls: 3.4877, loss: 3.4877 +2024-07-19 21:47:17,742 - pyskl - INFO - Epoch [99][600/3746] lr: 2.669e-02, eta: 1 day, 19:48:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6441, loss_cls: 3.5297, loss: 3.5297 +2024-07-19 21:48:40,070 - pyskl - INFO - Epoch [99][700/3746] lr: 2.666e-02, eta: 1 day, 19:46:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6531, loss_cls: 3.4468, loss: 3.4468 +2024-07-19 21:50:01,888 - pyskl - INFO - Epoch [99][800/3746] lr: 2.664e-02, eta: 1 day, 19:45:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3739, top5_acc: 0.6373, loss_cls: 3.5027, loss: 3.5027 +2024-07-19 21:51:23,194 - pyskl - INFO - Epoch [99][900/3746] lr: 2.661e-02, eta: 1 day, 19:44:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3812, top5_acc: 0.6403, loss_cls: 3.5057, loss: 3.5057 +2024-07-19 21:52:44,885 - pyskl - INFO - Epoch [99][1000/3746] lr: 2.659e-02, eta: 1 day, 19:42:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6480, loss_cls: 3.4567, loss: 3.4567 +2024-07-19 21:54:06,507 - pyskl - INFO - Epoch [99][1100/3746] lr: 2.656e-02, eta: 1 day, 19:41:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6405, loss_cls: 3.4881, loss: 3.4881 +2024-07-19 21:55:28,614 - pyskl - INFO - Epoch [99][1200/3746] lr: 2.654e-02, eta: 1 day, 19:40:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6528, loss_cls: 3.4369, loss: 3.4369 +2024-07-19 21:56:50,178 - pyskl - INFO - Epoch [99][1300/3746] lr: 2.651e-02, eta: 1 day, 19:38:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6370, loss_cls: 3.5005, loss: 3.5005 +2024-07-19 21:58:12,068 - pyskl - INFO - Epoch [99][1400/3746] lr: 2.649e-02, eta: 1 day, 19:37:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6375, loss_cls: 3.5115, loss: 3.5115 +2024-07-19 21:59:33,879 - pyskl - INFO - Epoch [99][1500/3746] lr: 2.646e-02, eta: 1 day, 19:36:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6414, loss_cls: 3.5195, loss: 3.5195 +2024-07-19 22:00:55,989 - pyskl - INFO - Epoch [99][1600/3746] lr: 2.644e-02, eta: 1 day, 19:34:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6392, loss_cls: 3.5096, loss: 3.5096 +2024-07-19 22:02:18,340 - pyskl - INFO - Epoch [99][1700/3746] lr: 2.642e-02, eta: 1 day, 19:33:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6367, loss_cls: 3.5224, loss: 3.5224 +2024-07-19 22:03:40,589 - pyskl - INFO - Epoch [99][1800/3746] lr: 2.639e-02, eta: 1 day, 19:32:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6300, loss_cls: 3.5695, loss: 3.5695 +2024-07-19 22:05:02,383 - pyskl - INFO - Epoch [99][1900/3746] lr: 2.637e-02, eta: 1 day, 19:30:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6270, loss_cls: 3.5538, loss: 3.5538 +2024-07-19 22:06:24,305 - pyskl - INFO - Epoch [99][2000/3746] lr: 2.634e-02, eta: 1 day, 19:29:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6416, loss_cls: 3.4977, loss: 3.4977 +2024-07-19 22:07:47,101 - pyskl - INFO - Epoch [99][2100/3746] lr: 2.632e-02, eta: 1 day, 19:28:00, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6269, loss_cls: 3.5198, loss: 3.5198 +2024-07-19 22:09:09,460 - pyskl - INFO - Epoch [99][2200/3746] lr: 2.629e-02, eta: 1 day, 19:26:39, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6458, loss_cls: 3.4847, loss: 3.4847 +2024-07-19 22:10:31,581 - pyskl - INFO - Epoch [99][2300/3746] lr: 2.627e-02, eta: 1 day, 19:25:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6348, loss_cls: 3.5758, loss: 3.5758 +2024-07-19 22:11:53,525 - pyskl - INFO - Epoch [99][2400/3746] lr: 2.624e-02, eta: 1 day, 19:23:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6380, loss_cls: 3.5201, loss: 3.5201 +2024-07-19 22:13:15,862 - pyskl - INFO - Epoch [99][2500/3746] lr: 2.622e-02, eta: 1 day, 19:22:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6425, loss_cls: 3.4833, loss: 3.4833 +2024-07-19 22:14:38,740 - pyskl - INFO - Epoch [99][2600/3746] lr: 2.619e-02, eta: 1 day, 19:21:17, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6456, loss_cls: 3.5259, loss: 3.5259 +2024-07-19 22:16:00,791 - pyskl - INFO - Epoch [99][2700/3746] lr: 2.617e-02, eta: 1 day, 19:19:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6458, loss_cls: 3.4827, loss: 3.4827 +2024-07-19 22:17:22,906 - pyskl - INFO - Epoch [99][2800/3746] lr: 2.614e-02, eta: 1 day, 19:18:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6391, loss_cls: 3.5097, loss: 3.5097 +2024-07-19 22:18:44,861 - pyskl - INFO - Epoch [99][2900/3746] lr: 2.612e-02, eta: 1 day, 19:17:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6364, loss_cls: 3.5234, loss: 3.5234 +2024-07-19 22:20:06,830 - pyskl - INFO - Epoch [99][3000/3746] lr: 2.610e-02, eta: 1 day, 19:15:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6398, loss_cls: 3.5400, loss: 3.5400 +2024-07-19 22:21:28,527 - pyskl - INFO - Epoch [99][3100/3746] lr: 2.607e-02, eta: 1 day, 19:14:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6330, loss_cls: 3.5145, loss: 3.5145 +2024-07-19 22:22:50,326 - pyskl - INFO - Epoch [99][3200/3746] lr: 2.605e-02, eta: 1 day, 19:13:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6327, loss_cls: 3.5151, loss: 3.5151 +2024-07-19 22:24:11,813 - pyskl - INFO - Epoch [99][3300/3746] lr: 2.602e-02, eta: 1 day, 19:11:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6381, loss_cls: 3.5349, loss: 3.5349 +2024-07-19 22:25:33,891 - pyskl - INFO - Epoch [99][3400/3746] lr: 2.600e-02, eta: 1 day, 19:10:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6431, loss_cls: 3.4813, loss: 3.4813 +2024-07-19 22:26:55,760 - pyskl - INFO - Epoch [99][3500/3746] lr: 2.597e-02, eta: 1 day, 19:09:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3741, top5_acc: 0.6311, loss_cls: 3.5338, loss: 3.5338 +2024-07-19 22:28:17,613 - pyskl - INFO - Epoch [99][3600/3746] lr: 2.595e-02, eta: 1 day, 19:07:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6459, loss_cls: 3.4447, loss: 3.4447 +2024-07-19 22:29:39,128 - pyskl - INFO - Epoch [99][3700/3746] lr: 2.592e-02, eta: 1 day, 19:06:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6386, loss_cls: 3.5739, loss: 3.5739 +2024-07-19 22:30:18,795 - pyskl - INFO - Saving checkpoint at 99 epochs +2024-07-19 22:32:08,650 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 22:32:09,330 - pyskl - INFO - +top1_acc 0.3217 +top5_acc 0.5865 +2024-07-19 22:32:09,333 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 22:32:09,376 - pyskl - INFO - +mean_acc 0.3215 +2024-07-19 22:32:09,381 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_97.pth was removed +2024-07-19 22:32:09,639 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_99.pth. +2024-07-19 22:32:09,640 - pyskl - INFO - Best top1_acc is 0.3217 at 99 epoch. +2024-07-19 22:32:09,654 - pyskl - INFO - Epoch(val) [99][309] top1_acc: 0.3217, top5_acc: 0.5865, mean_class_accuracy: 0.3215 +2024-07-19 22:36:01,642 - pyskl - INFO - Epoch [100][100/3746] lr: 2.589e-02, eta: 1 day, 19:05:27, time: 2.320, data_time: 1.338, memory: 15990, top1_acc: 0.3927, top5_acc: 0.6636, loss_cls: 3.3956, loss: 3.3956 +2024-07-19 22:37:23,885 - pyskl - INFO - Epoch [100][200/3746] lr: 2.586e-02, eta: 1 day, 19:04:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6550, loss_cls: 3.4204, loss: 3.4204 +2024-07-19 22:38:45,791 - pyskl - INFO - Epoch [100][300/3746] lr: 2.584e-02, eta: 1 day, 19:02:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6464, loss_cls: 3.4699, loss: 3.4699 +2024-07-19 22:40:07,595 - pyskl - INFO - Epoch [100][400/3746] lr: 2.581e-02, eta: 1 day, 19:01:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6527, loss_cls: 3.4435, loss: 3.4435 +2024-07-19 22:41:29,463 - pyskl - INFO - Epoch [100][500/3746] lr: 2.579e-02, eta: 1 day, 19:00:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6470, loss_cls: 3.4690, loss: 3.4690 +2024-07-19 22:42:51,157 - pyskl - INFO - Epoch [100][600/3746] lr: 2.577e-02, eta: 1 day, 18:58:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3859, top5_acc: 0.6353, loss_cls: 3.5000, loss: 3.5000 +2024-07-19 22:44:12,778 - pyskl - INFO - Epoch [100][700/3746] lr: 2.574e-02, eta: 1 day, 18:57:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6461, loss_cls: 3.4749, loss: 3.4749 +2024-07-19 22:45:34,643 - pyskl - INFO - Epoch [100][800/3746] lr: 2.572e-02, eta: 1 day, 18:56:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6497, loss_cls: 3.4640, loss: 3.4640 +2024-07-19 22:46:57,154 - pyskl - INFO - Epoch [100][900/3746] lr: 2.569e-02, eta: 1 day, 18:54:40, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6470, loss_cls: 3.4636, loss: 3.4636 +2024-07-19 22:48:19,027 - pyskl - INFO - Epoch [100][1000/3746] lr: 2.567e-02, eta: 1 day, 18:53:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6348, loss_cls: 3.5141, loss: 3.5141 +2024-07-19 22:49:40,787 - pyskl - INFO - Epoch [100][1100/3746] lr: 2.564e-02, eta: 1 day, 18:51:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6480, loss_cls: 3.4578, loss: 3.4578 +2024-07-19 22:51:02,155 - pyskl - INFO - Epoch [100][1200/3746] lr: 2.562e-02, eta: 1 day, 18:50:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6491, loss_cls: 3.4780, loss: 3.4780 +2024-07-19 22:52:23,818 - pyskl - INFO - Epoch [100][1300/3746] lr: 2.559e-02, eta: 1 day, 18:49:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6367, loss_cls: 3.5291, loss: 3.5291 +2024-07-19 22:53:45,191 - pyskl - INFO - Epoch [100][1400/3746] lr: 2.557e-02, eta: 1 day, 18:47:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6416, loss_cls: 3.4976, loss: 3.4976 +2024-07-19 22:55:07,634 - pyskl - INFO - Epoch [100][1500/3746] lr: 2.555e-02, eta: 1 day, 18:46:34, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6369, loss_cls: 3.5061, loss: 3.5061 +2024-07-19 22:56:29,592 - pyskl - INFO - Epoch [100][1600/3746] lr: 2.552e-02, eta: 1 day, 18:45:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3686, top5_acc: 0.6450, loss_cls: 3.5070, loss: 3.5070 +2024-07-19 22:57:51,530 - pyskl - INFO - Epoch [100][1700/3746] lr: 2.550e-02, eta: 1 day, 18:43:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6303, loss_cls: 3.5405, loss: 3.5405 +2024-07-19 22:59:13,743 - pyskl - INFO - Epoch [100][1800/3746] lr: 2.547e-02, eta: 1 day, 18:42:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6497, loss_cls: 3.4559, loss: 3.4559 +2024-07-19 23:00:35,644 - pyskl - INFO - Epoch [100][1900/3746] lr: 2.545e-02, eta: 1 day, 18:41:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3837, top5_acc: 0.6544, loss_cls: 3.4194, loss: 3.4194 +2024-07-19 23:01:57,790 - pyskl - INFO - Epoch [100][2000/3746] lr: 2.542e-02, eta: 1 day, 18:39:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6373, loss_cls: 3.5335, loss: 3.5335 +2024-07-19 23:03:20,197 - pyskl - INFO - Epoch [100][2100/3746] lr: 2.540e-02, eta: 1 day, 18:38:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6348, loss_cls: 3.5116, loss: 3.5116 +2024-07-19 23:04:42,030 - pyskl - INFO - Epoch [100][2200/3746] lr: 2.538e-02, eta: 1 day, 18:37:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3708, top5_acc: 0.6400, loss_cls: 3.5165, loss: 3.5165 +2024-07-19 23:06:04,459 - pyskl - INFO - Epoch [100][2300/3746] lr: 2.535e-02, eta: 1 day, 18:35:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6520, loss_cls: 3.4720, loss: 3.4720 +2024-07-19 23:07:26,747 - pyskl - INFO - Epoch [100][2400/3746] lr: 2.533e-02, eta: 1 day, 18:34:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6500, loss_cls: 3.4752, loss: 3.4752 +2024-07-19 23:08:48,881 - pyskl - INFO - Epoch [100][2500/3746] lr: 2.530e-02, eta: 1 day, 18:33:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6302, loss_cls: 3.5176, loss: 3.5176 +2024-07-19 23:10:11,277 - pyskl - INFO - Epoch [100][2600/3746] lr: 2.528e-02, eta: 1 day, 18:31:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6325, loss_cls: 3.5519, loss: 3.5519 +2024-07-19 23:11:33,355 - pyskl - INFO - Epoch [100][2700/3746] lr: 2.525e-02, eta: 1 day, 18:30:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6280, loss_cls: 3.5568, loss: 3.5568 +2024-07-19 23:12:55,340 - pyskl - INFO - Epoch [100][2800/3746] lr: 2.523e-02, eta: 1 day, 18:29:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6441, loss_cls: 3.4996, loss: 3.4996 +2024-07-19 23:14:17,188 - pyskl - INFO - Epoch [100][2900/3746] lr: 2.521e-02, eta: 1 day, 18:27:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6400, loss_cls: 3.5179, loss: 3.5179 +2024-07-19 23:15:39,537 - pyskl - INFO - Epoch [100][3000/3746] lr: 2.518e-02, eta: 1 day, 18:26:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6480, loss_cls: 3.4754, loss: 3.4754 +2024-07-19 23:17:01,330 - pyskl - INFO - Epoch [100][3100/3746] lr: 2.516e-02, eta: 1 day, 18:25:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6444, loss_cls: 3.4572, loss: 3.4572 +2024-07-19 23:18:22,941 - pyskl - INFO - Epoch [100][3200/3746] lr: 2.513e-02, eta: 1 day, 18:23:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6366, loss_cls: 3.5174, loss: 3.5174 +2024-07-19 23:19:44,867 - pyskl - INFO - Epoch [100][3300/3746] lr: 2.511e-02, eta: 1 day, 18:22:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6388, loss_cls: 3.5044, loss: 3.5044 +2024-07-19 23:21:07,092 - pyskl - INFO - Epoch [100][3400/3746] lr: 2.508e-02, eta: 1 day, 18:20:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6436, loss_cls: 3.4673, loss: 3.4673 +2024-07-19 23:22:28,910 - pyskl - INFO - Epoch [100][3500/3746] lr: 2.506e-02, eta: 1 day, 18:19:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6416, loss_cls: 3.5133, loss: 3.5133 +2024-07-19 23:23:51,084 - pyskl - INFO - Epoch [100][3600/3746] lr: 2.504e-02, eta: 1 day, 18:18:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6388, loss_cls: 3.4938, loss: 3.4938 +2024-07-19 23:25:12,992 - pyskl - INFO - Epoch [100][3700/3746] lr: 2.501e-02, eta: 1 day, 18:16:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6445, loss_cls: 3.4579, loss: 3.4579 +2024-07-19 23:25:53,227 - pyskl - INFO - Saving checkpoint at 100 epochs +2024-07-19 23:27:44,215 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 23:27:44,875 - pyskl - INFO - +top1_acc 0.2978 +top5_acc 0.5551 +2024-07-19 23:27:44,875 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 23:27:44,917 - pyskl - INFO - +mean_acc 0.2976 +2024-07-19 23:27:44,930 - pyskl - INFO - Epoch(val) [100][309] top1_acc: 0.2978, top5_acc: 0.5551, mean_class_accuracy: 0.2976 +2024-07-19 23:31:36,593 - pyskl - INFO - Epoch [101][100/3746] lr: 2.498e-02, eta: 1 day, 18:15:53, time: 2.317, data_time: 1.334, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6569, loss_cls: 3.3619, loss: 3.3619 +2024-07-19 23:32:59,383 - pyskl - INFO - Epoch [101][200/3746] lr: 2.495e-02, eta: 1 day, 18:14:33, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6506, loss_cls: 3.4276, loss: 3.4276 +2024-07-19 23:34:21,665 - pyskl - INFO - Epoch [101][300/3746] lr: 2.493e-02, eta: 1 day, 18:13:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6592, loss_cls: 3.4133, loss: 3.4133 +2024-07-19 23:35:43,559 - pyskl - INFO - Epoch [101][400/3746] lr: 2.490e-02, eta: 1 day, 18:11:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6536, loss_cls: 3.4101, loss: 3.4101 +2024-07-19 23:37:05,191 - pyskl - INFO - Epoch [101][500/3746] lr: 2.488e-02, eta: 1 day, 18:10:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6431, loss_cls: 3.4584, loss: 3.4584 +2024-07-19 23:38:26,690 - pyskl - INFO - Epoch [101][600/3746] lr: 2.486e-02, eta: 1 day, 18:09:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6505, loss_cls: 3.4187, loss: 3.4187 +2024-07-19 23:39:48,591 - pyskl - INFO - Epoch [101][700/3746] lr: 2.483e-02, eta: 1 day, 18:07:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3848, top5_acc: 0.6445, loss_cls: 3.4657, loss: 3.4657 +2024-07-19 23:41:09,976 - pyskl - INFO - Epoch [101][800/3746] lr: 2.481e-02, eta: 1 day, 18:06:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6572, loss_cls: 3.4178, loss: 3.4178 +2024-07-19 23:42:32,377 - pyskl - INFO - Epoch [101][900/3746] lr: 2.478e-02, eta: 1 day, 18:05:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6520, loss_cls: 3.4459, loss: 3.4459 +2024-07-19 23:43:54,231 - pyskl - INFO - Epoch [101][1000/3746] lr: 2.476e-02, eta: 1 day, 18:03:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6441, loss_cls: 3.5003, loss: 3.5003 +2024-07-19 23:45:16,175 - pyskl - INFO - Epoch [101][1100/3746] lr: 2.473e-02, eta: 1 day, 18:02:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6491, loss_cls: 3.4443, loss: 3.4443 +2024-07-19 23:46:37,767 - pyskl - INFO - Epoch [101][1200/3746] lr: 2.471e-02, eta: 1 day, 18:01:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6472, loss_cls: 3.4614, loss: 3.4614 +2024-07-19 23:47:59,546 - pyskl - INFO - Epoch [101][1300/3746] lr: 2.469e-02, eta: 1 day, 17:59:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6505, loss_cls: 3.4571, loss: 3.4571 +2024-07-19 23:49:21,129 - pyskl - INFO - Epoch [101][1400/3746] lr: 2.466e-02, eta: 1 day, 17:58:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6422, loss_cls: 3.4901, loss: 3.4901 +2024-07-19 23:50:43,484 - pyskl - INFO - Epoch [101][1500/3746] lr: 2.464e-02, eta: 1 day, 17:57:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6406, loss_cls: 3.4962, loss: 3.4962 +2024-07-19 23:52:05,414 - pyskl - INFO - Epoch [101][1600/3746] lr: 2.461e-02, eta: 1 day, 17:55:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6381, loss_cls: 3.5180, loss: 3.5180 +2024-07-19 23:53:27,548 - pyskl - INFO - Epoch [101][1700/3746] lr: 2.459e-02, eta: 1 day, 17:54:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6613, loss_cls: 3.4037, loss: 3.4037 +2024-07-19 23:54:49,508 - pyskl - INFO - Epoch [101][1800/3746] lr: 2.457e-02, eta: 1 day, 17:52:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6539, loss_cls: 3.4495, loss: 3.4495 +2024-07-19 23:56:11,617 - pyskl - INFO - Epoch [101][1900/3746] lr: 2.454e-02, eta: 1 day, 17:51:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6478, loss_cls: 3.4296, loss: 3.4296 +2024-07-19 23:57:33,956 - pyskl - INFO - Epoch [101][2000/3746] lr: 2.452e-02, eta: 1 day, 17:50:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6334, loss_cls: 3.4742, loss: 3.4742 +2024-07-19 23:58:56,718 - pyskl - INFO - Epoch [101][2100/3746] lr: 2.449e-02, eta: 1 day, 17:48:55, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6400, loss_cls: 3.4978, loss: 3.4978 +2024-07-20 00:00:19,047 - pyskl - INFO - Epoch [101][2200/3746] lr: 2.447e-02, eta: 1 day, 17:47:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6492, loss_cls: 3.4540, loss: 3.4540 +2024-07-20 00:01:40,806 - pyskl - INFO - Epoch [101][2300/3746] lr: 2.445e-02, eta: 1 day, 17:46:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6478, loss_cls: 3.4645, loss: 3.4645 +2024-07-20 00:03:02,836 - pyskl - INFO - Epoch [101][2400/3746] lr: 2.442e-02, eta: 1 day, 17:44:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6295, loss_cls: 3.5336, loss: 3.5336 +2024-07-20 00:04:25,536 - pyskl - INFO - Epoch [101][2500/3746] lr: 2.440e-02, eta: 1 day, 17:43:32, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6494, loss_cls: 3.4460, loss: 3.4460 +2024-07-20 00:05:46,940 - pyskl - INFO - Epoch [101][2600/3746] lr: 2.437e-02, eta: 1 day, 17:42:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6480, loss_cls: 3.4403, loss: 3.4403 +2024-07-20 00:07:08,891 - pyskl - INFO - Epoch [101][2700/3746] lr: 2.435e-02, eta: 1 day, 17:40:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6391, loss_cls: 3.5197, loss: 3.5197 +2024-07-20 00:08:31,568 - pyskl - INFO - Epoch [101][2800/3746] lr: 2.433e-02, eta: 1 day, 17:39:29, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6434, loss_cls: 3.5004, loss: 3.5004 +2024-07-20 00:09:53,571 - pyskl - INFO - Epoch [101][2900/3746] lr: 2.430e-02, eta: 1 day, 17:38:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6450, loss_cls: 3.4830, loss: 3.4830 +2024-07-20 00:11:15,672 - pyskl - INFO - Epoch [101][3000/3746] lr: 2.428e-02, eta: 1 day, 17:36:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6433, loss_cls: 3.4840, loss: 3.4840 +2024-07-20 00:12:37,277 - pyskl - INFO - Epoch [101][3100/3746] lr: 2.425e-02, eta: 1 day, 17:35:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6380, loss_cls: 3.5302, loss: 3.5302 +2024-07-20 00:13:59,114 - pyskl - INFO - Epoch [101][3200/3746] lr: 2.423e-02, eta: 1 day, 17:34:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6430, loss_cls: 3.4741, loss: 3.4741 +2024-07-20 00:15:21,215 - pyskl - INFO - Epoch [101][3300/3746] lr: 2.421e-02, eta: 1 day, 17:32:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6444, loss_cls: 3.4924, loss: 3.4924 +2024-07-20 00:16:42,706 - pyskl - INFO - Epoch [101][3400/3746] lr: 2.418e-02, eta: 1 day, 17:31:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6422, loss_cls: 3.4806, loss: 3.4806 +2024-07-20 00:18:05,105 - pyskl - INFO - Epoch [101][3500/3746] lr: 2.416e-02, eta: 1 day, 17:30:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6384, loss_cls: 3.5250, loss: 3.5250 +2024-07-20 00:19:26,537 - pyskl - INFO - Epoch [101][3600/3746] lr: 2.413e-02, eta: 1 day, 17:28:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6408, loss_cls: 3.5202, loss: 3.5202 +2024-07-20 00:20:48,473 - pyskl - INFO - Epoch [101][3700/3746] lr: 2.411e-02, eta: 1 day, 17:27:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3748, top5_acc: 0.6391, loss_cls: 3.5147, loss: 3.5147 +2024-07-20 00:21:28,265 - pyskl - INFO - Saving checkpoint at 101 epochs +2024-07-20 00:23:19,763 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 00:23:20,429 - pyskl - INFO - +top1_acc 0.3062 +top5_acc 0.5702 +2024-07-20 00:23:20,429 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 00:23:20,471 - pyskl - INFO - +mean_acc 0.3060 +2024-07-20 00:23:20,484 - pyskl - INFO - Epoch(val) [101][309] top1_acc: 0.3062, top5_acc: 0.5702, mean_class_accuracy: 0.3060 +2024-07-20 00:27:15,565 - pyskl - INFO - Epoch [102][100/3746] lr: 2.407e-02, eta: 1 day, 17:26:18, time: 2.351, data_time: 1.362, memory: 15990, top1_acc: 0.4016, top5_acc: 0.6656, loss_cls: 3.3464, loss: 3.3464 +2024-07-20 00:28:37,912 - pyskl - INFO - Epoch [102][200/3746] lr: 2.405e-02, eta: 1 day, 17:24:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6653, loss_cls: 3.3657, loss: 3.3657 +2024-07-20 00:29:59,456 - pyskl - INFO - Epoch [102][300/3746] lr: 2.403e-02, eta: 1 day, 17:23:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6506, loss_cls: 3.4567, loss: 3.4567 +2024-07-20 00:31:21,846 - pyskl - INFO - Epoch [102][400/3746] lr: 2.400e-02, eta: 1 day, 17:22:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6555, loss_cls: 3.4281, loss: 3.4281 +2024-07-20 00:32:44,013 - pyskl - INFO - Epoch [102][500/3746] lr: 2.398e-02, eta: 1 day, 17:20:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6525, loss_cls: 3.4113, loss: 3.4113 +2024-07-20 00:34:05,899 - pyskl - INFO - Epoch [102][600/3746] lr: 2.396e-02, eta: 1 day, 17:19:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6495, loss_cls: 3.4294, loss: 3.4294 +2024-07-20 00:35:27,355 - pyskl - INFO - Epoch [102][700/3746] lr: 2.393e-02, eta: 1 day, 17:18:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6525, loss_cls: 3.4430, loss: 3.4430 +2024-07-20 00:36:49,896 - pyskl - INFO - Epoch [102][800/3746] lr: 2.391e-02, eta: 1 day, 17:16:51, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3858, top5_acc: 0.6480, loss_cls: 3.4535, loss: 3.4535 +2024-07-20 00:38:11,787 - pyskl - INFO - Epoch [102][900/3746] lr: 2.388e-02, eta: 1 day, 17:15:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6534, loss_cls: 3.4360, loss: 3.4360 +2024-07-20 00:39:33,351 - pyskl - INFO - Epoch [102][1000/3746] lr: 2.386e-02, eta: 1 day, 17:14:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6467, loss_cls: 3.4470, loss: 3.4470 +2024-07-20 00:40:55,776 - pyskl - INFO - Epoch [102][1100/3746] lr: 2.384e-02, eta: 1 day, 17:12:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3739, top5_acc: 0.6355, loss_cls: 3.5234, loss: 3.5234 +2024-07-20 00:42:17,193 - pyskl - INFO - Epoch [102][1200/3746] lr: 2.381e-02, eta: 1 day, 17:11:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6545, loss_cls: 3.4307, loss: 3.4307 +2024-07-20 00:43:38,844 - pyskl - INFO - Epoch [102][1300/3746] lr: 2.379e-02, eta: 1 day, 17:10:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6461, loss_cls: 3.4484, loss: 3.4484 +2024-07-20 00:45:01,339 - pyskl - INFO - Epoch [102][1400/3746] lr: 2.376e-02, eta: 1 day, 17:08:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6370, loss_cls: 3.5006, loss: 3.5006 +2024-07-20 00:46:23,387 - pyskl - INFO - Epoch [102][1500/3746] lr: 2.374e-02, eta: 1 day, 17:07:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6406, loss_cls: 3.5023, loss: 3.5023 +2024-07-20 00:47:45,696 - pyskl - INFO - Epoch [102][1600/3746] lr: 2.372e-02, eta: 1 day, 17:06:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6577, loss_cls: 3.4119, loss: 3.4119 +2024-07-20 00:49:08,209 - pyskl - INFO - Epoch [102][1700/3746] lr: 2.369e-02, eta: 1 day, 17:04:42, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6433, loss_cls: 3.5017, loss: 3.5017 +2024-07-20 00:50:30,411 - pyskl - INFO - Epoch [102][1800/3746] lr: 2.367e-02, eta: 1 day, 17:03:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3720, top5_acc: 0.6467, loss_cls: 3.4664, loss: 3.4664 +2024-07-20 00:51:52,304 - pyskl - INFO - Epoch [102][1900/3746] lr: 2.365e-02, eta: 1 day, 17:02:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6402, loss_cls: 3.5151, loss: 3.5151 +2024-07-20 00:53:14,278 - pyskl - INFO - Epoch [102][2000/3746] lr: 2.362e-02, eta: 1 day, 17:00:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6484, loss_cls: 3.4269, loss: 3.4269 +2024-07-20 00:54:36,506 - pyskl - INFO - Epoch [102][2100/3746] lr: 2.360e-02, eta: 1 day, 16:59:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6533, loss_cls: 3.4233, loss: 3.4233 +2024-07-20 00:55:58,239 - pyskl - INFO - Epoch [102][2200/3746] lr: 2.357e-02, eta: 1 day, 16:57:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6523, loss_cls: 3.4326, loss: 3.4326 +2024-07-20 00:57:19,896 - pyskl - INFO - Epoch [102][2300/3746] lr: 2.355e-02, eta: 1 day, 16:56:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6505, loss_cls: 3.4645, loss: 3.4645 +2024-07-20 00:58:41,382 - pyskl - INFO - Epoch [102][2400/3746] lr: 2.353e-02, eta: 1 day, 16:55:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3762, top5_acc: 0.6420, loss_cls: 3.4917, loss: 3.4917 +2024-07-20 01:00:03,449 - pyskl - INFO - Epoch [102][2500/3746] lr: 2.350e-02, eta: 1 day, 16:53:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6450, loss_cls: 3.4898, loss: 3.4898 +2024-07-20 01:01:25,031 - pyskl - INFO - Epoch [102][2600/3746] lr: 2.348e-02, eta: 1 day, 16:52:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6444, loss_cls: 3.4411, loss: 3.4411 +2024-07-20 01:02:47,768 - pyskl - INFO - Epoch [102][2700/3746] lr: 2.346e-02, eta: 1 day, 16:51:12, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6450, loss_cls: 3.4542, loss: 3.4542 +2024-07-20 01:04:10,586 - pyskl - INFO - Epoch [102][2800/3746] lr: 2.343e-02, eta: 1 day, 16:49:51, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6444, loss_cls: 3.4630, loss: 3.4630 +2024-07-20 01:05:32,309 - pyskl - INFO - Epoch [102][2900/3746] lr: 2.341e-02, eta: 1 day, 16:48:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6516, loss_cls: 3.4329, loss: 3.4329 +2024-07-20 01:06:53,811 - pyskl - INFO - Epoch [102][3000/3746] lr: 2.339e-02, eta: 1 day, 16:47:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6506, loss_cls: 3.4319, loss: 3.4319 +2024-07-20 01:08:15,503 - pyskl - INFO - Epoch [102][3100/3746] lr: 2.336e-02, eta: 1 day, 16:45:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6402, loss_cls: 3.5157, loss: 3.5157 +2024-07-20 01:09:37,266 - pyskl - INFO - Epoch [102][3200/3746] lr: 2.334e-02, eta: 1 day, 16:44:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6497, loss_cls: 3.4366, loss: 3.4366 +2024-07-20 01:10:59,406 - pyskl - INFO - Epoch [102][3300/3746] lr: 2.331e-02, eta: 1 day, 16:43:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6417, loss_cls: 3.4791, loss: 3.4791 +2024-07-20 01:12:20,899 - pyskl - INFO - Epoch [102][3400/3746] lr: 2.329e-02, eta: 1 day, 16:41:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3923, top5_acc: 0.6462, loss_cls: 3.4227, loss: 3.4227 +2024-07-20 01:13:43,050 - pyskl - INFO - Epoch [102][3500/3746] lr: 2.327e-02, eta: 1 day, 16:40:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6358, loss_cls: 3.5047, loss: 3.5047 +2024-07-20 01:15:04,460 - pyskl - INFO - Epoch [102][3600/3746] lr: 2.324e-02, eta: 1 day, 16:39:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6494, loss_cls: 3.4769, loss: 3.4769 +2024-07-20 01:16:25,944 - pyskl - INFO - Epoch [102][3700/3746] lr: 2.322e-02, eta: 1 day, 16:37:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3909, top5_acc: 0.6577, loss_cls: 3.3892, loss: 3.3892 +2024-07-20 01:17:05,406 - pyskl - INFO - Saving checkpoint at 102 epochs +2024-07-20 01:18:56,648 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 01:18:57,372 - pyskl - INFO - +top1_acc 0.3257 +top5_acc 0.5853 +2024-07-20 01:18:57,372 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 01:18:57,414 - pyskl - INFO - +mean_acc 0.3254 +2024-07-20 01:18:57,419 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_99.pth was removed +2024-07-20 01:18:57,686 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_102.pth. +2024-07-20 01:18:57,687 - pyskl - INFO - Best top1_acc is 0.3257 at 102 epoch. +2024-07-20 01:18:57,700 - pyskl - INFO - Epoch(val) [102][309] top1_acc: 0.3257, top5_acc: 0.5853, mean_class_accuracy: 0.3254 +2024-07-20 01:22:50,828 - pyskl - INFO - Epoch [103][100/3746] lr: 2.319e-02, eta: 1 day, 16:36:36, time: 2.331, data_time: 1.346, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6623, loss_cls: 3.3852, loss: 3.3852 +2024-07-20 01:24:13,216 - pyskl - INFO - Epoch [103][200/3746] lr: 2.316e-02, eta: 1 day, 16:35:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3898, top5_acc: 0.6652, loss_cls: 3.3777, loss: 3.3777 +2024-07-20 01:25:35,113 - pyskl - INFO - Epoch [103][300/3746] lr: 2.314e-02, eta: 1 day, 16:33:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6522, loss_cls: 3.4052, loss: 3.4052 +2024-07-20 01:26:56,528 - pyskl - INFO - Epoch [103][400/3746] lr: 2.311e-02, eta: 1 day, 16:32:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3947, top5_acc: 0.6553, loss_cls: 3.4164, loss: 3.4164 +2024-07-20 01:28:18,073 - pyskl - INFO - Epoch [103][500/3746] lr: 2.309e-02, eta: 1 day, 16:31:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6616, loss_cls: 3.3746, loss: 3.3746 +2024-07-20 01:29:39,743 - pyskl - INFO - Epoch [103][600/3746] lr: 2.307e-02, eta: 1 day, 16:29:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6641, loss_cls: 3.3870, loss: 3.3870 +2024-07-20 01:31:01,519 - pyskl - INFO - Epoch [103][700/3746] lr: 2.304e-02, eta: 1 day, 16:28:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6500, loss_cls: 3.4243, loss: 3.4243 +2024-07-20 01:32:23,459 - pyskl - INFO - Epoch [103][800/3746] lr: 2.302e-02, eta: 1 day, 16:27:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6578, loss_cls: 3.4030, loss: 3.4030 +2024-07-20 01:33:45,211 - pyskl - INFO - Epoch [103][900/3746] lr: 2.300e-02, eta: 1 day, 16:25:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6475, loss_cls: 3.4098, loss: 3.4098 +2024-07-20 01:35:06,897 - pyskl - INFO - Epoch [103][1000/3746] lr: 2.297e-02, eta: 1 day, 16:24:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6564, loss_cls: 3.4120, loss: 3.4120 +2024-07-20 01:36:28,365 - pyskl - INFO - Epoch [103][1100/3746] lr: 2.295e-02, eta: 1 day, 16:23:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3923, top5_acc: 0.6527, loss_cls: 3.4274, loss: 3.4274 +2024-07-20 01:37:50,068 - pyskl - INFO - Epoch [103][1200/3746] lr: 2.293e-02, eta: 1 day, 16:21:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6394, loss_cls: 3.4957, loss: 3.4957 +2024-07-20 01:39:11,988 - pyskl - INFO - Epoch [103][1300/3746] lr: 2.290e-02, eta: 1 day, 16:20:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6514, loss_cls: 3.4518, loss: 3.4518 +2024-07-20 01:40:34,341 - pyskl - INFO - Epoch [103][1400/3746] lr: 2.288e-02, eta: 1 day, 16:19:01, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6484, loss_cls: 3.4191, loss: 3.4191 +2024-07-20 01:41:56,762 - pyskl - INFO - Epoch [103][1500/3746] lr: 2.286e-02, eta: 1 day, 16:17:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6536, loss_cls: 3.4152, loss: 3.4152 +2024-07-20 01:43:18,964 - pyskl - INFO - Epoch [103][1600/3746] lr: 2.283e-02, eta: 1 day, 16:16:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6573, loss_cls: 3.4068, loss: 3.4068 +2024-07-20 01:44:40,923 - pyskl - INFO - Epoch [103][1700/3746] lr: 2.281e-02, eta: 1 day, 16:14:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6505, loss_cls: 3.4087, loss: 3.4087 +2024-07-20 01:46:03,187 - pyskl - INFO - Epoch [103][1800/3746] lr: 2.279e-02, eta: 1 day, 16:13:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6488, loss_cls: 3.4276, loss: 3.4276 +2024-07-20 01:47:25,223 - pyskl - INFO - Epoch [103][1900/3746] lr: 2.276e-02, eta: 1 day, 16:12:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6428, loss_cls: 3.4816, loss: 3.4816 +2024-07-20 01:48:46,897 - pyskl - INFO - Epoch [103][2000/3746] lr: 2.274e-02, eta: 1 day, 16:10:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3903, top5_acc: 0.6475, loss_cls: 3.4330, loss: 3.4330 +2024-07-20 01:50:09,270 - pyskl - INFO - Epoch [103][2100/3746] lr: 2.272e-02, eta: 1 day, 16:09:34, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6456, loss_cls: 3.4386, loss: 3.4386 +2024-07-20 01:51:31,674 - pyskl - INFO - Epoch [103][2200/3746] lr: 2.269e-02, eta: 1 day, 16:08:13, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6488, loss_cls: 3.4591, loss: 3.4591 +2024-07-20 01:52:53,679 - pyskl - INFO - Epoch [103][2300/3746] lr: 2.267e-02, eta: 1 day, 16:06:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3977, top5_acc: 0.6495, loss_cls: 3.4031, loss: 3.4031 +2024-07-20 01:54:16,435 - pyskl - INFO - Epoch [103][2400/3746] lr: 2.264e-02, eta: 1 day, 16:05:32, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6558, loss_cls: 3.3919, loss: 3.3919 +2024-07-20 01:55:38,879 - pyskl - INFO - Epoch [103][2500/3746] lr: 2.262e-02, eta: 1 day, 16:04:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6587, loss_cls: 3.4178, loss: 3.4178 +2024-07-20 01:57:01,249 - pyskl - INFO - Epoch [103][2600/3746] lr: 2.260e-02, eta: 1 day, 16:02:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3933, top5_acc: 0.6520, loss_cls: 3.4097, loss: 3.4097 +2024-07-20 01:58:23,627 - pyskl - INFO - Epoch [103][2700/3746] lr: 2.257e-02, eta: 1 day, 16:01:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6495, loss_cls: 3.4718, loss: 3.4718 +2024-07-20 01:59:45,950 - pyskl - INFO - Epoch [103][2800/3746] lr: 2.255e-02, eta: 1 day, 16:00:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6488, loss_cls: 3.4302, loss: 3.4302 +2024-07-20 02:01:07,949 - pyskl - INFO - Epoch [103][2900/3746] lr: 2.253e-02, eta: 1 day, 15:58:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6489, loss_cls: 3.4428, loss: 3.4428 +2024-07-20 02:02:29,429 - pyskl - INFO - Epoch [103][3000/3746] lr: 2.250e-02, eta: 1 day, 15:57:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6364, loss_cls: 3.4966, loss: 3.4966 +2024-07-20 02:03:51,168 - pyskl - INFO - Epoch [103][3100/3746] lr: 2.248e-02, eta: 1 day, 15:56:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6484, loss_cls: 3.4295, loss: 3.4295 +2024-07-20 02:05:12,896 - pyskl - INFO - Epoch [103][3200/3746] lr: 2.246e-02, eta: 1 day, 15:54:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6448, loss_cls: 3.4540, loss: 3.4540 +2024-07-20 02:06:34,795 - pyskl - INFO - Epoch [103][3300/3746] lr: 2.243e-02, eta: 1 day, 15:53:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6322, loss_cls: 3.5331, loss: 3.5331 +2024-07-20 02:07:56,556 - pyskl - INFO - Epoch [103][3400/3746] lr: 2.241e-02, eta: 1 day, 15:52:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6514, loss_cls: 3.4435, loss: 3.4435 +2024-07-20 02:09:18,176 - pyskl - INFO - Epoch [103][3500/3746] lr: 2.239e-02, eta: 1 day, 15:50:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6567, loss_cls: 3.4282, loss: 3.4282 +2024-07-20 02:10:39,685 - pyskl - INFO - Epoch [103][3600/3746] lr: 2.236e-02, eta: 1 day, 15:49:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6417, loss_cls: 3.4849, loss: 3.4849 +2024-07-20 02:12:01,771 - pyskl - INFO - Epoch [103][3700/3746] lr: 2.234e-02, eta: 1 day, 15:47:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6536, loss_cls: 3.3989, loss: 3.3989 +2024-07-20 02:12:41,377 - pyskl - INFO - Saving checkpoint at 103 epochs +2024-07-20 02:14:32,958 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 02:14:33,638 - pyskl - INFO - +top1_acc 0.3180 +top5_acc 0.5761 +2024-07-20 02:14:33,638 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 02:14:33,680 - pyskl - INFO - +mean_acc 0.3177 +2024-07-20 02:14:33,693 - pyskl - INFO - Epoch(val) [103][309] top1_acc: 0.3180, top5_acc: 0.5761, mean_class_accuracy: 0.3177 +2024-07-20 02:18:27,257 - pyskl - INFO - Epoch [104][100/3746] lr: 2.231e-02, eta: 1 day, 15:46:51, time: 2.336, data_time: 1.329, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6623, loss_cls: 3.3564, loss: 3.3564 +2024-07-20 02:19:50,190 - pyskl - INFO - Epoch [104][200/3746] lr: 2.228e-02, eta: 1 day, 15:45:30, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4059, top5_acc: 0.6720, loss_cls: 3.3366, loss: 3.3366 +2024-07-20 02:21:12,522 - pyskl - INFO - Epoch [104][300/3746] lr: 2.226e-02, eta: 1 day, 15:44:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6552, loss_cls: 3.4135, loss: 3.4135 +2024-07-20 02:22:34,762 - pyskl - INFO - Epoch [104][400/3746] lr: 2.224e-02, eta: 1 day, 15:42:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3994, top5_acc: 0.6689, loss_cls: 3.3787, loss: 3.3787 +2024-07-20 02:23:56,904 - pyskl - INFO - Epoch [104][500/3746] lr: 2.221e-02, eta: 1 day, 15:41:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3903, top5_acc: 0.6589, loss_cls: 3.3995, loss: 3.3995 +2024-07-20 02:25:18,644 - pyskl - INFO - Epoch [104][600/3746] lr: 2.219e-02, eta: 1 day, 15:40:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6558, loss_cls: 3.4040, loss: 3.4040 +2024-07-20 02:26:39,997 - pyskl - INFO - Epoch [104][700/3746] lr: 2.217e-02, eta: 1 day, 15:38:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6658, loss_cls: 3.3642, loss: 3.3642 +2024-07-20 02:28:01,733 - pyskl - INFO - Epoch [104][800/3746] lr: 2.214e-02, eta: 1 day, 15:37:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6598, loss_cls: 3.3962, loss: 3.3962 +2024-07-20 02:29:23,267 - pyskl - INFO - Epoch [104][900/3746] lr: 2.212e-02, eta: 1 day, 15:36:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3941, top5_acc: 0.6614, loss_cls: 3.3845, loss: 3.3845 +2024-07-20 02:30:44,796 - pyskl - INFO - Epoch [104][1000/3746] lr: 2.210e-02, eta: 1 day, 15:34:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6587, loss_cls: 3.4104, loss: 3.4104 +2024-07-20 02:32:06,854 - pyskl - INFO - Epoch [104][1100/3746] lr: 2.208e-02, eta: 1 day, 15:33:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6569, loss_cls: 3.4000, loss: 3.4000 +2024-07-20 02:33:28,655 - pyskl - INFO - Epoch [104][1200/3746] lr: 2.205e-02, eta: 1 day, 15:31:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6519, loss_cls: 3.4367, loss: 3.4367 +2024-07-20 02:34:49,974 - pyskl - INFO - Epoch [104][1300/3746] lr: 2.203e-02, eta: 1 day, 15:30:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6542, loss_cls: 3.3686, loss: 3.3686 +2024-07-20 02:36:12,166 - pyskl - INFO - Epoch [104][1400/3746] lr: 2.201e-02, eta: 1 day, 15:29:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6553, loss_cls: 3.3941, loss: 3.3941 +2024-07-20 02:37:34,838 - pyskl - INFO - Epoch [104][1500/3746] lr: 2.198e-02, eta: 1 day, 15:27:55, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6566, loss_cls: 3.4437, loss: 3.4437 +2024-07-20 02:38:57,076 - pyskl - INFO - Epoch [104][1600/3746] lr: 2.196e-02, eta: 1 day, 15:26:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6584, loss_cls: 3.3818, loss: 3.3818 +2024-07-20 02:40:19,858 - pyskl - INFO - Epoch [104][1700/3746] lr: 2.194e-02, eta: 1 day, 15:25:13, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6433, loss_cls: 3.4822, loss: 3.4822 +2024-07-20 02:41:42,232 - pyskl - INFO - Epoch [104][1800/3746] lr: 2.191e-02, eta: 1 day, 15:23:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6547, loss_cls: 3.3966, loss: 3.3966 +2024-07-20 02:43:05,212 - pyskl - INFO - Epoch [104][1900/3746] lr: 2.189e-02, eta: 1 day, 15:22:32, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6564, loss_cls: 3.4050, loss: 3.4050 +2024-07-20 02:44:27,271 - pyskl - INFO - Epoch [104][2000/3746] lr: 2.187e-02, eta: 1 day, 15:21:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6567, loss_cls: 3.4091, loss: 3.4091 +2024-07-20 02:45:49,626 - pyskl - INFO - Epoch [104][2100/3746] lr: 2.184e-02, eta: 1 day, 15:19:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6539, loss_cls: 3.4452, loss: 3.4452 +2024-07-20 02:47:11,350 - pyskl - INFO - Epoch [104][2200/3746] lr: 2.182e-02, eta: 1 day, 15:18:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6566, loss_cls: 3.4305, loss: 3.4305 +2024-07-20 02:48:33,140 - pyskl - INFO - Epoch [104][2300/3746] lr: 2.180e-02, eta: 1 day, 15:17:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6456, loss_cls: 3.4700, loss: 3.4700 +2024-07-20 02:49:55,283 - pyskl - INFO - Epoch [104][2400/3746] lr: 2.177e-02, eta: 1 day, 15:15:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6514, loss_cls: 3.4196, loss: 3.4196 +2024-07-20 02:51:17,014 - pyskl - INFO - Epoch [104][2500/3746] lr: 2.175e-02, eta: 1 day, 15:14:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6530, loss_cls: 3.4063, loss: 3.4063 +2024-07-20 02:52:38,955 - pyskl - INFO - Epoch [104][2600/3746] lr: 2.173e-02, eta: 1 day, 15:13:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6567, loss_cls: 3.3973, loss: 3.3973 +2024-07-20 02:54:01,622 - pyskl - INFO - Epoch [104][2700/3746] lr: 2.171e-02, eta: 1 day, 15:11:43, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6558, loss_cls: 3.4141, loss: 3.4141 +2024-07-20 02:55:23,419 - pyskl - INFO - Epoch [104][2800/3746] lr: 2.168e-02, eta: 1 day, 15:10:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3994, top5_acc: 0.6594, loss_cls: 3.4313, loss: 3.4313 +2024-07-20 02:56:45,524 - pyskl - INFO - Epoch [104][2900/3746] lr: 2.166e-02, eta: 1 day, 15:09:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6616, loss_cls: 3.4087, loss: 3.4087 +2024-07-20 02:58:06,937 - pyskl - INFO - Epoch [104][3000/3746] lr: 2.164e-02, eta: 1 day, 15:07:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6541, loss_cls: 3.4062, loss: 3.4062 +2024-07-20 02:59:28,427 - pyskl - INFO - Epoch [104][3100/3746] lr: 2.161e-02, eta: 1 day, 15:06:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4025, top5_acc: 0.6580, loss_cls: 3.3685, loss: 3.3685 +2024-07-20 03:00:50,287 - pyskl - INFO - Epoch [104][3200/3746] lr: 2.159e-02, eta: 1 day, 15:04:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6484, loss_cls: 3.4845, loss: 3.4845 +2024-07-20 03:02:12,243 - pyskl - INFO - Epoch [104][3300/3746] lr: 2.157e-02, eta: 1 day, 15:03:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3970, top5_acc: 0.6598, loss_cls: 3.4107, loss: 3.4107 +2024-07-20 03:03:34,600 - pyskl - INFO - Epoch [104][3400/3746] lr: 2.154e-02, eta: 1 day, 15:02:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6517, loss_cls: 3.4131, loss: 3.4131 +2024-07-20 03:04:56,281 - pyskl - INFO - Epoch [104][3500/3746] lr: 2.152e-02, eta: 1 day, 15:00:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6531, loss_cls: 3.3997, loss: 3.3997 +2024-07-20 03:06:18,442 - pyskl - INFO - Epoch [104][3600/3746] lr: 2.150e-02, eta: 1 day, 14:59:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6545, loss_cls: 3.4404, loss: 3.4404 +2024-07-20 03:07:40,290 - pyskl - INFO - Epoch [104][3700/3746] lr: 2.148e-02, eta: 1 day, 14:58:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3927, top5_acc: 0.6597, loss_cls: 3.4116, loss: 3.4116 +2024-07-20 03:08:19,835 - pyskl - INFO - Saving checkpoint at 104 epochs +2024-07-20 03:10:12,017 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 03:10:12,708 - pyskl - INFO - +top1_acc 0.3331 +top5_acc 0.5937 +2024-07-20 03:10:12,708 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 03:10:12,750 - pyskl - INFO - +mean_acc 0.3328 +2024-07-20 03:10:12,755 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_102.pth was removed +2024-07-20 03:10:13,013 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_104.pth. +2024-07-20 03:10:13,014 - pyskl - INFO - Best top1_acc is 0.3331 at 104 epoch. +2024-07-20 03:10:13,027 - pyskl - INFO - Epoch(val) [104][309] top1_acc: 0.3331, top5_acc: 0.5937, mean_class_accuracy: 0.3328 +2024-07-20 03:14:07,765 - pyskl - INFO - Epoch [105][100/3746] lr: 2.144e-02, eta: 1 day, 14:57:04, time: 2.347, data_time: 1.358, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6708, loss_cls: 3.3145, loss: 3.3145 +2024-07-20 03:15:30,469 - pyskl - INFO - Epoch [105][200/3746] lr: 2.142e-02, eta: 1 day, 14:55:43, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6708, loss_cls: 3.3176, loss: 3.3176 +2024-07-20 03:16:52,846 - pyskl - INFO - Epoch [105][300/3746] lr: 2.140e-02, eta: 1 day, 14:54:22, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6620, loss_cls: 3.3828, loss: 3.3828 +2024-07-20 03:18:14,386 - pyskl - INFO - Epoch [105][400/3746] lr: 2.137e-02, eta: 1 day, 14:53:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6625, loss_cls: 3.3726, loss: 3.3726 +2024-07-20 03:19:35,911 - pyskl - INFO - Epoch [105][500/3746] lr: 2.135e-02, eta: 1 day, 14:51:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6503, loss_cls: 3.4171, loss: 3.4171 +2024-07-20 03:20:58,017 - pyskl - INFO - Epoch [105][600/3746] lr: 2.133e-02, eta: 1 day, 14:50:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3989, top5_acc: 0.6573, loss_cls: 3.4026, loss: 3.4026 +2024-07-20 03:22:19,759 - pyskl - INFO - Epoch [105][700/3746] lr: 2.130e-02, eta: 1 day, 14:48:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3980, top5_acc: 0.6648, loss_cls: 3.3602, loss: 3.3602 +2024-07-20 03:23:41,425 - pyskl - INFO - Epoch [105][800/3746] lr: 2.128e-02, eta: 1 day, 14:47:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6641, loss_cls: 3.3985, loss: 3.3985 +2024-07-20 03:25:03,041 - pyskl - INFO - Epoch [105][900/3746] lr: 2.126e-02, eta: 1 day, 14:46:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4005, top5_acc: 0.6587, loss_cls: 3.3610, loss: 3.3610 +2024-07-20 03:26:24,623 - pyskl - INFO - Epoch [105][1000/3746] lr: 2.124e-02, eta: 1 day, 14:44:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6534, loss_cls: 3.3518, loss: 3.3518 +2024-07-20 03:27:46,478 - pyskl - INFO - Epoch [105][1100/3746] lr: 2.121e-02, eta: 1 day, 14:43:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6514, loss_cls: 3.4002, loss: 3.4002 +2024-07-20 03:29:08,747 - pyskl - INFO - Epoch [105][1200/3746] lr: 2.119e-02, eta: 1 day, 14:42:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6656, loss_cls: 3.3416, loss: 3.3416 +2024-07-20 03:30:30,282 - pyskl - INFO - Epoch [105][1300/3746] lr: 2.117e-02, eta: 1 day, 14:40:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3994, top5_acc: 0.6536, loss_cls: 3.3786, loss: 3.3786 +2024-07-20 03:31:52,053 - pyskl - INFO - Epoch [105][1400/3746] lr: 2.114e-02, eta: 1 day, 14:39:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6634, loss_cls: 3.3762, loss: 3.3762 +2024-07-20 03:33:14,721 - pyskl - INFO - Epoch [105][1500/3746] lr: 2.112e-02, eta: 1 day, 14:38:07, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6552, loss_cls: 3.3988, loss: 3.3988 +2024-07-20 03:34:36,776 - pyskl - INFO - Epoch [105][1600/3746] lr: 2.110e-02, eta: 1 day, 14:36:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6616, loss_cls: 3.3422, loss: 3.3422 +2024-07-20 03:35:58,900 - pyskl - INFO - Epoch [105][1700/3746] lr: 2.108e-02, eta: 1 day, 14:35:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6547, loss_cls: 3.4102, loss: 3.4102 +2024-07-20 03:37:20,773 - pyskl - INFO - Epoch [105][1800/3746] lr: 2.105e-02, eta: 1 day, 14:34:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3903, top5_acc: 0.6519, loss_cls: 3.4276, loss: 3.4276 +2024-07-20 03:38:43,014 - pyskl - INFO - Epoch [105][1900/3746] lr: 2.103e-02, eta: 1 day, 14:32:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6550, loss_cls: 3.4029, loss: 3.4029 +2024-07-20 03:40:05,265 - pyskl - INFO - Epoch [105][2000/3746] lr: 2.101e-02, eta: 1 day, 14:31:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6545, loss_cls: 3.4196, loss: 3.4196 +2024-07-20 03:41:27,855 - pyskl - INFO - Epoch [105][2100/3746] lr: 2.098e-02, eta: 1 day, 14:30:00, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3997, top5_acc: 0.6681, loss_cls: 3.3591, loss: 3.3591 +2024-07-20 03:42:49,032 - pyskl - INFO - Epoch [105][2200/3746] lr: 2.096e-02, eta: 1 day, 14:28:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6642, loss_cls: 3.3906, loss: 3.3906 +2024-07-20 03:44:10,813 - pyskl - INFO - Epoch [105][2300/3746] lr: 2.094e-02, eta: 1 day, 14:27:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6480, loss_cls: 3.4444, loss: 3.4444 +2024-07-20 03:45:33,084 - pyskl - INFO - Epoch [105][2400/3746] lr: 2.092e-02, eta: 1 day, 14:25:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6562, loss_cls: 3.4112, loss: 3.4112 +2024-07-20 03:46:55,121 - pyskl - INFO - Epoch [105][2500/3746] lr: 2.089e-02, eta: 1 day, 14:24:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6536, loss_cls: 3.4072, loss: 3.4072 +2024-07-20 03:48:18,130 - pyskl - INFO - Epoch [105][2600/3746] lr: 2.087e-02, eta: 1 day, 14:23:14, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6506, loss_cls: 3.4087, loss: 3.4087 +2024-07-20 03:49:40,040 - pyskl - INFO - Epoch [105][2700/3746] lr: 2.085e-02, eta: 1 day, 14:21:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6422, loss_cls: 3.4396, loss: 3.4396 +2024-07-20 03:51:01,552 - pyskl - INFO - Epoch [105][2800/3746] lr: 2.083e-02, eta: 1 day, 14:20:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3966, top5_acc: 0.6575, loss_cls: 3.4061, loss: 3.4061 +2024-07-20 03:52:23,603 - pyskl - INFO - Epoch [105][2900/3746] lr: 2.080e-02, eta: 1 day, 14:19:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3945, top5_acc: 0.6630, loss_cls: 3.3713, loss: 3.3713 +2024-07-20 03:53:45,225 - pyskl - INFO - Epoch [105][3000/3746] lr: 2.078e-02, eta: 1 day, 14:17:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6594, loss_cls: 3.4072, loss: 3.4072 +2024-07-20 03:55:06,972 - pyskl - INFO - Epoch [105][3100/3746] lr: 2.076e-02, eta: 1 day, 14:16:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6503, loss_cls: 3.4056, loss: 3.4056 +2024-07-20 03:56:28,558 - pyskl - INFO - Epoch [105][3200/3746] lr: 2.073e-02, eta: 1 day, 14:15:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3970, top5_acc: 0.6684, loss_cls: 3.3710, loss: 3.3710 +2024-07-20 03:57:50,516 - pyskl - INFO - Epoch [105][3300/3746] lr: 2.071e-02, eta: 1 day, 14:13:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6567, loss_cls: 3.4015, loss: 3.4015 +2024-07-20 03:59:12,834 - pyskl - INFO - Epoch [105][3400/3746] lr: 2.069e-02, eta: 1 day, 14:12:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3909, top5_acc: 0.6514, loss_cls: 3.3914, loss: 3.3914 +2024-07-20 04:00:34,845 - pyskl - INFO - Epoch [105][3500/3746] lr: 2.067e-02, eta: 1 day, 14:11:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6509, loss_cls: 3.4120, loss: 3.4120 +2024-07-20 04:01:56,151 - pyskl - INFO - Epoch [105][3600/3746] lr: 2.064e-02, eta: 1 day, 14:09:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6600, loss_cls: 3.3822, loss: 3.3822 +2024-07-20 04:03:18,156 - pyskl - INFO - Epoch [105][3700/3746] lr: 2.062e-02, eta: 1 day, 14:08:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6719, loss_cls: 3.3501, loss: 3.3501 +2024-07-20 04:03:57,769 - pyskl - INFO - Saving checkpoint at 105 epochs +2024-07-20 04:05:49,775 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 04:05:50,485 - pyskl - INFO - +top1_acc 0.3075 +top5_acc 0.5669 +2024-07-20 04:05:50,485 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 04:05:50,528 - pyskl - INFO - +mean_acc 0.3072 +2024-07-20 04:05:50,541 - pyskl - INFO - Epoch(val) [105][309] top1_acc: 0.3075, top5_acc: 0.5669, mean_class_accuracy: 0.3072 +2024-07-20 04:09:42,537 - pyskl - INFO - Epoch [106][100/3746] lr: 2.059e-02, eta: 1 day, 14:07:10, time: 2.320, data_time: 1.333, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6725, loss_cls: 3.2962, loss: 3.2962 +2024-07-20 04:11:04,818 - pyskl - INFO - Epoch [106][200/3746] lr: 2.057e-02, eta: 1 day, 14:05:49, time: 0.823, data_time: 0.001, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6692, loss_cls: 3.3081, loss: 3.3081 +2024-07-20 04:12:26,698 - pyskl - INFO - Epoch [106][300/3746] lr: 2.054e-02, eta: 1 day, 14:04:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6711, loss_cls: 3.3545, loss: 3.3545 +2024-07-20 04:13:49,057 - pyskl - INFO - Epoch [106][400/3746] lr: 2.052e-02, eta: 1 day, 14:03:07, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3927, top5_acc: 0.6616, loss_cls: 3.3676, loss: 3.3676 +2024-07-20 04:15:10,912 - pyskl - INFO - Epoch [106][500/3746] lr: 2.050e-02, eta: 1 day, 14:01:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6670, loss_cls: 3.3638, loss: 3.3638 +2024-07-20 04:16:32,968 - pyskl - INFO - Epoch [106][600/3746] lr: 2.048e-02, eta: 1 day, 14:00:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6698, loss_cls: 3.3407, loss: 3.3407 +2024-07-20 04:17:54,581 - pyskl - INFO - Epoch [106][700/3746] lr: 2.045e-02, eta: 1 day, 13:59:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6611, loss_cls: 3.4010, loss: 3.4010 +2024-07-20 04:19:16,165 - pyskl - INFO - Epoch [106][800/3746] lr: 2.043e-02, eta: 1 day, 13:57:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3980, top5_acc: 0.6583, loss_cls: 3.3689, loss: 3.3689 +2024-07-20 04:20:37,794 - pyskl - INFO - Epoch [106][900/3746] lr: 2.041e-02, eta: 1 day, 13:56:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4211, top5_acc: 0.6695, loss_cls: 3.2981, loss: 3.2981 +2024-07-20 04:21:59,514 - pyskl - INFO - Epoch [106][1000/3746] lr: 2.039e-02, eta: 1 day, 13:54:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6614, loss_cls: 3.3793, loss: 3.3793 +2024-07-20 04:23:21,239 - pyskl - INFO - Epoch [106][1100/3746] lr: 2.036e-02, eta: 1 day, 13:53:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6633, loss_cls: 3.3498, loss: 3.3498 +2024-07-20 04:24:42,744 - pyskl - INFO - Epoch [106][1200/3746] lr: 2.034e-02, eta: 1 day, 13:52:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3997, top5_acc: 0.6620, loss_cls: 3.3558, loss: 3.3558 +2024-07-20 04:26:05,069 - pyskl - INFO - Epoch [106][1300/3746] lr: 2.032e-02, eta: 1 day, 13:50:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3980, top5_acc: 0.6677, loss_cls: 3.3466, loss: 3.3466 +2024-07-20 04:27:26,897 - pyskl - INFO - Epoch [106][1400/3746] lr: 2.030e-02, eta: 1 day, 13:49:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6691, loss_cls: 3.3215, loss: 3.3215 +2024-07-20 04:28:49,202 - pyskl - INFO - Epoch [106][1500/3746] lr: 2.027e-02, eta: 1 day, 13:48:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3992, top5_acc: 0.6686, loss_cls: 3.3467, loss: 3.3467 +2024-07-20 04:30:10,883 - pyskl - INFO - Epoch [106][1600/3746] lr: 2.025e-02, eta: 1 day, 13:46:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6547, loss_cls: 3.3868, loss: 3.3868 +2024-07-20 04:31:33,665 - pyskl - INFO - Epoch [106][1700/3746] lr: 2.023e-02, eta: 1 day, 13:45:30, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6609, loss_cls: 3.3916, loss: 3.3916 +2024-07-20 04:32:55,659 - pyskl - INFO - Epoch [106][1800/3746] lr: 2.021e-02, eta: 1 day, 13:44:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6573, loss_cls: 3.3739, loss: 3.3739 +2024-07-20 04:34:17,850 - pyskl - INFO - Epoch [106][1900/3746] lr: 2.018e-02, eta: 1 day, 13:42:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6603, loss_cls: 3.3543, loss: 3.3543 +2024-07-20 04:35:40,183 - pyskl - INFO - Epoch [106][2000/3746] lr: 2.016e-02, eta: 1 day, 13:41:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6541, loss_cls: 3.4055, loss: 3.4055 +2024-07-20 04:37:02,973 - pyskl - INFO - Epoch [106][2100/3746] lr: 2.014e-02, eta: 1 day, 13:40:06, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3989, top5_acc: 0.6603, loss_cls: 3.3819, loss: 3.3819 +2024-07-20 04:38:25,457 - pyskl - INFO - Epoch [106][2200/3746] lr: 2.012e-02, eta: 1 day, 13:38:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3898, top5_acc: 0.6681, loss_cls: 3.3741, loss: 3.3741 +2024-07-20 04:39:48,188 - pyskl - INFO - Epoch [106][2300/3746] lr: 2.009e-02, eta: 1 day, 13:37:24, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6627, loss_cls: 3.3656, loss: 3.3656 +2024-07-20 04:41:11,273 - pyskl - INFO - Epoch [106][2400/3746] lr: 2.007e-02, eta: 1 day, 13:36:03, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3909, top5_acc: 0.6567, loss_cls: 3.4269, loss: 3.4269 +2024-07-20 04:42:33,111 - pyskl - INFO - Epoch [106][2500/3746] lr: 2.005e-02, eta: 1 day, 13:34:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6637, loss_cls: 3.3886, loss: 3.3886 +2024-07-20 04:43:55,518 - pyskl - INFO - Epoch [106][2600/3746] lr: 2.003e-02, eta: 1 day, 13:33:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3972, top5_acc: 0.6558, loss_cls: 3.3865, loss: 3.3865 +2024-07-20 04:45:17,491 - pyskl - INFO - Epoch [106][2700/3746] lr: 2.000e-02, eta: 1 day, 13:31:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6645, loss_cls: 3.3930, loss: 3.3930 +2024-07-20 04:46:39,288 - pyskl - INFO - Epoch [106][2800/3746] lr: 1.998e-02, eta: 1 day, 13:30:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6580, loss_cls: 3.4002, loss: 3.4002 +2024-07-20 04:48:00,944 - pyskl - INFO - Epoch [106][2900/3746] lr: 1.996e-02, eta: 1 day, 13:29:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4009, top5_acc: 0.6581, loss_cls: 3.4164, loss: 3.4164 +2024-07-20 04:49:22,534 - pyskl - INFO - Epoch [106][3000/3746] lr: 1.994e-02, eta: 1 day, 13:27:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6631, loss_cls: 3.3539, loss: 3.3539 +2024-07-20 04:50:44,606 - pyskl - INFO - Epoch [106][3100/3746] lr: 1.991e-02, eta: 1 day, 13:26:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6672, loss_cls: 3.3561, loss: 3.3561 +2024-07-20 04:52:06,234 - pyskl - INFO - Epoch [106][3200/3746] lr: 1.989e-02, eta: 1 day, 13:25:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6559, loss_cls: 3.3760, loss: 3.3760 +2024-07-20 04:53:27,917 - pyskl - INFO - Epoch [106][3300/3746] lr: 1.987e-02, eta: 1 day, 13:23:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4005, top5_acc: 0.6653, loss_cls: 3.3639, loss: 3.3639 +2024-07-20 04:54:49,458 - pyskl - INFO - Epoch [106][3400/3746] lr: 1.985e-02, eta: 1 day, 13:22:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6594, loss_cls: 3.3734, loss: 3.3734 +2024-07-20 04:56:11,030 - pyskl - INFO - Epoch [106][3500/3746] lr: 1.983e-02, eta: 1 day, 13:21:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6578, loss_cls: 3.4004, loss: 3.4004 +2024-07-20 04:57:32,632 - pyskl - INFO - Epoch [106][3600/3746] lr: 1.980e-02, eta: 1 day, 13:19:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6553, loss_cls: 3.3819, loss: 3.3819 +2024-07-20 04:58:54,374 - pyskl - INFO - Epoch [106][3700/3746] lr: 1.978e-02, eta: 1 day, 13:18:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6645, loss_cls: 3.3629, loss: 3.3629 +2024-07-20 04:59:33,837 - pyskl - INFO - Saving checkpoint at 106 epochs +2024-07-20 05:01:25,223 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 05:01:25,893 - pyskl - INFO - +top1_acc 0.3334 +top5_acc 0.5952 +2024-07-20 05:01:25,893 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 05:01:25,937 - pyskl - INFO - +mean_acc 0.3330 +2024-07-20 05:01:25,942 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_104.pth was removed +2024-07-20 05:01:26,209 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_106.pth. +2024-07-20 05:01:26,210 - pyskl - INFO - Best top1_acc is 0.3334 at 106 epoch. +2024-07-20 05:01:26,224 - pyskl - INFO - Epoch(val) [106][309] top1_acc: 0.3334, top5_acc: 0.5952, mean_class_accuracy: 0.3330 +2024-07-20 05:05:20,132 - pyskl - INFO - Epoch [107][100/3746] lr: 1.975e-02, eta: 1 day, 13:17:14, time: 2.339, data_time: 1.353, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6839, loss_cls: 3.2363, loss: 3.2363 +2024-07-20 05:06:42,319 - pyskl - INFO - Epoch [107][200/3746] lr: 1.973e-02, eta: 1 day, 13:15:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6733, loss_cls: 3.3041, loss: 3.3041 +2024-07-20 05:08:04,492 - pyskl - INFO - Epoch [107][300/3746] lr: 1.970e-02, eta: 1 day, 13:14:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6731, loss_cls: 3.2866, loss: 3.2866 +2024-07-20 05:09:26,558 - pyskl - INFO - Epoch [107][400/3746] lr: 1.968e-02, eta: 1 day, 13:13:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6619, loss_cls: 3.3344, loss: 3.3344 +2024-07-20 05:10:48,545 - pyskl - INFO - Epoch [107][500/3746] lr: 1.966e-02, eta: 1 day, 13:11:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4117, top5_acc: 0.6798, loss_cls: 3.2565, loss: 3.2565 +2024-07-20 05:12:10,214 - pyskl - INFO - Epoch [107][600/3746] lr: 1.964e-02, eta: 1 day, 13:10:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6711, loss_cls: 3.3177, loss: 3.3177 +2024-07-20 05:13:32,863 - pyskl - INFO - Epoch [107][700/3746] lr: 1.961e-02, eta: 1 day, 13:09:07, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6620, loss_cls: 3.3309, loss: 3.3309 +2024-07-20 05:14:54,532 - pyskl - INFO - Epoch [107][800/3746] lr: 1.959e-02, eta: 1 day, 13:07:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4072, top5_acc: 0.6639, loss_cls: 3.3378, loss: 3.3378 +2024-07-20 05:16:16,194 - pyskl - INFO - Epoch [107][900/3746] lr: 1.957e-02, eta: 1 day, 13:06:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6592, loss_cls: 3.3598, loss: 3.3598 +2024-07-20 05:17:38,390 - pyskl - INFO - Epoch [107][1000/3746] lr: 1.955e-02, eta: 1 day, 13:05:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4070, top5_acc: 0.6714, loss_cls: 3.3249, loss: 3.3249 +2024-07-20 05:19:00,314 - pyskl - INFO - Epoch [107][1100/3746] lr: 1.953e-02, eta: 1 day, 13:03:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4091, top5_acc: 0.6661, loss_cls: 3.3118, loss: 3.3118 +2024-07-20 05:20:22,346 - pyskl - INFO - Epoch [107][1200/3746] lr: 1.950e-02, eta: 1 day, 13:02:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6533, loss_cls: 3.4115, loss: 3.4115 +2024-07-20 05:21:44,208 - pyskl - INFO - Epoch [107][1300/3746] lr: 1.948e-02, eta: 1 day, 13:00:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6791, loss_cls: 3.2956, loss: 3.2956 +2024-07-20 05:23:05,774 - pyskl - INFO - Epoch [107][1400/3746] lr: 1.946e-02, eta: 1 day, 12:59:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4098, top5_acc: 0.6783, loss_cls: 3.2766, loss: 3.2766 +2024-07-20 05:24:27,842 - pyskl - INFO - Epoch [107][1500/3746] lr: 1.944e-02, eta: 1 day, 12:58:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6614, loss_cls: 3.3529, loss: 3.3529 +2024-07-20 05:25:49,608 - pyskl - INFO - Epoch [107][1600/3746] lr: 1.942e-02, eta: 1 day, 12:56:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3997, top5_acc: 0.6633, loss_cls: 3.3788, loss: 3.3788 +2024-07-20 05:27:11,837 - pyskl - INFO - Epoch [107][1700/3746] lr: 1.939e-02, eta: 1 day, 12:55:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6652, loss_cls: 3.3693, loss: 3.3693 +2024-07-20 05:28:33,466 - pyskl - INFO - Epoch [107][1800/3746] lr: 1.937e-02, eta: 1 day, 12:54:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3898, top5_acc: 0.6655, loss_cls: 3.3859, loss: 3.3859 +2024-07-20 05:29:56,115 - pyskl - INFO - Epoch [107][1900/3746] lr: 1.935e-02, eta: 1 day, 12:52:51, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6545, loss_cls: 3.3909, loss: 3.3909 +2024-07-20 05:31:18,261 - pyskl - INFO - Epoch [107][2000/3746] lr: 1.933e-02, eta: 1 day, 12:51:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6611, loss_cls: 3.3867, loss: 3.3867 +2024-07-20 05:32:40,788 - pyskl - INFO - Epoch [107][2100/3746] lr: 1.930e-02, eta: 1 day, 12:50:09, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6692, loss_cls: 3.3504, loss: 3.3504 +2024-07-20 05:34:02,203 - pyskl - INFO - Epoch [107][2200/3746] lr: 1.928e-02, eta: 1 day, 12:48:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6536, loss_cls: 3.4105, loss: 3.4105 +2024-07-20 05:35:23,865 - pyskl - INFO - Epoch [107][2300/3746] lr: 1.926e-02, eta: 1 day, 12:47:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6642, loss_cls: 3.3473, loss: 3.3473 +2024-07-20 05:36:47,091 - pyskl - INFO - Epoch [107][2400/3746] lr: 1.924e-02, eta: 1 day, 12:46:05, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4006, top5_acc: 0.6673, loss_cls: 3.3528, loss: 3.3528 +2024-07-20 05:38:09,641 - pyskl - INFO - Epoch [107][2500/3746] lr: 1.922e-02, eta: 1 day, 12:44:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6591, loss_cls: 3.3688, loss: 3.3688 +2024-07-20 05:39:31,792 - pyskl - INFO - Epoch [107][2600/3746] lr: 1.919e-02, eta: 1 day, 12:43:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6625, loss_cls: 3.3553, loss: 3.3553 +2024-07-20 05:40:53,397 - pyskl - INFO - Epoch [107][2700/3746] lr: 1.917e-02, eta: 1 day, 12:42:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6561, loss_cls: 3.3776, loss: 3.3776 +2024-07-20 05:42:15,537 - pyskl - INFO - Epoch [107][2800/3746] lr: 1.915e-02, eta: 1 day, 12:40:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4059, top5_acc: 0.6631, loss_cls: 3.3340, loss: 3.3340 +2024-07-20 05:43:37,293 - pyskl - INFO - Epoch [107][2900/3746] lr: 1.913e-02, eta: 1 day, 12:39:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6637, loss_cls: 3.3737, loss: 3.3737 +2024-07-20 05:44:59,521 - pyskl - INFO - Epoch [107][3000/3746] lr: 1.911e-02, eta: 1 day, 12:37:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6602, loss_cls: 3.3853, loss: 3.3853 +2024-07-20 05:46:21,931 - pyskl - INFO - Epoch [107][3100/3746] lr: 1.908e-02, eta: 1 day, 12:36:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3994, top5_acc: 0.6658, loss_cls: 3.3518, loss: 3.3518 +2024-07-20 05:47:43,714 - pyskl - INFO - Epoch [107][3200/3746] lr: 1.906e-02, eta: 1 day, 12:35:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6534, loss_cls: 3.4357, loss: 3.4357 +2024-07-20 05:49:05,330 - pyskl - INFO - Epoch [107][3300/3746] lr: 1.904e-02, eta: 1 day, 12:33:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6686, loss_cls: 3.3467, loss: 3.3467 +2024-07-20 05:50:26,856 - pyskl - INFO - Epoch [107][3400/3746] lr: 1.902e-02, eta: 1 day, 12:32:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6550, loss_cls: 3.4256, loss: 3.4256 +2024-07-20 05:51:48,352 - pyskl - INFO - Epoch [107][3500/3746] lr: 1.900e-02, eta: 1 day, 12:31:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6556, loss_cls: 3.3892, loss: 3.3892 +2024-07-20 05:53:10,436 - pyskl - INFO - Epoch [107][3600/3746] lr: 1.897e-02, eta: 1 day, 12:29:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6630, loss_cls: 3.3581, loss: 3.3581 +2024-07-20 05:54:32,235 - pyskl - INFO - Epoch [107][3700/3746] lr: 1.895e-02, eta: 1 day, 12:28:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6650, loss_cls: 3.3286, loss: 3.3286 +2024-07-20 05:55:11,885 - pyskl - INFO - Saving checkpoint at 107 epochs +2024-07-20 05:57:05,050 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 05:57:05,715 - pyskl - INFO - +top1_acc 0.3233 +top5_acc 0.5854 +2024-07-20 05:57:05,715 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 05:57:05,759 - pyskl - INFO - +mean_acc 0.3231 +2024-07-20 05:57:05,773 - pyskl - INFO - Epoch(val) [107][309] top1_acc: 0.3233, top5_acc: 0.5854, mean_class_accuracy: 0.3231 +2024-07-20 06:00:53,994 - pyskl - INFO - Epoch [108][100/3746] lr: 1.892e-02, eta: 1 day, 12:27:13, time: 2.282, data_time: 1.292, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6759, loss_cls: 3.3032, loss: 3.3032 +2024-07-20 06:02:15,874 - pyskl - INFO - Epoch [108][200/3746] lr: 1.890e-02, eta: 1 day, 12:25:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4202, top5_acc: 0.6880, loss_cls: 3.2379, loss: 3.2379 +2024-07-20 06:03:38,020 - pyskl - INFO - Epoch [108][300/3746] lr: 1.888e-02, eta: 1 day, 12:24:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6689, loss_cls: 3.3027, loss: 3.3027 +2024-07-20 06:04:59,596 - pyskl - INFO - Epoch [108][400/3746] lr: 1.886e-02, eta: 1 day, 12:23:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6748, loss_cls: 3.2957, loss: 3.2957 +2024-07-20 06:06:21,195 - pyskl - INFO - Epoch [108][500/3746] lr: 1.883e-02, eta: 1 day, 12:21:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3997, top5_acc: 0.6711, loss_cls: 3.3346, loss: 3.3346 +2024-07-20 06:07:42,838 - pyskl - INFO - Epoch [108][600/3746] lr: 1.881e-02, eta: 1 day, 12:20:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6652, loss_cls: 3.3259, loss: 3.3259 +2024-07-20 06:09:04,345 - pyskl - INFO - Epoch [108][700/3746] lr: 1.879e-02, eta: 1 day, 12:19:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6648, loss_cls: 3.3546, loss: 3.3546 +2024-07-20 06:10:25,801 - pyskl - INFO - Epoch [108][800/3746] lr: 1.877e-02, eta: 1 day, 12:17:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6719, loss_cls: 3.2685, loss: 3.2685 +2024-07-20 06:11:47,742 - pyskl - INFO - Epoch [108][900/3746] lr: 1.875e-02, eta: 1 day, 12:16:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6755, loss_cls: 3.2880, loss: 3.2880 +2024-07-20 06:13:09,952 - pyskl - INFO - Epoch [108][1000/3746] lr: 1.872e-02, eta: 1 day, 12:15:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6681, loss_cls: 3.3337, loss: 3.3337 +2024-07-20 06:14:31,992 - pyskl - INFO - Epoch [108][1100/3746] lr: 1.870e-02, eta: 1 day, 12:13:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4042, top5_acc: 0.6687, loss_cls: 3.3102, loss: 3.3102 +2024-07-20 06:15:53,649 - pyskl - INFO - Epoch [108][1200/3746] lr: 1.868e-02, eta: 1 day, 12:12:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6714, loss_cls: 3.3480, loss: 3.3480 +2024-07-20 06:17:15,266 - pyskl - INFO - Epoch [108][1300/3746] lr: 1.866e-02, eta: 1 day, 12:10:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6727, loss_cls: 3.3111, loss: 3.3111 +2024-07-20 06:18:37,035 - pyskl - INFO - Epoch [108][1400/3746] lr: 1.864e-02, eta: 1 day, 12:09:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6783, loss_cls: 3.2876, loss: 3.2876 +2024-07-20 06:19:59,226 - pyskl - INFO - Epoch [108][1500/3746] lr: 1.862e-02, eta: 1 day, 12:08:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6613, loss_cls: 3.3550, loss: 3.3550 +2024-07-20 06:21:21,413 - pyskl - INFO - Epoch [108][1600/3746] lr: 1.859e-02, eta: 1 day, 12:06:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4078, top5_acc: 0.6714, loss_cls: 3.2933, loss: 3.2933 +2024-07-20 06:22:44,339 - pyskl - INFO - Epoch [108][1700/3746] lr: 1.857e-02, eta: 1 day, 12:05:31, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6552, loss_cls: 3.3352, loss: 3.3352 +2024-07-20 06:24:06,359 - pyskl - INFO - Epoch [108][1800/3746] lr: 1.855e-02, eta: 1 day, 12:04:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6784, loss_cls: 3.2755, loss: 3.2755 +2024-07-20 06:25:28,558 - pyskl - INFO - Epoch [108][1900/3746] lr: 1.853e-02, eta: 1 day, 12:02:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6609, loss_cls: 3.3834, loss: 3.3834 +2024-07-20 06:26:51,095 - pyskl - INFO - Epoch [108][2000/3746] lr: 1.851e-02, eta: 1 day, 12:01:27, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6594, loss_cls: 3.3664, loss: 3.3664 +2024-07-20 06:28:13,637 - pyskl - INFO - Epoch [108][2100/3746] lr: 1.848e-02, eta: 1 day, 12:00:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6733, loss_cls: 3.3398, loss: 3.3398 +2024-07-20 06:29:35,664 - pyskl - INFO - Epoch [108][2200/3746] lr: 1.846e-02, eta: 1 day, 11:58:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4059, top5_acc: 0.6689, loss_cls: 3.3241, loss: 3.3241 +2024-07-20 06:30:58,021 - pyskl - INFO - Epoch [108][2300/3746] lr: 1.844e-02, eta: 1 day, 11:57:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6684, loss_cls: 3.3028, loss: 3.3028 +2024-07-20 06:32:19,762 - pyskl - INFO - Epoch [108][2400/3746] lr: 1.842e-02, eta: 1 day, 11:56:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6655, loss_cls: 3.3334, loss: 3.3334 +2024-07-20 06:33:42,759 - pyskl - INFO - Epoch [108][2500/3746] lr: 1.840e-02, eta: 1 day, 11:54:41, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4005, top5_acc: 0.6613, loss_cls: 3.3808, loss: 3.3808 +2024-07-20 06:35:04,564 - pyskl - INFO - Epoch [108][2600/3746] lr: 1.838e-02, eta: 1 day, 11:53:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6581, loss_cls: 3.3932, loss: 3.3932 +2024-07-20 06:36:26,886 - pyskl - INFO - Epoch [108][2700/3746] lr: 1.835e-02, eta: 1 day, 11:51:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6555, loss_cls: 3.3953, loss: 3.3953 +2024-07-20 06:37:48,776 - pyskl - INFO - Epoch [108][2800/3746] lr: 1.833e-02, eta: 1 day, 11:50:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6659, loss_cls: 3.3464, loss: 3.3464 +2024-07-20 06:39:10,760 - pyskl - INFO - Epoch [108][2900/3746] lr: 1.831e-02, eta: 1 day, 11:49:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6687, loss_cls: 3.3428, loss: 3.3428 +2024-07-20 06:40:32,621 - pyskl - INFO - Epoch [108][3000/3746] lr: 1.829e-02, eta: 1 day, 11:47:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6695, loss_cls: 3.3167, loss: 3.3167 +2024-07-20 06:41:54,499 - pyskl - INFO - Epoch [108][3100/3746] lr: 1.827e-02, eta: 1 day, 11:46:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6739, loss_cls: 3.2956, loss: 3.2956 +2024-07-20 06:43:16,440 - pyskl - INFO - Epoch [108][3200/3746] lr: 1.825e-02, eta: 1 day, 11:45:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6681, loss_cls: 3.3273, loss: 3.3273 +2024-07-20 06:44:38,162 - pyskl - INFO - Epoch [108][3300/3746] lr: 1.823e-02, eta: 1 day, 11:43:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6648, loss_cls: 3.3782, loss: 3.3782 +2024-07-20 06:46:00,463 - pyskl - INFO - Epoch [108][3400/3746] lr: 1.820e-02, eta: 1 day, 11:42:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6675, loss_cls: 3.3429, loss: 3.3429 +2024-07-20 06:47:22,502 - pyskl - INFO - Epoch [108][3500/3746] lr: 1.818e-02, eta: 1 day, 11:41:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4006, top5_acc: 0.6603, loss_cls: 3.3622, loss: 3.3622 +2024-07-20 06:48:44,831 - pyskl - INFO - Epoch [108][3600/3746] lr: 1.816e-02, eta: 1 day, 11:39:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6627, loss_cls: 3.3309, loss: 3.3309 +2024-07-20 06:50:06,665 - pyskl - INFO - Epoch [108][3700/3746] lr: 1.814e-02, eta: 1 day, 11:38:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4059, top5_acc: 0.6752, loss_cls: 3.3028, loss: 3.3028 +2024-07-20 06:50:46,360 - pyskl - INFO - Saving checkpoint at 108 epochs +2024-07-20 06:52:38,850 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 06:52:39,523 - pyskl - INFO - +top1_acc 0.3313 +top5_acc 0.5941 +2024-07-20 06:52:39,524 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 06:52:39,567 - pyskl - INFO - +mean_acc 0.3311 +2024-07-20 06:52:39,580 - pyskl - INFO - Epoch(val) [108][309] top1_acc: 0.3313, top5_acc: 0.5941, mean_class_accuracy: 0.3311 +2024-07-20 06:56:33,754 - pyskl - INFO - Epoch [109][100/3746] lr: 1.811e-02, eta: 1 day, 11:37:11, time: 2.342, data_time: 1.347, memory: 15990, top1_acc: 0.4098, top5_acc: 0.6745, loss_cls: 3.2784, loss: 3.2784 +2024-07-20 06:57:55,781 - pyskl - INFO - Epoch [109][200/3746] lr: 1.809e-02, eta: 1 day, 11:35:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4142, top5_acc: 0.6833, loss_cls: 3.2459, loss: 3.2459 +2024-07-20 06:59:17,996 - pyskl - INFO - Epoch [109][300/3746] lr: 1.806e-02, eta: 1 day, 11:34:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4220, top5_acc: 0.6850, loss_cls: 3.2280, loss: 3.2280 +2024-07-20 07:00:39,797 - pyskl - INFO - Epoch [109][400/3746] lr: 1.804e-02, eta: 1 day, 11:33:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6703, loss_cls: 3.3048, loss: 3.3048 +2024-07-20 07:02:01,711 - pyskl - INFO - Epoch [109][500/3746] lr: 1.802e-02, eta: 1 day, 11:31:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6759, loss_cls: 3.2679, loss: 3.2679 +2024-07-20 07:03:23,894 - pyskl - INFO - Epoch [109][600/3746] lr: 1.800e-02, eta: 1 day, 11:30:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6787, loss_cls: 3.2334, loss: 3.2334 +2024-07-20 07:04:45,927 - pyskl - INFO - Epoch [109][700/3746] lr: 1.798e-02, eta: 1 day, 11:29:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4109, top5_acc: 0.6813, loss_cls: 3.2619, loss: 3.2619 +2024-07-20 07:06:07,667 - pyskl - INFO - Epoch [109][800/3746] lr: 1.796e-02, eta: 1 day, 11:27:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4138, top5_acc: 0.6725, loss_cls: 3.2922, loss: 3.2922 +2024-07-20 07:07:29,311 - pyskl - INFO - Epoch [109][900/3746] lr: 1.794e-02, eta: 1 day, 11:26:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6822, loss_cls: 3.2601, loss: 3.2601 +2024-07-20 07:08:51,359 - pyskl - INFO - Epoch [109][1000/3746] lr: 1.791e-02, eta: 1 day, 11:24:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6714, loss_cls: 3.3109, loss: 3.3109 +2024-07-20 07:10:13,482 - pyskl - INFO - Epoch [109][1100/3746] lr: 1.789e-02, eta: 1 day, 11:23:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6658, loss_cls: 3.3215, loss: 3.3215 +2024-07-20 07:11:35,276 - pyskl - INFO - Epoch [109][1200/3746] lr: 1.787e-02, eta: 1 day, 11:22:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6745, loss_cls: 3.3068, loss: 3.3068 +2024-07-20 07:12:57,196 - pyskl - INFO - Epoch [109][1300/3746] lr: 1.785e-02, eta: 1 day, 11:20:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6784, loss_cls: 3.2978, loss: 3.2978 +2024-07-20 07:14:18,666 - pyskl - INFO - Epoch [109][1400/3746] lr: 1.783e-02, eta: 1 day, 11:19:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6661, loss_cls: 3.3110, loss: 3.3110 +2024-07-20 07:15:41,399 - pyskl - INFO - Epoch [109][1500/3746] lr: 1.781e-02, eta: 1 day, 11:18:11, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3966, top5_acc: 0.6633, loss_cls: 3.3541, loss: 3.3541 +2024-07-20 07:17:03,470 - pyskl - INFO - Epoch [109][1600/3746] lr: 1.779e-02, eta: 1 day, 11:16:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6694, loss_cls: 3.3247, loss: 3.3247 +2024-07-20 07:18:25,649 - pyskl - INFO - Epoch [109][1700/3746] lr: 1.776e-02, eta: 1 day, 11:15:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6619, loss_cls: 3.3471, loss: 3.3471 +2024-07-20 07:19:47,786 - pyskl - INFO - Epoch [109][1800/3746] lr: 1.774e-02, eta: 1 day, 11:14:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6750, loss_cls: 3.3258, loss: 3.3258 +2024-07-20 07:21:10,184 - pyskl - INFO - Epoch [109][1900/3746] lr: 1.772e-02, eta: 1 day, 11:12:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6697, loss_cls: 3.3197, loss: 3.3197 +2024-07-20 07:22:32,382 - pyskl - INFO - Epoch [109][2000/3746] lr: 1.770e-02, eta: 1 day, 11:11:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6698, loss_cls: 3.3107, loss: 3.3107 +2024-07-20 07:23:54,228 - pyskl - INFO - Epoch [109][2100/3746] lr: 1.768e-02, eta: 1 day, 11:10:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6755, loss_cls: 3.3062, loss: 3.3062 +2024-07-20 07:25:16,281 - pyskl - INFO - Epoch [109][2200/3746] lr: 1.766e-02, eta: 1 day, 11:08:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6608, loss_cls: 3.3655, loss: 3.3655 +2024-07-20 07:26:38,147 - pyskl - INFO - Epoch [109][2300/3746] lr: 1.764e-02, eta: 1 day, 11:07:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4066, top5_acc: 0.6689, loss_cls: 3.3174, loss: 3.3174 +2024-07-20 07:27:59,695 - pyskl - INFO - Epoch [109][2400/3746] lr: 1.761e-02, eta: 1 day, 11:05:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6697, loss_cls: 3.3332, loss: 3.3332 +2024-07-20 07:29:22,192 - pyskl - INFO - Epoch [109][2500/3746] lr: 1.759e-02, eta: 1 day, 11:04:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6673, loss_cls: 3.3112, loss: 3.3112 +2024-07-20 07:30:44,326 - pyskl - INFO - Epoch [109][2600/3746] lr: 1.757e-02, eta: 1 day, 11:03:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6713, loss_cls: 3.3155, loss: 3.3155 +2024-07-20 07:32:05,546 - pyskl - INFO - Epoch [109][2700/3746] lr: 1.755e-02, eta: 1 day, 11:01:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6737, loss_cls: 3.3025, loss: 3.3025 +2024-07-20 07:33:27,967 - pyskl - INFO - Epoch [109][2800/3746] lr: 1.753e-02, eta: 1 day, 11:00:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6645, loss_cls: 3.3053, loss: 3.3053 +2024-07-20 07:34:49,800 - pyskl - INFO - Epoch [109][2900/3746] lr: 1.751e-02, eta: 1 day, 10:59:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6781, loss_cls: 3.3152, loss: 3.3152 +2024-07-20 07:36:11,643 - pyskl - INFO - Epoch [109][3000/3746] lr: 1.749e-02, eta: 1 day, 10:57:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6713, loss_cls: 3.3040, loss: 3.3040 +2024-07-20 07:37:33,245 - pyskl - INFO - Epoch [109][3100/3746] lr: 1.747e-02, eta: 1 day, 10:56:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6663, loss_cls: 3.3203, loss: 3.3203 +2024-07-20 07:38:55,625 - pyskl - INFO - Epoch [109][3200/3746] lr: 1.744e-02, eta: 1 day, 10:55:08, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6752, loss_cls: 3.2838, loss: 3.2838 +2024-07-20 07:40:17,631 - pyskl - INFO - Epoch [109][3300/3746] lr: 1.742e-02, eta: 1 day, 10:53:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6706, loss_cls: 3.3415, loss: 3.3415 +2024-07-20 07:41:39,518 - pyskl - INFO - Epoch [109][3400/3746] lr: 1.740e-02, eta: 1 day, 10:52:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4161, top5_acc: 0.6708, loss_cls: 3.2925, loss: 3.2925 +2024-07-20 07:43:01,581 - pyskl - INFO - Epoch [109][3500/3746] lr: 1.738e-02, eta: 1 day, 10:51:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6625, loss_cls: 3.3830, loss: 3.3830 +2024-07-20 07:44:23,408 - pyskl - INFO - Epoch [109][3600/3746] lr: 1.736e-02, eta: 1 day, 10:49:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6617, loss_cls: 3.3685, loss: 3.3685 +2024-07-20 07:45:44,876 - pyskl - INFO - Epoch [109][3700/3746] lr: 1.734e-02, eta: 1 day, 10:48:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6663, loss_cls: 3.3512, loss: 3.3512 +2024-07-20 07:46:24,519 - pyskl - INFO - Saving checkpoint at 109 epochs +2024-07-20 07:48:17,782 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 07:48:18,450 - pyskl - INFO - +top1_acc 0.3279 +top5_acc 0.5878 +2024-07-20 07:48:18,451 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 07:48:18,495 - pyskl - INFO - +mean_acc 0.3277 +2024-07-20 07:48:18,509 - pyskl - INFO - Epoch(val) [109][309] top1_acc: 0.3279, top5_acc: 0.5878, mean_class_accuracy: 0.3277 +2024-07-20 07:52:12,539 - pyskl - INFO - Epoch [110][100/3746] lr: 1.731e-02, eta: 1 day, 10:47:05, time: 2.340, data_time: 1.356, memory: 15990, top1_acc: 0.4273, top5_acc: 0.6917, loss_cls: 3.2113, loss: 3.2113 +2024-07-20 07:53:34,662 - pyskl - INFO - Epoch [110][200/3746] lr: 1.729e-02, eta: 1 day, 10:45:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4277, top5_acc: 0.6900, loss_cls: 3.2142, loss: 3.2142 +2024-07-20 07:54:56,400 - pyskl - INFO - Epoch [110][300/3746] lr: 1.727e-02, eta: 1 day, 10:44:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6745, loss_cls: 3.2556, loss: 3.2556 +2024-07-20 07:56:18,192 - pyskl - INFO - Epoch [110][400/3746] lr: 1.724e-02, eta: 1 day, 10:43:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4205, top5_acc: 0.6703, loss_cls: 3.2585, loss: 3.2585 +2024-07-20 07:57:40,698 - pyskl - INFO - Epoch [110][500/3746] lr: 1.722e-02, eta: 1 day, 10:41:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6787, loss_cls: 3.3076, loss: 3.3076 +2024-07-20 07:59:02,406 - pyskl - INFO - Epoch [110][600/3746] lr: 1.720e-02, eta: 1 day, 10:40:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4178, top5_acc: 0.6814, loss_cls: 3.2558, loss: 3.2558 +2024-07-20 08:00:24,147 - pyskl - INFO - Epoch [110][700/3746] lr: 1.718e-02, eta: 1 day, 10:38:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4044, top5_acc: 0.6763, loss_cls: 3.2958, loss: 3.2958 +2024-07-20 08:01:46,326 - pyskl - INFO - Epoch [110][800/3746] lr: 1.716e-02, eta: 1 day, 10:37:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6681, loss_cls: 3.2872, loss: 3.2872 +2024-07-20 08:03:08,147 - pyskl - INFO - Epoch [110][900/3746] lr: 1.714e-02, eta: 1 day, 10:36:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6675, loss_cls: 3.3018, loss: 3.3018 +2024-07-20 08:04:30,171 - pyskl - INFO - Epoch [110][1000/3746] lr: 1.712e-02, eta: 1 day, 10:34:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4195, top5_acc: 0.6886, loss_cls: 3.2465, loss: 3.2465 +2024-07-20 08:05:52,166 - pyskl - INFO - Epoch [110][1100/3746] lr: 1.710e-02, eta: 1 day, 10:33:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4086, top5_acc: 0.6698, loss_cls: 3.2980, loss: 3.2980 +2024-07-20 08:07:13,799 - pyskl - INFO - Epoch [110][1200/3746] lr: 1.708e-02, eta: 1 day, 10:32:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6827, loss_cls: 3.2443, loss: 3.2443 +2024-07-20 08:08:35,500 - pyskl - INFO - Epoch [110][1300/3746] lr: 1.705e-02, eta: 1 day, 10:30:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6778, loss_cls: 3.2741, loss: 3.2741 +2024-07-20 08:09:57,402 - pyskl - INFO - Epoch [110][1400/3746] lr: 1.703e-02, eta: 1 day, 10:29:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4134, top5_acc: 0.6814, loss_cls: 3.2305, loss: 3.2305 +2024-07-20 08:11:19,616 - pyskl - INFO - Epoch [110][1500/3746] lr: 1.701e-02, eta: 1 day, 10:28:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6750, loss_cls: 3.2665, loss: 3.2665 +2024-07-20 08:12:41,583 - pyskl - INFO - Epoch [110][1600/3746] lr: 1.699e-02, eta: 1 day, 10:26:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4161, top5_acc: 0.6798, loss_cls: 3.2631, loss: 3.2631 +2024-07-20 08:14:03,303 - pyskl - INFO - Epoch [110][1700/3746] lr: 1.697e-02, eta: 1 day, 10:25:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4086, top5_acc: 0.6678, loss_cls: 3.3134, loss: 3.3134 +2024-07-20 08:15:25,095 - pyskl - INFO - Epoch [110][1800/3746] lr: 1.695e-02, eta: 1 day, 10:24:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4163, top5_acc: 0.6844, loss_cls: 3.2446, loss: 3.2446 +2024-07-20 08:16:47,750 - pyskl - INFO - Epoch [110][1900/3746] lr: 1.693e-02, eta: 1 day, 10:22:39, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4142, top5_acc: 0.6823, loss_cls: 3.2761, loss: 3.2761 +2024-07-20 08:18:09,516 - pyskl - INFO - Epoch [110][2000/3746] lr: 1.691e-02, eta: 1 day, 10:21:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6734, loss_cls: 3.3097, loss: 3.3097 +2024-07-20 08:19:31,724 - pyskl - INFO - Epoch [110][2100/3746] lr: 1.689e-02, eta: 1 day, 10:19:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6809, loss_cls: 3.2858, loss: 3.2858 +2024-07-20 08:20:54,655 - pyskl - INFO - Epoch [110][2200/3746] lr: 1.687e-02, eta: 1 day, 10:18:35, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4117, top5_acc: 0.6791, loss_cls: 3.2824, loss: 3.2824 +2024-07-20 08:22:16,757 - pyskl - INFO - Epoch [110][2300/3746] lr: 1.685e-02, eta: 1 day, 10:17:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6717, loss_cls: 3.3355, loss: 3.3355 +2024-07-20 08:23:39,105 - pyskl - INFO - Epoch [110][2400/3746] lr: 1.682e-02, eta: 1 day, 10:15:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6727, loss_cls: 3.3239, loss: 3.3239 +2024-07-20 08:25:01,039 - pyskl - INFO - Epoch [110][2500/3746] lr: 1.680e-02, eta: 1 day, 10:14:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6714, loss_cls: 3.3175, loss: 3.3175 +2024-07-20 08:26:23,028 - pyskl - INFO - Epoch [110][2600/3746] lr: 1.678e-02, eta: 1 day, 10:13:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4188, top5_acc: 0.6786, loss_cls: 3.2617, loss: 3.2617 +2024-07-20 08:27:44,827 - pyskl - INFO - Epoch [110][2700/3746] lr: 1.676e-02, eta: 1 day, 10:11:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6689, loss_cls: 3.3107, loss: 3.3107 +2024-07-20 08:29:07,396 - pyskl - INFO - Epoch [110][2800/3746] lr: 1.674e-02, eta: 1 day, 10:10:26, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6767, loss_cls: 3.2647, loss: 3.2647 +2024-07-20 08:30:29,218 - pyskl - INFO - Epoch [110][2900/3746] lr: 1.672e-02, eta: 1 day, 10:09:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6795, loss_cls: 3.2718, loss: 3.2718 +2024-07-20 08:31:50,613 - pyskl - INFO - Epoch [110][3000/3746] lr: 1.670e-02, eta: 1 day, 10:07:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4072, top5_acc: 0.6636, loss_cls: 3.3251, loss: 3.3251 +2024-07-20 08:33:12,534 - pyskl - INFO - Epoch [110][3100/3746] lr: 1.668e-02, eta: 1 day, 10:06:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4008, top5_acc: 0.6622, loss_cls: 3.3322, loss: 3.3322 +2024-07-20 08:34:34,286 - pyskl - INFO - Epoch [110][3200/3746] lr: 1.666e-02, eta: 1 day, 10:05:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4134, top5_acc: 0.6695, loss_cls: 3.3279, loss: 3.3279 +2024-07-20 08:35:55,734 - pyskl - INFO - Epoch [110][3300/3746] lr: 1.664e-02, eta: 1 day, 10:03:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6722, loss_cls: 3.3029, loss: 3.3029 +2024-07-20 08:37:17,507 - pyskl - INFO - Epoch [110][3400/3746] lr: 1.662e-02, eta: 1 day, 10:02:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6695, loss_cls: 3.3418, loss: 3.3418 +2024-07-20 08:38:39,576 - pyskl - INFO - Epoch [110][3500/3746] lr: 1.659e-02, eta: 1 day, 10:00:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4145, top5_acc: 0.6750, loss_cls: 3.2605, loss: 3.2605 +2024-07-20 08:40:01,655 - pyskl - INFO - Epoch [110][3600/3746] lr: 1.657e-02, eta: 1 day, 9:59:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4017, top5_acc: 0.6703, loss_cls: 3.3287, loss: 3.3287 +2024-07-20 08:41:23,568 - pyskl - INFO - Epoch [110][3700/3746] lr: 1.655e-02, eta: 1 day, 9:58:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4106, top5_acc: 0.6750, loss_cls: 3.2919, loss: 3.2919 +2024-07-20 08:42:03,350 - pyskl - INFO - Saving checkpoint at 110 epochs +2024-07-20 08:43:55,118 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 08:43:55,785 - pyskl - INFO - +top1_acc 0.3537 +top5_acc 0.6122 +2024-07-20 08:43:55,785 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 08:43:55,828 - pyskl - INFO - +mean_acc 0.3535 +2024-07-20 08:43:55,833 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_106.pth was removed +2024-07-20 08:43:56,089 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_110.pth. +2024-07-20 08:43:56,090 - pyskl - INFO - Best top1_acc is 0.3537 at 110 epoch. +2024-07-20 08:43:56,103 - pyskl - INFO - Epoch(val) [110][309] top1_acc: 0.3537, top5_acc: 0.6122, mean_class_accuracy: 0.3535 +2024-07-20 08:47:45,355 - pyskl - INFO - Epoch [111][100/3746] lr: 1.652e-02, eta: 1 day, 9:56:53, time: 2.292, data_time: 1.311, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6959, loss_cls: 3.1876, loss: 3.1876 +2024-07-20 08:49:08,007 - pyskl - INFO - Epoch [111][200/3746] lr: 1.650e-02, eta: 1 day, 9:55:32, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6766, loss_cls: 3.2457, loss: 3.2457 +2024-07-20 08:50:30,251 - pyskl - INFO - Epoch [111][300/3746] lr: 1.648e-02, eta: 1 day, 9:54:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4303, top5_acc: 0.6919, loss_cls: 3.2047, loss: 3.2047 +2024-07-20 08:51:52,367 - pyskl - INFO - Epoch [111][400/3746] lr: 1.646e-02, eta: 1 day, 9:52:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6787, loss_cls: 3.2304, loss: 3.2304 +2024-07-20 08:53:14,431 - pyskl - INFO - Epoch [111][500/3746] lr: 1.644e-02, eta: 1 day, 9:51:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4327, top5_acc: 0.6897, loss_cls: 3.1692, loss: 3.1692 +2024-07-20 08:54:36,180 - pyskl - INFO - Epoch [111][600/3746] lr: 1.642e-02, eta: 1 day, 9:50:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4134, top5_acc: 0.6761, loss_cls: 3.2804, loss: 3.2804 +2024-07-20 08:55:58,357 - pyskl - INFO - Epoch [111][700/3746] lr: 1.640e-02, eta: 1 day, 9:48:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6741, loss_cls: 3.2532, loss: 3.2532 +2024-07-20 08:57:20,436 - pyskl - INFO - Epoch [111][800/3746] lr: 1.638e-02, eta: 1 day, 9:47:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6833, loss_cls: 3.2233, loss: 3.2233 +2024-07-20 08:58:42,004 - pyskl - INFO - Epoch [111][900/3746] lr: 1.636e-02, eta: 1 day, 9:46:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6778, loss_cls: 3.2868, loss: 3.2868 +2024-07-20 09:00:04,507 - pyskl - INFO - Epoch [111][1000/3746] lr: 1.634e-02, eta: 1 day, 9:44:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6739, loss_cls: 3.2704, loss: 3.2704 +2024-07-20 09:01:26,278 - pyskl - INFO - Epoch [111][1100/3746] lr: 1.632e-02, eta: 1 day, 9:43:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6727, loss_cls: 3.2800, loss: 3.2800 +2024-07-20 09:02:47,883 - pyskl - INFO - Epoch [111][1200/3746] lr: 1.630e-02, eta: 1 day, 9:41:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4266, top5_acc: 0.6877, loss_cls: 3.2212, loss: 3.2212 +2024-07-20 09:04:09,674 - pyskl - INFO - Epoch [111][1300/3746] lr: 1.627e-02, eta: 1 day, 9:40:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6880, loss_cls: 3.1945, loss: 3.1945 +2024-07-20 09:05:31,357 - pyskl - INFO - Epoch [111][1400/3746] lr: 1.625e-02, eta: 1 day, 9:39:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4138, top5_acc: 0.6731, loss_cls: 3.2872, loss: 3.2872 +2024-07-20 09:06:53,613 - pyskl - INFO - Epoch [111][1500/3746] lr: 1.623e-02, eta: 1 day, 9:37:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4006, top5_acc: 0.6700, loss_cls: 3.3271, loss: 3.3271 +2024-07-20 09:08:15,540 - pyskl - INFO - Epoch [111][1600/3746] lr: 1.621e-02, eta: 1 day, 9:36:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4156, top5_acc: 0.6736, loss_cls: 3.2385, loss: 3.2385 +2024-07-20 09:09:38,240 - pyskl - INFO - Epoch [111][1700/3746] lr: 1.619e-02, eta: 1 day, 9:35:10, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4153, top5_acc: 0.6881, loss_cls: 3.2225, loss: 3.2225 +2024-07-20 09:11:00,425 - pyskl - INFO - Epoch [111][1800/3746] lr: 1.617e-02, eta: 1 day, 9:33:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6758, loss_cls: 3.2843, loss: 3.2843 +2024-07-20 09:12:23,074 - pyskl - INFO - Epoch [111][1900/3746] lr: 1.615e-02, eta: 1 day, 9:32:27, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4170, top5_acc: 0.6730, loss_cls: 3.2993, loss: 3.2993 +2024-07-20 09:13:45,091 - pyskl - INFO - Epoch [111][2000/3746] lr: 1.613e-02, eta: 1 day, 9:31:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6823, loss_cls: 3.2223, loss: 3.2223 +2024-07-20 09:15:07,921 - pyskl - INFO - Epoch [111][2100/3746] lr: 1.611e-02, eta: 1 day, 9:29:45, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6755, loss_cls: 3.2624, loss: 3.2624 +2024-07-20 09:16:29,775 - pyskl - INFO - Epoch [111][2200/3746] lr: 1.609e-02, eta: 1 day, 9:28:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6750, loss_cls: 3.2832, loss: 3.2832 +2024-07-20 09:17:52,004 - pyskl - INFO - Epoch [111][2300/3746] lr: 1.607e-02, eta: 1 day, 9:27:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6739, loss_cls: 3.2924, loss: 3.2924 +2024-07-20 09:19:14,634 - pyskl - INFO - Epoch [111][2400/3746] lr: 1.605e-02, eta: 1 day, 9:25:41, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6767, loss_cls: 3.2962, loss: 3.2962 +2024-07-20 09:20:36,927 - pyskl - INFO - Epoch [111][2500/3746] lr: 1.603e-02, eta: 1 day, 9:24:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6770, loss_cls: 3.3120, loss: 3.3120 +2024-07-20 09:21:59,131 - pyskl - INFO - Epoch [111][2600/3746] lr: 1.601e-02, eta: 1 day, 9:22:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6763, loss_cls: 3.2610, loss: 3.2610 +2024-07-20 09:23:21,928 - pyskl - INFO - Epoch [111][2700/3746] lr: 1.599e-02, eta: 1 day, 9:21:37, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4072, top5_acc: 0.6859, loss_cls: 3.2608, loss: 3.2608 +2024-07-20 09:24:44,037 - pyskl - INFO - Epoch [111][2800/3746] lr: 1.597e-02, eta: 1 day, 9:20:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6839, loss_cls: 3.2313, loss: 3.2313 +2024-07-20 09:26:06,021 - pyskl - INFO - Epoch [111][2900/3746] lr: 1.595e-02, eta: 1 day, 9:18:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6705, loss_cls: 3.3308, loss: 3.3308 +2024-07-20 09:27:27,532 - pyskl - INFO - Epoch [111][3000/3746] lr: 1.593e-02, eta: 1 day, 9:17:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6683, loss_cls: 3.2973, loss: 3.2973 +2024-07-20 09:28:49,142 - pyskl - INFO - Epoch [111][3100/3746] lr: 1.590e-02, eta: 1 day, 9:16:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4252, top5_acc: 0.6887, loss_cls: 3.2060, loss: 3.2060 +2024-07-20 09:30:11,111 - pyskl - INFO - Epoch [111][3200/3746] lr: 1.588e-02, eta: 1 day, 9:14:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4117, top5_acc: 0.6778, loss_cls: 3.2854, loss: 3.2854 +2024-07-20 09:31:32,690 - pyskl - INFO - Epoch [111][3300/3746] lr: 1.586e-02, eta: 1 day, 9:13:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6791, loss_cls: 3.2750, loss: 3.2750 +2024-07-20 09:32:54,671 - pyskl - INFO - Epoch [111][3400/3746] lr: 1.584e-02, eta: 1 day, 9:12:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4142, top5_acc: 0.6787, loss_cls: 3.2661, loss: 3.2661 +2024-07-20 09:34:16,314 - pyskl - INFO - Epoch [111][3500/3746] lr: 1.582e-02, eta: 1 day, 9:10:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4109, top5_acc: 0.6734, loss_cls: 3.2972, loss: 3.2972 +2024-07-20 09:35:37,896 - pyskl - INFO - Epoch [111][3600/3746] lr: 1.580e-02, eta: 1 day, 9:09:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6828, loss_cls: 3.2571, loss: 3.2571 +2024-07-20 09:36:59,640 - pyskl - INFO - Epoch [111][3700/3746] lr: 1.578e-02, eta: 1 day, 9:08:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4183, top5_acc: 0.6772, loss_cls: 3.2602, loss: 3.2602 +2024-07-20 09:37:39,350 - pyskl - INFO - Saving checkpoint at 111 epochs +2024-07-20 09:39:31,205 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 09:39:31,949 - pyskl - INFO - +top1_acc 0.3478 +top5_acc 0.6050 +2024-07-20 09:39:31,949 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 09:39:31,992 - pyskl - INFO - +mean_acc 0.3474 +2024-07-20 09:39:32,005 - pyskl - INFO - Epoch(val) [111][309] top1_acc: 0.3478, top5_acc: 0.6050, mean_class_accuracy: 0.3474 +2024-07-20 09:43:27,659 - pyskl - INFO - Epoch [112][100/3746] lr: 1.575e-02, eta: 1 day, 9:06:43, time: 2.356, data_time: 1.355, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6992, loss_cls: 3.1677, loss: 3.1677 +2024-07-20 09:44:51,021 - pyskl - INFO - Epoch [112][200/3746] lr: 1.573e-02, eta: 1 day, 9:05:22, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4281, top5_acc: 0.6913, loss_cls: 3.1890, loss: 3.1890 +2024-07-20 09:46:13,903 - pyskl - INFO - Epoch [112][300/3746] lr: 1.571e-02, eta: 1 day, 9:04:00, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4284, top5_acc: 0.7028, loss_cls: 3.1541, loss: 3.1541 +2024-07-20 09:47:37,344 - pyskl - INFO - Epoch [112][400/3746] lr: 1.569e-02, eta: 1 day, 9:02:39, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6816, loss_cls: 3.2090, loss: 3.2090 +2024-07-20 09:49:00,259 - pyskl - INFO - Epoch [112][500/3746] lr: 1.567e-02, eta: 1 day, 9:01:18, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4219, top5_acc: 0.6817, loss_cls: 3.2097, loss: 3.2097 +2024-07-20 09:50:23,136 - pyskl - INFO - Epoch [112][600/3746] lr: 1.565e-02, eta: 1 day, 8:59:57, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4270, top5_acc: 0.6981, loss_cls: 3.1771, loss: 3.1771 +2024-07-20 09:51:46,119 - pyskl - INFO - Epoch [112][700/3746] lr: 1.563e-02, eta: 1 day, 8:58:36, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4177, top5_acc: 0.6803, loss_cls: 3.2367, loss: 3.2367 +2024-07-20 09:53:09,150 - pyskl - INFO - Epoch [112][800/3746] lr: 1.561e-02, eta: 1 day, 8:57:14, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4141, top5_acc: 0.6858, loss_cls: 3.2456, loss: 3.2456 +2024-07-20 09:54:32,504 - pyskl - INFO - Epoch [112][900/3746] lr: 1.559e-02, eta: 1 day, 8:55:53, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6792, loss_cls: 3.2282, loss: 3.2282 +2024-07-20 09:55:55,860 - pyskl - INFO - Epoch [112][1000/3746] lr: 1.557e-02, eta: 1 day, 8:54:32, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4117, top5_acc: 0.6795, loss_cls: 3.2458, loss: 3.2458 +2024-07-20 09:57:18,995 - pyskl - INFO - Epoch [112][1100/3746] lr: 1.555e-02, eta: 1 day, 8:53:11, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4156, top5_acc: 0.6816, loss_cls: 3.2145, loss: 3.2145 +2024-07-20 09:58:41,756 - pyskl - INFO - Epoch [112][1200/3746] lr: 1.553e-02, eta: 1 day, 8:51:50, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4189, top5_acc: 0.6787, loss_cls: 3.2570, loss: 3.2570 +2024-07-20 10:00:04,424 - pyskl - INFO - Epoch [112][1300/3746] lr: 1.551e-02, eta: 1 day, 8:50:29, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6789, loss_cls: 3.2375, loss: 3.2375 +2024-07-20 10:01:27,600 - pyskl - INFO - Epoch [112][1400/3746] lr: 1.549e-02, eta: 1 day, 8:49:08, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4339, top5_acc: 0.6981, loss_cls: 3.1450, loss: 3.1450 +2024-07-20 10:02:50,743 - pyskl - INFO - Epoch [112][1500/3746] lr: 1.547e-02, eta: 1 day, 8:47:46, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6798, loss_cls: 3.2244, loss: 3.2244 +2024-07-20 10:04:13,071 - pyskl - INFO - Epoch [112][1600/3746] lr: 1.545e-02, eta: 1 day, 8:46:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4139, top5_acc: 0.6683, loss_cls: 3.2894, loss: 3.2894 +2024-07-20 10:05:35,621 - pyskl - INFO - Epoch [112][1700/3746] lr: 1.543e-02, eta: 1 day, 8:45:04, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6794, loss_cls: 3.2694, loss: 3.2694 +2024-07-20 10:06:58,729 - pyskl - INFO - Epoch [112][1800/3746] lr: 1.541e-02, eta: 1 day, 8:43:42, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4177, top5_acc: 0.6798, loss_cls: 3.2455, loss: 3.2455 +2024-07-20 10:08:22,038 - pyskl - INFO - Epoch [112][1900/3746] lr: 1.539e-02, eta: 1 day, 8:42:21, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4142, top5_acc: 0.6794, loss_cls: 3.2556, loss: 3.2556 +2024-07-20 10:09:45,264 - pyskl - INFO - Epoch [112][2000/3746] lr: 1.537e-02, eta: 1 day, 8:41:00, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4164, top5_acc: 0.6748, loss_cls: 3.2967, loss: 3.2967 +2024-07-20 10:11:07,768 - pyskl - INFO - Epoch [112][2100/3746] lr: 1.535e-02, eta: 1 day, 8:39:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4211, top5_acc: 0.6809, loss_cls: 3.2631, loss: 3.2631 +2024-07-20 10:12:30,300 - pyskl - INFO - Epoch [112][2200/3746] lr: 1.533e-02, eta: 1 day, 8:38:18, time: 0.825, data_time: 0.001, memory: 15990, top1_acc: 0.4153, top5_acc: 0.6789, loss_cls: 3.2642, loss: 3.2642 +2024-07-20 10:13:52,335 - pyskl - INFO - Epoch [112][2300/3746] lr: 1.531e-02, eta: 1 day, 8:36:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6875, loss_cls: 3.2105, loss: 3.2105 +2024-07-20 10:15:13,905 - pyskl - INFO - Epoch [112][2400/3746] lr: 1.529e-02, eta: 1 day, 8:35:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6737, loss_cls: 3.2823, loss: 3.2823 +2024-07-20 10:16:36,187 - pyskl - INFO - Epoch [112][2500/3746] lr: 1.527e-02, eta: 1 day, 8:34:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4277, top5_acc: 0.6847, loss_cls: 3.1944, loss: 3.1944 +2024-07-20 10:17:58,422 - pyskl - INFO - Epoch [112][2600/3746] lr: 1.525e-02, eta: 1 day, 8:32:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4248, top5_acc: 0.6758, loss_cls: 3.2464, loss: 3.2464 +2024-07-20 10:19:21,338 - pyskl - INFO - Epoch [112][2700/3746] lr: 1.523e-02, eta: 1 day, 8:31:30, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6791, loss_cls: 3.2507, loss: 3.2507 +2024-07-20 10:20:44,039 - pyskl - INFO - Epoch [112][2800/3746] lr: 1.521e-02, eta: 1 day, 8:30:09, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6744, loss_cls: 3.2802, loss: 3.2802 +2024-07-20 10:22:06,525 - pyskl - INFO - Epoch [112][2900/3746] lr: 1.519e-02, eta: 1 day, 8:28:48, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4281, top5_acc: 0.6806, loss_cls: 3.2453, loss: 3.2453 +2024-07-20 10:23:29,342 - pyskl - INFO - Epoch [112][3000/3746] lr: 1.517e-02, eta: 1 day, 8:27:26, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6764, loss_cls: 3.2995, loss: 3.2995 +2024-07-20 10:24:51,707 - pyskl - INFO - Epoch [112][3100/3746] lr: 1.515e-02, eta: 1 day, 8:26:05, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4163, top5_acc: 0.6820, loss_cls: 3.2394, loss: 3.2394 +2024-07-20 10:26:14,408 - pyskl - INFO - Epoch [112][3200/3746] lr: 1.513e-02, eta: 1 day, 8:24:44, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6747, loss_cls: 3.2696, loss: 3.2696 +2024-07-20 10:27:36,842 - pyskl - INFO - Epoch [112][3300/3746] lr: 1.511e-02, eta: 1 day, 8:23:22, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6861, loss_cls: 3.2172, loss: 3.2172 +2024-07-20 10:28:59,371 - pyskl - INFO - Epoch [112][3400/3746] lr: 1.509e-02, eta: 1 day, 8:22:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6664, loss_cls: 3.3261, loss: 3.3261 +2024-07-20 10:30:22,294 - pyskl - INFO - Epoch [112][3500/3746] lr: 1.507e-02, eta: 1 day, 8:20:40, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6730, loss_cls: 3.2103, loss: 3.2103 +2024-07-20 10:31:45,005 - pyskl - INFO - Epoch [112][3600/3746] lr: 1.505e-02, eta: 1 day, 8:19:18, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4158, top5_acc: 0.6798, loss_cls: 3.2698, loss: 3.2698 +2024-07-20 10:33:07,811 - pyskl - INFO - Epoch [112][3700/3746] lr: 1.503e-02, eta: 1 day, 8:17:57, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6816, loss_cls: 3.2207, loss: 3.2207 +2024-07-20 10:33:47,767 - pyskl - INFO - Saving checkpoint at 112 epochs +2024-07-20 10:35:39,348 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 10:35:40,065 - pyskl - INFO - +top1_acc 0.3441 +top5_acc 0.6075 +2024-07-20 10:35:40,065 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 10:35:40,104 - pyskl - INFO - +mean_acc 0.3440 +2024-07-20 10:35:40,114 - pyskl - INFO - Epoch(val) [112][309] top1_acc: 0.3441, top5_acc: 0.6075, mean_class_accuracy: 0.3440 +2024-07-20 10:39:31,672 - pyskl - INFO - Epoch [113][100/3746] lr: 1.500e-02, eta: 1 day, 8:16:36, time: 2.315, data_time: 1.332, memory: 15990, top1_acc: 0.4442, top5_acc: 0.7072, loss_cls: 3.0840, loss: 3.0840 +2024-07-20 10:40:55,263 - pyskl - INFO - Epoch [113][200/3746] lr: 1.498e-02, eta: 1 day, 8:15:15, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4272, top5_acc: 0.6906, loss_cls: 3.1472, loss: 3.1472 +2024-07-20 10:42:18,901 - pyskl - INFO - Epoch [113][300/3746] lr: 1.496e-02, eta: 1 day, 8:13:54, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4341, top5_acc: 0.7100, loss_cls: 3.1201, loss: 3.1201 +2024-07-20 10:43:41,950 - pyskl - INFO - Epoch [113][400/3746] lr: 1.494e-02, eta: 1 day, 8:12:33, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6916, loss_cls: 3.1910, loss: 3.1910 +2024-07-20 10:45:05,466 - pyskl - INFO - Epoch [113][500/3746] lr: 1.492e-02, eta: 1 day, 8:11:12, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6892, loss_cls: 3.1924, loss: 3.1924 +2024-07-20 10:46:29,021 - pyskl - INFO - Epoch [113][600/3746] lr: 1.490e-02, eta: 1 day, 8:09:50, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6820, loss_cls: 3.2170, loss: 3.2170 +2024-07-20 10:47:52,739 - pyskl - INFO - Epoch [113][700/3746] lr: 1.488e-02, eta: 1 day, 8:08:29, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6889, loss_cls: 3.2058, loss: 3.2058 +2024-07-20 10:49:16,385 - pyskl - INFO - Epoch [113][800/3746] lr: 1.486e-02, eta: 1 day, 8:07:08, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6922, loss_cls: 3.2139, loss: 3.2139 +2024-07-20 10:50:39,691 - pyskl - INFO - Epoch [113][900/3746] lr: 1.484e-02, eta: 1 day, 8:05:47, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4372, top5_acc: 0.6936, loss_cls: 3.1330, loss: 3.1330 +2024-07-20 10:52:02,339 - pyskl - INFO - Epoch [113][1000/3746] lr: 1.482e-02, eta: 1 day, 8:04:26, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4280, top5_acc: 0.6913, loss_cls: 3.1916, loss: 3.1916 +2024-07-20 10:53:25,553 - pyskl - INFO - Epoch [113][1100/3746] lr: 1.480e-02, eta: 1 day, 8:03:05, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6791, loss_cls: 3.2260, loss: 3.2260 +2024-07-20 10:54:48,711 - pyskl - INFO - Epoch [113][1200/3746] lr: 1.478e-02, eta: 1 day, 8:01:44, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4292, top5_acc: 0.6881, loss_cls: 3.2128, loss: 3.2128 +2024-07-20 10:56:12,216 - pyskl - INFO - Epoch [113][1300/3746] lr: 1.476e-02, eta: 1 day, 8:00:22, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6894, loss_cls: 3.1957, loss: 3.1957 +2024-07-20 10:57:35,705 - pyskl - INFO - Epoch [113][1400/3746] lr: 1.474e-02, eta: 1 day, 7:59:01, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4195, top5_acc: 0.6852, loss_cls: 3.2315, loss: 3.2315 +2024-07-20 10:58:59,377 - pyskl - INFO - Epoch [113][1500/3746] lr: 1.472e-02, eta: 1 day, 7:57:40, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4213, top5_acc: 0.6803, loss_cls: 3.2269, loss: 3.2269 +2024-07-20 11:00:21,446 - pyskl - INFO - Epoch [113][1600/3746] lr: 1.470e-02, eta: 1 day, 7:56:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6845, loss_cls: 3.2315, loss: 3.2315 +2024-07-20 11:01:44,525 - pyskl - INFO - Epoch [113][1700/3746] lr: 1.468e-02, eta: 1 day, 7:54:58, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6778, loss_cls: 3.2972, loss: 3.2972 +2024-07-20 11:03:07,469 - pyskl - INFO - Epoch [113][1800/3746] lr: 1.466e-02, eta: 1 day, 7:53:36, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6802, loss_cls: 3.2183, loss: 3.2183 +2024-07-20 11:04:30,406 - pyskl - INFO - Epoch [113][1900/3746] lr: 1.464e-02, eta: 1 day, 7:52:15, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4245, top5_acc: 0.6889, loss_cls: 3.1883, loss: 3.1883 +2024-07-20 11:05:52,235 - pyskl - INFO - Epoch [113][2000/3746] lr: 1.462e-02, eta: 1 day, 7:50:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6853, loss_cls: 3.2083, loss: 3.2083 +2024-07-20 11:07:14,501 - pyskl - INFO - Epoch [113][2100/3746] lr: 1.460e-02, eta: 1 day, 7:49:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4272, top5_acc: 0.6930, loss_cls: 3.1974, loss: 3.1974 +2024-07-20 11:08:37,730 - pyskl - INFO - Epoch [113][2200/3746] lr: 1.458e-02, eta: 1 day, 7:48:11, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4286, top5_acc: 0.6889, loss_cls: 3.1892, loss: 3.1892 +2024-07-20 11:10:00,303 - pyskl - INFO - Epoch [113][2300/3746] lr: 1.456e-02, eta: 1 day, 7:46:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4202, top5_acc: 0.6848, loss_cls: 3.2204, loss: 3.2204 +2024-07-20 11:11:22,507 - pyskl - INFO - Epoch [113][2400/3746] lr: 1.454e-02, eta: 1 day, 7:45:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6764, loss_cls: 3.2523, loss: 3.2523 +2024-07-20 11:12:45,262 - pyskl - INFO - Epoch [113][2500/3746] lr: 1.452e-02, eta: 1 day, 7:44:06, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6828, loss_cls: 3.2381, loss: 3.2381 +2024-07-20 11:14:07,728 - pyskl - INFO - Epoch [113][2600/3746] lr: 1.450e-02, eta: 1 day, 7:42:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6786, loss_cls: 3.2808, loss: 3.2808 +2024-07-20 11:15:30,929 - pyskl - INFO - Epoch [113][2700/3746] lr: 1.448e-02, eta: 1 day, 7:41:24, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4195, top5_acc: 0.6783, loss_cls: 3.2405, loss: 3.2405 +2024-07-20 11:16:53,401 - pyskl - INFO - Epoch [113][2800/3746] lr: 1.446e-02, eta: 1 day, 7:40:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4195, top5_acc: 0.6777, loss_cls: 3.2395, loss: 3.2395 +2024-07-20 11:18:15,209 - pyskl - INFO - Epoch [113][2900/3746] lr: 1.444e-02, eta: 1 day, 7:38:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6817, loss_cls: 3.2332, loss: 3.2332 +2024-07-20 11:19:37,780 - pyskl - INFO - Epoch [113][3000/3746] lr: 1.442e-02, eta: 1 day, 7:37:19, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6919, loss_cls: 3.1994, loss: 3.1994 +2024-07-20 11:21:00,365 - pyskl - INFO - Epoch [113][3100/3746] lr: 1.440e-02, eta: 1 day, 7:35:58, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4300, top5_acc: 0.6881, loss_cls: 3.2033, loss: 3.2033 +2024-07-20 11:22:23,228 - pyskl - INFO - Epoch [113][3200/3746] lr: 1.438e-02, eta: 1 day, 7:34:37, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4163, top5_acc: 0.6764, loss_cls: 3.2551, loss: 3.2551 +2024-07-20 11:23:45,527 - pyskl - INFO - Epoch [113][3300/3746] lr: 1.436e-02, eta: 1 day, 7:33:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6842, loss_cls: 3.2443, loss: 3.2443 +2024-07-20 11:25:08,111 - pyskl - INFO - Epoch [113][3400/3746] lr: 1.434e-02, eta: 1 day, 7:31:54, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4167, top5_acc: 0.6805, loss_cls: 3.2715, loss: 3.2715 +2024-07-20 11:26:30,856 - pyskl - INFO - Epoch [113][3500/3746] lr: 1.432e-02, eta: 1 day, 7:30:32, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4173, top5_acc: 0.6816, loss_cls: 3.2376, loss: 3.2376 +2024-07-20 11:27:54,313 - pyskl - INFO - Epoch [113][3600/3746] lr: 1.431e-02, eta: 1 day, 7:29:11, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6775, loss_cls: 3.2488, loss: 3.2488 +2024-07-20 11:29:17,327 - pyskl - INFO - Epoch [113][3700/3746] lr: 1.429e-02, eta: 1 day, 7:27:50, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6805, loss_cls: 3.2316, loss: 3.2316 +2024-07-20 11:29:56,772 - pyskl - INFO - Saving checkpoint at 113 epochs +2024-07-20 11:31:47,633 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 11:31:48,300 - pyskl - INFO - +top1_acc 0.3449 +top5_acc 0.6046 +2024-07-20 11:31:48,300 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 11:31:48,340 - pyskl - INFO - +mean_acc 0.3447 +2024-07-20 11:31:48,351 - pyskl - INFO - Epoch(val) [113][309] top1_acc: 0.3449, top5_acc: 0.6046, mean_class_accuracy: 0.3447 +2024-07-20 11:35:45,389 - pyskl - INFO - Epoch [114][100/3746] lr: 1.426e-02, eta: 1 day, 7:26:29, time: 2.370, data_time: 1.376, memory: 15990, top1_acc: 0.4384, top5_acc: 0.7005, loss_cls: 3.1396, loss: 3.1396 +2024-07-20 11:37:09,247 - pyskl - INFO - Epoch [114][200/3746] lr: 1.424e-02, eta: 1 day, 7:25:08, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4252, top5_acc: 0.6897, loss_cls: 3.1833, loss: 3.1833 +2024-07-20 11:38:33,191 - pyskl - INFO - Epoch [114][300/3746] lr: 1.422e-02, eta: 1 day, 7:23:47, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4380, top5_acc: 0.7034, loss_cls: 3.1237, loss: 3.1237 +2024-07-20 11:39:56,891 - pyskl - INFO - Epoch [114][400/3746] lr: 1.420e-02, eta: 1 day, 7:22:26, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4392, top5_acc: 0.6981, loss_cls: 3.1243, loss: 3.1243 +2024-07-20 11:41:20,327 - pyskl - INFO - Epoch [114][500/3746] lr: 1.418e-02, eta: 1 day, 7:21:05, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4389, top5_acc: 0.6967, loss_cls: 3.1346, loss: 3.1346 +2024-07-20 11:42:43,525 - pyskl - INFO - Epoch [114][600/3746] lr: 1.416e-02, eta: 1 day, 7:19:44, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6866, loss_cls: 3.2065, loss: 3.2065 +2024-07-20 11:44:07,012 - pyskl - INFO - Epoch [114][700/3746] lr: 1.414e-02, eta: 1 day, 7:18:22, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.7016, loss_cls: 3.1345, loss: 3.1345 +2024-07-20 11:45:30,435 - pyskl - INFO - Epoch [114][800/3746] lr: 1.412e-02, eta: 1 day, 7:17:01, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4303, top5_acc: 0.6905, loss_cls: 3.1903, loss: 3.1903 +2024-07-20 11:46:54,090 - pyskl - INFO - Epoch [114][900/3746] lr: 1.410e-02, eta: 1 day, 7:15:40, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6984, loss_cls: 3.1477, loss: 3.1477 +2024-07-20 11:48:17,591 - pyskl - INFO - Epoch [114][1000/3746] lr: 1.408e-02, eta: 1 day, 7:14:19, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6920, loss_cls: 3.1665, loss: 3.1665 +2024-07-20 11:49:40,291 - pyskl - INFO - Epoch [114][1100/3746] lr: 1.406e-02, eta: 1 day, 7:12:58, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4269, top5_acc: 0.6839, loss_cls: 3.2054, loss: 3.2054 +2024-07-20 11:51:03,850 - pyskl - INFO - Epoch [114][1200/3746] lr: 1.404e-02, eta: 1 day, 7:11:36, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4331, top5_acc: 0.7048, loss_cls: 3.1195, loss: 3.1195 +2024-07-20 11:52:27,471 - pyskl - INFO - Epoch [114][1300/3746] lr: 1.402e-02, eta: 1 day, 7:10:15, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4281, top5_acc: 0.6897, loss_cls: 3.1903, loss: 3.1903 +2024-07-20 11:53:50,744 - pyskl - INFO - Epoch [114][1400/3746] lr: 1.400e-02, eta: 1 day, 7:08:54, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6969, loss_cls: 3.1715, loss: 3.1715 +2024-07-20 11:55:13,474 - pyskl - INFO - Epoch [114][1500/3746] lr: 1.398e-02, eta: 1 day, 7:07:33, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6819, loss_cls: 3.2439, loss: 3.2439 +2024-07-20 11:56:35,621 - pyskl - INFO - Epoch [114][1600/3746] lr: 1.397e-02, eta: 1 day, 7:06:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4323, top5_acc: 0.6922, loss_cls: 3.1775, loss: 3.1775 +2024-07-20 11:57:58,380 - pyskl - INFO - Epoch [114][1700/3746] lr: 1.395e-02, eta: 1 day, 7:04:50, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6861, loss_cls: 3.1704, loss: 3.1704 +2024-07-20 11:59:21,315 - pyskl - INFO - Epoch [114][1800/3746] lr: 1.393e-02, eta: 1 day, 7:03:28, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6853, loss_cls: 3.2286, loss: 3.2286 +2024-07-20 12:00:44,577 - pyskl - INFO - Epoch [114][1900/3746] lr: 1.391e-02, eta: 1 day, 7:02:07, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6786, loss_cls: 3.2641, loss: 3.2641 +2024-07-20 12:02:06,889 - pyskl - INFO - Epoch [114][2000/3746] lr: 1.389e-02, eta: 1 day, 7:00:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4320, top5_acc: 0.6847, loss_cls: 3.1921, loss: 3.1921 +2024-07-20 12:03:29,502 - pyskl - INFO - Epoch [114][2100/3746] lr: 1.387e-02, eta: 1 day, 6:59:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4167, top5_acc: 0.6880, loss_cls: 3.2222, loss: 3.2222 +2024-07-20 12:04:51,426 - pyskl - INFO - Epoch [114][2200/3746] lr: 1.385e-02, eta: 1 day, 6:58:02, time: 0.819, data_time: 0.001, memory: 15990, top1_acc: 0.4241, top5_acc: 0.6903, loss_cls: 3.1854, loss: 3.1854 +2024-07-20 12:06:13,599 - pyskl - INFO - Epoch [114][2300/3746] lr: 1.383e-02, eta: 1 day, 6:56:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6786, loss_cls: 3.2191, loss: 3.2191 +2024-07-20 12:07:35,343 - pyskl - INFO - Epoch [114][2400/3746] lr: 1.381e-02, eta: 1 day, 6:55:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6750, loss_cls: 3.2426, loss: 3.2426 +2024-07-20 12:08:56,767 - pyskl - INFO - Epoch [114][2500/3746] lr: 1.379e-02, eta: 1 day, 6:53:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6837, loss_cls: 3.1973, loss: 3.1973 +2024-07-20 12:10:18,206 - pyskl - INFO - Epoch [114][2600/3746] lr: 1.377e-02, eta: 1 day, 6:52:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6863, loss_cls: 3.2408, loss: 3.2408 +2024-07-20 12:11:39,734 - pyskl - INFO - Epoch [114][2700/3746] lr: 1.375e-02, eta: 1 day, 6:51:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6795, loss_cls: 3.2441, loss: 3.2441 +2024-07-20 12:13:01,594 - pyskl - INFO - Epoch [114][2800/3746] lr: 1.373e-02, eta: 1 day, 6:49:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4370, top5_acc: 0.6944, loss_cls: 3.1763, loss: 3.1763 +2024-07-20 12:14:23,400 - pyskl - INFO - Epoch [114][2900/3746] lr: 1.371e-02, eta: 1 day, 6:48:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4308, top5_acc: 0.6859, loss_cls: 3.2308, loss: 3.2308 +2024-07-20 12:15:45,960 - pyskl - INFO - Epoch [114][3000/3746] lr: 1.369e-02, eta: 1 day, 6:47:09, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4317, top5_acc: 0.6852, loss_cls: 3.1859, loss: 3.1859 +2024-07-20 12:17:08,237 - pyskl - INFO - Epoch [114][3100/3746] lr: 1.368e-02, eta: 1 day, 6:45:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6845, loss_cls: 3.2004, loss: 3.2004 +2024-07-20 12:18:29,767 - pyskl - INFO - Epoch [114][3200/3746] lr: 1.366e-02, eta: 1 day, 6:44:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6764, loss_cls: 3.2462, loss: 3.2462 +2024-07-20 12:19:51,774 - pyskl - INFO - Epoch [114][3300/3746] lr: 1.364e-02, eta: 1 day, 6:43:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6834, loss_cls: 3.2174, loss: 3.2174 +2024-07-20 12:21:13,235 - pyskl - INFO - Epoch [114][3400/3746] lr: 1.362e-02, eta: 1 day, 6:41:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4280, top5_acc: 0.6923, loss_cls: 3.1752, loss: 3.1752 +2024-07-20 12:22:35,279 - pyskl - INFO - Epoch [114][3500/3746] lr: 1.360e-02, eta: 1 day, 6:40:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6817, loss_cls: 3.2477, loss: 3.2477 +2024-07-20 12:23:57,526 - pyskl - INFO - Epoch [114][3600/3746] lr: 1.358e-02, eta: 1 day, 6:38:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4263, top5_acc: 0.6837, loss_cls: 3.2220, loss: 3.2220 +2024-07-20 12:25:19,741 - pyskl - INFO - Epoch [114][3700/3746] lr: 1.356e-02, eta: 1 day, 6:37:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6958, loss_cls: 3.1799, loss: 3.1799 +2024-07-20 12:25:59,165 - pyskl - INFO - Saving checkpoint at 114 epochs +2024-07-20 12:27:51,304 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 12:27:51,983 - pyskl - INFO - +top1_acc 0.3500 +top5_acc 0.6107 +2024-07-20 12:27:51,983 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 12:27:52,022 - pyskl - INFO - +mean_acc 0.3498 +2024-07-20 12:27:52,033 - pyskl - INFO - Epoch(val) [114][309] top1_acc: 0.3500, top5_acc: 0.6107, mean_class_accuracy: 0.3498 +2024-07-20 12:31:48,179 - pyskl - INFO - Epoch [115][100/3746] lr: 1.353e-02, eta: 1 day, 6:36:15, time: 2.361, data_time: 1.371, memory: 15990, top1_acc: 0.4462, top5_acc: 0.6984, loss_cls: 3.1117, loss: 3.1117 +2024-07-20 12:33:11,687 - pyskl - INFO - Epoch [115][200/3746] lr: 1.351e-02, eta: 1 day, 6:34:54, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4412, top5_acc: 0.7070, loss_cls: 3.1087, loss: 3.1087 +2024-07-20 12:34:35,098 - pyskl - INFO - Epoch [115][300/3746] lr: 1.349e-02, eta: 1 day, 6:33:32, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4308, top5_acc: 0.6959, loss_cls: 3.1411, loss: 3.1411 +2024-07-20 12:35:58,456 - pyskl - INFO - Epoch [115][400/3746] lr: 1.348e-02, eta: 1 day, 6:32:11, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4358, top5_acc: 0.6998, loss_cls: 3.1405, loss: 3.1405 +2024-07-20 12:37:21,576 - pyskl - INFO - Epoch [115][500/3746] lr: 1.346e-02, eta: 1 day, 6:30:50, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4445, top5_acc: 0.6994, loss_cls: 3.1166, loss: 3.1166 +2024-07-20 12:38:45,265 - pyskl - INFO - Epoch [115][600/3746] lr: 1.344e-02, eta: 1 day, 6:29:29, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4323, top5_acc: 0.7055, loss_cls: 3.1240, loss: 3.1240 +2024-07-20 12:40:08,859 - pyskl - INFO - Epoch [115][700/3746] lr: 1.342e-02, eta: 1 day, 6:28:07, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4403, top5_acc: 0.7044, loss_cls: 3.1105, loss: 3.1105 +2024-07-20 12:41:32,238 - pyskl - INFO - Epoch [115][800/3746] lr: 1.340e-02, eta: 1 day, 6:26:46, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.6916, loss_cls: 3.1518, loss: 3.1518 +2024-07-20 12:42:55,701 - pyskl - INFO - Epoch [115][900/3746] lr: 1.338e-02, eta: 1 day, 6:25:25, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4316, top5_acc: 0.6992, loss_cls: 3.1213, loss: 3.1213 +2024-07-20 12:44:19,253 - pyskl - INFO - Epoch [115][1000/3746] lr: 1.336e-02, eta: 1 day, 6:24:04, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4342, top5_acc: 0.6970, loss_cls: 3.1679, loss: 3.1679 +2024-07-20 12:45:42,641 - pyskl - INFO - Epoch [115][1100/3746] lr: 1.334e-02, eta: 1 day, 6:22:42, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4306, top5_acc: 0.6981, loss_cls: 3.1401, loss: 3.1401 +2024-07-20 12:47:06,314 - pyskl - INFO - Epoch [115][1200/3746] lr: 1.332e-02, eta: 1 day, 6:21:21, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6931, loss_cls: 3.1436, loss: 3.1436 +2024-07-20 12:48:29,590 - pyskl - INFO - Epoch [115][1300/3746] lr: 1.330e-02, eta: 1 day, 6:20:00, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4286, top5_acc: 0.6939, loss_cls: 3.1588, loss: 3.1588 +2024-07-20 12:49:52,824 - pyskl - INFO - Epoch [115][1400/3746] lr: 1.328e-02, eta: 1 day, 6:18:39, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4313, top5_acc: 0.6930, loss_cls: 3.1832, loss: 3.1832 +2024-07-20 12:51:15,559 - pyskl - INFO - Epoch [115][1500/3746] lr: 1.327e-02, eta: 1 day, 6:17:17, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.6967, loss_cls: 3.1620, loss: 3.1620 +2024-07-20 12:52:38,225 - pyskl - INFO - Epoch [115][1600/3746] lr: 1.325e-02, eta: 1 day, 6:15:56, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.7008, loss_cls: 3.1503, loss: 3.1503 +2024-07-20 12:54:01,494 - pyskl - INFO - Epoch [115][1700/3746] lr: 1.323e-02, eta: 1 day, 6:14:34, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.6908, loss_cls: 3.1622, loss: 3.1622 +2024-07-20 12:55:25,041 - pyskl - INFO - Epoch [115][1800/3746] lr: 1.321e-02, eta: 1 day, 6:13:13, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4245, top5_acc: 0.6922, loss_cls: 3.1934, loss: 3.1934 +2024-07-20 12:56:47,617 - pyskl - INFO - Epoch [115][1900/3746] lr: 1.319e-02, eta: 1 day, 6:11:52, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4372, top5_acc: 0.7020, loss_cls: 3.1212, loss: 3.1212 +2024-07-20 12:58:10,485 - pyskl - INFO - Epoch [115][2000/3746] lr: 1.317e-02, eta: 1 day, 6:10:30, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6909, loss_cls: 3.2160, loss: 3.2160 +2024-07-20 12:59:32,879 - pyskl - INFO - Epoch [115][2100/3746] lr: 1.315e-02, eta: 1 day, 6:09:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6797, loss_cls: 3.2425, loss: 3.2425 +2024-07-20 13:00:55,863 - pyskl - INFO - Epoch [115][2200/3746] lr: 1.313e-02, eta: 1 day, 6:07:47, time: 0.830, data_time: 0.001, memory: 15990, top1_acc: 0.4161, top5_acc: 0.6792, loss_cls: 3.2172, loss: 3.2172 +2024-07-20 13:02:19,052 - pyskl - INFO - Epoch [115][2300/3746] lr: 1.311e-02, eta: 1 day, 6:06:26, time: 0.832, data_time: 0.001, memory: 15990, top1_acc: 0.4264, top5_acc: 0.6889, loss_cls: 3.1727, loss: 3.1727 +2024-07-20 13:03:41,210 - pyskl - INFO - Epoch [115][2400/3746] lr: 1.310e-02, eta: 1 day, 6:05:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4275, top5_acc: 0.6848, loss_cls: 3.2087, loss: 3.2087 +2024-07-20 13:05:02,642 - pyskl - INFO - Epoch [115][2500/3746] lr: 1.308e-02, eta: 1 day, 6:03:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6934, loss_cls: 3.1783, loss: 3.1783 +2024-07-20 13:06:24,580 - pyskl - INFO - Epoch [115][2600/3746] lr: 1.306e-02, eta: 1 day, 6:02:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4177, top5_acc: 0.6777, loss_cls: 3.2491, loss: 3.2491 +2024-07-20 13:07:46,456 - pyskl - INFO - Epoch [115][2700/3746] lr: 1.304e-02, eta: 1 day, 6:00:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4366, top5_acc: 0.7000, loss_cls: 3.1148, loss: 3.1148 +2024-07-20 13:09:07,547 - pyskl - INFO - Epoch [115][2800/3746] lr: 1.302e-02, eta: 1 day, 5:59:37, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4277, top5_acc: 0.6903, loss_cls: 3.1771, loss: 3.1771 +2024-07-20 13:10:29,187 - pyskl - INFO - Epoch [115][2900/3746] lr: 1.300e-02, eta: 1 day, 5:58:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4317, top5_acc: 0.6936, loss_cls: 3.1890, loss: 3.1890 +2024-07-20 13:11:50,708 - pyskl - INFO - Epoch [115][3000/3746] lr: 1.298e-02, eta: 1 day, 5:56:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6834, loss_cls: 3.2019, loss: 3.2019 +2024-07-20 13:13:12,362 - pyskl - INFO - Epoch [115][3100/3746] lr: 1.296e-02, eta: 1 day, 5:55:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4323, top5_acc: 0.6839, loss_cls: 3.1873, loss: 3.1873 +2024-07-20 13:14:34,184 - pyskl - INFO - Epoch [115][3200/3746] lr: 1.295e-02, eta: 1 day, 5:54:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4339, top5_acc: 0.7009, loss_cls: 3.1481, loss: 3.1481 +2024-07-20 13:15:55,724 - pyskl - INFO - Epoch [115][3300/3746] lr: 1.293e-02, eta: 1 day, 5:52:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6895, loss_cls: 3.1946, loss: 3.1946 +2024-07-20 13:17:17,454 - pyskl - INFO - Epoch [115][3400/3746] lr: 1.291e-02, eta: 1 day, 5:51:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6795, loss_cls: 3.2136, loss: 3.2136 +2024-07-20 13:18:38,998 - pyskl - INFO - Epoch [115][3500/3746] lr: 1.289e-02, eta: 1 day, 5:50:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6878, loss_cls: 3.2147, loss: 3.2147 +2024-07-20 13:20:00,409 - pyskl - INFO - Epoch [115][3600/3746] lr: 1.287e-02, eta: 1 day, 5:48:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6898, loss_cls: 3.2246, loss: 3.2246 +2024-07-20 13:21:22,106 - pyskl - INFO - Epoch [115][3700/3746] lr: 1.285e-02, eta: 1 day, 5:47:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4400, top5_acc: 0.6934, loss_cls: 3.1795, loss: 3.1795 +2024-07-20 13:22:02,053 - pyskl - INFO - Saving checkpoint at 115 epochs +2024-07-20 13:23:52,443 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 13:23:53,130 - pyskl - INFO - +top1_acc 0.3670 +top5_acc 0.6287 +2024-07-20 13:23:53,130 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 13:23:53,170 - pyskl - INFO - +mean_acc 0.3668 +2024-07-20 13:23:53,175 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_110.pth was removed +2024-07-20 13:23:53,410 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_115.pth. +2024-07-20 13:23:53,411 - pyskl - INFO - Best top1_acc is 0.3670 at 115 epoch. +2024-07-20 13:23:53,422 - pyskl - INFO - Epoch(val) [115][309] top1_acc: 0.3670, top5_acc: 0.6287, mean_class_accuracy: 0.3668 +2024-07-20 13:27:43,063 - pyskl - INFO - Epoch [116][100/3746] lr: 1.282e-02, eta: 1 day, 5:45:55, time: 2.296, data_time: 1.324, memory: 15990, top1_acc: 0.4589, top5_acc: 0.7102, loss_cls: 3.0286, loss: 3.0286 +2024-07-20 13:29:05,807 - pyskl - INFO - Epoch [116][200/3746] lr: 1.281e-02, eta: 1 day, 5:44:33, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4534, top5_acc: 0.7166, loss_cls: 3.0231, loss: 3.0231 +2024-07-20 13:30:27,978 - pyskl - INFO - Epoch [116][300/3746] lr: 1.279e-02, eta: 1 day, 5:43:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4433, top5_acc: 0.7033, loss_cls: 3.1128, loss: 3.1128 +2024-07-20 13:31:50,001 - pyskl - INFO - Epoch [116][400/3746] lr: 1.277e-02, eta: 1 day, 5:41:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4470, top5_acc: 0.7091, loss_cls: 3.0581, loss: 3.0581 +2024-07-20 13:33:11,478 - pyskl - INFO - Epoch [116][500/3746] lr: 1.275e-02, eta: 1 day, 5:40:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4470, top5_acc: 0.6948, loss_cls: 3.1243, loss: 3.1243 +2024-07-20 13:34:33,376 - pyskl - INFO - Epoch [116][600/3746] lr: 1.273e-02, eta: 1 day, 5:39:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.6937, loss_cls: 3.1358, loss: 3.1358 +2024-07-20 13:35:55,073 - pyskl - INFO - Epoch [116][700/3746] lr: 1.271e-02, eta: 1 day, 5:37:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4314, top5_acc: 0.6878, loss_cls: 3.1719, loss: 3.1719 +2024-07-20 13:37:16,885 - pyskl - INFO - Epoch [116][800/3746] lr: 1.269e-02, eta: 1 day, 5:36:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4320, top5_acc: 0.6984, loss_cls: 3.1471, loss: 3.1471 +2024-07-20 13:38:38,310 - pyskl - INFO - Epoch [116][900/3746] lr: 1.268e-02, eta: 1 day, 5:35:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4297, top5_acc: 0.7002, loss_cls: 3.1215, loss: 3.1215 +2024-07-20 13:39:59,987 - pyskl - INFO - Epoch [116][1000/3746] lr: 1.266e-02, eta: 1 day, 5:33:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.6970, loss_cls: 3.1415, loss: 3.1415 +2024-07-20 13:41:21,268 - pyskl - INFO - Epoch [116][1100/3746] lr: 1.264e-02, eta: 1 day, 5:32:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4247, top5_acc: 0.6898, loss_cls: 3.1893, loss: 3.1893 +2024-07-20 13:42:42,734 - pyskl - INFO - Epoch [116][1200/3746] lr: 1.262e-02, eta: 1 day, 5:30:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4358, top5_acc: 0.7006, loss_cls: 3.1258, loss: 3.1258 +2024-07-20 13:44:05,236 - pyskl - INFO - Epoch [116][1300/3746] lr: 1.260e-02, eta: 1 day, 5:29:34, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4405, top5_acc: 0.6972, loss_cls: 3.1448, loss: 3.1448 +2024-07-20 13:45:27,620 - pyskl - INFO - Epoch [116][1400/3746] lr: 1.258e-02, eta: 1 day, 5:28:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.7011, loss_cls: 3.1143, loss: 3.1143 +2024-07-20 13:46:49,455 - pyskl - INFO - Epoch [116][1500/3746] lr: 1.256e-02, eta: 1 day, 5:26:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4298, top5_acc: 0.6931, loss_cls: 3.1481, loss: 3.1481 +2024-07-20 13:48:11,023 - pyskl - INFO - Epoch [116][1600/3746] lr: 1.255e-02, eta: 1 day, 5:25:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6980, loss_cls: 3.1641, loss: 3.1641 +2024-07-20 13:49:33,902 - pyskl - INFO - Epoch [116][1700/3746] lr: 1.253e-02, eta: 1 day, 5:24:07, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4403, top5_acc: 0.7059, loss_cls: 3.0890, loss: 3.0890 +2024-07-20 13:50:56,822 - pyskl - INFO - Epoch [116][1800/3746] lr: 1.251e-02, eta: 1 day, 5:22:45, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4380, top5_acc: 0.6952, loss_cls: 3.1361, loss: 3.1361 +2024-07-20 13:52:19,115 - pyskl - INFO - Epoch [116][1900/3746] lr: 1.249e-02, eta: 1 day, 5:21:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4317, top5_acc: 0.7022, loss_cls: 3.1247, loss: 3.1247 +2024-07-20 13:53:41,355 - pyskl - INFO - Epoch [116][2000/3746] lr: 1.247e-02, eta: 1 day, 5:20:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4331, top5_acc: 0.6937, loss_cls: 3.1704, loss: 3.1704 +2024-07-20 13:55:03,447 - pyskl - INFO - Epoch [116][2100/3746] lr: 1.245e-02, eta: 1 day, 5:18:40, time: 0.821, data_time: 0.001, memory: 15990, top1_acc: 0.4309, top5_acc: 0.6919, loss_cls: 3.1627, loss: 3.1627 +2024-07-20 13:56:25,649 - pyskl - INFO - Epoch [116][2200/3746] lr: 1.243e-02, eta: 1 day, 5:17:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4469, top5_acc: 0.7031, loss_cls: 3.1004, loss: 3.1004 +2024-07-20 13:57:49,246 - pyskl - INFO - Epoch [116][2300/3746] lr: 1.242e-02, eta: 1 day, 5:15:57, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4348, top5_acc: 0.6956, loss_cls: 3.1487, loss: 3.1487 +2024-07-20 13:59:10,511 - pyskl - INFO - Epoch [116][2400/3746] lr: 1.240e-02, eta: 1 day, 5:14:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4263, top5_acc: 0.6855, loss_cls: 3.1789, loss: 3.1789 +2024-07-20 14:00:31,991 - pyskl - INFO - Epoch [116][2500/3746] lr: 1.238e-02, eta: 1 day, 5:13:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6892, loss_cls: 3.1810, loss: 3.1810 +2024-07-20 14:01:54,149 - pyskl - INFO - Epoch [116][2600/3746] lr: 1.236e-02, eta: 1 day, 5:11:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4322, top5_acc: 0.6942, loss_cls: 3.1804, loss: 3.1804 +2024-07-20 14:03:15,878 - pyskl - INFO - Epoch [116][2700/3746] lr: 1.234e-02, eta: 1 day, 5:10:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6908, loss_cls: 3.1852, loss: 3.1852 +2024-07-20 14:04:36,990 - pyskl - INFO - Epoch [116][2800/3746] lr: 1.232e-02, eta: 1 day, 5:09:08, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6936, loss_cls: 3.1841, loss: 3.1841 +2024-07-20 14:05:58,189 - pyskl - INFO - Epoch [116][2900/3746] lr: 1.231e-02, eta: 1 day, 5:07:46, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6955, loss_cls: 3.1326, loss: 3.1326 +2024-07-20 14:07:19,259 - pyskl - INFO - Epoch [116][3000/3746] lr: 1.229e-02, eta: 1 day, 5:06:24, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4317, top5_acc: 0.6911, loss_cls: 3.1890, loss: 3.1890 +2024-07-20 14:08:40,297 - pyskl - INFO - Epoch [116][3100/3746] lr: 1.227e-02, eta: 1 day, 5:05:02, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4266, top5_acc: 0.6877, loss_cls: 3.2010, loss: 3.2010 +2024-07-20 14:10:01,378 - pyskl - INFO - Epoch [116][3200/3746] lr: 1.225e-02, eta: 1 day, 5:03:40, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4425, top5_acc: 0.6911, loss_cls: 3.1642, loss: 3.1642 +2024-07-20 14:11:22,606 - pyskl - INFO - Epoch [116][3300/3746] lr: 1.223e-02, eta: 1 day, 5:02:18, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4380, top5_acc: 0.7002, loss_cls: 3.1487, loss: 3.1487 +2024-07-20 14:12:44,123 - pyskl - INFO - Epoch [116][3400/3746] lr: 1.221e-02, eta: 1 day, 5:00:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4372, top5_acc: 0.6941, loss_cls: 3.1454, loss: 3.1454 +2024-07-20 14:14:05,577 - pyskl - INFO - Epoch [116][3500/3746] lr: 1.220e-02, eta: 1 day, 4:59:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4398, top5_acc: 0.6981, loss_cls: 3.1442, loss: 3.1442 +2024-07-20 14:15:28,016 - pyskl - INFO - Epoch [116][3600/3746] lr: 1.218e-02, eta: 1 day, 4:58:13, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4361, top5_acc: 0.6967, loss_cls: 3.1305, loss: 3.1305 +2024-07-20 14:16:49,719 - pyskl - INFO - Epoch [116][3700/3746] lr: 1.216e-02, eta: 1 day, 4:56:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4247, top5_acc: 0.6873, loss_cls: 3.1944, loss: 3.1944 +2024-07-20 14:17:29,652 - pyskl - INFO - Saving checkpoint at 116 epochs +2024-07-20 14:19:20,303 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 14:19:20,964 - pyskl - INFO - +top1_acc 0.3705 +top5_acc 0.6314 +2024-07-20 14:19:20,964 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 14:19:21,002 - pyskl - INFO - +mean_acc 0.3703 +2024-07-20 14:19:21,008 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_115.pth was removed +2024-07-20 14:19:21,317 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_116.pth. +2024-07-20 14:19:21,318 - pyskl - INFO - Best top1_acc is 0.3705 at 116 epoch. +2024-07-20 14:19:21,329 - pyskl - INFO - Epoch(val) [116][309] top1_acc: 0.3705, top5_acc: 0.6314, mean_class_accuracy: 0.3703 +2024-07-20 14:23:06,150 - pyskl - INFO - Epoch [117][100/3746] lr: 1.213e-02, eta: 1 day, 4:55:22, time: 2.248, data_time: 1.268, memory: 15990, top1_acc: 0.4555, top5_acc: 0.7195, loss_cls: 3.0293, loss: 3.0293 +2024-07-20 14:24:28,645 - pyskl - INFO - Epoch [117][200/3746] lr: 1.211e-02, eta: 1 day, 4:54:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4417, top5_acc: 0.7083, loss_cls: 3.0963, loss: 3.0963 +2024-07-20 14:25:50,875 - pyskl - INFO - Epoch [117][300/3746] lr: 1.210e-02, eta: 1 day, 4:52:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4605, top5_acc: 0.7205, loss_cls: 2.9893, loss: 2.9893 +2024-07-20 14:27:12,328 - pyskl - INFO - Epoch [117][400/3746] lr: 1.208e-02, eta: 1 day, 4:51:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4339, top5_acc: 0.6983, loss_cls: 3.1000, loss: 3.1000 +2024-07-20 14:28:33,601 - pyskl - INFO - Epoch [117][500/3746] lr: 1.206e-02, eta: 1 day, 4:49:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4437, top5_acc: 0.7045, loss_cls: 3.0755, loss: 3.0755 +2024-07-20 14:29:55,821 - pyskl - INFO - Epoch [117][600/3746] lr: 1.204e-02, eta: 1 day, 4:48:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4341, top5_acc: 0.6983, loss_cls: 3.1147, loss: 3.1147 +2024-07-20 14:31:17,381 - pyskl - INFO - Epoch [117][700/3746] lr: 1.202e-02, eta: 1 day, 4:47:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4295, top5_acc: 0.6955, loss_cls: 3.1845, loss: 3.1845 +2024-07-20 14:32:38,977 - pyskl - INFO - Epoch [117][800/3746] lr: 1.200e-02, eta: 1 day, 4:45:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4428, top5_acc: 0.7086, loss_cls: 3.1045, loss: 3.1045 +2024-07-20 14:34:00,860 - pyskl - INFO - Epoch [117][900/3746] lr: 1.199e-02, eta: 1 day, 4:44:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4506, top5_acc: 0.7013, loss_cls: 3.0927, loss: 3.0927 +2024-07-20 14:35:22,373 - pyskl - INFO - Epoch [117][1000/3746] lr: 1.197e-02, eta: 1 day, 4:43:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4475, top5_acc: 0.7078, loss_cls: 3.0908, loss: 3.0908 +2024-07-20 14:36:43,628 - pyskl - INFO - Epoch [117][1100/3746] lr: 1.195e-02, eta: 1 day, 4:41:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4464, top5_acc: 0.6992, loss_cls: 3.1289, loss: 3.1289 +2024-07-20 14:38:05,897 - pyskl - INFO - Epoch [117][1200/3746] lr: 1.193e-02, eta: 1 day, 4:40:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4369, top5_acc: 0.7048, loss_cls: 3.0917, loss: 3.0917 +2024-07-20 14:39:26,984 - pyskl - INFO - Epoch [117][1300/3746] lr: 1.191e-02, eta: 1 day, 4:39:00, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4327, top5_acc: 0.6933, loss_cls: 3.1388, loss: 3.1388 +2024-07-20 14:40:49,641 - pyskl - INFO - Epoch [117][1400/3746] lr: 1.190e-02, eta: 1 day, 4:37:39, time: 0.827, data_time: 0.001, memory: 15990, top1_acc: 0.4481, top5_acc: 0.7116, loss_cls: 3.0828, loss: 3.0828 +2024-07-20 14:42:11,801 - pyskl - INFO - Epoch [117][1500/3746] lr: 1.188e-02, eta: 1 day, 4:36:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.7006, loss_cls: 3.0918, loss: 3.0918 +2024-07-20 14:43:32,922 - pyskl - INFO - Epoch [117][1600/3746] lr: 1.186e-02, eta: 1 day, 4:34:55, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.7017, loss_cls: 3.1040, loss: 3.1040 +2024-07-20 14:44:54,882 - pyskl - INFO - Epoch [117][1700/3746] lr: 1.184e-02, eta: 1 day, 4:33:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4366, top5_acc: 0.6945, loss_cls: 3.1294, loss: 3.1294 +2024-07-20 14:46:17,325 - pyskl - INFO - Epoch [117][1800/3746] lr: 1.182e-02, eta: 1 day, 4:32:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4369, top5_acc: 0.7005, loss_cls: 3.1138, loss: 3.1138 +2024-07-20 14:47:39,738 - pyskl - INFO - Epoch [117][1900/3746] lr: 1.181e-02, eta: 1 day, 4:30:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4402, top5_acc: 0.7119, loss_cls: 3.0801, loss: 3.0801 +2024-07-20 14:49:01,535 - pyskl - INFO - Epoch [117][2000/3746] lr: 1.179e-02, eta: 1 day, 4:29:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6992, loss_cls: 3.1748, loss: 3.1748 +2024-07-20 14:50:23,769 - pyskl - INFO - Epoch [117][2100/3746] lr: 1.177e-02, eta: 1 day, 4:28:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4283, top5_acc: 0.6969, loss_cls: 3.1640, loss: 3.1640 +2024-07-20 14:51:45,927 - pyskl - INFO - Epoch [117][2200/3746] lr: 1.175e-02, eta: 1 day, 4:26:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4377, top5_acc: 0.6980, loss_cls: 3.1478, loss: 3.1478 +2024-07-20 14:53:08,459 - pyskl - INFO - Epoch [117][2300/3746] lr: 1.173e-02, eta: 1 day, 4:25:23, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4378, top5_acc: 0.7037, loss_cls: 3.1136, loss: 3.1136 +2024-07-20 14:54:29,841 - pyskl - INFO - Epoch [117][2400/3746] lr: 1.172e-02, eta: 1 day, 4:24:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.6961, loss_cls: 3.1784, loss: 3.1784 +2024-07-20 14:55:51,036 - pyskl - INFO - Epoch [117][2500/3746] lr: 1.170e-02, eta: 1 day, 4:22:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4308, top5_acc: 0.7033, loss_cls: 3.1398, loss: 3.1398 +2024-07-20 14:57:13,092 - pyskl - INFO - Epoch [117][2600/3746] lr: 1.168e-02, eta: 1 day, 4:21:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4380, top5_acc: 0.6944, loss_cls: 3.1159, loss: 3.1159 +2024-07-20 14:58:35,916 - pyskl - INFO - Epoch [117][2700/3746] lr: 1.166e-02, eta: 1 day, 4:19:56, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4458, top5_acc: 0.7045, loss_cls: 3.1205, loss: 3.1205 +2024-07-20 14:59:57,884 - pyskl - INFO - Epoch [117][2800/3746] lr: 1.164e-02, eta: 1 day, 4:18:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4341, top5_acc: 0.6991, loss_cls: 3.1492, loss: 3.1492 +2024-07-20 15:01:19,950 - pyskl - INFO - Epoch [117][2900/3746] lr: 1.163e-02, eta: 1 day, 4:17:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4445, top5_acc: 0.6966, loss_cls: 3.1249, loss: 3.1249 +2024-07-20 15:02:41,839 - pyskl - INFO - Epoch [117][3000/3746] lr: 1.161e-02, eta: 1 day, 4:15:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6970, loss_cls: 3.1530, loss: 3.1530 +2024-07-20 15:04:03,743 - pyskl - INFO - Epoch [117][3100/3746] lr: 1.159e-02, eta: 1 day, 4:14:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6977, loss_cls: 3.1474, loss: 3.1474 +2024-07-20 15:05:25,100 - pyskl - INFO - Epoch [117][3200/3746] lr: 1.157e-02, eta: 1 day, 4:13:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4338, top5_acc: 0.6980, loss_cls: 3.1214, loss: 3.1214 +2024-07-20 15:06:46,278 - pyskl - INFO - Epoch [117][3300/3746] lr: 1.155e-02, eta: 1 day, 4:11:45, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.6978, loss_cls: 3.1321, loss: 3.1321 +2024-07-20 15:08:07,998 - pyskl - INFO - Epoch [117][3400/3746] lr: 1.154e-02, eta: 1 day, 4:10:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4322, top5_acc: 0.6973, loss_cls: 3.1403, loss: 3.1403 +2024-07-20 15:09:30,017 - pyskl - INFO - Epoch [117][3500/3746] lr: 1.152e-02, eta: 1 day, 4:09:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4405, top5_acc: 0.6945, loss_cls: 3.1387, loss: 3.1387 +2024-07-20 15:10:51,479 - pyskl - INFO - Epoch [117][3600/3746] lr: 1.150e-02, eta: 1 day, 4:07:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4400, top5_acc: 0.6959, loss_cls: 3.1313, loss: 3.1313 +2024-07-20 15:12:13,701 - pyskl - INFO - Epoch [117][3700/3746] lr: 1.148e-02, eta: 1 day, 4:06:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4308, top5_acc: 0.7005, loss_cls: 3.1504, loss: 3.1504 +2024-07-20 15:12:53,459 - pyskl - INFO - Saving checkpoint at 117 epochs +2024-07-20 15:14:44,992 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 15:14:45,655 - pyskl - INFO - +top1_acc 0.3759 +top5_acc 0.6361 +2024-07-20 15:14:45,656 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 15:14:45,695 - pyskl - INFO - +mean_acc 0.3757 +2024-07-20 15:14:45,700 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_116.pth was removed +2024-07-20 15:14:45,961 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_117.pth. +2024-07-20 15:14:45,962 - pyskl - INFO - Best top1_acc is 0.3759 at 117 epoch. +2024-07-20 15:14:45,973 - pyskl - INFO - Epoch(val) [117][309] top1_acc: 0.3759, top5_acc: 0.6361, mean_class_accuracy: 0.3757 +2024-07-20 15:18:31,093 - pyskl - INFO - Epoch [118][100/3746] lr: 1.146e-02, eta: 1 day, 4:04:48, time: 2.251, data_time: 1.287, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7186, loss_cls: 3.0079, loss: 3.0079 +2024-07-20 15:19:53,276 - pyskl - INFO - Epoch [118][200/3746] lr: 1.144e-02, eta: 1 day, 4:03:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4528, top5_acc: 0.7137, loss_cls: 3.0402, loss: 3.0402 +2024-07-20 15:21:15,197 - pyskl - INFO - Epoch [118][300/3746] lr: 1.142e-02, eta: 1 day, 4:02:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4556, top5_acc: 0.7084, loss_cls: 3.0573, loss: 3.0573 +2024-07-20 15:22:36,865 - pyskl - INFO - Epoch [118][400/3746] lr: 1.140e-02, eta: 1 day, 4:00:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.7075, loss_cls: 3.0639, loss: 3.0639 +2024-07-20 15:23:58,500 - pyskl - INFO - Epoch [118][500/3746] lr: 1.139e-02, eta: 1 day, 3:59:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4567, top5_acc: 0.7141, loss_cls: 3.0372, loss: 3.0372 +2024-07-20 15:25:19,920 - pyskl - INFO - Epoch [118][600/3746] lr: 1.137e-02, eta: 1 day, 3:57:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4506, top5_acc: 0.7070, loss_cls: 3.0778, loss: 3.0778 +2024-07-20 15:26:41,186 - pyskl - INFO - Epoch [118][700/3746] lr: 1.135e-02, eta: 1 day, 3:56:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4400, top5_acc: 0.7008, loss_cls: 3.1267, loss: 3.1267 +2024-07-20 15:28:03,123 - pyskl - INFO - Epoch [118][800/3746] lr: 1.133e-02, eta: 1 day, 3:55:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.6944, loss_cls: 3.1209, loss: 3.1209 +2024-07-20 15:29:24,847 - pyskl - INFO - Epoch [118][900/3746] lr: 1.131e-02, eta: 1 day, 3:53:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4584, top5_acc: 0.7137, loss_cls: 3.0300, loss: 3.0300 +2024-07-20 15:30:47,087 - pyskl - INFO - Epoch [118][1000/3746] lr: 1.130e-02, eta: 1 day, 3:52:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4408, top5_acc: 0.7020, loss_cls: 3.1077, loss: 3.1077 +2024-07-20 15:32:08,624 - pyskl - INFO - Epoch [118][1100/3746] lr: 1.128e-02, eta: 1 day, 3:51:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4483, top5_acc: 0.7050, loss_cls: 3.0985, loss: 3.0985 +2024-07-20 15:33:30,279 - pyskl - INFO - Epoch [118][1200/3746] lr: 1.126e-02, eta: 1 day, 3:49:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4509, top5_acc: 0.7130, loss_cls: 3.0637, loss: 3.0637 +2024-07-20 15:34:51,645 - pyskl - INFO - Epoch [118][1300/3746] lr: 1.124e-02, eta: 1 day, 3:48:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4372, top5_acc: 0.6944, loss_cls: 3.1261, loss: 3.1261 +2024-07-20 15:36:13,801 - pyskl - INFO - Epoch [118][1400/3746] lr: 1.123e-02, eta: 1 day, 3:47:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4386, top5_acc: 0.7069, loss_cls: 3.0913, loss: 3.0913 +2024-07-20 15:37:35,785 - pyskl - INFO - Epoch [118][1500/3746] lr: 1.121e-02, eta: 1 day, 3:45:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.7100, loss_cls: 3.0772, loss: 3.0772 +2024-07-20 15:38:58,036 - pyskl - INFO - Epoch [118][1600/3746] lr: 1.119e-02, eta: 1 day, 3:44:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4378, top5_acc: 0.7072, loss_cls: 3.1213, loss: 3.1213 +2024-07-20 15:40:20,555 - pyskl - INFO - Epoch [118][1700/3746] lr: 1.117e-02, eta: 1 day, 3:42:59, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4455, top5_acc: 0.7013, loss_cls: 3.0769, loss: 3.0769 +2024-07-20 15:41:42,384 - pyskl - INFO - Epoch [118][1800/3746] lr: 1.116e-02, eta: 1 day, 3:41:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.7116, loss_cls: 3.0920, loss: 3.0920 +2024-07-20 15:43:05,542 - pyskl - INFO - Epoch [118][1900/3746] lr: 1.114e-02, eta: 1 day, 3:40:15, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4422, top5_acc: 0.7036, loss_cls: 3.0752, loss: 3.0752 +2024-07-20 15:44:27,127 - pyskl - INFO - Epoch [118][2000/3746] lr: 1.112e-02, eta: 1 day, 3:38:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4348, top5_acc: 0.6966, loss_cls: 3.1399, loss: 3.1399 +2024-07-20 15:45:49,263 - pyskl - INFO - Epoch [118][2100/3746] lr: 1.110e-02, eta: 1 day, 3:37:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.7025, loss_cls: 3.1220, loss: 3.1220 +2024-07-20 15:47:11,841 - pyskl - INFO - Epoch [118][2200/3746] lr: 1.109e-02, eta: 1 day, 3:36:10, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4577, top5_acc: 0.7116, loss_cls: 3.0378, loss: 3.0378 +2024-07-20 15:48:34,240 - pyskl - INFO - Epoch [118][2300/3746] lr: 1.107e-02, eta: 1 day, 3:34:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4514, top5_acc: 0.7153, loss_cls: 3.0670, loss: 3.0670 +2024-07-20 15:49:55,850 - pyskl - INFO - Epoch [118][2400/3746] lr: 1.105e-02, eta: 1 day, 3:33:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4447, top5_acc: 0.7023, loss_cls: 3.0875, loss: 3.0875 +2024-07-20 15:51:16,602 - pyskl - INFO - Epoch [118][2500/3746] lr: 1.103e-02, eta: 1 day, 3:32:04, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4467, top5_acc: 0.7066, loss_cls: 3.0784, loss: 3.0784 +2024-07-20 15:52:38,478 - pyskl - INFO - Epoch [118][2600/3746] lr: 1.102e-02, eta: 1 day, 3:30:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4311, top5_acc: 0.7003, loss_cls: 3.1181, loss: 3.1181 +2024-07-20 15:54:00,566 - pyskl - INFO - Epoch [118][2700/3746] lr: 1.100e-02, eta: 1 day, 3:29:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4475, top5_acc: 0.6987, loss_cls: 3.0798, loss: 3.0798 +2024-07-20 15:55:22,610 - pyskl - INFO - Epoch [118][2800/3746] lr: 1.098e-02, eta: 1 day, 3:27:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4419, top5_acc: 0.6994, loss_cls: 3.1054, loss: 3.1054 +2024-07-20 15:56:44,273 - pyskl - INFO - Epoch [118][2900/3746] lr: 1.096e-02, eta: 1 day, 3:26:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4328, top5_acc: 0.6895, loss_cls: 3.1562, loss: 3.1562 +2024-07-20 15:58:06,162 - pyskl - INFO - Epoch [118][3000/3746] lr: 1.095e-02, eta: 1 day, 3:25:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4448, top5_acc: 0.7009, loss_cls: 3.0950, loss: 3.0950 +2024-07-20 15:59:27,527 - pyskl - INFO - Epoch [118][3100/3746] lr: 1.093e-02, eta: 1 day, 3:23:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4412, top5_acc: 0.7092, loss_cls: 3.0870, loss: 3.0870 +2024-07-20 16:00:48,971 - pyskl - INFO - Epoch [118][3200/3746] lr: 1.091e-02, eta: 1 day, 3:22:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4386, top5_acc: 0.7086, loss_cls: 3.0876, loss: 3.0876 +2024-07-20 16:02:11,091 - pyskl - INFO - Epoch [118][3300/3746] lr: 1.089e-02, eta: 1 day, 3:21:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4345, top5_acc: 0.6969, loss_cls: 3.1347, loss: 3.1347 +2024-07-20 16:03:32,718 - pyskl - INFO - Epoch [118][3400/3746] lr: 1.088e-02, eta: 1 day, 3:19:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.7009, loss_cls: 3.1258, loss: 3.1258 +2024-07-20 16:04:54,365 - pyskl - INFO - Epoch [118][3500/3746] lr: 1.086e-02, eta: 1 day, 3:18:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4481, top5_acc: 0.7077, loss_cls: 3.0654, loss: 3.0654 +2024-07-20 16:06:15,731 - pyskl - INFO - Epoch [118][3600/3746] lr: 1.084e-02, eta: 1 day, 3:17:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4323, top5_acc: 0.7023, loss_cls: 3.1361, loss: 3.1361 +2024-07-20 16:07:38,589 - pyskl - INFO - Epoch [118][3700/3746] lr: 1.082e-02, eta: 1 day, 3:15:42, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4339, top5_acc: 0.7025, loss_cls: 3.1526, loss: 3.1526 +2024-07-20 16:08:17,960 - pyskl - INFO - Saving checkpoint at 118 epochs +2024-07-20 16:10:08,452 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 16:10:09,114 - pyskl - INFO - +top1_acc 0.3782 +top5_acc 0.6435 +2024-07-20 16:10:09,114 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 16:10:09,152 - pyskl - INFO - +mean_acc 0.3777 +2024-07-20 16:10:09,157 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_117.pth was removed +2024-07-20 16:10:09,405 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2024-07-20 16:10:09,406 - pyskl - INFO - Best top1_acc is 0.3782 at 118 epoch. +2024-07-20 16:10:09,417 - pyskl - INFO - Epoch(val) [118][309] top1_acc: 0.3782, top5_acc: 0.6435, mean_class_accuracy: 0.3777 +2024-07-20 16:13:53,624 - pyskl - INFO - Epoch [119][100/3746] lr: 1.080e-02, eta: 1 day, 3:14:11, time: 2.242, data_time: 1.276, memory: 15990, top1_acc: 0.4644, top5_acc: 0.7311, loss_cls: 2.9724, loss: 2.9724 +2024-07-20 16:15:15,761 - pyskl - INFO - Epoch [119][200/3746] lr: 1.078e-02, eta: 1 day, 3:12:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7216, loss_cls: 3.0019, loss: 3.0019 +2024-07-20 16:16:36,850 - pyskl - INFO - Epoch [119][300/3746] lr: 1.076e-02, eta: 1 day, 3:11:27, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4670, top5_acc: 0.7194, loss_cls: 2.9925, loss: 2.9925 +2024-07-20 16:17:58,337 - pyskl - INFO - Epoch [119][400/3746] lr: 1.075e-02, eta: 1 day, 3:10:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4541, top5_acc: 0.7114, loss_cls: 3.0248, loss: 3.0248 +2024-07-20 16:19:20,309 - pyskl - INFO - Epoch [119][500/3746] lr: 1.073e-02, eta: 1 day, 3:08:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4508, top5_acc: 0.7173, loss_cls: 3.0261, loss: 3.0261 +2024-07-20 16:20:41,491 - pyskl - INFO - Epoch [119][600/3746] lr: 1.071e-02, eta: 1 day, 3:07:21, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4478, top5_acc: 0.7089, loss_cls: 3.0809, loss: 3.0809 +2024-07-20 16:22:02,718 - pyskl - INFO - Epoch [119][700/3746] lr: 1.069e-02, eta: 1 day, 3:05:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4502, top5_acc: 0.7067, loss_cls: 3.0511, loss: 3.0511 +2024-07-20 16:23:23,808 - pyskl - INFO - Epoch [119][800/3746] lr: 1.068e-02, eta: 1 day, 3:04:37, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7120, loss_cls: 3.0322, loss: 3.0322 +2024-07-20 16:24:45,210 - pyskl - INFO - Epoch [119][900/3746] lr: 1.066e-02, eta: 1 day, 3:03:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4537, top5_acc: 0.7172, loss_cls: 3.0336, loss: 3.0336 +2024-07-20 16:26:06,602 - pyskl - INFO - Epoch [119][1000/3746] lr: 1.064e-02, eta: 1 day, 3:01:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4381, top5_acc: 0.7036, loss_cls: 3.0960, loss: 3.0960 +2024-07-20 16:27:28,221 - pyskl - INFO - Epoch [119][1100/3746] lr: 1.063e-02, eta: 1 day, 3:00:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4542, top5_acc: 0.7177, loss_cls: 3.0301, loss: 3.0301 +2024-07-20 16:28:50,459 - pyskl - INFO - Epoch [119][1200/3746] lr: 1.061e-02, eta: 1 day, 2:59:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7106, loss_cls: 3.0538, loss: 3.0538 +2024-07-20 16:30:12,589 - pyskl - INFO - Epoch [119][1300/3746] lr: 1.059e-02, eta: 1 day, 2:57:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4459, top5_acc: 0.7097, loss_cls: 3.0577, loss: 3.0577 +2024-07-20 16:31:34,943 - pyskl - INFO - Epoch [119][1400/3746] lr: 1.057e-02, eta: 1 day, 2:56:26, time: 0.824, data_time: 0.001, memory: 15990, top1_acc: 0.4519, top5_acc: 0.7072, loss_cls: 3.0493, loss: 3.0493 +2024-07-20 16:32:56,767 - pyskl - INFO - Epoch [119][1500/3746] lr: 1.056e-02, eta: 1 day, 2:55:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4511, top5_acc: 0.7045, loss_cls: 3.0551, loss: 3.0551 +2024-07-20 16:34:18,470 - pyskl - INFO - Epoch [119][1600/3746] lr: 1.054e-02, eta: 1 day, 2:53:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4519, top5_acc: 0.7144, loss_cls: 3.0239, loss: 3.0239 +2024-07-20 16:35:40,498 - pyskl - INFO - Epoch [119][1700/3746] lr: 1.052e-02, eta: 1 day, 2:52:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4458, top5_acc: 0.7128, loss_cls: 3.0247, loss: 3.0247 +2024-07-20 16:37:02,862 - pyskl - INFO - Epoch [119][1800/3746] lr: 1.050e-02, eta: 1 day, 2:50:59, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4539, top5_acc: 0.7089, loss_cls: 3.0364, loss: 3.0364 +2024-07-20 16:38:26,282 - pyskl - INFO - Epoch [119][1900/3746] lr: 1.049e-02, eta: 1 day, 2:49:37, time: 0.834, data_time: 0.001, memory: 15990, top1_acc: 0.4512, top5_acc: 0.7091, loss_cls: 3.0932, loss: 3.0932 +2024-07-20 16:39:48,410 - pyskl - INFO - Epoch [119][2000/3746] lr: 1.047e-02, eta: 1 day, 2:48:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4470, top5_acc: 0.7080, loss_cls: 3.0520, loss: 3.0520 +2024-07-20 16:41:10,189 - pyskl - INFO - Epoch [119][2100/3746] lr: 1.045e-02, eta: 1 day, 2:46:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4511, top5_acc: 0.7091, loss_cls: 3.0538, loss: 3.0538 +2024-07-20 16:42:33,137 - pyskl - INFO - Epoch [119][2200/3746] lr: 1.044e-02, eta: 1 day, 2:45:32, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4428, top5_acc: 0.7050, loss_cls: 3.0806, loss: 3.0806 +2024-07-20 16:43:56,326 - pyskl - INFO - Epoch [119][2300/3746] lr: 1.042e-02, eta: 1 day, 2:44:10, time: 0.832, data_time: 0.001, memory: 15990, top1_acc: 0.4528, top5_acc: 0.7064, loss_cls: 3.0448, loss: 3.0448 +2024-07-20 16:45:18,221 - pyskl - INFO - Epoch [119][2400/3746] lr: 1.040e-02, eta: 1 day, 2:42:48, time: 0.819, data_time: 0.001, memory: 15990, top1_acc: 0.4519, top5_acc: 0.7102, loss_cls: 3.0574, loss: 3.0574 +2024-07-20 16:46:39,489 - pyskl - INFO - Epoch [119][2500/3746] lr: 1.039e-02, eta: 1 day, 2:41:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4456, top5_acc: 0.7108, loss_cls: 3.0591, loss: 3.0591 +2024-07-20 16:48:02,237 - pyskl - INFO - Epoch [119][2600/3746] lr: 1.037e-02, eta: 1 day, 2:40:05, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4436, top5_acc: 0.7020, loss_cls: 3.0879, loss: 3.0879 +2024-07-20 16:49:25,044 - pyskl - INFO - Epoch [119][2700/3746] lr: 1.035e-02, eta: 1 day, 2:38:43, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6956, loss_cls: 3.1645, loss: 3.1645 +2024-07-20 16:50:47,351 - pyskl - INFO - Epoch [119][2800/3746] lr: 1.033e-02, eta: 1 day, 2:37:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4344, top5_acc: 0.7053, loss_cls: 3.1010, loss: 3.1010 +2024-07-20 16:52:09,776 - pyskl - INFO - Epoch [119][2900/3746] lr: 1.032e-02, eta: 1 day, 2:36:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4481, top5_acc: 0.7077, loss_cls: 3.0769, loss: 3.0769 +2024-07-20 16:53:31,413 - pyskl - INFO - Epoch [119][3000/3746] lr: 1.030e-02, eta: 1 day, 2:34:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4448, top5_acc: 0.7050, loss_cls: 3.0854, loss: 3.0854 +2024-07-20 16:54:52,880 - pyskl - INFO - Epoch [119][3100/3746] lr: 1.028e-02, eta: 1 day, 2:33:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4544, top5_acc: 0.7075, loss_cls: 3.0740, loss: 3.0740 +2024-07-20 16:56:14,352 - pyskl - INFO - Epoch [119][3200/3746] lr: 1.027e-02, eta: 1 day, 2:31:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4512, top5_acc: 0.7133, loss_cls: 3.0569, loss: 3.0569 +2024-07-20 16:57:36,034 - pyskl - INFO - Epoch [119][3300/3746] lr: 1.025e-02, eta: 1 day, 2:30:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.6969, loss_cls: 3.1098, loss: 3.1098 +2024-07-20 16:58:57,383 - pyskl - INFO - Epoch [119][3400/3746] lr: 1.023e-02, eta: 1 day, 2:29:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4425, top5_acc: 0.6983, loss_cls: 3.1238, loss: 3.1238 +2024-07-20 17:00:18,654 - pyskl - INFO - Epoch [119][3500/3746] lr: 1.022e-02, eta: 1 day, 2:27:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.7086, loss_cls: 3.0835, loss: 3.0835 +2024-07-20 17:01:40,211 - pyskl - INFO - Epoch [119][3600/3746] lr: 1.020e-02, eta: 1 day, 2:26:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4358, top5_acc: 0.6978, loss_cls: 3.1478, loss: 3.1478 +2024-07-20 17:03:02,561 - pyskl - INFO - Epoch [119][3700/3746] lr: 1.018e-02, eta: 1 day, 2:25:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4461, top5_acc: 0.7106, loss_cls: 3.0782, loss: 3.0782 +2024-07-20 17:03:41,667 - pyskl - INFO - Saving checkpoint at 119 epochs +2024-07-20 17:05:31,827 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 17:05:32,485 - pyskl - INFO - +top1_acc 0.3827 +top5_acc 0.6379 +2024-07-20 17:05:32,485 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 17:05:32,524 - pyskl - INFO - +mean_acc 0.3825 +2024-07-20 17:05:32,528 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_118.pth was removed +2024-07-20 17:05:32,779 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_119.pth. +2024-07-20 17:05:32,779 - pyskl - INFO - Best top1_acc is 0.3827 at 119 epoch. +2024-07-20 17:05:32,790 - pyskl - INFO - Epoch(val) [119][309] top1_acc: 0.3827, top5_acc: 0.6379, mean_class_accuracy: 0.3825 +2024-07-20 17:09:16,397 - pyskl - INFO - Epoch [120][100/3746] lr: 1.016e-02, eta: 1 day, 2:23:32, time: 2.236, data_time: 1.264, memory: 15990, top1_acc: 0.4656, top5_acc: 0.7252, loss_cls: 2.9622, loss: 2.9622 +2024-07-20 17:10:38,163 - pyskl - INFO - Epoch [120][200/3746] lr: 1.014e-02, eta: 1 day, 2:22:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4603, top5_acc: 0.7133, loss_cls: 2.9725, loss: 2.9725 +2024-07-20 17:12:00,355 - pyskl - INFO - Epoch [120][300/3746] lr: 1.012e-02, eta: 1 day, 2:20:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4606, top5_acc: 0.7139, loss_cls: 3.0005, loss: 3.0005 +2024-07-20 17:13:21,756 - pyskl - INFO - Epoch [120][400/3746] lr: 1.011e-02, eta: 1 day, 2:19:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.7225, loss_cls: 3.0350, loss: 3.0350 +2024-07-20 17:14:43,112 - pyskl - INFO - Epoch [120][500/3746] lr: 1.009e-02, eta: 1 day, 2:18:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4670, top5_acc: 0.7258, loss_cls: 2.9546, loss: 2.9546 +2024-07-20 17:16:04,860 - pyskl - INFO - Epoch [120][600/3746] lr: 1.007e-02, eta: 1 day, 2:16:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4500, top5_acc: 0.7233, loss_cls: 3.0091, loss: 3.0091 +2024-07-20 17:17:26,159 - pyskl - INFO - Epoch [120][700/3746] lr: 1.006e-02, eta: 1 day, 2:15:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4641, top5_acc: 0.7242, loss_cls: 2.9720, loss: 2.9720 +2024-07-20 17:18:47,388 - pyskl - INFO - Epoch [120][800/3746] lr: 1.004e-02, eta: 1 day, 2:13:58, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4603, top5_acc: 0.7136, loss_cls: 2.9999, loss: 2.9999 +2024-07-20 17:20:08,690 - pyskl - INFO - Epoch [120][900/3746] lr: 1.002e-02, eta: 1 day, 2:12:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4683, top5_acc: 0.7212, loss_cls: 2.9724, loss: 2.9724 +2024-07-20 17:21:30,374 - pyskl - INFO - Epoch [120][1000/3746] lr: 1.001e-02, eta: 1 day, 2:11:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4587, top5_acc: 0.7211, loss_cls: 2.9801, loss: 2.9801 +2024-07-20 17:22:51,383 - pyskl - INFO - Epoch [120][1100/3746] lr: 9.989e-03, eta: 1 day, 2:09:52, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4459, top5_acc: 0.7048, loss_cls: 3.0763, loss: 3.0763 +2024-07-20 17:24:12,958 - pyskl - INFO - Epoch [120][1200/3746] lr: 9.972e-03, eta: 1 day, 2:08:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4506, top5_acc: 0.7167, loss_cls: 3.0473, loss: 3.0473 +2024-07-20 17:25:34,974 - pyskl - INFO - Epoch [120][1300/3746] lr: 9.955e-03, eta: 1 day, 2:07:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4608, top5_acc: 0.7272, loss_cls: 2.9649, loss: 2.9649 +2024-07-20 17:26:56,995 - pyskl - INFO - Epoch [120][1400/3746] lr: 9.938e-03, eta: 1 day, 2:05:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4575, top5_acc: 0.7136, loss_cls: 3.0103, loss: 3.0103 +2024-07-20 17:28:20,115 - pyskl - INFO - Epoch [120][1500/3746] lr: 9.922e-03, eta: 1 day, 2:04:25, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4508, top5_acc: 0.7191, loss_cls: 3.0548, loss: 3.0548 +2024-07-20 17:29:41,897 - pyskl - INFO - Epoch [120][1600/3746] lr: 9.905e-03, eta: 1 day, 2:03:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4541, top5_acc: 0.7103, loss_cls: 3.0409, loss: 3.0409 +2024-07-20 17:31:04,550 - pyskl - INFO - Epoch [120][1700/3746] lr: 9.888e-03, eta: 1 day, 2:01:41, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4570, top5_acc: 0.7152, loss_cls: 3.0335, loss: 3.0335 +2024-07-20 17:32:26,759 - pyskl - INFO - Epoch [120][1800/3746] lr: 9.871e-03, eta: 1 day, 2:00:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4441, top5_acc: 0.7139, loss_cls: 3.0515, loss: 3.0515 +2024-07-20 17:33:49,735 - pyskl - INFO - Epoch [120][1900/3746] lr: 9.855e-03, eta: 1 day, 1:58:58, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4573, top5_acc: 0.7230, loss_cls: 3.0016, loss: 3.0016 +2024-07-20 17:35:12,527 - pyskl - INFO - Epoch [120][2000/3746] lr: 9.838e-03, eta: 1 day, 1:57:36, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4394, top5_acc: 0.6966, loss_cls: 3.0998, loss: 3.0998 +2024-07-20 17:36:34,863 - pyskl - INFO - Epoch [120][2100/3746] lr: 9.821e-03, eta: 1 day, 1:56:14, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4564, top5_acc: 0.7092, loss_cls: 3.0413, loss: 3.0413 +2024-07-20 17:37:57,407 - pyskl - INFO - Epoch [120][2200/3746] lr: 9.805e-03, eta: 1 day, 1:54:52, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7164, loss_cls: 3.0214, loss: 3.0214 +2024-07-20 17:39:20,272 - pyskl - INFO - Epoch [120][2300/3746] lr: 9.788e-03, eta: 1 day, 1:53:31, time: 0.829, data_time: 0.001, memory: 15990, top1_acc: 0.4545, top5_acc: 0.7073, loss_cls: 3.0863, loss: 3.0863 +2024-07-20 17:40:41,478 - pyskl - INFO - Epoch [120][2400/3746] lr: 9.772e-03, eta: 1 day, 1:52:09, time: 0.812, data_time: 0.001, memory: 15990, top1_acc: 0.4628, top5_acc: 0.7136, loss_cls: 3.0117, loss: 3.0117 +2024-07-20 17:42:02,839 - pyskl - INFO - Epoch [120][2500/3746] lr: 9.755e-03, eta: 1 day, 1:50:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.7075, loss_cls: 3.0757, loss: 3.0757 +2024-07-20 17:43:25,384 - pyskl - INFO - Epoch [120][2600/3746] lr: 9.738e-03, eta: 1 day, 1:49:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4475, top5_acc: 0.7134, loss_cls: 3.0575, loss: 3.0575 +2024-07-20 17:44:47,771 - pyskl - INFO - Epoch [120][2700/3746] lr: 9.722e-03, eta: 1 day, 1:48:03, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4519, top5_acc: 0.7148, loss_cls: 3.0433, loss: 3.0433 +2024-07-20 17:46:09,678 - pyskl - INFO - Epoch [120][2800/3746] lr: 9.705e-03, eta: 1 day, 1:46:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4520, top5_acc: 0.7047, loss_cls: 3.0680, loss: 3.0680 +2024-07-20 17:47:31,729 - pyskl - INFO - Epoch [120][2900/3746] lr: 9.689e-03, eta: 1 day, 1:45:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4603, top5_acc: 0.7211, loss_cls: 3.0025, loss: 3.0025 +2024-07-20 17:48:53,068 - pyskl - INFO - Epoch [120][3000/3746] lr: 9.672e-03, eta: 1 day, 1:43:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4550, top5_acc: 0.7078, loss_cls: 3.0624, loss: 3.0624 +2024-07-20 17:50:14,804 - pyskl - INFO - Epoch [120][3100/3746] lr: 9.656e-03, eta: 1 day, 1:42:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.7027, loss_cls: 3.0874, loss: 3.0874 +2024-07-20 17:51:36,738 - pyskl - INFO - Epoch [120][3200/3746] lr: 9.639e-03, eta: 1 day, 1:41:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4412, top5_acc: 0.7056, loss_cls: 3.0770, loss: 3.0770 +2024-07-20 17:52:58,326 - pyskl - INFO - Epoch [120][3300/3746] lr: 9.623e-03, eta: 1 day, 1:39:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.7003, loss_cls: 3.1229, loss: 3.1229 +2024-07-20 17:54:19,831 - pyskl - INFO - Epoch [120][3400/3746] lr: 9.606e-03, eta: 1 day, 1:38:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4431, top5_acc: 0.6994, loss_cls: 3.0975, loss: 3.0975 +2024-07-20 17:55:41,061 - pyskl - INFO - Epoch [120][3500/3746] lr: 9.590e-03, eta: 1 day, 1:37:08, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4508, top5_acc: 0.7153, loss_cls: 3.0319, loss: 3.0319 +2024-07-20 17:57:03,139 - pyskl - INFO - Epoch [120][3600/3746] lr: 9.573e-03, eta: 1 day, 1:35:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4548, top5_acc: 0.7097, loss_cls: 3.0637, loss: 3.0637 +2024-07-20 17:58:25,113 - pyskl - INFO - Epoch [120][3700/3746] lr: 9.557e-03, eta: 1 day, 1:34:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4500, top5_acc: 0.7006, loss_cls: 3.0712, loss: 3.0712 +2024-07-20 17:59:04,282 - pyskl - INFO - Saving checkpoint at 120 epochs +2024-07-20 18:00:54,461 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 18:00:55,120 - pyskl - INFO - +top1_acc 0.3792 +top5_acc 0.6396 +2024-07-20 18:00:55,120 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 18:00:55,159 - pyskl - INFO - +mean_acc 0.3789 +2024-07-20 18:00:55,170 - pyskl - INFO - Epoch(val) [120][309] top1_acc: 0.3792, top5_acc: 0.6396, mean_class_accuracy: 0.3789 +2024-07-20 18:04:40,844 - pyskl - INFO - Epoch [121][100/3746] lr: 9.533e-03, eta: 1 day, 1:32:51, time: 2.257, data_time: 1.287, memory: 15990, top1_acc: 0.4708, top5_acc: 0.7294, loss_cls: 2.9488, loss: 2.9488 +2024-07-20 18:06:02,869 - pyskl - INFO - Epoch [121][200/3746] lr: 9.516e-03, eta: 1 day, 1:31:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4537, top5_acc: 0.7255, loss_cls: 2.9901, loss: 2.9901 +2024-07-20 18:07:24,309 - pyskl - INFO - Epoch [121][300/3746] lr: 9.500e-03, eta: 1 day, 1:30:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4725, top5_acc: 0.7311, loss_cls: 2.9127, loss: 2.9127 +2024-07-20 18:08:46,108 - pyskl - INFO - Epoch [121][400/3746] lr: 9.484e-03, eta: 1 day, 1:28:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4670, top5_acc: 0.7316, loss_cls: 2.9517, loss: 2.9517 +2024-07-20 18:10:07,516 - pyskl - INFO - Epoch [121][500/3746] lr: 9.467e-03, eta: 1 day, 1:27:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4652, top5_acc: 0.7137, loss_cls: 2.9792, loss: 2.9792 +2024-07-20 18:11:29,495 - pyskl - INFO - Epoch [121][600/3746] lr: 9.451e-03, eta: 1 day, 1:26:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4519, top5_acc: 0.7139, loss_cls: 3.0551, loss: 3.0551 +2024-07-20 18:12:50,951 - pyskl - INFO - Epoch [121][700/3746] lr: 9.435e-03, eta: 1 day, 1:24:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4473, top5_acc: 0.7236, loss_cls: 3.0108, loss: 3.0108 +2024-07-20 18:14:12,409 - pyskl - INFO - Epoch [121][800/3746] lr: 9.418e-03, eta: 1 day, 1:23:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7231, loss_cls: 2.9723, loss: 2.9723 +2024-07-20 18:15:33,688 - pyskl - INFO - Epoch [121][900/3746] lr: 9.402e-03, eta: 1 day, 1:21:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4595, top5_acc: 0.7175, loss_cls: 3.0067, loss: 3.0067 +2024-07-20 18:16:55,433 - pyskl - INFO - Epoch [121][1000/3746] lr: 9.386e-03, eta: 1 day, 1:20:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.7200, loss_cls: 3.0108, loss: 3.0108 +2024-07-20 18:18:17,104 - pyskl - INFO - Epoch [121][1100/3746] lr: 9.369e-03, eta: 1 day, 1:19:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4666, top5_acc: 0.7223, loss_cls: 2.9675, loss: 2.9675 +2024-07-20 18:19:38,878 - pyskl - INFO - Epoch [121][1200/3746] lr: 9.353e-03, eta: 1 day, 1:17:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4612, top5_acc: 0.7205, loss_cls: 2.9931, loss: 2.9931 +2024-07-20 18:21:00,184 - pyskl - INFO - Epoch [121][1300/3746] lr: 9.337e-03, eta: 1 day, 1:16:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4484, top5_acc: 0.7109, loss_cls: 3.0431, loss: 3.0431 +2024-07-20 18:22:22,188 - pyskl - INFO - Epoch [121][1400/3746] lr: 9.321e-03, eta: 1 day, 1:15:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7177, loss_cls: 2.9984, loss: 2.9984 +2024-07-20 18:23:44,366 - pyskl - INFO - Epoch [121][1500/3746] lr: 9.304e-03, eta: 1 day, 1:13:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4644, top5_acc: 0.7214, loss_cls: 2.9677, loss: 2.9677 +2024-07-20 18:25:05,670 - pyskl - INFO - Epoch [121][1600/3746] lr: 9.288e-03, eta: 1 day, 1:12:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4575, top5_acc: 0.7242, loss_cls: 3.0144, loss: 3.0144 +2024-07-20 18:26:27,625 - pyskl - INFO - Epoch [121][1700/3746] lr: 9.272e-03, eta: 1 day, 1:10:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4636, top5_acc: 0.7269, loss_cls: 2.9578, loss: 2.9578 +2024-07-20 18:27:50,723 - pyskl - INFO - Epoch [121][1800/3746] lr: 9.256e-03, eta: 1 day, 1:09:38, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4608, top5_acc: 0.7163, loss_cls: 2.9986, loss: 2.9986 +2024-07-20 18:29:13,886 - pyskl - INFO - Epoch [121][1900/3746] lr: 9.239e-03, eta: 1 day, 1:08:16, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4419, top5_acc: 0.7048, loss_cls: 3.0680, loss: 3.0680 +2024-07-20 18:30:35,878 - pyskl - INFO - Epoch [121][2000/3746] lr: 9.223e-03, eta: 1 day, 1:06:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4545, top5_acc: 0.7142, loss_cls: 3.0222, loss: 3.0222 +2024-07-20 18:31:57,563 - pyskl - INFO - Epoch [121][2100/3746] lr: 9.207e-03, eta: 1 day, 1:05:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4567, top5_acc: 0.7170, loss_cls: 3.0020, loss: 3.0020 +2024-07-20 18:33:20,048 - pyskl - INFO - Epoch [121][2200/3746] lr: 9.191e-03, eta: 1 day, 1:04:10, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4505, top5_acc: 0.7097, loss_cls: 3.0491, loss: 3.0491 +2024-07-20 18:34:42,877 - pyskl - INFO - Epoch [121][2300/3746] lr: 9.175e-03, eta: 1 day, 1:02:49, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4612, top5_acc: 0.7191, loss_cls: 2.9885, loss: 2.9885 +2024-07-20 18:36:05,004 - pyskl - INFO - Epoch [121][2400/3746] lr: 9.159e-03, eta: 1 day, 1:01:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4523, top5_acc: 0.7148, loss_cls: 3.0109, loss: 3.0109 +2024-07-20 18:37:26,824 - pyskl - INFO - Epoch [121][2500/3746] lr: 9.142e-03, eta: 1 day, 1:00:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7063, loss_cls: 3.0391, loss: 3.0391 +2024-07-20 18:38:49,530 - pyskl - INFO - Epoch [121][2600/3746] lr: 9.126e-03, eta: 1 day, 0:58:43, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4591, top5_acc: 0.7119, loss_cls: 3.0142, loss: 3.0142 +2024-07-20 18:40:12,110 - pyskl - INFO - Epoch [121][2700/3746] lr: 9.110e-03, eta: 1 day, 0:57:22, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4536, top5_acc: 0.7113, loss_cls: 3.0168, loss: 3.0168 +2024-07-20 18:41:34,215 - pyskl - INFO - Epoch [121][2800/3746] lr: 9.094e-03, eta: 1 day, 0:56:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.7152, loss_cls: 3.0654, loss: 3.0654 +2024-07-20 18:42:56,455 - pyskl - INFO - Epoch [121][2900/3746] lr: 9.078e-03, eta: 1 day, 0:54:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4545, top5_acc: 0.7116, loss_cls: 3.0397, loss: 3.0397 +2024-07-20 18:44:17,919 - pyskl - INFO - Epoch [121][3000/3746] lr: 9.062e-03, eta: 1 day, 0:53:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4608, top5_acc: 0.7141, loss_cls: 3.0405, loss: 3.0405 +2024-07-20 18:45:39,454 - pyskl - INFO - Epoch [121][3100/3746] lr: 9.046e-03, eta: 1 day, 0:51:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4619, top5_acc: 0.7166, loss_cls: 3.0228, loss: 3.0228 +2024-07-20 18:47:00,845 - pyskl - INFO - Epoch [121][3200/3746] lr: 9.030e-03, eta: 1 day, 0:50:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4461, top5_acc: 0.7053, loss_cls: 3.0302, loss: 3.0302 +2024-07-20 18:48:22,241 - pyskl - INFO - Epoch [121][3300/3746] lr: 9.014e-03, eta: 1 day, 0:49:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4553, top5_acc: 0.7164, loss_cls: 2.9812, loss: 2.9812 +2024-07-20 18:49:43,755 - pyskl - INFO - Epoch [121][3400/3746] lr: 8.998e-03, eta: 1 day, 0:47:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4545, top5_acc: 0.7172, loss_cls: 3.0285, loss: 3.0285 +2024-07-20 18:51:05,351 - pyskl - INFO - Epoch [121][3500/3746] lr: 8.982e-03, eta: 1 day, 0:46:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4584, top5_acc: 0.7105, loss_cls: 3.0175, loss: 3.0175 +2024-07-20 18:52:27,205 - pyskl - INFO - Epoch [121][3600/3746] lr: 8.966e-03, eta: 1 day, 0:45:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7130, loss_cls: 3.0259, loss: 3.0259 +2024-07-20 18:53:49,708 - pyskl - INFO - Epoch [121][3700/3746] lr: 8.950e-03, eta: 1 day, 0:43:42, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4455, top5_acc: 0.7094, loss_cls: 3.0582, loss: 3.0582 +2024-07-20 18:54:29,202 - pyskl - INFO - Saving checkpoint at 121 epochs +2024-07-20 18:56:20,599 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 18:56:21,265 - pyskl - INFO - +top1_acc 0.3963 +top5_acc 0.6470 +2024-07-20 18:56:21,266 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 18:56:21,304 - pyskl - INFO - +mean_acc 0.3958 +2024-07-20 18:56:21,308 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_119.pth was removed +2024-07-20 18:56:21,565 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_121.pth. +2024-07-20 18:56:21,566 - pyskl - INFO - Best top1_acc is 0.3963 at 121 epoch. +2024-07-20 18:56:21,577 - pyskl - INFO - Epoch(val) [121][309] top1_acc: 0.3963, top5_acc: 0.6470, mean_class_accuracy: 0.3958 +2024-07-20 19:00:07,340 - pyskl - INFO - Epoch [122][100/3746] lr: 8.927e-03, eta: 1 day, 0:42:08, time: 2.258, data_time: 1.282, memory: 15990, top1_acc: 0.4830, top5_acc: 0.7359, loss_cls: 2.8645, loss: 2.8645 +2024-07-20 19:01:29,838 - pyskl - INFO - Epoch [122][200/3746] lr: 8.911e-03, eta: 1 day, 0:40:46, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4809, top5_acc: 0.7309, loss_cls: 2.9099, loss: 2.9099 +2024-07-20 19:02:52,231 - pyskl - INFO - Epoch [122][300/3746] lr: 8.895e-03, eta: 1 day, 0:39:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4739, top5_acc: 0.7316, loss_cls: 2.9148, loss: 2.9148 +2024-07-20 19:04:15,207 - pyskl - INFO - Epoch [122][400/3746] lr: 8.879e-03, eta: 1 day, 0:38:03, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4688, top5_acc: 0.7328, loss_cls: 2.9411, loss: 2.9411 +2024-07-20 19:05:37,175 - pyskl - INFO - Epoch [122][500/3746] lr: 8.863e-03, eta: 1 day, 0:36:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4689, top5_acc: 0.7273, loss_cls: 2.9281, loss: 2.9281 +2024-07-20 19:06:59,329 - pyskl - INFO - Epoch [122][600/3746] lr: 8.847e-03, eta: 1 day, 0:35:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4664, top5_acc: 0.7261, loss_cls: 2.9383, loss: 2.9383 +2024-07-20 19:08:21,084 - pyskl - INFO - Epoch [122][700/3746] lr: 8.831e-03, eta: 1 day, 0:33:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7189, loss_cls: 2.9989, loss: 2.9989 +2024-07-20 19:09:42,994 - pyskl - INFO - Epoch [122][800/3746] lr: 8.815e-03, eta: 1 day, 0:32:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4709, top5_acc: 0.7250, loss_cls: 2.9509, loss: 2.9509 +2024-07-20 19:11:04,817 - pyskl - INFO - Epoch [122][900/3746] lr: 8.800e-03, eta: 1 day, 0:31:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7208, loss_cls: 2.9748, loss: 2.9748 +2024-07-20 19:12:26,847 - pyskl - INFO - Epoch [122][1000/3746] lr: 8.784e-03, eta: 1 day, 0:29:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4717, top5_acc: 0.7259, loss_cls: 2.9457, loss: 2.9457 +2024-07-20 19:13:48,813 - pyskl - INFO - Epoch [122][1100/3746] lr: 8.768e-03, eta: 1 day, 0:28:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7327, loss_cls: 2.9336, loss: 2.9336 +2024-07-20 19:15:10,632 - pyskl - INFO - Epoch [122][1200/3746] lr: 8.752e-03, eta: 1 day, 0:27:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4713, top5_acc: 0.7283, loss_cls: 2.9268, loss: 2.9268 +2024-07-20 19:16:33,198 - pyskl - INFO - Epoch [122][1300/3746] lr: 8.736e-03, eta: 1 day, 0:25:45, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4577, top5_acc: 0.7205, loss_cls: 2.9935, loss: 2.9935 +2024-07-20 19:17:55,189 - pyskl - INFO - Epoch [122][1400/3746] lr: 8.721e-03, eta: 1 day, 0:24:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4552, top5_acc: 0.7205, loss_cls: 2.9847, loss: 2.9847 +2024-07-20 19:19:17,047 - pyskl - INFO - Epoch [122][1500/3746] lr: 8.705e-03, eta: 1 day, 0:23:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4644, top5_acc: 0.7195, loss_cls: 2.9707, loss: 2.9707 +2024-07-20 19:20:39,190 - pyskl - INFO - Epoch [122][1600/3746] lr: 8.689e-03, eta: 1 day, 0:21:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4630, top5_acc: 0.7222, loss_cls: 2.9807, loss: 2.9807 +2024-07-20 19:22:01,477 - pyskl - INFO - Epoch [122][1700/3746] lr: 8.673e-03, eta: 1 day, 0:20:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4625, top5_acc: 0.7203, loss_cls: 2.9758, loss: 2.9758 +2024-07-20 19:23:23,278 - pyskl - INFO - Epoch [122][1800/3746] lr: 8.658e-03, eta: 1 day, 0:18:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7297, loss_cls: 2.9535, loss: 2.9535 +2024-07-20 19:24:45,507 - pyskl - INFO - Epoch [122][1900/3746] lr: 8.642e-03, eta: 1 day, 0:17:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7220, loss_cls: 2.9854, loss: 2.9854 +2024-07-20 19:26:07,939 - pyskl - INFO - Epoch [122][2000/3746] lr: 8.626e-03, eta: 1 day, 0:16:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7189, loss_cls: 2.9748, loss: 2.9748 +2024-07-20 19:27:30,514 - pyskl - INFO - Epoch [122][2100/3746] lr: 8.610e-03, eta: 1 day, 0:14:50, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4678, top5_acc: 0.7242, loss_cls: 2.9576, loss: 2.9576 +2024-07-20 19:28:52,803 - pyskl - INFO - Epoch [122][2200/3746] lr: 8.595e-03, eta: 1 day, 0:13:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4503, top5_acc: 0.7114, loss_cls: 3.0353, loss: 3.0353 +2024-07-20 19:30:15,400 - pyskl - INFO - Epoch [122][2300/3746] lr: 8.579e-03, eta: 1 day, 0:12:07, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4530, top5_acc: 0.7091, loss_cls: 3.0243, loss: 3.0243 +2024-07-20 19:31:37,451 - pyskl - INFO - Epoch [122][2400/3746] lr: 8.563e-03, eta: 1 day, 0:10:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4637, top5_acc: 0.7222, loss_cls: 2.9819, loss: 2.9819 +2024-07-20 19:32:59,939 - pyskl - INFO - Epoch [122][2500/3746] lr: 8.548e-03, eta: 1 day, 0:09:23, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7223, loss_cls: 2.9833, loss: 2.9833 +2024-07-20 19:34:22,580 - pyskl - INFO - Epoch [122][2600/3746] lr: 8.532e-03, eta: 1 day, 0:08:01, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4641, top5_acc: 0.7109, loss_cls: 3.0135, loss: 3.0135 +2024-07-20 19:35:44,934 - pyskl - INFO - Epoch [122][2700/3746] lr: 8.517e-03, eta: 1 day, 0:06:39, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7252, loss_cls: 2.9592, loss: 2.9592 +2024-07-20 19:37:07,080 - pyskl - INFO - Epoch [122][2800/3746] lr: 8.501e-03, eta: 1 day, 0:05:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4756, top5_acc: 0.7295, loss_cls: 2.9483, loss: 2.9483 +2024-07-20 19:38:28,814 - pyskl - INFO - Epoch [122][2900/3746] lr: 8.485e-03, eta: 1 day, 0:03:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4637, top5_acc: 0.7131, loss_cls: 3.0003, loss: 3.0003 +2024-07-20 19:39:51,388 - pyskl - INFO - Epoch [122][3000/3746] lr: 8.470e-03, eta: 1 day, 0:02:34, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4478, top5_acc: 0.7150, loss_cls: 3.0369, loss: 3.0369 +2024-07-20 19:41:13,203 - pyskl - INFO - Epoch [122][3100/3746] lr: 8.454e-03, eta: 1 day, 0:01:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4661, top5_acc: 0.7194, loss_cls: 2.9938, loss: 2.9938 +2024-07-20 19:42:35,295 - pyskl - INFO - Epoch [122][3200/3746] lr: 8.439e-03, eta: 23:59:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4639, top5_acc: 0.7178, loss_cls: 3.0171, loss: 3.0171 +2024-07-20 19:43:57,012 - pyskl - INFO - Epoch [122][3300/3746] lr: 8.423e-03, eta: 23:58:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7131, loss_cls: 3.0144, loss: 3.0144 +2024-07-20 19:45:18,742 - pyskl - INFO - Epoch [122][3400/3746] lr: 8.408e-03, eta: 23:57:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4573, top5_acc: 0.7183, loss_cls: 2.9953, loss: 2.9953 +2024-07-20 19:46:40,972 - pyskl - INFO - Epoch [122][3500/3746] lr: 8.392e-03, eta: 23:55:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4542, top5_acc: 0.7178, loss_cls: 3.0257, loss: 3.0257 +2024-07-20 19:48:03,224 - pyskl - INFO - Epoch [122][3600/3746] lr: 8.377e-03, eta: 23:54:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4569, top5_acc: 0.7230, loss_cls: 2.9879, loss: 2.9879 +2024-07-20 19:49:25,114 - pyskl - INFO - Epoch [122][3700/3746] lr: 8.361e-03, eta: 23:53:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4525, top5_acc: 0.7191, loss_cls: 3.0151, loss: 3.0151 +2024-07-20 19:50:04,763 - pyskl - INFO - Saving checkpoint at 122 epochs +2024-07-20 19:51:57,299 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 19:51:57,972 - pyskl - INFO - +top1_acc 0.3937 +top5_acc 0.6503 +2024-07-20 19:51:57,972 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 19:51:58,014 - pyskl - INFO - +mean_acc 0.3933 +2024-07-20 19:51:58,026 - pyskl - INFO - Epoch(val) [122][309] top1_acc: 0.3937, top5_acc: 0.6503, mean_class_accuracy: 0.3933 +2024-07-20 19:55:45,999 - pyskl - INFO - Epoch [123][100/3746] lr: 8.339e-03, eta: 23:51:25, time: 2.280, data_time: 1.297, memory: 15990, top1_acc: 0.4775, top5_acc: 0.7364, loss_cls: 2.8923, loss: 2.8923 +2024-07-20 19:57:08,942 - pyskl - INFO - Epoch [123][200/3746] lr: 8.323e-03, eta: 23:50:03, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4873, top5_acc: 0.7438, loss_cls: 2.8369, loss: 2.8369 +2024-07-20 19:58:31,039 - pyskl - INFO - Epoch [123][300/3746] lr: 8.308e-03, eta: 23:48:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4744, top5_acc: 0.7377, loss_cls: 2.9122, loss: 2.9122 +2024-07-20 19:59:52,995 - pyskl - INFO - Epoch [123][400/3746] lr: 8.292e-03, eta: 23:47:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4683, top5_acc: 0.7286, loss_cls: 2.9476, loss: 2.9476 +2024-07-20 20:01:15,015 - pyskl - INFO - Epoch [123][500/3746] lr: 8.277e-03, eta: 23:45:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4866, top5_acc: 0.7398, loss_cls: 2.8771, loss: 2.8771 +2024-07-20 20:02:37,118 - pyskl - INFO - Epoch [123][600/3746] lr: 8.262e-03, eta: 23:44:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7258, loss_cls: 2.9327, loss: 2.9327 +2024-07-20 20:03:58,587 - pyskl - INFO - Epoch [123][700/3746] lr: 8.246e-03, eta: 23:43:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4816, top5_acc: 0.7362, loss_cls: 2.9010, loss: 2.9010 +2024-07-20 20:05:20,913 - pyskl - INFO - Epoch [123][800/3746] lr: 8.231e-03, eta: 23:41:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4688, top5_acc: 0.7256, loss_cls: 2.9427, loss: 2.9427 +2024-07-20 20:06:43,083 - pyskl - INFO - Epoch [123][900/3746] lr: 8.215e-03, eta: 23:40:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4828, top5_acc: 0.7291, loss_cls: 2.8905, loss: 2.8905 +2024-07-20 20:08:04,984 - pyskl - INFO - Epoch [123][1000/3746] lr: 8.200e-03, eta: 23:39:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4678, top5_acc: 0.7303, loss_cls: 2.9251, loss: 2.9251 +2024-07-20 20:09:26,944 - pyskl - INFO - Epoch [123][1100/3746] lr: 8.185e-03, eta: 23:37:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4798, top5_acc: 0.7377, loss_cls: 2.9057, loss: 2.9057 +2024-07-20 20:10:49,037 - pyskl - INFO - Epoch [123][1200/3746] lr: 8.169e-03, eta: 23:36:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4686, top5_acc: 0.7305, loss_cls: 2.9207, loss: 2.9207 +2024-07-20 20:12:10,766 - pyskl - INFO - Epoch [123][1300/3746] lr: 8.154e-03, eta: 23:35:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4764, top5_acc: 0.7328, loss_cls: 2.8850, loss: 2.8850 +2024-07-20 20:13:32,555 - pyskl - INFO - Epoch [123][1400/3746] lr: 8.139e-03, eta: 23:33:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4622, top5_acc: 0.7327, loss_cls: 2.9409, loss: 2.9409 +2024-07-20 20:14:54,370 - pyskl - INFO - Epoch [123][1500/3746] lr: 8.124e-03, eta: 23:32:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4691, top5_acc: 0.7180, loss_cls: 2.9836, loss: 2.9836 +2024-07-20 20:16:16,181 - pyskl - INFO - Epoch [123][1600/3746] lr: 8.108e-03, eta: 23:30:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4545, top5_acc: 0.7219, loss_cls: 3.0007, loss: 3.0007 +2024-07-20 20:17:38,263 - pyskl - INFO - Epoch [123][1700/3746] lr: 8.093e-03, eta: 23:29:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4658, top5_acc: 0.7178, loss_cls: 3.0097, loss: 3.0097 +2024-07-20 20:19:00,297 - pyskl - INFO - Epoch [123][1800/3746] lr: 8.078e-03, eta: 23:28:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4619, top5_acc: 0.7283, loss_cls: 2.9476, loss: 2.9476 +2024-07-20 20:20:22,284 - pyskl - INFO - Epoch [123][1900/3746] lr: 8.063e-03, eta: 23:26:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4647, top5_acc: 0.7189, loss_cls: 2.9634, loss: 2.9634 +2024-07-20 20:21:44,349 - pyskl - INFO - Epoch [123][2000/3746] lr: 8.047e-03, eta: 23:25:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4609, top5_acc: 0.7275, loss_cls: 2.9683, loss: 2.9683 +2024-07-20 20:23:06,464 - pyskl - INFO - Epoch [123][2100/3746] lr: 8.032e-03, eta: 23:24:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4633, top5_acc: 0.7253, loss_cls: 2.9590, loss: 2.9590 +2024-07-20 20:24:28,279 - pyskl - INFO - Epoch [123][2200/3746] lr: 8.017e-03, eta: 23:22:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4656, top5_acc: 0.7267, loss_cls: 2.9385, loss: 2.9385 +2024-07-20 20:25:50,148 - pyskl - INFO - Epoch [123][2300/3746] lr: 8.002e-03, eta: 23:21:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4684, top5_acc: 0.7269, loss_cls: 2.9185, loss: 2.9185 +2024-07-20 20:27:11,727 - pyskl - INFO - Epoch [123][2400/3746] lr: 7.987e-03, eta: 23:20:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4761, top5_acc: 0.7298, loss_cls: 2.9281, loss: 2.9281 +2024-07-20 20:28:33,454 - pyskl - INFO - Epoch [123][2500/3746] lr: 7.971e-03, eta: 23:18:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4556, top5_acc: 0.7150, loss_cls: 3.0220, loss: 3.0220 +2024-07-20 20:29:54,983 - pyskl - INFO - Epoch [123][2600/3746] lr: 7.956e-03, eta: 23:17:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7242, loss_cls: 2.9808, loss: 2.9808 +2024-07-20 20:31:16,375 - pyskl - INFO - Epoch [123][2700/3746] lr: 7.941e-03, eta: 23:15:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4728, top5_acc: 0.7345, loss_cls: 2.9286, loss: 2.9286 +2024-07-20 20:32:37,968 - pyskl - INFO - Epoch [123][2800/3746] lr: 7.926e-03, eta: 23:14:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7275, loss_cls: 2.9729, loss: 2.9729 +2024-07-20 20:33:59,597 - pyskl - INFO - Epoch [123][2900/3746] lr: 7.911e-03, eta: 23:13:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4594, top5_acc: 0.7136, loss_cls: 2.9804, loss: 2.9804 +2024-07-20 20:35:21,629 - pyskl - INFO - Epoch [123][3000/3746] lr: 7.896e-03, eta: 23:11:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4708, top5_acc: 0.7341, loss_cls: 2.9429, loss: 2.9429 +2024-07-20 20:36:43,506 - pyskl - INFO - Epoch [123][3100/3746] lr: 7.881e-03, eta: 23:10:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4580, top5_acc: 0.7206, loss_cls: 3.0020, loss: 3.0020 +2024-07-20 20:38:05,011 - pyskl - INFO - Epoch [123][3200/3746] lr: 7.866e-03, eta: 23:09:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7269, loss_cls: 2.9336, loss: 2.9336 +2024-07-20 20:39:27,221 - pyskl - INFO - Epoch [123][3300/3746] lr: 7.851e-03, eta: 23:07:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7259, loss_cls: 2.9453, loss: 2.9453 +2024-07-20 20:40:49,394 - pyskl - INFO - Epoch [123][3400/3746] lr: 7.836e-03, eta: 23:06:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4594, top5_acc: 0.7211, loss_cls: 2.9879, loss: 2.9879 +2024-07-20 20:42:12,071 - pyskl - INFO - Epoch [123][3500/3746] lr: 7.821e-03, eta: 23:04:58, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4730, top5_acc: 0.7308, loss_cls: 2.9481, loss: 2.9481 +2024-07-20 20:43:34,030 - pyskl - INFO - Epoch [123][3600/3746] lr: 7.806e-03, eta: 23:03:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4608, top5_acc: 0.7219, loss_cls: 3.0050, loss: 3.0050 +2024-07-20 20:44:55,934 - pyskl - INFO - Epoch [123][3700/3746] lr: 7.791e-03, eta: 23:02:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4689, top5_acc: 0.7233, loss_cls: 2.9591, loss: 2.9591 +2024-07-20 20:45:35,345 - pyskl - INFO - Saving checkpoint at 123 epochs +2024-07-20 20:47:27,329 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 20:47:28,068 - pyskl - INFO - +top1_acc 0.3908 +top5_acc 0.6504 +2024-07-20 20:47:28,068 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 20:47:28,118 - pyskl - INFO - +mean_acc 0.3906 +2024-07-20 20:47:28,131 - pyskl - INFO - Epoch(val) [123][309] top1_acc: 0.3908, top5_acc: 0.6504, mean_class_accuracy: 0.3906 +2024-07-20 20:51:23,860 - pyskl - INFO - Epoch [124][100/3746] lr: 7.769e-03, eta: 23:00:40, time: 2.357, data_time: 1.362, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7358, loss_cls: 2.9067, loss: 2.9067 +2024-07-20 20:52:47,896 - pyskl - INFO - Epoch [124][200/3746] lr: 7.754e-03, eta: 22:59:19, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.4844, top5_acc: 0.7361, loss_cls: 2.8533, loss: 2.8533 +2024-07-20 20:54:11,220 - pyskl - INFO - Epoch [124][300/3746] lr: 7.739e-03, eta: 22:57:57, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4888, top5_acc: 0.7441, loss_cls: 2.8448, loss: 2.8448 +2024-07-20 20:55:34,486 - pyskl - INFO - Epoch [124][400/3746] lr: 7.724e-03, eta: 22:56:35, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4770, top5_acc: 0.7434, loss_cls: 2.8807, loss: 2.8807 +2024-07-20 20:56:57,887 - pyskl - INFO - Epoch [124][500/3746] lr: 7.709e-03, eta: 22:55:14, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4786, top5_acc: 0.7288, loss_cls: 2.9004, loss: 2.9004 +2024-07-20 20:58:21,691 - pyskl - INFO - Epoch [124][600/3746] lr: 7.694e-03, eta: 22:53:52, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4764, top5_acc: 0.7352, loss_cls: 2.8811, loss: 2.8811 +2024-07-20 20:59:45,044 - pyskl - INFO - Epoch [124][700/3746] lr: 7.679e-03, eta: 22:52:30, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4777, top5_acc: 0.7328, loss_cls: 2.8937, loss: 2.8937 +2024-07-20 21:01:08,490 - pyskl - INFO - Epoch [124][800/3746] lr: 7.664e-03, eta: 22:51:09, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7372, loss_cls: 2.8730, loss: 2.8730 +2024-07-20 21:02:31,539 - pyskl - INFO - Epoch [124][900/3746] lr: 7.649e-03, eta: 22:49:47, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7209, loss_cls: 2.9398, loss: 2.9398 +2024-07-20 21:03:54,902 - pyskl - INFO - Epoch [124][1000/3746] lr: 7.635e-03, eta: 22:48:25, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4908, top5_acc: 0.7380, loss_cls: 2.8521, loss: 2.8521 +2024-07-20 21:05:17,725 - pyskl - INFO - Epoch [124][1100/3746] lr: 7.620e-03, eta: 22:47:03, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4748, top5_acc: 0.7316, loss_cls: 2.8990, loss: 2.8990 +2024-07-20 21:06:41,591 - pyskl - INFO - Epoch [124][1200/3746] lr: 7.605e-03, eta: 22:45:42, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4705, top5_acc: 0.7328, loss_cls: 2.9482, loss: 2.9482 +2024-07-20 21:08:04,778 - pyskl - INFO - Epoch [124][1300/3746] lr: 7.590e-03, eta: 22:44:20, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4783, top5_acc: 0.7359, loss_cls: 2.8913, loss: 2.8913 +2024-07-20 21:09:27,948 - pyskl - INFO - Epoch [124][1400/3746] lr: 7.575e-03, eta: 22:42:58, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4791, top5_acc: 0.7330, loss_cls: 2.8968, loss: 2.8968 +2024-07-20 21:10:51,357 - pyskl - INFO - Epoch [124][1500/3746] lr: 7.561e-03, eta: 22:41:37, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4683, top5_acc: 0.7259, loss_cls: 2.9418, loss: 2.9418 +2024-07-20 21:12:13,997 - pyskl - INFO - Epoch [124][1600/3746] lr: 7.546e-03, eta: 22:40:15, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4769, top5_acc: 0.7302, loss_cls: 2.9075, loss: 2.9075 +2024-07-20 21:13:37,160 - pyskl - INFO - Epoch [124][1700/3746] lr: 7.531e-03, eta: 22:38:53, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4713, top5_acc: 0.7278, loss_cls: 2.9306, loss: 2.9306 +2024-07-20 21:15:00,429 - pyskl - INFO - Epoch [124][1800/3746] lr: 7.516e-03, eta: 22:37:31, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4658, top5_acc: 0.7262, loss_cls: 2.9511, loss: 2.9511 +2024-07-20 21:16:23,748 - pyskl - INFO - Epoch [124][1900/3746] lr: 7.502e-03, eta: 22:36:10, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4827, top5_acc: 0.7405, loss_cls: 2.8812, loss: 2.8812 +2024-07-20 21:17:46,427 - pyskl - INFO - Epoch [124][2000/3746] lr: 7.487e-03, eta: 22:34:48, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4673, top5_acc: 0.7205, loss_cls: 2.9796, loss: 2.9796 +2024-07-20 21:19:09,160 - pyskl - INFO - Epoch [124][2100/3746] lr: 7.472e-03, eta: 22:33:26, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4616, top5_acc: 0.7219, loss_cls: 2.9516, loss: 2.9516 +2024-07-20 21:20:31,592 - pyskl - INFO - Epoch [124][2200/3746] lr: 7.457e-03, eta: 22:32:04, time: 0.824, data_time: 0.001, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7277, loss_cls: 2.9661, loss: 2.9661 +2024-07-20 21:21:54,534 - pyskl - INFO - Epoch [124][2300/3746] lr: 7.443e-03, eta: 22:30:42, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4798, top5_acc: 0.7381, loss_cls: 2.8857, loss: 2.8857 +2024-07-20 21:23:16,536 - pyskl - INFO - Epoch [124][2400/3746] lr: 7.428e-03, eta: 22:29:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4745, top5_acc: 0.7306, loss_cls: 2.9184, loss: 2.9184 +2024-07-20 21:24:39,403 - pyskl - INFO - Epoch [124][2500/3746] lr: 7.413e-03, eta: 22:27:58, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4767, top5_acc: 0.7377, loss_cls: 2.8615, loss: 2.8615 +2024-07-20 21:26:01,896 - pyskl - INFO - Epoch [124][2600/3746] lr: 7.399e-03, eta: 22:26:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4648, top5_acc: 0.7184, loss_cls: 2.9703, loss: 2.9703 +2024-07-20 21:27:24,148 - pyskl - INFO - Epoch [124][2700/3746] lr: 7.384e-03, eta: 22:25:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4784, top5_acc: 0.7278, loss_cls: 2.9281, loss: 2.9281 +2024-07-20 21:28:47,026 - pyskl - INFO - Epoch [124][2800/3746] lr: 7.370e-03, eta: 22:23:53, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7261, loss_cls: 2.9486, loss: 2.9486 +2024-07-20 21:30:09,714 - pyskl - INFO - Epoch [124][2900/3746] lr: 7.355e-03, eta: 22:22:31, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4752, top5_acc: 0.7311, loss_cls: 2.9397, loss: 2.9397 +2024-07-20 21:31:32,196 - pyskl - INFO - Epoch [124][3000/3746] lr: 7.340e-03, eta: 22:21:09, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4725, top5_acc: 0.7259, loss_cls: 2.9451, loss: 2.9451 +2024-07-20 21:32:54,781 - pyskl - INFO - Epoch [124][3100/3746] lr: 7.326e-03, eta: 22:19:47, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4652, top5_acc: 0.7275, loss_cls: 2.9333, loss: 2.9333 +2024-07-20 21:34:17,271 - pyskl - INFO - Epoch [124][3200/3746] lr: 7.311e-03, eta: 22:18:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4736, top5_acc: 0.7306, loss_cls: 2.9227, loss: 2.9227 +2024-07-20 21:35:40,243 - pyskl - INFO - Epoch [124][3300/3746] lr: 7.297e-03, eta: 22:17:04, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4636, top5_acc: 0.7341, loss_cls: 2.9262, loss: 2.9262 +2024-07-20 21:37:02,772 - pyskl - INFO - Epoch [124][3400/3746] lr: 7.282e-03, eta: 22:15:42, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7205, loss_cls: 2.9716, loss: 2.9716 +2024-07-20 21:38:25,970 - pyskl - INFO - Epoch [124][3500/3746] lr: 7.268e-03, eta: 22:14:20, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4672, top5_acc: 0.7311, loss_cls: 2.9383, loss: 2.9383 +2024-07-20 21:39:47,717 - pyskl - INFO - Epoch [124][3600/3746] lr: 7.253e-03, eta: 22:12:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4702, top5_acc: 0.7148, loss_cls: 2.9476, loss: 2.9476 +2024-07-20 21:41:09,963 - pyskl - INFO - Epoch [124][3700/3746] lr: 7.239e-03, eta: 22:11:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7170, loss_cls: 2.9697, loss: 2.9697 +2024-07-20 21:41:49,468 - pyskl - INFO - Saving checkpoint at 124 epochs +2024-07-20 21:43:40,544 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 21:43:41,208 - pyskl - INFO - +top1_acc 0.3932 +top5_acc 0.6494 +2024-07-20 21:43:41,208 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 21:43:41,248 - pyskl - INFO - +mean_acc 0.3931 +2024-07-20 21:43:41,259 - pyskl - INFO - Epoch(val) [124][309] top1_acc: 0.3932, top5_acc: 0.6494, mean_class_accuracy: 0.3931 +2024-07-20 21:47:36,511 - pyskl - INFO - Epoch [125][100/3746] lr: 7.217e-03, eta: 22:10:00, time: 2.352, data_time: 1.367, memory: 15990, top1_acc: 0.4998, top5_acc: 0.7511, loss_cls: 2.8082, loss: 2.8082 +2024-07-20 21:48:57,869 - pyskl - INFO - Epoch [125][200/3746] lr: 7.203e-03, eta: 22:08:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4886, top5_acc: 0.7442, loss_cls: 2.8212, loss: 2.8212 +2024-07-20 21:50:19,619 - pyskl - INFO - Epoch [125][300/3746] lr: 7.189e-03, eta: 22:07:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4930, top5_acc: 0.7403, loss_cls: 2.8365, loss: 2.8365 +2024-07-20 21:51:41,586 - pyskl - INFO - Epoch [125][400/3746] lr: 7.174e-03, eta: 22:05:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4792, top5_acc: 0.7472, loss_cls: 2.8384, loss: 2.8384 +2024-07-20 21:53:03,277 - pyskl - INFO - Epoch [125][500/3746] lr: 7.160e-03, eta: 22:04:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4859, top5_acc: 0.7408, loss_cls: 2.8530, loss: 2.8530 +2024-07-20 21:54:25,495 - pyskl - INFO - Epoch [125][600/3746] lr: 7.145e-03, eta: 22:03:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4894, top5_acc: 0.7450, loss_cls: 2.8037, loss: 2.8037 +2024-07-20 21:55:47,413 - pyskl - INFO - Epoch [125][700/3746] lr: 7.131e-03, eta: 22:01:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4780, top5_acc: 0.7458, loss_cls: 2.8645, loss: 2.8645 +2024-07-20 21:57:09,371 - pyskl - INFO - Epoch [125][800/3746] lr: 7.117e-03, eta: 22:00:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4873, top5_acc: 0.7328, loss_cls: 2.8872, loss: 2.8872 +2024-07-20 21:58:30,978 - pyskl - INFO - Epoch [125][900/3746] lr: 7.102e-03, eta: 21:59:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4809, top5_acc: 0.7356, loss_cls: 2.8846, loss: 2.8846 +2024-07-20 21:59:52,610 - pyskl - INFO - Epoch [125][1000/3746] lr: 7.088e-03, eta: 21:57:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4756, top5_acc: 0.7409, loss_cls: 2.8838, loss: 2.8838 +2024-07-20 22:01:14,344 - pyskl - INFO - Epoch [125][1100/3746] lr: 7.073e-03, eta: 21:56:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4822, top5_acc: 0.7405, loss_cls: 2.8513, loss: 2.8513 +2024-07-20 22:02:36,334 - pyskl - INFO - Epoch [125][1200/3746] lr: 7.059e-03, eta: 21:54:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4627, top5_acc: 0.7306, loss_cls: 2.9487, loss: 2.9487 +2024-07-20 22:03:58,077 - pyskl - INFO - Epoch [125][1300/3746] lr: 7.045e-03, eta: 21:53:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4659, top5_acc: 0.7245, loss_cls: 2.9627, loss: 2.9627 +2024-07-20 22:05:20,762 - pyskl - INFO - Epoch [125][1400/3746] lr: 7.031e-03, eta: 21:52:14, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4791, top5_acc: 0.7375, loss_cls: 2.8656, loss: 2.8656 +2024-07-20 22:06:42,738 - pyskl - INFO - Epoch [125][1500/3746] lr: 7.016e-03, eta: 21:50:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4763, top5_acc: 0.7275, loss_cls: 2.8929, loss: 2.8929 +2024-07-20 22:08:04,927 - pyskl - INFO - Epoch [125][1600/3746] lr: 7.002e-03, eta: 21:49:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4814, top5_acc: 0.7397, loss_cls: 2.8725, loss: 2.8725 +2024-07-20 22:09:26,525 - pyskl - INFO - Epoch [125][1700/3746] lr: 6.988e-03, eta: 21:48:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4838, top5_acc: 0.7400, loss_cls: 2.8670, loss: 2.8670 +2024-07-20 22:10:48,309 - pyskl - INFO - Epoch [125][1800/3746] lr: 6.973e-03, eta: 21:46:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4853, top5_acc: 0.7419, loss_cls: 2.8714, loss: 2.8714 +2024-07-20 22:12:10,702 - pyskl - INFO - Epoch [125][1900/3746] lr: 6.959e-03, eta: 21:45:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4847, top5_acc: 0.7356, loss_cls: 2.8615, loss: 2.8615 +2024-07-20 22:13:33,520 - pyskl - INFO - Epoch [125][2000/3746] lr: 6.945e-03, eta: 21:44:02, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4756, top5_acc: 0.7325, loss_cls: 2.8814, loss: 2.8814 +2024-07-20 22:14:55,463 - pyskl - INFO - Epoch [125][2100/3746] lr: 6.931e-03, eta: 21:42:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7472, loss_cls: 2.8540, loss: 2.8540 +2024-07-20 22:16:17,798 - pyskl - INFO - Epoch [125][2200/3746] lr: 6.917e-03, eta: 21:41:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4744, top5_acc: 0.7341, loss_cls: 2.8949, loss: 2.8949 +2024-07-20 22:17:39,885 - pyskl - INFO - Epoch [125][2300/3746] lr: 6.902e-03, eta: 21:39:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4620, top5_acc: 0.7227, loss_cls: 2.9693, loss: 2.9693 +2024-07-20 22:19:02,262 - pyskl - INFO - Epoch [125][2400/3746] lr: 6.888e-03, eta: 21:38:34, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4758, top5_acc: 0.7283, loss_cls: 2.9144, loss: 2.9144 +2024-07-20 22:20:24,974 - pyskl - INFO - Epoch [125][2500/3746] lr: 6.874e-03, eta: 21:37:12, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4834, top5_acc: 0.7409, loss_cls: 2.8576, loss: 2.8576 +2024-07-20 22:21:47,107 - pyskl - INFO - Epoch [125][2600/3746] lr: 6.860e-03, eta: 21:35:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4736, top5_acc: 0.7291, loss_cls: 2.9160, loss: 2.9160 +2024-07-20 22:23:08,934 - pyskl - INFO - Epoch [125][2700/3746] lr: 6.846e-03, eta: 21:34:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7375, loss_cls: 2.8871, loss: 2.8871 +2024-07-20 22:24:30,714 - pyskl - INFO - Epoch [125][2800/3746] lr: 6.832e-03, eta: 21:33:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4706, top5_acc: 0.7334, loss_cls: 2.9049, loss: 2.9049 +2024-07-20 22:25:53,349 - pyskl - INFO - Epoch [125][2900/3746] lr: 6.818e-03, eta: 21:31:44, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4756, top5_acc: 0.7352, loss_cls: 2.9032, loss: 2.9032 +2024-07-20 22:27:15,404 - pyskl - INFO - Epoch [125][3000/3746] lr: 6.804e-03, eta: 21:30:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4745, top5_acc: 0.7345, loss_cls: 2.9191, loss: 2.9191 +2024-07-20 22:28:37,521 - pyskl - INFO - Epoch [125][3100/3746] lr: 6.789e-03, eta: 21:29:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7369, loss_cls: 2.8823, loss: 2.8823 +2024-07-20 22:29:59,470 - pyskl - INFO - Epoch [125][3200/3746] lr: 6.775e-03, eta: 21:27:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4775, top5_acc: 0.7314, loss_cls: 2.8876, loss: 2.8876 +2024-07-20 22:31:21,324 - pyskl - INFO - Epoch [125][3300/3746] lr: 6.761e-03, eta: 21:26:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4747, top5_acc: 0.7264, loss_cls: 2.8961, loss: 2.8961 +2024-07-20 22:32:44,402 - pyskl - INFO - Epoch [125][3400/3746] lr: 6.747e-03, eta: 21:24:54, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4709, top5_acc: 0.7278, loss_cls: 2.9498, loss: 2.9498 +2024-07-20 22:34:06,762 - pyskl - INFO - Epoch [125][3500/3746] lr: 6.733e-03, eta: 21:23:32, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4781, top5_acc: 0.7411, loss_cls: 2.8776, loss: 2.8776 +2024-07-20 22:35:29,098 - pyskl - INFO - Epoch [125][3600/3746] lr: 6.719e-03, eta: 21:22:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4705, top5_acc: 0.7267, loss_cls: 2.9194, loss: 2.9194 +2024-07-20 22:36:51,496 - pyskl - INFO - Epoch [125][3700/3746] lr: 6.705e-03, eta: 21:20:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4784, top5_acc: 0.7262, loss_cls: 2.9260, loss: 2.9260 +2024-07-20 22:37:30,956 - pyskl - INFO - Saving checkpoint at 125 epochs +2024-07-20 22:39:23,183 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 22:39:23,848 - pyskl - INFO - +top1_acc 0.3974 +top5_acc 0.6595 +2024-07-20 22:39:23,849 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 22:39:23,889 - pyskl - INFO - +mean_acc 0.3972 +2024-07-20 22:39:23,894 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_121.pth was removed +2024-07-20 22:39:24,157 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_125.pth. +2024-07-20 22:39:24,158 - pyskl - INFO - Best top1_acc is 0.3974 at 125 epoch. +2024-07-20 22:39:24,170 - pyskl - INFO - Epoch(val) [125][309] top1_acc: 0.3974, top5_acc: 0.6595, mean_class_accuracy: 0.3972 +2024-07-20 22:43:13,710 - pyskl - INFO - Epoch [126][100/3746] lr: 6.685e-03, eta: 21:19:10, time: 2.295, data_time: 1.312, memory: 15990, top1_acc: 0.4900, top5_acc: 0.7448, loss_cls: 2.8044, loss: 2.8044 +2024-07-20 22:44:36,120 - pyskl - INFO - Epoch [126][200/3746] lr: 6.671e-03, eta: 21:17:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4944, top5_acc: 0.7423, loss_cls: 2.8042, loss: 2.8042 +2024-07-20 22:45:57,852 - pyskl - INFO - Epoch [126][300/3746] lr: 6.657e-03, eta: 21:16:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7481, loss_cls: 2.8284, loss: 2.8284 +2024-07-20 22:47:19,804 - pyskl - INFO - Epoch [126][400/3746] lr: 6.643e-03, eta: 21:15:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4931, top5_acc: 0.7519, loss_cls: 2.7960, loss: 2.7960 +2024-07-20 22:48:41,555 - pyskl - INFO - Epoch [126][500/3746] lr: 6.629e-03, eta: 21:13:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4831, top5_acc: 0.7400, loss_cls: 2.8647, loss: 2.8647 +2024-07-20 22:50:03,159 - pyskl - INFO - Epoch [126][600/3746] lr: 6.615e-03, eta: 21:12:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4889, top5_acc: 0.7384, loss_cls: 2.8177, loss: 2.8177 +2024-07-20 22:51:24,673 - pyskl - INFO - Epoch [126][700/3746] lr: 6.601e-03, eta: 21:10:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4827, top5_acc: 0.7386, loss_cls: 2.8732, loss: 2.8732 +2024-07-20 22:52:46,203 - pyskl - INFO - Epoch [126][800/3746] lr: 6.587e-03, eta: 21:09:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4928, top5_acc: 0.7430, loss_cls: 2.8498, loss: 2.8498 +2024-07-20 22:54:08,080 - pyskl - INFO - Epoch [126][900/3746] lr: 6.574e-03, eta: 21:08:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4822, top5_acc: 0.7372, loss_cls: 2.8708, loss: 2.8708 +2024-07-20 22:55:30,114 - pyskl - INFO - Epoch [126][1000/3746] lr: 6.560e-03, eta: 21:06:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4813, top5_acc: 0.7478, loss_cls: 2.8401, loss: 2.8401 +2024-07-20 22:56:52,112 - pyskl - INFO - Epoch [126][1100/3746] lr: 6.546e-03, eta: 21:05:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4838, top5_acc: 0.7453, loss_cls: 2.8336, loss: 2.8336 +2024-07-20 22:58:13,593 - pyskl - INFO - Epoch [126][1200/3746] lr: 6.532e-03, eta: 21:04:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4916, top5_acc: 0.7403, loss_cls: 2.8506, loss: 2.8506 +2024-07-20 22:59:35,269 - pyskl - INFO - Epoch [126][1300/3746] lr: 6.518e-03, eta: 21:02:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4798, top5_acc: 0.7419, loss_cls: 2.8591, loss: 2.8591 +2024-07-20 23:00:57,571 - pyskl - INFO - Epoch [126][1400/3746] lr: 6.505e-03, eta: 21:01:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4863, top5_acc: 0.7445, loss_cls: 2.8253, loss: 2.8253 +2024-07-20 23:02:19,528 - pyskl - INFO - Epoch [126][1500/3746] lr: 6.491e-03, eta: 21:00:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4881, top5_acc: 0.7408, loss_cls: 2.8579, loss: 2.8579 +2024-07-20 23:03:41,578 - pyskl - INFO - Epoch [126][1600/3746] lr: 6.477e-03, eta: 20:58:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4769, top5_acc: 0.7403, loss_cls: 2.8760, loss: 2.8760 +2024-07-20 23:05:03,289 - pyskl - INFO - Epoch [126][1700/3746] lr: 6.463e-03, eta: 20:57:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4922, top5_acc: 0.7411, loss_cls: 2.8332, loss: 2.8332 +2024-07-20 23:06:25,751 - pyskl - INFO - Epoch [126][1800/3746] lr: 6.449e-03, eta: 20:55:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4866, top5_acc: 0.7416, loss_cls: 2.8509, loss: 2.8509 +2024-07-20 23:07:48,009 - pyskl - INFO - Epoch [126][1900/3746] lr: 6.436e-03, eta: 20:54:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4841, top5_acc: 0.7481, loss_cls: 2.8254, loss: 2.8254 +2024-07-20 23:09:11,010 - pyskl - INFO - Epoch [126][2000/3746] lr: 6.422e-03, eta: 20:53:11, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4927, top5_acc: 0.7450, loss_cls: 2.8160, loss: 2.8160 +2024-07-20 23:10:33,098 - pyskl - INFO - Epoch [126][2100/3746] lr: 6.408e-03, eta: 20:51:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4908, top5_acc: 0.7469, loss_cls: 2.8263, loss: 2.8263 +2024-07-20 23:11:55,415 - pyskl - INFO - Epoch [126][2200/3746] lr: 6.395e-03, eta: 20:50:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4897, top5_acc: 0.7392, loss_cls: 2.8556, loss: 2.8556 +2024-07-20 23:13:17,403 - pyskl - INFO - Epoch [126][2300/3746] lr: 6.381e-03, eta: 20:49:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4745, top5_acc: 0.7294, loss_cls: 2.8883, loss: 2.8883 +2024-07-20 23:14:39,173 - pyskl - INFO - Epoch [126][2400/3746] lr: 6.367e-03, eta: 20:47:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7352, loss_cls: 2.8738, loss: 2.8738 +2024-07-20 23:16:01,039 - pyskl - INFO - Epoch [126][2500/3746] lr: 6.354e-03, eta: 20:46:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4828, top5_acc: 0.7422, loss_cls: 2.8799, loss: 2.8799 +2024-07-20 23:17:22,806 - pyskl - INFO - Epoch [126][2600/3746] lr: 6.340e-03, eta: 20:44:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4891, top5_acc: 0.7403, loss_cls: 2.8547, loss: 2.8547 +2024-07-20 23:18:44,298 - pyskl - INFO - Epoch [126][2700/3746] lr: 6.326e-03, eta: 20:43:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4806, top5_acc: 0.7402, loss_cls: 2.8665, loss: 2.8665 +2024-07-20 23:20:05,990 - pyskl - INFO - Epoch [126][2800/3746] lr: 6.313e-03, eta: 20:42:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4789, top5_acc: 0.7386, loss_cls: 2.8826, loss: 2.8826 +2024-07-20 23:21:28,415 - pyskl - INFO - Epoch [126][2900/3746] lr: 6.299e-03, eta: 20:40:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4788, top5_acc: 0.7292, loss_cls: 2.9000, loss: 2.9000 +2024-07-20 23:22:50,382 - pyskl - INFO - Epoch [126][3000/3746] lr: 6.286e-03, eta: 20:39:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4767, top5_acc: 0.7395, loss_cls: 2.8551, loss: 2.8551 +2024-07-20 23:24:12,195 - pyskl - INFO - Epoch [126][3100/3746] lr: 6.272e-03, eta: 20:38:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4850, top5_acc: 0.7375, loss_cls: 2.8589, loss: 2.8589 +2024-07-20 23:25:34,114 - pyskl - INFO - Epoch [126][3200/3746] lr: 6.259e-03, eta: 20:36:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4867, top5_acc: 0.7444, loss_cls: 2.8410, loss: 2.8410 +2024-07-20 23:26:56,452 - pyskl - INFO - Epoch [126][3300/3746] lr: 6.245e-03, eta: 20:35:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4773, top5_acc: 0.7389, loss_cls: 2.8702, loss: 2.8702 +2024-07-20 23:28:18,597 - pyskl - INFO - Epoch [126][3400/3746] lr: 6.231e-03, eta: 20:34:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4850, top5_acc: 0.7352, loss_cls: 2.8547, loss: 2.8547 +2024-07-20 23:29:40,967 - pyskl - INFO - Epoch [126][3500/3746] lr: 6.218e-03, eta: 20:32:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4786, top5_acc: 0.7370, loss_cls: 2.8794, loss: 2.8794 +2024-07-20 23:31:03,271 - pyskl - INFO - Epoch [126][3600/3746] lr: 6.204e-03, eta: 20:31:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4767, top5_acc: 0.7373, loss_cls: 2.8956, loss: 2.8956 +2024-07-20 23:32:25,687 - pyskl - INFO - Epoch [126][3700/3746] lr: 6.191e-03, eta: 20:29:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4833, top5_acc: 0.7406, loss_cls: 2.8358, loss: 2.8358 +2024-07-20 23:33:05,327 - pyskl - INFO - Saving checkpoint at 126 epochs +2024-07-20 23:34:56,443 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 23:34:57,137 - pyskl - INFO - +top1_acc 0.4042 +top5_acc 0.6588 +2024-07-20 23:34:57,137 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 23:34:57,185 - pyskl - INFO - +mean_acc 0.4039 +2024-07-20 23:34:57,191 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_125.pth was removed +2024-07-20 23:34:57,488 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_126.pth. +2024-07-20 23:34:57,489 - pyskl - INFO - Best top1_acc is 0.4042 at 126 epoch. +2024-07-20 23:34:57,502 - pyskl - INFO - Epoch(val) [126][309] top1_acc: 0.4042, top5_acc: 0.6588, mean_class_accuracy: 0.4039 +2024-07-20 23:38:45,913 - pyskl - INFO - Epoch [127][100/3746] lr: 6.171e-03, eta: 20:28:17, time: 2.284, data_time: 1.300, memory: 15990, top1_acc: 0.5077, top5_acc: 0.7569, loss_cls: 2.7359, loss: 2.7359 +2024-07-20 23:40:08,223 - pyskl - INFO - Epoch [127][200/3746] lr: 6.158e-03, eta: 20:26:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5036, top5_acc: 0.7584, loss_cls: 2.7559, loss: 2.7559 +2024-07-20 23:41:29,915 - pyskl - INFO - Epoch [127][300/3746] lr: 6.144e-03, eta: 20:25:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4933, top5_acc: 0.7517, loss_cls: 2.7919, loss: 2.7919 +2024-07-20 23:42:52,040 - pyskl - INFO - Epoch [127][400/3746] lr: 6.131e-03, eta: 20:24:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4916, top5_acc: 0.7505, loss_cls: 2.8051, loss: 2.8051 +2024-07-20 23:44:13,754 - pyskl - INFO - Epoch [127][500/3746] lr: 6.118e-03, eta: 20:22:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4923, top5_acc: 0.7434, loss_cls: 2.8044, loss: 2.8044 +2024-07-20 23:45:36,018 - pyskl - INFO - Epoch [127][600/3746] lr: 6.104e-03, eta: 20:21:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5031, top5_acc: 0.7548, loss_cls: 2.7682, loss: 2.7682 +2024-07-20 23:46:58,104 - pyskl - INFO - Epoch [127][700/3746] lr: 6.091e-03, eta: 20:20:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4898, top5_acc: 0.7509, loss_cls: 2.7902, loss: 2.7902 +2024-07-20 23:48:19,951 - pyskl - INFO - Epoch [127][800/3746] lr: 6.077e-03, eta: 20:18:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4984, top5_acc: 0.7506, loss_cls: 2.7661, loss: 2.7661 +2024-07-20 23:49:41,868 - pyskl - INFO - Epoch [127][900/3746] lr: 6.064e-03, eta: 20:17:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4903, top5_acc: 0.7525, loss_cls: 2.8136, loss: 2.8136 +2024-07-20 23:51:03,466 - pyskl - INFO - Epoch [127][1000/3746] lr: 6.051e-03, eta: 20:15:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7514, loss_cls: 2.7870, loss: 2.7870 +2024-07-20 23:52:25,194 - pyskl - INFO - Epoch [127][1100/3746] lr: 6.037e-03, eta: 20:14:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4841, top5_acc: 0.7428, loss_cls: 2.8358, loss: 2.8358 +2024-07-20 23:53:46,574 - pyskl - INFO - Epoch [127][1200/3746] lr: 6.024e-03, eta: 20:13:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4916, top5_acc: 0.7489, loss_cls: 2.8205, loss: 2.8205 +2024-07-20 23:55:08,309 - pyskl - INFO - Epoch [127][1300/3746] lr: 6.011e-03, eta: 20:11:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4923, top5_acc: 0.7455, loss_cls: 2.8223, loss: 2.8223 +2024-07-20 23:56:31,238 - pyskl - INFO - Epoch [127][1400/3746] lr: 5.998e-03, eta: 20:10:30, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4923, top5_acc: 0.7455, loss_cls: 2.8298, loss: 2.8298 +2024-07-20 23:57:53,753 - pyskl - INFO - Epoch [127][1500/3746] lr: 5.984e-03, eta: 20:09:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7422, loss_cls: 2.8066, loss: 2.8066 +2024-07-20 23:59:16,171 - pyskl - INFO - Epoch [127][1600/3746] lr: 5.971e-03, eta: 20:07:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4883, top5_acc: 0.7498, loss_cls: 2.8112, loss: 2.8112 +2024-07-21 00:00:37,592 - pyskl - INFO - Epoch [127][1700/3746] lr: 5.958e-03, eta: 20:06:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4933, top5_acc: 0.7508, loss_cls: 2.7963, loss: 2.7963 +2024-07-21 00:01:59,605 - pyskl - INFO - Epoch [127][1800/3746] lr: 5.945e-03, eta: 20:05:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4755, top5_acc: 0.7389, loss_cls: 2.9081, loss: 2.9081 +2024-07-21 00:03:21,427 - pyskl - INFO - Epoch [127][1900/3746] lr: 5.931e-03, eta: 20:03:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4906, top5_acc: 0.7542, loss_cls: 2.7878, loss: 2.7878 +2024-07-21 00:04:44,180 - pyskl - INFO - Epoch [127][2000/3746] lr: 5.918e-03, eta: 20:02:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4877, top5_acc: 0.7489, loss_cls: 2.8014, loss: 2.8014 +2024-07-21 00:06:07,260 - pyskl - INFO - Epoch [127][2100/3746] lr: 5.905e-03, eta: 20:00:56, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4755, top5_acc: 0.7356, loss_cls: 2.8818, loss: 2.8818 +2024-07-21 00:07:29,575 - pyskl - INFO - Epoch [127][2200/3746] lr: 5.892e-03, eta: 19:59:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4900, top5_acc: 0.7550, loss_cls: 2.8084, loss: 2.8084 +2024-07-21 00:08:52,013 - pyskl - INFO - Epoch [127][2300/3746] lr: 5.879e-03, eta: 19:58:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4913, top5_acc: 0.7472, loss_cls: 2.8093, loss: 2.8093 +2024-07-21 00:10:13,933 - pyskl - INFO - Epoch [127][2400/3746] lr: 5.866e-03, eta: 19:56:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4833, top5_acc: 0.7388, loss_cls: 2.8432, loss: 2.8432 +2024-07-21 00:11:35,783 - pyskl - INFO - Epoch [127][2500/3746] lr: 5.852e-03, eta: 19:55:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4881, top5_acc: 0.7452, loss_cls: 2.8335, loss: 2.8335 +2024-07-21 00:12:58,096 - pyskl - INFO - Epoch [127][2600/3746] lr: 5.839e-03, eta: 19:54:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4794, top5_acc: 0.7433, loss_cls: 2.8557, loss: 2.8557 +2024-07-21 00:14:20,098 - pyskl - INFO - Epoch [127][2700/3746] lr: 5.826e-03, eta: 19:52:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4775, top5_acc: 0.7234, loss_cls: 2.9193, loss: 2.9193 +2024-07-21 00:15:42,435 - pyskl - INFO - Epoch [127][2800/3746] lr: 5.813e-03, eta: 19:51:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4816, top5_acc: 0.7333, loss_cls: 2.8572, loss: 2.8572 +2024-07-21 00:17:04,142 - pyskl - INFO - Epoch [127][2900/3746] lr: 5.800e-03, eta: 19:50:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4994, top5_acc: 0.7509, loss_cls: 2.7956, loss: 2.7956 +2024-07-21 00:18:25,914 - pyskl - INFO - Epoch [127][3000/3746] lr: 5.787e-03, eta: 19:48:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4819, top5_acc: 0.7366, loss_cls: 2.8588, loss: 2.8588 +2024-07-21 00:19:47,835 - pyskl - INFO - Epoch [127][3100/3746] lr: 5.774e-03, eta: 19:47:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4894, top5_acc: 0.7469, loss_cls: 2.8226, loss: 2.8226 +2024-07-21 00:21:09,462 - pyskl - INFO - Epoch [127][3200/3746] lr: 5.761e-03, eta: 19:45:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4852, top5_acc: 0.7366, loss_cls: 2.8616, loss: 2.8616 +2024-07-21 00:22:31,413 - pyskl - INFO - Epoch [127][3300/3746] lr: 5.748e-03, eta: 19:44:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4858, top5_acc: 0.7456, loss_cls: 2.8364, loss: 2.8364 +2024-07-21 00:23:52,843 - pyskl - INFO - Epoch [127][3400/3746] lr: 5.735e-03, eta: 19:43:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4928, top5_acc: 0.7478, loss_cls: 2.8345, loss: 2.8345 +2024-07-21 00:25:14,766 - pyskl - INFO - Epoch [127][3500/3746] lr: 5.722e-03, eta: 19:41:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4880, top5_acc: 0.7444, loss_cls: 2.8283, loss: 2.8283 +2024-07-21 00:26:36,996 - pyskl - INFO - Epoch [127][3600/3746] lr: 5.709e-03, eta: 19:40:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4922, top5_acc: 0.7475, loss_cls: 2.7873, loss: 2.7873 +2024-07-21 00:27:59,259 - pyskl - INFO - Epoch [127][3700/3746] lr: 5.696e-03, eta: 19:39:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4859, top5_acc: 0.7372, loss_cls: 2.8507, loss: 2.8507 +2024-07-21 00:28:38,854 - pyskl - INFO - Saving checkpoint at 127 epochs +2024-07-21 00:30:30,727 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 00:30:31,395 - pyskl - INFO - +top1_acc 0.4042 +top5_acc 0.6607 +2024-07-21 00:30:31,395 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 00:30:31,436 - pyskl - INFO - +mean_acc 0.4041 +2024-07-21 00:30:31,442 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_126.pth was removed +2024-07-21 00:30:31,698 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_127.pth. +2024-07-21 00:30:31,698 - pyskl - INFO - Best top1_acc is 0.4042 at 127 epoch. +2024-07-21 00:30:31,711 - pyskl - INFO - Epoch(val) [127][309] top1_acc: 0.4042, top5_acc: 0.6607, mean_class_accuracy: 0.4041 +2024-07-21 00:34:21,351 - pyskl - INFO - Epoch [128][100/3746] lr: 5.677e-03, eta: 19:37:23, time: 2.296, data_time: 1.318, memory: 15990, top1_acc: 0.5134, top5_acc: 0.7658, loss_cls: 2.7010, loss: 2.7010 +2024-07-21 00:35:43,639 - pyskl - INFO - Epoch [128][200/3746] lr: 5.664e-03, eta: 19:36:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5008, top5_acc: 0.7612, loss_cls: 2.7402, loss: 2.7402 +2024-07-21 00:37:05,955 - pyskl - INFO - Epoch [128][300/3746] lr: 5.651e-03, eta: 19:34:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5055, top5_acc: 0.7573, loss_cls: 2.7260, loss: 2.7260 +2024-07-21 00:38:28,574 - pyskl - INFO - Epoch [128][400/3746] lr: 5.638e-03, eta: 19:33:17, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5186, top5_acc: 0.7625, loss_cls: 2.6679, loss: 2.6679 +2024-07-21 00:39:50,152 - pyskl - INFO - Epoch [128][500/3746] lr: 5.625e-03, eta: 19:31:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5000, top5_acc: 0.7594, loss_cls: 2.7416, loss: 2.7416 +2024-07-21 00:41:12,312 - pyskl - INFO - Epoch [128][600/3746] lr: 5.612e-03, eta: 19:30:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5084, top5_acc: 0.7611, loss_cls: 2.7313, loss: 2.7313 +2024-07-21 00:42:33,937 - pyskl - INFO - Epoch [128][700/3746] lr: 5.600e-03, eta: 19:29:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5011, top5_acc: 0.7628, loss_cls: 2.7379, loss: 2.7379 +2024-07-21 00:43:56,253 - pyskl - INFO - Epoch [128][800/3746] lr: 5.587e-03, eta: 19:27:48, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5025, top5_acc: 0.7550, loss_cls: 2.7473, loss: 2.7473 +2024-07-21 00:45:18,592 - pyskl - INFO - Epoch [128][900/3746] lr: 5.574e-03, eta: 19:26:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4950, top5_acc: 0.7502, loss_cls: 2.7794, loss: 2.7794 +2024-07-21 00:46:40,618 - pyskl - INFO - Epoch [128][1000/3746] lr: 5.561e-03, eta: 19:25:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4952, top5_acc: 0.7584, loss_cls: 2.7595, loss: 2.7595 +2024-07-21 00:48:02,514 - pyskl - INFO - Epoch [128][1100/3746] lr: 5.548e-03, eta: 19:23:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4842, top5_acc: 0.7478, loss_cls: 2.8251, loss: 2.8251 +2024-07-21 00:49:24,378 - pyskl - INFO - Epoch [128][1200/3746] lr: 5.536e-03, eta: 19:22:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4895, top5_acc: 0.7456, loss_cls: 2.7989, loss: 2.7989 +2024-07-21 00:50:47,139 - pyskl - INFO - Epoch [128][1300/3746] lr: 5.523e-03, eta: 19:20:58, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5041, top5_acc: 0.7555, loss_cls: 2.7391, loss: 2.7391 +2024-07-21 00:52:09,688 - pyskl - INFO - Epoch [128][1400/3746] lr: 5.510e-03, eta: 19:19:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4909, top5_acc: 0.7473, loss_cls: 2.8068, loss: 2.8068 +2024-07-21 00:53:31,871 - pyskl - INFO - Epoch [128][1500/3746] lr: 5.497e-03, eta: 19:18:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4981, top5_acc: 0.7567, loss_cls: 2.7409, loss: 2.7409 +2024-07-21 00:54:54,362 - pyskl - INFO - Epoch [128][1600/3746] lr: 5.485e-03, eta: 19:16:52, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5014, top5_acc: 0.7611, loss_cls: 2.7427, loss: 2.7427 +2024-07-21 00:56:16,518 - pyskl - INFO - Epoch [128][1700/3746] lr: 5.472e-03, eta: 19:15:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4817, top5_acc: 0.7453, loss_cls: 2.8339, loss: 2.8339 +2024-07-21 00:57:38,554 - pyskl - INFO - Epoch [128][1800/3746] lr: 5.459e-03, eta: 19:14:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5112, top5_acc: 0.7569, loss_cls: 2.7283, loss: 2.7283 +2024-07-21 00:59:01,043 - pyskl - INFO - Epoch [128][1900/3746] lr: 5.446e-03, eta: 19:12:46, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5033, top5_acc: 0.7584, loss_cls: 2.7474, loss: 2.7474 +2024-07-21 01:00:23,171 - pyskl - INFO - Epoch [128][2000/3746] lr: 5.434e-03, eta: 19:11:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5006, top5_acc: 0.7505, loss_cls: 2.7923, loss: 2.7923 +2024-07-21 01:01:45,609 - pyskl - INFO - Epoch [128][2100/3746] lr: 5.421e-03, eta: 19:10:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7392, loss_cls: 2.8483, loss: 2.8483 +2024-07-21 01:03:07,343 - pyskl - INFO - Epoch [128][2200/3746] lr: 5.408e-03, eta: 19:08:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4891, top5_acc: 0.7445, loss_cls: 2.8123, loss: 2.8123 +2024-07-21 01:04:30,152 - pyskl - INFO - Epoch [128][2300/3746] lr: 5.396e-03, eta: 19:07:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4913, top5_acc: 0.7486, loss_cls: 2.8061, loss: 2.8061 +2024-07-21 01:05:52,131 - pyskl - INFO - Epoch [128][2400/3746] lr: 5.383e-03, eta: 19:05:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4986, top5_acc: 0.7489, loss_cls: 2.8067, loss: 2.8067 +2024-07-21 01:07:13,910 - pyskl - INFO - Epoch [128][2500/3746] lr: 5.370e-03, eta: 19:04:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7400, loss_cls: 2.8278, loss: 2.8278 +2024-07-21 01:08:35,529 - pyskl - INFO - Epoch [128][2600/3746] lr: 5.358e-03, eta: 19:03:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4984, top5_acc: 0.7447, loss_cls: 2.8024, loss: 2.8024 +2024-07-21 01:09:56,888 - pyskl - INFO - Epoch [128][2700/3746] lr: 5.345e-03, eta: 19:01:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4963, top5_acc: 0.7481, loss_cls: 2.7877, loss: 2.7877 +2024-07-21 01:11:19,357 - pyskl - INFO - Epoch [128][2800/3746] lr: 5.333e-03, eta: 19:00:27, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4992, top5_acc: 0.7491, loss_cls: 2.7781, loss: 2.7781 +2024-07-21 01:12:41,428 - pyskl - INFO - Epoch [128][2900/3746] lr: 5.320e-03, eta: 18:59:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4838, top5_acc: 0.7408, loss_cls: 2.8585, loss: 2.8585 +2024-07-21 01:14:03,215 - pyskl - INFO - Epoch [128][3000/3746] lr: 5.308e-03, eta: 18:57:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7445, loss_cls: 2.8298, loss: 2.8298 +2024-07-21 01:15:24,876 - pyskl - INFO - Epoch [128][3100/3746] lr: 5.295e-03, eta: 18:56:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4936, top5_acc: 0.7506, loss_cls: 2.7669, loss: 2.7669 +2024-07-21 01:16:46,962 - pyskl - INFO - Epoch [128][3200/3746] lr: 5.283e-03, eta: 18:54:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4905, top5_acc: 0.7491, loss_cls: 2.7969, loss: 2.7969 +2024-07-21 01:18:08,947 - pyskl - INFO - Epoch [128][3300/3746] lr: 5.270e-03, eta: 18:53:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7444, loss_cls: 2.8322, loss: 2.8322 +2024-07-21 01:19:31,162 - pyskl - INFO - Epoch [128][3400/3746] lr: 5.258e-03, eta: 18:52:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4886, top5_acc: 0.7456, loss_cls: 2.8081, loss: 2.8081 +2024-07-21 01:20:53,762 - pyskl - INFO - Epoch [128][3500/3746] lr: 5.245e-03, eta: 18:50:52, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4947, top5_acc: 0.7425, loss_cls: 2.8085, loss: 2.8085 +2024-07-21 01:22:15,695 - pyskl - INFO - Epoch [128][3600/3746] lr: 5.233e-03, eta: 18:49:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4908, top5_acc: 0.7456, loss_cls: 2.8115, loss: 2.8115 +2024-07-21 01:23:37,555 - pyskl - INFO - Epoch [128][3700/3746] lr: 5.220e-03, eta: 18:48:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4920, top5_acc: 0.7447, loss_cls: 2.8097, loss: 2.8097 +2024-07-21 01:24:17,126 - pyskl - INFO - Saving checkpoint at 128 epochs +2024-07-21 01:26:08,514 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 01:26:09,183 - pyskl - INFO - +top1_acc 0.4171 +top5_acc 0.6725 +2024-07-21 01:26:09,184 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 01:26:09,226 - pyskl - INFO - +mean_acc 0.4169 +2024-07-21 01:26:09,232 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_127.pth was removed +2024-07-21 01:26:09,485 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_128.pth. +2024-07-21 01:26:09,486 - pyskl - INFO - Best top1_acc is 0.4171 at 128 epoch. +2024-07-21 01:26:09,499 - pyskl - INFO - Epoch(val) [128][309] top1_acc: 0.4171, top5_acc: 0.6725, mean_class_accuracy: 0.4169 +2024-07-21 01:29:58,983 - pyskl - INFO - Epoch [129][100/3746] lr: 5.202e-03, eta: 18:46:27, time: 2.295, data_time: 1.312, memory: 15990, top1_acc: 0.5270, top5_acc: 0.7694, loss_cls: 2.6313, loss: 2.6313 +2024-07-21 01:31:21,894 - pyskl - INFO - Epoch [129][200/3746] lr: 5.190e-03, eta: 18:45:05, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5245, top5_acc: 0.7761, loss_cls: 2.6137, loss: 2.6137 +2024-07-21 01:32:43,757 - pyskl - INFO - Epoch [129][300/3746] lr: 5.177e-03, eta: 18:43:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5081, top5_acc: 0.7572, loss_cls: 2.7123, loss: 2.7123 +2024-07-21 01:34:05,875 - pyskl - INFO - Epoch [129][400/3746] lr: 5.165e-03, eta: 18:42:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5172, top5_acc: 0.7689, loss_cls: 2.6813, loss: 2.6813 +2024-07-21 01:35:27,619 - pyskl - INFO - Epoch [129][500/3746] lr: 5.153e-03, eta: 18:40:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5094, top5_acc: 0.7712, loss_cls: 2.6918, loss: 2.6918 +2024-07-21 01:36:49,518 - pyskl - INFO - Epoch [129][600/3746] lr: 5.140e-03, eta: 18:39:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4995, top5_acc: 0.7591, loss_cls: 2.7545, loss: 2.7545 +2024-07-21 01:38:11,195 - pyskl - INFO - Epoch [129][700/3746] lr: 5.128e-03, eta: 18:38:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5186, top5_acc: 0.7661, loss_cls: 2.6964, loss: 2.6964 +2024-07-21 01:39:32,896 - pyskl - INFO - Epoch [129][800/3746] lr: 5.116e-03, eta: 18:36:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5005, top5_acc: 0.7514, loss_cls: 2.7497, loss: 2.7497 +2024-07-21 01:40:54,500 - pyskl - INFO - Epoch [129][900/3746] lr: 5.103e-03, eta: 18:35:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5005, top5_acc: 0.7477, loss_cls: 2.7598, loss: 2.7598 +2024-07-21 01:42:16,756 - pyskl - INFO - Epoch [129][1000/3746] lr: 5.091e-03, eta: 18:34:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5103, top5_acc: 0.7639, loss_cls: 2.6995, loss: 2.6995 +2024-07-21 01:43:38,667 - pyskl - INFO - Epoch [129][1100/3746] lr: 5.079e-03, eta: 18:32:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4989, top5_acc: 0.7508, loss_cls: 2.7607, loss: 2.7607 +2024-07-21 01:45:00,188 - pyskl - INFO - Epoch [129][1200/3746] lr: 5.066e-03, eta: 18:31:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5023, top5_acc: 0.7586, loss_cls: 2.7459, loss: 2.7459 +2024-07-21 01:46:22,003 - pyskl - INFO - Epoch [129][1300/3746] lr: 5.054e-03, eta: 18:30:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5005, top5_acc: 0.7519, loss_cls: 2.7428, loss: 2.7428 +2024-07-21 01:47:43,886 - pyskl - INFO - Epoch [129][1400/3746] lr: 5.042e-03, eta: 18:28:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4983, top5_acc: 0.7512, loss_cls: 2.7747, loss: 2.7747 +2024-07-21 01:49:05,662 - pyskl - INFO - Epoch [129][1500/3746] lr: 5.030e-03, eta: 18:27:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4914, top5_acc: 0.7498, loss_cls: 2.7771, loss: 2.7771 +2024-07-21 01:50:27,846 - pyskl - INFO - Epoch [129][1600/3746] lr: 5.017e-03, eta: 18:25:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5047, top5_acc: 0.7592, loss_cls: 2.7384, loss: 2.7384 +2024-07-21 01:51:50,433 - pyskl - INFO - Epoch [129][1700/3746] lr: 5.005e-03, eta: 18:24:33, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5031, top5_acc: 0.7608, loss_cls: 2.7265, loss: 2.7265 +2024-07-21 01:53:12,263 - pyskl - INFO - Epoch [129][1800/3746] lr: 4.993e-03, eta: 18:23:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4986, top5_acc: 0.7498, loss_cls: 2.7474, loss: 2.7474 +2024-07-21 01:54:34,462 - pyskl - INFO - Epoch [129][1900/3746] lr: 4.981e-03, eta: 18:21:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4964, top5_acc: 0.7512, loss_cls: 2.7638, loss: 2.7638 +2024-07-21 01:55:56,984 - pyskl - INFO - Epoch [129][2000/3746] lr: 4.969e-03, eta: 18:20:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4966, top5_acc: 0.7667, loss_cls: 2.7611, loss: 2.7611 +2024-07-21 01:57:19,250 - pyskl - INFO - Epoch [129][2100/3746] lr: 4.957e-03, eta: 18:19:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4997, top5_acc: 0.7584, loss_cls: 2.7373, loss: 2.7373 +2024-07-21 01:58:41,030 - pyskl - INFO - Epoch [129][2200/3746] lr: 4.944e-03, eta: 18:17:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4956, top5_acc: 0.7517, loss_cls: 2.7541, loss: 2.7541 +2024-07-21 02:00:03,022 - pyskl - INFO - Epoch [129][2300/3746] lr: 4.932e-03, eta: 18:16:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5045, top5_acc: 0.7556, loss_cls: 2.7502, loss: 2.7502 +2024-07-21 02:01:24,949 - pyskl - INFO - Epoch [129][2400/3746] lr: 4.920e-03, eta: 18:14:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4877, top5_acc: 0.7484, loss_cls: 2.8172, loss: 2.8172 +2024-07-21 02:02:46,231 - pyskl - INFO - Epoch [129][2500/3746] lr: 4.908e-03, eta: 18:13:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5102, top5_acc: 0.7577, loss_cls: 2.7269, loss: 2.7269 +2024-07-21 02:04:08,251 - pyskl - INFO - Epoch [129][2600/3746] lr: 4.896e-03, eta: 18:12:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4908, top5_acc: 0.7508, loss_cls: 2.7814, loss: 2.7814 +2024-07-21 02:05:29,689 - pyskl - INFO - Epoch [129][2700/3746] lr: 4.884e-03, eta: 18:10:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4872, top5_acc: 0.7500, loss_cls: 2.7919, loss: 2.7919 +2024-07-21 02:06:51,306 - pyskl - INFO - Epoch [129][2800/3746] lr: 4.872e-03, eta: 18:09:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5086, top5_acc: 0.7569, loss_cls: 2.7403, loss: 2.7403 +2024-07-21 02:08:13,385 - pyskl - INFO - Epoch [129][2900/3746] lr: 4.860e-03, eta: 18:08:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5011, top5_acc: 0.7602, loss_cls: 2.7278, loss: 2.7278 +2024-07-21 02:09:35,443 - pyskl - INFO - Epoch [129][3000/3746] lr: 4.848e-03, eta: 18:06:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4950, top5_acc: 0.7491, loss_cls: 2.7859, loss: 2.7859 +2024-07-21 02:10:57,382 - pyskl - INFO - Epoch [129][3100/3746] lr: 4.836e-03, eta: 18:05:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4852, top5_acc: 0.7389, loss_cls: 2.8259, loss: 2.8259 +2024-07-21 02:12:19,339 - pyskl - INFO - Epoch [129][3200/3746] lr: 4.824e-03, eta: 18:04:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5016, top5_acc: 0.7655, loss_cls: 2.7171, loss: 2.7171 +2024-07-21 02:13:41,647 - pyskl - INFO - Epoch [129][3300/3746] lr: 4.812e-03, eta: 18:02:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4841, top5_acc: 0.7456, loss_cls: 2.8166, loss: 2.8166 +2024-07-21 02:15:03,591 - pyskl - INFO - Epoch [129][3400/3746] lr: 4.800e-03, eta: 18:01:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4925, top5_acc: 0.7558, loss_cls: 2.7714, loss: 2.7714 +2024-07-21 02:16:25,909 - pyskl - INFO - Epoch [129][3500/3746] lr: 4.788e-03, eta: 17:59:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4973, top5_acc: 0.7467, loss_cls: 2.7682, loss: 2.7682 +2024-07-21 02:17:47,884 - pyskl - INFO - Epoch [129][3600/3746] lr: 4.776e-03, eta: 17:58:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4927, top5_acc: 0.7475, loss_cls: 2.7811, loss: 2.7811 +2024-07-21 02:19:09,632 - pyskl - INFO - Epoch [129][3700/3746] lr: 4.764e-03, eta: 17:57:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4966, top5_acc: 0.7506, loss_cls: 2.7997, loss: 2.7997 +2024-07-21 02:19:49,491 - pyskl - INFO - Saving checkpoint at 129 epochs +2024-07-21 02:21:40,640 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 02:21:41,308 - pyskl - INFO - +top1_acc 0.4101 +top5_acc 0.6696 +2024-07-21 02:21:41,308 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 02:21:41,349 - pyskl - INFO - +mean_acc 0.4099 +2024-07-21 02:21:41,361 - pyskl - INFO - Epoch(val) [129][309] top1_acc: 0.4101, top5_acc: 0.6696, mean_class_accuracy: 0.4099 +2024-07-21 02:25:30,247 - pyskl - INFO - Epoch [130][100/3746] lr: 4.747e-03, eta: 17:55:28, time: 2.289, data_time: 1.302, memory: 15990, top1_acc: 0.5212, top5_acc: 0.7666, loss_cls: 2.6772, loss: 2.6772 +2024-07-21 02:26:52,230 - pyskl - INFO - Epoch [130][200/3746] lr: 4.735e-03, eta: 17:54:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5300, top5_acc: 0.7741, loss_cls: 2.6060, loss: 2.6060 +2024-07-21 02:28:13,901 - pyskl - INFO - Epoch [130][300/3746] lr: 4.723e-03, eta: 17:52:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5247, top5_acc: 0.7777, loss_cls: 2.6068, loss: 2.6068 +2024-07-21 02:29:35,865 - pyskl - INFO - Epoch [130][400/3746] lr: 4.711e-03, eta: 17:51:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5177, top5_acc: 0.7659, loss_cls: 2.6857, loss: 2.6857 +2024-07-21 02:30:57,920 - pyskl - INFO - Epoch [130][500/3746] lr: 4.699e-03, eta: 17:49:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5258, top5_acc: 0.7761, loss_cls: 2.6395, loss: 2.6395 +2024-07-21 02:32:19,709 - pyskl - INFO - Epoch [130][600/3746] lr: 4.688e-03, eta: 17:48:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5186, top5_acc: 0.7703, loss_cls: 2.6618, loss: 2.6618 +2024-07-21 02:33:41,689 - pyskl - INFO - Epoch [130][700/3746] lr: 4.676e-03, eta: 17:47:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5045, top5_acc: 0.7594, loss_cls: 2.7035, loss: 2.7035 +2024-07-21 02:35:03,197 - pyskl - INFO - Epoch [130][800/3746] lr: 4.664e-03, eta: 17:45:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5225, top5_acc: 0.7731, loss_cls: 2.6274, loss: 2.6274 +2024-07-21 02:36:24,958 - pyskl - INFO - Epoch [130][900/3746] lr: 4.652e-03, eta: 17:44:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5136, top5_acc: 0.7656, loss_cls: 2.6736, loss: 2.6736 +2024-07-21 02:37:46,449 - pyskl - INFO - Epoch [130][1000/3746] lr: 4.640e-03, eta: 17:43:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5056, top5_acc: 0.7614, loss_cls: 2.7142, loss: 2.7142 +2024-07-21 02:39:08,289 - pyskl - INFO - Epoch [130][1100/3746] lr: 4.629e-03, eta: 17:41:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5131, top5_acc: 0.7602, loss_cls: 2.7032, loss: 2.7032 +2024-07-21 02:40:30,388 - pyskl - INFO - Epoch [130][1200/3746] lr: 4.617e-03, eta: 17:40:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5141, top5_acc: 0.7602, loss_cls: 2.7087, loss: 2.7087 +2024-07-21 02:41:52,639 - pyskl - INFO - Epoch [130][1300/3746] lr: 4.605e-03, eta: 17:39:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5150, top5_acc: 0.7661, loss_cls: 2.6854, loss: 2.6854 +2024-07-21 02:43:15,269 - pyskl - INFO - Epoch [130][1400/3746] lr: 4.594e-03, eta: 17:37:40, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5006, top5_acc: 0.7608, loss_cls: 2.6997, loss: 2.6997 +2024-07-21 02:44:37,189 - pyskl - INFO - Epoch [130][1500/3746] lr: 4.582e-03, eta: 17:36:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5127, top5_acc: 0.7534, loss_cls: 2.7233, loss: 2.7233 +2024-07-21 02:45:59,115 - pyskl - INFO - Epoch [130][1600/3746] lr: 4.570e-03, eta: 17:34:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5075, top5_acc: 0.7609, loss_cls: 2.7208, loss: 2.7208 +2024-07-21 02:47:21,687 - pyskl - INFO - Epoch [130][1700/3746] lr: 4.558e-03, eta: 17:33:33, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5084, top5_acc: 0.7731, loss_cls: 2.6885, loss: 2.6885 +2024-07-21 02:48:43,708 - pyskl - INFO - Epoch [130][1800/3746] lr: 4.547e-03, eta: 17:32:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5122, top5_acc: 0.7661, loss_cls: 2.6677, loss: 2.6677 +2024-07-21 02:50:06,042 - pyskl - INFO - Epoch [130][1900/3746] lr: 4.535e-03, eta: 17:30:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5034, top5_acc: 0.7634, loss_cls: 2.7186, loss: 2.7186 +2024-07-21 02:51:27,843 - pyskl - INFO - Epoch [130][2000/3746] lr: 4.524e-03, eta: 17:29:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5091, top5_acc: 0.7597, loss_cls: 2.7286, loss: 2.7286 +2024-07-21 02:52:50,190 - pyskl - INFO - Epoch [130][2100/3746] lr: 4.512e-03, eta: 17:28:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5028, top5_acc: 0.7561, loss_cls: 2.7425, loss: 2.7425 +2024-07-21 02:54:11,995 - pyskl - INFO - Epoch [130][2200/3746] lr: 4.500e-03, eta: 17:26:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4989, top5_acc: 0.7525, loss_cls: 2.7517, loss: 2.7517 +2024-07-21 02:55:34,262 - pyskl - INFO - Epoch [130][2300/3746] lr: 4.489e-03, eta: 17:25:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5058, top5_acc: 0.7636, loss_cls: 2.7231, loss: 2.7231 +2024-07-21 02:56:56,224 - pyskl - INFO - Epoch [130][2400/3746] lr: 4.477e-03, eta: 17:23:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5125, top5_acc: 0.7647, loss_cls: 2.7224, loss: 2.7224 +2024-07-21 02:58:18,734 - pyskl - INFO - Epoch [130][2500/3746] lr: 4.466e-03, eta: 17:22:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4997, top5_acc: 0.7525, loss_cls: 2.7551, loss: 2.7551 +2024-07-21 02:59:40,997 - pyskl - INFO - Epoch [130][2600/3746] lr: 4.454e-03, eta: 17:21:14, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5128, top5_acc: 0.7570, loss_cls: 2.7255, loss: 2.7255 +2024-07-21 03:01:02,464 - pyskl - INFO - Epoch [130][2700/3746] lr: 4.443e-03, eta: 17:19:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4878, top5_acc: 0.7478, loss_cls: 2.7679, loss: 2.7679 +2024-07-21 03:02:23,907 - pyskl - INFO - Epoch [130][2800/3746] lr: 4.431e-03, eta: 17:18:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5152, top5_acc: 0.7664, loss_cls: 2.7004, loss: 2.7004 +2024-07-21 03:03:45,332 - pyskl - INFO - Epoch [130][2900/3746] lr: 4.420e-03, eta: 17:17:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4977, top5_acc: 0.7502, loss_cls: 2.7626, loss: 2.7626 +2024-07-21 03:05:07,020 - pyskl - INFO - Epoch [130][3000/3746] lr: 4.408e-03, eta: 17:15:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5142, top5_acc: 0.7580, loss_cls: 2.7264, loss: 2.7264 +2024-07-21 03:06:28,396 - pyskl - INFO - Epoch [130][3100/3746] lr: 4.397e-03, eta: 17:14:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5058, top5_acc: 0.7559, loss_cls: 2.7485, loss: 2.7485 +2024-07-21 03:07:50,217 - pyskl - INFO - Epoch [130][3200/3746] lr: 4.385e-03, eta: 17:13:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5045, top5_acc: 0.7492, loss_cls: 2.7522, loss: 2.7522 +2024-07-21 03:09:11,758 - pyskl - INFO - Epoch [130][3300/3746] lr: 4.374e-03, eta: 17:11:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5050, top5_acc: 0.7502, loss_cls: 2.7320, loss: 2.7320 +2024-07-21 03:10:34,034 - pyskl - INFO - Epoch [130][3400/3746] lr: 4.362e-03, eta: 17:10:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5078, top5_acc: 0.7609, loss_cls: 2.7009, loss: 2.7009 +2024-07-21 03:11:56,827 - pyskl - INFO - Epoch [130][3500/3746] lr: 4.351e-03, eta: 17:08:54, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5077, top5_acc: 0.7484, loss_cls: 2.7563, loss: 2.7563 +2024-07-21 03:13:18,794 - pyskl - INFO - Epoch [130][3600/3746] lr: 4.339e-03, eta: 17:07:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4909, top5_acc: 0.7616, loss_cls: 2.7526, loss: 2.7526 +2024-07-21 03:14:40,653 - pyskl - INFO - Epoch [130][3700/3746] lr: 4.328e-03, eta: 17:06:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4959, top5_acc: 0.7506, loss_cls: 2.7637, loss: 2.7637 +2024-07-21 03:15:20,135 - pyskl - INFO - Saving checkpoint at 130 epochs +2024-07-21 03:17:11,786 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 03:17:12,452 - pyskl - INFO - +top1_acc 0.4247 +top5_acc 0.6785 +2024-07-21 03:17:12,452 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 03:17:12,498 - pyskl - INFO - +mean_acc 0.4244 +2024-07-21 03:17:12,506 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_128.pth was removed +2024-07-21 03:17:12,766 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_130.pth. +2024-07-21 03:17:12,767 - pyskl - INFO - Best top1_acc is 0.4247 at 130 epoch. +2024-07-21 03:17:12,783 - pyskl - INFO - Epoch(val) [130][309] top1_acc: 0.4247, top5_acc: 0.6785, mean_class_accuracy: 0.4244 +2024-07-21 03:21:02,608 - pyskl - INFO - Epoch [131][100/3746] lr: 4.311e-03, eta: 17:04:27, time: 2.298, data_time: 1.315, memory: 15990, top1_acc: 0.5400, top5_acc: 0.7762, loss_cls: 2.5719, loss: 2.5719 +2024-07-21 03:22:24,452 - pyskl - INFO - Epoch [131][200/3746] lr: 4.300e-03, eta: 17:03:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5297, top5_acc: 0.7722, loss_cls: 2.6226, loss: 2.6226 +2024-07-21 03:23:46,933 - pyskl - INFO - Epoch [131][300/3746] lr: 4.289e-03, eta: 17:01:43, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5288, top5_acc: 0.7733, loss_cls: 2.6225, loss: 2.6225 +2024-07-21 03:25:08,535 - pyskl - INFO - Epoch [131][400/3746] lr: 4.277e-03, eta: 17:00:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5222, top5_acc: 0.7714, loss_cls: 2.6400, loss: 2.6400 +2024-07-21 03:26:30,725 - pyskl - INFO - Epoch [131][500/3746] lr: 4.266e-03, eta: 16:58:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5225, top5_acc: 0.7717, loss_cls: 2.6461, loss: 2.6461 +2024-07-21 03:27:52,570 - pyskl - INFO - Epoch [131][600/3746] lr: 4.255e-03, eta: 16:57:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5269, top5_acc: 0.7678, loss_cls: 2.6375, loss: 2.6375 +2024-07-21 03:29:14,313 - pyskl - INFO - Epoch [131][700/3746] lr: 4.244e-03, eta: 16:56:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5259, top5_acc: 0.7805, loss_cls: 2.6132, loss: 2.6132 +2024-07-21 03:30:35,926 - pyskl - INFO - Epoch [131][800/3746] lr: 4.232e-03, eta: 16:54:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5138, top5_acc: 0.7697, loss_cls: 2.6420, loss: 2.6420 +2024-07-21 03:31:58,158 - pyskl - INFO - Epoch [131][900/3746] lr: 4.221e-03, eta: 16:53:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5105, top5_acc: 0.7741, loss_cls: 2.6540, loss: 2.6540 +2024-07-21 03:33:19,786 - pyskl - INFO - Epoch [131][1000/3746] lr: 4.210e-03, eta: 16:52:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5255, top5_acc: 0.7722, loss_cls: 2.6345, loss: 2.6345 +2024-07-21 03:34:41,476 - pyskl - INFO - Epoch [131][1100/3746] lr: 4.199e-03, eta: 16:50:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5205, top5_acc: 0.7773, loss_cls: 2.6166, loss: 2.6166 +2024-07-21 03:36:03,067 - pyskl - INFO - Epoch [131][1200/3746] lr: 4.187e-03, eta: 16:49:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5086, top5_acc: 0.7658, loss_cls: 2.6943, loss: 2.6943 +2024-07-21 03:37:24,687 - pyskl - INFO - Epoch [131][1300/3746] lr: 4.176e-03, eta: 16:48:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5181, top5_acc: 0.7711, loss_cls: 2.6490, loss: 2.6490 +2024-07-21 03:38:47,437 - pyskl - INFO - Epoch [131][1400/3746] lr: 4.165e-03, eta: 16:46:39, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5097, top5_acc: 0.7647, loss_cls: 2.7026, loss: 2.7026 +2024-07-21 03:40:09,614 - pyskl - INFO - Epoch [131][1500/3746] lr: 4.154e-03, eta: 16:45:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5142, top5_acc: 0.7673, loss_cls: 2.6613, loss: 2.6613 +2024-07-21 03:41:32,420 - pyskl - INFO - Epoch [131][1600/3746] lr: 4.143e-03, eta: 16:43:54, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5142, top5_acc: 0.7725, loss_cls: 2.6483, loss: 2.6483 +2024-07-21 03:42:54,693 - pyskl - INFO - Epoch [131][1700/3746] lr: 4.132e-03, eta: 16:42:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5084, top5_acc: 0.7572, loss_cls: 2.7416, loss: 2.7416 +2024-07-21 03:44:16,854 - pyskl - INFO - Epoch [131][1800/3746] lr: 4.120e-03, eta: 16:41:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5136, top5_acc: 0.7614, loss_cls: 2.6830, loss: 2.6830 +2024-07-21 03:45:39,497 - pyskl - INFO - Epoch [131][1900/3746] lr: 4.109e-03, eta: 16:39:48, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5220, top5_acc: 0.7703, loss_cls: 2.6618, loss: 2.6618 +2024-07-21 03:47:01,330 - pyskl - INFO - Epoch [131][2000/3746] lr: 4.098e-03, eta: 16:38:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5247, top5_acc: 0.7748, loss_cls: 2.6196, loss: 2.6196 +2024-07-21 03:48:23,271 - pyskl - INFO - Epoch [131][2100/3746] lr: 4.087e-03, eta: 16:37:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5023, top5_acc: 0.7570, loss_cls: 2.7321, loss: 2.7321 +2024-07-21 03:49:45,394 - pyskl - INFO - Epoch [131][2200/3746] lr: 4.076e-03, eta: 16:35:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5028, top5_acc: 0.7634, loss_cls: 2.7110, loss: 2.7110 +2024-07-21 03:51:07,222 - pyskl - INFO - Epoch [131][2300/3746] lr: 4.065e-03, eta: 16:34:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5072, top5_acc: 0.7677, loss_cls: 2.7075, loss: 2.7075 +2024-07-21 03:52:29,639 - pyskl - INFO - Epoch [131][2400/3746] lr: 4.054e-03, eta: 16:32:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5136, top5_acc: 0.7672, loss_cls: 2.6506, loss: 2.6506 +2024-07-21 03:53:51,318 - pyskl - INFO - Epoch [131][2500/3746] lr: 4.043e-03, eta: 16:31:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5109, top5_acc: 0.7634, loss_cls: 2.6753, loss: 2.6753 +2024-07-21 03:55:13,283 - pyskl - INFO - Epoch [131][2600/3746] lr: 4.032e-03, eta: 16:30:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5117, top5_acc: 0.7608, loss_cls: 2.7148, loss: 2.7148 +2024-07-21 03:56:35,473 - pyskl - INFO - Epoch [131][2700/3746] lr: 4.021e-03, eta: 16:28:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5162, top5_acc: 0.7620, loss_cls: 2.6796, loss: 2.6796 +2024-07-21 03:57:57,640 - pyskl - INFO - Epoch [131][2800/3746] lr: 4.010e-03, eta: 16:27:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5202, top5_acc: 0.7730, loss_cls: 2.6564, loss: 2.6564 +2024-07-21 03:59:19,091 - pyskl - INFO - Epoch [131][2900/3746] lr: 3.999e-03, eta: 16:26:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5062, top5_acc: 0.7620, loss_cls: 2.7188, loss: 2.7188 +2024-07-21 04:00:40,565 - pyskl - INFO - Epoch [131][3000/3746] lr: 3.988e-03, eta: 16:24:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5100, top5_acc: 0.7578, loss_cls: 2.7344, loss: 2.7344 +2024-07-21 04:02:02,191 - pyskl - INFO - Epoch [131][3100/3746] lr: 3.977e-03, eta: 16:23:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5059, top5_acc: 0.7641, loss_cls: 2.6963, loss: 2.6963 +2024-07-21 04:03:24,635 - pyskl - INFO - Epoch [131][3200/3746] lr: 3.966e-03, eta: 16:22:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5156, top5_acc: 0.7650, loss_cls: 2.6770, loss: 2.6770 +2024-07-21 04:04:46,809 - pyskl - INFO - Epoch [131][3300/3746] lr: 3.955e-03, eta: 16:20:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5084, top5_acc: 0.7603, loss_cls: 2.7048, loss: 2.7048 +2024-07-21 04:06:09,042 - pyskl - INFO - Epoch [131][3400/3746] lr: 3.945e-03, eta: 16:19:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5023, top5_acc: 0.7627, loss_cls: 2.7227, loss: 2.7227 +2024-07-21 04:07:31,103 - pyskl - INFO - Epoch [131][3500/3746] lr: 3.934e-03, eta: 16:17:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5078, top5_acc: 0.7639, loss_cls: 2.6780, loss: 2.6780 +2024-07-21 04:08:53,046 - pyskl - INFO - Epoch [131][3600/3746] lr: 3.923e-03, eta: 16:16:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5180, top5_acc: 0.7673, loss_cls: 2.6679, loss: 2.6679 +2024-07-21 04:10:14,623 - pyskl - INFO - Epoch [131][3700/3746] lr: 3.912e-03, eta: 16:15:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5088, top5_acc: 0.7633, loss_cls: 2.7239, loss: 2.7239 +2024-07-21 04:10:54,515 - pyskl - INFO - Saving checkpoint at 131 epochs +2024-07-21 04:12:45,657 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 04:12:46,320 - pyskl - INFO - +top1_acc 0.4243 +top5_acc 0.6821 +2024-07-21 04:12:46,320 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 04:12:46,362 - pyskl - INFO - +mean_acc 0.4239 +2024-07-21 04:12:46,375 - pyskl - INFO - Epoch(val) [131][309] top1_acc: 0.4243, top5_acc: 0.6821, mean_class_accuracy: 0.4239 +2024-07-21 04:16:35,105 - pyskl - INFO - Epoch [132][100/3746] lr: 3.896e-03, eta: 16:13:25, time: 2.287, data_time: 1.304, memory: 15990, top1_acc: 0.5428, top5_acc: 0.7884, loss_cls: 2.5332, loss: 2.5332 +2024-07-21 04:17:57,536 - pyskl - INFO - Epoch [132][200/3746] lr: 3.885e-03, eta: 16:12:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5316, top5_acc: 0.7762, loss_cls: 2.6100, loss: 2.6100 +2024-07-21 04:19:19,731 - pyskl - INFO - Epoch [132][300/3746] lr: 3.875e-03, eta: 16:10:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5316, top5_acc: 0.7784, loss_cls: 2.5869, loss: 2.5869 +2024-07-21 04:20:41,541 - pyskl - INFO - Epoch [132][400/3746] lr: 3.864e-03, eta: 16:09:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5459, top5_acc: 0.7830, loss_cls: 2.5380, loss: 2.5380 +2024-07-21 04:22:03,261 - pyskl - INFO - Epoch [132][500/3746] lr: 3.853e-03, eta: 16:07:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5323, top5_acc: 0.7867, loss_cls: 2.5583, loss: 2.5583 +2024-07-21 04:23:25,400 - pyskl - INFO - Epoch [132][600/3746] lr: 3.842e-03, eta: 16:06:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5188, top5_acc: 0.7658, loss_cls: 2.6557, loss: 2.6557 +2024-07-21 04:24:47,380 - pyskl - INFO - Epoch [132][700/3746] lr: 3.831e-03, eta: 16:05:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5312, top5_acc: 0.7825, loss_cls: 2.5738, loss: 2.5738 +2024-07-21 04:26:09,743 - pyskl - INFO - Epoch [132][800/3746] lr: 3.821e-03, eta: 16:03:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5370, top5_acc: 0.7811, loss_cls: 2.5947, loss: 2.5947 +2024-07-21 04:27:32,694 - pyskl - INFO - Epoch [132][900/3746] lr: 3.810e-03, eta: 16:02:27, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5216, top5_acc: 0.7823, loss_cls: 2.6083, loss: 2.6083 +2024-07-21 04:28:54,690 - pyskl - INFO - Epoch [132][1000/3746] lr: 3.799e-03, eta: 16:01:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5205, top5_acc: 0.7703, loss_cls: 2.6761, loss: 2.6761 +2024-07-21 04:30:16,661 - pyskl - INFO - Epoch [132][1100/3746] lr: 3.789e-03, eta: 15:59:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5255, top5_acc: 0.7789, loss_cls: 2.6157, loss: 2.6157 +2024-07-21 04:31:38,416 - pyskl - INFO - Epoch [132][1200/3746] lr: 3.778e-03, eta: 15:58:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5319, top5_acc: 0.7772, loss_cls: 2.6126, loss: 2.6126 +2024-07-21 04:33:00,354 - pyskl - INFO - Epoch [132][1300/3746] lr: 3.767e-03, eta: 15:56:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5206, top5_acc: 0.7759, loss_cls: 2.6199, loss: 2.6199 +2024-07-21 04:34:22,829 - pyskl - INFO - Epoch [132][1400/3746] lr: 3.757e-03, eta: 15:55:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5202, top5_acc: 0.7678, loss_cls: 2.6799, loss: 2.6799 +2024-07-21 04:35:44,597 - pyskl - INFO - Epoch [132][1500/3746] lr: 3.746e-03, eta: 15:54:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5239, top5_acc: 0.7777, loss_cls: 2.6097, loss: 2.6097 +2024-07-21 04:37:07,626 - pyskl - INFO - Epoch [132][1600/3746] lr: 3.735e-03, eta: 15:52:52, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5267, top5_acc: 0.7764, loss_cls: 2.6129, loss: 2.6129 +2024-07-21 04:38:30,264 - pyskl - INFO - Epoch [132][1700/3746] lr: 3.725e-03, eta: 15:51:30, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5247, top5_acc: 0.7722, loss_cls: 2.6186, loss: 2.6186 +2024-07-21 04:39:52,702 - pyskl - INFO - Epoch [132][1800/3746] lr: 3.714e-03, eta: 15:50:08, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5181, top5_acc: 0.7688, loss_cls: 2.6477, loss: 2.6477 +2024-07-21 04:41:14,642 - pyskl - INFO - Epoch [132][1900/3746] lr: 3.704e-03, eta: 15:48:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5194, top5_acc: 0.7728, loss_cls: 2.6236, loss: 2.6236 +2024-07-21 04:42:36,658 - pyskl - INFO - Epoch [132][2000/3746] lr: 3.693e-03, eta: 15:47:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5253, top5_acc: 0.7742, loss_cls: 2.6237, loss: 2.6237 +2024-07-21 04:43:58,920 - pyskl - INFO - Epoch [132][2100/3746] lr: 3.683e-03, eta: 15:46:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5164, top5_acc: 0.7605, loss_cls: 2.6714, loss: 2.6714 +2024-07-21 04:45:21,480 - pyskl - INFO - Epoch [132][2200/3746] lr: 3.672e-03, eta: 15:44:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5148, top5_acc: 0.7728, loss_cls: 2.6410, loss: 2.6410 +2024-07-21 04:46:43,903 - pyskl - INFO - Epoch [132][2300/3746] lr: 3.662e-03, eta: 15:43:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5202, top5_acc: 0.7727, loss_cls: 2.6361, loss: 2.6361 +2024-07-21 04:48:06,418 - pyskl - INFO - Epoch [132][2400/3746] lr: 3.651e-03, eta: 15:41:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5186, top5_acc: 0.7734, loss_cls: 2.6380, loss: 2.6380 +2024-07-21 04:49:28,530 - pyskl - INFO - Epoch [132][2500/3746] lr: 3.641e-03, eta: 15:40:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5292, top5_acc: 0.7777, loss_cls: 2.6056, loss: 2.6056 +2024-07-21 04:50:50,419 - pyskl - INFO - Epoch [132][2600/3746] lr: 3.630e-03, eta: 15:39:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5162, top5_acc: 0.7645, loss_cls: 2.6546, loss: 2.6546 +2024-07-21 04:52:11,848 - pyskl - INFO - Epoch [132][2700/3746] lr: 3.620e-03, eta: 15:37:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5327, top5_acc: 0.7692, loss_cls: 2.6249, loss: 2.6249 +2024-07-21 04:53:33,311 - pyskl - INFO - Epoch [132][2800/3746] lr: 3.609e-03, eta: 15:36:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5223, top5_acc: 0.7744, loss_cls: 2.6560, loss: 2.6560 +2024-07-21 04:54:55,366 - pyskl - INFO - Epoch [132][2900/3746] lr: 3.599e-03, eta: 15:35:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5244, top5_acc: 0.7631, loss_cls: 2.6513, loss: 2.6513 +2024-07-21 04:56:16,774 - pyskl - INFO - Epoch [132][3000/3746] lr: 3.588e-03, eta: 15:33:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5216, top5_acc: 0.7683, loss_cls: 2.6388, loss: 2.6388 +2024-07-21 04:57:38,642 - pyskl - INFO - Epoch [132][3100/3746] lr: 3.578e-03, eta: 15:32:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5220, top5_acc: 0.7767, loss_cls: 2.6117, loss: 2.6117 +2024-07-21 04:59:00,474 - pyskl - INFO - Epoch [132][3200/3746] lr: 3.568e-03, eta: 15:30:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5312, top5_acc: 0.7819, loss_cls: 2.5961, loss: 2.5961 +2024-07-21 05:00:22,458 - pyskl - INFO - Epoch [132][3300/3746] lr: 3.557e-03, eta: 15:29:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5141, top5_acc: 0.7659, loss_cls: 2.6664, loss: 2.6664 +2024-07-21 05:01:44,934 - pyskl - INFO - Epoch [132][3400/3746] lr: 3.547e-03, eta: 15:28:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5130, top5_acc: 0.7678, loss_cls: 2.6355, loss: 2.6355 +2024-07-21 05:03:06,705 - pyskl - INFO - Epoch [132][3500/3746] lr: 3.537e-03, eta: 15:26:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5131, top5_acc: 0.7612, loss_cls: 2.6957, loss: 2.6957 +2024-07-21 05:04:28,158 - pyskl - INFO - Epoch [132][3600/3746] lr: 3.526e-03, eta: 15:25:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5131, top5_acc: 0.7667, loss_cls: 2.6724, loss: 2.6724 +2024-07-21 05:05:50,075 - pyskl - INFO - Epoch [132][3700/3746] lr: 3.516e-03, eta: 15:24:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5175, top5_acc: 0.7636, loss_cls: 2.6852, loss: 2.6852 +2024-07-21 05:06:29,789 - pyskl - INFO - Saving checkpoint at 132 epochs +2024-07-21 05:08:21,642 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 05:08:22,313 - pyskl - INFO - +top1_acc 0.4297 +top5_acc 0.6845 +2024-07-21 05:08:22,313 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 05:08:22,356 - pyskl - INFO - +mean_acc 0.4294 +2024-07-21 05:08:22,361 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_130.pth was removed +2024-07-21 05:08:22,620 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_132.pth. +2024-07-21 05:08:22,621 - pyskl - INFO - Best top1_acc is 0.4297 at 132 epoch. +2024-07-21 05:08:22,639 - pyskl - INFO - Epoch(val) [132][309] top1_acc: 0.4297, top5_acc: 0.6845, mean_class_accuracy: 0.4294 +2024-07-21 05:12:14,129 - pyskl - INFO - Epoch [133][100/3746] lr: 3.501e-03, eta: 15:22:21, time: 2.315, data_time: 1.329, memory: 15990, top1_acc: 0.5430, top5_acc: 0.7873, loss_cls: 2.5309, loss: 2.5309 +2024-07-21 05:13:36,374 - pyskl - INFO - Epoch [133][200/3746] lr: 3.491e-03, eta: 15:20:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5389, top5_acc: 0.7897, loss_cls: 2.5265, loss: 2.5265 +2024-07-21 05:14:58,073 - pyskl - INFO - Epoch [133][300/3746] lr: 3.480e-03, eta: 15:19:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5423, top5_acc: 0.7906, loss_cls: 2.5193, loss: 2.5193 +2024-07-21 05:16:20,117 - pyskl - INFO - Epoch [133][400/3746] lr: 3.470e-03, eta: 15:18:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5408, top5_acc: 0.7883, loss_cls: 2.5236, loss: 2.5236 +2024-07-21 05:17:41,604 - pyskl - INFO - Epoch [133][500/3746] lr: 3.460e-03, eta: 15:16:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5477, top5_acc: 0.7906, loss_cls: 2.4985, loss: 2.4985 +2024-07-21 05:19:02,922 - pyskl - INFO - Epoch [133][600/3746] lr: 3.450e-03, eta: 15:15:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5366, top5_acc: 0.7875, loss_cls: 2.5225, loss: 2.5225 +2024-07-21 05:20:24,322 - pyskl - INFO - Epoch [133][700/3746] lr: 3.440e-03, eta: 15:14:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5355, top5_acc: 0.7820, loss_cls: 2.5542, loss: 2.5542 +2024-07-21 05:21:46,254 - pyskl - INFO - Epoch [133][800/3746] lr: 3.429e-03, eta: 15:12:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5416, top5_acc: 0.7891, loss_cls: 2.5317, loss: 2.5317 +2024-07-21 05:23:08,166 - pyskl - INFO - Epoch [133][900/3746] lr: 3.419e-03, eta: 15:11:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5352, top5_acc: 0.7797, loss_cls: 2.5757, loss: 2.5757 +2024-07-21 05:24:30,370 - pyskl - INFO - Epoch [133][1000/3746] lr: 3.409e-03, eta: 15:10:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5270, top5_acc: 0.7839, loss_cls: 2.5843, loss: 2.5843 +2024-07-21 05:25:52,610 - pyskl - INFO - Epoch [133][1100/3746] lr: 3.399e-03, eta: 15:08:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5231, top5_acc: 0.7731, loss_cls: 2.6053, loss: 2.6053 +2024-07-21 05:27:14,098 - pyskl - INFO - Epoch [133][1200/3746] lr: 3.389e-03, eta: 15:07:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5317, top5_acc: 0.7858, loss_cls: 2.5543, loss: 2.5543 +2024-07-21 05:28:36,275 - pyskl - INFO - Epoch [133][1300/3746] lr: 3.379e-03, eta: 15:05:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5295, top5_acc: 0.7745, loss_cls: 2.6016, loss: 2.6016 +2024-07-21 05:29:58,834 - pyskl - INFO - Epoch [133][1400/3746] lr: 3.369e-03, eta: 15:04:32, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5289, top5_acc: 0.7812, loss_cls: 2.5874, loss: 2.5874 +2024-07-21 05:31:20,648 - pyskl - INFO - Epoch [133][1500/3746] lr: 3.359e-03, eta: 15:03:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5364, top5_acc: 0.7913, loss_cls: 2.5325, loss: 2.5325 +2024-07-21 05:32:43,011 - pyskl - INFO - Epoch [133][1600/3746] lr: 3.348e-03, eta: 15:01:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5238, top5_acc: 0.7783, loss_cls: 2.6034, loss: 2.6034 +2024-07-21 05:34:05,124 - pyskl - INFO - Epoch [133][1700/3746] lr: 3.338e-03, eta: 15:00:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5156, top5_acc: 0.7680, loss_cls: 2.6474, loss: 2.6474 +2024-07-21 05:35:27,107 - pyskl - INFO - Epoch [133][1800/3746] lr: 3.328e-03, eta: 14:59:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5361, top5_acc: 0.7802, loss_cls: 2.5857, loss: 2.5857 +2024-07-21 05:36:49,021 - pyskl - INFO - Epoch [133][1900/3746] lr: 3.318e-03, eta: 14:57:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5331, top5_acc: 0.7767, loss_cls: 2.5940, loss: 2.5940 +2024-07-21 05:38:10,568 - pyskl - INFO - Epoch [133][2000/3746] lr: 3.308e-03, eta: 14:56:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5198, top5_acc: 0.7775, loss_cls: 2.6213, loss: 2.6213 +2024-07-21 05:39:32,626 - pyskl - INFO - Epoch [133][2100/3746] lr: 3.298e-03, eta: 14:54:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5402, top5_acc: 0.7762, loss_cls: 2.5551, loss: 2.5551 +2024-07-21 05:40:55,084 - pyskl - INFO - Epoch [133][2200/3746] lr: 3.288e-03, eta: 14:53:34, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5233, top5_acc: 0.7725, loss_cls: 2.6275, loss: 2.6275 +2024-07-21 05:42:17,092 - pyskl - INFO - Epoch [133][2300/3746] lr: 3.278e-03, eta: 14:52:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5270, top5_acc: 0.7745, loss_cls: 2.6200, loss: 2.6200 +2024-07-21 05:43:39,116 - pyskl - INFO - Epoch [133][2400/3746] lr: 3.268e-03, eta: 14:50:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5333, top5_acc: 0.7706, loss_cls: 2.6115, loss: 2.6115 +2024-07-21 05:45:01,268 - pyskl - INFO - Epoch [133][2500/3746] lr: 3.259e-03, eta: 14:49:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5142, top5_acc: 0.7734, loss_cls: 2.6465, loss: 2.6465 +2024-07-21 05:46:23,253 - pyskl - INFO - Epoch [133][2600/3746] lr: 3.249e-03, eta: 14:48:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5280, top5_acc: 0.7753, loss_cls: 2.5986, loss: 2.5986 +2024-07-21 05:47:44,879 - pyskl - INFO - Epoch [133][2700/3746] lr: 3.239e-03, eta: 14:46:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5192, top5_acc: 0.7703, loss_cls: 2.6414, loss: 2.6414 +2024-07-21 05:49:06,745 - pyskl - INFO - Epoch [133][2800/3746] lr: 3.229e-03, eta: 14:45:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5336, top5_acc: 0.7795, loss_cls: 2.5799, loss: 2.5799 +2024-07-21 05:50:28,881 - pyskl - INFO - Epoch [133][2900/3746] lr: 3.219e-03, eta: 14:43:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5381, top5_acc: 0.7922, loss_cls: 2.5596, loss: 2.5596 +2024-07-21 05:51:50,575 - pyskl - INFO - Epoch [133][3000/3746] lr: 3.209e-03, eta: 14:42:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7722, loss_cls: 2.6351, loss: 2.6351 +2024-07-21 05:53:12,685 - pyskl - INFO - Epoch [133][3100/3746] lr: 3.199e-03, eta: 14:41:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5353, top5_acc: 0.7773, loss_cls: 2.5618, loss: 2.5618 +2024-07-21 05:54:34,306 - pyskl - INFO - Epoch [133][3200/3746] lr: 3.189e-03, eta: 14:39:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5286, top5_acc: 0.7794, loss_cls: 2.5920, loss: 2.5920 +2024-07-21 05:55:56,590 - pyskl - INFO - Epoch [133][3300/3746] lr: 3.180e-03, eta: 14:38:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5209, top5_acc: 0.7739, loss_cls: 2.6238, loss: 2.6238 +2024-07-21 05:57:19,276 - pyskl - INFO - Epoch [133][3400/3746] lr: 3.170e-03, eta: 14:37:08, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5294, top5_acc: 0.7816, loss_cls: 2.5971, loss: 2.5971 +2024-07-21 05:58:41,274 - pyskl - INFO - Epoch [133][3500/3746] lr: 3.160e-03, eta: 14:35:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5319, top5_acc: 0.7778, loss_cls: 2.5764, loss: 2.5764 +2024-07-21 06:00:03,084 - pyskl - INFO - Epoch [133][3600/3746] lr: 3.150e-03, eta: 14:34:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5288, top5_acc: 0.7706, loss_cls: 2.6149, loss: 2.6149 +2024-07-21 06:01:25,042 - pyskl - INFO - Epoch [133][3700/3746] lr: 3.140e-03, eta: 14:33:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5247, top5_acc: 0.7797, loss_cls: 2.6097, loss: 2.6097 +2024-07-21 06:02:04,712 - pyskl - INFO - Saving checkpoint at 133 epochs +2024-07-21 06:03:56,797 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 06:03:57,476 - pyskl - INFO - +top1_acc 0.4372 +top5_acc 0.6895 +2024-07-21 06:03:57,476 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 06:03:57,517 - pyskl - INFO - +mean_acc 0.4369 +2024-07-21 06:03:57,522 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_132.pth was removed +2024-07-21 06:03:57,785 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_133.pth. +2024-07-21 06:03:57,786 - pyskl - INFO - Best top1_acc is 0.4372 at 133 epoch. +2024-07-21 06:03:57,800 - pyskl - INFO - Epoch(val) [133][309] top1_acc: 0.4372, top5_acc: 0.6895, mean_class_accuracy: 0.4369 +2024-07-21 06:07:50,134 - pyskl - INFO - Epoch [134][100/3746] lr: 3.126e-03, eta: 14:31:15, time: 2.323, data_time: 1.332, memory: 15990, top1_acc: 0.5584, top5_acc: 0.8020, loss_cls: 2.4379, loss: 2.4379 +2024-07-21 06:09:13,197 - pyskl - INFO - Epoch [134][200/3746] lr: 3.117e-03, eta: 14:29:53, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5534, top5_acc: 0.7925, loss_cls: 2.4775, loss: 2.4775 +2024-07-21 06:10:35,647 - pyskl - INFO - Epoch [134][300/3746] lr: 3.107e-03, eta: 14:28:31, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5400, top5_acc: 0.7914, loss_cls: 2.5201, loss: 2.5201 +2024-07-21 06:11:58,288 - pyskl - INFO - Epoch [134][400/3746] lr: 3.097e-03, eta: 14:27:09, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5505, top5_acc: 0.7959, loss_cls: 2.4806, loss: 2.4806 +2024-07-21 06:13:19,676 - pyskl - INFO - Epoch [134][500/3746] lr: 3.087e-03, eta: 14:25:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5459, top5_acc: 0.7859, loss_cls: 2.5174, loss: 2.5174 +2024-07-21 06:14:41,856 - pyskl - INFO - Epoch [134][600/3746] lr: 3.078e-03, eta: 14:24:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5506, top5_acc: 0.7911, loss_cls: 2.4934, loss: 2.4934 +2024-07-21 06:16:03,542 - pyskl - INFO - Epoch [134][700/3746] lr: 3.068e-03, eta: 14:23:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5434, top5_acc: 0.7956, loss_cls: 2.4894, loss: 2.4894 +2024-07-21 06:17:25,487 - pyskl - INFO - Epoch [134][800/3746] lr: 3.059e-03, eta: 14:21:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5355, top5_acc: 0.7864, loss_cls: 2.5342, loss: 2.5342 +2024-07-21 06:18:47,501 - pyskl - INFO - Epoch [134][900/3746] lr: 3.049e-03, eta: 14:20:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5403, top5_acc: 0.7867, loss_cls: 2.5348, loss: 2.5348 +2024-07-21 06:20:08,953 - pyskl - INFO - Epoch [134][1000/3746] lr: 3.039e-03, eta: 14:18:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5391, top5_acc: 0.7941, loss_cls: 2.5172, loss: 2.5172 +2024-07-21 06:21:30,798 - pyskl - INFO - Epoch [134][1100/3746] lr: 3.030e-03, eta: 14:17:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5367, top5_acc: 0.7886, loss_cls: 2.5477, loss: 2.5477 +2024-07-21 06:22:52,344 - pyskl - INFO - Epoch [134][1200/3746] lr: 3.020e-03, eta: 14:16:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5344, top5_acc: 0.7805, loss_cls: 2.5639, loss: 2.5639 +2024-07-21 06:24:14,273 - pyskl - INFO - Epoch [134][1300/3746] lr: 3.011e-03, eta: 14:14:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5444, top5_acc: 0.7805, loss_cls: 2.5436, loss: 2.5436 +2024-07-21 06:25:36,287 - pyskl - INFO - Epoch [134][1400/3746] lr: 3.001e-03, eta: 14:13:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5484, top5_acc: 0.7847, loss_cls: 2.5242, loss: 2.5242 +2024-07-21 06:26:58,188 - pyskl - INFO - Epoch [134][1500/3746] lr: 2.991e-03, eta: 14:12:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5472, top5_acc: 0.7873, loss_cls: 2.5142, loss: 2.5142 +2024-07-21 06:28:20,486 - pyskl - INFO - Epoch [134][1600/3746] lr: 2.982e-03, eta: 14:10:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5400, top5_acc: 0.7809, loss_cls: 2.5580, loss: 2.5580 +2024-07-21 06:29:42,465 - pyskl - INFO - Epoch [134][1700/3746] lr: 2.972e-03, eta: 14:09:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5441, top5_acc: 0.7963, loss_cls: 2.4813, loss: 2.4813 +2024-07-21 06:31:04,693 - pyskl - INFO - Epoch [134][1800/3746] lr: 2.963e-03, eta: 14:07:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5275, top5_acc: 0.7800, loss_cls: 2.6029, loss: 2.6029 +2024-07-21 06:32:26,909 - pyskl - INFO - Epoch [134][1900/3746] lr: 2.953e-03, eta: 14:06:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5302, top5_acc: 0.7722, loss_cls: 2.6051, loss: 2.6051 +2024-07-21 06:33:48,695 - pyskl - INFO - Epoch [134][2000/3746] lr: 2.944e-03, eta: 14:05:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5297, top5_acc: 0.7812, loss_cls: 2.5592, loss: 2.5592 +2024-07-21 06:35:10,740 - pyskl - INFO - Epoch [134][2100/3746] lr: 2.935e-03, eta: 14:03:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5413, top5_acc: 0.7861, loss_cls: 2.5093, loss: 2.5093 +2024-07-21 06:36:33,050 - pyskl - INFO - Epoch [134][2200/3746] lr: 2.925e-03, eta: 14:02:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5359, top5_acc: 0.7830, loss_cls: 2.5394, loss: 2.5394 +2024-07-21 06:37:55,294 - pyskl - INFO - Epoch [134][2300/3746] lr: 2.916e-03, eta: 14:01:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5305, top5_acc: 0.7730, loss_cls: 2.5857, loss: 2.5857 +2024-07-21 06:39:17,118 - pyskl - INFO - Epoch [134][2400/3746] lr: 2.906e-03, eta: 13:59:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5323, top5_acc: 0.7881, loss_cls: 2.5689, loss: 2.5689 +2024-07-21 06:40:39,172 - pyskl - INFO - Epoch [134][2500/3746] lr: 2.897e-03, eta: 13:58:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5392, top5_acc: 0.7830, loss_cls: 2.5579, loss: 2.5579 +2024-07-21 06:42:00,996 - pyskl - INFO - Epoch [134][2600/3746] lr: 2.888e-03, eta: 13:56:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5350, top5_acc: 0.7859, loss_cls: 2.5423, loss: 2.5423 +2024-07-21 06:43:22,701 - pyskl - INFO - Epoch [134][2700/3746] lr: 2.878e-03, eta: 13:55:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5281, top5_acc: 0.7820, loss_cls: 2.5766, loss: 2.5766 +2024-07-21 06:44:44,459 - pyskl - INFO - Epoch [134][2800/3746] lr: 2.869e-03, eta: 13:54:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5341, top5_acc: 0.7814, loss_cls: 2.5643, loss: 2.5643 +2024-07-21 06:46:06,303 - pyskl - INFO - Epoch [134][2900/3746] lr: 2.860e-03, eta: 13:52:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5430, top5_acc: 0.7853, loss_cls: 2.5336, loss: 2.5336 +2024-07-21 06:47:28,043 - pyskl - INFO - Epoch [134][3000/3746] lr: 2.850e-03, eta: 13:51:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5423, top5_acc: 0.7837, loss_cls: 2.5540, loss: 2.5540 +2024-07-21 06:48:50,458 - pyskl - INFO - Epoch [134][3100/3746] lr: 2.841e-03, eta: 13:50:08, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5353, top5_acc: 0.7852, loss_cls: 2.5619, loss: 2.5619 +2024-07-21 06:50:12,434 - pyskl - INFO - Epoch [134][3200/3746] lr: 2.832e-03, eta: 13:48:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5302, top5_acc: 0.7783, loss_cls: 2.5763, loss: 2.5763 +2024-07-21 06:51:35,265 - pyskl - INFO - Epoch [134][3300/3746] lr: 2.822e-03, eta: 13:47:24, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5325, top5_acc: 0.7814, loss_cls: 2.5645, loss: 2.5645 +2024-07-21 06:52:57,826 - pyskl - INFO - Epoch [134][3400/3746] lr: 2.813e-03, eta: 13:46:01, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5306, top5_acc: 0.7759, loss_cls: 2.5968, loss: 2.5968 +2024-07-21 06:54:19,229 - pyskl - INFO - Epoch [134][3500/3746] lr: 2.804e-03, eta: 13:44:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5414, top5_acc: 0.7844, loss_cls: 2.5351, loss: 2.5351 +2024-07-21 06:55:41,698 - pyskl - INFO - Epoch [134][3600/3746] lr: 2.795e-03, eta: 13:43:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5337, top5_acc: 0.7859, loss_cls: 2.5620, loss: 2.5620 +2024-07-21 06:57:03,560 - pyskl - INFO - Epoch [134][3700/3746] lr: 2.786e-03, eta: 13:41:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5308, top5_acc: 0.7820, loss_cls: 2.5763, loss: 2.5763 +2024-07-21 06:57:43,509 - pyskl - INFO - Saving checkpoint at 134 epochs +2024-07-21 06:59:35,616 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 06:59:36,277 - pyskl - INFO - +top1_acc 0.4374 +top5_acc 0.6879 +2024-07-21 06:59:36,277 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 06:59:36,318 - pyskl - INFO - +mean_acc 0.4371 +2024-07-21 06:59:36,323 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_133.pth was removed +2024-07-21 06:59:36,562 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_134.pth. +2024-07-21 06:59:36,563 - pyskl - INFO - Best top1_acc is 0.4374 at 134 epoch. +2024-07-21 06:59:36,575 - pyskl - INFO - Epoch(val) [134][309] top1_acc: 0.4374, top5_acc: 0.6879, mean_class_accuracy: 0.4371 +2024-07-21 07:03:23,790 - pyskl - INFO - Epoch [135][100/3746] lr: 2.772e-03, eta: 13:40:07, time: 2.272, data_time: 1.288, memory: 15990, top1_acc: 0.5555, top5_acc: 0.8009, loss_cls: 2.4436, loss: 2.4436 +2024-07-21 07:04:45,856 - pyskl - INFO - Epoch [135][200/3746] lr: 2.763e-03, eta: 13:38:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5661, top5_acc: 0.7975, loss_cls: 2.4423, loss: 2.4423 +2024-07-21 07:06:08,163 - pyskl - INFO - Epoch [135][300/3746] lr: 2.754e-03, eta: 13:37:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5550, top5_acc: 0.8006, loss_cls: 2.4441, loss: 2.4441 +2024-07-21 07:07:31,020 - pyskl - INFO - Epoch [135][400/3746] lr: 2.745e-03, eta: 13:36:01, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5641, top5_acc: 0.7950, loss_cls: 2.4482, loss: 2.4482 +2024-07-21 07:08:53,191 - pyskl - INFO - Epoch [135][500/3746] lr: 2.735e-03, eta: 13:34:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5578, top5_acc: 0.8006, loss_cls: 2.4444, loss: 2.4444 +2024-07-21 07:10:15,475 - pyskl - INFO - Epoch [135][600/3746] lr: 2.726e-03, eta: 13:33:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5634, top5_acc: 0.7986, loss_cls: 2.4227, loss: 2.4227 +2024-07-21 07:11:37,622 - pyskl - INFO - Epoch [135][700/3746] lr: 2.717e-03, eta: 13:31:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5545, top5_acc: 0.7894, loss_cls: 2.4754, loss: 2.4754 +2024-07-21 07:13:00,978 - pyskl - INFO - Epoch [135][800/3746] lr: 2.708e-03, eta: 13:30:32, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5598, top5_acc: 0.7986, loss_cls: 2.4401, loss: 2.4401 +2024-07-21 07:14:22,926 - pyskl - INFO - Epoch [135][900/3746] lr: 2.699e-03, eta: 13:29:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5553, top5_acc: 0.8036, loss_cls: 2.4491, loss: 2.4491 +2024-07-21 07:15:45,216 - pyskl - INFO - Epoch [135][1000/3746] lr: 2.690e-03, eta: 13:27:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5483, top5_acc: 0.7958, loss_cls: 2.4683, loss: 2.4683 +2024-07-21 07:17:07,510 - pyskl - INFO - Epoch [135][1100/3746] lr: 2.681e-03, eta: 13:26:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5467, top5_acc: 0.7934, loss_cls: 2.5162, loss: 2.5162 +2024-07-21 07:18:29,419 - pyskl - INFO - Epoch [135][1200/3746] lr: 2.672e-03, eta: 13:25:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5445, top5_acc: 0.7947, loss_cls: 2.5048, loss: 2.5048 +2024-07-21 07:19:51,501 - pyskl - INFO - Epoch [135][1300/3746] lr: 2.663e-03, eta: 13:23:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5486, top5_acc: 0.7878, loss_cls: 2.5004, loss: 2.5004 +2024-07-21 07:21:13,815 - pyskl - INFO - Epoch [135][1400/3746] lr: 2.654e-03, eta: 13:22:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5505, top5_acc: 0.7886, loss_cls: 2.4944, loss: 2.4944 +2024-07-21 07:22:36,350 - pyskl - INFO - Epoch [135][1500/3746] lr: 2.645e-03, eta: 13:20:56, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5339, top5_acc: 0.7908, loss_cls: 2.5212, loss: 2.5212 +2024-07-21 07:23:59,191 - pyskl - INFO - Epoch [135][1600/3746] lr: 2.636e-03, eta: 13:19:34, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5417, top5_acc: 0.7928, loss_cls: 2.4999, loss: 2.4999 +2024-07-21 07:25:21,580 - pyskl - INFO - Epoch [135][1700/3746] lr: 2.627e-03, eta: 13:18:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5519, top5_acc: 0.7911, loss_cls: 2.4747, loss: 2.4747 +2024-07-21 07:26:43,819 - pyskl - INFO - Epoch [135][1800/3746] lr: 2.618e-03, eta: 13:16:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5447, top5_acc: 0.7966, loss_cls: 2.5038, loss: 2.5038 +2024-07-21 07:28:05,918 - pyskl - INFO - Epoch [135][1900/3746] lr: 2.609e-03, eta: 13:15:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5517, top5_acc: 0.7922, loss_cls: 2.4725, loss: 2.4725 +2024-07-21 07:29:27,873 - pyskl - INFO - Epoch [135][2000/3746] lr: 2.600e-03, eta: 13:14:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5494, top5_acc: 0.8006, loss_cls: 2.4772, loss: 2.4772 +2024-07-21 07:30:49,587 - pyskl - INFO - Epoch [135][2100/3746] lr: 2.591e-03, eta: 13:12:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5459, top5_acc: 0.7950, loss_cls: 2.4727, loss: 2.4727 +2024-07-21 07:32:11,514 - pyskl - INFO - Epoch [135][2200/3746] lr: 2.583e-03, eta: 13:11:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5398, top5_acc: 0.7895, loss_cls: 2.5134, loss: 2.5134 +2024-07-21 07:33:33,563 - pyskl - INFO - Epoch [135][2300/3746] lr: 2.574e-03, eta: 13:09:58, time: 0.820, data_time: 0.001, memory: 15990, top1_acc: 0.5464, top5_acc: 0.7961, loss_cls: 2.4975, loss: 2.4975 +2024-07-21 07:34:56,072 - pyskl - INFO - Epoch [135][2400/3746] lr: 2.565e-03, eta: 13:08:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5355, top5_acc: 0.7761, loss_cls: 2.5578, loss: 2.5578 +2024-07-21 07:36:18,147 - pyskl - INFO - Epoch [135][2500/3746] lr: 2.556e-03, eta: 13:07:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5356, top5_acc: 0.7863, loss_cls: 2.5427, loss: 2.5427 +2024-07-21 07:37:40,129 - pyskl - INFO - Epoch [135][2600/3746] lr: 2.547e-03, eta: 13:05:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5483, top5_acc: 0.7898, loss_cls: 2.5192, loss: 2.5192 +2024-07-21 07:39:02,274 - pyskl - INFO - Epoch [135][2700/3746] lr: 2.538e-03, eta: 13:04:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5452, top5_acc: 0.7889, loss_cls: 2.5026, loss: 2.5026 +2024-07-21 07:40:23,969 - pyskl - INFO - Epoch [135][2800/3746] lr: 2.530e-03, eta: 13:03:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5425, top5_acc: 0.7836, loss_cls: 2.5163, loss: 2.5163 +2024-07-21 07:41:45,783 - pyskl - INFO - Epoch [135][2900/3746] lr: 2.521e-03, eta: 13:01:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5380, top5_acc: 0.7836, loss_cls: 2.5350, loss: 2.5350 +2024-07-21 07:43:07,524 - pyskl - INFO - Epoch [135][3000/3746] lr: 2.512e-03, eta: 13:00:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5311, top5_acc: 0.7852, loss_cls: 2.5601, loss: 2.5601 +2024-07-21 07:44:30,616 - pyskl - INFO - Epoch [135][3100/3746] lr: 2.503e-03, eta: 12:59:00, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5358, top5_acc: 0.7833, loss_cls: 2.5703, loss: 2.5703 +2024-07-21 07:45:52,701 - pyskl - INFO - Epoch [135][3200/3746] lr: 2.495e-03, eta: 12:57:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5441, top5_acc: 0.7905, loss_cls: 2.5125, loss: 2.5125 +2024-07-21 07:47:15,223 - pyskl - INFO - Epoch [135][3300/3746] lr: 2.486e-03, eta: 12:56:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5356, top5_acc: 0.7837, loss_cls: 2.5382, loss: 2.5382 +2024-07-21 07:48:37,036 - pyskl - INFO - Epoch [135][3400/3746] lr: 2.477e-03, eta: 12:54:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5481, top5_acc: 0.7956, loss_cls: 2.4843, loss: 2.4843 +2024-07-21 07:49:59,426 - pyskl - INFO - Epoch [135][3500/3746] lr: 2.469e-03, eta: 12:53:31, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5473, top5_acc: 0.7927, loss_cls: 2.4853, loss: 2.4853 +2024-07-21 07:51:21,662 - pyskl - INFO - Epoch [135][3600/3746] lr: 2.460e-03, eta: 12:52:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5425, top5_acc: 0.8000, loss_cls: 2.4671, loss: 2.4671 +2024-07-21 07:52:43,370 - pyskl - INFO - Epoch [135][3700/3746] lr: 2.451e-03, eta: 12:50:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5283, top5_acc: 0.7784, loss_cls: 2.5865, loss: 2.5865 +2024-07-21 07:53:23,247 - pyskl - INFO - Saving checkpoint at 135 epochs +2024-07-21 07:55:14,812 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 07:55:15,479 - pyskl - INFO - +top1_acc 0.4388 +top5_acc 0.6906 +2024-07-21 07:55:15,480 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 07:55:15,520 - pyskl - INFO - +mean_acc 0.4386 +2024-07-21 07:55:15,525 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_134.pth was removed +2024-07-21 07:55:15,770 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_135.pth. +2024-07-21 07:55:15,770 - pyskl - INFO - Best top1_acc is 0.4388 at 135 epoch. +2024-07-21 07:55:15,782 - pyskl - INFO - Epoch(val) [135][309] top1_acc: 0.4388, top5_acc: 0.6906, mean_class_accuracy: 0.4386 +2024-07-21 07:59:03,155 - pyskl - INFO - Epoch [136][100/3746] lr: 2.439e-03, eta: 12:48:58, time: 2.274, data_time: 1.290, memory: 15990, top1_acc: 0.5714, top5_acc: 0.8164, loss_cls: 2.3576, loss: 2.3576 +2024-07-21 08:00:25,225 - pyskl - INFO - Epoch [136][200/3746] lr: 2.430e-03, eta: 12:47:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5672, top5_acc: 0.8184, loss_cls: 2.3513, loss: 2.3513 +2024-07-21 08:01:46,977 - pyskl - INFO - Epoch [136][300/3746] lr: 2.421e-03, eta: 12:46:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5678, top5_acc: 0.8077, loss_cls: 2.3753, loss: 2.3753 +2024-07-21 08:03:08,991 - pyskl - INFO - Epoch [136][400/3746] lr: 2.413e-03, eta: 12:44:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5698, top5_acc: 0.8130, loss_cls: 2.3661, loss: 2.3661 +2024-07-21 08:04:30,902 - pyskl - INFO - Epoch [136][500/3746] lr: 2.404e-03, eta: 12:43:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5564, top5_acc: 0.7986, loss_cls: 2.4275, loss: 2.4275 +2024-07-21 08:05:52,406 - pyskl - INFO - Epoch [136][600/3746] lr: 2.396e-03, eta: 12:42:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5578, top5_acc: 0.8058, loss_cls: 2.3940, loss: 2.3940 +2024-07-21 08:07:14,658 - pyskl - INFO - Epoch [136][700/3746] lr: 2.387e-03, eta: 12:40:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5517, top5_acc: 0.7975, loss_cls: 2.4687, loss: 2.4687 +2024-07-21 08:08:36,571 - pyskl - INFO - Epoch [136][800/3746] lr: 2.379e-03, eta: 12:39:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5519, top5_acc: 0.7981, loss_cls: 2.4725, loss: 2.4725 +2024-07-21 08:09:58,719 - pyskl - INFO - Epoch [136][900/3746] lr: 2.370e-03, eta: 12:38:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5606, top5_acc: 0.8036, loss_cls: 2.4192, loss: 2.4192 +2024-07-21 08:11:20,966 - pyskl - INFO - Epoch [136][1000/3746] lr: 2.362e-03, eta: 12:36:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5523, top5_acc: 0.7958, loss_cls: 2.4408, loss: 2.4408 +2024-07-21 08:12:42,789 - pyskl - INFO - Epoch [136][1100/3746] lr: 2.353e-03, eta: 12:35:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5589, top5_acc: 0.8063, loss_cls: 2.4157, loss: 2.4157 +2024-07-21 08:14:04,351 - pyskl - INFO - Epoch [136][1200/3746] lr: 2.345e-03, eta: 12:33:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5708, top5_acc: 0.8081, loss_cls: 2.3932, loss: 2.3932 +2024-07-21 08:15:26,679 - pyskl - INFO - Epoch [136][1300/3746] lr: 2.336e-03, eta: 12:32:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5516, top5_acc: 0.7936, loss_cls: 2.4753, loss: 2.4753 +2024-07-21 08:16:49,071 - pyskl - INFO - Epoch [136][1400/3746] lr: 2.328e-03, eta: 12:31:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5644, top5_acc: 0.8006, loss_cls: 2.4384, loss: 2.4384 +2024-07-21 08:18:11,705 - pyskl - INFO - Epoch [136][1500/3746] lr: 2.319e-03, eta: 12:29:47, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5595, top5_acc: 0.8013, loss_cls: 2.4195, loss: 2.4195 +2024-07-21 08:19:34,464 - pyskl - INFO - Epoch [136][1600/3746] lr: 2.311e-03, eta: 12:28:24, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5592, top5_acc: 0.8037, loss_cls: 2.4285, loss: 2.4285 +2024-07-21 08:20:57,170 - pyskl - INFO - Epoch [136][1700/3746] lr: 2.303e-03, eta: 12:27:02, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5528, top5_acc: 0.7972, loss_cls: 2.4637, loss: 2.4637 +2024-07-21 08:22:19,508 - pyskl - INFO - Epoch [136][1800/3746] lr: 2.294e-03, eta: 12:25:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5525, top5_acc: 0.7939, loss_cls: 2.4687, loss: 2.4687 +2024-07-21 08:23:41,694 - pyskl - INFO - Epoch [136][1900/3746] lr: 2.286e-03, eta: 12:24:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5519, top5_acc: 0.7856, loss_cls: 2.4804, loss: 2.4804 +2024-07-21 08:25:03,636 - pyskl - INFO - Epoch [136][2000/3746] lr: 2.277e-03, eta: 12:22:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5591, top5_acc: 0.7952, loss_cls: 2.4476, loss: 2.4476 +2024-07-21 08:26:26,668 - pyskl - INFO - Epoch [136][2100/3746] lr: 2.269e-03, eta: 12:21:33, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5591, top5_acc: 0.8041, loss_cls: 2.4147, loss: 2.4147 +2024-07-21 08:27:49,105 - pyskl - INFO - Epoch [136][2200/3746] lr: 2.261e-03, eta: 12:20:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5478, top5_acc: 0.7969, loss_cls: 2.4997, loss: 2.4997 +2024-07-21 08:29:12,497 - pyskl - INFO - Epoch [136][2300/3746] lr: 2.253e-03, eta: 12:18:49, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5508, top5_acc: 0.7898, loss_cls: 2.4849, loss: 2.4849 +2024-07-21 08:30:34,388 - pyskl - INFO - Epoch [136][2400/3746] lr: 2.244e-03, eta: 12:17:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5645, top5_acc: 0.7994, loss_cls: 2.4373, loss: 2.4373 +2024-07-21 08:31:56,568 - pyskl - INFO - Epoch [136][2500/3746] lr: 2.236e-03, eta: 12:16:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5513, top5_acc: 0.7913, loss_cls: 2.4815, loss: 2.4815 +2024-07-21 08:33:18,916 - pyskl - INFO - Epoch [136][2600/3746] lr: 2.228e-03, eta: 12:14:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5447, top5_acc: 0.7928, loss_cls: 2.4899, loss: 2.4899 +2024-07-21 08:34:41,284 - pyskl - INFO - Epoch [136][2700/3746] lr: 2.219e-03, eta: 12:13:20, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5506, top5_acc: 0.7991, loss_cls: 2.4679, loss: 2.4679 +2024-07-21 08:36:03,084 - pyskl - INFO - Epoch [136][2800/3746] lr: 2.211e-03, eta: 12:11:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5536, top5_acc: 0.7941, loss_cls: 2.4691, loss: 2.4691 +2024-07-21 08:37:24,802 - pyskl - INFO - Epoch [136][2900/3746] lr: 2.203e-03, eta: 12:10:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5509, top5_acc: 0.7983, loss_cls: 2.4645, loss: 2.4645 +2024-07-21 08:38:47,046 - pyskl - INFO - Epoch [136][3000/3746] lr: 2.195e-03, eta: 12:09:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5513, top5_acc: 0.7989, loss_cls: 2.4450, loss: 2.4450 +2024-07-21 08:40:08,735 - pyskl - INFO - Epoch [136][3100/3746] lr: 2.187e-03, eta: 12:07:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5441, top5_acc: 0.7975, loss_cls: 2.4961, loss: 2.4961 +2024-07-21 08:41:31,336 - pyskl - INFO - Epoch [136][3200/3746] lr: 2.178e-03, eta: 12:06:28, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5511, top5_acc: 0.7984, loss_cls: 2.4539, loss: 2.4539 +2024-07-21 08:42:53,300 - pyskl - INFO - Epoch [136][3300/3746] lr: 2.170e-03, eta: 12:05:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5480, top5_acc: 0.7853, loss_cls: 2.5077, loss: 2.5077 +2024-07-21 08:44:16,456 - pyskl - INFO - Epoch [136][3400/3746] lr: 2.162e-03, eta: 12:03:44, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5491, top5_acc: 0.7875, loss_cls: 2.5097, loss: 2.5097 +2024-07-21 08:45:38,033 - pyskl - INFO - Epoch [136][3500/3746] lr: 2.154e-03, eta: 12:02:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5519, top5_acc: 0.7947, loss_cls: 2.4572, loss: 2.4572 +2024-07-21 08:47:00,081 - pyskl - INFO - Epoch [136][3600/3746] lr: 2.146e-03, eta: 12:00:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5563, top5_acc: 0.8013, loss_cls: 2.4584, loss: 2.4584 +2024-07-21 08:48:22,121 - pyskl - INFO - Epoch [136][3700/3746] lr: 2.138e-03, eta: 11:59:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5530, top5_acc: 0.7964, loss_cls: 2.4654, loss: 2.4654 +2024-07-21 08:49:01,726 - pyskl - INFO - Saving checkpoint at 136 epochs +2024-07-21 08:50:54,523 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 08:50:55,182 - pyskl - INFO - +top1_acc 0.4440 +top5_acc 0.6969 +2024-07-21 08:50:55,182 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 08:50:55,223 - pyskl - INFO - +mean_acc 0.4437 +2024-07-21 08:50:55,228 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_135.pth was removed +2024-07-21 08:50:55,482 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_136.pth. +2024-07-21 08:50:55,483 - pyskl - INFO - Best top1_acc is 0.4440 at 136 epoch. +2024-07-21 08:50:55,494 - pyskl - INFO - Epoch(val) [136][309] top1_acc: 0.4440, top5_acc: 0.6969, mean_class_accuracy: 0.4437 +2024-07-21 08:54:40,634 - pyskl - INFO - Epoch [137][100/3746] lr: 2.126e-03, eta: 11:57:48, time: 2.251, data_time: 1.271, memory: 15990, top1_acc: 0.5753, top5_acc: 0.8158, loss_cls: 2.3355, loss: 2.3355 +2024-07-21 08:56:02,579 - pyskl - INFO - Epoch [137][200/3746] lr: 2.118e-03, eta: 11:56:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5725, top5_acc: 0.8083, loss_cls: 2.3904, loss: 2.3904 +2024-07-21 08:57:25,013 - pyskl - INFO - Epoch [137][300/3746] lr: 2.110e-03, eta: 11:55:03, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5717, top5_acc: 0.8098, loss_cls: 2.3656, loss: 2.3656 +2024-07-21 08:58:47,045 - pyskl - INFO - Epoch [137][400/3746] lr: 2.102e-03, eta: 11:53:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5628, top5_acc: 0.7998, loss_cls: 2.4192, loss: 2.4192 +2024-07-21 09:00:08,839 - pyskl - INFO - Epoch [137][500/3746] lr: 2.094e-03, eta: 11:52:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5759, top5_acc: 0.8187, loss_cls: 2.3301, loss: 2.3301 +2024-07-21 09:01:30,689 - pyskl - INFO - Epoch [137][600/3746] lr: 2.086e-03, eta: 11:50:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5614, top5_acc: 0.8061, loss_cls: 2.3872, loss: 2.3872 +2024-07-21 09:02:53,050 - pyskl - INFO - Epoch [137][700/3746] lr: 2.078e-03, eta: 11:49:34, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5719, top5_acc: 0.8175, loss_cls: 2.3343, loss: 2.3343 +2024-07-21 09:04:15,426 - pyskl - INFO - Epoch [137][800/3746] lr: 2.070e-03, eta: 11:48:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5719, top5_acc: 0.8147, loss_cls: 2.3584, loss: 2.3584 +2024-07-21 09:05:37,375 - pyskl - INFO - Epoch [137][900/3746] lr: 2.062e-03, eta: 11:46:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5709, top5_acc: 0.8131, loss_cls: 2.3273, loss: 2.3273 +2024-07-21 09:06:59,263 - pyskl - INFO - Epoch [137][1000/3746] lr: 2.054e-03, eta: 11:45:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5691, top5_acc: 0.8117, loss_cls: 2.3732, loss: 2.3732 +2024-07-21 09:08:21,682 - pyskl - INFO - Epoch [137][1100/3746] lr: 2.046e-03, eta: 11:44:05, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5600, top5_acc: 0.8125, loss_cls: 2.3930, loss: 2.3930 +2024-07-21 09:09:43,458 - pyskl - INFO - Epoch [137][1200/3746] lr: 2.038e-03, eta: 11:42:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5630, top5_acc: 0.8022, loss_cls: 2.4055, loss: 2.4055 +2024-07-21 09:11:05,392 - pyskl - INFO - Epoch [137][1300/3746] lr: 2.030e-03, eta: 11:41:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5698, top5_acc: 0.8050, loss_cls: 2.4139, loss: 2.4139 +2024-07-21 09:12:27,423 - pyskl - INFO - Epoch [137][1400/3746] lr: 2.022e-03, eta: 11:39:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5681, top5_acc: 0.8059, loss_cls: 2.4130, loss: 2.4130 +2024-07-21 09:13:49,862 - pyskl - INFO - Epoch [137][1500/3746] lr: 2.015e-03, eta: 11:38:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5691, top5_acc: 0.8041, loss_cls: 2.3690, loss: 2.3690 +2024-07-21 09:15:12,664 - pyskl - INFO - Epoch [137][1600/3746] lr: 2.007e-03, eta: 11:37:13, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5705, top5_acc: 0.8050, loss_cls: 2.3885, loss: 2.3885 +2024-07-21 09:16:35,238 - pyskl - INFO - Epoch [137][1700/3746] lr: 1.999e-03, eta: 11:35:51, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5631, top5_acc: 0.7964, loss_cls: 2.4379, loss: 2.4379 +2024-07-21 09:17:57,008 - pyskl - INFO - Epoch [137][1800/3746] lr: 1.991e-03, eta: 11:34:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5669, top5_acc: 0.8070, loss_cls: 2.3824, loss: 2.3824 +2024-07-21 09:19:19,037 - pyskl - INFO - Epoch [137][1900/3746] lr: 1.983e-03, eta: 11:33:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5580, top5_acc: 0.7917, loss_cls: 2.4439, loss: 2.4439 +2024-07-21 09:20:40,853 - pyskl - INFO - Epoch [137][2000/3746] lr: 1.976e-03, eta: 11:31:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5598, top5_acc: 0.8014, loss_cls: 2.4215, loss: 2.4215 +2024-07-21 09:22:02,870 - pyskl - INFO - Epoch [137][2100/3746] lr: 1.968e-03, eta: 11:30:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5709, top5_acc: 0.8047, loss_cls: 2.3978, loss: 2.3978 +2024-07-21 09:23:25,045 - pyskl - INFO - Epoch [137][2200/3746] lr: 1.960e-03, eta: 11:28:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5605, top5_acc: 0.7980, loss_cls: 2.4227, loss: 2.4227 +2024-07-21 09:24:47,230 - pyskl - INFO - Epoch [137][2300/3746] lr: 1.952e-03, eta: 11:27:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5645, top5_acc: 0.8030, loss_cls: 2.3931, loss: 2.3931 +2024-07-21 09:26:10,323 - pyskl - INFO - Epoch [137][2400/3746] lr: 1.944e-03, eta: 11:26:15, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5558, top5_acc: 0.7961, loss_cls: 2.4492, loss: 2.4492 +2024-07-21 09:27:32,464 - pyskl - INFO - Epoch [137][2500/3746] lr: 1.937e-03, eta: 11:24:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5564, top5_acc: 0.8047, loss_cls: 2.4448, loss: 2.4448 +2024-07-21 09:28:54,877 - pyskl - INFO - Epoch [137][2600/3746] lr: 1.929e-03, eta: 11:23:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5700, top5_acc: 0.8058, loss_cls: 2.4026, loss: 2.4026 +2024-07-21 09:30:16,808 - pyskl - INFO - Epoch [137][2700/3746] lr: 1.921e-03, eta: 11:22:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5614, top5_acc: 0.7997, loss_cls: 2.4192, loss: 2.4192 +2024-07-21 09:31:38,521 - pyskl - INFO - Epoch [137][2800/3746] lr: 1.914e-03, eta: 11:20:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5616, top5_acc: 0.8053, loss_cls: 2.4029, loss: 2.4029 +2024-07-21 09:33:00,798 - pyskl - INFO - Epoch [137][2900/3746] lr: 1.906e-03, eta: 11:19:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5681, top5_acc: 0.8011, loss_cls: 2.3956, loss: 2.3956 +2024-07-21 09:34:23,278 - pyskl - INFO - Epoch [137][3000/3746] lr: 1.898e-03, eta: 11:18:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5630, top5_acc: 0.8102, loss_cls: 2.3937, loss: 2.3937 +2024-07-21 09:35:45,226 - pyskl - INFO - Epoch [137][3100/3746] lr: 1.891e-03, eta: 11:16:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5655, top5_acc: 0.8023, loss_cls: 2.3900, loss: 2.3900 +2024-07-21 09:37:07,425 - pyskl - INFO - Epoch [137][3200/3746] lr: 1.883e-03, eta: 11:15:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5613, top5_acc: 0.8075, loss_cls: 2.4055, loss: 2.4055 +2024-07-21 09:38:29,040 - pyskl - INFO - Epoch [137][3300/3746] lr: 1.876e-03, eta: 11:13:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5595, top5_acc: 0.7969, loss_cls: 2.4374, loss: 2.4374 +2024-07-21 09:39:51,800 - pyskl - INFO - Epoch [137][3400/3746] lr: 1.868e-03, eta: 11:12:32, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5598, top5_acc: 0.8028, loss_cls: 2.4118, loss: 2.4118 +2024-07-21 09:41:13,955 - pyskl - INFO - Epoch [137][3500/3746] lr: 1.860e-03, eta: 11:11:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5631, top5_acc: 0.7991, loss_cls: 2.4159, loss: 2.4159 +2024-07-21 09:42:35,988 - pyskl - INFO - Epoch [137][3600/3746] lr: 1.853e-03, eta: 11:09:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5650, top5_acc: 0.8002, loss_cls: 2.4266, loss: 2.4266 +2024-07-21 09:43:57,414 - pyskl - INFO - Epoch [137][3700/3746] lr: 1.845e-03, eta: 11:08:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5606, top5_acc: 0.8019, loss_cls: 2.4099, loss: 2.4099 +2024-07-21 09:44:37,274 - pyskl - INFO - Saving checkpoint at 137 epochs +2024-07-21 09:46:28,902 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 09:46:29,563 - pyskl - INFO - +top1_acc 0.4466 +top5_acc 0.7007 +2024-07-21 09:46:29,563 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 09:46:29,607 - pyskl - INFO - +mean_acc 0.4464 +2024-07-21 09:46:29,612 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_136.pth was removed +2024-07-21 09:46:29,869 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_137.pth. +2024-07-21 09:46:29,870 - pyskl - INFO - Best top1_acc is 0.4466 at 137 epoch. +2024-07-21 09:46:29,882 - pyskl - INFO - Epoch(val) [137][309] top1_acc: 0.4466, top5_acc: 0.7007, mean_class_accuracy: 0.4464 +2024-07-21 09:50:14,365 - pyskl - INFO - Epoch [138][100/3746] lr: 1.834e-03, eta: 11:06:35, time: 2.245, data_time: 1.257, memory: 15990, top1_acc: 0.5891, top5_acc: 0.8283, loss_cls: 2.2547, loss: 2.2547 +2024-07-21 09:51:36,125 - pyskl - INFO - Epoch [138][200/3746] lr: 1.827e-03, eta: 11:05:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5867, top5_acc: 0.8237, loss_cls: 2.2735, loss: 2.2735 +2024-07-21 09:52:57,951 - pyskl - INFO - Epoch [138][300/3746] lr: 1.819e-03, eta: 11:03:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5875, top5_acc: 0.8173, loss_cls: 2.2810, loss: 2.2810 +2024-07-21 09:54:19,895 - pyskl - INFO - Epoch [138][400/3746] lr: 1.812e-03, eta: 11:02:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5783, top5_acc: 0.8198, loss_cls: 2.3050, loss: 2.3050 +2024-07-21 09:55:41,751 - pyskl - INFO - Epoch [138][500/3746] lr: 1.805e-03, eta: 11:01:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5833, top5_acc: 0.8233, loss_cls: 2.2830, loss: 2.2830 +2024-07-21 09:57:03,683 - pyskl - INFO - Epoch [138][600/3746] lr: 1.797e-03, eta: 10:59:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5786, top5_acc: 0.8170, loss_cls: 2.3234, loss: 2.3234 +2024-07-21 09:58:25,734 - pyskl - INFO - Epoch [138][700/3746] lr: 1.790e-03, eta: 10:58:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5869, top5_acc: 0.8266, loss_cls: 2.2913, loss: 2.2913 +2024-07-21 09:59:47,910 - pyskl - INFO - Epoch [138][800/3746] lr: 1.782e-03, eta: 10:56:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5834, top5_acc: 0.8144, loss_cls: 2.3134, loss: 2.3134 +2024-07-21 10:01:09,641 - pyskl - INFO - Epoch [138][900/3746] lr: 1.775e-03, eta: 10:55:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5731, top5_acc: 0.8169, loss_cls: 2.3362, loss: 2.3362 +2024-07-21 10:02:31,784 - pyskl - INFO - Epoch [138][1000/3746] lr: 1.768e-03, eta: 10:54:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5728, top5_acc: 0.8100, loss_cls: 2.3401, loss: 2.3401 +2024-07-21 10:03:53,404 - pyskl - INFO - Epoch [138][1100/3746] lr: 1.760e-03, eta: 10:52:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5769, top5_acc: 0.8189, loss_cls: 2.3163, loss: 2.3163 +2024-07-21 10:05:15,419 - pyskl - INFO - Epoch [138][1200/3746] lr: 1.753e-03, eta: 10:51:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5692, top5_acc: 0.8109, loss_cls: 2.3550, loss: 2.3550 +2024-07-21 10:06:37,569 - pyskl - INFO - Epoch [138][1300/3746] lr: 1.745e-03, eta: 10:50:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5823, top5_acc: 0.8192, loss_cls: 2.3077, loss: 2.3077 +2024-07-21 10:07:59,968 - pyskl - INFO - Epoch [138][1400/3746] lr: 1.738e-03, eta: 10:48:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5706, top5_acc: 0.8128, loss_cls: 2.3690, loss: 2.3690 +2024-07-21 10:09:22,691 - pyskl - INFO - Epoch [138][1500/3746] lr: 1.731e-03, eta: 10:47:22, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5711, top5_acc: 0.8109, loss_cls: 2.3544, loss: 2.3544 +2024-07-21 10:10:44,747 - pyskl - INFO - Epoch [138][1600/3746] lr: 1.724e-03, eta: 10:46:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5761, top5_acc: 0.8061, loss_cls: 2.3644, loss: 2.3644 +2024-07-21 10:12:07,137 - pyskl - INFO - Epoch [138][1700/3746] lr: 1.716e-03, eta: 10:44:38, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5709, top5_acc: 0.8056, loss_cls: 2.3958, loss: 2.3958 +2024-07-21 10:13:28,756 - pyskl - INFO - Epoch [138][1800/3746] lr: 1.709e-03, eta: 10:43:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5761, top5_acc: 0.8163, loss_cls: 2.3237, loss: 2.3237 +2024-07-21 10:14:51,098 - pyskl - INFO - Epoch [138][1900/3746] lr: 1.702e-03, eta: 10:41:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5752, top5_acc: 0.8164, loss_cls: 2.3102, loss: 2.3102 +2024-07-21 10:16:13,014 - pyskl - INFO - Epoch [138][2000/3746] lr: 1.695e-03, eta: 10:40:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5783, top5_acc: 0.8255, loss_cls: 2.3177, loss: 2.3177 +2024-07-21 10:17:34,394 - pyskl - INFO - Epoch [138][2100/3746] lr: 1.687e-03, eta: 10:39:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5575, top5_acc: 0.8048, loss_cls: 2.4128, loss: 2.4128 +2024-07-21 10:18:56,301 - pyskl - INFO - Epoch [138][2200/3746] lr: 1.680e-03, eta: 10:37:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5761, top5_acc: 0.8066, loss_cls: 2.3698, loss: 2.3698 +2024-07-21 10:20:17,638 - pyskl - INFO - Epoch [138][2300/3746] lr: 1.673e-03, eta: 10:36:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5766, top5_acc: 0.8150, loss_cls: 2.3377, loss: 2.3377 +2024-07-21 10:21:39,213 - pyskl - INFO - Epoch [138][2400/3746] lr: 1.666e-03, eta: 10:35:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5644, top5_acc: 0.8097, loss_cls: 2.3687, loss: 2.3687 +2024-07-21 10:23:01,712 - pyskl - INFO - Epoch [138][2500/3746] lr: 1.659e-03, eta: 10:33:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5636, top5_acc: 0.8092, loss_cls: 2.3717, loss: 2.3717 +2024-07-21 10:24:23,587 - pyskl - INFO - Epoch [138][2600/3746] lr: 1.652e-03, eta: 10:32:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5633, top5_acc: 0.8080, loss_cls: 2.3813, loss: 2.3813 +2024-07-21 10:25:46,415 - pyskl - INFO - Epoch [138][2700/3746] lr: 1.644e-03, eta: 10:30:54, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5628, top5_acc: 0.8073, loss_cls: 2.4078, loss: 2.4078 +2024-07-21 10:27:08,062 - pyskl - INFO - Epoch [138][2800/3746] lr: 1.637e-03, eta: 10:29:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5678, top5_acc: 0.8067, loss_cls: 2.3804, loss: 2.3804 +2024-07-21 10:28:29,794 - pyskl - INFO - Epoch [138][2900/3746] lr: 1.630e-03, eta: 10:28:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5659, top5_acc: 0.8109, loss_cls: 2.3830, loss: 2.3830 +2024-07-21 10:29:51,855 - pyskl - INFO - Epoch [138][3000/3746] lr: 1.623e-03, eta: 10:26:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5723, top5_acc: 0.8056, loss_cls: 2.3920, loss: 2.3920 +2024-07-21 10:31:14,503 - pyskl - INFO - Epoch [138][3100/3746] lr: 1.616e-03, eta: 10:25:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5691, top5_acc: 0.8063, loss_cls: 2.3843, loss: 2.3843 +2024-07-21 10:32:36,500 - pyskl - INFO - Epoch [138][3200/3746] lr: 1.609e-03, eta: 10:24:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5769, top5_acc: 0.8087, loss_cls: 2.3391, loss: 2.3391 +2024-07-21 10:33:58,505 - pyskl - INFO - Epoch [138][3300/3746] lr: 1.602e-03, eta: 10:22:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5689, top5_acc: 0.8028, loss_cls: 2.3955, loss: 2.3955 +2024-07-21 10:35:20,614 - pyskl - INFO - Epoch [138][3400/3746] lr: 1.595e-03, eta: 10:21:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5609, top5_acc: 0.8106, loss_cls: 2.3921, loss: 2.3921 +2024-07-21 10:36:42,672 - pyskl - INFO - Epoch [138][3500/3746] lr: 1.588e-03, eta: 10:19:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5780, top5_acc: 0.8172, loss_cls: 2.3230, loss: 2.3230 +2024-07-21 10:38:04,312 - pyskl - INFO - Epoch [138][3600/3746] lr: 1.581e-03, eta: 10:18:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5750, top5_acc: 0.8058, loss_cls: 2.3476, loss: 2.3476 +2024-07-21 10:39:25,855 - pyskl - INFO - Epoch [138][3700/3746] lr: 1.574e-03, eta: 10:17:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5755, top5_acc: 0.8083, loss_cls: 2.3700, loss: 2.3700 +2024-07-21 10:40:05,501 - pyskl - INFO - Saving checkpoint at 138 epochs +2024-07-21 10:41:56,290 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 10:41:56,953 - pyskl - INFO - +top1_acc 0.4475 +top5_acc 0.6997 +2024-07-21 10:41:56,953 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 10:41:56,995 - pyskl - INFO - +mean_acc 0.4472 +2024-07-21 10:41:57,000 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_137.pth was removed +2024-07-21 10:41:57,255 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_138.pth. +2024-07-21 10:41:57,256 - pyskl - INFO - Best top1_acc is 0.4475 at 138 epoch. +2024-07-21 10:41:57,268 - pyskl - INFO - Epoch(val) [138][309] top1_acc: 0.4475, top5_acc: 0.6997, mean_class_accuracy: 0.4472 +2024-07-21 10:45:47,121 - pyskl - INFO - Epoch [139][100/3746] lr: 1.564e-03, eta: 10:15:20, time: 2.298, data_time: 1.274, memory: 15990, top1_acc: 0.5973, top5_acc: 0.8233, loss_cls: 2.2644, loss: 2.2644 +2024-07-21 10:47:08,900 - pyskl - INFO - Epoch [139][200/3746] lr: 1.557e-03, eta: 10:13:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5961, top5_acc: 0.8272, loss_cls: 2.2336, loss: 2.2336 +2024-07-21 10:48:30,838 - pyskl - INFO - Epoch [139][300/3746] lr: 1.550e-03, eta: 10:12:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5884, top5_acc: 0.8284, loss_cls: 2.2819, loss: 2.2819 +2024-07-21 10:49:52,552 - pyskl - INFO - Epoch [139][400/3746] lr: 1.543e-03, eta: 10:11:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6041, top5_acc: 0.8378, loss_cls: 2.1772, loss: 2.1772 +2024-07-21 10:51:14,395 - pyskl - INFO - Epoch [139][500/3746] lr: 1.536e-03, eta: 10:09:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5947, top5_acc: 0.8222, loss_cls: 2.2685, loss: 2.2685 +2024-07-21 10:52:36,507 - pyskl - INFO - Epoch [139][600/3746] lr: 1.529e-03, eta: 10:08:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5934, top5_acc: 0.8286, loss_cls: 2.2445, loss: 2.2445 +2024-07-21 10:53:58,225 - pyskl - INFO - Epoch [139][700/3746] lr: 1.523e-03, eta: 10:07:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5891, top5_acc: 0.8311, loss_cls: 2.2480, loss: 2.2480 +2024-07-21 10:55:20,050 - pyskl - INFO - Epoch [139][800/3746] lr: 1.516e-03, eta: 10:05:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5884, top5_acc: 0.8178, loss_cls: 2.2992, loss: 2.2992 +2024-07-21 10:56:42,275 - pyskl - INFO - Epoch [139][900/3746] lr: 1.509e-03, eta: 10:04:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5983, top5_acc: 0.8237, loss_cls: 2.2432, loss: 2.2432 +2024-07-21 10:58:04,152 - pyskl - INFO - Epoch [139][1000/3746] lr: 1.502e-03, eta: 10:02:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5914, top5_acc: 0.8222, loss_cls: 2.2523, loss: 2.2523 +2024-07-21 10:59:26,119 - pyskl - INFO - Epoch [139][1100/3746] lr: 1.495e-03, eta: 10:01:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5872, top5_acc: 0.8208, loss_cls: 2.2762, loss: 2.2762 +2024-07-21 11:00:47,972 - pyskl - INFO - Epoch [139][1200/3746] lr: 1.489e-03, eta: 10:00:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5898, top5_acc: 0.8208, loss_cls: 2.2751, loss: 2.2751 +2024-07-21 11:02:10,006 - pyskl - INFO - Epoch [139][1300/3746] lr: 1.482e-03, eta: 9:58:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5878, top5_acc: 0.8183, loss_cls: 2.2968, loss: 2.2968 +2024-07-21 11:03:33,110 - pyskl - INFO - Epoch [139][1400/3746] lr: 1.475e-03, eta: 9:57:30, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5809, top5_acc: 0.8252, loss_cls: 2.2811, loss: 2.2811 +2024-07-21 11:04:55,846 - pyskl - INFO - Epoch [139][1500/3746] lr: 1.468e-03, eta: 9:56:08, time: 0.827, data_time: 0.001, memory: 15990, top1_acc: 0.5867, top5_acc: 0.8175, loss_cls: 2.2987, loss: 2.2987 +2024-07-21 11:06:17,767 - pyskl - INFO - Epoch [139][1600/3746] lr: 1.462e-03, eta: 9:54:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5747, top5_acc: 0.8137, loss_cls: 2.3351, loss: 2.3351 +2024-07-21 11:07:40,125 - pyskl - INFO - Epoch [139][1700/3746] lr: 1.455e-03, eta: 9:53:23, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5831, top5_acc: 0.8191, loss_cls: 2.2943, loss: 2.2943 +2024-07-21 11:09:02,657 - pyskl - INFO - Epoch [139][1800/3746] lr: 1.448e-03, eta: 9:52:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5883, top5_acc: 0.8205, loss_cls: 2.2791, loss: 2.2791 +2024-07-21 11:10:24,363 - pyskl - INFO - Epoch [139][1900/3746] lr: 1.442e-03, eta: 9:50:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5730, top5_acc: 0.8139, loss_cls: 2.3218, loss: 2.3218 +2024-07-21 11:11:46,990 - pyskl - INFO - Epoch [139][2000/3746] lr: 1.435e-03, eta: 9:49:16, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5616, top5_acc: 0.8094, loss_cls: 2.3835, loss: 2.3835 +2024-07-21 11:13:09,192 - pyskl - INFO - Epoch [139][2100/3746] lr: 1.428e-03, eta: 9:47:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5736, top5_acc: 0.8148, loss_cls: 2.3323, loss: 2.3323 +2024-07-21 11:14:31,295 - pyskl - INFO - Epoch [139][2200/3746] lr: 1.422e-03, eta: 9:46:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5903, top5_acc: 0.8225, loss_cls: 2.2662, loss: 2.2662 +2024-07-21 11:15:53,206 - pyskl - INFO - Epoch [139][2300/3746] lr: 1.415e-03, eta: 9:45:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5834, top5_acc: 0.8164, loss_cls: 2.3139, loss: 2.3139 +2024-07-21 11:17:15,927 - pyskl - INFO - Epoch [139][2400/3746] lr: 1.408e-03, eta: 9:43:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5817, top5_acc: 0.8148, loss_cls: 2.3250, loss: 2.3250 +2024-07-21 11:18:39,050 - pyskl - INFO - Epoch [139][2500/3746] lr: 1.402e-03, eta: 9:42:25, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5773, top5_acc: 0.8163, loss_cls: 2.3098, loss: 2.3098 +2024-07-21 11:20:01,110 - pyskl - INFO - Epoch [139][2600/3746] lr: 1.395e-03, eta: 9:41:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5817, top5_acc: 0.8172, loss_cls: 2.2989, loss: 2.2989 +2024-07-21 11:21:23,243 - pyskl - INFO - Epoch [139][2700/3746] lr: 1.389e-03, eta: 9:39:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5808, top5_acc: 0.8183, loss_cls: 2.3079, loss: 2.3079 +2024-07-21 11:22:45,323 - pyskl - INFO - Epoch [139][2800/3746] lr: 1.382e-03, eta: 9:38:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5809, top5_acc: 0.8137, loss_cls: 2.3061, loss: 2.3061 +2024-07-21 11:24:08,185 - pyskl - INFO - Epoch [139][2900/3746] lr: 1.376e-03, eta: 9:36:55, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5806, top5_acc: 0.8270, loss_cls: 2.3064, loss: 2.3064 +2024-07-21 11:25:32,592 - pyskl - INFO - Epoch [139][3000/3746] lr: 1.369e-03, eta: 9:35:33, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5806, top5_acc: 0.8172, loss_cls: 2.3167, loss: 2.3167 +2024-07-21 11:26:55,399 - pyskl - INFO - Epoch [139][3100/3746] lr: 1.363e-03, eta: 9:34:11, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5875, top5_acc: 0.8231, loss_cls: 2.2865, loss: 2.2865 +2024-07-21 11:28:17,517 - pyskl - INFO - Epoch [139][3200/3746] lr: 1.356e-03, eta: 9:32:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5830, top5_acc: 0.8117, loss_cls: 2.3262, loss: 2.3262 +2024-07-21 11:29:39,965 - pyskl - INFO - Epoch [139][3300/3746] lr: 1.350e-03, eta: 9:31:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5773, top5_acc: 0.8198, loss_cls: 2.3163, loss: 2.3163 +2024-07-21 11:31:01,762 - pyskl - INFO - Epoch [139][3400/3746] lr: 1.343e-03, eta: 9:30:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5798, top5_acc: 0.8170, loss_cls: 2.3207, loss: 2.3207 +2024-07-21 11:32:23,918 - pyskl - INFO - Epoch [139][3500/3746] lr: 1.337e-03, eta: 9:28:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5811, top5_acc: 0.8245, loss_cls: 2.3015, loss: 2.3015 +2024-07-21 11:33:45,883 - pyskl - INFO - Epoch [139][3600/3746] lr: 1.330e-03, eta: 9:27:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5761, top5_acc: 0.8153, loss_cls: 2.3101, loss: 2.3101 +2024-07-21 11:35:07,997 - pyskl - INFO - Epoch [139][3700/3746] lr: 1.324e-03, eta: 9:25:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5794, top5_acc: 0.8209, loss_cls: 2.3090, loss: 2.3090 +2024-07-21 11:35:48,093 - pyskl - INFO - Saving checkpoint at 139 epochs +2024-07-21 11:37:39,948 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 11:37:40,613 - pyskl - INFO - +top1_acc 0.4556 +top5_acc 0.7036 +2024-07-21 11:37:40,613 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 11:37:40,653 - pyskl - INFO - +mean_acc 0.4554 +2024-07-21 11:37:40,659 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_138.pth was removed +2024-07-21 11:37:40,914 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_139.pth. +2024-07-21 11:37:40,915 - pyskl - INFO - Best top1_acc is 0.4556 at 139 epoch. +2024-07-21 11:37:40,927 - pyskl - INFO - Epoch(val) [139][309] top1_acc: 0.4556, top5_acc: 0.7036, mean_class_accuracy: 0.4554 +2024-07-21 11:41:32,318 - pyskl - INFO - Epoch [140][100/3746] lr: 1.315e-03, eta: 9:24:05, time: 2.314, data_time: 1.331, memory: 15990, top1_acc: 0.6000, top5_acc: 0.8281, loss_cls: 2.2176, loss: 2.2176 +2024-07-21 11:42:54,529 - pyskl - INFO - Epoch [140][200/3746] lr: 1.308e-03, eta: 9:22:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6064, top5_acc: 0.8411, loss_cls: 2.1853, loss: 2.1853 +2024-07-21 11:44:17,048 - pyskl - INFO - Epoch [140][300/3746] lr: 1.302e-03, eta: 9:21:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6016, top5_acc: 0.8263, loss_cls: 2.2169, loss: 2.2169 +2024-07-21 11:45:38,911 - pyskl - INFO - Epoch [140][400/3746] lr: 1.296e-03, eta: 9:19:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6077, top5_acc: 0.8348, loss_cls: 2.1729, loss: 2.1729 +2024-07-21 11:47:00,650 - pyskl - INFO - Epoch [140][500/3746] lr: 1.289e-03, eta: 9:18:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5900, top5_acc: 0.8313, loss_cls: 2.2327, loss: 2.2327 +2024-07-21 11:48:22,136 - pyskl - INFO - Epoch [140][600/3746] lr: 1.283e-03, eta: 9:17:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5933, top5_acc: 0.8305, loss_cls: 2.2118, loss: 2.2118 +2024-07-21 11:49:43,936 - pyskl - INFO - Epoch [140][700/3746] lr: 1.277e-03, eta: 9:15:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6003, top5_acc: 0.8328, loss_cls: 2.2095, loss: 2.2095 +2024-07-21 11:51:06,085 - pyskl - INFO - Epoch [140][800/3746] lr: 1.271e-03, eta: 9:14:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5978, top5_acc: 0.8292, loss_cls: 2.2121, loss: 2.2121 +2024-07-21 11:52:27,834 - pyskl - INFO - Epoch [140][900/3746] lr: 1.264e-03, eta: 9:13:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5919, top5_acc: 0.8223, loss_cls: 2.2274, loss: 2.2274 +2024-07-21 11:53:49,759 - pyskl - INFO - Epoch [140][1000/3746] lr: 1.258e-03, eta: 9:11:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5923, top5_acc: 0.8203, loss_cls: 2.2553, loss: 2.2553 +2024-07-21 11:55:11,612 - pyskl - INFO - Epoch [140][1100/3746] lr: 1.252e-03, eta: 9:10:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5887, top5_acc: 0.8239, loss_cls: 2.2489, loss: 2.2489 +2024-07-21 11:56:33,141 - pyskl - INFO - Epoch [140][1200/3746] lr: 1.246e-03, eta: 9:08:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6062, top5_acc: 0.8452, loss_cls: 2.1705, loss: 2.1705 +2024-07-21 11:57:55,058 - pyskl - INFO - Epoch [140][1300/3746] lr: 1.239e-03, eta: 9:07:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5908, top5_acc: 0.8231, loss_cls: 2.2599, loss: 2.2599 +2024-07-21 11:59:16,842 - pyskl - INFO - Epoch [140][1400/3746] lr: 1.233e-03, eta: 9:06:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5903, top5_acc: 0.8236, loss_cls: 2.2504, loss: 2.2504 +2024-07-21 12:00:39,110 - pyskl - INFO - Epoch [140][1500/3746] lr: 1.227e-03, eta: 9:04:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5889, top5_acc: 0.8233, loss_cls: 2.2621, loss: 2.2621 +2024-07-21 12:02:01,179 - pyskl - INFO - Epoch [140][1600/3746] lr: 1.221e-03, eta: 9:03:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5867, top5_acc: 0.8227, loss_cls: 2.2641, loss: 2.2641 +2024-07-21 12:03:23,650 - pyskl - INFO - Epoch [140][1700/3746] lr: 1.215e-03, eta: 9:02:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6023, top5_acc: 0.8253, loss_cls: 2.2009, loss: 2.2009 +2024-07-21 12:04:46,321 - pyskl - INFO - Epoch [140][1800/3746] lr: 1.209e-03, eta: 9:00:45, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5906, top5_acc: 0.8202, loss_cls: 2.2657, loss: 2.2657 +2024-07-21 12:06:08,312 - pyskl - INFO - Epoch [140][1900/3746] lr: 1.203e-03, eta: 8:59:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5908, top5_acc: 0.8220, loss_cls: 2.2608, loss: 2.2608 +2024-07-21 12:07:30,123 - pyskl - INFO - Epoch [140][2000/3746] lr: 1.196e-03, eta: 8:58:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5972, top5_acc: 0.8272, loss_cls: 2.2387, loss: 2.2387 +2024-07-21 12:08:52,184 - pyskl - INFO - Epoch [140][2100/3746] lr: 1.190e-03, eta: 8:56:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5848, top5_acc: 0.8269, loss_cls: 2.2759, loss: 2.2759 +2024-07-21 12:10:13,937 - pyskl - INFO - Epoch [140][2200/3746] lr: 1.184e-03, eta: 8:55:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5889, top5_acc: 0.8170, loss_cls: 2.2877, loss: 2.2877 +2024-07-21 12:11:35,780 - pyskl - INFO - Epoch [140][2300/3746] lr: 1.178e-03, eta: 8:53:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6019, top5_acc: 0.8356, loss_cls: 2.1948, loss: 2.1948 +2024-07-21 12:12:57,731 - pyskl - INFO - Epoch [140][2400/3746] lr: 1.172e-03, eta: 8:52:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5967, top5_acc: 0.8209, loss_cls: 2.2497, loss: 2.2497 +2024-07-21 12:14:19,424 - pyskl - INFO - Epoch [140][2500/3746] lr: 1.166e-03, eta: 8:51:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5856, top5_acc: 0.8272, loss_cls: 2.2656, loss: 2.2656 +2024-07-21 12:15:41,746 - pyskl - INFO - Epoch [140][2600/3746] lr: 1.160e-03, eta: 8:49:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6045, top5_acc: 0.8348, loss_cls: 2.1891, loss: 2.1891 +2024-07-21 12:17:04,124 - pyskl - INFO - Epoch [140][2700/3746] lr: 1.154e-03, eta: 8:48:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5869, top5_acc: 0.8222, loss_cls: 2.2538, loss: 2.2538 +2024-07-21 12:18:25,892 - pyskl - INFO - Epoch [140][2800/3746] lr: 1.148e-03, eta: 8:47:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5931, top5_acc: 0.8266, loss_cls: 2.2515, loss: 2.2515 +2024-07-21 12:19:48,226 - pyskl - INFO - Epoch [140][2900/3746] lr: 1.142e-03, eta: 8:45:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5839, top5_acc: 0.8295, loss_cls: 2.2617, loss: 2.2617 +2024-07-21 12:21:10,117 - pyskl - INFO - Epoch [140][3000/3746] lr: 1.136e-03, eta: 8:44:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5925, top5_acc: 0.8211, loss_cls: 2.2662, loss: 2.2662 +2024-07-21 12:22:32,593 - pyskl - INFO - Epoch [140][3100/3746] lr: 1.131e-03, eta: 8:42:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5925, top5_acc: 0.8297, loss_cls: 2.2352, loss: 2.2352 +2024-07-21 12:23:55,634 - pyskl - INFO - Epoch [140][3200/3746] lr: 1.125e-03, eta: 8:41:32, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5905, top5_acc: 0.8217, loss_cls: 2.2810, loss: 2.2810 +2024-07-21 12:25:17,033 - pyskl - INFO - Epoch [140][3300/3746] lr: 1.119e-03, eta: 8:40:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5847, top5_acc: 0.8247, loss_cls: 2.2707, loss: 2.2707 +2024-07-21 12:26:38,857 - pyskl - INFO - Epoch [140][3400/3746] lr: 1.113e-03, eta: 8:38:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5889, top5_acc: 0.8323, loss_cls: 2.2250, loss: 2.2250 +2024-07-21 12:28:00,707 - pyskl - INFO - Epoch [140][3500/3746] lr: 1.107e-03, eta: 8:37:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5923, top5_acc: 0.8239, loss_cls: 2.2436, loss: 2.2436 +2024-07-21 12:29:22,620 - pyskl - INFO - Epoch [140][3600/3746] lr: 1.101e-03, eta: 8:36:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5923, top5_acc: 0.8228, loss_cls: 2.2533, loss: 2.2533 +2024-07-21 12:30:44,130 - pyskl - INFO - Epoch [140][3700/3746] lr: 1.095e-03, eta: 8:34:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6008, top5_acc: 0.8292, loss_cls: 2.2183, loss: 2.2183 +2024-07-21 12:31:23,739 - pyskl - INFO - Saving checkpoint at 140 epochs +2024-07-21 12:33:15,366 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 12:33:16,035 - pyskl - INFO - +top1_acc 0.4587 +top5_acc 0.7091 +2024-07-21 12:33:16,035 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 12:33:16,077 - pyskl - INFO - +mean_acc 0.4584 +2024-07-21 12:33:16,082 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_139.pth was removed +2024-07-21 12:33:16,329 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_140.pth. +2024-07-21 12:33:16,330 - pyskl - INFO - Best top1_acc is 0.4587 at 140 epoch. +2024-07-21 12:33:16,343 - pyskl - INFO - Epoch(val) [140][309] top1_acc: 0.4587, top5_acc: 0.7091, mean_class_accuracy: 0.4584 +2024-07-21 12:37:06,113 - pyskl - INFO - Epoch [141][100/3746] lr: 1.087e-03, eta: 8:32:48, time: 2.298, data_time: 1.312, memory: 15990, top1_acc: 0.6138, top5_acc: 0.8445, loss_cls: 2.1138, loss: 2.1138 +2024-07-21 12:38:28,242 - pyskl - INFO - Epoch [141][200/3746] lr: 1.081e-03, eta: 8:31:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6220, top5_acc: 0.8375, loss_cls: 2.1449, loss: 2.1449 +2024-07-21 12:39:50,039 - pyskl - INFO - Epoch [141][300/3746] lr: 1.075e-03, eta: 8:30:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6106, top5_acc: 0.8405, loss_cls: 2.1541, loss: 2.1541 +2024-07-21 12:41:12,073 - pyskl - INFO - Epoch [141][400/3746] lr: 1.070e-03, eta: 8:28:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6069, top5_acc: 0.8395, loss_cls: 2.1425, loss: 2.1425 +2024-07-21 12:42:34,095 - pyskl - INFO - Epoch [141][500/3746] lr: 1.064e-03, eta: 8:27:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6198, top5_acc: 0.8405, loss_cls: 2.1369, loss: 2.1369 +2024-07-21 12:43:55,841 - pyskl - INFO - Epoch [141][600/3746] lr: 1.058e-03, eta: 8:25:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6050, top5_acc: 0.8328, loss_cls: 2.1727, loss: 2.1727 +2024-07-21 12:45:17,624 - pyskl - INFO - Epoch [141][700/3746] lr: 1.052e-03, eta: 8:24:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6172, top5_acc: 0.8398, loss_cls: 2.1423, loss: 2.1423 +2024-07-21 12:46:39,205 - pyskl - INFO - Epoch [141][800/3746] lr: 1.047e-03, eta: 8:23:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6080, top5_acc: 0.8286, loss_cls: 2.1785, loss: 2.1785 +2024-07-21 12:48:00,932 - pyskl - INFO - Epoch [141][900/3746] lr: 1.041e-03, eta: 8:21:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6066, top5_acc: 0.8420, loss_cls: 2.1475, loss: 2.1475 +2024-07-21 12:49:23,538 - pyskl - INFO - Epoch [141][1000/3746] lr: 1.035e-03, eta: 8:20:26, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5845, top5_acc: 0.8198, loss_cls: 2.2744, loss: 2.2744 +2024-07-21 12:50:45,407 - pyskl - INFO - Epoch [141][1100/3746] lr: 1.030e-03, eta: 8:19:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6020, top5_acc: 0.8397, loss_cls: 2.1789, loss: 2.1789 +2024-07-21 12:52:07,365 - pyskl - INFO - Epoch [141][1200/3746] lr: 1.024e-03, eta: 8:17:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6120, top5_acc: 0.8350, loss_cls: 2.1652, loss: 2.1652 +2024-07-21 12:53:29,453 - pyskl - INFO - Epoch [141][1300/3746] lr: 1.018e-03, eta: 8:16:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6117, top5_acc: 0.8386, loss_cls: 2.1681, loss: 2.1681 +2024-07-21 12:54:52,205 - pyskl - INFO - Epoch [141][1400/3746] lr: 1.013e-03, eta: 8:14:57, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5998, top5_acc: 0.8406, loss_cls: 2.1808, loss: 2.1808 +2024-07-21 12:56:14,751 - pyskl - INFO - Epoch [141][1500/3746] lr: 1.007e-03, eta: 8:13:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6133, top5_acc: 0.8373, loss_cls: 2.1519, loss: 2.1519 +2024-07-21 12:57:36,733 - pyskl - INFO - Epoch [141][1600/3746] lr: 1.002e-03, eta: 8:12:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6202, top5_acc: 0.8375, loss_cls: 2.1406, loss: 2.1406 +2024-07-21 12:58:59,642 - pyskl - INFO - Epoch [141][1700/3746] lr: 9.961e-04, eta: 8:10:50, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6036, top5_acc: 0.8375, loss_cls: 2.1923, loss: 2.1923 +2024-07-21 13:00:21,917 - pyskl - INFO - Epoch [141][1800/3746] lr: 9.905e-04, eta: 8:09:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5981, top5_acc: 0.8402, loss_cls: 2.1541, loss: 2.1541 +2024-07-21 13:01:44,168 - pyskl - INFO - Epoch [141][1900/3746] lr: 9.850e-04, eta: 8:08:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5955, top5_acc: 0.8325, loss_cls: 2.2067, loss: 2.2067 +2024-07-21 13:03:06,169 - pyskl - INFO - Epoch [141][2000/3746] lr: 9.795e-04, eta: 8:06:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5972, top5_acc: 0.8311, loss_cls: 2.2349, loss: 2.2349 +2024-07-21 13:04:28,352 - pyskl - INFO - Epoch [141][2100/3746] lr: 9.740e-04, eta: 8:05:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5983, top5_acc: 0.8352, loss_cls: 2.1909, loss: 2.1909 +2024-07-21 13:05:50,575 - pyskl - INFO - Epoch [141][2200/3746] lr: 9.685e-04, eta: 8:03:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6033, top5_acc: 0.8317, loss_cls: 2.1902, loss: 2.1902 +2024-07-21 13:07:12,538 - pyskl - INFO - Epoch [141][2300/3746] lr: 9.630e-04, eta: 8:02:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6111, top5_acc: 0.8337, loss_cls: 2.1847, loss: 2.1847 +2024-07-21 13:08:34,574 - pyskl - INFO - Epoch [141][2400/3746] lr: 9.576e-04, eta: 8:01:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6070, top5_acc: 0.8389, loss_cls: 2.1817, loss: 2.1817 +2024-07-21 13:09:56,578 - pyskl - INFO - Epoch [141][2500/3746] lr: 9.522e-04, eta: 7:59:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6067, top5_acc: 0.8358, loss_cls: 2.1735, loss: 2.1735 +2024-07-21 13:11:18,758 - pyskl - INFO - Epoch [141][2600/3746] lr: 9.467e-04, eta: 7:58:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6055, top5_acc: 0.8370, loss_cls: 2.1857, loss: 2.1857 +2024-07-21 13:12:40,815 - pyskl - INFO - Epoch [141][2700/3746] lr: 9.413e-04, eta: 7:57:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6050, top5_acc: 0.8231, loss_cls: 2.2014, loss: 2.2014 +2024-07-21 13:14:03,189 - pyskl - INFO - Epoch [141][2800/3746] lr: 9.359e-04, eta: 7:55:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5956, top5_acc: 0.8278, loss_cls: 2.2362, loss: 2.2362 +2024-07-21 13:15:25,477 - pyskl - INFO - Epoch [141][2900/3746] lr: 9.306e-04, eta: 7:54:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5995, top5_acc: 0.8305, loss_cls: 2.2218, loss: 2.2218 +2024-07-21 13:16:48,323 - pyskl - INFO - Epoch [141][3000/3746] lr: 9.252e-04, eta: 7:52:59, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6078, top5_acc: 0.8327, loss_cls: 2.1680, loss: 2.1680 +2024-07-21 13:18:10,757 - pyskl - INFO - Epoch [141][3100/3746] lr: 9.199e-04, eta: 7:51:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5984, top5_acc: 0.8272, loss_cls: 2.2231, loss: 2.2231 +2024-07-21 13:19:32,958 - pyskl - INFO - Epoch [141][3200/3746] lr: 9.145e-04, eta: 7:50:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6081, top5_acc: 0.8344, loss_cls: 2.1878, loss: 2.1878 +2024-07-21 13:20:55,191 - pyskl - INFO - Epoch [141][3300/3746] lr: 9.092e-04, eta: 7:48:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6195, top5_acc: 0.8402, loss_cls: 2.1314, loss: 2.1314 +2024-07-21 13:22:17,306 - pyskl - INFO - Epoch [141][3400/3746] lr: 9.039e-04, eta: 7:47:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6003, top5_acc: 0.8353, loss_cls: 2.1825, loss: 2.1825 +2024-07-21 13:23:39,386 - pyskl - INFO - Epoch [141][3500/3746] lr: 8.986e-04, eta: 7:46:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5923, top5_acc: 0.8269, loss_cls: 2.2359, loss: 2.2359 +2024-07-21 13:25:01,446 - pyskl - INFO - Epoch [141][3600/3746] lr: 8.934e-04, eta: 7:44:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5983, top5_acc: 0.8330, loss_cls: 2.2043, loss: 2.2043 +2024-07-21 13:26:22,963 - pyskl - INFO - Epoch [141][3700/3746] lr: 8.881e-04, eta: 7:43:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5952, top5_acc: 0.8319, loss_cls: 2.2040, loss: 2.2040 +2024-07-21 13:27:02,544 - pyskl - INFO - Saving checkpoint at 141 epochs +2024-07-21 13:28:54,772 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 13:28:55,437 - pyskl - INFO - +top1_acc 0.4609 +top5_acc 0.7106 +2024-07-21 13:28:55,437 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 13:28:55,478 - pyskl - INFO - +mean_acc 0.4606 +2024-07-21 13:28:55,484 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_140.pth was removed +2024-07-21 13:28:55,738 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_141.pth. +2024-07-21 13:28:55,739 - pyskl - INFO - Best top1_acc is 0.4609 at 141 epoch. +2024-07-21 13:28:55,751 - pyskl - INFO - Epoch(val) [141][309] top1_acc: 0.4609, top5_acc: 0.7106, mean_class_accuracy: 0.4606 +2024-07-21 13:32:43,619 - pyskl - INFO - Epoch [142][100/3746] lr: 8.805e-04, eta: 7:41:29, time: 2.279, data_time: 1.295, memory: 15990, top1_acc: 0.6305, top5_acc: 0.8533, loss_cls: 2.0671, loss: 2.0671 +2024-07-21 13:34:06,111 - pyskl - INFO - Epoch [142][200/3746] lr: 8.752e-04, eta: 7:40:07, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6214, top5_acc: 0.8428, loss_cls: 2.1277, loss: 2.1277 +2024-07-21 13:35:28,006 - pyskl - INFO - Epoch [142][300/3746] lr: 8.700e-04, eta: 7:38:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6177, top5_acc: 0.8377, loss_cls: 2.1275, loss: 2.1275 +2024-07-21 13:36:49,802 - pyskl - INFO - Epoch [142][400/3746] lr: 8.649e-04, eta: 7:37:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6311, top5_acc: 0.8484, loss_cls: 2.0882, loss: 2.0882 +2024-07-21 13:38:11,545 - pyskl - INFO - Epoch [142][500/3746] lr: 8.597e-04, eta: 7:36:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6194, top5_acc: 0.8389, loss_cls: 2.1326, loss: 2.1326 +2024-07-21 13:39:33,243 - pyskl - INFO - Epoch [142][600/3746] lr: 8.545e-04, eta: 7:34:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6136, top5_acc: 0.8397, loss_cls: 2.1524, loss: 2.1524 +2024-07-21 13:40:54,973 - pyskl - INFO - Epoch [142][700/3746] lr: 8.494e-04, eta: 7:33:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6144, top5_acc: 0.8400, loss_cls: 2.1242, loss: 2.1242 +2024-07-21 13:42:16,550 - pyskl - INFO - Epoch [142][800/3746] lr: 8.443e-04, eta: 7:31:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6102, top5_acc: 0.8369, loss_cls: 2.1342, loss: 2.1342 +2024-07-21 13:43:38,440 - pyskl - INFO - Epoch [142][900/3746] lr: 8.392e-04, eta: 7:30:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6272, top5_acc: 0.8483, loss_cls: 2.0930, loss: 2.0930 +2024-07-21 13:45:00,104 - pyskl - INFO - Epoch [142][1000/3746] lr: 8.341e-04, eta: 7:29:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6105, top5_acc: 0.8406, loss_cls: 2.1454, loss: 2.1454 +2024-07-21 13:46:21,825 - pyskl - INFO - Epoch [142][1100/3746] lr: 8.290e-04, eta: 7:27:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6211, top5_acc: 0.8425, loss_cls: 2.1043, loss: 2.1043 +2024-07-21 13:47:43,216 - pyskl - INFO - Epoch [142][1200/3746] lr: 8.239e-04, eta: 7:26:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6153, top5_acc: 0.8381, loss_cls: 2.1345, loss: 2.1345 +2024-07-21 13:49:04,739 - pyskl - INFO - Epoch [142][1300/3746] lr: 8.189e-04, eta: 7:25:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6238, top5_acc: 0.8470, loss_cls: 2.0843, loss: 2.0843 +2024-07-21 13:50:26,093 - pyskl - INFO - Epoch [142][1400/3746] lr: 8.139e-04, eta: 7:23:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6258, top5_acc: 0.8452, loss_cls: 2.0899, loss: 2.0899 +2024-07-21 13:51:48,657 - pyskl - INFO - Epoch [142][1500/3746] lr: 8.088e-04, eta: 7:22:15, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6077, top5_acc: 0.8400, loss_cls: 2.1534, loss: 2.1534 +2024-07-21 13:53:10,447 - pyskl - INFO - Epoch [142][1600/3746] lr: 8.038e-04, eta: 7:20:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6062, top5_acc: 0.8366, loss_cls: 2.1651, loss: 2.1651 +2024-07-21 13:54:33,187 - pyskl - INFO - Epoch [142][1700/3746] lr: 7.989e-04, eta: 7:19:31, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6067, top5_acc: 0.8342, loss_cls: 2.1649, loss: 2.1649 +2024-07-21 13:55:55,631 - pyskl - INFO - Epoch [142][1800/3746] lr: 7.939e-04, eta: 7:18:08, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6205, top5_acc: 0.8420, loss_cls: 2.1169, loss: 2.1169 +2024-07-21 13:57:17,958 - pyskl - INFO - Epoch [142][1900/3746] lr: 7.889e-04, eta: 7:16:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6334, top5_acc: 0.8511, loss_cls: 2.0413, loss: 2.0413 +2024-07-21 13:58:39,699 - pyskl - INFO - Epoch [142][2000/3746] lr: 7.840e-04, eta: 7:15:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6245, top5_acc: 0.8500, loss_cls: 2.0810, loss: 2.0810 +2024-07-21 14:00:01,798 - pyskl - INFO - Epoch [142][2100/3746] lr: 7.791e-04, eta: 7:14:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6119, top5_acc: 0.8356, loss_cls: 2.1380, loss: 2.1380 +2024-07-21 14:01:23,783 - pyskl - INFO - Epoch [142][2200/3746] lr: 7.742e-04, eta: 7:12:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6083, top5_acc: 0.8323, loss_cls: 2.1712, loss: 2.1712 +2024-07-21 14:02:45,329 - pyskl - INFO - Epoch [142][2300/3746] lr: 7.693e-04, eta: 7:11:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6077, top5_acc: 0.8363, loss_cls: 2.1525, loss: 2.1525 +2024-07-21 14:04:07,209 - pyskl - INFO - Epoch [142][2400/3746] lr: 7.644e-04, eta: 7:09:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6111, top5_acc: 0.8337, loss_cls: 2.1496, loss: 2.1496 +2024-07-21 14:05:29,535 - pyskl - INFO - Epoch [142][2500/3746] lr: 7.595e-04, eta: 7:08:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6161, top5_acc: 0.8372, loss_cls: 2.1463, loss: 2.1463 +2024-07-21 14:06:51,921 - pyskl - INFO - Epoch [142][2600/3746] lr: 7.547e-04, eta: 7:07:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6159, top5_acc: 0.8409, loss_cls: 2.1294, loss: 2.1294 +2024-07-21 14:08:13,602 - pyskl - INFO - Epoch [142][2700/3746] lr: 7.499e-04, eta: 7:05:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6112, top5_acc: 0.8389, loss_cls: 2.1476, loss: 2.1476 +2024-07-21 14:09:35,839 - pyskl - INFO - Epoch [142][2800/3746] lr: 7.450e-04, eta: 7:04:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6262, top5_acc: 0.8527, loss_cls: 2.0719, loss: 2.0719 +2024-07-21 14:10:58,038 - pyskl - INFO - Epoch [142][2900/3746] lr: 7.402e-04, eta: 7:03:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6066, top5_acc: 0.8358, loss_cls: 2.1542, loss: 2.1542 +2024-07-21 14:12:19,980 - pyskl - INFO - Epoch [142][3000/3746] lr: 7.355e-04, eta: 7:01:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6105, top5_acc: 0.8450, loss_cls: 2.1205, loss: 2.1205 +2024-07-21 14:13:41,872 - pyskl - INFO - Epoch [142][3100/3746] lr: 7.307e-04, eta: 7:00:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6155, top5_acc: 0.8386, loss_cls: 2.1411, loss: 2.1411 +2024-07-21 14:15:03,944 - pyskl - INFO - Epoch [142][3200/3746] lr: 7.259e-04, eta: 6:58:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6042, top5_acc: 0.8434, loss_cls: 2.1472, loss: 2.1472 +2024-07-21 14:16:26,337 - pyskl - INFO - Epoch [142][3300/3746] lr: 7.212e-04, eta: 6:57:32, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6039, top5_acc: 0.8377, loss_cls: 2.1779, loss: 2.1779 +2024-07-21 14:17:48,343 - pyskl - INFO - Epoch [142][3400/3746] lr: 7.165e-04, eta: 6:56:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6234, top5_acc: 0.8450, loss_cls: 2.1144, loss: 2.1144 +2024-07-21 14:19:10,339 - pyskl - INFO - Epoch [142][3500/3746] lr: 7.118e-04, eta: 6:54:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6148, top5_acc: 0.8411, loss_cls: 2.1210, loss: 2.1210 +2024-07-21 14:20:32,340 - pyskl - INFO - Epoch [142][3600/3746] lr: 7.071e-04, eta: 6:53:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6134, top5_acc: 0.8438, loss_cls: 2.1377, loss: 2.1377 +2024-07-21 14:21:54,147 - pyskl - INFO - Epoch [142][3700/3746] lr: 7.024e-04, eta: 6:52:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6150, top5_acc: 0.8381, loss_cls: 2.1252, loss: 2.1252 +2024-07-21 14:22:34,035 - pyskl - INFO - Saving checkpoint at 142 epochs +2024-07-21 14:24:25,968 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 14:24:26,643 - pyskl - INFO - +top1_acc 0.4611 +top5_acc 0.7111 +2024-07-21 14:24:26,643 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 14:24:26,687 - pyskl - INFO - +mean_acc 0.4608 +2024-07-21 14:24:26,691 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_141.pth was removed +2024-07-21 14:24:26,965 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_142.pth. +2024-07-21 14:24:26,966 - pyskl - INFO - Best top1_acc is 0.4611 at 142 epoch. +2024-07-21 14:24:26,978 - pyskl - INFO - Epoch(val) [142][309] top1_acc: 0.4611, top5_acc: 0.7111, mean_class_accuracy: 0.4608 +2024-07-21 14:28:18,088 - pyskl - INFO - Epoch [143][100/3746] lr: 6.956e-04, eta: 6:50:09, time: 2.311, data_time: 1.324, memory: 15990, top1_acc: 0.6372, top5_acc: 0.8569, loss_cls: 2.0160, loss: 2.0160 +2024-07-21 14:29:40,447 - pyskl - INFO - Epoch [143][200/3746] lr: 6.910e-04, eta: 6:48:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6253, top5_acc: 0.8527, loss_cls: 2.0531, loss: 2.0531 +2024-07-21 14:31:02,582 - pyskl - INFO - Epoch [143][300/3746] lr: 6.863e-04, eta: 6:47:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6247, top5_acc: 0.8477, loss_cls: 2.0569, loss: 2.0569 +2024-07-21 14:32:24,365 - pyskl - INFO - Epoch [143][400/3746] lr: 6.817e-04, eta: 6:46:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6338, top5_acc: 0.8491, loss_cls: 2.0528, loss: 2.0528 +2024-07-21 14:33:45,746 - pyskl - INFO - Epoch [143][500/3746] lr: 6.771e-04, eta: 6:44:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6281, top5_acc: 0.8514, loss_cls: 2.0796, loss: 2.0796 +2024-07-21 14:35:07,201 - pyskl - INFO - Epoch [143][600/3746] lr: 6.725e-04, eta: 6:43:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6433, top5_acc: 0.8486, loss_cls: 2.0407, loss: 2.0407 +2024-07-21 14:36:29,064 - pyskl - INFO - Epoch [143][700/3746] lr: 6.680e-04, eta: 6:41:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6220, top5_acc: 0.8552, loss_cls: 2.0408, loss: 2.0408 +2024-07-21 14:37:50,670 - pyskl - INFO - Epoch [143][800/3746] lr: 6.634e-04, eta: 6:40:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6283, top5_acc: 0.8522, loss_cls: 2.0466, loss: 2.0466 +2024-07-21 14:39:12,461 - pyskl - INFO - Epoch [143][900/3746] lr: 6.589e-04, eta: 6:39:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6298, top5_acc: 0.8530, loss_cls: 2.0582, loss: 2.0582 +2024-07-21 14:40:34,447 - pyskl - INFO - Epoch [143][1000/3746] lr: 6.544e-04, eta: 6:37:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6389, top5_acc: 0.8630, loss_cls: 2.0083, loss: 2.0083 +2024-07-21 14:41:56,251 - pyskl - INFO - Epoch [143][1100/3746] lr: 6.499e-04, eta: 6:36:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6255, top5_acc: 0.8448, loss_cls: 2.0775, loss: 2.0775 +2024-07-21 14:43:17,933 - pyskl - INFO - Epoch [143][1200/3746] lr: 6.454e-04, eta: 6:35:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6277, top5_acc: 0.8456, loss_cls: 2.0742, loss: 2.0742 +2024-07-21 14:44:39,615 - pyskl - INFO - Epoch [143][1300/3746] lr: 6.409e-04, eta: 6:33:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6236, top5_acc: 0.8500, loss_cls: 2.0704, loss: 2.0704 +2024-07-21 14:46:01,355 - pyskl - INFO - Epoch [143][1400/3746] lr: 6.365e-04, eta: 6:32:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6267, top5_acc: 0.8500, loss_cls: 2.0873, loss: 2.0873 +2024-07-21 14:47:23,450 - pyskl - INFO - Epoch [143][1500/3746] lr: 6.320e-04, eta: 6:30:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6269, top5_acc: 0.8477, loss_cls: 2.0699, loss: 2.0699 +2024-07-21 14:48:45,600 - pyskl - INFO - Epoch [143][1600/3746] lr: 6.276e-04, eta: 6:29:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6259, top5_acc: 0.8494, loss_cls: 2.0704, loss: 2.0704 +2024-07-21 14:50:08,134 - pyskl - INFO - Epoch [143][1700/3746] lr: 6.232e-04, eta: 6:28:10, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6294, top5_acc: 0.8488, loss_cls: 2.0527, loss: 2.0527 +2024-07-21 14:51:30,335 - pyskl - INFO - Epoch [143][1800/3746] lr: 6.188e-04, eta: 6:26:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6289, top5_acc: 0.8491, loss_cls: 2.0684, loss: 2.0684 +2024-07-21 14:52:52,233 - pyskl - INFO - Epoch [143][1900/3746] lr: 6.144e-04, eta: 6:25:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6155, top5_acc: 0.8420, loss_cls: 2.0877, loss: 2.0877 +2024-07-21 14:54:14,472 - pyskl - INFO - Epoch [143][2000/3746] lr: 6.101e-04, eta: 6:24:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6327, top5_acc: 0.8534, loss_cls: 2.0423, loss: 2.0423 +2024-07-21 14:55:37,041 - pyskl - INFO - Epoch [143][2100/3746] lr: 6.057e-04, eta: 6:22:40, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6295, top5_acc: 0.8477, loss_cls: 2.0681, loss: 2.0681 +2024-07-21 14:56:59,427 - pyskl - INFO - Epoch [143][2200/3746] lr: 6.014e-04, eta: 6:21:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6252, top5_acc: 0.8456, loss_cls: 2.0888, loss: 2.0888 +2024-07-21 14:58:21,570 - pyskl - INFO - Epoch [143][2300/3746] lr: 5.971e-04, eta: 6:19:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6214, top5_acc: 0.8450, loss_cls: 2.0991, loss: 2.0991 +2024-07-21 14:59:43,383 - pyskl - INFO - Epoch [143][2400/3746] lr: 5.928e-04, eta: 6:18:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6241, top5_acc: 0.8536, loss_cls: 2.0734, loss: 2.0734 +2024-07-21 15:01:05,868 - pyskl - INFO - Epoch [143][2500/3746] lr: 5.885e-04, eta: 6:17:11, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6198, top5_acc: 0.8422, loss_cls: 2.0975, loss: 2.0975 +2024-07-21 15:02:28,398 - pyskl - INFO - Epoch [143][2600/3746] lr: 5.842e-04, eta: 6:15:48, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6194, top5_acc: 0.8470, loss_cls: 2.1063, loss: 2.1063 +2024-07-21 15:03:50,144 - pyskl - INFO - Epoch [143][2700/3746] lr: 5.800e-04, eta: 6:14:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6211, top5_acc: 0.8444, loss_cls: 2.1078, loss: 2.1078 +2024-07-21 15:05:12,785 - pyskl - INFO - Epoch [143][2800/3746] lr: 5.757e-04, eta: 6:13:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6205, top5_acc: 0.8413, loss_cls: 2.1215, loss: 2.1215 +2024-07-21 15:06:34,823 - pyskl - INFO - Epoch [143][2900/3746] lr: 5.715e-04, eta: 6:11:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6269, top5_acc: 0.8534, loss_cls: 2.0629, loss: 2.0629 +2024-07-21 15:07:56,779 - pyskl - INFO - Epoch [143][3000/3746] lr: 5.673e-04, eta: 6:10:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6286, top5_acc: 0.8542, loss_cls: 2.0301, loss: 2.0301 +2024-07-21 15:09:18,516 - pyskl - INFO - Epoch [143][3100/3746] lr: 5.631e-04, eta: 6:08:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6297, top5_acc: 0.8448, loss_cls: 2.0784, loss: 2.0784 +2024-07-21 15:10:39,959 - pyskl - INFO - Epoch [143][3200/3746] lr: 5.590e-04, eta: 6:07:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6208, top5_acc: 0.8420, loss_cls: 2.0940, loss: 2.0940 +2024-07-21 15:12:01,873 - pyskl - INFO - Epoch [143][3300/3746] lr: 5.548e-04, eta: 6:06:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6294, top5_acc: 0.8542, loss_cls: 2.0391, loss: 2.0391 +2024-07-21 15:13:24,046 - pyskl - INFO - Epoch [143][3400/3746] lr: 5.506e-04, eta: 6:04:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6173, top5_acc: 0.8427, loss_cls: 2.1240, loss: 2.1240 +2024-07-21 15:14:45,598 - pyskl - INFO - Epoch [143][3500/3746] lr: 5.465e-04, eta: 6:03:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6267, top5_acc: 0.8422, loss_cls: 2.1042, loss: 2.1042 +2024-07-21 15:16:08,046 - pyskl - INFO - Epoch [143][3600/3746] lr: 5.424e-04, eta: 6:02:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6234, top5_acc: 0.8456, loss_cls: 2.0986, loss: 2.0986 +2024-07-21 15:17:30,106 - pyskl - INFO - Epoch [143][3700/3746] lr: 5.383e-04, eta: 6:00:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6269, top5_acc: 0.8508, loss_cls: 2.0483, loss: 2.0483 +2024-07-21 15:18:10,245 - pyskl - INFO - Saving checkpoint at 143 epochs +2024-07-21 15:20:02,876 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 15:20:03,540 - pyskl - INFO - +top1_acc 0.4615 +top5_acc 0.7134 +2024-07-21 15:20:03,540 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 15:20:03,582 - pyskl - INFO - +mean_acc 0.4613 +2024-07-21 15:20:03,586 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_142.pth was removed +2024-07-21 15:20:03,858 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_143.pth. +2024-07-21 15:20:03,858 - pyskl - INFO - Best top1_acc is 0.4615 at 143 epoch. +2024-07-21 15:20:03,871 - pyskl - INFO - Epoch(val) [143][309] top1_acc: 0.4615, top5_acc: 0.7134, mean_class_accuracy: 0.4613 +2024-07-21 15:23:51,160 - pyskl - INFO - Epoch [144][100/3746] lr: 5.323e-04, eta: 5:58:47, time: 2.273, data_time: 1.284, memory: 15990, top1_acc: 0.6562, top5_acc: 0.8647, loss_cls: 1.9508, loss: 1.9508 +2024-07-21 15:25:12,949 - pyskl - INFO - Epoch [144][200/3746] lr: 5.283e-04, eta: 5:57:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6394, top5_acc: 0.8633, loss_cls: 1.9834, loss: 1.9834 +2024-07-21 15:26:35,306 - pyskl - INFO - Epoch [144][300/3746] lr: 5.242e-04, eta: 5:56:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6477, top5_acc: 0.8612, loss_cls: 1.9878, loss: 1.9878 +2024-07-21 15:27:57,364 - pyskl - INFO - Epoch [144][400/3746] lr: 5.202e-04, eta: 5:54:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6395, top5_acc: 0.8608, loss_cls: 2.0002, loss: 2.0002 +2024-07-21 15:29:19,258 - pyskl - INFO - Epoch [144][500/3746] lr: 5.162e-04, eta: 5:53:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6470, top5_acc: 0.8584, loss_cls: 1.9952, loss: 1.9952 +2024-07-21 15:30:41,022 - pyskl - INFO - Epoch [144][600/3746] lr: 5.122e-04, eta: 5:51:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6498, top5_acc: 0.8578, loss_cls: 1.9903, loss: 1.9903 +2024-07-21 15:32:03,025 - pyskl - INFO - Epoch [144][700/3746] lr: 5.082e-04, eta: 5:50:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6569, top5_acc: 0.8636, loss_cls: 1.9370, loss: 1.9370 +2024-07-21 15:33:24,713 - pyskl - INFO - Epoch [144][800/3746] lr: 5.042e-04, eta: 5:49:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6384, top5_acc: 0.8573, loss_cls: 2.0114, loss: 2.0114 +2024-07-21 15:34:46,546 - pyskl - INFO - Epoch [144][900/3746] lr: 5.003e-04, eta: 5:47:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6503, top5_acc: 0.8611, loss_cls: 1.9814, loss: 1.9814 +2024-07-21 15:36:08,257 - pyskl - INFO - Epoch [144][1000/3746] lr: 4.964e-04, eta: 5:46:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6402, top5_acc: 0.8583, loss_cls: 2.0044, loss: 2.0044 +2024-07-21 15:37:30,509 - pyskl - INFO - Epoch [144][1100/3746] lr: 4.924e-04, eta: 5:45:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6336, top5_acc: 0.8525, loss_cls: 2.0358, loss: 2.0358 +2024-07-21 15:38:51,943 - pyskl - INFO - Epoch [144][1200/3746] lr: 4.885e-04, eta: 5:43:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6261, top5_acc: 0.8527, loss_cls: 2.0548, loss: 2.0548 +2024-07-21 15:40:13,590 - pyskl - INFO - Epoch [144][1300/3746] lr: 4.846e-04, eta: 5:42:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6383, top5_acc: 0.8545, loss_cls: 2.0231, loss: 2.0231 +2024-07-21 15:41:35,768 - pyskl - INFO - Epoch [144][1400/3746] lr: 4.808e-04, eta: 5:40:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6470, top5_acc: 0.8650, loss_cls: 1.9609, loss: 1.9609 +2024-07-21 15:42:57,537 - pyskl - INFO - Epoch [144][1500/3746] lr: 4.769e-04, eta: 5:39:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6383, top5_acc: 0.8602, loss_cls: 2.0185, loss: 2.0185 +2024-07-21 15:44:19,340 - pyskl - INFO - Epoch [144][1600/3746] lr: 4.731e-04, eta: 5:38:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6470, top5_acc: 0.8539, loss_cls: 1.9923, loss: 1.9923 +2024-07-21 15:45:41,768 - pyskl - INFO - Epoch [144][1700/3746] lr: 4.692e-04, eta: 5:36:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6361, top5_acc: 0.8555, loss_cls: 2.0331, loss: 2.0331 +2024-07-21 15:47:04,205 - pyskl - INFO - Epoch [144][1800/3746] lr: 4.654e-04, eta: 5:35:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6386, top5_acc: 0.8539, loss_cls: 2.0247, loss: 2.0247 +2024-07-21 15:48:25,998 - pyskl - INFO - Epoch [144][1900/3746] lr: 4.616e-04, eta: 5:34:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6406, top5_acc: 0.8541, loss_cls: 2.0302, loss: 2.0302 +2024-07-21 15:49:47,959 - pyskl - INFO - Epoch [144][2000/3746] lr: 4.578e-04, eta: 5:32:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6298, top5_acc: 0.8592, loss_cls: 2.0217, loss: 2.0217 +2024-07-21 15:51:09,595 - pyskl - INFO - Epoch [144][2100/3746] lr: 4.541e-04, eta: 5:31:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6338, top5_acc: 0.8514, loss_cls: 2.0436, loss: 2.0436 +2024-07-21 15:52:31,818 - pyskl - INFO - Epoch [144][2200/3746] lr: 4.503e-04, eta: 5:29:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6403, top5_acc: 0.8573, loss_cls: 2.0036, loss: 2.0036 +2024-07-21 15:53:53,745 - pyskl - INFO - Epoch [144][2300/3746] lr: 4.466e-04, eta: 5:28:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6364, top5_acc: 0.8589, loss_cls: 2.0212, loss: 2.0212 +2024-07-21 15:55:15,737 - pyskl - INFO - Epoch [144][2400/3746] lr: 4.429e-04, eta: 5:27:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6320, top5_acc: 0.8522, loss_cls: 2.0478, loss: 2.0478 +2024-07-21 15:56:37,954 - pyskl - INFO - Epoch [144][2500/3746] lr: 4.392e-04, eta: 5:25:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6386, top5_acc: 0.8572, loss_cls: 2.0184, loss: 2.0184 +2024-07-21 15:57:59,983 - pyskl - INFO - Epoch [144][2600/3746] lr: 4.355e-04, eta: 5:24:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6370, top5_acc: 0.8575, loss_cls: 2.0272, loss: 2.0272 +2024-07-21 15:59:22,425 - pyskl - INFO - Epoch [144][2700/3746] lr: 4.318e-04, eta: 5:23:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6408, top5_acc: 0.8488, loss_cls: 2.0070, loss: 2.0070 +2024-07-21 16:00:44,885 - pyskl - INFO - Epoch [144][2800/3746] lr: 4.281e-04, eta: 5:21:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6422, top5_acc: 0.8595, loss_cls: 2.0190, loss: 2.0190 +2024-07-21 16:02:07,382 - pyskl - INFO - Epoch [144][2900/3746] lr: 4.245e-04, eta: 5:20:19, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6370, top5_acc: 0.8527, loss_cls: 2.0294, loss: 2.0294 +2024-07-21 16:03:29,114 - pyskl - INFO - Epoch [144][3000/3746] lr: 4.209e-04, eta: 5:18:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6309, top5_acc: 0.8475, loss_cls: 2.0652, loss: 2.0652 +2024-07-21 16:04:50,728 - pyskl - INFO - Epoch [144][3100/3746] lr: 4.173e-04, eta: 5:17:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6312, top5_acc: 0.8519, loss_cls: 2.0531, loss: 2.0531 +2024-07-21 16:06:12,808 - pyskl - INFO - Epoch [144][3200/3746] lr: 4.137e-04, eta: 5:16:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6369, top5_acc: 0.8534, loss_cls: 2.0150, loss: 2.0150 +2024-07-21 16:07:34,582 - pyskl - INFO - Epoch [144][3300/3746] lr: 4.101e-04, eta: 5:14:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6377, top5_acc: 0.8547, loss_cls: 2.0295, loss: 2.0295 +2024-07-21 16:08:56,432 - pyskl - INFO - Epoch [144][3400/3746] lr: 4.065e-04, eta: 5:13:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6348, top5_acc: 0.8588, loss_cls: 2.0098, loss: 2.0098 +2024-07-21 16:10:17,955 - pyskl - INFO - Epoch [144][3500/3746] lr: 4.030e-04, eta: 5:12:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6317, top5_acc: 0.8503, loss_cls: 2.0560, loss: 2.0560 +2024-07-21 16:11:39,645 - pyskl - INFO - Epoch [144][3600/3746] lr: 3.994e-04, eta: 5:10:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6333, top5_acc: 0.8522, loss_cls: 2.0467, loss: 2.0467 +2024-07-21 16:13:01,469 - pyskl - INFO - Epoch [144][3700/3746] lr: 3.959e-04, eta: 5:09:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6220, top5_acc: 0.8512, loss_cls: 2.0516, loss: 2.0516 +2024-07-21 16:13:40,746 - pyskl - INFO - Saving checkpoint at 144 epochs +2024-07-21 16:15:30,683 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 16:15:31,349 - pyskl - INFO - +top1_acc 0.4665 +top5_acc 0.7157 +2024-07-21 16:15:31,349 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 16:15:31,391 - pyskl - INFO - +mean_acc 0.4663 +2024-07-21 16:15:31,396 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_143.pth was removed +2024-07-21 16:15:31,638 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_144.pth. +2024-07-21 16:15:31,638 - pyskl - INFO - Best top1_acc is 0.4665 at 144 epoch. +2024-07-21 16:15:31,651 - pyskl - INFO - Epoch(val) [144][309] top1_acc: 0.4665, top5_acc: 0.7157, mean_class_accuracy: 0.4663 +2024-07-21 16:19:22,300 - pyskl - INFO - Epoch [145][100/3746] lr: 3.908e-04, eta: 5:07:24, time: 2.306, data_time: 1.318, memory: 15990, top1_acc: 0.6444, top5_acc: 0.8608, loss_cls: 2.0046, loss: 2.0046 +2024-07-21 16:20:44,476 - pyskl - INFO - Epoch [145][200/3746] lr: 3.873e-04, eta: 5:06:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6469, top5_acc: 0.8617, loss_cls: 1.9741, loss: 1.9741 +2024-07-21 16:22:06,462 - pyskl - INFO - Epoch [145][300/3746] lr: 3.839e-04, eta: 5:04:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6495, top5_acc: 0.8638, loss_cls: 1.9688, loss: 1.9688 +2024-07-21 16:23:28,444 - pyskl - INFO - Epoch [145][400/3746] lr: 3.804e-04, eta: 5:03:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6561, top5_acc: 0.8603, loss_cls: 1.9512, loss: 1.9512 +2024-07-21 16:24:50,656 - pyskl - INFO - Epoch [145][500/3746] lr: 3.770e-04, eta: 5:01:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6411, top5_acc: 0.8600, loss_cls: 1.9690, loss: 1.9690 +2024-07-21 16:26:13,597 - pyskl - INFO - Epoch [145][600/3746] lr: 3.736e-04, eta: 5:00:31, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6489, top5_acc: 0.8650, loss_cls: 1.9692, loss: 1.9692 +2024-07-21 16:27:36,346 - pyskl - INFO - Epoch [145][700/3746] lr: 3.702e-04, eta: 4:59:09, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6530, top5_acc: 0.8708, loss_cls: 1.9239, loss: 1.9239 +2024-07-21 16:28:57,967 - pyskl - INFO - Epoch [145][800/3746] lr: 3.668e-04, eta: 4:57:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6544, top5_acc: 0.8669, loss_cls: 1.9719, loss: 1.9719 +2024-07-21 16:30:20,142 - pyskl - INFO - Epoch [145][900/3746] lr: 3.634e-04, eta: 4:56:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6581, top5_acc: 0.8709, loss_cls: 1.9487, loss: 1.9487 +2024-07-21 16:31:42,076 - pyskl - INFO - Epoch [145][1000/3746] lr: 3.600e-04, eta: 4:55:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6539, top5_acc: 0.8580, loss_cls: 1.9576, loss: 1.9576 +2024-07-21 16:33:03,942 - pyskl - INFO - Epoch [145][1100/3746] lr: 3.567e-04, eta: 4:53:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6522, top5_acc: 0.8611, loss_cls: 1.9474, loss: 1.9474 +2024-07-21 16:34:25,796 - pyskl - INFO - Epoch [145][1200/3746] lr: 3.534e-04, eta: 4:52:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6514, top5_acc: 0.8564, loss_cls: 1.9756, loss: 1.9756 +2024-07-21 16:35:47,426 - pyskl - INFO - Epoch [145][1300/3746] lr: 3.501e-04, eta: 4:50:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6495, top5_acc: 0.8647, loss_cls: 1.9584, loss: 1.9584 +2024-07-21 16:37:08,957 - pyskl - INFO - Epoch [145][1400/3746] lr: 3.468e-04, eta: 4:49:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6531, top5_acc: 0.8612, loss_cls: 1.9479, loss: 1.9479 +2024-07-21 16:38:30,837 - pyskl - INFO - Epoch [145][1500/3746] lr: 3.435e-04, eta: 4:48:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6502, top5_acc: 0.8641, loss_cls: 1.9696, loss: 1.9696 +2024-07-21 16:39:52,851 - pyskl - INFO - Epoch [145][1600/3746] lr: 3.402e-04, eta: 4:46:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6472, top5_acc: 0.8617, loss_cls: 1.9805, loss: 1.9805 +2024-07-21 16:41:15,005 - pyskl - INFO - Epoch [145][1700/3746] lr: 3.370e-04, eta: 4:45:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6481, top5_acc: 0.8623, loss_cls: 1.9792, loss: 1.9792 +2024-07-21 16:42:37,884 - pyskl - INFO - Epoch [145][1800/3746] lr: 3.337e-04, eta: 4:44:02, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6462, top5_acc: 0.8598, loss_cls: 1.9595, loss: 1.9595 +2024-07-21 16:43:59,698 - pyskl - INFO - Epoch [145][1900/3746] lr: 3.305e-04, eta: 4:42:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6566, top5_acc: 0.8675, loss_cls: 1.9398, loss: 1.9398 +2024-07-21 16:45:21,681 - pyskl - INFO - Epoch [145][2000/3746] lr: 3.273e-04, eta: 4:41:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6533, top5_acc: 0.8675, loss_cls: 1.9498, loss: 1.9498 +2024-07-21 16:46:43,514 - pyskl - INFO - Epoch [145][2100/3746] lr: 3.241e-04, eta: 4:39:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6442, top5_acc: 0.8648, loss_cls: 1.9719, loss: 1.9719 +2024-07-21 16:48:05,925 - pyskl - INFO - Epoch [145][2200/3746] lr: 3.210e-04, eta: 4:38:32, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6555, top5_acc: 0.8667, loss_cls: 1.9307, loss: 1.9307 +2024-07-21 16:49:27,802 - pyskl - INFO - Epoch [145][2300/3746] lr: 3.178e-04, eta: 4:37:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6527, top5_acc: 0.8614, loss_cls: 1.9617, loss: 1.9617 +2024-07-21 16:50:49,661 - pyskl - INFO - Epoch [145][2400/3746] lr: 3.147e-04, eta: 4:35:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6488, top5_acc: 0.8684, loss_cls: 1.9545, loss: 1.9545 +2024-07-21 16:52:12,266 - pyskl - INFO - Epoch [145][2500/3746] lr: 3.116e-04, eta: 4:34:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6386, top5_acc: 0.8542, loss_cls: 2.0016, loss: 2.0016 +2024-07-21 16:53:33,897 - pyskl - INFO - Epoch [145][2600/3746] lr: 3.084e-04, eta: 4:33:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6452, top5_acc: 0.8641, loss_cls: 1.9535, loss: 1.9535 +2024-07-21 16:54:56,218 - pyskl - INFO - Epoch [145][2700/3746] lr: 3.054e-04, eta: 4:31:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6552, top5_acc: 0.8669, loss_cls: 1.9531, loss: 1.9531 +2024-07-21 16:56:19,120 - pyskl - INFO - Epoch [145][2800/3746] lr: 3.023e-04, eta: 4:30:18, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6481, top5_acc: 0.8525, loss_cls: 2.0068, loss: 2.0068 +2024-07-21 16:57:41,356 - pyskl - INFO - Epoch [145][2900/3746] lr: 2.992e-04, eta: 4:28:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6542, top5_acc: 0.8575, loss_cls: 1.9732, loss: 1.9732 +2024-07-21 16:59:03,796 - pyskl - INFO - Epoch [145][3000/3746] lr: 2.962e-04, eta: 4:27:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6495, top5_acc: 0.8609, loss_cls: 1.9572, loss: 1.9572 +2024-07-21 17:00:26,030 - pyskl - INFO - Epoch [145][3100/3746] lr: 2.931e-04, eta: 4:26:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6459, top5_acc: 0.8656, loss_cls: 1.9831, loss: 1.9831 +2024-07-21 17:01:47,510 - pyskl - INFO - Epoch [145][3200/3746] lr: 2.901e-04, eta: 4:24:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6512, top5_acc: 0.8634, loss_cls: 1.9707, loss: 1.9707 +2024-07-21 17:03:09,332 - pyskl - INFO - Epoch [145][3300/3746] lr: 2.871e-04, eta: 4:23:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6423, top5_acc: 0.8531, loss_cls: 2.0048, loss: 2.0048 +2024-07-21 17:04:31,039 - pyskl - INFO - Epoch [145][3400/3746] lr: 2.841e-04, eta: 4:22:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6447, top5_acc: 0.8569, loss_cls: 1.9802, loss: 1.9802 +2024-07-21 17:05:52,426 - pyskl - INFO - Epoch [145][3500/3746] lr: 2.812e-04, eta: 4:20:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6475, top5_acc: 0.8616, loss_cls: 1.9579, loss: 1.9579 +2024-07-21 17:07:14,049 - pyskl - INFO - Epoch [145][3600/3746] lr: 2.782e-04, eta: 4:19:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6388, top5_acc: 0.8595, loss_cls: 1.9835, loss: 1.9835 +2024-07-21 17:08:35,778 - pyskl - INFO - Epoch [145][3700/3746] lr: 2.753e-04, eta: 4:17:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6609, top5_acc: 0.8656, loss_cls: 1.9284, loss: 1.9284 +2024-07-21 17:09:15,409 - pyskl - INFO - Saving checkpoint at 145 epochs +2024-07-21 17:11:05,174 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 17:11:05,850 - pyskl - INFO - +top1_acc 0.4667 +top5_acc 0.7137 +2024-07-21 17:11:05,850 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 17:11:05,895 - pyskl - INFO - +mean_acc 0.4665 +2024-07-21 17:11:05,900 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_144.pth was removed +2024-07-21 17:11:06,170 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_145.pth. +2024-07-21 17:11:06,171 - pyskl - INFO - Best top1_acc is 0.4667 at 145 epoch. +2024-07-21 17:11:06,184 - pyskl - INFO - Epoch(val) [145][309] top1_acc: 0.4667, top5_acc: 0.7137, mean_class_accuracy: 0.4665 +2024-07-21 17:14:54,748 - pyskl - INFO - Epoch [146][100/3746] lr: 2.710e-04, eta: 4:15:59, time: 2.286, data_time: 1.289, memory: 15990, top1_acc: 0.6622, top5_acc: 0.8691, loss_cls: 1.9146, loss: 1.9146 +2024-07-21 17:16:16,428 - pyskl - INFO - Epoch [146][200/3746] lr: 2.681e-04, eta: 4:14:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6705, top5_acc: 0.8695, loss_cls: 1.8772, loss: 1.8772 +2024-07-21 17:17:38,201 - pyskl - INFO - Epoch [146][300/3746] lr: 2.652e-04, eta: 4:13:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6691, top5_acc: 0.8723, loss_cls: 1.8849, loss: 1.8849 +2024-07-21 17:18:59,387 - pyskl - INFO - Epoch [146][400/3746] lr: 2.624e-04, eta: 4:11:52, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6564, top5_acc: 0.8636, loss_cls: 1.9327, loss: 1.9327 +2024-07-21 17:20:21,234 - pyskl - INFO - Epoch [146][500/3746] lr: 2.595e-04, eta: 4:10:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6514, top5_acc: 0.8659, loss_cls: 1.9621, loss: 1.9621 +2024-07-21 17:21:43,001 - pyskl - INFO - Epoch [146][600/3746] lr: 2.567e-04, eta: 4:09:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6516, top5_acc: 0.8616, loss_cls: 1.9500, loss: 1.9500 +2024-07-21 17:23:04,750 - pyskl - INFO - Epoch [146][700/3746] lr: 2.539e-04, eta: 4:07:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6619, top5_acc: 0.8703, loss_cls: 1.8979, loss: 1.8979 +2024-07-21 17:24:26,100 - pyskl - INFO - Epoch [146][800/3746] lr: 2.511e-04, eta: 4:06:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6731, top5_acc: 0.8719, loss_cls: 1.8833, loss: 1.8833 +2024-07-21 17:25:47,833 - pyskl - INFO - Epoch [146][900/3746] lr: 2.483e-04, eta: 4:04:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6595, top5_acc: 0.8692, loss_cls: 1.9171, loss: 1.9171 +2024-07-21 17:27:09,422 - pyskl - INFO - Epoch [146][1000/3746] lr: 2.455e-04, eta: 4:03:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6611, top5_acc: 0.8719, loss_cls: 1.8916, loss: 1.8916 +2024-07-21 17:28:31,548 - pyskl - INFO - Epoch [146][1100/3746] lr: 2.427e-04, eta: 4:02:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6641, top5_acc: 0.8733, loss_cls: 1.9122, loss: 1.9122 +2024-07-21 17:29:53,447 - pyskl - INFO - Epoch [146][1200/3746] lr: 2.400e-04, eta: 4:00:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6598, top5_acc: 0.8716, loss_cls: 1.8967, loss: 1.8967 +2024-07-21 17:31:14,819 - pyskl - INFO - Epoch [146][1300/3746] lr: 2.373e-04, eta: 3:59:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6508, top5_acc: 0.8653, loss_cls: 1.9452, loss: 1.9452 +2024-07-21 17:32:36,718 - pyskl - INFO - Epoch [146][1400/3746] lr: 2.345e-04, eta: 3:58:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6687, top5_acc: 0.8778, loss_cls: 1.8786, loss: 1.8786 +2024-07-21 17:33:58,663 - pyskl - INFO - Epoch [146][1500/3746] lr: 2.318e-04, eta: 3:56:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6594, top5_acc: 0.8727, loss_cls: 1.9183, loss: 1.9183 +2024-07-21 17:35:20,827 - pyskl - INFO - Epoch [146][1600/3746] lr: 2.292e-04, eta: 3:55:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6633, top5_acc: 0.8702, loss_cls: 1.9176, loss: 1.9176 +2024-07-21 17:36:43,074 - pyskl - INFO - Epoch [146][1700/3746] lr: 2.265e-04, eta: 3:54:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6498, top5_acc: 0.8612, loss_cls: 1.9639, loss: 1.9639 +2024-07-21 17:38:05,510 - pyskl - INFO - Epoch [146][1800/3746] lr: 2.239e-04, eta: 3:52:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6608, top5_acc: 0.8691, loss_cls: 1.8962, loss: 1.8962 +2024-07-21 17:39:27,784 - pyskl - INFO - Epoch [146][1900/3746] lr: 2.212e-04, eta: 3:51:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6589, top5_acc: 0.8697, loss_cls: 1.8934, loss: 1.8934 +2024-07-21 17:40:49,895 - pyskl - INFO - Epoch [146][2000/3746] lr: 2.186e-04, eta: 3:49:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6650, top5_acc: 0.8711, loss_cls: 1.8871, loss: 1.8871 +2024-07-21 17:42:12,273 - pyskl - INFO - Epoch [146][2100/3746] lr: 2.160e-04, eta: 3:48:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6441, top5_acc: 0.8631, loss_cls: 1.9782, loss: 1.9782 +2024-07-21 17:43:34,408 - pyskl - INFO - Epoch [146][2200/3746] lr: 2.134e-04, eta: 3:47:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6511, top5_acc: 0.8617, loss_cls: 1.9567, loss: 1.9567 +2024-07-21 17:44:56,295 - pyskl - INFO - Epoch [146][2300/3746] lr: 2.108e-04, eta: 3:45:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6495, top5_acc: 0.8645, loss_cls: 1.9507, loss: 1.9507 +2024-07-21 17:46:18,456 - pyskl - INFO - Epoch [146][2400/3746] lr: 2.083e-04, eta: 3:44:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6481, top5_acc: 0.8692, loss_cls: 1.9304, loss: 1.9304 +2024-07-21 17:47:40,472 - pyskl - INFO - Epoch [146][2500/3746] lr: 2.057e-04, eta: 3:43:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6594, top5_acc: 0.8648, loss_cls: 1.9122, loss: 1.9122 +2024-07-21 17:49:02,703 - pyskl - INFO - Epoch [146][2600/3746] lr: 2.032e-04, eta: 3:41:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6502, top5_acc: 0.8642, loss_cls: 1.9685, loss: 1.9685 +2024-07-21 17:50:25,589 - pyskl - INFO - Epoch [146][2700/3746] lr: 2.007e-04, eta: 3:40:15, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6567, top5_acc: 0.8616, loss_cls: 1.9169, loss: 1.9169 +2024-07-21 17:51:48,066 - pyskl - INFO - Epoch [146][2800/3746] lr: 1.982e-04, eta: 3:38:53, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6469, top5_acc: 0.8605, loss_cls: 1.9751, loss: 1.9751 +2024-07-21 17:53:10,521 - pyskl - INFO - Epoch [146][2900/3746] lr: 1.957e-04, eta: 3:37:30, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6519, top5_acc: 0.8667, loss_cls: 1.9394, loss: 1.9394 +2024-07-21 17:54:32,943 - pyskl - INFO - Epoch [146][3000/3746] lr: 1.933e-04, eta: 3:36:08, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6566, top5_acc: 0.8730, loss_cls: 1.9155, loss: 1.9155 +2024-07-21 17:55:54,614 - pyskl - INFO - Epoch [146][3100/3746] lr: 1.908e-04, eta: 3:34:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6556, top5_acc: 0.8655, loss_cls: 1.9427, loss: 1.9427 +2024-07-21 17:57:16,278 - pyskl - INFO - Epoch [146][3200/3746] lr: 1.884e-04, eta: 3:33:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6505, top5_acc: 0.8594, loss_cls: 1.9747, loss: 1.9747 +2024-07-21 17:58:37,766 - pyskl - INFO - Epoch [146][3300/3746] lr: 1.860e-04, eta: 3:32:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6592, top5_acc: 0.8698, loss_cls: 1.9041, loss: 1.9041 +2024-07-21 17:59:59,630 - pyskl - INFO - Epoch [146][3400/3746] lr: 1.836e-04, eta: 3:30:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6559, top5_acc: 0.8700, loss_cls: 1.9473, loss: 1.9473 +2024-07-21 18:01:21,188 - pyskl - INFO - Epoch [146][3500/3746] lr: 1.812e-04, eta: 3:29:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6666, top5_acc: 0.8656, loss_cls: 1.8990, loss: 1.8990 +2024-07-21 18:02:43,150 - pyskl - INFO - Epoch [146][3600/3746] lr: 1.788e-04, eta: 3:27:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6520, top5_acc: 0.8667, loss_cls: 1.9342, loss: 1.9342 +2024-07-21 18:04:04,961 - pyskl - INFO - Epoch [146][3700/3746] lr: 1.765e-04, eta: 3:26:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6561, top5_acc: 0.8625, loss_cls: 1.9169, loss: 1.9169 +2024-07-21 18:04:44,806 - pyskl - INFO - Saving checkpoint at 146 epochs +2024-07-21 18:06:34,190 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 18:06:34,850 - pyskl - INFO - +top1_acc 0.4687 +top5_acc 0.7154 +2024-07-21 18:06:34,850 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 18:06:34,892 - pyskl - INFO - +mean_acc 0.4684 +2024-07-21 18:06:34,896 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_145.pth was removed +2024-07-21 18:06:35,152 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_146.pth. +2024-07-21 18:06:35,153 - pyskl - INFO - Best top1_acc is 0.4687 at 146 epoch. +2024-07-21 18:06:35,166 - pyskl - INFO - Epoch(val) [146][309] top1_acc: 0.4687, top5_acc: 0.7154, mean_class_accuracy: 0.4684 +2024-07-21 18:10:22,259 - pyskl - INFO - Epoch [147][100/3746] lr: 1.730e-04, eta: 3:24:33, time: 2.271, data_time: 1.285, memory: 15990, top1_acc: 0.6669, top5_acc: 0.8741, loss_cls: 1.8759, loss: 1.8759 +2024-07-21 18:11:44,195 - pyskl - INFO - Epoch [147][200/3746] lr: 1.707e-04, eta: 3:23:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6656, top5_acc: 0.8734, loss_cls: 1.8910, loss: 1.8910 +2024-07-21 18:13:06,136 - pyskl - INFO - Epoch [147][300/3746] lr: 1.684e-04, eta: 3:21:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6619, top5_acc: 0.8733, loss_cls: 1.8906, loss: 1.8906 +2024-07-21 18:14:27,981 - pyskl - INFO - Epoch [147][400/3746] lr: 1.661e-04, eta: 3:20:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6761, top5_acc: 0.8747, loss_cls: 1.8534, loss: 1.8534 +2024-07-21 18:15:49,663 - pyskl - INFO - Epoch [147][500/3746] lr: 1.639e-04, eta: 3:19:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6655, top5_acc: 0.8748, loss_cls: 1.8953, loss: 1.8953 +2024-07-21 18:17:11,554 - pyskl - INFO - Epoch [147][600/3746] lr: 1.616e-04, eta: 3:17:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6677, top5_acc: 0.8700, loss_cls: 1.8776, loss: 1.8776 +2024-07-21 18:18:32,942 - pyskl - INFO - Epoch [147][700/3746] lr: 1.594e-04, eta: 3:16:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6625, top5_acc: 0.8702, loss_cls: 1.8830, loss: 1.8830 +2024-07-21 18:19:54,879 - pyskl - INFO - Epoch [147][800/3746] lr: 1.572e-04, eta: 3:14:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6620, top5_acc: 0.8634, loss_cls: 1.9227, loss: 1.9227 +2024-07-21 18:21:16,801 - pyskl - INFO - Epoch [147][900/3746] lr: 1.550e-04, eta: 3:13:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6648, top5_acc: 0.8730, loss_cls: 1.8849, loss: 1.8849 +2024-07-21 18:22:39,197 - pyskl - INFO - Epoch [147][1000/3746] lr: 1.528e-04, eta: 3:12:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6634, top5_acc: 0.8778, loss_cls: 1.8778, loss: 1.8778 +2024-07-21 18:24:00,585 - pyskl - INFO - Epoch [147][1100/3746] lr: 1.506e-04, eta: 3:10:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6658, top5_acc: 0.8733, loss_cls: 1.8855, loss: 1.8855 +2024-07-21 18:25:22,315 - pyskl - INFO - Epoch [147][1200/3746] lr: 1.484e-04, eta: 3:09:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6708, top5_acc: 0.8756, loss_cls: 1.8700, loss: 1.8700 +2024-07-21 18:26:44,491 - pyskl - INFO - Epoch [147][1300/3746] lr: 1.463e-04, eta: 3:08:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6600, top5_acc: 0.8725, loss_cls: 1.8984, loss: 1.8984 +2024-07-21 18:28:06,873 - pyskl - INFO - Epoch [147][1400/3746] lr: 1.442e-04, eta: 3:06:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6678, top5_acc: 0.8777, loss_cls: 1.8694, loss: 1.8694 +2024-07-21 18:29:28,604 - pyskl - INFO - Epoch [147][1500/3746] lr: 1.420e-04, eta: 3:05:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6628, top5_acc: 0.8688, loss_cls: 1.9142, loss: 1.9142 +2024-07-21 18:30:51,122 - pyskl - INFO - Epoch [147][1600/3746] lr: 1.399e-04, eta: 3:03:56, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6672, top5_acc: 0.8733, loss_cls: 1.8786, loss: 1.8786 +2024-07-21 18:32:12,905 - pyskl - INFO - Epoch [147][1700/3746] lr: 1.379e-04, eta: 3:02:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6713, top5_acc: 0.8717, loss_cls: 1.8769, loss: 1.8769 +2024-07-21 18:33:34,713 - pyskl - INFO - Epoch [147][1800/3746] lr: 1.358e-04, eta: 3:01:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6670, top5_acc: 0.8781, loss_cls: 1.8627, loss: 1.8627 +2024-07-21 18:34:57,114 - pyskl - INFO - Epoch [147][1900/3746] lr: 1.337e-04, eta: 2:59:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6552, top5_acc: 0.8680, loss_cls: 1.9361, loss: 1.9361 +2024-07-21 18:36:19,176 - pyskl - INFO - Epoch [147][2000/3746] lr: 1.317e-04, eta: 2:58:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6606, top5_acc: 0.8695, loss_cls: 1.9096, loss: 1.9096 +2024-07-21 18:37:41,652 - pyskl - INFO - Epoch [147][2100/3746] lr: 1.297e-04, eta: 2:57:04, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6694, top5_acc: 0.8766, loss_cls: 1.8850, loss: 1.8850 +2024-07-21 18:39:04,084 - pyskl - INFO - Epoch [147][2200/3746] lr: 1.277e-04, eta: 2:55:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6680, top5_acc: 0.8775, loss_cls: 1.8751, loss: 1.8751 +2024-07-21 18:40:26,599 - pyskl - INFO - Epoch [147][2300/3746] lr: 1.257e-04, eta: 2:54:19, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6670, top5_acc: 0.8702, loss_cls: 1.9108, loss: 1.9108 +2024-07-21 18:41:48,871 - pyskl - INFO - Epoch [147][2400/3746] lr: 1.237e-04, eta: 2:52:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6672, top5_acc: 0.8753, loss_cls: 1.8713, loss: 1.8713 +2024-07-21 18:43:10,509 - pyskl - INFO - Epoch [147][2500/3746] lr: 1.218e-04, eta: 2:51:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6538, top5_acc: 0.8697, loss_cls: 1.9097, loss: 1.9097 +2024-07-21 18:44:32,825 - pyskl - INFO - Epoch [147][2600/3746] lr: 1.198e-04, eta: 2:50:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6695, top5_acc: 0.8745, loss_cls: 1.8802, loss: 1.8802 +2024-07-21 18:45:55,203 - pyskl - INFO - Epoch [147][2700/3746] lr: 1.179e-04, eta: 2:48:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6711, top5_acc: 0.8791, loss_cls: 1.8461, loss: 1.8461 +2024-07-21 18:47:17,883 - pyskl - INFO - Epoch [147][2800/3746] lr: 1.160e-04, eta: 2:47:26, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6663, top5_acc: 0.8777, loss_cls: 1.8963, loss: 1.8963 +2024-07-21 18:48:40,510 - pyskl - INFO - Epoch [147][2900/3746] lr: 1.141e-04, eta: 2:46:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6637, top5_acc: 0.8767, loss_cls: 1.8831, loss: 1.8831 +2024-07-21 18:50:02,425 - pyskl - INFO - Epoch [147][3000/3746] lr: 1.122e-04, eta: 2:44:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6642, top5_acc: 0.8791, loss_cls: 1.8820, loss: 1.8820 +2024-07-21 18:51:24,040 - pyskl - INFO - Epoch [147][3100/3746] lr: 1.103e-04, eta: 2:43:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6719, top5_acc: 0.8761, loss_cls: 1.8601, loss: 1.8601 +2024-07-21 18:52:45,920 - pyskl - INFO - Epoch [147][3200/3746] lr: 1.085e-04, eta: 2:41:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6628, top5_acc: 0.8725, loss_cls: 1.8736, loss: 1.8736 +2024-07-21 18:54:07,871 - pyskl - INFO - Epoch [147][3300/3746] lr: 1.067e-04, eta: 2:40:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6744, top5_acc: 0.8814, loss_cls: 1.8487, loss: 1.8487 +2024-07-21 18:55:29,589 - pyskl - INFO - Epoch [147][3400/3746] lr: 1.048e-04, eta: 2:39:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6653, top5_acc: 0.8745, loss_cls: 1.8821, loss: 1.8821 +2024-07-21 18:56:51,543 - pyskl - INFO - Epoch [147][3500/3746] lr: 1.030e-04, eta: 2:37:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6655, top5_acc: 0.8678, loss_cls: 1.8902, loss: 1.8902 +2024-07-21 18:58:13,486 - pyskl - INFO - Epoch [147][3600/3746] lr: 1.013e-04, eta: 2:36:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6770, top5_acc: 0.8769, loss_cls: 1.8397, loss: 1.8397 +2024-07-21 18:59:34,982 - pyskl - INFO - Epoch [147][3700/3746] lr: 9.949e-05, eta: 2:35:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6589, top5_acc: 0.8759, loss_cls: 1.8711, loss: 1.8711 +2024-07-21 19:00:14,721 - pyskl - INFO - Saving checkpoint at 147 epochs +2024-07-21 19:02:07,355 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 19:02:08,013 - pyskl - INFO - +top1_acc 0.4684 +top5_acc 0.7168 +2024-07-21 19:02:08,013 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 19:02:08,055 - pyskl - INFO - +mean_acc 0.4681 +2024-07-21 19:02:08,067 - pyskl - INFO - Epoch(val) [147][309] top1_acc: 0.4684, top5_acc: 0.7168, mean_class_accuracy: 0.4681 +2024-07-21 19:05:55,510 - pyskl - INFO - Epoch [148][100/3746] lr: 9.693e-05, eta: 2:33:06, time: 2.274, data_time: 1.291, memory: 15990, top1_acc: 0.6709, top5_acc: 0.8761, loss_cls: 1.8672, loss: 1.8672 +2024-07-21 19:07:17,136 - pyskl - INFO - Epoch [148][200/3746] lr: 9.520e-05, eta: 2:31:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6689, top5_acc: 0.8739, loss_cls: 1.8614, loss: 1.8614 +2024-07-21 19:08:38,808 - pyskl - INFO - Epoch [148][300/3746] lr: 9.348e-05, eta: 2:30:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6831, top5_acc: 0.8788, loss_cls: 1.8307, loss: 1.8307 +2024-07-21 19:10:00,832 - pyskl - INFO - Epoch [148][400/3746] lr: 9.178e-05, eta: 2:28:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6808, top5_acc: 0.8808, loss_cls: 1.8493, loss: 1.8493 +2024-07-21 19:11:22,742 - pyskl - INFO - Epoch [148][500/3746] lr: 9.010e-05, eta: 2:27:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6664, top5_acc: 0.8781, loss_cls: 1.8694, loss: 1.8694 +2024-07-21 19:12:44,667 - pyskl - INFO - Epoch [148][600/3746] lr: 8.843e-05, eta: 2:26:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6666, top5_acc: 0.8703, loss_cls: 1.8795, loss: 1.8795 +2024-07-21 19:14:06,596 - pyskl - INFO - Epoch [148][700/3746] lr: 8.678e-05, eta: 2:24:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6678, top5_acc: 0.8741, loss_cls: 1.8961, loss: 1.8961 +2024-07-21 19:15:28,415 - pyskl - INFO - Epoch [148][800/3746] lr: 8.514e-05, eta: 2:23:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6875, top5_acc: 0.8825, loss_cls: 1.7953, loss: 1.7953 +2024-07-21 19:16:50,693 - pyskl - INFO - Epoch [148][900/3746] lr: 8.351e-05, eta: 2:22:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6842, top5_acc: 0.8894, loss_cls: 1.7916, loss: 1.7916 +2024-07-21 19:18:12,396 - pyskl - INFO - Epoch [148][1000/3746] lr: 8.191e-05, eta: 2:20:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6753, top5_acc: 0.8770, loss_cls: 1.8360, loss: 1.8360 +2024-07-21 19:19:34,363 - pyskl - INFO - Epoch [148][1100/3746] lr: 8.031e-05, eta: 2:19:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6759, top5_acc: 0.8777, loss_cls: 1.8345, loss: 1.8345 +2024-07-21 19:20:56,041 - pyskl - INFO - Epoch [148][1200/3746] lr: 7.874e-05, eta: 2:17:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6727, top5_acc: 0.8728, loss_cls: 1.8591, loss: 1.8591 +2024-07-21 19:22:17,697 - pyskl - INFO - Epoch [148][1300/3746] lr: 7.718e-05, eta: 2:16:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6741, top5_acc: 0.8722, loss_cls: 1.8814, loss: 1.8814 +2024-07-21 19:23:40,127 - pyskl - INFO - Epoch [148][1400/3746] lr: 7.563e-05, eta: 2:15:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6644, top5_acc: 0.8719, loss_cls: 1.8866, loss: 1.8866 +2024-07-21 19:25:02,292 - pyskl - INFO - Epoch [148][1500/3746] lr: 7.410e-05, eta: 2:13:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6741, top5_acc: 0.8798, loss_cls: 1.8531, loss: 1.8531 +2024-07-21 19:26:24,181 - pyskl - INFO - Epoch [148][1600/3746] lr: 7.259e-05, eta: 2:12:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6772, top5_acc: 0.8817, loss_cls: 1.8492, loss: 1.8492 +2024-07-21 19:27:46,721 - pyskl - INFO - Epoch [148][1700/3746] lr: 7.109e-05, eta: 2:11:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6727, top5_acc: 0.8739, loss_cls: 1.8510, loss: 1.8510 +2024-07-21 19:29:08,642 - pyskl - INFO - Epoch [148][1800/3746] lr: 6.961e-05, eta: 2:09:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6730, top5_acc: 0.8820, loss_cls: 1.8368, loss: 1.8368 +2024-07-21 19:30:31,004 - pyskl - INFO - Epoch [148][1900/3746] lr: 6.814e-05, eta: 2:08:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6711, top5_acc: 0.8767, loss_cls: 1.8424, loss: 1.8424 +2024-07-21 19:31:53,418 - pyskl - INFO - Epoch [148][2000/3746] lr: 6.669e-05, eta: 2:06:59, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6866, top5_acc: 0.8828, loss_cls: 1.8115, loss: 1.8115 +2024-07-21 19:33:15,491 - pyskl - INFO - Epoch [148][2100/3746] lr: 6.526e-05, eta: 2:05:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6648, top5_acc: 0.8706, loss_cls: 1.8950, loss: 1.8950 +2024-07-21 19:34:37,966 - pyskl - INFO - Epoch [148][2200/3746] lr: 6.384e-05, eta: 2:04:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6695, top5_acc: 0.8759, loss_cls: 1.8730, loss: 1.8730 +2024-07-21 19:35:59,819 - pyskl - INFO - Epoch [148][2300/3746] lr: 6.243e-05, eta: 2:02:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6748, top5_acc: 0.8752, loss_cls: 1.8543, loss: 1.8543 +2024-07-21 19:37:21,624 - pyskl - INFO - Epoch [148][2400/3746] lr: 6.104e-05, eta: 2:01:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6737, top5_acc: 0.8753, loss_cls: 1.8535, loss: 1.8535 +2024-07-21 19:38:43,537 - pyskl - INFO - Epoch [148][2500/3746] lr: 5.967e-05, eta: 2:00:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6559, top5_acc: 0.8700, loss_cls: 1.9149, loss: 1.9149 +2024-07-21 19:40:06,005 - pyskl - INFO - Epoch [148][2600/3746] lr: 5.831e-05, eta: 1:58:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6702, top5_acc: 0.8834, loss_cls: 1.8446, loss: 1.8446 +2024-07-21 19:41:27,654 - pyskl - INFO - Epoch [148][2700/3746] lr: 5.697e-05, eta: 1:57:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6622, top5_acc: 0.8745, loss_cls: 1.8699, loss: 1.8699 +2024-07-21 19:42:49,838 - pyskl - INFO - Epoch [148][2800/3746] lr: 5.564e-05, eta: 1:55:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6709, top5_acc: 0.8802, loss_cls: 1.8382, loss: 1.8382 +2024-07-21 19:44:12,265 - pyskl - INFO - Epoch [148][2900/3746] lr: 5.433e-05, eta: 1:54:36, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6713, top5_acc: 0.8767, loss_cls: 1.8433, loss: 1.8433 +2024-07-21 19:45:34,668 - pyskl - INFO - Epoch [148][3000/3746] lr: 5.304e-05, eta: 1:53:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6709, top5_acc: 0.8733, loss_cls: 1.8790, loss: 1.8790 +2024-07-21 19:46:56,489 - pyskl - INFO - Epoch [148][3100/3746] lr: 5.176e-05, eta: 1:51:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6683, top5_acc: 0.8738, loss_cls: 1.8657, loss: 1.8657 +2024-07-21 19:48:18,305 - pyskl - INFO - Epoch [148][3200/3746] lr: 5.050e-05, eta: 1:50:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6689, top5_acc: 0.8781, loss_cls: 1.8766, loss: 1.8766 +2024-07-21 19:49:39,786 - pyskl - INFO - Epoch [148][3300/3746] lr: 4.925e-05, eta: 1:49:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6777, top5_acc: 0.8825, loss_cls: 1.8220, loss: 1.8220 +2024-07-21 19:51:01,814 - pyskl - INFO - Epoch [148][3400/3746] lr: 4.801e-05, eta: 1:47:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6783, top5_acc: 0.8736, loss_cls: 1.8580, loss: 1.8580 +2024-07-21 19:52:24,708 - pyskl - INFO - Epoch [148][3500/3746] lr: 4.680e-05, eta: 1:46:21, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6653, top5_acc: 0.8662, loss_cls: 1.8927, loss: 1.8927 +2024-07-21 19:53:46,685 - pyskl - INFO - Epoch [148][3600/3746] lr: 4.560e-05, eta: 1:44:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6687, top5_acc: 0.8719, loss_cls: 1.8756, loss: 1.8756 +2024-07-21 19:55:08,163 - pyskl - INFO - Epoch [148][3700/3746] lr: 4.441e-05, eta: 1:43:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6667, top5_acc: 0.8662, loss_cls: 1.8840, loss: 1.8840 +2024-07-21 19:55:48,333 - pyskl - INFO - Saving checkpoint at 148 epochs +2024-07-21 19:57:40,083 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 19:57:40,837 - pyskl - INFO - +top1_acc 0.4683 +top5_acc 0.7153 +2024-07-21 19:57:40,838 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 19:57:40,880 - pyskl - INFO - +mean_acc 0.4680 +2024-07-21 19:57:40,892 - pyskl - INFO - Epoch(val) [148][309] top1_acc: 0.4683, top5_acc: 0.7153, mean_class_accuracy: 0.4680 +2024-07-21 20:01:27,537 - pyskl - INFO - Epoch [149][100/3746] lr: 4.271e-05, eta: 1:41:37, time: 2.266, data_time: 1.273, memory: 15990, top1_acc: 0.6639, top5_acc: 0.8705, loss_cls: 1.8847, loss: 1.8847 +2024-07-21 20:02:49,932 - pyskl - INFO - Epoch [149][200/3746] lr: 4.156e-05, eta: 1:40:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6839, top5_acc: 0.8828, loss_cls: 1.8175, loss: 1.8175 +2024-07-21 20:04:11,883 - pyskl - INFO - Epoch [149][300/3746] lr: 4.043e-05, eta: 1:38:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6844, top5_acc: 0.8772, loss_cls: 1.8336, loss: 1.8336 +2024-07-21 20:05:33,698 - pyskl - INFO - Epoch [149][400/3746] lr: 3.931e-05, eta: 1:37:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6827, top5_acc: 0.8869, loss_cls: 1.7980, loss: 1.7980 +2024-07-21 20:06:55,657 - pyskl - INFO - Epoch [149][500/3746] lr: 3.821e-05, eta: 1:36:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6673, top5_acc: 0.8719, loss_cls: 1.8790, loss: 1.8790 +2024-07-21 20:08:17,368 - pyskl - INFO - Epoch [149][600/3746] lr: 3.713e-05, eta: 1:34:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6747, top5_acc: 0.8722, loss_cls: 1.8521, loss: 1.8521 +2024-07-21 20:09:39,659 - pyskl - INFO - Epoch [149][700/3746] lr: 3.606e-05, eta: 1:33:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6755, top5_acc: 0.8820, loss_cls: 1.8253, loss: 1.8253 +2024-07-21 20:11:01,148 - pyskl - INFO - Epoch [149][800/3746] lr: 3.500e-05, eta: 1:32:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6744, top5_acc: 0.8780, loss_cls: 1.8523, loss: 1.8523 +2024-07-21 20:12:22,917 - pyskl - INFO - Epoch [149][900/3746] lr: 3.397e-05, eta: 1:30:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6836, top5_acc: 0.8822, loss_cls: 1.8041, loss: 1.8041 +2024-07-21 20:13:44,750 - pyskl - INFO - Epoch [149][1000/3746] lr: 3.294e-05, eta: 1:29:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6770, top5_acc: 0.8836, loss_cls: 1.8215, loss: 1.8215 +2024-07-21 20:15:06,337 - pyskl - INFO - Epoch [149][1100/3746] lr: 3.194e-05, eta: 1:27:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6669, top5_acc: 0.8730, loss_cls: 1.8750, loss: 1.8750 +2024-07-21 20:16:27,824 - pyskl - INFO - Epoch [149][1200/3746] lr: 3.095e-05, eta: 1:26:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6806, top5_acc: 0.8803, loss_cls: 1.8353, loss: 1.8353 +2024-07-21 20:17:50,120 - pyskl - INFO - Epoch [149][1300/3746] lr: 2.997e-05, eta: 1:25:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6752, top5_acc: 0.8756, loss_cls: 1.8465, loss: 1.8465 +2024-07-21 20:19:12,779 - pyskl - INFO - Epoch [149][1400/3746] lr: 2.901e-05, eta: 1:23:45, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6828, top5_acc: 0.8850, loss_cls: 1.8090, loss: 1.8090 +2024-07-21 20:20:34,750 - pyskl - INFO - Epoch [149][1500/3746] lr: 2.807e-05, eta: 1:22:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6858, top5_acc: 0.8798, loss_cls: 1.8135, loss: 1.8135 +2024-07-21 20:21:56,432 - pyskl - INFO - Epoch [149][1600/3746] lr: 2.714e-05, eta: 1:21:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6769, top5_acc: 0.8825, loss_cls: 1.8368, loss: 1.8368 +2024-07-21 20:23:18,385 - pyskl - INFO - Epoch [149][1700/3746] lr: 2.622e-05, eta: 1:19:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6761, top5_acc: 0.8762, loss_cls: 1.8321, loss: 1.8321 +2024-07-21 20:24:40,536 - pyskl - INFO - Epoch [149][1800/3746] lr: 2.533e-05, eta: 1:18:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6736, top5_acc: 0.8761, loss_cls: 1.8410, loss: 1.8410 +2024-07-21 20:26:02,883 - pyskl - INFO - Epoch [149][1900/3746] lr: 2.444e-05, eta: 1:16:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6786, top5_acc: 0.8823, loss_cls: 1.8431, loss: 1.8431 +2024-07-21 20:27:25,508 - pyskl - INFO - Epoch [149][2000/3746] lr: 2.358e-05, eta: 1:15:30, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6739, top5_acc: 0.8839, loss_cls: 1.8311, loss: 1.8311 +2024-07-21 20:28:47,607 - pyskl - INFO - Epoch [149][2100/3746] lr: 2.273e-05, eta: 1:14:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6747, top5_acc: 0.8820, loss_cls: 1.8137, loss: 1.8137 +2024-07-21 20:30:09,807 - pyskl - INFO - Epoch [149][2200/3746] lr: 2.189e-05, eta: 1:12:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6831, top5_acc: 0.8784, loss_cls: 1.8281, loss: 1.8281 +2024-07-21 20:31:32,155 - pyskl - INFO - Epoch [149][2300/3746] lr: 2.107e-05, eta: 1:11:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6716, top5_acc: 0.8772, loss_cls: 1.8489, loss: 1.8489 +2024-07-21 20:32:54,341 - pyskl - INFO - Epoch [149][2400/3746] lr: 2.027e-05, eta: 1:10:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6783, top5_acc: 0.8778, loss_cls: 1.8399, loss: 1.8399 +2024-07-21 20:34:16,107 - pyskl - INFO - Epoch [149][2500/3746] lr: 1.948e-05, eta: 1:08:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6808, top5_acc: 0.8816, loss_cls: 1.8049, loss: 1.8049 +2024-07-21 20:35:38,720 - pyskl - INFO - Epoch [149][2600/3746] lr: 1.871e-05, eta: 1:07:15, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6669, top5_acc: 0.8759, loss_cls: 1.8499, loss: 1.8499 +2024-07-21 20:37:00,944 - pyskl - INFO - Epoch [149][2700/3746] lr: 1.795e-05, eta: 1:05:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6791, top5_acc: 0.8814, loss_cls: 1.8098, loss: 1.8098 +2024-07-21 20:38:23,170 - pyskl - INFO - Epoch [149][2800/3746] lr: 1.721e-05, eta: 1:04:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6759, top5_acc: 0.8788, loss_cls: 1.8563, loss: 1.8563 +2024-07-21 20:39:45,457 - pyskl - INFO - Epoch [149][2900/3746] lr: 1.649e-05, eta: 1:03:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6770, top5_acc: 0.8823, loss_cls: 1.8254, loss: 1.8254 +2024-07-21 20:41:07,651 - pyskl - INFO - Epoch [149][3000/3746] lr: 1.578e-05, eta: 1:01:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6734, top5_acc: 0.8805, loss_cls: 1.8451, loss: 1.8451 +2024-07-21 20:42:29,658 - pyskl - INFO - Epoch [149][3100/3746] lr: 1.508e-05, eta: 1:00:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6766, top5_acc: 0.8823, loss_cls: 1.8599, loss: 1.8599 +2024-07-21 20:43:51,461 - pyskl - INFO - Epoch [149][3200/3746] lr: 1.440e-05, eta: 0:59:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6713, top5_acc: 0.8758, loss_cls: 1.8474, loss: 1.8474 +2024-07-21 20:45:12,948 - pyskl - INFO - Epoch [149][3300/3746] lr: 1.374e-05, eta: 0:57:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6705, top5_acc: 0.8770, loss_cls: 1.8523, loss: 1.8523 +2024-07-21 20:46:34,641 - pyskl - INFO - Epoch [149][3400/3746] lr: 1.309e-05, eta: 0:56:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6820, top5_acc: 0.8791, loss_cls: 1.8271, loss: 1.8271 +2024-07-21 20:47:56,495 - pyskl - INFO - Epoch [149][3500/3746] lr: 1.246e-05, eta: 0:54:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6697, top5_acc: 0.8786, loss_cls: 1.8579, loss: 1.8579 +2024-07-21 20:49:18,217 - pyskl - INFO - Epoch [149][3600/3746] lr: 1.184e-05, eta: 0:53:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6763, top5_acc: 0.8823, loss_cls: 1.8178, loss: 1.8178 +2024-07-21 20:50:40,575 - pyskl - INFO - Epoch [149][3700/3746] lr: 1.124e-05, eta: 0:52:08, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6730, top5_acc: 0.8762, loss_cls: 1.8498, loss: 1.8498 +2024-07-21 20:51:20,273 - pyskl - INFO - Saving checkpoint at 149 epochs +2024-07-21 20:53:11,716 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 20:53:12,388 - pyskl - INFO - +top1_acc 0.4692 +top5_acc 0.7169 +2024-07-21 20:53:12,389 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 20:53:12,436 - pyskl - INFO - +mean_acc 0.4689 +2024-07-21 20:53:12,441 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_146.pth was removed +2024-07-21 20:53:12,686 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_149.pth. +2024-07-21 20:53:12,687 - pyskl - INFO - Best top1_acc is 0.4692 at 149 epoch. +2024-07-21 20:53:12,700 - pyskl - INFO - Epoch(val) [149][309] top1_acc: 0.4692, top5_acc: 0.7169, mean_class_accuracy: 0.4689 +2024-07-21 20:56:57,244 - pyskl - INFO - Epoch [150][100/3746] lr: 1.039e-05, eta: 0:50:08, time: 2.245, data_time: 1.261, memory: 15990, top1_acc: 0.6725, top5_acc: 0.8775, loss_cls: 1.8551, loss: 1.8551 +2024-07-21 20:58:19,232 - pyskl - INFO - Epoch [150][200/3746] lr: 9.832e-06, eta: 0:48:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6734, top5_acc: 0.8819, loss_cls: 1.8416, loss: 1.8416 +2024-07-21 20:59:40,864 - pyskl - INFO - Epoch [150][300/3746] lr: 9.285e-06, eta: 0:47:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6863, top5_acc: 0.8828, loss_cls: 1.8115, loss: 1.8115 +2024-07-21 21:01:02,793 - pyskl - INFO - Epoch [150][400/3746] lr: 8.754e-06, eta: 0:46:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6797, top5_acc: 0.8808, loss_cls: 1.8150, loss: 1.8150 +2024-07-21 21:02:24,698 - pyskl - INFO - Epoch [150][500/3746] lr: 8.239e-06, eta: 0:44:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6822, top5_acc: 0.8772, loss_cls: 1.8461, loss: 1.8461 +2024-07-21 21:03:46,440 - pyskl - INFO - Epoch [150][600/3746] lr: 7.739e-06, eta: 0:43:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6798, top5_acc: 0.8750, loss_cls: 1.8460, loss: 1.8460 +2024-07-21 21:05:08,237 - pyskl - INFO - Epoch [150][700/3746] lr: 7.255e-06, eta: 0:41:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6761, top5_acc: 0.8820, loss_cls: 1.8289, loss: 1.8289 +2024-07-21 21:06:30,586 - pyskl - INFO - Epoch [150][800/3746] lr: 6.787e-06, eta: 0:40:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6816, top5_acc: 0.8856, loss_cls: 1.7915, loss: 1.7915 +2024-07-21 21:07:52,090 - pyskl - INFO - Epoch [150][900/3746] lr: 6.334e-06, eta: 0:39:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6722, top5_acc: 0.8833, loss_cls: 1.8195, loss: 1.8195 +2024-07-21 21:09:13,800 - pyskl - INFO - Epoch [150][1000/3746] lr: 5.897e-06, eta: 0:37:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6870, top5_acc: 0.8842, loss_cls: 1.8090, loss: 1.8090 +2024-07-21 21:10:35,555 - pyskl - INFO - Epoch [150][1100/3746] lr: 5.475e-06, eta: 0:36:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6802, top5_acc: 0.8858, loss_cls: 1.7853, loss: 1.7853 +2024-07-21 21:11:57,238 - pyskl - INFO - Epoch [150][1200/3746] lr: 5.070e-06, eta: 0:35:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6820, top5_acc: 0.8852, loss_cls: 1.8290, loss: 1.8290 +2024-07-21 21:13:18,847 - pyskl - INFO - Epoch [150][1300/3746] lr: 4.679e-06, eta: 0:33:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6795, top5_acc: 0.8809, loss_cls: 1.8198, loss: 1.8198 +2024-07-21 21:14:40,439 - pyskl - INFO - Epoch [150][1400/3746] lr: 4.305e-06, eta: 0:32:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6831, top5_acc: 0.8844, loss_cls: 1.8003, loss: 1.8003 +2024-07-21 21:16:02,833 - pyskl - INFO - Epoch [150][1500/3746] lr: 3.946e-06, eta: 0:30:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6794, top5_acc: 0.8803, loss_cls: 1.8084, loss: 1.8084 +2024-07-21 21:17:24,427 - pyskl - INFO - Epoch [150][1600/3746] lr: 3.602e-06, eta: 0:29:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6725, top5_acc: 0.8727, loss_cls: 1.8755, loss: 1.8755 +2024-07-21 21:18:47,049 - pyskl - INFO - Epoch [150][1700/3746] lr: 3.275e-06, eta: 0:28:08, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6819, top5_acc: 0.8775, loss_cls: 1.8263, loss: 1.8263 +2024-07-21 21:20:09,499 - pyskl - INFO - Epoch [150][1800/3746] lr: 2.962e-06, eta: 0:26:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6727, top5_acc: 0.8783, loss_cls: 1.8563, loss: 1.8563 +2024-07-21 21:21:31,666 - pyskl - INFO - Epoch [150][1900/3746] lr: 2.666e-06, eta: 0:25:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6839, top5_acc: 0.8834, loss_cls: 1.8047, loss: 1.8047 +2024-07-21 21:22:53,618 - pyskl - INFO - Epoch [150][2000/3746] lr: 2.385e-06, eta: 0:24:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6802, top5_acc: 0.8881, loss_cls: 1.8180, loss: 1.8180 +2024-07-21 21:24:15,136 - pyskl - INFO - Epoch [150][2100/3746] lr: 2.120e-06, eta: 0:22:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6780, top5_acc: 0.8781, loss_cls: 1.8216, loss: 1.8216 +2024-07-21 21:25:37,690 - pyskl - INFO - Epoch [150][2200/3746] lr: 1.870e-06, eta: 0:21:15, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6778, top5_acc: 0.8728, loss_cls: 1.8603, loss: 1.8603 +2024-07-21 21:26:59,151 - pyskl - INFO - Epoch [150][2300/3746] lr: 1.636e-06, eta: 0:19:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6753, top5_acc: 0.8747, loss_cls: 1.8476, loss: 1.8476 +2024-07-21 21:28:20,586 - pyskl - INFO - Epoch [150][2400/3746] lr: 1.418e-06, eta: 0:18:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6717, top5_acc: 0.8697, loss_cls: 1.8760, loss: 1.8760 +2024-07-21 21:29:42,903 - pyskl - INFO - Epoch [150][2500/3746] lr: 1.215e-06, eta: 0:17:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6794, top5_acc: 0.8784, loss_cls: 1.8455, loss: 1.8455 +2024-07-21 21:31:06,018 - pyskl - INFO - Epoch [150][2600/3746] lr: 1.028e-06, eta: 0:15:45, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6922, top5_acc: 0.8883, loss_cls: 1.7793, loss: 1.7793 +2024-07-21 21:32:28,330 - pyskl - INFO - Epoch [150][2700/3746] lr: 8.567e-07, eta: 0:14:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6783, top5_acc: 0.8789, loss_cls: 1.8320, loss: 1.8320 +2024-07-21 21:33:50,539 - pyskl - INFO - Epoch [150][2800/3746] lr: 7.008e-07, eta: 0:13:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6780, top5_acc: 0.8784, loss_cls: 1.8382, loss: 1.8382 +2024-07-21 21:35:11,628 - pyskl - INFO - Epoch [150][2900/3746] lr: 5.606e-07, eta: 0:11:38, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6769, top5_acc: 0.8823, loss_cls: 1.8115, loss: 1.8115 +2024-07-21 21:36:33,708 - pyskl - INFO - Epoch [150][3000/3746] lr: 4.361e-07, eta: 0:10:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6875, top5_acc: 0.8827, loss_cls: 1.7947, loss: 1.7947 +2024-07-21 21:37:55,650 - pyskl - INFO - Epoch [150][3100/3746] lr: 3.271e-07, eta: 0:08:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6717, top5_acc: 0.8752, loss_cls: 1.8500, loss: 1.8500 +2024-07-21 21:39:17,963 - pyskl - INFO - Epoch [150][3200/3746] lr: 2.338e-07, eta: 0:07:30, time: 0.823, data_time: 0.001, memory: 15990, top1_acc: 0.6828, top5_acc: 0.8880, loss_cls: 1.8091, loss: 1.8091 +2024-07-21 21:40:39,591 - pyskl - INFO - Epoch [150][3300/3746] lr: 1.561e-07, eta: 0:06:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6784, top5_acc: 0.8855, loss_cls: 1.8084, loss: 1.8084 +2024-07-21 21:42:01,502 - pyskl - INFO - Epoch [150][3400/3746] lr: 9.410e-08, eta: 0:04:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6703, top5_acc: 0.8773, loss_cls: 1.8503, loss: 1.8503 +2024-07-21 21:43:22,995 - pyskl - INFO - Epoch [150][3500/3746] lr: 4.768e-08, eta: 0:03:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6837, top5_acc: 0.8847, loss_cls: 1.8100, loss: 1.8100 +2024-07-21 21:44:45,295 - pyskl - INFO - Epoch [150][3600/3746] lr: 1.689e-08, eta: 0:02:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6878, top5_acc: 0.8866, loss_cls: 1.7774, loss: 1.7774 +2024-07-21 21:46:06,448 - pyskl - INFO - Epoch [150][3700/3746] lr: 1.726e-09, eta: 0:00:37, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6811, top5_acc: 0.8756, loss_cls: 1.8308, loss: 1.8308 +2024-07-21 21:46:46,476 - pyskl - INFO - Saving checkpoint at 150 epochs +2024-07-21 21:48:34,414 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 21:48:35,070 - pyskl - INFO - +top1_acc 0.4700 +top5_acc 0.7150 +2024-07-21 21:48:35,071 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 21:48:35,110 - pyskl - INFO - +mean_acc 0.4697 +2024-07-21 21:48:35,115 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_149.pth was removed +2024-07-21 21:48:35,527 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_150.pth. +2024-07-21 21:48:35,528 - pyskl - INFO - Best top1_acc is 0.4700 at 150 epoch. +2024-07-21 21:48:35,539 - pyskl - INFO - Epoch(val) [150][309] top1_acc: 0.4700, top5_acc: 0.7150, mean_class_accuracy: 0.4697 +2024-07-21 21:48:49,417 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-07-21 22:00:35,201 - pyskl - INFO - Testing results of the last checkpoint +2024-07-21 22:00:35,201 - pyskl - INFO - top1_acc: 0.4799 +2024-07-21 22:00:35,201 - pyskl - INFO - top5_acc: 0.7254 +2024-07-21 22:00:35,201 - pyskl - INFO - mean_class_accuracy: 0.4796 +2024-07-21 22:00:35,202 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/k400/j_3/best_top1_acc_epoch_150.pth +2024-07-21 22:12:12,714 - pyskl - INFO - Testing results of the best checkpoint +2024-07-21 22:12:12,714 - pyskl - INFO - top1_acc: 0.4799 +2024-07-21 22:12:12,714 - pyskl - INFO - top5_acc: 0.7254 +2024-07-21 22:12:12,714 - pyskl - INFO - mean_class_accuracy: 0.4796 diff --git a/k400/j_3/20240716_064615.log.json b/k400/j_3/20240716_064615.log.json new file mode 100644 index 0000000000000000000000000000000000000000..c136ae8d6b5b6814875740ef2caee69da4eb46de --- /dev/null +++ b/k400/j_3/20240716_064615.log.json @@ -0,0 +1,5701 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 1362284316, "config_name": "j_3.py", "work_dir": "j_3", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.1, "memory": 15990, "data_time": 1.17363, "top1_acc": 0.00531, "top5_acc": 0.02656, "loss_cls": 6.47773, "loss": 6.47773, "time": 1.88129} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.1, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.00875, "top5_acc": 0.04469, "loss_cls": 6.45059, "loss": 6.45059, "time": 0.70121} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.1, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.01625, "top5_acc": 0.06781, "loss_cls": 6.262, "loss": 6.262, "time": 0.69963} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.01906, "top5_acc": 0.07922, "loss_cls": 6.11266, "loss": 6.11266, "time": 0.69955} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.02516, "top5_acc": 0.10031, "loss_cls": 6.00581, "loss": 6.00581, "time": 0.702} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.1, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.02688, "top5_acc": 0.10859, "loss_cls": 5.88084, "loss": 5.88084, "time": 0.70082} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.03109, "top5_acc": 0.11578, "loss_cls": 5.85327, "loss": 5.85327, "time": 0.70252} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.03422, "top5_acc": 0.12031, "loss_cls": 5.81044, "loss": 5.81044, "time": 0.70346} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.03609, "top5_acc": 0.13297, "loss_cls": 5.76392, "loss": 5.76392, "time": 0.70114} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.03891, "top5_acc": 0.14359, "loss_cls": 5.71375, "loss": 5.71375, "time": 0.70152} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.04031, "top5_acc": 0.14516, "loss_cls": 5.72131, "loss": 5.72131, "time": 0.7004} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.04484, "top5_acc": 0.1625, "loss_cls": 5.63562, "loss": 5.63562, "time": 0.69807} +{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.04688, "top5_acc": 0.15797, "loss_cls": 5.63281, "loss": 5.63281, "time": 0.69871} +{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.04766, "top5_acc": 0.16625, "loss_cls": 5.57788, "loss": 5.57788, "time": 0.70118} +{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.04922, "top5_acc": 0.17156, "loss_cls": 5.54912, "loss": 5.54912, "time": 0.69963} +{"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.05016, "top5_acc": 0.16969, "loss_cls": 5.57921, "loss": 5.57921, "time": 0.69793} +{"mode": "train", "epoch": 1, "iter": 1700, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.05656, "top5_acc": 0.17859, "loss_cls": 5.5474, "loss": 5.5474, "time": 0.69913} +{"mode": "train", "epoch": 1, "iter": 1800, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.06125, "top5_acc": 0.19234, "loss_cls": 5.47329, "loss": 5.47329, "time": 0.70521} +{"mode": "train", "epoch": 1, "iter": 1900, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.0575, "top5_acc": 0.18328, "loss_cls": 5.50175, "loss": 5.50175, "time": 0.7038} +{"mode": "train", "epoch": 1, "iter": 2000, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.06547, "top5_acc": 0.20312, "loss_cls": 5.4467, "loss": 5.4467, "time": 0.70535} +{"mode": "train", "epoch": 1, "iter": 2100, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.06609, "top5_acc": 0.19859, "loss_cls": 5.43603, "loss": 5.43603, "time": 0.70288} +{"mode": "train", "epoch": 1, "iter": 2200, "lr": 0.1, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.06719, "top5_acc": 0.21484, "loss_cls": 5.39809, "loss": 5.39809, "time": 0.70366} +{"mode": "train", "epoch": 1, "iter": 2300, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.07, "top5_acc": 0.21047, "loss_cls": 5.38662, "loss": 5.38662, "time": 0.70437} +{"mode": "train", "epoch": 1, "iter": 2400, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.07234, "top5_acc": 0.21812, "loss_cls": 5.3961, "loss": 5.3961, "time": 0.70997} +{"mode": "train", "epoch": 1, "iter": 2500, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.07984, "top5_acc": 0.23609, "loss_cls": 5.30616, "loss": 5.30616, "time": 0.70483} +{"mode": "train", "epoch": 1, "iter": 2600, "lr": 0.09999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.08016, "top5_acc": 0.23016, "loss_cls": 5.30801, "loss": 5.30801, "time": 0.70397} +{"mode": "train", "epoch": 1, "iter": 2700, "lr": 0.09999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.08562, "top5_acc": 0.245, "loss_cls": 5.28657, "loss": 5.28657, "time": 0.71254} +{"mode": "train", "epoch": 1, "iter": 2800, "lr": 0.09999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.08812, "top5_acc": 0.24984, "loss_cls": 5.25084, "loss": 5.25084, "time": 0.70551} +{"mode": "train", "epoch": 1, "iter": 2900, "lr": 0.09999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.09031, "top5_acc": 0.25172, "loss_cls": 5.22136, "loss": 5.22136, "time": 0.70698} +{"mode": "train", "epoch": 1, "iter": 3000, "lr": 0.09999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.09672, "top5_acc": 0.26047, "loss_cls": 5.18167, "loss": 5.18167, "time": 0.71083} +{"mode": "train", "epoch": 1, "iter": 3100, "lr": 0.09999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.08953, "top5_acc": 0.26906, "loss_cls": 5.19317, "loss": 5.19317, "time": 0.70652} +{"mode": "train", "epoch": 1, "iter": 3200, "lr": 0.09999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.09844, "top5_acc": 0.27766, "loss_cls": 5.11936, "loss": 5.11936, "time": 0.70692} +{"mode": "train", "epoch": 1, "iter": 3300, "lr": 0.09999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.09922, "top5_acc": 0.27812, "loss_cls": 5.1242, "loss": 5.1242, "time": 0.7055} +{"mode": "train", "epoch": 1, "iter": 3400, "lr": 0.09999, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.10094, "top5_acc": 0.27016, "loss_cls": 5.12422, "loss": 5.12422, "time": 0.70744} +{"mode": "train", "epoch": 1, "iter": 3500, "lr": 0.09999, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.11719, "top5_acc": 0.29938, "loss_cls": 5.04526, "loss": 5.04526, "time": 0.70639} +{"mode": "train", "epoch": 1, "iter": 3600, "lr": 0.09999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.10375, "top5_acc": 0.29328, "loss_cls": 5.08923, "loss": 5.08923, "time": 0.70113} +{"mode": "train", "epoch": 1, "iter": 3700, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.12125, "top5_acc": 0.30828, "loss_cls": 5.0145, "loss": 5.0145, "time": 0.70408} +{"mode": "val", "epoch": 1, "iter": 309, "lr": 0.09999, "top1_acc": 0.08043, "top5_acc": 0.23507, "mean_class_accuracy": 0.0804} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.09999, "memory": 15990, "data_time": 1.27948, "top1_acc": 0.11406, "top5_acc": 0.30047, "loss_cls": 4.99508, "loss": 4.99508, "time": 1.992} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.09999, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.12438, "top5_acc": 0.31219, "loss_cls": 4.9427, "loss": 4.9427, "time": 0.71244} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.09999, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.11844, "top5_acc": 0.31406, "loss_cls": 4.96273, "loss": 4.96273, "time": 0.7055} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.09999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.12328, "top5_acc": 0.32, "loss_cls": 4.96738, "loss": 4.96738, "time": 0.70474} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.09999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.12516, "top5_acc": 0.32031, "loss_cls": 4.94147, "loss": 4.94147, "time": 0.7002} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.12219, "top5_acc": 0.32188, "loss_cls": 4.94222, "loss": 4.94222, "time": 0.70279} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.09998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.12812, "top5_acc": 0.33281, "loss_cls": 4.88013, "loss": 4.88013, "time": 0.70138} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.09998, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.13156, "top5_acc": 0.32719, "loss_cls": 4.90701, "loss": 4.90701, "time": 0.70263} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.09998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.13422, "top5_acc": 0.33156, "loss_cls": 4.88557, "loss": 4.88557, "time": 0.7053} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.09998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.13641, "top5_acc": 0.34516, "loss_cls": 4.8492, "loss": 4.8492, "time": 0.70453} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.09998, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.14203, "top5_acc": 0.35516, "loss_cls": 4.81741, "loss": 4.81741, "time": 0.70384} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.09998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.14312, "top5_acc": 0.34141, "loss_cls": 4.83031, "loss": 4.83031, "time": 0.7044} +{"mode": "train", "epoch": 2, "iter": 1300, "lr": 0.09998, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.13984, "top5_acc": 0.35062, "loss_cls": 4.81994, "loss": 4.81994, "time": 0.70156} +{"mode": "train", "epoch": 2, "iter": 1400, "lr": 0.09998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.14156, "top5_acc": 0.34672, "loss_cls": 4.83575, "loss": 4.83575, "time": 0.70221} +{"mode": "train", "epoch": 2, "iter": 1500, "lr": 0.09998, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.13938, "top5_acc": 0.34031, "loss_cls": 4.85071, "loss": 4.85071, "time": 0.70032} +{"mode": "train", "epoch": 2, "iter": 1600, "lr": 0.09998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.14375, "top5_acc": 0.35125, "loss_cls": 4.79557, "loss": 4.79557, "time": 0.7031} +{"mode": "train", "epoch": 2, "iter": 1700, "lr": 0.09998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.15078, "top5_acc": 0.36188, "loss_cls": 4.78109, "loss": 4.78109, "time": 0.70004} +{"mode": "train", "epoch": 2, "iter": 1800, "lr": 0.09998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.155, "top5_acc": 0.36844, "loss_cls": 4.74496, "loss": 4.74496, "time": 0.70006} +{"mode": "train", "epoch": 2, "iter": 1900, "lr": 0.09998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.15188, "top5_acc": 0.36438, "loss_cls": 4.75013, "loss": 4.75013, "time": 0.70434} +{"mode": "train", "epoch": 2, "iter": 2000, "lr": 0.09997, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.14969, "top5_acc": 0.36719, "loss_cls": 4.73494, "loss": 4.73494, "time": 0.70421} +{"mode": "train", "epoch": 2, "iter": 2100, "lr": 0.09997, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.15547, "top5_acc": 0.37672, "loss_cls": 4.71613, "loss": 4.71613, "time": 0.70925} +{"mode": "train", "epoch": 2, "iter": 2200, "lr": 0.09997, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.15766, "top5_acc": 0.36688, "loss_cls": 4.72829, "loss": 4.72829, "time": 0.70178} +{"mode": "train", "epoch": 2, "iter": 2300, "lr": 0.09997, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.15625, "top5_acc": 0.37406, "loss_cls": 4.72321, "loss": 4.72321, "time": 0.70445} +{"mode": "train", "epoch": 2, "iter": 2400, "lr": 0.09997, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.15172, "top5_acc": 0.37188, "loss_cls": 4.70824, "loss": 4.70824, "time": 0.71314} +{"mode": "train", "epoch": 2, "iter": 2500, "lr": 0.09997, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.15875, "top5_acc": 0.37328, "loss_cls": 4.74029, "loss": 4.74029, "time": 0.71265} +{"mode": "train", "epoch": 2, "iter": 2600, "lr": 0.09997, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.15547, "top5_acc": 0.36531, "loss_cls": 4.71309, "loss": 4.71309, "time": 0.71105} +{"mode": "train", "epoch": 2, "iter": 2700, "lr": 0.09997, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.14, "top5_acc": 0.35766, "loss_cls": 4.78548, "loss": 4.78548, "time": 0.71156} +{"mode": "train", "epoch": 2, "iter": 2800, "lr": 0.09997, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.15391, "top5_acc": 0.36938, "loss_cls": 4.7219, "loss": 4.7219, "time": 0.71299} +{"mode": "train", "epoch": 2, "iter": 2900, "lr": 0.09997, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.15781, "top5_acc": 0.37906, "loss_cls": 4.68048, "loss": 4.68048, "time": 0.71013} +{"mode": "train", "epoch": 2, "iter": 3000, "lr": 0.09996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.16516, "top5_acc": 0.38266, "loss_cls": 4.65102, "loss": 4.65102, "time": 0.7076} +{"mode": "train", "epoch": 2, "iter": 3100, "lr": 0.09996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.16547, "top5_acc": 0.38266, "loss_cls": 4.65053, "loss": 4.65053, "time": 0.70998} +{"mode": "train", "epoch": 2, "iter": 3200, "lr": 0.09996, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.16484, "top5_acc": 0.38625, "loss_cls": 4.67092, "loss": 4.67092, "time": 0.70738} +{"mode": "train", "epoch": 2, "iter": 3300, "lr": 0.09996, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.16953, "top5_acc": 0.39312, "loss_cls": 4.64521, "loss": 4.64521, "time": 0.70933} +{"mode": "train", "epoch": 2, "iter": 3400, "lr": 0.09996, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.17172, "top5_acc": 0.41531, "loss_cls": 4.5758, "loss": 4.5758, "time": 0.70925} +{"mode": "train", "epoch": 2, "iter": 3500, "lr": 0.09996, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.165, "top5_acc": 0.38516, "loss_cls": 4.64145, "loss": 4.64145, "time": 0.71065} +{"mode": "train", "epoch": 2, "iter": 3600, "lr": 0.09996, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.16359, "top5_acc": 0.37578, "loss_cls": 4.71787, "loss": 4.71787, "time": 0.71243} +{"mode": "train", "epoch": 2, "iter": 3700, "lr": 0.09996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17844, "top5_acc": 0.40406, "loss_cls": 4.59156, "loss": 4.59156, "time": 0.71099} +{"mode": "val", "epoch": 2, "iter": 309, "lr": 0.09996, "top1_acc": 0.09968, "top5_acc": 0.26105, "mean_class_accuracy": 0.09947} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.09995, "memory": 15990, "data_time": 1.28385, "top1_acc": 0.16656, "top5_acc": 0.39641, "loss_cls": 4.58694, "loss": 4.58694, "time": 1.99496} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.09995, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.18562, "top5_acc": 0.40391, "loss_cls": 4.54937, "loss": 4.54937, "time": 0.71484} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.09995, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.17766, "top5_acc": 0.40875, "loss_cls": 4.5681, "loss": 4.5681, "time": 0.70964} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.09995, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.1875, "top5_acc": 0.40156, "loss_cls": 4.55426, "loss": 4.55426, "time": 0.7036} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.09995, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.1775, "top5_acc": 0.40406, "loss_cls": 4.59198, "loss": 4.59198, "time": 0.70263} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.09995, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.17328, "top5_acc": 0.39703, "loss_cls": 4.59246, "loss": 4.59246, "time": 0.70387} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.09995, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.17859, "top5_acc": 0.41, "loss_cls": 4.53776, "loss": 4.53776, "time": 0.70339} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.09995, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.18047, "top5_acc": 0.40062, "loss_cls": 4.57734, "loss": 4.57734, "time": 0.70218} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.09994, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18219, "top5_acc": 0.41141, "loss_cls": 4.53824, "loss": 4.53824, "time": 0.70426} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.09994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17469, "top5_acc": 0.40984, "loss_cls": 4.54695, "loss": 4.54695, "time": 0.70188} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.09994, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.17594, "top5_acc": 0.41531, "loss_cls": 4.53857, "loss": 4.53857, "time": 0.70383} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.09994, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.17938, "top5_acc": 0.41969, "loss_cls": 4.51933, "loss": 4.51933, "time": 0.7039} +{"mode": "train", "epoch": 3, "iter": 1300, "lr": 0.09994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18531, "top5_acc": 0.41375, "loss_cls": 4.5234, "loss": 4.5234, "time": 0.70377} +{"mode": "train", "epoch": 3, "iter": 1400, "lr": 0.09994, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.18547, "top5_acc": 0.41266, "loss_cls": 4.54404, "loss": 4.54404, "time": 0.70377} +{"mode": "train", "epoch": 3, "iter": 1500, "lr": 0.09994, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.18812, "top5_acc": 0.41766, "loss_cls": 4.51746, "loss": 4.51746, "time": 0.69996} +{"mode": "train", "epoch": 3, "iter": 1600, "lr": 0.09994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17969, "top5_acc": 0.4125, "loss_cls": 4.52707, "loss": 4.52707, "time": 0.70302} +{"mode": "train", "epoch": 3, "iter": 1700, "lr": 0.09993, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18703, "top5_acc": 0.42281, "loss_cls": 4.51586, "loss": 4.51586, "time": 0.70684} +{"mode": "train", "epoch": 3, "iter": 1800, "lr": 0.09993, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18938, "top5_acc": 0.41094, "loss_cls": 4.49075, "loss": 4.49075, "time": 0.70608} +{"mode": "train", "epoch": 3, "iter": 1900, "lr": 0.09993, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18641, "top5_acc": 0.42625, "loss_cls": 4.48413, "loss": 4.48413, "time": 0.704} +{"mode": "train", "epoch": 3, "iter": 2000, "lr": 0.09993, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18516, "top5_acc": 0.41031, "loss_cls": 4.48961, "loss": 4.48961, "time": 0.70727} +{"mode": "train", "epoch": 3, "iter": 2100, "lr": 0.09993, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18, "top5_acc": 0.41562, "loss_cls": 4.48832, "loss": 4.48832, "time": 0.70752} +{"mode": "train", "epoch": 3, "iter": 2200, "lr": 0.09993, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19609, "top5_acc": 0.42625, "loss_cls": 4.43036, "loss": 4.43036, "time": 0.70711} +{"mode": "train", "epoch": 3, "iter": 2300, "lr": 0.09993, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.1925, "top5_acc": 0.42609, "loss_cls": 4.47692, "loss": 4.47692, "time": 0.70308} +{"mode": "train", "epoch": 3, "iter": 2400, "lr": 0.09992, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20328, "top5_acc": 0.43375, "loss_cls": 4.43463, "loss": 4.43463, "time": 0.71369} +{"mode": "train", "epoch": 3, "iter": 2500, "lr": 0.09992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18844, "top5_acc": 0.42453, "loss_cls": 4.47103, "loss": 4.47103, "time": 0.7104} +{"mode": "train", "epoch": 3, "iter": 2600, "lr": 0.09992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19234, "top5_acc": 0.41984, "loss_cls": 4.50267, "loss": 4.50267, "time": 0.70915} +{"mode": "train", "epoch": 3, "iter": 2700, "lr": 0.09992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19703, "top5_acc": 0.42641, "loss_cls": 4.48396, "loss": 4.48396, "time": 0.70507} +{"mode": "train", "epoch": 3, "iter": 2800, "lr": 0.09992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18516, "top5_acc": 0.41578, "loss_cls": 4.51544, "loss": 4.51544, "time": 0.70631} +{"mode": "train", "epoch": 3, "iter": 2900, "lr": 0.09992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20406, "top5_acc": 0.44016, "loss_cls": 4.40956, "loss": 4.40956, "time": 0.70761} +{"mode": "train", "epoch": 3, "iter": 3000, "lr": 0.09991, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20391, "top5_acc": 0.43969, "loss_cls": 4.41105, "loss": 4.41105, "time": 0.70894} +{"mode": "train", "epoch": 3, "iter": 3100, "lr": 0.09991, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19266, "top5_acc": 0.42328, "loss_cls": 4.48233, "loss": 4.48233, "time": 0.71162} +{"mode": "train", "epoch": 3, "iter": 3200, "lr": 0.09991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20219, "top5_acc": 0.42656, "loss_cls": 4.44012, "loss": 4.44012, "time": 0.70789} +{"mode": "train", "epoch": 3, "iter": 3300, "lr": 0.09991, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20547, "top5_acc": 0.44, "loss_cls": 4.40754, "loss": 4.40754, "time": 0.70534} +{"mode": "train", "epoch": 3, "iter": 3400, "lr": 0.09991, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.1975, "top5_acc": 0.43203, "loss_cls": 4.44086, "loss": 4.44086, "time": 0.71103} +{"mode": "train", "epoch": 3, "iter": 3500, "lr": 0.09991, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21172, "top5_acc": 0.45172, "loss_cls": 4.38446, "loss": 4.38446, "time": 0.70966} +{"mode": "train", "epoch": 3, "iter": 3600, "lr": 0.0999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20156, "top5_acc": 0.44844, "loss_cls": 4.37794, "loss": 4.37794, "time": 0.70562} +{"mode": "train", "epoch": 3, "iter": 3700, "lr": 0.0999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19875, "top5_acc": 0.43859, "loss_cls": 4.40404, "loss": 4.40404, "time": 0.70769} +{"mode": "val", "epoch": 3, "iter": 309, "lr": 0.0999, "top1_acc": 0.11022, "top5_acc": 0.29904, "mean_class_accuracy": 0.11002} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.0999, "memory": 15990, "data_time": 1.29444, "top1_acc": 0.20516, "top5_acc": 0.45109, "loss_cls": 4.37484, "loss": 4.37484, "time": 2.00438} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.0999, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.20219, "top5_acc": 0.43562, "loss_cls": 4.38356, "loss": 4.38356, "time": 0.71301} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.0999, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21734, "top5_acc": 0.46172, "loss_cls": 4.32345, "loss": 4.32345, "time": 0.70892} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.09989, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20672, "top5_acc": 0.4475, "loss_cls": 4.3831, "loss": 4.3831, "time": 0.70913} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.09989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19891, "top5_acc": 0.43625, "loss_cls": 4.42101, "loss": 4.42101, "time": 0.70375} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.09989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19953, "top5_acc": 0.44578, "loss_cls": 4.36822, "loss": 4.36822, "time": 0.70257} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.09989, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20438, "top5_acc": 0.44172, "loss_cls": 4.40204, "loss": 4.40204, "time": 0.70201} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.09989, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20031, "top5_acc": 0.44203, "loss_cls": 4.39054, "loss": 4.39054, "time": 0.70179} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.09988, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20703, "top5_acc": 0.45062, "loss_cls": 4.36675, "loss": 4.36675, "time": 0.70001} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.09988, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20766, "top5_acc": 0.43828, "loss_cls": 4.37919, "loss": 4.37919, "time": 0.69939} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.09988, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19891, "top5_acc": 0.43828, "loss_cls": 4.4354, "loss": 4.4354, "time": 0.70093} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.09988, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20719, "top5_acc": 0.44641, "loss_cls": 4.38742, "loss": 4.38742, "time": 0.70032} +{"mode": "train", "epoch": 4, "iter": 1300, "lr": 0.09988, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20891, "top5_acc": 0.45172, "loss_cls": 4.33414, "loss": 4.33414, "time": 0.70343} +{"mode": "train", "epoch": 4, "iter": 1400, "lr": 0.09988, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20031, "top5_acc": 0.44172, "loss_cls": 4.40275, "loss": 4.40275, "time": 0.70057} +{"mode": "train", "epoch": 4, "iter": 1500, "lr": 0.09987, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20734, "top5_acc": 0.44125, "loss_cls": 4.39262, "loss": 4.39262, "time": 0.69997} +{"mode": "train", "epoch": 4, "iter": 1600, "lr": 0.09987, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21078, "top5_acc": 0.44953, "loss_cls": 4.36903, "loss": 4.36903, "time": 0.70398} +{"mode": "train", "epoch": 4, "iter": 1700, "lr": 0.09987, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21797, "top5_acc": 0.45266, "loss_cls": 4.36521, "loss": 4.36521, "time": 0.70045} +{"mode": "train", "epoch": 4, "iter": 1800, "lr": 0.09987, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21203, "top5_acc": 0.44766, "loss_cls": 4.348, "loss": 4.348, "time": 0.70095} +{"mode": "train", "epoch": 4, "iter": 1900, "lr": 0.09987, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20594, "top5_acc": 0.44859, "loss_cls": 4.37659, "loss": 4.37659, "time": 0.70158} +{"mode": "train", "epoch": 4, "iter": 2000, "lr": 0.09986, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21062, "top5_acc": 0.45844, "loss_cls": 4.33082, "loss": 4.33082, "time": 0.6993} +{"mode": "train", "epoch": 4, "iter": 2100, "lr": 0.09986, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21422, "top5_acc": 0.45734, "loss_cls": 4.3264, "loss": 4.3264, "time": 0.70646} +{"mode": "train", "epoch": 4, "iter": 2200, "lr": 0.09986, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2175, "top5_acc": 0.46281, "loss_cls": 4.30589, "loss": 4.30589, "time": 0.69865} +{"mode": "train", "epoch": 4, "iter": 2300, "lr": 0.09986, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.20453, "top5_acc": 0.45109, "loss_cls": 4.34475, "loss": 4.34475, "time": 0.70465} +{"mode": "train", "epoch": 4, "iter": 2400, "lr": 0.09985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20953, "top5_acc": 0.44953, "loss_cls": 4.3784, "loss": 4.3784, "time": 0.71337} +{"mode": "train", "epoch": 4, "iter": 2500, "lr": 0.09985, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21047, "top5_acc": 0.44672, "loss_cls": 4.37955, "loss": 4.37955, "time": 0.70772} +{"mode": "train", "epoch": 4, "iter": 2600, "lr": 0.09985, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20734, "top5_acc": 0.45312, "loss_cls": 4.36197, "loss": 4.36197, "time": 0.70952} +{"mode": "train", "epoch": 4, "iter": 2700, "lr": 0.09985, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21406, "top5_acc": 0.45484, "loss_cls": 4.34028, "loss": 4.34028, "time": 0.70412} +{"mode": "train", "epoch": 4, "iter": 2800, "lr": 0.09985, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21344, "top5_acc": 0.4525, "loss_cls": 4.34865, "loss": 4.34865, "time": 0.70386} +{"mode": "train", "epoch": 4, "iter": 2900, "lr": 0.09984, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22281, "top5_acc": 0.46859, "loss_cls": 4.30813, "loss": 4.30813, "time": 0.70555} +{"mode": "train", "epoch": 4, "iter": 3000, "lr": 0.09984, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22047, "top5_acc": 0.45453, "loss_cls": 4.32799, "loss": 4.32799, "time": 0.70157} +{"mode": "train", "epoch": 4, "iter": 3100, "lr": 0.09984, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21406, "top5_acc": 0.45391, "loss_cls": 4.35191, "loss": 4.35191, "time": 0.70326} +{"mode": "train", "epoch": 4, "iter": 3200, "lr": 0.09984, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21094, "top5_acc": 0.44625, "loss_cls": 4.34836, "loss": 4.34836, "time": 0.70537} +{"mode": "train", "epoch": 4, "iter": 3300, "lr": 0.09983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20562, "top5_acc": 0.45438, "loss_cls": 4.34419, "loss": 4.34419, "time": 0.70926} +{"mode": "train", "epoch": 4, "iter": 3400, "lr": 0.09983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21438, "top5_acc": 0.46875, "loss_cls": 4.2978, "loss": 4.2978, "time": 0.7062} +{"mode": "train", "epoch": 4, "iter": 3500, "lr": 0.09983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21172, "top5_acc": 0.45281, "loss_cls": 4.29684, "loss": 4.29684, "time": 0.70628} +{"mode": "train", "epoch": 4, "iter": 3600, "lr": 0.09983, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22078, "top5_acc": 0.46109, "loss_cls": 4.31106, "loss": 4.31106, "time": 0.70828} +{"mode": "train", "epoch": 4, "iter": 3700, "lr": 0.09983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22453, "top5_acc": 0.46109, "loss_cls": 4.29486, "loss": 4.29486, "time": 0.705} +{"mode": "val", "epoch": 4, "iter": 309, "lr": 0.09982, "top1_acc": 0.14755, "top5_acc": 0.35572, "mean_class_accuracy": 0.14742} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.09982, "memory": 15990, "data_time": 1.23719, "top1_acc": 0.22344, "top5_acc": 0.46078, "loss_cls": 4.28279, "loss": 4.28279, "time": 1.95265} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.09982, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21688, "top5_acc": 0.45891, "loss_cls": 4.29151, "loss": 4.29151, "time": 0.71444} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.09982, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22344, "top5_acc": 0.46406, "loss_cls": 4.27551, "loss": 4.27551, "time": 0.70476} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.09982, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22391, "top5_acc": 0.46297, "loss_cls": 4.28451, "loss": 4.28451, "time": 0.70474} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.09981, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21859, "top5_acc": 0.46141, "loss_cls": 4.29852, "loss": 4.29852, "time": 0.70919} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.09981, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21469, "top5_acc": 0.45766, "loss_cls": 4.31867, "loss": 4.31867, "time": 0.7046} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.09981, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21859, "top5_acc": 0.46734, "loss_cls": 4.29592, "loss": 4.29592, "time": 0.70599} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.09981, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22484, "top5_acc": 0.46453, "loss_cls": 4.27145, "loss": 4.27145, "time": 0.70354} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.0998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20891, "top5_acc": 0.4525, "loss_cls": 4.3375, "loss": 4.3375, "time": 0.70467} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.0998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21641, "top5_acc": 0.46234, "loss_cls": 4.30715, "loss": 4.30715, "time": 0.70622} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.0998, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21875, "top5_acc": 0.46375, "loss_cls": 4.29984, "loss": 4.29984, "time": 0.7052} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.0998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22531, "top5_acc": 0.46844, "loss_cls": 4.26713, "loss": 4.26713, "time": 0.70518} +{"mode": "train", "epoch": 5, "iter": 1300, "lr": 0.09979, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22719, "top5_acc": 0.46734, "loss_cls": 4.27479, "loss": 4.27479, "time": 0.7064} +{"mode": "train", "epoch": 5, "iter": 1400, "lr": 0.09979, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2125, "top5_acc": 0.45688, "loss_cls": 4.33245, "loss": 4.33245, "time": 0.70196} +{"mode": "train", "epoch": 5, "iter": 1500, "lr": 0.09979, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22828, "top5_acc": 0.47188, "loss_cls": 4.26938, "loss": 4.26938, "time": 0.70338} +{"mode": "train", "epoch": 5, "iter": 1600, "lr": 0.09979, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22781, "top5_acc": 0.46859, "loss_cls": 4.26674, "loss": 4.26674, "time": 0.70342} +{"mode": "train", "epoch": 5, "iter": 1700, "lr": 0.09978, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22859, "top5_acc": 0.47891, "loss_cls": 4.25169, "loss": 4.25169, "time": 0.70139} +{"mode": "train", "epoch": 5, "iter": 1800, "lr": 0.09978, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22031, "top5_acc": 0.47406, "loss_cls": 4.28004, "loss": 4.28004, "time": 0.70831} +{"mode": "train", "epoch": 5, "iter": 1900, "lr": 0.09978, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21906, "top5_acc": 0.4625, "loss_cls": 4.27002, "loss": 4.27002, "time": 0.70559} +{"mode": "train", "epoch": 5, "iter": 2000, "lr": 0.09977, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22703, "top5_acc": 0.47062, "loss_cls": 4.25249, "loss": 4.25249, "time": 0.70357} +{"mode": "train", "epoch": 5, "iter": 2100, "lr": 0.09977, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22281, "top5_acc": 0.47438, "loss_cls": 4.24632, "loss": 4.24632, "time": 0.7082} +{"mode": "train", "epoch": 5, "iter": 2200, "lr": 0.09977, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21219, "top5_acc": 0.46219, "loss_cls": 4.30255, "loss": 4.30255, "time": 0.70527} +{"mode": "train", "epoch": 5, "iter": 2300, "lr": 0.09977, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23078, "top5_acc": 0.47203, "loss_cls": 4.26561, "loss": 4.26561, "time": 0.70546} +{"mode": "train", "epoch": 5, "iter": 2400, "lr": 0.09976, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23719, "top5_acc": 0.48594, "loss_cls": 4.22571, "loss": 4.22571, "time": 0.71285} +{"mode": "train", "epoch": 5, "iter": 2500, "lr": 0.09976, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20734, "top5_acc": 0.45141, "loss_cls": 4.31853, "loss": 4.31853, "time": 0.70227} +{"mode": "train", "epoch": 5, "iter": 2600, "lr": 0.09976, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22094, "top5_acc": 0.46703, "loss_cls": 4.2804, "loss": 4.2804, "time": 0.7102} +{"mode": "train", "epoch": 5, "iter": 2700, "lr": 0.09976, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22391, "top5_acc": 0.47031, "loss_cls": 4.2681, "loss": 4.2681, "time": 0.71233} +{"mode": "train", "epoch": 5, "iter": 2800, "lr": 0.09975, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22297, "top5_acc": 0.46, "loss_cls": 4.28116, "loss": 4.28116, "time": 0.70632} +{"mode": "train", "epoch": 5, "iter": 2900, "lr": 0.09975, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21422, "top5_acc": 0.46234, "loss_cls": 4.29831, "loss": 4.29831, "time": 0.70813} +{"mode": "train", "epoch": 5, "iter": 3000, "lr": 0.09975, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2175, "top5_acc": 0.46125, "loss_cls": 4.28865, "loss": 4.28865, "time": 0.71047} +{"mode": "train", "epoch": 5, "iter": 3100, "lr": 0.09974, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23234, "top5_acc": 0.46844, "loss_cls": 4.27599, "loss": 4.27599, "time": 0.70831} +{"mode": "train", "epoch": 5, "iter": 3200, "lr": 0.09974, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23141, "top5_acc": 0.47219, "loss_cls": 4.24373, "loss": 4.24373, "time": 0.70291} +{"mode": "train", "epoch": 5, "iter": 3300, "lr": 0.09974, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22016, "top5_acc": 0.45969, "loss_cls": 4.3162, "loss": 4.3162, "time": 0.70437} +{"mode": "train", "epoch": 5, "iter": 3400, "lr": 0.09974, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22672, "top5_acc": 0.46953, "loss_cls": 4.25789, "loss": 4.25789, "time": 0.70841} +{"mode": "train", "epoch": 5, "iter": 3500, "lr": 0.09973, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22859, "top5_acc": 0.46594, "loss_cls": 4.27063, "loss": 4.27063, "time": 0.71159} +{"mode": "train", "epoch": 5, "iter": 3600, "lr": 0.09973, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23562, "top5_acc": 0.47406, "loss_cls": 4.22236, "loss": 4.22236, "time": 0.70652} +{"mode": "train", "epoch": 5, "iter": 3700, "lr": 0.09973, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22062, "top5_acc": 0.46922, "loss_cls": 4.25836, "loss": 4.25836, "time": 0.70186} +{"mode": "val", "epoch": 5, "iter": 309, "lr": 0.09973, "top1_acc": 0.16755, "top5_acc": 0.38282, "mean_class_accuracy": 0.16745} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.09972, "memory": 15990, "data_time": 1.28805, "top1_acc": 0.22953, "top5_acc": 0.48188, "loss_cls": 4.23153, "loss": 4.23153, "time": 2.00072} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.09972, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23094, "top5_acc": 0.475, "loss_cls": 4.21465, "loss": 4.21465, "time": 0.71431} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.09972, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.22453, "top5_acc": 0.46938, "loss_cls": 4.25635, "loss": 4.25635, "time": 0.70488} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.09971, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.235, "top5_acc": 0.48375, "loss_cls": 4.18058, "loss": 4.18058, "time": 0.70332} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.09971, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23344, "top5_acc": 0.46719, "loss_cls": 4.23837, "loss": 4.23837, "time": 0.70371} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.09971, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22328, "top5_acc": 0.47266, "loss_cls": 4.27994, "loss": 4.27994, "time": 0.70346} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.09971, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22484, "top5_acc": 0.46781, "loss_cls": 4.2427, "loss": 4.2427, "time": 0.70021} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.0997, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22531, "top5_acc": 0.47484, "loss_cls": 4.22399, "loss": 4.22399, "time": 0.69931} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.0997, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23812, "top5_acc": 0.47922, "loss_cls": 4.21024, "loss": 4.21024, "time": 0.70138} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.0997, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23547, "top5_acc": 0.48219, "loss_cls": 4.18689, "loss": 4.18689, "time": 0.70487} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.09969, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22719, "top5_acc": 0.46547, "loss_cls": 4.2529, "loss": 4.2529, "time": 0.70363} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.09969, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2275, "top5_acc": 0.47328, "loss_cls": 4.22061, "loss": 4.22061, "time": 0.70212} +{"mode": "train", "epoch": 6, "iter": 1300, "lr": 0.09969, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23125, "top5_acc": 0.48312, "loss_cls": 4.22674, "loss": 4.22674, "time": 0.70001} +{"mode": "train", "epoch": 6, "iter": 1400, "lr": 0.09968, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23016, "top5_acc": 0.47531, "loss_cls": 4.23065, "loss": 4.23065, "time": 0.70061} +{"mode": "train", "epoch": 6, "iter": 1500, "lr": 0.09968, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22125, "top5_acc": 0.46562, "loss_cls": 4.28906, "loss": 4.28906, "time": 0.69938} +{"mode": "train", "epoch": 6, "iter": 1600, "lr": 0.09968, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22484, "top5_acc": 0.47203, "loss_cls": 4.25284, "loss": 4.25284, "time": 0.69957} +{"mode": "train", "epoch": 6, "iter": 1700, "lr": 0.09967, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23078, "top5_acc": 0.48703, "loss_cls": 4.22113, "loss": 4.22113, "time": 0.70087} +{"mode": "train", "epoch": 6, "iter": 1800, "lr": 0.09967, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22828, "top5_acc": 0.48375, "loss_cls": 4.22077, "loss": 4.22077, "time": 0.70213} +{"mode": "train", "epoch": 6, "iter": 1900, "lr": 0.09967, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2325, "top5_acc": 0.47859, "loss_cls": 4.19674, "loss": 4.19674, "time": 0.70033} +{"mode": "train", "epoch": 6, "iter": 2000, "lr": 0.09966, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.23641, "top5_acc": 0.49016, "loss_cls": 4.18813, "loss": 4.18813, "time": 0.70764} +{"mode": "train", "epoch": 6, "iter": 2100, "lr": 0.09966, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22812, "top5_acc": 0.47219, "loss_cls": 4.22893, "loss": 4.22893, "time": 0.70074} +{"mode": "train", "epoch": 6, "iter": 2200, "lr": 0.09966, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21984, "top5_acc": 0.46812, "loss_cls": 4.28729, "loss": 4.28729, "time": 0.70179} +{"mode": "train", "epoch": 6, "iter": 2300, "lr": 0.09965, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22219, "top5_acc": 0.48141, "loss_cls": 4.22382, "loss": 4.22382, "time": 0.70567} +{"mode": "train", "epoch": 6, "iter": 2400, "lr": 0.09965, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22781, "top5_acc": 0.47203, "loss_cls": 4.23398, "loss": 4.23398, "time": 0.71393} +{"mode": "train", "epoch": 6, "iter": 2500, "lr": 0.09965, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23734, "top5_acc": 0.48391, "loss_cls": 4.18802, "loss": 4.18802, "time": 0.70895} +{"mode": "train", "epoch": 6, "iter": 2600, "lr": 0.09964, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22656, "top5_acc": 0.47625, "loss_cls": 4.21216, "loss": 4.21216, "time": 0.70816} +{"mode": "train", "epoch": 6, "iter": 2700, "lr": 0.09964, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23438, "top5_acc": 0.47516, "loss_cls": 4.22929, "loss": 4.22929, "time": 0.71039} +{"mode": "train", "epoch": 6, "iter": 2800, "lr": 0.09964, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23562, "top5_acc": 0.47016, "loss_cls": 4.23065, "loss": 4.23065, "time": 0.70748} +{"mode": "train", "epoch": 6, "iter": 2900, "lr": 0.09963, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23984, "top5_acc": 0.48156, "loss_cls": 4.18468, "loss": 4.18468, "time": 0.70507} +{"mode": "train", "epoch": 6, "iter": 3000, "lr": 0.09963, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22875, "top5_acc": 0.46953, "loss_cls": 4.23044, "loss": 4.23044, "time": 0.70677} +{"mode": "train", "epoch": 6, "iter": 3100, "lr": 0.09963, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23219, "top5_acc": 0.48469, "loss_cls": 4.22637, "loss": 4.22637, "time": 0.70535} +{"mode": "train", "epoch": 6, "iter": 3200, "lr": 0.09962, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23344, "top5_acc": 0.48188, "loss_cls": 4.20216, "loss": 4.20216, "time": 0.70946} +{"mode": "train", "epoch": 6, "iter": 3300, "lr": 0.09962, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23141, "top5_acc": 0.47625, "loss_cls": 4.2185, "loss": 4.2185, "time": 0.7067} +{"mode": "train", "epoch": 6, "iter": 3400, "lr": 0.09962, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23141, "top5_acc": 0.47406, "loss_cls": 4.2169, "loss": 4.2169, "time": 0.70776} +{"mode": "train", "epoch": 6, "iter": 3500, "lr": 0.09961, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22641, "top5_acc": 0.48141, "loss_cls": 4.22011, "loss": 4.22011, "time": 0.70898} +{"mode": "train", "epoch": 6, "iter": 3600, "lr": 0.09961, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24, "top5_acc": 0.48344, "loss_cls": 4.19282, "loss": 4.19282, "time": 0.70567} +{"mode": "train", "epoch": 6, "iter": 3700, "lr": 0.09961, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23391, "top5_acc": 0.48641, "loss_cls": 4.19546, "loss": 4.19546, "time": 0.7049} +{"mode": "val", "epoch": 6, "iter": 309, "lr": 0.09961, "top1_acc": 0.17115, "top5_acc": 0.3893, "mean_class_accuracy": 0.17097} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0996, "memory": 15990, "data_time": 1.26729, "top1_acc": 0.23406, "top5_acc": 0.49078, "loss_cls": 4.16424, "loss": 4.16424, "time": 1.9748} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0996, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23266, "top5_acc": 0.48141, "loss_cls": 4.20343, "loss": 4.20343, "time": 0.70665} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.0996, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24922, "top5_acc": 0.49422, "loss_cls": 4.158, "loss": 4.158, "time": 0.70754} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.09959, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22484, "top5_acc": 0.47609, "loss_cls": 4.2137, "loss": 4.2137, "time": 0.7027} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.09959, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24141, "top5_acc": 0.48438, "loss_cls": 4.1419, "loss": 4.1419, "time": 0.70078} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.09958, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23219, "top5_acc": 0.48234, "loss_cls": 4.19392, "loss": 4.19392, "time": 0.70297} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.09958, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23594, "top5_acc": 0.48125, "loss_cls": 4.19153, "loss": 4.19153, "time": 0.70026} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.09958, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23984, "top5_acc": 0.48734, "loss_cls": 4.18759, "loss": 4.18759, "time": 0.70188} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.09957, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23219, "top5_acc": 0.485, "loss_cls": 4.21001, "loss": 4.21001, "time": 0.70063} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.09957, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23734, "top5_acc": 0.48906, "loss_cls": 4.17653, "loss": 4.17653, "time": 0.70063} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.09957, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2275, "top5_acc": 0.47094, "loss_cls": 4.21165, "loss": 4.21165, "time": 0.70189} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.09956, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23828, "top5_acc": 0.47766, "loss_cls": 4.21978, "loss": 4.21978, "time": 0.70244} +{"mode": "train", "epoch": 7, "iter": 1300, "lr": 0.09956, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23906, "top5_acc": 0.47984, "loss_cls": 4.20007, "loss": 4.20007, "time": 0.698} +{"mode": "train", "epoch": 7, "iter": 1400, "lr": 0.09956, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23281, "top5_acc": 0.48516, "loss_cls": 4.17932, "loss": 4.17932, "time": 0.69973} +{"mode": "train", "epoch": 7, "iter": 1500, "lr": 0.09955, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23156, "top5_acc": 0.48906, "loss_cls": 4.17993, "loss": 4.17993, "time": 0.69747} +{"mode": "train", "epoch": 7, "iter": 1600, "lr": 0.09955, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23609, "top5_acc": 0.47859, "loss_cls": 4.22228, "loss": 4.22228, "time": 0.69964} +{"mode": "train", "epoch": 7, "iter": 1700, "lr": 0.09954, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22984, "top5_acc": 0.47922, "loss_cls": 4.21948, "loss": 4.21948, "time": 0.6962} +{"mode": "train", "epoch": 7, "iter": 1800, "lr": 0.09954, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22922, "top5_acc": 0.48, "loss_cls": 4.20956, "loss": 4.20956, "time": 0.69858} +{"mode": "train", "epoch": 7, "iter": 1900, "lr": 0.09954, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23531, "top5_acc": 0.48281, "loss_cls": 4.1844, "loss": 4.1844, "time": 0.70096} +{"mode": "train", "epoch": 7, "iter": 2000, "lr": 0.09953, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23453, "top5_acc": 0.47422, "loss_cls": 4.20475, "loss": 4.20475, "time": 0.70316} +{"mode": "train", "epoch": 7, "iter": 2100, "lr": 0.09953, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23062, "top5_acc": 0.48781, "loss_cls": 4.17615, "loss": 4.17615, "time": 0.69954} +{"mode": "train", "epoch": 7, "iter": 2200, "lr": 0.09952, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23688, "top5_acc": 0.47984, "loss_cls": 4.18163, "loss": 4.18163, "time": 0.70014} +{"mode": "train", "epoch": 7, "iter": 2300, "lr": 0.09952, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23969, "top5_acc": 0.47703, "loss_cls": 4.19694, "loss": 4.19694, "time": 0.70323} +{"mode": "train", "epoch": 7, "iter": 2400, "lr": 0.09952, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23719, "top5_acc": 0.47594, "loss_cls": 4.20449, "loss": 4.20449, "time": 0.69868} +{"mode": "train", "epoch": 7, "iter": 2500, "lr": 0.09951, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22672, "top5_acc": 0.47172, "loss_cls": 4.23365, "loss": 4.23365, "time": 0.69856} +{"mode": "train", "epoch": 7, "iter": 2600, "lr": 0.09951, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23906, "top5_acc": 0.48172, "loss_cls": 4.21893, "loss": 4.21893, "time": 0.69771} +{"mode": "train", "epoch": 7, "iter": 2700, "lr": 0.09951, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23672, "top5_acc": 0.48141, "loss_cls": 4.1934, "loss": 4.1934, "time": 0.69709} +{"mode": "train", "epoch": 7, "iter": 2800, "lr": 0.0995, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23641, "top5_acc": 0.48156, "loss_cls": 4.17787, "loss": 4.17787, "time": 0.70008} +{"mode": "train", "epoch": 7, "iter": 2900, "lr": 0.0995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24, "top5_acc": 0.4875, "loss_cls": 4.1787, "loss": 4.1787, "time": 0.69672} +{"mode": "train", "epoch": 7, "iter": 3000, "lr": 0.09949, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24156, "top5_acc": 0.48688, "loss_cls": 4.16771, "loss": 4.16771, "time": 0.70267} +{"mode": "train", "epoch": 7, "iter": 3100, "lr": 0.09949, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23906, "top5_acc": 0.4875, "loss_cls": 4.20023, "loss": 4.20023, "time": 0.70245} +{"mode": "train", "epoch": 7, "iter": 3200, "lr": 0.09949, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2325, "top5_acc": 0.48734, "loss_cls": 4.19504, "loss": 4.19504, "time": 0.70123} +{"mode": "train", "epoch": 7, "iter": 3300, "lr": 0.09948, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23938, "top5_acc": 0.48812, "loss_cls": 4.19067, "loss": 4.19067, "time": 0.69896} +{"mode": "train", "epoch": 7, "iter": 3400, "lr": 0.09948, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23125, "top5_acc": 0.47766, "loss_cls": 4.20848, "loss": 4.20848, "time": 0.69787} +{"mode": "train", "epoch": 7, "iter": 3500, "lr": 0.09947, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2425, "top5_acc": 0.49656, "loss_cls": 4.17256, "loss": 4.17256, "time": 0.69671} +{"mode": "train", "epoch": 7, "iter": 3600, "lr": 0.09947, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24078, "top5_acc": 0.48781, "loss_cls": 4.1752, "loss": 4.1752, "time": 0.69618} +{"mode": "train", "epoch": 7, "iter": 3700, "lr": 0.09947, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22938, "top5_acc": 0.48125, "loss_cls": 4.2183, "loss": 4.2183, "time": 0.69954} +{"mode": "val", "epoch": 7, "iter": 309, "lr": 0.09946, "top1_acc": 0.1712, "top5_acc": 0.39422, "mean_class_accuracy": 0.17096} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.09946, "memory": 15990, "data_time": 1.30019, "top1_acc": 0.25141, "top5_acc": 0.49672, "loss_cls": 4.11995, "loss": 4.11995, "time": 2.00674} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.09946, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23266, "top5_acc": 0.47922, "loss_cls": 4.20757, "loss": 4.20757, "time": 0.70426} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.09945, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24031, "top5_acc": 0.48781, "loss_cls": 4.16281, "loss": 4.16281, "time": 0.70264} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.09945, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23406, "top5_acc": 0.48562, "loss_cls": 4.19554, "loss": 4.19554, "time": 0.70473} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.09944, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23344, "top5_acc": 0.48297, "loss_cls": 4.18664, "loss": 4.18664, "time": 0.69985} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.09944, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2325, "top5_acc": 0.48156, "loss_cls": 4.20646, "loss": 4.20646, "time": 0.69762} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.09943, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23828, "top5_acc": 0.48719, "loss_cls": 4.1588, "loss": 4.1588, "time": 0.69892} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.09943, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24172, "top5_acc": 0.49109, "loss_cls": 4.13224, "loss": 4.13224, "time": 0.69426} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.09943, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24047, "top5_acc": 0.48672, "loss_cls": 4.17862, "loss": 4.17862, "time": 0.69895} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.09942, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23375, "top5_acc": 0.48438, "loss_cls": 4.19562, "loss": 4.19562, "time": 0.69706} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.09942, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23797, "top5_acc": 0.49547, "loss_cls": 4.17169, "loss": 4.17169, "time": 0.69742} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.09941, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24344, "top5_acc": 0.49328, "loss_cls": 4.13382, "loss": 4.13382, "time": 0.69736} +{"mode": "train", "epoch": 8, "iter": 1300, "lr": 0.09941, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23516, "top5_acc": 0.49328, "loss_cls": 4.17057, "loss": 4.17057, "time": 0.69858} +{"mode": "train", "epoch": 8, "iter": 1400, "lr": 0.0994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24047, "top5_acc": 0.49266, "loss_cls": 4.15575, "loss": 4.15575, "time": 0.69953} +{"mode": "train", "epoch": 8, "iter": 1500, "lr": 0.0994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24312, "top5_acc": 0.49422, "loss_cls": 4.15962, "loss": 4.15962, "time": 0.69705} +{"mode": "train", "epoch": 8, "iter": 1600, "lr": 0.0994, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23672, "top5_acc": 0.48516, "loss_cls": 4.17886, "loss": 4.17886, "time": 0.69832} +{"mode": "train", "epoch": 8, "iter": 1700, "lr": 0.09939, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23266, "top5_acc": 0.48328, "loss_cls": 4.19702, "loss": 4.19702, "time": 0.69693} +{"mode": "train", "epoch": 8, "iter": 1800, "lr": 0.09939, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2375, "top5_acc": 0.49156, "loss_cls": 4.1517, "loss": 4.1517, "time": 0.699} +{"mode": "train", "epoch": 8, "iter": 1900, "lr": 0.09938, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24438, "top5_acc": 0.48953, "loss_cls": 4.14279, "loss": 4.14279, "time": 0.69705} +{"mode": "train", "epoch": 8, "iter": 2000, "lr": 0.09938, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2375, "top5_acc": 0.48547, "loss_cls": 4.17042, "loss": 4.17042, "time": 0.69985} +{"mode": "train", "epoch": 8, "iter": 2100, "lr": 0.09937, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24094, "top5_acc": 0.48484, "loss_cls": 4.15752, "loss": 4.15752, "time": 0.69937} +{"mode": "train", "epoch": 8, "iter": 2200, "lr": 0.09937, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23438, "top5_acc": 0.48297, "loss_cls": 4.17153, "loss": 4.17153, "time": 0.70163} +{"mode": "train", "epoch": 8, "iter": 2300, "lr": 0.09937, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24922, "top5_acc": 0.49688, "loss_cls": 4.12555, "loss": 4.12555, "time": 0.6983} +{"mode": "train", "epoch": 8, "iter": 2400, "lr": 0.09936, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23719, "top5_acc": 0.48891, "loss_cls": 4.15507, "loss": 4.15507, "time": 0.70015} +{"mode": "train", "epoch": 8, "iter": 2500, "lr": 0.09936, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23625, "top5_acc": 0.49531, "loss_cls": 4.17304, "loss": 4.17304, "time": 0.69857} +{"mode": "train", "epoch": 8, "iter": 2600, "lr": 0.09935, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24328, "top5_acc": 0.49016, "loss_cls": 4.15674, "loss": 4.15674, "time": 0.69769} +{"mode": "train", "epoch": 8, "iter": 2700, "lr": 0.09935, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23312, "top5_acc": 0.47641, "loss_cls": 4.21103, "loss": 4.21103, "time": 0.69676} +{"mode": "train", "epoch": 8, "iter": 2800, "lr": 0.09934, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23094, "top5_acc": 0.47406, "loss_cls": 4.22264, "loss": 4.22264, "time": 0.69807} +{"mode": "train", "epoch": 8, "iter": 2900, "lr": 0.09934, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24406, "top5_acc": 0.49031, "loss_cls": 4.16996, "loss": 4.16996, "time": 0.69898} +{"mode": "train", "epoch": 8, "iter": 3000, "lr": 0.09933, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24266, "top5_acc": 0.49641, "loss_cls": 4.11514, "loss": 4.11514, "time": 0.6982} +{"mode": "train", "epoch": 8, "iter": 3100, "lr": 0.09933, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23734, "top5_acc": 0.48312, "loss_cls": 4.15447, "loss": 4.15447, "time": 0.70193} +{"mode": "train", "epoch": 8, "iter": 3200, "lr": 0.09933, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23844, "top5_acc": 0.48938, "loss_cls": 4.17674, "loss": 4.17674, "time": 0.70121} +{"mode": "train", "epoch": 8, "iter": 3300, "lr": 0.09932, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23688, "top5_acc": 0.485, "loss_cls": 4.19963, "loss": 4.19963, "time": 0.69915} +{"mode": "train", "epoch": 8, "iter": 3400, "lr": 0.09932, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23531, "top5_acc": 0.48031, "loss_cls": 4.18956, "loss": 4.18956, "time": 0.70105} +{"mode": "train", "epoch": 8, "iter": 3500, "lr": 0.09931, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23562, "top5_acc": 0.49141, "loss_cls": 4.15249, "loss": 4.15249, "time": 0.7014} +{"mode": "train", "epoch": 8, "iter": 3600, "lr": 0.09931, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24312, "top5_acc": 0.48672, "loss_cls": 4.16802, "loss": 4.16802, "time": 0.69981} +{"mode": "train", "epoch": 8, "iter": 3700, "lr": 0.0993, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24312, "top5_acc": 0.50203, "loss_cls": 4.11828, "loss": 4.11828, "time": 0.69872} +{"mode": "val", "epoch": 8, "iter": 309, "lr": 0.0993, "top1_acc": 0.16239, "top5_acc": 0.37644, "mean_class_accuracy": 0.16228} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.0993, "memory": 15990, "data_time": 1.28151, "top1_acc": 0.24203, "top5_acc": 0.50562, "loss_cls": 4.10862, "loss": 4.10862, "time": 1.98457} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.09929, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24953, "top5_acc": 0.49094, "loss_cls": 4.12552, "loss": 4.12552, "time": 0.70566} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.09929, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24375, "top5_acc": 0.48609, "loss_cls": 4.17603, "loss": 4.17603, "time": 0.70143} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.09928, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24109, "top5_acc": 0.48844, "loss_cls": 4.17422, "loss": 4.17422, "time": 0.70132} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.09928, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24922, "top5_acc": 0.49969, "loss_cls": 4.11712, "loss": 4.11712, "time": 0.69852} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.09927, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23984, "top5_acc": 0.48719, "loss_cls": 4.14576, "loss": 4.14576, "time": 0.70133} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.09927, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23891, "top5_acc": 0.49297, "loss_cls": 4.16114, "loss": 4.16114, "time": 0.69878} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.09926, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23844, "top5_acc": 0.48719, "loss_cls": 4.13393, "loss": 4.13393, "time": 0.70004} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.09926, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24266, "top5_acc": 0.48891, "loss_cls": 4.13439, "loss": 4.13439, "time": 0.70379} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.09925, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24219, "top5_acc": 0.48781, "loss_cls": 4.13522, "loss": 4.13522, "time": 0.7048} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.09925, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24875, "top5_acc": 0.49703, "loss_cls": 4.13087, "loss": 4.13087, "time": 0.70078} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.09924, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24641, "top5_acc": 0.49734, "loss_cls": 4.13159, "loss": 4.13159, "time": 0.70164} +{"mode": "train", "epoch": 9, "iter": 1300, "lr": 0.09924, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24266, "top5_acc": 0.49609, "loss_cls": 4.12855, "loss": 4.12855, "time": 0.7034} +{"mode": "train", "epoch": 9, "iter": 1400, "lr": 0.09923, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24141, "top5_acc": 0.47688, "loss_cls": 4.19739, "loss": 4.19739, "time": 0.70175} +{"mode": "train", "epoch": 9, "iter": 1500, "lr": 0.09923, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24688, "top5_acc": 0.48984, "loss_cls": 4.13085, "loss": 4.13085, "time": 0.70442} +{"mode": "train", "epoch": 9, "iter": 1600, "lr": 0.09922, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23703, "top5_acc": 0.48516, "loss_cls": 4.16036, "loss": 4.16036, "time": 0.70134} +{"mode": "train", "epoch": 9, "iter": 1700, "lr": 0.09922, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24203, "top5_acc": 0.49094, "loss_cls": 4.16393, "loss": 4.16393, "time": 0.69673} +{"mode": "train", "epoch": 9, "iter": 1800, "lr": 0.09921, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24484, "top5_acc": 0.50094, "loss_cls": 4.1262, "loss": 4.1262, "time": 0.69929} +{"mode": "train", "epoch": 9, "iter": 1900, "lr": 0.09921, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24312, "top5_acc": 0.49672, "loss_cls": 4.15367, "loss": 4.15367, "time": 0.70289} +{"mode": "train", "epoch": 9, "iter": 2000, "lr": 0.0992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23281, "top5_acc": 0.49297, "loss_cls": 4.17566, "loss": 4.17566, "time": 0.70397} +{"mode": "train", "epoch": 9, "iter": 2100, "lr": 0.0992, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24484, "top5_acc": 0.49516, "loss_cls": 4.13726, "loss": 4.13726, "time": 0.70023} +{"mode": "train", "epoch": 9, "iter": 2200, "lr": 0.09919, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24188, "top5_acc": 0.49359, "loss_cls": 4.14158, "loss": 4.14158, "time": 0.70245} +{"mode": "train", "epoch": 9, "iter": 2300, "lr": 0.09919, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24219, "top5_acc": 0.49234, "loss_cls": 4.12264, "loss": 4.12264, "time": 0.69943} +{"mode": "train", "epoch": 9, "iter": 2400, "lr": 0.09918, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24, "top5_acc": 0.48484, "loss_cls": 4.17959, "loss": 4.17959, "time": 0.70169} +{"mode": "train", "epoch": 9, "iter": 2500, "lr": 0.09918, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24859, "top5_acc": 0.49797, "loss_cls": 4.12783, "loss": 4.12783, "time": 0.69776} +{"mode": "train", "epoch": 9, "iter": 2600, "lr": 0.09917, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23844, "top5_acc": 0.50016, "loss_cls": 4.14486, "loss": 4.14486, "time": 0.70023} +{"mode": "train", "epoch": 9, "iter": 2700, "lr": 0.09917, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24281, "top5_acc": 0.47891, "loss_cls": 4.17686, "loss": 4.17686, "time": 0.69877} +{"mode": "train", "epoch": 9, "iter": 2800, "lr": 0.09916, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23891, "top5_acc": 0.4925, "loss_cls": 4.12161, "loss": 4.12161, "time": 0.70081} +{"mode": "train", "epoch": 9, "iter": 2900, "lr": 0.09916, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24875, "top5_acc": 0.49328, "loss_cls": 4.13791, "loss": 4.13791, "time": 0.6979} +{"mode": "train", "epoch": 9, "iter": 3000, "lr": 0.09915, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2425, "top5_acc": 0.49344, "loss_cls": 4.12887, "loss": 4.12887, "time": 0.69776} +{"mode": "train", "epoch": 9, "iter": 3100, "lr": 0.09915, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24016, "top5_acc": 0.49094, "loss_cls": 4.1389, "loss": 4.1389, "time": 0.70189} +{"mode": "train", "epoch": 9, "iter": 3200, "lr": 0.09914, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23312, "top5_acc": 0.49141, "loss_cls": 4.18334, "loss": 4.18334, "time": 0.69851} +{"mode": "train", "epoch": 9, "iter": 3300, "lr": 0.09914, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23859, "top5_acc": 0.48719, "loss_cls": 4.16453, "loss": 4.16453, "time": 0.69838} +{"mode": "train", "epoch": 9, "iter": 3400, "lr": 0.09913, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24891, "top5_acc": 0.50031, "loss_cls": 4.13264, "loss": 4.13264, "time": 0.70277} +{"mode": "train", "epoch": 9, "iter": 3500, "lr": 0.09913, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24172, "top5_acc": 0.49672, "loss_cls": 4.13284, "loss": 4.13284, "time": 0.69751} +{"mode": "train", "epoch": 9, "iter": 3600, "lr": 0.09912, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23734, "top5_acc": 0.48297, "loss_cls": 4.17717, "loss": 4.17717, "time": 0.69874} +{"mode": "train", "epoch": 9, "iter": 3700, "lr": 0.09912, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24375, "top5_acc": 0.49516, "loss_cls": 4.11984, "loss": 4.11984, "time": 0.70142} +{"mode": "val", "epoch": 9, "iter": 309, "lr": 0.09911, "top1_acc": 0.16077, "top5_acc": 0.3772, "mean_class_accuracy": 0.16052} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.09911, "memory": 15990, "data_time": 1.28735, "top1_acc": 0.25219, "top5_acc": 0.5075, "loss_cls": 4.07562, "loss": 4.07562, "time": 1.98777} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.0991, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25859, "top5_acc": 0.50203, "loss_cls": 4.08785, "loss": 4.08785, "time": 0.70384} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.0991, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25344, "top5_acc": 0.50594, "loss_cls": 4.04565, "loss": 4.04565, "time": 0.70498} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.09909, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24656, "top5_acc": 0.49688, "loss_cls": 4.10972, "loss": 4.10972, "time": 0.69941} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.09909, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23922, "top5_acc": 0.4975, "loss_cls": 4.15541, "loss": 4.15541, "time": 0.69865} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.09908, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.235, "top5_acc": 0.49234, "loss_cls": 4.15444, "loss": 4.15444, "time": 0.69884} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.09908, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2525, "top5_acc": 0.49328, "loss_cls": 4.13647, "loss": 4.13647, "time": 0.69616} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.09907, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24672, "top5_acc": 0.49438, "loss_cls": 4.10462, "loss": 4.10462, "time": 0.69614} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.09907, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23875, "top5_acc": 0.48891, "loss_cls": 4.15158, "loss": 4.15158, "time": 0.69806} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.09906, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24719, "top5_acc": 0.49516, "loss_cls": 4.15388, "loss": 4.15388, "time": 0.69953} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.09906, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23281, "top5_acc": 0.48734, "loss_cls": 4.16378, "loss": 4.16378, "time": 0.69681} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.09905, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23625, "top5_acc": 0.48781, "loss_cls": 4.15216, "loss": 4.15216, "time": 0.69873} +{"mode": "train", "epoch": 10, "iter": 1300, "lr": 0.09905, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25297, "top5_acc": 0.50516, "loss_cls": 4.09097, "loss": 4.09097, "time": 0.70098} +{"mode": "train", "epoch": 10, "iter": 1400, "lr": 0.09904, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23859, "top5_acc": 0.49531, "loss_cls": 4.14952, "loss": 4.14952, "time": 0.69973} +{"mode": "train", "epoch": 10, "iter": 1500, "lr": 0.09903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24484, "top5_acc": 0.48969, "loss_cls": 4.15047, "loss": 4.15047, "time": 0.7045} +{"mode": "train", "epoch": 10, "iter": 1600, "lr": 0.09903, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24484, "top5_acc": 0.48438, "loss_cls": 4.1808, "loss": 4.1808, "time": 0.69564} +{"mode": "train", "epoch": 10, "iter": 1700, "lr": 0.09902, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25188, "top5_acc": 0.49719, "loss_cls": 4.09657, "loss": 4.09657, "time": 0.70143} +{"mode": "train", "epoch": 10, "iter": 1800, "lr": 0.09902, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24594, "top5_acc": 0.48625, "loss_cls": 4.11802, "loss": 4.11802, "time": 0.69827} +{"mode": "train", "epoch": 10, "iter": 1900, "lr": 0.09901, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23844, "top5_acc": 0.48906, "loss_cls": 4.12706, "loss": 4.12706, "time": 0.69691} +{"mode": "train", "epoch": 10, "iter": 2000, "lr": 0.09901, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24516, "top5_acc": 0.48922, "loss_cls": 4.11532, "loss": 4.11532, "time": 0.70368} +{"mode": "train", "epoch": 10, "iter": 2100, "lr": 0.099, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24625, "top5_acc": 0.49953, "loss_cls": 4.11546, "loss": 4.11546, "time": 0.70009} +{"mode": "train", "epoch": 10, "iter": 2200, "lr": 0.099, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23797, "top5_acc": 0.49203, "loss_cls": 4.13778, "loss": 4.13778, "time": 0.70531} +{"mode": "train", "epoch": 10, "iter": 2300, "lr": 0.09899, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25125, "top5_acc": 0.50062, "loss_cls": 4.09719, "loss": 4.09719, "time": 0.69805} +{"mode": "train", "epoch": 10, "iter": 2400, "lr": 0.09898, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24391, "top5_acc": 0.48891, "loss_cls": 4.16491, "loss": 4.16491, "time": 0.69663} +{"mode": "train", "epoch": 10, "iter": 2500, "lr": 0.09898, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23766, "top5_acc": 0.50266, "loss_cls": 4.10237, "loss": 4.10237, "time": 0.69631} +{"mode": "train", "epoch": 10, "iter": 2600, "lr": 0.09897, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24547, "top5_acc": 0.48562, "loss_cls": 4.1572, "loss": 4.1572, "time": 0.69891} +{"mode": "train", "epoch": 10, "iter": 2700, "lr": 0.09897, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23938, "top5_acc": 0.49016, "loss_cls": 4.14361, "loss": 4.14361, "time": 0.69825} +{"mode": "train", "epoch": 10, "iter": 2800, "lr": 0.09896, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24641, "top5_acc": 0.50234, "loss_cls": 4.10516, "loss": 4.10516, "time": 0.70023} +{"mode": "train", "epoch": 10, "iter": 2900, "lr": 0.09896, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23547, "top5_acc": 0.49312, "loss_cls": 4.13334, "loss": 4.13334, "time": 0.69815} +{"mode": "train", "epoch": 10, "iter": 3000, "lr": 0.09895, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24438, "top5_acc": 0.48812, "loss_cls": 4.16049, "loss": 4.16049, "time": 0.69765} +{"mode": "train", "epoch": 10, "iter": 3100, "lr": 0.09894, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24469, "top5_acc": 0.49328, "loss_cls": 4.10975, "loss": 4.10975, "time": 0.7013} +{"mode": "train", "epoch": 10, "iter": 3200, "lr": 0.09894, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23234, "top5_acc": 0.48844, "loss_cls": 4.17468, "loss": 4.17468, "time": 0.69691} +{"mode": "train", "epoch": 10, "iter": 3300, "lr": 0.09893, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25141, "top5_acc": 0.495, "loss_cls": 4.09959, "loss": 4.09959, "time": 0.69989} +{"mode": "train", "epoch": 10, "iter": 3400, "lr": 0.09893, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24875, "top5_acc": 0.49875, "loss_cls": 4.11229, "loss": 4.11229, "time": 0.69788} +{"mode": "train", "epoch": 10, "iter": 3500, "lr": 0.09892, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25094, "top5_acc": 0.49781, "loss_cls": 4.10646, "loss": 4.10646, "time": 0.69677} +{"mode": "train", "epoch": 10, "iter": 3600, "lr": 0.09892, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24656, "top5_acc": 0.48891, "loss_cls": 4.15252, "loss": 4.15252, "time": 0.69742} +{"mode": "train", "epoch": 10, "iter": 3700, "lr": 0.09891, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24359, "top5_acc": 0.49812, "loss_cls": 4.12995, "loss": 4.12995, "time": 0.70491} +{"mode": "val", "epoch": 10, "iter": 309, "lr": 0.09891, "top1_acc": 0.16689, "top5_acc": 0.38859, "mean_class_accuracy": 0.16669} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.0989, "memory": 15990, "data_time": 1.29392, "top1_acc": 0.255, "top5_acc": 0.49688, "loss_cls": 4.11601, "loss": 4.11601, "time": 2.00442} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.0989, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.2525, "top5_acc": 0.50016, "loss_cls": 4.10176, "loss": 4.10176, "time": 0.70546} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.09889, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24469, "top5_acc": 0.49609, "loss_cls": 4.11153, "loss": 4.11153, "time": 0.70487} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.09888, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23266, "top5_acc": 0.49031, "loss_cls": 4.14879, "loss": 4.14879, "time": 0.69826} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.09888, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24594, "top5_acc": 0.50594, "loss_cls": 4.0997, "loss": 4.0997, "time": 0.70042} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.09887, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25016, "top5_acc": 0.49422, "loss_cls": 4.13667, "loss": 4.13667, "time": 0.69844} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.09887, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24969, "top5_acc": 0.49938, "loss_cls": 4.08624, "loss": 4.08624, "time": 0.69622} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.09886, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23969, "top5_acc": 0.48609, "loss_cls": 4.15085, "loss": 4.15085, "time": 0.69972} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.09885, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23406, "top5_acc": 0.49047, "loss_cls": 4.16706, "loss": 4.16706, "time": 0.69825} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.09885, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25078, "top5_acc": 0.50609, "loss_cls": 4.08863, "loss": 4.08863, "time": 0.6997} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.09884, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24281, "top5_acc": 0.49844, "loss_cls": 4.11996, "loss": 4.11996, "time": 0.69841} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.09884, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25875, "top5_acc": 0.50422, "loss_cls": 4.06346, "loss": 4.06346, "time": 0.70016} +{"mode": "train", "epoch": 11, "iter": 1300, "lr": 0.09883, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24844, "top5_acc": 0.49688, "loss_cls": 4.12605, "loss": 4.12605, "time": 0.6998} +{"mode": "train", "epoch": 11, "iter": 1400, "lr": 0.09882, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25156, "top5_acc": 0.49938, "loss_cls": 4.10839, "loss": 4.10839, "time": 0.69805} +{"mode": "train", "epoch": 11, "iter": 1500, "lr": 0.09882, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23906, "top5_acc": 0.49266, "loss_cls": 4.14413, "loss": 4.14413, "time": 0.69971} +{"mode": "train", "epoch": 11, "iter": 1600, "lr": 0.09881, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24125, "top5_acc": 0.5, "loss_cls": 4.14822, "loss": 4.14822, "time": 0.69727} +{"mode": "train", "epoch": 11, "iter": 1700, "lr": 0.09881, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24109, "top5_acc": 0.49797, "loss_cls": 4.11946, "loss": 4.11946, "time": 0.6987} +{"mode": "train", "epoch": 11, "iter": 1800, "lr": 0.0988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25234, "top5_acc": 0.50219, "loss_cls": 4.08279, "loss": 4.08279, "time": 0.6973} +{"mode": "train", "epoch": 11, "iter": 1900, "lr": 0.09879, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24422, "top5_acc": 0.49906, "loss_cls": 4.10645, "loss": 4.10645, "time": 0.70045} +{"mode": "train", "epoch": 11, "iter": 2000, "lr": 0.09879, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24859, "top5_acc": 0.49938, "loss_cls": 4.12873, "loss": 4.12873, "time": 0.70287} +{"mode": "train", "epoch": 11, "iter": 2100, "lr": 0.09878, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24266, "top5_acc": 0.49594, "loss_cls": 4.12345, "loss": 4.12345, "time": 0.69938} +{"mode": "train", "epoch": 11, "iter": 2200, "lr": 0.09878, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23766, "top5_acc": 0.4925, "loss_cls": 4.12152, "loss": 4.12152, "time": 0.70202} +{"mode": "train", "epoch": 11, "iter": 2300, "lr": 0.09877, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25297, "top5_acc": 0.49812, "loss_cls": 4.08893, "loss": 4.08893, "time": 0.70114} +{"mode": "train", "epoch": 11, "iter": 2400, "lr": 0.09876, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23719, "top5_acc": 0.49344, "loss_cls": 4.13932, "loss": 4.13932, "time": 0.69954} +{"mode": "train", "epoch": 11, "iter": 2500, "lr": 0.09876, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25016, "top5_acc": 0.49672, "loss_cls": 4.12293, "loss": 4.12293, "time": 0.69591} +{"mode": "train", "epoch": 11, "iter": 2600, "lr": 0.09875, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25156, "top5_acc": 0.50641, "loss_cls": 4.09102, "loss": 4.09102, "time": 0.69712} +{"mode": "train", "epoch": 11, "iter": 2700, "lr": 0.09874, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25641, "top5_acc": 0.49422, "loss_cls": 4.11739, "loss": 4.11739, "time": 0.69882} +{"mode": "train", "epoch": 11, "iter": 2800, "lr": 0.09874, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25281, "top5_acc": 0.50141, "loss_cls": 4.08704, "loss": 4.08704, "time": 0.70275} +{"mode": "train", "epoch": 11, "iter": 2900, "lr": 0.09873, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25016, "top5_acc": 0.50469, "loss_cls": 4.12769, "loss": 4.12769, "time": 0.70046} +{"mode": "train", "epoch": 11, "iter": 3000, "lr": 0.09873, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25188, "top5_acc": 0.49328, "loss_cls": 4.12486, "loss": 4.12486, "time": 0.69866} +{"mode": "train", "epoch": 11, "iter": 3100, "lr": 0.09872, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25125, "top5_acc": 0.5, "loss_cls": 4.08686, "loss": 4.08686, "time": 0.69921} +{"mode": "train", "epoch": 11, "iter": 3200, "lr": 0.09871, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24641, "top5_acc": 0.48797, "loss_cls": 4.14877, "loss": 4.14877, "time": 0.69951} +{"mode": "train", "epoch": 11, "iter": 3300, "lr": 0.09871, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23984, "top5_acc": 0.49281, "loss_cls": 4.13825, "loss": 4.13825, "time": 0.69808} +{"mode": "train", "epoch": 11, "iter": 3400, "lr": 0.0987, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25094, "top5_acc": 0.49234, "loss_cls": 4.11465, "loss": 4.11465, "time": 0.70057} +{"mode": "train", "epoch": 11, "iter": 3500, "lr": 0.09869, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24125, "top5_acc": 0.49531, "loss_cls": 4.11224, "loss": 4.11224, "time": 0.69853} +{"mode": "train", "epoch": 11, "iter": 3600, "lr": 0.09869, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24328, "top5_acc": 0.49953, "loss_cls": 4.10239, "loss": 4.10239, "time": 0.69579} +{"mode": "train", "epoch": 11, "iter": 3700, "lr": 0.09868, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25094, "top5_acc": 0.50313, "loss_cls": 4.07216, "loss": 4.07216, "time": 0.69895} +{"mode": "val", "epoch": 11, "iter": 309, "lr": 0.09868, "top1_acc": 0.17692, "top5_acc": 0.4084, "mean_class_accuracy": 0.17673} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.09867, "memory": 15990, "data_time": 1.27543, "top1_acc": 0.24797, "top5_acc": 0.49969, "loss_cls": 4.10031, "loss": 4.10031, "time": 1.98238} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.09867, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24797, "top5_acc": 0.50906, "loss_cls": 4.07956, "loss": 4.07956, "time": 0.70754} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.09866, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25547, "top5_acc": 0.51172, "loss_cls": 4.05416, "loss": 4.05416, "time": 0.70136} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.09865, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25281, "top5_acc": 0.50187, "loss_cls": 4.06005, "loss": 4.06005, "time": 0.70065} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.09865, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24984, "top5_acc": 0.49188, "loss_cls": 4.11224, "loss": 4.11224, "time": 0.70048} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.09864, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24625, "top5_acc": 0.49938, "loss_cls": 4.10563, "loss": 4.10563, "time": 0.69836} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.09863, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25062, "top5_acc": 0.4975, "loss_cls": 4.07677, "loss": 4.07677, "time": 0.70102} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.09863, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24094, "top5_acc": 0.48391, "loss_cls": 4.13735, "loss": 4.13735, "time": 0.69955} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.09862, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24312, "top5_acc": 0.49203, "loss_cls": 4.11983, "loss": 4.11983, "time": 0.69975} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.09861, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24453, "top5_acc": 0.49766, "loss_cls": 4.13642, "loss": 4.13642, "time": 0.70013} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.09861, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24203, "top5_acc": 0.49938, "loss_cls": 4.09669, "loss": 4.09669, "time": 0.69924} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.0986, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24062, "top5_acc": 0.48969, "loss_cls": 4.13002, "loss": 4.13002, "time": 0.69822} +{"mode": "train", "epoch": 12, "iter": 1300, "lr": 0.09859, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25438, "top5_acc": 0.50297, "loss_cls": 4.09491, "loss": 4.09491, "time": 0.69993} +{"mode": "train", "epoch": 12, "iter": 1400, "lr": 0.09859, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25016, "top5_acc": 0.50297, "loss_cls": 4.09754, "loss": 4.09754, "time": 0.69867} +{"mode": "train", "epoch": 12, "iter": 1500, "lr": 0.09858, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.24578, "top5_acc": 0.49203, "loss_cls": 4.13444, "loss": 4.13444, "time": 0.70012} +{"mode": "train", "epoch": 12, "iter": 1600, "lr": 0.09857, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25625, "top5_acc": 0.49719, "loss_cls": 4.10766, "loss": 4.10766, "time": 0.69969} +{"mode": "train", "epoch": 12, "iter": 1700, "lr": 0.09857, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24281, "top5_acc": 0.48625, "loss_cls": 4.16066, "loss": 4.16066, "time": 0.69909} +{"mode": "train", "epoch": 12, "iter": 1800, "lr": 0.09856, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26125, "top5_acc": 0.51047, "loss_cls": 4.06458, "loss": 4.06458, "time": 0.69906} +{"mode": "train", "epoch": 12, "iter": 1900, "lr": 0.09855, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26016, "top5_acc": 0.51453, "loss_cls": 4.06914, "loss": 4.06914, "time": 0.69848} +{"mode": "train", "epoch": 12, "iter": 2000, "lr": 0.09855, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24297, "top5_acc": 0.50656, "loss_cls": 4.08862, "loss": 4.08862, "time": 0.704} +{"mode": "train", "epoch": 12, "iter": 2100, "lr": 0.09854, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25062, "top5_acc": 0.50938, "loss_cls": 4.07467, "loss": 4.07467, "time": 0.70046} +{"mode": "train", "epoch": 12, "iter": 2200, "lr": 0.09853, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23844, "top5_acc": 0.49062, "loss_cls": 4.14274, "loss": 4.14274, "time": 0.69907} +{"mode": "train", "epoch": 12, "iter": 2300, "lr": 0.09853, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23891, "top5_acc": 0.49031, "loss_cls": 4.13547, "loss": 4.13547, "time": 0.69921} +{"mode": "train", "epoch": 12, "iter": 2400, "lr": 0.09852, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25422, "top5_acc": 0.4975, "loss_cls": 4.10988, "loss": 4.10988, "time": 0.69897} +{"mode": "train", "epoch": 12, "iter": 2500, "lr": 0.09851, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24359, "top5_acc": 0.49438, "loss_cls": 4.11221, "loss": 4.11221, "time": 0.69576} +{"mode": "train", "epoch": 12, "iter": 2600, "lr": 0.09851, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24438, "top5_acc": 0.50016, "loss_cls": 4.08958, "loss": 4.08958, "time": 0.69898} +{"mode": "train", "epoch": 12, "iter": 2700, "lr": 0.0985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24391, "top5_acc": 0.50187, "loss_cls": 4.12831, "loss": 4.12831, "time": 0.69986} +{"mode": "train", "epoch": 12, "iter": 2800, "lr": 0.09849, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24547, "top5_acc": 0.48219, "loss_cls": 4.15231, "loss": 4.15231, "time": 0.69888} +{"mode": "train", "epoch": 12, "iter": 2900, "lr": 0.09849, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25484, "top5_acc": 0.50422, "loss_cls": 4.10045, "loss": 4.10045, "time": 0.6968} +{"mode": "train", "epoch": 12, "iter": 3000, "lr": 0.09848, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24203, "top5_acc": 0.48672, "loss_cls": 4.14543, "loss": 4.14543, "time": 0.69833} +{"mode": "train", "epoch": 12, "iter": 3100, "lr": 0.09847, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24906, "top5_acc": 0.49875, "loss_cls": 4.10281, "loss": 4.10281, "time": 0.69997} +{"mode": "train", "epoch": 12, "iter": 3200, "lr": 0.09847, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24109, "top5_acc": 0.48734, "loss_cls": 4.15442, "loss": 4.15442, "time": 0.69627} +{"mode": "train", "epoch": 12, "iter": 3300, "lr": 0.09846, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25781, "top5_acc": 0.50844, "loss_cls": 4.06199, "loss": 4.06199, "time": 0.69725} +{"mode": "train", "epoch": 12, "iter": 3400, "lr": 0.09845, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24891, "top5_acc": 0.49953, "loss_cls": 4.12274, "loss": 4.12274, "time": 0.69779} +{"mode": "train", "epoch": 12, "iter": 3500, "lr": 0.09845, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25172, "top5_acc": 0.50156, "loss_cls": 4.08997, "loss": 4.08997, "time": 0.69766} +{"mode": "train", "epoch": 12, "iter": 3600, "lr": 0.09844, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24891, "top5_acc": 0.49781, "loss_cls": 4.10367, "loss": 4.10367, "time": 0.69894} +{"mode": "train", "epoch": 12, "iter": 3700, "lr": 0.09843, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24422, "top5_acc": 0.50141, "loss_cls": 4.10901, "loss": 4.10901, "time": 0.69863} +{"mode": "val", "epoch": 12, "iter": 309, "lr": 0.09843, "top1_acc": 0.17728, "top5_acc": 0.4044, "mean_class_accuracy": 0.17707} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.09842, "memory": 15990, "data_time": 1.29221, "top1_acc": 0.25484, "top5_acc": 0.50187, "loss_cls": 4.06929, "loss": 4.06929, "time": 1.99678} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.09842, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25484, "top5_acc": 0.50219, "loss_cls": 4.07601, "loss": 4.07601, "time": 0.70633} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.09841, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26078, "top5_acc": 0.51234, "loss_cls": 4.0337, "loss": 4.0337, "time": 0.69836} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.0984, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24734, "top5_acc": 0.49734, "loss_cls": 4.10923, "loss": 4.10923, "time": 0.69894} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.09839, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25234, "top5_acc": 0.51344, "loss_cls": 4.05489, "loss": 4.05489, "time": 0.70725} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.09839, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25047, "top5_acc": 0.49734, "loss_cls": 4.12013, "loss": 4.12013, "time": 0.69832} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.09838, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25266, "top5_acc": 0.50078, "loss_cls": 4.07046, "loss": 4.07046, "time": 0.70001} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.09837, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24328, "top5_acc": 0.49797, "loss_cls": 4.12923, "loss": 4.12923, "time": 0.70275} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.09837, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24922, "top5_acc": 0.50313, "loss_cls": 4.09456, "loss": 4.09456, "time": 0.69943} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.09836, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24688, "top5_acc": 0.49938, "loss_cls": 4.10577, "loss": 4.10577, "time": 0.69893} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.09835, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24812, "top5_acc": 0.49719, "loss_cls": 4.1136, "loss": 4.1136, "time": 0.69783} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.09834, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24047, "top5_acc": 0.49875, "loss_cls": 4.12957, "loss": 4.12957, "time": 0.69602} +{"mode": "train", "epoch": 13, "iter": 1300, "lr": 0.09834, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24906, "top5_acc": 0.50656, "loss_cls": 4.07586, "loss": 4.07586, "time": 0.70128} +{"mode": "train", "epoch": 13, "iter": 1400, "lr": 0.09833, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25234, "top5_acc": 0.49781, "loss_cls": 4.06656, "loss": 4.06656, "time": 0.69827} +{"mode": "train", "epoch": 13, "iter": 1500, "lr": 0.09832, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24781, "top5_acc": 0.50297, "loss_cls": 4.08437, "loss": 4.08437, "time": 0.69827} +{"mode": "train", "epoch": 13, "iter": 1600, "lr": 0.09832, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26, "top5_acc": 0.50547, "loss_cls": 4.06853, "loss": 4.06853, "time": 0.69932} +{"mode": "train", "epoch": 13, "iter": 1700, "lr": 0.09831, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24953, "top5_acc": 0.50594, "loss_cls": 4.08231, "loss": 4.08231, "time": 0.69819} +{"mode": "train", "epoch": 13, "iter": 1800, "lr": 0.0983, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24797, "top5_acc": 0.4925, "loss_cls": 4.12655, "loss": 4.12655, "time": 0.69954} +{"mode": "train", "epoch": 13, "iter": 1900, "lr": 0.09829, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25141, "top5_acc": 0.50016, "loss_cls": 4.08121, "loss": 4.08121, "time": 0.69703} +{"mode": "train", "epoch": 13, "iter": 2000, "lr": 0.09829, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2425, "top5_acc": 0.48891, "loss_cls": 4.13348, "loss": 4.13348, "time": 0.69981} +{"mode": "train", "epoch": 13, "iter": 2100, "lr": 0.09828, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25625, "top5_acc": 0.50734, "loss_cls": 4.07077, "loss": 4.07077, "time": 0.69616} +{"mode": "train", "epoch": 13, "iter": 2200, "lr": 0.09827, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25406, "top5_acc": 0.50031, "loss_cls": 4.07047, "loss": 4.07047, "time": 0.70145} +{"mode": "train", "epoch": 13, "iter": 2300, "lr": 0.09827, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25312, "top5_acc": 0.50187, "loss_cls": 4.08822, "loss": 4.08822, "time": 0.69991} +{"mode": "train", "epoch": 13, "iter": 2400, "lr": 0.09826, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24688, "top5_acc": 0.49156, "loss_cls": 4.13998, "loss": 4.13998, "time": 0.69964} +{"mode": "train", "epoch": 13, "iter": 2500, "lr": 0.09825, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25125, "top5_acc": 0.50578, "loss_cls": 4.10388, "loss": 4.10388, "time": 0.69768} +{"mode": "train", "epoch": 13, "iter": 2600, "lr": 0.09824, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24328, "top5_acc": 0.49531, "loss_cls": 4.11142, "loss": 4.11142, "time": 0.70029} +{"mode": "train", "epoch": 13, "iter": 2700, "lr": 0.09824, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24172, "top5_acc": 0.49516, "loss_cls": 4.1412, "loss": 4.1412, "time": 0.69719} +{"mode": "train", "epoch": 13, "iter": 2800, "lr": 0.09823, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24156, "top5_acc": 0.50094, "loss_cls": 4.11469, "loss": 4.11469, "time": 0.69845} +{"mode": "train", "epoch": 13, "iter": 2900, "lr": 0.09822, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24953, "top5_acc": 0.49625, "loss_cls": 4.08848, "loss": 4.08848, "time": 0.70254} +{"mode": "train", "epoch": 13, "iter": 3000, "lr": 0.09821, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24797, "top5_acc": 0.48391, "loss_cls": 4.15602, "loss": 4.15602, "time": 0.69721} +{"mode": "train", "epoch": 13, "iter": 3100, "lr": 0.09821, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25625, "top5_acc": 0.51047, "loss_cls": 4.07, "loss": 4.07, "time": 0.70028} +{"mode": "train", "epoch": 13, "iter": 3200, "lr": 0.0982, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2525, "top5_acc": 0.5, "loss_cls": 4.12276, "loss": 4.12276, "time": 0.69766} +{"mode": "train", "epoch": 13, "iter": 3300, "lr": 0.09819, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24109, "top5_acc": 0.4975, "loss_cls": 4.13883, "loss": 4.13883, "time": 0.69772} +{"mode": "train", "epoch": 13, "iter": 3400, "lr": 0.09818, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25484, "top5_acc": 0.50562, "loss_cls": 4.08339, "loss": 4.08339, "time": 0.69892} +{"mode": "train", "epoch": 13, "iter": 3500, "lr": 0.09818, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24672, "top5_acc": 0.50141, "loss_cls": 4.12285, "loss": 4.12285, "time": 0.70021} +{"mode": "train", "epoch": 13, "iter": 3600, "lr": 0.09817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24969, "top5_acc": 0.50266, "loss_cls": 4.07987, "loss": 4.07987, "time": 0.70157} +{"mode": "train", "epoch": 13, "iter": 3700, "lr": 0.09816, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24953, "top5_acc": 0.49766, "loss_cls": 4.13124, "loss": 4.13124, "time": 0.70298} +{"mode": "val", "epoch": 13, "iter": 309, "lr": 0.09816, "top1_acc": 0.17181, "top5_acc": 0.39422, "mean_class_accuracy": 0.17148} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.09815, "memory": 15990, "data_time": 1.2771, "top1_acc": 0.25875, "top5_acc": 0.50922, "loss_cls": 4.05768, "loss": 4.05768, "time": 1.98145} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.09814, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25062, "top5_acc": 0.50891, "loss_cls": 4.06729, "loss": 4.06729, "time": 0.70502} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.09814, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26156, "top5_acc": 0.50766, "loss_cls": 4.06759, "loss": 4.06759, "time": 0.70174} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.09813, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24672, "top5_acc": 0.49312, "loss_cls": 4.10253, "loss": 4.10253, "time": 0.6969} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.09812, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25172, "top5_acc": 0.49953, "loss_cls": 4.09543, "loss": 4.09543, "time": 0.70272} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.09811, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24203, "top5_acc": 0.50078, "loss_cls": 4.11091, "loss": 4.11091, "time": 0.6961} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.09811, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25141, "top5_acc": 0.50062, "loss_cls": 4.10307, "loss": 4.10307, "time": 0.69842} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.0981, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25484, "top5_acc": 0.49328, "loss_cls": 4.0798, "loss": 4.0798, "time": 0.69867} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.09809, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25312, "top5_acc": 0.50234, "loss_cls": 4.10126, "loss": 4.10126, "time": 0.69922} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.09808, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26422, "top5_acc": 0.50313, "loss_cls": 4.07152, "loss": 4.07152, "time": 0.69934} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.09807, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24031, "top5_acc": 0.49438, "loss_cls": 4.12835, "loss": 4.12835, "time": 0.6972} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.09807, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25141, "top5_acc": 0.51094, "loss_cls": 4.08471, "loss": 4.08471, "time": 0.6981} +{"mode": "train", "epoch": 14, "iter": 1300, "lr": 0.09806, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25125, "top5_acc": 0.50344, "loss_cls": 4.05205, "loss": 4.05205, "time": 0.6974} +{"mode": "train", "epoch": 14, "iter": 1400, "lr": 0.09805, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25281, "top5_acc": 0.50766, "loss_cls": 4.06869, "loss": 4.06869, "time": 0.69791} +{"mode": "train", "epoch": 14, "iter": 1500, "lr": 0.09804, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25156, "top5_acc": 0.49641, "loss_cls": 4.11399, "loss": 4.11399, "time": 0.69877} +{"mode": "train", "epoch": 14, "iter": 1600, "lr": 0.09804, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25, "top5_acc": 0.50047, "loss_cls": 4.09149, "loss": 4.09149, "time": 0.69761} +{"mode": "train", "epoch": 14, "iter": 1700, "lr": 0.09803, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25531, "top5_acc": 0.50625, "loss_cls": 4.08575, "loss": 4.08575, "time": 0.69615} +{"mode": "train", "epoch": 14, "iter": 1800, "lr": 0.09802, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24672, "top5_acc": 0.50859, "loss_cls": 4.08317, "loss": 4.08317, "time": 0.69701} +{"mode": "train", "epoch": 14, "iter": 1900, "lr": 0.09801, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24766, "top5_acc": 0.50062, "loss_cls": 4.10574, "loss": 4.10574, "time": 0.70107} +{"mode": "train", "epoch": 14, "iter": 2000, "lr": 0.098, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26328, "top5_acc": 0.51641, "loss_cls": 4.03273, "loss": 4.03273, "time": 0.70204} +{"mode": "train", "epoch": 14, "iter": 2100, "lr": 0.098, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25047, "top5_acc": 0.49469, "loss_cls": 4.09897, "loss": 4.09897, "time": 0.69965} +{"mode": "train", "epoch": 14, "iter": 2200, "lr": 0.09799, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26234, "top5_acc": 0.49938, "loss_cls": 4.08465, "loss": 4.08465, "time": 0.70082} +{"mode": "train", "epoch": 14, "iter": 2300, "lr": 0.09798, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25234, "top5_acc": 0.50047, "loss_cls": 4.1103, "loss": 4.1103, "time": 0.69869} +{"mode": "train", "epoch": 14, "iter": 2400, "lr": 0.09797, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25062, "top5_acc": 0.50656, "loss_cls": 4.0471, "loss": 4.0471, "time": 0.70006} +{"mode": "train", "epoch": 14, "iter": 2500, "lr": 0.09797, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24953, "top5_acc": 0.50406, "loss_cls": 4.08621, "loss": 4.08621, "time": 0.69904} +{"mode": "train", "epoch": 14, "iter": 2600, "lr": 0.09796, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25188, "top5_acc": 0.50859, "loss_cls": 4.06972, "loss": 4.06972, "time": 0.69698} +{"mode": "train", "epoch": 14, "iter": 2700, "lr": 0.09795, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24984, "top5_acc": 0.50313, "loss_cls": 4.09907, "loss": 4.09907, "time": 0.69887} +{"mode": "train", "epoch": 14, "iter": 2800, "lr": 0.09794, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25156, "top5_acc": 0.51, "loss_cls": 4.06247, "loss": 4.06247, "time": 0.69989} +{"mode": "train", "epoch": 14, "iter": 2900, "lr": 0.09793, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24656, "top5_acc": 0.49781, "loss_cls": 4.09495, "loss": 4.09495, "time": 0.69884} +{"mode": "train", "epoch": 14, "iter": 3000, "lr": 0.09793, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24297, "top5_acc": 0.49609, "loss_cls": 4.13797, "loss": 4.13797, "time": 0.69989} +{"mode": "train", "epoch": 14, "iter": 3100, "lr": 0.09792, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24641, "top5_acc": 0.49453, "loss_cls": 4.11933, "loss": 4.11933, "time": 0.69916} +{"mode": "train", "epoch": 14, "iter": 3200, "lr": 0.09791, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24859, "top5_acc": 0.49, "loss_cls": 4.10457, "loss": 4.10457, "time": 0.6979} +{"mode": "train", "epoch": 14, "iter": 3300, "lr": 0.0979, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24703, "top5_acc": 0.49672, "loss_cls": 4.11563, "loss": 4.11563, "time": 0.6992} +{"mode": "train", "epoch": 14, "iter": 3400, "lr": 0.09789, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25438, "top5_acc": 0.50109, "loss_cls": 4.08015, "loss": 4.08015, "time": 0.69834} +{"mode": "train", "epoch": 14, "iter": 3500, "lr": 0.09789, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23984, "top5_acc": 0.50656, "loss_cls": 4.11625, "loss": 4.11625, "time": 0.69618} +{"mode": "train", "epoch": 14, "iter": 3600, "lr": 0.09788, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24781, "top5_acc": 0.50938, "loss_cls": 4.08599, "loss": 4.08599, "time": 0.69678} +{"mode": "train", "epoch": 14, "iter": 3700, "lr": 0.09787, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25469, "top5_acc": 0.50047, "loss_cls": 4.09675, "loss": 4.09675, "time": 0.69636} +{"mode": "val", "epoch": 14, "iter": 309, "lr": 0.09787, "top1_acc": 0.16426, "top5_acc": 0.38986, "mean_class_accuracy": 0.16404} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.09786, "memory": 15990, "data_time": 1.30804, "top1_acc": 0.24953, "top5_acc": 0.50969, "loss_cls": 4.08216, "loss": 4.08216, "time": 2.01259} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.09785, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25844, "top5_acc": 0.50766, "loss_cls": 4.04763, "loss": 4.04763, "time": 0.70282} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.09784, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25562, "top5_acc": 0.50562, "loss_cls": 4.06593, "loss": 4.06593, "time": 0.70345} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.09783, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25734, "top5_acc": 0.51203, "loss_cls": 4.00707, "loss": 4.00707, "time": 0.70308} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.09783, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25719, "top5_acc": 0.50969, "loss_cls": 4.05805, "loss": 4.05805, "time": 0.69974} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.09782, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25875, "top5_acc": 0.50734, "loss_cls": 4.06909, "loss": 4.06909, "time": 0.69705} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.09781, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25672, "top5_acc": 0.50859, "loss_cls": 4.04624, "loss": 4.04624, "time": 0.70134} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.0978, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25969, "top5_acc": 0.51688, "loss_cls": 4.01535, "loss": 4.01535, "time": 0.70126} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.09779, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25109, "top5_acc": 0.50266, "loss_cls": 4.08016, "loss": 4.08016, "time": 0.70073} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.09778, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24609, "top5_acc": 0.49953, "loss_cls": 4.11469, "loss": 4.11469, "time": 0.69749} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.09778, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24734, "top5_acc": 0.50062, "loss_cls": 4.09284, "loss": 4.09284, "time": 0.69683} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.09777, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26062, "top5_acc": 0.49734, "loss_cls": 4.11227, "loss": 4.11227, "time": 0.6998} +{"mode": "train", "epoch": 15, "iter": 1300, "lr": 0.09776, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25812, "top5_acc": 0.49797, "loss_cls": 4.09246, "loss": 4.09246, "time": 0.69835} +{"mode": "train", "epoch": 15, "iter": 1400, "lr": 0.09775, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25328, "top5_acc": 0.50125, "loss_cls": 4.08793, "loss": 4.08793, "time": 0.69943} +{"mode": "train", "epoch": 15, "iter": 1500, "lr": 0.09774, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25141, "top5_acc": 0.50078, "loss_cls": 4.08817, "loss": 4.08817, "time": 0.69854} +{"mode": "train", "epoch": 15, "iter": 1600, "lr": 0.09773, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25297, "top5_acc": 0.50953, "loss_cls": 4.0706, "loss": 4.0706, "time": 0.69845} +{"mode": "train", "epoch": 15, "iter": 1700, "lr": 0.09773, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25391, "top5_acc": 0.5025, "loss_cls": 4.06708, "loss": 4.06708, "time": 0.69776} +{"mode": "train", "epoch": 15, "iter": 1800, "lr": 0.09772, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24391, "top5_acc": 0.50328, "loss_cls": 4.07628, "loss": 4.07628, "time": 0.70321} +{"mode": "train", "epoch": 15, "iter": 1900, "lr": 0.09771, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25672, "top5_acc": 0.50703, "loss_cls": 4.09706, "loss": 4.09706, "time": 0.70034} +{"mode": "train", "epoch": 15, "iter": 2000, "lr": 0.0977, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25594, "top5_acc": 0.50938, "loss_cls": 4.04672, "loss": 4.04672, "time": 0.70262} +{"mode": "train", "epoch": 15, "iter": 2100, "lr": 0.09769, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25844, "top5_acc": 0.50406, "loss_cls": 4.07432, "loss": 4.07432, "time": 0.70308} +{"mode": "train", "epoch": 15, "iter": 2200, "lr": 0.09768, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24328, "top5_acc": 0.49547, "loss_cls": 4.13328, "loss": 4.13328, "time": 0.69899} +{"mode": "train", "epoch": 15, "iter": 2300, "lr": 0.09768, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25672, "top5_acc": 0.50859, "loss_cls": 4.04532, "loss": 4.04532, "time": 0.70222} +{"mode": "train", "epoch": 15, "iter": 2400, "lr": 0.09767, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24766, "top5_acc": 0.49531, "loss_cls": 4.14411, "loss": 4.14411, "time": 0.69921} +{"mode": "train", "epoch": 15, "iter": 2500, "lr": 0.09766, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25859, "top5_acc": 0.51328, "loss_cls": 4.04928, "loss": 4.04928, "time": 0.69927} +{"mode": "train", "epoch": 15, "iter": 2600, "lr": 0.09765, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25984, "top5_acc": 0.51438, "loss_cls": 4.0646, "loss": 4.0646, "time": 0.70041} +{"mode": "train", "epoch": 15, "iter": 2700, "lr": 0.09764, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24953, "top5_acc": 0.50891, "loss_cls": 4.07981, "loss": 4.07981, "time": 0.69948} +{"mode": "train", "epoch": 15, "iter": 2800, "lr": 0.09763, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25156, "top5_acc": 0.50203, "loss_cls": 4.08645, "loss": 4.08645, "time": 0.69778} +{"mode": "train", "epoch": 15, "iter": 2900, "lr": 0.09763, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25578, "top5_acc": 0.50438, "loss_cls": 4.07467, "loss": 4.07467, "time": 0.69594} +{"mode": "train", "epoch": 15, "iter": 3000, "lr": 0.09762, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24328, "top5_acc": 0.49562, "loss_cls": 4.13883, "loss": 4.13883, "time": 0.69872} +{"mode": "train", "epoch": 15, "iter": 3100, "lr": 0.09761, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25891, "top5_acc": 0.50031, "loss_cls": 4.08359, "loss": 4.08359, "time": 0.69572} +{"mode": "train", "epoch": 15, "iter": 3200, "lr": 0.0976, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25531, "top5_acc": 0.50438, "loss_cls": 4.06253, "loss": 4.06253, "time": 0.6979} +{"mode": "train", "epoch": 15, "iter": 3300, "lr": 0.09759, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25031, "top5_acc": 0.49547, "loss_cls": 4.11841, "loss": 4.11841, "time": 0.69936} +{"mode": "train", "epoch": 15, "iter": 3400, "lr": 0.09758, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25016, "top5_acc": 0.50031, "loss_cls": 4.14206, "loss": 4.14206, "time": 0.69753} +{"mode": "train", "epoch": 15, "iter": 3500, "lr": 0.09757, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24703, "top5_acc": 0.50125, "loss_cls": 4.1117, "loss": 4.1117, "time": 0.69749} +{"mode": "train", "epoch": 15, "iter": 3600, "lr": 0.09757, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25359, "top5_acc": 0.50719, "loss_cls": 4.08435, "loss": 4.08435, "time": 0.6995} +{"mode": "train", "epoch": 15, "iter": 3700, "lr": 0.09756, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24859, "top5_acc": 0.49719, "loss_cls": 4.11407, "loss": 4.11407, "time": 0.70216} +{"mode": "val", "epoch": 15, "iter": 309, "lr": 0.09755, "top1_acc": 0.17991, "top5_acc": 0.40065, "mean_class_accuracy": 0.17978} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.09754, "memory": 15990, "data_time": 1.29483, "top1_acc": 0.25609, "top5_acc": 0.515, "loss_cls": 4.00844, "loss": 4.00844, "time": 2.00327} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.09754, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25016, "top5_acc": 0.51219, "loss_cls": 4.0356, "loss": 4.0356, "time": 0.70634} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.09753, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25578, "top5_acc": 0.51313, "loss_cls": 4.01927, "loss": 4.01927, "time": 0.7041} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.09752, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25094, "top5_acc": 0.51109, "loss_cls": 4.05327, "loss": 4.05327, "time": 0.6992} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.09751, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25297, "top5_acc": 0.5025, "loss_cls": 4.10138, "loss": 4.10138, "time": 0.70371} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.0975, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25109, "top5_acc": 0.4975, "loss_cls": 4.06943, "loss": 4.06943, "time": 0.69977} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.09749, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24438, "top5_acc": 0.49031, "loss_cls": 4.14321, "loss": 4.14321, "time": 0.70113} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.09748, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24844, "top5_acc": 0.50953, "loss_cls": 4.05708, "loss": 4.05708, "time": 0.70213} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.09747, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26031, "top5_acc": 0.50453, "loss_cls": 4.07415, "loss": 4.07415, "time": 0.69793} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.09747, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25344, "top5_acc": 0.49859, "loss_cls": 4.10299, "loss": 4.10299, "time": 0.70177} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.09746, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25688, "top5_acc": 0.51656, "loss_cls": 4.03219, "loss": 4.03219, "time": 0.70129} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.09745, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25125, "top5_acc": 0.50984, "loss_cls": 4.07875, "loss": 4.07875, "time": 0.69818} +{"mode": "train", "epoch": 16, "iter": 1300, "lr": 0.09744, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24828, "top5_acc": 0.50375, "loss_cls": 4.08912, "loss": 4.08912, "time": 0.70098} +{"mode": "train", "epoch": 16, "iter": 1400, "lr": 0.09743, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24641, "top5_acc": 0.50734, "loss_cls": 4.07017, "loss": 4.07017, "time": 0.69965} +{"mode": "train", "epoch": 16, "iter": 1500, "lr": 0.09742, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25766, "top5_acc": 0.51313, "loss_cls": 4.0306, "loss": 4.0306, "time": 0.70039} +{"mode": "train", "epoch": 16, "iter": 1600, "lr": 0.09741, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24859, "top5_acc": 0.49828, "loss_cls": 4.12201, "loss": 4.12201, "time": 0.6982} +{"mode": "train", "epoch": 16, "iter": 1700, "lr": 0.0974, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24812, "top5_acc": 0.50109, "loss_cls": 4.10495, "loss": 4.10495, "time": 0.69775} +{"mode": "train", "epoch": 16, "iter": 1800, "lr": 0.0974, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25578, "top5_acc": 0.50922, "loss_cls": 4.05343, "loss": 4.05343, "time": 0.70235} +{"mode": "train", "epoch": 16, "iter": 1900, "lr": 0.09739, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24859, "top5_acc": 0.50094, "loss_cls": 4.13254, "loss": 4.13254, "time": 0.70303} +{"mode": "train", "epoch": 16, "iter": 2000, "lr": 0.09738, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24891, "top5_acc": 0.51016, "loss_cls": 4.05314, "loss": 4.05314, "time": 0.69949} +{"mode": "train", "epoch": 16, "iter": 2100, "lr": 0.09737, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25016, "top5_acc": 0.50406, "loss_cls": 4.09781, "loss": 4.09781, "time": 0.69822} +{"mode": "train", "epoch": 16, "iter": 2200, "lr": 0.09736, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26266, "top5_acc": 0.50594, "loss_cls": 4.06264, "loss": 4.06264, "time": 0.69985} +{"mode": "train", "epoch": 16, "iter": 2300, "lr": 0.09735, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25781, "top5_acc": 0.5075, "loss_cls": 4.08521, "loss": 4.08521, "time": 0.70211} +{"mode": "train", "epoch": 16, "iter": 2400, "lr": 0.09734, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25594, "top5_acc": 0.51531, "loss_cls": 4.07891, "loss": 4.07891, "time": 0.69792} +{"mode": "train", "epoch": 16, "iter": 2500, "lr": 0.09733, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25406, "top5_acc": 0.50469, "loss_cls": 4.05615, "loss": 4.05615, "time": 0.6982} +{"mode": "train", "epoch": 16, "iter": 2600, "lr": 0.09732, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25984, "top5_acc": 0.51, "loss_cls": 4.04849, "loss": 4.04849, "time": 0.70231} +{"mode": "train", "epoch": 16, "iter": 2700, "lr": 0.09731, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24078, "top5_acc": 0.48688, "loss_cls": 4.12804, "loss": 4.12804, "time": 0.70086} +{"mode": "train", "epoch": 16, "iter": 2800, "lr": 0.09731, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24562, "top5_acc": 0.48438, "loss_cls": 4.14915, "loss": 4.14915, "time": 0.70152} +{"mode": "train", "epoch": 16, "iter": 2900, "lr": 0.0973, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24969, "top5_acc": 0.50609, "loss_cls": 4.07188, "loss": 4.07188, "time": 0.69771} +{"mode": "train", "epoch": 16, "iter": 3000, "lr": 0.09729, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25719, "top5_acc": 0.50422, "loss_cls": 4.06746, "loss": 4.06746, "time": 0.70281} +{"mode": "train", "epoch": 16, "iter": 3100, "lr": 0.09728, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24875, "top5_acc": 0.49906, "loss_cls": 4.08857, "loss": 4.08857, "time": 0.69936} +{"mode": "train", "epoch": 16, "iter": 3200, "lr": 0.09727, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25594, "top5_acc": 0.50734, "loss_cls": 4.06318, "loss": 4.06318, "time": 0.70035} +{"mode": "train", "epoch": 16, "iter": 3300, "lr": 0.09726, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25047, "top5_acc": 0.50469, "loss_cls": 4.07954, "loss": 4.07954, "time": 0.69868} +{"mode": "train", "epoch": 16, "iter": 3400, "lr": 0.09725, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25328, "top5_acc": 0.50281, "loss_cls": 4.0987, "loss": 4.0987, "time": 0.69737} +{"mode": "train", "epoch": 16, "iter": 3500, "lr": 0.09724, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2525, "top5_acc": 0.50672, "loss_cls": 4.07837, "loss": 4.07837, "time": 0.69804} +{"mode": "train", "epoch": 16, "iter": 3600, "lr": 0.09723, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25422, "top5_acc": 0.50422, "loss_cls": 4.07258, "loss": 4.07258, "time": 0.70015} +{"mode": "train", "epoch": 16, "iter": 3700, "lr": 0.09722, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26344, "top5_acc": 0.51141, "loss_cls": 4.04984, "loss": 4.04984, "time": 0.70447} +{"mode": "val", "epoch": 16, "iter": 309, "lr": 0.09722, "top1_acc": 0.19131, "top5_acc": 0.42172, "mean_class_accuracy": 0.19124} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.09721, "memory": 15990, "data_time": 1.30646, "top1_acc": 0.26359, "top5_acc": 0.51703, "loss_cls": 3.99455, "loss": 3.99455, "time": 2.01225} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.0972, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26375, "top5_acc": 0.51422, "loss_cls": 4.02827, "loss": 4.02827, "time": 0.70515} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.09719, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26172, "top5_acc": 0.51547, "loss_cls": 4.02459, "loss": 4.02459, "time": 0.70631} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.09718, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25172, "top5_acc": 0.50672, "loss_cls": 4.04548, "loss": 4.04548, "time": 0.7007} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.09717, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26328, "top5_acc": 0.51797, "loss_cls": 4.03811, "loss": 4.03811, "time": 0.70221} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.09716, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25531, "top5_acc": 0.51234, "loss_cls": 4.06618, "loss": 4.06618, "time": 0.70093} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.09715, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24266, "top5_acc": 0.50562, "loss_cls": 4.09066, "loss": 4.09066, "time": 0.69877} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.09714, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24922, "top5_acc": 0.50062, "loss_cls": 4.0819, "loss": 4.0819, "time": 0.697} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.09714, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25469, "top5_acc": 0.50641, "loss_cls": 4.07649, "loss": 4.07649, "time": 0.70167} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.09713, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25266, "top5_acc": 0.50453, "loss_cls": 4.07083, "loss": 4.07083, "time": 0.69813} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.09712, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24766, "top5_acc": 0.50422, "loss_cls": 4.05938, "loss": 4.05938, "time": 0.70034} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.09711, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25312, "top5_acc": 0.50719, "loss_cls": 4.08258, "loss": 4.08258, "time": 0.69959} +{"mode": "train", "epoch": 17, "iter": 1300, "lr": 0.0971, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25312, "top5_acc": 0.50125, "loss_cls": 4.09838, "loss": 4.09838, "time": 0.69652} +{"mode": "train", "epoch": 17, "iter": 1400, "lr": 0.09709, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24438, "top5_acc": 0.50422, "loss_cls": 4.08998, "loss": 4.08998, "time": 0.69878} +{"mode": "train", "epoch": 17, "iter": 1500, "lr": 0.09708, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24406, "top5_acc": 0.49516, "loss_cls": 4.10567, "loss": 4.10567, "time": 0.69718} +{"mode": "train", "epoch": 17, "iter": 1600, "lr": 0.09707, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24953, "top5_acc": 0.49703, "loss_cls": 4.10503, "loss": 4.10503, "time": 0.69643} +{"mode": "train", "epoch": 17, "iter": 1700, "lr": 0.09706, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24703, "top5_acc": 0.48891, "loss_cls": 4.14012, "loss": 4.14012, "time": 0.70091} +{"mode": "train", "epoch": 17, "iter": 1800, "lr": 0.09705, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25359, "top5_acc": 0.50125, "loss_cls": 4.08445, "loss": 4.08445, "time": 0.69682} +{"mode": "train", "epoch": 17, "iter": 1900, "lr": 0.09704, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25578, "top5_acc": 0.51281, "loss_cls": 4.04039, "loss": 4.04039, "time": 0.69974} +{"mode": "train", "epoch": 17, "iter": 2000, "lr": 0.09703, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26, "top5_acc": 0.49922, "loss_cls": 4.08499, "loss": 4.08499, "time": 0.70255} +{"mode": "train", "epoch": 17, "iter": 2100, "lr": 0.09702, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25469, "top5_acc": 0.49766, "loss_cls": 4.07152, "loss": 4.07152, "time": 0.70274} +{"mode": "train", "epoch": 17, "iter": 2200, "lr": 0.09701, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26641, "top5_acc": 0.52438, "loss_cls": 3.9977, "loss": 3.9977, "time": 0.69951} +{"mode": "train", "epoch": 17, "iter": 2300, "lr": 0.097, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25953, "top5_acc": 0.50906, "loss_cls": 4.06325, "loss": 4.06325, "time": 0.70035} +{"mode": "train", "epoch": 17, "iter": 2400, "lr": 0.09699, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24844, "top5_acc": 0.49766, "loss_cls": 4.09463, "loss": 4.09463, "time": 0.7002} +{"mode": "train", "epoch": 17, "iter": 2500, "lr": 0.09698, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24188, "top5_acc": 0.50828, "loss_cls": 4.09729, "loss": 4.09729, "time": 0.69829} +{"mode": "train", "epoch": 17, "iter": 2600, "lr": 0.09697, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25266, "top5_acc": 0.50328, "loss_cls": 4.09767, "loss": 4.09767, "time": 0.69711} +{"mode": "train", "epoch": 17, "iter": 2700, "lr": 0.09697, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25125, "top5_acc": 0.50187, "loss_cls": 4.09809, "loss": 4.09809, "time": 0.69854} +{"mode": "train", "epoch": 17, "iter": 2800, "lr": 0.09696, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26094, "top5_acc": 0.51625, "loss_cls": 4.05198, "loss": 4.05198, "time": 0.69741} +{"mode": "train", "epoch": 17, "iter": 2900, "lr": 0.09695, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26109, "top5_acc": 0.50531, "loss_cls": 4.09467, "loss": 4.09467, "time": 0.69667} +{"mode": "train", "epoch": 17, "iter": 3000, "lr": 0.09694, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25266, "top5_acc": 0.50281, "loss_cls": 4.09719, "loss": 4.09719, "time": 0.7012} +{"mode": "train", "epoch": 17, "iter": 3100, "lr": 0.09693, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25234, "top5_acc": 0.50172, "loss_cls": 4.09281, "loss": 4.09281, "time": 0.69891} +{"mode": "train", "epoch": 17, "iter": 3200, "lr": 0.09692, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24422, "top5_acc": 0.50078, "loss_cls": 4.09695, "loss": 4.09695, "time": 0.69783} +{"mode": "train", "epoch": 17, "iter": 3300, "lr": 0.09691, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25188, "top5_acc": 0.50016, "loss_cls": 4.06185, "loss": 4.06185, "time": 0.69705} +{"mode": "train", "epoch": 17, "iter": 3400, "lr": 0.0969, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24438, "top5_acc": 0.49562, "loss_cls": 4.13451, "loss": 4.13451, "time": 0.69903} +{"mode": "train", "epoch": 17, "iter": 3500, "lr": 0.09689, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25406, "top5_acc": 0.50656, "loss_cls": 4.07153, "loss": 4.07153, "time": 0.69794} +{"mode": "train", "epoch": 17, "iter": 3600, "lr": 0.09688, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25375, "top5_acc": 0.50078, "loss_cls": 4.07103, "loss": 4.07103, "time": 0.70093} +{"mode": "train", "epoch": 17, "iter": 3700, "lr": 0.09687, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25219, "top5_acc": 0.51578, "loss_cls": 4.06424, "loss": 4.06424, "time": 0.70521} +{"mode": "val", "epoch": 17, "iter": 309, "lr": 0.09686, "top1_acc": 0.09462, "top5_acc": 0.24637, "mean_class_accuracy": 0.0947} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.09685, "memory": 15990, "data_time": 1.28579, "top1_acc": 0.25734, "top5_acc": 0.51859, "loss_cls": 4.01252, "loss": 4.01252, "time": 1.99304} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.09684, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25484, "top5_acc": 0.5, "loss_cls": 4.05331, "loss": 4.05331, "time": 0.70162} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.09683, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25734, "top5_acc": 0.50797, "loss_cls": 4.03917, "loss": 4.03917, "time": 0.70316} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.09683, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25328, "top5_acc": 0.51172, "loss_cls": 4.04449, "loss": 4.04449, "time": 0.70392} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.09682, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25078, "top5_acc": 0.50594, "loss_cls": 4.06901, "loss": 4.06901, "time": 0.70214} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.09681, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25141, "top5_acc": 0.50391, "loss_cls": 4.0672, "loss": 4.0672, "time": 0.69954} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.0968, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25094, "top5_acc": 0.49891, "loss_cls": 4.09105, "loss": 4.09105, "time": 0.70103} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.09679, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25703, "top5_acc": 0.51203, "loss_cls": 4.03863, "loss": 4.03863, "time": 0.69835} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.09678, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25047, "top5_acc": 0.50281, "loss_cls": 4.10672, "loss": 4.10672, "time": 0.69975} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.09677, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24609, "top5_acc": 0.49734, "loss_cls": 4.11484, "loss": 4.11484, "time": 0.69959} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.09676, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25469, "top5_acc": 0.50484, "loss_cls": 4.09732, "loss": 4.09732, "time": 0.69908} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.09675, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24828, "top5_acc": 0.50109, "loss_cls": 4.08268, "loss": 4.08268, "time": 0.6995} +{"mode": "train", "epoch": 18, "iter": 1300, "lr": 0.09674, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25047, "top5_acc": 0.50719, "loss_cls": 4.09196, "loss": 4.09196, "time": 0.69766} +{"mode": "train", "epoch": 18, "iter": 1400, "lr": 0.09673, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24422, "top5_acc": 0.49531, "loss_cls": 4.11764, "loss": 4.11764, "time": 0.69873} +{"mode": "train", "epoch": 18, "iter": 1500, "lr": 0.09672, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26062, "top5_acc": 0.50375, "loss_cls": 4.05057, "loss": 4.05057, "time": 0.69993} +{"mode": "train", "epoch": 18, "iter": 1600, "lr": 0.09671, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25, "top5_acc": 0.50438, "loss_cls": 4.08891, "loss": 4.08891, "time": 0.69846} +{"mode": "train", "epoch": 18, "iter": 1700, "lr": 0.0967, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25656, "top5_acc": 0.51359, "loss_cls": 4.05279, "loss": 4.05279, "time": 0.69872} +{"mode": "train", "epoch": 18, "iter": 1800, "lr": 0.09669, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25047, "top5_acc": 0.50172, "loss_cls": 4.09126, "loss": 4.09126, "time": 0.69692} +{"mode": "train", "epoch": 18, "iter": 1900, "lr": 0.09668, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25438, "top5_acc": 0.50156, "loss_cls": 4.07141, "loss": 4.07141, "time": 0.70405} +{"mode": "train", "epoch": 18, "iter": 2000, "lr": 0.09667, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26656, "top5_acc": 0.51734, "loss_cls": 4.00283, "loss": 4.00283, "time": 0.70707} +{"mode": "train", "epoch": 18, "iter": 2100, "lr": 0.09666, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25281, "top5_acc": 0.50969, "loss_cls": 4.04456, "loss": 4.04456, "time": 0.69859} +{"mode": "train", "epoch": 18, "iter": 2200, "lr": 0.09665, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25703, "top5_acc": 0.50656, "loss_cls": 4.06909, "loss": 4.06909, "time": 0.70285} +{"mode": "train", "epoch": 18, "iter": 2300, "lr": 0.09664, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26687, "top5_acc": 0.51844, "loss_cls": 4.01707, "loss": 4.01707, "time": 0.69957} +{"mode": "train", "epoch": 18, "iter": 2400, "lr": 0.09663, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25125, "top5_acc": 0.49875, "loss_cls": 4.10319, "loss": 4.10319, "time": 0.70106} +{"mode": "train", "epoch": 18, "iter": 2500, "lr": 0.09662, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26859, "top5_acc": 0.51438, "loss_cls": 4.00574, "loss": 4.00574, "time": 0.69849} +{"mode": "train", "epoch": 18, "iter": 2600, "lr": 0.09661, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26094, "top5_acc": 0.5225, "loss_cls": 4.01792, "loss": 4.01792, "time": 0.69737} +{"mode": "train", "epoch": 18, "iter": 2700, "lr": 0.0966, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25438, "top5_acc": 0.5125, "loss_cls": 4.03807, "loss": 4.03807, "time": 0.69832} +{"mode": "train", "epoch": 18, "iter": 2800, "lr": 0.09659, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2625, "top5_acc": 0.51812, "loss_cls": 4.02828, "loss": 4.02828, "time": 0.69846} +{"mode": "train", "epoch": 18, "iter": 2900, "lr": 0.09658, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24828, "top5_acc": 0.50047, "loss_cls": 4.10034, "loss": 4.10034, "time": 0.69809} +{"mode": "train", "epoch": 18, "iter": 3000, "lr": 0.09657, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26312, "top5_acc": 0.51156, "loss_cls": 4.03262, "loss": 4.03262, "time": 0.69859} +{"mode": "train", "epoch": 18, "iter": 3100, "lr": 0.09656, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25812, "top5_acc": 0.51625, "loss_cls": 4.03617, "loss": 4.03617, "time": 0.69826} +{"mode": "train", "epoch": 18, "iter": 3200, "lr": 0.09654, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24641, "top5_acc": 0.51047, "loss_cls": 4.0775, "loss": 4.0775, "time": 0.70235} +{"mode": "train", "epoch": 18, "iter": 3300, "lr": 0.09653, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25047, "top5_acc": 0.50484, "loss_cls": 4.07953, "loss": 4.07953, "time": 0.70002} +{"mode": "train", "epoch": 18, "iter": 3400, "lr": 0.09652, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25047, "top5_acc": 0.49891, "loss_cls": 4.11219, "loss": 4.11219, "time": 0.69725} +{"mode": "train", "epoch": 18, "iter": 3500, "lr": 0.09651, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24, "top5_acc": 0.49844, "loss_cls": 4.10986, "loss": 4.10986, "time": 0.6979} +{"mode": "train", "epoch": 18, "iter": 3600, "lr": 0.0965, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26922, "top5_acc": 0.50656, "loss_cls": 4.01781, "loss": 4.01781, "time": 0.69839} +{"mode": "train", "epoch": 18, "iter": 3700, "lr": 0.09649, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2475, "top5_acc": 0.49953, "loss_cls": 4.0982, "loss": 4.0982, "time": 0.70142} +{"mode": "val", "epoch": 18, "iter": 309, "lr": 0.09649, "top1_acc": 0.18022, "top5_acc": 0.41118, "mean_class_accuracy": 0.17993} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.09648, "memory": 15990, "data_time": 1.28819, "top1_acc": 0.25641, "top5_acc": 0.51734, "loss_cls": 4.01794, "loss": 4.01794, "time": 1.9971} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.09647, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25797, "top5_acc": 0.51313, "loss_cls": 4.03919, "loss": 4.03919, "time": 0.70502} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.09646, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27062, "top5_acc": 0.51672, "loss_cls": 4.02581, "loss": 4.02581, "time": 0.70546} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.09645, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25344, "top5_acc": 0.50609, "loss_cls": 4.06387, "loss": 4.06387, "time": 0.70317} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.09644, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25453, "top5_acc": 0.50813, "loss_cls": 4.06021, "loss": 4.06021, "time": 0.7005} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.09643, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26188, "top5_acc": 0.50797, "loss_cls": 4.04594, "loss": 4.04594, "time": 0.70003} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.09642, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25797, "top5_acc": 0.51562, "loss_cls": 4.04235, "loss": 4.04235, "time": 0.69731} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.09641, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26188, "top5_acc": 0.52031, "loss_cls": 4.01891, "loss": 4.01891, "time": 0.69555} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.0964, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25531, "top5_acc": 0.51344, "loss_cls": 4.04777, "loss": 4.04777, "time": 0.69939} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.09639, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2525, "top5_acc": 0.51203, "loss_cls": 4.03673, "loss": 4.03673, "time": 0.69918} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.09637, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25375, "top5_acc": 0.50313, "loss_cls": 4.06619, "loss": 4.06619, "time": 0.69681} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.09636, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25984, "top5_acc": 0.51469, "loss_cls": 4.05561, "loss": 4.05561, "time": 0.69798} +{"mode": "train", "epoch": 19, "iter": 1300, "lr": 0.09635, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26, "top5_acc": 0.50781, "loss_cls": 4.064, "loss": 4.064, "time": 0.70193} +{"mode": "train", "epoch": 19, "iter": 1400, "lr": 0.09634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25641, "top5_acc": 0.50656, "loss_cls": 4.07406, "loss": 4.07406, "time": 0.69985} +{"mode": "train", "epoch": 19, "iter": 1500, "lr": 0.09633, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25578, "top5_acc": 0.50469, "loss_cls": 4.08049, "loss": 4.08049, "time": 0.701} +{"mode": "train", "epoch": 19, "iter": 1600, "lr": 0.09632, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26219, "top5_acc": 0.50922, "loss_cls": 4.03703, "loss": 4.03703, "time": 0.69767} +{"mode": "train", "epoch": 19, "iter": 1700, "lr": 0.09631, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25078, "top5_acc": 0.50344, "loss_cls": 4.10158, "loss": 4.10158, "time": 0.70121} +{"mode": "train", "epoch": 19, "iter": 1800, "lr": 0.0963, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26234, "top5_acc": 0.50813, "loss_cls": 4.0638, "loss": 4.0638, "time": 0.69976} +{"mode": "train", "epoch": 19, "iter": 1900, "lr": 0.09629, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26062, "top5_acc": 0.50703, "loss_cls": 4.04291, "loss": 4.04291, "time": 0.70038} +{"mode": "train", "epoch": 19, "iter": 2000, "lr": 0.09628, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.255, "top5_acc": 0.49719, "loss_cls": 4.07743, "loss": 4.07743, "time": 0.70137} +{"mode": "train", "epoch": 19, "iter": 2100, "lr": 0.09627, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26562, "top5_acc": 0.51531, "loss_cls": 4.03052, "loss": 4.03052, "time": 0.70201} +{"mode": "train", "epoch": 19, "iter": 2200, "lr": 0.09626, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25078, "top5_acc": 0.50516, "loss_cls": 4.07426, "loss": 4.07426, "time": 0.69833} +{"mode": "train", "epoch": 19, "iter": 2300, "lr": 0.09625, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25875, "top5_acc": 0.50547, "loss_cls": 4.06233, "loss": 4.06233, "time": 0.69921} +{"mode": "train", "epoch": 19, "iter": 2400, "lr": 0.09624, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26547, "top5_acc": 0.51344, "loss_cls": 4.04257, "loss": 4.04257, "time": 0.70044} +{"mode": "train", "epoch": 19, "iter": 2500, "lr": 0.09623, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24797, "top5_acc": 0.49156, "loss_cls": 4.12693, "loss": 4.12693, "time": 0.6998} +{"mode": "train", "epoch": 19, "iter": 2600, "lr": 0.09622, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24297, "top5_acc": 0.5, "loss_cls": 4.0775, "loss": 4.0775, "time": 0.70088} +{"mode": "train", "epoch": 19, "iter": 2700, "lr": 0.09621, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25828, "top5_acc": 0.50594, "loss_cls": 4.06557, "loss": 4.06557, "time": 0.70117} +{"mode": "train", "epoch": 19, "iter": 2800, "lr": 0.0962, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25578, "top5_acc": 0.51, "loss_cls": 4.0885, "loss": 4.0885, "time": 0.69616} +{"mode": "train", "epoch": 19, "iter": 2900, "lr": 0.09618, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25516, "top5_acc": 0.51516, "loss_cls": 4.05468, "loss": 4.05468, "time": 0.70043} +{"mode": "train", "epoch": 19, "iter": 3000, "lr": 0.09617, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25406, "top5_acc": 0.50109, "loss_cls": 4.07813, "loss": 4.07813, "time": 0.69818} +{"mode": "train", "epoch": 19, "iter": 3100, "lr": 0.09616, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2725, "top5_acc": 0.51875, "loss_cls": 3.98297, "loss": 3.98297, "time": 0.69811} +{"mode": "train", "epoch": 19, "iter": 3200, "lr": 0.09615, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2525, "top5_acc": 0.49953, "loss_cls": 4.11652, "loss": 4.11652, "time": 0.69904} +{"mode": "train", "epoch": 19, "iter": 3300, "lr": 0.09614, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25938, "top5_acc": 0.50375, "loss_cls": 4.07052, "loss": 4.07052, "time": 0.69654} +{"mode": "train", "epoch": 19, "iter": 3400, "lr": 0.09613, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25094, "top5_acc": 0.50281, "loss_cls": 4.06726, "loss": 4.06726, "time": 0.69734} +{"mode": "train", "epoch": 19, "iter": 3500, "lr": 0.09612, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25609, "top5_acc": 0.50359, "loss_cls": 4.06241, "loss": 4.06241, "time": 0.69986} +{"mode": "train", "epoch": 19, "iter": 3600, "lr": 0.09611, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25641, "top5_acc": 0.50578, "loss_cls": 4.06844, "loss": 4.06844, "time": 0.69942} +{"mode": "train", "epoch": 19, "iter": 3700, "lr": 0.0961, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25969, "top5_acc": 0.51203, "loss_cls": 4.066, "loss": 4.066, "time": 0.70141} +{"mode": "val", "epoch": 19, "iter": 309, "lr": 0.09609, "top1_acc": 0.17723, "top5_acc": 0.40252, "mean_class_accuracy": 0.17711} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.09608, "memory": 15990, "data_time": 1.27631, "top1_acc": 0.26453, "top5_acc": 0.51453, "loss_cls": 4.03594, "loss": 4.03594, "time": 1.99105} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.09607, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25609, "top5_acc": 0.51172, "loss_cls": 4.04401, "loss": 4.04401, "time": 0.70421} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.09606, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25188, "top5_acc": 0.50609, "loss_cls": 4.08963, "loss": 4.08963, "time": 0.70324} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.09605, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2525, "top5_acc": 0.51641, "loss_cls": 4.01646, "loss": 4.01646, "time": 0.69995} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.09604, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26297, "top5_acc": 0.51922, "loss_cls": 4.03464, "loss": 4.03464, "time": 0.70178} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.09603, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26219, "top5_acc": 0.51969, "loss_cls": 3.99206, "loss": 3.99206, "time": 0.70177} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.09602, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26125, "top5_acc": 0.51547, "loss_cls": 4.05122, "loss": 4.05122, "time": 0.69915} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.09601, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26016, "top5_acc": 0.505, "loss_cls": 4.07212, "loss": 4.07212, "time": 0.69797} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.096, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26016, "top5_acc": 0.51844, "loss_cls": 4.02373, "loss": 4.02373, "time": 0.70043} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.09598, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25922, "top5_acc": 0.50922, "loss_cls": 4.03565, "loss": 4.03565, "time": 0.69851} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.09597, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26031, "top5_acc": 0.51062, "loss_cls": 4.07226, "loss": 4.07226, "time": 0.6999} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.09596, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25891, "top5_acc": 0.50922, "loss_cls": 4.06832, "loss": 4.06832, "time": 0.69747} +{"mode": "train", "epoch": 20, "iter": 1300, "lr": 0.09595, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25344, "top5_acc": 0.50719, "loss_cls": 4.06708, "loss": 4.06708, "time": 0.70154} +{"mode": "train", "epoch": 20, "iter": 1400, "lr": 0.09594, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26125, "top5_acc": 0.51078, "loss_cls": 4.05088, "loss": 4.05088, "time": 0.69969} +{"mode": "train", "epoch": 20, "iter": 1500, "lr": 0.09593, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26281, "top5_acc": 0.51281, "loss_cls": 4.01916, "loss": 4.01916, "time": 0.70031} +{"mode": "train", "epoch": 20, "iter": 1600, "lr": 0.09592, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26484, "top5_acc": 0.51078, "loss_cls": 4.03455, "loss": 4.03455, "time": 0.6976} +{"mode": "train", "epoch": 20, "iter": 1700, "lr": 0.09591, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26125, "top5_acc": 0.51297, "loss_cls": 4.0456, "loss": 4.0456, "time": 0.69824} +{"mode": "train", "epoch": 20, "iter": 1800, "lr": 0.0959, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26156, "top5_acc": 0.51, "loss_cls": 4.03641, "loss": 4.03641, "time": 0.69781} +{"mode": "train", "epoch": 20, "iter": 1900, "lr": 0.09588, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25844, "top5_acc": 0.50484, "loss_cls": 4.06919, "loss": 4.06919, "time": 0.69846} +{"mode": "train", "epoch": 20, "iter": 2000, "lr": 0.09587, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25266, "top5_acc": 0.50359, "loss_cls": 4.09422, "loss": 4.09422, "time": 0.70389} +{"mode": "train", "epoch": 20, "iter": 2100, "lr": 0.09586, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25062, "top5_acc": 0.50156, "loss_cls": 4.11472, "loss": 4.11472, "time": 0.6983} +{"mode": "train", "epoch": 20, "iter": 2200, "lr": 0.09585, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25922, "top5_acc": 0.50703, "loss_cls": 4.05517, "loss": 4.05517, "time": 0.70445} +{"mode": "train", "epoch": 20, "iter": 2300, "lr": 0.09584, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24766, "top5_acc": 0.50859, "loss_cls": 4.06688, "loss": 4.06688, "time": 0.69804} +{"mode": "train", "epoch": 20, "iter": 2400, "lr": 0.09583, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25516, "top5_acc": 0.49938, "loss_cls": 4.08987, "loss": 4.08987, "time": 0.69916} +{"mode": "train", "epoch": 20, "iter": 2500, "lr": 0.09582, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25141, "top5_acc": 0.49203, "loss_cls": 4.1352, "loss": 4.1352, "time": 0.69802} +{"mode": "train", "epoch": 20, "iter": 2600, "lr": 0.09581, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25062, "top5_acc": 0.5, "loss_cls": 4.09986, "loss": 4.09986, "time": 0.69665} +{"mode": "train", "epoch": 20, "iter": 2700, "lr": 0.0958, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25172, "top5_acc": 0.50172, "loss_cls": 4.07849, "loss": 4.07849, "time": 0.7038} +{"mode": "train", "epoch": 20, "iter": 2800, "lr": 0.09578, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25016, "top5_acc": 0.51562, "loss_cls": 4.0403, "loss": 4.0403, "time": 0.69751} +{"mode": "train", "epoch": 20, "iter": 2900, "lr": 0.09577, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26062, "top5_acc": 0.51438, "loss_cls": 3.9959, "loss": 3.9959, "time": 0.69822} +{"mode": "train", "epoch": 20, "iter": 3000, "lr": 0.09576, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25734, "top5_acc": 0.51078, "loss_cls": 4.03426, "loss": 4.03426, "time": 0.69775} +{"mode": "train", "epoch": 20, "iter": 3100, "lr": 0.09575, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24141, "top5_acc": 0.49672, "loss_cls": 4.11612, "loss": 4.11612, "time": 0.69954} +{"mode": "train", "epoch": 20, "iter": 3200, "lr": 0.09574, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24438, "top5_acc": 0.49625, "loss_cls": 4.12548, "loss": 4.12548, "time": 0.698} +{"mode": "train", "epoch": 20, "iter": 3300, "lr": 0.09573, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25188, "top5_acc": 0.50703, "loss_cls": 4.07205, "loss": 4.07205, "time": 0.69868} +{"mode": "train", "epoch": 20, "iter": 3400, "lr": 0.09572, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25844, "top5_acc": 0.50406, "loss_cls": 4.07252, "loss": 4.07252, "time": 0.69622} +{"mode": "train", "epoch": 20, "iter": 3500, "lr": 0.09571, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25422, "top5_acc": 0.50766, "loss_cls": 4.02607, "loss": 4.02607, "time": 0.70301} +{"mode": "train", "epoch": 20, "iter": 3600, "lr": 0.09569, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26, "top5_acc": 0.51594, "loss_cls": 4.04774, "loss": 4.04774, "time": 0.69918} +{"mode": "train", "epoch": 20, "iter": 3700, "lr": 0.09568, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26047, "top5_acc": 0.51016, "loss_cls": 4.03784, "loss": 4.03784, "time": 0.69965} +{"mode": "val", "epoch": 20, "iter": 309, "lr": 0.09568, "top1_acc": 0.19085, "top5_acc": 0.40779, "mean_class_accuracy": 0.19085} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.09567, "memory": 15990, "data_time": 1.27707, "top1_acc": 0.25953, "top5_acc": 0.51219, "loss_cls": 4.0083, "loss": 4.0083, "time": 1.98579} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.09565, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26047, "top5_acc": 0.515, "loss_cls": 4.0171, "loss": 4.0171, "time": 0.70822} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.09564, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25875, "top5_acc": 0.51094, "loss_cls": 4.0467, "loss": 4.0467, "time": 0.70595} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.09563, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25516, "top5_acc": 0.50297, "loss_cls": 4.06334, "loss": 4.06334, "time": 0.7017} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.09562, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25688, "top5_acc": 0.50719, "loss_cls": 4.08069, "loss": 4.08069, "time": 0.7027} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.09561, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25047, "top5_acc": 0.50359, "loss_cls": 4.06514, "loss": 4.06514, "time": 0.69778} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.0956, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25422, "top5_acc": 0.50797, "loss_cls": 4.06792, "loss": 4.06792, "time": 0.69869} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.09559, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25922, "top5_acc": 0.51516, "loss_cls": 4.01289, "loss": 4.01289, "time": 0.69682} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.09557, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26359, "top5_acc": 0.52094, "loss_cls": 4.02063, "loss": 4.02063, "time": 0.69772} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.09556, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25703, "top5_acc": 0.51359, "loss_cls": 4.0424, "loss": 4.0424, "time": 0.69561} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.09555, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26609, "top5_acc": 0.51641, "loss_cls": 4.0112, "loss": 4.0112, "time": 0.69715} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.09554, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25781, "top5_acc": 0.51203, "loss_cls": 4.02635, "loss": 4.02635, "time": 0.69882} +{"mode": "train", "epoch": 21, "iter": 1300, "lr": 0.09553, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26328, "top5_acc": 0.51094, "loss_cls": 4.05767, "loss": 4.05767, "time": 0.70074} +{"mode": "train", "epoch": 21, "iter": 1400, "lr": 0.09552, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25969, "top5_acc": 0.51156, "loss_cls": 4.0711, "loss": 4.0711, "time": 0.70025} +{"mode": "train", "epoch": 21, "iter": 1500, "lr": 0.09551, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2525, "top5_acc": 0.51078, "loss_cls": 4.05402, "loss": 4.05402, "time": 0.70144} +{"mode": "train", "epoch": 21, "iter": 1600, "lr": 0.09549, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2575, "top5_acc": 0.515, "loss_cls": 4.03346, "loss": 4.03346, "time": 0.69879} +{"mode": "train", "epoch": 21, "iter": 1700, "lr": 0.09548, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25984, "top5_acc": 0.51062, "loss_cls": 4.05304, "loss": 4.05304, "time": 0.69948} +{"mode": "train", "epoch": 21, "iter": 1800, "lr": 0.09547, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26094, "top5_acc": 0.51359, "loss_cls": 4.01956, "loss": 4.01956, "time": 0.69979} +{"mode": "train", "epoch": 21, "iter": 1900, "lr": 0.09546, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25359, "top5_acc": 0.50328, "loss_cls": 4.07701, "loss": 4.07701, "time": 0.69999} +{"mode": "train", "epoch": 21, "iter": 2000, "lr": 0.09545, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25188, "top5_acc": 0.51094, "loss_cls": 4.04879, "loss": 4.04879, "time": 0.70296} +{"mode": "train", "epoch": 21, "iter": 2100, "lr": 0.09544, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25516, "top5_acc": 0.51016, "loss_cls": 4.05699, "loss": 4.05699, "time": 0.69718} +{"mode": "train", "epoch": 21, "iter": 2200, "lr": 0.09542, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25562, "top5_acc": 0.50578, "loss_cls": 4.04429, "loss": 4.04429, "time": 0.7004} +{"mode": "train", "epoch": 21, "iter": 2300, "lr": 0.09541, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25109, "top5_acc": 0.50734, "loss_cls": 4.05703, "loss": 4.05703, "time": 0.69807} +{"mode": "train", "epoch": 21, "iter": 2400, "lr": 0.0954, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25297, "top5_acc": 0.505, "loss_cls": 4.06806, "loss": 4.06806, "time": 0.69785} +{"mode": "train", "epoch": 21, "iter": 2500, "lr": 0.09539, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26766, "top5_acc": 0.51375, "loss_cls": 4.02352, "loss": 4.02352, "time": 0.6964} +{"mode": "train", "epoch": 21, "iter": 2600, "lr": 0.09538, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24875, "top5_acc": 0.50562, "loss_cls": 4.06209, "loss": 4.06209, "time": 0.70182} +{"mode": "train", "epoch": 21, "iter": 2700, "lr": 0.09537, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.255, "top5_acc": 0.50078, "loss_cls": 4.08227, "loss": 4.08227, "time": 0.70106} +{"mode": "train", "epoch": 21, "iter": 2800, "lr": 0.09535, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25859, "top5_acc": 0.50734, "loss_cls": 4.07605, "loss": 4.07605, "time": 0.69754} +{"mode": "train", "epoch": 21, "iter": 2900, "lr": 0.09534, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25188, "top5_acc": 0.50688, "loss_cls": 4.07718, "loss": 4.07718, "time": 0.6978} +{"mode": "train", "epoch": 21, "iter": 3000, "lr": 0.09533, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26172, "top5_acc": 0.51219, "loss_cls": 4.05372, "loss": 4.05372, "time": 0.69932} +{"mode": "train", "epoch": 21, "iter": 3100, "lr": 0.09532, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25859, "top5_acc": 0.50266, "loss_cls": 4.08159, "loss": 4.08159, "time": 0.69878} +{"mode": "train", "epoch": 21, "iter": 3200, "lr": 0.09531, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26172, "top5_acc": 0.52312, "loss_cls": 4.017, "loss": 4.017, "time": 0.70144} +{"mode": "train", "epoch": 21, "iter": 3300, "lr": 0.09529, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24438, "top5_acc": 0.505, "loss_cls": 4.10066, "loss": 4.10066, "time": 0.69469} +{"mode": "train", "epoch": 21, "iter": 3400, "lr": 0.09528, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26734, "top5_acc": 0.50688, "loss_cls": 4.07297, "loss": 4.07297, "time": 0.69876} +{"mode": "train", "epoch": 21, "iter": 3500, "lr": 0.09527, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25125, "top5_acc": 0.50281, "loss_cls": 4.09189, "loss": 4.09189, "time": 0.69901} +{"mode": "train", "epoch": 21, "iter": 3600, "lr": 0.09526, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25234, "top5_acc": 0.5125, "loss_cls": 4.06619, "loss": 4.06619, "time": 0.69718} +{"mode": "train", "epoch": 21, "iter": 3700, "lr": 0.09525, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25578, "top5_acc": 0.50672, "loss_cls": 4.06124, "loss": 4.06124, "time": 0.69853} +{"mode": "val", "epoch": 21, "iter": 309, "lr": 0.09524, "top1_acc": 0.19273, "top5_acc": 0.42547, "mean_class_accuracy": 0.19274} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.09523, "memory": 15990, "data_time": 1.28093, "top1_acc": 0.26641, "top5_acc": 0.52984, "loss_cls": 3.98701, "loss": 3.98701, "time": 1.98683} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.09522, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26031, "top5_acc": 0.52406, "loss_cls": 4.02106, "loss": 4.02106, "time": 0.71162} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.09521, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26312, "top5_acc": 0.51547, "loss_cls": 4.02352, "loss": 4.02352, "time": 0.70388} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.09519, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26359, "top5_acc": 0.51625, "loss_cls": 4.00396, "loss": 4.00396, "time": 0.70213} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.09518, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27047, "top5_acc": 0.52031, "loss_cls": 3.998, "loss": 3.998, "time": 0.70695} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.09517, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26109, "top5_acc": 0.51172, "loss_cls": 4.05299, "loss": 4.05299, "time": 0.6998} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.09516, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.255, "top5_acc": 0.52016, "loss_cls": 4.02876, "loss": 4.02876, "time": 0.69807} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.09515, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26859, "top5_acc": 0.51594, "loss_cls": 4.02808, "loss": 4.02808, "time": 0.70105} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.09513, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25703, "top5_acc": 0.50547, "loss_cls": 4.04424, "loss": 4.04424, "time": 0.69902} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.09512, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26109, "top5_acc": 0.50578, "loss_cls": 4.03691, "loss": 4.03691, "time": 0.69853} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.09511, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26031, "top5_acc": 0.50578, "loss_cls": 4.06416, "loss": 4.06416, "time": 0.69692} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0951, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25547, "top5_acc": 0.51016, "loss_cls": 4.06185, "loss": 4.06185, "time": 0.6983} +{"mode": "train", "epoch": 22, "iter": 1300, "lr": 0.09509, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25172, "top5_acc": 0.51391, "loss_cls": 4.04691, "loss": 4.04691, "time": 0.698} +{"mode": "train", "epoch": 22, "iter": 1400, "lr": 0.09507, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26359, "top5_acc": 0.51078, "loss_cls": 4.03407, "loss": 4.03407, "time": 0.69744} +{"mode": "train", "epoch": 22, "iter": 1500, "lr": 0.09506, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25812, "top5_acc": 0.51031, "loss_cls": 4.06719, "loss": 4.06719, "time": 0.69541} +{"mode": "train", "epoch": 22, "iter": 1600, "lr": 0.09505, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26719, "top5_acc": 0.51109, "loss_cls": 4.02358, "loss": 4.02358, "time": 0.69599} +{"mode": "train", "epoch": 22, "iter": 1700, "lr": 0.09504, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26125, "top5_acc": 0.51766, "loss_cls": 4.02049, "loss": 4.02049, "time": 0.69778} +{"mode": "train", "epoch": 22, "iter": 1800, "lr": 0.09502, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25531, "top5_acc": 0.50422, "loss_cls": 4.08408, "loss": 4.08408, "time": 0.70078} +{"mode": "train", "epoch": 22, "iter": 1900, "lr": 0.09501, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25797, "top5_acc": 0.51109, "loss_cls": 4.03563, "loss": 4.03563, "time": 0.69815} +{"mode": "train", "epoch": 22, "iter": 2000, "lr": 0.095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25953, "top5_acc": 0.50813, "loss_cls": 4.06639, "loss": 4.06639, "time": 0.70228} +{"mode": "train", "epoch": 22, "iter": 2100, "lr": 0.09499, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25859, "top5_acc": 0.51344, "loss_cls": 4.01865, "loss": 4.01865, "time": 0.69847} +{"mode": "train", "epoch": 22, "iter": 2200, "lr": 0.09498, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25984, "top5_acc": 0.51969, "loss_cls": 4.02566, "loss": 4.02566, "time": 0.69944} +{"mode": "train", "epoch": 22, "iter": 2300, "lr": 0.09496, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25953, "top5_acc": 0.51188, "loss_cls": 4.04135, "loss": 4.04135, "time": 0.70093} +{"mode": "train", "epoch": 22, "iter": 2400, "lr": 0.09495, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.255, "top5_acc": 0.50672, "loss_cls": 4.03491, "loss": 4.03491, "time": 0.70117} +{"mode": "train", "epoch": 22, "iter": 2500, "lr": 0.09494, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25406, "top5_acc": 0.51109, "loss_cls": 4.0677, "loss": 4.0677, "time": 0.69797} +{"mode": "train", "epoch": 22, "iter": 2600, "lr": 0.09493, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25516, "top5_acc": 0.50688, "loss_cls": 4.06422, "loss": 4.06422, "time": 0.69896} +{"mode": "train", "epoch": 22, "iter": 2700, "lr": 0.09491, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25594, "top5_acc": 0.50578, "loss_cls": 4.04348, "loss": 4.04348, "time": 0.69937} +{"mode": "train", "epoch": 22, "iter": 2800, "lr": 0.0949, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24234, "top5_acc": 0.49891, "loss_cls": 4.07394, "loss": 4.07394, "time": 0.70031} +{"mode": "train", "epoch": 22, "iter": 2900, "lr": 0.09489, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25781, "top5_acc": 0.50875, "loss_cls": 4.0904, "loss": 4.0904, "time": 0.69632} +{"mode": "train", "epoch": 22, "iter": 3000, "lr": 0.09488, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25141, "top5_acc": 0.51, "loss_cls": 4.0437, "loss": 4.0437, "time": 0.69808} +{"mode": "train", "epoch": 22, "iter": 3100, "lr": 0.09487, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25938, "top5_acc": 0.51297, "loss_cls": 4.05001, "loss": 4.05001, "time": 0.70066} +{"mode": "train", "epoch": 22, "iter": 3200, "lr": 0.09485, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25188, "top5_acc": 0.50453, "loss_cls": 4.09154, "loss": 4.09154, "time": 0.69589} +{"mode": "train", "epoch": 22, "iter": 3300, "lr": 0.09484, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25953, "top5_acc": 0.50375, "loss_cls": 4.06897, "loss": 4.06897, "time": 0.69966} +{"mode": "train", "epoch": 22, "iter": 3400, "lr": 0.09483, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26297, "top5_acc": 0.515, "loss_cls": 4.02595, "loss": 4.02595, "time": 0.69825} +{"mode": "train", "epoch": 22, "iter": 3500, "lr": 0.09482, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24891, "top5_acc": 0.49875, "loss_cls": 4.09532, "loss": 4.09532, "time": 0.69862} +{"mode": "train", "epoch": 22, "iter": 3600, "lr": 0.0948, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25578, "top5_acc": 0.50062, "loss_cls": 4.06319, "loss": 4.06319, "time": 0.69852} +{"mode": "train", "epoch": 22, "iter": 3700, "lr": 0.09479, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24812, "top5_acc": 0.50766, "loss_cls": 4.07993, "loss": 4.07993, "time": 0.70429} +{"mode": "val", "epoch": 22, "iter": 309, "lr": 0.09479, "top1_acc": 0.17135, "top5_acc": 0.39315, "mean_class_accuracy": 0.17119} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.09477, "memory": 15990, "data_time": 1.26954, "top1_acc": 0.25734, "top5_acc": 0.51578, "loss_cls": 4.02014, "loss": 4.02014, "time": 1.97329} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.09476, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26516, "top5_acc": 0.51625, "loss_cls": 4.01146, "loss": 4.01146, "time": 0.70597} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.09475, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25672, "top5_acc": 0.50594, "loss_cls": 4.07546, "loss": 4.07546, "time": 0.70378} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.09474, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25438, "top5_acc": 0.50766, "loss_cls": 4.03461, "loss": 4.03461, "time": 0.7066} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.09472, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26937, "top5_acc": 0.52375, "loss_cls": 3.9833, "loss": 3.9833, "time": 0.70316} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.09471, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25953, "top5_acc": 0.50641, "loss_cls": 4.05104, "loss": 4.05104, "time": 0.69708} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.0947, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26094, "top5_acc": 0.52359, "loss_cls": 3.99703, "loss": 3.99703, "time": 0.7009} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.09469, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26516, "top5_acc": 0.52109, "loss_cls": 4.0032, "loss": 4.0032, "time": 0.7011} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.09467, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25734, "top5_acc": 0.50156, "loss_cls": 4.04799, "loss": 4.04799, "time": 0.69508} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.09466, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25922, "top5_acc": 0.51234, "loss_cls": 4.04101, "loss": 4.04101, "time": 0.70035} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.09465, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26844, "top5_acc": 0.51, "loss_cls": 4.04524, "loss": 4.04524, "time": 0.69901} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.09464, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25953, "top5_acc": 0.51281, "loss_cls": 4.02991, "loss": 4.02991, "time": 0.69836} +{"mode": "train", "epoch": 23, "iter": 1300, "lr": 0.09462, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24703, "top5_acc": 0.50641, "loss_cls": 4.05269, "loss": 4.05269, "time": 0.69804} +{"mode": "train", "epoch": 23, "iter": 1400, "lr": 0.09461, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26109, "top5_acc": 0.51594, "loss_cls": 4.04475, "loss": 4.04475, "time": 0.69917} +{"mode": "train", "epoch": 23, "iter": 1500, "lr": 0.0946, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26266, "top5_acc": 0.52281, "loss_cls": 3.99398, "loss": 3.99398, "time": 0.70018} +{"mode": "train", "epoch": 23, "iter": 1600, "lr": 0.09459, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26234, "top5_acc": 0.50938, "loss_cls": 4.03282, "loss": 4.03282, "time": 0.69712} +{"mode": "train", "epoch": 23, "iter": 1700, "lr": 0.09457, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26203, "top5_acc": 0.51578, "loss_cls": 4.04334, "loss": 4.04334, "time": 0.70021} +{"mode": "train", "epoch": 23, "iter": 1800, "lr": 0.09456, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26312, "top5_acc": 0.51281, "loss_cls": 4.03816, "loss": 4.03816, "time": 0.69965} +{"mode": "train", "epoch": 23, "iter": 1900, "lr": 0.09455, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25828, "top5_acc": 0.51656, "loss_cls": 4.02915, "loss": 4.02915, "time": 0.69748} +{"mode": "train", "epoch": 23, "iter": 2000, "lr": 0.09453, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26172, "top5_acc": 0.51359, "loss_cls": 4.02884, "loss": 4.02884, "time": 0.69914} +{"mode": "train", "epoch": 23, "iter": 2100, "lr": 0.09452, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26391, "top5_acc": 0.51344, "loss_cls": 4.01294, "loss": 4.01294, "time": 0.69819} +{"mode": "train", "epoch": 23, "iter": 2200, "lr": 0.09451, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26797, "top5_acc": 0.50891, "loss_cls": 4.06397, "loss": 4.06397, "time": 0.69881} +{"mode": "train", "epoch": 23, "iter": 2300, "lr": 0.0945, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24953, "top5_acc": 0.51531, "loss_cls": 4.0366, "loss": 4.0366, "time": 0.69956} +{"mode": "train", "epoch": 23, "iter": 2400, "lr": 0.09448, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25812, "top5_acc": 0.50781, "loss_cls": 4.05577, "loss": 4.05577, "time": 0.70058} +{"mode": "train", "epoch": 23, "iter": 2500, "lr": 0.09447, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25391, "top5_acc": 0.51078, "loss_cls": 4.05524, "loss": 4.05524, "time": 0.69997} +{"mode": "train", "epoch": 23, "iter": 2600, "lr": 0.09446, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24906, "top5_acc": 0.50594, "loss_cls": 4.07587, "loss": 4.07587, "time": 0.69779} +{"mode": "train", "epoch": 23, "iter": 2700, "lr": 0.09445, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26203, "top5_acc": 0.51672, "loss_cls": 4.02491, "loss": 4.02491, "time": 0.69644} +{"mode": "train", "epoch": 23, "iter": 2800, "lr": 0.09443, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25734, "top5_acc": 0.51219, "loss_cls": 4.0505, "loss": 4.0505, "time": 0.69769} +{"mode": "train", "epoch": 23, "iter": 2900, "lr": 0.09442, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25656, "top5_acc": 0.51125, "loss_cls": 4.04633, "loss": 4.04633, "time": 0.70079} +{"mode": "train", "epoch": 23, "iter": 3000, "lr": 0.09441, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26328, "top5_acc": 0.51203, "loss_cls": 4.04064, "loss": 4.04064, "time": 0.69769} +{"mode": "train", "epoch": 23, "iter": 3100, "lr": 0.09439, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24781, "top5_acc": 0.50969, "loss_cls": 4.05718, "loss": 4.05718, "time": 0.69587} +{"mode": "train", "epoch": 23, "iter": 3200, "lr": 0.09438, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25359, "top5_acc": 0.50453, "loss_cls": 4.06563, "loss": 4.06563, "time": 0.69825} +{"mode": "train", "epoch": 23, "iter": 3300, "lr": 0.09437, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25031, "top5_acc": 0.50734, "loss_cls": 4.05563, "loss": 4.05563, "time": 0.70062} +{"mode": "train", "epoch": 23, "iter": 3400, "lr": 0.09436, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25078, "top5_acc": 0.50297, "loss_cls": 4.08395, "loss": 4.08395, "time": 0.69661} +{"mode": "train", "epoch": 23, "iter": 3500, "lr": 0.09434, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25609, "top5_acc": 0.505, "loss_cls": 4.08499, "loss": 4.08499, "time": 0.6965} +{"mode": "train", "epoch": 23, "iter": 3600, "lr": 0.09433, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25438, "top5_acc": 0.50062, "loss_cls": 4.08037, "loss": 4.08037, "time": 0.70001} +{"mode": "train", "epoch": 23, "iter": 3700, "lr": 0.09432, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.255, "top5_acc": 0.50406, "loss_cls": 4.06571, "loss": 4.06571, "time": 0.70193} +{"mode": "val", "epoch": 23, "iter": 309, "lr": 0.09431, "top1_acc": 0.19951, "top5_acc": 0.43362, "mean_class_accuracy": 0.19932} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.0943, "memory": 15990, "data_time": 1.29709, "top1_acc": 0.25297, "top5_acc": 0.51812, "loss_cls": 3.99986, "loss": 3.99986, "time": 2.00319} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.09428, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26125, "top5_acc": 0.51375, "loss_cls": 4.02653, "loss": 4.02653, "time": 0.70979} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.09427, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26219, "top5_acc": 0.51078, "loss_cls": 4.05455, "loss": 4.05455, "time": 0.70129} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.09426, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2675, "top5_acc": 0.52453, "loss_cls": 3.98484, "loss": 3.98484, "time": 0.71159} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.09425, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25656, "top5_acc": 0.51172, "loss_cls": 4.05959, "loss": 4.05959, "time": 0.69948} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.09423, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25234, "top5_acc": 0.51219, "loss_cls": 4.04186, "loss": 4.04186, "time": 0.69758} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.09422, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26031, "top5_acc": 0.50953, "loss_cls": 4.06785, "loss": 4.06785, "time": 0.6981} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.09421, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26703, "top5_acc": 0.52062, "loss_cls": 4.01088, "loss": 4.01088, "time": 0.70105} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.09419, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25578, "top5_acc": 0.51141, "loss_cls": 4.00739, "loss": 4.00739, "time": 0.69908} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.09418, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26812, "top5_acc": 0.52047, "loss_cls": 3.9957, "loss": 3.9957, "time": 0.69863} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.09417, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25266, "top5_acc": 0.50328, "loss_cls": 4.06502, "loss": 4.06502, "time": 0.69878} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.09415, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26531, "top5_acc": 0.51766, "loss_cls": 4.01658, "loss": 4.01658, "time": 0.69842} +{"mode": "train", "epoch": 24, "iter": 1300, "lr": 0.09414, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25438, "top5_acc": 0.50625, "loss_cls": 4.0857, "loss": 4.0857, "time": 0.69784} +{"mode": "train", "epoch": 24, "iter": 1400, "lr": 0.09413, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26859, "top5_acc": 0.52344, "loss_cls": 3.99138, "loss": 3.99138, "time": 0.69807} +{"mode": "train", "epoch": 24, "iter": 1500, "lr": 0.09411, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26219, "top5_acc": 0.52562, "loss_cls": 3.9829, "loss": 3.9829, "time": 0.69796} +{"mode": "train", "epoch": 24, "iter": 1600, "lr": 0.0941, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26141, "top5_acc": 0.50266, "loss_cls": 4.05942, "loss": 4.05942, "time": 0.69799} +{"mode": "train", "epoch": 24, "iter": 1700, "lr": 0.09409, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26594, "top5_acc": 0.52156, "loss_cls": 4.01796, "loss": 4.01796, "time": 0.69733} +{"mode": "train", "epoch": 24, "iter": 1800, "lr": 0.09407, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26, "top5_acc": 0.51453, "loss_cls": 4.02489, "loss": 4.02489, "time": 0.69984} +{"mode": "train", "epoch": 24, "iter": 1900, "lr": 0.09406, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26078, "top5_acc": 0.51, "loss_cls": 4.04696, "loss": 4.04696, "time": 0.70016} +{"mode": "train", "epoch": 24, "iter": 2000, "lr": 0.09405, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26375, "top5_acc": 0.51672, "loss_cls": 4.01508, "loss": 4.01508, "time": 0.70288} +{"mode": "train", "epoch": 24, "iter": 2100, "lr": 0.09404, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26016, "top5_acc": 0.51641, "loss_cls": 4.01683, "loss": 4.01683, "time": 0.69934} +{"mode": "train", "epoch": 24, "iter": 2200, "lr": 0.09402, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25547, "top5_acc": 0.52109, "loss_cls": 4.04989, "loss": 4.04989, "time": 0.70001} +{"mode": "train", "epoch": 24, "iter": 2300, "lr": 0.09401, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25219, "top5_acc": 0.50125, "loss_cls": 4.06726, "loss": 4.06726, "time": 0.70153} +{"mode": "train", "epoch": 24, "iter": 2400, "lr": 0.094, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26672, "top5_acc": 0.51438, "loss_cls": 4.03233, "loss": 4.03233, "time": 0.69923} +{"mode": "train", "epoch": 24, "iter": 2500, "lr": 0.09398, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25922, "top5_acc": 0.51375, "loss_cls": 4.0413, "loss": 4.0413, "time": 0.69826} +{"mode": "train", "epoch": 24, "iter": 2600, "lr": 0.09397, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26344, "top5_acc": 0.51719, "loss_cls": 4.04096, "loss": 4.04096, "time": 0.69489} +{"mode": "train", "epoch": 24, "iter": 2700, "lr": 0.09396, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26141, "top5_acc": 0.5125, "loss_cls": 4.02731, "loss": 4.02731, "time": 0.69587} +{"mode": "train", "epoch": 24, "iter": 2800, "lr": 0.09394, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26312, "top5_acc": 0.51203, "loss_cls": 4.02947, "loss": 4.02947, "time": 0.69744} +{"mode": "train", "epoch": 24, "iter": 2900, "lr": 0.09393, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26125, "top5_acc": 0.51344, "loss_cls": 4.03127, "loss": 4.03127, "time": 0.69887} +{"mode": "train", "epoch": 24, "iter": 3000, "lr": 0.09392, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25766, "top5_acc": 0.50953, "loss_cls": 4.05387, "loss": 4.05387, "time": 0.69875} +{"mode": "train", "epoch": 24, "iter": 3100, "lr": 0.0939, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25875, "top5_acc": 0.50828, "loss_cls": 4.04996, "loss": 4.04996, "time": 0.69774} +{"mode": "train", "epoch": 24, "iter": 3200, "lr": 0.09389, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24531, "top5_acc": 0.50281, "loss_cls": 4.1028, "loss": 4.1028, "time": 0.69679} +{"mode": "train", "epoch": 24, "iter": 3300, "lr": 0.09388, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26094, "top5_acc": 0.51359, "loss_cls": 4.0143, "loss": 4.0143, "time": 0.6974} +{"mode": "train", "epoch": 24, "iter": 3400, "lr": 0.09386, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26859, "top5_acc": 0.51656, "loss_cls": 4.03449, "loss": 4.03449, "time": 0.69642} +{"mode": "train", "epoch": 24, "iter": 3500, "lr": 0.09385, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25594, "top5_acc": 0.50734, "loss_cls": 4.08031, "loss": 4.08031, "time": 0.699} +{"mode": "train", "epoch": 24, "iter": 3600, "lr": 0.09384, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26391, "top5_acc": 0.51203, "loss_cls": 4.03581, "loss": 4.03581, "time": 0.69902} +{"mode": "train", "epoch": 24, "iter": 3700, "lr": 0.09382, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24484, "top5_acc": 0.50297, "loss_cls": 4.08739, "loss": 4.08739, "time": 0.70077} +{"mode": "val", "epoch": 24, "iter": 309, "lr": 0.09382, "top1_acc": 0.17743, "top5_acc": 0.39948, "mean_class_accuracy": 0.17721} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.0938, "memory": 15990, "data_time": 1.27989, "top1_acc": 0.26828, "top5_acc": 0.52359, "loss_cls": 3.98924, "loss": 3.98924, "time": 1.98291} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.09379, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25812, "top5_acc": 0.52203, "loss_cls": 3.97686, "loss": 3.97686, "time": 0.71001} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.09378, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25234, "top5_acc": 0.50859, "loss_cls": 4.06689, "loss": 4.06689, "time": 0.70456} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.09376, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25516, "top5_acc": 0.50625, "loss_cls": 4.05, "loss": 4.05, "time": 0.70516} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.09375, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27, "top5_acc": 0.52641, "loss_cls": 3.98293, "loss": 3.98293, "time": 0.70457} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.09373, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25812, "top5_acc": 0.50578, "loss_cls": 4.07124, "loss": 4.07124, "time": 0.701} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.09372, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25453, "top5_acc": 0.50109, "loss_cls": 4.05443, "loss": 4.05443, "time": 0.69799} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.09371, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25828, "top5_acc": 0.51547, "loss_cls": 4.02906, "loss": 4.02906, "time": 0.69766} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.09369, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25109, "top5_acc": 0.50922, "loss_cls": 4.07612, "loss": 4.07612, "time": 0.69777} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.09368, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26703, "top5_acc": 0.52141, "loss_cls": 4.01124, "loss": 4.01124, "time": 0.69715} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.09367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25297, "top5_acc": 0.51453, "loss_cls": 4.04114, "loss": 4.04114, "time": 0.69769} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.09365, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25609, "top5_acc": 0.49984, "loss_cls": 4.07916, "loss": 4.07916, "time": 0.69661} +{"mode": "train", "epoch": 25, "iter": 1300, "lr": 0.09364, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25516, "top5_acc": 0.51766, "loss_cls": 4.05342, "loss": 4.05342, "time": 0.69666} +{"mode": "train", "epoch": 25, "iter": 1400, "lr": 0.09363, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25672, "top5_acc": 0.50625, "loss_cls": 4.05359, "loss": 4.05359, "time": 0.69996} +{"mode": "train", "epoch": 25, "iter": 1500, "lr": 0.09361, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27078, "top5_acc": 0.52188, "loss_cls": 3.99991, "loss": 3.99991, "time": 0.69944} +{"mode": "train", "epoch": 25, "iter": 1600, "lr": 0.0936, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25406, "top5_acc": 0.50688, "loss_cls": 4.04466, "loss": 4.04466, "time": 0.69812} +{"mode": "train", "epoch": 25, "iter": 1700, "lr": 0.09358, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26062, "top5_acc": 0.51641, "loss_cls": 4.03449, "loss": 4.03449, "time": 0.69871} +{"mode": "train", "epoch": 25, "iter": 1800, "lr": 0.09357, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25859, "top5_acc": 0.50766, "loss_cls": 4.05354, "loss": 4.05354, "time": 0.69806} +{"mode": "train", "epoch": 25, "iter": 1900, "lr": 0.09356, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26359, "top5_acc": 0.52203, "loss_cls": 3.98294, "loss": 3.98294, "time": 0.69964} +{"mode": "train", "epoch": 25, "iter": 2000, "lr": 0.09354, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27297, "top5_acc": 0.52484, "loss_cls": 3.97788, "loss": 3.97788, "time": 0.7032} +{"mode": "train", "epoch": 25, "iter": 2100, "lr": 0.09353, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26172, "top5_acc": 0.51891, "loss_cls": 3.97821, "loss": 3.97821, "time": 0.70369} +{"mode": "train", "epoch": 25, "iter": 2200, "lr": 0.09352, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26547, "top5_acc": 0.51156, "loss_cls": 4.02164, "loss": 4.02164, "time": 0.69956} +{"mode": "train", "epoch": 25, "iter": 2300, "lr": 0.0935, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25531, "top5_acc": 0.50984, "loss_cls": 4.05778, "loss": 4.05778, "time": 0.69795} +{"mode": "train", "epoch": 25, "iter": 2400, "lr": 0.09349, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25156, "top5_acc": 0.51016, "loss_cls": 4.06601, "loss": 4.06601, "time": 0.69813} +{"mode": "train", "epoch": 25, "iter": 2500, "lr": 0.09347, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26344, "top5_acc": 0.52359, "loss_cls": 4.01668, "loss": 4.01668, "time": 0.69968} +{"mode": "train", "epoch": 25, "iter": 2600, "lr": 0.09346, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26219, "top5_acc": 0.52234, "loss_cls": 3.99758, "loss": 3.99758, "time": 0.70058} +{"mode": "train", "epoch": 25, "iter": 2700, "lr": 0.09345, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25891, "top5_acc": 0.51562, "loss_cls": 4.01261, "loss": 4.01261, "time": 0.7017} +{"mode": "train", "epoch": 25, "iter": 2800, "lr": 0.09343, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26156, "top5_acc": 0.50391, "loss_cls": 4.04942, "loss": 4.04942, "time": 0.69908} +{"mode": "train", "epoch": 25, "iter": 2900, "lr": 0.09342, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26219, "top5_acc": 0.51609, "loss_cls": 4.02113, "loss": 4.02113, "time": 0.70171} +{"mode": "train", "epoch": 25, "iter": 3000, "lr": 0.09341, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25812, "top5_acc": 0.51047, "loss_cls": 4.0338, "loss": 4.0338, "time": 0.70046} +{"mode": "train", "epoch": 25, "iter": 3100, "lr": 0.09339, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27219, "top5_acc": 0.51766, "loss_cls": 3.99857, "loss": 3.99857, "time": 0.70006} +{"mode": "train", "epoch": 25, "iter": 3200, "lr": 0.09338, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25953, "top5_acc": 0.51969, "loss_cls": 4.0406, "loss": 4.0406, "time": 0.70008} +{"mode": "train", "epoch": 25, "iter": 3300, "lr": 0.09336, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26609, "top5_acc": 0.51812, "loss_cls": 4.03441, "loss": 4.03441, "time": 0.69958} +{"mode": "train", "epoch": 25, "iter": 3400, "lr": 0.09335, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25781, "top5_acc": 0.51625, "loss_cls": 4.04069, "loss": 4.04069, "time": 0.70067} +{"mode": "train", "epoch": 25, "iter": 3500, "lr": 0.09334, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25547, "top5_acc": 0.51062, "loss_cls": 4.04197, "loss": 4.04197, "time": 0.69965} +{"mode": "train", "epoch": 25, "iter": 3600, "lr": 0.09332, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26406, "top5_acc": 0.50344, "loss_cls": 4.08565, "loss": 4.08565, "time": 0.69935} +{"mode": "train", "epoch": 25, "iter": 3700, "lr": 0.09331, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25922, "top5_acc": 0.50844, "loss_cls": 4.04495, "loss": 4.04495, "time": 0.70197} +{"mode": "val", "epoch": 25, "iter": 309, "lr": 0.0933, "top1_acc": 0.16735, "top5_acc": 0.37421, "mean_class_accuracy": 0.16721} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.09329, "memory": 15990, "data_time": 1.32856, "top1_acc": 0.27297, "top5_acc": 0.52594, "loss_cls": 3.98284, "loss": 3.98284, "time": 2.0326} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.09327, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27078, "top5_acc": 0.51859, "loss_cls": 3.99104, "loss": 3.99104, "time": 0.71141} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.09326, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25609, "top5_acc": 0.51734, "loss_cls": 4.02073, "loss": 4.02073, "time": 0.70379} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.09325, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25984, "top5_acc": 0.51984, "loss_cls": 4.01137, "loss": 4.01137, "time": 0.70728} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.09323, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26719, "top5_acc": 0.51391, "loss_cls": 4.01079, "loss": 4.01079, "time": 0.70635} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.09322, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26578, "top5_acc": 0.51438, "loss_cls": 4.02925, "loss": 4.02925, "time": 0.701} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.0932, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26937, "top5_acc": 0.51891, "loss_cls": 3.99447, "loss": 3.99447, "time": 0.69988} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.09319, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25531, "top5_acc": 0.51375, "loss_cls": 4.03368, "loss": 4.03368, "time": 0.70322} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.09318, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25891, "top5_acc": 0.51313, "loss_cls": 4.04152, "loss": 4.04152, "time": 0.69844} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.09316, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25859, "top5_acc": 0.51812, "loss_cls": 4.0373, "loss": 4.0373, "time": 0.69813} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.09315, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2575, "top5_acc": 0.51328, "loss_cls": 4.03968, "loss": 4.03968, "time": 0.69999} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.09313, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27297, "top5_acc": 0.52203, "loss_cls": 3.95851, "loss": 3.95851, "time": 0.69925} +{"mode": "train", "epoch": 26, "iter": 1300, "lr": 0.09312, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25953, "top5_acc": 0.51281, "loss_cls": 4.0366, "loss": 4.0366, "time": 0.69987} +{"mode": "train", "epoch": 26, "iter": 1400, "lr": 0.0931, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25828, "top5_acc": 0.5025, "loss_cls": 4.07352, "loss": 4.07352, "time": 0.70023} +{"mode": "train", "epoch": 26, "iter": 1500, "lr": 0.09309, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27094, "top5_acc": 0.52641, "loss_cls": 3.98971, "loss": 3.98971, "time": 0.69971} +{"mode": "train", "epoch": 26, "iter": 1600, "lr": 0.09308, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26203, "top5_acc": 0.52406, "loss_cls": 4.00289, "loss": 4.00289, "time": 0.69883} +{"mode": "train", "epoch": 26, "iter": 1700, "lr": 0.09306, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26453, "top5_acc": 0.51922, "loss_cls": 3.99557, "loss": 3.99557, "time": 0.69962} +{"mode": "train", "epoch": 26, "iter": 1800, "lr": 0.09305, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26234, "top5_acc": 0.50531, "loss_cls": 4.04816, "loss": 4.04816, "time": 0.69973} +{"mode": "train", "epoch": 26, "iter": 1900, "lr": 0.09303, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25984, "top5_acc": 0.51656, "loss_cls": 4.05369, "loss": 4.05369, "time": 0.70067} +{"mode": "train", "epoch": 26, "iter": 2000, "lr": 0.09302, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24938, "top5_acc": 0.51375, "loss_cls": 4.0262, "loss": 4.0262, "time": 0.70292} +{"mode": "train", "epoch": 26, "iter": 2100, "lr": 0.093, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26734, "top5_acc": 0.51797, "loss_cls": 4.01471, "loss": 4.01471, "time": 0.70079} +{"mode": "train", "epoch": 26, "iter": 2200, "lr": 0.09299, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.26, "top5_acc": 0.50453, "loss_cls": 4.05219, "loss": 4.05219, "time": 0.69983} +{"mode": "train", "epoch": 26, "iter": 2300, "lr": 0.09298, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26906, "top5_acc": 0.51656, "loss_cls": 4.00092, "loss": 4.00092, "time": 0.70133} +{"mode": "train", "epoch": 26, "iter": 2400, "lr": 0.09296, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25984, "top5_acc": 0.52047, "loss_cls": 4.03192, "loss": 4.03192, "time": 0.7056} +{"mode": "train", "epoch": 26, "iter": 2500, "lr": 0.09295, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25953, "top5_acc": 0.52391, "loss_cls": 4.00856, "loss": 4.00856, "time": 0.70336} +{"mode": "train", "epoch": 26, "iter": 2600, "lr": 0.09293, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26141, "top5_acc": 0.51516, "loss_cls": 4.05008, "loss": 4.05008, "time": 0.70413} +{"mode": "train", "epoch": 26, "iter": 2700, "lr": 0.09292, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25766, "top5_acc": 0.51359, "loss_cls": 4.04897, "loss": 4.04897, "time": 0.69897} +{"mode": "train", "epoch": 26, "iter": 2800, "lr": 0.0929, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25344, "top5_acc": 0.51359, "loss_cls": 4.06556, "loss": 4.06556, "time": 0.70068} +{"mode": "train", "epoch": 26, "iter": 2900, "lr": 0.09289, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26141, "top5_acc": 0.50875, "loss_cls": 4.06442, "loss": 4.06442, "time": 0.6972} +{"mode": "train", "epoch": 26, "iter": 3000, "lr": 0.09288, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2575, "top5_acc": 0.51688, "loss_cls": 4.00011, "loss": 4.00011, "time": 0.70017} +{"mode": "train", "epoch": 26, "iter": 3100, "lr": 0.09286, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26406, "top5_acc": 0.51734, "loss_cls": 4.01601, "loss": 4.01601, "time": 0.69955} +{"mode": "train", "epoch": 26, "iter": 3200, "lr": 0.09285, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24906, "top5_acc": 0.49, "loss_cls": 4.11668, "loss": 4.11668, "time": 0.70025} +{"mode": "train", "epoch": 26, "iter": 3300, "lr": 0.09283, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25297, "top5_acc": 0.51516, "loss_cls": 4.06422, "loss": 4.06422, "time": 0.70212} +{"mode": "train", "epoch": 26, "iter": 3400, "lr": 0.09282, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27187, "top5_acc": 0.52016, "loss_cls": 3.99563, "loss": 3.99563, "time": 0.69875} +{"mode": "train", "epoch": 26, "iter": 3500, "lr": 0.0928, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25391, "top5_acc": 0.505, "loss_cls": 4.07513, "loss": 4.07513, "time": 0.69824} +{"mode": "train", "epoch": 26, "iter": 3600, "lr": 0.09279, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25859, "top5_acc": 0.51078, "loss_cls": 4.02783, "loss": 4.02783, "time": 0.70368} +{"mode": "train", "epoch": 26, "iter": 3700, "lr": 0.09278, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26031, "top5_acc": 0.51422, "loss_cls": 4.04211, "loss": 4.04211, "time": 0.70255} +{"mode": "val", "epoch": 26, "iter": 309, "lr": 0.09277, "top1_acc": 0.16234, "top5_acc": 0.36509, "mean_class_accuracy": 0.1622} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.09275, "memory": 15990, "data_time": 1.32795, "top1_acc": 0.25969, "top5_acc": 0.51422, "loss_cls": 4.02961, "loss": 4.02961, "time": 2.02985} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.09274, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25547, "top5_acc": 0.51828, "loss_cls": 4.02127, "loss": 4.02127, "time": 0.71216} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.09272, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25625, "top5_acc": 0.51516, "loss_cls": 4.01443, "loss": 4.01443, "time": 0.70496} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.09271, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26562, "top5_acc": 0.5175, "loss_cls": 4.00248, "loss": 4.00248, "time": 0.70262} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.0927, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26812, "top5_acc": 0.51531, "loss_cls": 4.0298, "loss": 4.0298, "time": 0.70522} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.09268, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25828, "top5_acc": 0.51578, "loss_cls": 4.02346, "loss": 4.02346, "time": 0.70093} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.09267, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25859, "top5_acc": 0.51328, "loss_cls": 4.02762, "loss": 4.02762, "time": 0.70063} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.09265, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26656, "top5_acc": 0.52859, "loss_cls": 4.00621, "loss": 4.00621, "time": 0.70056} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.09264, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26391, "top5_acc": 0.51281, "loss_cls": 4.03144, "loss": 4.03144, "time": 0.70027} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.09262, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26109, "top5_acc": 0.51516, "loss_cls": 4.03403, "loss": 4.03403, "time": 0.69814} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.09261, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26375, "top5_acc": 0.51344, "loss_cls": 3.98442, "loss": 3.98442, "time": 0.70239} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.09259, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25844, "top5_acc": 0.50266, "loss_cls": 4.04707, "loss": 4.04707, "time": 0.69804} +{"mode": "train", "epoch": 27, "iter": 1300, "lr": 0.09258, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.255, "top5_acc": 0.50797, "loss_cls": 4.04606, "loss": 4.04606, "time": 0.69887} +{"mode": "train", "epoch": 27, "iter": 1400, "lr": 0.09256, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26906, "top5_acc": 0.52484, "loss_cls": 3.9654, "loss": 3.9654, "time": 0.69922} +{"mode": "train", "epoch": 27, "iter": 1500, "lr": 0.09255, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24547, "top5_acc": 0.51188, "loss_cls": 4.05356, "loss": 4.05356, "time": 0.70003} +{"mode": "train", "epoch": 27, "iter": 1600, "lr": 0.09253, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25422, "top5_acc": 0.5125, "loss_cls": 4.04591, "loss": 4.04591, "time": 0.69984} +{"mode": "train", "epoch": 27, "iter": 1700, "lr": 0.09252, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25359, "top5_acc": 0.51, "loss_cls": 4.03494, "loss": 4.03494, "time": 0.70097} +{"mode": "train", "epoch": 27, "iter": 1800, "lr": 0.09251, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26125, "top5_acc": 0.50641, "loss_cls": 4.04959, "loss": 4.04959, "time": 0.69864} +{"mode": "train", "epoch": 27, "iter": 1900, "lr": 0.09249, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25047, "top5_acc": 0.50766, "loss_cls": 4.07153, "loss": 4.07153, "time": 0.69901} +{"mode": "train", "epoch": 27, "iter": 2000, "lr": 0.09248, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26422, "top5_acc": 0.51203, "loss_cls": 4.00956, "loss": 4.00956, "time": 0.70876} +{"mode": "train", "epoch": 27, "iter": 2100, "lr": 0.09246, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25641, "top5_acc": 0.51594, "loss_cls": 4.01584, "loss": 4.01584, "time": 0.69874} +{"mode": "train", "epoch": 27, "iter": 2200, "lr": 0.09245, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24969, "top5_acc": 0.50578, "loss_cls": 4.09631, "loss": 4.09631, "time": 0.70144} +{"mode": "train", "epoch": 27, "iter": 2300, "lr": 0.09243, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26031, "top5_acc": 0.51969, "loss_cls": 4.03931, "loss": 4.03931, "time": 0.69943} +{"mode": "train", "epoch": 27, "iter": 2400, "lr": 0.09242, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25812, "top5_acc": 0.51688, "loss_cls": 3.9943, "loss": 3.9943, "time": 0.70032} +{"mode": "train", "epoch": 27, "iter": 2500, "lr": 0.0924, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26438, "top5_acc": 0.51938, "loss_cls": 3.99261, "loss": 3.99261, "time": 0.69816} +{"mode": "train", "epoch": 27, "iter": 2600, "lr": 0.09239, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26687, "top5_acc": 0.51625, "loss_cls": 3.98478, "loss": 3.98478, "time": 0.69539} +{"mode": "train", "epoch": 27, "iter": 2700, "lr": 0.09237, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26328, "top5_acc": 0.51469, "loss_cls": 4.03901, "loss": 4.03901, "time": 0.69682} +{"mode": "train", "epoch": 27, "iter": 2800, "lr": 0.09236, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26734, "top5_acc": 0.51031, "loss_cls": 4.00874, "loss": 4.00874, "time": 0.69949} +{"mode": "train", "epoch": 27, "iter": 2900, "lr": 0.09234, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26969, "top5_acc": 0.51922, "loss_cls": 4.00993, "loss": 4.00993, "time": 0.6966} +{"mode": "train", "epoch": 27, "iter": 3000, "lr": 0.09233, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26547, "top5_acc": 0.51188, "loss_cls": 4.01765, "loss": 4.01765, "time": 0.69619} +{"mode": "train", "epoch": 27, "iter": 3100, "lr": 0.09231, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2625, "top5_acc": 0.51734, "loss_cls": 4.00386, "loss": 4.00386, "time": 0.69755} +{"mode": "train", "epoch": 27, "iter": 3200, "lr": 0.0923, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.265, "top5_acc": 0.52031, "loss_cls": 4.00841, "loss": 4.00841, "time": 0.69802} +{"mode": "train", "epoch": 27, "iter": 3300, "lr": 0.09228, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25531, "top5_acc": 0.49828, "loss_cls": 4.09569, "loss": 4.09569, "time": 0.69867} +{"mode": "train", "epoch": 27, "iter": 3400, "lr": 0.09227, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26875, "top5_acc": 0.52031, "loss_cls": 4.00903, "loss": 4.00903, "time": 0.69824} +{"mode": "train", "epoch": 27, "iter": 3500, "lr": 0.09225, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26109, "top5_acc": 0.50953, "loss_cls": 4.02398, "loss": 4.02398, "time": 0.69688} +{"mode": "train", "epoch": 27, "iter": 3600, "lr": 0.09224, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25719, "top5_acc": 0.50953, "loss_cls": 4.06265, "loss": 4.06265, "time": 0.69662} +{"mode": "train", "epoch": 27, "iter": 3700, "lr": 0.09222, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25688, "top5_acc": 0.50625, "loss_cls": 4.05882, "loss": 4.05882, "time": 0.6984} +{"mode": "val", "epoch": 27, "iter": 309, "lr": 0.09222, "top1_acc": 0.20053, "top5_acc": 0.43621, "mean_class_accuracy": 0.20042} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.0922, "memory": 15990, "data_time": 1.26669, "top1_acc": 0.26453, "top5_acc": 0.52219, "loss_cls": 3.99738, "loss": 3.99738, "time": 1.96822} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.09219, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27141, "top5_acc": 0.53188, "loss_cls": 3.91525, "loss": 3.91525, "time": 0.70529} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.09217, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25688, "top5_acc": 0.52078, "loss_cls": 4.01273, "loss": 4.01273, "time": 0.71028} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.09216, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26266, "top5_acc": 0.52047, "loss_cls": 4.01075, "loss": 4.01075, "time": 0.7031} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.09214, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27156, "top5_acc": 0.52344, "loss_cls": 3.95551, "loss": 3.95551, "time": 0.70225} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.09213, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25797, "top5_acc": 0.51453, "loss_cls": 4.04387, "loss": 4.04387, "time": 0.69939} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.09211, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25578, "top5_acc": 0.51422, "loss_cls": 4.02487, "loss": 4.02487, "time": 0.70014} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.0921, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26047, "top5_acc": 0.51812, "loss_cls": 3.99793, "loss": 3.99793, "time": 0.69945} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.09208, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26797, "top5_acc": 0.52812, "loss_cls": 3.99968, "loss": 3.99968, "time": 0.69667} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.09207, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26297, "top5_acc": 0.50969, "loss_cls": 4.04798, "loss": 4.04798, "time": 0.69611} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.09205, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26531, "top5_acc": 0.51641, "loss_cls": 4.02415, "loss": 4.02415, "time": 0.6975} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.09204, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26188, "top5_acc": 0.52109, "loss_cls": 3.99783, "loss": 3.99783, "time": 0.69644} +{"mode": "train", "epoch": 28, "iter": 1300, "lr": 0.09202, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25453, "top5_acc": 0.50922, "loss_cls": 4.05129, "loss": 4.05129, "time": 0.69696} +{"mode": "train", "epoch": 28, "iter": 1400, "lr": 0.09201, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26391, "top5_acc": 0.51766, "loss_cls": 4.01279, "loss": 4.01279, "time": 0.69695} +{"mode": "train", "epoch": 28, "iter": 1500, "lr": 0.09199, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26922, "top5_acc": 0.52, "loss_cls": 4.00612, "loss": 4.00612, "time": 0.69914} +{"mode": "train", "epoch": 28, "iter": 1600, "lr": 0.09198, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26516, "top5_acc": 0.52328, "loss_cls": 4.02224, "loss": 4.02224, "time": 0.69633} +{"mode": "train", "epoch": 28, "iter": 1700, "lr": 0.09196, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26766, "top5_acc": 0.51922, "loss_cls": 4.0027, "loss": 4.0027, "time": 0.69755} +{"mode": "train", "epoch": 28, "iter": 1800, "lr": 0.09194, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26422, "top5_acc": 0.50844, "loss_cls": 4.03024, "loss": 4.03024, "time": 0.69766} +{"mode": "train", "epoch": 28, "iter": 1900, "lr": 0.09193, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26016, "top5_acc": 0.50828, "loss_cls": 4.02268, "loss": 4.02268, "time": 0.70058} +{"mode": "train", "epoch": 28, "iter": 2000, "lr": 0.09191, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25672, "top5_acc": 0.51172, "loss_cls": 4.0473, "loss": 4.0473, "time": 0.70088} +{"mode": "train", "epoch": 28, "iter": 2100, "lr": 0.0919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26125, "top5_acc": 0.51344, "loss_cls": 4.02318, "loss": 4.02318, "time": 0.70163} +{"mode": "train", "epoch": 28, "iter": 2200, "lr": 0.09188, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.26188, "top5_acc": 0.51625, "loss_cls": 4.02034, "loss": 4.02034, "time": 0.70031} +{"mode": "train", "epoch": 28, "iter": 2300, "lr": 0.09187, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26391, "top5_acc": 0.51297, "loss_cls": 4.03814, "loss": 4.03814, "time": 0.69761} +{"mode": "train", "epoch": 28, "iter": 2400, "lr": 0.09185, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26141, "top5_acc": 0.51766, "loss_cls": 4.00616, "loss": 4.00616, "time": 0.70009} +{"mode": "train", "epoch": 28, "iter": 2500, "lr": 0.09184, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26609, "top5_acc": 0.52203, "loss_cls": 3.97812, "loss": 3.97812, "time": 0.69867} +{"mode": "train", "epoch": 28, "iter": 2600, "lr": 0.09182, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26094, "top5_acc": 0.51766, "loss_cls": 4.03737, "loss": 4.03737, "time": 0.70008} +{"mode": "train", "epoch": 28, "iter": 2700, "lr": 0.09181, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26875, "top5_acc": 0.52219, "loss_cls": 3.98766, "loss": 3.98766, "time": 0.69896} +{"mode": "train", "epoch": 28, "iter": 2800, "lr": 0.09179, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2625, "top5_acc": 0.50859, "loss_cls": 4.03804, "loss": 4.03804, "time": 0.69708} +{"mode": "train", "epoch": 28, "iter": 2900, "lr": 0.09178, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25922, "top5_acc": 0.51172, "loss_cls": 4.03937, "loss": 4.03937, "time": 0.69607} +{"mode": "train", "epoch": 28, "iter": 3000, "lr": 0.09176, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2575, "top5_acc": 0.51438, "loss_cls": 4.04445, "loss": 4.04445, "time": 0.69704} +{"mode": "train", "epoch": 28, "iter": 3100, "lr": 0.09175, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26828, "top5_acc": 0.51594, "loss_cls": 4.01268, "loss": 4.01268, "time": 0.69668} +{"mode": "train", "epoch": 28, "iter": 3200, "lr": 0.09173, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26469, "top5_acc": 0.52047, "loss_cls": 4.0033, "loss": 4.0033, "time": 0.69838} +{"mode": "train", "epoch": 28, "iter": 3300, "lr": 0.09172, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27141, "top5_acc": 0.52188, "loss_cls": 3.998, "loss": 3.998, "time": 0.69851} +{"mode": "train", "epoch": 28, "iter": 3400, "lr": 0.0917, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27062, "top5_acc": 0.52938, "loss_cls": 3.98207, "loss": 3.98207, "time": 0.69615} +{"mode": "train", "epoch": 28, "iter": 3500, "lr": 0.09168, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26297, "top5_acc": 0.50984, "loss_cls": 4.01212, "loss": 4.01212, "time": 0.69614} +{"mode": "train", "epoch": 28, "iter": 3600, "lr": 0.09167, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26516, "top5_acc": 0.51578, "loss_cls": 4.03973, "loss": 4.03973, "time": 0.69583} +{"mode": "train", "epoch": 28, "iter": 3700, "lr": 0.09165, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24844, "top5_acc": 0.5125, "loss_cls": 4.04266, "loss": 4.04266, "time": 0.70007} +{"mode": "val", "epoch": 28, "iter": 309, "lr": 0.09165, "top1_acc": 0.20762, "top5_acc": 0.43813, "mean_class_accuracy": 0.20737} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.09163, "memory": 15990, "data_time": 1.2751, "top1_acc": 0.27344, "top5_acc": 0.52953, "loss_cls": 3.96871, "loss": 3.96871, "time": 1.97564} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.09162, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25297, "top5_acc": 0.51656, "loss_cls": 4.0161, "loss": 4.0161, "time": 0.7026} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.0916, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27031, "top5_acc": 0.52984, "loss_cls": 3.96985, "loss": 3.96985, "time": 0.70696} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.09158, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26734, "top5_acc": 0.52078, "loss_cls": 3.98332, "loss": 3.98332, "time": 0.70378} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.09157, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27078, "top5_acc": 0.52406, "loss_cls": 3.97859, "loss": 3.97859, "time": 0.70803} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.09155, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26172, "top5_acc": 0.52125, "loss_cls": 3.99477, "loss": 3.99477, "time": 0.70063} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.09154, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26031, "top5_acc": 0.51406, "loss_cls": 3.99801, "loss": 3.99801, "time": 0.69846} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.09152, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26234, "top5_acc": 0.51422, "loss_cls": 4.0154, "loss": 4.0154, "time": 0.7005} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.09151, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25938, "top5_acc": 0.50953, "loss_cls": 4.0616, "loss": 4.0616, "time": 0.69599} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.09149, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26953, "top5_acc": 0.52734, "loss_cls": 3.97982, "loss": 3.97982, "time": 0.69925} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.09148, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.265, "top5_acc": 0.51859, "loss_cls": 4.0183, "loss": 4.0183, "time": 0.69756} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.09146, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2675, "top5_acc": 0.50609, "loss_cls": 4.03263, "loss": 4.03263, "time": 0.69845} +{"mode": "train", "epoch": 29, "iter": 1300, "lr": 0.09144, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27062, "top5_acc": 0.52625, "loss_cls": 3.98946, "loss": 3.98946, "time": 0.70067} +{"mode": "train", "epoch": 29, "iter": 1400, "lr": 0.09143, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27547, "top5_acc": 0.51453, "loss_cls": 4.00542, "loss": 4.00542, "time": 0.70119} +{"mode": "train", "epoch": 29, "iter": 1500, "lr": 0.09141, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26609, "top5_acc": 0.52469, "loss_cls": 3.97191, "loss": 3.97191, "time": 0.70023} +{"mode": "train", "epoch": 29, "iter": 1600, "lr": 0.0914, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25844, "top5_acc": 0.52438, "loss_cls": 3.98896, "loss": 3.98896, "time": 0.69733} +{"mode": "train", "epoch": 29, "iter": 1700, "lr": 0.09138, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26172, "top5_acc": 0.51094, "loss_cls": 4.04012, "loss": 4.04012, "time": 0.69678} +{"mode": "train", "epoch": 29, "iter": 1800, "lr": 0.09137, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26281, "top5_acc": 0.50953, "loss_cls": 4.03418, "loss": 4.03418, "time": 0.69748} +{"mode": "train", "epoch": 29, "iter": 1900, "lr": 0.09135, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26125, "top5_acc": 0.50828, "loss_cls": 4.04931, "loss": 4.04931, "time": 0.70024} +{"mode": "train", "epoch": 29, "iter": 2000, "lr": 0.09133, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25359, "top5_acc": 0.50422, "loss_cls": 4.0598, "loss": 4.0598, "time": 0.70383} +{"mode": "train", "epoch": 29, "iter": 2100, "lr": 0.09132, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26328, "top5_acc": 0.51906, "loss_cls": 4.01656, "loss": 4.01656, "time": 0.699} +{"mode": "train", "epoch": 29, "iter": 2200, "lr": 0.0913, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.25891, "top5_acc": 0.51719, "loss_cls": 4.03063, "loss": 4.03063, "time": 0.70012} +{"mode": "train", "epoch": 29, "iter": 2300, "lr": 0.09129, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26625, "top5_acc": 0.51781, "loss_cls": 4.01097, "loss": 4.01097, "time": 0.69899} +{"mode": "train", "epoch": 29, "iter": 2400, "lr": 0.09127, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26125, "top5_acc": 0.51078, "loss_cls": 4.05601, "loss": 4.05601, "time": 0.69912} +{"mode": "train", "epoch": 29, "iter": 2500, "lr": 0.09126, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25953, "top5_acc": 0.51656, "loss_cls": 4.02036, "loss": 4.02036, "time": 0.69873} +{"mode": "train", "epoch": 29, "iter": 2600, "lr": 0.09124, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25828, "top5_acc": 0.50703, "loss_cls": 4.05971, "loss": 4.05971, "time": 0.69604} +{"mode": "train", "epoch": 29, "iter": 2700, "lr": 0.09122, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28062, "top5_acc": 0.53359, "loss_cls": 3.9634, "loss": 3.9634, "time": 0.69754} +{"mode": "train", "epoch": 29, "iter": 2800, "lr": 0.09121, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26266, "top5_acc": 0.51109, "loss_cls": 4.01686, "loss": 4.01686, "time": 0.69865} +{"mode": "train", "epoch": 29, "iter": 2900, "lr": 0.09119, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26484, "top5_acc": 0.51953, "loss_cls": 4.00843, "loss": 4.00843, "time": 0.69818} +{"mode": "train", "epoch": 29, "iter": 3000, "lr": 0.09118, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26969, "top5_acc": 0.52516, "loss_cls": 3.97353, "loss": 3.97353, "time": 0.69768} +{"mode": "train", "epoch": 29, "iter": 3100, "lr": 0.09116, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25156, "top5_acc": 0.51844, "loss_cls": 4.02913, "loss": 4.02913, "time": 0.69795} +{"mode": "train", "epoch": 29, "iter": 3200, "lr": 0.09114, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27484, "top5_acc": 0.52047, "loss_cls": 4.00584, "loss": 4.00584, "time": 0.69631} +{"mode": "train", "epoch": 29, "iter": 3300, "lr": 0.09113, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26391, "top5_acc": 0.50469, "loss_cls": 4.03429, "loss": 4.03429, "time": 0.69775} +{"mode": "train", "epoch": 29, "iter": 3400, "lr": 0.09111, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26016, "top5_acc": 0.51609, "loss_cls": 4.00605, "loss": 4.00605, "time": 0.70064} +{"mode": "train", "epoch": 29, "iter": 3500, "lr": 0.0911, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26562, "top5_acc": 0.52953, "loss_cls": 3.99715, "loss": 3.99715, "time": 0.69752} +{"mode": "train", "epoch": 29, "iter": 3600, "lr": 0.09108, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25812, "top5_acc": 0.50766, "loss_cls": 4.05673, "loss": 4.05673, "time": 0.69835} +{"mode": "train", "epoch": 29, "iter": 3700, "lr": 0.09106, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26641, "top5_acc": 0.52484, "loss_cls": 4.02269, "loss": 4.02269, "time": 0.70233} +{"mode": "val", "epoch": 29, "iter": 309, "lr": 0.09106, "top1_acc": 0.16401, "top5_acc": 0.38216, "mean_class_accuracy": 0.16409} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.09104, "memory": 15990, "data_time": 1.28305, "top1_acc": 0.27312, "top5_acc": 0.5275, "loss_cls": 3.96199, "loss": 3.96199, "time": 2.08882} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.09103, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26734, "top5_acc": 0.52109, "loss_cls": 3.98152, "loss": 3.98152, "time": 0.81696} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.09101, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27406, "top5_acc": 0.53609, "loss_cls": 3.94528, "loss": 3.94528, "time": 0.81266} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.09099, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25938, "top5_acc": 0.51969, "loss_cls": 4.01773, "loss": 4.01773, "time": 0.80506} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.09098, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26531, "top5_acc": 0.51469, "loss_cls": 4.0207, "loss": 4.0207, "time": 0.80557} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.09096, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26172, "top5_acc": 0.51219, "loss_cls": 3.99957, "loss": 3.99957, "time": 0.80703} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.09095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26406, "top5_acc": 0.52359, "loss_cls": 3.98943, "loss": 3.98943, "time": 0.80455} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.09093, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26422, "top5_acc": 0.51672, "loss_cls": 3.9984, "loss": 3.9984, "time": 0.79882} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.09091, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27047, "top5_acc": 0.51109, "loss_cls": 4.0332, "loss": 4.0332, "time": 0.79951} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.0909, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26312, "top5_acc": 0.51719, "loss_cls": 3.98443, "loss": 3.98443, "time": 0.80487} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.09088, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26031, "top5_acc": 0.525, "loss_cls": 4.00358, "loss": 4.00358, "time": 0.80262} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.09087, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26891, "top5_acc": 0.52891, "loss_cls": 3.98591, "loss": 3.98591, "time": 0.80029} +{"mode": "train", "epoch": 30, "iter": 1300, "lr": 0.09085, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26672, "top5_acc": 0.51922, "loss_cls": 4.02197, "loss": 4.02197, "time": 0.80801} +{"mode": "train", "epoch": 30, "iter": 1400, "lr": 0.09083, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26641, "top5_acc": 0.51641, "loss_cls": 3.99656, "loss": 3.99656, "time": 0.80725} +{"mode": "train", "epoch": 30, "iter": 1500, "lr": 0.09082, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26687, "top5_acc": 0.51641, "loss_cls": 3.99326, "loss": 3.99326, "time": 0.80092} +{"mode": "train", "epoch": 30, "iter": 1600, "lr": 0.0908, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26297, "top5_acc": 0.51297, "loss_cls": 4.03526, "loss": 4.03526, "time": 0.80618} +{"mode": "train", "epoch": 30, "iter": 1700, "lr": 0.09078, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25719, "top5_acc": 0.51266, "loss_cls": 4.0344, "loss": 4.0344, "time": 0.80286} +{"mode": "train", "epoch": 30, "iter": 1800, "lr": 0.09077, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25516, "top5_acc": 0.51469, "loss_cls": 4.00895, "loss": 4.00895, "time": 0.80198} +{"mode": "train", "epoch": 30, "iter": 1900, "lr": 0.09075, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26, "top5_acc": 0.52031, "loss_cls": 4.01407, "loss": 4.01407, "time": 0.8018} +{"mode": "train", "epoch": 30, "iter": 2000, "lr": 0.09074, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25125, "top5_acc": 0.50562, "loss_cls": 4.09295, "loss": 4.09295, "time": 0.80738} +{"mode": "train", "epoch": 30, "iter": 2100, "lr": 0.09072, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26828, "top5_acc": 0.52484, "loss_cls": 3.95401, "loss": 3.95401, "time": 0.80462} +{"mode": "train", "epoch": 30, "iter": 2200, "lr": 0.0907, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25891, "top5_acc": 0.50984, "loss_cls": 4.05386, "loss": 4.05386, "time": 0.80277} +{"mode": "train", "epoch": 30, "iter": 2300, "lr": 0.09069, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26531, "top5_acc": 0.52125, "loss_cls": 4.01403, "loss": 4.01403, "time": 0.80176} +{"mode": "train", "epoch": 30, "iter": 2400, "lr": 0.09067, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27187, "top5_acc": 0.52062, "loss_cls": 3.99442, "loss": 3.99442, "time": 0.80448} +{"mode": "train", "epoch": 30, "iter": 2500, "lr": 0.09065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25594, "top5_acc": 0.51281, "loss_cls": 4.02049, "loss": 4.02049, "time": 0.80184} +{"mode": "train", "epoch": 30, "iter": 2600, "lr": 0.09064, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25984, "top5_acc": 0.51297, "loss_cls": 4.04412, "loss": 4.04412, "time": 0.80814} +{"mode": "train", "epoch": 30, "iter": 2700, "lr": 0.09062, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24984, "top5_acc": 0.51875, "loss_cls": 4.0348, "loss": 4.0348, "time": 0.80518} +{"mode": "train", "epoch": 30, "iter": 2800, "lr": 0.09061, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25969, "top5_acc": 0.51281, "loss_cls": 4.04093, "loss": 4.04093, "time": 0.81245} +{"mode": "train", "epoch": 30, "iter": 2900, "lr": 0.09059, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26469, "top5_acc": 0.51, "loss_cls": 4.01396, "loss": 4.01396, "time": 0.80307} +{"mode": "train", "epoch": 30, "iter": 3000, "lr": 0.09057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27344, "top5_acc": 0.52781, "loss_cls": 3.95399, "loss": 3.95399, "time": 0.80253} +{"mode": "train", "epoch": 30, "iter": 3100, "lr": 0.09056, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26859, "top5_acc": 0.51859, "loss_cls": 4.01197, "loss": 4.01197, "time": 0.80491} +{"mode": "train", "epoch": 30, "iter": 3200, "lr": 0.09054, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26266, "top5_acc": 0.51703, "loss_cls": 4.00782, "loss": 4.00782, "time": 0.80654} +{"mode": "train", "epoch": 30, "iter": 3300, "lr": 0.09052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26906, "top5_acc": 0.51203, "loss_cls": 4.0082, "loss": 4.0082, "time": 0.80138} +{"mode": "train", "epoch": 30, "iter": 3400, "lr": 0.09051, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27922, "top5_acc": 0.53344, "loss_cls": 3.93576, "loss": 3.93576, "time": 0.79916} +{"mode": "train", "epoch": 30, "iter": 3500, "lr": 0.09049, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2675, "top5_acc": 0.52484, "loss_cls": 3.97319, "loss": 3.97319, "time": 0.80724} +{"mode": "train", "epoch": 30, "iter": 3600, "lr": 0.09047, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26047, "top5_acc": 0.51375, "loss_cls": 4.04085, "loss": 4.04085, "time": 0.80251} +{"mode": "train", "epoch": 30, "iter": 3700, "lr": 0.09046, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26125, "top5_acc": 0.5125, "loss_cls": 4.03442, "loss": 4.03442, "time": 0.805} +{"mode": "val", "epoch": 30, "iter": 309, "lr": 0.09045, "top1_acc": 0.167, "top5_acc": 0.39168, "mean_class_accuracy": 0.16701} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.09043, "memory": 15990, "data_time": 1.33406, "top1_acc": 0.28094, "top5_acc": 0.54, "loss_cls": 4.13082, "loss": 4.13082, "time": 2.3145} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.09042, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26984, "top5_acc": 0.51484, "loss_cls": 4.21888, "loss": 4.21888, "time": 0.82665} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.0904, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26562, "top5_acc": 0.53844, "loss_cls": 4.16828, "loss": 4.16828, "time": 0.83021} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.09039, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26078, "top5_acc": 0.51469, "loss_cls": 4.22585, "loss": 4.22585, "time": 0.81863} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.09037, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27641, "top5_acc": 0.52469, "loss_cls": 4.20217, "loss": 4.20217, "time": 0.82471} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.09035, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26578, "top5_acc": 0.51812, "loss_cls": 4.199, "loss": 4.199, "time": 0.82911} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.09034, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27891, "top5_acc": 0.53391, "loss_cls": 4.15595, "loss": 4.15595, "time": 0.8263} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.09032, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25672, "top5_acc": 0.51609, "loss_cls": 4.23986, "loss": 4.23986, "time": 0.82728} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0903, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26172, "top5_acc": 0.52141, "loss_cls": 4.22091, "loss": 4.22091, "time": 0.81832} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.09029, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25609, "top5_acc": 0.51313, "loss_cls": 4.24917, "loss": 4.24917, "time": 0.82274} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.09027, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26625, "top5_acc": 0.52078, "loss_cls": 4.20301, "loss": 4.20301, "time": 0.81679} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.09025, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26469, "top5_acc": 0.5175, "loss_cls": 4.21155, "loss": 4.21155, "time": 0.81634} +{"mode": "train", "epoch": 31, "iter": 1300, "lr": 0.09024, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26156, "top5_acc": 0.51031, "loss_cls": 4.25446, "loss": 4.25446, "time": 0.81717} +{"mode": "train", "epoch": 31, "iter": 1400, "lr": 0.09022, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26516, "top5_acc": 0.53125, "loss_cls": 4.18103, "loss": 4.18103, "time": 0.82662} +{"mode": "train", "epoch": 31, "iter": 1500, "lr": 0.0902, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26406, "top5_acc": 0.51531, "loss_cls": 4.22531, "loss": 4.22531, "time": 0.82101} +{"mode": "train", "epoch": 31, "iter": 1600, "lr": 0.09019, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26578, "top5_acc": 0.51719, "loss_cls": 4.20119, "loss": 4.20119, "time": 0.82973} +{"mode": "train", "epoch": 31, "iter": 1700, "lr": 0.09017, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26484, "top5_acc": 0.51156, "loss_cls": 4.28402, "loss": 4.28402, "time": 0.82273} +{"mode": "train", "epoch": 31, "iter": 1800, "lr": 0.09015, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26281, "top5_acc": 0.51469, "loss_cls": 4.23434, "loss": 4.23434, "time": 0.82933} +{"mode": "train", "epoch": 31, "iter": 1900, "lr": 0.09014, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25953, "top5_acc": 0.52016, "loss_cls": 4.21977, "loss": 4.21977, "time": 0.82531} +{"mode": "train", "epoch": 31, "iter": 2000, "lr": 0.09012, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26672, "top5_acc": 0.52344, "loss_cls": 4.18897, "loss": 4.18897, "time": 0.83124} +{"mode": "train", "epoch": 31, "iter": 2100, "lr": 0.0901, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26594, "top5_acc": 0.5225, "loss_cls": 4.22393, "loss": 4.22393, "time": 0.82386} +{"mode": "train", "epoch": 31, "iter": 2200, "lr": 0.09009, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27359, "top5_acc": 0.51984, "loss_cls": 4.1972, "loss": 4.1972, "time": 0.81808} +{"mode": "train", "epoch": 31, "iter": 2300, "lr": 0.09007, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27109, "top5_acc": 0.52406, "loss_cls": 4.22202, "loss": 4.22202, "time": 0.83147} +{"mode": "train", "epoch": 31, "iter": 2400, "lr": 0.09005, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26, "top5_acc": 0.51125, "loss_cls": 4.23655, "loss": 4.23655, "time": 0.83096} +{"mode": "train", "epoch": 31, "iter": 2500, "lr": 0.09004, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26531, "top5_acc": 0.51422, "loss_cls": 4.23311, "loss": 4.23311, "time": 0.83035} +{"mode": "train", "epoch": 31, "iter": 2600, "lr": 0.09002, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26453, "top5_acc": 0.51656, "loss_cls": 4.1987, "loss": 4.1987, "time": 0.83476} +{"mode": "train", "epoch": 31, "iter": 2700, "lr": 0.09, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26391, "top5_acc": 0.51844, "loss_cls": 4.22864, "loss": 4.22864, "time": 0.83514} +{"mode": "train", "epoch": 31, "iter": 2800, "lr": 0.08999, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26516, "top5_acc": 0.52719, "loss_cls": 4.20894, "loss": 4.20894, "time": 0.82282} +{"mode": "train", "epoch": 31, "iter": 2900, "lr": 0.08997, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25828, "top5_acc": 0.51594, "loss_cls": 4.24524, "loss": 4.24524, "time": 0.8301} +{"mode": "train", "epoch": 31, "iter": 3000, "lr": 0.08995, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26047, "top5_acc": 0.51266, "loss_cls": 4.25754, "loss": 4.25754, "time": 0.82701} +{"mode": "train", "epoch": 31, "iter": 3100, "lr": 0.08994, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25906, "top5_acc": 0.51984, "loss_cls": 4.24054, "loss": 4.24054, "time": 0.82867} +{"mode": "train", "epoch": 31, "iter": 3200, "lr": 0.08992, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27266, "top5_acc": 0.53312, "loss_cls": 4.16, "loss": 4.16, "time": 0.82837} +{"mode": "train", "epoch": 31, "iter": 3300, "lr": 0.0899, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26312, "top5_acc": 0.51609, "loss_cls": 4.23307, "loss": 4.23307, "time": 0.82386} +{"mode": "train", "epoch": 31, "iter": 3400, "lr": 0.08989, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26937, "top5_acc": 0.52, "loss_cls": 4.2124, "loss": 4.2124, "time": 0.83286} +{"mode": "train", "epoch": 31, "iter": 3500, "lr": 0.08987, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26281, "top5_acc": 0.52359, "loss_cls": 4.21334, "loss": 4.21334, "time": 0.82366} +{"mode": "train", "epoch": 31, "iter": 3600, "lr": 0.08985, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27094, "top5_acc": 0.53141, "loss_cls": 4.19554, "loss": 4.19554, "time": 0.82497} +{"mode": "train", "epoch": 31, "iter": 3700, "lr": 0.08983, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25406, "top5_acc": 0.51438, "loss_cls": 4.28441, "loss": 4.28441, "time": 0.83785} +{"mode": "val", "epoch": 31, "iter": 309, "lr": 0.08983, "top1_acc": 0.20473, "top5_acc": 0.43388, "mean_class_accuracy": 0.20457} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.08981, "memory": 15990, "data_time": 1.27053, "top1_acc": 0.26797, "top5_acc": 0.53062, "loss_cls": 4.1783, "loss": 4.1783, "time": 2.25393} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.08979, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26656, "top5_acc": 0.53453, "loss_cls": 4.16664, "loss": 4.16664, "time": 0.82797} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.08978, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25672, "top5_acc": 0.51875, "loss_cls": 4.23461, "loss": 4.23461, "time": 0.83174} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.08976, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27234, "top5_acc": 0.51938, "loss_cls": 4.2011, "loss": 4.2011, "time": 0.82484} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.08974, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27578, "top5_acc": 0.54188, "loss_cls": 4.12917, "loss": 4.12917, "time": 0.82933} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.08973, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26453, "top5_acc": 0.52766, "loss_cls": 4.17981, "loss": 4.17981, "time": 0.83022} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.08971, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26703, "top5_acc": 0.52281, "loss_cls": 4.19099, "loss": 4.19099, "time": 0.83026} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.08969, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26391, "top5_acc": 0.52688, "loss_cls": 4.18839, "loss": 4.18839, "time": 0.81935} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.08967, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26219, "top5_acc": 0.52391, "loss_cls": 4.19735, "loss": 4.19735, "time": 0.81294} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.08966, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25953, "top5_acc": 0.50938, "loss_cls": 4.26811, "loss": 4.26811, "time": 0.81554} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.08964, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2725, "top5_acc": 0.52031, "loss_cls": 4.20766, "loss": 4.20766, "time": 0.8178} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.08962, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25828, "top5_acc": 0.5125, "loss_cls": 4.26218, "loss": 4.26218, "time": 0.81366} +{"mode": "train", "epoch": 32, "iter": 1300, "lr": 0.08961, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25656, "top5_acc": 0.51609, "loss_cls": 4.26872, "loss": 4.26872, "time": 0.81524} +{"mode": "train", "epoch": 32, "iter": 1400, "lr": 0.08959, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27875, "top5_acc": 0.52906, "loss_cls": 4.17953, "loss": 4.17953, "time": 0.80996} +{"mode": "train", "epoch": 32, "iter": 1500, "lr": 0.08957, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27078, "top5_acc": 0.54047, "loss_cls": 4.14839, "loss": 4.14839, "time": 0.81333} +{"mode": "train", "epoch": 32, "iter": 1600, "lr": 0.08955, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26844, "top5_acc": 0.51875, "loss_cls": 4.20612, "loss": 4.20612, "time": 0.81655} +{"mode": "train", "epoch": 32, "iter": 1700, "lr": 0.08954, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26312, "top5_acc": 0.52641, "loss_cls": 4.20079, "loss": 4.20079, "time": 0.82019} +{"mode": "train", "epoch": 32, "iter": 1800, "lr": 0.08952, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26344, "top5_acc": 0.51766, "loss_cls": 4.2443, "loss": 4.2443, "time": 0.82038} +{"mode": "train", "epoch": 32, "iter": 1900, "lr": 0.0895, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26094, "top5_acc": 0.51812, "loss_cls": 4.22238, "loss": 4.22238, "time": 0.80868} +{"mode": "train", "epoch": 32, "iter": 2000, "lr": 0.08949, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2625, "top5_acc": 0.51891, "loss_cls": 4.20206, "loss": 4.20206, "time": 0.81099} +{"mode": "train", "epoch": 32, "iter": 2100, "lr": 0.08947, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26844, "top5_acc": 0.51516, "loss_cls": 4.23024, "loss": 4.23024, "time": 0.81663} +{"mode": "train", "epoch": 32, "iter": 2200, "lr": 0.08945, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26719, "top5_acc": 0.51547, "loss_cls": 4.225, "loss": 4.225, "time": 0.82258} +{"mode": "train", "epoch": 32, "iter": 2300, "lr": 0.08943, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26656, "top5_acc": 0.52375, "loss_cls": 4.22819, "loss": 4.22819, "time": 0.82081} +{"mode": "train", "epoch": 32, "iter": 2400, "lr": 0.08942, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27641, "top5_acc": 0.51859, "loss_cls": 4.21214, "loss": 4.21214, "time": 0.81306} +{"mode": "train", "epoch": 32, "iter": 2500, "lr": 0.0894, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26922, "top5_acc": 0.51797, "loss_cls": 4.23664, "loss": 4.23664, "time": 0.81783} +{"mode": "train", "epoch": 32, "iter": 2600, "lr": 0.08938, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26375, "top5_acc": 0.51766, "loss_cls": 4.24409, "loss": 4.24409, "time": 0.81838} +{"mode": "train", "epoch": 32, "iter": 2700, "lr": 0.08937, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27094, "top5_acc": 0.51641, "loss_cls": 4.22395, "loss": 4.22395, "time": 0.81576} +{"mode": "train", "epoch": 32, "iter": 2800, "lr": 0.08935, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2625, "top5_acc": 0.51812, "loss_cls": 4.25907, "loss": 4.25907, "time": 0.81918} +{"mode": "train", "epoch": 32, "iter": 2900, "lr": 0.08933, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25797, "top5_acc": 0.51844, "loss_cls": 4.23859, "loss": 4.23859, "time": 0.81576} +{"mode": "train", "epoch": 32, "iter": 3000, "lr": 0.08931, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27281, "top5_acc": 0.52203, "loss_cls": 4.19521, "loss": 4.19521, "time": 0.8174} +{"mode": "train", "epoch": 32, "iter": 3100, "lr": 0.0893, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27688, "top5_acc": 0.52906, "loss_cls": 4.16552, "loss": 4.16552, "time": 0.8179} +{"mode": "train", "epoch": 32, "iter": 3200, "lr": 0.08928, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26766, "top5_acc": 0.51734, "loss_cls": 4.24534, "loss": 4.24534, "time": 0.81627} +{"mode": "train", "epoch": 32, "iter": 3300, "lr": 0.08926, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26391, "top5_acc": 0.51781, "loss_cls": 4.21207, "loss": 4.21207, "time": 0.81052} +{"mode": "train", "epoch": 32, "iter": 3400, "lr": 0.08924, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26359, "top5_acc": 0.51391, "loss_cls": 4.23786, "loss": 4.23786, "time": 0.81283} +{"mode": "train", "epoch": 32, "iter": 3500, "lr": 0.08923, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27, "top5_acc": 0.51938, "loss_cls": 4.2039, "loss": 4.2039, "time": 0.81929} +{"mode": "train", "epoch": 32, "iter": 3600, "lr": 0.08921, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26531, "top5_acc": 0.51453, "loss_cls": 4.25764, "loss": 4.25764, "time": 0.81839} +{"mode": "train", "epoch": 32, "iter": 3700, "lr": 0.08919, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27547, "top5_acc": 0.52938, "loss_cls": 4.18252, "loss": 4.18252, "time": 0.81835} +{"mode": "val", "epoch": 32, "iter": 309, "lr": 0.08918, "top1_acc": 0.19632, "top5_acc": 0.42369, "mean_class_accuracy": 0.19627} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.08917, "memory": 15990, "data_time": 1.19957, "top1_acc": 0.26828, "top5_acc": 0.52875, "loss_cls": 4.15794, "loss": 4.15794, "time": 2.18612} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.08915, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.26781, "top5_acc": 0.52328, "loss_cls": 4.21693, "loss": 4.21693, "time": 0.82368} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.08913, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.27734, "top5_acc": 0.52812, "loss_cls": 4.15539, "loss": 4.15539, "time": 0.82114} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.08912, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26844, "top5_acc": 0.53094, "loss_cls": 4.16179, "loss": 4.16179, "time": 0.82207} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.0891, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25953, "top5_acc": 0.51875, "loss_cls": 4.23318, "loss": 4.23318, "time": 0.82445} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.08908, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27812, "top5_acc": 0.53641, "loss_cls": 4.14216, "loss": 4.14216, "time": 0.82558} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.08906, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27297, "top5_acc": 0.53734, "loss_cls": 4.1965, "loss": 4.1965, "time": 0.82292} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.08905, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26406, "top5_acc": 0.52047, "loss_cls": 4.18527, "loss": 4.18527, "time": 0.82012} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.08903, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.275, "top5_acc": 0.51797, "loss_cls": 4.21167, "loss": 4.21167, "time": 0.81915} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.08901, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26266, "top5_acc": 0.515, "loss_cls": 4.25565, "loss": 4.25565, "time": 0.81308} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.08899, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26422, "top5_acc": 0.51719, "loss_cls": 4.22227, "loss": 4.22227, "time": 0.81308} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.08898, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27266, "top5_acc": 0.52625, "loss_cls": 4.17127, "loss": 4.17127, "time": 0.81722} +{"mode": "train", "epoch": 33, "iter": 1300, "lr": 0.08896, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25734, "top5_acc": 0.51812, "loss_cls": 4.21355, "loss": 4.21355, "time": 0.81509} +{"mode": "train", "epoch": 33, "iter": 1400, "lr": 0.08894, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27016, "top5_acc": 0.53016, "loss_cls": 4.18739, "loss": 4.18739, "time": 0.81611} +{"mode": "train", "epoch": 33, "iter": 1500, "lr": 0.08892, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26609, "top5_acc": 0.52203, "loss_cls": 4.20814, "loss": 4.20814, "time": 0.81396} +{"mode": "train", "epoch": 33, "iter": 1600, "lr": 0.08891, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26891, "top5_acc": 0.52516, "loss_cls": 4.17939, "loss": 4.17939, "time": 0.81796} +{"mode": "train", "epoch": 33, "iter": 1700, "lr": 0.08889, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27297, "top5_acc": 0.53094, "loss_cls": 4.19698, "loss": 4.19698, "time": 0.81669} +{"mode": "train", "epoch": 33, "iter": 1800, "lr": 0.08887, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26562, "top5_acc": 0.51328, "loss_cls": 4.22331, "loss": 4.22331, "time": 0.81253} +{"mode": "train", "epoch": 33, "iter": 1900, "lr": 0.08885, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27344, "top5_acc": 0.51891, "loss_cls": 4.20242, "loss": 4.20242, "time": 0.81651} +{"mode": "train", "epoch": 33, "iter": 2000, "lr": 0.08884, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26875, "top5_acc": 0.51688, "loss_cls": 4.20049, "loss": 4.20049, "time": 0.81507} +{"mode": "train", "epoch": 33, "iter": 2100, "lr": 0.08882, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26484, "top5_acc": 0.51625, "loss_cls": 4.22031, "loss": 4.22031, "time": 0.81377} +{"mode": "train", "epoch": 33, "iter": 2200, "lr": 0.0888, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26906, "top5_acc": 0.52531, "loss_cls": 4.18652, "loss": 4.18652, "time": 0.81809} +{"mode": "train", "epoch": 33, "iter": 2300, "lr": 0.08878, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26875, "top5_acc": 0.51812, "loss_cls": 4.20106, "loss": 4.20106, "time": 0.81628} +{"mode": "train", "epoch": 33, "iter": 2400, "lr": 0.08876, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26953, "top5_acc": 0.52547, "loss_cls": 4.18833, "loss": 4.18833, "time": 0.81509} +{"mode": "train", "epoch": 33, "iter": 2500, "lr": 0.08875, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27969, "top5_acc": 0.52812, "loss_cls": 4.16543, "loss": 4.16543, "time": 0.8142} +{"mode": "train", "epoch": 33, "iter": 2600, "lr": 0.08873, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25938, "top5_acc": 0.52, "loss_cls": 4.23724, "loss": 4.23724, "time": 0.81778} +{"mode": "train", "epoch": 33, "iter": 2700, "lr": 0.08871, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26828, "top5_acc": 0.53125, "loss_cls": 4.18487, "loss": 4.18487, "time": 0.82182} +{"mode": "train", "epoch": 33, "iter": 2800, "lr": 0.08869, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27062, "top5_acc": 0.52969, "loss_cls": 4.19687, "loss": 4.19687, "time": 0.81453} +{"mode": "train", "epoch": 33, "iter": 2900, "lr": 0.08868, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26062, "top5_acc": 0.51641, "loss_cls": 4.22371, "loss": 4.22371, "time": 0.81468} +{"mode": "train", "epoch": 33, "iter": 3000, "lr": 0.08866, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26031, "top5_acc": 0.51484, "loss_cls": 4.25336, "loss": 4.25336, "time": 0.81787} +{"mode": "train", "epoch": 33, "iter": 3100, "lr": 0.08864, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27344, "top5_acc": 0.51469, "loss_cls": 4.23472, "loss": 4.23472, "time": 0.813} +{"mode": "train", "epoch": 33, "iter": 3200, "lr": 0.08862, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26906, "top5_acc": 0.52734, "loss_cls": 4.19127, "loss": 4.19127, "time": 0.81784} +{"mode": "train", "epoch": 33, "iter": 3300, "lr": 0.08861, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27109, "top5_acc": 0.52484, "loss_cls": 4.19514, "loss": 4.19514, "time": 0.81578} +{"mode": "train", "epoch": 33, "iter": 3400, "lr": 0.08859, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27266, "top5_acc": 0.52641, "loss_cls": 4.17298, "loss": 4.17298, "time": 0.81084} +{"mode": "train", "epoch": 33, "iter": 3500, "lr": 0.08857, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26312, "top5_acc": 0.52859, "loss_cls": 4.22564, "loss": 4.22564, "time": 0.81724} +{"mode": "train", "epoch": 33, "iter": 3600, "lr": 0.08855, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26859, "top5_acc": 0.52484, "loss_cls": 4.19186, "loss": 4.19186, "time": 0.81311} +{"mode": "train", "epoch": 33, "iter": 3700, "lr": 0.08853, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26219, "top5_acc": 0.51594, "loss_cls": 4.25247, "loss": 4.25247, "time": 0.823} +{"mode": "val", "epoch": 33, "iter": 309, "lr": 0.08853, "top1_acc": 0.19653, "top5_acc": 0.42911, "mean_class_accuracy": 0.19655} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.08851, "memory": 15990, "data_time": 1.2097, "top1_acc": 0.27016, "top5_acc": 0.53109, "loss_cls": 4.16618, "loss": 4.16618, "time": 2.18001} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.08849, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26797, "top5_acc": 0.52062, "loss_cls": 4.20123, "loss": 4.20123, "time": 0.81705} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.08847, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27547, "top5_acc": 0.53641, "loss_cls": 4.12747, "loss": 4.12747, "time": 0.82343} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.08845, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25875, "top5_acc": 0.51125, "loss_cls": 4.23543, "loss": 4.23543, "time": 0.82418} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.08844, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26969, "top5_acc": 0.53203, "loss_cls": 4.17526, "loss": 4.17526, "time": 0.82332} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.08842, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26859, "top5_acc": 0.51859, "loss_cls": 4.21897, "loss": 4.21897, "time": 0.82895} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.0884, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25734, "top5_acc": 0.51641, "loss_cls": 4.23536, "loss": 4.23536, "time": 0.82838} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.08838, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2725, "top5_acc": 0.53453, "loss_cls": 4.15446, "loss": 4.15446, "time": 0.82021} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.08836, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27406, "top5_acc": 0.52516, "loss_cls": 4.17435, "loss": 4.17435, "time": 0.82384} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.08835, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26656, "top5_acc": 0.52219, "loss_cls": 4.17374, "loss": 4.17374, "time": 0.81841} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.08833, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27469, "top5_acc": 0.52859, "loss_cls": 4.17957, "loss": 4.17957, "time": 0.81872} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.08831, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27469, "top5_acc": 0.525, "loss_cls": 4.17598, "loss": 4.17598, "time": 0.81866} +{"mode": "train", "epoch": 34, "iter": 1300, "lr": 0.08829, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25812, "top5_acc": 0.52641, "loss_cls": 4.21339, "loss": 4.21339, "time": 0.81485} +{"mode": "train", "epoch": 34, "iter": 1400, "lr": 0.08828, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26234, "top5_acc": 0.53234, "loss_cls": 4.18551, "loss": 4.18551, "time": 0.81181} +{"mode": "train", "epoch": 34, "iter": 1500, "lr": 0.08826, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26578, "top5_acc": 0.52203, "loss_cls": 4.21563, "loss": 4.21563, "time": 0.82054} +{"mode": "train", "epoch": 34, "iter": 1600, "lr": 0.08824, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27297, "top5_acc": 0.52625, "loss_cls": 4.196, "loss": 4.196, "time": 0.81641} +{"mode": "train", "epoch": 34, "iter": 1700, "lr": 0.08822, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26359, "top5_acc": 0.52531, "loss_cls": 4.21429, "loss": 4.21429, "time": 0.81144} +{"mode": "train", "epoch": 34, "iter": 1800, "lr": 0.0882, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26781, "top5_acc": 0.52078, "loss_cls": 4.19573, "loss": 4.19573, "time": 0.81833} +{"mode": "train", "epoch": 34, "iter": 1900, "lr": 0.08819, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26687, "top5_acc": 0.52203, "loss_cls": 4.19254, "loss": 4.19254, "time": 0.81597} +{"mode": "train", "epoch": 34, "iter": 2000, "lr": 0.08817, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28125, "top5_acc": 0.53328, "loss_cls": 4.12159, "loss": 4.12159, "time": 0.81776} +{"mode": "train", "epoch": 34, "iter": 2100, "lr": 0.08815, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26859, "top5_acc": 0.52219, "loss_cls": 4.20291, "loss": 4.20291, "time": 0.81521} +{"mode": "train", "epoch": 34, "iter": 2200, "lr": 0.08813, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27234, "top5_acc": 0.53297, "loss_cls": 4.18605, "loss": 4.18605, "time": 0.81547} +{"mode": "train", "epoch": 34, "iter": 2300, "lr": 0.08811, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25344, "top5_acc": 0.50844, "loss_cls": 4.23765, "loss": 4.23765, "time": 0.81502} +{"mode": "train", "epoch": 34, "iter": 2400, "lr": 0.08809, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26188, "top5_acc": 0.50656, "loss_cls": 4.2481, "loss": 4.2481, "time": 0.81403} +{"mode": "train", "epoch": 34, "iter": 2500, "lr": 0.08808, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27766, "top5_acc": 0.53859, "loss_cls": 4.1502, "loss": 4.1502, "time": 0.82258} +{"mode": "train", "epoch": 34, "iter": 2600, "lr": 0.08806, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26422, "top5_acc": 0.51844, "loss_cls": 4.19268, "loss": 4.19268, "time": 0.81599} +{"mode": "train", "epoch": 34, "iter": 2700, "lr": 0.08804, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25875, "top5_acc": 0.52062, "loss_cls": 4.2101, "loss": 4.2101, "time": 0.81931} +{"mode": "train", "epoch": 34, "iter": 2800, "lr": 0.08802, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26547, "top5_acc": 0.51734, "loss_cls": 4.22056, "loss": 4.22056, "time": 0.8127} +{"mode": "train", "epoch": 34, "iter": 2900, "lr": 0.088, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28203, "top5_acc": 0.52766, "loss_cls": 4.1721, "loss": 4.1721, "time": 0.81514} +{"mode": "train", "epoch": 34, "iter": 3000, "lr": 0.08799, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27562, "top5_acc": 0.52703, "loss_cls": 4.15662, "loss": 4.15662, "time": 0.81623} +{"mode": "train", "epoch": 34, "iter": 3100, "lr": 0.08797, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26562, "top5_acc": 0.51531, "loss_cls": 4.22998, "loss": 4.22998, "time": 0.81995} +{"mode": "train", "epoch": 34, "iter": 3200, "lr": 0.08795, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26516, "top5_acc": 0.52125, "loss_cls": 4.22289, "loss": 4.22289, "time": 0.81337} +{"mode": "train", "epoch": 34, "iter": 3300, "lr": 0.08793, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26172, "top5_acc": 0.52281, "loss_cls": 4.21449, "loss": 4.21449, "time": 0.81854} +{"mode": "train", "epoch": 34, "iter": 3400, "lr": 0.08791, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26812, "top5_acc": 0.52516, "loss_cls": 4.20154, "loss": 4.20154, "time": 0.81491} +{"mode": "train", "epoch": 34, "iter": 3500, "lr": 0.08789, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26391, "top5_acc": 0.52281, "loss_cls": 4.20262, "loss": 4.20262, "time": 0.81844} +{"mode": "train", "epoch": 34, "iter": 3600, "lr": 0.08788, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27672, "top5_acc": 0.52922, "loss_cls": 4.17315, "loss": 4.17315, "time": 0.82324} +{"mode": "train", "epoch": 34, "iter": 3700, "lr": 0.08786, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28375, "top5_acc": 0.53938, "loss_cls": 4.13245, "loss": 4.13245, "time": 0.82601} +{"mode": "val", "epoch": 34, "iter": 309, "lr": 0.08785, "top1_acc": 0.15671, "top5_acc": 0.3735, "mean_class_accuracy": 0.15669} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.08783, "memory": 15990, "data_time": 1.19364, "top1_acc": 0.27047, "top5_acc": 0.53109, "loss_cls": 4.16975, "loss": 4.16975, "time": 2.16892} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.08781, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26438, "top5_acc": 0.52484, "loss_cls": 4.19007, "loss": 4.19007, "time": 0.82017} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.0878, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27578, "top5_acc": 0.5325, "loss_cls": 4.16437, "loss": 4.16437, "time": 0.82267} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.08778, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27547, "top5_acc": 0.53203, "loss_cls": 4.14836, "loss": 4.14836, "time": 0.82777} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.08776, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.27125, "top5_acc": 0.52609, "loss_cls": 4.18787, "loss": 4.18787, "time": 0.82452} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.08774, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28047, "top5_acc": 0.5375, "loss_cls": 4.15811, "loss": 4.15811, "time": 0.82283} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.08772, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26953, "top5_acc": 0.52297, "loss_cls": 4.18394, "loss": 4.18394, "time": 0.82621} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.0877, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26812, "top5_acc": 0.52156, "loss_cls": 4.1914, "loss": 4.1914, "time": 0.82095} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.08769, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2725, "top5_acc": 0.5225, "loss_cls": 4.18095, "loss": 4.18095, "time": 0.82313} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.08767, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27141, "top5_acc": 0.52156, "loss_cls": 4.19697, "loss": 4.19697, "time": 0.82349} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.08765, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26812, "top5_acc": 0.52531, "loss_cls": 4.16036, "loss": 4.16036, "time": 0.81667} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.08763, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26984, "top5_acc": 0.53141, "loss_cls": 4.13944, "loss": 4.13944, "time": 0.81962} +{"mode": "train", "epoch": 35, "iter": 1300, "lr": 0.08761, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27453, "top5_acc": 0.52391, "loss_cls": 4.18187, "loss": 4.18187, "time": 0.81621} +{"mode": "train", "epoch": 35, "iter": 1400, "lr": 0.08759, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27797, "top5_acc": 0.54016, "loss_cls": 4.13958, "loss": 4.13958, "time": 0.82015} +{"mode": "train", "epoch": 35, "iter": 1500, "lr": 0.08757, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27375, "top5_acc": 0.52094, "loss_cls": 4.17228, "loss": 4.17228, "time": 0.81464} +{"mode": "train", "epoch": 35, "iter": 1600, "lr": 0.08756, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26844, "top5_acc": 0.53297, "loss_cls": 4.17595, "loss": 4.17595, "time": 0.81253} +{"mode": "train", "epoch": 35, "iter": 1700, "lr": 0.08754, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26156, "top5_acc": 0.52812, "loss_cls": 4.17228, "loss": 4.17228, "time": 0.80775} +{"mode": "train", "epoch": 35, "iter": 1800, "lr": 0.08752, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25984, "top5_acc": 0.52641, "loss_cls": 4.21973, "loss": 4.21973, "time": 0.80921} +{"mode": "train", "epoch": 35, "iter": 1900, "lr": 0.0875, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27719, "top5_acc": 0.52516, "loss_cls": 4.17137, "loss": 4.17137, "time": 0.81099} +{"mode": "train", "epoch": 35, "iter": 2000, "lr": 0.08748, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26797, "top5_acc": 0.52141, "loss_cls": 4.21645, "loss": 4.21645, "time": 0.81708} +{"mode": "train", "epoch": 35, "iter": 2100, "lr": 0.08746, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26922, "top5_acc": 0.52688, "loss_cls": 4.16556, "loss": 4.16556, "time": 0.81474} +{"mode": "train", "epoch": 35, "iter": 2200, "lr": 0.08745, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27078, "top5_acc": 0.52938, "loss_cls": 4.17107, "loss": 4.17107, "time": 0.81518} +{"mode": "train", "epoch": 35, "iter": 2300, "lr": 0.08743, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2725, "top5_acc": 0.52625, "loss_cls": 4.18657, "loss": 4.18657, "time": 0.81838} +{"mode": "train", "epoch": 35, "iter": 2400, "lr": 0.08741, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26438, "top5_acc": 0.51547, "loss_cls": 4.22088, "loss": 4.22088, "time": 0.80826} +{"mode": "train", "epoch": 35, "iter": 2500, "lr": 0.08739, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26844, "top5_acc": 0.52656, "loss_cls": 4.21041, "loss": 4.21041, "time": 0.81529} +{"mode": "train", "epoch": 35, "iter": 2600, "lr": 0.08737, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26656, "top5_acc": 0.51719, "loss_cls": 4.21769, "loss": 4.21769, "time": 0.82263} +{"mode": "train", "epoch": 35, "iter": 2700, "lr": 0.08735, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27047, "top5_acc": 0.52125, "loss_cls": 4.22564, "loss": 4.22564, "time": 0.8174} +{"mode": "train", "epoch": 35, "iter": 2800, "lr": 0.08733, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26656, "top5_acc": 0.52266, "loss_cls": 4.18114, "loss": 4.18114, "time": 0.8157} +{"mode": "train", "epoch": 35, "iter": 2900, "lr": 0.08732, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26906, "top5_acc": 0.52922, "loss_cls": 4.16962, "loss": 4.16962, "time": 0.8169} +{"mode": "train", "epoch": 35, "iter": 3000, "lr": 0.0873, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27016, "top5_acc": 0.52062, "loss_cls": 4.19388, "loss": 4.19388, "time": 0.81549} +{"mode": "train", "epoch": 35, "iter": 3100, "lr": 0.08728, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27047, "top5_acc": 0.50813, "loss_cls": 4.22657, "loss": 4.22657, "time": 0.81329} +{"mode": "train", "epoch": 35, "iter": 3200, "lr": 0.08726, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25812, "top5_acc": 0.5125, "loss_cls": 4.26022, "loss": 4.26022, "time": 0.81803} +{"mode": "train", "epoch": 35, "iter": 3300, "lr": 0.08724, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26016, "top5_acc": 0.51234, "loss_cls": 4.25209, "loss": 4.25209, "time": 0.81508} +{"mode": "train", "epoch": 35, "iter": 3400, "lr": 0.08722, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26828, "top5_acc": 0.52703, "loss_cls": 4.18917, "loss": 4.18917, "time": 0.81912} +{"mode": "train", "epoch": 35, "iter": 3500, "lr": 0.0872, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26406, "top5_acc": 0.51328, "loss_cls": 4.23407, "loss": 4.23407, "time": 0.81515} +{"mode": "train", "epoch": 35, "iter": 3600, "lr": 0.08718, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27656, "top5_acc": 0.52406, "loss_cls": 4.1901, "loss": 4.1901, "time": 0.82014} +{"mode": "train", "epoch": 35, "iter": 3700, "lr": 0.08717, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26453, "top5_acc": 0.51562, "loss_cls": 4.22908, "loss": 4.22908, "time": 0.81781} +{"mode": "val", "epoch": 35, "iter": 309, "lr": 0.08716, "top1_acc": 0.18224, "top5_acc": 0.3928, "mean_class_accuracy": 0.18204} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.08714, "memory": 15990, "data_time": 1.19877, "top1_acc": 0.28188, "top5_acc": 0.54172, "loss_cls": 4.10816, "loss": 4.10816, "time": 2.17543} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.08712, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27297, "top5_acc": 0.53125, "loss_cls": 4.16434, "loss": 4.16434, "time": 0.82336} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.0871, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26672, "top5_acc": 0.52219, "loss_cls": 4.17572, "loss": 4.17572, "time": 0.82185} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.08708, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28109, "top5_acc": 0.53734, "loss_cls": 4.08252, "loss": 4.08252, "time": 0.8163} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.08706, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26531, "top5_acc": 0.52594, "loss_cls": 4.18586, "loss": 4.18586, "time": 0.82809} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.08704, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27797, "top5_acc": 0.52875, "loss_cls": 4.14837, "loss": 4.14837, "time": 0.82355} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.08703, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27344, "top5_acc": 0.52797, "loss_cls": 4.17276, "loss": 4.17276, "time": 0.81718} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.08701, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26875, "top5_acc": 0.52406, "loss_cls": 4.20053, "loss": 4.20053, "time": 0.82311} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.08699, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2675, "top5_acc": 0.51344, "loss_cls": 4.23078, "loss": 4.23078, "time": 0.81518} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.08697, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27266, "top5_acc": 0.53, "loss_cls": 4.18763, "loss": 4.18763, "time": 0.81751} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.08695, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28, "top5_acc": 0.53625, "loss_cls": 4.11735, "loss": 4.11735, "time": 0.82161} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.08693, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26438, "top5_acc": 0.52656, "loss_cls": 4.22091, "loss": 4.22091, "time": 0.81875} +{"mode": "train", "epoch": 36, "iter": 1300, "lr": 0.08691, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27234, "top5_acc": 0.51516, "loss_cls": 4.19479, "loss": 4.19479, "time": 0.81977} +{"mode": "train", "epoch": 36, "iter": 1400, "lr": 0.08689, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27328, "top5_acc": 0.52391, "loss_cls": 4.17824, "loss": 4.17824, "time": 0.82924} +{"mode": "train", "epoch": 36, "iter": 1500, "lr": 0.08688, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27141, "top5_acc": 0.52, "loss_cls": 4.17962, "loss": 4.17962, "time": 0.81936} +{"mode": "train", "epoch": 36, "iter": 1600, "lr": 0.08686, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27219, "top5_acc": 0.52891, "loss_cls": 4.17618, "loss": 4.17618, "time": 0.81686} +{"mode": "train", "epoch": 36, "iter": 1700, "lr": 0.08684, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27469, "top5_acc": 0.52453, "loss_cls": 4.2239, "loss": 4.2239, "time": 0.82114} +{"mode": "train", "epoch": 36, "iter": 1800, "lr": 0.08682, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26594, "top5_acc": 0.52094, "loss_cls": 4.21986, "loss": 4.21986, "time": 0.8105} +{"mode": "train", "epoch": 36, "iter": 1900, "lr": 0.0868, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2625, "top5_acc": 0.50703, "loss_cls": 4.24942, "loss": 4.24942, "time": 0.81437} +{"mode": "train", "epoch": 36, "iter": 2000, "lr": 0.08678, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27719, "top5_acc": 0.53797, "loss_cls": 4.15039, "loss": 4.15039, "time": 0.81736} +{"mode": "train", "epoch": 36, "iter": 2100, "lr": 0.08676, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26766, "top5_acc": 0.52031, "loss_cls": 4.22126, "loss": 4.22126, "time": 0.81305} +{"mode": "train", "epoch": 36, "iter": 2200, "lr": 0.08674, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27422, "top5_acc": 0.52969, "loss_cls": 4.16948, "loss": 4.16948, "time": 0.81792} +{"mode": "train", "epoch": 36, "iter": 2300, "lr": 0.08672, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28172, "top5_acc": 0.52891, "loss_cls": 4.15115, "loss": 4.15115, "time": 0.82348} +{"mode": "train", "epoch": 36, "iter": 2400, "lr": 0.08671, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28766, "top5_acc": 0.53734, "loss_cls": 4.12041, "loss": 4.12041, "time": 0.81443} +{"mode": "train", "epoch": 36, "iter": 2500, "lr": 0.08669, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27094, "top5_acc": 0.53062, "loss_cls": 4.17315, "loss": 4.17315, "time": 0.81956} +{"mode": "train", "epoch": 36, "iter": 2600, "lr": 0.08667, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28125, "top5_acc": 0.53266, "loss_cls": 4.14953, "loss": 4.14953, "time": 0.82111} +{"mode": "train", "epoch": 36, "iter": 2700, "lr": 0.08665, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26484, "top5_acc": 0.53188, "loss_cls": 4.17228, "loss": 4.17228, "time": 0.8193} +{"mode": "train", "epoch": 36, "iter": 2800, "lr": 0.08663, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27, "top5_acc": 0.52766, "loss_cls": 4.15976, "loss": 4.15976, "time": 0.81119} +{"mode": "train", "epoch": 36, "iter": 2900, "lr": 0.08661, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26672, "top5_acc": 0.53172, "loss_cls": 4.16736, "loss": 4.16736, "time": 0.81235} +{"mode": "train", "epoch": 36, "iter": 3000, "lr": 0.08659, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26906, "top5_acc": 0.52609, "loss_cls": 4.19521, "loss": 4.19521, "time": 0.82198} +{"mode": "train", "epoch": 36, "iter": 3100, "lr": 0.08657, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27156, "top5_acc": 0.51781, "loss_cls": 4.18668, "loss": 4.18668, "time": 0.81153} +{"mode": "train", "epoch": 36, "iter": 3200, "lr": 0.08655, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27688, "top5_acc": 0.52969, "loss_cls": 4.14114, "loss": 4.14114, "time": 0.81673} +{"mode": "train", "epoch": 36, "iter": 3300, "lr": 0.08653, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26547, "top5_acc": 0.52781, "loss_cls": 4.21664, "loss": 4.21664, "time": 0.81393} +{"mode": "train", "epoch": 36, "iter": 3400, "lr": 0.08651, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27844, "top5_acc": 0.52562, "loss_cls": 4.18306, "loss": 4.18306, "time": 0.8167} +{"mode": "train", "epoch": 36, "iter": 3500, "lr": 0.0865, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28203, "top5_acc": 0.53016, "loss_cls": 4.1533, "loss": 4.1533, "time": 0.81215} +{"mode": "train", "epoch": 36, "iter": 3600, "lr": 0.08648, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26969, "top5_acc": 0.52141, "loss_cls": 4.23171, "loss": 4.23171, "time": 0.81621} +{"mode": "train", "epoch": 36, "iter": 3700, "lr": 0.08646, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26344, "top5_acc": 0.52031, "loss_cls": 4.21139, "loss": 4.21139, "time": 0.81956} +{"mode": "val", "epoch": 36, "iter": 309, "lr": 0.08645, "top1_acc": 0.19095, "top5_acc": 0.43155, "mean_class_accuracy": 0.191} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.08643, "memory": 15990, "data_time": 1.21417, "top1_acc": 0.28156, "top5_acc": 0.53453, "loss_cls": 4.12468, "loss": 4.12468, "time": 2.19131} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.08641, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27812, "top5_acc": 0.52844, "loss_cls": 4.14471, "loss": 4.14471, "time": 0.82153} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.08639, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2775, "top5_acc": 0.52688, "loss_cls": 4.1761, "loss": 4.1761, "time": 0.82151} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.08637, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27688, "top5_acc": 0.52969, "loss_cls": 4.15837, "loss": 4.15837, "time": 0.81901} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.08635, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27141, "top5_acc": 0.51703, "loss_cls": 4.20533, "loss": 4.20533, "time": 0.83235} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.08633, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26641, "top5_acc": 0.52234, "loss_cls": 4.17739, "loss": 4.17739, "time": 0.8191} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.08631, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26937, "top5_acc": 0.51922, "loss_cls": 4.18971, "loss": 4.18971, "time": 0.82497} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0863, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26922, "top5_acc": 0.52688, "loss_cls": 4.18308, "loss": 4.18308, "time": 0.83029} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.08628, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28125, "top5_acc": 0.53891, "loss_cls": 4.14156, "loss": 4.14156, "time": 0.82263} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.08626, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27156, "top5_acc": 0.52797, "loss_cls": 4.16451, "loss": 4.16451, "time": 0.82724} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.08624, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26953, "top5_acc": 0.52531, "loss_cls": 4.16291, "loss": 4.16291, "time": 0.81709} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.08622, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25891, "top5_acc": 0.52172, "loss_cls": 4.23007, "loss": 4.23007, "time": 0.81801} +{"mode": "train", "epoch": 37, "iter": 1300, "lr": 0.0862, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27703, "top5_acc": 0.52125, "loss_cls": 4.17047, "loss": 4.17047, "time": 0.81487} +{"mode": "train", "epoch": 37, "iter": 1400, "lr": 0.08618, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27391, "top5_acc": 0.52156, "loss_cls": 4.19314, "loss": 4.19314, "time": 0.81733} +{"mode": "train", "epoch": 37, "iter": 1500, "lr": 0.08616, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27688, "top5_acc": 0.53531, "loss_cls": 4.15492, "loss": 4.15492, "time": 0.81802} +{"mode": "train", "epoch": 37, "iter": 1600, "lr": 0.08614, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26547, "top5_acc": 0.52312, "loss_cls": 4.18081, "loss": 4.18081, "time": 0.81765} +{"mode": "train", "epoch": 37, "iter": 1700, "lr": 0.08612, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26547, "top5_acc": 0.52234, "loss_cls": 4.17172, "loss": 4.17172, "time": 0.81024} +{"mode": "train", "epoch": 37, "iter": 1800, "lr": 0.0861, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26781, "top5_acc": 0.52141, "loss_cls": 4.18046, "loss": 4.18046, "time": 0.81932} +{"mode": "train", "epoch": 37, "iter": 1900, "lr": 0.08608, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26469, "top5_acc": 0.52703, "loss_cls": 4.17383, "loss": 4.17383, "time": 0.81755} +{"mode": "train", "epoch": 37, "iter": 2000, "lr": 0.08606, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2675, "top5_acc": 0.51766, "loss_cls": 4.20134, "loss": 4.20134, "time": 0.81965} +{"mode": "train", "epoch": 37, "iter": 2100, "lr": 0.08604, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27078, "top5_acc": 0.52328, "loss_cls": 4.18636, "loss": 4.18636, "time": 0.81407} +{"mode": "train", "epoch": 37, "iter": 2200, "lr": 0.08602, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27688, "top5_acc": 0.53406, "loss_cls": 4.13885, "loss": 4.13885, "time": 0.82093} +{"mode": "train", "epoch": 37, "iter": 2300, "lr": 0.08601, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26562, "top5_acc": 0.52422, "loss_cls": 4.2007, "loss": 4.2007, "time": 0.82028} +{"mode": "train", "epoch": 37, "iter": 2400, "lr": 0.08599, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26625, "top5_acc": 0.53156, "loss_cls": 4.19669, "loss": 4.19669, "time": 0.80994} +{"mode": "train", "epoch": 37, "iter": 2500, "lr": 0.08597, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27, "top5_acc": 0.52156, "loss_cls": 4.19186, "loss": 4.19186, "time": 0.81881} +{"mode": "train", "epoch": 37, "iter": 2600, "lr": 0.08595, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26937, "top5_acc": 0.525, "loss_cls": 4.21486, "loss": 4.21486, "time": 0.81763} +{"mode": "train", "epoch": 37, "iter": 2700, "lr": 0.08593, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26953, "top5_acc": 0.52844, "loss_cls": 4.19517, "loss": 4.19517, "time": 0.81929} +{"mode": "train", "epoch": 37, "iter": 2800, "lr": 0.08591, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27797, "top5_acc": 0.53453, "loss_cls": 4.14955, "loss": 4.14955, "time": 0.81398} +{"mode": "train", "epoch": 37, "iter": 2900, "lr": 0.08589, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28516, "top5_acc": 0.53828, "loss_cls": 4.12399, "loss": 4.12399, "time": 0.81708} +{"mode": "train", "epoch": 37, "iter": 3000, "lr": 0.08587, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26891, "top5_acc": 0.52328, "loss_cls": 4.18407, "loss": 4.18407, "time": 0.81658} +{"mode": "train", "epoch": 37, "iter": 3100, "lr": 0.08585, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26641, "top5_acc": 0.51875, "loss_cls": 4.21141, "loss": 4.21141, "time": 0.8197} +{"mode": "train", "epoch": 37, "iter": 3200, "lr": 0.08583, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26219, "top5_acc": 0.50969, "loss_cls": 4.2381, "loss": 4.2381, "time": 0.82359} +{"mode": "train", "epoch": 37, "iter": 3300, "lr": 0.08581, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26438, "top5_acc": 0.52328, "loss_cls": 4.16563, "loss": 4.16563, "time": 0.81885} +{"mode": "train", "epoch": 37, "iter": 3400, "lr": 0.08579, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27516, "top5_acc": 0.53297, "loss_cls": 4.15585, "loss": 4.15585, "time": 0.81692} +{"mode": "train", "epoch": 37, "iter": 3500, "lr": 0.08577, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26328, "top5_acc": 0.51953, "loss_cls": 4.2313, "loss": 4.2313, "time": 0.81555} +{"mode": "train", "epoch": 37, "iter": 3600, "lr": 0.08575, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26875, "top5_acc": 0.51906, "loss_cls": 4.21259, "loss": 4.21259, "time": 0.82464} +{"mode": "train", "epoch": 37, "iter": 3700, "lr": 0.08573, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27578, "top5_acc": 0.52984, "loss_cls": 4.13907, "loss": 4.13907, "time": 0.82178} +{"mode": "val", "epoch": 37, "iter": 309, "lr": 0.08572, "top1_acc": 0.19703, "top5_acc": 0.4281, "mean_class_accuracy": 0.19675} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.0857, "memory": 15990, "data_time": 1.20502, "top1_acc": 0.27625, "top5_acc": 0.54188, "loss_cls": 4.12215, "loss": 4.12215, "time": 2.18044} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.08568, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27109, "top5_acc": 0.52391, "loss_cls": 4.16771, "loss": 4.16771, "time": 0.8113} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.08567, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27016, "top5_acc": 0.53484, "loss_cls": 4.15226, "loss": 4.15226, "time": 0.81653} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.08565, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27016, "top5_acc": 0.53609, "loss_cls": 4.13218, "loss": 4.13218, "time": 0.83032} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.08563, "memory": 15990, "data_time": 0.00094, "top1_acc": 0.28, "top5_acc": 0.52984, "loss_cls": 4.15697, "loss": 4.15697, "time": 0.82905} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.08561, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27469, "top5_acc": 0.53375, "loss_cls": 4.12016, "loss": 4.12016, "time": 0.82268} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.08559, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27141, "top5_acc": 0.53641, "loss_cls": 4.15836, "loss": 4.15836, "time": 0.82354} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.08557, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26687, "top5_acc": 0.52375, "loss_cls": 4.21585, "loss": 4.21585, "time": 0.82076} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.08555, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26984, "top5_acc": 0.52625, "loss_cls": 4.17203, "loss": 4.17203, "time": 0.82173} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.08553, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26719, "top5_acc": 0.53125, "loss_cls": 4.18073, "loss": 4.18073, "time": 0.8143} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.08551, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27156, "top5_acc": 0.52078, "loss_cls": 4.19424, "loss": 4.19424, "time": 0.81409} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.08549, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27141, "top5_acc": 0.51469, "loss_cls": 4.18433, "loss": 4.18433, "time": 0.8199} +{"mode": "train", "epoch": 38, "iter": 1300, "lr": 0.08547, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27219, "top5_acc": 0.53188, "loss_cls": 4.14764, "loss": 4.14764, "time": 0.81069} +{"mode": "train", "epoch": 38, "iter": 1400, "lr": 0.08545, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26219, "top5_acc": 0.52062, "loss_cls": 4.22079, "loss": 4.22079, "time": 0.81726} +{"mode": "train", "epoch": 38, "iter": 1500, "lr": 0.08543, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2775, "top5_acc": 0.53188, "loss_cls": 4.16912, "loss": 4.16912, "time": 0.8204} +{"mode": "train", "epoch": 38, "iter": 1600, "lr": 0.08541, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27516, "top5_acc": 0.52578, "loss_cls": 4.17288, "loss": 4.17288, "time": 0.82225} +{"mode": "train", "epoch": 38, "iter": 1700, "lr": 0.08539, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2775, "top5_acc": 0.53594, "loss_cls": 4.13164, "loss": 4.13164, "time": 0.81351} +{"mode": "train", "epoch": 38, "iter": 1800, "lr": 0.08537, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27141, "top5_acc": 0.52203, "loss_cls": 4.20801, "loss": 4.20801, "time": 0.81531} +{"mode": "train", "epoch": 38, "iter": 1900, "lr": 0.08535, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26844, "top5_acc": 0.53578, "loss_cls": 4.15467, "loss": 4.15467, "time": 0.81519} +{"mode": "train", "epoch": 38, "iter": 2000, "lr": 0.08533, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27891, "top5_acc": 0.53141, "loss_cls": 4.12991, "loss": 4.12991, "time": 0.81136} +{"mode": "train", "epoch": 38, "iter": 2100, "lr": 0.08531, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27594, "top5_acc": 0.53344, "loss_cls": 4.12998, "loss": 4.12998, "time": 0.81633} +{"mode": "train", "epoch": 38, "iter": 2200, "lr": 0.08529, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2625, "top5_acc": 0.51234, "loss_cls": 4.22674, "loss": 4.22674, "time": 0.81183} +{"mode": "train", "epoch": 38, "iter": 2300, "lr": 0.08527, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26656, "top5_acc": 0.52469, "loss_cls": 4.20419, "loss": 4.20419, "time": 0.82003} +{"mode": "train", "epoch": 38, "iter": 2400, "lr": 0.08525, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.27891, "top5_acc": 0.52875, "loss_cls": 4.15835, "loss": 4.15835, "time": 0.81693} +{"mode": "train", "epoch": 38, "iter": 2500, "lr": 0.08523, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26438, "top5_acc": 0.51234, "loss_cls": 4.24936, "loss": 4.24936, "time": 0.81181} +{"mode": "train", "epoch": 38, "iter": 2600, "lr": 0.08521, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25906, "top5_acc": 0.51875, "loss_cls": 4.23213, "loss": 4.23213, "time": 0.82031} +{"mode": "train", "epoch": 38, "iter": 2700, "lr": 0.08519, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26406, "top5_acc": 0.52406, "loss_cls": 4.18989, "loss": 4.18989, "time": 0.81704} +{"mode": "train", "epoch": 38, "iter": 2800, "lr": 0.08517, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28172, "top5_acc": 0.52953, "loss_cls": 4.11421, "loss": 4.11421, "time": 0.81986} +{"mode": "train", "epoch": 38, "iter": 2900, "lr": 0.08515, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27156, "top5_acc": 0.52359, "loss_cls": 4.19266, "loss": 4.19266, "time": 0.81702} +{"mode": "train", "epoch": 38, "iter": 3000, "lr": 0.08513, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26578, "top5_acc": 0.52234, "loss_cls": 4.21703, "loss": 4.21703, "time": 0.8178} +{"mode": "train", "epoch": 38, "iter": 3100, "lr": 0.08511, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26578, "top5_acc": 0.52938, "loss_cls": 4.20068, "loss": 4.20068, "time": 0.82137} +{"mode": "train", "epoch": 38, "iter": 3200, "lr": 0.08509, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27469, "top5_acc": 0.54281, "loss_cls": 4.12476, "loss": 4.12476, "time": 0.82019} +{"mode": "train", "epoch": 38, "iter": 3300, "lr": 0.08507, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26703, "top5_acc": 0.51344, "loss_cls": 4.21402, "loss": 4.21402, "time": 0.81919} +{"mode": "train", "epoch": 38, "iter": 3400, "lr": 0.08505, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27, "top5_acc": 0.52719, "loss_cls": 4.17725, "loss": 4.17725, "time": 0.81897} +{"mode": "train", "epoch": 38, "iter": 3500, "lr": 0.08503, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27, "top5_acc": 0.52469, "loss_cls": 4.19067, "loss": 4.19067, "time": 0.81445} +{"mode": "train", "epoch": 38, "iter": 3600, "lr": 0.08501, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27344, "top5_acc": 0.53328, "loss_cls": 4.15435, "loss": 4.15435, "time": 0.82483} +{"mode": "train", "epoch": 38, "iter": 3700, "lr": 0.08499, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27141, "top5_acc": 0.51578, "loss_cls": 4.19089, "loss": 4.19089, "time": 0.81825} +{"mode": "val", "epoch": 38, "iter": 309, "lr": 0.08498, "top1_acc": 0.17257, "top5_acc": 0.39209, "mean_class_accuracy": 0.17226} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.08496, "memory": 15990, "data_time": 1.2023, "top1_acc": 0.28422, "top5_acc": 0.54641, "loss_cls": 4.10033, "loss": 4.10033, "time": 2.181} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.08494, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27781, "top5_acc": 0.54031, "loss_cls": 4.12921, "loss": 4.12921, "time": 0.81647} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.08492, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28203, "top5_acc": 0.53922, "loss_cls": 4.11205, "loss": 4.11205, "time": 0.81728} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.0849, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28203, "top5_acc": 0.53812, "loss_cls": 4.12633, "loss": 4.12633, "time": 0.83145} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.08488, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27469, "top5_acc": 0.53703, "loss_cls": 4.09872, "loss": 4.09872, "time": 0.81706} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.08486, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27469, "top5_acc": 0.52875, "loss_cls": 4.1691, "loss": 4.1691, "time": 0.82498} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.08484, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26969, "top5_acc": 0.53125, "loss_cls": 4.15547, "loss": 4.15547, "time": 0.82863} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.08482, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27469, "top5_acc": 0.53078, "loss_cls": 4.15649, "loss": 4.15649, "time": 0.82619} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.0848, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27578, "top5_acc": 0.53141, "loss_cls": 4.14479, "loss": 4.14479, "time": 0.82903} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.08478, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27234, "top5_acc": 0.53297, "loss_cls": 4.15578, "loss": 4.15578, "time": 0.82077} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.08476, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27187, "top5_acc": 0.52469, "loss_cls": 4.19862, "loss": 4.19862, "time": 0.82636} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.08474, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27031, "top5_acc": 0.52438, "loss_cls": 4.19516, "loss": 4.19516, "time": 0.81889} +{"mode": "train", "epoch": 39, "iter": 1300, "lr": 0.08472, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26594, "top5_acc": 0.5225, "loss_cls": 4.17869, "loss": 4.17869, "time": 0.82357} +{"mode": "train", "epoch": 39, "iter": 1400, "lr": 0.0847, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.275, "top5_acc": 0.52781, "loss_cls": 4.18065, "loss": 4.18065, "time": 0.81226} +{"mode": "train", "epoch": 39, "iter": 1500, "lr": 0.08468, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27844, "top5_acc": 0.53203, "loss_cls": 4.1435, "loss": 4.1435, "time": 0.81946} +{"mode": "train", "epoch": 39, "iter": 1600, "lr": 0.08466, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27078, "top5_acc": 0.52922, "loss_cls": 4.17011, "loss": 4.17011, "time": 0.81337} +{"mode": "train", "epoch": 39, "iter": 1700, "lr": 0.08464, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27, "top5_acc": 0.52656, "loss_cls": 4.16242, "loss": 4.16242, "time": 0.82172} +{"mode": "train", "epoch": 39, "iter": 1800, "lr": 0.08462, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27547, "top5_acc": 0.52781, "loss_cls": 4.1902, "loss": 4.1902, "time": 0.81954} +{"mode": "train", "epoch": 39, "iter": 1900, "lr": 0.0846, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27562, "top5_acc": 0.53734, "loss_cls": 4.12318, "loss": 4.12318, "time": 0.81136} +{"mode": "train", "epoch": 39, "iter": 2000, "lr": 0.08458, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26609, "top5_acc": 0.52672, "loss_cls": 4.18394, "loss": 4.18394, "time": 0.81874} +{"mode": "train", "epoch": 39, "iter": 2100, "lr": 0.08456, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27297, "top5_acc": 0.51984, "loss_cls": 4.17853, "loss": 4.17853, "time": 0.80829} +{"mode": "train", "epoch": 39, "iter": 2200, "lr": 0.08454, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26797, "top5_acc": 0.52469, "loss_cls": 4.20555, "loss": 4.20555, "time": 0.81413} +{"mode": "train", "epoch": 39, "iter": 2300, "lr": 0.08452, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.27187, "top5_acc": 0.53828, "loss_cls": 4.1457, "loss": 4.1457, "time": 0.82158} +{"mode": "train", "epoch": 39, "iter": 2400, "lr": 0.0845, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27047, "top5_acc": 0.52578, "loss_cls": 4.18022, "loss": 4.18022, "time": 0.81418} +{"mode": "train", "epoch": 39, "iter": 2500, "lr": 0.08448, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27078, "top5_acc": 0.51672, "loss_cls": 4.17032, "loss": 4.17032, "time": 0.81922} +{"mode": "train", "epoch": 39, "iter": 2600, "lr": 0.08446, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26031, "top5_acc": 0.51516, "loss_cls": 4.21649, "loss": 4.21649, "time": 0.8143} +{"mode": "train", "epoch": 39, "iter": 2700, "lr": 0.08444, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26297, "top5_acc": 0.51406, "loss_cls": 4.22842, "loss": 4.22842, "time": 0.81896} +{"mode": "train", "epoch": 39, "iter": 2800, "lr": 0.08442, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26625, "top5_acc": 0.52328, "loss_cls": 4.1643, "loss": 4.1643, "time": 0.82379} +{"mode": "train", "epoch": 39, "iter": 2900, "lr": 0.0844, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2675, "top5_acc": 0.52719, "loss_cls": 4.17725, "loss": 4.17725, "time": 0.81407} +{"mode": "train", "epoch": 39, "iter": 3000, "lr": 0.08438, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26906, "top5_acc": 0.52516, "loss_cls": 4.17027, "loss": 4.17027, "time": 0.82106} +{"mode": "train", "epoch": 39, "iter": 3100, "lr": 0.08436, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27547, "top5_acc": 0.51812, "loss_cls": 4.18829, "loss": 4.18829, "time": 0.81802} +{"mode": "train", "epoch": 39, "iter": 3200, "lr": 0.08434, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26281, "top5_acc": 0.51203, "loss_cls": 4.21225, "loss": 4.21225, "time": 0.81892} +{"mode": "train", "epoch": 39, "iter": 3300, "lr": 0.08432, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26125, "top5_acc": 0.52281, "loss_cls": 4.21478, "loss": 4.21478, "time": 0.80954} +{"mode": "train", "epoch": 39, "iter": 3400, "lr": 0.0843, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26656, "top5_acc": 0.52203, "loss_cls": 4.17752, "loss": 4.17752, "time": 0.80876} +{"mode": "train", "epoch": 39, "iter": 3500, "lr": 0.08428, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27, "top5_acc": 0.5275, "loss_cls": 4.17786, "loss": 4.17786, "time": 0.81473} +{"mode": "train", "epoch": 39, "iter": 3600, "lr": 0.08426, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26531, "top5_acc": 0.53562, "loss_cls": 4.1818, "loss": 4.1818, "time": 0.82567} +{"mode": "train", "epoch": 39, "iter": 3700, "lr": 0.08424, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27781, "top5_acc": 0.52484, "loss_cls": 4.1709, "loss": 4.1709, "time": 0.81772} +{"mode": "val", "epoch": 39, "iter": 309, "lr": 0.08423, "top1_acc": 0.15869, "top5_acc": 0.36195, "mean_class_accuracy": 0.15858} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.08421, "memory": 15990, "data_time": 1.21664, "top1_acc": 0.27969, "top5_acc": 0.53453, "loss_cls": 4.13224, "loss": 4.13224, "time": 2.18952} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.08419, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27328, "top5_acc": 0.53641, "loss_cls": 4.14921, "loss": 4.14921, "time": 0.8225} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.08417, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27469, "top5_acc": 0.53531, "loss_cls": 4.14891, "loss": 4.14891, "time": 0.8149} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.08415, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27766, "top5_acc": 0.53656, "loss_cls": 4.08637, "loss": 4.08637, "time": 0.82531} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.08413, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28672, "top5_acc": 0.54078, "loss_cls": 4.12605, "loss": 4.12605, "time": 0.81641} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.08411, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27875, "top5_acc": 0.53266, "loss_cls": 4.15064, "loss": 4.15064, "time": 0.82851} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.08408, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27766, "top5_acc": 0.53172, "loss_cls": 4.1381, "loss": 4.1381, "time": 0.81702} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.08406, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27234, "top5_acc": 0.52656, "loss_cls": 4.1451, "loss": 4.1451, "time": 0.82073} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.08404, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27781, "top5_acc": 0.53719, "loss_cls": 4.12064, "loss": 4.12064, "time": 0.81977} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.08402, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26641, "top5_acc": 0.52594, "loss_cls": 4.1699, "loss": 4.1699, "time": 0.8184} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.084, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27125, "top5_acc": 0.53422, "loss_cls": 4.15174, "loss": 4.15174, "time": 0.81626} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.08398, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26734, "top5_acc": 0.51938, "loss_cls": 4.18577, "loss": 4.18577, "time": 0.81401} +{"mode": "train", "epoch": 40, "iter": 1300, "lr": 0.08396, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26937, "top5_acc": 0.52484, "loss_cls": 4.19132, "loss": 4.19132, "time": 0.81806} +{"mode": "train", "epoch": 40, "iter": 1400, "lr": 0.08394, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26562, "top5_acc": 0.52641, "loss_cls": 4.18252, "loss": 4.18252, "time": 0.82275} +{"mode": "train", "epoch": 40, "iter": 1500, "lr": 0.08392, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27422, "top5_acc": 0.53641, "loss_cls": 4.17052, "loss": 4.17052, "time": 0.82242} +{"mode": "train", "epoch": 40, "iter": 1600, "lr": 0.0839, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26781, "top5_acc": 0.53641, "loss_cls": 4.15228, "loss": 4.15228, "time": 0.81935} +{"mode": "train", "epoch": 40, "iter": 1700, "lr": 0.08388, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26984, "top5_acc": 0.52828, "loss_cls": 4.17953, "loss": 4.17953, "time": 0.8176} +{"mode": "train", "epoch": 40, "iter": 1800, "lr": 0.08386, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27016, "top5_acc": 0.53141, "loss_cls": 4.1728, "loss": 4.1728, "time": 0.8136} +{"mode": "train", "epoch": 40, "iter": 1900, "lr": 0.08384, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27734, "top5_acc": 0.53562, "loss_cls": 4.15727, "loss": 4.15727, "time": 0.81769} +{"mode": "train", "epoch": 40, "iter": 2000, "lr": 0.08382, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27578, "top5_acc": 0.52422, "loss_cls": 4.17413, "loss": 4.17413, "time": 0.81735} +{"mode": "train", "epoch": 40, "iter": 2100, "lr": 0.0838, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27656, "top5_acc": 0.52859, "loss_cls": 4.15823, "loss": 4.15823, "time": 0.81184} +{"mode": "train", "epoch": 40, "iter": 2200, "lr": 0.08378, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27672, "top5_acc": 0.52922, "loss_cls": 4.15905, "loss": 4.15905, "time": 0.81059} +{"mode": "train", "epoch": 40, "iter": 2300, "lr": 0.08376, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2725, "top5_acc": 0.52438, "loss_cls": 4.18558, "loss": 4.18558, "time": 0.82257} +{"mode": "train", "epoch": 40, "iter": 2400, "lr": 0.08374, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27453, "top5_acc": 0.53031, "loss_cls": 4.1699, "loss": 4.1699, "time": 0.81644} +{"mode": "train", "epoch": 40, "iter": 2500, "lr": 0.08371, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27203, "top5_acc": 0.52156, "loss_cls": 4.19295, "loss": 4.19295, "time": 0.81655} +{"mode": "train", "epoch": 40, "iter": 2600, "lr": 0.08369, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27375, "top5_acc": 0.53234, "loss_cls": 4.18557, "loss": 4.18557, "time": 0.81892} +{"mode": "train", "epoch": 40, "iter": 2700, "lr": 0.08367, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26375, "top5_acc": 0.52812, "loss_cls": 4.19695, "loss": 4.19695, "time": 0.81513} +{"mode": "train", "epoch": 40, "iter": 2800, "lr": 0.08365, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27562, "top5_acc": 0.52969, "loss_cls": 4.16439, "loss": 4.16439, "time": 0.82269} +{"mode": "train", "epoch": 40, "iter": 2900, "lr": 0.08363, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26844, "top5_acc": 0.53234, "loss_cls": 4.17382, "loss": 4.17382, "time": 0.81883} +{"mode": "train", "epoch": 40, "iter": 3000, "lr": 0.08361, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27062, "top5_acc": 0.52578, "loss_cls": 4.17464, "loss": 4.17464, "time": 0.81315} +{"mode": "train", "epoch": 40, "iter": 3100, "lr": 0.08359, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26797, "top5_acc": 0.51969, "loss_cls": 4.16435, "loss": 4.16435, "time": 0.81248} +{"mode": "train", "epoch": 40, "iter": 3200, "lr": 0.08357, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27328, "top5_acc": 0.52594, "loss_cls": 4.17362, "loss": 4.17362, "time": 0.81062} +{"mode": "train", "epoch": 40, "iter": 3300, "lr": 0.08355, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27016, "top5_acc": 0.52625, "loss_cls": 4.1834, "loss": 4.1834, "time": 0.81048} +{"mode": "train", "epoch": 40, "iter": 3400, "lr": 0.08353, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27281, "top5_acc": 0.53203, "loss_cls": 4.17616, "loss": 4.17616, "time": 0.81771} +{"mode": "train", "epoch": 40, "iter": 3500, "lr": 0.08351, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26953, "top5_acc": 0.53109, "loss_cls": 4.18347, "loss": 4.18347, "time": 0.8107} +{"mode": "train", "epoch": 40, "iter": 3600, "lr": 0.08349, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27453, "top5_acc": 0.52781, "loss_cls": 4.15383, "loss": 4.15383, "time": 0.82681} +{"mode": "train", "epoch": 40, "iter": 3700, "lr": 0.08347, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27859, "top5_acc": 0.525, "loss_cls": 4.16757, "loss": 4.16757, "time": 0.81384} +{"mode": "val", "epoch": 40, "iter": 309, "lr": 0.08346, "top1_acc": 0.19698, "top5_acc": 0.4278, "mean_class_accuracy": 0.19706} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.08344, "memory": 15990, "data_time": 1.20507, "top1_acc": 0.27484, "top5_acc": 0.53828, "loss_cls": 4.12529, "loss": 4.12529, "time": 2.18336} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.08342, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28469, "top5_acc": 0.52812, "loss_cls": 4.14182, "loss": 4.14182, "time": 0.81723} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.08339, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27141, "top5_acc": 0.54266, "loss_cls": 4.13786, "loss": 4.13786, "time": 0.82309} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.08337, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.26609, "top5_acc": 0.52188, "loss_cls": 4.18772, "loss": 4.18772, "time": 0.82422} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.08335, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27969, "top5_acc": 0.53641, "loss_cls": 4.13358, "loss": 4.13358, "time": 0.82343} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.08333, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27859, "top5_acc": 0.53578, "loss_cls": 4.13902, "loss": 4.13902, "time": 0.82709} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.08331, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27953, "top5_acc": 0.53625, "loss_cls": 4.11514, "loss": 4.11514, "time": 0.82885} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.08329, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27234, "top5_acc": 0.52109, "loss_cls": 4.18304, "loss": 4.18304, "time": 0.82927} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.08327, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27625, "top5_acc": 0.53906, "loss_cls": 4.13138, "loss": 4.13138, "time": 0.82062} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.08325, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27797, "top5_acc": 0.53422, "loss_cls": 4.12129, "loss": 4.12129, "time": 0.81616} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.08323, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27781, "top5_acc": 0.53734, "loss_cls": 4.11237, "loss": 4.11237, "time": 0.81066} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.08321, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26969, "top5_acc": 0.52453, "loss_cls": 4.16948, "loss": 4.16948, "time": 0.81145} +{"mode": "train", "epoch": 41, "iter": 1300, "lr": 0.08319, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26906, "top5_acc": 0.52953, "loss_cls": 4.16705, "loss": 4.16705, "time": 0.81383} +{"mode": "train", "epoch": 41, "iter": 1400, "lr": 0.08316, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27922, "top5_acc": 0.53359, "loss_cls": 4.17473, "loss": 4.17473, "time": 0.81706} +{"mode": "train", "epoch": 41, "iter": 1500, "lr": 0.08314, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28016, "top5_acc": 0.53031, "loss_cls": 4.14964, "loss": 4.14964, "time": 0.81055} +{"mode": "train", "epoch": 41, "iter": 1600, "lr": 0.08312, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27594, "top5_acc": 0.5325, "loss_cls": 4.14988, "loss": 4.14988, "time": 0.813} +{"mode": "train", "epoch": 41, "iter": 1700, "lr": 0.0831, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2675, "top5_acc": 0.52078, "loss_cls": 4.2185, "loss": 4.2185, "time": 0.82008} +{"mode": "train", "epoch": 41, "iter": 1800, "lr": 0.08308, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27578, "top5_acc": 0.52547, "loss_cls": 4.17688, "loss": 4.17688, "time": 0.81777} +{"mode": "train", "epoch": 41, "iter": 1900, "lr": 0.08306, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26906, "top5_acc": 0.51641, "loss_cls": 4.19475, "loss": 4.19475, "time": 0.82118} +{"mode": "train", "epoch": 41, "iter": 2000, "lr": 0.08304, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27766, "top5_acc": 0.53125, "loss_cls": 4.14281, "loss": 4.14281, "time": 0.81874} +{"mode": "train", "epoch": 41, "iter": 2100, "lr": 0.08302, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27562, "top5_acc": 0.52828, "loss_cls": 4.14887, "loss": 4.14887, "time": 0.81972} +{"mode": "train", "epoch": 41, "iter": 2200, "lr": 0.083, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28094, "top5_acc": 0.53469, "loss_cls": 4.12383, "loss": 4.12383, "time": 0.81295} +{"mode": "train", "epoch": 41, "iter": 2300, "lr": 0.08298, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27328, "top5_acc": 0.5225, "loss_cls": 4.1727, "loss": 4.1727, "time": 0.82219} +{"mode": "train", "epoch": 41, "iter": 2400, "lr": 0.08296, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27609, "top5_acc": 0.52734, "loss_cls": 4.16005, "loss": 4.16005, "time": 0.81042} +{"mode": "train", "epoch": 41, "iter": 2500, "lr": 0.08293, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28578, "top5_acc": 0.5325, "loss_cls": 4.12049, "loss": 4.12049, "time": 0.81573} +{"mode": "train", "epoch": 41, "iter": 2600, "lr": 0.08291, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28203, "top5_acc": 0.53094, "loss_cls": 4.14022, "loss": 4.14022, "time": 0.81918} +{"mode": "train", "epoch": 41, "iter": 2700, "lr": 0.08289, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27391, "top5_acc": 0.52844, "loss_cls": 4.16711, "loss": 4.16711, "time": 0.82212} +{"mode": "train", "epoch": 41, "iter": 2800, "lr": 0.08287, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27906, "top5_acc": 0.53328, "loss_cls": 4.14268, "loss": 4.14268, "time": 0.81916} +{"mode": "train", "epoch": 41, "iter": 2900, "lr": 0.08285, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27844, "top5_acc": 0.53891, "loss_cls": 4.14555, "loss": 4.14555, "time": 0.81231} +{"mode": "train", "epoch": 41, "iter": 3000, "lr": 0.08283, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27141, "top5_acc": 0.52453, "loss_cls": 4.16506, "loss": 4.16506, "time": 0.81352} +{"mode": "train", "epoch": 41, "iter": 3100, "lr": 0.08281, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27156, "top5_acc": 0.52281, "loss_cls": 4.201, "loss": 4.201, "time": 0.82028} +{"mode": "train", "epoch": 41, "iter": 3200, "lr": 0.08279, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27156, "top5_acc": 0.53266, "loss_cls": 4.15988, "loss": 4.15988, "time": 0.81474} +{"mode": "train", "epoch": 41, "iter": 3300, "lr": 0.08277, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2625, "top5_acc": 0.51859, "loss_cls": 4.23477, "loss": 4.23477, "time": 0.8188} +{"mode": "train", "epoch": 41, "iter": 3400, "lr": 0.08274, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27047, "top5_acc": 0.52562, "loss_cls": 4.19015, "loss": 4.19015, "time": 0.82042} +{"mode": "train", "epoch": 41, "iter": 3500, "lr": 0.08272, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26609, "top5_acc": 0.51359, "loss_cls": 4.20228, "loss": 4.20228, "time": 0.82007} +{"mode": "train", "epoch": 41, "iter": 3600, "lr": 0.0827, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26531, "top5_acc": 0.52562, "loss_cls": 4.17788, "loss": 4.17788, "time": 0.83125} +{"mode": "train", "epoch": 41, "iter": 3700, "lr": 0.08268, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27047, "top5_acc": 0.52547, "loss_cls": 4.20582, "loss": 4.20582, "time": 0.81232} +{"mode": "val", "epoch": 41, "iter": 309, "lr": 0.08267, "top1_acc": 0.19941, "top5_acc": 0.43261, "mean_class_accuracy": 0.19909} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.08265, "memory": 15990, "data_time": 1.21163, "top1_acc": 0.27344, "top5_acc": 0.53031, "loss_cls": 4.14788, "loss": 4.14788, "time": 2.19564} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.08263, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27266, "top5_acc": 0.53062, "loss_cls": 4.15271, "loss": 4.15271, "time": 0.82039} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.08261, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26859, "top5_acc": 0.53016, "loss_cls": 4.17192, "loss": 4.17192, "time": 0.81515} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.08259, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2775, "top5_acc": 0.53266, "loss_cls": 4.13993, "loss": 4.13993, "time": 0.81877} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.08257, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27938, "top5_acc": 0.53719, "loss_cls": 4.13251, "loss": 4.13251, "time": 0.82589} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.08254, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27437, "top5_acc": 0.53609, "loss_cls": 4.13358, "loss": 4.13358, "time": 0.82066} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.08252, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27312, "top5_acc": 0.53547, "loss_cls": 4.16078, "loss": 4.16078, "time": 0.82545} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.0825, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27797, "top5_acc": 0.53234, "loss_cls": 4.12687, "loss": 4.12687, "time": 0.81815} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.08248, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28359, "top5_acc": 0.54047, "loss_cls": 4.10603, "loss": 4.10603, "time": 0.82078} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.08246, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28141, "top5_acc": 0.52938, "loss_cls": 4.13602, "loss": 4.13602, "time": 0.82007} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.08244, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27609, "top5_acc": 0.52688, "loss_cls": 4.17267, "loss": 4.17267, "time": 0.81234} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.08242, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25969, "top5_acc": 0.52969, "loss_cls": 4.18778, "loss": 4.18778, "time": 0.81722} +{"mode": "train", "epoch": 42, "iter": 1300, "lr": 0.0824, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27594, "top5_acc": 0.53516, "loss_cls": 4.16151, "loss": 4.16151, "time": 0.81628} +{"mode": "train", "epoch": 42, "iter": 1400, "lr": 0.08237, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27203, "top5_acc": 0.5375, "loss_cls": 4.14753, "loss": 4.14753, "time": 0.82031} +{"mode": "train", "epoch": 42, "iter": 1500, "lr": 0.08235, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28453, "top5_acc": 0.53797, "loss_cls": 4.11291, "loss": 4.11291, "time": 0.81509} +{"mode": "train", "epoch": 42, "iter": 1600, "lr": 0.08233, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25938, "top5_acc": 0.52641, "loss_cls": 4.19025, "loss": 4.19025, "time": 0.81634} +{"mode": "train", "epoch": 42, "iter": 1700, "lr": 0.08231, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26906, "top5_acc": 0.53812, "loss_cls": 4.15393, "loss": 4.15393, "time": 0.81832} +{"mode": "train", "epoch": 42, "iter": 1800, "lr": 0.08229, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27453, "top5_acc": 0.53828, "loss_cls": 4.15481, "loss": 4.15481, "time": 0.81701} +{"mode": "train", "epoch": 42, "iter": 1900, "lr": 0.08227, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28516, "top5_acc": 0.53188, "loss_cls": 4.14425, "loss": 4.14425, "time": 0.81585} +{"mode": "train", "epoch": 42, "iter": 2000, "lr": 0.08225, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27578, "top5_acc": 0.53438, "loss_cls": 4.12677, "loss": 4.12677, "time": 0.81704} +{"mode": "train", "epoch": 42, "iter": 2100, "lr": 0.08222, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27266, "top5_acc": 0.52844, "loss_cls": 4.17557, "loss": 4.17557, "time": 0.8126} +{"mode": "train", "epoch": 42, "iter": 2200, "lr": 0.0822, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27906, "top5_acc": 0.53, "loss_cls": 4.13346, "loss": 4.13346, "time": 0.8155} +{"mode": "train", "epoch": 42, "iter": 2300, "lr": 0.08218, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27594, "top5_acc": 0.52484, "loss_cls": 4.16653, "loss": 4.16653, "time": 0.82138} +{"mode": "train", "epoch": 42, "iter": 2400, "lr": 0.08216, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27828, "top5_acc": 0.52984, "loss_cls": 4.15079, "loss": 4.15079, "time": 0.81464} +{"mode": "train", "epoch": 42, "iter": 2500, "lr": 0.08214, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26906, "top5_acc": 0.52125, "loss_cls": 4.1868, "loss": 4.1868, "time": 0.81595} +{"mode": "train", "epoch": 42, "iter": 2600, "lr": 0.08212, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27781, "top5_acc": 0.53109, "loss_cls": 4.13574, "loss": 4.13574, "time": 0.81932} +{"mode": "train", "epoch": 42, "iter": 2700, "lr": 0.0821, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27781, "top5_acc": 0.53, "loss_cls": 4.15628, "loss": 4.15628, "time": 0.82089} +{"mode": "train", "epoch": 42, "iter": 2800, "lr": 0.08207, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2875, "top5_acc": 0.53688, "loss_cls": 4.13312, "loss": 4.13312, "time": 0.81102} +{"mode": "train", "epoch": 42, "iter": 2900, "lr": 0.08205, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27641, "top5_acc": 0.52953, "loss_cls": 4.15285, "loss": 4.15285, "time": 0.81211} +{"mode": "train", "epoch": 42, "iter": 3000, "lr": 0.08203, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28234, "top5_acc": 0.53531, "loss_cls": 4.14848, "loss": 4.14848, "time": 0.81607} +{"mode": "train", "epoch": 42, "iter": 3100, "lr": 0.08201, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27219, "top5_acc": 0.535, "loss_cls": 4.16278, "loss": 4.16278, "time": 0.81672} +{"mode": "train", "epoch": 42, "iter": 3200, "lr": 0.08199, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27562, "top5_acc": 0.52859, "loss_cls": 4.16123, "loss": 4.16123, "time": 0.81083} +{"mode": "train", "epoch": 42, "iter": 3300, "lr": 0.08197, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28172, "top5_acc": 0.53312, "loss_cls": 4.11963, "loss": 4.11963, "time": 0.81872} +{"mode": "train", "epoch": 42, "iter": 3400, "lr": 0.08195, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27891, "top5_acc": 0.53422, "loss_cls": 4.14649, "loss": 4.14649, "time": 0.81347} +{"mode": "train", "epoch": 42, "iter": 3500, "lr": 0.08192, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27828, "top5_acc": 0.52578, "loss_cls": 4.1655, "loss": 4.1655, "time": 0.81558} +{"mode": "train", "epoch": 42, "iter": 3600, "lr": 0.0819, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27672, "top5_acc": 0.53828, "loss_cls": 4.10991, "loss": 4.10991, "time": 0.82497} +{"mode": "train", "epoch": 42, "iter": 3700, "lr": 0.08188, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27828, "top5_acc": 0.53016, "loss_cls": 4.14008, "loss": 4.14008, "time": 0.81125} +{"mode": "val", "epoch": 42, "iter": 309, "lr": 0.08187, "top1_acc": 0.2104, "top5_acc": 0.44563, "mean_class_accuracy": 0.21017} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.08185, "memory": 15990, "data_time": 1.25978, "top1_acc": 0.27953, "top5_acc": 0.53828, "loss_cls": 4.12273, "loss": 4.12273, "time": 2.24608} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.08183, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28422, "top5_acc": 0.545, "loss_cls": 4.06009, "loss": 4.06009, "time": 0.82396} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.08181, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27219, "top5_acc": 0.53125, "loss_cls": 4.17416, "loss": 4.17416, "time": 0.82934} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.08179, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28125, "top5_acc": 0.53281, "loss_cls": 4.12759, "loss": 4.12759, "time": 0.82267} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.08176, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28078, "top5_acc": 0.54422, "loss_cls": 4.09477, "loss": 4.09477, "time": 0.82075} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.08174, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27547, "top5_acc": 0.53281, "loss_cls": 4.14064, "loss": 4.14064, "time": 0.81121} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.08172, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2775, "top5_acc": 0.53875, "loss_cls": 4.11508, "loss": 4.11508, "time": 0.82781} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.0817, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28547, "top5_acc": 0.54078, "loss_cls": 4.0697, "loss": 4.0697, "time": 0.82447} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.08168, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27562, "top5_acc": 0.52344, "loss_cls": 4.15736, "loss": 4.15736, "time": 0.82981} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.08166, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28359, "top5_acc": 0.53703, "loss_cls": 4.10493, "loss": 4.10493, "time": 0.82732} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.08163, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27734, "top5_acc": 0.5375, "loss_cls": 4.10768, "loss": 4.10768, "time": 0.8241} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.08161, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27156, "top5_acc": 0.53359, "loss_cls": 4.14413, "loss": 4.14413, "time": 0.82254} +{"mode": "train", "epoch": 43, "iter": 1300, "lr": 0.08159, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27516, "top5_acc": 0.53734, "loss_cls": 4.15174, "loss": 4.15174, "time": 0.81646} +{"mode": "train", "epoch": 43, "iter": 1400, "lr": 0.08157, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28234, "top5_acc": 0.53703, "loss_cls": 4.11539, "loss": 4.11539, "time": 0.81852} +{"mode": "train", "epoch": 43, "iter": 1500, "lr": 0.08155, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28375, "top5_acc": 0.53609, "loss_cls": 4.14885, "loss": 4.14885, "time": 0.81425} +{"mode": "train", "epoch": 43, "iter": 1600, "lr": 0.08153, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27266, "top5_acc": 0.53031, "loss_cls": 4.1428, "loss": 4.1428, "time": 0.81152} +{"mode": "train", "epoch": 43, "iter": 1700, "lr": 0.0815, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28906, "top5_acc": 0.53031, "loss_cls": 4.11837, "loss": 4.11837, "time": 0.8155} +{"mode": "train", "epoch": 43, "iter": 1800, "lr": 0.08148, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27891, "top5_acc": 0.53469, "loss_cls": 4.14741, "loss": 4.14741, "time": 0.81299} +{"mode": "train", "epoch": 43, "iter": 1900, "lr": 0.08146, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26781, "top5_acc": 0.53391, "loss_cls": 4.13879, "loss": 4.13879, "time": 0.81716} +{"mode": "train", "epoch": 43, "iter": 2000, "lr": 0.08144, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26766, "top5_acc": 0.51812, "loss_cls": 4.19074, "loss": 4.19074, "time": 0.81186} +{"mode": "train", "epoch": 43, "iter": 2100, "lr": 0.08142, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26844, "top5_acc": 0.52516, "loss_cls": 4.17758, "loss": 4.17758, "time": 0.81353} +{"mode": "train", "epoch": 43, "iter": 2200, "lr": 0.0814, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28391, "top5_acc": 0.52984, "loss_cls": 4.14731, "loss": 4.14731, "time": 0.81165} +{"mode": "train", "epoch": 43, "iter": 2300, "lr": 0.08137, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27016, "top5_acc": 0.52391, "loss_cls": 4.16201, "loss": 4.16201, "time": 0.81579} +{"mode": "train", "epoch": 43, "iter": 2400, "lr": 0.08135, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2725, "top5_acc": 0.52531, "loss_cls": 4.1621, "loss": 4.1621, "time": 0.81911} +{"mode": "train", "epoch": 43, "iter": 2500, "lr": 0.08133, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27406, "top5_acc": 0.53219, "loss_cls": 4.1418, "loss": 4.1418, "time": 0.81457} +{"mode": "train", "epoch": 43, "iter": 2600, "lr": 0.08131, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28172, "top5_acc": 0.52375, "loss_cls": 4.15539, "loss": 4.15539, "time": 0.82092} +{"mode": "train", "epoch": 43, "iter": 2700, "lr": 0.08129, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27297, "top5_acc": 0.53484, "loss_cls": 4.1485, "loss": 4.1485, "time": 0.81959} +{"mode": "train", "epoch": 43, "iter": 2800, "lr": 0.08126, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27281, "top5_acc": 0.52922, "loss_cls": 4.17465, "loss": 4.17465, "time": 0.81985} +{"mode": "train", "epoch": 43, "iter": 2900, "lr": 0.08124, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27516, "top5_acc": 0.53344, "loss_cls": 4.10692, "loss": 4.10692, "time": 0.81947} +{"mode": "train", "epoch": 43, "iter": 3000, "lr": 0.08122, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27406, "top5_acc": 0.525, "loss_cls": 4.16894, "loss": 4.16894, "time": 0.81835} +{"mode": "train", "epoch": 43, "iter": 3100, "lr": 0.0812, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27047, "top5_acc": 0.52562, "loss_cls": 4.17858, "loss": 4.17858, "time": 0.82015} +{"mode": "train", "epoch": 43, "iter": 3200, "lr": 0.08118, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27922, "top5_acc": 0.52516, "loss_cls": 4.14686, "loss": 4.14686, "time": 0.81048} +{"mode": "train", "epoch": 43, "iter": 3300, "lr": 0.08116, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27375, "top5_acc": 0.53266, "loss_cls": 4.1379, "loss": 4.1379, "time": 0.81114} +{"mode": "train", "epoch": 43, "iter": 3400, "lr": 0.08113, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28, "top5_acc": 0.53109, "loss_cls": 4.15373, "loss": 4.15373, "time": 0.81592} +{"mode": "train", "epoch": 43, "iter": 3500, "lr": 0.08111, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2725, "top5_acc": 0.52688, "loss_cls": 4.19678, "loss": 4.19678, "time": 0.8172} +{"mode": "train", "epoch": 43, "iter": 3600, "lr": 0.08109, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28, "top5_acc": 0.52594, "loss_cls": 4.17836, "loss": 4.17836, "time": 0.82461} +{"mode": "train", "epoch": 43, "iter": 3700, "lr": 0.08107, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27141, "top5_acc": 0.53703, "loss_cls": 4.12039, "loss": 4.12039, "time": 0.81881} +{"mode": "val", "epoch": 43, "iter": 309, "lr": 0.08106, "top1_acc": 0.20301, "top5_acc": 0.43073, "mean_class_accuracy": 0.20272} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.08104, "memory": 15990, "data_time": 1.25495, "top1_acc": 0.28609, "top5_acc": 0.55453, "loss_cls": 4.04669, "loss": 4.04669, "time": 2.22386} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.08101, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29656, "top5_acc": 0.54141, "loss_cls": 4.07172, "loss": 4.07172, "time": 0.81974} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.08099, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27875, "top5_acc": 0.54359, "loss_cls": 4.08388, "loss": 4.08388, "time": 0.82067} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.08097, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29188, "top5_acc": 0.54438, "loss_cls": 4.06137, "loss": 4.06137, "time": 0.8207} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.08095, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27641, "top5_acc": 0.53562, "loss_cls": 4.16788, "loss": 4.16788, "time": 0.82517} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.08093, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28922, "top5_acc": 0.53641, "loss_cls": 4.13424, "loss": 4.13424, "time": 0.81383} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.0809, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27359, "top5_acc": 0.52828, "loss_cls": 4.17101, "loss": 4.17101, "time": 0.82107} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.08088, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27609, "top5_acc": 0.54172, "loss_cls": 4.1249, "loss": 4.1249, "time": 0.82803} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.08086, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27406, "top5_acc": 0.52719, "loss_cls": 4.17874, "loss": 4.17874, "time": 0.82122} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.08084, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27437, "top5_acc": 0.51875, "loss_cls": 4.20029, "loss": 4.20029, "time": 0.81797} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.08082, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28156, "top5_acc": 0.53125, "loss_cls": 4.11519, "loss": 4.11519, "time": 0.81635} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.08079, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28141, "top5_acc": 0.5425, "loss_cls": 4.12574, "loss": 4.12574, "time": 0.81543} +{"mode": "train", "epoch": 44, "iter": 1300, "lr": 0.08077, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27422, "top5_acc": 0.53531, "loss_cls": 4.14112, "loss": 4.14112, "time": 0.82371} +{"mode": "train", "epoch": 44, "iter": 1400, "lr": 0.08075, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27344, "top5_acc": 0.5275, "loss_cls": 4.17452, "loss": 4.17452, "time": 0.81087} +{"mode": "train", "epoch": 44, "iter": 1500, "lr": 0.08073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28266, "top5_acc": 0.54062, "loss_cls": 4.08466, "loss": 4.08466, "time": 0.81315} +{"mode": "train", "epoch": 44, "iter": 1600, "lr": 0.08071, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26984, "top5_acc": 0.52891, "loss_cls": 4.16638, "loss": 4.16638, "time": 0.81296} +{"mode": "train", "epoch": 44, "iter": 1700, "lr": 0.08068, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27969, "top5_acc": 0.54234, "loss_cls": 4.10022, "loss": 4.10022, "time": 0.81384} +{"mode": "train", "epoch": 44, "iter": 1800, "lr": 0.08066, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27234, "top5_acc": 0.52688, "loss_cls": 4.15111, "loss": 4.15111, "time": 0.81646} +{"mode": "train", "epoch": 44, "iter": 1900, "lr": 0.08064, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27484, "top5_acc": 0.54016, "loss_cls": 4.12386, "loss": 4.12386, "time": 0.81775} +{"mode": "train", "epoch": 44, "iter": 2000, "lr": 0.08062, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28047, "top5_acc": 0.54406, "loss_cls": 4.09312, "loss": 4.09312, "time": 0.82097} +{"mode": "train", "epoch": 44, "iter": 2100, "lr": 0.0806, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26719, "top5_acc": 0.52672, "loss_cls": 4.18497, "loss": 4.18497, "time": 0.81465} +{"mode": "train", "epoch": 44, "iter": 2200, "lr": 0.08057, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28047, "top5_acc": 0.54375, "loss_cls": 4.11465, "loss": 4.11465, "time": 0.8149} +{"mode": "train", "epoch": 44, "iter": 2300, "lr": 0.08055, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27938, "top5_acc": 0.53031, "loss_cls": 4.15748, "loss": 4.15748, "time": 0.81893} +{"mode": "train", "epoch": 44, "iter": 2400, "lr": 0.08053, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26828, "top5_acc": 0.5225, "loss_cls": 4.18766, "loss": 4.18766, "time": 0.8132} +{"mode": "train", "epoch": 44, "iter": 2500, "lr": 0.08051, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28375, "top5_acc": 0.54328, "loss_cls": 4.09314, "loss": 4.09314, "time": 0.80989} +{"mode": "train", "epoch": 44, "iter": 2600, "lr": 0.08048, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27516, "top5_acc": 0.53312, "loss_cls": 4.13802, "loss": 4.13802, "time": 0.81853} +{"mode": "train", "epoch": 44, "iter": 2700, "lr": 0.08046, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27031, "top5_acc": 0.52672, "loss_cls": 4.15446, "loss": 4.15446, "time": 0.81524} +{"mode": "train", "epoch": 44, "iter": 2800, "lr": 0.08044, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27828, "top5_acc": 0.53703, "loss_cls": 4.1202, "loss": 4.1202, "time": 0.81186} +{"mode": "train", "epoch": 44, "iter": 2900, "lr": 0.08042, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28594, "top5_acc": 0.54172, "loss_cls": 4.09705, "loss": 4.09705, "time": 0.81748} +{"mode": "train", "epoch": 44, "iter": 3000, "lr": 0.0804, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2775, "top5_acc": 0.54078, "loss_cls": 4.12197, "loss": 4.12197, "time": 0.81321} +{"mode": "train", "epoch": 44, "iter": 3100, "lr": 0.08037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28266, "top5_acc": 0.53391, "loss_cls": 4.13339, "loss": 4.13339, "time": 0.81343} +{"mode": "train", "epoch": 44, "iter": 3200, "lr": 0.08035, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27672, "top5_acc": 0.53078, "loss_cls": 4.13783, "loss": 4.13783, "time": 0.81386} +{"mode": "train", "epoch": 44, "iter": 3300, "lr": 0.08033, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26984, "top5_acc": 0.52578, "loss_cls": 4.17368, "loss": 4.17368, "time": 0.8147} +{"mode": "train", "epoch": 44, "iter": 3400, "lr": 0.08031, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28938, "top5_acc": 0.54141, "loss_cls": 4.10208, "loss": 4.10208, "time": 0.81908} +{"mode": "train", "epoch": 44, "iter": 3500, "lr": 0.08028, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.275, "top5_acc": 0.52766, "loss_cls": 4.16767, "loss": 4.16767, "time": 0.81295} +{"mode": "train", "epoch": 44, "iter": 3600, "lr": 0.08026, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28344, "top5_acc": 0.54125, "loss_cls": 4.12881, "loss": 4.12881, "time": 0.82721} +{"mode": "train", "epoch": 44, "iter": 3700, "lr": 0.08024, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27297, "top5_acc": 0.53766, "loss_cls": 4.1593, "loss": 4.1593, "time": 0.82406} +{"mode": "val", "epoch": 44, "iter": 309, "lr": 0.08023, "top1_acc": 0.21729, "top5_acc": 0.45667, "mean_class_accuracy": 0.21699} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.08021, "memory": 15990, "data_time": 1.25676, "top1_acc": 0.27766, "top5_acc": 0.54375, "loss_cls": 4.08797, "loss": 4.08797, "time": 2.23181} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.08019, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28078, "top5_acc": 0.54672, "loss_cls": 4.07348, "loss": 4.07348, "time": 0.82452} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.08016, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27531, "top5_acc": 0.53734, "loss_cls": 4.1216, "loss": 4.1216, "time": 0.82023} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.08014, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27234, "top5_acc": 0.54484, "loss_cls": 4.11362, "loss": 4.11362, "time": 0.81545} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.08012, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27203, "top5_acc": 0.53141, "loss_cls": 4.14646, "loss": 4.14646, "time": 0.81875} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.0801, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29359, "top5_acc": 0.54766, "loss_cls": 4.05881, "loss": 4.05881, "time": 0.81622} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.08007, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28047, "top5_acc": 0.52938, "loss_cls": 4.14031, "loss": 4.14031, "time": 0.81597} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.08005, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28609, "top5_acc": 0.5425, "loss_cls": 4.09803, "loss": 4.09803, "time": 0.82877} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.08003, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2725, "top5_acc": 0.52828, "loss_cls": 4.17515, "loss": 4.17515, "time": 0.81964} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.08001, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28016, "top5_acc": 0.53062, "loss_cls": 4.14296, "loss": 4.14296, "time": 0.82522} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.07998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27266, "top5_acc": 0.53906, "loss_cls": 4.11267, "loss": 4.11267, "time": 0.82455} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.07996, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2825, "top5_acc": 0.54031, "loss_cls": 4.11161, "loss": 4.11161, "time": 0.81123} +{"mode": "train", "epoch": 45, "iter": 1300, "lr": 0.07994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28234, "top5_acc": 0.53844, "loss_cls": 4.11973, "loss": 4.11973, "time": 0.80988} +{"mode": "train", "epoch": 45, "iter": 1400, "lr": 0.07992, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27109, "top5_acc": 0.53922, "loss_cls": 4.10732, "loss": 4.10732, "time": 0.8137} +{"mode": "train", "epoch": 45, "iter": 1500, "lr": 0.0799, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27, "top5_acc": 0.52422, "loss_cls": 4.18713, "loss": 4.18713, "time": 0.81211} +{"mode": "train", "epoch": 45, "iter": 1600, "lr": 0.07987, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27562, "top5_acc": 0.53375, "loss_cls": 4.12236, "loss": 4.12236, "time": 0.81335} +{"mode": "train", "epoch": 45, "iter": 1700, "lr": 0.07985, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27688, "top5_acc": 0.53094, "loss_cls": 4.15512, "loss": 4.15512, "time": 0.81413} +{"mode": "train", "epoch": 45, "iter": 1800, "lr": 0.07983, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28047, "top5_acc": 0.52984, "loss_cls": 4.14793, "loss": 4.14793, "time": 0.81867} +{"mode": "train", "epoch": 45, "iter": 1900, "lr": 0.07981, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27594, "top5_acc": 0.53844, "loss_cls": 4.10863, "loss": 4.10863, "time": 0.81511} +{"mode": "train", "epoch": 45, "iter": 2000, "lr": 0.07978, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28234, "top5_acc": 0.53688, "loss_cls": 4.12143, "loss": 4.12143, "time": 0.81372} +{"mode": "train", "epoch": 45, "iter": 2100, "lr": 0.07976, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29078, "top5_acc": 0.54359, "loss_cls": 4.07115, "loss": 4.07115, "time": 0.81594} +{"mode": "train", "epoch": 45, "iter": 2200, "lr": 0.07974, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27609, "top5_acc": 0.53797, "loss_cls": 4.13006, "loss": 4.13006, "time": 0.81179} +{"mode": "train", "epoch": 45, "iter": 2300, "lr": 0.07972, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27734, "top5_acc": 0.52875, "loss_cls": 4.15234, "loss": 4.15234, "time": 0.81605} +{"mode": "train", "epoch": 45, "iter": 2400, "lr": 0.07969, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28141, "top5_acc": 0.53812, "loss_cls": 4.10635, "loss": 4.10635, "time": 0.82487} +{"mode": "train", "epoch": 45, "iter": 2500, "lr": 0.07967, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28625, "top5_acc": 0.545, "loss_cls": 4.08582, "loss": 4.08582, "time": 0.81326} +{"mode": "train", "epoch": 45, "iter": 2600, "lr": 0.07965, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27078, "top5_acc": 0.52859, "loss_cls": 4.15599, "loss": 4.15599, "time": 0.82241} +{"mode": "train", "epoch": 45, "iter": 2700, "lr": 0.07963, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28141, "top5_acc": 0.53562, "loss_cls": 4.13941, "loss": 4.13941, "time": 0.81977} +{"mode": "train", "epoch": 45, "iter": 2800, "lr": 0.0796, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28172, "top5_acc": 0.53062, "loss_cls": 4.13593, "loss": 4.13593, "time": 0.81176} +{"mode": "train", "epoch": 45, "iter": 2900, "lr": 0.07958, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26984, "top5_acc": 0.52328, "loss_cls": 4.17032, "loss": 4.17032, "time": 0.81465} +{"mode": "train", "epoch": 45, "iter": 3000, "lr": 0.07956, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27984, "top5_acc": 0.53547, "loss_cls": 4.13515, "loss": 4.13515, "time": 0.81067} +{"mode": "train", "epoch": 45, "iter": 3100, "lr": 0.07954, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28125, "top5_acc": 0.5425, "loss_cls": 4.11763, "loss": 4.11763, "time": 0.81439} +{"mode": "train", "epoch": 45, "iter": 3200, "lr": 0.07951, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27766, "top5_acc": 0.54, "loss_cls": 4.1324, "loss": 4.1324, "time": 0.8124} +{"mode": "train", "epoch": 45, "iter": 3300, "lr": 0.07949, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.275, "top5_acc": 0.53719, "loss_cls": 4.13696, "loss": 4.13696, "time": 0.81606} +{"mode": "train", "epoch": 45, "iter": 3400, "lr": 0.07947, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29047, "top5_acc": 0.54297, "loss_cls": 4.0615, "loss": 4.0615, "time": 0.81554} +{"mode": "train", "epoch": 45, "iter": 3500, "lr": 0.07945, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27453, "top5_acc": 0.53438, "loss_cls": 4.12991, "loss": 4.12991, "time": 0.82057} +{"mode": "train", "epoch": 45, "iter": 3600, "lr": 0.07942, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28156, "top5_acc": 0.53438, "loss_cls": 4.13349, "loss": 4.13349, "time": 0.82374} +{"mode": "train", "epoch": 45, "iter": 3700, "lr": 0.0794, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27953, "top5_acc": 0.53031, "loss_cls": 4.13144, "loss": 4.13144, "time": 0.81751} +{"mode": "val", "epoch": 45, "iter": 309, "lr": 0.07939, "top1_acc": 0.19779, "top5_acc": 0.42739, "mean_class_accuracy": 0.19749} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.07937, "memory": 15990, "data_time": 1.22839, "top1_acc": 0.27672, "top5_acc": 0.53484, "loss_cls": 4.13683, "loss": 4.13683, "time": 2.20107} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.07934, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28813, "top5_acc": 0.54797, "loss_cls": 4.05875, "loss": 4.05875, "time": 0.82038} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.07932, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28359, "top5_acc": 0.54312, "loss_cls": 4.08147, "loss": 4.08147, "time": 0.82403} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.0793, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28297, "top5_acc": 0.53891, "loss_cls": 4.09387, "loss": 4.09387, "time": 0.81429} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.07928, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.28547, "top5_acc": 0.54234, "loss_cls": 4.08097, "loss": 4.08097, "time": 0.82045} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.07925, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27859, "top5_acc": 0.52891, "loss_cls": 4.14316, "loss": 4.14316, "time": 0.82337} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.07923, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27453, "top5_acc": 0.52062, "loss_cls": 4.18421, "loss": 4.18421, "time": 0.81553} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.07921, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27688, "top5_acc": 0.5375, "loss_cls": 4.11388, "loss": 4.11388, "time": 0.82421} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.07919, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29516, "top5_acc": 0.54969, "loss_cls": 4.04384, "loss": 4.04384, "time": 0.82844} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.07916, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28516, "top5_acc": 0.53141, "loss_cls": 4.11632, "loss": 4.11632, "time": 0.81262} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.07914, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27094, "top5_acc": 0.52578, "loss_cls": 4.16593, "loss": 4.16593, "time": 0.81755} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.07912, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27437, "top5_acc": 0.52938, "loss_cls": 4.14232, "loss": 4.14232, "time": 0.81986} +{"mode": "train", "epoch": 46, "iter": 1300, "lr": 0.07909, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28094, "top5_acc": 0.53656, "loss_cls": 4.10073, "loss": 4.10073, "time": 0.82093} +{"mode": "train", "epoch": 46, "iter": 1400, "lr": 0.07907, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27859, "top5_acc": 0.53094, "loss_cls": 4.13268, "loss": 4.13268, "time": 0.81552} +{"mode": "train", "epoch": 46, "iter": 1500, "lr": 0.07905, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28156, "top5_acc": 0.53906, "loss_cls": 4.1304, "loss": 4.1304, "time": 0.81678} +{"mode": "train", "epoch": 46, "iter": 1600, "lr": 0.07903, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27531, "top5_acc": 0.53406, "loss_cls": 4.14902, "loss": 4.14902, "time": 0.82019} +{"mode": "train", "epoch": 46, "iter": 1700, "lr": 0.079, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28625, "top5_acc": 0.55062, "loss_cls": 4.07903, "loss": 4.07903, "time": 0.81365} +{"mode": "train", "epoch": 46, "iter": 1800, "lr": 0.07898, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29219, "top5_acc": 0.53766, "loss_cls": 4.06868, "loss": 4.06868, "time": 0.81791} +{"mode": "train", "epoch": 46, "iter": 1900, "lr": 0.07896, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.275, "top5_acc": 0.54172, "loss_cls": 4.09979, "loss": 4.09979, "time": 0.81637} +{"mode": "train", "epoch": 46, "iter": 2000, "lr": 0.07894, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27422, "top5_acc": 0.53203, "loss_cls": 4.13983, "loss": 4.13983, "time": 0.82105} +{"mode": "train", "epoch": 46, "iter": 2100, "lr": 0.07891, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28, "top5_acc": 0.53281, "loss_cls": 4.1084, "loss": 4.1084, "time": 0.81355} +{"mode": "train", "epoch": 46, "iter": 2200, "lr": 0.07889, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28781, "top5_acc": 0.54984, "loss_cls": 4.09787, "loss": 4.09787, "time": 0.81858} +{"mode": "train", "epoch": 46, "iter": 2300, "lr": 0.07887, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28266, "top5_acc": 0.53578, "loss_cls": 4.13136, "loss": 4.13136, "time": 0.81526} +{"mode": "train", "epoch": 46, "iter": 2400, "lr": 0.07884, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28469, "top5_acc": 0.54594, "loss_cls": 4.09388, "loss": 4.09388, "time": 0.81952} +{"mode": "train", "epoch": 46, "iter": 2500, "lr": 0.07882, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26734, "top5_acc": 0.52875, "loss_cls": 4.16212, "loss": 4.16212, "time": 0.81565} +{"mode": "train", "epoch": 46, "iter": 2600, "lr": 0.0788, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27672, "top5_acc": 0.54656, "loss_cls": 4.11419, "loss": 4.11419, "time": 0.81627} +{"mode": "train", "epoch": 46, "iter": 2700, "lr": 0.07878, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28703, "top5_acc": 0.53641, "loss_cls": 4.09766, "loss": 4.09766, "time": 0.82226} +{"mode": "train", "epoch": 46, "iter": 2800, "lr": 0.07875, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28406, "top5_acc": 0.53453, "loss_cls": 4.14369, "loss": 4.14369, "time": 0.81421} +{"mode": "train", "epoch": 46, "iter": 2900, "lr": 0.07873, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27797, "top5_acc": 0.54625, "loss_cls": 4.10355, "loss": 4.10355, "time": 0.81091} +{"mode": "train", "epoch": 46, "iter": 3000, "lr": 0.07871, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27531, "top5_acc": 0.54125, "loss_cls": 4.14218, "loss": 4.14218, "time": 0.81606} +{"mode": "train", "epoch": 46, "iter": 3100, "lr": 0.07868, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28391, "top5_acc": 0.53703, "loss_cls": 4.13764, "loss": 4.13764, "time": 0.81351} +{"mode": "train", "epoch": 46, "iter": 3200, "lr": 0.07866, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29031, "top5_acc": 0.54094, "loss_cls": 4.12049, "loss": 4.12049, "time": 0.82403} +{"mode": "train", "epoch": 46, "iter": 3300, "lr": 0.07864, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28031, "top5_acc": 0.53906, "loss_cls": 4.11251, "loss": 4.11251, "time": 0.81438} +{"mode": "train", "epoch": 46, "iter": 3400, "lr": 0.07862, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28922, "top5_acc": 0.54016, "loss_cls": 4.09969, "loss": 4.09969, "time": 0.81571} +{"mode": "train", "epoch": 46, "iter": 3500, "lr": 0.07859, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27391, "top5_acc": 0.52844, "loss_cls": 4.1547, "loss": 4.1547, "time": 0.81535} +{"mode": "train", "epoch": 46, "iter": 3600, "lr": 0.07857, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28031, "top5_acc": 0.53188, "loss_cls": 4.10158, "loss": 4.10158, "time": 0.82966} +{"mode": "train", "epoch": 46, "iter": 3700, "lr": 0.07855, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27516, "top5_acc": 0.54391, "loss_cls": 4.12907, "loss": 4.12907, "time": 0.81998} +{"mode": "val", "epoch": 46, "iter": 309, "lr": 0.07854, "top1_acc": 0.21243, "top5_acc": 0.45469, "mean_class_accuracy": 0.21231} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.07851, "memory": 15990, "data_time": 1.22343, "top1_acc": 0.29359, "top5_acc": 0.55234, "loss_cls": 4.01325, "loss": 4.01325, "time": 2.18607} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.07849, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27484, "top5_acc": 0.52812, "loss_cls": 4.16077, "loss": 4.16077, "time": 0.81681} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.07847, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29328, "top5_acc": 0.55656, "loss_cls": 4.04596, "loss": 4.04596, "time": 0.81623} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.07844, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28188, "top5_acc": 0.54016, "loss_cls": 4.07682, "loss": 4.07682, "time": 0.81568} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.07842, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27547, "top5_acc": 0.5425, "loss_cls": 4.11938, "loss": 4.11938, "time": 0.81019} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.0784, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27594, "top5_acc": 0.53406, "loss_cls": 4.14069, "loss": 4.14069, "time": 0.82426} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.07838, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28625, "top5_acc": 0.53531, "loss_cls": 4.09969, "loss": 4.09969, "time": 0.82178} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.07835, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28797, "top5_acc": 0.54703, "loss_cls": 4.08246, "loss": 4.08246, "time": 0.81659} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.07833, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28094, "top5_acc": 0.54062, "loss_cls": 4.11167, "loss": 4.11167, "time": 0.82356} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.07831, "memory": 15990, "data_time": 0.00075, "top1_acc": 0.27828, "top5_acc": 0.54094, "loss_cls": 4.09413, "loss": 4.09413, "time": 0.82966} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.07828, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27391, "top5_acc": 0.53594, "loss_cls": 4.13178, "loss": 4.13178, "time": 0.81663} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.07826, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27938, "top5_acc": 0.53344, "loss_cls": 4.13154, "loss": 4.13154, "time": 0.82407} +{"mode": "train", "epoch": 47, "iter": 1300, "lr": 0.07824, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28453, "top5_acc": 0.54172, "loss_cls": 4.09702, "loss": 4.09702, "time": 0.81628} +{"mode": "train", "epoch": 47, "iter": 1400, "lr": 0.07821, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28094, "top5_acc": 0.5325, "loss_cls": 4.13356, "loss": 4.13356, "time": 0.81598} +{"mode": "train", "epoch": 47, "iter": 1500, "lr": 0.07819, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27594, "top5_acc": 0.52781, "loss_cls": 4.15268, "loss": 4.15268, "time": 0.81207} +{"mode": "train", "epoch": 47, "iter": 1600, "lr": 0.07817, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27422, "top5_acc": 0.53453, "loss_cls": 4.16661, "loss": 4.16661, "time": 0.81262} +{"mode": "train", "epoch": 47, "iter": 1700, "lr": 0.07814, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27797, "top5_acc": 0.54469, "loss_cls": 4.08243, "loss": 4.08243, "time": 0.81514} +{"mode": "train", "epoch": 47, "iter": 1800, "lr": 0.07812, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28203, "top5_acc": 0.54531, "loss_cls": 4.08558, "loss": 4.08558, "time": 0.81583} +{"mode": "train", "epoch": 47, "iter": 1900, "lr": 0.0781, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27969, "top5_acc": 0.53828, "loss_cls": 4.10706, "loss": 4.10706, "time": 0.81559} +{"mode": "train", "epoch": 47, "iter": 2000, "lr": 0.07808, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27312, "top5_acc": 0.52578, "loss_cls": 4.16849, "loss": 4.16849, "time": 0.81381} +{"mode": "train", "epoch": 47, "iter": 2100, "lr": 0.07805, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28328, "top5_acc": 0.5375, "loss_cls": 4.12927, "loss": 4.12927, "time": 0.81396} +{"mode": "train", "epoch": 47, "iter": 2200, "lr": 0.07803, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28297, "top5_acc": 0.53375, "loss_cls": 4.12116, "loss": 4.12116, "time": 0.81438} +{"mode": "train", "epoch": 47, "iter": 2300, "lr": 0.07801, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27812, "top5_acc": 0.53125, "loss_cls": 4.11501, "loss": 4.11501, "time": 0.81537} +{"mode": "train", "epoch": 47, "iter": 2400, "lr": 0.07798, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27453, "top5_acc": 0.53109, "loss_cls": 4.16092, "loss": 4.16092, "time": 0.82583} +{"mode": "train", "epoch": 47, "iter": 2500, "lr": 0.07796, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27531, "top5_acc": 0.52984, "loss_cls": 4.13528, "loss": 4.13528, "time": 0.81354} +{"mode": "train", "epoch": 47, "iter": 2600, "lr": 0.07794, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27109, "top5_acc": 0.52953, "loss_cls": 4.14457, "loss": 4.14457, "time": 0.81281} +{"mode": "train", "epoch": 47, "iter": 2700, "lr": 0.07791, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26812, "top5_acc": 0.52812, "loss_cls": 4.17885, "loss": 4.17885, "time": 0.82111} +{"mode": "train", "epoch": 47, "iter": 2800, "lr": 0.07789, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27234, "top5_acc": 0.53516, "loss_cls": 4.13322, "loss": 4.13322, "time": 0.818} +{"mode": "train", "epoch": 47, "iter": 2900, "lr": 0.07787, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28672, "top5_acc": 0.54172, "loss_cls": 4.06544, "loss": 4.06544, "time": 0.81319} +{"mode": "train", "epoch": 47, "iter": 3000, "lr": 0.07784, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28469, "top5_acc": 0.53562, "loss_cls": 4.10345, "loss": 4.10345, "time": 0.8174} +{"mode": "train", "epoch": 47, "iter": 3100, "lr": 0.07782, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28047, "top5_acc": 0.53609, "loss_cls": 4.09031, "loss": 4.09031, "time": 0.81102} +{"mode": "train", "epoch": 47, "iter": 3200, "lr": 0.0778, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27344, "top5_acc": 0.53406, "loss_cls": 4.13429, "loss": 4.13429, "time": 0.81054} +{"mode": "train", "epoch": 47, "iter": 3300, "lr": 0.07777, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27234, "top5_acc": 0.52641, "loss_cls": 4.17353, "loss": 4.17353, "time": 0.81918} +{"mode": "train", "epoch": 47, "iter": 3400, "lr": 0.07775, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28328, "top5_acc": 0.53781, "loss_cls": 4.10608, "loss": 4.10608, "time": 0.81918} +{"mode": "train", "epoch": 47, "iter": 3500, "lr": 0.07773, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28297, "top5_acc": 0.53906, "loss_cls": 4.12037, "loss": 4.12037, "time": 0.81481} +{"mode": "train", "epoch": 47, "iter": 3600, "lr": 0.0777, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27859, "top5_acc": 0.53891, "loss_cls": 4.12252, "loss": 4.12252, "time": 0.82611} +{"mode": "train", "epoch": 47, "iter": 3700, "lr": 0.07768, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28094, "top5_acc": 0.53938, "loss_cls": 4.12806, "loss": 4.12806, "time": 0.82046} +{"mode": "val", "epoch": 47, "iter": 309, "lr": 0.07767, "top1_acc": 0.20433, "top5_acc": 0.43661, "mean_class_accuracy": 0.20408} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.07765, "memory": 15990, "data_time": 1.2534, "top1_acc": 0.28312, "top5_acc": 0.5525, "loss_cls": 4.05866, "loss": 4.05866, "time": 2.22519} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.07762, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29281, "top5_acc": 0.54984, "loss_cls": 4.0201, "loss": 4.0201, "time": 0.82795} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.0776, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27594, "top5_acc": 0.53828, "loss_cls": 4.12228, "loss": 4.12228, "time": 0.8265} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.07758, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2825, "top5_acc": 0.53875, "loss_cls": 4.10392, "loss": 4.10392, "time": 0.8215} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.07755, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.285, "top5_acc": 0.54312, "loss_cls": 4.0931, "loss": 4.0931, "time": 0.81869} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.07753, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28813, "top5_acc": 0.54031, "loss_cls": 4.09048, "loss": 4.09048, "time": 0.82809} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.07751, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28531, "top5_acc": 0.54484, "loss_cls": 4.11496, "loss": 4.11496, "time": 0.81092} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.07748, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29453, "top5_acc": 0.54266, "loss_cls": 4.0574, "loss": 4.0574, "time": 0.81824} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.07746, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28031, "top5_acc": 0.54047, "loss_cls": 4.10274, "loss": 4.10274, "time": 0.82369} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.07744, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27859, "top5_acc": 0.53922, "loss_cls": 4.08517, "loss": 4.08517, "time": 0.82254} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.07741, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29063, "top5_acc": 0.55672, "loss_cls": 4.03567, "loss": 4.03567, "time": 0.82497} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.07739, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27922, "top5_acc": 0.53234, "loss_cls": 4.12847, "loss": 4.12847, "time": 0.82027} +{"mode": "train", "epoch": 48, "iter": 1300, "lr": 0.07737, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27328, "top5_acc": 0.535, "loss_cls": 4.13939, "loss": 4.13939, "time": 0.82264} +{"mode": "train", "epoch": 48, "iter": 1400, "lr": 0.07734, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27297, "top5_acc": 0.53953, "loss_cls": 4.11475, "loss": 4.11475, "time": 0.81746} +{"mode": "train", "epoch": 48, "iter": 1500, "lr": 0.07732, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27984, "top5_acc": 0.53562, "loss_cls": 4.11172, "loss": 4.11172, "time": 0.81415} +{"mode": "train", "epoch": 48, "iter": 1600, "lr": 0.0773, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29281, "top5_acc": 0.54266, "loss_cls": 4.07708, "loss": 4.07708, "time": 0.81782} +{"mode": "train", "epoch": 48, "iter": 1700, "lr": 0.07727, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28609, "top5_acc": 0.53703, "loss_cls": 4.10871, "loss": 4.10871, "time": 0.81719} +{"mode": "train", "epoch": 48, "iter": 1800, "lr": 0.07725, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27922, "top5_acc": 0.53734, "loss_cls": 4.1123, "loss": 4.1123, "time": 0.8204} +{"mode": "train", "epoch": 48, "iter": 1900, "lr": 0.07723, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29063, "top5_acc": 0.54234, "loss_cls": 4.0667, "loss": 4.0667, "time": 0.81721} +{"mode": "train", "epoch": 48, "iter": 2000, "lr": 0.0772, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27344, "top5_acc": 0.53078, "loss_cls": 4.14586, "loss": 4.14586, "time": 0.8164} +{"mode": "train", "epoch": 48, "iter": 2100, "lr": 0.07718, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27781, "top5_acc": 0.54, "loss_cls": 4.11219, "loss": 4.11219, "time": 0.81572} +{"mode": "train", "epoch": 48, "iter": 2200, "lr": 0.07716, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27594, "top5_acc": 0.54156, "loss_cls": 4.15324, "loss": 4.15324, "time": 0.81765} +{"mode": "train", "epoch": 48, "iter": 2300, "lr": 0.07713, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28219, "top5_acc": 0.53625, "loss_cls": 4.1108, "loss": 4.1108, "time": 0.81492} +{"mode": "train", "epoch": 48, "iter": 2400, "lr": 0.07711, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27766, "top5_acc": 0.52859, "loss_cls": 4.16201, "loss": 4.16201, "time": 0.82032} +{"mode": "train", "epoch": 48, "iter": 2500, "lr": 0.07709, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27578, "top5_acc": 0.53438, "loss_cls": 4.14103, "loss": 4.14103, "time": 0.81155} +{"mode": "train", "epoch": 48, "iter": 2600, "lr": 0.07706, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27938, "top5_acc": 0.53266, "loss_cls": 4.10521, "loss": 4.10521, "time": 0.81459} +{"mode": "train", "epoch": 48, "iter": 2700, "lr": 0.07704, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28172, "top5_acc": 0.52938, "loss_cls": 4.15287, "loss": 4.15287, "time": 0.8206} +{"mode": "train", "epoch": 48, "iter": 2800, "lr": 0.07701, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28172, "top5_acc": 0.5475, "loss_cls": 4.08762, "loss": 4.08762, "time": 0.81662} +{"mode": "train", "epoch": 48, "iter": 2900, "lr": 0.07699, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27766, "top5_acc": 0.53312, "loss_cls": 4.16004, "loss": 4.16004, "time": 0.81751} +{"mode": "train", "epoch": 48, "iter": 3000, "lr": 0.07697, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27203, "top5_acc": 0.53531, "loss_cls": 4.11621, "loss": 4.11621, "time": 0.8239} +{"mode": "train", "epoch": 48, "iter": 3100, "lr": 0.07694, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27719, "top5_acc": 0.54266, "loss_cls": 4.10132, "loss": 4.10132, "time": 0.81587} +{"mode": "train", "epoch": 48, "iter": 3200, "lr": 0.07692, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27547, "top5_acc": 0.53891, "loss_cls": 4.12738, "loss": 4.12738, "time": 0.81876} +{"mode": "train", "epoch": 48, "iter": 3300, "lr": 0.0769, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27422, "top5_acc": 0.53031, "loss_cls": 4.12396, "loss": 4.12396, "time": 0.81925} +{"mode": "train", "epoch": 48, "iter": 3400, "lr": 0.07687, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28859, "top5_acc": 0.54016, "loss_cls": 4.13386, "loss": 4.13386, "time": 0.81808} +{"mode": "train", "epoch": 48, "iter": 3500, "lr": 0.07685, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28375, "top5_acc": 0.53547, "loss_cls": 4.11406, "loss": 4.11406, "time": 0.81198} +{"mode": "train", "epoch": 48, "iter": 3600, "lr": 0.07683, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27172, "top5_acc": 0.52844, "loss_cls": 4.14062, "loss": 4.14062, "time": 0.82465} +{"mode": "train", "epoch": 48, "iter": 3700, "lr": 0.0768, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28156, "top5_acc": 0.53141, "loss_cls": 4.13976, "loss": 4.13976, "time": 0.81068} +{"mode": "val", "epoch": 48, "iter": 309, "lr": 0.07679, "top1_acc": 0.19217, "top5_acc": 0.42202, "mean_class_accuracy": 0.19226} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.07677, "memory": 15990, "data_time": 1.22093, "top1_acc": 0.30016, "top5_acc": 0.56234, "loss_cls": 4.01713, "loss": 4.01713, "time": 2.18661} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.07674, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27906, "top5_acc": 0.53844, "loss_cls": 4.09, "loss": 4.09, "time": 0.83157} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.07672, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28891, "top5_acc": 0.55891, "loss_cls": 4.02757, "loss": 4.02757, "time": 0.82697} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.0767, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29625, "top5_acc": 0.54937, "loss_cls": 4.08298, "loss": 4.08298, "time": 0.81661} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.07667, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28594, "top5_acc": 0.53797, "loss_cls": 4.1108, "loss": 4.1108, "time": 0.81437} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.07665, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28719, "top5_acc": 0.55453, "loss_cls": 4.03203, "loss": 4.03203, "time": 0.82872} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.07663, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28094, "top5_acc": 0.53281, "loss_cls": 4.10933, "loss": 4.10933, "time": 0.81457} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.0766, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29016, "top5_acc": 0.54531, "loss_cls": 4.07857, "loss": 4.07857, "time": 0.8192} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.07658, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27766, "top5_acc": 0.54281, "loss_cls": 4.09978, "loss": 4.09978, "time": 0.82624} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.07656, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27094, "top5_acc": 0.53375, "loss_cls": 4.14871, "loss": 4.14871, "time": 0.81673} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.07653, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27797, "top5_acc": 0.53406, "loss_cls": 4.12257, "loss": 4.12257, "time": 0.82864} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.07651, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27406, "top5_acc": 0.53562, "loss_cls": 4.10692, "loss": 4.10692, "time": 0.82187} +{"mode": "train", "epoch": 49, "iter": 1300, "lr": 0.07648, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28938, "top5_acc": 0.54453, "loss_cls": 4.07038, "loss": 4.07038, "time": 0.818} +{"mode": "train", "epoch": 49, "iter": 1400, "lr": 0.07646, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27359, "top5_acc": 0.53281, "loss_cls": 4.10513, "loss": 4.10513, "time": 0.82034} +{"mode": "train", "epoch": 49, "iter": 1500, "lr": 0.07644, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28156, "top5_acc": 0.53672, "loss_cls": 4.10836, "loss": 4.10836, "time": 0.81847} +{"mode": "train", "epoch": 49, "iter": 1600, "lr": 0.07641, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27672, "top5_acc": 0.53703, "loss_cls": 4.10927, "loss": 4.10927, "time": 0.81898} +{"mode": "train", "epoch": 49, "iter": 1700, "lr": 0.07639, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27609, "top5_acc": 0.53891, "loss_cls": 4.10458, "loss": 4.10458, "time": 0.81183} +{"mode": "train", "epoch": 49, "iter": 1800, "lr": 0.07637, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.275, "top5_acc": 0.53688, "loss_cls": 4.11042, "loss": 4.11042, "time": 0.81619} +{"mode": "train", "epoch": 49, "iter": 1900, "lr": 0.07634, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27656, "top5_acc": 0.54297, "loss_cls": 4.11852, "loss": 4.11852, "time": 0.81456} +{"mode": "train", "epoch": 49, "iter": 2000, "lr": 0.07632, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28078, "top5_acc": 0.54078, "loss_cls": 4.10249, "loss": 4.10249, "time": 0.82176} +{"mode": "train", "epoch": 49, "iter": 2100, "lr": 0.07629, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27375, "top5_acc": 0.53438, "loss_cls": 4.15245, "loss": 4.15245, "time": 0.81646} +{"mode": "train", "epoch": 49, "iter": 2200, "lr": 0.07627, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28469, "top5_acc": 0.53703, "loss_cls": 4.11015, "loss": 4.11015, "time": 0.81345} +{"mode": "train", "epoch": 49, "iter": 2300, "lr": 0.07625, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28328, "top5_acc": 0.54328, "loss_cls": 4.07486, "loss": 4.07486, "time": 0.81295} +{"mode": "train", "epoch": 49, "iter": 2400, "lr": 0.07622, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27406, "top5_acc": 0.52734, "loss_cls": 4.1494, "loss": 4.1494, "time": 0.82241} +{"mode": "train", "epoch": 49, "iter": 2500, "lr": 0.0762, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28422, "top5_acc": 0.55156, "loss_cls": 4.05844, "loss": 4.05844, "time": 0.81023} +{"mode": "train", "epoch": 49, "iter": 2600, "lr": 0.07618, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28375, "top5_acc": 0.53922, "loss_cls": 4.11062, "loss": 4.11062, "time": 0.81204} +{"mode": "train", "epoch": 49, "iter": 2700, "lr": 0.07615, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27828, "top5_acc": 0.53422, "loss_cls": 4.13746, "loss": 4.13746, "time": 0.82305} +{"mode": "train", "epoch": 49, "iter": 2800, "lr": 0.07613, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28453, "top5_acc": 0.54594, "loss_cls": 4.0926, "loss": 4.0926, "time": 0.81778} +{"mode": "train", "epoch": 49, "iter": 2900, "lr": 0.0761, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28094, "top5_acc": 0.54125, "loss_cls": 4.11413, "loss": 4.11413, "time": 0.81695} +{"mode": "train", "epoch": 49, "iter": 3000, "lr": 0.07608, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28578, "top5_acc": 0.54, "loss_cls": 4.0943, "loss": 4.0943, "time": 0.81601} +{"mode": "train", "epoch": 49, "iter": 3100, "lr": 0.07606, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2825, "top5_acc": 0.53781, "loss_cls": 4.10635, "loss": 4.10635, "time": 0.81554} +{"mode": "train", "epoch": 49, "iter": 3200, "lr": 0.07603, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28531, "top5_acc": 0.53531, "loss_cls": 4.13308, "loss": 4.13308, "time": 0.81804} +{"mode": "train", "epoch": 49, "iter": 3300, "lr": 0.07601, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28312, "top5_acc": 0.53156, "loss_cls": 4.13518, "loss": 4.13518, "time": 0.81255} +{"mode": "train", "epoch": 49, "iter": 3400, "lr": 0.07598, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28406, "top5_acc": 0.54109, "loss_cls": 4.0815, "loss": 4.0815, "time": 0.80945} +{"mode": "train", "epoch": 49, "iter": 3500, "lr": 0.07596, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28281, "top5_acc": 0.545, "loss_cls": 4.10401, "loss": 4.10401, "time": 0.8153} +{"mode": "train", "epoch": 49, "iter": 3600, "lr": 0.07594, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28781, "top5_acc": 0.53859, "loss_cls": 4.10097, "loss": 4.10097, "time": 0.82819} +{"mode": "train", "epoch": 49, "iter": 3700, "lr": 0.07591, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28516, "top5_acc": 0.54125, "loss_cls": 4.08617, "loss": 4.08617, "time": 0.81899} +{"mode": "val", "epoch": 49, "iter": 309, "lr": 0.0759, "top1_acc": 0.22008, "top5_acc": 0.46644, "mean_class_accuracy": 0.21988} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.07588, "memory": 15990, "data_time": 1.22085, "top1_acc": 0.28594, "top5_acc": 0.53641, "loss_cls": 4.07957, "loss": 4.07957, "time": 2.19029} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.07585, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29469, "top5_acc": 0.54344, "loss_cls": 4.07538, "loss": 4.07538, "time": 0.82758} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.07583, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28594, "top5_acc": 0.55281, "loss_cls": 4.03832, "loss": 4.03832, "time": 0.82178} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.07581, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29031, "top5_acc": 0.55859, "loss_cls": 4.02212, "loss": 4.02212, "time": 0.8194} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.07578, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28031, "top5_acc": 0.53797, "loss_cls": 4.10647, "loss": 4.10647, "time": 0.81576} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.07576, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.29047, "top5_acc": 0.54703, "loss_cls": 4.05764, "loss": 4.05764, "time": 0.82134} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.07573, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29109, "top5_acc": 0.55109, "loss_cls": 4.08859, "loss": 4.08859, "time": 0.82545} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.07571, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.27625, "top5_acc": 0.5375, "loss_cls": 4.13165, "loss": 4.13165, "time": 0.81005} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.07569, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27969, "top5_acc": 0.55703, "loss_cls": 4.05014, "loss": 4.05014, "time": 0.82661} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.07566, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2825, "top5_acc": 0.53672, "loss_cls": 4.10535, "loss": 4.10535, "time": 0.82008} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.07564, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28578, "top5_acc": 0.53625, "loss_cls": 4.11177, "loss": 4.11177, "time": 0.81815} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.07561, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28625, "top5_acc": 0.54328, "loss_cls": 4.09139, "loss": 4.09139, "time": 0.82354} +{"mode": "train", "epoch": 50, "iter": 1300, "lr": 0.07559, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28672, "top5_acc": 0.55234, "loss_cls": 4.06776, "loss": 4.06776, "time": 0.82091} +{"mode": "train", "epoch": 50, "iter": 1400, "lr": 0.07557, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28047, "top5_acc": 0.53266, "loss_cls": 4.13267, "loss": 4.13267, "time": 0.81478} +{"mode": "train", "epoch": 50, "iter": 1500, "lr": 0.07554, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28766, "top5_acc": 0.53594, "loss_cls": 4.09281, "loss": 4.09281, "time": 0.81414} +{"mode": "train", "epoch": 50, "iter": 1600, "lr": 0.07552, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27797, "top5_acc": 0.53766, "loss_cls": 4.09241, "loss": 4.09241, "time": 0.81292} +{"mode": "train", "epoch": 50, "iter": 1700, "lr": 0.07549, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29391, "top5_acc": 0.54656, "loss_cls": 4.06362, "loss": 4.06362, "time": 0.81657} +{"mode": "train", "epoch": 50, "iter": 1800, "lr": 0.07547, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27938, "top5_acc": 0.53719, "loss_cls": 4.12101, "loss": 4.12101, "time": 0.80948} +{"mode": "train", "epoch": 50, "iter": 1900, "lr": 0.07545, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28594, "top5_acc": 0.54562, "loss_cls": 4.09535, "loss": 4.09535, "time": 0.81171} +{"mode": "train", "epoch": 50, "iter": 2000, "lr": 0.07542, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27953, "top5_acc": 0.53406, "loss_cls": 4.11497, "loss": 4.11497, "time": 0.81446} +{"mode": "train", "epoch": 50, "iter": 2100, "lr": 0.0754, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29297, "top5_acc": 0.54094, "loss_cls": 4.11048, "loss": 4.11048, "time": 0.81603} +{"mode": "train", "epoch": 50, "iter": 2200, "lr": 0.07537, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27797, "top5_acc": 0.54156, "loss_cls": 4.10241, "loss": 4.10241, "time": 0.81318} +{"mode": "train", "epoch": 50, "iter": 2300, "lr": 0.07535, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27406, "top5_acc": 0.53188, "loss_cls": 4.14725, "loss": 4.14725, "time": 0.8151} +{"mode": "train", "epoch": 50, "iter": 2400, "lr": 0.07533, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.27516, "top5_acc": 0.53344, "loss_cls": 4.12214, "loss": 4.12214, "time": 0.82351} +{"mode": "train", "epoch": 50, "iter": 2500, "lr": 0.0753, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28578, "top5_acc": 0.54656, "loss_cls": 4.10445, "loss": 4.10445, "time": 0.817} +{"mode": "train", "epoch": 50, "iter": 2600, "lr": 0.07528, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28141, "top5_acc": 0.53797, "loss_cls": 4.1097, "loss": 4.1097, "time": 0.81815} +{"mode": "train", "epoch": 50, "iter": 2700, "lr": 0.07525, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27531, "top5_acc": 0.53328, "loss_cls": 4.12624, "loss": 4.12624, "time": 0.82336} +{"mode": "train", "epoch": 50, "iter": 2800, "lr": 0.07523, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28344, "top5_acc": 0.54125, "loss_cls": 4.09753, "loss": 4.09753, "time": 0.81462} +{"mode": "train", "epoch": 50, "iter": 2900, "lr": 0.0752, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28078, "top5_acc": 0.53938, "loss_cls": 4.10401, "loss": 4.10401, "time": 0.81886} +{"mode": "train", "epoch": 50, "iter": 3000, "lr": 0.07518, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28984, "top5_acc": 0.54266, "loss_cls": 4.11335, "loss": 4.11335, "time": 0.80835} +{"mode": "train", "epoch": 50, "iter": 3100, "lr": 0.07516, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28094, "top5_acc": 0.54016, "loss_cls": 4.0986, "loss": 4.0986, "time": 0.81196} +{"mode": "train", "epoch": 50, "iter": 3200, "lr": 0.07513, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29469, "top5_acc": 0.54578, "loss_cls": 4.05975, "loss": 4.05975, "time": 0.81569} +{"mode": "train", "epoch": 50, "iter": 3300, "lr": 0.07511, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28312, "top5_acc": 0.54203, "loss_cls": 4.10112, "loss": 4.10112, "time": 0.81674} +{"mode": "train", "epoch": 50, "iter": 3400, "lr": 0.07508, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28547, "top5_acc": 0.54219, "loss_cls": 4.10404, "loss": 4.10404, "time": 0.81251} +{"mode": "train", "epoch": 50, "iter": 3500, "lr": 0.07506, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28984, "top5_acc": 0.53812, "loss_cls": 4.09333, "loss": 4.09333, "time": 0.81636} +{"mode": "train", "epoch": 50, "iter": 3600, "lr": 0.07504, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2825, "top5_acc": 0.53641, "loss_cls": 4.10942, "loss": 4.10942, "time": 0.82141} +{"mode": "train", "epoch": 50, "iter": 3700, "lr": 0.07501, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27766, "top5_acc": 0.53516, "loss_cls": 4.12782, "loss": 4.12782, "time": 0.81508} +{"mode": "val", "epoch": 50, "iter": 309, "lr": 0.075, "top1_acc": 0.206, "top5_acc": 0.44122, "mean_class_accuracy": 0.20582} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.07498, "memory": 15990, "data_time": 1.22443, "top1_acc": 0.29219, "top5_acc": 0.55719, "loss_cls": 4.0399, "loss": 4.0399, "time": 2.18792} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.07495, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28375, "top5_acc": 0.55406, "loss_cls": 4.06301, "loss": 4.06301, "time": 0.82323} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.07493, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28094, "top5_acc": 0.53703, "loss_cls": 4.11785, "loss": 4.11785, "time": 0.81334} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.0749, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27703, "top5_acc": 0.54688, "loss_cls": 4.0994, "loss": 4.0994, "time": 0.81528} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.07488, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2875, "top5_acc": 0.54203, "loss_cls": 4.06678, "loss": 4.06678, "time": 0.81266} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.07485, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29734, "top5_acc": 0.55516, "loss_cls": 4.05108, "loss": 4.05108, "time": 0.81203} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.07483, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28484, "top5_acc": 0.535, "loss_cls": 4.11273, "loss": 4.11273, "time": 0.8231} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.07481, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28062, "top5_acc": 0.53781, "loss_cls": 4.10535, "loss": 4.10535, "time": 0.81664} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.07478, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28188, "top5_acc": 0.54625, "loss_cls": 4.08013, "loss": 4.08013, "time": 0.81625} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.07476, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28234, "top5_acc": 0.53016, "loss_cls": 4.11396, "loss": 4.11396, "time": 0.82439} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.07473, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27828, "top5_acc": 0.53328, "loss_cls": 4.13637, "loss": 4.13637, "time": 0.81743} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.07471, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28141, "top5_acc": 0.5425, "loss_cls": 4.0908, "loss": 4.0908, "time": 0.82266} +{"mode": "train", "epoch": 51, "iter": 1300, "lr": 0.07468, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27703, "top5_acc": 0.53938, "loss_cls": 4.12169, "loss": 4.12169, "time": 0.81586} +{"mode": "train", "epoch": 51, "iter": 1400, "lr": 0.07466, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28219, "top5_acc": 0.55047, "loss_cls": 4.05785, "loss": 4.05785, "time": 0.81787} +{"mode": "train", "epoch": 51, "iter": 1500, "lr": 0.07464, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28172, "top5_acc": 0.53656, "loss_cls": 4.10124, "loss": 4.10124, "time": 0.81508} +{"mode": "train", "epoch": 51, "iter": 1600, "lr": 0.07461, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29313, "top5_acc": 0.54562, "loss_cls": 4.05172, "loss": 4.05172, "time": 0.8094} +{"mode": "train", "epoch": 51, "iter": 1700, "lr": 0.07459, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28172, "top5_acc": 0.53328, "loss_cls": 4.10619, "loss": 4.10619, "time": 0.80769} +{"mode": "train", "epoch": 51, "iter": 1800, "lr": 0.07456, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27828, "top5_acc": 0.54156, "loss_cls": 4.12334, "loss": 4.12334, "time": 0.81777} +{"mode": "train", "epoch": 51, "iter": 1900, "lr": 0.07454, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27938, "top5_acc": 0.53844, "loss_cls": 4.09983, "loss": 4.09983, "time": 0.82501} +{"mode": "train", "epoch": 51, "iter": 2000, "lr": 0.07451, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27938, "top5_acc": 0.53625, "loss_cls": 4.12423, "loss": 4.12423, "time": 0.81615} +{"mode": "train", "epoch": 51, "iter": 2100, "lr": 0.07449, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29094, "top5_acc": 0.55094, "loss_cls": 4.06318, "loss": 4.06318, "time": 0.81504} +{"mode": "train", "epoch": 51, "iter": 2200, "lr": 0.07447, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28734, "top5_acc": 0.53906, "loss_cls": 4.06988, "loss": 4.06988, "time": 0.81167} +{"mode": "train", "epoch": 51, "iter": 2300, "lr": 0.07444, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28891, "top5_acc": 0.54281, "loss_cls": 4.09467, "loss": 4.09467, "time": 0.8123} +{"mode": "train", "epoch": 51, "iter": 2400, "lr": 0.07442, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2975, "top5_acc": 0.55391, "loss_cls": 4.00123, "loss": 4.00123, "time": 0.81411} +{"mode": "train", "epoch": 51, "iter": 2500, "lr": 0.07439, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28609, "top5_acc": 0.53922, "loss_cls": 4.10368, "loss": 4.10368, "time": 0.82073} +{"mode": "train", "epoch": 51, "iter": 2600, "lr": 0.07437, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28734, "top5_acc": 0.54359, "loss_cls": 4.07978, "loss": 4.07978, "time": 0.81711} +{"mode": "train", "epoch": 51, "iter": 2700, "lr": 0.07434, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28922, "top5_acc": 0.55, "loss_cls": 4.04549, "loss": 4.04549, "time": 0.81935} +{"mode": "train", "epoch": 51, "iter": 2800, "lr": 0.07432, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29109, "top5_acc": 0.54312, "loss_cls": 4.06292, "loss": 4.06292, "time": 0.82658} +{"mode": "train", "epoch": 51, "iter": 2900, "lr": 0.07429, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27906, "top5_acc": 0.54203, "loss_cls": 4.07745, "loss": 4.07745, "time": 0.82041} +{"mode": "train", "epoch": 51, "iter": 3000, "lr": 0.07427, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27922, "top5_acc": 0.54125, "loss_cls": 4.11277, "loss": 4.11277, "time": 0.81702} +{"mode": "train", "epoch": 51, "iter": 3100, "lr": 0.07425, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28766, "top5_acc": 0.54172, "loss_cls": 4.0763, "loss": 4.0763, "time": 0.81155} +{"mode": "train", "epoch": 51, "iter": 3200, "lr": 0.07422, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27953, "top5_acc": 0.53672, "loss_cls": 4.08916, "loss": 4.08916, "time": 0.81431} +{"mode": "train", "epoch": 51, "iter": 3300, "lr": 0.0742, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29156, "top5_acc": 0.54672, "loss_cls": 4.0632, "loss": 4.0632, "time": 0.81338} +{"mode": "train", "epoch": 51, "iter": 3400, "lr": 0.07417, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2825, "top5_acc": 0.535, "loss_cls": 4.10638, "loss": 4.10638, "time": 0.81654} +{"mode": "train", "epoch": 51, "iter": 3500, "lr": 0.07415, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28781, "top5_acc": 0.54734, "loss_cls": 4.06684, "loss": 4.06684, "time": 0.817} +{"mode": "train", "epoch": 51, "iter": 3600, "lr": 0.07412, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28281, "top5_acc": 0.53125, "loss_cls": 4.13229, "loss": 4.13229, "time": 0.82674} +{"mode": "train", "epoch": 51, "iter": 3700, "lr": 0.0741, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29422, "top5_acc": 0.54641, "loss_cls": 4.05685, "loss": 4.05685, "time": 0.812} +{"mode": "val", "epoch": 51, "iter": 309, "lr": 0.07409, "top1_acc": 0.19075, "top5_acc": 0.40673, "mean_class_accuracy": 0.19065} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.07406, "memory": 15990, "data_time": 1.22356, "top1_acc": 0.29781, "top5_acc": 0.56078, "loss_cls": 4.00422, "loss": 4.00422, "time": 2.19597} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.07404, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28781, "top5_acc": 0.55719, "loss_cls": 3.99838, "loss": 3.99838, "time": 0.81926} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.07401, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28734, "top5_acc": 0.53422, "loss_cls": 4.09824, "loss": 4.09824, "time": 0.81435} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.07399, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28672, "top5_acc": 0.53656, "loss_cls": 4.09262, "loss": 4.09262, "time": 0.80968} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.07397, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28578, "top5_acc": 0.54797, "loss_cls": 4.06369, "loss": 4.06369, "time": 0.81412} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.07394, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27719, "top5_acc": 0.53891, "loss_cls": 4.09345, "loss": 4.09345, "time": 0.81876} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.07392, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2875, "top5_acc": 0.54766, "loss_cls": 4.04145, "loss": 4.04145, "time": 0.82418} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.07389, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27969, "top5_acc": 0.54016, "loss_cls": 4.12276, "loss": 4.12276, "time": 0.8153} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.07387, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28156, "top5_acc": 0.54672, "loss_cls": 4.09047, "loss": 4.09047, "time": 0.81925} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.07384, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.29719, "top5_acc": 0.54656, "loss_cls": 4.04198, "loss": 4.04198, "time": 0.83212} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.07382, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28969, "top5_acc": 0.54172, "loss_cls": 4.09488, "loss": 4.09488, "time": 0.82031} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.07379, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28359, "top5_acc": 0.54766, "loss_cls": 4.0749, "loss": 4.0749, "time": 0.81778} +{"mode": "train", "epoch": 52, "iter": 1300, "lr": 0.07377, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28391, "top5_acc": 0.54188, "loss_cls": 4.08486, "loss": 4.08486, "time": 0.82147} +{"mode": "train", "epoch": 52, "iter": 1400, "lr": 0.07374, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29672, "top5_acc": 0.55234, "loss_cls": 4.02779, "loss": 4.02779, "time": 0.816} +{"mode": "train", "epoch": 52, "iter": 1500, "lr": 0.07372, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28484, "top5_acc": 0.54469, "loss_cls": 4.11061, "loss": 4.11061, "time": 0.81487} +{"mode": "train", "epoch": 52, "iter": 1600, "lr": 0.0737, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28469, "top5_acc": 0.54062, "loss_cls": 4.08612, "loss": 4.08612, "time": 0.81505} +{"mode": "train", "epoch": 52, "iter": 1700, "lr": 0.07367, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.285, "top5_acc": 0.54406, "loss_cls": 4.09544, "loss": 4.09544, "time": 0.81282} +{"mode": "train", "epoch": 52, "iter": 1800, "lr": 0.07365, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28609, "top5_acc": 0.54547, "loss_cls": 4.06131, "loss": 4.06131, "time": 0.80954} +{"mode": "train", "epoch": 52, "iter": 1900, "lr": 0.07362, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27922, "top5_acc": 0.53188, "loss_cls": 4.13452, "loss": 4.13452, "time": 0.81387} +{"mode": "train", "epoch": 52, "iter": 2000, "lr": 0.0736, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28312, "top5_acc": 0.53578, "loss_cls": 4.0847, "loss": 4.0847, "time": 0.81524} +{"mode": "train", "epoch": 52, "iter": 2100, "lr": 0.07357, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29219, "top5_acc": 0.54453, "loss_cls": 4.06015, "loss": 4.06015, "time": 0.81489} +{"mode": "train", "epoch": 52, "iter": 2200, "lr": 0.07355, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28062, "top5_acc": 0.53547, "loss_cls": 4.10956, "loss": 4.10956, "time": 0.8152} +{"mode": "train", "epoch": 52, "iter": 2300, "lr": 0.07352, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29016, "top5_acc": 0.55094, "loss_cls": 4.05035, "loss": 4.05035, "time": 0.81676} +{"mode": "train", "epoch": 52, "iter": 2400, "lr": 0.0735, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29063, "top5_acc": 0.54234, "loss_cls": 4.05328, "loss": 4.05328, "time": 0.81494} +{"mode": "train", "epoch": 52, "iter": 2500, "lr": 0.07347, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28469, "top5_acc": 0.5425, "loss_cls": 4.08641, "loss": 4.08641, "time": 0.81648} +{"mode": "train", "epoch": 52, "iter": 2600, "lr": 0.07345, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27969, "top5_acc": 0.53375, "loss_cls": 4.14146, "loss": 4.14146, "time": 0.81217} +{"mode": "train", "epoch": 52, "iter": 2700, "lr": 0.07342, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27625, "top5_acc": 0.54078, "loss_cls": 4.10079, "loss": 4.10079, "time": 0.81587} +{"mode": "train", "epoch": 52, "iter": 2800, "lr": 0.0734, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29047, "top5_acc": 0.55297, "loss_cls": 4.05479, "loss": 4.05479, "time": 0.82503} +{"mode": "train", "epoch": 52, "iter": 2900, "lr": 0.07337, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28859, "top5_acc": 0.55, "loss_cls": 4.05353, "loss": 4.05353, "time": 0.81621} +{"mode": "train", "epoch": 52, "iter": 3000, "lr": 0.07335, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28109, "top5_acc": 0.55719, "loss_cls": 4.05533, "loss": 4.05533, "time": 0.81489} +{"mode": "train", "epoch": 52, "iter": 3100, "lr": 0.07332, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28141, "top5_acc": 0.53328, "loss_cls": 4.11574, "loss": 4.11574, "time": 0.81059} +{"mode": "train", "epoch": 52, "iter": 3200, "lr": 0.0733, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28984, "top5_acc": 0.54469, "loss_cls": 4.08754, "loss": 4.08754, "time": 0.81329} +{"mode": "train", "epoch": 52, "iter": 3300, "lr": 0.07328, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29094, "top5_acc": 0.54109, "loss_cls": 4.10905, "loss": 4.10905, "time": 0.81215} +{"mode": "train", "epoch": 52, "iter": 3400, "lr": 0.07325, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27781, "top5_acc": 0.54062, "loss_cls": 4.07881, "loss": 4.07881, "time": 0.81375} +{"mode": "train", "epoch": 52, "iter": 3500, "lr": 0.07323, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29, "top5_acc": 0.54531, "loss_cls": 4.0549, "loss": 4.0549, "time": 0.81484} +{"mode": "train", "epoch": 52, "iter": 3600, "lr": 0.0732, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28312, "top5_acc": 0.54719, "loss_cls": 4.07611, "loss": 4.07611, "time": 0.82083} +{"mode": "train", "epoch": 52, "iter": 3700, "lr": 0.07318, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28453, "top5_acc": 0.54188, "loss_cls": 4.09903, "loss": 4.09903, "time": 0.81843} +{"mode": "val", "epoch": 52, "iter": 309, "lr": 0.07317, "top1_acc": 0.21213, "top5_acc": 0.45013, "mean_class_accuracy": 0.21191} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.07314, "memory": 15990, "data_time": 1.21627, "top1_acc": 0.29859, "top5_acc": 0.56203, "loss_cls": 3.99477, "loss": 3.99477, "time": 2.18601} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.07312, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28688, "top5_acc": 0.55734, "loss_cls": 4.0205, "loss": 4.0205, "time": 0.82582} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.07309, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28797, "top5_acc": 0.54625, "loss_cls": 4.08843, "loss": 4.08843, "time": 0.81732} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.07307, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29516, "top5_acc": 0.54609, "loss_cls": 4.03056, "loss": 4.03056, "time": 0.82104} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.07304, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28344, "top5_acc": 0.54266, "loss_cls": 4.07139, "loss": 4.07139, "time": 0.8114} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.07302, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29172, "top5_acc": 0.55141, "loss_cls": 4.04205, "loss": 4.04205, "time": 0.81479} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.07299, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29234, "top5_acc": 0.55688, "loss_cls": 4.04061, "loss": 4.04061, "time": 0.82398} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.07297, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29484, "top5_acc": 0.55625, "loss_cls": 4.01036, "loss": 4.01036, "time": 0.82746} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.07294, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.285, "top5_acc": 0.54391, "loss_cls": 4.05575, "loss": 4.05575, "time": 0.81338} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.07292, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29391, "top5_acc": 0.54531, "loss_cls": 4.05323, "loss": 4.05323, "time": 0.82097} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.07289, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28688, "top5_acc": 0.54328, "loss_cls": 4.05758, "loss": 4.05758, "time": 0.82152} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.07287, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28625, "top5_acc": 0.53609, "loss_cls": 4.07091, "loss": 4.07091, "time": 0.81447} +{"mode": "train", "epoch": 53, "iter": 1300, "lr": 0.07284, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29547, "top5_acc": 0.54891, "loss_cls": 4.03391, "loss": 4.03391, "time": 0.81739} +{"mode": "train", "epoch": 53, "iter": 1400, "lr": 0.07282, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28953, "top5_acc": 0.54016, "loss_cls": 4.09729, "loss": 4.09729, "time": 0.81554} +{"mode": "train", "epoch": 53, "iter": 1500, "lr": 0.07279, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26969, "top5_acc": 0.53344, "loss_cls": 4.15152, "loss": 4.15152, "time": 0.81457} +{"mode": "train", "epoch": 53, "iter": 1600, "lr": 0.07277, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29422, "top5_acc": 0.54906, "loss_cls": 4.06728, "loss": 4.06728, "time": 0.81974} +{"mode": "train", "epoch": 53, "iter": 1700, "lr": 0.07274, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28547, "top5_acc": 0.54516, "loss_cls": 4.08819, "loss": 4.08819, "time": 0.81216} +{"mode": "train", "epoch": 53, "iter": 1800, "lr": 0.07272, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29047, "top5_acc": 0.54078, "loss_cls": 4.08795, "loss": 4.08795, "time": 0.80974} +{"mode": "train", "epoch": 53, "iter": 1900, "lr": 0.07269, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28438, "top5_acc": 0.54422, "loss_cls": 4.10516, "loss": 4.10516, "time": 0.81604} +{"mode": "train", "epoch": 53, "iter": 2000, "lr": 0.07267, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28109, "top5_acc": 0.53438, "loss_cls": 4.13229, "loss": 4.13229, "time": 0.81163} +{"mode": "train", "epoch": 53, "iter": 2100, "lr": 0.07264, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28625, "top5_acc": 0.54719, "loss_cls": 4.08457, "loss": 4.08457, "time": 0.8127} +{"mode": "train", "epoch": 53, "iter": 2200, "lr": 0.07262, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28531, "top5_acc": 0.54672, "loss_cls": 4.07903, "loss": 4.07903, "time": 0.80854} +{"mode": "train", "epoch": 53, "iter": 2300, "lr": 0.07259, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28953, "top5_acc": 0.54828, "loss_cls": 4.03894, "loss": 4.03894, "time": 0.80886} +{"mode": "train", "epoch": 53, "iter": 2400, "lr": 0.07257, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29063, "top5_acc": 0.54062, "loss_cls": 4.08591, "loss": 4.08591, "time": 0.80522} +{"mode": "train", "epoch": 53, "iter": 2500, "lr": 0.07254, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29656, "top5_acc": 0.55922, "loss_cls": 4.0067, "loss": 4.0067, "time": 0.8184} +{"mode": "train", "epoch": 53, "iter": 2600, "lr": 0.07252, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28891, "top5_acc": 0.54547, "loss_cls": 4.05292, "loss": 4.05292, "time": 0.81611} +{"mode": "train", "epoch": 53, "iter": 2700, "lr": 0.07249, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28656, "top5_acc": 0.55016, "loss_cls": 4.05739, "loss": 4.05739, "time": 0.81819} +{"mode": "train", "epoch": 53, "iter": 2800, "lr": 0.07247, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28438, "top5_acc": 0.54594, "loss_cls": 4.05622, "loss": 4.05622, "time": 0.82698} +{"mode": "train", "epoch": 53, "iter": 2900, "lr": 0.07244, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29922, "top5_acc": 0.55891, "loss_cls": 4.01156, "loss": 4.01156, "time": 0.81905} +{"mode": "train", "epoch": 53, "iter": 3000, "lr": 0.07242, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28688, "top5_acc": 0.53562, "loss_cls": 4.10912, "loss": 4.10912, "time": 0.81202} +{"mode": "train", "epoch": 53, "iter": 3100, "lr": 0.07239, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28828, "top5_acc": 0.54312, "loss_cls": 4.08473, "loss": 4.08473, "time": 0.8174} +{"mode": "train", "epoch": 53, "iter": 3200, "lr": 0.07237, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26703, "top5_acc": 0.52844, "loss_cls": 4.16451, "loss": 4.16451, "time": 0.80975} +{"mode": "train", "epoch": 53, "iter": 3300, "lr": 0.07234, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27391, "top5_acc": 0.53469, "loss_cls": 4.12955, "loss": 4.12955, "time": 0.81693} +{"mode": "train", "epoch": 53, "iter": 3400, "lr": 0.07232, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28328, "top5_acc": 0.54188, "loss_cls": 4.09883, "loss": 4.09883, "time": 0.81643} +{"mode": "train", "epoch": 53, "iter": 3500, "lr": 0.07229, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29969, "top5_acc": 0.55172, "loss_cls": 4.04274, "loss": 4.04274, "time": 0.81563} +{"mode": "train", "epoch": 53, "iter": 3600, "lr": 0.07227, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29047, "top5_acc": 0.54703, "loss_cls": 4.08546, "loss": 4.08546, "time": 0.82182} +{"mode": "train", "epoch": 53, "iter": 3700, "lr": 0.07224, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28031, "top5_acc": 0.53547, "loss_cls": 4.1357, "loss": 4.1357, "time": 0.8156} +{"mode": "val", "epoch": 53, "iter": 309, "lr": 0.07223, "top1_acc": 0.222, "top5_acc": 0.4634, "mean_class_accuracy": 0.22191} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.07221, "memory": 15990, "data_time": 1.22718, "top1_acc": 0.28984, "top5_acc": 0.55078, "loss_cls": 4.01996, "loss": 4.01996, "time": 2.19264} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.07218, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27844, "top5_acc": 0.5375, "loss_cls": 4.09575, "loss": 4.09575, "time": 0.82764} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.07216, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2925, "top5_acc": 0.54922, "loss_cls": 4.05211, "loss": 4.05211, "time": 0.8226} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.07213, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28016, "top5_acc": 0.53656, "loss_cls": 4.0906, "loss": 4.0906, "time": 0.81676} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.07211, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29406, "top5_acc": 0.55656, "loss_cls": 4.03491, "loss": 4.03491, "time": 0.81678} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.07208, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28969, "top5_acc": 0.55062, "loss_cls": 4.03709, "loss": 4.03709, "time": 0.815} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.07206, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28281, "top5_acc": 0.54703, "loss_cls": 4.07125, "loss": 4.07125, "time": 0.81437} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.07203, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27953, "top5_acc": 0.53922, "loss_cls": 4.11125, "loss": 4.11125, "time": 0.82375} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.07201, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28562, "top5_acc": 0.54031, "loss_cls": 4.08536, "loss": 4.08536, "time": 0.8133} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.07198, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27812, "top5_acc": 0.54344, "loss_cls": 4.094, "loss": 4.094, "time": 0.81389} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.07196, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29203, "top5_acc": 0.55406, "loss_cls": 4.00927, "loss": 4.00927, "time": 0.82827} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.07193, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29063, "top5_acc": 0.54781, "loss_cls": 4.04766, "loss": 4.04766, "time": 0.81776} +{"mode": "train", "epoch": 54, "iter": 1300, "lr": 0.07191, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29078, "top5_acc": 0.55688, "loss_cls": 4.02675, "loss": 4.02675, "time": 0.82331} +{"mode": "train", "epoch": 54, "iter": 1400, "lr": 0.07188, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28406, "top5_acc": 0.53812, "loss_cls": 4.09896, "loss": 4.09896, "time": 0.81447} +{"mode": "train", "epoch": 54, "iter": 1500, "lr": 0.07186, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28031, "top5_acc": 0.53969, "loss_cls": 4.09804, "loss": 4.09804, "time": 0.82036} +{"mode": "train", "epoch": 54, "iter": 1600, "lr": 0.07183, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28734, "top5_acc": 0.54344, "loss_cls": 4.06363, "loss": 4.06363, "time": 0.81585} +{"mode": "train", "epoch": 54, "iter": 1700, "lr": 0.07181, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29266, "top5_acc": 0.54859, "loss_cls": 4.03647, "loss": 4.03647, "time": 0.81228} +{"mode": "train", "epoch": 54, "iter": 1800, "lr": 0.07178, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28344, "top5_acc": 0.53672, "loss_cls": 4.10099, "loss": 4.10099, "time": 0.82003} +{"mode": "train", "epoch": 54, "iter": 1900, "lr": 0.07176, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29047, "top5_acc": 0.53922, "loss_cls": 4.06847, "loss": 4.06847, "time": 0.81389} +{"mode": "train", "epoch": 54, "iter": 2000, "lr": 0.07173, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29578, "top5_acc": 0.54953, "loss_cls": 4.051, "loss": 4.051, "time": 0.81986} +{"mode": "train", "epoch": 54, "iter": 2100, "lr": 0.0717, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27531, "top5_acc": 0.53562, "loss_cls": 4.09819, "loss": 4.09819, "time": 0.8207} +{"mode": "train", "epoch": 54, "iter": 2200, "lr": 0.07168, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28906, "top5_acc": 0.55469, "loss_cls": 4.0418, "loss": 4.0418, "time": 0.81715} +{"mode": "train", "epoch": 54, "iter": 2300, "lr": 0.07165, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28906, "top5_acc": 0.54312, "loss_cls": 4.07344, "loss": 4.07344, "time": 0.81508} +{"mode": "train", "epoch": 54, "iter": 2400, "lr": 0.07163, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28828, "top5_acc": 0.54734, "loss_cls": 4.0829, "loss": 4.0829, "time": 0.81705} +{"mode": "train", "epoch": 54, "iter": 2500, "lr": 0.0716, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28031, "top5_acc": 0.54031, "loss_cls": 4.08985, "loss": 4.08985, "time": 0.82938} +{"mode": "train", "epoch": 54, "iter": 2600, "lr": 0.07158, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28938, "top5_acc": 0.54625, "loss_cls": 4.06576, "loss": 4.06576, "time": 0.81307} +{"mode": "train", "epoch": 54, "iter": 2700, "lr": 0.07155, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29328, "top5_acc": 0.54937, "loss_cls": 4.05328, "loss": 4.05328, "time": 0.81291} +{"mode": "train", "epoch": 54, "iter": 2800, "lr": 0.07153, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28203, "top5_acc": 0.54234, "loss_cls": 4.07969, "loss": 4.07969, "time": 0.82301} +{"mode": "train", "epoch": 54, "iter": 2900, "lr": 0.0715, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28062, "top5_acc": 0.54484, "loss_cls": 4.08263, "loss": 4.08263, "time": 0.81174} +{"mode": "train", "epoch": 54, "iter": 3000, "lr": 0.07148, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29234, "top5_acc": 0.5425, "loss_cls": 4.05824, "loss": 4.05824, "time": 0.81132} +{"mode": "train", "epoch": 54, "iter": 3100, "lr": 0.07145, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28875, "top5_acc": 0.53969, "loss_cls": 4.11111, "loss": 4.11111, "time": 0.817} +{"mode": "train", "epoch": 54, "iter": 3200, "lr": 0.07143, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28188, "top5_acc": 0.53766, "loss_cls": 4.09096, "loss": 4.09096, "time": 0.81449} +{"mode": "train", "epoch": 54, "iter": 3300, "lr": 0.0714, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27609, "top5_acc": 0.535, "loss_cls": 4.11084, "loss": 4.11084, "time": 0.81726} +{"mode": "train", "epoch": 54, "iter": 3400, "lr": 0.07138, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28484, "top5_acc": 0.54875, "loss_cls": 4.04784, "loss": 4.04784, "time": 0.81365} +{"mode": "train", "epoch": 54, "iter": 3500, "lr": 0.07135, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29609, "top5_acc": 0.55062, "loss_cls": 4.01028, "loss": 4.01028, "time": 0.81473} +{"mode": "train", "epoch": 54, "iter": 3600, "lr": 0.07133, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.30047, "top5_acc": 0.54188, "loss_cls": 4.06907, "loss": 4.06907, "time": 0.83017} +{"mode": "train", "epoch": 54, "iter": 3700, "lr": 0.0713, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29641, "top5_acc": 0.54906, "loss_cls": 4.04363, "loss": 4.04363, "time": 0.82075} +{"mode": "val", "epoch": 54, "iter": 309, "lr": 0.07129, "top1_acc": 0.20113, "top5_acc": 0.43504, "mean_class_accuracy": 0.20096} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.07126, "memory": 15990, "data_time": 1.22251, "top1_acc": 0.29594, "top5_acc": 0.55406, "loss_cls": 4.0187, "loss": 4.0187, "time": 2.19563} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.07124, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29313, "top5_acc": 0.54891, "loss_cls": 4.05013, "loss": 4.05013, "time": 0.82074} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.07121, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29766, "top5_acc": 0.55344, "loss_cls": 3.99951, "loss": 3.99951, "time": 0.81335} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.07119, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29438, "top5_acc": 0.55188, "loss_cls": 4.03966, "loss": 4.03966, "time": 0.82104} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.07116, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29922, "top5_acc": 0.56188, "loss_cls": 3.99774, "loss": 3.99774, "time": 0.81693} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.07114, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29484, "top5_acc": 0.55359, "loss_cls": 4.03983, "loss": 4.03983, "time": 0.81712} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.07111, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28734, "top5_acc": 0.53859, "loss_cls": 4.1136, "loss": 4.1136, "time": 0.81979} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.07109, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29156, "top5_acc": 0.55484, "loss_cls": 4.01118, "loss": 4.01118, "time": 0.81957} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.07106, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28953, "top5_acc": 0.54719, "loss_cls": 4.0567, "loss": 4.0567, "time": 0.816} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.07104, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.29547, "top5_acc": 0.55062, "loss_cls": 4.03804, "loss": 4.03804, "time": 0.81254} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.07101, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.285, "top5_acc": 0.55203, "loss_cls": 4.04422, "loss": 4.04422, "time": 0.82602} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.07099, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29125, "top5_acc": 0.55953, "loss_cls": 4.02917, "loss": 4.02917, "time": 0.8145} +{"mode": "train", "epoch": 55, "iter": 1300, "lr": 0.07096, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27781, "top5_acc": 0.54391, "loss_cls": 4.0921, "loss": 4.0921, "time": 0.81523} +{"mode": "train", "epoch": 55, "iter": 1400, "lr": 0.07093, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29266, "top5_acc": 0.54766, "loss_cls": 4.03671, "loss": 4.03671, "time": 0.82651} +{"mode": "train", "epoch": 55, "iter": 1500, "lr": 0.07091, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28609, "top5_acc": 0.55109, "loss_cls": 4.05149, "loss": 4.05149, "time": 0.81236} +{"mode": "train", "epoch": 55, "iter": 1600, "lr": 0.07088, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.285, "top5_acc": 0.54703, "loss_cls": 4.07157, "loss": 4.07157, "time": 0.81525} +{"mode": "train", "epoch": 55, "iter": 1700, "lr": 0.07086, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28406, "top5_acc": 0.53703, "loss_cls": 4.12281, "loss": 4.12281, "time": 0.81889} +{"mode": "train", "epoch": 55, "iter": 1800, "lr": 0.07083, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28656, "top5_acc": 0.55109, "loss_cls": 4.07206, "loss": 4.07206, "time": 0.82026} +{"mode": "train", "epoch": 55, "iter": 1900, "lr": 0.07081, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28672, "top5_acc": 0.54562, "loss_cls": 4.05456, "loss": 4.05456, "time": 0.81705} +{"mode": "train", "epoch": 55, "iter": 2000, "lr": 0.07078, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28031, "top5_acc": 0.5425, "loss_cls": 4.09852, "loss": 4.09852, "time": 0.81437} +{"mode": "train", "epoch": 55, "iter": 2100, "lr": 0.07076, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29063, "top5_acc": 0.5425, "loss_cls": 4.07874, "loss": 4.07874, "time": 0.81273} +{"mode": "train", "epoch": 55, "iter": 2200, "lr": 0.07073, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28297, "top5_acc": 0.54578, "loss_cls": 4.06295, "loss": 4.06295, "time": 0.81455} +{"mode": "train", "epoch": 55, "iter": 2300, "lr": 0.07071, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29219, "top5_acc": 0.55641, "loss_cls": 4.02816, "loss": 4.02816, "time": 0.81121} +{"mode": "train", "epoch": 55, "iter": 2400, "lr": 0.07068, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29484, "top5_acc": 0.55641, "loss_cls": 4.01944, "loss": 4.01944, "time": 0.81324} +{"mode": "train", "epoch": 55, "iter": 2500, "lr": 0.07065, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28359, "top5_acc": 0.54859, "loss_cls": 4.07064, "loss": 4.07064, "time": 0.82364} +{"mode": "train", "epoch": 55, "iter": 2600, "lr": 0.07063, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28016, "top5_acc": 0.54, "loss_cls": 4.09936, "loss": 4.09936, "time": 0.81794} +{"mode": "train", "epoch": 55, "iter": 2700, "lr": 0.0706, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27734, "top5_acc": 0.53906, "loss_cls": 4.11932, "loss": 4.11932, "time": 0.81904} +{"mode": "train", "epoch": 55, "iter": 2800, "lr": 0.07058, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28484, "top5_acc": 0.54297, "loss_cls": 4.07994, "loss": 4.07994, "time": 0.82097} +{"mode": "train", "epoch": 55, "iter": 2900, "lr": 0.07055, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28641, "top5_acc": 0.55484, "loss_cls": 4.02558, "loss": 4.02558, "time": 0.81711} +{"mode": "train", "epoch": 55, "iter": 3000, "lr": 0.07053, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28484, "top5_acc": 0.54422, "loss_cls": 4.08277, "loss": 4.08277, "time": 0.819} +{"mode": "train", "epoch": 55, "iter": 3100, "lr": 0.0705, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29313, "top5_acc": 0.54656, "loss_cls": 4.06165, "loss": 4.06165, "time": 0.81714} +{"mode": "train", "epoch": 55, "iter": 3200, "lr": 0.07048, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29438, "top5_acc": 0.55297, "loss_cls": 4.03718, "loss": 4.03718, "time": 0.81464} +{"mode": "train", "epoch": 55, "iter": 3300, "lr": 0.07045, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28984, "top5_acc": 0.54484, "loss_cls": 4.04405, "loss": 4.04405, "time": 0.81538} +{"mode": "train", "epoch": 55, "iter": 3400, "lr": 0.07043, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28922, "top5_acc": 0.5525, "loss_cls": 4.03743, "loss": 4.03743, "time": 0.81495} +{"mode": "train", "epoch": 55, "iter": 3500, "lr": 0.0704, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28969, "top5_acc": 0.55656, "loss_cls": 4.04512, "loss": 4.04512, "time": 0.8105} +{"mode": "train", "epoch": 55, "iter": 3600, "lr": 0.07037, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.29109, "top5_acc": 0.54781, "loss_cls": 4.05274, "loss": 4.05274, "time": 0.82046} +{"mode": "train", "epoch": 55, "iter": 3700, "lr": 0.07035, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29047, "top5_acc": 0.55141, "loss_cls": 4.05208, "loss": 4.05208, "time": 0.81203} +{"mode": "val", "epoch": 55, "iter": 309, "lr": 0.07034, "top1_acc": 0.21668, "top5_acc": 0.44856, "mean_class_accuracy": 0.21653} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.07031, "memory": 15990, "data_time": 1.21849, "top1_acc": 0.30078, "top5_acc": 0.55969, "loss_cls": 3.95979, "loss": 3.95979, "time": 2.18841} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.07029, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28766, "top5_acc": 0.55219, "loss_cls": 4.00011, "loss": 4.00011, "time": 0.8232} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.07026, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30234, "top5_acc": 0.56109, "loss_cls": 3.96773, "loss": 3.96773, "time": 0.81218} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.07023, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29797, "top5_acc": 0.55344, "loss_cls": 4.02675, "loss": 4.02675, "time": 0.81463} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.07021, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29016, "top5_acc": 0.53969, "loss_cls": 4.08547, "loss": 4.08547, "time": 0.81778} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.07018, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28797, "top5_acc": 0.54734, "loss_cls": 4.0587, "loss": 4.0587, "time": 0.81155} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.07016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28969, "top5_acc": 0.55672, "loss_cls": 4.00844, "loss": 4.00844, "time": 0.81596} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.07013, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29141, "top5_acc": 0.55281, "loss_cls": 4.02137, "loss": 4.02137, "time": 0.81506} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.07011, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29328, "top5_acc": 0.55562, "loss_cls": 4.03, "loss": 4.03, "time": 0.81873} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.07008, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29219, "top5_acc": 0.55391, "loss_cls": 4.04674, "loss": 4.04674, "time": 0.81622} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.07006, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.29344, "top5_acc": 0.545, "loss_cls": 4.06378, "loss": 4.06378, "time": 0.82408} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.07003, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2875, "top5_acc": 0.54109, "loss_cls": 4.09118, "loss": 4.09118, "time": 0.82505} +{"mode": "train", "epoch": 56, "iter": 1300, "lr": 0.07, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28938, "top5_acc": 0.54719, "loss_cls": 4.06218, "loss": 4.06218, "time": 0.81646} +{"mode": "train", "epoch": 56, "iter": 1400, "lr": 0.06998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30141, "top5_acc": 0.56391, "loss_cls": 4.00468, "loss": 4.00468, "time": 0.8192} +{"mode": "train", "epoch": 56, "iter": 1500, "lr": 0.06995, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29969, "top5_acc": 0.55719, "loss_cls": 4.02392, "loss": 4.02392, "time": 0.82222} +{"mode": "train", "epoch": 56, "iter": 1600, "lr": 0.06993, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29031, "top5_acc": 0.54516, "loss_cls": 4.05351, "loss": 4.05351, "time": 0.81824} +{"mode": "train", "epoch": 56, "iter": 1700, "lr": 0.0699, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28266, "top5_acc": 0.54781, "loss_cls": 4.07044, "loss": 4.07044, "time": 0.82038} +{"mode": "train", "epoch": 56, "iter": 1800, "lr": 0.06988, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27828, "top5_acc": 0.54016, "loss_cls": 4.08405, "loss": 4.08405, "time": 0.81622} +{"mode": "train", "epoch": 56, "iter": 1900, "lr": 0.06985, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28734, "top5_acc": 0.55625, "loss_cls": 4.04381, "loss": 4.04381, "time": 0.81077} +{"mode": "train", "epoch": 56, "iter": 2000, "lr": 0.06983, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2875, "top5_acc": 0.54172, "loss_cls": 4.07977, "loss": 4.07977, "time": 0.81734} +{"mode": "train", "epoch": 56, "iter": 2100, "lr": 0.0698, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28891, "top5_acc": 0.54344, "loss_cls": 4.05542, "loss": 4.05542, "time": 0.81411} +{"mode": "train", "epoch": 56, "iter": 2200, "lr": 0.06977, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29516, "top5_acc": 0.55031, "loss_cls": 4.0425, "loss": 4.0425, "time": 0.8153} +{"mode": "train", "epoch": 56, "iter": 2300, "lr": 0.06975, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29109, "top5_acc": 0.54875, "loss_cls": 4.06815, "loss": 4.06815, "time": 0.81253} +{"mode": "train", "epoch": 56, "iter": 2400, "lr": 0.06972, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28844, "top5_acc": 0.55312, "loss_cls": 4.05195, "loss": 4.05195, "time": 0.81247} +{"mode": "train", "epoch": 56, "iter": 2500, "lr": 0.0697, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27953, "top5_acc": 0.54094, "loss_cls": 4.07777, "loss": 4.07777, "time": 0.82263} +{"mode": "train", "epoch": 56, "iter": 2600, "lr": 0.06967, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28422, "top5_acc": 0.54828, "loss_cls": 4.068, "loss": 4.068, "time": 0.81432} +{"mode": "train", "epoch": 56, "iter": 2700, "lr": 0.06965, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29969, "top5_acc": 0.55906, "loss_cls": 4.02496, "loss": 4.02496, "time": 0.81504} +{"mode": "train", "epoch": 56, "iter": 2800, "lr": 0.06962, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28781, "top5_acc": 0.54906, "loss_cls": 4.05851, "loss": 4.05851, "time": 0.82337} +{"mode": "train", "epoch": 56, "iter": 2900, "lr": 0.06959, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28297, "top5_acc": 0.53453, "loss_cls": 4.1183, "loss": 4.1183, "time": 0.82497} +{"mode": "train", "epoch": 56, "iter": 3000, "lr": 0.06957, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28484, "top5_acc": 0.54594, "loss_cls": 4.0941, "loss": 4.0941, "time": 0.81126} +{"mode": "train", "epoch": 56, "iter": 3100, "lr": 0.06954, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28672, "top5_acc": 0.55672, "loss_cls": 4.05248, "loss": 4.05248, "time": 0.81404} +{"mode": "train", "epoch": 56, "iter": 3200, "lr": 0.06952, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.275, "top5_acc": 0.53328, "loss_cls": 4.10781, "loss": 4.10781, "time": 0.80868} +{"mode": "train", "epoch": 56, "iter": 3300, "lr": 0.06949, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29953, "top5_acc": 0.55953, "loss_cls": 3.99993, "loss": 3.99993, "time": 0.81395} +{"mode": "train", "epoch": 56, "iter": 3400, "lr": 0.06947, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29313, "top5_acc": 0.54219, "loss_cls": 4.04791, "loss": 4.04791, "time": 0.81505} +{"mode": "train", "epoch": 56, "iter": 3500, "lr": 0.06944, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29563, "top5_acc": 0.54391, "loss_cls": 4.0446, "loss": 4.0446, "time": 0.81318} +{"mode": "train", "epoch": 56, "iter": 3600, "lr": 0.06941, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29375, "top5_acc": 0.54797, "loss_cls": 4.05356, "loss": 4.05356, "time": 0.82833} +{"mode": "train", "epoch": 56, "iter": 3700, "lr": 0.06939, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30047, "top5_acc": 0.5575, "loss_cls": 4.02529, "loss": 4.02529, "time": 0.81717} +{"mode": "val", "epoch": 56, "iter": 309, "lr": 0.06938, "top1_acc": 0.23158, "top5_acc": 0.48118, "mean_class_accuracy": 0.23129} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.06935, "memory": 15990, "data_time": 1.23349, "top1_acc": 0.30344, "top5_acc": 0.55688, "loss_cls": 4.002, "loss": 4.002, "time": 2.20542} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.06932, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30016, "top5_acc": 0.56344, "loss_cls": 3.95147, "loss": 3.95147, "time": 0.8235} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.0693, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30297, "top5_acc": 0.56984, "loss_cls": 3.93505, "loss": 3.93505, "time": 0.81975} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.06927, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28984, "top5_acc": 0.55266, "loss_cls": 4.05275, "loss": 4.05275, "time": 0.81644} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.06925, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29156, "top5_acc": 0.55437, "loss_cls": 4.02815, "loss": 4.02815, "time": 0.81379} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.06922, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29453, "top5_acc": 0.56156, "loss_cls": 4.00818, "loss": 4.00818, "time": 0.8148} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.0692, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29313, "top5_acc": 0.55328, "loss_cls": 4.03738, "loss": 4.03738, "time": 0.81442} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.06917, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28938, "top5_acc": 0.55437, "loss_cls": 4.04439, "loss": 4.04439, "time": 0.81155} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.06914, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29922, "top5_acc": 0.55969, "loss_cls": 4.01135, "loss": 4.01135, "time": 0.81748} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.06912, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28438, "top5_acc": 0.53906, "loss_cls": 4.10772, "loss": 4.10772, "time": 0.81348} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.06909, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29109, "top5_acc": 0.54312, "loss_cls": 4.07308, "loss": 4.07308, "time": 0.82025} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.06907, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28328, "top5_acc": 0.545, "loss_cls": 4.07123, "loss": 4.07123, "time": 0.82274} +{"mode": "train", "epoch": 57, "iter": 1300, "lr": 0.06904, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29797, "top5_acc": 0.55156, "loss_cls": 4.02627, "loss": 4.02627, "time": 0.81793} +{"mode": "train", "epoch": 57, "iter": 1400, "lr": 0.06901, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28875, "top5_acc": 0.54516, "loss_cls": 4.03768, "loss": 4.03768, "time": 0.81662} +{"mode": "train", "epoch": 57, "iter": 1500, "lr": 0.06899, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28453, "top5_acc": 0.54609, "loss_cls": 4.06958, "loss": 4.06958, "time": 0.82132} +{"mode": "train", "epoch": 57, "iter": 1600, "lr": 0.06896, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29094, "top5_acc": 0.55781, "loss_cls": 4.02665, "loss": 4.02665, "time": 0.81456} +{"mode": "train", "epoch": 57, "iter": 1700, "lr": 0.06894, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28672, "top5_acc": 0.5425, "loss_cls": 4.05835, "loss": 4.05835, "time": 0.8144} +{"mode": "train", "epoch": 57, "iter": 1800, "lr": 0.06891, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27812, "top5_acc": 0.53, "loss_cls": 4.12525, "loss": 4.12525, "time": 0.81543} +{"mode": "train", "epoch": 57, "iter": 1900, "lr": 0.06889, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29453, "top5_acc": 0.54344, "loss_cls": 4.0578, "loss": 4.0578, "time": 0.81837} +{"mode": "train", "epoch": 57, "iter": 2000, "lr": 0.06886, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30016, "top5_acc": 0.55437, "loss_cls": 4.01073, "loss": 4.01073, "time": 0.81925} +{"mode": "train", "epoch": 57, "iter": 2100, "lr": 0.06883, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30203, "top5_acc": 0.55859, "loss_cls": 4.0203, "loss": 4.0203, "time": 0.82184} +{"mode": "train", "epoch": 57, "iter": 2200, "lr": 0.06881, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29641, "top5_acc": 0.54937, "loss_cls": 4.03655, "loss": 4.03655, "time": 0.81034} +{"mode": "train", "epoch": 57, "iter": 2300, "lr": 0.06878, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28266, "top5_acc": 0.54141, "loss_cls": 4.10801, "loss": 4.10801, "time": 0.81745} +{"mode": "train", "epoch": 57, "iter": 2400, "lr": 0.06876, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29359, "top5_acc": 0.55781, "loss_cls": 4.01666, "loss": 4.01666, "time": 0.80932} +{"mode": "train", "epoch": 57, "iter": 2500, "lr": 0.06873, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30219, "top5_acc": 0.55984, "loss_cls": 3.9769, "loss": 3.9769, "time": 0.81583} +{"mode": "train", "epoch": 57, "iter": 2600, "lr": 0.0687, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.3025, "top5_acc": 0.55828, "loss_cls": 4.02424, "loss": 4.02424, "time": 0.81808} +{"mode": "train", "epoch": 57, "iter": 2700, "lr": 0.06868, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28797, "top5_acc": 0.55094, "loss_cls": 4.06408, "loss": 4.06408, "time": 0.80884} +{"mode": "train", "epoch": 57, "iter": 2800, "lr": 0.06865, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29812, "top5_acc": 0.55125, "loss_cls": 4.03292, "loss": 4.03292, "time": 0.81875} +{"mode": "train", "epoch": 57, "iter": 2900, "lr": 0.06863, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28656, "top5_acc": 0.54641, "loss_cls": 4.07107, "loss": 4.07107, "time": 0.81965} +{"mode": "train", "epoch": 57, "iter": 3000, "lr": 0.0686, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29313, "top5_acc": 0.55359, "loss_cls": 4.03626, "loss": 4.03626, "time": 0.81549} +{"mode": "train", "epoch": 57, "iter": 3100, "lr": 0.06857, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29156, "top5_acc": 0.55875, "loss_cls": 4.01591, "loss": 4.01591, "time": 0.81638} +{"mode": "train", "epoch": 57, "iter": 3200, "lr": 0.06855, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27875, "top5_acc": 0.53672, "loss_cls": 4.09493, "loss": 4.09493, "time": 0.81217} +{"mode": "train", "epoch": 57, "iter": 3300, "lr": 0.06852, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28859, "top5_acc": 0.54969, "loss_cls": 4.08447, "loss": 4.08447, "time": 0.81391} +{"mode": "train", "epoch": 57, "iter": 3400, "lr": 0.0685, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29156, "top5_acc": 0.55188, "loss_cls": 4.05735, "loss": 4.05735, "time": 0.8151} +{"mode": "train", "epoch": 57, "iter": 3500, "lr": 0.06847, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29078, "top5_acc": 0.55312, "loss_cls": 4.03507, "loss": 4.03507, "time": 0.81146} +{"mode": "train", "epoch": 57, "iter": 3600, "lr": 0.06844, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29203, "top5_acc": 0.54328, "loss_cls": 4.06314, "loss": 4.06314, "time": 0.8239} +{"mode": "train", "epoch": 57, "iter": 3700, "lr": 0.06842, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29219, "top5_acc": 0.54703, "loss_cls": 4.07436, "loss": 4.07436, "time": 0.81841} +{"mode": "val", "epoch": 57, "iter": 309, "lr": 0.06841, "top1_acc": 0.21258, "top5_acc": 0.44487, "mean_class_accuracy": 0.21248} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.06838, "memory": 15990, "data_time": 1.2235, "top1_acc": 0.29906, "top5_acc": 0.55953, "loss_cls": 3.9696, "loss": 3.9696, "time": 2.19563} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.06835, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28969, "top5_acc": 0.55797, "loss_cls": 4.01458, "loss": 4.01458, "time": 0.82719} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.06833, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28828, "top5_acc": 0.55406, "loss_cls": 3.99717, "loss": 3.99717, "time": 0.81807} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.0683, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29141, "top5_acc": 0.55875, "loss_cls": 4.00084, "loss": 4.00084, "time": 0.81312} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.06828, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29203, "top5_acc": 0.55672, "loss_cls": 4.03473, "loss": 4.03473, "time": 0.81638} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.06825, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30797, "top5_acc": 0.55484, "loss_cls": 3.99884, "loss": 3.99884, "time": 0.8159} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.06822, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28891, "top5_acc": 0.54047, "loss_cls": 4.05778, "loss": 4.05778, "time": 0.82376} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.0682, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29094, "top5_acc": 0.54781, "loss_cls": 4.04395, "loss": 4.04395, "time": 0.81307} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.06817, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29688, "top5_acc": 0.5525, "loss_cls": 4.02675, "loss": 4.02675, "time": 0.81868} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.06815, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29734, "top5_acc": 0.56141, "loss_cls": 3.98091, "loss": 3.98091, "time": 0.82085} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.06812, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29641, "top5_acc": 0.54984, "loss_cls": 4.01359, "loss": 4.01359, "time": 0.81388} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.06809, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28984, "top5_acc": 0.54812, "loss_cls": 4.03481, "loss": 4.03481, "time": 0.8295} +{"mode": "train", "epoch": 58, "iter": 1300, "lr": 0.06807, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28484, "top5_acc": 0.55234, "loss_cls": 4.03227, "loss": 4.03227, "time": 0.827} +{"mode": "train", "epoch": 58, "iter": 1400, "lr": 0.06804, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.29094, "top5_acc": 0.53641, "loss_cls": 4.08009, "loss": 4.08009, "time": 0.82162} +{"mode": "train", "epoch": 58, "iter": 1500, "lr": 0.06802, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29766, "top5_acc": 0.54969, "loss_cls": 3.9958, "loss": 3.9958, "time": 0.82207} +{"mode": "train", "epoch": 58, "iter": 1600, "lr": 0.06799, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29547, "top5_acc": 0.55734, "loss_cls": 3.99771, "loss": 3.99771, "time": 0.8191} +{"mode": "train", "epoch": 58, "iter": 1700, "lr": 0.06796, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29969, "top5_acc": 0.55562, "loss_cls": 4.02697, "loss": 4.02697, "time": 0.81877} +{"mode": "train", "epoch": 58, "iter": 1800, "lr": 0.06794, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29203, "top5_acc": 0.55375, "loss_cls": 4.06887, "loss": 4.06887, "time": 0.8188} +{"mode": "train", "epoch": 58, "iter": 1900, "lr": 0.06791, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29281, "top5_acc": 0.55359, "loss_cls": 4.04491, "loss": 4.04491, "time": 0.82047} +{"mode": "train", "epoch": 58, "iter": 2000, "lr": 0.06789, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28953, "top5_acc": 0.54828, "loss_cls": 4.04992, "loss": 4.04992, "time": 0.81611} +{"mode": "train", "epoch": 58, "iter": 2100, "lr": 0.06786, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29641, "top5_acc": 0.54875, "loss_cls": 4.04231, "loss": 4.04231, "time": 0.81583} +{"mode": "train", "epoch": 58, "iter": 2200, "lr": 0.06783, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29297, "top5_acc": 0.54375, "loss_cls": 4.05254, "loss": 4.05254, "time": 0.81749} +{"mode": "train", "epoch": 58, "iter": 2300, "lr": 0.06781, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29406, "top5_acc": 0.55828, "loss_cls": 3.99379, "loss": 3.99379, "time": 0.81781} +{"mode": "train", "epoch": 58, "iter": 2400, "lr": 0.06778, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28469, "top5_acc": 0.54766, "loss_cls": 4.04778, "loss": 4.04778, "time": 0.81403} +{"mode": "train", "epoch": 58, "iter": 2500, "lr": 0.06775, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28875, "top5_acc": 0.55141, "loss_cls": 4.05163, "loss": 4.05163, "time": 0.81897} +{"mode": "train", "epoch": 58, "iter": 2600, "lr": 0.06773, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29203, "top5_acc": 0.5525, "loss_cls": 4.03363, "loss": 4.03363, "time": 0.81947} +{"mode": "train", "epoch": 58, "iter": 2700, "lr": 0.0677, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29063, "top5_acc": 0.54453, "loss_cls": 4.04851, "loss": 4.04851, "time": 0.81014} +{"mode": "train", "epoch": 58, "iter": 2800, "lr": 0.06768, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.295, "top5_acc": 0.56031, "loss_cls": 4.02333, "loss": 4.02333, "time": 0.81686} +{"mode": "train", "epoch": 58, "iter": 2900, "lr": 0.06765, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28578, "top5_acc": 0.55469, "loss_cls": 4.04681, "loss": 4.04681, "time": 0.8192} +{"mode": "train", "epoch": 58, "iter": 3000, "lr": 0.06762, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29359, "top5_acc": 0.55359, "loss_cls": 4.05168, "loss": 4.05168, "time": 0.81685} +{"mode": "train", "epoch": 58, "iter": 3100, "lr": 0.0676, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28391, "top5_acc": 0.54297, "loss_cls": 4.1071, "loss": 4.1071, "time": 0.81666} +{"mode": "train", "epoch": 58, "iter": 3200, "lr": 0.06757, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29344, "top5_acc": 0.54281, "loss_cls": 4.06444, "loss": 4.06444, "time": 0.81225} +{"mode": "train", "epoch": 58, "iter": 3300, "lr": 0.06755, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29656, "top5_acc": 0.56141, "loss_cls": 4.0047, "loss": 4.0047, "time": 0.81542} +{"mode": "train", "epoch": 58, "iter": 3400, "lr": 0.06752, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29016, "top5_acc": 0.54312, "loss_cls": 4.06648, "loss": 4.06648, "time": 0.81657} +{"mode": "train", "epoch": 58, "iter": 3500, "lr": 0.06749, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29281, "top5_acc": 0.55172, "loss_cls": 4.02607, "loss": 4.02607, "time": 0.81196} +{"mode": "train", "epoch": 58, "iter": 3600, "lr": 0.06747, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28406, "top5_acc": 0.55453, "loss_cls": 4.05686, "loss": 4.05686, "time": 0.82628} +{"mode": "train", "epoch": 58, "iter": 3700, "lr": 0.06744, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29234, "top5_acc": 0.55297, "loss_cls": 4.02661, "loss": 4.02661, "time": 0.81642} +{"mode": "val", "epoch": 58, "iter": 309, "lr": 0.06743, "top1_acc": 0.18315, "top5_acc": 0.4048, "mean_class_accuracy": 0.18293} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.0674, "memory": 15990, "data_time": 1.22485, "top1_acc": 0.29766, "top5_acc": 0.55922, "loss_cls": 3.99915, "loss": 3.99915, "time": 2.19527} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.06738, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30844, "top5_acc": 0.56547, "loss_cls": 3.98561, "loss": 3.98561, "time": 0.82462} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.06735, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28984, "top5_acc": 0.55, "loss_cls": 4.04233, "loss": 4.04233, "time": 0.81836} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.06732, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28516, "top5_acc": 0.54641, "loss_cls": 4.03397, "loss": 4.03397, "time": 0.8149} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.0673, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29141, "top5_acc": 0.555, "loss_cls": 3.99688, "loss": 3.99688, "time": 0.82104} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.06727, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29688, "top5_acc": 0.56141, "loss_cls": 3.9691, "loss": 3.9691, "time": 0.81854} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.06725, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3, "top5_acc": 0.55797, "loss_cls": 4.01447, "loss": 4.01447, "time": 0.8135} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.06722, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28359, "top5_acc": 0.54594, "loss_cls": 4.04262, "loss": 4.04262, "time": 0.81088} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.06719, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29672, "top5_acc": 0.55484, "loss_cls": 4.01133, "loss": 4.01133, "time": 0.81223} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.06717, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30266, "top5_acc": 0.56031, "loss_cls": 3.97347, "loss": 3.97347, "time": 0.82192} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.06714, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30266, "top5_acc": 0.56359, "loss_cls": 3.99527, "loss": 3.99527, "time": 0.80813} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.06711, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28703, "top5_acc": 0.54609, "loss_cls": 4.07997, "loss": 4.07997, "time": 0.81966} +{"mode": "train", "epoch": 59, "iter": 1300, "lr": 0.06709, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29328, "top5_acc": 0.55437, "loss_cls": 4.05608, "loss": 4.05608, "time": 0.82576} +{"mode": "train", "epoch": 59, "iter": 1400, "lr": 0.06706, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30094, "top5_acc": 0.55969, "loss_cls": 4.00626, "loss": 4.00626, "time": 0.82125} +{"mode": "train", "epoch": 59, "iter": 1500, "lr": 0.06704, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29344, "top5_acc": 0.55547, "loss_cls": 4.01852, "loss": 4.01852, "time": 0.81819} +{"mode": "train", "epoch": 59, "iter": 1600, "lr": 0.06701, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30062, "top5_acc": 0.55422, "loss_cls": 4.01209, "loss": 4.01209, "time": 0.81644} +{"mode": "train", "epoch": 59, "iter": 1700, "lr": 0.06698, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29406, "top5_acc": 0.55703, "loss_cls": 3.99581, "loss": 3.99581, "time": 0.81937} +{"mode": "train", "epoch": 59, "iter": 1800, "lr": 0.06696, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29828, "top5_acc": 0.56234, "loss_cls": 3.99331, "loss": 3.99331, "time": 0.81318} +{"mode": "train", "epoch": 59, "iter": 1900, "lr": 0.06693, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30312, "top5_acc": 0.56406, "loss_cls": 3.99525, "loss": 3.99525, "time": 0.81677} +{"mode": "train", "epoch": 59, "iter": 2000, "lr": 0.0669, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28984, "top5_acc": 0.555, "loss_cls": 4.0383, "loss": 4.0383, "time": 0.81538} +{"mode": "train", "epoch": 59, "iter": 2100, "lr": 0.06688, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29, "top5_acc": 0.54266, "loss_cls": 4.0556, "loss": 4.0556, "time": 0.8148} +{"mode": "train", "epoch": 59, "iter": 2200, "lr": 0.06685, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29719, "top5_acc": 0.54969, "loss_cls": 4.04296, "loss": 4.04296, "time": 0.8156} +{"mode": "train", "epoch": 59, "iter": 2300, "lr": 0.06682, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29328, "top5_acc": 0.55109, "loss_cls": 4.00919, "loss": 4.00919, "time": 0.8148} +{"mode": "train", "epoch": 59, "iter": 2400, "lr": 0.0668, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29563, "top5_acc": 0.55203, "loss_cls": 4.0327, "loss": 4.0327, "time": 0.81655} +{"mode": "train", "epoch": 59, "iter": 2500, "lr": 0.06677, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.29375, "top5_acc": 0.55375, "loss_cls": 4.01445, "loss": 4.01445, "time": 0.8137} +{"mode": "train", "epoch": 59, "iter": 2600, "lr": 0.06675, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29547, "top5_acc": 0.55375, "loss_cls": 4.04028, "loss": 4.04028, "time": 0.81934} +{"mode": "train", "epoch": 59, "iter": 2700, "lr": 0.06672, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28406, "top5_acc": 0.55062, "loss_cls": 4.06761, "loss": 4.06761, "time": 0.812} +{"mode": "train", "epoch": 59, "iter": 2800, "lr": 0.06669, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29844, "top5_acc": 0.55656, "loss_cls": 4.01104, "loss": 4.01104, "time": 0.82261} +{"mode": "train", "epoch": 59, "iter": 2900, "lr": 0.06667, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28953, "top5_acc": 0.5375, "loss_cls": 4.07409, "loss": 4.07409, "time": 0.82278} +{"mode": "train", "epoch": 59, "iter": 3000, "lr": 0.06664, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28969, "top5_acc": 0.55406, "loss_cls": 4.02051, "loss": 4.02051, "time": 0.81789} +{"mode": "train", "epoch": 59, "iter": 3100, "lr": 0.06661, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30047, "top5_acc": 0.555, "loss_cls": 4.02883, "loss": 4.02883, "time": 0.819} +{"mode": "train", "epoch": 59, "iter": 3200, "lr": 0.06659, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29344, "top5_acc": 0.55547, "loss_cls": 3.99763, "loss": 3.99763, "time": 0.81461} +{"mode": "train", "epoch": 59, "iter": 3300, "lr": 0.06656, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30719, "top5_acc": 0.56375, "loss_cls": 3.95976, "loss": 3.95976, "time": 0.81878} +{"mode": "train", "epoch": 59, "iter": 3400, "lr": 0.06653, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28844, "top5_acc": 0.55, "loss_cls": 4.07154, "loss": 4.07154, "time": 0.81542} +{"mode": "train", "epoch": 59, "iter": 3500, "lr": 0.06651, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29406, "top5_acc": 0.55141, "loss_cls": 4.03834, "loss": 4.03834, "time": 0.81825} +{"mode": "train", "epoch": 59, "iter": 3600, "lr": 0.06648, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29438, "top5_acc": 0.55609, "loss_cls": 4.00842, "loss": 4.00842, "time": 0.81641} +{"mode": "train", "epoch": 59, "iter": 3700, "lr": 0.06646, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27688, "top5_acc": 0.54719, "loss_cls": 4.07107, "loss": 4.07107, "time": 0.81824} +{"mode": "val", "epoch": 59, "iter": 309, "lr": 0.06644, "top1_acc": 0.218, "top5_acc": 0.45834, "mean_class_accuracy": 0.21784} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.06642, "memory": 15990, "data_time": 1.21055, "top1_acc": 0.29625, "top5_acc": 0.5625, "loss_cls": 3.98151, "loss": 3.98151, "time": 2.17987} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.06639, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29594, "top5_acc": 0.56109, "loss_cls": 3.98024, "loss": 3.98024, "time": 0.82322} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.06636, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29875, "top5_acc": 0.55703, "loss_cls": 4.01897, "loss": 4.01897, "time": 0.81482} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.06634, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30406, "top5_acc": 0.54797, "loss_cls": 3.98749, "loss": 3.98749, "time": 0.81617} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.06631, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30578, "top5_acc": 0.56625, "loss_cls": 3.93836, "loss": 3.93836, "time": 0.81054} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.06629, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29688, "top5_acc": 0.56219, "loss_cls": 3.99887, "loss": 3.99887, "time": 0.81735} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.06626, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28844, "top5_acc": 0.54266, "loss_cls": 4.04106, "loss": 4.04106, "time": 0.82239} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.06623, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29906, "top5_acc": 0.55594, "loss_cls": 3.99317, "loss": 3.99317, "time": 0.8121} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.06621, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29484, "top5_acc": 0.55969, "loss_cls": 4.00706, "loss": 4.00706, "time": 0.8169} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.06618, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30031, "top5_acc": 0.55656, "loss_cls": 3.99602, "loss": 3.99602, "time": 0.82742} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.06615, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29375, "top5_acc": 0.55328, "loss_cls": 4.01489, "loss": 4.01489, "time": 0.81413} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.06613, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29953, "top5_acc": 0.56125, "loss_cls": 4.01995, "loss": 4.01995, "time": 0.81583} +{"mode": "train", "epoch": 60, "iter": 1300, "lr": 0.0661, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28766, "top5_acc": 0.54812, "loss_cls": 4.06013, "loss": 4.06013, "time": 0.82421} +{"mode": "train", "epoch": 60, "iter": 1400, "lr": 0.06607, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30188, "top5_acc": 0.5675, "loss_cls": 3.97114, "loss": 3.97114, "time": 0.81415} +{"mode": "train", "epoch": 60, "iter": 1500, "lr": 0.06605, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29656, "top5_acc": 0.55859, "loss_cls": 4.02987, "loss": 4.02987, "time": 0.81324} +{"mode": "train", "epoch": 60, "iter": 1600, "lr": 0.06602, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28359, "top5_acc": 0.54516, "loss_cls": 4.06664, "loss": 4.06664, "time": 0.82614} +{"mode": "train", "epoch": 60, "iter": 1700, "lr": 0.06599, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28922, "top5_acc": 0.55531, "loss_cls": 4.01222, "loss": 4.01222, "time": 0.81483} +{"mode": "train", "epoch": 60, "iter": 1800, "lr": 0.06597, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3, "top5_acc": 0.55469, "loss_cls": 4.02009, "loss": 4.02009, "time": 0.81469} +{"mode": "train", "epoch": 60, "iter": 1900, "lr": 0.06594, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29828, "top5_acc": 0.55703, "loss_cls": 4.0088, "loss": 4.0088, "time": 0.81416} +{"mode": "train", "epoch": 60, "iter": 2000, "lr": 0.06591, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29219, "top5_acc": 0.55719, "loss_cls": 4.00253, "loss": 4.00253, "time": 0.81628} +{"mode": "train", "epoch": 60, "iter": 2100, "lr": 0.06589, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29188, "top5_acc": 0.54797, "loss_cls": 4.06007, "loss": 4.06007, "time": 0.81524} +{"mode": "train", "epoch": 60, "iter": 2200, "lr": 0.06586, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30891, "top5_acc": 0.56391, "loss_cls": 3.98341, "loss": 3.98341, "time": 0.81458} +{"mode": "train", "epoch": 60, "iter": 2300, "lr": 0.06584, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29172, "top5_acc": 0.54937, "loss_cls": 4.03968, "loss": 4.03968, "time": 0.81139} +{"mode": "train", "epoch": 60, "iter": 2400, "lr": 0.06581, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29656, "top5_acc": 0.55375, "loss_cls": 4.01072, "loss": 4.01072, "time": 0.81315} +{"mode": "train", "epoch": 60, "iter": 2500, "lr": 0.06578, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29656, "top5_acc": 0.54656, "loss_cls": 4.07474, "loss": 4.07474, "time": 0.81057} +{"mode": "train", "epoch": 60, "iter": 2600, "lr": 0.06576, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29719, "top5_acc": 0.56047, "loss_cls": 4.02253, "loss": 4.02253, "time": 0.82279} +{"mode": "train", "epoch": 60, "iter": 2700, "lr": 0.06573, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29625, "top5_acc": 0.55547, "loss_cls": 4.02785, "loss": 4.02785, "time": 0.8134} +{"mode": "train", "epoch": 60, "iter": 2800, "lr": 0.0657, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29313, "top5_acc": 0.55203, "loss_cls": 4.01553, "loss": 4.01553, "time": 0.81682} +{"mode": "train", "epoch": 60, "iter": 2900, "lr": 0.06568, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29719, "top5_acc": 0.55906, "loss_cls": 4.00591, "loss": 4.00591, "time": 0.82671} +{"mode": "train", "epoch": 60, "iter": 3000, "lr": 0.06565, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29297, "top5_acc": 0.55953, "loss_cls": 3.99724, "loss": 3.99724, "time": 0.81587} +{"mode": "train", "epoch": 60, "iter": 3100, "lr": 0.06562, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30078, "top5_acc": 0.55891, "loss_cls": 3.96914, "loss": 3.96914, "time": 0.81419} +{"mode": "train", "epoch": 60, "iter": 3200, "lr": 0.0656, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30812, "top5_acc": 0.56625, "loss_cls": 3.96622, "loss": 3.96622, "time": 0.81747} +{"mode": "train", "epoch": 60, "iter": 3300, "lr": 0.06557, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29047, "top5_acc": 0.54328, "loss_cls": 4.05688, "loss": 4.05688, "time": 0.81758} +{"mode": "train", "epoch": 60, "iter": 3400, "lr": 0.06554, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29422, "top5_acc": 0.55266, "loss_cls": 4.03277, "loss": 4.03277, "time": 0.81067} +{"mode": "train", "epoch": 60, "iter": 3500, "lr": 0.06552, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30312, "top5_acc": 0.56266, "loss_cls": 3.9728, "loss": 3.9728, "time": 0.81361} +{"mode": "train", "epoch": 60, "iter": 3600, "lr": 0.06549, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29156, "top5_acc": 0.54562, "loss_cls": 4.02029, "loss": 4.02029, "time": 0.82264} +{"mode": "train", "epoch": 60, "iter": 3700, "lr": 0.06546, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27906, "top5_acc": 0.54391, "loss_cls": 4.05491, "loss": 4.05491, "time": 0.82073} +{"mode": "val", "epoch": 60, "iter": 309, "lr": 0.06545, "top1_acc": 0.21334, "top5_acc": 0.44988, "mean_class_accuracy": 0.21311} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.06542, "memory": 15990, "data_time": 1.28229, "top1_acc": 0.30516, "top5_acc": 0.56812, "loss_cls": 3.95471, "loss": 3.95471, "time": 2.26318} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.0654, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30703, "top5_acc": 0.56938, "loss_cls": 3.95999, "loss": 3.95999, "time": 0.83058} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.06537, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30078, "top5_acc": 0.56422, "loss_cls": 3.95519, "loss": 3.95519, "time": 0.82928} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.06534, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30391, "top5_acc": 0.56531, "loss_cls": 3.98136, "loss": 3.98136, "time": 0.82971} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.06532, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29719, "top5_acc": 0.56266, "loss_cls": 4.00054, "loss": 4.00054, "time": 0.82775} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.06529, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30391, "top5_acc": 0.56422, "loss_cls": 3.98738, "loss": 3.98738, "time": 0.8338} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.06526, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30375, "top5_acc": 0.56266, "loss_cls": 3.95583, "loss": 3.95583, "time": 0.83109} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.06524, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29703, "top5_acc": 0.55656, "loss_cls": 4.02471, "loss": 4.02471, "time": 0.8339} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.06521, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30109, "top5_acc": 0.57, "loss_cls": 3.95507, "loss": 3.95507, "time": 0.82939} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.06519, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29328, "top5_acc": 0.54734, "loss_cls": 4.01737, "loss": 4.01737, "time": 0.83139} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.06516, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29656, "top5_acc": 0.55797, "loss_cls": 4.00976, "loss": 4.00976, "time": 0.83021} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.06513, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.305, "top5_acc": 0.56344, "loss_cls": 3.97142, "loss": 3.97142, "time": 0.82778} +{"mode": "train", "epoch": 61, "iter": 1300, "lr": 0.06511, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29531, "top5_acc": 0.55953, "loss_cls": 4.02324, "loss": 4.02324, "time": 0.83059} +{"mode": "train", "epoch": 61, "iter": 1400, "lr": 0.06508, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.29688, "top5_acc": 0.55062, "loss_cls": 4.02257, "loss": 4.02257, "time": 0.82061} +{"mode": "train", "epoch": 61, "iter": 1500, "lr": 0.06505, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3, "top5_acc": 0.56344, "loss_cls": 3.99292, "loss": 3.99292, "time": 0.82411} +{"mode": "train", "epoch": 61, "iter": 1600, "lr": 0.06503, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29172, "top5_acc": 0.56188, "loss_cls": 4.00668, "loss": 4.00668, "time": 0.83161} +{"mode": "train", "epoch": 61, "iter": 1700, "lr": 0.065, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29406, "top5_acc": 0.55625, "loss_cls": 4.03449, "loss": 4.03449, "time": 0.82175} +{"mode": "train", "epoch": 61, "iter": 1800, "lr": 0.06497, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30609, "top5_acc": 0.55844, "loss_cls": 3.97565, "loss": 3.97565, "time": 0.82388} +{"mode": "train", "epoch": 61, "iter": 1900, "lr": 0.06495, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29891, "top5_acc": 0.55781, "loss_cls": 3.99278, "loss": 3.99278, "time": 0.81848} +{"mode": "train", "epoch": 61, "iter": 2000, "lr": 0.06492, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28719, "top5_acc": 0.54766, "loss_cls": 4.04338, "loss": 4.04338, "time": 0.81357} +{"mode": "train", "epoch": 61, "iter": 2100, "lr": 0.06489, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29797, "top5_acc": 0.56203, "loss_cls": 4.00247, "loss": 4.00247, "time": 0.8156} +{"mode": "train", "epoch": 61, "iter": 2200, "lr": 0.06487, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28891, "top5_acc": 0.545, "loss_cls": 4.05796, "loss": 4.05796, "time": 0.81846} +{"mode": "train", "epoch": 61, "iter": 2300, "lr": 0.06484, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29906, "top5_acc": 0.55875, "loss_cls": 4.00625, "loss": 4.00625, "time": 0.81473} +{"mode": "train", "epoch": 61, "iter": 2400, "lr": 0.06481, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30078, "top5_acc": 0.565, "loss_cls": 3.97129, "loss": 3.97129, "time": 0.81709} +{"mode": "train", "epoch": 61, "iter": 2500, "lr": 0.06478, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29219, "top5_acc": 0.55359, "loss_cls": 4.02973, "loss": 4.02973, "time": 0.81747} +{"mode": "train", "epoch": 61, "iter": 2600, "lr": 0.06476, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29328, "top5_acc": 0.55656, "loss_cls": 4.00356, "loss": 4.00356, "time": 0.82379} +{"mode": "train", "epoch": 61, "iter": 2700, "lr": 0.06473, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28844, "top5_acc": 0.55406, "loss_cls": 4.0195, "loss": 4.0195, "time": 0.81507} +{"mode": "train", "epoch": 61, "iter": 2800, "lr": 0.0647, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30391, "top5_acc": 0.56656, "loss_cls": 3.98402, "loss": 3.98402, "time": 0.81505} +{"mode": "train", "epoch": 61, "iter": 2900, "lr": 0.06468, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29703, "top5_acc": 0.55719, "loss_cls": 4.02234, "loss": 4.02234, "time": 0.82398} +{"mode": "train", "epoch": 61, "iter": 3000, "lr": 0.06465, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2975, "top5_acc": 0.55469, "loss_cls": 4.01159, "loss": 4.01159, "time": 0.82518} +{"mode": "train", "epoch": 61, "iter": 3100, "lr": 0.06462, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30516, "top5_acc": 0.55437, "loss_cls": 3.9955, "loss": 3.9955, "time": 0.8227} +{"mode": "train", "epoch": 61, "iter": 3200, "lr": 0.0646, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30016, "top5_acc": 0.55375, "loss_cls": 4.01477, "loss": 4.01477, "time": 0.81663} +{"mode": "train", "epoch": 61, "iter": 3300, "lr": 0.06457, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3, "top5_acc": 0.56437, "loss_cls": 3.98058, "loss": 3.98058, "time": 0.81681} +{"mode": "train", "epoch": 61, "iter": 3400, "lr": 0.06454, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29203, "top5_acc": 0.54391, "loss_cls": 4.05191, "loss": 4.05191, "time": 0.81773} +{"mode": "train", "epoch": 61, "iter": 3500, "lr": 0.06452, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30438, "top5_acc": 0.55469, "loss_cls": 4.00557, "loss": 4.00557, "time": 0.82021} +{"mode": "train", "epoch": 61, "iter": 3600, "lr": 0.06449, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29875, "top5_acc": 0.55906, "loss_cls": 3.99729, "loss": 3.99729, "time": 0.81383} +{"mode": "train", "epoch": 61, "iter": 3700, "lr": 0.06446, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3, "top5_acc": 0.55437, "loss_cls": 4.01747, "loss": 4.01747, "time": 0.82349} +{"mode": "val", "epoch": 61, "iter": 309, "lr": 0.06445, "top1_acc": 0.22347, "top5_acc": 0.46867, "mean_class_accuracy": 0.22322} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.06443, "memory": 15990, "data_time": 1.2333, "top1_acc": 0.30125, "top5_acc": 0.57, "loss_cls": 3.92034, "loss": 3.92034, "time": 2.21505} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.0644, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30812, "top5_acc": 0.55906, "loss_cls": 3.97083, "loss": 3.97083, "time": 0.81665} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.06437, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30016, "top5_acc": 0.55937, "loss_cls": 3.97994, "loss": 3.97994, "time": 0.81334} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.06434, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29547, "top5_acc": 0.55609, "loss_cls": 4.01174, "loss": 4.01174, "time": 0.81298} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.06432, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29016, "top5_acc": 0.56375, "loss_cls": 3.97575, "loss": 3.97575, "time": 0.81375} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.06429, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30297, "top5_acc": 0.56359, "loss_cls": 3.97158, "loss": 3.97158, "time": 0.8128} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.06426, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30688, "top5_acc": 0.56797, "loss_cls": 3.95106, "loss": 3.95106, "time": 0.81759} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.06424, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29328, "top5_acc": 0.55547, "loss_cls": 4.01858, "loss": 4.01858, "time": 0.81577} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.06421, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30312, "top5_acc": 0.56594, "loss_cls": 3.96415, "loss": 3.96415, "time": 0.81888} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.06418, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30094, "top5_acc": 0.55437, "loss_cls": 3.99546, "loss": 3.99546, "time": 0.82224} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.06416, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30297, "top5_acc": 0.55937, "loss_cls": 3.9829, "loss": 3.9829, "time": 0.81879} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.06413, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30234, "top5_acc": 0.55969, "loss_cls": 3.97733, "loss": 3.97733, "time": 0.81726} +{"mode": "train", "epoch": 62, "iter": 1300, "lr": 0.0641, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30844, "top5_acc": 0.56406, "loss_cls": 3.95301, "loss": 3.95301, "time": 0.82434} +{"mode": "train", "epoch": 62, "iter": 1400, "lr": 0.06408, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30453, "top5_acc": 0.56328, "loss_cls": 3.9657, "loss": 3.9657, "time": 0.82156} +{"mode": "train", "epoch": 62, "iter": 1500, "lr": 0.06405, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29703, "top5_acc": 0.55844, "loss_cls": 3.99211, "loss": 3.99211, "time": 0.81672} +{"mode": "train", "epoch": 62, "iter": 1600, "lr": 0.06402, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29953, "top5_acc": 0.56391, "loss_cls": 3.9869, "loss": 3.9869, "time": 0.82498} +{"mode": "train", "epoch": 62, "iter": 1700, "lr": 0.064, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30594, "top5_acc": 0.56578, "loss_cls": 3.97567, "loss": 3.97567, "time": 0.82087} +{"mode": "train", "epoch": 62, "iter": 1800, "lr": 0.06397, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30188, "top5_acc": 0.55781, "loss_cls": 3.98223, "loss": 3.98223, "time": 0.82189} +{"mode": "train", "epoch": 62, "iter": 1900, "lr": 0.06394, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30594, "top5_acc": 0.55797, "loss_cls": 3.98269, "loss": 3.98269, "time": 0.81214} +{"mode": "train", "epoch": 62, "iter": 2000, "lr": 0.06392, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29, "top5_acc": 0.55578, "loss_cls": 4.01276, "loss": 4.01276, "time": 0.8172} +{"mode": "train", "epoch": 62, "iter": 2100, "lr": 0.06389, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29812, "top5_acc": 0.55594, "loss_cls": 3.9945, "loss": 3.9945, "time": 0.82264} +{"mode": "train", "epoch": 62, "iter": 2200, "lr": 0.06386, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30453, "top5_acc": 0.55578, "loss_cls": 4.0072, "loss": 4.0072, "time": 0.81597} +{"mode": "train", "epoch": 62, "iter": 2300, "lr": 0.06384, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29141, "top5_acc": 0.55672, "loss_cls": 4.01838, "loss": 4.01838, "time": 0.81344} +{"mode": "train", "epoch": 62, "iter": 2400, "lr": 0.06381, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29516, "top5_acc": 0.55531, "loss_cls": 4.01342, "loss": 4.01342, "time": 0.81136} +{"mode": "train", "epoch": 62, "iter": 2500, "lr": 0.06378, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30562, "top5_acc": 0.56531, "loss_cls": 3.96851, "loss": 3.96851, "time": 0.81632} +{"mode": "train", "epoch": 62, "iter": 2600, "lr": 0.06375, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29688, "top5_acc": 0.55828, "loss_cls": 4.0132, "loss": 4.0132, "time": 0.82892} +{"mode": "train", "epoch": 62, "iter": 2700, "lr": 0.06373, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30312, "top5_acc": 0.56594, "loss_cls": 3.99345, "loss": 3.99345, "time": 0.82211} +{"mode": "train", "epoch": 62, "iter": 2800, "lr": 0.0637, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29953, "top5_acc": 0.5575, "loss_cls": 3.97025, "loss": 3.97025, "time": 0.81685} +{"mode": "train", "epoch": 62, "iter": 2900, "lr": 0.06367, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29984, "top5_acc": 0.55469, "loss_cls": 4.01425, "loss": 4.01425, "time": 0.82425} +{"mode": "train", "epoch": 62, "iter": 3000, "lr": 0.06365, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29219, "top5_acc": 0.54812, "loss_cls": 4.04309, "loss": 4.04309, "time": 0.81526} +{"mode": "train", "epoch": 62, "iter": 3100, "lr": 0.06362, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29438, "top5_acc": 0.555, "loss_cls": 4.0109, "loss": 4.0109, "time": 0.81777} +{"mode": "train", "epoch": 62, "iter": 3200, "lr": 0.06359, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29063, "top5_acc": 0.54531, "loss_cls": 4.0632, "loss": 4.0632, "time": 0.82211} +{"mode": "train", "epoch": 62, "iter": 3300, "lr": 0.06357, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29625, "top5_acc": 0.55766, "loss_cls": 4.01669, "loss": 4.01669, "time": 0.81881} +{"mode": "train", "epoch": 62, "iter": 3400, "lr": 0.06354, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30172, "top5_acc": 0.55359, "loss_cls": 4.01241, "loss": 4.01241, "time": 0.81732} +{"mode": "train", "epoch": 62, "iter": 3500, "lr": 0.06351, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29203, "top5_acc": 0.55141, "loss_cls": 4.00188, "loss": 4.00188, "time": 0.81646} +{"mode": "train", "epoch": 62, "iter": 3600, "lr": 0.06349, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30156, "top5_acc": 0.56719, "loss_cls": 3.97011, "loss": 3.97011, "time": 0.81255} +{"mode": "train", "epoch": 62, "iter": 3700, "lr": 0.06346, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29875, "top5_acc": 0.55031, "loss_cls": 4.03509, "loss": 4.03509, "time": 0.82935} +{"mode": "val", "epoch": 62, "iter": 309, "lr": 0.06345, "top1_acc": 0.23183, "top5_acc": 0.47353, "mean_class_accuracy": 0.23156} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.06342, "memory": 15990, "data_time": 1.24096, "top1_acc": 0.30734, "top5_acc": 0.57141, "loss_cls": 3.94302, "loss": 3.94302, "time": 2.22367} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.06339, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30234, "top5_acc": 0.56719, "loss_cls": 3.9686, "loss": 3.9686, "time": 0.82251} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.06337, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30703, "top5_acc": 0.56203, "loss_cls": 3.9461, "loss": 3.9461, "time": 0.82389} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.06334, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31031, "top5_acc": 0.57, "loss_cls": 3.93657, "loss": 3.93657, "time": 0.82102} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.06331, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30016, "top5_acc": 0.56484, "loss_cls": 3.96308, "loss": 3.96308, "time": 0.82237} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.06328, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30203, "top5_acc": 0.55297, "loss_cls": 4.00086, "loss": 4.00086, "time": 0.82394} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.06326, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29578, "top5_acc": 0.55625, "loss_cls": 3.97866, "loss": 3.97866, "time": 0.8272} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.06323, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29313, "top5_acc": 0.55641, "loss_cls": 4.03169, "loss": 4.03169, "time": 0.82115} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.0632, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30141, "top5_acc": 0.55812, "loss_cls": 3.96871, "loss": 3.96871, "time": 0.81955} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.06318, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.29359, "top5_acc": 0.56312, "loss_cls": 3.99136, "loss": 3.99136, "time": 0.82302} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.06315, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30219, "top5_acc": 0.56312, "loss_cls": 3.97337, "loss": 3.97337, "time": 0.81698} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.06312, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30375, "top5_acc": 0.56172, "loss_cls": 3.97154, "loss": 3.97154, "time": 0.81448} +{"mode": "train", "epoch": 63, "iter": 1300, "lr": 0.0631, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29984, "top5_acc": 0.56484, "loss_cls": 3.96116, "loss": 3.96116, "time": 0.82694} +{"mode": "train", "epoch": 63, "iter": 1400, "lr": 0.06307, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.29281, "top5_acc": 0.55062, "loss_cls": 4.01836, "loss": 4.01836, "time": 0.81798} +{"mode": "train", "epoch": 63, "iter": 1500, "lr": 0.06304, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31422, "top5_acc": 0.57359, "loss_cls": 3.92033, "loss": 3.92033, "time": 0.81346} +{"mode": "train", "epoch": 63, "iter": 1600, "lr": 0.06301, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29969, "top5_acc": 0.56297, "loss_cls": 3.97725, "loss": 3.97725, "time": 0.82522} +{"mode": "train", "epoch": 63, "iter": 1700, "lr": 0.06299, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30047, "top5_acc": 0.55344, "loss_cls": 4.00689, "loss": 4.00689, "time": 0.81832} +{"mode": "train", "epoch": 63, "iter": 1800, "lr": 0.06296, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29547, "top5_acc": 0.56, "loss_cls": 3.98295, "loss": 3.98295, "time": 0.81538} +{"mode": "train", "epoch": 63, "iter": 1900, "lr": 0.06293, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30453, "top5_acc": 0.56516, "loss_cls": 3.96863, "loss": 3.96863, "time": 0.81709} +{"mode": "train", "epoch": 63, "iter": 2000, "lr": 0.06291, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30438, "top5_acc": 0.56297, "loss_cls": 3.98628, "loss": 3.98628, "time": 0.81442} +{"mode": "train", "epoch": 63, "iter": 2100, "lr": 0.06288, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30688, "top5_acc": 0.56094, "loss_cls": 3.97621, "loss": 3.97621, "time": 0.81468} +{"mode": "train", "epoch": 63, "iter": 2200, "lr": 0.06285, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29609, "top5_acc": 0.55969, "loss_cls": 4.00532, "loss": 4.00532, "time": 0.81183} +{"mode": "train", "epoch": 63, "iter": 2300, "lr": 0.06283, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29703, "top5_acc": 0.55078, "loss_cls": 4.0272, "loss": 4.0272, "time": 0.81315} +{"mode": "train", "epoch": 63, "iter": 2400, "lr": 0.0628, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31406, "top5_acc": 0.56672, "loss_cls": 3.93028, "loss": 3.93028, "time": 0.81151} +{"mode": "train", "epoch": 63, "iter": 2500, "lr": 0.06277, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29891, "top5_acc": 0.55562, "loss_cls": 4.00162, "loss": 4.00162, "time": 0.81186} +{"mode": "train", "epoch": 63, "iter": 2600, "lr": 0.06274, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29969, "top5_acc": 0.56141, "loss_cls": 3.98525, "loss": 3.98525, "time": 0.82686} +{"mode": "train", "epoch": 63, "iter": 2700, "lr": 0.06272, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30109, "top5_acc": 0.56031, "loss_cls": 3.99686, "loss": 3.99686, "time": 0.813} +{"mode": "train", "epoch": 63, "iter": 2800, "lr": 0.06269, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30641, "top5_acc": 0.55594, "loss_cls": 3.97933, "loss": 3.97933, "time": 0.8151} +{"mode": "train", "epoch": 63, "iter": 2900, "lr": 0.06266, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29047, "top5_acc": 0.54922, "loss_cls": 4.02025, "loss": 4.02025, "time": 0.8221} +{"mode": "train", "epoch": 63, "iter": 3000, "lr": 0.06264, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30203, "top5_acc": 0.56109, "loss_cls": 4.00042, "loss": 4.00042, "time": 0.81044} +{"mode": "train", "epoch": 63, "iter": 3100, "lr": 0.06261, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28984, "top5_acc": 0.55453, "loss_cls": 4.039, "loss": 4.039, "time": 0.8128} +{"mode": "train", "epoch": 63, "iter": 3200, "lr": 0.06258, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30859, "top5_acc": 0.5625, "loss_cls": 3.97491, "loss": 3.97491, "time": 0.81401} +{"mode": "train", "epoch": 63, "iter": 3300, "lr": 0.06256, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30031, "top5_acc": 0.55437, "loss_cls": 3.99514, "loss": 3.99514, "time": 0.81773} +{"mode": "train", "epoch": 63, "iter": 3400, "lr": 0.06253, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31109, "top5_acc": 0.56609, "loss_cls": 3.93621, "loss": 3.93621, "time": 0.81182} +{"mode": "train", "epoch": 63, "iter": 3500, "lr": 0.0625, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30078, "top5_acc": 0.55203, "loss_cls": 4.01689, "loss": 4.01689, "time": 0.81732} +{"mode": "train", "epoch": 63, "iter": 3600, "lr": 0.06247, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2975, "top5_acc": 0.55922, "loss_cls": 3.97775, "loss": 3.97775, "time": 0.8134} +{"mode": "train", "epoch": 63, "iter": 3700, "lr": 0.06245, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29094, "top5_acc": 0.55672, "loss_cls": 4.01439, "loss": 4.01439, "time": 0.82328} +{"mode": "val", "epoch": 63, "iter": 309, "lr": 0.06243, "top1_acc": 0.21476, "top5_acc": 0.44081, "mean_class_accuracy": 0.21457} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.06241, "memory": 15990, "data_time": 1.28202, "top1_acc": 0.30922, "top5_acc": 0.56375, "loss_cls": 3.95377, "loss": 3.95377, "time": 2.27387} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.06238, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30391, "top5_acc": 0.56219, "loss_cls": 3.94586, "loss": 3.94586, "time": 0.82018} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.06235, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29766, "top5_acc": 0.55734, "loss_cls": 3.99879, "loss": 3.99879, "time": 0.82087} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.06233, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30344, "top5_acc": 0.57469, "loss_cls": 3.94802, "loss": 3.94802, "time": 0.81959} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.0623, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30672, "top5_acc": 0.56688, "loss_cls": 3.94528, "loss": 3.94528, "time": 0.8206} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.06227, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30969, "top5_acc": 0.57563, "loss_cls": 3.92648, "loss": 3.92648, "time": 0.81755} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.06225, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29906, "top5_acc": 0.56656, "loss_cls": 3.95805, "loss": 3.95805, "time": 0.82384} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.06222, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29516, "top5_acc": 0.55625, "loss_cls": 3.98844, "loss": 3.98844, "time": 0.82064} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.06219, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2975, "top5_acc": 0.56906, "loss_cls": 3.96715, "loss": 3.96715, "time": 0.82233} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.06216, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29469, "top5_acc": 0.55688, "loss_cls": 3.97993, "loss": 3.97993, "time": 0.82222} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.06214, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30609, "top5_acc": 0.56359, "loss_cls": 3.966, "loss": 3.966, "time": 0.81944} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.06211, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30016, "top5_acc": 0.56766, "loss_cls": 3.9225, "loss": 3.9225, "time": 0.82364} +{"mode": "train", "epoch": 64, "iter": 1300, "lr": 0.06208, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30281, "top5_acc": 0.56234, "loss_cls": 3.96187, "loss": 3.96187, "time": 0.82768} +{"mode": "train", "epoch": 64, "iter": 1400, "lr": 0.06206, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29172, "top5_acc": 0.56094, "loss_cls": 4.01531, "loss": 4.01531, "time": 0.82211} +{"mode": "train", "epoch": 64, "iter": 1500, "lr": 0.06203, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30984, "top5_acc": 0.57547, "loss_cls": 3.93066, "loss": 3.93066, "time": 0.81774} +{"mode": "train", "epoch": 64, "iter": 1600, "lr": 0.062, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30281, "top5_acc": 0.55797, "loss_cls": 3.9982, "loss": 3.9982, "time": 0.81667} +{"mode": "train", "epoch": 64, "iter": 1700, "lr": 0.06197, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30359, "top5_acc": 0.55812, "loss_cls": 3.97392, "loss": 3.97392, "time": 0.81265} +{"mode": "train", "epoch": 64, "iter": 1800, "lr": 0.06195, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30156, "top5_acc": 0.56547, "loss_cls": 3.98585, "loss": 3.98585, "time": 0.81689} +{"mode": "train", "epoch": 64, "iter": 1900, "lr": 0.06192, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29469, "top5_acc": 0.55328, "loss_cls": 4.01773, "loss": 4.01773, "time": 0.82316} +{"mode": "train", "epoch": 64, "iter": 2000, "lr": 0.06189, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29766, "top5_acc": 0.56281, "loss_cls": 3.99871, "loss": 3.99871, "time": 0.81756} +{"mode": "train", "epoch": 64, "iter": 2100, "lr": 0.06187, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30938, "top5_acc": 0.55953, "loss_cls": 3.97943, "loss": 3.97943, "time": 0.82124} +{"mode": "train", "epoch": 64, "iter": 2200, "lr": 0.06184, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29594, "top5_acc": 0.54828, "loss_cls": 4.02365, "loss": 4.02365, "time": 0.82283} +{"mode": "train", "epoch": 64, "iter": 2300, "lr": 0.06181, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31453, "top5_acc": 0.56359, "loss_cls": 3.93985, "loss": 3.93985, "time": 0.81798} +{"mode": "train", "epoch": 64, "iter": 2400, "lr": 0.06178, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30812, "top5_acc": 0.56969, "loss_cls": 3.92928, "loss": 3.92928, "time": 0.8166} +{"mode": "train", "epoch": 64, "iter": 2500, "lr": 0.06176, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29016, "top5_acc": 0.55031, "loss_cls": 4.03311, "loss": 4.03311, "time": 0.81688} +{"mode": "train", "epoch": 64, "iter": 2600, "lr": 0.06173, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30172, "top5_acc": 0.55766, "loss_cls": 3.99124, "loss": 3.99124, "time": 0.81833} +{"mode": "train", "epoch": 64, "iter": 2700, "lr": 0.0617, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.305, "top5_acc": 0.56297, "loss_cls": 3.97166, "loss": 3.97166, "time": 0.81674} +{"mode": "train", "epoch": 64, "iter": 2800, "lr": 0.06168, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32172, "top5_acc": 0.57422, "loss_cls": 3.88313, "loss": 3.88313, "time": 0.82164} +{"mode": "train", "epoch": 64, "iter": 2900, "lr": 0.06165, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29078, "top5_acc": 0.5425, "loss_cls": 4.03753, "loss": 4.03753, "time": 0.81899} +{"mode": "train", "epoch": 64, "iter": 3000, "lr": 0.06162, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31234, "top5_acc": 0.55969, "loss_cls": 3.95526, "loss": 3.95526, "time": 0.82224} +{"mode": "train", "epoch": 64, "iter": 3100, "lr": 0.06159, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28984, "top5_acc": 0.56359, "loss_cls": 3.9765, "loss": 3.9765, "time": 0.81936} +{"mode": "train", "epoch": 64, "iter": 3200, "lr": 0.06157, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30812, "top5_acc": 0.56063, "loss_cls": 3.96912, "loss": 3.96912, "time": 0.81628} +{"mode": "train", "epoch": 64, "iter": 3300, "lr": 0.06154, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30359, "top5_acc": 0.56219, "loss_cls": 3.96701, "loss": 3.96701, "time": 0.82029} +{"mode": "train", "epoch": 64, "iter": 3400, "lr": 0.06151, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30141, "top5_acc": 0.56359, "loss_cls": 3.99061, "loss": 3.99061, "time": 0.81916} +{"mode": "train", "epoch": 64, "iter": 3500, "lr": 0.06148, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28969, "top5_acc": 0.55359, "loss_cls": 4.05258, "loss": 4.05258, "time": 0.81946} +{"mode": "train", "epoch": 64, "iter": 3600, "lr": 0.06146, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29875, "top5_acc": 0.55672, "loss_cls": 4.00483, "loss": 4.00483, "time": 0.82441} +{"mode": "train", "epoch": 64, "iter": 3700, "lr": 0.06143, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30266, "top5_acc": 0.55937, "loss_cls": 3.99908, "loss": 3.99908, "time": 0.81975} +{"mode": "val", "epoch": 64, "iter": 309, "lr": 0.06142, "top1_acc": 0.22869, "top5_acc": 0.46021, "mean_class_accuracy": 0.22842} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.06139, "memory": 15990, "data_time": 1.32412, "top1_acc": 0.31234, "top5_acc": 0.56453, "loss_cls": 3.91976, "loss": 3.91976, "time": 2.31903} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.06136, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31438, "top5_acc": 0.57828, "loss_cls": 3.87352, "loss": 3.87352, "time": 0.83281} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.06134, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30547, "top5_acc": 0.56406, "loss_cls": 3.93547, "loss": 3.93547, "time": 0.83131} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.06131, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31391, "top5_acc": 0.56625, "loss_cls": 3.93883, "loss": 3.93883, "time": 0.83068} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.06128, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30297, "top5_acc": 0.57766, "loss_cls": 3.92744, "loss": 3.92744, "time": 0.83533} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.06125, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30859, "top5_acc": 0.56703, "loss_cls": 3.94671, "loss": 3.94671, "time": 0.82885} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.06123, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30797, "top5_acc": 0.55578, "loss_cls": 3.97587, "loss": 3.97587, "time": 0.83565} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0612, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31297, "top5_acc": 0.56719, "loss_cls": 3.92417, "loss": 3.92417, "time": 0.82378} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.06117, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30656, "top5_acc": 0.57594, "loss_cls": 3.92063, "loss": 3.92063, "time": 0.82597} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.06115, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3125, "top5_acc": 0.57375, "loss_cls": 3.95303, "loss": 3.95303, "time": 0.82544} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.06112, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29453, "top5_acc": 0.55484, "loss_cls": 3.99308, "loss": 3.99308, "time": 0.83242} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.06109, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30422, "top5_acc": 0.56375, "loss_cls": 3.95453, "loss": 3.95453, "time": 0.82562} +{"mode": "train", "epoch": 65, "iter": 1300, "lr": 0.06106, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.30969, "top5_acc": 0.56328, "loss_cls": 3.96913, "loss": 3.96913, "time": 0.82627} +{"mode": "train", "epoch": 65, "iter": 1400, "lr": 0.06104, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29688, "top5_acc": 0.57078, "loss_cls": 3.94321, "loss": 3.94321, "time": 0.82749} +{"mode": "train", "epoch": 65, "iter": 1500, "lr": 0.06101, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29672, "top5_acc": 0.56094, "loss_cls": 3.98752, "loss": 3.98752, "time": 0.82544} +{"mode": "train", "epoch": 65, "iter": 1600, "lr": 0.06098, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29313, "top5_acc": 0.55078, "loss_cls": 4.01375, "loss": 4.01375, "time": 0.827} +{"mode": "train", "epoch": 65, "iter": 1700, "lr": 0.06095, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30859, "top5_acc": 0.57703, "loss_cls": 3.92399, "loss": 3.92399, "time": 0.83214} +{"mode": "train", "epoch": 65, "iter": 1800, "lr": 0.06093, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29859, "top5_acc": 0.56422, "loss_cls": 3.96875, "loss": 3.96875, "time": 0.83269} +{"mode": "train", "epoch": 65, "iter": 1900, "lr": 0.0609, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30734, "top5_acc": 0.56812, "loss_cls": 3.95757, "loss": 3.95757, "time": 0.82763} +{"mode": "train", "epoch": 65, "iter": 2000, "lr": 0.06087, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29984, "top5_acc": 0.55953, "loss_cls": 3.96879, "loss": 3.96879, "time": 0.8236} +{"mode": "train", "epoch": 65, "iter": 2100, "lr": 0.06085, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30141, "top5_acc": 0.56312, "loss_cls": 3.98334, "loss": 3.98334, "time": 0.82751} +{"mode": "train", "epoch": 65, "iter": 2200, "lr": 0.06082, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29844, "top5_acc": 0.55703, "loss_cls": 3.98659, "loss": 3.98659, "time": 0.82769} +{"mode": "train", "epoch": 65, "iter": 2300, "lr": 0.06079, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30219, "top5_acc": 0.56219, "loss_cls": 3.97117, "loss": 3.97117, "time": 0.82988} +{"mode": "train", "epoch": 65, "iter": 2400, "lr": 0.06076, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29625, "top5_acc": 0.56094, "loss_cls": 3.97919, "loss": 3.97919, "time": 0.82381} +{"mode": "train", "epoch": 65, "iter": 2500, "lr": 0.06074, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30109, "top5_acc": 0.56031, "loss_cls": 3.97371, "loss": 3.97371, "time": 0.83311} +{"mode": "train", "epoch": 65, "iter": 2600, "lr": 0.06071, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30203, "top5_acc": 0.55688, "loss_cls": 3.99258, "loss": 3.99258, "time": 0.82599} +{"mode": "train", "epoch": 65, "iter": 2700, "lr": 0.06068, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31422, "top5_acc": 0.56969, "loss_cls": 3.94194, "loss": 3.94194, "time": 0.82262} +{"mode": "train", "epoch": 65, "iter": 2800, "lr": 0.06065, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29953, "top5_acc": 0.56734, "loss_cls": 3.95624, "loss": 3.95624, "time": 0.82199} +{"mode": "train", "epoch": 65, "iter": 2900, "lr": 0.06063, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30359, "top5_acc": 0.56656, "loss_cls": 3.95579, "loss": 3.95579, "time": 0.83144} +{"mode": "train", "epoch": 65, "iter": 3000, "lr": 0.0606, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29, "top5_acc": 0.55203, "loss_cls": 4.03261, "loss": 4.03261, "time": 0.82596} +{"mode": "train", "epoch": 65, "iter": 3100, "lr": 0.06057, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31109, "top5_acc": 0.57281, "loss_cls": 3.94627, "loss": 3.94627, "time": 0.82337} +{"mode": "train", "epoch": 65, "iter": 3200, "lr": 0.06055, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31203, "top5_acc": 0.56031, "loss_cls": 3.96016, "loss": 3.96016, "time": 0.82413} +{"mode": "train", "epoch": 65, "iter": 3300, "lr": 0.06052, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30375, "top5_acc": 0.57016, "loss_cls": 3.94951, "loss": 3.94951, "time": 0.82484} +{"mode": "train", "epoch": 65, "iter": 3400, "lr": 0.06049, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29938, "top5_acc": 0.5625, "loss_cls": 3.98807, "loss": 3.98807, "time": 0.82552} +{"mode": "train", "epoch": 65, "iter": 3500, "lr": 0.06046, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31047, "top5_acc": 0.56234, "loss_cls": 3.95075, "loss": 3.95075, "time": 0.82143} +{"mode": "train", "epoch": 65, "iter": 3600, "lr": 0.06044, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30312, "top5_acc": 0.5525, "loss_cls": 4.01807, "loss": 4.01807, "time": 0.83495} +{"mode": "train", "epoch": 65, "iter": 3700, "lr": 0.06041, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30203, "top5_acc": 0.56203, "loss_cls": 3.98291, "loss": 3.98291, "time": 0.82974} +{"mode": "val", "epoch": 65, "iter": 309, "lr": 0.0604, "top1_acc": 0.2296, "top5_acc": 0.46766, "mean_class_accuracy": 0.22938} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.06037, "memory": 15990, "data_time": 1.28766, "top1_acc": 0.30375, "top5_acc": 0.56891, "loss_cls": 3.95148, "loss": 3.95148, "time": 2.26883} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.06034, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31156, "top5_acc": 0.57469, "loss_cls": 3.90105, "loss": 3.90105, "time": 0.82401} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.06031, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31156, "top5_acc": 0.56797, "loss_cls": 3.94124, "loss": 3.94124, "time": 0.83291} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.06029, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.305, "top5_acc": 0.57094, "loss_cls": 3.92044, "loss": 3.92044, "time": 0.82761} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.06026, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31547, "top5_acc": 0.57828, "loss_cls": 3.87014, "loss": 3.87014, "time": 0.81656} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.06023, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30203, "top5_acc": 0.55734, "loss_cls": 3.98233, "loss": 3.98233, "time": 0.81812} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.0602, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32094, "top5_acc": 0.57984, "loss_cls": 3.84403, "loss": 3.84403, "time": 0.81754} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.06018, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30484, "top5_acc": 0.56906, "loss_cls": 3.95875, "loss": 3.95875, "time": 0.8191} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.06015, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30672, "top5_acc": 0.57359, "loss_cls": 3.94982, "loss": 3.94982, "time": 0.82844} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.06012, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31125, "top5_acc": 0.56625, "loss_cls": 3.94526, "loss": 3.94526, "time": 0.82052} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.06009, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30156, "top5_acc": 0.55953, "loss_cls": 3.97527, "loss": 3.97527, "time": 0.82467} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.06007, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31203, "top5_acc": 0.56594, "loss_cls": 3.94559, "loss": 3.94559, "time": 0.82077} +{"mode": "train", "epoch": 66, "iter": 1300, "lr": 0.06004, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29641, "top5_acc": 0.5525, "loss_cls": 3.98871, "loss": 3.98871, "time": 0.82917} +{"mode": "train", "epoch": 66, "iter": 1400, "lr": 0.06001, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30781, "top5_acc": 0.57266, "loss_cls": 3.94437, "loss": 3.94437, "time": 0.82059} +{"mode": "train", "epoch": 66, "iter": 1500, "lr": 0.05999, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30641, "top5_acc": 0.57047, "loss_cls": 3.92672, "loss": 3.92672, "time": 0.82642} +{"mode": "train", "epoch": 66, "iter": 1600, "lr": 0.05996, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30812, "top5_acc": 0.55797, "loss_cls": 3.98893, "loss": 3.98893, "time": 0.82644} +{"mode": "train", "epoch": 66, "iter": 1700, "lr": 0.05993, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31047, "top5_acc": 0.57281, "loss_cls": 3.91875, "loss": 3.91875, "time": 0.82756} +{"mode": "train", "epoch": 66, "iter": 1800, "lr": 0.0599, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30594, "top5_acc": 0.56688, "loss_cls": 3.98125, "loss": 3.98125, "time": 0.81777} +{"mode": "train", "epoch": 66, "iter": 1900, "lr": 0.05988, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30844, "top5_acc": 0.56281, "loss_cls": 3.9578, "loss": 3.9578, "time": 0.8221} +{"mode": "train", "epoch": 66, "iter": 2000, "lr": 0.05985, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30531, "top5_acc": 0.55719, "loss_cls": 3.9613, "loss": 3.9613, "time": 0.81702} +{"mode": "train", "epoch": 66, "iter": 2100, "lr": 0.05982, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30719, "top5_acc": 0.56078, "loss_cls": 3.96257, "loss": 3.96257, "time": 0.81497} +{"mode": "train", "epoch": 66, "iter": 2200, "lr": 0.05979, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31859, "top5_acc": 0.57578, "loss_cls": 3.89942, "loss": 3.89942, "time": 0.82174} +{"mode": "train", "epoch": 66, "iter": 2300, "lr": 0.05977, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29281, "top5_acc": 0.55719, "loss_cls": 4.02411, "loss": 4.02411, "time": 0.81961} +{"mode": "train", "epoch": 66, "iter": 2400, "lr": 0.05974, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31703, "top5_acc": 0.56594, "loss_cls": 3.94899, "loss": 3.94899, "time": 0.82083} +{"mode": "train", "epoch": 66, "iter": 2500, "lr": 0.05971, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31266, "top5_acc": 0.56922, "loss_cls": 3.93481, "loss": 3.93481, "time": 0.82223} +{"mode": "train", "epoch": 66, "iter": 2600, "lr": 0.05968, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29328, "top5_acc": 0.56109, "loss_cls": 4.00033, "loss": 4.00033, "time": 0.82032} +{"mode": "train", "epoch": 66, "iter": 2700, "lr": 0.05966, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30141, "top5_acc": 0.56063, "loss_cls": 3.96543, "loss": 3.96543, "time": 0.82318} +{"mode": "train", "epoch": 66, "iter": 2800, "lr": 0.05963, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30109, "top5_acc": 0.56547, "loss_cls": 3.97388, "loss": 3.97388, "time": 0.81686} +{"mode": "train", "epoch": 66, "iter": 2900, "lr": 0.0596, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29656, "top5_acc": 0.56422, "loss_cls": 3.9833, "loss": 3.9833, "time": 0.81974} +{"mode": "train", "epoch": 66, "iter": 3000, "lr": 0.05957, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32953, "top5_acc": 0.57922, "loss_cls": 3.89332, "loss": 3.89332, "time": 0.81374} +{"mode": "train", "epoch": 66, "iter": 3100, "lr": 0.05955, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29391, "top5_acc": 0.56328, "loss_cls": 3.97802, "loss": 3.97802, "time": 0.81597} +{"mode": "train", "epoch": 66, "iter": 3200, "lr": 0.05952, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30203, "top5_acc": 0.55891, "loss_cls": 3.98036, "loss": 3.98036, "time": 0.8262} +{"mode": "train", "epoch": 66, "iter": 3300, "lr": 0.05949, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29781, "top5_acc": 0.56125, "loss_cls": 3.97517, "loss": 3.97517, "time": 0.82587} +{"mode": "train", "epoch": 66, "iter": 3400, "lr": 0.05946, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30719, "top5_acc": 0.56312, "loss_cls": 3.93209, "loss": 3.93209, "time": 0.81964} +{"mode": "train", "epoch": 66, "iter": 3500, "lr": 0.05944, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.3075, "top5_acc": 0.56672, "loss_cls": 3.93107, "loss": 3.93107, "time": 0.82743} +{"mode": "train", "epoch": 66, "iter": 3600, "lr": 0.05941, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29688, "top5_acc": 0.56109, "loss_cls": 3.9953, "loss": 3.9953, "time": 0.82504} +{"mode": "train", "epoch": 66, "iter": 3700, "lr": 0.05938, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30156, "top5_acc": 0.55828, "loss_cls": 3.9801, "loss": 3.9801, "time": 0.82533} +{"mode": "val", "epoch": 66, "iter": 309, "lr": 0.05937, "top1_acc": 0.24753, "top5_acc": 0.4983, "mean_class_accuracy": 0.24724} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.05934, "memory": 15990, "data_time": 1.3058, "top1_acc": 0.31469, "top5_acc": 0.57766, "loss_cls": 3.87919, "loss": 3.87919, "time": 2.28328} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.05931, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32344, "top5_acc": 0.58031, "loss_cls": 3.85783, "loss": 3.85783, "time": 0.81632} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.05929, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31, "top5_acc": 0.55891, "loss_cls": 3.95439, "loss": 3.95439, "time": 0.82226} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.05926, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30891, "top5_acc": 0.56437, "loss_cls": 3.94633, "loss": 3.94633, "time": 0.82238} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.05923, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29672, "top5_acc": 0.56203, "loss_cls": 3.99623, "loss": 3.99623, "time": 0.81509} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.0592, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30922, "top5_acc": 0.57, "loss_cls": 3.92116, "loss": 3.92116, "time": 0.81817} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.05918, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30734, "top5_acc": 0.55547, "loss_cls": 3.95825, "loss": 3.95825, "time": 0.81719} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.05915, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30734, "top5_acc": 0.56375, "loss_cls": 3.98832, "loss": 3.98832, "time": 0.81837} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.05912, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30375, "top5_acc": 0.56344, "loss_cls": 3.93768, "loss": 3.93768, "time": 0.81414} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.05909, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30156, "top5_acc": 0.55641, "loss_cls": 4.00037, "loss": 4.00037, "time": 0.81919} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.05907, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29594, "top5_acc": 0.56234, "loss_cls": 3.96691, "loss": 3.96691, "time": 0.81491} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.05904, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31141, "top5_acc": 0.575, "loss_cls": 3.92775, "loss": 3.92775, "time": 0.81823} +{"mode": "train", "epoch": 67, "iter": 1300, "lr": 0.05901, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31562, "top5_acc": 0.57797, "loss_cls": 3.88129, "loss": 3.88129, "time": 0.82966} +{"mode": "train", "epoch": 67, "iter": 1400, "lr": 0.05898, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30391, "top5_acc": 0.56406, "loss_cls": 3.93481, "loss": 3.93481, "time": 0.82182} +{"mode": "train", "epoch": 67, "iter": 1500, "lr": 0.05896, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30719, "top5_acc": 0.56547, "loss_cls": 3.95241, "loss": 3.95241, "time": 0.82112} +{"mode": "train", "epoch": 67, "iter": 1600, "lr": 0.05893, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29812, "top5_acc": 0.56688, "loss_cls": 3.9827, "loss": 3.9827, "time": 0.81877} +{"mode": "train", "epoch": 67, "iter": 1700, "lr": 0.0589, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31547, "top5_acc": 0.57266, "loss_cls": 3.91663, "loss": 3.91663, "time": 0.81577} +{"mode": "train", "epoch": 67, "iter": 1800, "lr": 0.05887, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30781, "top5_acc": 0.56406, "loss_cls": 3.92694, "loss": 3.92694, "time": 0.81672} +{"mode": "train", "epoch": 67, "iter": 1900, "lr": 0.05885, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29688, "top5_acc": 0.56781, "loss_cls": 3.95977, "loss": 3.95977, "time": 0.81779} +{"mode": "train", "epoch": 67, "iter": 2000, "lr": 0.05882, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30312, "top5_acc": 0.57219, "loss_cls": 3.9342, "loss": 3.9342, "time": 0.81712} +{"mode": "train", "epoch": 67, "iter": 2100, "lr": 0.05879, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31312, "top5_acc": 0.56234, "loss_cls": 3.92831, "loss": 3.92831, "time": 0.81499} +{"mode": "train", "epoch": 67, "iter": 2200, "lr": 0.05876, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30297, "top5_acc": 0.56281, "loss_cls": 3.95394, "loss": 3.95394, "time": 0.8205} +{"mode": "train", "epoch": 67, "iter": 2300, "lr": 0.05874, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30328, "top5_acc": 0.55937, "loss_cls": 3.9652, "loss": 3.9652, "time": 0.81785} +{"mode": "train", "epoch": 67, "iter": 2400, "lr": 0.05871, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30844, "top5_acc": 0.56703, "loss_cls": 3.92451, "loss": 3.92451, "time": 0.81565} +{"mode": "train", "epoch": 67, "iter": 2500, "lr": 0.05868, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30203, "top5_acc": 0.56828, "loss_cls": 3.95257, "loss": 3.95257, "time": 0.8233} +{"mode": "train", "epoch": 67, "iter": 2600, "lr": 0.05865, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29891, "top5_acc": 0.55828, "loss_cls": 3.99326, "loss": 3.99326, "time": 0.82106} +{"mode": "train", "epoch": 67, "iter": 2700, "lr": 0.05863, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30781, "top5_acc": 0.575, "loss_cls": 3.8958, "loss": 3.8958, "time": 0.81755} +{"mode": "train", "epoch": 67, "iter": 2800, "lr": 0.0586, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29766, "top5_acc": 0.57406, "loss_cls": 3.95459, "loss": 3.95459, "time": 0.81965} +{"mode": "train", "epoch": 67, "iter": 2900, "lr": 0.05857, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30875, "top5_acc": 0.58109, "loss_cls": 3.9065, "loss": 3.9065, "time": 0.82004} +{"mode": "train", "epoch": 67, "iter": 3000, "lr": 0.05854, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30391, "top5_acc": 0.56984, "loss_cls": 3.93912, "loss": 3.93912, "time": 0.81771} +{"mode": "train", "epoch": 67, "iter": 3100, "lr": 0.05852, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32031, "top5_acc": 0.57047, "loss_cls": 3.91542, "loss": 3.91542, "time": 0.81632} +{"mode": "train", "epoch": 67, "iter": 3200, "lr": 0.05849, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31625, "top5_acc": 0.57781, "loss_cls": 3.91849, "loss": 3.91849, "time": 0.81644} +{"mode": "train", "epoch": 67, "iter": 3300, "lr": 0.05846, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31172, "top5_acc": 0.56609, "loss_cls": 3.9485, "loss": 3.9485, "time": 0.81558} +{"mode": "train", "epoch": 67, "iter": 3400, "lr": 0.05843, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29984, "top5_acc": 0.56297, "loss_cls": 3.94585, "loss": 3.94585, "time": 0.81614} +{"mode": "train", "epoch": 67, "iter": 3500, "lr": 0.05841, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31141, "top5_acc": 0.56859, "loss_cls": 3.92791, "loss": 3.92791, "time": 0.82773} +{"mode": "train", "epoch": 67, "iter": 3600, "lr": 0.05838, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29609, "top5_acc": 0.56266, "loss_cls": 3.99698, "loss": 3.99698, "time": 0.82516} +{"mode": "train", "epoch": 67, "iter": 3700, "lr": 0.05835, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30672, "top5_acc": 0.57375, "loss_cls": 3.94008, "loss": 3.94008, "time": 0.82618} +{"mode": "val", "epoch": 67, "iter": 309, "lr": 0.05834, "top1_acc": 0.22879, "top5_acc": 0.47627, "mean_class_accuracy": 0.22855} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.05831, "memory": 15990, "data_time": 1.30133, "top1_acc": 0.31984, "top5_acc": 0.585, "loss_cls": 3.84824, "loss": 3.84824, "time": 2.29098} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.05828, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31344, "top5_acc": 0.57531, "loss_cls": 3.88183, "loss": 3.88183, "time": 0.82214} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.05826, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30938, "top5_acc": 0.58109, "loss_cls": 3.87924, "loss": 3.87924, "time": 0.819} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.05823, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31797, "top5_acc": 0.5725, "loss_cls": 3.91365, "loss": 3.91365, "time": 0.81684} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.0582, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31875, "top5_acc": 0.57781, "loss_cls": 3.90891, "loss": 3.90891, "time": 0.81767} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.05817, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30594, "top5_acc": 0.56688, "loss_cls": 3.95222, "loss": 3.95222, "time": 0.81733} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.05815, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31812, "top5_acc": 0.565, "loss_cls": 3.93712, "loss": 3.93712, "time": 0.82036} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.05812, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31203, "top5_acc": 0.57812, "loss_cls": 3.91501, "loss": 3.91501, "time": 0.8163} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.05809, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31625, "top5_acc": 0.57203, "loss_cls": 3.90265, "loss": 3.90265, "time": 0.81941} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.05806, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29031, "top5_acc": 0.56719, "loss_cls": 3.9531, "loss": 3.9531, "time": 0.81681} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.05804, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30688, "top5_acc": 0.57391, "loss_cls": 3.93317, "loss": 3.93317, "time": 0.81911} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.05801, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30969, "top5_acc": 0.57828, "loss_cls": 3.92108, "loss": 3.92108, "time": 0.81868} +{"mode": "train", "epoch": 68, "iter": 1300, "lr": 0.05798, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30797, "top5_acc": 0.55469, "loss_cls": 3.98312, "loss": 3.98312, "time": 0.82556} +{"mode": "train", "epoch": 68, "iter": 1400, "lr": 0.05795, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31938, "top5_acc": 0.57266, "loss_cls": 3.91284, "loss": 3.91284, "time": 0.82282} +{"mode": "train", "epoch": 68, "iter": 1500, "lr": 0.05792, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31406, "top5_acc": 0.57516, "loss_cls": 3.88173, "loss": 3.88173, "time": 0.81924} +{"mode": "train", "epoch": 68, "iter": 1600, "lr": 0.0579, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31281, "top5_acc": 0.56766, "loss_cls": 3.91982, "loss": 3.91982, "time": 0.82076} +{"mode": "train", "epoch": 68, "iter": 1700, "lr": 0.05787, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30109, "top5_acc": 0.54797, "loss_cls": 3.98882, "loss": 3.98882, "time": 0.81487} +{"mode": "train", "epoch": 68, "iter": 1800, "lr": 0.05784, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30891, "top5_acc": 0.56969, "loss_cls": 3.94935, "loss": 3.94935, "time": 0.81538} +{"mode": "train", "epoch": 68, "iter": 1900, "lr": 0.05781, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31406, "top5_acc": 0.57234, "loss_cls": 3.91619, "loss": 3.91619, "time": 0.82165} +{"mode": "train", "epoch": 68, "iter": 2000, "lr": 0.05779, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31484, "top5_acc": 0.57641, "loss_cls": 3.91287, "loss": 3.91287, "time": 0.81838} +{"mode": "train", "epoch": 68, "iter": 2100, "lr": 0.05776, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29938, "top5_acc": 0.55859, "loss_cls": 3.97313, "loss": 3.97313, "time": 0.81709} +{"mode": "train", "epoch": 68, "iter": 2200, "lr": 0.05773, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31766, "top5_acc": 0.57391, "loss_cls": 3.90519, "loss": 3.90519, "time": 0.81642} +{"mode": "train", "epoch": 68, "iter": 2300, "lr": 0.0577, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30109, "top5_acc": 0.56422, "loss_cls": 3.9489, "loss": 3.9489, "time": 0.81655} +{"mode": "train", "epoch": 68, "iter": 2400, "lr": 0.05768, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30391, "top5_acc": 0.56469, "loss_cls": 3.96229, "loss": 3.96229, "time": 0.81719} +{"mode": "train", "epoch": 68, "iter": 2500, "lr": 0.05765, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30891, "top5_acc": 0.56891, "loss_cls": 3.94429, "loss": 3.94429, "time": 0.82115} +{"mode": "train", "epoch": 68, "iter": 2600, "lr": 0.05762, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31047, "top5_acc": 0.57297, "loss_cls": 3.90495, "loss": 3.90495, "time": 0.81518} +{"mode": "train", "epoch": 68, "iter": 2700, "lr": 0.05759, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30297, "top5_acc": 0.56266, "loss_cls": 3.98266, "loss": 3.98266, "time": 0.82218} +{"mode": "train", "epoch": 68, "iter": 2800, "lr": 0.05757, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30484, "top5_acc": 0.56516, "loss_cls": 3.94836, "loss": 3.94836, "time": 0.81862} +{"mode": "train", "epoch": 68, "iter": 2900, "lr": 0.05754, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30672, "top5_acc": 0.56859, "loss_cls": 3.93324, "loss": 3.93324, "time": 0.82409} +{"mode": "train", "epoch": 68, "iter": 3000, "lr": 0.05751, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31469, "top5_acc": 0.57375, "loss_cls": 3.91953, "loss": 3.91953, "time": 0.81511} +{"mode": "train", "epoch": 68, "iter": 3100, "lr": 0.05748, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32391, "top5_acc": 0.57609, "loss_cls": 3.90811, "loss": 3.90811, "time": 0.81877} +{"mode": "train", "epoch": 68, "iter": 3200, "lr": 0.05746, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31312, "top5_acc": 0.56734, "loss_cls": 3.93274, "loss": 3.93274, "time": 0.81515} +{"mode": "train", "epoch": 68, "iter": 3300, "lr": 0.05743, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30484, "top5_acc": 0.5625, "loss_cls": 3.94964, "loss": 3.94964, "time": 0.81426} +{"mode": "train", "epoch": 68, "iter": 3400, "lr": 0.0574, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30328, "top5_acc": 0.57359, "loss_cls": 3.92905, "loss": 3.92905, "time": 0.81949} +{"mode": "train", "epoch": 68, "iter": 3500, "lr": 0.05737, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30953, "top5_acc": 0.56625, "loss_cls": 3.91792, "loss": 3.91792, "time": 0.82541} +{"mode": "train", "epoch": 68, "iter": 3600, "lr": 0.05734, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31547, "top5_acc": 0.57219, "loss_cls": 3.92306, "loss": 3.92306, "time": 0.8218} +{"mode": "train", "epoch": 68, "iter": 3700, "lr": 0.05732, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31312, "top5_acc": 0.57016, "loss_cls": 3.90617, "loss": 3.90617, "time": 0.82678} +{"mode": "val", "epoch": 68, "iter": 309, "lr": 0.0573, "top1_acc": 0.24804, "top5_acc": 0.4904, "mean_class_accuracy": 0.24787} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.05728, "memory": 15990, "data_time": 1.30064, "top1_acc": 0.32688, "top5_acc": 0.59641, "loss_cls": 3.8085, "loss": 3.8085, "time": 2.29135} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.05725, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32109, "top5_acc": 0.59234, "loss_cls": 3.83513, "loss": 3.83513, "time": 0.82249} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.05722, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30828, "top5_acc": 0.56734, "loss_cls": 3.93297, "loss": 3.93297, "time": 0.81515} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.05719, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31016, "top5_acc": 0.5775, "loss_cls": 3.88527, "loss": 3.88527, "time": 0.81735} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.05717, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30875, "top5_acc": 0.57328, "loss_cls": 3.91347, "loss": 3.91347, "time": 0.81288} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.05714, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31781, "top5_acc": 0.58344, "loss_cls": 3.89627, "loss": 3.89627, "time": 0.82142} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.05711, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32141, "top5_acc": 0.58281, "loss_cls": 3.86376, "loss": 3.86376, "time": 0.81418} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.05708, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31406, "top5_acc": 0.57406, "loss_cls": 3.91003, "loss": 3.91003, "time": 0.82728} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.05706, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31422, "top5_acc": 0.57812, "loss_cls": 3.88831, "loss": 3.88831, "time": 0.8139} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.05703, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30203, "top5_acc": 0.56109, "loss_cls": 3.95276, "loss": 3.95276, "time": 0.81803} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.057, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30641, "top5_acc": 0.56484, "loss_cls": 3.97296, "loss": 3.97296, "time": 0.81887} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.05697, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31125, "top5_acc": 0.56547, "loss_cls": 3.92572, "loss": 3.92572, "time": 0.82154} +{"mode": "train", "epoch": 69, "iter": 1300, "lr": 0.05694, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30406, "top5_acc": 0.55672, "loss_cls": 3.96635, "loss": 3.96635, "time": 0.82704} +{"mode": "train", "epoch": 69, "iter": 1400, "lr": 0.05692, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31234, "top5_acc": 0.57563, "loss_cls": 3.90265, "loss": 3.90265, "time": 0.82404} +{"mode": "train", "epoch": 69, "iter": 1500, "lr": 0.05689, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31516, "top5_acc": 0.57891, "loss_cls": 3.92813, "loss": 3.92813, "time": 0.82353} +{"mode": "train", "epoch": 69, "iter": 1600, "lr": 0.05686, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31203, "top5_acc": 0.56609, "loss_cls": 3.95514, "loss": 3.95514, "time": 0.8225} +{"mode": "train", "epoch": 69, "iter": 1700, "lr": 0.05683, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30328, "top5_acc": 0.56969, "loss_cls": 3.95675, "loss": 3.95675, "time": 0.82171} +{"mode": "train", "epoch": 69, "iter": 1800, "lr": 0.05681, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30156, "top5_acc": 0.56547, "loss_cls": 3.94725, "loss": 3.94725, "time": 0.8177} +{"mode": "train", "epoch": 69, "iter": 1900, "lr": 0.05678, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32094, "top5_acc": 0.58, "loss_cls": 3.86976, "loss": 3.86976, "time": 0.81839} +{"mode": "train", "epoch": 69, "iter": 2000, "lr": 0.05675, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31359, "top5_acc": 0.57516, "loss_cls": 3.91224, "loss": 3.91224, "time": 0.81552} +{"mode": "train", "epoch": 69, "iter": 2100, "lr": 0.05672, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30703, "top5_acc": 0.57219, "loss_cls": 3.91275, "loss": 3.91275, "time": 0.8215} +{"mode": "train", "epoch": 69, "iter": 2200, "lr": 0.0567, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30484, "top5_acc": 0.56437, "loss_cls": 3.95349, "loss": 3.95349, "time": 0.81952} +{"mode": "train", "epoch": 69, "iter": 2300, "lr": 0.05667, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31328, "top5_acc": 0.57219, "loss_cls": 3.89789, "loss": 3.89789, "time": 0.81782} +{"mode": "train", "epoch": 69, "iter": 2400, "lr": 0.05664, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30609, "top5_acc": 0.5675, "loss_cls": 3.93351, "loss": 3.93351, "time": 0.81782} +{"mode": "train", "epoch": 69, "iter": 2500, "lr": 0.05661, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30766, "top5_acc": 0.56188, "loss_cls": 3.96539, "loss": 3.96539, "time": 0.82149} +{"mode": "train", "epoch": 69, "iter": 2600, "lr": 0.05658, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30938, "top5_acc": 0.57563, "loss_cls": 3.90703, "loss": 3.90703, "time": 0.81455} +{"mode": "train", "epoch": 69, "iter": 2700, "lr": 0.05656, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30453, "top5_acc": 0.56516, "loss_cls": 3.95168, "loss": 3.95168, "time": 0.81957} +{"mode": "train", "epoch": 69, "iter": 2800, "lr": 0.05653, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30562, "top5_acc": 0.57063, "loss_cls": 3.93466, "loss": 3.93466, "time": 0.82383} +{"mode": "train", "epoch": 69, "iter": 2900, "lr": 0.0565, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31359, "top5_acc": 0.57172, "loss_cls": 3.9198, "loss": 3.9198, "time": 0.81665} +{"mode": "train", "epoch": 69, "iter": 3000, "lr": 0.05647, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30547, "top5_acc": 0.57094, "loss_cls": 3.92638, "loss": 3.92638, "time": 0.82367} +{"mode": "train", "epoch": 69, "iter": 3100, "lr": 0.05645, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31594, "top5_acc": 0.57047, "loss_cls": 3.90111, "loss": 3.90111, "time": 0.81692} +{"mode": "train", "epoch": 69, "iter": 3200, "lr": 0.05642, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28828, "top5_acc": 0.55141, "loss_cls": 4.02381, "loss": 4.02381, "time": 0.82127} +{"mode": "train", "epoch": 69, "iter": 3300, "lr": 0.05639, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30609, "top5_acc": 0.56672, "loss_cls": 3.97452, "loss": 3.97452, "time": 0.81381} +{"mode": "train", "epoch": 69, "iter": 3400, "lr": 0.05636, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30672, "top5_acc": 0.57031, "loss_cls": 3.94691, "loss": 3.94691, "time": 0.81711} +{"mode": "train", "epoch": 69, "iter": 3500, "lr": 0.05634, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31484, "top5_acc": 0.56969, "loss_cls": 3.90716, "loss": 3.90716, "time": 0.82466} +{"mode": "train", "epoch": 69, "iter": 3600, "lr": 0.05631, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.31406, "top5_acc": 0.57547, "loss_cls": 3.92171, "loss": 3.92171, "time": 0.82642} +{"mode": "train", "epoch": 69, "iter": 3700, "lr": 0.05628, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31656, "top5_acc": 0.57406, "loss_cls": 3.9014, "loss": 3.9014, "time": 0.82339} +{"mode": "val", "epoch": 69, "iter": 309, "lr": 0.05627, "top1_acc": 0.23304, "top5_acc": 0.47485, "mean_class_accuracy": 0.23294} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.05624, "memory": 15990, "data_time": 1.29464, "top1_acc": 0.325, "top5_acc": 0.58547, "loss_cls": 3.83259, "loss": 3.83259, "time": 2.28891} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.05621, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30422, "top5_acc": 0.57297, "loss_cls": 3.89963, "loss": 3.89963, "time": 0.81791} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.05618, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31328, "top5_acc": 0.58156, "loss_cls": 3.88673, "loss": 3.88673, "time": 0.81534} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.05616, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30859, "top5_acc": 0.5675, "loss_cls": 3.9359, "loss": 3.9359, "time": 0.81653} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.05613, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31438, "top5_acc": 0.57938, "loss_cls": 3.89457, "loss": 3.89457, "time": 0.8198} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.0561, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31594, "top5_acc": 0.58297, "loss_cls": 3.84816, "loss": 3.84816, "time": 0.81829} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.05607, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31141, "top5_acc": 0.56703, "loss_cls": 3.93133, "loss": 3.93133, "time": 0.81632} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.05605, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31625, "top5_acc": 0.56734, "loss_cls": 3.89863, "loss": 3.89863, "time": 0.82259} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.05602, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31312, "top5_acc": 0.57141, "loss_cls": 3.95443, "loss": 3.95443, "time": 0.81675} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.05599, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30344, "top5_acc": 0.56641, "loss_cls": 3.95213, "loss": 3.95213, "time": 0.81915} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.05596, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30906, "top5_acc": 0.565, "loss_cls": 3.904, "loss": 3.904, "time": 0.82108} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.05593, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32078, "top5_acc": 0.58297, "loss_cls": 3.85867, "loss": 3.85867, "time": 0.81975} +{"mode": "train", "epoch": 70, "iter": 1300, "lr": 0.05591, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31438, "top5_acc": 0.57203, "loss_cls": 3.90709, "loss": 3.90709, "time": 0.82481} +{"mode": "train", "epoch": 70, "iter": 1400, "lr": 0.05588, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.315, "top5_acc": 0.57, "loss_cls": 3.93597, "loss": 3.93597, "time": 0.82459} +{"mode": "train", "epoch": 70, "iter": 1500, "lr": 0.05585, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30453, "top5_acc": 0.56359, "loss_cls": 3.91721, "loss": 3.91721, "time": 0.82505} +{"mode": "train", "epoch": 70, "iter": 1600, "lr": 0.05582, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30391, "top5_acc": 0.57172, "loss_cls": 3.92768, "loss": 3.92768, "time": 0.81858} +{"mode": "train", "epoch": 70, "iter": 1700, "lr": 0.0558, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30984, "top5_acc": 0.57891, "loss_cls": 3.92459, "loss": 3.92459, "time": 0.81597} +{"mode": "train", "epoch": 70, "iter": 1800, "lr": 0.05577, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31594, "top5_acc": 0.57641, "loss_cls": 3.91393, "loss": 3.91393, "time": 0.81581} +{"mode": "train", "epoch": 70, "iter": 1900, "lr": 0.05574, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30766, "top5_acc": 0.56422, "loss_cls": 3.92806, "loss": 3.92806, "time": 0.82262} +{"mode": "train", "epoch": 70, "iter": 2000, "lr": 0.05571, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3125, "top5_acc": 0.57719, "loss_cls": 3.91467, "loss": 3.91467, "time": 0.81284} +{"mode": "train", "epoch": 70, "iter": 2100, "lr": 0.05568, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30516, "top5_acc": 0.57359, "loss_cls": 3.92838, "loss": 3.92838, "time": 0.81607} +{"mode": "train", "epoch": 70, "iter": 2200, "lr": 0.05566, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31406, "top5_acc": 0.57563, "loss_cls": 3.90647, "loss": 3.90647, "time": 0.81898} +{"mode": "train", "epoch": 70, "iter": 2300, "lr": 0.05563, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31594, "top5_acc": 0.57547, "loss_cls": 3.89973, "loss": 3.89973, "time": 0.81692} +{"mode": "train", "epoch": 70, "iter": 2400, "lr": 0.0556, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30266, "top5_acc": 0.56328, "loss_cls": 3.95581, "loss": 3.95581, "time": 0.81719} +{"mode": "train", "epoch": 70, "iter": 2500, "lr": 0.05557, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31359, "top5_acc": 0.57422, "loss_cls": 3.9089, "loss": 3.9089, "time": 0.81758} +{"mode": "train", "epoch": 70, "iter": 2600, "lr": 0.05555, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31234, "top5_acc": 0.57484, "loss_cls": 3.88695, "loss": 3.88695, "time": 0.81852} +{"mode": "train", "epoch": 70, "iter": 2700, "lr": 0.05552, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31688, "top5_acc": 0.56703, "loss_cls": 3.90329, "loss": 3.90329, "time": 0.82248} +{"mode": "train", "epoch": 70, "iter": 2800, "lr": 0.05549, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32328, "top5_acc": 0.58953, "loss_cls": 3.80969, "loss": 3.80969, "time": 0.81602} +{"mode": "train", "epoch": 70, "iter": 2900, "lr": 0.05546, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31859, "top5_acc": 0.57375, "loss_cls": 3.88757, "loss": 3.88757, "time": 0.81924} +{"mode": "train", "epoch": 70, "iter": 3000, "lr": 0.05543, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30969, "top5_acc": 0.57109, "loss_cls": 3.94125, "loss": 3.94125, "time": 0.81529} +{"mode": "train", "epoch": 70, "iter": 3100, "lr": 0.05541, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31125, "top5_acc": 0.55969, "loss_cls": 3.96113, "loss": 3.96113, "time": 0.82264} +{"mode": "train", "epoch": 70, "iter": 3200, "lr": 0.05538, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30859, "top5_acc": 0.57344, "loss_cls": 3.90708, "loss": 3.90708, "time": 0.81497} +{"mode": "train", "epoch": 70, "iter": 3300, "lr": 0.05535, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30766, "top5_acc": 0.57453, "loss_cls": 3.9351, "loss": 3.9351, "time": 0.81994} +{"mode": "train", "epoch": 70, "iter": 3400, "lr": 0.05532, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31391, "top5_acc": 0.57609, "loss_cls": 3.90828, "loss": 3.90828, "time": 0.82233} +{"mode": "train", "epoch": 70, "iter": 3500, "lr": 0.0553, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31531, "top5_acc": 0.57078, "loss_cls": 3.93255, "loss": 3.93255, "time": 0.82771} +{"mode": "train", "epoch": 70, "iter": 3600, "lr": 0.05527, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30922, "top5_acc": 0.56469, "loss_cls": 3.94934, "loss": 3.94934, "time": 0.8207} +{"mode": "train", "epoch": 70, "iter": 3700, "lr": 0.05524, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31016, "top5_acc": 0.57625, "loss_cls": 3.92543, "loss": 3.92543, "time": 0.82743} +{"mode": "val", "epoch": 70, "iter": 309, "lr": 0.05523, "top1_acc": 0.18138, "top5_acc": 0.41301, "mean_class_accuracy": 0.18128} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.0552, "memory": 15990, "data_time": 1.32071, "top1_acc": 0.32094, "top5_acc": 0.58438, "loss_cls": 3.84847, "loss": 3.84847, "time": 2.30467} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.05517, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31141, "top5_acc": 0.57797, "loss_cls": 3.88178, "loss": 3.88178, "time": 0.8233} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.05514, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32562, "top5_acc": 0.5875, "loss_cls": 3.82583, "loss": 3.82583, "time": 0.82255} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.05512, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31344, "top5_acc": 0.58078, "loss_cls": 3.85755, "loss": 3.85755, "time": 0.81675} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.05509, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3075, "top5_acc": 0.57469, "loss_cls": 3.89821, "loss": 3.89821, "time": 0.81688} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.05506, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31656, "top5_acc": 0.57063, "loss_cls": 3.91253, "loss": 3.91253, "time": 0.81955} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.05503, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31922, "top5_acc": 0.57797, "loss_cls": 3.89616, "loss": 3.89616, "time": 0.81798} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.055, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31609, "top5_acc": 0.58109, "loss_cls": 3.9129, "loss": 3.9129, "time": 0.82037} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.05498, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3075, "top5_acc": 0.57297, "loss_cls": 3.90997, "loss": 3.90997, "time": 0.8212} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.05495, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32297, "top5_acc": 0.57938, "loss_cls": 3.8563, "loss": 3.8563, "time": 0.81959} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.05492, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3175, "top5_acc": 0.58281, "loss_cls": 3.89069, "loss": 3.89069, "time": 0.8173} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.05489, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3175, "top5_acc": 0.58547, "loss_cls": 3.86839, "loss": 3.86839, "time": 0.82711} +{"mode": "train", "epoch": 71, "iter": 1300, "lr": 0.05487, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31078, "top5_acc": 0.57688, "loss_cls": 3.89875, "loss": 3.89875, "time": 0.82349} +{"mode": "train", "epoch": 71, "iter": 1400, "lr": 0.05484, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30641, "top5_acc": 0.56031, "loss_cls": 3.94952, "loss": 3.94952, "time": 0.82076} +{"mode": "train", "epoch": 71, "iter": 1500, "lr": 0.05481, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31766, "top5_acc": 0.5775, "loss_cls": 3.87999, "loss": 3.87999, "time": 0.82376} +{"mode": "train", "epoch": 71, "iter": 1600, "lr": 0.05478, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31109, "top5_acc": 0.57719, "loss_cls": 3.92765, "loss": 3.92765, "time": 0.82098} +{"mode": "train", "epoch": 71, "iter": 1700, "lr": 0.05475, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32344, "top5_acc": 0.58578, "loss_cls": 3.85399, "loss": 3.85399, "time": 0.81626} +{"mode": "train", "epoch": 71, "iter": 1800, "lr": 0.05473, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30641, "top5_acc": 0.57734, "loss_cls": 3.89975, "loss": 3.89975, "time": 0.81756} +{"mode": "train", "epoch": 71, "iter": 1900, "lr": 0.0547, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30938, "top5_acc": 0.56984, "loss_cls": 3.91229, "loss": 3.91229, "time": 0.81993} +{"mode": "train", "epoch": 71, "iter": 2000, "lr": 0.05467, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31328, "top5_acc": 0.56531, "loss_cls": 3.95642, "loss": 3.95642, "time": 0.81487} +{"mode": "train", "epoch": 71, "iter": 2100, "lr": 0.05464, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30938, "top5_acc": 0.56703, "loss_cls": 3.92893, "loss": 3.92893, "time": 0.81655} +{"mode": "train", "epoch": 71, "iter": 2200, "lr": 0.05461, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31188, "top5_acc": 0.57609, "loss_cls": 3.9077, "loss": 3.9077, "time": 0.81749} +{"mode": "train", "epoch": 71, "iter": 2300, "lr": 0.05459, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31734, "top5_acc": 0.57469, "loss_cls": 3.91778, "loss": 3.91778, "time": 0.81624} +{"mode": "train", "epoch": 71, "iter": 2400, "lr": 0.05456, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31125, "top5_acc": 0.57797, "loss_cls": 3.91768, "loss": 3.91768, "time": 0.81724} +{"mode": "train", "epoch": 71, "iter": 2500, "lr": 0.05453, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31219, "top5_acc": 0.58266, "loss_cls": 3.88315, "loss": 3.88315, "time": 0.82149} +{"mode": "train", "epoch": 71, "iter": 2600, "lr": 0.0545, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30375, "top5_acc": 0.56344, "loss_cls": 3.95874, "loss": 3.95874, "time": 0.81988} +{"mode": "train", "epoch": 71, "iter": 2700, "lr": 0.05448, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31922, "top5_acc": 0.57469, "loss_cls": 3.88726, "loss": 3.88726, "time": 0.81695} +{"mode": "train", "epoch": 71, "iter": 2800, "lr": 0.05445, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3125, "top5_acc": 0.57688, "loss_cls": 3.87595, "loss": 3.87595, "time": 0.8194} +{"mode": "train", "epoch": 71, "iter": 2900, "lr": 0.05442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30547, "top5_acc": 0.57344, "loss_cls": 3.93119, "loss": 3.93119, "time": 0.81383} +{"mode": "train", "epoch": 71, "iter": 3000, "lr": 0.05439, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30938, "top5_acc": 0.57438, "loss_cls": 3.91372, "loss": 3.91372, "time": 0.81654} +{"mode": "train", "epoch": 71, "iter": 3100, "lr": 0.05436, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30688, "top5_acc": 0.57328, "loss_cls": 3.92166, "loss": 3.92166, "time": 0.81533} +{"mode": "train", "epoch": 71, "iter": 3200, "lr": 0.05434, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31594, "top5_acc": 0.57328, "loss_cls": 3.87417, "loss": 3.87417, "time": 0.81642} +{"mode": "train", "epoch": 71, "iter": 3300, "lr": 0.05431, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30891, "top5_acc": 0.57359, "loss_cls": 3.91378, "loss": 3.91378, "time": 0.81379} +{"mode": "train", "epoch": 71, "iter": 3400, "lr": 0.05428, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3175, "top5_acc": 0.57859, "loss_cls": 3.86781, "loss": 3.86781, "time": 0.82454} +{"mode": "train", "epoch": 71, "iter": 3500, "lr": 0.05425, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32078, "top5_acc": 0.57375, "loss_cls": 3.89385, "loss": 3.89385, "time": 0.81733} +{"mode": "train", "epoch": 71, "iter": 3600, "lr": 0.05422, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3175, "top5_acc": 0.57688, "loss_cls": 3.90227, "loss": 3.90227, "time": 0.82161} +{"mode": "train", "epoch": 71, "iter": 3700, "lr": 0.0542, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31344, "top5_acc": 0.57484, "loss_cls": 3.91301, "loss": 3.91301, "time": 0.81963} +{"mode": "val", "epoch": 71, "iter": 309, "lr": 0.05418, "top1_acc": 0.23674, "top5_acc": 0.47693, "mean_class_accuracy": 0.2365} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.05416, "memory": 15990, "data_time": 1.28897, "top1_acc": 0.32859, "top5_acc": 0.58484, "loss_cls": 3.83734, "loss": 3.83734, "time": 2.27117} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.05413, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32203, "top5_acc": 0.57766, "loss_cls": 3.87633, "loss": 3.87633, "time": 0.82038} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.0541, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31203, "top5_acc": 0.56578, "loss_cls": 3.92304, "loss": 3.92304, "time": 0.81742} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.05407, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32875, "top5_acc": 0.58281, "loss_cls": 3.86154, "loss": 3.86154, "time": 0.81323} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.05404, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30562, "top5_acc": 0.57641, "loss_cls": 3.8984, "loss": 3.8984, "time": 0.81726} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.05402, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32328, "top5_acc": 0.58484, "loss_cls": 3.83647, "loss": 3.83647, "time": 0.81954} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.05399, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31391, "top5_acc": 0.58, "loss_cls": 3.8774, "loss": 3.8774, "time": 0.81383} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.05396, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31266, "top5_acc": 0.57688, "loss_cls": 3.87345, "loss": 3.87345, "time": 0.81967} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.05393, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31453, "top5_acc": 0.58422, "loss_cls": 3.87867, "loss": 3.87867, "time": 0.81832} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.05391, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31844, "top5_acc": 0.58203, "loss_cls": 3.86393, "loss": 3.86393, "time": 0.81541} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.05388, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30953, "top5_acc": 0.57188, "loss_cls": 3.91566, "loss": 3.91566, "time": 0.81744} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.05385, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32047, "top5_acc": 0.57828, "loss_cls": 3.91048, "loss": 3.91048, "time": 0.81648} +{"mode": "train", "epoch": 72, "iter": 1300, "lr": 0.05382, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32266, "top5_acc": 0.57922, "loss_cls": 3.88468, "loss": 3.88468, "time": 0.82483} +{"mode": "train", "epoch": 72, "iter": 1400, "lr": 0.05379, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30359, "top5_acc": 0.56531, "loss_cls": 3.93957, "loss": 3.93957, "time": 0.82682} +{"mode": "train", "epoch": 72, "iter": 1500, "lr": 0.05377, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31891, "top5_acc": 0.57609, "loss_cls": 3.88848, "loss": 3.88848, "time": 0.83023} +{"mode": "train", "epoch": 72, "iter": 1600, "lr": 0.05374, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3075, "top5_acc": 0.57016, "loss_cls": 3.94552, "loss": 3.94552, "time": 0.81972} +{"mode": "train", "epoch": 72, "iter": 1700, "lr": 0.05371, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31562, "top5_acc": 0.58547, "loss_cls": 3.8708, "loss": 3.8708, "time": 0.81702} +{"mode": "train", "epoch": 72, "iter": 1800, "lr": 0.05368, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31609, "top5_acc": 0.58719, "loss_cls": 3.86174, "loss": 3.86174, "time": 0.817} +{"mode": "train", "epoch": 72, "iter": 1900, "lr": 0.05365, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31094, "top5_acc": 0.57625, "loss_cls": 3.86796, "loss": 3.86796, "time": 0.81625} +{"mode": "train", "epoch": 72, "iter": 2000, "lr": 0.05363, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31062, "top5_acc": 0.56984, "loss_cls": 3.93964, "loss": 3.93964, "time": 0.81359} +{"mode": "train", "epoch": 72, "iter": 2100, "lr": 0.0536, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32141, "top5_acc": 0.57875, "loss_cls": 3.88469, "loss": 3.88469, "time": 0.81785} +{"mode": "train", "epoch": 72, "iter": 2200, "lr": 0.05357, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30859, "top5_acc": 0.57672, "loss_cls": 3.90632, "loss": 3.90632, "time": 0.82275} +{"mode": "train", "epoch": 72, "iter": 2300, "lr": 0.05354, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31156, "top5_acc": 0.56719, "loss_cls": 3.94857, "loss": 3.94857, "time": 0.82041} +{"mode": "train", "epoch": 72, "iter": 2400, "lr": 0.05352, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31234, "top5_acc": 0.56703, "loss_cls": 3.91369, "loss": 3.91369, "time": 0.82164} +{"mode": "train", "epoch": 72, "iter": 2500, "lr": 0.05349, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31562, "top5_acc": 0.57578, "loss_cls": 3.87217, "loss": 3.87217, "time": 0.81821} +{"mode": "train", "epoch": 72, "iter": 2600, "lr": 0.05346, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31516, "top5_acc": 0.56609, "loss_cls": 3.92852, "loss": 3.92852, "time": 0.81907} +{"mode": "train", "epoch": 72, "iter": 2700, "lr": 0.05343, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31328, "top5_acc": 0.57906, "loss_cls": 3.89731, "loss": 3.89731, "time": 0.81503} +{"mode": "train", "epoch": 72, "iter": 2800, "lr": 0.0534, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31922, "top5_acc": 0.57719, "loss_cls": 3.89324, "loss": 3.89324, "time": 0.81211} +{"mode": "train", "epoch": 72, "iter": 2900, "lr": 0.05338, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.315, "top5_acc": 0.57453, "loss_cls": 3.85748, "loss": 3.85748, "time": 0.81175} +{"mode": "train", "epoch": 72, "iter": 3000, "lr": 0.05335, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31578, "top5_acc": 0.57922, "loss_cls": 3.88049, "loss": 3.88049, "time": 0.81599} +{"mode": "train", "epoch": 72, "iter": 3100, "lr": 0.05332, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31703, "top5_acc": 0.57203, "loss_cls": 3.89839, "loss": 3.89839, "time": 0.81474} +{"mode": "train", "epoch": 72, "iter": 3200, "lr": 0.05329, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31516, "top5_acc": 0.57781, "loss_cls": 3.89252, "loss": 3.89252, "time": 0.81794} +{"mode": "train", "epoch": 72, "iter": 3300, "lr": 0.05326, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30797, "top5_acc": 0.57328, "loss_cls": 3.92869, "loss": 3.92869, "time": 0.81508} +{"mode": "train", "epoch": 72, "iter": 3400, "lr": 0.05324, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31875, "top5_acc": 0.57875, "loss_cls": 3.83498, "loss": 3.83498, "time": 0.82508} +{"mode": "train", "epoch": 72, "iter": 3500, "lr": 0.05321, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31906, "top5_acc": 0.59031, "loss_cls": 3.82277, "loss": 3.82277, "time": 0.81863} +{"mode": "train", "epoch": 72, "iter": 3600, "lr": 0.05318, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30844, "top5_acc": 0.56188, "loss_cls": 3.93667, "loss": 3.93667, "time": 0.82589} +{"mode": "train", "epoch": 72, "iter": 3700, "lr": 0.05315, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31719, "top5_acc": 0.57375, "loss_cls": 3.89601, "loss": 3.89601, "time": 0.81452} +{"mode": "val", "epoch": 72, "iter": 309, "lr": 0.05314, "top1_acc": 0.23674, "top5_acc": 0.48144, "mean_class_accuracy": 0.23656} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.05311, "memory": 15990, "data_time": 1.29054, "top1_acc": 0.31391, "top5_acc": 0.57422, "loss_cls": 3.85569, "loss": 3.85569, "time": 2.27758} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.05308, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31531, "top5_acc": 0.58203, "loss_cls": 3.87888, "loss": 3.87888, "time": 0.82363} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.05306, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33078, "top5_acc": 0.58969, "loss_cls": 3.79664, "loss": 3.79664, "time": 0.81737} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.05303, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32, "top5_acc": 0.58094, "loss_cls": 3.87986, "loss": 3.87986, "time": 0.81771} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.053, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32547, "top5_acc": 0.59203, "loss_cls": 3.81414, "loss": 3.81414, "time": 0.81544} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.05297, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31219, "top5_acc": 0.575, "loss_cls": 3.88705, "loss": 3.88705, "time": 0.81754} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.05294, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32734, "top5_acc": 0.58406, "loss_cls": 3.82893, "loss": 3.82893, "time": 0.81869} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.05292, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31047, "top5_acc": 0.57922, "loss_cls": 3.86646, "loss": 3.86646, "time": 0.82124} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.05289, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31203, "top5_acc": 0.58234, "loss_cls": 3.86094, "loss": 3.86094, "time": 0.81522} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.05286, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31703, "top5_acc": 0.57656, "loss_cls": 3.85922, "loss": 3.85922, "time": 0.8147} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.05283, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31469, "top5_acc": 0.57938, "loss_cls": 3.88691, "loss": 3.88691, "time": 0.81858} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.0528, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32297, "top5_acc": 0.58406, "loss_cls": 3.85253, "loss": 3.85253, "time": 0.8178} +{"mode": "train", "epoch": 73, "iter": 1300, "lr": 0.05278, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32, "top5_acc": 0.57953, "loss_cls": 3.8831, "loss": 3.8831, "time": 0.82251} +{"mode": "train", "epoch": 73, "iter": 1400, "lr": 0.05275, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31484, "top5_acc": 0.58328, "loss_cls": 3.88163, "loss": 3.88163, "time": 0.83262} +{"mode": "train", "epoch": 73, "iter": 1500, "lr": 0.05272, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30953, "top5_acc": 0.57422, "loss_cls": 3.93316, "loss": 3.93316, "time": 0.82387} +{"mode": "train", "epoch": 73, "iter": 1600, "lr": 0.05269, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30531, "top5_acc": 0.57063, "loss_cls": 3.94248, "loss": 3.94248, "time": 0.82632} +{"mode": "train", "epoch": 73, "iter": 1700, "lr": 0.05267, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30094, "top5_acc": 0.57297, "loss_cls": 3.90894, "loss": 3.90894, "time": 0.81846} +{"mode": "train", "epoch": 73, "iter": 1800, "lr": 0.05264, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32531, "top5_acc": 0.57563, "loss_cls": 3.85485, "loss": 3.85485, "time": 0.81759} +{"mode": "train", "epoch": 73, "iter": 1900, "lr": 0.05261, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32547, "top5_acc": 0.57859, "loss_cls": 3.86057, "loss": 3.86057, "time": 0.81868} +{"mode": "train", "epoch": 73, "iter": 2000, "lr": 0.05258, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31531, "top5_acc": 0.57063, "loss_cls": 3.92448, "loss": 3.92448, "time": 0.81824} +{"mode": "train", "epoch": 73, "iter": 2100, "lr": 0.05255, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32438, "top5_acc": 0.58062, "loss_cls": 3.84375, "loss": 3.84375, "time": 0.81972} +{"mode": "train", "epoch": 73, "iter": 2200, "lr": 0.05253, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31484, "top5_acc": 0.57797, "loss_cls": 3.85582, "loss": 3.85582, "time": 0.82378} +{"mode": "train", "epoch": 73, "iter": 2300, "lr": 0.0525, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32031, "top5_acc": 0.57406, "loss_cls": 3.85909, "loss": 3.85909, "time": 0.82199} +{"mode": "train", "epoch": 73, "iter": 2400, "lr": 0.05247, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31078, "top5_acc": 0.58203, "loss_cls": 3.88045, "loss": 3.88045, "time": 0.82386} +{"mode": "train", "epoch": 73, "iter": 2500, "lr": 0.05244, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31922, "top5_acc": 0.58219, "loss_cls": 3.8432, "loss": 3.8432, "time": 0.82214} +{"mode": "train", "epoch": 73, "iter": 2600, "lr": 0.05241, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30625, "top5_acc": 0.56734, "loss_cls": 3.9294, "loss": 3.9294, "time": 0.82087} +{"mode": "train", "epoch": 73, "iter": 2700, "lr": 0.05239, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31281, "top5_acc": 0.57469, "loss_cls": 3.89756, "loss": 3.89756, "time": 0.81786} +{"mode": "train", "epoch": 73, "iter": 2800, "lr": 0.05236, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31234, "top5_acc": 0.57219, "loss_cls": 3.91564, "loss": 3.91564, "time": 0.82014} +{"mode": "train", "epoch": 73, "iter": 2900, "lr": 0.05233, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31625, "top5_acc": 0.58578, "loss_cls": 3.84832, "loss": 3.84832, "time": 0.81752} +{"mode": "train", "epoch": 73, "iter": 3000, "lr": 0.0523, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32094, "top5_acc": 0.57547, "loss_cls": 3.87144, "loss": 3.87144, "time": 0.81581} +{"mode": "train", "epoch": 73, "iter": 3100, "lr": 0.05227, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3175, "top5_acc": 0.57141, "loss_cls": 3.90501, "loss": 3.90501, "time": 0.81728} +{"mode": "train", "epoch": 73, "iter": 3200, "lr": 0.05225, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32391, "top5_acc": 0.58578, "loss_cls": 3.84768, "loss": 3.84768, "time": 0.81786} +{"mode": "train", "epoch": 73, "iter": 3300, "lr": 0.05222, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31594, "top5_acc": 0.57094, "loss_cls": 3.91411, "loss": 3.91411, "time": 0.81506} +{"mode": "train", "epoch": 73, "iter": 3400, "lr": 0.05219, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32094, "top5_acc": 0.57703, "loss_cls": 3.87699, "loss": 3.87699, "time": 0.82535} +{"mode": "train", "epoch": 73, "iter": 3500, "lr": 0.05216, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31641, "top5_acc": 0.58188, "loss_cls": 3.86096, "loss": 3.86096, "time": 0.81894} +{"mode": "train", "epoch": 73, "iter": 3600, "lr": 0.05213, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31141, "top5_acc": 0.57312, "loss_cls": 3.87817, "loss": 3.87817, "time": 0.82086} +{"mode": "train", "epoch": 73, "iter": 3700, "lr": 0.05211, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31219, "top5_acc": 0.57953, "loss_cls": 3.89003, "loss": 3.89003, "time": 0.82412} +{"mode": "val", "epoch": 73, "iter": 309, "lr": 0.05209, "top1_acc": 0.24176, "top5_acc": 0.47855, "mean_class_accuracy": 0.24161} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.05207, "memory": 15990, "data_time": 1.30207, "top1_acc": 0.31812, "top5_acc": 0.58125, "loss_cls": 3.84824, "loss": 3.84824, "time": 2.29308} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.05204, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32641, "top5_acc": 0.58359, "loss_cls": 3.83973, "loss": 3.83973, "time": 0.82211} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.05201, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31703, "top5_acc": 0.58641, "loss_cls": 3.83643, "loss": 3.83643, "time": 0.81698} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.05198, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32781, "top5_acc": 0.58156, "loss_cls": 3.83187, "loss": 3.83187, "time": 0.81681} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.05195, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3275, "top5_acc": 0.59266, "loss_cls": 3.80533, "loss": 3.80533, "time": 0.81776} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.05193, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31297, "top5_acc": 0.57266, "loss_cls": 3.8803, "loss": 3.8803, "time": 0.81683} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.0519, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31719, "top5_acc": 0.59047, "loss_cls": 3.85215, "loss": 3.85215, "time": 0.81937} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.05187, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32109, "top5_acc": 0.58359, "loss_cls": 3.84034, "loss": 3.84034, "time": 0.81671} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.05184, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31859, "top5_acc": 0.58266, "loss_cls": 3.8192, "loss": 3.8192, "time": 0.8205} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.05181, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31953, "top5_acc": 0.58891, "loss_cls": 3.8431, "loss": 3.8431, "time": 0.81441} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.05179, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31766, "top5_acc": 0.56938, "loss_cls": 3.90079, "loss": 3.90079, "time": 0.81545} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.05176, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32, "top5_acc": 0.58672, "loss_cls": 3.83018, "loss": 3.83018, "time": 0.81799} +{"mode": "train", "epoch": 74, "iter": 1300, "lr": 0.05173, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.315, "top5_acc": 0.58078, "loss_cls": 3.88363, "loss": 3.88363, "time": 0.81709} +{"mode": "train", "epoch": 74, "iter": 1400, "lr": 0.0517, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32078, "top5_acc": 0.58094, "loss_cls": 3.86771, "loss": 3.86771, "time": 0.82572} +{"mode": "train", "epoch": 74, "iter": 1500, "lr": 0.05168, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31516, "top5_acc": 0.57594, "loss_cls": 3.86507, "loss": 3.86507, "time": 0.82232} +{"mode": "train", "epoch": 74, "iter": 1600, "lr": 0.05165, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31828, "top5_acc": 0.58375, "loss_cls": 3.85482, "loss": 3.85482, "time": 0.82343} +{"mode": "train", "epoch": 74, "iter": 1700, "lr": 0.05162, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.305, "top5_acc": 0.56156, "loss_cls": 3.93091, "loss": 3.93091, "time": 0.82054} +{"mode": "train", "epoch": 74, "iter": 1800, "lr": 0.05159, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32203, "top5_acc": 0.58234, "loss_cls": 3.83229, "loss": 3.83229, "time": 0.81503} +{"mode": "train", "epoch": 74, "iter": 1900, "lr": 0.05156, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32469, "top5_acc": 0.5825, "loss_cls": 3.84295, "loss": 3.84295, "time": 0.81806} +{"mode": "train", "epoch": 74, "iter": 2000, "lr": 0.05154, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32266, "top5_acc": 0.58359, "loss_cls": 3.81218, "loss": 3.81218, "time": 0.82244} +{"mode": "train", "epoch": 74, "iter": 2100, "lr": 0.05151, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32047, "top5_acc": 0.58422, "loss_cls": 3.86916, "loss": 3.86916, "time": 0.82695} +{"mode": "train", "epoch": 74, "iter": 2200, "lr": 0.05148, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31844, "top5_acc": 0.57656, "loss_cls": 3.88397, "loss": 3.88397, "time": 0.81175} +{"mode": "train", "epoch": 74, "iter": 2300, "lr": 0.05145, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32234, "top5_acc": 0.57531, "loss_cls": 3.90864, "loss": 3.90864, "time": 0.82059} +{"mode": "train", "epoch": 74, "iter": 2400, "lr": 0.05142, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31812, "top5_acc": 0.57453, "loss_cls": 3.9022, "loss": 3.9022, "time": 0.82567} +{"mode": "train", "epoch": 74, "iter": 2500, "lr": 0.0514, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31766, "top5_acc": 0.58594, "loss_cls": 3.8691, "loss": 3.8691, "time": 0.8211} +{"mode": "train", "epoch": 74, "iter": 2600, "lr": 0.05137, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32297, "top5_acc": 0.58172, "loss_cls": 3.85642, "loss": 3.85642, "time": 0.82171} +{"mode": "train", "epoch": 74, "iter": 2700, "lr": 0.05134, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32016, "top5_acc": 0.59109, "loss_cls": 3.82132, "loss": 3.82132, "time": 0.8174} +{"mode": "train", "epoch": 74, "iter": 2800, "lr": 0.05131, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32203, "top5_acc": 0.58469, "loss_cls": 3.84957, "loss": 3.84957, "time": 0.8167} +{"mode": "train", "epoch": 74, "iter": 2900, "lr": 0.05128, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31938, "top5_acc": 0.58641, "loss_cls": 3.87146, "loss": 3.87146, "time": 0.82146} +{"mode": "train", "epoch": 74, "iter": 3000, "lr": 0.05126, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.315, "top5_acc": 0.56766, "loss_cls": 3.89798, "loss": 3.89798, "time": 0.81741} +{"mode": "train", "epoch": 74, "iter": 3100, "lr": 0.05123, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31781, "top5_acc": 0.58547, "loss_cls": 3.86623, "loss": 3.86623, "time": 0.81682} +{"mode": "train", "epoch": 74, "iter": 3200, "lr": 0.0512, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32047, "top5_acc": 0.57719, "loss_cls": 3.89701, "loss": 3.89701, "time": 0.8184} +{"mode": "train", "epoch": 74, "iter": 3300, "lr": 0.05117, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30922, "top5_acc": 0.58219, "loss_cls": 3.89225, "loss": 3.89225, "time": 0.8257} +{"mode": "train", "epoch": 74, "iter": 3400, "lr": 0.05114, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3225, "top5_acc": 0.57891, "loss_cls": 3.84028, "loss": 3.84028, "time": 0.81973} +{"mode": "train", "epoch": 74, "iter": 3500, "lr": 0.05112, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31312, "top5_acc": 0.57797, "loss_cls": 3.91119, "loss": 3.91119, "time": 0.82115} +{"mode": "train", "epoch": 74, "iter": 3600, "lr": 0.05109, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31547, "top5_acc": 0.56906, "loss_cls": 3.88152, "loss": 3.88152, "time": 0.81826} +{"mode": "train", "epoch": 74, "iter": 3700, "lr": 0.05106, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31375, "top5_acc": 0.56016, "loss_cls": 3.89774, "loss": 3.89774, "time": 0.82239} +{"mode": "val", "epoch": 74, "iter": 309, "lr": 0.05105, "top1_acc": 0.24581, "top5_acc": 0.49511, "mean_class_accuracy": 0.24572} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.05102, "memory": 15990, "data_time": 1.30369, "top1_acc": 0.31781, "top5_acc": 0.58, "loss_cls": 3.8749, "loss": 3.8749, "time": 2.28333} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.05099, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32641, "top5_acc": 0.59219, "loss_cls": 3.81325, "loss": 3.81325, "time": 0.82342} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.05096, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33156, "top5_acc": 0.58656, "loss_cls": 3.81335, "loss": 3.81335, "time": 0.81478} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.05094, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3225, "top5_acc": 0.58781, "loss_cls": 3.80724, "loss": 3.80724, "time": 0.82257} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.05091, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32219, "top5_acc": 0.58703, "loss_cls": 3.84857, "loss": 3.84857, "time": 0.81833} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.05088, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.315, "top5_acc": 0.57906, "loss_cls": 3.86733, "loss": 3.86733, "time": 0.8187} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.05085, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31625, "top5_acc": 0.58391, "loss_cls": 3.85665, "loss": 3.85665, "time": 0.81979} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.05082, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31969, "top5_acc": 0.58734, "loss_cls": 3.83749, "loss": 3.83749, "time": 0.8194} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.0508, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31812, "top5_acc": 0.57828, "loss_cls": 3.86435, "loss": 3.86435, "time": 0.81631} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.05077, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32359, "top5_acc": 0.58328, "loss_cls": 3.84433, "loss": 3.84433, "time": 0.8194} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.05074, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31688, "top5_acc": 0.57734, "loss_cls": 3.88183, "loss": 3.88183, "time": 0.82275} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.05071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31281, "top5_acc": 0.57844, "loss_cls": 3.88782, "loss": 3.88782, "time": 0.81587} +{"mode": "train", "epoch": 75, "iter": 1300, "lr": 0.05068, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33375, "top5_acc": 0.59453, "loss_cls": 3.78301, "loss": 3.78301, "time": 0.81783} +{"mode": "train", "epoch": 75, "iter": 1400, "lr": 0.05066, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32609, "top5_acc": 0.59859, "loss_cls": 3.7857, "loss": 3.7857, "time": 0.82618} +{"mode": "train", "epoch": 75, "iter": 1500, "lr": 0.05063, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31906, "top5_acc": 0.57422, "loss_cls": 3.87799, "loss": 3.87799, "time": 0.82599} +{"mode": "train", "epoch": 75, "iter": 1600, "lr": 0.0506, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3225, "top5_acc": 0.58547, "loss_cls": 3.84368, "loss": 3.84368, "time": 0.82072} +{"mode": "train", "epoch": 75, "iter": 1700, "lr": 0.05057, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31516, "top5_acc": 0.58328, "loss_cls": 3.87681, "loss": 3.87681, "time": 0.82076} +{"mode": "train", "epoch": 75, "iter": 1800, "lr": 0.05054, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31156, "top5_acc": 0.57781, "loss_cls": 3.90568, "loss": 3.90568, "time": 0.81811} +{"mode": "train", "epoch": 75, "iter": 1900, "lr": 0.05052, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32359, "top5_acc": 0.59297, "loss_cls": 3.81982, "loss": 3.81982, "time": 0.81915} +{"mode": "train", "epoch": 75, "iter": 2000, "lr": 0.05049, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31875, "top5_acc": 0.59016, "loss_cls": 3.84, "loss": 3.84, "time": 0.81328} +{"mode": "train", "epoch": 75, "iter": 2100, "lr": 0.05046, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30891, "top5_acc": 0.58266, "loss_cls": 3.8531, "loss": 3.8531, "time": 0.82021} +{"mode": "train", "epoch": 75, "iter": 2200, "lr": 0.05043, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31594, "top5_acc": 0.58188, "loss_cls": 3.87379, "loss": 3.87379, "time": 0.81565} +{"mode": "train", "epoch": 75, "iter": 2300, "lr": 0.0504, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32484, "top5_acc": 0.57812, "loss_cls": 3.85638, "loss": 3.85638, "time": 0.81796} +{"mode": "train", "epoch": 75, "iter": 2400, "lr": 0.05038, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30625, "top5_acc": 0.56625, "loss_cls": 3.93397, "loss": 3.93397, "time": 0.82091} +{"mode": "train", "epoch": 75, "iter": 2500, "lr": 0.05035, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.315, "top5_acc": 0.58641, "loss_cls": 3.84796, "loss": 3.84796, "time": 0.81391} +{"mode": "train", "epoch": 75, "iter": 2600, "lr": 0.05032, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32781, "top5_acc": 0.58938, "loss_cls": 3.83342, "loss": 3.83342, "time": 0.82311} +{"mode": "train", "epoch": 75, "iter": 2700, "lr": 0.05029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31641, "top5_acc": 0.57938, "loss_cls": 3.89083, "loss": 3.89083, "time": 0.81814} +{"mode": "train", "epoch": 75, "iter": 2800, "lr": 0.05026, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31594, "top5_acc": 0.58766, "loss_cls": 3.82269, "loss": 3.82269, "time": 0.82965} +{"mode": "train", "epoch": 75, "iter": 2900, "lr": 0.05024, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31797, "top5_acc": 0.57266, "loss_cls": 3.88765, "loss": 3.88765, "time": 0.82209} +{"mode": "train", "epoch": 75, "iter": 3000, "lr": 0.05021, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31766, "top5_acc": 0.58453, "loss_cls": 3.83615, "loss": 3.83615, "time": 0.81874} +{"mode": "train", "epoch": 75, "iter": 3100, "lr": 0.05018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31609, "top5_acc": 0.59531, "loss_cls": 3.80705, "loss": 3.80705, "time": 0.81959} +{"mode": "train", "epoch": 75, "iter": 3200, "lr": 0.05015, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31344, "top5_acc": 0.57391, "loss_cls": 3.92667, "loss": 3.92667, "time": 0.8194} +{"mode": "train", "epoch": 75, "iter": 3300, "lr": 0.05012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31891, "top5_acc": 0.58109, "loss_cls": 3.89054, "loss": 3.89054, "time": 0.82446} +{"mode": "train", "epoch": 75, "iter": 3400, "lr": 0.0501, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32875, "top5_acc": 0.58312, "loss_cls": 3.83037, "loss": 3.83037, "time": 0.81969} +{"mode": "train", "epoch": 75, "iter": 3500, "lr": 0.05007, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32203, "top5_acc": 0.58062, "loss_cls": 3.88287, "loss": 3.88287, "time": 0.8236} +{"mode": "train", "epoch": 75, "iter": 3600, "lr": 0.05004, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33344, "top5_acc": 0.58812, "loss_cls": 3.79045, "loss": 3.79045, "time": 0.81789} +{"mode": "train", "epoch": 75, "iter": 3700, "lr": 0.05001, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32469, "top5_acc": 0.5775, "loss_cls": 3.87741, "loss": 3.87741, "time": 0.81669} +{"mode": "val", "epoch": 75, "iter": 309, "lr": 0.05, "top1_acc": 0.25255, "top5_acc": 0.51137, "mean_class_accuracy": 0.25232} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.04997, "memory": 15990, "data_time": 1.2852, "top1_acc": 0.33453, "top5_acc": 0.59734, "loss_cls": 3.77271, "loss": 3.77271, "time": 2.2646} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.04994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32719, "top5_acc": 0.58938, "loss_cls": 3.8002, "loss": 3.8002, "time": 0.82173} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.04992, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33328, "top5_acc": 0.59375, "loss_cls": 3.79024, "loss": 3.79024, "time": 0.8219} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.04989, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32938, "top5_acc": 0.58812, "loss_cls": 3.8082, "loss": 3.8082, "time": 0.81816} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.04986, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32953, "top5_acc": 0.59109, "loss_cls": 3.81025, "loss": 3.81025, "time": 0.81836} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.04983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32094, "top5_acc": 0.58031, "loss_cls": 3.85207, "loss": 3.85207, "time": 0.81573} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.0498, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32297, "top5_acc": 0.59578, "loss_cls": 3.78042, "loss": 3.78042, "time": 0.81697} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.04978, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32281, "top5_acc": 0.58422, "loss_cls": 3.84912, "loss": 3.84912, "time": 0.82252} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.04975, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31891, "top5_acc": 0.57625, "loss_cls": 3.88489, "loss": 3.88489, "time": 0.81803} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.04972, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32797, "top5_acc": 0.58672, "loss_cls": 3.83817, "loss": 3.83817, "time": 0.8198} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.04969, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3175, "top5_acc": 0.58656, "loss_cls": 3.83838, "loss": 3.83838, "time": 0.81575} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.04966, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32344, "top5_acc": 0.58688, "loss_cls": 3.80481, "loss": 3.80481, "time": 0.81508} +{"mode": "train", "epoch": 76, "iter": 1300, "lr": 0.04964, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31703, "top5_acc": 0.58938, "loss_cls": 3.84832, "loss": 3.84832, "time": 0.82179} +{"mode": "train", "epoch": 76, "iter": 1400, "lr": 0.04961, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3275, "top5_acc": 0.58516, "loss_cls": 3.83701, "loss": 3.83701, "time": 0.82452} +{"mode": "train", "epoch": 76, "iter": 1500, "lr": 0.04958, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32672, "top5_acc": 0.59031, "loss_cls": 3.82504, "loss": 3.82504, "time": 0.82242} +{"mode": "train", "epoch": 76, "iter": 1600, "lr": 0.04955, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31375, "top5_acc": 0.58266, "loss_cls": 3.85794, "loss": 3.85794, "time": 0.81831} +{"mode": "train", "epoch": 76, "iter": 1700, "lr": 0.04953, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31672, "top5_acc": 0.58906, "loss_cls": 3.86146, "loss": 3.86146, "time": 0.8185} +{"mode": "train", "epoch": 76, "iter": 1800, "lr": 0.0495, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32344, "top5_acc": 0.57969, "loss_cls": 3.83518, "loss": 3.83518, "time": 0.81969} +{"mode": "train", "epoch": 76, "iter": 1900, "lr": 0.04947, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33109, "top5_acc": 0.59094, "loss_cls": 3.81764, "loss": 3.81764, "time": 0.81597} +{"mode": "train", "epoch": 76, "iter": 2000, "lr": 0.04944, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31688, "top5_acc": 0.57734, "loss_cls": 3.84647, "loss": 3.84647, "time": 0.81926} +{"mode": "train", "epoch": 76, "iter": 2100, "lr": 0.04941, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32344, "top5_acc": 0.58859, "loss_cls": 3.82419, "loss": 3.82419, "time": 0.8206} +{"mode": "train", "epoch": 76, "iter": 2200, "lr": 0.04939, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31781, "top5_acc": 0.58609, "loss_cls": 3.84494, "loss": 3.84494, "time": 0.81504} +{"mode": "train", "epoch": 76, "iter": 2300, "lr": 0.04936, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32672, "top5_acc": 0.57828, "loss_cls": 3.84521, "loss": 3.84521, "time": 0.81325} +{"mode": "train", "epoch": 76, "iter": 2400, "lr": 0.04933, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31797, "top5_acc": 0.57531, "loss_cls": 3.88364, "loss": 3.88364, "time": 0.82286} +{"mode": "train", "epoch": 76, "iter": 2500, "lr": 0.0493, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32328, "top5_acc": 0.57766, "loss_cls": 3.84795, "loss": 3.84795, "time": 0.81393} +{"mode": "train", "epoch": 76, "iter": 2600, "lr": 0.04927, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32, "top5_acc": 0.57938, "loss_cls": 3.85225, "loss": 3.85225, "time": 0.81794} +{"mode": "train", "epoch": 76, "iter": 2700, "lr": 0.04925, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32078, "top5_acc": 0.58266, "loss_cls": 3.86855, "loss": 3.86855, "time": 0.8175} +{"mode": "train", "epoch": 76, "iter": 2800, "lr": 0.04922, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32875, "top5_acc": 0.59203, "loss_cls": 3.8314, "loss": 3.8314, "time": 0.81771} +{"mode": "train", "epoch": 76, "iter": 2900, "lr": 0.04919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31219, "top5_acc": 0.57438, "loss_cls": 3.89516, "loss": 3.89516, "time": 0.81457} +{"mode": "train", "epoch": 76, "iter": 3000, "lr": 0.04916, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31172, "top5_acc": 0.58469, "loss_cls": 3.87933, "loss": 3.87933, "time": 0.81573} +{"mode": "train", "epoch": 76, "iter": 3100, "lr": 0.04913, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30891, "top5_acc": 0.57375, "loss_cls": 3.89415, "loss": 3.89415, "time": 0.81724} +{"mode": "train", "epoch": 76, "iter": 3200, "lr": 0.04911, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31641, "top5_acc": 0.57344, "loss_cls": 3.9026, "loss": 3.9026, "time": 0.81802} +{"mode": "train", "epoch": 76, "iter": 3300, "lr": 0.04908, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32547, "top5_acc": 0.58219, "loss_cls": 3.84815, "loss": 3.84815, "time": 0.82149} +{"mode": "train", "epoch": 76, "iter": 3400, "lr": 0.04905, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32016, "top5_acc": 0.57891, "loss_cls": 3.83317, "loss": 3.83317, "time": 0.81851} +{"mode": "train", "epoch": 76, "iter": 3500, "lr": 0.04902, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31859, "top5_acc": 0.58156, "loss_cls": 3.84733, "loss": 3.84733, "time": 0.82715} +{"mode": "train", "epoch": 76, "iter": 3600, "lr": 0.04899, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32172, "top5_acc": 0.58719, "loss_cls": 3.85607, "loss": 3.85607, "time": 0.82193} +{"mode": "train", "epoch": 76, "iter": 3700, "lr": 0.04897, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33047, "top5_acc": 0.58891, "loss_cls": 3.78829, "loss": 3.78829, "time": 0.81634} +{"mode": "val", "epoch": 76, "iter": 309, "lr": 0.04895, "top1_acc": 0.24556, "top5_acc": 0.50767, "mean_class_accuracy": 0.24537} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.04893, "memory": 15990, "data_time": 1.31103, "top1_acc": 0.34312, "top5_acc": 0.60453, "loss_cls": 3.73283, "loss": 3.73283, "time": 2.31301} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0489, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33062, "top5_acc": 0.59672, "loss_cls": 3.75502, "loss": 3.75502, "time": 0.82571} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.04887, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3275, "top5_acc": 0.59562, "loss_cls": 3.79607, "loss": 3.79607, "time": 0.82733} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.04884, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32391, "top5_acc": 0.58812, "loss_cls": 3.82396, "loss": 3.82396, "time": 0.81896} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.04881, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33719, "top5_acc": 0.59469, "loss_cls": 3.78619, "loss": 3.78619, "time": 0.81961} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.04879, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32719, "top5_acc": 0.59672, "loss_cls": 3.79372, "loss": 3.79372, "time": 0.82283} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.04876, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32016, "top5_acc": 0.5775, "loss_cls": 3.84718, "loss": 3.84718, "time": 0.82002} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.04873, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32719, "top5_acc": 0.58969, "loss_cls": 3.81009, "loss": 3.81009, "time": 0.81638} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.0487, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33156, "top5_acc": 0.59141, "loss_cls": 3.80045, "loss": 3.80045, "time": 0.82104} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.04867, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32859, "top5_acc": 0.58641, "loss_cls": 3.82375, "loss": 3.82375, "time": 0.81782} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.04865, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31672, "top5_acc": 0.57875, "loss_cls": 3.86327, "loss": 3.86327, "time": 0.81987} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.04862, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31766, "top5_acc": 0.58188, "loss_cls": 3.89509, "loss": 3.89509, "time": 0.81844} +{"mode": "train", "epoch": 77, "iter": 1300, "lr": 0.04859, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32094, "top5_acc": 0.5825, "loss_cls": 3.84869, "loss": 3.84869, "time": 0.81774} +{"mode": "train", "epoch": 77, "iter": 1400, "lr": 0.04856, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32266, "top5_acc": 0.58828, "loss_cls": 3.82179, "loss": 3.82179, "time": 0.82367} +{"mode": "train", "epoch": 77, "iter": 1500, "lr": 0.04853, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31938, "top5_acc": 0.58391, "loss_cls": 3.85301, "loss": 3.85301, "time": 0.81718} +{"mode": "train", "epoch": 77, "iter": 1600, "lr": 0.04851, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31906, "top5_acc": 0.58797, "loss_cls": 3.83428, "loss": 3.83428, "time": 0.8252} +{"mode": "train", "epoch": 77, "iter": 1700, "lr": 0.04848, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31812, "top5_acc": 0.58453, "loss_cls": 3.85345, "loss": 3.85345, "time": 0.81874} +{"mode": "train", "epoch": 77, "iter": 1800, "lr": 0.04845, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.325, "top5_acc": 0.59094, "loss_cls": 3.81894, "loss": 3.81894, "time": 0.82357} +{"mode": "train", "epoch": 77, "iter": 1900, "lr": 0.04842, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32516, "top5_acc": 0.58594, "loss_cls": 3.8319, "loss": 3.8319, "time": 0.82128} +{"mode": "train", "epoch": 77, "iter": 2000, "lr": 0.04839, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33922, "top5_acc": 0.58172, "loss_cls": 3.80768, "loss": 3.80768, "time": 0.81919} +{"mode": "train", "epoch": 77, "iter": 2100, "lr": 0.04837, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32219, "top5_acc": 0.58891, "loss_cls": 3.81724, "loss": 3.81724, "time": 0.81787} +{"mode": "train", "epoch": 77, "iter": 2200, "lr": 0.04834, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32922, "top5_acc": 0.58594, "loss_cls": 3.82169, "loss": 3.82169, "time": 0.81586} +{"mode": "train", "epoch": 77, "iter": 2300, "lr": 0.04831, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31609, "top5_acc": 0.57609, "loss_cls": 3.88446, "loss": 3.88446, "time": 0.82458} +{"mode": "train", "epoch": 77, "iter": 2400, "lr": 0.04828, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31875, "top5_acc": 0.57297, "loss_cls": 3.86616, "loss": 3.86616, "time": 0.82052} +{"mode": "train", "epoch": 77, "iter": 2500, "lr": 0.04825, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32859, "top5_acc": 0.59094, "loss_cls": 3.81317, "loss": 3.81317, "time": 0.81847} +{"mode": "train", "epoch": 77, "iter": 2600, "lr": 0.04823, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31297, "top5_acc": 0.58016, "loss_cls": 3.8697, "loss": 3.8697, "time": 0.82177} +{"mode": "train", "epoch": 77, "iter": 2700, "lr": 0.0482, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32375, "top5_acc": 0.58547, "loss_cls": 3.82873, "loss": 3.82873, "time": 0.82117} +{"mode": "train", "epoch": 77, "iter": 2800, "lr": 0.04817, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33, "top5_acc": 0.58641, "loss_cls": 3.79935, "loss": 3.79935, "time": 0.8233} +{"mode": "train", "epoch": 77, "iter": 2900, "lr": 0.04814, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32062, "top5_acc": 0.58656, "loss_cls": 3.83382, "loss": 3.83382, "time": 0.82303} +{"mode": "train", "epoch": 77, "iter": 3000, "lr": 0.04811, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32391, "top5_acc": 0.59031, "loss_cls": 3.81578, "loss": 3.81578, "time": 0.81972} +{"mode": "train", "epoch": 77, "iter": 3100, "lr": 0.04809, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32469, "top5_acc": 0.5825, "loss_cls": 3.84536, "loss": 3.84536, "time": 0.81514} +{"mode": "train", "epoch": 77, "iter": 3200, "lr": 0.04806, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32812, "top5_acc": 0.59375, "loss_cls": 3.80373, "loss": 3.80373, "time": 0.8238} +{"mode": "train", "epoch": 77, "iter": 3300, "lr": 0.04803, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31359, "top5_acc": 0.56688, "loss_cls": 3.88668, "loss": 3.88668, "time": 0.8216} +{"mode": "train", "epoch": 77, "iter": 3400, "lr": 0.048, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32391, "top5_acc": 0.57969, "loss_cls": 3.85569, "loss": 3.85569, "time": 0.82403} +{"mode": "train", "epoch": 77, "iter": 3500, "lr": 0.04798, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32016, "top5_acc": 0.58781, "loss_cls": 3.83159, "loss": 3.83159, "time": 0.82487} +{"mode": "train", "epoch": 77, "iter": 3600, "lr": 0.04795, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31844, "top5_acc": 0.58656, "loss_cls": 3.85967, "loss": 3.85967, "time": 0.81599} +{"mode": "train", "epoch": 77, "iter": 3700, "lr": 0.04792, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33422, "top5_acc": 0.59562, "loss_cls": 3.78083, "loss": 3.78083, "time": 0.82084} +{"mode": "val", "epoch": 77, "iter": 309, "lr": 0.04791, "top1_acc": 0.26004, "top5_acc": 0.50519, "mean_class_accuracy": 0.25987} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.04788, "memory": 15990, "data_time": 1.342, "top1_acc": 0.32875, "top5_acc": 0.59375, "loss_cls": 3.79322, "loss": 3.79322, "time": 2.32781} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.04785, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32969, "top5_acc": 0.59859, "loss_cls": 3.799, "loss": 3.799, "time": 0.81917} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.04782, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33141, "top5_acc": 0.59969, "loss_cls": 3.77194, "loss": 3.77194, "time": 0.82074} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.04779, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32172, "top5_acc": 0.58594, "loss_cls": 3.82504, "loss": 3.82504, "time": 0.81581} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.04777, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33219, "top5_acc": 0.59609, "loss_cls": 3.76287, "loss": 3.76287, "time": 0.8212} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.04774, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32812, "top5_acc": 0.59062, "loss_cls": 3.79196, "loss": 3.79196, "time": 0.81843} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.04771, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31984, "top5_acc": 0.58766, "loss_cls": 3.83111, "loss": 3.83111, "time": 0.82216} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.04768, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33391, "top5_acc": 0.59812, "loss_cls": 3.75545, "loss": 3.75545, "time": 0.81697} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.04766, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32641, "top5_acc": 0.58234, "loss_cls": 3.80889, "loss": 3.80889, "time": 0.82004} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.04763, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32078, "top5_acc": 0.58625, "loss_cls": 3.82518, "loss": 3.82518, "time": 0.81424} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.0476, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33844, "top5_acc": 0.59781, "loss_cls": 3.77276, "loss": 3.77276, "time": 0.81582} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.04757, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32578, "top5_acc": 0.59547, "loss_cls": 3.82195, "loss": 3.82195, "time": 0.83019} +{"mode": "train", "epoch": 78, "iter": 1300, "lr": 0.04754, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3225, "top5_acc": 0.58953, "loss_cls": 3.80775, "loss": 3.80775, "time": 0.81738} +{"mode": "train", "epoch": 78, "iter": 1400, "lr": 0.04752, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32859, "top5_acc": 0.58734, "loss_cls": 3.82729, "loss": 3.82729, "time": 0.81855} +{"mode": "train", "epoch": 78, "iter": 1500, "lr": 0.04749, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32703, "top5_acc": 0.58359, "loss_cls": 3.81514, "loss": 3.81514, "time": 0.81817} +{"mode": "train", "epoch": 78, "iter": 1600, "lr": 0.04746, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32688, "top5_acc": 0.58969, "loss_cls": 3.81563, "loss": 3.81563, "time": 0.83105} +{"mode": "train", "epoch": 78, "iter": 1700, "lr": 0.04743, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31406, "top5_acc": 0.58062, "loss_cls": 3.85064, "loss": 3.85064, "time": 0.81598} +{"mode": "train", "epoch": 78, "iter": 1800, "lr": 0.0474, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33031, "top5_acc": 0.59875, "loss_cls": 3.81187, "loss": 3.81187, "time": 0.82047} +{"mode": "train", "epoch": 78, "iter": 1900, "lr": 0.04738, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31438, "top5_acc": 0.58141, "loss_cls": 3.86884, "loss": 3.86884, "time": 0.81701} +{"mode": "train", "epoch": 78, "iter": 2000, "lr": 0.04735, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32578, "top5_acc": 0.57969, "loss_cls": 3.82669, "loss": 3.82669, "time": 0.8199} +{"mode": "train", "epoch": 78, "iter": 2100, "lr": 0.04732, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32859, "top5_acc": 0.59188, "loss_cls": 3.7976, "loss": 3.7976, "time": 0.81882} +{"mode": "train", "epoch": 78, "iter": 2200, "lr": 0.04729, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32016, "top5_acc": 0.57719, "loss_cls": 3.86851, "loss": 3.86851, "time": 0.81821} +{"mode": "train", "epoch": 78, "iter": 2300, "lr": 0.04726, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32391, "top5_acc": 0.58203, "loss_cls": 3.80926, "loss": 3.80926, "time": 0.82506} +{"mode": "train", "epoch": 78, "iter": 2400, "lr": 0.04724, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32875, "top5_acc": 0.59172, "loss_cls": 3.80719, "loss": 3.80719, "time": 0.82824} +{"mode": "train", "epoch": 78, "iter": 2500, "lr": 0.04721, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31156, "top5_acc": 0.57766, "loss_cls": 3.88209, "loss": 3.88209, "time": 0.82076} +{"mode": "train", "epoch": 78, "iter": 2600, "lr": 0.04718, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33125, "top5_acc": 0.59578, "loss_cls": 3.7783, "loss": 3.7783, "time": 0.81908} +{"mode": "train", "epoch": 78, "iter": 2700, "lr": 0.04715, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31641, "top5_acc": 0.58141, "loss_cls": 3.8449, "loss": 3.8449, "time": 0.8151} +{"mode": "train", "epoch": 78, "iter": 2800, "lr": 0.04712, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32844, "top5_acc": 0.59484, "loss_cls": 3.77648, "loss": 3.77648, "time": 0.81922} +{"mode": "train", "epoch": 78, "iter": 2900, "lr": 0.0471, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31203, "top5_acc": 0.57422, "loss_cls": 3.88324, "loss": 3.88324, "time": 0.81945} +{"mode": "train", "epoch": 78, "iter": 3000, "lr": 0.04707, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32297, "top5_acc": 0.58891, "loss_cls": 3.83047, "loss": 3.83047, "time": 0.81415} +{"mode": "train", "epoch": 78, "iter": 3100, "lr": 0.04704, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32703, "top5_acc": 0.59078, "loss_cls": 3.81849, "loss": 3.81849, "time": 0.81687} +{"mode": "train", "epoch": 78, "iter": 3200, "lr": 0.04701, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31375, "top5_acc": 0.5775, "loss_cls": 3.87595, "loss": 3.87595, "time": 0.823} +{"mode": "train", "epoch": 78, "iter": 3300, "lr": 0.04699, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32484, "top5_acc": 0.58047, "loss_cls": 3.85066, "loss": 3.85066, "time": 0.81694} +{"mode": "train", "epoch": 78, "iter": 3400, "lr": 0.04696, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.325, "top5_acc": 0.59312, "loss_cls": 3.78535, "loss": 3.78535, "time": 0.8236} +{"mode": "train", "epoch": 78, "iter": 3500, "lr": 0.04693, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33219, "top5_acc": 0.59812, "loss_cls": 3.76124, "loss": 3.76124, "time": 0.81717} +{"mode": "train", "epoch": 78, "iter": 3600, "lr": 0.0469, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32516, "top5_acc": 0.58203, "loss_cls": 3.83332, "loss": 3.83332, "time": 0.81653} +{"mode": "train", "epoch": 78, "iter": 3700, "lr": 0.04687, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32719, "top5_acc": 0.58719, "loss_cls": 3.82082, "loss": 3.82082, "time": 0.81362} +{"mode": "val", "epoch": 78, "iter": 309, "lr": 0.04686, "top1_acc": 0.24155, "top5_acc": 0.49405, "mean_class_accuracy": 0.24132} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.04683, "memory": 15990, "data_time": 1.33528, "top1_acc": 0.33516, "top5_acc": 0.60109, "loss_cls": 3.74739, "loss": 3.74739, "time": 2.32381} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.0468, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33516, "top5_acc": 0.58859, "loss_cls": 3.774, "loss": 3.774, "time": 0.81783} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.04678, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32703, "top5_acc": 0.59922, "loss_cls": 3.7978, "loss": 3.7978, "time": 0.82229} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.04675, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32797, "top5_acc": 0.595, "loss_cls": 3.77706, "loss": 3.77706, "time": 0.81953} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.04672, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34, "top5_acc": 0.60047, "loss_cls": 3.74427, "loss": 3.74427, "time": 0.81651} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.04669, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33906, "top5_acc": 0.60078, "loss_cls": 3.74801, "loss": 3.74801, "time": 0.81477} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.04667, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32688, "top5_acc": 0.58594, "loss_cls": 3.83048, "loss": 3.83048, "time": 0.81829} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.04664, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33391, "top5_acc": 0.58812, "loss_cls": 3.80169, "loss": 3.80169, "time": 0.81761} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.04661, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33906, "top5_acc": 0.60094, "loss_cls": 3.75768, "loss": 3.75768, "time": 0.81829} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.04658, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32281, "top5_acc": 0.59297, "loss_cls": 3.78605, "loss": 3.78605, "time": 0.81682} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.04655, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32641, "top5_acc": 0.58875, "loss_cls": 3.81872, "loss": 3.81872, "time": 0.816} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.04653, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32812, "top5_acc": 0.59609, "loss_cls": 3.78465, "loss": 3.78465, "time": 0.82057} +{"mode": "train", "epoch": 79, "iter": 1300, "lr": 0.0465, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33219, "top5_acc": 0.59953, "loss_cls": 3.78201, "loss": 3.78201, "time": 0.82} +{"mode": "train", "epoch": 79, "iter": 1400, "lr": 0.04647, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32484, "top5_acc": 0.59719, "loss_cls": 3.83321, "loss": 3.83321, "time": 0.82293} +{"mode": "train", "epoch": 79, "iter": 1500, "lr": 0.04644, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32312, "top5_acc": 0.58469, "loss_cls": 3.84236, "loss": 3.84236, "time": 0.82187} +{"mode": "train", "epoch": 79, "iter": 1600, "lr": 0.04641, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33188, "top5_acc": 0.59781, "loss_cls": 3.78234, "loss": 3.78234, "time": 0.8321} +{"mode": "train", "epoch": 79, "iter": 1700, "lr": 0.04639, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31156, "top5_acc": 0.56781, "loss_cls": 3.90445, "loss": 3.90445, "time": 0.81918} +{"mode": "train", "epoch": 79, "iter": 1800, "lr": 0.04636, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31422, "top5_acc": 0.57094, "loss_cls": 3.90177, "loss": 3.90177, "time": 0.81947} +{"mode": "train", "epoch": 79, "iter": 1900, "lr": 0.04633, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3225, "top5_acc": 0.58328, "loss_cls": 3.797, "loss": 3.797, "time": 0.81856} +{"mode": "train", "epoch": 79, "iter": 2000, "lr": 0.0463, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33297, "top5_acc": 0.58797, "loss_cls": 3.80246, "loss": 3.80246, "time": 0.82014} +{"mode": "train", "epoch": 79, "iter": 2100, "lr": 0.04628, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33297, "top5_acc": 0.58938, "loss_cls": 3.8068, "loss": 3.8068, "time": 0.8165} +{"mode": "train", "epoch": 79, "iter": 2200, "lr": 0.04625, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33031, "top5_acc": 0.59438, "loss_cls": 3.80794, "loss": 3.80794, "time": 0.8187} +{"mode": "train", "epoch": 79, "iter": 2300, "lr": 0.04622, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32922, "top5_acc": 0.59031, "loss_cls": 3.82445, "loss": 3.82445, "time": 0.82134} +{"mode": "train", "epoch": 79, "iter": 2400, "lr": 0.04619, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32938, "top5_acc": 0.59234, "loss_cls": 3.78934, "loss": 3.78934, "time": 0.82182} +{"mode": "train", "epoch": 79, "iter": 2500, "lr": 0.04616, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32078, "top5_acc": 0.58547, "loss_cls": 3.84167, "loss": 3.84167, "time": 0.8194} +{"mode": "train", "epoch": 79, "iter": 2600, "lr": 0.04614, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33203, "top5_acc": 0.59812, "loss_cls": 3.77299, "loss": 3.77299, "time": 0.8203} +{"mode": "train", "epoch": 79, "iter": 2700, "lr": 0.04611, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33188, "top5_acc": 0.58547, "loss_cls": 3.80567, "loss": 3.80567, "time": 0.81775} +{"mode": "train", "epoch": 79, "iter": 2800, "lr": 0.04608, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33594, "top5_acc": 0.59781, "loss_cls": 3.77175, "loss": 3.77175, "time": 0.82213} +{"mode": "train", "epoch": 79, "iter": 2900, "lr": 0.04605, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33875, "top5_acc": 0.60484, "loss_cls": 3.76757, "loss": 3.76757, "time": 0.81611} +{"mode": "train", "epoch": 79, "iter": 3000, "lr": 0.04602, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32469, "top5_acc": 0.59656, "loss_cls": 3.7945, "loss": 3.7945, "time": 0.81987} +{"mode": "train", "epoch": 79, "iter": 3100, "lr": 0.046, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32703, "top5_acc": 0.58375, "loss_cls": 3.8261, "loss": 3.8261, "time": 0.81534} +{"mode": "train", "epoch": 79, "iter": 3200, "lr": 0.04597, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32453, "top5_acc": 0.58766, "loss_cls": 3.80635, "loss": 3.80635, "time": 0.82602} +{"mode": "train", "epoch": 79, "iter": 3300, "lr": 0.04594, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33766, "top5_acc": 0.59516, "loss_cls": 3.77118, "loss": 3.77118, "time": 0.81683} +{"mode": "train", "epoch": 79, "iter": 3400, "lr": 0.04591, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33172, "top5_acc": 0.59516, "loss_cls": 3.79611, "loss": 3.79611, "time": 0.82307} +{"mode": "train", "epoch": 79, "iter": 3500, "lr": 0.04588, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33531, "top5_acc": 0.59156, "loss_cls": 3.78884, "loss": 3.78884, "time": 0.81694} +{"mode": "train", "epoch": 79, "iter": 3600, "lr": 0.04586, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30922, "top5_acc": 0.5725, "loss_cls": 3.88813, "loss": 3.88813, "time": 0.81679} +{"mode": "train", "epoch": 79, "iter": 3700, "lr": 0.04583, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32281, "top5_acc": 0.58078, "loss_cls": 3.85876, "loss": 3.85876, "time": 0.82017} +{"mode": "val", "epoch": 79, "iter": 309, "lr": 0.04582, "top1_acc": 0.26561, "top5_acc": 0.51603, "mean_class_accuracy": 0.26546} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.04579, "memory": 15990, "data_time": 1.35667, "top1_acc": 0.34609, "top5_acc": 0.60578, "loss_cls": 3.70526, "loss": 3.70526, "time": 2.34789} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.04576, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33734, "top5_acc": 0.60312, "loss_cls": 3.72811, "loss": 3.72811, "time": 0.83289} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.04573, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33938, "top5_acc": 0.59688, "loss_cls": 3.76253, "loss": 3.76253, "time": 0.83186} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.0457, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32375, "top5_acc": 0.58672, "loss_cls": 3.80701, "loss": 3.80701, "time": 0.82732} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.04568, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33266, "top5_acc": 0.59484, "loss_cls": 3.79044, "loss": 3.79044, "time": 0.83368} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.04565, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33406, "top5_acc": 0.59891, "loss_cls": 3.74596, "loss": 3.74596, "time": 0.83155} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.04562, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33609, "top5_acc": 0.59969, "loss_cls": 3.7676, "loss": 3.7676, "time": 0.82314} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.04559, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33281, "top5_acc": 0.60219, "loss_cls": 3.75127, "loss": 3.75127, "time": 0.81875} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.04557, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32844, "top5_acc": 0.60172, "loss_cls": 3.77263, "loss": 3.77263, "time": 0.81808} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.04554, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33344, "top5_acc": 0.58828, "loss_cls": 3.78583, "loss": 3.78583, "time": 0.81533} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.04551, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33453, "top5_acc": 0.59328, "loss_cls": 3.77478, "loss": 3.77478, "time": 0.81804} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.04548, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33672, "top5_acc": 0.59094, "loss_cls": 3.80437, "loss": 3.80437, "time": 0.81683} +{"mode": "train", "epoch": 80, "iter": 1300, "lr": 0.04545, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32938, "top5_acc": 0.59719, "loss_cls": 3.77371, "loss": 3.77371, "time": 0.81996} +{"mode": "train", "epoch": 80, "iter": 1400, "lr": 0.04543, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32891, "top5_acc": 0.59781, "loss_cls": 3.78358, "loss": 3.78358, "time": 0.8224} +{"mode": "train", "epoch": 80, "iter": 1500, "lr": 0.0454, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33328, "top5_acc": 0.59344, "loss_cls": 3.79979, "loss": 3.79979, "time": 0.82077} +{"mode": "train", "epoch": 80, "iter": 1600, "lr": 0.04537, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.325, "top5_acc": 0.57828, "loss_cls": 3.81726, "loss": 3.81726, "time": 0.82883} +{"mode": "train", "epoch": 80, "iter": 1700, "lr": 0.04534, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33734, "top5_acc": 0.60266, "loss_cls": 3.76346, "loss": 3.76346, "time": 0.81878} +{"mode": "train", "epoch": 80, "iter": 1800, "lr": 0.04532, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.32969, "top5_acc": 0.59578, "loss_cls": 3.79049, "loss": 3.79049, "time": 0.82177} +{"mode": "train", "epoch": 80, "iter": 1900, "lr": 0.04529, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32547, "top5_acc": 0.59188, "loss_cls": 3.81016, "loss": 3.81016, "time": 0.81919} +{"mode": "train", "epoch": 80, "iter": 2000, "lr": 0.04526, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32234, "top5_acc": 0.58328, "loss_cls": 3.85416, "loss": 3.85416, "time": 0.81532} +{"mode": "train", "epoch": 80, "iter": 2100, "lr": 0.04523, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33453, "top5_acc": 0.5875, "loss_cls": 3.79694, "loss": 3.79694, "time": 0.82095} +{"mode": "train", "epoch": 80, "iter": 2200, "lr": 0.0452, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32906, "top5_acc": 0.58516, "loss_cls": 3.78438, "loss": 3.78438, "time": 0.8195} +{"mode": "train", "epoch": 80, "iter": 2300, "lr": 0.04518, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32984, "top5_acc": 0.58609, "loss_cls": 3.81985, "loss": 3.81985, "time": 0.82476} +{"mode": "train", "epoch": 80, "iter": 2400, "lr": 0.04515, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33438, "top5_acc": 0.60172, "loss_cls": 3.75257, "loss": 3.75257, "time": 0.82112} +{"mode": "train", "epoch": 80, "iter": 2500, "lr": 0.04512, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32406, "top5_acc": 0.58156, "loss_cls": 3.84363, "loss": 3.84363, "time": 0.81792} +{"mode": "train", "epoch": 80, "iter": 2600, "lr": 0.04509, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33219, "top5_acc": 0.59891, "loss_cls": 3.77606, "loss": 3.77606, "time": 0.82277} +{"mode": "train", "epoch": 80, "iter": 2700, "lr": 0.04506, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32469, "top5_acc": 0.59641, "loss_cls": 3.79915, "loss": 3.79915, "time": 0.81859} +{"mode": "train", "epoch": 80, "iter": 2800, "lr": 0.04504, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32312, "top5_acc": 0.59125, "loss_cls": 3.82377, "loss": 3.82377, "time": 0.82169} +{"mode": "train", "epoch": 80, "iter": 2900, "lr": 0.04501, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32719, "top5_acc": 0.59016, "loss_cls": 3.79595, "loss": 3.79595, "time": 0.81829} +{"mode": "train", "epoch": 80, "iter": 3000, "lr": 0.04498, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32859, "top5_acc": 0.59141, "loss_cls": 3.77197, "loss": 3.77197, "time": 0.82526} +{"mode": "train", "epoch": 80, "iter": 3100, "lr": 0.04495, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32531, "top5_acc": 0.59406, "loss_cls": 3.82195, "loss": 3.82195, "time": 0.82399} +{"mode": "train", "epoch": 80, "iter": 3200, "lr": 0.04493, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33375, "top5_acc": 0.58578, "loss_cls": 3.80503, "loss": 3.80503, "time": 0.82464} +{"mode": "train", "epoch": 80, "iter": 3300, "lr": 0.0449, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32438, "top5_acc": 0.58094, "loss_cls": 3.855, "loss": 3.855, "time": 0.8233} +{"mode": "train", "epoch": 80, "iter": 3400, "lr": 0.04487, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33406, "top5_acc": 0.60438, "loss_cls": 3.76901, "loss": 3.76901, "time": 0.82707} +{"mode": "train", "epoch": 80, "iter": 3500, "lr": 0.04484, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33062, "top5_acc": 0.59297, "loss_cls": 3.78353, "loss": 3.78353, "time": 0.8175} +{"mode": "train", "epoch": 80, "iter": 3600, "lr": 0.04481, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33031, "top5_acc": 0.58781, "loss_cls": 3.8103, "loss": 3.8103, "time": 0.81974} +{"mode": "train", "epoch": 80, "iter": 3700, "lr": 0.04479, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33062, "top5_acc": 0.5875, "loss_cls": 3.80118, "loss": 3.80118, "time": 0.82046} +{"mode": "val", "epoch": 80, "iter": 309, "lr": 0.04477, "top1_acc": 0.26389, "top5_acc": 0.51826, "mean_class_accuracy": 0.26364} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.04475, "memory": 15990, "data_time": 1.2984, "top1_acc": 0.33469, "top5_acc": 0.60312, "loss_cls": 3.74453, "loss": 3.74453, "time": 2.28196} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.04472, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34094, "top5_acc": 0.60312, "loss_cls": 3.75462, "loss": 3.75462, "time": 0.82245} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.04469, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33125, "top5_acc": 0.59609, "loss_cls": 3.73771, "loss": 3.73771, "time": 0.82465} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.04466, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.345, "top5_acc": 0.60969, "loss_cls": 3.70787, "loss": 3.70787, "time": 0.82647} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.04463, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32984, "top5_acc": 0.59875, "loss_cls": 3.77046, "loss": 3.77046, "time": 0.82441} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.04461, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32859, "top5_acc": 0.58562, "loss_cls": 3.80838, "loss": 3.80838, "time": 0.82523} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.04458, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32188, "top5_acc": 0.59188, "loss_cls": 3.81609, "loss": 3.81609, "time": 0.8163} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.04455, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33078, "top5_acc": 0.59703, "loss_cls": 3.78387, "loss": 3.78387, "time": 0.81771} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.04452, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32922, "top5_acc": 0.58703, "loss_cls": 3.80707, "loss": 3.80707, "time": 0.82004} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.0445, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33359, "top5_acc": 0.58766, "loss_cls": 3.80491, "loss": 3.80491, "time": 0.81744} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.04447, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33703, "top5_acc": 0.59891, "loss_cls": 3.7535, "loss": 3.7535, "time": 0.82475} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.04444, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32719, "top5_acc": 0.58828, "loss_cls": 3.82685, "loss": 3.82685, "time": 0.81702} +{"mode": "train", "epoch": 81, "iter": 1300, "lr": 0.04441, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33281, "top5_acc": 0.60141, "loss_cls": 3.75985, "loss": 3.75985, "time": 0.81648} +{"mode": "train", "epoch": 81, "iter": 1400, "lr": 0.04438, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33047, "top5_acc": 0.5975, "loss_cls": 3.77861, "loss": 3.77861, "time": 0.82066} +{"mode": "train", "epoch": 81, "iter": 1500, "lr": 0.04436, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33016, "top5_acc": 0.60297, "loss_cls": 3.75298, "loss": 3.75298, "time": 0.81828} +{"mode": "train", "epoch": 81, "iter": 1600, "lr": 0.04433, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.32734, "top5_acc": 0.58531, "loss_cls": 3.82069, "loss": 3.82069, "time": 0.81993} +{"mode": "train", "epoch": 81, "iter": 1700, "lr": 0.0443, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33922, "top5_acc": 0.60047, "loss_cls": 3.75143, "loss": 3.75143, "time": 0.81725} +{"mode": "train", "epoch": 81, "iter": 1800, "lr": 0.04427, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32938, "top5_acc": 0.59328, "loss_cls": 3.76438, "loss": 3.76438, "time": 0.82865} +{"mode": "train", "epoch": 81, "iter": 1900, "lr": 0.04425, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32719, "top5_acc": 0.59734, "loss_cls": 3.78251, "loss": 3.78251, "time": 0.82024} +{"mode": "train", "epoch": 81, "iter": 2000, "lr": 0.04422, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32406, "top5_acc": 0.58984, "loss_cls": 3.8048, "loss": 3.8048, "time": 0.82045} +{"mode": "train", "epoch": 81, "iter": 2100, "lr": 0.04419, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33359, "top5_acc": 0.59781, "loss_cls": 3.76299, "loss": 3.76299, "time": 0.81534} +{"mode": "train", "epoch": 81, "iter": 2200, "lr": 0.04416, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34094, "top5_acc": 0.59812, "loss_cls": 3.76669, "loss": 3.76669, "time": 0.81659} +{"mode": "train", "epoch": 81, "iter": 2300, "lr": 0.04413, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34031, "top5_acc": 0.60312, "loss_cls": 3.75192, "loss": 3.75192, "time": 0.818} +{"mode": "train", "epoch": 81, "iter": 2400, "lr": 0.04411, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32266, "top5_acc": 0.58984, "loss_cls": 3.81767, "loss": 3.81767, "time": 0.81677} +{"mode": "train", "epoch": 81, "iter": 2500, "lr": 0.04408, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32734, "top5_acc": 0.59328, "loss_cls": 3.81728, "loss": 3.81728, "time": 0.8175} +{"mode": "train", "epoch": 81, "iter": 2600, "lr": 0.04405, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32469, "top5_acc": 0.58219, "loss_cls": 3.82012, "loss": 3.82012, "time": 0.82085} +{"mode": "train", "epoch": 81, "iter": 2700, "lr": 0.04402, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33234, "top5_acc": 0.59719, "loss_cls": 3.78748, "loss": 3.78748, "time": 0.81955} +{"mode": "train", "epoch": 81, "iter": 2800, "lr": 0.044, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34047, "top5_acc": 0.605, "loss_cls": 3.72738, "loss": 3.72738, "time": 0.82109} +{"mode": "train", "epoch": 81, "iter": 2900, "lr": 0.04397, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34375, "top5_acc": 0.60906, "loss_cls": 3.69956, "loss": 3.69956, "time": 0.81805} +{"mode": "train", "epoch": 81, "iter": 3000, "lr": 0.04394, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3325, "top5_acc": 0.59391, "loss_cls": 3.7889, "loss": 3.7889, "time": 0.81532} +{"mode": "train", "epoch": 81, "iter": 3100, "lr": 0.04391, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33375, "top5_acc": 0.59266, "loss_cls": 3.78374, "loss": 3.78374, "time": 0.82} +{"mode": "train", "epoch": 81, "iter": 3200, "lr": 0.04389, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33266, "top5_acc": 0.59609, "loss_cls": 3.78014, "loss": 3.78014, "time": 0.81763} +{"mode": "train", "epoch": 81, "iter": 3300, "lr": 0.04386, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32953, "top5_acc": 0.58453, "loss_cls": 3.82683, "loss": 3.82683, "time": 0.82466} +{"mode": "train", "epoch": 81, "iter": 3400, "lr": 0.04383, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32062, "top5_acc": 0.58516, "loss_cls": 3.8605, "loss": 3.8605, "time": 0.82123} +{"mode": "train", "epoch": 81, "iter": 3500, "lr": 0.0438, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33422, "top5_acc": 0.59562, "loss_cls": 3.7762, "loss": 3.7762, "time": 0.8161} +{"mode": "train", "epoch": 81, "iter": 3600, "lr": 0.04377, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33984, "top5_acc": 0.6, "loss_cls": 3.758, "loss": 3.758, "time": 0.82418} +{"mode": "train", "epoch": 81, "iter": 3700, "lr": 0.04375, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33203, "top5_acc": 0.60203, "loss_cls": 3.76792, "loss": 3.76792, "time": 0.81852} +{"mode": "val", "epoch": 81, "iter": 309, "lr": 0.04373, "top1_acc": 0.2723, "top5_acc": 0.52282, "mean_class_accuracy": 0.27217} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.04371, "memory": 15990, "data_time": 1.30951, "top1_acc": 0.33375, "top5_acc": 0.58453, "loss_cls": 3.78628, "loss": 3.78628, "time": 2.29135} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.04368, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34125, "top5_acc": 0.60781, "loss_cls": 3.69589, "loss": 3.69589, "time": 0.82149} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.04365, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33406, "top5_acc": 0.60562, "loss_cls": 3.76191, "loss": 3.76191, "time": 0.82159} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.04362, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34344, "top5_acc": 0.60734, "loss_cls": 3.70169, "loss": 3.70169, "time": 0.81521} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.04359, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33234, "top5_acc": 0.60531, "loss_cls": 3.77039, "loss": 3.77039, "time": 0.82616} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.04357, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33297, "top5_acc": 0.60031, "loss_cls": 3.76227, "loss": 3.76227, "time": 0.82059} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.04354, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34328, "top5_acc": 0.59875, "loss_cls": 3.74715, "loss": 3.74715, "time": 0.81796} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.04351, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3275, "top5_acc": 0.59016, "loss_cls": 3.78599, "loss": 3.78599, "time": 0.81523} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.04348, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34016, "top5_acc": 0.60016, "loss_cls": 3.72826, "loss": 3.72826, "time": 0.81891} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.04346, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33453, "top5_acc": 0.60047, "loss_cls": 3.75949, "loss": 3.75949, "time": 0.81731} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.04343, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33422, "top5_acc": 0.59875, "loss_cls": 3.72113, "loss": 3.72113, "time": 0.81499} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.0434, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33969, "top5_acc": 0.59312, "loss_cls": 3.74669, "loss": 3.74669, "time": 0.81663} +{"mode": "train", "epoch": 82, "iter": 1300, "lr": 0.04337, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33609, "top5_acc": 0.60312, "loss_cls": 3.76439, "loss": 3.76439, "time": 0.8173} +{"mode": "train", "epoch": 82, "iter": 1400, "lr": 0.04335, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33344, "top5_acc": 0.59484, "loss_cls": 3.78793, "loss": 3.78793, "time": 0.82615} +{"mode": "train", "epoch": 82, "iter": 1500, "lr": 0.04332, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33578, "top5_acc": 0.5875, "loss_cls": 3.79009, "loss": 3.79009, "time": 0.82031} +{"mode": "train", "epoch": 82, "iter": 1600, "lr": 0.04329, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33344, "top5_acc": 0.60297, "loss_cls": 3.76284, "loss": 3.76284, "time": 0.83039} +{"mode": "train", "epoch": 82, "iter": 1700, "lr": 0.04326, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34375, "top5_acc": 0.60781, "loss_cls": 3.7275, "loss": 3.7275, "time": 0.82179} +{"mode": "train", "epoch": 82, "iter": 1800, "lr": 0.04323, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32125, "top5_acc": 0.59359, "loss_cls": 3.79049, "loss": 3.79049, "time": 0.81985} +{"mode": "train", "epoch": 82, "iter": 1900, "lr": 0.04321, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33844, "top5_acc": 0.60188, "loss_cls": 3.72906, "loss": 3.72906, "time": 0.81993} +{"mode": "train", "epoch": 82, "iter": 2000, "lr": 0.04318, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33297, "top5_acc": 0.58516, "loss_cls": 3.79434, "loss": 3.79434, "time": 0.82086} +{"mode": "train", "epoch": 82, "iter": 2100, "lr": 0.04315, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32406, "top5_acc": 0.58906, "loss_cls": 3.81442, "loss": 3.81442, "time": 0.82046} +{"mode": "train", "epoch": 82, "iter": 2200, "lr": 0.04312, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32922, "top5_acc": 0.58797, "loss_cls": 3.80449, "loss": 3.80449, "time": 0.81828} +{"mode": "train", "epoch": 82, "iter": 2300, "lr": 0.0431, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34859, "top5_acc": 0.60312, "loss_cls": 3.71575, "loss": 3.71575, "time": 0.82054} +{"mode": "train", "epoch": 82, "iter": 2400, "lr": 0.04307, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33562, "top5_acc": 0.59141, "loss_cls": 3.7953, "loss": 3.7953, "time": 0.82418} +{"mode": "train", "epoch": 82, "iter": 2500, "lr": 0.04304, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33453, "top5_acc": 0.60828, "loss_cls": 3.73428, "loss": 3.73428, "time": 0.8193} +{"mode": "train", "epoch": 82, "iter": 2600, "lr": 0.04301, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32172, "top5_acc": 0.59156, "loss_cls": 3.82714, "loss": 3.82714, "time": 0.8234} +{"mode": "train", "epoch": 82, "iter": 2700, "lr": 0.04299, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33203, "top5_acc": 0.60312, "loss_cls": 3.74807, "loss": 3.74807, "time": 0.81678} +{"mode": "train", "epoch": 82, "iter": 2800, "lr": 0.04296, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33344, "top5_acc": 0.60172, "loss_cls": 3.75089, "loss": 3.75089, "time": 0.82194} +{"mode": "train", "epoch": 82, "iter": 2900, "lr": 0.04293, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32828, "top5_acc": 0.59641, "loss_cls": 3.79534, "loss": 3.79534, "time": 0.81722} +{"mode": "train", "epoch": 82, "iter": 3000, "lr": 0.0429, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32516, "top5_acc": 0.58344, "loss_cls": 3.84826, "loss": 3.84826, "time": 0.8157} +{"mode": "train", "epoch": 82, "iter": 3100, "lr": 0.04287, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33203, "top5_acc": 0.59844, "loss_cls": 3.77152, "loss": 3.77152, "time": 0.82939} +{"mode": "train", "epoch": 82, "iter": 3200, "lr": 0.04285, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33094, "top5_acc": 0.59938, "loss_cls": 3.76953, "loss": 3.76953, "time": 0.82561} +{"mode": "train", "epoch": 82, "iter": 3300, "lr": 0.04282, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34172, "top5_acc": 0.60562, "loss_cls": 3.73766, "loss": 3.73766, "time": 0.82417} +{"mode": "train", "epoch": 82, "iter": 3400, "lr": 0.04279, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32172, "top5_acc": 0.58469, "loss_cls": 3.81895, "loss": 3.81895, "time": 0.8246} +{"mode": "train", "epoch": 82, "iter": 3500, "lr": 0.04276, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33766, "top5_acc": 0.60297, "loss_cls": 3.76043, "loss": 3.76043, "time": 0.81578} +{"mode": "train", "epoch": 82, "iter": 3600, "lr": 0.04274, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33609, "top5_acc": 0.59766, "loss_cls": 3.76747, "loss": 3.76747, "time": 0.81605} +{"mode": "train", "epoch": 82, "iter": 3700, "lr": 0.04271, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3275, "top5_acc": 0.58625, "loss_cls": 3.82547, "loss": 3.82547, "time": 0.81732} +{"mode": "val", "epoch": 82, "iter": 309, "lr": 0.0427, "top1_acc": 0.27752, "top5_acc": 0.52935, "mean_class_accuracy": 0.27731} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.04267, "memory": 15990, "data_time": 1.33357, "top1_acc": 0.335, "top5_acc": 0.59312, "loss_cls": 3.73749, "loss": 3.73749, "time": 2.31548} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.04264, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33375, "top5_acc": 0.60203, "loss_cls": 3.74658, "loss": 3.74658, "time": 0.82645} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.04261, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34375, "top5_acc": 0.60875, "loss_cls": 3.72554, "loss": 3.72554, "time": 0.81949} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.04259, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33578, "top5_acc": 0.60422, "loss_cls": 3.74093, "loss": 3.74093, "time": 0.82335} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.04256, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34203, "top5_acc": 0.60656, "loss_cls": 3.70916, "loss": 3.70916, "time": 0.81707} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.04253, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33031, "top5_acc": 0.59703, "loss_cls": 3.77419, "loss": 3.77419, "time": 0.81693} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.0425, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33594, "top5_acc": 0.59562, "loss_cls": 3.7792, "loss": 3.7792, "time": 0.81888} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.04247, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34016, "top5_acc": 0.60516, "loss_cls": 3.7387, "loss": 3.7387, "time": 0.8232} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.04245, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33922, "top5_acc": 0.59906, "loss_cls": 3.7388, "loss": 3.7388, "time": 0.8194} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.04242, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33656, "top5_acc": 0.59875, "loss_cls": 3.7513, "loss": 3.7513, "time": 0.8211} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.04239, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33562, "top5_acc": 0.60203, "loss_cls": 3.74695, "loss": 3.74695, "time": 0.81761} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.04236, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33188, "top5_acc": 0.59266, "loss_cls": 3.76997, "loss": 3.76997, "time": 0.815} +{"mode": "train", "epoch": 83, "iter": 1300, "lr": 0.04234, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33812, "top5_acc": 0.60766, "loss_cls": 3.72192, "loss": 3.72192, "time": 0.82058} +{"mode": "train", "epoch": 83, "iter": 1400, "lr": 0.04231, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32219, "top5_acc": 0.59578, "loss_cls": 3.78848, "loss": 3.78848, "time": 0.82276} +{"mode": "train", "epoch": 83, "iter": 1500, "lr": 0.04228, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33719, "top5_acc": 0.5925, "loss_cls": 3.78179, "loss": 3.78179, "time": 0.81671} +{"mode": "train", "epoch": 83, "iter": 1600, "lr": 0.04225, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34078, "top5_acc": 0.60031, "loss_cls": 3.73214, "loss": 3.73214, "time": 0.82558} +{"mode": "train", "epoch": 83, "iter": 1700, "lr": 0.04223, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33406, "top5_acc": 0.59719, "loss_cls": 3.77623, "loss": 3.77623, "time": 0.82397} +{"mode": "train", "epoch": 83, "iter": 1800, "lr": 0.0422, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33406, "top5_acc": 0.58938, "loss_cls": 3.77462, "loss": 3.77462, "time": 0.82519} +{"mode": "train", "epoch": 83, "iter": 1900, "lr": 0.04217, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32688, "top5_acc": 0.5975, "loss_cls": 3.80007, "loss": 3.80007, "time": 0.81934} +{"mode": "train", "epoch": 83, "iter": 2000, "lr": 0.04214, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34359, "top5_acc": 0.60688, "loss_cls": 3.72449, "loss": 3.72449, "time": 0.81588} +{"mode": "train", "epoch": 83, "iter": 2100, "lr": 0.04212, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33203, "top5_acc": 0.59609, "loss_cls": 3.75683, "loss": 3.75683, "time": 0.81727} +{"mode": "train", "epoch": 83, "iter": 2200, "lr": 0.04209, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32938, "top5_acc": 0.59422, "loss_cls": 3.82311, "loss": 3.82311, "time": 0.82061} +{"mode": "train", "epoch": 83, "iter": 2300, "lr": 0.04206, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33953, "top5_acc": 0.60453, "loss_cls": 3.72393, "loss": 3.72393, "time": 0.82286} +{"mode": "train", "epoch": 83, "iter": 2400, "lr": 0.04203, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33141, "top5_acc": 0.58688, "loss_cls": 3.79889, "loss": 3.79889, "time": 0.82137} +{"mode": "train", "epoch": 83, "iter": 2500, "lr": 0.04201, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32875, "top5_acc": 0.59391, "loss_cls": 3.75356, "loss": 3.75356, "time": 0.82376} +{"mode": "train", "epoch": 83, "iter": 2600, "lr": 0.04198, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34547, "top5_acc": 0.60688, "loss_cls": 3.71658, "loss": 3.71658, "time": 0.81974} +{"mode": "train", "epoch": 83, "iter": 2700, "lr": 0.04195, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33422, "top5_acc": 0.60672, "loss_cls": 3.74154, "loss": 3.74154, "time": 0.81723} +{"mode": "train", "epoch": 83, "iter": 2800, "lr": 0.04192, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33125, "top5_acc": 0.59422, "loss_cls": 3.75374, "loss": 3.75374, "time": 0.81745} +{"mode": "train", "epoch": 83, "iter": 2900, "lr": 0.0419, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33219, "top5_acc": 0.58859, "loss_cls": 3.78564, "loss": 3.78564, "time": 0.82574} +{"mode": "train", "epoch": 83, "iter": 3000, "lr": 0.04187, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35047, "top5_acc": 0.60594, "loss_cls": 3.66631, "loss": 3.66631, "time": 0.81863} +{"mode": "train", "epoch": 83, "iter": 3100, "lr": 0.04184, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33719, "top5_acc": 0.59844, "loss_cls": 3.7686, "loss": 3.7686, "time": 0.82856} +{"mode": "train", "epoch": 83, "iter": 3200, "lr": 0.04181, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33797, "top5_acc": 0.60266, "loss_cls": 3.73123, "loss": 3.73123, "time": 0.82554} +{"mode": "train", "epoch": 83, "iter": 3300, "lr": 0.04178, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34938, "top5_acc": 0.59953, "loss_cls": 3.75043, "loss": 3.75043, "time": 0.82817} +{"mode": "train", "epoch": 83, "iter": 3400, "lr": 0.04176, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32875, "top5_acc": 0.59531, "loss_cls": 3.77876, "loss": 3.77876, "time": 0.82049} +{"mode": "train", "epoch": 83, "iter": 3500, "lr": 0.04173, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33625, "top5_acc": 0.59422, "loss_cls": 3.76368, "loss": 3.76368, "time": 0.82154} +{"mode": "train", "epoch": 83, "iter": 3600, "lr": 0.0417, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.345, "top5_acc": 0.60969, "loss_cls": 3.71416, "loss": 3.71416, "time": 0.82189} +{"mode": "train", "epoch": 83, "iter": 3700, "lr": 0.04167, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34594, "top5_acc": 0.59672, "loss_cls": 3.73314, "loss": 3.73314, "time": 0.81864} +{"mode": "val", "epoch": 83, "iter": 309, "lr": 0.04166, "top1_acc": 0.26409, "top5_acc": 0.51421, "mean_class_accuracy": 0.26382} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.04163, "memory": 15990, "data_time": 1.32096, "top1_acc": 0.35109, "top5_acc": 0.61656, "loss_cls": 3.64566, "loss": 3.64566, "time": 2.30917} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.04161, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34578, "top5_acc": 0.60469, "loss_cls": 3.71643, "loss": 3.71643, "time": 0.82366} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.04158, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33281, "top5_acc": 0.60078, "loss_cls": 3.76381, "loss": 3.76381, "time": 0.82166} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.04155, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.3375, "top5_acc": 0.6, "loss_cls": 3.74071, "loss": 3.74071, "time": 0.82264} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.04152, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33, "top5_acc": 0.59312, "loss_cls": 3.77154, "loss": 3.77154, "time": 0.82293} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.0415, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34312, "top5_acc": 0.61719, "loss_cls": 3.66958, "loss": 3.66958, "time": 0.82833} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.04147, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34141, "top5_acc": 0.60703, "loss_cls": 3.70195, "loss": 3.70195, "time": 0.82682} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.04144, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33672, "top5_acc": 0.6, "loss_cls": 3.75531, "loss": 3.75531, "time": 0.83041} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.04141, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33078, "top5_acc": 0.59453, "loss_cls": 3.75197, "loss": 3.75197, "time": 0.82411} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.04139, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33125, "top5_acc": 0.5975, "loss_cls": 3.75873, "loss": 3.75873, "time": 0.82209} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.04136, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33297, "top5_acc": 0.59312, "loss_cls": 3.77352, "loss": 3.77352, "time": 0.82633} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.04133, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33578, "top5_acc": 0.6, "loss_cls": 3.7583, "loss": 3.7583, "time": 0.82086} +{"mode": "train", "epoch": 84, "iter": 1300, "lr": 0.0413, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33984, "top5_acc": 0.59906, "loss_cls": 3.73828, "loss": 3.73828, "time": 0.81514} +{"mode": "train", "epoch": 84, "iter": 1400, "lr": 0.04128, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33766, "top5_acc": 0.60656, "loss_cls": 3.71731, "loss": 3.71731, "time": 0.82431} +{"mode": "train", "epoch": 84, "iter": 1500, "lr": 0.04125, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34312, "top5_acc": 0.60219, "loss_cls": 3.72637, "loss": 3.72637, "time": 0.82131} +{"mode": "train", "epoch": 84, "iter": 1600, "lr": 0.04122, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33531, "top5_acc": 0.60219, "loss_cls": 3.74214, "loss": 3.74214, "time": 0.8249} +{"mode": "train", "epoch": 84, "iter": 1700, "lr": 0.04119, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33625, "top5_acc": 0.59125, "loss_cls": 3.78234, "loss": 3.78234, "time": 0.82168} +{"mode": "train", "epoch": 84, "iter": 1800, "lr": 0.04117, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33531, "top5_acc": 0.60125, "loss_cls": 3.75444, "loss": 3.75444, "time": 0.82542} +{"mode": "train", "epoch": 84, "iter": 1900, "lr": 0.04114, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34281, "top5_acc": 0.60297, "loss_cls": 3.70576, "loss": 3.70576, "time": 0.82005} +{"mode": "train", "epoch": 84, "iter": 2000, "lr": 0.04111, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33547, "top5_acc": 0.59812, "loss_cls": 3.78039, "loss": 3.78039, "time": 0.81734} +{"mode": "train", "epoch": 84, "iter": 2100, "lr": 0.04108, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34688, "top5_acc": 0.60953, "loss_cls": 3.69044, "loss": 3.69044, "time": 0.81534} +{"mode": "train", "epoch": 84, "iter": 2200, "lr": 0.04106, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34203, "top5_acc": 0.60812, "loss_cls": 3.72159, "loss": 3.72159, "time": 0.81573} +{"mode": "train", "epoch": 84, "iter": 2300, "lr": 0.04103, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33875, "top5_acc": 0.60938, "loss_cls": 3.71903, "loss": 3.71903, "time": 0.82067} +{"mode": "train", "epoch": 84, "iter": 2400, "lr": 0.041, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33859, "top5_acc": 0.59109, "loss_cls": 3.7483, "loss": 3.7483, "time": 0.81845} +{"mode": "train", "epoch": 84, "iter": 2500, "lr": 0.04097, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34125, "top5_acc": 0.60016, "loss_cls": 3.72737, "loss": 3.72737, "time": 0.81799} +{"mode": "train", "epoch": 84, "iter": 2600, "lr": 0.04095, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33375, "top5_acc": 0.59578, "loss_cls": 3.75864, "loss": 3.75864, "time": 0.81537} +{"mode": "train", "epoch": 84, "iter": 2700, "lr": 0.04092, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33453, "top5_acc": 0.59938, "loss_cls": 3.74607, "loss": 3.74607, "time": 0.82102} +{"mode": "train", "epoch": 84, "iter": 2800, "lr": 0.04089, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33141, "top5_acc": 0.59328, "loss_cls": 3.75575, "loss": 3.75575, "time": 0.82034} +{"mode": "train", "epoch": 84, "iter": 2900, "lr": 0.04086, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33156, "top5_acc": 0.59344, "loss_cls": 3.79325, "loss": 3.79325, "time": 0.81874} +{"mode": "train", "epoch": 84, "iter": 3000, "lr": 0.04084, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33641, "top5_acc": 0.60562, "loss_cls": 3.72144, "loss": 3.72144, "time": 0.82658} +{"mode": "train", "epoch": 84, "iter": 3100, "lr": 0.04081, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34031, "top5_acc": 0.59859, "loss_cls": 3.76248, "loss": 3.76248, "time": 0.81876} +{"mode": "train", "epoch": 84, "iter": 3200, "lr": 0.04078, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.34297, "top5_acc": 0.60359, "loss_cls": 3.71689, "loss": 3.71689, "time": 0.82288} +{"mode": "train", "epoch": 84, "iter": 3300, "lr": 0.04075, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33172, "top5_acc": 0.6, "loss_cls": 3.76079, "loss": 3.76079, "time": 0.81795} +{"mode": "train", "epoch": 84, "iter": 3400, "lr": 0.04073, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33438, "top5_acc": 0.59719, "loss_cls": 3.78036, "loss": 3.78036, "time": 0.82038} +{"mode": "train", "epoch": 84, "iter": 3500, "lr": 0.0407, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34141, "top5_acc": 0.59703, "loss_cls": 3.75524, "loss": 3.75524, "time": 0.81685} +{"mode": "train", "epoch": 84, "iter": 3600, "lr": 0.04067, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33078, "top5_acc": 0.59453, "loss_cls": 3.7839, "loss": 3.7839, "time": 0.8189} +{"mode": "train", "epoch": 84, "iter": 3700, "lr": 0.04064, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33219, "top5_acc": 0.60469, "loss_cls": 3.73555, "loss": 3.73555, "time": 0.82357} +{"mode": "val", "epoch": 84, "iter": 309, "lr": 0.04063, "top1_acc": 0.2723, "top5_acc": 0.53432, "mean_class_accuracy": 0.27199} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.0406, "memory": 15990, "data_time": 1.31561, "top1_acc": 0.35781, "top5_acc": 0.61656, "loss_cls": 3.64082, "loss": 3.64082, "time": 2.29609} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.04058, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34875, "top5_acc": 0.62172, "loss_cls": 3.64337, "loss": 3.64337, "time": 0.82664} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.04055, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34672, "top5_acc": 0.61031, "loss_cls": 3.70178, "loss": 3.70178, "time": 0.8172} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.04052, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33641, "top5_acc": 0.60172, "loss_cls": 3.73656, "loss": 3.73656, "time": 0.82222} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.04049, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34109, "top5_acc": 0.60344, "loss_cls": 3.74771, "loss": 3.74771, "time": 0.81941} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.04047, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35203, "top5_acc": 0.61594, "loss_cls": 3.68038, "loss": 3.68038, "time": 0.82891} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.04044, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33953, "top5_acc": 0.61094, "loss_cls": 3.68345, "loss": 3.68345, "time": 0.81759} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.04041, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34078, "top5_acc": 0.60375, "loss_cls": 3.73129, "loss": 3.73129, "time": 0.81512} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.04038, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34719, "top5_acc": 0.61234, "loss_cls": 3.66411, "loss": 3.66411, "time": 0.8159} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.04036, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35, "top5_acc": 0.61047, "loss_cls": 3.70104, "loss": 3.70104, "time": 0.81859} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.04033, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34344, "top5_acc": 0.60594, "loss_cls": 3.71625, "loss": 3.71625, "time": 0.81544} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.0403, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34, "top5_acc": 0.60594, "loss_cls": 3.71792, "loss": 3.71792, "time": 0.81684} +{"mode": "train", "epoch": 85, "iter": 1300, "lr": 0.04027, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33781, "top5_acc": 0.59781, "loss_cls": 3.74219, "loss": 3.74219, "time": 0.82363} +{"mode": "train", "epoch": 85, "iter": 1400, "lr": 0.04025, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.335, "top5_acc": 0.59422, "loss_cls": 3.76263, "loss": 3.76263, "time": 0.82283} +{"mode": "train", "epoch": 85, "iter": 1500, "lr": 0.04022, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33344, "top5_acc": 0.60203, "loss_cls": 3.74759, "loss": 3.74759, "time": 0.82317} +{"mode": "train", "epoch": 85, "iter": 1600, "lr": 0.04019, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.33812, "top5_acc": 0.59781, "loss_cls": 3.77932, "loss": 3.77932, "time": 0.82758} +{"mode": "train", "epoch": 85, "iter": 1700, "lr": 0.04016, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34281, "top5_acc": 0.59703, "loss_cls": 3.72912, "loss": 3.72912, "time": 0.82794} +{"mode": "train", "epoch": 85, "iter": 1800, "lr": 0.04014, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.33188, "top5_acc": 0.59812, "loss_cls": 3.77429, "loss": 3.77429, "time": 0.81794} +{"mode": "train", "epoch": 85, "iter": 1900, "lr": 0.04011, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33156, "top5_acc": 0.59531, "loss_cls": 3.76262, "loss": 3.76262, "time": 0.82213} +{"mode": "train", "epoch": 85, "iter": 2000, "lr": 0.04008, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33656, "top5_acc": 0.60375, "loss_cls": 3.71328, "loss": 3.71328, "time": 0.82717} +{"mode": "train", "epoch": 85, "iter": 2100, "lr": 0.04006, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35438, "top5_acc": 0.61, "loss_cls": 3.66057, "loss": 3.66057, "time": 0.83471} +{"mode": "train", "epoch": 85, "iter": 2200, "lr": 0.04003, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34266, "top5_acc": 0.60578, "loss_cls": 3.72832, "loss": 3.72832, "time": 0.82724} +{"mode": "train", "epoch": 85, "iter": 2300, "lr": 0.04, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34016, "top5_acc": 0.60516, "loss_cls": 3.71223, "loss": 3.71223, "time": 0.81791} +{"mode": "train", "epoch": 85, "iter": 2400, "lr": 0.03997, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34656, "top5_acc": 0.60328, "loss_cls": 3.71049, "loss": 3.71049, "time": 0.82082} +{"mode": "train", "epoch": 85, "iter": 2500, "lr": 0.03995, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34, "top5_acc": 0.60203, "loss_cls": 3.72115, "loss": 3.72115, "time": 0.81046} +{"mode": "train", "epoch": 85, "iter": 2600, "lr": 0.03992, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35203, "top5_acc": 0.60953, "loss_cls": 3.67617, "loss": 3.67617, "time": 0.81905} +{"mode": "train", "epoch": 85, "iter": 2700, "lr": 0.03989, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32344, "top5_acc": 0.58953, "loss_cls": 3.7995, "loss": 3.7995, "time": 0.8233} +{"mode": "train", "epoch": 85, "iter": 2800, "lr": 0.03986, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34484, "top5_acc": 0.59875, "loss_cls": 3.75274, "loss": 3.75274, "time": 0.82644} +{"mode": "train", "epoch": 85, "iter": 2900, "lr": 0.03984, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33953, "top5_acc": 0.59172, "loss_cls": 3.7742, "loss": 3.7742, "time": 0.81678} +{"mode": "train", "epoch": 85, "iter": 3000, "lr": 0.03981, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34047, "top5_acc": 0.59797, "loss_cls": 3.75852, "loss": 3.75852, "time": 0.82278} +{"mode": "train", "epoch": 85, "iter": 3100, "lr": 0.03978, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34484, "top5_acc": 0.59984, "loss_cls": 3.73637, "loss": 3.73637, "time": 0.81997} +{"mode": "train", "epoch": 85, "iter": 3200, "lr": 0.03975, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34109, "top5_acc": 0.61125, "loss_cls": 3.72277, "loss": 3.72277, "time": 0.81531} +{"mode": "train", "epoch": 85, "iter": 3300, "lr": 0.03973, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33781, "top5_acc": 0.60719, "loss_cls": 3.71267, "loss": 3.71267, "time": 0.82177} +{"mode": "train", "epoch": 85, "iter": 3400, "lr": 0.0397, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35172, "top5_acc": 0.61141, "loss_cls": 3.68305, "loss": 3.68305, "time": 0.81783} +{"mode": "train", "epoch": 85, "iter": 3500, "lr": 0.03967, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.345, "top5_acc": 0.59781, "loss_cls": 3.7567, "loss": 3.7567, "time": 0.81456} +{"mode": "train", "epoch": 85, "iter": 3600, "lr": 0.03964, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34172, "top5_acc": 0.60812, "loss_cls": 3.72465, "loss": 3.72465, "time": 0.81596} +{"mode": "train", "epoch": 85, "iter": 3700, "lr": 0.03962, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33688, "top5_acc": 0.59094, "loss_cls": 3.7799, "loss": 3.7799, "time": 0.81505} +{"mode": "val", "epoch": 85, "iter": 309, "lr": 0.0396, "top1_acc": 0.25726, "top5_acc": 0.51796, "mean_class_accuracy": 0.25684} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.03958, "memory": 15990, "data_time": 1.28652, "top1_acc": 0.34484, "top5_acc": 0.61406, "loss_cls": 3.66759, "loss": 3.66759, "time": 2.25541} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.03955, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35016, "top5_acc": 0.61016, "loss_cls": 3.69394, "loss": 3.69394, "time": 0.81931} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.03952, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34406, "top5_acc": 0.61203, "loss_cls": 3.67971, "loss": 3.67971, "time": 0.82268} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.0395, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34672, "top5_acc": 0.60891, "loss_cls": 3.68254, "loss": 3.68254, "time": 0.81776} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.03947, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33328, "top5_acc": 0.60719, "loss_cls": 3.71266, "loss": 3.71266, "time": 0.82174} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.03944, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34359, "top5_acc": 0.61828, "loss_cls": 3.63936, "loss": 3.63936, "time": 0.81784} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.03941, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34, "top5_acc": 0.60984, "loss_cls": 3.72102, "loss": 3.72102, "time": 0.8193} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.03939, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33234, "top5_acc": 0.60875, "loss_cls": 3.75285, "loss": 3.75285, "time": 0.8154} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.03936, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34078, "top5_acc": 0.59812, "loss_cls": 3.72713, "loss": 3.72713, "time": 0.81733} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.03933, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34703, "top5_acc": 0.60359, "loss_cls": 3.70967, "loss": 3.70967, "time": 0.81855} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.0393, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34453, "top5_acc": 0.61172, "loss_cls": 3.67284, "loss": 3.67284, "time": 0.81939} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.03928, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34672, "top5_acc": 0.60391, "loss_cls": 3.71517, "loss": 3.71517, "time": 0.81173} +{"mode": "train", "epoch": 86, "iter": 1300, "lr": 0.03925, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34391, "top5_acc": 0.60703, "loss_cls": 3.70118, "loss": 3.70118, "time": 0.8167} +{"mode": "train", "epoch": 86, "iter": 1400, "lr": 0.03922, "memory": 15990, "data_time": 0.00068, "top1_acc": 0.34656, "top5_acc": 0.60891, "loss_cls": 3.67198, "loss": 3.67198, "time": 0.82144} +{"mode": "train", "epoch": 86, "iter": 1500, "lr": 0.03919, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34469, "top5_acc": 0.61328, "loss_cls": 3.70309, "loss": 3.70309, "time": 0.82397} +{"mode": "train", "epoch": 86, "iter": 1600, "lr": 0.03917, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33531, "top5_acc": 0.60469, "loss_cls": 3.72564, "loss": 3.72564, "time": 0.81534} +{"mode": "train", "epoch": 86, "iter": 1700, "lr": 0.03914, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34266, "top5_acc": 0.61266, "loss_cls": 3.69343, "loss": 3.69343, "time": 0.82406} +{"mode": "train", "epoch": 86, "iter": 1800, "lr": 0.03911, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34016, "top5_acc": 0.60141, "loss_cls": 3.77621, "loss": 3.77621, "time": 0.82075} +{"mode": "train", "epoch": 86, "iter": 1900, "lr": 0.03909, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34312, "top5_acc": 0.61453, "loss_cls": 3.67916, "loss": 3.67916, "time": 0.82929} +{"mode": "train", "epoch": 86, "iter": 2000, "lr": 0.03906, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33859, "top5_acc": 0.60172, "loss_cls": 3.74086, "loss": 3.74086, "time": 0.82598} +{"mode": "train", "epoch": 86, "iter": 2100, "lr": 0.03903, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34672, "top5_acc": 0.61094, "loss_cls": 3.70675, "loss": 3.70675, "time": 0.82271} +{"mode": "train", "epoch": 86, "iter": 2200, "lr": 0.039, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35188, "top5_acc": 0.61609, "loss_cls": 3.68086, "loss": 3.68086, "time": 0.81898} +{"mode": "train", "epoch": 86, "iter": 2300, "lr": 0.03898, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34766, "top5_acc": 0.60422, "loss_cls": 3.69819, "loss": 3.69819, "time": 0.82369} +{"mode": "train", "epoch": 86, "iter": 2400, "lr": 0.03895, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3425, "top5_acc": 0.60844, "loss_cls": 3.69748, "loss": 3.69748, "time": 0.82335} +{"mode": "train", "epoch": 86, "iter": 2500, "lr": 0.03892, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34578, "top5_acc": 0.60906, "loss_cls": 3.69005, "loss": 3.69005, "time": 0.81469} +{"mode": "train", "epoch": 86, "iter": 2600, "lr": 0.03889, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34578, "top5_acc": 0.60703, "loss_cls": 3.71522, "loss": 3.71522, "time": 0.81662} +{"mode": "train", "epoch": 86, "iter": 2700, "lr": 0.03887, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34469, "top5_acc": 0.60734, "loss_cls": 3.71174, "loss": 3.71174, "time": 0.82574} +{"mode": "train", "epoch": 86, "iter": 2800, "lr": 0.03884, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33031, "top5_acc": 0.59359, "loss_cls": 3.78279, "loss": 3.78279, "time": 0.82606} +{"mode": "train", "epoch": 86, "iter": 2900, "lr": 0.03881, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35016, "top5_acc": 0.61094, "loss_cls": 3.68548, "loss": 3.68548, "time": 0.82242} +{"mode": "train", "epoch": 86, "iter": 3000, "lr": 0.03879, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.35062, "top5_acc": 0.6075, "loss_cls": 3.6978, "loss": 3.6978, "time": 0.82428} +{"mode": "train", "epoch": 86, "iter": 3100, "lr": 0.03876, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33781, "top5_acc": 0.60344, "loss_cls": 3.71717, "loss": 3.71717, "time": 0.81681} +{"mode": "train", "epoch": 86, "iter": 3200, "lr": 0.03873, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33766, "top5_acc": 0.59531, "loss_cls": 3.75426, "loss": 3.75426, "time": 0.81866} +{"mode": "train", "epoch": 86, "iter": 3300, "lr": 0.0387, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34469, "top5_acc": 0.59875, "loss_cls": 3.71183, "loss": 3.71183, "time": 0.82573} +{"mode": "train", "epoch": 86, "iter": 3400, "lr": 0.03868, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33719, "top5_acc": 0.6, "loss_cls": 3.73335, "loss": 3.73335, "time": 0.81989} +{"mode": "train", "epoch": 86, "iter": 3500, "lr": 0.03865, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33875, "top5_acc": 0.60188, "loss_cls": 3.73986, "loss": 3.73986, "time": 0.82263} +{"mode": "train", "epoch": 86, "iter": 3600, "lr": 0.03862, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33844, "top5_acc": 0.60156, "loss_cls": 3.70292, "loss": 3.70292, "time": 0.81834} +{"mode": "train", "epoch": 86, "iter": 3700, "lr": 0.0386, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34719, "top5_acc": 0.61469, "loss_cls": 3.70062, "loss": 3.70062, "time": 0.81268} +{"mode": "val", "epoch": 86, "iter": 309, "lr": 0.03858, "top1_acc": 0.25406, "top5_acc": 0.5022, "mean_class_accuracy": 0.25392} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.03856, "memory": 15990, "data_time": 1.29419, "top1_acc": 0.36281, "top5_acc": 0.62703, "loss_cls": 3.5799, "loss": 3.5799, "time": 2.26479} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.03853, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33594, "top5_acc": 0.61172, "loss_cls": 3.67548, "loss": 3.67548, "time": 0.82187} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.0385, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34953, "top5_acc": 0.62313, "loss_cls": 3.62261, "loss": 3.62261, "time": 0.82111} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.03847, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34938, "top5_acc": 0.60656, "loss_cls": 3.70575, "loss": 3.70575, "time": 0.81699} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.03845, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33844, "top5_acc": 0.60312, "loss_cls": 3.72534, "loss": 3.72534, "time": 0.81423} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.03842, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35156, "top5_acc": 0.61094, "loss_cls": 3.66071, "loss": 3.66071, "time": 0.81391} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.03839, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34047, "top5_acc": 0.60938, "loss_cls": 3.702, "loss": 3.702, "time": 0.81622} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.03837, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34469, "top5_acc": 0.61312, "loss_cls": 3.68785, "loss": 3.68785, "time": 0.82104} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.03834, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33688, "top5_acc": 0.59969, "loss_cls": 3.72101, "loss": 3.72101, "time": 0.81922} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.03831, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33797, "top5_acc": 0.61422, "loss_cls": 3.66815, "loss": 3.66815, "time": 0.8174} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.03828, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35188, "top5_acc": 0.61391, "loss_cls": 3.66414, "loss": 3.66414, "time": 0.8195} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.03826, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34, "top5_acc": 0.60562, "loss_cls": 3.70935, "loss": 3.70935, "time": 0.81897} +{"mode": "train", "epoch": 87, "iter": 1300, "lr": 0.03823, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36047, "top5_acc": 0.61922, "loss_cls": 3.59607, "loss": 3.59607, "time": 0.81841} +{"mode": "train", "epoch": 87, "iter": 1400, "lr": 0.0382, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33875, "top5_acc": 0.60219, "loss_cls": 3.71658, "loss": 3.71658, "time": 0.81361} +{"mode": "train", "epoch": 87, "iter": 1500, "lr": 0.03817, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33953, "top5_acc": 0.60359, "loss_cls": 3.72915, "loss": 3.72915, "time": 0.82612} +{"mode": "train", "epoch": 87, "iter": 1600, "lr": 0.03815, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34359, "top5_acc": 0.61156, "loss_cls": 3.68497, "loss": 3.68497, "time": 0.81585} +{"mode": "train", "epoch": 87, "iter": 1700, "lr": 0.03812, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35422, "top5_acc": 0.60781, "loss_cls": 3.70682, "loss": 3.70682, "time": 0.82203} +{"mode": "train", "epoch": 87, "iter": 1800, "lr": 0.03809, "memory": 15990, "data_time": 0.00077, "top1_acc": 0.34734, "top5_acc": 0.60906, "loss_cls": 3.67868, "loss": 3.67868, "time": 0.82634} +{"mode": "train", "epoch": 87, "iter": 1900, "lr": 0.03807, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34531, "top5_acc": 0.59984, "loss_cls": 3.72327, "loss": 3.72327, "time": 0.82771} +{"mode": "train", "epoch": 87, "iter": 2000, "lr": 0.03804, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33906, "top5_acc": 0.59734, "loss_cls": 3.7439, "loss": 3.7439, "time": 0.82784} +{"mode": "train", "epoch": 87, "iter": 2100, "lr": 0.03801, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33859, "top5_acc": 0.60656, "loss_cls": 3.71039, "loss": 3.71039, "time": 0.82213} +{"mode": "train", "epoch": 87, "iter": 2200, "lr": 0.03798, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34781, "top5_acc": 0.60875, "loss_cls": 3.70345, "loss": 3.70345, "time": 0.82189} +{"mode": "train", "epoch": 87, "iter": 2300, "lr": 0.03796, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34484, "top5_acc": 0.60812, "loss_cls": 3.69727, "loss": 3.69727, "time": 0.82648} +{"mode": "train", "epoch": 87, "iter": 2400, "lr": 0.03793, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35016, "top5_acc": 0.61031, "loss_cls": 3.67153, "loss": 3.67153, "time": 0.81824} +{"mode": "train", "epoch": 87, "iter": 2500, "lr": 0.0379, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34469, "top5_acc": 0.60828, "loss_cls": 3.70607, "loss": 3.70607, "time": 0.81561} +{"mode": "train", "epoch": 87, "iter": 2600, "lr": 0.03788, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3425, "top5_acc": 0.60656, "loss_cls": 3.7076, "loss": 3.7076, "time": 0.81648} +{"mode": "train", "epoch": 87, "iter": 2700, "lr": 0.03785, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35438, "top5_acc": 0.61625, "loss_cls": 3.64561, "loss": 3.64561, "time": 0.8184} +{"mode": "train", "epoch": 87, "iter": 2800, "lr": 0.03782, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32938, "top5_acc": 0.585, "loss_cls": 3.81228, "loss": 3.81228, "time": 0.8255} +{"mode": "train", "epoch": 87, "iter": 2900, "lr": 0.03779, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33969, "top5_acc": 0.60766, "loss_cls": 3.7066, "loss": 3.7066, "time": 0.81926} +{"mode": "train", "epoch": 87, "iter": 3000, "lr": 0.03777, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33734, "top5_acc": 0.60109, "loss_cls": 3.72219, "loss": 3.72219, "time": 0.82526} +{"mode": "train", "epoch": 87, "iter": 3100, "lr": 0.03774, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34266, "top5_acc": 0.60766, "loss_cls": 3.691, "loss": 3.691, "time": 0.8099} +{"mode": "train", "epoch": 87, "iter": 3200, "lr": 0.03771, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34828, "top5_acc": 0.61891, "loss_cls": 3.62902, "loss": 3.62902, "time": 0.81885} +{"mode": "train", "epoch": 87, "iter": 3300, "lr": 0.03769, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34422, "top5_acc": 0.605, "loss_cls": 3.72192, "loss": 3.72192, "time": 0.82341} +{"mode": "train", "epoch": 87, "iter": 3400, "lr": 0.03766, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33484, "top5_acc": 0.59797, "loss_cls": 3.75707, "loss": 3.75707, "time": 0.82147} +{"mode": "train", "epoch": 87, "iter": 3500, "lr": 0.03763, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35125, "top5_acc": 0.60641, "loss_cls": 3.69883, "loss": 3.69883, "time": 0.81874} +{"mode": "train", "epoch": 87, "iter": 3600, "lr": 0.03761, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34312, "top5_acc": 0.60516, "loss_cls": 3.7275, "loss": 3.7275, "time": 0.82086} +{"mode": "train", "epoch": 87, "iter": 3700, "lr": 0.03758, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33938, "top5_acc": 0.60469, "loss_cls": 3.7173, "loss": 3.7173, "time": 0.8164} +{"mode": "val", "epoch": 87, "iter": 309, "lr": 0.03757, "top1_acc": 0.27792, "top5_acc": 0.53467, "mean_class_accuracy": 0.27753} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.03754, "memory": 15990, "data_time": 1.27987, "top1_acc": 0.35219, "top5_acc": 0.62687, "loss_cls": 3.61797, "loss": 3.61797, "time": 2.25039} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.03751, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35734, "top5_acc": 0.6175, "loss_cls": 3.61485, "loss": 3.61485, "time": 0.82751} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.03748, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35531, "top5_acc": 0.61594, "loss_cls": 3.64272, "loss": 3.64272, "time": 0.82642} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.03746, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34859, "top5_acc": 0.60719, "loss_cls": 3.66077, "loss": 3.66077, "time": 0.81814} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.03743, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33844, "top5_acc": 0.61172, "loss_cls": 3.70902, "loss": 3.70902, "time": 0.8123} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.0374, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34109, "top5_acc": 0.60891, "loss_cls": 3.71477, "loss": 3.71477, "time": 0.81837} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.03738, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35281, "top5_acc": 0.61766, "loss_cls": 3.63464, "loss": 3.63464, "time": 0.82004} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.03735, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33984, "top5_acc": 0.6025, "loss_cls": 3.73104, "loss": 3.73104, "time": 0.81491} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.03732, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35703, "top5_acc": 0.61891, "loss_cls": 3.61995, "loss": 3.61995, "time": 0.81778} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.0373, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35359, "top5_acc": 0.61781, "loss_cls": 3.65982, "loss": 3.65982, "time": 0.81785} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.03727, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35547, "top5_acc": 0.62422, "loss_cls": 3.62371, "loss": 3.62371, "time": 0.82128} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.03724, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35469, "top5_acc": 0.615, "loss_cls": 3.64191, "loss": 3.64191, "time": 0.81907} +{"mode": "train", "epoch": 88, "iter": 1300, "lr": 0.03721, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34938, "top5_acc": 0.62016, "loss_cls": 3.66221, "loss": 3.66221, "time": 0.81831} +{"mode": "train", "epoch": 88, "iter": 1400, "lr": 0.03719, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34234, "top5_acc": 0.62141, "loss_cls": 3.65866, "loss": 3.65866, "time": 0.81566} +{"mode": "train", "epoch": 88, "iter": 1500, "lr": 0.03716, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34156, "top5_acc": 0.60719, "loss_cls": 3.72824, "loss": 3.72824, "time": 0.81839} +{"mode": "train", "epoch": 88, "iter": 1600, "lr": 0.03713, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34047, "top5_acc": 0.60562, "loss_cls": 3.7124, "loss": 3.7124, "time": 0.81279} +{"mode": "train", "epoch": 88, "iter": 1700, "lr": 0.03711, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.33828, "top5_acc": 0.60391, "loss_cls": 3.73153, "loss": 3.73153, "time": 0.82513} +{"mode": "train", "epoch": 88, "iter": 1800, "lr": 0.03708, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34875, "top5_acc": 0.61984, "loss_cls": 3.64937, "loss": 3.64937, "time": 0.82624} +{"mode": "train", "epoch": 88, "iter": 1900, "lr": 0.03705, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.345, "top5_acc": 0.61172, "loss_cls": 3.69735, "loss": 3.69735, "time": 0.8248} +{"mode": "train", "epoch": 88, "iter": 2000, "lr": 0.03703, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34312, "top5_acc": 0.60562, "loss_cls": 3.69059, "loss": 3.69059, "time": 0.82574} +{"mode": "train", "epoch": 88, "iter": 2100, "lr": 0.037, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34016, "top5_acc": 0.5975, "loss_cls": 3.72578, "loss": 3.72578, "time": 0.82765} +{"mode": "train", "epoch": 88, "iter": 2200, "lr": 0.03697, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34562, "top5_acc": 0.60688, "loss_cls": 3.6922, "loss": 3.6922, "time": 0.82706} +{"mode": "train", "epoch": 88, "iter": 2300, "lr": 0.03694, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33594, "top5_acc": 0.605, "loss_cls": 3.72264, "loss": 3.72264, "time": 0.822} +{"mode": "train", "epoch": 88, "iter": 2400, "lr": 0.03692, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34281, "top5_acc": 0.60922, "loss_cls": 3.69181, "loss": 3.69181, "time": 0.82277} +{"mode": "train", "epoch": 88, "iter": 2500, "lr": 0.03689, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35109, "top5_acc": 0.60781, "loss_cls": 3.67249, "loss": 3.67249, "time": 0.81087} +{"mode": "train", "epoch": 88, "iter": 2600, "lr": 0.03686, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35562, "top5_acc": 0.61469, "loss_cls": 3.65137, "loss": 3.65137, "time": 0.82346} +{"mode": "train", "epoch": 88, "iter": 2700, "lr": 0.03684, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34578, "top5_acc": 0.60656, "loss_cls": 3.70233, "loss": 3.70233, "time": 0.82414} +{"mode": "train", "epoch": 88, "iter": 2800, "lr": 0.03681, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34562, "top5_acc": 0.6, "loss_cls": 3.73389, "loss": 3.73389, "time": 0.82128} +{"mode": "train", "epoch": 88, "iter": 2900, "lr": 0.03678, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34531, "top5_acc": 0.60781, "loss_cls": 3.68145, "loss": 3.68145, "time": 0.82106} +{"mode": "train", "epoch": 88, "iter": 3000, "lr": 0.03676, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34, "top5_acc": 0.60891, "loss_cls": 3.71476, "loss": 3.71476, "time": 0.82153} +{"mode": "train", "epoch": 88, "iter": 3100, "lr": 0.03673, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35812, "top5_acc": 0.62047, "loss_cls": 3.61533, "loss": 3.61533, "time": 0.81838} +{"mode": "train", "epoch": 88, "iter": 3200, "lr": 0.0367, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33625, "top5_acc": 0.61281, "loss_cls": 3.69819, "loss": 3.69819, "time": 0.8162} +{"mode": "train", "epoch": 88, "iter": 3300, "lr": 0.03667, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33766, "top5_acc": 0.61109, "loss_cls": 3.70579, "loss": 3.70579, "time": 0.82712} +{"mode": "train", "epoch": 88, "iter": 3400, "lr": 0.03665, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34469, "top5_acc": 0.61219, "loss_cls": 3.67175, "loss": 3.67175, "time": 0.81721} +{"mode": "train", "epoch": 88, "iter": 3500, "lr": 0.03662, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34641, "top5_acc": 0.61219, "loss_cls": 3.6852, "loss": 3.6852, "time": 0.81406} +{"mode": "train", "epoch": 88, "iter": 3600, "lr": 0.03659, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34609, "top5_acc": 0.61031, "loss_cls": 3.69474, "loss": 3.69474, "time": 0.819} +{"mode": "train", "epoch": 88, "iter": 3700, "lr": 0.03657, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34547, "top5_acc": 0.60109, "loss_cls": 3.69749, "loss": 3.69749, "time": 0.81964} +{"mode": "val", "epoch": 88, "iter": 309, "lr": 0.03655, "top1_acc": 0.27655, "top5_acc": 0.53148, "mean_class_accuracy": 0.2764} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.03653, "memory": 15990, "data_time": 1.29483, "top1_acc": 0.35688, "top5_acc": 0.62172, "loss_cls": 3.61879, "loss": 3.61879, "time": 2.26195} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0365, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36625, "top5_acc": 0.63438, "loss_cls": 3.55258, "loss": 3.55258, "time": 0.82287} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.03647, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34891, "top5_acc": 0.62297, "loss_cls": 3.63107, "loss": 3.63107, "time": 0.82149} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.03645, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34594, "top5_acc": 0.60719, "loss_cls": 3.67108, "loss": 3.67108, "time": 0.81962} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.03642, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35047, "top5_acc": 0.62516, "loss_cls": 3.62843, "loss": 3.62843, "time": 0.81855} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.03639, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33641, "top5_acc": 0.60938, "loss_cls": 3.71411, "loss": 3.71411, "time": 0.81795} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.03637, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35984, "top5_acc": 0.63125, "loss_cls": 3.59098, "loss": 3.59098, "time": 0.81499} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.03634, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34953, "top5_acc": 0.62062, "loss_cls": 3.64433, "loss": 3.64433, "time": 0.8122} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.03631, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35, "top5_acc": 0.60828, "loss_cls": 3.683, "loss": 3.683, "time": 0.82901} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.03629, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34312, "top5_acc": 0.61984, "loss_cls": 3.66953, "loss": 3.66953, "time": 0.82254} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.03626, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35578, "top5_acc": 0.61844, "loss_cls": 3.6426, "loss": 3.6426, "time": 0.81566} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.03623, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34969, "top5_acc": 0.62156, "loss_cls": 3.63136, "loss": 3.63136, "time": 0.81812} +{"mode": "train", "epoch": 89, "iter": 1300, "lr": 0.0362, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34109, "top5_acc": 0.60281, "loss_cls": 3.71722, "loss": 3.71722, "time": 0.82172} +{"mode": "train", "epoch": 89, "iter": 1400, "lr": 0.03618, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34438, "top5_acc": 0.60188, "loss_cls": 3.72471, "loss": 3.72471, "time": 0.81656} +{"mode": "train", "epoch": 89, "iter": 1500, "lr": 0.03615, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34859, "top5_acc": 0.60156, "loss_cls": 3.69082, "loss": 3.69082, "time": 0.82472} +{"mode": "train", "epoch": 89, "iter": 1600, "lr": 0.03612, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3525, "top5_acc": 0.61656, "loss_cls": 3.64951, "loss": 3.64951, "time": 0.81907} +{"mode": "train", "epoch": 89, "iter": 1700, "lr": 0.0361, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.35156, "top5_acc": 0.61797, "loss_cls": 3.6468, "loss": 3.6468, "time": 0.83528} +{"mode": "train", "epoch": 89, "iter": 1800, "lr": 0.03607, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35109, "top5_acc": 0.60891, "loss_cls": 3.70655, "loss": 3.70655, "time": 0.82589} +{"mode": "train", "epoch": 89, "iter": 1900, "lr": 0.03604, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35391, "top5_acc": 0.62109, "loss_cls": 3.64061, "loss": 3.64061, "time": 0.82571} +{"mode": "train", "epoch": 89, "iter": 2000, "lr": 0.03602, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35656, "top5_acc": 0.61266, "loss_cls": 3.65582, "loss": 3.65582, "time": 0.82603} +{"mode": "train", "epoch": 89, "iter": 2100, "lr": 0.03599, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34391, "top5_acc": 0.59953, "loss_cls": 3.72895, "loss": 3.72895, "time": 0.82094} +{"mode": "train", "epoch": 89, "iter": 2200, "lr": 0.03596, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34016, "top5_acc": 0.60047, "loss_cls": 3.71783, "loss": 3.71783, "time": 0.81739} +{"mode": "train", "epoch": 89, "iter": 2300, "lr": 0.03594, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.35156, "top5_acc": 0.61547, "loss_cls": 3.6476, "loss": 3.6476, "time": 0.82337} +{"mode": "train", "epoch": 89, "iter": 2400, "lr": 0.03591, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34359, "top5_acc": 0.60812, "loss_cls": 3.68984, "loss": 3.68984, "time": 0.81303} +{"mode": "train", "epoch": 89, "iter": 2500, "lr": 0.03588, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35, "top5_acc": 0.61734, "loss_cls": 3.66798, "loss": 3.66798, "time": 0.81116} +{"mode": "train", "epoch": 89, "iter": 2600, "lr": 0.03586, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35, "top5_acc": 0.61172, "loss_cls": 3.67118, "loss": 3.67118, "time": 0.81677} +{"mode": "train", "epoch": 89, "iter": 2700, "lr": 0.03583, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35312, "top5_acc": 0.61781, "loss_cls": 3.63144, "loss": 3.63144, "time": 0.82326} +{"mode": "train", "epoch": 89, "iter": 2800, "lr": 0.0358, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34188, "top5_acc": 0.60969, "loss_cls": 3.708, "loss": 3.708, "time": 0.81892} +{"mode": "train", "epoch": 89, "iter": 2900, "lr": 0.03578, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.34734, "top5_acc": 0.61203, "loss_cls": 3.67691, "loss": 3.67691, "time": 0.82256} +{"mode": "train", "epoch": 89, "iter": 3000, "lr": 0.03575, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35203, "top5_acc": 0.60641, "loss_cls": 3.6998, "loss": 3.6998, "time": 0.82183} +{"mode": "train", "epoch": 89, "iter": 3100, "lr": 0.03572, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34922, "top5_acc": 0.61562, "loss_cls": 3.64212, "loss": 3.64212, "time": 0.81469} +{"mode": "train", "epoch": 89, "iter": 3200, "lr": 0.03569, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34594, "top5_acc": 0.61531, "loss_cls": 3.6545, "loss": 3.6545, "time": 0.8197} +{"mode": "train", "epoch": 89, "iter": 3300, "lr": 0.03567, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34281, "top5_acc": 0.60688, "loss_cls": 3.68713, "loss": 3.68713, "time": 0.82272} +{"mode": "train", "epoch": 89, "iter": 3400, "lr": 0.03564, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34391, "top5_acc": 0.61938, "loss_cls": 3.67372, "loss": 3.67372, "time": 0.82429} +{"mode": "train", "epoch": 89, "iter": 3500, "lr": 0.03561, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33953, "top5_acc": 0.61016, "loss_cls": 3.67531, "loss": 3.67531, "time": 0.81681} +{"mode": "train", "epoch": 89, "iter": 3600, "lr": 0.03559, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35172, "top5_acc": 0.62078, "loss_cls": 3.64305, "loss": 3.64305, "time": 0.81656} +{"mode": "train", "epoch": 89, "iter": 3700, "lr": 0.03556, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35688, "top5_acc": 0.60969, "loss_cls": 3.6694, "loss": 3.6694, "time": 0.81251} +{"mode": "val", "epoch": 89, "iter": 309, "lr": 0.03555, "top1_acc": 0.26116, "top5_acc": 0.51299, "mean_class_accuracy": 0.26098} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.03552, "memory": 15990, "data_time": 1.31613, "top1_acc": 0.36672, "top5_acc": 0.62906, "loss_cls": 3.58299, "loss": 3.58299, "time": 2.29478} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.0355, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36047, "top5_acc": 0.63094, "loss_cls": 3.57502, "loss": 3.57502, "time": 0.82297} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.03547, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35891, "top5_acc": 0.62531, "loss_cls": 3.59115, "loss": 3.59115, "time": 0.82585} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.03544, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35062, "top5_acc": 0.62156, "loss_cls": 3.64081, "loss": 3.64081, "time": 0.81503} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.03541, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35578, "top5_acc": 0.61297, "loss_cls": 3.66094, "loss": 3.66094, "time": 0.81885} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.03539, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36141, "top5_acc": 0.62438, "loss_cls": 3.59702, "loss": 3.59702, "time": 0.81492} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.03536, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35656, "top5_acc": 0.62062, "loss_cls": 3.61679, "loss": 3.61679, "time": 0.81086} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.03533, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35328, "top5_acc": 0.615, "loss_cls": 3.66037, "loss": 3.66037, "time": 0.81429} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.03531, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34984, "top5_acc": 0.61187, "loss_cls": 3.67327, "loss": 3.67327, "time": 0.81845} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.03528, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35297, "top5_acc": 0.61187, "loss_cls": 3.67358, "loss": 3.67358, "time": 0.82458} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.03525, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35766, "top5_acc": 0.61547, "loss_cls": 3.66157, "loss": 3.66157, "time": 0.82206} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.03523, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34844, "top5_acc": 0.60953, "loss_cls": 3.69749, "loss": 3.69749, "time": 0.82216} +{"mode": "train", "epoch": 90, "iter": 1300, "lr": 0.0352, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36234, "top5_acc": 0.62359, "loss_cls": 3.61105, "loss": 3.61105, "time": 0.8203} +{"mode": "train", "epoch": 90, "iter": 1400, "lr": 0.03517, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35109, "top5_acc": 0.61297, "loss_cls": 3.64265, "loss": 3.64265, "time": 0.81459} +{"mode": "train", "epoch": 90, "iter": 1500, "lr": 0.03515, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35172, "top5_acc": 0.62281, "loss_cls": 3.62589, "loss": 3.62589, "time": 0.82815} +{"mode": "train", "epoch": 90, "iter": 1600, "lr": 0.03512, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35766, "top5_acc": 0.62281, "loss_cls": 3.6297, "loss": 3.6297, "time": 0.81761} +{"mode": "train", "epoch": 90, "iter": 1700, "lr": 0.03509, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.34844, "top5_acc": 0.60625, "loss_cls": 3.66447, "loss": 3.66447, "time": 0.8238} +{"mode": "train", "epoch": 90, "iter": 1800, "lr": 0.03507, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.35141, "top5_acc": 0.61328, "loss_cls": 3.6758, "loss": 3.6758, "time": 0.83415} +{"mode": "train", "epoch": 90, "iter": 1900, "lr": 0.03504, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.34344, "top5_acc": 0.61203, "loss_cls": 3.70201, "loss": 3.70201, "time": 0.82704} +{"mode": "train", "epoch": 90, "iter": 2000, "lr": 0.03501, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34891, "top5_acc": 0.61187, "loss_cls": 3.68261, "loss": 3.68261, "time": 0.82002} +{"mode": "train", "epoch": 90, "iter": 2100, "lr": 0.03499, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36, "top5_acc": 0.6275, "loss_cls": 3.59881, "loss": 3.59881, "time": 0.82346} +{"mode": "train", "epoch": 90, "iter": 2200, "lr": 0.03496, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.355, "top5_acc": 0.61297, "loss_cls": 3.67982, "loss": 3.67982, "time": 0.8191} +{"mode": "train", "epoch": 90, "iter": 2300, "lr": 0.03493, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3525, "top5_acc": 0.61641, "loss_cls": 3.67363, "loss": 3.67363, "time": 0.82072} +{"mode": "train", "epoch": 90, "iter": 2400, "lr": 0.03491, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34641, "top5_acc": 0.60219, "loss_cls": 3.68108, "loss": 3.68108, "time": 0.82034} +{"mode": "train", "epoch": 90, "iter": 2500, "lr": 0.03488, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35062, "top5_acc": 0.6125, "loss_cls": 3.6712, "loss": 3.6712, "time": 0.81493} +{"mode": "train", "epoch": 90, "iter": 2600, "lr": 0.03485, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35344, "top5_acc": 0.60797, "loss_cls": 3.65474, "loss": 3.65474, "time": 0.82812} +{"mode": "train", "epoch": 90, "iter": 2700, "lr": 0.03483, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34578, "top5_acc": 0.60984, "loss_cls": 3.69307, "loss": 3.69307, "time": 0.82124} +{"mode": "train", "epoch": 90, "iter": 2800, "lr": 0.0348, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.355, "top5_acc": 0.61469, "loss_cls": 3.65101, "loss": 3.65101, "time": 0.82115} +{"mode": "train", "epoch": 90, "iter": 2900, "lr": 0.03477, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35156, "top5_acc": 0.61875, "loss_cls": 3.65105, "loss": 3.65105, "time": 0.82084} +{"mode": "train", "epoch": 90, "iter": 3000, "lr": 0.03475, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34953, "top5_acc": 0.61281, "loss_cls": 3.69208, "loss": 3.69208, "time": 0.82227} +{"mode": "train", "epoch": 90, "iter": 3100, "lr": 0.03472, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35391, "top5_acc": 0.62062, "loss_cls": 3.6306, "loss": 3.6306, "time": 0.81616} +{"mode": "train", "epoch": 90, "iter": 3200, "lr": 0.03469, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34906, "top5_acc": 0.61625, "loss_cls": 3.6656, "loss": 3.6656, "time": 0.8221} +{"mode": "train", "epoch": 90, "iter": 3300, "lr": 0.03467, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35547, "top5_acc": 0.62031, "loss_cls": 3.63571, "loss": 3.63571, "time": 0.81835} +{"mode": "train", "epoch": 90, "iter": 3400, "lr": 0.03464, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35203, "top5_acc": 0.61438, "loss_cls": 3.66643, "loss": 3.66643, "time": 0.81727} +{"mode": "train", "epoch": 90, "iter": 3500, "lr": 0.03461, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35188, "top5_acc": 0.61672, "loss_cls": 3.64487, "loss": 3.64487, "time": 0.81243} +{"mode": "train", "epoch": 90, "iter": 3600, "lr": 0.03459, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3475, "top5_acc": 0.61641, "loss_cls": 3.66943, "loss": 3.66943, "time": 0.81863} +{"mode": "train", "epoch": 90, "iter": 3700, "lr": 0.03456, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35688, "top5_acc": 0.61203, "loss_cls": 3.67881, "loss": 3.67881, "time": 0.81981} +{"mode": "val", "epoch": 90, "iter": 309, "lr": 0.03455, "top1_acc": 0.28451, "top5_acc": 0.53624, "mean_class_accuracy": 0.28444} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.03452, "memory": 15990, "data_time": 1.2783, "top1_acc": 0.365, "top5_acc": 0.63297, "loss_cls": 3.5773, "loss": 3.5773, "time": 2.25397} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0345, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36141, "top5_acc": 0.63078, "loss_cls": 3.57745, "loss": 3.57745, "time": 0.83041} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.03447, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34812, "top5_acc": 0.62578, "loss_cls": 3.61746, "loss": 3.61746, "time": 0.83319} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.03444, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36062, "top5_acc": 0.62875, "loss_cls": 3.57377, "loss": 3.57377, "time": 0.82864} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.03442, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36047, "top5_acc": 0.62531, "loss_cls": 3.58774, "loss": 3.58774, "time": 0.82884} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.03439, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36922, "top5_acc": 0.63406, "loss_cls": 3.55937, "loss": 3.55937, "time": 0.83234} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.03436, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35516, "top5_acc": 0.62125, "loss_cls": 3.61528, "loss": 3.61528, "time": 0.82683} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.03434, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3625, "top5_acc": 0.62953, "loss_cls": 3.60202, "loss": 3.60202, "time": 0.8236} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.03431, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3575, "top5_acc": 0.62187, "loss_cls": 3.62616, "loss": 3.62616, "time": 0.82798} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.03428, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35703, "top5_acc": 0.62453, "loss_cls": 3.59699, "loss": 3.59699, "time": 0.82908} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.03426, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36234, "top5_acc": 0.62484, "loss_cls": 3.59121, "loss": 3.59121, "time": 0.82869} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.03423, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35141, "top5_acc": 0.62891, "loss_cls": 3.59371, "loss": 3.59371, "time": 0.83119} +{"mode": "train", "epoch": 91, "iter": 1300, "lr": 0.0342, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35156, "top5_acc": 0.61281, "loss_cls": 3.65328, "loss": 3.65328, "time": 0.82684} +{"mode": "train", "epoch": 91, "iter": 1400, "lr": 0.03418, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35141, "top5_acc": 0.62531, "loss_cls": 3.62258, "loss": 3.62258, "time": 0.82116} +{"mode": "train", "epoch": 91, "iter": 1500, "lr": 0.03415, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34984, "top5_acc": 0.60656, "loss_cls": 3.6934, "loss": 3.6934, "time": 0.82907} +{"mode": "train", "epoch": 91, "iter": 1600, "lr": 0.03412, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36484, "top5_acc": 0.61875, "loss_cls": 3.59785, "loss": 3.59785, "time": 0.81979} +{"mode": "train", "epoch": 91, "iter": 1700, "lr": 0.0341, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35922, "top5_acc": 0.61625, "loss_cls": 3.62696, "loss": 3.62696, "time": 0.82624} +{"mode": "train", "epoch": 91, "iter": 1800, "lr": 0.03407, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35312, "top5_acc": 0.60969, "loss_cls": 3.69196, "loss": 3.69196, "time": 0.82701} +{"mode": "train", "epoch": 91, "iter": 1900, "lr": 0.03405, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35281, "top5_acc": 0.62438, "loss_cls": 3.64102, "loss": 3.64102, "time": 0.83203} +{"mode": "train", "epoch": 91, "iter": 2000, "lr": 0.03402, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34031, "top5_acc": 0.61062, "loss_cls": 3.71819, "loss": 3.71819, "time": 0.82901} +{"mode": "train", "epoch": 91, "iter": 2100, "lr": 0.03399, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35422, "top5_acc": 0.61312, "loss_cls": 3.65028, "loss": 3.65028, "time": 0.82739} +{"mode": "train", "epoch": 91, "iter": 2200, "lr": 0.03397, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35031, "top5_acc": 0.61266, "loss_cls": 3.66203, "loss": 3.66203, "time": 0.82984} +{"mode": "train", "epoch": 91, "iter": 2300, "lr": 0.03394, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.34953, "top5_acc": 0.60281, "loss_cls": 3.66812, "loss": 3.66812, "time": 0.83139} +{"mode": "train", "epoch": 91, "iter": 2400, "lr": 0.03391, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35609, "top5_acc": 0.62156, "loss_cls": 3.62365, "loss": 3.62365, "time": 0.82385} +{"mode": "train", "epoch": 91, "iter": 2500, "lr": 0.03389, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35078, "top5_acc": 0.61609, "loss_cls": 3.65602, "loss": 3.65602, "time": 0.81939} +{"mode": "train", "epoch": 91, "iter": 2600, "lr": 0.03386, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35781, "top5_acc": 0.61906, "loss_cls": 3.62894, "loss": 3.62894, "time": 0.82957} +{"mode": "train", "epoch": 91, "iter": 2700, "lr": 0.03383, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34344, "top5_acc": 0.60391, "loss_cls": 3.70112, "loss": 3.70112, "time": 0.82504} +{"mode": "train", "epoch": 91, "iter": 2800, "lr": 0.03381, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35359, "top5_acc": 0.62141, "loss_cls": 3.65243, "loss": 3.65243, "time": 0.82878} +{"mode": "train", "epoch": 91, "iter": 2900, "lr": 0.03378, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35766, "top5_acc": 0.62234, "loss_cls": 3.62886, "loss": 3.62886, "time": 0.82837} +{"mode": "train", "epoch": 91, "iter": 3000, "lr": 0.03375, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.34172, "top5_acc": 0.60891, "loss_cls": 3.67495, "loss": 3.67495, "time": 0.82853} +{"mode": "train", "epoch": 91, "iter": 3100, "lr": 0.03373, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34438, "top5_acc": 0.60969, "loss_cls": 3.69491, "loss": 3.69491, "time": 0.83413} +{"mode": "train", "epoch": 91, "iter": 3200, "lr": 0.0337, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35344, "top5_acc": 0.61438, "loss_cls": 3.62822, "loss": 3.62822, "time": 0.82502} +{"mode": "train", "epoch": 91, "iter": 3300, "lr": 0.03367, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35484, "top5_acc": 0.62266, "loss_cls": 3.61038, "loss": 3.61038, "time": 0.82291} +{"mode": "train", "epoch": 91, "iter": 3400, "lr": 0.03365, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35172, "top5_acc": 0.60969, "loss_cls": 3.66955, "loss": 3.66955, "time": 0.83438} +{"mode": "train", "epoch": 91, "iter": 3500, "lr": 0.03362, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35109, "top5_acc": 0.60719, "loss_cls": 3.68192, "loss": 3.68192, "time": 0.82723} +{"mode": "train", "epoch": 91, "iter": 3600, "lr": 0.0336, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34078, "top5_acc": 0.61062, "loss_cls": 3.69333, "loss": 3.69333, "time": 0.82512} +{"mode": "train", "epoch": 91, "iter": 3700, "lr": 0.03357, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35266, "top5_acc": 0.61984, "loss_cls": 3.64728, "loss": 3.64728, "time": 0.82944} +{"mode": "val", "epoch": 91, "iter": 309, "lr": 0.03356, "top1_acc": 0.27564, "top5_acc": 0.53462, "mean_class_accuracy": 0.27518} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.03353, "memory": 15990, "data_time": 1.34662, "top1_acc": 0.37203, "top5_acc": 0.62469, "loss_cls": 3.57326, "loss": 3.57326, "time": 2.34893} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.0335, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36, "top5_acc": 0.62297, "loss_cls": 3.59535, "loss": 3.59535, "time": 0.83711} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.03348, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35828, "top5_acc": 0.62609, "loss_cls": 3.57033, "loss": 3.57033, "time": 0.83506} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.03345, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36875, "top5_acc": 0.63219, "loss_cls": 3.5972, "loss": 3.5972, "time": 0.82835} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.03342, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35078, "top5_acc": 0.61266, "loss_cls": 3.67039, "loss": 3.67039, "time": 0.83545} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.0334, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36547, "top5_acc": 0.63281, "loss_cls": 3.55882, "loss": 3.55882, "time": 0.83425} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.03337, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35672, "top5_acc": 0.62844, "loss_cls": 3.59693, "loss": 3.59693, "time": 0.83506} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.03335, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35078, "top5_acc": 0.61281, "loss_cls": 3.65433, "loss": 3.65433, "time": 0.83477} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.03332, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35656, "top5_acc": 0.62547, "loss_cls": 3.61424, "loss": 3.61424, "time": 0.83396} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.03329, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36203, "top5_acc": 0.625, "loss_cls": 3.59842, "loss": 3.59842, "time": 0.83717} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.03327, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35719, "top5_acc": 0.60859, "loss_cls": 3.67585, "loss": 3.67585, "time": 0.83245} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.03324, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35078, "top5_acc": 0.62094, "loss_cls": 3.64483, "loss": 3.64483, "time": 0.82892} +{"mode": "train", "epoch": 92, "iter": 1300, "lr": 0.03321, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3625, "top5_acc": 0.62953, "loss_cls": 3.59225, "loss": 3.59225, "time": 0.8261} +{"mode": "train", "epoch": 92, "iter": 1400, "lr": 0.03319, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36281, "top5_acc": 0.62266, "loss_cls": 3.63017, "loss": 3.63017, "time": 0.82331} +{"mode": "train", "epoch": 92, "iter": 1500, "lr": 0.03316, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35281, "top5_acc": 0.62328, "loss_cls": 3.629, "loss": 3.629, "time": 0.82695} +{"mode": "train", "epoch": 92, "iter": 1600, "lr": 0.03314, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36047, "top5_acc": 0.62562, "loss_cls": 3.6111, "loss": 3.6111, "time": 0.81925} +{"mode": "train", "epoch": 92, "iter": 1700, "lr": 0.03311, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34344, "top5_acc": 0.61531, "loss_cls": 3.66845, "loss": 3.66845, "time": 0.82042} +{"mode": "train", "epoch": 92, "iter": 1800, "lr": 0.03308, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36406, "top5_acc": 0.62344, "loss_cls": 3.59848, "loss": 3.59848, "time": 0.8225} +{"mode": "train", "epoch": 92, "iter": 1900, "lr": 0.03306, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35859, "top5_acc": 0.62891, "loss_cls": 3.58408, "loss": 3.58408, "time": 0.82641} +{"mode": "train", "epoch": 92, "iter": 2000, "lr": 0.03303, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35328, "top5_acc": 0.62281, "loss_cls": 3.6085, "loss": 3.6085, "time": 0.81952} +{"mode": "train", "epoch": 92, "iter": 2100, "lr": 0.033, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35609, "top5_acc": 0.61812, "loss_cls": 3.64343, "loss": 3.64343, "time": 0.81686} +{"mode": "train", "epoch": 92, "iter": 2200, "lr": 0.03298, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34812, "top5_acc": 0.61, "loss_cls": 3.67154, "loss": 3.67154, "time": 0.82604} +{"mode": "train", "epoch": 92, "iter": 2300, "lr": 0.03295, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35797, "top5_acc": 0.62234, "loss_cls": 3.63729, "loss": 3.63729, "time": 0.81603} +{"mode": "train", "epoch": 92, "iter": 2400, "lr": 0.03292, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35766, "top5_acc": 0.62234, "loss_cls": 3.61807, "loss": 3.61807, "time": 0.81551} +{"mode": "train", "epoch": 92, "iter": 2500, "lr": 0.0329, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35625, "top5_acc": 0.61703, "loss_cls": 3.64548, "loss": 3.64548, "time": 0.81784} +{"mode": "train", "epoch": 92, "iter": 2600, "lr": 0.03287, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35172, "top5_acc": 0.61359, "loss_cls": 3.65984, "loss": 3.65984, "time": 0.81837} +{"mode": "train", "epoch": 92, "iter": 2700, "lr": 0.03285, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35359, "top5_acc": 0.61672, "loss_cls": 3.6329, "loss": 3.6329, "time": 0.82381} +{"mode": "train", "epoch": 92, "iter": 2800, "lr": 0.03282, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36125, "top5_acc": 0.6225, "loss_cls": 3.60054, "loss": 3.60054, "time": 0.82239} +{"mode": "train", "epoch": 92, "iter": 2900, "lr": 0.03279, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35438, "top5_acc": 0.62453, "loss_cls": 3.60608, "loss": 3.60608, "time": 0.82476} +{"mode": "train", "epoch": 92, "iter": 3000, "lr": 0.03277, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35375, "top5_acc": 0.61453, "loss_cls": 3.66886, "loss": 3.66886, "time": 0.8242} +{"mode": "train", "epoch": 92, "iter": 3100, "lr": 0.03274, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36125, "top5_acc": 0.62484, "loss_cls": 3.64905, "loss": 3.64905, "time": 0.82214} +{"mode": "train", "epoch": 92, "iter": 3200, "lr": 0.03271, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36359, "top5_acc": 0.63078, "loss_cls": 3.59476, "loss": 3.59476, "time": 0.8184} +{"mode": "train", "epoch": 92, "iter": 3300, "lr": 0.03269, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35578, "top5_acc": 0.61094, "loss_cls": 3.64942, "loss": 3.64942, "time": 0.81739} +{"mode": "train", "epoch": 92, "iter": 3400, "lr": 0.03266, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36234, "top5_acc": 0.6175, "loss_cls": 3.63835, "loss": 3.63835, "time": 0.81808} +{"mode": "train", "epoch": 92, "iter": 3500, "lr": 0.03264, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34875, "top5_acc": 0.61453, "loss_cls": 3.65897, "loss": 3.65897, "time": 0.81778} +{"mode": "train", "epoch": 92, "iter": 3600, "lr": 0.03261, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36172, "top5_acc": 0.61844, "loss_cls": 3.59452, "loss": 3.59452, "time": 0.81902} +{"mode": "train", "epoch": 92, "iter": 3700, "lr": 0.03258, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36734, "top5_acc": 0.63344, "loss_cls": 3.55384, "loss": 3.55384, "time": 0.82205} +{"mode": "val", "epoch": 92, "iter": 309, "lr": 0.03257, "top1_acc": 0.27888, "top5_acc": 0.53133, "mean_class_accuracy": 0.27865} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.03255, "memory": 15990, "data_time": 1.32937, "top1_acc": 0.37219, "top5_acc": 0.63375, "loss_cls": 3.54842, "loss": 3.54842, "time": 2.31758} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.03252, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36562, "top5_acc": 0.63656, "loss_cls": 3.56322, "loss": 3.56322, "time": 0.82374} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.03249, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36562, "top5_acc": 0.62703, "loss_cls": 3.57544, "loss": 3.57544, "time": 0.82097} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.03247, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35656, "top5_acc": 0.62469, "loss_cls": 3.61192, "loss": 3.61192, "time": 0.81969} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.03244, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36312, "top5_acc": 0.63, "loss_cls": 3.58129, "loss": 3.58129, "time": 0.82064} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.03241, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35953, "top5_acc": 0.62313, "loss_cls": 3.60091, "loss": 3.60091, "time": 0.81688} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.03239, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35328, "top5_acc": 0.62875, "loss_cls": 3.59952, "loss": 3.59952, "time": 0.82273} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.03236, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35891, "top5_acc": 0.63266, "loss_cls": 3.5947, "loss": 3.5947, "time": 0.81671} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.03234, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36219, "top5_acc": 0.61797, "loss_cls": 3.63542, "loss": 3.63542, "time": 0.81629} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.03231, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36875, "top5_acc": 0.63641, "loss_cls": 3.54381, "loss": 3.54381, "time": 0.81707} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.03228, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34984, "top5_acc": 0.62234, "loss_cls": 3.60721, "loss": 3.60721, "time": 0.8167} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.03226, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36297, "top5_acc": 0.63062, "loss_cls": 3.57573, "loss": 3.57573, "time": 0.81608} +{"mode": "train", "epoch": 93, "iter": 1300, "lr": 0.03223, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36719, "top5_acc": 0.62969, "loss_cls": 3.5583, "loss": 3.5583, "time": 0.82004} +{"mode": "train", "epoch": 93, "iter": 1400, "lr": 0.03221, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36484, "top5_acc": 0.62797, "loss_cls": 3.57557, "loss": 3.57557, "time": 0.8227} +{"mode": "train", "epoch": 93, "iter": 1500, "lr": 0.03218, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35125, "top5_acc": 0.6225, "loss_cls": 3.63161, "loss": 3.63161, "time": 0.8198} +{"mode": "train", "epoch": 93, "iter": 1600, "lr": 0.03215, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35234, "top5_acc": 0.62156, "loss_cls": 3.63347, "loss": 3.63347, "time": 0.82791} +{"mode": "train", "epoch": 93, "iter": 1700, "lr": 0.03213, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35641, "top5_acc": 0.62797, "loss_cls": 3.58379, "loss": 3.58379, "time": 0.82216} +{"mode": "train", "epoch": 93, "iter": 1800, "lr": 0.0321, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35531, "top5_acc": 0.62547, "loss_cls": 3.59275, "loss": 3.59275, "time": 0.81939} +{"mode": "train", "epoch": 93, "iter": 1900, "lr": 0.03207, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36328, "top5_acc": 0.62516, "loss_cls": 3.58221, "loss": 3.58221, "time": 0.81816} +{"mode": "train", "epoch": 93, "iter": 2000, "lr": 0.03205, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35469, "top5_acc": 0.62844, "loss_cls": 3.60094, "loss": 3.60094, "time": 0.81979} +{"mode": "train", "epoch": 93, "iter": 2100, "lr": 0.03202, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36578, "top5_acc": 0.62938, "loss_cls": 3.57949, "loss": 3.57949, "time": 0.82118} +{"mode": "train", "epoch": 93, "iter": 2200, "lr": 0.032, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.355, "top5_acc": 0.61266, "loss_cls": 3.62059, "loss": 3.62059, "time": 0.82023} +{"mode": "train", "epoch": 93, "iter": 2300, "lr": 0.03197, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36, "top5_acc": 0.63297, "loss_cls": 3.58853, "loss": 3.58853, "time": 0.81616} +{"mode": "train", "epoch": 93, "iter": 2400, "lr": 0.03194, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36, "top5_acc": 0.61953, "loss_cls": 3.60714, "loss": 3.60714, "time": 0.81999} +{"mode": "train", "epoch": 93, "iter": 2500, "lr": 0.03192, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36906, "top5_acc": 0.63016, "loss_cls": 3.58252, "loss": 3.58252, "time": 0.81849} +{"mode": "train", "epoch": 93, "iter": 2600, "lr": 0.03189, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36562, "top5_acc": 0.63156, "loss_cls": 3.55162, "loss": 3.55162, "time": 0.81729} +{"mode": "train", "epoch": 93, "iter": 2700, "lr": 0.03187, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36781, "top5_acc": 0.62328, "loss_cls": 3.58474, "loss": 3.58474, "time": 0.8225} +{"mode": "train", "epoch": 93, "iter": 2800, "lr": 0.03184, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34875, "top5_acc": 0.62156, "loss_cls": 3.64053, "loss": 3.64053, "time": 0.81942} +{"mode": "train", "epoch": 93, "iter": 2900, "lr": 0.03181, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35375, "top5_acc": 0.62719, "loss_cls": 3.60226, "loss": 3.60226, "time": 0.82} +{"mode": "train", "epoch": 93, "iter": 3000, "lr": 0.03179, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36312, "top5_acc": 0.62562, "loss_cls": 3.55588, "loss": 3.55588, "time": 0.822} +{"mode": "train", "epoch": 93, "iter": 3100, "lr": 0.03176, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35047, "top5_acc": 0.61, "loss_cls": 3.65595, "loss": 3.65595, "time": 0.81684} +{"mode": "train", "epoch": 93, "iter": 3200, "lr": 0.03174, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35109, "top5_acc": 0.6175, "loss_cls": 3.68232, "loss": 3.68232, "time": 0.81826} +{"mode": "train", "epoch": 93, "iter": 3300, "lr": 0.03171, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35688, "top5_acc": 0.61891, "loss_cls": 3.60864, "loss": 3.60864, "time": 0.81641} +{"mode": "train", "epoch": 93, "iter": 3400, "lr": 0.03168, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35562, "top5_acc": 0.61141, "loss_cls": 3.67728, "loss": 3.67728, "time": 0.81683} +{"mode": "train", "epoch": 93, "iter": 3500, "lr": 0.03166, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35969, "top5_acc": 0.62078, "loss_cls": 3.62272, "loss": 3.62272, "time": 0.82283} +{"mode": "train", "epoch": 93, "iter": 3600, "lr": 0.03163, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35391, "top5_acc": 0.61953, "loss_cls": 3.66301, "loss": 3.66301, "time": 0.81556} +{"mode": "train", "epoch": 93, "iter": 3700, "lr": 0.03161, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35312, "top5_acc": 0.61328, "loss_cls": 3.64235, "loss": 3.64235, "time": 0.81646} +{"mode": "val", "epoch": 93, "iter": 309, "lr": 0.03159, "top1_acc": 0.29616, "top5_acc": 0.55265, "mean_class_accuracy": 0.29588} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.03157, "memory": 15990, "data_time": 1.30403, "top1_acc": 0.37156, "top5_acc": 0.635, "loss_cls": 3.54995, "loss": 3.54995, "time": 2.2908} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.03154, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37156, "top5_acc": 0.62766, "loss_cls": 3.57048, "loss": 3.57048, "time": 0.82598} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.03152, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36688, "top5_acc": 0.64141, "loss_cls": 3.51932, "loss": 3.51932, "time": 0.81923} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.03149, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36422, "top5_acc": 0.62813, "loss_cls": 3.55147, "loss": 3.55147, "time": 0.82103} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.03146, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35906, "top5_acc": 0.62359, "loss_cls": 3.60185, "loss": 3.60185, "time": 0.8171} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.03144, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37734, "top5_acc": 0.63766, "loss_cls": 3.51582, "loss": 3.51582, "time": 0.81849} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.03141, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36312, "top5_acc": 0.6275, "loss_cls": 3.58822, "loss": 3.58822, "time": 0.81443} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.03139, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36094, "top5_acc": 0.63016, "loss_cls": 3.57518, "loss": 3.57518, "time": 0.8158} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.03136, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35203, "top5_acc": 0.62234, "loss_cls": 3.63011, "loss": 3.63011, "time": 0.81918} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.03133, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35375, "top5_acc": 0.62109, "loss_cls": 3.59696, "loss": 3.59696, "time": 0.81623} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.03131, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36625, "top5_acc": 0.62687, "loss_cls": 3.5575, "loss": 3.5575, "time": 0.81696} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.03128, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35656, "top5_acc": 0.62, "loss_cls": 3.61201, "loss": 3.61201, "time": 0.81863} +{"mode": "train", "epoch": 94, "iter": 1300, "lr": 0.03126, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36188, "top5_acc": 0.62406, "loss_cls": 3.60895, "loss": 3.60895, "time": 0.81862} +{"mode": "train", "epoch": 94, "iter": 1400, "lr": 0.03123, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35375, "top5_acc": 0.61859, "loss_cls": 3.61409, "loss": 3.61409, "time": 0.8207} +{"mode": "train", "epoch": 94, "iter": 1500, "lr": 0.0312, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36672, "top5_acc": 0.62844, "loss_cls": 3.5725, "loss": 3.5725, "time": 0.8214} +{"mode": "train", "epoch": 94, "iter": 1600, "lr": 0.03118, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36094, "top5_acc": 0.62531, "loss_cls": 3.60426, "loss": 3.60426, "time": 0.81951} +{"mode": "train", "epoch": 94, "iter": 1700, "lr": 0.03115, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36156, "top5_acc": 0.62766, "loss_cls": 3.61003, "loss": 3.61003, "time": 0.82568} +{"mode": "train", "epoch": 94, "iter": 1800, "lr": 0.03113, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35516, "top5_acc": 0.61922, "loss_cls": 3.62973, "loss": 3.62973, "time": 0.82175} +{"mode": "train", "epoch": 94, "iter": 1900, "lr": 0.0311, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37031, "top5_acc": 0.62281, "loss_cls": 3.57883, "loss": 3.57883, "time": 0.81861} +{"mode": "train", "epoch": 94, "iter": 2000, "lr": 0.03108, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36078, "top5_acc": 0.63094, "loss_cls": 3.57398, "loss": 3.57398, "time": 0.82176} +{"mode": "train", "epoch": 94, "iter": 2100, "lr": 0.03105, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.355, "top5_acc": 0.61609, "loss_cls": 3.62758, "loss": 3.62758, "time": 0.81724} +{"mode": "train", "epoch": 94, "iter": 2200, "lr": 0.03102, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36188, "top5_acc": 0.62531, "loss_cls": 3.61508, "loss": 3.61508, "time": 0.817} +{"mode": "train", "epoch": 94, "iter": 2300, "lr": 0.031, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35828, "top5_acc": 0.62266, "loss_cls": 3.60814, "loss": 3.60814, "time": 0.81589} +{"mode": "train", "epoch": 94, "iter": 2400, "lr": 0.03097, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36016, "top5_acc": 0.62047, "loss_cls": 3.60813, "loss": 3.60813, "time": 0.81867} +{"mode": "train", "epoch": 94, "iter": 2500, "lr": 0.03095, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35359, "top5_acc": 0.62422, "loss_cls": 3.60145, "loss": 3.60145, "time": 0.8171} +{"mode": "train", "epoch": 94, "iter": 2600, "lr": 0.03092, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36031, "top5_acc": 0.62187, "loss_cls": 3.61603, "loss": 3.61603, "time": 0.82233} +{"mode": "train", "epoch": 94, "iter": 2700, "lr": 0.03089, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35047, "top5_acc": 0.61656, "loss_cls": 3.65057, "loss": 3.65057, "time": 0.8198} +{"mode": "train", "epoch": 94, "iter": 2800, "lr": 0.03087, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36297, "top5_acc": 0.62734, "loss_cls": 3.61619, "loss": 3.61619, "time": 0.8224} +{"mode": "train", "epoch": 94, "iter": 2900, "lr": 0.03084, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35562, "top5_acc": 0.63188, "loss_cls": 3.58323, "loss": 3.58323, "time": 0.82081} +{"mode": "train", "epoch": 94, "iter": 3000, "lr": 0.03082, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36516, "top5_acc": 0.63234, "loss_cls": 3.57072, "loss": 3.57072, "time": 0.81763} +{"mode": "train", "epoch": 94, "iter": 3100, "lr": 0.03079, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36641, "top5_acc": 0.63219, "loss_cls": 3.57582, "loss": 3.57582, "time": 0.81713} +{"mode": "train", "epoch": 94, "iter": 3200, "lr": 0.03077, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35922, "top5_acc": 0.62672, "loss_cls": 3.59034, "loss": 3.59034, "time": 0.8162} +{"mode": "train", "epoch": 94, "iter": 3300, "lr": 0.03074, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36828, "top5_acc": 0.63781, "loss_cls": 3.54581, "loss": 3.54581, "time": 0.8179} +{"mode": "train", "epoch": 94, "iter": 3400, "lr": 0.03071, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35438, "top5_acc": 0.60828, "loss_cls": 3.65229, "loss": 3.65229, "time": 0.82605} +{"mode": "train", "epoch": 94, "iter": 3500, "lr": 0.03069, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36266, "top5_acc": 0.62828, "loss_cls": 3.58672, "loss": 3.58672, "time": 0.81564} +{"mode": "train", "epoch": 94, "iter": 3600, "lr": 0.03066, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36734, "top5_acc": 0.61953, "loss_cls": 3.59649, "loss": 3.59649, "time": 0.82043} +{"mode": "train", "epoch": 94, "iter": 3700, "lr": 0.03064, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37453, "top5_acc": 0.64297, "loss_cls": 3.51903, "loss": 3.51903, "time": 0.81724} +{"mode": "val", "epoch": 94, "iter": 309, "lr": 0.03062, "top1_acc": 0.28522, "top5_acc": 0.53766, "mean_class_accuracy": 0.28497} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.0306, "memory": 15990, "data_time": 1.32183, "top1_acc": 0.36547, "top5_acc": 0.64094, "loss_cls": 3.49531, "loss": 3.49531, "time": 2.29783} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.03057, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36312, "top5_acc": 0.62656, "loss_cls": 3.57754, "loss": 3.57754, "time": 0.82103} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.03055, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37266, "top5_acc": 0.63625, "loss_cls": 3.51737, "loss": 3.51737, "time": 0.82685} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.03052, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37109, "top5_acc": 0.64328, "loss_cls": 3.53606, "loss": 3.53606, "time": 0.81914} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.0305, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.3675, "top5_acc": 0.63031, "loss_cls": 3.56138, "loss": 3.56138, "time": 0.82611} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.03047, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36828, "top5_acc": 0.62531, "loss_cls": 3.57885, "loss": 3.57885, "time": 0.81783} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.03044, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36078, "top5_acc": 0.62297, "loss_cls": 3.59065, "loss": 3.59065, "time": 0.82191} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.03042, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37031, "top5_acc": 0.63172, "loss_cls": 3.55207, "loss": 3.55207, "time": 0.81766} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.03039, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37766, "top5_acc": 0.64281, "loss_cls": 3.50681, "loss": 3.50681, "time": 0.81631} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.03037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37438, "top5_acc": 0.64469, "loss_cls": 3.51815, "loss": 3.51815, "time": 0.82141} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.03034, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36609, "top5_acc": 0.62031, "loss_cls": 3.57631, "loss": 3.57631, "time": 0.82189} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.03032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36359, "top5_acc": 0.62969, "loss_cls": 3.57695, "loss": 3.57695, "time": 0.81486} +{"mode": "train", "epoch": 95, "iter": 1300, "lr": 0.03029, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35875, "top5_acc": 0.62781, "loss_cls": 3.57862, "loss": 3.57862, "time": 0.81815} +{"mode": "train", "epoch": 95, "iter": 1400, "lr": 0.03026, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36422, "top5_acc": 0.62391, "loss_cls": 3.58481, "loss": 3.58481, "time": 0.82017} +{"mode": "train", "epoch": 95, "iter": 1500, "lr": 0.03024, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36531, "top5_acc": 0.62516, "loss_cls": 3.59341, "loss": 3.59341, "time": 0.82002} +{"mode": "train", "epoch": 95, "iter": 1600, "lr": 0.03021, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36734, "top5_acc": 0.63172, "loss_cls": 3.56103, "loss": 3.56103, "time": 0.82583} +{"mode": "train", "epoch": 95, "iter": 1700, "lr": 0.03019, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36266, "top5_acc": 0.62672, "loss_cls": 3.57782, "loss": 3.57782, "time": 0.82332} +{"mode": "train", "epoch": 95, "iter": 1800, "lr": 0.03016, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36297, "top5_acc": 0.63703, "loss_cls": 3.53583, "loss": 3.53583, "time": 0.82057} +{"mode": "train", "epoch": 95, "iter": 1900, "lr": 0.03014, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36375, "top5_acc": 0.62766, "loss_cls": 3.56772, "loss": 3.56772, "time": 0.82482} +{"mode": "train", "epoch": 95, "iter": 2000, "lr": 0.03011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35734, "top5_acc": 0.62469, "loss_cls": 3.58456, "loss": 3.58456, "time": 0.82112} +{"mode": "train", "epoch": 95, "iter": 2100, "lr": 0.03008, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36125, "top5_acc": 0.62938, "loss_cls": 3.58407, "loss": 3.58407, "time": 0.81668} +{"mode": "train", "epoch": 95, "iter": 2200, "lr": 0.03006, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36906, "top5_acc": 0.62156, "loss_cls": 3.59768, "loss": 3.59768, "time": 0.82556} +{"mode": "train", "epoch": 95, "iter": 2300, "lr": 0.03003, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36781, "top5_acc": 0.63938, "loss_cls": 3.53846, "loss": 3.53846, "time": 0.8166} +{"mode": "train", "epoch": 95, "iter": 2400, "lr": 0.03001, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35703, "top5_acc": 0.61672, "loss_cls": 3.62912, "loss": 3.62912, "time": 0.81928} +{"mode": "train", "epoch": 95, "iter": 2500, "lr": 0.02998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36031, "top5_acc": 0.62703, "loss_cls": 3.60526, "loss": 3.60526, "time": 0.81917} +{"mode": "train", "epoch": 95, "iter": 2600, "lr": 0.02996, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36594, "top5_acc": 0.63609, "loss_cls": 3.56484, "loss": 3.56484, "time": 0.81596} +{"mode": "train", "epoch": 95, "iter": 2700, "lr": 0.02993, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35422, "top5_acc": 0.62641, "loss_cls": 3.61037, "loss": 3.61037, "time": 0.82697} +{"mode": "train", "epoch": 95, "iter": 2800, "lr": 0.02991, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36531, "top5_acc": 0.61703, "loss_cls": 3.57468, "loss": 3.57468, "time": 0.82113} +{"mode": "train", "epoch": 95, "iter": 2900, "lr": 0.02988, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35312, "top5_acc": 0.61828, "loss_cls": 3.63752, "loss": 3.63752, "time": 0.8219} +{"mode": "train", "epoch": 95, "iter": 3000, "lr": 0.02985, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37031, "top5_acc": 0.63547, "loss_cls": 3.55634, "loss": 3.55634, "time": 0.82114} +{"mode": "train", "epoch": 95, "iter": 3100, "lr": 0.02983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36234, "top5_acc": 0.62719, "loss_cls": 3.58763, "loss": 3.58763, "time": 0.81511} +{"mode": "train", "epoch": 95, "iter": 3200, "lr": 0.0298, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35875, "top5_acc": 0.62172, "loss_cls": 3.61699, "loss": 3.61699, "time": 0.81676} +{"mode": "train", "epoch": 95, "iter": 3300, "lr": 0.02978, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37109, "top5_acc": 0.63078, "loss_cls": 3.53307, "loss": 3.53307, "time": 0.81496} +{"mode": "train", "epoch": 95, "iter": 3400, "lr": 0.02975, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35844, "top5_acc": 0.63, "loss_cls": 3.5933, "loss": 3.5933, "time": 0.81624} +{"mode": "train", "epoch": 95, "iter": 3500, "lr": 0.02973, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36578, "top5_acc": 0.63266, "loss_cls": 3.56738, "loss": 3.56738, "time": 0.81498} +{"mode": "train", "epoch": 95, "iter": 3600, "lr": 0.0297, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35438, "top5_acc": 0.61953, "loss_cls": 3.64461, "loss": 3.64461, "time": 0.82117} +{"mode": "train", "epoch": 95, "iter": 3700, "lr": 0.02968, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36078, "top5_acc": 0.62734, "loss_cls": 3.58958, "loss": 3.58958, "time": 0.81498} +{"mode": "val", "epoch": 95, "iter": 309, "lr": 0.02966, "top1_acc": 0.27225, "top5_acc": 0.53229, "mean_class_accuracy": 0.27213} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.02964, "memory": 15990, "data_time": 1.323, "top1_acc": 0.36359, "top5_acc": 0.63734, "loss_cls": 3.51483, "loss": 3.51483, "time": 2.30323} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.02961, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37438, "top5_acc": 0.63844, "loss_cls": 3.47999, "loss": 3.47999, "time": 0.82193} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.02959, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36906, "top5_acc": 0.63875, "loss_cls": 3.52788, "loss": 3.52788, "time": 0.82069} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.02956, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36312, "top5_acc": 0.63, "loss_cls": 3.57121, "loss": 3.57121, "time": 0.81736} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.02954, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35922, "top5_acc": 0.63078, "loss_cls": 3.59422, "loss": 3.59422, "time": 0.817} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.02951, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36625, "top5_acc": 0.63328, "loss_cls": 3.55648, "loss": 3.55648, "time": 0.81758} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.02948, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37891, "top5_acc": 0.64062, "loss_cls": 3.5027, "loss": 3.5027, "time": 0.81631} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.02946, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35906, "top5_acc": 0.63359, "loss_cls": 3.55341, "loss": 3.55341, "time": 0.81655} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.02943, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37547, "top5_acc": 0.64156, "loss_cls": 3.54532, "loss": 3.54532, "time": 0.82001} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.02941, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35969, "top5_acc": 0.62703, "loss_cls": 3.59067, "loss": 3.59067, "time": 0.82016} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.02938, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37188, "top5_acc": 0.63219, "loss_cls": 3.53582, "loss": 3.53582, "time": 0.81352} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.02936, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36734, "top5_acc": 0.63016, "loss_cls": 3.59069, "loss": 3.59069, "time": 0.81623} +{"mode": "train", "epoch": 96, "iter": 1300, "lr": 0.02933, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37406, "top5_acc": 0.63375, "loss_cls": 3.52687, "loss": 3.52687, "time": 0.82148} +{"mode": "train", "epoch": 96, "iter": 1400, "lr": 0.02931, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35438, "top5_acc": 0.61797, "loss_cls": 3.60401, "loss": 3.60401, "time": 0.8214} +{"mode": "train", "epoch": 96, "iter": 1500, "lr": 0.02928, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36375, "top5_acc": 0.63375, "loss_cls": 3.54376, "loss": 3.54376, "time": 0.82874} +{"mode": "train", "epoch": 96, "iter": 1600, "lr": 0.02926, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3775, "top5_acc": 0.63938, "loss_cls": 3.52742, "loss": 3.52742, "time": 0.81808} +{"mode": "train", "epoch": 96, "iter": 1700, "lr": 0.02923, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37234, "top5_acc": 0.63125, "loss_cls": 3.54538, "loss": 3.54538, "time": 0.82412} +{"mode": "train", "epoch": 96, "iter": 1800, "lr": 0.0292, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36953, "top5_acc": 0.63156, "loss_cls": 3.5609, "loss": 3.5609, "time": 0.8217} +{"mode": "train", "epoch": 96, "iter": 1900, "lr": 0.02918, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36984, "top5_acc": 0.63641, "loss_cls": 3.54376, "loss": 3.54376, "time": 0.82514} +{"mode": "train", "epoch": 96, "iter": 2000, "lr": 0.02915, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36781, "top5_acc": 0.62844, "loss_cls": 3.59761, "loss": 3.59761, "time": 0.82352} +{"mode": "train", "epoch": 96, "iter": 2100, "lr": 0.02913, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37125, "top5_acc": 0.63125, "loss_cls": 3.55817, "loss": 3.55817, "time": 0.81968} +{"mode": "train", "epoch": 96, "iter": 2200, "lr": 0.0291, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37562, "top5_acc": 0.63734, "loss_cls": 3.51056, "loss": 3.51056, "time": 0.82705} +{"mode": "train", "epoch": 96, "iter": 2300, "lr": 0.02908, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36672, "top5_acc": 0.62938, "loss_cls": 3.56901, "loss": 3.56901, "time": 0.81443} +{"mode": "train", "epoch": 96, "iter": 2400, "lr": 0.02905, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36391, "top5_acc": 0.62656, "loss_cls": 3.61151, "loss": 3.61151, "time": 0.82239} +{"mode": "train", "epoch": 96, "iter": 2500, "lr": 0.02903, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36328, "top5_acc": 0.62797, "loss_cls": 3.57519, "loss": 3.57519, "time": 0.81886} +{"mode": "train", "epoch": 96, "iter": 2600, "lr": 0.029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37062, "top5_acc": 0.62547, "loss_cls": 3.57602, "loss": 3.57602, "time": 0.81871} +{"mode": "train", "epoch": 96, "iter": 2700, "lr": 0.02898, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36328, "top5_acc": 0.62578, "loss_cls": 3.60283, "loss": 3.60283, "time": 0.82244} +{"mode": "train", "epoch": 96, "iter": 2800, "lr": 0.02895, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37016, "top5_acc": 0.63844, "loss_cls": 3.54148, "loss": 3.54148, "time": 0.81686} +{"mode": "train", "epoch": 96, "iter": 2900, "lr": 0.02893, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37547, "top5_acc": 0.64547, "loss_cls": 3.50293, "loss": 3.50293, "time": 0.82745} +{"mode": "train", "epoch": 96, "iter": 3000, "lr": 0.0289, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36266, "top5_acc": 0.63, "loss_cls": 3.59281, "loss": 3.59281, "time": 0.82053} +{"mode": "train", "epoch": 96, "iter": 3100, "lr": 0.02887, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36531, "top5_acc": 0.63141, "loss_cls": 3.56585, "loss": 3.56585, "time": 0.8178} +{"mode": "train", "epoch": 96, "iter": 3200, "lr": 0.02885, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36344, "top5_acc": 0.62828, "loss_cls": 3.55715, "loss": 3.55715, "time": 0.82006} +{"mode": "train", "epoch": 96, "iter": 3300, "lr": 0.02882, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36312, "top5_acc": 0.61875, "loss_cls": 3.63819, "loss": 3.63819, "time": 0.82056} +{"mode": "train", "epoch": 96, "iter": 3400, "lr": 0.0288, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35797, "top5_acc": 0.62719, "loss_cls": 3.57682, "loss": 3.57682, "time": 0.82343} +{"mode": "train", "epoch": 96, "iter": 3500, "lr": 0.02877, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36906, "top5_acc": 0.62844, "loss_cls": 3.56571, "loss": 3.56571, "time": 0.81813} +{"mode": "train", "epoch": 96, "iter": 3600, "lr": 0.02875, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37266, "top5_acc": 0.63172, "loss_cls": 3.55812, "loss": 3.55812, "time": 0.8182} +{"mode": "train", "epoch": 96, "iter": 3700, "lr": 0.02872, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36609, "top5_acc": 0.63281, "loss_cls": 3.57156, "loss": 3.57156, "time": 0.81614} +{"mode": "val", "epoch": 96, "iter": 309, "lr": 0.02871, "top1_acc": 0.29038, "top5_acc": 0.55123, "mean_class_accuracy": 0.29014} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.02869, "memory": 15990, "data_time": 1.30676, "top1_acc": 0.38031, "top5_acc": 0.64594, "loss_cls": 3.46637, "loss": 3.46637, "time": 2.28565} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.02866, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37641, "top5_acc": 0.64812, "loss_cls": 3.45636, "loss": 3.45636, "time": 0.82529} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.02864, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37297, "top5_acc": 0.63641, "loss_cls": 3.52491, "loss": 3.52491, "time": 0.81915} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.02861, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37172, "top5_acc": 0.63047, "loss_cls": 3.51997, "loss": 3.51997, "time": 0.82723} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.02858, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38094, "top5_acc": 0.64609, "loss_cls": 3.50256, "loss": 3.50256, "time": 0.82072} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.02856, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37125, "top5_acc": 0.63766, "loss_cls": 3.54598, "loss": 3.54598, "time": 0.82731} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.02853, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37141, "top5_acc": 0.64234, "loss_cls": 3.48584, "loss": 3.48584, "time": 0.82401} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.02851, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36875, "top5_acc": 0.63625, "loss_cls": 3.55091, "loss": 3.55091, "time": 0.82346} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.02848, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37781, "top5_acc": 0.64016, "loss_cls": 3.49208, "loss": 3.49208, "time": 0.82781} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.02846, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36984, "top5_acc": 0.62953, "loss_cls": 3.56299, "loss": 3.56299, "time": 0.82042} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.02843, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36531, "top5_acc": 0.63031, "loss_cls": 3.56316, "loss": 3.56316, "time": 0.82152} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.02841, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36016, "top5_acc": 0.6275, "loss_cls": 3.56204, "loss": 3.56204, "time": 0.82528} +{"mode": "train", "epoch": 97, "iter": 1300, "lr": 0.02838, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36391, "top5_acc": 0.63719, "loss_cls": 3.53712, "loss": 3.53712, "time": 0.82692} +{"mode": "train", "epoch": 97, "iter": 1400, "lr": 0.02836, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37172, "top5_acc": 0.63172, "loss_cls": 3.55269, "loss": 3.55269, "time": 0.82258} +{"mode": "train", "epoch": 97, "iter": 1500, "lr": 0.02833, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36312, "top5_acc": 0.63391, "loss_cls": 3.58644, "loss": 3.58644, "time": 0.83342} +{"mode": "train", "epoch": 97, "iter": 1600, "lr": 0.02831, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37391, "top5_acc": 0.62781, "loss_cls": 3.54745, "loss": 3.54745, "time": 0.82412} +{"mode": "train", "epoch": 97, "iter": 1700, "lr": 0.02828, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36281, "top5_acc": 0.62625, "loss_cls": 3.56544, "loss": 3.56544, "time": 0.82292} +{"mode": "train", "epoch": 97, "iter": 1800, "lr": 0.02826, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.36641, "top5_acc": 0.62984, "loss_cls": 3.56641, "loss": 3.56641, "time": 0.82478} +{"mode": "train", "epoch": 97, "iter": 1900, "lr": 0.02823, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37562, "top5_acc": 0.63984, "loss_cls": 3.48836, "loss": 3.48836, "time": 0.8326} +{"mode": "train", "epoch": 97, "iter": 2000, "lr": 0.02821, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3675, "top5_acc": 0.62438, "loss_cls": 3.56143, "loss": 3.56143, "time": 0.82167} +{"mode": "train", "epoch": 97, "iter": 2100, "lr": 0.02818, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37781, "top5_acc": 0.64344, "loss_cls": 3.49423, "loss": 3.49423, "time": 0.82188} +{"mode": "train", "epoch": 97, "iter": 2200, "lr": 0.02816, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37219, "top5_acc": 0.64172, "loss_cls": 3.49132, "loss": 3.49132, "time": 0.82847} +{"mode": "train", "epoch": 97, "iter": 2300, "lr": 0.02813, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36516, "top5_acc": 0.62531, "loss_cls": 3.57081, "loss": 3.57081, "time": 0.82455} +{"mode": "train", "epoch": 97, "iter": 2400, "lr": 0.02811, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35781, "top5_acc": 0.62656, "loss_cls": 3.59277, "loss": 3.59277, "time": 0.82084} +{"mode": "train", "epoch": 97, "iter": 2500, "lr": 0.02808, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36422, "top5_acc": 0.63672, "loss_cls": 3.52803, "loss": 3.52803, "time": 0.83029} +{"mode": "train", "epoch": 97, "iter": 2600, "lr": 0.02806, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37406, "top5_acc": 0.63172, "loss_cls": 3.53717, "loss": 3.53717, "time": 0.82718} +{"mode": "train", "epoch": 97, "iter": 2700, "lr": 0.02803, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37078, "top5_acc": 0.63719, "loss_cls": 3.53667, "loss": 3.53667, "time": 0.82658} +{"mode": "train", "epoch": 97, "iter": 2800, "lr": 0.02801, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36422, "top5_acc": 0.63109, "loss_cls": 3.55398, "loss": 3.55398, "time": 0.81855} +{"mode": "train", "epoch": 97, "iter": 2900, "lr": 0.02798, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37172, "top5_acc": 0.63188, "loss_cls": 3.53754, "loss": 3.53754, "time": 0.83392} +{"mode": "train", "epoch": 97, "iter": 3000, "lr": 0.02796, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37281, "top5_acc": 0.64078, "loss_cls": 3.54206, "loss": 3.54206, "time": 0.8276} +{"mode": "train", "epoch": 97, "iter": 3100, "lr": 0.02793, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36312, "top5_acc": 0.62969, "loss_cls": 3.57118, "loss": 3.57118, "time": 0.8213} +{"mode": "train", "epoch": 97, "iter": 3200, "lr": 0.02791, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36438, "top5_acc": 0.6275, "loss_cls": 3.58853, "loss": 3.58853, "time": 0.82495} +{"mode": "train", "epoch": 97, "iter": 3300, "lr": 0.02788, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37328, "top5_acc": 0.63109, "loss_cls": 3.53731, "loss": 3.53731, "time": 0.8213} +{"mode": "train", "epoch": 97, "iter": 3400, "lr": 0.02786, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37156, "top5_acc": 0.64516, "loss_cls": 3.52143, "loss": 3.52143, "time": 0.8237} +{"mode": "train", "epoch": 97, "iter": 3500, "lr": 0.02783, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35875, "top5_acc": 0.63391, "loss_cls": 3.56894, "loss": 3.56894, "time": 0.82449} +{"mode": "train", "epoch": 97, "iter": 3600, "lr": 0.02781, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36719, "top5_acc": 0.61156, "loss_cls": 3.62288, "loss": 3.62288, "time": 0.82656} +{"mode": "train", "epoch": 97, "iter": 3700, "lr": 0.02778, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35578, "top5_acc": 0.62453, "loss_cls": 3.59241, "loss": 3.59241, "time": 0.8244} +{"mode": "val", "epoch": 97, "iter": 309, "lr": 0.02777, "top1_acc": 0.31267, "top5_acc": 0.5718, "mean_class_accuracy": 0.31254} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.02774, "memory": 15990, "data_time": 1.3192, "top1_acc": 0.38219, "top5_acc": 0.65094, "loss_cls": 3.44254, "loss": 3.44254, "time": 2.30503} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.02772, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3775, "top5_acc": 0.64125, "loss_cls": 3.49844, "loss": 3.49844, "time": 0.82277} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.02769, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37562, "top5_acc": 0.63906, "loss_cls": 3.49073, "loss": 3.49073, "time": 0.82268} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.02767, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38672, "top5_acc": 0.64188, "loss_cls": 3.49911, "loss": 3.49911, "time": 0.82008} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.02764, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37641, "top5_acc": 0.64172, "loss_cls": 3.50001, "loss": 3.50001, "time": 0.82154} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.02762, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38156, "top5_acc": 0.65297, "loss_cls": 3.45548, "loss": 3.45548, "time": 0.81702} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.02759, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38734, "top5_acc": 0.64578, "loss_cls": 3.44592, "loss": 3.44592, "time": 0.81913} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.02757, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36859, "top5_acc": 0.63422, "loss_cls": 3.5156, "loss": 3.5156, "time": 0.81788} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.02754, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37469, "top5_acc": 0.63625, "loss_cls": 3.53211, "loss": 3.53211, "time": 0.81899} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.02752, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36891, "top5_acc": 0.62453, "loss_cls": 3.54298, "loss": 3.54298, "time": 0.81887} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.02749, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38203, "top5_acc": 0.64297, "loss_cls": 3.47916, "loss": 3.47916, "time": 0.82359} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.02747, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36, "top5_acc": 0.62938, "loss_cls": 3.57119, "loss": 3.57119, "time": 0.82013} +{"mode": "train", "epoch": 98, "iter": 1300, "lr": 0.02744, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37859, "top5_acc": 0.63781, "loss_cls": 3.50745, "loss": 3.50745, "time": 0.81664} +{"mode": "train", "epoch": 98, "iter": 1400, "lr": 0.02742, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37156, "top5_acc": 0.64094, "loss_cls": 3.51989, "loss": 3.51989, "time": 0.82303} +{"mode": "train", "epoch": 98, "iter": 1500, "lr": 0.02739, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37656, "top5_acc": 0.63703, "loss_cls": 3.51224, "loss": 3.51224, "time": 0.82293} +{"mode": "train", "epoch": 98, "iter": 1600, "lr": 0.02737, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37516, "top5_acc": 0.63453, "loss_cls": 3.54018, "loss": 3.54018, "time": 0.82333} +{"mode": "train", "epoch": 98, "iter": 1700, "lr": 0.02734, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37625, "top5_acc": 0.64344, "loss_cls": 3.50218, "loss": 3.50218, "time": 0.81874} +{"mode": "train", "epoch": 98, "iter": 1800, "lr": 0.02732, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35984, "top5_acc": 0.64, "loss_cls": 3.5456, "loss": 3.5456, "time": 0.82316} +{"mode": "train", "epoch": 98, "iter": 1900, "lr": 0.02729, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36406, "top5_acc": 0.62781, "loss_cls": 3.55955, "loss": 3.55955, "time": 0.81765} +{"mode": "train", "epoch": 98, "iter": 2000, "lr": 0.02727, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36625, "top5_acc": 0.63578, "loss_cls": 3.54468, "loss": 3.54468, "time": 0.81855} +{"mode": "train", "epoch": 98, "iter": 2100, "lr": 0.02724, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35984, "top5_acc": 0.62531, "loss_cls": 3.57471, "loss": 3.57471, "time": 0.8241} +{"mode": "train", "epoch": 98, "iter": 2200, "lr": 0.02722, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37422, "top5_acc": 0.63203, "loss_cls": 3.5289, "loss": 3.5289, "time": 0.81974} +{"mode": "train", "epoch": 98, "iter": 2300, "lr": 0.02719, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37141, "top5_acc": 0.63813, "loss_cls": 3.5226, "loss": 3.5226, "time": 0.81712} +{"mode": "train", "epoch": 98, "iter": 2400, "lr": 0.02717, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38047, "top5_acc": 0.63828, "loss_cls": 3.50789, "loss": 3.50789, "time": 0.82098} +{"mode": "train", "epoch": 98, "iter": 2500, "lr": 0.02714, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36875, "top5_acc": 0.62891, "loss_cls": 3.56801, "loss": 3.56801, "time": 0.82623} +{"mode": "train", "epoch": 98, "iter": 2600, "lr": 0.02712, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36672, "top5_acc": 0.63891, "loss_cls": 3.55366, "loss": 3.55366, "time": 0.82434} +{"mode": "train", "epoch": 98, "iter": 2700, "lr": 0.02709, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36406, "top5_acc": 0.63047, "loss_cls": 3.56382, "loss": 3.56382, "time": 0.81715} +{"mode": "train", "epoch": 98, "iter": 2800, "lr": 0.02707, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37594, "top5_acc": 0.64359, "loss_cls": 3.49164, "loss": 3.49164, "time": 0.81947} +{"mode": "train", "epoch": 98, "iter": 2900, "lr": 0.02705, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37562, "top5_acc": 0.64016, "loss_cls": 3.49126, "loss": 3.49126, "time": 0.82327} +{"mode": "train", "epoch": 98, "iter": 3000, "lr": 0.02702, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37328, "top5_acc": 0.63813, "loss_cls": 3.51659, "loss": 3.51659, "time": 0.82025} +{"mode": "train", "epoch": 98, "iter": 3100, "lr": 0.027, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37094, "top5_acc": 0.63406, "loss_cls": 3.54413, "loss": 3.54413, "time": 0.81983} +{"mode": "train", "epoch": 98, "iter": 3200, "lr": 0.02697, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36859, "top5_acc": 0.645, "loss_cls": 3.51589, "loss": 3.51589, "time": 0.81669} +{"mode": "train", "epoch": 98, "iter": 3300, "lr": 0.02695, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36531, "top5_acc": 0.62906, "loss_cls": 3.55857, "loss": 3.55857, "time": 0.81736} +{"mode": "train", "epoch": 98, "iter": 3400, "lr": 0.02692, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37531, "top5_acc": 0.64266, "loss_cls": 3.49432, "loss": 3.49432, "time": 0.81741} +{"mode": "train", "epoch": 98, "iter": 3500, "lr": 0.0269, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37797, "top5_acc": 0.64016, "loss_cls": 3.48936, "loss": 3.48936, "time": 0.81742} +{"mode": "train", "epoch": 98, "iter": 3600, "lr": 0.02687, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36891, "top5_acc": 0.62859, "loss_cls": 3.56288, "loss": 3.56288, "time": 0.81856} +{"mode": "train", "epoch": 98, "iter": 3700, "lr": 0.02685, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37609, "top5_acc": 0.63609, "loss_cls": 3.53311, "loss": 3.53311, "time": 0.81635} +{"mode": "val", "epoch": 98, "iter": 309, "lr": 0.02684, "top1_acc": 0.31019, "top5_acc": 0.55873, "mean_class_accuracy": 0.30997} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.02681, "memory": 15990, "data_time": 1.31818, "top1_acc": 0.38906, "top5_acc": 0.64453, "loss_cls": 3.42008, "loss": 3.42008, "time": 2.29779} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.02679, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39078, "top5_acc": 0.65281, "loss_cls": 3.423, "loss": 3.423, "time": 0.82167} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.02676, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38016, "top5_acc": 0.64172, "loss_cls": 3.49478, "loss": 3.49478, "time": 0.82677} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.02674, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38422, "top5_acc": 0.64625, "loss_cls": 3.43744, "loss": 3.43744, "time": 0.82742} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.02671, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37219, "top5_acc": 0.64062, "loss_cls": 3.48769, "loss": 3.48769, "time": 0.82508} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.02669, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36391, "top5_acc": 0.64406, "loss_cls": 3.5297, "loss": 3.5297, "time": 0.82035} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.02666, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38281, "top5_acc": 0.65312, "loss_cls": 3.4468, "loss": 3.4468, "time": 0.82327} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.02664, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37391, "top5_acc": 0.63734, "loss_cls": 3.50266, "loss": 3.50266, "time": 0.81817} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.02661, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38125, "top5_acc": 0.64031, "loss_cls": 3.50565, "loss": 3.50565, "time": 0.81306} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.02659, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38391, "top5_acc": 0.64797, "loss_cls": 3.45667, "loss": 3.45667, "time": 0.81689} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.02656, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38203, "top5_acc": 0.64047, "loss_cls": 3.48806, "loss": 3.48806, "time": 0.81621} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.02654, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38328, "top5_acc": 0.65281, "loss_cls": 3.43688, "loss": 3.43688, "time": 0.82106} +{"mode": "train", "epoch": 99, "iter": 1300, "lr": 0.02651, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37953, "top5_acc": 0.63703, "loss_cls": 3.50051, "loss": 3.50051, "time": 0.81563} +{"mode": "train", "epoch": 99, "iter": 1400, "lr": 0.02649, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37016, "top5_acc": 0.6375, "loss_cls": 3.51155, "loss": 3.51155, "time": 0.8189} +{"mode": "train", "epoch": 99, "iter": 1500, "lr": 0.02646, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37359, "top5_acc": 0.64141, "loss_cls": 3.5195, "loss": 3.5195, "time": 0.81809} +{"mode": "train", "epoch": 99, "iter": 1600, "lr": 0.02644, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37688, "top5_acc": 0.63922, "loss_cls": 3.50961, "loss": 3.50961, "time": 0.82109} +{"mode": "train", "epoch": 99, "iter": 1700, "lr": 0.02642, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37031, "top5_acc": 0.63672, "loss_cls": 3.5224, "loss": 3.5224, "time": 0.8235} +{"mode": "train", "epoch": 99, "iter": 1800, "lr": 0.02639, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36766, "top5_acc": 0.63, "loss_cls": 3.56952, "loss": 3.56952, "time": 0.82248} +{"mode": "train", "epoch": 99, "iter": 1900, "lr": 0.02637, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35953, "top5_acc": 0.62703, "loss_cls": 3.55381, "loss": 3.55381, "time": 0.81793} +{"mode": "train", "epoch": 99, "iter": 2000, "lr": 0.02634, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37156, "top5_acc": 0.64156, "loss_cls": 3.49771, "loss": 3.49771, "time": 0.81921} +{"mode": "train", "epoch": 99, "iter": 2100, "lr": 0.02632, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37266, "top5_acc": 0.62687, "loss_cls": 3.51976, "loss": 3.51976, "time": 0.82795} +{"mode": "train", "epoch": 99, "iter": 2200, "lr": 0.02629, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37922, "top5_acc": 0.64578, "loss_cls": 3.48465, "loss": 3.48465, "time": 0.82359} +{"mode": "train", "epoch": 99, "iter": 2300, "lr": 0.02627, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36312, "top5_acc": 0.63484, "loss_cls": 3.57583, "loss": 3.57583, "time": 0.8212} +{"mode": "train", "epoch": 99, "iter": 2400, "lr": 0.02624, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37531, "top5_acc": 0.63797, "loss_cls": 3.52012, "loss": 3.52012, "time": 0.81943} +{"mode": "train", "epoch": 99, "iter": 2500, "lr": 0.02622, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38234, "top5_acc": 0.6425, "loss_cls": 3.48326, "loss": 3.48326, "time": 0.82337} +{"mode": "train", "epoch": 99, "iter": 2600, "lr": 0.02619, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37297, "top5_acc": 0.64562, "loss_cls": 3.5259, "loss": 3.5259, "time": 0.82877} +{"mode": "train", "epoch": 99, "iter": 2700, "lr": 0.02617, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38156, "top5_acc": 0.64578, "loss_cls": 3.48272, "loss": 3.48272, "time": 0.8205} +{"mode": "train", "epoch": 99, "iter": 2800, "lr": 0.02614, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37375, "top5_acc": 0.63906, "loss_cls": 3.50973, "loss": 3.50973, "time": 0.82114} +{"mode": "train", "epoch": 99, "iter": 2900, "lr": 0.02612, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37156, "top5_acc": 0.63641, "loss_cls": 3.52338, "loss": 3.52338, "time": 0.81955} +{"mode": "train", "epoch": 99, "iter": 3000, "lr": 0.0261, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36969, "top5_acc": 0.63984, "loss_cls": 3.53996, "loss": 3.53996, "time": 0.81968} +{"mode": "train", "epoch": 99, "iter": 3100, "lr": 0.02607, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38109, "top5_acc": 0.63297, "loss_cls": 3.51447, "loss": 3.51447, "time": 0.81695} +{"mode": "train", "epoch": 99, "iter": 3200, "lr": 0.02605, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37594, "top5_acc": 0.63266, "loss_cls": 3.51515, "loss": 3.51515, "time": 0.81799} +{"mode": "train", "epoch": 99, "iter": 3300, "lr": 0.02602, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37188, "top5_acc": 0.63813, "loss_cls": 3.53494, "loss": 3.53494, "time": 0.81486} +{"mode": "train", "epoch": 99, "iter": 3400, "lr": 0.026, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37984, "top5_acc": 0.64312, "loss_cls": 3.4813, "loss": 3.4813, "time": 0.82077} +{"mode": "train", "epoch": 99, "iter": 3500, "lr": 0.02597, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37406, "top5_acc": 0.63109, "loss_cls": 3.53381, "loss": 3.53381, "time": 0.81868} +{"mode": "train", "epoch": 99, "iter": 3600, "lr": 0.02595, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38141, "top5_acc": 0.64594, "loss_cls": 3.44468, "loss": 3.44468, "time": 0.81852} +{"mode": "train", "epoch": 99, "iter": 3700, "lr": 0.02592, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36078, "top5_acc": 0.63859, "loss_cls": 3.57386, "loss": 3.57386, "time": 0.81515} +{"mode": "val", "epoch": 99, "iter": 309, "lr": 0.02591, "top1_acc": 0.32173, "top5_acc": 0.58649, "mean_class_accuracy": 0.32145} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.02589, "memory": 15990, "data_time": 1.33807, "top1_acc": 0.39266, "top5_acc": 0.66359, "loss_cls": 3.39562, "loss": 3.39562, "time": 2.31978} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.02586, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38703, "top5_acc": 0.655, "loss_cls": 3.4204, "loss": 3.4204, "time": 0.82242} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.02584, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3825, "top5_acc": 0.64641, "loss_cls": 3.46986, "loss": 3.46986, "time": 0.81905} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.02581, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.385, "top5_acc": 0.65266, "loss_cls": 3.44348, "loss": 3.44348, "time": 0.81803} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.02579, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38141, "top5_acc": 0.64703, "loss_cls": 3.46899, "loss": 3.46899, "time": 0.81866} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.02577, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38594, "top5_acc": 0.63531, "loss_cls": 3.49997, "loss": 3.49997, "time": 0.81693} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.02574, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37297, "top5_acc": 0.64609, "loss_cls": 3.47486, "loss": 3.47486, "time": 0.8162} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.02572, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3775, "top5_acc": 0.64969, "loss_cls": 3.46398, "loss": 3.46398, "time": 0.81864} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.02569, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38156, "top5_acc": 0.64703, "loss_cls": 3.46363, "loss": 3.46363, "time": 0.82509} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.02567, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37953, "top5_acc": 0.63484, "loss_cls": 3.51412, "loss": 3.51412, "time": 0.81873} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.02564, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37969, "top5_acc": 0.64797, "loss_cls": 3.45784, "loss": 3.45784, "time": 0.81759} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.02562, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37906, "top5_acc": 0.64906, "loss_cls": 3.47802, "loss": 3.47802, "time": 0.81368} +{"mode": "train", "epoch": 100, "iter": 1300, "lr": 0.02559, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36656, "top5_acc": 0.63672, "loss_cls": 3.52908, "loss": 3.52908, "time": 0.81661} +{"mode": "train", "epoch": 100, "iter": 1400, "lr": 0.02557, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38453, "top5_acc": 0.64156, "loss_cls": 3.49758, "loss": 3.49758, "time": 0.81372} +{"mode": "train", "epoch": 100, "iter": 1500, "lr": 0.02555, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37266, "top5_acc": 0.63688, "loss_cls": 3.50609, "loss": 3.50609, "time": 0.82442} +{"mode": "train", "epoch": 100, "iter": 1600, "lr": 0.02552, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36859, "top5_acc": 0.645, "loss_cls": 3.50705, "loss": 3.50705, "time": 0.81958} +{"mode": "train", "epoch": 100, "iter": 1700, "lr": 0.0255, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37547, "top5_acc": 0.63031, "loss_cls": 3.54045, "loss": 3.54045, "time": 0.81936} +{"mode": "train", "epoch": 100, "iter": 1800, "lr": 0.02547, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37891, "top5_acc": 0.64969, "loss_cls": 3.45593, "loss": 3.45593, "time": 0.82213} +{"mode": "train", "epoch": 100, "iter": 1900, "lr": 0.02545, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38375, "top5_acc": 0.65438, "loss_cls": 3.4194, "loss": 3.4194, "time": 0.819} +{"mode": "train", "epoch": 100, "iter": 2000, "lr": 0.02542, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36812, "top5_acc": 0.63734, "loss_cls": 3.53348, "loss": 3.53348, "time": 0.82145} +{"mode": "train", "epoch": 100, "iter": 2100, "lr": 0.0254, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37297, "top5_acc": 0.63484, "loss_cls": 3.51164, "loss": 3.51164, "time": 0.82406} +{"mode": "train", "epoch": 100, "iter": 2200, "lr": 0.02538, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37078, "top5_acc": 0.64, "loss_cls": 3.51645, "loss": 3.51645, "time": 0.81832} +{"mode": "train", "epoch": 100, "iter": 2300, "lr": 0.02535, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38078, "top5_acc": 0.65203, "loss_cls": 3.47197, "loss": 3.47197, "time": 0.82428} +{"mode": "train", "epoch": 100, "iter": 2400, "lr": 0.02533, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38141, "top5_acc": 0.65, "loss_cls": 3.47525, "loss": 3.47525, "time": 0.82286} +{"mode": "train", "epoch": 100, "iter": 2500, "lr": 0.0253, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36688, "top5_acc": 0.63016, "loss_cls": 3.51761, "loss": 3.51761, "time": 0.82134} +{"mode": "train", "epoch": 100, "iter": 2600, "lr": 0.02528, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36188, "top5_acc": 0.6325, "loss_cls": 3.55194, "loss": 3.55194, "time": 0.82394} +{"mode": "train", "epoch": 100, "iter": 2700, "lr": 0.02525, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37562, "top5_acc": 0.62797, "loss_cls": 3.55683, "loss": 3.55683, "time": 0.82077} +{"mode": "train", "epoch": 100, "iter": 2800, "lr": 0.02523, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38141, "top5_acc": 0.64406, "loss_cls": 3.49959, "loss": 3.49959, "time": 0.81984} +{"mode": "train", "epoch": 100, "iter": 2900, "lr": 0.02521, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37219, "top5_acc": 0.64, "loss_cls": 3.51787, "loss": 3.51787, "time": 0.81847} +{"mode": "train", "epoch": 100, "iter": 3000, "lr": 0.02518, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37641, "top5_acc": 0.64797, "loss_cls": 3.47538, "loss": 3.47538, "time": 0.82348} +{"mode": "train", "epoch": 100, "iter": 3100, "lr": 0.02516, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38031, "top5_acc": 0.64438, "loss_cls": 3.45716, "loss": 3.45716, "time": 0.81792} +{"mode": "train", "epoch": 100, "iter": 3200, "lr": 0.02513, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38062, "top5_acc": 0.63656, "loss_cls": 3.5174, "loss": 3.5174, "time": 0.8161} +{"mode": "train", "epoch": 100, "iter": 3300, "lr": 0.02511, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37578, "top5_acc": 0.63875, "loss_cls": 3.50436, "loss": 3.50436, "time": 0.81926} +{"mode": "train", "epoch": 100, "iter": 3400, "lr": 0.02508, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38891, "top5_acc": 0.64359, "loss_cls": 3.46733, "loss": 3.46733, "time": 0.82223} +{"mode": "train", "epoch": 100, "iter": 3500, "lr": 0.02506, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37547, "top5_acc": 0.64156, "loss_cls": 3.51334, "loss": 3.51334, "time": 0.81818} +{"mode": "train", "epoch": 100, "iter": 3600, "lr": 0.02504, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37938, "top5_acc": 0.63875, "loss_cls": 3.49377, "loss": 3.49377, "time": 0.82173} +{"mode": "train", "epoch": 100, "iter": 3700, "lr": 0.02501, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37906, "top5_acc": 0.64453, "loss_cls": 3.45792, "loss": 3.45792, "time": 0.81907} +{"mode": "val", "epoch": 100, "iter": 309, "lr": 0.025, "top1_acc": 0.29783, "top5_acc": 0.55513, "mean_class_accuracy": 0.29763} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.02498, "memory": 15990, "data_time": 1.33375, "top1_acc": 0.39516, "top5_acc": 0.65688, "loss_cls": 3.36187, "loss": 3.36187, "time": 2.31652} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.02495, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37922, "top5_acc": 0.65062, "loss_cls": 3.4276, "loss": 3.4276, "time": 0.82789} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.02493, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38812, "top5_acc": 0.65922, "loss_cls": 3.41328, "loss": 3.41328, "time": 0.82281} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.0249, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38656, "top5_acc": 0.65359, "loss_cls": 3.41011, "loss": 3.41011, "time": 0.81893} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.02488, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38844, "top5_acc": 0.64312, "loss_cls": 3.45843, "loss": 3.45843, "time": 0.8163} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.02486, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38891, "top5_acc": 0.65047, "loss_cls": 3.41867, "loss": 3.41867, "time": 0.81498} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.02483, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38484, "top5_acc": 0.64453, "loss_cls": 3.46574, "loss": 3.46574, "time": 0.819} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.02481, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38172, "top5_acc": 0.65719, "loss_cls": 3.41776, "loss": 3.41776, "time": 0.81384} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.02478, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38812, "top5_acc": 0.65203, "loss_cls": 3.44589, "loss": 3.44589, "time": 0.824} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.02476, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37875, "top5_acc": 0.64406, "loss_cls": 3.50027, "loss": 3.50027, "time": 0.81853} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.02473, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38672, "top5_acc": 0.64906, "loss_cls": 3.44434, "loss": 3.44434, "time": 0.81944} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.02471, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38672, "top5_acc": 0.64719, "loss_cls": 3.46142, "loss": 3.46142, "time": 0.81591} +{"mode": "train", "epoch": 101, "iter": 1300, "lr": 0.02469, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38141, "top5_acc": 0.65047, "loss_cls": 3.45709, "loss": 3.45709, "time": 0.81778} +{"mode": "train", "epoch": 101, "iter": 1400, "lr": 0.02466, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37641, "top5_acc": 0.64219, "loss_cls": 3.49015, "loss": 3.49015, "time": 0.81582} +{"mode": "train", "epoch": 101, "iter": 1500, "lr": 0.02464, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37703, "top5_acc": 0.64062, "loss_cls": 3.49616, "loss": 3.49616, "time": 0.82354} +{"mode": "train", "epoch": 101, "iter": 1600, "lr": 0.02461, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36969, "top5_acc": 0.63813, "loss_cls": 3.51801, "loss": 3.51801, "time": 0.81929} +{"mode": "train", "epoch": 101, "iter": 1700, "lr": 0.02459, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39391, "top5_acc": 0.66125, "loss_cls": 3.40368, "loss": 3.40368, "time": 0.82133} +{"mode": "train", "epoch": 101, "iter": 1800, "lr": 0.02457, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38203, "top5_acc": 0.65391, "loss_cls": 3.44947, "loss": 3.44947, "time": 0.8196} +{"mode": "train", "epoch": 101, "iter": 1900, "lr": 0.02454, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38797, "top5_acc": 0.64781, "loss_cls": 3.42964, "loss": 3.42964, "time": 0.82108} +{"mode": "train", "epoch": 101, "iter": 2000, "lr": 0.02452, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38266, "top5_acc": 0.63344, "loss_cls": 3.47424, "loss": 3.47424, "time": 0.82338} +{"mode": "train", "epoch": 101, "iter": 2100, "lr": 0.02449, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37328, "top5_acc": 0.64, "loss_cls": 3.49777, "loss": 3.49777, "time": 0.82761} +{"mode": "train", "epoch": 101, "iter": 2200, "lr": 0.02447, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38891, "top5_acc": 0.64922, "loss_cls": 3.454, "loss": 3.454, "time": 0.82329} +{"mode": "train", "epoch": 101, "iter": 2300, "lr": 0.02445, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38391, "top5_acc": 0.64781, "loss_cls": 3.46451, "loss": 3.46451, "time": 0.81758} +{"mode": "train", "epoch": 101, "iter": 2400, "lr": 0.02442, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37062, "top5_acc": 0.62953, "loss_cls": 3.53357, "loss": 3.53357, "time": 0.82029} +{"mode": "train", "epoch": 101, "iter": 2500, "lr": 0.0244, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38297, "top5_acc": 0.64938, "loss_cls": 3.44598, "loss": 3.44598, "time": 0.82698} +{"mode": "train", "epoch": 101, "iter": 2600, "lr": 0.02437, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38531, "top5_acc": 0.64797, "loss_cls": 3.44034, "loss": 3.44034, "time": 0.81404} +{"mode": "train", "epoch": 101, "iter": 2700, "lr": 0.02435, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37125, "top5_acc": 0.63906, "loss_cls": 3.51974, "loss": 3.51974, "time": 0.8195} +{"mode": "train", "epoch": 101, "iter": 2800, "lr": 0.02433, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37609, "top5_acc": 0.64344, "loss_cls": 3.5004, "loss": 3.5004, "time": 0.82676} +{"mode": "train", "epoch": 101, "iter": 2900, "lr": 0.0243, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37938, "top5_acc": 0.645, "loss_cls": 3.48295, "loss": 3.48295, "time": 0.82003} +{"mode": "train", "epoch": 101, "iter": 3000, "lr": 0.02428, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37875, "top5_acc": 0.64328, "loss_cls": 3.48397, "loss": 3.48397, "time": 0.821} +{"mode": "train", "epoch": 101, "iter": 3100, "lr": 0.02425, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37156, "top5_acc": 0.63797, "loss_cls": 3.53024, "loss": 3.53024, "time": 0.81605} +{"mode": "train", "epoch": 101, "iter": 3200, "lr": 0.02423, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37516, "top5_acc": 0.64297, "loss_cls": 3.4741, "loss": 3.4741, "time": 0.81836} +{"mode": "train", "epoch": 101, "iter": 3300, "lr": 0.02421, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37578, "top5_acc": 0.64438, "loss_cls": 3.49237, "loss": 3.49237, "time": 0.821} +{"mode": "train", "epoch": 101, "iter": 3400, "lr": 0.02418, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38734, "top5_acc": 0.64219, "loss_cls": 3.48064, "loss": 3.48064, "time": 0.8149} +{"mode": "train", "epoch": 101, "iter": 3500, "lr": 0.02416, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37875, "top5_acc": 0.63844, "loss_cls": 3.52501, "loss": 3.52501, "time": 0.82398} +{"mode": "train", "epoch": 101, "iter": 3600, "lr": 0.02413, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37438, "top5_acc": 0.64078, "loss_cls": 3.52023, "loss": 3.52023, "time": 0.81431} +{"mode": "train", "epoch": 101, "iter": 3700, "lr": 0.02411, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37484, "top5_acc": 0.63906, "loss_cls": 3.51465, "loss": 3.51465, "time": 0.81936} +{"mode": "val", "epoch": 101, "iter": 309, "lr": 0.0241, "top1_acc": 0.30624, "top5_acc": 0.57023, "mean_class_accuracy": 0.30602} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.02407, "memory": 15990, "data_time": 1.36152, "top1_acc": 0.40156, "top5_acc": 0.66562, "loss_cls": 3.34637, "loss": 3.34637, "time": 2.35071} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.02405, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40375, "top5_acc": 0.66531, "loss_cls": 3.36574, "loss": 3.36574, "time": 0.82345} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.02403, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39188, "top5_acc": 0.65062, "loss_cls": 3.45669, "loss": 3.45669, "time": 0.81543} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.024, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38266, "top5_acc": 0.65547, "loss_cls": 3.42812, "loss": 3.42812, "time": 0.82389} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.02398, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38562, "top5_acc": 0.6525, "loss_cls": 3.41126, "loss": 3.41126, "time": 0.82166} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.02396, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38516, "top5_acc": 0.64953, "loss_cls": 3.42937, "loss": 3.42937, "time": 0.81885} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.02393, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38094, "top5_acc": 0.6525, "loss_cls": 3.44304, "loss": 3.44304, "time": 0.81455} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.02391, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38578, "top5_acc": 0.64797, "loss_cls": 3.45351, "loss": 3.45351, "time": 0.8254} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.02388, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38141, "top5_acc": 0.65344, "loss_cls": 3.43603, "loss": 3.43603, "time": 0.81891} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.02386, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38266, "top5_acc": 0.64672, "loss_cls": 3.44696, "loss": 3.44696, "time": 0.81563} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.02384, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37391, "top5_acc": 0.63547, "loss_cls": 3.52341, "loss": 3.52341, "time": 0.82424} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.02381, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38641, "top5_acc": 0.65453, "loss_cls": 3.43071, "loss": 3.43071, "time": 0.81416} +{"mode": "train", "epoch": 102, "iter": 1300, "lr": 0.02379, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38094, "top5_acc": 0.64609, "loss_cls": 3.44837, "loss": 3.44837, "time": 0.8165} +{"mode": "train", "epoch": 102, "iter": 1400, "lr": 0.02376, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38109, "top5_acc": 0.63703, "loss_cls": 3.50058, "loss": 3.50058, "time": 0.82495} +{"mode": "train", "epoch": 102, "iter": 1500, "lr": 0.02374, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37562, "top5_acc": 0.64062, "loss_cls": 3.50233, "loss": 3.50233, "time": 0.82047} +{"mode": "train", "epoch": 102, "iter": 1600, "lr": 0.02372, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38781, "top5_acc": 0.65766, "loss_cls": 3.41195, "loss": 3.41195, "time": 0.82308} +{"mode": "train", "epoch": 102, "iter": 1700, "lr": 0.02369, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37562, "top5_acc": 0.64328, "loss_cls": 3.50169, "loss": 3.50169, "time": 0.82512} +{"mode": "train", "epoch": 102, "iter": 1800, "lr": 0.02367, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37203, "top5_acc": 0.64672, "loss_cls": 3.46644, "loss": 3.46644, "time": 0.82201} +{"mode": "train", "epoch": 102, "iter": 1900, "lr": 0.02365, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37828, "top5_acc": 0.64016, "loss_cls": 3.51509, "loss": 3.51509, "time": 0.81892} +{"mode": "train", "epoch": 102, "iter": 2000, "lr": 0.02362, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38938, "top5_acc": 0.64844, "loss_cls": 3.42688, "loss": 3.42688, "time": 0.81973} +{"mode": "train", "epoch": 102, "iter": 2100, "lr": 0.0236, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38797, "top5_acc": 0.65328, "loss_cls": 3.42326, "loss": 3.42326, "time": 0.82227} +{"mode": "train", "epoch": 102, "iter": 2200, "lr": 0.02357, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38938, "top5_acc": 0.65234, "loss_cls": 3.43257, "loss": 3.43257, "time": 0.81733} +{"mode": "train", "epoch": 102, "iter": 2300, "lr": 0.02355, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38516, "top5_acc": 0.65047, "loss_cls": 3.4645, "loss": 3.4645, "time": 0.81656} +{"mode": "train", "epoch": 102, "iter": 2400, "lr": 0.02353, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37625, "top5_acc": 0.64203, "loss_cls": 3.49168, "loss": 3.49168, "time": 0.81478} +{"mode": "train", "epoch": 102, "iter": 2500, "lr": 0.0235, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37125, "top5_acc": 0.645, "loss_cls": 3.48982, "loss": 3.48982, "time": 0.82067} +{"mode": "train", "epoch": 102, "iter": 2600, "lr": 0.02348, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39062, "top5_acc": 0.64438, "loss_cls": 3.44113, "loss": 3.44113, "time": 0.81581} +{"mode": "train", "epoch": 102, "iter": 2700, "lr": 0.02346, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.385, "top5_acc": 0.645, "loss_cls": 3.45415, "loss": 3.45415, "time": 0.82735} +{"mode": "train", "epoch": 102, "iter": 2800, "lr": 0.02343, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38938, "top5_acc": 0.64438, "loss_cls": 3.46298, "loss": 3.46298, "time": 0.82818} +{"mode": "train", "epoch": 102, "iter": 2900, "lr": 0.02341, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38281, "top5_acc": 0.65156, "loss_cls": 3.43288, "loss": 3.43288, "time": 0.81722} +{"mode": "train", "epoch": 102, "iter": 3000, "lr": 0.02339, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39016, "top5_acc": 0.65062, "loss_cls": 3.43187, "loss": 3.43187, "time": 0.81501} +{"mode": "train", "epoch": 102, "iter": 3100, "lr": 0.02336, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37141, "top5_acc": 0.64016, "loss_cls": 3.51565, "loss": 3.51565, "time": 0.81691} +{"mode": "train", "epoch": 102, "iter": 3200, "lr": 0.02334, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38562, "top5_acc": 0.64969, "loss_cls": 3.43661, "loss": 3.43661, "time": 0.81762} +{"mode": "train", "epoch": 102, "iter": 3300, "lr": 0.02331, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38062, "top5_acc": 0.64172, "loss_cls": 3.47907, "loss": 3.47907, "time": 0.82139} +{"mode": "train", "epoch": 102, "iter": 3400, "lr": 0.02329, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39234, "top5_acc": 0.64625, "loss_cls": 3.42273, "loss": 3.42273, "time": 0.81492} +{"mode": "train", "epoch": 102, "iter": 3500, "lr": 0.02327, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38016, "top5_acc": 0.63578, "loss_cls": 3.50474, "loss": 3.50474, "time": 0.8215} +{"mode": "train", "epoch": 102, "iter": 3600, "lr": 0.02324, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.375, "top5_acc": 0.64938, "loss_cls": 3.47686, "loss": 3.47686, "time": 0.81409} +{"mode": "train", "epoch": 102, "iter": 3700, "lr": 0.02322, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39094, "top5_acc": 0.65766, "loss_cls": 3.38925, "loss": 3.38925, "time": 0.81484} +{"mode": "val", "epoch": 102, "iter": 309, "lr": 0.02321, "top1_acc": 0.32569, "top5_acc": 0.58527, "mean_class_accuracy": 0.32539} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.02319, "memory": 15990, "data_time": 1.34624, "top1_acc": 0.38938, "top5_acc": 0.66234, "loss_cls": 3.38522, "loss": 3.38522, "time": 2.33118} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.02316, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38984, "top5_acc": 0.66516, "loss_cls": 3.37768, "loss": 3.37768, "time": 0.82387} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.02314, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38734, "top5_acc": 0.65219, "loss_cls": 3.40517, "loss": 3.40517, "time": 0.81896} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.02311, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39469, "top5_acc": 0.65531, "loss_cls": 3.41636, "loss": 3.41636, "time": 0.81415} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.02309, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39297, "top5_acc": 0.66156, "loss_cls": 3.37463, "loss": 3.37463, "time": 0.81543} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.02307, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39531, "top5_acc": 0.66406, "loss_cls": 3.38697, "loss": 3.38697, "time": 0.81669} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.02304, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39016, "top5_acc": 0.65, "loss_cls": 3.42426, "loss": 3.42426, "time": 0.81775} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.02302, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39516, "top5_acc": 0.65781, "loss_cls": 3.40296, "loss": 3.40296, "time": 0.81939} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.023, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39, "top5_acc": 0.6475, "loss_cls": 3.40979, "loss": 3.40979, "time": 0.81751} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.02297, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38641, "top5_acc": 0.65641, "loss_cls": 3.41197, "loss": 3.41197, "time": 0.81685} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.02295, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39234, "top5_acc": 0.65266, "loss_cls": 3.42745, "loss": 3.42745, "time": 0.81468} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.02293, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.375, "top5_acc": 0.63938, "loss_cls": 3.49571, "loss": 3.49571, "time": 0.81702} +{"mode": "train", "epoch": 103, "iter": 1300, "lr": 0.0229, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37719, "top5_acc": 0.65141, "loss_cls": 3.45184, "loss": 3.45184, "time": 0.81919} +{"mode": "train", "epoch": 103, "iter": 1400, "lr": 0.02288, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38875, "top5_acc": 0.64844, "loss_cls": 3.41907, "loss": 3.41907, "time": 0.82352} +{"mode": "train", "epoch": 103, "iter": 1500, "lr": 0.02286, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39312, "top5_acc": 0.65359, "loss_cls": 3.41522, "loss": 3.41522, "time": 0.8242} +{"mode": "train", "epoch": 103, "iter": 1600, "lr": 0.02283, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38766, "top5_acc": 0.65734, "loss_cls": 3.40679, "loss": 3.40679, "time": 0.82201} +{"mode": "train", "epoch": 103, "iter": 1700, "lr": 0.02281, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39359, "top5_acc": 0.65047, "loss_cls": 3.40867, "loss": 3.40867, "time": 0.81958} +{"mode": "train", "epoch": 103, "iter": 1800, "lr": 0.02279, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39281, "top5_acc": 0.64875, "loss_cls": 3.42762, "loss": 3.42762, "time": 0.82263} +{"mode": "train", "epoch": 103, "iter": 1900, "lr": 0.02276, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38344, "top5_acc": 0.64281, "loss_cls": 3.48157, "loss": 3.48157, "time": 0.82036} +{"mode": "train", "epoch": 103, "iter": 2000, "lr": 0.02274, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39031, "top5_acc": 0.6475, "loss_cls": 3.43303, "loss": 3.43303, "time": 0.81673} +{"mode": "train", "epoch": 103, "iter": 2100, "lr": 0.02272, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38297, "top5_acc": 0.64562, "loss_cls": 3.43864, "loss": 3.43864, "time": 0.82372} +{"mode": "train", "epoch": 103, "iter": 2200, "lr": 0.02269, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38656, "top5_acc": 0.64875, "loss_cls": 3.45908, "loss": 3.45908, "time": 0.82402} +{"mode": "train", "epoch": 103, "iter": 2300, "lr": 0.02267, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39766, "top5_acc": 0.64953, "loss_cls": 3.40313, "loss": 3.40313, "time": 0.82004} +{"mode": "train", "epoch": 103, "iter": 2400, "lr": 0.02264, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38938, "top5_acc": 0.65578, "loss_cls": 3.39194, "loss": 3.39194, "time": 0.82756} +{"mode": "train", "epoch": 103, "iter": 2500, "lr": 0.02262, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38953, "top5_acc": 0.65875, "loss_cls": 3.41784, "loss": 3.41784, "time": 0.82443} +{"mode": "train", "epoch": 103, "iter": 2600, "lr": 0.0226, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39328, "top5_acc": 0.65203, "loss_cls": 3.4097, "loss": 3.4097, "time": 0.82369} +{"mode": "train", "epoch": 103, "iter": 2700, "lr": 0.02257, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38797, "top5_acc": 0.64953, "loss_cls": 3.47177, "loss": 3.47177, "time": 0.82376} +{"mode": "train", "epoch": 103, "iter": 2800, "lr": 0.02255, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38734, "top5_acc": 0.64875, "loss_cls": 3.43023, "loss": 3.43023, "time": 0.82322} +{"mode": "train", "epoch": 103, "iter": 2900, "lr": 0.02253, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37891, "top5_acc": 0.64891, "loss_cls": 3.44279, "loss": 3.44279, "time": 0.81998} +{"mode": "train", "epoch": 103, "iter": 3000, "lr": 0.0225, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37703, "top5_acc": 0.63641, "loss_cls": 3.49657, "loss": 3.49657, "time": 0.81479} +{"mode": "train", "epoch": 103, "iter": 3100, "lr": 0.02248, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39281, "top5_acc": 0.64844, "loss_cls": 3.42953, "loss": 3.42953, "time": 0.81738} +{"mode": "train", "epoch": 103, "iter": 3200, "lr": 0.02246, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38688, "top5_acc": 0.64484, "loss_cls": 3.45401, "loss": 3.45401, "time": 0.81727} +{"mode": "train", "epoch": 103, "iter": 3300, "lr": 0.02243, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37219, "top5_acc": 0.63219, "loss_cls": 3.53314, "loss": 3.53314, "time": 0.81897} +{"mode": "train", "epoch": 103, "iter": 3400, "lr": 0.02241, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38547, "top5_acc": 0.65141, "loss_cls": 3.44353, "loss": 3.44353, "time": 0.81761} +{"mode": "train", "epoch": 103, "iter": 3500, "lr": 0.02239, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38016, "top5_acc": 0.65672, "loss_cls": 3.42821, "loss": 3.42821, "time": 0.81619} +{"mode": "train", "epoch": 103, "iter": 3600, "lr": 0.02236, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38062, "top5_acc": 0.64172, "loss_cls": 3.4849, "loss": 3.4849, "time": 0.81508} +{"mode": "train", "epoch": 103, "iter": 3700, "lr": 0.02234, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39484, "top5_acc": 0.65359, "loss_cls": 3.3989, "loss": 3.3989, "time": 0.82086} +{"mode": "val", "epoch": 103, "iter": 309, "lr": 0.02233, "top1_acc": 0.31799, "top5_acc": 0.57605, "mean_class_accuracy": 0.31774} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.02231, "memory": 15990, "data_time": 1.32932, "top1_acc": 0.39125, "top5_acc": 0.66234, "loss_cls": 3.35643, "loss": 3.35643, "time": 2.33553} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.02228, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.40594, "top5_acc": 0.67203, "loss_cls": 3.33658, "loss": 3.33658, "time": 0.82933} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.02226, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38891, "top5_acc": 0.65516, "loss_cls": 3.41351, "loss": 3.41351, "time": 0.8233} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.02224, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39938, "top5_acc": 0.66891, "loss_cls": 3.37871, "loss": 3.37871, "time": 0.82239} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.02221, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39031, "top5_acc": 0.65891, "loss_cls": 3.3995, "loss": 3.3995, "time": 0.82141} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.02219, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39438, "top5_acc": 0.65578, "loss_cls": 3.40401, "loss": 3.40401, "time": 0.81739} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.02217, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39391, "top5_acc": 0.66578, "loss_cls": 3.36423, "loss": 3.36423, "time": 0.81352} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.02214, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39391, "top5_acc": 0.65984, "loss_cls": 3.39623, "loss": 3.39623, "time": 0.81735} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.02212, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39406, "top5_acc": 0.66141, "loss_cls": 3.38454, "loss": 3.38454, "time": 0.81533} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.0221, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39188, "top5_acc": 0.65875, "loss_cls": 3.41036, "loss": 3.41036, "time": 0.81528} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.02208, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39, "top5_acc": 0.65688, "loss_cls": 3.39999, "loss": 3.39999, "time": 0.82057} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.02205, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38828, "top5_acc": 0.65188, "loss_cls": 3.43674, "loss": 3.43674, "time": 0.818} +{"mode": "train", "epoch": 104, "iter": 1300, "lr": 0.02203, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39625, "top5_acc": 0.65422, "loss_cls": 3.36861, "loss": 3.36861, "time": 0.81318} +{"mode": "train", "epoch": 104, "iter": 1400, "lr": 0.02201, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38688, "top5_acc": 0.65531, "loss_cls": 3.39408, "loss": 3.39408, "time": 0.82191} +{"mode": "train", "epoch": 104, "iter": 1500, "lr": 0.02198, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38109, "top5_acc": 0.65656, "loss_cls": 3.4437, "loss": 3.4437, "time": 0.8267} +{"mode": "train", "epoch": 104, "iter": 1600, "lr": 0.02196, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39531, "top5_acc": 0.65844, "loss_cls": 3.38183, "loss": 3.38183, "time": 0.82238} +{"mode": "train", "epoch": 104, "iter": 1700, "lr": 0.02194, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37828, "top5_acc": 0.64328, "loss_cls": 3.48222, "loss": 3.48222, "time": 0.82781} +{"mode": "train", "epoch": 104, "iter": 1800, "lr": 0.02191, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38969, "top5_acc": 0.65469, "loss_cls": 3.39658, "loss": 3.39658, "time": 0.82374} +{"mode": "train", "epoch": 104, "iter": 1900, "lr": 0.02189, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39641, "top5_acc": 0.65641, "loss_cls": 3.40504, "loss": 3.40504, "time": 0.82979} +{"mode": "train", "epoch": 104, "iter": 2000, "lr": 0.02187, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3875, "top5_acc": 0.65672, "loss_cls": 3.40908, "loss": 3.40908, "time": 0.82057} +{"mode": "train", "epoch": 104, "iter": 2100, "lr": 0.02184, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38953, "top5_acc": 0.65391, "loss_cls": 3.44522, "loss": 3.44522, "time": 0.82355} +{"mode": "train", "epoch": 104, "iter": 2200, "lr": 0.02182, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38797, "top5_acc": 0.65656, "loss_cls": 3.43054, "loss": 3.43054, "time": 0.81723} +{"mode": "train", "epoch": 104, "iter": 2300, "lr": 0.0218, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38234, "top5_acc": 0.64562, "loss_cls": 3.46999, "loss": 3.46999, "time": 0.81789} +{"mode": "train", "epoch": 104, "iter": 2400, "lr": 0.02177, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39125, "top5_acc": 0.65141, "loss_cls": 3.41957, "loss": 3.41957, "time": 0.82142} +{"mode": "train", "epoch": 104, "iter": 2500, "lr": 0.02175, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39047, "top5_acc": 0.65297, "loss_cls": 3.40631, "loss": 3.40631, "time": 0.8173} +{"mode": "train", "epoch": 104, "iter": 2600, "lr": 0.02173, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39812, "top5_acc": 0.65672, "loss_cls": 3.39732, "loss": 3.39732, "time": 0.8194} +{"mode": "train", "epoch": 104, "iter": 2700, "lr": 0.02171, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39312, "top5_acc": 0.65578, "loss_cls": 3.41414, "loss": 3.41414, "time": 0.82666} +{"mode": "train", "epoch": 104, "iter": 2800, "lr": 0.02168, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39938, "top5_acc": 0.65938, "loss_cls": 3.43131, "loss": 3.43131, "time": 0.81796} +{"mode": "train", "epoch": 104, "iter": 2900, "lr": 0.02166, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38297, "top5_acc": 0.66156, "loss_cls": 3.40868, "loss": 3.40868, "time": 0.82104} +{"mode": "train", "epoch": 104, "iter": 3000, "lr": 0.02164, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39531, "top5_acc": 0.65406, "loss_cls": 3.40616, "loss": 3.40616, "time": 0.81412} +{"mode": "train", "epoch": 104, "iter": 3100, "lr": 0.02161, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4025, "top5_acc": 0.65797, "loss_cls": 3.36846, "loss": 3.36846, "time": 0.81489} +{"mode": "train", "epoch": 104, "iter": 3200, "lr": 0.02159, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38922, "top5_acc": 0.64844, "loss_cls": 3.48447, "loss": 3.48447, "time": 0.8186} +{"mode": "train", "epoch": 104, "iter": 3300, "lr": 0.02157, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39703, "top5_acc": 0.65984, "loss_cls": 3.41068, "loss": 3.41068, "time": 0.81955} +{"mode": "train", "epoch": 104, "iter": 3400, "lr": 0.02154, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38641, "top5_acc": 0.65172, "loss_cls": 3.41312, "loss": 3.41312, "time": 0.82355} +{"mode": "train", "epoch": 104, "iter": 3500, "lr": 0.02152, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38328, "top5_acc": 0.65312, "loss_cls": 3.39967, "loss": 3.39967, "time": 0.81681} +{"mode": "train", "epoch": 104, "iter": 3600, "lr": 0.0215, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38156, "top5_acc": 0.65453, "loss_cls": 3.44036, "loss": 3.44036, "time": 0.8216} +{"mode": "train", "epoch": 104, "iter": 3700, "lr": 0.02148, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39266, "top5_acc": 0.65969, "loss_cls": 3.41165, "loss": 3.41165, "time": 0.81847} +{"mode": "val", "epoch": 104, "iter": 309, "lr": 0.02146, "top1_acc": 0.33313, "top5_acc": 0.59368, "mean_class_accuracy": 0.33282} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.02144, "memory": 15990, "data_time": 1.35832, "top1_acc": 0.40562, "top5_acc": 0.67078, "loss_cls": 3.31448, "loss": 3.31448, "time": 2.34728} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.02142, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40391, "top5_acc": 0.67078, "loss_cls": 3.31756, "loss": 3.31756, "time": 0.82703} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.0214, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39016, "top5_acc": 0.66203, "loss_cls": 3.38282, "loss": 3.38282, "time": 0.82376} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.02137, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40031, "top5_acc": 0.6625, "loss_cls": 3.37258, "loss": 3.37258, "time": 0.8154} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.02135, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38859, "top5_acc": 0.65031, "loss_cls": 3.41709, "loss": 3.41709, "time": 0.81523} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.02133, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39891, "top5_acc": 0.65734, "loss_cls": 3.40259, "loss": 3.40259, "time": 0.82105} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.0213, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39797, "top5_acc": 0.66484, "loss_cls": 3.36023, "loss": 3.36023, "time": 0.81741} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.02128, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38828, "top5_acc": 0.66406, "loss_cls": 3.39846, "loss": 3.39846, "time": 0.81665} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.02126, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40047, "top5_acc": 0.65875, "loss_cls": 3.36101, "loss": 3.36101, "time": 0.81615} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.02124, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40281, "top5_acc": 0.65344, "loss_cls": 3.35175, "loss": 3.35175, "time": 0.81582} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.02121, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39125, "top5_acc": 0.65141, "loss_cls": 3.40023, "loss": 3.40023, "time": 0.81854} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.02119, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40359, "top5_acc": 0.66562, "loss_cls": 3.34161, "loss": 3.34161, "time": 0.82268} +{"mode": "train", "epoch": 105, "iter": 1300, "lr": 0.02117, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39938, "top5_acc": 0.65359, "loss_cls": 3.37856, "loss": 3.37856, "time": 0.81534} +{"mode": "train", "epoch": 105, "iter": 1400, "lr": 0.02114, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40406, "top5_acc": 0.66344, "loss_cls": 3.3762, "loss": 3.3762, "time": 0.8177} +{"mode": "train", "epoch": 105, "iter": 1500, "lr": 0.02112, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39578, "top5_acc": 0.65516, "loss_cls": 3.39882, "loss": 3.39882, "time": 0.82667} +{"mode": "train", "epoch": 105, "iter": 1600, "lr": 0.0211, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40375, "top5_acc": 0.66156, "loss_cls": 3.34218, "loss": 3.34218, "time": 0.82055} +{"mode": "train", "epoch": 105, "iter": 1700, "lr": 0.02108, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38922, "top5_acc": 0.65469, "loss_cls": 3.41021, "loss": 3.41021, "time": 0.82123} +{"mode": "train", "epoch": 105, "iter": 1800, "lr": 0.02105, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39031, "top5_acc": 0.65188, "loss_cls": 3.42759, "loss": 3.42759, "time": 0.81872} +{"mode": "train", "epoch": 105, "iter": 1900, "lr": 0.02103, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39438, "top5_acc": 0.655, "loss_cls": 3.40287, "loss": 3.40287, "time": 0.8224} +{"mode": "train", "epoch": 105, "iter": 2000, "lr": 0.02101, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39359, "top5_acc": 0.65453, "loss_cls": 3.41964, "loss": 3.41964, "time": 0.8225} +{"mode": "train", "epoch": 105, "iter": 2100, "lr": 0.02098, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39969, "top5_acc": 0.66812, "loss_cls": 3.35905, "loss": 3.35905, "time": 0.82589} +{"mode": "train", "epoch": 105, "iter": 2200, "lr": 0.02096, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40406, "top5_acc": 0.66422, "loss_cls": 3.39059, "loss": 3.39059, "time": 0.81176} +{"mode": "train", "epoch": 105, "iter": 2300, "lr": 0.02094, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38469, "top5_acc": 0.64797, "loss_cls": 3.44437, "loss": 3.44437, "time": 0.8178} +{"mode": "train", "epoch": 105, "iter": 2400, "lr": 0.02092, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38703, "top5_acc": 0.65625, "loss_cls": 3.41121, "loss": 3.41121, "time": 0.8227} +{"mode": "train", "epoch": 105, "iter": 2500, "lr": 0.02089, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38656, "top5_acc": 0.65359, "loss_cls": 3.40718, "loss": 3.40718, "time": 0.82036} +{"mode": "train", "epoch": 105, "iter": 2600, "lr": 0.02087, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39281, "top5_acc": 0.65062, "loss_cls": 3.40868, "loss": 3.40868, "time": 0.83008} +{"mode": "train", "epoch": 105, "iter": 2700, "lr": 0.02085, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38891, "top5_acc": 0.64219, "loss_cls": 3.43963, "loss": 3.43963, "time": 0.81909} +{"mode": "train", "epoch": 105, "iter": 2800, "lr": 0.02083, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39656, "top5_acc": 0.6575, "loss_cls": 3.40612, "loss": 3.40612, "time": 0.8151} +{"mode": "train", "epoch": 105, "iter": 2900, "lr": 0.0208, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39453, "top5_acc": 0.66297, "loss_cls": 3.37132, "loss": 3.37132, "time": 0.8205} +{"mode": "train", "epoch": 105, "iter": 3000, "lr": 0.02078, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39578, "top5_acc": 0.65938, "loss_cls": 3.40722, "loss": 3.40722, "time": 0.81621} +{"mode": "train", "epoch": 105, "iter": 3100, "lr": 0.02076, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39812, "top5_acc": 0.65031, "loss_cls": 3.40557, "loss": 3.40557, "time": 0.81746} +{"mode": "train", "epoch": 105, "iter": 3200, "lr": 0.02073, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39703, "top5_acc": 0.66844, "loss_cls": 3.371, "loss": 3.371, "time": 0.81586} +{"mode": "train", "epoch": 105, "iter": 3300, "lr": 0.02071, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38891, "top5_acc": 0.65672, "loss_cls": 3.4015, "loss": 3.4015, "time": 0.81957} +{"mode": "train", "epoch": 105, "iter": 3400, "lr": 0.02069, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39094, "top5_acc": 0.65141, "loss_cls": 3.39142, "loss": 3.39142, "time": 0.82317} +{"mode": "train", "epoch": 105, "iter": 3500, "lr": 0.02067, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38906, "top5_acc": 0.65094, "loss_cls": 3.412, "loss": 3.412, "time": 0.82011} +{"mode": "train", "epoch": 105, "iter": 3600, "lr": 0.02064, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39594, "top5_acc": 0.66, "loss_cls": 3.38224, "loss": 3.38224, "time": 0.81305} +{"mode": "train", "epoch": 105, "iter": 3700, "lr": 0.02062, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40141, "top5_acc": 0.67188, "loss_cls": 3.3501, "loss": 3.3501, "time": 0.82003} +{"mode": "val", "epoch": 105, "iter": 309, "lr": 0.02061, "top1_acc": 0.3075, "top5_acc": 0.56694, "mean_class_accuracy": 0.30718} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.02059, "memory": 15990, "data_time": 1.33271, "top1_acc": 0.40641, "top5_acc": 0.6725, "loss_cls": 3.29615, "loss": 3.29615, "time": 2.31985} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.02057, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.41, "top5_acc": 0.66922, "loss_cls": 3.30809, "loss": 3.30809, "time": 0.8228} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.02054, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.395, "top5_acc": 0.67109, "loss_cls": 3.35447, "loss": 3.35447, "time": 0.81879} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.02052, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39266, "top5_acc": 0.66156, "loss_cls": 3.36757, "loss": 3.36757, "time": 0.82357} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.0205, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39125, "top5_acc": 0.66703, "loss_cls": 3.36383, "loss": 3.36383, "time": 0.81854} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.02048, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40688, "top5_acc": 0.66984, "loss_cls": 3.34074, "loss": 3.34074, "time": 0.82056} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.02045, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38703, "top5_acc": 0.66109, "loss_cls": 3.40103, "loss": 3.40103, "time": 0.81612} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.02043, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39797, "top5_acc": 0.65828, "loss_cls": 3.36891, "loss": 3.36891, "time": 0.81582} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.02041, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42109, "top5_acc": 0.66953, "loss_cls": 3.2981, "loss": 3.2981, "time": 0.81628} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.02039, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39422, "top5_acc": 0.66141, "loss_cls": 3.37929, "loss": 3.37929, "time": 0.81719} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.02036, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40469, "top5_acc": 0.66328, "loss_cls": 3.34984, "loss": 3.34984, "time": 0.81725} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.02034, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39969, "top5_acc": 0.66203, "loss_cls": 3.3558, "loss": 3.3558, "time": 0.81503} +{"mode": "train", "epoch": 106, "iter": 1300, "lr": 0.02032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39797, "top5_acc": 0.66766, "loss_cls": 3.34657, "loss": 3.34657, "time": 0.82325} +{"mode": "train", "epoch": 106, "iter": 1400, "lr": 0.0203, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40188, "top5_acc": 0.66906, "loss_cls": 3.32147, "loss": 3.32147, "time": 0.81827} +{"mode": "train", "epoch": 106, "iter": 1500, "lr": 0.02027, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39922, "top5_acc": 0.66859, "loss_cls": 3.3467, "loss": 3.3467, "time": 0.82304} +{"mode": "train", "epoch": 106, "iter": 1600, "lr": 0.02025, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38781, "top5_acc": 0.65469, "loss_cls": 3.38684, "loss": 3.38684, "time": 0.8168} +{"mode": "train", "epoch": 106, "iter": 1700, "lr": 0.02023, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39844, "top5_acc": 0.66094, "loss_cls": 3.39162, "loss": 3.39162, "time": 0.82781} +{"mode": "train", "epoch": 106, "iter": 1800, "lr": 0.02021, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39422, "top5_acc": 0.65734, "loss_cls": 3.37391, "loss": 3.37391, "time": 0.81994} +{"mode": "train", "epoch": 106, "iter": 1900, "lr": 0.02018, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39672, "top5_acc": 0.66031, "loss_cls": 3.35428, "loss": 3.35428, "time": 0.8219} +{"mode": "train", "epoch": 106, "iter": 2000, "lr": 0.02016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.385, "top5_acc": 0.65406, "loss_cls": 3.40546, "loss": 3.40546, "time": 0.82332} +{"mode": "train", "epoch": 106, "iter": 2100, "lr": 0.02014, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39891, "top5_acc": 0.66031, "loss_cls": 3.38185, "loss": 3.38185, "time": 0.82789} +{"mode": "train", "epoch": 106, "iter": 2200, "lr": 0.02012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38984, "top5_acc": 0.66812, "loss_cls": 3.37415, "loss": 3.37415, "time": 0.82484} +{"mode": "train", "epoch": 106, "iter": 2300, "lr": 0.02009, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.405, "top5_acc": 0.66266, "loss_cls": 3.36563, "loss": 3.36563, "time": 0.8273} +{"mode": "train", "epoch": 106, "iter": 2400, "lr": 0.02007, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39094, "top5_acc": 0.65672, "loss_cls": 3.42693, "loss": 3.42693, "time": 0.83083} +{"mode": "train", "epoch": 106, "iter": 2500, "lr": 0.02005, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39547, "top5_acc": 0.66375, "loss_cls": 3.38857, "loss": 3.38857, "time": 0.81837} +{"mode": "train", "epoch": 106, "iter": 2600, "lr": 0.02003, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39719, "top5_acc": 0.65578, "loss_cls": 3.38648, "loss": 3.38648, "time": 0.82407} +{"mode": "train", "epoch": 106, "iter": 2700, "lr": 0.02, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39359, "top5_acc": 0.66453, "loss_cls": 3.39298, "loss": 3.39298, "time": 0.81972} +{"mode": "train", "epoch": 106, "iter": 2800, "lr": 0.01998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39781, "top5_acc": 0.65797, "loss_cls": 3.40025, "loss": 3.40025, "time": 0.81796} +{"mode": "train", "epoch": 106, "iter": 2900, "lr": 0.01996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40094, "top5_acc": 0.65812, "loss_cls": 3.41638, "loss": 3.41638, "time": 0.81656} +{"mode": "train", "epoch": 106, "iter": 3000, "lr": 0.01994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40234, "top5_acc": 0.66312, "loss_cls": 3.35393, "loss": 3.35393, "time": 0.81589} +{"mode": "train", "epoch": 106, "iter": 3100, "lr": 0.01991, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40406, "top5_acc": 0.66719, "loss_cls": 3.35613, "loss": 3.35613, "time": 0.82071} +{"mode": "train", "epoch": 106, "iter": 3200, "lr": 0.01989, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39578, "top5_acc": 0.65594, "loss_cls": 3.37602, "loss": 3.37602, "time": 0.81627} +{"mode": "train", "epoch": 106, "iter": 3300, "lr": 0.01987, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40047, "top5_acc": 0.66531, "loss_cls": 3.36387, "loss": 3.36387, "time": 0.81682} +{"mode": "train", "epoch": 106, "iter": 3400, "lr": 0.01985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39578, "top5_acc": 0.65938, "loss_cls": 3.37341, "loss": 3.37341, "time": 0.8154} +{"mode": "train", "epoch": 106, "iter": 3500, "lr": 0.01983, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39375, "top5_acc": 0.65781, "loss_cls": 3.40039, "loss": 3.40039, "time": 0.81571} +{"mode": "train", "epoch": 106, "iter": 3600, "lr": 0.0198, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38703, "top5_acc": 0.65531, "loss_cls": 3.3819, "loss": 3.3819, "time": 0.81601} +{"mode": "train", "epoch": 106, "iter": 3700, "lr": 0.01978, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39516, "top5_acc": 0.66453, "loss_cls": 3.36292, "loss": 3.36292, "time": 0.81742} +{"mode": "val", "epoch": 106, "iter": 309, "lr": 0.01977, "top1_acc": 0.33338, "top5_acc": 0.5952, "mean_class_accuracy": 0.33304} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.01975, "memory": 15990, "data_time": 1.35265, "top1_acc": 0.41328, "top5_acc": 0.68391, "loss_cls": 3.23634, "loss": 3.23634, "time": 2.33898} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.01973, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40766, "top5_acc": 0.67328, "loss_cls": 3.30412, "loss": 3.30412, "time": 0.82185} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.0197, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40641, "top5_acc": 0.67312, "loss_cls": 3.28657, "loss": 3.28657, "time": 0.82173} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.01968, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40531, "top5_acc": 0.66188, "loss_cls": 3.33438, "loss": 3.33438, "time": 0.82064} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.01966, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41172, "top5_acc": 0.67984, "loss_cls": 3.25649, "loss": 3.25649, "time": 0.81987} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.01964, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40484, "top5_acc": 0.67109, "loss_cls": 3.31771, "loss": 3.31771, "time": 0.81666} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.01961, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39828, "top5_acc": 0.66203, "loss_cls": 3.33087, "loss": 3.33087, "time": 0.82649} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.01959, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40719, "top5_acc": 0.66391, "loss_cls": 3.33783, "loss": 3.33783, "time": 0.81668} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.01957, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40203, "top5_acc": 0.65922, "loss_cls": 3.35981, "loss": 3.35981, "time": 0.81661} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.01955, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40703, "top5_acc": 0.67141, "loss_cls": 3.32489, "loss": 3.32489, "time": 0.82195} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.01953, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40906, "top5_acc": 0.66609, "loss_cls": 3.31177, "loss": 3.31177, "time": 0.81923} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.0195, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39344, "top5_acc": 0.65328, "loss_cls": 3.41154, "loss": 3.41154, "time": 0.8203} +{"mode": "train", "epoch": 107, "iter": 1300, "lr": 0.01948, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40969, "top5_acc": 0.67906, "loss_cls": 3.29556, "loss": 3.29556, "time": 0.81861} +{"mode": "train", "epoch": 107, "iter": 1400, "lr": 0.01946, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40984, "top5_acc": 0.67828, "loss_cls": 3.27662, "loss": 3.27662, "time": 0.81564} +{"mode": "train", "epoch": 107, "iter": 1500, "lr": 0.01944, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40219, "top5_acc": 0.66141, "loss_cls": 3.35293, "loss": 3.35293, "time": 0.82068} +{"mode": "train", "epoch": 107, "iter": 1600, "lr": 0.01942, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39969, "top5_acc": 0.66328, "loss_cls": 3.37885, "loss": 3.37885, "time": 0.81765} +{"mode": "train", "epoch": 107, "iter": 1700, "lr": 0.01939, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39188, "top5_acc": 0.66516, "loss_cls": 3.36934, "loss": 3.36934, "time": 0.82228} +{"mode": "train", "epoch": 107, "iter": 1800, "lr": 0.01937, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38984, "top5_acc": 0.66547, "loss_cls": 3.3859, "loss": 3.3859, "time": 0.81628} +{"mode": "train", "epoch": 107, "iter": 1900, "lr": 0.01935, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39953, "top5_acc": 0.65453, "loss_cls": 3.39087, "loss": 3.39087, "time": 0.82649} +{"mode": "train", "epoch": 107, "iter": 2000, "lr": 0.01933, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38891, "top5_acc": 0.66109, "loss_cls": 3.38667, "loss": 3.38667, "time": 0.82145} +{"mode": "train", "epoch": 107, "iter": 2100, "lr": 0.0193, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39391, "top5_acc": 0.66922, "loss_cls": 3.35037, "loss": 3.35037, "time": 0.82526} +{"mode": "train", "epoch": 107, "iter": 2200, "lr": 0.01928, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38781, "top5_acc": 0.65359, "loss_cls": 3.41055, "loss": 3.41055, "time": 0.81415} +{"mode": "train", "epoch": 107, "iter": 2300, "lr": 0.01926, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40203, "top5_acc": 0.66422, "loss_cls": 3.34733, "loss": 3.34733, "time": 0.81661} +{"mode": "train", "epoch": 107, "iter": 2400, "lr": 0.01924, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40062, "top5_acc": 0.66734, "loss_cls": 3.35282, "loss": 3.35282, "time": 0.83225} +{"mode": "train", "epoch": 107, "iter": 2500, "lr": 0.01922, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40344, "top5_acc": 0.65906, "loss_cls": 3.36876, "loss": 3.36876, "time": 0.82549} +{"mode": "train", "epoch": 107, "iter": 2600, "lr": 0.01919, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39812, "top5_acc": 0.6625, "loss_cls": 3.35527, "loss": 3.35527, "time": 0.8215} +{"mode": "train", "epoch": 107, "iter": 2700, "lr": 0.01917, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40016, "top5_acc": 0.65609, "loss_cls": 3.37762, "loss": 3.37762, "time": 0.81604} +{"mode": "train", "epoch": 107, "iter": 2800, "lr": 0.01915, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40594, "top5_acc": 0.66312, "loss_cls": 3.33397, "loss": 3.33397, "time": 0.82139} +{"mode": "train", "epoch": 107, "iter": 2900, "lr": 0.01913, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39578, "top5_acc": 0.66375, "loss_cls": 3.37373, "loss": 3.37373, "time": 0.81755} +{"mode": "train", "epoch": 107, "iter": 3000, "lr": 0.01911, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39547, "top5_acc": 0.66016, "loss_cls": 3.38526, "loss": 3.38526, "time": 0.82227} +{"mode": "train", "epoch": 107, "iter": 3100, "lr": 0.01908, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39938, "top5_acc": 0.66578, "loss_cls": 3.35182, "loss": 3.35182, "time": 0.82409} +{"mode": "train", "epoch": 107, "iter": 3200, "lr": 0.01906, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38688, "top5_acc": 0.65344, "loss_cls": 3.43568, "loss": 3.43568, "time": 0.81782} +{"mode": "train", "epoch": 107, "iter": 3300, "lr": 0.01904, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40484, "top5_acc": 0.66859, "loss_cls": 3.34673, "loss": 3.34673, "time": 0.81615} +{"mode": "train", "epoch": 107, "iter": 3400, "lr": 0.01902, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38969, "top5_acc": 0.655, "loss_cls": 3.42559, "loss": 3.42559, "time": 0.81525} +{"mode": "train", "epoch": 107, "iter": 3500, "lr": 0.019, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38891, "top5_acc": 0.65562, "loss_cls": 3.38919, "loss": 3.38919, "time": 0.81495} +{"mode": "train", "epoch": 107, "iter": 3600, "lr": 0.01897, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40531, "top5_acc": 0.66297, "loss_cls": 3.35812, "loss": 3.35812, "time": 0.82084} +{"mode": "train", "epoch": 107, "iter": 3700, "lr": 0.01895, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40938, "top5_acc": 0.665, "loss_cls": 3.32856, "loss": 3.32856, "time": 0.81798} +{"mode": "val", "epoch": 107, "iter": 309, "lr": 0.01894, "top1_acc": 0.3233, "top5_acc": 0.58537, "mean_class_accuracy": 0.32313} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.01892, "memory": 15990, "data_time": 1.2923, "top1_acc": 0.40578, "top5_acc": 0.67594, "loss_cls": 3.30319, "loss": 3.30319, "time": 2.2821} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0189, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42016, "top5_acc": 0.68797, "loss_cls": 3.23787, "loss": 3.23787, "time": 0.81879} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.01888, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3975, "top5_acc": 0.66891, "loss_cls": 3.30269, "loss": 3.30269, "time": 0.82145} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.01886, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40969, "top5_acc": 0.67484, "loss_cls": 3.29573, "loss": 3.29573, "time": 0.81575} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.01883, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39969, "top5_acc": 0.67109, "loss_cls": 3.33455, "loss": 3.33455, "time": 0.81598} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.01881, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40562, "top5_acc": 0.66516, "loss_cls": 3.32588, "loss": 3.32588, "time": 0.81641} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.01879, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39578, "top5_acc": 0.66484, "loss_cls": 3.35461, "loss": 3.35461, "time": 0.81507} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.01877, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41219, "top5_acc": 0.67188, "loss_cls": 3.26853, "loss": 3.26853, "time": 0.81455} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.01875, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40344, "top5_acc": 0.67547, "loss_cls": 3.28804, "loss": 3.28804, "time": 0.8194} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.01872, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39562, "top5_acc": 0.66812, "loss_cls": 3.33368, "loss": 3.33368, "time": 0.82209} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.0187, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40422, "top5_acc": 0.66875, "loss_cls": 3.31024, "loss": 3.31024, "time": 0.82039} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.01868, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40188, "top5_acc": 0.67141, "loss_cls": 3.348, "loss": 3.348, "time": 0.81656} +{"mode": "train", "epoch": 108, "iter": 1300, "lr": 0.01866, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40109, "top5_acc": 0.67266, "loss_cls": 3.31115, "loss": 3.31115, "time": 0.81617} +{"mode": "train", "epoch": 108, "iter": 1400, "lr": 0.01864, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41328, "top5_acc": 0.67828, "loss_cls": 3.28763, "loss": 3.28763, "time": 0.81768} +{"mode": "train", "epoch": 108, "iter": 1500, "lr": 0.01862, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39312, "top5_acc": 0.66125, "loss_cls": 3.35498, "loss": 3.35498, "time": 0.8219} +{"mode": "train", "epoch": 108, "iter": 1600, "lr": 0.01859, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40781, "top5_acc": 0.67141, "loss_cls": 3.29333, "loss": 3.29333, "time": 0.82185} +{"mode": "train", "epoch": 108, "iter": 1700, "lr": 0.01857, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39375, "top5_acc": 0.65516, "loss_cls": 3.33523, "loss": 3.33523, "time": 0.82925} +{"mode": "train", "epoch": 108, "iter": 1800, "lr": 0.01855, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41109, "top5_acc": 0.67844, "loss_cls": 3.27546, "loss": 3.27546, "time": 0.82019} +{"mode": "train", "epoch": 108, "iter": 1900, "lr": 0.01853, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39578, "top5_acc": 0.66094, "loss_cls": 3.38335, "loss": 3.38335, "time": 0.82198} +{"mode": "train", "epoch": 108, "iter": 2000, "lr": 0.01851, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40031, "top5_acc": 0.65938, "loss_cls": 3.36637, "loss": 3.36637, "time": 0.82536} +{"mode": "train", "epoch": 108, "iter": 2100, "lr": 0.01848, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40266, "top5_acc": 0.67328, "loss_cls": 3.33977, "loss": 3.33977, "time": 0.8254} +{"mode": "train", "epoch": 108, "iter": 2200, "lr": 0.01846, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40594, "top5_acc": 0.66891, "loss_cls": 3.32411, "loss": 3.32411, "time": 0.82026} +{"mode": "train", "epoch": 108, "iter": 2300, "lr": 0.01844, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40531, "top5_acc": 0.66844, "loss_cls": 3.3028, "loss": 3.3028, "time": 0.82356} +{"mode": "train", "epoch": 108, "iter": 2400, "lr": 0.01842, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40797, "top5_acc": 0.66547, "loss_cls": 3.33336, "loss": 3.33336, "time": 0.8174} +{"mode": "train", "epoch": 108, "iter": 2500, "lr": 0.0184, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40047, "top5_acc": 0.66125, "loss_cls": 3.38077, "loss": 3.38077, "time": 0.82996} +{"mode": "train", "epoch": 108, "iter": 2600, "lr": 0.01838, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39312, "top5_acc": 0.65812, "loss_cls": 3.39315, "loss": 3.39315, "time": 0.81804} +{"mode": "train", "epoch": 108, "iter": 2700, "lr": 0.01835, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39984, "top5_acc": 0.65547, "loss_cls": 3.39532, "loss": 3.39532, "time": 0.8232} +{"mode": "train", "epoch": 108, "iter": 2800, "lr": 0.01833, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39734, "top5_acc": 0.66594, "loss_cls": 3.34642, "loss": 3.34642, "time": 0.8189} +{"mode": "train", "epoch": 108, "iter": 2900, "lr": 0.01831, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40547, "top5_acc": 0.66875, "loss_cls": 3.34275, "loss": 3.34275, "time": 0.81982} +{"mode": "train", "epoch": 108, "iter": 3000, "lr": 0.01829, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40484, "top5_acc": 0.66953, "loss_cls": 3.31669, "loss": 3.31669, "time": 0.81861} +{"mode": "train", "epoch": 108, "iter": 3100, "lr": 0.01827, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40266, "top5_acc": 0.67391, "loss_cls": 3.29556, "loss": 3.29556, "time": 0.81877} +{"mode": "train", "epoch": 108, "iter": 3200, "lr": 0.01825, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4075, "top5_acc": 0.66812, "loss_cls": 3.32735, "loss": 3.32735, "time": 0.8194} +{"mode": "train", "epoch": 108, "iter": 3300, "lr": 0.01823, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40516, "top5_acc": 0.66484, "loss_cls": 3.37824, "loss": 3.37824, "time": 0.81722} +{"mode": "train", "epoch": 108, "iter": 3400, "lr": 0.0182, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40297, "top5_acc": 0.6675, "loss_cls": 3.34288, "loss": 3.34288, "time": 0.823} +{"mode": "train", "epoch": 108, "iter": 3500, "lr": 0.01818, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40062, "top5_acc": 0.66031, "loss_cls": 3.36225, "loss": 3.36225, "time": 0.82038} +{"mode": "train", "epoch": 108, "iter": 3600, "lr": 0.01816, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4075, "top5_acc": 0.66266, "loss_cls": 3.33095, "loss": 3.33095, "time": 0.82328} +{"mode": "train", "epoch": 108, "iter": 3700, "lr": 0.01814, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40594, "top5_acc": 0.67516, "loss_cls": 3.3028, "loss": 3.3028, "time": 0.81833} +{"mode": "val", "epoch": 108, "iter": 309, "lr": 0.01813, "top1_acc": 0.33126, "top5_acc": 0.59408, "mean_class_accuracy": 0.33111} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.01811, "memory": 15990, "data_time": 1.34714, "top1_acc": 0.40984, "top5_acc": 0.67453, "loss_cls": 3.27836, "loss": 3.27836, "time": 2.34164} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.01809, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41422, "top5_acc": 0.68328, "loss_cls": 3.24594, "loss": 3.24594, "time": 0.82026} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.01806, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42203, "top5_acc": 0.685, "loss_cls": 3.22805, "loss": 3.22805, "time": 0.82213} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.01804, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40312, "top5_acc": 0.67031, "loss_cls": 3.30477, "loss": 3.30477, "time": 0.81801} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.01802, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40578, "top5_acc": 0.67594, "loss_cls": 3.26792, "loss": 3.26792, "time": 0.81913} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42, "top5_acc": 0.67875, "loss_cls": 3.23341, "loss": 3.23341, "time": 0.82182} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.01798, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41094, "top5_acc": 0.68125, "loss_cls": 3.2619, "loss": 3.2619, "time": 0.82032} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.01796, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41375, "top5_acc": 0.6725, "loss_cls": 3.29217, "loss": 3.29217, "time": 0.81739} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.01794, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41438, "top5_acc": 0.68219, "loss_cls": 3.26015, "loss": 3.26015, "time": 0.81643} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.01791, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40531, "top5_acc": 0.67141, "loss_cls": 3.31087, "loss": 3.31087, "time": 0.82047} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.01789, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40031, "top5_acc": 0.66578, "loss_cls": 3.32146, "loss": 3.32146, "time": 0.82122} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.01787, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40938, "top5_acc": 0.67453, "loss_cls": 3.30683, "loss": 3.30683, "time": 0.81793} +{"mode": "train", "epoch": 109, "iter": 1300, "lr": 0.01785, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40641, "top5_acc": 0.67844, "loss_cls": 3.29778, "loss": 3.29778, "time": 0.8192} +{"mode": "train", "epoch": 109, "iter": 1400, "lr": 0.01783, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40406, "top5_acc": 0.66609, "loss_cls": 3.31098, "loss": 3.31098, "time": 0.81469} +{"mode": "train", "epoch": 109, "iter": 1500, "lr": 0.01781, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39656, "top5_acc": 0.66328, "loss_cls": 3.3541, "loss": 3.3541, "time": 0.82731} +{"mode": "train", "epoch": 109, "iter": 1600, "lr": 0.01779, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41594, "top5_acc": 0.66938, "loss_cls": 3.32467, "loss": 3.32467, "time": 0.8207} +{"mode": "train", "epoch": 109, "iter": 1700, "lr": 0.01776, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3975, "top5_acc": 0.66188, "loss_cls": 3.34712, "loss": 3.34712, "time": 0.82178} +{"mode": "train", "epoch": 109, "iter": 1800, "lr": 0.01774, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40281, "top5_acc": 0.675, "loss_cls": 3.32575, "loss": 3.32575, "time": 0.82137} +{"mode": "train", "epoch": 109, "iter": 1900, "lr": 0.01772, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40969, "top5_acc": 0.66969, "loss_cls": 3.31971, "loss": 3.31971, "time": 0.82396} +{"mode": "train", "epoch": 109, "iter": 2000, "lr": 0.0177, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40812, "top5_acc": 0.66984, "loss_cls": 3.31068, "loss": 3.31068, "time": 0.82198} +{"mode": "train", "epoch": 109, "iter": 2100, "lr": 0.01768, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40812, "top5_acc": 0.67547, "loss_cls": 3.3062, "loss": 3.3062, "time": 0.81845} +{"mode": "train", "epoch": 109, "iter": 2200, "lr": 0.01766, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40516, "top5_acc": 0.66078, "loss_cls": 3.36555, "loss": 3.36555, "time": 0.82052} +{"mode": "train", "epoch": 109, "iter": 2300, "lr": 0.01764, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40656, "top5_acc": 0.66891, "loss_cls": 3.31739, "loss": 3.31739, "time": 0.81865} +{"mode": "train", "epoch": 109, "iter": 2400, "lr": 0.01761, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40141, "top5_acc": 0.66969, "loss_cls": 3.33315, "loss": 3.33315, "time": 0.81547} +{"mode": "train", "epoch": 109, "iter": 2500, "lr": 0.01759, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41438, "top5_acc": 0.66734, "loss_cls": 3.31121, "loss": 3.31121, "time": 0.82496} +{"mode": "train", "epoch": 109, "iter": 2600, "lr": 0.01757, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40125, "top5_acc": 0.67125, "loss_cls": 3.31547, "loss": 3.31547, "time": 0.82133} +{"mode": "train", "epoch": 109, "iter": 2700, "lr": 0.01755, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40391, "top5_acc": 0.67375, "loss_cls": 3.30248, "loss": 3.30248, "time": 0.8122} +{"mode": "train", "epoch": 109, "iter": 2800, "lr": 0.01753, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40469, "top5_acc": 0.66453, "loss_cls": 3.30533, "loss": 3.30533, "time": 0.82419} +{"mode": "train", "epoch": 109, "iter": 2900, "lr": 0.01751, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40375, "top5_acc": 0.67812, "loss_cls": 3.31517, "loss": 3.31517, "time": 0.81833} +{"mode": "train", "epoch": 109, "iter": 3000, "lr": 0.01749, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41078, "top5_acc": 0.67125, "loss_cls": 3.30402, "loss": 3.30402, "time": 0.81842} +{"mode": "train", "epoch": 109, "iter": 3100, "lr": 0.01747, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40844, "top5_acc": 0.66625, "loss_cls": 3.32035, "loss": 3.32035, "time": 0.81601} +{"mode": "train", "epoch": 109, "iter": 3200, "lr": 0.01744, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41031, "top5_acc": 0.67516, "loss_cls": 3.28382, "loss": 3.28382, "time": 0.82379} +{"mode": "train", "epoch": 109, "iter": 3300, "lr": 0.01742, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40547, "top5_acc": 0.67062, "loss_cls": 3.34151, "loss": 3.34151, "time": 0.82005} +{"mode": "train", "epoch": 109, "iter": 3400, "lr": 0.0174, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41609, "top5_acc": 0.67078, "loss_cls": 3.29251, "loss": 3.29251, "time": 0.81886} +{"mode": "train", "epoch": 109, "iter": 3500, "lr": 0.01738, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39609, "top5_acc": 0.6625, "loss_cls": 3.38295, "loss": 3.38295, "time": 0.82062} +{"mode": "train", "epoch": 109, "iter": 3600, "lr": 0.01736, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41031, "top5_acc": 0.66172, "loss_cls": 3.36849, "loss": 3.36849, "time": 0.81827} +{"mode": "train", "epoch": 109, "iter": 3700, "lr": 0.01734, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40922, "top5_acc": 0.66625, "loss_cls": 3.35116, "loss": 3.35116, "time": 0.81468} +{"mode": "val", "epoch": 109, "iter": 309, "lr": 0.01733, "top1_acc": 0.32786, "top5_acc": 0.58775, "mean_class_accuracy": 0.32768} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.01731, "memory": 15990, "data_time": 1.35598, "top1_acc": 0.42734, "top5_acc": 0.69172, "loss_cls": 3.21133, "loss": 3.21133, "time": 2.3402} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.01729, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42766, "top5_acc": 0.69, "loss_cls": 3.21422, "loss": 3.21422, "time": 0.82122} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.01727, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41484, "top5_acc": 0.67453, "loss_cls": 3.25556, "loss": 3.25556, "time": 0.81737} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.01724, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42047, "top5_acc": 0.67031, "loss_cls": 3.25846, "loss": 3.25846, "time": 0.81792} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.01722, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41031, "top5_acc": 0.67875, "loss_cls": 3.30759, "loss": 3.30759, "time": 0.82505} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.0172, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41781, "top5_acc": 0.68141, "loss_cls": 3.25582, "loss": 3.25582, "time": 0.81707} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.01718, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40438, "top5_acc": 0.67625, "loss_cls": 3.29585, "loss": 3.29585, "time": 0.81741} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.01716, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.415, "top5_acc": 0.66812, "loss_cls": 3.2872, "loss": 3.2872, "time": 0.82177} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.01714, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40797, "top5_acc": 0.6675, "loss_cls": 3.30177, "loss": 3.30177, "time": 0.8182} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.01712, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41953, "top5_acc": 0.68859, "loss_cls": 3.24653, "loss": 3.24653, "time": 0.82023} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.0171, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40859, "top5_acc": 0.66984, "loss_cls": 3.29797, "loss": 3.29797, "time": 0.81994} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.01708, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42422, "top5_acc": 0.68266, "loss_cls": 3.24428, "loss": 3.24428, "time": 0.81633} +{"mode": "train", "epoch": 110, "iter": 1300, "lr": 0.01705, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40609, "top5_acc": 0.67781, "loss_cls": 3.27412, "loss": 3.27412, "time": 0.817} +{"mode": "train", "epoch": 110, "iter": 1400, "lr": 0.01703, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41344, "top5_acc": 0.68141, "loss_cls": 3.23046, "loss": 3.23046, "time": 0.81901} +{"mode": "train", "epoch": 110, "iter": 1500, "lr": 0.01701, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41047, "top5_acc": 0.675, "loss_cls": 3.2665, "loss": 3.2665, "time": 0.82212} +{"mode": "train", "epoch": 110, "iter": 1600, "lr": 0.01699, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41609, "top5_acc": 0.67984, "loss_cls": 3.26312, "loss": 3.26312, "time": 0.81967} +{"mode": "train", "epoch": 110, "iter": 1700, "lr": 0.01697, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40859, "top5_acc": 0.66781, "loss_cls": 3.31343, "loss": 3.31343, "time": 0.81718} +{"mode": "train", "epoch": 110, "iter": 1800, "lr": 0.01695, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41625, "top5_acc": 0.68438, "loss_cls": 3.24463, "loss": 3.24463, "time": 0.81792} +{"mode": "train", "epoch": 110, "iter": 1900, "lr": 0.01693, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41422, "top5_acc": 0.68234, "loss_cls": 3.27607, "loss": 3.27607, "time": 0.82654} +{"mode": "train", "epoch": 110, "iter": 2000, "lr": 0.01691, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4075, "top5_acc": 0.67344, "loss_cls": 3.30967, "loss": 3.30967, "time": 0.81765} +{"mode": "train", "epoch": 110, "iter": 2100, "lr": 0.01689, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40609, "top5_acc": 0.68094, "loss_cls": 3.28576, "loss": 3.28576, "time": 0.82207} +{"mode": "train", "epoch": 110, "iter": 2200, "lr": 0.01687, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41172, "top5_acc": 0.67906, "loss_cls": 3.28237, "loss": 3.28237, "time": 0.82929} +{"mode": "train", "epoch": 110, "iter": 2300, "lr": 0.01685, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39875, "top5_acc": 0.67172, "loss_cls": 3.33547, "loss": 3.33547, "time": 0.82103} +{"mode": "train", "epoch": 110, "iter": 2400, "lr": 0.01682, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41031, "top5_acc": 0.67266, "loss_cls": 3.32386, "loss": 3.32386, "time": 0.82346} +{"mode": "train", "epoch": 110, "iter": 2500, "lr": 0.0168, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40891, "top5_acc": 0.67141, "loss_cls": 3.31751, "loss": 3.31751, "time": 0.81933} +{"mode": "train", "epoch": 110, "iter": 2600, "lr": 0.01678, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41875, "top5_acc": 0.67859, "loss_cls": 3.26165, "loss": 3.26165, "time": 0.81989} +{"mode": "train", "epoch": 110, "iter": 2700, "lr": 0.01676, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41031, "top5_acc": 0.66891, "loss_cls": 3.31066, "loss": 3.31066, "time": 0.81798} +{"mode": "train", "epoch": 110, "iter": 2800, "lr": 0.01674, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41484, "top5_acc": 0.67672, "loss_cls": 3.2647, "loss": 3.2647, "time": 0.82568} +{"mode": "train", "epoch": 110, "iter": 2900, "lr": 0.01672, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40922, "top5_acc": 0.67953, "loss_cls": 3.2718, "loss": 3.2718, "time": 0.81821} +{"mode": "train", "epoch": 110, "iter": 3000, "lr": 0.0167, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40719, "top5_acc": 0.66359, "loss_cls": 3.32507, "loss": 3.32507, "time": 0.81395} +{"mode": "train", "epoch": 110, "iter": 3100, "lr": 0.01668, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40078, "top5_acc": 0.66219, "loss_cls": 3.33218, "loss": 3.33218, "time": 0.8192} +{"mode": "train", "epoch": 110, "iter": 3200, "lr": 0.01666, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41344, "top5_acc": 0.66953, "loss_cls": 3.32793, "loss": 3.32793, "time": 0.81752} +{"mode": "train", "epoch": 110, "iter": 3300, "lr": 0.01664, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40547, "top5_acc": 0.67219, "loss_cls": 3.30287, "loss": 3.30287, "time": 0.81447} +{"mode": "train", "epoch": 110, "iter": 3400, "lr": 0.01662, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40578, "top5_acc": 0.66953, "loss_cls": 3.3418, "loss": 3.3418, "time": 0.81771} +{"mode": "train", "epoch": 110, "iter": 3500, "lr": 0.01659, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41453, "top5_acc": 0.675, "loss_cls": 3.26053, "loss": 3.26053, "time": 0.82068} +{"mode": "train", "epoch": 110, "iter": 3600, "lr": 0.01657, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40172, "top5_acc": 0.67031, "loss_cls": 3.32871, "loss": 3.32871, "time": 0.82079} +{"mode": "train", "epoch": 110, "iter": 3700, "lr": 0.01655, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41062, "top5_acc": 0.675, "loss_cls": 3.29185, "loss": 3.29185, "time": 0.81911} +{"mode": "val", "epoch": 110, "iter": 309, "lr": 0.01654, "top1_acc": 0.35369, "top5_acc": 0.61217, "mean_class_accuracy": 0.35352} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.01652, "memory": 15990, "data_time": 1.31142, "top1_acc": 0.42672, "top5_acc": 0.69594, "loss_cls": 3.1876, "loss": 3.1876, "time": 2.29242} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.0165, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41281, "top5_acc": 0.67656, "loss_cls": 3.24569, "loss": 3.24569, "time": 0.8265} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.01648, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43031, "top5_acc": 0.69188, "loss_cls": 3.20466, "loss": 3.20466, "time": 0.82244} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.01646, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41859, "top5_acc": 0.67875, "loss_cls": 3.2304, "loss": 3.2304, "time": 0.82114} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.01644, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43266, "top5_acc": 0.68969, "loss_cls": 3.1692, "loss": 3.1692, "time": 0.82062} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.01642, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41344, "top5_acc": 0.67609, "loss_cls": 3.28035, "loss": 3.28035, "time": 0.81748} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.0164, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42094, "top5_acc": 0.67406, "loss_cls": 3.25318, "loss": 3.25318, "time": 0.82176} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.01638, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42172, "top5_acc": 0.68328, "loss_cls": 3.22326, "loss": 3.22326, "time": 0.82078} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.01636, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40281, "top5_acc": 0.67781, "loss_cls": 3.2868, "loss": 3.2868, "time": 0.81567} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.01634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40641, "top5_acc": 0.67391, "loss_cls": 3.2704, "loss": 3.2704, "time": 0.82502} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.01632, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40688, "top5_acc": 0.67266, "loss_cls": 3.27998, "loss": 3.27998, "time": 0.8177} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.0163, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42656, "top5_acc": 0.68766, "loss_cls": 3.22118, "loss": 3.22118, "time": 0.81604} +{"mode": "train", "epoch": 111, "iter": 1300, "lr": 0.01627, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4225, "top5_acc": 0.68797, "loss_cls": 3.19449, "loss": 3.19449, "time": 0.81791} +{"mode": "train", "epoch": 111, "iter": 1400, "lr": 0.01625, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41375, "top5_acc": 0.67312, "loss_cls": 3.28715, "loss": 3.28715, "time": 0.81682} +{"mode": "train", "epoch": 111, "iter": 1500, "lr": 0.01623, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40062, "top5_acc": 0.67, "loss_cls": 3.32708, "loss": 3.32708, "time": 0.82254} +{"mode": "train", "epoch": 111, "iter": 1600, "lr": 0.01621, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41562, "top5_acc": 0.67359, "loss_cls": 3.23846, "loss": 3.23846, "time": 0.81926} +{"mode": "train", "epoch": 111, "iter": 1700, "lr": 0.01619, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41531, "top5_acc": 0.68812, "loss_cls": 3.22254, "loss": 3.22254, "time": 0.82699} +{"mode": "train", "epoch": 111, "iter": 1800, "lr": 0.01617, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41266, "top5_acc": 0.67578, "loss_cls": 3.28435, "loss": 3.28435, "time": 0.82184} +{"mode": "train", "epoch": 111, "iter": 1900, "lr": 0.01615, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41703, "top5_acc": 0.67297, "loss_cls": 3.29927, "loss": 3.29927, "time": 0.82648} +{"mode": "train", "epoch": 111, "iter": 2000, "lr": 0.01613, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42531, "top5_acc": 0.68234, "loss_cls": 3.22232, "loss": 3.22232, "time": 0.82016} +{"mode": "train", "epoch": 111, "iter": 2100, "lr": 0.01611, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41109, "top5_acc": 0.67547, "loss_cls": 3.26244, "loss": 3.26244, "time": 0.82829} +{"mode": "train", "epoch": 111, "iter": 2200, "lr": 0.01609, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41438, "top5_acc": 0.675, "loss_cls": 3.28324, "loss": 3.28324, "time": 0.81853} +{"mode": "train", "epoch": 111, "iter": 2300, "lr": 0.01607, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40922, "top5_acc": 0.67391, "loss_cls": 3.29242, "loss": 3.29242, "time": 0.82228} +{"mode": "train", "epoch": 111, "iter": 2400, "lr": 0.01605, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40969, "top5_acc": 0.67672, "loss_cls": 3.29624, "loss": 3.29624, "time": 0.82629} +{"mode": "train", "epoch": 111, "iter": 2500, "lr": 0.01603, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40547, "top5_acc": 0.67703, "loss_cls": 3.31199, "loss": 3.31199, "time": 0.82292} +{"mode": "train", "epoch": 111, "iter": 2600, "lr": 0.01601, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41719, "top5_acc": 0.67625, "loss_cls": 3.26098, "loss": 3.26098, "time": 0.82203} +{"mode": "train", "epoch": 111, "iter": 2700, "lr": 0.01599, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40719, "top5_acc": 0.68594, "loss_cls": 3.26078, "loss": 3.26078, "time": 0.82795} +{"mode": "train", "epoch": 111, "iter": 2800, "lr": 0.01597, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42328, "top5_acc": 0.68391, "loss_cls": 3.2313, "loss": 3.2313, "time": 0.82108} +{"mode": "train", "epoch": 111, "iter": 2900, "lr": 0.01595, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40359, "top5_acc": 0.67047, "loss_cls": 3.33083, "loss": 3.33083, "time": 0.81983} +{"mode": "train", "epoch": 111, "iter": 3000, "lr": 0.01593, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.405, "top5_acc": 0.66828, "loss_cls": 3.29731, "loss": 3.29731, "time": 0.8151} +{"mode": "train", "epoch": 111, "iter": 3100, "lr": 0.0159, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42516, "top5_acc": 0.68875, "loss_cls": 3.20602, "loss": 3.20602, "time": 0.8161} +{"mode": "train", "epoch": 111, "iter": 3200, "lr": 0.01588, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41172, "top5_acc": 0.67781, "loss_cls": 3.2854, "loss": 3.2854, "time": 0.81968} +{"mode": "train", "epoch": 111, "iter": 3300, "lr": 0.01586, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41312, "top5_acc": 0.67906, "loss_cls": 3.27496, "loss": 3.27496, "time": 0.81579} +{"mode": "train", "epoch": 111, "iter": 3400, "lr": 0.01584, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41422, "top5_acc": 0.67875, "loss_cls": 3.26613, "loss": 3.26613, "time": 0.8198} +{"mode": "train", "epoch": 111, "iter": 3500, "lr": 0.01582, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41094, "top5_acc": 0.67344, "loss_cls": 3.2972, "loss": 3.2972, "time": 0.81642} +{"mode": "train", "epoch": 111, "iter": 3600, "lr": 0.0158, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42359, "top5_acc": 0.68281, "loss_cls": 3.25712, "loss": 3.25712, "time": 0.81581} +{"mode": "train", "epoch": 111, "iter": 3700, "lr": 0.01578, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41828, "top5_acc": 0.67719, "loss_cls": 3.26023, "loss": 3.26023, "time": 0.81744} +{"mode": "val", "epoch": 111, "iter": 309, "lr": 0.01577, "top1_acc": 0.34777, "top5_acc": 0.60502, "mean_class_accuracy": 0.3474} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.01575, "memory": 15990, "data_time": 1.3552, "top1_acc": 0.42844, "top5_acc": 0.69922, "loss_cls": 3.16765, "loss": 3.16765, "time": 2.35643} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.01573, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42812, "top5_acc": 0.69125, "loss_cls": 3.18898, "loss": 3.18898, "time": 0.83361} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.01571, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42844, "top5_acc": 0.70281, "loss_cls": 3.1541, "loss": 3.1541, "time": 0.8288} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.01569, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.4175, "top5_acc": 0.68156, "loss_cls": 3.20898, "loss": 3.20898, "time": 0.8344} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.01567, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42188, "top5_acc": 0.68172, "loss_cls": 3.20974, "loss": 3.20974, "time": 0.82913} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.01565, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42703, "top5_acc": 0.69812, "loss_cls": 3.17714, "loss": 3.17714, "time": 0.82877} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.01563, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41766, "top5_acc": 0.68031, "loss_cls": 3.23666, "loss": 3.23666, "time": 0.82982} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.01561, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41406, "top5_acc": 0.68578, "loss_cls": 3.2456, "loss": 3.2456, "time": 0.83031} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.01559, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42031, "top5_acc": 0.67922, "loss_cls": 3.22817, "loss": 3.22817, "time": 0.83352} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.01557, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41172, "top5_acc": 0.67953, "loss_cls": 3.24577, "loss": 3.24577, "time": 0.83355} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.01555, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41562, "top5_acc": 0.68156, "loss_cls": 3.21446, "loss": 3.21446, "time": 0.83135} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.01553, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41891, "top5_acc": 0.67875, "loss_cls": 3.25697, "loss": 3.25697, "time": 0.8276} +{"mode": "train", "epoch": 112, "iter": 1300, "lr": 0.01551, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41859, "top5_acc": 0.67891, "loss_cls": 3.2375, "loss": 3.2375, "time": 0.82667} +{"mode": "train", "epoch": 112, "iter": 1400, "lr": 0.01549, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43391, "top5_acc": 0.69812, "loss_cls": 3.14502, "loss": 3.14502, "time": 0.83175} +{"mode": "train", "epoch": 112, "iter": 1500, "lr": 0.01547, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42359, "top5_acc": 0.67984, "loss_cls": 3.22444, "loss": 3.22444, "time": 0.83142} +{"mode": "train", "epoch": 112, "iter": 1600, "lr": 0.01545, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41391, "top5_acc": 0.66828, "loss_cls": 3.28941, "loss": 3.28941, "time": 0.82327} +{"mode": "train", "epoch": 112, "iter": 1700, "lr": 0.01543, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40797, "top5_acc": 0.67938, "loss_cls": 3.26937, "loss": 3.26937, "time": 0.82548} +{"mode": "train", "epoch": 112, "iter": 1800, "lr": 0.01541, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41766, "top5_acc": 0.67984, "loss_cls": 3.24555, "loss": 3.24555, "time": 0.83108} +{"mode": "train", "epoch": 112, "iter": 1900, "lr": 0.01539, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41422, "top5_acc": 0.67938, "loss_cls": 3.25559, "loss": 3.25559, "time": 0.83308} +{"mode": "train", "epoch": 112, "iter": 2000, "lr": 0.01537, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41641, "top5_acc": 0.67484, "loss_cls": 3.29666, "loss": 3.29666, "time": 0.83224} +{"mode": "train", "epoch": 112, "iter": 2100, "lr": 0.01535, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42109, "top5_acc": 0.68094, "loss_cls": 3.26309, "loss": 3.26309, "time": 0.82503} +{"mode": "train", "epoch": 112, "iter": 2200, "lr": 0.01533, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.41531, "top5_acc": 0.67891, "loss_cls": 3.26415, "loss": 3.26415, "time": 0.82531} +{"mode": "train", "epoch": 112, "iter": 2300, "lr": 0.01531, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.41797, "top5_acc": 0.6875, "loss_cls": 3.21052, "loss": 3.21052, "time": 0.82034} +{"mode": "train", "epoch": 112, "iter": 2400, "lr": 0.01529, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40844, "top5_acc": 0.67375, "loss_cls": 3.28232, "loss": 3.28232, "time": 0.81569} +{"mode": "train", "epoch": 112, "iter": 2500, "lr": 0.01527, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42766, "top5_acc": 0.68469, "loss_cls": 3.19437, "loss": 3.19437, "time": 0.82281} +{"mode": "train", "epoch": 112, "iter": 2600, "lr": 0.01525, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42484, "top5_acc": 0.67578, "loss_cls": 3.24637, "loss": 3.24637, "time": 0.82235} +{"mode": "train", "epoch": 112, "iter": 2700, "lr": 0.01523, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42062, "top5_acc": 0.67906, "loss_cls": 3.25067, "loss": 3.25067, "time": 0.82915} +{"mode": "train", "epoch": 112, "iter": 2800, "lr": 0.01521, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40812, "top5_acc": 0.67438, "loss_cls": 3.28015, "loss": 3.28015, "time": 0.827} +{"mode": "train", "epoch": 112, "iter": 2900, "lr": 0.01519, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42812, "top5_acc": 0.68062, "loss_cls": 3.24529, "loss": 3.24529, "time": 0.82486} +{"mode": "train", "epoch": 112, "iter": 3000, "lr": 0.01517, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40688, "top5_acc": 0.67641, "loss_cls": 3.29952, "loss": 3.29952, "time": 0.82815} +{"mode": "train", "epoch": 112, "iter": 3100, "lr": 0.01515, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41625, "top5_acc": 0.68203, "loss_cls": 3.23941, "loss": 3.23941, "time": 0.82364} +{"mode": "train", "epoch": 112, "iter": 3200, "lr": 0.01513, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41078, "top5_acc": 0.67469, "loss_cls": 3.26956, "loss": 3.26956, "time": 0.827} +{"mode": "train", "epoch": 112, "iter": 3300, "lr": 0.01511, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4225, "top5_acc": 0.68609, "loss_cls": 3.21717, "loss": 3.21717, "time": 0.82434} +{"mode": "train", "epoch": 112, "iter": 3400, "lr": 0.01509, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40578, "top5_acc": 0.66641, "loss_cls": 3.3261, "loss": 3.3261, "time": 0.82528} +{"mode": "train", "epoch": 112, "iter": 3500, "lr": 0.01507, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42672, "top5_acc": 0.67297, "loss_cls": 3.21028, "loss": 3.21028, "time": 0.82921} +{"mode": "train", "epoch": 112, "iter": 3600, "lr": 0.01505, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41578, "top5_acc": 0.67984, "loss_cls": 3.26982, "loss": 3.26982, "time": 0.8271} +{"mode": "train", "epoch": 112, "iter": 3700, "lr": 0.01503, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41984, "top5_acc": 0.68156, "loss_cls": 3.22067, "loss": 3.22067, "time": 0.82806} +{"mode": "val", "epoch": 112, "iter": 309, "lr": 0.01502, "top1_acc": 0.34412, "top5_acc": 0.60751, "mean_class_accuracy": 0.34399} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.015, "memory": 15990, "data_time": 1.3318, "top1_acc": 0.44422, "top5_acc": 0.70719, "loss_cls": 3.08403, "loss": 3.08403, "time": 2.31548} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.01498, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42719, "top5_acc": 0.69062, "loss_cls": 3.14718, "loss": 3.14718, "time": 0.8359} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.01496, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43406, "top5_acc": 0.71, "loss_cls": 3.12015, "loss": 3.12015, "time": 0.83636} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.01494, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42781, "top5_acc": 0.69156, "loss_cls": 3.19098, "loss": 3.19098, "time": 0.83048} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.01492, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42875, "top5_acc": 0.68922, "loss_cls": 3.19243, "loss": 3.19243, "time": 0.83514} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.0149, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42094, "top5_acc": 0.68203, "loss_cls": 3.21704, "loss": 3.21704, "time": 0.83553} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.01488, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42844, "top5_acc": 0.68891, "loss_cls": 3.20582, "loss": 3.20582, "time": 0.83717} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.01486, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42297, "top5_acc": 0.69219, "loss_cls": 3.21389, "loss": 3.21389, "time": 0.83645} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.01484, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43719, "top5_acc": 0.69359, "loss_cls": 3.13297, "loss": 3.13297, "time": 0.83305} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.01482, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42797, "top5_acc": 0.69125, "loss_cls": 3.19156, "loss": 3.19156, "time": 0.82647} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0148, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.415, "top5_acc": 0.67906, "loss_cls": 3.22602, "loss": 3.22602, "time": 0.83214} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.01478, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42922, "top5_acc": 0.68812, "loss_cls": 3.21278, "loss": 3.21278, "time": 0.83156} +{"mode": "train", "epoch": 113, "iter": 1300, "lr": 0.01476, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41594, "top5_acc": 0.68938, "loss_cls": 3.19565, "loss": 3.19565, "time": 0.83504} +{"mode": "train", "epoch": 113, "iter": 1400, "lr": 0.01474, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41953, "top5_acc": 0.68516, "loss_cls": 3.23145, "loss": 3.23145, "time": 0.83486} +{"mode": "train", "epoch": 113, "iter": 1500, "lr": 0.01472, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42125, "top5_acc": 0.68031, "loss_cls": 3.22689, "loss": 3.22689, "time": 0.83672} +{"mode": "train", "epoch": 113, "iter": 1600, "lr": 0.0147, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41031, "top5_acc": 0.68453, "loss_cls": 3.2315, "loss": 3.2315, "time": 0.82069} +{"mode": "train", "epoch": 113, "iter": 1700, "lr": 0.01468, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40344, "top5_acc": 0.67781, "loss_cls": 3.29721, "loss": 3.29721, "time": 0.83078} +{"mode": "train", "epoch": 113, "iter": 1800, "lr": 0.01466, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42391, "top5_acc": 0.68016, "loss_cls": 3.21828, "loss": 3.21828, "time": 0.82943} +{"mode": "train", "epoch": 113, "iter": 1900, "lr": 0.01464, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42453, "top5_acc": 0.68891, "loss_cls": 3.18826, "loss": 3.18826, "time": 0.82936} +{"mode": "train", "epoch": 113, "iter": 2000, "lr": 0.01462, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42172, "top5_acc": 0.68531, "loss_cls": 3.2083, "loss": 3.2083, "time": 0.81828} +{"mode": "train", "epoch": 113, "iter": 2100, "lr": 0.0146, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42719, "top5_acc": 0.69297, "loss_cls": 3.19736, "loss": 3.19736, "time": 0.82266} +{"mode": "train", "epoch": 113, "iter": 2200, "lr": 0.01458, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42859, "top5_acc": 0.68891, "loss_cls": 3.18917, "loss": 3.18917, "time": 0.83227} +{"mode": "train", "epoch": 113, "iter": 2300, "lr": 0.01456, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42016, "top5_acc": 0.68484, "loss_cls": 3.22043, "loss": 3.22043, "time": 0.82573} +{"mode": "train", "epoch": 113, "iter": 2400, "lr": 0.01454, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42609, "top5_acc": 0.67641, "loss_cls": 3.25228, "loss": 3.25228, "time": 0.82203} +{"mode": "train", "epoch": 113, "iter": 2500, "lr": 0.01452, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41109, "top5_acc": 0.68281, "loss_cls": 3.23812, "loss": 3.23812, "time": 0.82753} +{"mode": "train", "epoch": 113, "iter": 2600, "lr": 0.0145, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40844, "top5_acc": 0.67859, "loss_cls": 3.28078, "loss": 3.28078, "time": 0.82465} +{"mode": "train", "epoch": 113, "iter": 2700, "lr": 0.01448, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41953, "top5_acc": 0.67828, "loss_cls": 3.24045, "loss": 3.24045, "time": 0.832} +{"mode": "train", "epoch": 113, "iter": 2800, "lr": 0.01446, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41953, "top5_acc": 0.67766, "loss_cls": 3.23951, "loss": 3.23951, "time": 0.82471} +{"mode": "train", "epoch": 113, "iter": 2900, "lr": 0.01444, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42078, "top5_acc": 0.68172, "loss_cls": 3.23323, "loss": 3.23323, "time": 0.81807} +{"mode": "train", "epoch": 113, "iter": 3000, "lr": 0.01442, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42312, "top5_acc": 0.69188, "loss_cls": 3.19941, "loss": 3.19941, "time": 0.8257} +{"mode": "train", "epoch": 113, "iter": 3100, "lr": 0.0144, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43, "top5_acc": 0.68812, "loss_cls": 3.20332, "loss": 3.20332, "time": 0.82584} +{"mode": "train", "epoch": 113, "iter": 3200, "lr": 0.01438, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41625, "top5_acc": 0.67641, "loss_cls": 3.25506, "loss": 3.25506, "time": 0.82862} +{"mode": "train", "epoch": 113, "iter": 3300, "lr": 0.01436, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42547, "top5_acc": 0.68422, "loss_cls": 3.24431, "loss": 3.24431, "time": 0.82298} +{"mode": "train", "epoch": 113, "iter": 3400, "lr": 0.01434, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41672, "top5_acc": 0.68047, "loss_cls": 3.27151, "loss": 3.27151, "time": 0.82583} +{"mode": "train", "epoch": 113, "iter": 3500, "lr": 0.01432, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41734, "top5_acc": 0.68156, "loss_cls": 3.23756, "loss": 3.23756, "time": 0.82744} +{"mode": "train", "epoch": 113, "iter": 3600, "lr": 0.01431, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42297, "top5_acc": 0.6775, "loss_cls": 3.24879, "loss": 3.24879, "time": 0.83456} +{"mode": "train", "epoch": 113, "iter": 3700, "lr": 0.01429, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41984, "top5_acc": 0.68047, "loss_cls": 3.2316, "loss": 3.2316, "time": 0.83013} +{"mode": "val", "epoch": 113, "iter": 309, "lr": 0.01428, "top1_acc": 0.34493, "top5_acc": 0.60457, "mean_class_accuracy": 0.34465} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.01426, "memory": 15990, "data_time": 1.37572, "top1_acc": 0.43844, "top5_acc": 0.70047, "loss_cls": 3.13963, "loss": 3.13963, "time": 2.3703} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.01424, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42516, "top5_acc": 0.68969, "loss_cls": 3.18332, "loss": 3.18332, "time": 0.83857} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.01422, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43797, "top5_acc": 0.70344, "loss_cls": 3.12374, "loss": 3.12374, "time": 0.83942} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.0142, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43922, "top5_acc": 0.69812, "loss_cls": 3.12426, "loss": 3.12426, "time": 0.83699} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.01418, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43891, "top5_acc": 0.69672, "loss_cls": 3.13458, "loss": 3.13458, "time": 0.83434} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.01416, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42547, "top5_acc": 0.68656, "loss_cls": 3.20654, "loss": 3.20654, "time": 0.83196} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.01414, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.4325, "top5_acc": 0.70156, "loss_cls": 3.13453, "loss": 3.13453, "time": 0.83486} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.01412, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43031, "top5_acc": 0.69047, "loss_cls": 3.19034, "loss": 3.19034, "time": 0.83422} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.0141, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.425, "top5_acc": 0.69844, "loss_cls": 3.14766, "loss": 3.14766, "time": 0.83654} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.01408, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42781, "top5_acc": 0.69203, "loss_cls": 3.16647, "loss": 3.16647, "time": 0.835} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.01406, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42688, "top5_acc": 0.68391, "loss_cls": 3.20536, "loss": 3.20536, "time": 0.82699} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.01404, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43312, "top5_acc": 0.70484, "loss_cls": 3.11951, "loss": 3.11951, "time": 0.83558} +{"mode": "train", "epoch": 114, "iter": 1300, "lr": 0.01402, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42812, "top5_acc": 0.68969, "loss_cls": 3.19034, "loss": 3.19034, "time": 0.8362} +{"mode": "train", "epoch": 114, "iter": 1400, "lr": 0.014, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.425, "top5_acc": 0.69688, "loss_cls": 3.17145, "loss": 3.17145, "time": 0.83272} +{"mode": "train", "epoch": 114, "iter": 1500, "lr": 0.01398, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42344, "top5_acc": 0.68188, "loss_cls": 3.24393, "loss": 3.24393, "time": 0.82729} +{"mode": "train", "epoch": 114, "iter": 1600, "lr": 0.01397, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43234, "top5_acc": 0.69219, "loss_cls": 3.17748, "loss": 3.17748, "time": 0.82146} +{"mode": "train", "epoch": 114, "iter": 1700, "lr": 0.01395, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4325, "top5_acc": 0.68609, "loss_cls": 3.17043, "loss": 3.17043, "time": 0.82758} +{"mode": "train", "epoch": 114, "iter": 1800, "lr": 0.01393, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41719, "top5_acc": 0.68531, "loss_cls": 3.22863, "loss": 3.22863, "time": 0.82933} +{"mode": "train", "epoch": 114, "iter": 1900, "lr": 0.01391, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41281, "top5_acc": 0.67859, "loss_cls": 3.26415, "loss": 3.26415, "time": 0.83261} +{"mode": "train", "epoch": 114, "iter": 2000, "lr": 0.01389, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43203, "top5_acc": 0.68469, "loss_cls": 3.19205, "loss": 3.19205, "time": 0.82311} +{"mode": "train", "epoch": 114, "iter": 2100, "lr": 0.01387, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41672, "top5_acc": 0.68797, "loss_cls": 3.22218, "loss": 3.22218, "time": 0.82611} +{"mode": "train", "epoch": 114, "iter": 2200, "lr": 0.01385, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.42406, "top5_acc": 0.69031, "loss_cls": 3.18535, "loss": 3.18535, "time": 0.81924} +{"mode": "train", "epoch": 114, "iter": 2300, "lr": 0.01383, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.41906, "top5_acc": 0.67859, "loss_cls": 3.21912, "loss": 3.21912, "time": 0.82173} +{"mode": "train", "epoch": 114, "iter": 2400, "lr": 0.01381, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42062, "top5_acc": 0.675, "loss_cls": 3.24262, "loss": 3.24262, "time": 0.81743} +{"mode": "train", "epoch": 114, "iter": 2500, "lr": 0.01379, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42781, "top5_acc": 0.68375, "loss_cls": 3.19733, "loss": 3.19733, "time": 0.81423} +{"mode": "train", "epoch": 114, "iter": 2600, "lr": 0.01377, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41906, "top5_acc": 0.68625, "loss_cls": 3.24079, "loss": 3.24079, "time": 0.81437} +{"mode": "train", "epoch": 114, "iter": 2700, "lr": 0.01375, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42172, "top5_acc": 0.67953, "loss_cls": 3.24411, "loss": 3.24411, "time": 0.81528} +{"mode": "train", "epoch": 114, "iter": 2800, "lr": 0.01373, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43703, "top5_acc": 0.69438, "loss_cls": 3.17631, "loss": 3.17631, "time": 0.81858} +{"mode": "train", "epoch": 114, "iter": 2900, "lr": 0.01371, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43078, "top5_acc": 0.68594, "loss_cls": 3.23081, "loss": 3.23081, "time": 0.81806} +{"mode": "train", "epoch": 114, "iter": 3000, "lr": 0.01369, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43172, "top5_acc": 0.68516, "loss_cls": 3.18589, "loss": 3.18589, "time": 0.82559} +{"mode": "train", "epoch": 114, "iter": 3100, "lr": 0.01368, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42297, "top5_acc": 0.68453, "loss_cls": 3.20038, "loss": 3.20038, "time": 0.82275} +{"mode": "train", "epoch": 114, "iter": 3200, "lr": 0.01366, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.4175, "top5_acc": 0.67641, "loss_cls": 3.24617, "loss": 3.24617, "time": 0.8153} +{"mode": "train", "epoch": 114, "iter": 3300, "lr": 0.01364, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42312, "top5_acc": 0.68344, "loss_cls": 3.21737, "loss": 3.21737, "time": 0.82006} +{"mode": "train", "epoch": 114, "iter": 3400, "lr": 0.01362, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42797, "top5_acc": 0.69234, "loss_cls": 3.17525, "loss": 3.17525, "time": 0.81459} +{"mode": "train", "epoch": 114, "iter": 3500, "lr": 0.0136, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42156, "top5_acc": 0.68172, "loss_cls": 3.24767, "loss": 3.24767, "time": 0.82044} +{"mode": "train", "epoch": 114, "iter": 3600, "lr": 0.01358, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42625, "top5_acc": 0.68375, "loss_cls": 3.22203, "loss": 3.22203, "time": 0.82246} +{"mode": "train", "epoch": 114, "iter": 3700, "lr": 0.01356, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43188, "top5_acc": 0.69578, "loss_cls": 3.17988, "loss": 3.17988, "time": 0.82214} +{"mode": "val", "epoch": 114, "iter": 309, "lr": 0.01355, "top1_acc": 0.35, "top5_acc": 0.61075, "mean_class_accuracy": 0.34977} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.01353, "memory": 15990, "data_time": 1.37122, "top1_acc": 0.44625, "top5_acc": 0.69844, "loss_cls": 3.1117, "loss": 3.1117, "time": 2.36137} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.01351, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44125, "top5_acc": 0.70703, "loss_cls": 3.10871, "loss": 3.10871, "time": 0.83506} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.01349, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43078, "top5_acc": 0.69594, "loss_cls": 3.14111, "loss": 3.14111, "time": 0.8341} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.01348, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43578, "top5_acc": 0.69984, "loss_cls": 3.1405, "loss": 3.1405, "time": 0.83357} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.01346, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44453, "top5_acc": 0.69938, "loss_cls": 3.11661, "loss": 3.11661, "time": 0.83118} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.01344, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43234, "top5_acc": 0.70547, "loss_cls": 3.12404, "loss": 3.12404, "time": 0.83689} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.01342, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44031, "top5_acc": 0.70438, "loss_cls": 3.11055, "loss": 3.11055, "time": 0.83593} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.0134, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43047, "top5_acc": 0.69156, "loss_cls": 3.15182, "loss": 3.15182, "time": 0.83378} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.01338, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43156, "top5_acc": 0.69922, "loss_cls": 3.12135, "loss": 3.12135, "time": 0.83461} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.01336, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43422, "top5_acc": 0.69703, "loss_cls": 3.16792, "loss": 3.16792, "time": 0.8355} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.01334, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43062, "top5_acc": 0.69812, "loss_cls": 3.14013, "loss": 3.14013, "time": 0.83388} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.01332, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43359, "top5_acc": 0.69312, "loss_cls": 3.14359, "loss": 3.14359, "time": 0.83672} +{"mode": "train", "epoch": 115, "iter": 1300, "lr": 0.0133, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42859, "top5_acc": 0.69391, "loss_cls": 3.15882, "loss": 3.15882, "time": 0.83275} +{"mode": "train", "epoch": 115, "iter": 1400, "lr": 0.01328, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43125, "top5_acc": 0.69297, "loss_cls": 3.18323, "loss": 3.18323, "time": 0.83233} +{"mode": "train", "epoch": 115, "iter": 1500, "lr": 0.01327, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43047, "top5_acc": 0.69672, "loss_cls": 3.16205, "loss": 3.16205, "time": 0.82734} +{"mode": "train", "epoch": 115, "iter": 1600, "lr": 0.01325, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43047, "top5_acc": 0.70078, "loss_cls": 3.15026, "loss": 3.15026, "time": 0.82666} +{"mode": "train", "epoch": 115, "iter": 1700, "lr": 0.01323, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43047, "top5_acc": 0.69078, "loss_cls": 3.16217, "loss": 3.16217, "time": 0.83268} +{"mode": "train", "epoch": 115, "iter": 1800, "lr": 0.01321, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42453, "top5_acc": 0.69219, "loss_cls": 3.19344, "loss": 3.19344, "time": 0.83546} +{"mode": "train", "epoch": 115, "iter": 1900, "lr": 0.01319, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43719, "top5_acc": 0.70203, "loss_cls": 3.1212, "loss": 3.1212, "time": 0.82575} +{"mode": "train", "epoch": 115, "iter": 2000, "lr": 0.01317, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42172, "top5_acc": 0.69094, "loss_cls": 3.21602, "loss": 3.21602, "time": 0.82867} +{"mode": "train", "epoch": 115, "iter": 2100, "lr": 0.01315, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42078, "top5_acc": 0.67969, "loss_cls": 3.24248, "loss": 3.24248, "time": 0.82393} +{"mode": "train", "epoch": 115, "iter": 2200, "lr": 0.01313, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.41609, "top5_acc": 0.67922, "loss_cls": 3.21723, "loss": 3.21723, "time": 0.82983} +{"mode": "train", "epoch": 115, "iter": 2300, "lr": 0.01311, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.42641, "top5_acc": 0.68891, "loss_cls": 3.17273, "loss": 3.17273, "time": 0.83188} +{"mode": "train", "epoch": 115, "iter": 2400, "lr": 0.0131, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4275, "top5_acc": 0.68484, "loss_cls": 3.20866, "loss": 3.20866, "time": 0.82157} +{"mode": "train", "epoch": 115, "iter": 2500, "lr": 0.01308, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42672, "top5_acc": 0.69344, "loss_cls": 3.17834, "loss": 3.17834, "time": 0.81432} +{"mode": "train", "epoch": 115, "iter": 2600, "lr": 0.01306, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41766, "top5_acc": 0.67766, "loss_cls": 3.24913, "loss": 3.24913, "time": 0.81937} +{"mode": "train", "epoch": 115, "iter": 2700, "lr": 0.01304, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43656, "top5_acc": 0.7, "loss_cls": 3.11479, "loss": 3.11479, "time": 0.81875} +{"mode": "train", "epoch": 115, "iter": 2800, "lr": 0.01302, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42766, "top5_acc": 0.69031, "loss_cls": 3.17705, "loss": 3.17705, "time": 0.8109} +{"mode": "train", "epoch": 115, "iter": 2900, "lr": 0.013, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43172, "top5_acc": 0.69359, "loss_cls": 3.18903, "loss": 3.18903, "time": 0.8164} +{"mode": "train", "epoch": 115, "iter": 3000, "lr": 0.01298, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42156, "top5_acc": 0.68344, "loss_cls": 3.20187, "loss": 3.20187, "time": 0.8152} +{"mode": "train", "epoch": 115, "iter": 3100, "lr": 0.01296, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43234, "top5_acc": 0.68391, "loss_cls": 3.18729, "loss": 3.18729, "time": 0.81653} +{"mode": "train", "epoch": 115, "iter": 3200, "lr": 0.01295, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43391, "top5_acc": 0.70094, "loss_cls": 3.14807, "loss": 3.14807, "time": 0.81822} +{"mode": "train", "epoch": 115, "iter": 3300, "lr": 0.01293, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42156, "top5_acc": 0.68953, "loss_cls": 3.19463, "loss": 3.19463, "time": 0.81539} +{"mode": "train", "epoch": 115, "iter": 3400, "lr": 0.01291, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42672, "top5_acc": 0.67953, "loss_cls": 3.21359, "loss": 3.21359, "time": 0.81729} +{"mode": "train", "epoch": 115, "iter": 3500, "lr": 0.01289, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42031, "top5_acc": 0.68781, "loss_cls": 3.2147, "loss": 3.2147, "time": 0.81544} +{"mode": "train", "epoch": 115, "iter": 3600, "lr": 0.01287, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42312, "top5_acc": 0.68984, "loss_cls": 3.22461, "loss": 3.22461, "time": 0.8141} +{"mode": "train", "epoch": 115, "iter": 3700, "lr": 0.01285, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44, "top5_acc": 0.69344, "loss_cls": 3.17955, "loss": 3.17955, "time": 0.81697} +{"mode": "val", "epoch": 115, "iter": 309, "lr": 0.01284, "top1_acc": 0.36697, "top5_acc": 0.62873, "mean_class_accuracy": 0.36681} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.01282, "memory": 15990, "data_time": 1.32449, "top1_acc": 0.45891, "top5_acc": 0.71016, "loss_cls": 3.02862, "loss": 3.02862, "time": 2.29632} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.01281, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45344, "top5_acc": 0.71656, "loss_cls": 3.02307, "loss": 3.02307, "time": 0.82743} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.01279, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44328, "top5_acc": 0.70328, "loss_cls": 3.11275, "loss": 3.11275, "time": 0.8217} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.01277, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44703, "top5_acc": 0.70906, "loss_cls": 3.05811, "loss": 3.05811, "time": 0.82022} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.01275, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44703, "top5_acc": 0.69484, "loss_cls": 3.1243, "loss": 3.1243, "time": 0.81476} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.01273, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43672, "top5_acc": 0.69375, "loss_cls": 3.13578, "loss": 3.13578, "time": 0.81897} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.01271, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43141, "top5_acc": 0.68781, "loss_cls": 3.17191, "loss": 3.17191, "time": 0.81696} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.01269, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43203, "top5_acc": 0.69844, "loss_cls": 3.14714, "loss": 3.14714, "time": 0.81811} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.01268, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42969, "top5_acc": 0.70016, "loss_cls": 3.12152, "loss": 3.12152, "time": 0.81425} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.01266, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44094, "top5_acc": 0.69703, "loss_cls": 3.14147, "loss": 3.14147, "time": 0.81676} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.01264, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42469, "top5_acc": 0.68984, "loss_cls": 3.18927, "loss": 3.18927, "time": 0.8128} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.01262, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43578, "top5_acc": 0.70062, "loss_cls": 3.12579, "loss": 3.12579, "time": 0.81465} +{"mode": "train", "epoch": 116, "iter": 1300, "lr": 0.0126, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44047, "top5_acc": 0.69719, "loss_cls": 3.14483, "loss": 3.14483, "time": 0.82501} +{"mode": "train", "epoch": 116, "iter": 1400, "lr": 0.01258, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43641, "top5_acc": 0.70109, "loss_cls": 3.11427, "loss": 3.11427, "time": 0.82382} +{"mode": "train", "epoch": 116, "iter": 1500, "lr": 0.01256, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42984, "top5_acc": 0.69312, "loss_cls": 3.14809, "loss": 3.14809, "time": 0.81835} +{"mode": "train", "epoch": 116, "iter": 1600, "lr": 0.01255, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.42844, "top5_acc": 0.69797, "loss_cls": 3.1641, "loss": 3.1641, "time": 0.81567} +{"mode": "train", "epoch": 116, "iter": 1700, "lr": 0.01253, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44031, "top5_acc": 0.70594, "loss_cls": 3.08905, "loss": 3.08905, "time": 0.82879} +{"mode": "train", "epoch": 116, "iter": 1800, "lr": 0.01251, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43797, "top5_acc": 0.69516, "loss_cls": 3.13607, "loss": 3.13607, "time": 0.82918} +{"mode": "train", "epoch": 116, "iter": 1900, "lr": 0.01249, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43172, "top5_acc": 0.70219, "loss_cls": 3.12471, "loss": 3.12471, "time": 0.82292} +{"mode": "train", "epoch": 116, "iter": 2000, "lr": 0.01247, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43312, "top5_acc": 0.69375, "loss_cls": 3.17037, "loss": 3.17037, "time": 0.8224} +{"mode": "train", "epoch": 116, "iter": 2100, "lr": 0.01245, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.43094, "top5_acc": 0.69188, "loss_cls": 3.16266, "loss": 3.16266, "time": 0.82091} +{"mode": "train", "epoch": 116, "iter": 2200, "lr": 0.01243, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44688, "top5_acc": 0.70312, "loss_cls": 3.1004, "loss": 3.1004, "time": 0.82201} +{"mode": "train", "epoch": 116, "iter": 2300, "lr": 0.01242, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43484, "top5_acc": 0.69562, "loss_cls": 3.14867, "loss": 3.14867, "time": 0.83597} +{"mode": "train", "epoch": 116, "iter": 2400, "lr": 0.0124, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42625, "top5_acc": 0.68547, "loss_cls": 3.17886, "loss": 3.17886, "time": 0.81264} +{"mode": "train", "epoch": 116, "iter": 2500, "lr": 0.01238, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43188, "top5_acc": 0.68922, "loss_cls": 3.18102, "loss": 3.18102, "time": 0.81479} +{"mode": "train", "epoch": 116, "iter": 2600, "lr": 0.01236, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43219, "top5_acc": 0.69422, "loss_cls": 3.18042, "loss": 3.18042, "time": 0.82157} +{"mode": "train", "epoch": 116, "iter": 2700, "lr": 0.01234, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43516, "top5_acc": 0.69078, "loss_cls": 3.18518, "loss": 3.18518, "time": 0.81729} +{"mode": "train", "epoch": 116, "iter": 2800, "lr": 0.01232, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41938, "top5_acc": 0.69359, "loss_cls": 3.18409, "loss": 3.18409, "time": 0.81111} +{"mode": "train", "epoch": 116, "iter": 2900, "lr": 0.01231, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43328, "top5_acc": 0.69547, "loss_cls": 3.13257, "loss": 3.13257, "time": 0.81198} +{"mode": "train", "epoch": 116, "iter": 3000, "lr": 0.01229, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43172, "top5_acc": 0.69109, "loss_cls": 3.189, "loss": 3.189, "time": 0.81069} +{"mode": "train", "epoch": 116, "iter": 3100, "lr": 0.01227, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42656, "top5_acc": 0.68766, "loss_cls": 3.201, "loss": 3.201, "time": 0.81037} +{"mode": "train", "epoch": 116, "iter": 3200, "lr": 0.01225, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4425, "top5_acc": 0.69109, "loss_cls": 3.16423, "loss": 3.16423, "time": 0.8108} +{"mode": "train", "epoch": 116, "iter": 3300, "lr": 0.01223, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43797, "top5_acc": 0.70016, "loss_cls": 3.14865, "loss": 3.14865, "time": 0.81227} +{"mode": "train", "epoch": 116, "iter": 3400, "lr": 0.01221, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43719, "top5_acc": 0.69406, "loss_cls": 3.14545, "loss": 3.14545, "time": 0.81516} +{"mode": "train", "epoch": 116, "iter": 3500, "lr": 0.0122, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43984, "top5_acc": 0.69812, "loss_cls": 3.14424, "loss": 3.14424, "time": 0.81453} +{"mode": "train", "epoch": 116, "iter": 3600, "lr": 0.01218, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43609, "top5_acc": 0.69672, "loss_cls": 3.13054, "loss": 3.13054, "time": 0.82439} +{"mode": "train", "epoch": 116, "iter": 3700, "lr": 0.01216, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42469, "top5_acc": 0.68734, "loss_cls": 3.19438, "loss": 3.19438, "time": 0.81702} +{"mode": "val", "epoch": 116, "iter": 309, "lr": 0.01215, "top1_acc": 0.37046, "top5_acc": 0.63141, "mean_class_accuracy": 0.3703} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.01213, "memory": 15990, "data_time": 1.26846, "top1_acc": 0.45547, "top5_acc": 0.71953, "loss_cls": 3.02929, "loss": 3.02929, "time": 2.24811} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.01211, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44172, "top5_acc": 0.70828, "loss_cls": 3.09625, "loss": 3.09625, "time": 0.82494} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.0121, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46047, "top5_acc": 0.72047, "loss_cls": 2.98928, "loss": 2.98928, "time": 0.82228} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.01208, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43391, "top5_acc": 0.69828, "loss_cls": 3.10001, "loss": 3.10001, "time": 0.81452} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.01206, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44375, "top5_acc": 0.70453, "loss_cls": 3.07554, "loss": 3.07554, "time": 0.81273} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.01204, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43406, "top5_acc": 0.69828, "loss_cls": 3.11465, "loss": 3.11465, "time": 0.82219} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.01202, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42953, "top5_acc": 0.69547, "loss_cls": 3.18452, "loss": 3.18452, "time": 0.81559} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.012, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44281, "top5_acc": 0.70859, "loss_cls": 3.10447, "loss": 3.10447, "time": 0.81595} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.01199, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45062, "top5_acc": 0.70125, "loss_cls": 3.09266, "loss": 3.09266, "time": 0.81882} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.01197, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.4475, "top5_acc": 0.70781, "loss_cls": 3.09084, "loss": 3.09084, "time": 0.81512} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.01195, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44641, "top5_acc": 0.69922, "loss_cls": 3.12891, "loss": 3.12891, "time": 0.81254} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.01193, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43688, "top5_acc": 0.70484, "loss_cls": 3.09173, "loss": 3.09173, "time": 0.82267} +{"mode": "train", "epoch": 117, "iter": 1300, "lr": 0.01191, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43266, "top5_acc": 0.69328, "loss_cls": 3.13884, "loss": 3.13884, "time": 0.81087} +{"mode": "train", "epoch": 117, "iter": 1400, "lr": 0.0119, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.44812, "top5_acc": 0.71156, "loss_cls": 3.08282, "loss": 3.08282, "time": 0.82656} +{"mode": "train", "epoch": 117, "iter": 1500, "lr": 0.01188, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44875, "top5_acc": 0.70062, "loss_cls": 3.09182, "loss": 3.09182, "time": 0.82159} +{"mode": "train", "epoch": 117, "iter": 1600, "lr": 0.01186, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44297, "top5_acc": 0.70172, "loss_cls": 3.10402, "loss": 3.10402, "time": 0.8112} +{"mode": "train", "epoch": 117, "iter": 1700, "lr": 0.01184, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43656, "top5_acc": 0.69453, "loss_cls": 3.12941, "loss": 3.12941, "time": 0.81959} +{"mode": "train", "epoch": 117, "iter": 1800, "lr": 0.01182, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43688, "top5_acc": 0.70047, "loss_cls": 3.1138, "loss": 3.1138, "time": 0.82442} +{"mode": "train", "epoch": 117, "iter": 1900, "lr": 0.01181, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44016, "top5_acc": 0.71188, "loss_cls": 3.08013, "loss": 3.08013, "time": 0.82412} +{"mode": "train", "epoch": 117, "iter": 2000, "lr": 0.01179, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43359, "top5_acc": 0.69922, "loss_cls": 3.17475, "loss": 3.17475, "time": 0.81796} +{"mode": "train", "epoch": 117, "iter": 2100, "lr": 0.01177, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42828, "top5_acc": 0.69688, "loss_cls": 3.16401, "loss": 3.16401, "time": 0.82233} +{"mode": "train", "epoch": 117, "iter": 2200, "lr": 0.01175, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43766, "top5_acc": 0.69797, "loss_cls": 3.14779, "loss": 3.14779, "time": 0.82157} +{"mode": "train", "epoch": 117, "iter": 2300, "lr": 0.01173, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43781, "top5_acc": 0.70375, "loss_cls": 3.11356, "loss": 3.11356, "time": 0.82531} +{"mode": "train", "epoch": 117, "iter": 2400, "lr": 0.01172, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43047, "top5_acc": 0.69609, "loss_cls": 3.17844, "loss": 3.17844, "time": 0.81381} +{"mode": "train", "epoch": 117, "iter": 2500, "lr": 0.0117, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43078, "top5_acc": 0.70328, "loss_cls": 3.13976, "loss": 3.13976, "time": 0.81195} +{"mode": "train", "epoch": 117, "iter": 2600, "lr": 0.01168, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43797, "top5_acc": 0.69438, "loss_cls": 3.11592, "loss": 3.11592, "time": 0.82055} +{"mode": "train", "epoch": 117, "iter": 2700, "lr": 0.01166, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44578, "top5_acc": 0.70453, "loss_cls": 3.12051, "loss": 3.12051, "time": 0.82822} +{"mode": "train", "epoch": 117, "iter": 2800, "lr": 0.01164, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43406, "top5_acc": 0.69906, "loss_cls": 3.14915, "loss": 3.14915, "time": 0.81968} +{"mode": "train", "epoch": 117, "iter": 2900, "lr": 0.01163, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44453, "top5_acc": 0.69656, "loss_cls": 3.12495, "loss": 3.12495, "time": 0.82064} +{"mode": "train", "epoch": 117, "iter": 3000, "lr": 0.01161, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43516, "top5_acc": 0.69703, "loss_cls": 3.15301, "loss": 3.15301, "time": 0.81888} +{"mode": "train", "epoch": 117, "iter": 3100, "lr": 0.01159, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43328, "top5_acc": 0.69766, "loss_cls": 3.14738, "loss": 3.14738, "time": 0.81904} +{"mode": "train", "epoch": 117, "iter": 3200, "lr": 0.01157, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43375, "top5_acc": 0.69797, "loss_cls": 3.12141, "loss": 3.12141, "time": 0.81356} +{"mode": "train", "epoch": 117, "iter": 3300, "lr": 0.01155, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43672, "top5_acc": 0.69781, "loss_cls": 3.13213, "loss": 3.13213, "time": 0.81176} +{"mode": "train", "epoch": 117, "iter": 3400, "lr": 0.01154, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43219, "top5_acc": 0.69734, "loss_cls": 3.14032, "loss": 3.14032, "time": 0.8172} +{"mode": "train", "epoch": 117, "iter": 3500, "lr": 0.01152, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44047, "top5_acc": 0.69453, "loss_cls": 3.13866, "loss": 3.13866, "time": 0.82018} +{"mode": "train", "epoch": 117, "iter": 3600, "lr": 0.0115, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44, "top5_acc": 0.69594, "loss_cls": 3.13128, "loss": 3.13128, "time": 0.81461} +{"mode": "train", "epoch": 117, "iter": 3700, "lr": 0.01148, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43078, "top5_acc": 0.70047, "loss_cls": 3.15043, "loss": 3.15043, "time": 0.82221} +{"mode": "val", "epoch": 117, "iter": 309, "lr": 0.01147, "top1_acc": 0.37593, "top5_acc": 0.63607, "mean_class_accuracy": 0.37574} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.01146, "memory": 15990, "data_time": 1.2867, "top1_acc": 0.46172, "top5_acc": 0.71859, "loss_cls": 3.00787, "loss": 3.00787, "time": 2.25111} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.01144, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45281, "top5_acc": 0.71375, "loss_cls": 3.04021, "loss": 3.04021, "time": 0.82182} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.01142, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45562, "top5_acc": 0.70844, "loss_cls": 3.05734, "loss": 3.05734, "time": 0.8192} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.0114, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44234, "top5_acc": 0.7075, "loss_cls": 3.06391, "loss": 3.06391, "time": 0.81667} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.01139, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45672, "top5_acc": 0.71406, "loss_cls": 3.03725, "loss": 3.03725, "time": 0.81634} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.01137, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45062, "top5_acc": 0.70703, "loss_cls": 3.07782, "loss": 3.07782, "time": 0.8142} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.01135, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44, "top5_acc": 0.70078, "loss_cls": 3.12671, "loss": 3.12671, "time": 0.81264} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.01133, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43047, "top5_acc": 0.69438, "loss_cls": 3.12092, "loss": 3.12092, "time": 0.81936} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.01131, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45844, "top5_acc": 0.71375, "loss_cls": 3.03003, "loss": 3.03003, "time": 0.81723} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.0113, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44078, "top5_acc": 0.70203, "loss_cls": 3.10773, "loss": 3.10773, "time": 0.82239} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.01128, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44828, "top5_acc": 0.705, "loss_cls": 3.09848, "loss": 3.09848, "time": 0.81537} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.01126, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45094, "top5_acc": 0.71297, "loss_cls": 3.06369, "loss": 3.06369, "time": 0.81654} +{"mode": "train", "epoch": 118, "iter": 1300, "lr": 0.01124, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43719, "top5_acc": 0.69438, "loss_cls": 3.12614, "loss": 3.12614, "time": 0.81366} +{"mode": "train", "epoch": 118, "iter": 1400, "lr": 0.01123, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43859, "top5_acc": 0.70688, "loss_cls": 3.09125, "loss": 3.09125, "time": 0.82154} +{"mode": "train", "epoch": 118, "iter": 1500, "lr": 0.01121, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44297, "top5_acc": 0.71, "loss_cls": 3.07718, "loss": 3.07718, "time": 0.81983} +{"mode": "train", "epoch": 118, "iter": 1600, "lr": 0.01119, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43781, "top5_acc": 0.70719, "loss_cls": 3.12126, "loss": 3.12126, "time": 0.8225} +{"mode": "train", "epoch": 118, "iter": 1700, "lr": 0.01117, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44547, "top5_acc": 0.70125, "loss_cls": 3.07685, "loss": 3.07685, "time": 0.82518} +{"mode": "train", "epoch": 118, "iter": 1800, "lr": 0.01116, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43953, "top5_acc": 0.71156, "loss_cls": 3.09203, "loss": 3.09203, "time": 0.81828} +{"mode": "train", "epoch": 118, "iter": 1900, "lr": 0.01114, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44219, "top5_acc": 0.70359, "loss_cls": 3.07523, "loss": 3.07523, "time": 0.83156} +{"mode": "train", "epoch": 118, "iter": 2000, "lr": 0.01112, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.43484, "top5_acc": 0.69656, "loss_cls": 3.13994, "loss": 3.13994, "time": 0.81585} +{"mode": "train", "epoch": 118, "iter": 2100, "lr": 0.0111, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43641, "top5_acc": 0.7025, "loss_cls": 3.12197, "loss": 3.12197, "time": 0.82135} +{"mode": "train", "epoch": 118, "iter": 2200, "lr": 0.01109, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45766, "top5_acc": 0.71156, "loss_cls": 3.03781, "loss": 3.03781, "time": 0.82577} +{"mode": "train", "epoch": 118, "iter": 2300, "lr": 0.01107, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45141, "top5_acc": 0.71531, "loss_cls": 3.06695, "loss": 3.06695, "time": 0.82399} +{"mode": "train", "epoch": 118, "iter": 2400, "lr": 0.01105, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44469, "top5_acc": 0.70234, "loss_cls": 3.08745, "loss": 3.08745, "time": 0.81608} +{"mode": "train", "epoch": 118, "iter": 2500, "lr": 0.01103, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44672, "top5_acc": 0.70656, "loss_cls": 3.07836, "loss": 3.07836, "time": 0.80752} +{"mode": "train", "epoch": 118, "iter": 2600, "lr": 0.01102, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43109, "top5_acc": 0.70031, "loss_cls": 3.11808, "loss": 3.11808, "time": 0.81875} +{"mode": "train", "epoch": 118, "iter": 2700, "lr": 0.011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4475, "top5_acc": 0.69875, "loss_cls": 3.07981, "loss": 3.07981, "time": 0.82087} +{"mode": "train", "epoch": 118, "iter": 2800, "lr": 0.01098, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44188, "top5_acc": 0.69938, "loss_cls": 3.10537, "loss": 3.10537, "time": 0.82043} +{"mode": "train", "epoch": 118, "iter": 2900, "lr": 0.01096, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43281, "top5_acc": 0.68953, "loss_cls": 3.15622, "loss": 3.15622, "time": 0.81662} +{"mode": "train", "epoch": 118, "iter": 3000, "lr": 0.01095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44484, "top5_acc": 0.70094, "loss_cls": 3.09498, "loss": 3.09498, "time": 0.81889} +{"mode": "train", "epoch": 118, "iter": 3100, "lr": 0.01093, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44125, "top5_acc": 0.70922, "loss_cls": 3.087, "loss": 3.087, "time": 0.81364} +{"mode": "train", "epoch": 118, "iter": 3200, "lr": 0.01091, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43859, "top5_acc": 0.70859, "loss_cls": 3.08756, "loss": 3.08756, "time": 0.81443} +{"mode": "train", "epoch": 118, "iter": 3300, "lr": 0.01089, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43453, "top5_acc": 0.69688, "loss_cls": 3.13472, "loss": 3.13472, "time": 0.82119} +{"mode": "train", "epoch": 118, "iter": 3400, "lr": 0.01088, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43531, "top5_acc": 0.70094, "loss_cls": 3.12579, "loss": 3.12579, "time": 0.81626} +{"mode": "train", "epoch": 118, "iter": 3500, "lr": 0.01086, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44812, "top5_acc": 0.70766, "loss_cls": 3.06539, "loss": 3.06539, "time": 0.81646} +{"mode": "train", "epoch": 118, "iter": 3600, "lr": 0.01084, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43234, "top5_acc": 0.70234, "loss_cls": 3.13608, "loss": 3.13608, "time": 0.81365} +{"mode": "train", "epoch": 118, "iter": 3700, "lr": 0.01082, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.43391, "top5_acc": 0.7025, "loss_cls": 3.15262, "loss": 3.15262, "time": 0.82858} +{"mode": "val", "epoch": 118, "iter": 309, "lr": 0.01082, "top1_acc": 0.37816, "top5_acc": 0.64352, "mean_class_accuracy": 0.37773} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.0108, "memory": 15990, "data_time": 1.27553, "top1_acc": 0.46438, "top5_acc": 0.73109, "loss_cls": 2.97237, "loss": 2.97237, "time": 2.24199} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.01078, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45656, "top5_acc": 0.72156, "loss_cls": 3.00193, "loss": 3.00193, "time": 0.82135} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.01076, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46703, "top5_acc": 0.71938, "loss_cls": 2.99245, "loss": 2.99245, "time": 0.81088} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.01075, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45406, "top5_acc": 0.71141, "loss_cls": 3.02484, "loss": 3.02484, "time": 0.81486} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.01073, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45078, "top5_acc": 0.71734, "loss_cls": 3.02606, "loss": 3.02606, "time": 0.81971} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.01071, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44781, "top5_acc": 0.70891, "loss_cls": 3.08094, "loss": 3.08094, "time": 0.8118} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.01069, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45016, "top5_acc": 0.70672, "loss_cls": 3.05106, "loss": 3.05106, "time": 0.81226} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.01068, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46422, "top5_acc": 0.71203, "loss_cls": 3.03216, "loss": 3.03216, "time": 0.8109} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.01066, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45375, "top5_acc": 0.71719, "loss_cls": 3.03358, "loss": 3.03358, "time": 0.81401} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.01064, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43812, "top5_acc": 0.70359, "loss_cls": 3.09599, "loss": 3.09599, "time": 0.81391} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.01063, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45422, "top5_acc": 0.71766, "loss_cls": 3.0301, "loss": 3.0301, "time": 0.81618} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.01061, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45156, "top5_acc": 0.71062, "loss_cls": 3.05376, "loss": 3.05376, "time": 0.82236} +{"mode": "train", "epoch": 119, "iter": 1300, "lr": 0.01059, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44594, "top5_acc": 0.70969, "loss_cls": 3.05772, "loss": 3.05772, "time": 0.82129} +{"mode": "train", "epoch": 119, "iter": 1400, "lr": 0.01057, "memory": 15990, "data_time": 0.0007, "top1_acc": 0.45188, "top5_acc": 0.70719, "loss_cls": 3.04926, "loss": 3.04926, "time": 0.82354} +{"mode": "train", "epoch": 119, "iter": 1500, "lr": 0.01056, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45109, "top5_acc": 0.70453, "loss_cls": 3.05514, "loss": 3.05514, "time": 0.81823} +{"mode": "train", "epoch": 119, "iter": 1600, "lr": 0.01054, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45188, "top5_acc": 0.71438, "loss_cls": 3.02387, "loss": 3.02387, "time": 0.81702} +{"mode": "train", "epoch": 119, "iter": 1700, "lr": 0.01052, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44578, "top5_acc": 0.71281, "loss_cls": 3.02472, "loss": 3.02472, "time": 0.82027} +{"mode": "train", "epoch": 119, "iter": 1800, "lr": 0.0105, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45391, "top5_acc": 0.70891, "loss_cls": 3.03638, "loss": 3.03638, "time": 0.82363} +{"mode": "train", "epoch": 119, "iter": 1900, "lr": 0.01049, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.45125, "top5_acc": 0.70906, "loss_cls": 3.09316, "loss": 3.09316, "time": 0.83419} +{"mode": "train", "epoch": 119, "iter": 2000, "lr": 0.01047, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44703, "top5_acc": 0.70797, "loss_cls": 3.05203, "loss": 3.05203, "time": 0.82127} +{"mode": "train", "epoch": 119, "iter": 2100, "lr": 0.01045, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45109, "top5_acc": 0.70906, "loss_cls": 3.05376, "loss": 3.05376, "time": 0.81778} +{"mode": "train", "epoch": 119, "iter": 2200, "lr": 0.01044, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44281, "top5_acc": 0.705, "loss_cls": 3.08059, "loss": 3.08059, "time": 0.82947} +{"mode": "train", "epoch": 119, "iter": 2300, "lr": 0.01042, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.45281, "top5_acc": 0.70641, "loss_cls": 3.04482, "loss": 3.04482, "time": 0.83188} +{"mode": "train", "epoch": 119, "iter": 2400, "lr": 0.0104, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.45188, "top5_acc": 0.71016, "loss_cls": 3.05745, "loss": 3.05745, "time": 0.81893} +{"mode": "train", "epoch": 119, "iter": 2500, "lr": 0.01039, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44562, "top5_acc": 0.71078, "loss_cls": 3.05911, "loss": 3.05911, "time": 0.81267} +{"mode": "train", "epoch": 119, "iter": 2600, "lr": 0.01037, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44359, "top5_acc": 0.70203, "loss_cls": 3.08789, "loss": 3.08789, "time": 0.82748} +{"mode": "train", "epoch": 119, "iter": 2700, "lr": 0.01035, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42672, "top5_acc": 0.69562, "loss_cls": 3.16449, "loss": 3.16449, "time": 0.82806} +{"mode": "train", "epoch": 119, "iter": 2800, "lr": 0.01033, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43438, "top5_acc": 0.70531, "loss_cls": 3.10103, "loss": 3.10103, "time": 0.82305} +{"mode": "train", "epoch": 119, "iter": 2900, "lr": 0.01032, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44812, "top5_acc": 0.70766, "loss_cls": 3.07691, "loss": 3.07691, "time": 0.82425} +{"mode": "train", "epoch": 119, "iter": 3000, "lr": 0.0103, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44484, "top5_acc": 0.705, "loss_cls": 3.08541, "loss": 3.08541, "time": 0.81636} +{"mode": "train", "epoch": 119, "iter": 3100, "lr": 0.01028, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45438, "top5_acc": 0.7075, "loss_cls": 3.07398, "loss": 3.07398, "time": 0.81465} +{"mode": "train", "epoch": 119, "iter": 3200, "lr": 0.01027, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45125, "top5_acc": 0.71328, "loss_cls": 3.0569, "loss": 3.0569, "time": 0.81471} +{"mode": "train", "epoch": 119, "iter": 3300, "lr": 0.01025, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44234, "top5_acc": 0.69688, "loss_cls": 3.10982, "loss": 3.10982, "time": 0.81681} +{"mode": "train", "epoch": 119, "iter": 3400, "lr": 0.01023, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.4425, "top5_acc": 0.69828, "loss_cls": 3.12378, "loss": 3.12378, "time": 0.81348} +{"mode": "train", "epoch": 119, "iter": 3500, "lr": 0.01022, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44969, "top5_acc": 0.70859, "loss_cls": 3.08346, "loss": 3.08346, "time": 0.8127} +{"mode": "train", "epoch": 119, "iter": 3600, "lr": 0.0102, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43578, "top5_acc": 0.69781, "loss_cls": 3.14779, "loss": 3.14779, "time": 0.81556} +{"mode": "train", "epoch": 119, "iter": 3700, "lr": 0.01018, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44609, "top5_acc": 0.71062, "loss_cls": 3.07818, "loss": 3.07818, "time": 0.82349} +{"mode": "val", "epoch": 119, "iter": 309, "lr": 0.01017, "top1_acc": 0.38272, "top5_acc": 0.6379, "mean_class_accuracy": 0.38254} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.01016, "memory": 15990, "data_time": 1.26358, "top1_acc": 0.46562, "top5_acc": 0.72516, "loss_cls": 2.96225, "loss": 2.96225, "time": 2.23596} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.01014, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46031, "top5_acc": 0.71328, "loss_cls": 2.97247, "loss": 2.97247, "time": 0.81766} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.01012, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46062, "top5_acc": 0.71391, "loss_cls": 3.00048, "loss": 3.00048, "time": 0.82191} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.01011, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44875, "top5_acc": 0.7225, "loss_cls": 3.03503, "loss": 3.03503, "time": 0.81399} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.01009, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46703, "top5_acc": 0.72578, "loss_cls": 2.9546, "loss": 2.9546, "time": 0.81355} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.01007, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45, "top5_acc": 0.72328, "loss_cls": 3.00912, "loss": 3.00912, "time": 0.81746} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.01006, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46406, "top5_acc": 0.72422, "loss_cls": 2.97196, "loss": 2.97196, "time": 0.81299} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.01004, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46031, "top5_acc": 0.71359, "loss_cls": 2.99986, "loss": 2.99986, "time": 0.81228} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.01002, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46828, "top5_acc": 0.72125, "loss_cls": 2.97242, "loss": 2.97242, "time": 0.81301} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.01001, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45875, "top5_acc": 0.72109, "loss_cls": 2.98008, "loss": 2.98008, "time": 0.81683} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00999, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44594, "top5_acc": 0.70484, "loss_cls": 3.07627, "loss": 3.07627, "time": 0.81008} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.00997, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45062, "top5_acc": 0.71672, "loss_cls": 3.04732, "loss": 3.04732, "time": 0.81574} +{"mode": "train", "epoch": 120, "iter": 1300, "lr": 0.00996, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46078, "top5_acc": 0.72719, "loss_cls": 2.96492, "loss": 2.96492, "time": 0.82015} +{"mode": "train", "epoch": 120, "iter": 1400, "lr": 0.00994, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.4575, "top5_acc": 0.71359, "loss_cls": 3.01029, "loss": 3.01029, "time": 0.8202} +{"mode": "train", "epoch": 120, "iter": 1500, "lr": 0.00992, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45078, "top5_acc": 0.71906, "loss_cls": 3.05484, "loss": 3.05484, "time": 0.83119} +{"mode": "train", "epoch": 120, "iter": 1600, "lr": 0.0099, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45406, "top5_acc": 0.71031, "loss_cls": 3.04087, "loss": 3.04087, "time": 0.81781} +{"mode": "train", "epoch": 120, "iter": 1700, "lr": 0.00989, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45703, "top5_acc": 0.71516, "loss_cls": 3.03351, "loss": 3.03351, "time": 0.82653} +{"mode": "train", "epoch": 120, "iter": 1800, "lr": 0.00987, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44406, "top5_acc": 0.71391, "loss_cls": 3.05145, "loss": 3.05145, "time": 0.82208} +{"mode": "train", "epoch": 120, "iter": 1900, "lr": 0.00985, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.45734, "top5_acc": 0.72297, "loss_cls": 3.00159, "loss": 3.00159, "time": 0.82975} +{"mode": "train", "epoch": 120, "iter": 2000, "lr": 0.00984, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43938, "top5_acc": 0.69656, "loss_cls": 3.0998, "loss": 3.0998, "time": 0.82792} +{"mode": "train", "epoch": 120, "iter": 2100, "lr": 0.00982, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45641, "top5_acc": 0.70922, "loss_cls": 3.04134, "loss": 3.04134, "time": 0.82334} +{"mode": "train", "epoch": 120, "iter": 2200, "lr": 0.0098, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.46172, "top5_acc": 0.71641, "loss_cls": 3.02138, "loss": 3.02138, "time": 0.82543} +{"mode": "train", "epoch": 120, "iter": 2300, "lr": 0.00979, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.45453, "top5_acc": 0.70734, "loss_cls": 3.08626, "loss": 3.08626, "time": 0.82864} +{"mode": "train", "epoch": 120, "iter": 2400, "lr": 0.00977, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.46281, "top5_acc": 0.71359, "loss_cls": 3.0117, "loss": 3.0117, "time": 0.81205} +{"mode": "train", "epoch": 120, "iter": 2500, "lr": 0.00976, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43906, "top5_acc": 0.7075, "loss_cls": 3.07574, "loss": 3.07574, "time": 0.81359} +{"mode": "train", "epoch": 120, "iter": 2600, "lr": 0.00974, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4475, "top5_acc": 0.71344, "loss_cls": 3.05754, "loss": 3.05754, "time": 0.82544} +{"mode": "train", "epoch": 120, "iter": 2700, "lr": 0.00972, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45188, "top5_acc": 0.71484, "loss_cls": 3.04327, "loss": 3.04327, "time": 0.82387} +{"mode": "train", "epoch": 120, "iter": 2800, "lr": 0.00971, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45203, "top5_acc": 0.70469, "loss_cls": 3.068, "loss": 3.068, "time": 0.81905} +{"mode": "train", "epoch": 120, "iter": 2900, "lr": 0.00969, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46031, "top5_acc": 0.72109, "loss_cls": 3.00251, "loss": 3.00251, "time": 0.8205} +{"mode": "train", "epoch": 120, "iter": 3000, "lr": 0.00967, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.455, "top5_acc": 0.70781, "loss_cls": 3.06235, "loss": 3.06235, "time": 0.81338} +{"mode": "train", "epoch": 120, "iter": 3100, "lr": 0.00966, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44109, "top5_acc": 0.70266, "loss_cls": 3.08744, "loss": 3.08744, "time": 0.81735} +{"mode": "train", "epoch": 120, "iter": 3200, "lr": 0.00964, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44125, "top5_acc": 0.70562, "loss_cls": 3.07695, "loss": 3.07695, "time": 0.81934} +{"mode": "train", "epoch": 120, "iter": 3300, "lr": 0.00962, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43516, "top5_acc": 0.70031, "loss_cls": 3.12294, "loss": 3.12294, "time": 0.81587} +{"mode": "train", "epoch": 120, "iter": 3400, "lr": 0.00961, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44312, "top5_acc": 0.69938, "loss_cls": 3.09751, "loss": 3.09751, "time": 0.81504} +{"mode": "train", "epoch": 120, "iter": 3500, "lr": 0.00959, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45078, "top5_acc": 0.71531, "loss_cls": 3.03188, "loss": 3.03188, "time": 0.81229} +{"mode": "train", "epoch": 120, "iter": 3600, "lr": 0.00957, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45484, "top5_acc": 0.70969, "loss_cls": 3.06374, "loss": 3.06374, "time": 0.82077} +{"mode": "train", "epoch": 120, "iter": 3700, "lr": 0.00956, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.45, "top5_acc": 0.70062, "loss_cls": 3.07122, "loss": 3.07122, "time": 0.81974} +{"mode": "val", "epoch": 120, "iter": 309, "lr": 0.00955, "top1_acc": 0.37917, "top5_acc": 0.63962, "mean_class_accuracy": 0.37892} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00953, "memory": 15990, "data_time": 1.28738, "top1_acc": 0.47078, "top5_acc": 0.72938, "loss_cls": 2.9488, "loss": 2.9488, "time": 2.25666} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00952, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45375, "top5_acc": 0.72547, "loss_cls": 2.99007, "loss": 2.99007, "time": 0.82024} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.0095, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.4725, "top5_acc": 0.73109, "loss_cls": 2.91267, "loss": 2.91267, "time": 0.81439} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00948, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46703, "top5_acc": 0.73156, "loss_cls": 2.95172, "loss": 2.95172, "time": 0.81798} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00947, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46516, "top5_acc": 0.71375, "loss_cls": 2.97922, "loss": 2.97922, "time": 0.81407} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00945, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45188, "top5_acc": 0.71391, "loss_cls": 3.0551, "loss": 3.0551, "time": 0.81977} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.00943, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44734, "top5_acc": 0.72359, "loss_cls": 3.01083, "loss": 3.01083, "time": 0.81455} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00942, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46672, "top5_acc": 0.72312, "loss_cls": 2.97228, "loss": 2.97228, "time": 0.81458} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.0094, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45953, "top5_acc": 0.7175, "loss_cls": 3.00674, "loss": 3.00674, "time": 0.81277} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00939, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44875, "top5_acc": 0.72, "loss_cls": 3.01079, "loss": 3.01079, "time": 0.81744} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00937, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46656, "top5_acc": 0.72234, "loss_cls": 2.96751, "loss": 2.96751, "time": 0.81671} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00935, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46125, "top5_acc": 0.72047, "loss_cls": 2.99315, "loss": 2.99315, "time": 0.81773} +{"mode": "train", "epoch": 121, "iter": 1300, "lr": 0.00934, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44844, "top5_acc": 0.71094, "loss_cls": 3.04307, "loss": 3.04307, "time": 0.81305} +{"mode": "train", "epoch": 121, "iter": 1400, "lr": 0.00932, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45156, "top5_acc": 0.71766, "loss_cls": 2.99843, "loss": 2.99843, "time": 0.82003} +{"mode": "train", "epoch": 121, "iter": 1500, "lr": 0.0093, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46438, "top5_acc": 0.72141, "loss_cls": 2.96765, "loss": 2.96765, "time": 0.82177} +{"mode": "train", "epoch": 121, "iter": 1600, "lr": 0.00929, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4575, "top5_acc": 0.72422, "loss_cls": 3.01439, "loss": 3.01439, "time": 0.81303} +{"mode": "train", "epoch": 121, "iter": 1700, "lr": 0.00927, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46359, "top5_acc": 0.72688, "loss_cls": 2.95784, "loss": 2.95784, "time": 0.81954} +{"mode": "train", "epoch": 121, "iter": 1800, "lr": 0.00926, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46078, "top5_acc": 0.71625, "loss_cls": 2.99855, "loss": 2.99855, "time": 0.83097} +{"mode": "train", "epoch": 121, "iter": 1900, "lr": 0.00924, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44188, "top5_acc": 0.70484, "loss_cls": 3.06797, "loss": 3.06797, "time": 0.83161} +{"mode": "train", "epoch": 121, "iter": 2000, "lr": 0.00922, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45453, "top5_acc": 0.71422, "loss_cls": 3.02221, "loss": 3.02221, "time": 0.81991} +{"mode": "train", "epoch": 121, "iter": 2100, "lr": 0.00921, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45672, "top5_acc": 0.71703, "loss_cls": 3.00204, "loss": 3.00204, "time": 0.81685} +{"mode": "train", "epoch": 121, "iter": 2200, "lr": 0.00919, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45047, "top5_acc": 0.70969, "loss_cls": 3.04908, "loss": 3.04908, "time": 0.82483} +{"mode": "train", "epoch": 121, "iter": 2300, "lr": 0.00917, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46125, "top5_acc": 0.71906, "loss_cls": 2.9885, "loss": 2.9885, "time": 0.82828} +{"mode": "train", "epoch": 121, "iter": 2400, "lr": 0.00916, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45234, "top5_acc": 0.71484, "loss_cls": 3.01088, "loss": 3.01088, "time": 0.82126} +{"mode": "train", "epoch": 121, "iter": 2500, "lr": 0.00914, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45312, "top5_acc": 0.70625, "loss_cls": 3.03914, "loss": 3.03914, "time": 0.8182} +{"mode": "train", "epoch": 121, "iter": 2600, "lr": 0.00913, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45906, "top5_acc": 0.71188, "loss_cls": 3.01421, "loss": 3.01421, "time": 0.82705} +{"mode": "train", "epoch": 121, "iter": 2700, "lr": 0.00911, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45359, "top5_acc": 0.71125, "loss_cls": 3.01676, "loss": 3.01676, "time": 0.82579} +{"mode": "train", "epoch": 121, "iter": 2800, "lr": 0.00909, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44203, "top5_acc": 0.71516, "loss_cls": 3.06542, "loss": 3.06542, "time": 0.82104} +{"mode": "train", "epoch": 121, "iter": 2900, "lr": 0.00908, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45453, "top5_acc": 0.71156, "loss_cls": 3.03968, "loss": 3.03968, "time": 0.82239} +{"mode": "train", "epoch": 121, "iter": 3000, "lr": 0.00906, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46078, "top5_acc": 0.71406, "loss_cls": 3.04055, "loss": 3.04055, "time": 0.81463} +{"mode": "train", "epoch": 121, "iter": 3100, "lr": 0.00905, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46188, "top5_acc": 0.71656, "loss_cls": 3.02284, "loss": 3.02284, "time": 0.81535} +{"mode": "train", "epoch": 121, "iter": 3200, "lr": 0.00903, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44609, "top5_acc": 0.70531, "loss_cls": 3.03025, "loss": 3.03025, "time": 0.8139} +{"mode": "train", "epoch": 121, "iter": 3300, "lr": 0.00901, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45531, "top5_acc": 0.71641, "loss_cls": 2.98125, "loss": 2.98125, "time": 0.81395} +{"mode": "train", "epoch": 121, "iter": 3400, "lr": 0.009, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45453, "top5_acc": 0.71719, "loss_cls": 3.02853, "loss": 3.02853, "time": 0.81513} +{"mode": "train", "epoch": 121, "iter": 3500, "lr": 0.00898, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45844, "top5_acc": 0.71047, "loss_cls": 3.01751, "loss": 3.01751, "time": 0.81596} +{"mode": "train", "epoch": 121, "iter": 3600, "lr": 0.00897, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.45812, "top5_acc": 0.71297, "loss_cls": 3.02592, "loss": 3.02592, "time": 0.81852} +{"mode": "train", "epoch": 121, "iter": 3700, "lr": 0.00895, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44547, "top5_acc": 0.70938, "loss_cls": 3.05816, "loss": 3.05816, "time": 0.82502} +{"mode": "val", "epoch": 121, "iter": 309, "lr": 0.00894, "top1_acc": 0.39629, "top5_acc": 0.64696, "mean_class_accuracy": 0.39584} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00893, "memory": 15990, "data_time": 1.28208, "top1_acc": 0.48297, "top5_acc": 0.73594, "loss_cls": 2.86447, "loss": 2.86447, "time": 2.25754} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00891, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48094, "top5_acc": 0.73094, "loss_cls": 2.90989, "loss": 2.90989, "time": 0.82497} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.00889, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47391, "top5_acc": 0.73156, "loss_cls": 2.91481, "loss": 2.91481, "time": 0.82391} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00888, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46875, "top5_acc": 0.73281, "loss_cls": 2.94111, "loss": 2.94111, "time": 0.82976} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00886, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46891, "top5_acc": 0.72734, "loss_cls": 2.92806, "loss": 2.92806, "time": 0.81966} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00885, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46641, "top5_acc": 0.72609, "loss_cls": 2.93831, "loss": 2.93831, "time": 0.82153} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00883, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46109, "top5_acc": 0.71891, "loss_cls": 2.99894, "loss": 2.99894, "time": 0.81754} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00882, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47094, "top5_acc": 0.725, "loss_cls": 2.95094, "loss": 2.95094, "time": 0.81909} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.0088, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47, "top5_acc": 0.72078, "loss_cls": 2.97481, "loss": 2.97481, "time": 0.81822} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00878, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47172, "top5_acc": 0.72594, "loss_cls": 2.94566, "loss": 2.94566, "time": 0.82028} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00877, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46672, "top5_acc": 0.73266, "loss_cls": 2.93364, "loss": 2.93364, "time": 0.81965} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.00875, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47125, "top5_acc": 0.72828, "loss_cls": 2.92678, "loss": 2.92678, "time": 0.81819} +{"mode": "train", "epoch": 122, "iter": 1300, "lr": 0.00874, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45766, "top5_acc": 0.72047, "loss_cls": 2.99348, "loss": 2.99348, "time": 0.82565} +{"mode": "train", "epoch": 122, "iter": 1400, "lr": 0.00872, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45516, "top5_acc": 0.72047, "loss_cls": 2.98467, "loss": 2.98467, "time": 0.81991} +{"mode": "train", "epoch": 122, "iter": 1500, "lr": 0.0087, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46438, "top5_acc": 0.71953, "loss_cls": 2.97068, "loss": 2.97068, "time": 0.81857} +{"mode": "train", "epoch": 122, "iter": 1600, "lr": 0.00869, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46297, "top5_acc": 0.72219, "loss_cls": 2.98073, "loss": 2.98073, "time": 0.82142} +{"mode": "train", "epoch": 122, "iter": 1700, "lr": 0.00867, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.4625, "top5_acc": 0.72031, "loss_cls": 2.97584, "loss": 2.97584, "time": 0.82285} +{"mode": "train", "epoch": 122, "iter": 1800, "lr": 0.00866, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46672, "top5_acc": 0.72969, "loss_cls": 2.95348, "loss": 2.95348, "time": 0.81801} +{"mode": "train", "epoch": 122, "iter": 1900, "lr": 0.00864, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46172, "top5_acc": 0.72203, "loss_cls": 2.98545, "loss": 2.98545, "time": 0.82228} +{"mode": "train", "epoch": 122, "iter": 2000, "lr": 0.00863, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46109, "top5_acc": 0.71891, "loss_cls": 2.97479, "loss": 2.97479, "time": 0.82432} +{"mode": "train", "epoch": 122, "iter": 2100, "lr": 0.00861, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46781, "top5_acc": 0.72422, "loss_cls": 2.95755, "loss": 2.95755, "time": 0.82573} +{"mode": "train", "epoch": 122, "iter": 2200, "lr": 0.00859, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45031, "top5_acc": 0.71141, "loss_cls": 3.0353, "loss": 3.0353, "time": 0.82288} +{"mode": "train", "epoch": 122, "iter": 2300, "lr": 0.00858, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45297, "top5_acc": 0.70906, "loss_cls": 3.02429, "loss": 3.02429, "time": 0.82596} +{"mode": "train", "epoch": 122, "iter": 2400, "lr": 0.00856, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46375, "top5_acc": 0.72219, "loss_cls": 2.98189, "loss": 2.98189, "time": 0.8205} +{"mode": "train", "epoch": 122, "iter": 2500, "lr": 0.00855, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46, "top5_acc": 0.72234, "loss_cls": 2.98331, "loss": 2.98331, "time": 0.82487} +{"mode": "train", "epoch": 122, "iter": 2600, "lr": 0.00853, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46406, "top5_acc": 0.71094, "loss_cls": 3.01348, "loss": 3.01348, "time": 0.8264} +{"mode": "train", "epoch": 122, "iter": 2700, "lr": 0.00852, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46172, "top5_acc": 0.72516, "loss_cls": 2.95924, "loss": 2.95924, "time": 0.82353} +{"mode": "train", "epoch": 122, "iter": 2800, "lr": 0.0085, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47562, "top5_acc": 0.72953, "loss_cls": 2.94832, "loss": 2.94832, "time": 0.82145} +{"mode": "train", "epoch": 122, "iter": 2900, "lr": 0.00849, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46375, "top5_acc": 0.71312, "loss_cls": 3.00031, "loss": 3.00031, "time": 0.81733} +{"mode": "train", "epoch": 122, "iter": 3000, "lr": 0.00847, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44781, "top5_acc": 0.715, "loss_cls": 3.03692, "loss": 3.03692, "time": 0.82573} +{"mode": "train", "epoch": 122, "iter": 3100, "lr": 0.00845, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46609, "top5_acc": 0.71938, "loss_cls": 2.99376, "loss": 2.99376, "time": 0.81814} +{"mode": "train", "epoch": 122, "iter": 3200, "lr": 0.00844, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46391, "top5_acc": 0.71781, "loss_cls": 3.01709, "loss": 3.01709, "time": 0.82092} +{"mode": "train", "epoch": 122, "iter": 3300, "lr": 0.00842, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46422, "top5_acc": 0.71312, "loss_cls": 3.01442, "loss": 3.01442, "time": 0.81715} +{"mode": "train", "epoch": 122, "iter": 3400, "lr": 0.00841, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45734, "top5_acc": 0.71828, "loss_cls": 2.99526, "loss": 2.99526, "time": 0.8173} +{"mode": "train", "epoch": 122, "iter": 3500, "lr": 0.00839, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45422, "top5_acc": 0.71781, "loss_cls": 3.0257, "loss": 3.0257, "time": 0.82229} +{"mode": "train", "epoch": 122, "iter": 3600, "lr": 0.00838, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45688, "top5_acc": 0.72297, "loss_cls": 2.98791, "loss": 2.98791, "time": 0.82251} +{"mode": "train", "epoch": 122, "iter": 3700, "lr": 0.00836, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.4525, "top5_acc": 0.71906, "loss_cls": 3.0151, "loss": 3.0151, "time": 0.81889} +{"mode": "val", "epoch": 122, "iter": 309, "lr": 0.00835, "top1_acc": 0.39366, "top5_acc": 0.65031, "mean_class_accuracy": 0.39327} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00834, "memory": 15990, "data_time": 1.2975, "top1_acc": 0.4775, "top5_acc": 0.73641, "loss_cls": 2.89229, "loss": 2.89229, "time": 2.27964} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00832, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48734, "top5_acc": 0.74375, "loss_cls": 2.83692, "loss": 2.83692, "time": 0.82941} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00831, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47438, "top5_acc": 0.73766, "loss_cls": 2.9122, "loss": 2.9122, "time": 0.82095} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00829, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46828, "top5_acc": 0.72859, "loss_cls": 2.94763, "loss": 2.94763, "time": 0.81955} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00828, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48656, "top5_acc": 0.73984, "loss_cls": 2.87705, "loss": 2.87705, "time": 0.82019} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00826, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47594, "top5_acc": 0.72578, "loss_cls": 2.93272, "loss": 2.93272, "time": 0.82102} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00825, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48156, "top5_acc": 0.73625, "loss_cls": 2.90099, "loss": 2.90099, "time": 0.81468} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.00823, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46875, "top5_acc": 0.72562, "loss_cls": 2.94274, "loss": 2.94274, "time": 0.82324} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00822, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48281, "top5_acc": 0.72906, "loss_cls": 2.89053, "loss": 2.89053, "time": 0.8217} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.0082, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46781, "top5_acc": 0.73031, "loss_cls": 2.92506, "loss": 2.92506, "time": 0.81899} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00818, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47984, "top5_acc": 0.73766, "loss_cls": 2.90567, "loss": 2.90567, "time": 0.8196} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00817, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46859, "top5_acc": 0.73047, "loss_cls": 2.9207, "loss": 2.9207, "time": 0.82092} +{"mode": "train", "epoch": 123, "iter": 1300, "lr": 0.00815, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47641, "top5_acc": 0.73281, "loss_cls": 2.88499, "loss": 2.88499, "time": 0.81728} +{"mode": "train", "epoch": 123, "iter": 1400, "lr": 0.00814, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46219, "top5_acc": 0.73266, "loss_cls": 2.94088, "loss": 2.94088, "time": 0.81787} +{"mode": "train", "epoch": 123, "iter": 1500, "lr": 0.00812, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46906, "top5_acc": 0.71797, "loss_cls": 2.98357, "loss": 2.98357, "time": 0.81814} +{"mode": "train", "epoch": 123, "iter": 1600, "lr": 0.00811, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45453, "top5_acc": 0.72188, "loss_cls": 3.00069, "loss": 3.00069, "time": 0.8181} +{"mode": "train", "epoch": 123, "iter": 1700, "lr": 0.00809, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46578, "top5_acc": 0.71781, "loss_cls": 3.00966, "loss": 3.00966, "time": 0.82081} +{"mode": "train", "epoch": 123, "iter": 1800, "lr": 0.00808, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46188, "top5_acc": 0.72828, "loss_cls": 2.94762, "loss": 2.94762, "time": 0.82033} +{"mode": "train", "epoch": 123, "iter": 1900, "lr": 0.00806, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46469, "top5_acc": 0.71891, "loss_cls": 2.96336, "loss": 2.96336, "time": 0.81985} +{"mode": "train", "epoch": 123, "iter": 2000, "lr": 0.00805, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46094, "top5_acc": 0.7275, "loss_cls": 2.9683, "loss": 2.9683, "time": 0.82065} +{"mode": "train", "epoch": 123, "iter": 2100, "lr": 0.00803, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46328, "top5_acc": 0.72531, "loss_cls": 2.959, "loss": 2.959, "time": 0.82114} +{"mode": "train", "epoch": 123, "iter": 2200, "lr": 0.00802, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46562, "top5_acc": 0.72672, "loss_cls": 2.93854, "loss": 2.93854, "time": 0.81814} +{"mode": "train", "epoch": 123, "iter": 2300, "lr": 0.008, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46844, "top5_acc": 0.72688, "loss_cls": 2.91849, "loss": 2.91849, "time": 0.81868} +{"mode": "train", "epoch": 123, "iter": 2400, "lr": 0.00799, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47609, "top5_acc": 0.72984, "loss_cls": 2.92805, "loss": 2.92805, "time": 0.81577} +{"mode": "train", "epoch": 123, "iter": 2500, "lr": 0.00797, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45562, "top5_acc": 0.715, "loss_cls": 3.02202, "loss": 3.02202, "time": 0.81726} +{"mode": "train", "epoch": 123, "iter": 2600, "lr": 0.00796, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46, "top5_acc": 0.72422, "loss_cls": 2.98075, "loss": 2.98075, "time": 0.81528} +{"mode": "train", "epoch": 123, "iter": 2700, "lr": 0.00794, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47281, "top5_acc": 0.73453, "loss_cls": 2.92855, "loss": 2.92855, "time": 0.81391} +{"mode": "train", "epoch": 123, "iter": 2800, "lr": 0.00793, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47, "top5_acc": 0.7275, "loss_cls": 2.97294, "loss": 2.97294, "time": 0.81592} +{"mode": "train", "epoch": 123, "iter": 2900, "lr": 0.00791, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45938, "top5_acc": 0.71359, "loss_cls": 2.98043, "loss": 2.98043, "time": 0.81628} +{"mode": "train", "epoch": 123, "iter": 3000, "lr": 0.0079, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47078, "top5_acc": 0.73406, "loss_cls": 2.94292, "loss": 2.94292, "time": 0.82031} +{"mode": "train", "epoch": 123, "iter": 3100, "lr": 0.00788, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45797, "top5_acc": 0.72062, "loss_cls": 3.00195, "loss": 3.00195, "time": 0.81876} +{"mode": "train", "epoch": 123, "iter": 3200, "lr": 0.00787, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46422, "top5_acc": 0.72688, "loss_cls": 2.9336, "loss": 2.9336, "time": 0.81504} +{"mode": "train", "epoch": 123, "iter": 3300, "lr": 0.00785, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46422, "top5_acc": 0.72594, "loss_cls": 2.9453, "loss": 2.9453, "time": 0.8221} +{"mode": "train", "epoch": 123, "iter": 3400, "lr": 0.00784, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45938, "top5_acc": 0.72109, "loss_cls": 2.98794, "loss": 2.98794, "time": 0.82172} +{"mode": "train", "epoch": 123, "iter": 3500, "lr": 0.00782, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47297, "top5_acc": 0.73078, "loss_cls": 2.94809, "loss": 2.94809, "time": 0.82676} +{"mode": "train", "epoch": 123, "iter": 3600, "lr": 0.00781, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46078, "top5_acc": 0.72188, "loss_cls": 3.005, "loss": 3.005, "time": 0.81958} +{"mode": "train", "epoch": 123, "iter": 3700, "lr": 0.00779, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.46891, "top5_acc": 0.72328, "loss_cls": 2.95912, "loss": 2.95912, "time": 0.81903} +{"mode": "val", "epoch": 123, "iter": 309, "lr": 0.00778, "top1_acc": 0.39082, "top5_acc": 0.65041, "mean_class_accuracy": 0.39055} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00777, "memory": 15990, "data_time": 1.36238, "top1_acc": 0.47594, "top5_acc": 0.73578, "loss_cls": 2.90671, "loss": 2.90671, "time": 2.35718} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00775, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48438, "top5_acc": 0.73609, "loss_cls": 2.85328, "loss": 2.85328, "time": 0.84035} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00774, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48875, "top5_acc": 0.74406, "loss_cls": 2.84475, "loss": 2.84475, "time": 0.83323} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.00772, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47703, "top5_acc": 0.74344, "loss_cls": 2.88075, "loss": 2.88075, "time": 0.83264} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00771, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47859, "top5_acc": 0.72875, "loss_cls": 2.9004, "loss": 2.9004, "time": 0.834} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00769, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47641, "top5_acc": 0.73516, "loss_cls": 2.88109, "loss": 2.88109, "time": 0.83804} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00768, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47766, "top5_acc": 0.73281, "loss_cls": 2.89367, "loss": 2.89367, "time": 0.83351} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00766, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48078, "top5_acc": 0.73719, "loss_cls": 2.87297, "loss": 2.87297, "time": 0.83445} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00765, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47, "top5_acc": 0.72094, "loss_cls": 2.93977, "loss": 2.93977, "time": 0.83048} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00763, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49078, "top5_acc": 0.73797, "loss_cls": 2.8521, "loss": 2.8521, "time": 0.83362} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00762, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47484, "top5_acc": 0.73156, "loss_cls": 2.89898, "loss": 2.89898, "time": 0.82822} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.0076, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47047, "top5_acc": 0.73281, "loss_cls": 2.94816, "loss": 2.94816, "time": 0.83864} +{"mode": "train", "epoch": 124, "iter": 1300, "lr": 0.00759, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.47828, "top5_acc": 0.73594, "loss_cls": 2.89134, "loss": 2.89134, "time": 0.83187} +{"mode": "train", "epoch": 124, "iter": 1400, "lr": 0.00758, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.47906, "top5_acc": 0.73297, "loss_cls": 2.89676, "loss": 2.89676, "time": 0.83168} +{"mode": "train", "epoch": 124, "iter": 1500, "lr": 0.00756, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46828, "top5_acc": 0.72594, "loss_cls": 2.94178, "loss": 2.94178, "time": 0.83408} +{"mode": "train", "epoch": 124, "iter": 1600, "lr": 0.00755, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47688, "top5_acc": 0.73016, "loss_cls": 2.90748, "loss": 2.90748, "time": 0.82639} +{"mode": "train", "epoch": 124, "iter": 1700, "lr": 0.00753, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47125, "top5_acc": 0.72781, "loss_cls": 2.93058, "loss": 2.93058, "time": 0.83161} +{"mode": "train", "epoch": 124, "iter": 1800, "lr": 0.00752, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46578, "top5_acc": 0.72625, "loss_cls": 2.95111, "loss": 2.95111, "time": 0.83268} +{"mode": "train", "epoch": 124, "iter": 1900, "lr": 0.0075, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48266, "top5_acc": 0.74047, "loss_cls": 2.88124, "loss": 2.88124, "time": 0.83318} +{"mode": "train", "epoch": 124, "iter": 2000, "lr": 0.00749, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46734, "top5_acc": 0.72047, "loss_cls": 2.97961, "loss": 2.97961, "time": 0.82678} +{"mode": "train", "epoch": 124, "iter": 2100, "lr": 0.00747, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46156, "top5_acc": 0.72188, "loss_cls": 2.9516, "loss": 2.9516, "time": 0.82732} +{"mode": "train", "epoch": 124, "iter": 2200, "lr": 0.00746, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.46672, "top5_acc": 0.72766, "loss_cls": 2.96606, "loss": 2.96606, "time": 0.82431} +{"mode": "train", "epoch": 124, "iter": 2300, "lr": 0.00744, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47984, "top5_acc": 0.73812, "loss_cls": 2.88571, "loss": 2.88571, "time": 0.82941} +{"mode": "train", "epoch": 124, "iter": 2400, "lr": 0.00743, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47453, "top5_acc": 0.73062, "loss_cls": 2.91838, "loss": 2.91838, "time": 0.82001} +{"mode": "train", "epoch": 124, "iter": 2500, "lr": 0.00741, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47672, "top5_acc": 0.73766, "loss_cls": 2.86154, "loss": 2.86154, "time": 0.82867} +{"mode": "train", "epoch": 124, "iter": 2600, "lr": 0.0074, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46484, "top5_acc": 0.71844, "loss_cls": 2.9703, "loss": 2.9703, "time": 0.82492} +{"mode": "train", "epoch": 124, "iter": 2700, "lr": 0.00738, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47844, "top5_acc": 0.72781, "loss_cls": 2.92809, "loss": 2.92809, "time": 0.82251} +{"mode": "train", "epoch": 124, "iter": 2800, "lr": 0.00737, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46, "top5_acc": 0.72609, "loss_cls": 2.9486, "loss": 2.9486, "time": 0.82877} +{"mode": "train", "epoch": 124, "iter": 2900, "lr": 0.00735, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47516, "top5_acc": 0.73109, "loss_cls": 2.93974, "loss": 2.93974, "time": 0.82687} +{"mode": "train", "epoch": 124, "iter": 3000, "lr": 0.00734, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.4725, "top5_acc": 0.72594, "loss_cls": 2.94514, "loss": 2.94514, "time": 0.82481} +{"mode": "train", "epoch": 124, "iter": 3100, "lr": 0.00733, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46516, "top5_acc": 0.7275, "loss_cls": 2.93331, "loss": 2.93331, "time": 0.82585} +{"mode": "train", "epoch": 124, "iter": 3200, "lr": 0.00731, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47359, "top5_acc": 0.73062, "loss_cls": 2.92267, "loss": 2.92267, "time": 0.82488} +{"mode": "train", "epoch": 124, "iter": 3300, "lr": 0.0073, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46359, "top5_acc": 0.73406, "loss_cls": 2.92615, "loss": 2.92615, "time": 0.82972} +{"mode": "train", "epoch": 124, "iter": 3400, "lr": 0.00728, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47, "top5_acc": 0.72047, "loss_cls": 2.97156, "loss": 2.97156, "time": 0.82528} +{"mode": "train", "epoch": 124, "iter": 3500, "lr": 0.00727, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46719, "top5_acc": 0.73109, "loss_cls": 2.9383, "loss": 2.9383, "time": 0.83197} +{"mode": "train", "epoch": 124, "iter": 3600, "lr": 0.00725, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47016, "top5_acc": 0.71484, "loss_cls": 2.94762, "loss": 2.94762, "time": 0.81746} +{"mode": "train", "epoch": 124, "iter": 3700, "lr": 0.00724, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46672, "top5_acc": 0.71703, "loss_cls": 2.9697, "loss": 2.9697, "time": 0.82246} +{"mode": "val", "epoch": 124, "iter": 309, "lr": 0.00723, "top1_acc": 0.3932, "top5_acc": 0.64945, "mean_class_accuracy": 0.39306} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.00722, "memory": 15990, "data_time": 1.3666, "top1_acc": 0.49984, "top5_acc": 0.75109, "loss_cls": 2.80823, "loss": 2.80823, "time": 2.35242} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.0072, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48859, "top5_acc": 0.74422, "loss_cls": 2.82125, "loss": 2.82125, "time": 0.81357} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00719, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49297, "top5_acc": 0.74031, "loss_cls": 2.83646, "loss": 2.83646, "time": 0.81749} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00717, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47922, "top5_acc": 0.74719, "loss_cls": 2.83842, "loss": 2.83842, "time": 0.81966} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00716, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48594, "top5_acc": 0.74078, "loss_cls": 2.85297, "loss": 2.85297, "time": 0.8169} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00715, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48938, "top5_acc": 0.745, "loss_cls": 2.80368, "loss": 2.80368, "time": 0.82217} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00713, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47797, "top5_acc": 0.74578, "loss_cls": 2.86447, "loss": 2.86447, "time": 0.81917} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00712, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48734, "top5_acc": 0.73281, "loss_cls": 2.88721, "loss": 2.88721, "time": 0.81957} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.0071, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48094, "top5_acc": 0.73562, "loss_cls": 2.88456, "loss": 2.88456, "time": 0.81606} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.00709, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47562, "top5_acc": 0.74094, "loss_cls": 2.88382, "loss": 2.88382, "time": 0.81631} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00707, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48219, "top5_acc": 0.74047, "loss_cls": 2.85126, "loss": 2.85126, "time": 0.81733} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00706, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46266, "top5_acc": 0.73062, "loss_cls": 2.94866, "loss": 2.94866, "time": 0.8199} +{"mode": "train", "epoch": 125, "iter": 1300, "lr": 0.00704, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46594, "top5_acc": 0.72453, "loss_cls": 2.96269, "loss": 2.96269, "time": 0.81741} +{"mode": "train", "epoch": 125, "iter": 1400, "lr": 0.00703, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47906, "top5_acc": 0.7375, "loss_cls": 2.86563, "loss": 2.86563, "time": 0.82685} +{"mode": "train", "epoch": 125, "iter": 1500, "lr": 0.00702, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47625, "top5_acc": 0.7275, "loss_cls": 2.89286, "loss": 2.89286, "time": 0.81975} +{"mode": "train", "epoch": 125, "iter": 1600, "lr": 0.007, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48141, "top5_acc": 0.73969, "loss_cls": 2.8725, "loss": 2.8725, "time": 0.82189} +{"mode": "train", "epoch": 125, "iter": 1700, "lr": 0.00699, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48375, "top5_acc": 0.74, "loss_cls": 2.86697, "loss": 2.86697, "time": 0.81597} +{"mode": "train", "epoch": 125, "iter": 1800, "lr": 0.00697, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48531, "top5_acc": 0.74188, "loss_cls": 2.87137, "loss": 2.87137, "time": 0.81783} +{"mode": "train", "epoch": 125, "iter": 1900, "lr": 0.00696, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48469, "top5_acc": 0.73562, "loss_cls": 2.86146, "loss": 2.86146, "time": 0.82392} +{"mode": "train", "epoch": 125, "iter": 2000, "lr": 0.00694, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47562, "top5_acc": 0.7325, "loss_cls": 2.88136, "loss": 2.88136, "time": 0.82817} +{"mode": "train", "epoch": 125, "iter": 2100, "lr": 0.00693, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48484, "top5_acc": 0.74719, "loss_cls": 2.85395, "loss": 2.85395, "time": 0.81942} +{"mode": "train", "epoch": 125, "iter": 2200, "lr": 0.00692, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47438, "top5_acc": 0.73406, "loss_cls": 2.8949, "loss": 2.8949, "time": 0.82335} +{"mode": "train", "epoch": 125, "iter": 2300, "lr": 0.0069, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46203, "top5_acc": 0.72266, "loss_cls": 2.96925, "loss": 2.96925, "time": 0.82086} +{"mode": "train", "epoch": 125, "iter": 2400, "lr": 0.00689, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47578, "top5_acc": 0.72828, "loss_cls": 2.91439, "loss": 2.91439, "time": 0.82377} +{"mode": "train", "epoch": 125, "iter": 2500, "lr": 0.00687, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48344, "top5_acc": 0.74094, "loss_cls": 2.85763, "loss": 2.85763, "time": 0.82711} +{"mode": "train", "epoch": 125, "iter": 2600, "lr": 0.00686, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47359, "top5_acc": 0.72906, "loss_cls": 2.916, "loss": 2.916, "time": 0.82132} +{"mode": "train", "epoch": 125, "iter": 2700, "lr": 0.00685, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47594, "top5_acc": 0.7375, "loss_cls": 2.8871, "loss": 2.8871, "time": 0.81827} +{"mode": "train", "epoch": 125, "iter": 2800, "lr": 0.00683, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47062, "top5_acc": 0.73344, "loss_cls": 2.90492, "loss": 2.90492, "time": 0.81779} +{"mode": "train", "epoch": 125, "iter": 2900, "lr": 0.00682, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47562, "top5_acc": 0.73516, "loss_cls": 2.90325, "loss": 2.90325, "time": 0.82634} +{"mode": "train", "epoch": 125, "iter": 3000, "lr": 0.0068, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47453, "top5_acc": 0.73453, "loss_cls": 2.91914, "loss": 2.91914, "time": 0.82054} +{"mode": "train", "epoch": 125, "iter": 3100, "lr": 0.00679, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48078, "top5_acc": 0.73688, "loss_cls": 2.88226, "loss": 2.88226, "time": 0.82116} +{"mode": "train", "epoch": 125, "iter": 3200, "lr": 0.00678, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4775, "top5_acc": 0.73141, "loss_cls": 2.8876, "loss": 2.8876, "time": 0.81948} +{"mode": "train", "epoch": 125, "iter": 3300, "lr": 0.00676, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47469, "top5_acc": 0.72641, "loss_cls": 2.89605, "loss": 2.89605, "time": 0.81853} +{"mode": "train", "epoch": 125, "iter": 3400, "lr": 0.00675, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47094, "top5_acc": 0.72781, "loss_cls": 2.94976, "loss": 2.94976, "time": 0.83075} +{"mode": "train", "epoch": 125, "iter": 3500, "lr": 0.00673, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47812, "top5_acc": 0.74109, "loss_cls": 2.87763, "loss": 2.87763, "time": 0.8236} +{"mode": "train", "epoch": 125, "iter": 3600, "lr": 0.00672, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47047, "top5_acc": 0.72672, "loss_cls": 2.91937, "loss": 2.91937, "time": 0.82335} +{"mode": "train", "epoch": 125, "iter": 3700, "lr": 0.00671, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47844, "top5_acc": 0.72625, "loss_cls": 2.92598, "loss": 2.92598, "time": 0.82397} +{"mode": "val", "epoch": 125, "iter": 309, "lr": 0.0067, "top1_acc": 0.39736, "top5_acc": 0.65952, "mean_class_accuracy": 0.39718} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00668, "memory": 15990, "data_time": 1.31219, "top1_acc": 0.49, "top5_acc": 0.74484, "loss_cls": 2.80435, "loss": 2.80435, "time": 2.2953} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00667, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49438, "top5_acc": 0.74234, "loss_cls": 2.80424, "loss": 2.80424, "time": 0.8241} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00666, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48562, "top5_acc": 0.74812, "loss_cls": 2.82837, "loss": 2.82837, "time": 0.81731} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00664, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49312, "top5_acc": 0.75188, "loss_cls": 2.79603, "loss": 2.79603, "time": 0.81951} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00663, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48312, "top5_acc": 0.74, "loss_cls": 2.86473, "loss": 2.86473, "time": 0.8175} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00662, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48891, "top5_acc": 0.73844, "loss_cls": 2.81769, "loss": 2.81769, "time": 0.81603} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0066, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48266, "top5_acc": 0.73859, "loss_cls": 2.87319, "loss": 2.87319, "time": 0.81513} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00659, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49281, "top5_acc": 0.74297, "loss_cls": 2.84985, "loss": 2.84985, "time": 0.81529} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00657, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48219, "top5_acc": 0.73719, "loss_cls": 2.8708, "loss": 2.8708, "time": 0.81876} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00656, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48125, "top5_acc": 0.74781, "loss_cls": 2.84012, "loss": 2.84012, "time": 0.82033} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00655, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48375, "top5_acc": 0.74531, "loss_cls": 2.83364, "loss": 2.83364, "time": 0.81997} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00653, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49156, "top5_acc": 0.74031, "loss_cls": 2.8506, "loss": 2.8506, "time": 0.8148} +{"mode": "train", "epoch": 126, "iter": 1300, "lr": 0.00652, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47984, "top5_acc": 0.74188, "loss_cls": 2.85906, "loss": 2.85906, "time": 0.81675} +{"mode": "train", "epoch": 126, "iter": 1400, "lr": 0.0065, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48625, "top5_acc": 0.74453, "loss_cls": 2.82528, "loss": 2.82528, "time": 0.82301} +{"mode": "train", "epoch": 126, "iter": 1500, "lr": 0.00649, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48812, "top5_acc": 0.74078, "loss_cls": 2.8579, "loss": 2.8579, "time": 0.81956} +{"mode": "train", "epoch": 126, "iter": 1600, "lr": 0.00648, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47688, "top5_acc": 0.74031, "loss_cls": 2.87596, "loss": 2.87596, "time": 0.8205} +{"mode": "train", "epoch": 126, "iter": 1700, "lr": 0.00646, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49219, "top5_acc": 0.74109, "loss_cls": 2.83319, "loss": 2.83319, "time": 0.8171} +{"mode": "train", "epoch": 126, "iter": 1800, "lr": 0.00645, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48656, "top5_acc": 0.74156, "loss_cls": 2.85087, "loss": 2.85087, "time": 0.82461} +{"mode": "train", "epoch": 126, "iter": 1900, "lr": 0.00644, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48406, "top5_acc": 0.74812, "loss_cls": 2.82544, "loss": 2.82544, "time": 0.82257} +{"mode": "train", "epoch": 126, "iter": 2000, "lr": 0.00642, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49266, "top5_acc": 0.745, "loss_cls": 2.81603, "loss": 2.81603, "time": 0.83} +{"mode": "train", "epoch": 126, "iter": 2100, "lr": 0.00641, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49078, "top5_acc": 0.74688, "loss_cls": 2.82626, "loss": 2.82626, "time": 0.82087} +{"mode": "train", "epoch": 126, "iter": 2200, "lr": 0.00639, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48969, "top5_acc": 0.73922, "loss_cls": 2.85557, "loss": 2.85557, "time": 0.82317} +{"mode": "train", "epoch": 126, "iter": 2300, "lr": 0.00638, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47453, "top5_acc": 0.72938, "loss_cls": 2.88834, "loss": 2.88834, "time": 0.81987} +{"mode": "train", "epoch": 126, "iter": 2400, "lr": 0.00637, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48078, "top5_acc": 0.73516, "loss_cls": 2.87385, "loss": 2.87385, "time": 0.81769} +{"mode": "train", "epoch": 126, "iter": 2500, "lr": 0.00635, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48281, "top5_acc": 0.74219, "loss_cls": 2.8799, "loss": 2.8799, "time": 0.81865} +{"mode": "train", "epoch": 126, "iter": 2600, "lr": 0.00634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48906, "top5_acc": 0.74031, "loss_cls": 2.85474, "loss": 2.85474, "time": 0.81766} +{"mode": "train", "epoch": 126, "iter": 2700, "lr": 0.00633, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48062, "top5_acc": 0.74016, "loss_cls": 2.86646, "loss": 2.86646, "time": 0.81491} +{"mode": "train", "epoch": 126, "iter": 2800, "lr": 0.00631, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47891, "top5_acc": 0.73859, "loss_cls": 2.88255, "loss": 2.88255, "time": 0.81691} +{"mode": "train", "epoch": 126, "iter": 2900, "lr": 0.0063, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47875, "top5_acc": 0.72922, "loss_cls": 2.9, "loss": 2.9, "time": 0.82424} +{"mode": "train", "epoch": 126, "iter": 3000, "lr": 0.00629, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47672, "top5_acc": 0.73953, "loss_cls": 2.85508, "loss": 2.85508, "time": 0.81966} +{"mode": "train", "epoch": 126, "iter": 3100, "lr": 0.00627, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.485, "top5_acc": 0.7375, "loss_cls": 2.85885, "loss": 2.85885, "time": 0.81812} +{"mode": "train", "epoch": 126, "iter": 3200, "lr": 0.00626, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48672, "top5_acc": 0.74438, "loss_cls": 2.84099, "loss": 2.84099, "time": 0.81918} +{"mode": "train", "epoch": 126, "iter": 3300, "lr": 0.00625, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47734, "top5_acc": 0.73891, "loss_cls": 2.8702, "loss": 2.8702, "time": 0.82336} +{"mode": "train", "epoch": 126, "iter": 3400, "lr": 0.00623, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.485, "top5_acc": 0.73516, "loss_cls": 2.85467, "loss": 2.85467, "time": 0.82144} +{"mode": "train", "epoch": 126, "iter": 3500, "lr": 0.00622, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47859, "top5_acc": 0.73703, "loss_cls": 2.87937, "loss": 2.87937, "time": 0.82369} +{"mode": "train", "epoch": 126, "iter": 3600, "lr": 0.0062, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47672, "top5_acc": 0.73734, "loss_cls": 2.8956, "loss": 2.8956, "time": 0.82304} +{"mode": "train", "epoch": 126, "iter": 3700, "lr": 0.00619, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48328, "top5_acc": 0.74062, "loss_cls": 2.8358, "loss": 2.8358, "time": 0.82415} +{"mode": "val", "epoch": 126, "iter": 309, "lr": 0.00618, "top1_acc": 0.40419, "top5_acc": 0.65882, "mean_class_accuracy": 0.40392} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00617, "memory": 15990, "data_time": 1.29982, "top1_acc": 0.50766, "top5_acc": 0.75688, "loss_cls": 2.73591, "loss": 2.73591, "time": 2.284} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00616, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.50359, "top5_acc": 0.75844, "loss_cls": 2.75589, "loss": 2.75589, "time": 0.82309} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00614, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49328, "top5_acc": 0.75172, "loss_cls": 2.79193, "loss": 2.79193, "time": 0.81691} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00613, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49156, "top5_acc": 0.75047, "loss_cls": 2.80512, "loss": 2.80512, "time": 0.82123} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.00612, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49234, "top5_acc": 0.74344, "loss_cls": 2.80444, "loss": 2.80444, "time": 0.81713} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.0061, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50313, "top5_acc": 0.75484, "loss_cls": 2.76824, "loss": 2.76824, "time": 0.82263} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00609, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48984, "top5_acc": 0.75094, "loss_cls": 2.79024, "loss": 2.79024, "time": 0.82085} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00608, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49844, "top5_acc": 0.75062, "loss_cls": 2.7661, "loss": 2.7661, "time": 0.81845} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00606, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.49031, "top5_acc": 0.7525, "loss_cls": 2.81357, "loss": 2.81357, "time": 0.81917} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00605, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49578, "top5_acc": 0.75141, "loss_cls": 2.78702, "loss": 2.78702, "time": 0.81597} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00604, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48406, "top5_acc": 0.74281, "loss_cls": 2.83584, "loss": 2.83584, "time": 0.81727} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00602, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49156, "top5_acc": 0.74891, "loss_cls": 2.82054, "loss": 2.82054, "time": 0.81379} +{"mode": "train", "epoch": 127, "iter": 1300, "lr": 0.00601, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49234, "top5_acc": 0.74547, "loss_cls": 2.8223, "loss": 2.8223, "time": 0.81734} +{"mode": "train", "epoch": 127, "iter": 1400, "lr": 0.006, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49234, "top5_acc": 0.74547, "loss_cls": 2.82984, "loss": 2.82984, "time": 0.82928} +{"mode": "train", "epoch": 127, "iter": 1500, "lr": 0.00598, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49688, "top5_acc": 0.74219, "loss_cls": 2.8066, "loss": 2.8066, "time": 0.82514} +{"mode": "train", "epoch": 127, "iter": 1600, "lr": 0.00597, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.48828, "top5_acc": 0.74984, "loss_cls": 2.81117, "loss": 2.81117, "time": 0.82418} +{"mode": "train", "epoch": 127, "iter": 1700, "lr": 0.00596, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49328, "top5_acc": 0.75078, "loss_cls": 2.79627, "loss": 2.79627, "time": 0.81419} +{"mode": "train", "epoch": 127, "iter": 1800, "lr": 0.00594, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.47547, "top5_acc": 0.73891, "loss_cls": 2.90815, "loss": 2.90815, "time": 0.82012} +{"mode": "train", "epoch": 127, "iter": 1900, "lr": 0.00593, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49062, "top5_acc": 0.75422, "loss_cls": 2.78782, "loss": 2.78782, "time": 0.8182} +{"mode": "train", "epoch": 127, "iter": 2000, "lr": 0.00592, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48766, "top5_acc": 0.74891, "loss_cls": 2.80145, "loss": 2.80145, "time": 0.82752} +{"mode": "train", "epoch": 127, "iter": 2100, "lr": 0.00591, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47547, "top5_acc": 0.73562, "loss_cls": 2.88182, "loss": 2.88182, "time": 0.83079} +{"mode": "train", "epoch": 127, "iter": 2200, "lr": 0.00589, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49, "top5_acc": 0.755, "loss_cls": 2.80835, "loss": 2.80835, "time": 0.82314} +{"mode": "train", "epoch": 127, "iter": 2300, "lr": 0.00588, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49125, "top5_acc": 0.74719, "loss_cls": 2.80926, "loss": 2.80926, "time": 0.82437} +{"mode": "train", "epoch": 127, "iter": 2400, "lr": 0.00587, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48328, "top5_acc": 0.73875, "loss_cls": 2.84321, "loss": 2.84321, "time": 0.81919} +{"mode": "train", "epoch": 127, "iter": 2500, "lr": 0.00585, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48812, "top5_acc": 0.74516, "loss_cls": 2.83348, "loss": 2.83348, "time": 0.81849} +{"mode": "train", "epoch": 127, "iter": 2600, "lr": 0.00584, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47938, "top5_acc": 0.74328, "loss_cls": 2.85569, "loss": 2.85569, "time": 0.82312} +{"mode": "train", "epoch": 127, "iter": 2700, "lr": 0.00583, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4775, "top5_acc": 0.72344, "loss_cls": 2.91933, "loss": 2.91933, "time": 0.82} +{"mode": "train", "epoch": 127, "iter": 2800, "lr": 0.00581, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48156, "top5_acc": 0.73328, "loss_cls": 2.85725, "loss": 2.85725, "time": 0.82337} +{"mode": "train", "epoch": 127, "iter": 2900, "lr": 0.0058, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49938, "top5_acc": 0.75094, "loss_cls": 2.79559, "loss": 2.79559, "time": 0.81706} +{"mode": "train", "epoch": 127, "iter": 3000, "lr": 0.00579, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48188, "top5_acc": 0.73656, "loss_cls": 2.85879, "loss": 2.85879, "time": 0.81772} +{"mode": "train", "epoch": 127, "iter": 3100, "lr": 0.00577, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48938, "top5_acc": 0.74688, "loss_cls": 2.82257, "loss": 2.82257, "time": 0.81919} +{"mode": "train", "epoch": 127, "iter": 3200, "lr": 0.00576, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48516, "top5_acc": 0.73656, "loss_cls": 2.86159, "loss": 2.86159, "time": 0.81626} +{"mode": "train", "epoch": 127, "iter": 3300, "lr": 0.00575, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48578, "top5_acc": 0.74562, "loss_cls": 2.83643, "loss": 2.83643, "time": 0.81951} +{"mode": "train", "epoch": 127, "iter": 3400, "lr": 0.00573, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49281, "top5_acc": 0.74781, "loss_cls": 2.83452, "loss": 2.83452, "time": 0.81429} +{"mode": "train", "epoch": 127, "iter": 3500, "lr": 0.00572, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48797, "top5_acc": 0.74438, "loss_cls": 2.8283, "loss": 2.8283, "time": 0.81923} +{"mode": "train", "epoch": 127, "iter": 3600, "lr": 0.00571, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49219, "top5_acc": 0.7475, "loss_cls": 2.78734, "loss": 2.78734, "time": 0.82229} +{"mode": "train", "epoch": 127, "iter": 3700, "lr": 0.0057, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48594, "top5_acc": 0.73719, "loss_cls": 2.85074, "loss": 2.85074, "time": 0.82263} +{"mode": "val", "epoch": 127, "iter": 309, "lr": 0.00569, "top1_acc": 0.40424, "top5_acc": 0.66069, "mean_class_accuracy": 0.40408} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00568, "memory": 15990, "data_time": 1.31757, "top1_acc": 0.51344, "top5_acc": 0.76578, "loss_cls": 2.70099, "loss": 2.70099, "time": 2.29631} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.00566, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50078, "top5_acc": 0.76125, "loss_cls": 2.74024, "loss": 2.74024, "time": 0.82287} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00565, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50547, "top5_acc": 0.75734, "loss_cls": 2.72601, "loss": 2.72601, "time": 0.82315} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00564, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51859, "top5_acc": 0.7625, "loss_cls": 2.66789, "loss": 2.66789, "time": 0.82618} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00563, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.5, "top5_acc": 0.75938, "loss_cls": 2.74162, "loss": 2.74162, "time": 0.81577} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00561, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50844, "top5_acc": 0.76109, "loss_cls": 2.73128, "loss": 2.73128, "time": 0.8216} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.0056, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50109, "top5_acc": 0.76281, "loss_cls": 2.73789, "loss": 2.73789, "time": 0.81624} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00559, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.5025, "top5_acc": 0.755, "loss_cls": 2.74731, "loss": 2.74731, "time": 0.82315} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00557, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.495, "top5_acc": 0.75016, "loss_cls": 2.77944, "loss": 2.77944, "time": 0.82338} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00556, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49516, "top5_acc": 0.75844, "loss_cls": 2.75946, "loss": 2.75946, "time": 0.82025} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00555, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48422, "top5_acc": 0.74781, "loss_cls": 2.82507, "loss": 2.82507, "time": 0.81895} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00554, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48953, "top5_acc": 0.74562, "loss_cls": 2.79887, "loss": 2.79887, "time": 0.81862} +{"mode": "train", "epoch": 128, "iter": 1300, "lr": 0.00552, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50406, "top5_acc": 0.75547, "loss_cls": 2.73909, "loss": 2.73909, "time": 0.8276} +{"mode": "train", "epoch": 128, "iter": 1400, "lr": 0.00551, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49094, "top5_acc": 0.74734, "loss_cls": 2.80676, "loss": 2.80676, "time": 0.82549} +{"mode": "train", "epoch": 128, "iter": 1500, "lr": 0.0055, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49812, "top5_acc": 0.75672, "loss_cls": 2.74092, "loss": 2.74092, "time": 0.82181} +{"mode": "train", "epoch": 128, "iter": 1600, "lr": 0.00548, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50141, "top5_acc": 0.76109, "loss_cls": 2.74265, "loss": 2.74265, "time": 0.8249} +{"mode": "train", "epoch": 128, "iter": 1700, "lr": 0.00547, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48172, "top5_acc": 0.74531, "loss_cls": 2.83393, "loss": 2.83393, "time": 0.82155} +{"mode": "train", "epoch": 128, "iter": 1800, "lr": 0.00546, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51125, "top5_acc": 0.75688, "loss_cls": 2.72834, "loss": 2.72834, "time": 0.82035} +{"mode": "train", "epoch": 128, "iter": 1900, "lr": 0.00545, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.50328, "top5_acc": 0.75844, "loss_cls": 2.74745, "loss": 2.74745, "time": 0.82488} +{"mode": "train", "epoch": 128, "iter": 2000, "lr": 0.00543, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50062, "top5_acc": 0.75047, "loss_cls": 2.79231, "loss": 2.79231, "time": 0.82127} +{"mode": "train", "epoch": 128, "iter": 2100, "lr": 0.00542, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49109, "top5_acc": 0.73922, "loss_cls": 2.84827, "loss": 2.84827, "time": 0.82438} +{"mode": "train", "epoch": 128, "iter": 2200, "lr": 0.00541, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48906, "top5_acc": 0.74453, "loss_cls": 2.81226, "loss": 2.81226, "time": 0.81733} +{"mode": "train", "epoch": 128, "iter": 2300, "lr": 0.0054, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49125, "top5_acc": 0.74859, "loss_cls": 2.80612, "loss": 2.80612, "time": 0.82808} +{"mode": "train", "epoch": 128, "iter": 2400, "lr": 0.00538, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49859, "top5_acc": 0.74891, "loss_cls": 2.80666, "loss": 2.80666, "time": 0.81977} +{"mode": "train", "epoch": 128, "iter": 2500, "lr": 0.00537, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48562, "top5_acc": 0.74, "loss_cls": 2.8278, "loss": 2.8278, "time": 0.81779} +{"mode": "train", "epoch": 128, "iter": 2600, "lr": 0.00536, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49844, "top5_acc": 0.74469, "loss_cls": 2.80244, "loss": 2.80244, "time": 0.81617} +{"mode": "train", "epoch": 128, "iter": 2700, "lr": 0.00535, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49625, "top5_acc": 0.74812, "loss_cls": 2.78773, "loss": 2.78773, "time": 0.81358} +{"mode": "train", "epoch": 128, "iter": 2800, "lr": 0.00533, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49922, "top5_acc": 0.74906, "loss_cls": 2.77809, "loss": 2.77809, "time": 0.82468} +{"mode": "train", "epoch": 128, "iter": 2900, "lr": 0.00532, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48375, "top5_acc": 0.74078, "loss_cls": 2.85853, "loss": 2.85853, "time": 0.8207} +{"mode": "train", "epoch": 128, "iter": 3000, "lr": 0.00531, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49109, "top5_acc": 0.74453, "loss_cls": 2.82984, "loss": 2.82984, "time": 0.81786} +{"mode": "train", "epoch": 128, "iter": 3100, "lr": 0.0053, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49359, "top5_acc": 0.75062, "loss_cls": 2.76695, "loss": 2.76695, "time": 0.8166} +{"mode": "train", "epoch": 128, "iter": 3200, "lr": 0.00528, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49047, "top5_acc": 0.74906, "loss_cls": 2.79689, "loss": 2.79689, "time": 0.82085} +{"mode": "train", "epoch": 128, "iter": 3300, "lr": 0.00527, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48562, "top5_acc": 0.74438, "loss_cls": 2.83223, "loss": 2.83223, "time": 0.81984} +{"mode": "train", "epoch": 128, "iter": 3400, "lr": 0.00526, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.48859, "top5_acc": 0.74562, "loss_cls": 2.80811, "loss": 2.80811, "time": 0.82214} +{"mode": "train", "epoch": 128, "iter": 3500, "lr": 0.00525, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49469, "top5_acc": 0.7425, "loss_cls": 2.80853, "loss": 2.80853, "time": 0.82599} +{"mode": "train", "epoch": 128, "iter": 3600, "lr": 0.00523, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49078, "top5_acc": 0.74562, "loss_cls": 2.81145, "loss": 2.81145, "time": 0.81933} +{"mode": "train", "epoch": 128, "iter": 3700, "lr": 0.00522, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49203, "top5_acc": 0.74469, "loss_cls": 2.8097, "loss": 2.8097, "time": 0.81859} +{"mode": "val", "epoch": 128, "iter": 309, "lr": 0.00521, "top1_acc": 0.41711, "top5_acc": 0.67249, "mean_class_accuracy": 0.41694} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.0052, "memory": 15990, "data_time": 1.31234, "top1_acc": 0.52703, "top5_acc": 0.76938, "loss_cls": 2.63128, "loss": 2.63128, "time": 2.29473} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00519, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.52453, "top5_acc": 0.77609, "loss_cls": 2.61369, "loss": 2.61369, "time": 0.8291} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00518, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50813, "top5_acc": 0.75719, "loss_cls": 2.71227, "loss": 2.71227, "time": 0.81862} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00516, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51719, "top5_acc": 0.76891, "loss_cls": 2.68132, "loss": 2.68132, "time": 0.82117} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00515, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50938, "top5_acc": 0.77125, "loss_cls": 2.69179, "loss": 2.69179, "time": 0.81743} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00514, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49953, "top5_acc": 0.75906, "loss_cls": 2.75448, "loss": 2.75448, "time": 0.81894} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00513, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51859, "top5_acc": 0.76609, "loss_cls": 2.69636, "loss": 2.69636, "time": 0.8168} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00512, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50047, "top5_acc": 0.75141, "loss_cls": 2.74972, "loss": 2.74972, "time": 0.817} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.0051, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50047, "top5_acc": 0.74766, "loss_cls": 2.75978, "loss": 2.75978, "time": 0.81603} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00509, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51031, "top5_acc": 0.76391, "loss_cls": 2.6995, "loss": 2.6995, "time": 0.82255} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00508, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49891, "top5_acc": 0.75078, "loss_cls": 2.76073, "loss": 2.76073, "time": 0.8191} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.00507, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50234, "top5_acc": 0.75859, "loss_cls": 2.74591, "loss": 2.74591, "time": 0.8152} +{"mode": "train", "epoch": 129, "iter": 1300, "lr": 0.00505, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50047, "top5_acc": 0.75188, "loss_cls": 2.74279, "loss": 2.74279, "time": 0.81814} +{"mode": "train", "epoch": 129, "iter": 1400, "lr": 0.00504, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49828, "top5_acc": 0.75125, "loss_cls": 2.77471, "loss": 2.77471, "time": 0.81882} +{"mode": "train", "epoch": 129, "iter": 1500, "lr": 0.00503, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49141, "top5_acc": 0.74984, "loss_cls": 2.77707, "loss": 2.77707, "time": 0.81774} +{"mode": "train", "epoch": 129, "iter": 1600, "lr": 0.00502, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50469, "top5_acc": 0.75922, "loss_cls": 2.73843, "loss": 2.73843, "time": 0.82183} +{"mode": "train", "epoch": 129, "iter": 1700, "lr": 0.00501, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50313, "top5_acc": 0.76078, "loss_cls": 2.72651, "loss": 2.72651, "time": 0.82587} +{"mode": "train", "epoch": 129, "iter": 1800, "lr": 0.00499, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49859, "top5_acc": 0.74984, "loss_cls": 2.74736, "loss": 2.74736, "time": 0.81828} +{"mode": "train", "epoch": 129, "iter": 1900, "lr": 0.00498, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49641, "top5_acc": 0.75125, "loss_cls": 2.76379, "loss": 2.76379, "time": 0.82199} +{"mode": "train", "epoch": 129, "iter": 2000, "lr": 0.00497, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49656, "top5_acc": 0.76672, "loss_cls": 2.76109, "loss": 2.76109, "time": 0.82521} +{"mode": "train", "epoch": 129, "iter": 2100, "lr": 0.00496, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49969, "top5_acc": 0.75844, "loss_cls": 2.73727, "loss": 2.73727, "time": 0.82265} +{"mode": "train", "epoch": 129, "iter": 2200, "lr": 0.00494, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49562, "top5_acc": 0.75172, "loss_cls": 2.75409, "loss": 2.75409, "time": 0.81778} +{"mode": "train", "epoch": 129, "iter": 2300, "lr": 0.00493, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.50453, "top5_acc": 0.75562, "loss_cls": 2.75024, "loss": 2.75024, "time": 0.81992} +{"mode": "train", "epoch": 129, "iter": 2400, "lr": 0.00492, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48766, "top5_acc": 0.74844, "loss_cls": 2.8172, "loss": 2.8172, "time": 0.81925} +{"mode": "train", "epoch": 129, "iter": 2500, "lr": 0.00491, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51016, "top5_acc": 0.75766, "loss_cls": 2.72687, "loss": 2.72687, "time": 0.81282} +{"mode": "train", "epoch": 129, "iter": 2600, "lr": 0.0049, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49078, "top5_acc": 0.75078, "loss_cls": 2.7814, "loss": 2.7814, "time": 0.82019} +{"mode": "train", "epoch": 129, "iter": 2700, "lr": 0.00488, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48719, "top5_acc": 0.75, "loss_cls": 2.79191, "loss": 2.79191, "time": 0.81437} +{"mode": "train", "epoch": 129, "iter": 2800, "lr": 0.00487, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50859, "top5_acc": 0.75688, "loss_cls": 2.7403, "loss": 2.7403, "time": 0.81616} +{"mode": "train", "epoch": 129, "iter": 2900, "lr": 0.00486, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50109, "top5_acc": 0.76016, "loss_cls": 2.72776, "loss": 2.72776, "time": 0.82078} +{"mode": "train", "epoch": 129, "iter": 3000, "lr": 0.00485, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.495, "top5_acc": 0.74906, "loss_cls": 2.78586, "loss": 2.78586, "time": 0.82057} +{"mode": "train", "epoch": 129, "iter": 3100, "lr": 0.00484, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48516, "top5_acc": 0.73891, "loss_cls": 2.82586, "loss": 2.82586, "time": 0.81938} +{"mode": "train", "epoch": 129, "iter": 3200, "lr": 0.00482, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50156, "top5_acc": 0.76547, "loss_cls": 2.71709, "loss": 2.71709, "time": 0.81956} +{"mode": "train", "epoch": 129, "iter": 3300, "lr": 0.00481, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48406, "top5_acc": 0.74562, "loss_cls": 2.81659, "loss": 2.81659, "time": 0.82307} +{"mode": "train", "epoch": 129, "iter": 3400, "lr": 0.0048, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4925, "top5_acc": 0.75578, "loss_cls": 2.77138, "loss": 2.77138, "time": 0.81943} +{"mode": "train", "epoch": 129, "iter": 3500, "lr": 0.00479, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49734, "top5_acc": 0.74672, "loss_cls": 2.76821, "loss": 2.76821, "time": 0.82317} +{"mode": "train", "epoch": 129, "iter": 3600, "lr": 0.00478, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49266, "top5_acc": 0.7475, "loss_cls": 2.78113, "loss": 2.78113, "time": 0.81975} +{"mode": "train", "epoch": 129, "iter": 3700, "lr": 0.00476, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49656, "top5_acc": 0.75062, "loss_cls": 2.79968, "loss": 2.79968, "time": 0.81747} +{"mode": "val", "epoch": 129, "iter": 309, "lr": 0.00476, "top1_acc": 0.41012, "top5_acc": 0.66955, "mean_class_accuracy": 0.40985} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00475, "memory": 15990, "data_time": 1.30199, "top1_acc": 0.52125, "top5_acc": 0.76656, "loss_cls": 2.67718, "loss": 2.67718, "time": 2.28875} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00473, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53, "top5_acc": 0.77406, "loss_cls": 2.60595, "loss": 2.60595, "time": 0.81982} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00472, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52469, "top5_acc": 0.77766, "loss_cls": 2.60678, "loss": 2.60678, "time": 0.81669} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00471, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51766, "top5_acc": 0.76594, "loss_cls": 2.6857, "loss": 2.6857, "time": 0.81963} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.0047, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52578, "top5_acc": 0.77609, "loss_cls": 2.63951, "loss": 2.63951, "time": 0.82054} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00469, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51859, "top5_acc": 0.77031, "loss_cls": 2.66182, "loss": 2.66182, "time": 0.81788} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00468, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50453, "top5_acc": 0.75938, "loss_cls": 2.70351, "loss": 2.70351, "time": 0.81978} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00466, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5225, "top5_acc": 0.77312, "loss_cls": 2.62739, "loss": 2.62739, "time": 0.81507} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00465, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51359, "top5_acc": 0.76562, "loss_cls": 2.67359, "loss": 2.67359, "time": 0.8176} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.00464, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50562, "top5_acc": 0.76141, "loss_cls": 2.71415, "loss": 2.71415, "time": 0.8149} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.00463, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51313, "top5_acc": 0.76016, "loss_cls": 2.70318, "loss": 2.70318, "time": 0.81839} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00462, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51406, "top5_acc": 0.76016, "loss_cls": 2.70866, "loss": 2.70866, "time": 0.82098} +{"mode": "train", "epoch": 130, "iter": 1300, "lr": 0.00461, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.515, "top5_acc": 0.76609, "loss_cls": 2.68541, "loss": 2.68541, "time": 0.8225} +{"mode": "train", "epoch": 130, "iter": 1400, "lr": 0.00459, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50062, "top5_acc": 0.76078, "loss_cls": 2.69965, "loss": 2.69965, "time": 0.82629} +{"mode": "train", "epoch": 130, "iter": 1500, "lr": 0.00458, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51266, "top5_acc": 0.75344, "loss_cls": 2.72334, "loss": 2.72334, "time": 0.81918} +{"mode": "train", "epoch": 130, "iter": 1600, "lr": 0.00457, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.5075, "top5_acc": 0.76094, "loss_cls": 2.72078, "loss": 2.72078, "time": 0.81926} +{"mode": "train", "epoch": 130, "iter": 1700, "lr": 0.00456, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50844, "top5_acc": 0.77312, "loss_cls": 2.68854, "loss": 2.68854, "time": 0.82571} +{"mode": "train", "epoch": 130, "iter": 1800, "lr": 0.00455, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51219, "top5_acc": 0.76609, "loss_cls": 2.66769, "loss": 2.66769, "time": 0.8202} +{"mode": "train", "epoch": 130, "iter": 1900, "lr": 0.00454, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.50344, "top5_acc": 0.76344, "loss_cls": 2.71862, "loss": 2.71862, "time": 0.82333} +{"mode": "train", "epoch": 130, "iter": 2000, "lr": 0.00452, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50906, "top5_acc": 0.75969, "loss_cls": 2.72863, "loss": 2.72863, "time": 0.81799} +{"mode": "train", "epoch": 130, "iter": 2100, "lr": 0.00451, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50281, "top5_acc": 0.75609, "loss_cls": 2.74251, "loss": 2.74251, "time": 0.82346} +{"mode": "train", "epoch": 130, "iter": 2200, "lr": 0.0045, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49891, "top5_acc": 0.7525, "loss_cls": 2.7517, "loss": 2.7517, "time": 0.81805} +{"mode": "train", "epoch": 130, "iter": 2300, "lr": 0.00449, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50578, "top5_acc": 0.76359, "loss_cls": 2.72305, "loss": 2.72305, "time": 0.82266} +{"mode": "train", "epoch": 130, "iter": 2400, "lr": 0.00448, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.5125, "top5_acc": 0.76469, "loss_cls": 2.72238, "loss": 2.72238, "time": 0.81961} +{"mode": "train", "epoch": 130, "iter": 2500, "lr": 0.00447, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49969, "top5_acc": 0.7525, "loss_cls": 2.75513, "loss": 2.75513, "time": 0.82509} +{"mode": "train", "epoch": 130, "iter": 2600, "lr": 0.00445, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51281, "top5_acc": 0.75703, "loss_cls": 2.72547, "loss": 2.72547, "time": 0.82263} +{"mode": "train", "epoch": 130, "iter": 2700, "lr": 0.00444, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48781, "top5_acc": 0.74781, "loss_cls": 2.76795, "loss": 2.76795, "time": 0.81466} +{"mode": "train", "epoch": 130, "iter": 2800, "lr": 0.00443, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51516, "top5_acc": 0.76641, "loss_cls": 2.70035, "loss": 2.70035, "time": 0.81441} +{"mode": "train", "epoch": 130, "iter": 2900, "lr": 0.00442, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49766, "top5_acc": 0.75016, "loss_cls": 2.76265, "loss": 2.76265, "time": 0.81424} +{"mode": "train", "epoch": 130, "iter": 3000, "lr": 0.00441, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51422, "top5_acc": 0.75797, "loss_cls": 2.72642, "loss": 2.72642, "time": 0.81687} +{"mode": "train", "epoch": 130, "iter": 3100, "lr": 0.0044, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50578, "top5_acc": 0.75594, "loss_cls": 2.74847, "loss": 2.74847, "time": 0.81375} +{"mode": "train", "epoch": 130, "iter": 3200, "lr": 0.00439, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.50453, "top5_acc": 0.74922, "loss_cls": 2.75217, "loss": 2.75217, "time": 0.8182} +{"mode": "train", "epoch": 130, "iter": 3300, "lr": 0.00437, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.505, "top5_acc": 0.75016, "loss_cls": 2.73197, "loss": 2.73197, "time": 0.8154} +{"mode": "train", "epoch": 130, "iter": 3400, "lr": 0.00436, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50781, "top5_acc": 0.76094, "loss_cls": 2.70088, "loss": 2.70088, "time": 0.82275} +{"mode": "train", "epoch": 130, "iter": 3500, "lr": 0.00435, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50766, "top5_acc": 0.74844, "loss_cls": 2.75633, "loss": 2.75633, "time": 0.82792} +{"mode": "train", "epoch": 130, "iter": 3600, "lr": 0.00434, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49094, "top5_acc": 0.76156, "loss_cls": 2.75265, "loss": 2.75265, "time": 0.81966} +{"mode": "train", "epoch": 130, "iter": 3700, "lr": 0.00433, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49594, "top5_acc": 0.75062, "loss_cls": 2.76371, "loss": 2.76371, "time": 0.81858} +{"mode": "val", "epoch": 130, "iter": 309, "lr": 0.00432, "top1_acc": 0.42466, "top5_acc": 0.67852, "mean_class_accuracy": 0.42439} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00431, "memory": 15990, "data_time": 1.31459, "top1_acc": 0.54, "top5_acc": 0.77625, "loss_cls": 2.57189, "loss": 2.57189, "time": 2.29815} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.0043, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52969, "top5_acc": 0.77219, "loss_cls": 2.62263, "loss": 2.62263, "time": 0.81843} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00429, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52875, "top5_acc": 0.77328, "loss_cls": 2.62247, "loss": 2.62247, "time": 0.8248} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00428, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52219, "top5_acc": 0.77141, "loss_cls": 2.64002, "loss": 2.64002, "time": 0.816} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00427, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5225, "top5_acc": 0.77172, "loss_cls": 2.64607, "loss": 2.64607, "time": 0.8219} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00425, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52688, "top5_acc": 0.76781, "loss_cls": 2.63752, "loss": 2.63752, "time": 0.81843} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00424, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52594, "top5_acc": 0.78047, "loss_cls": 2.61321, "loss": 2.61321, "time": 0.81742} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00423, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51375, "top5_acc": 0.76969, "loss_cls": 2.64203, "loss": 2.64203, "time": 0.81612} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00422, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51047, "top5_acc": 0.77406, "loss_cls": 2.65397, "loss": 2.65397, "time": 0.82231} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.00421, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52547, "top5_acc": 0.77219, "loss_cls": 2.63453, "loss": 2.63453, "time": 0.81628} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.0042, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52047, "top5_acc": 0.77734, "loss_cls": 2.61657, "loss": 2.61657, "time": 0.81689} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00419, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50859, "top5_acc": 0.76578, "loss_cls": 2.69432, "loss": 2.69432, "time": 0.81589} +{"mode": "train", "epoch": 131, "iter": 1300, "lr": 0.00418, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51812, "top5_acc": 0.77109, "loss_cls": 2.649, "loss": 2.649, "time": 0.81619} +{"mode": "train", "epoch": 131, "iter": 1400, "lr": 0.00417, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50969, "top5_acc": 0.76469, "loss_cls": 2.70263, "loss": 2.70263, "time": 0.82749} +{"mode": "train", "epoch": 131, "iter": 1500, "lr": 0.00415, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51422, "top5_acc": 0.76734, "loss_cls": 2.66135, "loss": 2.66135, "time": 0.82177} +{"mode": "train", "epoch": 131, "iter": 1600, "lr": 0.00414, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.51422, "top5_acc": 0.7725, "loss_cls": 2.64826, "loss": 2.64826, "time": 0.82804} +{"mode": "train", "epoch": 131, "iter": 1700, "lr": 0.00413, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.50844, "top5_acc": 0.75719, "loss_cls": 2.74164, "loss": 2.74164, "time": 0.82272} +{"mode": "train", "epoch": 131, "iter": 1800, "lr": 0.00412, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51359, "top5_acc": 0.76141, "loss_cls": 2.68301, "loss": 2.68301, "time": 0.82161} +{"mode": "train", "epoch": 131, "iter": 1900, "lr": 0.00411, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52203, "top5_acc": 0.77031, "loss_cls": 2.66185, "loss": 2.66185, "time": 0.82642} +{"mode": "train", "epoch": 131, "iter": 2000, "lr": 0.0041, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52469, "top5_acc": 0.77484, "loss_cls": 2.61962, "loss": 2.61962, "time": 0.81833} +{"mode": "train", "epoch": 131, "iter": 2100, "lr": 0.00409, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50234, "top5_acc": 0.75703, "loss_cls": 2.73209, "loss": 2.73209, "time": 0.8194} +{"mode": "train", "epoch": 131, "iter": 2200, "lr": 0.00408, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50281, "top5_acc": 0.76344, "loss_cls": 2.71098, "loss": 2.71098, "time": 0.82122} +{"mode": "train", "epoch": 131, "iter": 2300, "lr": 0.00407, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50719, "top5_acc": 0.76766, "loss_cls": 2.70747, "loss": 2.70747, "time": 0.81827} +{"mode": "train", "epoch": 131, "iter": 2400, "lr": 0.00405, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51359, "top5_acc": 0.76719, "loss_cls": 2.65063, "loss": 2.65063, "time": 0.82416} +{"mode": "train", "epoch": 131, "iter": 2500, "lr": 0.00404, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51094, "top5_acc": 0.76344, "loss_cls": 2.67527, "loss": 2.67527, "time": 0.81679} +{"mode": "train", "epoch": 131, "iter": 2600, "lr": 0.00403, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51172, "top5_acc": 0.76078, "loss_cls": 2.71475, "loss": 2.71475, "time": 0.81964} +{"mode": "train", "epoch": 131, "iter": 2700, "lr": 0.00402, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51625, "top5_acc": 0.76203, "loss_cls": 2.67963, "loss": 2.67963, "time": 0.82189} +{"mode": "train", "epoch": 131, "iter": 2800, "lr": 0.00401, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52016, "top5_acc": 0.77297, "loss_cls": 2.65644, "loss": 2.65644, "time": 0.82166} +{"mode": "train", "epoch": 131, "iter": 2900, "lr": 0.004, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50625, "top5_acc": 0.76203, "loss_cls": 2.71884, "loss": 2.71884, "time": 0.8145} +{"mode": "train", "epoch": 131, "iter": 3000, "lr": 0.00399, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51, "top5_acc": 0.75781, "loss_cls": 2.73439, "loss": 2.73439, "time": 0.81474} +{"mode": "train", "epoch": 131, "iter": 3100, "lr": 0.00398, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50594, "top5_acc": 0.76406, "loss_cls": 2.69631, "loss": 2.69631, "time": 0.81625} +{"mode": "train", "epoch": 131, "iter": 3200, "lr": 0.00397, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.51562, "top5_acc": 0.765, "loss_cls": 2.67696, "loss": 2.67696, "time": 0.82443} +{"mode": "train", "epoch": 131, "iter": 3300, "lr": 0.00396, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50844, "top5_acc": 0.76031, "loss_cls": 2.70479, "loss": 2.70479, "time": 0.82173} +{"mode": "train", "epoch": 131, "iter": 3400, "lr": 0.00394, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50234, "top5_acc": 0.76266, "loss_cls": 2.72269, "loss": 2.72269, "time": 0.82231} +{"mode": "train", "epoch": 131, "iter": 3500, "lr": 0.00393, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50781, "top5_acc": 0.76391, "loss_cls": 2.67799, "loss": 2.67799, "time": 0.82061} +{"mode": "train", "epoch": 131, "iter": 3600, "lr": 0.00392, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51797, "top5_acc": 0.76734, "loss_cls": 2.66787, "loss": 2.66787, "time": 0.81942} +{"mode": "train", "epoch": 131, "iter": 3700, "lr": 0.00391, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50875, "top5_acc": 0.76328, "loss_cls": 2.72386, "loss": 2.72386, "time": 0.81577} +{"mode": "val", "epoch": 131, "iter": 309, "lr": 0.00391, "top1_acc": 0.42425, "top5_acc": 0.68212, "mean_class_accuracy": 0.42393} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.0039, "memory": 15990, "data_time": 1.30373, "top1_acc": 0.54281, "top5_acc": 0.78844, "loss_cls": 2.53317, "loss": 2.53317, "time": 2.2872} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00389, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53156, "top5_acc": 0.77625, "loss_cls": 2.60999, "loss": 2.60999, "time": 0.8243} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00387, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53156, "top5_acc": 0.77844, "loss_cls": 2.58695, "loss": 2.58695, "time": 0.82194} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00386, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54594, "top5_acc": 0.78297, "loss_cls": 2.538, "loss": 2.538, "time": 0.81809} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00385, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53234, "top5_acc": 0.78672, "loss_cls": 2.55832, "loss": 2.55832, "time": 0.81719} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00384, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51875, "top5_acc": 0.76578, "loss_cls": 2.65572, "loss": 2.65572, "time": 0.82138} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00383, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53125, "top5_acc": 0.7825, "loss_cls": 2.57379, "loss": 2.57379, "time": 0.81979} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00382, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53703, "top5_acc": 0.78109, "loss_cls": 2.59473, "loss": 2.59473, "time": 0.82362} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00381, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52156, "top5_acc": 0.78234, "loss_cls": 2.60831, "loss": 2.60831, "time": 0.8295} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0038, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52047, "top5_acc": 0.77031, "loss_cls": 2.67614, "loss": 2.67614, "time": 0.81995} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00379, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52547, "top5_acc": 0.77891, "loss_cls": 2.61568, "loss": 2.61568, "time": 0.81969} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00378, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53188, "top5_acc": 0.77719, "loss_cls": 2.61257, "loss": 2.61257, "time": 0.81755} +{"mode": "train", "epoch": 132, "iter": 1300, "lr": 0.00377, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52062, "top5_acc": 0.77594, "loss_cls": 2.61991, "loss": 2.61991, "time": 0.81937} +{"mode": "train", "epoch": 132, "iter": 1400, "lr": 0.00376, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.52016, "top5_acc": 0.76781, "loss_cls": 2.67994, "loss": 2.67994, "time": 0.82474} +{"mode": "train", "epoch": 132, "iter": 1500, "lr": 0.00375, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52391, "top5_acc": 0.77766, "loss_cls": 2.60969, "loss": 2.60969, "time": 0.81767} +{"mode": "train", "epoch": 132, "iter": 1600, "lr": 0.00374, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.52672, "top5_acc": 0.77641, "loss_cls": 2.61289, "loss": 2.61289, "time": 0.83028} +{"mode": "train", "epoch": 132, "iter": 1700, "lr": 0.00372, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52469, "top5_acc": 0.77219, "loss_cls": 2.61864, "loss": 2.61864, "time": 0.82636} +{"mode": "train", "epoch": 132, "iter": 1800, "lr": 0.00371, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51812, "top5_acc": 0.76875, "loss_cls": 2.64768, "loss": 2.64768, "time": 0.82438} +{"mode": "train", "epoch": 132, "iter": 1900, "lr": 0.0037, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51938, "top5_acc": 0.77281, "loss_cls": 2.62355, "loss": 2.62355, "time": 0.81939} +{"mode": "train", "epoch": 132, "iter": 2000, "lr": 0.00369, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52531, "top5_acc": 0.77422, "loss_cls": 2.62375, "loss": 2.62375, "time": 0.82015} +{"mode": "train", "epoch": 132, "iter": 2100, "lr": 0.00368, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51641, "top5_acc": 0.76047, "loss_cls": 2.67135, "loss": 2.67135, "time": 0.82261} +{"mode": "train", "epoch": 132, "iter": 2200, "lr": 0.00367, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51484, "top5_acc": 0.77281, "loss_cls": 2.64098, "loss": 2.64098, "time": 0.82559} +{"mode": "train", "epoch": 132, "iter": 2300, "lr": 0.00366, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.52016, "top5_acc": 0.77266, "loss_cls": 2.63611, "loss": 2.63611, "time": 0.82422} +{"mode": "train", "epoch": 132, "iter": 2400, "lr": 0.00365, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.51859, "top5_acc": 0.77344, "loss_cls": 2.63804, "loss": 2.63804, "time": 0.82514} +{"mode": "train", "epoch": 132, "iter": 2500, "lr": 0.00364, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52922, "top5_acc": 0.77766, "loss_cls": 2.6056, "loss": 2.6056, "time": 0.82111} +{"mode": "train", "epoch": 132, "iter": 2600, "lr": 0.00363, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51625, "top5_acc": 0.76453, "loss_cls": 2.65462, "loss": 2.65462, "time": 0.81887} +{"mode": "train", "epoch": 132, "iter": 2700, "lr": 0.00362, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53266, "top5_acc": 0.76922, "loss_cls": 2.62492, "loss": 2.62492, "time": 0.81428} +{"mode": "train", "epoch": 132, "iter": 2800, "lr": 0.00361, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52234, "top5_acc": 0.77438, "loss_cls": 2.65603, "loss": 2.65603, "time": 0.81462} +{"mode": "train", "epoch": 132, "iter": 2900, "lr": 0.0036, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52438, "top5_acc": 0.76312, "loss_cls": 2.65128, "loss": 2.65128, "time": 0.82055} +{"mode": "train", "epoch": 132, "iter": 3000, "lr": 0.00359, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52156, "top5_acc": 0.76828, "loss_cls": 2.63875, "loss": 2.63875, "time": 0.81407} +{"mode": "train", "epoch": 132, "iter": 3100, "lr": 0.00358, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52203, "top5_acc": 0.77672, "loss_cls": 2.61168, "loss": 2.61168, "time": 0.81867} +{"mode": "train", "epoch": 132, "iter": 3200, "lr": 0.00357, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.53125, "top5_acc": 0.78188, "loss_cls": 2.59609, "loss": 2.59609, "time": 0.81831} +{"mode": "train", "epoch": 132, "iter": 3300, "lr": 0.00356, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51406, "top5_acc": 0.76594, "loss_cls": 2.66638, "loss": 2.66638, "time": 0.81983} +{"mode": "train", "epoch": 132, "iter": 3400, "lr": 0.00355, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51297, "top5_acc": 0.76781, "loss_cls": 2.63548, "loss": 2.63548, "time": 0.82475} +{"mode": "train", "epoch": 132, "iter": 3500, "lr": 0.00354, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51313, "top5_acc": 0.76125, "loss_cls": 2.69566, "loss": 2.69566, "time": 0.81771} +{"mode": "train", "epoch": 132, "iter": 3600, "lr": 0.00353, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51313, "top5_acc": 0.76672, "loss_cls": 2.67244, "loss": 2.67244, "time": 0.81453} +{"mode": "train", "epoch": 132, "iter": 3700, "lr": 0.00352, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.5175, "top5_acc": 0.76359, "loss_cls": 2.68516, "loss": 2.68516, "time": 0.81915} +{"mode": "val", "epoch": 132, "iter": 309, "lr": 0.00351, "top1_acc": 0.42967, "top5_acc": 0.68455, "mean_class_accuracy": 0.42939} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.0035, "memory": 15990, "data_time": 1.32941, "top1_acc": 0.54297, "top5_acc": 0.78734, "loss_cls": 2.53092, "loss": 2.53092, "time": 2.3148} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00349, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53891, "top5_acc": 0.78969, "loss_cls": 2.52646, "loss": 2.52646, "time": 0.82244} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00348, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54234, "top5_acc": 0.79062, "loss_cls": 2.51935, "loss": 2.51935, "time": 0.81698} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00347, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54078, "top5_acc": 0.78828, "loss_cls": 2.52357, "loss": 2.52357, "time": 0.82043} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00346, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54766, "top5_acc": 0.79062, "loss_cls": 2.4985, "loss": 2.4985, "time": 0.81486} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00345, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53656, "top5_acc": 0.7875, "loss_cls": 2.5225, "loss": 2.5225, "time": 0.81317} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00344, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53547, "top5_acc": 0.78203, "loss_cls": 2.55424, "loss": 2.55424, "time": 0.814} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00343, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.54156, "top5_acc": 0.78906, "loss_cls": 2.53166, "loss": 2.53166, "time": 0.81931} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00342, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53516, "top5_acc": 0.77969, "loss_cls": 2.57571, "loss": 2.57571, "time": 0.81911} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.00341, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52703, "top5_acc": 0.78391, "loss_cls": 2.58434, "loss": 2.58434, "time": 0.82203} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0034, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52312, "top5_acc": 0.77312, "loss_cls": 2.60529, "loss": 2.60529, "time": 0.82238} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00339, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53172, "top5_acc": 0.78578, "loss_cls": 2.55426, "loss": 2.55426, "time": 0.81488} +{"mode": "train", "epoch": 133, "iter": 1300, "lr": 0.00338, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52953, "top5_acc": 0.77453, "loss_cls": 2.60161, "loss": 2.60161, "time": 0.82175} +{"mode": "train", "epoch": 133, "iter": 1400, "lr": 0.00337, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.52891, "top5_acc": 0.78125, "loss_cls": 2.58737, "loss": 2.58737, "time": 0.82558} +{"mode": "train", "epoch": 133, "iter": 1500, "lr": 0.00336, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53641, "top5_acc": 0.79125, "loss_cls": 2.53246, "loss": 2.53246, "time": 0.81813} +{"mode": "train", "epoch": 133, "iter": 1600, "lr": 0.00335, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52375, "top5_acc": 0.77828, "loss_cls": 2.60337, "loss": 2.60337, "time": 0.82362} +{"mode": "train", "epoch": 133, "iter": 1700, "lr": 0.00334, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.51562, "top5_acc": 0.76797, "loss_cls": 2.64745, "loss": 2.64745, "time": 0.82112} +{"mode": "train", "epoch": 133, "iter": 1800, "lr": 0.00333, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53609, "top5_acc": 0.78016, "loss_cls": 2.58571, "loss": 2.58571, "time": 0.81982} +{"mode": "train", "epoch": 133, "iter": 1900, "lr": 0.00332, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53312, "top5_acc": 0.77672, "loss_cls": 2.59404, "loss": 2.59404, "time": 0.81913} +{"mode": "train", "epoch": 133, "iter": 2000, "lr": 0.00331, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51984, "top5_acc": 0.7775, "loss_cls": 2.62127, "loss": 2.62127, "time": 0.81545} +{"mode": "train", "epoch": 133, "iter": 2100, "lr": 0.0033, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.54016, "top5_acc": 0.77625, "loss_cls": 2.55512, "loss": 2.55512, "time": 0.82057} +{"mode": "train", "epoch": 133, "iter": 2200, "lr": 0.00329, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52328, "top5_acc": 0.7725, "loss_cls": 2.62745, "loss": 2.62745, "time": 0.82457} +{"mode": "train", "epoch": 133, "iter": 2300, "lr": 0.00328, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52703, "top5_acc": 0.77453, "loss_cls": 2.62002, "loss": 2.62002, "time": 0.82007} +{"mode": "train", "epoch": 133, "iter": 2400, "lr": 0.00327, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53328, "top5_acc": 0.77062, "loss_cls": 2.6115, "loss": 2.6115, "time": 0.82023} +{"mode": "train", "epoch": 133, "iter": 2500, "lr": 0.00326, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51422, "top5_acc": 0.77344, "loss_cls": 2.64649, "loss": 2.64649, "time": 0.82151} +{"mode": "train", "epoch": 133, "iter": 2600, "lr": 0.00325, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52797, "top5_acc": 0.77531, "loss_cls": 2.59856, "loss": 2.59856, "time": 0.81983} +{"mode": "train", "epoch": 133, "iter": 2700, "lr": 0.00324, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51922, "top5_acc": 0.77031, "loss_cls": 2.64137, "loss": 2.64137, "time": 0.81626} +{"mode": "train", "epoch": 133, "iter": 2800, "lr": 0.00323, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53359, "top5_acc": 0.77953, "loss_cls": 2.5799, "loss": 2.5799, "time": 0.81865} +{"mode": "train", "epoch": 133, "iter": 2900, "lr": 0.00322, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53812, "top5_acc": 0.79219, "loss_cls": 2.55965, "loss": 2.55965, "time": 0.82136} +{"mode": "train", "epoch": 133, "iter": 3000, "lr": 0.00321, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52281, "top5_acc": 0.77219, "loss_cls": 2.63507, "loss": 2.63507, "time": 0.81692} +{"mode": "train", "epoch": 133, "iter": 3100, "lr": 0.0032, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53531, "top5_acc": 0.77734, "loss_cls": 2.56183, "loss": 2.56183, "time": 0.82109} +{"mode": "train", "epoch": 133, "iter": 3200, "lr": 0.00319, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52859, "top5_acc": 0.77938, "loss_cls": 2.59199, "loss": 2.59199, "time": 0.81621} +{"mode": "train", "epoch": 133, "iter": 3300, "lr": 0.00318, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52094, "top5_acc": 0.77391, "loss_cls": 2.62381, "loss": 2.62381, "time": 0.82283} +{"mode": "train", "epoch": 133, "iter": 3400, "lr": 0.00317, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52938, "top5_acc": 0.78156, "loss_cls": 2.59706, "loss": 2.59706, "time": 0.82686} +{"mode": "train", "epoch": 133, "iter": 3500, "lr": 0.00316, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53188, "top5_acc": 0.77781, "loss_cls": 2.57641, "loss": 2.57641, "time": 0.81997} +{"mode": "train", "epoch": 133, "iter": 3600, "lr": 0.00315, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52875, "top5_acc": 0.77062, "loss_cls": 2.61493, "loss": 2.61493, "time": 0.81809} +{"mode": "train", "epoch": 133, "iter": 3700, "lr": 0.00314, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52469, "top5_acc": 0.77969, "loss_cls": 2.6097, "loss": 2.6097, "time": 0.81957} +{"mode": "val", "epoch": 133, "iter": 309, "lr": 0.00314, "top1_acc": 0.43717, "top5_acc": 0.68946, "mean_class_accuracy": 0.43693} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00313, "memory": 15990, "data_time": 1.33233, "top1_acc": 0.55844, "top5_acc": 0.80203, "loss_cls": 2.43786, "loss": 2.43786, "time": 2.32323} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00312, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.55344, "top5_acc": 0.7925, "loss_cls": 2.47746, "loss": 2.47746, "time": 0.83062} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00311, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54, "top5_acc": 0.79141, "loss_cls": 2.52014, "loss": 2.52014, "time": 0.82448} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.0031, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55047, "top5_acc": 0.79594, "loss_cls": 2.48062, "loss": 2.48062, "time": 0.8264} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00309, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54594, "top5_acc": 0.78594, "loss_cls": 2.51742, "loss": 2.51742, "time": 0.81386} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00308, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55062, "top5_acc": 0.79109, "loss_cls": 2.49337, "loss": 2.49337, "time": 0.8218} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00307, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54344, "top5_acc": 0.79562, "loss_cls": 2.48939, "loss": 2.48939, "time": 0.81685} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00306, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53547, "top5_acc": 0.78641, "loss_cls": 2.53416, "loss": 2.53416, "time": 0.81944} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00305, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54031, "top5_acc": 0.78672, "loss_cls": 2.53477, "loss": 2.53477, "time": 0.82013} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00304, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53906, "top5_acc": 0.79406, "loss_cls": 2.51725, "loss": 2.51725, "time": 0.8145} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00303, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53672, "top5_acc": 0.78859, "loss_cls": 2.54768, "loss": 2.54768, "time": 0.81844} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.00302, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53438, "top5_acc": 0.78047, "loss_cls": 2.56393, "loss": 2.56393, "time": 0.81546} +{"mode": "train", "epoch": 134, "iter": 1300, "lr": 0.00301, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54438, "top5_acc": 0.78047, "loss_cls": 2.54357, "loss": 2.54357, "time": 0.81928} +{"mode": "train", "epoch": 134, "iter": 1400, "lr": 0.003, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54844, "top5_acc": 0.78469, "loss_cls": 2.52419, "loss": 2.52419, "time": 0.82013} +{"mode": "train", "epoch": 134, "iter": 1500, "lr": 0.00299, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54719, "top5_acc": 0.78734, "loss_cls": 2.51421, "loss": 2.51421, "time": 0.819} +{"mode": "train", "epoch": 134, "iter": 1600, "lr": 0.00298, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.54, "top5_acc": 0.78094, "loss_cls": 2.55796, "loss": 2.55796, "time": 0.82297} +{"mode": "train", "epoch": 134, "iter": 1700, "lr": 0.00297, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54406, "top5_acc": 0.79625, "loss_cls": 2.48133, "loss": 2.48133, "time": 0.81978} +{"mode": "train", "epoch": 134, "iter": 1800, "lr": 0.00296, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.5275, "top5_acc": 0.78, "loss_cls": 2.60293, "loss": 2.60293, "time": 0.82227} +{"mode": "train", "epoch": 134, "iter": 1900, "lr": 0.00295, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53016, "top5_acc": 0.77219, "loss_cls": 2.60511, "loss": 2.60511, "time": 0.82216} +{"mode": "train", "epoch": 134, "iter": 2000, "lr": 0.00294, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.52969, "top5_acc": 0.78125, "loss_cls": 2.55923, "loss": 2.55923, "time": 0.81784} +{"mode": "train", "epoch": 134, "iter": 2100, "lr": 0.00293, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54125, "top5_acc": 0.78609, "loss_cls": 2.50932, "loss": 2.50932, "time": 0.82044} +{"mode": "train", "epoch": 134, "iter": 2200, "lr": 0.00293, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53594, "top5_acc": 0.78297, "loss_cls": 2.53937, "loss": 2.53937, "time": 0.82309} +{"mode": "train", "epoch": 134, "iter": 2300, "lr": 0.00292, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53047, "top5_acc": 0.77297, "loss_cls": 2.5857, "loss": 2.5857, "time": 0.82243} +{"mode": "train", "epoch": 134, "iter": 2400, "lr": 0.00291, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53234, "top5_acc": 0.78812, "loss_cls": 2.56887, "loss": 2.56887, "time": 0.81823} +{"mode": "train", "epoch": 134, "iter": 2500, "lr": 0.0029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53922, "top5_acc": 0.78297, "loss_cls": 2.55793, "loss": 2.55793, "time": 0.82053} +{"mode": "train", "epoch": 134, "iter": 2600, "lr": 0.00289, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.535, "top5_acc": 0.78594, "loss_cls": 2.54232, "loss": 2.54232, "time": 0.81824} +{"mode": "train", "epoch": 134, "iter": 2700, "lr": 0.00288, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52812, "top5_acc": 0.78203, "loss_cls": 2.57664, "loss": 2.57664, "time": 0.81704} +{"mode": "train", "epoch": 134, "iter": 2800, "lr": 0.00287, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53406, "top5_acc": 0.78141, "loss_cls": 2.56433, "loss": 2.56433, "time": 0.81757} +{"mode": "train", "epoch": 134, "iter": 2900, "lr": 0.00286, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54297, "top5_acc": 0.78531, "loss_cls": 2.53362, "loss": 2.53362, "time": 0.81843} +{"mode": "train", "epoch": 134, "iter": 3000, "lr": 0.00285, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54234, "top5_acc": 0.78375, "loss_cls": 2.55401, "loss": 2.55401, "time": 0.81739} +{"mode": "train", "epoch": 134, "iter": 3100, "lr": 0.00284, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.53531, "top5_acc": 0.78516, "loss_cls": 2.56187, "loss": 2.56187, "time": 0.82414} +{"mode": "train", "epoch": 134, "iter": 3200, "lr": 0.00283, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53016, "top5_acc": 0.77828, "loss_cls": 2.57627, "loss": 2.57627, "time": 0.81975} +{"mode": "train", "epoch": 134, "iter": 3300, "lr": 0.00282, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.5325, "top5_acc": 0.78141, "loss_cls": 2.56454, "loss": 2.56454, "time": 0.8283} +{"mode": "train", "epoch": 134, "iter": 3400, "lr": 0.00281, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53062, "top5_acc": 0.77594, "loss_cls": 2.59684, "loss": 2.59684, "time": 0.8256} +{"mode": "train", "epoch": 134, "iter": 3500, "lr": 0.0028, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54141, "top5_acc": 0.78438, "loss_cls": 2.53512, "loss": 2.53512, "time": 0.81402} +{"mode": "train", "epoch": 134, "iter": 3600, "lr": 0.00279, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53375, "top5_acc": 0.78594, "loss_cls": 2.56197, "loss": 2.56197, "time": 0.82468} +{"mode": "train", "epoch": 134, "iter": 3700, "lr": 0.00279, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53078, "top5_acc": 0.78203, "loss_cls": 2.5763, "loss": 2.5763, "time": 0.81861} +{"mode": "val", "epoch": 134, "iter": 309, "lr": 0.00278, "top1_acc": 0.43737, "top5_acc": 0.68789, "mean_class_accuracy": 0.43708} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00277, "memory": 15990, "data_time": 1.28807, "top1_acc": 0.55547, "top5_acc": 0.80094, "loss_cls": 2.4436, "loss": 2.4436, "time": 2.27205} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00276, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56609, "top5_acc": 0.7975, "loss_cls": 2.44235, "loss": 2.44235, "time": 0.82065} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00275, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.555, "top5_acc": 0.80062, "loss_cls": 2.44407, "loss": 2.44407, "time": 0.82306} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00274, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56406, "top5_acc": 0.795, "loss_cls": 2.44822, "loss": 2.44822, "time": 0.82856} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00274, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55781, "top5_acc": 0.80062, "loss_cls": 2.44441, "loss": 2.44441, "time": 0.8217} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00273, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56344, "top5_acc": 0.79859, "loss_cls": 2.4227, "loss": 2.4227, "time": 0.82283} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00272, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55453, "top5_acc": 0.78938, "loss_cls": 2.47539, "loss": 2.47539, "time": 0.82146} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00271, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.55984, "top5_acc": 0.79859, "loss_cls": 2.44005, "loss": 2.44005, "time": 0.83355} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.0027, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55531, "top5_acc": 0.80359, "loss_cls": 2.4491, "loss": 2.4491, "time": 0.81947} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00269, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54828, "top5_acc": 0.79578, "loss_cls": 2.46835, "loss": 2.46835, "time": 0.82289} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00268, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54672, "top5_acc": 0.79344, "loss_cls": 2.5162, "loss": 2.5162, "time": 0.82293} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00267, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54453, "top5_acc": 0.79469, "loss_cls": 2.50476, "loss": 2.50476, "time": 0.81908} +{"mode": "train", "epoch": 135, "iter": 1300, "lr": 0.00266, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54859, "top5_acc": 0.78781, "loss_cls": 2.50037, "loss": 2.50037, "time": 0.82081} +{"mode": "train", "epoch": 135, "iter": 1400, "lr": 0.00265, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55047, "top5_acc": 0.78859, "loss_cls": 2.49441, "loss": 2.49441, "time": 0.82313} +{"mode": "train", "epoch": 135, "iter": 1500, "lr": 0.00265, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.53391, "top5_acc": 0.79078, "loss_cls": 2.52117, "loss": 2.52117, "time": 0.82534} +{"mode": "train", "epoch": 135, "iter": 1600, "lr": 0.00264, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.54172, "top5_acc": 0.79281, "loss_cls": 2.49986, "loss": 2.49986, "time": 0.8284} +{"mode": "train", "epoch": 135, "iter": 1700, "lr": 0.00263, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55188, "top5_acc": 0.79109, "loss_cls": 2.47469, "loss": 2.47469, "time": 0.82388} +{"mode": "train", "epoch": 135, "iter": 1800, "lr": 0.00262, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54469, "top5_acc": 0.79656, "loss_cls": 2.50382, "loss": 2.50382, "time": 0.82239} +{"mode": "train", "epoch": 135, "iter": 1900, "lr": 0.00261, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55172, "top5_acc": 0.79219, "loss_cls": 2.47252, "loss": 2.47252, "time": 0.82098} +{"mode": "train", "epoch": 135, "iter": 2000, "lr": 0.0026, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54937, "top5_acc": 0.80062, "loss_cls": 2.47719, "loss": 2.47719, "time": 0.81954} +{"mode": "train", "epoch": 135, "iter": 2100, "lr": 0.00259, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54594, "top5_acc": 0.795, "loss_cls": 2.47268, "loss": 2.47268, "time": 0.81713} +{"mode": "train", "epoch": 135, "iter": 2200, "lr": 0.00258, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53984, "top5_acc": 0.78953, "loss_cls": 2.51342, "loss": 2.51342, "time": 0.81927} +{"mode": "train", "epoch": 135, "iter": 2300, "lr": 0.00257, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.54641, "top5_acc": 0.79609, "loss_cls": 2.49749, "loss": 2.49749, "time": 0.82048} +{"mode": "train", "epoch": 135, "iter": 2400, "lr": 0.00256, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53547, "top5_acc": 0.77609, "loss_cls": 2.55785, "loss": 2.55785, "time": 0.82508} +{"mode": "train", "epoch": 135, "iter": 2500, "lr": 0.00256, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53562, "top5_acc": 0.78625, "loss_cls": 2.54265, "loss": 2.54265, "time": 0.82075} +{"mode": "train", "epoch": 135, "iter": 2600, "lr": 0.00255, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54828, "top5_acc": 0.78984, "loss_cls": 2.5192, "loss": 2.5192, "time": 0.81981} +{"mode": "train", "epoch": 135, "iter": 2700, "lr": 0.00254, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54516, "top5_acc": 0.78891, "loss_cls": 2.50256, "loss": 2.50256, "time": 0.82143} +{"mode": "train", "epoch": 135, "iter": 2800, "lr": 0.00253, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5425, "top5_acc": 0.78359, "loss_cls": 2.51629, "loss": 2.51629, "time": 0.81694} +{"mode": "train", "epoch": 135, "iter": 2900, "lr": 0.00252, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53797, "top5_acc": 0.78359, "loss_cls": 2.53495, "loss": 2.53495, "time": 0.81813} +{"mode": "train", "epoch": 135, "iter": 3000, "lr": 0.00251, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53109, "top5_acc": 0.78516, "loss_cls": 2.56005, "loss": 2.56005, "time": 0.8174} +{"mode": "train", "epoch": 135, "iter": 3100, "lr": 0.0025, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53578, "top5_acc": 0.78328, "loss_cls": 2.57026, "loss": 2.57026, "time": 0.83091} +{"mode": "train", "epoch": 135, "iter": 3200, "lr": 0.00249, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54406, "top5_acc": 0.79047, "loss_cls": 2.51248, "loss": 2.51248, "time": 0.82084} +{"mode": "train", "epoch": 135, "iter": 3300, "lr": 0.00249, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.53562, "top5_acc": 0.78375, "loss_cls": 2.53823, "loss": 2.53823, "time": 0.82522} +{"mode": "train", "epoch": 135, "iter": 3400, "lr": 0.00248, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.54812, "top5_acc": 0.79562, "loss_cls": 2.4843, "loss": 2.4843, "time": 0.81812} +{"mode": "train", "epoch": 135, "iter": 3500, "lr": 0.00247, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54734, "top5_acc": 0.79266, "loss_cls": 2.48534, "loss": 2.48534, "time": 0.82389} +{"mode": "train", "epoch": 135, "iter": 3600, "lr": 0.00246, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5425, "top5_acc": 0.8, "loss_cls": 2.46707, "loss": 2.46707, "time": 0.82235} +{"mode": "train", "epoch": 135, "iter": 3700, "lr": 0.00245, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52828, "top5_acc": 0.77844, "loss_cls": 2.58649, "loss": 2.58649, "time": 0.81706} +{"mode": "val", "epoch": 135, "iter": 309, "lr": 0.00245, "top1_acc": 0.43884, "top5_acc": 0.69062, "mean_class_accuracy": 0.4386} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00244, "memory": 15990, "data_time": 1.28985, "top1_acc": 0.57141, "top5_acc": 0.81641, "loss_cls": 2.35759, "loss": 2.35759, "time": 2.27364} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.00243, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56719, "top5_acc": 0.81844, "loss_cls": 2.35132, "loss": 2.35132, "time": 0.82069} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00242, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56781, "top5_acc": 0.80766, "loss_cls": 2.3753, "loss": 2.3753, "time": 0.81751} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00241, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56984, "top5_acc": 0.81297, "loss_cls": 2.36607, "loss": 2.36607, "time": 0.82012} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.0024, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.55641, "top5_acc": 0.79859, "loss_cls": 2.42751, "loss": 2.42751, "time": 0.81911} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.0024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55781, "top5_acc": 0.80578, "loss_cls": 2.39401, "loss": 2.39401, "time": 0.81503} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00239, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55172, "top5_acc": 0.7975, "loss_cls": 2.46869, "loss": 2.46869, "time": 0.8225} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00238, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55188, "top5_acc": 0.79812, "loss_cls": 2.47249, "loss": 2.47249, "time": 0.81913} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00237, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56063, "top5_acc": 0.80359, "loss_cls": 2.41921, "loss": 2.41921, "time": 0.82147} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00236, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55234, "top5_acc": 0.79578, "loss_cls": 2.44081, "loss": 2.44081, "time": 0.82247} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00235, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55891, "top5_acc": 0.80625, "loss_cls": 2.41573, "loss": 2.41573, "time": 0.81822} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00234, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57078, "top5_acc": 0.80812, "loss_cls": 2.39321, "loss": 2.39321, "time": 0.81561} +{"mode": "train", "epoch": 136, "iter": 1300, "lr": 0.00234, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55156, "top5_acc": 0.79359, "loss_cls": 2.47529, "loss": 2.47529, "time": 0.82327} +{"mode": "train", "epoch": 136, "iter": 1400, "lr": 0.00233, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56437, "top5_acc": 0.80062, "loss_cls": 2.43839, "loss": 2.43839, "time": 0.82391} +{"mode": "train", "epoch": 136, "iter": 1500, "lr": 0.00232, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55953, "top5_acc": 0.80125, "loss_cls": 2.41947, "loss": 2.41947, "time": 0.82633} +{"mode": "train", "epoch": 136, "iter": 1600, "lr": 0.00231, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55922, "top5_acc": 0.80375, "loss_cls": 2.42852, "loss": 2.42852, "time": 0.82759} +{"mode": "train", "epoch": 136, "iter": 1700, "lr": 0.0023, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55281, "top5_acc": 0.79719, "loss_cls": 2.46374, "loss": 2.46374, "time": 0.82705} +{"mode": "train", "epoch": 136, "iter": 1800, "lr": 0.00229, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.5525, "top5_acc": 0.79391, "loss_cls": 2.46871, "loss": 2.46871, "time": 0.82338} +{"mode": "train", "epoch": 136, "iter": 1900, "lr": 0.00229, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55188, "top5_acc": 0.78562, "loss_cls": 2.48043, "loss": 2.48043, "time": 0.82185} +{"mode": "train", "epoch": 136, "iter": 2000, "lr": 0.00228, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55906, "top5_acc": 0.79516, "loss_cls": 2.44759, "loss": 2.44759, "time": 0.81941} +{"mode": "train", "epoch": 136, "iter": 2100, "lr": 0.00227, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55906, "top5_acc": 0.80406, "loss_cls": 2.41472, "loss": 2.41472, "time": 0.8303} +{"mode": "train", "epoch": 136, "iter": 2200, "lr": 0.00226, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54781, "top5_acc": 0.79688, "loss_cls": 2.4997, "loss": 2.4997, "time": 0.82437} +{"mode": "train", "epoch": 136, "iter": 2300, "lr": 0.00225, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.55078, "top5_acc": 0.78984, "loss_cls": 2.48486, "loss": 2.48486, "time": 0.83391} +{"mode": "train", "epoch": 136, "iter": 2400, "lr": 0.00224, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56453, "top5_acc": 0.79938, "loss_cls": 2.43731, "loss": 2.43731, "time": 0.8189} +{"mode": "train", "epoch": 136, "iter": 2500, "lr": 0.00224, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55125, "top5_acc": 0.79125, "loss_cls": 2.48148, "loss": 2.48148, "time": 0.8218} +{"mode": "train", "epoch": 136, "iter": 2600, "lr": 0.00223, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54469, "top5_acc": 0.79281, "loss_cls": 2.48991, "loss": 2.48991, "time": 0.82347} +{"mode": "train", "epoch": 136, "iter": 2700, "lr": 0.00222, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55062, "top5_acc": 0.79906, "loss_cls": 2.46791, "loss": 2.46791, "time": 0.82368} +{"mode": "train", "epoch": 136, "iter": 2800, "lr": 0.00221, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55359, "top5_acc": 0.79406, "loss_cls": 2.46912, "loss": 2.46912, "time": 0.81798} +{"mode": "train", "epoch": 136, "iter": 2900, "lr": 0.0022, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55094, "top5_acc": 0.79828, "loss_cls": 2.46454, "loss": 2.46454, "time": 0.81718} +{"mode": "train", "epoch": 136, "iter": 3000, "lr": 0.00219, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55125, "top5_acc": 0.79891, "loss_cls": 2.44497, "loss": 2.44497, "time": 0.82242} +{"mode": "train", "epoch": 136, "iter": 3100, "lr": 0.00219, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54406, "top5_acc": 0.7975, "loss_cls": 2.49614, "loss": 2.49614, "time": 0.81689} +{"mode": "train", "epoch": 136, "iter": 3200, "lr": 0.00218, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55109, "top5_acc": 0.79844, "loss_cls": 2.45385, "loss": 2.45385, "time": 0.826} +{"mode": "train", "epoch": 136, "iter": 3300, "lr": 0.00217, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54797, "top5_acc": 0.78531, "loss_cls": 2.50774, "loss": 2.50774, "time": 0.81963} +{"mode": "train", "epoch": 136, "iter": 3400, "lr": 0.00216, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54906, "top5_acc": 0.7875, "loss_cls": 2.50973, "loss": 2.50973, "time": 0.83155} +{"mode": "train", "epoch": 136, "iter": 3500, "lr": 0.00215, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.55188, "top5_acc": 0.79469, "loss_cls": 2.45716, "loss": 2.45716, "time": 0.81577} +{"mode": "train", "epoch": 136, "iter": 3600, "lr": 0.00215, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55625, "top5_acc": 0.80125, "loss_cls": 2.4584, "loss": 2.4584, "time": 0.82047} +{"mode": "train", "epoch": 136, "iter": 3700, "lr": 0.00214, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55297, "top5_acc": 0.79641, "loss_cls": 2.46542, "loss": 2.46542, "time": 0.82039} +{"mode": "val", "epoch": 136, "iter": 309, "lr": 0.00213, "top1_acc": 0.44395, "top5_acc": 0.69691, "mean_class_accuracy": 0.44373} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00213, "memory": 15990, "data_time": 1.27093, "top1_acc": 0.57531, "top5_acc": 0.81578, "loss_cls": 2.33552, "loss": 2.33552, "time": 2.2513} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00212, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5725, "top5_acc": 0.80828, "loss_cls": 2.3904, "loss": 2.3904, "time": 0.81945} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00211, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57172, "top5_acc": 0.80984, "loss_cls": 2.3656, "loss": 2.3656, "time": 0.82433} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.0021, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56281, "top5_acc": 0.79984, "loss_cls": 2.41916, "loss": 2.41916, "time": 0.82031} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.00209, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57594, "top5_acc": 0.81875, "loss_cls": 2.33013, "loss": 2.33013, "time": 0.81793} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.00209, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56141, "top5_acc": 0.80609, "loss_cls": 2.3872, "loss": 2.3872, "time": 0.81849} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00208, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57188, "top5_acc": 0.8175, "loss_cls": 2.33431, "loss": 2.33431, "time": 0.8236} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00207, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57188, "top5_acc": 0.81469, "loss_cls": 2.35836, "loss": 2.35836, "time": 0.82375} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00206, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57094, "top5_acc": 0.81312, "loss_cls": 2.32729, "loss": 2.32729, "time": 0.81949} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00205, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56906, "top5_acc": 0.81172, "loss_cls": 2.37324, "loss": 2.37324, "time": 0.81887} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00205, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56, "top5_acc": 0.8125, "loss_cls": 2.39299, "loss": 2.39299, "time": 0.82418} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00204, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56297, "top5_acc": 0.80219, "loss_cls": 2.40548, "loss": 2.40548, "time": 0.81775} +{"mode": "train", "epoch": 137, "iter": 1300, "lr": 0.00203, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56984, "top5_acc": 0.805, "loss_cls": 2.41392, "loss": 2.41392, "time": 0.81933} +{"mode": "train", "epoch": 137, "iter": 1400, "lr": 0.00202, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56812, "top5_acc": 0.80594, "loss_cls": 2.41302, "loss": 2.41302, "time": 0.8203} +{"mode": "train", "epoch": 137, "iter": 1500, "lr": 0.00201, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56906, "top5_acc": 0.80406, "loss_cls": 2.369, "loss": 2.369, "time": 0.82438} +{"mode": "train", "epoch": 137, "iter": 1600, "lr": 0.00201, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.57047, "top5_acc": 0.805, "loss_cls": 2.3885, "loss": 2.3885, "time": 0.82801} +{"mode": "train", "epoch": 137, "iter": 1700, "lr": 0.002, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.56312, "top5_acc": 0.79641, "loss_cls": 2.43791, "loss": 2.43791, "time": 0.82574} +{"mode": "train", "epoch": 137, "iter": 1800, "lr": 0.00199, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56688, "top5_acc": 0.80703, "loss_cls": 2.38235, "loss": 2.38235, "time": 0.81768} +{"mode": "train", "epoch": 137, "iter": 1900, "lr": 0.00198, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55797, "top5_acc": 0.79172, "loss_cls": 2.4439, "loss": 2.4439, "time": 0.82029} +{"mode": "train", "epoch": 137, "iter": 2000, "lr": 0.00198, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55984, "top5_acc": 0.80141, "loss_cls": 2.42148, "loss": 2.42148, "time": 0.81816} +{"mode": "train", "epoch": 137, "iter": 2100, "lr": 0.00197, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57094, "top5_acc": 0.80469, "loss_cls": 2.39776, "loss": 2.39776, "time": 0.82015} +{"mode": "train", "epoch": 137, "iter": 2200, "lr": 0.00196, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56047, "top5_acc": 0.79797, "loss_cls": 2.42269, "loss": 2.42269, "time": 0.82174} +{"mode": "train", "epoch": 137, "iter": 2300, "lr": 0.00195, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56453, "top5_acc": 0.80297, "loss_cls": 2.39312, "loss": 2.39312, "time": 0.82184} +{"mode": "train", "epoch": 137, "iter": 2400, "lr": 0.00194, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.55578, "top5_acc": 0.79609, "loss_cls": 2.44919, "loss": 2.44919, "time": 0.83092} +{"mode": "train", "epoch": 137, "iter": 2500, "lr": 0.00194, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55641, "top5_acc": 0.80469, "loss_cls": 2.44483, "loss": 2.44483, "time": 0.8214} +{"mode": "train", "epoch": 137, "iter": 2600, "lr": 0.00193, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.57, "top5_acc": 0.80578, "loss_cls": 2.40256, "loss": 2.40256, "time": 0.82412} +{"mode": "train", "epoch": 137, "iter": 2700, "lr": 0.00192, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56141, "top5_acc": 0.79969, "loss_cls": 2.41925, "loss": 2.41925, "time": 0.81931} +{"mode": "train", "epoch": 137, "iter": 2800, "lr": 0.00191, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56156, "top5_acc": 0.80531, "loss_cls": 2.40292, "loss": 2.40292, "time": 0.81712} +{"mode": "train", "epoch": 137, "iter": 2900, "lr": 0.00191, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56812, "top5_acc": 0.80109, "loss_cls": 2.39562, "loss": 2.39562, "time": 0.82277} +{"mode": "train", "epoch": 137, "iter": 3000, "lr": 0.0019, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56297, "top5_acc": 0.81016, "loss_cls": 2.39367, "loss": 2.39367, "time": 0.82479} +{"mode": "train", "epoch": 137, "iter": 3100, "lr": 0.00189, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56547, "top5_acc": 0.80234, "loss_cls": 2.39002, "loss": 2.39002, "time": 0.81947} +{"mode": "train", "epoch": 137, "iter": 3200, "lr": 0.00188, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56125, "top5_acc": 0.8075, "loss_cls": 2.40548, "loss": 2.40548, "time": 0.82198} +{"mode": "train", "epoch": 137, "iter": 3300, "lr": 0.00188, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55953, "top5_acc": 0.79688, "loss_cls": 2.43744, "loss": 2.43744, "time": 0.81615} +{"mode": "train", "epoch": 137, "iter": 3400, "lr": 0.00187, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55984, "top5_acc": 0.80281, "loss_cls": 2.41176, "loss": 2.41176, "time": 0.82759} +{"mode": "train", "epoch": 137, "iter": 3500, "lr": 0.00186, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56312, "top5_acc": 0.79906, "loss_cls": 2.41588, "loss": 2.41588, "time": 0.82154} +{"mode": "train", "epoch": 137, "iter": 3600, "lr": 0.00185, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.565, "top5_acc": 0.80016, "loss_cls": 2.42663, "loss": 2.42663, "time": 0.82032} +{"mode": "train", "epoch": 137, "iter": 3700, "lr": 0.00185, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.56063, "top5_acc": 0.80188, "loss_cls": 2.4099, "loss": 2.4099, "time": 0.81426} +{"mode": "val", "epoch": 137, "iter": 309, "lr": 0.00184, "top1_acc": 0.44664, "top5_acc": 0.70065, "mean_class_accuracy": 0.4464} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00183, "memory": 15990, "data_time": 1.25653, "top1_acc": 0.58906, "top5_acc": 0.82828, "loss_cls": 2.2547, "loss": 2.2547, "time": 2.24474} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00183, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58672, "top5_acc": 0.82375, "loss_cls": 2.27345, "loss": 2.27345, "time": 0.81758} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00182, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5875, "top5_acc": 0.81734, "loss_cls": 2.28097, "loss": 2.28097, "time": 0.81826} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00181, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57828, "top5_acc": 0.81984, "loss_cls": 2.305, "loss": 2.305, "time": 0.81943} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.0018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58328, "top5_acc": 0.82328, "loss_cls": 2.28305, "loss": 2.28305, "time": 0.81856} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.0018, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57859, "top5_acc": 0.81703, "loss_cls": 2.32341, "loss": 2.32341, "time": 0.81931} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00179, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58688, "top5_acc": 0.82656, "loss_cls": 2.29133, "loss": 2.29133, "time": 0.8205} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00178, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58344, "top5_acc": 0.81438, "loss_cls": 2.31341, "loss": 2.31341, "time": 0.82175} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00177, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.57312, "top5_acc": 0.81688, "loss_cls": 2.33618, "loss": 2.33618, "time": 0.8173} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00177, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57281, "top5_acc": 0.81, "loss_cls": 2.34011, "loss": 2.34011, "time": 0.82142} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.00176, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57688, "top5_acc": 0.81891, "loss_cls": 2.31629, "loss": 2.31629, "time": 0.81619} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.00175, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56922, "top5_acc": 0.81094, "loss_cls": 2.355, "loss": 2.355, "time": 0.82014} +{"mode": "train", "epoch": 138, "iter": 1300, "lr": 0.00175, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.58234, "top5_acc": 0.81922, "loss_cls": 2.30774, "loss": 2.30774, "time": 0.82149} +{"mode": "train", "epoch": 138, "iter": 1400, "lr": 0.00174, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57063, "top5_acc": 0.81281, "loss_cls": 2.36903, "loss": 2.36903, "time": 0.82399} +{"mode": "train", "epoch": 138, "iter": 1500, "lr": 0.00173, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57109, "top5_acc": 0.81094, "loss_cls": 2.35442, "loss": 2.35442, "time": 0.82721} +{"mode": "train", "epoch": 138, "iter": 1600, "lr": 0.00172, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57609, "top5_acc": 0.80609, "loss_cls": 2.36441, "loss": 2.36441, "time": 0.82056} +{"mode": "train", "epoch": 138, "iter": 1700, "lr": 0.00172, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57094, "top5_acc": 0.80562, "loss_cls": 2.39579, "loss": 2.39579, "time": 0.82389} +{"mode": "train", "epoch": 138, "iter": 1800, "lr": 0.00171, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57609, "top5_acc": 0.81625, "loss_cls": 2.32374, "loss": 2.32374, "time": 0.81619} +{"mode": "train", "epoch": 138, "iter": 1900, "lr": 0.0017, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57516, "top5_acc": 0.81641, "loss_cls": 2.31017, "loss": 2.31017, "time": 0.8234} +{"mode": "train", "epoch": 138, "iter": 2000, "lr": 0.00169, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57828, "top5_acc": 0.82547, "loss_cls": 2.31774, "loss": 2.31774, "time": 0.81915} +{"mode": "train", "epoch": 138, "iter": 2100, "lr": 0.00169, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5575, "top5_acc": 0.80484, "loss_cls": 2.41281, "loss": 2.41281, "time": 0.81379} +{"mode": "train", "epoch": 138, "iter": 2200, "lr": 0.00168, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57609, "top5_acc": 0.80656, "loss_cls": 2.36979, "loss": 2.36979, "time": 0.81906} +{"mode": "train", "epoch": 138, "iter": 2300, "lr": 0.00167, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.57656, "top5_acc": 0.815, "loss_cls": 2.33772, "loss": 2.33772, "time": 0.81336} +{"mode": "train", "epoch": 138, "iter": 2400, "lr": 0.00167, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56437, "top5_acc": 0.80969, "loss_cls": 2.36874, "loss": 2.36874, "time": 0.81575} +{"mode": "train", "epoch": 138, "iter": 2500, "lr": 0.00166, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.56359, "top5_acc": 0.80922, "loss_cls": 2.37171, "loss": 2.37171, "time": 0.82498} +{"mode": "train", "epoch": 138, "iter": 2600, "lr": 0.00165, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56328, "top5_acc": 0.80797, "loss_cls": 2.38127, "loss": 2.38127, "time": 0.81874} +{"mode": "train", "epoch": 138, "iter": 2700, "lr": 0.00164, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56281, "top5_acc": 0.80734, "loss_cls": 2.40779, "loss": 2.40779, "time": 0.82826} +{"mode": "train", "epoch": 138, "iter": 2800, "lr": 0.00164, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56781, "top5_acc": 0.80672, "loss_cls": 2.38035, "loss": 2.38035, "time": 0.81647} +{"mode": "train", "epoch": 138, "iter": 2900, "lr": 0.00163, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56594, "top5_acc": 0.81094, "loss_cls": 2.38304, "loss": 2.38304, "time": 0.81731} +{"mode": "train", "epoch": 138, "iter": 3000, "lr": 0.00162, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57234, "top5_acc": 0.80562, "loss_cls": 2.39201, "loss": 2.39201, "time": 0.8206} +{"mode": "train", "epoch": 138, "iter": 3100, "lr": 0.00162, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.56906, "top5_acc": 0.80625, "loss_cls": 2.38425, "loss": 2.38425, "time": 0.82647} +{"mode": "train", "epoch": 138, "iter": 3200, "lr": 0.00161, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57688, "top5_acc": 0.80875, "loss_cls": 2.33906, "loss": 2.33906, "time": 0.81996} +{"mode": "train", "epoch": 138, "iter": 3300, "lr": 0.0016, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56891, "top5_acc": 0.80281, "loss_cls": 2.39546, "loss": 2.39546, "time": 0.82005} +{"mode": "train", "epoch": 138, "iter": 3400, "lr": 0.0016, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56094, "top5_acc": 0.81062, "loss_cls": 2.39214, "loss": 2.39214, "time": 0.82108} +{"mode": "train", "epoch": 138, "iter": 3500, "lr": 0.00159, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57797, "top5_acc": 0.81719, "loss_cls": 2.32303, "loss": 2.32303, "time": 0.82056} +{"mode": "train", "epoch": 138, "iter": 3600, "lr": 0.00158, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.575, "top5_acc": 0.80578, "loss_cls": 2.34759, "loss": 2.34759, "time": 0.8164} +{"mode": "train", "epoch": 138, "iter": 3700, "lr": 0.00157, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57547, "top5_acc": 0.80828, "loss_cls": 2.36998, "loss": 2.36998, "time": 0.81542} +{"mode": "val", "epoch": 138, "iter": 309, "lr": 0.00157, "top1_acc": 0.4475, "top5_acc": 0.69974, "mean_class_accuracy": 0.44721} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00156, "memory": 15990, "data_time": 1.27352, "top1_acc": 0.59734, "top5_acc": 0.82328, "loss_cls": 2.26439, "loss": 2.26439, "time": 2.29844} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00156, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59609, "top5_acc": 0.82719, "loss_cls": 2.23364, "loss": 2.23364, "time": 0.81777} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00155, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58844, "top5_acc": 0.82844, "loss_cls": 2.28194, "loss": 2.28194, "time": 0.81938} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00154, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.60406, "top5_acc": 0.83781, "loss_cls": 2.1772, "loss": 2.1772, "time": 0.81713} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00154, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59469, "top5_acc": 0.82219, "loss_cls": 2.26854, "loss": 2.26854, "time": 0.81842} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00153, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59344, "top5_acc": 0.82859, "loss_cls": 2.24454, "loss": 2.24454, "time": 0.82111} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00152, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58906, "top5_acc": 0.83109, "loss_cls": 2.24798, "loss": 2.24798, "time": 0.81717} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00152, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58844, "top5_acc": 0.81781, "loss_cls": 2.29917, "loss": 2.29917, "time": 0.81825} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00151, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59828, "top5_acc": 0.82375, "loss_cls": 2.24319, "loss": 2.24319, "time": 0.82224} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.0015, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59141, "top5_acc": 0.82219, "loss_cls": 2.25235, "loss": 2.25235, "time": 0.81876} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.0015, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58719, "top5_acc": 0.82078, "loss_cls": 2.27624, "loss": 2.27624, "time": 0.81966} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00149, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58984, "top5_acc": 0.82078, "loss_cls": 2.27506, "loss": 2.27506, "time": 0.81853} +{"mode": "train", "epoch": 139, "iter": 1300, "lr": 0.00148, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58781, "top5_acc": 0.81828, "loss_cls": 2.29681, "loss": 2.29681, "time": 0.82033} +{"mode": "train", "epoch": 139, "iter": 1400, "lr": 0.00148, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58094, "top5_acc": 0.82516, "loss_cls": 2.28107, "loss": 2.28107, "time": 0.83103} +{"mode": "train", "epoch": 139, "iter": 1500, "lr": 0.00147, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.58672, "top5_acc": 0.8175, "loss_cls": 2.29872, "loss": 2.29872, "time": 0.82735} +{"mode": "train", "epoch": 139, "iter": 1600, "lr": 0.00146, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57469, "top5_acc": 0.81375, "loss_cls": 2.33508, "loss": 2.33508, "time": 0.8192} +{"mode": "train", "epoch": 139, "iter": 1700, "lr": 0.00145, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.58312, "top5_acc": 0.81906, "loss_cls": 2.29432, "loss": 2.29432, "time": 0.82357} +{"mode": "train", "epoch": 139, "iter": 1800, "lr": 0.00145, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58828, "top5_acc": 0.82047, "loss_cls": 2.2791, "loss": 2.2791, "time": 0.82531} +{"mode": "train", "epoch": 139, "iter": 1900, "lr": 0.00144, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57297, "top5_acc": 0.81391, "loss_cls": 2.32182, "loss": 2.32182, "time": 0.81706} +{"mode": "train", "epoch": 139, "iter": 2000, "lr": 0.00143, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56156, "top5_acc": 0.80938, "loss_cls": 2.38353, "loss": 2.38353, "time": 0.82626} +{"mode": "train", "epoch": 139, "iter": 2100, "lr": 0.00143, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57359, "top5_acc": 0.81484, "loss_cls": 2.3323, "loss": 2.3323, "time": 0.822} +{"mode": "train", "epoch": 139, "iter": 2200, "lr": 0.00142, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.59031, "top5_acc": 0.8225, "loss_cls": 2.26618, "loss": 2.26618, "time": 0.82102} +{"mode": "train", "epoch": 139, "iter": 2300, "lr": 0.00142, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58344, "top5_acc": 0.81641, "loss_cls": 2.31395, "loss": 2.31395, "time": 0.8191} +{"mode": "train", "epoch": 139, "iter": 2400, "lr": 0.00141, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58172, "top5_acc": 0.81484, "loss_cls": 2.32498, "loss": 2.32498, "time": 0.8272} +{"mode": "train", "epoch": 139, "iter": 2500, "lr": 0.0014, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57734, "top5_acc": 0.81625, "loss_cls": 2.30982, "loss": 2.30982, "time": 0.83122} +{"mode": "train", "epoch": 139, "iter": 2600, "lr": 0.0014, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58172, "top5_acc": 0.81719, "loss_cls": 2.29886, "loss": 2.29886, "time": 0.82059} +{"mode": "train", "epoch": 139, "iter": 2700, "lr": 0.00139, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.58078, "top5_acc": 0.81828, "loss_cls": 2.30787, "loss": 2.30787, "time": 0.82132} +{"mode": "train", "epoch": 139, "iter": 2800, "lr": 0.00138, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58094, "top5_acc": 0.81375, "loss_cls": 2.30614, "loss": 2.30614, "time": 0.82079} +{"mode": "train", "epoch": 139, "iter": 2900, "lr": 0.00138, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.58062, "top5_acc": 0.82703, "loss_cls": 2.3064, "loss": 2.3064, "time": 0.82861} +{"mode": "train", "epoch": 139, "iter": 3000, "lr": 0.00137, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.58062, "top5_acc": 0.81719, "loss_cls": 2.31674, "loss": 2.31674, "time": 0.84406} +{"mode": "train", "epoch": 139, "iter": 3100, "lr": 0.00136, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.5875, "top5_acc": 0.82312, "loss_cls": 2.28649, "loss": 2.28649, "time": 0.82806} +{"mode": "train", "epoch": 139, "iter": 3200, "lr": 0.00136, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58297, "top5_acc": 0.81172, "loss_cls": 2.32617, "loss": 2.32617, "time": 0.82116} +{"mode": "train", "epoch": 139, "iter": 3300, "lr": 0.00135, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57734, "top5_acc": 0.81984, "loss_cls": 2.3163, "loss": 2.3163, "time": 0.82447} +{"mode": "train", "epoch": 139, "iter": 3400, "lr": 0.00134, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57984, "top5_acc": 0.81703, "loss_cls": 2.32066, "loss": 2.32066, "time": 0.81797} +{"mode": "train", "epoch": 139, "iter": 3500, "lr": 0.00134, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58109, "top5_acc": 0.82453, "loss_cls": 2.30146, "loss": 2.30146, "time": 0.82155} +{"mode": "train", "epoch": 139, "iter": 3600, "lr": 0.00133, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57609, "top5_acc": 0.81531, "loss_cls": 2.31014, "loss": 2.31014, "time": 0.81964} +{"mode": "train", "epoch": 139, "iter": 3700, "lr": 0.00132, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57938, "top5_acc": 0.82094, "loss_cls": 2.30897, "loss": 2.30897, "time": 0.82113} +{"mode": "val", "epoch": 139, "iter": 309, "lr": 0.00132, "top1_acc": 0.4556, "top5_acc": 0.70359, "mean_class_accuracy": 0.45538} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00131, "memory": 15990, "data_time": 1.33141, "top1_acc": 0.6, "top5_acc": 0.82812, "loss_cls": 2.21759, "loss": 2.21759, "time": 2.3138} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00131, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.60641, "top5_acc": 0.84109, "loss_cls": 2.18534, "loss": 2.18534, "time": 0.82209} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.0013, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60156, "top5_acc": 0.82625, "loss_cls": 2.21694, "loss": 2.21694, "time": 0.82518} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.0013, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.60766, "top5_acc": 0.83484, "loss_cls": 2.17288, "loss": 2.17288, "time": 0.81862} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00129, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59, "top5_acc": 0.83125, "loss_cls": 2.23268, "loss": 2.23268, "time": 0.81738} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.00128, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59328, "top5_acc": 0.83047, "loss_cls": 2.21176, "loss": 2.21176, "time": 0.81485} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.00128, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60031, "top5_acc": 0.83281, "loss_cls": 2.20955, "loss": 2.20955, "time": 0.81798} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00127, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59781, "top5_acc": 0.82922, "loss_cls": 2.2121, "loss": 2.2121, "time": 0.82149} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00126, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59188, "top5_acc": 0.82234, "loss_cls": 2.22743, "loss": 2.22743, "time": 0.81748} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00126, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59234, "top5_acc": 0.82031, "loss_cls": 2.25532, "loss": 2.25532, "time": 0.81923} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00125, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58875, "top5_acc": 0.82391, "loss_cls": 2.24887, "loss": 2.24887, "time": 0.81853} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00125, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60625, "top5_acc": 0.84516, "loss_cls": 2.17051, "loss": 2.17051, "time": 0.81528} +{"mode": "train", "epoch": 140, "iter": 1300, "lr": 0.00124, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59078, "top5_acc": 0.82312, "loss_cls": 2.25987, "loss": 2.25987, "time": 0.81916} +{"mode": "train", "epoch": 140, "iter": 1400, "lr": 0.00123, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59031, "top5_acc": 0.82359, "loss_cls": 2.25043, "loss": 2.25043, "time": 0.81784} +{"mode": "train", "epoch": 140, "iter": 1500, "lr": 0.00123, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58891, "top5_acc": 0.82328, "loss_cls": 2.26207, "loss": 2.26207, "time": 0.82267} +{"mode": "train", "epoch": 140, "iter": 1600, "lr": 0.00122, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58672, "top5_acc": 0.82266, "loss_cls": 2.26412, "loss": 2.26412, "time": 0.82068} +{"mode": "train", "epoch": 140, "iter": 1700, "lr": 0.00121, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.60234, "top5_acc": 0.82531, "loss_cls": 2.20086, "loss": 2.20086, "time": 0.8247} +{"mode": "train", "epoch": 140, "iter": 1800, "lr": 0.00121, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59062, "top5_acc": 0.82016, "loss_cls": 2.26575, "loss": 2.26575, "time": 0.8267} +{"mode": "train", "epoch": 140, "iter": 1900, "lr": 0.0012, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59078, "top5_acc": 0.82203, "loss_cls": 2.26081, "loss": 2.26081, "time": 0.8199} +{"mode": "train", "epoch": 140, "iter": 2000, "lr": 0.0012, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59719, "top5_acc": 0.82719, "loss_cls": 2.23875, "loss": 2.23875, "time": 0.81811} +{"mode": "train", "epoch": 140, "iter": 2100, "lr": 0.00119, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58484, "top5_acc": 0.82688, "loss_cls": 2.27586, "loss": 2.27586, "time": 0.82059} +{"mode": "train", "epoch": 140, "iter": 2200, "lr": 0.00118, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58891, "top5_acc": 0.81703, "loss_cls": 2.28767, "loss": 2.28767, "time": 0.81752} +{"mode": "train", "epoch": 140, "iter": 2300, "lr": 0.00118, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60188, "top5_acc": 0.83562, "loss_cls": 2.19477, "loss": 2.19477, "time": 0.81842} +{"mode": "train", "epoch": 140, "iter": 2400, "lr": 0.00117, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59672, "top5_acc": 0.82094, "loss_cls": 2.2497, "loss": 2.2497, "time": 0.81949} +{"mode": "train", "epoch": 140, "iter": 2500, "lr": 0.00117, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58562, "top5_acc": 0.82719, "loss_cls": 2.26564, "loss": 2.26564, "time": 0.81693} +{"mode": "train", "epoch": 140, "iter": 2600, "lr": 0.00116, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.60453, "top5_acc": 0.83484, "loss_cls": 2.1891, "loss": 2.1891, "time": 0.82321} +{"mode": "train", "epoch": 140, "iter": 2700, "lr": 0.00115, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58688, "top5_acc": 0.82219, "loss_cls": 2.25375, "loss": 2.25375, "time": 0.82377} +{"mode": "train", "epoch": 140, "iter": 2800, "lr": 0.00115, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59312, "top5_acc": 0.82656, "loss_cls": 2.25148, "loss": 2.25148, "time": 0.81767} +{"mode": "train", "epoch": 140, "iter": 2900, "lr": 0.00114, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58391, "top5_acc": 0.82953, "loss_cls": 2.26174, "loss": 2.26174, "time": 0.82334} +{"mode": "train", "epoch": 140, "iter": 3000, "lr": 0.00114, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5925, "top5_acc": 0.82109, "loss_cls": 2.26617, "loss": 2.26617, "time": 0.81889} +{"mode": "train", "epoch": 140, "iter": 3100, "lr": 0.00113, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.5925, "top5_acc": 0.82969, "loss_cls": 2.23518, "loss": 2.23518, "time": 0.82475} +{"mode": "train", "epoch": 140, "iter": 3200, "lr": 0.00112, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59047, "top5_acc": 0.82172, "loss_cls": 2.28095, "loss": 2.28095, "time": 0.83041} +{"mode": "train", "epoch": 140, "iter": 3300, "lr": 0.00112, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58469, "top5_acc": 0.82469, "loss_cls": 2.2707, "loss": 2.2707, "time": 0.81398} +{"mode": "train", "epoch": 140, "iter": 3400, "lr": 0.00111, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58891, "top5_acc": 0.83234, "loss_cls": 2.22496, "loss": 2.22496, "time": 0.81822} +{"mode": "train", "epoch": 140, "iter": 3500, "lr": 0.00111, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59234, "top5_acc": 0.82391, "loss_cls": 2.24362, "loss": 2.24362, "time": 0.81849} +{"mode": "train", "epoch": 140, "iter": 3600, "lr": 0.0011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59234, "top5_acc": 0.82281, "loss_cls": 2.2533, "loss": 2.2533, "time": 0.81913} +{"mode": "train", "epoch": 140, "iter": 3700, "lr": 0.0011, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60078, "top5_acc": 0.82922, "loss_cls": 2.21833, "loss": 2.21833, "time": 0.8151} +{"mode": "val", "epoch": 140, "iter": 309, "lr": 0.00109, "top1_acc": 0.45869, "top5_acc": 0.70906, "mean_class_accuracy": 0.45844} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00109, "memory": 15990, "data_time": 1.31196, "top1_acc": 0.61375, "top5_acc": 0.84453, "loss_cls": 2.11376, "loss": 2.11376, "time": 2.2976} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00108, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.62203, "top5_acc": 0.8375, "loss_cls": 2.14489, "loss": 2.14489, "time": 0.82128} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00108, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61062, "top5_acc": 0.84047, "loss_cls": 2.15414, "loss": 2.15414, "time": 0.81796} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00107, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.60688, "top5_acc": 0.83953, "loss_cls": 2.14251, "loss": 2.14251, "time": 0.82033} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00106, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61984, "top5_acc": 0.84047, "loss_cls": 2.13694, "loss": 2.13694, "time": 0.82021} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00106, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.605, "top5_acc": 0.83281, "loss_cls": 2.1727, "loss": 2.1727, "time": 0.81744} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00105, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61719, "top5_acc": 0.83984, "loss_cls": 2.14232, "loss": 2.14232, "time": 0.81783} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00105, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60797, "top5_acc": 0.82859, "loss_cls": 2.17851, "loss": 2.17851, "time": 0.8158} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00104, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60656, "top5_acc": 0.84203, "loss_cls": 2.14749, "loss": 2.14749, "time": 0.81726} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00104, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58453, "top5_acc": 0.81984, "loss_cls": 2.2744, "loss": 2.2744, "time": 0.82605} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00103, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60203, "top5_acc": 0.83969, "loss_cls": 2.17889, "loss": 2.17889, "time": 0.81868} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00102, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61203, "top5_acc": 0.835, "loss_cls": 2.16523, "loss": 2.16523, "time": 0.81957} +{"mode": "train", "epoch": 141, "iter": 1300, "lr": 0.00102, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61172, "top5_acc": 0.83859, "loss_cls": 2.16809, "loss": 2.16809, "time": 0.82087} +{"mode": "train", "epoch": 141, "iter": 1400, "lr": 0.00101, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59984, "top5_acc": 0.84062, "loss_cls": 2.18084, "loss": 2.18084, "time": 0.82752} +{"mode": "train", "epoch": 141, "iter": 1500, "lr": 0.00101, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.61328, "top5_acc": 0.83734, "loss_cls": 2.15194, "loss": 2.15194, "time": 0.82544} +{"mode": "train", "epoch": 141, "iter": 1600, "lr": 0.001, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.62016, "top5_acc": 0.8375, "loss_cls": 2.14058, "loss": 2.14058, "time": 0.81981} +{"mode": "train", "epoch": 141, "iter": 1700, "lr": 0.001, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.60359, "top5_acc": 0.8375, "loss_cls": 2.19233, "loss": 2.19233, "time": 0.82908} +{"mode": "train", "epoch": 141, "iter": 1800, "lr": 0.00099, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59812, "top5_acc": 0.84016, "loss_cls": 2.15413, "loss": 2.15413, "time": 0.82274} +{"mode": "train", "epoch": 141, "iter": 1900, "lr": 0.00099, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.59547, "top5_acc": 0.8325, "loss_cls": 2.20666, "loss": 2.20666, "time": 0.8225} +{"mode": "train", "epoch": 141, "iter": 2000, "lr": 0.00098, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59719, "top5_acc": 0.83109, "loss_cls": 2.23491, "loss": 2.23491, "time": 0.82} +{"mode": "train", "epoch": 141, "iter": 2100, "lr": 0.00097, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.59828, "top5_acc": 0.83516, "loss_cls": 2.19092, "loss": 2.19092, "time": 0.82181} +{"mode": "train", "epoch": 141, "iter": 2200, "lr": 0.00097, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.60328, "top5_acc": 0.83172, "loss_cls": 2.19024, "loss": 2.19024, "time": 0.82222} +{"mode": "train", "epoch": 141, "iter": 2300, "lr": 0.00096, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61109, "top5_acc": 0.83375, "loss_cls": 2.18471, "loss": 2.18471, "time": 0.81963} +{"mode": "train", "epoch": 141, "iter": 2400, "lr": 0.00096, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.60703, "top5_acc": 0.83891, "loss_cls": 2.18173, "loss": 2.18173, "time": 0.82034} +{"mode": "train", "epoch": 141, "iter": 2500, "lr": 0.00095, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60672, "top5_acc": 0.83578, "loss_cls": 2.17351, "loss": 2.17351, "time": 0.82003} +{"mode": "train", "epoch": 141, "iter": 2600, "lr": 0.00095, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60547, "top5_acc": 0.83703, "loss_cls": 2.18572, "loss": 2.18572, "time": 0.8218} +{"mode": "train", "epoch": 141, "iter": 2700, "lr": 0.00094, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.605, "top5_acc": 0.82312, "loss_cls": 2.20136, "loss": 2.20136, "time": 0.82055} +{"mode": "train", "epoch": 141, "iter": 2800, "lr": 0.00094, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.59562, "top5_acc": 0.82781, "loss_cls": 2.2362, "loss": 2.2362, "time": 0.82373} +{"mode": "train", "epoch": 141, "iter": 2900, "lr": 0.00093, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59953, "top5_acc": 0.83047, "loss_cls": 2.22183, "loss": 2.22183, "time": 0.82288} +{"mode": "train", "epoch": 141, "iter": 3000, "lr": 0.00093, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.60781, "top5_acc": 0.83266, "loss_cls": 2.168, "loss": 2.168, "time": 0.82844} +{"mode": "train", "epoch": 141, "iter": 3100, "lr": 0.00092, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59844, "top5_acc": 0.82719, "loss_cls": 2.22315, "loss": 2.22315, "time": 0.82434} +{"mode": "train", "epoch": 141, "iter": 3200, "lr": 0.00091, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60812, "top5_acc": 0.83438, "loss_cls": 2.18776, "loss": 2.18776, "time": 0.822} +{"mode": "train", "epoch": 141, "iter": 3300, "lr": 0.00091, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61953, "top5_acc": 0.84016, "loss_cls": 2.13136, "loss": 2.13136, "time": 0.82232} +{"mode": "train", "epoch": 141, "iter": 3400, "lr": 0.0009, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60031, "top5_acc": 0.83531, "loss_cls": 2.18251, "loss": 2.18251, "time": 0.82114} +{"mode": "train", "epoch": 141, "iter": 3500, "lr": 0.0009, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59234, "top5_acc": 0.82688, "loss_cls": 2.23585, "loss": 2.23585, "time": 0.8208} +{"mode": "train", "epoch": 141, "iter": 3600, "lr": 0.00089, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.59828, "top5_acc": 0.83297, "loss_cls": 2.20428, "loss": 2.20428, "time": 0.8206} +{"mode": "train", "epoch": 141, "iter": 3700, "lr": 0.00089, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59516, "top5_acc": 0.83188, "loss_cls": 2.20396, "loss": 2.20396, "time": 0.81515} +{"mode": "val", "epoch": 141, "iter": 309, "lr": 0.00089, "top1_acc": 0.46092, "top5_acc": 0.71063, "mean_class_accuracy": 0.46064} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00088, "memory": 15990, "data_time": 1.29501, "top1_acc": 0.63047, "top5_acc": 0.85328, "loss_cls": 2.06706, "loss": 2.06706, "time": 2.27858} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00088, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.62141, "top5_acc": 0.84281, "loss_cls": 2.1277, "loss": 2.1277, "time": 0.82491} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00087, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61766, "top5_acc": 0.83766, "loss_cls": 2.12752, "loss": 2.12752, "time": 0.81894} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00086, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63109, "top5_acc": 0.84844, "loss_cls": 2.0882, "loss": 2.0882, "time": 0.81795} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.00086, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61938, "top5_acc": 0.83891, "loss_cls": 2.1326, "loss": 2.1326, "time": 0.81743} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.00085, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61359, "top5_acc": 0.83969, "loss_cls": 2.15235, "loss": 2.15235, "time": 0.81696} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.00085, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61438, "top5_acc": 0.84, "loss_cls": 2.12419, "loss": 2.12419, "time": 0.8173} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00084, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61016, "top5_acc": 0.83688, "loss_cls": 2.13421, "loss": 2.13421, "time": 0.81576} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00084, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62719, "top5_acc": 0.84828, "loss_cls": 2.09296, "loss": 2.09296, "time": 0.81889} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00083, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61047, "top5_acc": 0.84062, "loss_cls": 2.14545, "loss": 2.14545, "time": 0.81663} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00083, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62109, "top5_acc": 0.8425, "loss_cls": 2.10431, "loss": 2.10431, "time": 0.8172} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00082, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61531, "top5_acc": 0.83812, "loss_cls": 2.13452, "loss": 2.13452, "time": 0.8139} +{"mode": "train", "epoch": 142, "iter": 1300, "lr": 0.00082, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62375, "top5_acc": 0.84703, "loss_cls": 2.08433, "loss": 2.08433, "time": 0.81522} +{"mode": "train", "epoch": 142, "iter": 1400, "lr": 0.00081, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62578, "top5_acc": 0.84516, "loss_cls": 2.08989, "loss": 2.08989, "time": 0.81354} +{"mode": "train", "epoch": 142, "iter": 1500, "lr": 0.00081, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.60766, "top5_acc": 0.84, "loss_cls": 2.15343, "loss": 2.15343, "time": 0.82563} +{"mode": "train", "epoch": 142, "iter": 1600, "lr": 0.0008, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60625, "top5_acc": 0.83656, "loss_cls": 2.16511, "loss": 2.16511, "time": 0.81788} +{"mode": "train", "epoch": 142, "iter": 1700, "lr": 0.0008, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.60672, "top5_acc": 0.83422, "loss_cls": 2.16487, "loss": 2.16487, "time": 0.82739} +{"mode": "train", "epoch": 142, "iter": 1800, "lr": 0.00079, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62047, "top5_acc": 0.84203, "loss_cls": 2.11695, "loss": 2.11695, "time": 0.82443} +{"mode": "train", "epoch": 142, "iter": 1900, "lr": 0.00079, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.63344, "top5_acc": 0.85109, "loss_cls": 2.04125, "loss": 2.04125, "time": 0.82327} +{"mode": "train", "epoch": 142, "iter": 2000, "lr": 0.00078, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62453, "top5_acc": 0.85, "loss_cls": 2.08097, "loss": 2.08097, "time": 0.81741} +{"mode": "train", "epoch": 142, "iter": 2100, "lr": 0.00078, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61187, "top5_acc": 0.83562, "loss_cls": 2.13798, "loss": 2.13798, "time": 0.82097} +{"mode": "train", "epoch": 142, "iter": 2200, "lr": 0.00077, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.60828, "top5_acc": 0.83234, "loss_cls": 2.17123, "loss": 2.17123, "time": 0.81985} +{"mode": "train", "epoch": 142, "iter": 2300, "lr": 0.00077, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60766, "top5_acc": 0.83625, "loss_cls": 2.15255, "loss": 2.15255, "time": 0.81545} +{"mode": "train", "epoch": 142, "iter": 2400, "lr": 0.00076, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.61109, "top5_acc": 0.83375, "loss_cls": 2.14961, "loss": 2.14961, "time": 0.81879} +{"mode": "train", "epoch": 142, "iter": 2500, "lr": 0.00076, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.61609, "top5_acc": 0.83719, "loss_cls": 2.14635, "loss": 2.14635, "time": 0.82325} +{"mode": "train", "epoch": 142, "iter": 2600, "lr": 0.00075, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61594, "top5_acc": 0.84094, "loss_cls": 2.12936, "loss": 2.12936, "time": 0.82385} +{"mode": "train", "epoch": 142, "iter": 2700, "lr": 0.00075, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61125, "top5_acc": 0.83891, "loss_cls": 2.14759, "loss": 2.14759, "time": 0.8168} +{"mode": "train", "epoch": 142, "iter": 2800, "lr": 0.00075, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.62625, "top5_acc": 0.85266, "loss_cls": 2.07187, "loss": 2.07187, "time": 0.82236} +{"mode": "train", "epoch": 142, "iter": 2900, "lr": 0.00074, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.60656, "top5_acc": 0.83578, "loss_cls": 2.15423, "loss": 2.15423, "time": 0.82198} +{"mode": "train", "epoch": 142, "iter": 3000, "lr": 0.00074, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.61047, "top5_acc": 0.845, "loss_cls": 2.12046, "loss": 2.12046, "time": 0.81941} +{"mode": "train", "epoch": 142, "iter": 3100, "lr": 0.00073, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61547, "top5_acc": 0.83859, "loss_cls": 2.14115, "loss": 2.14115, "time": 0.81891} +{"mode": "train", "epoch": 142, "iter": 3200, "lr": 0.00073, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60422, "top5_acc": 0.84344, "loss_cls": 2.14717, "loss": 2.14717, "time": 0.82072} +{"mode": "train", "epoch": 142, "iter": 3300, "lr": 0.00072, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60391, "top5_acc": 0.83766, "loss_cls": 2.17787, "loss": 2.17787, "time": 0.82392} +{"mode": "train", "epoch": 142, "iter": 3400, "lr": 0.00072, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62344, "top5_acc": 0.845, "loss_cls": 2.11442, "loss": 2.11442, "time": 0.82005} +{"mode": "train", "epoch": 142, "iter": 3500, "lr": 0.00071, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61484, "top5_acc": 0.84109, "loss_cls": 2.12098, "loss": 2.12098, "time": 0.81995} +{"mode": "train", "epoch": 142, "iter": 3600, "lr": 0.00071, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61344, "top5_acc": 0.84375, "loss_cls": 2.13766, "loss": 2.13766, "time": 0.82} +{"mode": "train", "epoch": 142, "iter": 3700, "lr": 0.0007, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.615, "top5_acc": 0.83812, "loss_cls": 2.12523, "loss": 2.12523, "time": 0.81806} +{"mode": "val", "epoch": 142, "iter": 309, "lr": 0.0007, "top1_acc": 0.46107, "top5_acc": 0.71109, "mean_class_accuracy": 0.46081} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.0007, "memory": 15990, "data_time": 1.32427, "top1_acc": 0.63719, "top5_acc": 0.85688, "loss_cls": 2.01599, "loss": 2.01599, "time": 2.31099} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00069, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.62531, "top5_acc": 0.85266, "loss_cls": 2.05311, "loss": 2.05311, "time": 0.82358} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00069, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.62469, "top5_acc": 0.84766, "loss_cls": 2.0569, "loss": 2.0569, "time": 0.82134} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00068, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63375, "top5_acc": 0.84906, "loss_cls": 2.0528, "loss": 2.0528, "time": 0.81782} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00068, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62813, "top5_acc": 0.85141, "loss_cls": 2.07962, "loss": 2.07962, "time": 0.8138} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00067, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64328, "top5_acc": 0.84859, "loss_cls": 2.04072, "loss": 2.04072, "time": 0.81454} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00067, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62203, "top5_acc": 0.85516, "loss_cls": 2.04085, "loss": 2.04085, "time": 0.81862} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00066, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62828, "top5_acc": 0.85219, "loss_cls": 2.04656, "loss": 2.04656, "time": 0.81604} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00066, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62984, "top5_acc": 0.85297, "loss_cls": 2.0582, "loss": 2.0582, "time": 0.81791} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63891, "top5_acc": 0.86297, "loss_cls": 2.00835, "loss": 2.00835, "time": 0.81985} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62547, "top5_acc": 0.84484, "loss_cls": 2.07749, "loss": 2.07749, "time": 0.81804} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00065, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.62766, "top5_acc": 0.84562, "loss_cls": 2.07417, "loss": 2.07417, "time": 0.81681} +{"mode": "train", "epoch": 143, "iter": 1300, "lr": 0.00064, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62359, "top5_acc": 0.85, "loss_cls": 2.0704, "loss": 2.0704, "time": 0.81681} +{"mode": "train", "epoch": 143, "iter": 1400, "lr": 0.00064, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62672, "top5_acc": 0.85, "loss_cls": 2.08729, "loss": 2.08729, "time": 0.81739} +{"mode": "train", "epoch": 143, "iter": 1500, "lr": 0.00063, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62687, "top5_acc": 0.84766, "loss_cls": 2.06991, "loss": 2.06991, "time": 0.82094} +{"mode": "train", "epoch": 143, "iter": 1600, "lr": 0.00063, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62594, "top5_acc": 0.84938, "loss_cls": 2.07045, "loss": 2.07045, "time": 0.82149} +{"mode": "train", "epoch": 143, "iter": 1700, "lr": 0.00062, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.62938, "top5_acc": 0.84875, "loss_cls": 2.05268, "loss": 2.05268, "time": 0.82533} +{"mode": "train", "epoch": 143, "iter": 1800, "lr": 0.00062, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.62891, "top5_acc": 0.84906, "loss_cls": 2.06842, "loss": 2.06842, "time": 0.822} +{"mode": "train", "epoch": 143, "iter": 1900, "lr": 0.00061, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61547, "top5_acc": 0.84203, "loss_cls": 2.08772, "loss": 2.08772, "time": 0.81897} +{"mode": "train", "epoch": 143, "iter": 2000, "lr": 0.00061, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63266, "top5_acc": 0.85344, "loss_cls": 2.04229, "loss": 2.04229, "time": 0.82238} +{"mode": "train", "epoch": 143, "iter": 2100, "lr": 0.00061, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62953, "top5_acc": 0.84766, "loss_cls": 2.06809, "loss": 2.06809, "time": 0.82569} +{"mode": "train", "epoch": 143, "iter": 2200, "lr": 0.0006, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62516, "top5_acc": 0.84562, "loss_cls": 2.08881, "loss": 2.08881, "time": 0.82384} +{"mode": "train", "epoch": 143, "iter": 2300, "lr": 0.0006, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62141, "top5_acc": 0.845, "loss_cls": 2.09911, "loss": 2.09911, "time": 0.82143} +{"mode": "train", "epoch": 143, "iter": 2400, "lr": 0.00059, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.62406, "top5_acc": 0.85359, "loss_cls": 2.07339, "loss": 2.07339, "time": 0.81812} +{"mode": "train", "epoch": 143, "iter": 2500, "lr": 0.00059, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61984, "top5_acc": 0.84219, "loss_cls": 2.09749, "loss": 2.09749, "time": 0.82485} +{"mode": "train", "epoch": 143, "iter": 2600, "lr": 0.00058, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61938, "top5_acc": 0.84703, "loss_cls": 2.10634, "loss": 2.10634, "time": 0.82529} +{"mode": "train", "epoch": 143, "iter": 2700, "lr": 0.00058, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62109, "top5_acc": 0.84438, "loss_cls": 2.10778, "loss": 2.10778, "time": 0.81745} +{"mode": "train", "epoch": 143, "iter": 2800, "lr": 0.00058, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.62047, "top5_acc": 0.84125, "loss_cls": 2.12149, "loss": 2.12149, "time": 0.82641} +{"mode": "train", "epoch": 143, "iter": 2900, "lr": 0.00057, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.62687, "top5_acc": 0.85344, "loss_cls": 2.06287, "loss": 2.06287, "time": 0.82036} +{"mode": "train", "epoch": 143, "iter": 3000, "lr": 0.00057, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62859, "top5_acc": 0.85422, "loss_cls": 2.03007, "loss": 2.03007, "time": 0.81955} +{"mode": "train", "epoch": 143, "iter": 3100, "lr": 0.00056, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62969, "top5_acc": 0.84484, "loss_cls": 2.07839, "loss": 2.07839, "time": 0.81737} +{"mode": "train", "epoch": 143, "iter": 3200, "lr": 0.00056, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62078, "top5_acc": 0.84203, "loss_cls": 2.09403, "loss": 2.09403, "time": 0.81442} +{"mode": "train", "epoch": 143, "iter": 3300, "lr": 0.00055, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62938, "top5_acc": 0.85422, "loss_cls": 2.03908, "loss": 2.03908, "time": 0.81913} +{"mode": "train", "epoch": 143, "iter": 3400, "lr": 0.00055, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61734, "top5_acc": 0.84266, "loss_cls": 2.12402, "loss": 2.12402, "time": 0.82173} +{"mode": "train", "epoch": 143, "iter": 3500, "lr": 0.00055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62672, "top5_acc": 0.84219, "loss_cls": 2.10422, "loss": 2.10422, "time": 0.81551} +{"mode": "train", "epoch": 143, "iter": 3600, "lr": 0.00054, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62344, "top5_acc": 0.84562, "loss_cls": 2.09865, "loss": 2.09865, "time": 0.82447} +{"mode": "train", "epoch": 143, "iter": 3700, "lr": 0.00054, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62687, "top5_acc": 0.85078, "loss_cls": 2.04831, "loss": 2.04831, "time": 0.82059} +{"mode": "val", "epoch": 143, "iter": 309, "lr": 0.00054, "top1_acc": 0.46148, "top5_acc": 0.71337, "mean_class_accuracy": 0.4613} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00053, "memory": 15990, "data_time": 1.28433, "top1_acc": 0.65625, "top5_acc": 0.86469, "loss_cls": 1.95081, "loss": 1.95081, "time": 2.2728} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00053, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63938, "top5_acc": 0.86328, "loss_cls": 1.98341, "loss": 1.98341, "time": 0.81788} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00052, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.64766, "top5_acc": 0.86125, "loss_cls": 1.98776, "loss": 1.98776, "time": 0.82356} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63953, "top5_acc": 0.86078, "loss_cls": 2.00022, "loss": 2.00022, "time": 0.82057} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00052, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64703, "top5_acc": 0.85844, "loss_cls": 1.99524, "loss": 1.99524, "time": 0.81892} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00051, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64984, "top5_acc": 0.85781, "loss_cls": 1.99026, "loss": 1.99026, "time": 0.81763} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00051, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65688, "top5_acc": 0.86359, "loss_cls": 1.93697, "loss": 1.93697, "time": 0.82002} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.0005, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63844, "top5_acc": 0.85734, "loss_cls": 2.01142, "loss": 2.01142, "time": 0.81687} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.0005, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65031, "top5_acc": 0.86109, "loss_cls": 1.98141, "loss": 1.98141, "time": 0.81833} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.0005, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64016, "top5_acc": 0.85828, "loss_cls": 2.00435, "loss": 2.00435, "time": 0.8171} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.00049, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63359, "top5_acc": 0.8525, "loss_cls": 2.03584, "loss": 2.03584, "time": 0.82251} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.00049, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62609, "top5_acc": 0.85266, "loss_cls": 2.05478, "loss": 2.05478, "time": 0.81434} +{"mode": "train", "epoch": 144, "iter": 1300, "lr": 0.00048, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63828, "top5_acc": 0.85453, "loss_cls": 2.02314, "loss": 2.02314, "time": 0.81646} +{"mode": "train", "epoch": 144, "iter": 1400, "lr": 0.00048, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64703, "top5_acc": 0.865, "loss_cls": 1.9609, "loss": 1.9609, "time": 0.82178} +{"mode": "train", "epoch": 144, "iter": 1500, "lr": 0.00048, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.63828, "top5_acc": 0.86016, "loss_cls": 2.01855, "loss": 2.01855, "time": 0.81768} +{"mode": "train", "epoch": 144, "iter": 1600, "lr": 0.00047, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64703, "top5_acc": 0.85391, "loss_cls": 1.99228, "loss": 1.99228, "time": 0.81802} +{"mode": "train", "epoch": 144, "iter": 1700, "lr": 0.00047, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.63609, "top5_acc": 0.85547, "loss_cls": 2.03314, "loss": 2.03314, "time": 0.82426} +{"mode": "train", "epoch": 144, "iter": 1800, "lr": 0.00047, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63859, "top5_acc": 0.85391, "loss_cls": 2.02473, "loss": 2.02473, "time": 0.82437} +{"mode": "train", "epoch": 144, "iter": 1900, "lr": 0.00046, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64062, "top5_acc": 0.85406, "loss_cls": 2.03015, "loss": 2.03015, "time": 0.81792} +{"mode": "train", "epoch": 144, "iter": 2000, "lr": 0.00046, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62984, "top5_acc": 0.85922, "loss_cls": 2.02169, "loss": 2.02169, "time": 0.81961} +{"mode": "train", "epoch": 144, "iter": 2100, "lr": 0.00045, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63375, "top5_acc": 0.85141, "loss_cls": 2.0436, "loss": 2.0436, "time": 0.81634} +{"mode": "train", "epoch": 144, "iter": 2200, "lr": 0.00045, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64031, "top5_acc": 0.85734, "loss_cls": 2.00364, "loss": 2.00364, "time": 0.82222} +{"mode": "train", "epoch": 144, "iter": 2300, "lr": 0.00045, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63641, "top5_acc": 0.85891, "loss_cls": 2.02119, "loss": 2.02119, "time": 0.81927} +{"mode": "train", "epoch": 144, "iter": 2400, "lr": 0.00044, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63203, "top5_acc": 0.85219, "loss_cls": 2.04777, "loss": 2.04777, "time": 0.81991} +{"mode": "train", "epoch": 144, "iter": 2500, "lr": 0.00044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63859, "top5_acc": 0.85719, "loss_cls": 2.01837, "loss": 2.01837, "time": 0.82216} +{"mode": "train", "epoch": 144, "iter": 2600, "lr": 0.00044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63703, "top5_acc": 0.8575, "loss_cls": 2.02723, "loss": 2.02723, "time": 0.82028} +{"mode": "train", "epoch": 144, "iter": 2700, "lr": 0.00043, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64078, "top5_acc": 0.84875, "loss_cls": 2.00703, "loss": 2.00703, "time": 0.8244} +{"mode": "train", "epoch": 144, "iter": 2800, "lr": 0.00043, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.64219, "top5_acc": 0.85953, "loss_cls": 2.01895, "loss": 2.01895, "time": 0.82459} +{"mode": "train", "epoch": 144, "iter": 2900, "lr": 0.00042, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.63703, "top5_acc": 0.85266, "loss_cls": 2.02943, "loss": 2.02943, "time": 0.82496} +{"mode": "train", "epoch": 144, "iter": 3000, "lr": 0.00042, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63094, "top5_acc": 0.8475, "loss_cls": 2.06516, "loss": 2.06516, "time": 0.81731} +{"mode": "train", "epoch": 144, "iter": 3100, "lr": 0.00042, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63125, "top5_acc": 0.85188, "loss_cls": 2.05309, "loss": 2.05309, "time": 0.81613} +{"mode": "train", "epoch": 144, "iter": 3200, "lr": 0.00041, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63688, "top5_acc": 0.85344, "loss_cls": 2.015, "loss": 2.015, "time": 0.8208} +{"mode": "train", "epoch": 144, "iter": 3300, "lr": 0.00041, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63766, "top5_acc": 0.85469, "loss_cls": 2.0295, "loss": 2.0295, "time": 0.81773} +{"mode": "train", "epoch": 144, "iter": 3400, "lr": 0.00041, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63484, "top5_acc": 0.85875, "loss_cls": 2.00985, "loss": 2.00985, "time": 0.81849} +{"mode": "train", "epoch": 144, "iter": 3500, "lr": 0.0004, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63172, "top5_acc": 0.85031, "loss_cls": 2.05596, "loss": 2.05596, "time": 0.81523} +{"mode": "train", "epoch": 144, "iter": 3600, "lr": 0.0004, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.63328, "top5_acc": 0.85219, "loss_cls": 2.04669, "loss": 2.04669, "time": 0.81689} +{"mode": "train", "epoch": 144, "iter": 3700, "lr": 0.0004, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62203, "top5_acc": 0.85125, "loss_cls": 2.05161, "loss": 2.05161, "time": 0.81823} +{"mode": "val", "epoch": 144, "iter": 309, "lr": 0.00039, "top1_acc": 0.46649, "top5_acc": 0.71575, "mean_class_accuracy": 0.46629} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.00039, "memory": 15990, "data_time": 1.31763, "top1_acc": 0.64438, "top5_acc": 0.86078, "loss_cls": 2.00457, "loss": 2.00457, "time": 2.30638} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 0.00039, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64688, "top5_acc": 0.86172, "loss_cls": 1.97407, "loss": 1.97407, "time": 0.82176} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 0.00038, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64953, "top5_acc": 0.86375, "loss_cls": 1.96877, "loss": 1.96877, "time": 0.81984} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 0.00038, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65609, "top5_acc": 0.86031, "loss_cls": 1.95121, "loss": 1.95121, "time": 0.81981} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 0.00038, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64109, "top5_acc": 0.86, "loss_cls": 1.96898, "loss": 1.96898, "time": 0.82211} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 0.00037, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64891, "top5_acc": 0.865, "loss_cls": 1.96918, "loss": 1.96918, "time": 0.82939} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 0.00037, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65297, "top5_acc": 0.87078, "loss_cls": 1.92393, "loss": 1.92393, "time": 0.82748} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 0.00037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65438, "top5_acc": 0.86688, "loss_cls": 1.97189, "loss": 1.97189, "time": 0.8162} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 0.00036, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65812, "top5_acc": 0.87094, "loss_cls": 1.94869, "loss": 1.94869, "time": 0.82174} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 0.00036, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65391, "top5_acc": 0.85797, "loss_cls": 1.95764, "loss": 1.95764, "time": 0.81933} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 0.00036, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65219, "top5_acc": 0.86109, "loss_cls": 1.94736, "loss": 1.94736, "time": 0.81865} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 0.00035, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65141, "top5_acc": 0.85641, "loss_cls": 1.97562, "loss": 1.97562, "time": 0.81853} +{"mode": "train", "epoch": 145, "iter": 1300, "lr": 0.00035, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64953, "top5_acc": 0.86469, "loss_cls": 1.9584, "loss": 1.9584, "time": 0.8163} +{"mode": "train", "epoch": 145, "iter": 1400, "lr": 0.00035, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65312, "top5_acc": 0.86125, "loss_cls": 1.94787, "loss": 1.94787, "time": 0.8153} +{"mode": "train", "epoch": 145, "iter": 1500, "lr": 0.00034, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65016, "top5_acc": 0.86406, "loss_cls": 1.96962, "loss": 1.96962, "time": 0.81879} +{"mode": "train", "epoch": 145, "iter": 1600, "lr": 0.00034, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64719, "top5_acc": 0.86172, "loss_cls": 1.9805, "loss": 1.9805, "time": 0.82013} +{"mode": "train", "epoch": 145, "iter": 1700, "lr": 0.00034, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.64812, "top5_acc": 0.86234, "loss_cls": 1.97918, "loss": 1.97918, "time": 0.82153} +{"mode": "train", "epoch": 145, "iter": 1800, "lr": 0.00033, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.64625, "top5_acc": 0.85984, "loss_cls": 1.95954, "loss": 1.95954, "time": 0.82878} +{"mode": "train", "epoch": 145, "iter": 1900, "lr": 0.00033, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65656, "top5_acc": 0.8675, "loss_cls": 1.93978, "loss": 1.93978, "time": 0.81812} +{"mode": "train", "epoch": 145, "iter": 2000, "lr": 0.00033, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.65328, "top5_acc": 0.8675, "loss_cls": 1.94979, "loss": 1.94979, "time": 0.81982} +{"mode": "train", "epoch": 145, "iter": 2100, "lr": 0.00032, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64422, "top5_acc": 0.86484, "loss_cls": 1.97186, "loss": 1.97186, "time": 0.81832} +{"mode": "train", "epoch": 145, "iter": 2200, "lr": 0.00032, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.65547, "top5_acc": 0.86672, "loss_cls": 1.93069, "loss": 1.93069, "time": 0.82411} +{"mode": "train", "epoch": 145, "iter": 2300, "lr": 0.00032, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.65266, "top5_acc": 0.86141, "loss_cls": 1.96174, "loss": 1.96174, "time": 0.81875} +{"mode": "train", "epoch": 145, "iter": 2400, "lr": 0.00031, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.64875, "top5_acc": 0.86844, "loss_cls": 1.95448, "loss": 1.95448, "time": 0.81859} +{"mode": "train", "epoch": 145, "iter": 2500, "lr": 0.00031, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63859, "top5_acc": 0.85422, "loss_cls": 2.00157, "loss": 2.00157, "time": 0.82604} +{"mode": "train", "epoch": 145, "iter": 2600, "lr": 0.00031, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64516, "top5_acc": 0.86406, "loss_cls": 1.9535, "loss": 1.9535, "time": 0.81631} +{"mode": "train", "epoch": 145, "iter": 2700, "lr": 0.00031, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65516, "top5_acc": 0.86688, "loss_cls": 1.95311, "loss": 1.95311, "time": 0.82319} +{"mode": "train", "epoch": 145, "iter": 2800, "lr": 0.0003, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.64812, "top5_acc": 0.8525, "loss_cls": 2.0068, "loss": 2.0068, "time": 0.82902} +{"mode": "train", "epoch": 145, "iter": 2900, "lr": 0.0003, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.65422, "top5_acc": 0.8575, "loss_cls": 1.97315, "loss": 1.97315, "time": 0.82235} +{"mode": "train", "epoch": 145, "iter": 3000, "lr": 0.0003, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64953, "top5_acc": 0.86094, "loss_cls": 1.95715, "loss": 1.95715, "time": 0.82439} +{"mode": "train", "epoch": 145, "iter": 3100, "lr": 0.00029, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64594, "top5_acc": 0.86562, "loss_cls": 1.98309, "loss": 1.98309, "time": 0.82233} +{"mode": "train", "epoch": 145, "iter": 3200, "lr": 0.00029, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65125, "top5_acc": 0.86344, "loss_cls": 1.97068, "loss": 1.97068, "time": 0.81479} +{"mode": "train", "epoch": 145, "iter": 3300, "lr": 0.00029, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64234, "top5_acc": 0.85312, "loss_cls": 2.00483, "loss": 2.00483, "time": 0.81821} +{"mode": "train", "epoch": 145, "iter": 3400, "lr": 0.00028, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64469, "top5_acc": 0.85688, "loss_cls": 1.98017, "loss": 1.98017, "time": 0.81707} +{"mode": "train", "epoch": 145, "iter": 3500, "lr": 0.00028, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.6475, "top5_acc": 0.86156, "loss_cls": 1.95785, "loss": 1.95785, "time": 0.81386} +{"mode": "train", "epoch": 145, "iter": 3600, "lr": 0.00028, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63875, "top5_acc": 0.85953, "loss_cls": 1.98354, "loss": 1.98354, "time": 0.81622} +{"mode": "train", "epoch": 145, "iter": 3700, "lr": 0.00028, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66094, "top5_acc": 0.86562, "loss_cls": 1.9284, "loss": 1.9284, "time": 0.81729} +{"mode": "val", "epoch": 145, "iter": 309, "lr": 0.00027, "top1_acc": 0.46675, "top5_acc": 0.71372, "mean_class_accuracy": 0.46652} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 0.00027, "memory": 15990, "data_time": 1.28864, "top1_acc": 0.66219, "top5_acc": 0.86906, "loss_cls": 1.91457, "loss": 1.91457, "time": 2.28554} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 0.00027, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67047, "top5_acc": 0.86953, "loss_cls": 1.87716, "loss": 1.87716, "time": 0.81679} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 0.00027, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66906, "top5_acc": 0.87234, "loss_cls": 1.8849, "loss": 1.8849, "time": 0.81772} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 0.00026, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65641, "top5_acc": 0.86359, "loss_cls": 1.93268, "loss": 1.93268, "time": 0.81185} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 0.00026, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65141, "top5_acc": 0.86594, "loss_cls": 1.96208, "loss": 1.96208, "time": 0.81846} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 0.00026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65156, "top5_acc": 0.86156, "loss_cls": 1.94995, "loss": 1.94995, "time": 0.81766} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 0.00025, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66188, "top5_acc": 0.87031, "loss_cls": 1.89789, "loss": 1.89789, "time": 0.81748} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 0.00025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67312, "top5_acc": 0.87188, "loss_cls": 1.88331, "loss": 1.88331, "time": 0.81349} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 0.00025, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65953, "top5_acc": 0.86922, "loss_cls": 1.91705, "loss": 1.91705, "time": 0.81732} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 0.00025, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66109, "top5_acc": 0.87188, "loss_cls": 1.89164, "loss": 1.89164, "time": 0.81588} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 0.00024, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66406, "top5_acc": 0.87328, "loss_cls": 1.91215, "loss": 1.91215, "time": 0.82126} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 0.00024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65984, "top5_acc": 0.87156, "loss_cls": 1.89665, "loss": 1.89665, "time": 0.81898} +{"mode": "train", "epoch": 146, "iter": 1300, "lr": 0.00024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65078, "top5_acc": 0.86531, "loss_cls": 1.9452, "loss": 1.9452, "time": 0.81371} +{"mode": "train", "epoch": 146, "iter": 1400, "lr": 0.00023, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66875, "top5_acc": 0.87781, "loss_cls": 1.87861, "loss": 1.87861, "time": 0.81898} +{"mode": "train", "epoch": 146, "iter": 1500, "lr": 0.00023, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.65938, "top5_acc": 0.87266, "loss_cls": 1.91833, "loss": 1.91833, "time": 0.81945} +{"mode": "train", "epoch": 146, "iter": 1600, "lr": 0.00023, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66328, "top5_acc": 0.87016, "loss_cls": 1.91759, "loss": 1.91759, "time": 0.82163} +{"mode": "train", "epoch": 146, "iter": 1700, "lr": 0.00023, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.64984, "top5_acc": 0.86125, "loss_cls": 1.96393, "loss": 1.96393, "time": 0.82246} +{"mode": "train", "epoch": 146, "iter": 1800, "lr": 0.00022, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66078, "top5_acc": 0.86906, "loss_cls": 1.89616, "loss": 1.89616, "time": 0.82435} +{"mode": "train", "epoch": 146, "iter": 1900, "lr": 0.00022, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65891, "top5_acc": 0.86969, "loss_cls": 1.89338, "loss": 1.89338, "time": 0.82273} +{"mode": "train", "epoch": 146, "iter": 2000, "lr": 0.00022, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.665, "top5_acc": 0.87109, "loss_cls": 1.88706, "loss": 1.88706, "time": 0.8211} +{"mode": "train", "epoch": 146, "iter": 2100, "lr": 0.00022, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64406, "top5_acc": 0.86312, "loss_cls": 1.97824, "loss": 1.97824, "time": 0.82377} +{"mode": "train", "epoch": 146, "iter": 2200, "lr": 0.00021, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65109, "top5_acc": 0.86172, "loss_cls": 1.95675, "loss": 1.95675, "time": 0.82135} +{"mode": "train", "epoch": 146, "iter": 2300, "lr": 0.00021, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64953, "top5_acc": 0.86453, "loss_cls": 1.95072, "loss": 1.95072, "time": 0.81885} +{"mode": "train", "epoch": 146, "iter": 2400, "lr": 0.00021, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.64812, "top5_acc": 0.86922, "loss_cls": 1.93037, "loss": 1.93037, "time": 0.82161} +{"mode": "train", "epoch": 146, "iter": 2500, "lr": 0.00021, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65938, "top5_acc": 0.86484, "loss_cls": 1.91215, "loss": 1.91215, "time": 0.82015} +{"mode": "train", "epoch": 146, "iter": 2600, "lr": 0.0002, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65016, "top5_acc": 0.86422, "loss_cls": 1.96846, "loss": 1.96846, "time": 0.8223} +{"mode": "train", "epoch": 146, "iter": 2700, "lr": 0.0002, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.65672, "top5_acc": 0.86156, "loss_cls": 1.91693, "loss": 1.91693, "time": 0.82885} +{"mode": "train", "epoch": 146, "iter": 2800, "lr": 0.0002, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64688, "top5_acc": 0.86047, "loss_cls": 1.97508, "loss": 1.97508, "time": 0.82476} +{"mode": "train", "epoch": 146, "iter": 2900, "lr": 0.0002, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.65188, "top5_acc": 0.86672, "loss_cls": 1.93936, "loss": 1.93936, "time": 0.82454} +{"mode": "train", "epoch": 146, "iter": 3000, "lr": 0.00019, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65656, "top5_acc": 0.87297, "loss_cls": 1.91549, "loss": 1.91549, "time": 0.82421} +{"mode": "train", "epoch": 146, "iter": 3100, "lr": 0.00019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65562, "top5_acc": 0.86547, "loss_cls": 1.94271, "loss": 1.94271, "time": 0.8167} +{"mode": "train", "epoch": 146, "iter": 3200, "lr": 0.00019, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.65047, "top5_acc": 0.85938, "loss_cls": 1.97475, "loss": 1.97475, "time": 0.81664} +{"mode": "train", "epoch": 146, "iter": 3300, "lr": 0.00019, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65922, "top5_acc": 0.86984, "loss_cls": 1.90408, "loss": 1.90408, "time": 0.81487} +{"mode": "train", "epoch": 146, "iter": 3400, "lr": 0.00018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65594, "top5_acc": 0.87, "loss_cls": 1.94728, "loss": 1.94728, "time": 0.81863} +{"mode": "train", "epoch": 146, "iter": 3500, "lr": 0.00018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66656, "top5_acc": 0.86562, "loss_cls": 1.89899, "loss": 1.89899, "time": 0.81557} +{"mode": "train", "epoch": 146, "iter": 3600, "lr": 0.00018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65203, "top5_acc": 0.86672, "loss_cls": 1.9342, "loss": 1.9342, "time": 0.81961} +{"mode": "train", "epoch": 146, "iter": 3700, "lr": 0.00018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65609, "top5_acc": 0.8625, "loss_cls": 1.9169, "loss": 1.9169, "time": 0.8181} +{"mode": "val", "epoch": 146, "iter": 309, "lr": 0.00018, "top1_acc": 0.46872, "top5_acc": 0.71539, "mean_class_accuracy": 0.46844} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 0.00017, "memory": 15990, "data_time": 1.28534, "top1_acc": 0.66688, "top5_acc": 0.87406, "loss_cls": 1.87587, "loss": 1.87587, "time": 2.27084} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 0.00017, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66562, "top5_acc": 0.87344, "loss_cls": 1.89099, "loss": 1.89099, "time": 0.81935} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 0.00017, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66188, "top5_acc": 0.87328, "loss_cls": 1.89064, "loss": 1.89064, "time": 0.8194} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 0.00017, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67609, "top5_acc": 0.87469, "loss_cls": 1.8534, "loss": 1.8534, "time": 0.81844} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 0.00016, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66547, "top5_acc": 0.87484, "loss_cls": 1.89525, "loss": 1.89525, "time": 0.8168} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 0.00016, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66766, "top5_acc": 0.87, "loss_cls": 1.87762, "loss": 1.87762, "time": 0.81891} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 0.00016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.6625, "top5_acc": 0.87016, "loss_cls": 1.883, "loss": 1.883, "time": 0.81387} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 0.00016, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66203, "top5_acc": 0.86344, "loss_cls": 1.92272, "loss": 1.92272, "time": 0.81935} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 0.00015, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66484, "top5_acc": 0.87297, "loss_cls": 1.88492, "loss": 1.88492, "time": 0.81922} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 0.00015, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66344, "top5_acc": 0.87781, "loss_cls": 1.87783, "loss": 1.87783, "time": 0.82395} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 0.00015, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66578, "top5_acc": 0.87328, "loss_cls": 1.88554, "loss": 1.88554, "time": 0.81387} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 0.00015, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67078, "top5_acc": 0.87562, "loss_cls": 1.87002, "loss": 1.87002, "time": 0.8173} +{"mode": "train", "epoch": 147, "iter": 1300, "lr": 0.00015, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66, "top5_acc": 0.8725, "loss_cls": 1.8984, "loss": 1.8984, "time": 0.82174} +{"mode": "train", "epoch": 147, "iter": 1400, "lr": 0.00014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66781, "top5_acc": 0.87766, "loss_cls": 1.86943, "loss": 1.86943, "time": 0.82382} +{"mode": "train", "epoch": 147, "iter": 1500, "lr": 0.00014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66281, "top5_acc": 0.86875, "loss_cls": 1.91419, "loss": 1.91419, "time": 0.8173} +{"mode": "train", "epoch": 147, "iter": 1600, "lr": 0.00014, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66719, "top5_acc": 0.87328, "loss_cls": 1.87862, "loss": 1.87862, "time": 0.82516} +{"mode": "train", "epoch": 147, "iter": 1700, "lr": 0.00014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67125, "top5_acc": 0.87172, "loss_cls": 1.87689, "loss": 1.87689, "time": 0.81782} +{"mode": "train", "epoch": 147, "iter": 1800, "lr": 0.00014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66703, "top5_acc": 0.87812, "loss_cls": 1.86267, "loss": 1.86267, "time": 0.81807} +{"mode": "train", "epoch": 147, "iter": 1900, "lr": 0.00013, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65516, "top5_acc": 0.86797, "loss_cls": 1.93614, "loss": 1.93614, "time": 0.824} +{"mode": "train", "epoch": 147, "iter": 2000, "lr": 0.00013, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66062, "top5_acc": 0.86953, "loss_cls": 1.90957, "loss": 1.90957, "time": 0.8206} +{"mode": "train", "epoch": 147, "iter": 2100, "lr": 0.00013, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66938, "top5_acc": 0.87656, "loss_cls": 1.88501, "loss": 1.88501, "time": 0.82475} +{"mode": "train", "epoch": 147, "iter": 2200, "lr": 0.00013, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66797, "top5_acc": 0.8775, "loss_cls": 1.87509, "loss": 1.87509, "time": 0.82432} +{"mode": "train", "epoch": 147, "iter": 2300, "lr": 0.00013, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66703, "top5_acc": 0.87016, "loss_cls": 1.91076, "loss": 1.91076, "time": 0.82514} +{"mode": "train", "epoch": 147, "iter": 2400, "lr": 0.00012, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66719, "top5_acc": 0.87531, "loss_cls": 1.87133, "loss": 1.87133, "time": 0.8227} +{"mode": "train", "epoch": 147, "iter": 2500, "lr": 0.00012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65375, "top5_acc": 0.86969, "loss_cls": 1.90966, "loss": 1.90966, "time": 0.81637} +{"mode": "train", "epoch": 147, "iter": 2600, "lr": 0.00012, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.66953, "top5_acc": 0.87453, "loss_cls": 1.88015, "loss": 1.88015, "time": 0.82315} +{"mode": "train", "epoch": 147, "iter": 2700, "lr": 0.00012, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67109, "top5_acc": 0.87906, "loss_cls": 1.84611, "loss": 1.84611, "time": 0.82378} +{"mode": "train", "epoch": 147, "iter": 2800, "lr": 0.00012, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66625, "top5_acc": 0.87766, "loss_cls": 1.89635, "loss": 1.89635, "time": 0.82679} +{"mode": "train", "epoch": 147, "iter": 2900, "lr": 0.00011, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.66375, "top5_acc": 0.87672, "loss_cls": 1.88314, "loss": 1.88314, "time": 0.82626} +{"mode": "train", "epoch": 147, "iter": 3000, "lr": 0.00011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66422, "top5_acc": 0.87906, "loss_cls": 1.88205, "loss": 1.88205, "time": 0.81914} +{"mode": "train", "epoch": 147, "iter": 3100, "lr": 0.00011, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67188, "top5_acc": 0.87609, "loss_cls": 1.86007, "loss": 1.86007, "time": 0.81614} +{"mode": "train", "epoch": 147, "iter": 3200, "lr": 0.00011, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66281, "top5_acc": 0.8725, "loss_cls": 1.8736, "loss": 1.8736, "time": 0.81879} +{"mode": "train", "epoch": 147, "iter": 3300, "lr": 0.00011, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67438, "top5_acc": 0.88141, "loss_cls": 1.84866, "loss": 1.84866, "time": 0.8195} +{"mode": "train", "epoch": 147, "iter": 3400, "lr": 0.0001, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66531, "top5_acc": 0.87453, "loss_cls": 1.88211, "loss": 1.88211, "time": 0.81717} +{"mode": "train", "epoch": 147, "iter": 3500, "lr": 0.0001, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66547, "top5_acc": 0.86781, "loss_cls": 1.89016, "loss": 1.89016, "time": 0.81953} +{"mode": "train", "epoch": 147, "iter": 3600, "lr": 0.0001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67703, "top5_acc": 0.87688, "loss_cls": 1.83974, "loss": 1.83974, "time": 0.81942} +{"mode": "train", "epoch": 147, "iter": 3700, "lr": 0.0001, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65891, "top5_acc": 0.87594, "loss_cls": 1.87107, "loss": 1.87107, "time": 0.81496} +{"mode": "val", "epoch": 147, "iter": 309, "lr": 0.0001, "top1_acc": 0.46837, "top5_acc": 0.71676, "mean_class_accuracy": 0.46813} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 0.0001, "memory": 15990, "data_time": 1.29052, "top1_acc": 0.67094, "top5_acc": 0.87609, "loss_cls": 1.86725, "loss": 1.86725, "time": 2.27433} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 0.0001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66891, "top5_acc": 0.87391, "loss_cls": 1.86137, "loss": 1.86137, "time": 0.81625} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 9e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68312, "top5_acc": 0.87875, "loss_cls": 1.83073, "loss": 1.83073, "time": 0.81671} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 9e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.68078, "top5_acc": 0.88078, "loss_cls": 1.84928, "loss": 1.84928, "time": 0.82023} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 9e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66641, "top5_acc": 0.87812, "loss_cls": 1.86942, "loss": 1.86942, "time": 0.81909} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 9e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66656, "top5_acc": 0.87031, "loss_cls": 1.8795, "loss": 1.8795, "time": 0.81924} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 9e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66781, "top5_acc": 0.87406, "loss_cls": 1.89608, "loss": 1.89608, "time": 0.81928} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 9e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.6875, "top5_acc": 0.8825, "loss_cls": 1.79528, "loss": 1.79528, "time": 0.81818} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 8e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.68422, "top5_acc": 0.88938, "loss_cls": 1.79161, "loss": 1.79161, "time": 0.82277} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 8e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67531, "top5_acc": 0.87703, "loss_cls": 1.83597, "loss": 1.83597, "time": 0.81702} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 8e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67594, "top5_acc": 0.87766, "loss_cls": 1.83455, "loss": 1.83455, "time": 0.81967} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 8e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67266, "top5_acc": 0.87281, "loss_cls": 1.85909, "loss": 1.85909, "time": 0.81677} +{"mode": "train", "epoch": 148, "iter": 1300, "lr": 8e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67406, "top5_acc": 0.87219, "loss_cls": 1.88143, "loss": 1.88143, "time": 0.81655} +{"mode": "train", "epoch": 148, "iter": 1400, "lr": 8e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66438, "top5_acc": 0.87188, "loss_cls": 1.88657, "loss": 1.88657, "time": 0.8243} +{"mode": "train", "epoch": 148, "iter": 1500, "lr": 7e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67406, "top5_acc": 0.87984, "loss_cls": 1.85312, "loss": 1.85312, "time": 0.82163} +{"mode": "train", "epoch": 148, "iter": 1600, "lr": 7e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67719, "top5_acc": 0.88172, "loss_cls": 1.84915, "loss": 1.84915, "time": 0.81888} +{"mode": "train", "epoch": 148, "iter": 1700, "lr": 7e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67266, "top5_acc": 0.87391, "loss_cls": 1.85095, "loss": 1.85095, "time": 0.82539} +{"mode": "train", "epoch": 148, "iter": 1800, "lr": 7e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67297, "top5_acc": 0.88203, "loss_cls": 1.83681, "loss": 1.83681, "time": 0.81921} +{"mode": "train", "epoch": 148, "iter": 1900, "lr": 7e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67109, "top5_acc": 0.87672, "loss_cls": 1.84244, "loss": 1.84244, "time": 0.82361} +{"mode": "train", "epoch": 148, "iter": 2000, "lr": 7e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.68656, "top5_acc": 0.88281, "loss_cls": 1.81152, "loss": 1.81152, "time": 0.82413} +{"mode": "train", "epoch": 148, "iter": 2100, "lr": 7e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66484, "top5_acc": 0.87062, "loss_cls": 1.89499, "loss": 1.89499, "time": 0.82072} +{"mode": "train", "epoch": 148, "iter": 2200, "lr": 6e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66953, "top5_acc": 0.87594, "loss_cls": 1.87299, "loss": 1.87299, "time": 0.82475} +{"mode": "train", "epoch": 148, "iter": 2300, "lr": 6e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67484, "top5_acc": 0.87516, "loss_cls": 1.85432, "loss": 1.85432, "time": 0.81852} +{"mode": "train", "epoch": 148, "iter": 2400, "lr": 6e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67375, "top5_acc": 0.87531, "loss_cls": 1.85351, "loss": 1.85351, "time": 0.81803} +{"mode": "train", "epoch": 148, "iter": 2500, "lr": 6e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65594, "top5_acc": 0.87, "loss_cls": 1.91494, "loss": 1.91494, "time": 0.81912} +{"mode": "train", "epoch": 148, "iter": 2600, "lr": 6e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.67016, "top5_acc": 0.88344, "loss_cls": 1.8446, "loss": 1.8446, "time": 0.82468} +{"mode": "train", "epoch": 148, "iter": 2700, "lr": 6e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66219, "top5_acc": 0.87453, "loss_cls": 1.86986, "loss": 1.86986, "time": 0.81648} +{"mode": "train", "epoch": 148, "iter": 2800, "lr": 6e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.67094, "top5_acc": 0.88016, "loss_cls": 1.83823, "loss": 1.83823, "time": 0.82183} +{"mode": "train", "epoch": 148, "iter": 2900, "lr": 5e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67125, "top5_acc": 0.87672, "loss_cls": 1.8433, "loss": 1.8433, "time": 0.82425} +{"mode": "train", "epoch": 148, "iter": 3000, "lr": 5e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67094, "top5_acc": 0.87328, "loss_cls": 1.87901, "loss": 1.87901, "time": 0.82403} +{"mode": "train", "epoch": 148, "iter": 3100, "lr": 5e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66828, "top5_acc": 0.87375, "loss_cls": 1.86574, "loss": 1.86574, "time": 0.8182} +{"mode": "train", "epoch": 148, "iter": 3200, "lr": 5e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66891, "top5_acc": 0.87812, "loss_cls": 1.87663, "loss": 1.87663, "time": 0.81815} +{"mode": "train", "epoch": 148, "iter": 3300, "lr": 5e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67766, "top5_acc": 0.8825, "loss_cls": 1.82195, "loss": 1.82195, "time": 0.8148} +{"mode": "train", "epoch": 148, "iter": 3400, "lr": 5e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67828, "top5_acc": 0.87359, "loss_cls": 1.858, "loss": 1.858, "time": 0.82028} +{"mode": "train", "epoch": 148, "iter": 3500, "lr": 5e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66531, "top5_acc": 0.86625, "loss_cls": 1.89273, "loss": 1.89273, "time": 0.82893} +{"mode": "train", "epoch": 148, "iter": 3600, "lr": 5e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66875, "top5_acc": 0.87188, "loss_cls": 1.87557, "loss": 1.87557, "time": 0.81977} +{"mode": "train", "epoch": 148, "iter": 3700, "lr": 4e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66672, "top5_acc": 0.86625, "loss_cls": 1.88397, "loss": 1.88397, "time": 0.81477} +{"mode": "val", "epoch": 148, "iter": 309, "lr": 4e-05, "top1_acc": 0.46827, "top5_acc": 0.71529, "mean_class_accuracy": 0.468} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 4e-05, "memory": 15990, "data_time": 1.27313, "top1_acc": 0.66391, "top5_acc": 0.87047, "loss_cls": 1.88474, "loss": 1.88474, "time": 2.26634} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 4e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.68391, "top5_acc": 0.88281, "loss_cls": 1.81752, "loss": 1.81752, "time": 0.82394} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 4e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.68438, "top5_acc": 0.87719, "loss_cls": 1.83362, "loss": 1.83362, "time": 0.8195} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 4e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68266, "top5_acc": 0.88688, "loss_cls": 1.79803, "loss": 1.79803, "time": 0.81815} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 4e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66734, "top5_acc": 0.87188, "loss_cls": 1.879, "loss": 1.879, "time": 0.81958} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 4e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67469, "top5_acc": 0.87219, "loss_cls": 1.85206, "loss": 1.85206, "time": 0.8171} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 4e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67547, "top5_acc": 0.88203, "loss_cls": 1.82532, "loss": 1.82532, "time": 0.8229} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 4e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67438, "top5_acc": 0.87797, "loss_cls": 1.85229, "loss": 1.85229, "time": 0.81488} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 3e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68359, "top5_acc": 0.88219, "loss_cls": 1.80406, "loss": 1.80406, "time": 0.81768} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67703, "top5_acc": 0.88359, "loss_cls": 1.82151, "loss": 1.82151, "time": 0.81832} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66688, "top5_acc": 0.87297, "loss_cls": 1.87499, "loss": 1.87499, "time": 0.81586} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68062, "top5_acc": 0.88031, "loss_cls": 1.83532, "loss": 1.83532, "time": 0.81486} +{"mode": "train", "epoch": 149, "iter": 1300, "lr": 3e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67516, "top5_acc": 0.87562, "loss_cls": 1.84649, "loss": 1.84649, "time": 0.82295} +{"mode": "train", "epoch": 149, "iter": 1400, "lr": 3e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68281, "top5_acc": 0.885, "loss_cls": 1.80903, "loss": 1.80903, "time": 0.82659} +{"mode": "train", "epoch": 149, "iter": 1500, "lr": 3e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68578, "top5_acc": 0.87984, "loss_cls": 1.81348, "loss": 1.81348, "time": 0.8197} +{"mode": "train", "epoch": 149, "iter": 1600, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67688, "top5_acc": 0.8825, "loss_cls": 1.83676, "loss": 1.83676, "time": 0.81681} +{"mode": "train", "epoch": 149, "iter": 1700, "lr": 3e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67609, "top5_acc": 0.87625, "loss_cls": 1.8321, "loss": 1.8321, "time": 0.81952} +{"mode": "train", "epoch": 149, "iter": 1800, "lr": 3e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67359, "top5_acc": 0.87609, "loss_cls": 1.84095, "loss": 1.84095, "time": 0.8215} +{"mode": "train", "epoch": 149, "iter": 1900, "lr": 2e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67859, "top5_acc": 0.88234, "loss_cls": 1.84307, "loss": 1.84307, "time": 0.82346} +{"mode": "train", "epoch": 149, "iter": 2000, "lr": 2e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.67391, "top5_acc": 0.88391, "loss_cls": 1.83108, "loss": 1.83108, "time": 0.82624} +{"mode": "train", "epoch": 149, "iter": 2100, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67469, "top5_acc": 0.88203, "loss_cls": 1.81371, "loss": 1.81371, "time": 0.82098} +{"mode": "train", "epoch": 149, "iter": 2200, "lr": 2e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.68312, "top5_acc": 0.87844, "loss_cls": 1.82812, "loss": 1.82812, "time": 0.82199} +{"mode": "train", "epoch": 149, "iter": 2300, "lr": 2e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.67156, "top5_acc": 0.87719, "loss_cls": 1.84894, "loss": 1.84894, "time": 0.82348} +{"mode": "train", "epoch": 149, "iter": 2400, "lr": 2e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.67828, "top5_acc": 0.87781, "loss_cls": 1.83986, "loss": 1.83986, "time": 0.82185} +{"mode": "train", "epoch": 149, "iter": 2500, "lr": 2e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68078, "top5_acc": 0.88156, "loss_cls": 1.80492, "loss": 1.80492, "time": 0.81765} +{"mode": "train", "epoch": 149, "iter": 2600, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66688, "top5_acc": 0.87594, "loss_cls": 1.84992, "loss": 1.84992, "time": 0.82613} +{"mode": "train", "epoch": 149, "iter": 2700, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67906, "top5_acc": 0.88141, "loss_cls": 1.80979, "loss": 1.80979, "time": 0.82223} +{"mode": "train", "epoch": 149, "iter": 2800, "lr": 2e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67594, "top5_acc": 0.87875, "loss_cls": 1.85632, "loss": 1.85632, "time": 0.82226} +{"mode": "train", "epoch": 149, "iter": 2900, "lr": 2e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.67703, "top5_acc": 0.88234, "loss_cls": 1.82537, "loss": 1.82537, "time": 0.82286} +{"mode": "train", "epoch": 149, "iter": 3000, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67344, "top5_acc": 0.88047, "loss_cls": 1.8451, "loss": 1.8451, "time": 0.82194} +{"mode": "train", "epoch": 149, "iter": 3100, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67656, "top5_acc": 0.88234, "loss_cls": 1.85995, "loss": 1.85995, "time": 0.82006} +{"mode": "train", "epoch": 149, "iter": 3200, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67125, "top5_acc": 0.87578, "loss_cls": 1.84735, "loss": 1.84735, "time": 0.81802} +{"mode": "train", "epoch": 149, "iter": 3300, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67047, "top5_acc": 0.87703, "loss_cls": 1.85226, "loss": 1.85226, "time": 0.81485} +{"mode": "train", "epoch": 149, "iter": 3400, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68203, "top5_acc": 0.87906, "loss_cls": 1.82713, "loss": 1.82713, "time": 0.81692} +{"mode": "train", "epoch": 149, "iter": 3500, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66969, "top5_acc": 0.87859, "loss_cls": 1.85787, "loss": 1.85787, "time": 0.81853} +{"mode": "train", "epoch": 149, "iter": 3600, "lr": 1e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67625, "top5_acc": 0.88234, "loss_cls": 1.81783, "loss": 1.81783, "time": 0.81722} +{"mode": "train", "epoch": 149, "iter": 3700, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67297, "top5_acc": 0.87625, "loss_cls": 1.84984, "loss": 1.84984, "time": 0.82357} +{"mode": "val", "epoch": 149, "iter": 309, "lr": 1e-05, "top1_acc": 0.46918, "top5_acc": 0.71691, "mean_class_accuracy": 0.46894} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 1e-05, "memory": 15990, "data_time": 1.26136, "top1_acc": 0.6725, "top5_acc": 0.8775, "loss_cls": 1.85513, "loss": 1.85513, "time": 2.24534} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 1e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67344, "top5_acc": 0.88188, "loss_cls": 1.84156, "loss": 1.84156, "time": 0.81988} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 1e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.68625, "top5_acc": 0.88281, "loss_cls": 1.81154, "loss": 1.81154, "time": 0.81631} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67969, "top5_acc": 0.88078, "loss_cls": 1.81496, "loss": 1.81496, "time": 0.81927} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 1e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.68219, "top5_acc": 0.87719, "loss_cls": 1.84607, "loss": 1.84607, "time": 0.81904} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67984, "top5_acc": 0.875, "loss_cls": 1.84601, "loss": 1.84601, "time": 0.81741} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67609, "top5_acc": 0.88203, "loss_cls": 1.82893, "loss": 1.82893, "time": 0.81796} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 1e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.68156, "top5_acc": 0.88562, "loss_cls": 1.79153, "loss": 1.79153, "time": 0.82348} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67219, "top5_acc": 0.88328, "loss_cls": 1.81952, "loss": 1.81952, "time": 0.81503} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68703, "top5_acc": 0.88422, "loss_cls": 1.80897, "loss": 1.80897, "time": 0.8171} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68016, "top5_acc": 0.88578, "loss_cls": 1.78531, "loss": 1.78531, "time": 0.81754} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68203, "top5_acc": 0.88516, "loss_cls": 1.82901, "loss": 1.82901, "time": 0.81682} +{"mode": "train", "epoch": 150, "iter": 1300, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.67953, "top5_acc": 0.88094, "loss_cls": 1.81977, "loss": 1.81977, "time": 0.81608} +{"mode": "train", "epoch": 150, "iter": 1400, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68312, "top5_acc": 0.88438, "loss_cls": 1.80031, "loss": 1.80031, "time": 0.81591} +{"mode": "train", "epoch": 150, "iter": 1500, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67938, "top5_acc": 0.88031, "loss_cls": 1.8084, "loss": 1.8084, "time": 0.82394} +{"mode": "train", "epoch": 150, "iter": 1600, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.6725, "top5_acc": 0.87266, "loss_cls": 1.87554, "loss": 1.87554, "time": 0.81593} +{"mode": "train", "epoch": 150, "iter": 1700, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.68188, "top5_acc": 0.8775, "loss_cls": 1.82625, "loss": 1.82625, "time": 0.8262} +{"mode": "train", "epoch": 150, "iter": 1800, "lr": 0.0, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.67266, "top5_acc": 0.87828, "loss_cls": 1.85631, "loss": 1.85631, "time": 0.8245} +{"mode": "train", "epoch": 150, "iter": 1900, "lr": 0.0, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.68391, "top5_acc": 0.88344, "loss_cls": 1.80469, "loss": 1.80469, "time": 0.82166} +{"mode": "train", "epoch": 150, "iter": 2000, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68016, "top5_acc": 0.88812, "loss_cls": 1.818, "loss": 1.818, "time": 0.81951} +{"mode": "train", "epoch": 150, "iter": 2100, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67797, "top5_acc": 0.87812, "loss_cls": 1.82158, "loss": 1.82158, "time": 0.81516} +{"mode": "train", "epoch": 150, "iter": 2200, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67781, "top5_acc": 0.87281, "loss_cls": 1.86034, "loss": 1.86034, "time": 0.82553} +{"mode": "train", "epoch": 150, "iter": 2300, "lr": 0.0, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.67531, "top5_acc": 0.87469, "loss_cls": 1.84758, "loss": 1.84758, "time": 0.81461} +{"mode": "train", "epoch": 150, "iter": 2400, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67172, "top5_acc": 0.86969, "loss_cls": 1.87598, "loss": 1.87598, "time": 0.81433} +{"mode": "train", "epoch": 150, "iter": 2500, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67938, "top5_acc": 0.87844, "loss_cls": 1.84555, "loss": 1.84555, "time": 0.82317} +{"mode": "train", "epoch": 150, "iter": 2600, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.69219, "top5_acc": 0.88828, "loss_cls": 1.77935, "loss": 1.77935, "time": 0.83114} +{"mode": "train", "epoch": 150, "iter": 2700, "lr": 0.0, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.67828, "top5_acc": 0.87891, "loss_cls": 1.83196, "loss": 1.83196, "time": 0.82311} +{"mode": "train", "epoch": 150, "iter": 2800, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67797, "top5_acc": 0.87844, "loss_cls": 1.83816, "loss": 1.83816, "time": 0.82209} +{"mode": "train", "epoch": 150, "iter": 2900, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67688, "top5_acc": 0.88234, "loss_cls": 1.81155, "loss": 1.81155, "time": 0.81088} +{"mode": "train", "epoch": 150, "iter": 3000, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.6875, "top5_acc": 0.88266, "loss_cls": 1.79466, "loss": 1.79466, "time": 0.82079} +{"mode": "train", "epoch": 150, "iter": 3100, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67172, "top5_acc": 0.87516, "loss_cls": 1.85004, "loss": 1.85004, "time": 0.81941} +{"mode": "train", "epoch": 150, "iter": 3200, "lr": 0.0, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.68281, "top5_acc": 0.88797, "loss_cls": 1.80908, "loss": 1.80908, "time": 0.82312} +{"mode": "train", "epoch": 150, "iter": 3300, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67844, "top5_acc": 0.88547, "loss_cls": 1.80838, "loss": 1.80838, "time": 0.81628} +{"mode": "train", "epoch": 150, "iter": 3400, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67031, "top5_acc": 0.87734, "loss_cls": 1.85031, "loss": 1.85031, "time": 0.8191} +{"mode": "train", "epoch": 150, "iter": 3500, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.68375, "top5_acc": 0.88469, "loss_cls": 1.80998, "loss": 1.80998, "time": 0.81492} +{"mode": "train", "epoch": 150, "iter": 3600, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.68781, "top5_acc": 0.88656, "loss_cls": 1.77743, "loss": 1.77743, "time": 0.82299} +{"mode": "train", "epoch": 150, "iter": 3700, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.68109, "top5_acc": 0.87562, "loss_cls": 1.83077, "loss": 1.83077, "time": 0.81153} +{"mode": "val", "epoch": 150, "iter": 309, "lr": 0.0, "top1_acc": 0.47004, "top5_acc": 0.71499, "mean_class_accuracy": 0.46975} diff --git a/k400/j_3/best_pred.pkl b/k400/j_3/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..e54b005ef6d22496c069eeefc38c88b9339c0208 --- /dev/null +++ b/k400/j_3/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4e645ea48b9cf3e3deb0efa50740971d7b4c0b32f1f2ea7923d958475bcab96e +size 44884097 diff --git a/k400/j_3/best_top1_acc_epoch_150.pth b/k400/j_3/best_top1_acc_epoch_150.pth new file mode 100644 index 0000000000000000000000000000000000000000..b8ad21a8e37c77b62e11148d4dc481ebf3af950c --- /dev/null +++ b/k400/j_3/best_top1_acc_epoch_150.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ec9d1b1342f9f337661aeecfdbd95f53523c3fd567b82dbec28d7c64ccd2a00d +size 32926705 diff --git a/k400/j_3/j_3.py b/k400/j_3/j_3.py new file mode 100644 index 0000000000000000000000000000000000000000..e5f3bfafd00f74229d5dff51a71a53909dd9bb80 --- /dev/null +++ b/k400/j_3/j_3.py @@ -0,0 +1,133 @@ +modality = 'j' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/j_3' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['j']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/k400/jm/20240716_064836.log b/k400/jm/20240716_064836.log new file mode 100644 index 0000000000000000000000000000000000000000..62f1271988f917bcccd16977dc09d2acdb231aeb --- /dev/null +++ b/k400/jm/20240716_064836.log @@ -0,0 +1,7304 @@ +2024-07-16 06:48:36,081 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2024-07-16 06:48:36,463 - pyskl - INFO - Config: modality = 'jm' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/jm' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2024-07-16 06:48:36,463 - pyskl - INFO - Set random seed to 1004529236, deterministic: False +2024-07-16 06:48:47,117 - pyskl - INFO - 239737 videos remain after valid thresholding +2024-07-16 06:49:02,168 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-07-16 06:49:02,173 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm +2024-07-16 06:49:02,182 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2024-07-16 06:49:02,208 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2024-07-16 06:49:02,218 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm by HardDiskBackend. +2024-07-16 06:52:18,699 - pyskl - INFO - Epoch [1][100/3746] lr: 1.000e-01, eta: 12 days, 18:35:24, time: 1.965, data_time: 1.257, memory: 15990, top1_acc: 0.0059, top5_acc: 0.0244, loss_cls: 6.4920, loss: 6.4920 +2024-07-16 06:53:28,651 - pyskl - INFO - Epoch [1][200/3746] lr: 1.000e-01, eta: 8 days, 15:50:21, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0100, top5_acc: 0.0491, loss_cls: 6.4611, loss: 6.4611 +2024-07-16 06:54:39,007 - pyskl - INFO - Epoch [1][300/3746] lr: 1.000e-01, eta: 7 days, 7:07:10, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0155, top5_acc: 0.0616, loss_cls: 6.2925, loss: 6.2925 +2024-07-16 06:55:49,085 - pyskl - INFO - Epoch [1][400/3746] lr: 1.000e-01, eta: 6 days, 14:38:28, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0167, top5_acc: 0.0675, loss_cls: 6.2095, loss: 6.2095 +2024-07-16 06:56:59,517 - pyskl - INFO - Epoch [1][500/3746] lr: 1.000e-01, eta: 6 days, 4:51:25, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0184, top5_acc: 0.0788, loss_cls: 6.1341, loss: 6.1341 +2024-07-16 06:58:09,409 - pyskl - INFO - Epoch [1][600/3746] lr: 1.000e-01, eta: 5 days, 22:11:15, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0244, top5_acc: 0.0895, loss_cls: 6.0555, loss: 6.0555 +2024-07-16 06:59:19,445 - pyskl - INFO - Epoch [1][700/3746] lr: 1.000e-01, eta: 5 days, 17:27:00, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0262, top5_acc: 0.1030, loss_cls: 5.9585, loss: 5.9585 +2024-07-16 07:00:29,359 - pyskl - INFO - Epoch [1][800/3746] lr: 1.000e-01, eta: 5 days, 13:52:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0305, top5_acc: 0.1105, loss_cls: 5.9263, loss: 5.9263 +2024-07-16 07:01:39,640 - pyskl - INFO - Epoch [1][900/3746] lr: 1.000e-01, eta: 5 days, 11:08:29, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0320, top5_acc: 0.1136, loss_cls: 5.8930, loss: 5.8930 +2024-07-16 07:02:49,929 - pyskl - INFO - Epoch [1][1000/3746] lr: 1.000e-01, eta: 5 days, 8:57:28, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0359, top5_acc: 0.1247, loss_cls: 5.8343, loss: 5.8343 +2024-07-16 07:03:59,796 - pyskl - INFO - Epoch [1][1100/3746] lr: 1.000e-01, eta: 5 days, 7:06:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0309, top5_acc: 0.1239, loss_cls: 5.8006, loss: 5.8006 +2024-07-16 07:05:09,820 - pyskl - INFO - Epoch [1][1200/3746] lr: 1.000e-01, eta: 5 days, 5:34:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0344, top5_acc: 0.1375, loss_cls: 5.7691, loss: 5.7691 +2024-07-16 07:06:19,624 - pyskl - INFO - Epoch [1][1300/3746] lr: 1.000e-01, eta: 5 days, 4:15:48, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.0384, top5_acc: 0.1316, loss_cls: 5.7441, loss: 5.7441 +2024-07-16 07:07:29,259 - pyskl - INFO - Epoch [1][1400/3746] lr: 1.000e-01, eta: 5 days, 3:06:39, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.0431, top5_acc: 0.1363, loss_cls: 5.7240, loss: 5.7240 +2024-07-16 07:08:39,090 - pyskl - INFO - Epoch [1][1500/3746] lr: 1.000e-01, eta: 5 days, 2:07:47, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.0419, top5_acc: 0.1517, loss_cls: 5.6760, loss: 5.6760 +2024-07-16 07:09:49,126 - pyskl - INFO - Epoch [1][1600/3746] lr: 1.000e-01, eta: 5 days, 1:17:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0441, top5_acc: 0.1483, loss_cls: 5.6663, loss: 5.6663 +2024-07-16 07:10:58,878 - pyskl - INFO - Epoch [1][1700/3746] lr: 1.000e-01, eta: 5 days, 0:31:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.0481, top5_acc: 0.1512, loss_cls: 5.6629, loss: 5.6629 +2024-07-16 07:12:08,733 - pyskl - INFO - Epoch [1][1800/3746] lr: 1.000e-01, eta: 4 days, 23:50:26, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0473, top5_acc: 0.1616, loss_cls: 5.6229, loss: 5.6229 +2024-07-16 07:13:19,356 - pyskl - INFO - Epoch [1][1900/3746] lr: 1.000e-01, eta: 4 days, 23:17:42, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.0475, top5_acc: 0.1595, loss_cls: 5.6283, loss: 5.6283 +2024-07-16 07:14:29,303 - pyskl - INFO - Epoch [1][2000/3746] lr: 1.000e-01, eta: 4 days, 22:44:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0522, top5_acc: 0.1716, loss_cls: 5.5893, loss: 5.5893 +2024-07-16 07:15:39,932 - pyskl - INFO - Epoch [1][2100/3746] lr: 1.000e-01, eta: 4 days, 22:18:15, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.0555, top5_acc: 0.1778, loss_cls: 5.5468, loss: 5.5468 +2024-07-16 07:16:49,648 - pyskl - INFO - Epoch [1][2200/3746] lr: 1.000e-01, eta: 4 days, 21:50:00, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.0537, top5_acc: 0.1802, loss_cls: 5.5506, loss: 5.5506 +2024-07-16 07:17:59,381 - pyskl - INFO - Epoch [1][2300/3746] lr: 1.000e-01, eta: 4 days, 21:24:10, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.0555, top5_acc: 0.1805, loss_cls: 5.5375, loss: 5.5375 +2024-07-16 07:19:09,028 - pyskl - INFO - Epoch [1][2400/3746] lr: 1.000e-01, eta: 4 days, 21:00:03, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.0508, top5_acc: 0.1817, loss_cls: 5.5218, loss: 5.5218 +2024-07-16 07:20:19,097 - pyskl - INFO - Epoch [1][2500/3746] lr: 1.000e-01, eta: 4 days, 20:39:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0575, top5_acc: 0.1972, loss_cls: 5.4719, loss: 5.4719 +2024-07-16 07:21:28,877 - pyskl - INFO - Epoch [1][2600/3746] lr: 9.999e-02, eta: 4 days, 20:19:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.0605, top5_acc: 0.1925, loss_cls: 5.4587, loss: 5.4587 +2024-07-16 07:22:38,787 - pyskl - INFO - Epoch [1][2700/3746] lr: 9.999e-02, eta: 4 days, 20:00:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0666, top5_acc: 0.1967, loss_cls: 5.4648, loss: 5.4648 +2024-07-16 07:23:48,666 - pyskl - INFO - Epoch [1][2800/3746] lr: 9.999e-02, eta: 4 days, 19:43:30, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0644, top5_acc: 0.2031, loss_cls: 5.4512, loss: 5.4512 +2024-07-16 07:24:58,660 - pyskl - INFO - Epoch [1][2900/3746] lr: 9.999e-02, eta: 4 days, 19:27:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0619, top5_acc: 0.2114, loss_cls: 5.4204, loss: 5.4204 +2024-07-16 07:26:08,393 - pyskl - INFO - Epoch [1][3000/3746] lr: 9.999e-02, eta: 4 days, 19:12:08, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.0691, top5_acc: 0.2147, loss_cls: 5.4104, loss: 5.4104 +2024-07-16 07:27:18,150 - pyskl - INFO - Epoch [1][3100/3746] lr: 9.999e-02, eta: 4 days, 18:57:32, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.0730, top5_acc: 0.2173, loss_cls: 5.3435, loss: 5.3435 +2024-07-16 07:28:28,106 - pyskl - INFO - Epoch [1][3200/3746] lr: 9.999e-02, eta: 4 days, 18:44:21, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0762, top5_acc: 0.2169, loss_cls: 5.3847, loss: 5.3847 +2024-07-16 07:29:38,088 - pyskl - INFO - Epoch [1][3300/3746] lr: 9.999e-02, eta: 4 days, 18:31:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0734, top5_acc: 0.2225, loss_cls: 5.3827, loss: 5.3827 +2024-07-16 07:30:47,782 - pyskl - INFO - Epoch [1][3400/3746] lr: 9.999e-02, eta: 4 days, 18:19:28, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.0756, top5_acc: 0.2284, loss_cls: 5.3320, loss: 5.3320 +2024-07-16 07:31:57,569 - pyskl - INFO - Epoch [1][3500/3746] lr: 9.999e-02, eta: 4 days, 18:07:51, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.0834, top5_acc: 0.2352, loss_cls: 5.2962, loss: 5.2962 +2024-07-16 07:33:07,687 - pyskl - INFO - Epoch [1][3600/3746] lr: 9.999e-02, eta: 4 days, 17:57:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0767, top5_acc: 0.2352, loss_cls: 5.2941, loss: 5.2941 +2024-07-16 07:34:18,063 - pyskl - INFO - Epoch [1][3700/3746] lr: 9.999e-02, eta: 4 days, 17:48:38, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0834, top5_acc: 0.2439, loss_cls: 5.2410, loss: 5.2410 +2024-07-16 07:34:52,969 - pyskl - INFO - Saving checkpoint at 1 epochs +2024-07-16 07:36:44,188 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 07:36:44,854 - pyskl - INFO - +top1_acc 0.0632 +top5_acc 0.1924 +2024-07-16 07:36:44,854 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 07:36:44,894 - pyskl - INFO - +mean_acc 0.0632 +2024-07-16 07:36:45,120 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2024-07-16 07:36:45,121 - pyskl - INFO - Best top1_acc is 0.0632 at 1 epoch. +2024-07-16 07:36:45,131 - pyskl - INFO - Epoch(val) [1][309] top1_acc: 0.0632, top5_acc: 0.1924, mean_class_accuracy: 0.0632 +2024-07-16 07:40:05,198 - pyskl - INFO - Epoch [2][100/3746] lr: 9.999e-02, eta: 4 days, 21:31:30, time: 2.001, data_time: 1.301, memory: 15990, top1_acc: 0.0886, top5_acc: 0.2591, loss_cls: 5.2392, loss: 5.2392 +2024-07-16 07:41:15,127 - pyskl - INFO - Epoch [2][200/3746] lr: 9.999e-02, eta: 4 days, 21:16:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.0978, top5_acc: 0.2664, loss_cls: 5.1573, loss: 5.1573 +2024-07-16 07:42:25,139 - pyskl - INFO - Epoch [2][300/3746] lr: 9.999e-02, eta: 4 days, 21:02:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0912, top5_acc: 0.2634, loss_cls: 5.1901, loss: 5.1901 +2024-07-16 07:43:35,261 - pyskl - INFO - Epoch [2][400/3746] lr: 9.999e-02, eta: 4 days, 20:48:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0902, top5_acc: 0.2603, loss_cls: 5.2043, loss: 5.2043 +2024-07-16 07:44:45,042 - pyskl - INFO - Epoch [2][500/3746] lr: 9.999e-02, eta: 4 days, 20:35:11, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.0928, top5_acc: 0.2644, loss_cls: 5.1522, loss: 5.1522 +2024-07-16 07:45:55,129 - pyskl - INFO - Epoch [2][600/3746] lr: 9.999e-02, eta: 4 days, 20:22:51, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1089, top5_acc: 0.2916, loss_cls: 5.0609, loss: 5.0609 +2024-07-16 07:47:05,264 - pyskl - INFO - Epoch [2][700/3746] lr: 9.998e-02, eta: 4 days, 20:11:08, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1073, top5_acc: 0.2861, loss_cls: 5.0798, loss: 5.0798 +2024-07-16 07:48:15,217 - pyskl - INFO - Epoch [2][800/3746] lr: 9.998e-02, eta: 4 days, 19:59:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1095, top5_acc: 0.2948, loss_cls: 5.0860, loss: 5.0860 +2024-07-16 07:49:25,062 - pyskl - INFO - Epoch [2][900/3746] lr: 9.998e-02, eta: 4 days, 19:48:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1027, top5_acc: 0.2856, loss_cls: 5.0925, loss: 5.0925 +2024-07-16 07:50:35,480 - pyskl - INFO - Epoch [2][1000/3746] lr: 9.998e-02, eta: 4 days, 19:38:15, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1089, top5_acc: 0.2869, loss_cls: 5.0489, loss: 5.0489 +2024-07-16 07:51:45,702 - pyskl - INFO - Epoch [2][1100/3746] lr: 9.998e-02, eta: 4 days, 19:28:24, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1014, top5_acc: 0.2775, loss_cls: 5.1194, loss: 5.1194 +2024-07-16 07:52:55,862 - pyskl - INFO - Epoch [2][1200/3746] lr: 9.998e-02, eta: 4 days, 19:18:46, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1058, top5_acc: 0.2950, loss_cls: 5.0432, loss: 5.0432 +2024-07-16 07:54:06,095 - pyskl - INFO - Epoch [2][1300/3746] lr: 9.998e-02, eta: 4 days, 19:09:37, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1183, top5_acc: 0.3039, loss_cls: 5.0152, loss: 5.0152 +2024-07-16 07:55:16,337 - pyskl - INFO - Epoch [2][1400/3746] lr: 9.998e-02, eta: 4 days, 19:00:47, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1147, top5_acc: 0.3105, loss_cls: 5.0124, loss: 5.0124 +2024-07-16 07:56:26,277 - pyskl - INFO - Epoch [2][1500/3746] lr: 9.998e-02, eta: 4 days, 18:51:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1080, top5_acc: 0.2927, loss_cls: 5.0604, loss: 5.0604 +2024-07-16 07:57:36,179 - pyskl - INFO - Epoch [2][1600/3746] lr: 9.998e-02, eta: 4 days, 18:42:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1102, top5_acc: 0.2934, loss_cls: 5.0335, loss: 5.0335 +2024-07-16 07:58:45,956 - pyskl - INFO - Epoch [2][1700/3746] lr: 9.998e-02, eta: 4 days, 18:34:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1159, top5_acc: 0.3056, loss_cls: 4.9827, loss: 4.9827 +2024-07-16 07:59:55,886 - pyskl - INFO - Epoch [2][1800/3746] lr: 9.998e-02, eta: 4 days, 18:25:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1159, top5_acc: 0.3022, loss_cls: 4.9985, loss: 4.9985 +2024-07-16 08:01:06,520 - pyskl - INFO - Epoch [2][1900/3746] lr: 9.998e-02, eta: 4 days, 18:19:01, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1198, top5_acc: 0.3172, loss_cls: 4.9676, loss: 4.9676 +2024-07-16 08:02:16,209 - pyskl - INFO - Epoch [2][2000/3746] lr: 9.997e-02, eta: 4 days, 18:10:51, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1220, top5_acc: 0.3191, loss_cls: 4.9813, loss: 4.9813 +2024-07-16 08:03:26,116 - pyskl - INFO - Epoch [2][2100/3746] lr: 9.997e-02, eta: 4 days, 18:03:17, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1242, top5_acc: 0.3202, loss_cls: 4.9461, loss: 4.9461 +2024-07-16 08:04:36,347 - pyskl - INFO - Epoch [2][2200/3746] lr: 9.997e-02, eta: 4 days, 17:56:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1177, top5_acc: 0.3153, loss_cls: 4.9883, loss: 4.9883 +2024-07-16 08:05:46,396 - pyskl - INFO - Epoch [2][2300/3746] lr: 9.997e-02, eta: 4 days, 17:49:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1187, top5_acc: 0.3127, loss_cls: 4.9876, loss: 4.9876 +2024-07-16 08:06:56,294 - pyskl - INFO - Epoch [2][2400/3746] lr: 9.997e-02, eta: 4 days, 17:42:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1256, top5_acc: 0.3189, loss_cls: 4.9408, loss: 4.9408 +2024-07-16 08:08:06,175 - pyskl - INFO - Epoch [2][2500/3746] lr: 9.997e-02, eta: 4 days, 17:35:39, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1294, top5_acc: 0.3167, loss_cls: 4.9404, loss: 4.9404 +2024-07-16 08:09:16,249 - pyskl - INFO - Epoch [2][2600/3746] lr: 9.997e-02, eta: 4 days, 17:29:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1239, top5_acc: 0.3220, loss_cls: 4.9460, loss: 4.9460 +2024-07-16 08:10:25,955 - pyskl - INFO - Epoch [2][2700/3746] lr: 9.997e-02, eta: 4 days, 17:22:33, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1327, top5_acc: 0.3300, loss_cls: 4.8942, loss: 4.8942 +2024-07-16 08:11:35,616 - pyskl - INFO - Epoch [2][2800/3746] lr: 9.997e-02, eta: 4 days, 17:15:56, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1284, top5_acc: 0.3322, loss_cls: 4.8897, loss: 4.8897 +2024-07-16 08:12:45,244 - pyskl - INFO - Epoch [2][2900/3746] lr: 9.997e-02, eta: 4 days, 17:09:25, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1255, top5_acc: 0.3230, loss_cls: 4.9432, loss: 4.9432 +2024-07-16 08:13:55,178 - pyskl - INFO - Epoch [2][3000/3746] lr: 9.996e-02, eta: 4 days, 17:03:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1345, top5_acc: 0.3452, loss_cls: 4.8825, loss: 4.8825 +2024-07-16 08:15:04,826 - pyskl - INFO - Epoch [2][3100/3746] lr: 9.996e-02, eta: 4 days, 16:57:19, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1311, top5_acc: 0.3306, loss_cls: 4.8863, loss: 4.8863 +2024-07-16 08:16:15,097 - pyskl - INFO - Epoch [2][3200/3746] lr: 9.996e-02, eta: 4 days, 16:52:06, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1339, top5_acc: 0.3447, loss_cls: 4.8540, loss: 4.8540 +2024-07-16 08:17:25,210 - pyskl - INFO - Epoch [2][3300/3746] lr: 9.996e-02, eta: 4 days, 16:46:49, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1447, top5_acc: 0.3423, loss_cls: 4.8431, loss: 4.8431 +2024-07-16 08:18:35,294 - pyskl - INFO - Epoch [2][3400/3746] lr: 9.996e-02, eta: 4 days, 16:41:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1411, top5_acc: 0.3445, loss_cls: 4.8351, loss: 4.8351 +2024-07-16 08:19:45,420 - pyskl - INFO - Epoch [2][3500/3746] lr: 9.996e-02, eta: 4 days, 16:36:32, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1392, top5_acc: 0.3439, loss_cls: 4.8695, loss: 4.8695 +2024-07-16 08:20:55,539 - pyskl - INFO - Epoch [2][3600/3746] lr: 9.996e-02, eta: 4 days, 16:31:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1372, top5_acc: 0.3305, loss_cls: 4.9000, loss: 4.9000 +2024-07-16 08:22:06,432 - pyskl - INFO - Epoch [2][3700/3746] lr: 9.996e-02, eta: 4 days, 16:27:41, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1388, top5_acc: 0.3453, loss_cls: 4.8440, loss: 4.8440 +2024-07-16 08:22:41,041 - pyskl - INFO - Saving checkpoint at 2 epochs +2024-07-16 08:24:33,030 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 08:24:33,718 - pyskl - INFO - +top1_acc 0.0658 +top5_acc 0.2125 +2024-07-16 08:24:33,718 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 08:24:33,757 - pyskl - INFO - +mean_acc 0.0656 +2024-07-16 08:24:33,761 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_1.pth was removed +2024-07-16 08:24:33,984 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2024-07-16 08:24:33,985 - pyskl - INFO - Best top1_acc is 0.0658 at 2 epoch. +2024-07-16 08:24:33,997 - pyskl - INFO - Epoch(val) [2][309] top1_acc: 0.0658, top5_acc: 0.2125, mean_class_accuracy: 0.0656 +2024-07-16 08:27:57,214 - pyskl - INFO - Epoch [3][100/3746] lr: 9.995e-02, eta: 4 days, 18:23:28, time: 2.032, data_time: 1.326, memory: 15990, top1_acc: 0.1383, top5_acc: 0.3428, loss_cls: 4.8232, loss: 4.8232 +2024-07-16 08:29:07,520 - pyskl - INFO - Epoch [3][200/3746] lr: 9.995e-02, eta: 4 days, 18:17:26, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1369, top5_acc: 0.3480, loss_cls: 4.8221, loss: 4.8221 +2024-07-16 08:30:17,498 - pyskl - INFO - Epoch [3][300/3746] lr: 9.995e-02, eta: 4 days, 18:11:09, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1333, top5_acc: 0.3514, loss_cls: 4.8271, loss: 4.8271 +2024-07-16 08:31:27,516 - pyskl - INFO - Epoch [3][400/3746] lr: 9.995e-02, eta: 4 days, 18:05:02, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1388, top5_acc: 0.3484, loss_cls: 4.8258, loss: 4.8258 +2024-07-16 08:32:37,520 - pyskl - INFO - Epoch [3][500/3746] lr: 9.995e-02, eta: 4 days, 17:59:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1414, top5_acc: 0.3555, loss_cls: 4.7921, loss: 4.7921 +2024-07-16 08:33:47,499 - pyskl - INFO - Epoch [3][600/3746] lr: 9.995e-02, eta: 4 days, 17:53:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1373, top5_acc: 0.3511, loss_cls: 4.8466, loss: 4.8466 +2024-07-16 08:34:57,710 - pyskl - INFO - Epoch [3][700/3746] lr: 9.995e-02, eta: 4 days, 17:47:34, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1497, top5_acc: 0.3617, loss_cls: 4.7758, loss: 4.7758 +2024-07-16 08:36:07,523 - pyskl - INFO - Epoch [3][800/3746] lr: 9.995e-02, eta: 4 days, 17:41:42, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1444, top5_acc: 0.3459, loss_cls: 4.8100, loss: 4.8100 +2024-07-16 08:37:17,616 - pyskl - INFO - Epoch [3][900/3746] lr: 9.994e-02, eta: 4 days, 17:36:14, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1523, top5_acc: 0.3647, loss_cls: 4.7599, loss: 4.7599 +2024-07-16 08:38:27,957 - pyskl - INFO - Epoch [3][1000/3746] lr: 9.994e-02, eta: 4 days, 17:31:09, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1439, top5_acc: 0.3556, loss_cls: 4.7895, loss: 4.7895 +2024-07-16 08:39:37,714 - pyskl - INFO - Epoch [3][1100/3746] lr: 9.994e-02, eta: 4 days, 17:25:32, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1533, top5_acc: 0.3620, loss_cls: 4.7824, loss: 4.7824 +2024-07-16 08:40:47,731 - pyskl - INFO - Epoch [3][1200/3746] lr: 9.994e-02, eta: 4 days, 17:20:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1475, top5_acc: 0.3608, loss_cls: 4.7525, loss: 4.7525 +2024-07-16 08:41:58,008 - pyskl - INFO - Epoch [3][1300/3746] lr: 9.994e-02, eta: 4 days, 17:15:25, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1448, top5_acc: 0.3608, loss_cls: 4.7638, loss: 4.7638 +2024-07-16 08:43:08,079 - pyskl - INFO - Epoch [3][1400/3746] lr: 9.994e-02, eta: 4 days, 17:10:25, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1552, top5_acc: 0.3606, loss_cls: 4.7744, loss: 4.7744 +2024-07-16 08:44:18,201 - pyskl - INFO - Epoch [3][1500/3746] lr: 9.994e-02, eta: 4 days, 17:05:32, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1545, top5_acc: 0.3616, loss_cls: 4.7655, loss: 4.7655 +2024-07-16 08:45:28,060 - pyskl - INFO - Epoch [3][1600/3746] lr: 9.994e-02, eta: 4 days, 17:00:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1480, top5_acc: 0.3528, loss_cls: 4.7856, loss: 4.7856 +2024-07-16 08:46:38,153 - pyskl - INFO - Epoch [3][1700/3746] lr: 9.993e-02, eta: 4 days, 16:55:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1528, top5_acc: 0.3644, loss_cls: 4.7180, loss: 4.7180 +2024-07-16 08:47:48,330 - pyskl - INFO - Epoch [3][1800/3746] lr: 9.993e-02, eta: 4 days, 16:51:11, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1430, top5_acc: 0.3611, loss_cls: 4.7879, loss: 4.7879 +2024-07-16 08:48:58,614 - pyskl - INFO - Epoch [3][1900/3746] lr: 9.993e-02, eta: 4 days, 16:46:47, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1566, top5_acc: 0.3716, loss_cls: 4.7160, loss: 4.7160 +2024-07-16 08:50:08,220 - pyskl - INFO - Epoch [3][2000/3746] lr: 9.993e-02, eta: 4 days, 16:41:47, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1467, top5_acc: 0.3598, loss_cls: 4.7671, loss: 4.7671 +2024-07-16 08:51:18,282 - pyskl - INFO - Epoch [3][2100/3746] lr: 9.993e-02, eta: 4 days, 16:37:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1461, top5_acc: 0.3588, loss_cls: 4.7786, loss: 4.7786 +2024-07-16 08:52:28,077 - pyskl - INFO - Epoch [3][2200/3746] lr: 9.993e-02, eta: 4 days, 16:32:40, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1544, top5_acc: 0.3814, loss_cls: 4.6906, loss: 4.6906 +2024-07-16 08:53:38,064 - pyskl - INFO - Epoch [3][2300/3746] lr: 9.993e-02, eta: 4 days, 16:28:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1580, top5_acc: 0.3739, loss_cls: 4.7164, loss: 4.7164 +2024-07-16 08:54:47,674 - pyskl - INFO - Epoch [3][2400/3746] lr: 9.992e-02, eta: 4 days, 16:23:34, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1636, top5_acc: 0.3695, loss_cls: 4.6905, loss: 4.6905 +2024-07-16 08:55:57,563 - pyskl - INFO - Epoch [3][2500/3746] lr: 9.992e-02, eta: 4 days, 16:19:13, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1616, top5_acc: 0.3844, loss_cls: 4.6950, loss: 4.6950 +2024-07-16 08:57:07,482 - pyskl - INFO - Epoch [3][2600/3746] lr: 9.992e-02, eta: 4 days, 16:14:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1561, top5_acc: 0.3761, loss_cls: 4.7425, loss: 4.7425 +2024-07-16 08:58:17,554 - pyskl - INFO - Epoch [3][2700/3746] lr: 9.992e-02, eta: 4 days, 16:10:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1658, top5_acc: 0.3805, loss_cls: 4.6871, loss: 4.6871 +2024-07-16 08:59:27,253 - pyskl - INFO - Epoch [3][2800/3746] lr: 9.992e-02, eta: 4 days, 16:06:31, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1517, top5_acc: 0.3673, loss_cls: 4.7326, loss: 4.7326 +2024-07-16 09:00:37,196 - pyskl - INFO - Epoch [3][2900/3746] lr: 9.992e-02, eta: 4 days, 16:02:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1534, top5_acc: 0.3741, loss_cls: 4.7520, loss: 4.7520 +2024-07-16 09:01:47,111 - pyskl - INFO - Epoch [3][3000/3746] lr: 9.991e-02, eta: 4 days, 15:58:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1569, top5_acc: 0.3714, loss_cls: 4.7163, loss: 4.7163 +2024-07-16 09:02:57,343 - pyskl - INFO - Epoch [3][3100/3746] lr: 9.991e-02, eta: 4 days, 15:54:42, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1591, top5_acc: 0.3742, loss_cls: 4.6997, loss: 4.6997 +2024-07-16 09:04:06,987 - pyskl - INFO - Epoch [3][3200/3746] lr: 9.991e-02, eta: 4 days, 15:50:32, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1556, top5_acc: 0.3663, loss_cls: 4.7164, loss: 4.7164 +2024-07-16 09:05:17,049 - pyskl - INFO - Epoch [3][3300/3746] lr: 9.991e-02, eta: 4 days, 15:46:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1539, top5_acc: 0.3688, loss_cls: 4.7384, loss: 4.7384 +2024-07-16 09:06:26,788 - pyskl - INFO - Epoch [3][3400/3746] lr: 9.991e-02, eta: 4 days, 15:42:47, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1636, top5_acc: 0.3847, loss_cls: 4.6643, loss: 4.6643 +2024-07-16 09:07:36,851 - pyskl - INFO - Epoch [3][3500/3746] lr: 9.991e-02, eta: 4 days, 15:39:08, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1642, top5_acc: 0.3798, loss_cls: 4.6524, loss: 4.6524 +2024-07-16 09:08:46,860 - pyskl - INFO - Epoch [3][3600/3746] lr: 9.990e-02, eta: 4 days, 15:35:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1652, top5_acc: 0.3802, loss_cls: 4.6705, loss: 4.6705 +2024-07-16 09:09:57,774 - pyskl - INFO - Epoch [3][3700/3746] lr: 9.990e-02, eta: 4 days, 15:32:36, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1605, top5_acc: 0.3780, loss_cls: 4.6976, loss: 4.6976 +2024-07-16 09:10:32,931 - pyskl - INFO - Saving checkpoint at 3 epochs +2024-07-16 09:12:24,376 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 09:12:25,039 - pyskl - INFO - +top1_acc 0.0919 +top5_acc 0.2475 +2024-07-16 09:12:25,040 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 09:12:25,081 - pyskl - INFO - +mean_acc 0.0919 +2024-07-16 09:12:25,086 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_2.pth was removed +2024-07-16 09:12:25,315 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2024-07-16 09:12:25,316 - pyskl - INFO - Best top1_acc is 0.0919 at 3 epoch. +2024-07-16 09:12:25,326 - pyskl - INFO - Epoch(val) [3][309] top1_acc: 0.0919, top5_acc: 0.2475, mean_class_accuracy: 0.0919 +2024-07-16 09:15:47,297 - pyskl - INFO - Epoch [4][100/3746] lr: 9.990e-02, eta: 4 days, 16:48:07, time: 2.020, data_time: 1.315, memory: 15990, top1_acc: 0.1656, top5_acc: 0.3803, loss_cls: 4.6743, loss: 4.6743 +2024-07-16 09:16:57,298 - pyskl - INFO - Epoch [4][200/3746] lr: 9.990e-02, eta: 4 days, 16:43:52, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1663, top5_acc: 0.3880, loss_cls: 4.6288, loss: 4.6288 +2024-07-16 09:18:07,194 - pyskl - INFO - Epoch [4][300/3746] lr: 9.990e-02, eta: 4 days, 16:39:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1644, top5_acc: 0.3870, loss_cls: 4.6412, loss: 4.6412 +2024-07-16 09:19:17,310 - pyskl - INFO - Epoch [4][400/3746] lr: 9.989e-02, eta: 4 days, 16:35:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1736, top5_acc: 0.3877, loss_cls: 4.6270, loss: 4.6270 +2024-07-16 09:20:27,258 - pyskl - INFO - Epoch [4][500/3746] lr: 9.989e-02, eta: 4 days, 16:31:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1656, top5_acc: 0.3933, loss_cls: 4.6231, loss: 4.6231 +2024-07-16 09:21:37,086 - pyskl - INFO - Epoch [4][600/3746] lr: 9.989e-02, eta: 4 days, 16:27:15, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1709, top5_acc: 0.3803, loss_cls: 4.6915, loss: 4.6915 +2024-07-16 09:22:47,119 - pyskl - INFO - Epoch [4][700/3746] lr: 9.989e-02, eta: 4 days, 16:23:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1727, top5_acc: 0.3903, loss_cls: 4.6291, loss: 4.6291 +2024-07-16 09:23:56,818 - pyskl - INFO - Epoch [4][800/3746] lr: 9.989e-02, eta: 4 days, 16:19:07, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1672, top5_acc: 0.3962, loss_cls: 4.6248, loss: 4.6248 +2024-07-16 09:25:06,726 - pyskl - INFO - Epoch [4][900/3746] lr: 9.988e-02, eta: 4 days, 16:15:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1780, top5_acc: 0.3953, loss_cls: 4.5872, loss: 4.5872 +2024-07-16 09:26:16,737 - pyskl - INFO - Epoch [4][1000/3746] lr: 9.988e-02, eta: 4 days, 16:11:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1655, top5_acc: 0.3945, loss_cls: 4.6350, loss: 4.6350 +2024-07-16 09:27:26,793 - pyskl - INFO - Epoch [4][1100/3746] lr: 9.988e-02, eta: 4 days, 16:07:32, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1675, top5_acc: 0.3905, loss_cls: 4.6182, loss: 4.6182 +2024-07-16 09:28:36,699 - pyskl - INFO - Epoch [4][1200/3746] lr: 9.988e-02, eta: 4 days, 16:03:42, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1680, top5_acc: 0.3917, loss_cls: 4.6389, loss: 4.6389 +2024-07-16 09:29:46,619 - pyskl - INFO - Epoch [4][1300/3746] lr: 9.988e-02, eta: 4 days, 15:59:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1750, top5_acc: 0.3945, loss_cls: 4.6060, loss: 4.6060 +2024-07-16 09:30:56,636 - pyskl - INFO - Epoch [4][1400/3746] lr: 9.988e-02, eta: 4 days, 15:56:15, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1655, top5_acc: 0.3869, loss_cls: 4.6544, loss: 4.6544 +2024-07-16 09:32:06,547 - pyskl - INFO - Epoch [4][1500/3746] lr: 9.987e-02, eta: 4 days, 15:52:33, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1694, top5_acc: 0.3969, loss_cls: 4.6103, loss: 4.6103 +2024-07-16 09:33:16,464 - pyskl - INFO - Epoch [4][1600/3746] lr: 9.987e-02, eta: 4 days, 15:48:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1725, top5_acc: 0.3955, loss_cls: 4.6242, loss: 4.6242 +2024-07-16 09:34:26,557 - pyskl - INFO - Epoch [4][1700/3746] lr: 9.987e-02, eta: 4 days, 15:45:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1717, top5_acc: 0.3969, loss_cls: 4.5988, loss: 4.5988 +2024-07-16 09:35:36,494 - pyskl - INFO - Epoch [4][1800/3746] lr: 9.987e-02, eta: 4 days, 15:41:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1653, top5_acc: 0.3850, loss_cls: 4.6422, loss: 4.6422 +2024-07-16 09:36:46,661 - pyskl - INFO - Epoch [4][1900/3746] lr: 9.987e-02, eta: 4 days, 15:38:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1708, top5_acc: 0.3936, loss_cls: 4.6344, loss: 4.6344 +2024-07-16 09:37:56,693 - pyskl - INFO - Epoch [4][2000/3746] lr: 9.986e-02, eta: 4 days, 15:35:00, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1716, top5_acc: 0.3955, loss_cls: 4.6135, loss: 4.6135 +2024-07-16 09:39:06,821 - pyskl - INFO - Epoch [4][2100/3746] lr: 9.986e-02, eta: 4 days, 15:31:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1688, top5_acc: 0.3903, loss_cls: 4.6015, loss: 4.6015 +2024-07-16 09:40:17,037 - pyskl - INFO - Epoch [4][2200/3746] lr: 9.986e-02, eta: 4 days, 15:28:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1677, top5_acc: 0.3777, loss_cls: 4.6535, loss: 4.6535 +2024-07-16 09:41:27,114 - pyskl - INFO - Epoch [4][2300/3746] lr: 9.986e-02, eta: 4 days, 15:25:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1736, top5_acc: 0.3937, loss_cls: 4.6133, loss: 4.6133 +2024-07-16 09:42:37,006 - pyskl - INFO - Epoch [4][2400/3746] lr: 9.985e-02, eta: 4 days, 15:21:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1630, top5_acc: 0.3823, loss_cls: 4.6541, loss: 4.6541 +2024-07-16 09:43:47,561 - pyskl - INFO - Epoch [4][2500/3746] lr: 9.985e-02, eta: 4 days, 15:18:47, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1659, top5_acc: 0.3872, loss_cls: 4.6557, loss: 4.6557 +2024-07-16 09:44:57,315 - pyskl - INFO - Epoch [4][2600/3746] lr: 9.985e-02, eta: 4 days, 15:15:21, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1784, top5_acc: 0.4020, loss_cls: 4.6121, loss: 4.6121 +2024-07-16 09:46:07,274 - pyskl - INFO - Epoch [4][2700/3746] lr: 9.985e-02, eta: 4 days, 15:12:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1711, top5_acc: 0.3961, loss_cls: 4.5840, loss: 4.5840 +2024-07-16 09:47:17,141 - pyskl - INFO - Epoch [4][2800/3746] lr: 9.985e-02, eta: 4 days, 15:08:48, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1647, top5_acc: 0.3880, loss_cls: 4.6512, loss: 4.6512 +2024-07-16 09:48:27,240 - pyskl - INFO - Epoch [4][2900/3746] lr: 9.984e-02, eta: 4 days, 15:05:41, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1723, top5_acc: 0.3945, loss_cls: 4.6058, loss: 4.6058 +2024-07-16 09:49:37,411 - pyskl - INFO - Epoch [4][3000/3746] lr: 9.984e-02, eta: 4 days, 15:02:39, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1708, top5_acc: 0.3973, loss_cls: 4.6185, loss: 4.6185 +2024-07-16 09:50:47,413 - pyskl - INFO - Epoch [4][3100/3746] lr: 9.984e-02, eta: 4 days, 14:59:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1867, top5_acc: 0.4089, loss_cls: 4.5799, loss: 4.5799 +2024-07-16 09:51:57,906 - pyskl - INFO - Epoch [4][3200/3746] lr: 9.984e-02, eta: 4 days, 14:56:44, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1684, top5_acc: 0.3914, loss_cls: 4.6369, loss: 4.6369 +2024-07-16 09:53:07,770 - pyskl - INFO - Epoch [4][3300/3746] lr: 9.983e-02, eta: 4 days, 14:53:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1717, top5_acc: 0.3873, loss_cls: 4.6286, loss: 4.6286 +2024-07-16 09:54:17,616 - pyskl - INFO - Epoch [4][3400/3746] lr: 9.983e-02, eta: 4 days, 14:50:26, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1820, top5_acc: 0.3994, loss_cls: 4.5833, loss: 4.5833 +2024-07-16 09:55:27,724 - pyskl - INFO - Epoch [4][3500/3746] lr: 9.983e-02, eta: 4 days, 14:47:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1789, top5_acc: 0.3931, loss_cls: 4.6207, loss: 4.6207 +2024-07-16 09:56:37,803 - pyskl - INFO - Epoch [4][3600/3746] lr: 9.983e-02, eta: 4 days, 14:44:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1737, top5_acc: 0.3905, loss_cls: 4.6140, loss: 4.6140 +2024-07-16 09:57:48,860 - pyskl - INFO - Epoch [4][3700/3746] lr: 9.983e-02, eta: 4 days, 14:42:13, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1778, top5_acc: 0.4022, loss_cls: 4.5748, loss: 4.5748 +2024-07-16 09:58:23,996 - pyskl - INFO - Saving checkpoint at 4 epochs +2024-07-16 10:00:15,292 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 10:00:15,983 - pyskl - INFO - +top1_acc 0.1104 +top5_acc 0.2859 +2024-07-16 10:00:15,983 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 10:00:16,023 - pyskl - INFO - +mean_acc 0.1104 +2024-07-16 10:00:16,028 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_3.pth was removed +2024-07-16 10:00:16,443 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2024-07-16 10:00:16,444 - pyskl - INFO - Best top1_acc is 0.1104 at 4 epoch. +2024-07-16 10:00:16,456 - pyskl - INFO - Epoch(val) [4][309] top1_acc: 0.1104, top5_acc: 0.2859, mean_class_accuracy: 0.1104 +2024-07-16 10:03:38,848 - pyskl - INFO - Epoch [5][100/3746] lr: 9.982e-02, eta: 4 days, 15:38:27, time: 2.024, data_time: 1.321, memory: 15990, top1_acc: 0.1830, top5_acc: 0.4141, loss_cls: 4.5109, loss: 4.5109 +2024-07-16 10:04:49,085 - pyskl - INFO - Epoch [5][200/3746] lr: 9.982e-02, eta: 4 days, 15:35:16, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1831, top5_acc: 0.4022, loss_cls: 4.5615, loss: 4.5615 +2024-07-16 10:05:59,248 - pyskl - INFO - Epoch [5][300/3746] lr: 9.982e-02, eta: 4 days, 15:32:04, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1787, top5_acc: 0.3998, loss_cls: 4.5681, loss: 4.5681 +2024-07-16 10:07:09,177 - pyskl - INFO - Epoch [5][400/3746] lr: 9.982e-02, eta: 4 days, 15:28:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1752, top5_acc: 0.3977, loss_cls: 4.5883, loss: 4.5883 +2024-07-16 10:08:19,294 - pyskl - INFO - Epoch [5][500/3746] lr: 9.981e-02, eta: 4 days, 15:25:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1752, top5_acc: 0.3995, loss_cls: 4.5977, loss: 4.5977 +2024-07-16 10:09:28,755 - pyskl - INFO - Epoch [5][600/3746] lr: 9.981e-02, eta: 4 days, 15:22:03, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.1731, top5_acc: 0.4014, loss_cls: 4.5782, loss: 4.5782 +2024-07-16 10:10:38,527 - pyskl - INFO - Epoch [5][700/3746] lr: 9.981e-02, eta: 4 days, 15:18:43, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1795, top5_acc: 0.3984, loss_cls: 4.5876, loss: 4.5876 +2024-07-16 10:11:48,426 - pyskl - INFO - Epoch [5][800/3746] lr: 9.981e-02, eta: 4 days, 15:15:30, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1842, top5_acc: 0.4123, loss_cls: 4.5270, loss: 4.5270 +2024-07-16 10:12:58,459 - pyskl - INFO - Epoch [5][900/3746] lr: 9.980e-02, eta: 4 days, 15:12:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1745, top5_acc: 0.3989, loss_cls: 4.5812, loss: 4.5812 +2024-07-16 10:14:08,382 - pyskl - INFO - Epoch [5][1000/3746] lr: 9.980e-02, eta: 4 days, 15:09:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1820, top5_acc: 0.4098, loss_cls: 4.5741, loss: 4.5741 +2024-07-16 10:15:18,197 - pyskl - INFO - Epoch [5][1100/3746] lr: 9.980e-02, eta: 4 days, 15:06:02, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1808, top5_acc: 0.4142, loss_cls: 4.5708, loss: 4.5708 +2024-07-16 10:16:28,043 - pyskl - INFO - Epoch [5][1200/3746] lr: 9.980e-02, eta: 4 days, 15:02:53, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1837, top5_acc: 0.4062, loss_cls: 4.5686, loss: 4.5686 +2024-07-16 10:17:37,709 - pyskl - INFO - Epoch [5][1300/3746] lr: 9.979e-02, eta: 4 days, 14:59:39, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1919, top5_acc: 0.4133, loss_cls: 4.5393, loss: 4.5393 +2024-07-16 10:18:47,702 - pyskl - INFO - Epoch [5][1400/3746] lr: 9.979e-02, eta: 4 days, 14:56:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1872, top5_acc: 0.4169, loss_cls: 4.4979, loss: 4.4979 +2024-07-16 10:19:57,757 - pyskl - INFO - Epoch [5][1500/3746] lr: 9.979e-02, eta: 4 days, 14:53:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1812, top5_acc: 0.4066, loss_cls: 4.5580, loss: 4.5580 +2024-07-16 10:21:07,653 - pyskl - INFO - Epoch [5][1600/3746] lr: 9.979e-02, eta: 4 days, 14:50:39, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1783, top5_acc: 0.4097, loss_cls: 4.5569, loss: 4.5569 +2024-07-16 10:22:17,620 - pyskl - INFO - Epoch [5][1700/3746] lr: 9.978e-02, eta: 4 days, 14:47:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1800, top5_acc: 0.4164, loss_cls: 4.5395, loss: 4.5395 +2024-07-16 10:23:27,217 - pyskl - INFO - Epoch [5][1800/3746] lr: 9.978e-02, eta: 4 days, 14:44:32, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1791, top5_acc: 0.4217, loss_cls: 4.5464, loss: 4.5464 +2024-07-16 10:24:37,549 - pyskl - INFO - Epoch [5][1900/3746] lr: 9.978e-02, eta: 4 days, 14:41:48, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1711, top5_acc: 0.3973, loss_cls: 4.5790, loss: 4.5790 +2024-07-16 10:25:47,822 - pyskl - INFO - Epoch [5][2000/3746] lr: 9.977e-02, eta: 4 days, 14:39:04, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1891, top5_acc: 0.4086, loss_cls: 4.5713, loss: 4.5713 +2024-07-16 10:26:57,672 - pyskl - INFO - Epoch [5][2100/3746] lr: 9.977e-02, eta: 4 days, 14:36:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1764, top5_acc: 0.3966, loss_cls: 4.5821, loss: 4.5821 +2024-07-16 10:28:07,865 - pyskl - INFO - Epoch [5][2200/3746] lr: 9.977e-02, eta: 4 days, 14:33:22, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1880, top5_acc: 0.4150, loss_cls: 4.5325, loss: 4.5325 +2024-07-16 10:29:18,183 - pyskl - INFO - Epoch [5][2300/3746] lr: 9.977e-02, eta: 4 days, 14:30:43, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1870, top5_acc: 0.4191, loss_cls: 4.4917, loss: 4.4917 +2024-07-16 10:30:28,175 - pyskl - INFO - Epoch [5][2400/3746] lr: 9.976e-02, eta: 4 days, 14:27:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1842, top5_acc: 0.4145, loss_cls: 4.5468, loss: 4.5468 +2024-07-16 10:31:38,001 - pyskl - INFO - Epoch [5][2500/3746] lr: 9.976e-02, eta: 4 days, 14:25:01, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1814, top5_acc: 0.4113, loss_cls: 4.5298, loss: 4.5298 +2024-07-16 10:32:47,796 - pyskl - INFO - Epoch [5][2600/3746] lr: 9.976e-02, eta: 4 days, 14:22:08, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1922, top5_acc: 0.4239, loss_cls: 4.4807, loss: 4.4807 +2024-07-16 10:33:57,607 - pyskl - INFO - Epoch [5][2700/3746] lr: 9.976e-02, eta: 4 days, 14:19:17, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1862, top5_acc: 0.4070, loss_cls: 4.5587, loss: 4.5587 +2024-07-16 10:35:07,510 - pyskl - INFO - Epoch [5][2800/3746] lr: 9.975e-02, eta: 4 days, 14:16:30, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1878, top5_acc: 0.4138, loss_cls: 4.5411, loss: 4.5411 +2024-07-16 10:36:17,536 - pyskl - INFO - Epoch [5][2900/3746] lr: 9.975e-02, eta: 4 days, 14:13:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1905, top5_acc: 0.4088, loss_cls: 4.5267, loss: 4.5267 +2024-07-16 10:37:27,749 - pyskl - INFO - Epoch [5][3000/3746] lr: 9.975e-02, eta: 4 days, 14:11:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1767, top5_acc: 0.4113, loss_cls: 4.5327, loss: 4.5327 +2024-07-16 10:38:37,460 - pyskl - INFO - Epoch [5][3100/3746] lr: 9.974e-02, eta: 4 days, 14:08:22, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1819, top5_acc: 0.4106, loss_cls: 4.5438, loss: 4.5438 +2024-07-16 10:39:47,294 - pyskl - INFO - Epoch [5][3200/3746] lr: 9.974e-02, eta: 4 days, 14:05:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1819, top5_acc: 0.4072, loss_cls: 4.5435, loss: 4.5435 +2024-07-16 10:40:57,101 - pyskl - INFO - Epoch [5][3300/3746] lr: 9.974e-02, eta: 4 days, 14:02:53, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1900, top5_acc: 0.4080, loss_cls: 4.5374, loss: 4.5374 +2024-07-16 10:42:07,205 - pyskl - INFO - Epoch [5][3400/3746] lr: 9.974e-02, eta: 4 days, 14:00:18, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1759, top5_acc: 0.4014, loss_cls: 4.5851, loss: 4.5851 +2024-07-16 10:43:17,375 - pyskl - INFO - Epoch [5][3500/3746] lr: 9.973e-02, eta: 4 days, 13:57:46, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1925, top5_acc: 0.4263, loss_cls: 4.5012, loss: 4.5012 +2024-07-16 10:44:27,066 - pyskl - INFO - Epoch [5][3600/3746] lr: 9.973e-02, eta: 4 days, 13:55:01, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1797, top5_acc: 0.4108, loss_cls: 4.5566, loss: 4.5566 +2024-07-16 10:45:37,558 - pyskl - INFO - Epoch [5][3700/3746] lr: 9.973e-02, eta: 4 days, 13:52:40, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1873, top5_acc: 0.4142, loss_cls: 4.5368, loss: 4.5368 +2024-07-16 10:46:12,415 - pyskl - INFO - Saving checkpoint at 5 epochs +2024-07-16 10:48:01,495 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 10:48:02,149 - pyskl - INFO - +top1_acc 0.1148 +top5_acc 0.2953 +2024-07-16 10:48:02,149 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 10:48:02,187 - pyskl - INFO - +mean_acc 0.1146 +2024-07-16 10:48:02,192 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_4.pth was removed +2024-07-16 10:48:02,409 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2024-07-16 10:48:02,410 - pyskl - INFO - Best top1_acc is 0.1148 at 5 epoch. +2024-07-16 10:48:02,419 - pyskl - INFO - Epoch(val) [5][309] top1_acc: 0.1148, top5_acc: 0.2953, mean_class_accuracy: 0.1146 +2024-07-16 10:51:19,924 - pyskl - INFO - Epoch [6][100/3746] lr: 9.972e-02, eta: 4 days, 14:34:43, time: 1.975, data_time: 1.272, memory: 15990, top1_acc: 0.1963, top5_acc: 0.4322, loss_cls: 4.4449, loss: 4.4449 +2024-07-16 10:52:29,954 - pyskl - INFO - Epoch [6][200/3746] lr: 9.972e-02, eta: 4 days, 14:31:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4227, loss_cls: 4.4836, loss: 4.4836 +2024-07-16 10:53:39,611 - pyskl - INFO - Epoch [6][300/3746] lr: 9.972e-02, eta: 4 days, 14:28:59, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1878, top5_acc: 0.4172, loss_cls: 4.4969, loss: 4.4969 +2024-07-16 10:54:49,885 - pyskl - INFO - Epoch [6][400/3746] lr: 9.971e-02, eta: 4 days, 14:26:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1895, top5_acc: 0.4205, loss_cls: 4.4905, loss: 4.4905 +2024-07-16 10:55:59,922 - pyskl - INFO - Epoch [6][500/3746] lr: 9.971e-02, eta: 4 days, 14:23:37, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1967, top5_acc: 0.4258, loss_cls: 4.4955, loss: 4.4955 +2024-07-16 10:57:09,709 - pyskl - INFO - Epoch [6][600/3746] lr: 9.971e-02, eta: 4 days, 14:20:47, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1920, top5_acc: 0.4266, loss_cls: 4.4673, loss: 4.4673 +2024-07-16 10:58:19,603 - pyskl - INFO - Epoch [6][700/3746] lr: 9.971e-02, eta: 4 days, 14:18:01, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1916, top5_acc: 0.4269, loss_cls: 4.4961, loss: 4.4961 +2024-07-16 10:59:29,641 - pyskl - INFO - Epoch [6][800/3746] lr: 9.970e-02, eta: 4 days, 14:15:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1903, top5_acc: 0.4178, loss_cls: 4.4950, loss: 4.4950 +2024-07-16 11:00:39,384 - pyskl - INFO - Epoch [6][900/3746] lr: 9.970e-02, eta: 4 days, 14:12:32, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1927, top5_acc: 0.4167, loss_cls: 4.4989, loss: 4.4989 +2024-07-16 11:01:49,471 - pyskl - INFO - Epoch [6][1000/3746] lr: 9.970e-02, eta: 4 days, 14:09:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1967, top5_acc: 0.4178, loss_cls: 4.4880, loss: 4.4880 +2024-07-16 11:02:59,185 - pyskl - INFO - Epoch [6][1100/3746] lr: 9.969e-02, eta: 4 days, 14:07:07, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1883, top5_acc: 0.4181, loss_cls: 4.4972, loss: 4.4972 +2024-07-16 11:04:08,954 - pyskl - INFO - Epoch [6][1200/3746] lr: 9.969e-02, eta: 4 days, 14:04:22, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1864, top5_acc: 0.4197, loss_cls: 4.5151, loss: 4.5151 +2024-07-16 11:05:18,630 - pyskl - INFO - Epoch [6][1300/3746] lr: 9.969e-02, eta: 4 days, 14:01:36, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1923, top5_acc: 0.4220, loss_cls: 4.4864, loss: 4.4864 +2024-07-16 11:06:28,306 - pyskl - INFO - Epoch [6][1400/3746] lr: 9.968e-02, eta: 4 days, 13:58:51, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1902, top5_acc: 0.4259, loss_cls: 4.4684, loss: 4.4684 +2024-07-16 11:07:37,831 - pyskl - INFO - Epoch [6][1500/3746] lr: 9.968e-02, eta: 4 days, 13:56:03, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.1859, top5_acc: 0.4141, loss_cls: 4.5360, loss: 4.5360 +2024-07-16 11:08:47,814 - pyskl - INFO - Epoch [6][1600/3746] lr: 9.968e-02, eta: 4 days, 13:53:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1875, top5_acc: 0.4173, loss_cls: 4.5368, loss: 4.5368 +2024-07-16 11:09:57,672 - pyskl - INFO - Epoch [6][1700/3746] lr: 9.967e-02, eta: 4 days, 13:50:50, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1841, top5_acc: 0.4089, loss_cls: 4.5455, loss: 4.5455 +2024-07-16 11:11:07,416 - pyskl - INFO - Epoch [6][1800/3746] lr: 9.967e-02, eta: 4 days, 13:48:10, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1836, top5_acc: 0.4106, loss_cls: 4.5514, loss: 4.5514 +2024-07-16 11:12:17,924 - pyskl - INFO - Epoch [6][1900/3746] lr: 9.967e-02, eta: 4 days, 13:45:51, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1847, top5_acc: 0.4117, loss_cls: 4.5134, loss: 4.5134 +2024-07-16 11:13:27,578 - pyskl - INFO - Epoch [6][2000/3746] lr: 9.966e-02, eta: 4 days, 13:43:11, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1791, top5_acc: 0.4019, loss_cls: 4.5518, loss: 4.5518 +2024-07-16 11:14:37,738 - pyskl - INFO - Epoch [6][2100/3746] lr: 9.966e-02, eta: 4 days, 13:40:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1908, top5_acc: 0.4169, loss_cls: 4.4967, loss: 4.4967 +2024-07-16 11:15:48,211 - pyskl - INFO - Epoch [6][2200/3746] lr: 9.966e-02, eta: 4 days, 13:38:27, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1903, top5_acc: 0.4302, loss_cls: 4.4877, loss: 4.4877 +2024-07-16 11:16:58,079 - pyskl - INFO - Epoch [6][2300/3746] lr: 9.965e-02, eta: 4 days, 13:35:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1878, top5_acc: 0.4116, loss_cls: 4.5229, loss: 4.5229 +2024-07-16 11:18:07,977 - pyskl - INFO - Epoch [6][2400/3746] lr: 9.965e-02, eta: 4 days, 13:33:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1825, top5_acc: 0.4075, loss_cls: 4.5614, loss: 4.5614 +2024-07-16 11:19:17,744 - pyskl - INFO - Epoch [6][2500/3746] lr: 9.965e-02, eta: 4 days, 13:30:50, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1892, top5_acc: 0.4088, loss_cls: 4.5252, loss: 4.5252 +2024-07-16 11:20:27,775 - pyskl - INFO - Epoch [6][2600/3746] lr: 9.964e-02, eta: 4 days, 13:28:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1875, top5_acc: 0.4186, loss_cls: 4.4897, loss: 4.4897 +2024-07-16 11:21:37,416 - pyskl - INFO - Epoch [6][2700/3746] lr: 9.964e-02, eta: 4 days, 13:25:48, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1955, top5_acc: 0.4220, loss_cls: 4.5010, loss: 4.5010 +2024-07-16 11:22:47,253 - pyskl - INFO - Epoch [6][2800/3746] lr: 9.964e-02, eta: 4 days, 13:23:19, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1883, top5_acc: 0.4125, loss_cls: 4.5193, loss: 4.5193 +2024-07-16 11:23:57,140 - pyskl - INFO - Epoch [6][2900/3746] lr: 9.963e-02, eta: 4 days, 13:20:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1944, top5_acc: 0.4227, loss_cls: 4.4964, loss: 4.4964 +2024-07-16 11:25:06,989 - pyskl - INFO - Epoch [6][3000/3746] lr: 9.963e-02, eta: 4 days, 13:18:23, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1803, top5_acc: 0.4097, loss_cls: 4.5297, loss: 4.5297 +2024-07-16 11:26:17,201 - pyskl - INFO - Epoch [6][3100/3746] lr: 9.963e-02, eta: 4 days, 13:16:05, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1891, top5_acc: 0.4111, loss_cls: 4.5261, loss: 4.5261 +2024-07-16 11:27:27,277 - pyskl - INFO - Epoch [6][3200/3746] lr: 9.962e-02, eta: 4 days, 13:13:44, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1928, top5_acc: 0.4245, loss_cls: 4.4821, loss: 4.4821 +2024-07-16 11:28:37,309 - pyskl - INFO - Epoch [6][3300/3746] lr: 9.962e-02, eta: 4 days, 13:11:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1995, top5_acc: 0.4331, loss_cls: 4.4428, loss: 4.4428 +2024-07-16 11:29:47,500 - pyskl - INFO - Epoch [6][3400/3746] lr: 9.962e-02, eta: 4 days, 13:09:06, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1906, top5_acc: 0.4134, loss_cls: 4.5193, loss: 4.5193 +2024-07-16 11:30:57,405 - pyskl - INFO - Epoch [6][3500/3746] lr: 9.961e-02, eta: 4 days, 13:06:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1917, top5_acc: 0.4227, loss_cls: 4.4947, loss: 4.4947 +2024-07-16 11:32:07,250 - pyskl - INFO - Epoch [6][3600/3746] lr: 9.961e-02, eta: 4 days, 13:04:19, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1911, top5_acc: 0.4186, loss_cls: 4.5299, loss: 4.5299 +2024-07-16 11:33:17,690 - pyskl - INFO - Epoch [6][3700/3746] lr: 9.961e-02, eta: 4 days, 13:02:11, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1939, top5_acc: 0.4213, loss_cls: 4.5191, loss: 4.5191 +2024-07-16 11:33:52,625 - pyskl - INFO - Saving checkpoint at 6 epochs +2024-07-16 11:35:41,316 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 11:35:41,969 - pyskl - INFO - +top1_acc 0.1340 +top5_acc 0.3300 +2024-07-16 11:35:41,969 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 11:35:42,008 - pyskl - INFO - +mean_acc 0.1338 +2024-07-16 11:35:42,012 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_5.pth was removed +2024-07-16 11:35:42,223 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_6.pth. +2024-07-16 11:35:42,224 - pyskl - INFO - Best top1_acc is 0.1340 at 6 epoch. +2024-07-16 11:35:42,233 - pyskl - INFO - Epoch(val) [6][309] top1_acc: 0.1340, top5_acc: 0.3300, mean_class_accuracy: 0.1338 +2024-07-16 11:38:59,386 - pyskl - INFO - Epoch [7][100/3746] lr: 9.960e-02, eta: 4 days, 13:36:36, time: 1.971, data_time: 1.266, memory: 15990, top1_acc: 0.2036, top5_acc: 0.4327, loss_cls: 4.4291, loss: 4.4291 +2024-07-16 11:40:09,715 - pyskl - INFO - Epoch [7][200/3746] lr: 9.960e-02, eta: 4 days, 13:34:16, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1886, top5_acc: 0.4192, loss_cls: 4.5105, loss: 4.5105 +2024-07-16 11:41:19,366 - pyskl - INFO - Epoch [7][300/3746] lr: 9.960e-02, eta: 4 days, 13:31:39, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1966, top5_acc: 0.4253, loss_cls: 4.4844, loss: 4.4844 +2024-07-16 11:42:29,105 - pyskl - INFO - Epoch [7][400/3746] lr: 9.959e-02, eta: 4 days, 13:29:06, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4145, loss_cls: 4.4977, loss: 4.4977 +2024-07-16 11:43:38,875 - pyskl - INFO - Epoch [7][500/3746] lr: 9.959e-02, eta: 4 days, 13:26:34, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1958, top5_acc: 0.4156, loss_cls: 4.4596, loss: 4.4596 +2024-07-16 11:44:48,596 - pyskl - INFO - Epoch [7][600/3746] lr: 9.958e-02, eta: 4 days, 13:24:02, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1950, top5_acc: 0.4205, loss_cls: 4.4752, loss: 4.4752 +2024-07-16 11:45:58,500 - pyskl - INFO - Epoch [7][700/3746] lr: 9.958e-02, eta: 4 days, 13:21:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1920, top5_acc: 0.4291, loss_cls: 4.4933, loss: 4.4933 +2024-07-16 11:47:08,449 - pyskl - INFO - Epoch [7][800/3746] lr: 9.958e-02, eta: 4 days, 13:19:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1953, top5_acc: 0.4303, loss_cls: 4.4746, loss: 4.4746 +2024-07-16 11:48:18,517 - pyskl - INFO - Epoch [7][900/3746] lr: 9.957e-02, eta: 4 days, 13:16:47, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1963, top5_acc: 0.4250, loss_cls: 4.4682, loss: 4.4682 +2024-07-16 11:49:28,312 - pyskl - INFO - Epoch [7][1000/3746] lr: 9.957e-02, eta: 4 days, 13:14:20, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1945, top5_acc: 0.4148, loss_cls: 4.4900, loss: 4.4900 +2024-07-16 11:50:37,980 - pyskl - INFO - Epoch [7][1100/3746] lr: 9.957e-02, eta: 4 days, 13:11:50, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1886, top5_acc: 0.4191, loss_cls: 4.4927, loss: 4.4927 +2024-07-16 11:51:47,524 - pyskl - INFO - Epoch [7][1200/3746] lr: 9.956e-02, eta: 4 days, 13:09:17, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4377, loss_cls: 4.4197, loss: 4.4197 +2024-07-16 11:52:57,169 - pyskl - INFO - Epoch [7][1300/3746] lr: 9.956e-02, eta: 4 days, 13:06:48, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1923, top5_acc: 0.4155, loss_cls: 4.5169, loss: 4.5169 +2024-07-16 11:54:07,189 - pyskl - INFO - Epoch [7][1400/3746] lr: 9.956e-02, eta: 4 days, 13:04:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1947, top5_acc: 0.4289, loss_cls: 4.4565, loss: 4.4565 +2024-07-16 11:55:16,778 - pyskl - INFO - Epoch [7][1500/3746] lr: 9.955e-02, eta: 4 days, 13:01:59, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4328, loss_cls: 4.4453, loss: 4.4453 +2024-07-16 11:56:26,908 - pyskl - INFO - Epoch [7][1600/3746] lr: 9.955e-02, eta: 4 days, 12:59:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1878, top5_acc: 0.4205, loss_cls: 4.4914, loss: 4.4914 +2024-07-16 11:57:36,571 - pyskl - INFO - Epoch [7][1700/3746] lr: 9.954e-02, eta: 4 days, 12:57:16, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1873, top5_acc: 0.4208, loss_cls: 4.4825, loss: 4.4825 +2024-07-16 11:58:46,241 - pyskl - INFO - Epoch [7][1800/3746] lr: 9.954e-02, eta: 4 days, 12:54:51, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1978, top5_acc: 0.4269, loss_cls: 4.4800, loss: 4.4800 +2024-07-16 11:59:56,450 - pyskl - INFO - Epoch [7][1900/3746] lr: 9.954e-02, eta: 4 days, 12:52:38, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1973, top5_acc: 0.4233, loss_cls: 4.4654, loss: 4.4654 +2024-07-16 12:01:06,545 - pyskl - INFO - Epoch [7][2000/3746] lr: 9.953e-02, eta: 4 days, 12:50:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1958, top5_acc: 0.4216, loss_cls: 4.4642, loss: 4.4642 +2024-07-16 12:02:16,402 - pyskl - INFO - Epoch [7][2100/3746] lr: 9.953e-02, eta: 4 days, 12:48:03, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1953, top5_acc: 0.4298, loss_cls: 4.4805, loss: 4.4805 +2024-07-16 12:03:26,287 - pyskl - INFO - Epoch [7][2200/3746] lr: 9.952e-02, eta: 4 days, 12:45:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1916, top5_acc: 0.4177, loss_cls: 4.5074, loss: 4.5074 +2024-07-16 12:04:36,071 - pyskl - INFO - Epoch [7][2300/3746] lr: 9.952e-02, eta: 4 days, 12:43:25, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1920, top5_acc: 0.4278, loss_cls: 4.4601, loss: 4.4601 +2024-07-16 12:05:45,698 - pyskl - INFO - Epoch [7][2400/3746] lr: 9.952e-02, eta: 4 days, 12:41:02, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1945, top5_acc: 0.4206, loss_cls: 4.4908, loss: 4.4908 +2024-07-16 12:06:55,476 - pyskl - INFO - Epoch [7][2500/3746] lr: 9.951e-02, eta: 4 days, 12:38:43, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1952, top5_acc: 0.4238, loss_cls: 4.4979, loss: 4.4979 +2024-07-16 12:08:05,149 - pyskl - INFO - Epoch [7][2600/3746] lr: 9.951e-02, eta: 4 days, 12:36:22, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2028, top5_acc: 0.4320, loss_cls: 4.4230, loss: 4.4230 +2024-07-16 12:09:14,777 - pyskl - INFO - Epoch [7][2700/3746] lr: 9.951e-02, eta: 4 days, 12:34:01, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1963, top5_acc: 0.4303, loss_cls: 4.4758, loss: 4.4758 +2024-07-16 12:10:24,685 - pyskl - INFO - Epoch [7][2800/3746] lr: 9.950e-02, eta: 4 days, 12:31:46, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1923, top5_acc: 0.4267, loss_cls: 4.4679, loss: 4.4679 +2024-07-16 12:11:34,493 - pyskl - INFO - Epoch [7][2900/3746] lr: 9.950e-02, eta: 4 days, 12:29:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1923, top5_acc: 0.4214, loss_cls: 4.4632, loss: 4.4632 +2024-07-16 12:12:44,360 - pyskl - INFO - Epoch [7][3000/3746] lr: 9.949e-02, eta: 4 days, 12:27:15, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4300, loss_cls: 4.4810, loss: 4.4810 +2024-07-16 12:13:54,437 - pyskl - INFO - Epoch [7][3100/3746] lr: 9.949e-02, eta: 4 days, 12:25:06, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1941, top5_acc: 0.4227, loss_cls: 4.4944, loss: 4.4944 +2024-07-16 12:15:04,306 - pyskl - INFO - Epoch [7][3200/3746] lr: 9.949e-02, eta: 4 days, 12:22:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4295, loss_cls: 4.4582, loss: 4.4582 +2024-07-16 12:16:13,870 - pyskl - INFO - Epoch [7][3300/3746] lr: 9.948e-02, eta: 4 days, 12:20:33, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1917, top5_acc: 0.4192, loss_cls: 4.5080, loss: 4.5080 +2024-07-16 12:17:23,418 - pyskl - INFO - Epoch [7][3400/3746] lr: 9.948e-02, eta: 4 days, 12:18:14, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.1956, top5_acc: 0.4261, loss_cls: 4.4846, loss: 4.4846 +2024-07-16 12:18:33,364 - pyskl - INFO - Epoch [7][3500/3746] lr: 9.947e-02, eta: 4 days, 12:16:04, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1972, top5_acc: 0.4281, loss_cls: 4.4515, loss: 4.4515 +2024-07-16 12:19:43,184 - pyskl - INFO - Epoch [7][3600/3746] lr: 9.947e-02, eta: 4 days, 12:13:51, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4398, loss_cls: 4.4249, loss: 4.4249 +2024-07-16 12:20:53,345 - pyskl - INFO - Epoch [7][3700/3746] lr: 9.947e-02, eta: 4 days, 12:11:46, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4278, loss_cls: 4.4609, loss: 4.4609 +2024-07-16 12:21:27,997 - pyskl - INFO - Saving checkpoint at 7 epochs +2024-07-16 12:23:17,548 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 12:23:18,247 - pyskl - INFO - +top1_acc 0.1398 +top5_acc 0.3357 +2024-07-16 12:23:18,247 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 12:23:18,297 - pyskl - INFO - +mean_acc 0.1396 +2024-07-16 12:23:18,302 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_6.pth was removed +2024-07-16 12:23:18,594 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2024-07-16 12:23:18,595 - pyskl - INFO - Best top1_acc is 0.1398 at 7 epoch. +2024-07-16 12:23:18,611 - pyskl - INFO - Epoch(val) [7][309] top1_acc: 0.1398, top5_acc: 0.3357, mean_class_accuracy: 0.1396 +2024-07-16 12:26:44,567 - pyskl - INFO - Epoch [8][100/3746] lr: 9.946e-02, eta: 4 days, 12:43:50, time: 2.059, data_time: 1.340, memory: 15990, top1_acc: 0.2009, top5_acc: 0.4353, loss_cls: 4.4273, loss: 4.4273 +2024-07-16 12:27:56,020 - pyskl - INFO - Epoch [8][200/3746] lr: 9.946e-02, eta: 4 days, 12:42:04, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4342, loss_cls: 4.4234, loss: 4.4234 +2024-07-16 12:29:06,986 - pyskl - INFO - Epoch [8][300/3746] lr: 9.945e-02, eta: 4 days, 12:40:08, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1978, top5_acc: 0.4273, loss_cls: 4.4496, loss: 4.4496 +2024-07-16 12:30:17,810 - pyskl - INFO - Epoch [8][400/3746] lr: 9.945e-02, eta: 4 days, 12:38:10, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1897, top5_acc: 0.4180, loss_cls: 4.4758, loss: 4.4758 +2024-07-16 12:31:28,071 - pyskl - INFO - Epoch [8][500/3746] lr: 9.944e-02, eta: 4 days, 12:36:01, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1959, top5_acc: 0.4294, loss_cls: 4.4660, loss: 4.4660 +2024-07-16 12:32:38,041 - pyskl - INFO - Epoch [8][600/3746] lr: 9.944e-02, eta: 4 days, 12:33:46, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1906, top5_acc: 0.4184, loss_cls: 4.5031, loss: 4.5031 +2024-07-16 12:33:48,256 - pyskl - INFO - Epoch [8][700/3746] lr: 9.943e-02, eta: 4 days, 12:31:37, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2027, top5_acc: 0.4281, loss_cls: 4.4416, loss: 4.4416 +2024-07-16 12:34:58,516 - pyskl - INFO - Epoch [8][800/3746] lr: 9.943e-02, eta: 4 days, 12:29:29, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1917, top5_acc: 0.4256, loss_cls: 4.4942, loss: 4.4942 +2024-07-16 12:36:08,866 - pyskl - INFO - Epoch [8][900/3746] lr: 9.943e-02, eta: 4 days, 12:27:24, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4300, loss_cls: 4.4461, loss: 4.4461 +2024-07-16 12:37:19,097 - pyskl - INFO - Epoch [8][1000/3746] lr: 9.942e-02, eta: 4 days, 12:25:16, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1989, top5_acc: 0.4222, loss_cls: 4.4471, loss: 4.4471 +2024-07-16 12:38:29,232 - pyskl - INFO - Epoch [8][1100/3746] lr: 9.942e-02, eta: 4 days, 12:23:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1963, top5_acc: 0.4247, loss_cls: 4.4714, loss: 4.4714 +2024-07-16 12:39:39,027 - pyskl - INFO - Epoch [8][1200/3746] lr: 9.941e-02, eta: 4 days, 12:20:52, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1964, top5_acc: 0.4313, loss_cls: 4.4302, loss: 4.4302 +2024-07-16 12:40:49,164 - pyskl - INFO - Epoch [8][1300/3746] lr: 9.941e-02, eta: 4 days, 12:18:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1877, top5_acc: 0.4245, loss_cls: 4.4643, loss: 4.4643 +2024-07-16 12:41:58,973 - pyskl - INFO - Epoch [8][1400/3746] lr: 9.940e-02, eta: 4 days, 12:16:29, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2092, top5_acc: 0.4316, loss_cls: 4.4032, loss: 4.4032 +2024-07-16 12:43:09,033 - pyskl - INFO - Epoch [8][1500/3746] lr: 9.940e-02, eta: 4 days, 12:14:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4319, loss_cls: 4.4157, loss: 4.4157 +2024-07-16 12:44:18,813 - pyskl - INFO - Epoch [8][1600/3746] lr: 9.940e-02, eta: 4 days, 12:12:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4384, loss_cls: 4.4031, loss: 4.4031 +2024-07-16 12:45:28,693 - pyskl - INFO - Epoch [8][1700/3746] lr: 9.939e-02, eta: 4 days, 12:09:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4319, loss_cls: 4.4571, loss: 4.4571 +2024-07-16 12:46:38,431 - pyskl - INFO - Epoch [8][1800/3746] lr: 9.939e-02, eta: 4 days, 12:07:42, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1956, top5_acc: 0.4364, loss_cls: 4.4324, loss: 4.4324 +2024-07-16 12:47:48,804 - pyskl - INFO - Epoch [8][1900/3746] lr: 9.938e-02, eta: 4 days, 12:05:41, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1977, top5_acc: 0.4359, loss_cls: 4.4492, loss: 4.4492 +2024-07-16 12:48:58,561 - pyskl - INFO - Epoch [8][2000/3746] lr: 9.938e-02, eta: 4 days, 12:03:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1916, top5_acc: 0.4270, loss_cls: 4.4592, loss: 4.4592 +2024-07-16 12:50:08,753 - pyskl - INFO - Epoch [8][2100/3746] lr: 9.937e-02, eta: 4 days, 12:01:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4269, loss_cls: 4.4580, loss: 4.4580 +2024-07-16 12:51:18,978 - pyskl - INFO - Epoch [8][2200/3746] lr: 9.937e-02, eta: 4 days, 11:59:22, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1978, top5_acc: 0.4266, loss_cls: 4.4575, loss: 4.4575 +2024-07-16 12:52:29,064 - pyskl - INFO - Epoch [8][2300/3746] lr: 9.937e-02, eta: 4 days, 11:57:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1947, top5_acc: 0.4298, loss_cls: 4.4820, loss: 4.4820 +2024-07-16 12:53:38,892 - pyskl - INFO - Epoch [8][2400/3746] lr: 9.936e-02, eta: 4 days, 11:55:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4358, loss_cls: 4.4508, loss: 4.4508 +2024-07-16 12:54:48,691 - pyskl - INFO - Epoch [8][2500/3746] lr: 9.936e-02, eta: 4 days, 11:52:58, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1917, top5_acc: 0.4181, loss_cls: 4.4976, loss: 4.4976 +2024-07-16 12:55:58,376 - pyskl - INFO - Epoch [8][2600/3746] lr: 9.935e-02, eta: 4 days, 11:50:46, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4363, loss_cls: 4.4487, loss: 4.4487 +2024-07-16 12:57:07,986 - pyskl - INFO - Epoch [8][2700/3746] lr: 9.935e-02, eta: 4 days, 11:48:34, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2027, top5_acc: 0.4284, loss_cls: 4.4360, loss: 4.4360 +2024-07-16 12:58:17,865 - pyskl - INFO - Epoch [8][2800/3746] lr: 9.934e-02, eta: 4 days, 11:46:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4402, loss_cls: 4.4468, loss: 4.4468 +2024-07-16 12:59:27,450 - pyskl - INFO - Epoch [8][2900/3746] lr: 9.934e-02, eta: 4 days, 11:44:16, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2033, top5_acc: 0.4298, loss_cls: 4.4701, loss: 4.4701 +2024-07-16 13:00:37,336 - pyskl - INFO - Epoch [8][3000/3746] lr: 9.933e-02, eta: 4 days, 11:42:10, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1967, top5_acc: 0.4203, loss_cls: 4.4476, loss: 4.4476 +2024-07-16 13:01:47,208 - pyskl - INFO - Epoch [8][3100/3746] lr: 9.933e-02, eta: 4 days, 11:40:04, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1947, top5_acc: 0.4327, loss_cls: 4.4206, loss: 4.4206 +2024-07-16 13:02:56,923 - pyskl - INFO - Epoch [8][3200/3746] lr: 9.933e-02, eta: 4 days, 11:37:56, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1984, top5_acc: 0.4239, loss_cls: 4.4788, loss: 4.4788 +2024-07-16 13:04:06,741 - pyskl - INFO - Epoch [8][3300/3746] lr: 9.932e-02, eta: 4 days, 11:35:50, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4322, loss_cls: 4.4348, loss: 4.4348 +2024-07-16 13:05:16,873 - pyskl - INFO - Epoch [8][3400/3746] lr: 9.932e-02, eta: 4 days, 11:33:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4191, loss_cls: 4.4606, loss: 4.4606 +2024-07-16 13:06:27,154 - pyskl - INFO - Epoch [8][3500/3746] lr: 9.931e-02, eta: 4 days, 11:31:53, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2027, top5_acc: 0.4375, loss_cls: 4.4124, loss: 4.4124 +2024-07-16 13:07:36,934 - pyskl - INFO - Epoch [8][3600/3746] lr: 9.931e-02, eta: 4 days, 11:29:47, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2047, top5_acc: 0.4336, loss_cls: 4.4334, loss: 4.4334 +2024-07-16 13:08:47,591 - pyskl - INFO - Epoch [8][3700/3746] lr: 9.930e-02, eta: 4 days, 11:27:58, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4338, loss_cls: 4.4284, loss: 4.4284 +2024-07-16 13:09:22,075 - pyskl - INFO - Saving checkpoint at 8 epochs +2024-07-16 13:11:13,375 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 13:11:14,087 - pyskl - INFO - +top1_acc 0.1292 +top5_acc 0.3123 +2024-07-16 13:11:14,088 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 13:11:14,138 - pyskl - INFO - +mean_acc 0.1292 +2024-07-16 13:11:14,155 - pyskl - INFO - Epoch(val) [8][309] top1_acc: 0.1292, top5_acc: 0.3123, mean_class_accuracy: 0.1292 +2024-07-16 13:14:43,495 - pyskl - INFO - Epoch [9][100/3746] lr: 9.930e-02, eta: 4 days, 11:56:36, time: 2.093, data_time: 1.376, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4317, loss_cls: 4.4270, loss: 4.4270 +2024-07-16 13:15:54,509 - pyskl - INFO - Epoch [9][200/3746] lr: 9.929e-02, eta: 4 days, 11:54:47, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4359, loss_cls: 4.4057, loss: 4.4057 +2024-07-16 13:17:05,672 - pyskl - INFO - Epoch [9][300/3746] lr: 9.929e-02, eta: 4 days, 11:53:00, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4352, loss_cls: 4.4567, loss: 4.4567 +2024-07-16 13:18:16,574 - pyskl - INFO - Epoch [9][400/3746] lr: 9.928e-02, eta: 4 days, 11:51:09, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4331, loss_cls: 4.3985, loss: 4.3985 +2024-07-16 13:19:27,498 - pyskl - INFO - Epoch [9][500/3746] lr: 9.928e-02, eta: 4 days, 11:49:19, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4419, loss_cls: 4.4218, loss: 4.4218 +2024-07-16 13:20:38,465 - pyskl - INFO - Epoch [9][600/3746] lr: 9.927e-02, eta: 4 days, 11:47:30, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4442, loss_cls: 4.3853, loss: 4.3853 +2024-07-16 13:21:49,502 - pyskl - INFO - Epoch [9][700/3746] lr: 9.927e-02, eta: 4 days, 11:45:43, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2059, top5_acc: 0.4430, loss_cls: 4.4044, loss: 4.4044 +2024-07-16 13:23:00,249 - pyskl - INFO - Epoch [9][800/3746] lr: 9.926e-02, eta: 4 days, 11:43:50, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4227, loss_cls: 4.4661, loss: 4.4661 +2024-07-16 13:24:10,871 - pyskl - INFO - Epoch [9][900/3746] lr: 9.926e-02, eta: 4 days, 11:41:56, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4267, loss_cls: 4.4305, loss: 4.4305 +2024-07-16 13:25:22,029 - pyskl - INFO - Epoch [9][1000/3746] lr: 9.925e-02, eta: 4 days, 11:40:11, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4327, loss_cls: 4.4451, loss: 4.4451 +2024-07-16 13:26:32,857 - pyskl - INFO - Epoch [9][1100/3746] lr: 9.925e-02, eta: 4 days, 11:38:21, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1953, top5_acc: 0.4248, loss_cls: 4.4537, loss: 4.4537 +2024-07-16 13:27:43,833 - pyskl - INFO - Epoch [9][1200/3746] lr: 9.924e-02, eta: 4 days, 11:36:33, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1947, top5_acc: 0.4252, loss_cls: 4.4946, loss: 4.4946 +2024-07-16 13:28:55,328 - pyskl - INFO - Epoch [9][1300/3746] lr: 9.924e-02, eta: 4 days, 11:34:55, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4372, loss_cls: 4.4264, loss: 4.4264 +2024-07-16 13:30:06,810 - pyskl - INFO - Epoch [9][1400/3746] lr: 9.923e-02, eta: 4 days, 11:33:16, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4252, loss_cls: 4.4492, loss: 4.4492 +2024-07-16 13:31:18,279 - pyskl - INFO - Epoch [9][1500/3746] lr: 9.923e-02, eta: 4 days, 11:31:38, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1931, top5_acc: 0.4263, loss_cls: 4.4473, loss: 4.4473 +2024-07-16 13:32:28,883 - pyskl - INFO - Epoch [9][1600/3746] lr: 9.922e-02, eta: 4 days, 11:29:45, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4395, loss_cls: 4.4192, loss: 4.4192 +2024-07-16 13:33:39,919 - pyskl - INFO - Epoch [9][1700/3746] lr: 9.922e-02, eta: 4 days, 11:28:00, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1938, top5_acc: 0.4309, loss_cls: 4.4523, loss: 4.4523 +2024-07-16 13:34:50,390 - pyskl - INFO - Epoch [9][1800/3746] lr: 9.921e-02, eta: 4 days, 11:26:05, time: 0.705, data_time: 0.001, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4313, loss_cls: 4.4731, loss: 4.4731 +2024-07-16 13:36:01,593 - pyskl - INFO - Epoch [9][1900/3746] lr: 9.921e-02, eta: 4 days, 11:24:23, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1963, top5_acc: 0.4277, loss_cls: 4.4607, loss: 4.4607 +2024-07-16 13:37:11,922 - pyskl - INFO - Epoch [9][2000/3746] lr: 9.920e-02, eta: 4 days, 11:22:27, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4439, loss_cls: 4.4261, loss: 4.4261 +2024-07-16 13:38:22,545 - pyskl - INFO - Epoch [9][2100/3746] lr: 9.920e-02, eta: 4 days, 11:20:35, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1970, top5_acc: 0.4314, loss_cls: 4.4465, loss: 4.4465 +2024-07-16 13:39:33,779 - pyskl - INFO - Epoch [9][2200/3746] lr: 9.919e-02, eta: 4 days, 11:18:54, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1973, top5_acc: 0.4367, loss_cls: 4.4196, loss: 4.4196 +2024-07-16 13:40:44,883 - pyskl - INFO - Epoch [9][2300/3746] lr: 9.919e-02, eta: 4 days, 11:17:11, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4252, loss_cls: 4.4527, loss: 4.4527 +2024-07-16 13:41:55,950 - pyskl - INFO - Epoch [9][2400/3746] lr: 9.918e-02, eta: 4 days, 11:15:28, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1964, top5_acc: 0.4339, loss_cls: 4.4348, loss: 4.4348 +2024-07-16 13:43:07,150 - pyskl - INFO - Epoch [9][2500/3746] lr: 9.918e-02, eta: 4 days, 11:13:47, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4334, loss_cls: 4.4542, loss: 4.4542 +2024-07-16 13:44:18,658 - pyskl - INFO - Epoch [9][2600/3746] lr: 9.917e-02, eta: 4 days, 11:12:11, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2000, top5_acc: 0.4242, loss_cls: 4.4461, loss: 4.4461 +2024-07-16 13:45:29,470 - pyskl - INFO - Epoch [9][2700/3746] lr: 9.917e-02, eta: 4 days, 11:10:24, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1992, top5_acc: 0.4309, loss_cls: 4.4369, loss: 4.4369 +2024-07-16 13:46:41,032 - pyskl - INFO - Epoch [9][2800/3746] lr: 9.916e-02, eta: 4 days, 11:08:50, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4366, loss_cls: 4.4134, loss: 4.4134 +2024-07-16 13:47:52,333 - pyskl - INFO - Epoch [9][2900/3746] lr: 9.916e-02, eta: 4 days, 11:07:11, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1953, top5_acc: 0.4239, loss_cls: 4.4682, loss: 4.4682 +2024-07-16 13:49:03,368 - pyskl - INFO - Epoch [9][3000/3746] lr: 9.915e-02, eta: 4 days, 11:05:28, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4325, loss_cls: 4.4504, loss: 4.4504 +2024-07-16 13:50:14,186 - pyskl - INFO - Epoch [9][3100/3746] lr: 9.915e-02, eta: 4 days, 11:03:42, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4359, loss_cls: 4.4215, loss: 4.4215 +2024-07-16 13:51:24,992 - pyskl - INFO - Epoch [9][3200/3746] lr: 9.914e-02, eta: 4 days, 11:01:56, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1967, top5_acc: 0.4355, loss_cls: 4.4267, loss: 4.4267 +2024-07-16 13:52:36,573 - pyskl - INFO - Epoch [9][3300/3746] lr: 9.914e-02, eta: 4 days, 11:00:23, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2047, top5_acc: 0.4336, loss_cls: 4.4308, loss: 4.4308 +2024-07-16 13:53:47,893 - pyskl - INFO - Epoch [9][3400/3746] lr: 9.913e-02, eta: 4 days, 10:58:46, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2036, top5_acc: 0.4444, loss_cls: 4.3910, loss: 4.3910 +2024-07-16 13:54:58,896 - pyskl - INFO - Epoch [9][3500/3746] lr: 9.913e-02, eta: 4 days, 10:57:03, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2059, top5_acc: 0.4416, loss_cls: 4.4202, loss: 4.4202 +2024-07-16 13:56:10,356 - pyskl - INFO - Epoch [9][3600/3746] lr: 9.912e-02, eta: 4 days, 10:55:28, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4269, loss_cls: 4.4247, loss: 4.4247 +2024-07-16 13:57:21,970 - pyskl - INFO - Epoch [9][3700/3746] lr: 9.912e-02, eta: 4 days, 10:53:56, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4397, loss_cls: 4.4074, loss: 4.4074 +2024-07-16 13:57:56,803 - pyskl - INFO - Saving checkpoint at 9 epochs +2024-07-16 13:59:49,072 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 13:59:49,787 - pyskl - INFO - +top1_acc 0.1191 +top5_acc 0.2994 +2024-07-16 13:59:49,787 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 13:59:49,826 - pyskl - INFO - +mean_acc 0.1189 +2024-07-16 13:59:49,838 - pyskl - INFO - Epoch(val) [9][309] top1_acc: 0.1191, top5_acc: 0.2994, mean_class_accuracy: 0.1189 +2024-07-16 14:03:05,392 - pyskl - INFO - Epoch [10][100/3746] lr: 9.911e-02, eta: 4 days, 11:15:22, time: 1.955, data_time: 1.245, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4455, loss_cls: 4.3638, loss: 4.3638 +2024-07-16 14:04:16,506 - pyskl - INFO - Epoch [10][200/3746] lr: 9.910e-02, eta: 4 days, 11:13:38, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2069, top5_acc: 0.4519, loss_cls: 4.3685, loss: 4.3685 +2024-07-16 14:05:27,297 - pyskl - INFO - Epoch [10][300/3746] lr: 9.910e-02, eta: 4 days, 11:11:49, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1983, top5_acc: 0.4406, loss_cls: 4.4137, loss: 4.4137 +2024-07-16 14:06:37,854 - pyskl - INFO - Epoch [10][400/3746] lr: 9.909e-02, eta: 4 days, 11:09:56, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4316, loss_cls: 4.4573, loss: 4.4573 +2024-07-16 14:07:47,824 - pyskl - INFO - Epoch [10][500/3746] lr: 9.909e-02, eta: 4 days, 11:07:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4341, loss_cls: 4.3920, loss: 4.3920 +2024-07-16 14:08:58,170 - pyskl - INFO - Epoch [10][600/3746] lr: 9.908e-02, eta: 4 days, 11:06:00, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1995, top5_acc: 0.4373, loss_cls: 4.4112, loss: 4.4112 +2024-07-16 14:10:08,017 - pyskl - INFO - Epoch [10][700/3746] lr: 9.908e-02, eta: 4 days, 11:03:57, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4298, loss_cls: 4.4439, loss: 4.4439 +2024-07-16 14:11:18,082 - pyskl - INFO - Epoch [10][800/3746] lr: 9.907e-02, eta: 4 days, 11:01:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1998, top5_acc: 0.4317, loss_cls: 4.4254, loss: 4.4254 +2024-07-16 14:12:28,289 - pyskl - INFO - Epoch [10][900/3746] lr: 9.907e-02, eta: 4 days, 11:00:01, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4420, loss_cls: 4.3715, loss: 4.3715 +2024-07-16 14:13:38,485 - pyskl - INFO - Epoch [10][1000/3746] lr: 9.906e-02, eta: 4 days, 10:58:05, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1975, top5_acc: 0.4242, loss_cls: 4.4460, loss: 4.4460 +2024-07-16 14:14:48,853 - pyskl - INFO - Epoch [10][1100/3746] lr: 9.906e-02, eta: 4 days, 10:56:11, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4348, loss_cls: 4.4253, loss: 4.4253 +2024-07-16 14:15:59,004 - pyskl - INFO - Epoch [10][1200/3746] lr: 9.905e-02, eta: 4 days, 10:54:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4344, loss_cls: 4.4348, loss: 4.4348 +2024-07-16 14:17:09,008 - pyskl - INFO - Epoch [10][1300/3746] lr: 9.905e-02, eta: 4 days, 10:52:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4434, loss_cls: 4.4067, loss: 4.4067 +2024-07-16 14:18:19,023 - pyskl - INFO - Epoch [10][1400/3746] lr: 9.904e-02, eta: 4 days, 10:50:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2030, top5_acc: 0.4452, loss_cls: 4.3905, loss: 4.3905 +2024-07-16 14:19:28,894 - pyskl - INFO - Epoch [10][1500/3746] lr: 9.903e-02, eta: 4 days, 10:48:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4353, loss_cls: 4.4338, loss: 4.4338 +2024-07-16 14:20:38,537 - pyskl - INFO - Epoch [10][1600/3746] lr: 9.903e-02, eta: 4 days, 10:46:15, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2056, top5_acc: 0.4334, loss_cls: 4.4215, loss: 4.4215 +2024-07-16 14:21:48,633 - pyskl - INFO - Epoch [10][1700/3746] lr: 9.902e-02, eta: 4 days, 10:44:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2019, top5_acc: 0.4341, loss_cls: 4.4327, loss: 4.4327 +2024-07-16 14:22:58,915 - pyskl - INFO - Epoch [10][1800/3746] lr: 9.902e-02, eta: 4 days, 10:42:26, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1959, top5_acc: 0.4264, loss_cls: 4.4616, loss: 4.4616 +2024-07-16 14:24:08,715 - pyskl - INFO - Epoch [10][1900/3746] lr: 9.901e-02, eta: 4 days, 10:40:26, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2047, top5_acc: 0.4363, loss_cls: 4.4216, loss: 4.4216 +2024-07-16 14:25:18,821 - pyskl - INFO - Epoch [10][2000/3746] lr: 9.901e-02, eta: 4 days, 10:38:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1995, top5_acc: 0.4427, loss_cls: 4.4135, loss: 4.4135 +2024-07-16 14:26:29,151 - pyskl - INFO - Epoch [10][2100/3746] lr: 9.900e-02, eta: 4 days, 10:36:39, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4405, loss_cls: 4.3904, loss: 4.3904 +2024-07-16 14:27:38,935 - pyskl - INFO - Epoch [10][2200/3746] lr: 9.900e-02, eta: 4 days, 10:34:40, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4270, loss_cls: 4.4333, loss: 4.4333 +2024-07-16 14:28:48,701 - pyskl - INFO - Epoch [10][2300/3746] lr: 9.899e-02, eta: 4 days, 10:32:40, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4369, loss_cls: 4.4156, loss: 4.4156 +2024-07-16 14:29:58,440 - pyskl - INFO - Epoch [10][2400/3746] lr: 9.898e-02, eta: 4 days, 10:30:41, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1983, top5_acc: 0.4353, loss_cls: 4.4370, loss: 4.4370 +2024-07-16 14:31:08,464 - pyskl - INFO - Epoch [10][2500/3746] lr: 9.898e-02, eta: 4 days, 10:28:46, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4355, loss_cls: 4.3999, loss: 4.3999 +2024-07-16 14:32:18,575 - pyskl - INFO - Epoch [10][2600/3746] lr: 9.897e-02, eta: 4 days, 10:26:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4500, loss_cls: 4.3973, loss: 4.3973 +2024-07-16 14:33:28,648 - pyskl - INFO - Epoch [10][2700/3746] lr: 9.897e-02, eta: 4 days, 10:24:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4403, loss_cls: 4.3783, loss: 4.3783 +2024-07-16 14:34:38,524 - pyskl - INFO - Epoch [10][2800/3746] lr: 9.896e-02, eta: 4 days, 10:23:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4361, loss_cls: 4.4191, loss: 4.4191 +2024-07-16 14:35:48,254 - pyskl - INFO - Epoch [10][2900/3746] lr: 9.896e-02, eta: 4 days, 10:21:03, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4400, loss_cls: 4.3938, loss: 4.3938 +2024-07-16 14:36:58,280 - pyskl - INFO - Epoch [10][3000/3746] lr: 9.895e-02, eta: 4 days, 10:19:10, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4363, loss_cls: 4.4186, loss: 4.4186 +2024-07-16 14:38:07,975 - pyskl - INFO - Epoch [10][3100/3746] lr: 9.894e-02, eta: 4 days, 10:17:11, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2059, top5_acc: 0.4305, loss_cls: 4.4432, loss: 4.4432 +2024-07-16 14:39:18,048 - pyskl - INFO - Epoch [10][3200/3746] lr: 9.894e-02, eta: 4 days, 10:15:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2030, top5_acc: 0.4328, loss_cls: 4.4554, loss: 4.4554 +2024-07-16 14:40:28,013 - pyskl - INFO - Epoch [10][3300/3746] lr: 9.893e-02, eta: 4 days, 10:13:25, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4341, loss_cls: 4.4210, loss: 4.4210 +2024-07-16 14:41:37,772 - pyskl - INFO - Epoch [10][3400/3746] lr: 9.893e-02, eta: 4 days, 10:11:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2011, top5_acc: 0.4389, loss_cls: 4.4077, loss: 4.4077 +2024-07-16 14:42:47,480 - pyskl - INFO - Epoch [10][3500/3746] lr: 9.892e-02, eta: 4 days, 10:09:31, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4378, loss_cls: 4.4386, loss: 4.4386 +2024-07-16 14:43:57,711 - pyskl - INFO - Epoch [10][3600/3746] lr: 9.892e-02, eta: 4 days, 10:07:41, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4447, loss_cls: 4.4045, loss: 4.4045 +2024-07-16 14:45:08,841 - pyskl - INFO - Epoch [10][3700/3746] lr: 9.891e-02, eta: 4 days, 10:06:05, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1936, top5_acc: 0.4191, loss_cls: 4.4840, loss: 4.4840 +2024-07-16 14:45:42,808 - pyskl - INFO - Saving checkpoint at 10 epochs +2024-07-16 14:47:35,637 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 14:47:36,421 - pyskl - INFO - +top1_acc 0.1505 +top5_acc 0.3596 +2024-07-16 14:47:36,421 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 14:47:36,471 - pyskl - INFO - +mean_acc 0.1504 +2024-07-16 14:47:36,478 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_7.pth was removed +2024-07-16 14:47:36,820 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_10.pth. +2024-07-16 14:47:36,821 - pyskl - INFO - Best top1_acc is 0.1505 at 10 epoch. +2024-07-16 14:47:36,837 - pyskl - INFO - Epoch(val) [10][309] top1_acc: 0.1505, top5_acc: 0.3596, mean_class_accuracy: 0.1504 +2024-07-16 14:50:51,255 - pyskl - INFO - Epoch [11][100/3746] lr: 9.890e-02, eta: 4 days, 10:24:48, time: 1.944, data_time: 1.232, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4366, loss_cls: 4.3990, loss: 4.3990 +2024-07-16 14:52:01,604 - pyskl - INFO - Epoch [11][200/3746] lr: 9.890e-02, eta: 4 days, 10:22:57, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4334, loss_cls: 4.4228, loss: 4.4228 +2024-07-16 14:53:12,459 - pyskl - INFO - Epoch [11][300/3746] lr: 9.889e-02, eta: 4 days, 10:21:14, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4411, loss_cls: 4.4041, loss: 4.4041 +2024-07-16 14:54:22,921 - pyskl - INFO - Epoch [11][400/3746] lr: 9.888e-02, eta: 4 days, 10:19:25, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4470, loss_cls: 4.3703, loss: 4.3703 +2024-07-16 14:55:33,250 - pyskl - INFO - Epoch [11][500/3746] lr: 9.888e-02, eta: 4 days, 10:17:34, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2009, top5_acc: 0.4392, loss_cls: 4.3939, loss: 4.3939 +2024-07-16 14:56:43,288 - pyskl - INFO - Epoch [11][600/3746] lr: 9.887e-02, eta: 4 days, 10:15:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4375, loss_cls: 4.3995, loss: 4.3995 +2024-07-16 14:57:53,672 - pyskl - INFO - Epoch [11][700/3746] lr: 9.887e-02, eta: 4 days, 10:13:51, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4456, loss_cls: 4.4148, loss: 4.4148 +2024-07-16 14:59:04,090 - pyskl - INFO - Epoch [11][800/3746] lr: 9.886e-02, eta: 4 days, 10:12:02, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4452, loss_cls: 4.3713, loss: 4.3713 +2024-07-16 15:00:14,272 - pyskl - INFO - Epoch [11][900/3746] lr: 9.885e-02, eta: 4 days, 10:10:10, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4525, loss_cls: 4.3782, loss: 4.3782 +2024-07-16 15:01:24,282 - pyskl - INFO - Epoch [11][1000/3746] lr: 9.885e-02, eta: 4 days, 10:08:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4442, loss_cls: 4.3947, loss: 4.3947 +2024-07-16 15:02:34,408 - pyskl - INFO - Epoch [11][1100/3746] lr: 9.884e-02, eta: 4 days, 10:06:25, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1878, top5_acc: 0.4281, loss_cls: 4.4509, loss: 4.4509 +2024-07-16 15:03:44,555 - pyskl - INFO - Epoch [11][1200/3746] lr: 9.884e-02, eta: 4 days, 10:04:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4452, loss_cls: 4.3773, loss: 4.3773 +2024-07-16 15:04:54,715 - pyskl - INFO - Epoch [11][1300/3746] lr: 9.883e-02, eta: 4 days, 10:02:42, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1936, top5_acc: 0.4308, loss_cls: 4.4620, loss: 4.4620 +2024-07-16 15:06:04,868 - pyskl - INFO - Epoch [11][1400/3746] lr: 9.882e-02, eta: 4 days, 10:00:51, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4263, loss_cls: 4.4753, loss: 4.4753 +2024-07-16 15:07:14,950 - pyskl - INFO - Epoch [11][1500/3746] lr: 9.882e-02, eta: 4 days, 9:58:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4484, loss_cls: 4.3899, loss: 4.3899 +2024-07-16 15:08:25,224 - pyskl - INFO - Epoch [11][1600/3746] lr: 9.881e-02, eta: 4 days, 9:57:10, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4405, loss_cls: 4.4060, loss: 4.4060 +2024-07-16 15:09:35,102 - pyskl - INFO - Epoch [11][1700/3746] lr: 9.881e-02, eta: 4 days, 9:55:16, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4470, loss_cls: 4.3997, loss: 4.3997 +2024-07-16 15:10:45,074 - pyskl - INFO - Epoch [11][1800/3746] lr: 9.880e-02, eta: 4 days, 9:53:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2008, top5_acc: 0.4322, loss_cls: 4.4205, loss: 4.4205 +2024-07-16 15:11:55,465 - pyskl - INFO - Epoch [11][1900/3746] lr: 9.879e-02, eta: 4 days, 9:51:37, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1955, top5_acc: 0.4303, loss_cls: 4.4420, loss: 4.4420 +2024-07-16 15:13:05,537 - pyskl - INFO - Epoch [11][2000/3746] lr: 9.879e-02, eta: 4 days, 9:49:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2037, top5_acc: 0.4378, loss_cls: 4.4157, loss: 4.4157 +2024-07-16 15:14:15,644 - pyskl - INFO - Epoch [11][2100/3746] lr: 9.878e-02, eta: 4 days, 9:47:56, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4437, loss_cls: 4.3824, loss: 4.3824 +2024-07-16 15:15:25,617 - pyskl - INFO - Epoch [11][2200/3746] lr: 9.878e-02, eta: 4 days, 9:46:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4530, loss_cls: 4.3512, loss: 4.3512 +2024-07-16 15:16:35,273 - pyskl - INFO - Epoch [11][2300/3746] lr: 9.877e-02, eta: 4 days, 9:44:09, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1939, top5_acc: 0.4234, loss_cls: 4.4916, loss: 4.4916 +2024-07-16 15:17:45,168 - pyskl - INFO - Epoch [11][2400/3746] lr: 9.876e-02, eta: 4 days, 9:42:16, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2069, top5_acc: 0.4408, loss_cls: 4.4181, loss: 4.4181 +2024-07-16 15:18:54,787 - pyskl - INFO - Epoch [11][2500/3746] lr: 9.876e-02, eta: 4 days, 9:40:21, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4233, loss_cls: 4.4780, loss: 4.4780 +2024-07-16 15:20:04,693 - pyskl - INFO - Epoch [11][2600/3746] lr: 9.875e-02, eta: 4 days, 9:38:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4494, loss_cls: 4.3559, loss: 4.3559 +2024-07-16 15:21:14,665 - pyskl - INFO - Epoch [11][2700/3746] lr: 9.874e-02, eta: 4 days, 9:36:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4423, loss_cls: 4.3812, loss: 4.3812 +2024-07-16 15:22:24,601 - pyskl - INFO - Epoch [11][2800/3746] lr: 9.874e-02, eta: 4 days, 9:34:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4320, loss_cls: 4.4312, loss: 4.4312 +2024-07-16 15:23:34,588 - pyskl - INFO - Epoch [11][2900/3746] lr: 9.873e-02, eta: 4 days, 9:32:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2045, top5_acc: 0.4387, loss_cls: 4.4096, loss: 4.4096 +2024-07-16 15:24:44,573 - pyskl - INFO - Epoch [11][3000/3746] lr: 9.873e-02, eta: 4 days, 9:31:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4512, loss_cls: 4.3824, loss: 4.3824 +2024-07-16 15:25:54,144 - pyskl - INFO - Epoch [11][3100/3746] lr: 9.872e-02, eta: 4 days, 9:29:13, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.1984, top5_acc: 0.4231, loss_cls: 4.4717, loss: 4.4717 +2024-07-16 15:27:04,010 - pyskl - INFO - Epoch [11][3200/3746] lr: 9.871e-02, eta: 4 days, 9:27:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4423, loss_cls: 4.4017, loss: 4.4017 +2024-07-16 15:28:13,809 - pyskl - INFO - Epoch [11][3300/3746] lr: 9.871e-02, eta: 4 days, 9:25:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4345, loss_cls: 4.4319, loss: 4.4319 +2024-07-16 15:29:23,340 - pyskl - INFO - Epoch [11][3400/3746] lr: 9.870e-02, eta: 4 days, 9:23:35, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.1977, top5_acc: 0.4386, loss_cls: 4.4165, loss: 4.4165 +2024-07-16 15:30:33,237 - pyskl - INFO - Epoch [11][3500/3746] lr: 9.869e-02, eta: 4 days, 9:21:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1998, top5_acc: 0.4361, loss_cls: 4.3889, loss: 4.3889 +2024-07-16 15:31:43,533 - pyskl - INFO - Epoch [11][3600/3746] lr: 9.869e-02, eta: 4 days, 9:20:00, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4375, loss_cls: 4.4308, loss: 4.4308 +2024-07-16 15:32:53,856 - pyskl - INFO - Epoch [11][3700/3746] lr: 9.868e-02, eta: 4 days, 9:18:16, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4495, loss_cls: 4.3622, loss: 4.3622 +2024-07-16 15:33:27,856 - pyskl - INFO - Saving checkpoint at 11 epochs +2024-07-16 15:35:19,081 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 15:35:19,761 - pyskl - INFO - +top1_acc 0.1233 +top5_acc 0.3130 +2024-07-16 15:35:19,761 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 15:35:19,800 - pyskl - INFO - +mean_acc 0.1232 +2024-07-16 15:35:19,812 - pyskl - INFO - Epoch(val) [11][309] top1_acc: 0.1233, top5_acc: 0.3130, mean_class_accuracy: 0.1232 +2024-07-16 15:38:35,508 - pyskl - INFO - Epoch [12][100/3746] lr: 9.867e-02, eta: 4 days, 9:35:16, time: 1.957, data_time: 1.245, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4433, loss_cls: 4.3718, loss: 4.3718 +2024-07-16 15:39:46,886 - pyskl - INFO - Epoch [12][200/3746] lr: 9.867e-02, eta: 4 days, 9:33:43, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4541, loss_cls: 4.3527, loss: 4.3527 +2024-07-16 15:40:57,829 - pyskl - INFO - Epoch [12][300/3746] lr: 9.866e-02, eta: 4 days, 9:32:04, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4494, loss_cls: 4.3808, loss: 4.3808 +2024-07-16 15:42:08,340 - pyskl - INFO - Epoch [12][400/3746] lr: 9.865e-02, eta: 4 days, 9:30:20, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4523, loss_cls: 4.3739, loss: 4.3739 +2024-07-16 15:43:19,352 - pyskl - INFO - Epoch [12][500/3746] lr: 9.865e-02, eta: 4 days, 9:28:42, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4437, loss_cls: 4.3747, loss: 4.3747 +2024-07-16 15:44:29,919 - pyskl - INFO - Epoch [12][600/3746] lr: 9.864e-02, eta: 4 days, 9:26:59, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4487, loss_cls: 4.3831, loss: 4.3831 +2024-07-16 15:45:40,254 - pyskl - INFO - Epoch [12][700/3746] lr: 9.863e-02, eta: 4 days, 9:25:13, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2047, top5_acc: 0.4378, loss_cls: 4.4110, loss: 4.4110 +2024-07-16 15:46:50,928 - pyskl - INFO - Epoch [12][800/3746] lr: 9.863e-02, eta: 4 days, 9:23:31, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4558, loss_cls: 4.3510, loss: 4.3510 +2024-07-16 15:48:00,952 - pyskl - INFO - Epoch [12][900/3746] lr: 9.862e-02, eta: 4 days, 9:21:42, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2108, top5_acc: 0.4387, loss_cls: 4.4010, loss: 4.4010 +2024-07-16 15:49:11,143 - pyskl - INFO - Epoch [12][1000/3746] lr: 9.861e-02, eta: 4 days, 9:19:54, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1972, top5_acc: 0.4414, loss_cls: 4.4007, loss: 4.4007 +2024-07-16 15:50:21,409 - pyskl - INFO - Epoch [12][1100/3746] lr: 9.861e-02, eta: 4 days, 9:18:08, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4420, loss_cls: 4.4071, loss: 4.4071 +2024-07-16 15:51:31,590 - pyskl - INFO - Epoch [12][1200/3746] lr: 9.860e-02, eta: 4 days, 9:16:21, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2025, top5_acc: 0.4412, loss_cls: 4.3935, loss: 4.3935 +2024-07-16 15:52:42,338 - pyskl - INFO - Epoch [12][1300/3746] lr: 9.859e-02, eta: 4 days, 9:14:41, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4328, loss_cls: 4.4182, loss: 4.4182 +2024-07-16 15:53:52,738 - pyskl - INFO - Epoch [12][1400/3746] lr: 9.859e-02, eta: 4 days, 9:12:57, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4384, loss_cls: 4.4154, loss: 4.4154 +2024-07-16 15:55:03,088 - pyskl - INFO - Epoch [12][1500/3746] lr: 9.858e-02, eta: 4 days, 9:11:13, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4328, loss_cls: 4.4451, loss: 4.4451 +2024-07-16 15:56:13,209 - pyskl - INFO - Epoch [12][1600/3746] lr: 9.857e-02, eta: 4 days, 9:09:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4500, loss_cls: 4.3761, loss: 4.3761 +2024-07-16 15:57:22,950 - pyskl - INFO - Epoch [12][1700/3746] lr: 9.857e-02, eta: 4 days, 9:07:34, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2002, top5_acc: 0.4353, loss_cls: 4.4152, loss: 4.4152 +2024-07-16 15:58:33,168 - pyskl - INFO - Epoch [12][1800/3746] lr: 9.856e-02, eta: 4 days, 9:05:49, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4472, loss_cls: 4.3731, loss: 4.3731 +2024-07-16 15:59:43,168 - pyskl - INFO - Epoch [12][1900/3746] lr: 9.855e-02, eta: 4 days, 9:04:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4409, loss_cls: 4.3891, loss: 4.3891 +2024-07-16 16:00:53,132 - pyskl - INFO - Epoch [12][2000/3746] lr: 9.855e-02, eta: 4 days, 9:02:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1983, top5_acc: 0.4417, loss_cls: 4.3998, loss: 4.3998 +2024-07-16 16:02:03,237 - pyskl - INFO - Epoch [12][2100/3746] lr: 9.854e-02, eta: 4 days, 9:00:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4325, loss_cls: 4.4309, loss: 4.4309 +2024-07-16 16:03:13,371 - pyskl - INFO - Epoch [12][2200/3746] lr: 9.853e-02, eta: 4 days, 8:58:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4417, loss_cls: 4.3747, loss: 4.3747 +2024-07-16 16:04:23,024 - pyskl - INFO - Epoch [12][2300/3746] lr: 9.853e-02, eta: 4 days, 8:56:49, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2047, top5_acc: 0.4341, loss_cls: 4.4123, loss: 4.4123 +2024-07-16 16:05:32,909 - pyskl - INFO - Epoch [12][2400/3746] lr: 9.852e-02, eta: 4 days, 8:55:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4470, loss_cls: 4.3841, loss: 4.3841 +2024-07-16 16:06:42,728 - pyskl - INFO - Epoch [12][2500/3746] lr: 9.851e-02, eta: 4 days, 8:53:11, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4352, loss_cls: 4.4221, loss: 4.4221 +2024-07-16 16:07:52,505 - pyskl - INFO - Epoch [12][2600/3746] lr: 9.851e-02, eta: 4 days, 8:51:22, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2027, top5_acc: 0.4298, loss_cls: 4.4211, loss: 4.4211 +2024-07-16 16:09:02,370 - pyskl - INFO - Epoch [12][2700/3746] lr: 9.850e-02, eta: 4 days, 8:49:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2019, top5_acc: 0.4366, loss_cls: 4.4151, loss: 4.4151 +2024-07-16 16:10:12,053 - pyskl - INFO - Epoch [12][2800/3746] lr: 9.849e-02, eta: 4 days, 8:47:43, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4531, loss_cls: 4.3826, loss: 4.3826 +2024-07-16 16:11:21,739 - pyskl - INFO - Epoch [12][2900/3746] lr: 9.849e-02, eta: 4 days, 8:45:53, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4334, loss_cls: 4.4477, loss: 4.4477 +2024-07-16 16:12:31,610 - pyskl - INFO - Epoch [12][3000/3746] lr: 9.848e-02, eta: 4 days, 8:44:06, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4466, loss_cls: 4.3663, loss: 4.3663 +2024-07-16 16:13:41,569 - pyskl - INFO - Epoch [12][3100/3746] lr: 9.847e-02, eta: 4 days, 8:42:19, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4512, loss_cls: 4.3581, loss: 4.3581 +2024-07-16 16:14:51,199 - pyskl - INFO - Epoch [12][3200/3746] lr: 9.847e-02, eta: 4 days, 8:40:29, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4433, loss_cls: 4.3905, loss: 4.3905 +2024-07-16 16:16:00,760 - pyskl - INFO - Epoch [12][3300/3746] lr: 9.846e-02, eta: 4 days, 8:38:39, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4398, loss_cls: 4.4169, loss: 4.4169 +2024-07-16 16:17:10,506 - pyskl - INFO - Epoch [12][3400/3746] lr: 9.845e-02, eta: 4 days, 8:36:50, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1973, top5_acc: 0.4381, loss_cls: 4.4193, loss: 4.4193 +2024-07-16 16:18:20,468 - pyskl - INFO - Epoch [12][3500/3746] lr: 9.845e-02, eta: 4 days, 8:35:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2131, top5_acc: 0.4431, loss_cls: 4.3726, loss: 4.3726 +2024-07-16 16:19:30,995 - pyskl - INFO - Epoch [12][3600/3746] lr: 9.844e-02, eta: 4 days, 8:33:26, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2009, top5_acc: 0.4352, loss_cls: 4.4524, loss: 4.4524 +2024-07-16 16:20:41,239 - pyskl - INFO - Epoch [12][3700/3746] lr: 9.843e-02, eta: 4 days, 8:31:43, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4411, loss_cls: 4.3867, loss: 4.3867 +2024-07-16 16:21:15,748 - pyskl - INFO - Saving checkpoint at 12 epochs +2024-07-16 16:23:07,231 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 16:23:07,942 - pyskl - INFO - +top1_acc 0.1290 +top5_acc 0.3267 +2024-07-16 16:23:07,942 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 16:23:07,982 - pyskl - INFO - +mean_acc 0.1290 +2024-07-16 16:23:07,998 - pyskl - INFO - Epoch(val) [12][309] top1_acc: 0.1290, top5_acc: 0.3267, mean_class_accuracy: 0.1290 +2024-07-16 16:26:26,318 - pyskl - INFO - Epoch [13][100/3746] lr: 9.842e-02, eta: 4 days, 8:47:33, time: 1.983, data_time: 1.269, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4527, loss_cls: 4.3432, loss: 4.3432 +2024-07-16 16:27:36,998 - pyskl - INFO - Epoch [13][200/3746] lr: 9.842e-02, eta: 4 days, 8:45:54, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4408, loss_cls: 4.3748, loss: 4.3748 +2024-07-16 16:28:48,006 - pyskl - INFO - Epoch [13][300/3746] lr: 9.841e-02, eta: 4 days, 8:44:18, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4361, loss_cls: 4.3963, loss: 4.3963 +2024-07-16 16:29:58,792 - pyskl - INFO - Epoch [13][400/3746] lr: 9.840e-02, eta: 4 days, 8:42:40, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4552, loss_cls: 4.3671, loss: 4.3671 +2024-07-16 16:31:09,210 - pyskl - INFO - Epoch [13][500/3746] lr: 9.839e-02, eta: 4 days, 8:40:58, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4439, loss_cls: 4.3776, loss: 4.3776 +2024-07-16 16:32:19,316 - pyskl - INFO - Epoch [13][600/3746] lr: 9.839e-02, eta: 4 days, 8:39:13, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4455, loss_cls: 4.3604, loss: 4.3604 +2024-07-16 16:33:29,404 - pyskl - INFO - Epoch [13][700/3746] lr: 9.838e-02, eta: 4 days, 8:37:27, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4461, loss_cls: 4.3761, loss: 4.3761 +2024-07-16 16:34:39,308 - pyskl - INFO - Epoch [13][800/3746] lr: 9.837e-02, eta: 4 days, 8:35:40, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2114, top5_acc: 0.4459, loss_cls: 4.3556, loss: 4.3556 +2024-07-16 16:35:49,277 - pyskl - INFO - Epoch [13][900/3746] lr: 9.837e-02, eta: 4 days, 8:33:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4405, loss_cls: 4.3916, loss: 4.3916 +2024-07-16 16:36:59,367 - pyskl - INFO - Epoch [13][1000/3746] lr: 9.836e-02, eta: 4 days, 8:32:08, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2069, top5_acc: 0.4397, loss_cls: 4.3953, loss: 4.3953 +2024-07-16 16:38:09,309 - pyskl - INFO - Epoch [13][1100/3746] lr: 9.835e-02, eta: 4 days, 8:30:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4484, loss_cls: 4.3665, loss: 4.3665 +2024-07-16 16:39:19,491 - pyskl - INFO - Epoch [13][1200/3746] lr: 9.834e-02, eta: 4 days, 8:28:38, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4328, loss_cls: 4.4392, loss: 4.4392 +2024-07-16 16:40:29,498 - pyskl - INFO - Epoch [13][1300/3746] lr: 9.834e-02, eta: 4 days, 8:26:52, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4436, loss_cls: 4.4104, loss: 4.4104 +2024-07-16 16:41:39,454 - pyskl - INFO - Epoch [13][1400/3746] lr: 9.833e-02, eta: 4 days, 8:25:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4466, loss_cls: 4.3567, loss: 4.3567 +2024-07-16 16:42:49,184 - pyskl - INFO - Epoch [13][1500/3746] lr: 9.832e-02, eta: 4 days, 8:23:18, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4366, loss_cls: 4.4345, loss: 4.4345 +2024-07-16 16:43:59,071 - pyskl - INFO - Epoch [13][1600/3746] lr: 9.832e-02, eta: 4 days, 8:21:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4436, loss_cls: 4.4059, loss: 4.4059 +2024-07-16 16:45:08,709 - pyskl - INFO - Epoch [13][1700/3746] lr: 9.831e-02, eta: 4 days, 8:19:43, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4347, loss_cls: 4.4046, loss: 4.4046 +2024-07-16 16:46:18,801 - pyskl - INFO - Epoch [13][1800/3746] lr: 9.830e-02, eta: 4 days, 8:17:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2078, top5_acc: 0.4436, loss_cls: 4.3545, loss: 4.3545 +2024-07-16 16:47:28,732 - pyskl - INFO - Epoch [13][1900/3746] lr: 9.829e-02, eta: 4 days, 8:16:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4537, loss_cls: 4.3437, loss: 4.3437 +2024-07-16 16:48:38,808 - pyskl - INFO - Epoch [13][2000/3746] lr: 9.829e-02, eta: 4 days, 8:14:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2045, top5_acc: 0.4364, loss_cls: 4.4286, loss: 4.4286 +2024-07-16 16:49:49,013 - pyskl - INFO - Epoch [13][2100/3746] lr: 9.828e-02, eta: 4 days, 8:12:48, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4478, loss_cls: 4.3836, loss: 4.3836 +2024-07-16 16:50:59,123 - pyskl - INFO - Epoch [13][2200/3746] lr: 9.827e-02, eta: 4 days, 8:11:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4403, loss_cls: 4.3889, loss: 4.3889 +2024-07-16 16:52:08,823 - pyskl - INFO - Epoch [13][2300/3746] lr: 9.827e-02, eta: 4 days, 8:09:18, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4378, loss_cls: 4.4046, loss: 4.4046 +2024-07-16 16:53:19,138 - pyskl - INFO - Epoch [13][2400/3746] lr: 9.826e-02, eta: 4 days, 8:07:37, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4455, loss_cls: 4.3920, loss: 4.3920 +2024-07-16 16:54:28,791 - pyskl - INFO - Epoch [13][2500/3746] lr: 9.825e-02, eta: 4 days, 8:05:50, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4419, loss_cls: 4.4059, loss: 4.4059 +2024-07-16 16:55:38,626 - pyskl - INFO - Epoch [13][2600/3746] lr: 9.824e-02, eta: 4 days, 8:04:04, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2069, top5_acc: 0.4458, loss_cls: 4.3923, loss: 4.3923 +2024-07-16 16:56:48,238 - pyskl - INFO - Epoch [13][2700/3746] lr: 9.824e-02, eta: 4 days, 8:02:16, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4423, loss_cls: 4.3790, loss: 4.3790 +2024-07-16 16:57:58,031 - pyskl - INFO - Epoch [13][2800/3746] lr: 9.823e-02, eta: 4 days, 8:00:31, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4447, loss_cls: 4.3677, loss: 4.3677 +2024-07-16 16:59:07,885 - pyskl - INFO - Epoch [13][2900/3746] lr: 9.822e-02, eta: 4 days, 7:58:46, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4467, loss_cls: 4.3752, loss: 4.3752 +2024-07-16 17:00:17,462 - pyskl - INFO - Epoch [13][3000/3746] lr: 9.821e-02, eta: 4 days, 7:56:59, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4431, loss_cls: 4.4044, loss: 4.4044 +2024-07-16 17:01:27,473 - pyskl - INFO - Epoch [13][3100/3746] lr: 9.821e-02, eta: 4 days, 7:55:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4358, loss_cls: 4.4270, loss: 4.4270 +2024-07-16 17:02:37,250 - pyskl - INFO - Epoch [13][3200/3746] lr: 9.820e-02, eta: 4 days, 7:53:31, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4428, loss_cls: 4.3776, loss: 4.3776 +2024-07-16 17:03:46,814 - pyskl - INFO - Epoch [13][3300/3746] lr: 9.819e-02, eta: 4 days, 7:51:43, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4409, loss_cls: 4.4214, loss: 4.4214 +2024-07-16 17:04:56,662 - pyskl - INFO - Epoch [13][3400/3746] lr: 9.818e-02, eta: 4 days, 7:49:59, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4361, loss_cls: 4.4343, loss: 4.4343 +2024-07-16 17:06:06,674 - pyskl - INFO - Epoch [13][3500/3746] lr: 9.818e-02, eta: 4 days, 7:48:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4475, loss_cls: 4.3710, loss: 4.3710 +2024-07-16 17:07:17,193 - pyskl - INFO - Epoch [13][3600/3746] lr: 9.817e-02, eta: 4 days, 7:46:40, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2037, top5_acc: 0.4380, loss_cls: 4.3947, loss: 4.3947 +2024-07-16 17:08:27,322 - pyskl - INFO - Epoch [13][3700/3746] lr: 9.816e-02, eta: 4 days, 7:45:00, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2078, top5_acc: 0.4341, loss_cls: 4.4096, loss: 4.4096 +2024-07-16 17:09:02,085 - pyskl - INFO - Saving checkpoint at 13 epochs +2024-07-16 17:10:53,913 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 17:10:54,574 - pyskl - INFO - +top1_acc 0.1388 +top5_acc 0.3379 +2024-07-16 17:10:54,575 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 17:10:54,614 - pyskl - INFO - +mean_acc 0.1387 +2024-07-16 17:10:54,626 - pyskl - INFO - Epoch(val) [13][309] top1_acc: 0.1388, top5_acc: 0.3379, mean_class_accuracy: 0.1387 +2024-07-16 17:14:15,901 - pyskl - INFO - Epoch [14][100/3746] lr: 9.815e-02, eta: 4 days, 7:59:52, time: 2.013, data_time: 1.304, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4508, loss_cls: 4.3520, loss: 4.3520 +2024-07-16 17:15:27,106 - pyskl - INFO - Epoch [14][200/3746] lr: 9.814e-02, eta: 4 days, 7:58:21, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4558, loss_cls: 4.3226, loss: 4.3226 +2024-07-16 17:16:37,775 - pyskl - INFO - Epoch [14][300/3746] lr: 9.814e-02, eta: 4 days, 7:56:44, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2078, top5_acc: 0.4441, loss_cls: 4.3696, loss: 4.3696 +2024-07-16 17:17:48,354 - pyskl - INFO - Epoch [14][400/3746] lr: 9.813e-02, eta: 4 days, 7:55:06, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4358, loss_cls: 4.4025, loss: 4.4025 +2024-07-16 17:18:58,853 - pyskl - INFO - Epoch [14][500/3746] lr: 9.812e-02, eta: 4 days, 7:53:28, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4481, loss_cls: 4.3681, loss: 4.3681 +2024-07-16 17:20:08,796 - pyskl - INFO - Epoch [14][600/3746] lr: 9.811e-02, eta: 4 days, 7:51:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4569, loss_cls: 4.3279, loss: 4.3279 +2024-07-16 17:21:18,997 - pyskl - INFO - Epoch [14][700/3746] lr: 9.811e-02, eta: 4 days, 7:50:02, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4414, loss_cls: 4.4087, loss: 4.4087 +2024-07-16 17:22:29,122 - pyskl - INFO - Epoch [14][800/3746] lr: 9.810e-02, eta: 4 days, 7:48:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4423, loss_cls: 4.3713, loss: 4.3713 +2024-07-16 17:23:39,197 - pyskl - INFO - Epoch [14][900/3746] lr: 9.809e-02, eta: 4 days, 7:46:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4420, loss_cls: 4.3973, loss: 4.3973 +2024-07-16 17:24:49,620 - pyskl - INFO - Epoch [14][1000/3746] lr: 9.808e-02, eta: 4 days, 7:44:59, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4412, loss_cls: 4.3817, loss: 4.3817 +2024-07-16 17:25:59,668 - pyskl - INFO - Epoch [14][1100/3746] lr: 9.807e-02, eta: 4 days, 7:43:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4505, loss_cls: 4.3638, loss: 4.3638 +2024-07-16 17:27:09,850 - pyskl - INFO - Epoch [14][1200/3746] lr: 9.807e-02, eta: 4 days, 7:41:35, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4461, loss_cls: 4.3622, loss: 4.3622 +2024-07-16 17:28:20,066 - pyskl - INFO - Epoch [14][1300/3746] lr: 9.806e-02, eta: 4 days, 7:39:55, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4453, loss_cls: 4.4220, loss: 4.4220 +2024-07-16 17:29:29,693 - pyskl - INFO - Epoch [14][1400/3746] lr: 9.805e-02, eta: 4 days, 7:38:09, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4420, loss_cls: 4.4161, loss: 4.4161 +2024-07-16 17:30:39,848 - pyskl - INFO - Epoch [14][1500/3746] lr: 9.804e-02, eta: 4 days, 7:36:28, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2092, top5_acc: 0.4369, loss_cls: 4.3839, loss: 4.3839 +2024-07-16 17:31:49,668 - pyskl - INFO - Epoch [14][1600/3746] lr: 9.804e-02, eta: 4 days, 7:34:44, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4411, loss_cls: 4.4184, loss: 4.4184 +2024-07-16 17:32:59,670 - pyskl - INFO - Epoch [14][1700/3746] lr: 9.803e-02, eta: 4 days, 7:33:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4423, loss_cls: 4.4008, loss: 4.4008 +2024-07-16 17:34:09,785 - pyskl - INFO - Epoch [14][1800/3746] lr: 9.802e-02, eta: 4 days, 7:31:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4394, loss_cls: 4.3948, loss: 4.3948 +2024-07-16 17:35:19,848 - pyskl - INFO - Epoch [14][1900/3746] lr: 9.801e-02, eta: 4 days, 7:29:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4395, loss_cls: 4.4184, loss: 4.4184 +2024-07-16 17:36:30,211 - pyskl - INFO - Epoch [14][2000/3746] lr: 9.800e-02, eta: 4 days, 7:28:01, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2108, top5_acc: 0.4552, loss_cls: 4.3699, loss: 4.3699 +2024-07-16 17:37:40,385 - pyskl - INFO - Epoch [14][2100/3746] lr: 9.800e-02, eta: 4 days, 7:26:21, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4436, loss_cls: 4.3896, loss: 4.3896 +2024-07-16 17:38:50,226 - pyskl - INFO - Epoch [14][2200/3746] lr: 9.799e-02, eta: 4 days, 7:24:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4453, loss_cls: 4.3738, loss: 4.3738 +2024-07-16 17:40:00,086 - pyskl - INFO - Epoch [14][2300/3746] lr: 9.798e-02, eta: 4 days, 7:22:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4447, loss_cls: 4.3714, loss: 4.3714 +2024-07-16 17:41:09,929 - pyskl - INFO - Epoch [14][2400/3746] lr: 9.797e-02, eta: 4 days, 7:21:12, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4405, loss_cls: 4.4201, loss: 4.4201 +2024-07-16 17:42:19,751 - pyskl - INFO - Epoch [14][2500/3746] lr: 9.797e-02, eta: 4 days, 7:19:29, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2114, top5_acc: 0.4453, loss_cls: 4.3801, loss: 4.3801 +2024-07-16 17:43:29,674 - pyskl - INFO - Epoch [14][2600/3746] lr: 9.796e-02, eta: 4 days, 7:17:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1888, top5_acc: 0.4234, loss_cls: 4.4614, loss: 4.4614 +2024-07-16 17:44:39,559 - pyskl - INFO - Epoch [14][2700/3746] lr: 9.795e-02, eta: 4 days, 7:16:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4430, loss_cls: 4.4070, loss: 4.4070 +2024-07-16 17:45:49,259 - pyskl - INFO - Epoch [14][2800/3746] lr: 9.794e-02, eta: 4 days, 7:14:22, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4509, loss_cls: 4.3609, loss: 4.3609 +2024-07-16 17:46:59,296 - pyskl - INFO - Epoch [14][2900/3746] lr: 9.793e-02, eta: 4 days, 7:12:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4545, loss_cls: 4.3336, loss: 4.3336 +2024-07-16 17:48:09,050 - pyskl - INFO - Epoch [14][3000/3746] lr: 9.793e-02, eta: 4 days, 7:10:58, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4464, loss_cls: 4.4024, loss: 4.4024 +2024-07-16 17:49:19,384 - pyskl - INFO - Epoch [14][3100/3746] lr: 9.792e-02, eta: 4 days, 7:09:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4692, loss_cls: 4.3257, loss: 4.3257 +2024-07-16 17:50:29,092 - pyskl - INFO - Epoch [14][3200/3746] lr: 9.791e-02, eta: 4 days, 7:07:38, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4409, loss_cls: 4.3740, loss: 4.3740 +2024-07-16 17:51:38,920 - pyskl - INFO - Epoch [14][3300/3746] lr: 9.790e-02, eta: 4 days, 7:05:56, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4497, loss_cls: 4.3542, loss: 4.3542 +2024-07-16 17:52:48,521 - pyskl - INFO - Epoch [14][3400/3746] lr: 9.789e-02, eta: 4 days, 7:04:12, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4514, loss_cls: 4.3574, loss: 4.3574 +2024-07-16 17:53:58,287 - pyskl - INFO - Epoch [14][3500/3746] lr: 9.789e-02, eta: 4 days, 7:02:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4462, loss_cls: 4.3686, loss: 4.3686 +2024-07-16 17:55:09,088 - pyskl - INFO - Epoch [14][3600/3746] lr: 9.788e-02, eta: 4 days, 7:00:58, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4517, loss_cls: 4.4015, loss: 4.4015 +2024-07-16 17:56:19,182 - pyskl - INFO - Epoch [14][3700/3746] lr: 9.787e-02, eta: 4 days, 6:59:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1972, top5_acc: 0.4320, loss_cls: 4.4385, loss: 4.4385 +2024-07-16 17:56:53,342 - pyskl - INFO - Saving checkpoint at 14 epochs +2024-07-16 17:58:45,739 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 17:58:46,429 - pyskl - INFO - +top1_acc 0.1316 +top5_acc 0.3247 +2024-07-16 17:58:46,429 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 17:58:46,474 - pyskl - INFO - +mean_acc 0.1317 +2024-07-16 17:58:46,486 - pyskl - INFO - Epoch(val) [14][309] top1_acc: 0.1316, top5_acc: 0.3247, mean_class_accuracy: 0.1317 +2024-07-16 18:02:11,769 - pyskl - INFO - Epoch [15][100/3746] lr: 9.786e-02, eta: 4 days, 7:13:33, time: 2.053, data_time: 1.340, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4437, loss_cls: 4.3904, loss: 4.3904 +2024-07-16 18:03:23,104 - pyskl - INFO - Epoch [15][200/3746] lr: 9.785e-02, eta: 4 days, 7:12:04, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4539, loss_cls: 4.3429, loss: 4.3429 +2024-07-16 18:04:34,101 - pyskl - INFO - Epoch [15][300/3746] lr: 9.784e-02, eta: 4 days, 7:10:32, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4481, loss_cls: 4.3721, loss: 4.3721 +2024-07-16 18:05:44,731 - pyskl - INFO - Epoch [15][400/3746] lr: 9.783e-02, eta: 4 days, 7:08:57, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4450, loss_cls: 4.3487, loss: 4.3487 +2024-07-16 18:06:55,220 - pyskl - INFO - Epoch [15][500/3746] lr: 9.783e-02, eta: 4 days, 7:07:20, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4383, loss_cls: 4.4049, loss: 4.4049 +2024-07-16 18:08:05,338 - pyskl - INFO - Epoch [15][600/3746] lr: 9.782e-02, eta: 4 days, 7:05:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2045, top5_acc: 0.4408, loss_cls: 4.4005, loss: 4.4005 +2024-07-16 18:09:15,468 - pyskl - INFO - Epoch [15][700/3746] lr: 9.781e-02, eta: 4 days, 7:04:00, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4536, loss_cls: 4.3570, loss: 4.3570 +2024-07-16 18:10:25,407 - pyskl - INFO - Epoch [15][800/3746] lr: 9.780e-02, eta: 4 days, 7:02:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4548, loss_cls: 4.3387, loss: 4.3387 +2024-07-16 18:11:35,377 - pyskl - INFO - Epoch [15][900/3746] lr: 9.779e-02, eta: 4 days, 7:00:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2194, top5_acc: 0.4520, loss_cls: 4.3089, loss: 4.3089 +2024-07-16 18:12:45,601 - pyskl - INFO - Epoch [15][1000/3746] lr: 9.778e-02, eta: 4 days, 6:58:59, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4505, loss_cls: 4.3299, loss: 4.3299 +2024-07-16 18:13:55,647 - pyskl - INFO - Epoch [15][1100/3746] lr: 9.778e-02, eta: 4 days, 6:57:19, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4455, loss_cls: 4.3809, loss: 4.3809 +2024-07-16 18:15:05,651 - pyskl - INFO - Epoch [15][1200/3746] lr: 9.777e-02, eta: 4 days, 6:55:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4405, loss_cls: 4.3965, loss: 4.3965 +2024-07-16 18:16:15,898 - pyskl - INFO - Epoch [15][1300/3746] lr: 9.776e-02, eta: 4 days, 6:54:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2036, top5_acc: 0.4378, loss_cls: 4.4126, loss: 4.4126 +2024-07-16 18:17:25,975 - pyskl - INFO - Epoch [15][1400/3746] lr: 9.775e-02, eta: 4 days, 6:52:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4478, loss_cls: 4.4195, loss: 4.4195 +2024-07-16 18:18:36,011 - pyskl - INFO - Epoch [15][1500/3746] lr: 9.774e-02, eta: 4 days, 6:50:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4467, loss_cls: 4.3690, loss: 4.3690 +2024-07-16 18:19:46,142 - pyskl - INFO - Epoch [15][1600/3746] lr: 9.773e-02, eta: 4 days, 6:49:02, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4458, loss_cls: 4.3978, loss: 4.3978 +2024-07-16 18:20:56,922 - pyskl - INFO - Epoch [15][1700/3746] lr: 9.773e-02, eta: 4 days, 6:47:30, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1995, top5_acc: 0.4447, loss_cls: 4.4035, loss: 4.4035 +2024-07-16 18:22:08,051 - pyskl - INFO - Epoch [15][1800/3746] lr: 9.772e-02, eta: 4 days, 6:46:00, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4509, loss_cls: 4.3395, loss: 4.3395 +2024-07-16 18:23:18,366 - pyskl - INFO - Epoch [15][1900/3746] lr: 9.771e-02, eta: 4 days, 6:44:24, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4456, loss_cls: 4.3893, loss: 4.3893 +2024-07-16 18:24:29,062 - pyskl - INFO - Epoch [15][2000/3746] lr: 9.770e-02, eta: 4 days, 6:42:50, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4323, loss_cls: 4.4356, loss: 4.4356 +2024-07-16 18:25:39,666 - pyskl - INFO - Epoch [15][2100/3746] lr: 9.769e-02, eta: 4 days, 6:41:16, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2069, top5_acc: 0.4516, loss_cls: 4.3731, loss: 4.3731 +2024-07-16 18:26:50,365 - pyskl - INFO - Epoch [15][2200/3746] lr: 9.768e-02, eta: 4 days, 6:39:44, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4466, loss_cls: 4.3813, loss: 4.3813 +2024-07-16 18:28:01,057 - pyskl - INFO - Epoch [15][2300/3746] lr: 9.768e-02, eta: 4 days, 6:38:11, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4484, loss_cls: 4.3496, loss: 4.3496 +2024-07-16 18:29:11,560 - pyskl - INFO - Epoch [15][2400/3746] lr: 9.767e-02, eta: 4 days, 6:36:36, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1998, top5_acc: 0.4431, loss_cls: 4.4176, loss: 4.4176 +2024-07-16 18:30:21,849 - pyskl - INFO - Epoch [15][2500/3746] lr: 9.766e-02, eta: 4 days, 6:34:59, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4541, loss_cls: 4.3364, loss: 4.3364 +2024-07-16 18:31:32,443 - pyskl - INFO - Epoch [15][2600/3746] lr: 9.765e-02, eta: 4 days, 6:33:26, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4511, loss_cls: 4.3525, loss: 4.3525 +2024-07-16 18:32:42,729 - pyskl - INFO - Epoch [15][2700/3746] lr: 9.764e-02, eta: 4 days, 6:31:50, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4464, loss_cls: 4.3485, loss: 4.3485 +2024-07-16 18:33:52,689 - pyskl - INFO - Epoch [15][2800/3746] lr: 9.763e-02, eta: 4 days, 6:30:10, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4437, loss_cls: 4.3896, loss: 4.3896 +2024-07-16 18:35:02,769 - pyskl - INFO - Epoch [15][2900/3746] lr: 9.763e-02, eta: 4 days, 6:28:32, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4419, loss_cls: 4.3896, loss: 4.3896 +2024-07-16 18:36:12,831 - pyskl - INFO - Epoch [15][3000/3746] lr: 9.762e-02, eta: 4 days, 6:26:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4408, loss_cls: 4.4045, loss: 4.4045 +2024-07-16 18:37:23,077 - pyskl - INFO - Epoch [15][3100/3746] lr: 9.761e-02, eta: 4 days, 6:25:18, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4516, loss_cls: 4.3609, loss: 4.3609 +2024-07-16 18:38:33,250 - pyskl - INFO - Epoch [15][3200/3746] lr: 9.760e-02, eta: 4 days, 6:23:41, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4541, loss_cls: 4.3510, loss: 4.3510 +2024-07-16 18:39:43,513 - pyskl - INFO - Epoch [15][3300/3746] lr: 9.759e-02, eta: 4 days, 6:22:05, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4313, loss_cls: 4.3989, loss: 4.3989 +2024-07-16 18:40:53,882 - pyskl - INFO - Epoch [15][3400/3746] lr: 9.758e-02, eta: 4 days, 6:20:30, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2037, top5_acc: 0.4356, loss_cls: 4.3907, loss: 4.3907 +2024-07-16 18:42:04,341 - pyskl - INFO - Epoch [15][3500/3746] lr: 9.757e-02, eta: 4 days, 6:18:56, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2008, top5_acc: 0.4412, loss_cls: 4.4115, loss: 4.4115 +2024-07-16 18:43:15,186 - pyskl - INFO - Epoch [15][3600/3746] lr: 9.757e-02, eta: 4 days, 6:17:26, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4336, loss_cls: 4.4237, loss: 4.4237 +2024-07-16 18:44:25,938 - pyskl - INFO - Epoch [15][3700/3746] lr: 9.756e-02, eta: 4 days, 6:15:54, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4500, loss_cls: 4.3817, loss: 4.3817 +2024-07-16 18:45:00,193 - pyskl - INFO - Saving checkpoint at 15 epochs +2024-07-16 18:46:51,708 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 18:46:52,407 - pyskl - INFO - +top1_acc 0.1063 +top5_acc 0.2810 +2024-07-16 18:46:52,407 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 18:46:52,450 - pyskl - INFO - +mean_acc 0.1062 +2024-07-16 18:46:52,463 - pyskl - INFO - Epoch(val) [15][309] top1_acc: 0.1063, top5_acc: 0.2810, mean_class_accuracy: 0.1062 +2024-07-16 18:50:08,435 - pyskl - INFO - Epoch [16][100/3746] lr: 9.754e-02, eta: 4 days, 6:27:34, time: 1.960, data_time: 1.248, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4580, loss_cls: 4.3400, loss: 4.3400 +2024-07-16 18:51:19,475 - pyskl - INFO - Epoch [16][200/3746] lr: 9.754e-02, eta: 4 days, 6:26:04, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4558, loss_cls: 4.3232, loss: 4.3232 +2024-07-16 18:52:30,041 - pyskl - INFO - Epoch [16][300/3746] lr: 9.753e-02, eta: 4 days, 6:24:29, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4450, loss_cls: 4.3501, loss: 4.3501 +2024-07-16 18:53:40,542 - pyskl - INFO - Epoch [16][400/3746] lr: 9.752e-02, eta: 4 days, 6:22:54, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4527, loss_cls: 4.3525, loss: 4.3525 +2024-07-16 18:54:50,629 - pyskl - INFO - Epoch [16][500/3746] lr: 9.751e-02, eta: 4 days, 6:21:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4384, loss_cls: 4.4200, loss: 4.4200 +2024-07-16 18:56:00,556 - pyskl - INFO - Epoch [16][600/3746] lr: 9.750e-02, eta: 4 days, 6:19:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4378, loss_cls: 4.3937, loss: 4.3937 +2024-07-16 18:57:10,858 - pyskl - INFO - Epoch [16][700/3746] lr: 9.749e-02, eta: 4 days, 6:18:00, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2092, top5_acc: 0.4494, loss_cls: 4.3456, loss: 4.3456 +2024-07-16 18:58:21,152 - pyskl - INFO - Epoch [16][800/3746] lr: 9.748e-02, eta: 4 days, 6:16:24, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4470, loss_cls: 4.3702, loss: 4.3702 +2024-07-16 18:59:31,589 - pyskl - INFO - Epoch [16][900/3746] lr: 9.747e-02, eta: 4 days, 6:14:49, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1997, top5_acc: 0.4359, loss_cls: 4.4078, loss: 4.4078 +2024-07-16 19:00:41,962 - pyskl - INFO - Epoch [16][1000/3746] lr: 9.747e-02, eta: 4 days, 6:13:14, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2108, top5_acc: 0.4447, loss_cls: 4.3737, loss: 4.3737 +2024-07-16 19:01:52,222 - pyskl - INFO - Epoch [16][1100/3746] lr: 9.746e-02, eta: 4 days, 6:11:37, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4481, loss_cls: 4.3678, loss: 4.3678 +2024-07-16 19:03:02,278 - pyskl - INFO - Epoch [16][1200/3746] lr: 9.745e-02, eta: 4 days, 6:09:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1973, top5_acc: 0.4345, loss_cls: 4.4247, loss: 4.4247 +2024-07-16 19:04:12,695 - pyskl - INFO - Epoch [16][1300/3746] lr: 9.744e-02, eta: 4 days, 6:08:25, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4670, loss_cls: 4.3051, loss: 4.3051 +2024-07-16 19:05:22,953 - pyskl - INFO - Epoch [16][1400/3746] lr: 9.743e-02, eta: 4 days, 6:06:49, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4492, loss_cls: 4.3910, loss: 4.3910 +2024-07-16 19:06:33,320 - pyskl - INFO - Epoch [16][1500/3746] lr: 9.742e-02, eta: 4 days, 6:05:14, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4472, loss_cls: 4.3878, loss: 4.3878 +2024-07-16 19:07:43,451 - pyskl - INFO - Epoch [16][1600/3746] lr: 9.741e-02, eta: 4 days, 6:03:37, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4534, loss_cls: 4.3361, loss: 4.3361 +2024-07-16 19:08:53,692 - pyskl - INFO - Epoch [16][1700/3746] lr: 9.740e-02, eta: 4 days, 6:02:01, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4430, loss_cls: 4.3792, loss: 4.3792 +2024-07-16 19:10:03,958 - pyskl - INFO - Epoch [16][1800/3746] lr: 9.740e-02, eta: 4 days, 6:00:25, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4527, loss_cls: 4.3576, loss: 4.3576 +2024-07-16 19:11:14,200 - pyskl - INFO - Epoch [16][1900/3746] lr: 9.739e-02, eta: 4 days, 5:58:49, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4503, loss_cls: 4.3304, loss: 4.3304 +2024-07-16 19:12:24,468 - pyskl - INFO - Epoch [16][2000/3746] lr: 9.738e-02, eta: 4 days, 5:57:14, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2030, top5_acc: 0.4345, loss_cls: 4.4197, loss: 4.4197 +2024-07-16 19:13:34,700 - pyskl - INFO - Epoch [16][2100/3746] lr: 9.737e-02, eta: 4 days, 5:55:38, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4391, loss_cls: 4.3953, loss: 4.3953 +2024-07-16 19:14:44,744 - pyskl - INFO - Epoch [16][2200/3746] lr: 9.736e-02, eta: 4 days, 5:54:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4423, loss_cls: 4.3830, loss: 4.3830 +2024-07-16 19:15:54,425 - pyskl - INFO - Epoch [16][2300/3746] lr: 9.735e-02, eta: 4 days, 5:52:21, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4462, loss_cls: 4.3658, loss: 4.3658 +2024-07-16 19:17:04,479 - pyskl - INFO - Epoch [16][2400/3746] lr: 9.734e-02, eta: 4 days, 5:50:44, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4408, loss_cls: 4.4161, loss: 4.4161 +2024-07-16 19:18:14,425 - pyskl - INFO - Epoch [16][2500/3746] lr: 9.733e-02, eta: 4 days, 5:49:06, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4336, loss_cls: 4.4267, loss: 4.4267 +2024-07-16 19:19:24,552 - pyskl - INFO - Epoch [16][2600/3746] lr: 9.732e-02, eta: 4 days, 5:47:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2037, top5_acc: 0.4391, loss_cls: 4.4123, loss: 4.4123 +2024-07-16 19:20:34,366 - pyskl - INFO - Epoch [16][2700/3746] lr: 9.731e-02, eta: 4 days, 5:45:52, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4512, loss_cls: 4.3465, loss: 4.3465 +2024-07-16 19:21:44,390 - pyskl - INFO - Epoch [16][2800/3746] lr: 9.731e-02, eta: 4 days, 5:44:15, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2092, top5_acc: 0.4480, loss_cls: 4.3975, loss: 4.3975 +2024-07-16 19:22:53,994 - pyskl - INFO - Epoch [16][2900/3746] lr: 9.730e-02, eta: 4 days, 5:42:35, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4486, loss_cls: 4.3781, loss: 4.3781 +2024-07-16 19:24:03,959 - pyskl - INFO - Epoch [16][3000/3746] lr: 9.729e-02, eta: 4 days, 5:40:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4447, loss_cls: 4.3696, loss: 4.3696 +2024-07-16 19:25:14,097 - pyskl - INFO - Epoch [16][3100/3746] lr: 9.728e-02, eta: 4 days, 5:39:22, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4428, loss_cls: 4.3977, loss: 4.3977 +2024-07-16 19:26:23,992 - pyskl - INFO - Epoch [16][3200/3746] lr: 9.727e-02, eta: 4 days, 5:37:44, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4361, loss_cls: 4.4030, loss: 4.4030 +2024-07-16 19:27:33,668 - pyskl - INFO - Epoch [16][3300/3746] lr: 9.726e-02, eta: 4 days, 5:36:05, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4458, loss_cls: 4.3765, loss: 4.3765 +2024-07-16 19:28:43,448 - pyskl - INFO - Epoch [16][3400/3746] lr: 9.725e-02, eta: 4 days, 5:34:27, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4495, loss_cls: 4.3589, loss: 4.3589 +2024-07-16 19:29:53,559 - pyskl - INFO - Epoch [16][3500/3746] lr: 9.724e-02, eta: 4 days, 5:32:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4439, loss_cls: 4.3780, loss: 4.3780 +2024-07-16 19:31:04,436 - pyskl - INFO - Epoch [16][3600/3746] lr: 9.723e-02, eta: 4 days, 5:31:23, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4522, loss_cls: 4.3529, loss: 4.3529 +2024-07-16 19:32:14,696 - pyskl - INFO - Epoch [16][3700/3746] lr: 9.722e-02, eta: 4 days, 5:29:49, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4402, loss_cls: 4.3839, loss: 4.3839 +2024-07-16 19:32:49,052 - pyskl - INFO - Saving checkpoint at 16 epochs +2024-07-16 19:34:41,192 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 19:34:41,882 - pyskl - INFO - +top1_acc 0.1307 +top5_acc 0.3113 +2024-07-16 19:34:41,882 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 19:34:41,921 - pyskl - INFO - +mean_acc 0.1305 +2024-07-16 19:34:41,933 - pyskl - INFO - Epoch(val) [16][309] top1_acc: 0.1307, top5_acc: 0.3113, mean_class_accuracy: 0.1305 +2024-07-16 19:37:58,252 - pyskl - INFO - Epoch [17][100/3746] lr: 9.721e-02, eta: 4 days, 5:40:35, time: 1.963, data_time: 1.253, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4559, loss_cls: 4.3404, loss: 4.3404 +2024-07-16 19:39:09,234 - pyskl - INFO - Epoch [17][200/3746] lr: 9.720e-02, eta: 4 days, 5:39:06, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4477, loss_cls: 4.3554, loss: 4.3554 +2024-07-16 19:40:20,407 - pyskl - INFO - Epoch [17][300/3746] lr: 9.719e-02, eta: 4 days, 5:37:38, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4608, loss_cls: 4.3542, loss: 4.3542 +2024-07-16 19:41:31,317 - pyskl - INFO - Epoch [17][400/3746] lr: 9.718e-02, eta: 4 days, 5:36:09, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4594, loss_cls: 4.3280, loss: 4.3280 +2024-07-16 19:42:41,483 - pyskl - INFO - Epoch [17][500/3746] lr: 9.717e-02, eta: 4 days, 5:34:33, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4597, loss_cls: 4.3412, loss: 4.3412 +2024-07-16 19:43:51,779 - pyskl - INFO - Epoch [17][600/3746] lr: 9.716e-02, eta: 4 days, 5:32:58, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4383, loss_cls: 4.3984, loss: 4.3984 +2024-07-16 19:45:01,703 - pyskl - INFO - Epoch [17][700/3746] lr: 9.715e-02, eta: 4 days, 5:31:20, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4512, loss_cls: 4.3449, loss: 4.3449 +2024-07-16 19:46:11,610 - pyskl - INFO - Epoch [17][800/3746] lr: 9.714e-02, eta: 4 days, 5:29:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4459, loss_cls: 4.3580, loss: 4.3580 +2024-07-16 19:47:21,600 - pyskl - INFO - Epoch [17][900/3746] lr: 9.714e-02, eta: 4 days, 5:28:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4444, loss_cls: 4.3860, loss: 4.3860 +2024-07-16 19:48:31,608 - pyskl - INFO - Epoch [17][1000/3746] lr: 9.713e-02, eta: 4 days, 5:26:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4462, loss_cls: 4.3449, loss: 4.3449 +2024-07-16 19:49:41,659 - pyskl - INFO - Epoch [17][1100/3746] lr: 9.712e-02, eta: 4 days, 5:24:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4486, loss_cls: 4.3767, loss: 4.3767 +2024-07-16 19:50:51,706 - pyskl - INFO - Epoch [17][1200/3746] lr: 9.711e-02, eta: 4 days, 5:23:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4495, loss_cls: 4.3388, loss: 4.3388 +2024-07-16 19:52:01,820 - pyskl - INFO - Epoch [17][1300/3746] lr: 9.710e-02, eta: 4 days, 5:21:41, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4492, loss_cls: 4.3560, loss: 4.3560 +2024-07-16 19:53:11,934 - pyskl - INFO - Epoch [17][1400/3746] lr: 9.709e-02, eta: 4 days, 5:20:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1959, top5_acc: 0.4400, loss_cls: 4.4100, loss: 4.4100 +2024-07-16 19:54:21,764 - pyskl - INFO - Epoch [17][1500/3746] lr: 9.708e-02, eta: 4 days, 5:18:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4448, loss_cls: 4.3653, loss: 4.3653 +2024-07-16 19:55:31,582 - pyskl - INFO - Epoch [17][1600/3746] lr: 9.707e-02, eta: 4 days, 5:16:50, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4478, loss_cls: 4.3711, loss: 4.3711 +2024-07-16 19:56:41,992 - pyskl - INFO - Epoch [17][1700/3746] lr: 9.706e-02, eta: 4 days, 5:15:17, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4441, loss_cls: 4.3922, loss: 4.3922 +2024-07-16 19:57:52,798 - pyskl - INFO - Epoch [17][1800/3746] lr: 9.705e-02, eta: 4 days, 5:13:48, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4478, loss_cls: 4.3623, loss: 4.3623 +2024-07-16 19:59:02,908 - pyskl - INFO - Epoch [17][1900/3746] lr: 9.704e-02, eta: 4 days, 5:12:13, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4497, loss_cls: 4.3621, loss: 4.3621 +2024-07-16 20:00:12,850 - pyskl - INFO - Epoch [17][2000/3746] lr: 9.703e-02, eta: 4 days, 5:10:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4405, loss_cls: 4.3877, loss: 4.3877 +2024-07-16 20:01:23,915 - pyskl - INFO - Epoch [17][2100/3746] lr: 9.702e-02, eta: 4 days, 5:09:09, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4491, loss_cls: 4.3624, loss: 4.3624 +2024-07-16 20:02:34,232 - pyskl - INFO - Epoch [17][2200/3746] lr: 9.701e-02, eta: 4 days, 5:07:36, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4486, loss_cls: 4.3593, loss: 4.3593 +2024-07-16 20:03:44,318 - pyskl - INFO - Epoch [17][2300/3746] lr: 9.700e-02, eta: 4 days, 5:06:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2114, top5_acc: 0.4555, loss_cls: 4.3360, loss: 4.3360 +2024-07-16 20:04:54,078 - pyskl - INFO - Epoch [17][2400/3746] lr: 9.699e-02, eta: 4 days, 5:04:23, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4473, loss_cls: 4.3774, loss: 4.3774 +2024-07-16 20:06:03,947 - pyskl - INFO - Epoch [17][2500/3746] lr: 9.698e-02, eta: 4 days, 5:02:46, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4450, loss_cls: 4.3603, loss: 4.3603 +2024-07-16 20:07:14,273 - pyskl - INFO - Epoch [17][2600/3746] lr: 9.697e-02, eta: 4 days, 5:01:14, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2030, top5_acc: 0.4477, loss_cls: 4.3667, loss: 4.3667 +2024-07-16 20:08:24,385 - pyskl - INFO - Epoch [17][2700/3746] lr: 9.697e-02, eta: 4 days, 4:59:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4511, loss_cls: 4.3492, loss: 4.3492 +2024-07-16 20:09:34,159 - pyskl - INFO - Epoch [17][2800/3746] lr: 9.696e-02, eta: 4 days, 4:58:02, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4430, loss_cls: 4.3816, loss: 4.3816 +2024-07-16 20:10:44,230 - pyskl - INFO - Epoch [17][2900/3746] lr: 9.695e-02, eta: 4 days, 4:56:27, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4414, loss_cls: 4.3881, loss: 4.3881 +2024-07-16 20:11:54,248 - pyskl - INFO - Epoch [17][3000/3746] lr: 9.694e-02, eta: 4 days, 4:54:52, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4472, loss_cls: 4.3818, loss: 4.3818 +2024-07-16 20:13:04,037 - pyskl - INFO - Epoch [17][3100/3746] lr: 9.693e-02, eta: 4 days, 4:53:16, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4484, loss_cls: 4.3704, loss: 4.3704 +2024-07-16 20:14:14,410 - pyskl - INFO - Epoch [17][3200/3746] lr: 9.692e-02, eta: 4 days, 4:51:44, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4481, loss_cls: 4.3862, loss: 4.3862 +2024-07-16 20:15:24,294 - pyskl - INFO - Epoch [17][3300/3746] lr: 9.691e-02, eta: 4 days, 4:50:08, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2025, top5_acc: 0.4400, loss_cls: 4.3980, loss: 4.3980 +2024-07-16 20:16:34,437 - pyskl - INFO - Epoch [17][3400/3746] lr: 9.690e-02, eta: 4 days, 4:48:34, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4525, loss_cls: 4.3625, loss: 4.3625 +2024-07-16 20:17:44,708 - pyskl - INFO - Epoch [17][3500/3746] lr: 9.689e-02, eta: 4 days, 4:47:02, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4523, loss_cls: 4.3484, loss: 4.3484 +2024-07-16 20:18:56,042 - pyskl - INFO - Epoch [17][3600/3746] lr: 9.688e-02, eta: 4 days, 4:45:37, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4570, loss_cls: 4.3555, loss: 4.3555 +2024-07-16 20:20:06,608 - pyskl - INFO - Epoch [17][3700/3746] lr: 9.687e-02, eta: 4 days, 4:44:07, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4367, loss_cls: 4.4108, loss: 4.4108 +2024-07-16 20:20:40,764 - pyskl - INFO - Saving checkpoint at 17 epochs +2024-07-16 20:22:31,426 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 20:22:32,100 - pyskl - INFO - +top1_acc 0.1254 +top5_acc 0.3098 +2024-07-16 20:22:32,100 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 20:22:32,139 - pyskl - INFO - +mean_acc 0.1255 +2024-07-16 20:22:32,152 - pyskl - INFO - Epoch(val) [17][309] top1_acc: 0.1254, top5_acc: 0.3098, mean_class_accuracy: 0.1255 +2024-07-16 20:25:47,814 - pyskl - INFO - Epoch [18][100/3746] lr: 9.685e-02, eta: 4 days, 4:53:59, time: 1.957, data_time: 1.250, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4530, loss_cls: 4.3218, loss: 4.3218 +2024-07-16 20:26:59,069 - pyskl - INFO - Epoch [18][200/3746] lr: 9.684e-02, eta: 4 days, 4:52:33, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4592, loss_cls: 4.3216, loss: 4.3216 +2024-07-16 20:28:09,720 - pyskl - INFO - Epoch [18][300/3746] lr: 9.683e-02, eta: 4 days, 4:51:03, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4541, loss_cls: 4.3474, loss: 4.3474 +2024-07-16 20:29:20,235 - pyskl - INFO - Epoch [18][400/3746] lr: 9.683e-02, eta: 4 days, 4:49:31, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4416, loss_cls: 4.4026, loss: 4.4026 +2024-07-16 20:30:30,819 - pyskl - INFO - Epoch [18][500/3746] lr: 9.682e-02, eta: 4 days, 4:48:00, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1997, top5_acc: 0.4427, loss_cls: 4.4090, loss: 4.4090 +2024-07-16 20:31:41,132 - pyskl - INFO - Epoch [18][600/3746] lr: 9.681e-02, eta: 4 days, 4:46:27, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4480, loss_cls: 4.3362, loss: 4.3362 +2024-07-16 20:32:51,393 - pyskl - INFO - Epoch [18][700/3746] lr: 9.680e-02, eta: 4 days, 4:44:54, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4589, loss_cls: 4.3152, loss: 4.3152 +2024-07-16 20:34:01,526 - pyskl - INFO - Epoch [18][800/3746] lr: 9.679e-02, eta: 4 days, 4:43:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4506, loss_cls: 4.3598, loss: 4.3598 +2024-07-16 20:35:11,612 - pyskl - INFO - Epoch [18][900/3746] lr: 9.678e-02, eta: 4 days, 4:41:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4553, loss_cls: 4.3113, loss: 4.3113 +2024-07-16 20:36:22,129 - pyskl - INFO - Epoch [18][1000/3746] lr: 9.677e-02, eta: 4 days, 4:40:14, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4500, loss_cls: 4.3564, loss: 4.3564 +2024-07-16 20:37:32,350 - pyskl - INFO - Epoch [18][1100/3746] lr: 9.676e-02, eta: 4 days, 4:38:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4462, loss_cls: 4.3615, loss: 4.3615 +2024-07-16 20:38:42,696 - pyskl - INFO - Epoch [18][1200/3746] lr: 9.675e-02, eta: 4 days, 4:37:08, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4447, loss_cls: 4.3803, loss: 4.3803 +2024-07-16 20:39:52,861 - pyskl - INFO - Epoch [18][1300/3746] lr: 9.674e-02, eta: 4 days, 4:35:34, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4477, loss_cls: 4.3537, loss: 4.3537 +2024-07-16 20:41:02,873 - pyskl - INFO - Epoch [18][1400/3746] lr: 9.673e-02, eta: 4 days, 4:34:00, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4548, loss_cls: 4.3162, loss: 4.3162 +2024-07-16 20:42:13,108 - pyskl - INFO - Epoch [18][1500/3746] lr: 9.672e-02, eta: 4 days, 4:32:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4608, loss_cls: 4.3230, loss: 4.3230 +2024-07-16 20:43:23,339 - pyskl - INFO - Epoch [18][1600/3746] lr: 9.671e-02, eta: 4 days, 4:30:54, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4558, loss_cls: 4.3426, loss: 4.3426 +2024-07-16 20:44:33,658 - pyskl - INFO - Epoch [18][1700/3746] lr: 9.670e-02, eta: 4 days, 4:29:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4516, loss_cls: 4.3493, loss: 4.3493 +2024-07-16 20:45:44,557 - pyskl - INFO - Epoch [18][1800/3746] lr: 9.669e-02, eta: 4 days, 4:27:54, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4536, loss_cls: 4.3386, loss: 4.3386 +2024-07-16 20:46:55,318 - pyskl - INFO - Epoch [18][1900/3746] lr: 9.668e-02, eta: 4 days, 4:26:25, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4591, loss_cls: 4.3150, loss: 4.3150 +2024-07-16 20:48:05,742 - pyskl - INFO - Epoch [18][2000/3746] lr: 9.667e-02, eta: 4 days, 4:24:54, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2078, top5_acc: 0.4433, loss_cls: 4.3990, loss: 4.3990 +2024-07-16 20:49:16,582 - pyskl - INFO - Epoch [18][2100/3746] lr: 9.666e-02, eta: 4 days, 4:23:26, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4439, loss_cls: 4.3713, loss: 4.3713 +2024-07-16 20:50:26,847 - pyskl - INFO - Epoch [18][2200/3746] lr: 9.665e-02, eta: 4 days, 4:21:54, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4539, loss_cls: 4.3378, loss: 4.3378 +2024-07-16 20:51:37,205 - pyskl - INFO - Epoch [18][2300/3746] lr: 9.664e-02, eta: 4 days, 4:20:22, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4587, loss_cls: 4.3509, loss: 4.3509 +2024-07-16 20:52:47,825 - pyskl - INFO - Epoch [18][2400/3746] lr: 9.663e-02, eta: 4 days, 4:18:52, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4461, loss_cls: 4.3811, loss: 4.3811 +2024-07-16 20:53:58,125 - pyskl - INFO - Epoch [18][2500/3746] lr: 9.662e-02, eta: 4 days, 4:17:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4484, loss_cls: 4.3631, loss: 4.3631 +2024-07-16 20:55:08,371 - pyskl - INFO - Epoch [18][2600/3746] lr: 9.661e-02, eta: 4 days, 4:15:48, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4531, loss_cls: 4.3383, loss: 4.3383 +2024-07-16 20:56:18,445 - pyskl - INFO - Epoch [18][2700/3746] lr: 9.660e-02, eta: 4 days, 4:14:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4441, loss_cls: 4.3880, loss: 4.3880 +2024-07-16 20:57:28,796 - pyskl - INFO - Epoch [18][2800/3746] lr: 9.659e-02, eta: 4 days, 4:12:44, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4389, loss_cls: 4.3952, loss: 4.3952 +2024-07-16 20:58:39,040 - pyskl - INFO - Epoch [18][2900/3746] lr: 9.658e-02, eta: 4 days, 4:11:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2114, top5_acc: 0.4492, loss_cls: 4.3560, loss: 4.3560 +2024-07-16 20:59:49,549 - pyskl - INFO - Epoch [18][3000/3746] lr: 9.657e-02, eta: 4 days, 4:09:42, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4484, loss_cls: 4.3710, loss: 4.3710 +2024-07-16 21:01:00,033 - pyskl - INFO - Epoch [18][3100/3746] lr: 9.656e-02, eta: 4 days, 4:08:12, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4497, loss_cls: 4.3504, loss: 4.3504 +2024-07-16 21:02:10,113 - pyskl - INFO - Epoch [18][3200/3746] lr: 9.654e-02, eta: 4 days, 4:06:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4661, loss_cls: 4.3026, loss: 4.3026 +2024-07-16 21:03:20,353 - pyskl - INFO - Epoch [18][3300/3746] lr: 9.653e-02, eta: 4 days, 4:05:07, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4444, loss_cls: 4.3833, loss: 4.3833 +2024-07-16 21:04:30,557 - pyskl - INFO - Epoch [18][3400/3746] lr: 9.652e-02, eta: 4 days, 4:03:35, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2033, top5_acc: 0.4414, loss_cls: 4.4283, loss: 4.4283 +2024-07-16 21:05:40,910 - pyskl - INFO - Epoch [18][3500/3746] lr: 9.651e-02, eta: 4 days, 4:02:04, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2005, top5_acc: 0.4405, loss_cls: 4.4043, loss: 4.4043 +2024-07-16 21:06:51,998 - pyskl - INFO - Epoch [18][3600/3746] lr: 9.650e-02, eta: 4 days, 4:00:39, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4612, loss_cls: 4.3212, loss: 4.3212 +2024-07-16 21:08:02,788 - pyskl - INFO - Epoch [18][3700/3746] lr: 9.649e-02, eta: 4 days, 3:59:11, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4314, loss_cls: 4.4074, loss: 4.4074 +2024-07-16 21:08:37,280 - pyskl - INFO - Saving checkpoint at 18 epochs +2024-07-16 21:10:27,465 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 21:10:28,189 - pyskl - INFO - +top1_acc 0.1568 +top5_acc 0.3581 +2024-07-16 21:10:28,189 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 21:10:28,228 - pyskl - INFO - +mean_acc 0.1568 +2024-07-16 21:10:28,233 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_10.pth was removed +2024-07-16 21:10:28,463 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_18.pth. +2024-07-16 21:10:28,464 - pyskl - INFO - Best top1_acc is 0.1568 at 18 epoch. +2024-07-16 21:10:28,475 - pyskl - INFO - Epoch(val) [18][309] top1_acc: 0.1568, top5_acc: 0.3581, mean_class_accuracy: 0.1568 +2024-07-16 21:13:43,515 - pyskl - INFO - Epoch [19][100/3746] lr: 9.648e-02, eta: 4 days, 4:08:15, time: 1.950, data_time: 1.246, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4605, loss_cls: 4.3255, loss: 4.3255 +2024-07-16 21:14:54,774 - pyskl - INFO - Epoch [19][200/3746] lr: 9.647e-02, eta: 4 days, 4:06:50, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4570, loss_cls: 4.3306, loss: 4.3306 +2024-07-16 21:16:06,098 - pyskl - INFO - Epoch [19][300/3746] lr: 9.646e-02, eta: 4 days, 4:05:25, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4719, loss_cls: 4.2680, loss: 4.2680 +2024-07-16 21:17:16,626 - pyskl - INFO - Epoch [19][400/3746] lr: 9.645e-02, eta: 4 days, 4:03:55, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4505, loss_cls: 4.3617, loss: 4.3617 +2024-07-16 21:18:26,994 - pyskl - INFO - Epoch [19][500/3746] lr: 9.644e-02, eta: 4 days, 4:02:24, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4494, loss_cls: 4.3468, loss: 4.3468 +2024-07-16 21:19:37,044 - pyskl - INFO - Epoch [19][600/3746] lr: 9.643e-02, eta: 4 days, 4:00:50, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4492, loss_cls: 4.3754, loss: 4.3754 +2024-07-16 21:20:47,953 - pyskl - INFO - Epoch [19][700/3746] lr: 9.642e-02, eta: 4 days, 3:59:23, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2131, top5_acc: 0.4527, loss_cls: 4.3145, loss: 4.3145 +2024-07-16 21:21:57,958 - pyskl - INFO - Epoch [19][800/3746] lr: 9.641e-02, eta: 4 days, 3:57:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4427, loss_cls: 4.3565, loss: 4.3565 +2024-07-16 21:23:08,253 - pyskl - INFO - Epoch [19][900/3746] lr: 9.640e-02, eta: 4 days, 3:56:17, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4562, loss_cls: 4.3573, loss: 4.3573 +2024-07-16 21:24:18,235 - pyskl - INFO - Epoch [19][1000/3746] lr: 9.639e-02, eta: 4 days, 3:54:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4491, loss_cls: 4.3607, loss: 4.3607 +2024-07-16 21:25:28,051 - pyskl - INFO - Epoch [19][1100/3746] lr: 9.637e-02, eta: 4 days, 3:53:08, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4420, loss_cls: 4.3914, loss: 4.3914 +2024-07-16 21:26:37,865 - pyskl - INFO - Epoch [19][1200/3746] lr: 9.636e-02, eta: 4 days, 3:51:33, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4484, loss_cls: 4.3308, loss: 4.3308 +2024-07-16 21:27:47,858 - pyskl - INFO - Epoch [19][1300/3746] lr: 9.635e-02, eta: 4 days, 3:50:00, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4355, loss_cls: 4.4209, loss: 4.4209 +2024-07-16 21:28:58,035 - pyskl - INFO - Epoch [19][1400/3746] lr: 9.634e-02, eta: 4 days, 3:48:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4436, loss_cls: 4.4083, loss: 4.4083 +2024-07-16 21:30:08,336 - pyskl - INFO - Epoch [19][1500/3746] lr: 9.633e-02, eta: 4 days, 3:46:56, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4555, loss_cls: 4.3344, loss: 4.3344 +2024-07-16 21:31:18,484 - pyskl - INFO - Epoch [19][1600/3746] lr: 9.632e-02, eta: 4 days, 3:45:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4514, loss_cls: 4.3590, loss: 4.3590 +2024-07-16 21:32:28,664 - pyskl - INFO - Epoch [19][1700/3746] lr: 9.631e-02, eta: 4 days, 3:43:52, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4617, loss_cls: 4.3322, loss: 4.3322 +2024-07-16 21:33:39,391 - pyskl - INFO - Epoch [19][1800/3746] lr: 9.630e-02, eta: 4 days, 3:42:24, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4545, loss_cls: 4.3298, loss: 4.3298 +2024-07-16 21:34:49,565 - pyskl - INFO - Epoch [19][1900/3746] lr: 9.629e-02, eta: 4 days, 3:40:52, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4509, loss_cls: 4.3255, loss: 4.3255 +2024-07-16 21:36:00,050 - pyskl - INFO - Epoch [19][2000/3746] lr: 9.628e-02, eta: 4 days, 3:39:22, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4506, loss_cls: 4.3490, loss: 4.3490 +2024-07-16 21:37:10,728 - pyskl - INFO - Epoch [19][2100/3746] lr: 9.627e-02, eta: 4 days, 3:37:54, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4441, loss_cls: 4.3638, loss: 4.3638 +2024-07-16 21:38:21,091 - pyskl - INFO - Epoch [19][2200/3746] lr: 9.626e-02, eta: 4 days, 3:36:24, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4591, loss_cls: 4.3300, loss: 4.3300 +2024-07-16 21:39:31,146 - pyskl - INFO - Epoch [19][2300/3746] lr: 9.625e-02, eta: 4 days, 3:34:51, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4423, loss_cls: 4.3848, loss: 4.3848 +2024-07-16 21:40:41,408 - pyskl - INFO - Epoch [19][2400/3746] lr: 9.624e-02, eta: 4 days, 3:33:20, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4416, loss_cls: 4.3838, loss: 4.3838 +2024-07-16 21:41:51,343 - pyskl - INFO - Epoch [19][2500/3746] lr: 9.623e-02, eta: 4 days, 3:31:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2092, top5_acc: 0.4455, loss_cls: 4.3739, loss: 4.3739 +2024-07-16 21:43:01,317 - pyskl - INFO - Epoch [19][2600/3746] lr: 9.622e-02, eta: 4 days, 3:30:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4475, loss_cls: 4.3600, loss: 4.3600 +2024-07-16 21:44:11,167 - pyskl - INFO - Epoch [19][2700/3746] lr: 9.621e-02, eta: 4 days, 3:28:40, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4448, loss_cls: 4.3608, loss: 4.3608 +2024-07-16 21:45:21,349 - pyskl - INFO - Epoch [19][2800/3746] lr: 9.620e-02, eta: 4 days, 3:27:09, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4469, loss_cls: 4.3997, loss: 4.3997 +2024-07-16 21:46:31,364 - pyskl - INFO - Epoch [19][2900/3746] lr: 9.618e-02, eta: 4 days, 3:25:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4459, loss_cls: 4.3930, loss: 4.3930 +2024-07-16 21:47:41,266 - pyskl - INFO - Epoch [19][3000/3746] lr: 9.617e-02, eta: 4 days, 3:24:03, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4698, loss_cls: 4.2781, loss: 4.2781 +2024-07-16 21:48:51,428 - pyskl - INFO - Epoch [19][3100/3746] lr: 9.616e-02, eta: 4 days, 3:22:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1997, top5_acc: 0.4342, loss_cls: 4.4066, loss: 4.4066 +2024-07-16 21:50:01,395 - pyskl - INFO - Epoch [19][3200/3746] lr: 9.615e-02, eta: 4 days, 3:20:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4497, loss_cls: 4.3546, loss: 4.3546 +2024-07-16 21:51:11,323 - pyskl - INFO - Epoch [19][3300/3746] lr: 9.614e-02, eta: 4 days, 3:19:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4550, loss_cls: 4.3580, loss: 4.3580 +2024-07-16 21:52:21,176 - pyskl - INFO - Epoch [19][3400/3746] lr: 9.613e-02, eta: 4 days, 3:17:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4447, loss_cls: 4.3920, loss: 4.3920 +2024-07-16 21:53:32,116 - pyskl - INFO - Epoch [19][3500/3746] lr: 9.612e-02, eta: 4 days, 3:16:28, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4439, loss_cls: 4.3892, loss: 4.3892 +2024-07-16 21:54:43,176 - pyskl - INFO - Epoch [19][3600/3746] lr: 9.611e-02, eta: 4 days, 3:15:03, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4480, loss_cls: 4.3678, loss: 4.3678 +2024-07-16 21:55:53,838 - pyskl - INFO - Epoch [19][3700/3746] lr: 9.610e-02, eta: 4 days, 3:13:36, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4609, loss_cls: 4.3392, loss: 4.3392 +2024-07-16 21:56:27,883 - pyskl - INFO - Saving checkpoint at 19 epochs +2024-07-16 21:58:17,737 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 21:58:18,427 - pyskl - INFO - +top1_acc 0.1441 +top5_acc 0.3422 +2024-07-16 21:58:18,427 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 21:58:18,467 - pyskl - INFO - +mean_acc 0.1438 +2024-07-16 21:58:18,478 - pyskl - INFO - Epoch(val) [19][309] top1_acc: 0.1441, top5_acc: 0.3422, mean_class_accuracy: 0.1438 +2024-07-16 22:01:34,216 - pyskl - INFO - Epoch [20][100/3746] lr: 9.608e-02, eta: 4 days, 3:22:05, time: 1.957, data_time: 1.247, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4508, loss_cls: 4.3086, loss: 4.3086 +2024-07-16 22:02:45,664 - pyskl - INFO - Epoch [20][200/3746] lr: 9.607e-02, eta: 4 days, 3:20:42, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4625, loss_cls: 4.2866, loss: 4.2866 +2024-07-16 22:03:56,422 - pyskl - INFO - Epoch [20][300/3746] lr: 9.606e-02, eta: 4 days, 3:19:14, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4466, loss_cls: 4.3394, loss: 4.3394 +2024-07-16 22:05:07,057 - pyskl - INFO - Epoch [20][400/3746] lr: 9.605e-02, eta: 4 days, 3:17:46, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4408, loss_cls: 4.3684, loss: 4.3684 +2024-07-16 22:06:16,979 - pyskl - INFO - Epoch [20][500/3746] lr: 9.604e-02, eta: 4 days, 3:16:13, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4583, loss_cls: 4.3340, loss: 4.3340 +2024-07-16 22:07:26,867 - pyskl - INFO - Epoch [20][600/3746] lr: 9.603e-02, eta: 4 days, 3:14:39, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2059, top5_acc: 0.4531, loss_cls: 4.3677, loss: 4.3677 +2024-07-16 22:08:36,950 - pyskl - INFO - Epoch [20][700/3746] lr: 9.602e-02, eta: 4 days, 3:13:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4442, loss_cls: 4.3571, loss: 4.3571 +2024-07-16 22:09:46,878 - pyskl - INFO - Epoch [20][800/3746] lr: 9.601e-02, eta: 4 days, 3:11:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4453, loss_cls: 4.3838, loss: 4.3838 +2024-07-16 22:10:56,840 - pyskl - INFO - Epoch [20][900/3746] lr: 9.600e-02, eta: 4 days, 3:10:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4631, loss_cls: 4.3232, loss: 4.3232 +2024-07-16 22:12:06,946 - pyskl - INFO - Epoch [20][1000/3746] lr: 9.598e-02, eta: 4 days, 3:08:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4486, loss_cls: 4.3733, loss: 4.3733 +2024-07-16 22:13:16,989 - pyskl - INFO - Epoch [20][1100/3746] lr: 9.597e-02, eta: 4 days, 3:06:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4411, loss_cls: 4.3735, loss: 4.3735 +2024-07-16 22:14:27,322 - pyskl - INFO - Epoch [20][1200/3746] lr: 9.596e-02, eta: 4 days, 3:05:27, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4533, loss_cls: 4.3271, loss: 4.3271 +2024-07-16 22:15:37,323 - pyskl - INFO - Epoch [20][1300/3746] lr: 9.595e-02, eta: 4 days, 3:03:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4508, loss_cls: 4.3248, loss: 4.3248 +2024-07-16 22:16:47,376 - pyskl - INFO - Epoch [20][1400/3746] lr: 9.594e-02, eta: 4 days, 3:02:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4434, loss_cls: 4.3665, loss: 4.3665 +2024-07-16 22:17:57,472 - pyskl - INFO - Epoch [20][1500/3746] lr: 9.593e-02, eta: 4 days, 3:00:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4486, loss_cls: 4.3630, loss: 4.3630 +2024-07-16 22:19:07,303 - pyskl - INFO - Epoch [20][1600/3746] lr: 9.592e-02, eta: 4 days, 2:59:18, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4573, loss_cls: 4.3164, loss: 4.3164 +2024-07-16 22:20:17,781 - pyskl - INFO - Epoch [20][1700/3746] lr: 9.591e-02, eta: 4 days, 2:57:50, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4569, loss_cls: 4.3236, loss: 4.3236 +2024-07-16 22:21:28,865 - pyskl - INFO - Epoch [20][1800/3746] lr: 9.590e-02, eta: 4 days, 2:56:25, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4553, loss_cls: 4.3490, loss: 4.3490 +2024-07-16 22:22:38,792 - pyskl - INFO - Epoch [20][1900/3746] lr: 9.588e-02, eta: 4 days, 2:54:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4437, loss_cls: 4.3944, loss: 4.3944 +2024-07-16 22:23:49,161 - pyskl - INFO - Epoch [20][2000/3746] lr: 9.587e-02, eta: 4 days, 2:53:23, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4530, loss_cls: 4.3429, loss: 4.3429 +2024-07-16 22:24:59,841 - pyskl - INFO - Epoch [20][2100/3746] lr: 9.586e-02, eta: 4 days, 2:51:56, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4419, loss_cls: 4.3798, loss: 4.3798 +2024-07-16 22:26:10,194 - pyskl - INFO - Epoch [20][2200/3746] lr: 9.585e-02, eta: 4 days, 2:50:26, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4523, loss_cls: 4.3153, loss: 4.3153 +2024-07-16 22:27:20,056 - pyskl - INFO - Epoch [20][2300/3746] lr: 9.584e-02, eta: 4 days, 2:48:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4500, loss_cls: 4.3411, loss: 4.3411 +2024-07-16 22:28:30,167 - pyskl - INFO - Epoch [20][2400/3746] lr: 9.583e-02, eta: 4 days, 2:47:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4464, loss_cls: 4.3720, loss: 4.3720 +2024-07-16 22:29:40,265 - pyskl - INFO - Epoch [20][2500/3746] lr: 9.582e-02, eta: 4 days, 2:45:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4447, loss_cls: 4.3872, loss: 4.3872 +2024-07-16 22:30:50,129 - pyskl - INFO - Epoch [20][2600/3746] lr: 9.581e-02, eta: 4 days, 2:44:20, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4684, loss_cls: 4.2889, loss: 4.2889 +2024-07-16 22:32:00,241 - pyskl - INFO - Epoch [20][2700/3746] lr: 9.580e-02, eta: 4 days, 2:42:49, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4544, loss_cls: 4.3407, loss: 4.3407 +2024-07-16 22:33:10,396 - pyskl - INFO - Epoch [20][2800/3746] lr: 9.578e-02, eta: 4 days, 2:41:18, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4491, loss_cls: 4.3702, loss: 4.3702 +2024-07-16 22:34:20,355 - pyskl - INFO - Epoch [20][2900/3746] lr: 9.577e-02, eta: 4 days, 2:39:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4466, loss_cls: 4.3794, loss: 4.3794 +2024-07-16 22:35:30,746 - pyskl - INFO - Epoch [20][3000/3746] lr: 9.576e-02, eta: 4 days, 2:38:18, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4562, loss_cls: 4.3375, loss: 4.3375 +2024-07-16 22:36:40,899 - pyskl - INFO - Epoch [20][3100/3746] lr: 9.575e-02, eta: 4 days, 2:36:48, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4442, loss_cls: 4.3864, loss: 4.3864 +2024-07-16 22:37:50,949 - pyskl - INFO - Epoch [20][3200/3746] lr: 9.574e-02, eta: 4 days, 2:35:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4480, loss_cls: 4.3649, loss: 4.3649 +2024-07-16 22:39:01,032 - pyskl - INFO - Epoch [20][3300/3746] lr: 9.573e-02, eta: 4 days, 2:33:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4456, loss_cls: 4.3857, loss: 4.3857 +2024-07-16 22:40:11,003 - pyskl - INFO - Epoch [20][3400/3746] lr: 9.572e-02, eta: 4 days, 2:32:15, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2108, top5_acc: 0.4533, loss_cls: 4.3524, loss: 4.3524 +2024-07-16 22:41:22,067 - pyskl - INFO - Epoch [20][3500/3746] lr: 9.571e-02, eta: 4 days, 2:30:51, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4514, loss_cls: 4.3299, loss: 4.3299 +2024-07-16 22:42:32,765 - pyskl - INFO - Epoch [20][3600/3746] lr: 9.569e-02, eta: 4 days, 2:29:25, time: 0.707, data_time: 0.001, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4428, loss_cls: 4.3751, loss: 4.3751 +2024-07-16 22:43:43,114 - pyskl - INFO - Epoch [20][3700/3746] lr: 9.568e-02, eta: 4 days, 2:27:56, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4481, loss_cls: 4.3561, loss: 4.3561 +2024-07-16 22:44:17,312 - pyskl - INFO - Saving checkpoint at 20 epochs +2024-07-16 22:46:07,276 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 22:46:07,953 - pyskl - INFO - +top1_acc 0.1559 +top5_acc 0.3608 +2024-07-16 22:46:07,954 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 22:46:07,992 - pyskl - INFO - +mean_acc 0.1558 +2024-07-16 22:46:08,004 - pyskl - INFO - Epoch(val) [20][309] top1_acc: 0.1559, top5_acc: 0.3608, mean_class_accuracy: 0.1558 +2024-07-16 22:49:24,506 - pyskl - INFO - Epoch [21][100/3746] lr: 9.567e-02, eta: 4 days, 2:35:55, time: 1.965, data_time: 1.259, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4558, loss_cls: 4.3093, loss: 4.3093 +2024-07-16 22:50:35,582 - pyskl - INFO - Epoch [21][200/3746] lr: 9.565e-02, eta: 4 days, 2:34:31, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4555, loss_cls: 4.3293, loss: 4.3293 +2024-07-16 22:51:46,731 - pyskl - INFO - Epoch [21][300/3746] lr: 9.564e-02, eta: 4 days, 2:33:06, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4500, loss_cls: 4.3166, loss: 4.3166 +2024-07-16 22:52:57,498 - pyskl - INFO - Epoch [21][400/3746] lr: 9.563e-02, eta: 4 days, 2:31:40, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4475, loss_cls: 4.3348, loss: 4.3348 +2024-07-16 22:54:07,856 - pyskl - INFO - Epoch [21][500/3746] lr: 9.562e-02, eta: 4 days, 2:30:10, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4605, loss_cls: 4.3118, loss: 4.3118 +2024-07-16 22:55:17,738 - pyskl - INFO - Epoch [21][600/3746] lr: 9.561e-02, eta: 4 days, 2:28:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4508, loss_cls: 4.3355, loss: 4.3355 +2024-07-16 22:56:27,672 - pyskl - INFO - Epoch [21][700/3746] lr: 9.560e-02, eta: 4 days, 2:27:06, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4445, loss_cls: 4.3612, loss: 4.3612 +2024-07-16 22:57:37,731 - pyskl - INFO - Epoch [21][800/3746] lr: 9.559e-02, eta: 4 days, 2:25:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4486, loss_cls: 4.3539, loss: 4.3539 +2024-07-16 22:58:47,787 - pyskl - INFO - Epoch [21][900/3746] lr: 9.557e-02, eta: 4 days, 2:24:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4511, loss_cls: 4.3218, loss: 4.3218 +2024-07-16 22:59:57,709 - pyskl - INFO - Epoch [21][1000/3746] lr: 9.556e-02, eta: 4 days, 2:22:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4603, loss_cls: 4.3100, loss: 4.3100 +2024-07-16 23:01:07,753 - pyskl - INFO - Epoch [21][1100/3746] lr: 9.555e-02, eta: 4 days, 2:21:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4634, loss_cls: 4.2936, loss: 4.2936 +2024-07-16 23:02:18,038 - pyskl - INFO - Epoch [21][1200/3746] lr: 9.554e-02, eta: 4 days, 2:19:32, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4397, loss_cls: 4.3863, loss: 4.3863 +2024-07-16 23:03:28,099 - pyskl - INFO - Epoch [21][1300/3746] lr: 9.553e-02, eta: 4 days, 2:18:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4414, loss_cls: 4.3705, loss: 4.3705 +2024-07-16 23:04:38,283 - pyskl - INFO - Epoch [21][1400/3746] lr: 9.552e-02, eta: 4 days, 2:16:31, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4417, loss_cls: 4.3738, loss: 4.3738 +2024-07-16 23:05:48,155 - pyskl - INFO - Epoch [21][1500/3746] lr: 9.551e-02, eta: 4 days, 2:14:59, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4530, loss_cls: 4.3531, loss: 4.3531 +2024-07-16 23:06:58,080 - pyskl - INFO - Epoch [21][1600/3746] lr: 9.549e-02, eta: 4 days, 2:13:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4536, loss_cls: 4.3436, loss: 4.3436 +2024-07-16 23:08:08,238 - pyskl - INFO - Epoch [21][1700/3746] lr: 9.548e-02, eta: 4 days, 2:11:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4533, loss_cls: 4.3415, loss: 4.3415 +2024-07-16 23:09:19,071 - pyskl - INFO - Epoch [21][1800/3746] lr: 9.547e-02, eta: 4 days, 2:10:32, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2155, top5_acc: 0.4550, loss_cls: 4.3229, loss: 4.3229 +2024-07-16 23:10:28,995 - pyskl - INFO - Epoch [21][1900/3746] lr: 9.546e-02, eta: 4 days, 2:09:01, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4506, loss_cls: 4.3425, loss: 4.3425 +2024-07-16 23:11:39,372 - pyskl - INFO - Epoch [21][2000/3746] lr: 9.545e-02, eta: 4 days, 2:07:33, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4370, loss_cls: 4.3964, loss: 4.3964 +2024-07-16 23:12:49,909 - pyskl - INFO - Epoch [21][2100/3746] lr: 9.544e-02, eta: 4 days, 2:06:05, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4522, loss_cls: 4.3387, loss: 4.3387 +2024-07-16 23:14:00,806 - pyskl - INFO - Epoch [21][2200/3746] lr: 9.542e-02, eta: 4 days, 2:04:40, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4520, loss_cls: 4.3219, loss: 4.3219 +2024-07-16 23:15:11,678 - pyskl - INFO - Epoch [21][2300/3746] lr: 9.541e-02, eta: 4 days, 2:03:15, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4550, loss_cls: 4.3183, loss: 4.3183 +2024-07-16 23:16:21,641 - pyskl - INFO - Epoch [21][2400/3746] lr: 9.540e-02, eta: 4 days, 2:01:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4477, loss_cls: 4.3411, loss: 4.3411 +2024-07-16 23:17:31,521 - pyskl - INFO - Epoch [21][2500/3746] lr: 9.539e-02, eta: 4 days, 2:00:13, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4547, loss_cls: 4.3361, loss: 4.3361 +2024-07-16 23:18:41,111 - pyskl - INFO - Epoch [21][2600/3746] lr: 9.538e-02, eta: 4 days, 1:58:40, time: 0.696, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4373, loss_cls: 4.3789, loss: 4.3789 +2024-07-16 23:19:51,115 - pyskl - INFO - Epoch [21][2700/3746] lr: 9.537e-02, eta: 4 days, 1:57:10, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4503, loss_cls: 4.3769, loss: 4.3769 +2024-07-16 23:21:01,278 - pyskl - INFO - Epoch [21][2800/3746] lr: 9.535e-02, eta: 4 days, 1:55:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4486, loss_cls: 4.3548, loss: 4.3548 +2024-07-16 23:22:11,407 - pyskl - INFO - Epoch [21][2900/3746] lr: 9.534e-02, eta: 4 days, 1:54:11, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4537, loss_cls: 4.3231, loss: 4.3231 +2024-07-16 23:23:21,429 - pyskl - INFO - Epoch [21][3000/3746] lr: 9.533e-02, eta: 4 days, 1:52:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4545, loss_cls: 4.3374, loss: 4.3374 +2024-07-16 23:24:31,405 - pyskl - INFO - Epoch [21][3100/3746] lr: 9.532e-02, eta: 4 days, 1:51:10, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4481, loss_cls: 4.3661, loss: 4.3661 +2024-07-16 23:25:41,326 - pyskl - INFO - Epoch [21][3200/3746] lr: 9.531e-02, eta: 4 days, 1:49:40, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4469, loss_cls: 4.3768, loss: 4.3768 +2024-07-16 23:26:51,200 - pyskl - INFO - Epoch [21][3300/3746] lr: 9.529e-02, eta: 4 days, 1:48:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4408, loss_cls: 4.3728, loss: 4.3728 +2024-07-16 23:28:00,928 - pyskl - INFO - Epoch [21][3400/3746] lr: 9.528e-02, eta: 4 days, 1:46:37, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2131, top5_acc: 0.4467, loss_cls: 4.3744, loss: 4.3744 +2024-07-16 23:29:11,753 - pyskl - INFO - Epoch [21][3500/3746] lr: 9.527e-02, eta: 4 days, 1:45:12, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4595, loss_cls: 4.3250, loss: 4.3250 +2024-07-16 23:30:22,561 - pyskl - INFO - Epoch [21][3600/3746] lr: 9.526e-02, eta: 4 days, 1:43:47, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4456, loss_cls: 4.3828, loss: 4.3828 +2024-07-16 23:31:32,679 - pyskl - INFO - Epoch [21][3700/3746] lr: 9.525e-02, eta: 4 days, 1:42:18, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4452, loss_cls: 4.3770, loss: 4.3770 +2024-07-16 23:32:07,026 - pyskl - INFO - Saving checkpoint at 21 epochs +2024-07-16 23:33:56,255 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 23:33:56,957 - pyskl - INFO - +top1_acc 0.1226 +top5_acc 0.3123 +2024-07-16 23:33:56,957 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 23:33:56,997 - pyskl - INFO - +mean_acc 0.1225 +2024-07-16 23:33:57,010 - pyskl - INFO - Epoch(val) [21][309] top1_acc: 0.1226, top5_acc: 0.3123, mean_class_accuracy: 0.1225 +2024-07-16 23:37:11,770 - pyskl - INFO - Epoch [22][100/3746] lr: 9.523e-02, eta: 4 days, 1:49:35, time: 1.947, data_time: 1.239, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4567, loss_cls: 4.3291, loss: 4.3291 +2024-07-16 23:38:23,098 - pyskl - INFO - Epoch [22][200/3746] lr: 9.522e-02, eta: 4 days, 1:48:12, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4494, loss_cls: 4.3489, loss: 4.3489 +2024-07-16 23:39:33,365 - pyskl - INFO - Epoch [22][300/3746] lr: 9.521e-02, eta: 4 days, 1:46:43, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4603, loss_cls: 4.2842, loss: 4.2842 +2024-07-16 23:40:43,494 - pyskl - INFO - Epoch [22][400/3746] lr: 9.519e-02, eta: 4 days, 1:45:13, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4556, loss_cls: 4.3403, loss: 4.3403 +2024-07-16 23:41:53,481 - pyskl - INFO - Epoch [22][500/3746] lr: 9.518e-02, eta: 4 days, 1:43:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4517, loss_cls: 4.3120, loss: 4.3120 +2024-07-16 23:43:03,470 - pyskl - INFO - Epoch [22][600/3746] lr: 9.517e-02, eta: 4 days, 1:42:12, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4580, loss_cls: 4.3210, loss: 4.3210 +2024-07-16 23:44:13,790 - pyskl - INFO - Epoch [22][700/3746] lr: 9.516e-02, eta: 4 days, 1:40:44, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4542, loss_cls: 4.3297, loss: 4.3297 +2024-07-16 23:45:23,637 - pyskl - INFO - Epoch [22][800/3746] lr: 9.515e-02, eta: 4 days, 1:39:13, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4609, loss_cls: 4.3363, loss: 4.3363 +2024-07-16 23:46:33,629 - pyskl - INFO - Epoch [22][900/3746] lr: 9.513e-02, eta: 4 days, 1:37:42, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4506, loss_cls: 4.3565, loss: 4.3565 +2024-07-16 23:47:43,373 - pyskl - INFO - Epoch [22][1000/3746] lr: 9.512e-02, eta: 4 days, 1:36:11, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4550, loss_cls: 4.3353, loss: 4.3353 +2024-07-16 23:48:53,159 - pyskl - INFO - Epoch [22][1100/3746] lr: 9.511e-02, eta: 4 days, 1:34:39, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4384, loss_cls: 4.3854, loss: 4.3854 +2024-07-16 23:50:03,077 - pyskl - INFO - Epoch [22][1200/3746] lr: 9.510e-02, eta: 4 days, 1:33:08, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4555, loss_cls: 4.3345, loss: 4.3345 +2024-07-16 23:51:13,231 - pyskl - INFO - Epoch [22][1300/3746] lr: 9.509e-02, eta: 4 days, 1:31:39, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4542, loss_cls: 4.3396, loss: 4.3396 +2024-07-16 23:52:23,076 - pyskl - INFO - Epoch [22][1400/3746] lr: 9.507e-02, eta: 4 days, 1:30:08, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2155, top5_acc: 0.4464, loss_cls: 4.3570, loss: 4.3570 +2024-07-16 23:53:32,841 - pyskl - INFO - Epoch [22][1500/3746] lr: 9.506e-02, eta: 4 days, 1:28:37, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4487, loss_cls: 4.3657, loss: 4.3657 +2024-07-16 23:54:42,830 - pyskl - INFO - Epoch [22][1600/3746] lr: 9.505e-02, eta: 4 days, 1:27:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4512, loss_cls: 4.3477, loss: 4.3477 +2024-07-16 23:55:53,108 - pyskl - INFO - Epoch [22][1700/3746] lr: 9.504e-02, eta: 4 days, 1:25:39, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4489, loss_cls: 4.3585, loss: 4.3585 +2024-07-16 23:57:03,910 - pyskl - INFO - Epoch [22][1800/3746] lr: 9.502e-02, eta: 4 days, 1:24:14, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4550, loss_cls: 4.3476, loss: 4.3476 +2024-07-16 23:58:13,753 - pyskl - INFO - Epoch [22][1900/3746] lr: 9.501e-02, eta: 4 days, 1:22:43, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4472, loss_cls: 4.3637, loss: 4.3637 +2024-07-16 23:59:23,796 - pyskl - INFO - Epoch [22][2000/3746] lr: 9.500e-02, eta: 4 days, 1:21:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2108, top5_acc: 0.4517, loss_cls: 4.3800, loss: 4.3800 +2024-07-17 00:00:34,333 - pyskl - INFO - Epoch [22][2100/3746] lr: 9.499e-02, eta: 4 days, 1:19:47, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4509, loss_cls: 4.3208, loss: 4.3208 +2024-07-17 00:01:44,479 - pyskl - INFO - Epoch [22][2200/3746] lr: 9.498e-02, eta: 4 days, 1:18:18, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4497, loss_cls: 4.3714, loss: 4.3714 +2024-07-17 00:02:54,602 - pyskl - INFO - Epoch [22][2300/3746] lr: 9.496e-02, eta: 4 days, 1:16:49, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4567, loss_cls: 4.3225, loss: 4.3225 +2024-07-17 00:04:04,539 - pyskl - INFO - Epoch [22][2400/3746] lr: 9.495e-02, eta: 4 days, 1:15:20, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4511, loss_cls: 4.3709, loss: 4.3709 +2024-07-17 00:05:14,404 - pyskl - INFO - Epoch [22][2500/3746] lr: 9.494e-02, eta: 4 days, 1:13:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4602, loss_cls: 4.3370, loss: 4.3370 +2024-07-17 00:06:24,233 - pyskl - INFO - Epoch [22][2600/3746] lr: 9.493e-02, eta: 4 days, 1:12:19, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4458, loss_cls: 4.3690, loss: 4.3690 +2024-07-17 00:07:34,169 - pyskl - INFO - Epoch [22][2700/3746] lr: 9.491e-02, eta: 4 days, 1:10:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4481, loss_cls: 4.3816, loss: 4.3816 +2024-07-17 00:08:44,291 - pyskl - INFO - Epoch [22][2800/3746] lr: 9.490e-02, eta: 4 days, 1:09:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4602, loss_cls: 4.3028, loss: 4.3028 +2024-07-17 00:09:54,073 - pyskl - INFO - Epoch [22][2900/3746] lr: 9.489e-02, eta: 4 days, 1:07:50, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4452, loss_cls: 4.3760, loss: 4.3760 +2024-07-17 00:11:04,028 - pyskl - INFO - Epoch [22][3000/3746] lr: 9.488e-02, eta: 4 days, 1:06:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4564, loss_cls: 4.3162, loss: 4.3162 +2024-07-17 00:12:13,893 - pyskl - INFO - Epoch [22][3100/3746] lr: 9.487e-02, eta: 4 days, 1:04:50, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4477, loss_cls: 4.3438, loss: 4.3438 +2024-07-17 00:13:24,016 - pyskl - INFO - Epoch [22][3200/3746] lr: 9.485e-02, eta: 4 days, 1:03:22, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4444, loss_cls: 4.3414, loss: 4.3414 +2024-07-17 00:14:33,816 - pyskl - INFO - Epoch [22][3300/3746] lr: 9.484e-02, eta: 4 days, 1:01:52, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4602, loss_cls: 4.3314, loss: 4.3314 +2024-07-17 00:15:43,878 - pyskl - INFO - Epoch [22][3400/3746] lr: 9.483e-02, eta: 4 days, 1:00:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4564, loss_cls: 4.3207, loss: 4.3207 +2024-07-17 00:16:54,377 - pyskl - INFO - Epoch [22][3500/3746] lr: 9.482e-02, eta: 4 days, 0:58:57, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4456, loss_cls: 4.3696, loss: 4.3696 +2024-07-17 00:18:04,939 - pyskl - INFO - Epoch [22][3600/3746] lr: 9.480e-02, eta: 4 days, 0:57:31, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4502, loss_cls: 4.3493, loss: 4.3493 +2024-07-17 00:19:15,133 - pyskl - INFO - Epoch [22][3700/3746] lr: 9.479e-02, eta: 4 days, 0:56:03, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4564, loss_cls: 4.3112, loss: 4.3112 +2024-07-17 00:19:49,455 - pyskl - INFO - Saving checkpoint at 22 epochs +2024-07-17 00:21:39,088 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 00:21:39,750 - pyskl - INFO - +top1_acc 0.1222 +top5_acc 0.2975 +2024-07-17 00:21:39,751 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 00:21:39,790 - pyskl - INFO - +mean_acc 0.1221 +2024-07-17 00:21:39,801 - pyskl - INFO - Epoch(val) [22][309] top1_acc: 0.1222, top5_acc: 0.2975, mean_class_accuracy: 0.1221 +2024-07-17 00:24:56,906 - pyskl - INFO - Epoch [23][100/3746] lr: 9.477e-02, eta: 4 days, 1:03:05, time: 1.971, data_time: 1.266, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4548, loss_cls: 4.3104, loss: 4.3104 +2024-07-17 00:26:07,996 - pyskl - INFO - Epoch [23][200/3746] lr: 9.476e-02, eta: 4 days, 1:01:42, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4586, loss_cls: 4.3218, loss: 4.3218 +2024-07-17 00:27:18,609 - pyskl - INFO - Epoch [23][300/3746] lr: 9.475e-02, eta: 4 days, 1:00:16, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4608, loss_cls: 4.2898, loss: 4.2898 +2024-07-17 00:28:28,969 - pyskl - INFO - Epoch [23][400/3746] lr: 9.474e-02, eta: 4 days, 0:58:48, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4566, loss_cls: 4.3124, loss: 4.3124 +2024-07-17 00:29:38,979 - pyskl - INFO - Epoch [23][500/3746] lr: 9.472e-02, eta: 4 days, 0:57:19, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4544, loss_cls: 4.3343, loss: 4.3343 +2024-07-17 00:30:49,325 - pyskl - INFO - Epoch [23][600/3746] lr: 9.471e-02, eta: 4 days, 0:55:51, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4578, loss_cls: 4.2901, loss: 4.2901 +2024-07-17 00:31:59,099 - pyskl - INFO - Epoch [23][700/3746] lr: 9.470e-02, eta: 4 days, 0:54:21, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4636, loss_cls: 4.2847, loss: 4.2847 +2024-07-17 00:33:09,190 - pyskl - INFO - Epoch [23][800/3746] lr: 9.469e-02, eta: 4 days, 0:52:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4498, loss_cls: 4.3353, loss: 4.3353 +2024-07-17 00:34:19,332 - pyskl - INFO - Epoch [23][900/3746] lr: 9.467e-02, eta: 4 days, 0:51:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4514, loss_cls: 4.3502, loss: 4.3502 +2024-07-17 00:35:29,283 - pyskl - INFO - Epoch [23][1000/3746] lr: 9.466e-02, eta: 4 days, 0:49:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4530, loss_cls: 4.3617, loss: 4.3617 +2024-07-17 00:36:39,261 - pyskl - INFO - Epoch [23][1100/3746] lr: 9.465e-02, eta: 4 days, 0:48:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4483, loss_cls: 4.3540, loss: 4.3540 +2024-07-17 00:37:49,764 - pyskl - INFO - Epoch [23][1200/3746] lr: 9.464e-02, eta: 4 days, 0:46:58, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4605, loss_cls: 4.3072, loss: 4.3072 +2024-07-17 00:38:59,741 - pyskl - INFO - Epoch [23][1300/3746] lr: 9.462e-02, eta: 4 days, 0:45:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4544, loss_cls: 4.3268, loss: 4.3268 +2024-07-17 00:40:09,525 - pyskl - INFO - Epoch [23][1400/3746] lr: 9.461e-02, eta: 4 days, 0:43:59, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4498, loss_cls: 4.3484, loss: 4.3484 +2024-07-17 00:41:19,494 - pyskl - INFO - Epoch [23][1500/3746] lr: 9.460e-02, eta: 4 days, 0:42:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2108, top5_acc: 0.4647, loss_cls: 4.3136, loss: 4.3136 +2024-07-17 00:42:29,821 - pyskl - INFO - Epoch [23][1600/3746] lr: 9.459e-02, eta: 4 days, 0:41:02, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4572, loss_cls: 4.3205, loss: 4.3205 +2024-07-17 00:43:40,500 - pyskl - INFO - Epoch [23][1700/3746] lr: 9.457e-02, eta: 4 days, 0:39:37, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4545, loss_cls: 4.3418, loss: 4.3418 +2024-07-17 00:44:51,029 - pyskl - INFO - Epoch [23][1800/3746] lr: 9.456e-02, eta: 4 days, 0:38:11, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4569, loss_cls: 4.3081, loss: 4.3081 +2024-07-17 00:46:00,739 - pyskl - INFO - Epoch [23][1900/3746] lr: 9.455e-02, eta: 4 days, 0:36:41, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4431, loss_cls: 4.3707, loss: 4.3707 +2024-07-17 00:47:11,371 - pyskl - INFO - Epoch [23][2000/3746] lr: 9.453e-02, eta: 4 days, 0:35:16, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4537, loss_cls: 4.3278, loss: 4.3278 +2024-07-17 00:48:22,315 - pyskl - INFO - Epoch [23][2100/3746] lr: 9.452e-02, eta: 4 days, 0:33:52, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4636, loss_cls: 4.2943, loss: 4.2943 +2024-07-17 00:49:32,528 - pyskl - INFO - Epoch [23][2200/3746] lr: 9.451e-02, eta: 4 days, 0:32:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4669, loss_cls: 4.2815, loss: 4.2815 +2024-07-17 00:50:42,476 - pyskl - INFO - Epoch [23][2300/3746] lr: 9.450e-02, eta: 4 days, 0:30:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4466, loss_cls: 4.3768, loss: 4.3768 +2024-07-17 00:51:52,614 - pyskl - INFO - Epoch [23][2400/3746] lr: 9.448e-02, eta: 4 days, 0:29:28, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2194, top5_acc: 0.4581, loss_cls: 4.3412, loss: 4.3412 +2024-07-17 00:53:02,639 - pyskl - INFO - Epoch [23][2500/3746] lr: 9.447e-02, eta: 4 days, 0:27:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4514, loss_cls: 4.3544, loss: 4.3544 +2024-07-17 00:54:12,613 - pyskl - INFO - Epoch [23][2600/3746] lr: 9.446e-02, eta: 4 days, 0:26:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4525, loss_cls: 4.3744, loss: 4.3744 +2024-07-17 00:55:22,566 - pyskl - INFO - Epoch [23][2700/3746] lr: 9.445e-02, eta: 4 days, 0:25:02, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4398, loss_cls: 4.3978, loss: 4.3978 +2024-07-17 00:56:32,460 - pyskl - INFO - Epoch [23][2800/3746] lr: 9.443e-02, eta: 4 days, 0:23:33, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4628, loss_cls: 4.3081, loss: 4.3081 +2024-07-17 00:57:42,546 - pyskl - INFO - Epoch [23][2900/3746] lr: 9.442e-02, eta: 4 days, 0:22:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4550, loss_cls: 4.3506, loss: 4.3506 +2024-07-17 00:58:52,469 - pyskl - INFO - Epoch [23][3000/3746] lr: 9.441e-02, eta: 4 days, 0:20:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4508, loss_cls: 4.3521, loss: 4.3521 +2024-07-17 01:00:02,373 - pyskl - INFO - Epoch [23][3100/3746] lr: 9.439e-02, eta: 4 days, 0:19:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2059, top5_acc: 0.4420, loss_cls: 4.3689, loss: 4.3689 +2024-07-17 01:01:12,325 - pyskl - INFO - Epoch [23][3200/3746] lr: 9.438e-02, eta: 4 days, 0:17:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4575, loss_cls: 4.3061, loss: 4.3061 +2024-07-17 01:02:22,258 - pyskl - INFO - Epoch [23][3300/3746] lr: 9.437e-02, eta: 4 days, 0:16:10, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4597, loss_cls: 4.3373, loss: 4.3373 +2024-07-17 01:03:32,321 - pyskl - INFO - Epoch [23][3400/3746] lr: 9.436e-02, eta: 4 days, 0:14:42, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4462, loss_cls: 4.3794, loss: 4.3794 +2024-07-17 01:04:43,486 - pyskl - INFO - Epoch [23][3500/3746] lr: 9.434e-02, eta: 4 days, 0:13:20, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4502, loss_cls: 4.3387, loss: 4.3387 +2024-07-17 01:05:54,482 - pyskl - INFO - Epoch [23][3600/3746] lr: 9.433e-02, eta: 4 days, 0:11:58, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4486, loss_cls: 4.3517, loss: 4.3517 +2024-07-17 01:07:04,487 - pyskl - INFO - Epoch [23][3700/3746] lr: 9.432e-02, eta: 4 days, 0:10:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4509, loss_cls: 4.3522, loss: 4.3522 +2024-07-17 01:07:39,156 - pyskl - INFO - Saving checkpoint at 23 epochs +2024-07-17 01:09:28,086 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 01:09:28,755 - pyskl - INFO - +top1_acc 0.1082 +top5_acc 0.2865 +2024-07-17 01:09:28,755 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 01:09:28,793 - pyskl - INFO - +mean_acc 0.1080 +2024-07-17 01:09:28,804 - pyskl - INFO - Epoch(val) [23][309] top1_acc: 0.1082, top5_acc: 0.2865, mean_class_accuracy: 0.1080 +2024-07-17 01:12:45,272 - pyskl - INFO - Epoch [24][100/3746] lr: 9.430e-02, eta: 4 days, 0:17:01, time: 1.965, data_time: 1.258, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4653, loss_cls: 4.2707, loss: 4.2707 +2024-07-17 01:13:56,013 - pyskl - INFO - Epoch [24][200/3746] lr: 9.428e-02, eta: 4 days, 0:15:36, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4592, loss_cls: 4.2850, loss: 4.2850 +2024-07-17 01:15:06,813 - pyskl - INFO - Epoch [24][300/3746] lr: 9.427e-02, eta: 4 days, 0:14:12, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4648, loss_cls: 4.2822, loss: 4.2822 +2024-07-17 01:16:17,185 - pyskl - INFO - Epoch [24][400/3746] lr: 9.426e-02, eta: 4 days, 0:12:45, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4622, loss_cls: 4.3086, loss: 4.3086 +2024-07-17 01:17:27,507 - pyskl - INFO - Epoch [24][500/3746] lr: 9.425e-02, eta: 4 days, 0:11:19, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4559, loss_cls: 4.3349, loss: 4.3349 +2024-07-17 01:18:37,687 - pyskl - INFO - Epoch [24][600/3746] lr: 9.423e-02, eta: 4 days, 0:09:51, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4591, loss_cls: 4.3390, loss: 4.3390 +2024-07-17 01:19:47,516 - pyskl - INFO - Epoch [24][700/3746] lr: 9.422e-02, eta: 4 days, 0:08:22, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4508, loss_cls: 4.3275, loss: 4.3275 +2024-07-17 01:20:57,464 - pyskl - INFO - Epoch [24][800/3746] lr: 9.421e-02, eta: 4 days, 0:06:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4586, loss_cls: 4.3113, loss: 4.3113 +2024-07-17 01:22:07,350 - pyskl - INFO - Epoch [24][900/3746] lr: 9.419e-02, eta: 4 days, 0:05:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4512, loss_cls: 4.3482, loss: 4.3482 +2024-07-17 01:23:17,652 - pyskl - INFO - Epoch [24][1000/3746] lr: 9.418e-02, eta: 4 days, 0:03:57, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4598, loss_cls: 4.3206, loss: 4.3206 +2024-07-17 01:24:27,613 - pyskl - INFO - Epoch [24][1100/3746] lr: 9.417e-02, eta: 4 days, 0:02:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2131, top5_acc: 0.4480, loss_cls: 4.3478, loss: 4.3478 +2024-07-17 01:25:37,496 - pyskl - INFO - Epoch [24][1200/3746] lr: 9.415e-02, eta: 4 days, 0:01:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2194, top5_acc: 0.4509, loss_cls: 4.3108, loss: 4.3108 +2024-07-17 01:26:47,388 - pyskl - INFO - Epoch [24][1300/3746] lr: 9.414e-02, eta: 3 days, 23:59:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4491, loss_cls: 4.3343, loss: 4.3343 +2024-07-17 01:27:57,222 - pyskl - INFO - Epoch [24][1400/3746] lr: 9.413e-02, eta: 3 days, 23:58:02, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2131, top5_acc: 0.4595, loss_cls: 4.3294, loss: 4.3294 +2024-07-17 01:29:07,178 - pyskl - INFO - Epoch [24][1500/3746] lr: 9.411e-02, eta: 3 days, 23:56:33, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4609, loss_cls: 4.3052, loss: 4.3052 +2024-07-17 01:30:16,900 - pyskl - INFO - Epoch [24][1600/3746] lr: 9.410e-02, eta: 3 days, 23:55:04, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4587, loss_cls: 4.3222, loss: 4.3222 +2024-07-17 01:31:27,128 - pyskl - INFO - Epoch [24][1700/3746] lr: 9.409e-02, eta: 3 days, 23:53:37, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4589, loss_cls: 4.3206, loss: 4.3206 +2024-07-17 01:32:37,872 - pyskl - INFO - Epoch [24][1800/3746] lr: 9.407e-02, eta: 3 days, 23:52:13, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4558, loss_cls: 4.3358, loss: 4.3358 +2024-07-17 01:33:48,195 - pyskl - INFO - Epoch [24][1900/3746] lr: 9.406e-02, eta: 3 days, 23:50:46, time: 0.703, data_time: 0.001, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4545, loss_cls: 4.3247, loss: 4.3247 +2024-07-17 01:34:58,719 - pyskl - INFO - Epoch [24][2000/3746] lr: 9.405e-02, eta: 3 days, 23:49:21, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4506, loss_cls: 4.3546, loss: 4.3546 +2024-07-17 01:36:09,299 - pyskl - INFO - Epoch [24][2100/3746] lr: 9.404e-02, eta: 3 days, 23:47:56, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4527, loss_cls: 4.3493, loss: 4.3493 +2024-07-17 01:37:19,294 - pyskl - INFO - Epoch [24][2200/3746] lr: 9.402e-02, eta: 3 days, 23:46:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4642, loss_cls: 4.3003, loss: 4.3003 +2024-07-17 01:38:29,270 - pyskl - INFO - Epoch [24][2300/3746] lr: 9.401e-02, eta: 3 days, 23:45:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4647, loss_cls: 4.3027, loss: 4.3027 +2024-07-17 01:39:39,221 - pyskl - INFO - Epoch [24][2400/3746] lr: 9.400e-02, eta: 3 days, 23:43:32, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2047, top5_acc: 0.4459, loss_cls: 4.3881, loss: 4.3881 +2024-07-17 01:40:48,951 - pyskl - INFO - Epoch [24][2500/3746] lr: 9.398e-02, eta: 3 days, 23:42:03, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4533, loss_cls: 4.3249, loss: 4.3249 +2024-07-17 01:41:58,975 - pyskl - INFO - Epoch [24][2600/3746] lr: 9.397e-02, eta: 3 days, 23:40:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4644, loss_cls: 4.3071, loss: 4.3071 +2024-07-17 01:43:08,743 - pyskl - INFO - Epoch [24][2700/3746] lr: 9.396e-02, eta: 3 days, 23:39:07, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4498, loss_cls: 4.3617, loss: 4.3617 +2024-07-17 01:44:18,771 - pyskl - INFO - Epoch [24][2800/3746] lr: 9.394e-02, eta: 3 days, 23:37:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4523, loss_cls: 4.3591, loss: 4.3591 +2024-07-17 01:45:28,445 - pyskl - INFO - Epoch [24][2900/3746] lr: 9.393e-02, eta: 3 days, 23:36:10, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4575, loss_cls: 4.3274, loss: 4.3274 +2024-07-17 01:46:38,329 - pyskl - INFO - Epoch [24][3000/3746] lr: 9.392e-02, eta: 3 days, 23:34:42, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4603, loss_cls: 4.3080, loss: 4.3080 +2024-07-17 01:47:48,260 - pyskl - INFO - Epoch [24][3100/3746] lr: 9.390e-02, eta: 3 days, 23:33:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4577, loss_cls: 4.3363, loss: 4.3363 +2024-07-17 01:48:57,987 - pyskl - INFO - Epoch [24][3200/3746] lr: 9.389e-02, eta: 3 days, 23:31:45, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4569, loss_cls: 4.3295, loss: 4.3295 +2024-07-17 01:50:07,993 - pyskl - INFO - Epoch [24][3300/3746] lr: 9.388e-02, eta: 3 days, 23:30:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4619, loss_cls: 4.3144, loss: 4.3144 +2024-07-17 01:51:18,464 - pyskl - INFO - Epoch [24][3400/3746] lr: 9.386e-02, eta: 3 days, 23:28:52, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4575, loss_cls: 4.3203, loss: 4.3203 +2024-07-17 01:52:28,950 - pyskl - INFO - Epoch [24][3500/3746] lr: 9.385e-02, eta: 3 days, 23:27:28, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4531, loss_cls: 4.3619, loss: 4.3619 +2024-07-17 01:53:40,071 - pyskl - INFO - Epoch [24][3600/3746] lr: 9.384e-02, eta: 3 days, 23:26:06, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4355, loss_cls: 4.3851, loss: 4.3851 +2024-07-17 01:54:50,789 - pyskl - INFO - Epoch [24][3700/3746] lr: 9.382e-02, eta: 3 days, 23:24:43, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4545, loss_cls: 4.3189, loss: 4.3189 +2024-07-17 01:55:25,248 - pyskl - INFO - Saving checkpoint at 24 epochs +2024-07-17 01:57:15,511 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 01:57:16,173 - pyskl - INFO - +top1_acc 0.1645 +top5_acc 0.3746 +2024-07-17 01:57:16,173 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 01:57:16,213 - pyskl - INFO - +mean_acc 0.1646 +2024-07-17 01:57:16,218 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_18.pth was removed +2024-07-17 01:57:16,444 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_24.pth. +2024-07-17 01:57:16,445 - pyskl - INFO - Best top1_acc is 0.1645 at 24 epoch. +2024-07-17 01:57:16,456 - pyskl - INFO - Epoch(val) [24][309] top1_acc: 0.1645, top5_acc: 0.3746, mean_class_accuracy: 0.1646 +2024-07-17 02:00:33,119 - pyskl - INFO - Epoch [25][100/3746] lr: 9.380e-02, eta: 3 days, 23:30:50, time: 1.967, data_time: 1.260, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4552, loss_cls: 4.3104, loss: 4.3104 +2024-07-17 02:01:43,993 - pyskl - INFO - Epoch [25][200/3746] lr: 9.379e-02, eta: 3 days, 23:29:27, time: 0.709, data_time: 0.001, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4567, loss_cls: 4.3035, loss: 4.3035 +2024-07-17 02:02:55,012 - pyskl - INFO - Epoch [25][300/3746] lr: 9.378e-02, eta: 3 days, 23:28:05, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4542, loss_cls: 4.3238, loss: 4.3238 +2024-07-17 02:04:05,083 - pyskl - INFO - Epoch [25][400/3746] lr: 9.376e-02, eta: 3 days, 23:26:37, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4597, loss_cls: 4.3116, loss: 4.3116 +2024-07-17 02:05:15,166 - pyskl - INFO - Epoch [25][500/3746] lr: 9.375e-02, eta: 3 days, 23:25:10, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4686, loss_cls: 4.3053, loss: 4.3053 +2024-07-17 02:06:25,229 - pyskl - INFO - Epoch [25][600/3746] lr: 9.373e-02, eta: 3 days, 23:23:42, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4595, loss_cls: 4.3137, loss: 4.3137 +2024-07-17 02:07:35,137 - pyskl - INFO - Epoch [25][700/3746] lr: 9.372e-02, eta: 3 days, 23:22:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4556, loss_cls: 4.3643, loss: 4.3643 +2024-07-17 02:08:44,957 - pyskl - INFO - Epoch [25][800/3746] lr: 9.371e-02, eta: 3 days, 23:20:46, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4527, loss_cls: 4.3419, loss: 4.3419 +2024-07-17 02:09:54,997 - pyskl - INFO - Epoch [25][900/3746] lr: 9.369e-02, eta: 3 days, 23:19:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4631, loss_cls: 4.2920, loss: 4.2920 +2024-07-17 02:11:04,922 - pyskl - INFO - Epoch [25][1000/3746] lr: 9.368e-02, eta: 3 days, 23:17:50, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2131, top5_acc: 0.4555, loss_cls: 4.3064, loss: 4.3064 +2024-07-17 02:12:15,144 - pyskl - INFO - Epoch [25][1100/3746] lr: 9.367e-02, eta: 3 days, 23:16:24, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4673, loss_cls: 4.2597, loss: 4.2597 +2024-07-17 02:13:25,112 - pyskl - INFO - Epoch [25][1200/3746] lr: 9.365e-02, eta: 3 days, 23:14:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4434, loss_cls: 4.3595, loss: 4.3595 +2024-07-17 02:14:34,924 - pyskl - INFO - Epoch [25][1300/3746] lr: 9.364e-02, eta: 3 days, 23:13:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4600, loss_cls: 4.3169, loss: 4.3169 +2024-07-17 02:15:45,048 - pyskl - INFO - Epoch [25][1400/3746] lr: 9.363e-02, eta: 3 days, 23:12:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4559, loss_cls: 4.3511, loss: 4.3511 +2024-07-17 02:16:55,041 - pyskl - INFO - Epoch [25][1500/3746] lr: 9.361e-02, eta: 3 days, 23:10:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4570, loss_cls: 4.3156, loss: 4.3156 +2024-07-17 02:18:05,041 - pyskl - INFO - Epoch [25][1600/3746] lr: 9.360e-02, eta: 3 days, 23:09:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4409, loss_cls: 4.3530, loss: 4.3530 +2024-07-17 02:19:15,482 - pyskl - INFO - Epoch [25][1700/3746] lr: 9.358e-02, eta: 3 days, 23:07:41, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4484, loss_cls: 4.3508, loss: 4.3508 +2024-07-17 02:20:26,365 - pyskl - INFO - Epoch [25][1800/3746] lr: 9.357e-02, eta: 3 days, 23:06:19, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4562, loss_cls: 4.3397, loss: 4.3397 +2024-07-17 02:21:36,909 - pyskl - INFO - Epoch [25][1900/3746] lr: 9.356e-02, eta: 3 days, 23:04:54, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4383, loss_cls: 4.4041, loss: 4.4041 +2024-07-17 02:22:47,181 - pyskl - INFO - Epoch [25][2000/3746] lr: 9.354e-02, eta: 3 days, 23:03:28, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4514, loss_cls: 4.3316, loss: 4.3316 +2024-07-17 02:23:57,833 - pyskl - INFO - Epoch [25][2100/3746] lr: 9.353e-02, eta: 3 days, 23:02:04, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4477, loss_cls: 4.3569, loss: 4.3569 +2024-07-17 02:25:08,017 - pyskl - INFO - Epoch [25][2200/3746] lr: 9.352e-02, eta: 3 days, 23:00:38, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4553, loss_cls: 4.3302, loss: 4.3302 +2024-07-17 02:26:18,254 - pyskl - INFO - Epoch [25][2300/3746] lr: 9.350e-02, eta: 3 days, 22:59:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4533, loss_cls: 4.3173, loss: 4.3173 +2024-07-17 02:27:28,307 - pyskl - INFO - Epoch [25][2400/3746] lr: 9.349e-02, eta: 3 days, 22:57:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4569, loss_cls: 4.3085, loss: 4.3085 +2024-07-17 02:28:38,227 - pyskl - INFO - Epoch [25][2500/3746] lr: 9.347e-02, eta: 3 days, 22:56:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4527, loss_cls: 4.3228, loss: 4.3228 +2024-07-17 02:29:48,001 - pyskl - INFO - Epoch [25][2600/3746] lr: 9.346e-02, eta: 3 days, 22:54:50, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4591, loss_cls: 4.3163, loss: 4.3163 +2024-07-17 02:30:58,138 - pyskl - INFO - Epoch [25][2700/3746] lr: 9.345e-02, eta: 3 days, 22:53:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4520, loss_cls: 4.3373, loss: 4.3373 +2024-07-17 02:32:08,288 - pyskl - INFO - Epoch [25][2800/3746] lr: 9.343e-02, eta: 3 days, 22:51:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4505, loss_cls: 4.3369, loss: 4.3369 +2024-07-17 02:33:18,318 - pyskl - INFO - Epoch [25][2900/3746] lr: 9.342e-02, eta: 3 days, 22:50:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4498, loss_cls: 4.3768, loss: 4.3768 +2024-07-17 02:34:28,510 - pyskl - INFO - Epoch [25][3000/3746] lr: 9.341e-02, eta: 3 days, 22:49:05, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4572, loss_cls: 4.3116, loss: 4.3116 +2024-07-17 02:35:38,570 - pyskl - INFO - Epoch [25][3100/3746] lr: 9.339e-02, eta: 3 days, 22:47:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4622, loss_cls: 4.2920, loss: 4.2920 +2024-07-17 02:36:48,813 - pyskl - INFO - Epoch [25][3200/3746] lr: 9.338e-02, eta: 3 days, 22:46:13, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4434, loss_cls: 4.3496, loss: 4.3496 +2024-07-17 02:37:58,711 - pyskl - INFO - Epoch [25][3300/3746] lr: 9.336e-02, eta: 3 days, 22:44:46, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4405, loss_cls: 4.3885, loss: 4.3885 +2024-07-17 02:39:08,623 - pyskl - INFO - Epoch [25][3400/3746] lr: 9.335e-02, eta: 3 days, 22:43:18, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4556, loss_cls: 4.3192, loss: 4.3192 +2024-07-17 02:40:19,563 - pyskl - INFO - Epoch [25][3500/3746] lr: 9.334e-02, eta: 3 days, 22:41:56, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4542, loss_cls: 4.3244, loss: 4.3244 +2024-07-17 02:41:30,295 - pyskl - INFO - Epoch [25][3600/3746] lr: 9.332e-02, eta: 3 days, 22:40:34, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4600, loss_cls: 4.2946, loss: 4.2946 +2024-07-17 02:42:40,873 - pyskl - INFO - Epoch [25][3700/3746] lr: 9.331e-02, eta: 3 days, 22:39:10, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4575, loss_cls: 4.3325, loss: 4.3325 +2024-07-17 02:43:15,266 - pyskl - INFO - Saving checkpoint at 25 epochs +2024-07-17 02:45:05,811 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 02:45:06,474 - pyskl - INFO - +top1_acc 0.1243 +top5_acc 0.3044 +2024-07-17 02:45:06,475 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 02:45:06,513 - pyskl - INFO - +mean_acc 0.1243 +2024-07-17 02:45:06,525 - pyskl - INFO - Epoch(val) [25][309] top1_acc: 0.1243, top5_acc: 0.3044, mean_class_accuracy: 0.1243 +2024-07-17 02:48:21,125 - pyskl - INFO - Epoch [26][100/3746] lr: 9.329e-02, eta: 3 days, 22:44:45, time: 1.946, data_time: 1.240, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4667, loss_cls: 4.2907, loss: 4.2907 +2024-07-17 02:49:31,840 - pyskl - INFO - Epoch [26][200/3746] lr: 9.327e-02, eta: 3 days, 22:43:21, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4662, loss_cls: 4.2562, loss: 4.2562 +2024-07-17 02:50:42,641 - pyskl - INFO - Epoch [26][300/3746] lr: 9.326e-02, eta: 3 days, 22:41:58, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4639, loss_cls: 4.3090, loss: 4.3090 +2024-07-17 02:51:53,137 - pyskl - INFO - Epoch [26][400/3746] lr: 9.325e-02, eta: 3 days, 22:40:34, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4577, loss_cls: 4.2791, loss: 4.2791 +2024-07-17 02:53:03,402 - pyskl - INFO - Epoch [26][500/3746] lr: 9.323e-02, eta: 3 days, 22:39:08, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4616, loss_cls: 4.2914, loss: 4.2914 +2024-07-17 02:54:13,386 - pyskl - INFO - Epoch [26][600/3746] lr: 9.322e-02, eta: 3 days, 22:37:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2155, top5_acc: 0.4584, loss_cls: 4.3090, loss: 4.3090 +2024-07-17 02:55:23,444 - pyskl - INFO - Epoch [26][700/3746] lr: 9.320e-02, eta: 3 days, 22:36:14, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4497, loss_cls: 4.3269, loss: 4.3269 +2024-07-17 02:56:33,754 - pyskl - INFO - Epoch [26][800/3746] lr: 9.319e-02, eta: 3 days, 22:34:49, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4673, loss_cls: 4.2935, loss: 4.2935 +2024-07-17 02:57:43,833 - pyskl - INFO - Epoch [26][900/3746] lr: 9.318e-02, eta: 3 days, 22:33:22, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4516, loss_cls: 4.3676, loss: 4.3676 +2024-07-17 02:58:53,816 - pyskl - INFO - Epoch [26][1000/3746] lr: 9.316e-02, eta: 3 days, 22:31:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4747, loss_cls: 4.2415, loss: 4.2415 +2024-07-17 03:00:03,721 - pyskl - INFO - Epoch [26][1100/3746] lr: 9.315e-02, eta: 3 days, 22:30:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4644, loss_cls: 4.2934, loss: 4.2934 +2024-07-17 03:01:13,795 - pyskl - INFO - Epoch [26][1200/3746] lr: 9.313e-02, eta: 3 days, 22:29:02, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4564, loss_cls: 4.3068, loss: 4.3068 +2024-07-17 03:02:23,924 - pyskl - INFO - Epoch [26][1300/3746] lr: 9.312e-02, eta: 3 days, 22:27:36, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4647, loss_cls: 4.2964, loss: 4.2964 +2024-07-17 03:03:34,115 - pyskl - INFO - Epoch [26][1400/3746] lr: 9.310e-02, eta: 3 days, 22:26:10, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4617, loss_cls: 4.2932, loss: 4.2932 +2024-07-17 03:04:44,493 - pyskl - INFO - Epoch [26][1500/3746] lr: 9.309e-02, eta: 3 days, 22:24:45, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4555, loss_cls: 4.3359, loss: 4.3359 +2024-07-17 03:05:54,325 - pyskl - INFO - Epoch [26][1600/3746] lr: 9.308e-02, eta: 3 days, 22:23:17, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4686, loss_cls: 4.3065, loss: 4.3065 +2024-07-17 03:07:04,792 - pyskl - INFO - Epoch [26][1700/3746] lr: 9.306e-02, eta: 3 days, 22:21:53, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4519, loss_cls: 4.3498, loss: 4.3498 +2024-07-17 03:08:15,697 - pyskl - INFO - Epoch [26][1800/3746] lr: 9.305e-02, eta: 3 days, 22:20:31, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4544, loss_cls: 4.3267, loss: 4.3267 +2024-07-17 03:09:26,405 - pyskl - INFO - Epoch [26][1900/3746] lr: 9.303e-02, eta: 3 days, 22:19:08, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4631, loss_cls: 4.3280, loss: 4.3280 +2024-07-17 03:10:36,473 - pyskl - INFO - Epoch [26][2000/3746] lr: 9.302e-02, eta: 3 days, 22:17:42, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4500, loss_cls: 4.3421, loss: 4.3421 +2024-07-17 03:11:47,075 - pyskl - INFO - Epoch [26][2100/3746] lr: 9.300e-02, eta: 3 days, 22:16:18, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4569, loss_cls: 4.3298, loss: 4.3298 +2024-07-17 03:12:56,991 - pyskl - INFO - Epoch [26][2200/3746] lr: 9.299e-02, eta: 3 days, 22:14:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4500, loss_cls: 4.3288, loss: 4.3288 +2024-07-17 03:14:07,244 - pyskl - INFO - Epoch [26][2300/3746] lr: 9.298e-02, eta: 3 days, 22:13:26, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4536, loss_cls: 4.3283, loss: 4.3283 +2024-07-17 03:15:17,273 - pyskl - INFO - Epoch [26][2400/3746] lr: 9.296e-02, eta: 3 days, 22:12:00, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4472, loss_cls: 4.3554, loss: 4.3554 +2024-07-17 03:16:27,431 - pyskl - INFO - Epoch [26][2500/3746] lr: 9.295e-02, eta: 3 days, 22:10:34, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4567, loss_cls: 4.2936, loss: 4.2936 +2024-07-17 03:17:37,363 - pyskl - INFO - Epoch [26][2600/3746] lr: 9.293e-02, eta: 3 days, 22:09:08, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4534, loss_cls: 4.3622, loss: 4.3622 +2024-07-17 03:18:47,480 - pyskl - INFO - Epoch [26][2700/3746] lr: 9.292e-02, eta: 3 days, 22:07:42, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4617, loss_cls: 4.2770, loss: 4.2770 +2024-07-17 03:19:57,643 - pyskl - INFO - Epoch [26][2800/3746] lr: 9.290e-02, eta: 3 days, 22:06:17, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4556, loss_cls: 4.3216, loss: 4.3216 +2024-07-17 03:21:07,700 - pyskl - INFO - Epoch [26][2900/3746] lr: 9.289e-02, eta: 3 days, 22:04:51, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4503, loss_cls: 4.3344, loss: 4.3344 +2024-07-17 03:22:18,339 - pyskl - INFO - Epoch [26][3000/3746] lr: 9.288e-02, eta: 3 days, 22:03:27, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4603, loss_cls: 4.3242, loss: 4.3242 +2024-07-17 03:23:28,315 - pyskl - INFO - Epoch [26][3100/3746] lr: 9.286e-02, eta: 3 days, 22:02:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4619, loss_cls: 4.3036, loss: 4.3036 +2024-07-17 03:24:38,094 - pyskl - INFO - Epoch [26][3200/3746] lr: 9.285e-02, eta: 3 days, 22:00:34, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4480, loss_cls: 4.3434, loss: 4.3434 +2024-07-17 03:25:48,535 - pyskl - INFO - Epoch [26][3300/3746] lr: 9.283e-02, eta: 3 days, 21:59:10, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4531, loss_cls: 4.3455, loss: 4.3455 +2024-07-17 03:26:58,672 - pyskl - INFO - Epoch [26][3400/3746] lr: 9.282e-02, eta: 3 days, 21:57:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4628, loss_cls: 4.3056, loss: 4.3056 +2024-07-17 03:28:10,066 - pyskl - INFO - Epoch [26][3500/3746] lr: 9.280e-02, eta: 3 days, 21:56:25, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4575, loss_cls: 4.3373, loss: 4.3373 +2024-07-17 03:29:21,368 - pyskl - INFO - Epoch [26][3600/3746] lr: 9.279e-02, eta: 3 days, 21:55:06, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4475, loss_cls: 4.3739, loss: 4.3739 +2024-07-17 03:30:31,932 - pyskl - INFO - Epoch [26][3700/3746] lr: 9.278e-02, eta: 3 days, 21:53:42, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2037, top5_acc: 0.4409, loss_cls: 4.3932, loss: 4.3932 +2024-07-17 03:31:06,478 - pyskl - INFO - Saving checkpoint at 26 epochs +2024-07-17 03:32:56,741 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 03:32:57,407 - pyskl - INFO - +top1_acc 0.1433 +top5_acc 0.3398 +2024-07-17 03:32:57,407 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 03:32:57,449 - pyskl - INFO - +mean_acc 0.1433 +2024-07-17 03:32:57,462 - pyskl - INFO - Epoch(val) [26][309] top1_acc: 0.1433, top5_acc: 0.3398, mean_class_accuracy: 0.1433 +2024-07-17 03:36:14,770 - pyskl - INFO - Epoch [27][100/3746] lr: 9.275e-02, eta: 3 days, 21:59:10, time: 1.973, data_time: 1.266, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4680, loss_cls: 4.2534, loss: 4.2534 +2024-07-17 03:37:25,748 - pyskl - INFO - Epoch [27][200/3746] lr: 9.274e-02, eta: 3 days, 21:57:48, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4647, loss_cls: 4.2643, loss: 4.2643 +2024-07-17 03:38:36,858 - pyskl - INFO - Epoch [27][300/3746] lr: 9.272e-02, eta: 3 days, 21:56:27, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4461, loss_cls: 4.3528, loss: 4.3528 +2024-07-17 03:39:47,180 - pyskl - INFO - Epoch [27][400/3746] lr: 9.271e-02, eta: 3 days, 21:55:02, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4619, loss_cls: 4.3162, loss: 4.3162 +2024-07-17 03:40:57,057 - pyskl - INFO - Epoch [27][500/3746] lr: 9.270e-02, eta: 3 days, 21:53:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4603, loss_cls: 4.2912, loss: 4.2912 +2024-07-17 03:42:07,466 - pyskl - INFO - Epoch [27][600/3746] lr: 9.268e-02, eta: 3 days, 21:52:11, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4562, loss_cls: 4.3229, loss: 4.3229 +2024-07-17 03:43:17,771 - pyskl - INFO - Epoch [27][700/3746] lr: 9.267e-02, eta: 3 days, 21:50:46, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4617, loss_cls: 4.2995, loss: 4.2995 +2024-07-17 03:44:27,874 - pyskl - INFO - Epoch [27][800/3746] lr: 9.265e-02, eta: 3 days, 21:49:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4556, loss_cls: 4.3206, loss: 4.3206 +2024-07-17 03:45:37,913 - pyskl - INFO - Epoch [27][900/3746] lr: 9.264e-02, eta: 3 days, 21:47:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4542, loss_cls: 4.3180, loss: 4.3180 +2024-07-17 03:46:47,831 - pyskl - INFO - Epoch [27][1000/3746] lr: 9.262e-02, eta: 3 days, 21:46:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4645, loss_cls: 4.2817, loss: 4.2817 +2024-07-17 03:47:57,942 - pyskl - INFO - Epoch [27][1100/3746] lr: 9.261e-02, eta: 3 days, 21:45:02, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4536, loss_cls: 4.3139, loss: 4.3139 +2024-07-17 03:49:07,776 - pyskl - INFO - Epoch [27][1200/3746] lr: 9.259e-02, eta: 3 days, 21:43:35, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4561, loss_cls: 4.3345, loss: 4.3345 +2024-07-17 03:50:17,669 - pyskl - INFO - Epoch [27][1300/3746] lr: 9.258e-02, eta: 3 days, 21:42:08, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4625, loss_cls: 4.2919, loss: 4.2919 +2024-07-17 03:51:27,762 - pyskl - INFO - Epoch [27][1400/3746] lr: 9.256e-02, eta: 3 days, 21:40:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4525, loss_cls: 4.3428, loss: 4.3428 +2024-07-17 03:52:37,790 - pyskl - INFO - Epoch [27][1500/3746] lr: 9.255e-02, eta: 3 days, 21:39:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4541, loss_cls: 4.2899, loss: 4.2899 +2024-07-17 03:53:47,762 - pyskl - INFO - Epoch [27][1600/3746] lr: 9.253e-02, eta: 3 days, 21:37:51, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4542, loss_cls: 4.3431, loss: 4.3431 +2024-07-17 03:54:58,624 - pyskl - INFO - Epoch [27][1700/3746] lr: 9.252e-02, eta: 3 days, 21:36:29, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4559, loss_cls: 4.3118, loss: 4.3118 +2024-07-17 03:56:09,638 - pyskl - INFO - Epoch [27][1800/3746] lr: 9.251e-02, eta: 3 days, 21:35:07, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4592, loss_cls: 4.3342, loss: 4.3342 +2024-07-17 03:57:20,197 - pyskl - INFO - Epoch [27][1900/3746] lr: 9.249e-02, eta: 3 days, 21:33:44, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4534, loss_cls: 4.3063, loss: 4.3063 +2024-07-17 03:58:31,132 - pyskl - INFO - Epoch [27][2000/3746] lr: 9.248e-02, eta: 3 days, 21:32:22, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4642, loss_cls: 4.2674, loss: 4.2674 +2024-07-17 03:59:41,505 - pyskl - INFO - Epoch [27][2100/3746] lr: 9.246e-02, eta: 3 days, 21:30:58, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4502, loss_cls: 4.3536, loss: 4.3536 +2024-07-17 04:00:51,577 - pyskl - INFO - Epoch [27][2200/3746] lr: 9.245e-02, eta: 3 days, 21:29:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4559, loss_cls: 4.3313, loss: 4.3313 +2024-07-17 04:02:01,529 - pyskl - INFO - Epoch [27][2300/3746] lr: 9.243e-02, eta: 3 days, 21:28:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4561, loss_cls: 4.3147, loss: 4.3147 +2024-07-17 04:03:11,545 - pyskl - INFO - Epoch [27][2400/3746] lr: 9.242e-02, eta: 3 days, 21:26:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4519, loss_cls: 4.3155, loss: 4.3155 +2024-07-17 04:04:21,527 - pyskl - INFO - Epoch [27][2500/3746] lr: 9.240e-02, eta: 3 days, 21:25:15, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4573, loss_cls: 4.3221, loss: 4.3221 +2024-07-17 04:05:31,263 - pyskl - INFO - Epoch [27][2600/3746] lr: 9.239e-02, eta: 3 days, 21:23:48, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4541, loss_cls: 4.3557, loss: 4.3557 +2024-07-17 04:06:41,029 - pyskl - INFO - Epoch [27][2700/3746] lr: 9.237e-02, eta: 3 days, 21:22:22, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4637, loss_cls: 4.2947, loss: 4.2947 +2024-07-17 04:07:50,992 - pyskl - INFO - Epoch [27][2800/3746] lr: 9.236e-02, eta: 3 days, 21:20:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4656, loss_cls: 4.2751, loss: 4.2751 +2024-07-17 04:09:00,736 - pyskl - INFO - Epoch [27][2900/3746] lr: 9.234e-02, eta: 3 days, 21:19:29, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4650, loss_cls: 4.2957, loss: 4.2957 +2024-07-17 04:10:10,423 - pyskl - INFO - Epoch [27][3000/3746] lr: 9.233e-02, eta: 3 days, 21:18:02, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4550, loss_cls: 4.3560, loss: 4.3560 +2024-07-17 04:11:20,309 - pyskl - INFO - Epoch [27][3100/3746] lr: 9.231e-02, eta: 3 days, 21:16:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4450, loss_cls: 4.3572, loss: 4.3572 +2024-07-17 04:12:30,069 - pyskl - INFO - Epoch [27][3200/3746] lr: 9.230e-02, eta: 3 days, 21:15:09, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4542, loss_cls: 4.3289, loss: 4.3289 +2024-07-17 04:13:39,877 - pyskl - INFO - Epoch [27][3300/3746] lr: 9.228e-02, eta: 3 days, 21:13:43, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4548, loss_cls: 4.3184, loss: 4.3184 +2024-07-17 04:14:50,049 - pyskl - INFO - Epoch [27][3400/3746] lr: 9.227e-02, eta: 3 days, 21:12:18, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4453, loss_cls: 4.3750, loss: 4.3750 +2024-07-17 04:16:00,277 - pyskl - INFO - Epoch [27][3500/3746] lr: 9.225e-02, eta: 3 days, 21:10:54, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4628, loss_cls: 4.2903, loss: 4.2903 +2024-07-17 04:17:11,239 - pyskl - INFO - Epoch [27][3600/3746] lr: 9.224e-02, eta: 3 days, 21:09:33, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4647, loss_cls: 4.3055, loss: 4.3055 +2024-07-17 04:18:21,464 - pyskl - INFO - Epoch [27][3700/3746] lr: 9.222e-02, eta: 3 days, 21:08:09, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4548, loss_cls: 4.3590, loss: 4.3590 +2024-07-17 04:18:55,624 - pyskl - INFO - Saving checkpoint at 27 epochs +2024-07-17 04:20:45,625 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 04:20:46,295 - pyskl - INFO - +top1_acc 0.1498 +top5_acc 0.3609 +2024-07-17 04:20:46,295 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 04:20:46,336 - pyskl - INFO - +mean_acc 0.1497 +2024-07-17 04:20:46,347 - pyskl - INFO - Epoch(val) [27][309] top1_acc: 0.1498, top5_acc: 0.3609, mean_class_accuracy: 0.1497 +2024-07-17 04:24:01,371 - pyskl - INFO - Epoch [28][100/3746] lr: 9.220e-02, eta: 3 days, 21:13:06, time: 1.950, data_time: 1.241, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4633, loss_cls: 4.2649, loss: 4.2649 +2024-07-17 04:25:12,306 - pyskl - INFO - Epoch [28][200/3746] lr: 9.219e-02, eta: 3 days, 21:11:45, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4603, loss_cls: 4.2644, loss: 4.2644 +2024-07-17 04:26:23,340 - pyskl - INFO - Epoch [28][300/3746] lr: 9.217e-02, eta: 3 days, 21:10:24, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4542, loss_cls: 4.3154, loss: 4.3154 +2024-07-17 04:27:33,917 - pyskl - INFO - Epoch [28][400/3746] lr: 9.216e-02, eta: 3 days, 21:09:00, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4572, loss_cls: 4.2983, loss: 4.2983 +2024-07-17 04:28:43,978 - pyskl - INFO - Epoch [28][500/3746] lr: 9.214e-02, eta: 3 days, 21:07:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4675, loss_cls: 4.2851, loss: 4.2851 +2024-07-17 04:29:54,103 - pyskl - INFO - Epoch [28][600/3746] lr: 9.213e-02, eta: 3 days, 21:06:10, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4636, loss_cls: 4.2990, loss: 4.2990 +2024-07-17 04:31:04,150 - pyskl - INFO - Epoch [28][700/3746] lr: 9.211e-02, eta: 3 days, 21:04:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4448, loss_cls: 4.3509, loss: 4.3509 +2024-07-17 04:32:14,209 - pyskl - INFO - Epoch [28][800/3746] lr: 9.210e-02, eta: 3 days, 21:03:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4597, loss_cls: 4.3421, loss: 4.3421 +2024-07-17 04:33:24,289 - pyskl - INFO - Epoch [28][900/3746] lr: 9.208e-02, eta: 3 days, 21:01:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4580, loss_cls: 4.3385, loss: 4.3385 +2024-07-17 04:34:34,337 - pyskl - INFO - Epoch [28][1000/3746] lr: 9.207e-02, eta: 3 days, 21:00:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4570, loss_cls: 4.3068, loss: 4.3068 +2024-07-17 04:35:44,475 - pyskl - INFO - Epoch [28][1100/3746] lr: 9.205e-02, eta: 3 days, 20:59:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4534, loss_cls: 4.3255, loss: 4.3255 +2024-07-17 04:36:54,626 - pyskl - INFO - Epoch [28][1200/3746] lr: 9.204e-02, eta: 3 days, 20:57:39, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4713, loss_cls: 4.2983, loss: 4.2983 +2024-07-17 04:38:04,532 - pyskl - INFO - Epoch [28][1300/3746] lr: 9.202e-02, eta: 3 days, 20:56:13, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4595, loss_cls: 4.3036, loss: 4.3036 +2024-07-17 04:39:14,417 - pyskl - INFO - Epoch [28][1400/3746] lr: 9.201e-02, eta: 3 days, 20:54:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2114, top5_acc: 0.4483, loss_cls: 4.3360, loss: 4.3360 +2024-07-17 04:40:24,176 - pyskl - INFO - Epoch [28][1500/3746] lr: 9.199e-02, eta: 3 days, 20:53:20, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4630, loss_cls: 4.3118, loss: 4.3118 +2024-07-17 04:41:33,965 - pyskl - INFO - Epoch [28][1600/3746] lr: 9.198e-02, eta: 3 days, 20:51:54, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4572, loss_cls: 4.3127, loss: 4.3127 +2024-07-17 04:42:44,132 - pyskl - INFO - Epoch [28][1700/3746] lr: 9.196e-02, eta: 3 days, 20:50:29, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4548, loss_cls: 4.3200, loss: 4.3200 +2024-07-17 04:43:54,922 - pyskl - INFO - Epoch [28][1800/3746] lr: 9.194e-02, eta: 3 days, 20:49:08, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4517, loss_cls: 4.3554, loss: 4.3554 +2024-07-17 04:45:05,154 - pyskl - INFO - Epoch [28][1900/3746] lr: 9.193e-02, eta: 3 days, 20:47:43, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4620, loss_cls: 4.2897, loss: 4.2897 +2024-07-17 04:46:15,521 - pyskl - INFO - Epoch [28][2000/3746] lr: 9.191e-02, eta: 3 days, 20:46:20, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4480, loss_cls: 4.3725, loss: 4.3725 +2024-07-17 04:47:25,884 - pyskl - INFO - Epoch [28][2100/3746] lr: 9.190e-02, eta: 3 days, 20:44:56, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4633, loss_cls: 4.3010, loss: 4.3010 +2024-07-17 04:48:36,039 - pyskl - INFO - Epoch [28][2200/3746] lr: 9.188e-02, eta: 3 days, 20:43:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4561, loss_cls: 4.3268, loss: 4.3268 +2024-07-17 04:49:45,846 - pyskl - INFO - Epoch [28][2300/3746] lr: 9.187e-02, eta: 3 days, 20:42:05, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4594, loss_cls: 4.3180, loss: 4.3180 +2024-07-17 04:50:55,801 - pyskl - INFO - Epoch [28][2400/3746] lr: 9.185e-02, eta: 3 days, 20:40:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4658, loss_cls: 4.2923, loss: 4.2923 +2024-07-17 04:52:05,554 - pyskl - INFO - Epoch [28][2500/3746] lr: 9.184e-02, eta: 3 days, 20:39:14, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4555, loss_cls: 4.3258, loss: 4.3258 +2024-07-17 04:53:15,274 - pyskl - INFO - Epoch [28][2600/3746] lr: 9.182e-02, eta: 3 days, 20:37:48, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4612, loss_cls: 4.2812, loss: 4.2812 +2024-07-17 04:54:24,982 - pyskl - INFO - Epoch [28][2700/3746] lr: 9.181e-02, eta: 3 days, 20:36:21, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4645, loss_cls: 4.3006, loss: 4.3006 +2024-07-17 04:55:34,870 - pyskl - INFO - Epoch [28][2800/3746] lr: 9.179e-02, eta: 3 days, 20:34:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4416, loss_cls: 4.3723, loss: 4.3723 +2024-07-17 04:56:44,968 - pyskl - INFO - Epoch [28][2900/3746] lr: 9.178e-02, eta: 3 days, 20:33:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4566, loss_cls: 4.3282, loss: 4.3282 +2024-07-17 04:57:54,751 - pyskl - INFO - Epoch [28][3000/3746] lr: 9.176e-02, eta: 3 days, 20:32:05, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4631, loss_cls: 4.2923, loss: 4.2923 +2024-07-17 04:59:04,598 - pyskl - INFO - Epoch [28][3100/3746] lr: 9.175e-02, eta: 3 days, 20:30:40, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4534, loss_cls: 4.3253, loss: 4.3253 +2024-07-17 05:00:14,503 - pyskl - INFO - Epoch [28][3200/3746] lr: 9.173e-02, eta: 3 days, 20:29:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4522, loss_cls: 4.3517, loss: 4.3517 +2024-07-17 05:01:24,410 - pyskl - INFO - Epoch [28][3300/3746] lr: 9.172e-02, eta: 3 days, 20:27:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4505, loss_cls: 4.3333, loss: 4.3333 +2024-07-17 05:02:34,579 - pyskl - INFO - Epoch [28][3400/3746] lr: 9.170e-02, eta: 3 days, 20:26:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4555, loss_cls: 4.3147, loss: 4.3147 +2024-07-17 05:03:45,529 - pyskl - INFO - Epoch [28][3500/3746] lr: 9.168e-02, eta: 3 days, 20:25:04, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4480, loss_cls: 4.3510, loss: 4.3510 +2024-07-17 05:04:56,257 - pyskl - INFO - Epoch [28][3600/3746] lr: 9.167e-02, eta: 3 days, 20:23:42, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4528, loss_cls: 4.3223, loss: 4.3223 +2024-07-17 05:06:06,548 - pyskl - INFO - Epoch [28][3700/3746] lr: 9.165e-02, eta: 3 days, 20:22:19, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4564, loss_cls: 4.3172, loss: 4.3172 +2024-07-17 05:06:40,899 - pyskl - INFO - Saving checkpoint at 28 epochs +2024-07-17 05:08:30,875 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 05:08:31,545 - pyskl - INFO - +top1_acc 0.1360 +top5_acc 0.3277 +2024-07-17 05:08:31,545 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 05:08:31,585 - pyskl - INFO - +mean_acc 0.1358 +2024-07-17 05:08:31,597 - pyskl - INFO - Epoch(val) [28][309] top1_acc: 0.1360, top5_acc: 0.3277, mean_class_accuracy: 0.1358 +2024-07-17 05:11:47,588 - pyskl - INFO - Epoch [29][100/3746] lr: 9.163e-02, eta: 3 days, 20:27:03, time: 1.960, data_time: 1.249, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4639, loss_cls: 4.2605, loss: 4.2605 +2024-07-17 05:12:58,439 - pyskl - INFO - Epoch [29][200/3746] lr: 9.162e-02, eta: 3 days, 20:25:42, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4559, loss_cls: 4.2968, loss: 4.2968 +2024-07-17 05:14:09,444 - pyskl - INFO - Epoch [29][300/3746] lr: 9.160e-02, eta: 3 days, 20:24:21, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4616, loss_cls: 4.2998, loss: 4.2998 +2024-07-17 05:15:19,764 - pyskl - INFO - Epoch [29][400/3746] lr: 9.158e-02, eta: 3 days, 20:22:57, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4620, loss_cls: 4.2855, loss: 4.2855 +2024-07-17 05:16:30,067 - pyskl - INFO - Epoch [29][500/3746] lr: 9.157e-02, eta: 3 days, 20:21:33, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4622, loss_cls: 4.3100, loss: 4.3100 +2024-07-17 05:17:40,029 - pyskl - INFO - Epoch [29][600/3746] lr: 9.155e-02, eta: 3 days, 20:20:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4639, loss_cls: 4.2654, loss: 4.2654 +2024-07-17 05:18:49,805 - pyskl - INFO - Epoch [29][700/3746] lr: 9.154e-02, eta: 3 days, 20:18:42, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4583, loss_cls: 4.3187, loss: 4.3187 +2024-07-17 05:19:59,893 - pyskl - INFO - Epoch [29][800/3746] lr: 9.152e-02, eta: 3 days, 20:17:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4670, loss_cls: 4.2913, loss: 4.2913 +2024-07-17 05:21:09,778 - pyskl - INFO - Epoch [29][900/3746] lr: 9.151e-02, eta: 3 days, 20:15:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4630, loss_cls: 4.3307, loss: 4.3307 +2024-07-17 05:22:19,793 - pyskl - INFO - Epoch [29][1000/3746] lr: 9.149e-02, eta: 3 days, 20:14:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4620, loss_cls: 4.3138, loss: 4.3138 +2024-07-17 05:23:30,130 - pyskl - INFO - Epoch [29][1100/3746] lr: 9.148e-02, eta: 3 days, 20:13:04, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4675, loss_cls: 4.2879, loss: 4.2879 +2024-07-17 05:24:39,937 - pyskl - INFO - Epoch [29][1200/3746] lr: 9.146e-02, eta: 3 days, 20:11:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4589, loss_cls: 4.3221, loss: 4.3221 +2024-07-17 05:25:49,617 - pyskl - INFO - Epoch [29][1300/3746] lr: 9.144e-02, eta: 3 days, 20:10:12, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4609, loss_cls: 4.2901, loss: 4.2901 +2024-07-17 05:26:59,162 - pyskl - INFO - Epoch [29][1400/3746] lr: 9.143e-02, eta: 3 days, 20:08:45, time: 0.695, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4567, loss_cls: 4.3186, loss: 4.3186 +2024-07-17 05:28:08,870 - pyskl - INFO - Epoch [29][1500/3746] lr: 9.141e-02, eta: 3 days, 20:07:19, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4611, loss_cls: 4.3283, loss: 4.3283 +2024-07-17 05:29:18,654 - pyskl - INFO - Epoch [29][1600/3746] lr: 9.140e-02, eta: 3 days, 20:05:53, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4553, loss_cls: 4.3262, loss: 4.3262 +2024-07-17 05:30:28,647 - pyskl - INFO - Epoch [29][1700/3746] lr: 9.138e-02, eta: 3 days, 20:04:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4566, loss_cls: 4.3224, loss: 4.3224 +2024-07-17 05:31:39,156 - pyskl - INFO - Epoch [29][1800/3746] lr: 9.137e-02, eta: 3 days, 20:03:05, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4703, loss_cls: 4.2480, loss: 4.2480 +2024-07-17 05:32:49,202 - pyskl - INFO - Epoch [29][1900/3746] lr: 9.135e-02, eta: 3 days, 20:01:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4616, loss_cls: 4.2952, loss: 4.2952 +2024-07-17 05:33:59,413 - pyskl - INFO - Epoch [29][2000/3746] lr: 9.133e-02, eta: 3 days, 20:00:17, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4609, loss_cls: 4.3171, loss: 4.3171 +2024-07-17 05:35:09,861 - pyskl - INFO - Epoch [29][2100/3746] lr: 9.132e-02, eta: 3 days, 19:58:54, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4608, loss_cls: 4.3115, loss: 4.3115 +2024-07-17 05:36:20,330 - pyskl - INFO - Epoch [29][2200/3746] lr: 9.130e-02, eta: 3 days, 19:57:32, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4589, loss_cls: 4.3093, loss: 4.3093 +2024-07-17 05:37:30,323 - pyskl - INFO - Epoch [29][2300/3746] lr: 9.129e-02, eta: 3 days, 19:56:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4581, loss_cls: 4.2938, loss: 4.2938 +2024-07-17 05:38:40,365 - pyskl - INFO - Epoch [29][2400/3746] lr: 9.127e-02, eta: 3 days, 19:54:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4569, loss_cls: 4.3041, loss: 4.3041 +2024-07-17 05:39:50,545 - pyskl - INFO - Epoch [29][2500/3746] lr: 9.126e-02, eta: 3 days, 19:53:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4544, loss_cls: 4.3206, loss: 4.3206 +2024-07-17 05:41:00,409 - pyskl - INFO - Epoch [29][2600/3746] lr: 9.124e-02, eta: 3 days, 19:51:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4595, loss_cls: 4.2985, loss: 4.2985 +2024-07-17 05:42:10,470 - pyskl - INFO - Epoch [29][2700/3746] lr: 9.122e-02, eta: 3 days, 19:50:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4506, loss_cls: 4.3197, loss: 4.3197 +2024-07-17 05:43:20,237 - pyskl - INFO - Epoch [29][2800/3746] lr: 9.121e-02, eta: 3 days, 19:49:04, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4569, loss_cls: 4.3380, loss: 4.3380 +2024-07-17 05:44:30,117 - pyskl - INFO - Epoch [29][2900/3746] lr: 9.119e-02, eta: 3 days, 19:47:39, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4472, loss_cls: 4.3581, loss: 4.3581 +2024-07-17 05:45:40,174 - pyskl - INFO - Epoch [29][3000/3746] lr: 9.118e-02, eta: 3 days, 19:46:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4523, loss_cls: 4.3423, loss: 4.3423 +2024-07-17 05:46:50,133 - pyskl - INFO - Epoch [29][3100/3746] lr: 9.116e-02, eta: 3 days, 19:44:50, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4530, loss_cls: 4.3121, loss: 4.3121 +2024-07-17 05:47:59,834 - pyskl - INFO - Epoch [29][3200/3746] lr: 9.114e-02, eta: 3 days, 19:43:25, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4467, loss_cls: 4.3803, loss: 4.3803 +2024-07-17 05:49:09,852 - pyskl - INFO - Epoch [29][3300/3746] lr: 9.113e-02, eta: 3 days, 19:42:00, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4644, loss_cls: 4.3093, loss: 4.3093 +2024-07-17 05:50:19,866 - pyskl - INFO - Epoch [29][3400/3746] lr: 9.111e-02, eta: 3 days, 19:40:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4603, loss_cls: 4.2917, loss: 4.2917 +2024-07-17 05:51:30,393 - pyskl - INFO - Epoch [29][3500/3746] lr: 9.110e-02, eta: 3 days, 19:39:14, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4603, loss_cls: 4.3167, loss: 4.3167 +2024-07-17 05:52:41,314 - pyskl - INFO - Epoch [29][3600/3746] lr: 9.108e-02, eta: 3 days, 19:37:54, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4603, loss_cls: 4.3263, loss: 4.3263 +2024-07-17 05:53:52,478 - pyskl - INFO - Epoch [29][3700/3746] lr: 9.106e-02, eta: 3 days, 19:36:34, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4633, loss_cls: 4.2850, loss: 4.2850 +2024-07-17 05:54:26,890 - pyskl - INFO - Saving checkpoint at 29 epochs +2024-07-17 05:56:16,664 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 05:56:17,331 - pyskl - INFO - +top1_acc 0.1502 +top5_acc 0.3489 +2024-07-17 05:56:17,331 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 05:56:17,370 - pyskl - INFO - +mean_acc 0.1499 +2024-07-17 05:56:17,382 - pyskl - INFO - Epoch(val) [29][309] top1_acc: 0.1502, top5_acc: 0.3489, mean_class_accuracy: 0.1499 +2024-07-17 05:59:43,217 - pyskl - INFO - Epoch [30][100/3746] lr: 9.104e-02, eta: 3 days, 19:41:43, time: 2.058, data_time: 1.240, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4730, loss_cls: 4.2211, loss: 4.2211 +2024-07-17 06:01:04,061 - pyskl - INFO - Epoch [30][200/3746] lr: 9.103e-02, eta: 3 days, 19:41:04, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4678, loss_cls: 4.2619, loss: 4.2619 +2024-07-17 06:02:26,180 - pyskl - INFO - Epoch [30][300/3746] lr: 9.101e-02, eta: 3 days, 19:40:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4623, loss_cls: 4.2798, loss: 4.2798 +2024-07-17 06:03:47,768 - pyskl - INFO - Epoch [30][400/3746] lr: 9.099e-02, eta: 3 days, 19:39:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4700, loss_cls: 4.2699, loss: 4.2699 +2024-07-17 06:05:08,622 - pyskl - INFO - Epoch [30][500/3746] lr: 9.098e-02, eta: 3 days, 19:39:13, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4636, loss_cls: 4.3140, loss: 4.3140 +2024-07-17 06:06:29,788 - pyskl - INFO - Epoch [30][600/3746] lr: 9.096e-02, eta: 3 days, 19:38:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4508, loss_cls: 4.3511, loss: 4.3511 +2024-07-17 06:07:50,628 - pyskl - INFO - Epoch [30][700/3746] lr: 9.095e-02, eta: 3 days, 19:37:54, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4572, loss_cls: 4.2986, loss: 4.2986 +2024-07-17 06:09:11,103 - pyskl - INFO - Epoch [30][800/3746] lr: 9.093e-02, eta: 3 days, 19:37:13, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4639, loss_cls: 4.2914, loss: 4.2914 +2024-07-17 06:10:32,179 - pyskl - INFO - Epoch [30][900/3746] lr: 9.091e-02, eta: 3 days, 19:36:34, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4631, loss_cls: 4.2725, loss: 4.2725 +2024-07-17 06:11:53,151 - pyskl - INFO - Epoch [30][1000/3746] lr: 9.090e-02, eta: 3 days, 19:35:54, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4603, loss_cls: 4.3397, loss: 4.3397 +2024-07-17 06:13:13,782 - pyskl - INFO - Epoch [30][1100/3746] lr: 9.088e-02, eta: 3 days, 19:35:13, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4672, loss_cls: 4.3033, loss: 4.3033 +2024-07-17 06:14:35,461 - pyskl - INFO - Epoch [30][1200/3746] lr: 9.087e-02, eta: 3 days, 19:34:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4586, loss_cls: 4.3075, loss: 4.3075 +2024-07-17 06:15:56,563 - pyskl - INFO - Epoch [30][1300/3746] lr: 9.085e-02, eta: 3 days, 19:33:57, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4677, loss_cls: 4.2740, loss: 4.2740 +2024-07-17 06:17:17,585 - pyskl - INFO - Epoch [30][1400/3746] lr: 9.083e-02, eta: 3 days, 19:33:17, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4648, loss_cls: 4.2855, loss: 4.2855 +2024-07-17 06:18:38,199 - pyskl - INFO - Epoch [30][1500/3746] lr: 9.082e-02, eta: 3 days, 19:32:36, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4552, loss_cls: 4.3256, loss: 4.3256 +2024-07-17 06:19:59,346 - pyskl - INFO - Epoch [30][1600/3746] lr: 9.080e-02, eta: 3 days, 19:31:56, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4562, loss_cls: 4.2955, loss: 4.2955 +2024-07-17 06:21:20,719 - pyskl - INFO - Epoch [30][1700/3746] lr: 9.078e-02, eta: 3 days, 19:31:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2194, top5_acc: 0.4475, loss_cls: 4.3590, loss: 4.3590 +2024-07-17 06:22:41,708 - pyskl - INFO - Epoch [30][1800/3746] lr: 9.077e-02, eta: 3 days, 19:30:38, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4597, loss_cls: 4.2955, loss: 4.2955 +2024-07-17 06:24:02,654 - pyskl - INFO - Epoch [30][1900/3746] lr: 9.075e-02, eta: 3 days, 19:29:57, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4541, loss_cls: 4.3306, loss: 4.3306 +2024-07-17 06:25:24,708 - pyskl - INFO - Epoch [30][2000/3746] lr: 9.074e-02, eta: 3 days, 19:29:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4606, loss_cls: 4.3028, loss: 4.3028 +2024-07-17 06:26:45,932 - pyskl - INFO - Epoch [30][2100/3746] lr: 9.072e-02, eta: 3 days, 19:28:42, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4705, loss_cls: 4.2641, loss: 4.2641 +2024-07-17 06:28:07,626 - pyskl - INFO - Epoch [30][2200/3746] lr: 9.070e-02, eta: 3 days, 19:28:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4553, loss_cls: 4.3368, loss: 4.3368 +2024-07-17 06:29:29,938 - pyskl - INFO - Epoch [30][2300/3746] lr: 9.069e-02, eta: 3 days, 19:27:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4594, loss_cls: 4.2877, loss: 4.2877 +2024-07-17 06:30:51,603 - pyskl - INFO - Epoch [30][2400/3746] lr: 9.067e-02, eta: 3 days, 19:26:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4528, loss_cls: 4.3597, loss: 4.3597 +2024-07-17 06:32:12,472 - pyskl - INFO - Epoch [30][2500/3746] lr: 9.065e-02, eta: 3 days, 19:26:10, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4675, loss_cls: 4.2568, loss: 4.2568 +2024-07-17 06:33:33,325 - pyskl - INFO - Epoch [30][2600/3746] lr: 9.064e-02, eta: 3 days, 19:25:29, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4583, loss_cls: 4.3142, loss: 4.3142 +2024-07-17 06:34:54,060 - pyskl - INFO - Epoch [30][2700/3746] lr: 9.062e-02, eta: 3 days, 19:24:47, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4598, loss_cls: 4.2922, loss: 4.2922 +2024-07-17 06:36:14,808 - pyskl - INFO - Epoch [30][2800/3746] lr: 9.061e-02, eta: 3 days, 19:24:05, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4584, loss_cls: 4.3208, loss: 4.3208 +2024-07-17 06:37:35,929 - pyskl - INFO - Epoch [30][2900/3746] lr: 9.059e-02, eta: 3 days, 19:23:25, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4556, loss_cls: 4.3347, loss: 4.3347 +2024-07-17 06:38:56,534 - pyskl - INFO - Epoch [30][3000/3746] lr: 9.057e-02, eta: 3 days, 19:22:42, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4480, loss_cls: 4.3510, loss: 4.3510 +2024-07-17 06:40:17,556 - pyskl - INFO - Epoch [30][3100/3746] lr: 9.056e-02, eta: 3 days, 19:22:01, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4634, loss_cls: 4.2927, loss: 4.2927 +2024-07-17 06:41:38,461 - pyskl - INFO - Epoch [30][3200/3746] lr: 9.054e-02, eta: 3 days, 19:21:19, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4642, loss_cls: 4.2648, loss: 4.2648 +2024-07-17 06:42:58,969 - pyskl - INFO - Epoch [30][3300/3746] lr: 9.052e-02, eta: 3 days, 19:20:36, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2155, top5_acc: 0.4623, loss_cls: 4.2993, loss: 4.2993 +2024-07-17 06:44:20,059 - pyskl - INFO - Epoch [30][3400/3746] lr: 9.051e-02, eta: 3 days, 19:19:55, time: 0.811, data_time: 0.001, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4577, loss_cls: 4.3164, loss: 4.3164 +2024-07-17 06:45:41,344 - pyskl - INFO - Epoch [30][3500/3746] lr: 9.049e-02, eta: 3 days, 19:19:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4578, loss_cls: 4.3369, loss: 4.3369 +2024-07-17 06:47:03,236 - pyskl - INFO - Epoch [30][3600/3746] lr: 9.047e-02, eta: 3 days, 19:18:37, time: 0.819, data_time: 0.001, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4684, loss_cls: 4.2870, loss: 4.2870 +2024-07-17 06:48:24,708 - pyskl - INFO - Epoch [30][3700/3746] lr: 9.046e-02, eta: 3 days, 19:17:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4606, loss_cls: 4.3244, loss: 4.3244 +2024-07-17 06:49:04,262 - pyskl - INFO - Saving checkpoint at 30 epochs +2024-07-17 06:50:54,520 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 06:50:55,516 - pyskl - INFO - +top1_acc 0.1447 +top5_acc 0.3424 +2024-07-17 06:50:55,516 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 06:50:55,556 - pyskl - INFO - +mean_acc 0.1446 +2024-07-17 06:50:55,568 - pyskl - INFO - Epoch(val) [30][309] top1_acc: 0.1447, top5_acc: 0.3424, mean_class_accuracy: 0.1446 +2024-07-17 06:54:45,288 - pyskl - INFO - Epoch [31][100/3746] lr: 9.043e-02, eta: 3 days, 19:24:22, time: 2.297, data_time: 1.307, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4686, loss_cls: 4.5089, loss: 4.5089 +2024-07-17 06:56:08,219 - pyskl - INFO - Epoch [31][200/3746] lr: 9.042e-02, eta: 3 days, 19:23:48, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4653, loss_cls: 4.5479, loss: 4.5479 +2024-07-17 06:57:31,673 - pyskl - INFO - Epoch [31][300/3746] lr: 9.040e-02, eta: 3 days, 19:23:15, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4720, loss_cls: 4.4695, loss: 4.4695 +2024-07-17 06:58:55,402 - pyskl - INFO - Epoch [31][400/3746] lr: 9.039e-02, eta: 3 days, 19:22:44, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4525, loss_cls: 4.5560, loss: 4.5560 +2024-07-17 07:00:18,184 - pyskl - INFO - Epoch [31][500/3746] lr: 9.037e-02, eta: 3 days, 19:22:09, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4636, loss_cls: 4.5425, loss: 4.5425 +2024-07-17 07:01:40,977 - pyskl - INFO - Epoch [31][600/3746] lr: 9.035e-02, eta: 3 days, 19:21:33, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4620, loss_cls: 4.5172, loss: 4.5172 +2024-07-17 07:03:03,468 - pyskl - INFO - Epoch [31][700/3746] lr: 9.034e-02, eta: 3 days, 19:20:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4517, loss_cls: 4.5672, loss: 4.5672 +2024-07-17 07:04:25,755 - pyskl - INFO - Epoch [31][800/3746] lr: 9.032e-02, eta: 3 days, 19:20:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4617, loss_cls: 4.5406, loss: 4.5406 +2024-07-17 07:05:48,288 - pyskl - INFO - Epoch [31][900/3746] lr: 9.030e-02, eta: 3 days, 19:19:42, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4569, loss_cls: 4.5447, loss: 4.5447 +2024-07-17 07:07:10,457 - pyskl - INFO - Epoch [31][1000/3746] lr: 9.029e-02, eta: 3 days, 19:19:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4703, loss_cls: 4.4820, loss: 4.4820 +2024-07-17 07:08:32,323 - pyskl - INFO - Epoch [31][1100/3746] lr: 9.027e-02, eta: 3 days, 19:18:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4533, loss_cls: 4.5622, loss: 4.5622 +2024-07-17 07:09:54,067 - pyskl - INFO - Epoch [31][1200/3746] lr: 9.025e-02, eta: 3 days, 19:17:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4689, loss_cls: 4.4939, loss: 4.4939 +2024-07-17 07:11:16,293 - pyskl - INFO - Epoch [31][1300/3746] lr: 9.024e-02, eta: 3 days, 19:17:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4506, loss_cls: 4.5608, loss: 4.5608 +2024-07-17 07:12:38,412 - pyskl - INFO - Epoch [31][1400/3746] lr: 9.022e-02, eta: 3 days, 19:16:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4642, loss_cls: 4.5285, loss: 4.5285 +2024-07-17 07:14:00,601 - pyskl - INFO - Epoch [31][1500/3746] lr: 9.020e-02, eta: 3 days, 19:15:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4434, loss_cls: 4.6121, loss: 4.6121 +2024-07-17 07:15:22,787 - pyskl - INFO - Epoch [31][1600/3746] lr: 9.019e-02, eta: 3 days, 19:15:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4781, loss_cls: 4.5074, loss: 4.5074 +2024-07-17 07:16:45,086 - pyskl - INFO - Epoch [31][1700/3746] lr: 9.017e-02, eta: 3 days, 19:14:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4531, loss_cls: 4.5505, loss: 4.5505 +2024-07-17 07:18:07,074 - pyskl - INFO - Epoch [31][1800/3746] lr: 9.015e-02, eta: 3 days, 19:13:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4497, loss_cls: 4.5621, loss: 4.5621 +2024-07-17 07:19:29,688 - pyskl - INFO - Epoch [31][1900/3746] lr: 9.014e-02, eta: 3 days, 19:13:16, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4591, loss_cls: 4.5192, loss: 4.5192 +2024-07-17 07:20:53,042 - pyskl - INFO - Epoch [31][2000/3746] lr: 9.012e-02, eta: 3 days, 19:12:42, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4605, loss_cls: 4.5557, loss: 4.5557 +2024-07-17 07:22:15,627 - pyskl - INFO - Epoch [31][2100/3746] lr: 9.010e-02, eta: 3 days, 19:12:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4600, loss_cls: 4.5343, loss: 4.5343 +2024-07-17 07:23:38,350 - pyskl - INFO - Epoch [31][2200/3746] lr: 9.009e-02, eta: 3 days, 19:11:28, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4472, loss_cls: 4.5839, loss: 4.5839 +2024-07-17 07:25:01,983 - pyskl - INFO - Epoch [31][2300/3746] lr: 9.007e-02, eta: 3 days, 19:10:54, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4611, loss_cls: 4.5368, loss: 4.5368 +2024-07-17 07:26:25,852 - pyskl - INFO - Epoch [31][2400/3746] lr: 9.005e-02, eta: 3 days, 19:10:22, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4592, loss_cls: 4.5695, loss: 4.5695 +2024-07-17 07:27:48,847 - pyskl - INFO - Epoch [31][2500/3746] lr: 9.004e-02, eta: 3 days, 19:09:45, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4609, loss_cls: 4.5228, loss: 4.5228 +2024-07-17 07:29:11,354 - pyskl - INFO - Epoch [31][2600/3746] lr: 9.002e-02, eta: 3 days, 19:09:07, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4677, loss_cls: 4.5318, loss: 4.5318 +2024-07-17 07:30:33,540 - pyskl - INFO - Epoch [31][2700/3746] lr: 9.000e-02, eta: 3 days, 19:08:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4523, loss_cls: 4.5709, loss: 4.5709 +2024-07-17 07:31:55,745 - pyskl - INFO - Epoch [31][2800/3746] lr: 8.999e-02, eta: 3 days, 19:07:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4667, loss_cls: 4.5147, loss: 4.5147 +2024-07-17 07:33:18,374 - pyskl - INFO - Epoch [31][2900/3746] lr: 8.997e-02, eta: 3 days, 19:07:11, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4517, loss_cls: 4.5615, loss: 4.5615 +2024-07-17 07:34:40,583 - pyskl - INFO - Epoch [31][3000/3746] lr: 8.995e-02, eta: 3 days, 19:06:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4430, loss_cls: 4.5974, loss: 4.5974 +2024-07-17 07:36:02,501 - pyskl - INFO - Epoch [31][3100/3746] lr: 8.994e-02, eta: 3 days, 19:05:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4603, loss_cls: 4.5229, loss: 4.5229 +2024-07-17 07:37:24,925 - pyskl - INFO - Epoch [31][3200/3746] lr: 8.992e-02, eta: 3 days, 19:05:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4602, loss_cls: 4.5222, loss: 4.5222 +2024-07-17 07:38:47,228 - pyskl - INFO - Epoch [31][3300/3746] lr: 8.990e-02, eta: 3 days, 19:04:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4417, loss_cls: 4.6027, loss: 4.6027 +2024-07-17 07:40:10,751 - pyskl - INFO - Epoch [31][3400/3746] lr: 8.989e-02, eta: 3 days, 19:03:57, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4630, loss_cls: 4.5453, loss: 4.5453 +2024-07-17 07:41:33,446 - pyskl - INFO - Epoch [31][3500/3746] lr: 8.987e-02, eta: 3 days, 19:03:19, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4545, loss_cls: 4.5807, loss: 4.5807 +2024-07-17 07:42:56,237 - pyskl - INFO - Epoch [31][3600/3746] lr: 8.985e-02, eta: 3 days, 19:02:42, time: 0.828, data_time: 0.001, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4508, loss_cls: 4.5742, loss: 4.5742 +2024-07-17 07:44:19,287 - pyskl - INFO - Epoch [31][3700/3746] lr: 8.983e-02, eta: 3 days, 19:02:05, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4627, loss_cls: 4.5036, loss: 4.5036 +2024-07-17 07:44:58,954 - pyskl - INFO - Saving checkpoint at 31 epochs +2024-07-17 07:46:49,705 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 07:46:50,394 - pyskl - INFO - +top1_acc 0.1667 +top5_acc 0.3821 +2024-07-17 07:46:50,394 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 07:46:50,434 - pyskl - INFO - +mean_acc 0.1665 +2024-07-17 07:46:50,439 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_24.pth was removed +2024-07-17 07:46:50,667 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_31.pth. +2024-07-17 07:46:50,668 - pyskl - INFO - Best top1_acc is 0.1667 at 31 epoch. +2024-07-17 07:46:50,680 - pyskl - INFO - Epoch(val) [31][309] top1_acc: 0.1667, top5_acc: 0.3821, mean_class_accuracy: 0.1665 +2024-07-17 07:50:38,897 - pyskl - INFO - Epoch [32][100/3746] lr: 8.981e-02, eta: 3 days, 19:08:01, time: 2.282, data_time: 1.293, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4669, loss_cls: 4.4991, loss: 4.4991 +2024-07-17 07:52:02,209 - pyskl - INFO - Epoch [32][200/3746] lr: 8.979e-02, eta: 3 days, 19:07:24, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4622, loss_cls: 4.5302, loss: 4.5302 +2024-07-17 07:53:26,100 - pyskl - INFO - Epoch [32][300/3746] lr: 8.978e-02, eta: 3 days, 19:06:50, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4839, loss_cls: 4.4446, loss: 4.4446 +2024-07-17 07:54:49,471 - pyskl - INFO - Epoch [32][400/3746] lr: 8.976e-02, eta: 3 days, 19:06:14, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4455, loss_cls: 4.5893, loss: 4.5893 +2024-07-17 07:56:12,277 - pyskl - INFO - Epoch [32][500/3746] lr: 8.974e-02, eta: 3 days, 19:05:35, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4667, loss_cls: 4.5616, loss: 4.5616 +2024-07-17 07:57:34,167 - pyskl - INFO - Epoch [32][600/3746] lr: 8.973e-02, eta: 3 days, 19:04:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4561, loss_cls: 4.5594, loss: 4.5594 +2024-07-17 07:58:56,509 - pyskl - INFO - Epoch [32][700/3746] lr: 8.971e-02, eta: 3 days, 19:04:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4733, loss_cls: 4.4979, loss: 4.4979 +2024-07-17 08:00:18,578 - pyskl - INFO - Epoch [32][800/3746] lr: 8.969e-02, eta: 3 days, 19:03:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4603, loss_cls: 4.5388, loss: 4.5388 +2024-07-17 08:01:40,721 - pyskl - INFO - Epoch [32][900/3746] lr: 8.967e-02, eta: 3 days, 19:02:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4445, loss_cls: 4.5729, loss: 4.5729 +2024-07-17 08:03:02,575 - pyskl - INFO - Epoch [32][1000/3746] lr: 8.966e-02, eta: 3 days, 19:02:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4609, loss_cls: 4.5469, loss: 4.5469 +2024-07-17 08:04:24,972 - pyskl - INFO - Epoch [32][1100/3746] lr: 8.964e-02, eta: 3 days, 19:01:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4645, loss_cls: 4.5283, loss: 4.5283 +2024-07-17 08:05:46,956 - pyskl - INFO - Epoch [32][1200/3746] lr: 8.962e-02, eta: 3 days, 19:00:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4706, loss_cls: 4.4963, loss: 4.4963 +2024-07-17 08:07:08,849 - pyskl - INFO - Epoch [32][1300/3746] lr: 8.961e-02, eta: 3 days, 19:00:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4642, loss_cls: 4.5285, loss: 4.5285 +2024-07-17 08:08:30,455 - pyskl - INFO - Epoch [32][1400/3746] lr: 8.959e-02, eta: 3 days, 18:59:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4519, loss_cls: 4.5985, loss: 4.5985 +2024-07-17 08:09:51,973 - pyskl - INFO - Epoch [32][1500/3746] lr: 8.957e-02, eta: 3 days, 18:58:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4658, loss_cls: 4.5426, loss: 4.5426 +2024-07-17 08:11:13,803 - pyskl - INFO - Epoch [32][1600/3746] lr: 8.955e-02, eta: 3 days, 18:57:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4559, loss_cls: 4.5632, loss: 4.5632 +2024-07-17 08:12:35,476 - pyskl - INFO - Epoch [32][1700/3746] lr: 8.954e-02, eta: 3 days, 18:57:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4659, loss_cls: 4.5250, loss: 4.5250 +2024-07-17 08:13:57,360 - pyskl - INFO - Epoch [32][1800/3746] lr: 8.952e-02, eta: 3 days, 18:56:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4652, loss_cls: 4.5075, loss: 4.5075 +2024-07-17 08:15:19,349 - pyskl - INFO - Epoch [32][1900/3746] lr: 8.950e-02, eta: 3 days, 18:55:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4475, loss_cls: 4.5747, loss: 4.5747 +2024-07-17 08:16:40,596 - pyskl - INFO - Epoch [32][2000/3746] lr: 8.949e-02, eta: 3 days, 18:54:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4602, loss_cls: 4.5388, loss: 4.5388 +2024-07-17 08:18:02,528 - pyskl - INFO - Epoch [32][2100/3746] lr: 8.947e-02, eta: 3 days, 18:54:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4553, loss_cls: 4.5935, loss: 4.5935 +2024-07-17 08:19:23,645 - pyskl - INFO - Epoch [32][2200/3746] lr: 8.945e-02, eta: 3 days, 18:53:25, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4617, loss_cls: 4.5281, loss: 4.5281 +2024-07-17 08:20:44,814 - pyskl - INFO - Epoch [32][2300/3746] lr: 8.943e-02, eta: 3 days, 18:52:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4517, loss_cls: 4.5802, loss: 4.5802 +2024-07-17 08:22:06,389 - pyskl - INFO - Epoch [32][2400/3746] lr: 8.942e-02, eta: 3 days, 18:51:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4636, loss_cls: 4.5631, loss: 4.5631 +2024-07-17 08:23:27,096 - pyskl - INFO - Epoch [32][2500/3746] lr: 8.940e-02, eta: 3 days, 18:51:07, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4561, loss_cls: 4.5372, loss: 4.5372 +2024-07-17 08:24:48,426 - pyskl - INFO - Epoch [32][2600/3746] lr: 8.938e-02, eta: 3 days, 18:50:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4583, loss_cls: 4.5534, loss: 4.5534 +2024-07-17 08:26:09,909 - pyskl - INFO - Epoch [32][2700/3746] lr: 8.937e-02, eta: 3 days, 18:49:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4644, loss_cls: 4.5081, loss: 4.5081 +2024-07-17 08:27:31,538 - pyskl - INFO - Epoch [32][2800/3746] lr: 8.935e-02, eta: 3 days, 18:48:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4605, loss_cls: 4.5097, loss: 4.5097 +2024-07-17 08:28:53,119 - pyskl - INFO - Epoch [32][2900/3746] lr: 8.933e-02, eta: 3 days, 18:48:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4602, loss_cls: 4.5433, loss: 4.5433 +2024-07-17 08:30:14,275 - pyskl - INFO - Epoch [32][3000/3746] lr: 8.931e-02, eta: 3 days, 18:47:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4595, loss_cls: 4.5091, loss: 4.5091 +2024-07-17 08:31:35,497 - pyskl - INFO - Epoch [32][3100/3746] lr: 8.930e-02, eta: 3 days, 18:46:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4659, loss_cls: 4.5399, loss: 4.5399 +2024-07-17 08:32:57,638 - pyskl - INFO - Epoch [32][3200/3746] lr: 8.928e-02, eta: 3 days, 18:45:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4511, loss_cls: 4.5944, loss: 4.5944 +2024-07-17 08:34:19,483 - pyskl - INFO - Epoch [32][3300/3746] lr: 8.926e-02, eta: 3 days, 18:45:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4605, loss_cls: 4.5097, loss: 4.5097 +2024-07-17 08:35:40,749 - pyskl - INFO - Epoch [32][3400/3746] lr: 8.924e-02, eta: 3 days, 18:44:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4645, loss_cls: 4.5498, loss: 4.5498 +2024-07-17 08:37:02,903 - pyskl - INFO - Epoch [32][3500/3746] lr: 8.923e-02, eta: 3 days, 18:43:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4594, loss_cls: 4.5309, loss: 4.5309 +2024-07-17 08:38:24,342 - pyskl - INFO - Epoch [32][3600/3746] lr: 8.921e-02, eta: 3 days, 18:42:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4719, loss_cls: 4.4864, loss: 4.4864 +2024-07-17 08:39:46,410 - pyskl - INFO - Epoch [32][3700/3746] lr: 8.919e-02, eta: 3 days, 18:42:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4597, loss_cls: 4.5386, loss: 4.5386 +2024-07-17 08:40:25,777 - pyskl - INFO - Saving checkpoint at 32 epochs +2024-07-17 08:42:17,056 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 08:42:17,749 - pyskl - INFO - +top1_acc 0.1558 +top5_acc 0.3482 +2024-07-17 08:42:17,749 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 08:42:17,793 - pyskl - INFO - +mean_acc 0.1558 +2024-07-17 08:42:17,806 - pyskl - INFO - Epoch(val) [32][309] top1_acc: 0.1558, top5_acc: 0.3482, mean_class_accuracy: 0.1558 +2024-07-17 08:46:09,144 - pyskl - INFO - Epoch [33][100/3746] lr: 8.917e-02, eta: 3 days, 18:47:55, time: 2.313, data_time: 1.325, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4666, loss_cls: 4.5154, loss: 4.5154 +2024-07-17 08:47:32,713 - pyskl - INFO - Epoch [33][200/3746] lr: 8.915e-02, eta: 3 days, 18:47:16, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4692, loss_cls: 4.4872, loss: 4.4872 +2024-07-17 08:48:56,509 - pyskl - INFO - Epoch [33][300/3746] lr: 8.913e-02, eta: 3 days, 18:46:38, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4650, loss_cls: 4.5101, loss: 4.5101 +2024-07-17 08:50:19,540 - pyskl - INFO - Epoch [33][400/3746] lr: 8.912e-02, eta: 3 days, 18:45:58, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4552, loss_cls: 4.5558, loss: 4.5558 +2024-07-17 08:51:42,246 - pyskl - INFO - Epoch [33][500/3746] lr: 8.910e-02, eta: 3 days, 18:45:16, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4716, loss_cls: 4.4896, loss: 4.4896 +2024-07-17 08:53:05,065 - pyskl - INFO - Epoch [33][600/3746] lr: 8.908e-02, eta: 3 days, 18:44:34, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4703, loss_cls: 4.5022, loss: 4.5022 +2024-07-17 08:54:27,318 - pyskl - INFO - Epoch [33][700/3746] lr: 8.906e-02, eta: 3 days, 18:43:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4698, loss_cls: 4.5030, loss: 4.5030 +2024-07-17 08:55:49,877 - pyskl - INFO - Epoch [33][800/3746] lr: 8.905e-02, eta: 3 days, 18:43:07, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4669, loss_cls: 4.5366, loss: 4.5366 +2024-07-17 08:57:12,208 - pyskl - INFO - Epoch [33][900/3746] lr: 8.903e-02, eta: 3 days, 18:42:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4631, loss_cls: 4.5239, loss: 4.5239 +2024-07-17 08:58:35,390 - pyskl - INFO - Epoch [33][1000/3746] lr: 8.901e-02, eta: 3 days, 18:41:43, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4566, loss_cls: 4.5367, loss: 4.5367 +2024-07-17 08:59:57,762 - pyskl - INFO - Epoch [33][1100/3746] lr: 8.899e-02, eta: 3 days, 18:40:59, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4567, loss_cls: 4.5350, loss: 4.5350 +2024-07-17 09:01:19,920 - pyskl - INFO - Epoch [33][1200/3746] lr: 8.898e-02, eta: 3 days, 18:40:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4600, loss_cls: 4.5336, loss: 4.5336 +2024-07-17 09:02:42,148 - pyskl - INFO - Epoch [33][1300/3746] lr: 8.896e-02, eta: 3 days, 18:39:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4689, loss_cls: 4.5183, loss: 4.5183 +2024-07-17 09:04:04,663 - pyskl - INFO - Epoch [33][1400/3746] lr: 8.894e-02, eta: 3 days, 18:38:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4642, loss_cls: 4.5228, loss: 4.5228 +2024-07-17 09:05:26,622 - pyskl - INFO - Epoch [33][1500/3746] lr: 8.892e-02, eta: 3 days, 18:38:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4516, loss_cls: 4.5688, loss: 4.5688 +2024-07-17 09:06:49,034 - pyskl - INFO - Epoch [33][1600/3746] lr: 8.891e-02, eta: 3 days, 18:37:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4658, loss_cls: 4.5357, loss: 4.5357 +2024-07-17 09:08:11,972 - pyskl - INFO - Epoch [33][1700/3746] lr: 8.889e-02, eta: 3 days, 18:36:36, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4644, loss_cls: 4.5068, loss: 4.5068 +2024-07-17 09:09:35,179 - pyskl - INFO - Epoch [33][1800/3746] lr: 8.887e-02, eta: 3 days, 18:35:55, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4469, loss_cls: 4.5750, loss: 4.5750 +2024-07-17 09:10:57,633 - pyskl - INFO - Epoch [33][1900/3746] lr: 8.885e-02, eta: 3 days, 18:35:11, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4583, loss_cls: 4.5376, loss: 4.5376 +2024-07-17 09:12:18,853 - pyskl - INFO - Epoch [33][2000/3746] lr: 8.884e-02, eta: 3 days, 18:34:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4597, loss_cls: 4.5295, loss: 4.5295 +2024-07-17 09:13:40,779 - pyskl - INFO - Epoch [33][2100/3746] lr: 8.882e-02, eta: 3 days, 18:33:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4537, loss_cls: 4.5506, loss: 4.5506 +2024-07-17 09:15:02,022 - pyskl - INFO - Epoch [33][2200/3746] lr: 8.880e-02, eta: 3 days, 18:32:49, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4527, loss_cls: 4.5689, loss: 4.5689 +2024-07-17 09:16:23,623 - pyskl - INFO - Epoch [33][2300/3746] lr: 8.878e-02, eta: 3 days, 18:32:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4627, loss_cls: 4.5276, loss: 4.5276 +2024-07-17 09:17:45,368 - pyskl - INFO - Epoch [33][2400/3746] lr: 8.876e-02, eta: 3 days, 18:31:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4600, loss_cls: 4.5115, loss: 4.5115 +2024-07-17 09:19:06,038 - pyskl - INFO - Epoch [33][2500/3746] lr: 8.875e-02, eta: 3 days, 18:30:24, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4609, loss_cls: 4.5049, loss: 4.5049 +2024-07-17 09:20:26,894 - pyskl - INFO - Epoch [33][2600/3746] lr: 8.873e-02, eta: 3 days, 18:29:34, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4681, loss_cls: 4.5050, loss: 4.5050 +2024-07-17 09:21:48,067 - pyskl - INFO - Epoch [33][2700/3746] lr: 8.871e-02, eta: 3 days, 18:28:45, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4698, loss_cls: 4.4989, loss: 4.4989 +2024-07-17 09:23:09,394 - pyskl - INFO - Epoch [33][2800/3746] lr: 8.869e-02, eta: 3 days, 18:27:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4555, loss_cls: 4.5442, loss: 4.5442 +2024-07-17 09:24:31,086 - pyskl - INFO - Epoch [33][2900/3746] lr: 8.868e-02, eta: 3 days, 18:27:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4709, loss_cls: 4.5143, loss: 4.5143 +2024-07-17 09:25:52,221 - pyskl - INFO - Epoch [33][3000/3746] lr: 8.866e-02, eta: 3 days, 18:26:20, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4805, loss_cls: 4.4434, loss: 4.4434 +2024-07-17 09:27:12,785 - pyskl - INFO - Epoch [33][3100/3746] lr: 8.864e-02, eta: 3 days, 18:25:29, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4670, loss_cls: 4.5360, loss: 4.5360 +2024-07-17 09:28:33,403 - pyskl - INFO - Epoch [33][3200/3746] lr: 8.862e-02, eta: 3 days, 18:24:38, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4612, loss_cls: 4.5138, loss: 4.5138 +2024-07-17 09:29:54,851 - pyskl - INFO - Epoch [33][3300/3746] lr: 8.861e-02, eta: 3 days, 18:23:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4537, loss_cls: 4.5375, loss: 4.5375 +2024-07-17 09:31:16,059 - pyskl - INFO - Epoch [33][3400/3746] lr: 8.859e-02, eta: 3 days, 18:23:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4628, loss_cls: 4.5175, loss: 4.5175 +2024-07-17 09:32:38,301 - pyskl - INFO - Epoch [33][3500/3746] lr: 8.857e-02, eta: 3 days, 18:22:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4553, loss_cls: 4.5517, loss: 4.5517 +2024-07-17 09:34:00,127 - pyskl - INFO - Epoch [33][3600/3746] lr: 8.855e-02, eta: 3 days, 18:21:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4558, loss_cls: 4.5688, loss: 4.5688 +2024-07-17 09:35:21,694 - pyskl - INFO - Epoch [33][3700/3746] lr: 8.853e-02, eta: 3 days, 18:20:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4597, loss_cls: 4.5490, loss: 4.5490 +2024-07-17 09:36:01,152 - pyskl - INFO - Saving checkpoint at 33 epochs +2024-07-17 09:37:52,543 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 09:37:53,203 - pyskl - INFO - +top1_acc 0.1310 +top5_acc 0.3222 +2024-07-17 09:37:53,203 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 09:37:53,244 - pyskl - INFO - +mean_acc 0.1308 +2024-07-17 09:37:53,257 - pyskl - INFO - Epoch(val) [33][309] top1_acc: 0.1310, top5_acc: 0.3222, mean_class_accuracy: 0.1308 +2024-07-17 09:41:41,257 - pyskl - INFO - Epoch [34][100/3746] lr: 8.851e-02, eta: 3 days, 18:25:55, time: 2.280, data_time: 1.309, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4770, loss_cls: 4.4664, loss: 4.4664 +2024-07-17 09:43:02,370 - pyskl - INFO - Epoch [34][200/3746] lr: 8.849e-02, eta: 3 days, 18:25:05, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4550, loss_cls: 4.5464, loss: 4.5464 +2024-07-17 09:44:23,022 - pyskl - INFO - Epoch [34][300/3746] lr: 8.847e-02, eta: 3 days, 18:24:13, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4645, loss_cls: 4.5189, loss: 4.5189 +2024-07-17 09:45:44,406 - pyskl - INFO - Epoch [34][400/3746] lr: 8.845e-02, eta: 3 days, 18:23:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4612, loss_cls: 4.5314, loss: 4.5314 +2024-07-17 09:47:05,882 - pyskl - INFO - Epoch [34][500/3746] lr: 8.844e-02, eta: 3 days, 18:22:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4569, loss_cls: 4.5171, loss: 4.5171 +2024-07-17 09:48:27,003 - pyskl - INFO - Epoch [34][600/3746] lr: 8.842e-02, eta: 3 days, 18:21:45, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4600, loss_cls: 4.5462, loss: 4.5462 +2024-07-17 09:49:47,754 - pyskl - INFO - Epoch [34][700/3746] lr: 8.840e-02, eta: 3 days, 18:20:53, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4733, loss_cls: 4.4930, loss: 4.4930 +2024-07-17 09:51:08,780 - pyskl - INFO - Epoch [34][800/3746] lr: 8.838e-02, eta: 3 days, 18:20:02, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4630, loss_cls: 4.5162, loss: 4.5162 +2024-07-17 09:52:29,810 - pyskl - INFO - Epoch [34][900/3746] lr: 8.836e-02, eta: 3 days, 18:19:11, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4552, loss_cls: 4.5396, loss: 4.5396 +2024-07-17 09:53:51,109 - pyskl - INFO - Epoch [34][1000/3746] lr: 8.835e-02, eta: 3 days, 18:18:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4725, loss_cls: 4.4941, loss: 4.4941 +2024-07-17 09:55:12,288 - pyskl - INFO - Epoch [34][1100/3746] lr: 8.833e-02, eta: 3 days, 18:17:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4628, loss_cls: 4.5096, loss: 4.5096 +2024-07-17 09:56:33,134 - pyskl - INFO - Epoch [34][1200/3746] lr: 8.831e-02, eta: 3 days, 18:16:39, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4662, loss_cls: 4.4983, loss: 4.4983 +2024-07-17 09:57:53,515 - pyskl - INFO - Epoch [34][1300/3746] lr: 8.829e-02, eta: 3 days, 18:15:46, time: 0.804, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4642, loss_cls: 4.5179, loss: 4.5179 +2024-07-17 09:59:14,289 - pyskl - INFO - Epoch [34][1400/3746] lr: 8.828e-02, eta: 3 days, 18:14:54, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4727, loss_cls: 4.5211, loss: 4.5211 +2024-07-17 10:00:35,091 - pyskl - INFO - Epoch [34][1500/3746] lr: 8.826e-02, eta: 3 days, 18:14:02, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4636, loss_cls: 4.5305, loss: 4.5305 +2024-07-17 10:01:56,279 - pyskl - INFO - Epoch [34][1600/3746] lr: 8.824e-02, eta: 3 days, 18:13:12, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4567, loss_cls: 4.5445, loss: 4.5445 +2024-07-17 10:03:17,718 - pyskl - INFO - Epoch [34][1700/3746] lr: 8.822e-02, eta: 3 days, 18:12:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4716, loss_cls: 4.4840, loss: 4.4840 +2024-07-17 10:04:38,879 - pyskl - INFO - Epoch [34][1800/3746] lr: 8.820e-02, eta: 3 days, 18:11:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4702, loss_cls: 4.4628, loss: 4.4628 +2024-07-17 10:06:00,442 - pyskl - INFO - Epoch [34][1900/3746] lr: 8.819e-02, eta: 3 days, 18:10:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4664, loss_cls: 4.5176, loss: 4.5176 +2024-07-17 10:07:21,566 - pyskl - INFO - Epoch [34][2000/3746] lr: 8.817e-02, eta: 3 days, 18:09:51, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4700, loss_cls: 4.4941, loss: 4.4941 +2024-07-17 10:08:43,101 - pyskl - INFO - Epoch [34][2100/3746] lr: 8.815e-02, eta: 3 days, 18:09:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4600, loss_cls: 4.5532, loss: 4.5532 +2024-07-17 10:10:05,321 - pyskl - INFO - Epoch [34][2200/3746] lr: 8.813e-02, eta: 3 days, 18:08:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4658, loss_cls: 4.5132, loss: 4.5132 +2024-07-17 10:11:26,224 - pyskl - INFO - Epoch [34][2300/3746] lr: 8.811e-02, eta: 3 days, 18:07:22, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2194, top5_acc: 0.4498, loss_cls: 4.5676, loss: 4.5676 +2024-07-17 10:12:46,962 - pyskl - INFO - Epoch [34][2400/3746] lr: 8.809e-02, eta: 3 days, 18:06:29, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4667, loss_cls: 4.4954, loss: 4.4954 +2024-07-17 10:14:07,766 - pyskl - INFO - Epoch [34][2500/3746] lr: 8.808e-02, eta: 3 days, 18:05:37, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4678, loss_cls: 4.5199, loss: 4.5199 +2024-07-17 10:15:28,803 - pyskl - INFO - Epoch [34][2600/3746] lr: 8.806e-02, eta: 3 days, 18:04:45, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4678, loss_cls: 4.5036, loss: 4.5036 +2024-07-17 10:16:49,544 - pyskl - INFO - Epoch [34][2700/3746] lr: 8.804e-02, eta: 3 days, 18:03:52, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4716, loss_cls: 4.5172, loss: 4.5172 +2024-07-17 10:18:10,691 - pyskl - INFO - Epoch [34][2800/3746] lr: 8.802e-02, eta: 3 days, 18:03:01, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4580, loss_cls: 4.5536, loss: 4.5536 +2024-07-17 10:19:31,552 - pyskl - INFO - Epoch [34][2900/3746] lr: 8.800e-02, eta: 3 days, 18:02:09, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4595, loss_cls: 4.5127, loss: 4.5127 +2024-07-17 10:20:52,569 - pyskl - INFO - Epoch [34][3000/3746] lr: 8.799e-02, eta: 3 days, 18:01:17, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4606, loss_cls: 4.5232, loss: 4.5232 +2024-07-17 10:22:13,672 - pyskl - INFO - Epoch [34][3100/3746] lr: 8.797e-02, eta: 3 days, 18:00:25, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4566, loss_cls: 4.5656, loss: 4.5656 +2024-07-17 10:23:34,726 - pyskl - INFO - Epoch [34][3200/3746] lr: 8.795e-02, eta: 3 days, 17:59:33, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4614, loss_cls: 4.5390, loss: 4.5390 +2024-07-17 10:24:56,737 - pyskl - INFO - Epoch [34][3300/3746] lr: 8.793e-02, eta: 3 days, 17:58:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4606, loss_cls: 4.5252, loss: 4.5252 +2024-07-17 10:26:18,330 - pyskl - INFO - Epoch [34][3400/3746] lr: 8.791e-02, eta: 3 days, 17:57:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4548, loss_cls: 4.5197, loss: 4.5197 +2024-07-17 10:27:40,758 - pyskl - INFO - Epoch [34][3500/3746] lr: 8.789e-02, eta: 3 days, 17:57:07, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4653, loss_cls: 4.5395, loss: 4.5395 +2024-07-17 10:29:02,291 - pyskl - INFO - Epoch [34][3600/3746] lr: 8.788e-02, eta: 3 days, 17:56:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4711, loss_cls: 4.4904, loss: 4.4904 +2024-07-17 10:30:24,077 - pyskl - INFO - Epoch [34][3700/3746] lr: 8.786e-02, eta: 3 days, 17:55:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4720, loss_cls: 4.5091, loss: 4.5091 +2024-07-17 10:31:03,408 - pyskl - INFO - Saving checkpoint at 34 epochs +2024-07-17 10:32:52,430 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 10:32:53,096 - pyskl - INFO - +top1_acc 0.1620 +top5_acc 0.3762 +2024-07-17 10:32:53,096 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 10:32:53,135 - pyskl - INFO - +mean_acc 0.1619 +2024-07-17 10:32:53,145 - pyskl - INFO - Epoch(val) [34][309] top1_acc: 0.1620, top5_acc: 0.3762, mean_class_accuracy: 0.1619 +2024-07-17 10:36:43,614 - pyskl - INFO - Epoch [35][100/3746] lr: 8.783e-02, eta: 3 days, 18:00:34, time: 2.305, data_time: 1.325, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4717, loss_cls: 4.4811, loss: 4.4811 +2024-07-17 10:38:05,502 - pyskl - INFO - Epoch [35][200/3746] lr: 8.781e-02, eta: 3 days, 17:59:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4692, loss_cls: 4.4874, loss: 4.4874 +2024-07-17 10:39:26,695 - pyskl - INFO - Epoch [35][300/3746] lr: 8.780e-02, eta: 3 days, 17:58:52, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4706, loss_cls: 4.4815, loss: 4.4815 +2024-07-17 10:40:47,780 - pyskl - INFO - Epoch [35][400/3746] lr: 8.778e-02, eta: 3 days, 17:57:59, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4750, loss_cls: 4.4688, loss: 4.4688 +2024-07-17 10:42:08,392 - pyskl - INFO - Epoch [35][500/3746] lr: 8.776e-02, eta: 3 days, 17:57:05, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4714, loss_cls: 4.4815, loss: 4.4815 +2024-07-17 10:43:28,903 - pyskl - INFO - Epoch [35][600/3746] lr: 8.774e-02, eta: 3 days, 17:56:10, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4716, loss_cls: 4.4661, loss: 4.4661 +2024-07-17 10:44:49,964 - pyskl - INFO - Epoch [35][700/3746] lr: 8.772e-02, eta: 3 days, 17:55:18, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4620, loss_cls: 4.5209, loss: 4.5209 +2024-07-17 10:46:11,194 - pyskl - INFO - Epoch [35][800/3746] lr: 8.770e-02, eta: 3 days, 17:54:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4527, loss_cls: 4.5507, loss: 4.5507 +2024-07-17 10:47:32,207 - pyskl - INFO - Epoch [35][900/3746] lr: 8.769e-02, eta: 3 days, 17:53:32, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4667, loss_cls: 4.4877, loss: 4.4877 +2024-07-17 10:48:53,915 - pyskl - INFO - Epoch [35][1000/3746] lr: 8.767e-02, eta: 3 days, 17:52:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4672, loss_cls: 4.5153, loss: 4.5153 +2024-07-17 10:50:14,728 - pyskl - INFO - Epoch [35][1100/3746] lr: 8.765e-02, eta: 3 days, 17:51:48, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4647, loss_cls: 4.5370, loss: 4.5370 +2024-07-17 10:51:36,184 - pyskl - INFO - Epoch [35][1200/3746] lr: 8.763e-02, eta: 3 days, 17:50:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4656, loss_cls: 4.5318, loss: 4.5318 +2024-07-17 10:52:57,405 - pyskl - INFO - Epoch [35][1300/3746] lr: 8.761e-02, eta: 3 days, 17:50:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4697, loss_cls: 4.4969, loss: 4.4969 +2024-07-17 10:54:18,611 - pyskl - INFO - Epoch [35][1400/3746] lr: 8.759e-02, eta: 3 days, 17:49:11, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4772, loss_cls: 4.4651, loss: 4.4651 +2024-07-17 10:55:39,441 - pyskl - INFO - Epoch [35][1500/3746] lr: 8.757e-02, eta: 3 days, 17:48:17, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4545, loss_cls: 4.5330, loss: 4.5330 +2024-07-17 10:57:00,347 - pyskl - INFO - Epoch [35][1600/3746] lr: 8.756e-02, eta: 3 days, 17:47:23, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4653, loss_cls: 4.5270, loss: 4.5270 +2024-07-17 10:58:21,863 - pyskl - INFO - Epoch [35][1700/3746] lr: 8.754e-02, eta: 3 days, 17:46:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4672, loss_cls: 4.5116, loss: 4.5116 +2024-07-17 10:59:42,915 - pyskl - INFO - Epoch [35][1800/3746] lr: 8.752e-02, eta: 3 days, 17:45:38, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4614, loss_cls: 4.5254, loss: 4.5254 +2024-07-17 11:01:04,350 - pyskl - INFO - Epoch [35][1900/3746] lr: 8.750e-02, eta: 3 days, 17:44:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4561, loss_cls: 4.5514, loss: 4.5514 +2024-07-17 11:02:26,268 - pyskl - INFO - Epoch [35][2000/3746] lr: 8.748e-02, eta: 3 days, 17:43:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4612, loss_cls: 4.5009, loss: 4.5009 +2024-07-17 11:03:47,447 - pyskl - INFO - Epoch [35][2100/3746] lr: 8.746e-02, eta: 3 days, 17:43:03, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4620, loss_cls: 4.5297, loss: 4.5297 +2024-07-17 11:05:08,733 - pyskl - INFO - Epoch [35][2200/3746] lr: 8.745e-02, eta: 3 days, 17:42:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4608, loss_cls: 4.5217, loss: 4.5217 +2024-07-17 11:06:30,159 - pyskl - INFO - Epoch [35][2300/3746] lr: 8.743e-02, eta: 3 days, 17:41:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4589, loss_cls: 4.5293, loss: 4.5293 +2024-07-17 11:07:52,211 - pyskl - INFO - Epoch [35][2400/3746] lr: 8.741e-02, eta: 3 days, 17:40:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4609, loss_cls: 4.4972, loss: 4.4972 +2024-07-17 11:09:14,159 - pyskl - INFO - Epoch [35][2500/3746] lr: 8.739e-02, eta: 3 days, 17:39:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4645, loss_cls: 4.5177, loss: 4.5177 +2024-07-17 11:10:36,145 - pyskl - INFO - Epoch [35][2600/3746] lr: 8.737e-02, eta: 3 days, 17:38:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4505, loss_cls: 4.5800, loss: 4.5800 +2024-07-17 11:11:56,855 - pyskl - INFO - Epoch [35][2700/3746] lr: 8.735e-02, eta: 3 days, 17:37:52, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4519, loss_cls: 4.5526, loss: 4.5526 +2024-07-17 11:13:18,005 - pyskl - INFO - Epoch [35][2800/3746] lr: 8.733e-02, eta: 3 days, 17:36:58, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4580, loss_cls: 4.5460, loss: 4.5460 +2024-07-17 11:14:39,111 - pyskl - INFO - Epoch [35][2900/3746] lr: 8.732e-02, eta: 3 days, 17:36:05, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4598, loss_cls: 4.5258, loss: 4.5258 +2024-07-17 11:15:59,888 - pyskl - INFO - Epoch [35][3000/3746] lr: 8.730e-02, eta: 3 days, 17:35:10, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4597, loss_cls: 4.5140, loss: 4.5140 +2024-07-17 11:17:21,115 - pyskl - INFO - Epoch [35][3100/3746] lr: 8.728e-02, eta: 3 days, 17:34:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4622, loss_cls: 4.5236, loss: 4.5236 +2024-07-17 11:18:42,410 - pyskl - INFO - Epoch [35][3200/3746] lr: 8.726e-02, eta: 3 days, 17:33:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4658, loss_cls: 4.4995, loss: 4.4995 +2024-07-17 11:20:03,991 - pyskl - INFO - Epoch [35][3300/3746] lr: 8.724e-02, eta: 3 days, 17:32:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4641, loss_cls: 4.5135, loss: 4.5135 +2024-07-17 11:21:26,340 - pyskl - INFO - Epoch [35][3400/3746] lr: 8.722e-02, eta: 3 days, 17:31:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4570, loss_cls: 4.5555, loss: 4.5555 +2024-07-17 11:22:48,973 - pyskl - INFO - Epoch [35][3500/3746] lr: 8.720e-02, eta: 3 days, 17:30:53, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4639, loss_cls: 4.5306, loss: 4.5306 +2024-07-17 11:24:10,555 - pyskl - INFO - Epoch [35][3600/3746] lr: 8.718e-02, eta: 3 days, 17:30:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4686, loss_cls: 4.4864, loss: 4.4864 +2024-07-17 11:25:31,940 - pyskl - INFO - Epoch [35][3700/3746] lr: 8.717e-02, eta: 3 days, 17:29:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4727, loss_cls: 4.4947, loss: 4.4947 +2024-07-17 11:26:11,570 - pyskl - INFO - Saving checkpoint at 35 epochs +2024-07-17 11:28:01,198 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 11:28:01,854 - pyskl - INFO - +top1_acc 0.1557 +top5_acc 0.3676 +2024-07-17 11:28:01,854 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 11:28:01,894 - pyskl - INFO - +mean_acc 0.1555 +2024-07-17 11:28:01,906 - pyskl - INFO - Epoch(val) [35][309] top1_acc: 0.1557, top5_acc: 0.3676, mean_class_accuracy: 0.1555 +2024-07-17 11:31:49,315 - pyskl - INFO - Epoch [36][100/3746] lr: 8.714e-02, eta: 3 days, 17:33:47, time: 2.274, data_time: 1.303, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4653, loss_cls: 4.4965, loss: 4.4965 +2024-07-17 11:33:10,692 - pyskl - INFO - Epoch [36][200/3746] lr: 8.712e-02, eta: 3 days, 17:32:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4744, loss_cls: 4.4539, loss: 4.4539 +2024-07-17 11:34:31,789 - pyskl - INFO - Epoch [36][300/3746] lr: 8.710e-02, eta: 3 days, 17:31:59, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4583, loss_cls: 4.5129, loss: 4.5129 +2024-07-17 11:35:53,192 - pyskl - INFO - Epoch [36][400/3746] lr: 8.708e-02, eta: 3 days, 17:31:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4730, loss_cls: 4.4875, loss: 4.4875 +2024-07-17 11:37:14,150 - pyskl - INFO - Epoch [36][500/3746] lr: 8.706e-02, eta: 3 days, 17:30:10, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4586, loss_cls: 4.5394, loss: 4.5394 +2024-07-17 11:38:34,926 - pyskl - INFO - Epoch [36][600/3746] lr: 8.704e-02, eta: 3 days, 17:29:15, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4698, loss_cls: 4.4768, loss: 4.4768 +2024-07-17 11:39:55,983 - pyskl - INFO - Epoch [36][700/3746] lr: 8.703e-02, eta: 3 days, 17:28:20, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4667, loss_cls: 4.5462, loss: 4.5462 +2024-07-17 11:41:17,180 - pyskl - INFO - Epoch [36][800/3746] lr: 8.701e-02, eta: 3 days, 17:27:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4661, loss_cls: 4.4983, loss: 4.4983 +2024-07-17 11:42:38,217 - pyskl - INFO - Epoch [36][900/3746] lr: 8.699e-02, eta: 3 days, 17:26:31, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4552, loss_cls: 4.5263, loss: 4.5263 +2024-07-17 11:43:59,331 - pyskl - INFO - Epoch [36][1000/3746] lr: 8.697e-02, eta: 3 days, 17:25:36, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4591, loss_cls: 4.5340, loss: 4.5340 +2024-07-17 11:45:20,794 - pyskl - INFO - Epoch [36][1100/3746] lr: 8.695e-02, eta: 3 days, 17:24:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4650, loss_cls: 4.4947, loss: 4.4947 +2024-07-17 11:46:41,786 - pyskl - INFO - Epoch [36][1200/3746] lr: 8.693e-02, eta: 3 days, 17:23:47, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4666, loss_cls: 4.4909, loss: 4.4909 +2024-07-17 11:48:02,525 - pyskl - INFO - Epoch [36][1300/3746] lr: 8.691e-02, eta: 3 days, 17:22:51, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4669, loss_cls: 4.4886, loss: 4.4886 +2024-07-17 11:49:23,786 - pyskl - INFO - Epoch [36][1400/3746] lr: 8.689e-02, eta: 3 days, 17:21:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4577, loss_cls: 4.5203, loss: 4.5203 +2024-07-17 11:50:45,320 - pyskl - INFO - Epoch [36][1500/3746] lr: 8.688e-02, eta: 3 days, 17:21:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4728, loss_cls: 4.4991, loss: 4.4991 +2024-07-17 11:52:06,030 - pyskl - INFO - Epoch [36][1600/3746] lr: 8.686e-02, eta: 3 days, 17:20:07, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4773, loss_cls: 4.4623, loss: 4.4623 +2024-07-17 11:53:26,923 - pyskl - INFO - Epoch [36][1700/3746] lr: 8.684e-02, eta: 3 days, 17:19:11, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4647, loss_cls: 4.5028, loss: 4.5028 +2024-07-17 11:54:47,658 - pyskl - INFO - Epoch [36][1800/3746] lr: 8.682e-02, eta: 3 days, 17:18:15, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4713, loss_cls: 4.4870, loss: 4.4870 +2024-07-17 11:56:08,880 - pyskl - INFO - Epoch [36][1900/3746] lr: 8.680e-02, eta: 3 days, 17:17:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4667, loss_cls: 4.5263, loss: 4.5263 +2024-07-17 11:57:30,087 - pyskl - INFO - Epoch [36][2000/3746] lr: 8.678e-02, eta: 3 days, 17:16:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4736, loss_cls: 4.4747, loss: 4.4747 +2024-07-17 11:58:52,629 - pyskl - INFO - Epoch [36][2100/3746] lr: 8.676e-02, eta: 3 days, 17:15:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4609, loss_cls: 4.5416, loss: 4.5416 +2024-07-17 12:00:13,296 - pyskl - INFO - Epoch [36][2200/3746] lr: 8.674e-02, eta: 3 days, 17:14:38, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4652, loss_cls: 4.5030, loss: 4.5030 +2024-07-17 12:01:34,367 - pyskl - INFO - Epoch [36][2300/3746] lr: 8.672e-02, eta: 3 days, 17:13:43, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4730, loss_cls: 4.4881, loss: 4.4881 +2024-07-17 12:02:55,506 - pyskl - INFO - Epoch [36][2400/3746] lr: 8.671e-02, eta: 3 days, 17:12:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4622, loss_cls: 4.5247, loss: 4.5247 +2024-07-17 12:04:16,679 - pyskl - INFO - Epoch [36][2500/3746] lr: 8.669e-02, eta: 3 days, 17:11:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4606, loss_cls: 4.5340, loss: 4.5340 +2024-07-17 12:05:37,699 - pyskl - INFO - Epoch [36][2600/3746] lr: 8.667e-02, eta: 3 days, 17:10:57, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4717, loss_cls: 4.5049, loss: 4.5049 +2024-07-17 12:06:58,505 - pyskl - INFO - Epoch [36][2700/3746] lr: 8.665e-02, eta: 3 days, 17:10:01, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4627, loss_cls: 4.5374, loss: 4.5374 +2024-07-17 12:08:19,512 - pyskl - INFO - Epoch [36][2800/3746] lr: 8.663e-02, eta: 3 days, 17:09:05, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4581, loss_cls: 4.5409, loss: 4.5409 +2024-07-17 12:09:40,577 - pyskl - INFO - Epoch [36][2900/3746] lr: 8.661e-02, eta: 3 days, 17:08:09, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4673, loss_cls: 4.4882, loss: 4.4882 +2024-07-17 12:11:01,894 - pyskl - INFO - Epoch [36][3000/3746] lr: 8.659e-02, eta: 3 days, 17:07:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4552, loss_cls: 4.5431, loss: 4.5431 +2024-07-17 12:12:23,475 - pyskl - INFO - Epoch [36][3100/3746] lr: 8.657e-02, eta: 3 days, 17:06:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4641, loss_cls: 4.5099, loss: 4.5099 +2024-07-17 12:13:45,037 - pyskl - INFO - Epoch [36][3200/3746] lr: 8.655e-02, eta: 3 days, 17:05:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4698, loss_cls: 4.4671, loss: 4.4671 +2024-07-17 12:15:06,929 - pyskl - INFO - Epoch [36][3300/3746] lr: 8.653e-02, eta: 3 days, 17:04:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4572, loss_cls: 4.5261, loss: 4.5261 +2024-07-17 12:16:27,814 - pyskl - INFO - Epoch [36][3400/3746] lr: 8.651e-02, eta: 3 days, 17:03:37, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4512, loss_cls: 4.5549, loss: 4.5549 +2024-07-17 12:17:49,627 - pyskl - INFO - Epoch [36][3500/3746] lr: 8.650e-02, eta: 3 days, 17:02:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4609, loss_cls: 4.5491, loss: 4.5491 +2024-07-17 12:19:11,167 - pyskl - INFO - Epoch [36][3600/3746] lr: 8.648e-02, eta: 3 days, 17:01:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4633, loss_cls: 4.5083, loss: 4.5083 +2024-07-17 12:20:32,808 - pyskl - INFO - Epoch [36][3700/3746] lr: 8.646e-02, eta: 3 days, 17:00:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4689, loss_cls: 4.5266, loss: 4.5266 +2024-07-17 12:21:12,871 - pyskl - INFO - Saving checkpoint at 36 epochs +2024-07-17 12:23:02,496 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 12:23:03,155 - pyskl - INFO - +top1_acc 0.1426 +top5_acc 0.3441 +2024-07-17 12:23:03,155 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 12:23:03,192 - pyskl - INFO - +mean_acc 0.1424 +2024-07-17 12:23:03,202 - pyskl - INFO - Epoch(val) [36][309] top1_acc: 0.1426, top5_acc: 0.3441, mean_class_accuracy: 0.1424 +2024-07-17 12:26:46,050 - pyskl - INFO - Epoch [37][100/3746] lr: 8.643e-02, eta: 3 days, 17:05:04, time: 2.228, data_time: 1.258, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4688, loss_cls: 4.5027, loss: 4.5027 +2024-07-17 12:28:08,085 - pyskl - INFO - Epoch [37][200/3746] lr: 8.641e-02, eta: 3 days, 17:04:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4659, loss_cls: 4.4905, loss: 4.4905 +2024-07-17 12:29:29,169 - pyskl - INFO - Epoch [37][300/3746] lr: 8.639e-02, eta: 3 days, 17:03:14, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4700, loss_cls: 4.4582, loss: 4.4582 +2024-07-17 12:30:50,293 - pyskl - INFO - Epoch [37][400/3746] lr: 8.637e-02, eta: 3 days, 17:02:18, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4655, loss_cls: 4.5313, loss: 4.5313 +2024-07-17 12:32:10,839 - pyskl - INFO - Epoch [37][500/3746] lr: 8.635e-02, eta: 3 days, 17:01:20, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4727, loss_cls: 4.4804, loss: 4.4804 +2024-07-17 12:33:31,571 - pyskl - INFO - Epoch [37][600/3746] lr: 8.633e-02, eta: 3 days, 17:00:23, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4883, loss_cls: 4.4220, loss: 4.4220 +2024-07-17 12:34:52,685 - pyskl - INFO - Epoch [37][700/3746] lr: 8.631e-02, eta: 3 days, 16:59:26, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4597, loss_cls: 4.5218, loss: 4.5218 +2024-07-17 12:36:13,889 - pyskl - INFO - Epoch [37][800/3746] lr: 8.630e-02, eta: 3 days, 16:58:30, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4628, loss_cls: 4.5299, loss: 4.5299 +2024-07-17 12:37:34,830 - pyskl - INFO - Epoch [37][900/3746] lr: 8.628e-02, eta: 3 days, 16:57:33, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4723, loss_cls: 4.4556, loss: 4.4556 +2024-07-17 12:38:56,014 - pyskl - INFO - Epoch [37][1000/3746] lr: 8.626e-02, eta: 3 days, 16:56:37, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4555, loss_cls: 4.5520, loss: 4.5520 +2024-07-17 12:40:17,924 - pyskl - INFO - Epoch [37][1100/3746] lr: 8.624e-02, eta: 3 days, 16:55:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4691, loss_cls: 4.5015, loss: 4.5015 +2024-07-17 12:41:38,732 - pyskl - INFO - Epoch [37][1200/3746] lr: 8.622e-02, eta: 3 days, 16:54:46, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4689, loss_cls: 4.5305, loss: 4.5305 +2024-07-17 12:42:59,929 - pyskl - INFO - Epoch [37][1300/3746] lr: 8.620e-02, eta: 3 days, 16:53:49, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4664, loss_cls: 4.4762, loss: 4.4762 +2024-07-17 12:44:21,364 - pyskl - INFO - Epoch [37][1400/3746] lr: 8.618e-02, eta: 3 days, 16:52:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4642, loss_cls: 4.5109, loss: 4.5109 +2024-07-17 12:45:42,491 - pyskl - INFO - Epoch [37][1500/3746] lr: 8.616e-02, eta: 3 days, 16:51:57, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4672, loss_cls: 4.4586, loss: 4.4586 +2024-07-17 12:47:03,700 - pyskl - INFO - Epoch [37][1600/3746] lr: 8.614e-02, eta: 3 days, 16:51:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4775, loss_cls: 4.4410, loss: 4.4410 +2024-07-17 12:48:24,969 - pyskl - INFO - Epoch [37][1700/3746] lr: 8.612e-02, eta: 3 days, 16:50:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4627, loss_cls: 4.5134, loss: 4.5134 +2024-07-17 12:49:45,576 - pyskl - INFO - Epoch [37][1800/3746] lr: 8.610e-02, eta: 3 days, 16:49:06, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4708, loss_cls: 4.5033, loss: 4.5033 +2024-07-17 12:51:07,368 - pyskl - INFO - Epoch [37][1900/3746] lr: 8.608e-02, eta: 3 days, 16:48:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4691, loss_cls: 4.4973, loss: 4.4973 +2024-07-17 12:52:28,649 - pyskl - INFO - Epoch [37][2000/3746] lr: 8.606e-02, eta: 3 days, 16:47:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4700, loss_cls: 4.4958, loss: 4.4958 +2024-07-17 12:53:49,888 - pyskl - INFO - Epoch [37][2100/3746] lr: 8.604e-02, eta: 3 days, 16:46:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4813, loss_cls: 4.4516, loss: 4.4516 +2024-07-17 12:55:11,245 - pyskl - INFO - Epoch [37][2200/3746] lr: 8.602e-02, eta: 3 days, 16:45:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4570, loss_cls: 4.5359, loss: 4.5359 +2024-07-17 12:56:32,337 - pyskl - INFO - Epoch [37][2300/3746] lr: 8.601e-02, eta: 3 days, 16:44:26, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4641, loss_cls: 4.5168, loss: 4.5168 +2024-07-17 12:57:53,837 - pyskl - INFO - Epoch [37][2400/3746] lr: 8.599e-02, eta: 3 days, 16:43:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4669, loss_cls: 4.4884, loss: 4.4884 +2024-07-17 12:59:15,132 - pyskl - INFO - Epoch [37][2500/3746] lr: 8.597e-02, eta: 3 days, 16:42:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4625, loss_cls: 4.5406, loss: 4.5406 +2024-07-17 13:00:36,371 - pyskl - INFO - Epoch [37][2600/3746] lr: 8.595e-02, eta: 3 days, 16:41:37, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4597, loss_cls: 4.5507, loss: 4.5507 +2024-07-17 13:01:56,984 - pyskl - INFO - Epoch [37][2700/3746] lr: 8.593e-02, eta: 3 days, 16:40:38, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4675, loss_cls: 4.4929, loss: 4.4929 +2024-07-17 13:03:18,089 - pyskl - INFO - Epoch [37][2800/3746] lr: 8.591e-02, eta: 3 days, 16:39:41, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4667, loss_cls: 4.5371, loss: 4.5371 +2024-07-17 13:04:39,093 - pyskl - INFO - Epoch [37][2900/3746] lr: 8.589e-02, eta: 3 days, 16:38:43, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4658, loss_cls: 4.4988, loss: 4.4988 +2024-07-17 13:05:59,955 - pyskl - INFO - Epoch [37][3000/3746] lr: 8.587e-02, eta: 3 days, 16:37:45, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4614, loss_cls: 4.5000, loss: 4.5000 +2024-07-17 13:07:20,703 - pyskl - INFO - Epoch [37][3100/3746] lr: 8.585e-02, eta: 3 days, 16:36:47, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4661, loss_cls: 4.5199, loss: 4.5199 +2024-07-17 13:08:42,201 - pyskl - INFO - Epoch [37][3200/3746] lr: 8.583e-02, eta: 3 days, 16:35:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4628, loss_cls: 4.5394, loss: 4.5394 +2024-07-17 13:10:03,148 - pyskl - INFO - Epoch [37][3300/3746] lr: 8.581e-02, eta: 3 days, 16:34:53, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4759, loss_cls: 4.4973, loss: 4.4973 +2024-07-17 13:11:24,188 - pyskl - INFO - Epoch [37][3400/3746] lr: 8.579e-02, eta: 3 days, 16:33:56, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4637, loss_cls: 4.5469, loss: 4.5469 +2024-07-17 13:12:46,009 - pyskl - INFO - Epoch [37][3500/3746] lr: 8.577e-02, eta: 3 days, 16:33:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4766, loss_cls: 4.4891, loss: 4.4891 +2024-07-17 13:14:07,571 - pyskl - INFO - Epoch [37][3600/3746] lr: 8.575e-02, eta: 3 days, 16:32:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4570, loss_cls: 4.5635, loss: 4.5635 +2024-07-17 13:15:29,916 - pyskl - INFO - Epoch [37][3700/3746] lr: 8.573e-02, eta: 3 days, 16:31:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4662, loss_cls: 4.4914, loss: 4.4914 +2024-07-17 13:16:09,724 - pyskl - INFO - Saving checkpoint at 37 epochs +2024-07-17 13:17:59,830 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 13:18:00,491 - pyskl - INFO - +top1_acc 0.1732 +top5_acc 0.3902 +2024-07-17 13:18:00,491 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 13:18:00,531 - pyskl - INFO - +mean_acc 0.1733 +2024-07-17 13:18:00,536 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_31.pth was removed +2024-07-17 13:18:00,767 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_37.pth. +2024-07-17 13:18:00,767 - pyskl - INFO - Best top1_acc is 0.1732 at 37 epoch. +2024-07-17 13:18:00,778 - pyskl - INFO - Epoch(val) [37][309] top1_acc: 0.1732, top5_acc: 0.3902, mean_class_accuracy: 0.1733 +2024-07-17 13:21:42,432 - pyskl - INFO - Epoch [38][100/3746] lr: 8.570e-02, eta: 3 days, 16:35:02, time: 2.216, data_time: 1.248, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4678, loss_cls: 4.4944, loss: 4.4944 +2024-07-17 13:23:04,649 - pyskl - INFO - Epoch [38][200/3746] lr: 8.568e-02, eta: 3 days, 16:34:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4731, loss_cls: 4.4878, loss: 4.4878 +2024-07-17 13:24:26,067 - pyskl - INFO - Epoch [38][300/3746] lr: 8.567e-02, eta: 3 days, 16:33:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4628, loss_cls: 4.4998, loss: 4.4998 +2024-07-17 13:25:46,610 - pyskl - INFO - Epoch [38][400/3746] lr: 8.565e-02, eta: 3 days, 16:32:11, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4722, loss_cls: 4.4840, loss: 4.4840 +2024-07-17 13:27:07,449 - pyskl - INFO - Epoch [38][500/3746] lr: 8.563e-02, eta: 3 days, 16:31:12, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4720, loss_cls: 4.4791, loss: 4.4791 +2024-07-17 13:28:28,298 - pyskl - INFO - Epoch [38][600/3746] lr: 8.561e-02, eta: 3 days, 16:30:14, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4639, loss_cls: 4.4976, loss: 4.4976 +2024-07-17 13:29:49,617 - pyskl - INFO - Epoch [38][700/3746] lr: 8.559e-02, eta: 3 days, 16:29:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4677, loss_cls: 4.4949, loss: 4.4949 +2024-07-17 13:31:10,710 - pyskl - INFO - Epoch [38][800/3746] lr: 8.557e-02, eta: 3 days, 16:28:18, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4736, loss_cls: 4.4561, loss: 4.4561 +2024-07-17 13:32:32,242 - pyskl - INFO - Epoch [38][900/3746] lr: 8.555e-02, eta: 3 days, 16:27:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4612, loss_cls: 4.5005, loss: 4.5005 +2024-07-17 13:33:53,220 - pyskl - INFO - Epoch [38][1000/3746] lr: 8.553e-02, eta: 3 days, 16:26:23, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4642, loss_cls: 4.5090, loss: 4.5090 +2024-07-17 13:35:14,040 - pyskl - INFO - Epoch [38][1100/3746] lr: 8.551e-02, eta: 3 days, 16:25:24, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4633, loss_cls: 4.5085, loss: 4.5085 +2024-07-17 13:36:34,748 - pyskl - INFO - Epoch [38][1200/3746] lr: 8.549e-02, eta: 3 days, 16:24:24, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4598, loss_cls: 4.5436, loss: 4.5436 +2024-07-17 13:37:55,926 - pyskl - INFO - Epoch [38][1300/3746] lr: 8.547e-02, eta: 3 days, 16:23:27, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4636, loss_cls: 4.5124, loss: 4.5124 +2024-07-17 13:39:17,155 - pyskl - INFO - Epoch [38][1400/3746] lr: 8.545e-02, eta: 3 days, 16:22:29, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4739, loss_cls: 4.4830, loss: 4.4830 +2024-07-17 13:40:38,407 - pyskl - INFO - Epoch [38][1500/3746] lr: 8.543e-02, eta: 3 days, 16:21:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4647, loss_cls: 4.4987, loss: 4.4987 +2024-07-17 13:41:59,484 - pyskl - INFO - Epoch [38][1600/3746] lr: 8.541e-02, eta: 3 days, 16:20:32, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4698, loss_cls: 4.5289, loss: 4.5289 +2024-07-17 13:43:20,370 - pyskl - INFO - Epoch [38][1700/3746] lr: 8.539e-02, eta: 3 days, 16:19:33, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4686, loss_cls: 4.4892, loss: 4.4892 +2024-07-17 13:44:41,586 - pyskl - INFO - Epoch [38][1800/3746] lr: 8.537e-02, eta: 3 days, 16:18:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4716, loss_cls: 4.4867, loss: 4.4867 +2024-07-17 13:46:02,393 - pyskl - INFO - Epoch [38][1900/3746] lr: 8.535e-02, eta: 3 days, 16:17:36, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4678, loss_cls: 4.5130, loss: 4.5130 +2024-07-17 13:47:24,279 - pyskl - INFO - Epoch [38][2000/3746] lr: 8.533e-02, eta: 3 days, 16:16:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4700, loss_cls: 4.4765, loss: 4.4765 +2024-07-17 13:48:46,435 - pyskl - INFO - Epoch [38][2100/3746] lr: 8.531e-02, eta: 3 days, 16:15:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4681, loss_cls: 4.5099, loss: 4.5099 +2024-07-17 13:50:08,236 - pyskl - INFO - Epoch [38][2200/3746] lr: 8.529e-02, eta: 3 days, 16:14:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4666, loss_cls: 4.5113, loss: 4.5113 +2024-07-17 13:51:29,762 - pyskl - INFO - Epoch [38][2300/3746] lr: 8.527e-02, eta: 3 days, 16:13:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4664, loss_cls: 4.5011, loss: 4.5011 +2024-07-17 13:52:50,884 - pyskl - INFO - Epoch [38][2400/3746] lr: 8.525e-02, eta: 3 days, 16:12:53, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4725, loss_cls: 4.4978, loss: 4.4978 +2024-07-17 13:54:12,035 - pyskl - INFO - Epoch [38][2500/3746] lr: 8.523e-02, eta: 3 days, 16:11:54, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4813, loss_cls: 4.4148, loss: 4.4148 +2024-07-17 13:55:33,036 - pyskl - INFO - Epoch [38][2600/3746] lr: 8.521e-02, eta: 3 days, 16:10:55, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4698, loss_cls: 4.5217, loss: 4.5217 +2024-07-17 13:56:53,877 - pyskl - INFO - Epoch [38][2700/3746] lr: 8.519e-02, eta: 3 days, 16:09:56, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4553, loss_cls: 4.5481, loss: 4.5481 +2024-07-17 13:58:14,959 - pyskl - INFO - Epoch [38][2800/3746] lr: 8.517e-02, eta: 3 days, 16:08:57, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4709, loss_cls: 4.4700, loss: 4.4700 +2024-07-17 13:59:36,107 - pyskl - INFO - Epoch [38][2900/3746] lr: 8.515e-02, eta: 3 days, 16:07:58, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4725, loss_cls: 4.4626, loss: 4.4626 +2024-07-17 14:00:57,095 - pyskl - INFO - Epoch [38][3000/3746] lr: 8.513e-02, eta: 3 days, 16:06:59, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4641, loss_cls: 4.4947, loss: 4.4947 +2024-07-17 14:02:18,091 - pyskl - INFO - Epoch [38][3100/3746] lr: 8.511e-02, eta: 3 days, 16:06:00, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4727, loss_cls: 4.4772, loss: 4.4772 +2024-07-17 14:03:39,564 - pyskl - INFO - Epoch [38][3200/3746] lr: 8.509e-02, eta: 3 days, 16:05:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4625, loss_cls: 4.4998, loss: 4.4998 +2024-07-17 14:05:00,920 - pyskl - INFO - Epoch [38][3300/3746] lr: 8.507e-02, eta: 3 days, 16:04:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4708, loss_cls: 4.4622, loss: 4.4622 +2024-07-17 14:06:22,360 - pyskl - INFO - Epoch [38][3400/3746] lr: 8.505e-02, eta: 3 days, 16:03:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4769, loss_cls: 4.4645, loss: 4.4645 +2024-07-17 14:07:43,454 - pyskl - INFO - Epoch [38][3500/3746] lr: 8.503e-02, eta: 3 days, 16:02:08, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4647, loss_cls: 4.5000, loss: 4.5000 +2024-07-17 14:09:05,397 - pyskl - INFO - Epoch [38][3600/3746] lr: 8.501e-02, eta: 3 days, 16:01:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4691, loss_cls: 4.4849, loss: 4.4849 +2024-07-17 14:10:26,808 - pyskl - INFO - Epoch [38][3700/3746] lr: 8.499e-02, eta: 3 days, 16:00:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4648, loss_cls: 4.5253, loss: 4.5253 +2024-07-17 14:11:06,867 - pyskl - INFO - Saving checkpoint at 38 epochs +2024-07-17 14:12:56,363 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 14:12:57,026 - pyskl - INFO - +top1_acc 0.1547 +top5_acc 0.3733 +2024-07-17 14:12:57,027 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 14:12:57,065 - pyskl - INFO - +mean_acc 0.1547 +2024-07-17 14:12:57,075 - pyskl - INFO - Epoch(val) [38][309] top1_acc: 0.1547, top5_acc: 0.3733, mean_class_accuracy: 0.1547 +2024-07-17 14:16:40,383 - pyskl - INFO - Epoch [39][100/3746] lr: 8.496e-02, eta: 3 days, 16:03:56, time: 2.233, data_time: 1.261, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4723, loss_cls: 4.4661, loss: 4.4661 +2024-07-17 14:18:01,926 - pyskl - INFO - Epoch [39][200/3746] lr: 8.494e-02, eta: 3 days, 16:02:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4661, loss_cls: 4.4694, loss: 4.4694 +2024-07-17 14:19:23,738 - pyskl - INFO - Epoch [39][300/3746] lr: 8.492e-02, eta: 3 days, 16:02:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4694, loss_cls: 4.5032, loss: 4.5032 +2024-07-17 14:20:45,110 - pyskl - INFO - Epoch [39][400/3746] lr: 8.490e-02, eta: 3 days, 16:01:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4584, loss_cls: 4.5134, loss: 4.5134 +2024-07-17 14:22:06,206 - pyskl - INFO - Epoch [39][500/3746] lr: 8.488e-02, eta: 3 days, 16:00:02, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4784, loss_cls: 4.4594, loss: 4.4594 +2024-07-17 14:23:27,001 - pyskl - INFO - Epoch [39][600/3746] lr: 8.486e-02, eta: 3 days, 15:59:02, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4711, loss_cls: 4.5166, loss: 4.5166 +2024-07-17 14:24:47,953 - pyskl - INFO - Epoch [39][700/3746] lr: 8.484e-02, eta: 3 days, 15:58:02, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4688, loss_cls: 4.4653, loss: 4.4653 +2024-07-17 14:26:08,812 - pyskl - INFO - Epoch [39][800/3746] lr: 8.482e-02, eta: 3 days, 15:57:02, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4763, loss_cls: 4.4562, loss: 4.4562 +2024-07-17 14:27:29,491 - pyskl - INFO - Epoch [39][900/3746] lr: 8.480e-02, eta: 3 days, 15:56:01, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4748, loss_cls: 4.4530, loss: 4.4530 +2024-07-17 14:28:50,845 - pyskl - INFO - Epoch [39][1000/3746] lr: 8.478e-02, eta: 3 days, 15:55:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4703, loss_cls: 4.4869, loss: 4.4869 +2024-07-17 14:30:11,811 - pyskl - INFO - Epoch [39][1100/3746] lr: 8.476e-02, eta: 3 days, 15:54:02, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4686, loss_cls: 4.4949, loss: 4.4949 +2024-07-17 14:31:32,567 - pyskl - INFO - Epoch [39][1200/3746] lr: 8.474e-02, eta: 3 days, 15:53:01, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4731, loss_cls: 4.4418, loss: 4.4418 +2024-07-17 14:32:53,741 - pyskl - INFO - Epoch [39][1300/3746] lr: 8.472e-02, eta: 3 days, 15:52:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4755, loss_cls: 4.4888, loss: 4.4888 +2024-07-17 14:34:14,527 - pyskl - INFO - Epoch [39][1400/3746] lr: 8.470e-02, eta: 3 days, 15:51:01, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4844, loss_cls: 4.4568, loss: 4.4568 +2024-07-17 14:35:35,576 - pyskl - INFO - Epoch [39][1500/3746] lr: 8.468e-02, eta: 3 days, 15:50:01, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4656, loss_cls: 4.5013, loss: 4.5013 +2024-07-17 14:36:56,919 - pyskl - INFO - Epoch [39][1600/3746] lr: 8.466e-02, eta: 3 days, 15:49:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4641, loss_cls: 4.4953, loss: 4.4953 +2024-07-17 14:38:17,978 - pyskl - INFO - Epoch [39][1700/3746] lr: 8.464e-02, eta: 3 days, 15:48:02, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4741, loss_cls: 4.4896, loss: 4.4896 +2024-07-17 14:39:39,286 - pyskl - INFO - Epoch [39][1800/3746] lr: 8.462e-02, eta: 3 days, 15:47:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4594, loss_cls: 4.5483, loss: 4.5483 +2024-07-17 14:41:00,411 - pyskl - INFO - Epoch [39][1900/3746] lr: 8.460e-02, eta: 3 days, 15:46:03, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4686, loss_cls: 4.4864, loss: 4.4864 +2024-07-17 14:42:21,931 - pyskl - INFO - Epoch [39][2000/3746] lr: 8.458e-02, eta: 3 days, 15:45:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4612, loss_cls: 4.5173, loss: 4.5173 +2024-07-17 14:43:43,709 - pyskl - INFO - Epoch [39][2100/3746] lr: 8.456e-02, eta: 3 days, 15:44:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4695, loss_cls: 4.4727, loss: 4.4727 +2024-07-17 14:45:05,460 - pyskl - INFO - Epoch [39][2200/3746] lr: 8.454e-02, eta: 3 days, 15:43:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4686, loss_cls: 4.4829, loss: 4.4829 +2024-07-17 14:46:27,174 - pyskl - INFO - Epoch [39][2300/3746] lr: 8.452e-02, eta: 3 days, 15:42:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4684, loss_cls: 4.5044, loss: 4.5044 +2024-07-17 14:47:48,427 - pyskl - INFO - Epoch [39][2400/3746] lr: 8.450e-02, eta: 3 days, 15:41:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4648, loss_cls: 4.4849, loss: 4.4849 +2024-07-17 14:49:09,291 - pyskl - INFO - Epoch [39][2500/3746] lr: 8.448e-02, eta: 3 days, 15:40:10, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4662, loss_cls: 4.4864, loss: 4.4864 +2024-07-17 14:50:30,458 - pyskl - INFO - Epoch [39][2600/3746] lr: 8.446e-02, eta: 3 days, 15:39:10, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4686, loss_cls: 4.4935, loss: 4.4935 +2024-07-17 14:51:52,253 - pyskl - INFO - Epoch [39][2700/3746] lr: 8.444e-02, eta: 3 days, 15:38:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4639, loss_cls: 4.5331, loss: 4.5331 +2024-07-17 14:53:13,519 - pyskl - INFO - Epoch [39][2800/3746] lr: 8.442e-02, eta: 3 days, 15:37:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4647, loss_cls: 4.4961, loss: 4.4961 +2024-07-17 14:54:34,757 - pyskl - INFO - Epoch [39][2900/3746] lr: 8.440e-02, eta: 3 days, 15:36:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4730, loss_cls: 4.4713, loss: 4.4713 +2024-07-17 14:55:55,911 - pyskl - INFO - Epoch [39][3000/3746] lr: 8.438e-02, eta: 3 days, 15:35:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4602, loss_cls: 4.5168, loss: 4.5168 +2024-07-17 14:57:16,509 - pyskl - INFO - Epoch [39][3100/3746] lr: 8.436e-02, eta: 3 days, 15:34:11, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4670, loss_cls: 4.4694, loss: 4.4694 +2024-07-17 14:58:38,081 - pyskl - INFO - Epoch [39][3200/3746] lr: 8.434e-02, eta: 3 days, 15:33:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4627, loss_cls: 4.4896, loss: 4.4896 +2024-07-17 14:59:59,565 - pyskl - INFO - Epoch [39][3300/3746] lr: 8.432e-02, eta: 3 days, 15:32:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4625, loss_cls: 4.5000, loss: 4.5000 +2024-07-17 15:01:20,790 - pyskl - INFO - Epoch [39][3400/3746] lr: 8.430e-02, eta: 3 days, 15:31:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4767, loss_cls: 4.4546, loss: 4.4546 +2024-07-17 15:02:42,194 - pyskl - INFO - Epoch [39][3500/3746] lr: 8.428e-02, eta: 3 days, 15:30:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4652, loss_cls: 4.5170, loss: 4.5170 +2024-07-17 15:04:03,889 - pyskl - INFO - Epoch [39][3600/3746] lr: 8.426e-02, eta: 3 days, 15:29:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4714, loss_cls: 4.4532, loss: 4.4532 +2024-07-17 15:05:25,748 - pyskl - INFO - Epoch [39][3700/3746] lr: 8.424e-02, eta: 3 days, 15:28:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4677, loss_cls: 4.5087, loss: 4.5087 +2024-07-17 15:06:05,188 - pyskl - INFO - Saving checkpoint at 39 epochs +2024-07-17 15:07:55,845 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 15:07:56,503 - pyskl - INFO - +top1_acc 0.1568 +top5_acc 0.3699 +2024-07-17 15:07:56,503 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 15:07:56,540 - pyskl - INFO - +mean_acc 0.1567 +2024-07-17 15:07:56,550 - pyskl - INFO - Epoch(val) [39][309] top1_acc: 0.1568, top5_acc: 0.3699, mean_class_accuracy: 0.1567 +2024-07-17 15:11:42,581 - pyskl - INFO - Epoch [40][100/3746] lr: 8.421e-02, eta: 3 days, 15:31:54, time: 2.260, data_time: 1.251, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4808, loss_cls: 4.4316, loss: 4.4316 +2024-07-17 15:13:03,888 - pyskl - INFO - Epoch [40][200/3746] lr: 8.419e-02, eta: 3 days, 15:30:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4723, loss_cls: 4.4345, loss: 4.4345 +2024-07-17 15:14:25,485 - pyskl - INFO - Epoch [40][300/3746] lr: 8.417e-02, eta: 3 days, 15:29:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4702, loss_cls: 4.4717, loss: 4.4717 +2024-07-17 15:15:47,530 - pyskl - INFO - Epoch [40][400/3746] lr: 8.415e-02, eta: 3 days, 15:28:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4716, loss_cls: 4.4654, loss: 4.4654 +2024-07-17 15:17:08,462 - pyskl - INFO - Epoch [40][500/3746] lr: 8.413e-02, eta: 3 days, 15:27:55, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4695, loss_cls: 4.5049, loss: 4.5049 +2024-07-17 15:18:30,212 - pyskl - INFO - Epoch [40][600/3746] lr: 8.411e-02, eta: 3 days, 15:26:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4717, loss_cls: 4.4586, loss: 4.4586 +2024-07-17 15:19:51,325 - pyskl - INFO - Epoch [40][700/3746] lr: 8.408e-02, eta: 3 days, 15:25:55, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4728, loss_cls: 4.4645, loss: 4.4645 +2024-07-17 15:21:12,508 - pyskl - INFO - Epoch [40][800/3746] lr: 8.406e-02, eta: 3 days, 15:24:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4820, loss_cls: 4.4290, loss: 4.4290 +2024-07-17 15:22:33,509 - pyskl - INFO - Epoch [40][900/3746] lr: 8.404e-02, eta: 3 days, 15:23:53, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4756, loss_cls: 4.4644, loss: 4.4644 +2024-07-17 15:23:54,716 - pyskl - INFO - Epoch [40][1000/3746] lr: 8.402e-02, eta: 3 days, 15:22:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4706, loss_cls: 4.4791, loss: 4.4791 +2024-07-17 15:25:16,387 - pyskl - INFO - Epoch [40][1100/3746] lr: 8.400e-02, eta: 3 days, 15:21:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4744, loss_cls: 4.4434, loss: 4.4434 +2024-07-17 15:26:38,225 - pyskl - INFO - Epoch [40][1200/3746] lr: 8.398e-02, eta: 3 days, 15:20:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4600, loss_cls: 4.5362, loss: 4.5362 +2024-07-17 15:27:59,408 - pyskl - INFO - Epoch [40][1300/3746] lr: 8.396e-02, eta: 3 days, 15:19:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4662, loss_cls: 4.4867, loss: 4.4867 +2024-07-17 15:29:20,415 - pyskl - INFO - Epoch [40][1400/3746] lr: 8.394e-02, eta: 3 days, 15:18:52, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4644, loss_cls: 4.5354, loss: 4.5354 +2024-07-17 15:30:41,285 - pyskl - INFO - Epoch [40][1500/3746] lr: 8.392e-02, eta: 3 days, 15:17:50, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4866, loss_cls: 4.4236, loss: 4.4236 +2024-07-17 15:32:02,736 - pyskl - INFO - Epoch [40][1600/3746] lr: 8.390e-02, eta: 3 days, 15:16:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4725, loss_cls: 4.4624, loss: 4.4624 +2024-07-17 15:33:23,877 - pyskl - INFO - Epoch [40][1700/3746] lr: 8.388e-02, eta: 3 days, 15:15:49, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4527, loss_cls: 4.5537, loss: 4.5537 +2024-07-17 15:34:44,577 - pyskl - INFO - Epoch [40][1800/3746] lr: 8.386e-02, eta: 3 days, 15:14:47, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4570, loss_cls: 4.5225, loss: 4.5225 +2024-07-17 15:36:05,313 - pyskl - INFO - Epoch [40][1900/3746] lr: 8.384e-02, eta: 3 days, 15:13:45, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4716, loss_cls: 4.4689, loss: 4.4689 +2024-07-17 15:37:26,789 - pyskl - INFO - Epoch [40][2000/3746] lr: 8.382e-02, eta: 3 days, 15:12:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4764, loss_cls: 4.4673, loss: 4.4673 +2024-07-17 15:38:48,558 - pyskl - INFO - Epoch [40][2100/3746] lr: 8.380e-02, eta: 3 days, 15:11:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4711, loss_cls: 4.4874, loss: 4.4874 +2024-07-17 15:40:10,334 - pyskl - INFO - Epoch [40][2200/3746] lr: 8.378e-02, eta: 3 days, 15:10:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4711, loss_cls: 4.4687, loss: 4.4687 +2024-07-17 15:41:31,996 - pyskl - INFO - Epoch [40][2300/3746] lr: 8.376e-02, eta: 3 days, 15:09:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4730, loss_cls: 4.4698, loss: 4.4698 +2024-07-17 15:42:53,046 - pyskl - INFO - Epoch [40][2400/3746] lr: 8.374e-02, eta: 3 days, 15:08:44, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4658, loss_cls: 4.4917, loss: 4.4917 +2024-07-17 15:44:13,820 - pyskl - INFO - Epoch [40][2500/3746] lr: 8.371e-02, eta: 3 days, 15:07:42, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4659, loss_cls: 4.5162, loss: 4.5162 +2024-07-17 15:45:34,584 - pyskl - INFO - Epoch [40][2600/3746] lr: 8.369e-02, eta: 3 days, 15:06:40, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4745, loss_cls: 4.4602, loss: 4.4602 +2024-07-17 15:46:55,656 - pyskl - INFO - Epoch [40][2700/3746] lr: 8.367e-02, eta: 3 days, 15:05:38, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4595, loss_cls: 4.5198, loss: 4.5198 +2024-07-17 15:48:17,340 - pyskl - INFO - Epoch [40][2800/3746] lr: 8.365e-02, eta: 3 days, 15:04:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4544, loss_cls: 4.5328, loss: 4.5328 +2024-07-17 15:49:38,079 - pyskl - INFO - Epoch [40][2900/3746] lr: 8.363e-02, eta: 3 days, 15:03:36, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4647, loss_cls: 4.5419, loss: 4.5419 +2024-07-17 15:50:59,033 - pyskl - INFO - Epoch [40][3000/3746] lr: 8.361e-02, eta: 3 days, 15:02:34, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4738, loss_cls: 4.4725, loss: 4.4725 +2024-07-17 15:52:20,244 - pyskl - INFO - Epoch [40][3100/3746] lr: 8.359e-02, eta: 3 days, 15:01:33, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4667, loss_cls: 4.5226, loss: 4.5226 +2024-07-17 15:53:40,890 - pyskl - INFO - Epoch [40][3200/3746] lr: 8.357e-02, eta: 3 days, 15:00:30, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4725, loss_cls: 4.4658, loss: 4.4658 +2024-07-17 15:55:02,006 - pyskl - INFO - Epoch [40][3300/3746] lr: 8.355e-02, eta: 3 days, 14:59:28, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4598, loss_cls: 4.5072, loss: 4.5072 +2024-07-17 15:56:23,291 - pyskl - INFO - Epoch [40][3400/3746] lr: 8.353e-02, eta: 3 days, 14:58:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4680, loss_cls: 4.5287, loss: 4.5287 +2024-07-17 15:57:44,907 - pyskl - INFO - Epoch [40][3500/3746] lr: 8.351e-02, eta: 3 days, 14:57:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4716, loss_cls: 4.4654, loss: 4.4654 +2024-07-17 15:59:06,516 - pyskl - INFO - Epoch [40][3600/3746] lr: 8.349e-02, eta: 3 days, 14:56:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4681, loss_cls: 4.4719, loss: 4.4719 +2024-07-17 16:00:28,356 - pyskl - INFO - Epoch [40][3700/3746] lr: 8.347e-02, eta: 3 days, 14:55:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4742, loss_cls: 4.4737, loss: 4.4737 +2024-07-17 16:01:07,689 - pyskl - INFO - Saving checkpoint at 40 epochs +2024-07-17 16:02:58,615 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 16:02:59,271 - pyskl - INFO - +top1_acc 0.1713 +top5_acc 0.3794 +2024-07-17 16:02:59,271 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 16:02:59,310 - pyskl - INFO - +mean_acc 0.1711 +2024-07-17 16:02:59,320 - pyskl - INFO - Epoch(val) [40][309] top1_acc: 0.1713, top5_acc: 0.3794, mean_class_accuracy: 0.1711 +2024-07-17 16:06:44,491 - pyskl - INFO - Epoch [41][100/3746] lr: 8.344e-02, eta: 3 days, 14:58:50, time: 2.252, data_time: 1.277, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4778, loss_cls: 4.4491, loss: 4.4491 +2024-07-17 16:08:06,106 - pyskl - INFO - Epoch [41][200/3746] lr: 8.342e-02, eta: 3 days, 14:57:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4772, loss_cls: 4.4391, loss: 4.4391 +2024-07-17 16:09:27,890 - pyskl - INFO - Epoch [41][300/3746] lr: 8.339e-02, eta: 3 days, 14:56:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4731, loss_cls: 4.4426, loss: 4.4426 +2024-07-17 16:10:50,095 - pyskl - INFO - Epoch [41][400/3746] lr: 8.337e-02, eta: 3 days, 14:55:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4662, loss_cls: 4.4865, loss: 4.4865 +2024-07-17 16:12:11,129 - pyskl - INFO - Epoch [41][500/3746] lr: 8.335e-02, eta: 3 days, 14:54:48, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4689, loss_cls: 4.4772, loss: 4.4772 +2024-07-17 16:13:31,975 - pyskl - INFO - Epoch [41][600/3746] lr: 8.333e-02, eta: 3 days, 14:53:45, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4827, loss_cls: 4.4434, loss: 4.4434 +2024-07-17 16:14:53,044 - pyskl - INFO - Epoch [41][700/3746] lr: 8.331e-02, eta: 3 days, 14:52:43, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4773, loss_cls: 4.4394, loss: 4.4394 +2024-07-17 16:16:13,819 - pyskl - INFO - Epoch [41][800/3746] lr: 8.329e-02, eta: 3 days, 14:51:40, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4772, loss_cls: 4.4707, loss: 4.4707 +2024-07-17 16:17:34,430 - pyskl - INFO - Epoch [41][900/3746] lr: 8.327e-02, eta: 3 days, 14:50:36, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4809, loss_cls: 4.4623, loss: 4.4623 +2024-07-17 16:18:55,942 - pyskl - INFO - Epoch [41][1000/3746] lr: 8.325e-02, eta: 3 days, 14:49:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4641, loss_cls: 4.5060, loss: 4.5060 +2024-07-17 16:20:17,192 - pyskl - INFO - Epoch [41][1100/3746] lr: 8.323e-02, eta: 3 days, 14:48:33, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4630, loss_cls: 4.5301, loss: 4.5301 +2024-07-17 16:21:38,591 - pyskl - INFO - Epoch [41][1200/3746] lr: 8.321e-02, eta: 3 days, 14:47:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4634, loss_cls: 4.5108, loss: 4.5108 +2024-07-17 16:22:59,612 - pyskl - INFO - Epoch [41][1300/3746] lr: 8.319e-02, eta: 3 days, 14:46:29, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4722, loss_cls: 4.4610, loss: 4.4610 +2024-07-17 16:24:20,496 - pyskl - INFO - Epoch [41][1400/3746] lr: 8.316e-02, eta: 3 days, 14:45:26, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4711, loss_cls: 4.4462, loss: 4.4462 +2024-07-17 16:25:41,676 - pyskl - INFO - Epoch [41][1500/3746] lr: 8.314e-02, eta: 3 days, 14:44:24, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4692, loss_cls: 4.4928, loss: 4.4928 +2024-07-17 16:27:02,940 - pyskl - INFO - Epoch [41][1600/3746] lr: 8.312e-02, eta: 3 days, 14:43:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4623, loss_cls: 4.4938, loss: 4.4938 +2024-07-17 16:28:23,959 - pyskl - INFO - Epoch [41][1700/3746] lr: 8.310e-02, eta: 3 days, 14:42:20, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4766, loss_cls: 4.4740, loss: 4.4740 +2024-07-17 16:29:44,854 - pyskl - INFO - Epoch [41][1800/3746] lr: 8.308e-02, eta: 3 days, 14:41:17, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4678, loss_cls: 4.4737, loss: 4.4737 +2024-07-17 16:31:06,031 - pyskl - INFO - Epoch [41][1900/3746] lr: 8.306e-02, eta: 3 days, 14:40:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4695, loss_cls: 4.4786, loss: 4.4786 +2024-07-17 16:32:27,195 - pyskl - INFO - Epoch [41][2000/3746] lr: 8.304e-02, eta: 3 days, 14:39:12, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4695, loss_cls: 4.4909, loss: 4.4909 +2024-07-17 16:33:49,422 - pyskl - INFO - Epoch [41][2100/3746] lr: 8.302e-02, eta: 3 days, 14:38:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4689, loss_cls: 4.4735, loss: 4.4735 +2024-07-17 16:35:10,785 - pyskl - INFO - Epoch [41][2200/3746] lr: 8.300e-02, eta: 3 days, 14:37:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4652, loss_cls: 4.5155, loss: 4.5155 +2024-07-17 16:36:32,661 - pyskl - INFO - Epoch [41][2300/3746] lr: 8.298e-02, eta: 3 days, 14:36:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4802, loss_cls: 4.4728, loss: 4.4728 +2024-07-17 16:37:53,464 - pyskl - INFO - Epoch [41][2400/3746] lr: 8.296e-02, eta: 3 days, 14:35:07, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4636, loss_cls: 4.4874, loss: 4.4874 +2024-07-17 16:39:14,579 - pyskl - INFO - Epoch [41][2500/3746] lr: 8.293e-02, eta: 3 days, 14:34:04, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4769, loss_cls: 4.4653, loss: 4.4653 +2024-07-17 16:40:35,506 - pyskl - INFO - Epoch [41][2600/3746] lr: 8.291e-02, eta: 3 days, 14:33:01, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4736, loss_cls: 4.4812, loss: 4.4812 +2024-07-17 16:41:56,845 - pyskl - INFO - Epoch [41][2700/3746] lr: 8.289e-02, eta: 3 days, 14:31:59, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4650, loss_cls: 4.5349, loss: 4.5349 +2024-07-17 16:43:18,364 - pyskl - INFO - Epoch [41][2800/3746] lr: 8.287e-02, eta: 3 days, 14:30:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4664, loss_cls: 4.4651, loss: 4.4651 +2024-07-17 16:44:39,723 - pyskl - INFO - Epoch [41][2900/3746] lr: 8.285e-02, eta: 3 days, 14:29:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4720, loss_cls: 4.4488, loss: 4.4488 +2024-07-17 16:46:00,716 - pyskl - INFO - Epoch [41][3000/3746] lr: 8.283e-02, eta: 3 days, 14:28:52, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4656, loss_cls: 4.4911, loss: 4.4911 +2024-07-17 16:47:21,976 - pyskl - INFO - Epoch [41][3100/3746] lr: 8.281e-02, eta: 3 days, 14:27:50, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4708, loss_cls: 4.4951, loss: 4.4951 +2024-07-17 16:48:43,253 - pyskl - INFO - Epoch [41][3200/3746] lr: 8.279e-02, eta: 3 days, 14:26:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4675, loss_cls: 4.4931, loss: 4.4931 +2024-07-17 16:50:04,540 - pyskl - INFO - Epoch [41][3300/3746] lr: 8.277e-02, eta: 3 days, 14:25:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4717, loss_cls: 4.4838, loss: 4.4838 +2024-07-17 16:51:26,381 - pyskl - INFO - Epoch [41][3400/3746] lr: 8.274e-02, eta: 3 days, 14:24:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4723, loss_cls: 4.4594, loss: 4.4594 +2024-07-17 16:52:47,552 - pyskl - INFO - Epoch [41][3500/3746] lr: 8.272e-02, eta: 3 days, 14:23:42, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4717, loss_cls: 4.4865, loss: 4.4865 +2024-07-17 16:54:09,253 - pyskl - INFO - Epoch [41][3600/3746] lr: 8.270e-02, eta: 3 days, 14:22:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4752, loss_cls: 4.4311, loss: 4.4311 +2024-07-17 16:55:30,247 - pyskl - INFO - Epoch [41][3700/3746] lr: 8.268e-02, eta: 3 days, 14:21:37, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4717, loss_cls: 4.5089, loss: 4.5089 +2024-07-17 16:56:10,331 - pyskl - INFO - Saving checkpoint at 41 epochs +2024-07-17 16:58:01,804 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 16:58:02,459 - pyskl - INFO - +top1_acc 0.1633 +top5_acc 0.3637 +2024-07-17 16:58:02,459 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 16:58:02,500 - pyskl - INFO - +mean_acc 0.1632 +2024-07-17 16:58:02,512 - pyskl - INFO - Epoch(val) [41][309] top1_acc: 0.1633, top5_acc: 0.3637, mean_class_accuracy: 0.1632 +2024-07-17 17:01:45,449 - pyskl - INFO - Epoch [42][100/3746] lr: 8.265e-02, eta: 3 days, 14:24:43, time: 2.229, data_time: 1.271, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4841, loss_cls: 4.4312, loss: 4.4312 +2024-07-17 17:03:06,878 - pyskl - INFO - Epoch [42][200/3746] lr: 8.263e-02, eta: 3 days, 14:23:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4700, loss_cls: 4.4658, loss: 4.4658 +2024-07-17 17:04:27,846 - pyskl - INFO - Epoch [42][300/3746] lr: 8.261e-02, eta: 3 days, 14:22:37, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4844, loss_cls: 4.4526, loss: 4.4526 +2024-07-17 17:05:50,026 - pyskl - INFO - Epoch [42][400/3746] lr: 8.259e-02, eta: 3 days, 14:21:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4775, loss_cls: 4.4382, loss: 4.4382 +2024-07-17 17:07:12,028 - pyskl - INFO - Epoch [42][500/3746] lr: 8.257e-02, eta: 3 days, 14:20:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4678, loss_cls: 4.4817, loss: 4.4817 +2024-07-17 17:08:33,458 - pyskl - INFO - Epoch [42][600/3746] lr: 8.254e-02, eta: 3 days, 14:19:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4781, loss_cls: 4.4544, loss: 4.4544 +2024-07-17 17:09:54,115 - pyskl - INFO - Epoch [42][700/3746] lr: 8.252e-02, eta: 3 days, 14:18:29, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4611, loss_cls: 4.4958, loss: 4.4958 +2024-07-17 17:11:15,771 - pyskl - INFO - Epoch [42][800/3746] lr: 8.250e-02, eta: 3 days, 14:17:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4719, loss_cls: 4.4448, loss: 4.4448 +2024-07-17 17:12:37,111 - pyskl - INFO - Epoch [42][900/3746] lr: 8.248e-02, eta: 3 days, 14:16:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4716, loss_cls: 4.4630, loss: 4.4630 +2024-07-17 17:13:58,067 - pyskl - INFO - Epoch [42][1000/3746] lr: 8.246e-02, eta: 3 days, 14:15:21, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4631, loss_cls: 4.5101, loss: 4.5101 +2024-07-17 17:15:20,142 - pyskl - INFO - Epoch [42][1100/3746] lr: 8.244e-02, eta: 3 days, 14:14:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4731, loss_cls: 4.4831, loss: 4.4831 +2024-07-17 17:16:41,438 - pyskl - INFO - Epoch [42][1200/3746] lr: 8.242e-02, eta: 3 days, 14:13:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4739, loss_cls: 4.4686, loss: 4.4686 +2024-07-17 17:18:02,614 - pyskl - INFO - Epoch [42][1300/3746] lr: 8.240e-02, eta: 3 days, 14:12:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4745, loss_cls: 4.4803, loss: 4.4803 +2024-07-17 17:19:23,837 - pyskl - INFO - Epoch [42][1400/3746] lr: 8.237e-02, eta: 3 days, 14:11:10, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4686, loss_cls: 4.4623, loss: 4.4623 +2024-07-17 17:20:44,854 - pyskl - INFO - Epoch [42][1500/3746] lr: 8.235e-02, eta: 3 days, 14:10:06, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4695, loss_cls: 4.5049, loss: 4.5049 +2024-07-17 17:22:06,038 - pyskl - INFO - Epoch [42][1600/3746] lr: 8.233e-02, eta: 3 days, 14:09:03, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4600, loss_cls: 4.5119, loss: 4.5119 +2024-07-17 17:23:27,323 - pyskl - INFO - Epoch [42][1700/3746] lr: 8.231e-02, eta: 3 days, 14:08:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4811, loss_cls: 4.4253, loss: 4.4253 +2024-07-17 17:24:49,133 - pyskl - INFO - Epoch [42][1800/3746] lr: 8.229e-02, eta: 3 days, 14:06:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4653, loss_cls: 4.4693, loss: 4.4693 +2024-07-17 17:26:10,447 - pyskl - INFO - Epoch [42][1900/3746] lr: 8.227e-02, eta: 3 days, 14:05:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4655, loss_cls: 4.4910, loss: 4.4910 +2024-07-17 17:27:31,210 - pyskl - INFO - Epoch [42][2000/3746] lr: 8.225e-02, eta: 3 days, 14:04:51, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4650, loss_cls: 4.5176, loss: 4.5176 +2024-07-17 17:28:52,711 - pyskl - INFO - Epoch [42][2100/3746] lr: 8.222e-02, eta: 3 days, 14:03:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4670, loss_cls: 4.4891, loss: 4.4891 +2024-07-17 17:30:14,210 - pyskl - INFO - Epoch [42][2200/3746] lr: 8.220e-02, eta: 3 days, 14:02:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4728, loss_cls: 4.4476, loss: 4.4476 +2024-07-17 17:31:35,695 - pyskl - INFO - Epoch [42][2300/3746] lr: 8.218e-02, eta: 3 days, 14:01:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4627, loss_cls: 4.5017, loss: 4.5017 +2024-07-17 17:32:56,623 - pyskl - INFO - Epoch [42][2400/3746] lr: 8.216e-02, eta: 3 days, 14:00:39, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4702, loss_cls: 4.5054, loss: 4.5054 +2024-07-17 17:34:18,623 - pyskl - INFO - Epoch [42][2500/3746] lr: 8.214e-02, eta: 3 days, 13:59:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4795, loss_cls: 4.4549, loss: 4.4549 +2024-07-17 17:35:40,509 - pyskl - INFO - Epoch [42][2600/3746] lr: 8.212e-02, eta: 3 days, 13:58:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4834, loss_cls: 4.4090, loss: 4.4090 +2024-07-17 17:37:01,948 - pyskl - INFO - Epoch [42][2700/3746] lr: 8.210e-02, eta: 3 days, 13:57:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4702, loss_cls: 4.4700, loss: 4.4700 +2024-07-17 17:38:22,978 - pyskl - INFO - Epoch [42][2800/3746] lr: 8.207e-02, eta: 3 days, 13:56:28, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4800, loss_cls: 4.4449, loss: 4.4449 +2024-07-17 17:39:43,871 - pyskl - INFO - Epoch [42][2900/3746] lr: 8.205e-02, eta: 3 days, 13:55:24, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4650, loss_cls: 4.4936, loss: 4.4936 +2024-07-17 17:41:04,714 - pyskl - INFO - Epoch [42][3000/3746] lr: 8.203e-02, eta: 3 days, 13:54:20, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4705, loss_cls: 4.4578, loss: 4.4578 +2024-07-17 17:42:25,786 - pyskl - INFO - Epoch [42][3100/3746] lr: 8.201e-02, eta: 3 days, 13:53:16, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4775, loss_cls: 4.4477, loss: 4.4477 +2024-07-17 17:43:47,032 - pyskl - INFO - Epoch [42][3200/3746] lr: 8.199e-02, eta: 3 days, 13:52:12, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4697, loss_cls: 4.4587, loss: 4.4587 +2024-07-17 17:45:08,401 - pyskl - INFO - Epoch [42][3300/3746] lr: 8.197e-02, eta: 3 days, 13:51:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4722, loss_cls: 4.4485, loss: 4.4485 +2024-07-17 17:46:29,951 - pyskl - INFO - Epoch [42][3400/3746] lr: 8.195e-02, eta: 3 days, 13:50:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4719, loss_cls: 4.4654, loss: 4.4654 +2024-07-17 17:47:51,498 - pyskl - INFO - Epoch [42][3500/3746] lr: 8.192e-02, eta: 3 days, 13:49:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4697, loss_cls: 4.5072, loss: 4.5072 +2024-07-17 17:49:12,990 - pyskl - INFO - Epoch [42][3600/3746] lr: 8.190e-02, eta: 3 days, 13:48:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4738, loss_cls: 4.4864, loss: 4.4864 +2024-07-17 17:50:34,776 - pyskl - INFO - Epoch [42][3700/3746] lr: 8.188e-02, eta: 3 days, 13:46:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4631, loss_cls: 4.4903, loss: 4.4903 +2024-07-17 17:51:13,898 - pyskl - INFO - Saving checkpoint at 42 epochs +2024-07-17 17:53:04,395 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 17:53:05,049 - pyskl - INFO - +top1_acc 0.1537 +top5_acc 0.3558 +2024-07-17 17:53:05,049 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 17:53:05,087 - pyskl - INFO - +mean_acc 0.1536 +2024-07-17 17:53:05,096 - pyskl - INFO - Epoch(val) [42][309] top1_acc: 0.1537, top5_acc: 0.3558, mean_class_accuracy: 0.1536 +2024-07-17 17:56:44,235 - pyskl - INFO - Epoch [43][100/3746] lr: 8.185e-02, eta: 3 days, 13:49:43, time: 2.191, data_time: 1.226, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4661, loss_cls: 4.4597, loss: 4.4597 +2024-07-17 17:58:05,695 - pyskl - INFO - Epoch [43][200/3746] lr: 8.183e-02, eta: 3 days, 13:48:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4755, loss_cls: 4.4380, loss: 4.4380 +2024-07-17 17:59:28,230 - pyskl - INFO - Epoch [43][300/3746] lr: 8.181e-02, eta: 3 days, 13:47:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4783, loss_cls: 4.4704, loss: 4.4704 +2024-07-17 18:00:49,554 - pyskl - INFO - Epoch [43][400/3746] lr: 8.179e-02, eta: 3 days, 13:46:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4802, loss_cls: 4.4352, loss: 4.4352 +2024-07-17 18:02:11,168 - pyskl - INFO - Epoch [43][500/3746] lr: 8.176e-02, eta: 3 days, 13:45:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4730, loss_cls: 4.4353, loss: 4.4353 +2024-07-17 18:03:32,532 - pyskl - INFO - Epoch [43][600/3746] lr: 8.174e-02, eta: 3 days, 13:44:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4759, loss_cls: 4.4742, loss: 4.4742 +2024-07-17 18:04:53,422 - pyskl - INFO - Epoch [43][700/3746] lr: 8.172e-02, eta: 3 days, 13:43:24, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4608, loss_cls: 4.5139, loss: 4.5139 +2024-07-17 18:06:14,163 - pyskl - INFO - Epoch [43][800/3746] lr: 8.170e-02, eta: 3 days, 13:42:18, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4733, loss_cls: 4.4289, loss: 4.4289 +2024-07-17 18:07:35,250 - pyskl - INFO - Epoch [43][900/3746] lr: 8.168e-02, eta: 3 days, 13:41:14, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4806, loss_cls: 4.4416, loss: 4.4416 +2024-07-17 18:08:56,779 - pyskl - INFO - Epoch [43][1000/3746] lr: 8.166e-02, eta: 3 days, 13:40:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4742, loss_cls: 4.4515, loss: 4.4515 +2024-07-17 18:10:17,763 - pyskl - INFO - Epoch [43][1100/3746] lr: 8.163e-02, eta: 3 days, 13:39:06, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4842, loss_cls: 4.4297, loss: 4.4297 +2024-07-17 18:11:38,310 - pyskl - INFO - Epoch [43][1200/3746] lr: 8.161e-02, eta: 3 days, 13:38:00, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4703, loss_cls: 4.4772, loss: 4.4772 +2024-07-17 18:12:59,363 - pyskl - INFO - Epoch [43][1300/3746] lr: 8.159e-02, eta: 3 days, 13:36:55, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4750, loss_cls: 4.4684, loss: 4.4684 +2024-07-17 18:14:20,713 - pyskl - INFO - Epoch [43][1400/3746] lr: 8.157e-02, eta: 3 days, 13:35:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4573, loss_cls: 4.5316, loss: 4.5316 +2024-07-17 18:15:42,365 - pyskl - INFO - Epoch [43][1500/3746] lr: 8.155e-02, eta: 3 days, 13:34:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4763, loss_cls: 4.4886, loss: 4.4886 +2024-07-17 18:17:03,626 - pyskl - INFO - Epoch [43][1600/3746] lr: 8.153e-02, eta: 3 days, 13:33:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4705, loss_cls: 4.4938, loss: 4.4938 +2024-07-17 18:18:24,736 - pyskl - INFO - Epoch [43][1700/3746] lr: 8.150e-02, eta: 3 days, 13:32:39, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4723, loss_cls: 4.4709, loss: 4.4709 +2024-07-17 18:19:45,755 - pyskl - INFO - Epoch [43][1800/3746] lr: 8.148e-02, eta: 3 days, 13:31:34, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4745, loss_cls: 4.4372, loss: 4.4372 +2024-07-17 18:21:06,644 - pyskl - INFO - Epoch [43][1900/3746] lr: 8.146e-02, eta: 3 days, 13:30:29, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4769, loss_cls: 4.4651, loss: 4.4651 +2024-07-17 18:22:28,473 - pyskl - INFO - Epoch [43][2000/3746] lr: 8.144e-02, eta: 3 days, 13:29:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4734, loss_cls: 4.4819, loss: 4.4819 +2024-07-17 18:23:49,434 - pyskl - INFO - Epoch [43][2100/3746] lr: 8.142e-02, eta: 3 days, 13:28:21, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4625, loss_cls: 4.5233, loss: 4.5233 +2024-07-17 18:25:10,633 - pyskl - INFO - Epoch [43][2200/3746] lr: 8.140e-02, eta: 3 days, 13:27:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4722, loss_cls: 4.4798, loss: 4.4798 +2024-07-17 18:26:32,093 - pyskl - INFO - Epoch [43][2300/3746] lr: 8.137e-02, eta: 3 days, 13:26:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4883, loss_cls: 4.3854, loss: 4.3854 +2024-07-17 18:27:54,273 - pyskl - INFO - Epoch [43][2400/3746] lr: 8.135e-02, eta: 3 days, 13:25:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4639, loss_cls: 4.4700, loss: 4.4700 +2024-07-17 18:29:15,256 - pyskl - INFO - Epoch [43][2500/3746] lr: 8.133e-02, eta: 3 days, 13:24:06, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4728, loss_cls: 4.4648, loss: 4.4648 +2024-07-17 18:30:36,495 - pyskl - INFO - Epoch [43][2600/3746] lr: 8.131e-02, eta: 3 days, 13:23:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4786, loss_cls: 4.4725, loss: 4.4725 +2024-07-17 18:31:57,334 - pyskl - INFO - Epoch [43][2700/3746] lr: 8.129e-02, eta: 3 days, 13:21:56, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4659, loss_cls: 4.4668, loss: 4.4668 +2024-07-17 18:33:19,129 - pyskl - INFO - Epoch [43][2800/3746] lr: 8.126e-02, eta: 3 days, 13:20:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4691, loss_cls: 4.4735, loss: 4.4735 +2024-07-17 18:34:40,257 - pyskl - INFO - Epoch [43][2900/3746] lr: 8.124e-02, eta: 3 days, 13:19:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4747, loss_cls: 4.4606, loss: 4.4606 +2024-07-17 18:36:01,393 - pyskl - INFO - Epoch [43][3000/3746] lr: 8.122e-02, eta: 3 days, 13:18:43, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4722, loss_cls: 4.4702, loss: 4.4702 +2024-07-17 18:37:22,498 - pyskl - INFO - Epoch [43][3100/3746] lr: 8.120e-02, eta: 3 days, 13:17:38, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4786, loss_cls: 4.4277, loss: 4.4277 +2024-07-17 18:38:44,068 - pyskl - INFO - Epoch [43][3200/3746] lr: 8.118e-02, eta: 3 days, 13:16:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4564, loss_cls: 4.5392, loss: 4.5392 +2024-07-17 18:40:04,843 - pyskl - INFO - Epoch [43][3300/3746] lr: 8.116e-02, eta: 3 days, 13:15:28, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4758, loss_cls: 4.4519, loss: 4.4519 +2024-07-17 18:41:26,326 - pyskl - INFO - Epoch [43][3400/3746] lr: 8.113e-02, eta: 3 days, 13:14:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4833, loss_cls: 4.4249, loss: 4.4249 +2024-07-17 18:42:47,426 - pyskl - INFO - Epoch [43][3500/3746] lr: 8.111e-02, eta: 3 days, 13:13:19, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4652, loss_cls: 4.4800, loss: 4.4800 +2024-07-17 18:44:08,993 - pyskl - INFO - Epoch [43][3600/3746] lr: 8.109e-02, eta: 3 days, 13:12:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4741, loss_cls: 4.4546, loss: 4.4546 +2024-07-17 18:45:30,417 - pyskl - INFO - Epoch [43][3700/3746] lr: 8.107e-02, eta: 3 days, 13:11:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4719, loss_cls: 4.4939, loss: 4.4939 +2024-07-17 18:46:09,568 - pyskl - INFO - Saving checkpoint at 43 epochs +2024-07-17 18:48:00,247 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 18:48:00,907 - pyskl - INFO - +top1_acc 0.1850 +top5_acc 0.4064 +2024-07-17 18:48:00,907 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 18:48:00,945 - pyskl - INFO - +mean_acc 0.1849 +2024-07-17 18:48:00,950 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_37.pth was removed +2024-07-17 18:48:01,175 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_43.pth. +2024-07-17 18:48:01,176 - pyskl - INFO - Best top1_acc is 0.1850 at 43 epoch. +2024-07-17 18:48:01,186 - pyskl - INFO - Epoch(val) [43][309] top1_acc: 0.1850, top5_acc: 0.4064, mean_class_accuracy: 0.1849 +2024-07-17 18:51:44,882 - pyskl - INFO - Epoch [44][100/3746] lr: 8.104e-02, eta: 3 days, 13:13:58, time: 2.237, data_time: 1.266, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4736, loss_cls: 4.4486, loss: 4.4486 +2024-07-17 18:53:06,375 - pyskl - INFO - Epoch [44][200/3746] lr: 8.101e-02, eta: 3 days, 13:12:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4859, loss_cls: 4.4081, loss: 4.4081 +2024-07-17 18:54:28,198 - pyskl - INFO - Epoch [44][300/3746] lr: 8.099e-02, eta: 3 days, 13:11:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4750, loss_cls: 4.4716, loss: 4.4716 +2024-07-17 18:55:49,481 - pyskl - INFO - Epoch [44][400/3746] lr: 8.097e-02, eta: 3 days, 13:10:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4880, loss_cls: 4.4102, loss: 4.4102 +2024-07-17 18:57:11,305 - pyskl - INFO - Epoch [44][500/3746] lr: 8.095e-02, eta: 3 days, 13:09:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4758, loss_cls: 4.4677, loss: 4.4677 +2024-07-17 18:58:33,254 - pyskl - INFO - Epoch [44][600/3746] lr: 8.093e-02, eta: 3 days, 13:08:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4752, loss_cls: 4.4256, loss: 4.4256 +2024-07-17 18:59:54,611 - pyskl - INFO - Epoch [44][700/3746] lr: 8.090e-02, eta: 3 days, 13:07:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4813, loss_cls: 4.4851, loss: 4.4851 +2024-07-17 19:01:16,130 - pyskl - INFO - Epoch [44][800/3746] lr: 8.088e-02, eta: 3 days, 13:06:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4738, loss_cls: 4.4631, loss: 4.4631 +2024-07-17 19:02:37,453 - pyskl - INFO - Epoch [44][900/3746] lr: 8.086e-02, eta: 3 days, 13:05:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4669, loss_cls: 4.4563, loss: 4.4563 +2024-07-17 19:03:58,627 - pyskl - INFO - Epoch [44][1000/3746] lr: 8.084e-02, eta: 3 days, 13:04:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4695, loss_cls: 4.4673, loss: 4.4673 +2024-07-17 19:05:19,421 - pyskl - INFO - Epoch [44][1100/3746] lr: 8.082e-02, eta: 3 days, 13:03:13, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4778, loss_cls: 4.4004, loss: 4.4004 +2024-07-17 19:06:40,844 - pyskl - INFO - Epoch [44][1200/3746] lr: 8.079e-02, eta: 3 days, 13:02:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4636, loss_cls: 4.5035, loss: 4.5035 +2024-07-17 19:08:02,043 - pyskl - INFO - Epoch [44][1300/3746] lr: 8.077e-02, eta: 3 days, 13:01:03, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4831, loss_cls: 4.4082, loss: 4.4082 +2024-07-17 19:09:23,766 - pyskl - INFO - Epoch [44][1400/3746] lr: 8.075e-02, eta: 3 days, 12:59:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4745, loss_cls: 4.4616, loss: 4.4616 +2024-07-17 19:10:44,796 - pyskl - INFO - Epoch [44][1500/3746] lr: 8.073e-02, eta: 3 days, 12:58:53, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4753, loss_cls: 4.4374, loss: 4.4374 +2024-07-17 19:12:05,477 - pyskl - INFO - Epoch [44][1600/3746] lr: 8.071e-02, eta: 3 days, 12:57:46, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4739, loss_cls: 4.4435, loss: 4.4435 +2024-07-17 19:13:26,953 - pyskl - INFO - Epoch [44][1700/3746] lr: 8.068e-02, eta: 3 days, 12:56:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4723, loss_cls: 4.4479, loss: 4.4479 +2024-07-17 19:14:48,070 - pyskl - INFO - Epoch [44][1800/3746] lr: 8.066e-02, eta: 3 days, 12:55:36, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4725, loss_cls: 4.4546, loss: 4.4546 +2024-07-17 19:16:09,649 - pyskl - INFO - Epoch [44][1900/3746] lr: 8.064e-02, eta: 3 days, 12:54:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4761, loss_cls: 4.4401, loss: 4.4401 +2024-07-17 19:17:30,704 - pyskl - INFO - Epoch [44][2000/3746] lr: 8.062e-02, eta: 3 days, 12:53:26, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4644, loss_cls: 4.4985, loss: 4.4985 +2024-07-17 19:18:52,149 - pyskl - INFO - Epoch [44][2100/3746] lr: 8.060e-02, eta: 3 days, 12:52:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4644, loss_cls: 4.4708, loss: 4.4708 +2024-07-17 19:20:13,792 - pyskl - INFO - Epoch [44][2200/3746] lr: 8.057e-02, eta: 3 days, 12:51:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4733, loss_cls: 4.4538, loss: 4.4538 +2024-07-17 19:21:35,184 - pyskl - INFO - Epoch [44][2300/3746] lr: 8.055e-02, eta: 3 days, 12:50:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4723, loss_cls: 4.4856, loss: 4.4856 +2024-07-17 19:22:56,958 - pyskl - INFO - Epoch [44][2400/3746] lr: 8.053e-02, eta: 3 days, 12:49:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4645, loss_cls: 4.4860, loss: 4.4860 +2024-07-17 19:24:18,038 - pyskl - INFO - Epoch [44][2500/3746] lr: 8.051e-02, eta: 3 days, 12:48:01, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4673, loss_cls: 4.4848, loss: 4.4848 +2024-07-17 19:25:39,338 - pyskl - INFO - Epoch [44][2600/3746] lr: 8.048e-02, eta: 3 days, 12:46:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4745, loss_cls: 4.4242, loss: 4.4242 +2024-07-17 19:27:00,456 - pyskl - INFO - Epoch [44][2700/3746] lr: 8.046e-02, eta: 3 days, 12:45:50, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4795, loss_cls: 4.4386, loss: 4.4386 +2024-07-17 19:28:21,346 - pyskl - INFO - Epoch [44][2800/3746] lr: 8.044e-02, eta: 3 days, 12:44:44, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4722, loss_cls: 4.4931, loss: 4.4931 +2024-07-17 19:29:42,841 - pyskl - INFO - Epoch [44][2900/3746] lr: 8.042e-02, eta: 3 days, 12:43:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4766, loss_cls: 4.4309, loss: 4.4309 +2024-07-17 19:31:03,802 - pyskl - INFO - Epoch [44][3000/3746] lr: 8.040e-02, eta: 3 days, 12:42:33, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4753, loss_cls: 4.4619, loss: 4.4619 +2024-07-17 19:32:24,420 - pyskl - INFO - Epoch [44][3100/3746] lr: 8.037e-02, eta: 3 days, 12:41:26, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4717, loss_cls: 4.4386, loss: 4.4386 +2024-07-17 19:33:46,114 - pyskl - INFO - Epoch [44][3200/3746] lr: 8.035e-02, eta: 3 days, 12:40:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4750, loss_cls: 4.4849, loss: 4.4849 +2024-07-17 19:35:07,827 - pyskl - INFO - Epoch [44][3300/3746] lr: 8.033e-02, eta: 3 days, 12:39:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4688, loss_cls: 4.4872, loss: 4.4872 +2024-07-17 19:36:29,347 - pyskl - INFO - Epoch [44][3400/3746] lr: 8.031e-02, eta: 3 days, 12:38:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4830, loss_cls: 4.4174, loss: 4.4174 +2024-07-17 19:37:50,367 - pyskl - INFO - Epoch [44][3500/3746] lr: 8.028e-02, eta: 3 days, 12:37:06, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4767, loss_cls: 4.4484, loss: 4.4484 +2024-07-17 19:39:12,357 - pyskl - INFO - Epoch [44][3600/3746] lr: 8.026e-02, eta: 3 days, 12:36:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4731, loss_cls: 4.4514, loss: 4.4514 +2024-07-17 19:40:33,820 - pyskl - INFO - Epoch [44][3700/3746] lr: 8.024e-02, eta: 3 days, 12:34:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4820, loss_cls: 4.4183, loss: 4.4183 +2024-07-17 19:41:12,857 - pyskl - INFO - Saving checkpoint at 44 epochs +2024-07-17 19:43:03,090 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 19:43:03,746 - pyskl - INFO - +top1_acc 0.1893 +top5_acc 0.4156 +2024-07-17 19:43:03,747 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 19:43:03,786 - pyskl - INFO - +mean_acc 0.1892 +2024-07-17 19:43:03,790 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_43.pth was removed +2024-07-17 19:43:04,042 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_44.pth. +2024-07-17 19:43:04,042 - pyskl - INFO - Best top1_acc is 0.1893 at 44 epoch. +2024-07-17 19:43:04,052 - pyskl - INFO - Epoch(val) [44][309] top1_acc: 0.1893, top5_acc: 0.4156, mean_class_accuracy: 0.1892 +2024-07-17 19:46:50,315 - pyskl - INFO - Epoch [45][100/3746] lr: 8.021e-02, eta: 3 days, 12:37:40, time: 2.263, data_time: 1.288, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4813, loss_cls: 4.4336, loss: 4.4336 +2024-07-17 19:48:11,763 - pyskl - INFO - Epoch [45][200/3746] lr: 8.019e-02, eta: 3 days, 12:36:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4845, loss_cls: 4.4083, loss: 4.4083 +2024-07-17 19:49:34,069 - pyskl - INFO - Epoch [45][300/3746] lr: 8.016e-02, eta: 3 days, 12:35:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4877, loss_cls: 4.3853, loss: 4.3853 +2024-07-17 19:50:55,312 - pyskl - INFO - Epoch [45][400/3746] lr: 8.014e-02, eta: 3 days, 12:34:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4706, loss_cls: 4.4977, loss: 4.4977 +2024-07-17 19:52:16,503 - pyskl - INFO - Epoch [45][500/3746] lr: 8.012e-02, eta: 3 days, 12:33:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4708, loss_cls: 4.4599, loss: 4.4599 +2024-07-17 19:53:38,325 - pyskl - INFO - Epoch [45][600/3746] lr: 8.010e-02, eta: 3 days, 12:32:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4722, loss_cls: 4.4636, loss: 4.4636 +2024-07-17 19:54:59,024 - pyskl - INFO - Epoch [45][700/3746] lr: 8.007e-02, eta: 3 days, 12:31:07, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4750, loss_cls: 4.4423, loss: 4.4423 +2024-07-17 19:56:21,110 - pyskl - INFO - Epoch [45][800/3746] lr: 8.005e-02, eta: 3 days, 12:30:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4836, loss_cls: 4.4296, loss: 4.4296 +2024-07-17 19:57:41,984 - pyskl - INFO - Epoch [45][900/3746] lr: 8.003e-02, eta: 3 days, 12:28:56, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4900, loss_cls: 4.4064, loss: 4.4064 +2024-07-17 19:59:03,128 - pyskl - INFO - Epoch [45][1000/3746] lr: 8.001e-02, eta: 3 days, 12:27:50, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4703, loss_cls: 4.4687, loss: 4.4687 +2024-07-17 20:00:24,387 - pyskl - INFO - Epoch [45][1100/3746] lr: 7.998e-02, eta: 3 days, 12:26:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4727, loss_cls: 4.4628, loss: 4.4628 +2024-07-17 20:01:45,932 - pyskl - INFO - Epoch [45][1200/3746] lr: 7.996e-02, eta: 3 days, 12:25:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4669, loss_cls: 4.4908, loss: 4.4908 +2024-07-17 20:03:07,623 - pyskl - INFO - Epoch [45][1300/3746] lr: 7.994e-02, eta: 3 days, 12:24:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4878, loss_cls: 4.3688, loss: 4.3688 +2024-07-17 20:04:28,926 - pyskl - INFO - Epoch [45][1400/3746] lr: 7.992e-02, eta: 3 days, 12:23:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4725, loss_cls: 4.4565, loss: 4.4565 +2024-07-17 20:05:49,893 - pyskl - INFO - Epoch [45][1500/3746] lr: 7.990e-02, eta: 3 days, 12:22:20, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4598, loss_cls: 4.5133, loss: 4.5133 +2024-07-17 20:07:10,870 - pyskl - INFO - Epoch [45][1600/3746] lr: 7.987e-02, eta: 3 days, 12:21:14, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4820, loss_cls: 4.4231, loss: 4.4231 +2024-07-17 20:08:33,006 - pyskl - INFO - Epoch [45][1700/3746] lr: 7.985e-02, eta: 3 days, 12:20:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4652, loss_cls: 4.4762, loss: 4.4762 +2024-07-17 20:09:54,499 - pyskl - INFO - Epoch [45][1800/3746] lr: 7.983e-02, eta: 3 days, 12:19:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4683, loss_cls: 4.4565, loss: 4.4565 +2024-07-17 20:11:15,621 - pyskl - INFO - Epoch [45][1900/3746] lr: 7.981e-02, eta: 3 days, 12:17:57, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4736, loss_cls: 4.4376, loss: 4.4376 +2024-07-17 20:12:36,696 - pyskl - INFO - Epoch [45][2000/3746] lr: 7.978e-02, eta: 3 days, 12:16:51, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4769, loss_cls: 4.4490, loss: 4.4490 +2024-07-17 20:13:58,437 - pyskl - INFO - Epoch [45][2100/3746] lr: 7.976e-02, eta: 3 days, 12:15:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4709, loss_cls: 4.4698, loss: 4.4698 +2024-07-17 20:15:20,431 - pyskl - INFO - Epoch [45][2200/3746] lr: 7.974e-02, eta: 3 days, 12:14:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4753, loss_cls: 4.4309, loss: 4.4309 +2024-07-17 20:16:42,058 - pyskl - INFO - Epoch [45][2300/3746] lr: 7.972e-02, eta: 3 days, 12:13:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4695, loss_cls: 4.4519, loss: 4.4519 +2024-07-17 20:18:03,649 - pyskl - INFO - Epoch [45][2400/3746] lr: 7.969e-02, eta: 3 days, 12:12:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4647, loss_cls: 4.4947, loss: 4.4947 +2024-07-17 20:19:24,470 - pyskl - INFO - Epoch [45][2500/3746] lr: 7.967e-02, eta: 3 days, 12:11:23, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4731, loss_cls: 4.4554, loss: 4.4554 +2024-07-17 20:20:46,104 - pyskl - INFO - Epoch [45][2600/3746] lr: 7.965e-02, eta: 3 days, 12:10:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4702, loss_cls: 4.4850, loss: 4.4850 +2024-07-17 20:22:07,738 - pyskl - INFO - Epoch [45][2700/3746] lr: 7.963e-02, eta: 3 days, 12:09:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4698, loss_cls: 4.4461, loss: 4.4461 +2024-07-17 20:23:28,691 - pyskl - INFO - Epoch [45][2800/3746] lr: 7.960e-02, eta: 3 days, 12:08:05, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4788, loss_cls: 4.4214, loss: 4.4214 +2024-07-17 20:24:50,539 - pyskl - INFO - Epoch [45][2900/3746] lr: 7.958e-02, eta: 3 days, 12:07:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4823, loss_cls: 4.4209, loss: 4.4209 +2024-07-17 20:26:11,908 - pyskl - INFO - Epoch [45][3000/3746] lr: 7.956e-02, eta: 3 days, 12:05:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4773, loss_cls: 4.4394, loss: 4.4394 +2024-07-17 20:27:32,528 - pyskl - INFO - Epoch [45][3100/3746] lr: 7.954e-02, eta: 3 days, 12:04:46, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4727, loss_cls: 4.4599, loss: 4.4599 +2024-07-17 20:28:53,462 - pyskl - INFO - Epoch [45][3200/3746] lr: 7.951e-02, eta: 3 days, 12:03:38, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4823, loss_cls: 4.3985, loss: 4.3985 +2024-07-17 20:30:14,330 - pyskl - INFO - Epoch [45][3300/3746] lr: 7.949e-02, eta: 3 days, 12:02:31, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4767, loss_cls: 4.4585, loss: 4.4585 +2024-07-17 20:31:35,406 - pyskl - INFO - Epoch [45][3400/3746] lr: 7.947e-02, eta: 3 days, 12:01:24, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4841, loss_cls: 4.4096, loss: 4.4096 +2024-07-17 20:32:56,542 - pyskl - INFO - Epoch [45][3500/3746] lr: 7.945e-02, eta: 3 days, 12:00:17, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4769, loss_cls: 4.4384, loss: 4.4384 +2024-07-17 20:34:17,625 - pyskl - INFO - Epoch [45][3600/3746] lr: 7.942e-02, eta: 3 days, 11:59:10, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4700, loss_cls: 4.4848, loss: 4.4848 +2024-07-17 20:35:39,443 - pyskl - INFO - Epoch [45][3700/3746] lr: 7.940e-02, eta: 3 days, 11:58:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4655, loss_cls: 4.4704, loss: 4.4704 +2024-07-17 20:36:18,989 - pyskl - INFO - Saving checkpoint at 45 epochs +2024-07-17 20:38:10,267 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 20:38:10,927 - pyskl - INFO - +top1_acc 0.1463 +top5_acc 0.3446 +2024-07-17 20:38:10,927 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 20:38:10,965 - pyskl - INFO - +mean_acc 0.1463 +2024-07-17 20:38:10,976 - pyskl - INFO - Epoch(val) [45][309] top1_acc: 0.1463, top5_acc: 0.3446, mean_class_accuracy: 0.1463 +2024-07-17 20:42:00,708 - pyskl - INFO - Epoch [46][100/3746] lr: 7.937e-02, eta: 3 days, 12:00:47, time: 2.297, data_time: 1.318, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4888, loss_cls: 4.3594, loss: 4.3594 +2024-07-17 20:43:21,907 - pyskl - INFO - Epoch [46][200/3746] lr: 7.934e-02, eta: 3 days, 11:59:40, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4734, loss_cls: 4.4314, loss: 4.4314 +2024-07-17 20:44:43,285 - pyskl - INFO - Epoch [46][300/3746] lr: 7.932e-02, eta: 3 days, 11:58:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4898, loss_cls: 4.4006, loss: 4.4006 +2024-07-17 20:46:05,082 - pyskl - INFO - Epoch [46][400/3746] lr: 7.930e-02, eta: 3 days, 11:57:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4805, loss_cls: 4.4269, loss: 4.4269 +2024-07-17 20:47:26,200 - pyskl - INFO - Epoch [46][500/3746] lr: 7.928e-02, eta: 3 days, 11:56:21, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4792, loss_cls: 4.4259, loss: 4.4259 +2024-07-17 20:48:47,914 - pyskl - INFO - Epoch [46][600/3746] lr: 7.925e-02, eta: 3 days, 11:55:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4813, loss_cls: 4.4184, loss: 4.4184 +2024-07-17 20:50:08,792 - pyskl - INFO - Epoch [46][700/3746] lr: 7.923e-02, eta: 3 days, 11:54:07, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4739, loss_cls: 4.4471, loss: 4.4471 +2024-07-17 20:51:30,084 - pyskl - INFO - Epoch [46][800/3746] lr: 7.921e-02, eta: 3 days, 11:53:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4675, loss_cls: 4.4575, loss: 4.4575 +2024-07-17 20:52:51,539 - pyskl - INFO - Epoch [46][900/3746] lr: 7.919e-02, eta: 3 days, 11:51:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4725, loss_cls: 4.4657, loss: 4.4657 +2024-07-17 20:54:13,058 - pyskl - INFO - Epoch [46][1000/3746] lr: 7.916e-02, eta: 3 days, 11:50:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4833, loss_cls: 4.3942, loss: 4.3942 +2024-07-17 20:55:34,057 - pyskl - INFO - Epoch [46][1100/3746] lr: 7.914e-02, eta: 3 days, 11:49:40, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4794, loss_cls: 4.4239, loss: 4.4239 +2024-07-17 20:56:55,406 - pyskl - INFO - Epoch [46][1200/3746] lr: 7.912e-02, eta: 3 days, 11:48:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4711, loss_cls: 4.4483, loss: 4.4483 +2024-07-17 20:58:16,616 - pyskl - INFO - Epoch [46][1300/3746] lr: 7.909e-02, eta: 3 days, 11:47:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4772, loss_cls: 4.4459, loss: 4.4459 +2024-07-17 20:59:37,838 - pyskl - INFO - Epoch [46][1400/3746] lr: 7.907e-02, eta: 3 days, 11:46:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4822, loss_cls: 4.4395, loss: 4.4395 +2024-07-17 21:00:59,149 - pyskl - INFO - Epoch [46][1500/3746] lr: 7.905e-02, eta: 3 days, 11:45:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4730, loss_cls: 4.4182, loss: 4.4182 +2024-07-17 21:02:20,802 - pyskl - INFO - Epoch [46][1600/3746] lr: 7.903e-02, eta: 3 days, 11:44:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4730, loss_cls: 4.4372, loss: 4.4372 +2024-07-17 21:03:42,107 - pyskl - INFO - Epoch [46][1700/3746] lr: 7.900e-02, eta: 3 days, 11:42:59, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4722, loss_cls: 4.4630, loss: 4.4630 +2024-07-17 21:05:03,593 - pyskl - INFO - Epoch [46][1800/3746] lr: 7.898e-02, eta: 3 days, 11:41:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4714, loss_cls: 4.4650, loss: 4.4650 +2024-07-17 21:06:24,843 - pyskl - INFO - Epoch [46][1900/3746] lr: 7.896e-02, eta: 3 days, 11:40:46, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4708, loss_cls: 4.4746, loss: 4.4746 +2024-07-17 21:07:46,146 - pyskl - INFO - Epoch [46][2000/3746] lr: 7.894e-02, eta: 3 days, 11:39:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4750, loss_cls: 4.4208, loss: 4.4208 +2024-07-17 21:09:08,042 - pyskl - INFO - Epoch [46][2100/3746] lr: 7.891e-02, eta: 3 days, 11:38:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4800, loss_cls: 4.4283, loss: 4.4283 +2024-07-17 21:10:29,438 - pyskl - INFO - Epoch [46][2200/3746] lr: 7.889e-02, eta: 3 days, 11:37:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4769, loss_cls: 4.4080, loss: 4.4080 +2024-07-17 21:11:50,745 - pyskl - INFO - Epoch [46][2300/3746] lr: 7.887e-02, eta: 3 days, 11:36:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4645, loss_cls: 4.4717, loss: 4.4717 +2024-07-17 21:13:12,283 - pyskl - INFO - Epoch [46][2400/3746] lr: 7.884e-02, eta: 3 days, 11:35:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4773, loss_cls: 4.4648, loss: 4.4648 +2024-07-17 21:14:33,507 - pyskl - INFO - Epoch [46][2500/3746] lr: 7.882e-02, eta: 3 days, 11:34:05, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4808, loss_cls: 4.4465, loss: 4.4465 +2024-07-17 21:15:54,690 - pyskl - INFO - Epoch [46][2600/3746] lr: 7.880e-02, eta: 3 days, 11:32:58, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4773, loss_cls: 4.4323, loss: 4.4323 +2024-07-17 21:17:15,532 - pyskl - INFO - Epoch [46][2700/3746] lr: 7.878e-02, eta: 3 days, 11:31:50, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4883, loss_cls: 4.4154, loss: 4.4154 +2024-07-17 21:18:36,814 - pyskl - INFO - Epoch [46][2800/3746] lr: 7.875e-02, eta: 3 days, 11:30:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4664, loss_cls: 4.4824, loss: 4.4824 +2024-07-17 21:19:58,454 - pyskl - INFO - Epoch [46][2900/3746] lr: 7.873e-02, eta: 3 days, 11:29:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4738, loss_cls: 4.4394, loss: 4.4394 +2024-07-17 21:21:19,297 - pyskl - INFO - Epoch [46][3000/3746] lr: 7.871e-02, eta: 3 days, 11:28:28, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4817, loss_cls: 4.4173, loss: 4.4173 +2024-07-17 21:22:40,402 - pyskl - INFO - Epoch [46][3100/3746] lr: 7.868e-02, eta: 3 days, 11:27:20, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4777, loss_cls: 4.4496, loss: 4.4496 +2024-07-17 21:24:01,257 - pyskl - INFO - Epoch [46][3200/3746] lr: 7.866e-02, eta: 3 days, 11:26:12, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4670, loss_cls: 4.5090, loss: 4.5090 +2024-07-17 21:25:22,745 - pyskl - INFO - Epoch [46][3300/3746] lr: 7.864e-02, eta: 3 days, 11:25:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4752, loss_cls: 4.4207, loss: 4.4207 +2024-07-17 21:26:43,580 - pyskl - INFO - Epoch [46][3400/3746] lr: 7.862e-02, eta: 3 days, 11:23:57, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4677, loss_cls: 4.4396, loss: 4.4396 +2024-07-17 21:28:05,062 - pyskl - INFO - Epoch [46][3500/3746] lr: 7.859e-02, eta: 3 days, 11:22:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4744, loss_cls: 4.4577, loss: 4.4577 +2024-07-17 21:29:27,262 - pyskl - INFO - Epoch [46][3600/3746] lr: 7.857e-02, eta: 3 days, 11:21:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4719, loss_cls: 4.4772, loss: 4.4772 +2024-07-17 21:30:49,555 - pyskl - INFO - Epoch [46][3700/3746] lr: 7.855e-02, eta: 3 days, 11:20:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4847, loss_cls: 4.4368, loss: 4.4368 +2024-07-17 21:31:29,166 - pyskl - INFO - Saving checkpoint at 46 epochs +2024-07-17 21:33:19,580 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 21:33:20,236 - pyskl - INFO - +top1_acc 0.1387 +top5_acc 0.3487 +2024-07-17 21:33:20,236 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 21:33:20,274 - pyskl - INFO - +mean_acc 0.1384 +2024-07-17 21:33:20,285 - pyskl - INFO - Epoch(val) [46][309] top1_acc: 0.1387, top5_acc: 0.3487, mean_class_accuracy: 0.1384 +2024-07-17 21:37:04,150 - pyskl - INFO - Epoch [47][100/3746] lr: 7.851e-02, eta: 3 days, 11:22:59, time: 2.239, data_time: 1.276, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4777, loss_cls: 4.4034, loss: 4.4034 +2024-07-17 21:38:25,451 - pyskl - INFO - Epoch [47][200/3746] lr: 7.849e-02, eta: 3 days, 11:21:52, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4814, loss_cls: 4.4158, loss: 4.4158 +2024-07-17 21:39:46,437 - pyskl - INFO - Epoch [47][300/3746] lr: 7.847e-02, eta: 3 days, 11:20:43, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4878, loss_cls: 4.3986, loss: 4.3986 +2024-07-17 21:41:07,941 - pyskl - INFO - Epoch [47][400/3746] lr: 7.844e-02, eta: 3 days, 11:19:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4741, loss_cls: 4.4660, loss: 4.4660 +2024-07-17 21:42:29,369 - pyskl - INFO - Epoch [47][500/3746] lr: 7.842e-02, eta: 3 days, 11:18:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4711, loss_cls: 4.4375, loss: 4.4375 +2024-07-17 21:43:51,546 - pyskl - INFO - Epoch [47][600/3746] lr: 7.840e-02, eta: 3 days, 11:17:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4831, loss_cls: 4.4107, loss: 4.4107 +2024-07-17 21:45:12,231 - pyskl - INFO - Epoch [47][700/3746] lr: 7.838e-02, eta: 3 days, 11:16:14, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4886, loss_cls: 4.3885, loss: 4.3885 +2024-07-17 21:46:33,723 - pyskl - INFO - Epoch [47][800/3746] lr: 7.835e-02, eta: 3 days, 11:15:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4836, loss_cls: 4.3733, loss: 4.3733 +2024-07-17 21:47:54,911 - pyskl - INFO - Epoch [47][900/3746] lr: 7.833e-02, eta: 3 days, 11:13:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4728, loss_cls: 4.4343, loss: 4.4343 +2024-07-17 21:49:15,775 - pyskl - INFO - Epoch [47][1000/3746] lr: 7.831e-02, eta: 3 days, 11:12:51, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4878, loss_cls: 4.3922, loss: 4.3922 +2024-07-17 21:50:37,169 - pyskl - INFO - Epoch [47][1100/3746] lr: 7.828e-02, eta: 3 days, 11:11:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4775, loss_cls: 4.4164, loss: 4.4164 +2024-07-17 21:51:58,123 - pyskl - INFO - Epoch [47][1200/3746] lr: 7.826e-02, eta: 3 days, 11:10:35, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4755, loss_cls: 4.4346, loss: 4.4346 +2024-07-17 21:53:18,954 - pyskl - INFO - Epoch [47][1300/3746] lr: 7.824e-02, eta: 3 days, 11:09:26, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4680, loss_cls: 4.4600, loss: 4.4600 +2024-07-17 21:54:39,599 - pyskl - INFO - Epoch [47][1400/3746] lr: 7.821e-02, eta: 3 days, 11:08:17, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4792, loss_cls: 4.4227, loss: 4.4227 +2024-07-17 21:56:00,552 - pyskl - INFO - Epoch [47][1500/3746] lr: 7.819e-02, eta: 3 days, 11:07:08, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4800, loss_cls: 4.4260, loss: 4.4260 +2024-07-17 21:57:22,087 - pyskl - INFO - Epoch [47][1600/3746] lr: 7.817e-02, eta: 3 days, 11:06:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4817, loss_cls: 4.4196, loss: 4.4196 +2024-07-17 21:58:43,452 - pyskl - INFO - Epoch [47][1700/3746] lr: 7.814e-02, eta: 3 days, 11:04:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4822, loss_cls: 4.4219, loss: 4.4219 +2024-07-17 22:00:04,388 - pyskl - INFO - Epoch [47][1800/3746] lr: 7.812e-02, eta: 3 days, 11:03:45, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4709, loss_cls: 4.4548, loss: 4.4548 +2024-07-17 22:01:24,900 - pyskl - INFO - Epoch [47][1900/3746] lr: 7.810e-02, eta: 3 days, 11:02:35, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4752, loss_cls: 4.4393, loss: 4.4393 +2024-07-17 22:02:45,779 - pyskl - INFO - Epoch [47][2000/3746] lr: 7.808e-02, eta: 3 days, 11:01:27, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4769, loss_cls: 4.4285, loss: 4.4285 +2024-07-17 22:04:06,477 - pyskl - INFO - Epoch [47][2100/3746] lr: 7.805e-02, eta: 3 days, 11:00:17, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4764, loss_cls: 4.4454, loss: 4.4454 +2024-07-17 22:05:27,966 - pyskl - INFO - Epoch [47][2200/3746] lr: 7.803e-02, eta: 3 days, 10:59:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4848, loss_cls: 4.4342, loss: 4.4342 +2024-07-17 22:06:49,867 - pyskl - INFO - Epoch [47][2300/3746] lr: 7.801e-02, eta: 3 days, 10:58:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4789, loss_cls: 4.3953, loss: 4.3953 +2024-07-17 22:08:10,690 - pyskl - INFO - Epoch [47][2400/3746] lr: 7.798e-02, eta: 3 days, 10:56:55, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4777, loss_cls: 4.4290, loss: 4.4290 +2024-07-17 22:09:32,051 - pyskl - INFO - Epoch [47][2500/3746] lr: 7.796e-02, eta: 3 days, 10:55:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4858, loss_cls: 4.4326, loss: 4.4326 +2024-07-17 22:10:52,997 - pyskl - INFO - Epoch [47][2600/3746] lr: 7.794e-02, eta: 3 days, 10:54:38, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4695, loss_cls: 4.4879, loss: 4.4879 +2024-07-17 22:12:14,124 - pyskl - INFO - Epoch [47][2700/3746] lr: 7.791e-02, eta: 3 days, 10:53:30, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4745, loss_cls: 4.4567, loss: 4.4567 +2024-07-17 22:13:34,849 - pyskl - INFO - Epoch [47][2800/3746] lr: 7.789e-02, eta: 3 days, 10:52:21, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4808, loss_cls: 4.4104, loss: 4.4104 +2024-07-17 22:14:55,487 - pyskl - INFO - Epoch [47][2900/3746] lr: 7.787e-02, eta: 3 days, 10:51:11, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4716, loss_cls: 4.4474, loss: 4.4474 +2024-07-17 22:16:16,444 - pyskl - INFO - Epoch [47][3000/3746] lr: 7.784e-02, eta: 3 days, 10:50:02, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4863, loss_cls: 4.4021, loss: 4.4021 +2024-07-17 22:17:37,933 - pyskl - INFO - Epoch [47][3100/3746] lr: 7.782e-02, eta: 3 days, 10:48:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4658, loss_cls: 4.4795, loss: 4.4795 +2024-07-17 22:18:59,595 - pyskl - INFO - Epoch [47][3200/3746] lr: 7.780e-02, eta: 3 days, 10:47:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4688, loss_cls: 4.4727, loss: 4.4727 +2024-07-17 22:20:21,102 - pyskl - INFO - Epoch [47][3300/3746] lr: 7.777e-02, eta: 3 days, 10:46:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4808, loss_cls: 4.4520, loss: 4.4520 +2024-07-17 22:21:42,413 - pyskl - INFO - Epoch [47][3400/3746] lr: 7.775e-02, eta: 3 days, 10:45:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4775, loss_cls: 4.4465, loss: 4.4465 +2024-07-17 22:23:04,189 - pyskl - INFO - Epoch [47][3500/3746] lr: 7.773e-02, eta: 3 days, 10:44:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4834, loss_cls: 4.4004, loss: 4.4004 +2024-07-17 22:24:25,978 - pyskl - INFO - Epoch [47][3600/3746] lr: 7.770e-02, eta: 3 days, 10:43:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4778, loss_cls: 4.4521, loss: 4.4521 +2024-07-17 22:25:48,076 - pyskl - INFO - Epoch [47][3700/3746] lr: 7.768e-02, eta: 3 days, 10:42:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4681, loss_cls: 4.4842, loss: 4.4842 +2024-07-17 22:26:27,811 - pyskl - INFO - Saving checkpoint at 47 epochs +2024-07-17 22:28:19,738 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 22:28:20,394 - pyskl - INFO - +top1_acc 0.1620 +top5_acc 0.3669 +2024-07-17 22:28:20,394 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 22:28:20,433 - pyskl - INFO - +mean_acc 0.1619 +2024-07-17 22:28:20,443 - pyskl - INFO - Epoch(val) [47][309] top1_acc: 0.1620, top5_acc: 0.3669, mean_class_accuracy: 0.1619 +2024-07-17 22:32:07,523 - pyskl - INFO - Epoch [48][100/3746] lr: 7.765e-02, eta: 3 days, 10:44:30, time: 2.271, data_time: 1.297, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4883, loss_cls: 4.3761, loss: 4.3761 +2024-07-17 22:33:29,524 - pyskl - INFO - Epoch [48][200/3746] lr: 7.762e-02, eta: 3 days, 10:43:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4888, loss_cls: 4.3860, loss: 4.3860 +2024-07-17 22:34:51,105 - pyskl - INFO - Epoch [48][300/3746] lr: 7.760e-02, eta: 3 days, 10:42:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4731, loss_cls: 4.4399, loss: 4.4399 +2024-07-17 22:36:12,400 - pyskl - INFO - Epoch [48][400/3746] lr: 7.758e-02, eta: 3 days, 10:41:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4708, loss_cls: 4.4561, loss: 4.4561 +2024-07-17 22:37:34,414 - pyskl - INFO - Epoch [48][500/3746] lr: 7.755e-02, eta: 3 days, 10:40:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4795, loss_cls: 4.4487, loss: 4.4487 +2024-07-17 22:38:56,034 - pyskl - INFO - Epoch [48][600/3746] lr: 7.753e-02, eta: 3 days, 10:38:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4797, loss_cls: 4.4190, loss: 4.4190 +2024-07-17 22:40:18,176 - pyskl - INFO - Epoch [48][700/3746] lr: 7.751e-02, eta: 3 days, 10:37:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4800, loss_cls: 4.4243, loss: 4.4243 +2024-07-17 22:41:39,086 - pyskl - INFO - Epoch [48][800/3746] lr: 7.748e-02, eta: 3 days, 10:36:37, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4805, loss_cls: 4.4159, loss: 4.4159 +2024-07-17 22:43:00,138 - pyskl - INFO - Epoch [48][900/3746] lr: 7.746e-02, eta: 3 days, 10:35:28, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4778, loss_cls: 4.4260, loss: 4.4260 +2024-07-17 22:44:21,098 - pyskl - INFO - Epoch [48][1000/3746] lr: 7.744e-02, eta: 3 days, 10:34:19, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4828, loss_cls: 4.4131, loss: 4.4131 +2024-07-17 22:45:42,947 - pyskl - INFO - Epoch [48][1100/3746] lr: 7.741e-02, eta: 3 days, 10:33:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4747, loss_cls: 4.4397, loss: 4.4397 +2024-07-17 22:47:04,498 - pyskl - INFO - Epoch [48][1200/3746] lr: 7.739e-02, eta: 3 days, 10:32:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4727, loss_cls: 4.4311, loss: 4.4311 +2024-07-17 22:48:25,739 - pyskl - INFO - Epoch [48][1300/3746] lr: 7.737e-02, eta: 3 days, 10:30:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4858, loss_cls: 4.3998, loss: 4.3998 +2024-07-17 22:49:46,989 - pyskl - INFO - Epoch [48][1400/3746] lr: 7.734e-02, eta: 3 days, 10:29:46, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4798, loss_cls: 4.3904, loss: 4.3904 +2024-07-17 22:51:08,341 - pyskl - INFO - Epoch [48][1500/3746] lr: 7.732e-02, eta: 3 days, 10:28:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4830, loss_cls: 4.4242, loss: 4.4242 +2024-07-17 22:52:29,494 - pyskl - INFO - Epoch [48][1600/3746] lr: 7.730e-02, eta: 3 days, 10:27:29, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4783, loss_cls: 4.4150, loss: 4.4150 +2024-07-17 22:53:50,930 - pyskl - INFO - Epoch [48][1700/3746] lr: 7.727e-02, eta: 3 days, 10:26:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4736, loss_cls: 4.4423, loss: 4.4423 +2024-07-17 22:55:12,304 - pyskl - INFO - Epoch [48][1800/3746] lr: 7.725e-02, eta: 3 days, 10:25:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4764, loss_cls: 4.3956, loss: 4.3956 +2024-07-17 22:56:33,147 - pyskl - INFO - Epoch [48][1900/3746] lr: 7.723e-02, eta: 3 days, 10:24:03, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4764, loss_cls: 4.4539, loss: 4.4539 +2024-07-17 22:57:54,135 - pyskl - INFO - Epoch [48][2000/3746] lr: 7.720e-02, eta: 3 days, 10:22:54, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4831, loss_cls: 4.4232, loss: 4.4232 +2024-07-17 22:59:15,666 - pyskl - INFO - Epoch [48][2100/3746] lr: 7.718e-02, eta: 3 days, 10:21:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4922, loss_cls: 4.3687, loss: 4.3687 +2024-07-17 23:00:37,520 - pyskl - INFO - Epoch [48][2200/3746] lr: 7.716e-02, eta: 3 days, 10:20:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4722, loss_cls: 4.4403, loss: 4.4403 +2024-07-17 23:01:59,739 - pyskl - INFO - Epoch [48][2300/3746] lr: 7.713e-02, eta: 3 days, 10:19:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4662, loss_cls: 4.4688, loss: 4.4688 +2024-07-17 23:03:20,991 - pyskl - INFO - Epoch [48][2400/3746] lr: 7.711e-02, eta: 3 days, 10:18:23, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4805, loss_cls: 4.4347, loss: 4.4347 +2024-07-17 23:04:42,366 - pyskl - INFO - Epoch [48][2500/3746] lr: 7.709e-02, eta: 3 days, 10:17:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4831, loss_cls: 4.4000, loss: 4.4000 +2024-07-17 23:06:03,798 - pyskl - INFO - Epoch [48][2600/3746] lr: 7.706e-02, eta: 3 days, 10:16:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4831, loss_cls: 4.4049, loss: 4.4049 +2024-07-17 23:07:25,278 - pyskl - INFO - Epoch [48][2700/3746] lr: 7.704e-02, eta: 3 days, 10:14:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4806, loss_cls: 4.4238, loss: 4.4238 +2024-07-17 23:08:47,190 - pyskl - INFO - Epoch [48][2800/3746] lr: 7.701e-02, eta: 3 days, 10:13:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4800, loss_cls: 4.4014, loss: 4.4014 +2024-07-17 23:10:08,105 - pyskl - INFO - Epoch [48][2900/3746] lr: 7.699e-02, eta: 3 days, 10:12:41, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4764, loss_cls: 4.4392, loss: 4.4392 +2024-07-17 23:11:28,888 - pyskl - INFO - Epoch [48][3000/3746] lr: 7.697e-02, eta: 3 days, 10:11:31, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4803, loss_cls: 4.4179, loss: 4.4179 +2024-07-17 23:12:50,086 - pyskl - INFO - Epoch [48][3100/3746] lr: 7.694e-02, eta: 3 days, 10:10:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4777, loss_cls: 4.4336, loss: 4.4336 +2024-07-17 23:14:11,890 - pyskl - INFO - Epoch [48][3200/3746] lr: 7.692e-02, eta: 3 days, 10:09:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4923, loss_cls: 4.4086, loss: 4.4086 +2024-07-17 23:15:33,040 - pyskl - INFO - Epoch [48][3300/3746] lr: 7.690e-02, eta: 3 days, 10:08:05, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4730, loss_cls: 4.4592, loss: 4.4592 +2024-07-17 23:16:54,012 - pyskl - INFO - Epoch [48][3400/3746] lr: 7.687e-02, eta: 3 days, 10:06:56, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4708, loss_cls: 4.4138, loss: 4.4138 +2024-07-17 23:18:15,743 - pyskl - INFO - Epoch [48][3500/3746] lr: 7.685e-02, eta: 3 days, 10:05:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4905, loss_cls: 4.3706, loss: 4.3706 +2024-07-17 23:19:36,647 - pyskl - INFO - Epoch [48][3600/3746] lr: 7.683e-02, eta: 3 days, 10:04:39, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4716, loss_cls: 4.4543, loss: 4.4543 +2024-07-17 23:20:58,807 - pyskl - INFO - Epoch [48][3700/3746] lr: 7.680e-02, eta: 3 days, 10:03:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4884, loss_cls: 4.3795, loss: 4.3795 +2024-07-17 23:21:38,705 - pyskl - INFO - Saving checkpoint at 48 epochs +2024-07-17 23:23:30,016 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 23:23:30,678 - pyskl - INFO - +top1_acc 0.1765 +top5_acc 0.3962 +2024-07-17 23:23:30,678 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 23:23:30,721 - pyskl - INFO - +mean_acc 0.1765 +2024-07-17 23:23:30,732 - pyskl - INFO - Epoch(val) [48][309] top1_acc: 0.1765, top5_acc: 0.3962, mean_class_accuracy: 0.1765 +2024-07-17 23:27:20,434 - pyskl - INFO - Epoch [49][100/3746] lr: 7.677e-02, eta: 3 days, 10:05:47, time: 2.297, data_time: 1.320, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4895, loss_cls: 4.3760, loss: 4.3760 +2024-07-17 23:28:42,125 - pyskl - INFO - Epoch [49][200/3746] lr: 7.674e-02, eta: 3 days, 10:04:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4842, loss_cls: 4.3882, loss: 4.3882 +2024-07-17 23:30:04,090 - pyskl - INFO - Epoch [49][300/3746] lr: 7.672e-02, eta: 3 days, 10:03:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4839, loss_cls: 4.3823, loss: 4.3823 +2024-07-17 23:31:25,673 - pyskl - INFO - Epoch [49][400/3746] lr: 7.670e-02, eta: 3 days, 10:02:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4820, loss_cls: 4.4307, loss: 4.4307 +2024-07-17 23:32:47,148 - pyskl - INFO - Epoch [49][500/3746] lr: 7.667e-02, eta: 3 days, 10:01:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4808, loss_cls: 4.3906, loss: 4.3906 +2024-07-17 23:34:08,125 - pyskl - INFO - Epoch [49][600/3746] lr: 7.665e-02, eta: 3 days, 10:00:04, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4878, loss_cls: 4.3709, loss: 4.3709 +2024-07-17 23:35:29,823 - pyskl - INFO - Epoch [49][700/3746] lr: 7.663e-02, eta: 3 days, 9:58:56, time: 0.817, data_time: 0.001, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4716, loss_cls: 4.4428, loss: 4.4428 +2024-07-17 23:36:50,841 - pyskl - INFO - Epoch [49][800/3746] lr: 7.660e-02, eta: 3 days, 9:57:46, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4928, loss_cls: 4.3541, loss: 4.3541 +2024-07-17 23:38:11,918 - pyskl - INFO - Epoch [49][900/3746] lr: 7.658e-02, eta: 3 days, 9:56:36, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4916, loss_cls: 4.3793, loss: 4.3793 +2024-07-17 23:39:33,774 - pyskl - INFO - Epoch [49][1000/3746] lr: 7.656e-02, eta: 3 days, 9:55:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4814, loss_cls: 4.4087, loss: 4.4087 +2024-07-17 23:40:55,184 - pyskl - INFO - Epoch [49][1100/3746] lr: 7.653e-02, eta: 3 days, 9:54:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4766, loss_cls: 4.4292, loss: 4.4292 +2024-07-17 23:42:17,150 - pyskl - INFO - Epoch [49][1200/3746] lr: 7.651e-02, eta: 3 days, 9:53:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4844, loss_cls: 4.4053, loss: 4.4053 +2024-07-17 23:43:38,020 - pyskl - INFO - Epoch [49][1300/3746] lr: 7.648e-02, eta: 3 days, 9:52:02, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4853, loss_cls: 4.3892, loss: 4.3892 +2024-07-17 23:44:58,731 - pyskl - INFO - Epoch [49][1400/3746] lr: 7.646e-02, eta: 3 days, 9:50:51, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4841, loss_cls: 4.3767, loss: 4.3767 +2024-07-17 23:46:19,994 - pyskl - INFO - Epoch [49][1500/3746] lr: 7.644e-02, eta: 3 days, 9:49:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4833, loss_cls: 4.4093, loss: 4.4093 +2024-07-17 23:47:41,310 - pyskl - INFO - Epoch [49][1600/3746] lr: 7.641e-02, eta: 3 days, 9:48:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4875, loss_cls: 4.3924, loss: 4.3924 +2024-07-17 23:49:02,090 - pyskl - INFO - Epoch [49][1700/3746] lr: 7.639e-02, eta: 3 days, 9:47:22, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4783, loss_cls: 4.4258, loss: 4.4258 +2024-07-17 23:50:23,106 - pyskl - INFO - Epoch [49][1800/3746] lr: 7.637e-02, eta: 3 days, 9:46:12, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4903, loss_cls: 4.3499, loss: 4.3499 +2024-07-17 23:51:44,815 - pyskl - INFO - Epoch [49][1900/3746] lr: 7.634e-02, eta: 3 days, 9:45:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4892, loss_cls: 4.3748, loss: 4.3748 +2024-07-17 23:53:05,542 - pyskl - INFO - Epoch [49][2000/3746] lr: 7.632e-02, eta: 3 days, 9:43:54, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4817, loss_cls: 4.4428, loss: 4.4428 +2024-07-17 23:54:26,395 - pyskl - INFO - Epoch [49][2100/3746] lr: 7.629e-02, eta: 3 days, 9:42:43, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4820, loss_cls: 4.4290, loss: 4.4290 +2024-07-17 23:55:47,307 - pyskl - INFO - Epoch [49][2200/3746] lr: 7.627e-02, eta: 3 days, 9:41:33, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4891, loss_cls: 4.3763, loss: 4.3763 +2024-07-17 23:57:08,860 - pyskl - INFO - Epoch [49][2300/3746] lr: 7.625e-02, eta: 3 days, 9:40:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4791, loss_cls: 4.3903, loss: 4.3903 +2024-07-17 23:58:29,701 - pyskl - INFO - Epoch [49][2400/3746] lr: 7.622e-02, eta: 3 days, 9:39:14, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4928, loss_cls: 4.3740, loss: 4.3740 +2024-07-17 23:59:52,102 - pyskl - INFO - Epoch [49][2500/3746] lr: 7.620e-02, eta: 3 days, 9:38:07, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4806, loss_cls: 4.4332, loss: 4.4332 +2024-07-18 00:01:13,044 - pyskl - INFO - Epoch [49][2600/3746] lr: 7.618e-02, eta: 3 days, 9:36:57, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4828, loss_cls: 4.4144, loss: 4.4144 +2024-07-18 00:02:33,936 - pyskl - INFO - Epoch [49][2700/3746] lr: 7.615e-02, eta: 3 days, 9:35:47, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4870, loss_cls: 4.3675, loss: 4.3675 +2024-07-18 00:03:54,927 - pyskl - INFO - Epoch [49][2800/3746] lr: 7.613e-02, eta: 3 days, 9:34:37, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4823, loss_cls: 4.4273, loss: 4.4273 +2024-07-18 00:05:15,922 - pyskl - INFO - Epoch [49][2900/3746] lr: 7.610e-02, eta: 3 days, 9:33:27, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4722, loss_cls: 4.4525, loss: 4.4525 +2024-07-18 00:06:36,958 - pyskl - INFO - Epoch [49][3000/3746] lr: 7.608e-02, eta: 3 days, 9:32:17, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4844, loss_cls: 4.4022, loss: 4.4022 +2024-07-18 00:07:58,010 - pyskl - INFO - Epoch [49][3100/3746] lr: 7.606e-02, eta: 3 days, 9:31:07, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4839, loss_cls: 4.4072, loss: 4.4072 +2024-07-18 00:09:18,941 - pyskl - INFO - Epoch [49][3200/3746] lr: 7.603e-02, eta: 3 days, 9:29:57, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4723, loss_cls: 4.4518, loss: 4.4518 +2024-07-18 00:10:39,678 - pyskl - INFO - Epoch [49][3300/3746] lr: 7.601e-02, eta: 3 days, 9:28:46, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4788, loss_cls: 4.4073, loss: 4.4073 +2024-07-18 00:12:00,546 - pyskl - INFO - Epoch [49][3400/3746] lr: 7.598e-02, eta: 3 days, 9:27:36, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4931, loss_cls: 4.3310, loss: 4.3310 +2024-07-18 00:13:22,311 - pyskl - INFO - Epoch [49][3500/3746] lr: 7.596e-02, eta: 3 days, 9:26:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4819, loss_cls: 4.4321, loss: 4.4321 +2024-07-18 00:14:43,707 - pyskl - INFO - Epoch [49][3600/3746] lr: 7.594e-02, eta: 3 days, 9:25:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4689, loss_cls: 4.4617, loss: 4.4617 +2024-07-18 00:16:05,640 - pyskl - INFO - Epoch [49][3700/3746] lr: 7.591e-02, eta: 3 days, 9:24:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4942, loss_cls: 4.3610, loss: 4.3610 +2024-07-18 00:16:45,136 - pyskl - INFO - Saving checkpoint at 49 epochs +2024-07-18 00:18:36,575 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 00:18:37,240 - pyskl - INFO - +top1_acc 0.1760 +top5_acc 0.3984 +2024-07-18 00:18:37,240 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 00:18:37,281 - pyskl - INFO - +mean_acc 0.1758 +2024-07-18 00:18:37,294 - pyskl - INFO - Epoch(val) [49][309] top1_acc: 0.1760, top5_acc: 0.3984, mean_class_accuracy: 0.1758 +2024-07-18 00:22:26,360 - pyskl - INFO - Epoch [50][100/3746] lr: 7.588e-02, eta: 3 days, 9:26:16, time: 2.291, data_time: 1.309, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4928, loss_cls: 4.3746, loss: 4.3746 +2024-07-18 00:23:47,544 - pyskl - INFO - Epoch [50][200/3746] lr: 7.585e-02, eta: 3 days, 9:25:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4927, loss_cls: 4.3434, loss: 4.3434 +2024-07-18 00:25:08,821 - pyskl - INFO - Epoch [50][300/3746] lr: 7.583e-02, eta: 3 days, 9:23:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4941, loss_cls: 4.3577, loss: 4.3577 +2024-07-18 00:26:30,721 - pyskl - INFO - Epoch [50][400/3746] lr: 7.581e-02, eta: 3 days, 9:22:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4880, loss_cls: 4.3946, loss: 4.3946 +2024-07-18 00:27:53,059 - pyskl - INFO - Epoch [50][500/3746] lr: 7.578e-02, eta: 3 days, 9:21:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4864, loss_cls: 4.4033, loss: 4.4033 +2024-07-18 00:29:14,188 - pyskl - INFO - Epoch [50][600/3746] lr: 7.576e-02, eta: 3 days, 9:20:30, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4828, loss_cls: 4.4077, loss: 4.4077 +2024-07-18 00:30:35,592 - pyskl - INFO - Epoch [50][700/3746] lr: 7.573e-02, eta: 3 days, 9:19:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4780, loss_cls: 4.4463, loss: 4.4463 +2024-07-18 00:31:57,327 - pyskl - INFO - Epoch [50][800/3746] lr: 7.571e-02, eta: 3 days, 9:18:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4866, loss_cls: 4.3701, loss: 4.3701 +2024-07-18 00:33:17,929 - pyskl - INFO - Epoch [50][900/3746] lr: 7.569e-02, eta: 3 days, 9:17:00, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4856, loss_cls: 4.4021, loss: 4.4021 +2024-07-18 00:34:39,163 - pyskl - INFO - Epoch [50][1000/3746] lr: 7.566e-02, eta: 3 days, 9:15:50, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4792, loss_cls: 4.3942, loss: 4.3942 +2024-07-18 00:36:00,037 - pyskl - INFO - Epoch [50][1100/3746] lr: 7.564e-02, eta: 3 days, 9:14:39, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4952, loss_cls: 4.3730, loss: 4.3730 +2024-07-18 00:37:21,389 - pyskl - INFO - Epoch [50][1200/3746] lr: 7.561e-02, eta: 3 days, 9:13:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4823, loss_cls: 4.3919, loss: 4.3919 +2024-07-18 00:38:42,812 - pyskl - INFO - Epoch [50][1300/3746] lr: 7.559e-02, eta: 3 days, 9:12:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4922, loss_cls: 4.3785, loss: 4.3785 +2024-07-18 00:40:03,923 - pyskl - INFO - Epoch [50][1400/3746] lr: 7.557e-02, eta: 3 days, 9:11:10, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4827, loss_cls: 4.3898, loss: 4.3898 +2024-07-18 00:41:24,542 - pyskl - INFO - Epoch [50][1500/3746] lr: 7.554e-02, eta: 3 days, 9:09:58, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4897, loss_cls: 4.3814, loss: 4.3814 +2024-07-18 00:42:45,308 - pyskl - INFO - Epoch [50][1600/3746] lr: 7.552e-02, eta: 3 days, 9:08:47, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4877, loss_cls: 4.3737, loss: 4.3737 +2024-07-18 00:44:06,153 - pyskl - INFO - Epoch [50][1700/3746] lr: 7.549e-02, eta: 3 days, 9:07:37, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4806, loss_cls: 4.4036, loss: 4.4036 +2024-07-18 00:45:26,724 - pyskl - INFO - Epoch [50][1800/3746] lr: 7.547e-02, eta: 3 days, 9:06:25, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4711, loss_cls: 4.4410, loss: 4.4410 +2024-07-18 00:46:47,710 - pyskl - INFO - Epoch [50][1900/3746] lr: 7.545e-02, eta: 3 days, 9:05:15, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4856, loss_cls: 4.4283, loss: 4.4283 +2024-07-18 00:48:08,654 - pyskl - INFO - Epoch [50][2000/3746] lr: 7.542e-02, eta: 3 days, 9:04:04, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4909, loss_cls: 4.4105, loss: 4.4105 +2024-07-18 00:49:29,594 - pyskl - INFO - Epoch [50][2100/3746] lr: 7.540e-02, eta: 3 days, 9:02:53, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4891, loss_cls: 4.3804, loss: 4.3804 +2024-07-18 00:50:50,260 - pyskl - INFO - Epoch [50][2200/3746] lr: 7.537e-02, eta: 3 days, 9:01:42, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4841, loss_cls: 4.3817, loss: 4.3817 +2024-07-18 00:52:12,232 - pyskl - INFO - Epoch [50][2300/3746] lr: 7.535e-02, eta: 3 days, 9:00:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4770, loss_cls: 4.4193, loss: 4.4193 +2024-07-18 00:53:33,444 - pyskl - INFO - Epoch [50][2400/3746] lr: 7.533e-02, eta: 3 days, 8:59:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4870, loss_cls: 4.4069, loss: 4.4069 +2024-07-18 00:54:55,661 - pyskl - INFO - Epoch [50][2500/3746] lr: 7.530e-02, eta: 3 days, 8:58:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4913, loss_cls: 4.3640, loss: 4.3640 +2024-07-18 00:56:16,885 - pyskl - INFO - Epoch [50][2600/3746] lr: 7.528e-02, eta: 3 days, 8:57:05, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4875, loss_cls: 4.3835, loss: 4.3835 +2024-07-18 00:57:38,720 - pyskl - INFO - Epoch [50][2700/3746] lr: 7.525e-02, eta: 3 days, 8:55:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4919, loss_cls: 4.3477, loss: 4.3477 +2024-07-18 00:59:00,245 - pyskl - INFO - Epoch [50][2800/3746] lr: 7.523e-02, eta: 3 days, 8:54:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4856, loss_cls: 4.3760, loss: 4.3760 +2024-07-18 01:00:21,807 - pyskl - INFO - Epoch [50][2900/3746] lr: 7.520e-02, eta: 3 days, 8:53:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4784, loss_cls: 4.4242, loss: 4.4242 +2024-07-18 01:01:42,878 - pyskl - INFO - Epoch [50][3000/3746] lr: 7.518e-02, eta: 3 days, 8:52:27, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4809, loss_cls: 4.4142, loss: 4.4142 +2024-07-18 01:03:03,648 - pyskl - INFO - Epoch [50][3100/3746] lr: 7.516e-02, eta: 3 days, 8:51:15, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.4863, loss_cls: 4.3610, loss: 4.3610 +2024-07-18 01:04:24,746 - pyskl - INFO - Epoch [50][3200/3746] lr: 7.513e-02, eta: 3 days, 8:50:05, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4875, loss_cls: 4.3902, loss: 4.3902 +2024-07-18 01:05:45,972 - pyskl - INFO - Epoch [50][3300/3746] lr: 7.511e-02, eta: 3 days, 8:48:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4930, loss_cls: 4.3556, loss: 4.3556 +2024-07-18 01:07:07,737 - pyskl - INFO - Epoch [50][3400/3746] lr: 7.508e-02, eta: 3 days, 8:47:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4930, loss_cls: 4.3756, loss: 4.3756 +2024-07-18 01:08:29,095 - pyskl - INFO - Epoch [50][3500/3746] lr: 7.506e-02, eta: 3 days, 8:46:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4861, loss_cls: 4.3917, loss: 4.3917 +2024-07-18 01:09:50,562 - pyskl - INFO - Epoch [50][3600/3746] lr: 7.504e-02, eta: 3 days, 8:45:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4883, loss_cls: 4.3788, loss: 4.3788 +2024-07-18 01:11:12,455 - pyskl - INFO - Epoch [50][3700/3746] lr: 7.501e-02, eta: 3 days, 8:44:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4852, loss_cls: 4.3919, loss: 4.3919 +2024-07-18 01:11:52,698 - pyskl - INFO - Saving checkpoint at 50 epochs +2024-07-18 01:13:45,008 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 01:13:45,762 - pyskl - INFO - +top1_acc 0.1766 +top5_acc 0.3995 +2024-07-18 01:13:45,762 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 01:13:45,810 - pyskl - INFO - +mean_acc 0.1764 +2024-07-18 01:13:45,824 - pyskl - INFO - Epoch(val) [50][309] top1_acc: 0.1766, top5_acc: 0.3995, mean_class_accuracy: 0.1764 +2024-07-18 01:17:36,776 - pyskl - INFO - Epoch [51][100/3746] lr: 7.498e-02, eta: 3 days, 8:46:19, time: 2.309, data_time: 1.324, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4848, loss_cls: 4.3856, loss: 4.3856 +2024-07-18 01:18:58,284 - pyskl - INFO - Epoch [51][200/3746] lr: 7.495e-02, eta: 3 days, 8:45:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5019, loss_cls: 4.3139, loss: 4.3139 +2024-07-18 01:20:19,661 - pyskl - INFO - Epoch [51][300/3746] lr: 7.493e-02, eta: 3 days, 8:43:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4859, loss_cls: 4.3959, loss: 4.3959 +2024-07-18 01:21:40,498 - pyskl - INFO - Epoch [51][400/3746] lr: 7.490e-02, eta: 3 days, 8:42:47, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4920, loss_cls: 4.3480, loss: 4.3480 +2024-07-18 01:23:01,628 - pyskl - INFO - Epoch [51][500/3746] lr: 7.488e-02, eta: 3 days, 8:41:37, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4956, loss_cls: 4.3492, loss: 4.3492 +2024-07-18 01:24:22,998 - pyskl - INFO - Epoch [51][600/3746] lr: 7.485e-02, eta: 3 days, 8:40:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4955, loss_cls: 4.3201, loss: 4.3201 +2024-07-18 01:25:44,637 - pyskl - INFO - Epoch [51][700/3746] lr: 7.483e-02, eta: 3 days, 8:39:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4881, loss_cls: 4.3806, loss: 4.3806 +2024-07-18 01:27:06,627 - pyskl - INFO - Epoch [51][800/3746] lr: 7.481e-02, eta: 3 days, 8:38:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.4995, loss_cls: 4.3077, loss: 4.3077 +2024-07-18 01:28:28,223 - pyskl - INFO - Epoch [51][900/3746] lr: 7.478e-02, eta: 3 days, 8:36:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4981, loss_cls: 4.3445, loss: 4.3445 +2024-07-18 01:29:49,268 - pyskl - INFO - Epoch [51][1000/3746] lr: 7.476e-02, eta: 3 days, 8:35:47, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.5017, loss_cls: 4.3324, loss: 4.3324 +2024-07-18 01:31:10,439 - pyskl - INFO - Epoch [51][1100/3746] lr: 7.473e-02, eta: 3 days, 8:34:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4859, loss_cls: 4.3755, loss: 4.3755 +2024-07-18 01:32:31,644 - pyskl - INFO - Epoch [51][1200/3746] lr: 7.471e-02, eta: 3 days, 8:33:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4855, loss_cls: 4.3957, loss: 4.3957 +2024-07-18 01:33:53,577 - pyskl - INFO - Epoch [51][1300/3746] lr: 7.468e-02, eta: 3 days, 8:32:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.4973, loss_cls: 4.3561, loss: 4.3561 +2024-07-18 01:35:14,740 - pyskl - INFO - Epoch [51][1400/3746] lr: 7.466e-02, eta: 3 days, 8:31:05, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4916, loss_cls: 4.3533, loss: 4.3533 +2024-07-18 01:36:36,183 - pyskl - INFO - Epoch [51][1500/3746] lr: 7.464e-02, eta: 3 days, 8:29:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4853, loss_cls: 4.3745, loss: 4.3745 +2024-07-18 01:37:57,788 - pyskl - INFO - Epoch [51][1600/3746] lr: 7.461e-02, eta: 3 days, 8:28:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4930, loss_cls: 4.3618, loss: 4.3618 +2024-07-18 01:39:18,496 - pyskl - INFO - Epoch [51][1700/3746] lr: 7.459e-02, eta: 3 days, 8:27:34, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4748, loss_cls: 4.3836, loss: 4.3836 +2024-07-18 01:40:39,851 - pyskl - INFO - Epoch [51][1800/3746] lr: 7.456e-02, eta: 3 days, 8:26:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4964, loss_cls: 4.3420, loss: 4.3420 +2024-07-18 01:42:01,433 - pyskl - INFO - Epoch [51][1900/3746] lr: 7.454e-02, eta: 3 days, 8:25:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4795, loss_cls: 4.4057, loss: 4.4057 +2024-07-18 01:43:22,607 - pyskl - INFO - Epoch [51][2000/3746] lr: 7.451e-02, eta: 3 days, 8:24:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4892, loss_cls: 4.3849, loss: 4.3849 +2024-07-18 01:44:44,004 - pyskl - INFO - Epoch [51][2100/3746] lr: 7.449e-02, eta: 3 days, 8:22:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4877, loss_cls: 4.3836, loss: 4.3836 +2024-07-18 01:46:05,666 - pyskl - INFO - Epoch [51][2200/3746] lr: 7.447e-02, eta: 3 days, 8:21:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4941, loss_cls: 4.3568, loss: 4.3568 +2024-07-18 01:47:27,952 - pyskl - INFO - Epoch [51][2300/3746] lr: 7.444e-02, eta: 3 days, 8:20:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4788, loss_cls: 4.4147, loss: 4.4147 +2024-07-18 01:48:48,926 - pyskl - INFO - Epoch [51][2400/3746] lr: 7.442e-02, eta: 3 days, 8:19:22, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4894, loss_cls: 4.3601, loss: 4.3601 +2024-07-18 01:50:10,139 - pyskl - INFO - Epoch [51][2500/3746] lr: 7.439e-02, eta: 3 days, 8:18:11, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4784, loss_cls: 4.3954, loss: 4.3954 +2024-07-18 01:51:31,654 - pyskl - INFO - Epoch [51][2600/3746] lr: 7.437e-02, eta: 3 days, 8:17:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.4955, loss_cls: 4.3473, loss: 4.3473 +2024-07-18 01:52:52,963 - pyskl - INFO - Epoch [51][2700/3746] lr: 7.434e-02, eta: 3 days, 8:15:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4783, loss_cls: 4.3880, loss: 4.3880 +2024-07-18 01:54:14,357 - pyskl - INFO - Epoch [51][2800/3746] lr: 7.432e-02, eta: 3 days, 8:14:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4755, loss_cls: 4.4400, loss: 4.4400 +2024-07-18 01:55:35,408 - pyskl - INFO - Epoch [51][2900/3746] lr: 7.429e-02, eta: 3 days, 8:13:29, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4752, loss_cls: 4.4326, loss: 4.4326 +2024-07-18 01:56:56,361 - pyskl - INFO - Epoch [51][3000/3746] lr: 7.427e-02, eta: 3 days, 8:12:18, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4909, loss_cls: 4.3721, loss: 4.3721 +2024-07-18 01:58:17,163 - pyskl - INFO - Epoch [51][3100/3746] lr: 7.425e-02, eta: 3 days, 8:11:06, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4913, loss_cls: 4.3868, loss: 4.3868 +2024-07-18 01:59:38,439 - pyskl - INFO - Epoch [51][3200/3746] lr: 7.422e-02, eta: 3 days, 8:09:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4814, loss_cls: 4.4165, loss: 4.4165 +2024-07-18 02:01:00,221 - pyskl - INFO - Epoch [51][3300/3746] lr: 7.420e-02, eta: 3 days, 8:08:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4852, loss_cls: 4.3905, loss: 4.3905 +2024-07-18 02:02:21,780 - pyskl - INFO - Epoch [51][3400/3746] lr: 7.417e-02, eta: 3 days, 8:07:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4786, loss_cls: 4.4050, loss: 4.4050 +2024-07-18 02:03:43,503 - pyskl - INFO - Epoch [51][3500/3746] lr: 7.415e-02, eta: 3 days, 8:06:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4817, loss_cls: 4.4195, loss: 4.4195 +2024-07-18 02:05:04,352 - pyskl - INFO - Epoch [51][3600/3746] lr: 7.412e-02, eta: 3 days, 8:05:14, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4875, loss_cls: 4.3874, loss: 4.3874 +2024-07-18 02:06:25,688 - pyskl - INFO - Epoch [51][3700/3746] lr: 7.410e-02, eta: 3 days, 8:04:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4805, loss_cls: 4.3997, loss: 4.3997 +2024-07-18 02:07:05,835 - pyskl - INFO - Saving checkpoint at 51 epochs +2024-07-18 02:08:56,609 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 02:08:57,268 - pyskl - INFO - +top1_acc 0.1776 +top5_acc 0.3947 +2024-07-18 02:08:57,268 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 02:08:57,312 - pyskl - INFO - +mean_acc 0.1774 +2024-07-18 02:08:57,324 - pyskl - INFO - Epoch(val) [51][309] top1_acc: 0.1776, top5_acc: 0.3947, mean_class_accuracy: 0.1774 +2024-07-18 02:12:50,264 - pyskl - INFO - Epoch [52][100/3746] lr: 7.406e-02, eta: 3 days, 8:06:01, time: 2.329, data_time: 1.347, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4913, loss_cls: 4.3510, loss: 4.3510 +2024-07-18 02:14:12,119 - pyskl - INFO - Epoch [52][200/3746] lr: 7.404e-02, eta: 3 days, 8:04:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4909, loss_cls: 4.3602, loss: 4.3602 +2024-07-18 02:15:32,905 - pyskl - INFO - Epoch [52][300/3746] lr: 7.401e-02, eta: 3 days, 8:03:39, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4998, loss_cls: 4.3173, loss: 4.3173 +2024-07-18 02:16:54,073 - pyskl - INFO - Epoch [52][400/3746] lr: 7.399e-02, eta: 3 days, 8:02:28, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4973, loss_cls: 4.3117, loss: 4.3117 +2024-07-18 02:18:15,485 - pyskl - INFO - Epoch [52][500/3746] lr: 7.397e-02, eta: 3 days, 8:01:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4922, loss_cls: 4.3620, loss: 4.3620 +2024-07-18 02:19:37,843 - pyskl - INFO - Epoch [52][600/3746] lr: 7.394e-02, eta: 3 days, 8:00:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5011, loss_cls: 4.2906, loss: 4.2906 +2024-07-18 02:20:58,728 - pyskl - INFO - Epoch [52][700/3746] lr: 7.392e-02, eta: 3 days, 7:58:57, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4853, loss_cls: 4.3824, loss: 4.3824 +2024-07-18 02:22:20,620 - pyskl - INFO - Epoch [52][800/3746] lr: 7.389e-02, eta: 3 days, 7:57:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4959, loss_cls: 4.3391, loss: 4.3391 +2024-07-18 02:23:41,958 - pyskl - INFO - Epoch [52][900/3746] lr: 7.387e-02, eta: 3 days, 7:56:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4931, loss_cls: 4.3368, loss: 4.3368 +2024-07-18 02:25:04,003 - pyskl - INFO - Epoch [52][1000/3746] lr: 7.384e-02, eta: 3 days, 7:55:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4836, loss_cls: 4.3736, loss: 4.3736 +2024-07-18 02:26:25,457 - pyskl - INFO - Epoch [52][1100/3746] lr: 7.382e-02, eta: 3 days, 7:54:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4864, loss_cls: 4.3771, loss: 4.3771 +2024-07-18 02:27:46,511 - pyskl - INFO - Epoch [52][1200/3746] lr: 7.379e-02, eta: 3 days, 7:53:04, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4884, loss_cls: 4.4075, loss: 4.4075 +2024-07-18 02:29:07,322 - pyskl - INFO - Epoch [52][1300/3746] lr: 7.377e-02, eta: 3 days, 7:51:52, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4945, loss_cls: 4.3318, loss: 4.3318 +2024-07-18 02:30:28,187 - pyskl - INFO - Epoch [52][1400/3746] lr: 7.374e-02, eta: 3 days, 7:50:40, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4888, loss_cls: 4.3805, loss: 4.3805 +2024-07-18 02:31:49,172 - pyskl - INFO - Epoch [52][1500/3746] lr: 7.372e-02, eta: 3 days, 7:49:28, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4830, loss_cls: 4.4055, loss: 4.4055 +2024-07-18 02:33:10,095 - pyskl - INFO - Epoch [52][1600/3746] lr: 7.370e-02, eta: 3 days, 7:48:17, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4869, loss_cls: 4.3842, loss: 4.3842 +2024-07-18 02:34:31,679 - pyskl - INFO - Epoch [52][1700/3746] lr: 7.367e-02, eta: 3 days, 7:47:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4969, loss_cls: 4.3471, loss: 4.3471 +2024-07-18 02:35:52,604 - pyskl - INFO - Epoch [52][1800/3746] lr: 7.365e-02, eta: 3 days, 7:45:54, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4880, loss_cls: 4.4160, loss: 4.4160 +2024-07-18 02:37:13,685 - pyskl - INFO - Epoch [52][1900/3746] lr: 7.362e-02, eta: 3 days, 7:44:43, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4981, loss_cls: 4.3424, loss: 4.3424 +2024-07-18 02:38:34,867 - pyskl - INFO - Epoch [52][2000/3746] lr: 7.360e-02, eta: 3 days, 7:43:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4864, loss_cls: 4.3654, loss: 4.3654 +2024-07-18 02:39:55,961 - pyskl - INFO - Epoch [52][2100/3746] lr: 7.357e-02, eta: 3 days, 7:42:20, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4995, loss_cls: 4.3354, loss: 4.3354 +2024-07-18 02:41:17,190 - pyskl - INFO - Epoch [52][2200/3746] lr: 7.355e-02, eta: 3 days, 7:41:08, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4933, loss_cls: 4.3598, loss: 4.3598 +2024-07-18 02:42:38,281 - pyskl - INFO - Epoch [52][2300/3746] lr: 7.352e-02, eta: 3 days, 7:39:57, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4905, loss_cls: 4.3307, loss: 4.3307 +2024-07-18 02:43:59,612 - pyskl - INFO - Epoch [52][2400/3746] lr: 7.350e-02, eta: 3 days, 7:38:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4986, loss_cls: 4.3507, loss: 4.3507 +2024-07-18 02:45:21,670 - pyskl - INFO - Epoch [52][2500/3746] lr: 7.347e-02, eta: 3 days, 7:37:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4825, loss_cls: 4.3947, loss: 4.3947 +2024-07-18 02:46:43,234 - pyskl - INFO - Epoch [52][2600/3746] lr: 7.345e-02, eta: 3 days, 7:36:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4930, loss_cls: 4.3709, loss: 4.3709 +2024-07-18 02:48:04,400 - pyskl - INFO - Epoch [52][2700/3746] lr: 7.342e-02, eta: 3 days, 7:35:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4866, loss_cls: 4.3907, loss: 4.3907 +2024-07-18 02:49:25,758 - pyskl - INFO - Epoch [52][2800/3746] lr: 7.340e-02, eta: 3 days, 7:34:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4816, loss_cls: 4.4077, loss: 4.4077 +2024-07-18 02:50:46,540 - pyskl - INFO - Epoch [52][2900/3746] lr: 7.337e-02, eta: 3 days, 7:32:50, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4927, loss_cls: 4.3381, loss: 4.3381 +2024-07-18 02:52:07,952 - pyskl - INFO - Epoch [52][3000/3746] lr: 7.335e-02, eta: 3 days, 7:31:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4898, loss_cls: 4.3712, loss: 4.3712 +2024-07-18 02:53:29,575 - pyskl - INFO - Epoch [52][3100/3746] lr: 7.332e-02, eta: 3 days, 7:30:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4852, loss_cls: 4.3476, loss: 4.3476 +2024-07-18 02:54:51,573 - pyskl - INFO - Epoch [52][3200/3746] lr: 7.330e-02, eta: 3 days, 7:29:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4973, loss_cls: 4.3299, loss: 4.3299 +2024-07-18 02:56:12,686 - pyskl - INFO - Epoch [52][3300/3746] lr: 7.328e-02, eta: 3 days, 7:28:07, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4906, loss_cls: 4.3439, loss: 4.3439 +2024-07-18 02:57:33,651 - pyskl - INFO - Epoch [52][3400/3746] lr: 7.325e-02, eta: 3 days, 7:26:55, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4900, loss_cls: 4.3710, loss: 4.3710 +2024-07-18 02:58:55,507 - pyskl - INFO - Epoch [52][3500/3746] lr: 7.323e-02, eta: 3 days, 7:25:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4892, loss_cls: 4.3520, loss: 4.3520 +2024-07-18 03:00:16,581 - pyskl - INFO - Epoch [52][3600/3746] lr: 7.320e-02, eta: 3 days, 7:24:33, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4817, loss_cls: 4.3946, loss: 4.3946 +2024-07-18 03:01:38,480 - pyskl - INFO - Epoch [52][3700/3746] lr: 7.318e-02, eta: 3 days, 7:23:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4820, loss_cls: 4.3948, loss: 4.3948 +2024-07-18 03:02:17,751 - pyskl - INFO - Saving checkpoint at 52 epochs +2024-07-18 03:04:09,163 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 03:04:09,824 - pyskl - INFO - +top1_acc 0.1762 +top5_acc 0.4033 +2024-07-18 03:04:09,825 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 03:04:09,863 - pyskl - INFO - +mean_acc 0.1760 +2024-07-18 03:04:09,873 - pyskl - INFO - Epoch(val) [52][309] top1_acc: 0.1762, top5_acc: 0.4033, mean_class_accuracy: 0.1760 +2024-07-18 03:07:57,559 - pyskl - INFO - Epoch [53][100/3746] lr: 7.314e-02, eta: 3 days, 7:25:04, time: 2.277, data_time: 1.284, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5005, loss_cls: 4.3093, loss: 4.3093 +2024-07-18 03:09:19,578 - pyskl - INFO - Epoch [53][200/3746] lr: 7.312e-02, eta: 3 days, 7:23:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4850, loss_cls: 4.3760, loss: 4.3760 +2024-07-18 03:10:40,834 - pyskl - INFO - Epoch [53][300/3746] lr: 7.309e-02, eta: 3 days, 7:22:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4902, loss_cls: 4.3434, loss: 4.3434 +2024-07-18 03:12:02,499 - pyskl - INFO - Epoch [53][400/3746] lr: 7.307e-02, eta: 3 days, 7:21:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4917, loss_cls: 4.3709, loss: 4.3709 +2024-07-18 03:13:23,749 - pyskl - INFO - Epoch [53][500/3746] lr: 7.304e-02, eta: 3 days, 7:20:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4911, loss_cls: 4.3519, loss: 4.3519 +2024-07-18 03:14:45,512 - pyskl - INFO - Epoch [53][600/3746] lr: 7.302e-02, eta: 3 days, 7:19:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4958, loss_cls: 4.3428, loss: 4.3428 +2024-07-18 03:16:06,803 - pyskl - INFO - Epoch [53][700/3746] lr: 7.299e-02, eta: 3 days, 7:17:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4703, loss_cls: 4.4125, loss: 4.4125 +2024-07-18 03:17:27,841 - pyskl - INFO - Epoch [53][800/3746] lr: 7.297e-02, eta: 3 days, 7:16:46, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.5025, loss_cls: 4.3337, loss: 4.3337 +2024-07-18 03:18:50,113 - pyskl - INFO - Epoch [53][900/3746] lr: 7.294e-02, eta: 3 days, 7:15:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4838, loss_cls: 4.3917, loss: 4.3917 +2024-07-18 03:20:11,258 - pyskl - INFO - Epoch [53][1000/3746] lr: 7.292e-02, eta: 3 days, 7:14:24, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.4981, loss_cls: 4.3127, loss: 4.3127 +2024-07-18 03:21:32,099 - pyskl - INFO - Epoch [53][1100/3746] lr: 7.289e-02, eta: 3 days, 7:13:11, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4923, loss_cls: 4.3261, loss: 4.3261 +2024-07-18 03:22:53,361 - pyskl - INFO - Epoch [53][1200/3746] lr: 7.287e-02, eta: 3 days, 7:12:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4791, loss_cls: 4.4021, loss: 4.4021 +2024-07-18 03:24:14,091 - pyskl - INFO - Epoch [53][1300/3746] lr: 7.284e-02, eta: 3 days, 7:10:47, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.5025, loss_cls: 4.3421, loss: 4.3421 +2024-07-18 03:25:35,215 - pyskl - INFO - Epoch [53][1400/3746] lr: 7.282e-02, eta: 3 days, 7:09:35, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4903, loss_cls: 4.3464, loss: 4.3464 +2024-07-18 03:26:56,356 - pyskl - INFO - Epoch [53][1500/3746] lr: 7.279e-02, eta: 3 days, 7:08:23, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4947, loss_cls: 4.3469, loss: 4.3469 +2024-07-18 03:28:17,528 - pyskl - INFO - Epoch [53][1600/3746] lr: 7.277e-02, eta: 3 days, 7:07:11, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4888, loss_cls: 4.3620, loss: 4.3620 +2024-07-18 03:29:39,259 - pyskl - INFO - Epoch [53][1700/3746] lr: 7.274e-02, eta: 3 days, 7:06:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4894, loss_cls: 4.3269, loss: 4.3269 +2024-07-18 03:31:00,377 - pyskl - INFO - Epoch [53][1800/3746] lr: 7.272e-02, eta: 3 days, 7:04:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5009, loss_cls: 4.2933, loss: 4.2933 +2024-07-18 03:32:21,875 - pyskl - INFO - Epoch [53][1900/3746] lr: 7.269e-02, eta: 3 days, 7:03:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4872, loss_cls: 4.3980, loss: 4.3980 +2024-07-18 03:33:43,017 - pyskl - INFO - Epoch [53][2000/3746] lr: 7.267e-02, eta: 3 days, 7:02:25, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5036, loss_cls: 4.3298, loss: 4.3298 +2024-07-18 03:35:03,978 - pyskl - INFO - Epoch [53][2100/3746] lr: 7.264e-02, eta: 3 days, 7:01:13, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4856, loss_cls: 4.3433, loss: 4.3433 +2024-07-18 03:36:24,889 - pyskl - INFO - Epoch [53][2200/3746] lr: 7.262e-02, eta: 3 days, 7:00:00, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4936, loss_cls: 4.3366, loss: 4.3366 +2024-07-18 03:37:45,955 - pyskl - INFO - Epoch [53][2300/3746] lr: 7.259e-02, eta: 3 days, 6:58:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4944, loss_cls: 4.3406, loss: 4.3406 +2024-07-18 03:39:07,862 - pyskl - INFO - Epoch [53][2400/3746] lr: 7.257e-02, eta: 3 days, 6:57:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4975, loss_cls: 4.3205, loss: 4.3205 +2024-07-18 03:40:29,236 - pyskl - INFO - Epoch [53][2500/3746] lr: 7.254e-02, eta: 3 days, 6:56:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4966, loss_cls: 4.3353, loss: 4.3353 +2024-07-18 03:41:50,781 - pyskl - INFO - Epoch [53][2600/3746] lr: 7.252e-02, eta: 3 days, 6:55:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4867, loss_cls: 4.3573, loss: 4.3573 +2024-07-18 03:43:12,268 - pyskl - INFO - Epoch [53][2700/3746] lr: 7.249e-02, eta: 3 days, 6:54:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4953, loss_cls: 4.3544, loss: 4.3544 +2024-07-18 03:44:33,836 - pyskl - INFO - Epoch [53][2800/3746] lr: 7.247e-02, eta: 3 days, 6:52:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4867, loss_cls: 4.3593, loss: 4.3593 +2024-07-18 03:45:54,863 - pyskl - INFO - Epoch [53][2900/3746] lr: 7.244e-02, eta: 3 days, 6:51:40, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4894, loss_cls: 4.3497, loss: 4.3497 +2024-07-18 03:47:15,691 - pyskl - INFO - Epoch [53][3000/3746] lr: 7.242e-02, eta: 3 days, 6:50:27, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4889, loss_cls: 4.3561, loss: 4.3561 +2024-07-18 03:48:37,045 - pyskl - INFO - Epoch [53][3100/3746] lr: 7.239e-02, eta: 3 days, 6:49:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4997, loss_cls: 4.3270, loss: 4.3270 +2024-07-18 03:49:58,562 - pyskl - INFO - Epoch [53][3200/3746] lr: 7.237e-02, eta: 3 days, 6:48:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4911, loss_cls: 4.3627, loss: 4.3627 +2024-07-18 03:51:19,919 - pyskl - INFO - Epoch [53][3300/3746] lr: 7.234e-02, eta: 3 days, 6:46:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4892, loss_cls: 4.3336, loss: 4.3336 +2024-07-18 03:52:41,062 - pyskl - INFO - Epoch [53][3400/3746] lr: 7.232e-02, eta: 3 days, 6:45:40, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4913, loss_cls: 4.3777, loss: 4.3777 +2024-07-18 03:54:03,020 - pyskl - INFO - Epoch [53][3500/3746] lr: 7.229e-02, eta: 3 days, 6:44:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5020, loss_cls: 4.3320, loss: 4.3320 +2024-07-18 03:55:25,368 - pyskl - INFO - Epoch [53][3600/3746] lr: 7.227e-02, eta: 3 days, 6:43:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4952, loss_cls: 4.3548, loss: 4.3548 +2024-07-18 03:56:46,925 - pyskl - INFO - Epoch [53][3700/3746] lr: 7.224e-02, eta: 3 days, 6:42:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4794, loss_cls: 4.3900, loss: 4.3900 +2024-07-18 03:57:27,274 - pyskl - INFO - Saving checkpoint at 53 epochs +2024-07-18 03:59:17,927 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 03:59:18,590 - pyskl - INFO - +top1_acc 0.1739 +top5_acc 0.3939 +2024-07-18 03:59:18,590 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 03:59:18,628 - pyskl - INFO - +mean_acc 0.1738 +2024-07-18 03:59:18,638 - pyskl - INFO - Epoch(val) [53][309] top1_acc: 0.1739, top5_acc: 0.3939, mean_class_accuracy: 0.1738 +2024-07-18 04:03:05,114 - pyskl - INFO - Epoch [54][100/3746] lr: 7.221e-02, eta: 3 days, 6:43:40, time: 2.265, data_time: 1.282, memory: 15990, top1_acc: 0.2598, top5_acc: 0.4983, loss_cls: 4.3197, loss: 4.3197 +2024-07-18 04:04:27,465 - pyskl - INFO - Epoch [54][200/3746] lr: 7.218e-02, eta: 3 days, 6:42:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4963, loss_cls: 4.3197, loss: 4.3197 +2024-07-18 04:05:48,520 - pyskl - INFO - Epoch [54][300/3746] lr: 7.216e-02, eta: 3 days, 6:41:18, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4983, loss_cls: 4.3187, loss: 4.3187 +2024-07-18 04:07:09,736 - pyskl - INFO - Epoch [54][400/3746] lr: 7.213e-02, eta: 3 days, 6:40:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4947, loss_cls: 4.3175, loss: 4.3175 +2024-07-18 04:08:30,647 - pyskl - INFO - Epoch [54][500/3746] lr: 7.211e-02, eta: 3 days, 6:38:53, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4888, loss_cls: 4.3643, loss: 4.3643 +2024-07-18 04:09:51,666 - pyskl - INFO - Epoch [54][600/3746] lr: 7.208e-02, eta: 3 days, 6:37:40, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5028, loss_cls: 4.3121, loss: 4.3121 +2024-07-18 04:11:13,287 - pyskl - INFO - Epoch [54][700/3746] lr: 7.206e-02, eta: 3 days, 6:36:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4961, loss_cls: 4.3108, loss: 4.3108 +2024-07-18 04:12:34,596 - pyskl - INFO - Epoch [54][800/3746] lr: 7.203e-02, eta: 3 days, 6:35:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5023, loss_cls: 4.3207, loss: 4.3207 +2024-07-18 04:13:56,304 - pyskl - INFO - Epoch [54][900/3746] lr: 7.201e-02, eta: 3 days, 6:34:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4917, loss_cls: 4.3485, loss: 4.3485 +2024-07-18 04:15:17,141 - pyskl - INFO - Epoch [54][1000/3746] lr: 7.198e-02, eta: 3 days, 6:32:52, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4919, loss_cls: 4.3550, loss: 4.3550 +2024-07-18 04:16:38,614 - pyskl - INFO - Epoch [54][1100/3746] lr: 7.196e-02, eta: 3 days, 6:31:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5052, loss_cls: 4.3406, loss: 4.3406 +2024-07-18 04:17:59,841 - pyskl - INFO - Epoch [54][1200/3746] lr: 7.193e-02, eta: 3 days, 6:30:28, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4925, loss_cls: 4.3194, loss: 4.3194 +2024-07-18 04:19:20,885 - pyskl - INFO - Epoch [54][1300/3746] lr: 7.191e-02, eta: 3 days, 6:29:16, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4958, loss_cls: 4.3451, loss: 4.3451 +2024-07-18 04:20:42,102 - pyskl - INFO - Epoch [54][1400/3746] lr: 7.188e-02, eta: 3 days, 6:28:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4983, loss_cls: 4.3280, loss: 4.3280 +2024-07-18 04:22:03,498 - pyskl - INFO - Epoch [54][1500/3746] lr: 7.186e-02, eta: 3 days, 6:26:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.5022, loss_cls: 4.3168, loss: 4.3168 +2024-07-18 04:23:24,660 - pyskl - INFO - Epoch [54][1600/3746] lr: 7.183e-02, eta: 3 days, 6:25:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4948, loss_cls: 4.3573, loss: 4.3573 +2024-07-18 04:24:46,565 - pyskl - INFO - Epoch [54][1700/3746] lr: 7.181e-02, eta: 3 days, 6:24:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4847, loss_cls: 4.3566, loss: 4.3566 +2024-07-18 04:26:07,573 - pyskl - INFO - Epoch [54][1800/3746] lr: 7.178e-02, eta: 3 days, 6:23:15, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4975, loss_cls: 4.3507, loss: 4.3507 +2024-07-18 04:27:28,849 - pyskl - INFO - Epoch [54][1900/3746] lr: 7.176e-02, eta: 3 days, 6:22:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4844, loss_cls: 4.3948, loss: 4.3948 +2024-07-18 04:28:50,177 - pyskl - INFO - Epoch [54][2000/3746] lr: 7.173e-02, eta: 3 days, 6:20:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5027, loss_cls: 4.3119, loss: 4.3119 +2024-07-18 04:30:11,907 - pyskl - INFO - Epoch [54][2100/3746] lr: 7.170e-02, eta: 3 days, 6:19:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4916, loss_cls: 4.3594, loss: 4.3594 +2024-07-18 04:31:32,896 - pyskl - INFO - Epoch [54][2200/3746] lr: 7.168e-02, eta: 3 days, 6:18:27, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4959, loss_cls: 4.3566, loss: 4.3566 +2024-07-18 04:32:54,208 - pyskl - INFO - Epoch [54][2300/3746] lr: 7.165e-02, eta: 3 days, 6:17:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4922, loss_cls: 4.3291, loss: 4.3291 +2024-07-18 04:34:15,918 - pyskl - INFO - Epoch [54][2400/3746] lr: 7.163e-02, eta: 3 days, 6:16:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4917, loss_cls: 4.3525, loss: 4.3525 +2024-07-18 04:35:36,932 - pyskl - INFO - Epoch [54][2500/3746] lr: 7.160e-02, eta: 3 days, 6:14:50, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4941, loss_cls: 4.3401, loss: 4.3401 +2024-07-18 04:36:58,232 - pyskl - INFO - Epoch [54][2600/3746] lr: 7.158e-02, eta: 3 days, 6:13:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4986, loss_cls: 4.2872, loss: 4.2872 +2024-07-18 04:38:19,095 - pyskl - INFO - Epoch [54][2700/3746] lr: 7.155e-02, eta: 3 days, 6:12:25, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4934, loss_cls: 4.3463, loss: 4.3463 +2024-07-18 04:39:40,006 - pyskl - INFO - Epoch [54][2800/3746] lr: 7.153e-02, eta: 3 days, 6:11:12, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4894, loss_cls: 4.3623, loss: 4.3623 +2024-07-18 04:41:01,510 - pyskl - INFO - Epoch [54][2900/3746] lr: 7.150e-02, eta: 3 days, 6:10:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5034, loss_cls: 4.3192, loss: 4.3192 +2024-07-18 04:42:22,741 - pyskl - INFO - Epoch [54][3000/3746] lr: 7.148e-02, eta: 3 days, 6:08:48, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4909, loss_cls: 4.3663, loss: 4.3663 +2024-07-18 04:43:44,054 - pyskl - INFO - Epoch [54][3100/3746] lr: 7.145e-02, eta: 3 days, 6:07:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4844, loss_cls: 4.4108, loss: 4.4108 +2024-07-18 04:45:04,982 - pyskl - INFO - Epoch [54][3200/3746] lr: 7.143e-02, eta: 3 days, 6:06:22, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4894, loss_cls: 4.3729, loss: 4.3729 +2024-07-18 04:46:26,098 - pyskl - INFO - Epoch [54][3300/3746] lr: 7.140e-02, eta: 3 days, 6:05:10, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4894, loss_cls: 4.3575, loss: 4.3575 +2024-07-18 04:47:47,078 - pyskl - INFO - Epoch [54][3400/3746] lr: 7.138e-02, eta: 3 days, 6:03:57, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4989, loss_cls: 4.3281, loss: 4.3281 +2024-07-18 04:49:09,287 - pyskl - INFO - Epoch [54][3500/3746] lr: 7.135e-02, eta: 3 days, 6:02:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5033, loss_cls: 4.3142, loss: 4.3142 +2024-07-18 04:50:30,578 - pyskl - INFO - Epoch [54][3600/3746] lr: 7.133e-02, eta: 3 days, 6:01:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4884, loss_cls: 4.3596, loss: 4.3596 +2024-07-18 04:51:52,202 - pyskl - INFO - Epoch [54][3700/3746] lr: 7.130e-02, eta: 3 days, 6:00:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.5047, loss_cls: 4.2941, loss: 4.2941 +2024-07-18 04:52:32,486 - pyskl - INFO - Saving checkpoint at 54 epochs +2024-07-18 04:54:23,992 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 04:54:24,779 - pyskl - INFO - +top1_acc 0.1591 +top5_acc 0.3702 +2024-07-18 04:54:24,779 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 04:54:24,828 - pyskl - INFO - +mean_acc 0.1590 +2024-07-18 04:54:24,840 - pyskl - INFO - Epoch(val) [54][309] top1_acc: 0.1591, top5_acc: 0.3702, mean_class_accuracy: 0.1590 +2024-07-18 04:58:14,563 - pyskl - INFO - Epoch [55][100/3746] lr: 7.126e-02, eta: 3 days, 6:01:54, time: 2.297, data_time: 1.322, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5083, loss_cls: 4.2507, loss: 4.2507 +2024-07-18 04:59:36,096 - pyskl - INFO - Epoch [55][200/3746] lr: 7.124e-02, eta: 3 days, 6:00:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5072, loss_cls: 4.2952, loss: 4.2952 +2024-07-18 05:00:57,449 - pyskl - INFO - Epoch [55][300/3746] lr: 7.121e-02, eta: 3 days, 5:59:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5002, loss_cls: 4.2952, loss: 4.2952 +2024-07-18 05:02:19,317 - pyskl - INFO - Epoch [55][400/3746] lr: 7.119e-02, eta: 3 days, 5:58:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4881, loss_cls: 4.3589, loss: 4.3589 +2024-07-18 05:03:40,908 - pyskl - INFO - Epoch [55][500/3746] lr: 7.116e-02, eta: 3 days, 5:57:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.5053, loss_cls: 4.3016, loss: 4.3016 +2024-07-18 05:05:01,747 - pyskl - INFO - Epoch [55][600/3746] lr: 7.114e-02, eta: 3 days, 5:55:52, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4969, loss_cls: 4.3404, loss: 4.3404 +2024-07-18 05:06:23,056 - pyskl - INFO - Epoch [55][700/3746] lr: 7.111e-02, eta: 3 days, 5:54:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4867, loss_cls: 4.3579, loss: 4.3579 +2024-07-18 05:07:43,789 - pyskl - INFO - Epoch [55][800/3746] lr: 7.109e-02, eta: 3 days, 5:53:26, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5044, loss_cls: 4.2821, loss: 4.2821 +2024-07-18 05:09:05,269 - pyskl - INFO - Epoch [55][900/3746] lr: 7.106e-02, eta: 3 days, 5:52:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.4994, loss_cls: 4.3259, loss: 4.3259 +2024-07-18 05:10:26,769 - pyskl - INFO - Epoch [55][1000/3746] lr: 7.104e-02, eta: 3 days, 5:51:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5000, loss_cls: 4.2782, loss: 4.2782 +2024-07-18 05:11:47,700 - pyskl - INFO - Epoch [55][1100/3746] lr: 7.101e-02, eta: 3 days, 5:49:48, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5023, loss_cls: 4.3353, loss: 4.3353 +2024-07-18 05:13:09,323 - pyskl - INFO - Epoch [55][1200/3746] lr: 7.099e-02, eta: 3 days, 5:48:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5006, loss_cls: 4.2891, loss: 4.2891 +2024-07-18 05:14:30,511 - pyskl - INFO - Epoch [55][1300/3746] lr: 7.096e-02, eta: 3 days, 5:47:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4945, loss_cls: 4.3455, loss: 4.3455 +2024-07-18 05:15:51,401 - pyskl - INFO - Epoch [55][1400/3746] lr: 7.093e-02, eta: 3 days, 5:46:10, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4980, loss_cls: 4.3364, loss: 4.3364 +2024-07-18 05:17:12,881 - pyskl - INFO - Epoch [55][1500/3746] lr: 7.091e-02, eta: 3 days, 5:44:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4916, loss_cls: 4.3598, loss: 4.3598 +2024-07-18 05:18:33,724 - pyskl - INFO - Epoch [55][1600/3746] lr: 7.088e-02, eta: 3 days, 5:43:44, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.5020, loss_cls: 4.3390, loss: 4.3390 +2024-07-18 05:19:55,208 - pyskl - INFO - Epoch [55][1700/3746] lr: 7.086e-02, eta: 3 days, 5:42:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.4973, loss_cls: 4.2824, loss: 4.2824 +2024-07-18 05:21:16,135 - pyskl - INFO - Epoch [55][1800/3746] lr: 7.083e-02, eta: 3 days, 5:41:19, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5055, loss_cls: 4.2920, loss: 4.2920 +2024-07-18 05:22:36,758 - pyskl - INFO - Epoch [55][1900/3746] lr: 7.081e-02, eta: 3 days, 5:40:05, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.4997, loss_cls: 4.3067, loss: 4.3067 +2024-07-18 05:23:58,051 - pyskl - INFO - Epoch [55][2000/3746] lr: 7.078e-02, eta: 3 days, 5:38:52, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5002, loss_cls: 4.3321, loss: 4.3321 +2024-07-18 05:25:18,993 - pyskl - INFO - Epoch [55][2100/3746] lr: 7.076e-02, eta: 3 days, 5:37:39, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4834, loss_cls: 4.4114, loss: 4.4114 +2024-07-18 05:26:40,705 - pyskl - INFO - Epoch [55][2200/3746] lr: 7.073e-02, eta: 3 days, 5:36:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5019, loss_cls: 4.2856, loss: 4.2856 +2024-07-18 05:28:01,501 - pyskl - INFO - Epoch [55][2300/3746] lr: 7.071e-02, eta: 3 days, 5:35:13, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4936, loss_cls: 4.3715, loss: 4.3715 +2024-07-18 05:29:22,571 - pyskl - INFO - Epoch [55][2400/3746] lr: 7.068e-02, eta: 3 days, 5:34:00, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4978, loss_cls: 4.3341, loss: 4.3341 +2024-07-18 05:30:44,076 - pyskl - INFO - Epoch [55][2500/3746] lr: 7.065e-02, eta: 3 days, 5:32:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4950, loss_cls: 4.3758, loss: 4.3758 +2024-07-18 05:32:05,622 - pyskl - INFO - Epoch [55][2600/3746] lr: 7.063e-02, eta: 3 days, 5:31:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4947, loss_cls: 4.3465, loss: 4.3465 +2024-07-18 05:33:27,283 - pyskl - INFO - Epoch [55][2700/3746] lr: 7.060e-02, eta: 3 days, 5:30:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4977, loss_cls: 4.3187, loss: 4.3187 +2024-07-18 05:34:48,963 - pyskl - INFO - Epoch [55][2800/3746] lr: 7.058e-02, eta: 3 days, 5:29:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4895, loss_cls: 4.3410, loss: 4.3410 +2024-07-18 05:36:09,877 - pyskl - INFO - Epoch [55][2900/3746] lr: 7.055e-02, eta: 3 days, 5:27:58, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.5036, loss_cls: 4.3142, loss: 4.3142 +2024-07-18 05:37:31,367 - pyskl - INFO - Epoch [55][3000/3746] lr: 7.053e-02, eta: 3 days, 5:26:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4964, loss_cls: 4.3385, loss: 4.3385 +2024-07-18 05:38:52,476 - pyskl - INFO - Epoch [55][3100/3746] lr: 7.050e-02, eta: 3 days, 5:25:32, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4909, loss_cls: 4.3693, loss: 4.3693 +2024-07-18 05:40:13,388 - pyskl - INFO - Epoch [55][3200/3746] lr: 7.048e-02, eta: 3 days, 5:24:19, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4983, loss_cls: 4.3133, loss: 4.3133 +2024-07-18 05:41:34,383 - pyskl - INFO - Epoch [55][3300/3746] lr: 7.045e-02, eta: 3 days, 5:23:06, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4883, loss_cls: 4.3313, loss: 4.3313 +2024-07-18 05:42:55,185 - pyskl - INFO - Epoch [55][3400/3746] lr: 7.043e-02, eta: 3 days, 5:21:52, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5011, loss_cls: 4.2982, loss: 4.2982 +2024-07-18 05:44:16,637 - pyskl - INFO - Epoch [55][3500/3746] lr: 7.040e-02, eta: 3 days, 5:20:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4886, loss_cls: 4.3885, loss: 4.3885 +2024-07-18 05:45:37,376 - pyskl - INFO - Epoch [55][3600/3746] lr: 7.037e-02, eta: 3 days, 5:19:26, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5044, loss_cls: 4.2866, loss: 4.2866 +2024-07-18 05:46:58,922 - pyskl - INFO - Epoch [55][3700/3746] lr: 7.035e-02, eta: 3 days, 5:18:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4930, loss_cls: 4.3303, loss: 4.3303 +2024-07-18 05:47:38,870 - pyskl - INFO - Saving checkpoint at 55 epochs +2024-07-18 05:49:30,425 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 05:49:31,084 - pyskl - INFO - +top1_acc 0.1867 +top5_acc 0.4130 +2024-07-18 05:49:31,084 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 05:49:31,122 - pyskl - INFO - +mean_acc 0.1866 +2024-07-18 05:49:31,132 - pyskl - INFO - Epoch(val) [55][309] top1_acc: 0.1867, top5_acc: 0.4130, mean_class_accuracy: 0.1866 +2024-07-18 05:53:19,581 - pyskl - INFO - Epoch [56][100/3746] lr: 7.031e-02, eta: 3 days, 5:19:36, time: 2.284, data_time: 1.313, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5088, loss_cls: 4.2708, loss: 4.2708 +2024-07-18 05:54:41,516 - pyskl - INFO - Epoch [56][200/3746] lr: 7.029e-02, eta: 3 days, 5:18:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5080, loss_cls: 4.2800, loss: 4.2800 +2024-07-18 05:56:02,644 - pyskl - INFO - Epoch [56][300/3746] lr: 7.026e-02, eta: 3 days, 5:17:11, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4981, loss_cls: 4.3299, loss: 4.3299 +2024-07-18 05:57:23,723 - pyskl - INFO - Epoch [56][400/3746] lr: 7.023e-02, eta: 3 days, 5:15:58, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5009, loss_cls: 4.2920, loss: 4.2920 +2024-07-18 05:58:44,785 - pyskl - INFO - Epoch [56][500/3746] lr: 7.021e-02, eta: 3 days, 5:14:44, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4922, loss_cls: 4.2960, loss: 4.2960 +2024-07-18 06:00:06,073 - pyskl - INFO - Epoch [56][600/3746] lr: 7.018e-02, eta: 3 days, 5:13:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5127, loss_cls: 4.2435, loss: 4.2435 +2024-07-18 06:01:27,317 - pyskl - INFO - Epoch [56][700/3746] lr: 7.016e-02, eta: 3 days, 5:12:18, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.4983, loss_cls: 4.3283, loss: 4.3283 +2024-07-18 06:02:48,521 - pyskl - INFO - Epoch [56][800/3746] lr: 7.013e-02, eta: 3 days, 5:11:05, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5067, loss_cls: 4.2733, loss: 4.2733 +2024-07-18 06:04:10,152 - pyskl - INFO - Epoch [56][900/3746] lr: 7.011e-02, eta: 3 days, 5:09:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.5011, loss_cls: 4.3164, loss: 4.3164 +2024-07-18 06:05:31,797 - pyskl - INFO - Epoch [56][1000/3746] lr: 7.008e-02, eta: 3 days, 5:08:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.4988, loss_cls: 4.3034, loss: 4.3034 +2024-07-18 06:06:53,315 - pyskl - INFO - Epoch [56][1100/3746] lr: 7.006e-02, eta: 3 days, 5:07:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5036, loss_cls: 4.2789, loss: 4.2789 +2024-07-18 06:08:14,987 - pyskl - INFO - Epoch [56][1200/3746] lr: 7.003e-02, eta: 3 days, 5:06:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4967, loss_cls: 4.3334, loss: 4.3334 +2024-07-18 06:09:35,952 - pyskl - INFO - Epoch [56][1300/3746] lr: 7.000e-02, eta: 3 days, 5:05:01, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4950, loss_cls: 4.3380, loss: 4.3380 +2024-07-18 06:10:57,227 - pyskl - INFO - Epoch [56][1400/3746] lr: 6.998e-02, eta: 3 days, 5:03:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5070, loss_cls: 4.2903, loss: 4.2903 +2024-07-18 06:12:18,412 - pyskl - INFO - Epoch [56][1500/3746] lr: 6.995e-02, eta: 3 days, 5:02:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4967, loss_cls: 4.3238, loss: 4.3238 +2024-07-18 06:13:40,141 - pyskl - INFO - Epoch [56][1600/3746] lr: 6.993e-02, eta: 3 days, 5:01:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5038, loss_cls: 4.3201, loss: 4.3201 +2024-07-18 06:15:01,579 - pyskl - INFO - Epoch [56][1700/3746] lr: 6.990e-02, eta: 3 days, 5:00:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5033, loss_cls: 4.3133, loss: 4.3133 +2024-07-18 06:16:23,626 - pyskl - INFO - Epoch [56][1800/3746] lr: 6.988e-02, eta: 3 days, 4:58:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.4973, loss_cls: 4.3029, loss: 4.3029 +2024-07-18 06:17:44,978 - pyskl - INFO - Epoch [56][1900/3746] lr: 6.985e-02, eta: 3 days, 4:57:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4939, loss_cls: 4.3539, loss: 4.3539 +2024-07-18 06:19:06,114 - pyskl - INFO - Epoch [56][2000/3746] lr: 6.983e-02, eta: 3 days, 4:56:31, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4991, loss_cls: 4.3316, loss: 4.3316 +2024-07-18 06:20:27,126 - pyskl - INFO - Epoch [56][2100/3746] lr: 6.980e-02, eta: 3 days, 4:55:18, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4947, loss_cls: 4.3097, loss: 4.3097 +2024-07-18 06:21:48,144 - pyskl - INFO - Epoch [56][2200/3746] lr: 6.977e-02, eta: 3 days, 4:54:04, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5030, loss_cls: 4.2994, loss: 4.2994 +2024-07-18 06:23:09,560 - pyskl - INFO - Epoch [56][2300/3746] lr: 6.975e-02, eta: 3 days, 4:52:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4991, loss_cls: 4.3383, loss: 4.3383 +2024-07-18 06:24:31,044 - pyskl - INFO - Epoch [56][2400/3746] lr: 6.972e-02, eta: 3 days, 4:51:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4933, loss_cls: 4.3660, loss: 4.3660 +2024-07-18 06:25:52,525 - pyskl - INFO - Epoch [56][2500/3746] lr: 6.970e-02, eta: 3 days, 4:50:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4952, loss_cls: 4.3770, loss: 4.3770 +2024-07-18 06:27:13,809 - pyskl - INFO - Epoch [56][2600/3746] lr: 6.967e-02, eta: 3 days, 4:49:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4913, loss_cls: 4.3595, loss: 4.3595 +2024-07-18 06:28:34,948 - pyskl - INFO - Epoch [56][2700/3746] lr: 6.965e-02, eta: 3 days, 4:47:59, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4978, loss_cls: 4.3145, loss: 4.3145 +2024-07-18 06:29:56,348 - pyskl - INFO - Epoch [56][2800/3746] lr: 6.962e-02, eta: 3 days, 4:46:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4969, loss_cls: 4.3301, loss: 4.3301 +2024-07-18 06:31:17,766 - pyskl - INFO - Epoch [56][2900/3746] lr: 6.959e-02, eta: 3 days, 4:45:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4969, loss_cls: 4.3255, loss: 4.3255 +2024-07-18 06:32:38,777 - pyskl - INFO - Epoch [56][3000/3746] lr: 6.957e-02, eta: 3 days, 4:44:19, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4986, loss_cls: 4.3065, loss: 4.3065 +2024-07-18 06:34:00,038 - pyskl - INFO - Epoch [56][3100/3746] lr: 6.954e-02, eta: 3 days, 4:43:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5089, loss_cls: 4.2891, loss: 4.2891 +2024-07-18 06:35:21,287 - pyskl - INFO - Epoch [56][3200/3746] lr: 6.952e-02, eta: 3 days, 4:41:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4889, loss_cls: 4.3607, loss: 4.3607 +2024-07-18 06:36:42,688 - pyskl - INFO - Epoch [56][3300/3746] lr: 6.949e-02, eta: 3 days, 4:40:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5003, loss_cls: 4.3161, loss: 4.3161 +2024-07-18 06:38:03,936 - pyskl - INFO - Epoch [56][3400/3746] lr: 6.947e-02, eta: 3 days, 4:39:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4975, loss_cls: 4.3010, loss: 4.3010 +2024-07-18 06:39:25,383 - pyskl - INFO - Epoch [56][3500/3746] lr: 6.944e-02, eta: 3 days, 4:38:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5069, loss_cls: 4.2598, loss: 4.2598 +2024-07-18 06:40:46,824 - pyskl - INFO - Epoch [56][3600/3746] lr: 6.941e-02, eta: 3 days, 4:37:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4992, loss_cls: 4.3348, loss: 4.3348 +2024-07-18 06:42:08,153 - pyskl - INFO - Epoch [56][3700/3746] lr: 6.939e-02, eta: 3 days, 4:35:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4927, loss_cls: 4.3323, loss: 4.3323 +2024-07-18 06:42:47,583 - pyskl - INFO - Saving checkpoint at 56 epochs +2024-07-18 06:44:38,663 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 06:44:39,324 - pyskl - INFO - +top1_acc 0.1578 +top5_acc 0.3543 +2024-07-18 06:44:39,324 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 06:44:39,363 - pyskl - INFO - +mean_acc 0.1578 +2024-07-18 06:44:39,373 - pyskl - INFO - Epoch(val) [56][309] top1_acc: 0.1578, top5_acc: 0.3543, mean_class_accuracy: 0.1578 +2024-07-18 06:48:31,731 - pyskl - INFO - Epoch [57][100/3746] lr: 6.935e-02, eta: 3 days, 4:37:11, time: 2.323, data_time: 1.336, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5056, loss_cls: 4.2894, loss: 4.2894 +2024-07-18 06:49:53,388 - pyskl - INFO - Epoch [57][200/3746] lr: 6.932e-02, eta: 3 days, 4:35:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5098, loss_cls: 4.2492, loss: 4.2492 +2024-07-18 06:51:15,126 - pyskl - INFO - Epoch [57][300/3746] lr: 6.930e-02, eta: 3 days, 4:34:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5103, loss_cls: 4.2553, loss: 4.2553 +2024-07-18 06:52:36,754 - pyskl - INFO - Epoch [57][400/3746] lr: 6.927e-02, eta: 3 days, 4:33:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5220, loss_cls: 4.2391, loss: 4.2391 +2024-07-18 06:53:57,831 - pyskl - INFO - Epoch [57][500/3746] lr: 6.925e-02, eta: 3 days, 4:32:18, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5022, loss_cls: 4.2984, loss: 4.2984 +2024-07-18 06:55:18,904 - pyskl - INFO - Epoch [57][600/3746] lr: 6.922e-02, eta: 3 days, 4:31:05, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5092, loss_cls: 4.2645, loss: 4.2645 +2024-07-18 06:56:40,391 - pyskl - INFO - Epoch [57][700/3746] lr: 6.920e-02, eta: 3 days, 4:29:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5052, loss_cls: 4.3067, loss: 4.3067 +2024-07-18 06:58:02,875 - pyskl - INFO - Epoch [57][800/3746] lr: 6.917e-02, eta: 3 days, 4:28:40, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4984, loss_cls: 4.3101, loss: 4.3101 +2024-07-18 06:59:24,584 - pyskl - INFO - Epoch [57][900/3746] lr: 6.914e-02, eta: 3 days, 4:27:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5016, loss_cls: 4.3133, loss: 4.3133 +2024-07-18 07:00:46,360 - pyskl - INFO - Epoch [57][1000/3746] lr: 6.912e-02, eta: 3 days, 4:26:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5092, loss_cls: 4.2703, loss: 4.2703 +2024-07-18 07:02:07,822 - pyskl - INFO - Epoch [57][1100/3746] lr: 6.909e-02, eta: 3 days, 4:25:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5073, loss_cls: 4.2808, loss: 4.2808 +2024-07-18 07:03:28,720 - pyskl - INFO - Epoch [57][1200/3746] lr: 6.907e-02, eta: 3 days, 4:23:47, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5052, loss_cls: 4.2975, loss: 4.2975 +2024-07-18 07:04:49,451 - pyskl - INFO - Epoch [57][1300/3746] lr: 6.904e-02, eta: 3 days, 4:22:33, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5148, loss_cls: 4.2630, loss: 4.2630 +2024-07-18 07:06:10,423 - pyskl - INFO - Epoch [57][1400/3746] lr: 6.901e-02, eta: 3 days, 4:21:19, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.5091, loss_cls: 4.3098, loss: 4.3098 +2024-07-18 07:07:31,825 - pyskl - INFO - Epoch [57][1500/3746] lr: 6.899e-02, eta: 3 days, 4:20:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5012, loss_cls: 4.2838, loss: 4.2838 +2024-07-18 07:08:52,964 - pyskl - INFO - Epoch [57][1600/3746] lr: 6.896e-02, eta: 3 days, 4:18:51, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4897, loss_cls: 4.3670, loss: 4.3670 +2024-07-18 07:10:14,277 - pyskl - INFO - Epoch [57][1700/3746] lr: 6.894e-02, eta: 3 days, 4:17:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5011, loss_cls: 4.3580, loss: 4.3580 +2024-07-18 07:11:35,421 - pyskl - INFO - Epoch [57][1800/3746] lr: 6.891e-02, eta: 3 days, 4:16:24, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4906, loss_cls: 4.3494, loss: 4.3494 +2024-07-18 07:12:57,269 - pyskl - INFO - Epoch [57][1900/3746] lr: 6.889e-02, eta: 3 days, 4:15:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4925, loss_cls: 4.3402, loss: 4.3402 +2024-07-18 07:14:18,903 - pyskl - INFO - Epoch [57][2000/3746] lr: 6.886e-02, eta: 3 days, 4:13:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4975, loss_cls: 4.3128, loss: 4.3128 +2024-07-18 07:15:40,231 - pyskl - INFO - Epoch [57][2100/3746] lr: 6.883e-02, eta: 3 days, 4:12:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5055, loss_cls: 4.2813, loss: 4.2813 +2024-07-18 07:17:01,474 - pyskl - INFO - Epoch [57][2200/3746] lr: 6.881e-02, eta: 3 days, 4:11:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5003, loss_cls: 4.3360, loss: 4.3360 +2024-07-18 07:18:22,671 - pyskl - INFO - Epoch [57][2300/3746] lr: 6.878e-02, eta: 3 days, 4:10:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4920, loss_cls: 4.3394, loss: 4.3394 +2024-07-18 07:19:44,234 - pyskl - INFO - Epoch [57][2400/3746] lr: 6.876e-02, eta: 3 days, 4:09:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4991, loss_cls: 4.3030, loss: 4.3030 +2024-07-18 07:21:05,597 - pyskl - INFO - Epoch [57][2500/3746] lr: 6.873e-02, eta: 3 days, 4:07:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4972, loss_cls: 4.3252, loss: 4.3252 +2024-07-18 07:22:27,161 - pyskl - INFO - Epoch [57][2600/3746] lr: 6.870e-02, eta: 3 days, 4:06:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5055, loss_cls: 4.3032, loss: 4.3032 +2024-07-18 07:23:48,144 - pyskl - INFO - Epoch [57][2700/3746] lr: 6.868e-02, eta: 3 days, 4:05:24, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4947, loss_cls: 4.3396, loss: 4.3396 +2024-07-18 07:25:09,297 - pyskl - INFO - Epoch [57][2800/3746] lr: 6.865e-02, eta: 3 days, 4:04:10, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5006, loss_cls: 4.3017, loss: 4.3017 +2024-07-18 07:26:30,137 - pyskl - INFO - Epoch [57][2900/3746] lr: 6.863e-02, eta: 3 days, 4:02:55, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5105, loss_cls: 4.2726, loss: 4.2726 +2024-07-18 07:27:51,679 - pyskl - INFO - Epoch [57][3000/3746] lr: 6.860e-02, eta: 3 days, 4:01:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4892, loss_cls: 4.3472, loss: 4.3472 +2024-07-18 07:29:12,849 - pyskl - INFO - Epoch [57][3100/3746] lr: 6.857e-02, eta: 3 days, 4:00:28, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4941, loss_cls: 4.3481, loss: 4.3481 +2024-07-18 07:30:33,749 - pyskl - INFO - Epoch [57][3200/3746] lr: 6.855e-02, eta: 3 days, 3:59:14, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4978, loss_cls: 4.3269, loss: 4.3269 +2024-07-18 07:31:54,751 - pyskl - INFO - Epoch [57][3300/3746] lr: 6.852e-02, eta: 3 days, 3:58:00, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4922, loss_cls: 4.3245, loss: 4.3245 +2024-07-18 07:33:15,917 - pyskl - INFO - Epoch [57][3400/3746] lr: 6.850e-02, eta: 3 days, 3:56:46, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4945, loss_cls: 4.3206, loss: 4.3206 +2024-07-18 07:34:37,673 - pyskl - INFO - Epoch [57][3500/3746] lr: 6.847e-02, eta: 3 days, 3:55:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4963, loss_cls: 4.3013, loss: 4.3013 +2024-07-18 07:35:58,843 - pyskl - INFO - Epoch [57][3600/3746] lr: 6.844e-02, eta: 3 days, 3:54:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5081, loss_cls: 4.2619, loss: 4.2619 +2024-07-18 07:37:20,919 - pyskl - INFO - Epoch [57][3700/3746] lr: 6.842e-02, eta: 3 days, 3:53:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.5019, loss_cls: 4.3243, loss: 4.3243 +2024-07-18 07:38:00,537 - pyskl - INFO - Saving checkpoint at 57 epochs +2024-07-18 07:39:50,885 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 07:39:51,543 - pyskl - INFO - +top1_acc 0.1785 +top5_acc 0.4012 +2024-07-18 07:39:51,543 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 07:39:51,582 - pyskl - INFO - +mean_acc 0.1785 +2024-07-18 07:39:51,592 - pyskl - INFO - Epoch(val) [57][309] top1_acc: 0.1785, top5_acc: 0.4012, mean_class_accuracy: 0.1785 +2024-07-18 07:43:38,502 - pyskl - INFO - Epoch [58][100/3746] lr: 6.838e-02, eta: 3 days, 3:54:15, time: 2.269, data_time: 1.291, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5044, loss_cls: 4.2739, loss: 4.2739 +2024-07-18 07:44:59,790 - pyskl - INFO - Epoch [58][200/3746] lr: 6.835e-02, eta: 3 days, 3:53:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.4975, loss_cls: 4.3038, loss: 4.3038 +2024-07-18 07:46:21,643 - pyskl - INFO - Epoch [58][300/3746] lr: 6.833e-02, eta: 3 days, 3:51:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5050, loss_cls: 4.2527, loss: 4.2527 +2024-07-18 07:47:42,948 - pyskl - INFO - Epoch [58][400/3746] lr: 6.830e-02, eta: 3 days, 3:50:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4977, loss_cls: 4.3082, loss: 4.3082 +2024-07-18 07:49:04,446 - pyskl - INFO - Epoch [58][500/3746] lr: 6.828e-02, eta: 3 days, 3:49:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5059, loss_cls: 4.2764, loss: 4.2764 +2024-07-18 07:50:25,483 - pyskl - INFO - Epoch [58][600/3746] lr: 6.825e-02, eta: 3 days, 3:48:07, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5111, loss_cls: 4.2767, loss: 4.2767 +2024-07-18 07:51:46,570 - pyskl - INFO - Epoch [58][700/3746] lr: 6.822e-02, eta: 3 days, 3:46:53, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5053, loss_cls: 4.2943, loss: 4.2943 +2024-07-18 07:53:08,661 - pyskl - INFO - Epoch [58][800/3746] lr: 6.820e-02, eta: 3 days, 3:45:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5019, loss_cls: 4.2988, loss: 4.2988 +2024-07-18 07:54:30,039 - pyskl - INFO - Epoch [58][900/3746] lr: 6.817e-02, eta: 3 days, 3:44:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5058, loss_cls: 4.2763, loss: 4.2763 +2024-07-18 07:55:51,499 - pyskl - INFO - Epoch [58][1000/3746] lr: 6.815e-02, eta: 3 days, 3:43:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5002, loss_cls: 4.2908, loss: 4.2908 +2024-07-18 07:57:12,741 - pyskl - INFO - Epoch [58][1100/3746] lr: 6.812e-02, eta: 3 days, 3:41:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5216, loss_cls: 4.2320, loss: 4.2320 +2024-07-18 07:58:33,519 - pyskl - INFO - Epoch [58][1200/3746] lr: 6.809e-02, eta: 3 days, 3:40:44, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5030, loss_cls: 4.2532, loss: 4.2532 +2024-07-18 07:59:54,179 - pyskl - INFO - Epoch [58][1300/3746] lr: 6.807e-02, eta: 3 days, 3:39:29, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5045, loss_cls: 4.2607, loss: 4.2607 +2024-07-18 08:01:15,114 - pyskl - INFO - Epoch [58][1400/3746] lr: 6.804e-02, eta: 3 days, 3:38:15, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5034, loss_cls: 4.2840, loss: 4.2840 +2024-07-18 08:02:36,421 - pyskl - INFO - Epoch [58][1500/3746] lr: 6.802e-02, eta: 3 days, 3:37:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4938, loss_cls: 4.3463, loss: 4.3463 +2024-07-18 08:03:57,690 - pyskl - INFO - Epoch [58][1600/3746] lr: 6.799e-02, eta: 3 days, 3:35:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5062, loss_cls: 4.2774, loss: 4.2774 +2024-07-18 08:05:19,104 - pyskl - INFO - Epoch [58][1700/3746] lr: 6.796e-02, eta: 3 days, 3:34:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.5103, loss_cls: 4.2887, loss: 4.2887 +2024-07-18 08:06:40,167 - pyskl - INFO - Epoch [58][1800/3746] lr: 6.794e-02, eta: 3 days, 3:33:19, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5116, loss_cls: 4.2683, loss: 4.2683 +2024-07-18 08:08:01,276 - pyskl - INFO - Epoch [58][1900/3746] lr: 6.791e-02, eta: 3 days, 3:32:04, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5173, loss_cls: 4.2751, loss: 4.2751 +2024-07-18 08:09:23,088 - pyskl - INFO - Epoch [58][2000/3746] lr: 6.789e-02, eta: 3 days, 3:30:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4977, loss_cls: 4.3326, loss: 4.3326 +2024-07-18 08:10:44,207 - pyskl - INFO - Epoch [58][2100/3746] lr: 6.786e-02, eta: 3 days, 3:29:37, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4908, loss_cls: 4.3669, loss: 4.3669 +2024-07-18 08:12:06,174 - pyskl - INFO - Epoch [58][2200/3746] lr: 6.783e-02, eta: 3 days, 3:28:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4913, loss_cls: 4.3711, loss: 4.3711 +2024-07-18 08:13:27,539 - pyskl - INFO - Epoch [58][2300/3746] lr: 6.781e-02, eta: 3 days, 3:27:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5159, loss_cls: 4.2424, loss: 4.2424 +2024-07-18 08:14:49,071 - pyskl - INFO - Epoch [58][2400/3746] lr: 6.778e-02, eta: 3 days, 3:25:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5117, loss_cls: 4.2780, loss: 4.2780 +2024-07-18 08:16:10,668 - pyskl - INFO - Epoch [58][2500/3746] lr: 6.775e-02, eta: 3 days, 3:24:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5111, loss_cls: 4.2522, loss: 4.2522 +2024-07-18 08:17:31,817 - pyskl - INFO - Epoch [58][2600/3746] lr: 6.773e-02, eta: 3 days, 3:23:29, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5028, loss_cls: 4.3124, loss: 4.3124 +2024-07-18 08:18:52,927 - pyskl - INFO - Epoch [58][2700/3746] lr: 6.770e-02, eta: 3 days, 3:22:15, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5070, loss_cls: 4.2985, loss: 4.2985 +2024-07-18 08:20:14,343 - pyskl - INFO - Epoch [58][2800/3746] lr: 6.768e-02, eta: 3 days, 3:21:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5112, loss_cls: 4.2832, loss: 4.2832 +2024-07-18 08:21:35,697 - pyskl - INFO - Epoch [58][2900/3746] lr: 6.765e-02, eta: 3 days, 3:19:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5011, loss_cls: 4.2767, loss: 4.2767 +2024-07-18 08:22:56,745 - pyskl - INFO - Epoch [58][3000/3746] lr: 6.762e-02, eta: 3 days, 3:18:32, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4923, loss_cls: 4.3539, loss: 4.3539 +2024-07-18 08:24:18,483 - pyskl - INFO - Epoch [58][3100/3746] lr: 6.760e-02, eta: 3 days, 3:17:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5050, loss_cls: 4.2736, loss: 4.2736 +2024-07-18 08:25:39,489 - pyskl - INFO - Epoch [58][3200/3746] lr: 6.757e-02, eta: 3 days, 3:16:05, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5075, loss_cls: 4.2769, loss: 4.2769 +2024-07-18 08:27:00,439 - pyskl - INFO - Epoch [58][3300/3746] lr: 6.755e-02, eta: 3 days, 3:14:50, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5041, loss_cls: 4.3335, loss: 4.3335 +2024-07-18 08:28:21,559 - pyskl - INFO - Epoch [58][3400/3746] lr: 6.752e-02, eta: 3 days, 3:13:36, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4953, loss_cls: 4.3070, loss: 4.3070 +2024-07-18 08:29:43,794 - pyskl - INFO - Epoch [58][3500/3746] lr: 6.749e-02, eta: 3 days, 3:12:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5019, loss_cls: 4.3003, loss: 4.3003 +2024-07-18 08:31:05,124 - pyskl - INFO - Epoch [58][3600/3746] lr: 6.747e-02, eta: 3 days, 3:11:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4906, loss_cls: 4.3537, loss: 4.3537 +2024-07-18 08:32:27,108 - pyskl - INFO - Epoch [58][3700/3746] lr: 6.744e-02, eta: 3 days, 3:09:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4969, loss_cls: 4.3220, loss: 4.3220 +2024-07-18 08:33:06,904 - pyskl - INFO - Saving checkpoint at 58 epochs +2024-07-18 08:34:57,204 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 08:34:57,867 - pyskl - INFO - +top1_acc 0.1615 +top5_acc 0.3668 +2024-07-18 08:34:57,867 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 08:34:57,907 - pyskl - INFO - +mean_acc 0.1615 +2024-07-18 08:34:57,918 - pyskl - INFO - Epoch(val) [58][309] top1_acc: 0.1615, top5_acc: 0.3668, mean_class_accuracy: 0.1615 +2024-07-18 08:38:46,840 - pyskl - INFO - Epoch [59][100/3746] lr: 6.740e-02, eta: 3 days, 3:11:03, time: 2.289, data_time: 1.310, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5108, loss_cls: 4.2429, loss: 4.2429 +2024-07-18 08:40:08,416 - pyskl - INFO - Epoch [59][200/3746] lr: 6.738e-02, eta: 3 days, 3:09:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4994, loss_cls: 4.2843, loss: 4.2843 +2024-07-18 08:41:30,224 - pyskl - INFO - Epoch [59][300/3746] lr: 6.735e-02, eta: 3 days, 3:08:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5106, loss_cls: 4.2420, loss: 4.2420 +2024-07-18 08:42:51,464 - pyskl - INFO - Epoch [59][400/3746] lr: 6.732e-02, eta: 3 days, 3:07:21, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5036, loss_cls: 4.3088, loss: 4.3088 +2024-07-18 08:44:13,555 - pyskl - INFO - Epoch [59][500/3746] lr: 6.730e-02, eta: 3 days, 3:06:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5022, loss_cls: 4.2787, loss: 4.2787 +2024-07-18 08:45:34,859 - pyskl - INFO - Epoch [59][600/3746] lr: 6.727e-02, eta: 3 days, 3:04:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5061, loss_cls: 4.2382, loss: 4.2382 +2024-07-18 08:46:55,736 - pyskl - INFO - Epoch [59][700/3746] lr: 6.725e-02, eta: 3 days, 3:03:39, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5039, loss_cls: 4.2789, loss: 4.2789 +2024-07-18 08:48:16,697 - pyskl - INFO - Epoch [59][800/3746] lr: 6.722e-02, eta: 3 days, 3:02:24, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.4980, loss_cls: 4.3064, loss: 4.3064 +2024-07-18 08:49:37,986 - pyskl - INFO - Epoch [59][900/3746] lr: 6.719e-02, eta: 3 days, 3:01:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5147, loss_cls: 4.2340, loss: 4.2340 +2024-07-18 08:50:59,808 - pyskl - INFO - Epoch [59][1000/3746] lr: 6.717e-02, eta: 3 days, 2:59:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5166, loss_cls: 4.2533, loss: 4.2533 +2024-07-18 08:52:21,161 - pyskl - INFO - Epoch [59][1100/3746] lr: 6.714e-02, eta: 3 days, 2:58:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5147, loss_cls: 4.2733, loss: 4.2733 +2024-07-18 08:53:42,895 - pyskl - INFO - Epoch [59][1200/3746] lr: 6.711e-02, eta: 3 days, 2:57:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5042, loss_cls: 4.2695, loss: 4.2695 +2024-07-18 08:55:03,938 - pyskl - INFO - Epoch [59][1300/3746] lr: 6.709e-02, eta: 3 days, 2:56:14, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4973, loss_cls: 4.3162, loss: 4.3162 +2024-07-18 08:56:24,721 - pyskl - INFO - Epoch [59][1400/3746] lr: 6.706e-02, eta: 3 days, 2:54:59, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5091, loss_cls: 4.2954, loss: 4.2954 +2024-07-18 08:57:45,250 - pyskl - INFO - Epoch [59][1500/3746] lr: 6.704e-02, eta: 3 days, 2:53:43, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4927, loss_cls: 4.3152, loss: 4.3152 +2024-07-18 08:59:06,382 - pyskl - INFO - Epoch [59][1600/3746] lr: 6.701e-02, eta: 3 days, 2:52:29, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4953, loss_cls: 4.3258, loss: 4.3258 +2024-07-18 09:00:27,603 - pyskl - INFO - Epoch [59][1700/3746] lr: 6.698e-02, eta: 3 days, 2:51:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5114, loss_cls: 4.2671, loss: 4.2671 +2024-07-18 09:01:49,162 - pyskl - INFO - Epoch [59][1800/3746] lr: 6.696e-02, eta: 3 days, 2:50:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4952, loss_cls: 4.3062, loss: 4.3062 +2024-07-18 09:03:09,840 - pyskl - INFO - Epoch [59][1900/3746] lr: 6.693e-02, eta: 3 days, 2:48:45, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5016, loss_cls: 4.3029, loss: 4.3029 +2024-07-18 09:04:30,784 - pyskl - INFO - Epoch [59][2000/3746] lr: 6.690e-02, eta: 3 days, 2:47:30, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5011, loss_cls: 4.3043, loss: 4.3043 +2024-07-18 09:05:51,441 - pyskl - INFO - Epoch [59][2100/3746] lr: 6.688e-02, eta: 3 days, 2:46:15, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5134, loss_cls: 4.2618, loss: 4.2618 +2024-07-18 09:07:12,958 - pyskl - INFO - Epoch [59][2200/3746] lr: 6.685e-02, eta: 3 days, 2:45:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4953, loss_cls: 4.3126, loss: 4.3126 +2024-07-18 09:08:34,666 - pyskl - INFO - Epoch [59][2300/3746] lr: 6.682e-02, eta: 3 days, 2:43:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4939, loss_cls: 4.3300, loss: 4.3300 +2024-07-18 09:09:56,056 - pyskl - INFO - Epoch [59][2400/3746] lr: 6.680e-02, eta: 3 days, 2:42:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4947, loss_cls: 4.3166, loss: 4.3166 +2024-07-18 09:11:17,275 - pyskl - INFO - Epoch [59][2500/3746] lr: 6.677e-02, eta: 3 days, 2:41:18, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5009, loss_cls: 4.3055, loss: 4.3055 +2024-07-18 09:12:38,447 - pyskl - INFO - Epoch [59][2600/3746] lr: 6.675e-02, eta: 3 days, 2:40:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5064, loss_cls: 4.2981, loss: 4.2981 +2024-07-18 09:13:59,659 - pyskl - INFO - Epoch [59][2700/3746] lr: 6.672e-02, eta: 3 days, 2:38:49, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5098, loss_cls: 4.2505, loss: 4.2505 +2024-07-18 09:15:20,490 - pyskl - INFO - Epoch [59][2800/3746] lr: 6.669e-02, eta: 3 days, 2:37:34, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5092, loss_cls: 4.2921, loss: 4.2921 +2024-07-18 09:16:42,140 - pyskl - INFO - Epoch [59][2900/3746] lr: 6.667e-02, eta: 3 days, 2:36:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4988, loss_cls: 4.2930, loss: 4.2930 +2024-07-18 09:18:02,808 - pyskl - INFO - Epoch [59][3000/3746] lr: 6.664e-02, eta: 3 days, 2:35:05, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5005, loss_cls: 4.3073, loss: 4.3073 +2024-07-18 09:19:23,734 - pyskl - INFO - Epoch [59][3100/3746] lr: 6.661e-02, eta: 3 days, 2:33:50, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4989, loss_cls: 4.3017, loss: 4.3017 +2024-07-18 09:20:45,247 - pyskl - INFO - Epoch [59][3200/3746] lr: 6.659e-02, eta: 3 days, 2:32:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4927, loss_cls: 4.3121, loss: 4.3121 +2024-07-18 09:22:06,583 - pyskl - INFO - Epoch [59][3300/3746] lr: 6.656e-02, eta: 3 days, 2:31:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4992, loss_cls: 4.3261, loss: 4.3261 +2024-07-18 09:23:27,566 - pyskl - INFO - Epoch [59][3400/3746] lr: 6.653e-02, eta: 3 days, 2:30:07, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5136, loss_cls: 4.2364, loss: 4.2364 +2024-07-18 09:24:48,838 - pyskl - INFO - Epoch [59][3500/3746] lr: 6.651e-02, eta: 3 days, 2:28:52, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5002, loss_cls: 4.2785, loss: 4.2785 +2024-07-18 09:26:10,200 - pyskl - INFO - Epoch [59][3600/3746] lr: 6.648e-02, eta: 3 days, 2:27:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5075, loss_cls: 4.2651, loss: 4.2651 +2024-07-18 09:27:31,528 - pyskl - INFO - Epoch [59][3700/3746] lr: 6.646e-02, eta: 3 days, 2:26:23, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4955, loss_cls: 4.3326, loss: 4.3326 +2024-07-18 09:28:11,232 - pyskl - INFO - Saving checkpoint at 59 epochs +2024-07-18 09:30:03,164 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 09:30:03,906 - pyskl - INFO - +top1_acc 0.1872 +top5_acc 0.4120 +2024-07-18 09:30:03,906 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 09:30:03,949 - pyskl - INFO - +mean_acc 0.1872 +2024-07-18 09:30:03,960 - pyskl - INFO - Epoch(val) [59][309] top1_acc: 0.1872, top5_acc: 0.4120, mean_class_accuracy: 0.1872 +2024-07-18 09:33:54,414 - pyskl - INFO - Epoch [60][100/3746] lr: 6.642e-02, eta: 3 days, 2:27:27, time: 2.304, data_time: 1.328, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5186, loss_cls: 4.2134, loss: 4.2134 +2024-07-18 09:35:16,147 - pyskl - INFO - Epoch [60][200/3746] lr: 6.639e-02, eta: 3 days, 2:26:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5167, loss_cls: 4.2133, loss: 4.2133 +2024-07-18 09:36:36,863 - pyskl - INFO - Epoch [60][300/3746] lr: 6.636e-02, eta: 3 days, 2:24:57, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5111, loss_cls: 4.2353, loss: 4.2353 +2024-07-18 09:37:57,701 - pyskl - INFO - Epoch [60][400/3746] lr: 6.634e-02, eta: 3 days, 2:23:42, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5047, loss_cls: 4.2811, loss: 4.2811 +2024-07-18 09:39:18,927 - pyskl - INFO - Epoch [60][500/3746] lr: 6.631e-02, eta: 3 days, 2:22:27, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5019, loss_cls: 4.2990, loss: 4.2990 +2024-07-18 09:40:40,134 - pyskl - INFO - Epoch [60][600/3746] lr: 6.629e-02, eta: 3 days, 2:21:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5098, loss_cls: 4.2709, loss: 4.2709 +2024-07-18 09:42:01,261 - pyskl - INFO - Epoch [60][700/3746] lr: 6.626e-02, eta: 3 days, 2:19:58, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5128, loss_cls: 4.2621, loss: 4.2621 +2024-07-18 09:43:22,101 - pyskl - INFO - Epoch [60][800/3746] lr: 6.623e-02, eta: 3 days, 2:18:43, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5144, loss_cls: 4.2279, loss: 4.2279 +2024-07-18 09:44:44,453 - pyskl - INFO - Epoch [60][900/3746] lr: 6.621e-02, eta: 3 days, 2:17:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5102, loss_cls: 4.2628, loss: 4.2628 +2024-07-18 09:46:05,540 - pyskl - INFO - Epoch [60][1000/3746] lr: 6.618e-02, eta: 3 days, 2:16:15, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5091, loss_cls: 4.2877, loss: 4.2877 +2024-07-18 09:47:26,742 - pyskl - INFO - Epoch [60][1100/3746] lr: 6.615e-02, eta: 3 days, 2:15:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5022, loss_cls: 4.2640, loss: 4.2640 +2024-07-18 09:48:48,983 - pyskl - INFO - Epoch [60][1200/3746] lr: 6.613e-02, eta: 3 days, 2:13:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5002, loss_cls: 4.2832, loss: 4.2832 +2024-07-18 09:50:10,186 - pyskl - INFO - Epoch [60][1300/3746] lr: 6.610e-02, eta: 3 days, 2:12:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.5027, loss_cls: 4.3122, loss: 4.3122 +2024-07-18 09:51:31,656 - pyskl - INFO - Epoch [60][1400/3746] lr: 6.607e-02, eta: 3 days, 2:11:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5008, loss_cls: 4.2913, loss: 4.2913 +2024-07-18 09:52:52,721 - pyskl - INFO - Epoch [60][1500/3746] lr: 6.605e-02, eta: 3 days, 2:10:02, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.5044, loss_cls: 4.3283, loss: 4.3283 +2024-07-18 09:54:13,626 - pyskl - INFO - Epoch [60][1600/3746] lr: 6.602e-02, eta: 3 days, 2:08:47, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5055, loss_cls: 4.3071, loss: 4.3071 +2024-07-18 09:55:34,519 - pyskl - INFO - Epoch [60][1700/3746] lr: 6.599e-02, eta: 3 days, 2:07:32, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5045, loss_cls: 4.2617, loss: 4.2617 +2024-07-18 09:56:56,107 - pyskl - INFO - Epoch [60][1800/3746] lr: 6.597e-02, eta: 3 days, 2:06:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5044, loss_cls: 4.2850, loss: 4.2850 +2024-07-18 09:58:17,018 - pyskl - INFO - Epoch [60][1900/3746] lr: 6.594e-02, eta: 3 days, 2:05:02, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5052, loss_cls: 4.2659, loss: 4.2659 +2024-07-18 09:59:37,970 - pyskl - INFO - Epoch [60][2000/3746] lr: 6.591e-02, eta: 3 days, 2:03:47, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5184, loss_cls: 4.2347, loss: 4.2347 +2024-07-18 10:00:59,206 - pyskl - INFO - Epoch [60][2100/3746] lr: 6.589e-02, eta: 3 days, 2:02:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5177, loss_cls: 4.2475, loss: 4.2475 +2024-07-18 10:02:20,348 - pyskl - INFO - Epoch [60][2200/3746] lr: 6.586e-02, eta: 3 days, 2:01:17, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4950, loss_cls: 4.3048, loss: 4.3048 +2024-07-18 10:03:41,591 - pyskl - INFO - Epoch [60][2300/3746] lr: 6.584e-02, eta: 3 days, 2:00:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.4963, loss_cls: 4.2926, loss: 4.2926 +2024-07-18 10:05:02,415 - pyskl - INFO - Epoch [60][2400/3746] lr: 6.581e-02, eta: 3 days, 1:58:47, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5127, loss_cls: 4.2568, loss: 4.2568 +2024-07-18 10:06:23,776 - pyskl - INFO - Epoch [60][2500/3746] lr: 6.578e-02, eta: 3 days, 1:57:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5167, loss_cls: 4.2142, loss: 4.2142 +2024-07-18 10:07:45,110 - pyskl - INFO - Epoch [60][2600/3746] lr: 6.576e-02, eta: 3 days, 1:56:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5005, loss_cls: 4.2909, loss: 4.2909 +2024-07-18 10:09:06,564 - pyskl - INFO - Epoch [60][2700/3746] lr: 6.573e-02, eta: 3 days, 1:55:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5014, loss_cls: 4.2780, loss: 4.2780 +2024-07-18 10:10:27,863 - pyskl - INFO - Epoch [60][2800/3746] lr: 6.570e-02, eta: 3 days, 1:53:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5048, loss_cls: 4.3128, loss: 4.3128 +2024-07-18 10:11:49,254 - pyskl - INFO - Epoch [60][2900/3746] lr: 6.568e-02, eta: 3 days, 1:52:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4981, loss_cls: 4.3116, loss: 4.3116 +2024-07-18 10:13:11,045 - pyskl - INFO - Epoch [60][3000/3746] lr: 6.565e-02, eta: 3 days, 1:51:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5103, loss_cls: 4.2268, loss: 4.2268 +2024-07-18 10:14:32,688 - pyskl - INFO - Epoch [60][3100/3746] lr: 6.562e-02, eta: 3 days, 1:50:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4986, loss_cls: 4.3167, loss: 4.3167 +2024-07-18 10:15:53,695 - pyskl - INFO - Epoch [60][3200/3746] lr: 6.560e-02, eta: 3 days, 1:48:50, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5016, loss_cls: 4.2975, loss: 4.2975 +2024-07-18 10:17:15,226 - pyskl - INFO - Epoch [60][3300/3746] lr: 6.557e-02, eta: 3 days, 1:47:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4958, loss_cls: 4.2968, loss: 4.2968 +2024-07-18 10:18:36,812 - pyskl - INFO - Epoch [60][3400/3746] lr: 6.554e-02, eta: 3 days, 1:46:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4950, loss_cls: 4.3566, loss: 4.3566 +2024-07-18 10:19:57,644 - pyskl - INFO - Epoch [60][3500/3746] lr: 6.552e-02, eta: 3 days, 1:45:06, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5030, loss_cls: 4.2628, loss: 4.2628 +2024-07-18 10:21:19,340 - pyskl - INFO - Epoch [60][3600/3746] lr: 6.549e-02, eta: 3 days, 1:43:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5067, loss_cls: 4.2817, loss: 4.2817 +2024-07-18 10:22:40,605 - pyskl - INFO - Epoch [60][3700/3746] lr: 6.546e-02, eta: 3 days, 1:42:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5066, loss_cls: 4.2585, loss: 4.2585 +2024-07-18 10:23:20,123 - pyskl - INFO - Saving checkpoint at 60 epochs +2024-07-18 10:25:10,657 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 10:25:11,310 - pyskl - INFO - +top1_acc 0.1870 +top5_acc 0.4214 +2024-07-18 10:25:11,311 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 10:25:11,351 - pyskl - INFO - +mean_acc 0.1869 +2024-07-18 10:25:11,363 - pyskl - INFO - Epoch(val) [60][309] top1_acc: 0.1870, top5_acc: 0.4214, mean_class_accuracy: 0.1869 +2024-07-18 10:28:57,464 - pyskl - INFO - Epoch [61][100/3746] lr: 6.542e-02, eta: 3 days, 1:43:29, time: 2.261, data_time: 1.288, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5178, loss_cls: 4.2077, loss: 4.2077 +2024-07-18 10:30:18,468 - pyskl - INFO - Epoch [61][200/3746] lr: 6.540e-02, eta: 3 days, 1:42:14, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5112, loss_cls: 4.2465, loss: 4.2465 +2024-07-18 10:31:39,810 - pyskl - INFO - Epoch [61][300/3746] lr: 6.537e-02, eta: 3 days, 1:40:59, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5177, loss_cls: 4.2286, loss: 4.2286 +2024-07-18 10:33:00,893 - pyskl - INFO - Epoch [61][400/3746] lr: 6.534e-02, eta: 3 days, 1:39:43, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5161, loss_cls: 4.2318, loss: 4.2318 +2024-07-18 10:34:21,840 - pyskl - INFO - Epoch [61][500/3746] lr: 6.532e-02, eta: 3 days, 1:38:28, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5158, loss_cls: 4.2707, loss: 4.2707 +2024-07-18 10:35:43,400 - pyskl - INFO - Epoch [61][600/3746] lr: 6.529e-02, eta: 3 days, 1:37:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5064, loss_cls: 4.2466, loss: 4.2466 +2024-07-18 10:37:04,047 - pyskl - INFO - Epoch [61][700/3746] lr: 6.526e-02, eta: 3 days, 1:35:57, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5080, loss_cls: 4.2918, loss: 4.2918 +2024-07-18 10:38:24,837 - pyskl - INFO - Epoch [61][800/3746] lr: 6.524e-02, eta: 3 days, 1:34:42, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5136, loss_cls: 4.2475, loss: 4.2475 +2024-07-18 10:39:46,046 - pyskl - INFO - Epoch [61][900/3746] lr: 6.521e-02, eta: 3 days, 1:33:27, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5083, loss_cls: 4.2413, loss: 4.2413 +2024-07-18 10:41:07,979 - pyskl - INFO - Epoch [61][1000/3746] lr: 6.519e-02, eta: 3 days, 1:32:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5270, loss_cls: 4.1802, loss: 4.1802 +2024-07-18 10:42:29,658 - pyskl - INFO - Epoch [61][1100/3746] lr: 6.516e-02, eta: 3 days, 1:30:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5048, loss_cls: 4.2399, loss: 4.2399 +2024-07-18 10:43:51,111 - pyskl - INFO - Epoch [61][1200/3746] lr: 6.513e-02, eta: 3 days, 1:29:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5112, loss_cls: 4.2622, loss: 4.2622 +2024-07-18 10:45:12,746 - pyskl - INFO - Epoch [61][1300/3746] lr: 6.511e-02, eta: 3 days, 1:28:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5084, loss_cls: 4.2653, loss: 4.2653 +2024-07-18 10:46:33,900 - pyskl - INFO - Epoch [61][1400/3746] lr: 6.508e-02, eta: 3 days, 1:27:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.4977, loss_cls: 4.3082, loss: 4.3082 +2024-07-18 10:47:55,055 - pyskl - INFO - Epoch [61][1500/3746] lr: 6.505e-02, eta: 3 days, 1:25:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5111, loss_cls: 4.2387, loss: 4.2387 +2024-07-18 10:49:15,782 - pyskl - INFO - Epoch [61][1600/3746] lr: 6.503e-02, eta: 3 days, 1:24:43, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4964, loss_cls: 4.3033, loss: 4.3033 +2024-07-18 10:50:36,787 - pyskl - INFO - Epoch [61][1700/3746] lr: 6.500e-02, eta: 3 days, 1:23:27, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4994, loss_cls: 4.3227, loss: 4.3227 +2024-07-18 10:51:57,814 - pyskl - INFO - Epoch [61][1800/3746] lr: 6.497e-02, eta: 3 days, 1:22:12, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5030, loss_cls: 4.2812, loss: 4.2812 +2024-07-18 10:53:18,509 - pyskl - INFO - Epoch [61][1900/3746] lr: 6.495e-02, eta: 3 days, 1:20:56, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.5078, loss_cls: 4.2893, loss: 4.2893 +2024-07-18 10:54:39,428 - pyskl - INFO - Epoch [61][2000/3746] lr: 6.492e-02, eta: 3 days, 1:19:40, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5064, loss_cls: 4.2797, loss: 4.2797 +2024-07-18 10:56:00,414 - pyskl - INFO - Epoch [61][2100/3746] lr: 6.489e-02, eta: 3 days, 1:18:25, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5038, loss_cls: 4.3191, loss: 4.3191 +2024-07-18 10:57:21,158 - pyskl - INFO - Epoch [61][2200/3746] lr: 6.487e-02, eta: 3 days, 1:17:09, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5136, loss_cls: 4.2465, loss: 4.2465 +2024-07-18 10:58:41,674 - pyskl - INFO - Epoch [61][2300/3746] lr: 6.484e-02, eta: 3 days, 1:15:53, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5084, loss_cls: 4.2812, loss: 4.2812 +2024-07-18 11:00:02,640 - pyskl - INFO - Epoch [61][2400/3746] lr: 6.481e-02, eta: 3 days, 1:14:37, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5162, loss_cls: 4.2293, loss: 4.2293 +2024-07-18 11:01:23,499 - pyskl - INFO - Epoch [61][2500/3746] lr: 6.478e-02, eta: 3 days, 1:13:22, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5106, loss_cls: 4.2449, loss: 4.2449 +2024-07-18 11:02:44,780 - pyskl - INFO - Epoch [61][2600/3746] lr: 6.476e-02, eta: 3 days, 1:12:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5088, loss_cls: 4.2575, loss: 4.2575 +2024-07-18 11:04:06,626 - pyskl - INFO - Epoch [61][2700/3746] lr: 6.473e-02, eta: 3 days, 1:10:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5061, loss_cls: 4.2734, loss: 4.2734 +2024-07-18 11:05:27,543 - pyskl - INFO - Epoch [61][2800/3746] lr: 6.470e-02, eta: 3 days, 1:09:37, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5077, loss_cls: 4.2696, loss: 4.2696 +2024-07-18 11:06:48,943 - pyskl - INFO - Epoch [61][2900/3746] lr: 6.468e-02, eta: 3 days, 1:08:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5098, loss_cls: 4.2363, loss: 4.2363 +2024-07-18 11:08:09,797 - pyskl - INFO - Epoch [61][3000/3746] lr: 6.465e-02, eta: 3 days, 1:07:06, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5156, loss_cls: 4.2545, loss: 4.2545 +2024-07-18 11:09:31,777 - pyskl - INFO - Epoch [61][3100/3746] lr: 6.462e-02, eta: 3 days, 1:05:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5097, loss_cls: 4.2626, loss: 4.2626 +2024-07-18 11:10:52,498 - pyskl - INFO - Epoch [61][3200/3746] lr: 6.460e-02, eta: 3 days, 1:04:36, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4991, loss_cls: 4.3252, loss: 4.3252 +2024-07-18 11:12:13,923 - pyskl - INFO - Epoch [61][3300/3746] lr: 6.457e-02, eta: 3 days, 1:03:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5150, loss_cls: 4.2386, loss: 4.2386 +2024-07-18 11:13:34,620 - pyskl - INFO - Epoch [61][3400/3746] lr: 6.454e-02, eta: 3 days, 1:02:05, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5073, loss_cls: 4.2449, loss: 4.2449 +2024-07-18 11:14:55,470 - pyskl - INFO - Epoch [61][3500/3746] lr: 6.452e-02, eta: 3 days, 1:00:49, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5119, loss_cls: 4.2422, loss: 4.2422 +2024-07-18 11:16:16,623 - pyskl - INFO - Epoch [61][3600/3746] lr: 6.449e-02, eta: 3 days, 0:59:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4942, loss_cls: 4.3276, loss: 4.3276 +2024-07-18 11:17:38,657 - pyskl - INFO - Epoch [61][3700/3746] lr: 6.446e-02, eta: 3 days, 0:58:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5120, loss_cls: 4.2788, loss: 4.2788 +2024-07-18 11:18:17,681 - pyskl - INFO - Saving checkpoint at 61 epochs +2024-07-18 11:20:07,788 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 11:20:08,451 - pyskl - INFO - +top1_acc 0.1949 +top5_acc 0.4288 +2024-07-18 11:20:08,451 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 11:20:08,490 - pyskl - INFO - +mean_acc 0.1948 +2024-07-18 11:20:08,494 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_44.pth was removed +2024-07-18 11:20:08,729 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_61.pth. +2024-07-18 11:20:08,729 - pyskl - INFO - Best top1_acc is 0.1949 at 61 epoch. +2024-07-18 11:20:08,743 - pyskl - INFO - Epoch(val) [61][309] top1_acc: 0.1949, top5_acc: 0.4288, mean_class_accuracy: 0.1948 +2024-07-18 11:23:56,397 - pyskl - INFO - Epoch [62][100/3746] lr: 6.443e-02, eta: 3 days, 0:59:09, time: 2.276, data_time: 1.306, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5089, loss_cls: 4.2143, loss: 4.2143 +2024-07-18 11:25:18,276 - pyskl - INFO - Epoch [62][200/3746] lr: 6.440e-02, eta: 3 days, 0:57:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5205, loss_cls: 4.1967, loss: 4.1967 +2024-07-18 11:26:39,628 - pyskl - INFO - Epoch [62][300/3746] lr: 6.437e-02, eta: 3 days, 0:56:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5097, loss_cls: 4.2518, loss: 4.2518 +2024-07-18 11:28:00,355 - pyskl - INFO - Epoch [62][400/3746] lr: 6.434e-02, eta: 3 days, 0:55:23, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5002, loss_cls: 4.2583, loss: 4.2583 +2024-07-18 11:29:22,001 - pyskl - INFO - Epoch [62][500/3746] lr: 6.432e-02, eta: 3 days, 0:54:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5133, loss_cls: 4.2393, loss: 4.2393 +2024-07-18 11:30:43,185 - pyskl - INFO - Epoch [62][600/3746] lr: 6.429e-02, eta: 3 days, 0:52:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5098, loss_cls: 4.2466, loss: 4.2466 +2024-07-18 11:32:04,461 - pyskl - INFO - Epoch [62][700/3746] lr: 6.426e-02, eta: 3 days, 0:51:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5172, loss_cls: 4.2093, loss: 4.2093 +2024-07-18 11:33:25,680 - pyskl - INFO - Epoch [62][800/3746] lr: 6.424e-02, eta: 3 days, 0:50:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5114, loss_cls: 4.2792, loss: 4.2792 +2024-07-18 11:34:47,014 - pyskl - INFO - Epoch [62][900/3746] lr: 6.421e-02, eta: 3 days, 0:49:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5103, loss_cls: 4.2533, loss: 4.2533 +2024-07-18 11:36:09,251 - pyskl - INFO - Epoch [62][1000/3746] lr: 6.418e-02, eta: 3 days, 0:47:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5178, loss_cls: 4.2241, loss: 4.2241 +2024-07-18 11:37:30,323 - pyskl - INFO - Epoch [62][1100/3746] lr: 6.416e-02, eta: 3 days, 0:46:38, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5130, loss_cls: 4.2524, loss: 4.2524 +2024-07-18 11:38:51,896 - pyskl - INFO - Epoch [62][1200/3746] lr: 6.413e-02, eta: 3 days, 0:45:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5194, loss_cls: 4.2285, loss: 4.2285 +2024-07-18 11:40:12,840 - pyskl - INFO - Epoch [62][1300/3746] lr: 6.410e-02, eta: 3 days, 0:44:07, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5241, loss_cls: 4.2066, loss: 4.2066 +2024-07-18 11:41:33,921 - pyskl - INFO - Epoch [62][1400/3746] lr: 6.408e-02, eta: 3 days, 0:42:51, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5138, loss_cls: 4.2435, loss: 4.2435 +2024-07-18 11:42:55,692 - pyskl - INFO - Epoch [62][1500/3746] lr: 6.405e-02, eta: 3 days, 0:41:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5017, loss_cls: 4.2410, loss: 4.2410 +2024-07-18 11:44:16,581 - pyskl - INFO - Epoch [62][1600/3746] lr: 6.402e-02, eta: 3 days, 0:40:21, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5038, loss_cls: 4.2917, loss: 4.2917 +2024-07-18 11:45:37,929 - pyskl - INFO - Epoch [62][1700/3746] lr: 6.400e-02, eta: 3 days, 0:39:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5052, loss_cls: 4.2693, loss: 4.2693 +2024-07-18 11:46:59,097 - pyskl - INFO - Epoch [62][1800/3746] lr: 6.397e-02, eta: 3 days, 0:37:50, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5212, loss_cls: 4.2304, loss: 4.2304 +2024-07-18 11:48:20,170 - pyskl - INFO - Epoch [62][1900/3746] lr: 6.394e-02, eta: 3 days, 0:36:34, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5114, loss_cls: 4.2556, loss: 4.2556 +2024-07-18 11:49:41,228 - pyskl - INFO - Epoch [62][2000/3746] lr: 6.392e-02, eta: 3 days, 0:35:19, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.4986, loss_cls: 4.3089, loss: 4.3089 +2024-07-18 11:51:02,723 - pyskl - INFO - Epoch [62][2100/3746] lr: 6.389e-02, eta: 3 days, 0:34:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5045, loss_cls: 4.2751, loss: 4.2751 +2024-07-18 11:52:23,700 - pyskl - INFO - Epoch [62][2200/3746] lr: 6.386e-02, eta: 3 days, 0:32:48, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5064, loss_cls: 4.2516, loss: 4.2516 +2024-07-18 11:53:45,270 - pyskl - INFO - Epoch [62][2300/3746] lr: 6.384e-02, eta: 3 days, 0:31:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5000, loss_cls: 4.2998, loss: 4.2998 +2024-07-18 11:55:06,322 - pyskl - INFO - Epoch [62][2400/3746] lr: 6.381e-02, eta: 3 days, 0:30:17, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5002, loss_cls: 4.2862, loss: 4.2862 +2024-07-18 11:56:27,426 - pyskl - INFO - Epoch [62][2500/3746] lr: 6.378e-02, eta: 3 days, 0:29:02, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5083, loss_cls: 4.2862, loss: 4.2862 +2024-07-18 11:57:49,025 - pyskl - INFO - Epoch [62][2600/3746] lr: 6.375e-02, eta: 3 days, 0:27:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5080, loss_cls: 4.2561, loss: 4.2561 +2024-07-18 11:59:10,521 - pyskl - INFO - Epoch [62][2700/3746] lr: 6.373e-02, eta: 3 days, 0:26:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5069, loss_cls: 4.2436, loss: 4.2436 +2024-07-18 12:00:31,947 - pyskl - INFO - Epoch [62][2800/3746] lr: 6.370e-02, eta: 3 days, 0:25:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4992, loss_cls: 4.3076, loss: 4.3076 +2024-07-18 12:01:53,363 - pyskl - INFO - Epoch [62][2900/3746] lr: 6.367e-02, eta: 3 days, 0:24:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5183, loss_cls: 4.2369, loss: 4.2369 +2024-07-18 12:03:14,306 - pyskl - INFO - Epoch [62][3000/3746] lr: 6.365e-02, eta: 3 days, 0:22:45, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5212, loss_cls: 4.2259, loss: 4.2259 +2024-07-18 12:04:35,514 - pyskl - INFO - Epoch [62][3100/3746] lr: 6.362e-02, eta: 3 days, 0:21:30, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5188, loss_cls: 4.2143, loss: 4.2143 +2024-07-18 12:05:56,479 - pyskl - INFO - Epoch [62][3200/3746] lr: 6.359e-02, eta: 3 days, 0:20:14, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5133, loss_cls: 4.2223, loss: 4.2223 +2024-07-18 12:07:18,542 - pyskl - INFO - Epoch [62][3300/3746] lr: 6.357e-02, eta: 3 days, 0:19:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5027, loss_cls: 4.2777, loss: 4.2777 +2024-07-18 12:08:39,970 - pyskl - INFO - Epoch [62][3400/3746] lr: 6.354e-02, eta: 3 days, 0:17:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5025, loss_cls: 4.2731, loss: 4.2731 +2024-07-18 12:10:01,277 - pyskl - INFO - Epoch [62][3500/3746] lr: 6.351e-02, eta: 3 days, 0:16:29, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5130, loss_cls: 4.2295, loss: 4.2295 +2024-07-18 12:11:23,004 - pyskl - INFO - Epoch [62][3600/3746] lr: 6.349e-02, eta: 3 days, 0:15:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5161, loss_cls: 4.2162, loss: 4.2162 +2024-07-18 12:12:44,538 - pyskl - INFO - Epoch [62][3700/3746] lr: 6.346e-02, eta: 3 days, 0:13:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5102, loss_cls: 4.2881, loss: 4.2881 +2024-07-18 12:13:23,867 - pyskl - INFO - Saving checkpoint at 62 epochs +2024-07-18 12:15:14,778 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 12:15:15,460 - pyskl - INFO - +top1_acc 0.1839 +top5_acc 0.4164 +2024-07-18 12:15:15,461 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 12:15:15,516 - pyskl - INFO - +mean_acc 0.1838 +2024-07-18 12:15:15,535 - pyskl - INFO - Epoch(val) [62][309] top1_acc: 0.1839, top5_acc: 0.4164, mean_class_accuracy: 0.1838 +2024-07-18 12:19:06,116 - pyskl - INFO - Epoch [63][100/3746] lr: 6.342e-02, eta: 3 days, 0:14:48, time: 2.306, data_time: 1.304, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5139, loss_cls: 4.2361, loss: 4.2361 +2024-07-18 12:20:29,130 - pyskl - INFO - Epoch [63][200/3746] lr: 6.339e-02, eta: 3 days, 0:13:34, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5148, loss_cls: 4.2420, loss: 4.2420 +2024-07-18 12:21:52,386 - pyskl - INFO - Epoch [63][300/3746] lr: 6.337e-02, eta: 3 days, 0:12:22, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5169, loss_cls: 4.2138, loss: 4.2138 +2024-07-18 12:23:15,092 - pyskl - INFO - Epoch [63][400/3746] lr: 6.334e-02, eta: 3 days, 0:11:08, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5139, loss_cls: 4.2321, loss: 4.2321 +2024-07-18 12:24:38,072 - pyskl - INFO - Epoch [63][500/3746] lr: 6.331e-02, eta: 3 days, 0:09:55, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5214, loss_cls: 4.2202, loss: 4.2202 +2024-07-18 12:26:01,259 - pyskl - INFO - Epoch [63][600/3746] lr: 6.328e-02, eta: 3 days, 0:08:42, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5192, loss_cls: 4.2176, loss: 4.2176 +2024-07-18 12:27:24,648 - pyskl - INFO - Epoch [63][700/3746] lr: 6.326e-02, eta: 3 days, 0:07:29, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5116, loss_cls: 4.2308, loss: 4.2308 +2024-07-18 12:28:47,881 - pyskl - INFO - Epoch [63][800/3746] lr: 6.323e-02, eta: 3 days, 0:06:16, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5067, loss_cls: 4.2937, loss: 4.2937 +2024-07-18 12:30:10,459 - pyskl - INFO - Epoch [63][900/3746] lr: 6.320e-02, eta: 3 days, 0:05:03, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5036, loss_cls: 4.2740, loss: 4.2740 +2024-07-18 12:31:32,670 - pyskl - INFO - Epoch [63][1000/3746] lr: 6.318e-02, eta: 3 days, 0:03:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5211, loss_cls: 4.2126, loss: 4.2126 +2024-07-18 12:32:53,828 - pyskl - INFO - Epoch [63][1100/3746] lr: 6.315e-02, eta: 3 days, 0:02:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5039, loss_cls: 4.2524, loss: 4.2524 +2024-07-18 12:34:15,111 - pyskl - INFO - Epoch [63][1200/3746] lr: 6.312e-02, eta: 3 days, 0:01:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5130, loss_cls: 4.2262, loss: 4.2262 +2024-07-18 12:35:37,031 - pyskl - INFO - Epoch [63][1300/3746] lr: 6.310e-02, eta: 3 days, 0:00:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5061, loss_cls: 4.2646, loss: 4.2646 +2024-07-18 12:36:57,875 - pyskl - INFO - Epoch [63][1400/3746] lr: 6.307e-02, eta: 2 days, 23:58:46, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5155, loss_cls: 4.2451, loss: 4.2451 +2024-07-18 12:38:19,220 - pyskl - INFO - Epoch [63][1500/3746] lr: 6.304e-02, eta: 2 days, 23:57:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5102, loss_cls: 4.2464, loss: 4.2464 +2024-07-18 12:39:40,394 - pyskl - INFO - Epoch [63][1600/3746] lr: 6.301e-02, eta: 2 days, 23:56:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5036, loss_cls: 4.2663, loss: 4.2663 +2024-07-18 12:41:01,379 - pyskl - INFO - Epoch [63][1700/3746] lr: 6.299e-02, eta: 2 days, 23:54:58, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4991, loss_cls: 4.2785, loss: 4.2785 +2024-07-18 12:42:22,107 - pyskl - INFO - Epoch [63][1800/3746] lr: 6.296e-02, eta: 2 days, 23:53:42, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5022, loss_cls: 4.2895, loss: 4.2895 +2024-07-18 12:43:43,324 - pyskl - INFO - Epoch [63][1900/3746] lr: 6.293e-02, eta: 2 days, 23:52:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5195, loss_cls: 4.1934, loss: 4.1934 +2024-07-18 12:45:04,199 - pyskl - INFO - Epoch [63][2000/3746] lr: 6.291e-02, eta: 2 days, 23:51:10, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5148, loss_cls: 4.2257, loss: 4.2257 +2024-07-18 12:46:25,922 - pyskl - INFO - Epoch [63][2100/3746] lr: 6.288e-02, eta: 2 days, 23:49:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.4966, loss_cls: 4.2869, loss: 4.2869 +2024-07-18 12:47:47,775 - pyskl - INFO - Epoch [63][2200/3746] lr: 6.285e-02, eta: 2 days, 23:48:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4916, loss_cls: 4.3221, loss: 4.3221 +2024-07-18 12:49:08,865 - pyskl - INFO - Epoch [63][2300/3746] lr: 6.283e-02, eta: 2 days, 23:47:24, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5164, loss_cls: 4.2291, loss: 4.2291 +2024-07-18 12:50:29,866 - pyskl - INFO - Epoch [63][2400/3746] lr: 6.280e-02, eta: 2 days, 23:46:08, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5186, loss_cls: 4.2270, loss: 4.2270 +2024-07-18 12:51:51,348 - pyskl - INFO - Epoch [63][2500/3746] lr: 6.277e-02, eta: 2 days, 23:44:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.5017, loss_cls: 4.3024, loss: 4.3024 +2024-07-18 12:53:12,521 - pyskl - INFO - Epoch [63][2600/3746] lr: 6.274e-02, eta: 2 days, 23:43:37, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5173, loss_cls: 4.1898, loss: 4.1898 +2024-07-18 12:54:34,030 - pyskl - INFO - Epoch [63][2700/3746] lr: 6.272e-02, eta: 2 days, 23:42:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5008, loss_cls: 4.2771, loss: 4.2771 +2024-07-18 12:55:55,141 - pyskl - INFO - Epoch [63][2800/3746] lr: 6.269e-02, eta: 2 days, 23:41:05, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5128, loss_cls: 4.2134, loss: 4.2134 +2024-07-18 12:57:16,116 - pyskl - INFO - Epoch [63][2900/3746] lr: 6.266e-02, eta: 2 days, 23:39:49, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5161, loss_cls: 4.2163, loss: 4.2163 +2024-07-18 12:58:37,236 - pyskl - INFO - Epoch [63][3000/3746] lr: 6.264e-02, eta: 2 days, 23:38:33, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5183, loss_cls: 4.1852, loss: 4.1852 +2024-07-18 12:59:58,664 - pyskl - INFO - Epoch [63][3100/3746] lr: 6.261e-02, eta: 2 days, 23:37:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5216, loss_cls: 4.2019, loss: 4.2019 +2024-07-18 13:01:19,646 - pyskl - INFO - Epoch [63][3200/3746] lr: 6.258e-02, eta: 2 days, 23:36:01, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5114, loss_cls: 4.2662, loss: 4.2662 +2024-07-18 13:02:41,206 - pyskl - INFO - Epoch [63][3300/3746] lr: 6.256e-02, eta: 2 days, 23:34:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5061, loss_cls: 4.2688, loss: 4.2688 +2024-07-18 13:04:02,760 - pyskl - INFO - Epoch [63][3400/3746] lr: 6.253e-02, eta: 2 days, 23:33:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5117, loss_cls: 4.2508, loss: 4.2508 +2024-07-18 13:05:23,894 - pyskl - INFO - Epoch [63][3500/3746] lr: 6.250e-02, eta: 2 days, 23:32:15, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5162, loss_cls: 4.2449, loss: 4.2449 +2024-07-18 13:06:45,518 - pyskl - INFO - Epoch [63][3600/3746] lr: 6.247e-02, eta: 2 days, 23:30:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5156, loss_cls: 4.2222, loss: 4.2222 +2024-07-18 13:08:07,350 - pyskl - INFO - Epoch [63][3700/3746] lr: 6.245e-02, eta: 2 days, 23:29:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5173, loss_cls: 4.2171, loss: 4.2171 +2024-07-18 13:08:46,588 - pyskl - INFO - Saving checkpoint at 63 epochs +2024-07-18 13:10:38,563 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 13:10:39,241 - pyskl - INFO - +top1_acc 0.2008 +top5_acc 0.4293 +2024-07-18 13:10:39,241 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 13:10:39,290 - pyskl - INFO - +mean_acc 0.2006 +2024-07-18 13:10:39,294 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_61.pth was removed +2024-07-18 13:10:39,541 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_63.pth. +2024-07-18 13:10:39,542 - pyskl - INFO - Best top1_acc is 0.2008 at 63 epoch. +2024-07-18 13:10:39,555 - pyskl - INFO - Epoch(val) [63][309] top1_acc: 0.2008, top5_acc: 0.4293, mean_class_accuracy: 0.2006 +2024-07-18 13:14:28,182 - pyskl - INFO - Epoch [64][100/3746] lr: 6.241e-02, eta: 2 days, 23:30:26, time: 2.286, data_time: 1.292, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5217, loss_cls: 4.1937, loss: 4.1937 +2024-07-18 13:15:50,744 - pyskl - INFO - Epoch [64][200/3746] lr: 6.238e-02, eta: 2 days, 23:29:11, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5302, loss_cls: 4.1349, loss: 4.1349 +2024-07-18 13:17:13,055 - pyskl - INFO - Epoch [64][300/3746] lr: 6.235e-02, eta: 2 days, 23:27:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5283, loss_cls: 4.1813, loss: 4.1813 +2024-07-18 13:18:34,777 - pyskl - INFO - Epoch [64][400/3746] lr: 6.233e-02, eta: 2 days, 23:26:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5062, loss_cls: 4.2442, loss: 4.2442 +2024-07-18 13:19:56,691 - pyskl - INFO - Epoch [64][500/3746] lr: 6.230e-02, eta: 2 days, 23:25:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5114, loss_cls: 4.2540, loss: 4.2540 +2024-07-18 13:21:19,005 - pyskl - INFO - Epoch [64][600/3746] lr: 6.227e-02, eta: 2 days, 23:24:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5070, loss_cls: 4.2351, loss: 4.2351 +2024-07-18 13:22:41,198 - pyskl - INFO - Epoch [64][700/3746] lr: 6.225e-02, eta: 2 days, 23:22:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5183, loss_cls: 4.2545, loss: 4.2545 +2024-07-18 13:24:03,460 - pyskl - INFO - Epoch [64][800/3746] lr: 6.222e-02, eta: 2 days, 23:21:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5117, loss_cls: 4.2445, loss: 4.2445 +2024-07-18 13:25:25,804 - pyskl - INFO - Epoch [64][900/3746] lr: 6.219e-02, eta: 2 days, 23:20:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.5002, loss_cls: 4.3043, loss: 4.3043 +2024-07-18 13:26:47,733 - pyskl - INFO - Epoch [64][1000/3746] lr: 6.216e-02, eta: 2 days, 23:19:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5062, loss_cls: 4.2610, loss: 4.2610 +2024-07-18 13:28:09,925 - pyskl - INFO - Epoch [64][1100/3746] lr: 6.214e-02, eta: 2 days, 23:17:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5084, loss_cls: 4.2320, loss: 4.2320 +2024-07-18 13:29:31,867 - pyskl - INFO - Epoch [64][1200/3746] lr: 6.211e-02, eta: 2 days, 23:16:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5205, loss_cls: 4.1889, loss: 4.1889 +2024-07-18 13:30:53,875 - pyskl - INFO - Epoch [64][1300/3746] lr: 6.208e-02, eta: 2 days, 23:15:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5159, loss_cls: 4.2269, loss: 4.2269 +2024-07-18 13:32:15,839 - pyskl - INFO - Epoch [64][1400/3746] lr: 6.206e-02, eta: 2 days, 23:14:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5177, loss_cls: 4.2235, loss: 4.2235 +2024-07-18 13:33:37,242 - pyskl - INFO - Epoch [64][1500/3746] lr: 6.203e-02, eta: 2 days, 23:12:58, time: 0.814, data_time: 0.001, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5123, loss_cls: 4.2474, loss: 4.2474 +2024-07-18 13:34:59,327 - pyskl - INFO - Epoch [64][1600/3746] lr: 6.200e-02, eta: 2 days, 23:11:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5130, loss_cls: 4.2563, loss: 4.2563 +2024-07-18 13:36:20,674 - pyskl - INFO - Epoch [64][1700/3746] lr: 6.197e-02, eta: 2 days, 23:10:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5238, loss_cls: 4.2075, loss: 4.2075 +2024-07-18 13:37:41,681 - pyskl - INFO - Epoch [64][1800/3746] lr: 6.195e-02, eta: 2 days, 23:09:11, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5212, loss_cls: 4.2071, loss: 4.2071 +2024-07-18 13:39:02,941 - pyskl - INFO - Epoch [64][1900/3746] lr: 6.192e-02, eta: 2 days, 23:07:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5181, loss_cls: 4.2520, loss: 4.2520 +2024-07-18 13:40:23,803 - pyskl - INFO - Epoch [64][2000/3746] lr: 6.189e-02, eta: 2 days, 23:06:38, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5159, loss_cls: 4.2202, loss: 4.2202 +2024-07-18 13:41:44,744 - pyskl - INFO - Epoch [64][2100/3746] lr: 6.187e-02, eta: 2 days, 23:05:22, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5220, loss_cls: 4.2108, loss: 4.2108 +2024-07-18 13:43:05,758 - pyskl - INFO - Epoch [64][2200/3746] lr: 6.184e-02, eta: 2 days, 23:04:05, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5156, loss_cls: 4.2390, loss: 4.2390 +2024-07-18 13:44:27,447 - pyskl - INFO - Epoch [64][2300/3746] lr: 6.181e-02, eta: 2 days, 23:02:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5055, loss_cls: 4.2612, loss: 4.2612 +2024-07-18 13:45:48,224 - pyskl - INFO - Epoch [64][2400/3746] lr: 6.178e-02, eta: 2 days, 23:01:33, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5131, loss_cls: 4.2277, loss: 4.2277 +2024-07-18 13:47:09,481 - pyskl - INFO - Epoch [64][2500/3746] lr: 6.176e-02, eta: 2 days, 23:00:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5155, loss_cls: 4.2260, loss: 4.2260 +2024-07-18 13:48:31,313 - pyskl - INFO - Epoch [64][2600/3746] lr: 6.173e-02, eta: 2 days, 22:59:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5117, loss_cls: 4.2398, loss: 4.2398 +2024-07-18 13:49:52,253 - pyskl - INFO - Epoch [64][2700/3746] lr: 6.170e-02, eta: 2 days, 22:57:46, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5056, loss_cls: 4.2462, loss: 4.2462 +2024-07-18 13:51:13,650 - pyskl - INFO - Epoch [64][2800/3746] lr: 6.168e-02, eta: 2 days, 22:56:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5122, loss_cls: 4.2052, loss: 4.2052 +2024-07-18 13:52:35,316 - pyskl - INFO - Epoch [64][2900/3746] lr: 6.165e-02, eta: 2 days, 22:55:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5119, loss_cls: 4.2323, loss: 4.2323 +2024-07-18 13:53:56,625 - pyskl - INFO - Epoch [64][3000/3746] lr: 6.162e-02, eta: 2 days, 22:53:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5123, loss_cls: 4.2633, loss: 4.2633 +2024-07-18 13:55:18,310 - pyskl - INFO - Epoch [64][3100/3746] lr: 6.159e-02, eta: 2 days, 22:52:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5069, loss_cls: 4.2652, loss: 4.2652 +2024-07-18 13:56:39,909 - pyskl - INFO - Epoch [64][3200/3746] lr: 6.157e-02, eta: 2 days, 22:51:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5108, loss_cls: 4.2633, loss: 4.2633 +2024-07-18 13:58:00,628 - pyskl - INFO - Epoch [64][3300/3746] lr: 6.154e-02, eta: 2 days, 22:50:10, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5194, loss_cls: 4.2072, loss: 4.2072 +2024-07-18 13:59:21,513 - pyskl - INFO - Epoch [64][3400/3746] lr: 6.151e-02, eta: 2 days, 22:48:54, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5025, loss_cls: 4.2867, loss: 4.2867 +2024-07-18 14:00:42,543 - pyskl - INFO - Epoch [64][3500/3746] lr: 6.148e-02, eta: 2 days, 22:47:37, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5142, loss_cls: 4.2570, loss: 4.2570 +2024-07-18 14:02:04,172 - pyskl - INFO - Epoch [64][3600/3746] lr: 6.146e-02, eta: 2 days, 22:46:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5206, loss_cls: 4.2036, loss: 4.2036 +2024-07-18 14:03:25,862 - pyskl - INFO - Epoch [64][3700/3746] lr: 6.143e-02, eta: 2 days, 22:45:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5130, loss_cls: 4.2425, loss: 4.2425 +2024-07-18 14:04:05,549 - pyskl - INFO - Saving checkpoint at 64 epochs +2024-07-18 14:05:58,316 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 14:05:58,978 - pyskl - INFO - +top1_acc 0.1691 +top5_acc 0.3814 +2024-07-18 14:05:58,979 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 14:05:59,018 - pyskl - INFO - +mean_acc 0.1688 +2024-07-18 14:05:59,030 - pyskl - INFO - Epoch(val) [64][309] top1_acc: 0.1691, top5_acc: 0.3814, mean_class_accuracy: 0.1688 +2024-07-18 14:09:57,698 - pyskl - INFO - Epoch [65][100/3746] lr: 6.139e-02, eta: 2 days, 22:45:56, time: 2.387, data_time: 1.373, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5195, loss_cls: 4.1898, loss: 4.1898 +2024-07-18 14:11:21,565 - pyskl - INFO - Epoch [65][200/3746] lr: 6.136e-02, eta: 2 days, 22:44:44, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5228, loss_cls: 4.2063, loss: 4.2063 +2024-07-18 14:12:44,934 - pyskl - INFO - Epoch [65][300/3746] lr: 6.134e-02, eta: 2 days, 22:43:30, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5164, loss_cls: 4.2196, loss: 4.2196 +2024-07-18 14:14:08,001 - pyskl - INFO - Epoch [65][400/3746] lr: 6.131e-02, eta: 2 days, 22:42:16, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5227, loss_cls: 4.1622, loss: 4.1622 +2024-07-18 14:15:31,088 - pyskl - INFO - Epoch [65][500/3746] lr: 6.128e-02, eta: 2 days, 22:41:03, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5206, loss_cls: 4.2140, loss: 4.2140 +2024-07-18 14:16:54,665 - pyskl - INFO - Epoch [65][600/3746] lr: 6.125e-02, eta: 2 days, 22:39:50, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5181, loss_cls: 4.2075, loss: 4.2075 +2024-07-18 14:18:17,678 - pyskl - INFO - Epoch [65][700/3746] lr: 6.123e-02, eta: 2 days, 22:38:36, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5128, loss_cls: 4.2212, loss: 4.2212 +2024-07-18 14:19:40,028 - pyskl - INFO - Epoch [65][800/3746] lr: 6.120e-02, eta: 2 days, 22:37:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5178, loss_cls: 4.2313, loss: 4.2313 +2024-07-18 14:21:01,523 - pyskl - INFO - Epoch [65][900/3746] lr: 6.117e-02, eta: 2 days, 22:36:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5208, loss_cls: 4.1895, loss: 4.1895 +2024-07-18 14:22:22,825 - pyskl - INFO - Epoch [65][1000/3746] lr: 6.115e-02, eta: 2 days, 22:34:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5166, loss_cls: 4.2381, loss: 4.2381 +2024-07-18 14:23:44,518 - pyskl - INFO - Epoch [65][1100/3746] lr: 6.112e-02, eta: 2 days, 22:33:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5131, loss_cls: 4.2317, loss: 4.2317 +2024-07-18 14:25:05,983 - pyskl - INFO - Epoch [65][1200/3746] lr: 6.109e-02, eta: 2 days, 22:32:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5128, loss_cls: 4.2152, loss: 4.2152 +2024-07-18 14:26:27,610 - pyskl - INFO - Epoch [65][1300/3746] lr: 6.106e-02, eta: 2 days, 22:31:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5234, loss_cls: 4.2025, loss: 4.2025 +2024-07-18 14:27:49,498 - pyskl - INFO - Epoch [65][1400/3746] lr: 6.104e-02, eta: 2 days, 22:29:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5267, loss_cls: 4.2031, loss: 4.2031 +2024-07-18 14:29:10,859 - pyskl - INFO - Epoch [65][1500/3746] lr: 6.101e-02, eta: 2 days, 22:28:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.5047, loss_cls: 4.2818, loss: 4.2818 +2024-07-18 14:30:32,082 - pyskl - INFO - Epoch [65][1600/3746] lr: 6.098e-02, eta: 2 days, 22:27:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5223, loss_cls: 4.1943, loss: 4.1943 +2024-07-18 14:31:54,031 - pyskl - INFO - Epoch [65][1700/3746] lr: 6.095e-02, eta: 2 days, 22:25:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5138, loss_cls: 4.2281, loss: 4.2281 +2024-07-18 14:33:15,496 - pyskl - INFO - Epoch [65][1800/3746] lr: 6.093e-02, eta: 2 days, 22:24:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5097, loss_cls: 4.2522, loss: 4.2522 +2024-07-18 14:34:36,519 - pyskl - INFO - Epoch [65][1900/3746] lr: 6.090e-02, eta: 2 days, 22:23:25, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5159, loss_cls: 4.2453, loss: 4.2453 +2024-07-18 14:35:58,084 - pyskl - INFO - Epoch [65][2000/3746] lr: 6.087e-02, eta: 2 days, 22:22:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5062, loss_cls: 4.2854, loss: 4.2854 +2024-07-18 14:37:19,836 - pyskl - INFO - Epoch [65][2100/3746] lr: 6.085e-02, eta: 2 days, 22:20:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5170, loss_cls: 4.2149, loss: 4.2149 +2024-07-18 14:38:40,802 - pyskl - INFO - Epoch [65][2200/3746] lr: 6.082e-02, eta: 2 days, 22:19:37, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5105, loss_cls: 4.2274, loss: 4.2274 +2024-07-18 14:40:02,138 - pyskl - INFO - Epoch [65][2300/3746] lr: 6.079e-02, eta: 2 days, 22:18:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5208, loss_cls: 4.1865, loss: 4.1865 +2024-07-18 14:41:23,155 - pyskl - INFO - Epoch [65][2400/3746] lr: 6.076e-02, eta: 2 days, 22:17:04, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5108, loss_cls: 4.2586, loss: 4.2586 +2024-07-18 14:42:44,626 - pyskl - INFO - Epoch [65][2500/3746] lr: 6.074e-02, eta: 2 days, 22:15:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5153, loss_cls: 4.2184, loss: 4.2184 +2024-07-18 14:44:05,791 - pyskl - INFO - Epoch [65][2600/3746] lr: 6.071e-02, eta: 2 days, 22:14:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5139, loss_cls: 4.2202, loss: 4.2202 +2024-07-18 14:45:27,934 - pyskl - INFO - Epoch [65][2700/3746] lr: 6.068e-02, eta: 2 days, 22:13:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5158, loss_cls: 4.2380, loss: 4.2380 +2024-07-18 14:46:49,074 - pyskl - INFO - Epoch [65][2800/3746] lr: 6.065e-02, eta: 2 days, 22:12:00, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5152, loss_cls: 4.2014, loss: 4.2014 +2024-07-18 14:48:10,021 - pyskl - INFO - Epoch [65][2900/3746] lr: 6.063e-02, eta: 2 days, 22:10:43, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5103, loss_cls: 4.2655, loss: 4.2655 +2024-07-18 14:49:31,197 - pyskl - INFO - Epoch [65][3000/3746] lr: 6.060e-02, eta: 2 days, 22:09:27, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5150, loss_cls: 4.2208, loss: 4.2208 +2024-07-18 14:50:53,259 - pyskl - INFO - Epoch [65][3100/3746] lr: 6.057e-02, eta: 2 days, 22:08:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5194, loss_cls: 4.2270, loss: 4.2270 +2024-07-18 14:52:14,496 - pyskl - INFO - Epoch [65][3200/3746] lr: 6.055e-02, eta: 2 days, 22:06:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5127, loss_cls: 4.2413, loss: 4.2413 +2024-07-18 14:53:35,595 - pyskl - INFO - Epoch [65][3300/3746] lr: 6.052e-02, eta: 2 days, 22:05:39, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5066, loss_cls: 4.2578, loss: 4.2578 +2024-07-18 14:54:56,774 - pyskl - INFO - Epoch [65][3400/3746] lr: 6.049e-02, eta: 2 days, 22:04:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5136, loss_cls: 4.2715, loss: 4.2715 +2024-07-18 14:56:17,890 - pyskl - INFO - Epoch [65][3500/3746] lr: 6.046e-02, eta: 2 days, 22:03:06, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5280, loss_cls: 4.1702, loss: 4.1702 +2024-07-18 14:57:39,413 - pyskl - INFO - Epoch [65][3600/3746] lr: 6.044e-02, eta: 2 days, 22:01:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5130, loss_cls: 4.2728, loss: 4.2728 +2024-07-18 14:59:00,643 - pyskl - INFO - Epoch [65][3700/3746] lr: 6.041e-02, eta: 2 days, 22:00:33, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5034, loss_cls: 4.2235, loss: 4.2235 +2024-07-18 14:59:40,141 - pyskl - INFO - Saving checkpoint at 65 epochs +2024-07-18 15:01:30,878 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 15:01:31,594 - pyskl - INFO - +top1_acc 0.1881 +top5_acc 0.4133 +2024-07-18 15:01:31,594 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 15:01:31,639 - pyskl - INFO - +mean_acc 0.1878 +2024-07-18 15:01:31,651 - pyskl - INFO - Epoch(val) [65][309] top1_acc: 0.1881, top5_acc: 0.4133, mean_class_accuracy: 0.1878 +2024-07-18 15:05:14,859 - pyskl - INFO - Epoch [66][100/3746] lr: 6.037e-02, eta: 2 days, 22:00:58, time: 2.232, data_time: 1.273, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5144, loss_cls: 4.2219, loss: 4.2219 +2024-07-18 15:06:36,970 - pyskl - INFO - Epoch [66][200/3746] lr: 6.034e-02, eta: 2 days, 21:59:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5275, loss_cls: 4.1786, loss: 4.1786 +2024-07-18 15:07:58,241 - pyskl - INFO - Epoch [66][300/3746] lr: 6.031e-02, eta: 2 days, 21:58:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5205, loss_cls: 4.1839, loss: 4.1839 +2024-07-18 15:09:19,250 - pyskl - INFO - Epoch [66][400/3746] lr: 6.029e-02, eta: 2 days, 21:57:10, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5164, loss_cls: 4.2158, loss: 4.2158 +2024-07-18 15:10:40,345 - pyskl - INFO - Epoch [66][500/3746] lr: 6.026e-02, eta: 2 days, 21:55:53, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5039, loss_cls: 4.2572, loss: 4.2572 +2024-07-18 15:12:01,476 - pyskl - INFO - Epoch [66][600/3746] lr: 6.023e-02, eta: 2 days, 21:54:36, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5248, loss_cls: 4.1864, loss: 4.1864 +2024-07-18 15:13:22,302 - pyskl - INFO - Epoch [66][700/3746] lr: 6.020e-02, eta: 2 days, 21:53:19, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5236, loss_cls: 4.2234, loss: 4.2234 +2024-07-18 15:14:43,465 - pyskl - INFO - Epoch [66][800/3746] lr: 6.018e-02, eta: 2 days, 21:52:03, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5283, loss_cls: 4.2039, loss: 4.2039 +2024-07-18 15:16:04,604 - pyskl - INFO - Epoch [66][900/3746] lr: 6.015e-02, eta: 2 days, 21:50:46, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5206, loss_cls: 4.2200, loss: 4.2200 +2024-07-18 15:17:25,759 - pyskl - INFO - Epoch [66][1000/3746] lr: 6.012e-02, eta: 2 days, 21:49:29, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5242, loss_cls: 4.1716, loss: 4.1716 +2024-07-18 15:18:47,329 - pyskl - INFO - Epoch [66][1100/3746] lr: 6.009e-02, eta: 2 days, 21:48:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5177, loss_cls: 4.2004, loss: 4.2004 +2024-07-18 15:20:08,996 - pyskl - INFO - Epoch [66][1200/3746] lr: 6.007e-02, eta: 2 days, 21:46:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5080, loss_cls: 4.2638, loss: 4.2638 +2024-07-18 15:21:30,423 - pyskl - INFO - Epoch [66][1300/3746] lr: 6.004e-02, eta: 2 days, 21:45:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5245, loss_cls: 4.1810, loss: 4.1810 +2024-07-18 15:22:52,161 - pyskl - INFO - Epoch [66][1400/3746] lr: 6.001e-02, eta: 2 days, 21:44:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5195, loss_cls: 4.2035, loss: 4.2035 +2024-07-18 15:24:14,058 - pyskl - INFO - Epoch [66][1500/3746] lr: 5.999e-02, eta: 2 days, 21:43:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5123, loss_cls: 4.2212, loss: 4.2212 +2024-07-18 15:25:35,293 - pyskl - INFO - Epoch [66][1600/3746] lr: 5.996e-02, eta: 2 days, 21:41:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5175, loss_cls: 4.2151, loss: 4.2151 +2024-07-18 15:26:55,825 - pyskl - INFO - Epoch [66][1700/3746] lr: 5.993e-02, eta: 2 days, 21:40:35, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5178, loss_cls: 4.2207, loss: 4.2207 +2024-07-18 15:28:17,445 - pyskl - INFO - Epoch [66][1800/3746] lr: 5.990e-02, eta: 2 days, 21:39:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5089, loss_cls: 4.2227, loss: 4.2227 +2024-07-18 15:29:39,581 - pyskl - INFO - Epoch [66][1900/3746] lr: 5.988e-02, eta: 2 days, 21:38:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5230, loss_cls: 4.2110, loss: 4.2110 +2024-07-18 15:31:00,896 - pyskl - INFO - Epoch [66][2000/3746] lr: 5.985e-02, eta: 2 days, 21:36:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5103, loss_cls: 4.2557, loss: 4.2557 +2024-07-18 15:32:22,016 - pyskl - INFO - Epoch [66][2100/3746] lr: 5.982e-02, eta: 2 days, 21:35:30, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5227, loss_cls: 4.1991, loss: 4.1991 +2024-07-18 15:33:43,001 - pyskl - INFO - Epoch [66][2200/3746] lr: 5.979e-02, eta: 2 days, 21:34:14, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5238, loss_cls: 4.1785, loss: 4.1785 +2024-07-18 15:35:04,146 - pyskl - INFO - Epoch [66][2300/3746] lr: 5.977e-02, eta: 2 days, 21:32:57, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5044, loss_cls: 4.2651, loss: 4.2651 +2024-07-18 15:36:25,214 - pyskl - INFO - Epoch [66][2400/3746] lr: 5.974e-02, eta: 2 days, 21:31:40, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5142, loss_cls: 4.2380, loss: 4.2380 +2024-07-18 15:37:46,309 - pyskl - INFO - Epoch [66][2500/3746] lr: 5.971e-02, eta: 2 days, 21:30:23, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5120, loss_cls: 4.2146, loss: 4.2146 +2024-07-18 15:39:08,171 - pyskl - INFO - Epoch [66][2600/3746] lr: 5.968e-02, eta: 2 days, 21:29:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5180, loss_cls: 4.1905, loss: 4.1905 +2024-07-18 15:40:29,486 - pyskl - INFO - Epoch [66][2700/3746] lr: 5.966e-02, eta: 2 days, 21:27:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5273, loss_cls: 4.1793, loss: 4.1793 +2024-07-18 15:41:51,319 - pyskl - INFO - Epoch [66][2800/3746] lr: 5.963e-02, eta: 2 days, 21:26:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5119, loss_cls: 4.2160, loss: 4.2160 +2024-07-18 15:43:12,434 - pyskl - INFO - Epoch [66][2900/3746] lr: 5.960e-02, eta: 2 days, 21:25:18, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5230, loss_cls: 4.2077, loss: 4.2077 +2024-07-18 15:44:33,347 - pyskl - INFO - Epoch [66][3000/3746] lr: 5.957e-02, eta: 2 days, 21:24:01, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5216, loss_cls: 4.2231, loss: 4.2231 +2024-07-18 15:45:55,210 - pyskl - INFO - Epoch [66][3100/3746] lr: 5.955e-02, eta: 2 days, 21:22:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5241, loss_cls: 4.2098, loss: 4.2098 +2024-07-18 15:47:16,271 - pyskl - INFO - Epoch [66][3200/3746] lr: 5.952e-02, eta: 2 days, 21:21:29, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5289, loss_cls: 4.1680, loss: 4.1680 +2024-07-18 15:48:36,919 - pyskl - INFO - Epoch [66][3300/3746] lr: 5.949e-02, eta: 2 days, 21:20:11, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5128, loss_cls: 4.2403, loss: 4.2403 +2024-07-18 15:49:58,071 - pyskl - INFO - Epoch [66][3400/3746] lr: 5.946e-02, eta: 2 days, 21:18:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5152, loss_cls: 4.2091, loss: 4.2091 +2024-07-18 15:51:19,499 - pyskl - INFO - Epoch [66][3500/3746] lr: 5.944e-02, eta: 2 days, 21:17:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5250, loss_cls: 4.1695, loss: 4.1695 +2024-07-18 15:52:41,274 - pyskl - INFO - Epoch [66][3600/3746] lr: 5.941e-02, eta: 2 days, 21:16:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5122, loss_cls: 4.2529, loss: 4.2529 +2024-07-18 15:54:02,910 - pyskl - INFO - Epoch [66][3700/3746] lr: 5.938e-02, eta: 2 days, 21:15:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5078, loss_cls: 4.2339, loss: 4.2339 +2024-07-18 15:54:42,276 - pyskl - INFO - Saving checkpoint at 66 epochs +2024-07-18 15:56:32,776 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 15:56:33,434 - pyskl - INFO - +top1_acc 0.2112 +top5_acc 0.4435 +2024-07-18 15:56:33,434 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 15:56:33,472 - pyskl - INFO - +mean_acc 0.2108 +2024-07-18 15:56:33,477 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_63.pth was removed +2024-07-18 15:56:33,760 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_66.pth. +2024-07-18 15:56:33,760 - pyskl - INFO - Best top1_acc is 0.2112 at 66 epoch. +2024-07-18 15:56:33,771 - pyskl - INFO - Epoch(val) [66][309] top1_acc: 0.2112, top5_acc: 0.4435, mean_class_accuracy: 0.2108 +2024-07-18 16:00:19,012 - pyskl - INFO - Epoch [67][100/3746] lr: 5.934e-02, eta: 2 days, 21:15:30, time: 2.252, data_time: 1.286, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5270, loss_cls: 4.1487, loss: 4.1487 +2024-07-18 16:01:40,571 - pyskl - INFO - Epoch [67][200/3746] lr: 5.931e-02, eta: 2 days, 21:14:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5291, loss_cls: 4.1368, loss: 4.1368 +2024-07-18 16:03:02,293 - pyskl - INFO - Epoch [67][300/3746] lr: 5.929e-02, eta: 2 days, 21:12:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5264, loss_cls: 4.2012, loss: 4.2012 +2024-07-18 16:04:23,302 - pyskl - INFO - Epoch [67][400/3746] lr: 5.926e-02, eta: 2 days, 21:11:40, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5295, loss_cls: 4.1527, loss: 4.1527 +2024-07-18 16:05:44,510 - pyskl - INFO - Epoch [67][500/3746] lr: 5.923e-02, eta: 2 days, 21:10:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5173, loss_cls: 4.1799, loss: 4.1799 +2024-07-18 16:07:05,837 - pyskl - INFO - Epoch [67][600/3746] lr: 5.920e-02, eta: 2 days, 21:09:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5230, loss_cls: 4.1916, loss: 4.1916 +2024-07-18 16:08:27,501 - pyskl - INFO - Epoch [67][700/3746] lr: 5.918e-02, eta: 2 days, 21:07:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5361, loss_cls: 4.1510, loss: 4.1510 +2024-07-18 16:09:48,484 - pyskl - INFO - Epoch [67][800/3746] lr: 5.915e-02, eta: 2 days, 21:06:33, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5311, loss_cls: 4.1310, loss: 4.1310 +2024-07-18 16:11:09,881 - pyskl - INFO - Epoch [67][900/3746] lr: 5.912e-02, eta: 2 days, 21:05:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5183, loss_cls: 4.2239, loss: 4.2239 +2024-07-18 16:12:31,945 - pyskl - INFO - Epoch [67][1000/3746] lr: 5.909e-02, eta: 2 days, 21:04:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5138, loss_cls: 4.2188, loss: 4.2188 +2024-07-18 16:13:53,479 - pyskl - INFO - Epoch [67][1100/3746] lr: 5.907e-02, eta: 2 days, 21:02:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5328, loss_cls: 4.1561, loss: 4.1561 +2024-07-18 16:15:14,336 - pyskl - INFO - Epoch [67][1200/3746] lr: 5.904e-02, eta: 2 days, 21:01:27, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5069, loss_cls: 4.2560, loss: 4.2560 +2024-07-18 16:16:35,732 - pyskl - INFO - Epoch [67][1300/3746] lr: 5.901e-02, eta: 2 days, 21:00:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5147, loss_cls: 4.2211, loss: 4.2211 +2024-07-18 16:17:57,399 - pyskl - INFO - Epoch [67][1400/3746] lr: 5.898e-02, eta: 2 days, 20:58:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5172, loss_cls: 4.2033, loss: 4.2033 +2024-07-18 16:19:18,375 - pyskl - INFO - Epoch [67][1500/3746] lr: 5.896e-02, eta: 2 days, 20:57:37, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5159, loss_cls: 4.2254, loss: 4.2254 +2024-07-18 16:20:40,131 - pyskl - INFO - Epoch [67][1600/3746] lr: 5.893e-02, eta: 2 days, 20:56:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5080, loss_cls: 4.2334, loss: 4.2334 +2024-07-18 16:22:01,521 - pyskl - INFO - Epoch [67][1700/3746] lr: 5.890e-02, eta: 2 days, 20:55:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5180, loss_cls: 4.2004, loss: 4.2004 +2024-07-18 16:23:22,898 - pyskl - INFO - Epoch [67][1800/3746] lr: 5.887e-02, eta: 2 days, 20:53:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5116, loss_cls: 4.2138, loss: 4.2138 +2024-07-18 16:24:44,249 - pyskl - INFO - Epoch [67][1900/3746] lr: 5.885e-02, eta: 2 days, 20:52:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5327, loss_cls: 4.1586, loss: 4.1586 +2024-07-18 16:26:05,213 - pyskl - INFO - Epoch [67][2000/3746] lr: 5.882e-02, eta: 2 days, 20:51:14, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5212, loss_cls: 4.1875, loss: 4.1875 +2024-07-18 16:27:26,340 - pyskl - INFO - Epoch [67][2100/3746] lr: 5.879e-02, eta: 2 days, 20:49:57, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5302, loss_cls: 4.1653, loss: 4.1653 +2024-07-18 16:28:47,121 - pyskl - INFO - Epoch [67][2200/3746] lr: 5.876e-02, eta: 2 days, 20:48:40, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5256, loss_cls: 4.1921, loss: 4.1921 +2024-07-18 16:30:07,971 - pyskl - INFO - Epoch [67][2300/3746] lr: 5.874e-02, eta: 2 days, 20:47:22, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5183, loss_cls: 4.2209, loss: 4.2209 +2024-07-18 16:31:29,239 - pyskl - INFO - Epoch [67][2400/3746] lr: 5.871e-02, eta: 2 days, 20:46:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5066, loss_cls: 4.2712, loss: 4.2712 +2024-07-18 16:32:50,299 - pyskl - INFO - Epoch [67][2500/3746] lr: 5.868e-02, eta: 2 days, 20:44:49, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5192, loss_cls: 4.2571, loss: 4.2571 +2024-07-18 16:34:12,046 - pyskl - INFO - Epoch [67][2600/3746] lr: 5.865e-02, eta: 2 days, 20:43:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5266, loss_cls: 4.1717, loss: 4.1717 +2024-07-18 16:35:33,030 - pyskl - INFO - Epoch [67][2700/3746] lr: 5.863e-02, eta: 2 days, 20:42:15, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5164, loss_cls: 4.2138, loss: 4.2138 +2024-07-18 16:36:54,817 - pyskl - INFO - Epoch [67][2800/3746] lr: 5.860e-02, eta: 2 days, 20:40:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5244, loss_cls: 4.1767, loss: 4.1767 +2024-07-18 16:38:16,029 - pyskl - INFO - Epoch [67][2900/3746] lr: 5.857e-02, eta: 2 days, 20:39:42, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5109, loss_cls: 4.2589, loss: 4.2589 +2024-07-18 16:39:37,188 - pyskl - INFO - Epoch [67][3000/3746] lr: 5.854e-02, eta: 2 days, 20:38:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5148, loss_cls: 4.2556, loss: 4.2556 +2024-07-18 16:40:57,995 - pyskl - INFO - Epoch [67][3100/3746] lr: 5.852e-02, eta: 2 days, 20:37:08, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5086, loss_cls: 4.2821, loss: 4.2821 +2024-07-18 16:42:18,894 - pyskl - INFO - Epoch [67][3200/3746] lr: 5.849e-02, eta: 2 days, 20:35:51, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5275, loss_cls: 4.2057, loss: 4.2057 +2024-07-18 16:43:40,337 - pyskl - INFO - Epoch [67][3300/3746] lr: 5.846e-02, eta: 2 days, 20:34:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5286, loss_cls: 4.1794, loss: 4.1794 +2024-07-18 16:45:01,539 - pyskl - INFO - Epoch [67][3400/3746] lr: 5.843e-02, eta: 2 days, 20:33:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5225, loss_cls: 4.1869, loss: 4.1869 +2024-07-18 16:46:22,324 - pyskl - INFO - Epoch [67][3500/3746] lr: 5.841e-02, eta: 2 days, 20:32:00, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5255, loss_cls: 4.1811, loss: 4.1811 +2024-07-18 16:47:43,536 - pyskl - INFO - Epoch [67][3600/3746] lr: 5.838e-02, eta: 2 days, 20:30:43, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5169, loss_cls: 4.2170, loss: 4.2170 +2024-07-18 16:49:04,784 - pyskl - INFO - Epoch [67][3700/3746] lr: 5.835e-02, eta: 2 days, 20:29:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5269, loss_cls: 4.1902, loss: 4.1902 +2024-07-18 16:49:44,243 - pyskl - INFO - Saving checkpoint at 67 epochs +2024-07-18 16:51:34,809 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 16:51:35,470 - pyskl - INFO - +top1_acc 0.1827 +top5_acc 0.4118 +2024-07-18 16:51:35,471 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 16:51:35,511 - pyskl - INFO - +mean_acc 0.1829 +2024-07-18 16:51:35,522 - pyskl - INFO - Epoch(val) [67][309] top1_acc: 0.1827, top5_acc: 0.4118, mean_class_accuracy: 0.1829 +2024-07-18 16:55:18,851 - pyskl - INFO - Epoch [68][100/3746] lr: 5.831e-02, eta: 2 days, 20:29:43, time: 2.233, data_time: 1.267, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5334, loss_cls: 4.1256, loss: 4.1256 +2024-07-18 16:56:39,907 - pyskl - INFO - Epoch [68][200/3746] lr: 5.828e-02, eta: 2 days, 20:28:26, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5261, loss_cls: 4.1558, loss: 4.1558 +2024-07-18 16:58:00,863 - pyskl - INFO - Epoch [68][300/3746] lr: 5.826e-02, eta: 2 days, 20:27:09, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5219, loss_cls: 4.1694, loss: 4.1694 +2024-07-18 16:59:21,642 - pyskl - INFO - Epoch [68][400/3746] lr: 5.823e-02, eta: 2 days, 20:25:51, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5212, loss_cls: 4.1923, loss: 4.1923 +2024-07-18 17:00:42,612 - pyskl - INFO - Epoch [68][500/3746] lr: 5.820e-02, eta: 2 days, 20:24:34, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5177, loss_cls: 4.1966, loss: 4.1966 +2024-07-18 17:02:03,973 - pyskl - INFO - Epoch [68][600/3746] lr: 5.817e-02, eta: 2 days, 20:23:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5264, loss_cls: 4.1738, loss: 4.1738 +2024-07-18 17:03:25,202 - pyskl - INFO - Epoch [68][700/3746] lr: 5.815e-02, eta: 2 days, 20:22:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5281, loss_cls: 4.1335, loss: 4.1335 +2024-07-18 17:04:46,084 - pyskl - INFO - Epoch [68][800/3746] lr: 5.812e-02, eta: 2 days, 20:20:43, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5294, loss_cls: 4.1337, loss: 4.1337 +2024-07-18 17:06:07,253 - pyskl - INFO - Epoch [68][900/3746] lr: 5.809e-02, eta: 2 days, 20:19:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5184, loss_cls: 4.2083, loss: 4.2083 +2024-07-18 17:07:28,234 - pyskl - INFO - Epoch [68][1000/3746] lr: 5.806e-02, eta: 2 days, 20:18:08, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5289, loss_cls: 4.1263, loss: 4.1263 +2024-07-18 17:08:49,444 - pyskl - INFO - Epoch [68][1100/3746] lr: 5.804e-02, eta: 2 days, 20:16:51, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5089, loss_cls: 4.2179, loss: 4.2179 +2024-07-18 17:10:10,263 - pyskl - INFO - Epoch [68][1200/3746] lr: 5.801e-02, eta: 2 days, 20:15:34, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5247, loss_cls: 4.1864, loss: 4.1864 +2024-07-18 17:11:32,503 - pyskl - INFO - Epoch [68][1300/3746] lr: 5.798e-02, eta: 2 days, 20:14:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5267, loss_cls: 4.1629, loss: 4.1629 +2024-07-18 17:12:53,584 - pyskl - INFO - Epoch [68][1400/3746] lr: 5.795e-02, eta: 2 days, 20:13:01, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5220, loss_cls: 4.2010, loss: 4.2010 +2024-07-18 17:14:15,282 - pyskl - INFO - Epoch [68][1500/3746] lr: 5.792e-02, eta: 2 days, 20:11:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5217, loss_cls: 4.1837, loss: 4.1837 +2024-07-18 17:15:36,806 - pyskl - INFO - Epoch [68][1600/3746] lr: 5.790e-02, eta: 2 days, 20:10:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5119, loss_cls: 4.2421, loss: 4.2421 +2024-07-18 17:16:58,209 - pyskl - INFO - Epoch [68][1700/3746] lr: 5.787e-02, eta: 2 days, 20:09:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5220, loss_cls: 4.2008, loss: 4.2008 +2024-07-18 17:18:19,310 - pyskl - INFO - Epoch [68][1800/3746] lr: 5.784e-02, eta: 2 days, 20:07:53, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5217, loss_cls: 4.2206, loss: 4.2206 +2024-07-18 17:19:40,409 - pyskl - INFO - Epoch [68][1900/3746] lr: 5.781e-02, eta: 2 days, 20:06:36, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5170, loss_cls: 4.2200, loss: 4.2200 +2024-07-18 17:21:01,664 - pyskl - INFO - Epoch [68][2000/3746] lr: 5.779e-02, eta: 2 days, 20:05:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5150, loss_cls: 4.2103, loss: 4.2103 +2024-07-18 17:22:22,624 - pyskl - INFO - Epoch [68][2100/3746] lr: 5.776e-02, eta: 2 days, 20:04:02, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5059, loss_cls: 4.2552, loss: 4.2552 +2024-07-18 17:23:43,781 - pyskl - INFO - Epoch [68][2200/3746] lr: 5.773e-02, eta: 2 days, 20:02:45, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5148, loss_cls: 4.2014, loss: 4.2014 +2024-07-18 17:25:04,971 - pyskl - INFO - Epoch [68][2300/3746] lr: 5.770e-02, eta: 2 days, 20:01:28, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5203, loss_cls: 4.2257, loss: 4.2257 +2024-07-18 17:26:25,820 - pyskl - INFO - Epoch [68][2400/3746] lr: 5.768e-02, eta: 2 days, 20:00:10, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5219, loss_cls: 4.1778, loss: 4.1778 +2024-07-18 17:27:46,770 - pyskl - INFO - Epoch [68][2500/3746] lr: 5.765e-02, eta: 2 days, 19:58:53, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5209, loss_cls: 4.1976, loss: 4.1976 +2024-07-18 17:29:07,952 - pyskl - INFO - Epoch [68][2600/3746] lr: 5.762e-02, eta: 2 days, 19:57:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5269, loss_cls: 4.1766, loss: 4.1766 +2024-07-18 17:30:29,764 - pyskl - INFO - Epoch [68][2700/3746] lr: 5.759e-02, eta: 2 days, 19:56:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5241, loss_cls: 4.2137, loss: 4.2137 +2024-07-18 17:31:51,587 - pyskl - INFO - Epoch [68][2800/3746] lr: 5.757e-02, eta: 2 days, 19:55:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5234, loss_cls: 4.1895, loss: 4.1895 +2024-07-18 17:33:13,093 - pyskl - INFO - Epoch [68][2900/3746] lr: 5.754e-02, eta: 2 days, 19:53:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5220, loss_cls: 4.2062, loss: 4.2062 +2024-07-18 17:34:34,153 - pyskl - INFO - Epoch [68][3000/3746] lr: 5.751e-02, eta: 2 days, 19:52:29, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5181, loss_cls: 4.2284, loss: 4.2284 +2024-07-18 17:35:54,823 - pyskl - INFO - Epoch [68][3100/3746] lr: 5.748e-02, eta: 2 days, 19:51:11, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5122, loss_cls: 4.2587, loss: 4.2587 +2024-07-18 17:37:15,675 - pyskl - INFO - Epoch [68][3200/3746] lr: 5.746e-02, eta: 2 days, 19:49:53, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5188, loss_cls: 4.1871, loss: 4.1871 +2024-07-18 17:38:36,518 - pyskl - INFO - Epoch [68][3300/3746] lr: 5.743e-02, eta: 2 days, 19:48:36, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5334, loss_cls: 4.1363, loss: 4.1363 +2024-07-18 17:39:57,587 - pyskl - INFO - Epoch [68][3400/3746] lr: 5.740e-02, eta: 2 days, 19:47:19, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5109, loss_cls: 4.2425, loss: 4.2425 +2024-07-18 17:41:18,491 - pyskl - INFO - Epoch [68][3500/3746] lr: 5.737e-02, eta: 2 days, 19:46:01, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5241, loss_cls: 4.2049, loss: 4.2049 +2024-07-18 17:42:40,041 - pyskl - INFO - Epoch [68][3600/3746] lr: 5.734e-02, eta: 2 days, 19:44:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5192, loss_cls: 4.1794, loss: 4.1794 +2024-07-18 17:44:01,802 - pyskl - INFO - Epoch [68][3700/3746] lr: 5.732e-02, eta: 2 days, 19:43:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5250, loss_cls: 4.1899, loss: 4.1899 +2024-07-18 17:44:41,047 - pyskl - INFO - Saving checkpoint at 68 epochs +2024-07-18 17:46:30,631 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 17:46:31,307 - pyskl - INFO - +top1_acc 0.2151 +top5_acc 0.4472 +2024-07-18 17:46:31,307 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 17:46:31,346 - pyskl - INFO - +mean_acc 0.2149 +2024-07-18 17:46:31,350 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_66.pth was removed +2024-07-18 17:46:31,592 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_68.pth. +2024-07-18 17:46:31,593 - pyskl - INFO - Best top1_acc is 0.2151 at 68 epoch. +2024-07-18 17:46:31,603 - pyskl - INFO - Epoch(val) [68][309] top1_acc: 0.2151, top5_acc: 0.4472, mean_class_accuracy: 0.2149 +2024-07-18 17:50:27,994 - pyskl - INFO - Epoch [69][100/3746] lr: 5.728e-02, eta: 2 days, 19:43:57, time: 2.364, data_time: 1.351, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5356, loss_cls: 4.1468, loss: 4.1468 +2024-07-18 17:51:50,841 - pyskl - INFO - Epoch [69][200/3746] lr: 5.725e-02, eta: 2 days, 19:42:42, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5281, loss_cls: 4.1700, loss: 4.1700 +2024-07-18 17:53:14,318 - pyskl - INFO - Epoch [69][300/3746] lr: 5.722e-02, eta: 2 days, 19:41:28, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5175, loss_cls: 4.2268, loss: 4.2268 +2024-07-18 17:54:37,907 - pyskl - INFO - Epoch [69][400/3746] lr: 5.719e-02, eta: 2 days, 19:40:13, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5219, loss_cls: 4.1879, loss: 4.1879 +2024-07-18 17:56:01,204 - pyskl - INFO - Epoch [69][500/3746] lr: 5.717e-02, eta: 2 days, 19:38:58, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5294, loss_cls: 4.1746, loss: 4.1746 +2024-07-18 17:57:23,960 - pyskl - INFO - Epoch [69][600/3746] lr: 5.714e-02, eta: 2 days, 19:37:43, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5277, loss_cls: 4.1497, loss: 4.1497 +2024-07-18 17:58:46,703 - pyskl - INFO - Epoch [69][700/3746] lr: 5.711e-02, eta: 2 days, 19:36:28, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5281, loss_cls: 4.1918, loss: 4.1918 +2024-07-18 18:00:09,387 - pyskl - INFO - Epoch [69][800/3746] lr: 5.708e-02, eta: 2 days, 19:35:12, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5325, loss_cls: 4.1401, loss: 4.1401 +2024-07-18 18:01:31,969 - pyskl - INFO - Epoch [69][900/3746] lr: 5.706e-02, eta: 2 days, 19:33:56, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5242, loss_cls: 4.1425, loss: 4.1425 +2024-07-18 18:02:54,300 - pyskl - INFO - Epoch [69][1000/3746] lr: 5.703e-02, eta: 2 days, 19:32:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5219, loss_cls: 4.1866, loss: 4.1866 +2024-07-18 18:04:16,970 - pyskl - INFO - Epoch [69][1100/3746] lr: 5.700e-02, eta: 2 days, 19:31:25, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5064, loss_cls: 4.2380, loss: 4.2380 +2024-07-18 18:05:38,457 - pyskl - INFO - Epoch [69][1200/3746] lr: 5.697e-02, eta: 2 days, 19:30:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5109, loss_cls: 4.2556, loss: 4.2556 +2024-07-18 18:07:00,494 - pyskl - INFO - Epoch [69][1300/3746] lr: 5.694e-02, eta: 2 days, 19:28:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5202, loss_cls: 4.1908, loss: 4.1908 +2024-07-18 18:08:21,855 - pyskl - INFO - Epoch [69][1400/3746] lr: 5.692e-02, eta: 2 days, 19:27:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5303, loss_cls: 4.1094, loss: 4.1094 +2024-07-18 18:09:43,663 - pyskl - INFO - Epoch [69][1500/3746] lr: 5.689e-02, eta: 2 days, 19:26:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5155, loss_cls: 4.2210, loss: 4.2210 +2024-07-18 18:11:05,648 - pyskl - INFO - Epoch [69][1600/3746] lr: 5.686e-02, eta: 2 days, 19:25:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5170, loss_cls: 4.1973, loss: 4.1973 +2024-07-18 18:12:27,026 - pyskl - INFO - Epoch [69][1700/3746] lr: 5.683e-02, eta: 2 days, 19:23:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5216, loss_cls: 4.1501, loss: 4.1501 +2024-07-18 18:13:48,561 - pyskl - INFO - Epoch [69][1800/3746] lr: 5.681e-02, eta: 2 days, 19:22:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5209, loss_cls: 4.1816, loss: 4.1816 +2024-07-18 18:15:10,304 - pyskl - INFO - Epoch [69][1900/3746] lr: 5.678e-02, eta: 2 days, 19:21:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5294, loss_cls: 4.1306, loss: 4.1306 +2024-07-18 18:16:31,574 - pyskl - INFO - Epoch [69][2000/3746] lr: 5.675e-02, eta: 2 days, 19:19:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5152, loss_cls: 4.2096, loss: 4.2096 +2024-07-18 18:17:52,668 - pyskl - INFO - Epoch [69][2100/3746] lr: 5.672e-02, eta: 2 days, 19:18:36, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5186, loss_cls: 4.2213, loss: 4.2213 +2024-07-18 18:19:13,498 - pyskl - INFO - Epoch [69][2200/3746] lr: 5.670e-02, eta: 2 days, 19:17:18, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5155, loss_cls: 4.2261, loss: 4.2261 +2024-07-18 18:20:34,814 - pyskl - INFO - Epoch [69][2300/3746] lr: 5.667e-02, eta: 2 days, 19:16:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5205, loss_cls: 4.1697, loss: 4.1697 +2024-07-18 18:21:55,921 - pyskl - INFO - Epoch [69][2400/3746] lr: 5.664e-02, eta: 2 days, 19:14:44, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5191, loss_cls: 4.2059, loss: 4.2059 +2024-07-18 18:23:17,126 - pyskl - INFO - Epoch [69][2500/3746] lr: 5.661e-02, eta: 2 days, 19:13:27, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5225, loss_cls: 4.1995, loss: 4.1995 +2024-07-18 18:24:38,386 - pyskl - INFO - Epoch [69][2600/3746] lr: 5.658e-02, eta: 2 days, 19:12:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5394, loss_cls: 4.1276, loss: 4.1276 +2024-07-18 18:25:59,922 - pyskl - INFO - Epoch [69][2700/3746] lr: 5.656e-02, eta: 2 days, 19:10:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5312, loss_cls: 4.1604, loss: 4.1604 +2024-07-18 18:27:21,665 - pyskl - INFO - Epoch [69][2800/3746] lr: 5.653e-02, eta: 2 days, 19:09:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5178, loss_cls: 4.2200, loss: 4.2200 +2024-07-18 18:28:43,222 - pyskl - INFO - Epoch [69][2900/3746] lr: 5.650e-02, eta: 2 days, 19:08:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5228, loss_cls: 4.2042, loss: 4.2042 +2024-07-18 18:30:04,607 - pyskl - INFO - Epoch [69][3000/3746] lr: 5.647e-02, eta: 2 days, 19:07:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5195, loss_cls: 4.2335, loss: 4.2335 +2024-07-18 18:31:25,541 - pyskl - INFO - Epoch [69][3100/3746] lr: 5.645e-02, eta: 2 days, 19:05:44, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5203, loss_cls: 4.1641, loss: 4.1641 +2024-07-18 18:32:46,418 - pyskl - INFO - Epoch [69][3200/3746] lr: 5.642e-02, eta: 2 days, 19:04:26, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5177, loss_cls: 4.1914, loss: 4.1914 +2024-07-18 18:34:06,910 - pyskl - INFO - Epoch [69][3300/3746] lr: 5.639e-02, eta: 2 days, 19:03:08, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5161, loss_cls: 4.2075, loss: 4.2075 +2024-07-18 18:35:28,377 - pyskl - INFO - Epoch [69][3400/3746] lr: 5.636e-02, eta: 2 days, 19:01:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5233, loss_cls: 4.1884, loss: 4.1884 +2024-07-18 18:36:49,726 - pyskl - INFO - Epoch [69][3500/3746] lr: 5.634e-02, eta: 2 days, 19:00:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5145, loss_cls: 4.2130, loss: 4.2130 +2024-07-18 18:38:11,346 - pyskl - INFO - Epoch [69][3600/3746] lr: 5.631e-02, eta: 2 days, 18:59:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5308, loss_cls: 4.1535, loss: 4.1535 +2024-07-18 18:39:32,685 - pyskl - INFO - Epoch [69][3700/3746] lr: 5.628e-02, eta: 2 days, 18:58:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5162, loss_cls: 4.2158, loss: 4.2158 +2024-07-18 18:40:11,751 - pyskl - INFO - Saving checkpoint at 69 epochs +2024-07-18 18:42:03,201 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 18:42:03,877 - pyskl - INFO - +top1_acc 0.2127 +top5_acc 0.4385 +2024-07-18 18:42:03,878 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 18:42:03,921 - pyskl - INFO - +mean_acc 0.2128 +2024-07-18 18:42:03,933 - pyskl - INFO - Epoch(val) [69][309] top1_acc: 0.2127, top5_acc: 0.4385, mean_class_accuracy: 0.2128 +2024-07-18 18:45:56,431 - pyskl - INFO - Epoch [70][100/3746] lr: 5.624e-02, eta: 2 days, 18:58:20, time: 2.325, data_time: 1.326, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5333, loss_cls: 4.1312, loss: 4.1312 +2024-07-18 18:47:19,590 - pyskl - INFO - Epoch [70][200/3746] lr: 5.621e-02, eta: 2 days, 18:57:05, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5267, loss_cls: 4.1559, loss: 4.1559 +2024-07-18 18:48:43,815 - pyskl - INFO - Epoch [70][300/3746] lr: 5.618e-02, eta: 2 days, 18:55:51, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5183, loss_cls: 4.1967, loss: 4.1967 +2024-07-18 18:50:07,563 - pyskl - INFO - Epoch [70][400/3746] lr: 5.616e-02, eta: 2 days, 18:54:37, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5244, loss_cls: 4.1610, loss: 4.1610 +2024-07-18 18:51:31,003 - pyskl - INFO - Epoch [70][500/3746] lr: 5.613e-02, eta: 2 days, 18:53:22, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5281, loss_cls: 4.1861, loss: 4.1861 +2024-07-18 18:52:54,462 - pyskl - INFO - Epoch [70][600/3746] lr: 5.610e-02, eta: 2 days, 18:52:07, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5250, loss_cls: 4.1751, loss: 4.1751 +2024-07-18 18:54:18,162 - pyskl - INFO - Epoch [70][700/3746] lr: 5.607e-02, eta: 2 days, 18:50:52, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5277, loss_cls: 4.1520, loss: 4.1520 +2024-07-18 18:55:41,303 - pyskl - INFO - Epoch [70][800/3746] lr: 5.605e-02, eta: 2 days, 18:49:37, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5359, loss_cls: 4.1348, loss: 4.1348 +2024-07-18 18:57:05,101 - pyskl - INFO - Epoch [70][900/3746] lr: 5.602e-02, eta: 2 days, 18:48:23, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5433, loss_cls: 4.1302, loss: 4.1302 +2024-07-18 18:58:28,849 - pyskl - INFO - Epoch [70][1000/3746] lr: 5.599e-02, eta: 2 days, 18:47:08, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5248, loss_cls: 4.1738, loss: 4.1738 +2024-07-18 18:59:52,358 - pyskl - INFO - Epoch [70][1100/3746] lr: 5.596e-02, eta: 2 days, 18:45:53, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5153, loss_cls: 4.2098, loss: 4.2098 +2024-07-18 19:01:15,834 - pyskl - INFO - Epoch [70][1200/3746] lr: 5.593e-02, eta: 2 days, 18:44:38, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5255, loss_cls: 4.1608, loss: 4.1608 +2024-07-18 19:02:39,401 - pyskl - INFO - Epoch [70][1300/3746] lr: 5.591e-02, eta: 2 days, 18:43:24, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5184, loss_cls: 4.1646, loss: 4.1646 +2024-07-18 19:04:02,710 - pyskl - INFO - Epoch [70][1400/3746] lr: 5.588e-02, eta: 2 days, 18:42:09, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5230, loss_cls: 4.1536, loss: 4.1536 +2024-07-18 19:05:26,509 - pyskl - INFO - Epoch [70][1500/3746] lr: 5.585e-02, eta: 2 days, 18:40:54, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5152, loss_cls: 4.1916, loss: 4.1916 +2024-07-18 19:06:48,789 - pyskl - INFO - Epoch [70][1600/3746] lr: 5.582e-02, eta: 2 days, 18:39:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5330, loss_cls: 4.1583, loss: 4.1583 +2024-07-18 19:08:11,375 - pyskl - INFO - Epoch [70][1700/3746] lr: 5.580e-02, eta: 2 days, 18:38:22, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5252, loss_cls: 4.1846, loss: 4.1846 +2024-07-18 19:09:34,833 - pyskl - INFO - Epoch [70][1800/3746] lr: 5.577e-02, eta: 2 days, 18:37:07, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5169, loss_cls: 4.2236, loss: 4.2236 +2024-07-18 19:10:58,457 - pyskl - INFO - Epoch [70][1900/3746] lr: 5.574e-02, eta: 2 days, 18:35:52, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5258, loss_cls: 4.1567, loss: 4.1567 +2024-07-18 19:12:21,025 - pyskl - INFO - Epoch [70][2000/3746] lr: 5.571e-02, eta: 2 days, 18:34:36, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5227, loss_cls: 4.1948, loss: 4.1948 +2024-07-18 19:13:44,214 - pyskl - INFO - Epoch [70][2100/3746] lr: 5.568e-02, eta: 2 days, 18:33:21, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5239, loss_cls: 4.1725, loss: 4.1725 +2024-07-18 19:15:06,838 - pyskl - INFO - Epoch [70][2200/3746] lr: 5.566e-02, eta: 2 days, 18:32:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5291, loss_cls: 4.1349, loss: 4.1349 +2024-07-18 19:16:29,309 - pyskl - INFO - Epoch [70][2300/3746] lr: 5.563e-02, eta: 2 days, 18:30:49, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5395, loss_cls: 4.1287, loss: 4.1287 +2024-07-18 19:17:51,670 - pyskl - INFO - Epoch [70][2400/3746] lr: 5.560e-02, eta: 2 days, 18:29:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5334, loss_cls: 4.1557, loss: 4.1557 +2024-07-18 19:19:13,678 - pyskl - INFO - Epoch [70][2500/3746] lr: 5.557e-02, eta: 2 days, 18:28:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5195, loss_cls: 4.1795, loss: 4.1795 +2024-07-18 19:20:36,044 - pyskl - INFO - Epoch [70][2600/3746] lr: 5.555e-02, eta: 2 days, 18:27:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5203, loss_cls: 4.2006, loss: 4.2006 +2024-07-18 19:21:59,039 - pyskl - INFO - Epoch [70][2700/3746] lr: 5.552e-02, eta: 2 days, 18:25:44, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5361, loss_cls: 4.1299, loss: 4.1299 +2024-07-18 19:23:21,288 - pyskl - INFO - Epoch [70][2800/3746] lr: 5.549e-02, eta: 2 days, 18:24:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5227, loss_cls: 4.1973, loss: 4.1973 +2024-07-18 19:24:44,258 - pyskl - INFO - Epoch [70][2900/3746] lr: 5.546e-02, eta: 2 days, 18:23:12, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5250, loss_cls: 4.1977, loss: 4.1977 +2024-07-18 19:26:07,274 - pyskl - INFO - Epoch [70][3000/3746] lr: 5.543e-02, eta: 2 days, 18:21:57, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5214, loss_cls: 4.1867, loss: 4.1867 +2024-07-18 19:27:29,268 - pyskl - INFO - Epoch [70][3100/3746] lr: 5.541e-02, eta: 2 days, 18:20:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5248, loss_cls: 4.1600, loss: 4.1600 +2024-07-18 19:28:51,031 - pyskl - INFO - Epoch [70][3200/3746] lr: 5.538e-02, eta: 2 days, 18:19:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5308, loss_cls: 4.1728, loss: 4.1728 +2024-07-18 19:30:13,009 - pyskl - INFO - Epoch [70][3300/3746] lr: 5.535e-02, eta: 2 days, 18:18:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5195, loss_cls: 4.1976, loss: 4.1976 +2024-07-18 19:31:35,047 - pyskl - INFO - Epoch [70][3400/3746] lr: 5.532e-02, eta: 2 days, 18:16:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5227, loss_cls: 4.1746, loss: 4.1746 +2024-07-18 19:32:57,783 - pyskl - INFO - Epoch [70][3500/3746] lr: 5.530e-02, eta: 2 days, 18:15:34, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5275, loss_cls: 4.1700, loss: 4.1700 +2024-07-18 19:34:20,878 - pyskl - INFO - Epoch [70][3600/3746] lr: 5.527e-02, eta: 2 days, 18:14:18, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5239, loss_cls: 4.1996, loss: 4.1996 +2024-07-18 19:35:43,295 - pyskl - INFO - Epoch [70][3700/3746] lr: 5.524e-02, eta: 2 days, 18:13:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5336, loss_cls: 4.1355, loss: 4.1355 +2024-07-18 19:36:23,047 - pyskl - INFO - Saving checkpoint at 70 epochs +2024-07-18 19:38:14,739 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 19:38:15,414 - pyskl - INFO - +top1_acc 0.1932 +top5_acc 0.4244 +2024-07-18 19:38:15,414 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 19:38:15,458 - pyskl - INFO - +mean_acc 0.1931 +2024-07-18 19:38:15,471 - pyskl - INFO - Epoch(val) [70][309] top1_acc: 0.1932, top5_acc: 0.4244, mean_class_accuracy: 0.1931 +2024-07-18 19:42:00,821 - pyskl - INFO - Epoch [71][100/3746] lr: 5.520e-02, eta: 2 days, 18:13:11, time: 2.253, data_time: 1.271, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5402, loss_cls: 4.0841, loss: 4.0841 +2024-07-18 19:43:23,865 - pyskl - INFO - Epoch [71][200/3746] lr: 5.517e-02, eta: 2 days, 18:11:55, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5434, loss_cls: 4.0919, loss: 4.0919 +2024-07-18 19:44:46,786 - pyskl - INFO - Epoch [71][300/3746] lr: 5.514e-02, eta: 2 days, 18:10:39, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5481, loss_cls: 4.0486, loss: 4.0486 +2024-07-18 19:46:09,546 - pyskl - INFO - Epoch [71][400/3746] lr: 5.512e-02, eta: 2 days, 18:09:23, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5330, loss_cls: 4.1355, loss: 4.1355 +2024-07-18 19:47:31,565 - pyskl - INFO - Epoch [71][500/3746] lr: 5.509e-02, eta: 2 days, 18:08:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5267, loss_cls: 4.1853, loss: 4.1853 +2024-07-18 19:48:54,682 - pyskl - INFO - Epoch [71][600/3746] lr: 5.506e-02, eta: 2 days, 18:06:51, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5209, loss_cls: 4.1791, loss: 4.1791 +2024-07-18 19:50:17,329 - pyskl - INFO - Epoch [71][700/3746] lr: 5.503e-02, eta: 2 days, 18:05:35, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5305, loss_cls: 4.1310, loss: 4.1310 +2024-07-18 19:51:39,965 - pyskl - INFO - Epoch [71][800/3746] lr: 5.500e-02, eta: 2 days, 18:04:19, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5291, loss_cls: 4.1664, loss: 4.1664 +2024-07-18 19:53:02,862 - pyskl - INFO - Epoch [71][900/3746] lr: 5.498e-02, eta: 2 days, 18:03:03, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5427, loss_cls: 4.0908, loss: 4.0908 +2024-07-18 19:54:25,081 - pyskl - INFO - Epoch [71][1000/3746] lr: 5.495e-02, eta: 2 days, 18:01:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5188, loss_cls: 4.1901, loss: 4.1901 +2024-07-18 19:55:47,762 - pyskl - INFO - Epoch [71][1100/3746] lr: 5.492e-02, eta: 2 days, 18:00:30, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5236, loss_cls: 4.1710, loss: 4.1710 +2024-07-18 19:57:11,047 - pyskl - INFO - Epoch [71][1200/3746] lr: 5.489e-02, eta: 2 days, 17:59:15, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5183, loss_cls: 4.1647, loss: 4.1647 +2024-07-18 19:58:33,671 - pyskl - INFO - Epoch [71][1300/3746] lr: 5.487e-02, eta: 2 days, 17:57:59, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5222, loss_cls: 4.1792, loss: 4.1792 +2024-07-18 19:59:57,353 - pyskl - INFO - Epoch [71][1400/3746] lr: 5.484e-02, eta: 2 days, 17:56:44, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5236, loss_cls: 4.1662, loss: 4.1662 +2024-07-18 20:01:20,528 - pyskl - INFO - Epoch [71][1500/3746] lr: 5.481e-02, eta: 2 days, 17:55:28, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5391, loss_cls: 4.1178, loss: 4.1178 +2024-07-18 20:02:43,102 - pyskl - INFO - Epoch [71][1600/3746] lr: 5.478e-02, eta: 2 days, 17:54:12, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5220, loss_cls: 4.1591, loss: 4.1591 +2024-07-18 20:04:05,715 - pyskl - INFO - Epoch [71][1700/3746] lr: 5.475e-02, eta: 2 days, 17:52:56, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5189, loss_cls: 4.1979, loss: 4.1979 +2024-07-18 20:05:29,343 - pyskl - INFO - Epoch [71][1800/3746] lr: 5.473e-02, eta: 2 days, 17:51:41, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5280, loss_cls: 4.1783, loss: 4.1783 +2024-07-18 20:06:52,683 - pyskl - INFO - Epoch [71][1900/3746] lr: 5.470e-02, eta: 2 days, 17:50:25, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5356, loss_cls: 4.1004, loss: 4.1004 +2024-07-18 20:08:15,540 - pyskl - INFO - Epoch [71][2000/3746] lr: 5.467e-02, eta: 2 days, 17:49:10, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5339, loss_cls: 4.1599, loss: 4.1599 +2024-07-18 20:09:38,438 - pyskl - INFO - Epoch [71][2100/3746] lr: 5.464e-02, eta: 2 days, 17:47:54, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5327, loss_cls: 4.1386, loss: 4.1386 +2024-07-18 20:11:00,671 - pyskl - INFO - Epoch [71][2200/3746] lr: 5.461e-02, eta: 2 days, 17:46:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5283, loss_cls: 4.1637, loss: 4.1637 +2024-07-18 20:12:23,091 - pyskl - INFO - Epoch [71][2300/3746] lr: 5.459e-02, eta: 2 days, 17:45:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5244, loss_cls: 4.1846, loss: 4.1846 +2024-07-18 20:13:45,905 - pyskl - INFO - Epoch [71][2400/3746] lr: 5.456e-02, eta: 2 days, 17:44:05, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5302, loss_cls: 4.1397, loss: 4.1397 +2024-07-18 20:15:08,452 - pyskl - INFO - Epoch [71][2500/3746] lr: 5.453e-02, eta: 2 days, 17:42:48, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5330, loss_cls: 4.1137, loss: 4.1137 +2024-07-18 20:16:31,912 - pyskl - INFO - Epoch [71][2600/3746] lr: 5.450e-02, eta: 2 days, 17:41:33, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5284, loss_cls: 4.1572, loss: 4.1572 +2024-07-18 20:17:54,160 - pyskl - INFO - Epoch [71][2700/3746] lr: 5.448e-02, eta: 2 days, 17:40:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5212, loss_cls: 4.1898, loss: 4.1898 +2024-07-18 20:19:16,709 - pyskl - INFO - Epoch [71][2800/3746] lr: 5.445e-02, eta: 2 days, 17:39:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5208, loss_cls: 4.2115, loss: 4.2115 +2024-07-18 20:20:40,170 - pyskl - INFO - Epoch [71][2900/3746] lr: 5.442e-02, eta: 2 days, 17:37:45, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5147, loss_cls: 4.2320, loss: 4.2320 +2024-07-18 20:22:03,303 - pyskl - INFO - Epoch [71][3000/3746] lr: 5.439e-02, eta: 2 days, 17:36:29, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5305, loss_cls: 4.1453, loss: 4.1453 +2024-07-18 20:23:25,810 - pyskl - INFO - Epoch [71][3100/3746] lr: 5.436e-02, eta: 2 days, 17:35:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5372, loss_cls: 4.1470, loss: 4.1470 +2024-07-18 20:24:48,674 - pyskl - INFO - Epoch [71][3200/3746] lr: 5.434e-02, eta: 2 days, 17:33:57, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5294, loss_cls: 4.1513, loss: 4.1513 +2024-07-18 20:26:11,234 - pyskl - INFO - Epoch [71][3300/3746] lr: 5.431e-02, eta: 2 days, 17:32:40, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5181, loss_cls: 4.1840, loss: 4.1840 +2024-07-18 20:27:34,073 - pyskl - INFO - Epoch [71][3400/3746] lr: 5.428e-02, eta: 2 days, 17:31:24, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5261, loss_cls: 4.1959, loss: 4.1959 +2024-07-18 20:28:56,614 - pyskl - INFO - Epoch [71][3500/3746] lr: 5.425e-02, eta: 2 days, 17:30:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5348, loss_cls: 4.1508, loss: 4.1508 +2024-07-18 20:30:19,081 - pyskl - INFO - Epoch [71][3600/3746] lr: 5.422e-02, eta: 2 days, 17:28:52, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5239, loss_cls: 4.1651, loss: 4.1651 +2024-07-18 20:31:41,580 - pyskl - INFO - Epoch [71][3700/3746] lr: 5.420e-02, eta: 2 days, 17:27:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5264, loss_cls: 4.1771, loss: 4.1771 +2024-07-18 20:32:21,230 - pyskl - INFO - Saving checkpoint at 71 epochs +2024-07-18 20:34:11,853 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 20:34:12,518 - pyskl - INFO - +top1_acc 0.1888 +top5_acc 0.4122 +2024-07-18 20:34:12,518 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 20:34:12,564 - pyskl - INFO - +mean_acc 0.1887 +2024-07-18 20:34:12,577 - pyskl - INFO - Epoch(val) [71][309] top1_acc: 0.1888, top5_acc: 0.4122, mean_class_accuracy: 0.1887 +2024-07-18 20:38:02,895 - pyskl - INFO - Epoch [72][100/3746] lr: 5.416e-02, eta: 2 days, 17:27:46, time: 2.303, data_time: 1.305, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5347, loss_cls: 4.1083, loss: 4.1083 +2024-07-18 20:39:26,329 - pyskl - INFO - Epoch [72][200/3746] lr: 5.413e-02, eta: 2 days, 17:26:30, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5417, loss_cls: 4.1077, loss: 4.1077 +2024-07-18 20:40:49,502 - pyskl - INFO - Epoch [72][300/3746] lr: 5.410e-02, eta: 2 days, 17:25:14, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5270, loss_cls: 4.1608, loss: 4.1608 +2024-07-18 20:42:12,939 - pyskl - INFO - Epoch [72][400/3746] lr: 5.407e-02, eta: 2 days, 17:23:59, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5317, loss_cls: 4.1301, loss: 4.1301 +2024-07-18 20:43:35,984 - pyskl - INFO - Epoch [72][500/3746] lr: 5.404e-02, eta: 2 days, 17:22:43, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5323, loss_cls: 4.1500, loss: 4.1500 +2024-07-18 20:44:58,582 - pyskl - INFO - Epoch [72][600/3746] lr: 5.402e-02, eta: 2 days, 17:21:27, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5269, loss_cls: 4.1933, loss: 4.1933 +2024-07-18 20:46:21,163 - pyskl - INFO - Epoch [72][700/3746] lr: 5.399e-02, eta: 2 days, 17:20:10, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5283, loss_cls: 4.1517, loss: 4.1517 +2024-07-18 20:47:44,099 - pyskl - INFO - Epoch [72][800/3746] lr: 5.396e-02, eta: 2 days, 17:18:54, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5272, loss_cls: 4.1646, loss: 4.1646 +2024-07-18 20:49:06,583 - pyskl - INFO - Epoch [72][900/3746] lr: 5.393e-02, eta: 2 days, 17:17:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5323, loss_cls: 4.1584, loss: 4.1584 +2024-07-18 20:50:29,210 - pyskl - INFO - Epoch [72][1000/3746] lr: 5.391e-02, eta: 2 days, 17:16:21, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5375, loss_cls: 4.1187, loss: 4.1187 +2024-07-18 20:51:51,767 - pyskl - INFO - Epoch [72][1100/3746] lr: 5.388e-02, eta: 2 days, 17:15:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5316, loss_cls: 4.1616, loss: 4.1616 +2024-07-18 20:53:14,719 - pyskl - INFO - Epoch [72][1200/3746] lr: 5.385e-02, eta: 2 days, 17:13:49, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5264, loss_cls: 4.1431, loss: 4.1431 +2024-07-18 20:54:36,882 - pyskl - INFO - Epoch [72][1300/3746] lr: 5.382e-02, eta: 2 days, 17:12:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5247, loss_cls: 4.1515, loss: 4.1515 +2024-07-18 20:55:59,028 - pyskl - INFO - Epoch [72][1400/3746] lr: 5.379e-02, eta: 2 days, 17:11:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5280, loss_cls: 4.1555, loss: 4.1555 +2024-07-18 20:57:22,659 - pyskl - INFO - Epoch [72][1500/3746] lr: 5.377e-02, eta: 2 days, 17:09:59, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5302, loss_cls: 4.1539, loss: 4.1539 +2024-07-18 20:58:45,418 - pyskl - INFO - Epoch [72][1600/3746] lr: 5.374e-02, eta: 2 days, 17:08:43, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5378, loss_cls: 4.0978, loss: 4.0978 +2024-07-18 21:00:08,154 - pyskl - INFO - Epoch [72][1700/3746] lr: 5.371e-02, eta: 2 days, 17:07:27, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5238, loss_cls: 4.1509, loss: 4.1509 +2024-07-18 21:01:31,546 - pyskl - INFO - Epoch [72][1800/3746] lr: 5.368e-02, eta: 2 days, 17:06:11, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5181, loss_cls: 4.1835, loss: 4.1835 +2024-07-18 21:02:54,919 - pyskl - INFO - Epoch [72][1900/3746] lr: 5.365e-02, eta: 2 days, 17:04:56, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5383, loss_cls: 4.1297, loss: 4.1297 +2024-07-18 21:04:18,665 - pyskl - INFO - Epoch [72][2000/3746] lr: 5.363e-02, eta: 2 days, 17:03:40, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5387, loss_cls: 4.1302, loss: 4.1302 +2024-07-18 21:05:41,904 - pyskl - INFO - Epoch [72][2100/3746] lr: 5.360e-02, eta: 2 days, 17:02:25, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5269, loss_cls: 4.1594, loss: 4.1594 +2024-07-18 21:07:05,827 - pyskl - INFO - Epoch [72][2200/3746] lr: 5.357e-02, eta: 2 days, 17:01:10, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5188, loss_cls: 4.1785, loss: 4.1785 +2024-07-18 21:08:29,695 - pyskl - INFO - Epoch [72][2300/3746] lr: 5.354e-02, eta: 2 days, 16:59:54, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5295, loss_cls: 4.1492, loss: 4.1492 +2024-07-18 21:09:53,464 - pyskl - INFO - Epoch [72][2400/3746] lr: 5.352e-02, eta: 2 days, 16:58:39, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5336, loss_cls: 4.1256, loss: 4.1256 +2024-07-18 21:11:16,973 - pyskl - INFO - Epoch [72][2500/3746] lr: 5.349e-02, eta: 2 days, 16:57:24, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5341, loss_cls: 4.1279, loss: 4.1279 +2024-07-18 21:12:40,078 - pyskl - INFO - Epoch [72][2600/3746] lr: 5.346e-02, eta: 2 days, 16:56:08, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5316, loss_cls: 4.1644, loss: 4.1644 +2024-07-18 21:14:02,607 - pyskl - INFO - Epoch [72][2700/3746] lr: 5.343e-02, eta: 2 days, 16:54:51, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5311, loss_cls: 4.1190, loss: 4.1190 +2024-07-18 21:15:25,745 - pyskl - INFO - Epoch [72][2800/3746] lr: 5.340e-02, eta: 2 days, 16:53:35, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5283, loss_cls: 4.1693, loss: 4.1693 +2024-07-18 21:16:49,278 - pyskl - INFO - Epoch [72][2900/3746] lr: 5.338e-02, eta: 2 days, 16:52:20, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5364, loss_cls: 4.1340, loss: 4.1340 +2024-07-18 21:18:12,103 - pyskl - INFO - Epoch [72][3000/3746] lr: 5.335e-02, eta: 2 days, 16:51:03, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5252, loss_cls: 4.1745, loss: 4.1745 +2024-07-18 21:19:34,131 - pyskl - INFO - Epoch [72][3100/3746] lr: 5.332e-02, eta: 2 days, 16:49:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5331, loss_cls: 4.1372, loss: 4.1372 +2024-07-18 21:20:56,368 - pyskl - INFO - Epoch [72][3200/3746] lr: 5.329e-02, eta: 2 days, 16:48:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5216, loss_cls: 4.1483, loss: 4.1483 +2024-07-18 21:22:18,764 - pyskl - INFO - Epoch [72][3300/3746] lr: 5.326e-02, eta: 2 days, 16:47:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5300, loss_cls: 4.1333, loss: 4.1333 +2024-07-18 21:23:41,066 - pyskl - INFO - Epoch [72][3400/3746] lr: 5.324e-02, eta: 2 days, 16:45:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5269, loss_cls: 4.1633, loss: 4.1633 +2024-07-18 21:25:04,125 - pyskl - INFO - Epoch [72][3500/3746] lr: 5.321e-02, eta: 2 days, 16:44:40, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5167, loss_cls: 4.1878, loss: 4.1878 +2024-07-18 21:26:27,200 - pyskl - INFO - Epoch [72][3600/3746] lr: 5.318e-02, eta: 2 days, 16:43:23, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5369, loss_cls: 4.1159, loss: 4.1159 +2024-07-18 21:27:49,554 - pyskl - INFO - Epoch [72][3700/3746] lr: 5.315e-02, eta: 2 days, 16:42:07, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5238, loss_cls: 4.1598, loss: 4.1598 +2024-07-18 21:28:30,083 - pyskl - INFO - Saving checkpoint at 72 epochs +2024-07-18 21:30:21,834 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 21:30:22,502 - pyskl - INFO - +top1_acc 0.2106 +top5_acc 0.4463 +2024-07-18 21:30:22,503 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 21:30:22,544 - pyskl - INFO - +mean_acc 0.2104 +2024-07-18 21:30:22,557 - pyskl - INFO - Epoch(val) [72][309] top1_acc: 0.2106, top5_acc: 0.4463, mean_class_accuracy: 0.2104 +2024-07-18 21:34:15,939 - pyskl - INFO - Epoch [73][100/3746] lr: 5.311e-02, eta: 2 days, 16:42:17, time: 2.334, data_time: 1.336, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5467, loss_cls: 4.0605, loss: 4.0605 +2024-07-18 21:35:39,695 - pyskl - INFO - Epoch [73][200/3746] lr: 5.308e-02, eta: 2 days, 16:41:02, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5442, loss_cls: 4.0913, loss: 4.0913 +2024-07-18 21:37:03,492 - pyskl - INFO - Epoch [73][300/3746] lr: 5.306e-02, eta: 2 days, 16:39:46, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5311, loss_cls: 4.1009, loss: 4.1009 +2024-07-18 21:38:26,152 - pyskl - INFO - Epoch [73][400/3746] lr: 5.303e-02, eta: 2 days, 16:38:30, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5348, loss_cls: 4.1178, loss: 4.1178 +2024-07-18 21:39:48,708 - pyskl - INFO - Epoch [73][500/3746] lr: 5.300e-02, eta: 2 days, 16:37:13, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5372, loss_cls: 4.0879, loss: 4.0879 +2024-07-18 21:41:10,867 - pyskl - INFO - Epoch [73][600/3746] lr: 5.297e-02, eta: 2 days, 16:35:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5355, loss_cls: 4.1333, loss: 4.1333 +2024-07-18 21:42:32,920 - pyskl - INFO - Epoch [73][700/3746] lr: 5.294e-02, eta: 2 days, 16:34:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5373, loss_cls: 4.1001, loss: 4.1001 +2024-07-18 21:43:55,173 - pyskl - INFO - Epoch [73][800/3746] lr: 5.292e-02, eta: 2 days, 16:33:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5181, loss_cls: 4.1658, loss: 4.1658 +2024-07-18 21:45:17,709 - pyskl - INFO - Epoch [73][900/3746] lr: 5.289e-02, eta: 2 days, 16:32:05, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5308, loss_cls: 4.1944, loss: 4.1944 +2024-07-18 21:46:39,959 - pyskl - INFO - Epoch [73][1000/3746] lr: 5.286e-02, eta: 2 days, 16:30:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5220, loss_cls: 4.1827, loss: 4.1827 +2024-07-18 21:48:02,744 - pyskl - INFO - Epoch [73][1100/3746] lr: 5.283e-02, eta: 2 days, 16:29:31, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5308, loss_cls: 4.1327, loss: 4.1327 +2024-07-18 21:49:24,815 - pyskl - INFO - Epoch [73][1200/3746] lr: 5.280e-02, eta: 2 days, 16:28:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5334, loss_cls: 4.1256, loss: 4.1256 +2024-07-18 21:50:45,884 - pyskl - INFO - Epoch [73][1300/3746] lr: 5.278e-02, eta: 2 days, 16:26:55, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5195, loss_cls: 4.1657, loss: 4.1657 +2024-07-18 21:52:07,237 - pyskl - INFO - Epoch [73][1400/3746] lr: 5.275e-02, eta: 2 days, 16:25:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5364, loss_cls: 4.1037, loss: 4.1037 +2024-07-18 21:53:28,489 - pyskl - INFO - Epoch [73][1500/3746] lr: 5.272e-02, eta: 2 days, 16:24:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5364, loss_cls: 4.1039, loss: 4.1039 +2024-07-18 21:54:50,497 - pyskl - INFO - Epoch [73][1600/3746] lr: 5.269e-02, eta: 2 days, 16:23:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5294, loss_cls: 4.1594, loss: 4.1594 +2024-07-18 21:56:11,805 - pyskl - INFO - Epoch [73][1700/3746] lr: 5.267e-02, eta: 2 days, 16:21:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5322, loss_cls: 4.1137, loss: 4.1137 +2024-07-18 21:57:33,107 - pyskl - INFO - Epoch [73][1800/3746] lr: 5.264e-02, eta: 2 days, 16:20:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5223, loss_cls: 4.1747, loss: 4.1747 +2024-07-18 21:58:54,956 - pyskl - INFO - Epoch [73][1900/3746] lr: 5.261e-02, eta: 2 days, 16:19:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5284, loss_cls: 4.1609, loss: 4.1609 +2024-07-18 22:00:16,102 - pyskl - INFO - Epoch [73][2000/3746] lr: 5.258e-02, eta: 2 days, 16:17:50, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5380, loss_cls: 4.1179, loss: 4.1179 +2024-07-18 22:01:37,588 - pyskl - INFO - Epoch [73][2100/3746] lr: 5.255e-02, eta: 2 days, 16:16:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5381, loss_cls: 4.1122, loss: 4.1122 +2024-07-18 22:02:58,767 - pyskl - INFO - Epoch [73][2200/3746] lr: 5.253e-02, eta: 2 days, 16:15:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5284, loss_cls: 4.1570, loss: 4.1570 +2024-07-18 22:04:20,231 - pyskl - INFO - Epoch [73][2300/3746] lr: 5.250e-02, eta: 2 days, 16:13:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5300, loss_cls: 4.1273, loss: 4.1273 +2024-07-18 22:05:41,581 - pyskl - INFO - Epoch [73][2400/3746] lr: 5.247e-02, eta: 2 days, 16:12:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5284, loss_cls: 4.1560, loss: 4.1560 +2024-07-18 22:07:03,814 - pyskl - INFO - Epoch [73][2500/3746] lr: 5.244e-02, eta: 2 days, 16:11:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5141, loss_cls: 4.2012, loss: 4.2012 +2024-07-18 22:08:25,308 - pyskl - INFO - Epoch [73][2600/3746] lr: 5.241e-02, eta: 2 days, 16:10:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5294, loss_cls: 4.1227, loss: 4.1227 +2024-07-18 22:09:46,999 - pyskl - INFO - Epoch [73][2700/3746] lr: 5.239e-02, eta: 2 days, 16:08:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5414, loss_cls: 4.1140, loss: 4.1140 +2024-07-18 22:11:08,483 - pyskl - INFO - Epoch [73][2800/3746] lr: 5.236e-02, eta: 2 days, 16:07:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5286, loss_cls: 4.1562, loss: 4.1562 +2024-07-18 22:12:29,865 - pyskl - INFO - Epoch [73][2900/3746] lr: 5.233e-02, eta: 2 days, 16:06:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5233, loss_cls: 4.1632, loss: 4.1632 +2024-07-18 22:13:51,236 - pyskl - INFO - Epoch [73][3000/3746] lr: 5.230e-02, eta: 2 days, 16:04:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5309, loss_cls: 4.1610, loss: 4.1610 +2024-07-18 22:15:12,124 - pyskl - INFO - Epoch [73][3100/3746] lr: 5.227e-02, eta: 2 days, 16:03:32, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5319, loss_cls: 4.1488, loss: 4.1488 +2024-07-18 22:16:33,269 - pyskl - INFO - Epoch [73][3200/3746] lr: 5.225e-02, eta: 2 days, 16:02:14, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5242, loss_cls: 4.1366, loss: 4.1366 +2024-07-18 22:17:54,355 - pyskl - INFO - Epoch [73][3300/3746] lr: 5.222e-02, eta: 2 days, 16:00:55, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5339, loss_cls: 4.1200, loss: 4.1200 +2024-07-18 22:19:16,365 - pyskl - INFO - Epoch [73][3400/3746] lr: 5.219e-02, eta: 2 days, 15:59:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5333, loss_cls: 4.1463, loss: 4.1463 +2024-07-18 22:20:37,874 - pyskl - INFO - Epoch [73][3500/3746] lr: 5.216e-02, eta: 2 days, 15:58:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5303, loss_cls: 4.1476, loss: 4.1476 +2024-07-18 22:21:58,980 - pyskl - INFO - Epoch [73][3600/3746] lr: 5.213e-02, eta: 2 days, 15:57:02, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5378, loss_cls: 4.1301, loss: 4.1301 +2024-07-18 22:23:19,890 - pyskl - INFO - Epoch [73][3700/3746] lr: 5.211e-02, eta: 2 days, 15:55:43, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5383, loss_cls: 4.1210, loss: 4.1210 +2024-07-18 22:23:59,140 - pyskl - INFO - Saving checkpoint at 73 epochs +2024-07-18 22:25:50,415 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 22:25:51,120 - pyskl - INFO - +top1_acc 0.1959 +top5_acc 0.4234 +2024-07-18 22:25:51,120 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 22:25:51,167 - pyskl - INFO - +mean_acc 0.1957 +2024-07-18 22:25:51,183 - pyskl - INFO - Epoch(val) [73][309] top1_acc: 0.1959, top5_acc: 0.4234, mean_class_accuracy: 0.1957 +2024-07-18 22:29:44,178 - pyskl - INFO - Epoch [74][100/3746] lr: 5.207e-02, eta: 2 days, 15:55:49, time: 2.330, data_time: 1.336, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5414, loss_cls: 4.0730, loss: 4.0730 +2024-07-18 22:31:07,775 - pyskl - INFO - Epoch [74][200/3746] lr: 5.204e-02, eta: 2 days, 15:54:33, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5403, loss_cls: 4.0715, loss: 4.0715 +2024-07-18 22:32:31,443 - pyskl - INFO - Epoch [74][300/3746] lr: 5.201e-02, eta: 2 days, 15:53:18, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5472, loss_cls: 4.0624, loss: 4.0624 +2024-07-18 22:33:54,123 - pyskl - INFO - Epoch [74][400/3746] lr: 5.198e-02, eta: 2 days, 15:52:01, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5486, loss_cls: 4.0933, loss: 4.0933 +2024-07-18 22:35:17,152 - pyskl - INFO - Epoch [74][500/3746] lr: 5.195e-02, eta: 2 days, 15:50:44, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5392, loss_cls: 4.0932, loss: 4.0932 +2024-07-18 22:36:39,280 - pyskl - INFO - Epoch [74][600/3746] lr: 5.193e-02, eta: 2 days, 15:49:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5327, loss_cls: 4.1139, loss: 4.1139 +2024-07-18 22:38:01,923 - pyskl - INFO - Epoch [74][700/3746] lr: 5.190e-02, eta: 2 days, 15:48:10, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5439, loss_cls: 4.1030, loss: 4.1030 +2024-07-18 22:39:24,572 - pyskl - INFO - Epoch [74][800/3746] lr: 5.187e-02, eta: 2 days, 15:46:53, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5417, loss_cls: 4.0685, loss: 4.0685 +2024-07-18 22:40:47,142 - pyskl - INFO - Epoch [74][900/3746] lr: 5.184e-02, eta: 2 days, 15:45:36, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5466, loss_cls: 4.0920, loss: 4.0920 +2024-07-18 22:42:09,961 - pyskl - INFO - Epoch [74][1000/3746] lr: 5.181e-02, eta: 2 days, 15:44:20, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5330, loss_cls: 4.1216, loss: 4.1216 +2024-07-18 22:43:32,875 - pyskl - INFO - Epoch [74][1100/3746] lr: 5.179e-02, eta: 2 days, 15:43:03, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5352, loss_cls: 4.1423, loss: 4.1423 +2024-07-18 22:44:55,034 - pyskl - INFO - Epoch [74][1200/3746] lr: 5.176e-02, eta: 2 days, 15:41:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5342, loss_cls: 4.1136, loss: 4.1136 +2024-07-18 22:46:17,567 - pyskl - INFO - Epoch [74][1300/3746] lr: 5.173e-02, eta: 2 days, 15:40:29, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5286, loss_cls: 4.1542, loss: 4.1542 +2024-07-18 22:47:40,718 - pyskl - INFO - Epoch [74][1400/3746] lr: 5.170e-02, eta: 2 days, 15:39:12, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5475, loss_cls: 4.1038, loss: 4.1038 +2024-07-18 22:49:03,341 - pyskl - INFO - Epoch [74][1500/3746] lr: 5.168e-02, eta: 2 days, 15:37:55, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5341, loss_cls: 4.1366, loss: 4.1366 +2024-07-18 22:50:26,088 - pyskl - INFO - Epoch [74][1600/3746] lr: 5.165e-02, eta: 2 days, 15:36:39, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5433, loss_cls: 4.0975, loss: 4.0975 +2024-07-18 22:51:49,383 - pyskl - INFO - Epoch [74][1700/3746] lr: 5.162e-02, eta: 2 days, 15:35:22, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5255, loss_cls: 4.1647, loss: 4.1647 +2024-07-18 22:53:13,016 - pyskl - INFO - Epoch [74][1800/3746] lr: 5.159e-02, eta: 2 days, 15:34:06, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5353, loss_cls: 4.1281, loss: 4.1281 +2024-07-18 22:54:35,552 - pyskl - INFO - Epoch [74][1900/3746] lr: 5.156e-02, eta: 2 days, 15:32:49, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5278, loss_cls: 4.1536, loss: 4.1536 +2024-07-18 22:55:58,410 - pyskl - INFO - Epoch [74][2000/3746] lr: 5.154e-02, eta: 2 days, 15:31:33, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5238, loss_cls: 4.1634, loss: 4.1634 +2024-07-18 22:57:20,793 - pyskl - INFO - Epoch [74][2100/3746] lr: 5.151e-02, eta: 2 days, 15:30:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5398, loss_cls: 4.0682, loss: 4.0682 +2024-07-18 22:58:43,107 - pyskl - INFO - Epoch [74][2200/3746] lr: 5.148e-02, eta: 2 days, 15:28:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5277, loss_cls: 4.1411, loss: 4.1411 +2024-07-18 23:00:05,254 - pyskl - INFO - Epoch [74][2300/3746] lr: 5.145e-02, eta: 2 days, 15:27:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5272, loss_cls: 4.1379, loss: 4.1379 +2024-07-18 23:01:28,563 - pyskl - INFO - Epoch [74][2400/3746] lr: 5.142e-02, eta: 2 days, 15:26:25, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5372, loss_cls: 4.1359, loss: 4.1359 +2024-07-18 23:02:51,913 - pyskl - INFO - Epoch [74][2500/3746] lr: 5.140e-02, eta: 2 days, 15:25:08, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5231, loss_cls: 4.1451, loss: 4.1451 +2024-07-18 23:04:14,672 - pyskl - INFO - Epoch [74][2600/3746] lr: 5.137e-02, eta: 2 days, 15:23:51, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5230, loss_cls: 4.1540, loss: 4.1540 +2024-07-18 23:05:37,934 - pyskl - INFO - Epoch [74][2700/3746] lr: 5.134e-02, eta: 2 days, 15:22:35, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5278, loss_cls: 4.1725, loss: 4.1725 +2024-07-18 23:07:02,025 - pyskl - INFO - Epoch [74][2800/3746] lr: 5.131e-02, eta: 2 days, 15:21:20, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5269, loss_cls: 4.1579, loss: 4.1579 +2024-07-18 23:08:25,703 - pyskl - INFO - Epoch [74][2900/3746] lr: 5.128e-02, eta: 2 days, 15:20:04, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5369, loss_cls: 4.0906, loss: 4.0906 +2024-07-18 23:09:48,732 - pyskl - INFO - Epoch [74][3000/3746] lr: 5.126e-02, eta: 2 days, 15:18:47, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5216, loss_cls: 4.1498, loss: 4.1498 +2024-07-18 23:11:12,047 - pyskl - INFO - Epoch [74][3100/3746] lr: 5.123e-02, eta: 2 days, 15:17:31, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5255, loss_cls: 4.1636, loss: 4.1636 +2024-07-18 23:12:34,572 - pyskl - INFO - Epoch [74][3200/3746] lr: 5.120e-02, eta: 2 days, 15:16:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5323, loss_cls: 4.1299, loss: 4.1299 +2024-07-18 23:13:57,049 - pyskl - INFO - Epoch [74][3300/3746] lr: 5.117e-02, eta: 2 days, 15:14:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5306, loss_cls: 4.1327, loss: 4.1327 +2024-07-18 23:15:20,340 - pyskl - INFO - Epoch [74][3400/3746] lr: 5.114e-02, eta: 2 days, 15:13:40, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5303, loss_cls: 4.1467, loss: 4.1467 +2024-07-18 23:16:42,732 - pyskl - INFO - Epoch [74][3500/3746] lr: 5.112e-02, eta: 2 days, 15:12:23, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5370, loss_cls: 4.1257, loss: 4.1257 +2024-07-18 23:18:05,334 - pyskl - INFO - Epoch [74][3600/3746] lr: 5.109e-02, eta: 2 days, 15:11:06, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5272, loss_cls: 4.1532, loss: 4.1532 +2024-07-18 23:19:29,242 - pyskl - INFO - Epoch [74][3700/3746] lr: 5.106e-02, eta: 2 days, 15:09:50, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5333, loss_cls: 4.1295, loss: 4.1295 +2024-07-18 23:20:09,455 - pyskl - INFO - Saving checkpoint at 74 epochs +2024-07-18 23:22:01,797 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 23:22:02,479 - pyskl - INFO - +top1_acc 0.2228 +top5_acc 0.4652 +2024-07-18 23:22:02,479 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 23:22:02,524 - pyskl - INFO - +mean_acc 0.2225 +2024-07-18 23:22:02,528 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_68.pth was removed +2024-07-18 23:22:02,837 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_74.pth. +2024-07-18 23:22:02,838 - pyskl - INFO - Best top1_acc is 0.2228 at 74 epoch. +2024-07-18 23:22:02,850 - pyskl - INFO - Epoch(val) [74][309] top1_acc: 0.2228, top5_acc: 0.4652, mean_class_accuracy: 0.2225 +2024-07-18 23:25:53,898 - pyskl - INFO - Epoch [75][100/3746] lr: 5.102e-02, eta: 2 days, 15:09:51, time: 2.310, data_time: 1.313, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5406, loss_cls: 4.0651, loss: 4.0651 +2024-07-18 23:27:17,384 - pyskl - INFO - Epoch [75][200/3746] lr: 5.099e-02, eta: 2 days, 15:08:35, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5436, loss_cls: 4.1052, loss: 4.1052 +2024-07-18 23:28:41,089 - pyskl - INFO - Epoch [75][300/3746] lr: 5.096e-02, eta: 2 days, 15:07:19, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5480, loss_cls: 4.0435, loss: 4.0435 +2024-07-18 23:30:04,737 - pyskl - INFO - Epoch [75][400/3746] lr: 5.094e-02, eta: 2 days, 15:06:03, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5273, loss_cls: 4.1497, loss: 4.1497 +2024-07-18 23:31:28,627 - pyskl - INFO - Epoch [75][500/3746] lr: 5.091e-02, eta: 2 days, 15:04:47, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5439, loss_cls: 4.0920, loss: 4.0920 +2024-07-18 23:32:52,573 - pyskl - INFO - Epoch [75][600/3746] lr: 5.088e-02, eta: 2 days, 15:03:31, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5295, loss_cls: 4.1312, loss: 4.1312 +2024-07-18 23:34:16,246 - pyskl - INFO - Epoch [75][700/3746] lr: 5.085e-02, eta: 2 days, 15:02:15, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5384, loss_cls: 4.1036, loss: 4.1036 +2024-07-18 23:35:39,708 - pyskl - INFO - Epoch [75][800/3746] lr: 5.082e-02, eta: 2 days, 15:00:59, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5419, loss_cls: 4.0880, loss: 4.0880 +2024-07-18 23:37:03,923 - pyskl - INFO - Epoch [75][900/3746] lr: 5.080e-02, eta: 2 days, 14:59:44, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5397, loss_cls: 4.1024, loss: 4.1024 +2024-07-18 23:38:27,985 - pyskl - INFO - Epoch [75][1000/3746] lr: 5.077e-02, eta: 2 days, 14:58:28, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5383, loss_cls: 4.0928, loss: 4.0928 +2024-07-18 23:39:51,437 - pyskl - INFO - Epoch [75][1100/3746] lr: 5.074e-02, eta: 2 days, 14:57:12, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5352, loss_cls: 4.1225, loss: 4.1225 +2024-07-18 23:41:15,154 - pyskl - INFO - Epoch [75][1200/3746] lr: 5.071e-02, eta: 2 days, 14:55:56, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5420, loss_cls: 4.1346, loss: 4.1346 +2024-07-18 23:42:38,370 - pyskl - INFO - Epoch [75][1300/3746] lr: 5.068e-02, eta: 2 days, 14:54:39, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5198, loss_cls: 4.1877, loss: 4.1877 +2024-07-18 23:44:01,804 - pyskl - INFO - Epoch [75][1400/3746] lr: 5.066e-02, eta: 2 days, 14:53:23, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5333, loss_cls: 4.1291, loss: 4.1291 +2024-07-18 23:45:24,657 - pyskl - INFO - Epoch [75][1500/3746] lr: 5.063e-02, eta: 2 days, 14:52:06, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5366, loss_cls: 4.1017, loss: 4.1017 +2024-07-18 23:46:47,713 - pyskl - INFO - Epoch [75][1600/3746] lr: 5.060e-02, eta: 2 days, 14:50:49, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5336, loss_cls: 4.1481, loss: 4.1481 +2024-07-18 23:48:11,361 - pyskl - INFO - Epoch [75][1700/3746] lr: 5.057e-02, eta: 2 days, 14:49:33, time: 0.836, data_time: 0.001, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5383, loss_cls: 4.1144, loss: 4.1144 +2024-07-18 23:49:33,700 - pyskl - INFO - Epoch [75][1800/3746] lr: 5.054e-02, eta: 2 days, 14:48:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5322, loss_cls: 4.1247, loss: 4.1247 +2024-07-18 23:50:56,404 - pyskl - INFO - Epoch [75][1900/3746] lr: 5.052e-02, eta: 2 days, 14:46:58, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5273, loss_cls: 4.1505, loss: 4.1505 +2024-07-18 23:52:18,705 - pyskl - INFO - Epoch [75][2000/3746] lr: 5.049e-02, eta: 2 days, 14:45:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5345, loss_cls: 4.1243, loss: 4.1243 +2024-07-18 23:53:40,900 - pyskl - INFO - Epoch [75][2100/3746] lr: 5.046e-02, eta: 2 days, 14:44:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5484, loss_cls: 4.0893, loss: 4.0893 +2024-07-18 23:55:02,959 - pyskl - INFO - Epoch [75][2200/3746] lr: 5.043e-02, eta: 2 days, 14:43:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5437, loss_cls: 4.0591, loss: 4.0591 +2024-07-18 23:56:24,991 - pyskl - INFO - Epoch [75][2300/3746] lr: 5.040e-02, eta: 2 days, 14:41:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5322, loss_cls: 4.1324, loss: 4.1324 +2024-07-18 23:57:47,850 - pyskl - INFO - Epoch [75][2400/3746] lr: 5.038e-02, eta: 2 days, 14:40:31, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5245, loss_cls: 4.1391, loss: 4.1391 +2024-07-18 23:59:10,375 - pyskl - INFO - Epoch [75][2500/3746] lr: 5.035e-02, eta: 2 days, 14:39:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5289, loss_cls: 4.1360, loss: 4.1360 +2024-07-19 00:00:33,092 - pyskl - INFO - Epoch [75][2600/3746] lr: 5.032e-02, eta: 2 days, 14:37:56, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5258, loss_cls: 4.1632, loss: 4.1632 +2024-07-19 00:01:56,781 - pyskl - INFO - Epoch [75][2700/3746] lr: 5.029e-02, eta: 2 days, 14:36:40, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5381, loss_cls: 4.1031, loss: 4.1031 +2024-07-19 00:03:20,045 - pyskl - INFO - Epoch [75][2800/3746] lr: 5.026e-02, eta: 2 days, 14:35:23, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5288, loss_cls: 4.1445, loss: 4.1445 +2024-07-19 00:04:43,187 - pyskl - INFO - Epoch [75][2900/3746] lr: 5.024e-02, eta: 2 days, 14:34:07, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5348, loss_cls: 4.1205, loss: 4.1205 +2024-07-19 00:06:06,765 - pyskl - INFO - Epoch [75][3000/3746] lr: 5.021e-02, eta: 2 days, 14:32:50, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5230, loss_cls: 4.1741, loss: 4.1741 +2024-07-19 00:07:30,620 - pyskl - INFO - Epoch [75][3100/3746] lr: 5.018e-02, eta: 2 days, 14:31:34, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5323, loss_cls: 4.1383, loss: 4.1383 +2024-07-19 00:08:54,148 - pyskl - INFO - Epoch [75][3200/3746] lr: 5.015e-02, eta: 2 days, 14:30:18, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5381, loss_cls: 4.1144, loss: 4.1144 +2024-07-19 00:10:17,609 - pyskl - INFO - Epoch [75][3300/3746] lr: 5.012e-02, eta: 2 days, 14:29:02, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5363, loss_cls: 4.0929, loss: 4.0929 +2024-07-19 00:11:39,921 - pyskl - INFO - Epoch [75][3400/3746] lr: 5.010e-02, eta: 2 days, 14:27:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5433, loss_cls: 4.1002, loss: 4.1002 +2024-07-19 00:13:02,889 - pyskl - INFO - Epoch [75][3500/3746] lr: 5.007e-02, eta: 2 days, 14:26:27, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5434, loss_cls: 4.1019, loss: 4.1019 +2024-07-19 00:14:26,331 - pyskl - INFO - Epoch [75][3600/3746] lr: 5.004e-02, eta: 2 days, 14:25:11, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5352, loss_cls: 4.1212, loss: 4.1212 +2024-07-19 00:15:49,781 - pyskl - INFO - Epoch [75][3700/3746] lr: 5.001e-02, eta: 2 days, 14:23:54, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5283, loss_cls: 4.1206, loss: 4.1206 +2024-07-19 00:16:29,873 - pyskl - INFO - Saving checkpoint at 75 epochs +2024-07-19 00:18:22,361 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 00:18:23,038 - pyskl - INFO - +top1_acc 0.2029 +top5_acc 0.4267 +2024-07-19 00:18:23,038 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 00:18:23,088 - pyskl - INFO - +mean_acc 0.2029 +2024-07-19 00:18:23,102 - pyskl - INFO - Epoch(val) [75][309] top1_acc: 0.2029, top5_acc: 0.4267, mean_class_accuracy: 0.2029 +2024-07-19 00:22:12,826 - pyskl - INFO - Epoch [76][100/3746] lr: 4.997e-02, eta: 2 days, 14:23:51, time: 2.297, data_time: 1.297, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5444, loss_cls: 4.0825, loss: 4.0825 +2024-07-19 00:23:36,392 - pyskl - INFO - Epoch [76][200/3746] lr: 4.994e-02, eta: 2 days, 14:22:34, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5305, loss_cls: 4.1328, loss: 4.1328 +2024-07-19 00:25:00,267 - pyskl - INFO - Epoch [76][300/3746] lr: 4.992e-02, eta: 2 days, 14:21:18, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5373, loss_cls: 4.1033, loss: 4.1033 +2024-07-19 00:26:24,253 - pyskl - INFO - Epoch [76][400/3746] lr: 4.989e-02, eta: 2 days, 14:20:02, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5437, loss_cls: 4.0866, loss: 4.0866 +2024-07-19 00:27:48,122 - pyskl - INFO - Epoch [76][500/3746] lr: 4.986e-02, eta: 2 days, 14:18:46, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5425, loss_cls: 4.0964, loss: 4.0964 +2024-07-19 00:29:11,739 - pyskl - INFO - Epoch [76][600/3746] lr: 4.983e-02, eta: 2 days, 14:17:30, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5413, loss_cls: 4.0675, loss: 4.0675 +2024-07-19 00:30:35,685 - pyskl - INFO - Epoch [76][700/3746] lr: 4.980e-02, eta: 2 days, 14:16:14, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5414, loss_cls: 4.0971, loss: 4.0971 +2024-07-19 00:31:58,786 - pyskl - INFO - Epoch [76][800/3746] lr: 4.978e-02, eta: 2 days, 14:14:57, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5472, loss_cls: 4.0718, loss: 4.0718 +2024-07-19 00:33:22,571 - pyskl - INFO - Epoch [76][900/3746] lr: 4.975e-02, eta: 2 days, 14:13:40, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5434, loss_cls: 4.0865, loss: 4.0865 +2024-07-19 00:34:45,976 - pyskl - INFO - Epoch [76][1000/3746] lr: 4.972e-02, eta: 2 days, 14:12:24, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5394, loss_cls: 4.1081, loss: 4.1081 +2024-07-19 00:36:09,463 - pyskl - INFO - Epoch [76][1100/3746] lr: 4.969e-02, eta: 2 days, 14:11:07, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5386, loss_cls: 4.0940, loss: 4.0940 +2024-07-19 00:37:32,485 - pyskl - INFO - Epoch [76][1200/3746] lr: 4.966e-02, eta: 2 days, 14:09:50, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5391, loss_cls: 4.1105, loss: 4.1105 +2024-07-19 00:38:55,950 - pyskl - INFO - Epoch [76][1300/3746] lr: 4.964e-02, eta: 2 days, 14:08:34, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5380, loss_cls: 4.1003, loss: 4.1003 +2024-07-19 00:40:18,479 - pyskl - INFO - Epoch [76][1400/3746] lr: 4.961e-02, eta: 2 days, 14:07:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5406, loss_cls: 4.0988, loss: 4.0988 +2024-07-19 00:41:41,511 - pyskl - INFO - Epoch [76][1500/3746] lr: 4.958e-02, eta: 2 days, 14:05:59, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5480, loss_cls: 4.0487, loss: 4.0487 +2024-07-19 00:43:04,977 - pyskl - INFO - Epoch [76][1600/3746] lr: 4.955e-02, eta: 2 days, 14:04:43, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5322, loss_cls: 4.0996, loss: 4.0996 +2024-07-19 00:44:28,912 - pyskl - INFO - Epoch [76][1700/3746] lr: 4.953e-02, eta: 2 days, 14:03:27, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5408, loss_cls: 4.0656, loss: 4.0656 +2024-07-19 00:45:51,224 - pyskl - INFO - Epoch [76][1800/3746] lr: 4.950e-02, eta: 2 days, 14:02:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5309, loss_cls: 4.1511, loss: 4.1511 +2024-07-19 00:47:13,505 - pyskl - INFO - Epoch [76][1900/3746] lr: 4.947e-02, eta: 2 days, 14:00:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5353, loss_cls: 4.1232, loss: 4.1232 +2024-07-19 00:48:35,844 - pyskl - INFO - Epoch [76][2000/3746] lr: 4.944e-02, eta: 2 days, 13:59:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5394, loss_cls: 4.0995, loss: 4.0995 +2024-07-19 00:49:58,493 - pyskl - INFO - Epoch [76][2100/3746] lr: 4.941e-02, eta: 2 days, 13:58:16, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5369, loss_cls: 4.1130, loss: 4.1130 +2024-07-19 00:51:20,352 - pyskl - INFO - Epoch [76][2200/3746] lr: 4.939e-02, eta: 2 days, 13:56:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5480, loss_cls: 4.0518, loss: 4.0518 +2024-07-19 00:52:43,272 - pyskl - INFO - Epoch [76][2300/3746] lr: 4.936e-02, eta: 2 days, 13:55:41, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5272, loss_cls: 4.1167, loss: 4.1167 +2024-07-19 00:54:06,116 - pyskl - INFO - Epoch [76][2400/3746] lr: 4.933e-02, eta: 2 days, 13:54:23, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5281, loss_cls: 4.1132, loss: 4.1132 +2024-07-19 00:55:28,628 - pyskl - INFO - Epoch [76][2500/3746] lr: 4.930e-02, eta: 2 days, 13:53:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5391, loss_cls: 4.0841, loss: 4.0841 +2024-07-19 00:56:51,395 - pyskl - INFO - Epoch [76][2600/3746] lr: 4.927e-02, eta: 2 days, 13:51:49, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5277, loss_cls: 4.1438, loss: 4.1438 +2024-07-19 00:58:14,398 - pyskl - INFO - Epoch [76][2700/3746] lr: 4.925e-02, eta: 2 days, 13:50:31, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5370, loss_cls: 4.1017, loss: 4.1017 +2024-07-19 00:59:36,807 - pyskl - INFO - Epoch [76][2800/3746] lr: 4.922e-02, eta: 2 days, 13:49:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5342, loss_cls: 4.1164, loss: 4.1164 +2024-07-19 01:00:59,118 - pyskl - INFO - Epoch [76][2900/3746] lr: 4.919e-02, eta: 2 days, 13:47:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5369, loss_cls: 4.1048, loss: 4.1048 +2024-07-19 01:02:21,507 - pyskl - INFO - Epoch [76][3000/3746] lr: 4.916e-02, eta: 2 days, 13:46:38, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5381, loss_cls: 4.1190, loss: 4.1190 +2024-07-19 01:03:43,930 - pyskl - INFO - Epoch [76][3100/3746] lr: 4.913e-02, eta: 2 days, 13:45:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5405, loss_cls: 4.0809, loss: 4.0809 +2024-07-19 01:05:07,202 - pyskl - INFO - Epoch [76][3200/3746] lr: 4.911e-02, eta: 2 days, 13:44:04, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5381, loss_cls: 4.1194, loss: 4.1194 +2024-07-19 01:06:29,489 - pyskl - INFO - Epoch [76][3300/3746] lr: 4.908e-02, eta: 2 days, 13:42:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5445, loss_cls: 4.0925, loss: 4.0925 +2024-07-19 01:07:51,486 - pyskl - INFO - Epoch [76][3400/3746] lr: 4.905e-02, eta: 2 days, 13:41:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5369, loss_cls: 4.1164, loss: 4.1164 +2024-07-19 01:09:14,534 - pyskl - INFO - Epoch [76][3500/3746] lr: 4.902e-02, eta: 2 days, 13:40:11, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5273, loss_cls: 4.1508, loss: 4.1508 +2024-07-19 01:10:37,124 - pyskl - INFO - Epoch [76][3600/3746] lr: 4.899e-02, eta: 2 days, 13:38:53, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5337, loss_cls: 4.1202, loss: 4.1202 +2024-07-19 01:12:00,164 - pyskl - INFO - Epoch [76][3700/3746] lr: 4.897e-02, eta: 2 days, 13:37:36, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5386, loss_cls: 4.0939, loss: 4.0939 +2024-07-19 01:12:40,162 - pyskl - INFO - Saving checkpoint at 76 epochs +2024-07-19 01:14:31,950 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 01:14:32,608 - pyskl - INFO - +top1_acc 0.1931 +top5_acc 0.4136 +2024-07-19 01:14:32,608 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 01:14:32,649 - pyskl - INFO - +mean_acc 0.1932 +2024-07-19 01:14:32,662 - pyskl - INFO - Epoch(val) [76][309] top1_acc: 0.1931, top5_acc: 0.4136, mean_class_accuracy: 0.1932 +2024-07-19 01:18:17,320 - pyskl - INFO - Epoch [77][100/3746] lr: 4.893e-02, eta: 2 days, 13:37:24, time: 2.246, data_time: 1.251, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5494, loss_cls: 4.0492, loss: 4.0492 +2024-07-19 01:19:40,084 - pyskl - INFO - Epoch [77][200/3746] lr: 4.890e-02, eta: 2 days, 13:36:07, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5453, loss_cls: 4.0752, loss: 4.0752 +2024-07-19 01:21:02,899 - pyskl - INFO - Epoch [77][300/3746] lr: 4.887e-02, eta: 2 days, 13:34:50, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5486, loss_cls: 4.0375, loss: 4.0375 +2024-07-19 01:22:25,573 - pyskl - INFO - Epoch [77][400/3746] lr: 4.884e-02, eta: 2 days, 13:33:32, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5491, loss_cls: 4.0533, loss: 4.0533 +2024-07-19 01:23:48,149 - pyskl - INFO - Epoch [77][500/3746] lr: 4.881e-02, eta: 2 days, 13:32:14, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5395, loss_cls: 4.0671, loss: 4.0671 +2024-07-19 01:25:10,080 - pyskl - INFO - Epoch [77][600/3746] lr: 4.879e-02, eta: 2 days, 13:30:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5425, loss_cls: 4.0931, loss: 4.0931 +2024-07-19 01:26:32,536 - pyskl - INFO - Epoch [77][700/3746] lr: 4.876e-02, eta: 2 days, 13:29:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5308, loss_cls: 4.1341, loss: 4.1341 +2024-07-19 01:27:54,601 - pyskl - INFO - Epoch [77][800/3746] lr: 4.873e-02, eta: 2 days, 13:28:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5423, loss_cls: 4.0793, loss: 4.0793 +2024-07-19 01:29:16,839 - pyskl - INFO - Epoch [77][900/3746] lr: 4.870e-02, eta: 2 days, 13:27:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5502, loss_cls: 4.0825, loss: 4.0825 +2024-07-19 01:30:39,036 - pyskl - INFO - Epoch [77][1000/3746] lr: 4.867e-02, eta: 2 days, 13:25:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5411, loss_cls: 4.1207, loss: 4.1207 +2024-07-19 01:32:01,430 - pyskl - INFO - Epoch [77][1100/3746] lr: 4.865e-02, eta: 2 days, 13:24:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5427, loss_cls: 4.0750, loss: 4.0750 +2024-07-19 01:33:23,240 - pyskl - INFO - Epoch [77][1200/3746] lr: 4.862e-02, eta: 2 days, 13:23:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5403, loss_cls: 4.1116, loss: 4.1116 +2024-07-19 01:34:47,063 - pyskl - INFO - Epoch [77][1300/3746] lr: 4.859e-02, eta: 2 days, 13:21:52, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5448, loss_cls: 4.1014, loss: 4.1014 +2024-07-19 01:36:09,184 - pyskl - INFO - Epoch [77][1400/3746] lr: 4.856e-02, eta: 2 days, 13:20:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5353, loss_cls: 4.1114, loss: 4.1114 +2024-07-19 01:37:32,184 - pyskl - INFO - Epoch [77][1500/3746] lr: 4.853e-02, eta: 2 days, 13:19:16, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5473, loss_cls: 4.0574, loss: 4.0574 +2024-07-19 01:38:55,163 - pyskl - INFO - Epoch [77][1600/3746] lr: 4.851e-02, eta: 2 days, 13:17:59, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5345, loss_cls: 4.1409, loss: 4.1409 +2024-07-19 01:40:18,109 - pyskl - INFO - Epoch [77][1700/3746] lr: 4.848e-02, eta: 2 days, 13:16:42, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5397, loss_cls: 4.0966, loss: 4.0966 +2024-07-19 01:41:40,327 - pyskl - INFO - Epoch [77][1800/3746] lr: 4.845e-02, eta: 2 days, 13:15:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5472, loss_cls: 4.0483, loss: 4.0483 +2024-07-19 01:43:03,176 - pyskl - INFO - Epoch [77][1900/3746] lr: 4.842e-02, eta: 2 days, 13:14:06, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5298, loss_cls: 4.1255, loss: 4.1255 +2024-07-19 01:44:25,794 - pyskl - INFO - Epoch [77][2000/3746] lr: 4.839e-02, eta: 2 days, 13:12:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5366, loss_cls: 4.0857, loss: 4.0857 +2024-07-19 01:45:48,090 - pyskl - INFO - Epoch [77][2100/3746] lr: 4.837e-02, eta: 2 days, 13:11:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5411, loss_cls: 4.0927, loss: 4.0927 +2024-07-19 01:47:09,664 - pyskl - INFO - Epoch [77][2200/3746] lr: 4.834e-02, eta: 2 days, 13:10:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5413, loss_cls: 4.0705, loss: 4.0705 +2024-07-19 01:48:32,549 - pyskl - INFO - Epoch [77][2300/3746] lr: 4.831e-02, eta: 2 days, 13:08:55, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5336, loss_cls: 4.1056, loss: 4.1056 +2024-07-19 01:49:55,213 - pyskl - INFO - Epoch [77][2400/3746] lr: 4.828e-02, eta: 2 days, 13:07:37, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5344, loss_cls: 4.1095, loss: 4.1095 +2024-07-19 01:51:17,979 - pyskl - INFO - Epoch [77][2500/3746] lr: 4.825e-02, eta: 2 days, 13:06:20, time: 0.828, data_time: 0.001, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5478, loss_cls: 4.0389, loss: 4.0389 +2024-07-19 01:52:41,173 - pyskl - INFO - Epoch [77][2600/3746] lr: 4.823e-02, eta: 2 days, 13:05:03, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5420, loss_cls: 4.0796, loss: 4.0796 +2024-07-19 01:54:04,073 - pyskl - INFO - Epoch [77][2700/3746] lr: 4.820e-02, eta: 2 days, 13:03:45, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5427, loss_cls: 4.0915, loss: 4.0915 +2024-07-19 01:55:26,346 - pyskl - INFO - Epoch [77][2800/3746] lr: 4.817e-02, eta: 2 days, 13:02:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5444, loss_cls: 4.0885, loss: 4.0885 +2024-07-19 01:56:49,110 - pyskl - INFO - Epoch [77][2900/3746] lr: 4.814e-02, eta: 2 days, 13:01:10, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5453, loss_cls: 4.0617, loss: 4.0617 +2024-07-19 01:58:11,996 - pyskl - INFO - Epoch [77][3000/3746] lr: 4.811e-02, eta: 2 days, 12:59:52, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5492, loss_cls: 4.0463, loss: 4.0463 +2024-07-19 01:59:35,238 - pyskl - INFO - Epoch [77][3100/3746] lr: 4.809e-02, eta: 2 days, 12:58:35, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5356, loss_cls: 4.1084, loss: 4.1084 +2024-07-19 02:00:57,757 - pyskl - INFO - Epoch [77][3200/3746] lr: 4.806e-02, eta: 2 days, 12:57:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5387, loss_cls: 4.0894, loss: 4.0894 +2024-07-19 02:02:19,585 - pyskl - INFO - Epoch [77][3300/3746] lr: 4.803e-02, eta: 2 days, 12:55:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5308, loss_cls: 4.1517, loss: 4.1517 +2024-07-19 02:03:42,003 - pyskl - INFO - Epoch [77][3400/3746] lr: 4.800e-02, eta: 2 days, 12:54:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5422, loss_cls: 4.0803, loss: 4.0803 +2024-07-19 02:05:05,187 - pyskl - INFO - Epoch [77][3500/3746] lr: 4.798e-02, eta: 2 days, 12:53:24, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5345, loss_cls: 4.1085, loss: 4.1085 +2024-07-19 02:06:27,369 - pyskl - INFO - Epoch [77][3600/3746] lr: 4.795e-02, eta: 2 days, 12:52:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5391, loss_cls: 4.1352, loss: 4.1352 +2024-07-19 02:07:50,332 - pyskl - INFO - Epoch [77][3700/3746] lr: 4.792e-02, eta: 2 days, 12:50:49, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5337, loss_cls: 4.1186, loss: 4.1186 +2024-07-19 02:08:30,829 - pyskl - INFO - Saving checkpoint at 77 epochs +2024-07-19 02:10:22,393 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 02:10:23,052 - pyskl - INFO - +top1_acc 0.2250 +top5_acc 0.4650 +2024-07-19 02:10:23,052 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 02:10:23,094 - pyskl - INFO - +mean_acc 0.2249 +2024-07-19 02:10:23,099 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_74.pth was removed +2024-07-19 02:10:23,337 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_77.pth. +2024-07-19 02:10:23,338 - pyskl - INFO - Best top1_acc is 0.2250 at 77 epoch. +2024-07-19 02:10:23,350 - pyskl - INFO - Epoch(val) [77][309] top1_acc: 0.2250, top5_acc: 0.4650, mean_class_accuracy: 0.2249 +2024-07-19 02:14:06,311 - pyskl - INFO - Epoch [78][100/3746] lr: 4.788e-02, eta: 2 days, 12:50:32, time: 2.230, data_time: 1.241, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5566, loss_cls: 4.0027, loss: 4.0027 +2024-07-19 02:15:28,488 - pyskl - INFO - Epoch [78][200/3746] lr: 4.785e-02, eta: 2 days, 12:49:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5467, loss_cls: 4.0233, loss: 4.0233 +2024-07-19 02:16:51,301 - pyskl - INFO - Epoch [78][300/3746] lr: 4.782e-02, eta: 2 days, 12:47:56, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5469, loss_cls: 4.0425, loss: 4.0425 +2024-07-19 02:18:13,670 - pyskl - INFO - Epoch [78][400/3746] lr: 4.779e-02, eta: 2 days, 12:46:38, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5380, loss_cls: 4.0801, loss: 4.0801 +2024-07-19 02:19:35,753 - pyskl - INFO - Epoch [78][500/3746] lr: 4.777e-02, eta: 2 days, 12:45:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5475, loss_cls: 4.0526, loss: 4.0526 +2024-07-19 02:20:58,644 - pyskl - INFO - Epoch [78][600/3746] lr: 4.774e-02, eta: 2 days, 12:44:02, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5367, loss_cls: 4.0821, loss: 4.0821 +2024-07-19 02:22:20,874 - pyskl - INFO - Epoch [78][700/3746] lr: 4.771e-02, eta: 2 days, 12:42:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5539, loss_cls: 4.0021, loss: 4.0021 +2024-07-19 02:23:43,298 - pyskl - INFO - Epoch [78][800/3746] lr: 4.768e-02, eta: 2 days, 12:41:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5487, loss_cls: 4.0680, loss: 4.0680 +2024-07-19 02:25:05,006 - pyskl - INFO - Epoch [78][900/3746] lr: 4.766e-02, eta: 2 days, 12:40:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5489, loss_cls: 4.0289, loss: 4.0289 +2024-07-19 02:26:26,797 - pyskl - INFO - Epoch [78][1000/3746] lr: 4.763e-02, eta: 2 days, 12:38:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5453, loss_cls: 4.0603, loss: 4.0603 +2024-07-19 02:27:48,696 - pyskl - INFO - Epoch [78][1100/3746] lr: 4.760e-02, eta: 2 days, 12:37:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5428, loss_cls: 4.1008, loss: 4.1008 +2024-07-19 02:29:11,085 - pyskl - INFO - Epoch [78][1200/3746] lr: 4.757e-02, eta: 2 days, 12:36:13, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5353, loss_cls: 4.1114, loss: 4.1114 +2024-07-19 02:30:34,555 - pyskl - INFO - Epoch [78][1300/3746] lr: 4.754e-02, eta: 2 days, 12:34:56, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5441, loss_cls: 4.0889, loss: 4.0889 +2024-07-19 02:31:56,325 - pyskl - INFO - Epoch [78][1400/3746] lr: 4.752e-02, eta: 2 days, 12:33:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5428, loss_cls: 4.1163, loss: 4.1163 +2024-07-19 02:33:19,986 - pyskl - INFO - Epoch [78][1500/3746] lr: 4.749e-02, eta: 2 days, 12:32:20, time: 0.837, data_time: 0.001, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5502, loss_cls: 4.0484, loss: 4.0484 +2024-07-19 02:34:43,552 - pyskl - INFO - Epoch [78][1600/3746] lr: 4.746e-02, eta: 2 days, 12:31:03, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5387, loss_cls: 4.0722, loss: 4.0722 +2024-07-19 02:36:06,404 - pyskl - INFO - Epoch [78][1700/3746] lr: 4.743e-02, eta: 2 days, 12:29:46, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5508, loss_cls: 4.0562, loss: 4.0562 +2024-07-19 02:37:29,461 - pyskl - INFO - Epoch [78][1800/3746] lr: 4.740e-02, eta: 2 days, 12:28:28, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5439, loss_cls: 4.0478, loss: 4.0478 +2024-07-19 02:38:51,884 - pyskl - INFO - Epoch [78][1900/3746] lr: 4.738e-02, eta: 2 days, 12:27:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5484, loss_cls: 4.0798, loss: 4.0798 +2024-07-19 02:40:14,503 - pyskl - INFO - Epoch [78][2000/3746] lr: 4.735e-02, eta: 2 days, 12:25:52, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5333, loss_cls: 4.1013, loss: 4.1013 +2024-07-19 02:41:36,897 - pyskl - INFO - Epoch [78][2100/3746] lr: 4.732e-02, eta: 2 days, 12:24:34, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5398, loss_cls: 4.0779, loss: 4.0779 +2024-07-19 02:42:59,444 - pyskl - INFO - Epoch [78][2200/3746] lr: 4.729e-02, eta: 2 days, 12:23:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5369, loss_cls: 4.0995, loss: 4.0995 +2024-07-19 02:44:22,670 - pyskl - INFO - Epoch [78][2300/3746] lr: 4.726e-02, eta: 2 days, 12:21:59, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5423, loss_cls: 4.0977, loss: 4.0977 +2024-07-19 02:45:45,127 - pyskl - INFO - Epoch [78][2400/3746] lr: 4.724e-02, eta: 2 days, 12:20:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5425, loss_cls: 4.0558, loss: 4.0558 +2024-07-19 02:47:07,639 - pyskl - INFO - Epoch [78][2500/3746] lr: 4.721e-02, eta: 2 days, 12:19:23, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5366, loss_cls: 4.0893, loss: 4.0893 +2024-07-19 02:48:30,799 - pyskl - INFO - Epoch [78][2600/3746] lr: 4.718e-02, eta: 2 days, 12:18:06, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5281, loss_cls: 4.1879, loss: 4.1879 +2024-07-19 02:49:54,419 - pyskl - INFO - Epoch [78][2700/3746] lr: 4.715e-02, eta: 2 days, 12:16:49, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5428, loss_cls: 4.0809, loss: 4.0809 +2024-07-19 02:51:17,184 - pyskl - INFO - Epoch [78][2800/3746] lr: 4.712e-02, eta: 2 days, 12:15:31, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5450, loss_cls: 4.0586, loss: 4.0586 +2024-07-19 02:52:39,782 - pyskl - INFO - Epoch [78][2900/3746] lr: 4.710e-02, eta: 2 days, 12:14:13, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5466, loss_cls: 4.0752, loss: 4.0752 +2024-07-19 02:54:02,070 - pyskl - INFO - Epoch [78][3000/3746] lr: 4.707e-02, eta: 2 days, 12:12:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5403, loss_cls: 4.0510, loss: 4.0510 +2024-07-19 02:55:25,993 - pyskl - INFO - Epoch [78][3100/3746] lr: 4.704e-02, eta: 2 days, 12:11:39, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5448, loss_cls: 4.0920, loss: 4.0920 +2024-07-19 02:56:48,236 - pyskl - INFO - Epoch [78][3200/3746] lr: 4.701e-02, eta: 2 days, 12:10:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5380, loss_cls: 4.1077, loss: 4.1077 +2024-07-19 02:58:10,866 - pyskl - INFO - Epoch [78][3300/3746] lr: 4.699e-02, eta: 2 days, 12:09:02, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5397, loss_cls: 4.0897, loss: 4.0897 +2024-07-19 02:59:34,444 - pyskl - INFO - Epoch [78][3400/3746] lr: 4.696e-02, eta: 2 days, 12:07:46, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5383, loss_cls: 4.0996, loss: 4.0996 +2024-07-19 03:00:56,923 - pyskl - INFO - Epoch [78][3500/3746] lr: 4.693e-02, eta: 2 days, 12:06:27, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5361, loss_cls: 4.1030, loss: 4.1030 +2024-07-19 03:02:18,859 - pyskl - INFO - Epoch [78][3600/3746] lr: 4.690e-02, eta: 2 days, 12:05:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5405, loss_cls: 4.0844, loss: 4.0844 +2024-07-19 03:03:41,764 - pyskl - INFO - Epoch [78][3700/3746] lr: 4.687e-02, eta: 2 days, 12:03:51, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5417, loss_cls: 4.0785, loss: 4.0785 +2024-07-19 03:04:21,997 - pyskl - INFO - Saving checkpoint at 78 epochs +2024-07-19 03:06:12,913 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 03:06:13,577 - pyskl - INFO - +top1_acc 0.2133 +top5_acc 0.4504 +2024-07-19 03:06:13,577 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 03:06:13,617 - pyskl - INFO - +mean_acc 0.2131 +2024-07-19 03:06:13,629 - pyskl - INFO - Epoch(val) [78][309] top1_acc: 0.2133, top5_acc: 0.4504, mean_class_accuracy: 0.2131 +2024-07-19 03:09:58,456 - pyskl - INFO - Epoch [79][100/3746] lr: 4.683e-02, eta: 2 days, 12:03:34, time: 2.248, data_time: 1.267, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5570, loss_cls: 4.0239, loss: 4.0239 +2024-07-19 03:11:21,858 - pyskl - INFO - Epoch [79][200/3746] lr: 4.680e-02, eta: 2 days, 12:02:17, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5489, loss_cls: 4.0213, loss: 4.0213 +2024-07-19 03:12:44,292 - pyskl - INFO - Epoch [79][300/3746] lr: 4.678e-02, eta: 2 days, 12:00:58, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5452, loss_cls: 4.0317, loss: 4.0317 +2024-07-19 03:14:06,516 - pyskl - INFO - Epoch [79][400/3746] lr: 4.675e-02, eta: 2 days, 11:59:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5502, loss_cls: 4.0165, loss: 4.0165 +2024-07-19 03:15:28,527 - pyskl - INFO - Epoch [79][500/3746] lr: 4.672e-02, eta: 2 days, 11:58:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5528, loss_cls: 4.0606, loss: 4.0606 +2024-07-19 03:16:51,493 - pyskl - INFO - Epoch [79][600/3746] lr: 4.669e-02, eta: 2 days, 11:57:04, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5467, loss_cls: 4.0817, loss: 4.0817 +2024-07-19 03:18:14,118 - pyskl - INFO - Epoch [79][700/3746] lr: 4.667e-02, eta: 2 days, 11:55:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5403, loss_cls: 4.0973, loss: 4.0973 +2024-07-19 03:19:36,325 - pyskl - INFO - Epoch [79][800/3746] lr: 4.664e-02, eta: 2 days, 11:54:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5467, loss_cls: 4.0559, loss: 4.0559 +2024-07-19 03:20:58,805 - pyskl - INFO - Epoch [79][900/3746] lr: 4.661e-02, eta: 2 days, 11:53:09, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5437, loss_cls: 4.0477, loss: 4.0477 +2024-07-19 03:22:21,218 - pyskl - INFO - Epoch [79][1000/3746] lr: 4.658e-02, eta: 2 days, 11:51:51, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5366, loss_cls: 4.0813, loss: 4.0813 +2024-07-19 03:23:43,425 - pyskl - INFO - Epoch [79][1100/3746] lr: 4.655e-02, eta: 2 days, 11:50:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5459, loss_cls: 4.0405, loss: 4.0405 +2024-07-19 03:25:06,434 - pyskl - INFO - Epoch [79][1200/3746] lr: 4.653e-02, eta: 2 days, 11:49:15, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5417, loss_cls: 4.0931, loss: 4.0931 +2024-07-19 03:26:29,485 - pyskl - INFO - Epoch [79][1300/3746] lr: 4.650e-02, eta: 2 days, 11:47:58, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5403, loss_cls: 4.0769, loss: 4.0769 +2024-07-19 03:27:51,805 - pyskl - INFO - Epoch [79][1400/3746] lr: 4.647e-02, eta: 2 days, 11:46:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5495, loss_cls: 4.0606, loss: 4.0606 +2024-07-19 03:29:15,353 - pyskl - INFO - Epoch [79][1500/3746] lr: 4.644e-02, eta: 2 days, 11:45:22, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5367, loss_cls: 4.1143, loss: 4.1143 +2024-07-19 03:30:38,472 - pyskl - INFO - Epoch [79][1600/3746] lr: 4.641e-02, eta: 2 days, 11:44:05, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5452, loss_cls: 4.0465, loss: 4.0465 +2024-07-19 03:32:01,182 - pyskl - INFO - Epoch [79][1700/3746] lr: 4.639e-02, eta: 2 days, 11:42:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5419, loss_cls: 4.1185, loss: 4.1185 +2024-07-19 03:33:24,117 - pyskl - INFO - Epoch [79][1800/3746] lr: 4.636e-02, eta: 2 days, 11:41:29, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5437, loss_cls: 4.0758, loss: 4.0758 +2024-07-19 03:34:46,672 - pyskl - INFO - Epoch [79][1900/3746] lr: 4.633e-02, eta: 2 days, 11:40:11, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5366, loss_cls: 4.0951, loss: 4.0951 +2024-07-19 03:36:08,545 - pyskl - INFO - Epoch [79][2000/3746] lr: 4.630e-02, eta: 2 days, 11:38:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5466, loss_cls: 4.0536, loss: 4.0536 +2024-07-19 03:37:31,360 - pyskl - INFO - Epoch [79][2100/3746] lr: 4.628e-02, eta: 2 days, 11:37:34, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5428, loss_cls: 4.0402, loss: 4.0402 +2024-07-19 03:38:53,377 - pyskl - INFO - Epoch [79][2200/3746] lr: 4.625e-02, eta: 2 days, 11:36:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5417, loss_cls: 4.0562, loss: 4.0562 +2024-07-19 03:40:16,323 - pyskl - INFO - Epoch [79][2300/3746] lr: 4.622e-02, eta: 2 days, 11:34:58, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5487, loss_cls: 4.0436, loss: 4.0436 +2024-07-19 03:41:38,699 - pyskl - INFO - Epoch [79][2400/3746] lr: 4.619e-02, eta: 2 days, 11:33:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5458, loss_cls: 4.0510, loss: 4.0510 +2024-07-19 03:43:01,128 - pyskl - INFO - Epoch [79][2500/3746] lr: 4.616e-02, eta: 2 days, 11:32:22, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5423, loss_cls: 4.0966, loss: 4.0966 +2024-07-19 03:44:24,176 - pyskl - INFO - Epoch [79][2600/3746] lr: 4.614e-02, eta: 2 days, 11:31:04, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5542, loss_cls: 4.0379, loss: 4.0379 +2024-07-19 03:45:46,387 - pyskl - INFO - Epoch [79][2700/3746] lr: 4.611e-02, eta: 2 days, 11:29:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5425, loss_cls: 4.0915, loss: 4.0915 +2024-07-19 03:47:08,392 - pyskl - INFO - Epoch [79][2800/3746] lr: 4.608e-02, eta: 2 days, 11:28:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5466, loss_cls: 4.0317, loss: 4.0317 +2024-07-19 03:48:30,286 - pyskl - INFO - Epoch [79][2900/3746] lr: 4.605e-02, eta: 2 days, 11:27:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5539, loss_cls: 4.0411, loss: 4.0411 +2024-07-19 03:49:53,061 - pyskl - INFO - Epoch [79][3000/3746] lr: 4.602e-02, eta: 2 days, 11:25:50, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5434, loss_cls: 4.0655, loss: 4.0655 +2024-07-19 03:51:15,873 - pyskl - INFO - Epoch [79][3100/3746] lr: 4.600e-02, eta: 2 days, 11:24:33, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5502, loss_cls: 4.0251, loss: 4.0251 +2024-07-19 03:52:38,764 - pyskl - INFO - Epoch [79][3200/3746] lr: 4.597e-02, eta: 2 days, 11:23:15, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5430, loss_cls: 4.1000, loss: 4.1000 +2024-07-19 03:54:01,299 - pyskl - INFO - Epoch [79][3300/3746] lr: 4.594e-02, eta: 2 days, 11:21:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5373, loss_cls: 4.0959, loss: 4.0959 +2024-07-19 03:55:24,145 - pyskl - INFO - Epoch [79][3400/3746] lr: 4.591e-02, eta: 2 days, 11:20:39, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5441, loss_cls: 4.0592, loss: 4.0592 +2024-07-19 03:56:45,884 - pyskl - INFO - Epoch [79][3500/3746] lr: 4.588e-02, eta: 2 days, 11:19:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5422, loss_cls: 4.1065, loss: 4.1065 +2024-07-19 03:58:07,768 - pyskl - INFO - Epoch [79][3600/3746] lr: 4.586e-02, eta: 2 days, 11:18:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5542, loss_cls: 4.0297, loss: 4.0297 +2024-07-19 03:59:30,556 - pyskl - INFO - Epoch [79][3700/3746] lr: 4.583e-02, eta: 2 days, 11:16:43, time: 0.828, data_time: 0.001, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5572, loss_cls: 4.0038, loss: 4.0038 +2024-07-19 04:00:10,406 - pyskl - INFO - Saving checkpoint at 79 epochs +2024-07-19 04:02:02,122 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 04:02:02,785 - pyskl - INFO - +top1_acc 0.2280 +top5_acc 0.4679 +2024-07-19 04:02:02,786 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 04:02:02,827 - pyskl - INFO - +mean_acc 0.2277 +2024-07-19 04:02:02,835 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_77.pth was removed +2024-07-19 04:02:03,075 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_79.pth. +2024-07-19 04:02:03,076 - pyskl - INFO - Best top1_acc is 0.2280 at 79 epoch. +2024-07-19 04:02:03,088 - pyskl - INFO - Epoch(val) [79][309] top1_acc: 0.2280, top5_acc: 0.4679, mean_class_accuracy: 0.2277 +2024-07-19 04:05:46,452 - pyskl - INFO - Epoch [80][100/3746] lr: 4.579e-02, eta: 2 days, 11:16:21, time: 2.234, data_time: 1.241, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5608, loss_cls: 3.9713, loss: 3.9713 +2024-07-19 04:07:09,014 - pyskl - INFO - Epoch [80][200/3746] lr: 4.576e-02, eta: 2 days, 11:15:03, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5483, loss_cls: 3.9898, loss: 3.9898 +2024-07-19 04:08:31,066 - pyskl - INFO - Epoch [80][300/3746] lr: 4.573e-02, eta: 2 days, 11:13:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5564, loss_cls: 4.0042, loss: 4.0042 +2024-07-19 04:09:53,590 - pyskl - INFO - Epoch [80][400/3746] lr: 4.570e-02, eta: 2 days, 11:12:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5466, loss_cls: 4.0553, loss: 4.0553 +2024-07-19 04:11:15,751 - pyskl - INFO - Epoch [80][500/3746] lr: 4.568e-02, eta: 2 days, 11:11:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5534, loss_cls: 4.0386, loss: 4.0386 +2024-07-19 04:12:38,032 - pyskl - INFO - Epoch [80][600/3746] lr: 4.565e-02, eta: 2 days, 11:09:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5575, loss_cls: 4.0078, loss: 4.0078 +2024-07-19 04:14:00,254 - pyskl - INFO - Epoch [80][700/3746] lr: 4.562e-02, eta: 2 days, 11:08:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5558, loss_cls: 4.0297, loss: 4.0297 +2024-07-19 04:15:22,207 - pyskl - INFO - Epoch [80][800/3746] lr: 4.559e-02, eta: 2 days, 11:07:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5467, loss_cls: 4.0199, loss: 4.0199 +2024-07-19 04:16:43,800 - pyskl - INFO - Epoch [80][900/3746] lr: 4.557e-02, eta: 2 days, 11:05:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5487, loss_cls: 4.0150, loss: 4.0150 +2024-07-19 04:18:05,612 - pyskl - INFO - Epoch [80][1000/3746] lr: 4.554e-02, eta: 2 days, 11:04:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5447, loss_cls: 4.0396, loss: 4.0396 +2024-07-19 04:19:28,338 - pyskl - INFO - Epoch [80][1100/3746] lr: 4.551e-02, eta: 2 days, 11:03:16, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5537, loss_cls: 4.0227, loss: 4.0227 +2024-07-19 04:20:50,594 - pyskl - INFO - Epoch [80][1200/3746] lr: 4.548e-02, eta: 2 days, 11:01:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5480, loss_cls: 4.0542, loss: 4.0542 +2024-07-19 04:22:13,083 - pyskl - INFO - Epoch [80][1300/3746] lr: 4.545e-02, eta: 2 days, 11:00:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5536, loss_cls: 4.0330, loss: 4.0330 +2024-07-19 04:23:34,938 - pyskl - INFO - Epoch [80][1400/3746] lr: 4.543e-02, eta: 2 days, 10:59:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5544, loss_cls: 4.0243, loss: 4.0243 +2024-07-19 04:24:58,039 - pyskl - INFO - Epoch [80][1500/3746] lr: 4.540e-02, eta: 2 days, 10:58:03, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5486, loss_cls: 4.0465, loss: 4.0465 +2024-07-19 04:26:20,815 - pyskl - INFO - Epoch [80][1600/3746] lr: 4.537e-02, eta: 2 days, 10:56:45, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5433, loss_cls: 4.0943, loss: 4.0943 +2024-07-19 04:27:42,973 - pyskl - INFO - Epoch [80][1700/3746] lr: 4.534e-02, eta: 2 days, 10:55:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5469, loss_cls: 4.0559, loss: 4.0559 +2024-07-19 04:29:06,744 - pyskl - INFO - Epoch [80][1800/3746] lr: 4.532e-02, eta: 2 days, 10:54:09, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5442, loss_cls: 4.0644, loss: 4.0644 +2024-07-19 04:30:29,565 - pyskl - INFO - Epoch [80][1900/3746] lr: 4.529e-02, eta: 2 days, 10:52:51, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5486, loss_cls: 4.0307, loss: 4.0307 +2024-07-19 04:31:51,688 - pyskl - INFO - Epoch [80][2000/3746] lr: 4.526e-02, eta: 2 days, 10:51:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5500, loss_cls: 4.0655, loss: 4.0655 +2024-07-19 04:33:14,238 - pyskl - INFO - Epoch [80][2100/3746] lr: 4.523e-02, eta: 2 days, 10:50:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5403, loss_cls: 4.1027, loss: 4.1027 +2024-07-19 04:34:36,999 - pyskl - INFO - Epoch [80][2200/3746] lr: 4.520e-02, eta: 2 days, 10:48:56, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5444, loss_cls: 4.0794, loss: 4.0794 +2024-07-19 04:36:00,365 - pyskl - INFO - Epoch [80][2300/3746] lr: 4.518e-02, eta: 2 days, 10:47:38, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5423, loss_cls: 4.0677, loss: 4.0677 +2024-07-19 04:37:22,753 - pyskl - INFO - Epoch [80][2400/3746] lr: 4.515e-02, eta: 2 days, 10:46:20, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5519, loss_cls: 4.0095, loss: 4.0095 +2024-07-19 04:38:45,621 - pyskl - INFO - Epoch [80][2500/3746] lr: 4.512e-02, eta: 2 days, 10:45:02, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5500, loss_cls: 4.0533, loss: 4.0533 +2024-07-19 04:40:08,846 - pyskl - INFO - Epoch [80][2600/3746] lr: 4.509e-02, eta: 2 days, 10:43:44, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5416, loss_cls: 4.0956, loss: 4.0956 +2024-07-19 04:41:31,237 - pyskl - INFO - Epoch [80][2700/3746] lr: 4.506e-02, eta: 2 days, 10:42:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5383, loss_cls: 4.1004, loss: 4.1004 +2024-07-19 04:42:53,414 - pyskl - INFO - Epoch [80][2800/3746] lr: 4.504e-02, eta: 2 days, 10:41:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5480, loss_cls: 4.0253, loss: 4.0253 +2024-07-19 04:44:15,273 - pyskl - INFO - Epoch [80][2900/3746] lr: 4.501e-02, eta: 2 days, 10:39:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5361, loss_cls: 4.1012, loss: 4.1012 +2024-07-19 04:45:37,820 - pyskl - INFO - Epoch [80][3000/3746] lr: 4.498e-02, eta: 2 days, 10:38:30, time: 0.825, data_time: 0.001, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5352, loss_cls: 4.0786, loss: 4.0786 +2024-07-19 04:47:00,700 - pyskl - INFO - Epoch [80][3100/3746] lr: 4.495e-02, eta: 2 days, 10:37:12, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5534, loss_cls: 4.0325, loss: 4.0325 +2024-07-19 04:48:22,898 - pyskl - INFO - Epoch [80][3200/3746] lr: 4.493e-02, eta: 2 days, 10:35:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5441, loss_cls: 4.0665, loss: 4.0665 +2024-07-19 04:49:45,970 - pyskl - INFO - Epoch [80][3300/3746] lr: 4.490e-02, eta: 2 days, 10:34:36, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5395, loss_cls: 4.1077, loss: 4.1077 +2024-07-19 04:51:08,732 - pyskl - INFO - Epoch [80][3400/3746] lr: 4.487e-02, eta: 2 days, 10:33:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5566, loss_cls: 4.0424, loss: 4.0424 +2024-07-19 04:52:31,184 - pyskl - INFO - Epoch [80][3500/3746] lr: 4.484e-02, eta: 2 days, 10:31:59, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5423, loss_cls: 4.0771, loss: 4.0771 +2024-07-19 04:53:53,652 - pyskl - INFO - Epoch [80][3600/3746] lr: 4.481e-02, eta: 2 days, 10:30:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5453, loss_cls: 4.0731, loss: 4.0731 +2024-07-19 04:55:16,930 - pyskl - INFO - Epoch [80][3700/3746] lr: 4.479e-02, eta: 2 days, 10:29:23, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5445, loss_cls: 4.0589, loss: 4.0589 +2024-07-19 04:55:56,261 - pyskl - INFO - Saving checkpoint at 80 epochs +2024-07-19 04:57:46,810 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 04:57:47,472 - pyskl - INFO - +top1_acc 0.2156 +top5_acc 0.4583 +2024-07-19 04:57:47,472 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 04:57:47,512 - pyskl - INFO - +mean_acc 0.2153 +2024-07-19 04:57:47,523 - pyskl - INFO - Epoch(val) [80][309] top1_acc: 0.2156, top5_acc: 0.4583, mean_class_accuracy: 0.2153 +2024-07-19 05:01:30,320 - pyskl - INFO - Epoch [81][100/3746] lr: 4.475e-02, eta: 2 days, 10:28:58, time: 2.228, data_time: 1.235, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5556, loss_cls: 4.0177, loss: 4.0177 +2024-07-19 05:02:52,272 - pyskl - INFO - Epoch [81][200/3746] lr: 4.472e-02, eta: 2 days, 10:27:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5622, loss_cls: 3.9667, loss: 3.9667 +2024-07-19 05:04:14,602 - pyskl - INFO - Epoch [81][300/3746] lr: 4.469e-02, eta: 2 days, 10:26:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5653, loss_cls: 3.9833, loss: 3.9833 +2024-07-19 05:05:36,863 - pyskl - INFO - Epoch [81][400/3746] lr: 4.466e-02, eta: 2 days, 10:25:02, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5452, loss_cls: 4.0703, loss: 4.0703 +2024-07-19 05:06:59,187 - pyskl - INFO - Epoch [81][500/3746] lr: 4.463e-02, eta: 2 days, 10:23:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5634, loss_cls: 4.0007, loss: 4.0007 +2024-07-19 05:08:21,169 - pyskl - INFO - Epoch [81][600/3746] lr: 4.461e-02, eta: 2 days, 10:22:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5437, loss_cls: 4.0620, loss: 4.0620 +2024-07-19 05:09:43,584 - pyskl - INFO - Epoch [81][700/3746] lr: 4.458e-02, eta: 2 days, 10:21:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5531, loss_cls: 4.0239, loss: 4.0239 +2024-07-19 05:11:05,632 - pyskl - INFO - Epoch [81][800/3746] lr: 4.455e-02, eta: 2 days, 10:19:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5527, loss_cls: 4.0079, loss: 4.0079 +2024-07-19 05:12:28,211 - pyskl - INFO - Epoch [81][900/3746] lr: 4.452e-02, eta: 2 days, 10:18:29, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5455, loss_cls: 4.0968, loss: 4.0968 +2024-07-19 05:13:50,572 - pyskl - INFO - Epoch [81][1000/3746] lr: 4.450e-02, eta: 2 days, 10:17:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5514, loss_cls: 4.0541, loss: 4.0541 +2024-07-19 05:15:12,680 - pyskl - INFO - Epoch [81][1100/3746] lr: 4.447e-02, eta: 2 days, 10:15:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5506, loss_cls: 4.0162, loss: 4.0162 +2024-07-19 05:16:34,547 - pyskl - INFO - Epoch [81][1200/3746] lr: 4.444e-02, eta: 2 days, 10:14:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5495, loss_cls: 4.0488, loss: 4.0488 +2024-07-19 05:17:57,303 - pyskl - INFO - Epoch [81][1300/3746] lr: 4.441e-02, eta: 2 days, 10:13:14, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5464, loss_cls: 4.0568, loss: 4.0568 +2024-07-19 05:19:19,377 - pyskl - INFO - Epoch [81][1400/3746] lr: 4.438e-02, eta: 2 days, 10:11:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5417, loss_cls: 4.0633, loss: 4.0633 +2024-07-19 05:20:42,578 - pyskl - INFO - Epoch [81][1500/3746] lr: 4.436e-02, eta: 2 days, 10:10:38, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5580, loss_cls: 4.0185, loss: 4.0185 +2024-07-19 05:22:05,291 - pyskl - INFO - Epoch [81][1600/3746] lr: 4.433e-02, eta: 2 days, 10:09:19, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5473, loss_cls: 4.0618, loss: 4.0618 +2024-07-19 05:23:27,588 - pyskl - INFO - Epoch [81][1700/3746] lr: 4.430e-02, eta: 2 days, 10:08:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5381, loss_cls: 4.0718, loss: 4.0718 +2024-07-19 05:24:50,171 - pyskl - INFO - Epoch [81][1800/3746] lr: 4.427e-02, eta: 2 days, 10:06:42, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5464, loss_cls: 4.0663, loss: 4.0663 +2024-07-19 05:26:12,824 - pyskl - INFO - Epoch [81][1900/3746] lr: 4.425e-02, eta: 2 days, 10:05:24, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5548, loss_cls: 4.0204, loss: 4.0204 +2024-07-19 05:27:34,971 - pyskl - INFO - Epoch [81][2000/3746] lr: 4.422e-02, eta: 2 days, 10:04:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5472, loss_cls: 4.0408, loss: 4.0408 +2024-07-19 05:28:56,799 - pyskl - INFO - Epoch [81][2100/3746] lr: 4.419e-02, eta: 2 days, 10:02:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5463, loss_cls: 4.0357, loss: 4.0357 +2024-07-19 05:30:19,538 - pyskl - INFO - Epoch [81][2200/3746] lr: 4.416e-02, eta: 2 days, 10:01:28, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5511, loss_cls: 4.0273, loss: 4.0273 +2024-07-19 05:31:41,678 - pyskl - INFO - Epoch [81][2300/3746] lr: 4.413e-02, eta: 2 days, 10:00:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5489, loss_cls: 4.0562, loss: 4.0562 +2024-07-19 05:33:03,157 - pyskl - INFO - Epoch [81][2400/3746] lr: 4.411e-02, eta: 2 days, 9:58:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5373, loss_cls: 4.0809, loss: 4.0809 +2024-07-19 05:34:26,204 - pyskl - INFO - Epoch [81][2500/3746] lr: 4.408e-02, eta: 2 days, 9:57:32, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5530, loss_cls: 3.9853, loss: 3.9853 +2024-07-19 05:35:48,767 - pyskl - INFO - Epoch [81][2600/3746] lr: 4.405e-02, eta: 2 days, 9:56:13, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5414, loss_cls: 4.0597, loss: 4.0597 +2024-07-19 05:37:10,781 - pyskl - INFO - Epoch [81][2700/3746] lr: 4.402e-02, eta: 2 days, 9:54:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5375, loss_cls: 4.0833, loss: 4.0833 +2024-07-19 05:38:32,631 - pyskl - INFO - Epoch [81][2800/3746] lr: 4.400e-02, eta: 2 days, 9:53:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5459, loss_cls: 4.0636, loss: 4.0636 +2024-07-19 05:39:54,415 - pyskl - INFO - Epoch [81][2900/3746] lr: 4.397e-02, eta: 2 days, 9:52:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5506, loss_cls: 4.0463, loss: 4.0463 +2024-07-19 05:41:17,202 - pyskl - INFO - Epoch [81][3000/3746] lr: 4.394e-02, eta: 2 days, 9:50:58, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5425, loss_cls: 4.0494, loss: 4.0494 +2024-07-19 05:42:39,310 - pyskl - INFO - Epoch [81][3100/3746] lr: 4.391e-02, eta: 2 days, 9:49:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5558, loss_cls: 4.0325, loss: 4.0325 +2024-07-19 05:44:01,607 - pyskl - INFO - Epoch [81][3200/3746] lr: 4.389e-02, eta: 2 days, 9:48:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5508, loss_cls: 4.0337, loss: 4.0337 +2024-07-19 05:45:24,670 - pyskl - INFO - Epoch [81][3300/3746] lr: 4.386e-02, eta: 2 days, 9:47:03, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5550, loss_cls: 4.0451, loss: 4.0451 +2024-07-19 05:46:47,196 - pyskl - INFO - Epoch [81][3400/3746] lr: 4.383e-02, eta: 2 days, 9:45:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5441, loss_cls: 4.0656, loss: 4.0656 +2024-07-19 05:48:09,699 - pyskl - INFO - Epoch [81][3500/3746] lr: 4.380e-02, eta: 2 days, 9:44:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5397, loss_cls: 4.0819, loss: 4.0819 +2024-07-19 05:49:31,666 - pyskl - INFO - Epoch [81][3600/3746] lr: 4.377e-02, eta: 2 days, 9:43:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5672, loss_cls: 3.9712, loss: 3.9712 +2024-07-19 05:50:55,052 - pyskl - INFO - Epoch [81][3700/3746] lr: 4.375e-02, eta: 2 days, 9:41:49, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5544, loss_cls: 4.0191, loss: 4.0191 +2024-07-19 05:51:34,565 - pyskl - INFO - Saving checkpoint at 81 epochs +2024-07-19 05:53:25,563 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 05:53:26,224 - pyskl - INFO - +top1_acc 0.2043 +top5_acc 0.4287 +2024-07-19 05:53:26,224 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 05:53:26,264 - pyskl - INFO - +mean_acc 0.2042 +2024-07-19 05:53:26,275 - pyskl - INFO - Epoch(val) [81][309] top1_acc: 0.2043, top5_acc: 0.4287, mean_class_accuracy: 0.2042 +2024-07-19 05:57:05,671 - pyskl - INFO - Epoch [82][100/3746] lr: 4.371e-02, eta: 2 days, 9:41:19, time: 2.194, data_time: 1.209, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5630, loss_cls: 3.9831, loss: 3.9831 +2024-07-19 05:58:27,457 - pyskl - INFO - Epoch [82][200/3746] lr: 4.368e-02, eta: 2 days, 9:39:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5522, loss_cls: 4.0070, loss: 4.0070 +2024-07-19 05:59:49,938 - pyskl - INFO - Epoch [82][300/3746] lr: 4.365e-02, eta: 2 days, 9:38:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5519, loss_cls: 4.0201, loss: 4.0201 +2024-07-19 06:01:11,960 - pyskl - INFO - Epoch [82][400/3746] lr: 4.362e-02, eta: 2 days, 9:37:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5489, loss_cls: 4.0387, loss: 4.0387 +2024-07-19 06:02:34,446 - pyskl - INFO - Epoch [82][500/3746] lr: 4.359e-02, eta: 2 days, 9:36:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5469, loss_cls: 4.0508, loss: 4.0508 +2024-07-19 06:03:56,428 - pyskl - INFO - Epoch [82][600/3746] lr: 4.357e-02, eta: 2 days, 9:34:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5542, loss_cls: 4.0123, loss: 4.0123 +2024-07-19 06:05:18,388 - pyskl - INFO - Epoch [82][700/3746] lr: 4.354e-02, eta: 2 days, 9:33:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5508, loss_cls: 4.0423, loss: 4.0423 +2024-07-19 06:06:40,043 - pyskl - INFO - Epoch [82][800/3746] lr: 4.351e-02, eta: 2 days, 9:32:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5578, loss_cls: 4.0070, loss: 4.0070 +2024-07-19 06:08:02,178 - pyskl - INFO - Epoch [82][900/3746] lr: 4.348e-02, eta: 2 days, 9:30:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5487, loss_cls: 4.0149, loss: 4.0149 +2024-07-19 06:09:24,140 - pyskl - INFO - Epoch [82][1000/3746] lr: 4.346e-02, eta: 2 days, 9:29:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5577, loss_cls: 4.0249, loss: 4.0249 +2024-07-19 06:10:46,416 - pyskl - INFO - Epoch [82][1100/3746] lr: 4.343e-02, eta: 2 days, 9:28:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5563, loss_cls: 3.9954, loss: 3.9954 +2024-07-19 06:12:08,783 - pyskl - INFO - Epoch [82][1200/3746] lr: 4.340e-02, eta: 2 days, 9:26:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5480, loss_cls: 4.0387, loss: 4.0387 +2024-07-19 06:13:31,704 - pyskl - INFO - Epoch [82][1300/3746] lr: 4.337e-02, eta: 2 days, 9:25:32, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5581, loss_cls: 3.9919, loss: 3.9919 +2024-07-19 06:14:53,852 - pyskl - INFO - Epoch [82][1400/3746] lr: 4.335e-02, eta: 2 days, 9:24:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5450, loss_cls: 4.0622, loss: 4.0622 +2024-07-19 06:16:17,039 - pyskl - INFO - Epoch [82][1500/3746] lr: 4.332e-02, eta: 2 days, 9:22:55, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5506, loss_cls: 4.0647, loss: 4.0647 +2024-07-19 06:17:39,568 - pyskl - INFO - Epoch [82][1600/3746] lr: 4.329e-02, eta: 2 days, 9:21:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5516, loss_cls: 4.0411, loss: 4.0411 +2024-07-19 06:19:02,542 - pyskl - INFO - Epoch [82][1700/3746] lr: 4.326e-02, eta: 2 days, 9:20:18, time: 0.830, data_time: 0.001, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5377, loss_cls: 4.0780, loss: 4.0780 +2024-07-19 06:20:25,825 - pyskl - INFO - Epoch [82][1800/3746] lr: 4.323e-02, eta: 2 days, 9:19:00, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5486, loss_cls: 4.0397, loss: 4.0397 +2024-07-19 06:21:48,322 - pyskl - INFO - Epoch [82][1900/3746] lr: 4.321e-02, eta: 2 days, 9:17:42, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5556, loss_cls: 3.9857, loss: 3.9857 +2024-07-19 06:23:10,459 - pyskl - INFO - Epoch [82][2000/3746] lr: 4.318e-02, eta: 2 days, 9:16:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5437, loss_cls: 4.0679, loss: 4.0679 +2024-07-19 06:24:32,744 - pyskl - INFO - Epoch [82][2100/3746] lr: 4.315e-02, eta: 2 days, 9:15:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5469, loss_cls: 4.0389, loss: 4.0389 +2024-07-19 06:25:55,620 - pyskl - INFO - Epoch [82][2200/3746] lr: 4.312e-02, eta: 2 days, 9:13:46, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5430, loss_cls: 4.0847, loss: 4.0847 +2024-07-19 06:27:17,814 - pyskl - INFO - Epoch [82][2300/3746] lr: 4.310e-02, eta: 2 days, 9:12:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5506, loss_cls: 4.0139, loss: 4.0139 +2024-07-19 06:28:39,805 - pyskl - INFO - Epoch [82][2400/3746] lr: 4.307e-02, eta: 2 days, 9:11:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5478, loss_cls: 4.0158, loss: 4.0158 +2024-07-19 06:30:02,578 - pyskl - INFO - Epoch [82][2500/3746] lr: 4.304e-02, eta: 2 days, 9:09:49, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5469, loss_cls: 4.0680, loss: 4.0680 +2024-07-19 06:31:25,372 - pyskl - INFO - Epoch [82][2600/3746] lr: 4.301e-02, eta: 2 days, 9:08:31, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5561, loss_cls: 4.0161, loss: 4.0161 +2024-07-19 06:32:47,847 - pyskl - INFO - Epoch [82][2700/3746] lr: 4.299e-02, eta: 2 days, 9:07:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5495, loss_cls: 4.0018, loss: 4.0018 +2024-07-19 06:34:09,952 - pyskl - INFO - Epoch [82][2800/3746] lr: 4.296e-02, eta: 2 days, 9:05:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5452, loss_cls: 4.0363, loss: 4.0363 +2024-07-19 06:35:32,126 - pyskl - INFO - Epoch [82][2900/3746] lr: 4.293e-02, eta: 2 days, 9:04:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5505, loss_cls: 4.0268, loss: 4.0268 +2024-07-19 06:36:55,201 - pyskl - INFO - Epoch [82][3000/3746] lr: 4.290e-02, eta: 2 days, 9:03:16, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5522, loss_cls: 3.9957, loss: 3.9957 +2024-07-19 06:38:17,447 - pyskl - INFO - Epoch [82][3100/3746] lr: 4.287e-02, eta: 2 days, 9:01:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5483, loss_cls: 4.0040, loss: 4.0040 +2024-07-19 06:39:39,829 - pyskl - INFO - Epoch [82][3200/3746] lr: 4.285e-02, eta: 2 days, 9:00:39, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5472, loss_cls: 4.0239, loss: 4.0239 +2024-07-19 06:41:02,861 - pyskl - INFO - Epoch [82][3300/3746] lr: 4.282e-02, eta: 2 days, 8:59:21, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5500, loss_cls: 4.0345, loss: 4.0345 +2024-07-19 06:42:25,284 - pyskl - INFO - Epoch [82][3400/3746] lr: 4.279e-02, eta: 2 days, 8:58:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5461, loss_cls: 4.0580, loss: 4.0580 +2024-07-19 06:43:47,631 - pyskl - INFO - Epoch [82][3500/3746] lr: 4.276e-02, eta: 2 days, 8:56:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5423, loss_cls: 4.0354, loss: 4.0354 +2024-07-19 06:45:10,310 - pyskl - INFO - Epoch [82][3600/3746] lr: 4.274e-02, eta: 2 days, 8:55:25, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5578, loss_cls: 4.0048, loss: 4.0048 +2024-07-19 06:46:33,152 - pyskl - INFO - Epoch [82][3700/3746] lr: 4.271e-02, eta: 2 days, 8:54:06, time: 0.828, data_time: 0.001, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5514, loss_cls: 4.0497, loss: 4.0497 +2024-07-19 06:47:12,877 - pyskl - INFO - Saving checkpoint at 82 epochs +2024-07-19 06:49:03,472 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 06:49:04,130 - pyskl - INFO - +top1_acc 0.2241 +top5_acc 0.4608 +2024-07-19 06:49:04,130 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 06:49:04,172 - pyskl - INFO - +mean_acc 0.2239 +2024-07-19 06:49:04,184 - pyskl - INFO - Epoch(val) [82][309] top1_acc: 0.2241, top5_acc: 0.4608, mean_class_accuracy: 0.2239 +2024-07-19 06:52:45,855 - pyskl - INFO - Epoch [83][100/3746] lr: 4.267e-02, eta: 2 days, 8:53:35, time: 2.217, data_time: 1.233, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5602, loss_cls: 3.9819, loss: 3.9819 +2024-07-19 06:54:07,887 - pyskl - INFO - Epoch [83][200/3746] lr: 4.264e-02, eta: 2 days, 8:52:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5687, loss_cls: 3.9113, loss: 3.9113 +2024-07-19 06:55:30,129 - pyskl - INFO - Epoch [83][300/3746] lr: 4.261e-02, eta: 2 days, 8:50:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5553, loss_cls: 3.9917, loss: 3.9917 +2024-07-19 06:56:51,835 - pyskl - INFO - Epoch [83][400/3746] lr: 4.259e-02, eta: 2 days, 8:49:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5452, loss_cls: 4.0147, loss: 4.0147 +2024-07-19 06:58:14,221 - pyskl - INFO - Epoch [83][500/3746] lr: 4.256e-02, eta: 2 days, 8:48:19, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5533, loss_cls: 4.0154, loss: 4.0154 +2024-07-19 06:59:36,246 - pyskl - INFO - Epoch [83][600/3746] lr: 4.253e-02, eta: 2 days, 8:47:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5541, loss_cls: 4.0045, loss: 4.0045 +2024-07-19 07:00:58,153 - pyskl - INFO - Epoch [83][700/3746] lr: 4.250e-02, eta: 2 days, 8:45:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5569, loss_cls: 3.9976, loss: 3.9976 +2024-07-19 07:02:20,299 - pyskl - INFO - Epoch [83][800/3746] lr: 4.247e-02, eta: 2 days, 8:44:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5552, loss_cls: 4.0023, loss: 4.0023 +2024-07-19 07:03:42,865 - pyskl - INFO - Epoch [83][900/3746] lr: 4.245e-02, eta: 2 days, 8:43:03, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5475, loss_cls: 4.0244, loss: 4.0244 +2024-07-19 07:05:05,298 - pyskl - INFO - Epoch [83][1000/3746] lr: 4.242e-02, eta: 2 days, 8:41:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5570, loss_cls: 4.0100, loss: 4.0100 +2024-07-19 07:06:27,865 - pyskl - INFO - Epoch [83][1100/3746] lr: 4.239e-02, eta: 2 days, 8:40:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5628, loss_cls: 4.0041, loss: 4.0041 +2024-07-19 07:07:50,384 - pyskl - INFO - Epoch [83][1200/3746] lr: 4.236e-02, eta: 2 days, 8:39:07, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5545, loss_cls: 4.0237, loss: 4.0237 +2024-07-19 07:09:13,310 - pyskl - INFO - Epoch [83][1300/3746] lr: 4.234e-02, eta: 2 days, 8:37:48, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5541, loss_cls: 3.9975, loss: 3.9975 +2024-07-19 07:10:35,256 - pyskl - INFO - Epoch [83][1400/3746] lr: 4.231e-02, eta: 2 days, 8:36:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5544, loss_cls: 4.0096, loss: 4.0096 +2024-07-19 07:11:58,721 - pyskl - INFO - Epoch [83][1500/3746] lr: 4.228e-02, eta: 2 days, 8:35:11, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5481, loss_cls: 4.0259, loss: 4.0259 +2024-07-19 07:13:21,563 - pyskl - INFO - Epoch [83][1600/3746] lr: 4.225e-02, eta: 2 days, 8:33:52, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5528, loss_cls: 4.0018, loss: 4.0018 +2024-07-19 07:14:44,430 - pyskl - INFO - Epoch [83][1700/3746] lr: 4.223e-02, eta: 2 days, 8:32:34, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5403, loss_cls: 4.0503, loss: 4.0503 +2024-07-19 07:16:06,813 - pyskl - INFO - Epoch [83][1800/3746] lr: 4.220e-02, eta: 2 days, 8:31:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5525, loss_cls: 4.0467, loss: 4.0467 +2024-07-19 07:17:29,631 - pyskl - INFO - Epoch [83][1900/3746] lr: 4.217e-02, eta: 2 days, 8:29:57, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5539, loss_cls: 4.0402, loss: 4.0402 +2024-07-19 07:18:52,375 - pyskl - INFO - Epoch [83][2000/3746] lr: 4.214e-02, eta: 2 days, 8:28:38, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5552, loss_cls: 4.0153, loss: 4.0153 +2024-07-19 07:20:14,896 - pyskl - INFO - Epoch [83][2100/3746] lr: 4.212e-02, eta: 2 days, 8:27:19, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5463, loss_cls: 4.0485, loss: 4.0485 +2024-07-19 07:21:37,968 - pyskl - INFO - Epoch [83][2200/3746] lr: 4.209e-02, eta: 2 days, 8:26:01, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5536, loss_cls: 3.9988, loss: 3.9988 +2024-07-19 07:22:59,832 - pyskl - INFO - Epoch [83][2300/3746] lr: 4.206e-02, eta: 2 days, 8:24:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5602, loss_cls: 4.0304, loss: 4.0304 +2024-07-19 07:24:22,497 - pyskl - INFO - Epoch [83][2400/3746] lr: 4.203e-02, eta: 2 days, 8:23:23, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5380, loss_cls: 4.0795, loss: 4.0795 +2024-07-19 07:25:45,902 - pyskl - INFO - Epoch [83][2500/3746] lr: 4.201e-02, eta: 2 days, 8:22:05, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5491, loss_cls: 4.0251, loss: 4.0251 +2024-07-19 07:27:08,076 - pyskl - INFO - Epoch [83][2600/3746] lr: 4.198e-02, eta: 2 days, 8:20:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5527, loss_cls: 4.0353, loss: 4.0353 +2024-07-19 07:28:30,394 - pyskl - INFO - Epoch [83][2700/3746] lr: 4.195e-02, eta: 2 days, 8:19:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5620, loss_cls: 3.9883, loss: 3.9883 +2024-07-19 07:29:52,291 - pyskl - INFO - Epoch [83][2800/3746] lr: 4.192e-02, eta: 2 days, 8:18:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5463, loss_cls: 4.0498, loss: 4.0498 +2024-07-19 07:31:14,938 - pyskl - INFO - Epoch [83][2900/3746] lr: 4.190e-02, eta: 2 days, 8:16:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5609, loss_cls: 3.9778, loss: 3.9778 +2024-07-19 07:32:37,892 - pyskl - INFO - Epoch [83][3000/3746] lr: 4.187e-02, eta: 2 days, 8:15:31, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5563, loss_cls: 3.9737, loss: 3.9737 +2024-07-19 07:34:00,285 - pyskl - INFO - Epoch [83][3100/3746] lr: 4.184e-02, eta: 2 days, 8:14:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5500, loss_cls: 4.0235, loss: 4.0235 +2024-07-19 07:35:23,274 - pyskl - INFO - Epoch [83][3200/3746] lr: 4.181e-02, eta: 2 days, 8:12:53, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5469, loss_cls: 4.0320, loss: 4.0320 +2024-07-19 07:36:46,019 - pyskl - INFO - Epoch [83][3300/3746] lr: 4.178e-02, eta: 2 days, 8:11:35, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5475, loss_cls: 4.0386, loss: 4.0386 +2024-07-19 07:38:07,869 - pyskl - INFO - Epoch [83][3400/3746] lr: 4.176e-02, eta: 2 days, 8:10:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5620, loss_cls: 4.0172, loss: 4.0172 +2024-07-19 07:39:29,475 - pyskl - INFO - Epoch [83][3500/3746] lr: 4.173e-02, eta: 2 days, 8:08:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5602, loss_cls: 3.9606, loss: 3.9606 +2024-07-19 07:40:51,538 - pyskl - INFO - Epoch [83][3600/3746] lr: 4.170e-02, eta: 2 days, 8:07:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5452, loss_cls: 4.0703, loss: 4.0703 +2024-07-19 07:42:14,354 - pyskl - INFO - Epoch [83][3700/3746] lr: 4.167e-02, eta: 2 days, 8:06:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5553, loss_cls: 4.0429, loss: 4.0429 +2024-07-19 07:42:54,350 - pyskl - INFO - Saving checkpoint at 83 epochs +2024-07-19 07:44:44,884 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 07:44:45,559 - pyskl - INFO - +top1_acc 0.2100 +top5_acc 0.4430 +2024-07-19 07:44:45,559 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 07:44:45,603 - pyskl - INFO - +mean_acc 0.2100 +2024-07-19 07:44:45,615 - pyskl - INFO - Epoch(val) [83][309] top1_acc: 0.2100, top5_acc: 0.4430, mean_class_accuracy: 0.2100 +2024-07-19 07:48:31,013 - pyskl - INFO - Epoch [84][100/3746] lr: 4.163e-02, eta: 2 days, 8:05:48, time: 2.254, data_time: 1.262, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5595, loss_cls: 3.9815, loss: 3.9815 +2024-07-19 07:49:53,538 - pyskl - INFO - Epoch [84][200/3746] lr: 4.161e-02, eta: 2 days, 8:04:29, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5480, loss_cls: 4.0195, loss: 4.0195 +2024-07-19 07:51:15,763 - pyskl - INFO - Epoch [84][300/3746] lr: 4.158e-02, eta: 2 days, 8:03:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5578, loss_cls: 3.9823, loss: 3.9823 +2024-07-19 07:52:38,486 - pyskl - INFO - Epoch [84][400/3746] lr: 4.155e-02, eta: 2 days, 8:01:51, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5631, loss_cls: 3.9478, loss: 3.9478 +2024-07-19 07:54:01,853 - pyskl - INFO - Epoch [84][500/3746] lr: 4.152e-02, eta: 2 days, 8:00:33, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5739, loss_cls: 3.9073, loss: 3.9073 +2024-07-19 07:55:24,510 - pyskl - INFO - Epoch [84][600/3746] lr: 4.150e-02, eta: 2 days, 7:59:14, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5519, loss_cls: 4.0153, loss: 4.0153 +2024-07-19 07:56:46,686 - pyskl - INFO - Epoch [84][700/3746] lr: 4.147e-02, eta: 2 days, 7:57:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5600, loss_cls: 3.9935, loss: 3.9935 +2024-07-19 07:58:08,524 - pyskl - INFO - Epoch [84][800/3746] lr: 4.144e-02, eta: 2 days, 7:56:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5555, loss_cls: 4.0321, loss: 4.0321 +2024-07-19 07:59:30,433 - pyskl - INFO - Epoch [84][900/3746] lr: 4.141e-02, eta: 2 days, 7:55:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5544, loss_cls: 4.0089, loss: 4.0089 +2024-07-19 08:00:52,428 - pyskl - INFO - Epoch [84][1000/3746] lr: 4.139e-02, eta: 2 days, 7:53:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5534, loss_cls: 4.0067, loss: 4.0067 +2024-07-19 08:02:14,662 - pyskl - INFO - Epoch [84][1100/3746] lr: 4.136e-02, eta: 2 days, 7:52:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5487, loss_cls: 3.9985, loss: 3.9985 +2024-07-19 08:03:37,300 - pyskl - INFO - Epoch [84][1200/3746] lr: 4.133e-02, eta: 2 days, 7:51:19, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5534, loss_cls: 4.0242, loss: 4.0242 +2024-07-19 08:05:00,519 - pyskl - INFO - Epoch [84][1300/3746] lr: 4.130e-02, eta: 2 days, 7:50:01, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5466, loss_cls: 4.0064, loss: 4.0064 +2024-07-19 08:06:22,969 - pyskl - INFO - Epoch [84][1400/3746] lr: 4.128e-02, eta: 2 days, 7:48:42, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5664, loss_cls: 3.9699, loss: 3.9699 +2024-07-19 08:07:46,302 - pyskl - INFO - Epoch [84][1500/3746] lr: 4.125e-02, eta: 2 days, 7:47:23, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5597, loss_cls: 3.9827, loss: 3.9827 +2024-07-19 08:09:09,115 - pyskl - INFO - Epoch [84][1600/3746] lr: 4.122e-02, eta: 2 days, 7:46:05, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5675, loss_cls: 3.9459, loss: 3.9459 +2024-07-19 08:10:32,175 - pyskl - INFO - Epoch [84][1700/3746] lr: 4.119e-02, eta: 2 days, 7:44:46, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5347, loss_cls: 4.0543, loss: 4.0543 +2024-07-19 08:11:54,990 - pyskl - INFO - Epoch [84][1800/3746] lr: 4.117e-02, eta: 2 days, 7:43:28, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5645, loss_cls: 3.9964, loss: 3.9964 +2024-07-19 08:13:17,084 - pyskl - INFO - Epoch [84][1900/3746] lr: 4.114e-02, eta: 2 days, 7:42:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5514, loss_cls: 4.0277, loss: 4.0277 +2024-07-19 08:14:39,465 - pyskl - INFO - Epoch [84][2000/3746] lr: 4.111e-02, eta: 2 days, 7:40:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5550, loss_cls: 4.0454, loss: 4.0454 +2024-07-19 08:16:01,908 - pyskl - INFO - Epoch [84][2100/3746] lr: 4.108e-02, eta: 2 days, 7:39:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5458, loss_cls: 4.0492, loss: 4.0492 +2024-07-19 08:17:25,133 - pyskl - INFO - Epoch [84][2200/3746] lr: 4.106e-02, eta: 2 days, 7:38:12, time: 0.832, data_time: 0.001, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5559, loss_cls: 3.9924, loss: 3.9924 +2024-07-19 08:18:47,014 - pyskl - INFO - Epoch [84][2300/3746] lr: 4.103e-02, eta: 2 days, 7:36:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5648, loss_cls: 3.9708, loss: 3.9708 +2024-07-19 08:20:09,278 - pyskl - INFO - Epoch [84][2400/3746] lr: 4.100e-02, eta: 2 days, 7:35:33, time: 0.823, data_time: 0.001, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5578, loss_cls: 4.0224, loss: 4.0224 +2024-07-19 08:21:31,946 - pyskl - INFO - Epoch [84][2500/3746] lr: 4.097e-02, eta: 2 days, 7:34:15, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5600, loss_cls: 3.9849, loss: 3.9849 +2024-07-19 08:22:53,962 - pyskl - INFO - Epoch [84][2600/3746] lr: 4.095e-02, eta: 2 days, 7:32:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5517, loss_cls: 4.0208, loss: 4.0208 +2024-07-19 08:24:16,403 - pyskl - INFO - Epoch [84][2700/3746] lr: 4.092e-02, eta: 2 days, 7:31:36, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5584, loss_cls: 4.0214, loss: 4.0214 +2024-07-19 08:25:38,990 - pyskl - INFO - Epoch [84][2800/3746] lr: 4.089e-02, eta: 2 days, 7:30:18, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5583, loss_cls: 3.9960, loss: 3.9960 +2024-07-19 08:27:01,907 - pyskl - INFO - Epoch [84][2900/3746] lr: 4.086e-02, eta: 2 days, 7:28:59, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5525, loss_cls: 4.0422, loss: 4.0422 +2024-07-19 08:28:24,321 - pyskl - INFO - Epoch [84][3000/3746] lr: 4.084e-02, eta: 2 days, 7:27:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5644, loss_cls: 3.9838, loss: 3.9838 +2024-07-19 08:29:46,826 - pyskl - INFO - Epoch [84][3100/3746] lr: 4.081e-02, eta: 2 days, 7:26:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5628, loss_cls: 3.9601, loss: 3.9601 +2024-07-19 08:31:09,799 - pyskl - INFO - Epoch [84][3200/3746] lr: 4.078e-02, eta: 2 days, 7:25:02, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5478, loss_cls: 4.0418, loss: 4.0418 +2024-07-19 08:32:31,773 - pyskl - INFO - Epoch [84][3300/3746] lr: 4.075e-02, eta: 2 days, 7:23:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5672, loss_cls: 3.9332, loss: 3.9332 +2024-07-19 08:33:54,312 - pyskl - INFO - Epoch [84][3400/3746] lr: 4.073e-02, eta: 2 days, 7:22:24, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5514, loss_cls: 4.0353, loss: 4.0353 +2024-07-19 08:35:17,381 - pyskl - INFO - Epoch [84][3500/3746] lr: 4.070e-02, eta: 2 days, 7:21:06, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5505, loss_cls: 4.0061, loss: 4.0061 +2024-07-19 08:36:39,870 - pyskl - INFO - Epoch [84][3600/3746] lr: 4.067e-02, eta: 2 days, 7:19:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5553, loss_cls: 3.9907, loss: 3.9907 +2024-07-19 08:38:03,285 - pyskl - INFO - Epoch [84][3700/3746] lr: 4.064e-02, eta: 2 days, 7:18:28, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5516, loss_cls: 4.0247, loss: 4.0247 +2024-07-19 08:38:43,012 - pyskl - INFO - Saving checkpoint at 84 epochs +2024-07-19 08:40:33,446 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 08:40:34,141 - pyskl - INFO - +top1_acc 0.2245 +top5_acc 0.4685 +2024-07-19 08:40:34,141 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 08:40:34,181 - pyskl - INFO - +mean_acc 0.2243 +2024-07-19 08:40:34,192 - pyskl - INFO - Epoch(val) [84][309] top1_acc: 0.2245, top5_acc: 0.4685, mean_class_accuracy: 0.2243 +2024-07-19 08:44:19,475 - pyskl - INFO - Epoch [85][100/3746] lr: 4.060e-02, eta: 2 days, 7:17:55, time: 2.253, data_time: 1.255, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5772, loss_cls: 3.9187, loss: 3.9187 +2024-07-19 08:45:42,666 - pyskl - INFO - Epoch [85][200/3746] lr: 4.058e-02, eta: 2 days, 7:16:37, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5591, loss_cls: 4.0052, loss: 4.0052 +2024-07-19 08:47:05,638 - pyskl - INFO - Epoch [85][300/3746] lr: 4.055e-02, eta: 2 days, 7:15:18, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5602, loss_cls: 3.9832, loss: 3.9832 +2024-07-19 08:48:28,930 - pyskl - INFO - Epoch [85][400/3746] lr: 4.052e-02, eta: 2 days, 7:14:00, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5658, loss_cls: 3.9410, loss: 3.9410 +2024-07-19 08:49:50,775 - pyskl - INFO - Epoch [85][500/3746] lr: 4.049e-02, eta: 2 days, 7:12:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5684, loss_cls: 3.9386, loss: 3.9386 +2024-07-19 08:51:12,535 - pyskl - INFO - Epoch [85][600/3746] lr: 4.047e-02, eta: 2 days, 7:11:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5666, loss_cls: 3.9309, loss: 3.9309 +2024-07-19 08:52:34,148 - pyskl - INFO - Epoch [85][700/3746] lr: 4.044e-02, eta: 2 days, 7:10:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5766, loss_cls: 3.8791, loss: 3.8791 +2024-07-19 08:53:55,346 - pyskl - INFO - Epoch [85][800/3746] lr: 4.041e-02, eta: 2 days, 7:08:41, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5563, loss_cls: 4.0038, loss: 4.0038 +2024-07-19 08:55:16,831 - pyskl - INFO - Epoch [85][900/3746] lr: 4.038e-02, eta: 2 days, 7:07:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5587, loss_cls: 3.9744, loss: 3.9744 +2024-07-19 08:56:38,007 - pyskl - INFO - Epoch [85][1000/3746] lr: 4.036e-02, eta: 2 days, 7:06:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5528, loss_cls: 4.0163, loss: 4.0163 +2024-07-19 08:57:58,913 - pyskl - INFO - Epoch [85][1100/3746] lr: 4.033e-02, eta: 2 days, 7:04:41, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5544, loss_cls: 3.9924, loss: 3.9924 +2024-07-19 08:59:20,606 - pyskl - INFO - Epoch [85][1200/3746] lr: 4.030e-02, eta: 2 days, 7:03:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5717, loss_cls: 3.9217, loss: 3.9217 +2024-07-19 09:00:41,749 - pyskl - INFO - Epoch [85][1300/3746] lr: 4.027e-02, eta: 2 days, 7:02:01, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5620, loss_cls: 3.9585, loss: 3.9585 +2024-07-19 09:02:04,028 - pyskl - INFO - Epoch [85][1400/3746] lr: 4.025e-02, eta: 2 days, 7:00:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5683, loss_cls: 3.9787, loss: 3.9787 +2024-07-19 09:03:25,715 - pyskl - INFO - Epoch [85][1500/3746] lr: 4.022e-02, eta: 2 days, 6:59:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5559, loss_cls: 3.9989, loss: 3.9989 +2024-07-19 09:04:47,209 - pyskl - INFO - Epoch [85][1600/3746] lr: 4.019e-02, eta: 2 days, 6:58:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5564, loss_cls: 4.0070, loss: 4.0070 +2024-07-19 09:06:08,363 - pyskl - INFO - Epoch [85][1700/3746] lr: 4.016e-02, eta: 2 days, 6:56:42, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5530, loss_cls: 4.0038, loss: 4.0038 +2024-07-19 09:07:29,576 - pyskl - INFO - Epoch [85][1800/3746] lr: 4.014e-02, eta: 2 days, 6:55:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5570, loss_cls: 4.0045, loss: 4.0045 +2024-07-19 09:08:51,089 - pyskl - INFO - Epoch [85][1900/3746] lr: 4.011e-02, eta: 2 days, 6:54:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5425, loss_cls: 4.0399, loss: 4.0399 +2024-07-19 09:10:13,128 - pyskl - INFO - Epoch [85][2000/3746] lr: 4.008e-02, eta: 2 days, 6:52:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5659, loss_cls: 3.9755, loss: 3.9755 +2024-07-19 09:11:34,489 - pyskl - INFO - Epoch [85][2100/3746] lr: 4.006e-02, eta: 2 days, 6:51:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5486, loss_cls: 4.0207, loss: 4.0207 +2024-07-19 09:12:56,518 - pyskl - INFO - Epoch [85][2200/3746] lr: 4.003e-02, eta: 2 days, 6:50:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5598, loss_cls: 4.0113, loss: 4.0113 +2024-07-19 09:14:17,645 - pyskl - INFO - Epoch [85][2300/3746] lr: 4.000e-02, eta: 2 days, 6:48:43, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5553, loss_cls: 3.9788, loss: 3.9788 +2024-07-19 09:15:39,194 - pyskl - INFO - Epoch [85][2400/3746] lr: 3.997e-02, eta: 2 days, 6:47:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5530, loss_cls: 3.9685, loss: 3.9685 +2024-07-19 09:17:00,982 - pyskl - INFO - Epoch [85][2500/3746] lr: 3.995e-02, eta: 2 days, 6:46:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5563, loss_cls: 3.9941, loss: 3.9941 +2024-07-19 09:18:22,687 - pyskl - INFO - Epoch [85][2600/3746] lr: 3.992e-02, eta: 2 days, 6:44:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5653, loss_cls: 3.9508, loss: 3.9508 +2024-07-19 09:19:44,366 - pyskl - INFO - Epoch [85][2700/3746] lr: 3.989e-02, eta: 2 days, 6:43:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5592, loss_cls: 3.9889, loss: 3.9889 +2024-07-19 09:21:06,158 - pyskl - INFO - Epoch [85][2800/3746] lr: 3.986e-02, eta: 2 days, 6:42:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5525, loss_cls: 4.0335, loss: 4.0335 +2024-07-19 09:22:27,227 - pyskl - INFO - Epoch [85][2900/3746] lr: 3.984e-02, eta: 2 days, 6:40:45, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5620, loss_cls: 3.9541, loss: 3.9541 +2024-07-19 09:23:48,792 - pyskl - INFO - Epoch [85][3000/3746] lr: 3.981e-02, eta: 2 days, 6:39:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5694, loss_cls: 3.9456, loss: 3.9456 +2024-07-19 09:25:10,650 - pyskl - INFO - Epoch [85][3100/3746] lr: 3.978e-02, eta: 2 days, 6:38:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5628, loss_cls: 3.9775, loss: 3.9775 +2024-07-19 09:26:32,091 - pyskl - INFO - Epoch [85][3200/3746] lr: 3.975e-02, eta: 2 days, 6:36:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5480, loss_cls: 4.0320, loss: 4.0320 +2024-07-19 09:27:53,688 - pyskl - INFO - Epoch [85][3300/3746] lr: 3.973e-02, eta: 2 days, 6:35:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5514, loss_cls: 4.0206, loss: 4.0206 +2024-07-19 09:29:15,219 - pyskl - INFO - Epoch [85][3400/3746] lr: 3.970e-02, eta: 2 days, 6:34:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5772, loss_cls: 3.9352, loss: 3.9352 +2024-07-19 09:30:36,612 - pyskl - INFO - Epoch [85][3500/3746] lr: 3.967e-02, eta: 2 days, 6:32:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5495, loss_cls: 4.0328, loss: 4.0328 +2024-07-19 09:31:58,423 - pyskl - INFO - Epoch [85][3600/3746] lr: 3.964e-02, eta: 2 days, 6:31:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5508, loss_cls: 4.0246, loss: 4.0246 +2024-07-19 09:33:20,197 - pyskl - INFO - Epoch [85][3700/3746] lr: 3.962e-02, eta: 2 days, 6:30:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5564, loss_cls: 3.9757, loss: 3.9757 +2024-07-19 09:33:59,432 - pyskl - INFO - Saving checkpoint at 85 epochs +2024-07-19 09:35:50,622 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 09:35:51,393 - pyskl - INFO - +top1_acc 0.2189 +top5_acc 0.4588 +2024-07-19 09:35:51,393 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 09:35:51,438 - pyskl - INFO - +mean_acc 0.2189 +2024-07-19 09:35:51,451 - pyskl - INFO - Epoch(val) [85][309] top1_acc: 0.2189, top5_acc: 0.4588, mean_class_accuracy: 0.2189 +2024-07-19 09:39:51,358 - pyskl - INFO - Epoch [86][100/3746] lr: 3.958e-02, eta: 2 days, 6:29:42, time: 2.399, data_time: 1.414, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5653, loss_cls: 3.9306, loss: 3.9306 +2024-07-19 09:41:13,951 - pyskl - INFO - Epoch [86][200/3746] lr: 3.955e-02, eta: 2 days, 6:28:23, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5789, loss_cls: 3.8712, loss: 3.8712 +2024-07-19 09:42:36,047 - pyskl - INFO - Epoch [86][300/3746] lr: 3.952e-02, eta: 2 days, 6:27:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5809, loss_cls: 3.8896, loss: 3.8896 +2024-07-19 09:43:58,475 - pyskl - INFO - Epoch [86][400/3746] lr: 3.950e-02, eta: 2 days, 6:25:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5641, loss_cls: 3.9564, loss: 3.9564 +2024-07-19 09:45:20,334 - pyskl - INFO - Epoch [86][500/3746] lr: 3.947e-02, eta: 2 days, 6:24:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5552, loss_cls: 3.9994, loss: 3.9994 +2024-07-19 09:46:42,136 - pyskl - INFO - Epoch [86][600/3746] lr: 3.944e-02, eta: 2 days, 6:23:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5603, loss_cls: 3.9740, loss: 3.9740 +2024-07-19 09:48:03,358 - pyskl - INFO - Epoch [86][700/3746] lr: 3.941e-02, eta: 2 days, 6:21:45, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5584, loss_cls: 3.9510, loss: 3.9510 +2024-07-19 09:49:24,535 - pyskl - INFO - Epoch [86][800/3746] lr: 3.939e-02, eta: 2 days, 6:20:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5647, loss_cls: 3.9874, loss: 3.9874 +2024-07-19 09:50:45,597 - pyskl - INFO - Epoch [86][900/3746] lr: 3.936e-02, eta: 2 days, 6:19:05, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5605, loss_cls: 3.9897, loss: 3.9897 +2024-07-19 09:52:06,888 - pyskl - INFO - Epoch [86][1000/3746] lr: 3.933e-02, eta: 2 days, 6:17:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5675, loss_cls: 3.9616, loss: 3.9616 +2024-07-19 09:53:27,952 - pyskl - INFO - Epoch [86][1100/3746] lr: 3.930e-02, eta: 2 days, 6:16:24, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5633, loss_cls: 3.9744, loss: 3.9744 +2024-07-19 09:54:49,309 - pyskl - INFO - Epoch [86][1200/3746] lr: 3.928e-02, eta: 2 days, 6:15:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5684, loss_cls: 3.9495, loss: 3.9495 +2024-07-19 09:56:10,877 - pyskl - INFO - Epoch [86][1300/3746] lr: 3.925e-02, eta: 2 days, 6:13:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5625, loss_cls: 3.9488, loss: 3.9488 +2024-07-19 09:57:32,547 - pyskl - INFO - Epoch [86][1400/3746] lr: 3.922e-02, eta: 2 days, 6:12:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5611, loss_cls: 3.9798, loss: 3.9798 +2024-07-19 09:58:53,694 - pyskl - INFO - Epoch [86][1500/3746] lr: 3.919e-02, eta: 2 days, 6:11:04, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5684, loss_cls: 3.9759, loss: 3.9759 +2024-07-19 10:00:15,211 - pyskl - INFO - Epoch [86][1600/3746] lr: 3.917e-02, eta: 2 days, 6:09:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5583, loss_cls: 4.0084, loss: 4.0084 +2024-07-19 10:01:36,406 - pyskl - INFO - Epoch [86][1700/3746] lr: 3.914e-02, eta: 2 days, 6:08:24, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5534, loss_cls: 4.0219, loss: 4.0219 +2024-07-19 10:02:57,472 - pyskl - INFO - Epoch [86][1800/3746] lr: 3.911e-02, eta: 2 days, 6:07:04, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5587, loss_cls: 3.9520, loss: 3.9520 +2024-07-19 10:04:18,271 - pyskl - INFO - Epoch [86][1900/3746] lr: 3.909e-02, eta: 2 days, 6:05:44, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5573, loss_cls: 4.0081, loss: 4.0081 +2024-07-19 10:05:39,642 - pyskl - INFO - Epoch [86][2000/3746] lr: 3.906e-02, eta: 2 days, 6:04:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5630, loss_cls: 3.9896, loss: 3.9896 +2024-07-19 10:07:00,780 - pyskl - INFO - Epoch [86][2100/3746] lr: 3.903e-02, eta: 2 days, 6:03:03, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5453, loss_cls: 4.0563, loss: 4.0563 +2024-07-19 10:08:22,003 - pyskl - INFO - Epoch [86][2200/3746] lr: 3.900e-02, eta: 2 days, 6:01:43, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5569, loss_cls: 3.9724, loss: 3.9724 +2024-07-19 10:09:43,312 - pyskl - INFO - Epoch [86][2300/3746] lr: 3.898e-02, eta: 2 days, 6:00:23, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5644, loss_cls: 3.9528, loss: 3.9528 +2024-07-19 10:11:04,619 - pyskl - INFO - Epoch [86][2400/3746] lr: 3.895e-02, eta: 2 days, 5:59:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5631, loss_cls: 3.9503, loss: 3.9503 +2024-07-19 10:12:26,347 - pyskl - INFO - Epoch [86][2500/3746] lr: 3.892e-02, eta: 2 days, 5:57:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5603, loss_cls: 4.0126, loss: 4.0126 +2024-07-19 10:13:47,949 - pyskl - INFO - Epoch [86][2600/3746] lr: 3.889e-02, eta: 2 days, 5:56:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5472, loss_cls: 4.0291, loss: 4.0291 +2024-07-19 10:15:09,006 - pyskl - INFO - Epoch [86][2700/3746] lr: 3.887e-02, eta: 2 days, 5:55:03, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5533, loss_cls: 4.0087, loss: 4.0087 +2024-07-19 10:16:30,642 - pyskl - INFO - Epoch [86][2800/3746] lr: 3.884e-02, eta: 2 days, 5:53:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5623, loss_cls: 3.9715, loss: 3.9715 +2024-07-19 10:17:51,414 - pyskl - INFO - Epoch [86][2900/3746] lr: 3.881e-02, eta: 2 days, 5:52:23, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5550, loss_cls: 3.9931, loss: 3.9931 +2024-07-19 10:19:13,784 - pyskl - INFO - Epoch [86][3000/3746] lr: 3.879e-02, eta: 2 days, 5:51:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5548, loss_cls: 3.9915, loss: 3.9915 +2024-07-19 10:20:35,383 - pyskl - INFO - Epoch [86][3100/3746] lr: 3.876e-02, eta: 2 days, 5:49:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5683, loss_cls: 3.9072, loss: 3.9072 +2024-07-19 10:21:57,076 - pyskl - INFO - Epoch [86][3200/3746] lr: 3.873e-02, eta: 2 days, 5:48:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5770, loss_cls: 3.9028, loss: 3.9028 +2024-07-19 10:23:19,006 - pyskl - INFO - Epoch [86][3300/3746] lr: 3.870e-02, eta: 2 days, 5:47:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5617, loss_cls: 3.9590, loss: 3.9590 +2024-07-19 10:24:39,699 - pyskl - INFO - Epoch [86][3400/3746] lr: 3.868e-02, eta: 2 days, 5:45:44, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5605, loss_cls: 3.9937, loss: 3.9937 +2024-07-19 10:26:01,039 - pyskl - INFO - Epoch [86][3500/3746] lr: 3.865e-02, eta: 2 days, 5:44:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5508, loss_cls: 4.0195, loss: 4.0195 +2024-07-19 10:27:22,819 - pyskl - INFO - Epoch [86][3600/3746] lr: 3.862e-02, eta: 2 days, 5:43:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5583, loss_cls: 3.9893, loss: 3.9893 +2024-07-19 10:28:44,090 - pyskl - INFO - Epoch [86][3700/3746] lr: 3.860e-02, eta: 2 days, 5:41:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5555, loss_cls: 3.9782, loss: 3.9782 +2024-07-19 10:29:23,470 - pyskl - INFO - Saving checkpoint at 86 epochs +2024-07-19 10:31:14,461 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 10:31:15,124 - pyskl - INFO - +top1_acc 0.2342 +top5_acc 0.4731 +2024-07-19 10:31:15,124 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 10:31:15,164 - pyskl - INFO - +mean_acc 0.2339 +2024-07-19 10:31:15,170 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_79.pth was removed +2024-07-19 10:31:15,399 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_86.pth. +2024-07-19 10:31:15,400 - pyskl - INFO - Best top1_acc is 0.2342 at 86 epoch. +2024-07-19 10:31:15,411 - pyskl - INFO - Epoch(val) [86][309] top1_acc: 0.2342, top5_acc: 0.4731, mean_class_accuracy: 0.2339 +2024-07-19 10:35:04,806 - pyskl - INFO - Epoch [87][100/3746] lr: 3.856e-02, eta: 2 days, 5:41:09, time: 2.294, data_time: 1.315, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5567, loss_cls: 3.9717, loss: 3.9717 +2024-07-19 10:36:26,593 - pyskl - INFO - Epoch [87][200/3746] lr: 3.853e-02, eta: 2 days, 5:39:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5667, loss_cls: 3.9072, loss: 3.9072 +2024-07-19 10:37:47,368 - pyskl - INFO - Epoch [87][300/3746] lr: 3.850e-02, eta: 2 days, 5:38:29, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5716, loss_cls: 3.9463, loss: 3.9463 +2024-07-19 10:39:08,126 - pyskl - INFO - Epoch [87][400/3746] lr: 3.847e-02, eta: 2 days, 5:37:08, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5666, loss_cls: 3.9187, loss: 3.9187 +2024-07-19 10:40:29,071 - pyskl - INFO - Epoch [87][500/3746] lr: 3.845e-02, eta: 2 days, 5:35:48, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5642, loss_cls: 3.9414, loss: 3.9414 +2024-07-19 10:41:50,385 - pyskl - INFO - Epoch [87][600/3746] lr: 3.842e-02, eta: 2 days, 5:34:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5609, loss_cls: 3.9888, loss: 3.9888 +2024-07-19 10:43:11,667 - pyskl - INFO - Epoch [87][700/3746] lr: 3.839e-02, eta: 2 days, 5:33:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5655, loss_cls: 3.9485, loss: 3.9485 +2024-07-19 10:44:33,338 - pyskl - INFO - Epoch [87][800/3746] lr: 3.837e-02, eta: 2 days, 5:31:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5594, loss_cls: 3.9803, loss: 3.9803 +2024-07-19 10:45:54,645 - pyskl - INFO - Epoch [87][900/3746] lr: 3.834e-02, eta: 2 days, 5:30:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5656, loss_cls: 3.9450, loss: 3.9450 +2024-07-19 10:47:15,773 - pyskl - INFO - Epoch [87][1000/3746] lr: 3.831e-02, eta: 2 days, 5:29:07, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5678, loss_cls: 3.9438, loss: 3.9438 +2024-07-19 10:48:36,840 - pyskl - INFO - Epoch [87][1100/3746] lr: 3.828e-02, eta: 2 days, 5:27:47, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5733, loss_cls: 3.9437, loss: 3.9437 +2024-07-19 10:49:57,677 - pyskl - INFO - Epoch [87][1200/3746] lr: 3.826e-02, eta: 2 days, 5:26:26, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5578, loss_cls: 3.9987, loss: 3.9987 +2024-07-19 10:51:19,144 - pyskl - INFO - Epoch [87][1300/3746] lr: 3.823e-02, eta: 2 days, 5:25:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5575, loss_cls: 3.9929, loss: 3.9929 +2024-07-19 10:52:39,892 - pyskl - INFO - Epoch [87][1400/3746] lr: 3.820e-02, eta: 2 days, 5:23:46, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5697, loss_cls: 3.9496, loss: 3.9496 +2024-07-19 10:54:01,935 - pyskl - INFO - Epoch [87][1500/3746] lr: 3.817e-02, eta: 2 days, 5:22:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5811, loss_cls: 3.8946, loss: 3.8946 +2024-07-19 10:55:23,061 - pyskl - INFO - Epoch [87][1600/3746] lr: 3.815e-02, eta: 2 days, 5:21:06, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5606, loss_cls: 3.9831, loss: 3.9831 +2024-07-19 10:56:43,836 - pyskl - INFO - Epoch [87][1700/3746] lr: 3.812e-02, eta: 2 days, 5:19:45, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5702, loss_cls: 3.9375, loss: 3.9375 +2024-07-19 10:58:04,521 - pyskl - INFO - Epoch [87][1800/3746] lr: 3.809e-02, eta: 2 days, 5:18:24, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5611, loss_cls: 3.9411, loss: 3.9411 +2024-07-19 10:59:26,061 - pyskl - INFO - Epoch [87][1900/3746] lr: 3.807e-02, eta: 2 days, 5:17:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5658, loss_cls: 3.9205, loss: 3.9205 +2024-07-19 11:00:47,441 - pyskl - INFO - Epoch [87][2000/3746] lr: 3.804e-02, eta: 2 days, 5:15:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5611, loss_cls: 3.9751, loss: 3.9751 +2024-07-19 11:02:08,669 - pyskl - INFO - Epoch [87][2100/3746] lr: 3.801e-02, eta: 2 days, 5:14:24, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5517, loss_cls: 4.0015, loss: 4.0015 +2024-07-19 11:03:29,966 - pyskl - INFO - Epoch [87][2200/3746] lr: 3.798e-02, eta: 2 days, 5:13:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5775, loss_cls: 3.9172, loss: 3.9172 +2024-07-19 11:04:51,341 - pyskl - INFO - Epoch [87][2300/3746] lr: 3.796e-02, eta: 2 days, 5:11:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5620, loss_cls: 3.9612, loss: 3.9612 +2024-07-19 11:06:13,639 - pyskl - INFO - Epoch [87][2400/3746] lr: 3.793e-02, eta: 2 days, 5:10:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5586, loss_cls: 3.9853, loss: 3.9853 +2024-07-19 11:07:34,857 - pyskl - INFO - Epoch [87][2500/3746] lr: 3.790e-02, eta: 2 days, 5:09:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5637, loss_cls: 3.9501, loss: 3.9501 +2024-07-19 11:08:56,442 - pyskl - INFO - Epoch [87][2600/3746] lr: 3.788e-02, eta: 2 days, 5:07:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5614, loss_cls: 3.9444, loss: 3.9444 +2024-07-19 11:10:17,422 - pyskl - INFO - Epoch [87][2700/3746] lr: 3.785e-02, eta: 2 days, 5:06:24, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5614, loss_cls: 3.9801, loss: 3.9801 +2024-07-19 11:11:38,916 - pyskl - INFO - Epoch [87][2800/3746] lr: 3.782e-02, eta: 2 days, 5:05:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5583, loss_cls: 3.9647, loss: 3.9647 +2024-07-19 11:13:00,115 - pyskl - INFO - Epoch [87][2900/3746] lr: 3.779e-02, eta: 2 days, 5:03:43, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5525, loss_cls: 3.9726, loss: 3.9726 +2024-07-19 11:14:21,246 - pyskl - INFO - Epoch [87][3000/3746] lr: 3.777e-02, eta: 2 days, 5:02:23, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5458, loss_cls: 4.0213, loss: 4.0213 +2024-07-19 11:15:42,528 - pyskl - INFO - Epoch [87][3100/3746] lr: 3.774e-02, eta: 2 days, 5:01:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5508, loss_cls: 3.9868, loss: 3.9868 +2024-07-19 11:17:03,599 - pyskl - INFO - Epoch [87][3200/3746] lr: 3.771e-02, eta: 2 days, 4:59:43, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5555, loss_cls: 3.9878, loss: 3.9878 +2024-07-19 11:18:24,703 - pyskl - INFO - Epoch [87][3300/3746] lr: 3.769e-02, eta: 2 days, 4:58:22, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5528, loss_cls: 3.9890, loss: 3.9890 +2024-07-19 11:19:45,958 - pyskl - INFO - Epoch [87][3400/3746] lr: 3.766e-02, eta: 2 days, 4:57:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5625, loss_cls: 3.9678, loss: 3.9678 +2024-07-19 11:21:06,919 - pyskl - INFO - Epoch [87][3500/3746] lr: 3.763e-02, eta: 2 days, 4:55:42, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5545, loss_cls: 3.9972, loss: 3.9972 +2024-07-19 11:22:27,684 - pyskl - INFO - Epoch [87][3600/3746] lr: 3.761e-02, eta: 2 days, 4:54:21, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5709, loss_cls: 3.9200, loss: 3.9200 +2024-07-19 11:23:49,371 - pyskl - INFO - Epoch [87][3700/3746] lr: 3.758e-02, eta: 2 days, 4:53:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5637, loss_cls: 3.9354, loss: 3.9354 +2024-07-19 11:24:29,637 - pyskl - INFO - Saving checkpoint at 87 epochs +2024-07-19 11:26:19,861 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 11:26:20,519 - pyskl - INFO - +top1_acc 0.2342 +top5_acc 0.4817 +2024-07-19 11:26:20,519 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 11:26:20,557 - pyskl - INFO - +mean_acc 0.2340 +2024-07-19 11:26:20,562 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_86.pth was removed +2024-07-19 11:26:20,773 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_87.pth. +2024-07-19 11:26:20,774 - pyskl - INFO - Best top1_acc is 0.2342 at 87 epoch. +2024-07-19 11:26:20,784 - pyskl - INFO - Epoch(val) [87][309] top1_acc: 0.2342, top5_acc: 0.4817, mean_class_accuracy: 0.2340 +2024-07-19 11:30:08,826 - pyskl - INFO - Epoch [88][100/3746] lr: 3.754e-02, eta: 2 days, 4:52:23, time: 2.280, data_time: 1.302, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5783, loss_cls: 3.8689, loss: 3.8689 +2024-07-19 11:31:30,913 - pyskl - INFO - Epoch [88][200/3746] lr: 3.751e-02, eta: 2 days, 4:51:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5736, loss_cls: 3.8776, loss: 3.8776 +2024-07-19 11:32:52,242 - pyskl - INFO - Epoch [88][300/3746] lr: 3.748e-02, eta: 2 days, 4:49:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5778, loss_cls: 3.8829, loss: 3.8829 +2024-07-19 11:34:13,357 - pyskl - INFO - Epoch [88][400/3746] lr: 3.746e-02, eta: 2 days, 4:48:23, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5786, loss_cls: 3.8720, loss: 3.8720 +2024-07-19 11:35:34,956 - pyskl - INFO - Epoch [88][500/3746] lr: 3.743e-02, eta: 2 days, 4:47:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5716, loss_cls: 3.9267, loss: 3.9267 +2024-07-19 11:36:56,411 - pyskl - INFO - Epoch [88][600/3746] lr: 3.740e-02, eta: 2 days, 4:45:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5700, loss_cls: 3.8930, loss: 3.8930 +2024-07-19 11:38:17,879 - pyskl - INFO - Epoch [88][700/3746] lr: 3.738e-02, eta: 2 days, 4:44:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5741, loss_cls: 3.9128, loss: 3.9128 +2024-07-19 11:39:39,350 - pyskl - INFO - Epoch [88][800/3746] lr: 3.735e-02, eta: 2 days, 4:43:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5597, loss_cls: 3.9732, loss: 3.9732 +2024-07-19 11:41:00,508 - pyskl - INFO - Epoch [88][900/3746] lr: 3.732e-02, eta: 2 days, 4:41:42, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5586, loss_cls: 3.9477, loss: 3.9477 +2024-07-19 11:42:21,353 - pyskl - INFO - Epoch [88][1000/3746] lr: 3.730e-02, eta: 2 days, 4:40:21, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5761, loss_cls: 3.8877, loss: 3.8877 +2024-07-19 11:43:42,806 - pyskl - INFO - Epoch [88][1100/3746] lr: 3.727e-02, eta: 2 days, 4:39:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5575, loss_cls: 4.0034, loss: 4.0034 +2024-07-19 11:45:04,058 - pyskl - INFO - Epoch [88][1200/3746] lr: 3.724e-02, eta: 2 days, 4:37:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5630, loss_cls: 3.9476, loss: 3.9476 +2024-07-19 11:46:25,354 - pyskl - INFO - Epoch [88][1300/3746] lr: 3.721e-02, eta: 2 days, 4:36:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5700, loss_cls: 3.8955, loss: 3.8955 +2024-07-19 11:47:46,121 - pyskl - INFO - Epoch [88][1400/3746] lr: 3.719e-02, eta: 2 days, 4:35:00, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5714, loss_cls: 3.9220, loss: 3.9220 +2024-07-19 11:49:07,526 - pyskl - INFO - Epoch [88][1500/3746] lr: 3.716e-02, eta: 2 days, 4:33:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5750, loss_cls: 3.9034, loss: 3.9034 +2024-07-19 11:50:28,424 - pyskl - INFO - Epoch [88][1600/3746] lr: 3.713e-02, eta: 2 days, 4:32:19, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5681, loss_cls: 3.9409, loss: 3.9409 +2024-07-19 11:51:49,958 - pyskl - INFO - Epoch [88][1700/3746] lr: 3.711e-02, eta: 2 days, 4:30:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5602, loss_cls: 3.9422, loss: 3.9422 +2024-07-19 11:53:11,671 - pyskl - INFO - Epoch [88][1800/3746] lr: 3.708e-02, eta: 2 days, 4:29:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5572, loss_cls: 3.9825, loss: 3.9825 +2024-07-19 11:54:32,589 - pyskl - INFO - Epoch [88][1900/3746] lr: 3.705e-02, eta: 2 days, 4:28:19, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5648, loss_cls: 3.9653, loss: 3.9653 +2024-07-19 11:55:53,396 - pyskl - INFO - Epoch [88][2000/3746] lr: 3.703e-02, eta: 2 days, 4:26:58, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5545, loss_cls: 3.9784, loss: 3.9784 +2024-07-19 11:57:14,282 - pyskl - INFO - Epoch [88][2100/3746] lr: 3.700e-02, eta: 2 days, 4:25:38, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5655, loss_cls: 3.9348, loss: 3.9348 +2024-07-19 11:58:36,681 - pyskl - INFO - Epoch [88][2200/3746] lr: 3.697e-02, eta: 2 days, 4:24:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5652, loss_cls: 3.9439, loss: 3.9439 +2024-07-19 11:59:58,242 - pyskl - INFO - Epoch [88][2300/3746] lr: 3.694e-02, eta: 2 days, 4:22:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5605, loss_cls: 3.9644, loss: 3.9644 +2024-07-19 12:01:19,710 - pyskl - INFO - Epoch [88][2400/3746] lr: 3.692e-02, eta: 2 days, 4:21:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5600, loss_cls: 3.9561, loss: 3.9561 +2024-07-19 12:02:40,605 - pyskl - INFO - Epoch [88][2500/3746] lr: 3.689e-02, eta: 2 days, 4:20:17, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5608, loss_cls: 3.9632, loss: 3.9632 +2024-07-19 12:04:01,631 - pyskl - INFO - Epoch [88][2600/3746] lr: 3.686e-02, eta: 2 days, 4:18:57, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5592, loss_cls: 3.9575, loss: 3.9575 +2024-07-19 12:05:22,507 - pyskl - INFO - Epoch [88][2700/3746] lr: 3.684e-02, eta: 2 days, 4:17:36, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5645, loss_cls: 3.9388, loss: 3.9388 +2024-07-19 12:06:44,096 - pyskl - INFO - Epoch [88][2800/3746] lr: 3.681e-02, eta: 2 days, 4:16:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5572, loss_cls: 3.9734, loss: 3.9734 +2024-07-19 12:08:05,767 - pyskl - INFO - Epoch [88][2900/3746] lr: 3.678e-02, eta: 2 days, 4:14:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5556, loss_cls: 3.9862, loss: 3.9862 +2024-07-19 12:09:27,354 - pyskl - INFO - Epoch [88][3000/3746] lr: 3.676e-02, eta: 2 days, 4:13:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5494, loss_cls: 4.0350, loss: 4.0350 +2024-07-19 12:10:49,136 - pyskl - INFO - Epoch [88][3100/3746] lr: 3.673e-02, eta: 2 days, 4:12:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5675, loss_cls: 3.9112, loss: 3.9112 +2024-07-19 12:12:10,094 - pyskl - INFO - Epoch [88][3200/3746] lr: 3.670e-02, eta: 2 days, 4:10:56, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5608, loss_cls: 3.9859, loss: 3.9859 +2024-07-19 12:13:30,967 - pyskl - INFO - Epoch [88][3300/3746] lr: 3.667e-02, eta: 2 days, 4:09:35, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5550, loss_cls: 4.0095, loss: 4.0095 +2024-07-19 12:14:51,802 - pyskl - INFO - Epoch [88][3400/3746] lr: 3.665e-02, eta: 2 days, 4:08:15, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5644, loss_cls: 3.9420, loss: 3.9420 +2024-07-19 12:16:13,025 - pyskl - INFO - Epoch [88][3500/3746] lr: 3.662e-02, eta: 2 days, 4:06:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5614, loss_cls: 3.9708, loss: 3.9708 +2024-07-19 12:17:33,992 - pyskl - INFO - Epoch [88][3600/3746] lr: 3.659e-02, eta: 2 days, 4:05:34, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5752, loss_cls: 3.9068, loss: 3.9068 +2024-07-19 12:18:55,772 - pyskl - INFO - Epoch [88][3700/3746] lr: 3.657e-02, eta: 2 days, 4:04:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5641, loss_cls: 3.9562, loss: 3.9562 +2024-07-19 12:19:35,587 - pyskl - INFO - Saving checkpoint at 88 epochs +2024-07-19 12:21:26,359 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 12:21:27,025 - pyskl - INFO - +top1_acc 0.2465 +top5_acc 0.4908 +2024-07-19 12:21:27,025 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 12:21:27,064 - pyskl - INFO - +mean_acc 0.2464 +2024-07-19 12:21:27,069 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_87.pth was removed +2024-07-19 12:21:27,293 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_88.pth. +2024-07-19 12:21:27,294 - pyskl - INFO - Best top1_acc is 0.2465 at 88 epoch. +2024-07-19 12:21:27,305 - pyskl - INFO - Epoch(val) [88][309] top1_acc: 0.2465, top5_acc: 0.4908, mean_class_accuracy: 0.2464 +2024-07-19 12:25:15,928 - pyskl - INFO - Epoch [89][100/3746] lr: 3.653e-02, eta: 2 days, 4:03:34, time: 2.286, data_time: 1.310, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5841, loss_cls: 3.8633, loss: 3.8633 +2024-07-19 12:26:37,912 - pyskl - INFO - Epoch [89][200/3746] lr: 3.650e-02, eta: 2 days, 4:02:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5817, loss_cls: 3.8857, loss: 3.8857 +2024-07-19 12:27:59,491 - pyskl - INFO - Epoch [89][300/3746] lr: 3.647e-02, eta: 2 days, 4:00:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5795, loss_cls: 3.8999, loss: 3.8999 +2024-07-19 12:29:20,620 - pyskl - INFO - Epoch [89][400/3746] lr: 3.645e-02, eta: 2 days, 3:59:34, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5687, loss_cls: 3.9079, loss: 3.9079 +2024-07-19 12:30:41,505 - pyskl - INFO - Epoch [89][500/3746] lr: 3.642e-02, eta: 2 days, 3:58:13, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5708, loss_cls: 3.9394, loss: 3.9394 +2024-07-19 12:32:02,605 - pyskl - INFO - Epoch [89][600/3746] lr: 3.639e-02, eta: 2 days, 3:56:53, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5725, loss_cls: 3.9007, loss: 3.9007 +2024-07-19 12:33:23,615 - pyskl - INFO - Epoch [89][700/3746] lr: 3.637e-02, eta: 2 days, 3:55:32, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5720, loss_cls: 3.9095, loss: 3.9095 +2024-07-19 12:34:44,463 - pyskl - INFO - Epoch [89][800/3746] lr: 3.634e-02, eta: 2 days, 3:54:11, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5698, loss_cls: 3.9331, loss: 3.9331 +2024-07-19 12:36:05,796 - pyskl - INFO - Epoch [89][900/3746] lr: 3.631e-02, eta: 2 days, 3:52:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5669, loss_cls: 3.9391, loss: 3.9391 +2024-07-19 12:37:27,759 - pyskl - INFO - Epoch [89][1000/3746] lr: 3.629e-02, eta: 2 days, 3:51:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5722, loss_cls: 3.8597, loss: 3.8597 +2024-07-19 12:38:48,711 - pyskl - INFO - Epoch [89][1100/3746] lr: 3.626e-02, eta: 2 days, 3:50:11, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5717, loss_cls: 3.9432, loss: 3.9432 +2024-07-19 12:40:10,091 - pyskl - INFO - Epoch [89][1200/3746] lr: 3.623e-02, eta: 2 days, 3:48:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5692, loss_cls: 3.9432, loss: 3.9432 +2024-07-19 12:41:30,909 - pyskl - INFO - Epoch [89][1300/3746] lr: 3.620e-02, eta: 2 days, 3:47:30, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5661, loss_cls: 3.9279, loss: 3.9279 +2024-07-19 12:42:52,054 - pyskl - INFO - Epoch [89][1400/3746] lr: 3.618e-02, eta: 2 days, 3:46:09, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5583, loss_cls: 3.9639, loss: 3.9639 +2024-07-19 12:44:12,587 - pyskl - INFO - Epoch [89][1500/3746] lr: 3.615e-02, eta: 2 days, 3:44:48, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5706, loss_cls: 3.9205, loss: 3.9205 +2024-07-19 12:45:34,268 - pyskl - INFO - Epoch [89][1600/3746] lr: 3.612e-02, eta: 2 days, 3:43:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5739, loss_cls: 3.9066, loss: 3.9066 +2024-07-19 12:46:55,768 - pyskl - INFO - Epoch [89][1700/3746] lr: 3.610e-02, eta: 2 days, 3:42:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5663, loss_cls: 3.9087, loss: 3.9087 +2024-07-19 12:48:16,728 - pyskl - INFO - Epoch [89][1800/3746] lr: 3.607e-02, eta: 2 days, 3:40:48, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5656, loss_cls: 3.9382, loss: 3.9382 +2024-07-19 12:49:38,054 - pyskl - INFO - Epoch [89][1900/3746] lr: 3.604e-02, eta: 2 days, 3:39:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5702, loss_cls: 3.9361, loss: 3.9361 +2024-07-19 12:50:59,342 - pyskl - INFO - Epoch [89][2000/3746] lr: 3.602e-02, eta: 2 days, 3:38:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5645, loss_cls: 3.9603, loss: 3.9603 +2024-07-19 12:52:20,315 - pyskl - INFO - Epoch [89][2100/3746] lr: 3.599e-02, eta: 2 days, 3:36:46, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5689, loss_cls: 3.9494, loss: 3.9494 +2024-07-19 12:53:42,539 - pyskl - INFO - Epoch [89][2200/3746] lr: 3.596e-02, eta: 2 days, 3:35:27, time: 0.822, data_time: 0.001, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5703, loss_cls: 3.9194, loss: 3.9194 +2024-07-19 12:55:03,795 - pyskl - INFO - Epoch [89][2300/3746] lr: 3.594e-02, eta: 2 days, 3:34:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5587, loss_cls: 3.9951, loss: 3.9951 +2024-07-19 12:56:24,887 - pyskl - INFO - Epoch [89][2400/3746] lr: 3.591e-02, eta: 2 days, 3:32:46, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5630, loss_cls: 3.9332, loss: 3.9332 +2024-07-19 12:57:45,846 - pyskl - INFO - Epoch [89][2500/3746] lr: 3.588e-02, eta: 2 days, 3:31:25, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5528, loss_cls: 3.9870, loss: 3.9870 +2024-07-19 12:59:06,753 - pyskl - INFO - Epoch [89][2600/3746] lr: 3.586e-02, eta: 2 days, 3:30:05, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5564, loss_cls: 3.9742, loss: 3.9742 +2024-07-19 13:00:27,851 - pyskl - INFO - Epoch [89][2700/3746] lr: 3.583e-02, eta: 2 days, 3:28:44, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5684, loss_cls: 3.9281, loss: 3.9281 +2024-07-19 13:01:49,084 - pyskl - INFO - Epoch [89][2800/3746] lr: 3.580e-02, eta: 2 days, 3:27:24, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5747, loss_cls: 3.9187, loss: 3.9187 +2024-07-19 13:03:10,791 - pyskl - INFO - Epoch [89][2900/3746] lr: 3.578e-02, eta: 2 days, 3:26:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5631, loss_cls: 3.9487, loss: 3.9487 +2024-07-19 13:04:32,078 - pyskl - INFO - Epoch [89][3000/3746] lr: 3.575e-02, eta: 2 days, 3:24:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5634, loss_cls: 3.9295, loss: 3.9295 +2024-07-19 13:05:52,946 - pyskl - INFO - Epoch [89][3100/3746] lr: 3.572e-02, eta: 2 days, 3:23:23, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5650, loss_cls: 3.9438, loss: 3.9438 +2024-07-19 13:07:13,757 - pyskl - INFO - Epoch [89][3200/3746] lr: 3.569e-02, eta: 2 days, 3:22:02, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5759, loss_cls: 3.9091, loss: 3.9091 +2024-07-19 13:08:34,987 - pyskl - INFO - Epoch [89][3300/3746] lr: 3.567e-02, eta: 2 days, 3:20:42, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5770, loss_cls: 3.8836, loss: 3.8836 +2024-07-19 13:09:55,812 - pyskl - INFO - Epoch [89][3400/3746] lr: 3.564e-02, eta: 2 days, 3:19:21, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5666, loss_cls: 3.9267, loss: 3.9267 +2024-07-19 13:11:17,247 - pyskl - INFO - Epoch [89][3500/3746] lr: 3.561e-02, eta: 2 days, 3:18:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5589, loss_cls: 3.9388, loss: 3.9388 +2024-07-19 13:12:38,831 - pyskl - INFO - Epoch [89][3600/3746] lr: 3.559e-02, eta: 2 days, 3:16:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5591, loss_cls: 3.9607, loss: 3.9607 +2024-07-19 13:13:59,829 - pyskl - INFO - Epoch [89][3700/3746] lr: 3.556e-02, eta: 2 days, 3:15:20, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5681, loss_cls: 3.9089, loss: 3.9089 +2024-07-19 13:14:39,836 - pyskl - INFO - Saving checkpoint at 89 epochs +2024-07-19 13:16:30,452 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 13:16:31,124 - pyskl - INFO - +top1_acc 0.2430 +top5_acc 0.4833 +2024-07-19 13:16:31,124 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 13:16:31,169 - pyskl - INFO - +mean_acc 0.2429 +2024-07-19 13:16:31,182 - pyskl - INFO - Epoch(val) [89][309] top1_acc: 0.2430, top5_acc: 0.4833, mean_class_accuracy: 0.2429 +2024-07-19 13:20:22,324 - pyskl - INFO - Epoch [90][100/3746] lr: 3.552e-02, eta: 2 days, 3:14:40, time: 2.311, data_time: 1.337, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5795, loss_cls: 3.8652, loss: 3.8652 +2024-07-19 13:21:44,651 - pyskl - INFO - Epoch [90][200/3746] lr: 3.550e-02, eta: 2 days, 3:13:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5877, loss_cls: 3.8197, loss: 3.8197 +2024-07-19 13:23:05,993 - pyskl - INFO - Epoch [90][300/3746] lr: 3.547e-02, eta: 2 days, 3:12:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5773, loss_cls: 3.8747, loss: 3.8747 +2024-07-19 13:24:27,370 - pyskl - INFO - Epoch [90][400/3746] lr: 3.544e-02, eta: 2 days, 3:10:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5787, loss_cls: 3.8781, loss: 3.8781 +2024-07-19 13:25:48,023 - pyskl - INFO - Epoch [90][500/3746] lr: 3.541e-02, eta: 2 days, 3:09:18, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5687, loss_cls: 3.8859, loss: 3.8859 +2024-07-19 13:27:09,958 - pyskl - INFO - Epoch [90][600/3746] lr: 3.539e-02, eta: 2 days, 3:07:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5748, loss_cls: 3.8919, loss: 3.8919 +2024-07-19 13:28:30,879 - pyskl - INFO - Epoch [90][700/3746] lr: 3.536e-02, eta: 2 days, 3:06:38, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5736, loss_cls: 3.8663, loss: 3.8663 +2024-07-19 13:29:52,060 - pyskl - INFO - Epoch [90][800/3746] lr: 3.533e-02, eta: 2 days, 3:05:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5719, loss_cls: 3.9111, loss: 3.9111 +2024-07-19 13:31:13,312 - pyskl - INFO - Epoch [90][900/3746] lr: 3.531e-02, eta: 2 days, 3:03:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5673, loss_cls: 3.9100, loss: 3.9100 +2024-07-19 13:32:34,214 - pyskl - INFO - Epoch [90][1000/3746] lr: 3.528e-02, eta: 2 days, 3:02:36, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5767, loss_cls: 3.9131, loss: 3.9131 +2024-07-19 13:33:55,563 - pyskl - INFO - Epoch [90][1100/3746] lr: 3.525e-02, eta: 2 days, 3:01:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5669, loss_cls: 3.9412, loss: 3.9412 +2024-07-19 13:35:16,764 - pyskl - INFO - Epoch [90][1200/3746] lr: 3.523e-02, eta: 2 days, 2:59:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5742, loss_cls: 3.8956, loss: 3.8956 +2024-07-19 13:36:37,739 - pyskl - INFO - Epoch [90][1300/3746] lr: 3.520e-02, eta: 2 days, 2:58:35, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5713, loss_cls: 3.8972, loss: 3.8972 +2024-07-19 13:37:59,566 - pyskl - INFO - Epoch [90][1400/3746] lr: 3.517e-02, eta: 2 days, 2:57:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5642, loss_cls: 3.9149, loss: 3.9149 +2024-07-19 13:39:20,618 - pyskl - INFO - Epoch [90][1500/3746] lr: 3.515e-02, eta: 2 days, 2:55:54, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5711, loss_cls: 3.9003, loss: 3.9003 +2024-07-19 13:40:42,587 - pyskl - INFO - Epoch [90][1600/3746] lr: 3.512e-02, eta: 2 days, 2:54:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5731, loss_cls: 3.8960, loss: 3.8960 +2024-07-19 13:42:04,070 - pyskl - INFO - Epoch [90][1700/3746] lr: 3.509e-02, eta: 2 days, 2:53:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5694, loss_cls: 3.9239, loss: 3.9239 +2024-07-19 13:43:25,727 - pyskl - INFO - Epoch [90][1800/3746] lr: 3.507e-02, eta: 2 days, 2:51:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5633, loss_cls: 3.9601, loss: 3.9601 +2024-07-19 13:44:47,742 - pyskl - INFO - Epoch [90][1900/3746] lr: 3.504e-02, eta: 2 days, 2:50:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5756, loss_cls: 3.9003, loss: 3.9003 +2024-07-19 13:46:09,528 - pyskl - INFO - Epoch [90][2000/3746] lr: 3.501e-02, eta: 2 days, 2:49:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5700, loss_cls: 3.9117, loss: 3.9117 +2024-07-19 13:47:30,427 - pyskl - INFO - Epoch [90][2100/3746] lr: 3.499e-02, eta: 2 days, 2:47:53, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5708, loss_cls: 3.8985, loss: 3.8985 +2024-07-19 13:48:51,972 - pyskl - INFO - Epoch [90][2200/3746] lr: 3.496e-02, eta: 2 days, 2:46:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5719, loss_cls: 3.9039, loss: 3.9039 +2024-07-19 13:50:12,877 - pyskl - INFO - Epoch [90][2300/3746] lr: 3.493e-02, eta: 2 days, 2:45:12, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5630, loss_cls: 3.9512, loss: 3.9512 +2024-07-19 13:51:34,483 - pyskl - INFO - Epoch [90][2400/3746] lr: 3.491e-02, eta: 2 days, 2:43:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5736, loss_cls: 3.9203, loss: 3.9203 +2024-07-19 13:52:55,857 - pyskl - INFO - Epoch [90][2500/3746] lr: 3.488e-02, eta: 2 days, 2:42:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5633, loss_cls: 3.9592, loss: 3.9592 +2024-07-19 13:54:16,952 - pyskl - INFO - Epoch [90][2600/3746] lr: 3.485e-02, eta: 2 days, 2:41:11, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5703, loss_cls: 3.9409, loss: 3.9409 +2024-07-19 13:55:38,176 - pyskl - INFO - Epoch [90][2700/3746] lr: 3.483e-02, eta: 2 days, 2:39:50, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5727, loss_cls: 3.8855, loss: 3.8855 +2024-07-19 13:56:59,855 - pyskl - INFO - Epoch [90][2800/3746] lr: 3.480e-02, eta: 2 days, 2:38:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5637, loss_cls: 3.9458, loss: 3.9458 +2024-07-19 13:58:21,664 - pyskl - INFO - Epoch [90][2900/3746] lr: 3.477e-02, eta: 2 days, 2:37:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5663, loss_cls: 3.9248, loss: 3.9248 +2024-07-19 13:59:43,474 - pyskl - INFO - Epoch [90][3000/3746] lr: 3.475e-02, eta: 2 days, 2:35:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5650, loss_cls: 3.9293, loss: 3.9293 +2024-07-19 14:01:05,034 - pyskl - INFO - Epoch [90][3100/3746] lr: 3.472e-02, eta: 2 days, 2:34:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5834, loss_cls: 3.8650, loss: 3.8650 +2024-07-19 14:02:25,950 - pyskl - INFO - Epoch [90][3200/3746] lr: 3.469e-02, eta: 2 days, 2:33:09, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5591, loss_cls: 3.9612, loss: 3.9612 +2024-07-19 14:03:47,010 - pyskl - INFO - Epoch [90][3300/3746] lr: 3.467e-02, eta: 2 days, 2:31:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5691, loss_cls: 3.9298, loss: 3.9298 +2024-07-19 14:05:07,859 - pyskl - INFO - Epoch [90][3400/3746] lr: 3.464e-02, eta: 2 days, 2:30:28, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5636, loss_cls: 3.9374, loss: 3.9374 +2024-07-19 14:06:29,002 - pyskl - INFO - Epoch [90][3500/3746] lr: 3.461e-02, eta: 2 days, 2:29:07, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5747, loss_cls: 3.8922, loss: 3.8922 +2024-07-19 14:07:50,543 - pyskl - INFO - Epoch [90][3600/3746] lr: 3.459e-02, eta: 2 days, 2:27:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5747, loss_cls: 3.8845, loss: 3.8845 +2024-07-19 14:09:11,970 - pyskl - INFO - Epoch [90][3700/3746] lr: 3.456e-02, eta: 2 days, 2:26:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5770, loss_cls: 3.8748, loss: 3.8748 +2024-07-19 14:09:51,470 - pyskl - INFO - Saving checkpoint at 90 epochs +2024-07-19 14:11:41,906 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 14:11:42,664 - pyskl - INFO - +top1_acc 0.2467 +top5_acc 0.4978 +2024-07-19 14:11:42,664 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 14:11:42,723 - pyskl - INFO - +mean_acc 0.2467 +2024-07-19 14:11:42,728 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_88.pth was removed +2024-07-19 14:11:43,051 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_90.pth. +2024-07-19 14:11:43,051 - pyskl - INFO - Best top1_acc is 0.2467 at 90 epoch. +2024-07-19 14:11:43,070 - pyskl - INFO - Epoch(val) [90][309] top1_acc: 0.2467, top5_acc: 0.4978, mean_class_accuracy: 0.2467 +2024-07-19 14:15:40,983 - pyskl - INFO - Epoch [91][100/3746] lr: 3.452e-02, eta: 2 days, 2:25:49, time: 2.379, data_time: 1.383, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5889, loss_cls: 3.8341, loss: 3.8341 +2024-07-19 14:17:04,745 - pyskl - INFO - Epoch [91][200/3746] lr: 3.450e-02, eta: 2 days, 2:24:30, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5686, loss_cls: 3.8876, loss: 3.8876 +2024-07-19 14:18:28,462 - pyskl - INFO - Epoch [91][300/3746] lr: 3.447e-02, eta: 2 days, 2:23:11, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5833, loss_cls: 3.8479, loss: 3.8479 +2024-07-19 14:19:52,282 - pyskl - INFO - Epoch [91][400/3746] lr: 3.444e-02, eta: 2 days, 2:21:52, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5831, loss_cls: 3.8518, loss: 3.8518 +2024-07-19 14:21:16,505 - pyskl - INFO - Epoch [91][500/3746] lr: 3.442e-02, eta: 2 days, 2:20:34, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5911, loss_cls: 3.8527, loss: 3.8527 +2024-07-19 14:22:40,889 - pyskl - INFO - Epoch [91][600/3746] lr: 3.439e-02, eta: 2 days, 2:19:15, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5709, loss_cls: 3.9088, loss: 3.9088 +2024-07-19 14:24:04,359 - pyskl - INFO - Epoch [91][700/3746] lr: 3.436e-02, eta: 2 days, 2:17:56, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5747, loss_cls: 3.9022, loss: 3.9022 +2024-07-19 14:25:27,683 - pyskl - INFO - Epoch [91][800/3746] lr: 3.434e-02, eta: 2 days, 2:16:37, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5761, loss_cls: 3.8609, loss: 3.8609 +2024-07-19 14:26:51,788 - pyskl - INFO - Epoch [91][900/3746] lr: 3.431e-02, eta: 2 days, 2:15:18, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5689, loss_cls: 3.9313, loss: 3.9313 +2024-07-19 14:28:15,395 - pyskl - INFO - Epoch [91][1000/3746] lr: 3.428e-02, eta: 2 days, 2:13:59, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5755, loss_cls: 3.8991, loss: 3.8991 +2024-07-19 14:29:39,235 - pyskl - INFO - Epoch [91][1100/3746] lr: 3.426e-02, eta: 2 days, 2:12:40, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5811, loss_cls: 3.8530, loss: 3.8530 +2024-07-19 14:31:02,769 - pyskl - INFO - Epoch [91][1200/3746] lr: 3.423e-02, eta: 2 days, 2:11:21, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5703, loss_cls: 3.8910, loss: 3.8910 +2024-07-19 14:32:26,033 - pyskl - INFO - Epoch [91][1300/3746] lr: 3.420e-02, eta: 2 days, 2:10:02, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5708, loss_cls: 3.8883, loss: 3.8883 +2024-07-19 14:33:49,439 - pyskl - INFO - Epoch [91][1400/3746] lr: 3.418e-02, eta: 2 days, 2:08:43, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5759, loss_cls: 3.9234, loss: 3.9234 +2024-07-19 14:35:12,881 - pyskl - INFO - Epoch [91][1500/3746] lr: 3.415e-02, eta: 2 days, 2:07:24, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5714, loss_cls: 3.9095, loss: 3.9095 +2024-07-19 14:36:35,827 - pyskl - INFO - Epoch [91][1600/3746] lr: 3.412e-02, eta: 2 days, 2:06:05, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5706, loss_cls: 3.8821, loss: 3.8821 +2024-07-19 14:37:59,386 - pyskl - INFO - Epoch [91][1700/3746] lr: 3.410e-02, eta: 2 days, 2:04:46, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5753, loss_cls: 3.9127, loss: 3.9127 +2024-07-19 14:39:23,103 - pyskl - INFO - Epoch [91][1800/3746] lr: 3.407e-02, eta: 2 days, 2:03:27, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5766, loss_cls: 3.8699, loss: 3.8699 +2024-07-19 14:40:47,080 - pyskl - INFO - Epoch [91][1900/3746] lr: 3.405e-02, eta: 2 days, 2:02:08, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5791, loss_cls: 3.8679, loss: 3.8679 +2024-07-19 14:42:10,814 - pyskl - INFO - Epoch [91][2000/3746] lr: 3.402e-02, eta: 2 days, 2:00:49, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5744, loss_cls: 3.8846, loss: 3.8846 +2024-07-19 14:43:34,453 - pyskl - INFO - Epoch [91][2100/3746] lr: 3.399e-02, eta: 2 days, 1:59:30, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5720, loss_cls: 3.9037, loss: 3.9037 +2024-07-19 14:44:58,546 - pyskl - INFO - Epoch [91][2200/3746] lr: 3.397e-02, eta: 2 days, 1:58:11, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5773, loss_cls: 3.8753, loss: 3.8753 +2024-07-19 14:46:22,717 - pyskl - INFO - Epoch [91][2300/3746] lr: 3.394e-02, eta: 2 days, 1:56:53, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5647, loss_cls: 3.9198, loss: 3.9198 +2024-07-19 14:47:46,067 - pyskl - INFO - Epoch [91][2400/3746] lr: 3.391e-02, eta: 2 days, 1:55:33, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5734, loss_cls: 3.9013, loss: 3.9013 +2024-07-19 14:49:09,514 - pyskl - INFO - Epoch [91][2500/3746] lr: 3.389e-02, eta: 2 days, 1:54:14, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5769, loss_cls: 3.9072, loss: 3.9072 +2024-07-19 14:50:33,263 - pyskl - INFO - Epoch [91][2600/3746] lr: 3.386e-02, eta: 2 days, 1:52:55, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5733, loss_cls: 3.8777, loss: 3.8777 +2024-07-19 14:51:56,564 - pyskl - INFO - Epoch [91][2700/3746] lr: 3.383e-02, eta: 2 days, 1:51:36, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5586, loss_cls: 3.9415, loss: 3.9415 +2024-07-19 14:53:20,408 - pyskl - INFO - Epoch [91][2800/3746] lr: 3.381e-02, eta: 2 days, 1:50:17, time: 0.838, data_time: 0.001, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5736, loss_cls: 3.9132, loss: 3.9132 +2024-07-19 14:54:43,482 - pyskl - INFO - Epoch [91][2900/3746] lr: 3.378e-02, eta: 2 days, 1:48:58, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5636, loss_cls: 3.9285, loss: 3.9285 +2024-07-19 14:56:06,990 - pyskl - INFO - Epoch [91][3000/3746] lr: 3.375e-02, eta: 2 days, 1:47:39, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5739, loss_cls: 3.8813, loss: 3.8813 +2024-07-19 14:57:31,041 - pyskl - INFO - Epoch [91][3100/3746] lr: 3.373e-02, eta: 2 days, 1:46:20, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5728, loss_cls: 3.9191, loss: 3.9191 +2024-07-19 14:58:54,683 - pyskl - INFO - Epoch [91][3200/3746] lr: 3.370e-02, eta: 2 days, 1:45:01, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5606, loss_cls: 3.9395, loss: 3.9395 +2024-07-19 15:00:18,447 - pyskl - INFO - Epoch [91][3300/3746] lr: 3.367e-02, eta: 2 days, 1:43:42, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5742, loss_cls: 3.8651, loss: 3.8651 +2024-07-19 15:01:42,512 - pyskl - INFO - Epoch [91][3400/3746] lr: 3.365e-02, eta: 2 days, 1:42:23, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5711, loss_cls: 3.9097, loss: 3.9097 +2024-07-19 15:03:06,457 - pyskl - INFO - Epoch [91][3500/3746] lr: 3.362e-02, eta: 2 days, 1:41:05, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5617, loss_cls: 3.9550, loss: 3.9550 +2024-07-19 15:04:30,461 - pyskl - INFO - Epoch [91][3600/3746] lr: 3.360e-02, eta: 2 days, 1:39:46, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5808, loss_cls: 3.8831, loss: 3.8831 +2024-07-19 15:05:53,855 - pyskl - INFO - Epoch [91][3700/3746] lr: 3.357e-02, eta: 2 days, 1:38:27, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5750, loss_cls: 3.9169, loss: 3.9169 +2024-07-19 15:06:34,552 - pyskl - INFO - Saving checkpoint at 91 epochs +2024-07-19 15:08:27,204 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 15:08:27,932 - pyskl - INFO - +top1_acc 0.2328 +top5_acc 0.4697 +2024-07-19 15:08:27,933 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 15:08:27,979 - pyskl - INFO - +mean_acc 0.2325 +2024-07-19 15:08:27,991 - pyskl - INFO - Epoch(val) [91][309] top1_acc: 0.2328, top5_acc: 0.4697, mean_class_accuracy: 0.2325 +2024-07-19 15:12:25,178 - pyskl - INFO - Epoch [92][100/3746] lr: 3.353e-02, eta: 2 days, 1:37:46, time: 2.372, data_time: 1.382, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5792, loss_cls: 3.8421, loss: 3.8421 +2024-07-19 15:13:46,298 - pyskl - INFO - Epoch [92][200/3746] lr: 3.350e-02, eta: 2 days, 1:36:25, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5856, loss_cls: 3.8277, loss: 3.8277 +2024-07-19 15:15:08,227 - pyskl - INFO - Epoch [92][300/3746] lr: 3.348e-02, eta: 2 days, 1:35:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5816, loss_cls: 3.8351, loss: 3.8351 +2024-07-19 15:16:29,851 - pyskl - INFO - Epoch [92][400/3746] lr: 3.345e-02, eta: 2 days, 1:33:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5750, loss_cls: 3.8843, loss: 3.8843 +2024-07-19 15:17:51,171 - pyskl - INFO - Epoch [92][500/3746] lr: 3.342e-02, eta: 2 days, 1:32:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5794, loss_cls: 3.8659, loss: 3.8659 +2024-07-19 15:19:12,258 - pyskl - INFO - Epoch [92][600/3746] lr: 3.340e-02, eta: 2 days, 1:31:03, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5723, loss_cls: 3.8889, loss: 3.8889 +2024-07-19 15:20:33,258 - pyskl - INFO - Epoch [92][700/3746] lr: 3.337e-02, eta: 2 days, 1:29:42, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5889, loss_cls: 3.8296, loss: 3.8296 +2024-07-19 15:21:54,858 - pyskl - INFO - Epoch [92][800/3746] lr: 3.335e-02, eta: 2 days, 1:28:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5745, loss_cls: 3.9069, loss: 3.9069 +2024-07-19 15:23:15,373 - pyskl - INFO - Epoch [92][900/3746] lr: 3.332e-02, eta: 2 days, 1:27:01, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5913, loss_cls: 3.8133, loss: 3.8133 +2024-07-19 15:24:36,025 - pyskl - INFO - Epoch [92][1000/3746] lr: 3.329e-02, eta: 2 days, 1:25:40, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5703, loss_cls: 3.8998, loss: 3.8998 +2024-07-19 15:25:57,548 - pyskl - INFO - Epoch [92][1100/3746] lr: 3.327e-02, eta: 2 days, 1:24:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5675, loss_cls: 3.9288, loss: 3.9288 +2024-07-19 15:27:18,481 - pyskl - INFO - Epoch [92][1200/3746] lr: 3.324e-02, eta: 2 days, 1:22:59, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5675, loss_cls: 3.8771, loss: 3.8771 +2024-07-19 15:28:40,309 - pyskl - INFO - Epoch [92][1300/3746] lr: 3.321e-02, eta: 2 days, 1:21:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5830, loss_cls: 3.8569, loss: 3.8569 +2024-07-19 15:30:01,699 - pyskl - INFO - Epoch [92][1400/3746] lr: 3.319e-02, eta: 2 days, 1:20:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5922, loss_cls: 3.8171, loss: 3.8171 +2024-07-19 15:31:22,564 - pyskl - INFO - Epoch [92][1500/3746] lr: 3.316e-02, eta: 2 days, 1:18:57, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5802, loss_cls: 3.8466, loss: 3.8466 +2024-07-19 15:32:44,156 - pyskl - INFO - Epoch [92][1600/3746] lr: 3.314e-02, eta: 2 days, 1:17:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5823, loss_cls: 3.8662, loss: 3.8662 +2024-07-19 15:34:05,121 - pyskl - INFO - Epoch [92][1700/3746] lr: 3.311e-02, eta: 2 days, 1:16:16, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5787, loss_cls: 3.8808, loss: 3.8808 +2024-07-19 15:35:27,044 - pyskl - INFO - Epoch [92][1800/3746] lr: 3.308e-02, eta: 2 days, 1:14:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5841, loss_cls: 3.8415, loss: 3.8415 +2024-07-19 15:36:48,320 - pyskl - INFO - Epoch [92][1900/3746] lr: 3.306e-02, eta: 2 days, 1:13:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5806, loss_cls: 3.8717, loss: 3.8717 +2024-07-19 15:38:10,046 - pyskl - INFO - Epoch [92][2000/3746] lr: 3.303e-02, eta: 2 days, 1:12:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5770, loss_cls: 3.8797, loss: 3.8797 +2024-07-19 15:39:31,208 - pyskl - INFO - Epoch [92][2100/3746] lr: 3.300e-02, eta: 2 days, 1:10:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5736, loss_cls: 3.9234, loss: 3.9234 +2024-07-19 15:40:52,502 - pyskl - INFO - Epoch [92][2200/3746] lr: 3.298e-02, eta: 2 days, 1:09:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5772, loss_cls: 3.9094, loss: 3.9094 +2024-07-19 15:42:13,464 - pyskl - INFO - Epoch [92][2300/3746] lr: 3.295e-02, eta: 2 days, 1:08:12, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5703, loss_cls: 3.9064, loss: 3.9064 +2024-07-19 15:43:35,083 - pyskl - INFO - Epoch [92][2400/3746] lr: 3.292e-02, eta: 2 days, 1:06:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5609, loss_cls: 3.9385, loss: 3.9385 +2024-07-19 15:44:56,468 - pyskl - INFO - Epoch [92][2500/3746] lr: 3.290e-02, eta: 2 days, 1:05:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5653, loss_cls: 3.9443, loss: 3.9443 +2024-07-19 15:46:17,519 - pyskl - INFO - Epoch [92][2600/3746] lr: 3.287e-02, eta: 2 days, 1:04:11, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5720, loss_cls: 3.9224, loss: 3.9224 +2024-07-19 15:47:39,412 - pyskl - INFO - Epoch [92][2700/3746] lr: 3.285e-02, eta: 2 days, 1:02:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5819, loss_cls: 3.8592, loss: 3.8592 +2024-07-19 15:49:00,966 - pyskl - INFO - Epoch [92][2800/3746] lr: 3.282e-02, eta: 2 days, 1:01:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5717, loss_cls: 3.9051, loss: 3.9051 +2024-07-19 15:50:22,335 - pyskl - INFO - Epoch [92][2900/3746] lr: 3.279e-02, eta: 2 days, 1:00:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5744, loss_cls: 3.8914, loss: 3.8914 +2024-07-19 15:51:43,854 - pyskl - INFO - Epoch [92][3000/3746] lr: 3.277e-02, eta: 2 days, 0:58:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5619, loss_cls: 3.9313, loss: 3.9313 +2024-07-19 15:53:05,294 - pyskl - INFO - Epoch [92][3100/3746] lr: 3.274e-02, eta: 2 days, 0:57:28, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5773, loss_cls: 3.8621, loss: 3.8621 +2024-07-19 15:54:26,891 - pyskl - INFO - Epoch [92][3200/3746] lr: 3.271e-02, eta: 2 days, 0:56:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5737, loss_cls: 3.9023, loss: 3.9023 +2024-07-19 15:55:48,003 - pyskl - INFO - Epoch [92][3300/3746] lr: 3.269e-02, eta: 2 days, 0:54:47, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5705, loss_cls: 3.9020, loss: 3.9020 +2024-07-19 15:57:09,322 - pyskl - INFO - Epoch [92][3400/3746] lr: 3.266e-02, eta: 2 days, 0:53:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5837, loss_cls: 3.8363, loss: 3.8363 +2024-07-19 15:58:30,615 - pyskl - INFO - Epoch [92][3500/3746] lr: 3.264e-02, eta: 2 days, 0:52:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5697, loss_cls: 3.8829, loss: 3.8829 +2024-07-19 15:59:52,162 - pyskl - INFO - Epoch [92][3600/3746] lr: 3.261e-02, eta: 2 days, 0:50:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5783, loss_cls: 3.8481, loss: 3.8481 +2024-07-19 16:01:13,329 - pyskl - INFO - Epoch [92][3700/3746] lr: 3.258e-02, eta: 2 days, 0:49:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5687, loss_cls: 3.9240, loss: 3.9240 +2024-07-19 16:01:53,070 - pyskl - INFO - Saving checkpoint at 92 epochs +2024-07-19 16:03:44,689 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 16:03:45,372 - pyskl - INFO - +top1_acc 0.2617 +top5_acc 0.5090 +2024-07-19 16:03:45,373 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 16:03:45,418 - pyskl - INFO - +mean_acc 0.2615 +2024-07-19 16:03:45,423 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_90.pth was removed +2024-07-19 16:03:45,677 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_92.pth. +2024-07-19 16:03:45,678 - pyskl - INFO - Best top1_acc is 0.2617 at 92 epoch. +2024-07-19 16:03:45,693 - pyskl - INFO - Epoch(val) [92][309] top1_acc: 0.2617, top5_acc: 0.5090, mean_class_accuracy: 0.2615 +2024-07-19 16:07:38,136 - pyskl - INFO - Epoch [93][100/3746] lr: 3.255e-02, eta: 2 days, 0:48:39, time: 2.324, data_time: 1.322, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5872, loss_cls: 3.7949, loss: 3.7949 +2024-07-19 16:09:01,918 - pyskl - INFO - Epoch [93][200/3746] lr: 3.252e-02, eta: 2 days, 0:47:20, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5802, loss_cls: 3.8356, loss: 3.8356 +2024-07-19 16:10:24,665 - pyskl - INFO - Epoch [93][300/3746] lr: 3.249e-02, eta: 2 days, 0:46:00, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5953, loss_cls: 3.7745, loss: 3.7745 +2024-07-19 16:11:47,651 - pyskl - INFO - Epoch [93][400/3746] lr: 3.247e-02, eta: 2 days, 0:44:40, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5787, loss_cls: 3.8492, loss: 3.8492 +2024-07-19 16:13:10,106 - pyskl - INFO - Epoch [93][500/3746] lr: 3.244e-02, eta: 2 days, 0:43:20, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5831, loss_cls: 3.8345, loss: 3.8345 +2024-07-19 16:14:32,455 - pyskl - INFO - Epoch [93][600/3746] lr: 3.241e-02, eta: 2 days, 0:42:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5847, loss_cls: 3.8455, loss: 3.8455 +2024-07-19 16:15:55,112 - pyskl - INFO - Epoch [93][700/3746] lr: 3.239e-02, eta: 2 days, 0:40:40, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5767, loss_cls: 3.9042, loss: 3.9042 +2024-07-19 16:17:17,438 - pyskl - INFO - Epoch [93][800/3746] lr: 3.236e-02, eta: 2 days, 0:39:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5686, loss_cls: 3.9248, loss: 3.9248 +2024-07-19 16:18:39,926 - pyskl - INFO - Epoch [93][900/3746] lr: 3.234e-02, eta: 2 days, 0:38:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5803, loss_cls: 3.8620, loss: 3.8620 +2024-07-19 16:20:02,558 - pyskl - INFO - Epoch [93][1000/3746] lr: 3.231e-02, eta: 2 days, 0:36:41, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5878, loss_cls: 3.8117, loss: 3.8117 +2024-07-19 16:21:25,226 - pyskl - INFO - Epoch [93][1100/3746] lr: 3.228e-02, eta: 2 days, 0:35:21, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5681, loss_cls: 3.8908, loss: 3.8908 +2024-07-19 16:22:48,316 - pyskl - INFO - Epoch [93][1200/3746] lr: 3.226e-02, eta: 2 days, 0:34:01, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5772, loss_cls: 3.8697, loss: 3.8697 +2024-07-19 16:24:10,592 - pyskl - INFO - Epoch [93][1300/3746] lr: 3.223e-02, eta: 2 days, 0:32:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5820, loss_cls: 3.8894, loss: 3.8894 +2024-07-19 16:25:32,198 - pyskl - INFO - Epoch [93][1400/3746] lr: 3.221e-02, eta: 2 days, 0:31:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5695, loss_cls: 3.9116, loss: 3.9116 +2024-07-19 16:26:53,496 - pyskl - INFO - Epoch [93][1500/3746] lr: 3.218e-02, eta: 2 days, 0:30:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5653, loss_cls: 3.9289, loss: 3.9289 +2024-07-19 16:28:15,233 - pyskl - INFO - Epoch [93][1600/3746] lr: 3.215e-02, eta: 2 days, 0:28:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5816, loss_cls: 3.8237, loss: 3.8237 +2024-07-19 16:29:37,054 - pyskl - INFO - Epoch [93][1700/3746] lr: 3.213e-02, eta: 2 days, 0:27:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5797, loss_cls: 3.8877, loss: 3.8877 +2024-07-19 16:30:58,306 - pyskl - INFO - Epoch [93][1800/3746] lr: 3.210e-02, eta: 2 days, 0:25:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5781, loss_cls: 3.8719, loss: 3.8719 +2024-07-19 16:32:19,781 - pyskl - INFO - Epoch [93][1900/3746] lr: 3.207e-02, eta: 2 days, 0:24:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5778, loss_cls: 3.8807, loss: 3.8807 +2024-07-19 16:33:40,973 - pyskl - INFO - Epoch [93][2000/3746] lr: 3.205e-02, eta: 2 days, 0:23:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5702, loss_cls: 3.8737, loss: 3.8737 +2024-07-19 16:35:02,146 - pyskl - INFO - Epoch [93][2100/3746] lr: 3.202e-02, eta: 2 days, 0:21:56, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5944, loss_cls: 3.7779, loss: 3.7779 +2024-07-19 16:36:23,776 - pyskl - INFO - Epoch [93][2200/3746] lr: 3.200e-02, eta: 2 days, 0:20:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5837, loss_cls: 3.8467, loss: 3.8467 +2024-07-19 16:37:45,379 - pyskl - INFO - Epoch [93][2300/3746] lr: 3.197e-02, eta: 2 days, 0:19:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5833, loss_cls: 3.8681, loss: 3.8681 +2024-07-19 16:39:07,106 - pyskl - INFO - Epoch [93][2400/3746] lr: 3.194e-02, eta: 2 days, 0:17:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5852, loss_cls: 3.8224, loss: 3.8224 +2024-07-19 16:40:28,340 - pyskl - INFO - Epoch [93][2500/3746] lr: 3.192e-02, eta: 2 days, 0:16:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5713, loss_cls: 3.9028, loss: 3.9028 +2024-07-19 16:41:49,389 - pyskl - INFO - Epoch [93][2600/3746] lr: 3.189e-02, eta: 2 days, 0:15:13, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5764, loss_cls: 3.8625, loss: 3.8625 +2024-07-19 16:43:10,831 - pyskl - INFO - Epoch [93][2700/3746] lr: 3.187e-02, eta: 2 days, 0:13:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5752, loss_cls: 3.8895, loss: 3.8895 +2024-07-19 16:44:32,389 - pyskl - INFO - Epoch [93][2800/3746] lr: 3.184e-02, eta: 2 days, 0:12:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5736, loss_cls: 3.8864, loss: 3.8864 +2024-07-19 16:45:54,543 - pyskl - INFO - Epoch [93][2900/3746] lr: 3.181e-02, eta: 2 days, 0:11:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5781, loss_cls: 3.9156, loss: 3.9156 +2024-07-19 16:47:15,814 - pyskl - INFO - Epoch [93][3000/3746] lr: 3.179e-02, eta: 2 days, 0:09:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5884, loss_cls: 3.8329, loss: 3.8329 +2024-07-19 16:48:37,160 - pyskl - INFO - Epoch [93][3100/3746] lr: 3.176e-02, eta: 2 days, 0:08:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5792, loss_cls: 3.8631, loss: 3.8631 +2024-07-19 16:49:58,416 - pyskl - INFO - Epoch [93][3200/3746] lr: 3.174e-02, eta: 2 days, 0:07:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5744, loss_cls: 3.8950, loss: 3.8950 +2024-07-19 16:51:19,154 - pyskl - INFO - Epoch [93][3300/3746] lr: 3.171e-02, eta: 2 days, 0:05:49, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5789, loss_cls: 3.8500, loss: 3.8500 +2024-07-19 16:52:40,383 - pyskl - INFO - Epoch [93][3400/3746] lr: 3.168e-02, eta: 2 days, 0:04:28, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5727, loss_cls: 3.8554, loss: 3.8554 +2024-07-19 16:54:01,140 - pyskl - INFO - Epoch [93][3500/3746] lr: 3.166e-02, eta: 2 days, 0:03:07, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5731, loss_cls: 3.9073, loss: 3.9073 +2024-07-19 16:55:22,149 - pyskl - INFO - Epoch [93][3600/3746] lr: 3.163e-02, eta: 2 days, 0:01:46, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5686, loss_cls: 3.8867, loss: 3.8867 +2024-07-19 16:56:43,219 - pyskl - INFO - Epoch [93][3700/3746] lr: 3.161e-02, eta: 2 days, 0:00:25, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5766, loss_cls: 3.9011, loss: 3.9011 +2024-07-19 16:57:23,061 - pyskl - INFO - Saving checkpoint at 93 epochs +2024-07-19 16:59:14,787 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 16:59:15,450 - pyskl - INFO - +top1_acc 0.2491 +top5_acc 0.4945 +2024-07-19 16:59:15,451 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 16:59:15,494 - pyskl - INFO - +mean_acc 0.2488 +2024-07-19 16:59:15,506 - pyskl - INFO - Epoch(val) [93][309] top1_acc: 0.2491, top5_acc: 0.4945, mean_class_accuracy: 0.2488 +2024-07-19 17:03:09,608 - pyskl - INFO - Epoch [94][100/3746] lr: 3.157e-02, eta: 1 day, 23:59:38, time: 2.341, data_time: 1.344, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5916, loss_cls: 3.7790, loss: 3.7790 +2024-07-19 17:04:32,720 - pyskl - INFO - Epoch [94][200/3746] lr: 3.154e-02, eta: 1 day, 23:58:19, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5969, loss_cls: 3.7895, loss: 3.7895 +2024-07-19 17:05:55,536 - pyskl - INFO - Epoch [94][300/3746] lr: 3.152e-02, eta: 1 day, 23:56:59, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5814, loss_cls: 3.8719, loss: 3.8719 +2024-07-19 17:07:18,475 - pyskl - INFO - Epoch [94][400/3746] lr: 3.149e-02, eta: 1 day, 23:55:39, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5867, loss_cls: 3.8029, loss: 3.8029 +2024-07-19 17:08:41,525 - pyskl - INFO - Epoch [94][500/3746] lr: 3.146e-02, eta: 1 day, 23:54:19, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5939, loss_cls: 3.7889, loss: 3.7889 +2024-07-19 17:10:04,851 - pyskl - INFO - Epoch [94][600/3746] lr: 3.144e-02, eta: 1 day, 23:53:00, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5827, loss_cls: 3.8381, loss: 3.8381 +2024-07-19 17:11:27,958 - pyskl - INFO - Epoch [94][700/3746] lr: 3.141e-02, eta: 1 day, 23:51:40, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5792, loss_cls: 3.8219, loss: 3.8219 +2024-07-19 17:12:51,007 - pyskl - INFO - Epoch [94][800/3746] lr: 3.139e-02, eta: 1 day, 23:50:21, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5886, loss_cls: 3.8158, loss: 3.8158 +2024-07-19 17:14:13,951 - pyskl - INFO - Epoch [94][900/3746] lr: 3.136e-02, eta: 1 day, 23:49:01, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5795, loss_cls: 3.8463, loss: 3.8463 +2024-07-19 17:15:36,952 - pyskl - INFO - Epoch [94][1000/3746] lr: 3.133e-02, eta: 1 day, 23:47:41, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5814, loss_cls: 3.8802, loss: 3.8802 +2024-07-19 17:17:00,403 - pyskl - INFO - Epoch [94][1100/3746] lr: 3.131e-02, eta: 1 day, 23:46:22, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5780, loss_cls: 3.8997, loss: 3.8997 +2024-07-19 17:18:23,298 - pyskl - INFO - Epoch [94][1200/3746] lr: 3.128e-02, eta: 1 day, 23:45:02, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5825, loss_cls: 3.8520, loss: 3.8520 +2024-07-19 17:19:46,062 - pyskl - INFO - Epoch [94][1300/3746] lr: 3.126e-02, eta: 1 day, 23:43:42, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5769, loss_cls: 3.8818, loss: 3.8818 +2024-07-19 17:21:09,611 - pyskl - INFO - Epoch [94][1400/3746] lr: 3.123e-02, eta: 1 day, 23:42:23, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5770, loss_cls: 3.8884, loss: 3.8884 +2024-07-19 17:22:32,287 - pyskl - INFO - Epoch [94][1500/3746] lr: 3.120e-02, eta: 1 day, 23:41:03, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5800, loss_cls: 3.8599, loss: 3.8599 +2024-07-19 17:23:55,267 - pyskl - INFO - Epoch [94][1600/3746] lr: 3.118e-02, eta: 1 day, 23:39:43, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5870, loss_cls: 3.8162, loss: 3.8162 +2024-07-19 17:25:18,308 - pyskl - INFO - Epoch [94][1700/3746] lr: 3.115e-02, eta: 1 day, 23:38:23, time: 0.830, data_time: 0.001, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5683, loss_cls: 3.8929, loss: 3.8929 +2024-07-19 17:26:42,211 - pyskl - INFO - Epoch [94][1800/3746] lr: 3.113e-02, eta: 1 day, 23:37:04, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5811, loss_cls: 3.8603, loss: 3.8603 +2024-07-19 17:28:05,223 - pyskl - INFO - Epoch [94][1900/3746] lr: 3.110e-02, eta: 1 day, 23:35:44, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5742, loss_cls: 3.8570, loss: 3.8570 +2024-07-19 17:29:27,617 - pyskl - INFO - Epoch [94][2000/3746] lr: 3.108e-02, eta: 1 day, 23:34:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5875, loss_cls: 3.8297, loss: 3.8297 +2024-07-19 17:30:50,001 - pyskl - INFO - Epoch [94][2100/3746] lr: 3.105e-02, eta: 1 day, 23:33:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5759, loss_cls: 3.8431, loss: 3.8431 +2024-07-19 17:32:13,504 - pyskl - INFO - Epoch [94][2200/3746] lr: 3.102e-02, eta: 1 day, 23:31:45, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5763, loss_cls: 3.8533, loss: 3.8533 +2024-07-19 17:33:36,405 - pyskl - INFO - Epoch [94][2300/3746] lr: 3.100e-02, eta: 1 day, 23:30:25, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5847, loss_cls: 3.8484, loss: 3.8484 +2024-07-19 17:34:58,909 - pyskl - INFO - Epoch [94][2400/3746] lr: 3.097e-02, eta: 1 day, 23:29:05, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5908, loss_cls: 3.8331, loss: 3.8331 +2024-07-19 17:36:22,494 - pyskl - INFO - Epoch [94][2500/3746] lr: 3.095e-02, eta: 1 day, 23:27:45, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5884, loss_cls: 3.7985, loss: 3.7985 +2024-07-19 17:37:45,792 - pyskl - INFO - Epoch [94][2600/3746] lr: 3.092e-02, eta: 1 day, 23:26:26, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5817, loss_cls: 3.8772, loss: 3.8772 +2024-07-19 17:39:09,384 - pyskl - INFO - Epoch [94][2700/3746] lr: 3.089e-02, eta: 1 day, 23:25:06, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5833, loss_cls: 3.8775, loss: 3.8775 +2024-07-19 17:40:31,700 - pyskl - INFO - Epoch [94][2800/3746] lr: 3.087e-02, eta: 1 day, 23:23:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5811, loss_cls: 3.8398, loss: 3.8398 +2024-07-19 17:41:53,831 - pyskl - INFO - Epoch [94][2900/3746] lr: 3.084e-02, eta: 1 day, 23:22:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5727, loss_cls: 3.8772, loss: 3.8772 +2024-07-19 17:43:17,306 - pyskl - INFO - Epoch [94][3000/3746] lr: 3.082e-02, eta: 1 day, 23:21:07, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5734, loss_cls: 3.8576, loss: 3.8576 +2024-07-19 17:44:40,676 - pyskl - INFO - Epoch [94][3100/3746] lr: 3.079e-02, eta: 1 day, 23:19:47, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5844, loss_cls: 3.8470, loss: 3.8470 +2024-07-19 17:46:03,191 - pyskl - INFO - Epoch [94][3200/3746] lr: 3.077e-02, eta: 1 day, 23:18:27, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5798, loss_cls: 3.8596, loss: 3.8596 +2024-07-19 17:47:26,070 - pyskl - INFO - Epoch [94][3300/3746] lr: 3.074e-02, eta: 1 day, 23:17:07, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5694, loss_cls: 3.9044, loss: 3.9044 +2024-07-19 17:48:48,085 - pyskl - INFO - Epoch [94][3400/3746] lr: 3.071e-02, eta: 1 day, 23:15:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5847, loss_cls: 3.8434, loss: 3.8434 +2024-07-19 17:50:10,853 - pyskl - INFO - Epoch [94][3500/3746] lr: 3.069e-02, eta: 1 day, 23:14:27, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5772, loss_cls: 3.8681, loss: 3.8681 +2024-07-19 17:51:32,780 - pyskl - INFO - Epoch [94][3600/3746] lr: 3.066e-02, eta: 1 day, 23:13:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5834, loss_cls: 3.8457, loss: 3.8457 +2024-07-19 17:52:55,371 - pyskl - INFO - Epoch [94][3700/3746] lr: 3.064e-02, eta: 1 day, 23:11:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5761, loss_cls: 3.9025, loss: 3.9025 +2024-07-19 17:53:35,964 - pyskl - INFO - Saving checkpoint at 94 epochs +2024-07-19 17:55:27,000 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 17:55:27,760 - pyskl - INFO - +top1_acc 0.2570 +top5_acc 0.5107 +2024-07-19 17:55:27,760 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 17:55:27,800 - pyskl - INFO - +mean_acc 0.2568 +2024-07-19 17:55:27,811 - pyskl - INFO - Epoch(val) [94][309] top1_acc: 0.2570, top5_acc: 0.5107, mean_class_accuracy: 0.2568 +2024-07-19 17:59:20,281 - pyskl - INFO - Epoch [95][100/3746] lr: 3.060e-02, eta: 1 day, 23:10:56, time: 2.325, data_time: 1.328, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5997, loss_cls: 3.7225, loss: 3.7225 +2024-07-19 18:00:43,374 - pyskl - INFO - Epoch [95][200/3746] lr: 3.057e-02, eta: 1 day, 23:09:36, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5905, loss_cls: 3.8211, loss: 3.8211 +2024-07-19 18:02:06,467 - pyskl - INFO - Epoch [95][300/3746] lr: 3.055e-02, eta: 1 day, 23:08:17, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5858, loss_cls: 3.7935, loss: 3.7935 +2024-07-19 18:03:28,859 - pyskl - INFO - Epoch [95][400/3746] lr: 3.052e-02, eta: 1 day, 23:06:56, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5859, loss_cls: 3.8322, loss: 3.8322 +2024-07-19 18:04:51,410 - pyskl - INFO - Epoch [95][500/3746] lr: 3.050e-02, eta: 1 day, 23:05:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5870, loss_cls: 3.8203, loss: 3.8203 +2024-07-19 18:06:14,004 - pyskl - INFO - Epoch [95][600/3746] lr: 3.047e-02, eta: 1 day, 23:04:16, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5906, loss_cls: 3.8056, loss: 3.8056 +2024-07-19 18:07:36,110 - pyskl - INFO - Epoch [95][700/3746] lr: 3.044e-02, eta: 1 day, 23:02:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5969, loss_cls: 3.7690, loss: 3.7690 +2024-07-19 18:08:58,695 - pyskl - INFO - Epoch [95][800/3746] lr: 3.042e-02, eta: 1 day, 23:01:36, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5786, loss_cls: 3.8554, loss: 3.8554 +2024-07-19 18:10:21,506 - pyskl - INFO - Epoch [95][900/3746] lr: 3.039e-02, eta: 1 day, 23:00:16, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.5991, loss_cls: 3.7723, loss: 3.7723 +2024-07-19 18:11:44,610 - pyskl - INFO - Epoch [95][1000/3746] lr: 3.037e-02, eta: 1 day, 22:58:56, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5933, loss_cls: 3.7919, loss: 3.7919 +2024-07-19 18:13:07,212 - pyskl - INFO - Epoch [95][1100/3746] lr: 3.034e-02, eta: 1 day, 22:57:36, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5758, loss_cls: 3.8503, loss: 3.8503 +2024-07-19 18:14:29,743 - pyskl - INFO - Epoch [95][1200/3746] lr: 3.032e-02, eta: 1 day, 22:56:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5866, loss_cls: 3.8284, loss: 3.8284 +2024-07-19 18:15:52,069 - pyskl - INFO - Epoch [95][1300/3746] lr: 3.029e-02, eta: 1 day, 22:54:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5767, loss_cls: 3.8520, loss: 3.8520 +2024-07-19 18:17:15,169 - pyskl - INFO - Epoch [95][1400/3746] lr: 3.026e-02, eta: 1 day, 22:53:36, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5953, loss_cls: 3.7990, loss: 3.7990 +2024-07-19 18:18:37,214 - pyskl - INFO - Epoch [95][1500/3746] lr: 3.024e-02, eta: 1 day, 22:52:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5761, loss_cls: 3.8772, loss: 3.8772 +2024-07-19 18:19:58,961 - pyskl - INFO - Epoch [95][1600/3746] lr: 3.021e-02, eta: 1 day, 22:50:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5741, loss_cls: 3.8923, loss: 3.8923 +2024-07-19 18:21:22,083 - pyskl - INFO - Epoch [95][1700/3746] lr: 3.019e-02, eta: 1 day, 22:49:35, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5873, loss_cls: 3.8174, loss: 3.8174 +2024-07-19 18:22:45,785 - pyskl - INFO - Epoch [95][1800/3746] lr: 3.016e-02, eta: 1 day, 22:48:16, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5763, loss_cls: 3.8464, loss: 3.8464 +2024-07-19 18:24:07,894 - pyskl - INFO - Epoch [95][1900/3746] lr: 3.014e-02, eta: 1 day, 22:46:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5839, loss_cls: 3.8587, loss: 3.8587 +2024-07-19 18:25:30,867 - pyskl - INFO - Epoch [95][2000/3746] lr: 3.011e-02, eta: 1 day, 22:45:35, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5830, loss_cls: 3.8652, loss: 3.8652 +2024-07-19 18:26:53,784 - pyskl - INFO - Epoch [95][2100/3746] lr: 3.008e-02, eta: 1 day, 22:44:16, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5831, loss_cls: 3.8587, loss: 3.8587 +2024-07-19 18:28:15,649 - pyskl - INFO - Epoch [95][2200/3746] lr: 3.006e-02, eta: 1 day, 22:42:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5794, loss_cls: 3.8173, loss: 3.8173 +2024-07-19 18:29:38,003 - pyskl - INFO - Epoch [95][2300/3746] lr: 3.003e-02, eta: 1 day, 22:41:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5853, loss_cls: 3.8542, loss: 3.8542 +2024-07-19 18:31:01,400 - pyskl - INFO - Epoch [95][2400/3746] lr: 3.001e-02, eta: 1 day, 22:40:15, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5861, loss_cls: 3.8690, loss: 3.8690 +2024-07-19 18:32:23,152 - pyskl - INFO - Epoch [95][2500/3746] lr: 2.998e-02, eta: 1 day, 22:38:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5734, loss_cls: 3.8500, loss: 3.8500 +2024-07-19 18:33:46,285 - pyskl - INFO - Epoch [95][2600/3746] lr: 2.996e-02, eta: 1 day, 22:37:35, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5727, loss_cls: 3.8873, loss: 3.8873 +2024-07-19 18:35:07,981 - pyskl - INFO - Epoch [95][2700/3746] lr: 2.993e-02, eta: 1 day, 22:36:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5936, loss_cls: 3.8099, loss: 3.8099 +2024-07-19 18:36:30,396 - pyskl - INFO - Epoch [95][2800/3746] lr: 2.991e-02, eta: 1 day, 22:34:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5897, loss_cls: 3.8297, loss: 3.8297 +2024-07-19 18:37:52,908 - pyskl - INFO - Epoch [95][2900/3746] lr: 2.988e-02, eta: 1 day, 22:33:34, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5858, loss_cls: 3.8111, loss: 3.8111 +2024-07-19 18:39:15,409 - pyskl - INFO - Epoch [95][3000/3746] lr: 2.985e-02, eta: 1 day, 22:32:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5880, loss_cls: 3.8308, loss: 3.8308 +2024-07-19 18:40:38,009 - pyskl - INFO - Epoch [95][3100/3746] lr: 2.983e-02, eta: 1 day, 22:30:54, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5858, loss_cls: 3.8219, loss: 3.8219 +2024-07-19 18:42:00,756 - pyskl - INFO - Epoch [95][3200/3746] lr: 2.980e-02, eta: 1 day, 22:29:33, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5742, loss_cls: 3.8673, loss: 3.8673 +2024-07-19 18:43:23,052 - pyskl - INFO - Epoch [95][3300/3746] lr: 2.978e-02, eta: 1 day, 22:28:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5894, loss_cls: 3.8040, loss: 3.8040 +2024-07-19 18:44:45,309 - pyskl - INFO - Epoch [95][3400/3746] lr: 2.975e-02, eta: 1 day, 22:26:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5795, loss_cls: 3.8378, loss: 3.8378 +2024-07-19 18:46:07,210 - pyskl - INFO - Epoch [95][3500/3746] lr: 2.973e-02, eta: 1 day, 22:25:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5816, loss_cls: 3.8863, loss: 3.8863 +2024-07-19 18:47:29,069 - pyskl - INFO - Epoch [95][3600/3746] lr: 2.970e-02, eta: 1 day, 22:24:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5920, loss_cls: 3.8069, loss: 3.8069 +2024-07-19 18:48:52,486 - pyskl - INFO - Epoch [95][3700/3746] lr: 2.968e-02, eta: 1 day, 22:22:52, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5813, loss_cls: 3.8581, loss: 3.8581 +2024-07-19 18:49:32,514 - pyskl - INFO - Saving checkpoint at 95 epochs +2024-07-19 18:51:23,840 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 18:51:24,506 - pyskl - INFO - +top1_acc 0.2564 +top5_acc 0.4951 +2024-07-19 18:51:24,506 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 18:51:24,546 - pyskl - INFO - +mean_acc 0.2563 +2024-07-19 18:51:24,559 - pyskl - INFO - Epoch(val) [95][309] top1_acc: 0.2564, top5_acc: 0.4951, mean_class_accuracy: 0.2563 +2024-07-19 18:55:14,960 - pyskl - INFO - Epoch [96][100/3746] lr: 2.964e-02, eta: 1 day, 22:21:59, time: 2.304, data_time: 1.310, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6058, loss_cls: 3.6969, loss: 3.6969 +2024-07-19 18:56:37,972 - pyskl - INFO - Epoch [96][200/3746] lr: 2.961e-02, eta: 1 day, 22:20:39, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5942, loss_cls: 3.7670, loss: 3.7670 +2024-07-19 18:58:00,435 - pyskl - INFO - Epoch [96][300/3746] lr: 2.959e-02, eta: 1 day, 22:19:19, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5972, loss_cls: 3.7708, loss: 3.7708 +2024-07-19 18:59:22,814 - pyskl - INFO - Epoch [96][400/3746] lr: 2.956e-02, eta: 1 day, 22:17:58, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5938, loss_cls: 3.7595, loss: 3.7595 +2024-07-19 19:00:44,965 - pyskl - INFO - Epoch [96][500/3746] lr: 2.954e-02, eta: 1 day, 22:16:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5823, loss_cls: 3.8467, loss: 3.8467 +2024-07-19 19:02:06,844 - pyskl - INFO - Epoch [96][600/3746] lr: 2.951e-02, eta: 1 day, 22:15:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5908, loss_cls: 3.8096, loss: 3.8096 +2024-07-19 19:03:29,084 - pyskl - INFO - Epoch [96][700/3746] lr: 2.948e-02, eta: 1 day, 22:13:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5964, loss_cls: 3.7669, loss: 3.7669 +2024-07-19 19:04:50,872 - pyskl - INFO - Epoch [96][800/3746] lr: 2.946e-02, eta: 1 day, 22:12:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5964, loss_cls: 3.7773, loss: 3.7773 +2024-07-19 19:06:12,799 - pyskl - INFO - Epoch [96][900/3746] lr: 2.943e-02, eta: 1 day, 22:11:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5927, loss_cls: 3.8110, loss: 3.8110 +2024-07-19 19:07:35,177 - pyskl - INFO - Epoch [96][1000/3746] lr: 2.941e-02, eta: 1 day, 22:09:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5852, loss_cls: 3.8188, loss: 3.8188 +2024-07-19 19:08:57,410 - pyskl - INFO - Epoch [96][1100/3746] lr: 2.938e-02, eta: 1 day, 22:08:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5802, loss_cls: 3.8588, loss: 3.8588 +2024-07-19 19:10:19,740 - pyskl - INFO - Epoch [96][1200/3746] lr: 2.936e-02, eta: 1 day, 22:07:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5875, loss_cls: 3.7984, loss: 3.7984 +2024-07-19 19:11:42,223 - pyskl - INFO - Epoch [96][1300/3746] lr: 2.933e-02, eta: 1 day, 22:05:54, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5817, loss_cls: 3.8443, loss: 3.8443 +2024-07-19 19:13:04,707 - pyskl - INFO - Epoch [96][1400/3746] lr: 2.931e-02, eta: 1 day, 22:04:34, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5797, loss_cls: 3.8206, loss: 3.8206 +2024-07-19 19:14:26,512 - pyskl - INFO - Epoch [96][1500/3746] lr: 2.928e-02, eta: 1 day, 22:03:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5900, loss_cls: 3.8105, loss: 3.8105 +2024-07-19 19:15:49,235 - pyskl - INFO - Epoch [96][1600/3746] lr: 2.926e-02, eta: 1 day, 22:01:53, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5855, loss_cls: 3.8664, loss: 3.8664 +2024-07-19 19:17:12,651 - pyskl - INFO - Epoch [96][1700/3746] lr: 2.923e-02, eta: 1 day, 22:00:34, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5920, loss_cls: 3.8036, loss: 3.8036 +2024-07-19 19:18:35,494 - pyskl - INFO - Epoch [96][1800/3746] lr: 2.920e-02, eta: 1 day, 21:59:14, time: 0.828, data_time: 0.001, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5866, loss_cls: 3.8321, loss: 3.8321 +2024-07-19 19:19:57,580 - pyskl - INFO - Epoch [96][1900/3746] lr: 2.918e-02, eta: 1 day, 21:57:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5820, loss_cls: 3.8364, loss: 3.8364 +2024-07-19 19:21:20,052 - pyskl - INFO - Epoch [96][2000/3746] lr: 2.915e-02, eta: 1 day, 21:56:33, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5859, loss_cls: 3.8099, loss: 3.8099 +2024-07-19 19:22:43,750 - pyskl - INFO - Epoch [96][2100/3746] lr: 2.913e-02, eta: 1 day, 21:55:13, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.6028, loss_cls: 3.7529, loss: 3.7529 +2024-07-19 19:24:06,304 - pyskl - INFO - Epoch [96][2200/3746] lr: 2.910e-02, eta: 1 day, 21:53:53, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5758, loss_cls: 3.8549, loss: 3.8549 +2024-07-19 19:25:28,859 - pyskl - INFO - Epoch [96][2300/3746] lr: 2.908e-02, eta: 1 day, 21:52:33, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5891, loss_cls: 3.7973, loss: 3.7973 +2024-07-19 19:26:52,282 - pyskl - INFO - Epoch [96][2400/3746] lr: 2.905e-02, eta: 1 day, 21:51:13, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5814, loss_cls: 3.8417, loss: 3.8417 +2024-07-19 19:28:14,704 - pyskl - INFO - Epoch [96][2500/3746] lr: 2.903e-02, eta: 1 day, 21:49:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5850, loss_cls: 3.8209, loss: 3.8209 +2024-07-19 19:29:37,860 - pyskl - INFO - Epoch [96][2600/3746] lr: 2.900e-02, eta: 1 day, 21:48:33, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5872, loss_cls: 3.8165, loss: 3.8165 +2024-07-19 19:31:00,084 - pyskl - INFO - Epoch [96][2700/3746] lr: 2.898e-02, eta: 1 day, 21:47:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5836, loss_cls: 3.8567, loss: 3.8567 +2024-07-19 19:32:22,579 - pyskl - INFO - Epoch [96][2800/3746] lr: 2.895e-02, eta: 1 day, 21:45:52, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5823, loss_cls: 3.8345, loss: 3.8345 +2024-07-19 19:33:45,019 - pyskl - INFO - Epoch [96][2900/3746] lr: 2.893e-02, eta: 1 day, 21:44:32, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5973, loss_cls: 3.7793, loss: 3.7793 +2024-07-19 19:35:07,033 - pyskl - INFO - Epoch [96][3000/3746] lr: 2.890e-02, eta: 1 day, 21:43:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5745, loss_cls: 3.8866, loss: 3.8866 +2024-07-19 19:36:29,233 - pyskl - INFO - Epoch [96][3100/3746] lr: 2.887e-02, eta: 1 day, 21:41:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5836, loss_cls: 3.8458, loss: 3.8458 +2024-07-19 19:37:51,747 - pyskl - INFO - Epoch [96][3200/3746] lr: 2.885e-02, eta: 1 day, 21:40:31, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5819, loss_cls: 3.8276, loss: 3.8276 +2024-07-19 19:39:14,294 - pyskl - INFO - Epoch [96][3300/3746] lr: 2.882e-02, eta: 1 day, 21:39:11, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5905, loss_cls: 3.8101, loss: 3.8101 +2024-07-19 19:40:36,495 - pyskl - INFO - Epoch [96][3400/3746] lr: 2.880e-02, eta: 1 day, 21:37:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5730, loss_cls: 3.8575, loss: 3.8575 +2024-07-19 19:41:58,041 - pyskl - INFO - Epoch [96][3500/3746] lr: 2.877e-02, eta: 1 day, 21:36:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5969, loss_cls: 3.8236, loss: 3.8236 +2024-07-19 19:43:20,041 - pyskl - INFO - Epoch [96][3600/3746] lr: 2.875e-02, eta: 1 day, 21:35:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5861, loss_cls: 3.8308, loss: 3.8308 +2024-07-19 19:44:43,004 - pyskl - INFO - Epoch [96][3700/3746] lr: 2.872e-02, eta: 1 day, 21:33:49, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5773, loss_cls: 3.8580, loss: 3.8580 +2024-07-19 19:45:22,899 - pyskl - INFO - Saving checkpoint at 96 epochs +2024-07-19 19:47:13,096 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 19:47:13,758 - pyskl - INFO - +top1_acc 0.2757 +top5_acc 0.5293 +2024-07-19 19:47:13,758 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 19:47:13,800 - pyskl - INFO - +mean_acc 0.2755 +2024-07-19 19:47:13,806 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_92.pth was removed +2024-07-19 19:47:14,033 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_96.pth. +2024-07-19 19:47:14,034 - pyskl - INFO - Best top1_acc is 0.2757 at 96 epoch. +2024-07-19 19:47:14,046 - pyskl - INFO - Epoch(val) [96][309] top1_acc: 0.2757, top5_acc: 0.5293, mean_class_accuracy: 0.2755 +2024-07-19 19:50:59,122 - pyskl - INFO - Epoch [97][100/3746] lr: 2.869e-02, eta: 1 day, 21:32:50, time: 2.251, data_time: 1.261, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5992, loss_cls: 3.7624, loss: 3.7624 +2024-07-19 19:52:22,305 - pyskl - INFO - Epoch [97][200/3746] lr: 2.866e-02, eta: 1 day, 21:31:31, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5953, loss_cls: 3.7931, loss: 3.7931 +2024-07-19 19:53:45,639 - pyskl - INFO - Epoch [97][300/3746] lr: 2.864e-02, eta: 1 day, 21:30:11, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5939, loss_cls: 3.7735, loss: 3.7735 +2024-07-19 19:55:08,853 - pyskl - INFO - Epoch [97][400/3746] lr: 2.861e-02, eta: 1 day, 21:28:51, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6042, loss_cls: 3.7259, loss: 3.7259 +2024-07-19 19:56:31,566 - pyskl - INFO - Epoch [97][500/3746] lr: 2.858e-02, eta: 1 day, 21:27:31, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5889, loss_cls: 3.8129, loss: 3.8129 +2024-07-19 19:57:54,313 - pyskl - INFO - Epoch [97][600/3746] lr: 2.856e-02, eta: 1 day, 21:26:10, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5867, loss_cls: 3.8328, loss: 3.8328 +2024-07-19 19:59:17,449 - pyskl - INFO - Epoch [97][700/3746] lr: 2.853e-02, eta: 1 day, 21:24:50, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5928, loss_cls: 3.7983, loss: 3.7983 +2024-07-19 20:00:40,451 - pyskl - INFO - Epoch [97][800/3746] lr: 2.851e-02, eta: 1 day, 21:23:30, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5908, loss_cls: 3.8009, loss: 3.8009 +2024-07-19 20:02:03,824 - pyskl - INFO - Epoch [97][900/3746] lr: 2.848e-02, eta: 1 day, 21:22:11, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.5989, loss_cls: 3.7448, loss: 3.7448 +2024-07-19 20:03:27,073 - pyskl - INFO - Epoch [97][1000/3746] lr: 2.846e-02, eta: 1 day, 21:20:51, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6009, loss_cls: 3.7358, loss: 3.7358 +2024-07-19 20:04:49,743 - pyskl - INFO - Epoch [97][1100/3746] lr: 2.843e-02, eta: 1 day, 21:19:30, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5961, loss_cls: 3.7693, loss: 3.7693 +2024-07-19 20:06:12,711 - pyskl - INFO - Epoch [97][1200/3746] lr: 2.841e-02, eta: 1 day, 21:18:10, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5763, loss_cls: 3.8776, loss: 3.8776 +2024-07-19 20:07:36,362 - pyskl - INFO - Epoch [97][1300/3746] lr: 2.838e-02, eta: 1 day, 21:16:51, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5945, loss_cls: 3.8123, loss: 3.8123 +2024-07-19 20:08:58,988 - pyskl - INFO - Epoch [97][1400/3746] lr: 2.836e-02, eta: 1 day, 21:15:30, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5855, loss_cls: 3.8550, loss: 3.8550 +2024-07-19 20:10:21,414 - pyskl - INFO - Epoch [97][1500/3746] lr: 2.833e-02, eta: 1 day, 21:14:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5841, loss_cls: 3.8486, loss: 3.8486 +2024-07-19 20:11:44,643 - pyskl - INFO - Epoch [97][1600/3746] lr: 2.831e-02, eta: 1 day, 21:12:50, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5934, loss_cls: 3.8166, loss: 3.8166 +2024-07-19 20:13:08,786 - pyskl - INFO - Epoch [97][1700/3746] lr: 2.828e-02, eta: 1 day, 21:11:31, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5800, loss_cls: 3.8342, loss: 3.8342 +2024-07-19 20:14:31,662 - pyskl - INFO - Epoch [97][1800/3746] lr: 2.826e-02, eta: 1 day, 21:10:11, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5814, loss_cls: 3.8180, loss: 3.8180 +2024-07-19 20:15:54,428 - pyskl - INFO - Epoch [97][1900/3746] lr: 2.823e-02, eta: 1 day, 21:08:50, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5873, loss_cls: 3.8359, loss: 3.8359 +2024-07-19 20:17:17,768 - pyskl - INFO - Epoch [97][2000/3746] lr: 2.821e-02, eta: 1 day, 21:07:30, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5966, loss_cls: 3.7925, loss: 3.7925 +2024-07-19 20:18:41,359 - pyskl - INFO - Epoch [97][2100/3746] lr: 2.818e-02, eta: 1 day, 21:06:11, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5953, loss_cls: 3.7873, loss: 3.7873 +2024-07-19 20:20:03,693 - pyskl - INFO - Epoch [97][2200/3746] lr: 2.816e-02, eta: 1 day, 21:04:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5773, loss_cls: 3.8545, loss: 3.8545 +2024-07-19 20:21:26,519 - pyskl - INFO - Epoch [97][2300/3746] lr: 2.813e-02, eta: 1 day, 21:03:30, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5873, loss_cls: 3.8253, loss: 3.8253 +2024-07-19 20:22:49,905 - pyskl - INFO - Epoch [97][2400/3746] lr: 2.811e-02, eta: 1 day, 21:02:10, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5850, loss_cls: 3.8004, loss: 3.8004 +2024-07-19 20:24:13,609 - pyskl - INFO - Epoch [97][2500/3746] lr: 2.808e-02, eta: 1 day, 21:00:51, time: 0.837, data_time: 0.001, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5725, loss_cls: 3.8401, loss: 3.8401 +2024-07-19 20:25:36,933 - pyskl - INFO - Epoch [97][2600/3746] lr: 2.806e-02, eta: 1 day, 20:59:31, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.5958, loss_cls: 3.8007, loss: 3.8007 +2024-07-19 20:26:59,647 - pyskl - INFO - Epoch [97][2700/3746] lr: 2.803e-02, eta: 1 day, 20:58:10, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.6006, loss_cls: 3.7748, loss: 3.7748 +2024-07-19 20:28:23,020 - pyskl - INFO - Epoch [97][2800/3746] lr: 2.801e-02, eta: 1 day, 20:56:51, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5905, loss_cls: 3.7921, loss: 3.7921 +2024-07-19 20:29:45,274 - pyskl - INFO - Epoch [97][2900/3746] lr: 2.798e-02, eta: 1 day, 20:55:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5903, loss_cls: 3.7943, loss: 3.7943 +2024-07-19 20:31:07,177 - pyskl - INFO - Epoch [97][3000/3746] lr: 2.796e-02, eta: 1 day, 20:54:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5834, loss_cls: 3.8131, loss: 3.8131 +2024-07-19 20:32:29,806 - pyskl - INFO - Epoch [97][3100/3746] lr: 2.793e-02, eta: 1 day, 20:52:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5919, loss_cls: 3.7906, loss: 3.7906 +2024-07-19 20:33:52,090 - pyskl - INFO - Epoch [97][3200/3746] lr: 2.791e-02, eta: 1 day, 20:51:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5952, loss_cls: 3.7724, loss: 3.7724 +2024-07-19 20:35:14,541 - pyskl - INFO - Epoch [97][3300/3746] lr: 2.788e-02, eta: 1 day, 20:50:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5780, loss_cls: 3.8568, loss: 3.8568 +2024-07-19 20:36:36,718 - pyskl - INFO - Epoch [97][3400/3746] lr: 2.786e-02, eta: 1 day, 20:48:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5878, loss_cls: 3.8037, loss: 3.8037 +2024-07-19 20:37:58,716 - pyskl - INFO - Epoch [97][3500/3746] lr: 2.783e-02, eta: 1 day, 20:47:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5847, loss_cls: 3.8326, loss: 3.8326 +2024-07-19 20:39:20,844 - pyskl - INFO - Epoch [97][3600/3746] lr: 2.781e-02, eta: 1 day, 20:46:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.5962, loss_cls: 3.7578, loss: 3.7578 +2024-07-19 20:40:44,597 - pyskl - INFO - Epoch [97][3700/3746] lr: 2.778e-02, eta: 1 day, 20:44:47, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.5977, loss_cls: 3.7680, loss: 3.7680 +2024-07-19 20:41:24,225 - pyskl - INFO - Saving checkpoint at 97 epochs +2024-07-19 20:43:15,338 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 20:43:16,096 - pyskl - INFO - +top1_acc 0.2610 +top5_acc 0.4997 +2024-07-19 20:43:16,096 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 20:43:16,138 - pyskl - INFO - +mean_acc 0.2608 +2024-07-19 20:43:16,150 - pyskl - INFO - Epoch(val) [97][309] top1_acc: 0.2610, top5_acc: 0.4997, mean_class_accuracy: 0.2608 +2024-07-19 20:47:08,549 - pyskl - INFO - Epoch [98][100/3746] lr: 2.774e-02, eta: 1 day, 20:43:51, time: 2.324, data_time: 1.333, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5972, loss_cls: 3.7443, loss: 3.7443 +2024-07-19 20:48:31,968 - pyskl - INFO - Epoch [98][200/3746] lr: 2.772e-02, eta: 1 day, 20:42:31, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5919, loss_cls: 3.7809, loss: 3.7809 +2024-07-19 20:49:54,651 - pyskl - INFO - Epoch [98][300/3746] lr: 2.769e-02, eta: 1 day, 20:41:10, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5997, loss_cls: 3.7457, loss: 3.7457 +2024-07-19 20:51:16,736 - pyskl - INFO - Epoch [98][400/3746] lr: 2.767e-02, eta: 1 day, 20:39:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.5986, loss_cls: 3.7376, loss: 3.7376 +2024-07-19 20:52:39,671 - pyskl - INFO - Epoch [98][500/3746] lr: 2.764e-02, eta: 1 day, 20:38:30, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5830, loss_cls: 3.8244, loss: 3.8244 +2024-07-19 20:54:02,593 - pyskl - INFO - Epoch [98][600/3746] lr: 2.762e-02, eta: 1 day, 20:37:09, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5931, loss_cls: 3.7677, loss: 3.7677 +2024-07-19 20:55:25,037 - pyskl - INFO - Epoch [98][700/3746] lr: 2.759e-02, eta: 1 day, 20:35:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.6022, loss_cls: 3.7526, loss: 3.7526 +2024-07-19 20:56:47,445 - pyskl - INFO - Epoch [98][800/3746] lr: 2.757e-02, eta: 1 day, 20:34:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5988, loss_cls: 3.7654, loss: 3.7654 +2024-07-19 20:58:10,224 - pyskl - INFO - Epoch [98][900/3746] lr: 2.754e-02, eta: 1 day, 20:33:08, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5930, loss_cls: 3.7684, loss: 3.7684 +2024-07-19 20:59:32,924 - pyskl - INFO - Epoch [98][1000/3746] lr: 2.752e-02, eta: 1 day, 20:31:48, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5855, loss_cls: 3.7865, loss: 3.7865 +2024-07-19 21:00:54,728 - pyskl - INFO - Epoch [98][1100/3746] lr: 2.749e-02, eta: 1 day, 20:30:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5941, loss_cls: 3.7823, loss: 3.7823 +2024-07-19 21:02:16,950 - pyskl - INFO - Epoch [98][1200/3746] lr: 2.747e-02, eta: 1 day, 20:29:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5905, loss_cls: 3.7857, loss: 3.7857 +2024-07-19 21:03:40,225 - pyskl - INFO - Epoch [98][1300/3746] lr: 2.744e-02, eta: 1 day, 20:27:47, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5820, loss_cls: 3.8304, loss: 3.8304 +2024-07-19 21:05:02,498 - pyskl - INFO - Epoch [98][1400/3746] lr: 2.742e-02, eta: 1 day, 20:26:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5913, loss_cls: 3.7909, loss: 3.7909 +2024-07-19 21:06:24,762 - pyskl - INFO - Epoch [98][1500/3746] lr: 2.739e-02, eta: 1 day, 20:25:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5903, loss_cls: 3.8083, loss: 3.8083 +2024-07-19 21:07:48,043 - pyskl - INFO - Epoch [98][1600/3746] lr: 2.737e-02, eta: 1 day, 20:23:45, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5892, loss_cls: 3.8142, loss: 3.8142 +2024-07-19 21:09:12,015 - pyskl - INFO - Epoch [98][1700/3746] lr: 2.734e-02, eta: 1 day, 20:22:26, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.6025, loss_cls: 3.7513, loss: 3.7513 +2024-07-19 21:10:34,124 - pyskl - INFO - Epoch [98][1800/3746] lr: 2.732e-02, eta: 1 day, 20:21:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5919, loss_cls: 3.7417, loss: 3.7417 +2024-07-19 21:11:56,345 - pyskl - INFO - Epoch [98][1900/3746] lr: 2.729e-02, eta: 1 day, 20:19:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.6019, loss_cls: 3.7597, loss: 3.7597 +2024-07-19 21:13:19,679 - pyskl - INFO - Epoch [98][2000/3746] lr: 2.727e-02, eta: 1 day, 20:18:25, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5863, loss_cls: 3.8386, loss: 3.8386 +2024-07-19 21:14:42,165 - pyskl - INFO - Epoch [98][2100/3746] lr: 2.724e-02, eta: 1 day, 20:17:04, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5923, loss_cls: 3.7718, loss: 3.7718 +2024-07-19 21:16:04,349 - pyskl - INFO - Epoch [98][2200/3746] lr: 2.722e-02, eta: 1 day, 20:15:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5891, loss_cls: 3.8101, loss: 3.8101 +2024-07-19 21:17:27,155 - pyskl - INFO - Epoch [98][2300/3746] lr: 2.719e-02, eta: 1 day, 20:14:23, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5864, loss_cls: 3.7970, loss: 3.7970 +2024-07-19 21:18:50,590 - pyskl - INFO - Epoch [98][2400/3746] lr: 2.717e-02, eta: 1 day, 20:13:03, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5905, loss_cls: 3.7994, loss: 3.7994 +2024-07-19 21:20:13,302 - pyskl - INFO - Epoch [98][2500/3746] lr: 2.714e-02, eta: 1 day, 20:11:43, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5975, loss_cls: 3.7822, loss: 3.7822 +2024-07-19 21:21:35,318 - pyskl - INFO - Epoch [98][2600/3746] lr: 2.712e-02, eta: 1 day, 20:10:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5880, loss_cls: 3.8024, loss: 3.8024 +2024-07-19 21:22:58,274 - pyskl - INFO - Epoch [98][2700/3746] lr: 2.709e-02, eta: 1 day, 20:09:02, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5884, loss_cls: 3.7972, loss: 3.7972 +2024-07-19 21:24:20,457 - pyskl - INFO - Epoch [98][2800/3746] lr: 2.707e-02, eta: 1 day, 20:07:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5936, loss_cls: 3.8221, loss: 3.8221 +2024-07-19 21:25:42,783 - pyskl - INFO - Epoch [98][2900/3746] lr: 2.705e-02, eta: 1 day, 20:06:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5825, loss_cls: 3.8262, loss: 3.8262 +2024-07-19 21:27:04,559 - pyskl - INFO - Epoch [98][3000/3746] lr: 2.702e-02, eta: 1 day, 20:05:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5941, loss_cls: 3.8118, loss: 3.8118 +2024-07-19 21:28:26,774 - pyskl - INFO - Epoch [98][3100/3746] lr: 2.700e-02, eta: 1 day, 20:03:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5897, loss_cls: 3.8152, loss: 3.8152 +2024-07-19 21:29:49,238 - pyskl - INFO - Epoch [98][3200/3746] lr: 2.697e-02, eta: 1 day, 20:02:19, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5914, loss_cls: 3.7809, loss: 3.7809 +2024-07-19 21:31:11,978 - pyskl - INFO - Epoch [98][3300/3746] lr: 2.695e-02, eta: 1 day, 20:00:59, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.5911, loss_cls: 3.7835, loss: 3.7835 +2024-07-19 21:32:34,116 - pyskl - INFO - Epoch [98][3400/3746] lr: 2.692e-02, eta: 1 day, 19:59:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5975, loss_cls: 3.7687, loss: 3.7687 +2024-07-19 21:33:56,253 - pyskl - INFO - Epoch [98][3500/3746] lr: 2.690e-02, eta: 1 day, 19:58:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5948, loss_cls: 3.7886, loss: 3.7886 +2024-07-19 21:35:18,185 - pyskl - INFO - Epoch [98][3600/3746] lr: 2.687e-02, eta: 1 day, 19:56:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5919, loss_cls: 3.8241, loss: 3.8241 +2024-07-19 21:36:41,135 - pyskl - INFO - Epoch [98][3700/3746] lr: 2.685e-02, eta: 1 day, 19:55:36, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5884, loss_cls: 3.7889, loss: 3.7889 +2024-07-19 21:37:20,736 - pyskl - INFO - Saving checkpoint at 98 epochs +2024-07-19 21:39:11,347 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 21:39:12,012 - pyskl - INFO - +top1_acc 0.2497 +top5_acc 0.4922 +2024-07-19 21:39:12,012 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 21:39:12,054 - pyskl - INFO - +mean_acc 0.2492 +2024-07-19 21:39:12,066 - pyskl - INFO - Epoch(val) [98][309] top1_acc: 0.2497, top5_acc: 0.4922, mean_class_accuracy: 0.2492 +2024-07-19 21:42:55,485 - pyskl - INFO - Epoch [99][100/3746] lr: 2.681e-02, eta: 1 day, 19:54:34, time: 2.234, data_time: 1.243, memory: 15990, top1_acc: 0.3467, top5_acc: 0.5980, loss_cls: 3.7091, loss: 3.7091 +2024-07-19 21:44:17,996 - pyskl - INFO - Epoch [99][200/3746] lr: 2.679e-02, eta: 1 day, 19:53:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6075, loss_cls: 3.7273, loss: 3.7273 +2024-07-19 21:45:40,682 - pyskl - INFO - Epoch [99][300/3746] lr: 2.676e-02, eta: 1 day, 19:51:53, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.5995, loss_cls: 3.7572, loss: 3.7572 +2024-07-19 21:47:03,147 - pyskl - INFO - Epoch [99][400/3746] lr: 2.674e-02, eta: 1 day, 19:50:32, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.5877, loss_cls: 3.7767, loss: 3.7767 +2024-07-19 21:48:25,829 - pyskl - INFO - Epoch [99][500/3746] lr: 2.671e-02, eta: 1 day, 19:49:12, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.6016, loss_cls: 3.7559, loss: 3.7559 +2024-07-19 21:49:48,336 - pyskl - INFO - Epoch [99][600/3746] lr: 2.669e-02, eta: 1 day, 19:47:51, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.6050, loss_cls: 3.7576, loss: 3.7576 +2024-07-19 21:51:10,100 - pyskl - INFO - Epoch [99][700/3746] lr: 2.666e-02, eta: 1 day, 19:46:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.5889, loss_cls: 3.7904, loss: 3.7904 +2024-07-19 21:52:31,966 - pyskl - INFO - Epoch [99][800/3746] lr: 2.664e-02, eta: 1 day, 19:45:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5958, loss_cls: 3.7644, loss: 3.7644 +2024-07-19 21:53:53,531 - pyskl - INFO - Epoch [99][900/3746] lr: 2.661e-02, eta: 1 day, 19:43:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6062, loss_cls: 3.7218, loss: 3.7218 +2024-07-19 21:55:15,896 - pyskl - INFO - Epoch [99][1000/3746] lr: 2.659e-02, eta: 1 day, 19:42:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6038, loss_cls: 3.7124, loss: 3.7124 +2024-07-19 21:56:38,104 - pyskl - INFO - Epoch [99][1100/3746] lr: 2.656e-02, eta: 1 day, 19:41:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5827, loss_cls: 3.8538, loss: 3.8538 +2024-07-19 21:57:59,824 - pyskl - INFO - Epoch [99][1200/3746] lr: 2.654e-02, eta: 1 day, 19:39:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6061, loss_cls: 3.6887, loss: 3.6887 +2024-07-19 21:59:22,310 - pyskl - INFO - Epoch [99][1300/3746] lr: 2.651e-02, eta: 1 day, 19:38:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5961, loss_cls: 3.7521, loss: 3.7521 +2024-07-19 22:00:44,553 - pyskl - INFO - Epoch [99][1400/3746] lr: 2.649e-02, eta: 1 day, 19:37:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6031, loss_cls: 3.7487, loss: 3.7487 +2024-07-19 22:02:07,156 - pyskl - INFO - Epoch [99][1500/3746] lr: 2.646e-02, eta: 1 day, 19:35:45, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5845, loss_cls: 3.7967, loss: 3.7967 +2024-07-19 22:03:30,383 - pyskl - INFO - Epoch [99][1600/3746] lr: 2.644e-02, eta: 1 day, 19:34:25, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5923, loss_cls: 3.7912, loss: 3.7912 +2024-07-19 22:04:53,953 - pyskl - INFO - Epoch [99][1700/3746] lr: 2.642e-02, eta: 1 day, 19:33:05, time: 0.836, data_time: 0.001, memory: 15990, top1_acc: 0.3406, top5_acc: 0.5966, loss_cls: 3.7572, loss: 3.7572 +2024-07-19 22:06:15,873 - pyskl - INFO - Epoch [99][1800/3746] lr: 2.639e-02, eta: 1 day, 19:31:44, time: 0.819, data_time: 0.001, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5841, loss_cls: 3.7986, loss: 3.7986 +2024-07-19 22:07:39,112 - pyskl - INFO - Epoch [99][1900/3746] lr: 2.637e-02, eta: 1 day, 19:30:24, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.5959, loss_cls: 3.7712, loss: 3.7712 +2024-07-19 22:09:03,193 - pyskl - INFO - Epoch [99][2000/3746] lr: 2.634e-02, eta: 1 day, 19:29:04, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5920, loss_cls: 3.7797, loss: 3.7797 +2024-07-19 22:10:25,805 - pyskl - INFO - Epoch [99][2100/3746] lr: 2.632e-02, eta: 1 day, 19:27:44, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.5964, loss_cls: 3.7703, loss: 3.7703 +2024-07-19 22:11:48,363 - pyskl - INFO - Epoch [99][2200/3746] lr: 2.629e-02, eta: 1 day, 19:26:23, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5981, loss_cls: 3.7752, loss: 3.7752 +2024-07-19 22:13:11,680 - pyskl - INFO - Epoch [99][2300/3746] lr: 2.627e-02, eta: 1 day, 19:25:03, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5841, loss_cls: 3.8165, loss: 3.8165 +2024-07-19 22:14:34,790 - pyskl - INFO - Epoch [99][2400/3746] lr: 2.624e-02, eta: 1 day, 19:23:43, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5903, loss_cls: 3.8121, loss: 3.8121 +2024-07-19 22:15:57,431 - pyskl - INFO - Epoch [99][2500/3746] lr: 2.622e-02, eta: 1 day, 19:22:22, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6081, loss_cls: 3.7209, loss: 3.7209 +2024-07-19 22:17:19,840 - pyskl - INFO - Epoch [99][2600/3746] lr: 2.619e-02, eta: 1 day, 19:21:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5913, loss_cls: 3.7823, loss: 3.7823 +2024-07-19 22:18:42,822 - pyskl - INFO - Epoch [99][2700/3746] lr: 2.617e-02, eta: 1 day, 19:19:41, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.6005, loss_cls: 3.7724, loss: 3.7724 +2024-07-19 22:20:05,537 - pyskl - INFO - Epoch [99][2800/3746] lr: 2.614e-02, eta: 1 day, 19:18:21, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5906, loss_cls: 3.7725, loss: 3.7725 +2024-07-19 22:21:27,802 - pyskl - INFO - Epoch [99][2900/3746] lr: 2.612e-02, eta: 1 day, 19:17:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.5975, loss_cls: 3.7293, loss: 3.7293 +2024-07-19 22:22:49,590 - pyskl - INFO - Epoch [99][3000/3746] lr: 2.610e-02, eta: 1 day, 19:15:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5897, loss_cls: 3.7941, loss: 3.7941 +2024-07-19 22:24:11,854 - pyskl - INFO - Epoch [99][3100/3746] lr: 2.607e-02, eta: 1 day, 19:14:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5978, loss_cls: 3.7848, loss: 3.7848 +2024-07-19 22:25:33,973 - pyskl - INFO - Epoch [99][3200/3746] lr: 2.605e-02, eta: 1 day, 19:12:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5909, loss_cls: 3.8049, loss: 3.8049 +2024-07-19 22:26:56,425 - pyskl - INFO - Epoch [99][3300/3746] lr: 2.602e-02, eta: 1 day, 19:11:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5873, loss_cls: 3.8132, loss: 3.8132 +2024-07-19 22:28:18,528 - pyskl - INFO - Epoch [99][3400/3746] lr: 2.600e-02, eta: 1 day, 19:10:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5919, loss_cls: 3.7778, loss: 3.7778 +2024-07-19 22:29:40,575 - pyskl - INFO - Epoch [99][3500/3746] lr: 2.597e-02, eta: 1 day, 19:08:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5900, loss_cls: 3.8002, loss: 3.8002 +2024-07-19 22:31:03,096 - pyskl - INFO - Epoch [99][3600/3746] lr: 2.595e-02, eta: 1 day, 19:07:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.5995, loss_cls: 3.7429, loss: 3.7429 +2024-07-19 22:32:26,078 - pyskl - INFO - Epoch [99][3700/3746] lr: 2.592e-02, eta: 1 day, 19:06:15, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5939, loss_cls: 3.7631, loss: 3.7631 +2024-07-19 22:33:05,439 - pyskl - INFO - Saving checkpoint at 99 epochs +2024-07-19 22:34:56,031 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 22:34:56,695 - pyskl - INFO - +top1_acc 0.2508 +top5_acc 0.4969 +2024-07-19 22:34:56,695 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 22:34:56,736 - pyskl - INFO - +mean_acc 0.2504 +2024-07-19 22:34:56,748 - pyskl - INFO - Epoch(val) [99][309] top1_acc: 0.2508, top5_acc: 0.4969, mean_class_accuracy: 0.2504 +2024-07-19 22:38:38,959 - pyskl - INFO - Epoch [100][100/3746] lr: 2.589e-02, eta: 1 day, 19:05:10, time: 2.222, data_time: 1.242, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6131, loss_cls: 3.6578, loss: 3.6578 +2024-07-19 22:40:00,977 - pyskl - INFO - Epoch [100][200/3746] lr: 2.586e-02, eta: 1 day, 19:03:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6094, loss_cls: 3.7132, loss: 3.7132 +2024-07-19 22:41:23,368 - pyskl - INFO - Epoch [100][300/3746] lr: 2.584e-02, eta: 1 day, 19:02:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6045, loss_cls: 3.6918, loss: 3.6918 +2024-07-19 22:42:45,626 - pyskl - INFO - Epoch [100][400/3746] lr: 2.581e-02, eta: 1 day, 19:01:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.6069, loss_cls: 3.7056, loss: 3.7056 +2024-07-19 22:44:07,731 - pyskl - INFO - Epoch [100][500/3746] lr: 2.579e-02, eta: 1 day, 18:59:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6080, loss_cls: 3.6970, loss: 3.6970 +2024-07-19 22:45:29,880 - pyskl - INFO - Epoch [100][600/3746] lr: 2.577e-02, eta: 1 day, 18:58:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5997, loss_cls: 3.7478, loss: 3.7478 +2024-07-19 22:46:52,545 - pyskl - INFO - Epoch [100][700/3746] lr: 2.574e-02, eta: 1 day, 18:57:06, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.6028, loss_cls: 3.7311, loss: 3.7311 +2024-07-19 22:48:14,291 - pyskl - INFO - Epoch [100][800/3746] lr: 2.572e-02, eta: 1 day, 18:55:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6114, loss_cls: 3.7153, loss: 3.7153 +2024-07-19 22:49:36,493 - pyskl - INFO - Epoch [100][900/3746] lr: 2.569e-02, eta: 1 day, 18:54:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5967, loss_cls: 3.7495, loss: 3.7495 +2024-07-19 22:50:58,342 - pyskl - INFO - Epoch [100][1000/3746] lr: 2.567e-02, eta: 1 day, 18:53:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6130, loss_cls: 3.7058, loss: 3.7058 +2024-07-19 22:52:20,786 - pyskl - INFO - Epoch [100][1100/3746] lr: 2.564e-02, eta: 1 day, 18:51:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5947, loss_cls: 3.7623, loss: 3.7623 +2024-07-19 22:53:42,886 - pyskl - INFO - Epoch [100][1200/3746] lr: 2.562e-02, eta: 1 day, 18:50:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.5889, loss_cls: 3.7732, loss: 3.7732 +2024-07-19 22:55:05,777 - pyskl - INFO - Epoch [100][1300/3746] lr: 2.559e-02, eta: 1 day, 18:49:01, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.6045, loss_cls: 3.7639, loss: 3.7639 +2024-07-19 22:56:27,925 - pyskl - INFO - Epoch [100][1400/3746] lr: 2.557e-02, eta: 1 day, 18:47:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5955, loss_cls: 3.7219, loss: 3.7219 +2024-07-19 22:57:49,873 - pyskl - INFO - Epoch [100][1500/3746] lr: 2.555e-02, eta: 1 day, 18:46:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.6066, loss_cls: 3.7324, loss: 3.7324 +2024-07-19 22:59:13,246 - pyskl - INFO - Epoch [100][1600/3746] lr: 2.552e-02, eta: 1 day, 18:45:00, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5977, loss_cls: 3.7524, loss: 3.7524 +2024-07-19 23:00:37,122 - pyskl - INFO - Epoch [100][1700/3746] lr: 2.550e-02, eta: 1 day, 18:43:40, time: 0.839, data_time: 0.001, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5911, loss_cls: 3.7995, loss: 3.7995 +2024-07-19 23:01:59,772 - pyskl - INFO - Epoch [100][1800/3746] lr: 2.547e-02, eta: 1 day, 18:42:19, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5916, loss_cls: 3.7946, loss: 3.7946 +2024-07-19 23:03:22,481 - pyskl - INFO - Epoch [100][1900/3746] lr: 2.545e-02, eta: 1 day, 18:40:59, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.5953, loss_cls: 3.7499, loss: 3.7499 +2024-07-19 23:04:46,357 - pyskl - INFO - Epoch [100][2000/3746] lr: 2.542e-02, eta: 1 day, 18:39:39, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5956, loss_cls: 3.7570, loss: 3.7570 +2024-07-19 23:06:09,105 - pyskl - INFO - Epoch [100][2100/3746] lr: 2.540e-02, eta: 1 day, 18:38:18, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5991, loss_cls: 3.7200, loss: 3.7200 +2024-07-19 23:07:31,326 - pyskl - INFO - Epoch [100][2200/3746] lr: 2.538e-02, eta: 1 day, 18:36:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5945, loss_cls: 3.7872, loss: 3.7872 +2024-07-19 23:08:54,590 - pyskl - INFO - Epoch [100][2300/3746] lr: 2.535e-02, eta: 1 day, 18:35:37, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5930, loss_cls: 3.7468, loss: 3.7468 +2024-07-19 23:10:18,404 - pyskl - INFO - Epoch [100][2400/3746] lr: 2.533e-02, eta: 1 day, 18:34:17, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.5975, loss_cls: 3.7557, loss: 3.7557 +2024-07-19 23:11:40,491 - pyskl - INFO - Epoch [100][2500/3746] lr: 2.530e-02, eta: 1 day, 18:32:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.6083, loss_cls: 3.7341, loss: 3.7341 +2024-07-19 23:13:03,039 - pyskl - INFO - Epoch [100][2600/3746] lr: 2.528e-02, eta: 1 day, 18:31:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5980, loss_cls: 3.8012, loss: 3.8012 +2024-07-19 23:14:26,045 - pyskl - INFO - Epoch [100][2700/3746] lr: 2.525e-02, eta: 1 day, 18:30:16, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5975, loss_cls: 3.7399, loss: 3.7399 +2024-07-19 23:15:48,856 - pyskl - INFO - Epoch [100][2800/3746] lr: 2.523e-02, eta: 1 day, 18:28:55, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.5916, loss_cls: 3.7779, loss: 3.7779 +2024-07-19 23:17:11,136 - pyskl - INFO - Epoch [100][2900/3746] lr: 2.521e-02, eta: 1 day, 18:27:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5983, loss_cls: 3.7592, loss: 3.7592 +2024-07-19 23:18:33,687 - pyskl - INFO - Epoch [100][3000/3746] lr: 2.518e-02, eta: 1 day, 18:26:14, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6052, loss_cls: 3.7070, loss: 3.7070 +2024-07-19 23:19:56,797 - pyskl - INFO - Epoch [100][3100/3746] lr: 2.516e-02, eta: 1 day, 18:24:54, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5875, loss_cls: 3.8075, loss: 3.8075 +2024-07-19 23:21:19,417 - pyskl - INFO - Epoch [100][3200/3746] lr: 2.513e-02, eta: 1 day, 18:23:33, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5966, loss_cls: 3.7607, loss: 3.7607 +2024-07-19 23:22:41,791 - pyskl - INFO - Epoch [100][3300/3746] lr: 2.511e-02, eta: 1 day, 18:22:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.6005, loss_cls: 3.7734, loss: 3.7734 +2024-07-19 23:24:03,776 - pyskl - INFO - Epoch [100][3400/3746] lr: 2.508e-02, eta: 1 day, 18:20:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3452, top5_acc: 0.5947, loss_cls: 3.7639, loss: 3.7639 +2024-07-19 23:25:25,893 - pyskl - INFO - Epoch [100][3500/3746] lr: 2.506e-02, eta: 1 day, 18:19:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6045, loss_cls: 3.7222, loss: 3.7222 +2024-07-19 23:26:48,631 - pyskl - INFO - Epoch [100][3600/3746] lr: 2.504e-02, eta: 1 day, 18:18:10, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.5938, loss_cls: 3.7745, loss: 3.7745 +2024-07-19 23:28:11,676 - pyskl - INFO - Epoch [100][3700/3746] lr: 2.501e-02, eta: 1 day, 18:16:50, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5845, loss_cls: 3.8321, loss: 3.8321 +2024-07-19 23:28:51,107 - pyskl - INFO - Saving checkpoint at 100 epochs +2024-07-19 23:30:41,228 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 23:30:41,890 - pyskl - INFO - +top1_acc 0.2855 +top5_acc 0.5349 +2024-07-19 23:30:41,890 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 23:30:41,934 - pyskl - INFO - +mean_acc 0.2852 +2024-07-19 23:30:41,938 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_96.pth was removed +2024-07-19 23:30:42,171 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_100.pth. +2024-07-19 23:30:42,172 - pyskl - INFO - Best top1_acc is 0.2855 at 100 epoch. +2024-07-19 23:30:42,191 - pyskl - INFO - Epoch(val) [100][309] top1_acc: 0.2855, top5_acc: 0.5349, mean_class_accuracy: 0.2852 +2024-07-19 23:34:24,141 - pyskl - INFO - Epoch [101][100/3746] lr: 2.498e-02, eta: 1 day, 18:15:43, time: 2.219, data_time: 1.239, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6152, loss_cls: 3.6738, loss: 3.6738 +2024-07-19 23:35:46,157 - pyskl - INFO - Epoch [101][200/3746] lr: 2.495e-02, eta: 1 day, 18:14:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.6028, loss_cls: 3.7270, loss: 3.7270 +2024-07-19 23:37:08,358 - pyskl - INFO - Epoch [101][300/3746] lr: 2.493e-02, eta: 1 day, 18:13:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6089, loss_cls: 3.6958, loss: 3.6958 +2024-07-19 23:38:30,014 - pyskl - INFO - Epoch [101][400/3746] lr: 2.490e-02, eta: 1 day, 18:11:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6044, loss_cls: 3.7203, loss: 3.7203 +2024-07-19 23:39:52,100 - pyskl - INFO - Epoch [101][500/3746] lr: 2.488e-02, eta: 1 day, 18:10:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6002, loss_cls: 3.7024, loss: 3.7024 +2024-07-19 23:41:13,824 - pyskl - INFO - Epoch [101][600/3746] lr: 2.486e-02, eta: 1 day, 18:08:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.5964, loss_cls: 3.7248, loss: 3.7248 +2024-07-19 23:42:36,303 - pyskl - INFO - Epoch [101][700/3746] lr: 2.483e-02, eta: 1 day, 18:07:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6084, loss_cls: 3.7061, loss: 3.7061 +2024-07-19 23:43:57,986 - pyskl - INFO - Epoch [101][800/3746] lr: 2.481e-02, eta: 1 day, 18:06:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6014, loss_cls: 3.7402, loss: 3.7402 +2024-07-19 23:45:20,761 - pyskl - INFO - Epoch [101][900/3746] lr: 2.478e-02, eta: 1 day, 18:04:56, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6055, loss_cls: 3.7211, loss: 3.7211 +2024-07-19 23:46:43,068 - pyskl - INFO - Epoch [101][1000/3746] lr: 2.476e-02, eta: 1 day, 18:03:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6022, loss_cls: 3.7270, loss: 3.7270 +2024-07-19 23:48:05,087 - pyskl - INFO - Epoch [101][1100/3746] lr: 2.473e-02, eta: 1 day, 18:02:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.5973, loss_cls: 3.7097, loss: 3.7097 +2024-07-19 23:49:26,643 - pyskl - INFO - Epoch [101][1200/3746] lr: 2.471e-02, eta: 1 day, 18:00:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5983, loss_cls: 3.7411, loss: 3.7411 +2024-07-19 23:50:49,487 - pyskl - INFO - Epoch [101][1300/3746] lr: 2.469e-02, eta: 1 day, 17:59:32, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6133, loss_cls: 3.6943, loss: 3.6943 +2024-07-19 23:52:11,417 - pyskl - INFO - Epoch [101][1400/3746] lr: 2.466e-02, eta: 1 day, 17:58:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5977, loss_cls: 3.7622, loss: 3.7622 +2024-07-19 23:53:33,642 - pyskl - INFO - Epoch [101][1500/3746] lr: 2.464e-02, eta: 1 day, 17:56:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5961, loss_cls: 3.7437, loss: 3.7437 +2024-07-19 23:54:57,156 - pyskl - INFO - Epoch [101][1600/3746] lr: 2.461e-02, eta: 1 day, 17:55:31, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6119, loss_cls: 3.6950, loss: 3.6950 +2024-07-19 23:56:20,691 - pyskl - INFO - Epoch [101][1700/3746] lr: 2.459e-02, eta: 1 day, 17:54:10, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.6003, loss_cls: 3.7315, loss: 3.7315 +2024-07-19 23:57:43,264 - pyskl - INFO - Epoch [101][1800/3746] lr: 2.457e-02, eta: 1 day, 17:52:50, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.5980, loss_cls: 3.7429, loss: 3.7429 +2024-07-19 23:59:05,916 - pyskl - INFO - Epoch [101][1900/3746] lr: 2.454e-02, eta: 1 day, 17:51:29, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6056, loss_cls: 3.7024, loss: 3.7024 +2024-07-20 00:00:29,796 - pyskl - INFO - Epoch [101][2000/3746] lr: 2.452e-02, eta: 1 day, 17:50:09, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.5989, loss_cls: 3.7457, loss: 3.7457 +2024-07-20 00:01:51,685 - pyskl - INFO - Epoch [101][2100/3746] lr: 2.449e-02, eta: 1 day, 17:48:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5953, loss_cls: 3.7502, loss: 3.7502 +2024-07-20 00:03:14,481 - pyskl - INFO - Epoch [101][2200/3746] lr: 2.447e-02, eta: 1 day, 17:47:28, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.6009, loss_cls: 3.7258, loss: 3.7258 +2024-07-20 00:04:38,098 - pyskl - INFO - Epoch [101][2300/3746] lr: 2.445e-02, eta: 1 day, 17:46:07, time: 0.836, data_time: 0.001, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6128, loss_cls: 3.6789, loss: 3.6789 +2024-07-20 00:06:00,594 - pyskl - INFO - Epoch [101][2400/3746] lr: 2.442e-02, eta: 1 day, 17:44:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5991, loss_cls: 3.7334, loss: 3.7334 +2024-07-20 00:07:22,434 - pyskl - INFO - Epoch [101][2500/3746] lr: 2.440e-02, eta: 1 day, 17:43:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.5991, loss_cls: 3.7311, loss: 3.7311 +2024-07-20 00:08:45,307 - pyskl - INFO - Epoch [101][2600/3746] lr: 2.437e-02, eta: 1 day, 17:42:05, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5983, loss_cls: 3.7711, loss: 3.7711 +2024-07-20 00:10:08,499 - pyskl - INFO - Epoch [101][2700/3746] lr: 2.435e-02, eta: 1 day, 17:40:45, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5939, loss_cls: 3.7819, loss: 3.7819 +2024-07-20 00:11:30,801 - pyskl - INFO - Epoch [101][2800/3746] lr: 2.433e-02, eta: 1 day, 17:39:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6044, loss_cls: 3.7115, loss: 3.7115 +2024-07-20 00:12:53,294 - pyskl - INFO - Epoch [101][2900/3746] lr: 2.430e-02, eta: 1 day, 17:38:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6055, loss_cls: 3.6970, loss: 3.6970 +2024-07-20 00:14:15,731 - pyskl - INFO - Epoch [101][3000/3746] lr: 2.428e-02, eta: 1 day, 17:36:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.6006, loss_cls: 3.7462, loss: 3.7462 +2024-07-20 00:15:38,098 - pyskl - INFO - Epoch [101][3100/3746] lr: 2.425e-02, eta: 1 day, 17:35:22, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6011, loss_cls: 3.7240, loss: 3.7240 +2024-07-20 00:17:00,827 - pyskl - INFO - Epoch [101][3200/3746] lr: 2.423e-02, eta: 1 day, 17:34:01, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5825, loss_cls: 3.8077, loss: 3.8077 +2024-07-20 00:18:23,224 - pyskl - INFO - Epoch [101][3300/3746] lr: 2.421e-02, eta: 1 day, 17:32:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5894, loss_cls: 3.7858, loss: 3.7858 +2024-07-20 00:19:45,361 - pyskl - INFO - Epoch [101][3400/3746] lr: 2.418e-02, eta: 1 day, 17:31:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.5992, loss_cls: 3.7257, loss: 3.7257 +2024-07-20 00:21:08,060 - pyskl - INFO - Epoch [101][3500/3746] lr: 2.416e-02, eta: 1 day, 17:29:59, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5872, loss_cls: 3.8065, loss: 3.8065 +2024-07-20 00:22:30,931 - pyskl - INFO - Epoch [101][3600/3746] lr: 2.413e-02, eta: 1 day, 17:28:39, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.5969, loss_cls: 3.7497, loss: 3.7497 +2024-07-20 00:23:53,864 - pyskl - INFO - Epoch [101][3700/3746] lr: 2.411e-02, eta: 1 day, 17:27:18, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5922, loss_cls: 3.7600, loss: 3.7600 +2024-07-20 00:24:33,283 - pyskl - INFO - Saving checkpoint at 101 epochs +2024-07-20 00:26:23,809 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 00:26:24,470 - pyskl - INFO - +top1_acc 0.2822 +top5_acc 0.5304 +2024-07-20 00:26:24,470 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 00:26:24,511 - pyskl - INFO - +mean_acc 0.2822 +2024-07-20 00:26:24,522 - pyskl - INFO - Epoch(val) [101][309] top1_acc: 0.2822, top5_acc: 0.5304, mean_class_accuracy: 0.2822 +2024-07-20 00:30:08,573 - pyskl - INFO - Epoch [102][100/3746] lr: 2.407e-02, eta: 1 day, 17:26:11, time: 2.240, data_time: 1.253, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6164, loss_cls: 3.6433, loss: 3.6433 +2024-07-20 00:31:31,011 - pyskl - INFO - Epoch [102][200/3746] lr: 2.405e-02, eta: 1 day, 17:24:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6219, loss_cls: 3.6229, loss: 3.6229 +2024-07-20 00:32:53,469 - pyskl - INFO - Epoch [102][300/3746] lr: 2.403e-02, eta: 1 day, 17:23:29, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6000, loss_cls: 3.7251, loss: 3.7251 +2024-07-20 00:34:15,321 - pyskl - INFO - Epoch [102][400/3746] lr: 2.400e-02, eta: 1 day, 17:22:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6139, loss_cls: 3.6761, loss: 3.6761 +2024-07-20 00:35:37,085 - pyskl - INFO - Epoch [102][500/3746] lr: 2.398e-02, eta: 1 day, 17:20:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6034, loss_cls: 3.7116, loss: 3.7116 +2024-07-20 00:36:59,402 - pyskl - INFO - Epoch [102][600/3746] lr: 2.396e-02, eta: 1 day, 17:19:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.5970, loss_cls: 3.7297, loss: 3.7297 +2024-07-20 00:38:21,268 - pyskl - INFO - Epoch [102][700/3746] lr: 2.393e-02, eta: 1 day, 17:18:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3425, top5_acc: 0.6062, loss_cls: 3.7058, loss: 3.7058 +2024-07-20 00:39:43,313 - pyskl - INFO - Epoch [102][800/3746] lr: 2.391e-02, eta: 1 day, 17:16:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.6052, loss_cls: 3.6958, loss: 3.6958 +2024-07-20 00:41:05,895 - pyskl - INFO - Epoch [102][900/3746] lr: 2.388e-02, eta: 1 day, 17:15:23, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6106, loss_cls: 3.6589, loss: 3.6589 +2024-07-20 00:42:28,628 - pyskl - INFO - Epoch [102][1000/3746] lr: 2.386e-02, eta: 1 day, 17:14:03, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.6050, loss_cls: 3.7294, loss: 3.7294 +2024-07-20 00:43:50,887 - pyskl - INFO - Epoch [102][1100/3746] lr: 2.384e-02, eta: 1 day, 17:12:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6166, loss_cls: 3.6448, loss: 3.6448 +2024-07-20 00:45:13,294 - pyskl - INFO - Epoch [102][1200/3746] lr: 2.381e-02, eta: 1 day, 17:11:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6097, loss_cls: 3.6763, loss: 3.6763 +2024-07-20 00:46:36,571 - pyskl - INFO - Epoch [102][1300/3746] lr: 2.379e-02, eta: 1 day, 17:10:01, time: 0.833, data_time: 0.001, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6053, loss_cls: 3.6969, loss: 3.6969 +2024-07-20 00:47:58,656 - pyskl - INFO - Epoch [102][1400/3746] lr: 2.376e-02, eta: 1 day, 17:08:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.5992, loss_cls: 3.7305, loss: 3.7305 +2024-07-20 00:49:20,996 - pyskl - INFO - Epoch [102][1500/3746] lr: 2.374e-02, eta: 1 day, 17:07:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5970, loss_cls: 3.7357, loss: 3.7357 +2024-07-20 00:50:44,171 - pyskl - INFO - Epoch [102][1600/3746] lr: 2.372e-02, eta: 1 day, 17:05:59, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.6083, loss_cls: 3.7054, loss: 3.7054 +2024-07-20 00:52:07,755 - pyskl - INFO - Epoch [102][1700/3746] lr: 2.369e-02, eta: 1 day, 17:04:38, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5955, loss_cls: 3.7605, loss: 3.7605 +2024-07-20 00:53:29,775 - pyskl - INFO - Epoch [102][1800/3746] lr: 2.367e-02, eta: 1 day, 17:03:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6103, loss_cls: 3.6753, loss: 3.6753 +2024-07-20 00:54:52,791 - pyskl - INFO - Epoch [102][1900/3746] lr: 2.365e-02, eta: 1 day, 17:01:57, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6138, loss_cls: 3.6702, loss: 3.6702 +2024-07-20 00:56:15,863 - pyskl - INFO - Epoch [102][2000/3746] lr: 2.362e-02, eta: 1 day, 17:00:36, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.5995, loss_cls: 3.7344, loss: 3.7344 +2024-07-20 00:57:38,430 - pyskl - INFO - Epoch [102][2100/3746] lr: 2.360e-02, eta: 1 day, 16:59:16, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6058, loss_cls: 3.7015, loss: 3.7015 +2024-07-20 00:59:01,112 - pyskl - INFO - Epoch [102][2200/3746] lr: 2.357e-02, eta: 1 day, 16:57:55, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6069, loss_cls: 3.6813, loss: 3.6813 +2024-07-20 01:00:24,139 - pyskl - INFO - Epoch [102][2300/3746] lr: 2.355e-02, eta: 1 day, 16:56:34, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.5967, loss_cls: 3.7415, loss: 3.7415 +2024-07-20 01:01:46,186 - pyskl - INFO - Epoch [102][2400/3746] lr: 2.353e-02, eta: 1 day, 16:55:13, time: 0.820, data_time: 0.001, memory: 15990, top1_acc: 0.3428, top5_acc: 0.5978, loss_cls: 3.7330, loss: 3.7330 +2024-07-20 01:03:08,323 - pyskl - INFO - Epoch [102][2500/3746] lr: 2.350e-02, eta: 1 day, 16:53:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5934, loss_cls: 3.7833, loss: 3.7833 +2024-07-20 01:04:31,361 - pyskl - INFO - Epoch [102][2600/3746] lr: 2.348e-02, eta: 1 day, 16:52:32, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6162, loss_cls: 3.6672, loss: 3.6672 +2024-07-20 01:05:52,881 - pyskl - INFO - Epoch [102][2700/3746] lr: 2.346e-02, eta: 1 day, 16:51:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6169, loss_cls: 3.6627, loss: 3.6627 +2024-07-20 01:07:14,672 - pyskl - INFO - Epoch [102][2800/3746] lr: 2.343e-02, eta: 1 day, 16:49:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6033, loss_cls: 3.7125, loss: 3.7125 +2024-07-20 01:08:36,948 - pyskl - INFO - Epoch [102][2900/3746] lr: 2.341e-02, eta: 1 day, 16:48:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.6022, loss_cls: 3.7557, loss: 3.7557 +2024-07-20 01:09:59,019 - pyskl - INFO - Epoch [102][3000/3746] lr: 2.339e-02, eta: 1 day, 16:47:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5889, loss_cls: 3.7822, loss: 3.7822 +2024-07-20 01:11:21,172 - pyskl - INFO - Epoch [102][3100/3746] lr: 2.336e-02, eta: 1 day, 16:45:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.6031, loss_cls: 3.7237, loss: 3.7237 +2024-07-20 01:12:43,041 - pyskl - INFO - Epoch [102][3200/3746] lr: 2.334e-02, eta: 1 day, 16:44:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.5995, loss_cls: 3.7163, loss: 3.7163 +2024-07-20 01:14:04,631 - pyskl - INFO - Epoch [102][3300/3746] lr: 2.331e-02, eta: 1 day, 16:43:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6009, loss_cls: 3.7446, loss: 3.7446 +2024-07-20 01:15:25,864 - pyskl - INFO - Epoch [102][3400/3746] lr: 2.329e-02, eta: 1 day, 16:41:43, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.5939, loss_cls: 3.7769, loss: 3.7769 +2024-07-20 01:16:47,814 - pyskl - INFO - Epoch [102][3500/3746] lr: 2.327e-02, eta: 1 day, 16:40:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5911, loss_cls: 3.7526, loss: 3.7526 +2024-07-20 01:18:11,180 - pyskl - INFO - Epoch [102][3600/3746] lr: 2.324e-02, eta: 1 day, 16:39:02, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.6070, loss_cls: 3.7149, loss: 3.7149 +2024-07-20 01:19:33,078 - pyskl - INFO - Epoch [102][3700/3746] lr: 2.322e-02, eta: 1 day, 16:37:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5969, loss_cls: 3.7326, loss: 3.7326 +2024-07-20 01:20:12,631 - pyskl - INFO - Saving checkpoint at 102 epochs +2024-07-20 01:22:03,638 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 01:22:04,306 - pyskl - INFO - +top1_acc 0.2497 +top5_acc 0.4986 +2024-07-20 01:22:04,306 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 01:22:04,347 - pyskl - INFO - +mean_acc 0.2496 +2024-07-20 01:22:04,359 - pyskl - INFO - Epoch(val) [102][309] top1_acc: 0.2497, top5_acc: 0.4986, mean_class_accuracy: 0.2496 +2024-07-20 01:25:47,973 - pyskl - INFO - Epoch [103][100/3746] lr: 2.319e-02, eta: 1 day, 16:36:31, time: 2.236, data_time: 1.256, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6194, loss_cls: 3.6278, loss: 3.6278 +2024-07-20 01:27:10,724 - pyskl - INFO - Epoch [103][200/3746] lr: 2.316e-02, eta: 1 day, 16:35:11, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6186, loss_cls: 3.6026, loss: 3.6026 +2024-07-20 01:28:32,741 - pyskl - INFO - Epoch [103][300/3746] lr: 2.314e-02, eta: 1 day, 16:33:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6130, loss_cls: 3.6596, loss: 3.6596 +2024-07-20 01:29:54,988 - pyskl - INFO - Epoch [103][400/3746] lr: 2.311e-02, eta: 1 day, 16:32:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6128, loss_cls: 3.6590, loss: 3.6590 +2024-07-20 01:31:17,393 - pyskl - INFO - Epoch [103][500/3746] lr: 2.309e-02, eta: 1 day, 16:31:08, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6073, loss_cls: 3.6760, loss: 3.6760 +2024-07-20 01:32:39,294 - pyskl - INFO - Epoch [103][600/3746] lr: 2.307e-02, eta: 1 day, 16:29:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.6122, loss_cls: 3.7021, loss: 3.7021 +2024-07-20 01:34:02,279 - pyskl - INFO - Epoch [103][700/3746] lr: 2.304e-02, eta: 1 day, 16:28:26, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6133, loss_cls: 3.6576, loss: 3.6576 +2024-07-20 01:35:25,016 - pyskl - INFO - Epoch [103][800/3746] lr: 2.302e-02, eta: 1 day, 16:27:06, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6097, loss_cls: 3.6793, loss: 3.6793 +2024-07-20 01:36:47,324 - pyskl - INFO - Epoch [103][900/3746] lr: 2.300e-02, eta: 1 day, 16:25:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.6058, loss_cls: 3.7052, loss: 3.7052 +2024-07-20 01:38:09,759 - pyskl - INFO - Epoch [103][1000/3746] lr: 2.297e-02, eta: 1 day, 16:24:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5958, loss_cls: 3.7756, loss: 3.7756 +2024-07-20 01:39:32,005 - pyskl - INFO - Epoch [103][1100/3746] lr: 2.295e-02, eta: 1 day, 16:23:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3452, top5_acc: 0.6167, loss_cls: 3.6686, loss: 3.6686 +2024-07-20 01:40:54,682 - pyskl - INFO - Epoch [103][1200/3746] lr: 2.293e-02, eta: 1 day, 16:21:42, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6116, loss_cls: 3.6827, loss: 3.6827 +2024-07-20 01:42:17,293 - pyskl - INFO - Epoch [103][1300/3746] lr: 2.290e-02, eta: 1 day, 16:20:21, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6133, loss_cls: 3.6626, loss: 3.6626 +2024-07-20 01:43:39,547 - pyskl - INFO - Epoch [103][1400/3746] lr: 2.288e-02, eta: 1 day, 16:19:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6048, loss_cls: 3.7017, loss: 3.7017 +2024-07-20 01:45:01,713 - pyskl - INFO - Epoch [103][1500/3746] lr: 2.286e-02, eta: 1 day, 16:17:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6011, loss_cls: 3.6831, loss: 3.6831 +2024-07-20 01:46:24,986 - pyskl - INFO - Epoch [103][1600/3746] lr: 2.283e-02, eta: 1 day, 16:16:19, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.6017, loss_cls: 3.7100, loss: 3.7100 +2024-07-20 01:47:48,181 - pyskl - INFO - Epoch [103][1700/3746] lr: 2.281e-02, eta: 1 day, 16:14:59, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6081, loss_cls: 3.6987, loss: 3.6987 +2024-07-20 01:49:10,354 - pyskl - INFO - Epoch [103][1800/3746] lr: 2.279e-02, eta: 1 day, 16:13:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6047, loss_cls: 3.7109, loss: 3.7109 +2024-07-20 01:50:33,677 - pyskl - INFO - Epoch [103][1900/3746] lr: 2.276e-02, eta: 1 day, 16:12:17, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6052, loss_cls: 3.7168, loss: 3.7168 +2024-07-20 01:51:56,598 - pyskl - INFO - Epoch [103][2000/3746] lr: 2.274e-02, eta: 1 day, 16:10:56, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6106, loss_cls: 3.6995, loss: 3.6995 +2024-07-20 01:53:19,496 - pyskl - INFO - Epoch [103][2100/3746] lr: 2.272e-02, eta: 1 day, 16:09:36, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6103, loss_cls: 3.6980, loss: 3.6980 +2024-07-20 01:54:42,266 - pyskl - INFO - Epoch [103][2200/3746] lr: 2.269e-02, eta: 1 day, 16:08:15, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6025, loss_cls: 3.6998, loss: 3.6998 +2024-07-20 01:56:05,870 - pyskl - INFO - Epoch [103][2300/3746] lr: 2.267e-02, eta: 1 day, 16:06:55, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6030, loss_cls: 3.7104, loss: 3.7104 +2024-07-20 01:57:27,590 - pyskl - INFO - Epoch [103][2400/3746] lr: 2.264e-02, eta: 1 day, 16:05:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6088, loss_cls: 3.7200, loss: 3.7200 +2024-07-20 01:58:50,498 - pyskl - INFO - Epoch [103][2500/3746] lr: 2.262e-02, eta: 1 day, 16:04:13, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6184, loss_cls: 3.6464, loss: 3.6464 +2024-07-20 02:00:13,561 - pyskl - INFO - Epoch [103][2600/3746] lr: 2.260e-02, eta: 1 day, 16:02:52, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6109, loss_cls: 3.6906, loss: 3.6906 +2024-07-20 02:01:36,184 - pyskl - INFO - Epoch [103][2700/3746] lr: 2.257e-02, eta: 1 day, 16:01:32, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.5969, loss_cls: 3.7499, loss: 3.7499 +2024-07-20 02:02:58,336 - pyskl - INFO - Epoch [103][2800/3746] lr: 2.255e-02, eta: 1 day, 16:00:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6036, loss_cls: 3.6949, loss: 3.6949 +2024-07-20 02:04:20,175 - pyskl - INFO - Epoch [103][2900/3746] lr: 2.253e-02, eta: 1 day, 15:58:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.6042, loss_cls: 3.7321, loss: 3.7321 +2024-07-20 02:05:41,968 - pyskl - INFO - Epoch [103][3000/3746] lr: 2.250e-02, eta: 1 day, 15:57:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6011, loss_cls: 3.7287, loss: 3.7287 +2024-07-20 02:07:03,820 - pyskl - INFO - Epoch [103][3100/3746] lr: 2.248e-02, eta: 1 day, 15:56:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6108, loss_cls: 3.6647, loss: 3.6647 +2024-07-20 02:08:25,593 - pyskl - INFO - Epoch [103][3200/3746] lr: 2.246e-02, eta: 1 day, 15:54:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6116, loss_cls: 3.6593, loss: 3.6593 +2024-07-20 02:09:47,398 - pyskl - INFO - Epoch [103][3300/3746] lr: 2.243e-02, eta: 1 day, 15:53:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.6033, loss_cls: 3.7357, loss: 3.7357 +2024-07-20 02:11:09,328 - pyskl - INFO - Epoch [103][3400/3746] lr: 2.241e-02, eta: 1 day, 15:52:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6158, loss_cls: 3.6853, loss: 3.6853 +2024-07-20 02:12:31,176 - pyskl - INFO - Epoch [103][3500/3746] lr: 2.239e-02, eta: 1 day, 15:50:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6058, loss_cls: 3.6986, loss: 3.6986 +2024-07-20 02:13:54,267 - pyskl - INFO - Epoch [103][3600/3746] lr: 2.236e-02, eta: 1 day, 15:49:22, time: 0.831, data_time: 0.001, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6000, loss_cls: 3.7132, loss: 3.7132 +2024-07-20 02:15:16,729 - pyskl - INFO - Epoch [103][3700/3746] lr: 2.234e-02, eta: 1 day, 15:48:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5966, loss_cls: 3.7331, loss: 3.7331 +2024-07-20 02:15:56,312 - pyskl - INFO - Saving checkpoint at 103 epochs +2024-07-20 02:17:46,429 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 02:17:47,102 - pyskl - INFO - +top1_acc 0.2775 +top5_acc 0.5265 +2024-07-20 02:17:47,103 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 02:17:47,144 - pyskl - INFO - +mean_acc 0.2772 +2024-07-20 02:17:47,157 - pyskl - INFO - Epoch(val) [103][309] top1_acc: 0.2775, top5_acc: 0.5265, mean_class_accuracy: 0.2772 +2024-07-20 02:21:30,582 - pyskl - INFO - Epoch [104][100/3746] lr: 2.231e-02, eta: 1 day, 15:46:50, time: 2.234, data_time: 1.252, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6256, loss_cls: 3.6314, loss: 3.6314 +2024-07-20 02:22:52,800 - pyskl - INFO - Epoch [104][200/3746] lr: 2.228e-02, eta: 1 day, 15:45:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6286, loss_cls: 3.5925, loss: 3.5925 +2024-07-20 02:24:15,141 - pyskl - INFO - Epoch [104][300/3746] lr: 2.226e-02, eta: 1 day, 15:44:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6133, loss_cls: 3.6435, loss: 3.6435 +2024-07-20 02:25:37,632 - pyskl - INFO - Epoch [104][400/3746] lr: 2.224e-02, eta: 1 day, 15:42:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6278, loss_cls: 3.5827, loss: 3.5827 +2024-07-20 02:26:59,302 - pyskl - INFO - Epoch [104][500/3746] lr: 2.221e-02, eta: 1 day, 15:41:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6141, loss_cls: 3.6346, loss: 3.6346 +2024-07-20 02:28:21,562 - pyskl - INFO - Epoch [104][600/3746] lr: 2.219e-02, eta: 1 day, 15:40:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6139, loss_cls: 3.6928, loss: 3.6928 +2024-07-20 02:29:44,122 - pyskl - INFO - Epoch [104][700/3746] lr: 2.217e-02, eta: 1 day, 15:38:44, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6152, loss_cls: 3.6421, loss: 3.6421 +2024-07-20 02:31:06,388 - pyskl - INFO - Epoch [104][800/3746] lr: 2.214e-02, eta: 1 day, 15:37:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6066, loss_cls: 3.6826, loss: 3.6826 +2024-07-20 02:32:28,162 - pyskl - INFO - Epoch [104][900/3746] lr: 2.212e-02, eta: 1 day, 15:36:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6170, loss_cls: 3.6589, loss: 3.6589 +2024-07-20 02:33:50,403 - pyskl - INFO - Epoch [104][1000/3746] lr: 2.210e-02, eta: 1 day, 15:34:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6100, loss_cls: 3.6434, loss: 3.6434 +2024-07-20 02:35:12,117 - pyskl - INFO - Epoch [104][1100/3746] lr: 2.208e-02, eta: 1 day, 15:33:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.5989, loss_cls: 3.7274, loss: 3.7274 +2024-07-20 02:36:34,110 - pyskl - INFO - Epoch [104][1200/3746] lr: 2.205e-02, eta: 1 day, 15:31:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.5952, loss_cls: 3.7068, loss: 3.7068 +2024-07-20 02:37:56,726 - pyskl - INFO - Epoch [104][1300/3746] lr: 2.203e-02, eta: 1 day, 15:30:38, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6139, loss_cls: 3.6785, loss: 3.6785 +2024-07-20 02:39:19,410 - pyskl - INFO - Epoch [104][1400/3746] lr: 2.201e-02, eta: 1 day, 15:29:17, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6156, loss_cls: 3.6425, loss: 3.6425 +2024-07-20 02:40:42,071 - pyskl - INFO - Epoch [104][1500/3746] lr: 2.198e-02, eta: 1 day, 15:27:56, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6141, loss_cls: 3.6799, loss: 3.6799 +2024-07-20 02:42:04,820 - pyskl - INFO - Epoch [104][1600/3746] lr: 2.196e-02, eta: 1 day, 15:26:35, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6045, loss_cls: 3.7052, loss: 3.7052 +2024-07-20 02:43:28,319 - pyskl - INFO - Epoch [104][1700/3746] lr: 2.194e-02, eta: 1 day, 15:25:15, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6089, loss_cls: 3.6892, loss: 3.6892 +2024-07-20 02:44:50,913 - pyskl - INFO - Epoch [104][1800/3746] lr: 2.191e-02, eta: 1 day, 15:23:54, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6131, loss_cls: 3.6885, loss: 3.6885 +2024-07-20 02:46:13,800 - pyskl - INFO - Epoch [104][1900/3746] lr: 2.189e-02, eta: 1 day, 15:22:33, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6234, loss_cls: 3.6488, loss: 3.6488 +2024-07-20 02:47:37,365 - pyskl - INFO - Epoch [104][2000/3746] lr: 2.187e-02, eta: 1 day, 15:21:13, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.6045, loss_cls: 3.7353, loss: 3.7353 +2024-07-20 02:48:59,977 - pyskl - INFO - Epoch [104][2100/3746] lr: 2.184e-02, eta: 1 day, 15:19:52, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6102, loss_cls: 3.6750, loss: 3.6750 +2024-07-20 02:50:23,800 - pyskl - INFO - Epoch [104][2200/3746] lr: 2.182e-02, eta: 1 day, 15:18:32, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6073, loss_cls: 3.7050, loss: 3.7050 +2024-07-20 02:51:46,948 - pyskl - INFO - Epoch [104][2300/3746] lr: 2.180e-02, eta: 1 day, 15:17:11, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6128, loss_cls: 3.6630, loss: 3.6630 +2024-07-20 02:53:09,390 - pyskl - INFO - Epoch [104][2400/3746] lr: 2.177e-02, eta: 1 day, 15:15:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.5988, loss_cls: 3.7129, loss: 3.7129 +2024-07-20 02:54:32,754 - pyskl - INFO - Epoch [104][2500/3746] lr: 2.175e-02, eta: 1 day, 15:14:30, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6064, loss_cls: 3.7027, loss: 3.7027 +2024-07-20 02:55:55,705 - pyskl - INFO - Epoch [104][2600/3746] lr: 2.173e-02, eta: 1 day, 15:13:09, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.6070, loss_cls: 3.6896, loss: 3.6896 +2024-07-20 02:57:18,416 - pyskl - INFO - Epoch [104][2700/3746] lr: 2.171e-02, eta: 1 day, 15:11:48, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6123, loss_cls: 3.6550, loss: 3.6550 +2024-07-20 02:58:40,612 - pyskl - INFO - Epoch [104][2800/3746] lr: 2.168e-02, eta: 1 day, 15:10:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6072, loss_cls: 3.6889, loss: 3.6889 +2024-07-20 03:00:02,913 - pyskl - INFO - Epoch [104][2900/3746] lr: 2.166e-02, eta: 1 day, 15:09:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6112, loss_cls: 3.7262, loss: 3.7262 +2024-07-20 03:01:25,449 - pyskl - INFO - Epoch [104][3000/3746] lr: 2.164e-02, eta: 1 day, 15:07:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6139, loss_cls: 3.6414, loss: 3.6414 +2024-07-20 03:02:47,270 - pyskl - INFO - Epoch [104][3100/3746] lr: 2.161e-02, eta: 1 day, 15:06:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6016, loss_cls: 3.7029, loss: 3.7029 +2024-07-20 03:04:09,922 - pyskl - INFO - Epoch [104][3200/3746] lr: 2.159e-02, eta: 1 day, 15:05:03, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6139, loss_cls: 3.6435, loss: 3.6435 +2024-07-20 03:05:32,280 - pyskl - INFO - Epoch [104][3300/3746] lr: 2.157e-02, eta: 1 day, 15:03:42, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.6033, loss_cls: 3.7168, loss: 3.7168 +2024-07-20 03:06:54,557 - pyskl - INFO - Epoch [104][3400/3746] lr: 2.154e-02, eta: 1 day, 15:02:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6155, loss_cls: 3.6645, loss: 3.6645 +2024-07-20 03:08:16,614 - pyskl - INFO - Epoch [104][3500/3746] lr: 2.152e-02, eta: 1 day, 15:01:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6123, loss_cls: 3.6683, loss: 3.6683 +2024-07-20 03:09:40,404 - pyskl - INFO - Epoch [104][3600/3746] lr: 2.150e-02, eta: 1 day, 14:59:40, time: 0.838, data_time: 0.001, memory: 15990, top1_acc: 0.3409, top5_acc: 0.5986, loss_cls: 3.7337, loss: 3.7337 +2024-07-20 03:11:02,364 - pyskl - INFO - Epoch [104][3700/3746] lr: 2.148e-02, eta: 1 day, 14:58:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6075, loss_cls: 3.7193, loss: 3.7193 +2024-07-20 03:11:41,880 - pyskl - INFO - Saving checkpoint at 104 epochs +2024-07-20 03:13:32,227 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 03:13:32,900 - pyskl - INFO - +top1_acc 0.2846 +top5_acc 0.5339 +2024-07-20 03:13:32,900 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 03:13:32,945 - pyskl - INFO - +mean_acc 0.2844 +2024-07-20 03:13:32,958 - pyskl - INFO - Epoch(val) [104][309] top1_acc: 0.2846, top5_acc: 0.5339, mean_class_accuracy: 0.2844 +2024-07-20 03:17:17,709 - pyskl - INFO - Epoch [105][100/3746] lr: 2.144e-02, eta: 1 day, 14:57:07, time: 2.247, data_time: 1.261, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6200, loss_cls: 3.6096, loss: 3.6096 +2024-07-20 03:18:40,142 - pyskl - INFO - Epoch [105][200/3746] lr: 2.142e-02, eta: 1 day, 14:55:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6244, loss_cls: 3.5874, loss: 3.5874 +2024-07-20 03:20:03,123 - pyskl - INFO - Epoch [105][300/3746] lr: 2.140e-02, eta: 1 day, 14:54:25, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3541, top5_acc: 0.6142, loss_cls: 3.6172, loss: 3.6172 +2024-07-20 03:21:25,415 - pyskl - INFO - Epoch [105][400/3746] lr: 2.137e-02, eta: 1 day, 14:53:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6172, loss_cls: 3.6455, loss: 3.6455 +2024-07-20 03:22:48,162 - pyskl - INFO - Epoch [105][500/3746] lr: 2.135e-02, eta: 1 day, 14:51:43, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6073, loss_cls: 3.7280, loss: 3.7280 +2024-07-20 03:24:10,250 - pyskl - INFO - Epoch [105][600/3746] lr: 2.133e-02, eta: 1 day, 14:50:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6130, loss_cls: 3.6603, loss: 3.6603 +2024-07-20 03:25:32,686 - pyskl - INFO - Epoch [105][700/3746] lr: 2.130e-02, eta: 1 day, 14:49:01, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6116, loss_cls: 3.6928, loss: 3.6928 +2024-07-20 03:26:55,195 - pyskl - INFO - Epoch [105][800/3746] lr: 2.128e-02, eta: 1 day, 14:47:40, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3541, top5_acc: 0.6186, loss_cls: 3.6302, loss: 3.6302 +2024-07-20 03:28:17,263 - pyskl - INFO - Epoch [105][900/3746] lr: 2.126e-02, eta: 1 day, 14:46:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6130, loss_cls: 3.6755, loss: 3.6755 +2024-07-20 03:29:39,378 - pyskl - INFO - Epoch [105][1000/3746] lr: 2.124e-02, eta: 1 day, 14:44:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6117, loss_cls: 3.6658, loss: 3.6658 +2024-07-20 03:31:01,666 - pyskl - INFO - Epoch [105][1100/3746] lr: 2.121e-02, eta: 1 day, 14:43:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6208, loss_cls: 3.6202, loss: 3.6202 +2024-07-20 03:32:23,835 - pyskl - INFO - Epoch [105][1200/3746] lr: 2.119e-02, eta: 1 day, 14:42:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6084, loss_cls: 3.6604, loss: 3.6604 +2024-07-20 03:33:46,859 - pyskl - INFO - Epoch [105][1300/3746] lr: 2.117e-02, eta: 1 day, 14:40:55, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6277, loss_cls: 3.6077, loss: 3.6077 +2024-07-20 03:35:09,796 - pyskl - INFO - Epoch [105][1400/3746] lr: 2.114e-02, eta: 1 day, 14:39:34, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6156, loss_cls: 3.6508, loss: 3.6508 +2024-07-20 03:36:32,654 - pyskl - INFO - Epoch [105][1500/3746] lr: 2.112e-02, eta: 1 day, 14:38:13, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6156, loss_cls: 3.6314, loss: 3.6314 +2024-07-20 03:37:55,684 - pyskl - INFO - Epoch [105][1600/3746] lr: 2.110e-02, eta: 1 day, 14:36:53, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6131, loss_cls: 3.6638, loss: 3.6638 +2024-07-20 03:39:18,673 - pyskl - INFO - Epoch [105][1700/3746] lr: 2.108e-02, eta: 1 day, 14:35:32, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6194, loss_cls: 3.6263, loss: 3.6263 +2024-07-20 03:40:41,021 - pyskl - INFO - Epoch [105][1800/3746] lr: 2.105e-02, eta: 1 day, 14:34:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6214, loss_cls: 3.6278, loss: 3.6278 +2024-07-20 03:42:04,238 - pyskl - INFO - Epoch [105][1900/3746] lr: 2.103e-02, eta: 1 day, 14:32:50, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6197, loss_cls: 3.6371, loss: 3.6371 +2024-07-20 03:43:26,315 - pyskl - INFO - Epoch [105][2000/3746] lr: 2.101e-02, eta: 1 day, 14:31:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6116, loss_cls: 3.6550, loss: 3.6550 +2024-07-20 03:44:48,686 - pyskl - INFO - Epoch [105][2100/3746] lr: 2.098e-02, eta: 1 day, 14:30:08, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6191, loss_cls: 3.6353, loss: 3.6353 +2024-07-20 03:46:12,611 - pyskl - INFO - Epoch [105][2200/3746] lr: 2.096e-02, eta: 1 day, 14:28:48, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6145, loss_cls: 3.6699, loss: 3.6699 +2024-07-20 03:47:35,383 - pyskl - INFO - Epoch [105][2300/3746] lr: 2.094e-02, eta: 1 day, 14:27:27, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6152, loss_cls: 3.6727, loss: 3.6727 +2024-07-20 03:48:57,878 - pyskl - INFO - Epoch [105][2400/3746] lr: 2.092e-02, eta: 1 day, 14:26:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6120, loss_cls: 3.6787, loss: 3.6787 +2024-07-20 03:50:20,918 - pyskl - INFO - Epoch [105][2500/3746] lr: 2.089e-02, eta: 1 day, 14:24:45, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6203, loss_cls: 3.6535, loss: 3.6535 +2024-07-20 03:51:43,857 - pyskl - INFO - Epoch [105][2600/3746] lr: 2.087e-02, eta: 1 day, 14:23:24, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6038, loss_cls: 3.6760, loss: 3.6760 +2024-07-20 03:53:06,415 - pyskl - INFO - Epoch [105][2700/3746] lr: 2.085e-02, eta: 1 day, 14:22:03, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6052, loss_cls: 3.6869, loss: 3.6869 +2024-07-20 03:54:28,075 - pyskl - INFO - Epoch [105][2800/3746] lr: 2.083e-02, eta: 1 day, 14:20:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6069, loss_cls: 3.7048, loss: 3.7048 +2024-07-20 03:55:50,340 - pyskl - INFO - Epoch [105][2900/3746] lr: 2.080e-02, eta: 1 day, 14:19:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6175, loss_cls: 3.6225, loss: 3.6225 +2024-07-20 03:57:12,566 - pyskl - INFO - Epoch [105][3000/3746] lr: 2.078e-02, eta: 1 day, 14:18:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6128, loss_cls: 3.6833, loss: 3.6833 +2024-07-20 03:58:34,974 - pyskl - INFO - Epoch [105][3100/3746] lr: 2.076e-02, eta: 1 day, 14:16:39, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6094, loss_cls: 3.6690, loss: 3.6690 +2024-07-20 03:59:57,658 - pyskl - INFO - Epoch [105][3200/3746] lr: 2.073e-02, eta: 1 day, 14:15:18, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6111, loss_cls: 3.6693, loss: 3.6693 +2024-07-20 04:01:20,706 - pyskl - INFO - Epoch [105][3300/3746] lr: 2.071e-02, eta: 1 day, 14:13:57, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6056, loss_cls: 3.6833, loss: 3.6833 +2024-07-20 04:02:43,611 - pyskl - INFO - Epoch [105][3400/3746] lr: 2.069e-02, eta: 1 day, 14:12:36, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6105, loss_cls: 3.6664, loss: 3.6664 +2024-07-20 04:04:05,989 - pyskl - INFO - Epoch [105][3500/3746] lr: 2.067e-02, eta: 1 day, 14:11:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6080, loss_cls: 3.6444, loss: 3.6444 +2024-07-20 04:05:29,942 - pyskl - INFO - Epoch [105][3600/3746] lr: 2.064e-02, eta: 1 day, 14:09:55, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6108, loss_cls: 3.6806, loss: 3.6806 +2024-07-20 04:06:52,271 - pyskl - INFO - Epoch [105][3700/3746] lr: 2.062e-02, eta: 1 day, 14:08:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6097, loss_cls: 3.6818, loss: 3.6818 +2024-07-20 04:07:31,645 - pyskl - INFO - Saving checkpoint at 105 epochs +2024-07-20 04:09:21,924 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 04:09:22,597 - pyskl - INFO - +top1_acc 0.2946 +top5_acc 0.5485 +2024-07-20 04:09:22,597 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 04:09:22,640 - pyskl - INFO - +mean_acc 0.2944 +2024-07-20 04:09:22,644 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_100.pth was removed +2024-07-20 04:09:22,871 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_105.pth. +2024-07-20 04:09:22,872 - pyskl - INFO - Best top1_acc is 0.2946 at 105 epoch. +2024-07-20 04:09:22,884 - pyskl - INFO - Epoch(val) [105][309] top1_acc: 0.2946, top5_acc: 0.5485, mean_class_accuracy: 0.2944 +2024-07-20 04:13:10,889 - pyskl - INFO - Epoch [106][100/3746] lr: 2.059e-02, eta: 1 day, 14:07:22, time: 2.280, data_time: 1.297, memory: 15990, top1_acc: 0.3594, top5_acc: 0.6250, loss_cls: 3.5864, loss: 3.5864 +2024-07-20 04:14:33,299 - pyskl - INFO - Epoch [106][200/3746] lr: 2.057e-02, eta: 1 day, 14:06:01, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3594, top5_acc: 0.6255, loss_cls: 3.5912, loss: 3.5912 +2024-07-20 04:15:56,247 - pyskl - INFO - Epoch [106][300/3746] lr: 2.054e-02, eta: 1 day, 14:04:40, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3739, top5_acc: 0.6292, loss_cls: 3.5579, loss: 3.5579 +2024-07-20 04:17:18,616 - pyskl - INFO - Epoch [106][400/3746] lr: 2.052e-02, eta: 1 day, 14:03:19, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6214, loss_cls: 3.6176, loss: 3.6176 +2024-07-20 04:18:40,819 - pyskl - INFO - Epoch [106][500/3746] lr: 2.050e-02, eta: 1 day, 14:01:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6181, loss_cls: 3.6084, loss: 3.6084 +2024-07-20 04:20:03,262 - pyskl - INFO - Epoch [106][600/3746] lr: 2.048e-02, eta: 1 day, 14:00:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6200, loss_cls: 3.6410, loss: 3.6410 +2024-07-20 04:21:25,184 - pyskl - INFO - Epoch [106][700/3746] lr: 2.045e-02, eta: 1 day, 13:59:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6264, loss_cls: 3.6185, loss: 3.6185 +2024-07-20 04:22:47,315 - pyskl - INFO - Epoch [106][800/3746] lr: 2.043e-02, eta: 1 day, 13:57:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6105, loss_cls: 3.6494, loss: 3.6494 +2024-07-20 04:24:09,308 - pyskl - INFO - Epoch [106][900/3746] lr: 2.041e-02, eta: 1 day, 13:56:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6219, loss_cls: 3.6098, loss: 3.6098 +2024-07-20 04:25:31,254 - pyskl - INFO - Epoch [106][1000/3746] lr: 2.039e-02, eta: 1 day, 13:55:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6180, loss_cls: 3.6555, loss: 3.6555 +2024-07-20 04:26:53,396 - pyskl - INFO - Epoch [106][1100/3746] lr: 2.036e-02, eta: 1 day, 13:53:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6164, loss_cls: 3.6844, loss: 3.6844 +2024-07-20 04:28:16,297 - pyskl - INFO - Epoch [106][1200/3746] lr: 2.034e-02, eta: 1 day, 13:52:30, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6105, loss_cls: 3.6360, loss: 3.6360 +2024-07-20 04:29:38,518 - pyskl - INFO - Epoch [106][1300/3746] lr: 2.032e-02, eta: 1 day, 13:51:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6352, loss_cls: 3.5488, loss: 3.5488 +2024-07-20 04:31:01,071 - pyskl - INFO - Epoch [106][1400/3746] lr: 2.030e-02, eta: 1 day, 13:49:47, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6117, loss_cls: 3.6368, loss: 3.6368 +2024-07-20 04:32:23,560 - pyskl - INFO - Epoch [106][1500/3746] lr: 2.027e-02, eta: 1 day, 13:48:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6119, loss_cls: 3.6595, loss: 3.6595 +2024-07-20 04:33:46,262 - pyskl - INFO - Epoch [106][1600/3746] lr: 2.025e-02, eta: 1 day, 13:47:05, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6119, loss_cls: 3.6484, loss: 3.6484 +2024-07-20 04:35:09,067 - pyskl - INFO - Epoch [106][1700/3746] lr: 2.023e-02, eta: 1 day, 13:45:44, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6166, loss_cls: 3.6373, loss: 3.6373 +2024-07-20 04:36:31,444 - pyskl - INFO - Epoch [106][1800/3746] lr: 2.021e-02, eta: 1 day, 13:44:23, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6150, loss_cls: 3.6474, loss: 3.6474 +2024-07-20 04:37:54,657 - pyskl - INFO - Epoch [106][1900/3746] lr: 2.018e-02, eta: 1 day, 13:43:03, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6203, loss_cls: 3.6379, loss: 3.6379 +2024-07-20 04:39:18,140 - pyskl - INFO - Epoch [106][2000/3746] lr: 2.016e-02, eta: 1 day, 13:41:42, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6175, loss_cls: 3.6536, loss: 3.6536 +2024-07-20 04:40:40,895 - pyskl - INFO - Epoch [106][2100/3746] lr: 2.014e-02, eta: 1 day, 13:40:21, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6138, loss_cls: 3.6289, loss: 3.6289 +2024-07-20 04:42:03,923 - pyskl - INFO - Epoch [106][2200/3746] lr: 2.012e-02, eta: 1 day, 13:39:00, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6191, loss_cls: 3.6443, loss: 3.6443 +2024-07-20 04:43:25,996 - pyskl - INFO - Epoch [106][2300/3746] lr: 2.009e-02, eta: 1 day, 13:37:39, time: 0.821, data_time: 0.001, memory: 15990, top1_acc: 0.3594, top5_acc: 0.6192, loss_cls: 3.6307, loss: 3.6307 +2024-07-20 04:44:49,393 - pyskl - INFO - Epoch [106][2400/3746] lr: 2.007e-02, eta: 1 day, 13:36:18, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6220, loss_cls: 3.6063, loss: 3.6063 +2024-07-20 04:46:12,470 - pyskl - INFO - Epoch [106][2500/3746] lr: 2.005e-02, eta: 1 day, 13:34:58, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6200, loss_cls: 3.6221, loss: 3.6221 +2024-07-20 04:47:34,877 - pyskl - INFO - Epoch [106][2600/3746] lr: 2.003e-02, eta: 1 day, 13:33:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6262, loss_cls: 3.6139, loss: 3.6139 +2024-07-20 04:48:57,010 - pyskl - INFO - Epoch [106][2700/3746] lr: 2.000e-02, eta: 1 day, 13:32:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6084, loss_cls: 3.6932, loss: 3.6932 +2024-07-20 04:50:19,168 - pyskl - INFO - Epoch [106][2800/3746] lr: 1.998e-02, eta: 1 day, 13:30:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.6092, loss_cls: 3.6909, loss: 3.6909 +2024-07-20 04:51:41,639 - pyskl - INFO - Epoch [106][2900/3746] lr: 1.996e-02, eta: 1 day, 13:29:33, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6125, loss_cls: 3.6414, loss: 3.6414 +2024-07-20 04:53:03,888 - pyskl - INFO - Epoch [106][3000/3746] lr: 1.994e-02, eta: 1 day, 13:28:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6155, loss_cls: 3.6554, loss: 3.6554 +2024-07-20 04:54:26,769 - pyskl - INFO - Epoch [106][3100/3746] lr: 1.991e-02, eta: 1 day, 13:26:51, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6189, loss_cls: 3.6458, loss: 3.6458 +2024-07-20 04:55:49,080 - pyskl - INFO - Epoch [106][3200/3746] lr: 1.989e-02, eta: 1 day, 13:25:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6089, loss_cls: 3.6537, loss: 3.6537 +2024-07-20 04:57:11,367 - pyskl - INFO - Epoch [106][3300/3746] lr: 1.987e-02, eta: 1 day, 13:24:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6044, loss_cls: 3.6578, loss: 3.6578 +2024-07-20 04:58:33,757 - pyskl - INFO - Epoch [106][3400/3746] lr: 1.985e-02, eta: 1 day, 13:22:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6175, loss_cls: 3.5909, loss: 3.5909 +2024-07-20 04:59:56,545 - pyskl - INFO - Epoch [106][3500/3746] lr: 1.983e-02, eta: 1 day, 13:21:27, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6108, loss_cls: 3.6555, loss: 3.6555 +2024-07-20 05:01:19,620 - pyskl - INFO - Epoch [106][3600/3746] lr: 1.980e-02, eta: 1 day, 13:20:06, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6142, loss_cls: 3.6624, loss: 3.6624 +2024-07-20 05:02:41,661 - pyskl - INFO - Epoch [106][3700/3746] lr: 1.978e-02, eta: 1 day, 13:18:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6078, loss_cls: 3.6471, loss: 3.6471 +2024-07-20 05:03:21,223 - pyskl - INFO - Saving checkpoint at 106 epochs +2024-07-20 05:05:11,697 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 05:05:12,368 - pyskl - INFO - +top1_acc 0.2923 +top5_acc 0.5459 +2024-07-20 05:05:12,368 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 05:05:12,410 - pyskl - INFO - +mean_acc 0.2921 +2024-07-20 05:05:12,423 - pyskl - INFO - Epoch(val) [106][309] top1_acc: 0.2923, top5_acc: 0.5459, mean_class_accuracy: 0.2921 +2024-07-20 05:08:58,836 - pyskl - INFO - Epoch [107][100/3746] lr: 1.975e-02, eta: 1 day, 13:17:30, time: 2.264, data_time: 1.285, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6264, loss_cls: 3.5744, loss: 3.5744 +2024-07-20 05:10:21,619 - pyskl - INFO - Epoch [107][200/3746] lr: 1.973e-02, eta: 1 day, 13:16:09, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3594, top5_acc: 0.6297, loss_cls: 3.5499, loss: 3.5499 +2024-07-20 05:11:44,307 - pyskl - INFO - Epoch [107][300/3746] lr: 1.970e-02, eta: 1 day, 13:14:48, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6238, loss_cls: 3.5970, loss: 3.5970 +2024-07-20 05:13:06,527 - pyskl - INFO - Epoch [107][400/3746] lr: 1.968e-02, eta: 1 day, 13:13:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6236, loss_cls: 3.5644, loss: 3.5644 +2024-07-20 05:14:28,741 - pyskl - INFO - Epoch [107][500/3746] lr: 1.966e-02, eta: 1 day, 13:12:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6289, loss_cls: 3.5745, loss: 3.5745 +2024-07-20 05:15:50,805 - pyskl - INFO - Epoch [107][600/3746] lr: 1.964e-02, eta: 1 day, 13:10:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6242, loss_cls: 3.5754, loss: 3.5754 +2024-07-20 05:17:13,115 - pyskl - INFO - Epoch [107][700/3746] lr: 1.961e-02, eta: 1 day, 13:09:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6259, loss_cls: 3.6089, loss: 3.6089 +2024-07-20 05:18:35,230 - pyskl - INFO - Epoch [107][800/3746] lr: 1.959e-02, eta: 1 day, 13:08:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3588, top5_acc: 0.6217, loss_cls: 3.6104, loss: 3.6104 +2024-07-20 05:19:57,379 - pyskl - INFO - Epoch [107][900/3746] lr: 1.957e-02, eta: 1 day, 13:06:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6180, loss_cls: 3.5925, loss: 3.5925 +2024-07-20 05:21:19,476 - pyskl - INFO - Epoch [107][1000/3746] lr: 1.955e-02, eta: 1 day, 13:05:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6267, loss_cls: 3.6027, loss: 3.6027 +2024-07-20 05:22:41,786 - pyskl - INFO - Epoch [107][1100/3746] lr: 1.953e-02, eta: 1 day, 13:03:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6250, loss_cls: 3.6090, loss: 3.6090 +2024-07-20 05:24:04,290 - pyskl - INFO - Epoch [107][1200/3746] lr: 1.950e-02, eta: 1 day, 13:02:38, time: 0.825, data_time: 0.001, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6214, loss_cls: 3.6002, loss: 3.6002 +2024-07-20 05:25:26,811 - pyskl - INFO - Epoch [107][1300/3746] lr: 1.948e-02, eta: 1 day, 13:01:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6197, loss_cls: 3.6442, loss: 3.6442 +2024-07-20 05:26:49,076 - pyskl - INFO - Epoch [107][1400/3746] lr: 1.946e-02, eta: 1 day, 12:59:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6188, loss_cls: 3.6051, loss: 3.6051 +2024-07-20 05:28:11,684 - pyskl - INFO - Epoch [107][1500/3746] lr: 1.944e-02, eta: 1 day, 12:58:34, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6267, loss_cls: 3.5786, loss: 3.5786 +2024-07-20 05:29:34,881 - pyskl - INFO - Epoch [107][1600/3746] lr: 1.942e-02, eta: 1 day, 12:57:13, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6136, loss_cls: 3.6168, loss: 3.6168 +2024-07-20 05:30:57,693 - pyskl - INFO - Epoch [107][1700/3746] lr: 1.939e-02, eta: 1 day, 12:55:52, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6205, loss_cls: 3.6278, loss: 3.6278 +2024-07-20 05:32:20,344 - pyskl - INFO - Epoch [107][1800/3746] lr: 1.937e-02, eta: 1 day, 12:54:31, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6161, loss_cls: 3.6646, loss: 3.6646 +2024-07-20 05:33:43,951 - pyskl - INFO - Epoch [107][1900/3746] lr: 1.935e-02, eta: 1 day, 12:53:11, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6181, loss_cls: 3.6526, loss: 3.6526 +2024-07-20 05:35:06,925 - pyskl - INFO - Epoch [107][2000/3746] lr: 1.933e-02, eta: 1 day, 12:51:50, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6191, loss_cls: 3.6045, loss: 3.6045 +2024-07-20 05:36:31,387 - pyskl - INFO - Epoch [107][2100/3746] lr: 1.930e-02, eta: 1 day, 12:50:30, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6183, loss_cls: 3.6194, loss: 3.6194 +2024-07-20 05:37:54,586 - pyskl - INFO - Epoch [107][2200/3746] lr: 1.928e-02, eta: 1 day, 12:49:09, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6125, loss_cls: 3.6586, loss: 3.6586 +2024-07-20 05:39:17,673 - pyskl - INFO - Epoch [107][2300/3746] lr: 1.926e-02, eta: 1 day, 12:47:48, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6058, loss_cls: 3.6812, loss: 3.6812 +2024-07-20 05:40:41,322 - pyskl - INFO - Epoch [107][2400/3746] lr: 1.924e-02, eta: 1 day, 12:46:27, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6166, loss_cls: 3.6479, loss: 3.6479 +2024-07-20 05:42:05,634 - pyskl - INFO - Epoch [107][2500/3746] lr: 1.922e-02, eta: 1 day, 12:45:07, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6170, loss_cls: 3.6212, loss: 3.6212 +2024-07-20 05:43:28,973 - pyskl - INFO - Epoch [107][2600/3746] lr: 1.919e-02, eta: 1 day, 12:43:46, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6336, loss_cls: 3.5679, loss: 3.5679 +2024-07-20 05:44:52,682 - pyskl - INFO - Epoch [107][2700/3746] lr: 1.917e-02, eta: 1 day, 12:42:26, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6209, loss_cls: 3.6270, loss: 3.6270 +2024-07-20 05:46:16,401 - pyskl - INFO - Epoch [107][2800/3746] lr: 1.915e-02, eta: 1 day, 12:41:05, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6325, loss_cls: 3.5475, loss: 3.5475 +2024-07-20 05:47:40,174 - pyskl - INFO - Epoch [107][2900/3746] lr: 1.913e-02, eta: 1 day, 12:39:44, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6195, loss_cls: 3.6029, loss: 3.6029 +2024-07-20 05:49:03,981 - pyskl - INFO - Epoch [107][3000/3746] lr: 1.911e-02, eta: 1 day, 12:38:24, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6278, loss_cls: 3.6292, loss: 3.6292 +2024-07-20 05:50:27,407 - pyskl - INFO - Epoch [107][3100/3746] lr: 1.908e-02, eta: 1 day, 12:37:03, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6169, loss_cls: 3.6537, loss: 3.6537 +2024-07-20 05:51:51,033 - pyskl - INFO - Epoch [107][3200/3746] lr: 1.906e-02, eta: 1 day, 12:35:42, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6192, loss_cls: 3.6449, loss: 3.6449 +2024-07-20 05:53:14,435 - pyskl - INFO - Epoch [107][3300/3746] lr: 1.904e-02, eta: 1 day, 12:34:22, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6086, loss_cls: 3.6387, loss: 3.6387 +2024-07-20 05:54:38,154 - pyskl - INFO - Epoch [107][3400/3746] lr: 1.902e-02, eta: 1 day, 12:33:01, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6147, loss_cls: 3.6522, loss: 3.6522 +2024-07-20 05:56:02,013 - pyskl - INFO - Epoch [107][3500/3746] lr: 1.900e-02, eta: 1 day, 12:31:40, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6167, loss_cls: 3.6315, loss: 3.6315 +2024-07-20 05:57:25,021 - pyskl - INFO - Epoch [107][3600/3746] lr: 1.897e-02, eta: 1 day, 12:30:19, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6147, loss_cls: 3.6156, loss: 3.6156 +2024-07-20 05:58:47,541 - pyskl - INFO - Epoch [107][3700/3746] lr: 1.895e-02, eta: 1 day, 12:28:58, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6205, loss_cls: 3.6246, loss: 3.6246 +2024-07-20 05:59:27,507 - pyskl - INFO - Saving checkpoint at 107 epochs +2024-07-20 06:01:18,383 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 06:01:19,054 - pyskl - INFO - +top1_acc 0.2929 +top5_acc 0.5390 +2024-07-20 06:01:19,055 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 06:01:19,096 - pyskl - INFO - +mean_acc 0.2927 +2024-07-20 06:01:19,110 - pyskl - INFO - Epoch(val) [107][309] top1_acc: 0.2929, top5_acc: 0.5390, mean_class_accuracy: 0.2927 +2024-07-20 06:05:13,619 - pyskl - INFO - Epoch [108][100/3746] lr: 1.892e-02, eta: 1 day, 12:27:46, time: 2.345, data_time: 1.339, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6392, loss_cls: 3.5002, loss: 3.5002 +2024-07-20 06:06:36,452 - pyskl - INFO - Epoch [108][200/3746] lr: 1.890e-02, eta: 1 day, 12:26:25, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6295, loss_cls: 3.5486, loss: 3.5486 +2024-07-20 06:07:59,938 - pyskl - INFO - Epoch [108][300/3746] lr: 1.888e-02, eta: 1 day, 12:25:04, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6317, loss_cls: 3.5369, loss: 3.5369 +2024-07-20 06:09:22,009 - pyskl - INFO - Epoch [108][400/3746] lr: 1.886e-02, eta: 1 day, 12:23:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6300, loss_cls: 3.5677, loss: 3.5677 +2024-07-20 06:10:43,887 - pyskl - INFO - Epoch [108][500/3746] lr: 1.883e-02, eta: 1 day, 12:22:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6238, loss_cls: 3.5786, loss: 3.5786 +2024-07-20 06:12:05,986 - pyskl - INFO - Epoch [108][600/3746] lr: 1.881e-02, eta: 1 day, 12:21:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6308, loss_cls: 3.5665, loss: 3.5665 +2024-07-20 06:13:27,515 - pyskl - INFO - Epoch [108][700/3746] lr: 1.879e-02, eta: 1 day, 12:19:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6220, loss_cls: 3.6108, loss: 3.6108 +2024-07-20 06:14:49,145 - pyskl - INFO - Epoch [108][800/3746] lr: 1.877e-02, eta: 1 day, 12:18:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6145, loss_cls: 3.6534, loss: 3.6534 +2024-07-20 06:16:10,742 - pyskl - INFO - Epoch [108][900/3746] lr: 1.875e-02, eta: 1 day, 12:16:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6341, loss_cls: 3.5571, loss: 3.5571 +2024-07-20 06:17:31,906 - pyskl - INFO - Epoch [108][1000/3746] lr: 1.872e-02, eta: 1 day, 12:15:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6253, loss_cls: 3.5903, loss: 3.5903 +2024-07-20 06:18:53,155 - pyskl - INFO - Epoch [108][1100/3746] lr: 1.870e-02, eta: 1 day, 12:14:12, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6216, loss_cls: 3.5830, loss: 3.5830 +2024-07-20 06:20:14,293 - pyskl - INFO - Epoch [108][1200/3746] lr: 1.868e-02, eta: 1 day, 12:12:50, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6181, loss_cls: 3.6138, loss: 3.6138 +2024-07-20 06:21:35,246 - pyskl - INFO - Epoch [108][1300/3746] lr: 1.866e-02, eta: 1 day, 12:11:28, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6220, loss_cls: 3.5701, loss: 3.5701 +2024-07-20 06:22:56,706 - pyskl - INFO - Epoch [108][1400/3746] lr: 1.864e-02, eta: 1 day, 12:10:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6264, loss_cls: 3.5929, loss: 3.5929 +2024-07-20 06:24:17,809 - pyskl - INFO - Epoch [108][1500/3746] lr: 1.862e-02, eta: 1 day, 12:08:45, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6230, loss_cls: 3.5772, loss: 3.5772 +2024-07-20 06:25:39,536 - pyskl - INFO - Epoch [108][1600/3746] lr: 1.859e-02, eta: 1 day, 12:07:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6194, loss_cls: 3.6284, loss: 3.6284 +2024-07-20 06:27:00,873 - pyskl - INFO - Epoch [108][1700/3746] lr: 1.857e-02, eta: 1 day, 12:06:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6291, loss_cls: 3.5574, loss: 3.5574 +2024-07-20 06:28:22,811 - pyskl - INFO - Epoch [108][1800/3746] lr: 1.855e-02, eta: 1 day, 12:04:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6320, loss_cls: 3.5612, loss: 3.5612 +2024-07-20 06:29:44,343 - pyskl - INFO - Epoch [108][1900/3746] lr: 1.853e-02, eta: 1 day, 12:03:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6203, loss_cls: 3.6373, loss: 3.6373 +2024-07-20 06:31:06,128 - pyskl - INFO - Epoch [108][2000/3746] lr: 1.851e-02, eta: 1 day, 12:01:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6234, loss_cls: 3.5906, loss: 3.5906 +2024-07-20 06:32:27,591 - pyskl - INFO - Epoch [108][2100/3746] lr: 1.848e-02, eta: 1 day, 12:00:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6197, loss_cls: 3.6130, loss: 3.6130 +2024-07-20 06:33:49,298 - pyskl - INFO - Epoch [108][2200/3746] lr: 1.846e-02, eta: 1 day, 11:59:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6197, loss_cls: 3.6177, loss: 3.6177 +2024-07-20 06:35:11,108 - pyskl - INFO - Epoch [108][2300/3746] lr: 1.844e-02, eta: 1 day, 11:57:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6253, loss_cls: 3.5951, loss: 3.5951 +2024-07-20 06:36:32,611 - pyskl - INFO - Epoch [108][2400/3746] lr: 1.842e-02, eta: 1 day, 11:56:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6252, loss_cls: 3.5760, loss: 3.5760 +2024-07-20 06:37:54,247 - pyskl - INFO - Epoch [108][2500/3746] lr: 1.840e-02, eta: 1 day, 11:55:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6205, loss_cls: 3.5941, loss: 3.5941 +2024-07-20 06:39:15,651 - pyskl - INFO - Epoch [108][2600/3746] lr: 1.838e-02, eta: 1 day, 11:53:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6230, loss_cls: 3.5903, loss: 3.5903 +2024-07-20 06:40:36,645 - pyskl - INFO - Epoch [108][2700/3746] lr: 1.835e-02, eta: 1 day, 11:52:27, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6178, loss_cls: 3.6011, loss: 3.6011 +2024-07-20 06:41:58,410 - pyskl - INFO - Epoch [108][2800/3746] lr: 1.833e-02, eta: 1 day, 11:51:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6223, loss_cls: 3.6006, loss: 3.6006 +2024-07-20 06:43:19,476 - pyskl - INFO - Epoch [108][2900/3746] lr: 1.831e-02, eta: 1 day, 11:49:44, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6267, loss_cls: 3.6008, loss: 3.6008 +2024-07-20 06:44:41,080 - pyskl - INFO - Epoch [108][3000/3746] lr: 1.829e-02, eta: 1 day, 11:48:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6150, loss_cls: 3.5850, loss: 3.5850 +2024-07-20 06:46:02,253 - pyskl - INFO - Epoch [108][3100/3746] lr: 1.827e-02, eta: 1 day, 11:47:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6170, loss_cls: 3.6385, loss: 3.6385 +2024-07-20 06:47:23,690 - pyskl - INFO - Epoch [108][3200/3746] lr: 1.825e-02, eta: 1 day, 11:45:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3594, top5_acc: 0.6152, loss_cls: 3.6293, loss: 3.6293 +2024-07-20 06:48:44,906 - pyskl - INFO - Epoch [108][3300/3746] lr: 1.823e-02, eta: 1 day, 11:44:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6181, loss_cls: 3.6287, loss: 3.6287 +2024-07-20 06:50:06,618 - pyskl - INFO - Epoch [108][3400/3746] lr: 1.820e-02, eta: 1 day, 11:42:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6103, loss_cls: 3.6329, loss: 3.6329 +2024-07-20 06:51:28,197 - pyskl - INFO - Epoch [108][3500/3746] lr: 1.818e-02, eta: 1 day, 11:41:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6339, loss_cls: 3.5929, loss: 3.5929 +2024-07-20 06:52:49,982 - pyskl - INFO - Epoch [108][3600/3746] lr: 1.816e-02, eta: 1 day, 11:40:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6194, loss_cls: 3.6159, loss: 3.6159 +2024-07-20 06:54:11,386 - pyskl - INFO - Epoch [108][3700/3746] lr: 1.814e-02, eta: 1 day, 11:38:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6231, loss_cls: 3.5775, loss: 3.5775 +2024-07-20 06:54:50,623 - pyskl - INFO - Saving checkpoint at 108 epochs +2024-07-20 06:56:42,536 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 06:56:43,209 - pyskl - INFO - +top1_acc 0.3050 +top5_acc 0.5597 +2024-07-20 06:56:43,210 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 06:56:43,253 - pyskl - INFO - +mean_acc 0.3047 +2024-07-20 06:56:43,258 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_105.pth was removed +2024-07-20 06:56:43,484 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_108.pth. +2024-07-20 06:56:43,485 - pyskl - INFO - Best top1_acc is 0.3050 at 108 epoch. +2024-07-20 06:56:43,498 - pyskl - INFO - Epoch(val) [108][309] top1_acc: 0.3050, top5_acc: 0.5597, mean_class_accuracy: 0.3047 +2024-07-20 07:00:38,970 - pyskl - INFO - Epoch [109][100/3746] lr: 1.811e-02, eta: 1 day, 11:37:37, time: 2.355, data_time: 1.363, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6391, loss_cls: 3.5256, loss: 3.5256 +2024-07-20 07:02:00,625 - pyskl - INFO - Epoch [109][200/3746] lr: 1.809e-02, eta: 1 day, 11:36:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6280, loss_cls: 3.5673, loss: 3.5673 +2024-07-20 07:03:22,505 - pyskl - INFO - Epoch [109][300/3746] lr: 1.806e-02, eta: 1 day, 11:34:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6319, loss_cls: 3.5440, loss: 3.5440 +2024-07-20 07:04:44,370 - pyskl - INFO - Epoch [109][400/3746] lr: 1.804e-02, eta: 1 day, 11:33:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3720, top5_acc: 0.6342, loss_cls: 3.5213, loss: 3.5213 +2024-07-20 07:06:05,882 - pyskl - INFO - Epoch [109][500/3746] lr: 1.802e-02, eta: 1 day, 11:32:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6234, loss_cls: 3.5681, loss: 3.5681 +2024-07-20 07:07:26,833 - pyskl - INFO - Epoch [109][600/3746] lr: 1.800e-02, eta: 1 day, 11:30:49, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6414, loss_cls: 3.5356, loss: 3.5356 +2024-07-20 07:08:47,691 - pyskl - INFO - Epoch [109][700/3746] lr: 1.798e-02, eta: 1 day, 11:29:27, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6358, loss_cls: 3.5250, loss: 3.5250 +2024-07-20 07:10:09,009 - pyskl - INFO - Epoch [109][800/3746] lr: 1.796e-02, eta: 1 day, 11:28:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6295, loss_cls: 3.5468, loss: 3.5468 +2024-07-20 07:11:30,009 - pyskl - INFO - Epoch [109][900/3746] lr: 1.794e-02, eta: 1 day, 11:26:44, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6145, loss_cls: 3.6071, loss: 3.6071 +2024-07-20 07:12:50,842 - pyskl - INFO - Epoch [109][1000/3746] lr: 1.791e-02, eta: 1 day, 11:25:22, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6255, loss_cls: 3.6045, loss: 3.6045 +2024-07-20 07:14:12,024 - pyskl - INFO - Epoch [109][1100/3746] lr: 1.789e-02, eta: 1 day, 11:24:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6273, loss_cls: 3.5830, loss: 3.5830 +2024-07-20 07:15:33,530 - pyskl - INFO - Epoch [109][1200/3746] lr: 1.787e-02, eta: 1 day, 11:22:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6350, loss_cls: 3.5657, loss: 3.5657 +2024-07-20 07:16:54,271 - pyskl - INFO - Epoch [109][1300/3746] lr: 1.785e-02, eta: 1 day, 11:21:17, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6256, loss_cls: 3.5670, loss: 3.5670 +2024-07-20 07:18:15,472 - pyskl - INFO - Epoch [109][1400/3746] lr: 1.783e-02, eta: 1 day, 11:19:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6305, loss_cls: 3.5850, loss: 3.5850 +2024-07-20 07:19:37,141 - pyskl - INFO - Epoch [109][1500/3746] lr: 1.781e-02, eta: 1 day, 11:18:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6292, loss_cls: 3.5931, loss: 3.5931 +2024-07-20 07:20:58,580 - pyskl - INFO - Epoch [109][1600/3746] lr: 1.779e-02, eta: 1 day, 11:17:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6284, loss_cls: 3.5823, loss: 3.5823 +2024-07-20 07:22:19,623 - pyskl - INFO - Epoch [109][1700/3746] lr: 1.776e-02, eta: 1 day, 11:15:50, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6392, loss_cls: 3.5162, loss: 3.5162 +2024-07-20 07:23:41,440 - pyskl - INFO - Epoch [109][1800/3746] lr: 1.774e-02, eta: 1 day, 11:14:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6352, loss_cls: 3.5141, loss: 3.5141 +2024-07-20 07:25:03,998 - pyskl - INFO - Epoch [109][1900/3746] lr: 1.772e-02, eta: 1 day, 11:13:08, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6342, loss_cls: 3.5318, loss: 3.5318 +2024-07-20 07:26:25,366 - pyskl - INFO - Epoch [109][2000/3746] lr: 1.770e-02, eta: 1 day, 11:11:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6286, loss_cls: 3.5990, loss: 3.5990 +2024-07-20 07:27:46,636 - pyskl - INFO - Epoch [109][2100/3746] lr: 1.768e-02, eta: 1 day, 11:10:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6234, loss_cls: 3.6270, loss: 3.6270 +2024-07-20 07:29:08,145 - pyskl - INFO - Epoch [109][2200/3746] lr: 1.766e-02, eta: 1 day, 11:09:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6183, loss_cls: 3.6034, loss: 3.6034 +2024-07-20 07:30:29,760 - pyskl - INFO - Epoch [109][2300/3746] lr: 1.764e-02, eta: 1 day, 11:07:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6191, loss_cls: 3.6129, loss: 3.6129 +2024-07-20 07:31:51,422 - pyskl - INFO - Epoch [109][2400/3746] lr: 1.761e-02, eta: 1 day, 11:06:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6338, loss_cls: 3.5178, loss: 3.5178 +2024-07-20 07:33:13,166 - pyskl - INFO - Epoch [109][2500/3746] lr: 1.759e-02, eta: 1 day, 11:04:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6397, loss_cls: 3.5126, loss: 3.5126 +2024-07-20 07:34:34,588 - pyskl - INFO - Epoch [109][2600/3746] lr: 1.757e-02, eta: 1 day, 11:03:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6270, loss_cls: 3.5923, loss: 3.5923 +2024-07-20 07:35:56,129 - pyskl - INFO - Epoch [109][2700/3746] lr: 1.755e-02, eta: 1 day, 11:02:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6233, loss_cls: 3.6075, loss: 3.6075 +2024-07-20 07:37:17,336 - pyskl - INFO - Epoch [109][2800/3746] lr: 1.753e-02, eta: 1 day, 11:00:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6300, loss_cls: 3.5819, loss: 3.5819 +2024-07-20 07:38:38,923 - pyskl - INFO - Epoch [109][2900/3746] lr: 1.751e-02, eta: 1 day, 10:59:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6280, loss_cls: 3.5703, loss: 3.5703 +2024-07-20 07:39:59,933 - pyskl - INFO - Epoch [109][3000/3746] lr: 1.749e-02, eta: 1 day, 10:58:10, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6325, loss_cls: 3.5473, loss: 3.5473 +2024-07-20 07:41:21,241 - pyskl - INFO - Epoch [109][3100/3746] lr: 1.747e-02, eta: 1 day, 10:56:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6158, loss_cls: 3.5856, loss: 3.5856 +2024-07-20 07:42:42,218 - pyskl - INFO - Epoch [109][3200/3746] lr: 1.744e-02, eta: 1 day, 10:55:26, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6078, loss_cls: 3.6876, loss: 3.6876 +2024-07-20 07:44:03,861 - pyskl - INFO - Epoch [109][3300/3746] lr: 1.742e-02, eta: 1 day, 10:54:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6189, loss_cls: 3.6300, loss: 3.6300 +2024-07-20 07:45:25,576 - pyskl - INFO - Epoch [109][3400/3746] lr: 1.740e-02, eta: 1 day, 10:52:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6169, loss_cls: 3.6261, loss: 3.6261 +2024-07-20 07:46:46,793 - pyskl - INFO - Epoch [109][3500/3746] lr: 1.738e-02, eta: 1 day, 10:51:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6277, loss_cls: 3.5770, loss: 3.5770 +2024-07-20 07:48:08,701 - pyskl - INFO - Epoch [109][3600/3746] lr: 1.736e-02, eta: 1 day, 10:50:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6216, loss_cls: 3.5979, loss: 3.5979 +2024-07-20 07:49:29,953 - pyskl - INFO - Epoch [109][3700/3746] lr: 1.734e-02, eta: 1 day, 10:48:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6045, loss_cls: 3.6474, loss: 3.6474 +2024-07-20 07:50:08,847 - pyskl - INFO - Saving checkpoint at 109 epochs +2024-07-20 07:51:59,995 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 07:52:00,737 - pyskl - INFO - +top1_acc 0.2957 +top5_acc 0.5524 +2024-07-20 07:52:00,737 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 07:52:00,782 - pyskl - INFO - +mean_acc 0.2955 +2024-07-20 07:52:00,795 - pyskl - INFO - Epoch(val) [109][309] top1_acc: 0.2957, top5_acc: 0.5524, mean_class_accuracy: 0.2955 +2024-07-20 07:55:57,725 - pyskl - INFO - Epoch [110][100/3746] lr: 1.731e-02, eta: 1 day, 10:47:24, time: 2.369, data_time: 1.378, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6405, loss_cls: 3.4695, loss: 3.4695 +2024-07-20 07:57:19,727 - pyskl - INFO - Epoch [110][200/3746] lr: 1.729e-02, eta: 1 day, 10:46:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6345, loss_cls: 3.5411, loss: 3.5411 +2024-07-20 07:58:41,372 - pyskl - INFO - Epoch [110][300/3746] lr: 1.727e-02, eta: 1 day, 10:44:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6469, loss_cls: 3.4853, loss: 3.4853 +2024-07-20 08:00:02,867 - pyskl - INFO - Epoch [110][400/3746] lr: 1.724e-02, eta: 1 day, 10:43:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6445, loss_cls: 3.4981, loss: 3.4981 +2024-07-20 08:01:23,658 - pyskl - INFO - Epoch [110][500/3746] lr: 1.722e-02, eta: 1 day, 10:41:57, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6375, loss_cls: 3.5044, loss: 3.5044 +2024-07-20 08:02:44,961 - pyskl - INFO - Epoch [110][600/3746] lr: 1.720e-02, eta: 1 day, 10:40:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6417, loss_cls: 3.5116, loss: 3.5116 +2024-07-20 08:04:06,159 - pyskl - INFO - Epoch [110][700/3746] lr: 1.718e-02, eta: 1 day, 10:39:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6319, loss_cls: 3.5172, loss: 3.5172 +2024-07-20 08:05:27,232 - pyskl - INFO - Epoch [110][800/3746] lr: 1.716e-02, eta: 1 day, 10:37:52, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6245, loss_cls: 3.5651, loss: 3.5651 +2024-07-20 08:06:48,473 - pyskl - INFO - Epoch [110][900/3746] lr: 1.714e-02, eta: 1 day, 10:36:30, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6161, loss_cls: 3.5985, loss: 3.5985 +2024-07-20 08:08:10,614 - pyskl - INFO - Epoch [110][1000/3746] lr: 1.712e-02, eta: 1 day, 10:35:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6241, loss_cls: 3.5832, loss: 3.5832 +2024-07-20 08:09:32,009 - pyskl - INFO - Epoch [110][1100/3746] lr: 1.710e-02, eta: 1 day, 10:33:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6336, loss_cls: 3.5521, loss: 3.5521 +2024-07-20 08:10:53,194 - pyskl - INFO - Epoch [110][1200/3746] lr: 1.708e-02, eta: 1 day, 10:32:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6166, loss_cls: 3.6426, loss: 3.6426 +2024-07-20 08:12:14,139 - pyskl - INFO - Epoch [110][1300/3746] lr: 1.705e-02, eta: 1 day, 10:31:03, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6297, loss_cls: 3.5729, loss: 3.5729 +2024-07-20 08:13:35,283 - pyskl - INFO - Epoch [110][1400/3746] lr: 1.703e-02, eta: 1 day, 10:29:42, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6361, loss_cls: 3.5275, loss: 3.5275 +2024-07-20 08:14:56,425 - pyskl - INFO - Epoch [110][1500/3746] lr: 1.701e-02, eta: 1 day, 10:28:20, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6352, loss_cls: 3.5667, loss: 3.5667 +2024-07-20 08:16:18,506 - pyskl - INFO - Epoch [110][1600/3746] lr: 1.699e-02, eta: 1 day, 10:26:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6331, loss_cls: 3.5413, loss: 3.5413 +2024-07-20 08:17:39,473 - pyskl - INFO - Epoch [110][1700/3746] lr: 1.697e-02, eta: 1 day, 10:25:37, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6288, loss_cls: 3.5532, loss: 3.5532 +2024-07-20 08:19:00,758 - pyskl - INFO - Epoch [110][1800/3746] lr: 1.695e-02, eta: 1 day, 10:24:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6373, loss_cls: 3.5533, loss: 3.5533 +2024-07-20 08:20:22,527 - pyskl - INFO - Epoch [110][1900/3746] lr: 1.693e-02, eta: 1 day, 10:22:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6239, loss_cls: 3.5795, loss: 3.5795 +2024-07-20 08:21:43,930 - pyskl - INFO - Epoch [110][2000/3746] lr: 1.691e-02, eta: 1 day, 10:21:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6230, loss_cls: 3.5690, loss: 3.5690 +2024-07-20 08:23:05,580 - pyskl - INFO - Epoch [110][2100/3746] lr: 1.689e-02, eta: 1 day, 10:20:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6320, loss_cls: 3.5481, loss: 3.5481 +2024-07-20 08:24:27,200 - pyskl - INFO - Epoch [110][2200/3746] lr: 1.687e-02, eta: 1 day, 10:18:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6378, loss_cls: 3.5105, loss: 3.5105 +2024-07-20 08:25:48,080 - pyskl - INFO - Epoch [110][2300/3746] lr: 1.685e-02, eta: 1 day, 10:17:27, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6269, loss_cls: 3.5548, loss: 3.5548 +2024-07-20 08:27:09,033 - pyskl - INFO - Epoch [110][2400/3746] lr: 1.682e-02, eta: 1 day, 10:16:05, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6181, loss_cls: 3.5712, loss: 3.5712 +2024-07-20 08:28:30,117 - pyskl - INFO - Epoch [110][2500/3746] lr: 1.680e-02, eta: 1 day, 10:14:43, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6344, loss_cls: 3.5561, loss: 3.5561 +2024-07-20 08:29:51,366 - pyskl - INFO - Epoch [110][2600/3746] lr: 1.678e-02, eta: 1 day, 10:13:21, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6238, loss_cls: 3.5885, loss: 3.5885 +2024-07-20 08:31:12,856 - pyskl - INFO - Epoch [110][2700/3746] lr: 1.676e-02, eta: 1 day, 10:12:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6139, loss_cls: 3.6175, loss: 3.6175 +2024-07-20 08:32:34,554 - pyskl - INFO - Epoch [110][2800/3746] lr: 1.674e-02, eta: 1 day, 10:10:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3622, top5_acc: 0.6270, loss_cls: 3.5844, loss: 3.5844 +2024-07-20 08:33:55,829 - pyskl - INFO - Epoch [110][2900/3746] lr: 1.672e-02, eta: 1 day, 10:09:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6377, loss_cls: 3.5199, loss: 3.5199 +2024-07-20 08:35:17,363 - pyskl - INFO - Epoch [110][3000/3746] lr: 1.670e-02, eta: 1 day, 10:07:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6397, loss_cls: 3.5093, loss: 3.5093 +2024-07-20 08:36:37,818 - pyskl - INFO - Epoch [110][3100/3746] lr: 1.668e-02, eta: 1 day, 10:06:33, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6325, loss_cls: 3.5534, loss: 3.5534 +2024-07-20 08:37:58,725 - pyskl - INFO - Epoch [110][3200/3746] lr: 1.666e-02, eta: 1 day, 10:05:11, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6297, loss_cls: 3.5957, loss: 3.5957 +2024-07-20 08:39:19,481 - pyskl - INFO - Epoch [110][3300/3746] lr: 1.664e-02, eta: 1 day, 10:03:49, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6311, loss_cls: 3.5391, loss: 3.5391 +2024-07-20 08:40:40,518 - pyskl - INFO - Epoch [110][3400/3746] lr: 1.662e-02, eta: 1 day, 10:02:27, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6197, loss_cls: 3.5907, loss: 3.5907 +2024-07-20 08:42:01,338 - pyskl - INFO - Epoch [110][3500/3746] lr: 1.659e-02, eta: 1 day, 10:01:05, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6295, loss_cls: 3.5468, loss: 3.5468 +2024-07-20 08:43:22,698 - pyskl - INFO - Epoch [110][3600/3746] lr: 1.657e-02, eta: 1 day, 9:59:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6283, loss_cls: 3.5560, loss: 3.5560 +2024-07-20 08:44:43,799 - pyskl - INFO - Epoch [110][3700/3746] lr: 1.655e-02, eta: 1 day, 9:58:22, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6264, loss_cls: 3.5794, loss: 3.5794 +2024-07-20 08:45:23,166 - pyskl - INFO - Saving checkpoint at 110 epochs +2024-07-20 08:47:15,276 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 08:47:15,934 - pyskl - INFO - +top1_acc 0.3026 +top5_acc 0.5596 +2024-07-20 08:47:15,935 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 08:47:15,974 - pyskl - INFO - +mean_acc 0.3025 +2024-07-20 08:47:15,985 - pyskl - INFO - Epoch(val) [110][309] top1_acc: 0.3026, top5_acc: 0.5596, mean_class_accuracy: 0.3025 +2024-07-20 08:51:00,652 - pyskl - INFO - Epoch [111][100/3746] lr: 1.652e-02, eta: 1 day, 9:57:01, time: 2.247, data_time: 1.275, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6320, loss_cls: 3.5324, loss: 3.5324 +2024-07-20 08:52:21,965 - pyskl - INFO - Epoch [111][200/3746] lr: 1.650e-02, eta: 1 day, 9:55:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6425, loss_cls: 3.5189, loss: 3.5189 +2024-07-20 08:53:43,525 - pyskl - INFO - Epoch [111][300/3746] lr: 1.648e-02, eta: 1 day, 9:54:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6417, loss_cls: 3.4901, loss: 3.4901 +2024-07-20 08:55:04,446 - pyskl - INFO - Epoch [111][400/3746] lr: 1.646e-02, eta: 1 day, 9:52:56, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6366, loss_cls: 3.5074, loss: 3.5074 +2024-07-20 08:56:25,768 - pyskl - INFO - Epoch [111][500/3746] lr: 1.644e-02, eta: 1 day, 9:51:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6356, loss_cls: 3.5195, loss: 3.5195 +2024-07-20 08:57:46,688 - pyskl - INFO - Epoch [111][600/3746] lr: 1.642e-02, eta: 1 day, 9:50:12, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6347, loss_cls: 3.5450, loss: 3.5450 +2024-07-20 08:59:07,717 - pyskl - INFO - Epoch [111][700/3746] lr: 1.640e-02, eta: 1 day, 9:48:50, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6383, loss_cls: 3.5300, loss: 3.5300 +2024-07-20 09:00:28,864 - pyskl - INFO - Epoch [111][800/3746] lr: 1.638e-02, eta: 1 day, 9:47:29, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3741, top5_acc: 0.6339, loss_cls: 3.5398, loss: 3.5398 +2024-07-20 09:01:50,709 - pyskl - INFO - Epoch [111][900/3746] lr: 1.636e-02, eta: 1 day, 9:46:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6483, loss_cls: 3.4913, loss: 3.4913 +2024-07-20 09:03:11,633 - pyskl - INFO - Epoch [111][1000/3746] lr: 1.634e-02, eta: 1 day, 9:44:45, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6375, loss_cls: 3.5347, loss: 3.5347 +2024-07-20 09:04:33,246 - pyskl - INFO - Epoch [111][1100/3746] lr: 1.632e-02, eta: 1 day, 9:43:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6428, loss_cls: 3.4997, loss: 3.4997 +2024-07-20 09:05:54,449 - pyskl - INFO - Epoch [111][1200/3746] lr: 1.630e-02, eta: 1 day, 9:42:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6384, loss_cls: 3.5074, loss: 3.5074 +2024-07-20 09:07:16,063 - pyskl - INFO - Epoch [111][1300/3746] lr: 1.627e-02, eta: 1 day, 9:40:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6347, loss_cls: 3.5229, loss: 3.5229 +2024-07-20 09:08:37,093 - pyskl - INFO - Epoch [111][1400/3746] lr: 1.625e-02, eta: 1 day, 9:39:18, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6295, loss_cls: 3.5515, loss: 3.5515 +2024-07-20 09:09:58,031 - pyskl - INFO - Epoch [111][1500/3746] lr: 1.623e-02, eta: 1 day, 9:37:56, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6273, loss_cls: 3.5502, loss: 3.5502 +2024-07-20 09:11:18,810 - pyskl - INFO - Epoch [111][1600/3746] lr: 1.621e-02, eta: 1 day, 9:36:35, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6305, loss_cls: 3.5593, loss: 3.5593 +2024-07-20 09:12:40,911 - pyskl - INFO - Epoch [111][1700/3746] lr: 1.619e-02, eta: 1 day, 9:35:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6295, loss_cls: 3.5545, loss: 3.5545 +2024-07-20 09:14:02,372 - pyskl - INFO - Epoch [111][1800/3746] lr: 1.617e-02, eta: 1 day, 9:33:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6350, loss_cls: 3.5680, loss: 3.5680 +2024-07-20 09:15:24,116 - pyskl - INFO - Epoch [111][1900/3746] lr: 1.615e-02, eta: 1 day, 9:32:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6270, loss_cls: 3.5565, loss: 3.5565 +2024-07-20 09:16:45,913 - pyskl - INFO - Epoch [111][2000/3746] lr: 1.613e-02, eta: 1 day, 9:31:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6295, loss_cls: 3.5620, loss: 3.5620 +2024-07-20 09:18:07,233 - pyskl - INFO - Epoch [111][2100/3746] lr: 1.611e-02, eta: 1 day, 9:29:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6231, loss_cls: 3.5556, loss: 3.5556 +2024-07-20 09:19:28,793 - pyskl - INFO - Epoch [111][2200/3746] lr: 1.609e-02, eta: 1 day, 9:28:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6408, loss_cls: 3.5260, loss: 3.5260 +2024-07-20 09:20:50,366 - pyskl - INFO - Epoch [111][2300/3746] lr: 1.607e-02, eta: 1 day, 9:27:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6298, loss_cls: 3.5486, loss: 3.5486 +2024-07-20 09:22:10,974 - pyskl - INFO - Epoch [111][2400/3746] lr: 1.605e-02, eta: 1 day, 9:25:41, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6330, loss_cls: 3.5138, loss: 3.5138 +2024-07-20 09:23:31,785 - pyskl - INFO - Epoch [111][2500/3746] lr: 1.603e-02, eta: 1 day, 9:24:19, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3708, top5_acc: 0.6320, loss_cls: 3.5714, loss: 3.5714 +2024-07-20 09:24:53,147 - pyskl - INFO - Epoch [111][2600/3746] lr: 1.601e-02, eta: 1 day, 9:22:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6430, loss_cls: 3.4832, loss: 3.4832 +2024-07-20 09:26:14,380 - pyskl - INFO - Epoch [111][2700/3746] lr: 1.599e-02, eta: 1 day, 9:21:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6364, loss_cls: 3.5240, loss: 3.5240 +2024-07-20 09:27:35,780 - pyskl - INFO - Epoch [111][2800/3746] lr: 1.597e-02, eta: 1 day, 9:20:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6314, loss_cls: 3.5326, loss: 3.5326 +2024-07-20 09:28:57,082 - pyskl - INFO - Epoch [111][2900/3746] lr: 1.595e-02, eta: 1 day, 9:18:52, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6373, loss_cls: 3.5089, loss: 3.5089 +2024-07-20 09:30:18,253 - pyskl - INFO - Epoch [111][3000/3746] lr: 1.593e-02, eta: 1 day, 9:17:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6366, loss_cls: 3.5215, loss: 3.5215 +2024-07-20 09:31:39,880 - pyskl - INFO - Epoch [111][3100/3746] lr: 1.590e-02, eta: 1 day, 9:16:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6203, loss_cls: 3.5879, loss: 3.5879 +2024-07-20 09:33:00,984 - pyskl - INFO - Epoch [111][3200/3746] lr: 1.588e-02, eta: 1 day, 9:14:47, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6317, loss_cls: 3.5146, loss: 3.5146 +2024-07-20 09:34:22,162 - pyskl - INFO - Epoch [111][3300/3746] lr: 1.586e-02, eta: 1 day, 9:13:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6362, loss_cls: 3.5418, loss: 3.5418 +2024-07-20 09:35:43,286 - pyskl - INFO - Epoch [111][3400/3746] lr: 1.584e-02, eta: 1 day, 9:12:04, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6295, loss_cls: 3.5756, loss: 3.5756 +2024-07-20 09:37:04,325 - pyskl - INFO - Epoch [111][3500/3746] lr: 1.582e-02, eta: 1 day, 9:10:42, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6412, loss_cls: 3.5133, loss: 3.5133 +2024-07-20 09:38:25,734 - pyskl - INFO - Epoch [111][3600/3746] lr: 1.580e-02, eta: 1 day, 9:09:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6308, loss_cls: 3.5581, loss: 3.5581 +2024-07-20 09:39:47,209 - pyskl - INFO - Epoch [111][3700/3746] lr: 1.578e-02, eta: 1 day, 9:07:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6303, loss_cls: 3.5590, loss: 3.5590 +2024-07-20 09:40:26,322 - pyskl - INFO - Saving checkpoint at 111 epochs +2024-07-20 09:42:18,006 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 09:42:18,754 - pyskl - INFO - +top1_acc 0.2881 +top5_acc 0.5418 +2024-07-20 09:42:18,754 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 09:42:18,807 - pyskl - INFO - +mean_acc 0.2879 +2024-07-20 09:42:18,822 - pyskl - INFO - Epoch(val) [111][309] top1_acc: 0.2881, top5_acc: 0.5418, mean_class_accuracy: 0.2879 +2024-07-20 09:46:08,877 - pyskl - INFO - Epoch [112][100/3746] lr: 1.575e-02, eta: 1 day, 9:06:38, time: 2.300, data_time: 1.320, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6466, loss_cls: 3.4735, loss: 3.4735 +2024-07-20 09:47:30,524 - pyskl - INFO - Epoch [112][200/3746] lr: 1.573e-02, eta: 1 day, 9:05:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6464, loss_cls: 3.4591, loss: 3.4591 +2024-07-20 09:48:51,976 - pyskl - INFO - Epoch [112][300/3746] lr: 1.571e-02, eta: 1 day, 9:03:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3741, top5_acc: 0.6341, loss_cls: 3.5442, loss: 3.5442 +2024-07-20 09:50:12,945 - pyskl - INFO - Epoch [112][400/3746] lr: 1.569e-02, eta: 1 day, 9:02:33, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6470, loss_cls: 3.4934, loss: 3.4934 +2024-07-20 09:51:34,316 - pyskl - INFO - Epoch [112][500/3746] lr: 1.567e-02, eta: 1 day, 9:01:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6356, loss_cls: 3.4947, loss: 3.4947 +2024-07-20 09:52:55,642 - pyskl - INFO - Epoch [112][600/3746] lr: 1.565e-02, eta: 1 day, 8:59:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6483, loss_cls: 3.4913, loss: 3.4913 +2024-07-20 09:54:17,030 - pyskl - INFO - Epoch [112][700/3746] lr: 1.563e-02, eta: 1 day, 8:58:28, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6453, loss_cls: 3.4894, loss: 3.4894 +2024-07-20 09:55:37,926 - pyskl - INFO - Epoch [112][800/3746] lr: 1.561e-02, eta: 1 day, 8:57:06, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6423, loss_cls: 3.4833, loss: 3.4833 +2024-07-20 09:56:59,486 - pyskl - INFO - Epoch [112][900/3746] lr: 1.559e-02, eta: 1 day, 8:55:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6345, loss_cls: 3.5155, loss: 3.5155 +2024-07-20 09:58:21,050 - pyskl - INFO - Epoch [112][1000/3746] lr: 1.557e-02, eta: 1 day, 8:54:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6411, loss_cls: 3.4988, loss: 3.4988 +2024-07-20 09:59:42,149 - pyskl - INFO - Epoch [112][1100/3746] lr: 1.555e-02, eta: 1 day, 8:53:01, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6430, loss_cls: 3.4954, loss: 3.4954 +2024-07-20 10:01:03,221 - pyskl - INFO - Epoch [112][1200/3746] lr: 1.553e-02, eta: 1 day, 8:51:39, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6372, loss_cls: 3.5123, loss: 3.5123 +2024-07-20 10:02:24,766 - pyskl - INFO - Epoch [112][1300/3746] lr: 1.551e-02, eta: 1 day, 8:50:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6409, loss_cls: 3.5203, loss: 3.5203 +2024-07-20 10:03:45,881 - pyskl - INFO - Epoch [112][1400/3746] lr: 1.549e-02, eta: 1 day, 8:48:55, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6366, loss_cls: 3.5427, loss: 3.5427 +2024-07-20 10:05:07,047 - pyskl - INFO - Epoch [112][1500/3746] lr: 1.547e-02, eta: 1 day, 8:47:33, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6428, loss_cls: 3.4781, loss: 3.4781 +2024-07-20 10:06:27,715 - pyskl - INFO - Epoch [112][1600/3746] lr: 1.545e-02, eta: 1 day, 8:46:12, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6378, loss_cls: 3.5099, loss: 3.5099 +2024-07-20 10:07:49,562 - pyskl - INFO - Epoch [112][1700/3746] lr: 1.543e-02, eta: 1 day, 8:44:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6530, loss_cls: 3.4348, loss: 3.4348 +2024-07-20 10:09:10,880 - pyskl - INFO - Epoch [112][1800/3746] lr: 1.541e-02, eta: 1 day, 8:43:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6281, loss_cls: 3.5447, loss: 3.5447 +2024-07-20 10:10:32,111 - pyskl - INFO - Epoch [112][1900/3746] lr: 1.539e-02, eta: 1 day, 8:42:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6322, loss_cls: 3.5471, loss: 3.5471 +2024-07-20 10:11:53,885 - pyskl - INFO - Epoch [112][2000/3746] lr: 1.537e-02, eta: 1 day, 8:40:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6420, loss_cls: 3.5159, loss: 3.5159 +2024-07-20 10:13:15,884 - pyskl - INFO - Epoch [112][2100/3746] lr: 1.535e-02, eta: 1 day, 8:39:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6400, loss_cls: 3.5069, loss: 3.5069 +2024-07-20 10:14:37,148 - pyskl - INFO - Epoch [112][2200/3746] lr: 1.533e-02, eta: 1 day, 8:38:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6358, loss_cls: 3.5394, loss: 3.5394 +2024-07-20 10:15:58,205 - pyskl - INFO - Epoch [112][2300/3746] lr: 1.531e-02, eta: 1 day, 8:36:40, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6319, loss_cls: 3.5552, loss: 3.5552 +2024-07-20 10:17:19,911 - pyskl - INFO - Epoch [112][2400/3746] lr: 1.529e-02, eta: 1 day, 8:35:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6398, loss_cls: 3.4983, loss: 3.4983 +2024-07-20 10:18:41,130 - pyskl - INFO - Epoch [112][2500/3746] lr: 1.527e-02, eta: 1 day, 8:33:56, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6297, loss_cls: 3.5553, loss: 3.5553 +2024-07-20 10:20:02,436 - pyskl - INFO - Epoch [112][2600/3746] lr: 1.525e-02, eta: 1 day, 8:32:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6419, loss_cls: 3.4708, loss: 3.4708 +2024-07-20 10:21:23,297 - pyskl - INFO - Epoch [112][2700/3746] lr: 1.523e-02, eta: 1 day, 8:31:13, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6316, loss_cls: 3.5198, loss: 3.5198 +2024-07-20 10:22:44,407 - pyskl - INFO - Epoch [112][2800/3746] lr: 1.521e-02, eta: 1 day, 8:29:51, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6327, loss_cls: 3.4970, loss: 3.4970 +2024-07-20 10:24:05,603 - pyskl - INFO - Epoch [112][2900/3746] lr: 1.519e-02, eta: 1 day, 8:28:29, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6303, loss_cls: 3.5554, loss: 3.5554 +2024-07-20 10:25:27,162 - pyskl - INFO - Epoch [112][3000/3746] lr: 1.517e-02, eta: 1 day, 8:27:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6317, loss_cls: 3.5467, loss: 3.5467 +2024-07-20 10:26:48,133 - pyskl - INFO - Epoch [112][3100/3746] lr: 1.515e-02, eta: 1 day, 8:25:45, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6381, loss_cls: 3.4862, loss: 3.4862 +2024-07-20 10:28:09,097 - pyskl - INFO - Epoch [112][3200/3746] lr: 1.513e-02, eta: 1 day, 8:24:24, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6319, loss_cls: 3.5494, loss: 3.5494 +2024-07-20 10:29:29,859 - pyskl - INFO - Epoch [112][3300/3746] lr: 1.511e-02, eta: 1 day, 8:23:02, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6298, loss_cls: 3.5522, loss: 3.5522 +2024-07-20 10:30:50,639 - pyskl - INFO - Epoch [112][3400/3746] lr: 1.509e-02, eta: 1 day, 8:21:40, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6366, loss_cls: 3.4955, loss: 3.4955 +2024-07-20 10:32:11,786 - pyskl - INFO - Epoch [112][3500/3746] lr: 1.507e-02, eta: 1 day, 8:20:18, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6384, loss_cls: 3.5014, loss: 3.5014 +2024-07-20 10:33:33,195 - pyskl - INFO - Epoch [112][3600/3746] lr: 1.505e-02, eta: 1 day, 8:18:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6427, loss_cls: 3.4859, loss: 3.4859 +2024-07-20 10:34:55,297 - pyskl - INFO - Epoch [112][3700/3746] lr: 1.503e-02, eta: 1 day, 8:17:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6434, loss_cls: 3.5151, loss: 3.5151 +2024-07-20 10:35:34,541 - pyskl - INFO - Saving checkpoint at 112 epochs +2024-07-20 10:37:25,101 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 10:37:25,753 - pyskl - INFO - +top1_acc 0.3086 +top5_acc 0.5649 +2024-07-20 10:37:25,753 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 10:37:25,792 - pyskl - INFO - +mean_acc 0.3084 +2024-07-20 10:37:25,797 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_108.pth was removed +2024-07-20 10:37:26,025 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_112.pth. +2024-07-20 10:37:26,025 - pyskl - INFO - Best top1_acc is 0.3086 at 112 epoch. +2024-07-20 10:37:26,036 - pyskl - INFO - Epoch(val) [112][309] top1_acc: 0.3086, top5_acc: 0.5649, mean_class_accuracy: 0.3084 +2024-07-20 10:41:11,310 - pyskl - INFO - Epoch [113][100/3746] lr: 1.500e-02, eta: 1 day, 8:16:11, time: 2.253, data_time: 1.280, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6542, loss_cls: 3.3936, loss: 3.3936 +2024-07-20 10:42:32,715 - pyskl - INFO - Epoch [113][200/3746] lr: 1.498e-02, eta: 1 day, 8:14:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6477, loss_cls: 3.4315, loss: 3.4315 +2024-07-20 10:43:55,024 - pyskl - INFO - Epoch [113][300/3746] lr: 1.496e-02, eta: 1 day, 8:13:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6514, loss_cls: 3.4333, loss: 3.4333 +2024-07-20 10:45:16,137 - pyskl - INFO - Epoch [113][400/3746] lr: 1.494e-02, eta: 1 day, 8:12:06, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6494, loss_cls: 3.4166, loss: 3.4166 +2024-07-20 10:46:38,863 - pyskl - INFO - Epoch [113][500/3746] lr: 1.492e-02, eta: 1 day, 8:10:45, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6458, loss_cls: 3.4591, loss: 3.4591 +2024-07-20 10:48:00,707 - pyskl - INFO - Epoch [113][600/3746] lr: 1.490e-02, eta: 1 day, 8:09:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6483, loss_cls: 3.4341, loss: 3.4341 +2024-07-20 10:49:22,407 - pyskl - INFO - Epoch [113][700/3746] lr: 1.488e-02, eta: 1 day, 8:08:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6392, loss_cls: 3.5114, loss: 3.5114 +2024-07-20 10:50:43,523 - pyskl - INFO - Epoch [113][800/3746] lr: 1.486e-02, eta: 1 day, 8:06:40, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6481, loss_cls: 3.4638, loss: 3.4638 +2024-07-20 10:52:04,260 - pyskl - INFO - Epoch [113][900/3746] lr: 1.484e-02, eta: 1 day, 8:05:18, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6434, loss_cls: 3.4445, loss: 3.4445 +2024-07-20 10:53:25,798 - pyskl - INFO - Epoch [113][1000/3746] lr: 1.482e-02, eta: 1 day, 8:03:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6503, loss_cls: 3.4526, loss: 3.4526 +2024-07-20 10:54:47,495 - pyskl - INFO - Epoch [113][1100/3746] lr: 1.480e-02, eta: 1 day, 8:02:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6381, loss_cls: 3.4989, loss: 3.4989 +2024-07-20 10:56:09,857 - pyskl - INFO - Epoch [113][1200/3746] lr: 1.478e-02, eta: 1 day, 8:01:13, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6533, loss_cls: 3.4146, loss: 3.4146 +2024-07-20 10:57:31,432 - pyskl - INFO - Epoch [113][1300/3746] lr: 1.476e-02, eta: 1 day, 7:59:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6395, loss_cls: 3.5225, loss: 3.5225 +2024-07-20 10:58:52,612 - pyskl - INFO - Epoch [113][1400/3746] lr: 1.474e-02, eta: 1 day, 7:58:30, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6364, loss_cls: 3.4957, loss: 3.4957 +2024-07-20 11:00:14,082 - pyskl - INFO - Epoch [113][1500/3746] lr: 1.472e-02, eta: 1 day, 7:57:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6359, loss_cls: 3.5550, loss: 3.5550 +2024-07-20 11:01:35,551 - pyskl - INFO - Epoch [113][1600/3746] lr: 1.470e-02, eta: 1 day, 7:55:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6489, loss_cls: 3.4526, loss: 3.4526 +2024-07-20 11:02:56,824 - pyskl - INFO - Epoch [113][1700/3746] lr: 1.468e-02, eta: 1 day, 7:54:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6384, loss_cls: 3.4927, loss: 3.4927 +2024-07-20 11:04:18,534 - pyskl - INFO - Epoch [113][1800/3746] lr: 1.466e-02, eta: 1 day, 7:53:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6470, loss_cls: 3.4692, loss: 3.4692 +2024-07-20 11:05:39,876 - pyskl - INFO - Epoch [113][1900/3746] lr: 1.464e-02, eta: 1 day, 7:51:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6417, loss_cls: 3.4556, loss: 3.4556 +2024-07-20 11:07:02,171 - pyskl - INFO - Epoch [113][2000/3746] lr: 1.462e-02, eta: 1 day, 7:50:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3844, top5_acc: 0.6402, loss_cls: 3.4912, loss: 3.4912 +2024-07-20 11:08:23,358 - pyskl - INFO - Epoch [113][2100/3746] lr: 1.460e-02, eta: 1 day, 7:48:58, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6362, loss_cls: 3.5159, loss: 3.5159 +2024-07-20 11:09:44,953 - pyskl - INFO - Epoch [113][2200/3746] lr: 1.458e-02, eta: 1 day, 7:47:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6342, loss_cls: 3.5049, loss: 3.5049 +2024-07-20 11:11:06,303 - pyskl - INFO - Epoch [113][2300/3746] lr: 1.456e-02, eta: 1 day, 7:46:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6462, loss_cls: 3.4739, loss: 3.4739 +2024-07-20 11:12:27,405 - pyskl - INFO - Epoch [113][2400/3746] lr: 1.454e-02, eta: 1 day, 7:44:52, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6327, loss_cls: 3.5482, loss: 3.5482 +2024-07-20 11:13:48,797 - pyskl - INFO - Epoch [113][2500/3746] lr: 1.452e-02, eta: 1 day, 7:43:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6362, loss_cls: 3.5243, loss: 3.5243 +2024-07-20 11:15:10,213 - pyskl - INFO - Epoch [113][2600/3746] lr: 1.450e-02, eta: 1 day, 7:42:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3723, top5_acc: 0.6364, loss_cls: 3.5258, loss: 3.5258 +2024-07-20 11:16:31,232 - pyskl - INFO - Epoch [113][2700/3746] lr: 1.448e-02, eta: 1 day, 7:40:47, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6466, loss_cls: 3.4822, loss: 3.4822 +2024-07-20 11:17:52,482 - pyskl - INFO - Epoch [113][2800/3746] lr: 1.446e-02, eta: 1 day, 7:39:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6295, loss_cls: 3.5453, loss: 3.5453 +2024-07-20 11:19:13,952 - pyskl - INFO - Epoch [113][2900/3746] lr: 1.444e-02, eta: 1 day, 7:38:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6488, loss_cls: 3.4676, loss: 3.4676 +2024-07-20 11:20:35,452 - pyskl - INFO - Epoch [113][3000/3746] lr: 1.442e-02, eta: 1 day, 7:36:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6305, loss_cls: 3.5110, loss: 3.5110 +2024-07-20 11:21:56,284 - pyskl - INFO - Epoch [113][3100/3746] lr: 1.440e-02, eta: 1 day, 7:35:20, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6428, loss_cls: 3.4813, loss: 3.4813 +2024-07-20 11:23:17,264 - pyskl - INFO - Epoch [113][3200/3746] lr: 1.438e-02, eta: 1 day, 7:33:58, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6330, loss_cls: 3.5348, loss: 3.5348 +2024-07-20 11:24:38,249 - pyskl - INFO - Epoch [113][3300/3746] lr: 1.436e-02, eta: 1 day, 7:32:36, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6402, loss_cls: 3.5026, loss: 3.5026 +2024-07-20 11:25:59,639 - pyskl - INFO - Epoch [113][3400/3746] lr: 1.434e-02, eta: 1 day, 7:31:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6344, loss_cls: 3.5118, loss: 3.5118 +2024-07-20 11:27:20,632 - pyskl - INFO - Epoch [113][3500/3746] lr: 1.432e-02, eta: 1 day, 7:29:52, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6319, loss_cls: 3.5445, loss: 3.5445 +2024-07-20 11:28:41,426 - pyskl - INFO - Epoch [113][3600/3746] lr: 1.431e-02, eta: 1 day, 7:28:30, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3762, top5_acc: 0.6277, loss_cls: 3.5335, loss: 3.5335 +2024-07-20 11:30:02,347 - pyskl - INFO - Epoch [113][3700/3746] lr: 1.429e-02, eta: 1 day, 7:27:08, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6361, loss_cls: 3.4972, loss: 3.4972 +2024-07-20 11:30:42,105 - pyskl - INFO - Saving checkpoint at 113 epochs +2024-07-20 11:32:32,002 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 11:32:32,662 - pyskl - INFO - +top1_acc 0.3162 +top5_acc 0.5730 +2024-07-20 11:32:32,662 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 11:32:32,701 - pyskl - INFO - +mean_acc 0.3159 +2024-07-20 11:32:32,705 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_112.pth was removed +2024-07-20 11:32:32,934 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_113.pth. +2024-07-20 11:32:32,935 - pyskl - INFO - Best top1_acc is 0.3162 at 113 epoch. +2024-07-20 11:32:32,945 - pyskl - INFO - Epoch(val) [113][309] top1_acc: 0.3162, top5_acc: 0.5730, mean_class_accuracy: 0.3159 +2024-07-20 11:36:19,529 - pyskl - INFO - Epoch [114][100/3746] lr: 1.426e-02, eta: 1 day, 7:25:44, time: 2.266, data_time: 1.289, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6506, loss_cls: 3.4183, loss: 3.4183 +2024-07-20 11:37:41,038 - pyskl - INFO - Epoch [114][200/3746] lr: 1.424e-02, eta: 1 day, 7:24:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6581, loss_cls: 3.3806, loss: 3.3806 +2024-07-20 11:39:02,051 - pyskl - INFO - Epoch [114][300/3746] lr: 1.422e-02, eta: 1 day, 7:23:01, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6505, loss_cls: 3.4524, loss: 3.4524 +2024-07-20 11:40:23,240 - pyskl - INFO - Epoch [114][400/3746] lr: 1.420e-02, eta: 1 day, 7:21:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6495, loss_cls: 3.4509, loss: 3.4509 +2024-07-20 11:41:44,695 - pyskl - INFO - Epoch [114][500/3746] lr: 1.418e-02, eta: 1 day, 7:20:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6459, loss_cls: 3.4573, loss: 3.4573 +2024-07-20 11:43:06,391 - pyskl - INFO - Epoch [114][600/3746] lr: 1.416e-02, eta: 1 day, 7:18:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6469, loss_cls: 3.4527, loss: 3.4527 +2024-07-20 11:44:27,715 - pyskl - INFO - Epoch [114][700/3746] lr: 1.414e-02, eta: 1 day, 7:17:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6536, loss_cls: 3.4364, loss: 3.4364 +2024-07-20 11:45:48,910 - pyskl - INFO - Epoch [114][800/3746] lr: 1.412e-02, eta: 1 day, 7:16:12, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6486, loss_cls: 3.4952, loss: 3.4952 +2024-07-20 11:47:10,226 - pyskl - INFO - Epoch [114][900/3746] lr: 1.410e-02, eta: 1 day, 7:14:50, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6428, loss_cls: 3.4409, loss: 3.4409 +2024-07-20 11:48:31,030 - pyskl - INFO - Epoch [114][1000/3746] lr: 1.408e-02, eta: 1 day, 7:13:28, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6528, loss_cls: 3.4740, loss: 3.4740 +2024-07-20 11:49:52,282 - pyskl - INFO - Epoch [114][1100/3746] lr: 1.406e-02, eta: 1 day, 7:12:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6583, loss_cls: 3.4523, loss: 3.4523 +2024-07-20 11:51:13,958 - pyskl - INFO - Epoch [114][1200/3746] lr: 1.404e-02, eta: 1 day, 7:10:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3837, top5_acc: 0.6483, loss_cls: 3.4548, loss: 3.4548 +2024-07-20 11:52:35,532 - pyskl - INFO - Epoch [114][1300/3746] lr: 1.402e-02, eta: 1 day, 7:09:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6464, loss_cls: 3.4467, loss: 3.4467 +2024-07-20 11:53:57,135 - pyskl - INFO - Epoch [114][1400/3746] lr: 1.400e-02, eta: 1 day, 7:08:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6425, loss_cls: 3.4619, loss: 3.4619 +2024-07-20 11:55:18,397 - pyskl - INFO - Epoch [114][1500/3746] lr: 1.398e-02, eta: 1 day, 7:06:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3819, top5_acc: 0.6439, loss_cls: 3.4467, loss: 3.4467 +2024-07-20 11:56:39,458 - pyskl - INFO - Epoch [114][1600/3746] lr: 1.397e-02, eta: 1 day, 7:05:17, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6477, loss_cls: 3.4472, loss: 3.4472 +2024-07-20 11:58:00,211 - pyskl - INFO - Epoch [114][1700/3746] lr: 1.395e-02, eta: 1 day, 7:03:55, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6484, loss_cls: 3.4477, loss: 3.4477 +2024-07-20 11:59:22,262 - pyskl - INFO - Epoch [114][1800/3746] lr: 1.393e-02, eta: 1 day, 7:02:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6394, loss_cls: 3.4840, loss: 3.4840 +2024-07-20 12:00:43,401 - pyskl - INFO - Epoch [114][1900/3746] lr: 1.391e-02, eta: 1 day, 7:01:12, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6408, loss_cls: 3.4820, loss: 3.4820 +2024-07-20 12:02:05,680 - pyskl - INFO - Epoch [114][2000/3746] lr: 1.389e-02, eta: 1 day, 6:59:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6395, loss_cls: 3.5099, loss: 3.5099 +2024-07-20 12:03:26,885 - pyskl - INFO - Epoch [114][2100/3746] lr: 1.387e-02, eta: 1 day, 6:58:28, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6506, loss_cls: 3.4353, loss: 3.4353 +2024-07-20 12:04:48,701 - pyskl - INFO - Epoch [114][2200/3746] lr: 1.385e-02, eta: 1 day, 6:57:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3914, top5_acc: 0.6445, loss_cls: 3.4554, loss: 3.4554 +2024-07-20 12:06:10,112 - pyskl - INFO - Epoch [114][2300/3746] lr: 1.383e-02, eta: 1 day, 6:55:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6384, loss_cls: 3.4905, loss: 3.4905 +2024-07-20 12:07:31,486 - pyskl - INFO - Epoch [114][2400/3746] lr: 1.381e-02, eta: 1 day, 6:54:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6444, loss_cls: 3.4684, loss: 3.4684 +2024-07-20 12:08:53,004 - pyskl - INFO - Epoch [114][2500/3746] lr: 1.379e-02, eta: 1 day, 6:53:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6550, loss_cls: 3.4283, loss: 3.4283 +2024-07-20 12:10:14,328 - pyskl - INFO - Epoch [114][2600/3746] lr: 1.377e-02, eta: 1 day, 6:51:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6341, loss_cls: 3.4991, loss: 3.4991 +2024-07-20 12:11:35,280 - pyskl - INFO - Epoch [114][2700/3746] lr: 1.375e-02, eta: 1 day, 6:50:18, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6427, loss_cls: 3.4812, loss: 3.4812 +2024-07-20 12:12:55,890 - pyskl - INFO - Epoch [114][2800/3746] lr: 1.373e-02, eta: 1 day, 6:48:56, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6455, loss_cls: 3.4617, loss: 3.4617 +2024-07-20 12:14:17,685 - pyskl - INFO - Epoch [114][2900/3746] lr: 1.371e-02, eta: 1 day, 6:47:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6427, loss_cls: 3.5051, loss: 3.5051 +2024-07-20 12:15:38,813 - pyskl - INFO - Epoch [114][3000/3746] lr: 1.369e-02, eta: 1 day, 6:46:12, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6455, loss_cls: 3.4320, loss: 3.4320 +2024-07-20 12:17:00,271 - pyskl - INFO - Epoch [114][3100/3746] lr: 1.368e-02, eta: 1 day, 6:44:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6409, loss_cls: 3.4660, loss: 3.4660 +2024-07-20 12:18:21,417 - pyskl - INFO - Epoch [114][3200/3746] lr: 1.366e-02, eta: 1 day, 6:43:28, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6434, loss_cls: 3.5026, loss: 3.5026 +2024-07-20 12:19:42,761 - pyskl - INFO - Epoch [114][3300/3746] lr: 1.364e-02, eta: 1 day, 6:42:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6448, loss_cls: 3.4998, loss: 3.4998 +2024-07-20 12:21:03,660 - pyskl - INFO - Epoch [114][3400/3746] lr: 1.362e-02, eta: 1 day, 6:40:45, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6538, loss_cls: 3.4394, loss: 3.4394 +2024-07-20 12:22:24,819 - pyskl - INFO - Epoch [114][3500/3746] lr: 1.360e-02, eta: 1 day, 6:39:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6339, loss_cls: 3.4981, loss: 3.4981 +2024-07-20 12:23:45,786 - pyskl - INFO - Epoch [114][3600/3746] lr: 1.358e-02, eta: 1 day, 6:38:01, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6458, loss_cls: 3.4679, loss: 3.4679 +2024-07-20 12:25:07,222 - pyskl - INFO - Epoch [114][3700/3746] lr: 1.356e-02, eta: 1 day, 6:36:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3837, top5_acc: 0.6458, loss_cls: 3.4599, loss: 3.4599 +2024-07-20 12:25:46,413 - pyskl - INFO - Saving checkpoint at 114 epochs +2024-07-20 12:27:37,754 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 12:27:38,409 - pyskl - INFO - +top1_acc 0.3166 +top5_acc 0.5753 +2024-07-20 12:27:38,409 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 12:27:38,447 - pyskl - INFO - +mean_acc 0.3165 +2024-07-20 12:27:38,451 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_113.pth was removed +2024-07-20 12:27:38,679 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_114.pth. +2024-07-20 12:27:38,680 - pyskl - INFO - Best top1_acc is 0.3166 at 114 epoch. +2024-07-20 12:27:38,691 - pyskl - INFO - Epoch(val) [114][309] top1_acc: 0.3166, top5_acc: 0.5753, mean_class_accuracy: 0.3165 +2024-07-20 12:31:24,355 - pyskl - INFO - Epoch [115][100/3746] lr: 1.353e-02, eta: 1 day, 6:35:13, time: 2.257, data_time: 1.285, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6667, loss_cls: 3.3585, loss: 3.3585 +2024-07-20 12:32:45,774 - pyskl - INFO - Epoch [115][200/3746] lr: 1.351e-02, eta: 1 day, 6:33:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6577, loss_cls: 3.3891, loss: 3.3891 +2024-07-20 12:34:07,487 - pyskl - INFO - Epoch [115][300/3746] lr: 1.349e-02, eta: 1 day, 6:32:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6544, loss_cls: 3.4315, loss: 3.4315 +2024-07-20 12:35:28,630 - pyskl - INFO - Epoch [115][400/3746] lr: 1.348e-02, eta: 1 day, 6:31:08, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3997, top5_acc: 0.6559, loss_cls: 3.4026, loss: 3.4026 +2024-07-20 12:36:49,331 - pyskl - INFO - Epoch [115][500/3746] lr: 1.346e-02, eta: 1 day, 6:29:46, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6519, loss_cls: 3.4714, loss: 3.4714 +2024-07-20 12:38:10,626 - pyskl - INFO - Epoch [115][600/3746] lr: 1.344e-02, eta: 1 day, 6:28:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6527, loss_cls: 3.4124, loss: 3.4124 +2024-07-20 12:39:32,165 - pyskl - INFO - Epoch [115][700/3746] lr: 1.342e-02, eta: 1 day, 6:27:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6562, loss_cls: 3.4234, loss: 3.4234 +2024-07-20 12:40:53,583 - pyskl - INFO - Epoch [115][800/3746] lr: 1.340e-02, eta: 1 day, 6:25:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3837, top5_acc: 0.6502, loss_cls: 3.4432, loss: 3.4432 +2024-07-20 12:42:14,931 - pyskl - INFO - Epoch [115][900/3746] lr: 1.338e-02, eta: 1 day, 6:24:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6502, loss_cls: 3.4147, loss: 3.4147 +2024-07-20 12:43:36,374 - pyskl - INFO - Epoch [115][1000/3746] lr: 1.336e-02, eta: 1 day, 6:22:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3970, top5_acc: 0.6597, loss_cls: 3.4137, loss: 3.4137 +2024-07-20 12:44:57,589 - pyskl - INFO - Epoch [115][1100/3746] lr: 1.334e-02, eta: 1 day, 6:21:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6495, loss_cls: 3.4545, loss: 3.4545 +2024-07-20 12:46:18,830 - pyskl - INFO - Epoch [115][1200/3746] lr: 1.332e-02, eta: 1 day, 6:20:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3908, top5_acc: 0.6462, loss_cls: 3.4424, loss: 3.4424 +2024-07-20 12:47:39,901 - pyskl - INFO - Epoch [115][1300/3746] lr: 1.330e-02, eta: 1 day, 6:18:51, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6522, loss_cls: 3.4290, loss: 3.4290 +2024-07-20 12:49:01,230 - pyskl - INFO - Epoch [115][1400/3746] lr: 1.328e-02, eta: 1 day, 6:17:29, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6597, loss_cls: 3.3647, loss: 3.3647 +2024-07-20 12:50:22,339 - pyskl - INFO - Epoch [115][1500/3746] lr: 1.327e-02, eta: 1 day, 6:16:08, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6636, loss_cls: 3.3945, loss: 3.3945 +2024-07-20 12:51:43,564 - pyskl - INFO - Epoch [115][1600/3746] lr: 1.325e-02, eta: 1 day, 6:14:46, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6480, loss_cls: 3.4668, loss: 3.4668 +2024-07-20 12:53:05,030 - pyskl - INFO - Epoch [115][1700/3746] lr: 1.323e-02, eta: 1 day, 6:13:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6561, loss_cls: 3.4260, loss: 3.4260 +2024-07-20 12:54:26,691 - pyskl - INFO - Epoch [115][1800/3746] lr: 1.321e-02, eta: 1 day, 6:12:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6511, loss_cls: 3.4475, loss: 3.4475 +2024-07-20 12:55:48,206 - pyskl - INFO - Epoch [115][1900/3746] lr: 1.319e-02, eta: 1 day, 6:10:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6406, loss_cls: 3.4706, loss: 3.4706 +2024-07-20 12:57:09,924 - pyskl - INFO - Epoch [115][2000/3746] lr: 1.317e-02, eta: 1 day, 6:09:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6527, loss_cls: 3.4292, loss: 3.4292 +2024-07-20 12:58:31,960 - pyskl - INFO - Epoch [115][2100/3746] lr: 1.315e-02, eta: 1 day, 6:07:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6495, loss_cls: 3.4670, loss: 3.4670 +2024-07-20 12:59:54,198 - pyskl - INFO - Epoch [115][2200/3746] lr: 1.313e-02, eta: 1 day, 6:06:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6530, loss_cls: 3.4283, loss: 3.4283 +2024-07-20 13:01:15,590 - pyskl - INFO - Epoch [115][2300/3746] lr: 1.311e-02, eta: 1 day, 6:05:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6578, loss_cls: 3.4002, loss: 3.4002 +2024-07-20 13:02:36,855 - pyskl - INFO - Epoch [115][2400/3746] lr: 1.310e-02, eta: 1 day, 6:03:52, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6444, loss_cls: 3.4891, loss: 3.4891 +2024-07-20 13:03:58,285 - pyskl - INFO - Epoch [115][2500/3746] lr: 1.308e-02, eta: 1 day, 6:02:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3822, top5_acc: 0.6477, loss_cls: 3.4541, loss: 3.4541 +2024-07-20 13:05:19,573 - pyskl - INFO - Epoch [115][2600/3746] lr: 1.306e-02, eta: 1 day, 6:01:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6408, loss_cls: 3.4826, loss: 3.4826 +2024-07-20 13:06:40,419 - pyskl - INFO - Epoch [115][2700/3746] lr: 1.304e-02, eta: 1 day, 5:59:46, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6455, loss_cls: 3.4397, loss: 3.4397 +2024-07-20 13:08:01,552 - pyskl - INFO - Epoch [115][2800/3746] lr: 1.302e-02, eta: 1 day, 5:58:24, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6562, loss_cls: 3.4302, loss: 3.4302 +2024-07-20 13:09:22,922 - pyskl - INFO - Epoch [115][2900/3746] lr: 1.300e-02, eta: 1 day, 5:57:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3941, top5_acc: 0.6530, loss_cls: 3.4324, loss: 3.4324 +2024-07-20 13:10:44,202 - pyskl - INFO - Epoch [115][3000/3746] lr: 1.298e-02, eta: 1 day, 5:55:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6383, loss_cls: 3.4625, loss: 3.4625 +2024-07-20 13:12:04,958 - pyskl - INFO - Epoch [115][3100/3746] lr: 1.296e-02, eta: 1 day, 5:54:19, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6464, loss_cls: 3.4412, loss: 3.4412 +2024-07-20 13:13:26,508 - pyskl - INFO - Epoch [115][3200/3746] lr: 1.295e-02, eta: 1 day, 5:52:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6384, loss_cls: 3.4853, loss: 3.4853 +2024-07-20 13:14:47,917 - pyskl - INFO - Epoch [115][3300/3746] lr: 1.293e-02, eta: 1 day, 5:51:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6477, loss_cls: 3.4533, loss: 3.4533 +2024-07-20 13:16:09,169 - pyskl - INFO - Epoch [115][3400/3746] lr: 1.291e-02, eta: 1 day, 5:50:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3858, top5_acc: 0.6477, loss_cls: 3.4501, loss: 3.4501 +2024-07-20 13:17:30,380 - pyskl - INFO - Epoch [115][3500/3746] lr: 1.289e-02, eta: 1 day, 5:48:51, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6447, loss_cls: 3.4795, loss: 3.4795 +2024-07-20 13:18:51,132 - pyskl - INFO - Epoch [115][3600/3746] lr: 1.287e-02, eta: 1 day, 5:47:29, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6417, loss_cls: 3.4814, loss: 3.4814 +2024-07-20 13:20:12,105 - pyskl - INFO - Epoch [115][3700/3746] lr: 1.285e-02, eta: 1 day, 5:46:07, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6414, loss_cls: 3.4943, loss: 3.4943 +2024-07-20 13:20:51,335 - pyskl - INFO - Saving checkpoint at 115 epochs +2024-07-20 13:22:42,052 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 13:22:42,777 - pyskl - INFO - +top1_acc 0.3239 +top5_acc 0.5774 +2024-07-20 13:22:42,777 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 13:22:42,820 - pyskl - INFO - +mean_acc 0.3237 +2024-07-20 13:22:42,825 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_114.pth was removed +2024-07-20 13:22:43,045 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_115.pth. +2024-07-20 13:22:43,046 - pyskl - INFO - Best top1_acc is 0.3239 at 115 epoch. +2024-07-20 13:22:43,059 - pyskl - INFO - Epoch(val) [115][309] top1_acc: 0.3239, top5_acc: 0.5774, mean_class_accuracy: 0.3237 +2024-07-20 13:26:31,641 - pyskl - INFO - Epoch [116][100/3746] lr: 1.282e-02, eta: 1 day, 5:44:41, time: 2.286, data_time: 1.310, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6595, loss_cls: 3.3599, loss: 3.3599 +2024-07-20 13:27:54,068 - pyskl - INFO - Epoch [116][200/3746] lr: 1.281e-02, eta: 1 day, 5:43:20, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4005, top5_acc: 0.6642, loss_cls: 3.3424, loss: 3.3424 +2024-07-20 13:29:16,363 - pyskl - INFO - Epoch [116][300/3746] lr: 1.279e-02, eta: 1 day, 5:41:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3972, top5_acc: 0.6605, loss_cls: 3.3771, loss: 3.3771 +2024-07-20 13:30:38,262 - pyskl - INFO - Epoch [116][400/3746] lr: 1.277e-02, eta: 1 day, 5:40:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6577, loss_cls: 3.4032, loss: 3.4032 +2024-07-20 13:31:59,906 - pyskl - INFO - Epoch [116][500/3746] lr: 1.275e-02, eta: 1 day, 5:39:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3947, top5_acc: 0.6589, loss_cls: 3.3799, loss: 3.3799 +2024-07-20 13:33:21,179 - pyskl - INFO - Epoch [116][600/3746] lr: 1.273e-02, eta: 1 day, 5:37:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6562, loss_cls: 3.4003, loss: 3.4003 +2024-07-20 13:34:42,349 - pyskl - INFO - Epoch [116][700/3746] lr: 1.271e-02, eta: 1 day, 5:36:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4070, top5_acc: 0.6761, loss_cls: 3.3200, loss: 3.3200 +2024-07-20 13:36:03,442 - pyskl - INFO - Epoch [116][800/3746] lr: 1.269e-02, eta: 1 day, 5:35:09, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3980, top5_acc: 0.6528, loss_cls: 3.4079, loss: 3.4079 +2024-07-20 13:37:24,485 - pyskl - INFO - Epoch [116][900/3746] lr: 1.268e-02, eta: 1 day, 5:33:47, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6559, loss_cls: 3.4313, loss: 3.4313 +2024-07-20 13:38:45,976 - pyskl - INFO - Epoch [116][1000/3746] lr: 1.266e-02, eta: 1 day, 5:32:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6520, loss_cls: 3.4406, loss: 3.4406 +2024-07-20 13:40:07,917 - pyskl - INFO - Epoch [116][1100/3746] lr: 1.264e-02, eta: 1 day, 5:31:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6453, loss_cls: 3.4305, loss: 3.4305 +2024-07-20 13:41:29,922 - pyskl - INFO - Epoch [116][1200/3746] lr: 1.262e-02, eta: 1 day, 5:29:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6592, loss_cls: 3.3616, loss: 3.3616 +2024-07-20 13:42:51,331 - pyskl - INFO - Epoch [116][1300/3746] lr: 1.260e-02, eta: 1 day, 5:28:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6520, loss_cls: 3.3940, loss: 3.3940 +2024-07-20 13:44:12,945 - pyskl - INFO - Epoch [116][1400/3746] lr: 1.258e-02, eta: 1 day, 5:26:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6442, loss_cls: 3.4387, loss: 3.4387 +2024-07-20 13:45:34,207 - pyskl - INFO - Epoch [116][1500/3746] lr: 1.256e-02, eta: 1 day, 5:25:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3858, top5_acc: 0.6489, loss_cls: 3.4398, loss: 3.4398 +2024-07-20 13:46:55,715 - pyskl - INFO - Epoch [116][1600/3746] lr: 1.255e-02, eta: 1 day, 5:24:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6648, loss_cls: 3.3623, loss: 3.3623 +2024-07-20 13:48:16,808 - pyskl - INFO - Epoch [116][1700/3746] lr: 1.253e-02, eta: 1 day, 5:22:53, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6491, loss_cls: 3.4386, loss: 3.4386 +2024-07-20 13:49:38,041 - pyskl - INFO - Epoch [116][1800/3746] lr: 1.251e-02, eta: 1 day, 5:21:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6564, loss_cls: 3.4071, loss: 3.4071 +2024-07-20 13:50:59,412 - pyskl - INFO - Epoch [116][1900/3746] lr: 1.249e-02, eta: 1 day, 5:20:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6481, loss_cls: 3.4350, loss: 3.4350 +2024-07-20 13:52:21,191 - pyskl - INFO - Epoch [116][2000/3746] lr: 1.247e-02, eta: 1 day, 5:18:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6539, loss_cls: 3.4188, loss: 3.4188 +2024-07-20 13:53:42,867 - pyskl - INFO - Epoch [116][2100/3746] lr: 1.245e-02, eta: 1 day, 5:17:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6522, loss_cls: 3.4073, loss: 3.4073 +2024-07-20 13:55:04,330 - pyskl - INFO - Epoch [116][2200/3746] lr: 1.243e-02, eta: 1 day, 5:16:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6456, loss_cls: 3.4271, loss: 3.4271 +2024-07-20 13:56:25,768 - pyskl - INFO - Epoch [116][2300/3746] lr: 1.242e-02, eta: 1 day, 5:14:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6444, loss_cls: 3.4685, loss: 3.4685 +2024-07-20 13:57:47,329 - pyskl - INFO - Epoch [116][2400/3746] lr: 1.240e-02, eta: 1 day, 5:13:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6484, loss_cls: 3.4238, loss: 3.4238 +2024-07-20 13:59:08,404 - pyskl - INFO - Epoch [116][2500/3746] lr: 1.238e-02, eta: 1 day, 5:11:58, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3992, top5_acc: 0.6578, loss_cls: 3.3943, loss: 3.3943 +2024-07-20 14:00:30,041 - pyskl - INFO - Epoch [116][2600/3746] lr: 1.236e-02, eta: 1 day, 5:10:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6520, loss_cls: 3.3927, loss: 3.3927 +2024-07-20 14:01:50,777 - pyskl - INFO - Epoch [116][2700/3746] lr: 1.234e-02, eta: 1 day, 5:09:14, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6494, loss_cls: 3.4239, loss: 3.4239 +2024-07-20 14:03:12,370 - pyskl - INFO - Epoch [116][2800/3746] lr: 1.232e-02, eta: 1 day, 5:07:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6542, loss_cls: 3.4066, loss: 3.4066 +2024-07-20 14:04:33,295 - pyskl - INFO - Epoch [116][2900/3746] lr: 1.231e-02, eta: 1 day, 5:06:31, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6512, loss_cls: 3.4316, loss: 3.4316 +2024-07-20 14:05:54,803 - pyskl - INFO - Epoch [116][3000/3746] lr: 1.229e-02, eta: 1 day, 5:05:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6455, loss_cls: 3.4386, loss: 3.4386 +2024-07-20 14:07:15,597 - pyskl - INFO - Epoch [116][3100/3746] lr: 1.227e-02, eta: 1 day, 5:03:47, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6505, loss_cls: 3.4204, loss: 3.4204 +2024-07-20 14:08:37,033 - pyskl - INFO - Epoch [116][3200/3746] lr: 1.225e-02, eta: 1 day, 5:02:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6462, loss_cls: 3.4104, loss: 3.4104 +2024-07-20 14:09:58,279 - pyskl - INFO - Epoch [116][3300/3746] lr: 1.223e-02, eta: 1 day, 5:01:03, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6494, loss_cls: 3.4417, loss: 3.4417 +2024-07-20 14:11:19,405 - pyskl - INFO - Epoch [116][3400/3746] lr: 1.221e-02, eta: 1 day, 4:59:41, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6400, loss_cls: 3.4783, loss: 3.4783 +2024-07-20 14:12:40,372 - pyskl - INFO - Epoch [116][3500/3746] lr: 1.220e-02, eta: 1 day, 4:58:19, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6469, loss_cls: 3.4290, loss: 3.4290 +2024-07-20 14:14:01,269 - pyskl - INFO - Epoch [116][3600/3746] lr: 1.218e-02, eta: 1 day, 4:56:57, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6334, loss_cls: 3.4905, loss: 3.4905 +2024-07-20 14:15:22,086 - pyskl - INFO - Epoch [116][3700/3746] lr: 1.216e-02, eta: 1 day, 4:55:35, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6389, loss_cls: 3.4758, loss: 3.4758 +2024-07-20 14:16:01,412 - pyskl - INFO - Saving checkpoint at 116 epochs +2024-07-20 14:17:52,455 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 14:17:53,138 - pyskl - INFO - +top1_acc 0.3160 +top5_acc 0.5715 +2024-07-20 14:17:53,138 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 14:17:53,179 - pyskl - INFO - +mean_acc 0.3159 +2024-07-20 14:17:53,191 - pyskl - INFO - Epoch(val) [116][309] top1_acc: 0.3160, top5_acc: 0.5715, mean_class_accuracy: 0.3159 +2024-07-20 14:21:46,006 - pyskl - INFO - Epoch [117][100/3746] lr: 1.213e-02, eta: 1 day, 4:54:09, time: 2.328, data_time: 1.345, memory: 15990, top1_acc: 0.4017, top5_acc: 0.6661, loss_cls: 3.3448, loss: 3.3448 +2024-07-20 14:23:08,483 - pyskl - INFO - Epoch [117][200/3746] lr: 1.211e-02, eta: 1 day, 4:52:48, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4091, top5_acc: 0.6664, loss_cls: 3.3547, loss: 3.3547 +2024-07-20 14:24:30,353 - pyskl - INFO - Epoch [117][300/3746] lr: 1.210e-02, eta: 1 day, 4:51:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3966, top5_acc: 0.6541, loss_cls: 3.4043, loss: 3.4043 +2024-07-20 14:25:52,131 - pyskl - INFO - Epoch [117][400/3746] lr: 1.208e-02, eta: 1 day, 4:50:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4113, top5_acc: 0.6694, loss_cls: 3.2995, loss: 3.2995 +2024-07-20 14:27:13,490 - pyskl - INFO - Epoch [117][500/3746] lr: 1.206e-02, eta: 1 day, 4:48:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4033, top5_acc: 0.6645, loss_cls: 3.3455, loss: 3.3455 +2024-07-20 14:28:34,869 - pyskl - INFO - Epoch [117][600/3746] lr: 1.204e-02, eta: 1 day, 4:47:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3997, top5_acc: 0.6633, loss_cls: 3.3716, loss: 3.3716 +2024-07-20 14:29:56,427 - pyskl - INFO - Epoch [117][700/3746] lr: 1.202e-02, eta: 1 day, 4:45:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6673, loss_cls: 3.3500, loss: 3.3500 +2024-07-20 14:31:17,979 - pyskl - INFO - Epoch [117][800/3746] lr: 1.200e-02, eta: 1 day, 4:44:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4025, top5_acc: 0.6611, loss_cls: 3.3503, loss: 3.3503 +2024-07-20 14:32:39,076 - pyskl - INFO - Epoch [117][900/3746] lr: 1.199e-02, eta: 1 day, 4:43:15, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6597, loss_cls: 3.3874, loss: 3.3874 +2024-07-20 14:34:00,442 - pyskl - INFO - Epoch [117][1000/3746] lr: 1.197e-02, eta: 1 day, 4:41:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6527, loss_cls: 3.4070, loss: 3.4070 +2024-07-20 14:35:21,481 - pyskl - INFO - Epoch [117][1100/3746] lr: 1.195e-02, eta: 1 day, 4:40:31, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6550, loss_cls: 3.3861, loss: 3.3861 +2024-07-20 14:36:42,556 - pyskl - INFO - Epoch [117][1200/3746] lr: 1.193e-02, eta: 1 day, 4:39:09, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6577, loss_cls: 3.3859, loss: 3.3859 +2024-07-20 14:38:03,291 - pyskl - INFO - Epoch [117][1300/3746] lr: 1.191e-02, eta: 1 day, 4:37:47, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6595, loss_cls: 3.3764, loss: 3.3764 +2024-07-20 14:39:24,389 - pyskl - INFO - Epoch [117][1400/3746] lr: 1.190e-02, eta: 1 day, 4:36:25, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6500, loss_cls: 3.4186, loss: 3.4186 +2024-07-20 14:40:45,734 - pyskl - INFO - Epoch [117][1500/3746] lr: 1.188e-02, eta: 1 day, 4:35:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6467, loss_cls: 3.4270, loss: 3.4270 +2024-07-20 14:42:06,804 - pyskl - INFO - Epoch [117][1600/3746] lr: 1.186e-02, eta: 1 day, 4:33:41, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6625, loss_cls: 3.3429, loss: 3.3429 +2024-07-20 14:43:27,620 - pyskl - INFO - Epoch [117][1700/3746] lr: 1.184e-02, eta: 1 day, 4:32:19, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6545, loss_cls: 3.4343, loss: 3.4343 +2024-07-20 14:44:48,320 - pyskl - INFO - Epoch [117][1800/3746] lr: 1.182e-02, eta: 1 day, 4:30:57, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.3997, top5_acc: 0.6622, loss_cls: 3.3667, loss: 3.3667 +2024-07-20 14:46:10,235 - pyskl - INFO - Epoch [117][1900/3746] lr: 1.181e-02, eta: 1 day, 4:29:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6531, loss_cls: 3.4038, loss: 3.4038 +2024-07-20 14:47:31,853 - pyskl - INFO - Epoch [117][2000/3746] lr: 1.179e-02, eta: 1 day, 4:28:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6514, loss_cls: 3.4227, loss: 3.4227 +2024-07-20 14:48:52,885 - pyskl - INFO - Epoch [117][2100/3746] lr: 1.177e-02, eta: 1 day, 4:26:52, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6592, loss_cls: 3.3961, loss: 3.3961 +2024-07-20 14:50:14,350 - pyskl - INFO - Epoch [117][2200/3746] lr: 1.175e-02, eta: 1 day, 4:25:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6525, loss_cls: 3.4210, loss: 3.4210 +2024-07-20 14:51:35,709 - pyskl - INFO - Epoch [117][2300/3746] lr: 1.173e-02, eta: 1 day, 4:24:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4009, top5_acc: 0.6641, loss_cls: 3.3764, loss: 3.3764 +2024-07-20 14:52:57,243 - pyskl - INFO - Epoch [117][2400/3746] lr: 1.172e-02, eta: 1 day, 4:22:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6498, loss_cls: 3.4157, loss: 3.4157 +2024-07-20 14:54:18,346 - pyskl - INFO - Epoch [117][2500/3746] lr: 1.170e-02, eta: 1 day, 4:21:24, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6512, loss_cls: 3.4343, loss: 3.4343 +2024-07-20 14:55:39,857 - pyskl - INFO - Epoch [117][2600/3746] lr: 1.168e-02, eta: 1 day, 4:20:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4009, top5_acc: 0.6584, loss_cls: 3.3921, loss: 3.3921 +2024-07-20 14:57:00,800 - pyskl - INFO - Epoch [117][2700/3746] lr: 1.166e-02, eta: 1 day, 4:18:40, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6545, loss_cls: 3.3983, loss: 3.3983 +2024-07-20 14:58:21,719 - pyskl - INFO - Epoch [117][2800/3746] lr: 1.164e-02, eta: 1 day, 4:17:18, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6606, loss_cls: 3.4050, loss: 3.4050 +2024-07-20 14:59:42,664 - pyskl - INFO - Epoch [117][2900/3746] lr: 1.163e-02, eta: 1 day, 4:15:56, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4073, top5_acc: 0.6613, loss_cls: 3.3609, loss: 3.3609 +2024-07-20 15:01:03,806 - pyskl - INFO - Epoch [117][3000/3746] lr: 1.161e-02, eta: 1 day, 4:14:34, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6530, loss_cls: 3.3963, loss: 3.3963 +2024-07-20 15:02:25,428 - pyskl - INFO - Epoch [117][3100/3746] lr: 1.159e-02, eta: 1 day, 4:13:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6453, loss_cls: 3.4327, loss: 3.4327 +2024-07-20 15:03:47,079 - pyskl - INFO - Epoch [117][3200/3746] lr: 1.157e-02, eta: 1 day, 4:11:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6447, loss_cls: 3.4111, loss: 3.4111 +2024-07-20 15:05:07,720 - pyskl - INFO - Epoch [117][3300/3746] lr: 1.155e-02, eta: 1 day, 4:10:29, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.3859, top5_acc: 0.6505, loss_cls: 3.4395, loss: 3.4395 +2024-07-20 15:06:28,939 - pyskl - INFO - Epoch [117][3400/3746] lr: 1.154e-02, eta: 1 day, 4:09:07, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6600, loss_cls: 3.3788, loss: 3.3788 +2024-07-20 15:07:49,724 - pyskl - INFO - Epoch [117][3500/3746] lr: 1.152e-02, eta: 1 day, 4:07:45, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6503, loss_cls: 3.4103, loss: 3.4103 +2024-07-20 15:09:10,490 - pyskl - INFO - Epoch [117][3600/3746] lr: 1.150e-02, eta: 1 day, 4:06:23, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6581, loss_cls: 3.3914, loss: 3.3914 +2024-07-20 15:10:31,652 - pyskl - INFO - Epoch [117][3700/3746] lr: 1.148e-02, eta: 1 day, 4:05:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6650, loss_cls: 3.3835, loss: 3.3835 +2024-07-20 15:11:11,017 - pyskl - INFO - Saving checkpoint at 117 epochs +2024-07-20 15:13:02,537 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 15:13:03,213 - pyskl - INFO - +top1_acc 0.3138 +top5_acc 0.5737 +2024-07-20 15:13:03,213 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 15:13:03,253 - pyskl - INFO - +mean_acc 0.3136 +2024-07-20 15:13:03,264 - pyskl - INFO - Epoch(val) [117][309] top1_acc: 0.3138, top5_acc: 0.5737, mean_class_accuracy: 0.3136 +2024-07-20 15:16:53,378 - pyskl - INFO - Epoch [118][100/3746] lr: 1.146e-02, eta: 1 day, 4:03:33, time: 2.301, data_time: 1.325, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6737, loss_cls: 3.2810, loss: 3.2810 +2024-07-20 15:18:15,275 - pyskl - INFO - Epoch [118][200/3746] lr: 1.144e-02, eta: 1 day, 4:02:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4119, top5_acc: 0.6730, loss_cls: 3.3020, loss: 3.3020 +2024-07-20 15:19:36,742 - pyskl - INFO - Epoch [118][300/3746] lr: 1.142e-02, eta: 1 day, 4:00:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6737, loss_cls: 3.2918, loss: 3.2918 +2024-07-20 15:20:57,826 - pyskl - INFO - Epoch [118][400/3746] lr: 1.140e-02, eta: 1 day, 3:59:27, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6684, loss_cls: 3.3211, loss: 3.3211 +2024-07-20 15:22:19,548 - pyskl - INFO - Epoch [118][500/3746] lr: 1.139e-02, eta: 1 day, 3:58:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6644, loss_cls: 3.3688, loss: 3.3688 +2024-07-20 15:23:40,649 - pyskl - INFO - Epoch [118][600/3746] lr: 1.137e-02, eta: 1 day, 3:56:43, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6673, loss_cls: 3.3395, loss: 3.3395 +2024-07-20 15:25:01,635 - pyskl - INFO - Epoch [118][700/3746] lr: 1.135e-02, eta: 1 day, 3:55:21, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6663, loss_cls: 3.3827, loss: 3.3827 +2024-07-20 15:26:22,353 - pyskl - INFO - Epoch [118][800/3746] lr: 1.133e-02, eta: 1 day, 3:53:59, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6520, loss_cls: 3.3987, loss: 3.3987 +2024-07-20 15:27:43,530 - pyskl - INFO - Epoch [118][900/3746] lr: 1.131e-02, eta: 1 day, 3:52:37, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6659, loss_cls: 3.3347, loss: 3.3347 +2024-07-20 15:29:04,611 - pyskl - INFO - Epoch [118][1000/3746] lr: 1.130e-02, eta: 1 day, 3:51:15, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6573, loss_cls: 3.3769, loss: 3.3769 +2024-07-20 15:30:25,490 - pyskl - INFO - Epoch [118][1100/3746] lr: 1.128e-02, eta: 1 day, 3:49:53, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6473, loss_cls: 3.3891, loss: 3.3891 +2024-07-20 15:31:47,364 - pyskl - INFO - Epoch [118][1200/3746] lr: 1.126e-02, eta: 1 day, 3:48:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6680, loss_cls: 3.3377, loss: 3.3377 +2024-07-20 15:33:08,767 - pyskl - INFO - Epoch [118][1300/3746] lr: 1.124e-02, eta: 1 day, 3:47:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6550, loss_cls: 3.3936, loss: 3.3936 +2024-07-20 15:34:29,860 - pyskl - INFO - Epoch [118][1400/3746] lr: 1.123e-02, eta: 1 day, 3:45:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6695, loss_cls: 3.3474, loss: 3.3474 +2024-07-20 15:35:51,331 - pyskl - INFO - Epoch [118][1500/3746] lr: 1.121e-02, eta: 1 day, 3:44:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6473, loss_cls: 3.4275, loss: 3.4275 +2024-07-20 15:37:12,387 - pyskl - INFO - Epoch [118][1600/3746] lr: 1.119e-02, eta: 1 day, 3:43:04, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6534, loss_cls: 3.4098, loss: 3.4098 +2024-07-20 15:38:34,131 - pyskl - INFO - Epoch [118][1700/3746] lr: 1.117e-02, eta: 1 day, 3:41:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6673, loss_cls: 3.3596, loss: 3.3596 +2024-07-20 15:39:55,086 - pyskl - INFO - Epoch [118][1800/3746] lr: 1.116e-02, eta: 1 day, 3:40:20, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6564, loss_cls: 3.3965, loss: 3.3965 +2024-07-20 15:41:16,507 - pyskl - INFO - Epoch [118][1900/3746] lr: 1.114e-02, eta: 1 day, 3:38:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4066, top5_acc: 0.6598, loss_cls: 3.3407, loss: 3.3407 +2024-07-20 15:42:38,395 - pyskl - INFO - Epoch [118][2000/3746] lr: 1.112e-02, eta: 1 day, 3:37:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6659, loss_cls: 3.3483, loss: 3.3483 +2024-07-20 15:43:59,666 - pyskl - INFO - Epoch [118][2100/3746] lr: 1.110e-02, eta: 1 day, 3:36:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6558, loss_cls: 3.3838, loss: 3.3838 +2024-07-20 15:45:21,053 - pyskl - INFO - Epoch [118][2200/3746] lr: 1.109e-02, eta: 1 day, 3:34:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3994, top5_acc: 0.6575, loss_cls: 3.3604, loss: 3.3604 +2024-07-20 15:46:42,722 - pyskl - INFO - Epoch [118][2300/3746] lr: 1.107e-02, eta: 1 day, 3:33:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6619, loss_cls: 3.3648, loss: 3.3648 +2024-07-20 15:48:03,973 - pyskl - INFO - Epoch [118][2400/3746] lr: 1.105e-02, eta: 1 day, 3:32:09, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4033, top5_acc: 0.6608, loss_cls: 3.3612, loss: 3.3612 +2024-07-20 15:49:25,379 - pyskl - INFO - Epoch [118][2500/3746] lr: 1.103e-02, eta: 1 day, 3:30:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6480, loss_cls: 3.4250, loss: 3.4250 +2024-07-20 15:50:46,534 - pyskl - INFO - Epoch [118][2600/3746] lr: 1.102e-02, eta: 1 day, 3:29:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6569, loss_cls: 3.4041, loss: 3.4041 +2024-07-20 15:52:07,686 - pyskl - INFO - Epoch [118][2700/3746] lr: 1.100e-02, eta: 1 day, 3:28:03, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6495, loss_cls: 3.4224, loss: 3.4224 +2024-07-20 15:53:29,527 - pyskl - INFO - Epoch [118][2800/3746] lr: 1.098e-02, eta: 1 day, 3:26:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6591, loss_cls: 3.3695, loss: 3.3695 +2024-07-20 15:54:50,603 - pyskl - INFO - Epoch [118][2900/3746] lr: 1.096e-02, eta: 1 day, 3:25:19, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6578, loss_cls: 3.3719, loss: 3.3719 +2024-07-20 15:56:11,498 - pyskl - INFO - Epoch [118][3000/3746] lr: 1.095e-02, eta: 1 day, 3:23:57, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6600, loss_cls: 3.3623, loss: 3.3623 +2024-07-20 15:57:32,690 - pyskl - INFO - Epoch [118][3100/3746] lr: 1.093e-02, eta: 1 day, 3:22:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6616, loss_cls: 3.3508, loss: 3.3508 +2024-07-20 15:58:53,398 - pyskl - INFO - Epoch [118][3200/3746] lr: 1.091e-02, eta: 1 day, 3:21:13, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6653, loss_cls: 3.3690, loss: 3.3690 +2024-07-20 16:00:14,780 - pyskl - INFO - Epoch [118][3300/3746] lr: 1.089e-02, eta: 1 day, 3:19:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6587, loss_cls: 3.4034, loss: 3.4034 +2024-07-20 16:01:35,873 - pyskl - INFO - Epoch [118][3400/3746] lr: 1.088e-02, eta: 1 day, 3:18:30, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6598, loss_cls: 3.4030, loss: 3.4030 +2024-07-20 16:02:56,957 - pyskl - INFO - Epoch [118][3500/3746] lr: 1.086e-02, eta: 1 day, 3:17:08, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6534, loss_cls: 3.3844, loss: 3.3844 +2024-07-20 16:04:17,704 - pyskl - INFO - Epoch [118][3600/3746] lr: 1.084e-02, eta: 1 day, 3:15:46, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.3914, top5_acc: 0.6486, loss_cls: 3.4175, loss: 3.4175 +2024-07-20 16:05:38,373 - pyskl - INFO - Epoch [118][3700/3746] lr: 1.082e-02, eta: 1 day, 3:14:24, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6631, loss_cls: 3.3201, loss: 3.3201 +2024-07-20 16:06:17,582 - pyskl - INFO - Saving checkpoint at 118 epochs +2024-07-20 16:08:08,305 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 16:08:09,079 - pyskl - INFO - +top1_acc 0.3286 +top5_acc 0.5861 +2024-07-20 16:08:09,080 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 16:08:09,127 - pyskl - INFO - +mean_acc 0.3285 +2024-07-20 16:08:09,132 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_115.pth was removed +2024-07-20 16:08:09,360 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2024-07-20 16:08:09,361 - pyskl - INFO - Best top1_acc is 0.3286 at 118 epoch. +2024-07-20 16:08:09,375 - pyskl - INFO - Epoch(val) [118][309] top1_acc: 0.3286, top5_acc: 0.5861, mean_class_accuracy: 0.3285 +2024-07-20 16:11:57,766 - pyskl - INFO - Epoch [119][100/3746] lr: 1.080e-02, eta: 1 day, 3:12:54, time: 2.284, data_time: 1.306, memory: 15990, top1_acc: 0.4214, top5_acc: 0.6791, loss_cls: 3.2466, loss: 3.2466 +2024-07-20 16:13:19,711 - pyskl - INFO - Epoch [119][200/3746] lr: 1.078e-02, eta: 1 day, 3:11:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4167, top5_acc: 0.6864, loss_cls: 3.2317, loss: 3.2317 +2024-07-20 16:14:41,995 - pyskl - INFO - Epoch [119][300/3746] lr: 1.076e-02, eta: 1 day, 3:10:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4116, top5_acc: 0.6667, loss_cls: 3.2884, loss: 3.2884 +2024-07-20 16:16:03,449 - pyskl - INFO - Epoch [119][400/3746] lr: 1.075e-02, eta: 1 day, 3:08:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4025, top5_acc: 0.6613, loss_cls: 3.3552, loss: 3.3552 +2024-07-20 16:17:25,218 - pyskl - INFO - Epoch [119][500/3746] lr: 1.073e-02, eta: 1 day, 3:07:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4156, top5_acc: 0.6786, loss_cls: 3.2850, loss: 3.2850 +2024-07-20 16:18:46,550 - pyskl - INFO - Epoch [119][600/3746] lr: 1.071e-02, eta: 1 day, 3:06:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3997, top5_acc: 0.6663, loss_cls: 3.3359, loss: 3.3359 +2024-07-20 16:20:07,902 - pyskl - INFO - Epoch [119][700/3746] lr: 1.069e-02, eta: 1 day, 3:04:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6680, loss_cls: 3.3212, loss: 3.3212 +2024-07-20 16:21:29,201 - pyskl - INFO - Epoch [119][800/3746] lr: 1.068e-02, eta: 1 day, 3:03:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6661, loss_cls: 3.3383, loss: 3.3383 +2024-07-20 16:22:50,245 - pyskl - INFO - Epoch [119][900/3746] lr: 1.066e-02, eta: 1 day, 3:01:59, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4059, top5_acc: 0.6602, loss_cls: 3.3451, loss: 3.3451 +2024-07-20 16:24:11,572 - pyskl - INFO - Epoch [119][1000/3746] lr: 1.064e-02, eta: 1 day, 3:00:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4088, top5_acc: 0.6709, loss_cls: 3.2867, loss: 3.2867 +2024-07-20 16:25:32,399 - pyskl - INFO - Epoch [119][1100/3746] lr: 1.063e-02, eta: 1 day, 2:59:15, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6650, loss_cls: 3.3470, loss: 3.3470 +2024-07-20 16:26:53,609 - pyskl - INFO - Epoch [119][1200/3746] lr: 1.061e-02, eta: 1 day, 2:57:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6703, loss_cls: 3.2999, loss: 3.2999 +2024-07-20 16:28:14,924 - pyskl - INFO - Epoch [119][1300/3746] lr: 1.059e-02, eta: 1 day, 2:56:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6725, loss_cls: 3.2784, loss: 3.2784 +2024-07-20 16:29:36,178 - pyskl - INFO - Epoch [119][1400/3746] lr: 1.057e-02, eta: 1 day, 2:55:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6716, loss_cls: 3.3170, loss: 3.3170 +2024-07-20 16:30:57,417 - pyskl - INFO - Epoch [119][1500/3746] lr: 1.056e-02, eta: 1 day, 2:53:47, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6672, loss_cls: 3.3157, loss: 3.3157 +2024-07-20 16:32:18,755 - pyskl - INFO - Epoch [119][1600/3746] lr: 1.054e-02, eta: 1 day, 2:52:25, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6722, loss_cls: 3.3250, loss: 3.3250 +2024-07-20 16:33:40,653 - pyskl - INFO - Epoch [119][1700/3746] lr: 1.052e-02, eta: 1 day, 2:51:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6666, loss_cls: 3.3333, loss: 3.3333 +2024-07-20 16:35:02,417 - pyskl - INFO - Epoch [119][1800/3746] lr: 1.050e-02, eta: 1 day, 2:49:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4106, top5_acc: 0.6641, loss_cls: 3.3232, loss: 3.3232 +2024-07-20 16:36:23,696 - pyskl - INFO - Epoch [119][1900/3746] lr: 1.049e-02, eta: 1 day, 2:48:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6570, loss_cls: 3.3718, loss: 3.3718 +2024-07-20 16:37:45,925 - pyskl - INFO - Epoch [119][2000/3746] lr: 1.047e-02, eta: 1 day, 2:46:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6642, loss_cls: 3.3315, loss: 3.3315 +2024-07-20 16:39:07,394 - pyskl - INFO - Epoch [119][2100/3746] lr: 1.045e-02, eta: 1 day, 2:45:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6538, loss_cls: 3.4200, loss: 3.4200 +2024-07-20 16:40:28,488 - pyskl - INFO - Epoch [119][2200/3746] lr: 1.044e-02, eta: 1 day, 2:44:14, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6631, loss_cls: 3.3495, loss: 3.3495 +2024-07-20 16:41:50,119 - pyskl - INFO - Epoch [119][2300/3746] lr: 1.042e-02, eta: 1 day, 2:42:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6502, loss_cls: 3.3903, loss: 3.3903 +2024-07-20 16:43:11,504 - pyskl - INFO - Epoch [119][2400/3746] lr: 1.040e-02, eta: 1 day, 2:41:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6573, loss_cls: 3.3914, loss: 3.3914 +2024-07-20 16:44:32,923 - pyskl - INFO - Epoch [119][2500/3746] lr: 1.039e-02, eta: 1 day, 2:40:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3898, top5_acc: 0.6577, loss_cls: 3.3885, loss: 3.3885 +2024-07-20 16:45:54,627 - pyskl - INFO - Epoch [119][2600/3746] lr: 1.037e-02, eta: 1 day, 2:38:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4017, top5_acc: 0.6719, loss_cls: 3.3507, loss: 3.3507 +2024-07-20 16:47:15,897 - pyskl - INFO - Epoch [119][2700/3746] lr: 1.035e-02, eta: 1 day, 2:37:25, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3980, top5_acc: 0.6559, loss_cls: 3.3813, loss: 3.3813 +2024-07-20 16:48:37,163 - pyskl - INFO - Epoch [119][2800/3746] lr: 1.033e-02, eta: 1 day, 2:36:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4095, top5_acc: 0.6703, loss_cls: 3.3189, loss: 3.3189 +2024-07-20 16:49:58,035 - pyskl - INFO - Epoch [119][2900/3746] lr: 1.032e-02, eta: 1 day, 2:34:41, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6619, loss_cls: 3.3615, loss: 3.3615 +2024-07-20 16:51:19,510 - pyskl - INFO - Epoch [119][3000/3746] lr: 1.030e-02, eta: 1 day, 2:33:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6569, loss_cls: 3.3946, loss: 3.3946 +2024-07-20 16:52:40,105 - pyskl - INFO - Epoch [119][3100/3746] lr: 1.028e-02, eta: 1 day, 2:31:57, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6692, loss_cls: 3.3098, loss: 3.3098 +2024-07-20 16:54:01,022 - pyskl - INFO - Epoch [119][3200/3746] lr: 1.027e-02, eta: 1 day, 2:30:35, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6573, loss_cls: 3.3621, loss: 3.3621 +2024-07-20 16:55:22,625 - pyskl - INFO - Epoch [119][3300/3746] lr: 1.025e-02, eta: 1 day, 2:29:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6578, loss_cls: 3.3692, loss: 3.3692 +2024-07-20 16:56:44,105 - pyskl - INFO - Epoch [119][3400/3746] lr: 1.023e-02, eta: 1 day, 2:27:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6633, loss_cls: 3.3736, loss: 3.3736 +2024-07-20 16:58:05,536 - pyskl - INFO - Epoch [119][3500/3746] lr: 1.022e-02, eta: 1 day, 2:26:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6586, loss_cls: 3.3885, loss: 3.3885 +2024-07-20 16:59:27,195 - pyskl - INFO - Epoch [119][3600/3746] lr: 1.020e-02, eta: 1 day, 2:25:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4033, top5_acc: 0.6658, loss_cls: 3.3538, loss: 3.3538 +2024-07-20 17:00:48,188 - pyskl - INFO - Epoch [119][3700/3746] lr: 1.018e-02, eta: 1 day, 2:23:45, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6597, loss_cls: 3.3964, loss: 3.3964 +2024-07-20 17:01:27,704 - pyskl - INFO - Saving checkpoint at 119 epochs +2024-07-20 17:03:17,753 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 17:03:18,416 - pyskl - INFO - +top1_acc 0.3309 +top5_acc 0.5884 +2024-07-20 17:03:18,416 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 17:03:18,456 - pyskl - INFO - +mean_acc 0.3306 +2024-07-20 17:03:18,461 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_118.pth was removed +2024-07-20 17:03:18,696 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_119.pth. +2024-07-20 17:03:18,697 - pyskl - INFO - Best top1_acc is 0.3309 at 119 epoch. +2024-07-20 17:03:18,708 - pyskl - INFO - Epoch(val) [119][309] top1_acc: 0.3309, top5_acc: 0.5884, mean_class_accuracy: 0.3306 +2024-07-20 17:07:04,400 - pyskl - INFO - Epoch [120][100/3746] lr: 1.016e-02, eta: 1 day, 2:22:13, time: 2.257, data_time: 1.283, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6886, loss_cls: 3.2097, loss: 3.2097 +2024-07-20 17:08:26,333 - pyskl - INFO - Epoch [120][200/3746] lr: 1.014e-02, eta: 1 day, 2:20:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6814, loss_cls: 3.2485, loss: 3.2485 +2024-07-20 17:09:47,674 - pyskl - INFO - Epoch [120][300/3746] lr: 1.012e-02, eta: 1 day, 2:19:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4155, top5_acc: 0.6725, loss_cls: 3.2784, loss: 3.2784 +2024-07-20 17:11:09,021 - pyskl - INFO - Epoch [120][400/3746] lr: 1.011e-02, eta: 1 day, 2:18:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4211, top5_acc: 0.6820, loss_cls: 3.2345, loss: 3.2345 +2024-07-20 17:12:29,745 - pyskl - INFO - Epoch [120][500/3746] lr: 1.009e-02, eta: 1 day, 2:16:46, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.4078, top5_acc: 0.6728, loss_cls: 3.2857, loss: 3.2857 +2024-07-20 17:13:50,563 - pyskl - INFO - Epoch [120][600/3746] lr: 1.007e-02, eta: 1 day, 2:15:23, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6797, loss_cls: 3.2777, loss: 3.2777 +2024-07-20 17:15:11,893 - pyskl - INFO - Epoch [120][700/3746] lr: 1.006e-02, eta: 1 day, 2:14:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6644, loss_cls: 3.3539, loss: 3.3539 +2024-07-20 17:16:32,726 - pyskl - INFO - Epoch [120][800/3746] lr: 1.004e-02, eta: 1 day, 2:12:39, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6650, loss_cls: 3.3384, loss: 3.3384 +2024-07-20 17:17:54,094 - pyskl - INFO - Epoch [120][900/3746] lr: 1.002e-02, eta: 1 day, 2:11:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6700, loss_cls: 3.2752, loss: 3.2752 +2024-07-20 17:19:14,949 - pyskl - INFO - Epoch [120][1000/3746] lr: 1.001e-02, eta: 1 day, 2:09:55, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4125, top5_acc: 0.6786, loss_cls: 3.2983, loss: 3.2983 +2024-07-20 17:20:36,029 - pyskl - INFO - Epoch [120][1100/3746] lr: 9.989e-03, eta: 1 day, 2:08:33, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6731, loss_cls: 3.2989, loss: 3.2989 +2024-07-20 17:21:57,333 - pyskl - INFO - Epoch [120][1200/3746] lr: 9.972e-03, eta: 1 day, 2:07:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6655, loss_cls: 3.2769, loss: 3.2769 +2024-07-20 17:23:18,386 - pyskl - INFO - Epoch [120][1300/3746] lr: 9.955e-03, eta: 1 day, 2:05:49, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3994, top5_acc: 0.6659, loss_cls: 3.3401, loss: 3.3401 +2024-07-20 17:24:40,073 - pyskl - INFO - Epoch [120][1400/3746] lr: 9.938e-03, eta: 1 day, 2:04:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6677, loss_cls: 3.3414, loss: 3.3414 +2024-07-20 17:26:01,333 - pyskl - INFO - Epoch [120][1500/3746] lr: 9.922e-03, eta: 1 day, 2:03:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4136, top5_acc: 0.6827, loss_cls: 3.2744, loss: 3.2744 +2024-07-20 17:27:22,249 - pyskl - INFO - Epoch [120][1600/3746] lr: 9.905e-03, eta: 1 day, 2:01:44, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6595, loss_cls: 3.3757, loss: 3.3757 +2024-07-20 17:28:43,396 - pyskl - INFO - Epoch [120][1700/3746] lr: 9.888e-03, eta: 1 day, 2:00:22, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6691, loss_cls: 3.3156, loss: 3.3156 +2024-07-20 17:30:04,447 - pyskl - INFO - Epoch [120][1800/3746] lr: 9.871e-03, eta: 1 day, 1:59:00, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6611, loss_cls: 3.3396, loss: 3.3396 +2024-07-20 17:31:25,647 - pyskl - INFO - Epoch [120][1900/3746] lr: 9.855e-03, eta: 1 day, 1:57:38, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6575, loss_cls: 3.3782, loss: 3.3782 +2024-07-20 17:32:46,688 - pyskl - INFO - Epoch [120][2000/3746] lr: 9.838e-03, eta: 1 day, 1:56:16, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6666, loss_cls: 3.3371, loss: 3.3371 +2024-07-20 17:34:08,404 - pyskl - INFO - Epoch [120][2100/3746] lr: 9.821e-03, eta: 1 day, 1:54:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6747, loss_cls: 3.3210, loss: 3.3210 +2024-07-20 17:35:29,720 - pyskl - INFO - Epoch [120][2200/3746] lr: 9.805e-03, eta: 1 day, 1:53:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6686, loss_cls: 3.3026, loss: 3.3026 +2024-07-20 17:36:50,877 - pyskl - INFO - Epoch [120][2300/3746] lr: 9.788e-03, eta: 1 day, 1:52:10, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6687, loss_cls: 3.3338, loss: 3.3338 +2024-07-20 17:38:12,631 - pyskl - INFO - Epoch [120][2400/3746] lr: 9.772e-03, eta: 1 day, 1:50:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6711, loss_cls: 3.3259, loss: 3.3259 +2024-07-20 17:39:33,736 - pyskl - INFO - Epoch [120][2500/3746] lr: 9.755e-03, eta: 1 day, 1:49:26, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6577, loss_cls: 3.3382, loss: 3.3382 +2024-07-20 17:40:55,138 - pyskl - INFO - Epoch [120][2600/3746] lr: 9.738e-03, eta: 1 day, 1:48:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6634, loss_cls: 3.3280, loss: 3.3280 +2024-07-20 17:42:16,766 - pyskl - INFO - Epoch [120][2700/3746] lr: 9.722e-03, eta: 1 day, 1:46:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6578, loss_cls: 3.3528, loss: 3.3528 +2024-07-20 17:43:37,705 - pyskl - INFO - Epoch [120][2800/3746] lr: 9.705e-03, eta: 1 day, 1:45:20, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4066, top5_acc: 0.6584, loss_cls: 3.3437, loss: 3.3437 +2024-07-20 17:44:59,483 - pyskl - INFO - Epoch [120][2900/3746] lr: 9.689e-03, eta: 1 day, 1:43:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6719, loss_cls: 3.3168, loss: 3.3168 +2024-07-20 17:46:20,666 - pyskl - INFO - Epoch [120][3000/3746] lr: 9.672e-03, eta: 1 day, 1:42:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6652, loss_cls: 3.3448, loss: 3.3448 +2024-07-20 17:47:41,897 - pyskl - INFO - Epoch [120][3100/3746] lr: 9.656e-03, eta: 1 day, 1:41:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6572, loss_cls: 3.3675, loss: 3.3675 +2024-07-20 17:49:03,059 - pyskl - INFO - Epoch [120][3200/3746] lr: 9.639e-03, eta: 1 day, 1:39:52, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6663, loss_cls: 3.3286, loss: 3.3286 +2024-07-20 17:50:24,399 - pyskl - INFO - Epoch [120][3300/3746] lr: 9.623e-03, eta: 1 day, 1:38:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6641, loss_cls: 3.3257, loss: 3.3257 +2024-07-20 17:51:45,699 - pyskl - INFO - Epoch [120][3400/3746] lr: 9.606e-03, eta: 1 day, 1:37:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4125, top5_acc: 0.6697, loss_cls: 3.2913, loss: 3.2913 +2024-07-20 17:53:06,882 - pyskl - INFO - Epoch [120][3500/3746] lr: 9.590e-03, eta: 1 day, 1:35:46, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6728, loss_cls: 3.3194, loss: 3.3194 +2024-07-20 17:54:27,549 - pyskl - INFO - Epoch [120][3600/3746] lr: 9.573e-03, eta: 1 day, 1:34:24, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6658, loss_cls: 3.3422, loss: 3.3422 +2024-07-20 17:55:48,698 - pyskl - INFO - Epoch [120][3700/3746] lr: 9.557e-03, eta: 1 day, 1:33:02, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4017, top5_acc: 0.6667, loss_cls: 3.3212, loss: 3.3212 +2024-07-20 17:56:28,337 - pyskl - INFO - Saving checkpoint at 120 epochs +2024-07-20 17:58:20,683 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 17:58:21,344 - pyskl - INFO - +top1_acc 0.3439 +top5_acc 0.5990 +2024-07-20 17:58:21,345 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 17:58:21,384 - pyskl - INFO - +mean_acc 0.3438 +2024-07-20 17:58:21,388 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_119.pth was removed +2024-07-20 17:58:21,617 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_120.pth. +2024-07-20 17:58:21,618 - pyskl - INFO - Best top1_acc is 0.3439 at 120 epoch. +2024-07-20 17:58:21,629 - pyskl - INFO - Epoch(val) [120][309] top1_acc: 0.3439, top5_acc: 0.5990, mean_class_accuracy: 0.3438 +2024-07-20 18:02:10,106 - pyskl - INFO - Epoch [121][100/3746] lr: 9.533e-03, eta: 1 day, 1:31:30, time: 2.285, data_time: 1.307, memory: 15990, top1_acc: 0.4359, top5_acc: 0.6869, loss_cls: 3.1927, loss: 3.1927 +2024-07-20 18:03:32,396 - pyskl - INFO - Epoch [121][200/3746] lr: 9.516e-03, eta: 1 day, 1:30:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4220, top5_acc: 0.6770, loss_cls: 3.2353, loss: 3.2353 +2024-07-20 18:04:53,958 - pyskl - INFO - Epoch [121][300/3746] lr: 9.500e-03, eta: 1 day, 1:28:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4119, top5_acc: 0.6786, loss_cls: 3.2727, loss: 3.2727 +2024-07-20 18:06:15,557 - pyskl - INFO - Epoch [121][400/3746] lr: 9.484e-03, eta: 1 day, 1:27:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6770, loss_cls: 3.2611, loss: 3.2611 +2024-07-20 18:07:37,191 - pyskl - INFO - Epoch [121][500/3746] lr: 9.467e-03, eta: 1 day, 1:26:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4117, top5_acc: 0.6739, loss_cls: 3.3078, loss: 3.3078 +2024-07-20 18:08:58,230 - pyskl - INFO - Epoch [121][600/3746] lr: 9.451e-03, eta: 1 day, 1:24:41, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6805, loss_cls: 3.2556, loss: 3.2556 +2024-07-20 18:10:20,045 - pyskl - INFO - Epoch [121][700/3746] lr: 9.435e-03, eta: 1 day, 1:23:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6816, loss_cls: 3.2647, loss: 3.2647 +2024-07-20 18:11:41,463 - pyskl - INFO - Epoch [121][800/3746] lr: 9.418e-03, eta: 1 day, 1:21:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6825, loss_cls: 3.2545, loss: 3.2545 +2024-07-20 18:13:02,872 - pyskl - INFO - Epoch [121][900/3746] lr: 9.402e-03, eta: 1 day, 1:20:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6727, loss_cls: 3.3020, loss: 3.3020 +2024-07-20 18:14:24,025 - pyskl - INFO - Epoch [121][1000/3746] lr: 9.386e-03, eta: 1 day, 1:19:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6748, loss_cls: 3.2848, loss: 3.2848 +2024-07-20 18:15:44,867 - pyskl - INFO - Epoch [121][1100/3746] lr: 9.369e-03, eta: 1 day, 1:17:51, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6697, loss_cls: 3.2934, loss: 3.2934 +2024-07-20 18:17:05,685 - pyskl - INFO - Epoch [121][1200/3746] lr: 9.353e-03, eta: 1 day, 1:16:29, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4178, top5_acc: 0.6811, loss_cls: 3.2417, loss: 3.2417 +2024-07-20 18:18:26,445 - pyskl - INFO - Epoch [121][1300/3746] lr: 9.337e-03, eta: 1 day, 1:15:07, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6814, loss_cls: 3.2207, loss: 3.2207 +2024-07-20 18:19:47,867 - pyskl - INFO - Epoch [121][1400/3746] lr: 9.321e-03, eta: 1 day, 1:13:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6713, loss_cls: 3.2810, loss: 3.2810 +2024-07-20 18:21:09,079 - pyskl - INFO - Epoch [121][1500/3746] lr: 9.304e-03, eta: 1 day, 1:12:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6677, loss_cls: 3.2969, loss: 3.2969 +2024-07-20 18:22:30,577 - pyskl - INFO - Epoch [121][1600/3746] lr: 9.288e-03, eta: 1 day, 1:11:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6747, loss_cls: 3.2647, loss: 3.2647 +2024-07-20 18:23:51,704 - pyskl - INFO - Epoch [121][1700/3746] lr: 9.272e-03, eta: 1 day, 1:09:39, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6764, loss_cls: 3.2775, loss: 3.2775 +2024-07-20 18:25:12,986 - pyskl - INFO - Epoch [121][1800/3746] lr: 9.256e-03, eta: 1 day, 1:08:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6714, loss_cls: 3.2915, loss: 3.2915 +2024-07-20 18:26:34,286 - pyskl - INFO - Epoch [121][1900/3746] lr: 9.239e-03, eta: 1 day, 1:06:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4130, top5_acc: 0.6672, loss_cls: 3.3174, loss: 3.3174 +2024-07-20 18:27:55,862 - pyskl - INFO - Epoch [121][2000/3746] lr: 9.223e-03, eta: 1 day, 1:05:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4025, top5_acc: 0.6720, loss_cls: 3.3066, loss: 3.3066 +2024-07-20 18:29:18,010 - pyskl - INFO - Epoch [121][2100/3746] lr: 9.207e-03, eta: 1 day, 1:04:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6630, loss_cls: 3.3604, loss: 3.3604 +2024-07-20 18:30:40,314 - pyskl - INFO - Epoch [121][2200/3746] lr: 9.191e-03, eta: 1 day, 1:02:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6661, loss_cls: 3.2985, loss: 3.2985 +2024-07-20 18:32:01,926 - pyskl - INFO - Epoch [121][2300/3746] lr: 9.175e-03, eta: 1 day, 1:01:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4078, top5_acc: 0.6639, loss_cls: 3.3249, loss: 3.3249 +2024-07-20 18:33:23,591 - pyskl - INFO - Epoch [121][2400/3746] lr: 9.159e-03, eta: 1 day, 1:00:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6611, loss_cls: 3.3527, loss: 3.3527 +2024-07-20 18:34:45,060 - pyskl - INFO - Epoch [121][2500/3746] lr: 9.142e-03, eta: 1 day, 0:58:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4102, top5_acc: 0.6716, loss_cls: 3.2929, loss: 3.2929 +2024-07-20 18:36:05,862 - pyskl - INFO - Epoch [121][2600/3746] lr: 9.126e-03, eta: 1 day, 0:57:22, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6664, loss_cls: 3.3013, loss: 3.3013 +2024-07-20 18:37:27,066 - pyskl - INFO - Epoch [121][2700/3746] lr: 9.110e-03, eta: 1 day, 0:56:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4095, top5_acc: 0.6673, loss_cls: 3.2927, loss: 3.2927 +2024-07-20 18:38:47,927 - pyskl - INFO - Epoch [121][2800/3746] lr: 9.094e-03, eta: 1 day, 0:54:38, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6717, loss_cls: 3.2881, loss: 3.2881 +2024-07-20 18:40:08,767 - pyskl - INFO - Epoch [121][2900/3746] lr: 9.078e-03, eta: 1 day, 0:53:16, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6650, loss_cls: 3.2959, loss: 3.2959 +2024-07-20 18:41:29,763 - pyskl - INFO - Epoch [121][3000/3746] lr: 9.062e-03, eta: 1 day, 0:51:53, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4106, top5_acc: 0.6666, loss_cls: 3.3238, loss: 3.3238 +2024-07-20 18:42:50,740 - pyskl - INFO - Epoch [121][3100/3746] lr: 9.046e-03, eta: 1 day, 0:50:31, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6658, loss_cls: 3.3182, loss: 3.3182 +2024-07-20 18:44:11,849 - pyskl - INFO - Epoch [121][3200/3746] lr: 9.030e-03, eta: 1 day, 0:49:09, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4136, top5_acc: 0.6739, loss_cls: 3.2872, loss: 3.2872 +2024-07-20 18:45:32,972 - pyskl - INFO - Epoch [121][3300/3746] lr: 9.014e-03, eta: 1 day, 0:47:47, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6713, loss_cls: 3.3264, loss: 3.3264 +2024-07-20 18:46:54,668 - pyskl - INFO - Epoch [121][3400/3746] lr: 8.998e-03, eta: 1 day, 0:46:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4106, top5_acc: 0.6753, loss_cls: 3.2837, loss: 3.2837 +2024-07-20 18:48:15,549 - pyskl - INFO - Epoch [121][3500/3746] lr: 8.982e-03, eta: 1 day, 0:45:03, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6736, loss_cls: 3.3082, loss: 3.3082 +2024-07-20 18:49:36,738 - pyskl - INFO - Epoch [121][3600/3746] lr: 8.966e-03, eta: 1 day, 0:43:41, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6702, loss_cls: 3.3201, loss: 3.3201 +2024-07-20 18:50:58,145 - pyskl - INFO - Epoch [121][3700/3746] lr: 8.950e-03, eta: 1 day, 0:42:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4102, top5_acc: 0.6647, loss_cls: 3.3135, loss: 3.3135 +2024-07-20 18:51:37,492 - pyskl - INFO - Saving checkpoint at 121 epochs +2024-07-20 18:53:26,495 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 18:53:27,149 - pyskl - INFO - +top1_acc 0.3485 +top5_acc 0.6071 +2024-07-20 18:53:27,149 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 18:53:27,189 - pyskl - INFO - +mean_acc 0.3483 +2024-07-20 18:53:27,194 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_120.pth was removed +2024-07-20 18:53:27,423 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_121.pth. +2024-07-20 18:53:27,424 - pyskl - INFO - Best top1_acc is 0.3485 at 121 epoch. +2024-07-20 18:53:27,435 - pyskl - INFO - Epoch(val) [121][309] top1_acc: 0.3485, top5_acc: 0.6071, mean_class_accuracy: 0.3483 +2024-07-20 18:57:14,576 - pyskl - INFO - Epoch [122][100/3746] lr: 8.927e-03, eta: 1 day, 0:40:46, time: 2.271, data_time: 1.305, memory: 15990, top1_acc: 0.4134, top5_acc: 0.6770, loss_cls: 3.2424, loss: 3.2424 +2024-07-20 18:58:35,757 - pyskl - INFO - Epoch [122][200/3746] lr: 8.911e-03, eta: 1 day, 0:39:24, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4258, top5_acc: 0.6789, loss_cls: 3.2278, loss: 3.2278 +2024-07-20 18:59:57,606 - pyskl - INFO - Epoch [122][300/3746] lr: 8.895e-03, eta: 1 day, 0:38:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4303, top5_acc: 0.6900, loss_cls: 3.1791, loss: 3.1791 +2024-07-20 19:01:18,741 - pyskl - INFO - Epoch [122][400/3746] lr: 8.879e-03, eta: 1 day, 0:36:40, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4300, top5_acc: 0.6916, loss_cls: 3.1905, loss: 3.1905 +2024-07-20 19:02:39,783 - pyskl - INFO - Epoch [122][500/3746] lr: 8.863e-03, eta: 1 day, 0:35:18, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4277, top5_acc: 0.6845, loss_cls: 3.2016, loss: 3.2016 +2024-07-20 19:04:01,190 - pyskl - INFO - Epoch [122][600/3746] lr: 8.847e-03, eta: 1 day, 0:33:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6787, loss_cls: 3.2424, loss: 3.2424 +2024-07-20 19:05:22,438 - pyskl - INFO - Epoch [122][700/3746] lr: 8.831e-03, eta: 1 day, 0:32:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4098, top5_acc: 0.6777, loss_cls: 3.2672, loss: 3.2672 +2024-07-20 19:06:43,582 - pyskl - INFO - Epoch [122][800/3746] lr: 8.815e-03, eta: 1 day, 0:31:12, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6831, loss_cls: 3.2165, loss: 3.2165 +2024-07-20 19:08:04,688 - pyskl - INFO - Epoch [122][900/3746] lr: 8.800e-03, eta: 1 day, 0:29:50, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6783, loss_cls: 3.2599, loss: 3.2599 +2024-07-20 19:09:25,721 - pyskl - INFO - Epoch [122][1000/3746] lr: 8.784e-03, eta: 1 day, 0:28:28, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4177, top5_acc: 0.6800, loss_cls: 3.2527, loss: 3.2527 +2024-07-20 19:10:46,430 - pyskl - INFO - Epoch [122][1100/3746] lr: 8.768e-03, eta: 1 day, 0:27:06, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6713, loss_cls: 3.3195, loss: 3.3195 +2024-07-20 19:12:07,767 - pyskl - INFO - Epoch [122][1200/3746] lr: 8.752e-03, eta: 1 day, 0:25:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6725, loss_cls: 3.2687, loss: 3.2687 +2024-07-20 19:13:29,374 - pyskl - INFO - Epoch [122][1300/3746] lr: 8.736e-03, eta: 1 day, 0:24:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4188, top5_acc: 0.6859, loss_cls: 3.2250, loss: 3.2250 +2024-07-20 19:14:50,607 - pyskl - INFO - Epoch [122][1400/3746] lr: 8.721e-03, eta: 1 day, 0:23:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6822, loss_cls: 3.2347, loss: 3.2347 +2024-07-20 19:16:12,284 - pyskl - INFO - Epoch [122][1500/3746] lr: 8.705e-03, eta: 1 day, 0:21:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6748, loss_cls: 3.2943, loss: 3.2943 +2024-07-20 19:17:33,710 - pyskl - INFO - Epoch [122][1600/3746] lr: 8.689e-03, eta: 1 day, 0:20:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4202, top5_acc: 0.6769, loss_cls: 3.2598, loss: 3.2598 +2024-07-20 19:18:54,210 - pyskl - INFO - Epoch [122][1700/3746] lr: 8.673e-03, eta: 1 day, 0:18:54, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6655, loss_cls: 3.3014, loss: 3.3014 +2024-07-20 19:20:15,635 - pyskl - INFO - Epoch [122][1800/3746] lr: 8.658e-03, eta: 1 day, 0:17:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4181, top5_acc: 0.6758, loss_cls: 3.2901, loss: 3.2901 +2024-07-20 19:21:36,919 - pyskl - INFO - Epoch [122][1900/3746] lr: 8.642e-03, eta: 1 day, 0:16:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4152, top5_acc: 0.6769, loss_cls: 3.2691, loss: 3.2691 +2024-07-20 19:22:57,631 - pyskl - INFO - Epoch [122][2000/3746] lr: 8.626e-03, eta: 1 day, 0:14:48, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6863, loss_cls: 3.2092, loss: 3.2092 +2024-07-20 19:24:19,261 - pyskl - INFO - Epoch [122][2100/3746] lr: 8.610e-03, eta: 1 day, 0:13:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4142, top5_acc: 0.6708, loss_cls: 3.2982, loss: 3.2982 +2024-07-20 19:25:40,680 - pyskl - INFO - Epoch [122][2200/3746] lr: 8.595e-03, eta: 1 day, 0:12:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6892, loss_cls: 3.2011, loss: 3.2011 +2024-07-20 19:27:02,689 - pyskl - INFO - Epoch [122][2300/3746] lr: 8.579e-03, eta: 1 day, 0:10:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4166, top5_acc: 0.6781, loss_cls: 3.2658, loss: 3.2658 +2024-07-20 19:28:24,350 - pyskl - INFO - Epoch [122][2400/3746] lr: 8.563e-03, eta: 1 day, 0:09:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4134, top5_acc: 0.6694, loss_cls: 3.2577, loss: 3.2577 +2024-07-20 19:29:45,926 - pyskl - INFO - Epoch [122][2500/3746] lr: 8.548e-03, eta: 1 day, 0:07:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6716, loss_cls: 3.2583, loss: 3.2583 +2024-07-20 19:31:07,173 - pyskl - INFO - Epoch [122][2600/3746] lr: 8.532e-03, eta: 1 day, 0:06:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4091, top5_acc: 0.6677, loss_cls: 3.3150, loss: 3.3150 +2024-07-20 19:32:29,288 - pyskl - INFO - Epoch [122][2700/3746] lr: 8.517e-03, eta: 1 day, 0:05:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4095, top5_acc: 0.6725, loss_cls: 3.3052, loss: 3.3052 +2024-07-20 19:33:50,901 - pyskl - INFO - Epoch [122][2800/3746] lr: 8.501e-03, eta: 1 day, 0:03:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6755, loss_cls: 3.2557, loss: 3.2557 +2024-07-20 19:35:11,897 - pyskl - INFO - Epoch [122][2900/3746] lr: 8.485e-03, eta: 1 day, 0:02:30, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4213, top5_acc: 0.6731, loss_cls: 3.2825, loss: 3.2825 +2024-07-20 19:36:32,798 - pyskl - INFO - Epoch [122][3000/3746] lr: 8.470e-03, eta: 1 day, 0:01:08, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6853, loss_cls: 3.2447, loss: 3.2447 +2024-07-20 19:37:53,605 - pyskl - INFO - Epoch [122][3100/3746] lr: 8.454e-03, eta: 23:59:46, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6677, loss_cls: 3.3434, loss: 3.3434 +2024-07-20 19:39:14,840 - pyskl - INFO - Epoch [122][3200/3746] lr: 8.439e-03, eta: 23:58:24, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6767, loss_cls: 3.2579, loss: 3.2579 +2024-07-20 19:40:35,625 - pyskl - INFO - Epoch [122][3300/3746] lr: 8.423e-03, eta: 23:57:02, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6800, loss_cls: 3.2729, loss: 3.2729 +2024-07-20 19:41:56,526 - pyskl - INFO - Epoch [122][3400/3746] lr: 8.408e-03, eta: 23:55:40, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6783, loss_cls: 3.2558, loss: 3.2558 +2024-07-20 19:43:17,589 - pyskl - INFO - Epoch [122][3500/3746] lr: 8.392e-03, eta: 23:54:18, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6797, loss_cls: 3.2845, loss: 3.2845 +2024-07-20 19:44:39,350 - pyskl - INFO - Epoch [122][3600/3746] lr: 8.377e-03, eta: 23:52:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6700, loss_cls: 3.2868, loss: 3.2868 +2024-07-20 19:46:00,304 - pyskl - INFO - Epoch [122][3700/3746] lr: 8.361e-03, eta: 23:51:34, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6811, loss_cls: 3.2496, loss: 3.2496 +2024-07-20 19:46:39,996 - pyskl - INFO - Saving checkpoint at 122 epochs +2024-07-20 19:48:31,035 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 19:48:31,696 - pyskl - INFO - +top1_acc 0.3349 +top5_acc 0.5941 +2024-07-20 19:48:31,696 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 19:48:31,735 - pyskl - INFO - +mean_acc 0.3346 +2024-07-20 19:48:31,746 - pyskl - INFO - Epoch(val) [122][309] top1_acc: 0.3349, top5_acc: 0.5941, mean_class_accuracy: 0.3346 +2024-07-20 19:52:19,787 - pyskl - INFO - Epoch [123][100/3746] lr: 8.339e-03, eta: 23:49:59, time: 2.280, data_time: 1.306, memory: 15990, top1_acc: 0.4291, top5_acc: 0.6886, loss_cls: 3.1776, loss: 3.1776 +2024-07-20 19:53:41,316 - pyskl - INFO - Epoch [123][200/3746] lr: 8.323e-03, eta: 23:48:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4214, top5_acc: 0.6834, loss_cls: 3.2437, loss: 3.2437 +2024-07-20 19:55:02,376 - pyskl - INFO - Epoch [123][300/3746] lr: 8.308e-03, eta: 23:47:15, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4300, top5_acc: 0.6920, loss_cls: 3.1936, loss: 3.1936 +2024-07-20 19:56:23,919 - pyskl - INFO - Epoch [123][400/3746] lr: 8.292e-03, eta: 23:45:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6922, loss_cls: 3.1925, loss: 3.1925 +2024-07-20 19:57:45,143 - pyskl - INFO - Epoch [123][500/3746] lr: 8.277e-03, eta: 23:44:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4330, top5_acc: 0.6989, loss_cls: 3.1678, loss: 3.1678 +2024-07-20 19:59:06,668 - pyskl - INFO - Epoch [123][600/3746] lr: 8.262e-03, eta: 23:43:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6800, loss_cls: 3.2407, loss: 3.2407 +2024-07-20 20:00:27,541 - pyskl - INFO - Epoch [123][700/3746] lr: 8.246e-03, eta: 23:41:47, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4283, top5_acc: 0.6859, loss_cls: 3.2076, loss: 3.2076 +2024-07-20 20:01:48,966 - pyskl - INFO - Epoch [123][800/3746] lr: 8.231e-03, eta: 23:40:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4361, top5_acc: 0.6973, loss_cls: 3.1839, loss: 3.1839 +2024-07-20 20:03:09,831 - pyskl - INFO - Epoch [123][900/3746] lr: 8.215e-03, eta: 23:39:03, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4291, top5_acc: 0.6897, loss_cls: 3.2001, loss: 3.2001 +2024-07-20 20:04:30,489 - pyskl - INFO - Epoch [123][1000/3746] lr: 8.200e-03, eta: 23:37:41, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6789, loss_cls: 3.2721, loss: 3.2721 +2024-07-20 20:05:51,290 - pyskl - INFO - Epoch [123][1100/3746] lr: 8.185e-03, eta: 23:36:19, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6797, loss_cls: 3.2302, loss: 3.2302 +2024-07-20 20:07:12,402 - pyskl - INFO - Epoch [123][1200/3746] lr: 8.169e-03, eta: 23:34:57, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4220, top5_acc: 0.6867, loss_cls: 3.1906, loss: 3.1906 +2024-07-20 20:08:33,602 - pyskl - INFO - Epoch [123][1300/3746] lr: 8.154e-03, eta: 23:33:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6850, loss_cls: 3.2161, loss: 3.2161 +2024-07-20 20:09:54,469 - pyskl - INFO - Epoch [123][1400/3746] lr: 8.139e-03, eta: 23:32:13, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4177, top5_acc: 0.6661, loss_cls: 3.2689, loss: 3.2689 +2024-07-20 20:11:15,295 - pyskl - INFO - Epoch [123][1500/3746] lr: 8.124e-03, eta: 23:30:51, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6847, loss_cls: 3.2060, loss: 3.2060 +2024-07-20 20:12:36,155 - pyskl - INFO - Epoch [123][1600/3746] lr: 8.108e-03, eta: 23:29:28, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6833, loss_cls: 3.2216, loss: 3.2216 +2024-07-20 20:13:57,824 - pyskl - INFO - Epoch [123][1700/3746] lr: 8.093e-03, eta: 23:28:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6825, loss_cls: 3.2316, loss: 3.2316 +2024-07-20 20:15:18,841 - pyskl - INFO - Epoch [123][1800/3746] lr: 8.078e-03, eta: 23:26:44, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6759, loss_cls: 3.2697, loss: 3.2697 +2024-07-20 20:16:40,290 - pyskl - INFO - Epoch [123][1900/3746] lr: 8.063e-03, eta: 23:25:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6767, loss_cls: 3.2229, loss: 3.2229 +2024-07-20 20:18:01,793 - pyskl - INFO - Epoch [123][2000/3746] lr: 8.047e-03, eta: 23:24:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6802, loss_cls: 3.2444, loss: 3.2444 +2024-07-20 20:19:23,823 - pyskl - INFO - Epoch [123][2100/3746] lr: 8.032e-03, eta: 23:22:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6878, loss_cls: 3.2118, loss: 3.2118 +2024-07-20 20:20:45,185 - pyskl - INFO - Epoch [123][2200/3746] lr: 8.017e-03, eta: 23:21:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6761, loss_cls: 3.2507, loss: 3.2507 +2024-07-20 20:22:06,658 - pyskl - INFO - Epoch [123][2300/3746] lr: 8.002e-03, eta: 23:19:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6819, loss_cls: 3.2384, loss: 3.2384 +2024-07-20 20:23:28,347 - pyskl - INFO - Epoch [123][2400/3746] lr: 7.987e-03, eta: 23:18:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4167, top5_acc: 0.6780, loss_cls: 3.2305, loss: 3.2305 +2024-07-20 20:24:49,881 - pyskl - INFO - Epoch [123][2500/3746] lr: 7.971e-03, eta: 23:17:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6819, loss_cls: 3.2441, loss: 3.2441 +2024-07-20 20:26:11,540 - pyskl - INFO - Epoch [123][2600/3746] lr: 7.956e-03, eta: 23:15:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6673, loss_cls: 3.3238, loss: 3.3238 +2024-07-20 20:27:32,457 - pyskl - INFO - Epoch [123][2700/3746] lr: 7.941e-03, eta: 23:14:27, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4178, top5_acc: 0.6784, loss_cls: 3.2498, loss: 3.2498 +2024-07-20 20:28:53,917 - pyskl - INFO - Epoch [123][2800/3746] lr: 7.926e-03, eta: 23:13:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4350, top5_acc: 0.6933, loss_cls: 3.1877, loss: 3.1877 +2024-07-20 20:30:15,099 - pyskl - INFO - Epoch [123][2900/3746] lr: 7.911e-03, eta: 23:11:43, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4156, top5_acc: 0.6717, loss_cls: 3.2824, loss: 3.2824 +2024-07-20 20:31:35,696 - pyskl - INFO - Epoch [123][3000/3746] lr: 7.896e-03, eta: 23:10:21, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6844, loss_cls: 3.2328, loss: 3.2328 +2024-07-20 20:32:57,216 - pyskl - INFO - Epoch [123][3100/3746] lr: 7.881e-03, eta: 23:08:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4219, top5_acc: 0.6747, loss_cls: 3.2368, loss: 3.2368 +2024-07-20 20:34:18,459 - pyskl - INFO - Epoch [123][3200/3746] lr: 7.866e-03, eta: 23:07:37, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6819, loss_cls: 3.2413, loss: 3.2413 +2024-07-20 20:35:39,742 - pyskl - INFO - Epoch [123][3300/3746] lr: 7.851e-03, eta: 23:06:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6719, loss_cls: 3.2765, loss: 3.2765 +2024-07-20 20:37:00,843 - pyskl - INFO - Epoch [123][3400/3746] lr: 7.836e-03, eta: 23:04:53, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6911, loss_cls: 3.2306, loss: 3.2306 +2024-07-20 20:38:22,061 - pyskl - INFO - Epoch [123][3500/3746] lr: 7.821e-03, eta: 23:03:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6763, loss_cls: 3.2621, loss: 3.2621 +2024-07-20 20:39:43,085 - pyskl - INFO - Epoch [123][3600/3746] lr: 7.806e-03, eta: 23:02:09, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4141, top5_acc: 0.6767, loss_cls: 3.2489, loss: 3.2489 +2024-07-20 20:41:03,782 - pyskl - INFO - Epoch [123][3700/3746] lr: 7.791e-03, eta: 23:00:46, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.4266, top5_acc: 0.6856, loss_cls: 3.2115, loss: 3.2115 +2024-07-20 20:41:43,688 - pyskl - INFO - Saving checkpoint at 123 epochs +2024-07-20 20:43:35,193 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 20:43:35,847 - pyskl - INFO - +top1_acc 0.3438 +top5_acc 0.6037 +2024-07-20 20:43:35,847 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 20:43:35,887 - pyskl - INFO - +mean_acc 0.3437 +2024-07-20 20:43:35,898 - pyskl - INFO - Epoch(val) [123][309] top1_acc: 0.3438, top5_acc: 0.6037, mean_class_accuracy: 0.3437 +2024-07-20 20:47:19,178 - pyskl - INFO - Epoch [124][100/3746] lr: 7.769e-03, eta: 22:59:10, time: 2.233, data_time: 1.257, memory: 15990, top1_acc: 0.4383, top5_acc: 0.7047, loss_cls: 3.1401, loss: 3.1401 +2024-07-20 20:48:40,242 - pyskl - INFO - Epoch [124][200/3746] lr: 7.754e-03, eta: 22:57:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4316, top5_acc: 0.6998, loss_cls: 3.1370, loss: 3.1370 +2024-07-20 20:50:01,787 - pyskl - INFO - Epoch [124][300/3746] lr: 7.739e-03, eta: 22:56:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.6995, loss_cls: 3.1177, loss: 3.1177 +2024-07-20 20:51:22,345 - pyskl - INFO - Epoch [124][400/3746] lr: 7.724e-03, eta: 22:55:03, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.4383, top5_acc: 0.6997, loss_cls: 3.1497, loss: 3.1497 +2024-07-20 20:52:43,695 - pyskl - INFO - Epoch [124][500/3746] lr: 7.709e-03, eta: 22:53:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4247, top5_acc: 0.6909, loss_cls: 3.1840, loss: 3.1840 +2024-07-20 20:54:04,557 - pyskl - INFO - Epoch [124][600/3746] lr: 7.694e-03, eta: 22:52:19, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.7016, loss_cls: 3.1399, loss: 3.1399 +2024-07-20 20:55:25,418 - pyskl - INFO - Epoch [124][700/3746] lr: 7.679e-03, eta: 22:50:57, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4286, top5_acc: 0.6853, loss_cls: 3.2048, loss: 3.2048 +2024-07-20 20:56:46,574 - pyskl - INFO - Epoch [124][800/3746] lr: 7.664e-03, eta: 22:49:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.7014, loss_cls: 3.1317, loss: 3.1317 +2024-07-20 20:58:07,801 - pyskl - INFO - Epoch [124][900/3746] lr: 7.649e-03, eta: 22:48:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4398, top5_acc: 0.6934, loss_cls: 3.1474, loss: 3.1474 +2024-07-20 20:59:29,046 - pyskl - INFO - Epoch [124][1000/3746] lr: 7.635e-03, eta: 22:46:51, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4275, top5_acc: 0.6984, loss_cls: 3.1703, loss: 3.1703 +2024-07-20 21:00:50,283 - pyskl - INFO - Epoch [124][1100/3746] lr: 7.620e-03, eta: 22:45:29, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4316, top5_acc: 0.6816, loss_cls: 3.2207, loss: 3.2207 +2024-07-20 21:02:11,640 - pyskl - INFO - Epoch [124][1200/3746] lr: 7.605e-03, eta: 22:44:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6825, loss_cls: 3.2178, loss: 3.2178 +2024-07-20 21:03:33,081 - pyskl - INFO - Epoch [124][1300/3746] lr: 7.590e-03, eta: 22:42:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4280, top5_acc: 0.6834, loss_cls: 3.2145, loss: 3.2145 +2024-07-20 21:04:54,636 - pyskl - INFO - Epoch [124][1400/3746] lr: 7.575e-03, eta: 22:41:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4205, top5_acc: 0.6814, loss_cls: 3.2351, loss: 3.2351 +2024-07-20 21:06:15,760 - pyskl - INFO - Epoch [124][1500/3746] lr: 7.561e-03, eta: 22:40:01, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6889, loss_cls: 3.1857, loss: 3.1857 +2024-07-20 21:07:37,013 - pyskl - INFO - Epoch [124][1600/3746] lr: 7.546e-03, eta: 22:38:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4219, top5_acc: 0.6923, loss_cls: 3.1977, loss: 3.1977 +2024-07-20 21:08:58,843 - pyskl - INFO - Epoch [124][1700/3746] lr: 7.531e-03, eta: 22:37:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6880, loss_cls: 3.2185, loss: 3.2185 +2024-07-20 21:10:19,933 - pyskl - INFO - Epoch [124][1800/3746] lr: 7.516e-03, eta: 22:35:55, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6906, loss_cls: 3.2094, loss: 3.2094 +2024-07-20 21:11:40,996 - pyskl - INFO - Epoch [124][1900/3746] lr: 7.502e-03, eta: 22:34:33, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4295, top5_acc: 0.6877, loss_cls: 3.1834, loss: 3.1834 +2024-07-20 21:13:02,540 - pyskl - INFO - Epoch [124][2000/3746] lr: 7.487e-03, eta: 22:33:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6827, loss_cls: 3.2306, loss: 3.2306 +2024-07-20 21:14:23,727 - pyskl - INFO - Epoch [124][2100/3746] lr: 7.472e-03, eta: 22:31:49, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4341, top5_acc: 0.6950, loss_cls: 3.1490, loss: 3.1490 +2024-07-20 21:15:45,265 - pyskl - INFO - Epoch [124][2200/3746] lr: 7.457e-03, eta: 22:30:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4138, top5_acc: 0.6795, loss_cls: 3.2702, loss: 3.2702 +2024-07-20 21:17:06,660 - pyskl - INFO - Epoch [124][2300/3746] lr: 7.443e-03, eta: 22:29:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4258, top5_acc: 0.6931, loss_cls: 3.1837, loss: 3.1837 +2024-07-20 21:18:28,079 - pyskl - INFO - Epoch [124][2400/3746] lr: 7.428e-03, eta: 22:27:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6834, loss_cls: 3.2207, loss: 3.2207 +2024-07-20 21:19:49,505 - pyskl - INFO - Epoch [124][2500/3746] lr: 7.413e-03, eta: 22:26:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6791, loss_cls: 3.2885, loss: 3.2885 +2024-07-20 21:21:11,165 - pyskl - INFO - Epoch [124][2600/3746] lr: 7.399e-03, eta: 22:24:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4228, top5_acc: 0.6816, loss_cls: 3.2474, loss: 3.2474 +2024-07-20 21:22:33,072 - pyskl - INFO - Epoch [124][2700/3746] lr: 7.384e-03, eta: 22:23:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6777, loss_cls: 3.2499, loss: 3.2499 +2024-07-20 21:23:54,125 - pyskl - INFO - Epoch [124][2800/3746] lr: 7.370e-03, eta: 22:22:15, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4252, top5_acc: 0.6928, loss_cls: 3.1691, loss: 3.1691 +2024-07-20 21:25:15,693 - pyskl - INFO - Epoch [124][2900/3746] lr: 7.355e-03, eta: 22:20:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4264, top5_acc: 0.6766, loss_cls: 3.2363, loss: 3.2363 +2024-07-20 21:26:37,222 - pyskl - INFO - Epoch [124][3000/3746] lr: 7.340e-03, eta: 22:19:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6823, loss_cls: 3.2224, loss: 3.2224 +2024-07-20 21:27:58,956 - pyskl - INFO - Epoch [124][3100/3746] lr: 7.326e-03, eta: 22:18:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4227, top5_acc: 0.6878, loss_cls: 3.2125, loss: 3.2125 +2024-07-20 21:29:19,909 - pyskl - INFO - Epoch [124][3200/3746] lr: 7.311e-03, eta: 22:16:47, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4213, top5_acc: 0.6830, loss_cls: 3.2337, loss: 3.2337 +2024-07-20 21:30:40,930 - pyskl - INFO - Epoch [124][3300/3746] lr: 7.297e-03, eta: 22:15:25, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6792, loss_cls: 3.2656, loss: 3.2656 +2024-07-20 21:32:02,585 - pyskl - INFO - Epoch [124][3400/3746] lr: 7.282e-03, eta: 22:14:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6827, loss_cls: 3.2179, loss: 3.2179 +2024-07-20 21:33:23,693 - pyskl - INFO - Epoch [124][3500/3746] lr: 7.268e-03, eta: 22:12:41, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6839, loss_cls: 3.2015, loss: 3.2015 +2024-07-20 21:34:45,194 - pyskl - INFO - Epoch [124][3600/3746] lr: 7.253e-03, eta: 22:11:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4256, top5_acc: 0.6886, loss_cls: 3.2193, loss: 3.2193 +2024-07-20 21:36:06,429 - pyskl - INFO - Epoch [124][3700/3746] lr: 7.239e-03, eta: 22:09:57, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6859, loss_cls: 3.1943, loss: 3.1943 +2024-07-20 21:36:45,989 - pyskl - INFO - Saving checkpoint at 124 epochs +2024-07-20 21:38:36,465 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 21:38:37,127 - pyskl - INFO - +top1_acc 0.3436 +top5_acc 0.6002 +2024-07-20 21:38:37,128 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 21:38:37,172 - pyskl - INFO - +mean_acc 0.3435 +2024-07-20 21:38:37,185 - pyskl - INFO - Epoch(val) [124][309] top1_acc: 0.3436, top5_acc: 0.6002, mean_class_accuracy: 0.3435 +2024-07-20 21:42:18,626 - pyskl - INFO - Epoch [125][100/3746] lr: 7.217e-03, eta: 22:08:19, time: 2.214, data_time: 1.244, memory: 15990, top1_acc: 0.4439, top5_acc: 0.6955, loss_cls: 3.1116, loss: 3.1116 +2024-07-20 21:43:40,045 - pyskl - INFO - Epoch [125][200/3746] lr: 7.203e-03, eta: 22:06:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4378, top5_acc: 0.7017, loss_cls: 3.1198, loss: 3.1198 +2024-07-20 21:45:01,385 - pyskl - INFO - Epoch [125][300/3746] lr: 7.189e-03, eta: 22:05:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4530, top5_acc: 0.7100, loss_cls: 3.0579, loss: 3.0579 +2024-07-20 21:46:22,912 - pyskl - INFO - Epoch [125][400/3746] lr: 7.174e-03, eta: 22:04:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4495, top5_acc: 0.6945, loss_cls: 3.1004, loss: 3.1004 +2024-07-20 21:47:43,803 - pyskl - INFO - Epoch [125][500/3746] lr: 7.160e-03, eta: 22:02:51, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4366, top5_acc: 0.6977, loss_cls: 3.1377, loss: 3.1377 +2024-07-20 21:49:04,618 - pyskl - INFO - Epoch [125][600/3746] lr: 7.145e-03, eta: 22:01:28, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4292, top5_acc: 0.6978, loss_cls: 3.1539, loss: 3.1539 +2024-07-20 21:50:26,262 - pyskl - INFO - Epoch [125][700/3746] lr: 7.131e-03, eta: 22:00:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.7019, loss_cls: 3.1094, loss: 3.1094 +2024-07-20 21:51:47,317 - pyskl - INFO - Epoch [125][800/3746] lr: 7.117e-03, eta: 21:58:44, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4311, top5_acc: 0.6936, loss_cls: 3.1663, loss: 3.1663 +2024-07-20 21:53:08,529 - pyskl - INFO - Epoch [125][900/3746] lr: 7.102e-03, eta: 21:57:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4381, top5_acc: 0.6941, loss_cls: 3.1571, loss: 3.1571 +2024-07-20 21:54:29,472 - pyskl - INFO - Epoch [125][1000/3746] lr: 7.088e-03, eta: 21:56:00, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4375, top5_acc: 0.6933, loss_cls: 3.1618, loss: 3.1618 +2024-07-20 21:55:50,675 - pyskl - INFO - Epoch [125][1100/3746] lr: 7.073e-03, eta: 21:54:38, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4252, top5_acc: 0.6848, loss_cls: 3.1748, loss: 3.1748 +2024-07-20 21:57:12,187 - pyskl - INFO - Epoch [125][1200/3746] lr: 7.059e-03, eta: 21:53:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4375, top5_acc: 0.6908, loss_cls: 3.1688, loss: 3.1688 +2024-07-20 21:58:33,656 - pyskl - INFO - Epoch [125][1300/3746] lr: 7.045e-03, eta: 21:51:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.6980, loss_cls: 3.1319, loss: 3.1319 +2024-07-20 21:59:54,405 - pyskl - INFO - Epoch [125][1400/3746] lr: 7.031e-03, eta: 21:50:32, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.4272, top5_acc: 0.6891, loss_cls: 3.1876, loss: 3.1876 +2024-07-20 22:01:15,410 - pyskl - INFO - Epoch [125][1500/3746] lr: 7.016e-03, eta: 21:49:10, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6887, loss_cls: 3.1944, loss: 3.1944 +2024-07-20 22:02:36,544 - pyskl - INFO - Epoch [125][1600/3746] lr: 7.002e-03, eta: 21:47:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4323, top5_acc: 0.7045, loss_cls: 3.1373, loss: 3.1373 +2024-07-20 22:03:57,303 - pyskl - INFO - Epoch [125][1700/3746] lr: 6.988e-03, eta: 21:46:26, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4295, top5_acc: 0.6883, loss_cls: 3.2058, loss: 3.2058 +2024-07-20 22:05:18,857 - pyskl - INFO - Epoch [125][1800/3746] lr: 6.973e-03, eta: 21:45:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4205, top5_acc: 0.6941, loss_cls: 3.1805, loss: 3.1805 +2024-07-20 22:06:39,633 - pyskl - INFO - Epoch [125][1900/3746] lr: 6.959e-03, eta: 21:43:42, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4363, top5_acc: 0.6914, loss_cls: 3.1618, loss: 3.1618 +2024-07-20 22:08:00,787 - pyskl - INFO - Epoch [125][2000/3746] lr: 6.945e-03, eta: 21:42:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4370, top5_acc: 0.6937, loss_cls: 3.1479, loss: 3.1479 +2024-07-20 22:09:21,912 - pyskl - INFO - Epoch [125][2100/3746] lr: 6.931e-03, eta: 21:40:57, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4358, top5_acc: 0.6986, loss_cls: 3.1504, loss: 3.1504 +2024-07-20 22:10:43,207 - pyskl - INFO - Epoch [125][2200/3746] lr: 6.917e-03, eta: 21:39:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6783, loss_cls: 3.2595, loss: 3.2595 +2024-07-20 22:12:05,068 - pyskl - INFO - Epoch [125][2300/3746] lr: 6.902e-03, eta: 21:38:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.6980, loss_cls: 3.1379, loss: 3.1379 +2024-07-20 22:13:26,820 - pyskl - INFO - Epoch [125][2400/3746] lr: 6.888e-03, eta: 21:36:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4331, top5_acc: 0.6900, loss_cls: 3.1860, loss: 3.1860 +2024-07-20 22:14:48,772 - pyskl - INFO - Epoch [125][2500/3746] lr: 6.874e-03, eta: 21:35:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4220, top5_acc: 0.6830, loss_cls: 3.2217, loss: 3.2217 +2024-07-20 22:16:10,488 - pyskl - INFO - Epoch [125][2600/3746] lr: 6.860e-03, eta: 21:34:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6791, loss_cls: 3.2119, loss: 3.2119 +2024-07-20 22:17:32,493 - pyskl - INFO - Epoch [125][2700/3746] lr: 6.846e-03, eta: 21:32:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6997, loss_cls: 3.1272, loss: 3.1272 +2024-07-20 22:18:54,441 - pyskl - INFO - Epoch [125][2800/3746] lr: 6.832e-03, eta: 21:31:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4291, top5_acc: 0.6786, loss_cls: 3.2355, loss: 3.2355 +2024-07-20 22:20:15,957 - pyskl - INFO - Epoch [125][2900/3746] lr: 6.818e-03, eta: 21:30:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4297, top5_acc: 0.6927, loss_cls: 3.1874, loss: 3.1874 +2024-07-20 22:21:37,424 - pyskl - INFO - Epoch [125][3000/3746] lr: 6.804e-03, eta: 21:28:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4339, top5_acc: 0.6842, loss_cls: 3.1829, loss: 3.1829 +2024-07-20 22:22:58,887 - pyskl - INFO - Epoch [125][3100/3746] lr: 6.789e-03, eta: 21:27:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4264, top5_acc: 0.6764, loss_cls: 3.2223, loss: 3.2223 +2024-07-20 22:24:20,058 - pyskl - INFO - Epoch [125][3200/3746] lr: 6.775e-03, eta: 21:25:56, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4355, top5_acc: 0.6913, loss_cls: 3.1429, loss: 3.1429 +2024-07-20 22:25:41,252 - pyskl - INFO - Epoch [125][3300/3746] lr: 6.761e-03, eta: 21:24:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4197, top5_acc: 0.6828, loss_cls: 3.2054, loss: 3.2054 +2024-07-20 22:27:02,315 - pyskl - INFO - Epoch [125][3400/3746] lr: 6.747e-03, eta: 21:23:12, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4372, top5_acc: 0.6945, loss_cls: 3.1732, loss: 3.1732 +2024-07-20 22:28:22,929 - pyskl - INFO - Epoch [125][3500/3746] lr: 6.733e-03, eta: 21:21:49, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.4297, top5_acc: 0.6895, loss_cls: 3.1839, loss: 3.1839 +2024-07-20 22:29:44,105 - pyskl - INFO - Epoch [125][3600/3746] lr: 6.719e-03, eta: 21:20:27, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4280, top5_acc: 0.6859, loss_cls: 3.2056, loss: 3.2056 +2024-07-20 22:31:05,292 - pyskl - INFO - Epoch [125][3700/3746] lr: 6.705e-03, eta: 21:19:05, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6902, loss_cls: 3.1684, loss: 3.1684 +2024-07-20 22:31:44,827 - pyskl - INFO - Saving checkpoint at 125 epochs +2024-07-20 22:33:36,478 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 22:33:37,137 - pyskl - INFO - +top1_acc 0.3519 +top5_acc 0.6089 +2024-07-20 22:33:37,137 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 22:33:37,176 - pyskl - INFO - +mean_acc 0.3517 +2024-07-20 22:33:37,181 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_121.pth was removed +2024-07-20 22:33:37,408 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_125.pth. +2024-07-20 22:33:37,409 - pyskl - INFO - Best top1_acc is 0.3519 at 125 epoch. +2024-07-20 22:33:37,420 - pyskl - INFO - Epoch(val) [125][309] top1_acc: 0.3519, top5_acc: 0.6089, mean_class_accuracy: 0.3517 +2024-07-20 22:37:21,101 - pyskl - INFO - Epoch [126][100/3746] lr: 6.685e-03, eta: 21:17:27, time: 2.237, data_time: 1.261, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7113, loss_cls: 3.0473, loss: 3.0473 +2024-07-20 22:38:42,912 - pyskl - INFO - Epoch [126][200/3746] lr: 6.671e-03, eta: 21:16:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4414, top5_acc: 0.6958, loss_cls: 3.1362, loss: 3.1362 +2024-07-20 22:40:03,864 - pyskl - INFO - Epoch [126][300/3746] lr: 6.657e-03, eta: 21:14:42, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.6977, loss_cls: 3.0947, loss: 3.0947 +2024-07-20 22:41:25,296 - pyskl - INFO - Epoch [126][400/3746] lr: 6.643e-03, eta: 21:13:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4381, top5_acc: 0.7058, loss_cls: 3.1201, loss: 3.1201 +2024-07-20 22:42:46,271 - pyskl - INFO - Epoch [126][500/3746] lr: 6.629e-03, eta: 21:11:58, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4344, top5_acc: 0.6952, loss_cls: 3.1451, loss: 3.1451 +2024-07-20 22:44:07,693 - pyskl - INFO - Epoch [126][600/3746] lr: 6.615e-03, eta: 21:10:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4366, top5_acc: 0.6950, loss_cls: 3.1344, loss: 3.1344 +2024-07-20 22:45:29,052 - pyskl - INFO - Epoch [126][700/3746] lr: 6.601e-03, eta: 21:09:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.7011, loss_cls: 3.0703, loss: 3.0703 +2024-07-20 22:46:49,951 - pyskl - INFO - Epoch [126][800/3746] lr: 6.587e-03, eta: 21:07:52, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4461, top5_acc: 0.7044, loss_cls: 3.0888, loss: 3.0888 +2024-07-20 22:48:11,446 - pyskl - INFO - Epoch [126][900/3746] lr: 6.574e-03, eta: 21:06:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4313, top5_acc: 0.6978, loss_cls: 3.1369, loss: 3.1369 +2024-07-20 22:49:32,803 - pyskl - INFO - Epoch [126][1000/3746] lr: 6.560e-03, eta: 21:05:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4402, top5_acc: 0.7019, loss_cls: 3.1303, loss: 3.1303 +2024-07-20 22:50:53,951 - pyskl - INFO - Epoch [126][1100/3746] lr: 6.546e-03, eta: 21:03:46, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4383, top5_acc: 0.6953, loss_cls: 3.1392, loss: 3.1392 +2024-07-20 22:52:15,103 - pyskl - INFO - Epoch [126][1200/3746] lr: 6.532e-03, eta: 21:02:24, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4370, top5_acc: 0.6983, loss_cls: 3.1552, loss: 3.1552 +2024-07-20 22:53:36,373 - pyskl - INFO - Epoch [126][1300/3746] lr: 6.518e-03, eta: 21:01:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4400, top5_acc: 0.6925, loss_cls: 3.1218, loss: 3.1218 +2024-07-20 22:54:57,933 - pyskl - INFO - Epoch [126][1400/3746] lr: 6.505e-03, eta: 20:59:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4414, top5_acc: 0.6984, loss_cls: 3.1154, loss: 3.1154 +2024-07-20 22:56:18,931 - pyskl - INFO - Epoch [126][1500/3746] lr: 6.491e-03, eta: 20:58:18, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6950, loss_cls: 3.1450, loss: 3.1450 +2024-07-20 22:57:39,950 - pyskl - INFO - Epoch [126][1600/3746] lr: 6.477e-03, eta: 20:56:56, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6992, loss_cls: 3.1209, loss: 3.1209 +2024-07-20 22:59:01,387 - pyskl - INFO - Epoch [126][1700/3746] lr: 6.463e-03, eta: 20:55:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6817, loss_cls: 3.2008, loss: 3.2008 +2024-07-20 23:00:22,697 - pyskl - INFO - Epoch [126][1800/3746] lr: 6.449e-03, eta: 20:54:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4387, top5_acc: 0.6959, loss_cls: 3.1270, loss: 3.1270 +2024-07-20 23:01:43,625 - pyskl - INFO - Epoch [126][1900/3746] lr: 6.436e-03, eta: 20:52:49, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.6972, loss_cls: 3.1264, loss: 3.1264 +2024-07-20 23:03:04,531 - pyskl - INFO - Epoch [126][2000/3746] lr: 6.422e-03, eta: 20:51:27, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4363, top5_acc: 0.6905, loss_cls: 3.1395, loss: 3.1395 +2024-07-20 23:04:25,747 - pyskl - INFO - Epoch [126][2100/3746] lr: 6.408e-03, eta: 20:50:05, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4298, top5_acc: 0.6959, loss_cls: 3.1583, loss: 3.1583 +2024-07-20 23:05:47,023 - pyskl - INFO - Epoch [126][2200/3746] lr: 6.395e-03, eta: 20:48:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4408, top5_acc: 0.6964, loss_cls: 3.1093, loss: 3.1093 +2024-07-20 23:07:08,087 - pyskl - INFO - Epoch [126][2300/3746] lr: 6.381e-03, eta: 20:47:21, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4328, top5_acc: 0.7039, loss_cls: 3.1160, loss: 3.1160 +2024-07-20 23:08:30,376 - pyskl - INFO - Epoch [126][2400/3746] lr: 6.367e-03, eta: 20:45:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6887, loss_cls: 3.1720, loss: 3.1720 +2024-07-20 23:09:52,492 - pyskl - INFO - Epoch [126][2500/3746] lr: 6.354e-03, eta: 20:44:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4509, top5_acc: 0.7037, loss_cls: 3.1079, loss: 3.1079 +2024-07-20 23:11:14,402 - pyskl - INFO - Epoch [126][2600/3746] lr: 6.340e-03, eta: 20:43:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6903, loss_cls: 3.1794, loss: 3.1794 +2024-07-20 23:12:35,593 - pyskl - INFO - Epoch [126][2700/3746] lr: 6.326e-03, eta: 20:41:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4300, top5_acc: 0.6887, loss_cls: 3.1768, loss: 3.1768 +2024-07-20 23:13:57,023 - pyskl - INFO - Epoch [126][2800/3746] lr: 6.313e-03, eta: 20:40:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4347, top5_acc: 0.6867, loss_cls: 3.1849, loss: 3.1849 +2024-07-20 23:15:18,613 - pyskl - INFO - Epoch [126][2900/3746] lr: 6.299e-03, eta: 20:39:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4303, top5_acc: 0.6913, loss_cls: 3.1859, loss: 3.1859 +2024-07-20 23:16:39,981 - pyskl - INFO - Epoch [126][3000/3746] lr: 6.286e-03, eta: 20:37:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4363, top5_acc: 0.7045, loss_cls: 3.1288, loss: 3.1288 +2024-07-20 23:18:00,802 - pyskl - INFO - Epoch [126][3100/3746] lr: 6.272e-03, eta: 20:36:25, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4292, top5_acc: 0.6884, loss_cls: 3.1759, loss: 3.1759 +2024-07-20 23:19:21,720 - pyskl - INFO - Epoch [126][3200/3746] lr: 6.259e-03, eta: 20:35:03, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4348, top5_acc: 0.6934, loss_cls: 3.1339, loss: 3.1339 +2024-07-20 23:20:42,649 - pyskl - INFO - Epoch [126][3300/3746] lr: 6.245e-03, eta: 20:33:41, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6844, loss_cls: 3.1931, loss: 3.1931 +2024-07-20 23:22:03,871 - pyskl - INFO - Epoch [126][3400/3746] lr: 6.231e-03, eta: 20:32:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.7017, loss_cls: 3.1508, loss: 3.1508 +2024-07-20 23:23:24,817 - pyskl - INFO - Epoch [126][3500/3746] lr: 6.218e-03, eta: 20:30:57, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.7019, loss_cls: 3.1263, loss: 3.1263 +2024-07-20 23:24:45,888 - pyskl - INFO - Epoch [126][3600/3746] lr: 6.204e-03, eta: 20:29:35, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4389, top5_acc: 0.6931, loss_cls: 3.1389, loss: 3.1389 +2024-07-20 23:26:06,884 - pyskl - INFO - Epoch [126][3700/3746] lr: 6.191e-03, eta: 20:28:13, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4330, top5_acc: 0.6919, loss_cls: 3.1602, loss: 3.1602 +2024-07-20 23:26:46,250 - pyskl - INFO - Saving checkpoint at 126 epochs +2024-07-20 23:28:36,996 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 23:28:37,651 - pyskl - INFO - +top1_acc 0.3578 +top5_acc 0.6165 +2024-07-20 23:28:37,652 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 23:28:37,691 - pyskl - INFO - +mean_acc 0.3575 +2024-07-20 23:28:37,696 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_125.pth was removed +2024-07-20 23:28:37,921 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_126.pth. +2024-07-20 23:28:37,921 - pyskl - INFO - Best top1_acc is 0.3578 at 126 epoch. +2024-07-20 23:28:37,932 - pyskl - INFO - Epoch(val) [126][309] top1_acc: 0.3578, top5_acc: 0.6165, mean_class_accuracy: 0.3575 +2024-07-20 23:32:22,419 - pyskl - INFO - Epoch [127][100/3746] lr: 6.171e-03, eta: 20:26:33, time: 2.245, data_time: 1.268, memory: 15990, top1_acc: 0.4572, top5_acc: 0.7170, loss_cls: 3.0294, loss: 3.0294 +2024-07-20 23:33:44,250 - pyskl - INFO - Epoch [127][200/3746] lr: 6.158e-03, eta: 20:25:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4500, top5_acc: 0.7155, loss_cls: 3.0408, loss: 3.0408 +2024-07-20 23:35:05,907 - pyskl - INFO - Epoch [127][300/3746] lr: 6.144e-03, eta: 20:23:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4545, top5_acc: 0.7077, loss_cls: 3.0771, loss: 3.0771 +2024-07-20 23:36:27,779 - pyskl - INFO - Epoch [127][400/3746] lr: 6.131e-03, eta: 20:22:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4489, top5_acc: 0.6994, loss_cls: 3.0789, loss: 3.0789 +2024-07-20 23:37:49,289 - pyskl - INFO - Epoch [127][500/3746] lr: 6.118e-03, eta: 20:21:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4489, top5_acc: 0.7023, loss_cls: 3.0728, loss: 3.0728 +2024-07-20 23:39:11,342 - pyskl - INFO - Epoch [127][600/3746] lr: 6.104e-03, eta: 20:19:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.6995, loss_cls: 3.1103, loss: 3.1103 +2024-07-20 23:40:32,186 - pyskl - INFO - Epoch [127][700/3746] lr: 6.091e-03, eta: 20:18:21, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4544, top5_acc: 0.7080, loss_cls: 3.0448, loss: 3.0448 +2024-07-20 23:41:53,221 - pyskl - INFO - Epoch [127][800/3746] lr: 6.077e-03, eta: 20:16:59, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4356, top5_acc: 0.6991, loss_cls: 3.1318, loss: 3.1318 +2024-07-20 23:43:14,307 - pyskl - INFO - Epoch [127][900/3746] lr: 6.064e-03, eta: 20:15:37, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4511, top5_acc: 0.7078, loss_cls: 3.0892, loss: 3.0892 +2024-07-20 23:44:35,715 - pyskl - INFO - Epoch [127][1000/3746] lr: 6.051e-03, eta: 20:14:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4431, top5_acc: 0.6944, loss_cls: 3.1225, loss: 3.1225 +2024-07-20 23:45:56,654 - pyskl - INFO - Epoch [127][1100/3746] lr: 6.037e-03, eta: 20:12:52, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.7061, loss_cls: 3.1224, loss: 3.1224 +2024-07-20 23:47:17,631 - pyskl - INFO - Epoch [127][1200/3746] lr: 6.024e-03, eta: 20:11:30, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4481, top5_acc: 0.7216, loss_cls: 3.0434, loss: 3.0434 +2024-07-20 23:48:38,484 - pyskl - INFO - Epoch [127][1300/3746] lr: 6.011e-03, eta: 20:10:08, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4348, top5_acc: 0.6995, loss_cls: 3.1277, loss: 3.1277 +2024-07-20 23:50:00,059 - pyskl - INFO - Epoch [127][1400/3746] lr: 5.998e-03, eta: 20:08:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4461, top5_acc: 0.7067, loss_cls: 3.0719, loss: 3.0719 +2024-07-20 23:51:21,036 - pyskl - INFO - Epoch [127][1500/3746] lr: 5.984e-03, eta: 20:07:24, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4491, top5_acc: 0.7053, loss_cls: 3.0890, loss: 3.0890 +2024-07-20 23:52:42,197 - pyskl - INFO - Epoch [127][1600/3746] lr: 5.971e-03, eta: 20:06:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4414, top5_acc: 0.6969, loss_cls: 3.1234, loss: 3.1234 +2024-07-20 23:54:03,152 - pyskl - INFO - Epoch [127][1700/3746] lr: 5.958e-03, eta: 20:04:40, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4467, top5_acc: 0.7045, loss_cls: 3.0995, loss: 3.0995 +2024-07-20 23:55:24,191 - pyskl - INFO - Epoch [127][1800/3746] lr: 5.945e-03, eta: 20:03:18, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.7131, loss_cls: 3.0595, loss: 3.0595 +2024-07-20 23:56:45,723 - pyskl - INFO - Epoch [127][1900/3746] lr: 5.931e-03, eta: 20:01:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4361, top5_acc: 0.7087, loss_cls: 3.1003, loss: 3.1003 +2024-07-20 23:58:06,679 - pyskl - INFO - Epoch [127][2000/3746] lr: 5.918e-03, eta: 20:00:33, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.6975, loss_cls: 3.1198, loss: 3.1198 +2024-07-20 23:59:28,008 - pyskl - INFO - Epoch [127][2100/3746] lr: 5.905e-03, eta: 19:59:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4389, top5_acc: 0.7037, loss_cls: 3.1024, loss: 3.1024 +2024-07-21 00:00:49,290 - pyskl - INFO - Epoch [127][2200/3746] lr: 5.892e-03, eta: 19:57:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4256, top5_acc: 0.6959, loss_cls: 3.1401, loss: 3.1401 +2024-07-21 00:02:10,822 - pyskl - INFO - Epoch [127][2300/3746] lr: 5.879e-03, eta: 19:56:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6923, loss_cls: 3.1480, loss: 3.1480 +2024-07-21 00:03:32,427 - pyskl - INFO - Epoch [127][2400/3746] lr: 5.866e-03, eta: 19:55:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4431, top5_acc: 0.7056, loss_cls: 3.0748, loss: 3.0748 +2024-07-21 00:04:55,033 - pyskl - INFO - Epoch [127][2500/3746] lr: 5.852e-03, eta: 19:53:43, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4461, top5_acc: 0.7059, loss_cls: 3.1011, loss: 3.1011 +2024-07-21 00:06:16,921 - pyskl - INFO - Epoch [127][2600/3746] lr: 5.839e-03, eta: 19:52:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4434, top5_acc: 0.6989, loss_cls: 3.1193, loss: 3.1193 +2024-07-21 00:07:37,807 - pyskl - INFO - Epoch [127][2700/3746] lr: 5.826e-03, eta: 19:50:59, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4425, top5_acc: 0.6934, loss_cls: 3.1262, loss: 3.1262 +2024-07-21 00:09:00,334 - pyskl - INFO - Epoch [127][2800/3746] lr: 5.813e-03, eta: 19:49:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4398, top5_acc: 0.6998, loss_cls: 3.1253, loss: 3.1253 +2024-07-21 00:10:21,829 - pyskl - INFO - Epoch [127][2900/3746] lr: 5.800e-03, eta: 19:48:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.6872, loss_cls: 3.1305, loss: 3.1305 +2024-07-21 00:11:43,128 - pyskl - INFO - Epoch [127][3000/3746] lr: 5.787e-03, eta: 19:46:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4473, top5_acc: 0.7078, loss_cls: 3.0903, loss: 3.0903 +2024-07-21 00:13:04,289 - pyskl - INFO - Epoch [127][3100/3746] lr: 5.774e-03, eta: 19:45:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.6931, loss_cls: 3.1528, loss: 3.1528 +2024-07-21 00:14:25,144 - pyskl - INFO - Epoch [127][3200/3746] lr: 5.761e-03, eta: 19:44:09, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6864, loss_cls: 3.1951, loss: 3.1951 +2024-07-21 00:15:46,119 - pyskl - INFO - Epoch [127][3300/3746] lr: 5.748e-03, eta: 19:42:47, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4527, top5_acc: 0.7053, loss_cls: 3.0660, loss: 3.0660 +2024-07-21 00:17:07,299 - pyskl - INFO - Epoch [127][3400/3746] lr: 5.735e-03, eta: 19:41:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4448, top5_acc: 0.7039, loss_cls: 3.1050, loss: 3.1050 +2024-07-21 00:18:27,907 - pyskl - INFO - Epoch [127][3500/3746] lr: 5.722e-03, eta: 19:40:03, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.4427, top5_acc: 0.6956, loss_cls: 3.1463, loss: 3.1463 +2024-07-21 00:19:48,968 - pyskl - INFO - Epoch [127][3600/3746] lr: 5.709e-03, eta: 19:38:41, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4286, top5_acc: 0.6891, loss_cls: 3.1562, loss: 3.1562 +2024-07-21 00:21:10,271 - pyskl - INFO - Epoch [127][3700/3746] lr: 5.696e-03, eta: 19:37:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4441, top5_acc: 0.7042, loss_cls: 3.1158, loss: 3.1158 +2024-07-21 00:21:49,767 - pyskl - INFO - Saving checkpoint at 127 epochs +2024-07-21 00:23:40,830 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 00:23:41,489 - pyskl - INFO - +top1_acc 0.3694 +top5_acc 0.6258 +2024-07-21 00:23:41,489 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 00:23:41,527 - pyskl - INFO - +mean_acc 0.3691 +2024-07-21 00:23:41,532 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_126.pth was removed +2024-07-21 00:23:41,762 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_127.pth. +2024-07-21 00:23:41,763 - pyskl - INFO - Best top1_acc is 0.3694 at 127 epoch. +2024-07-21 00:23:41,774 - pyskl - INFO - Epoch(val) [127][309] top1_acc: 0.3694, top5_acc: 0.6258, mean_class_accuracy: 0.3691 +2024-07-21 00:27:27,064 - pyskl - INFO - Epoch [128][100/3746] lr: 5.677e-03, eta: 19:35:38, time: 2.253, data_time: 1.279, memory: 15990, top1_acc: 0.4559, top5_acc: 0.7247, loss_cls: 2.9867, loss: 2.9867 +2024-07-21 00:28:49,505 - pyskl - INFO - Epoch [128][200/3746] lr: 5.664e-03, eta: 19:34:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4681, top5_acc: 0.7194, loss_cls: 2.9819, loss: 2.9819 +2024-07-21 00:30:11,132 - pyskl - INFO - Epoch [128][300/3746] lr: 5.651e-03, eta: 19:32:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4553, top5_acc: 0.7212, loss_cls: 3.0085, loss: 3.0085 +2024-07-21 00:31:32,590 - pyskl - INFO - Epoch [128][400/3746] lr: 5.638e-03, eta: 19:31:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4544, top5_acc: 0.7150, loss_cls: 3.0285, loss: 3.0285 +2024-07-21 00:32:54,169 - pyskl - INFO - Epoch [128][500/3746] lr: 5.625e-03, eta: 19:30:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4730, top5_acc: 0.7278, loss_cls: 2.9547, loss: 2.9547 +2024-07-21 00:34:15,955 - pyskl - INFO - Epoch [128][600/3746] lr: 5.612e-03, eta: 19:28:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.7136, loss_cls: 3.0343, loss: 3.0343 +2024-07-21 00:35:37,363 - pyskl - INFO - Epoch [128][700/3746] lr: 5.600e-03, eta: 19:27:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4555, top5_acc: 0.7153, loss_cls: 3.0418, loss: 3.0418 +2024-07-21 00:36:58,858 - pyskl - INFO - Epoch [128][800/3746] lr: 5.587e-03, eta: 19:26:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4489, top5_acc: 0.7053, loss_cls: 3.0704, loss: 3.0704 +2024-07-21 00:38:19,761 - pyskl - INFO - Epoch [128][900/3746] lr: 5.574e-03, eta: 19:24:42, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4578, top5_acc: 0.7139, loss_cls: 3.0290, loss: 3.0290 +2024-07-21 00:39:40,807 - pyskl - INFO - Epoch [128][1000/3746] lr: 5.561e-03, eta: 19:23:20, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4477, top5_acc: 0.7094, loss_cls: 3.0282, loss: 3.0282 +2024-07-21 00:41:01,782 - pyskl - INFO - Epoch [128][1100/3746] lr: 5.548e-03, eta: 19:21:58, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4361, top5_acc: 0.7047, loss_cls: 3.1107, loss: 3.1107 +2024-07-21 00:42:22,778 - pyskl - INFO - Epoch [128][1200/3746] lr: 5.536e-03, eta: 19:20:35, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4478, top5_acc: 0.7028, loss_cls: 3.0445, loss: 3.0445 +2024-07-21 00:43:43,673 - pyskl - INFO - Epoch [128][1300/3746] lr: 5.523e-03, eta: 19:19:13, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4458, top5_acc: 0.7020, loss_cls: 3.1207, loss: 3.1207 +2024-07-21 00:45:04,604 - pyskl - INFO - Epoch [128][1400/3746] lr: 5.510e-03, eta: 19:17:51, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.7005, loss_cls: 3.1092, loss: 3.1092 +2024-07-21 00:46:25,710 - pyskl - INFO - Epoch [128][1500/3746] lr: 5.497e-03, eta: 19:16:29, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4608, top5_acc: 0.7109, loss_cls: 3.0381, loss: 3.0381 +2024-07-21 00:47:47,174 - pyskl - INFO - Epoch [128][1600/3746] lr: 5.485e-03, eta: 19:15:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4477, top5_acc: 0.7052, loss_cls: 3.0974, loss: 3.0974 +2024-07-21 00:49:07,893 - pyskl - INFO - Epoch [128][1700/3746] lr: 5.472e-03, eta: 19:13:45, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.4442, top5_acc: 0.7030, loss_cls: 3.0870, loss: 3.0870 +2024-07-21 00:50:28,835 - pyskl - INFO - Epoch [128][1800/3746] lr: 5.459e-03, eta: 19:12:23, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4448, top5_acc: 0.7059, loss_cls: 3.0949, loss: 3.0949 +2024-07-21 00:51:50,689 - pyskl - INFO - Epoch [128][1900/3746] lr: 5.446e-03, eta: 19:11:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4459, top5_acc: 0.7011, loss_cls: 3.0868, loss: 3.0868 +2024-07-21 00:53:12,344 - pyskl - INFO - Epoch [128][2000/3746] lr: 5.434e-03, eta: 19:09:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.7002, loss_cls: 3.1109, loss: 3.1109 +2024-07-21 00:54:33,486 - pyskl - INFO - Epoch [128][2100/3746] lr: 5.421e-03, eta: 19:08:17, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4481, top5_acc: 0.7080, loss_cls: 3.0751, loss: 3.0751 +2024-07-21 00:55:54,985 - pyskl - INFO - Epoch [128][2200/3746] lr: 5.408e-03, eta: 19:06:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4533, top5_acc: 0.7073, loss_cls: 3.0547, loss: 3.0547 +2024-07-21 00:57:16,827 - pyskl - INFO - Epoch [128][2300/3746] lr: 5.396e-03, eta: 19:05:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4459, top5_acc: 0.7078, loss_cls: 3.0690, loss: 3.0690 +2024-07-21 00:58:39,185 - pyskl - INFO - Epoch [128][2400/3746] lr: 5.383e-03, eta: 19:04:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4394, top5_acc: 0.6942, loss_cls: 3.1515, loss: 3.1515 +2024-07-21 01:00:00,674 - pyskl - INFO - Epoch [128][2500/3746] lr: 5.370e-03, eta: 19:02:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4448, top5_acc: 0.7111, loss_cls: 3.0804, loss: 3.0804 +2024-07-21 01:01:22,497 - pyskl - INFO - Epoch [128][2600/3746] lr: 5.358e-03, eta: 19:01:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4437, top5_acc: 0.7041, loss_cls: 3.0937, loss: 3.0937 +2024-07-21 01:02:44,533 - pyskl - INFO - Epoch [128][2700/3746] lr: 5.345e-03, eta: 19:00:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4533, top5_acc: 0.7064, loss_cls: 3.0612, loss: 3.0612 +2024-07-21 01:04:05,758 - pyskl - INFO - Epoch [128][2800/3746] lr: 5.333e-03, eta: 18:58:42, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.7000, loss_cls: 3.1051, loss: 3.1051 +2024-07-21 01:05:26,944 - pyskl - INFO - Epoch [128][2900/3746] lr: 5.320e-03, eta: 18:57:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4517, top5_acc: 0.7103, loss_cls: 3.0758, loss: 3.0758 +2024-07-21 01:06:47,974 - pyskl - INFO - Epoch [128][3000/3746] lr: 5.308e-03, eta: 18:55:58, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4412, top5_acc: 0.6989, loss_cls: 3.1174, loss: 3.1174 +2024-07-21 01:08:08,867 - pyskl - INFO - Epoch [128][3100/3746] lr: 5.295e-03, eta: 18:54:36, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4437, top5_acc: 0.7045, loss_cls: 3.0918, loss: 3.0918 +2024-07-21 01:09:29,512 - pyskl - INFO - Epoch [128][3200/3746] lr: 5.283e-03, eta: 18:53:14, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.4359, top5_acc: 0.7016, loss_cls: 3.1160, loss: 3.1160 +2024-07-21 01:10:50,664 - pyskl - INFO - Epoch [128][3300/3746] lr: 5.270e-03, eta: 18:51:52, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.7130, loss_cls: 3.0592, loss: 3.0592 +2024-07-21 01:12:11,549 - pyskl - INFO - Epoch [128][3400/3746] lr: 5.258e-03, eta: 18:50:30, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4442, top5_acc: 0.7041, loss_cls: 3.0864, loss: 3.0864 +2024-07-21 01:13:32,573 - pyskl - INFO - Epoch [128][3500/3746] lr: 5.245e-03, eta: 18:49:08, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4431, top5_acc: 0.7078, loss_cls: 3.0802, loss: 3.0802 +2024-07-21 01:14:53,613 - pyskl - INFO - Epoch [128][3600/3746] lr: 5.233e-03, eta: 18:47:45, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4405, top5_acc: 0.6980, loss_cls: 3.1101, loss: 3.1101 +2024-07-21 01:16:14,459 - pyskl - INFO - Epoch [128][3700/3746] lr: 5.220e-03, eta: 18:46:23, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4477, top5_acc: 0.7077, loss_cls: 3.0981, loss: 3.0981 +2024-07-21 01:16:53,802 - pyskl - INFO - Saving checkpoint at 128 epochs +2024-07-21 01:18:45,407 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 01:18:46,071 - pyskl - INFO - +top1_acc 0.3671 +top5_acc 0.6256 +2024-07-21 01:18:46,071 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 01:18:46,115 - pyskl - INFO - +mean_acc 0.3669 +2024-07-21 01:18:46,126 - pyskl - INFO - Epoch(val) [128][309] top1_acc: 0.3671, top5_acc: 0.6256, mean_class_accuracy: 0.3669 +2024-07-21 01:22:37,616 - pyskl - INFO - Epoch [129][100/3746] lr: 5.202e-03, eta: 18:44:43, time: 2.315, data_time: 1.305, memory: 15990, top1_acc: 0.4589, top5_acc: 0.7242, loss_cls: 2.9839, loss: 2.9839 +2024-07-21 01:23:59,838 - pyskl - INFO - Epoch [129][200/3746] lr: 5.190e-03, eta: 18:43:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4639, top5_acc: 0.7242, loss_cls: 2.9640, loss: 2.9640 +2024-07-21 01:25:21,901 - pyskl - INFO - Epoch [129][300/3746] lr: 5.177e-03, eta: 18:41:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4652, top5_acc: 0.7188, loss_cls: 2.9912, loss: 2.9912 +2024-07-21 01:26:43,439 - pyskl - INFO - Epoch [129][400/3746] lr: 5.165e-03, eta: 18:40:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4503, top5_acc: 0.7092, loss_cls: 3.0459, loss: 3.0459 +2024-07-21 01:28:05,168 - pyskl - INFO - Epoch [129][500/3746] lr: 5.153e-03, eta: 18:39:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4575, top5_acc: 0.7150, loss_cls: 3.0218, loss: 3.0218 +2024-07-21 01:29:26,440 - pyskl - INFO - Epoch [129][600/3746] lr: 5.140e-03, eta: 18:37:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4616, top5_acc: 0.7197, loss_cls: 2.9886, loss: 2.9886 +2024-07-21 01:30:47,370 - pyskl - INFO - Epoch [129][700/3746] lr: 5.128e-03, eta: 18:36:31, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4556, top5_acc: 0.7155, loss_cls: 3.0475, loss: 3.0475 +2024-07-21 01:32:08,045 - pyskl - INFO - Epoch [129][800/3746] lr: 5.116e-03, eta: 18:35:08, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7081, loss_cls: 3.0365, loss: 3.0365 +2024-07-21 01:33:29,238 - pyskl - INFO - Epoch [129][900/3746] lr: 5.103e-03, eta: 18:33:46, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4553, top5_acc: 0.7155, loss_cls: 3.0289, loss: 3.0289 +2024-07-21 01:34:49,680 - pyskl - INFO - Epoch [129][1000/3746] lr: 5.091e-03, eta: 18:32:24, time: 0.804, data_time: 0.000, memory: 15990, top1_acc: 0.4555, top5_acc: 0.7136, loss_cls: 3.0336, loss: 3.0336 +2024-07-21 01:36:10,923 - pyskl - INFO - Epoch [129][1100/3746] lr: 5.079e-03, eta: 18:31:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4648, top5_acc: 0.7144, loss_cls: 3.0011, loss: 3.0011 +2024-07-21 01:37:32,331 - pyskl - INFO - Epoch [129][1200/3746] lr: 5.066e-03, eta: 18:29:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4508, top5_acc: 0.7087, loss_cls: 3.0711, loss: 3.0711 +2024-07-21 01:38:53,208 - pyskl - INFO - Epoch [129][1300/3746] lr: 5.054e-03, eta: 18:28:18, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4572, top5_acc: 0.7120, loss_cls: 3.0217, loss: 3.0217 +2024-07-21 01:40:13,959 - pyskl - INFO - Epoch [129][1400/3746] lr: 5.042e-03, eta: 18:26:55, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4669, top5_acc: 0.7181, loss_cls: 3.0027, loss: 3.0027 +2024-07-21 01:41:35,253 - pyskl - INFO - Epoch [129][1500/3746] lr: 5.030e-03, eta: 18:25:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4520, top5_acc: 0.7145, loss_cls: 3.0309, loss: 3.0309 +2024-07-21 01:42:56,334 - pyskl - INFO - Epoch [129][1600/3746] lr: 5.017e-03, eta: 18:24:11, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4548, top5_acc: 0.7155, loss_cls: 3.0337, loss: 3.0337 +2024-07-21 01:44:17,477 - pyskl - INFO - Epoch [129][1700/3746] lr: 5.005e-03, eta: 18:22:49, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4533, top5_acc: 0.7170, loss_cls: 3.0311, loss: 3.0311 +2024-07-21 01:45:38,893 - pyskl - INFO - Epoch [129][1800/3746] lr: 4.993e-03, eta: 18:21:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4616, top5_acc: 0.7173, loss_cls: 3.0060, loss: 3.0060 +2024-07-21 01:47:00,533 - pyskl - INFO - Epoch [129][1900/3746] lr: 4.981e-03, eta: 18:20:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4506, top5_acc: 0.7089, loss_cls: 3.0487, loss: 3.0487 +2024-07-21 01:48:21,732 - pyskl - INFO - Epoch [129][2000/3746] lr: 4.969e-03, eta: 18:18:43, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4445, top5_acc: 0.7008, loss_cls: 3.0783, loss: 3.0783 +2024-07-21 01:49:42,741 - pyskl - INFO - Epoch [129][2100/3746] lr: 4.957e-03, eta: 18:17:21, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4564, top5_acc: 0.7092, loss_cls: 3.0443, loss: 3.0443 +2024-07-21 01:51:03,858 - pyskl - INFO - Epoch [129][2200/3746] lr: 4.944e-03, eta: 18:15:59, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4622, top5_acc: 0.7200, loss_cls: 2.9869, loss: 2.9869 +2024-07-21 01:52:26,326 - pyskl - INFO - Epoch [129][2300/3746] lr: 4.932e-03, eta: 18:14:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4533, top5_acc: 0.7114, loss_cls: 3.0580, loss: 3.0580 +2024-07-21 01:53:47,660 - pyskl - INFO - Epoch [129][2400/3746] lr: 4.920e-03, eta: 18:13:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4662, top5_acc: 0.7119, loss_cls: 3.0494, loss: 3.0494 +2024-07-21 01:55:09,350 - pyskl - INFO - Epoch [129][2500/3746] lr: 4.908e-03, eta: 18:11:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4534, top5_acc: 0.7095, loss_cls: 3.0682, loss: 3.0682 +2024-07-21 01:56:31,199 - pyskl - INFO - Epoch [129][2600/3746] lr: 4.896e-03, eta: 18:10:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.7133, loss_cls: 3.0502, loss: 3.0502 +2024-07-21 01:57:52,645 - pyskl - INFO - Epoch [129][2700/3746] lr: 4.884e-03, eta: 18:09:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4569, top5_acc: 0.7111, loss_cls: 3.0385, loss: 3.0385 +2024-07-21 01:59:14,308 - pyskl - INFO - Epoch [129][2800/3746] lr: 4.872e-03, eta: 18:07:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4453, top5_acc: 0.7133, loss_cls: 3.0540, loss: 3.0540 +2024-07-21 02:00:36,361 - pyskl - INFO - Epoch [129][2900/3746] lr: 4.860e-03, eta: 18:06:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4597, top5_acc: 0.7147, loss_cls: 3.0119, loss: 3.0119 +2024-07-21 02:01:57,102 - pyskl - INFO - Epoch [129][3000/3746] lr: 4.848e-03, eta: 18:05:02, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7127, loss_cls: 3.0358, loss: 3.0358 +2024-07-21 02:03:18,172 - pyskl - INFO - Epoch [129][3100/3746] lr: 4.836e-03, eta: 18:03:40, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.6992, loss_cls: 3.1217, loss: 3.1217 +2024-07-21 02:04:38,987 - pyskl - INFO - Epoch [129][3200/3746] lr: 4.824e-03, eta: 18:02:18, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4544, top5_acc: 0.7113, loss_cls: 3.0516, loss: 3.0516 +2024-07-21 02:05:59,931 - pyskl - INFO - Epoch [129][3300/3746] lr: 4.812e-03, eta: 18:00:56, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4559, top5_acc: 0.7097, loss_cls: 3.0412, loss: 3.0412 +2024-07-21 02:07:20,739 - pyskl - INFO - Epoch [129][3400/3746] lr: 4.800e-03, eta: 17:59:34, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4603, top5_acc: 0.7073, loss_cls: 3.0513, loss: 3.0513 +2024-07-21 02:08:41,792 - pyskl - INFO - Epoch [129][3500/3746] lr: 4.788e-03, eta: 17:58:12, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4517, top5_acc: 0.7148, loss_cls: 3.0492, loss: 3.0492 +2024-07-21 02:10:03,015 - pyskl - INFO - Epoch [129][3600/3746] lr: 4.776e-03, eta: 17:56:49, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4492, top5_acc: 0.7136, loss_cls: 3.0576, loss: 3.0576 +2024-07-21 02:11:24,997 - pyskl - INFO - Epoch [129][3700/3746] lr: 4.764e-03, eta: 17:55:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7133, loss_cls: 3.0559, loss: 3.0559 +2024-07-21 02:12:04,738 - pyskl - INFO - Saving checkpoint at 129 epochs +2024-07-21 02:13:56,390 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 02:13:57,055 - pyskl - INFO - +top1_acc 0.3789 +top5_acc 0.6332 +2024-07-21 02:13:57,055 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 02:13:57,095 - pyskl - INFO - +mean_acc 0.3788 +2024-07-21 02:13:57,100 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_127.pth was removed +2024-07-21 02:13:57,328 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_129.pth. +2024-07-21 02:13:57,329 - pyskl - INFO - Best top1_acc is 0.3789 at 129 epoch. +2024-07-21 02:13:57,340 - pyskl - INFO - Epoch(val) [129][309] top1_acc: 0.3789, top5_acc: 0.6332, mean_class_accuracy: 0.3788 +2024-07-21 02:17:43,163 - pyskl - INFO - Epoch [130][100/3746] lr: 4.747e-03, eta: 17:53:45, time: 2.258, data_time: 1.277, memory: 15990, top1_acc: 0.4744, top5_acc: 0.7258, loss_cls: 2.9287, loss: 2.9287 +2024-07-21 02:19:05,480 - pyskl - INFO - Epoch [130][200/3746] lr: 4.735e-03, eta: 17:52:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4730, top5_acc: 0.7302, loss_cls: 2.9284, loss: 2.9284 +2024-07-21 02:20:27,439 - pyskl - INFO - Epoch [130][300/3746] lr: 4.723e-03, eta: 17:51:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4781, top5_acc: 0.7317, loss_cls: 2.9340, loss: 2.9340 +2024-07-21 02:21:49,077 - pyskl - INFO - Epoch [130][400/3746] lr: 4.711e-03, eta: 17:49:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4686, top5_acc: 0.7259, loss_cls: 2.9494, loss: 2.9494 +2024-07-21 02:23:10,804 - pyskl - INFO - Epoch [130][500/3746] lr: 4.699e-03, eta: 17:48:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7222, loss_cls: 2.9892, loss: 2.9892 +2024-07-21 02:24:32,024 - pyskl - INFO - Epoch [130][600/3746] lr: 4.688e-03, eta: 17:46:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7209, loss_cls: 2.9689, loss: 2.9689 +2024-07-21 02:25:52,851 - pyskl - INFO - Epoch [130][700/3746] lr: 4.676e-03, eta: 17:45:33, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4656, top5_acc: 0.7188, loss_cls: 2.9736, loss: 2.9736 +2024-07-21 02:27:14,109 - pyskl - INFO - Epoch [130][800/3746] lr: 4.664e-03, eta: 17:44:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4669, top5_acc: 0.7186, loss_cls: 2.9743, loss: 2.9743 +2024-07-21 02:28:35,235 - pyskl - INFO - Epoch [130][900/3746] lr: 4.652e-03, eta: 17:42:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4673, top5_acc: 0.7225, loss_cls: 2.9886, loss: 2.9886 +2024-07-21 02:29:56,518 - pyskl - INFO - Epoch [130][1000/3746] lr: 4.640e-03, eta: 17:41:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4583, top5_acc: 0.7178, loss_cls: 3.0065, loss: 3.0065 +2024-07-21 02:31:17,994 - pyskl - INFO - Epoch [130][1100/3746] lr: 4.629e-03, eta: 17:40:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4580, top5_acc: 0.7161, loss_cls: 3.0230, loss: 3.0230 +2024-07-21 02:32:38,902 - pyskl - INFO - Epoch [130][1200/3746] lr: 4.617e-03, eta: 17:38:42, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4602, top5_acc: 0.7181, loss_cls: 2.9986, loss: 2.9986 +2024-07-21 02:34:00,072 - pyskl - INFO - Epoch [130][1300/3746] lr: 4.605e-03, eta: 17:37:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4684, top5_acc: 0.7211, loss_cls: 3.0002, loss: 3.0002 +2024-07-21 02:35:21,384 - pyskl - INFO - Epoch [130][1400/3746] lr: 4.594e-03, eta: 17:35:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4612, top5_acc: 0.7136, loss_cls: 3.0023, loss: 3.0023 +2024-07-21 02:36:42,951 - pyskl - INFO - Epoch [130][1500/3746] lr: 4.582e-03, eta: 17:34:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4572, top5_acc: 0.7147, loss_cls: 3.0137, loss: 3.0137 +2024-07-21 02:38:04,255 - pyskl - INFO - Epoch [130][1600/3746] lr: 4.570e-03, eta: 17:33:14, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4450, top5_acc: 0.7103, loss_cls: 3.0411, loss: 3.0411 +2024-07-21 02:39:25,783 - pyskl - INFO - Epoch [130][1700/3746] lr: 4.558e-03, eta: 17:31:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4567, top5_acc: 0.7217, loss_cls: 2.9975, loss: 2.9975 +2024-07-21 02:40:46,551 - pyskl - INFO - Epoch [130][1800/3746] lr: 4.547e-03, eta: 17:30:29, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4587, top5_acc: 0.7222, loss_cls: 2.9771, loss: 2.9771 +2024-07-21 02:42:07,677 - pyskl - INFO - Epoch [130][1900/3746] lr: 4.535e-03, eta: 17:29:07, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4741, top5_acc: 0.7255, loss_cls: 2.9429, loss: 2.9429 +2024-07-21 02:43:29,812 - pyskl - INFO - Epoch [130][2000/3746] lr: 4.524e-03, eta: 17:27:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7069, loss_cls: 3.0625, loss: 3.0625 +2024-07-21 02:44:50,698 - pyskl - INFO - Epoch [130][2100/3746] lr: 4.512e-03, eta: 17:26:23, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4578, top5_acc: 0.7219, loss_cls: 2.9993, loss: 2.9993 +2024-07-21 02:46:12,423 - pyskl - INFO - Epoch [130][2200/3746] lr: 4.500e-03, eta: 17:25:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4752, top5_acc: 0.7294, loss_cls: 2.9372, loss: 2.9372 +2024-07-21 02:47:34,060 - pyskl - INFO - Epoch [130][2300/3746] lr: 4.489e-03, eta: 17:23:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4648, top5_acc: 0.7206, loss_cls: 3.0122, loss: 3.0122 +2024-07-21 02:48:55,920 - pyskl - INFO - Epoch [130][2400/3746] lr: 4.477e-03, eta: 17:22:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7122, loss_cls: 3.0258, loss: 3.0258 +2024-07-21 02:50:18,064 - pyskl - INFO - Epoch [130][2500/3746] lr: 4.466e-03, eta: 17:20:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4597, top5_acc: 0.7180, loss_cls: 2.9849, loss: 2.9849 +2024-07-21 02:51:39,683 - pyskl - INFO - Epoch [130][2600/3746] lr: 4.454e-03, eta: 17:19:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4597, top5_acc: 0.7172, loss_cls: 3.0051, loss: 3.0051 +2024-07-21 02:53:01,982 - pyskl - INFO - Epoch [130][2700/3746] lr: 4.443e-03, eta: 17:18:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4561, top5_acc: 0.7103, loss_cls: 3.0445, loss: 3.0445 +2024-07-21 02:54:23,193 - pyskl - INFO - Epoch [130][2800/3746] lr: 4.431e-03, eta: 17:16:49, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4511, top5_acc: 0.7094, loss_cls: 3.0313, loss: 3.0313 +2024-07-21 02:55:44,699 - pyskl - INFO - Epoch [130][2900/3746] lr: 4.420e-03, eta: 17:15:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7102, loss_cls: 3.0493, loss: 3.0493 +2024-07-21 02:57:05,961 - pyskl - INFO - Epoch [130][3000/3746] lr: 4.408e-03, eta: 17:14:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4556, top5_acc: 0.7175, loss_cls: 2.9834, loss: 2.9834 +2024-07-21 02:58:26,876 - pyskl - INFO - Epoch [130][3100/3746] lr: 4.397e-03, eta: 17:12:42, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4553, top5_acc: 0.7152, loss_cls: 3.0250, loss: 3.0250 +2024-07-21 02:59:47,978 - pyskl - INFO - Epoch [130][3200/3746] lr: 4.385e-03, eta: 17:11:20, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7052, loss_cls: 3.0605, loss: 3.0605 +2024-07-21 03:01:09,014 - pyskl - INFO - Epoch [130][3300/3746] lr: 4.374e-03, eta: 17:09:58, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4512, top5_acc: 0.7169, loss_cls: 3.0164, loss: 3.0164 +2024-07-21 03:02:29,972 - pyskl - INFO - Epoch [130][3400/3746] lr: 4.362e-03, eta: 17:08:36, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4662, top5_acc: 0.7198, loss_cls: 2.9661, loss: 2.9661 +2024-07-21 03:03:50,964 - pyskl - INFO - Epoch [130][3500/3746] lr: 4.351e-03, eta: 17:07:14, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4612, top5_acc: 0.7116, loss_cls: 3.0255, loss: 3.0255 +2024-07-21 03:05:12,082 - pyskl - INFO - Epoch [130][3600/3746] lr: 4.339e-03, eta: 17:05:52, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4505, top5_acc: 0.7122, loss_cls: 3.0449, loss: 3.0449 +2024-07-21 03:06:33,388 - pyskl - INFO - Epoch [130][3700/3746] lr: 4.328e-03, eta: 17:04:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4462, top5_acc: 0.7092, loss_cls: 3.0685, loss: 3.0685 +2024-07-21 03:07:12,749 - pyskl - INFO - Saving checkpoint at 130 epochs +2024-07-21 03:09:03,969 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 03:09:04,685 - pyskl - INFO - +top1_acc 0.3828 +top5_acc 0.6344 +2024-07-21 03:09:04,685 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 03:09:04,728 - pyskl - INFO - +mean_acc 0.3826 +2024-07-21 03:09:04,733 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_129.pth was removed +2024-07-21 03:09:04,967 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_130.pth. +2024-07-21 03:09:04,968 - pyskl - INFO - Best top1_acc is 0.3828 at 130 epoch. +2024-07-21 03:09:04,980 - pyskl - INFO - Epoch(val) [130][309] top1_acc: 0.3828, top5_acc: 0.6344, mean_class_accuracy: 0.3826 +2024-07-21 03:12:52,291 - pyskl - INFO - Epoch [131][100/3746] lr: 4.311e-03, eta: 17:02:46, time: 2.273, data_time: 1.294, memory: 15990, top1_acc: 0.4789, top5_acc: 0.7327, loss_cls: 2.9060, loss: 2.9060 +2024-07-21 03:14:14,827 - pyskl - INFO - Epoch [131][200/3746] lr: 4.300e-03, eta: 17:01:24, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4813, top5_acc: 0.7336, loss_cls: 2.9132, loss: 2.9132 +2024-07-21 03:15:36,880 - pyskl - INFO - Epoch [131][300/3746] lr: 4.289e-03, eta: 17:00:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4766, top5_acc: 0.7341, loss_cls: 2.9100, loss: 2.9100 +2024-07-21 03:16:58,977 - pyskl - INFO - Epoch [131][400/3746] lr: 4.277e-03, eta: 16:58:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4688, top5_acc: 0.7219, loss_cls: 2.9517, loss: 2.9517 +2024-07-21 03:18:20,320 - pyskl - INFO - Epoch [131][500/3746] lr: 4.266e-03, eta: 16:57:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4761, top5_acc: 0.7311, loss_cls: 2.9003, loss: 2.9003 +2024-07-21 03:19:41,660 - pyskl - INFO - Epoch [131][600/3746] lr: 4.255e-03, eta: 16:55:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4655, top5_acc: 0.7233, loss_cls: 2.9339, loss: 2.9339 +2024-07-21 03:21:02,714 - pyskl - INFO - Epoch [131][700/3746] lr: 4.244e-03, eta: 16:54:34, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4769, top5_acc: 0.7373, loss_cls: 2.9008, loss: 2.9008 +2024-07-21 03:22:23,461 - pyskl - INFO - Epoch [131][800/3746] lr: 4.232e-03, eta: 16:53:12, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.4817, top5_acc: 0.7291, loss_cls: 2.9246, loss: 2.9246 +2024-07-21 03:23:44,212 - pyskl - INFO - Epoch [131][900/3746] lr: 4.221e-03, eta: 16:51:50, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4769, top5_acc: 0.7327, loss_cls: 2.9037, loss: 2.9037 +2024-07-21 03:25:05,961 - pyskl - INFO - Epoch [131][1000/3746] lr: 4.210e-03, eta: 16:50:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4602, top5_acc: 0.7261, loss_cls: 2.9566, loss: 2.9566 +2024-07-21 03:26:27,561 - pyskl - INFO - Epoch [131][1100/3746] lr: 4.199e-03, eta: 16:49:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4786, top5_acc: 0.7334, loss_cls: 2.9176, loss: 2.9176 +2024-07-21 03:27:49,137 - pyskl - INFO - Epoch [131][1200/3746] lr: 4.187e-03, eta: 16:47:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7303, loss_cls: 2.9449, loss: 2.9449 +2024-07-21 03:29:10,179 - pyskl - INFO - Epoch [131][1300/3746] lr: 4.176e-03, eta: 16:46:21, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4688, top5_acc: 0.7211, loss_cls: 2.9517, loss: 2.9517 +2024-07-21 03:30:31,331 - pyskl - INFO - Epoch [131][1400/3746] lr: 4.165e-03, eta: 16:44:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7308, loss_cls: 2.9323, loss: 2.9323 +2024-07-21 03:31:51,918 - pyskl - INFO - Epoch [131][1500/3746] lr: 4.154e-03, eta: 16:43:37, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.4648, top5_acc: 0.7178, loss_cls: 2.9748, loss: 2.9748 +2024-07-21 03:33:13,891 - pyskl - INFO - Epoch [131][1600/3746] lr: 4.143e-03, eta: 16:42:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4623, top5_acc: 0.7252, loss_cls: 3.0033, loss: 3.0033 +2024-07-21 03:34:35,499 - pyskl - INFO - Epoch [131][1700/3746] lr: 4.132e-03, eta: 16:40:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4561, top5_acc: 0.7166, loss_cls: 2.9948, loss: 2.9948 +2024-07-21 03:35:56,602 - pyskl - INFO - Epoch [131][1800/3746] lr: 4.120e-03, eta: 16:39:31, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4662, top5_acc: 0.7230, loss_cls: 2.9551, loss: 2.9551 +2024-07-21 03:37:17,820 - pyskl - INFO - Epoch [131][1900/3746] lr: 4.109e-03, eta: 16:38:08, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4614, top5_acc: 0.7270, loss_cls: 2.9674, loss: 2.9674 +2024-07-21 03:38:39,137 - pyskl - INFO - Epoch [131][2000/3746] lr: 4.098e-03, eta: 16:36:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4586, top5_acc: 0.7169, loss_cls: 3.0086, loss: 3.0086 +2024-07-21 03:40:00,032 - pyskl - INFO - Epoch [131][2100/3746] lr: 4.087e-03, eta: 16:35:24, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4553, top5_acc: 0.7156, loss_cls: 3.0143, loss: 3.0143 +2024-07-21 03:41:20,993 - pyskl - INFO - Epoch [131][2200/3746] lr: 4.076e-03, eta: 16:34:02, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4677, top5_acc: 0.7192, loss_cls: 2.9926, loss: 2.9926 +2024-07-21 03:42:42,426 - pyskl - INFO - Epoch [131][2300/3746] lr: 4.065e-03, eta: 16:32:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4653, top5_acc: 0.7161, loss_cls: 2.9970, loss: 2.9970 +2024-07-21 03:44:03,718 - pyskl - INFO - Epoch [131][2400/3746] lr: 4.054e-03, eta: 16:31:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4627, top5_acc: 0.7163, loss_cls: 3.0199, loss: 3.0199 +2024-07-21 03:45:24,934 - pyskl - INFO - Epoch [131][2500/3746] lr: 4.043e-03, eta: 16:29:56, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4705, top5_acc: 0.7198, loss_cls: 2.9802, loss: 2.9802 +2024-07-21 03:46:46,197 - pyskl - INFO - Epoch [131][2600/3746] lr: 4.032e-03, eta: 16:28:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4695, top5_acc: 0.7219, loss_cls: 2.9520, loss: 2.9520 +2024-07-21 03:48:07,344 - pyskl - INFO - Epoch [131][2700/3746] lr: 4.021e-03, eta: 16:27:11, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4673, top5_acc: 0.7259, loss_cls: 2.9458, loss: 2.9458 +2024-07-21 03:49:29,293 - pyskl - INFO - Epoch [131][2800/3746] lr: 4.010e-03, eta: 16:25:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4717, top5_acc: 0.7328, loss_cls: 2.9296, loss: 2.9296 +2024-07-21 03:50:50,450 - pyskl - INFO - Epoch [131][2900/3746] lr: 3.999e-03, eta: 16:24:27, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4677, top5_acc: 0.7294, loss_cls: 2.9524, loss: 2.9524 +2024-07-21 03:52:12,669 - pyskl - INFO - Epoch [131][3000/3746] lr: 3.988e-03, eta: 16:23:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4673, top5_acc: 0.7183, loss_cls: 2.9623, loss: 2.9623 +2024-07-21 03:53:33,799 - pyskl - INFO - Epoch [131][3100/3746] lr: 3.977e-03, eta: 16:21:43, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4580, top5_acc: 0.7111, loss_cls: 3.0257, loss: 3.0257 +2024-07-21 03:54:54,646 - pyskl - INFO - Epoch [131][3200/3746] lr: 3.966e-03, eta: 16:20:21, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7136, loss_cls: 2.9954, loss: 2.9954 +2024-07-21 03:56:16,547 - pyskl - INFO - Epoch [131][3300/3746] lr: 3.955e-03, eta: 16:18:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4603, top5_acc: 0.7134, loss_cls: 3.0096, loss: 3.0096 +2024-07-21 03:57:38,038 - pyskl - INFO - Epoch [131][3400/3746] lr: 3.945e-03, eta: 16:17:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4564, top5_acc: 0.7141, loss_cls: 3.0199, loss: 3.0199 +2024-07-21 03:58:59,140 - pyskl - INFO - Epoch [131][3500/3746] lr: 3.934e-03, eta: 16:16:15, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4695, top5_acc: 0.7275, loss_cls: 2.9564, loss: 2.9564 +2024-07-21 04:00:20,356 - pyskl - INFO - Epoch [131][3600/3746] lr: 3.923e-03, eta: 16:14:52, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4692, top5_acc: 0.7264, loss_cls: 2.9657, loss: 2.9657 +2024-07-21 04:01:41,270 - pyskl - INFO - Epoch [131][3700/3746] lr: 3.912e-03, eta: 16:13:30, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4692, top5_acc: 0.7247, loss_cls: 2.9456, loss: 2.9456 +2024-07-21 04:02:20,723 - pyskl - INFO - Saving checkpoint at 131 epochs +2024-07-21 04:04:11,705 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 04:04:12,418 - pyskl - INFO - +top1_acc 0.3797 +top5_acc 0.6351 +2024-07-21 04:04:12,418 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 04:04:12,458 - pyskl - INFO - +mean_acc 0.3795 +2024-07-21 04:04:12,469 - pyskl - INFO - Epoch(val) [131][309] top1_acc: 0.3797, top5_acc: 0.6351, mean_class_accuracy: 0.3795 +2024-07-21 04:08:01,269 - pyskl - INFO - Epoch [132][100/3746] lr: 3.896e-03, eta: 16:11:46, time: 2.288, data_time: 1.314, memory: 15990, top1_acc: 0.4830, top5_acc: 0.7389, loss_cls: 2.8897, loss: 2.8897 +2024-07-21 04:09:23,871 - pyskl - INFO - Epoch [132][200/3746] lr: 3.885e-03, eta: 16:10:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4805, top5_acc: 0.7395, loss_cls: 2.8673, loss: 2.8673 +2024-07-21 04:10:46,499 - pyskl - INFO - Epoch [132][300/3746] lr: 3.875e-03, eta: 16:09:02, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4952, top5_acc: 0.7436, loss_cls: 2.8034, loss: 2.8034 +2024-07-21 04:12:08,949 - pyskl - INFO - Epoch [132][400/3746] lr: 3.864e-03, eta: 16:07:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4852, top5_acc: 0.7341, loss_cls: 2.8896, loss: 2.8896 +2024-07-21 04:13:30,392 - pyskl - INFO - Epoch [132][500/3746] lr: 3.853e-03, eta: 16:06:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7355, loss_cls: 2.8794, loss: 2.8794 +2024-07-21 04:14:51,709 - pyskl - INFO - Epoch [132][600/3746] lr: 3.842e-03, eta: 16:04:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4664, top5_acc: 0.7291, loss_cls: 2.9273, loss: 2.9273 +2024-07-21 04:16:12,916 - pyskl - INFO - Epoch [132][700/3746] lr: 3.831e-03, eta: 16:03:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4766, top5_acc: 0.7334, loss_cls: 2.8933, loss: 2.8933 +2024-07-21 04:17:33,874 - pyskl - INFO - Epoch [132][800/3746] lr: 3.821e-03, eta: 16:02:12, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4798, top5_acc: 0.7323, loss_cls: 2.8838, loss: 2.8838 +2024-07-21 04:18:55,077 - pyskl - INFO - Epoch [132][900/3746] lr: 3.810e-03, eta: 16:00:50, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4727, top5_acc: 0.7384, loss_cls: 2.8759, loss: 2.8759 +2024-07-21 04:20:16,115 - pyskl - INFO - Epoch [132][1000/3746] lr: 3.799e-03, eta: 15:59:27, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4806, top5_acc: 0.7286, loss_cls: 2.9043, loss: 2.9043 +2024-07-21 04:21:37,743 - pyskl - INFO - Epoch [132][1100/3746] lr: 3.789e-03, eta: 15:58:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7352, loss_cls: 2.9176, loss: 2.9176 +2024-07-21 04:22:59,133 - pyskl - INFO - Epoch [132][1200/3746] lr: 3.778e-03, eta: 15:56:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4878, top5_acc: 0.7495, loss_cls: 2.8241, loss: 2.8241 +2024-07-21 04:24:20,441 - pyskl - INFO - Epoch [132][1300/3746] lr: 3.767e-03, eta: 15:55:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7384, loss_cls: 2.8763, loss: 2.8763 +2024-07-21 04:25:41,809 - pyskl - INFO - Epoch [132][1400/3746] lr: 3.757e-03, eta: 15:53:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4714, top5_acc: 0.7322, loss_cls: 2.9484, loss: 2.9484 +2024-07-21 04:27:03,687 - pyskl - INFO - Epoch [132][1500/3746] lr: 3.746e-03, eta: 15:52:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4763, top5_acc: 0.7303, loss_cls: 2.9362, loss: 2.9362 +2024-07-21 04:28:24,406 - pyskl - INFO - Epoch [132][1600/3746] lr: 3.735e-03, eta: 15:51:15, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.4698, top5_acc: 0.7269, loss_cls: 2.9386, loss: 2.9386 +2024-07-21 04:29:45,894 - pyskl - INFO - Epoch [132][1700/3746] lr: 3.725e-03, eta: 15:49:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4739, top5_acc: 0.7292, loss_cls: 2.9263, loss: 2.9263 +2024-07-21 04:31:07,158 - pyskl - INFO - Epoch [132][1800/3746] lr: 3.714e-03, eta: 15:48:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4767, top5_acc: 0.7272, loss_cls: 2.9002, loss: 2.9002 +2024-07-21 04:32:28,357 - pyskl - INFO - Epoch [132][1900/3746] lr: 3.704e-03, eta: 15:47:08, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4789, top5_acc: 0.7283, loss_cls: 2.9025, loss: 2.9025 +2024-07-21 04:33:49,678 - pyskl - INFO - Epoch [132][2000/3746] lr: 3.693e-03, eta: 15:45:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4631, top5_acc: 0.7211, loss_cls: 2.9591, loss: 2.9591 +2024-07-21 04:35:10,522 - pyskl - INFO - Epoch [132][2100/3746] lr: 3.683e-03, eta: 15:44:24, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4881, top5_acc: 0.7288, loss_cls: 2.8953, loss: 2.8953 +2024-07-21 04:36:31,608 - pyskl - INFO - Epoch [132][2200/3746] lr: 3.672e-03, eta: 15:43:02, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4739, top5_acc: 0.7294, loss_cls: 2.9042, loss: 2.9042 +2024-07-21 04:37:53,167 - pyskl - INFO - Epoch [132][2300/3746] lr: 3.662e-03, eta: 15:41:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4672, top5_acc: 0.7250, loss_cls: 2.9727, loss: 2.9727 +2024-07-21 04:39:14,159 - pyskl - INFO - Epoch [132][2400/3746] lr: 3.651e-03, eta: 15:40:17, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4672, top5_acc: 0.7295, loss_cls: 2.9329, loss: 2.9329 +2024-07-21 04:40:35,574 - pyskl - INFO - Epoch [132][2500/3746] lr: 3.641e-03, eta: 15:38:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4658, top5_acc: 0.7233, loss_cls: 2.9769, loss: 2.9769 +2024-07-21 04:41:56,789 - pyskl - INFO - Epoch [132][2600/3746] lr: 3.630e-03, eta: 15:37:33, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4720, top5_acc: 0.7323, loss_cls: 2.9448, loss: 2.9448 +2024-07-21 04:43:17,715 - pyskl - INFO - Epoch [132][2700/3746] lr: 3.620e-03, eta: 15:36:11, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4747, top5_acc: 0.7302, loss_cls: 2.8904, loss: 2.8904 +2024-07-21 04:44:39,404 - pyskl - INFO - Epoch [132][2800/3746] lr: 3.609e-03, eta: 15:34:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4716, top5_acc: 0.7305, loss_cls: 2.9474, loss: 2.9474 +2024-07-21 04:46:00,868 - pyskl - INFO - Epoch [132][2900/3746] lr: 3.599e-03, eta: 15:33:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4763, top5_acc: 0.7327, loss_cls: 2.9008, loss: 2.9008 +2024-07-21 04:47:23,179 - pyskl - INFO - Epoch [132][3000/3746] lr: 3.588e-03, eta: 15:32:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4637, top5_acc: 0.7155, loss_cls: 2.9755, loss: 2.9755 +2024-07-21 04:48:44,741 - pyskl - INFO - Epoch [132][3100/3746] lr: 3.578e-03, eta: 15:30:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4627, top5_acc: 0.7200, loss_cls: 2.9666, loss: 2.9666 +2024-07-21 04:50:05,820 - pyskl - INFO - Epoch [132][3200/3746] lr: 3.568e-03, eta: 15:29:21, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4630, top5_acc: 0.7198, loss_cls: 2.9823, loss: 2.9823 +2024-07-21 04:51:26,797 - pyskl - INFO - Epoch [132][3300/3746] lr: 3.557e-03, eta: 15:27:58, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7259, loss_cls: 2.9460, loss: 2.9460 +2024-07-21 04:52:47,897 - pyskl - INFO - Epoch [132][3400/3746] lr: 3.547e-03, eta: 15:26:36, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4623, top5_acc: 0.7223, loss_cls: 2.9815, loss: 2.9815 +2024-07-21 04:54:08,861 - pyskl - INFO - Epoch [132][3500/3746] lr: 3.537e-03, eta: 15:25:14, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4602, top5_acc: 0.7302, loss_cls: 2.9596, loss: 2.9596 +2024-07-21 04:55:29,903 - pyskl - INFO - Epoch [132][3600/3746] lr: 3.526e-03, eta: 15:23:52, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4672, top5_acc: 0.7137, loss_cls: 2.9920, loss: 2.9920 +2024-07-21 04:56:51,172 - pyskl - INFO - Epoch [132][3700/3746] lr: 3.516e-03, eta: 15:22:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7172, loss_cls: 2.9795, loss: 2.9795 +2024-07-21 04:57:30,653 - pyskl - INFO - Saving checkpoint at 132 epochs +2024-07-21 04:59:20,902 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 04:59:21,570 - pyskl - INFO - +top1_acc 0.3894 +top5_acc 0.6449 +2024-07-21 04:59:21,570 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 04:59:21,611 - pyskl - INFO - +mean_acc 0.3892 +2024-07-21 04:59:21,615 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_130.pth was removed +2024-07-21 04:59:21,848 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_132.pth. +2024-07-21 04:59:21,849 - pyskl - INFO - Best top1_acc is 0.3894 at 132 epoch. +2024-07-21 04:59:21,865 - pyskl - INFO - Epoch(val) [132][309] top1_acc: 0.3894, top5_acc: 0.6449, mean_class_accuracy: 0.3892 +2024-07-21 05:03:11,308 - pyskl - INFO - Epoch [133][100/3746] lr: 3.501e-03, eta: 15:20:45, time: 2.294, data_time: 1.316, memory: 15990, top1_acc: 0.4919, top5_acc: 0.7434, loss_cls: 2.8282, loss: 2.8282 +2024-07-21 05:04:33,523 - pyskl - INFO - Epoch [133][200/3746] lr: 3.491e-03, eta: 15:19:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4855, top5_acc: 0.7480, loss_cls: 2.8303, loss: 2.8303 +2024-07-21 05:05:55,120 - pyskl - INFO - Epoch [133][300/3746] lr: 3.480e-03, eta: 15:18:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4897, top5_acc: 0.7414, loss_cls: 2.8481, loss: 2.8481 +2024-07-21 05:07:16,978 - pyskl - INFO - Epoch [133][400/3746] lr: 3.470e-03, eta: 15:16:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4853, top5_acc: 0.7428, loss_cls: 2.8544, loss: 2.8544 +2024-07-21 05:08:39,413 - pyskl - INFO - Epoch [133][500/3746] lr: 3.460e-03, eta: 15:15:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4909, top5_acc: 0.7467, loss_cls: 2.8247, loss: 2.8247 +2024-07-21 05:10:00,767 - pyskl - INFO - Epoch [133][600/3746] lr: 3.450e-03, eta: 15:13:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4861, top5_acc: 0.7409, loss_cls: 2.8467, loss: 2.8467 +2024-07-21 05:11:22,628 - pyskl - INFO - Epoch [133][700/3746] lr: 3.440e-03, eta: 15:12:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7411, loss_cls: 2.8803, loss: 2.8803 +2024-07-21 05:12:44,511 - pyskl - INFO - Epoch [133][800/3746] lr: 3.429e-03, eta: 15:11:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4897, top5_acc: 0.7452, loss_cls: 2.8111, loss: 2.8111 +2024-07-21 05:14:06,174 - pyskl - INFO - Epoch [133][900/3746] lr: 3.419e-03, eta: 15:09:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4819, top5_acc: 0.7377, loss_cls: 2.8812, loss: 2.8812 +2024-07-21 05:15:27,165 - pyskl - INFO - Epoch [133][1000/3746] lr: 3.409e-03, eta: 15:08:26, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4898, top5_acc: 0.7423, loss_cls: 2.8506, loss: 2.8506 +2024-07-21 05:16:48,274 - pyskl - INFO - Epoch [133][1100/3746] lr: 3.399e-03, eta: 15:07:04, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4786, top5_acc: 0.7416, loss_cls: 2.8614, loss: 2.8614 +2024-07-21 05:18:09,468 - pyskl - INFO - Epoch [133][1200/3746] lr: 3.389e-03, eta: 15:05:42, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4777, top5_acc: 0.7295, loss_cls: 2.9258, loss: 2.9258 +2024-07-21 05:19:30,463 - pyskl - INFO - Epoch [133][1300/3746] lr: 3.379e-03, eta: 15:04:20, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4742, top5_acc: 0.7316, loss_cls: 2.9145, loss: 2.9145 +2024-07-21 05:20:51,408 - pyskl - INFO - Epoch [133][1400/3746] lr: 3.369e-03, eta: 15:02:57, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4766, top5_acc: 0.7292, loss_cls: 2.9007, loss: 2.9007 +2024-07-21 05:22:12,430 - pyskl - INFO - Epoch [133][1500/3746] lr: 3.359e-03, eta: 15:01:35, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4731, top5_acc: 0.7348, loss_cls: 2.9049, loss: 2.9049 +2024-07-21 05:23:33,665 - pyskl - INFO - Epoch [133][1600/3746] lr: 3.348e-03, eta: 15:00:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4883, top5_acc: 0.7392, loss_cls: 2.8535, loss: 2.8535 +2024-07-21 05:24:54,470 - pyskl - INFO - Epoch [133][1700/3746] lr: 3.338e-03, eta: 14:58:51, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4809, top5_acc: 0.7386, loss_cls: 2.8731, loss: 2.8731 +2024-07-21 05:26:16,000 - pyskl - INFO - Epoch [133][1800/3746] lr: 3.328e-03, eta: 14:57:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4730, top5_acc: 0.7269, loss_cls: 2.9171, loss: 2.9171 +2024-07-21 05:27:37,139 - pyskl - INFO - Epoch [133][1900/3746] lr: 3.318e-03, eta: 14:56:06, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4777, top5_acc: 0.7380, loss_cls: 2.8791, loss: 2.8791 +2024-07-21 05:28:58,315 - pyskl - INFO - Epoch [133][2000/3746] lr: 3.308e-03, eta: 14:54:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4813, top5_acc: 0.7341, loss_cls: 2.8894, loss: 2.8894 +2024-07-21 05:30:19,881 - pyskl - INFO - Epoch [133][2100/3746] lr: 3.298e-03, eta: 14:53:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4845, top5_acc: 0.7336, loss_cls: 2.8978, loss: 2.8978 +2024-07-21 05:31:40,646 - pyskl - INFO - Epoch [133][2200/3746] lr: 3.288e-03, eta: 14:52:00, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4847, top5_acc: 0.7359, loss_cls: 2.8553, loss: 2.8553 +2024-07-21 05:33:02,205 - pyskl - INFO - Epoch [133][2300/3746] lr: 3.278e-03, eta: 14:50:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4867, top5_acc: 0.7409, loss_cls: 2.8544, loss: 2.8544 +2024-07-21 05:34:23,570 - pyskl - INFO - Epoch [133][2400/3746] lr: 3.268e-03, eta: 14:49:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7341, loss_cls: 2.8959, loss: 2.8959 +2024-07-21 05:35:45,572 - pyskl - INFO - Epoch [133][2500/3746] lr: 3.259e-03, eta: 14:47:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4630, top5_acc: 0.7247, loss_cls: 2.9546, loss: 2.9546 +2024-07-21 05:37:06,516 - pyskl - INFO - Epoch [133][2600/3746] lr: 3.249e-03, eta: 14:46:31, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4864, top5_acc: 0.7308, loss_cls: 2.8805, loss: 2.8805 +2024-07-21 05:38:27,503 - pyskl - INFO - Epoch [133][2700/3746] lr: 3.239e-03, eta: 14:45:09, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4689, top5_acc: 0.7316, loss_cls: 2.9069, loss: 2.9069 +2024-07-21 05:39:49,065 - pyskl - INFO - Epoch [133][2800/3746] lr: 3.229e-03, eta: 14:43:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4698, top5_acc: 0.7203, loss_cls: 2.9405, loss: 2.9405 +2024-07-21 05:41:10,730 - pyskl - INFO - Epoch [133][2900/3746] lr: 3.219e-03, eta: 14:42:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4741, top5_acc: 0.7323, loss_cls: 2.9006, loss: 2.9006 +2024-07-21 05:42:31,999 - pyskl - INFO - Epoch [133][3000/3746] lr: 3.209e-03, eta: 14:41:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4770, top5_acc: 0.7378, loss_cls: 2.9018, loss: 2.9018 +2024-07-21 05:43:53,639 - pyskl - INFO - Epoch [133][3100/3746] lr: 3.199e-03, eta: 14:39:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4706, top5_acc: 0.7352, loss_cls: 2.9151, loss: 2.9151 +2024-07-21 05:45:14,886 - pyskl - INFO - Epoch [133][3200/3746] lr: 3.189e-03, eta: 14:38:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4763, top5_acc: 0.7298, loss_cls: 2.9033, loss: 2.9033 +2024-07-21 05:46:36,321 - pyskl - INFO - Epoch [133][3300/3746] lr: 3.180e-03, eta: 14:36:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4705, top5_acc: 0.7320, loss_cls: 2.8882, loss: 2.8882 +2024-07-21 05:47:58,104 - pyskl - INFO - Epoch [133][3400/3746] lr: 3.170e-03, eta: 14:35:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4692, top5_acc: 0.7297, loss_cls: 2.9175, loss: 2.9175 +2024-07-21 05:49:19,547 - pyskl - INFO - Epoch [133][3500/3746] lr: 3.160e-03, eta: 14:34:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4973, top5_acc: 0.7370, loss_cls: 2.8517, loss: 2.8517 +2024-07-21 05:50:40,642 - pyskl - INFO - Epoch [133][3600/3746] lr: 3.150e-03, eta: 14:32:50, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4820, top5_acc: 0.7339, loss_cls: 2.8883, loss: 2.8883 +2024-07-21 05:52:01,704 - pyskl - INFO - Epoch [133][3700/3746] lr: 3.140e-03, eta: 14:31:28, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4703, top5_acc: 0.7227, loss_cls: 2.9538, loss: 2.9538 +2024-07-21 05:52:40,896 - pyskl - INFO - Saving checkpoint at 133 epochs +2024-07-21 05:54:31,998 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 05:54:32,672 - pyskl - INFO - +top1_acc 0.3876 +top5_acc 0.6443 +2024-07-21 05:54:32,672 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 05:54:32,716 - pyskl - INFO - +mean_acc 0.3874 +2024-07-21 05:54:32,728 - pyskl - INFO - Epoch(val) [133][309] top1_acc: 0.3876, top5_acc: 0.6443, mean_class_accuracy: 0.3874 +2024-07-21 05:58:21,995 - pyskl - INFO - Epoch [134][100/3746] lr: 3.126e-03, eta: 14:29:42, time: 2.293, data_time: 1.319, memory: 15990, top1_acc: 0.5123, top5_acc: 0.7631, loss_cls: 2.7300, loss: 2.7300 +2024-07-21 05:59:43,481 - pyskl - INFO - Epoch [134][200/3746] lr: 3.117e-03, eta: 14:28:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5008, top5_acc: 0.7558, loss_cls: 2.7353, loss: 2.7353 +2024-07-21 06:01:04,750 - pyskl - INFO - Epoch [134][300/3746] lr: 3.107e-03, eta: 14:26:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4998, top5_acc: 0.7497, loss_cls: 2.7951, loss: 2.7951 +2024-07-21 06:02:26,607 - pyskl - INFO - Epoch [134][400/3746] lr: 3.097e-03, eta: 14:25:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4909, top5_acc: 0.7484, loss_cls: 2.8180, loss: 2.8180 +2024-07-21 06:03:48,118 - pyskl - INFO - Epoch [134][500/3746] lr: 3.087e-03, eta: 14:24:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4955, top5_acc: 0.7488, loss_cls: 2.8144, loss: 2.8144 +2024-07-21 06:05:09,726 - pyskl - INFO - Epoch [134][600/3746] lr: 3.078e-03, eta: 14:22:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4944, top5_acc: 0.7461, loss_cls: 2.7738, loss: 2.7738 +2024-07-21 06:06:31,289 - pyskl - INFO - Epoch [134][700/3746] lr: 3.068e-03, eta: 14:21:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4847, top5_acc: 0.7406, loss_cls: 2.8348, loss: 2.8348 +2024-07-21 06:07:52,253 - pyskl - INFO - Epoch [134][800/3746] lr: 3.059e-03, eta: 14:20:07, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4931, top5_acc: 0.7441, loss_cls: 2.8282, loss: 2.8282 +2024-07-21 06:09:13,534 - pyskl - INFO - Epoch [134][900/3746] lr: 3.049e-03, eta: 14:18:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4908, top5_acc: 0.7528, loss_cls: 2.8125, loss: 2.8125 +2024-07-21 06:10:35,487 - pyskl - INFO - Epoch [134][1000/3746] lr: 3.039e-03, eta: 14:17:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4991, top5_acc: 0.7548, loss_cls: 2.7808, loss: 2.7808 +2024-07-21 06:11:56,718 - pyskl - INFO - Epoch [134][1100/3746] lr: 3.030e-03, eta: 14:16:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4947, top5_acc: 0.7444, loss_cls: 2.8162, loss: 2.8162 +2024-07-21 06:13:17,943 - pyskl - INFO - Epoch [134][1200/3746] lr: 3.020e-03, eta: 14:14:38, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4836, top5_acc: 0.7364, loss_cls: 2.8373, loss: 2.8373 +2024-07-21 06:14:38,986 - pyskl - INFO - Epoch [134][1300/3746] lr: 3.011e-03, eta: 14:13:16, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4919, top5_acc: 0.7372, loss_cls: 2.8395, loss: 2.8395 +2024-07-21 06:16:00,842 - pyskl - INFO - Epoch [134][1400/3746] lr: 3.001e-03, eta: 14:11:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4992, top5_acc: 0.7456, loss_cls: 2.8047, loss: 2.8047 +2024-07-21 06:17:22,213 - pyskl - INFO - Epoch [134][1500/3746] lr: 2.991e-03, eta: 14:10:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4928, top5_acc: 0.7397, loss_cls: 2.8228, loss: 2.8228 +2024-07-21 06:18:43,539 - pyskl - INFO - Epoch [134][1600/3746] lr: 2.982e-03, eta: 14:09:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4873, top5_acc: 0.7384, loss_cls: 2.8582, loss: 2.8582 +2024-07-21 06:20:04,512 - pyskl - INFO - Epoch [134][1700/3746] lr: 2.972e-03, eta: 14:07:48, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4786, top5_acc: 0.7444, loss_cls: 2.8649, loss: 2.8649 +2024-07-21 06:21:25,333 - pyskl - INFO - Epoch [134][1800/3746] lr: 2.963e-03, eta: 14:06:25, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4803, top5_acc: 0.7441, loss_cls: 2.8558, loss: 2.8558 +2024-07-21 06:22:47,055 - pyskl - INFO - Epoch [134][1900/3746] lr: 2.953e-03, eta: 14:05:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7306, loss_cls: 2.8540, loss: 2.8540 +2024-07-21 06:24:07,983 - pyskl - INFO - Epoch [134][2000/3746] lr: 2.944e-03, eta: 14:03:41, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4953, top5_acc: 0.7466, loss_cls: 2.8142, loss: 2.8142 +2024-07-21 06:25:29,065 - pyskl - INFO - Epoch [134][2100/3746] lr: 2.935e-03, eta: 14:02:19, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4833, top5_acc: 0.7373, loss_cls: 2.8779, loss: 2.8779 +2024-07-21 06:26:50,355 - pyskl - INFO - Epoch [134][2200/3746] lr: 2.925e-03, eta: 14:00:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4897, top5_acc: 0.7375, loss_cls: 2.8374, loss: 2.8374 +2024-07-21 06:28:12,066 - pyskl - INFO - Epoch [134][2300/3746] lr: 2.916e-03, eta: 13:59:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4853, top5_acc: 0.7519, loss_cls: 2.8138, loss: 2.8138 +2024-07-21 06:29:33,059 - pyskl - INFO - Epoch [134][2400/3746] lr: 2.906e-03, eta: 13:58:12, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4764, top5_acc: 0.7261, loss_cls: 2.8918, loss: 2.8918 +2024-07-21 06:30:55,193 - pyskl - INFO - Epoch [134][2500/3746] lr: 2.897e-03, eta: 13:56:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4855, top5_acc: 0.7386, loss_cls: 2.8689, loss: 2.8689 +2024-07-21 06:32:16,544 - pyskl - INFO - Epoch [134][2600/3746] lr: 2.888e-03, eta: 13:55:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4883, top5_acc: 0.7377, loss_cls: 2.8592, loss: 2.8592 +2024-07-21 06:33:37,969 - pyskl - INFO - Epoch [134][2700/3746] lr: 2.878e-03, eta: 13:54:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4828, top5_acc: 0.7436, loss_cls: 2.8382, loss: 2.8382 +2024-07-21 06:34:59,190 - pyskl - INFO - Epoch [134][2800/3746] lr: 2.869e-03, eta: 13:52:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4809, top5_acc: 0.7461, loss_cls: 2.8531, loss: 2.8531 +2024-07-21 06:36:20,373 - pyskl - INFO - Epoch [134][2900/3746] lr: 2.860e-03, eta: 13:51:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4847, top5_acc: 0.7420, loss_cls: 2.8659, loss: 2.8659 +2024-07-21 06:37:41,765 - pyskl - INFO - Epoch [134][3000/3746] lr: 2.850e-03, eta: 13:50:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4814, top5_acc: 0.7358, loss_cls: 2.9002, loss: 2.9002 +2024-07-21 06:39:03,032 - pyskl - INFO - Epoch [134][3100/3746] lr: 2.841e-03, eta: 13:48:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4811, top5_acc: 0.7383, loss_cls: 2.8656, loss: 2.8656 +2024-07-21 06:40:24,336 - pyskl - INFO - Epoch [134][3200/3746] lr: 2.832e-03, eta: 13:47:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4877, top5_acc: 0.7308, loss_cls: 2.8904, loss: 2.8904 +2024-07-21 06:41:45,247 - pyskl - INFO - Epoch [134][3300/3746] lr: 2.822e-03, eta: 13:45:53, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4883, top5_acc: 0.7403, loss_cls: 2.8356, loss: 2.8356 +2024-07-21 06:43:05,944 - pyskl - INFO - Epoch [134][3400/3746] lr: 2.813e-03, eta: 13:44:31, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7334, loss_cls: 2.9110, loss: 2.9110 +2024-07-21 06:44:27,199 - pyskl - INFO - Epoch [134][3500/3746] lr: 2.804e-03, eta: 13:43:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4866, top5_acc: 0.7405, loss_cls: 2.8263, loss: 2.8263 +2024-07-21 06:45:48,320 - pyskl - INFO - Epoch [134][3600/3746] lr: 2.795e-03, eta: 13:41:46, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4795, top5_acc: 0.7336, loss_cls: 2.8872, loss: 2.8872 +2024-07-21 06:47:09,748 - pyskl - INFO - Epoch [134][3700/3746] lr: 2.786e-03, eta: 13:40:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4817, top5_acc: 0.7331, loss_cls: 2.8881, loss: 2.8881 +2024-07-21 06:47:49,229 - pyskl - INFO - Saving checkpoint at 134 epochs +2024-07-21 06:49:40,865 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 06:49:41,529 - pyskl - INFO - +top1_acc 0.3914 +top5_acc 0.6500 +2024-07-21 06:49:41,529 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 06:49:41,569 - pyskl - INFO - +mean_acc 0.3912 +2024-07-21 06:49:41,574 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_132.pth was removed +2024-07-21 06:49:41,808 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_134.pth. +2024-07-21 06:49:41,808 - pyskl - INFO - Best top1_acc is 0.3914 at 134 epoch. +2024-07-21 06:49:41,824 - pyskl - INFO - Epoch(val) [134][309] top1_acc: 0.3914, top5_acc: 0.6500, mean_class_accuracy: 0.3912 +2024-07-21 06:53:28,985 - pyskl - INFO - Epoch [135][100/3746] lr: 2.772e-03, eta: 13:38:37, time: 2.272, data_time: 1.296, memory: 15990, top1_acc: 0.5075, top5_acc: 0.7616, loss_cls: 2.7277, loss: 2.7277 +2024-07-21 06:54:50,224 - pyskl - INFO - Epoch [135][200/3746] lr: 2.763e-03, eta: 13:37:15, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5206, top5_acc: 0.7706, loss_cls: 2.6637, loss: 2.6637 +2024-07-21 06:56:11,616 - pyskl - INFO - Epoch [135][300/3746] lr: 2.754e-03, eta: 13:35:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5131, top5_acc: 0.7570, loss_cls: 2.7378, loss: 2.7378 +2024-07-21 06:57:32,725 - pyskl - INFO - Epoch [135][400/3746] lr: 2.745e-03, eta: 13:34:31, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4953, top5_acc: 0.7469, loss_cls: 2.8073, loss: 2.8073 +2024-07-21 06:58:54,628 - pyskl - INFO - Epoch [135][500/3746] lr: 2.735e-03, eta: 13:33:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5120, top5_acc: 0.7580, loss_cls: 2.7397, loss: 2.7397 +2024-07-21 07:00:16,185 - pyskl - INFO - Epoch [135][600/3746] lr: 2.726e-03, eta: 13:31:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7503, loss_cls: 2.7758, loss: 2.7758 +2024-07-21 07:01:38,647 - pyskl - INFO - Epoch [135][700/3746] lr: 2.717e-03, eta: 13:30:24, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4975, top5_acc: 0.7497, loss_cls: 2.7787, loss: 2.7787 +2024-07-21 07:03:00,183 - pyskl - INFO - Epoch [135][800/3746] lr: 2.708e-03, eta: 13:29:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4970, top5_acc: 0.7559, loss_cls: 2.7849, loss: 2.7849 +2024-07-21 07:04:21,492 - pyskl - INFO - Epoch [135][900/3746] lr: 2.699e-03, eta: 13:27:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4942, top5_acc: 0.7552, loss_cls: 2.7769, loss: 2.7769 +2024-07-21 07:05:42,937 - pyskl - INFO - Epoch [135][1000/3746] lr: 2.690e-03, eta: 13:26:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4934, top5_acc: 0.7512, loss_cls: 2.7802, loss: 2.7802 +2024-07-21 07:07:03,954 - pyskl - INFO - Epoch [135][1100/3746] lr: 2.681e-03, eta: 13:24:56, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4977, top5_acc: 0.7481, loss_cls: 2.7850, loss: 2.7850 +2024-07-21 07:08:25,179 - pyskl - INFO - Epoch [135][1200/3746] lr: 2.672e-03, eta: 13:23:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4991, top5_acc: 0.7517, loss_cls: 2.7784, loss: 2.7784 +2024-07-21 07:09:46,364 - pyskl - INFO - Epoch [135][1300/3746] lr: 2.663e-03, eta: 13:22:11, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4884, top5_acc: 0.7467, loss_cls: 2.8189, loss: 2.8189 +2024-07-21 07:11:07,837 - pyskl - INFO - Epoch [135][1400/3746] lr: 2.654e-03, eta: 13:20:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5094, top5_acc: 0.7656, loss_cls: 2.7103, loss: 2.7103 +2024-07-21 07:12:30,544 - pyskl - INFO - Epoch [135][1500/3746] lr: 2.645e-03, eta: 13:19:27, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4963, top5_acc: 0.7462, loss_cls: 2.8118, loss: 2.8118 +2024-07-21 07:13:52,886 - pyskl - INFO - Epoch [135][1600/3746] lr: 2.636e-03, eta: 13:18:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7467, loss_cls: 2.8091, loss: 2.8091 +2024-07-21 07:15:13,688 - pyskl - INFO - Epoch [135][1700/3746] lr: 2.627e-03, eta: 13:16:43, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4973, top5_acc: 0.7458, loss_cls: 2.8069, loss: 2.8069 +2024-07-21 07:16:35,190 - pyskl - INFO - Epoch [135][1800/3746] lr: 2.618e-03, eta: 13:15:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4884, top5_acc: 0.7466, loss_cls: 2.8277, loss: 2.8277 +2024-07-21 07:17:56,351 - pyskl - INFO - Epoch [135][1900/3746] lr: 2.609e-03, eta: 13:13:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5027, top5_acc: 0.7469, loss_cls: 2.8021, loss: 2.8021 +2024-07-21 07:19:17,617 - pyskl - INFO - Epoch [135][2000/3746] lr: 2.600e-03, eta: 13:12:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4894, top5_acc: 0.7469, loss_cls: 2.8218, loss: 2.8218 +2024-07-21 07:20:38,938 - pyskl - INFO - Epoch [135][2100/3746] lr: 2.591e-03, eta: 13:11:14, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4902, top5_acc: 0.7584, loss_cls: 2.7821, loss: 2.7821 +2024-07-21 07:22:00,509 - pyskl - INFO - Epoch [135][2200/3746] lr: 2.583e-03, eta: 13:09:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4770, top5_acc: 0.7298, loss_cls: 2.9004, loss: 2.9004 +2024-07-21 07:23:22,070 - pyskl - INFO - Epoch [135][2300/3746] lr: 2.574e-03, eta: 13:08:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4961, top5_acc: 0.7511, loss_cls: 2.7794, loss: 2.7794 +2024-07-21 07:24:43,738 - pyskl - INFO - Epoch [135][2400/3746] lr: 2.565e-03, eta: 13:07:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5017, top5_acc: 0.7469, loss_cls: 2.7865, loss: 2.7865 +2024-07-21 07:26:05,250 - pyskl - INFO - Epoch [135][2500/3746] lr: 2.556e-03, eta: 13:05:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5028, top5_acc: 0.7488, loss_cls: 2.7991, loss: 2.7991 +2024-07-21 07:27:26,069 - pyskl - INFO - Epoch [135][2600/3746] lr: 2.547e-03, eta: 13:04:23, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4861, top5_acc: 0.7411, loss_cls: 2.8330, loss: 2.8330 +2024-07-21 07:28:47,018 - pyskl - INFO - Epoch [135][2700/3746] lr: 2.538e-03, eta: 13:03:01, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4920, top5_acc: 0.7414, loss_cls: 2.8426, loss: 2.8426 +2024-07-21 07:30:08,232 - pyskl - INFO - Epoch [135][2800/3746] lr: 2.530e-03, eta: 13:01:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7447, loss_cls: 2.8364, loss: 2.8364 +2024-07-21 07:31:29,637 - pyskl - INFO - Epoch [135][2900/3746] lr: 2.521e-03, eta: 13:00:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4842, top5_acc: 0.7423, loss_cls: 2.8112, loss: 2.8112 +2024-07-21 07:32:51,469 - pyskl - INFO - Epoch [135][3000/3746] lr: 2.512e-03, eta: 12:58:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4902, top5_acc: 0.7409, loss_cls: 2.8293, loss: 2.8293 +2024-07-21 07:34:12,936 - pyskl - INFO - Epoch [135][3100/3746] lr: 2.503e-03, eta: 12:57:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4928, top5_acc: 0.7403, loss_cls: 2.8207, loss: 2.8207 +2024-07-21 07:35:34,682 - pyskl - INFO - Epoch [135][3200/3746] lr: 2.495e-03, eta: 12:56:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4886, top5_acc: 0.7370, loss_cls: 2.8620, loss: 2.8620 +2024-07-21 07:36:56,697 - pyskl - INFO - Epoch [135][3300/3746] lr: 2.486e-03, eta: 12:54:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4853, top5_acc: 0.7461, loss_cls: 2.8335, loss: 2.8335 +2024-07-21 07:38:17,934 - pyskl - INFO - Epoch [135][3400/3746] lr: 2.477e-03, eta: 12:53:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4934, top5_acc: 0.7459, loss_cls: 2.8344, loss: 2.8344 +2024-07-21 07:39:38,942 - pyskl - INFO - Epoch [135][3500/3746] lr: 2.469e-03, eta: 12:52:04, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4961, top5_acc: 0.7412, loss_cls: 2.8079, loss: 2.8079 +2024-07-21 07:40:59,698 - pyskl - INFO - Epoch [135][3600/3746] lr: 2.460e-03, eta: 12:50:42, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7408, loss_cls: 2.8422, loss: 2.8422 +2024-07-21 07:42:20,711 - pyskl - INFO - Epoch [135][3700/3746] lr: 2.451e-03, eta: 12:49:20, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4877, top5_acc: 0.7456, loss_cls: 2.8257, loss: 2.8257 +2024-07-21 07:43:00,090 - pyskl - INFO - Saving checkpoint at 135 epochs +2024-07-21 07:44:51,534 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 07:44:52,201 - pyskl - INFO - +top1_acc 0.3982 +top5_acc 0.6549 +2024-07-21 07:44:52,201 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 07:44:52,241 - pyskl - INFO - +mean_acc 0.3980 +2024-07-21 07:44:52,245 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_134.pth was removed +2024-07-21 07:44:52,476 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_135.pth. +2024-07-21 07:44:52,476 - pyskl - INFO - Best top1_acc is 0.3982 at 135 epoch. +2024-07-21 07:44:52,488 - pyskl - INFO - Epoch(val) [135][309] top1_acc: 0.3982, top5_acc: 0.6549, mean_class_accuracy: 0.3980 +2024-07-21 07:48:36,449 - pyskl - INFO - Epoch [136][100/3746] lr: 2.439e-03, eta: 12:47:31, time: 2.240, data_time: 1.268, memory: 15990, top1_acc: 0.5042, top5_acc: 0.7595, loss_cls: 2.7315, loss: 2.7315 +2024-07-21 07:49:57,676 - pyskl - INFO - Epoch [136][200/3746] lr: 2.430e-03, eta: 12:46:09, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5078, top5_acc: 0.7684, loss_cls: 2.6952, loss: 2.6952 +2024-07-21 07:51:19,070 - pyskl - INFO - Epoch [136][300/3746] lr: 2.421e-03, eta: 12:44:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5022, top5_acc: 0.7581, loss_cls: 2.7531, loss: 2.7531 +2024-07-21 07:52:39,869 - pyskl - INFO - Epoch [136][400/3746] lr: 2.413e-03, eta: 12:43:25, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.5091, top5_acc: 0.7633, loss_cls: 2.7212, loss: 2.7212 +2024-07-21 07:54:01,991 - pyskl - INFO - Epoch [136][500/3746] lr: 2.404e-03, eta: 12:42:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5097, top5_acc: 0.7644, loss_cls: 2.7155, loss: 2.7155 +2024-07-21 07:55:23,443 - pyskl - INFO - Epoch [136][600/3746] lr: 2.396e-03, eta: 12:40:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5048, top5_acc: 0.7627, loss_cls: 2.7251, loss: 2.7251 +2024-07-21 07:56:45,140 - pyskl - INFO - Epoch [136][700/3746] lr: 2.387e-03, eta: 12:39:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5102, top5_acc: 0.7606, loss_cls: 2.7207, loss: 2.7207 +2024-07-21 07:58:06,891 - pyskl - INFO - Epoch [136][800/3746] lr: 2.379e-03, eta: 12:37:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4939, top5_acc: 0.7573, loss_cls: 2.7571, loss: 2.7571 +2024-07-21 07:59:28,086 - pyskl - INFO - Epoch [136][900/3746] lr: 2.370e-03, eta: 12:36:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5070, top5_acc: 0.7522, loss_cls: 2.7362, loss: 2.7362 +2024-07-21 08:00:48,989 - pyskl - INFO - Epoch [136][1000/3746] lr: 2.362e-03, eta: 12:35:12, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5100, top5_acc: 0.7580, loss_cls: 2.7126, loss: 2.7126 +2024-07-21 08:02:10,493 - pyskl - INFO - Epoch [136][1100/3746] lr: 2.353e-03, eta: 12:33:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5017, top5_acc: 0.7509, loss_cls: 2.7585, loss: 2.7585 +2024-07-21 08:03:31,682 - pyskl - INFO - Epoch [136][1200/3746] lr: 2.345e-03, eta: 12:32:27, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4923, top5_acc: 0.7491, loss_cls: 2.7926, loss: 2.7926 +2024-07-21 08:04:53,231 - pyskl - INFO - Epoch [136][1300/3746] lr: 2.336e-03, eta: 12:31:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5038, top5_acc: 0.7553, loss_cls: 2.7185, loss: 2.7185 +2024-07-21 08:06:14,848 - pyskl - INFO - Epoch [136][1400/3746] lr: 2.328e-03, eta: 12:29:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5008, top5_acc: 0.7558, loss_cls: 2.7815, loss: 2.7815 +2024-07-21 08:07:35,960 - pyskl - INFO - Epoch [136][1500/3746] lr: 2.319e-03, eta: 12:28:21, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5002, top5_acc: 0.7541, loss_cls: 2.7564, loss: 2.7564 +2024-07-21 08:08:56,914 - pyskl - INFO - Epoch [136][1600/3746] lr: 2.311e-03, eta: 12:26:59, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5044, top5_acc: 0.7561, loss_cls: 2.7644, loss: 2.7644 +2024-07-21 08:10:18,313 - pyskl - INFO - Epoch [136][1700/3746] lr: 2.303e-03, eta: 12:25:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4950, top5_acc: 0.7569, loss_cls: 2.7605, loss: 2.7605 +2024-07-21 08:11:39,305 - pyskl - INFO - Epoch [136][1800/3746] lr: 2.294e-03, eta: 12:24:14, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5008, top5_acc: 0.7419, loss_cls: 2.7994, loss: 2.7994 +2024-07-21 08:13:00,831 - pyskl - INFO - Epoch [136][1900/3746] lr: 2.286e-03, eta: 12:22:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4977, top5_acc: 0.7508, loss_cls: 2.7778, loss: 2.7778 +2024-07-21 08:14:22,169 - pyskl - INFO - Epoch [136][2000/3746] lr: 2.277e-03, eta: 12:21:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5072, top5_acc: 0.7523, loss_cls: 2.7449, loss: 2.7449 +2024-07-21 08:15:43,627 - pyskl - INFO - Epoch [136][2100/3746] lr: 2.269e-03, eta: 12:20:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4975, top5_acc: 0.7589, loss_cls: 2.7440, loss: 2.7440 +2024-07-21 08:17:05,558 - pyskl - INFO - Epoch [136][2200/3746] lr: 2.261e-03, eta: 12:18:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4959, top5_acc: 0.7536, loss_cls: 2.7766, loss: 2.7766 +2024-07-21 08:18:27,810 - pyskl - INFO - Epoch [136][2300/3746] lr: 2.253e-03, eta: 12:17:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5059, top5_acc: 0.7575, loss_cls: 2.7503, loss: 2.7503 +2024-07-21 08:19:49,329 - pyskl - INFO - Epoch [136][2400/3746] lr: 2.244e-03, eta: 12:16:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5017, top5_acc: 0.7627, loss_cls: 2.7274, loss: 2.7274 +2024-07-21 08:21:10,787 - pyskl - INFO - Epoch [136][2500/3746] lr: 2.236e-03, eta: 12:14:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4961, top5_acc: 0.7527, loss_cls: 2.7810, loss: 2.7810 +2024-07-21 08:22:31,972 - pyskl - INFO - Epoch [136][2600/3746] lr: 2.228e-03, eta: 12:13:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4989, top5_acc: 0.7552, loss_cls: 2.7826, loss: 2.7826 +2024-07-21 08:23:53,568 - pyskl - INFO - Epoch [136][2700/3746] lr: 2.219e-03, eta: 12:11:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4994, top5_acc: 0.7445, loss_cls: 2.7938, loss: 2.7938 +2024-07-21 08:25:14,558 - pyskl - INFO - Epoch [136][2800/3746] lr: 2.211e-03, eta: 12:10:33, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5059, top5_acc: 0.7508, loss_cls: 2.7707, loss: 2.7707 +2024-07-21 08:26:36,447 - pyskl - INFO - Epoch [136][2900/3746] lr: 2.203e-03, eta: 12:09:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4981, top5_acc: 0.7519, loss_cls: 2.7909, loss: 2.7909 +2024-07-21 08:27:57,683 - pyskl - INFO - Epoch [136][3000/3746] lr: 2.195e-03, eta: 12:07:48, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5042, top5_acc: 0.7498, loss_cls: 2.7604, loss: 2.7604 +2024-07-21 08:29:19,752 - pyskl - INFO - Epoch [136][3100/3746] lr: 2.187e-03, eta: 12:06:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5050, top5_acc: 0.7559, loss_cls: 2.7522, loss: 2.7522 +2024-07-21 08:30:40,813 - pyskl - INFO - Epoch [136][3200/3746] lr: 2.178e-03, eta: 12:05:04, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4964, top5_acc: 0.7569, loss_cls: 2.7588, loss: 2.7588 +2024-07-21 08:32:02,201 - pyskl - INFO - Epoch [136][3300/3746] lr: 2.170e-03, eta: 12:03:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4995, top5_acc: 0.7530, loss_cls: 2.7638, loss: 2.7638 +2024-07-21 08:33:23,767 - pyskl - INFO - Epoch [136][3400/3746] lr: 2.162e-03, eta: 12:02:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5066, top5_acc: 0.7536, loss_cls: 2.7497, loss: 2.7497 +2024-07-21 08:34:44,717 - pyskl - INFO - Epoch [136][3500/3746] lr: 2.154e-03, eta: 12:00:57, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5014, top5_acc: 0.7488, loss_cls: 2.7798, loss: 2.7798 +2024-07-21 08:36:06,346 - pyskl - INFO - Epoch [136][3600/3746] lr: 2.146e-03, eta: 11:59:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5031, top5_acc: 0.7520, loss_cls: 2.7578, loss: 2.7578 +2024-07-21 08:37:27,347 - pyskl - INFO - Epoch [136][3700/3746] lr: 2.138e-03, eta: 11:58:13, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4927, top5_acc: 0.7492, loss_cls: 2.7930, loss: 2.7930 +2024-07-21 08:38:06,876 - pyskl - INFO - Saving checkpoint at 136 epochs +2024-07-21 08:39:57,894 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 08:39:58,549 - pyskl - INFO - +top1_acc 0.3952 +top5_acc 0.6510 +2024-07-21 08:39:58,549 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 08:39:58,587 - pyskl - INFO - +mean_acc 0.3950 +2024-07-21 08:39:58,598 - pyskl - INFO - Epoch(val) [136][309] top1_acc: 0.3952, top5_acc: 0.6510, mean_class_accuracy: 0.3950 +2024-07-21 08:43:42,314 - pyskl - INFO - Epoch [137][100/3746] lr: 2.126e-03, eta: 11:56:24, time: 2.237, data_time: 1.269, memory: 15990, top1_acc: 0.5212, top5_acc: 0.7694, loss_cls: 2.6609, loss: 2.6609 +2024-07-21 08:45:04,159 - pyskl - INFO - Epoch [137][200/3746] lr: 2.118e-03, eta: 11:55:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5148, top5_acc: 0.7606, loss_cls: 2.7002, loss: 2.7002 +2024-07-21 08:46:25,594 - pyskl - INFO - Epoch [137][300/3746] lr: 2.110e-03, eta: 11:53:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5250, top5_acc: 0.7727, loss_cls: 2.6302, loss: 2.6302 +2024-07-21 08:47:47,164 - pyskl - INFO - Epoch [137][400/3746] lr: 2.102e-03, eta: 11:52:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5133, top5_acc: 0.7698, loss_cls: 2.6708, loss: 2.6708 +2024-07-21 08:49:09,271 - pyskl - INFO - Epoch [137][500/3746] lr: 2.094e-03, eta: 11:50:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5291, top5_acc: 0.7739, loss_cls: 2.6218, loss: 2.6218 +2024-07-21 08:50:31,160 - pyskl - INFO - Epoch [137][600/3746] lr: 2.086e-03, eta: 11:49:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5144, top5_acc: 0.7658, loss_cls: 2.6807, loss: 2.6807 +2024-07-21 08:51:52,312 - pyskl - INFO - Epoch [137][700/3746] lr: 2.078e-03, eta: 11:48:11, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5105, top5_acc: 0.7659, loss_cls: 2.6935, loss: 2.6935 +2024-07-21 08:53:13,890 - pyskl - INFO - Epoch [137][800/3746] lr: 2.070e-03, eta: 11:46:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5245, top5_acc: 0.7670, loss_cls: 2.6712, loss: 2.6712 +2024-07-21 08:54:34,936 - pyskl - INFO - Epoch [137][900/3746] lr: 2.062e-03, eta: 11:45:26, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5020, top5_acc: 0.7638, loss_cls: 2.7012, loss: 2.7012 +2024-07-21 08:55:56,569 - pyskl - INFO - Epoch [137][1000/3746] lr: 2.054e-03, eta: 11:44:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5094, top5_acc: 0.7677, loss_cls: 2.6705, loss: 2.6705 +2024-07-21 08:57:17,914 - pyskl - INFO - Epoch [137][1100/3746] lr: 2.046e-03, eta: 11:42:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5139, top5_acc: 0.7625, loss_cls: 2.7072, loss: 2.7072 +2024-07-21 08:58:39,480 - pyskl - INFO - Epoch [137][1200/3746] lr: 2.038e-03, eta: 11:41:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5159, top5_acc: 0.7708, loss_cls: 2.6574, loss: 2.6574 +2024-07-21 09:00:00,531 - pyskl - INFO - Epoch [137][1300/3746] lr: 2.030e-03, eta: 11:39:58, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5183, top5_acc: 0.7667, loss_cls: 2.6785, loss: 2.6785 +2024-07-21 09:01:22,084 - pyskl - INFO - Epoch [137][1400/3746] lr: 2.022e-03, eta: 11:38:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5080, top5_acc: 0.7622, loss_cls: 2.7129, loss: 2.7129 +2024-07-21 09:02:42,918 - pyskl - INFO - Epoch [137][1500/3746] lr: 2.015e-03, eta: 11:37:13, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.5211, top5_acc: 0.7733, loss_cls: 2.6421, loss: 2.6421 +2024-07-21 09:04:04,438 - pyskl - INFO - Epoch [137][1600/3746] lr: 2.007e-03, eta: 11:35:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5072, top5_acc: 0.7611, loss_cls: 2.7302, loss: 2.7302 +2024-07-21 09:05:25,336 - pyskl - INFO - Epoch [137][1700/3746] lr: 1.999e-03, eta: 11:34:29, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5073, top5_acc: 0.7561, loss_cls: 2.7486, loss: 2.7486 +2024-07-21 09:06:46,262 - pyskl - INFO - Epoch [137][1800/3746] lr: 1.991e-03, eta: 11:33:07, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5106, top5_acc: 0.7611, loss_cls: 2.7431, loss: 2.7431 +2024-07-21 09:08:07,139 - pyskl - INFO - Epoch [137][1900/3746] lr: 1.983e-03, eta: 11:31:44, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5198, top5_acc: 0.7598, loss_cls: 2.6969, loss: 2.6969 +2024-07-21 09:09:28,413 - pyskl - INFO - Epoch [137][2000/3746] lr: 1.976e-03, eta: 11:30:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5120, top5_acc: 0.7639, loss_cls: 2.6842, loss: 2.6842 +2024-07-21 09:10:50,202 - pyskl - INFO - Epoch [137][2100/3746] lr: 1.968e-03, eta: 11:29:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5105, top5_acc: 0.7550, loss_cls: 2.7452, loss: 2.7452 +2024-07-21 09:12:11,224 - pyskl - INFO - Epoch [137][2200/3746] lr: 1.960e-03, eta: 11:27:38, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4986, top5_acc: 0.7562, loss_cls: 2.7671, loss: 2.7671 +2024-07-21 09:13:32,708 - pyskl - INFO - Epoch [137][2300/3746] lr: 1.952e-03, eta: 11:26:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4992, top5_acc: 0.7575, loss_cls: 2.7782, loss: 2.7782 +2024-07-21 09:14:54,606 - pyskl - INFO - Epoch [137][2400/3746] lr: 1.944e-03, eta: 11:24:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5131, top5_acc: 0.7694, loss_cls: 2.6753, loss: 2.6753 +2024-07-21 09:16:16,439 - pyskl - INFO - Epoch [137][2500/3746] lr: 1.937e-03, eta: 11:23:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5117, top5_acc: 0.7645, loss_cls: 2.7033, loss: 2.7033 +2024-07-21 09:17:37,364 - pyskl - INFO - Epoch [137][2600/3746] lr: 1.929e-03, eta: 11:22:09, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5083, top5_acc: 0.7641, loss_cls: 2.7319, loss: 2.7319 +2024-07-21 09:18:58,920 - pyskl - INFO - Epoch [137][2700/3746] lr: 1.921e-03, eta: 11:20:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5053, top5_acc: 0.7558, loss_cls: 2.7478, loss: 2.7478 +2024-07-21 09:20:20,394 - pyskl - INFO - Epoch [137][2800/3746] lr: 1.914e-03, eta: 11:19:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5038, top5_acc: 0.7598, loss_cls: 2.7244, loss: 2.7244 +2024-07-21 09:21:41,324 - pyskl - INFO - Epoch [137][2900/3746] lr: 1.906e-03, eta: 11:18:03, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.4983, top5_acc: 0.7505, loss_cls: 2.7604, loss: 2.7604 +2024-07-21 09:23:01,808 - pyskl - INFO - Epoch [137][3000/3746] lr: 1.898e-03, eta: 11:16:40, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.5134, top5_acc: 0.7639, loss_cls: 2.6890, loss: 2.6890 +2024-07-21 09:24:23,430 - pyskl - INFO - Epoch [137][3100/3746] lr: 1.891e-03, eta: 11:15:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4986, top5_acc: 0.7592, loss_cls: 2.7296, loss: 2.7296 +2024-07-21 09:25:45,198 - pyskl - INFO - Epoch [137][3200/3746] lr: 1.883e-03, eta: 11:13:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4984, top5_acc: 0.7489, loss_cls: 2.7666, loss: 2.7666 +2024-07-21 09:27:07,097 - pyskl - INFO - Epoch [137][3300/3746] lr: 1.876e-03, eta: 11:12:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5083, top5_acc: 0.7539, loss_cls: 2.7363, loss: 2.7363 +2024-07-21 09:28:28,150 - pyskl - INFO - Epoch [137][3400/3746] lr: 1.868e-03, eta: 11:11:12, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5100, top5_acc: 0.7523, loss_cls: 2.7493, loss: 2.7493 +2024-07-21 09:29:49,579 - pyskl - INFO - Epoch [137][3500/3746] lr: 1.860e-03, eta: 11:09:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5120, top5_acc: 0.7655, loss_cls: 2.7051, loss: 2.7051 +2024-07-21 09:31:10,267 - pyskl - INFO - Epoch [137][3600/3746] lr: 1.853e-03, eta: 11:08:27, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.5122, top5_acc: 0.7516, loss_cls: 2.7226, loss: 2.7226 +2024-07-21 09:32:31,155 - pyskl - INFO - Epoch [137][3700/3746] lr: 1.845e-03, eta: 11:07:05, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5070, top5_acc: 0.7539, loss_cls: 2.7621, loss: 2.7621 +2024-07-21 09:33:11,286 - pyskl - INFO - Saving checkpoint at 137 epochs +2024-07-21 09:35:02,274 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 09:35:02,981 - pyskl - INFO - +top1_acc 0.4024 +top5_acc 0.6587 +2024-07-21 09:35:02,981 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 09:35:03,021 - pyskl - INFO - +mean_acc 0.4022 +2024-07-21 09:35:03,025 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_135.pth was removed +2024-07-21 09:35:03,260 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_137.pth. +2024-07-21 09:35:03,261 - pyskl - INFO - Best top1_acc is 0.4024 at 137 epoch. +2024-07-21 09:35:03,272 - pyskl - INFO - Epoch(val) [137][309] top1_acc: 0.4024, top5_acc: 0.6587, mean_class_accuracy: 0.4022 +2024-07-21 09:38:46,380 - pyskl - INFO - Epoch [138][100/3746] lr: 1.834e-03, eta: 11:05:15, time: 2.231, data_time: 1.259, memory: 15990, top1_acc: 0.5288, top5_acc: 0.7786, loss_cls: 2.6087, loss: 2.6087 +2024-07-21 09:40:07,477 - pyskl - INFO - Epoch [138][200/3746] lr: 1.827e-03, eta: 11:03:53, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5291, top5_acc: 0.7837, loss_cls: 2.5850, loss: 2.5850 +2024-07-21 09:41:28,932 - pyskl - INFO - Epoch [138][300/3746] lr: 1.819e-03, eta: 11:02:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5311, top5_acc: 0.7791, loss_cls: 2.6181, loss: 2.6181 +2024-07-21 09:42:50,623 - pyskl - INFO - Epoch [138][400/3746] lr: 1.812e-03, eta: 11:01:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5234, top5_acc: 0.7655, loss_cls: 2.6730, loss: 2.6730 +2024-07-21 09:44:12,130 - pyskl - INFO - Epoch [138][500/3746] lr: 1.805e-03, eta: 10:59:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5397, top5_acc: 0.7842, loss_cls: 2.5578, loss: 2.5578 +2024-07-21 09:45:34,037 - pyskl - INFO - Epoch [138][600/3746] lr: 1.797e-03, eta: 10:58:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5123, top5_acc: 0.7714, loss_cls: 2.6880, loss: 2.6880 +2024-07-21 09:46:55,215 - pyskl - INFO - Epoch [138][700/3746] lr: 1.790e-03, eta: 10:57:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5264, top5_acc: 0.7728, loss_cls: 2.6456, loss: 2.6456 +2024-07-21 09:48:17,596 - pyskl - INFO - Epoch [138][800/3746] lr: 1.782e-03, eta: 10:55:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5205, top5_acc: 0.7678, loss_cls: 2.6646, loss: 2.6646 +2024-07-21 09:49:39,645 - pyskl - INFO - Epoch [138][900/3746] lr: 1.775e-03, eta: 10:54:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5341, top5_acc: 0.7822, loss_cls: 2.5731, loss: 2.5731 +2024-07-21 09:51:01,438 - pyskl - INFO - Epoch [138][1000/3746] lr: 1.768e-03, eta: 10:52:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5144, top5_acc: 0.7717, loss_cls: 2.6629, loss: 2.6629 +2024-07-21 09:52:22,947 - pyskl - INFO - Epoch [138][1100/3746] lr: 1.760e-03, eta: 10:51:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5253, top5_acc: 0.7703, loss_cls: 2.6435, loss: 2.6435 +2024-07-21 09:53:44,271 - pyskl - INFO - Epoch [138][1200/3746] lr: 1.753e-03, eta: 10:50:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5164, top5_acc: 0.7698, loss_cls: 2.6368, loss: 2.6368 +2024-07-21 09:55:05,246 - pyskl - INFO - Epoch [138][1300/3746] lr: 1.745e-03, eta: 10:48:49, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5098, top5_acc: 0.7577, loss_cls: 2.7037, loss: 2.7037 +2024-07-21 09:56:27,176 - pyskl - INFO - Epoch [138][1400/3746] lr: 1.738e-03, eta: 10:47:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5155, top5_acc: 0.7739, loss_cls: 2.6597, loss: 2.6597 +2024-07-21 09:57:48,640 - pyskl - INFO - Epoch [138][1500/3746] lr: 1.731e-03, eta: 10:46:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5158, top5_acc: 0.7717, loss_cls: 2.6715, loss: 2.6715 +2024-07-21 09:59:09,687 - pyskl - INFO - Epoch [138][1600/3746] lr: 1.724e-03, eta: 10:44:42, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5320, top5_acc: 0.7775, loss_cls: 2.6093, loss: 2.6093 +2024-07-21 10:00:31,037 - pyskl - INFO - Epoch [138][1700/3746] lr: 1.716e-03, eta: 10:43:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5283, top5_acc: 0.7650, loss_cls: 2.6530, loss: 2.6530 +2024-07-21 10:01:52,593 - pyskl - INFO - Epoch [138][1800/3746] lr: 1.709e-03, eta: 10:41:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5192, top5_acc: 0.7616, loss_cls: 2.6956, loss: 2.6956 +2024-07-21 10:03:13,862 - pyskl - INFO - Epoch [138][1900/3746] lr: 1.702e-03, eta: 10:40:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5197, top5_acc: 0.7673, loss_cls: 2.6744, loss: 2.6744 +2024-07-21 10:04:35,189 - pyskl - INFO - Epoch [138][2000/3746] lr: 1.695e-03, eta: 10:39:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5244, top5_acc: 0.7678, loss_cls: 2.6585, loss: 2.6585 +2024-07-21 10:05:56,214 - pyskl - INFO - Epoch [138][2100/3746] lr: 1.687e-03, eta: 10:37:51, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5200, top5_acc: 0.7773, loss_cls: 2.6106, loss: 2.6106 +2024-07-21 10:07:17,578 - pyskl - INFO - Epoch [138][2200/3746] lr: 1.680e-03, eta: 10:36:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5245, top5_acc: 0.7598, loss_cls: 2.6566, loss: 2.6566 +2024-07-21 10:08:39,163 - pyskl - INFO - Epoch [138][2300/3746] lr: 1.673e-03, eta: 10:35:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5123, top5_acc: 0.7614, loss_cls: 2.7245, loss: 2.7245 +2024-07-21 10:09:59,888 - pyskl - INFO - Epoch [138][2400/3746] lr: 1.666e-03, eta: 10:33:44, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.5130, top5_acc: 0.7636, loss_cls: 2.6868, loss: 2.6868 +2024-07-21 10:11:21,898 - pyskl - INFO - Epoch [138][2500/3746] lr: 1.659e-03, eta: 10:32:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5262, top5_acc: 0.7708, loss_cls: 2.6291, loss: 2.6291 +2024-07-21 10:12:43,235 - pyskl - INFO - Epoch [138][2600/3746] lr: 1.652e-03, eta: 10:31:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5294, top5_acc: 0.7712, loss_cls: 2.6460, loss: 2.6460 +2024-07-21 10:14:04,130 - pyskl - INFO - Epoch [138][2700/3746] lr: 1.644e-03, eta: 10:29:38, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5162, top5_acc: 0.7630, loss_cls: 2.6799, loss: 2.6799 +2024-07-21 10:15:25,775 - pyskl - INFO - Epoch [138][2800/3746] lr: 1.637e-03, eta: 10:28:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5052, top5_acc: 0.7659, loss_cls: 2.7050, loss: 2.7050 +2024-07-21 10:16:46,936 - pyskl - INFO - Epoch [138][2900/3746] lr: 1.630e-03, eta: 10:26:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5127, top5_acc: 0.7595, loss_cls: 2.7111, loss: 2.7111 +2024-07-21 10:18:07,853 - pyskl - INFO - Epoch [138][3000/3746] lr: 1.623e-03, eta: 10:25:31, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5220, top5_acc: 0.7728, loss_cls: 2.6360, loss: 2.6360 +2024-07-21 10:19:29,705 - pyskl - INFO - Epoch [138][3100/3746] lr: 1.616e-03, eta: 10:24:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5128, top5_acc: 0.7655, loss_cls: 2.6840, loss: 2.6840 +2024-07-21 10:20:51,018 - pyskl - INFO - Epoch [138][3200/3746] lr: 1.609e-03, eta: 10:22:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5136, top5_acc: 0.7647, loss_cls: 2.6850, loss: 2.6850 +2024-07-21 10:22:13,202 - pyskl - INFO - Epoch [138][3300/3746] lr: 1.602e-03, eta: 10:21:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5206, top5_acc: 0.7664, loss_cls: 2.6653, loss: 2.6653 +2024-07-21 10:23:34,573 - pyskl - INFO - Epoch [138][3400/3746] lr: 1.595e-03, eta: 10:20:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5064, top5_acc: 0.7638, loss_cls: 2.6968, loss: 2.6968 +2024-07-21 10:24:56,356 - pyskl - INFO - Epoch [138][3500/3746] lr: 1.588e-03, eta: 10:18:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5130, top5_acc: 0.7664, loss_cls: 2.6838, loss: 2.6838 +2024-07-21 10:26:17,139 - pyskl - INFO - Epoch [138][3600/3746] lr: 1.581e-03, eta: 10:17:18, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.5202, top5_acc: 0.7647, loss_cls: 2.6675, loss: 2.6675 +2024-07-21 10:27:38,499 - pyskl - INFO - Epoch [138][3700/3746] lr: 1.574e-03, eta: 10:15:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5222, top5_acc: 0.7688, loss_cls: 2.6684, loss: 2.6684 +2024-07-21 10:28:18,127 - pyskl - INFO - Saving checkpoint at 138 epochs +2024-07-21 10:30:07,898 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 10:30:08,555 - pyskl - INFO - +top1_acc 0.4087 +top5_acc 0.6674 +2024-07-21 10:30:08,555 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 10:30:08,594 - pyskl - INFO - +mean_acc 0.4084 +2024-07-21 10:30:08,598 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_137.pth was removed +2024-07-21 10:30:08,825 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_138.pth. +2024-07-21 10:30:08,826 - pyskl - INFO - Best top1_acc is 0.4087 at 138 epoch. +2024-07-21 10:30:08,837 - pyskl - INFO - Epoch(val) [138][309] top1_acc: 0.4087, top5_acc: 0.6674, mean_class_accuracy: 0.4084 +2024-07-21 10:33:50,655 - pyskl - INFO - Epoch [139][100/3746] lr: 1.564e-03, eta: 10:14:05, time: 2.218, data_time: 1.253, memory: 15990, top1_acc: 0.5387, top5_acc: 0.7914, loss_cls: 2.5399, loss: 2.5399 +2024-07-21 10:35:11,848 - pyskl - INFO - Epoch [139][200/3746] lr: 1.557e-03, eta: 10:12:43, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5369, top5_acc: 0.7869, loss_cls: 2.5612, loss: 2.5612 +2024-07-21 10:36:33,176 - pyskl - INFO - Epoch [139][300/3746] lr: 1.550e-03, eta: 10:11:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5442, top5_acc: 0.7900, loss_cls: 2.5309, loss: 2.5309 +2024-07-21 10:37:54,679 - pyskl - INFO - Epoch [139][400/3746] lr: 1.543e-03, eta: 10:09:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5314, top5_acc: 0.7766, loss_cls: 2.5927, loss: 2.5927 +2024-07-21 10:39:16,675 - pyskl - INFO - Epoch [139][500/3746] lr: 1.536e-03, eta: 10:08:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5253, top5_acc: 0.7758, loss_cls: 2.6005, loss: 2.6005 +2024-07-21 10:40:37,871 - pyskl - INFO - Epoch [139][600/3746] lr: 1.529e-03, eta: 10:07:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5373, top5_acc: 0.7850, loss_cls: 2.5609, loss: 2.5609 +2024-07-21 10:41:59,588 - pyskl - INFO - Epoch [139][700/3746] lr: 1.523e-03, eta: 10:05:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5242, top5_acc: 0.7736, loss_cls: 2.6227, loss: 2.6227 +2024-07-21 10:43:20,919 - pyskl - INFO - Epoch [139][800/3746] lr: 1.516e-03, eta: 10:04:29, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5309, top5_acc: 0.7794, loss_cls: 2.6015, loss: 2.6015 +2024-07-21 10:44:42,124 - pyskl - INFO - Epoch [139][900/3746] lr: 1.509e-03, eta: 10:03:07, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5394, top5_acc: 0.7775, loss_cls: 2.5654, loss: 2.5654 +2024-07-21 10:46:03,547 - pyskl - INFO - Epoch [139][1000/3746] lr: 1.502e-03, eta: 10:01:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5256, top5_acc: 0.7806, loss_cls: 2.5944, loss: 2.5944 +2024-07-21 10:47:24,893 - pyskl - INFO - Epoch [139][1100/3746] lr: 1.495e-03, eta: 10:00:23, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5383, top5_acc: 0.7780, loss_cls: 2.5637, loss: 2.5637 +2024-07-21 10:48:46,856 - pyskl - INFO - Epoch [139][1200/3746] lr: 1.489e-03, eta: 9:59:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5355, top5_acc: 0.7725, loss_cls: 2.5970, loss: 2.5970 +2024-07-21 10:50:08,213 - pyskl - INFO - Epoch [139][1300/3746] lr: 1.482e-03, eta: 9:57:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5214, top5_acc: 0.7775, loss_cls: 2.5965, loss: 2.5965 +2024-07-21 10:51:29,360 - pyskl - INFO - Epoch [139][1400/3746] lr: 1.475e-03, eta: 9:56:16, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5283, top5_acc: 0.7803, loss_cls: 2.6082, loss: 2.6082 +2024-07-21 10:52:50,509 - pyskl - INFO - Epoch [139][1500/3746] lr: 1.468e-03, eta: 9:54:54, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5288, top5_acc: 0.7759, loss_cls: 2.6150, loss: 2.6150 +2024-07-21 10:54:11,566 - pyskl - INFO - Epoch [139][1600/3746] lr: 1.462e-03, eta: 9:53:32, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5203, top5_acc: 0.7717, loss_cls: 2.6480, loss: 2.6480 +2024-07-21 10:55:32,925 - pyskl - INFO - Epoch [139][1700/3746] lr: 1.455e-03, eta: 9:52:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5369, top5_acc: 0.7778, loss_cls: 2.6023, loss: 2.6023 +2024-07-21 10:56:53,783 - pyskl - INFO - Epoch [139][1800/3746] lr: 1.448e-03, eta: 9:50:47, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5328, top5_acc: 0.7775, loss_cls: 2.5801, loss: 2.5801 +2024-07-21 10:58:14,516 - pyskl - INFO - Epoch [139][1900/3746] lr: 1.442e-03, eta: 9:49:25, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.5256, top5_acc: 0.7762, loss_cls: 2.6088, loss: 2.6088 +2024-07-21 10:59:35,781 - pyskl - INFO - Epoch [139][2000/3746] lr: 1.435e-03, eta: 9:48:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5166, top5_acc: 0.7683, loss_cls: 2.6619, loss: 2.6619 +2024-07-21 11:00:57,418 - pyskl - INFO - Epoch [139][2100/3746] lr: 1.428e-03, eta: 9:46:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5297, top5_acc: 0.7741, loss_cls: 2.6223, loss: 2.6223 +2024-07-21 11:02:18,729 - pyskl - INFO - Epoch [139][2200/3746] lr: 1.422e-03, eta: 9:45:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5250, top5_acc: 0.7752, loss_cls: 2.6185, loss: 2.6185 +2024-07-21 11:03:39,637 - pyskl - INFO - Epoch [139][2300/3746] lr: 1.415e-03, eta: 9:43:56, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5241, top5_acc: 0.7702, loss_cls: 2.6496, loss: 2.6496 +2024-07-21 11:05:00,952 - pyskl - INFO - Epoch [139][2400/3746] lr: 1.408e-03, eta: 9:42:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5205, top5_acc: 0.7720, loss_cls: 2.6094, loss: 2.6094 +2024-07-21 11:06:21,848 - pyskl - INFO - Epoch [139][2500/3746] lr: 1.402e-03, eta: 9:41:12, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5380, top5_acc: 0.7709, loss_cls: 2.5968, loss: 2.5968 +2024-07-21 11:07:43,081 - pyskl - INFO - Epoch [139][2600/3746] lr: 1.395e-03, eta: 9:39:49, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5297, top5_acc: 0.7691, loss_cls: 2.6359, loss: 2.6359 +2024-07-21 11:09:04,935 - pyskl - INFO - Epoch [139][2700/3746] lr: 1.389e-03, eta: 9:38:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5300, top5_acc: 0.7734, loss_cls: 2.6192, loss: 2.6192 +2024-07-21 11:10:26,386 - pyskl - INFO - Epoch [139][2800/3746] lr: 1.382e-03, eta: 9:37:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5203, top5_acc: 0.7670, loss_cls: 2.6580, loss: 2.6580 +2024-07-21 11:11:47,847 - pyskl - INFO - Epoch [139][2900/3746] lr: 1.376e-03, eta: 9:35:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5258, top5_acc: 0.7700, loss_cls: 2.6451, loss: 2.6451 +2024-07-21 11:13:09,635 - pyskl - INFO - Epoch [139][3000/3746] lr: 1.369e-03, eta: 9:34:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5225, top5_acc: 0.7647, loss_cls: 2.6594, loss: 2.6594 +2024-07-21 11:14:31,311 - pyskl - INFO - Epoch [139][3100/3746] lr: 1.363e-03, eta: 9:32:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5195, top5_acc: 0.7752, loss_cls: 2.6443, loss: 2.6443 +2024-07-21 11:15:52,491 - pyskl - INFO - Epoch [139][3200/3746] lr: 1.356e-03, eta: 9:31:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5248, top5_acc: 0.7762, loss_cls: 2.6029, loss: 2.6029 +2024-07-21 11:17:14,479 - pyskl - INFO - Epoch [139][3300/3746] lr: 1.350e-03, eta: 9:30:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5355, top5_acc: 0.7859, loss_cls: 2.5726, loss: 2.5726 +2024-07-21 11:18:36,537 - pyskl - INFO - Epoch [139][3400/3746] lr: 1.343e-03, eta: 9:28:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5253, top5_acc: 0.7661, loss_cls: 2.6579, loss: 2.6579 +2024-07-21 11:19:57,551 - pyskl - INFO - Epoch [139][3500/3746] lr: 1.337e-03, eta: 9:27:30, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5200, top5_acc: 0.7711, loss_cls: 2.6346, loss: 2.6346 +2024-07-21 11:21:19,436 - pyskl - INFO - Epoch [139][3600/3746] lr: 1.330e-03, eta: 9:26:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5262, top5_acc: 0.7702, loss_cls: 2.6361, loss: 2.6361 +2024-07-21 11:22:41,635 - pyskl - INFO - Epoch [139][3700/3746] lr: 1.324e-03, eta: 9:24:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5291, top5_acc: 0.7770, loss_cls: 2.6322, loss: 2.6322 +2024-07-21 11:23:21,371 - pyskl - INFO - Saving checkpoint at 139 epochs +2024-07-21 11:25:13,642 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 11:25:14,309 - pyskl - INFO - +top1_acc 0.4107 +top5_acc 0.6689 +2024-07-21 11:25:14,309 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 11:25:14,350 - pyskl - INFO - +mean_acc 0.4105 +2024-07-21 11:25:14,355 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_138.pth was removed +2024-07-21 11:25:14,587 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_139.pth. +2024-07-21 11:25:14,588 - pyskl - INFO - Best top1_acc is 0.4107 at 139 epoch. +2024-07-21 11:25:14,601 - pyskl - INFO - Epoch(val) [139][309] top1_acc: 0.4107, top5_acc: 0.6689, mean_class_accuracy: 0.4105 +2024-07-21 11:29:04,570 - pyskl - INFO - Epoch [140][100/3746] lr: 1.315e-03, eta: 9:22:54, time: 2.300, data_time: 1.324, memory: 15990, top1_acc: 0.5464, top5_acc: 0.7972, loss_cls: 2.4920, loss: 2.4920 +2024-07-21 11:30:26,807 - pyskl - INFO - Epoch [140][200/3746] lr: 1.308e-03, eta: 9:21:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5497, top5_acc: 0.7859, loss_cls: 2.5080, loss: 2.5080 +2024-07-21 11:31:48,634 - pyskl - INFO - Epoch [140][300/3746] lr: 1.302e-03, eta: 9:20:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5489, top5_acc: 0.7841, loss_cls: 2.5380, loss: 2.5380 +2024-07-21 11:33:10,030 - pyskl - INFO - Epoch [140][400/3746] lr: 1.296e-03, eta: 9:18:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5484, top5_acc: 0.7906, loss_cls: 2.5052, loss: 2.5052 +2024-07-21 11:34:31,496 - pyskl - INFO - Epoch [140][500/3746] lr: 1.289e-03, eta: 9:17:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5420, top5_acc: 0.7778, loss_cls: 2.5628, loss: 2.5628 +2024-07-21 11:35:53,301 - pyskl - INFO - Epoch [140][600/3746] lr: 1.283e-03, eta: 9:16:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5431, top5_acc: 0.7855, loss_cls: 2.5457, loss: 2.5457 +2024-07-21 11:37:14,917 - pyskl - INFO - Epoch [140][700/3746] lr: 1.277e-03, eta: 9:14:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5498, top5_acc: 0.7869, loss_cls: 2.5443, loss: 2.5443 +2024-07-21 11:38:37,239 - pyskl - INFO - Epoch [140][800/3746] lr: 1.271e-03, eta: 9:13:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5366, top5_acc: 0.7788, loss_cls: 2.5572, loss: 2.5572 +2024-07-21 11:39:58,504 - pyskl - INFO - Epoch [140][900/3746] lr: 1.264e-03, eta: 9:11:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5380, top5_acc: 0.7792, loss_cls: 2.5564, loss: 2.5564 +2024-07-21 11:41:19,155 - pyskl - INFO - Epoch [140][1000/3746] lr: 1.258e-03, eta: 9:10:34, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.5413, top5_acc: 0.7913, loss_cls: 2.5382, loss: 2.5382 +2024-07-21 11:42:40,301 - pyskl - INFO - Epoch [140][1100/3746] lr: 1.252e-03, eta: 9:09:12, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5350, top5_acc: 0.7884, loss_cls: 2.5657, loss: 2.5657 +2024-07-21 11:44:01,551 - pyskl - INFO - Epoch [140][1200/3746] lr: 1.246e-03, eta: 9:07:50, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5469, top5_acc: 0.7859, loss_cls: 2.5237, loss: 2.5237 +2024-07-21 11:45:22,867 - pyskl - INFO - Epoch [140][1300/3746] lr: 1.239e-03, eta: 9:06:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5275, top5_acc: 0.7731, loss_cls: 2.6235, loss: 2.6235 +2024-07-21 11:46:44,132 - pyskl - INFO - Epoch [140][1400/3746] lr: 1.233e-03, eta: 9:05:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5373, top5_acc: 0.7797, loss_cls: 2.5750, loss: 2.5750 +2024-07-21 11:48:05,174 - pyskl - INFO - Epoch [140][1500/3746] lr: 1.227e-03, eta: 9:03:43, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5411, top5_acc: 0.7798, loss_cls: 2.5687, loss: 2.5687 +2024-07-21 11:49:26,475 - pyskl - INFO - Epoch [140][1600/3746] lr: 1.221e-03, eta: 9:02:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5467, top5_acc: 0.7861, loss_cls: 2.5214, loss: 2.5214 +2024-07-21 11:50:47,453 - pyskl - INFO - Epoch [140][1700/3746] lr: 1.215e-03, eta: 9:00:59, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5466, top5_acc: 0.7872, loss_cls: 2.5146, loss: 2.5146 +2024-07-21 11:52:08,503 - pyskl - INFO - Epoch [140][1800/3746] lr: 1.209e-03, eta: 8:59:36, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5336, top5_acc: 0.7781, loss_cls: 2.5987, loss: 2.5987 +2024-07-21 11:53:29,765 - pyskl - INFO - Epoch [140][1900/3746] lr: 1.203e-03, eta: 8:58:14, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5458, top5_acc: 0.7877, loss_cls: 2.5364, loss: 2.5364 +2024-07-21 11:54:50,780 - pyskl - INFO - Epoch [140][2000/3746] lr: 1.196e-03, eta: 8:56:52, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5386, top5_acc: 0.7822, loss_cls: 2.5708, loss: 2.5708 +2024-07-21 11:56:12,204 - pyskl - INFO - Epoch [140][2100/3746] lr: 1.190e-03, eta: 8:55:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5258, top5_acc: 0.7723, loss_cls: 2.5943, loss: 2.5943 +2024-07-21 11:57:33,870 - pyskl - INFO - Epoch [140][2200/3746] lr: 1.184e-03, eta: 8:54:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5408, top5_acc: 0.7728, loss_cls: 2.5889, loss: 2.5889 +2024-07-21 11:58:55,329 - pyskl - INFO - Epoch [140][2300/3746] lr: 1.178e-03, eta: 8:52:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5394, top5_acc: 0.7947, loss_cls: 2.5181, loss: 2.5181 +2024-07-21 12:00:17,293 - pyskl - INFO - Epoch [140][2400/3746] lr: 1.172e-03, eta: 8:51:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5309, top5_acc: 0.7791, loss_cls: 2.5884, loss: 2.5884 +2024-07-21 12:01:39,177 - pyskl - INFO - Epoch [140][2500/3746] lr: 1.166e-03, eta: 8:50:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5363, top5_acc: 0.7772, loss_cls: 2.5846, loss: 2.5846 +2024-07-21 12:03:00,380 - pyskl - INFO - Epoch [140][2600/3746] lr: 1.160e-03, eta: 8:48:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5347, top5_acc: 0.7837, loss_cls: 2.5715, loss: 2.5715 +2024-07-21 12:04:22,113 - pyskl - INFO - Epoch [140][2700/3746] lr: 1.154e-03, eta: 8:47:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5295, top5_acc: 0.7811, loss_cls: 2.5780, loss: 2.5780 +2024-07-21 12:05:43,476 - pyskl - INFO - Epoch [140][2800/3746] lr: 1.148e-03, eta: 8:45:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5433, top5_acc: 0.7777, loss_cls: 2.5653, loss: 2.5653 +2024-07-21 12:07:05,553 - pyskl - INFO - Epoch [140][2900/3746] lr: 1.142e-03, eta: 8:44:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5336, top5_acc: 0.7781, loss_cls: 2.5700, loss: 2.5700 +2024-07-21 12:08:26,410 - pyskl - INFO - Epoch [140][3000/3746] lr: 1.136e-03, eta: 8:43:10, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5389, top5_acc: 0.7836, loss_cls: 2.5516, loss: 2.5516 +2024-07-21 12:09:48,131 - pyskl - INFO - Epoch [140][3100/3746] lr: 1.131e-03, eta: 8:41:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5289, top5_acc: 0.7841, loss_cls: 2.5747, loss: 2.5747 +2024-07-21 12:11:09,686 - pyskl - INFO - Epoch [140][3200/3746] lr: 1.125e-03, eta: 8:40:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5314, top5_acc: 0.7808, loss_cls: 2.5869, loss: 2.5869 +2024-07-21 12:12:31,866 - pyskl - INFO - Epoch [140][3300/3746] lr: 1.119e-03, eta: 8:39:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5337, top5_acc: 0.7848, loss_cls: 2.5718, loss: 2.5718 +2024-07-21 12:13:53,306 - pyskl - INFO - Epoch [140][3400/3746] lr: 1.113e-03, eta: 8:37:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5330, top5_acc: 0.7755, loss_cls: 2.5891, loss: 2.5891 +2024-07-21 12:15:14,904 - pyskl - INFO - Epoch [140][3500/3746] lr: 1.107e-03, eta: 8:36:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5262, top5_acc: 0.7798, loss_cls: 2.6040, loss: 2.6040 +2024-07-21 12:16:36,442 - pyskl - INFO - Epoch [140][3600/3746] lr: 1.101e-03, eta: 8:34:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5361, top5_acc: 0.7870, loss_cls: 2.5521, loss: 2.5521 +2024-07-21 12:17:57,737 - pyskl - INFO - Epoch [140][3700/3746] lr: 1.095e-03, eta: 8:33:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5363, top5_acc: 0.7880, loss_cls: 2.5343, loss: 2.5343 +2024-07-21 12:18:37,541 - pyskl - INFO - Saving checkpoint at 140 epochs +2024-07-21 12:20:27,739 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 12:20:28,404 - pyskl - INFO - +top1_acc 0.4117 +top5_acc 0.6715 +2024-07-21 12:20:28,404 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 12:20:28,444 - pyskl - INFO - +mean_acc 0.4116 +2024-07-21 12:20:28,449 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_139.pth was removed +2024-07-21 12:20:28,678 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_140.pth. +2024-07-21 12:20:28,679 - pyskl - INFO - Best top1_acc is 0.4117 at 140 epoch. +2024-07-21 12:20:28,691 - pyskl - INFO - Epoch(val) [140][309] top1_acc: 0.4117, top5_acc: 0.6715, mean_class_accuracy: 0.4116 +2024-07-21 12:24:14,423 - pyskl - INFO - Epoch [141][100/3746] lr: 1.087e-03, eta: 8:31:42, time: 2.257, data_time: 1.282, memory: 15990, top1_acc: 0.5558, top5_acc: 0.8014, loss_cls: 2.4507, loss: 2.4507 +2024-07-21 12:25:36,391 - pyskl - INFO - Epoch [141][200/3746] lr: 1.081e-03, eta: 8:30:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5481, top5_acc: 0.7936, loss_cls: 2.4953, loss: 2.4953 +2024-07-21 12:26:58,238 - pyskl - INFO - Epoch [141][300/3746] lr: 1.075e-03, eta: 8:28:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5487, top5_acc: 0.7948, loss_cls: 2.4988, loss: 2.4988 +2024-07-21 12:28:20,131 - pyskl - INFO - Epoch [141][400/3746] lr: 1.070e-03, eta: 8:27:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5602, top5_acc: 0.7898, loss_cls: 2.4590, loss: 2.4590 +2024-07-21 12:29:41,496 - pyskl - INFO - Epoch [141][500/3746] lr: 1.064e-03, eta: 8:26:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5466, top5_acc: 0.7839, loss_cls: 2.5359, loss: 2.5359 +2024-07-21 12:31:02,529 - pyskl - INFO - Epoch [141][600/3746] lr: 1.058e-03, eta: 8:24:51, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5503, top5_acc: 0.7911, loss_cls: 2.5047, loss: 2.5047 +2024-07-21 12:32:23,849 - pyskl - INFO - Epoch [141][700/3746] lr: 1.052e-03, eta: 8:23:29, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5416, top5_acc: 0.7942, loss_cls: 2.5113, loss: 2.5113 +2024-07-21 12:33:44,868 - pyskl - INFO - Epoch [141][800/3746] lr: 1.047e-03, eta: 8:22:06, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5508, top5_acc: 0.7902, loss_cls: 2.4991, loss: 2.4991 +2024-07-21 12:35:06,259 - pyskl - INFO - Epoch [141][900/3746] lr: 1.041e-03, eta: 8:20:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5522, top5_acc: 0.7936, loss_cls: 2.5071, loss: 2.5071 +2024-07-21 12:36:27,344 - pyskl - INFO - Epoch [141][1000/3746] lr: 1.035e-03, eta: 8:19:22, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5491, top5_acc: 0.7869, loss_cls: 2.5136, loss: 2.5136 +2024-07-21 12:37:48,555 - pyskl - INFO - Epoch [141][1100/3746] lr: 1.030e-03, eta: 8:18:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5470, top5_acc: 0.8000, loss_cls: 2.4846, loss: 2.4846 +2024-07-21 12:39:10,090 - pyskl - INFO - Epoch [141][1200/3746] lr: 1.024e-03, eta: 8:16:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5483, top5_acc: 0.7987, loss_cls: 2.4937, loss: 2.4937 +2024-07-21 12:40:31,624 - pyskl - INFO - Epoch [141][1300/3746] lr: 1.018e-03, eta: 8:15:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5427, top5_acc: 0.7848, loss_cls: 2.5577, loss: 2.5577 +2024-07-21 12:41:52,431 - pyskl - INFO - Epoch [141][1400/3746] lr: 1.013e-03, eta: 8:13:53, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.5556, top5_acc: 0.7897, loss_cls: 2.5092, loss: 2.5092 +2024-07-21 12:43:13,419 - pyskl - INFO - Epoch [141][1500/3746] lr: 1.007e-03, eta: 8:12:31, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5519, top5_acc: 0.7942, loss_cls: 2.4826, loss: 2.4826 +2024-07-21 12:44:34,584 - pyskl - INFO - Epoch [141][1600/3746] lr: 1.002e-03, eta: 8:11:08, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5459, top5_acc: 0.7906, loss_cls: 2.5185, loss: 2.5185 +2024-07-21 12:45:55,432 - pyskl - INFO - Epoch [141][1700/3746] lr: 9.961e-04, eta: 8:09:46, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.5487, top5_acc: 0.7933, loss_cls: 2.4912, loss: 2.4912 +2024-07-21 12:47:16,661 - pyskl - INFO - Epoch [141][1800/3746] lr: 9.905e-04, eta: 8:08:24, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5453, top5_acc: 0.7830, loss_cls: 2.5482, loss: 2.5482 +2024-07-21 12:48:37,953 - pyskl - INFO - Epoch [141][1900/3746] lr: 9.850e-04, eta: 8:07:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5625, top5_acc: 0.7942, loss_cls: 2.4574, loss: 2.4574 +2024-07-21 12:49:59,009 - pyskl - INFO - Epoch [141][2000/3746] lr: 9.795e-04, eta: 8:05:39, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5458, top5_acc: 0.7886, loss_cls: 2.5209, loss: 2.5209 +2024-07-21 12:51:20,134 - pyskl - INFO - Epoch [141][2100/3746] lr: 9.740e-04, eta: 8:04:17, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5509, top5_acc: 0.7892, loss_cls: 2.5020, loss: 2.5020 +2024-07-21 12:52:41,324 - pyskl - INFO - Epoch [141][2200/3746] lr: 9.685e-04, eta: 8:02:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5509, top5_acc: 0.7952, loss_cls: 2.4879, loss: 2.4879 +2024-07-21 12:54:02,319 - pyskl - INFO - Epoch [141][2300/3746] lr: 9.630e-04, eta: 8:01:33, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5403, top5_acc: 0.7897, loss_cls: 2.5173, loss: 2.5173 +2024-07-21 12:55:23,445 - pyskl - INFO - Epoch [141][2400/3746] lr: 9.576e-04, eta: 8:00:10, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5514, top5_acc: 0.7892, loss_cls: 2.4976, loss: 2.4976 +2024-07-21 12:56:44,592 - pyskl - INFO - Epoch [141][2500/3746] lr: 9.522e-04, eta: 7:58:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5494, top5_acc: 0.7925, loss_cls: 2.4992, loss: 2.4992 +2024-07-21 12:58:06,228 - pyskl - INFO - Epoch [141][2600/3746] lr: 9.467e-04, eta: 7:57:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5434, top5_acc: 0.7852, loss_cls: 2.5243, loss: 2.5243 +2024-07-21 12:59:27,911 - pyskl - INFO - Epoch [141][2700/3746] lr: 9.413e-04, eta: 7:56:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5437, top5_acc: 0.7884, loss_cls: 2.5436, loss: 2.5436 +2024-07-21 13:00:49,619 - pyskl - INFO - Epoch [141][2800/3746] lr: 9.359e-04, eta: 7:54:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5533, top5_acc: 0.7967, loss_cls: 2.4699, loss: 2.4699 +2024-07-21 13:02:11,052 - pyskl - INFO - Epoch [141][2900/3746] lr: 9.306e-04, eta: 7:53:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5494, top5_acc: 0.7887, loss_cls: 2.5143, loss: 2.5143 +2024-07-21 13:03:33,057 - pyskl - INFO - Epoch [141][3000/3746] lr: 9.252e-04, eta: 7:51:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5567, top5_acc: 0.7863, loss_cls: 2.4908, loss: 2.4908 +2024-07-21 13:04:54,616 - pyskl - INFO - Epoch [141][3100/3746] lr: 9.199e-04, eta: 7:50:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5359, top5_acc: 0.7814, loss_cls: 2.5380, loss: 2.5380 +2024-07-21 13:06:16,047 - pyskl - INFO - Epoch [141][3200/3746] lr: 9.145e-04, eta: 7:49:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5472, top5_acc: 0.7895, loss_cls: 2.5178, loss: 2.5178 +2024-07-21 13:07:37,747 - pyskl - INFO - Epoch [141][3300/3746] lr: 9.092e-04, eta: 7:47:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5430, top5_acc: 0.7881, loss_cls: 2.5209, loss: 2.5209 +2024-07-21 13:08:59,313 - pyskl - INFO - Epoch [141][3400/3746] lr: 9.039e-04, eta: 7:46:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5475, top5_acc: 0.7908, loss_cls: 2.5222, loss: 2.5222 +2024-07-21 13:10:21,030 - pyskl - INFO - Epoch [141][3500/3746] lr: 8.986e-04, eta: 7:45:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5381, top5_acc: 0.7884, loss_cls: 2.5745, loss: 2.5745 +2024-07-21 13:11:42,237 - pyskl - INFO - Epoch [141][3600/3746] lr: 8.934e-04, eta: 7:43:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5336, top5_acc: 0.7913, loss_cls: 2.5404, loss: 2.5404 +2024-07-21 13:13:03,651 - pyskl - INFO - Epoch [141][3700/3746] lr: 8.881e-04, eta: 7:42:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5419, top5_acc: 0.7853, loss_cls: 2.5454, loss: 2.5454 +2024-07-21 13:13:43,090 - pyskl - INFO - Saving checkpoint at 141 epochs +2024-07-21 13:15:33,421 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 13:15:34,105 - pyskl - INFO - +top1_acc 0.4174 +top5_acc 0.6714 +2024-07-21 13:15:34,105 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 13:15:34,145 - pyskl - INFO - +mean_acc 0.4172 +2024-07-21 13:15:34,150 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_140.pth was removed +2024-07-21 13:15:34,382 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_141.pth. +2024-07-21 13:15:34,383 - pyskl - INFO - Best top1_acc is 0.4174 at 141 epoch. +2024-07-21 13:15:34,396 - pyskl - INFO - Epoch(val) [141][309] top1_acc: 0.4174, top5_acc: 0.6714, mean_class_accuracy: 0.4172 +2024-07-21 13:19:19,688 - pyskl - INFO - Epoch [142][100/3746] lr: 8.805e-04, eta: 7:40:28, time: 2.253, data_time: 1.282, memory: 15990, top1_acc: 0.5503, top5_acc: 0.7964, loss_cls: 2.4702, loss: 2.4702 +2024-07-21 13:20:41,200 - pyskl - INFO - Epoch [142][200/3746] lr: 8.752e-04, eta: 7:39:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5637, top5_acc: 0.8020, loss_cls: 2.4419, loss: 2.4419 +2024-07-21 13:22:02,462 - pyskl - INFO - Epoch [142][300/3746] lr: 8.700e-04, eta: 7:37:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5675, top5_acc: 0.8081, loss_cls: 2.4077, loss: 2.4077 +2024-07-21 13:23:23,744 - pyskl - INFO - Epoch [142][400/3746] lr: 8.649e-04, eta: 7:36:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5614, top5_acc: 0.7966, loss_cls: 2.4473, loss: 2.4473 +2024-07-21 13:24:44,720 - pyskl - INFO - Epoch [142][500/3746] lr: 8.597e-04, eta: 7:34:59, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5752, top5_acc: 0.8153, loss_cls: 2.3489, loss: 2.3489 +2024-07-21 13:26:05,405 - pyskl - INFO - Epoch [142][600/3746] lr: 8.545e-04, eta: 7:33:37, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.5616, top5_acc: 0.8047, loss_cls: 2.4276, loss: 2.4276 +2024-07-21 13:27:26,843 - pyskl - INFO - Epoch [142][700/3746] lr: 8.494e-04, eta: 7:32:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5623, top5_acc: 0.8005, loss_cls: 2.4237, loss: 2.4237 +2024-07-21 13:28:48,197 - pyskl - INFO - Epoch [142][800/3746] lr: 8.443e-04, eta: 7:30:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5617, top5_acc: 0.8019, loss_cls: 2.4329, loss: 2.4329 +2024-07-21 13:30:10,344 - pyskl - INFO - Epoch [142][900/3746] lr: 8.392e-04, eta: 7:29:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5672, top5_acc: 0.8036, loss_cls: 2.4149, loss: 2.4149 +2024-07-21 13:31:32,305 - pyskl - INFO - Epoch [142][1000/3746] lr: 8.341e-04, eta: 7:28:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5650, top5_acc: 0.8030, loss_cls: 2.4224, loss: 2.4224 +2024-07-21 13:32:54,550 - pyskl - INFO - Epoch [142][1100/3746] lr: 8.290e-04, eta: 7:26:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5616, top5_acc: 0.7963, loss_cls: 2.4431, loss: 2.4431 +2024-07-21 13:34:15,570 - pyskl - INFO - Epoch [142][1200/3746] lr: 8.239e-04, eta: 7:25:24, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5667, top5_acc: 0.7927, loss_cls: 2.4612, loss: 2.4612 +2024-07-21 13:35:36,310 - pyskl - INFO - Epoch [142][1300/3746] lr: 8.189e-04, eta: 7:24:01, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.5625, top5_acc: 0.8014, loss_cls: 2.4440, loss: 2.4440 +2024-07-21 13:36:57,292 - pyskl - INFO - Epoch [142][1400/3746] lr: 8.139e-04, eta: 7:22:39, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5606, top5_acc: 0.8048, loss_cls: 2.4444, loss: 2.4444 +2024-07-21 13:38:18,554 - pyskl - INFO - Epoch [142][1500/3746] lr: 8.088e-04, eta: 7:21:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5559, top5_acc: 0.7997, loss_cls: 2.4480, loss: 2.4480 +2024-07-21 13:39:39,880 - pyskl - INFO - Epoch [142][1600/3746] lr: 8.038e-04, eta: 7:19:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5648, top5_acc: 0.7994, loss_cls: 2.4320, loss: 2.4320 +2024-07-21 13:41:00,631 - pyskl - INFO - Epoch [142][1700/3746] lr: 7.989e-04, eta: 7:18:32, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.5595, top5_acc: 0.8023, loss_cls: 2.4406, loss: 2.4406 +2024-07-21 13:42:21,674 - pyskl - INFO - Epoch [142][1800/3746] lr: 7.939e-04, eta: 7:17:10, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5530, top5_acc: 0.7928, loss_cls: 2.4718, loss: 2.4718 +2024-07-21 13:43:43,138 - pyskl - INFO - Epoch [142][1900/3746] lr: 7.889e-04, eta: 7:15:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5487, top5_acc: 0.7933, loss_cls: 2.4913, loss: 2.4913 +2024-07-21 13:45:04,061 - pyskl - INFO - Epoch [142][2000/3746] lr: 7.840e-04, eta: 7:14:26, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5569, top5_acc: 0.7933, loss_cls: 2.4442, loss: 2.4442 +2024-07-21 13:46:24,845 - pyskl - INFO - Epoch [142][2100/3746] lr: 7.791e-04, eta: 7:13:03, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.5475, top5_acc: 0.7937, loss_cls: 2.4920, loss: 2.4920 +2024-07-21 13:47:46,044 - pyskl - INFO - Epoch [142][2200/3746] lr: 7.742e-04, eta: 7:11:41, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5505, top5_acc: 0.7923, loss_cls: 2.4841, loss: 2.4841 +2024-07-21 13:49:07,194 - pyskl - INFO - Epoch [142][2300/3746] lr: 7.693e-04, eta: 7:10:19, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5566, top5_acc: 0.7969, loss_cls: 2.4516, loss: 2.4516 +2024-07-21 13:50:28,372 - pyskl - INFO - Epoch [142][2400/3746] lr: 7.644e-04, eta: 7:08:57, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5675, top5_acc: 0.7977, loss_cls: 2.4270, loss: 2.4270 +2024-07-21 13:51:49,307 - pyskl - INFO - Epoch [142][2500/3746] lr: 7.595e-04, eta: 7:07:34, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5545, top5_acc: 0.7939, loss_cls: 2.5003, loss: 2.5003 +2024-07-21 13:53:09,892 - pyskl - INFO - Epoch [142][2600/3746] lr: 7.547e-04, eta: 7:06:12, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.5539, top5_acc: 0.7936, loss_cls: 2.4929, loss: 2.4929 +2024-07-21 13:54:32,237 - pyskl - INFO - Epoch [142][2700/3746] lr: 7.499e-04, eta: 7:04:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5597, top5_acc: 0.7995, loss_cls: 2.4564, loss: 2.4564 +2024-07-21 13:55:53,263 - pyskl - INFO - Epoch [142][2800/3746] lr: 7.450e-04, eta: 7:03:28, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5406, top5_acc: 0.7877, loss_cls: 2.5269, loss: 2.5269 +2024-07-21 13:57:15,048 - pyskl - INFO - Epoch [142][2900/3746] lr: 7.402e-04, eta: 7:02:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5472, top5_acc: 0.7916, loss_cls: 2.5150, loss: 2.5150 +2024-07-21 13:58:36,075 - pyskl - INFO - Epoch [142][3000/3746] lr: 7.355e-04, eta: 7:00:43, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5530, top5_acc: 0.7956, loss_cls: 2.4816, loss: 2.4816 +2024-07-21 13:59:57,427 - pyskl - INFO - Epoch [142][3100/3746] lr: 7.307e-04, eta: 6:59:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5567, top5_acc: 0.8025, loss_cls: 2.4347, loss: 2.4347 +2024-07-21 14:01:18,897 - pyskl - INFO - Epoch [142][3200/3746] lr: 7.259e-04, eta: 6:57:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5486, top5_acc: 0.7906, loss_cls: 2.4872, loss: 2.4872 +2024-07-21 14:02:39,972 - pyskl - INFO - Epoch [142][3300/3746] lr: 7.212e-04, eta: 6:56:36, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5541, top5_acc: 0.7955, loss_cls: 2.4666, loss: 2.4666 +2024-07-21 14:04:01,320 - pyskl - INFO - Epoch [142][3400/3746] lr: 7.165e-04, eta: 6:55:14, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5567, top5_acc: 0.7856, loss_cls: 2.5075, loss: 2.5075 +2024-07-21 14:05:22,641 - pyskl - INFO - Epoch [142][3500/3746] lr: 7.118e-04, eta: 6:53:52, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5387, top5_acc: 0.7939, loss_cls: 2.5050, loss: 2.5050 +2024-07-21 14:06:44,798 - pyskl - INFO - Epoch [142][3600/3746] lr: 7.071e-04, eta: 6:52:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5550, top5_acc: 0.7973, loss_cls: 2.4615, loss: 2.4615 +2024-07-21 14:08:06,381 - pyskl - INFO - Epoch [142][3700/3746] lr: 7.024e-04, eta: 6:51:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5567, top5_acc: 0.7931, loss_cls: 2.4759, loss: 2.4759 +2024-07-21 14:08:46,143 - pyskl - INFO - Saving checkpoint at 142 epochs +2024-07-21 14:10:36,719 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 14:10:37,387 - pyskl - INFO - +top1_acc 0.4155 +top5_acc 0.6719 +2024-07-21 14:10:37,387 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 14:10:37,430 - pyskl - INFO - +mean_acc 0.4154 +2024-07-21 14:10:37,443 - pyskl - INFO - Epoch(val) [142][309] top1_acc: 0.4155, top5_acc: 0.6719, mean_class_accuracy: 0.4154 +2024-07-21 14:14:25,600 - pyskl - INFO - Epoch [143][100/3746] lr: 6.956e-04, eta: 6:49:14, time: 2.281, data_time: 1.305, memory: 15990, top1_acc: 0.5767, top5_acc: 0.8111, loss_cls: 2.3605, loss: 2.3605 +2024-07-21 14:15:48,058 - pyskl - INFO - Epoch [143][200/3746] lr: 6.910e-04, eta: 6:47:51, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5730, top5_acc: 0.8125, loss_cls: 2.3612, loss: 2.3612 +2024-07-21 14:17:09,715 - pyskl - INFO - Epoch [143][300/3746] lr: 6.863e-04, eta: 6:46:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5761, top5_acc: 0.8103, loss_cls: 2.3776, loss: 2.3776 +2024-07-21 14:18:30,665 - pyskl - INFO - Epoch [143][400/3746] lr: 6.817e-04, eta: 6:45:07, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5709, top5_acc: 0.8081, loss_cls: 2.3785, loss: 2.3785 +2024-07-21 14:19:51,967 - pyskl - INFO - Epoch [143][500/3746] lr: 6.771e-04, eta: 6:43:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5642, top5_acc: 0.8042, loss_cls: 2.4341, loss: 2.4341 +2024-07-21 14:21:12,643 - pyskl - INFO - Epoch [143][600/3746] lr: 6.725e-04, eta: 6:42:22, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.5648, top5_acc: 0.8042, loss_cls: 2.3942, loss: 2.3942 +2024-07-21 14:22:33,512 - pyskl - INFO - Epoch [143][700/3746] lr: 6.680e-04, eta: 6:41:00, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5731, top5_acc: 0.8094, loss_cls: 2.4003, loss: 2.4003 +2024-07-21 14:23:55,173 - pyskl - INFO - Epoch [143][800/3746] lr: 6.634e-04, eta: 6:39:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5670, top5_acc: 0.8006, loss_cls: 2.4231, loss: 2.4231 +2024-07-21 14:25:16,407 - pyskl - INFO - Epoch [143][900/3746] lr: 6.589e-04, eta: 6:38:16, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5548, top5_acc: 0.8020, loss_cls: 2.4247, loss: 2.4247 +2024-07-21 14:26:37,443 - pyskl - INFO - Epoch [143][1000/3746] lr: 6.544e-04, eta: 6:36:53, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5736, top5_acc: 0.8053, loss_cls: 2.3792, loss: 2.3792 +2024-07-21 14:27:58,507 - pyskl - INFO - Epoch [143][1100/3746] lr: 6.499e-04, eta: 6:35:31, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5731, top5_acc: 0.8123, loss_cls: 2.3546, loss: 2.3546 +2024-07-21 14:29:19,783 - pyskl - INFO - Epoch [143][1200/3746] lr: 6.454e-04, eta: 6:34:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5742, top5_acc: 0.8072, loss_cls: 2.3670, loss: 2.3670 +2024-07-21 14:30:40,743 - pyskl - INFO - Epoch [143][1300/3746] lr: 6.409e-04, eta: 6:32:46, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5719, top5_acc: 0.8070, loss_cls: 2.3844, loss: 2.3844 +2024-07-21 14:32:01,807 - pyskl - INFO - Epoch [143][1400/3746] lr: 6.365e-04, eta: 6:31:24, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5550, top5_acc: 0.7963, loss_cls: 2.4617, loss: 2.4617 +2024-07-21 14:33:23,149 - pyskl - INFO - Epoch [143][1500/3746] lr: 6.320e-04, eta: 6:30:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5639, top5_acc: 0.8066, loss_cls: 2.4082, loss: 2.4082 +2024-07-21 14:34:44,791 - pyskl - INFO - Epoch [143][1600/3746] lr: 6.276e-04, eta: 6:28:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5664, top5_acc: 0.8059, loss_cls: 2.4054, loss: 2.4054 +2024-07-21 14:36:06,365 - pyskl - INFO - Epoch [143][1700/3746] lr: 6.232e-04, eta: 6:27:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5666, top5_acc: 0.8023, loss_cls: 2.4264, loss: 2.4264 +2024-07-21 14:37:27,539 - pyskl - INFO - Epoch [143][1800/3746] lr: 6.188e-04, eta: 6:25:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5702, top5_acc: 0.8039, loss_cls: 2.3930, loss: 2.3930 +2024-07-21 14:38:48,854 - pyskl - INFO - Epoch [143][1900/3746] lr: 6.144e-04, eta: 6:24:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5708, top5_acc: 0.8025, loss_cls: 2.3927, loss: 2.3927 +2024-07-21 14:40:10,175 - pyskl - INFO - Epoch [143][2000/3746] lr: 6.101e-04, eta: 6:23:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5714, top5_acc: 0.8022, loss_cls: 2.3890, loss: 2.3890 +2024-07-21 14:41:31,531 - pyskl - INFO - Epoch [143][2100/3746] lr: 6.057e-04, eta: 6:21:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5617, top5_acc: 0.8017, loss_cls: 2.4213, loss: 2.4213 +2024-07-21 14:42:53,192 - pyskl - INFO - Epoch [143][2200/3746] lr: 6.014e-04, eta: 6:20:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5687, top5_acc: 0.8119, loss_cls: 2.3713, loss: 2.3713 +2024-07-21 14:44:14,462 - pyskl - INFO - Epoch [143][2300/3746] lr: 5.971e-04, eta: 6:19:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5709, top5_acc: 0.8063, loss_cls: 2.4093, loss: 2.4093 +2024-07-21 14:45:35,703 - pyskl - INFO - Epoch [143][2400/3746] lr: 5.928e-04, eta: 6:17:42, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5689, top5_acc: 0.7997, loss_cls: 2.4156, loss: 2.4156 +2024-07-21 14:46:57,133 - pyskl - INFO - Epoch [143][2500/3746] lr: 5.885e-04, eta: 6:16:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5594, top5_acc: 0.7984, loss_cls: 2.4358, loss: 2.4358 +2024-07-21 14:48:18,025 - pyskl - INFO - Epoch [143][2600/3746] lr: 5.842e-04, eta: 6:14:57, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5642, top5_acc: 0.8036, loss_cls: 2.4306, loss: 2.4306 +2024-07-21 14:49:39,502 - pyskl - INFO - Epoch [143][2700/3746] lr: 5.800e-04, eta: 6:13:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5625, top5_acc: 0.8041, loss_cls: 2.4113, loss: 2.4113 +2024-07-21 14:51:00,481 - pyskl - INFO - Epoch [143][2800/3746] lr: 5.757e-04, eta: 6:12:13, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5747, top5_acc: 0.8037, loss_cls: 2.4099, loss: 2.4099 +2024-07-21 14:52:22,083 - pyskl - INFO - Epoch [143][2900/3746] lr: 5.715e-04, eta: 6:10:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5655, top5_acc: 0.8028, loss_cls: 2.4251, loss: 2.4251 +2024-07-21 14:53:43,021 - pyskl - INFO - Epoch [143][3000/3746] lr: 5.673e-04, eta: 6:09:28, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5620, top5_acc: 0.8050, loss_cls: 2.4187, loss: 2.4187 +2024-07-21 14:55:04,519 - pyskl - INFO - Epoch [143][3100/3746] lr: 5.631e-04, eta: 6:08:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5567, top5_acc: 0.7997, loss_cls: 2.4226, loss: 2.4226 +2024-07-21 14:56:25,915 - pyskl - INFO - Epoch [143][3200/3746] lr: 5.590e-04, eta: 6:06:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5709, top5_acc: 0.8067, loss_cls: 2.3944, loss: 2.3944 +2024-07-21 14:57:47,572 - pyskl - INFO - Epoch [143][3300/3746] lr: 5.548e-04, eta: 6:05:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5523, top5_acc: 0.7947, loss_cls: 2.4748, loss: 2.4748 +2024-07-21 14:59:09,022 - pyskl - INFO - Epoch [143][3400/3746] lr: 5.506e-04, eta: 6:03:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5622, top5_acc: 0.8000, loss_cls: 2.4422, loss: 2.4422 +2024-07-21 15:00:30,222 - pyskl - INFO - Epoch [143][3500/3746] lr: 5.465e-04, eta: 6:02:37, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5664, top5_acc: 0.8034, loss_cls: 2.3992, loss: 2.3992 +2024-07-21 15:01:51,570 - pyskl - INFO - Epoch [143][3600/3746] lr: 5.424e-04, eta: 6:01:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5592, top5_acc: 0.7991, loss_cls: 2.4492, loss: 2.4492 +2024-07-21 15:03:12,623 - pyskl - INFO - Epoch [143][3700/3746] lr: 5.383e-04, eta: 5:59:52, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5630, top5_acc: 0.8064, loss_cls: 2.3947, loss: 2.3947 +2024-07-21 15:03:52,143 - pyskl - INFO - Saving checkpoint at 143 epochs +2024-07-21 15:05:42,462 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 15:05:43,127 - pyskl - INFO - +top1_acc 0.4200 +top5_acc 0.6752 +2024-07-21 15:05:43,127 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 15:05:43,166 - pyskl - INFO - +mean_acc 0.4199 +2024-07-21 15:05:43,171 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_141.pth was removed +2024-07-21 15:05:43,404 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_143.pth. +2024-07-21 15:05:43,405 - pyskl - INFO - Best top1_acc is 0.4200 at 143 epoch. +2024-07-21 15:05:43,418 - pyskl - INFO - Epoch(val) [143][309] top1_acc: 0.4200, top5_acc: 0.6752, mean_class_accuracy: 0.4199 +2024-07-21 15:09:26,346 - pyskl - INFO - Epoch [144][100/3746] lr: 5.323e-04, eta: 5:57:57, time: 2.229, data_time: 1.262, memory: 15990, top1_acc: 0.5802, top5_acc: 0.8197, loss_cls: 2.3320, loss: 2.3320 +2024-07-21 15:10:47,519 - pyskl - INFO - Epoch [144][200/3746] lr: 5.283e-04, eta: 5:56:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5780, top5_acc: 0.8122, loss_cls: 2.3469, loss: 2.3469 +2024-07-21 15:12:08,406 - pyskl - INFO - Epoch [144][300/3746] lr: 5.242e-04, eta: 5:55:13, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5856, top5_acc: 0.8197, loss_cls: 2.3153, loss: 2.3153 +2024-07-21 15:13:30,120 - pyskl - INFO - Epoch [144][400/3746] lr: 5.202e-04, eta: 5:53:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5886, top5_acc: 0.8164, loss_cls: 2.3173, loss: 2.3173 +2024-07-21 15:14:51,261 - pyskl - INFO - Epoch [144][500/3746] lr: 5.162e-04, eta: 5:52:28, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5744, top5_acc: 0.8147, loss_cls: 2.3581, loss: 2.3581 +2024-07-21 15:16:12,916 - pyskl - INFO - Epoch [144][600/3746] lr: 5.122e-04, eta: 5:51:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5733, top5_acc: 0.8136, loss_cls: 2.3608, loss: 2.3608 +2024-07-21 15:17:33,646 - pyskl - INFO - Epoch [144][700/3746] lr: 5.082e-04, eta: 5:49:44, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.5736, top5_acc: 0.8089, loss_cls: 2.3693, loss: 2.3693 +2024-07-21 15:18:54,937 - pyskl - INFO - Epoch [144][800/3746] lr: 5.042e-04, eta: 5:48:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5773, top5_acc: 0.8089, loss_cls: 2.3684, loss: 2.3684 +2024-07-21 15:20:15,968 - pyskl - INFO - Epoch [144][900/3746] lr: 5.003e-04, eta: 5:46:59, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5773, top5_acc: 0.8136, loss_cls: 2.3325, loss: 2.3325 +2024-07-21 15:21:36,917 - pyskl - INFO - Epoch [144][1000/3746] lr: 4.964e-04, eta: 5:45:37, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5853, top5_acc: 0.8183, loss_cls: 2.3143, loss: 2.3143 +2024-07-21 15:22:58,109 - pyskl - INFO - Epoch [144][1100/3746] lr: 4.924e-04, eta: 5:44:15, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5839, top5_acc: 0.8133, loss_cls: 2.3437, loss: 2.3437 +2024-07-21 15:24:19,847 - pyskl - INFO - Epoch [144][1200/3746] lr: 4.885e-04, eta: 5:42:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5745, top5_acc: 0.8059, loss_cls: 2.3703, loss: 2.3703 +2024-07-21 15:25:41,299 - pyskl - INFO - Epoch [144][1300/3746] lr: 4.846e-04, eta: 5:41:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5772, top5_acc: 0.8147, loss_cls: 2.3612, loss: 2.3612 +2024-07-21 15:27:02,310 - pyskl - INFO - Epoch [144][1400/3746] lr: 4.808e-04, eta: 5:40:08, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5739, top5_acc: 0.8084, loss_cls: 2.3858, loss: 2.3858 +2024-07-21 15:28:23,752 - pyskl - INFO - Epoch [144][1500/3746] lr: 4.769e-04, eta: 5:38:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5764, top5_acc: 0.8206, loss_cls: 2.3522, loss: 2.3522 +2024-07-21 15:29:44,757 - pyskl - INFO - Epoch [144][1600/3746] lr: 4.731e-04, eta: 5:37:23, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5748, top5_acc: 0.8187, loss_cls: 2.3463, loss: 2.3463 +2024-07-21 15:31:05,698 - pyskl - INFO - Epoch [144][1700/3746] lr: 4.692e-04, eta: 5:36:01, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5717, top5_acc: 0.8059, loss_cls: 2.3965, loss: 2.3965 +2024-07-21 15:32:27,087 - pyskl - INFO - Epoch [144][1800/3746] lr: 4.654e-04, eta: 5:34:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5673, top5_acc: 0.8078, loss_cls: 2.3950, loss: 2.3950 +2024-07-21 15:33:48,664 - pyskl - INFO - Epoch [144][1900/3746] lr: 4.616e-04, eta: 5:33:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5728, top5_acc: 0.8127, loss_cls: 2.3679, loss: 2.3679 +2024-07-21 15:35:09,716 - pyskl - INFO - Epoch [144][2000/3746] lr: 4.578e-04, eta: 5:31:54, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5802, top5_acc: 0.8131, loss_cls: 2.3444, loss: 2.3444 +2024-07-21 15:36:31,267 - pyskl - INFO - Epoch [144][2100/3746] lr: 4.541e-04, eta: 5:30:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5850, top5_acc: 0.8100, loss_cls: 2.3536, loss: 2.3536 +2024-07-21 15:37:52,587 - pyskl - INFO - Epoch [144][2200/3746] lr: 4.503e-04, eta: 5:29:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5809, top5_acc: 0.8130, loss_cls: 2.3523, loss: 2.3523 +2024-07-21 15:39:14,744 - pyskl - INFO - Epoch [144][2300/3746] lr: 4.466e-04, eta: 5:27:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5755, top5_acc: 0.8095, loss_cls: 2.3711, loss: 2.3711 +2024-07-21 15:40:35,907 - pyskl - INFO - Epoch [144][2400/3746] lr: 4.429e-04, eta: 5:26:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5809, top5_acc: 0.8156, loss_cls: 2.3449, loss: 2.3449 +2024-07-21 15:41:56,632 - pyskl - INFO - Epoch [144][2500/3746] lr: 4.392e-04, eta: 5:25:03, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.5767, top5_acc: 0.8131, loss_cls: 2.3509, loss: 2.3509 +2024-07-21 15:43:17,769 - pyskl - INFO - Epoch [144][2600/3746] lr: 4.355e-04, eta: 5:23:41, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5778, top5_acc: 0.8078, loss_cls: 2.3732, loss: 2.3732 +2024-07-21 15:44:38,556 - pyskl - INFO - Epoch [144][2700/3746] lr: 4.318e-04, eta: 5:22:19, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.5813, top5_acc: 0.8169, loss_cls: 2.3312, loss: 2.3312 +2024-07-21 15:45:59,929 - pyskl - INFO - Epoch [144][2800/3746] lr: 4.281e-04, eta: 5:20:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5639, top5_acc: 0.8056, loss_cls: 2.3902, loss: 2.3902 +2024-07-21 15:47:21,059 - pyskl - INFO - Epoch [144][2900/3746] lr: 4.245e-04, eta: 5:19:34, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5805, top5_acc: 0.8103, loss_cls: 2.3524, loss: 2.3524 +2024-07-21 15:48:43,347 - pyskl - INFO - Epoch [144][3000/3746] lr: 4.209e-04, eta: 5:18:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5737, top5_acc: 0.8097, loss_cls: 2.3547, loss: 2.3547 +2024-07-21 15:50:03,966 - pyskl - INFO - Epoch [144][3100/3746] lr: 4.173e-04, eta: 5:16:50, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.5758, top5_acc: 0.8106, loss_cls: 2.3579, loss: 2.3579 +2024-07-21 15:51:24,998 - pyskl - INFO - Epoch [144][3200/3746] lr: 4.137e-04, eta: 5:15:27, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5698, top5_acc: 0.8047, loss_cls: 2.3786, loss: 2.3786 +2024-07-21 15:52:46,668 - pyskl - INFO - Epoch [144][3300/3746] lr: 4.101e-04, eta: 5:14:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5820, top5_acc: 0.8137, loss_cls: 2.3435, loss: 2.3435 +2024-07-21 15:54:08,327 - pyskl - INFO - Epoch [144][3400/3746] lr: 4.065e-04, eta: 5:12:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5652, top5_acc: 0.8014, loss_cls: 2.4008, loss: 2.4008 +2024-07-21 15:55:29,550 - pyskl - INFO - Epoch [144][3500/3746] lr: 4.030e-04, eta: 5:11:21, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5731, top5_acc: 0.8055, loss_cls: 2.3910, loss: 2.3910 +2024-07-21 15:56:50,375 - pyskl - INFO - Epoch [144][3600/3746] lr: 3.994e-04, eta: 5:09:58, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.5839, top5_acc: 0.8172, loss_cls: 2.3177, loss: 2.3177 +2024-07-21 15:58:11,837 - pyskl - INFO - Epoch [144][3700/3746] lr: 3.959e-04, eta: 5:08:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5769, top5_acc: 0.8091, loss_cls: 2.3500, loss: 2.3500 +2024-07-21 15:58:51,686 - pyskl - INFO - Saving checkpoint at 144 epochs +2024-07-21 16:00:41,945 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 16:00:42,612 - pyskl - INFO - +top1_acc 0.4239 +top5_acc 0.6769 +2024-07-21 16:00:42,613 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 16:00:42,652 - pyskl - INFO - +mean_acc 0.4237 +2024-07-21 16:00:42,657 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_143.pth was removed +2024-07-21 16:00:42,892 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_144.pth. +2024-07-21 16:00:42,893 - pyskl - INFO - Best top1_acc is 0.4239 at 144 epoch. +2024-07-21 16:00:42,904 - pyskl - INFO - Epoch(val) [144][309] top1_acc: 0.4239, top5_acc: 0.6769, mean_class_accuracy: 0.4237 +2024-07-21 16:04:35,486 - pyskl - INFO - Epoch [145][100/3746] lr: 3.908e-04, eta: 5:06:41, time: 2.326, data_time: 1.346, memory: 15990, top1_acc: 0.5902, top5_acc: 0.8225, loss_cls: 2.2710, loss: 2.2710 +2024-07-21 16:05:58,544 - pyskl - INFO - Epoch [145][200/3746] lr: 3.873e-04, eta: 5:05:18, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5855, top5_acc: 0.8194, loss_cls: 2.2936, loss: 2.2936 +2024-07-21 16:07:21,313 - pyskl - INFO - Epoch [145][300/3746] lr: 3.839e-04, eta: 5:03:56, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5836, top5_acc: 0.8161, loss_cls: 2.2960, loss: 2.2960 +2024-07-21 16:08:44,021 - pyskl - INFO - Epoch [145][400/3746] lr: 3.804e-04, eta: 5:02:34, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5903, top5_acc: 0.8223, loss_cls: 2.2661, loss: 2.2661 +2024-07-21 16:10:06,208 - pyskl - INFO - Epoch [145][500/3746] lr: 3.770e-04, eta: 5:01:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5933, top5_acc: 0.8228, loss_cls: 2.2729, loss: 2.2729 +2024-07-21 16:11:28,487 - pyskl - INFO - Epoch [145][600/3746] lr: 3.736e-04, eta: 4:59:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5841, top5_acc: 0.8216, loss_cls: 2.3198, loss: 2.3198 +2024-07-21 16:12:51,468 - pyskl - INFO - Epoch [145][700/3746] lr: 3.702e-04, eta: 4:58:27, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5764, top5_acc: 0.8172, loss_cls: 2.3402, loss: 2.3402 +2024-07-21 16:14:13,291 - pyskl - INFO - Epoch [145][800/3746] lr: 3.668e-04, eta: 4:57:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5834, top5_acc: 0.8161, loss_cls: 2.3140, loss: 2.3140 +2024-07-21 16:15:35,188 - pyskl - INFO - Epoch [145][900/3746] lr: 3.634e-04, eta: 4:55:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5808, top5_acc: 0.8230, loss_cls: 2.3094, loss: 2.3094 +2024-07-21 16:16:56,660 - pyskl - INFO - Epoch [145][1000/3746] lr: 3.600e-04, eta: 4:54:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5897, top5_acc: 0.8228, loss_cls: 2.3030, loss: 2.3030 +2024-07-21 16:18:18,435 - pyskl - INFO - Epoch [145][1100/3746] lr: 3.567e-04, eta: 4:52:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5952, top5_acc: 0.8189, loss_cls: 2.2958, loss: 2.2958 +2024-07-21 16:19:39,599 - pyskl - INFO - Epoch [145][1200/3746] lr: 3.534e-04, eta: 4:51:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5845, top5_acc: 0.8183, loss_cls: 2.3193, loss: 2.3193 +2024-07-21 16:21:01,203 - pyskl - INFO - Epoch [145][1300/3746] lr: 3.501e-04, eta: 4:50:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5933, top5_acc: 0.8228, loss_cls: 2.2758, loss: 2.2758 +2024-07-21 16:22:22,512 - pyskl - INFO - Epoch [145][1400/3746] lr: 3.468e-04, eta: 4:48:52, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5928, top5_acc: 0.8200, loss_cls: 2.2856, loss: 2.2856 +2024-07-21 16:23:43,666 - pyskl - INFO - Epoch [145][1500/3746] lr: 3.435e-04, eta: 4:47:29, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5886, top5_acc: 0.8114, loss_cls: 2.3274, loss: 2.3274 +2024-07-21 16:25:04,830 - pyskl - INFO - Epoch [145][1600/3746] lr: 3.402e-04, eta: 4:46:07, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5969, top5_acc: 0.8281, loss_cls: 2.2718, loss: 2.2718 +2024-07-21 16:26:25,493 - pyskl - INFO - Epoch [145][1700/3746] lr: 3.370e-04, eta: 4:44:45, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.5798, top5_acc: 0.8144, loss_cls: 2.3297, loss: 2.3297 +2024-07-21 16:27:46,355 - pyskl - INFO - Epoch [145][1800/3746] lr: 3.337e-04, eta: 4:43:22, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5787, top5_acc: 0.8105, loss_cls: 2.3597, loss: 2.3597 +2024-07-21 16:29:07,191 - pyskl - INFO - Epoch [145][1900/3746] lr: 3.305e-04, eta: 4:42:00, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.5758, top5_acc: 0.8187, loss_cls: 2.3159, loss: 2.3159 +2024-07-21 16:30:27,804 - pyskl - INFO - Epoch [145][2000/3746] lr: 3.273e-04, eta: 4:40:38, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.5950, top5_acc: 0.8281, loss_cls: 2.2607, loss: 2.2607 +2024-07-21 16:31:48,639 - pyskl - INFO - Epoch [145][2100/3746] lr: 3.241e-04, eta: 4:39:16, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.5852, top5_acc: 0.8217, loss_cls: 2.3046, loss: 2.3046 +2024-07-21 16:33:09,955 - pyskl - INFO - Epoch [145][2200/3746] lr: 3.210e-04, eta: 4:37:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5847, top5_acc: 0.8200, loss_cls: 2.2977, loss: 2.2977 +2024-07-21 16:34:31,296 - pyskl - INFO - Epoch [145][2300/3746] lr: 3.178e-04, eta: 4:36:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5870, top5_acc: 0.8144, loss_cls: 2.3399, loss: 2.3399 +2024-07-21 16:35:52,294 - pyskl - INFO - Epoch [145][2400/3746] lr: 3.147e-04, eta: 4:35:09, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5809, top5_acc: 0.8123, loss_cls: 2.3390, loss: 2.3390 +2024-07-21 16:37:13,020 - pyskl - INFO - Epoch [145][2500/3746] lr: 3.116e-04, eta: 4:33:46, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.5764, top5_acc: 0.8175, loss_cls: 2.3293, loss: 2.3293 +2024-07-21 16:38:34,648 - pyskl - INFO - Epoch [145][2600/3746] lr: 3.084e-04, eta: 4:32:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5869, top5_acc: 0.8187, loss_cls: 2.3088, loss: 2.3088 +2024-07-21 16:39:55,359 - pyskl - INFO - Epoch [145][2700/3746] lr: 3.054e-04, eta: 4:31:02, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.5872, top5_acc: 0.8203, loss_cls: 2.3059, loss: 2.3059 +2024-07-21 16:41:17,581 - pyskl - INFO - Epoch [145][2800/3746] lr: 3.023e-04, eta: 4:29:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5866, top5_acc: 0.8147, loss_cls: 2.3169, loss: 2.3169 +2024-07-21 16:42:39,266 - pyskl - INFO - Epoch [145][2900/3746] lr: 2.992e-04, eta: 4:28:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5769, top5_acc: 0.8153, loss_cls: 2.3326, loss: 2.3326 +2024-07-21 16:44:00,398 - pyskl - INFO - Epoch [145][3000/3746] lr: 2.962e-04, eta: 4:26:55, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5680, top5_acc: 0.8045, loss_cls: 2.3775, loss: 2.3775 +2024-07-21 16:45:22,037 - pyskl - INFO - Epoch [145][3100/3746] lr: 2.931e-04, eta: 4:25:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5855, top5_acc: 0.8216, loss_cls: 2.3147, loss: 2.3147 +2024-07-21 16:46:43,061 - pyskl - INFO - Epoch [145][3200/3746] lr: 2.901e-04, eta: 4:24:11, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5905, top5_acc: 0.8280, loss_cls: 2.2728, loss: 2.2728 +2024-07-21 16:48:05,143 - pyskl - INFO - Epoch [145][3300/3746] lr: 2.871e-04, eta: 4:22:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5934, top5_acc: 0.8177, loss_cls: 2.3113, loss: 2.3113 +2024-07-21 16:49:26,519 - pyskl - INFO - Epoch [145][3400/3746] lr: 2.841e-04, eta: 4:21:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5816, top5_acc: 0.8167, loss_cls: 2.3237, loss: 2.3237 +2024-07-21 16:50:48,386 - pyskl - INFO - Epoch [145][3500/3746] lr: 2.812e-04, eta: 4:20:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5878, top5_acc: 0.8214, loss_cls: 2.3040, loss: 2.3040 +2024-07-21 16:52:09,424 - pyskl - INFO - Epoch [145][3600/3746] lr: 2.782e-04, eta: 4:18:42, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5758, top5_acc: 0.8136, loss_cls: 2.3353, loss: 2.3353 +2024-07-21 16:53:30,830 - pyskl - INFO - Epoch [145][3700/3746] lr: 2.753e-04, eta: 4:17:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5767, top5_acc: 0.8123, loss_cls: 2.3386, loss: 2.3386 +2024-07-21 16:54:10,077 - pyskl - INFO - Saving checkpoint at 145 epochs +2024-07-21 16:56:02,267 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 16:56:02,938 - pyskl - INFO - +top1_acc 0.4240 +top5_acc 0.6762 +2024-07-21 16:56:02,938 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 16:56:02,979 - pyskl - INFO - +mean_acc 0.4239 +2024-07-21 16:56:02,983 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_144.pth was removed +2024-07-21 16:56:03,224 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_145.pth. +2024-07-21 16:56:03,225 - pyskl - INFO - Best top1_acc is 0.4240 at 145 epoch. +2024-07-21 16:56:03,238 - pyskl - INFO - Epoch(val) [145][309] top1_acc: 0.4240, top5_acc: 0.6762, mean_class_accuracy: 0.4239 +2024-07-21 16:59:50,333 - pyskl - INFO - Epoch [146][100/3746] lr: 2.710e-04, eta: 4:15:23, time: 2.271, data_time: 1.295, memory: 15990, top1_acc: 0.6081, top5_acc: 0.8342, loss_cls: 2.2145, loss: 2.2145 +2024-07-21 17:01:11,485 - pyskl - INFO - Epoch [146][200/3746] lr: 2.681e-04, eta: 4:14:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5956, top5_acc: 0.8291, loss_cls: 2.2520, loss: 2.2520 +2024-07-21 17:02:32,707 - pyskl - INFO - Epoch [146][300/3746] lr: 2.652e-04, eta: 4:12:38, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6012, top5_acc: 0.8314, loss_cls: 2.2224, loss: 2.2224 +2024-07-21 17:03:53,774 - pyskl - INFO - Epoch [146][400/3746] lr: 2.624e-04, eta: 4:11:16, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6008, top5_acc: 0.8287, loss_cls: 2.2440, loss: 2.2440 +2024-07-21 17:05:14,935 - pyskl - INFO - Epoch [146][500/3746] lr: 2.595e-04, eta: 4:09:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5941, top5_acc: 0.8219, loss_cls: 2.2851, loss: 2.2851 +2024-07-21 17:06:35,814 - pyskl - INFO - Epoch [146][600/3746] lr: 2.567e-04, eta: 4:08:31, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6009, top5_acc: 0.8255, loss_cls: 2.2390, loss: 2.2390 +2024-07-21 17:07:56,714 - pyskl - INFO - Epoch [146][700/3746] lr: 2.539e-04, eta: 4:07:09, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6038, top5_acc: 0.8267, loss_cls: 2.2294, loss: 2.2294 +2024-07-21 17:09:17,743 - pyskl - INFO - Epoch [146][800/3746] lr: 2.511e-04, eta: 4:05:47, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5852, top5_acc: 0.8181, loss_cls: 2.3015, loss: 2.3015 +2024-07-21 17:10:39,060 - pyskl - INFO - Epoch [146][900/3746] lr: 2.483e-04, eta: 4:04:25, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5969, top5_acc: 0.8283, loss_cls: 2.2503, loss: 2.2503 +2024-07-21 17:12:00,136 - pyskl - INFO - Epoch [146][1000/3746] lr: 2.455e-04, eta: 4:03:02, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5936, top5_acc: 0.8198, loss_cls: 2.2806, loss: 2.2806 +2024-07-21 17:13:21,579 - pyskl - INFO - Epoch [146][1100/3746] lr: 2.427e-04, eta: 4:01:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5956, top5_acc: 0.8267, loss_cls: 2.2515, loss: 2.2515 +2024-07-21 17:14:43,209 - pyskl - INFO - Epoch [146][1200/3746] lr: 2.400e-04, eta: 4:00:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5900, top5_acc: 0.8167, loss_cls: 2.3055, loss: 2.3055 +2024-07-21 17:16:04,115 - pyskl - INFO - Epoch [146][1300/3746] lr: 2.373e-04, eta: 3:58:55, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5973, top5_acc: 0.8252, loss_cls: 2.2634, loss: 2.2634 +2024-07-21 17:17:25,245 - pyskl - INFO - Epoch [146][1400/3746] lr: 2.345e-04, eta: 3:57:33, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5914, top5_acc: 0.8261, loss_cls: 2.2680, loss: 2.2680 +2024-07-21 17:18:46,278 - pyskl - INFO - Epoch [146][1500/3746] lr: 2.318e-04, eta: 3:56:11, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5962, top5_acc: 0.8191, loss_cls: 2.2965, loss: 2.2965 +2024-07-21 17:20:07,799 - pyskl - INFO - Epoch [146][1600/3746] lr: 2.292e-04, eta: 3:54:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5863, top5_acc: 0.8245, loss_cls: 2.3039, loss: 2.3039 +2024-07-21 17:21:29,267 - pyskl - INFO - Epoch [146][1700/3746] lr: 2.265e-04, eta: 3:53:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5984, top5_acc: 0.8258, loss_cls: 2.2661, loss: 2.2661 +2024-07-21 17:22:50,648 - pyskl - INFO - Epoch [146][1800/3746] lr: 2.239e-04, eta: 3:52:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6030, top5_acc: 0.8283, loss_cls: 2.2427, loss: 2.2427 +2024-07-21 17:24:12,037 - pyskl - INFO - Epoch [146][1900/3746] lr: 2.212e-04, eta: 3:50:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5970, top5_acc: 0.8347, loss_cls: 2.2461, loss: 2.2461 +2024-07-21 17:25:32,895 - pyskl - INFO - Epoch [146][2000/3746] lr: 2.186e-04, eta: 3:49:20, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5869, top5_acc: 0.8230, loss_cls: 2.2895, loss: 2.2895 +2024-07-21 17:26:53,450 - pyskl - INFO - Epoch [146][2100/3746] lr: 2.160e-04, eta: 3:47:57, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.5902, top5_acc: 0.8250, loss_cls: 2.2890, loss: 2.2890 +2024-07-21 17:28:14,324 - pyskl - INFO - Epoch [146][2200/3746] lr: 2.134e-04, eta: 3:46:35, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5806, top5_acc: 0.8211, loss_cls: 2.2999, loss: 2.2999 +2024-07-21 17:29:36,157 - pyskl - INFO - Epoch [146][2300/3746] lr: 2.108e-04, eta: 3:45:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5923, top5_acc: 0.8302, loss_cls: 2.2464, loss: 2.2464 +2024-07-21 17:30:56,829 - pyskl - INFO - Epoch [146][2400/3746] lr: 2.083e-04, eta: 3:43:50, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.6022, top5_acc: 0.8281, loss_cls: 2.2320, loss: 2.2320 +2024-07-21 17:32:19,257 - pyskl - INFO - Epoch [146][2500/3746] lr: 2.057e-04, eta: 3:42:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5925, top5_acc: 0.8222, loss_cls: 2.2619, loss: 2.2619 +2024-07-21 17:33:40,446 - pyskl - INFO - Epoch [146][2600/3746] lr: 2.032e-04, eta: 3:41:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5944, top5_acc: 0.8216, loss_cls: 2.2888, loss: 2.2888 +2024-07-21 17:35:01,631 - pyskl - INFO - Epoch [146][2700/3746] lr: 2.007e-04, eta: 3:39:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5853, top5_acc: 0.8184, loss_cls: 2.3018, loss: 2.3018 +2024-07-21 17:36:22,494 - pyskl - INFO - Epoch [146][2800/3746] lr: 1.982e-04, eta: 3:38:21, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5928, top5_acc: 0.8202, loss_cls: 2.2650, loss: 2.2650 +2024-07-21 17:37:44,236 - pyskl - INFO - Epoch [146][2900/3746] lr: 1.957e-04, eta: 3:36:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5855, top5_acc: 0.8247, loss_cls: 2.2677, loss: 2.2677 +2024-07-21 17:39:05,882 - pyskl - INFO - Epoch [146][3000/3746] lr: 1.933e-04, eta: 3:35:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5873, top5_acc: 0.8206, loss_cls: 2.2716, loss: 2.2716 +2024-07-21 17:40:27,223 - pyskl - INFO - Epoch [146][3100/3746] lr: 1.908e-04, eta: 3:34:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5906, top5_acc: 0.8294, loss_cls: 2.2712, loss: 2.2712 +2024-07-21 17:41:49,215 - pyskl - INFO - Epoch [146][3200/3746] lr: 1.884e-04, eta: 3:32:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5936, top5_acc: 0.8242, loss_cls: 2.2629, loss: 2.2629 +2024-07-21 17:43:11,384 - pyskl - INFO - Epoch [146][3300/3746] lr: 1.860e-04, eta: 3:31:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6091, top5_acc: 0.8336, loss_cls: 2.2132, loss: 2.2132 +2024-07-21 17:44:32,562 - pyskl - INFO - Epoch [146][3400/3746] lr: 1.836e-04, eta: 3:30:08, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5831, top5_acc: 0.8156, loss_cls: 2.3090, loss: 2.3090 +2024-07-21 17:45:53,978 - pyskl - INFO - Epoch [146][3500/3746] lr: 1.812e-04, eta: 3:28:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5998, top5_acc: 0.8298, loss_cls: 2.2589, loss: 2.2589 +2024-07-21 17:47:15,380 - pyskl - INFO - Epoch [146][3600/3746] lr: 1.788e-04, eta: 3:27:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5964, top5_acc: 0.8225, loss_cls: 2.2476, loss: 2.2476 +2024-07-21 17:48:36,013 - pyskl - INFO - Epoch [146][3700/3746] lr: 1.765e-04, eta: 3:26:01, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.5947, top5_acc: 0.8155, loss_cls: 2.3060, loss: 2.3060 +2024-07-21 17:49:15,285 - pyskl - INFO - Saving checkpoint at 146 epochs +2024-07-21 17:51:05,996 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 17:51:06,661 - pyskl - INFO - +top1_acc 0.4263 +top5_acc 0.6775 +2024-07-21 17:51:06,661 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 17:51:06,700 - pyskl - INFO - +mean_acc 0.4261 +2024-07-21 17:51:06,705 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_145.pth was removed +2024-07-21 17:51:06,936 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_146.pth. +2024-07-21 17:51:06,936 - pyskl - INFO - Best top1_acc is 0.4263 at 146 epoch. +2024-07-21 17:51:06,952 - pyskl - INFO - Epoch(val) [146][309] top1_acc: 0.4263, top5_acc: 0.6775, mean_class_accuracy: 0.4261 +2024-07-21 17:54:52,919 - pyskl - INFO - Epoch [147][100/3746] lr: 1.730e-04, eta: 3:24:04, time: 2.260, data_time: 1.282, memory: 15990, top1_acc: 0.6039, top5_acc: 0.8350, loss_cls: 2.1976, loss: 2.1976 +2024-07-21 17:56:14,220 - pyskl - INFO - Epoch [147][200/3746] lr: 1.707e-04, eta: 3:22:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6036, top5_acc: 0.8295, loss_cls: 2.2199, loss: 2.2199 +2024-07-21 17:57:34,962 - pyskl - INFO - Epoch [147][300/3746] lr: 1.684e-04, eta: 3:21:19, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.6100, top5_acc: 0.8347, loss_cls: 2.1833, loss: 2.1833 +2024-07-21 17:58:56,329 - pyskl - INFO - Epoch [147][400/3746] lr: 1.661e-04, eta: 3:19:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6052, top5_acc: 0.8289, loss_cls: 2.2314, loss: 2.2314 +2024-07-21 18:00:17,171 - pyskl - INFO - Epoch [147][500/3746] lr: 1.639e-04, eta: 3:18:35, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.6003, top5_acc: 0.8344, loss_cls: 2.2173, loss: 2.2173 +2024-07-21 18:01:38,276 - pyskl - INFO - Epoch [147][600/3746] lr: 1.616e-04, eta: 3:17:12, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6095, top5_acc: 0.8264, loss_cls: 2.2355, loss: 2.2355 +2024-07-21 18:02:59,310 - pyskl - INFO - Epoch [147][700/3746] lr: 1.594e-04, eta: 3:15:50, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6066, top5_acc: 0.8292, loss_cls: 2.2251, loss: 2.2251 +2024-07-21 18:04:20,399 - pyskl - INFO - Epoch [147][800/3746] lr: 1.572e-04, eta: 3:14:28, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6122, top5_acc: 0.8423, loss_cls: 2.1642, loss: 2.1642 +2024-07-21 18:05:42,067 - pyskl - INFO - Epoch [147][900/3746] lr: 1.550e-04, eta: 3:13:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5950, top5_acc: 0.8261, loss_cls: 2.2550, loss: 2.2550 +2024-07-21 18:07:03,407 - pyskl - INFO - Epoch [147][1000/3746] lr: 1.528e-04, eta: 3:11:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5995, top5_acc: 0.8234, loss_cls: 2.2352, loss: 2.2352 +2024-07-21 18:08:24,586 - pyskl - INFO - Epoch [147][1100/3746] lr: 1.506e-04, eta: 3:10:21, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5958, top5_acc: 0.8316, loss_cls: 2.2505, loss: 2.2505 +2024-07-21 18:09:45,771 - pyskl - INFO - Epoch [147][1200/3746] lr: 1.484e-04, eta: 3:08:58, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5995, top5_acc: 0.8287, loss_cls: 2.2231, loss: 2.2231 +2024-07-21 18:11:06,931 - pyskl - INFO - Epoch [147][1300/3746] lr: 1.463e-04, eta: 3:07:36, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5855, top5_acc: 0.8244, loss_cls: 2.2706, loss: 2.2706 +2024-07-21 18:12:27,741 - pyskl - INFO - Epoch [147][1400/3746] lr: 1.442e-04, eta: 3:06:14, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.6011, top5_acc: 0.8323, loss_cls: 2.2294, loss: 2.2294 +2024-07-21 18:13:49,322 - pyskl - INFO - Epoch [147][1500/3746] lr: 1.420e-04, eta: 3:04:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6141, top5_acc: 0.8370, loss_cls: 2.1781, loss: 2.1781 +2024-07-21 18:15:10,301 - pyskl - INFO - Epoch [147][1600/3746] lr: 1.399e-04, eta: 3:03:29, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5966, top5_acc: 0.8311, loss_cls: 2.2543, loss: 2.2543 +2024-07-21 18:16:30,744 - pyskl - INFO - Epoch [147][1700/3746] lr: 1.379e-04, eta: 3:02:07, time: 0.804, data_time: 0.000, memory: 15990, top1_acc: 0.5983, top5_acc: 0.8234, loss_cls: 2.2391, loss: 2.2391 +2024-07-21 18:17:51,766 - pyskl - INFO - Epoch [147][1800/3746] lr: 1.358e-04, eta: 3:00:45, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6017, top5_acc: 0.8253, loss_cls: 2.2326, loss: 2.2326 +2024-07-21 18:19:13,074 - pyskl - INFO - Epoch [147][1900/3746] lr: 1.337e-04, eta: 2:59:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5941, top5_acc: 0.8306, loss_cls: 2.2425, loss: 2.2425 +2024-07-21 18:20:34,349 - pyskl - INFO - Epoch [147][2000/3746] lr: 1.317e-04, eta: 2:58:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5944, top5_acc: 0.8319, loss_cls: 2.2375, loss: 2.2375 +2024-07-21 18:21:55,672 - pyskl - INFO - Epoch [147][2100/3746] lr: 1.297e-04, eta: 2:56:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5948, top5_acc: 0.8236, loss_cls: 2.2649, loss: 2.2649 +2024-07-21 18:23:16,833 - pyskl - INFO - Epoch [147][2200/3746] lr: 1.277e-04, eta: 2:55:16, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6017, top5_acc: 0.8280, loss_cls: 2.2278, loss: 2.2278 +2024-07-21 18:24:37,602 - pyskl - INFO - Epoch [147][2300/3746] lr: 1.257e-04, eta: 2:53:53, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.5962, top5_acc: 0.8270, loss_cls: 2.2433, loss: 2.2433 +2024-07-21 18:25:58,677 - pyskl - INFO - Epoch [147][2400/3746] lr: 1.237e-04, eta: 2:52:31, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6081, top5_acc: 0.8300, loss_cls: 2.2152, loss: 2.2152 +2024-07-21 18:27:20,003 - pyskl - INFO - Epoch [147][2500/3746] lr: 1.218e-04, eta: 2:51:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5944, top5_acc: 0.8261, loss_cls: 2.2439, loss: 2.2439 +2024-07-21 18:28:40,646 - pyskl - INFO - Epoch [147][2600/3746] lr: 1.198e-04, eta: 2:49:46, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.5934, top5_acc: 0.8213, loss_cls: 2.2770, loss: 2.2770 +2024-07-21 18:30:01,200 - pyskl - INFO - Epoch [147][2700/3746] lr: 1.179e-04, eta: 2:48:24, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.5981, top5_acc: 0.8245, loss_cls: 2.2671, loss: 2.2671 +2024-07-21 18:31:22,174 - pyskl - INFO - Epoch [147][2800/3746] lr: 1.160e-04, eta: 2:47:02, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5797, top5_acc: 0.8116, loss_cls: 2.2927, loss: 2.2927 +2024-07-21 18:32:43,886 - pyskl - INFO - Epoch [147][2900/3746] lr: 1.141e-04, eta: 2:45:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6091, top5_acc: 0.8361, loss_cls: 2.1985, loss: 2.1985 +2024-07-21 18:34:05,458 - pyskl - INFO - Epoch [147][3000/3746] lr: 1.122e-04, eta: 2:44:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6006, top5_acc: 0.8230, loss_cls: 2.2319, loss: 2.2319 +2024-07-21 18:35:26,453 - pyskl - INFO - Epoch [147][3100/3746] lr: 1.103e-04, eta: 2:42:55, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5970, top5_acc: 0.8277, loss_cls: 2.2468, loss: 2.2468 +2024-07-21 18:36:47,723 - pyskl - INFO - Epoch [147][3200/3746] lr: 1.085e-04, eta: 2:41:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6105, top5_acc: 0.8387, loss_cls: 2.1848, loss: 2.1848 +2024-07-21 18:38:09,839 - pyskl - INFO - Epoch [147][3300/3746] lr: 1.067e-04, eta: 2:40:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5991, top5_acc: 0.8314, loss_cls: 2.2563, loss: 2.2563 +2024-07-21 18:39:31,675 - pyskl - INFO - Epoch [147][3400/3746] lr: 1.048e-04, eta: 2:38:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5955, top5_acc: 0.8203, loss_cls: 2.2709, loss: 2.2709 +2024-07-21 18:40:52,978 - pyskl - INFO - Epoch [147][3500/3746] lr: 1.030e-04, eta: 2:37:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6030, top5_acc: 0.8225, loss_cls: 2.2448, loss: 2.2448 +2024-07-21 18:42:14,185 - pyskl - INFO - Epoch [147][3600/3746] lr: 1.013e-04, eta: 2:36:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5966, top5_acc: 0.8255, loss_cls: 2.2481, loss: 2.2481 +2024-07-21 18:43:35,122 - pyskl - INFO - Epoch [147][3700/3746] lr: 9.949e-05, eta: 2:34:41, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6089, top5_acc: 0.8298, loss_cls: 2.2281, loss: 2.2281 +2024-07-21 18:44:14,218 - pyskl - INFO - Saving checkpoint at 147 epochs +2024-07-21 18:46:05,185 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 18:46:05,865 - pyskl - INFO - +top1_acc 0.4274 +top5_acc 0.6790 +2024-07-21 18:46:05,865 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 18:46:05,907 - pyskl - INFO - +mean_acc 0.4272 +2024-07-21 18:46:05,912 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_146.pth was removed +2024-07-21 18:46:06,146 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_147.pth. +2024-07-21 18:46:06,146 - pyskl - INFO - Best top1_acc is 0.4274 at 147 epoch. +2024-07-21 18:46:06,160 - pyskl - INFO - Epoch(val) [147][309] top1_acc: 0.4274, top5_acc: 0.6790, mean_class_accuracy: 0.4272 +2024-07-21 18:49:56,181 - pyskl - INFO - Epoch [148][100/3746] lr: 9.693e-05, eta: 2:32:44, time: 2.300, data_time: 1.327, memory: 15990, top1_acc: 0.6042, top5_acc: 0.8375, loss_cls: 2.2210, loss: 2.2210 +2024-07-21 18:51:17,465 - pyskl - INFO - Epoch [148][200/3746] lr: 9.520e-05, eta: 2:31:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6080, top5_acc: 0.8375, loss_cls: 2.1731, loss: 2.1731 +2024-07-21 18:52:39,106 - pyskl - INFO - Epoch [148][300/3746] lr: 9.348e-05, eta: 2:29:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6075, top5_acc: 0.8320, loss_cls: 2.2223, loss: 2.2223 +2024-07-21 18:54:00,234 - pyskl - INFO - Epoch [148][400/3746] lr: 9.178e-05, eta: 2:28:37, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6055, top5_acc: 0.8334, loss_cls: 2.2134, loss: 2.2134 +2024-07-21 18:55:21,438 - pyskl - INFO - Epoch [148][500/3746] lr: 9.010e-05, eta: 2:27:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5970, top5_acc: 0.8270, loss_cls: 2.2533, loss: 2.2533 +2024-07-21 18:56:42,374 - pyskl - INFO - Epoch [148][600/3746] lr: 8.843e-05, eta: 2:25:52, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6069, top5_acc: 0.8253, loss_cls: 2.2206, loss: 2.2206 +2024-07-21 18:58:03,849 - pyskl - INFO - Epoch [148][700/3746] lr: 8.678e-05, eta: 2:24:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5988, top5_acc: 0.8264, loss_cls: 2.2360, loss: 2.2360 +2024-07-21 18:59:25,160 - pyskl - INFO - Epoch [148][800/3746] lr: 8.514e-05, eta: 2:23:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6133, top5_acc: 0.8342, loss_cls: 2.1919, loss: 2.1919 +2024-07-21 19:00:47,320 - pyskl - INFO - Epoch [148][900/3746] lr: 8.351e-05, eta: 2:21:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5962, top5_acc: 0.8291, loss_cls: 2.2328, loss: 2.2328 +2024-07-21 19:02:08,793 - pyskl - INFO - Epoch [148][1000/3746] lr: 8.191e-05, eta: 2:20:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6136, top5_acc: 0.8264, loss_cls: 2.2122, loss: 2.2122 +2024-07-21 19:03:29,981 - pyskl - INFO - Epoch [148][1100/3746] lr: 8.031e-05, eta: 2:19:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6061, top5_acc: 0.8228, loss_cls: 2.2361, loss: 2.2361 +2024-07-21 19:04:50,908 - pyskl - INFO - Epoch [148][1200/3746] lr: 7.874e-05, eta: 2:17:38, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5894, top5_acc: 0.8183, loss_cls: 2.2686, loss: 2.2686 +2024-07-21 19:06:11,960 - pyskl - INFO - Epoch [148][1300/3746] lr: 7.718e-05, eta: 2:16:16, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6125, top5_acc: 0.8327, loss_cls: 2.1869, loss: 2.1869 +2024-07-21 19:07:33,062 - pyskl - INFO - Epoch [148][1400/3746] lr: 7.563e-05, eta: 2:14:54, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5981, top5_acc: 0.8328, loss_cls: 2.2191, loss: 2.2191 +2024-07-21 19:08:54,511 - pyskl - INFO - Epoch [148][1500/3746] lr: 7.410e-05, eta: 2:13:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5975, top5_acc: 0.8264, loss_cls: 2.2372, loss: 2.2372 +2024-07-21 19:10:15,683 - pyskl - INFO - Epoch [148][1600/3746] lr: 7.259e-05, eta: 2:12:09, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6023, top5_acc: 0.8330, loss_cls: 2.2268, loss: 2.2268 +2024-07-21 19:11:37,170 - pyskl - INFO - Epoch [148][1700/3746] lr: 7.109e-05, eta: 2:10:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6072, top5_acc: 0.8284, loss_cls: 2.2117, loss: 2.2117 +2024-07-21 19:12:58,182 - pyskl - INFO - Epoch [148][1800/3746] lr: 6.961e-05, eta: 2:09:25, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6125, top5_acc: 0.8353, loss_cls: 2.2012, loss: 2.2012 +2024-07-21 19:14:19,284 - pyskl - INFO - Epoch [148][1900/3746] lr: 6.814e-05, eta: 2:08:02, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6056, top5_acc: 0.8350, loss_cls: 2.2068, loss: 2.2068 +2024-07-21 19:15:40,039 - pyskl - INFO - Epoch [148][2000/3746] lr: 6.669e-05, eta: 2:06:40, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.6023, top5_acc: 0.8341, loss_cls: 2.1986, loss: 2.1986 +2024-07-21 19:17:01,266 - pyskl - INFO - Epoch [148][2100/3746] lr: 6.526e-05, eta: 2:05:18, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6038, top5_acc: 0.8344, loss_cls: 2.2191, loss: 2.2191 +2024-07-21 19:18:22,409 - pyskl - INFO - Epoch [148][2200/3746] lr: 6.384e-05, eta: 2:03:55, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6045, top5_acc: 0.8397, loss_cls: 2.1818, loss: 2.1818 +2024-07-21 19:19:43,318 - pyskl - INFO - Epoch [148][2300/3746] lr: 6.243e-05, eta: 2:02:33, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6067, top5_acc: 0.8392, loss_cls: 2.1907, loss: 2.1907 +2024-07-21 19:21:04,805 - pyskl - INFO - Epoch [148][2400/3746] lr: 6.104e-05, eta: 2:01:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6131, top5_acc: 0.8386, loss_cls: 2.1820, loss: 2.1820 +2024-07-21 19:22:25,733 - pyskl - INFO - Epoch [148][2500/3746] lr: 5.967e-05, eta: 1:59:48, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6023, top5_acc: 0.8248, loss_cls: 2.2355, loss: 2.2355 +2024-07-21 19:23:46,930 - pyskl - INFO - Epoch [148][2600/3746] lr: 5.831e-05, eta: 1:58:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5975, top5_acc: 0.8278, loss_cls: 2.2265, loss: 2.2265 +2024-07-21 19:25:07,965 - pyskl - INFO - Epoch [148][2700/3746] lr: 5.697e-05, eta: 1:57:04, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6033, top5_acc: 0.8359, loss_cls: 2.1904, loss: 2.1904 +2024-07-21 19:26:29,057 - pyskl - INFO - Epoch [148][2800/3746] lr: 5.564e-05, eta: 1:55:42, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6009, top5_acc: 0.8328, loss_cls: 2.2256, loss: 2.2256 +2024-07-21 19:27:49,991 - pyskl - INFO - Epoch [148][2900/3746] lr: 5.433e-05, eta: 1:54:19, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6038, top5_acc: 0.8286, loss_cls: 2.2193, loss: 2.2193 +2024-07-21 19:29:11,024 - pyskl - INFO - Epoch [148][3000/3746] lr: 5.304e-05, eta: 1:52:57, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6072, top5_acc: 0.8356, loss_cls: 2.1880, loss: 2.1880 +2024-07-21 19:30:32,174 - pyskl - INFO - Epoch [148][3100/3746] lr: 5.176e-05, eta: 1:51:35, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6100, top5_acc: 0.8309, loss_cls: 2.2004, loss: 2.2004 +2024-07-21 19:31:53,642 - pyskl - INFO - Epoch [148][3200/3746] lr: 5.050e-05, eta: 1:50:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6100, top5_acc: 0.8377, loss_cls: 2.1722, loss: 2.1722 +2024-07-21 19:33:14,646 - pyskl - INFO - Epoch [148][3300/3746] lr: 4.925e-05, eta: 1:48:50, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5953, top5_acc: 0.8208, loss_cls: 2.2725, loss: 2.2725 +2024-07-21 19:34:36,290 - pyskl - INFO - Epoch [148][3400/3746] lr: 4.801e-05, eta: 1:47:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6066, top5_acc: 0.8352, loss_cls: 2.1941, loss: 2.1941 +2024-07-21 19:35:57,488 - pyskl - INFO - Epoch [148][3500/3746] lr: 4.680e-05, eta: 1:46:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6075, top5_acc: 0.8358, loss_cls: 2.1785, loss: 2.1785 +2024-07-21 19:37:19,219 - pyskl - INFO - Epoch [148][3600/3746] lr: 4.560e-05, eta: 1:44:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6097, top5_acc: 0.8369, loss_cls: 2.1816, loss: 2.1816 +2024-07-21 19:38:40,801 - pyskl - INFO - Epoch [148][3700/3746] lr: 4.441e-05, eta: 1:43:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6105, top5_acc: 0.8308, loss_cls: 2.1912, loss: 2.1912 +2024-07-21 19:39:20,050 - pyskl - INFO - Saving checkpoint at 148 epochs +2024-07-21 19:41:11,888 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 19:41:12,577 - pyskl - INFO - +top1_acc 0.4282 +top5_acc 0.6796 +2024-07-21 19:41:12,577 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 19:41:12,621 - pyskl - INFO - +mean_acc 0.4280 +2024-07-21 19:41:12,626 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_147.pth was removed +2024-07-21 19:41:12,882 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_148.pth. +2024-07-21 19:41:12,883 - pyskl - INFO - Best top1_acc is 0.4282 at 148 epoch. +2024-07-21 19:41:12,896 - pyskl - INFO - Epoch(val) [148][309] top1_acc: 0.4282, top5_acc: 0.6796, mean_class_accuracy: 0.4280 +2024-07-21 19:44:57,770 - pyskl - INFO - Epoch [149][100/3746] lr: 4.271e-05, eta: 1:41:22, time: 2.249, data_time: 1.275, memory: 15990, top1_acc: 0.6067, top5_acc: 0.8328, loss_cls: 2.2092, loss: 2.2092 +2024-07-21 19:46:19,554 - pyskl - INFO - Epoch [149][200/3746] lr: 4.156e-05, eta: 1:40:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6120, top5_acc: 0.8342, loss_cls: 2.1657, loss: 2.1657 +2024-07-21 19:47:41,103 - pyskl - INFO - Epoch [149][300/3746] lr: 4.043e-05, eta: 1:38:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6028, top5_acc: 0.8442, loss_cls: 2.1785, loss: 2.1785 +2024-07-21 19:49:02,825 - pyskl - INFO - Epoch [149][400/3746] lr: 3.931e-05, eta: 1:37:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6181, top5_acc: 0.8414, loss_cls: 2.1744, loss: 2.1744 +2024-07-21 19:50:23,624 - pyskl - INFO - Epoch [149][500/3746] lr: 3.821e-05, eta: 1:35:53, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.5978, top5_acc: 0.8263, loss_cls: 2.2274, loss: 2.2274 +2024-07-21 19:51:44,945 - pyskl - INFO - Epoch [149][600/3746] lr: 3.713e-05, eta: 1:34:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6072, top5_acc: 0.8331, loss_cls: 2.2119, loss: 2.2119 +2024-07-21 19:53:06,160 - pyskl - INFO - Epoch [149][700/3746] lr: 3.606e-05, eta: 1:33:09, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6197, top5_acc: 0.8348, loss_cls: 2.1704, loss: 2.1704 +2024-07-21 19:54:26,976 - pyskl - INFO - Epoch [149][800/3746] lr: 3.500e-05, eta: 1:31:46, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.6088, top5_acc: 0.8350, loss_cls: 2.1976, loss: 2.1976 +2024-07-21 19:55:48,595 - pyskl - INFO - Epoch [149][900/3746] lr: 3.397e-05, eta: 1:30:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6084, top5_acc: 0.8295, loss_cls: 2.2363, loss: 2.2363 +2024-07-21 19:57:10,555 - pyskl - INFO - Epoch [149][1000/3746] lr: 3.294e-05, eta: 1:29:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6153, top5_acc: 0.8337, loss_cls: 2.1801, loss: 2.1801 +2024-07-21 19:58:32,010 - pyskl - INFO - Epoch [149][1100/3746] lr: 3.194e-05, eta: 1:27:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6114, top5_acc: 0.8345, loss_cls: 2.1796, loss: 2.1796 +2024-07-21 19:59:53,366 - pyskl - INFO - Epoch [149][1200/3746] lr: 3.095e-05, eta: 1:26:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6066, top5_acc: 0.8327, loss_cls: 2.2094, loss: 2.2094 +2024-07-21 20:01:15,185 - pyskl - INFO - Epoch [149][1300/3746] lr: 2.997e-05, eta: 1:24:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5981, top5_acc: 0.8289, loss_cls: 2.2150, loss: 2.2150 +2024-07-21 20:02:36,622 - pyskl - INFO - Epoch [149][1400/3746] lr: 2.901e-05, eta: 1:23:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6136, top5_acc: 0.8367, loss_cls: 2.1715, loss: 2.1715 +2024-07-21 20:03:57,756 - pyskl - INFO - Epoch [149][1500/3746] lr: 2.807e-05, eta: 1:22:10, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6131, top5_acc: 0.8366, loss_cls: 2.1646, loss: 2.1646 +2024-07-21 20:05:19,070 - pyskl - INFO - Epoch [149][1600/3746] lr: 2.714e-05, eta: 1:20:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6138, top5_acc: 0.8367, loss_cls: 2.1926, loss: 2.1926 +2024-07-21 20:06:40,231 - pyskl - INFO - Epoch [149][1700/3746] lr: 2.622e-05, eta: 1:19:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6139, top5_acc: 0.8398, loss_cls: 2.1594, loss: 2.1594 +2024-07-21 20:08:01,405 - pyskl - INFO - Epoch [149][1800/3746] lr: 2.533e-05, eta: 1:18:03, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6097, top5_acc: 0.8300, loss_cls: 2.1792, loss: 2.1792 +2024-07-21 20:09:22,517 - pyskl - INFO - Epoch [149][1900/3746] lr: 2.444e-05, eta: 1:16:41, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6130, top5_acc: 0.8300, loss_cls: 2.2125, loss: 2.2125 +2024-07-21 20:10:43,262 - pyskl - INFO - Epoch [149][2000/3746] lr: 2.358e-05, eta: 1:15:19, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.6044, top5_acc: 0.8377, loss_cls: 2.1939, loss: 2.1939 +2024-07-21 20:12:04,515 - pyskl - INFO - Epoch [149][2100/3746] lr: 2.273e-05, eta: 1:13:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6016, top5_acc: 0.8306, loss_cls: 2.2100, loss: 2.2100 +2024-07-21 20:13:25,888 - pyskl - INFO - Epoch [149][2200/3746] lr: 2.189e-05, eta: 1:12:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6095, top5_acc: 0.8345, loss_cls: 2.1845, loss: 2.1845 +2024-07-21 20:14:47,408 - pyskl - INFO - Epoch [149][2300/3746] lr: 2.107e-05, eta: 1:11:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6086, top5_acc: 0.8397, loss_cls: 2.1610, loss: 2.1610 +2024-07-21 20:16:08,627 - pyskl - INFO - Epoch [149][2400/3746] lr: 2.027e-05, eta: 1:09:49, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6206, top5_acc: 0.8391, loss_cls: 2.1662, loss: 2.1662 +2024-07-21 20:17:30,371 - pyskl - INFO - Epoch [149][2500/3746] lr: 1.948e-05, eta: 1:08:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6162, top5_acc: 0.8422, loss_cls: 2.1659, loss: 2.1659 +2024-07-21 20:18:51,284 - pyskl - INFO - Epoch [149][2600/3746] lr: 1.871e-05, eta: 1:07:05, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6003, top5_acc: 0.8252, loss_cls: 2.2400, loss: 2.2400 +2024-07-21 20:20:12,144 - pyskl - INFO - Epoch [149][2700/3746] lr: 1.795e-05, eta: 1:05:43, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6056, top5_acc: 0.8344, loss_cls: 2.1855, loss: 2.1855 +2024-07-21 20:21:33,355 - pyskl - INFO - Epoch [149][2800/3746] lr: 1.721e-05, eta: 1:04:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6053, top5_acc: 0.8309, loss_cls: 2.2323, loss: 2.2323 +2024-07-21 20:22:54,630 - pyskl - INFO - Epoch [149][2900/3746] lr: 1.649e-05, eta: 1:02:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6092, top5_acc: 0.8391, loss_cls: 2.1688, loss: 2.1688 +2024-07-21 20:24:16,513 - pyskl - INFO - Epoch [149][3000/3746] lr: 1.578e-05, eta: 1:01:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6153, top5_acc: 0.8342, loss_cls: 2.1804, loss: 2.1804 +2024-07-21 20:25:37,795 - pyskl - INFO - Epoch [149][3100/3746] lr: 1.508e-05, eta: 1:00:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6103, top5_acc: 0.8387, loss_cls: 2.1783, loss: 2.1783 +2024-07-21 20:26:59,298 - pyskl - INFO - Epoch [149][3200/3746] lr: 1.440e-05, eta: 0:58:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6055, top5_acc: 0.8309, loss_cls: 2.2031, loss: 2.2031 +2024-07-21 20:28:20,674 - pyskl - INFO - Epoch [149][3300/3746] lr: 1.374e-05, eta: 0:57:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6066, top5_acc: 0.8391, loss_cls: 2.1919, loss: 2.1919 +2024-07-21 20:29:42,376 - pyskl - INFO - Epoch [149][3400/3746] lr: 1.309e-05, eta: 0:56:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6030, top5_acc: 0.8264, loss_cls: 2.2353, loss: 2.2353 +2024-07-21 20:31:03,072 - pyskl - INFO - Epoch [149][3500/3746] lr: 1.246e-05, eta: 0:54:44, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.6152, top5_acc: 0.8337, loss_cls: 2.1879, loss: 2.1879 +2024-07-21 20:32:24,674 - pyskl - INFO - Epoch [149][3600/3746] lr: 1.184e-05, eta: 0:53:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6105, top5_acc: 0.8295, loss_cls: 2.1887, loss: 2.1887 +2024-07-21 20:33:45,878 - pyskl - INFO - Epoch [149][3700/3746] lr: 1.124e-05, eta: 0:52:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6091, top5_acc: 0.8302, loss_cls: 2.1990, loss: 2.1990 +2024-07-21 20:34:25,303 - pyskl - INFO - Saving checkpoint at 149 epochs +2024-07-21 20:36:17,640 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 20:36:18,300 - pyskl - INFO - +top1_acc 0.4277 +top5_acc 0.6820 +2024-07-21 20:36:18,301 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 20:36:18,340 - pyskl - INFO - +mean_acc 0.4276 +2024-07-21 20:36:18,351 - pyskl - INFO - Epoch(val) [149][309] top1_acc: 0.4277, top5_acc: 0.6820, mean_class_accuracy: 0.4276 +2024-07-21 20:40:03,202 - pyskl - INFO - Epoch [150][100/3746] lr: 1.039e-05, eta: 0:50:00, time: 2.248, data_time: 1.274, memory: 15990, top1_acc: 0.6133, top5_acc: 0.8377, loss_cls: 2.1807, loss: 2.1807 +2024-07-21 20:41:24,399 - pyskl - INFO - Epoch [150][200/3746] lr: 9.832e-06, eta: 0:48:38, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5930, top5_acc: 0.8286, loss_cls: 2.2400, loss: 2.2400 +2024-07-21 20:42:45,852 - pyskl - INFO - Epoch [150][300/3746] lr: 9.285e-06, eta: 0:47:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5991, top5_acc: 0.8336, loss_cls: 2.2327, loss: 2.2327 +2024-07-21 20:44:06,914 - pyskl - INFO - Epoch [150][400/3746] lr: 8.754e-06, eta: 0:45:53, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6094, top5_acc: 0.8336, loss_cls: 2.2049, loss: 2.2049 +2024-07-21 20:45:28,132 - pyskl - INFO - Epoch [150][500/3746] lr: 8.239e-06, eta: 0:44:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6128, top5_acc: 0.8359, loss_cls: 2.1564, loss: 2.1564 +2024-07-21 20:46:50,275 - pyskl - INFO - Epoch [150][600/3746] lr: 7.739e-06, eta: 0:43:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6134, top5_acc: 0.8397, loss_cls: 2.1743, loss: 2.1743 +2024-07-21 20:48:11,739 - pyskl - INFO - Epoch [150][700/3746] lr: 7.255e-06, eta: 0:41:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6116, top5_acc: 0.8380, loss_cls: 2.1963, loss: 2.1963 +2024-07-21 20:49:33,050 - pyskl - INFO - Epoch [150][800/3746] lr: 6.787e-06, eta: 0:40:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6169, top5_acc: 0.8381, loss_cls: 2.1600, loss: 2.1600 +2024-07-21 20:50:53,686 - pyskl - INFO - Epoch [150][900/3746] lr: 6.334e-06, eta: 0:39:02, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.6198, top5_acc: 0.8364, loss_cls: 2.1622, loss: 2.1622 +2024-07-21 20:52:15,641 - pyskl - INFO - Epoch [150][1000/3746] lr: 5.897e-06, eta: 0:37:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6095, top5_acc: 0.8403, loss_cls: 2.1697, loss: 2.1697 +2024-07-21 20:53:36,875 - pyskl - INFO - Epoch [150][1100/3746] lr: 5.475e-06, eta: 0:36:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6039, top5_acc: 0.8341, loss_cls: 2.2088, loss: 2.2088 +2024-07-21 20:54:58,058 - pyskl - INFO - Epoch [150][1200/3746] lr: 5.070e-06, eta: 0:34:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6217, top5_acc: 0.8414, loss_cls: 2.1463, loss: 2.1463 +2024-07-21 20:56:19,198 - pyskl - INFO - Epoch [150][1300/3746] lr: 4.679e-06, eta: 0:33:33, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6128, top5_acc: 0.8436, loss_cls: 2.1764, loss: 2.1764 +2024-07-21 20:57:40,734 - pyskl - INFO - Epoch [150][1400/3746] lr: 4.305e-06, eta: 0:32:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6039, top5_acc: 0.8409, loss_cls: 2.1807, loss: 2.1807 +2024-07-21 20:59:01,893 - pyskl - INFO - Epoch [150][1500/3746] lr: 3.946e-06, eta: 0:30:48, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6161, top5_acc: 0.8361, loss_cls: 2.1842, loss: 2.1842 +2024-07-21 21:00:22,978 - pyskl - INFO - Epoch [150][1600/3746] lr: 3.602e-06, eta: 0:29:26, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6077, top5_acc: 0.8375, loss_cls: 2.2280, loss: 2.2280 +2024-07-21 21:01:43,938 - pyskl - INFO - Epoch [150][1700/3746] lr: 3.275e-06, eta: 0:28:03, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6092, top5_acc: 0.8314, loss_cls: 2.1745, loss: 2.1745 +2024-07-21 21:03:05,161 - pyskl - INFO - Epoch [150][1800/3746] lr: 2.962e-06, eta: 0:26:41, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6105, top5_acc: 0.8373, loss_cls: 2.1849, loss: 2.1849 +2024-07-21 21:04:26,945 - pyskl - INFO - Epoch [150][1900/3746] lr: 2.666e-06, eta: 0:25:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6067, top5_acc: 0.8372, loss_cls: 2.1905, loss: 2.1905 +2024-07-21 21:05:47,644 - pyskl - INFO - Epoch [150][2000/3746] lr: 2.385e-06, eta: 0:23:56, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.6136, top5_acc: 0.8403, loss_cls: 2.1609, loss: 2.1609 +2024-07-21 21:07:08,516 - pyskl - INFO - Epoch [150][2100/3746] lr: 2.120e-06, eta: 0:22:34, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6097, top5_acc: 0.8355, loss_cls: 2.1776, loss: 2.1776 +2024-07-21 21:08:30,077 - pyskl - INFO - Epoch [150][2200/3746] lr: 1.870e-06, eta: 0:21:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6150, top5_acc: 0.8348, loss_cls: 2.1725, loss: 2.1725 +2024-07-21 21:09:51,611 - pyskl - INFO - Epoch [150][2300/3746] lr: 1.636e-06, eta: 0:19:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6106, top5_acc: 0.8280, loss_cls: 2.2116, loss: 2.2116 +2024-07-21 21:11:12,752 - pyskl - INFO - Epoch [150][2400/3746] lr: 1.418e-06, eta: 0:18:27, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6069, top5_acc: 0.8409, loss_cls: 2.1621, loss: 2.1621 +2024-07-21 21:12:34,334 - pyskl - INFO - Epoch [150][2500/3746] lr: 1.215e-06, eta: 0:17:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6139, top5_acc: 0.8375, loss_cls: 2.1644, loss: 2.1644 +2024-07-21 21:13:55,609 - pyskl - INFO - Epoch [150][2600/3746] lr: 1.028e-06, eta: 0:15:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6055, top5_acc: 0.8292, loss_cls: 2.2253, loss: 2.2253 +2024-07-21 21:15:16,601 - pyskl - INFO - Epoch [150][2700/3746] lr: 8.567e-07, eta: 0:14:20, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6128, top5_acc: 0.8397, loss_cls: 2.1565, loss: 2.1565 +2024-07-21 21:16:37,632 - pyskl - INFO - Epoch [150][2800/3746] lr: 7.008e-07, eta: 0:12:58, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6127, top5_acc: 0.8456, loss_cls: 2.1398, loss: 2.1398 +2024-07-21 21:17:58,895 - pyskl - INFO - Epoch [150][2900/3746] lr: 5.606e-07, eta: 0:11:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6220, top5_acc: 0.8397, loss_cls: 2.1361, loss: 2.1361 +2024-07-21 21:19:20,122 - pyskl - INFO - Epoch [150][3000/3746] lr: 4.361e-07, eta: 0:10:13, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6030, top5_acc: 0.8320, loss_cls: 2.2086, loss: 2.2086 +2024-07-21 21:20:41,924 - pyskl - INFO - Epoch [150][3100/3746] lr: 3.271e-07, eta: 0:08:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6134, top5_acc: 0.8416, loss_cls: 2.1629, loss: 2.1629 +2024-07-21 21:22:02,999 - pyskl - INFO - Epoch [150][3200/3746] lr: 2.338e-07, eta: 0:07:29, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6078, top5_acc: 0.8309, loss_cls: 2.1858, loss: 2.1858 +2024-07-21 21:23:24,229 - pyskl - INFO - Epoch [150][3300/3746] lr: 1.561e-07, eta: 0:06:07, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6062, top5_acc: 0.8248, loss_cls: 2.2311, loss: 2.2311 +2024-07-21 21:24:45,862 - pyskl - INFO - Epoch [150][3400/3746] lr: 9.410e-08, eta: 0:04:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6089, top5_acc: 0.8347, loss_cls: 2.1846, loss: 2.1846 +2024-07-21 21:26:07,070 - pyskl - INFO - Epoch [150][3500/3746] lr: 4.768e-08, eta: 0:03:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6066, top5_acc: 0.8345, loss_cls: 2.2016, loss: 2.2016 +2024-07-21 21:27:28,328 - pyskl - INFO - Epoch [150][3600/3746] lr: 1.689e-08, eta: 0:02:00, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6127, top5_acc: 0.8355, loss_cls: 2.1863, loss: 2.1863 +2024-07-21 21:28:49,325 - pyskl - INFO - Epoch [150][3700/3746] lr: 1.726e-09, eta: 0:00:37, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6047, top5_acc: 0.8303, loss_cls: 2.1863, loss: 2.1863 +2024-07-21 21:29:29,254 - pyskl - INFO - Saving checkpoint at 150 epochs +2024-07-21 21:31:18,983 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 21:31:19,646 - pyskl - INFO - +top1_acc 0.4283 +top5_acc 0.6808 +2024-07-21 21:31:19,646 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 21:31:19,684 - pyskl - INFO - +mean_acc 0.4281 +2024-07-21 21:31:19,688 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_148.pth was removed +2024-07-21 21:31:19,923 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_150.pth. +2024-07-21 21:31:19,924 - pyskl - INFO - Best top1_acc is 0.4283 at 150 epoch. +2024-07-21 21:31:19,935 - pyskl - INFO - Epoch(val) [150][309] top1_acc: 0.4283, top5_acc: 0.6808, mean_class_accuracy: 0.4281 +2024-07-21 21:31:33,879 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-07-21 21:43:29,181 - pyskl - INFO - Testing results of the last checkpoint +2024-07-21 21:43:29,182 - pyskl - INFO - top1_acc: 0.4443 +2024-07-21 21:43:29,182 - pyskl - INFO - top5_acc: 0.6976 +2024-07-21 21:43:29,182 - pyskl - INFO - mean_class_accuracy: 0.4441 +2024-07-21 21:43:29,182 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/k400/jm/best_top1_acc_epoch_150.pth +2024-07-21 21:55:17,486 - pyskl - INFO - Testing results of the best checkpoint +2024-07-21 21:55:17,486 - pyskl - INFO - top1_acc: 0.4443 +2024-07-21 21:55:17,486 - pyskl - INFO - top5_acc: 0.6976 +2024-07-21 21:55:17,486 - pyskl - INFO - mean_class_accuracy: 0.4441 diff --git a/k400/jm/20240716_064836.log.json b/k400/jm/20240716_064836.log.json new file mode 100644 index 0000000000000000000000000000000000000000..5aeda29ad9c352b28b620a59bd16caa7e696891d --- /dev/null +++ b/k400/jm/20240716_064836.log.json @@ -0,0 +1,5701 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 1004529236, "config_name": "jm.py", "work_dir": "jm", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.1, "memory": 15990, "data_time": 1.25717, "top1_acc": 0.00594, "top5_acc": 0.02438, "loss_cls": 6.49195, "loss": 6.49195, "time": 1.96462} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.01, "top5_acc": 0.04906, "loss_cls": 6.46111, "loss": 6.46111, "time": 0.69951} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.01547, "top5_acc": 0.06156, "loss_cls": 6.29252, "loss": 6.29252, "time": 0.70355} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.01672, "top5_acc": 0.0675, "loss_cls": 6.20954, "loss": 6.20954, "time": 0.70077} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.01844, "top5_acc": 0.07875, "loss_cls": 6.13413, "loss": 6.13413, "time": 0.70431} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.02438, "top5_acc": 0.08953, "loss_cls": 6.05546, "loss": 6.05546, "time": 0.69892} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.02625, "top5_acc": 0.10297, "loss_cls": 5.95854, "loss": 5.95854, "time": 0.70035} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.03047, "top5_acc": 0.11047, "loss_cls": 5.92633, "loss": 5.92633, "time": 0.69913} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.03203, "top5_acc": 0.11359, "loss_cls": 5.89304, "loss": 5.89304, "time": 0.7028} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.03594, "top5_acc": 0.12469, "loss_cls": 5.83432, "loss": 5.83432, "time": 0.70289} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.1, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.03094, "top5_acc": 0.12391, "loss_cls": 5.80063, "loss": 5.80063, "time": 0.69866} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.03438, "top5_acc": 0.1375, "loss_cls": 5.76908, "loss": 5.76908, "time": 0.70023} +{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.03844, "top5_acc": 0.13156, "loss_cls": 5.74415, "loss": 5.74415, "time": 0.69803} +{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.04312, "top5_acc": 0.13625, "loss_cls": 5.72398, "loss": 5.72398, "time": 0.69634} +{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.1, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.04188, "top5_acc": 0.15172, "loss_cls": 5.67605, "loss": 5.67605, "time": 0.6983} +{"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.04406, "top5_acc": 0.14828, "loss_cls": 5.66633, "loss": 5.66633, "time": 0.70035} +{"mode": "train", "epoch": 1, "iter": 1700, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.04812, "top5_acc": 0.15125, "loss_cls": 5.66291, "loss": 5.66291, "time": 0.69751} +{"mode": "train", "epoch": 1, "iter": 1800, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.04734, "top5_acc": 0.16156, "loss_cls": 5.62286, "loss": 5.62286, "time": 0.69854} +{"mode": "train", "epoch": 1, "iter": 1900, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.0475, "top5_acc": 0.15953, "loss_cls": 5.62828, "loss": 5.62828, "time": 0.70623} +{"mode": "train", "epoch": 1, "iter": 2000, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.05219, "top5_acc": 0.17156, "loss_cls": 5.58927, "loss": 5.58927, "time": 0.69946} +{"mode": "train", "epoch": 1, "iter": 2100, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.05547, "top5_acc": 0.17781, "loss_cls": 5.5468, "loss": 5.5468, "time": 0.70628} +{"mode": "train", "epoch": 1, "iter": 2200, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.05375, "top5_acc": 0.18016, "loss_cls": 5.55064, "loss": 5.55064, "time": 0.69716} +{"mode": "train", "epoch": 1, "iter": 2300, "lr": 0.1, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.05547, "top5_acc": 0.18047, "loss_cls": 5.53755, "loss": 5.53755, "time": 0.69732} +{"mode": "train", "epoch": 1, "iter": 2400, "lr": 0.1, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.05078, "top5_acc": 0.18172, "loss_cls": 5.52185, "loss": 5.52185, "time": 0.69646} +{"mode": "train", "epoch": 1, "iter": 2500, "lr": 0.1, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.0575, "top5_acc": 0.19719, "loss_cls": 5.47193, "loss": 5.47193, "time": 0.70069} +{"mode": "train", "epoch": 1, "iter": 2600, "lr": 0.09999, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.06047, "top5_acc": 0.1925, "loss_cls": 5.45874, "loss": 5.45874, "time": 0.69779} +{"mode": "train", "epoch": 1, "iter": 2700, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.06656, "top5_acc": 0.19672, "loss_cls": 5.46477, "loss": 5.46477, "time": 0.6991} +{"mode": "train", "epoch": 1, "iter": 2800, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.06438, "top5_acc": 0.20312, "loss_cls": 5.45121, "loss": 5.45121, "time": 0.69877} +{"mode": "train", "epoch": 1, "iter": 2900, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.06188, "top5_acc": 0.21141, "loss_cls": 5.42039, "loss": 5.42039, "time": 0.69994} +{"mode": "train", "epoch": 1, "iter": 3000, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.06906, "top5_acc": 0.21469, "loss_cls": 5.41037, "loss": 5.41037, "time": 0.69732} +{"mode": "train", "epoch": 1, "iter": 3100, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.07297, "top5_acc": 0.21734, "loss_cls": 5.34347, "loss": 5.34347, "time": 0.69757} +{"mode": "train", "epoch": 1, "iter": 3200, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.07625, "top5_acc": 0.21688, "loss_cls": 5.38469, "loss": 5.38469, "time": 0.69955} +{"mode": "train", "epoch": 1, "iter": 3300, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.07344, "top5_acc": 0.2225, "loss_cls": 5.38267, "loss": 5.38267, "time": 0.69981} +{"mode": "train", "epoch": 1, "iter": 3400, "lr": 0.09999, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.07562, "top5_acc": 0.22844, "loss_cls": 5.33197, "loss": 5.33197, "time": 0.69694} +{"mode": "train", "epoch": 1, "iter": 3500, "lr": 0.09999, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.08344, "top5_acc": 0.23516, "loss_cls": 5.29619, "loss": 5.29619, "time": 0.69786} +{"mode": "train", "epoch": 1, "iter": 3600, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.07672, "top5_acc": 0.23516, "loss_cls": 5.29412, "loss": 5.29412, "time": 0.70117} +{"mode": "train", "epoch": 1, "iter": 3700, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.08344, "top5_acc": 0.24391, "loss_cls": 5.24098, "loss": 5.24098, "time": 0.70375} +{"mode": "val", "epoch": 1, "iter": 309, "lr": 0.09999, "top1_acc": 0.06316, "top5_acc": 0.19237, "mean_class_accuracy": 0.06323} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.09999, "memory": 15990, "data_time": 1.30078, "top1_acc": 0.08859, "top5_acc": 0.25906, "loss_cls": 5.23918, "loss": 5.23918, "time": 2.00058} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.09781, "top5_acc": 0.26641, "loss_cls": 5.15727, "loss": 5.15727, "time": 0.69928} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.09999, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.09125, "top5_acc": 0.26344, "loss_cls": 5.19008, "loss": 5.19008, "time": 0.70011} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.09016, "top5_acc": 0.26031, "loss_cls": 5.20427, "loss": 5.20427, "time": 0.70121} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.09281, "top5_acc": 0.26438, "loss_cls": 5.15217, "loss": 5.15217, "time": 0.69781} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.10891, "top5_acc": 0.29156, "loss_cls": 5.06091, "loss": 5.06091, "time": 0.70085} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.10734, "top5_acc": 0.28609, "loss_cls": 5.07977, "loss": 5.07977, "time": 0.70134} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.10953, "top5_acc": 0.29484, "loss_cls": 5.08601, "loss": 5.08601, "time": 0.69952} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.09998, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.10266, "top5_acc": 0.28562, "loss_cls": 5.09252, "loss": 5.09252, "time": 0.69844} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.09998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.10891, "top5_acc": 0.28688, "loss_cls": 5.04894, "loss": 5.04894, "time": 0.70418} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.10141, "top5_acc": 0.2775, "loss_cls": 5.11942, "loss": 5.11942, "time": 0.70221} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.10578, "top5_acc": 0.295, "loss_cls": 5.04324, "loss": 5.04324, "time": 0.70159} +{"mode": "train", "epoch": 2, "iter": 1300, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.11828, "top5_acc": 0.30391, "loss_cls": 5.01517, "loss": 5.01517, "time": 0.70233} +{"mode": "train", "epoch": 2, "iter": 1400, "lr": 0.09998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.11469, "top5_acc": 0.31047, "loss_cls": 5.0124, "loss": 5.0124, "time": 0.7024} +{"mode": "train", "epoch": 2, "iter": 1500, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.10797, "top5_acc": 0.29266, "loss_cls": 5.06045, "loss": 5.06045, "time": 0.69939} +{"mode": "train", "epoch": 2, "iter": 1600, "lr": 0.09998, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.11016, "top5_acc": 0.29344, "loss_cls": 5.03349, "loss": 5.03349, "time": 0.69901} +{"mode": "train", "epoch": 2, "iter": 1700, "lr": 0.09998, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.11594, "top5_acc": 0.30562, "loss_cls": 4.98273, "loss": 4.98273, "time": 0.69776} +{"mode": "train", "epoch": 2, "iter": 1800, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.11594, "top5_acc": 0.30219, "loss_cls": 4.99847, "loss": 4.99847, "time": 0.69928} +{"mode": "train", "epoch": 2, "iter": 1900, "lr": 0.09998, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.11984, "top5_acc": 0.31719, "loss_cls": 4.96757, "loss": 4.96757, "time": 0.70635} +{"mode": "train", "epoch": 2, "iter": 2000, "lr": 0.09997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.12203, "top5_acc": 0.31906, "loss_cls": 4.98129, "loss": 4.98129, "time": 0.69688} +{"mode": "train", "epoch": 2, "iter": 2100, "lr": 0.09997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.12422, "top5_acc": 0.32016, "loss_cls": 4.94613, "loss": 4.94613, "time": 0.69906} +{"mode": "train", "epoch": 2, "iter": 2200, "lr": 0.09997, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.11766, "top5_acc": 0.31531, "loss_cls": 4.9883, "loss": 4.9883, "time": 0.70229} +{"mode": "train", "epoch": 2, "iter": 2300, "lr": 0.09997, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.11875, "top5_acc": 0.31266, "loss_cls": 4.98757, "loss": 4.98757, "time": 0.70049} +{"mode": "train", "epoch": 2, "iter": 2400, "lr": 0.09997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.12562, "top5_acc": 0.31891, "loss_cls": 4.94079, "loss": 4.94079, "time": 0.69898} +{"mode": "train", "epoch": 2, "iter": 2500, "lr": 0.09997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.12937, "top5_acc": 0.31672, "loss_cls": 4.94039, "loss": 4.94039, "time": 0.69881} +{"mode": "train", "epoch": 2, "iter": 2600, "lr": 0.09997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.12391, "top5_acc": 0.32203, "loss_cls": 4.94597, "loss": 4.94597, "time": 0.70073} +{"mode": "train", "epoch": 2, "iter": 2700, "lr": 0.09997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.13266, "top5_acc": 0.33, "loss_cls": 4.89418, "loss": 4.89418, "time": 0.69705} +{"mode": "train", "epoch": 2, "iter": 2800, "lr": 0.09997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.12844, "top5_acc": 0.33219, "loss_cls": 4.88973, "loss": 4.88973, "time": 0.6966} +{"mode": "train", "epoch": 2, "iter": 2900, "lr": 0.09997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.12547, "top5_acc": 0.32297, "loss_cls": 4.94321, "loss": 4.94321, "time": 0.69628} +{"mode": "train", "epoch": 2, "iter": 3000, "lr": 0.09996, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.13453, "top5_acc": 0.34516, "loss_cls": 4.88252, "loss": 4.88252, "time": 0.69933} +{"mode": "train", "epoch": 2, "iter": 3100, "lr": 0.09996, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.13109, "top5_acc": 0.33062, "loss_cls": 4.88633, "loss": 4.88633, "time": 0.69648} +{"mode": "train", "epoch": 2, "iter": 3200, "lr": 0.09996, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.13391, "top5_acc": 0.34469, "loss_cls": 4.85395, "loss": 4.85395, "time": 0.70269} +{"mode": "train", "epoch": 2, "iter": 3300, "lr": 0.09996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.14469, "top5_acc": 0.34234, "loss_cls": 4.84307, "loss": 4.84307, "time": 0.70113} +{"mode": "train", "epoch": 2, "iter": 3400, "lr": 0.09996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.14109, "top5_acc": 0.34453, "loss_cls": 4.83505, "loss": 4.83505, "time": 0.70082} +{"mode": "train", "epoch": 2, "iter": 3500, "lr": 0.09996, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.13922, "top5_acc": 0.34391, "loss_cls": 4.86952, "loss": 4.86952, "time": 0.70126} +{"mode": "train", "epoch": 2, "iter": 3600, "lr": 0.09996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.13719, "top5_acc": 0.33047, "loss_cls": 4.90002, "loss": 4.90002, "time": 0.70119} +{"mode": "train", "epoch": 2, "iter": 3700, "lr": 0.09996, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.13875, "top5_acc": 0.34531, "loss_cls": 4.84397, "loss": 4.84397, "time": 0.70892} +{"mode": "val", "epoch": 2, "iter": 309, "lr": 0.09996, "top1_acc": 0.0658, "top5_acc": 0.21248, "mean_class_accuracy": 0.06565} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.09995, "memory": 15990, "data_time": 1.32628, "top1_acc": 0.13828, "top5_acc": 0.34281, "loss_cls": 4.82321, "loss": 4.82321, "time": 2.03208} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.09995, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.13688, "top5_acc": 0.34797, "loss_cls": 4.82207, "loss": 4.82207, "time": 0.70306} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.09995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.13328, "top5_acc": 0.35141, "loss_cls": 4.82708, "loss": 4.82708, "time": 0.69977} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.09995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.13875, "top5_acc": 0.34844, "loss_cls": 4.82578, "loss": 4.82578, "time": 0.70018} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.09995, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.14141, "top5_acc": 0.35547, "loss_cls": 4.79206, "loss": 4.79206, "time": 0.70003} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.09995, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.13734, "top5_acc": 0.35109, "loss_cls": 4.84658, "loss": 4.84658, "time": 0.69978} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.09995, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.14969, "top5_acc": 0.36172, "loss_cls": 4.77578, "loss": 4.77578, "time": 0.7021} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.09995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.14438, "top5_acc": 0.34594, "loss_cls": 4.81, "loss": 4.81, "time": 0.69812} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.09994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15234, "top5_acc": 0.36469, "loss_cls": 4.75988, "loss": 4.75988, "time": 0.70092} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.09994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.14391, "top5_acc": 0.35562, "loss_cls": 4.78952, "loss": 4.78952, "time": 0.7034} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.09994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15328, "top5_acc": 0.36203, "loss_cls": 4.78242, "loss": 4.78242, "time": 0.69756} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.09994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.1475, "top5_acc": 0.36078, "loss_cls": 4.75246, "loss": 4.75246, "time": 0.70016} +{"mode": "train", "epoch": 3, "iter": 1300, "lr": 0.09994, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.14484, "top5_acc": 0.36078, "loss_cls": 4.76381, "loss": 4.76381, "time": 0.70276} +{"mode": "train", "epoch": 3, "iter": 1400, "lr": 0.09994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15516, "top5_acc": 0.36062, "loss_cls": 4.77435, "loss": 4.77435, "time": 0.70071} +{"mode": "train", "epoch": 3, "iter": 1500, "lr": 0.09994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15453, "top5_acc": 0.36156, "loss_cls": 4.76546, "loss": 4.76546, "time": 0.70121} +{"mode": "train", "epoch": 3, "iter": 1600, "lr": 0.09994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.14797, "top5_acc": 0.35281, "loss_cls": 4.78556, "loss": 4.78556, "time": 0.69858} +{"mode": "train", "epoch": 3, "iter": 1700, "lr": 0.09993, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15281, "top5_acc": 0.36438, "loss_cls": 4.71799, "loss": 4.71799, "time": 0.70093} +{"mode": "train", "epoch": 3, "iter": 1800, "lr": 0.09993, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.14297, "top5_acc": 0.36109, "loss_cls": 4.7879, "loss": 4.7879, "time": 0.70176} +{"mode": "train", "epoch": 3, "iter": 1900, "lr": 0.09993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15656, "top5_acc": 0.37156, "loss_cls": 4.71595, "loss": 4.71595, "time": 0.70284} +{"mode": "train", "epoch": 3, "iter": 2000, "lr": 0.09993, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.14672, "top5_acc": 0.35984, "loss_cls": 4.76705, "loss": 4.76705, "time": 0.69605} +{"mode": "train", "epoch": 3, "iter": 2100, "lr": 0.09993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.14609, "top5_acc": 0.35875, "loss_cls": 4.77865, "loss": 4.77865, "time": 0.70061} +{"mode": "train", "epoch": 3, "iter": 2200, "lr": 0.09993, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.15438, "top5_acc": 0.38141, "loss_cls": 4.69062, "loss": 4.69062, "time": 0.69795} +{"mode": "train", "epoch": 3, "iter": 2300, "lr": 0.09993, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.15797, "top5_acc": 0.37391, "loss_cls": 4.71643, "loss": 4.71643, "time": 0.69986} +{"mode": "train", "epoch": 3, "iter": 2400, "lr": 0.09992, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.16359, "top5_acc": 0.36953, "loss_cls": 4.69047, "loss": 4.69047, "time": 0.6961} +{"mode": "train", "epoch": 3, "iter": 2500, "lr": 0.09992, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16156, "top5_acc": 0.38438, "loss_cls": 4.69504, "loss": 4.69504, "time": 0.69888} +{"mode": "train", "epoch": 3, "iter": 2600, "lr": 0.09992, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.15609, "top5_acc": 0.37609, "loss_cls": 4.74246, "loss": 4.74246, "time": 0.69918} +{"mode": "train", "epoch": 3, "iter": 2700, "lr": 0.09992, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.16578, "top5_acc": 0.38047, "loss_cls": 4.68709, "loss": 4.68709, "time": 0.70071} +{"mode": "train", "epoch": 3, "iter": 2800, "lr": 0.09992, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.15172, "top5_acc": 0.36734, "loss_cls": 4.73258, "loss": 4.73258, "time": 0.69699} +{"mode": "train", "epoch": 3, "iter": 2900, "lr": 0.09992, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15344, "top5_acc": 0.37406, "loss_cls": 4.752, "loss": 4.752, "time": 0.69942} +{"mode": "train", "epoch": 3, "iter": 3000, "lr": 0.09991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15687, "top5_acc": 0.37141, "loss_cls": 4.71633, "loss": 4.71633, "time": 0.69915} +{"mode": "train", "epoch": 3, "iter": 3100, "lr": 0.09991, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15906, "top5_acc": 0.37422, "loss_cls": 4.69965, "loss": 4.69965, "time": 0.70231} +{"mode": "train", "epoch": 3, "iter": 3200, "lr": 0.09991, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.15563, "top5_acc": 0.36625, "loss_cls": 4.71638, "loss": 4.71638, "time": 0.69643} +{"mode": "train", "epoch": 3, "iter": 3300, "lr": 0.09991, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.15391, "top5_acc": 0.36875, "loss_cls": 4.73835, "loss": 4.73835, "time": 0.70062} +{"mode": "train", "epoch": 3, "iter": 3400, "lr": 0.09991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16359, "top5_acc": 0.38469, "loss_cls": 4.66429, "loss": 4.66429, "time": 0.69738} +{"mode": "train", "epoch": 3, "iter": 3500, "lr": 0.09991, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.16422, "top5_acc": 0.37984, "loss_cls": 4.65241, "loss": 4.65241, "time": 0.70062} +{"mode": "train", "epoch": 3, "iter": 3600, "lr": 0.0999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16516, "top5_acc": 0.38016, "loss_cls": 4.67051, "loss": 4.67051, "time": 0.70008} +{"mode": "train", "epoch": 3, "iter": 3700, "lr": 0.0999, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.16047, "top5_acc": 0.37797, "loss_cls": 4.69755, "loss": 4.69755, "time": 0.70913} +{"mode": "val", "epoch": 3, "iter": 309, "lr": 0.0999, "top1_acc": 0.09188, "top5_acc": 0.24748, "mean_class_accuracy": 0.09186} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.0999, "memory": 15990, "data_time": 1.31527, "top1_acc": 0.16562, "top5_acc": 0.38031, "loss_cls": 4.67427, "loss": 4.67427, "time": 2.01962} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.0999, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.16625, "top5_acc": 0.38797, "loss_cls": 4.62883, "loss": 4.62883, "time": 0.69999} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.0999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16438, "top5_acc": 0.38703, "loss_cls": 4.6412, "loss": 4.6412, "time": 0.69896} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.09989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17359, "top5_acc": 0.38766, "loss_cls": 4.62696, "loss": 4.62696, "time": 0.70115} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.09989, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.16562, "top5_acc": 0.39328, "loss_cls": 4.62311, "loss": 4.62311, "time": 0.69947} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.09989, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17094, "top5_acc": 0.38031, "loss_cls": 4.69154, "loss": 4.69154, "time": 0.69827} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.09989, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17266, "top5_acc": 0.39031, "loss_cls": 4.62908, "loss": 4.62908, "time": 0.70033} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.09989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16719, "top5_acc": 0.39625, "loss_cls": 4.62483, "loss": 4.62483, "time": 0.69699} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.09988, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17797, "top5_acc": 0.39531, "loss_cls": 4.58716, "loss": 4.58716, "time": 0.69907} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.09988, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16547, "top5_acc": 0.39453, "loss_cls": 4.63497, "loss": 4.63497, "time": 0.70011} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.09988, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.1675, "top5_acc": 0.39047, "loss_cls": 4.61824, "loss": 4.61824, "time": 0.70055} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.09988, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16797, "top5_acc": 0.39172, "loss_cls": 4.63886, "loss": 4.63886, "time": 0.69905} +{"mode": "train", "epoch": 4, "iter": 1300, "lr": 0.09988, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.175, "top5_acc": 0.39453, "loss_cls": 4.606, "loss": 4.606, "time": 0.69919} +{"mode": "train", "epoch": 4, "iter": 1400, "lr": 0.09988, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.16547, "top5_acc": 0.38688, "loss_cls": 4.65442, "loss": 4.65442, "time": 0.70016} +{"mode": "train", "epoch": 4, "iter": 1500, "lr": 0.09987, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16938, "top5_acc": 0.39688, "loss_cls": 4.6103, "loss": 4.6103, "time": 0.6991} +{"mode": "train", "epoch": 4, "iter": 1600, "lr": 0.09987, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.1725, "top5_acc": 0.39547, "loss_cls": 4.62416, "loss": 4.62416, "time": 0.69916} +{"mode": "train", "epoch": 4, "iter": 1700, "lr": 0.09987, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17172, "top5_acc": 0.39688, "loss_cls": 4.5988, "loss": 4.5988, "time": 0.70093} +{"mode": "train", "epoch": 4, "iter": 1800, "lr": 0.09987, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.16531, "top5_acc": 0.385, "loss_cls": 4.64223, "loss": 4.64223, "time": 0.69936} +{"mode": "train", "epoch": 4, "iter": 1900, "lr": 0.09987, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.17078, "top5_acc": 0.39359, "loss_cls": 4.63435, "loss": 4.63435, "time": 0.70166} +{"mode": "train", "epoch": 4, "iter": 2000, "lr": 0.09986, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.17156, "top5_acc": 0.39547, "loss_cls": 4.61354, "loss": 4.61354, "time": 0.70031} +{"mode": "train", "epoch": 4, "iter": 2100, "lr": 0.09986, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.16875, "top5_acc": 0.39031, "loss_cls": 4.60147, "loss": 4.60147, "time": 0.70127} +{"mode": "train", "epoch": 4, "iter": 2200, "lr": 0.09986, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.16766, "top5_acc": 0.37766, "loss_cls": 4.65354, "loss": 4.65354, "time": 0.70215} +{"mode": "train", "epoch": 4, "iter": 2300, "lr": 0.09986, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17359, "top5_acc": 0.39375, "loss_cls": 4.61328, "loss": 4.61328, "time": 0.70076} +{"mode": "train", "epoch": 4, "iter": 2400, "lr": 0.09985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16297, "top5_acc": 0.38234, "loss_cls": 4.65412, "loss": 4.65412, "time": 0.69891} +{"mode": "train", "epoch": 4, "iter": 2500, "lr": 0.09985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16594, "top5_acc": 0.38719, "loss_cls": 4.65572, "loss": 4.65572, "time": 0.70554} +{"mode": "train", "epoch": 4, "iter": 2600, "lr": 0.09985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17844, "top5_acc": 0.40203, "loss_cls": 4.61206, "loss": 4.61206, "time": 0.69754} +{"mode": "train", "epoch": 4, "iter": 2700, "lr": 0.09985, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.17109, "top5_acc": 0.39609, "loss_cls": 4.58401, "loss": 4.58401, "time": 0.69958} +{"mode": "train", "epoch": 4, "iter": 2800, "lr": 0.09985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16469, "top5_acc": 0.38797, "loss_cls": 4.65125, "loss": 4.65125, "time": 0.69866} +{"mode": "train", "epoch": 4, "iter": 2900, "lr": 0.09984, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17234, "top5_acc": 0.39453, "loss_cls": 4.60578, "loss": 4.60578, "time": 0.70098} +{"mode": "train", "epoch": 4, "iter": 3000, "lr": 0.09984, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17078, "top5_acc": 0.39734, "loss_cls": 4.61854, "loss": 4.61854, "time": 0.7017} +{"mode": "train", "epoch": 4, "iter": 3100, "lr": 0.09984, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18672, "top5_acc": 0.40891, "loss_cls": 4.57995, "loss": 4.57995, "time": 0.70001} +{"mode": "train", "epoch": 4, "iter": 3200, "lr": 0.09984, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.16844, "top5_acc": 0.39141, "loss_cls": 4.63688, "loss": 4.63688, "time": 0.70493} +{"mode": "train", "epoch": 4, "iter": 3300, "lr": 0.09983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17172, "top5_acc": 0.38734, "loss_cls": 4.62856, "loss": 4.62856, "time": 0.69863} +{"mode": "train", "epoch": 4, "iter": 3400, "lr": 0.09983, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18203, "top5_acc": 0.39938, "loss_cls": 4.58333, "loss": 4.58333, "time": 0.69845} +{"mode": "train", "epoch": 4, "iter": 3500, "lr": 0.09983, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17891, "top5_acc": 0.39312, "loss_cls": 4.62074, "loss": 4.62074, "time": 0.70107} +{"mode": "train", "epoch": 4, "iter": 3600, "lr": 0.09983, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17375, "top5_acc": 0.39047, "loss_cls": 4.61402, "loss": 4.61402, "time": 0.70079} +{"mode": "train", "epoch": 4, "iter": 3700, "lr": 0.09983, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.17781, "top5_acc": 0.40219, "loss_cls": 4.57485, "loss": 4.57485, "time": 0.71056} +{"mode": "val", "epoch": 4, "iter": 309, "lr": 0.09982, "top1_acc": 0.11042, "top5_acc": 0.28592, "mean_class_accuracy": 0.11039} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.09982, "memory": 15990, "data_time": 1.32102, "top1_acc": 0.18297, "top5_acc": 0.41406, "loss_cls": 4.51092, "loss": 4.51092, "time": 2.02383} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.09982, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18312, "top5_acc": 0.40219, "loss_cls": 4.5615, "loss": 4.5615, "time": 0.70236} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.09982, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17875, "top5_acc": 0.39984, "loss_cls": 4.56807, "loss": 4.56807, "time": 0.70162} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.09982, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17516, "top5_acc": 0.39766, "loss_cls": 4.58834, "loss": 4.58834, "time": 0.69928} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.09981, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17516, "top5_acc": 0.39953, "loss_cls": 4.5977, "loss": 4.5977, "time": 0.70116} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.09981, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17312, "top5_acc": 0.40141, "loss_cls": 4.57822, "loss": 4.57822, "time": 0.69461} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.09981, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.17953, "top5_acc": 0.39844, "loss_cls": 4.58764, "loss": 4.58764, "time": 0.69771} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.09981, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.18422, "top5_acc": 0.41234, "loss_cls": 4.52705, "loss": 4.52705, "time": 0.69899} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.0998, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.17453, "top5_acc": 0.39891, "loss_cls": 4.58124, "loss": 4.58124, "time": 0.70032} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.0998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18203, "top5_acc": 0.40984, "loss_cls": 4.5741, "loss": 4.5741, "time": 0.69922} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.0998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18078, "top5_acc": 0.41422, "loss_cls": 4.57083, "loss": 4.57083, "time": 0.69815} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.0998, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.18375, "top5_acc": 0.40625, "loss_cls": 4.56859, "loss": 4.56859, "time": 0.69845} +{"mode": "train", "epoch": 5, "iter": 1300, "lr": 0.09979, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19188, "top5_acc": 0.41328, "loss_cls": 4.5393, "loss": 4.5393, "time": 0.69666} +{"mode": "train", "epoch": 5, "iter": 1400, "lr": 0.09979, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18719, "top5_acc": 0.41688, "loss_cls": 4.49789, "loss": 4.49789, "time": 0.69992} +{"mode": "train", "epoch": 5, "iter": 1500, "lr": 0.09979, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.18125, "top5_acc": 0.40656, "loss_cls": 4.55803, "loss": 4.55803, "time": 0.70055} +{"mode": "train", "epoch": 5, "iter": 1600, "lr": 0.09979, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.17828, "top5_acc": 0.40969, "loss_cls": 4.55687, "loss": 4.55687, "time": 0.69895} +{"mode": "train", "epoch": 5, "iter": 1700, "lr": 0.09978, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18, "top5_acc": 0.41641, "loss_cls": 4.53947, "loss": 4.53947, "time": 0.69967} +{"mode": "train", "epoch": 5, "iter": 1800, "lr": 0.09978, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17906, "top5_acc": 0.42172, "loss_cls": 4.5464, "loss": 4.5464, "time": 0.69596} +{"mode": "train", "epoch": 5, "iter": 1900, "lr": 0.09978, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.17109, "top5_acc": 0.39734, "loss_cls": 4.57898, "loss": 4.57898, "time": 0.70331} +{"mode": "train", "epoch": 5, "iter": 2000, "lr": 0.09977, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18906, "top5_acc": 0.40859, "loss_cls": 4.57125, "loss": 4.57125, "time": 0.70273} +{"mode": "train", "epoch": 5, "iter": 2100, "lr": 0.09977, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17641, "top5_acc": 0.39656, "loss_cls": 4.58211, "loss": 4.58211, "time": 0.69848} +{"mode": "train", "epoch": 5, "iter": 2200, "lr": 0.09977, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18797, "top5_acc": 0.415, "loss_cls": 4.53251, "loss": 4.53251, "time": 0.70193} +{"mode": "train", "epoch": 5, "iter": 2300, "lr": 0.09977, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18703, "top5_acc": 0.41906, "loss_cls": 4.49174, "loss": 4.49174, "time": 0.70318} +{"mode": "train", "epoch": 5, "iter": 2400, "lr": 0.09976, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18422, "top5_acc": 0.41453, "loss_cls": 4.54676, "loss": 4.54676, "time": 0.69991} +{"mode": "train", "epoch": 5, "iter": 2500, "lr": 0.09976, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18141, "top5_acc": 0.41125, "loss_cls": 4.52982, "loss": 4.52982, "time": 0.69825} +{"mode": "train", "epoch": 5, "iter": 2600, "lr": 0.09976, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19219, "top5_acc": 0.42391, "loss_cls": 4.48066, "loss": 4.48066, "time": 0.69795} +{"mode": "train", "epoch": 5, "iter": 2700, "lr": 0.09976, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.18625, "top5_acc": 0.40703, "loss_cls": 4.55874, "loss": 4.55874, "time": 0.6981} +{"mode": "train", "epoch": 5, "iter": 2800, "lr": 0.09975, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18781, "top5_acc": 0.41375, "loss_cls": 4.54111, "loss": 4.54111, "time": 0.69903} +{"mode": "train", "epoch": 5, "iter": 2900, "lr": 0.09975, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19047, "top5_acc": 0.40875, "loss_cls": 4.52672, "loss": 4.52672, "time": 0.70024} +{"mode": "train", "epoch": 5, "iter": 3000, "lr": 0.09975, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17672, "top5_acc": 0.41125, "loss_cls": 4.53271, "loss": 4.53271, "time": 0.70213} +{"mode": "train", "epoch": 5, "iter": 3100, "lr": 0.09974, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.18188, "top5_acc": 0.41062, "loss_cls": 4.54375, "loss": 4.54375, "time": 0.6971} +{"mode": "train", "epoch": 5, "iter": 3200, "lr": 0.09974, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.18188, "top5_acc": 0.40719, "loss_cls": 4.54354, "loss": 4.54354, "time": 0.69834} +{"mode": "train", "epoch": 5, "iter": 3300, "lr": 0.09974, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19, "top5_acc": 0.40797, "loss_cls": 4.53741, "loss": 4.53741, "time": 0.69806} +{"mode": "train", "epoch": 5, "iter": 3400, "lr": 0.09974, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.17594, "top5_acc": 0.40141, "loss_cls": 4.58508, "loss": 4.58508, "time": 0.70102} +{"mode": "train", "epoch": 5, "iter": 3500, "lr": 0.09973, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.1925, "top5_acc": 0.42625, "loss_cls": 4.50122, "loss": 4.50122, "time": 0.7017} +{"mode": "train", "epoch": 5, "iter": 3600, "lr": 0.09973, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.17969, "top5_acc": 0.41078, "loss_cls": 4.55665, "loss": 4.55665, "time": 0.69691} +{"mode": "train", "epoch": 5, "iter": 3700, "lr": 0.09973, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.18734, "top5_acc": 0.41422, "loss_cls": 4.53678, "loss": 4.53678, "time": 0.70491} +{"mode": "val", "epoch": 5, "iter": 309, "lr": 0.09973, "top1_acc": 0.11477, "top5_acc": 0.29529, "mean_class_accuracy": 0.11459} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.09972, "memory": 15990, "data_time": 1.27243, "top1_acc": 0.19625, "top5_acc": 0.43219, "loss_cls": 4.44492, "loss": 4.44492, "time": 1.97497} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.09972, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19688, "top5_acc": 0.42266, "loss_cls": 4.48356, "loss": 4.48356, "time": 0.70029} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.09972, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.18781, "top5_acc": 0.41719, "loss_cls": 4.49692, "loss": 4.49692, "time": 0.69656} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.09971, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18953, "top5_acc": 0.42047, "loss_cls": 4.49053, "loss": 4.49053, "time": 0.70274} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.09971, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19672, "top5_acc": 0.42578, "loss_cls": 4.4955, "loss": 4.4955, "time": 0.70036} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.09971, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19203, "top5_acc": 0.42656, "loss_cls": 4.46727, "loss": 4.46727, "time": 0.69786} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.09971, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19156, "top5_acc": 0.42688, "loss_cls": 4.49607, "loss": 4.49607, "time": 0.69893} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.0997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19031, "top5_acc": 0.41781, "loss_cls": 4.49501, "loss": 4.49501, "time": 0.70037} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.0997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19266, "top5_acc": 0.41672, "loss_cls": 4.49887, "loss": 4.49887, "time": 0.69743} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.0997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19672, "top5_acc": 0.41781, "loss_cls": 4.48801, "loss": 4.48801, "time": 0.70085} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.09969, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18828, "top5_acc": 0.41812, "loss_cls": 4.49716, "loss": 4.49716, "time": 0.69714} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.09969, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.18641, "top5_acc": 0.41969, "loss_cls": 4.51515, "loss": 4.51515, "time": 0.69769} +{"mode": "train", "epoch": 6, "iter": 1300, "lr": 0.09969, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19234, "top5_acc": 0.42203, "loss_cls": 4.48643, "loss": 4.48643, "time": 0.69675} +{"mode": "train", "epoch": 6, "iter": 1400, "lr": 0.09968, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19016, "top5_acc": 0.42594, "loss_cls": 4.46835, "loss": 4.46835, "time": 0.69675} +{"mode": "train", "epoch": 6, "iter": 1500, "lr": 0.09968, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.18594, "top5_acc": 0.41406, "loss_cls": 4.53596, "loss": 4.53596, "time": 0.69524} +{"mode": "train", "epoch": 6, "iter": 1600, "lr": 0.09968, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.1875, "top5_acc": 0.41734, "loss_cls": 4.53675, "loss": 4.53675, "time": 0.69983} +{"mode": "train", "epoch": 6, "iter": 1700, "lr": 0.09967, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18406, "top5_acc": 0.40891, "loss_cls": 4.54555, "loss": 4.54555, "time": 0.69857} +{"mode": "train", "epoch": 6, "iter": 1800, "lr": 0.09967, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18359, "top5_acc": 0.41062, "loss_cls": 4.5514, "loss": 4.5514, "time": 0.69743} +{"mode": "train", "epoch": 6, "iter": 1900, "lr": 0.09967, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.18469, "top5_acc": 0.41172, "loss_cls": 4.51345, "loss": 4.51345, "time": 0.70507} +{"mode": "train", "epoch": 6, "iter": 2000, "lr": 0.09966, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17906, "top5_acc": 0.40188, "loss_cls": 4.55182, "loss": 4.55182, "time": 0.69654} +{"mode": "train", "epoch": 6, "iter": 2100, "lr": 0.09966, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19078, "top5_acc": 0.41688, "loss_cls": 4.49669, "loss": 4.49669, "time": 0.70159} +{"mode": "train", "epoch": 6, "iter": 2200, "lr": 0.09966, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19031, "top5_acc": 0.43016, "loss_cls": 4.48773, "loss": 4.48773, "time": 0.70472} +{"mode": "train", "epoch": 6, "iter": 2300, "lr": 0.09965, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18781, "top5_acc": 0.41156, "loss_cls": 4.52289, "loss": 4.52289, "time": 0.69867} +{"mode": "train", "epoch": 6, "iter": 2400, "lr": 0.09965, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.1825, "top5_acc": 0.4075, "loss_cls": 4.56135, "loss": 4.56135, "time": 0.69897} +{"mode": "train", "epoch": 6, "iter": 2500, "lr": 0.09965, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18922, "top5_acc": 0.40875, "loss_cls": 4.52524, "loss": 4.52524, "time": 0.69767} +{"mode": "train", "epoch": 6, "iter": 2600, "lr": 0.09964, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.1875, "top5_acc": 0.41859, "loss_cls": 4.48972, "loss": 4.48972, "time": 0.7003} +{"mode": "train", "epoch": 6, "iter": 2700, "lr": 0.09964, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19547, "top5_acc": 0.42203, "loss_cls": 4.50098, "loss": 4.50098, "time": 0.69641} +{"mode": "train", "epoch": 6, "iter": 2800, "lr": 0.09964, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18828, "top5_acc": 0.4125, "loss_cls": 4.51925, "loss": 4.51925, "time": 0.69836} +{"mode": "train", "epoch": 6, "iter": 2900, "lr": 0.09963, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19438, "top5_acc": 0.42266, "loss_cls": 4.49637, "loss": 4.49637, "time": 0.69886} +{"mode": "train", "epoch": 6, "iter": 3000, "lr": 0.09963, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.18031, "top5_acc": 0.40969, "loss_cls": 4.52968, "loss": 4.52968, "time": 0.69848} +{"mode": "train", "epoch": 6, "iter": 3100, "lr": 0.09963, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18906, "top5_acc": 0.41109, "loss_cls": 4.52607, "loss": 4.52607, "time": 0.70212} +{"mode": "train", "epoch": 6, "iter": 3200, "lr": 0.09962, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19281, "top5_acc": 0.42453, "loss_cls": 4.48214, "loss": 4.48214, "time": 0.70075} +{"mode": "train", "epoch": 6, "iter": 3300, "lr": 0.09962, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19953, "top5_acc": 0.43312, "loss_cls": 4.44279, "loss": 4.44279, "time": 0.70032} +{"mode": "train", "epoch": 6, "iter": 3400, "lr": 0.09962, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19062, "top5_acc": 0.41344, "loss_cls": 4.51933, "loss": 4.51933, "time": 0.7019} +{"mode": "train", "epoch": 6, "iter": 3500, "lr": 0.09961, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19172, "top5_acc": 0.42266, "loss_cls": 4.49469, "loss": 4.49469, "time": 0.69905} +{"mode": "train", "epoch": 6, "iter": 3600, "lr": 0.09961, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19109, "top5_acc": 0.41859, "loss_cls": 4.52992, "loss": 4.52992, "time": 0.69844} +{"mode": "train", "epoch": 6, "iter": 3700, "lr": 0.09961, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19391, "top5_acc": 0.42125, "loss_cls": 4.51915, "loss": 4.51915, "time": 0.70439} +{"mode": "val", "epoch": 6, "iter": 309, "lr": 0.09961, "top1_acc": 0.13397, "top5_acc": 0.33004, "mean_class_accuracy": 0.13382} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0996, "memory": 15990, "data_time": 1.26643, "top1_acc": 0.20359, "top5_acc": 0.43266, "loss_cls": 4.42906, "loss": 4.42906, "time": 1.97145} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18859, "top5_acc": 0.41922, "loss_cls": 4.51045, "loss": 4.51045, "time": 0.70328} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.0996, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19656, "top5_acc": 0.42531, "loss_cls": 4.4844, "loss": 4.4844, "time": 0.69649} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.09959, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19938, "top5_acc": 0.41453, "loss_cls": 4.49768, "loss": 4.49768, "time": 0.69739} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.09959, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19578, "top5_acc": 0.41562, "loss_cls": 4.45956, "loss": 4.45956, "time": 0.69769} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.09958, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.195, "top5_acc": 0.42047, "loss_cls": 4.4752, "loss": 4.4752, "time": 0.6972} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.09958, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19203, "top5_acc": 0.42906, "loss_cls": 4.49334, "loss": 4.49334, "time": 0.69904} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.09958, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19531, "top5_acc": 0.43031, "loss_cls": 4.47458, "loss": 4.47458, "time": 0.69948} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.09957, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19625, "top5_acc": 0.425, "loss_cls": 4.46816, "loss": 4.46816, "time": 0.70067} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.09957, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19453, "top5_acc": 0.41484, "loss_cls": 4.49001, "loss": 4.49001, "time": 0.69794} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.09957, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.18859, "top5_acc": 0.41906, "loss_cls": 4.49269, "loss": 4.49269, "time": 0.69667} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.09956, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20438, "top5_acc": 0.43766, "loss_cls": 4.41974, "loss": 4.41974, "time": 0.69543} +{"mode": "train", "epoch": 7, "iter": 1300, "lr": 0.09956, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19234, "top5_acc": 0.41547, "loss_cls": 4.51687, "loss": 4.51687, "time": 0.69645} +{"mode": "train", "epoch": 7, "iter": 1400, "lr": 0.09956, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19469, "top5_acc": 0.42891, "loss_cls": 4.45647, "loss": 4.45647, "time": 0.70019} +{"mode": "train", "epoch": 7, "iter": 1500, "lr": 0.09955, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19938, "top5_acc": 0.43281, "loss_cls": 4.4453, "loss": 4.4453, "time": 0.69588} +{"mode": "train", "epoch": 7, "iter": 1600, "lr": 0.09955, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18781, "top5_acc": 0.42047, "loss_cls": 4.49138, "loss": 4.49138, "time": 0.70129} +{"mode": "train", "epoch": 7, "iter": 1700, "lr": 0.09954, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.18734, "top5_acc": 0.42078, "loss_cls": 4.4825, "loss": 4.4825, "time": 0.69663} +{"mode": "train", "epoch": 7, "iter": 1800, "lr": 0.09954, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19781, "top5_acc": 0.42688, "loss_cls": 4.47995, "loss": 4.47995, "time": 0.69669} +{"mode": "train", "epoch": 7, "iter": 1900, "lr": 0.09954, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19734, "top5_acc": 0.42328, "loss_cls": 4.46544, "loss": 4.46544, "time": 0.70207} +{"mode": "train", "epoch": 7, "iter": 2000, "lr": 0.09953, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19578, "top5_acc": 0.42156, "loss_cls": 4.46418, "loss": 4.46418, "time": 0.70094} +{"mode": "train", "epoch": 7, "iter": 2100, "lr": 0.09953, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19531, "top5_acc": 0.42984, "loss_cls": 4.48045, "loss": 4.48045, "time": 0.69857} +{"mode": "train", "epoch": 7, "iter": 2200, "lr": 0.09952, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19156, "top5_acc": 0.41766, "loss_cls": 4.50744, "loss": 4.50744, "time": 0.69884} +{"mode": "train", "epoch": 7, "iter": 2300, "lr": 0.09952, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19203, "top5_acc": 0.42781, "loss_cls": 4.46008, "loss": 4.46008, "time": 0.69783} +{"mode": "train", "epoch": 7, "iter": 2400, "lr": 0.09952, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19453, "top5_acc": 0.42062, "loss_cls": 4.49082, "loss": 4.49082, "time": 0.69626} +{"mode": "train", "epoch": 7, "iter": 2500, "lr": 0.09951, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19516, "top5_acc": 0.42375, "loss_cls": 4.49791, "loss": 4.49791, "time": 0.69777} +{"mode": "train", "epoch": 7, "iter": 2600, "lr": 0.09951, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20281, "top5_acc": 0.43203, "loss_cls": 4.42297, "loss": 4.42297, "time": 0.69672} +{"mode": "train", "epoch": 7, "iter": 2700, "lr": 0.09951, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19625, "top5_acc": 0.43031, "loss_cls": 4.47578, "loss": 4.47578, "time": 0.69627} +{"mode": "train", "epoch": 7, "iter": 2800, "lr": 0.0995, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19234, "top5_acc": 0.42672, "loss_cls": 4.46786, "loss": 4.46786, "time": 0.69907} +{"mode": "train", "epoch": 7, "iter": 2900, "lr": 0.0995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19234, "top5_acc": 0.42141, "loss_cls": 4.4632, "loss": 4.4632, "time": 0.69808} +{"mode": "train", "epoch": 7, "iter": 3000, "lr": 0.09949, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19688, "top5_acc": 0.43, "loss_cls": 4.481, "loss": 4.481, "time": 0.69865} +{"mode": "train", "epoch": 7, "iter": 3100, "lr": 0.09949, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19406, "top5_acc": 0.42266, "loss_cls": 4.49435, "loss": 4.49435, "time": 0.70077} +{"mode": "train", "epoch": 7, "iter": 3200, "lr": 0.09949, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19812, "top5_acc": 0.42953, "loss_cls": 4.45821, "loss": 4.45821, "time": 0.69869} +{"mode": "train", "epoch": 7, "iter": 3300, "lr": 0.09948, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19172, "top5_acc": 0.41922, "loss_cls": 4.50803, "loss": 4.50803, "time": 0.69563} +{"mode": "train", "epoch": 7, "iter": 3400, "lr": 0.09948, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19562, "top5_acc": 0.42609, "loss_cls": 4.48456, "loss": 4.48456, "time": 0.69548} +{"mode": "train", "epoch": 7, "iter": 3500, "lr": 0.09947, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19719, "top5_acc": 0.42812, "loss_cls": 4.45152, "loss": 4.45152, "time": 0.69944} +{"mode": "train", "epoch": 7, "iter": 3600, "lr": 0.09947, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.20125, "top5_acc": 0.43984, "loss_cls": 4.42486, "loss": 4.42486, "time": 0.6982} +{"mode": "train", "epoch": 7, "iter": 3700, "lr": 0.09947, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20234, "top5_acc": 0.42781, "loss_cls": 4.46094, "loss": 4.46094, "time": 0.70159} +{"mode": "val", "epoch": 7, "iter": 309, "lr": 0.09946, "top1_acc": 0.1398, "top5_acc": 0.33566, "mean_class_accuracy": 0.13963} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.09946, "memory": 15990, "data_time": 1.33975, "top1_acc": 0.20094, "top5_acc": 0.43531, "loss_cls": 4.42731, "loss": 4.42731, "time": 2.05946} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.09946, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20969, "top5_acc": 0.43422, "loss_cls": 4.42345, "loss": 4.42345, "time": 0.71452} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.09945, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19781, "top5_acc": 0.42734, "loss_cls": 4.44963, "loss": 4.44963, "time": 0.70965} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.09945, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18969, "top5_acc": 0.41797, "loss_cls": 4.47578, "loss": 4.47578, "time": 0.70823} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.09944, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19594, "top5_acc": 0.42938, "loss_cls": 4.46603, "loss": 4.46603, "time": 0.7026} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.09944, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19062, "top5_acc": 0.41844, "loss_cls": 4.5031, "loss": 4.5031, "time": 0.69969} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.09943, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20266, "top5_acc": 0.42812, "loss_cls": 4.44165, "loss": 4.44165, "time": 0.70214} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.09943, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19172, "top5_acc": 0.42562, "loss_cls": 4.49425, "loss": 4.49425, "time": 0.70259} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.09943, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20172, "top5_acc": 0.43, "loss_cls": 4.44605, "loss": 4.44605, "time": 0.70348} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.09942, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19891, "top5_acc": 0.42219, "loss_cls": 4.4471, "loss": 4.4471, "time": 0.7023} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.09942, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.19625, "top5_acc": 0.42469, "loss_cls": 4.4714, "loss": 4.4714, "time": 0.70135} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.09941, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19641, "top5_acc": 0.43125, "loss_cls": 4.43019, "loss": 4.43019, "time": 0.69793} +{"mode": "train", "epoch": 8, "iter": 1300, "lr": 0.09941, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18766, "top5_acc": 0.42453, "loss_cls": 4.46431, "loss": 4.46431, "time": 0.70136} +{"mode": "train", "epoch": 8, "iter": 1400, "lr": 0.0994, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20922, "top5_acc": 0.43156, "loss_cls": 4.40315, "loss": 4.40315, "time": 0.69808} +{"mode": "train", "epoch": 8, "iter": 1500, "lr": 0.0994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19797, "top5_acc": 0.43188, "loss_cls": 4.41571, "loss": 4.41571, "time": 0.70059} +{"mode": "train", "epoch": 8, "iter": 1600, "lr": 0.0994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20531, "top5_acc": 0.43844, "loss_cls": 4.40313, "loss": 4.40313, "time": 0.69779} +{"mode": "train", "epoch": 8, "iter": 1700, "lr": 0.09939, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19906, "top5_acc": 0.43188, "loss_cls": 4.45708, "loss": 4.45708, "time": 0.6988} +{"mode": "train", "epoch": 8, "iter": 1800, "lr": 0.09939, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19562, "top5_acc": 0.43641, "loss_cls": 4.43241, "loss": 4.43241, "time": 0.69736} +{"mode": "train", "epoch": 8, "iter": 1900, "lr": 0.09938, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.19766, "top5_acc": 0.43594, "loss_cls": 4.4492, "loss": 4.4492, "time": 0.70372} +{"mode": "train", "epoch": 8, "iter": 2000, "lr": 0.09938, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19156, "top5_acc": 0.42703, "loss_cls": 4.4592, "loss": 4.4592, "time": 0.69756} +{"mode": "train", "epoch": 8, "iter": 2100, "lr": 0.09937, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19938, "top5_acc": 0.42688, "loss_cls": 4.45801, "loss": 4.45801, "time": 0.70191} +{"mode": "train", "epoch": 8, "iter": 2200, "lr": 0.09937, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19781, "top5_acc": 0.42656, "loss_cls": 4.45747, "loss": 4.45747, "time": 0.70225} +{"mode": "train", "epoch": 8, "iter": 2300, "lr": 0.09937, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19469, "top5_acc": 0.42984, "loss_cls": 4.48201, "loss": 4.48201, "time": 0.70085} +{"mode": "train", "epoch": 8, "iter": 2400, "lr": 0.09936, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19875, "top5_acc": 0.43578, "loss_cls": 4.45079, "loss": 4.45079, "time": 0.69827} +{"mode": "train", "epoch": 8, "iter": 2500, "lr": 0.09936, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19172, "top5_acc": 0.41812, "loss_cls": 4.49756, "loss": 4.49756, "time": 0.69798} +{"mode": "train", "epoch": 8, "iter": 2600, "lr": 0.09935, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19797, "top5_acc": 0.43625, "loss_cls": 4.44873, "loss": 4.44873, "time": 0.69684} +{"mode": "train", "epoch": 8, "iter": 2700, "lr": 0.09935, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20266, "top5_acc": 0.42844, "loss_cls": 4.436, "loss": 4.436, "time": 0.69609} +{"mode": "train", "epoch": 8, "iter": 2800, "lr": 0.09934, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20953, "top5_acc": 0.44016, "loss_cls": 4.44676, "loss": 4.44676, "time": 0.69878} +{"mode": "train", "epoch": 8, "iter": 2900, "lr": 0.09934, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20328, "top5_acc": 0.42984, "loss_cls": 4.47011, "loss": 4.47011, "time": 0.69584} +{"mode": "train", "epoch": 8, "iter": 3000, "lr": 0.09933, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.19672, "top5_acc": 0.42031, "loss_cls": 4.44759, "loss": 4.44759, "time": 0.69885} +{"mode": "train", "epoch": 8, "iter": 3100, "lr": 0.09933, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19469, "top5_acc": 0.43266, "loss_cls": 4.42056, "loss": 4.42056, "time": 0.69871} +{"mode": "train", "epoch": 8, "iter": 3200, "lr": 0.09933, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19844, "top5_acc": 0.42391, "loss_cls": 4.47878, "loss": 4.47878, "time": 0.69714} +{"mode": "train", "epoch": 8, "iter": 3300, "lr": 0.09932, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20391, "top5_acc": 0.43219, "loss_cls": 4.4348, "loss": 4.4348, "time": 0.69818} +{"mode": "train", "epoch": 8, "iter": 3400, "lr": 0.09932, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19812, "top5_acc": 0.41906, "loss_cls": 4.46064, "loss": 4.46064, "time": 0.7013} +{"mode": "train", "epoch": 8, "iter": 3500, "lr": 0.09931, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20266, "top5_acc": 0.4375, "loss_cls": 4.41239, "loss": 4.41239, "time": 0.7028} +{"mode": "train", "epoch": 8, "iter": 3600, "lr": 0.09931, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20469, "top5_acc": 0.43359, "loss_cls": 4.43341, "loss": 4.43341, "time": 0.6978} +{"mode": "train", "epoch": 8, "iter": 3700, "lr": 0.0993, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20609, "top5_acc": 0.43375, "loss_cls": 4.42841, "loss": 4.42841, "time": 0.70655} +{"mode": "val", "epoch": 8, "iter": 309, "lr": 0.0993, "top1_acc": 0.12921, "top5_acc": 0.31231, "mean_class_accuracy": 0.12921} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.0993, "memory": 15990, "data_time": 1.37632, "top1_acc": 0.19875, "top5_acc": 0.43172, "loss_cls": 4.42697, "loss": 4.42697, "time": 2.09328} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.09929, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20422, "top5_acc": 0.43594, "loss_cls": 4.40569, "loss": 4.40569, "time": 0.71013} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.09929, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.19797, "top5_acc": 0.43516, "loss_cls": 4.45666, "loss": 4.45666, "time": 0.71162} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.09928, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20516, "top5_acc": 0.43312, "loss_cls": 4.39854, "loss": 4.39854, "time": 0.709} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.09928, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20031, "top5_acc": 0.44188, "loss_cls": 4.42182, "loss": 4.42182, "time": 0.70923} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.09927, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21188, "top5_acc": 0.44422, "loss_cls": 4.3853, "loss": 4.3853, "time": 0.70966} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.09927, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20594, "top5_acc": 0.44297, "loss_cls": 4.40436, "loss": 4.40436, "time": 0.71036} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.09926, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20031, "top5_acc": 0.42266, "loss_cls": 4.46606, "loss": 4.46606, "time": 0.70746} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.09926, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20422, "top5_acc": 0.42672, "loss_cls": 4.43048, "loss": 4.43048, "time": 0.70621} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.09925, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20344, "top5_acc": 0.43266, "loss_cls": 4.44508, "loss": 4.44508, "time": 0.71157} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.09925, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19531, "top5_acc": 0.42484, "loss_cls": 4.45369, "loss": 4.45369, "time": 0.70828} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.09924, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19469, "top5_acc": 0.42516, "loss_cls": 4.4946, "loss": 4.4946, "time": 0.70975} +{"mode": "train", "epoch": 9, "iter": 1300, "lr": 0.09924, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20547, "top5_acc": 0.43719, "loss_cls": 4.42643, "loss": 4.42643, "time": 0.71493} +{"mode": "train", "epoch": 9, "iter": 1400, "lr": 0.09923, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20578, "top5_acc": 0.42516, "loss_cls": 4.44923, "loss": 4.44923, "time": 0.71481} +{"mode": "train", "epoch": 9, "iter": 1500, "lr": 0.09923, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.19312, "top5_acc": 0.42625, "loss_cls": 4.44731, "loss": 4.44731, "time": 0.71468} +{"mode": "train", "epoch": 9, "iter": 1600, "lr": 0.09922, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19797, "top5_acc": 0.43953, "loss_cls": 4.4192, "loss": 4.4192, "time": 0.70604} +{"mode": "train", "epoch": 9, "iter": 1700, "lr": 0.09922, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.19375, "top5_acc": 0.43094, "loss_cls": 4.45233, "loss": 4.45233, "time": 0.71034} +{"mode": "train", "epoch": 9, "iter": 1800, "lr": 0.09921, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.19688, "top5_acc": 0.43125, "loss_cls": 4.47314, "loss": 4.47314, "time": 0.70469} +{"mode": "train", "epoch": 9, "iter": 1900, "lr": 0.09921, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.19625, "top5_acc": 0.42766, "loss_cls": 4.46067, "loss": 4.46067, "time": 0.71203} +{"mode": "train", "epoch": 9, "iter": 2000, "lr": 0.0992, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20578, "top5_acc": 0.44391, "loss_cls": 4.42614, "loss": 4.42614, "time": 0.70328} +{"mode": "train", "epoch": 9, "iter": 2100, "lr": 0.0992, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19703, "top5_acc": 0.43141, "loss_cls": 4.44651, "loss": 4.44651, "time": 0.70622} +{"mode": "train", "epoch": 9, "iter": 2200, "lr": 0.09919, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19734, "top5_acc": 0.43672, "loss_cls": 4.41963, "loss": 4.41963, "time": 0.71233} +{"mode": "train", "epoch": 9, "iter": 2300, "lr": 0.09919, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20031, "top5_acc": 0.42516, "loss_cls": 4.45273, "loss": 4.45273, "time": 0.71103} +{"mode": "train", "epoch": 9, "iter": 2400, "lr": 0.09918, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.19641, "top5_acc": 0.43391, "loss_cls": 4.43478, "loss": 4.43478, "time": 0.71066} +{"mode": "train", "epoch": 9, "iter": 2500, "lr": 0.09918, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19875, "top5_acc": 0.43344, "loss_cls": 4.45417, "loss": 4.45417, "time": 0.71199} +{"mode": "train", "epoch": 9, "iter": 2600, "lr": 0.09917, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2, "top5_acc": 0.42422, "loss_cls": 4.44614, "loss": 4.44614, "time": 0.71508} +{"mode": "train", "epoch": 9, "iter": 2700, "lr": 0.09917, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19922, "top5_acc": 0.43094, "loss_cls": 4.43686, "loss": 4.43686, "time": 0.70811} +{"mode": "train", "epoch": 9, "iter": 2800, "lr": 0.09916, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20391, "top5_acc": 0.43656, "loss_cls": 4.41339, "loss": 4.41339, "time": 0.71561} +{"mode": "train", "epoch": 9, "iter": 2900, "lr": 0.09916, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19531, "top5_acc": 0.42391, "loss_cls": 4.46819, "loss": 4.46819, "time": 0.71299} +{"mode": "train", "epoch": 9, "iter": 3000, "lr": 0.09915, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20031, "top5_acc": 0.4325, "loss_cls": 4.45043, "loss": 4.45043, "time": 0.71034} +{"mode": "train", "epoch": 9, "iter": 3100, "lr": 0.09915, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20719, "top5_acc": 0.43594, "loss_cls": 4.4215, "loss": 4.4215, "time": 0.70818} +{"mode": "train", "epoch": 9, "iter": 3200, "lr": 0.09914, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19672, "top5_acc": 0.43547, "loss_cls": 4.42667, "loss": 4.42667, "time": 0.70804} +{"mode": "train", "epoch": 9, "iter": 3300, "lr": 0.09914, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20469, "top5_acc": 0.43359, "loss_cls": 4.43077, "loss": 4.43077, "time": 0.7158} +{"mode": "train", "epoch": 9, "iter": 3400, "lr": 0.09913, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20359, "top5_acc": 0.44438, "loss_cls": 4.39103, "loss": 4.39103, "time": 0.7132} +{"mode": "train", "epoch": 9, "iter": 3500, "lr": 0.09913, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20594, "top5_acc": 0.44156, "loss_cls": 4.42017, "loss": 4.42017, "time": 0.71002} +{"mode": "train", "epoch": 9, "iter": 3600, "lr": 0.09912, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.20234, "top5_acc": 0.42688, "loss_cls": 4.42465, "loss": 4.42465, "time": 0.71459} +{"mode": "train", "epoch": 9, "iter": 3700, "lr": 0.09912, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20312, "top5_acc": 0.43969, "loss_cls": 4.40743, "loss": 4.40743, "time": 0.71613} +{"mode": "val", "epoch": 9, "iter": 309, "lr": 0.09911, "top1_acc": 0.11908, "top5_acc": 0.2994, "mean_class_accuracy": 0.11889} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.09911, "memory": 15990, "data_time": 1.24517, "top1_acc": 0.21109, "top5_acc": 0.44547, "loss_cls": 4.36381, "loss": 4.36381, "time": 1.95545} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.0991, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20688, "top5_acc": 0.45188, "loss_cls": 4.36848, "loss": 4.36848, "time": 0.71111} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.0991, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19828, "top5_acc": 0.44062, "loss_cls": 4.41365, "loss": 4.41365, "time": 0.70791} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.09909, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19797, "top5_acc": 0.43156, "loss_cls": 4.45734, "loss": 4.45734, "time": 0.70556} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.09909, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19906, "top5_acc": 0.43406, "loss_cls": 4.39204, "loss": 4.39204, "time": 0.6997} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.09908, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19953, "top5_acc": 0.43734, "loss_cls": 4.41124, "loss": 4.41124, "time": 0.70345} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.09908, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20391, "top5_acc": 0.42984, "loss_cls": 4.44392, "loss": 4.44392, "time": 0.69846} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.09907, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19984, "top5_acc": 0.43172, "loss_cls": 4.42536, "loss": 4.42536, "time": 0.70064} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.09907, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21406, "top5_acc": 0.44203, "loss_cls": 4.37148, "loss": 4.37148, "time": 0.70205} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.09906, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.1975, "top5_acc": 0.42422, "loss_cls": 4.44604, "loss": 4.44604, "time": 0.70197} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.09906, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20234, "top5_acc": 0.43484, "loss_cls": 4.42527, "loss": 4.42527, "time": 0.70366} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.09905, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20531, "top5_acc": 0.43438, "loss_cls": 4.43484, "loss": 4.43484, "time": 0.70151} +{"mode": "train", "epoch": 10, "iter": 1300, "lr": 0.09905, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20422, "top5_acc": 0.44344, "loss_cls": 4.40672, "loss": 4.40672, "time": 0.70002} +{"mode": "train", "epoch": 10, "iter": 1400, "lr": 0.09904, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20297, "top5_acc": 0.44516, "loss_cls": 4.39047, "loss": 4.39047, "time": 0.70014} +{"mode": "train", "epoch": 10, "iter": 1500, "lr": 0.09903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19875, "top5_acc": 0.43531, "loss_cls": 4.43376, "loss": 4.43376, "time": 0.69871} +{"mode": "train", "epoch": 10, "iter": 1600, "lr": 0.09903, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20562, "top5_acc": 0.43344, "loss_cls": 4.42151, "loss": 4.42151, "time": 0.69642} +{"mode": "train", "epoch": 10, "iter": 1700, "lr": 0.09902, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20188, "top5_acc": 0.43406, "loss_cls": 4.43274, "loss": 4.43274, "time": 0.70095} +{"mode": "train", "epoch": 10, "iter": 1800, "lr": 0.09902, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19594, "top5_acc": 0.42641, "loss_cls": 4.46163, "loss": 4.46163, "time": 0.7028} +{"mode": "train", "epoch": 10, "iter": 1900, "lr": 0.09901, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20469, "top5_acc": 0.43625, "loss_cls": 4.42163, "loss": 4.42163, "time": 0.69799} +{"mode": "train", "epoch": 10, "iter": 2000, "lr": 0.09901, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.19953, "top5_acc": 0.44266, "loss_cls": 4.41347, "loss": 4.41347, "time": 0.70105} +{"mode": "train", "epoch": 10, "iter": 2100, "lr": 0.099, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20406, "top5_acc": 0.44047, "loss_cls": 4.39039, "loss": 4.39039, "time": 0.70329} +{"mode": "train", "epoch": 10, "iter": 2200, "lr": 0.099, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20422, "top5_acc": 0.42703, "loss_cls": 4.4333, "loss": 4.4333, "time": 0.69783} +{"mode": "train", "epoch": 10, "iter": 2300, "lr": 0.09899, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20828, "top5_acc": 0.43688, "loss_cls": 4.41558, "loss": 4.41558, "time": 0.69765} +{"mode": "train", "epoch": 10, "iter": 2400, "lr": 0.09898, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19828, "top5_acc": 0.43531, "loss_cls": 4.43696, "loss": 4.43696, "time": 0.69738} +{"mode": "train", "epoch": 10, "iter": 2500, "lr": 0.09898, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21031, "top5_acc": 0.43547, "loss_cls": 4.39992, "loss": 4.39992, "time": 0.70023} +{"mode": "train", "epoch": 10, "iter": 2600, "lr": 0.09897, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21859, "top5_acc": 0.45, "loss_cls": 4.39735, "loss": 4.39735, "time": 0.70111} +{"mode": "train", "epoch": 10, "iter": 2700, "lr": 0.09897, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21156, "top5_acc": 0.44031, "loss_cls": 4.3783, "loss": 4.3783, "time": 0.70071} +{"mode": "train", "epoch": 10, "iter": 2800, "lr": 0.09896, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20719, "top5_acc": 0.43609, "loss_cls": 4.41906, "loss": 4.41906, "time": 0.69875} +{"mode": "train", "epoch": 10, "iter": 2900, "lr": 0.09896, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21016, "top5_acc": 0.44, "loss_cls": 4.39376, "loss": 4.39376, "time": 0.69729} +{"mode": "train", "epoch": 10, "iter": 3000, "lr": 0.09895, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19812, "top5_acc": 0.43625, "loss_cls": 4.41862, "loss": 4.41862, "time": 0.70025} +{"mode": "train", "epoch": 10, "iter": 3100, "lr": 0.09894, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20594, "top5_acc": 0.43047, "loss_cls": 4.44316, "loss": 4.44316, "time": 0.69694} +{"mode": "train", "epoch": 10, "iter": 3200, "lr": 0.09894, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20297, "top5_acc": 0.43281, "loss_cls": 4.45536, "loss": 4.45536, "time": 0.70073} +{"mode": "train", "epoch": 10, "iter": 3300, "lr": 0.09893, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20219, "top5_acc": 0.43406, "loss_cls": 4.42105, "loss": 4.42105, "time": 0.69964} +{"mode": "train", "epoch": 10, "iter": 3400, "lr": 0.09893, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20109, "top5_acc": 0.43891, "loss_cls": 4.40766, "loss": 4.40766, "time": 0.69758} +{"mode": "train", "epoch": 10, "iter": 3500, "lr": 0.09892, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19812, "top5_acc": 0.43781, "loss_cls": 4.43858, "loss": 4.43858, "time": 0.69708} +{"mode": "train", "epoch": 10, "iter": 3600, "lr": 0.09892, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2075, "top5_acc": 0.44469, "loss_cls": 4.40448, "loss": 4.40448, "time": 0.7023} +{"mode": "train", "epoch": 10, "iter": 3700, "lr": 0.09891, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19359, "top5_acc": 0.41906, "loss_cls": 4.484, "loss": 4.484, "time": 0.7113} +{"mode": "val", "epoch": 10, "iter": 309, "lr": 0.09891, "top1_acc": 0.15053, "top5_acc": 0.35957, "mean_class_accuracy": 0.15044} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.0989, "memory": 15990, "data_time": 1.23181, "top1_acc": 0.20234, "top5_acc": 0.43656, "loss_cls": 4.39905, "loss": 4.39905, "time": 1.94406} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.0989, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20969, "top5_acc": 0.43344, "loss_cls": 4.42284, "loss": 4.42284, "time": 0.70348} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.09889, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20891, "top5_acc": 0.44109, "loss_cls": 4.40414, "loss": 4.40414, "time": 0.70855} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.09888, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21125, "top5_acc": 0.44703, "loss_cls": 4.37026, "loss": 4.37026, "time": 0.7046} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.09888, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20094, "top5_acc": 0.43922, "loss_cls": 4.39392, "loss": 4.39392, "time": 0.70328} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.09887, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20656, "top5_acc": 0.4375, "loss_cls": 4.39951, "loss": 4.39951, "time": 0.70037} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.09887, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20344, "top5_acc": 0.44562, "loss_cls": 4.41477, "loss": 4.41477, "time": 0.70382} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.09886, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20906, "top5_acc": 0.44516, "loss_cls": 4.37128, "loss": 4.37128, "time": 0.70418} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.09885, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20828, "top5_acc": 0.4525, "loss_cls": 4.37819, "loss": 4.37819, "time": 0.70181} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.09885, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20844, "top5_acc": 0.44422, "loss_cls": 4.39466, "loss": 4.39466, "time": 0.70009} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.09884, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.18781, "top5_acc": 0.42812, "loss_cls": 4.45091, "loss": 4.45091, "time": 0.70125} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.09884, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21281, "top5_acc": 0.44516, "loss_cls": 4.37733, "loss": 4.37733, "time": 0.70146} +{"mode": "train", "epoch": 11, "iter": 1300, "lr": 0.09883, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19359, "top5_acc": 0.43078, "loss_cls": 4.46202, "loss": 4.46202, "time": 0.70159} +{"mode": "train", "epoch": 11, "iter": 1400, "lr": 0.09882, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20422, "top5_acc": 0.42625, "loss_cls": 4.47533, "loss": 4.47533, "time": 0.70153} +{"mode": "train", "epoch": 11, "iter": 1500, "lr": 0.09882, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20609, "top5_acc": 0.44844, "loss_cls": 4.38989, "loss": 4.38989, "time": 0.70081} +{"mode": "train", "epoch": 11, "iter": 1600, "lr": 0.09881, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20609, "top5_acc": 0.44047, "loss_cls": 4.406, "loss": 4.406, "time": 0.70273} +{"mode": "train", "epoch": 11, "iter": 1700, "lr": 0.09881, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21188, "top5_acc": 0.44703, "loss_cls": 4.39969, "loss": 4.39969, "time": 0.69877} +{"mode": "train", "epoch": 11, "iter": 1800, "lr": 0.0988, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20078, "top5_acc": 0.43219, "loss_cls": 4.42047, "loss": 4.42047, "time": 0.69972} +{"mode": "train", "epoch": 11, "iter": 1900, "lr": 0.09879, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.19547, "top5_acc": 0.43031, "loss_cls": 4.442, "loss": 4.442, "time": 0.7039} +{"mode": "train", "epoch": 11, "iter": 2000, "lr": 0.09879, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20375, "top5_acc": 0.43781, "loss_cls": 4.41566, "loss": 4.41566, "time": 0.7007} +{"mode": "train", "epoch": 11, "iter": 2100, "lr": 0.09878, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20719, "top5_acc": 0.44375, "loss_cls": 4.38242, "loss": 4.38242, "time": 0.70106} +{"mode": "train", "epoch": 11, "iter": 2200, "lr": 0.09878, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20984, "top5_acc": 0.45297, "loss_cls": 4.35118, "loss": 4.35118, "time": 0.69973} +{"mode": "train", "epoch": 11, "iter": 2300, "lr": 0.09877, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19391, "top5_acc": 0.42344, "loss_cls": 4.49159, "loss": 4.49159, "time": 0.69655} +{"mode": "train", "epoch": 11, "iter": 2400, "lr": 0.09876, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20688, "top5_acc": 0.44078, "loss_cls": 4.41815, "loss": 4.41815, "time": 0.69894} +{"mode": "train", "epoch": 11, "iter": 2500, "lr": 0.09876, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20156, "top5_acc": 0.42328, "loss_cls": 4.47798, "loss": 4.47798, "time": 0.69618} +{"mode": "train", "epoch": 11, "iter": 2600, "lr": 0.09875, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21516, "top5_acc": 0.44938, "loss_cls": 4.35592, "loss": 4.35592, "time": 0.69906} +{"mode": "train", "epoch": 11, "iter": 2700, "lr": 0.09874, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20859, "top5_acc": 0.44234, "loss_cls": 4.38117, "loss": 4.38117, "time": 0.69971} +{"mode": "train", "epoch": 11, "iter": 2800, "lr": 0.09874, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20844, "top5_acc": 0.43203, "loss_cls": 4.43123, "loss": 4.43123, "time": 0.69936} +{"mode": "train", "epoch": 11, "iter": 2900, "lr": 0.09873, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20453, "top5_acc": 0.43875, "loss_cls": 4.40962, "loss": 4.40962, "time": 0.69985} +{"mode": "train", "epoch": 11, "iter": 3000, "lr": 0.09873, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21219, "top5_acc": 0.45125, "loss_cls": 4.3824, "loss": 4.3824, "time": 0.69985} +{"mode": "train", "epoch": 11, "iter": 3100, "lr": 0.09872, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19844, "top5_acc": 0.42312, "loss_cls": 4.47174, "loss": 4.47174, "time": 0.6957} +{"mode": "train", "epoch": 11, "iter": 3200, "lr": 0.09871, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20156, "top5_acc": 0.44234, "loss_cls": 4.40171, "loss": 4.40171, "time": 0.69865} +{"mode": "train", "epoch": 11, "iter": 3300, "lr": 0.09871, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20219, "top5_acc": 0.43453, "loss_cls": 4.43192, "loss": 4.43192, "time": 0.69798} +{"mode": "train", "epoch": 11, "iter": 3400, "lr": 0.0987, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19766, "top5_acc": 0.43859, "loss_cls": 4.41646, "loss": 4.41646, "time": 0.6953} +{"mode": "train", "epoch": 11, "iter": 3500, "lr": 0.09869, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19984, "top5_acc": 0.43609, "loss_cls": 4.38894, "loss": 4.38894, "time": 0.69897} +{"mode": "train", "epoch": 11, "iter": 3600, "lr": 0.09869, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19812, "top5_acc": 0.4375, "loss_cls": 4.43077, "loss": 4.43077, "time": 0.70294} +{"mode": "train", "epoch": 11, "iter": 3700, "lr": 0.09868, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21375, "top5_acc": 0.44953, "loss_cls": 4.36222, "loss": 4.36222, "time": 0.70323} +{"mode": "val", "epoch": 11, "iter": 309, "lr": 0.09868, "top1_acc": 0.12333, "top5_acc": 0.31297, "mean_class_accuracy": 0.1232} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.09867, "memory": 15990, "data_time": 1.24484, "top1_acc": 0.21234, "top5_acc": 0.44328, "loss_cls": 4.37177, "loss": 4.37177, "time": 1.95686} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.09867, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20656, "top5_acc": 0.45406, "loss_cls": 4.35273, "loss": 4.35273, "time": 0.71376} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.09866, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.215, "top5_acc": 0.44938, "loss_cls": 4.38076, "loss": 4.38076, "time": 0.70943} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.09865, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21016, "top5_acc": 0.45234, "loss_cls": 4.37385, "loss": 4.37385, "time": 0.7051} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.09865, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21766, "top5_acc": 0.44375, "loss_cls": 4.37471, "loss": 4.37471, "time": 0.7101} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.09864, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21031, "top5_acc": 0.44875, "loss_cls": 4.38305, "loss": 4.38305, "time": 0.70565} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.09863, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20469, "top5_acc": 0.43781, "loss_cls": 4.41096, "loss": 4.41096, "time": 0.70334} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.09863, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20719, "top5_acc": 0.45578, "loss_cls": 4.35103, "loss": 4.35103, "time": 0.70674} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.09862, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21078, "top5_acc": 0.43875, "loss_cls": 4.401, "loss": 4.401, "time": 0.70023} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.09861, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19719, "top5_acc": 0.44141, "loss_cls": 4.4007, "loss": 4.4007, "time": 0.7019} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.09861, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21062, "top5_acc": 0.44203, "loss_cls": 4.40709, "loss": 4.40709, "time": 0.70265} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.0986, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2025, "top5_acc": 0.44125, "loss_cls": 4.39348, "loss": 4.39348, "time": 0.70181} +{"mode": "train", "epoch": 12, "iter": 1300, "lr": 0.09859, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19859, "top5_acc": 0.43281, "loss_cls": 4.41825, "loss": 4.41825, "time": 0.70746} +{"mode": "train", "epoch": 12, "iter": 1400, "lr": 0.09859, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2075, "top5_acc": 0.43844, "loss_cls": 4.41542, "loss": 4.41542, "time": 0.70399} +{"mode": "train", "epoch": 12, "iter": 1500, "lr": 0.09858, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19906, "top5_acc": 0.43281, "loss_cls": 4.44508, "loss": 4.44508, "time": 0.7035} +{"mode": "train", "epoch": 12, "iter": 1600, "lr": 0.09857, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20984, "top5_acc": 0.45, "loss_cls": 4.37614, "loss": 4.37614, "time": 0.7012} +{"mode": "train", "epoch": 12, "iter": 1700, "lr": 0.09857, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20016, "top5_acc": 0.43531, "loss_cls": 4.4152, "loss": 4.4152, "time": 0.69739} +{"mode": "train", "epoch": 12, "iter": 1800, "lr": 0.09856, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21156, "top5_acc": 0.44719, "loss_cls": 4.3731, "loss": 4.3731, "time": 0.70217} +{"mode": "train", "epoch": 12, "iter": 1900, "lr": 0.09855, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20828, "top5_acc": 0.44094, "loss_cls": 4.38911, "loss": 4.38911, "time": 0.7} +{"mode": "train", "epoch": 12, "iter": 2000, "lr": 0.09855, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19828, "top5_acc": 0.44172, "loss_cls": 4.39982, "loss": 4.39982, "time": 0.69963} +{"mode": "train", "epoch": 12, "iter": 2100, "lr": 0.09854, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20125, "top5_acc": 0.4325, "loss_cls": 4.43093, "loss": 4.43093, "time": 0.70104} +{"mode": "train", "epoch": 12, "iter": 2200, "lr": 0.09853, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21016, "top5_acc": 0.44172, "loss_cls": 4.37466, "loss": 4.37466, "time": 0.70133} +{"mode": "train", "epoch": 12, "iter": 2300, "lr": 0.09853, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20469, "top5_acc": 0.43406, "loss_cls": 4.41229, "loss": 4.41229, "time": 0.69652} +{"mode": "train", "epoch": 12, "iter": 2400, "lr": 0.09852, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21094, "top5_acc": 0.44703, "loss_cls": 4.38409, "loss": 4.38409, "time": 0.69884} +{"mode": "train", "epoch": 12, "iter": 2500, "lr": 0.09851, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21125, "top5_acc": 0.43516, "loss_cls": 4.42206, "loss": 4.42206, "time": 0.69819} +{"mode": "train", "epoch": 12, "iter": 2600, "lr": 0.09851, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20266, "top5_acc": 0.42984, "loss_cls": 4.42111, "loss": 4.42111, "time": 0.69777} +{"mode": "train", "epoch": 12, "iter": 2700, "lr": 0.0985, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20188, "top5_acc": 0.43656, "loss_cls": 4.41513, "loss": 4.41513, "time": 0.69864} +{"mode": "train", "epoch": 12, "iter": 2800, "lr": 0.09849, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21, "top5_acc": 0.45312, "loss_cls": 4.38256, "loss": 4.38256, "time": 0.69682} +{"mode": "train", "epoch": 12, "iter": 2900, "lr": 0.09849, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19797, "top5_acc": 0.43344, "loss_cls": 4.44775, "loss": 4.44775, "time": 0.69685} +{"mode": "train", "epoch": 12, "iter": 3000, "lr": 0.09848, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22375, "top5_acc": 0.44656, "loss_cls": 4.36634, "loss": 4.36634, "time": 0.6987} +{"mode": "train", "epoch": 12, "iter": 3100, "lr": 0.09847, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20844, "top5_acc": 0.45125, "loss_cls": 4.35811, "loss": 4.35811, "time": 0.69959} +{"mode": "train", "epoch": 12, "iter": 3200, "lr": 0.09847, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20828, "top5_acc": 0.44328, "loss_cls": 4.39051, "loss": 4.39051, "time": 0.69629} +{"mode": "train", "epoch": 12, "iter": 3300, "lr": 0.09846, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20578, "top5_acc": 0.43984, "loss_cls": 4.41688, "loss": 4.41688, "time": 0.6956} +{"mode": "train", "epoch": 12, "iter": 3400, "lr": 0.09845, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19734, "top5_acc": 0.43812, "loss_cls": 4.41925, "loss": 4.41925, "time": 0.69745} +{"mode": "train", "epoch": 12, "iter": 3500, "lr": 0.09845, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21312, "top5_acc": 0.44312, "loss_cls": 4.37261, "loss": 4.37261, "time": 0.69961} +{"mode": "train", "epoch": 12, "iter": 3600, "lr": 0.09844, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20094, "top5_acc": 0.43516, "loss_cls": 4.45235, "loss": 4.45235, "time": 0.70526} +{"mode": "train", "epoch": 12, "iter": 3700, "lr": 0.09843, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21453, "top5_acc": 0.44109, "loss_cls": 4.38665, "loss": 4.38665, "time": 0.70242} +{"mode": "val", "epoch": 12, "iter": 309, "lr": 0.09843, "top1_acc": 0.12901, "top5_acc": 0.32675, "mean_class_accuracy": 0.12897} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.09842, "memory": 15990, "data_time": 1.26879, "top1_acc": 0.21203, "top5_acc": 0.45266, "loss_cls": 4.34315, "loss": 4.34315, "time": 1.98309} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.09842, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20844, "top5_acc": 0.44078, "loss_cls": 4.37478, "loss": 4.37478, "time": 0.70679} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.09841, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21, "top5_acc": 0.43609, "loss_cls": 4.39633, "loss": 4.39633, "time": 0.71007} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.0984, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21219, "top5_acc": 0.45516, "loss_cls": 4.36705, "loss": 4.36705, "time": 0.70785} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.09839, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20875, "top5_acc": 0.44391, "loss_cls": 4.3776, "loss": 4.3776, "time": 0.70417} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.09839, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20734, "top5_acc": 0.44547, "loss_cls": 4.36043, "loss": 4.36043, "time": 0.70104} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.09838, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21, "top5_acc": 0.44609, "loss_cls": 4.37612, "loss": 4.37612, "time": 0.70088} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.09837, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21141, "top5_acc": 0.44594, "loss_cls": 4.35565, "loss": 4.35565, "time": 0.69903} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.09837, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20641, "top5_acc": 0.44047, "loss_cls": 4.39158, "loss": 4.39158, "time": 0.69968} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.09836, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20688, "top5_acc": 0.43969, "loss_cls": 4.39533, "loss": 4.39533, "time": 0.70089} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.09835, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21812, "top5_acc": 0.44844, "loss_cls": 4.36648, "loss": 4.36648, "time": 0.69941} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.09834, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20312, "top5_acc": 0.43281, "loss_cls": 4.43916, "loss": 4.43916, "time": 0.70181} +{"mode": "train", "epoch": 13, "iter": 1300, "lr": 0.09834, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20578, "top5_acc": 0.44359, "loss_cls": 4.41044, "loss": 4.41044, "time": 0.70005} +{"mode": "train", "epoch": 13, "iter": 1400, "lr": 0.09833, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2125, "top5_acc": 0.44656, "loss_cls": 4.35675, "loss": 4.35675, "time": 0.69955} +{"mode": "train", "epoch": 13, "iter": 1500, "lr": 0.09832, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19688, "top5_acc": 0.43656, "loss_cls": 4.43455, "loss": 4.43455, "time": 0.69729} +{"mode": "train", "epoch": 13, "iter": 1600, "lr": 0.09832, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21016, "top5_acc": 0.44359, "loss_cls": 4.40585, "loss": 4.40585, "time": 0.69887} +{"mode": "train", "epoch": 13, "iter": 1700, "lr": 0.09831, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21047, "top5_acc": 0.43469, "loss_cls": 4.40456, "loss": 4.40456, "time": 0.69637} +{"mode": "train", "epoch": 13, "iter": 1800, "lr": 0.0983, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20781, "top5_acc": 0.44359, "loss_cls": 4.35453, "loss": 4.35453, "time": 0.70091} +{"mode": "train", "epoch": 13, "iter": 1900, "lr": 0.09829, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21047, "top5_acc": 0.45375, "loss_cls": 4.34366, "loss": 4.34366, "time": 0.69929} +{"mode": "train", "epoch": 13, "iter": 2000, "lr": 0.09829, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20453, "top5_acc": 0.43641, "loss_cls": 4.42855, "loss": 4.42855, "time": 0.70075} +{"mode": "train", "epoch": 13, "iter": 2100, "lr": 0.09828, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.205, "top5_acc": 0.44781, "loss_cls": 4.38361, "loss": 4.38361, "time": 0.70204} +{"mode": "train", "epoch": 13, "iter": 2200, "lr": 0.09827, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21328, "top5_acc": 0.44031, "loss_cls": 4.38891, "loss": 4.38891, "time": 0.7011} +{"mode": "train", "epoch": 13, "iter": 2300, "lr": 0.09827, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20656, "top5_acc": 0.43781, "loss_cls": 4.40462, "loss": 4.40462, "time": 0.69699} +{"mode": "train", "epoch": 13, "iter": 2400, "lr": 0.09826, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20734, "top5_acc": 0.44547, "loss_cls": 4.39204, "loss": 4.39204, "time": 0.70315} +{"mode": "train", "epoch": 13, "iter": 2500, "lr": 0.09825, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20875, "top5_acc": 0.44188, "loss_cls": 4.4059, "loss": 4.4059, "time": 0.69652} +{"mode": "train", "epoch": 13, "iter": 2600, "lr": 0.09824, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20688, "top5_acc": 0.44578, "loss_cls": 4.39233, "loss": 4.39233, "time": 0.69834} +{"mode": "train", "epoch": 13, "iter": 2700, "lr": 0.09824, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20438, "top5_acc": 0.44234, "loss_cls": 4.37899, "loss": 4.37899, "time": 0.69611} +{"mode": "train", "epoch": 13, "iter": 2800, "lr": 0.09823, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21391, "top5_acc": 0.44469, "loss_cls": 4.36773, "loss": 4.36773, "time": 0.69792} +{"mode": "train", "epoch": 13, "iter": 2900, "lr": 0.09822, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21438, "top5_acc": 0.44672, "loss_cls": 4.37524, "loss": 4.37524, "time": 0.69853} +{"mode": "train", "epoch": 13, "iter": 3000, "lr": 0.09821, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21344, "top5_acc": 0.44312, "loss_cls": 4.40442, "loss": 4.40442, "time": 0.69576} +{"mode": "train", "epoch": 13, "iter": 3100, "lr": 0.09821, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20656, "top5_acc": 0.43578, "loss_cls": 4.42704, "loss": 4.42704, "time": 0.70011} +{"mode": "train", "epoch": 13, "iter": 3200, "lr": 0.0982, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.215, "top5_acc": 0.44281, "loss_cls": 4.37763, "loss": 4.37763, "time": 0.69776} +{"mode": "train", "epoch": 13, "iter": 3300, "lr": 0.09819, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.20344, "top5_acc": 0.44094, "loss_cls": 4.42137, "loss": 4.42137, "time": 0.69564} +{"mode": "train", "epoch": 13, "iter": 3400, "lr": 0.09818, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20344, "top5_acc": 0.43609, "loss_cls": 4.43433, "loss": 4.43433, "time": 0.69846} +{"mode": "train", "epoch": 13, "iter": 3500, "lr": 0.09818, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21125, "top5_acc": 0.4475, "loss_cls": 4.37104, "loss": 4.37104, "time": 0.70012} +{"mode": "train", "epoch": 13, "iter": 3600, "lr": 0.09817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20375, "top5_acc": 0.43797, "loss_cls": 4.39472, "loss": 4.39472, "time": 0.70518} +{"mode": "train", "epoch": 13, "iter": 3700, "lr": 0.09816, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20781, "top5_acc": 0.43406, "loss_cls": 4.4096, "loss": 4.4096, "time": 0.70128} +{"mode": "val", "epoch": 13, "iter": 309, "lr": 0.09816, "top1_acc": 0.13878, "top5_acc": 0.33794, "mean_class_accuracy": 0.13869} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.09815, "memory": 15990, "data_time": 1.30381, "top1_acc": 0.20219, "top5_acc": 0.45078, "loss_cls": 4.35205, "loss": 4.35205, "time": 2.01264} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.09814, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22031, "top5_acc": 0.45578, "loss_cls": 4.32256, "loss": 4.32256, "time": 0.71205} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.09814, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20781, "top5_acc": 0.44406, "loss_cls": 4.36965, "loss": 4.36965, "time": 0.70668} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.09813, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19875, "top5_acc": 0.43578, "loss_cls": 4.40245, "loss": 4.40245, "time": 0.70577} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.09812, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20938, "top5_acc": 0.44812, "loss_cls": 4.36812, "loss": 4.36812, "time": 0.70498} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.09811, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21094, "top5_acc": 0.45688, "loss_cls": 4.32792, "loss": 4.32792, "time": 0.69942} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.09811, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21266, "top5_acc": 0.44141, "loss_cls": 4.40867, "loss": 4.40867, "time": 0.702} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.0981, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21391, "top5_acc": 0.44234, "loss_cls": 4.37129, "loss": 4.37129, "time": 0.70124} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.09809, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20547, "top5_acc": 0.44203, "loss_cls": 4.39725, "loss": 4.39725, "time": 0.70074} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.09808, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2125, "top5_acc": 0.44125, "loss_cls": 4.38174, "loss": 4.38174, "time": 0.70422} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.09807, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21453, "top5_acc": 0.45047, "loss_cls": 4.36385, "loss": 4.36385, "time": 0.70047} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.09807, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21016, "top5_acc": 0.44609, "loss_cls": 4.36221, "loss": 4.36221, "time": 0.70181} +{"mode": "train", "epoch": 14, "iter": 1300, "lr": 0.09806, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20531, "top5_acc": 0.44531, "loss_cls": 4.42205, "loss": 4.42205, "time": 0.70215} +{"mode": "train", "epoch": 14, "iter": 1400, "lr": 0.09805, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20969, "top5_acc": 0.44203, "loss_cls": 4.41605, "loss": 4.41605, "time": 0.69626} +{"mode": "train", "epoch": 14, "iter": 1500, "lr": 0.09804, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20922, "top5_acc": 0.43688, "loss_cls": 4.38391, "loss": 4.38391, "time": 0.70154} +{"mode": "train", "epoch": 14, "iter": 1600, "lr": 0.09804, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20859, "top5_acc": 0.44109, "loss_cls": 4.4184, "loss": 4.4184, "time": 0.69819} +{"mode": "train", "epoch": 14, "iter": 1700, "lr": 0.09803, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2075, "top5_acc": 0.44234, "loss_cls": 4.40083, "loss": 4.40083, "time": 0.70001} +{"mode": "train", "epoch": 14, "iter": 1800, "lr": 0.09802, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20703, "top5_acc": 0.43938, "loss_cls": 4.39481, "loss": 4.39481, "time": 0.70114} +{"mode": "train", "epoch": 14, "iter": 1900, "lr": 0.09801, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19938, "top5_acc": 0.43953, "loss_cls": 4.4184, "loss": 4.4184, "time": 0.70062} +{"mode": "train", "epoch": 14, "iter": 2000, "lr": 0.098, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21078, "top5_acc": 0.45516, "loss_cls": 4.3699, "loss": 4.3699, "time": 0.70362} +{"mode": "train", "epoch": 14, "iter": 2100, "lr": 0.098, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21047, "top5_acc": 0.44359, "loss_cls": 4.38964, "loss": 4.38964, "time": 0.70173} +{"mode": "train", "epoch": 14, "iter": 2200, "lr": 0.09799, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20984, "top5_acc": 0.44531, "loss_cls": 4.3738, "loss": 4.3738, "time": 0.6984} +{"mode": "train", "epoch": 14, "iter": 2300, "lr": 0.09798, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21219, "top5_acc": 0.44469, "loss_cls": 4.37143, "loss": 4.37143, "time": 0.6986} +{"mode": "train", "epoch": 14, "iter": 2400, "lr": 0.09797, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20766, "top5_acc": 0.44047, "loss_cls": 4.42011, "loss": 4.42011, "time": 0.69842} +{"mode": "train", "epoch": 14, "iter": 2500, "lr": 0.09797, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21141, "top5_acc": 0.44531, "loss_cls": 4.38012, "loss": 4.38012, "time": 0.6982} +{"mode": "train", "epoch": 14, "iter": 2600, "lr": 0.09796, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18875, "top5_acc": 0.42344, "loss_cls": 4.46136, "loss": 4.46136, "time": 0.69922} +{"mode": "train", "epoch": 14, "iter": 2700, "lr": 0.09795, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20969, "top5_acc": 0.44297, "loss_cls": 4.40699, "loss": 4.40699, "time": 0.69884} +{"mode": "train", "epoch": 14, "iter": 2800, "lr": 0.09794, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21359, "top5_acc": 0.45094, "loss_cls": 4.36092, "loss": 4.36092, "time": 0.69698} +{"mode": "train", "epoch": 14, "iter": 2900, "lr": 0.09793, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21578, "top5_acc": 0.45453, "loss_cls": 4.33365, "loss": 4.33365, "time": 0.70036} +{"mode": "train", "epoch": 14, "iter": 3000, "lr": 0.09793, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21219, "top5_acc": 0.44641, "loss_cls": 4.40238, "loss": 4.40238, "time": 0.69753} +{"mode": "train", "epoch": 14, "iter": 3100, "lr": 0.09792, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21422, "top5_acc": 0.46922, "loss_cls": 4.3257, "loss": 4.3257, "time": 0.70334} +{"mode": "train", "epoch": 14, "iter": 3200, "lr": 0.09791, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20859, "top5_acc": 0.44094, "loss_cls": 4.37403, "loss": 4.37403, "time": 0.69707} +{"mode": "train", "epoch": 14, "iter": 3300, "lr": 0.0979, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21094, "top5_acc": 0.44969, "loss_cls": 4.35415, "loss": 4.35415, "time": 0.69827} +{"mode": "train", "epoch": 14, "iter": 3400, "lr": 0.09789, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21031, "top5_acc": 0.45141, "loss_cls": 4.35737, "loss": 4.35737, "time": 0.696} +{"mode": "train", "epoch": 14, "iter": 3500, "lr": 0.09789, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21484, "top5_acc": 0.44625, "loss_cls": 4.36864, "loss": 4.36864, "time": 0.69765} +{"mode": "train", "epoch": 14, "iter": 3600, "lr": 0.09788, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20906, "top5_acc": 0.45172, "loss_cls": 4.40149, "loss": 4.40149, "time": 0.708} +{"mode": "train", "epoch": 14, "iter": 3700, "lr": 0.09787, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19719, "top5_acc": 0.43203, "loss_cls": 4.43854, "loss": 4.43854, "time": 0.70093} +{"mode": "val", "epoch": 14, "iter": 309, "lr": 0.09787, "top1_acc": 0.13159, "top5_acc": 0.32472, "mean_class_accuracy": 0.13169} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.09786, "memory": 15990, "data_time": 1.33965, "top1_acc": 0.20969, "top5_acc": 0.44375, "loss_cls": 4.39038, "loss": 4.39038, "time": 2.05272} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.09785, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21203, "top5_acc": 0.45391, "loss_cls": 4.34294, "loss": 4.34294, "time": 0.71335} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.09784, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20312, "top5_acc": 0.44812, "loss_cls": 4.37215, "loss": 4.37215, "time": 0.70995} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.09783, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21344, "top5_acc": 0.445, "loss_cls": 4.34873, "loss": 4.34873, "time": 0.70628} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.09783, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20547, "top5_acc": 0.43828, "loss_cls": 4.40492, "loss": 4.40492, "time": 0.70488} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.09782, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20453, "top5_acc": 0.44078, "loss_cls": 4.40053, "loss": 4.40053, "time": 0.70117} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.09781, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21516, "top5_acc": 0.45359, "loss_cls": 4.35699, "loss": 4.35699, "time": 0.70129} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.0978, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21688, "top5_acc": 0.45484, "loss_cls": 4.33874, "loss": 4.33874, "time": 0.69938} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.09779, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21938, "top5_acc": 0.45203, "loss_cls": 4.30891, "loss": 4.30891, "time": 0.69969} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.09778, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21719, "top5_acc": 0.45047, "loss_cls": 4.32993, "loss": 4.32993, "time": 0.70224} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.09778, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20625, "top5_acc": 0.44547, "loss_cls": 4.38092, "loss": 4.38092, "time": 0.70045} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.09777, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21031, "top5_acc": 0.44047, "loss_cls": 4.39651, "loss": 4.39651, "time": 0.70004} +{"mode": "train", "epoch": 15, "iter": 1300, "lr": 0.09776, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20359, "top5_acc": 0.43781, "loss_cls": 4.4126, "loss": 4.4126, "time": 0.70246} +{"mode": "train", "epoch": 15, "iter": 1400, "lr": 0.09775, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20656, "top5_acc": 0.44781, "loss_cls": 4.41949, "loss": 4.41949, "time": 0.70076} +{"mode": "train", "epoch": 15, "iter": 1500, "lr": 0.09774, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20828, "top5_acc": 0.44672, "loss_cls": 4.36897, "loss": 4.36897, "time": 0.70034} +{"mode": "train", "epoch": 15, "iter": 1600, "lr": 0.09773, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21031, "top5_acc": 0.44578, "loss_cls": 4.39783, "loss": 4.39783, "time": 0.7013} +{"mode": "train", "epoch": 15, "iter": 1700, "lr": 0.09773, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.19953, "top5_acc": 0.44469, "loss_cls": 4.4035, "loss": 4.4035, "time": 0.70779} +{"mode": "train", "epoch": 15, "iter": 1800, "lr": 0.09772, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22516, "top5_acc": 0.45094, "loss_cls": 4.33948, "loss": 4.33948, "time": 0.71128} +{"mode": "train", "epoch": 15, "iter": 1900, "lr": 0.09771, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20312, "top5_acc": 0.44562, "loss_cls": 4.38929, "loss": 4.38929, "time": 0.70314} +{"mode": "train", "epoch": 15, "iter": 2000, "lr": 0.0977, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19906, "top5_acc": 0.43234, "loss_cls": 4.43564, "loss": 4.43564, "time": 0.70695} +{"mode": "train", "epoch": 15, "iter": 2100, "lr": 0.09769, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20688, "top5_acc": 0.45156, "loss_cls": 4.37315, "loss": 4.37315, "time": 0.70602} +{"mode": "train", "epoch": 15, "iter": 2200, "lr": 0.09768, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20656, "top5_acc": 0.44656, "loss_cls": 4.38131, "loss": 4.38131, "time": 0.70699} +{"mode": "train", "epoch": 15, "iter": 2300, "lr": 0.09768, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21359, "top5_acc": 0.44844, "loss_cls": 4.34961, "loss": 4.34961, "time": 0.70691} +{"mode": "train", "epoch": 15, "iter": 2400, "lr": 0.09767, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19984, "top5_acc": 0.44312, "loss_cls": 4.41756, "loss": 4.41756, "time": 0.70501} +{"mode": "train", "epoch": 15, "iter": 2500, "lr": 0.09766, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21922, "top5_acc": 0.45406, "loss_cls": 4.33641, "loss": 4.33641, "time": 0.70288} +{"mode": "train", "epoch": 15, "iter": 2600, "lr": 0.09765, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21125, "top5_acc": 0.45109, "loss_cls": 4.35248, "loss": 4.35248, "time": 0.70593} +{"mode": "train", "epoch": 15, "iter": 2700, "lr": 0.09764, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20516, "top5_acc": 0.44641, "loss_cls": 4.34849, "loss": 4.34849, "time": 0.70285} +{"mode": "train", "epoch": 15, "iter": 2800, "lr": 0.09763, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21609, "top5_acc": 0.44375, "loss_cls": 4.38964, "loss": 4.38964, "time": 0.69959} +{"mode": "train", "epoch": 15, "iter": 2900, "lr": 0.09763, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20734, "top5_acc": 0.44188, "loss_cls": 4.38955, "loss": 4.38955, "time": 0.7008} +{"mode": "train", "epoch": 15, "iter": 3000, "lr": 0.09762, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20422, "top5_acc": 0.44078, "loss_cls": 4.40448, "loss": 4.40448, "time": 0.7006} +{"mode": "train", "epoch": 15, "iter": 3100, "lr": 0.09761, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20984, "top5_acc": 0.45156, "loss_cls": 4.36086, "loss": 4.36086, "time": 0.70246} +{"mode": "train", "epoch": 15, "iter": 3200, "lr": 0.0976, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20406, "top5_acc": 0.45406, "loss_cls": 4.35096, "loss": 4.35096, "time": 0.70172} +{"mode": "train", "epoch": 15, "iter": 3300, "lr": 0.09759, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20766, "top5_acc": 0.43125, "loss_cls": 4.39893, "loss": 4.39893, "time": 0.70261} +{"mode": "train", "epoch": 15, "iter": 3400, "lr": 0.09758, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20375, "top5_acc": 0.43562, "loss_cls": 4.39074, "loss": 4.39074, "time": 0.70368} +{"mode": "train", "epoch": 15, "iter": 3500, "lr": 0.09757, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20078, "top5_acc": 0.44125, "loss_cls": 4.41148, "loss": 4.41148, "time": 0.70459} +{"mode": "train", "epoch": 15, "iter": 3600, "lr": 0.09757, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.205, "top5_acc": 0.43359, "loss_cls": 4.42374, "loss": 4.42374, "time": 0.70844} +{"mode": "train", "epoch": 15, "iter": 3700, "lr": 0.09756, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20766, "top5_acc": 0.45, "loss_cls": 4.38174, "loss": 4.38174, "time": 0.7075} +{"mode": "val", "epoch": 15, "iter": 309, "lr": 0.09755, "top1_acc": 0.10627, "top5_acc": 0.28096, "mean_class_accuracy": 0.10623} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.09754, "memory": 15990, "data_time": 1.248, "top1_acc": 0.21734, "top5_acc": 0.45797, "loss_cls": 4.33996, "loss": 4.33996, "time": 1.95962} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.09754, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2225, "top5_acc": 0.45578, "loss_cls": 4.32321, "loss": 4.32321, "time": 0.71039} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.09753, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21266, "top5_acc": 0.445, "loss_cls": 4.35013, "loss": 4.35013, "time": 0.70565} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.09752, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20641, "top5_acc": 0.45266, "loss_cls": 4.35251, "loss": 4.35251, "time": 0.70501} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.09751, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20547, "top5_acc": 0.43844, "loss_cls": 4.42005, "loss": 4.42005, "time": 0.70086} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.0975, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20891, "top5_acc": 0.43781, "loss_cls": 4.39368, "loss": 4.39368, "time": 0.69926} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.09749, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20922, "top5_acc": 0.44938, "loss_cls": 4.34565, "loss": 4.34565, "time": 0.703} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.09748, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20938, "top5_acc": 0.44703, "loss_cls": 4.37021, "loss": 4.37021, "time": 0.70294} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.09747, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19969, "top5_acc": 0.43594, "loss_cls": 4.40784, "loss": 4.40784, "time": 0.70435} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.09747, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21078, "top5_acc": 0.44469, "loss_cls": 4.37366, "loss": 4.37366, "time": 0.70372} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.09746, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21203, "top5_acc": 0.44812, "loss_cls": 4.3678, "loss": 4.3678, "time": 0.70259} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.09745, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19734, "top5_acc": 0.43453, "loss_cls": 4.42469, "loss": 4.42469, "time": 0.70054} +{"mode": "train", "epoch": 16, "iter": 1300, "lr": 0.09744, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21688, "top5_acc": 0.46703, "loss_cls": 4.30514, "loss": 4.30514, "time": 0.70416} +{"mode": "train", "epoch": 16, "iter": 1400, "lr": 0.09743, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21609, "top5_acc": 0.44922, "loss_cls": 4.39096, "loss": 4.39096, "time": 0.70257} +{"mode": "train", "epoch": 16, "iter": 1500, "lr": 0.09742, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21438, "top5_acc": 0.44719, "loss_cls": 4.3878, "loss": 4.3878, "time": 0.70366} +{"mode": "train", "epoch": 16, "iter": 1600, "lr": 0.09741, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21344, "top5_acc": 0.45344, "loss_cls": 4.33611, "loss": 4.33611, "time": 0.70129} +{"mode": "train", "epoch": 16, "iter": 1700, "lr": 0.0974, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20906, "top5_acc": 0.44297, "loss_cls": 4.37924, "loss": 4.37924, "time": 0.7024} +{"mode": "train", "epoch": 16, "iter": 1800, "lr": 0.0974, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20172, "top5_acc": 0.45266, "loss_cls": 4.35762, "loss": 4.35762, "time": 0.70265} +{"mode": "train", "epoch": 16, "iter": 1900, "lr": 0.09739, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22062, "top5_acc": 0.45031, "loss_cls": 4.33039, "loss": 4.33039, "time": 0.70241} +{"mode": "train", "epoch": 16, "iter": 2000, "lr": 0.09738, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20297, "top5_acc": 0.43453, "loss_cls": 4.41967, "loss": 4.41967, "time": 0.70267} +{"mode": "train", "epoch": 16, "iter": 2100, "lr": 0.09737, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20797, "top5_acc": 0.43906, "loss_cls": 4.39531, "loss": 4.39531, "time": 0.70232} +{"mode": "train", "epoch": 16, "iter": 2200, "lr": 0.09736, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20828, "top5_acc": 0.44234, "loss_cls": 4.38299, "loss": 4.38299, "time": 0.70043} +{"mode": "train", "epoch": 16, "iter": 2300, "lr": 0.09735, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20219, "top5_acc": 0.44625, "loss_cls": 4.36585, "loss": 4.36585, "time": 0.6968} +{"mode": "train", "epoch": 16, "iter": 2400, "lr": 0.09734, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21734, "top5_acc": 0.44078, "loss_cls": 4.41611, "loss": 4.41611, "time": 0.70053} +{"mode": "train", "epoch": 16, "iter": 2500, "lr": 0.09733, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20844, "top5_acc": 0.43359, "loss_cls": 4.42672, "loss": 4.42672, "time": 0.69945} +{"mode": "train", "epoch": 16, "iter": 2600, "lr": 0.09732, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20375, "top5_acc": 0.43906, "loss_cls": 4.41234, "loss": 4.41234, "time": 0.70127} +{"mode": "train", "epoch": 16, "iter": 2700, "lr": 0.09731, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21031, "top5_acc": 0.45125, "loss_cls": 4.34647, "loss": 4.34647, "time": 0.69813} +{"mode": "train", "epoch": 16, "iter": 2800, "lr": 0.09731, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20922, "top5_acc": 0.44797, "loss_cls": 4.39753, "loss": 4.39753, "time": 0.70024} +{"mode": "train", "epoch": 16, "iter": 2900, "lr": 0.0973, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20547, "top5_acc": 0.44859, "loss_cls": 4.37811, "loss": 4.37811, "time": 0.69602} +{"mode": "train", "epoch": 16, "iter": 3000, "lr": 0.09729, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20312, "top5_acc": 0.44469, "loss_cls": 4.36961, "loss": 4.36961, "time": 0.69964} +{"mode": "train", "epoch": 16, "iter": 3100, "lr": 0.09728, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20531, "top5_acc": 0.44281, "loss_cls": 4.39773, "loss": 4.39773, "time": 0.70137} +{"mode": "train", "epoch": 16, "iter": 3200, "lr": 0.09727, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19938, "top5_acc": 0.43609, "loss_cls": 4.403, "loss": 4.403, "time": 0.69894} +{"mode": "train", "epoch": 16, "iter": 3300, "lr": 0.09726, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2075, "top5_acc": 0.44578, "loss_cls": 4.37654, "loss": 4.37654, "time": 0.69676} +{"mode": "train", "epoch": 16, "iter": 3400, "lr": 0.09725, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21656, "top5_acc": 0.44953, "loss_cls": 4.35891, "loss": 4.35891, "time": 0.69779} +{"mode": "train", "epoch": 16, "iter": 3500, "lr": 0.09724, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21438, "top5_acc": 0.44391, "loss_cls": 4.37797, "loss": 4.37797, "time": 0.7011} +{"mode": "train", "epoch": 16, "iter": 3600, "lr": 0.09723, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21109, "top5_acc": 0.45219, "loss_cls": 4.35288, "loss": 4.35288, "time": 0.70876} +{"mode": "train", "epoch": 16, "iter": 3700, "lr": 0.09722, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20703, "top5_acc": 0.44016, "loss_cls": 4.38388, "loss": 4.38388, "time": 0.7026} +{"mode": "val", "epoch": 16, "iter": 309, "lr": 0.09722, "top1_acc": 0.13068, "top5_acc": 0.3113, "mean_class_accuracy": 0.1305} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.09721, "memory": 15990, "data_time": 1.25269, "top1_acc": 0.21641, "top5_acc": 0.45594, "loss_cls": 4.34036, "loss": 4.34036, "time": 1.96309} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.0972, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20734, "top5_acc": 0.44766, "loss_cls": 4.35535, "loss": 4.35535, "time": 0.7098} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.09719, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20672, "top5_acc": 0.46078, "loss_cls": 4.35419, "loss": 4.35419, "time": 0.71173} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.09718, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21344, "top5_acc": 0.45938, "loss_cls": 4.32804, "loss": 4.32804, "time": 0.70908} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.09717, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21703, "top5_acc": 0.45969, "loss_cls": 4.34119, "loss": 4.34119, "time": 0.70165} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.09716, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20672, "top5_acc": 0.43828, "loss_cls": 4.3984, "loss": 4.3984, "time": 0.70295} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.09715, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21562, "top5_acc": 0.45125, "loss_cls": 4.34491, "loss": 4.34491, "time": 0.69923} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.09714, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21, "top5_acc": 0.44594, "loss_cls": 4.35799, "loss": 4.35799, "time": 0.69906} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.09714, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21094, "top5_acc": 0.44438, "loss_cls": 4.38603, "loss": 4.38603, "time": 0.69989} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.09713, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20844, "top5_acc": 0.44625, "loss_cls": 4.34488, "loss": 4.34488, "time": 0.70007} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.09712, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21172, "top5_acc": 0.44859, "loss_cls": 4.3767, "loss": 4.3767, "time": 0.7005} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.09711, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21594, "top5_acc": 0.44953, "loss_cls": 4.33876, "loss": 4.33876, "time": 0.70046} +{"mode": "train", "epoch": 17, "iter": 1300, "lr": 0.0971, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20641, "top5_acc": 0.44922, "loss_cls": 4.35602, "loss": 4.35602, "time": 0.70113} +{"mode": "train", "epoch": 17, "iter": 1400, "lr": 0.09709, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19594, "top5_acc": 0.44, "loss_cls": 4.40995, "loss": 4.40995, "time": 0.70113} +{"mode": "train", "epoch": 17, "iter": 1500, "lr": 0.09708, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21781, "top5_acc": 0.44484, "loss_cls": 4.36527, "loss": 4.36527, "time": 0.6983} +{"mode": "train", "epoch": 17, "iter": 1600, "lr": 0.09707, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21266, "top5_acc": 0.44781, "loss_cls": 4.37112, "loss": 4.37112, "time": 0.69816} +{"mode": "train", "epoch": 17, "iter": 1700, "lr": 0.09706, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20438, "top5_acc": 0.44406, "loss_cls": 4.39221, "loss": 4.39221, "time": 0.70409} +{"mode": "train", "epoch": 17, "iter": 1800, "lr": 0.09705, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21281, "top5_acc": 0.44781, "loss_cls": 4.36229, "loss": 4.36229, "time": 0.70805} +{"mode": "train", "epoch": 17, "iter": 1900, "lr": 0.09704, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20844, "top5_acc": 0.44969, "loss_cls": 4.36213, "loss": 4.36213, "time": 0.7011} +{"mode": "train", "epoch": 17, "iter": 2000, "lr": 0.09703, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20734, "top5_acc": 0.44047, "loss_cls": 4.38771, "loss": 4.38771, "time": 0.69941} +{"mode": "train", "epoch": 17, "iter": 2100, "lr": 0.09702, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20828, "top5_acc": 0.44906, "loss_cls": 4.36242, "loss": 4.36242, "time": 0.71063} +{"mode": "train", "epoch": 17, "iter": 2200, "lr": 0.09701, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21453, "top5_acc": 0.44859, "loss_cls": 4.35931, "loss": 4.35931, "time": 0.70316} +{"mode": "train", "epoch": 17, "iter": 2300, "lr": 0.097, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21141, "top5_acc": 0.45547, "loss_cls": 4.33598, "loss": 4.33598, "time": 0.70085} +{"mode": "train", "epoch": 17, "iter": 2400, "lr": 0.09699, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21531, "top5_acc": 0.44734, "loss_cls": 4.37744, "loss": 4.37744, "time": 0.69759} +{"mode": "train", "epoch": 17, "iter": 2500, "lr": 0.09698, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21328, "top5_acc": 0.445, "loss_cls": 4.36028, "loss": 4.36028, "time": 0.69868} +{"mode": "train", "epoch": 17, "iter": 2600, "lr": 0.09697, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20297, "top5_acc": 0.44766, "loss_cls": 4.36674, "loss": 4.36674, "time": 0.70326} +{"mode": "train", "epoch": 17, "iter": 2700, "lr": 0.09697, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21656, "top5_acc": 0.45109, "loss_cls": 4.34922, "loss": 4.34922, "time": 0.70111} +{"mode": "train", "epoch": 17, "iter": 2800, "lr": 0.09696, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20984, "top5_acc": 0.44297, "loss_cls": 4.38161, "loss": 4.38161, "time": 0.69773} +{"mode": "train", "epoch": 17, "iter": 2900, "lr": 0.09695, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20906, "top5_acc": 0.44141, "loss_cls": 4.3881, "loss": 4.3881, "time": 0.7007} +{"mode": "train", "epoch": 17, "iter": 3000, "lr": 0.09694, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20641, "top5_acc": 0.44719, "loss_cls": 4.38184, "loss": 4.38184, "time": 0.70017} +{"mode": "train", "epoch": 17, "iter": 3100, "lr": 0.09693, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21219, "top5_acc": 0.44844, "loss_cls": 4.37039, "loss": 4.37039, "time": 0.69788} +{"mode": "train", "epoch": 17, "iter": 3200, "lr": 0.09692, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21, "top5_acc": 0.44812, "loss_cls": 4.38618, "loss": 4.38618, "time": 0.70372} +{"mode": "train", "epoch": 17, "iter": 3300, "lr": 0.09691, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2025, "top5_acc": 0.44, "loss_cls": 4.39796, "loss": 4.39796, "time": 0.69883} +{"mode": "train", "epoch": 17, "iter": 3400, "lr": 0.0969, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21094, "top5_acc": 0.4525, "loss_cls": 4.3625, "loss": 4.3625, "time": 0.70142} +{"mode": "train", "epoch": 17, "iter": 3500, "lr": 0.09689, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21625, "top5_acc": 0.45234, "loss_cls": 4.34839, "loss": 4.34839, "time": 0.70269} +{"mode": "train", "epoch": 17, "iter": 3600, "lr": 0.09688, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21172, "top5_acc": 0.45703, "loss_cls": 4.35552, "loss": 4.35552, "time": 0.71333} +{"mode": "train", "epoch": 17, "iter": 3700, "lr": 0.09687, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20578, "top5_acc": 0.43672, "loss_cls": 4.41082, "loss": 4.41082, "time": 0.70565} +{"mode": "val", "epoch": 17, "iter": 309, "lr": 0.09686, "top1_acc": 0.12536, "top5_acc": 0.30983, "mean_class_accuracy": 0.12546} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.09685, "memory": 15990, "data_time": 1.24983, "top1_acc": 0.20828, "top5_acc": 0.45297, "loss_cls": 4.32176, "loss": 4.32176, "time": 1.95653} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.09684, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21625, "top5_acc": 0.45922, "loss_cls": 4.32158, "loss": 4.32158, "time": 0.71254} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.09683, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21531, "top5_acc": 0.45406, "loss_cls": 4.34742, "loss": 4.34742, "time": 0.7065} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.09683, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20812, "top5_acc": 0.44156, "loss_cls": 4.40261, "loss": 4.40261, "time": 0.70514} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.09682, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19969, "top5_acc": 0.44266, "loss_cls": 4.40896, "loss": 4.40896, "time": 0.70582} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.09681, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21047, "top5_acc": 0.44797, "loss_cls": 4.33616, "loss": 4.33616, "time": 0.70313} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.0968, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21469, "top5_acc": 0.45891, "loss_cls": 4.31517, "loss": 4.31517, "time": 0.7026} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.09679, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21, "top5_acc": 0.45062, "loss_cls": 4.35983, "loss": 4.35983, "time": 0.70132} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.09678, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21656, "top5_acc": 0.45531, "loss_cls": 4.31133, "loss": 4.31133, "time": 0.70085} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.09677, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20938, "top5_acc": 0.45, "loss_cls": 4.35637, "loss": 4.35637, "time": 0.70516} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.09676, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20656, "top5_acc": 0.44625, "loss_cls": 4.36151, "loss": 4.36151, "time": 0.70221} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.09675, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21062, "top5_acc": 0.44469, "loss_cls": 4.38028, "loss": 4.38028, "time": 0.70345} +{"mode": "train", "epoch": 18, "iter": 1300, "lr": 0.09674, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21438, "top5_acc": 0.44766, "loss_cls": 4.35373, "loss": 4.35373, "time": 0.70164} +{"mode": "train", "epoch": 18, "iter": 1400, "lr": 0.09673, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21594, "top5_acc": 0.45484, "loss_cls": 4.31623, "loss": 4.31623, "time": 0.70011} +{"mode": "train", "epoch": 18, "iter": 1500, "lr": 0.09672, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21656, "top5_acc": 0.46078, "loss_cls": 4.32301, "loss": 4.32301, "time": 0.70234} +{"mode": "train", "epoch": 18, "iter": 1600, "lr": 0.09671, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21781, "top5_acc": 0.45578, "loss_cls": 4.34256, "loss": 4.34256, "time": 0.7023} +{"mode": "train", "epoch": 18, "iter": 1700, "lr": 0.0967, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21125, "top5_acc": 0.45156, "loss_cls": 4.34925, "loss": 4.34925, "time": 0.70318} +{"mode": "train", "epoch": 18, "iter": 1800, "lr": 0.09669, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21641, "top5_acc": 0.45359, "loss_cls": 4.33859, "loss": 4.33859, "time": 0.70898} +{"mode": "train", "epoch": 18, "iter": 1900, "lr": 0.09668, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2175, "top5_acc": 0.45906, "loss_cls": 4.31496, "loss": 4.31496, "time": 0.7076} +{"mode": "train", "epoch": 18, "iter": 2000, "lr": 0.09667, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20781, "top5_acc": 0.44328, "loss_cls": 4.39899, "loss": 4.39899, "time": 0.70423} +{"mode": "train", "epoch": 18, "iter": 2100, "lr": 0.09666, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21828, "top5_acc": 0.44391, "loss_cls": 4.37127, "loss": 4.37127, "time": 0.70839} +{"mode": "train", "epoch": 18, "iter": 2200, "lr": 0.09665, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21641, "top5_acc": 0.45391, "loss_cls": 4.33779, "loss": 4.33779, "time": 0.70265} +{"mode": "train", "epoch": 18, "iter": 2300, "lr": 0.09664, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21422, "top5_acc": 0.45875, "loss_cls": 4.35086, "loss": 4.35086, "time": 0.70357} +{"mode": "train", "epoch": 18, "iter": 2400, "lr": 0.09663, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20797, "top5_acc": 0.44609, "loss_cls": 4.38112, "loss": 4.38112, "time": 0.70618} +{"mode": "train", "epoch": 18, "iter": 2500, "lr": 0.09662, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21766, "top5_acc": 0.44844, "loss_cls": 4.36308, "loss": 4.36308, "time": 0.70299} +{"mode": "train", "epoch": 18, "iter": 2600, "lr": 0.09661, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21109, "top5_acc": 0.45312, "loss_cls": 4.33829, "loss": 4.33829, "time": 0.70246} +{"mode": "train", "epoch": 18, "iter": 2700, "lr": 0.0966, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20891, "top5_acc": 0.44406, "loss_cls": 4.38803, "loss": 4.38803, "time": 0.70073} +{"mode": "train", "epoch": 18, "iter": 2800, "lr": 0.09659, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20484, "top5_acc": 0.43891, "loss_cls": 4.39524, "loss": 4.39524, "time": 0.70349} +{"mode": "train", "epoch": 18, "iter": 2900, "lr": 0.09658, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21141, "top5_acc": 0.44922, "loss_cls": 4.35601, "loss": 4.35601, "time": 0.70244} +{"mode": "train", "epoch": 18, "iter": 3000, "lr": 0.09657, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20703, "top5_acc": 0.44844, "loss_cls": 4.37099, "loss": 4.37099, "time": 0.70507} +{"mode": "train", "epoch": 18, "iter": 3100, "lr": 0.09656, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21359, "top5_acc": 0.44969, "loss_cls": 4.35042, "loss": 4.35042, "time": 0.70483} +{"mode": "train", "epoch": 18, "iter": 3200, "lr": 0.09654, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21828, "top5_acc": 0.46609, "loss_cls": 4.30257, "loss": 4.30257, "time": 0.70079} +{"mode": "train", "epoch": 18, "iter": 3300, "lr": 0.09653, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2075, "top5_acc": 0.44438, "loss_cls": 4.38335, "loss": 4.38335, "time": 0.7024} +{"mode": "train", "epoch": 18, "iter": 3400, "lr": 0.09652, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20328, "top5_acc": 0.44141, "loss_cls": 4.42826, "loss": 4.42826, "time": 0.70203} +{"mode": "train", "epoch": 18, "iter": 3500, "lr": 0.09651, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20047, "top5_acc": 0.44047, "loss_cls": 4.40431, "loss": 4.40431, "time": 0.70353} +{"mode": "train", "epoch": 18, "iter": 3600, "lr": 0.0965, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22078, "top5_acc": 0.46125, "loss_cls": 4.32119, "loss": 4.32119, "time": 0.71087} +{"mode": "train", "epoch": 18, "iter": 3700, "lr": 0.09649, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2075, "top5_acc": 0.43141, "loss_cls": 4.40743, "loss": 4.40743, "time": 0.70789} +{"mode": "val", "epoch": 18, "iter": 309, "lr": 0.09649, "top1_acc": 0.15676, "top5_acc": 0.35805, "mean_class_accuracy": 0.1568} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.09648, "memory": 15990, "data_time": 1.24629, "top1_acc": 0.21594, "top5_acc": 0.46047, "loss_cls": 4.32547, "loss": 4.32547, "time": 1.95031} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.09647, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21922, "top5_acc": 0.45703, "loss_cls": 4.33059, "loss": 4.33059, "time": 0.71258} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.09646, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22422, "top5_acc": 0.47188, "loss_cls": 4.26801, "loss": 4.26801, "time": 0.71322} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.09645, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21219, "top5_acc": 0.45047, "loss_cls": 4.36172, "loss": 4.36172, "time": 0.70528} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.09644, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21844, "top5_acc": 0.44938, "loss_cls": 4.34683, "loss": 4.34683, "time": 0.70367} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.09643, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20984, "top5_acc": 0.44922, "loss_cls": 4.37541, "loss": 4.37541, "time": 0.70049} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.09642, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21312, "top5_acc": 0.45266, "loss_cls": 4.3145, "loss": 4.3145, "time": 0.70908} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.09641, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21109, "top5_acc": 0.44266, "loss_cls": 4.35651, "loss": 4.35651, "time": 0.70004} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.0964, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21828, "top5_acc": 0.45625, "loss_cls": 4.35729, "loss": 4.35729, "time": 0.70294} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.09639, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20906, "top5_acc": 0.44906, "loss_cls": 4.36065, "loss": 4.36065, "time": 0.69981} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.09637, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21281, "top5_acc": 0.44203, "loss_cls": 4.39139, "loss": 4.39139, "time": 0.69815} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.09636, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21719, "top5_acc": 0.44844, "loss_cls": 4.33082, "loss": 4.33082, "time": 0.69813} +{"mode": "train", "epoch": 19, "iter": 1300, "lr": 0.09635, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19938, "top5_acc": 0.43547, "loss_cls": 4.42093, "loss": 4.42093, "time": 0.69992} +{"mode": "train", "epoch": 19, "iter": 1400, "lr": 0.09634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20844, "top5_acc": 0.44359, "loss_cls": 4.40834, "loss": 4.40834, "time": 0.70176} +{"mode": "train", "epoch": 19, "iter": 1500, "lr": 0.09633, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20953, "top5_acc": 0.45547, "loss_cls": 4.33444, "loss": 4.33444, "time": 0.703} +{"mode": "train", "epoch": 19, "iter": 1600, "lr": 0.09632, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21422, "top5_acc": 0.45141, "loss_cls": 4.359, "loss": 4.359, "time": 0.70148} +{"mode": "train", "epoch": 19, "iter": 1700, "lr": 0.09631, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21203, "top5_acc": 0.46172, "loss_cls": 4.3322, "loss": 4.3322, "time": 0.70179} +{"mode": "train", "epoch": 19, "iter": 1800, "lr": 0.0963, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.21203, "top5_acc": 0.45453, "loss_cls": 4.32984, "loss": 4.32984, "time": 0.70726} +{"mode": "train", "epoch": 19, "iter": 1900, "lr": 0.09629, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21359, "top5_acc": 0.45094, "loss_cls": 4.32553, "loss": 4.32553, "time": 0.70173} +{"mode": "train", "epoch": 19, "iter": 2000, "lr": 0.09628, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21297, "top5_acc": 0.45062, "loss_cls": 4.34897, "loss": 4.34897, "time": 0.70484} +{"mode": "train", "epoch": 19, "iter": 2100, "lr": 0.09627, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21359, "top5_acc": 0.44406, "loss_cls": 4.36382, "loss": 4.36382, "time": 0.70677} +{"mode": "train", "epoch": 19, "iter": 2200, "lr": 0.09626, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21281, "top5_acc": 0.45906, "loss_cls": 4.32999, "loss": 4.32999, "time": 0.70362} +{"mode": "train", "epoch": 19, "iter": 2300, "lr": 0.09625, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20984, "top5_acc": 0.44234, "loss_cls": 4.38478, "loss": 4.38478, "time": 0.70055} +{"mode": "train", "epoch": 19, "iter": 2400, "lr": 0.09624, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20984, "top5_acc": 0.44156, "loss_cls": 4.38384, "loss": 4.38384, "time": 0.70261} +{"mode": "train", "epoch": 19, "iter": 2500, "lr": 0.09623, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20922, "top5_acc": 0.44547, "loss_cls": 4.37389, "loss": 4.37389, "time": 0.69934} +{"mode": "train", "epoch": 19, "iter": 2600, "lr": 0.09622, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21422, "top5_acc": 0.4475, "loss_cls": 4.36002, "loss": 4.36002, "time": 0.69974} +{"mode": "train", "epoch": 19, "iter": 2700, "lr": 0.09621, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20484, "top5_acc": 0.44484, "loss_cls": 4.3608, "loss": 4.3608, "time": 0.69849} +{"mode": "train", "epoch": 19, "iter": 2800, "lr": 0.0962, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20484, "top5_acc": 0.44688, "loss_cls": 4.39967, "loss": 4.39967, "time": 0.70181} +{"mode": "train", "epoch": 19, "iter": 2900, "lr": 0.09618, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21375, "top5_acc": 0.44594, "loss_cls": 4.39305, "loss": 4.39305, "time": 0.70014} +{"mode": "train", "epoch": 19, "iter": 3000, "lr": 0.09617, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21578, "top5_acc": 0.46984, "loss_cls": 4.27814, "loss": 4.27814, "time": 0.69901} +{"mode": "train", "epoch": 19, "iter": 3100, "lr": 0.09616, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19969, "top5_acc": 0.43422, "loss_cls": 4.40658, "loss": 4.40658, "time": 0.7016} +{"mode": "train", "epoch": 19, "iter": 3200, "lr": 0.09615, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21047, "top5_acc": 0.44969, "loss_cls": 4.35456, "loss": 4.35456, "time": 0.69967} +{"mode": "train", "epoch": 19, "iter": 3300, "lr": 0.09614, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21031, "top5_acc": 0.455, "loss_cls": 4.358, "loss": 4.358, "time": 0.69927} +{"mode": "train", "epoch": 19, "iter": 3400, "lr": 0.09613, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20891, "top5_acc": 0.44469, "loss_cls": 4.39201, "loss": 4.39201, "time": 0.69852} +{"mode": "train", "epoch": 19, "iter": 3500, "lr": 0.09612, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20484, "top5_acc": 0.44391, "loss_cls": 4.38916, "loss": 4.38916, "time": 0.70939} +{"mode": "train", "epoch": 19, "iter": 3600, "lr": 0.09611, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21406, "top5_acc": 0.44797, "loss_cls": 4.36776, "loss": 4.36776, "time": 0.71059} +{"mode": "train", "epoch": 19, "iter": 3700, "lr": 0.0961, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21984, "top5_acc": 0.46094, "loss_cls": 4.3392, "loss": 4.3392, "time": 0.7066} +{"mode": "val", "epoch": 19, "iter": 309, "lr": 0.09609, "top1_acc": 0.1441, "top5_acc": 0.3422, "mean_class_accuracy": 0.14379} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.09608, "memory": 15990, "data_time": 1.24653, "top1_acc": 0.21922, "top5_acc": 0.45078, "loss_cls": 4.30856, "loss": 4.30856, "time": 1.95727} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.09607, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22547, "top5_acc": 0.4625, "loss_cls": 4.28663, "loss": 4.28663, "time": 0.71447} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.09606, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21406, "top5_acc": 0.44656, "loss_cls": 4.3394, "loss": 4.3394, "time": 0.70757} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.09605, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21438, "top5_acc": 0.44078, "loss_cls": 4.36843, "loss": 4.36843, "time": 0.70634} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.09604, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21703, "top5_acc": 0.45828, "loss_cls": 4.33403, "loss": 4.33403, "time": 0.69922} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.09603, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20594, "top5_acc": 0.45312, "loss_cls": 4.36766, "loss": 4.36766, "time": 0.69886} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.09602, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21406, "top5_acc": 0.44422, "loss_cls": 4.35708, "loss": 4.35708, "time": 0.70082} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.09601, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20531, "top5_acc": 0.44531, "loss_cls": 4.38379, "loss": 4.38379, "time": 0.69927} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.096, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21781, "top5_acc": 0.46312, "loss_cls": 4.32319, "loss": 4.32319, "time": 0.6996} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.09598, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21094, "top5_acc": 0.44859, "loss_cls": 4.37334, "loss": 4.37334, "time": 0.70106} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.09597, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21016, "top5_acc": 0.44109, "loss_cls": 4.37347, "loss": 4.37347, "time": 0.70043} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.09596, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21688, "top5_acc": 0.45328, "loss_cls": 4.32709, "loss": 4.32709, "time": 0.70332} +{"mode": "train", "epoch": 20, "iter": 1300, "lr": 0.09595, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21812, "top5_acc": 0.45078, "loss_cls": 4.32476, "loss": 4.32476, "time": 0.7} +{"mode": "train", "epoch": 20, "iter": 1400, "lr": 0.09594, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21031, "top5_acc": 0.44344, "loss_cls": 4.36653, "loss": 4.36653, "time": 0.70051} +{"mode": "train", "epoch": 20, "iter": 1500, "lr": 0.09593, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21703, "top5_acc": 0.44859, "loss_cls": 4.36296, "loss": 4.36296, "time": 0.70096} +{"mode": "train", "epoch": 20, "iter": 1600, "lr": 0.09592, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21625, "top5_acc": 0.45734, "loss_cls": 4.3164, "loss": 4.3164, "time": 0.6983} +{"mode": "train", "epoch": 20, "iter": 1700, "lr": 0.09591, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21203, "top5_acc": 0.45688, "loss_cls": 4.32357, "loss": 4.32357, "time": 0.70477} +{"mode": "train", "epoch": 20, "iter": 1800, "lr": 0.0959, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21562, "top5_acc": 0.45531, "loss_cls": 4.34899, "loss": 4.34899, "time": 0.71083} +{"mode": "train", "epoch": 20, "iter": 1900, "lr": 0.09588, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20891, "top5_acc": 0.44375, "loss_cls": 4.39439, "loss": 4.39439, "time": 0.69926} +{"mode": "train", "epoch": 20, "iter": 2000, "lr": 0.09587, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21578, "top5_acc": 0.45297, "loss_cls": 4.34289, "loss": 4.34289, "time": 0.70368} +{"mode": "train", "epoch": 20, "iter": 2100, "lr": 0.09586, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20875, "top5_acc": 0.44188, "loss_cls": 4.37979, "loss": 4.37979, "time": 0.70679} +{"mode": "train", "epoch": 20, "iter": 2200, "lr": 0.09585, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22281, "top5_acc": 0.45234, "loss_cls": 4.31528, "loss": 4.31528, "time": 0.70352} +{"mode": "train", "epoch": 20, "iter": 2300, "lr": 0.09584, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2075, "top5_acc": 0.45, "loss_cls": 4.34113, "loss": 4.34113, "time": 0.69861} +{"mode": "train", "epoch": 20, "iter": 2400, "lr": 0.09583, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21484, "top5_acc": 0.44641, "loss_cls": 4.37204, "loss": 4.37204, "time": 0.70111} +{"mode": "train", "epoch": 20, "iter": 2500, "lr": 0.09582, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20672, "top5_acc": 0.44469, "loss_cls": 4.38723, "loss": 4.38723, "time": 0.70097} +{"mode": "train", "epoch": 20, "iter": 2600, "lr": 0.09581, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22328, "top5_acc": 0.46844, "loss_cls": 4.28891, "loss": 4.28891, "time": 0.69864} +{"mode": "train", "epoch": 20, "iter": 2700, "lr": 0.0958, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21344, "top5_acc": 0.45438, "loss_cls": 4.34065, "loss": 4.34065, "time": 0.70111} +{"mode": "train", "epoch": 20, "iter": 2800, "lr": 0.09578, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21375, "top5_acc": 0.44906, "loss_cls": 4.37022, "loss": 4.37022, "time": 0.70154} +{"mode": "train", "epoch": 20, "iter": 2900, "lr": 0.09577, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21406, "top5_acc": 0.44656, "loss_cls": 4.37944, "loss": 4.37944, "time": 0.69958} +{"mode": "train", "epoch": 20, "iter": 3000, "lr": 0.09576, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21906, "top5_acc": 0.45625, "loss_cls": 4.33755, "loss": 4.33755, "time": 0.7039} +{"mode": "train", "epoch": 20, "iter": 3100, "lr": 0.09575, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21047, "top5_acc": 0.44422, "loss_cls": 4.38638, "loss": 4.38638, "time": 0.70153} +{"mode": "train", "epoch": 20, "iter": 3200, "lr": 0.09574, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21594, "top5_acc": 0.44797, "loss_cls": 4.3649, "loss": 4.3649, "time": 0.70049} +{"mode": "train", "epoch": 20, "iter": 3300, "lr": 0.09573, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21125, "top5_acc": 0.44562, "loss_cls": 4.38574, "loss": 4.38574, "time": 0.70083} +{"mode": "train", "epoch": 20, "iter": 3400, "lr": 0.09572, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21078, "top5_acc": 0.45328, "loss_cls": 4.35241, "loss": 4.35241, "time": 0.6997} +{"mode": "train", "epoch": 20, "iter": 3500, "lr": 0.09571, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22031, "top5_acc": 0.45141, "loss_cls": 4.32989, "loss": 4.32989, "time": 0.71062} +{"mode": "train", "epoch": 20, "iter": 3600, "lr": 0.09569, "memory": 15990, "data_time": 0.00082, "top1_acc": 0.20484, "top5_acc": 0.44281, "loss_cls": 4.37512, "loss": 4.37512, "time": 0.70698} +{"mode": "train", "epoch": 20, "iter": 3700, "lr": 0.09568, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20656, "top5_acc": 0.44812, "loss_cls": 4.35611, "loss": 4.35611, "time": 0.70348} +{"mode": "val", "epoch": 20, "iter": 309, "lr": 0.09568, "top1_acc": 0.1559, "top5_acc": 0.36084, "mean_class_accuracy": 0.1558} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.09567, "memory": 15990, "data_time": 1.25852, "top1_acc": 0.21375, "top5_acc": 0.45578, "loss_cls": 4.30934, "loss": 4.30934, "time": 1.96493} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.09565, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21172, "top5_acc": 0.45547, "loss_cls": 4.32929, "loss": 4.32929, "time": 0.71075} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.09564, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21984, "top5_acc": 0.45, "loss_cls": 4.31665, "loss": 4.31665, "time": 0.71148} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.09563, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21062, "top5_acc": 0.4475, "loss_cls": 4.33483, "loss": 4.33483, "time": 0.70766} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.09562, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22625, "top5_acc": 0.46047, "loss_cls": 4.31176, "loss": 4.31176, "time": 0.70357} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.09561, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20984, "top5_acc": 0.45078, "loss_cls": 4.33546, "loss": 4.33546, "time": 0.69881} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.0956, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21219, "top5_acc": 0.44453, "loss_cls": 4.36123, "loss": 4.36123, "time": 0.69933} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.09559, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21109, "top5_acc": 0.44859, "loss_cls": 4.35395, "loss": 4.35395, "time": 0.70058} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.09557, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20969, "top5_acc": 0.45109, "loss_cls": 4.32178, "loss": 4.32178, "time": 0.70055} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.09556, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22188, "top5_acc": 0.46031, "loss_cls": 4.31, "loss": 4.31, "time": 0.69921} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.09555, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23062, "top5_acc": 0.46344, "loss_cls": 4.29362, "loss": 4.29362, "time": 0.70043} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.09554, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21375, "top5_acc": 0.43969, "loss_cls": 4.38627, "loss": 4.38627, "time": 0.70284} +{"mode": "train", "epoch": 21, "iter": 1300, "lr": 0.09553, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21031, "top5_acc": 0.44141, "loss_cls": 4.37046, "loss": 4.37046, "time": 0.7006} +{"mode": "train", "epoch": 21, "iter": 1400, "lr": 0.09552, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21297, "top5_acc": 0.44172, "loss_cls": 4.37379, "loss": 4.37379, "time": 0.70183} +{"mode": "train", "epoch": 21, "iter": 1500, "lr": 0.09551, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2175, "top5_acc": 0.45297, "loss_cls": 4.35309, "loss": 4.35309, "time": 0.69872} +{"mode": "train", "epoch": 21, "iter": 1600, "lr": 0.09549, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21797, "top5_acc": 0.45359, "loss_cls": 4.34359, "loss": 4.34359, "time": 0.69924} +{"mode": "train", "epoch": 21, "iter": 1700, "lr": 0.09548, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21172, "top5_acc": 0.45328, "loss_cls": 4.3415, "loss": 4.3415, "time": 0.70157} +{"mode": "train", "epoch": 21, "iter": 1800, "lr": 0.09547, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21547, "top5_acc": 0.455, "loss_cls": 4.32291, "loss": 4.32291, "time": 0.70832} +{"mode": "train", "epoch": 21, "iter": 1900, "lr": 0.09546, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22516, "top5_acc": 0.45062, "loss_cls": 4.34246, "loss": 4.34246, "time": 0.69923} +{"mode": "train", "epoch": 21, "iter": 2000, "lr": 0.09545, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20844, "top5_acc": 0.43703, "loss_cls": 4.39636, "loss": 4.39636, "time": 0.70377} +{"mode": "train", "epoch": 21, "iter": 2100, "lr": 0.09544, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21469, "top5_acc": 0.45219, "loss_cls": 4.33872, "loss": 4.33872, "time": 0.70536} +{"mode": "train", "epoch": 21, "iter": 2200, "lr": 0.09542, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22531, "top5_acc": 0.45203, "loss_cls": 4.32193, "loss": 4.32193, "time": 0.70896} +{"mode": "train", "epoch": 21, "iter": 2300, "lr": 0.09541, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22, "top5_acc": 0.455, "loss_cls": 4.31828, "loss": 4.31828, "time": 0.7087} +{"mode": "train", "epoch": 21, "iter": 2400, "lr": 0.0954, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21062, "top5_acc": 0.44766, "loss_cls": 4.3411, "loss": 4.3411, "time": 0.69962} +{"mode": "train", "epoch": 21, "iter": 2500, "lr": 0.09539, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21266, "top5_acc": 0.45469, "loss_cls": 4.33614, "loss": 4.33614, "time": 0.69879} +{"mode": "train", "epoch": 21, "iter": 2600, "lr": 0.09538, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20391, "top5_acc": 0.43734, "loss_cls": 4.37885, "loss": 4.37885, "time": 0.69589} +{"mode": "train", "epoch": 21, "iter": 2700, "lr": 0.09537, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21219, "top5_acc": 0.45031, "loss_cls": 4.3769, "loss": 4.3769, "time": 0.70003} +{"mode": "train", "epoch": 21, "iter": 2800, "lr": 0.09535, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20969, "top5_acc": 0.44859, "loss_cls": 4.3548, "loss": 4.3548, "time": 0.70162} +{"mode": "train", "epoch": 21, "iter": 2900, "lr": 0.09534, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21719, "top5_acc": 0.45375, "loss_cls": 4.32308, "loss": 4.32308, "time": 0.70128} +{"mode": "train", "epoch": 21, "iter": 3000, "lr": 0.09533, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20984, "top5_acc": 0.45453, "loss_cls": 4.33739, "loss": 4.33739, "time": 0.70022} +{"mode": "train", "epoch": 21, "iter": 3100, "lr": 0.09532, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21344, "top5_acc": 0.44812, "loss_cls": 4.36606, "loss": 4.36606, "time": 0.69975} +{"mode": "train", "epoch": 21, "iter": 3200, "lr": 0.09531, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21, "top5_acc": 0.44688, "loss_cls": 4.37678, "loss": 4.37678, "time": 0.6992} +{"mode": "train", "epoch": 21, "iter": 3300, "lr": 0.09529, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21531, "top5_acc": 0.44078, "loss_cls": 4.3728, "loss": 4.3728, "time": 0.69874} +{"mode": "train", "epoch": 21, "iter": 3400, "lr": 0.09528, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21312, "top5_acc": 0.44672, "loss_cls": 4.37436, "loss": 4.37436, "time": 0.69727} +{"mode": "train", "epoch": 21, "iter": 3500, "lr": 0.09527, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22078, "top5_acc": 0.45953, "loss_cls": 4.32502, "loss": 4.32502, "time": 0.70823} +{"mode": "train", "epoch": 21, "iter": 3600, "lr": 0.09526, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21328, "top5_acc": 0.44562, "loss_cls": 4.38281, "loss": 4.38281, "time": 0.70806} +{"mode": "train", "epoch": 21, "iter": 3700, "lr": 0.09525, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21016, "top5_acc": 0.44516, "loss_cls": 4.37701, "loss": 4.37701, "time": 0.70118} +{"mode": "val", "epoch": 21, "iter": 309, "lr": 0.09524, "top1_acc": 0.12263, "top5_acc": 0.31226, "mean_class_accuracy": 0.12246} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.09523, "memory": 15990, "data_time": 1.23879, "top1_acc": 0.215, "top5_acc": 0.45672, "loss_cls": 4.32912, "loss": 4.32912, "time": 1.9475} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.09522, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20938, "top5_acc": 0.44938, "loss_cls": 4.34895, "loss": 4.34895, "time": 0.71326} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.09521, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22906, "top5_acc": 0.46031, "loss_cls": 4.28421, "loss": 4.28421, "time": 0.70266} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.09519, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21641, "top5_acc": 0.45562, "loss_cls": 4.34028, "loss": 4.34028, "time": 0.70128} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.09518, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21641, "top5_acc": 0.45172, "loss_cls": 4.31198, "loss": 4.31198, "time": 0.69985} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.09517, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21859, "top5_acc": 0.45797, "loss_cls": 4.32099, "loss": 4.32099, "time": 0.69987} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.09516, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20969, "top5_acc": 0.45422, "loss_cls": 4.32974, "loss": 4.32974, "time": 0.7032} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.09515, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21094, "top5_acc": 0.46094, "loss_cls": 4.33626, "loss": 4.33626, "time": 0.69846} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.09513, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21359, "top5_acc": 0.45062, "loss_cls": 4.35652, "loss": 4.35652, "time": 0.69991} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.09512, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22203, "top5_acc": 0.455, "loss_cls": 4.33529, "loss": 4.33529, "time": 0.69743} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.09511, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20766, "top5_acc": 0.43844, "loss_cls": 4.38543, "loss": 4.38543, "time": 0.69785} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0951, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21391, "top5_acc": 0.45547, "loss_cls": 4.33454, "loss": 4.33454, "time": 0.69917} +{"mode": "train", "epoch": 22, "iter": 1300, "lr": 0.09509, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21, "top5_acc": 0.45422, "loss_cls": 4.33958, "loss": 4.33958, "time": 0.70153} +{"mode": "train", "epoch": 22, "iter": 1400, "lr": 0.09507, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21547, "top5_acc": 0.44641, "loss_cls": 4.357, "loss": 4.357, "time": 0.69844} +{"mode": "train", "epoch": 22, "iter": 1500, "lr": 0.09506, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20953, "top5_acc": 0.44875, "loss_cls": 4.36574, "loss": 4.36574, "time": 0.69764} +{"mode": "train", "epoch": 22, "iter": 1600, "lr": 0.09505, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.215, "top5_acc": 0.45125, "loss_cls": 4.34771, "loss": 4.34771, "time": 0.69989} +{"mode": "train", "epoch": 22, "iter": 1700, "lr": 0.09504, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21422, "top5_acc": 0.44891, "loss_cls": 4.35855, "loss": 4.35855, "time": 0.70277} +{"mode": "train", "epoch": 22, "iter": 1800, "lr": 0.09502, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21766, "top5_acc": 0.455, "loss_cls": 4.34762, "loss": 4.34762, "time": 0.70801} +{"mode": "train", "epoch": 22, "iter": 1900, "lr": 0.09501, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21219, "top5_acc": 0.44719, "loss_cls": 4.36369, "loss": 4.36369, "time": 0.69842} +{"mode": "train", "epoch": 22, "iter": 2000, "lr": 0.095, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21078, "top5_acc": 0.45172, "loss_cls": 4.37999, "loss": 4.37999, "time": 0.70043} +{"mode": "train", "epoch": 22, "iter": 2100, "lr": 0.09499, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22375, "top5_acc": 0.45094, "loss_cls": 4.32076, "loss": 4.32076, "time": 0.70536} +{"mode": "train", "epoch": 22, "iter": 2200, "lr": 0.09498, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20953, "top5_acc": 0.44969, "loss_cls": 4.3714, "loss": 4.3714, "time": 0.70145} +{"mode": "train", "epoch": 22, "iter": 2300, "lr": 0.09496, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22172, "top5_acc": 0.45672, "loss_cls": 4.32246, "loss": 4.32246, "time": 0.70122} +{"mode": "train", "epoch": 22, "iter": 2400, "lr": 0.09495, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21531, "top5_acc": 0.45109, "loss_cls": 4.37093, "loss": 4.37093, "time": 0.69936} +{"mode": "train", "epoch": 22, "iter": 2500, "lr": 0.09494, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21453, "top5_acc": 0.46016, "loss_cls": 4.33701, "loss": 4.33701, "time": 0.69864} +{"mode": "train", "epoch": 22, "iter": 2600, "lr": 0.09493, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21266, "top5_acc": 0.44578, "loss_cls": 4.369, "loss": 4.369, "time": 0.69828} +{"mode": "train", "epoch": 22, "iter": 2700, "lr": 0.09491, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2125, "top5_acc": 0.44812, "loss_cls": 4.38156, "loss": 4.38156, "time": 0.69936} +{"mode": "train", "epoch": 22, "iter": 2800, "lr": 0.0949, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22344, "top5_acc": 0.46016, "loss_cls": 4.30277, "loss": 4.30277, "time": 0.7012} +{"mode": "train", "epoch": 22, "iter": 2900, "lr": 0.09489, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20156, "top5_acc": 0.44516, "loss_cls": 4.37598, "loss": 4.37598, "time": 0.69782} +{"mode": "train", "epoch": 22, "iter": 3000, "lr": 0.09488, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21891, "top5_acc": 0.45641, "loss_cls": 4.31625, "loss": 4.31625, "time": 0.69953} +{"mode": "train", "epoch": 22, "iter": 3100, "lr": 0.09487, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22094, "top5_acc": 0.44766, "loss_cls": 4.34376, "loss": 4.34376, "time": 0.69865} +{"mode": "train", "epoch": 22, "iter": 3200, "lr": 0.09485, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21891, "top5_acc": 0.44438, "loss_cls": 4.34138, "loss": 4.34138, "time": 0.70122} +{"mode": "train", "epoch": 22, "iter": 3300, "lr": 0.09484, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22422, "top5_acc": 0.46016, "loss_cls": 4.33138, "loss": 4.33138, "time": 0.69799} +{"mode": "train", "epoch": 22, "iter": 3400, "lr": 0.09483, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21688, "top5_acc": 0.45641, "loss_cls": 4.32074, "loss": 4.32074, "time": 0.70061} +{"mode": "train", "epoch": 22, "iter": 3500, "lr": 0.09482, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.20859, "top5_acc": 0.44562, "loss_cls": 4.36964, "loss": 4.36964, "time": 0.70498} +{"mode": "train", "epoch": 22, "iter": 3600, "lr": 0.0948, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21047, "top5_acc": 0.45016, "loss_cls": 4.34926, "loss": 4.34926, "time": 0.70562} +{"mode": "train", "epoch": 22, "iter": 3700, "lr": 0.09479, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21438, "top5_acc": 0.45641, "loss_cls": 4.31123, "loss": 4.31123, "time": 0.70193} +{"mode": "val", "epoch": 22, "iter": 309, "lr": 0.09479, "top1_acc": 0.12222, "top5_acc": 0.29747, "mean_class_accuracy": 0.12214} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.09477, "memory": 15990, "data_time": 1.26589, "top1_acc": 0.21984, "top5_acc": 0.45484, "loss_cls": 4.31037, "loss": 4.31037, "time": 1.97097} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.09476, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22359, "top5_acc": 0.45859, "loss_cls": 4.32184, "loss": 4.32184, "time": 0.71088} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.09475, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22531, "top5_acc": 0.46078, "loss_cls": 4.28977, "loss": 4.28977, "time": 0.70612} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.09474, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21328, "top5_acc": 0.45656, "loss_cls": 4.31245, "loss": 4.31245, "time": 0.7036} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.09472, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20812, "top5_acc": 0.45438, "loss_cls": 4.33433, "loss": 4.33433, "time": 0.70009} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.09471, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21453, "top5_acc": 0.45781, "loss_cls": 4.29007, "loss": 4.29007, "time": 0.70345} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.0947, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22891, "top5_acc": 0.46359, "loss_cls": 4.28468, "loss": 4.28468, "time": 0.69773} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.09469, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22188, "top5_acc": 0.44984, "loss_cls": 4.33533, "loss": 4.33533, "time": 0.7009} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.09467, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21484, "top5_acc": 0.45141, "loss_cls": 4.35018, "loss": 4.35018, "time": 0.70141} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.09466, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21781, "top5_acc": 0.45297, "loss_cls": 4.36166, "loss": 4.36166, "time": 0.69951} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.09465, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21484, "top5_acc": 0.44828, "loss_cls": 4.35403, "loss": 4.35403, "time": 0.69976} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.09464, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21859, "top5_acc": 0.46047, "loss_cls": 4.30725, "loss": 4.30725, "time": 0.70503} +{"mode": "train", "epoch": 23, "iter": 1300, "lr": 0.09462, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21484, "top5_acc": 0.45438, "loss_cls": 4.32678, "loss": 4.32678, "time": 0.69976} +{"mode": "train", "epoch": 23, "iter": 1400, "lr": 0.09461, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21812, "top5_acc": 0.44984, "loss_cls": 4.34839, "loss": 4.34839, "time": 0.69783} +{"mode": "train", "epoch": 23, "iter": 1500, "lr": 0.0946, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21078, "top5_acc": 0.46469, "loss_cls": 4.31356, "loss": 4.31356, "time": 0.69968} +{"mode": "train", "epoch": 23, "iter": 1600, "lr": 0.09459, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21688, "top5_acc": 0.45719, "loss_cls": 4.32053, "loss": 4.32053, "time": 0.70326} +{"mode": "train", "epoch": 23, "iter": 1700, "lr": 0.09457, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20578, "top5_acc": 0.45453, "loss_cls": 4.34176, "loss": 4.34176, "time": 0.70678} +{"mode": "train", "epoch": 23, "iter": 1800, "lr": 0.09456, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22484, "top5_acc": 0.45688, "loss_cls": 4.30812, "loss": 4.30812, "time": 0.70529} +{"mode": "train", "epoch": 23, "iter": 1900, "lr": 0.09455, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20656, "top5_acc": 0.44312, "loss_cls": 4.37066, "loss": 4.37066, "time": 0.69709} +{"mode": "train", "epoch": 23, "iter": 2000, "lr": 0.09453, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21703, "top5_acc": 0.45375, "loss_cls": 4.32783, "loss": 4.32783, "time": 0.70631} +{"mode": "train", "epoch": 23, "iter": 2100, "lr": 0.09452, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22469, "top5_acc": 0.46359, "loss_cls": 4.2943, "loss": 4.2943, "time": 0.70944} +{"mode": "train", "epoch": 23, "iter": 2200, "lr": 0.09451, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22547, "top5_acc": 0.46688, "loss_cls": 4.28145, "loss": 4.28145, "time": 0.70212} +{"mode": "train", "epoch": 23, "iter": 2300, "lr": 0.0945, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21562, "top5_acc": 0.44656, "loss_cls": 4.37676, "loss": 4.37676, "time": 0.69948} +{"mode": "train", "epoch": 23, "iter": 2400, "lr": 0.09448, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21938, "top5_acc": 0.45812, "loss_cls": 4.3412, "loss": 4.3412, "time": 0.70137} +{"mode": "train", "epoch": 23, "iter": 2500, "lr": 0.09447, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20953, "top5_acc": 0.45141, "loss_cls": 4.35442, "loss": 4.35442, "time": 0.70024} +{"mode": "train", "epoch": 23, "iter": 2600, "lr": 0.09446, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21703, "top5_acc": 0.4525, "loss_cls": 4.37436, "loss": 4.37436, "time": 0.69973} +{"mode": "train", "epoch": 23, "iter": 2700, "lr": 0.09445, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20719, "top5_acc": 0.43984, "loss_cls": 4.39782, "loss": 4.39782, "time": 0.69952} +{"mode": "train", "epoch": 23, "iter": 2800, "lr": 0.09443, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21594, "top5_acc": 0.46281, "loss_cls": 4.30813, "loss": 4.30813, "time": 0.69894} +{"mode": "train", "epoch": 23, "iter": 2900, "lr": 0.09442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21609, "top5_acc": 0.455, "loss_cls": 4.35058, "loss": 4.35058, "time": 0.70085} +{"mode": "train", "epoch": 23, "iter": 3000, "lr": 0.09441, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21094, "top5_acc": 0.45078, "loss_cls": 4.35207, "loss": 4.35207, "time": 0.69922} +{"mode": "train", "epoch": 23, "iter": 3100, "lr": 0.09439, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20594, "top5_acc": 0.44203, "loss_cls": 4.3689, "loss": 4.3689, "time": 0.69904} +{"mode": "train", "epoch": 23, "iter": 3200, "lr": 0.09438, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21359, "top5_acc": 0.4575, "loss_cls": 4.30613, "loss": 4.30613, "time": 0.69952} +{"mode": "train", "epoch": 23, "iter": 3300, "lr": 0.09437, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21016, "top5_acc": 0.45969, "loss_cls": 4.33727, "loss": 4.33727, "time": 0.69932} +{"mode": "train", "epoch": 23, "iter": 3400, "lr": 0.09436, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20484, "top5_acc": 0.44625, "loss_cls": 4.37945, "loss": 4.37945, "time": 0.70062} +{"mode": "train", "epoch": 23, "iter": 3500, "lr": 0.09434, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21828, "top5_acc": 0.45016, "loss_cls": 4.33874, "loss": 4.33874, "time": 0.71164} +{"mode": "train", "epoch": 23, "iter": 3600, "lr": 0.09433, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21516, "top5_acc": 0.44859, "loss_cls": 4.35171, "loss": 4.35171, "time": 0.70995} +{"mode": "train", "epoch": 23, "iter": 3700, "lr": 0.09432, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21188, "top5_acc": 0.45094, "loss_cls": 4.35221, "loss": 4.35221, "time": 0.70004} +{"mode": "val", "epoch": 23, "iter": 309, "lr": 0.09431, "top1_acc": 0.10819, "top5_acc": 0.28648, "mean_class_accuracy": 0.10796} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.0943, "memory": 15990, "data_time": 1.25847, "top1_acc": 0.22531, "top5_acc": 0.46531, "loss_cls": 4.27068, "loss": 4.27068, "time": 1.96459} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.09428, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21969, "top5_acc": 0.45922, "loss_cls": 4.28497, "loss": 4.28497, "time": 0.7074} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.09427, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22516, "top5_acc": 0.46484, "loss_cls": 4.28217, "loss": 4.28217, "time": 0.70799} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.09426, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21719, "top5_acc": 0.46219, "loss_cls": 4.30863, "loss": 4.30863, "time": 0.70371} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.09425, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21219, "top5_acc": 0.45594, "loss_cls": 4.33493, "loss": 4.33493, "time": 0.70322} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.09423, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21406, "top5_acc": 0.45906, "loss_cls": 4.33896, "loss": 4.33896, "time": 0.70179} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.09422, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21391, "top5_acc": 0.45078, "loss_cls": 4.32746, "loss": 4.32746, "time": 0.69828} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.09421, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21406, "top5_acc": 0.45859, "loss_cls": 4.31134, "loss": 4.31134, "time": 0.69947} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.09419, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21062, "top5_acc": 0.45125, "loss_cls": 4.34816, "loss": 4.34816, "time": 0.69885} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.09418, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22203, "top5_acc": 0.45984, "loss_cls": 4.32058, "loss": 4.32058, "time": 0.70301} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.09417, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21312, "top5_acc": 0.44797, "loss_cls": 4.34778, "loss": 4.34778, "time": 0.6996} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.09415, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21938, "top5_acc": 0.45094, "loss_cls": 4.31083, "loss": 4.31083, "time": 0.69882} +{"mode": "train", "epoch": 24, "iter": 1300, "lr": 0.09414, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21266, "top5_acc": 0.44906, "loss_cls": 4.33431, "loss": 4.33431, "time": 0.69891} +{"mode": "train", "epoch": 24, "iter": 1400, "lr": 0.09413, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21312, "top5_acc": 0.45953, "loss_cls": 4.32937, "loss": 4.32937, "time": 0.69834} +{"mode": "train", "epoch": 24, "iter": 1500, "lr": 0.09411, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21406, "top5_acc": 0.46094, "loss_cls": 4.3052, "loss": 4.3052, "time": 0.69955} +{"mode": "train", "epoch": 24, "iter": 1600, "lr": 0.0941, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21281, "top5_acc": 0.45875, "loss_cls": 4.32218, "loss": 4.32218, "time": 0.69721} +{"mode": "train", "epoch": 24, "iter": 1700, "lr": 0.09409, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22109, "top5_acc": 0.45891, "loss_cls": 4.32065, "loss": 4.32065, "time": 0.70227} +{"mode": "train", "epoch": 24, "iter": 1800, "lr": 0.09407, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21203, "top5_acc": 0.45578, "loss_cls": 4.3358, "loss": 4.3358, "time": 0.70742} +{"mode": "train", "epoch": 24, "iter": 1900, "lr": 0.09406, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.20891, "top5_acc": 0.45453, "loss_cls": 4.3247, "loss": 4.3247, "time": 0.70323} +{"mode": "train", "epoch": 24, "iter": 2000, "lr": 0.09405, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.215, "top5_acc": 0.45062, "loss_cls": 4.35457, "loss": 4.35457, "time": 0.70523} +{"mode": "train", "epoch": 24, "iter": 2100, "lr": 0.09404, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21281, "top5_acc": 0.45266, "loss_cls": 4.34933, "loss": 4.34933, "time": 0.70579} +{"mode": "train", "epoch": 24, "iter": 2200, "lr": 0.09402, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22703, "top5_acc": 0.46422, "loss_cls": 4.30027, "loss": 4.30027, "time": 0.69994} +{"mode": "train", "epoch": 24, "iter": 2300, "lr": 0.09401, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2175, "top5_acc": 0.46469, "loss_cls": 4.30266, "loss": 4.30266, "time": 0.69975} +{"mode": "train", "epoch": 24, "iter": 2400, "lr": 0.094, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20469, "top5_acc": 0.44594, "loss_cls": 4.38815, "loss": 4.38815, "time": 0.69951} +{"mode": "train", "epoch": 24, "iter": 2500, "lr": 0.09398, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21562, "top5_acc": 0.45328, "loss_cls": 4.32492, "loss": 4.32492, "time": 0.69729} +{"mode": "train", "epoch": 24, "iter": 2600, "lr": 0.09397, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21891, "top5_acc": 0.46438, "loss_cls": 4.30706, "loss": 4.30706, "time": 0.70023} +{"mode": "train", "epoch": 24, "iter": 2700, "lr": 0.09396, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21094, "top5_acc": 0.44984, "loss_cls": 4.36173, "loss": 4.36173, "time": 0.69767} +{"mode": "train", "epoch": 24, "iter": 2800, "lr": 0.09394, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.205, "top5_acc": 0.45234, "loss_cls": 4.35905, "loss": 4.35905, "time": 0.70026} +{"mode": "train", "epoch": 24, "iter": 2900, "lr": 0.09393, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21688, "top5_acc": 0.4575, "loss_cls": 4.32743, "loss": 4.32743, "time": 0.69673} +{"mode": "train", "epoch": 24, "iter": 3000, "lr": 0.09392, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21922, "top5_acc": 0.46031, "loss_cls": 4.30799, "loss": 4.30799, "time": 0.69884} +{"mode": "train", "epoch": 24, "iter": 3100, "lr": 0.0939, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20969, "top5_acc": 0.45766, "loss_cls": 4.33628, "loss": 4.33628, "time": 0.69931} +{"mode": "train", "epoch": 24, "iter": 3200, "lr": 0.09389, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2175, "top5_acc": 0.45688, "loss_cls": 4.3295, "loss": 4.3295, "time": 0.69726} +{"mode": "train", "epoch": 24, "iter": 3300, "lr": 0.09388, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2225, "top5_acc": 0.46188, "loss_cls": 4.31441, "loss": 4.31441, "time": 0.70005} +{"mode": "train", "epoch": 24, "iter": 3400, "lr": 0.09386, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21656, "top5_acc": 0.4575, "loss_cls": 4.32031, "loss": 4.32031, "time": 0.7047} +{"mode": "train", "epoch": 24, "iter": 3500, "lr": 0.09385, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21406, "top5_acc": 0.45312, "loss_cls": 4.36187, "loss": 4.36187, "time": 0.70485} +{"mode": "train", "epoch": 24, "iter": 3600, "lr": 0.09384, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.20391, "top5_acc": 0.43547, "loss_cls": 4.38511, "loss": 4.38511, "time": 0.71119} +{"mode": "train", "epoch": 24, "iter": 3700, "lr": 0.09382, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.225, "top5_acc": 0.45453, "loss_cls": 4.31887, "loss": 4.31887, "time": 0.70718} +{"mode": "val", "epoch": 24, "iter": 309, "lr": 0.09382, "top1_acc": 0.16451, "top5_acc": 0.37456, "mean_class_accuracy": 0.1646} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.0938, "memory": 15990, "data_time": 1.25958, "top1_acc": 0.22062, "top5_acc": 0.45516, "loss_cls": 4.31037, "loss": 4.31037, "time": 1.96655} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.09379, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.21203, "top5_acc": 0.45672, "loss_cls": 4.30348, "loss": 4.30348, "time": 0.70872} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.09378, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21625, "top5_acc": 0.45422, "loss_cls": 4.32379, "loss": 4.32379, "time": 0.71018} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.09376, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21781, "top5_acc": 0.45969, "loss_cls": 4.31163, "loss": 4.31163, "time": 0.7007} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.09375, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22406, "top5_acc": 0.46859, "loss_cls": 4.30533, "loss": 4.30533, "time": 0.70082} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.09373, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22672, "top5_acc": 0.45953, "loss_cls": 4.31369, "loss": 4.31369, "time": 0.70061} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.09372, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.215, "top5_acc": 0.45562, "loss_cls": 4.3643, "loss": 4.3643, "time": 0.69908} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.09371, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21328, "top5_acc": 0.45266, "loss_cls": 4.34189, "loss": 4.34189, "time": 0.69819} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.09369, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22141, "top5_acc": 0.46312, "loss_cls": 4.29201, "loss": 4.29201, "time": 0.70039} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.09368, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21312, "top5_acc": 0.45547, "loss_cls": 4.30637, "loss": 4.30637, "time": 0.69924} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.09367, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22781, "top5_acc": 0.46734, "loss_cls": 4.25968, "loss": 4.25968, "time": 0.70221} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.09365, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21328, "top5_acc": 0.44344, "loss_cls": 4.35953, "loss": 4.35953, "time": 0.69967} +{"mode": "train", "epoch": 25, "iter": 1300, "lr": 0.09364, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21484, "top5_acc": 0.46, "loss_cls": 4.31691, "loss": 4.31691, "time": 0.69811} +{"mode": "train", "epoch": 25, "iter": 1400, "lr": 0.09363, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21328, "top5_acc": 0.45594, "loss_cls": 4.35111, "loss": 4.35111, "time": 0.70123} +{"mode": "train", "epoch": 25, "iter": 1500, "lr": 0.09361, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21984, "top5_acc": 0.45703, "loss_cls": 4.31562, "loss": 4.31562, "time": 0.69992} +{"mode": "train", "epoch": 25, "iter": 1600, "lr": 0.0936, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21781, "top5_acc": 0.44094, "loss_cls": 4.35295, "loss": 4.35295, "time": 0.7} +{"mode": "train", "epoch": 25, "iter": 1700, "lr": 0.09358, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21344, "top5_acc": 0.44844, "loss_cls": 4.35084, "loss": 4.35084, "time": 0.70439} +{"mode": "train", "epoch": 25, "iter": 1800, "lr": 0.09357, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22078, "top5_acc": 0.45625, "loss_cls": 4.3397, "loss": 4.3397, "time": 0.70883} +{"mode": "train", "epoch": 25, "iter": 1900, "lr": 0.09356, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20938, "top5_acc": 0.43828, "loss_cls": 4.40413, "loss": 4.40413, "time": 0.70543} +{"mode": "train", "epoch": 25, "iter": 2000, "lr": 0.09354, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21719, "top5_acc": 0.45141, "loss_cls": 4.33163, "loss": 4.33163, "time": 0.70271} +{"mode": "train", "epoch": 25, "iter": 2100, "lr": 0.09353, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21172, "top5_acc": 0.44766, "loss_cls": 4.35692, "loss": 4.35692, "time": 0.70651} +{"mode": "train", "epoch": 25, "iter": 2200, "lr": 0.09352, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21969, "top5_acc": 0.45531, "loss_cls": 4.33018, "loss": 4.33018, "time": 0.70183} +{"mode": "train", "epoch": 25, "iter": 2300, "lr": 0.0935, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22312, "top5_acc": 0.45328, "loss_cls": 4.31726, "loss": 4.31726, "time": 0.70237} +{"mode": "train", "epoch": 25, "iter": 2400, "lr": 0.09349, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22031, "top5_acc": 0.45688, "loss_cls": 4.30845, "loss": 4.30845, "time": 0.70051} +{"mode": "train", "epoch": 25, "iter": 2500, "lr": 0.09347, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21625, "top5_acc": 0.45266, "loss_cls": 4.32277, "loss": 4.32277, "time": 0.69919} +{"mode": "train", "epoch": 25, "iter": 2600, "lr": 0.09346, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22172, "top5_acc": 0.45906, "loss_cls": 4.31628, "loss": 4.31628, "time": 0.69774} +{"mode": "train", "epoch": 25, "iter": 2700, "lr": 0.09345, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21484, "top5_acc": 0.45203, "loss_cls": 4.33728, "loss": 4.33728, "time": 0.70136} +{"mode": "train", "epoch": 25, "iter": 2800, "lr": 0.09343, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21391, "top5_acc": 0.45047, "loss_cls": 4.33694, "loss": 4.33694, "time": 0.70148} +{"mode": "train", "epoch": 25, "iter": 2900, "lr": 0.09342, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20812, "top5_acc": 0.44984, "loss_cls": 4.37678, "loss": 4.37678, "time": 0.7003} +{"mode": "train", "epoch": 25, "iter": 3000, "lr": 0.09341, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22125, "top5_acc": 0.45719, "loss_cls": 4.3116, "loss": 4.3116, "time": 0.70191} +{"mode": "train", "epoch": 25, "iter": 3100, "lr": 0.09339, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22062, "top5_acc": 0.46219, "loss_cls": 4.29203, "loss": 4.29203, "time": 0.70059} +{"mode": "train", "epoch": 25, "iter": 3200, "lr": 0.09338, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21, "top5_acc": 0.44344, "loss_cls": 4.34955, "loss": 4.34955, "time": 0.70243} +{"mode": "train", "epoch": 25, "iter": 3300, "lr": 0.09336, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2075, "top5_acc": 0.44047, "loss_cls": 4.38851, "loss": 4.38851, "time": 0.69897} +{"mode": "train", "epoch": 25, "iter": 3400, "lr": 0.09335, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22578, "top5_acc": 0.45562, "loss_cls": 4.31924, "loss": 4.31924, "time": 0.69911} +{"mode": "train", "epoch": 25, "iter": 3500, "lr": 0.09334, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21766, "top5_acc": 0.45422, "loss_cls": 4.32439, "loss": 4.32439, "time": 0.70939} +{"mode": "train", "epoch": 25, "iter": 3600, "lr": 0.09332, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21688, "top5_acc": 0.46, "loss_cls": 4.2946, "loss": 4.2946, "time": 0.70731} +{"mode": "train", "epoch": 25, "iter": 3700, "lr": 0.09331, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20953, "top5_acc": 0.4575, "loss_cls": 4.33252, "loss": 4.33252, "time": 0.70577} +{"mode": "val", "epoch": 25, "iter": 309, "lr": 0.0933, "top1_acc": 0.12435, "top5_acc": 0.30436, "mean_class_accuracy": 0.12426} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.09329, "memory": 15990, "data_time": 1.23953, "top1_acc": 0.22016, "top5_acc": 0.46672, "loss_cls": 4.29067, "loss": 4.29067, "time": 1.94592} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.09327, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22203, "top5_acc": 0.46625, "loss_cls": 4.2562, "loss": 4.2562, "time": 0.70712} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.09326, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22125, "top5_acc": 0.46391, "loss_cls": 4.30904, "loss": 4.30904, "time": 0.70801} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.09325, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22359, "top5_acc": 0.45766, "loss_cls": 4.27907, "loss": 4.27907, "time": 0.70494} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.09323, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22266, "top5_acc": 0.46156, "loss_cls": 4.29136, "loss": 4.29136, "time": 0.70264} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.09322, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21547, "top5_acc": 0.45844, "loss_cls": 4.30903, "loss": 4.30903, "time": 0.69983} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.0932, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21203, "top5_acc": 0.44969, "loss_cls": 4.32687, "loss": 4.32687, "time": 0.70057} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.09319, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22188, "top5_acc": 0.46734, "loss_cls": 4.29353, "loss": 4.29353, "time": 0.70309} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.09318, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21609, "top5_acc": 0.45156, "loss_cls": 4.36758, "loss": 4.36758, "time": 0.70078} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.09316, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22641, "top5_acc": 0.47469, "loss_cls": 4.2415, "loss": 4.2415, "time": 0.69982} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.09315, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21719, "top5_acc": 0.46438, "loss_cls": 4.29344, "loss": 4.29344, "time": 0.69904} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.09313, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21219, "top5_acc": 0.45641, "loss_cls": 4.30677, "loss": 4.30677, "time": 0.70074} +{"mode": "train", "epoch": 26, "iter": 1300, "lr": 0.09312, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22109, "top5_acc": 0.46469, "loss_cls": 4.29639, "loss": 4.29639, "time": 0.70128} +{"mode": "train", "epoch": 26, "iter": 1400, "lr": 0.0931, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21734, "top5_acc": 0.46172, "loss_cls": 4.29316, "loss": 4.29316, "time": 0.7019} +{"mode": "train", "epoch": 26, "iter": 1500, "lr": 0.09309, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21844, "top5_acc": 0.45547, "loss_cls": 4.33594, "loss": 4.33594, "time": 0.70377} +{"mode": "train", "epoch": 26, "iter": 1600, "lr": 0.09308, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22578, "top5_acc": 0.46859, "loss_cls": 4.30649, "loss": 4.30649, "time": 0.69831} +{"mode": "train", "epoch": 26, "iter": 1700, "lr": 0.09306, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21391, "top5_acc": 0.45188, "loss_cls": 4.34976, "loss": 4.34976, "time": 0.70465} +{"mode": "train", "epoch": 26, "iter": 1800, "lr": 0.09305, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22109, "top5_acc": 0.45438, "loss_cls": 4.32675, "loss": 4.32675, "time": 0.70904} +{"mode": "train", "epoch": 26, "iter": 1900, "lr": 0.09303, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21688, "top5_acc": 0.46312, "loss_cls": 4.32797, "loss": 4.32797, "time": 0.70707} +{"mode": "train", "epoch": 26, "iter": 2000, "lr": 0.09302, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21531, "top5_acc": 0.45, "loss_cls": 4.34214, "loss": 4.34214, "time": 0.70067} +{"mode": "train", "epoch": 26, "iter": 2100, "lr": 0.093, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21812, "top5_acc": 0.45688, "loss_cls": 4.32977, "loss": 4.32977, "time": 0.70601} +{"mode": "train", "epoch": 26, "iter": 2200, "lr": 0.09299, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21344, "top5_acc": 0.45, "loss_cls": 4.32878, "loss": 4.32878, "time": 0.69914} +{"mode": "train", "epoch": 26, "iter": 2300, "lr": 0.09298, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22281, "top5_acc": 0.45359, "loss_cls": 4.32827, "loss": 4.32827, "time": 0.70252} +{"mode": "train", "epoch": 26, "iter": 2400, "lr": 0.09296, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21719, "top5_acc": 0.44719, "loss_cls": 4.35536, "loss": 4.35536, "time": 0.70029} +{"mode": "train", "epoch": 26, "iter": 2500, "lr": 0.09295, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22094, "top5_acc": 0.45672, "loss_cls": 4.29364, "loss": 4.29364, "time": 0.70157} +{"mode": "train", "epoch": 26, "iter": 2600, "lr": 0.09293, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21859, "top5_acc": 0.45344, "loss_cls": 4.36219, "loss": 4.36219, "time": 0.69932} +{"mode": "train", "epoch": 26, "iter": 2700, "lr": 0.09292, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22578, "top5_acc": 0.46172, "loss_cls": 4.27701, "loss": 4.27701, "time": 0.70116} +{"mode": "train", "epoch": 26, "iter": 2800, "lr": 0.0929, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21719, "top5_acc": 0.45562, "loss_cls": 4.32158, "loss": 4.32158, "time": 0.70162} +{"mode": "train", "epoch": 26, "iter": 2900, "lr": 0.09289, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21422, "top5_acc": 0.45031, "loss_cls": 4.33437, "loss": 4.33437, "time": 0.70056} +{"mode": "train", "epoch": 26, "iter": 3000, "lr": 0.09288, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22203, "top5_acc": 0.46031, "loss_cls": 4.32424, "loss": 4.32424, "time": 0.70638} +{"mode": "train", "epoch": 26, "iter": 3100, "lr": 0.09286, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22219, "top5_acc": 0.46188, "loss_cls": 4.3036, "loss": 4.3036, "time": 0.69975} +{"mode": "train", "epoch": 26, "iter": 3200, "lr": 0.09285, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21922, "top5_acc": 0.44797, "loss_cls": 4.3434, "loss": 4.3434, "time": 0.69779} +{"mode": "train", "epoch": 26, "iter": 3300, "lr": 0.09283, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21719, "top5_acc": 0.45312, "loss_cls": 4.34546, "loss": 4.34546, "time": 0.70439} +{"mode": "train", "epoch": 26, "iter": 3400, "lr": 0.09282, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22016, "top5_acc": 0.46281, "loss_cls": 4.3056, "loss": 4.3056, "time": 0.70137} +{"mode": "train", "epoch": 26, "iter": 3500, "lr": 0.0928, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21859, "top5_acc": 0.4575, "loss_cls": 4.3373, "loss": 4.3373, "time": 0.71393} +{"mode": "train", "epoch": 26, "iter": 3600, "lr": 0.09279, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.2075, "top5_acc": 0.4475, "loss_cls": 4.37394, "loss": 4.37394, "time": 0.71301} +{"mode": "train", "epoch": 26, "iter": 3700, "lr": 0.09278, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20375, "top5_acc": 0.44094, "loss_cls": 4.39318, "loss": 4.39318, "time": 0.70563} +{"mode": "val", "epoch": 26, "iter": 309, "lr": 0.09277, "top1_acc": 0.14329, "top5_acc": 0.33977, "mean_class_accuracy": 0.14326} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.09275, "memory": 15990, "data_time": 1.26603, "top1_acc": 0.22547, "top5_acc": 0.46797, "loss_cls": 4.25335, "loss": 4.25335, "time": 1.97299} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.09274, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22422, "top5_acc": 0.46469, "loss_cls": 4.26427, "loss": 4.26427, "time": 0.70977} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.09272, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.21469, "top5_acc": 0.44609, "loss_cls": 4.35284, "loss": 4.35284, "time": 0.71108} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.09271, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22031, "top5_acc": 0.46188, "loss_cls": 4.31618, "loss": 4.31618, "time": 0.70321} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.0927, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21609, "top5_acc": 0.46031, "loss_cls": 4.2912, "loss": 4.2912, "time": 0.69876} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.09268, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21516, "top5_acc": 0.45625, "loss_cls": 4.32286, "loss": 4.32286, "time": 0.70408} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.09267, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22719, "top5_acc": 0.46172, "loss_cls": 4.29951, "loss": 4.29951, "time": 0.70304} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.09265, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22062, "top5_acc": 0.45562, "loss_cls": 4.32061, "loss": 4.32061, "time": 0.70101} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.09264, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21281, "top5_acc": 0.45422, "loss_cls": 4.31796, "loss": 4.31796, "time": 0.70038} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.09262, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21766, "top5_acc": 0.46453, "loss_cls": 4.28171, "loss": 4.28171, "time": 0.69918} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.09261, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22438, "top5_acc": 0.45359, "loss_cls": 4.31389, "loss": 4.31389, "time": 0.7011} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.09259, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21344, "top5_acc": 0.45609, "loss_cls": 4.33454, "loss": 4.33454, "time": 0.69834} +{"mode": "train", "epoch": 27, "iter": 1300, "lr": 0.09258, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22828, "top5_acc": 0.4625, "loss_cls": 4.29189, "loss": 4.29189, "time": 0.69892} +{"mode": "train", "epoch": 27, "iter": 1400, "lr": 0.09256, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22062, "top5_acc": 0.4525, "loss_cls": 4.34281, "loss": 4.34281, "time": 0.70092} +{"mode": "train", "epoch": 27, "iter": 1500, "lr": 0.09255, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22641, "top5_acc": 0.45406, "loss_cls": 4.28993, "loss": 4.28993, "time": 0.70027} +{"mode": "train", "epoch": 27, "iter": 1600, "lr": 0.09253, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22094, "top5_acc": 0.45422, "loss_cls": 4.3431, "loss": 4.3431, "time": 0.69971} +{"mode": "train", "epoch": 27, "iter": 1700, "lr": 0.09252, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21562, "top5_acc": 0.45594, "loss_cls": 4.31176, "loss": 4.31176, "time": 0.7086} +{"mode": "train", "epoch": 27, "iter": 1800, "lr": 0.09251, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21656, "top5_acc": 0.45922, "loss_cls": 4.3342, "loss": 4.3342, "time": 0.71014} +{"mode": "train", "epoch": 27, "iter": 1900, "lr": 0.09249, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21891, "top5_acc": 0.45344, "loss_cls": 4.3063, "loss": 4.3063, "time": 0.70557} +{"mode": "train", "epoch": 27, "iter": 2000, "lr": 0.09248, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22453, "top5_acc": 0.46422, "loss_cls": 4.26739, "loss": 4.26739, "time": 0.70934} +{"mode": "train", "epoch": 27, "iter": 2100, "lr": 0.09246, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21875, "top5_acc": 0.45016, "loss_cls": 4.35365, "loss": 4.35365, "time": 0.70372} +{"mode": "train", "epoch": 27, "iter": 2200, "lr": 0.09245, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21812, "top5_acc": 0.45594, "loss_cls": 4.33133, "loss": 4.33133, "time": 0.70071} +{"mode": "train", "epoch": 27, "iter": 2300, "lr": 0.09243, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21719, "top5_acc": 0.45609, "loss_cls": 4.31468, "loss": 4.31468, "time": 0.69952} +{"mode": "train", "epoch": 27, "iter": 2400, "lr": 0.09242, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22141, "top5_acc": 0.45188, "loss_cls": 4.31549, "loss": 4.31549, "time": 0.70014} +{"mode": "train", "epoch": 27, "iter": 2500, "lr": 0.0924, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22156, "top5_acc": 0.45734, "loss_cls": 4.32212, "loss": 4.32212, "time": 0.69982} +{"mode": "train", "epoch": 27, "iter": 2600, "lr": 0.09239, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21688, "top5_acc": 0.45406, "loss_cls": 4.35569, "loss": 4.35569, "time": 0.69735} +{"mode": "train", "epoch": 27, "iter": 2700, "lr": 0.09237, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22172, "top5_acc": 0.46375, "loss_cls": 4.29471, "loss": 4.29471, "time": 0.69765} +{"mode": "train", "epoch": 27, "iter": 2800, "lr": 0.09236, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22484, "top5_acc": 0.46562, "loss_cls": 4.27512, "loss": 4.27512, "time": 0.69963} +{"mode": "train", "epoch": 27, "iter": 2900, "lr": 0.09234, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22422, "top5_acc": 0.465, "loss_cls": 4.29572, "loss": 4.29572, "time": 0.69743} +{"mode": "train", "epoch": 27, "iter": 3000, "lr": 0.09233, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22047, "top5_acc": 0.455, "loss_cls": 4.35599, "loss": 4.35599, "time": 0.69686} +{"mode": "train", "epoch": 27, "iter": 3100, "lr": 0.09231, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21578, "top5_acc": 0.445, "loss_cls": 4.35717, "loss": 4.35717, "time": 0.69886} +{"mode": "train", "epoch": 27, "iter": 3200, "lr": 0.0923, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21703, "top5_acc": 0.45422, "loss_cls": 4.3289, "loss": 4.3289, "time": 0.69759} +{"mode": "train", "epoch": 27, "iter": 3300, "lr": 0.09228, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21719, "top5_acc": 0.45484, "loss_cls": 4.31842, "loss": 4.31842, "time": 0.69808} +{"mode": "train", "epoch": 27, "iter": 3400, "lr": 0.09227, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20984, "top5_acc": 0.44531, "loss_cls": 4.37499, "loss": 4.37499, "time": 0.70171} +{"mode": "train", "epoch": 27, "iter": 3500, "lr": 0.09225, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22, "top5_acc": 0.46281, "loss_cls": 4.29032, "loss": 4.29032, "time": 0.70227} +{"mode": "train", "epoch": 27, "iter": 3600, "lr": 0.09224, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22516, "top5_acc": 0.46469, "loss_cls": 4.30552, "loss": 4.30552, "time": 0.70961} +{"mode": "train", "epoch": 27, "iter": 3700, "lr": 0.09222, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.215, "top5_acc": 0.45484, "loss_cls": 4.35901, "loss": 4.35901, "time": 0.70224} +{"mode": "val", "epoch": 27, "iter": 309, "lr": 0.09222, "top1_acc": 0.14983, "top5_acc": 0.36089, "mean_class_accuracy": 0.1497} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.0922, "memory": 15990, "data_time": 1.24074, "top1_acc": 0.22484, "top5_acc": 0.46328, "loss_cls": 4.26493, "loss": 4.26493, "time": 1.95015} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.09219, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22219, "top5_acc": 0.46031, "loss_cls": 4.26438, "loss": 4.26438, "time": 0.70933} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.09217, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22234, "top5_acc": 0.45422, "loss_cls": 4.31539, "loss": 4.31539, "time": 0.71033} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.09216, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21891, "top5_acc": 0.45719, "loss_cls": 4.29835, "loss": 4.29835, "time": 0.70575} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.09214, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22438, "top5_acc": 0.4675, "loss_cls": 4.28514, "loss": 4.28514, "time": 0.7006} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.09213, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21609, "top5_acc": 0.46359, "loss_cls": 4.29896, "loss": 4.29896, "time": 0.70124} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.09211, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20906, "top5_acc": 0.44484, "loss_cls": 4.3509, "loss": 4.3509, "time": 0.70046} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.0921, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21844, "top5_acc": 0.45969, "loss_cls": 4.34211, "loss": 4.34211, "time": 0.70058} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.09208, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21281, "top5_acc": 0.45797, "loss_cls": 4.33853, "loss": 4.33853, "time": 0.70079} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.09207, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22219, "top5_acc": 0.45703, "loss_cls": 4.30683, "loss": 4.30683, "time": 0.70047} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.09205, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20641, "top5_acc": 0.45344, "loss_cls": 4.32545, "loss": 4.32545, "time": 0.70137} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.09204, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22, "top5_acc": 0.47125, "loss_cls": 4.29826, "loss": 4.29826, "time": 0.7015} +{"mode": "train", "epoch": 28, "iter": 1300, "lr": 0.09202, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22297, "top5_acc": 0.45953, "loss_cls": 4.30357, "loss": 4.30357, "time": 0.69904} +{"mode": "train", "epoch": 28, "iter": 1400, "lr": 0.09201, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21141, "top5_acc": 0.44828, "loss_cls": 4.33597, "loss": 4.33597, "time": 0.69885} +{"mode": "train", "epoch": 28, "iter": 1500, "lr": 0.09199, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22516, "top5_acc": 0.46297, "loss_cls": 4.31182, "loss": 4.31182, "time": 0.69758} +{"mode": "train", "epoch": 28, "iter": 1600, "lr": 0.09198, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21516, "top5_acc": 0.45719, "loss_cls": 4.31274, "loss": 4.31274, "time": 0.69788} +{"mode": "train", "epoch": 28, "iter": 1700, "lr": 0.09196, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21906, "top5_acc": 0.45484, "loss_cls": 4.31996, "loss": 4.31996, "time": 0.70166} +{"mode": "train", "epoch": 28, "iter": 1800, "lr": 0.09194, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22125, "top5_acc": 0.45172, "loss_cls": 4.35541, "loss": 4.35541, "time": 0.70789} +{"mode": "train", "epoch": 28, "iter": 1900, "lr": 0.09193, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21297, "top5_acc": 0.46203, "loss_cls": 4.28972, "loss": 4.28972, "time": 0.70231} +{"mode": "train", "epoch": 28, "iter": 2000, "lr": 0.09191, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20891, "top5_acc": 0.44797, "loss_cls": 4.37246, "loss": 4.37246, "time": 0.70366} +{"mode": "train", "epoch": 28, "iter": 2100, "lr": 0.0919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22203, "top5_acc": 0.46328, "loss_cls": 4.30101, "loss": 4.30101, "time": 0.70361} +{"mode": "train", "epoch": 28, "iter": 2200, "lr": 0.09188, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21625, "top5_acc": 0.45609, "loss_cls": 4.32678, "loss": 4.32678, "time": 0.70154} +{"mode": "train", "epoch": 28, "iter": 2300, "lr": 0.09187, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22031, "top5_acc": 0.45938, "loss_cls": 4.31802, "loss": 4.31802, "time": 0.69806} +{"mode": "train", "epoch": 28, "iter": 2400, "lr": 0.09185, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22891, "top5_acc": 0.46578, "loss_cls": 4.29235, "loss": 4.29235, "time": 0.69955} +{"mode": "train", "epoch": 28, "iter": 2500, "lr": 0.09184, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21875, "top5_acc": 0.45547, "loss_cls": 4.3258, "loss": 4.3258, "time": 0.69752} +{"mode": "train", "epoch": 28, "iter": 2600, "lr": 0.09182, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22469, "top5_acc": 0.46125, "loss_cls": 4.28124, "loss": 4.28124, "time": 0.69719} +{"mode": "train", "epoch": 28, "iter": 2700, "lr": 0.09181, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21859, "top5_acc": 0.46453, "loss_cls": 4.30064, "loss": 4.30064, "time": 0.69707} +{"mode": "train", "epoch": 28, "iter": 2800, "lr": 0.09179, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20547, "top5_acc": 0.44156, "loss_cls": 4.37232, "loss": 4.37232, "time": 0.69887} +{"mode": "train", "epoch": 28, "iter": 2900, "lr": 0.09178, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21812, "top5_acc": 0.45656, "loss_cls": 4.3282, "loss": 4.3282, "time": 0.70098} +{"mode": "train", "epoch": 28, "iter": 3000, "lr": 0.09176, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22484, "top5_acc": 0.46312, "loss_cls": 4.29231, "loss": 4.29231, "time": 0.69782} +{"mode": "train", "epoch": 28, "iter": 3100, "lr": 0.09175, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21234, "top5_acc": 0.45344, "loss_cls": 4.32535, "loss": 4.32535, "time": 0.69845} +{"mode": "train", "epoch": 28, "iter": 3200, "lr": 0.09173, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21, "top5_acc": 0.45219, "loss_cls": 4.35172, "loss": 4.35172, "time": 0.69905} +{"mode": "train", "epoch": 28, "iter": 3300, "lr": 0.09172, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21516, "top5_acc": 0.45047, "loss_cls": 4.3333, "loss": 4.3333, "time": 0.69906} +{"mode": "train", "epoch": 28, "iter": 3400, "lr": 0.0917, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22203, "top5_acc": 0.45547, "loss_cls": 4.31474, "loss": 4.31474, "time": 0.70168} +{"mode": "train", "epoch": 28, "iter": 3500, "lr": 0.09168, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20391, "top5_acc": 0.44797, "loss_cls": 4.35096, "loss": 4.35096, "time": 0.70949} +{"mode": "train", "epoch": 28, "iter": 3600, "lr": 0.09167, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21594, "top5_acc": 0.45281, "loss_cls": 4.32234, "loss": 4.32234, "time": 0.70728} +{"mode": "train", "epoch": 28, "iter": 3700, "lr": 0.09165, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21953, "top5_acc": 0.45641, "loss_cls": 4.31718, "loss": 4.31718, "time": 0.7029} +{"mode": "val", "epoch": 28, "iter": 309, "lr": 0.09165, "top1_acc": 0.13605, "top5_acc": 0.32766, "mean_class_accuracy": 0.13585} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.09163, "memory": 15990, "data_time": 1.24885, "top1_acc": 0.22406, "top5_acc": 0.46391, "loss_cls": 4.26051, "loss": 4.26051, "time": 1.95982} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.09162, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21891, "top5_acc": 0.45594, "loss_cls": 4.2968, "loss": 4.2968, "time": 0.70849} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.0916, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22359, "top5_acc": 0.46156, "loss_cls": 4.29983, "loss": 4.29983, "time": 0.71004} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.09158, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22156, "top5_acc": 0.46203, "loss_cls": 4.28547, "loss": 4.28547, "time": 0.70319} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.09157, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21656, "top5_acc": 0.46219, "loss_cls": 4.31002, "loss": 4.31002, "time": 0.70302} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.09155, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22844, "top5_acc": 0.46391, "loss_cls": 4.26543, "loss": 4.26543, "time": 0.6996} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.09154, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22328, "top5_acc": 0.45828, "loss_cls": 4.31868, "loss": 4.31868, "time": 0.69776} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.09152, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22422, "top5_acc": 0.46703, "loss_cls": 4.29135, "loss": 4.29135, "time": 0.70087} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.09151, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22172, "top5_acc": 0.46297, "loss_cls": 4.33067, "loss": 4.33067, "time": 0.69883} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.09149, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21781, "top5_acc": 0.46203, "loss_cls": 4.31384, "loss": 4.31384, "time": 0.70014} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.09148, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22312, "top5_acc": 0.4675, "loss_cls": 4.28785, "loss": 4.28785, "time": 0.70336} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.09146, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22203, "top5_acc": 0.45891, "loss_cls": 4.32207, "loss": 4.32207, "time": 0.69806} +{"mode": "train", "epoch": 29, "iter": 1300, "lr": 0.09144, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22438, "top5_acc": 0.46094, "loss_cls": 4.29011, "loss": 4.29011, "time": 0.69679} +{"mode": "train", "epoch": 29, "iter": 1400, "lr": 0.09143, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21953, "top5_acc": 0.45672, "loss_cls": 4.31864, "loss": 4.31864, "time": 0.69545} +{"mode": "train", "epoch": 29, "iter": 1500, "lr": 0.09141, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21516, "top5_acc": 0.46109, "loss_cls": 4.32826, "loss": 4.32826, "time": 0.69706} +{"mode": "train", "epoch": 29, "iter": 1600, "lr": 0.0914, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21812, "top5_acc": 0.45531, "loss_cls": 4.32623, "loss": 4.32623, "time": 0.69784} +{"mode": "train", "epoch": 29, "iter": 1700, "lr": 0.09138, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21734, "top5_acc": 0.45656, "loss_cls": 4.32243, "loss": 4.32243, "time": 0.69992} +{"mode": "train", "epoch": 29, "iter": 1800, "lr": 0.09137, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22969, "top5_acc": 0.47031, "loss_cls": 4.24798, "loss": 4.24798, "time": 0.70508} +{"mode": "train", "epoch": 29, "iter": 1900, "lr": 0.09135, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22531, "top5_acc": 0.46156, "loss_cls": 4.29523, "loss": 4.29523, "time": 0.70045} +{"mode": "train", "epoch": 29, "iter": 2000, "lr": 0.09133, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21625, "top5_acc": 0.46094, "loss_cls": 4.31714, "loss": 4.31714, "time": 0.7021} +{"mode": "train", "epoch": 29, "iter": 2100, "lr": 0.09132, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22797, "top5_acc": 0.46078, "loss_cls": 4.31151, "loss": 4.31151, "time": 0.70447} +{"mode": "train", "epoch": 29, "iter": 2200, "lr": 0.0913, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21969, "top5_acc": 0.45891, "loss_cls": 4.30929, "loss": 4.30929, "time": 0.70469} +{"mode": "train", "epoch": 29, "iter": 2300, "lr": 0.09129, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21688, "top5_acc": 0.45812, "loss_cls": 4.29377, "loss": 4.29377, "time": 0.69992} +{"mode": "train", "epoch": 29, "iter": 2400, "lr": 0.09127, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22, "top5_acc": 0.45688, "loss_cls": 4.30412, "loss": 4.30412, "time": 0.7004} +{"mode": "train", "epoch": 29, "iter": 2500, "lr": 0.09126, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22156, "top5_acc": 0.45438, "loss_cls": 4.32058, "loss": 4.32058, "time": 0.7018} +{"mode": "train", "epoch": 29, "iter": 2600, "lr": 0.09124, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22, "top5_acc": 0.45953, "loss_cls": 4.29852, "loss": 4.29852, "time": 0.69863} +{"mode": "train", "epoch": 29, "iter": 2700, "lr": 0.09122, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20984, "top5_acc": 0.45062, "loss_cls": 4.3197, "loss": 4.3197, "time": 0.7006} +{"mode": "train", "epoch": 29, "iter": 2800, "lr": 0.09121, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21453, "top5_acc": 0.45688, "loss_cls": 4.33802, "loss": 4.33802, "time": 0.69765} +{"mode": "train", "epoch": 29, "iter": 2900, "lr": 0.09119, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20984, "top5_acc": 0.44719, "loss_cls": 4.35813, "loss": 4.35813, "time": 0.6988} +{"mode": "train", "epoch": 29, "iter": 3000, "lr": 0.09118, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21469, "top5_acc": 0.45234, "loss_cls": 4.34235, "loss": 4.34235, "time": 0.70056} +{"mode": "train", "epoch": 29, "iter": 3100, "lr": 0.09116, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22047, "top5_acc": 0.45297, "loss_cls": 4.31211, "loss": 4.31211, "time": 0.69958} +{"mode": "train", "epoch": 29, "iter": 3200, "lr": 0.09114, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21, "top5_acc": 0.44672, "loss_cls": 4.38031, "loss": 4.38031, "time": 0.69701} +{"mode": "train", "epoch": 29, "iter": 3300, "lr": 0.09113, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22562, "top5_acc": 0.46438, "loss_cls": 4.3093, "loss": 4.3093, "time": 0.70017} +{"mode": "train", "epoch": 29, "iter": 3400, "lr": 0.09111, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22406, "top5_acc": 0.46031, "loss_cls": 4.29169, "loss": 4.29169, "time": 0.70013} +{"mode": "train", "epoch": 29, "iter": 3500, "lr": 0.0911, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21875, "top5_acc": 0.46031, "loss_cls": 4.31665, "loss": 4.31665, "time": 0.70526} +{"mode": "train", "epoch": 29, "iter": 3600, "lr": 0.09108, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.21906, "top5_acc": 0.46031, "loss_cls": 4.32625, "loss": 4.32625, "time": 0.7092} +{"mode": "train", "epoch": 29, "iter": 3700, "lr": 0.09106, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21656, "top5_acc": 0.46328, "loss_cls": 4.28496, "loss": 4.28496, "time": 0.71164} +{"mode": "val", "epoch": 29, "iter": 309, "lr": 0.09106, "top1_acc": 0.15023, "top5_acc": 0.34888, "mean_class_accuracy": 0.14988} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.09104, "memory": 15990, "data_time": 1.24004, "top1_acc": 0.23125, "top5_acc": 0.47297, "loss_cls": 4.22111, "loss": 4.22111, "time": 2.05826} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.09103, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23219, "top5_acc": 0.46781, "loss_cls": 4.26186, "loss": 4.26186, "time": 0.80843} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.09101, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21875, "top5_acc": 0.46234, "loss_cls": 4.27977, "loss": 4.27977, "time": 0.82117} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.09099, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23047, "top5_acc": 0.47, "loss_cls": 4.26986, "loss": 4.26986, "time": 0.81587} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.09098, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21688, "top5_acc": 0.46359, "loss_cls": 4.31397, "loss": 4.31397, "time": 0.80854} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.09096, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21875, "top5_acc": 0.45078, "loss_cls": 4.3511, "loss": 4.3511, "time": 0.81165} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.09095, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22297, "top5_acc": 0.45719, "loss_cls": 4.29861, "loss": 4.29861, "time": 0.80839} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.09093, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22719, "top5_acc": 0.46391, "loss_cls": 4.29139, "loss": 4.29139, "time": 0.80475} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.09091, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22375, "top5_acc": 0.46312, "loss_cls": 4.27254, "loss": 4.27254, "time": 0.81075} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.0909, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22188, "top5_acc": 0.46031, "loss_cls": 4.33974, "loss": 4.33974, "time": 0.8097} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.09088, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21969, "top5_acc": 0.46719, "loss_cls": 4.30331, "loss": 4.30331, "time": 0.80631} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.09087, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21969, "top5_acc": 0.45859, "loss_cls": 4.30748, "loss": 4.30748, "time": 0.81677} +{"mode": "train", "epoch": 30, "iter": 1300, "lr": 0.09085, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22469, "top5_acc": 0.46766, "loss_cls": 4.27396, "loss": 4.27396, "time": 0.81101} +{"mode": "train", "epoch": 30, "iter": 1400, "lr": 0.09083, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22328, "top5_acc": 0.46484, "loss_cls": 4.28553, "loss": 4.28553, "time": 0.81021} +{"mode": "train", "epoch": 30, "iter": 1500, "lr": 0.09082, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20953, "top5_acc": 0.45516, "loss_cls": 4.32563, "loss": 4.32563, "time": 0.80613} +{"mode": "train", "epoch": 30, "iter": 1600, "lr": 0.0908, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22625, "top5_acc": 0.45625, "loss_cls": 4.29553, "loss": 4.29553, "time": 0.81146} +{"mode": "train", "epoch": 30, "iter": 1700, "lr": 0.09078, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21938, "top5_acc": 0.4475, "loss_cls": 4.35902, "loss": 4.35902, "time": 0.81373} +{"mode": "train", "epoch": 30, "iter": 1800, "lr": 0.09077, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22531, "top5_acc": 0.45969, "loss_cls": 4.29547, "loss": 4.29547, "time": 0.80987} +{"mode": "train", "epoch": 30, "iter": 1900, "lr": 0.09075, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21672, "top5_acc": 0.45406, "loss_cls": 4.33059, "loss": 4.33059, "time": 0.80945} +{"mode": "train", "epoch": 30, "iter": 2000, "lr": 0.09074, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22344, "top5_acc": 0.46062, "loss_cls": 4.30276, "loss": 4.30276, "time": 0.82053} +{"mode": "train", "epoch": 30, "iter": 2100, "lr": 0.09072, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22297, "top5_acc": 0.47047, "loss_cls": 4.26414, "loss": 4.26414, "time": 0.81223} +{"mode": "train", "epoch": 30, "iter": 2200, "lr": 0.0907, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21203, "top5_acc": 0.45531, "loss_cls": 4.33678, "loss": 4.33678, "time": 0.81693} +{"mode": "train", "epoch": 30, "iter": 2300, "lr": 0.09069, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23016, "top5_acc": 0.45938, "loss_cls": 4.28772, "loss": 4.28772, "time": 0.82311} +{"mode": "train", "epoch": 30, "iter": 2400, "lr": 0.09067, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22266, "top5_acc": 0.45281, "loss_cls": 4.35974, "loss": 4.35974, "time": 0.81664} +{"mode": "train", "epoch": 30, "iter": 2500, "lr": 0.09065, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23047, "top5_acc": 0.4675, "loss_cls": 4.25678, "loss": 4.25678, "time": 0.80869} +{"mode": "train", "epoch": 30, "iter": 2600, "lr": 0.09064, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22703, "top5_acc": 0.45828, "loss_cls": 4.31424, "loss": 4.31424, "time": 0.80852} +{"mode": "train", "epoch": 30, "iter": 2700, "lr": 0.09062, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22406, "top5_acc": 0.45984, "loss_cls": 4.29216, "loss": 4.29216, "time": 0.80734} +{"mode": "train", "epoch": 30, "iter": 2800, "lr": 0.09061, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21219, "top5_acc": 0.45844, "loss_cls": 4.32084, "loss": 4.32084, "time": 0.80747} +{"mode": "train", "epoch": 30, "iter": 2900, "lr": 0.09059, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21594, "top5_acc": 0.45562, "loss_cls": 4.33466, "loss": 4.33466, "time": 0.81121} +{"mode": "train", "epoch": 30, "iter": 3000, "lr": 0.09057, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21672, "top5_acc": 0.44797, "loss_cls": 4.35097, "loss": 4.35097, "time": 0.80604} +{"mode": "train", "epoch": 30, "iter": 3100, "lr": 0.09056, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22281, "top5_acc": 0.46344, "loss_cls": 4.29268, "loss": 4.29268, "time": 0.81022} +{"mode": "train", "epoch": 30, "iter": 3200, "lr": 0.09054, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22344, "top5_acc": 0.46422, "loss_cls": 4.26479, "loss": 4.26479, "time": 0.80904} +{"mode": "train", "epoch": 30, "iter": 3300, "lr": 0.09052, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21547, "top5_acc": 0.46234, "loss_cls": 4.29932, "loss": 4.29932, "time": 0.80507} +{"mode": "train", "epoch": 30, "iter": 3400, "lr": 0.09051, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.225, "top5_acc": 0.45766, "loss_cls": 4.31642, "loss": 4.31642, "time": 0.8109} +{"mode": "train", "epoch": 30, "iter": 3500, "lr": 0.09049, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21719, "top5_acc": 0.45781, "loss_cls": 4.33686, "loss": 4.33686, "time": 0.81284} +{"mode": "train", "epoch": 30, "iter": 3600, "lr": 0.09047, "memory": 15990, "data_time": 0.00084, "top1_acc": 0.22469, "top5_acc": 0.46844, "loss_cls": 4.28697, "loss": 4.28697, "time": 0.81891} +{"mode": "train", "epoch": 30, "iter": 3700, "lr": 0.09046, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21875, "top5_acc": 0.46062, "loss_cls": 4.32437, "loss": 4.32437, "time": 0.81471} +{"mode": "val", "epoch": 30, "iter": 309, "lr": 0.09045, "top1_acc": 0.14466, "top5_acc": 0.3424, "mean_class_accuracy": 0.14459} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.09043, "memory": 15990, "data_time": 1.30745, "top1_acc": 0.22828, "top5_acc": 0.46859, "loss_cls": 4.50886, "loss": 4.50886, "time": 2.29712} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.09042, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.215, "top5_acc": 0.46531, "loss_cls": 4.54791, "loss": 4.54791, "time": 0.8293} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.0904, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22766, "top5_acc": 0.47203, "loss_cls": 4.46952, "loss": 4.46952, "time": 0.83453} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.09039, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21578, "top5_acc": 0.4525, "loss_cls": 4.55595, "loss": 4.55595, "time": 0.83728} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.09037, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22312, "top5_acc": 0.46359, "loss_cls": 4.5425, "loss": 4.5425, "time": 0.82781} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.09035, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22531, "top5_acc": 0.46203, "loss_cls": 4.51721, "loss": 4.51721, "time": 0.82792} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.09034, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21594, "top5_acc": 0.45172, "loss_cls": 4.56719, "loss": 4.56719, "time": 0.82491} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.09032, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22062, "top5_acc": 0.46172, "loss_cls": 4.54058, "loss": 4.54058, "time": 0.82285} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0903, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22188, "top5_acc": 0.45688, "loss_cls": 4.54474, "loss": 4.54474, "time": 0.82532} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.09029, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23328, "top5_acc": 0.47031, "loss_cls": 4.48196, "loss": 4.48196, "time": 0.82169} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.09027, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21625, "top5_acc": 0.45328, "loss_cls": 4.5622, "loss": 4.5622, "time": 0.81864} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.09025, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22719, "top5_acc": 0.46891, "loss_cls": 4.49395, "loss": 4.49395, "time": 0.81743} +{"mode": "train", "epoch": 31, "iter": 1300, "lr": 0.09024, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.225, "top5_acc": 0.45062, "loss_cls": 4.56077, "loss": 4.56077, "time": 0.82225} +{"mode": "train", "epoch": 31, "iter": 1400, "lr": 0.09022, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21859, "top5_acc": 0.46422, "loss_cls": 4.52853, "loss": 4.52853, "time": 0.82118} +{"mode": "train", "epoch": 31, "iter": 1500, "lr": 0.0902, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20406, "top5_acc": 0.44344, "loss_cls": 4.61215, "loss": 4.61215, "time": 0.82187} +{"mode": "train", "epoch": 31, "iter": 1600, "lr": 0.09019, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22734, "top5_acc": 0.47812, "loss_cls": 4.50742, "loss": 4.50742, "time": 0.82185} +{"mode": "train", "epoch": 31, "iter": 1700, "lr": 0.09017, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21125, "top5_acc": 0.45312, "loss_cls": 4.5505, "loss": 4.5505, "time": 0.82299} +{"mode": "train", "epoch": 31, "iter": 1800, "lr": 0.09015, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22266, "top5_acc": 0.44969, "loss_cls": 4.5621, "loss": 4.5621, "time": 0.81986} +{"mode": "train", "epoch": 31, "iter": 1900, "lr": 0.09014, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22531, "top5_acc": 0.45906, "loss_cls": 4.51924, "loss": 4.51924, "time": 0.82613} +{"mode": "train", "epoch": 31, "iter": 2000, "lr": 0.09012, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22047, "top5_acc": 0.46047, "loss_cls": 4.55574, "loss": 4.55574, "time": 0.83353} +{"mode": "train", "epoch": 31, "iter": 2100, "lr": 0.0901, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21922, "top5_acc": 0.46, "loss_cls": 4.53431, "loss": 4.53431, "time": 0.82584} +{"mode": "train", "epoch": 31, "iter": 2200, "lr": 0.09009, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21734, "top5_acc": 0.44719, "loss_cls": 4.58389, "loss": 4.58389, "time": 0.82722} +{"mode": "train", "epoch": 31, "iter": 2300, "lr": 0.09007, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22, "top5_acc": 0.46109, "loss_cls": 4.53678, "loss": 4.53678, "time": 0.83632} +{"mode": "train", "epoch": 31, "iter": 2400, "lr": 0.09005, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21609, "top5_acc": 0.45922, "loss_cls": 4.56952, "loss": 4.56952, "time": 0.83868} +{"mode": "train", "epoch": 31, "iter": 2500, "lr": 0.09004, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22562, "top5_acc": 0.46094, "loss_cls": 4.52279, "loss": 4.52279, "time": 0.82994} +{"mode": "train", "epoch": 31, "iter": 2600, "lr": 0.09002, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21672, "top5_acc": 0.46766, "loss_cls": 4.53183, "loss": 4.53183, "time": 0.82507} +{"mode": "train", "epoch": 31, "iter": 2700, "lr": 0.09, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22266, "top5_acc": 0.45234, "loss_cls": 4.57086, "loss": 4.57086, "time": 0.82185} +{"mode": "train", "epoch": 31, "iter": 2800, "lr": 0.08999, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22781, "top5_acc": 0.46672, "loss_cls": 4.51475, "loss": 4.51475, "time": 0.82204} +{"mode": "train", "epoch": 31, "iter": 2900, "lr": 0.08997, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21969, "top5_acc": 0.45172, "loss_cls": 4.56151, "loss": 4.56151, "time": 0.82628} +{"mode": "train", "epoch": 31, "iter": 3000, "lr": 0.08995, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21125, "top5_acc": 0.44297, "loss_cls": 4.59737, "loss": 4.59737, "time": 0.82209} +{"mode": "train", "epoch": 31, "iter": 3100, "lr": 0.08994, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22594, "top5_acc": 0.46031, "loss_cls": 4.52293, "loss": 4.52293, "time": 0.81917} +{"mode": "train", "epoch": 31, "iter": 3200, "lr": 0.08992, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22203, "top5_acc": 0.46016, "loss_cls": 4.52216, "loss": 4.52216, "time": 0.82423} +{"mode": "train", "epoch": 31, "iter": 3300, "lr": 0.0899, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21406, "top5_acc": 0.44172, "loss_cls": 4.6027, "loss": 4.6027, "time": 0.82302} +{"mode": "train", "epoch": 31, "iter": 3400, "lr": 0.08989, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22344, "top5_acc": 0.46297, "loss_cls": 4.54526, "loss": 4.54526, "time": 0.83522} +{"mode": "train", "epoch": 31, "iter": 3500, "lr": 0.08987, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21703, "top5_acc": 0.45453, "loss_cls": 4.58073, "loss": 4.58073, "time": 0.82694} +{"mode": "train", "epoch": 31, "iter": 3600, "lr": 0.08985, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.21594, "top5_acc": 0.45078, "loss_cls": 4.57417, "loss": 4.57417, "time": 0.82791} +{"mode": "train", "epoch": 31, "iter": 3700, "lr": 0.08983, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22594, "top5_acc": 0.46266, "loss_cls": 4.50361, "loss": 4.50361, "time": 0.83049} +{"mode": "val", "epoch": 31, "iter": 309, "lr": 0.08983, "top1_acc": 0.16674, "top5_acc": 0.38211, "mean_class_accuracy": 0.16647} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.08981, "memory": 15990, "data_time": 1.29348, "top1_acc": 0.23406, "top5_acc": 0.46688, "loss_cls": 4.49912, "loss": 4.49912, "time": 2.28208} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.08979, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.22484, "top5_acc": 0.46219, "loss_cls": 4.53016, "loss": 4.53016, "time": 0.83311} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.08978, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23625, "top5_acc": 0.48391, "loss_cls": 4.4446, "loss": 4.4446, "time": 0.8389} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.08976, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21453, "top5_acc": 0.44547, "loss_cls": 4.58929, "loss": 4.58929, "time": 0.8337} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.08974, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22109, "top5_acc": 0.46672, "loss_cls": 4.56161, "loss": 4.56161, "time": 0.82804} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.08973, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22219, "top5_acc": 0.45609, "loss_cls": 4.55936, "loss": 4.55936, "time": 0.81889} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.08971, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2275, "top5_acc": 0.47328, "loss_cls": 4.49787, "loss": 4.49787, "time": 0.8234} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.08969, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22297, "top5_acc": 0.46031, "loss_cls": 4.53875, "loss": 4.53875, "time": 0.82068} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.08967, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21891, "top5_acc": 0.44453, "loss_cls": 4.57292, "loss": 4.57292, "time": 0.82142} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.08966, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21625, "top5_acc": 0.46094, "loss_cls": 4.54687, "loss": 4.54687, "time": 0.81854} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.08964, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2225, "top5_acc": 0.46453, "loss_cls": 4.52833, "loss": 4.52833, "time": 0.82395} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.08962, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22141, "top5_acc": 0.47062, "loss_cls": 4.49625, "loss": 4.49625, "time": 0.81984} +{"mode": "train", "epoch": 32, "iter": 1300, "lr": 0.08961, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23219, "top5_acc": 0.46422, "loss_cls": 4.52853, "loss": 4.52853, "time": 0.81893} +{"mode": "train", "epoch": 32, "iter": 1400, "lr": 0.08959, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21609, "top5_acc": 0.45188, "loss_cls": 4.59845, "loss": 4.59845, "time": 0.81604} +{"mode": "train", "epoch": 32, "iter": 1500, "lr": 0.08957, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21797, "top5_acc": 0.46578, "loss_cls": 4.54261, "loss": 4.54261, "time": 0.81517} +{"mode": "train", "epoch": 32, "iter": 1600, "lr": 0.08955, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21375, "top5_acc": 0.45594, "loss_cls": 4.56324, "loss": 4.56324, "time": 0.8183} +{"mode": "train", "epoch": 32, "iter": 1700, "lr": 0.08954, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22203, "top5_acc": 0.46594, "loss_cls": 4.52496, "loss": 4.52496, "time": 0.81672} +{"mode": "train", "epoch": 32, "iter": 1800, "lr": 0.08952, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22594, "top5_acc": 0.46516, "loss_cls": 4.5075, "loss": 4.5075, "time": 0.81883} +{"mode": "train", "epoch": 32, "iter": 1900, "lr": 0.0895, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21391, "top5_acc": 0.4475, "loss_cls": 4.57474, "loss": 4.57474, "time": 0.81988} +{"mode": "train", "epoch": 32, "iter": 2000, "lr": 0.08949, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22219, "top5_acc": 0.46016, "loss_cls": 4.53876, "loss": 4.53876, "time": 0.81246} +{"mode": "train", "epoch": 32, "iter": 2100, "lr": 0.08947, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20656, "top5_acc": 0.45531, "loss_cls": 4.59352, "loss": 4.59352, "time": 0.81931} +{"mode": "train", "epoch": 32, "iter": 2200, "lr": 0.08945, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22891, "top5_acc": 0.46172, "loss_cls": 4.52807, "loss": 4.52807, "time": 0.81117} +{"mode": "train", "epoch": 32, "iter": 2300, "lr": 0.08943, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22047, "top5_acc": 0.45172, "loss_cls": 4.58023, "loss": 4.58023, "time": 0.81168} +{"mode": "train", "epoch": 32, "iter": 2400, "lr": 0.08942, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2175, "top5_acc": 0.46359, "loss_cls": 4.56307, "loss": 4.56307, "time": 0.81575} +{"mode": "train", "epoch": 32, "iter": 2500, "lr": 0.0894, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21469, "top5_acc": 0.45609, "loss_cls": 4.53723, "loss": 4.53723, "time": 0.80705} +{"mode": "train", "epoch": 32, "iter": 2600, "lr": 0.08938, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22188, "top5_acc": 0.45828, "loss_cls": 4.5534, "loss": 4.5534, "time": 0.81329} +{"mode": "train", "epoch": 32, "iter": 2700, "lr": 0.08937, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22953, "top5_acc": 0.46438, "loss_cls": 4.5081, "loss": 4.5081, "time": 0.81482} +{"mode": "train", "epoch": 32, "iter": 2800, "lr": 0.08935, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22875, "top5_acc": 0.46047, "loss_cls": 4.50974, "loss": 4.50974, "time": 0.81629} +{"mode": "train", "epoch": 32, "iter": 2900, "lr": 0.08933, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22078, "top5_acc": 0.46016, "loss_cls": 4.54329, "loss": 4.54329, "time": 0.8158} +{"mode": "train", "epoch": 32, "iter": 3000, "lr": 0.08931, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22062, "top5_acc": 0.45953, "loss_cls": 4.50914, "loss": 4.50914, "time": 0.81156} +{"mode": "train", "epoch": 32, "iter": 3100, "lr": 0.0893, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22078, "top5_acc": 0.46594, "loss_cls": 4.53992, "loss": 4.53992, "time": 0.81221} +{"mode": "train", "epoch": 32, "iter": 3200, "lr": 0.08928, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21203, "top5_acc": 0.45109, "loss_cls": 4.5944, "loss": 4.5944, "time": 0.82141} +{"mode": "train", "epoch": 32, "iter": 3300, "lr": 0.08926, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22625, "top5_acc": 0.46047, "loss_cls": 4.50972, "loss": 4.50972, "time": 0.81843} +{"mode": "train", "epoch": 32, "iter": 3400, "lr": 0.08924, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21703, "top5_acc": 0.46453, "loss_cls": 4.5498, "loss": 4.5498, "time": 0.81265} +{"mode": "train", "epoch": 32, "iter": 3500, "lr": 0.08923, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21875, "top5_acc": 0.45938, "loss_cls": 4.53092, "loss": 4.53092, "time": 0.82154} +{"mode": "train", "epoch": 32, "iter": 3600, "lr": 0.08921, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23109, "top5_acc": 0.47188, "loss_cls": 4.48639, "loss": 4.48639, "time": 0.81438} +{"mode": "train", "epoch": 32, "iter": 3700, "lr": 0.08919, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22203, "top5_acc": 0.45969, "loss_cls": 4.53865, "loss": 4.53865, "time": 0.82067} +{"mode": "val", "epoch": 32, "iter": 309, "lr": 0.08918, "top1_acc": 0.15575, "top5_acc": 0.34822, "mean_class_accuracy": 0.1558} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.08917, "memory": 15990, "data_time": 1.32533, "top1_acc": 0.22062, "top5_acc": 0.46656, "loss_cls": 4.51535, "loss": 4.51535, "time": 2.31328} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.08915, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22516, "top5_acc": 0.46922, "loss_cls": 4.48718, "loss": 4.48718, "time": 0.83568} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.08913, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22125, "top5_acc": 0.465, "loss_cls": 4.51006, "loss": 4.51006, "time": 0.83794} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.08912, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21781, "top5_acc": 0.45516, "loss_cls": 4.55581, "loss": 4.55581, "time": 0.83031} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.0891, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22719, "top5_acc": 0.47156, "loss_cls": 4.4896, "loss": 4.4896, "time": 0.82705} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.08908, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22172, "top5_acc": 0.47031, "loss_cls": 4.50221, "loss": 4.50221, "time": 0.82818} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.08906, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22141, "top5_acc": 0.46984, "loss_cls": 4.50295, "loss": 4.50295, "time": 0.82252} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.08905, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21641, "top5_acc": 0.46688, "loss_cls": 4.53663, "loss": 4.53663, "time": 0.82557} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.08903, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21766, "top5_acc": 0.46312, "loss_cls": 4.52387, "loss": 4.52387, "time": 0.82331} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.08901, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22297, "top5_acc": 0.45656, "loss_cls": 4.5367, "loss": 4.5367, "time": 0.83181} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.08899, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21703, "top5_acc": 0.45672, "loss_cls": 4.53502, "loss": 4.53502, "time": 0.82371} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.08898, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21531, "top5_acc": 0.46, "loss_cls": 4.53362, "loss": 4.53362, "time": 0.82157} +{"mode": "train", "epoch": 33, "iter": 1300, "lr": 0.08896, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22219, "top5_acc": 0.46891, "loss_cls": 4.51831, "loss": 4.51831, "time": 0.82227} +{"mode": "train", "epoch": 33, "iter": 1400, "lr": 0.08894, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22141, "top5_acc": 0.46422, "loss_cls": 4.5228, "loss": 4.5228, "time": 0.82515} +{"mode": "train", "epoch": 33, "iter": 1500, "lr": 0.08892, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21984, "top5_acc": 0.45156, "loss_cls": 4.56879, "loss": 4.56879, "time": 0.81958} +{"mode": "train", "epoch": 33, "iter": 1600, "lr": 0.08891, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22219, "top5_acc": 0.46578, "loss_cls": 4.53572, "loss": 4.53572, "time": 0.8241} +{"mode": "train", "epoch": 33, "iter": 1700, "lr": 0.08889, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21797, "top5_acc": 0.46438, "loss_cls": 4.50679, "loss": 4.50679, "time": 0.82937} +{"mode": "train", "epoch": 33, "iter": 1800, "lr": 0.08887, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21484, "top5_acc": 0.44688, "loss_cls": 4.57502, "loss": 4.57502, "time": 0.83206} +{"mode": "train", "epoch": 33, "iter": 1900, "lr": 0.08885, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21094, "top5_acc": 0.45828, "loss_cls": 4.53758, "loss": 4.53758, "time": 0.82452} +{"mode": "train", "epoch": 33, "iter": 2000, "lr": 0.08884, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22281, "top5_acc": 0.45969, "loss_cls": 4.52948, "loss": 4.52948, "time": 0.8122} +{"mode": "train", "epoch": 33, "iter": 2100, "lr": 0.08882, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22281, "top5_acc": 0.45375, "loss_cls": 4.55064, "loss": 4.55064, "time": 0.81925} +{"mode": "train", "epoch": 33, "iter": 2200, "lr": 0.0888, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21438, "top5_acc": 0.45266, "loss_cls": 4.56892, "loss": 4.56892, "time": 0.81242} +{"mode": "train", "epoch": 33, "iter": 2300, "lr": 0.08878, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21891, "top5_acc": 0.46266, "loss_cls": 4.52763, "loss": 4.52763, "time": 0.81599} +{"mode": "train", "epoch": 33, "iter": 2400, "lr": 0.08876, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22312, "top5_acc": 0.46, "loss_cls": 4.51153, "loss": 4.51153, "time": 0.81745} +{"mode": "train", "epoch": 33, "iter": 2500, "lr": 0.08875, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22719, "top5_acc": 0.46094, "loss_cls": 4.50495, "loss": 4.50495, "time": 0.8067} +{"mode": "train", "epoch": 33, "iter": 2600, "lr": 0.08873, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23188, "top5_acc": 0.46812, "loss_cls": 4.50501, "loss": 4.50501, "time": 0.80855} +{"mode": "train", "epoch": 33, "iter": 2700, "lr": 0.08871, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23422, "top5_acc": 0.46984, "loss_cls": 4.49889, "loss": 4.49889, "time": 0.81172} +{"mode": "train", "epoch": 33, "iter": 2800, "lr": 0.08869, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22656, "top5_acc": 0.45547, "loss_cls": 4.54417, "loss": 4.54417, "time": 0.81327} +{"mode": "train", "epoch": 33, "iter": 2900, "lr": 0.08868, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22328, "top5_acc": 0.47094, "loss_cls": 4.51428, "loss": 4.51428, "time": 0.81691} +{"mode": "train", "epoch": 33, "iter": 3000, "lr": 0.08866, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22828, "top5_acc": 0.48047, "loss_cls": 4.44343, "loss": 4.44343, "time": 0.81134} +{"mode": "train", "epoch": 33, "iter": 3100, "lr": 0.08864, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22719, "top5_acc": 0.46703, "loss_cls": 4.53595, "loss": 4.53595, "time": 0.80563} +{"mode": "train", "epoch": 33, "iter": 3200, "lr": 0.08862, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22922, "top5_acc": 0.46125, "loss_cls": 4.51377, "loss": 4.51377, "time": 0.80617} +{"mode": "train", "epoch": 33, "iter": 3300, "lr": 0.08861, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22188, "top5_acc": 0.45375, "loss_cls": 4.53752, "loss": 4.53752, "time": 0.81447} +{"mode": "train", "epoch": 33, "iter": 3400, "lr": 0.08859, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22219, "top5_acc": 0.46281, "loss_cls": 4.51748, "loss": 4.51748, "time": 0.81207} +{"mode": "train", "epoch": 33, "iter": 3500, "lr": 0.08857, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21969, "top5_acc": 0.45531, "loss_cls": 4.55167, "loss": 4.55167, "time": 0.82241} +{"mode": "train", "epoch": 33, "iter": 3600, "lr": 0.08855, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22172, "top5_acc": 0.45578, "loss_cls": 4.56877, "loss": 4.56877, "time": 0.81825} +{"mode": "train", "epoch": 33, "iter": 3700, "lr": 0.08853, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21812, "top5_acc": 0.45969, "loss_cls": 4.549, "loss": 4.549, "time": 0.81566} +{"mode": "val", "epoch": 33, "iter": 309, "lr": 0.08853, "top1_acc": 0.13103, "top5_acc": 0.32219, "mean_class_accuracy": 0.13083} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.08851, "memory": 15990, "data_time": 1.30912, "top1_acc": 0.23281, "top5_acc": 0.47703, "loss_cls": 4.46642, "loss": 4.46642, "time": 2.27989} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.08849, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21859, "top5_acc": 0.455, "loss_cls": 4.54641, "loss": 4.54641, "time": 0.81113} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.08847, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22266, "top5_acc": 0.46453, "loss_cls": 4.51892, "loss": 4.51892, "time": 0.80651} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.08845, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22469, "top5_acc": 0.46125, "loss_cls": 4.53139, "loss": 4.53139, "time": 0.81383} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.08844, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22984, "top5_acc": 0.45688, "loss_cls": 4.51715, "loss": 4.51715, "time": 0.81475} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.08842, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21953, "top5_acc": 0.46, "loss_cls": 4.54618, "loss": 4.54618, "time": 0.8112} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.0884, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22938, "top5_acc": 0.47328, "loss_cls": 4.493, "loss": 4.493, "time": 0.80751} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.08838, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21844, "top5_acc": 0.46297, "loss_cls": 4.51615, "loss": 4.51615, "time": 0.81025} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.08836, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22031, "top5_acc": 0.45516, "loss_cls": 4.53964, "loss": 4.53964, "time": 0.81029} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.08835, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23234, "top5_acc": 0.4725, "loss_cls": 4.49406, "loss": 4.49406, "time": 0.81299} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.08833, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21734, "top5_acc": 0.46281, "loss_cls": 4.50956, "loss": 4.50956, "time": 0.81178} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.08831, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22922, "top5_acc": 0.46625, "loss_cls": 4.49831, "loss": 4.49831, "time": 0.80845} +{"mode": "train", "epoch": 34, "iter": 1300, "lr": 0.08829, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21828, "top5_acc": 0.46422, "loss_cls": 4.51792, "loss": 4.51792, "time": 0.8038} +{"mode": "train", "epoch": 34, "iter": 1400, "lr": 0.08828, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22766, "top5_acc": 0.47266, "loss_cls": 4.52112, "loss": 4.52112, "time": 0.80773} +{"mode": "train", "epoch": 34, "iter": 1500, "lr": 0.08826, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22469, "top5_acc": 0.46359, "loss_cls": 4.53053, "loss": 4.53053, "time": 0.80801} +{"mode": "train", "epoch": 34, "iter": 1600, "lr": 0.08824, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22078, "top5_acc": 0.45672, "loss_cls": 4.54452, "loss": 4.54452, "time": 0.81188} +{"mode": "train", "epoch": 34, "iter": 1700, "lr": 0.08822, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23391, "top5_acc": 0.47156, "loss_cls": 4.48403, "loss": 4.48403, "time": 0.81438} +{"mode": "train", "epoch": 34, "iter": 1800, "lr": 0.0882, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23188, "top5_acc": 0.47016, "loss_cls": 4.46285, "loss": 4.46285, "time": 0.8116} +{"mode": "train", "epoch": 34, "iter": 1900, "lr": 0.08819, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.225, "top5_acc": 0.46641, "loss_cls": 4.51755, "loss": 4.51755, "time": 0.81562} +{"mode": "train", "epoch": 34, "iter": 2000, "lr": 0.08817, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23469, "top5_acc": 0.47, "loss_cls": 4.49408, "loss": 4.49408, "time": 0.81123} +{"mode": "train", "epoch": 34, "iter": 2100, "lr": 0.08815, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22094, "top5_acc": 0.46, "loss_cls": 4.55324, "loss": 4.55324, "time": 0.81535} +{"mode": "train", "epoch": 34, "iter": 2200, "lr": 0.08813, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22516, "top5_acc": 0.46578, "loss_cls": 4.51319, "loss": 4.51319, "time": 0.82219} +{"mode": "train", "epoch": 34, "iter": 2300, "lr": 0.08811, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21938, "top5_acc": 0.44984, "loss_cls": 4.56757, "loss": 4.56757, "time": 0.80902} +{"mode": "train", "epoch": 34, "iter": 2400, "lr": 0.08809, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22453, "top5_acc": 0.46672, "loss_cls": 4.4954, "loss": 4.4954, "time": 0.80737} +{"mode": "train", "epoch": 34, "iter": 2500, "lr": 0.08808, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.225, "top5_acc": 0.46781, "loss_cls": 4.51994, "loss": 4.51994, "time": 0.80803} +{"mode": "train", "epoch": 34, "iter": 2600, "lr": 0.08806, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22547, "top5_acc": 0.46781, "loss_cls": 4.50358, "loss": 4.50358, "time": 0.81036} +{"mode": "train", "epoch": 34, "iter": 2700, "lr": 0.08804, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21859, "top5_acc": 0.47156, "loss_cls": 4.51722, "loss": 4.51722, "time": 0.8074} +{"mode": "train", "epoch": 34, "iter": 2800, "lr": 0.08802, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22359, "top5_acc": 0.45797, "loss_cls": 4.55363, "loss": 4.55363, "time": 0.81146} +{"mode": "train", "epoch": 34, "iter": 2900, "lr": 0.088, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21922, "top5_acc": 0.45953, "loss_cls": 4.51269, "loss": 4.51269, "time": 0.80861} +{"mode": "train", "epoch": 34, "iter": 3000, "lr": 0.08799, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22391, "top5_acc": 0.46062, "loss_cls": 4.52316, "loss": 4.52316, "time": 0.81015} +{"mode": "train", "epoch": 34, "iter": 3100, "lr": 0.08797, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22312, "top5_acc": 0.45656, "loss_cls": 4.56558, "loss": 4.56558, "time": 0.81102} +{"mode": "train", "epoch": 34, "iter": 3200, "lr": 0.08795, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21969, "top5_acc": 0.46141, "loss_cls": 4.53903, "loss": 4.53903, "time": 0.81054} +{"mode": "train", "epoch": 34, "iter": 3300, "lr": 0.08793, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22109, "top5_acc": 0.46062, "loss_cls": 4.52524, "loss": 4.52524, "time": 0.8201} +{"mode": "train", "epoch": 34, "iter": 3400, "lr": 0.08791, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22453, "top5_acc": 0.45484, "loss_cls": 4.5197, "loss": 4.5197, "time": 0.81591} +{"mode": "train", "epoch": 34, "iter": 3500, "lr": 0.08789, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22703, "top5_acc": 0.46531, "loss_cls": 4.53949, "loss": 4.53949, "time": 0.82428} +{"mode": "train", "epoch": 34, "iter": 3600, "lr": 0.08788, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21781, "top5_acc": 0.47109, "loss_cls": 4.49041, "loss": 4.49041, "time": 0.81532} +{"mode": "train", "epoch": 34, "iter": 3700, "lr": 0.08786, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22172, "top5_acc": 0.47203, "loss_cls": 4.5091, "loss": 4.5091, "time": 0.81786} +{"mode": "val", "epoch": 34, "iter": 309, "lr": 0.08785, "top1_acc": 0.16198, "top5_acc": 0.37618, "mean_class_accuracy": 0.16186} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.08783, "memory": 15990, "data_time": 1.32536, "top1_acc": 0.23391, "top5_acc": 0.47172, "loss_cls": 4.48115, "loss": 4.48115, "time": 2.3046} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.08781, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23, "top5_acc": 0.46922, "loss_cls": 4.4874, "loss": 4.4874, "time": 0.81887} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.0878, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21906, "top5_acc": 0.47062, "loss_cls": 4.48148, "loss": 4.48148, "time": 0.81193} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.08778, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22781, "top5_acc": 0.475, "loss_cls": 4.46884, "loss": 4.46884, "time": 0.81084} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.08776, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22828, "top5_acc": 0.47141, "loss_cls": 4.4815, "loss": 4.4815, "time": 0.80611} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.08774, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.23516, "top5_acc": 0.47156, "loss_cls": 4.46613, "loss": 4.46613, "time": 0.8051} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.08772, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22141, "top5_acc": 0.46203, "loss_cls": 4.52089, "loss": 4.52089, "time": 0.8106} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.0877, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21859, "top5_acc": 0.45266, "loss_cls": 4.55071, "loss": 4.55071, "time": 0.81229} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.08769, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22594, "top5_acc": 0.46672, "loss_cls": 4.48765, "loss": 4.48765, "time": 0.81013} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.08767, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22938, "top5_acc": 0.46719, "loss_cls": 4.51529, "loss": 4.51529, "time": 0.81708} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.08765, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22312, "top5_acc": 0.46469, "loss_cls": 4.53703, "loss": 4.53703, "time": 0.80812} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.08763, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22, "top5_acc": 0.46562, "loss_cls": 4.53181, "loss": 4.53181, "time": 0.81455} +{"mode": "train", "epoch": 35, "iter": 1300, "lr": 0.08761, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22562, "top5_acc": 0.46969, "loss_cls": 4.49694, "loss": 4.49694, "time": 0.8122} +{"mode": "train", "epoch": 35, "iter": 1400, "lr": 0.08759, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22656, "top5_acc": 0.47719, "loss_cls": 4.46508, "loss": 4.46508, "time": 0.81205} +{"mode": "train", "epoch": 35, "iter": 1500, "lr": 0.08757, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21906, "top5_acc": 0.45453, "loss_cls": 4.53301, "loss": 4.53301, "time": 0.80829} +{"mode": "train", "epoch": 35, "iter": 1600, "lr": 0.08756, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22359, "top5_acc": 0.46531, "loss_cls": 4.52702, "loss": 4.52702, "time": 0.80905} +{"mode": "train", "epoch": 35, "iter": 1700, "lr": 0.08754, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22469, "top5_acc": 0.46719, "loss_cls": 4.51161, "loss": 4.51161, "time": 0.81515} +{"mode": "train", "epoch": 35, "iter": 1800, "lr": 0.08752, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22625, "top5_acc": 0.46141, "loss_cls": 4.52545, "loss": 4.52545, "time": 0.81052} +{"mode": "train", "epoch": 35, "iter": 1900, "lr": 0.0875, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21828, "top5_acc": 0.45609, "loss_cls": 4.55138, "loss": 4.55138, "time": 0.81434} +{"mode": "train", "epoch": 35, "iter": 2000, "lr": 0.08748, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23031, "top5_acc": 0.46125, "loss_cls": 4.50091, "loss": 4.50091, "time": 0.81917} +{"mode": "train", "epoch": 35, "iter": 2100, "lr": 0.08746, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22203, "top5_acc": 0.46203, "loss_cls": 4.52965, "loss": 4.52965, "time": 0.81178} +{"mode": "train", "epoch": 35, "iter": 2200, "lr": 0.08745, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22609, "top5_acc": 0.46078, "loss_cls": 4.5217, "loss": 4.5217, "time": 0.81286} +{"mode": "train", "epoch": 35, "iter": 2300, "lr": 0.08743, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21688, "top5_acc": 0.45891, "loss_cls": 4.52935, "loss": 4.52935, "time": 0.81425} +{"mode": "train", "epoch": 35, "iter": 2400, "lr": 0.08741, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22203, "top5_acc": 0.46094, "loss_cls": 4.49724, "loss": 4.49724, "time": 0.82051} +{"mode": "train", "epoch": 35, "iter": 2500, "lr": 0.08739, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23062, "top5_acc": 0.46453, "loss_cls": 4.51771, "loss": 4.51771, "time": 0.81947} +{"mode": "train", "epoch": 35, "iter": 2600, "lr": 0.08737, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21203, "top5_acc": 0.45047, "loss_cls": 4.57999, "loss": 4.57999, "time": 0.81986} +{"mode": "train", "epoch": 35, "iter": 2700, "lr": 0.08735, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21672, "top5_acc": 0.45188, "loss_cls": 4.55264, "loss": 4.55264, "time": 0.8071} +{"mode": "train", "epoch": 35, "iter": 2800, "lr": 0.08733, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21969, "top5_acc": 0.45797, "loss_cls": 4.54605, "loss": 4.54605, "time": 0.81149} +{"mode": "train", "epoch": 35, "iter": 2900, "lr": 0.08732, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22359, "top5_acc": 0.45984, "loss_cls": 4.52581, "loss": 4.52581, "time": 0.81105} +{"mode": "train", "epoch": 35, "iter": 3000, "lr": 0.0873, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22797, "top5_acc": 0.45969, "loss_cls": 4.51402, "loss": 4.51402, "time": 0.80776} +{"mode": "train", "epoch": 35, "iter": 3100, "lr": 0.08728, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22312, "top5_acc": 0.46219, "loss_cls": 4.52362, "loss": 4.52362, "time": 0.81226} +{"mode": "train", "epoch": 35, "iter": 3200, "lr": 0.08726, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22281, "top5_acc": 0.46578, "loss_cls": 4.4995, "loss": 4.4995, "time": 0.81294} +{"mode": "train", "epoch": 35, "iter": 3300, "lr": 0.08724, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22391, "top5_acc": 0.46406, "loss_cls": 4.51351, "loss": 4.51351, "time": 0.8158} +{"mode": "train", "epoch": 35, "iter": 3400, "lr": 0.08722, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21344, "top5_acc": 0.45703, "loss_cls": 4.55547, "loss": 4.55547, "time": 0.82348} +{"mode": "train", "epoch": 35, "iter": 3500, "lr": 0.0872, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22422, "top5_acc": 0.46391, "loss_cls": 4.53058, "loss": 4.53058, "time": 0.82632} +{"mode": "train", "epoch": 35, "iter": 3600, "lr": 0.08718, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22938, "top5_acc": 0.46859, "loss_cls": 4.48641, "loss": 4.48641, "time": 0.81582} +{"mode": "train", "epoch": 35, "iter": 3700, "lr": 0.08717, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22812, "top5_acc": 0.47266, "loss_cls": 4.49466, "loss": 4.49466, "time": 0.81384} +{"mode": "val", "epoch": 35, "iter": 309, "lr": 0.08716, "top1_acc": 0.15565, "top5_acc": 0.36762, "mean_class_accuracy": 0.15548} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.08714, "memory": 15990, "data_time": 1.3026, "top1_acc": 0.22562, "top5_acc": 0.46531, "loss_cls": 4.4965, "loss": 4.4965, "time": 2.274} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.08712, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2325, "top5_acc": 0.47438, "loss_cls": 4.4539, "loss": 4.4539, "time": 0.81375} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.0871, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22234, "top5_acc": 0.45828, "loss_cls": 4.51291, "loss": 4.51291, "time": 0.81097} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.08708, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22391, "top5_acc": 0.47297, "loss_cls": 4.4875, "loss": 4.4875, "time": 0.81402} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.08706, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21562, "top5_acc": 0.45859, "loss_cls": 4.53939, "loss": 4.53939, "time": 0.80958} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.08704, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22672, "top5_acc": 0.46984, "loss_cls": 4.47677, "loss": 4.47677, "time": 0.80775} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.08703, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22188, "top5_acc": 0.46672, "loss_cls": 4.54621, "loss": 4.54621, "time": 0.81056} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.08701, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22719, "top5_acc": 0.46609, "loss_cls": 4.49828, "loss": 4.49828, "time": 0.81196} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.08699, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22328, "top5_acc": 0.45516, "loss_cls": 4.52635, "loss": 4.52635, "time": 0.81036} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.08697, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22484, "top5_acc": 0.45906, "loss_cls": 4.53399, "loss": 4.53399, "time": 0.81113} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.08695, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22344, "top5_acc": 0.465, "loss_cls": 4.49468, "loss": 4.49468, "time": 0.81462} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.08693, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22734, "top5_acc": 0.46656, "loss_cls": 4.49093, "loss": 4.49093, "time": 0.80992} +{"mode": "train", "epoch": 36, "iter": 1300, "lr": 0.08691, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22656, "top5_acc": 0.46688, "loss_cls": 4.4886, "loss": 4.4886, "time": 0.80738} +{"mode": "train", "epoch": 36, "iter": 1400, "lr": 0.08689, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22031, "top5_acc": 0.45766, "loss_cls": 4.5203, "loss": 4.5203, "time": 0.81261} +{"mode": "train", "epoch": 36, "iter": 1500, "lr": 0.08688, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22594, "top5_acc": 0.47281, "loss_cls": 4.49913, "loss": 4.49913, "time": 0.81532} +{"mode": "train", "epoch": 36, "iter": 1600, "lr": 0.08686, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23484, "top5_acc": 0.47734, "loss_cls": 4.46226, "loss": 4.46226, "time": 0.80709} +{"mode": "train", "epoch": 36, "iter": 1700, "lr": 0.08684, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22453, "top5_acc": 0.46469, "loss_cls": 4.5028, "loss": 4.5028, "time": 0.80892} +{"mode": "train", "epoch": 36, "iter": 1800, "lr": 0.08682, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22703, "top5_acc": 0.47125, "loss_cls": 4.48704, "loss": 4.48704, "time": 0.80735} +{"mode": "train", "epoch": 36, "iter": 1900, "lr": 0.0868, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22203, "top5_acc": 0.46672, "loss_cls": 4.52633, "loss": 4.52633, "time": 0.81221} +{"mode": "train", "epoch": 36, "iter": 2000, "lr": 0.08678, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22969, "top5_acc": 0.47359, "loss_cls": 4.47465, "loss": 4.47465, "time": 0.81206} +{"mode": "train", "epoch": 36, "iter": 2100, "lr": 0.08676, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21984, "top5_acc": 0.46094, "loss_cls": 4.54158, "loss": 4.54158, "time": 0.82541} +{"mode": "train", "epoch": 36, "iter": 2200, "lr": 0.08674, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22078, "top5_acc": 0.46516, "loss_cls": 4.50298, "loss": 4.50298, "time": 0.80666} +{"mode": "train", "epoch": 36, "iter": 2300, "lr": 0.08672, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2275, "top5_acc": 0.47297, "loss_cls": 4.48815, "loss": 4.48815, "time": 0.81071} +{"mode": "train", "epoch": 36, "iter": 2400, "lr": 0.08671, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22984, "top5_acc": 0.46219, "loss_cls": 4.52469, "loss": 4.52469, "time": 0.81138} +{"mode": "train", "epoch": 36, "iter": 2500, "lr": 0.08669, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21859, "top5_acc": 0.46062, "loss_cls": 4.53395, "loss": 4.53395, "time": 0.81172} +{"mode": "train", "epoch": 36, "iter": 2600, "lr": 0.08667, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22531, "top5_acc": 0.47172, "loss_cls": 4.5049, "loss": 4.5049, "time": 0.81019} +{"mode": "train", "epoch": 36, "iter": 2700, "lr": 0.08665, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21812, "top5_acc": 0.46266, "loss_cls": 4.5374, "loss": 4.5374, "time": 0.80805} +{"mode": "train", "epoch": 36, "iter": 2800, "lr": 0.08663, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22531, "top5_acc": 0.45812, "loss_cls": 4.54091, "loss": 4.54091, "time": 0.81006} +{"mode": "train", "epoch": 36, "iter": 2900, "lr": 0.08661, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22422, "top5_acc": 0.46734, "loss_cls": 4.48824, "loss": 4.48824, "time": 0.81064} +{"mode": "train", "epoch": 36, "iter": 3000, "lr": 0.08659, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21781, "top5_acc": 0.45516, "loss_cls": 4.54312, "loss": 4.54312, "time": 0.81317} +{"mode": "train", "epoch": 36, "iter": 3100, "lr": 0.08657, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22031, "top5_acc": 0.46406, "loss_cls": 4.50988, "loss": 4.50988, "time": 0.8158} +{"mode": "train", "epoch": 36, "iter": 3200, "lr": 0.08655, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23203, "top5_acc": 0.46984, "loss_cls": 4.46708, "loss": 4.46708, "time": 0.81561} +{"mode": "train", "epoch": 36, "iter": 3300, "lr": 0.08653, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22219, "top5_acc": 0.45719, "loss_cls": 4.52609, "loss": 4.52609, "time": 0.81891} +{"mode": "train", "epoch": 36, "iter": 3400, "lr": 0.08651, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22625, "top5_acc": 0.45125, "loss_cls": 4.55493, "loss": 4.55493, "time": 0.80884} +{"mode": "train", "epoch": 36, "iter": 3500, "lr": 0.0865, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21859, "top5_acc": 0.46094, "loss_cls": 4.54912, "loss": 4.54912, "time": 0.81812} +{"mode": "train", "epoch": 36, "iter": 3600, "lr": 0.08648, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22906, "top5_acc": 0.46328, "loss_cls": 4.50832, "loss": 4.50832, "time": 0.81539} +{"mode": "train", "epoch": 36, "iter": 3700, "lr": 0.08646, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21984, "top5_acc": 0.46891, "loss_cls": 4.52663, "loss": 4.52663, "time": 0.8164} +{"mode": "val", "epoch": 36, "iter": 309, "lr": 0.08645, "top1_acc": 0.14258, "top5_acc": 0.34407, "mean_class_accuracy": 0.14237} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.08643, "memory": 15990, "data_time": 1.25832, "top1_acc": 0.22484, "top5_acc": 0.46875, "loss_cls": 4.50271, "loss": 4.50271, "time": 2.22839} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.08641, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22484, "top5_acc": 0.46594, "loss_cls": 4.49048, "loss": 4.49048, "time": 0.82034} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.08639, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23391, "top5_acc": 0.47, "loss_cls": 4.45817, "loss": 4.45817, "time": 0.81083} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.08637, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22172, "top5_acc": 0.46547, "loss_cls": 4.53131, "loss": 4.53131, "time": 0.81123} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.08635, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22922, "top5_acc": 0.47266, "loss_cls": 4.48039, "loss": 4.48039, "time": 0.80545} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.08633, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.235, "top5_acc": 0.48828, "loss_cls": 4.42198, "loss": 4.42198, "time": 0.80731} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.08631, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22641, "top5_acc": 0.45969, "loss_cls": 4.52183, "loss": 4.52183, "time": 0.81114} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0863, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22188, "top5_acc": 0.46281, "loss_cls": 4.52994, "loss": 4.52994, "time": 0.81203} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.08628, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23578, "top5_acc": 0.47234, "loss_cls": 4.45563, "loss": 4.45563, "time": 0.80941} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.08626, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22125, "top5_acc": 0.45547, "loss_cls": 4.55195, "loss": 4.55195, "time": 0.81183} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.08624, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22781, "top5_acc": 0.46906, "loss_cls": 4.50153, "loss": 4.50153, "time": 0.81909} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.08622, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21891, "top5_acc": 0.46891, "loss_cls": 4.53048, "loss": 4.53048, "time": 0.80808} +{"mode": "train", "epoch": 37, "iter": 1300, "lr": 0.0862, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23203, "top5_acc": 0.46641, "loss_cls": 4.47617, "loss": 4.47617, "time": 0.81195} +{"mode": "train", "epoch": 37, "iter": 1400, "lr": 0.08618, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22141, "top5_acc": 0.46422, "loss_cls": 4.51089, "loss": 4.51089, "time": 0.81435} +{"mode": "train", "epoch": 37, "iter": 1500, "lr": 0.08616, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23562, "top5_acc": 0.46719, "loss_cls": 4.45864, "loss": 4.45864, "time": 0.81126} +{"mode": "train", "epoch": 37, "iter": 1600, "lr": 0.08614, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22578, "top5_acc": 0.4775, "loss_cls": 4.44101, "loss": 4.44101, "time": 0.81208} +{"mode": "train", "epoch": 37, "iter": 1700, "lr": 0.08612, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22766, "top5_acc": 0.46266, "loss_cls": 4.51344, "loss": 4.51344, "time": 0.81268} +{"mode": "train", "epoch": 37, "iter": 1800, "lr": 0.0861, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22578, "top5_acc": 0.47078, "loss_cls": 4.50334, "loss": 4.50334, "time": 0.80606} +{"mode": "train", "epoch": 37, "iter": 1900, "lr": 0.08608, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22859, "top5_acc": 0.46906, "loss_cls": 4.49727, "loss": 4.49727, "time": 0.81791} +{"mode": "train", "epoch": 37, "iter": 2000, "lr": 0.08606, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23016, "top5_acc": 0.47, "loss_cls": 4.49584, "loss": 4.49584, "time": 0.8128} +{"mode": "train", "epoch": 37, "iter": 2100, "lr": 0.08604, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23812, "top5_acc": 0.48125, "loss_cls": 4.4516, "loss": 4.4516, "time": 0.81239} +{"mode": "train", "epoch": 37, "iter": 2200, "lr": 0.08602, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22594, "top5_acc": 0.45703, "loss_cls": 4.53595, "loss": 4.53595, "time": 0.81356} +{"mode": "train", "epoch": 37, "iter": 2300, "lr": 0.08601, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22188, "top5_acc": 0.46406, "loss_cls": 4.51685, "loss": 4.51685, "time": 0.81091} +{"mode": "train", "epoch": 37, "iter": 2400, "lr": 0.08599, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21891, "top5_acc": 0.46688, "loss_cls": 4.48837, "loss": 4.48837, "time": 0.81499} +{"mode": "train", "epoch": 37, "iter": 2500, "lr": 0.08597, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22406, "top5_acc": 0.4625, "loss_cls": 4.54064, "loss": 4.54064, "time": 0.81294} +{"mode": "train", "epoch": 37, "iter": 2600, "lr": 0.08595, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21906, "top5_acc": 0.45969, "loss_cls": 4.55074, "loss": 4.55074, "time": 0.81239} +{"mode": "train", "epoch": 37, "iter": 2700, "lr": 0.08593, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22375, "top5_acc": 0.4675, "loss_cls": 4.49286, "loss": 4.49286, "time": 0.80612} +{"mode": "train", "epoch": 37, "iter": 2800, "lr": 0.08591, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21906, "top5_acc": 0.46672, "loss_cls": 4.5371, "loss": 4.5371, "time": 0.81104} +{"mode": "train", "epoch": 37, "iter": 2900, "lr": 0.08589, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22375, "top5_acc": 0.46578, "loss_cls": 4.49882, "loss": 4.49882, "time": 0.81004} +{"mode": "train", "epoch": 37, "iter": 3000, "lr": 0.08587, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22812, "top5_acc": 0.46141, "loss_cls": 4.50005, "loss": 4.50005, "time": 0.80861} +{"mode": "train", "epoch": 37, "iter": 3100, "lr": 0.08585, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21719, "top5_acc": 0.46609, "loss_cls": 4.51991, "loss": 4.51991, "time": 0.80747} +{"mode": "train", "epoch": 37, "iter": 3200, "lr": 0.08583, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22109, "top5_acc": 0.46281, "loss_cls": 4.53945, "loss": 4.53945, "time": 0.81497} +{"mode": "train", "epoch": 37, "iter": 3300, "lr": 0.08581, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22922, "top5_acc": 0.47594, "loss_cls": 4.49731, "loss": 4.49731, "time": 0.80947} +{"mode": "train", "epoch": 37, "iter": 3400, "lr": 0.08579, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21438, "top5_acc": 0.46375, "loss_cls": 4.5469, "loss": 4.5469, "time": 0.81039} +{"mode": "train", "epoch": 37, "iter": 3500, "lr": 0.08577, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23719, "top5_acc": 0.47656, "loss_cls": 4.48911, "loss": 4.48911, "time": 0.81821} +{"mode": "train", "epoch": 37, "iter": 3600, "lr": 0.08575, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.215, "top5_acc": 0.45703, "loss_cls": 4.56354, "loss": 4.56354, "time": 0.81561} +{"mode": "train", "epoch": 37, "iter": 3700, "lr": 0.08573, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22328, "top5_acc": 0.46625, "loss_cls": 4.49139, "loss": 4.49139, "time": 0.82345} +{"mode": "val", "epoch": 37, "iter": 309, "lr": 0.08572, "top1_acc": 0.17318, "top5_acc": 0.39021, "mean_class_accuracy": 0.17328} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.0857, "memory": 15990, "data_time": 1.24822, "top1_acc": 0.2225, "top5_acc": 0.46781, "loss_cls": 4.49438, "loss": 4.49438, "time": 2.21647} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.08568, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22859, "top5_acc": 0.47312, "loss_cls": 4.48776, "loss": 4.48776, "time": 0.82215} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.08567, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.225, "top5_acc": 0.46281, "loss_cls": 4.4998, "loss": 4.4998, "time": 0.81417} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.08565, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.225, "top5_acc": 0.47219, "loss_cls": 4.48401, "loss": 4.48401, "time": 0.80542} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.08563, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22516, "top5_acc": 0.47203, "loss_cls": 4.47911, "loss": 4.47911, "time": 0.80838} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.08561, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22875, "top5_acc": 0.46391, "loss_cls": 4.49756, "loss": 4.49756, "time": 0.80848} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.08559, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22969, "top5_acc": 0.46766, "loss_cls": 4.49488, "loss": 4.49488, "time": 0.81318} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.08557, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23203, "top5_acc": 0.47359, "loss_cls": 4.45605, "loss": 4.45605, "time": 0.81092} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.08555, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22781, "top5_acc": 0.46125, "loss_cls": 4.50054, "loss": 4.50054, "time": 0.81531} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.08553, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22859, "top5_acc": 0.46422, "loss_cls": 4.50898, "loss": 4.50898, "time": 0.80977} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.08551, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22906, "top5_acc": 0.46328, "loss_cls": 4.50852, "loss": 4.50852, "time": 0.80819} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.08549, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22484, "top5_acc": 0.45984, "loss_cls": 4.54362, "loss": 4.54362, "time": 0.80708} +{"mode": "train", "epoch": 38, "iter": 1300, "lr": 0.08547, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22125, "top5_acc": 0.46359, "loss_cls": 4.51242, "loss": 4.51242, "time": 0.81178} +{"mode": "train", "epoch": 38, "iter": 1400, "lr": 0.08545, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22453, "top5_acc": 0.47391, "loss_cls": 4.48298, "loss": 4.48298, "time": 0.81228} +{"mode": "train", "epoch": 38, "iter": 1500, "lr": 0.08543, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22812, "top5_acc": 0.46469, "loss_cls": 4.4987, "loss": 4.4987, "time": 0.81251} +{"mode": "train", "epoch": 38, "iter": 1600, "lr": 0.08541, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22016, "top5_acc": 0.46984, "loss_cls": 4.52893, "loss": 4.52893, "time": 0.81077} +{"mode": "train", "epoch": 38, "iter": 1700, "lr": 0.08539, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23172, "top5_acc": 0.46859, "loss_cls": 4.48922, "loss": 4.48922, "time": 0.80884} +{"mode": "train", "epoch": 38, "iter": 1800, "lr": 0.08537, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22094, "top5_acc": 0.47156, "loss_cls": 4.4867, "loss": 4.4867, "time": 0.81215} +{"mode": "train", "epoch": 38, "iter": 1900, "lr": 0.08535, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23141, "top5_acc": 0.46781, "loss_cls": 4.51297, "loss": 4.51297, "time": 0.80807} +{"mode": "train", "epoch": 38, "iter": 2000, "lr": 0.08533, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23016, "top5_acc": 0.47, "loss_cls": 4.47651, "loss": 4.47651, "time": 0.81885} +{"mode": "train", "epoch": 38, "iter": 2100, "lr": 0.08531, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22359, "top5_acc": 0.46812, "loss_cls": 4.50986, "loss": 4.50986, "time": 0.82155} +{"mode": "train", "epoch": 38, "iter": 2200, "lr": 0.08529, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22562, "top5_acc": 0.46656, "loss_cls": 4.5113, "loss": 4.5113, "time": 0.818} +{"mode": "train", "epoch": 38, "iter": 2300, "lr": 0.08527, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2225, "top5_acc": 0.46641, "loss_cls": 4.50115, "loss": 4.50115, "time": 0.81526} +{"mode": "train", "epoch": 38, "iter": 2400, "lr": 0.08525, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.225, "top5_acc": 0.4725, "loss_cls": 4.49778, "loss": 4.49778, "time": 0.81121} +{"mode": "train", "epoch": 38, "iter": 2500, "lr": 0.08523, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23703, "top5_acc": 0.48125, "loss_cls": 4.41478, "loss": 4.41478, "time": 0.81149} +{"mode": "train", "epoch": 38, "iter": 2600, "lr": 0.08521, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22406, "top5_acc": 0.46984, "loss_cls": 4.52169, "loss": 4.52169, "time": 0.81001} +{"mode": "train", "epoch": 38, "iter": 2700, "lr": 0.08519, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21281, "top5_acc": 0.45531, "loss_cls": 4.5481, "loss": 4.5481, "time": 0.8084} +{"mode": "train", "epoch": 38, "iter": 2800, "lr": 0.08517, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22562, "top5_acc": 0.47094, "loss_cls": 4.47, "loss": 4.47, "time": 0.81081} +{"mode": "train", "epoch": 38, "iter": 2900, "lr": 0.08515, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22609, "top5_acc": 0.4725, "loss_cls": 4.46264, "loss": 4.46264, "time": 0.81147} +{"mode": "train", "epoch": 38, "iter": 3000, "lr": 0.08513, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22344, "top5_acc": 0.46406, "loss_cls": 4.49472, "loss": 4.49472, "time": 0.80987} +{"mode": "train", "epoch": 38, "iter": 3100, "lr": 0.08511, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23078, "top5_acc": 0.47266, "loss_cls": 4.47722, "loss": 4.47722, "time": 0.80995} +{"mode": "train", "epoch": 38, "iter": 3200, "lr": 0.08509, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22453, "top5_acc": 0.4625, "loss_cls": 4.49978, "loss": 4.49978, "time": 0.81473} +{"mode": "train", "epoch": 38, "iter": 3300, "lr": 0.08507, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23094, "top5_acc": 0.47078, "loss_cls": 4.46223, "loss": 4.46223, "time": 0.81355} +{"mode": "train", "epoch": 38, "iter": 3400, "lr": 0.08505, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23062, "top5_acc": 0.47688, "loss_cls": 4.46448, "loss": 4.46448, "time": 0.81438} +{"mode": "train", "epoch": 38, "iter": 3500, "lr": 0.08503, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22609, "top5_acc": 0.46469, "loss_cls": 4.5, "loss": 4.5, "time": 0.81094} +{"mode": "train", "epoch": 38, "iter": 3600, "lr": 0.08501, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22516, "top5_acc": 0.46906, "loss_cls": 4.48486, "loss": 4.48486, "time": 0.81943} +{"mode": "train", "epoch": 38, "iter": 3700, "lr": 0.08499, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22375, "top5_acc": 0.46484, "loss_cls": 4.52533, "loss": 4.52533, "time": 0.8141} +{"mode": "val", "epoch": 38, "iter": 309, "lr": 0.08498, "top1_acc": 0.15469, "top5_acc": 0.3733, "mean_class_accuracy": 0.15469} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.08496, "memory": 15990, "data_time": 1.26115, "top1_acc": 0.23109, "top5_acc": 0.47234, "loss_cls": 4.46609, "loss": 4.46609, "time": 2.23299} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.08494, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23516, "top5_acc": 0.46609, "loss_cls": 4.46944, "loss": 4.46944, "time": 0.81543} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.08492, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23141, "top5_acc": 0.46938, "loss_cls": 4.5032, "loss": 4.5032, "time": 0.81811} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.0849, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21422, "top5_acc": 0.45844, "loss_cls": 4.51344, "loss": 4.51344, "time": 0.81371} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.08488, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23531, "top5_acc": 0.47844, "loss_cls": 4.45939, "loss": 4.45939, "time": 0.81095} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.08486, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21969, "top5_acc": 0.47109, "loss_cls": 4.51662, "loss": 4.51662, "time": 0.80795} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.08484, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22391, "top5_acc": 0.46875, "loss_cls": 4.46533, "loss": 4.46533, "time": 0.80951} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.08482, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23625, "top5_acc": 0.47625, "loss_cls": 4.45624, "loss": 4.45624, "time": 0.80858} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.0848, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23312, "top5_acc": 0.47484, "loss_cls": 4.45295, "loss": 4.45295, "time": 0.80678} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.08478, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22719, "top5_acc": 0.47031, "loss_cls": 4.48685, "loss": 4.48685, "time": 0.81353} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.08476, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22562, "top5_acc": 0.46859, "loss_cls": 4.49489, "loss": 4.49489, "time": 0.80965} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.08474, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22766, "top5_acc": 0.47312, "loss_cls": 4.44177, "loss": 4.44177, "time": 0.80755} +{"mode": "train", "epoch": 39, "iter": 1300, "lr": 0.08472, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22688, "top5_acc": 0.47547, "loss_cls": 4.48883, "loss": 4.48883, "time": 0.81173} +{"mode": "train", "epoch": 39, "iter": 1400, "lr": 0.0847, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23578, "top5_acc": 0.48438, "loss_cls": 4.45684, "loss": 4.45684, "time": 0.80784} +{"mode": "train", "epoch": 39, "iter": 1500, "lr": 0.08468, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21906, "top5_acc": 0.46562, "loss_cls": 4.50128, "loss": 4.50128, "time": 0.81049} +{"mode": "train", "epoch": 39, "iter": 1600, "lr": 0.08466, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22016, "top5_acc": 0.46406, "loss_cls": 4.49529, "loss": 4.49529, "time": 0.81342} +{"mode": "train", "epoch": 39, "iter": 1700, "lr": 0.08464, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22688, "top5_acc": 0.47406, "loss_cls": 4.48957, "loss": 4.48957, "time": 0.81057} +{"mode": "train", "epoch": 39, "iter": 1800, "lr": 0.08462, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21766, "top5_acc": 0.45938, "loss_cls": 4.54825, "loss": 4.54825, "time": 0.81308} +{"mode": "train", "epoch": 39, "iter": 1900, "lr": 0.0846, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22312, "top5_acc": 0.46859, "loss_cls": 4.4864, "loss": 4.4864, "time": 0.81124} +{"mode": "train", "epoch": 39, "iter": 2000, "lr": 0.08458, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21531, "top5_acc": 0.46125, "loss_cls": 4.51732, "loss": 4.51732, "time": 0.81519} +{"mode": "train", "epoch": 39, "iter": 2100, "lr": 0.08456, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23797, "top5_acc": 0.46953, "loss_cls": 4.47268, "loss": 4.47268, "time": 0.81778} +{"mode": "train", "epoch": 39, "iter": 2200, "lr": 0.08454, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22641, "top5_acc": 0.46859, "loss_cls": 4.48295, "loss": 4.48295, "time": 0.8175} +{"mode": "train", "epoch": 39, "iter": 2300, "lr": 0.08452, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22219, "top5_acc": 0.46844, "loss_cls": 4.50444, "loss": 4.50444, "time": 0.81713} +{"mode": "train", "epoch": 39, "iter": 2400, "lr": 0.0845, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22781, "top5_acc": 0.46484, "loss_cls": 4.48494, "loss": 4.48494, "time": 0.81252} +{"mode": "train", "epoch": 39, "iter": 2500, "lr": 0.08448, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22984, "top5_acc": 0.46625, "loss_cls": 4.48639, "loss": 4.48639, "time": 0.80863} +{"mode": "train", "epoch": 39, "iter": 2600, "lr": 0.08446, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23047, "top5_acc": 0.46859, "loss_cls": 4.49354, "loss": 4.49354, "time": 0.81167} +{"mode": "train", "epoch": 39, "iter": 2700, "lr": 0.08444, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22641, "top5_acc": 0.46391, "loss_cls": 4.53313, "loss": 4.53313, "time": 0.81795} +{"mode": "train", "epoch": 39, "iter": 2800, "lr": 0.08442, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22781, "top5_acc": 0.46469, "loss_cls": 4.49611, "loss": 4.49611, "time": 0.81265} +{"mode": "train", "epoch": 39, "iter": 2900, "lr": 0.0844, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22156, "top5_acc": 0.47297, "loss_cls": 4.47133, "loss": 4.47133, "time": 0.81238} +{"mode": "train", "epoch": 39, "iter": 3000, "lr": 0.08438, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2275, "top5_acc": 0.46016, "loss_cls": 4.51676, "loss": 4.51676, "time": 0.81153} +{"mode": "train", "epoch": 39, "iter": 3100, "lr": 0.08436, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23062, "top5_acc": 0.46703, "loss_cls": 4.46938, "loss": 4.46938, "time": 0.80597} +{"mode": "train", "epoch": 39, "iter": 3200, "lr": 0.08434, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22891, "top5_acc": 0.46266, "loss_cls": 4.48956, "loss": 4.48956, "time": 0.81571} +{"mode": "train", "epoch": 39, "iter": 3300, "lr": 0.08432, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22656, "top5_acc": 0.4625, "loss_cls": 4.50004, "loss": 4.50004, "time": 0.81483} +{"mode": "train", "epoch": 39, "iter": 3400, "lr": 0.0843, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23234, "top5_acc": 0.47672, "loss_cls": 4.45457, "loss": 4.45457, "time": 0.81224} +{"mode": "train", "epoch": 39, "iter": 3500, "lr": 0.08428, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22391, "top5_acc": 0.46516, "loss_cls": 4.51705, "loss": 4.51705, "time": 0.81403} +{"mode": "train", "epoch": 39, "iter": 3600, "lr": 0.08426, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22875, "top5_acc": 0.47141, "loss_cls": 4.4532, "loss": 4.4532, "time": 0.81694} +{"mode": "train", "epoch": 39, "iter": 3700, "lr": 0.08424, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22703, "top5_acc": 0.46766, "loss_cls": 4.50872, "loss": 4.50872, "time": 0.81858} +{"mode": "val", "epoch": 39, "iter": 309, "lr": 0.08423, "top1_acc": 0.15676, "top5_acc": 0.36985, "mean_class_accuracy": 0.15672} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.08421, "memory": 15990, "data_time": 1.25146, "top1_acc": 0.23156, "top5_acc": 0.48078, "loss_cls": 4.43161, "loss": 4.43161, "time": 2.26023} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.08419, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22234, "top5_acc": 0.47234, "loss_cls": 4.43449, "loss": 4.43449, "time": 0.81306} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.08417, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23422, "top5_acc": 0.47016, "loss_cls": 4.47172, "loss": 4.47172, "time": 0.81596} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.08415, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.23031, "top5_acc": 0.47156, "loss_cls": 4.46539, "loss": 4.46539, "time": 0.82044} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.08413, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22375, "top5_acc": 0.46953, "loss_cls": 4.50492, "loss": 4.50492, "time": 0.8093} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.08411, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23484, "top5_acc": 0.47172, "loss_cls": 4.45858, "loss": 4.45858, "time": 0.8175} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.08408, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22688, "top5_acc": 0.47281, "loss_cls": 4.4645, "loss": 4.4645, "time": 0.81112} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.08406, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22906, "top5_acc": 0.48203, "loss_cls": 4.42899, "loss": 4.42899, "time": 0.81182} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.08404, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22266, "top5_acc": 0.47562, "loss_cls": 4.46437, "loss": 4.46437, "time": 0.81} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.08402, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22938, "top5_acc": 0.47062, "loss_cls": 4.47915, "loss": 4.47915, "time": 0.81206} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.084, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23406, "top5_acc": 0.47438, "loss_cls": 4.44343, "loss": 4.44343, "time": 0.81671} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.08398, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22625, "top5_acc": 0.46, "loss_cls": 4.53617, "loss": 4.53617, "time": 0.81837} +{"mode": "train", "epoch": 40, "iter": 1300, "lr": 0.08396, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22766, "top5_acc": 0.46625, "loss_cls": 4.48668, "loss": 4.48668, "time": 0.81182} +{"mode": "train", "epoch": 40, "iter": 1400, "lr": 0.08394, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21906, "top5_acc": 0.46438, "loss_cls": 4.53541, "loss": 4.53541, "time": 0.81006} +{"mode": "train", "epoch": 40, "iter": 1500, "lr": 0.08392, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23547, "top5_acc": 0.48656, "loss_cls": 4.42362, "loss": 4.42362, "time": 0.80869} +{"mode": "train", "epoch": 40, "iter": 1600, "lr": 0.0839, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2275, "top5_acc": 0.4725, "loss_cls": 4.46242, "loss": 4.46242, "time": 0.8145} +{"mode": "train", "epoch": 40, "iter": 1700, "lr": 0.08388, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20969, "top5_acc": 0.45266, "loss_cls": 4.55373, "loss": 4.55373, "time": 0.8114} +{"mode": "train", "epoch": 40, "iter": 1800, "lr": 0.08386, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.225, "top5_acc": 0.45703, "loss_cls": 4.52255, "loss": 4.52255, "time": 0.80699} +{"mode": "train", "epoch": 40, "iter": 1900, "lr": 0.08384, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23312, "top5_acc": 0.47156, "loss_cls": 4.46885, "loss": 4.46885, "time": 0.80736} +{"mode": "train", "epoch": 40, "iter": 2000, "lr": 0.08382, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22547, "top5_acc": 0.47641, "loss_cls": 4.46725, "loss": 4.46725, "time": 0.81475} +{"mode": "train", "epoch": 40, "iter": 2100, "lr": 0.0838, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22922, "top5_acc": 0.47109, "loss_cls": 4.48744, "loss": 4.48744, "time": 0.81769} +{"mode": "train", "epoch": 40, "iter": 2200, "lr": 0.08378, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23641, "top5_acc": 0.47109, "loss_cls": 4.46867, "loss": 4.46867, "time": 0.81775} +{"mode": "train", "epoch": 40, "iter": 2300, "lr": 0.08376, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22391, "top5_acc": 0.47297, "loss_cls": 4.46976, "loss": 4.46976, "time": 0.81661} +{"mode": "train", "epoch": 40, "iter": 2400, "lr": 0.08374, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22281, "top5_acc": 0.46578, "loss_cls": 4.49174, "loss": 4.49174, "time": 0.81048} +{"mode": "train", "epoch": 40, "iter": 2500, "lr": 0.08371, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21656, "top5_acc": 0.46594, "loss_cls": 4.51615, "loss": 4.51615, "time": 0.80773} +{"mode": "train", "epoch": 40, "iter": 2600, "lr": 0.08369, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22703, "top5_acc": 0.47453, "loss_cls": 4.46021, "loss": 4.46021, "time": 0.80764} +{"mode": "train", "epoch": 40, "iter": 2700, "lr": 0.08367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22125, "top5_acc": 0.45953, "loss_cls": 4.51976, "loss": 4.51976, "time": 0.81071} +{"mode": "train", "epoch": 40, "iter": 2800, "lr": 0.08365, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2225, "top5_acc": 0.45438, "loss_cls": 4.53285, "loss": 4.53285, "time": 0.81683} +{"mode": "train", "epoch": 40, "iter": 2900, "lr": 0.08363, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22391, "top5_acc": 0.46469, "loss_cls": 4.54191, "loss": 4.54191, "time": 0.80739} +{"mode": "train", "epoch": 40, "iter": 3000, "lr": 0.08361, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22734, "top5_acc": 0.47375, "loss_cls": 4.4725, "loss": 4.4725, "time": 0.80952} +{"mode": "train", "epoch": 40, "iter": 3100, "lr": 0.08359, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22109, "top5_acc": 0.46672, "loss_cls": 4.52257, "loss": 4.52257, "time": 0.81211} +{"mode": "train", "epoch": 40, "iter": 3200, "lr": 0.08357, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22828, "top5_acc": 0.4725, "loss_cls": 4.46582, "loss": 4.46582, "time": 0.80645} +{"mode": "train", "epoch": 40, "iter": 3300, "lr": 0.08355, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23016, "top5_acc": 0.45984, "loss_cls": 4.50719, "loss": 4.50719, "time": 0.81116} +{"mode": "train", "epoch": 40, "iter": 3400, "lr": 0.08353, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22078, "top5_acc": 0.46797, "loss_cls": 4.52874, "loss": 4.52874, "time": 0.81284} +{"mode": "train", "epoch": 40, "iter": 3500, "lr": 0.08351, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22688, "top5_acc": 0.47156, "loss_cls": 4.46542, "loss": 4.46542, "time": 0.81615} +{"mode": "train", "epoch": 40, "iter": 3600, "lr": 0.08349, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23281, "top5_acc": 0.46812, "loss_cls": 4.47192, "loss": 4.47192, "time": 0.81608} +{"mode": "train", "epoch": 40, "iter": 3700, "lr": 0.08347, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22953, "top5_acc": 0.47422, "loss_cls": 4.47365, "loss": 4.47365, "time": 0.8184} +{"mode": "val", "epoch": 40, "iter": 309, "lr": 0.08346, "top1_acc": 0.1713, "top5_acc": 0.37937, "mean_class_accuracy": 0.17112} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.08344, "memory": 15990, "data_time": 1.27706, "top1_acc": 0.23812, "top5_acc": 0.47781, "loss_cls": 4.44913, "loss": 4.44913, "time": 2.25162} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.08342, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23766, "top5_acc": 0.47719, "loss_cls": 4.43914, "loss": 4.43914, "time": 0.81615} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.08339, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23375, "top5_acc": 0.47312, "loss_cls": 4.44258, "loss": 4.44258, "time": 0.81782} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.08337, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22641, "top5_acc": 0.46625, "loss_cls": 4.48646, "loss": 4.48646, "time": 0.82204} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.08335, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23203, "top5_acc": 0.46891, "loss_cls": 4.47719, "loss": 4.47719, "time": 0.81033} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.08333, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23938, "top5_acc": 0.48266, "loss_cls": 4.44341, "loss": 4.44341, "time": 0.80846} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.08331, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22875, "top5_acc": 0.47734, "loss_cls": 4.43945, "loss": 4.43945, "time": 0.81068} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.08329, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22672, "top5_acc": 0.47719, "loss_cls": 4.4707, "loss": 4.4707, "time": 0.80774} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.08327, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23016, "top5_acc": 0.48094, "loss_cls": 4.46233, "loss": 4.46233, "time": 0.8061} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.08325, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22359, "top5_acc": 0.46406, "loss_cls": 4.50598, "loss": 4.50598, "time": 0.81511} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.08323, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22375, "top5_acc": 0.46297, "loss_cls": 4.53011, "loss": 4.53011, "time": 0.81248} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.08321, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22688, "top5_acc": 0.46344, "loss_cls": 4.51081, "loss": 4.51081, "time": 0.81399} +{"mode": "train", "epoch": 41, "iter": 1300, "lr": 0.08319, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22219, "top5_acc": 0.47219, "loss_cls": 4.46096, "loss": 4.46096, "time": 0.8102} +{"mode": "train", "epoch": 41, "iter": 1400, "lr": 0.08316, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23719, "top5_acc": 0.47109, "loss_cls": 4.44616, "loss": 4.44616, "time": 0.80883} +{"mode": "train", "epoch": 41, "iter": 1500, "lr": 0.08314, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23016, "top5_acc": 0.46922, "loss_cls": 4.49281, "loss": 4.49281, "time": 0.8118} +{"mode": "train", "epoch": 41, "iter": 1600, "lr": 0.08312, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22906, "top5_acc": 0.46234, "loss_cls": 4.49383, "loss": 4.49383, "time": 0.81263} +{"mode": "train", "epoch": 41, "iter": 1700, "lr": 0.0831, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23125, "top5_acc": 0.47656, "loss_cls": 4.47401, "loss": 4.47401, "time": 0.81018} +{"mode": "train", "epoch": 41, "iter": 1800, "lr": 0.08308, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22359, "top5_acc": 0.46781, "loss_cls": 4.47371, "loss": 4.47371, "time": 0.80894} +{"mode": "train", "epoch": 41, "iter": 1900, "lr": 0.08306, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22781, "top5_acc": 0.46953, "loss_cls": 4.47859, "loss": 4.47859, "time": 0.81176} +{"mode": "train", "epoch": 41, "iter": 2000, "lr": 0.08304, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22016, "top5_acc": 0.46953, "loss_cls": 4.49085, "loss": 4.49085, "time": 0.81163} +{"mode": "train", "epoch": 41, "iter": 2100, "lr": 0.08302, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23375, "top5_acc": 0.46891, "loss_cls": 4.47351, "loss": 4.47351, "time": 0.82226} +{"mode": "train", "epoch": 41, "iter": 2200, "lr": 0.083, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22484, "top5_acc": 0.46516, "loss_cls": 4.51553, "loss": 4.51553, "time": 0.81363} +{"mode": "train", "epoch": 41, "iter": 2300, "lr": 0.08298, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22391, "top5_acc": 0.48016, "loss_cls": 4.47277, "loss": 4.47277, "time": 0.81875} +{"mode": "train", "epoch": 41, "iter": 2400, "lr": 0.08296, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22703, "top5_acc": 0.46359, "loss_cls": 4.48742, "loss": 4.48742, "time": 0.80802} +{"mode": "train", "epoch": 41, "iter": 2500, "lr": 0.08293, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23031, "top5_acc": 0.47688, "loss_cls": 4.46534, "loss": 4.46534, "time": 0.81114} +{"mode": "train", "epoch": 41, "iter": 2600, "lr": 0.08291, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22703, "top5_acc": 0.47359, "loss_cls": 4.48118, "loss": 4.48118, "time": 0.80927} +{"mode": "train", "epoch": 41, "iter": 2700, "lr": 0.08289, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22141, "top5_acc": 0.465, "loss_cls": 4.5349, "loss": 4.5349, "time": 0.81337} +{"mode": "train", "epoch": 41, "iter": 2800, "lr": 0.08287, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23484, "top5_acc": 0.46641, "loss_cls": 4.46512, "loss": 4.46512, "time": 0.81519} +{"mode": "train", "epoch": 41, "iter": 2900, "lr": 0.08285, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23641, "top5_acc": 0.47203, "loss_cls": 4.44878, "loss": 4.44878, "time": 0.81358} +{"mode": "train", "epoch": 41, "iter": 3000, "lr": 0.08283, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22953, "top5_acc": 0.46562, "loss_cls": 4.49107, "loss": 4.49107, "time": 0.80992} +{"mode": "train", "epoch": 41, "iter": 3100, "lr": 0.08281, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22922, "top5_acc": 0.47078, "loss_cls": 4.49514, "loss": 4.49514, "time": 0.81259} +{"mode": "train", "epoch": 41, "iter": 3200, "lr": 0.08279, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22438, "top5_acc": 0.4675, "loss_cls": 4.49313, "loss": 4.49313, "time": 0.81276} +{"mode": "train", "epoch": 41, "iter": 3300, "lr": 0.08277, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22844, "top5_acc": 0.47172, "loss_cls": 4.48381, "loss": 4.48381, "time": 0.81285} +{"mode": "train", "epoch": 41, "iter": 3400, "lr": 0.08274, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22766, "top5_acc": 0.47234, "loss_cls": 4.4594, "loss": 4.4594, "time": 0.81841} +{"mode": "train", "epoch": 41, "iter": 3500, "lr": 0.08272, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22859, "top5_acc": 0.47172, "loss_cls": 4.48648, "loss": 4.48648, "time": 0.8117} +{"mode": "train", "epoch": 41, "iter": 3600, "lr": 0.0827, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23469, "top5_acc": 0.47516, "loss_cls": 4.43107, "loss": 4.43107, "time": 0.817} +{"mode": "train", "epoch": 41, "iter": 3700, "lr": 0.08268, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22391, "top5_acc": 0.47172, "loss_cls": 4.50893, "loss": 4.50893, "time": 0.80993} +{"mode": "val", "epoch": 41, "iter": 309, "lr": 0.08267, "top1_acc": 0.16335, "top5_acc": 0.36372, "mean_class_accuracy": 0.16317} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.08265, "memory": 15990, "data_time": 1.27104, "top1_acc": 0.2425, "top5_acc": 0.48406, "loss_cls": 4.43118, "loss": 4.43118, "time": 2.22928} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.08263, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23, "top5_acc": 0.47, "loss_cls": 4.46578, "loss": 4.46578, "time": 0.81429} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.08261, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23219, "top5_acc": 0.48438, "loss_cls": 4.45263, "loss": 4.45263, "time": 0.80966} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.08259, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22859, "top5_acc": 0.4775, "loss_cls": 4.4382, "loss": 4.4382, "time": 0.82179} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.08257, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22406, "top5_acc": 0.46781, "loss_cls": 4.4817, "loss": 4.4817, "time": 0.82} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.08254, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23312, "top5_acc": 0.47812, "loss_cls": 4.45437, "loss": 4.45437, "time": 0.8143} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.08252, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22484, "top5_acc": 0.46109, "loss_cls": 4.49582, "loss": 4.49582, "time": 0.80656} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.0825, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23281, "top5_acc": 0.47188, "loss_cls": 4.44485, "loss": 4.44485, "time": 0.81655} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.08248, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22562, "top5_acc": 0.47156, "loss_cls": 4.46301, "loss": 4.46301, "time": 0.81339} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.08246, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22328, "top5_acc": 0.46312, "loss_cls": 4.51015, "loss": 4.51015, "time": 0.80956} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.08244, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23141, "top5_acc": 0.47312, "loss_cls": 4.48307, "loss": 4.48307, "time": 0.82074} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.08242, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22781, "top5_acc": 0.47391, "loss_cls": 4.46856, "loss": 4.46856, "time": 0.81296} +{"mode": "train", "epoch": 42, "iter": 1300, "lr": 0.0824, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23094, "top5_acc": 0.47453, "loss_cls": 4.48027, "loss": 4.48027, "time": 0.81175} +{"mode": "train", "epoch": 42, "iter": 1400, "lr": 0.08237, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23312, "top5_acc": 0.46859, "loss_cls": 4.46234, "loss": 4.46234, "time": 0.81222} +{"mode": "train", "epoch": 42, "iter": 1500, "lr": 0.08235, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23344, "top5_acc": 0.46953, "loss_cls": 4.50494, "loss": 4.50494, "time": 0.81017} +{"mode": "train", "epoch": 42, "iter": 1600, "lr": 0.08233, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22516, "top5_acc": 0.46, "loss_cls": 4.51188, "loss": 4.51188, "time": 0.81183} +{"mode": "train", "epoch": 42, "iter": 1700, "lr": 0.08231, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24031, "top5_acc": 0.48109, "loss_cls": 4.42534, "loss": 4.42534, "time": 0.81284} +{"mode": "train", "epoch": 42, "iter": 1800, "lr": 0.08229, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22594, "top5_acc": 0.46531, "loss_cls": 4.46926, "loss": 4.46926, "time": 0.8181} +{"mode": "train", "epoch": 42, "iter": 1900, "lr": 0.08227, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22281, "top5_acc": 0.46547, "loss_cls": 4.49099, "loss": 4.49099, "time": 0.81313} +{"mode": "train", "epoch": 42, "iter": 2000, "lr": 0.08225, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22609, "top5_acc": 0.465, "loss_cls": 4.51765, "loss": 4.51765, "time": 0.80762} +{"mode": "train", "epoch": 42, "iter": 2100, "lr": 0.08222, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21859, "top5_acc": 0.46703, "loss_cls": 4.48913, "loss": 4.48913, "time": 0.815} +{"mode": "train", "epoch": 42, "iter": 2200, "lr": 0.0822, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22953, "top5_acc": 0.47281, "loss_cls": 4.44762, "loss": 4.44762, "time": 0.81499} +{"mode": "train", "epoch": 42, "iter": 2300, "lr": 0.08218, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22531, "top5_acc": 0.46266, "loss_cls": 4.5017, "loss": 4.5017, "time": 0.81484} +{"mode": "train", "epoch": 42, "iter": 2400, "lr": 0.08216, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22562, "top5_acc": 0.47016, "loss_cls": 4.50544, "loss": 4.50544, "time": 0.80927} +{"mode": "train", "epoch": 42, "iter": 2500, "lr": 0.08214, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22938, "top5_acc": 0.47953, "loss_cls": 4.45492, "loss": 4.45492, "time": 0.81999} +{"mode": "train", "epoch": 42, "iter": 2600, "lr": 0.08212, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24125, "top5_acc": 0.48344, "loss_cls": 4.40901, "loss": 4.40901, "time": 0.81885} +{"mode": "train", "epoch": 42, "iter": 2700, "lr": 0.0821, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22812, "top5_acc": 0.47016, "loss_cls": 4.47, "loss": 4.47, "time": 0.81438} +{"mode": "train", "epoch": 42, "iter": 2800, "lr": 0.08207, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23562, "top5_acc": 0.48, "loss_cls": 4.44492, "loss": 4.44492, "time": 0.81029} +{"mode": "train", "epoch": 42, "iter": 2900, "lr": 0.08205, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22875, "top5_acc": 0.465, "loss_cls": 4.49364, "loss": 4.49364, "time": 0.80893} +{"mode": "train", "epoch": 42, "iter": 3000, "lr": 0.08203, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22672, "top5_acc": 0.47047, "loss_cls": 4.45783, "loss": 4.45783, "time": 0.80842} +{"mode": "train", "epoch": 42, "iter": 3100, "lr": 0.08201, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23594, "top5_acc": 0.4775, "loss_cls": 4.44772, "loss": 4.44772, "time": 0.81071} +{"mode": "train", "epoch": 42, "iter": 3200, "lr": 0.08199, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23281, "top5_acc": 0.46969, "loss_cls": 4.45872, "loss": 4.45872, "time": 0.81245} +{"mode": "train", "epoch": 42, "iter": 3300, "lr": 0.08197, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23594, "top5_acc": 0.47219, "loss_cls": 4.44851, "loss": 4.44851, "time": 0.81369} +{"mode": "train", "epoch": 42, "iter": 3400, "lr": 0.08195, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22578, "top5_acc": 0.47188, "loss_cls": 4.46542, "loss": 4.46542, "time": 0.81549} +{"mode": "train", "epoch": 42, "iter": 3500, "lr": 0.08192, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22328, "top5_acc": 0.46969, "loss_cls": 4.50717, "loss": 4.50717, "time": 0.81546} +{"mode": "train", "epoch": 42, "iter": 3600, "lr": 0.0819, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23578, "top5_acc": 0.47375, "loss_cls": 4.48638, "loss": 4.48638, "time": 0.81491} +{"mode": "train", "epoch": 42, "iter": 3700, "lr": 0.08188, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23594, "top5_acc": 0.46312, "loss_cls": 4.49025, "loss": 4.49025, "time": 0.81786} +{"mode": "val", "epoch": 42, "iter": 309, "lr": 0.08187, "top1_acc": 0.15367, "top5_acc": 0.35577, "mean_class_accuracy": 0.15358} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.08185, "memory": 15990, "data_time": 1.22595, "top1_acc": 0.22984, "top5_acc": 0.46609, "loss_cls": 4.45973, "loss": 4.45973, "time": 2.19131} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.08183, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22875, "top5_acc": 0.47547, "loss_cls": 4.43796, "loss": 4.43796, "time": 0.81457} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.08181, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23219, "top5_acc": 0.47828, "loss_cls": 4.4704, "loss": 4.4704, "time": 0.82535} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.08179, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22625, "top5_acc": 0.48016, "loss_cls": 4.43525, "loss": 4.43525, "time": 0.81323} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.08176, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23297, "top5_acc": 0.47297, "loss_cls": 4.43532, "loss": 4.43532, "time": 0.81614} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.08174, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23109, "top5_acc": 0.47594, "loss_cls": 4.47421, "loss": 4.47421, "time": 0.81363} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.08172, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22234, "top5_acc": 0.46078, "loss_cls": 4.51393, "loss": 4.51393, "time": 0.80889} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.0817, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24562, "top5_acc": 0.47328, "loss_cls": 4.42888, "loss": 4.42888, "time": 0.8074} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.08168, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23797, "top5_acc": 0.48062, "loss_cls": 4.44156, "loss": 4.44156, "time": 0.81086} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.08166, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23594, "top5_acc": 0.47422, "loss_cls": 4.45152, "loss": 4.45152, "time": 0.81528} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.08163, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22922, "top5_acc": 0.48422, "loss_cls": 4.42971, "loss": 4.42971, "time": 0.80983} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.08161, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23359, "top5_acc": 0.47031, "loss_cls": 4.47721, "loss": 4.47721, "time": 0.80547} +{"mode": "train", "epoch": 43, "iter": 1300, "lr": 0.08159, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22844, "top5_acc": 0.475, "loss_cls": 4.46838, "loss": 4.46838, "time": 0.81052} +{"mode": "train", "epoch": 43, "iter": 1400, "lr": 0.08157, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22266, "top5_acc": 0.45734, "loss_cls": 4.53162, "loss": 4.53162, "time": 0.81349} +{"mode": "train", "epoch": 43, "iter": 1500, "lr": 0.08155, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22812, "top5_acc": 0.47625, "loss_cls": 4.48857, "loss": 4.48857, "time": 0.81652} +{"mode": "train", "epoch": 43, "iter": 1600, "lr": 0.08153, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22562, "top5_acc": 0.47047, "loss_cls": 4.49383, "loss": 4.49383, "time": 0.8126} +{"mode": "train", "epoch": 43, "iter": 1700, "lr": 0.0815, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23078, "top5_acc": 0.47234, "loss_cls": 4.47095, "loss": 4.47095, "time": 0.81109} +{"mode": "train", "epoch": 43, "iter": 1800, "lr": 0.08148, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22672, "top5_acc": 0.47453, "loss_cls": 4.43724, "loss": 4.43724, "time": 0.81018} +{"mode": "train", "epoch": 43, "iter": 1900, "lr": 0.08146, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22797, "top5_acc": 0.47688, "loss_cls": 4.46514, "loss": 4.46514, "time": 0.80888} +{"mode": "train", "epoch": 43, "iter": 2000, "lr": 0.08144, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23453, "top5_acc": 0.47344, "loss_cls": 4.48191, "loss": 4.48191, "time": 0.81828} +{"mode": "train", "epoch": 43, "iter": 2100, "lr": 0.08142, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21781, "top5_acc": 0.4625, "loss_cls": 4.52328, "loss": 4.52328, "time": 0.80961} +{"mode": "train", "epoch": 43, "iter": 2200, "lr": 0.0814, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23234, "top5_acc": 0.47219, "loss_cls": 4.47981, "loss": 4.47981, "time": 0.81198} +{"mode": "train", "epoch": 43, "iter": 2300, "lr": 0.08137, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23625, "top5_acc": 0.48828, "loss_cls": 4.3854, "loss": 4.3854, "time": 0.81459} +{"mode": "train", "epoch": 43, "iter": 2400, "lr": 0.08135, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22375, "top5_acc": 0.46391, "loss_cls": 4.47002, "loss": 4.47002, "time": 0.8218} +{"mode": "train", "epoch": 43, "iter": 2500, "lr": 0.08133, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22875, "top5_acc": 0.47281, "loss_cls": 4.4648, "loss": 4.4648, "time": 0.80982} +{"mode": "train", "epoch": 43, "iter": 2600, "lr": 0.08131, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23219, "top5_acc": 0.47859, "loss_cls": 4.47253, "loss": 4.47253, "time": 0.81238} +{"mode": "train", "epoch": 43, "iter": 2700, "lr": 0.08129, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22625, "top5_acc": 0.46594, "loss_cls": 4.4668, "loss": 4.4668, "time": 0.80838} +{"mode": "train", "epoch": 43, "iter": 2800, "lr": 0.08126, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22297, "top5_acc": 0.46906, "loss_cls": 4.47348, "loss": 4.47348, "time": 0.81795} +{"mode": "train", "epoch": 43, "iter": 2900, "lr": 0.08124, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23094, "top5_acc": 0.47469, "loss_cls": 4.46065, "loss": 4.46065, "time": 0.81127} +{"mode": "train", "epoch": 43, "iter": 3000, "lr": 0.08122, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23125, "top5_acc": 0.47219, "loss_cls": 4.47025, "loss": 4.47025, "time": 0.81135} +{"mode": "train", "epoch": 43, "iter": 3100, "lr": 0.0812, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23062, "top5_acc": 0.47859, "loss_cls": 4.42774, "loss": 4.42774, "time": 0.81105} +{"mode": "train", "epoch": 43, "iter": 3200, "lr": 0.08118, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22203, "top5_acc": 0.45641, "loss_cls": 4.53921, "loss": 4.53921, "time": 0.81569} +{"mode": "train", "epoch": 43, "iter": 3300, "lr": 0.08116, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23125, "top5_acc": 0.47578, "loss_cls": 4.4519, "loss": 4.4519, "time": 0.80774} +{"mode": "train", "epoch": 43, "iter": 3400, "lr": 0.08113, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23906, "top5_acc": 0.48328, "loss_cls": 4.42487, "loss": 4.42487, "time": 0.81483} +{"mode": "train", "epoch": 43, "iter": 3500, "lr": 0.08111, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23, "top5_acc": 0.46516, "loss_cls": 4.48003, "loss": 4.48003, "time": 0.81099} +{"mode": "train", "epoch": 43, "iter": 3600, "lr": 0.08109, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22797, "top5_acc": 0.47406, "loss_cls": 4.45464, "loss": 4.45464, "time": 0.81566} +{"mode": "train", "epoch": 43, "iter": 3700, "lr": 0.08107, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22453, "top5_acc": 0.47188, "loss_cls": 4.49389, "loss": 4.49389, "time": 0.81423} +{"mode": "val", "epoch": 43, "iter": 309, "lr": 0.08106, "top1_acc": 0.18498, "top5_acc": 0.40642, "mean_class_accuracy": 0.18489} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.08104, "memory": 15990, "data_time": 1.26646, "top1_acc": 0.23609, "top5_acc": 0.47359, "loss_cls": 4.44856, "loss": 4.44856, "time": 2.23687} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.08101, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23984, "top5_acc": 0.48594, "loss_cls": 4.40808, "loss": 4.40808, "time": 0.81492} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.08099, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23016, "top5_acc": 0.475, "loss_cls": 4.47158, "loss": 4.47158, "time": 0.81822} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.08097, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23781, "top5_acc": 0.48797, "loss_cls": 4.41016, "loss": 4.41016, "time": 0.81282} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.08095, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22984, "top5_acc": 0.47578, "loss_cls": 4.46767, "loss": 4.46767, "time": 0.81823} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.08093, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23688, "top5_acc": 0.47516, "loss_cls": 4.4256, "loss": 4.4256, "time": 0.81948} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.0809, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22859, "top5_acc": 0.48125, "loss_cls": 4.48515, "loss": 4.48515, "time": 0.81356} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.08088, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23016, "top5_acc": 0.47375, "loss_cls": 4.46314, "loss": 4.46314, "time": 0.81518} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.08086, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22375, "top5_acc": 0.46688, "loss_cls": 4.45627, "loss": 4.45627, "time": 0.81322} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.08084, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22453, "top5_acc": 0.46953, "loss_cls": 4.46734, "loss": 4.46734, "time": 0.81173} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.08082, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24109, "top5_acc": 0.47781, "loss_cls": 4.40037, "loss": 4.40037, "time": 0.80794} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.08079, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22922, "top5_acc": 0.46359, "loss_cls": 4.50349, "loss": 4.50349, "time": 0.81421} +{"mode": "train", "epoch": 44, "iter": 1300, "lr": 0.08077, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2375, "top5_acc": 0.48312, "loss_cls": 4.40819, "loss": 4.40819, "time": 0.81199} +{"mode": "train", "epoch": 44, "iter": 1400, "lr": 0.08075, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23828, "top5_acc": 0.47453, "loss_cls": 4.46161, "loss": 4.46161, "time": 0.81722} +{"mode": "train", "epoch": 44, "iter": 1500, "lr": 0.08073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22812, "top5_acc": 0.47531, "loss_cls": 4.43737, "loss": 4.43737, "time": 0.81029} +{"mode": "train", "epoch": 44, "iter": 1600, "lr": 0.08071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22688, "top5_acc": 0.47391, "loss_cls": 4.44348, "loss": 4.44348, "time": 0.8068} +{"mode": "train", "epoch": 44, "iter": 1700, "lr": 0.08068, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23422, "top5_acc": 0.47234, "loss_cls": 4.44793, "loss": 4.44793, "time": 0.81476} +{"mode": "train", "epoch": 44, "iter": 1800, "lr": 0.08066, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23203, "top5_acc": 0.4725, "loss_cls": 4.45456, "loss": 4.45456, "time": 0.81116} +{"mode": "train", "epoch": 44, "iter": 1900, "lr": 0.08064, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23016, "top5_acc": 0.47609, "loss_cls": 4.44008, "loss": 4.44008, "time": 0.81578} +{"mode": "train", "epoch": 44, "iter": 2000, "lr": 0.08062, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22422, "top5_acc": 0.46438, "loss_cls": 4.49851, "loss": 4.49851, "time": 0.81054} +{"mode": "train", "epoch": 44, "iter": 2100, "lr": 0.0806, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2275, "top5_acc": 0.46438, "loss_cls": 4.47078, "loss": 4.47078, "time": 0.81444} +{"mode": "train", "epoch": 44, "iter": 2200, "lr": 0.08057, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23203, "top5_acc": 0.47328, "loss_cls": 4.45383, "loss": 4.45383, "time": 0.81643} +{"mode": "train", "epoch": 44, "iter": 2300, "lr": 0.08055, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22109, "top5_acc": 0.47234, "loss_cls": 4.48561, "loss": 4.48561, "time": 0.81391} +{"mode": "train", "epoch": 44, "iter": 2400, "lr": 0.08053, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23062, "top5_acc": 0.46453, "loss_cls": 4.48604, "loss": 4.48604, "time": 0.81773} +{"mode": "train", "epoch": 44, "iter": 2500, "lr": 0.08051, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22547, "top5_acc": 0.46734, "loss_cls": 4.4848, "loss": 4.4848, "time": 0.8108} +{"mode": "train", "epoch": 44, "iter": 2600, "lr": 0.08048, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23078, "top5_acc": 0.47453, "loss_cls": 4.42419, "loss": 4.42419, "time": 0.81299} +{"mode": "train", "epoch": 44, "iter": 2700, "lr": 0.08046, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23703, "top5_acc": 0.47953, "loss_cls": 4.43855, "loss": 4.43855, "time": 0.81117} +{"mode": "train", "epoch": 44, "iter": 2800, "lr": 0.08044, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22234, "top5_acc": 0.47219, "loss_cls": 4.49314, "loss": 4.49314, "time": 0.80889} +{"mode": "train", "epoch": 44, "iter": 2900, "lr": 0.08042, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23828, "top5_acc": 0.47656, "loss_cls": 4.43093, "loss": 4.43093, "time": 0.81494} +{"mode": "train", "epoch": 44, "iter": 3000, "lr": 0.0804, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23516, "top5_acc": 0.47531, "loss_cls": 4.46191, "loss": 4.46191, "time": 0.8096} +{"mode": "train", "epoch": 44, "iter": 3100, "lr": 0.08037, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22875, "top5_acc": 0.47172, "loss_cls": 4.43864, "loss": 4.43864, "time": 0.80618} +{"mode": "train", "epoch": 44, "iter": 3200, "lr": 0.08035, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22359, "top5_acc": 0.475, "loss_cls": 4.48494, "loss": 4.48494, "time": 0.81693} +{"mode": "train", "epoch": 44, "iter": 3300, "lr": 0.08033, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23078, "top5_acc": 0.46875, "loss_cls": 4.48721, "loss": 4.48721, "time": 0.81713} +{"mode": "train", "epoch": 44, "iter": 3400, "lr": 0.08031, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23828, "top5_acc": 0.48297, "loss_cls": 4.41741, "loss": 4.41741, "time": 0.81519} +{"mode": "train", "epoch": 44, "iter": 3500, "lr": 0.08028, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23031, "top5_acc": 0.47672, "loss_cls": 4.44838, "loss": 4.44838, "time": 0.81019} +{"mode": "train", "epoch": 44, "iter": 3600, "lr": 0.08026, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22422, "top5_acc": 0.47312, "loss_cls": 4.45137, "loss": 4.45137, "time": 0.8199} +{"mode": "train", "epoch": 44, "iter": 3700, "lr": 0.08024, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23828, "top5_acc": 0.48203, "loss_cls": 4.41831, "loss": 4.41831, "time": 0.81462} +{"mode": "val", "epoch": 44, "iter": 309, "lr": 0.08023, "top1_acc": 0.18928, "top5_acc": 0.41564, "mean_class_accuracy": 0.1892} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.08021, "memory": 15990, "data_time": 1.28757, "top1_acc": 0.23766, "top5_acc": 0.48125, "loss_cls": 4.43365, "loss": 4.43365, "time": 2.26255} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.08019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23312, "top5_acc": 0.48453, "loss_cls": 4.40835, "loss": 4.40835, "time": 0.81447} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.08016, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23703, "top5_acc": 0.48766, "loss_cls": 4.38532, "loss": 4.38532, "time": 0.82304} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.08014, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22656, "top5_acc": 0.47062, "loss_cls": 4.49774, "loss": 4.49774, "time": 0.81242} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.08012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23562, "top5_acc": 0.47078, "loss_cls": 4.45995, "loss": 4.45995, "time": 0.8119} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.0801, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22281, "top5_acc": 0.47219, "loss_cls": 4.46361, "loss": 4.46361, "time": 0.81821} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.08007, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23875, "top5_acc": 0.475, "loss_cls": 4.44227, "loss": 4.44227, "time": 0.80698} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.08005, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23562, "top5_acc": 0.48359, "loss_cls": 4.42959, "loss": 4.42959, "time": 0.82085} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.08003, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23234, "top5_acc": 0.49, "loss_cls": 4.40642, "loss": 4.40642, "time": 0.80873} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.08001, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22766, "top5_acc": 0.47031, "loss_cls": 4.46868, "loss": 4.46868, "time": 0.81143} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.07998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23672, "top5_acc": 0.47266, "loss_cls": 4.46284, "loss": 4.46284, "time": 0.81259} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.07996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22688, "top5_acc": 0.46688, "loss_cls": 4.49079, "loss": 4.49079, "time": 0.81544} +{"mode": "train", "epoch": 45, "iter": 1300, "lr": 0.07994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24984, "top5_acc": 0.48781, "loss_cls": 4.36878, "loss": 4.36878, "time": 0.81689} +{"mode": "train", "epoch": 45, "iter": 1400, "lr": 0.07992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23328, "top5_acc": 0.4725, "loss_cls": 4.45649, "loss": 4.45649, "time": 0.81302} +{"mode": "train", "epoch": 45, "iter": 1500, "lr": 0.0799, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22875, "top5_acc": 0.45984, "loss_cls": 4.51327, "loss": 4.51327, "time": 0.80967} +{"mode": "train", "epoch": 45, "iter": 1600, "lr": 0.07987, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23062, "top5_acc": 0.48203, "loss_cls": 4.42313, "loss": 4.42313, "time": 0.80976} +{"mode": "train", "epoch": 45, "iter": 1700, "lr": 0.07985, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22344, "top5_acc": 0.46516, "loss_cls": 4.47616, "loss": 4.47616, "time": 0.82135} +{"mode": "train", "epoch": 45, "iter": 1800, "lr": 0.07983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23016, "top5_acc": 0.46828, "loss_cls": 4.45651, "loss": 4.45651, "time": 0.81492} +{"mode": "train", "epoch": 45, "iter": 1900, "lr": 0.07981, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23688, "top5_acc": 0.47359, "loss_cls": 4.43765, "loss": 4.43765, "time": 0.81121} +{"mode": "train", "epoch": 45, "iter": 2000, "lr": 0.07978, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23156, "top5_acc": 0.47688, "loss_cls": 4.44896, "loss": 4.44896, "time": 0.81074} +{"mode": "train", "epoch": 45, "iter": 2100, "lr": 0.07976, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23062, "top5_acc": 0.47094, "loss_cls": 4.46976, "loss": 4.46976, "time": 0.81741} +{"mode": "train", "epoch": 45, "iter": 2200, "lr": 0.07974, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23266, "top5_acc": 0.47531, "loss_cls": 4.43088, "loss": 4.43088, "time": 0.81993} +{"mode": "train", "epoch": 45, "iter": 2300, "lr": 0.07972, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.235, "top5_acc": 0.46953, "loss_cls": 4.45191, "loss": 4.45191, "time": 0.81626} +{"mode": "train", "epoch": 45, "iter": 2400, "lr": 0.07969, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22859, "top5_acc": 0.46469, "loss_cls": 4.49465, "loss": 4.49465, "time": 0.81591} +{"mode": "train", "epoch": 45, "iter": 2500, "lr": 0.07967, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23328, "top5_acc": 0.47312, "loss_cls": 4.45537, "loss": 4.45537, "time": 0.8082} +{"mode": "train", "epoch": 45, "iter": 2600, "lr": 0.07965, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23031, "top5_acc": 0.47016, "loss_cls": 4.48497, "loss": 4.48497, "time": 0.81633} +{"mode": "train", "epoch": 45, "iter": 2700, "lr": 0.07963, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22516, "top5_acc": 0.46984, "loss_cls": 4.44613, "loss": 4.44613, "time": 0.81633} +{"mode": "train", "epoch": 45, "iter": 2800, "lr": 0.0796, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23578, "top5_acc": 0.47875, "loss_cls": 4.42142, "loss": 4.42142, "time": 0.80953} +{"mode": "train", "epoch": 45, "iter": 2900, "lr": 0.07958, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23719, "top5_acc": 0.48234, "loss_cls": 4.42087, "loss": 4.42087, "time": 0.81847} +{"mode": "train", "epoch": 45, "iter": 3000, "lr": 0.07956, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23125, "top5_acc": 0.47734, "loss_cls": 4.43936, "loss": 4.43936, "time": 0.81369} +{"mode": "train", "epoch": 45, "iter": 3100, "lr": 0.07954, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23672, "top5_acc": 0.47266, "loss_cls": 4.45986, "loss": 4.45986, "time": 0.80619} +{"mode": "train", "epoch": 45, "iter": 3200, "lr": 0.07951, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23422, "top5_acc": 0.48234, "loss_cls": 4.39852, "loss": 4.39852, "time": 0.80934} +{"mode": "train", "epoch": 45, "iter": 3300, "lr": 0.07949, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23031, "top5_acc": 0.47672, "loss_cls": 4.45854, "loss": 4.45854, "time": 0.80868} +{"mode": "train", "epoch": 45, "iter": 3400, "lr": 0.07947, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23922, "top5_acc": 0.48406, "loss_cls": 4.40964, "loss": 4.40964, "time": 0.81075} +{"mode": "train", "epoch": 45, "iter": 3500, "lr": 0.07945, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23484, "top5_acc": 0.47688, "loss_cls": 4.43839, "loss": 4.43839, "time": 0.81135} +{"mode": "train", "epoch": 45, "iter": 3600, "lr": 0.07942, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22438, "top5_acc": 0.47, "loss_cls": 4.48478, "loss": 4.48478, "time": 0.81083} +{"mode": "train", "epoch": 45, "iter": 3700, "lr": 0.0794, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22969, "top5_acc": 0.46547, "loss_cls": 4.47038, "loss": 4.47038, "time": 0.81817} +{"mode": "val", "epoch": 45, "iter": 309, "lr": 0.07939, "top1_acc": 0.14633, "top5_acc": 0.34458, "mean_class_accuracy": 0.14634} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.07937, "memory": 15990, "data_time": 1.31768, "top1_acc": 0.23781, "top5_acc": 0.48875, "loss_cls": 4.35937, "loss": 4.35937, "time": 2.29723} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.07934, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23812, "top5_acc": 0.47344, "loss_cls": 4.43136, "loss": 4.43136, "time": 0.81199} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.07932, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24578, "top5_acc": 0.48984, "loss_cls": 4.40062, "loss": 4.40062, "time": 0.81377} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.0793, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23172, "top5_acc": 0.48047, "loss_cls": 4.42689, "loss": 4.42689, "time": 0.81796} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.07928, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24312, "top5_acc": 0.47922, "loss_cls": 4.42586, "loss": 4.42586, "time": 0.81116} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.07925, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23812, "top5_acc": 0.48125, "loss_cls": 4.41839, "loss": 4.41839, "time": 0.81714} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.07923, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23312, "top5_acc": 0.47391, "loss_cls": 4.44709, "loss": 4.44709, "time": 0.80877} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.07921, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22625, "top5_acc": 0.4675, "loss_cls": 4.45751, "loss": 4.45751, "time": 0.81291} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.07919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22766, "top5_acc": 0.4725, "loss_cls": 4.46572, "loss": 4.46572, "time": 0.81453} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.07916, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2425, "top5_acc": 0.48328, "loss_cls": 4.39418, "loss": 4.39418, "time": 0.81514} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.07914, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2425, "top5_acc": 0.47938, "loss_cls": 4.42389, "loss": 4.42389, "time": 0.81002} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.07912, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23438, "top5_acc": 0.47109, "loss_cls": 4.44827, "loss": 4.44827, "time": 0.81348} +{"mode": "train", "epoch": 46, "iter": 1300, "lr": 0.07909, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23297, "top5_acc": 0.47719, "loss_cls": 4.44591, "loss": 4.44591, "time": 0.81209} +{"mode": "train", "epoch": 46, "iter": 1400, "lr": 0.07907, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24078, "top5_acc": 0.48219, "loss_cls": 4.43954, "loss": 4.43954, "time": 0.81221} +{"mode": "train", "epoch": 46, "iter": 1500, "lr": 0.07905, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.235, "top5_acc": 0.47297, "loss_cls": 4.41822, "loss": 4.41822, "time": 0.81311} +{"mode": "train", "epoch": 46, "iter": 1600, "lr": 0.07903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23562, "top5_acc": 0.47297, "loss_cls": 4.43721, "loss": 4.43721, "time": 0.81652} +{"mode": "train", "epoch": 46, "iter": 1700, "lr": 0.079, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22844, "top5_acc": 0.47219, "loss_cls": 4.46304, "loss": 4.46304, "time": 0.81305} +{"mode": "train", "epoch": 46, "iter": 1800, "lr": 0.07898, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24203, "top5_acc": 0.47141, "loss_cls": 4.465, "loss": 4.465, "time": 0.81485} +{"mode": "train", "epoch": 46, "iter": 1900, "lr": 0.07896, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23766, "top5_acc": 0.47078, "loss_cls": 4.47457, "loss": 4.47457, "time": 0.8125} +{"mode": "train", "epoch": 46, "iter": 2000, "lr": 0.07894, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23609, "top5_acc": 0.475, "loss_cls": 4.42082, "loss": 4.42082, "time": 0.81302} +{"mode": "train", "epoch": 46, "iter": 2100, "lr": 0.07891, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23812, "top5_acc": 0.48, "loss_cls": 4.42826, "loss": 4.42826, "time": 0.81895} +{"mode": "train", "epoch": 46, "iter": 2200, "lr": 0.07889, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24453, "top5_acc": 0.47688, "loss_cls": 4.408, "loss": 4.408, "time": 0.81396} +{"mode": "train", "epoch": 46, "iter": 2300, "lr": 0.07887, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23312, "top5_acc": 0.46453, "loss_cls": 4.47172, "loss": 4.47172, "time": 0.81307} +{"mode": "train", "epoch": 46, "iter": 2400, "lr": 0.07884, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23469, "top5_acc": 0.47734, "loss_cls": 4.46483, "loss": 4.46483, "time": 0.81537} +{"mode": "train", "epoch": 46, "iter": 2500, "lr": 0.07882, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.235, "top5_acc": 0.48078, "loss_cls": 4.44651, "loss": 4.44651, "time": 0.81223} +{"mode": "train", "epoch": 46, "iter": 2600, "lr": 0.0788, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23344, "top5_acc": 0.47734, "loss_cls": 4.43226, "loss": 4.43226, "time": 0.81182} +{"mode": "train", "epoch": 46, "iter": 2700, "lr": 0.07878, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23422, "top5_acc": 0.48828, "loss_cls": 4.4154, "loss": 4.4154, "time": 0.80841} +{"mode": "train", "epoch": 46, "iter": 2800, "lr": 0.07875, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23, "top5_acc": 0.46641, "loss_cls": 4.4824, "loss": 4.4824, "time": 0.81281} +{"mode": "train", "epoch": 46, "iter": 2900, "lr": 0.07873, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22609, "top5_acc": 0.47375, "loss_cls": 4.43945, "loss": 4.43945, "time": 0.8164} +{"mode": "train", "epoch": 46, "iter": 3000, "lr": 0.07871, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24078, "top5_acc": 0.48172, "loss_cls": 4.41734, "loss": 4.41734, "time": 0.80842} +{"mode": "train", "epoch": 46, "iter": 3100, "lr": 0.07868, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22844, "top5_acc": 0.47766, "loss_cls": 4.44957, "loss": 4.44957, "time": 0.81103} +{"mode": "train", "epoch": 46, "iter": 3200, "lr": 0.07866, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21984, "top5_acc": 0.46703, "loss_cls": 4.50896, "loss": 4.50896, "time": 0.80855} +{"mode": "train", "epoch": 46, "iter": 3300, "lr": 0.07864, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23609, "top5_acc": 0.47516, "loss_cls": 4.42071, "loss": 4.42071, "time": 0.81488} +{"mode": "train", "epoch": 46, "iter": 3400, "lr": 0.07862, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23188, "top5_acc": 0.46766, "loss_cls": 4.4396, "loss": 4.4396, "time": 0.80834} +{"mode": "train", "epoch": 46, "iter": 3500, "lr": 0.07859, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23281, "top5_acc": 0.47438, "loss_cls": 4.45771, "loss": 4.45771, "time": 0.81481} +{"mode": "train", "epoch": 46, "iter": 3600, "lr": 0.07857, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22812, "top5_acc": 0.47188, "loss_cls": 4.47718, "loss": 4.47718, "time": 0.82199} +{"mode": "train", "epoch": 46, "iter": 3700, "lr": 0.07855, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23391, "top5_acc": 0.48469, "loss_cls": 4.43685, "loss": 4.43685, "time": 0.82293} +{"mode": "val", "epoch": 46, "iter": 309, "lr": 0.07854, "top1_acc": 0.13868, "top5_acc": 0.34873, "mean_class_accuracy": 0.13844} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.07851, "memory": 15990, "data_time": 1.27595, "top1_acc": 0.24094, "top5_acc": 0.47766, "loss_cls": 4.40337, "loss": 4.40337, "time": 2.23856} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.07849, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24141, "top5_acc": 0.48141, "loss_cls": 4.41578, "loss": 4.41578, "time": 0.813} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.07847, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23328, "top5_acc": 0.48781, "loss_cls": 4.39855, "loss": 4.39855, "time": 0.80984} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.07844, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22266, "top5_acc": 0.47406, "loss_cls": 4.46602, "loss": 4.46602, "time": 0.81504} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.07842, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22828, "top5_acc": 0.47109, "loss_cls": 4.43754, "loss": 4.43754, "time": 0.81426} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.0784, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23516, "top5_acc": 0.48312, "loss_cls": 4.41065, "loss": 4.41065, "time": 0.82177} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.07838, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23328, "top5_acc": 0.48859, "loss_cls": 4.38846, "loss": 4.38846, "time": 0.80683} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.07835, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24656, "top5_acc": 0.48359, "loss_cls": 4.37327, "loss": 4.37327, "time": 0.81492} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.07833, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23609, "top5_acc": 0.47281, "loss_cls": 4.43426, "loss": 4.43426, "time": 0.81187} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.07831, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24297, "top5_acc": 0.48781, "loss_cls": 4.39224, "loss": 4.39224, "time": 0.80858} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.07828, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23484, "top5_acc": 0.4775, "loss_cls": 4.41638, "loss": 4.41638, "time": 0.81398} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.07826, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23297, "top5_acc": 0.47547, "loss_cls": 4.43456, "loss": 4.43456, "time": 0.80953} +{"mode": "train", "epoch": 47, "iter": 1300, "lr": 0.07824, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23094, "top5_acc": 0.46797, "loss_cls": 4.45998, "loss": 4.45998, "time": 0.8083} +{"mode": "train", "epoch": 47, "iter": 1400, "lr": 0.07821, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24484, "top5_acc": 0.47922, "loss_cls": 4.42265, "loss": 4.42265, "time": 0.80645} +{"mode": "train", "epoch": 47, "iter": 1500, "lr": 0.07819, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23391, "top5_acc": 0.48, "loss_cls": 4.42595, "loss": 4.42595, "time": 0.80952} +{"mode": "train", "epoch": 47, "iter": 1600, "lr": 0.07817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23328, "top5_acc": 0.48172, "loss_cls": 4.4196, "loss": 4.4196, "time": 0.81534} +{"mode": "train", "epoch": 47, "iter": 1700, "lr": 0.07814, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23188, "top5_acc": 0.48219, "loss_cls": 4.4219, "loss": 4.4219, "time": 0.81364} +{"mode": "train", "epoch": 47, "iter": 1800, "lr": 0.07812, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22672, "top5_acc": 0.47094, "loss_cls": 4.4548, "loss": 4.4548, "time": 0.80935} +{"mode": "train", "epoch": 47, "iter": 1900, "lr": 0.0781, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22844, "top5_acc": 0.47516, "loss_cls": 4.43926, "loss": 4.43926, "time": 0.80511} +{"mode": "train", "epoch": 47, "iter": 2000, "lr": 0.07808, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23906, "top5_acc": 0.47688, "loss_cls": 4.42848, "loss": 4.42848, "time": 0.80878} +{"mode": "train", "epoch": 47, "iter": 2100, "lr": 0.07805, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23281, "top5_acc": 0.47641, "loss_cls": 4.44536, "loss": 4.44536, "time": 0.80697} +{"mode": "train", "epoch": 47, "iter": 2200, "lr": 0.07803, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23656, "top5_acc": 0.48484, "loss_cls": 4.43418, "loss": 4.43418, "time": 0.81488} +{"mode": "train", "epoch": 47, "iter": 2300, "lr": 0.07801, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24016, "top5_acc": 0.47891, "loss_cls": 4.39535, "loss": 4.39535, "time": 0.819} +{"mode": "train", "epoch": 47, "iter": 2400, "lr": 0.07798, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22797, "top5_acc": 0.47766, "loss_cls": 4.42902, "loss": 4.42902, "time": 0.80821} +{"mode": "train", "epoch": 47, "iter": 2500, "lr": 0.07796, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23328, "top5_acc": 0.48578, "loss_cls": 4.43258, "loss": 4.43258, "time": 0.81361} +{"mode": "train", "epoch": 47, "iter": 2600, "lr": 0.07794, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22172, "top5_acc": 0.46953, "loss_cls": 4.48794, "loss": 4.48794, "time": 0.80945} +{"mode": "train", "epoch": 47, "iter": 2700, "lr": 0.07791, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23594, "top5_acc": 0.47453, "loss_cls": 4.45666, "loss": 4.45666, "time": 0.81126} +{"mode": "train", "epoch": 47, "iter": 2800, "lr": 0.07789, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24062, "top5_acc": 0.48078, "loss_cls": 4.41035, "loss": 4.41035, "time": 0.80724} +{"mode": "train", "epoch": 47, "iter": 2900, "lr": 0.07787, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22891, "top5_acc": 0.47156, "loss_cls": 4.4474, "loss": 4.4474, "time": 0.80637} +{"mode": "train", "epoch": 47, "iter": 3000, "lr": 0.07784, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23469, "top5_acc": 0.48625, "loss_cls": 4.4021, "loss": 4.4021, "time": 0.80955} +{"mode": "train", "epoch": 47, "iter": 3100, "lr": 0.07782, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22734, "top5_acc": 0.46578, "loss_cls": 4.47952, "loss": 4.47952, "time": 0.81489} +{"mode": "train", "epoch": 47, "iter": 3200, "lr": 0.0778, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23328, "top5_acc": 0.46875, "loss_cls": 4.4727, "loss": 4.4727, "time": 0.81662} +{"mode": "train", "epoch": 47, "iter": 3300, "lr": 0.07777, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23562, "top5_acc": 0.48078, "loss_cls": 4.45199, "loss": 4.45199, "time": 0.81506} +{"mode": "train", "epoch": 47, "iter": 3400, "lr": 0.07775, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2325, "top5_acc": 0.4775, "loss_cls": 4.44652, "loss": 4.44652, "time": 0.8131} +{"mode": "train", "epoch": 47, "iter": 3500, "lr": 0.07773, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23625, "top5_acc": 0.48344, "loss_cls": 4.40038, "loss": 4.40038, "time": 0.81775} +{"mode": "train", "epoch": 47, "iter": 3600, "lr": 0.0777, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23312, "top5_acc": 0.47781, "loss_cls": 4.45213, "loss": 4.45213, "time": 0.81789} +{"mode": "train", "epoch": 47, "iter": 3700, "lr": 0.07768, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22938, "top5_acc": 0.46812, "loss_cls": 4.48423, "loss": 4.48423, "time": 0.82097} +{"mode": "val", "epoch": 47, "iter": 309, "lr": 0.07767, "top1_acc": 0.16203, "top5_acc": 0.36691, "mean_class_accuracy": 0.16189} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.07765, "memory": 15990, "data_time": 1.29681, "top1_acc": 0.24188, "top5_acc": 0.48828, "loss_cls": 4.37612, "loss": 4.37612, "time": 2.27072} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.07762, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23781, "top5_acc": 0.48875, "loss_cls": 4.38595, "loss": 4.38595, "time": 0.81999} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.0776, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23328, "top5_acc": 0.47312, "loss_cls": 4.43987, "loss": 4.43987, "time": 0.8158} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.07758, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23094, "top5_acc": 0.47078, "loss_cls": 4.45606, "loss": 4.45606, "time": 0.81294} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.07755, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23422, "top5_acc": 0.47953, "loss_cls": 4.44867, "loss": 4.44867, "time": 0.82013} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.07753, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23703, "top5_acc": 0.47969, "loss_cls": 4.41898, "loss": 4.41898, "time": 0.8162} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.07751, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23188, "top5_acc": 0.48, "loss_cls": 4.42429, "loss": 4.42429, "time": 0.8214} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.07748, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24328, "top5_acc": 0.48047, "loss_cls": 4.41587, "loss": 4.41587, "time": 0.80909} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.07746, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23859, "top5_acc": 0.47781, "loss_cls": 4.42602, "loss": 4.42602, "time": 0.81051} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.07744, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23281, "top5_acc": 0.48281, "loss_cls": 4.41312, "loss": 4.41312, "time": 0.8096} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.07741, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23453, "top5_acc": 0.47469, "loss_cls": 4.4397, "loss": 4.4397, "time": 0.81848} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.07739, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23516, "top5_acc": 0.47266, "loss_cls": 4.43114, "loss": 4.43114, "time": 0.8155} +{"mode": "train", "epoch": 48, "iter": 1300, "lr": 0.07737, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24016, "top5_acc": 0.48578, "loss_cls": 4.39985, "loss": 4.39985, "time": 0.8124} +{"mode": "train", "epoch": 48, "iter": 1400, "lr": 0.07734, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24172, "top5_acc": 0.47984, "loss_cls": 4.39043, "loss": 4.39043, "time": 0.8125} +{"mode": "train", "epoch": 48, "iter": 1500, "lr": 0.07732, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24609, "top5_acc": 0.48297, "loss_cls": 4.42422, "loss": 4.42422, "time": 0.81351} +{"mode": "train", "epoch": 48, "iter": 1600, "lr": 0.0773, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24125, "top5_acc": 0.47828, "loss_cls": 4.41498, "loss": 4.41498, "time": 0.81153} +{"mode": "train", "epoch": 48, "iter": 1700, "lr": 0.07727, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23578, "top5_acc": 0.47359, "loss_cls": 4.44229, "loss": 4.44229, "time": 0.81434} +{"mode": "train", "epoch": 48, "iter": 1800, "lr": 0.07725, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24969, "top5_acc": 0.47641, "loss_cls": 4.39555, "loss": 4.39555, "time": 0.81373} +{"mode": "train", "epoch": 48, "iter": 1900, "lr": 0.07723, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23812, "top5_acc": 0.47641, "loss_cls": 4.45386, "loss": 4.45386, "time": 0.80843} +{"mode": "train", "epoch": 48, "iter": 2000, "lr": 0.0772, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23547, "top5_acc": 0.48312, "loss_cls": 4.4232, "loss": 4.4232, "time": 0.80987} +{"mode": "train", "epoch": 48, "iter": 2100, "lr": 0.07718, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23953, "top5_acc": 0.49219, "loss_cls": 4.3687, "loss": 4.3687, "time": 0.8153} +{"mode": "train", "epoch": 48, "iter": 2200, "lr": 0.07716, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23656, "top5_acc": 0.47219, "loss_cls": 4.44029, "loss": 4.44029, "time": 0.81854} +{"mode": "train", "epoch": 48, "iter": 2300, "lr": 0.07713, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22719, "top5_acc": 0.46625, "loss_cls": 4.46875, "loss": 4.46875, "time": 0.82218} +{"mode": "train", "epoch": 48, "iter": 2400, "lr": 0.07711, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23359, "top5_acc": 0.48047, "loss_cls": 4.43472, "loss": 4.43472, "time": 0.81251} +{"mode": "train", "epoch": 48, "iter": 2500, "lr": 0.07709, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23953, "top5_acc": 0.48312, "loss_cls": 4.39998, "loss": 4.39998, "time": 0.81375} +{"mode": "train", "epoch": 48, "iter": 2600, "lr": 0.07706, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23703, "top5_acc": 0.48312, "loss_cls": 4.40492, "loss": 4.40492, "time": 0.81431} +{"mode": "train", "epoch": 48, "iter": 2700, "lr": 0.07704, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23266, "top5_acc": 0.48062, "loss_cls": 4.42376, "loss": 4.42376, "time": 0.81479} +{"mode": "train", "epoch": 48, "iter": 2800, "lr": 0.07701, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24234, "top5_acc": 0.48, "loss_cls": 4.40138, "loss": 4.40138, "time": 0.81912} +{"mode": "train", "epoch": 48, "iter": 2900, "lr": 0.07699, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23594, "top5_acc": 0.47641, "loss_cls": 4.43923, "loss": 4.43923, "time": 0.80914} +{"mode": "train", "epoch": 48, "iter": 3000, "lr": 0.07697, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24219, "top5_acc": 0.48031, "loss_cls": 4.41787, "loss": 4.41787, "time": 0.80782} +{"mode": "train", "epoch": 48, "iter": 3100, "lr": 0.07694, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.235, "top5_acc": 0.47766, "loss_cls": 4.4336, "loss": 4.4336, "time": 0.81197} +{"mode": "train", "epoch": 48, "iter": 3200, "lr": 0.07692, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23594, "top5_acc": 0.49234, "loss_cls": 4.40862, "loss": 4.40862, "time": 0.81803} +{"mode": "train", "epoch": 48, "iter": 3300, "lr": 0.0769, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22984, "top5_acc": 0.47297, "loss_cls": 4.45919, "loss": 4.45919, "time": 0.8115} +{"mode": "train", "epoch": 48, "iter": 3400, "lr": 0.07687, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24172, "top5_acc": 0.47078, "loss_cls": 4.41378, "loss": 4.41378, "time": 0.80971} +{"mode": "train", "epoch": 48, "iter": 3500, "lr": 0.07685, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24625, "top5_acc": 0.49047, "loss_cls": 4.37056, "loss": 4.37056, "time": 0.81731} +{"mode": "train", "epoch": 48, "iter": 3600, "lr": 0.07683, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23172, "top5_acc": 0.47156, "loss_cls": 4.4543, "loss": 4.4543, "time": 0.80903} +{"mode": "train", "epoch": 48, "iter": 3700, "lr": 0.0768, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23891, "top5_acc": 0.48844, "loss_cls": 4.37952, "loss": 4.37952, "time": 0.82159} +{"mode": "val", "epoch": 48, "iter": 309, "lr": 0.07679, "top1_acc": 0.17652, "top5_acc": 0.39619, "mean_class_accuracy": 0.17654} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.07677, "memory": 15990, "data_time": 1.31984, "top1_acc": 0.24984, "top5_acc": 0.48953, "loss_cls": 4.37597, "loss": 4.37597, "time": 2.29693} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.07674, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24062, "top5_acc": 0.48422, "loss_cls": 4.38825, "loss": 4.38825, "time": 0.8169} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.07672, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23562, "top5_acc": 0.48391, "loss_cls": 4.38228, "loss": 4.38228, "time": 0.81964} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.0767, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23469, "top5_acc": 0.48203, "loss_cls": 4.43073, "loss": 4.43073, "time": 0.81582} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.07667, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24688, "top5_acc": 0.48078, "loss_cls": 4.39065, "loss": 4.39065, "time": 0.81474} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.07665, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24312, "top5_acc": 0.48781, "loss_cls": 4.37095, "loss": 4.37095, "time": 0.80977} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.07663, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.22938, "top5_acc": 0.47156, "loss_cls": 4.44278, "loss": 4.44278, "time": 0.81697} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.0766, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24094, "top5_acc": 0.49281, "loss_cls": 4.35407, "loss": 4.35407, "time": 0.81018} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.07658, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24594, "top5_acc": 0.49156, "loss_cls": 4.37926, "loss": 4.37926, "time": 0.81076} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.07656, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23172, "top5_acc": 0.48141, "loss_cls": 4.40866, "loss": 4.40866, "time": 0.81855} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.07653, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23453, "top5_acc": 0.47656, "loss_cls": 4.42922, "loss": 4.42922, "time": 0.8141} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.07651, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23547, "top5_acc": 0.48438, "loss_cls": 4.40533, "loss": 4.40533, "time": 0.81964} +{"mode": "train", "epoch": 49, "iter": 1300, "lr": 0.07648, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24328, "top5_acc": 0.48531, "loss_cls": 4.38924, "loss": 4.38924, "time": 0.8087} +{"mode": "train", "epoch": 49, "iter": 1400, "lr": 0.07646, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24219, "top5_acc": 0.48406, "loss_cls": 4.3767, "loss": 4.3767, "time": 0.8071} +{"mode": "train", "epoch": 49, "iter": 1500, "lr": 0.07644, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23578, "top5_acc": 0.48328, "loss_cls": 4.40928, "loss": 4.40928, "time": 0.81263} +{"mode": "train", "epoch": 49, "iter": 1600, "lr": 0.07641, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23312, "top5_acc": 0.4875, "loss_cls": 4.39236, "loss": 4.39236, "time": 0.81315} +{"mode": "train", "epoch": 49, "iter": 1700, "lr": 0.07639, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24109, "top5_acc": 0.47828, "loss_cls": 4.42579, "loss": 4.42579, "time": 0.80778} +{"mode": "train", "epoch": 49, "iter": 1800, "lr": 0.07637, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24875, "top5_acc": 0.49031, "loss_cls": 4.34992, "loss": 4.34992, "time": 0.81015} +{"mode": "train", "epoch": 49, "iter": 1900, "lr": 0.07634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24516, "top5_acc": 0.48922, "loss_cls": 4.3748, "loss": 4.3748, "time": 0.81708} +{"mode": "train", "epoch": 49, "iter": 2000, "lr": 0.07632, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23547, "top5_acc": 0.48172, "loss_cls": 4.4428, "loss": 4.4428, "time": 0.80727} +{"mode": "train", "epoch": 49, "iter": 2100, "lr": 0.07629, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23469, "top5_acc": 0.48203, "loss_cls": 4.42899, "loss": 4.42899, "time": 0.80851} +{"mode": "train", "epoch": 49, "iter": 2200, "lr": 0.07627, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24859, "top5_acc": 0.48906, "loss_cls": 4.37626, "loss": 4.37626, "time": 0.80911} +{"mode": "train", "epoch": 49, "iter": 2300, "lr": 0.07625, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.235, "top5_acc": 0.47906, "loss_cls": 4.39031, "loss": 4.39031, "time": 0.81553} +{"mode": "train", "epoch": 49, "iter": 2400, "lr": 0.07622, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23812, "top5_acc": 0.49281, "loss_cls": 4.37404, "loss": 4.37404, "time": 0.8084} +{"mode": "train", "epoch": 49, "iter": 2500, "lr": 0.0762, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22797, "top5_acc": 0.48062, "loss_cls": 4.4332, "loss": 4.4332, "time": 0.824} +{"mode": "train", "epoch": 49, "iter": 2600, "lr": 0.07618, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24516, "top5_acc": 0.48281, "loss_cls": 4.41442, "loss": 4.41442, "time": 0.80941} +{"mode": "train", "epoch": 49, "iter": 2700, "lr": 0.07615, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24891, "top5_acc": 0.48703, "loss_cls": 4.36748, "loss": 4.36748, "time": 0.80891} +{"mode": "train", "epoch": 49, "iter": 2800, "lr": 0.07613, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24484, "top5_acc": 0.48234, "loss_cls": 4.4273, "loss": 4.4273, "time": 0.8099} +{"mode": "train", "epoch": 49, "iter": 2900, "lr": 0.0761, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23156, "top5_acc": 0.47219, "loss_cls": 4.45249, "loss": 4.45249, "time": 0.80994} +{"mode": "train", "epoch": 49, "iter": 3000, "lr": 0.07608, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23891, "top5_acc": 0.48438, "loss_cls": 4.40217, "loss": 4.40217, "time": 0.81036} +{"mode": "train", "epoch": 49, "iter": 3100, "lr": 0.07606, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23922, "top5_acc": 0.48391, "loss_cls": 4.40725, "loss": 4.40725, "time": 0.8105} +{"mode": "train", "epoch": 49, "iter": 3200, "lr": 0.07603, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23219, "top5_acc": 0.47234, "loss_cls": 4.45175, "loss": 4.45175, "time": 0.80931} +{"mode": "train", "epoch": 49, "iter": 3300, "lr": 0.07601, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23438, "top5_acc": 0.47875, "loss_cls": 4.40729, "loss": 4.40729, "time": 0.80736} +{"mode": "train", "epoch": 49, "iter": 3400, "lr": 0.07598, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24844, "top5_acc": 0.49312, "loss_cls": 4.33102, "loss": 4.33102, "time": 0.80867} +{"mode": "train", "epoch": 49, "iter": 3500, "lr": 0.07596, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23016, "top5_acc": 0.48188, "loss_cls": 4.43207, "loss": 4.43207, "time": 0.81765} +{"mode": "train", "epoch": 49, "iter": 3600, "lr": 0.07594, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23047, "top5_acc": 0.46891, "loss_cls": 4.46166, "loss": 4.46166, "time": 0.81394} +{"mode": "train", "epoch": 49, "iter": 3700, "lr": 0.07591, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24969, "top5_acc": 0.49422, "loss_cls": 4.36104, "loss": 4.36104, "time": 0.81933} +{"mode": "val", "epoch": 49, "iter": 309, "lr": 0.0759, "top1_acc": 0.17601, "top5_acc": 0.39842, "mean_class_accuracy": 0.1758} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.07588, "memory": 15990, "data_time": 1.30896, "top1_acc": 0.24375, "top5_acc": 0.49281, "loss_cls": 4.37459, "loss": 4.37459, "time": 2.29057} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.07585, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24891, "top5_acc": 0.49266, "loss_cls": 4.34341, "loss": 4.34341, "time": 0.81183} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.07583, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25609, "top5_acc": 0.49406, "loss_cls": 4.35767, "loss": 4.35767, "time": 0.81276} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.07581, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24312, "top5_acc": 0.48797, "loss_cls": 4.39463, "loss": 4.39463, "time": 0.81899} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.07578, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24062, "top5_acc": 0.48641, "loss_cls": 4.40332, "loss": 4.40332, "time": 0.82337} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.07576, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23609, "top5_acc": 0.48281, "loss_cls": 4.40771, "loss": 4.40771, "time": 0.81128} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.07573, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22938, "top5_acc": 0.47797, "loss_cls": 4.44627, "loss": 4.44627, "time": 0.81402} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.07571, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24281, "top5_acc": 0.48656, "loss_cls": 4.37007, "loss": 4.37007, "time": 0.81735} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.07569, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23203, "top5_acc": 0.48562, "loss_cls": 4.40211, "loss": 4.40211, "time": 0.806} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.07566, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23984, "top5_acc": 0.47922, "loss_cls": 4.39423, "loss": 4.39423, "time": 0.81233} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.07564, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24109, "top5_acc": 0.49516, "loss_cls": 4.37302, "loss": 4.37302, "time": 0.80874} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.07561, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24234, "top5_acc": 0.48234, "loss_cls": 4.3919, "loss": 4.3919, "time": 0.8135} +{"mode": "train", "epoch": 50, "iter": 1300, "lr": 0.07559, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24109, "top5_acc": 0.49219, "loss_cls": 4.37849, "loss": 4.37849, "time": 0.81422} +{"mode": "train", "epoch": 50, "iter": 1400, "lr": 0.07557, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24281, "top5_acc": 0.48266, "loss_cls": 4.3898, "loss": 4.3898, "time": 0.8111} +{"mode": "train", "epoch": 50, "iter": 1500, "lr": 0.07554, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24844, "top5_acc": 0.48969, "loss_cls": 4.38143, "loss": 4.38143, "time": 0.80619} +{"mode": "train", "epoch": 50, "iter": 1600, "lr": 0.07552, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24594, "top5_acc": 0.48766, "loss_cls": 4.37369, "loss": 4.37369, "time": 0.80765} +{"mode": "train", "epoch": 50, "iter": 1700, "lr": 0.07549, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23422, "top5_acc": 0.48062, "loss_cls": 4.40365, "loss": 4.40365, "time": 0.80844} +{"mode": "train", "epoch": 50, "iter": 1800, "lr": 0.07547, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23297, "top5_acc": 0.47109, "loss_cls": 4.44098, "loss": 4.44098, "time": 0.80571} +{"mode": "train", "epoch": 50, "iter": 1900, "lr": 0.07545, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23141, "top5_acc": 0.48562, "loss_cls": 4.42834, "loss": 4.42834, "time": 0.80985} +{"mode": "train", "epoch": 50, "iter": 2000, "lr": 0.07542, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23203, "top5_acc": 0.49094, "loss_cls": 4.41045, "loss": 4.41045, "time": 0.80943} +{"mode": "train", "epoch": 50, "iter": 2100, "lr": 0.0754, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2425, "top5_acc": 0.48906, "loss_cls": 4.38042, "loss": 4.38042, "time": 0.80939} +{"mode": "train", "epoch": 50, "iter": 2200, "lr": 0.07537, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24297, "top5_acc": 0.48406, "loss_cls": 4.38173, "loss": 4.38173, "time": 0.80665} +{"mode": "train", "epoch": 50, "iter": 2300, "lr": 0.07535, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23672, "top5_acc": 0.47703, "loss_cls": 4.41932, "loss": 4.41932, "time": 0.81971} +{"mode": "train", "epoch": 50, "iter": 2400, "lr": 0.07533, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23188, "top5_acc": 0.48703, "loss_cls": 4.40686, "loss": 4.40686, "time": 0.81211} +{"mode": "train", "epoch": 50, "iter": 2500, "lr": 0.0753, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24375, "top5_acc": 0.49125, "loss_cls": 4.36401, "loss": 4.36401, "time": 0.82216} +{"mode": "train", "epoch": 50, "iter": 2600, "lr": 0.07528, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24219, "top5_acc": 0.4875, "loss_cls": 4.38353, "loss": 4.38353, "time": 0.81224} +{"mode": "train", "epoch": 50, "iter": 2700, "lr": 0.07525, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23578, "top5_acc": 0.49188, "loss_cls": 4.34771, "loss": 4.34771, "time": 0.81833} +{"mode": "train", "epoch": 50, "iter": 2800, "lr": 0.07523, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24078, "top5_acc": 0.48562, "loss_cls": 4.37601, "loss": 4.37601, "time": 0.81524} +{"mode": "train", "epoch": 50, "iter": 2900, "lr": 0.0752, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.235, "top5_acc": 0.47844, "loss_cls": 4.42419, "loss": 4.42419, "time": 0.81561} +{"mode": "train", "epoch": 50, "iter": 3000, "lr": 0.07518, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23656, "top5_acc": 0.48094, "loss_cls": 4.41424, "loss": 4.41424, "time": 0.8107} +{"mode": "train", "epoch": 50, "iter": 3100, "lr": 0.07516, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25234, "top5_acc": 0.48625, "loss_cls": 4.36097, "loss": 4.36097, "time": 0.80769} +{"mode": "train", "epoch": 50, "iter": 3200, "lr": 0.07513, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24438, "top5_acc": 0.4875, "loss_cls": 4.39022, "loss": 4.39022, "time": 0.81097} +{"mode": "train", "epoch": 50, "iter": 3300, "lr": 0.07511, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25328, "top5_acc": 0.49297, "loss_cls": 4.35558, "loss": 4.35558, "time": 0.81225} +{"mode": "train", "epoch": 50, "iter": 3400, "lr": 0.07508, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2475, "top5_acc": 0.49297, "loss_cls": 4.37564, "loss": 4.37564, "time": 0.81765} +{"mode": "train", "epoch": 50, "iter": 3500, "lr": 0.07506, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24047, "top5_acc": 0.48609, "loss_cls": 4.39172, "loss": 4.39172, "time": 0.81357} +{"mode": "train", "epoch": 50, "iter": 3600, "lr": 0.07504, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24062, "top5_acc": 0.48828, "loss_cls": 4.37882, "loss": 4.37882, "time": 0.81466} +{"mode": "train", "epoch": 50, "iter": 3700, "lr": 0.07501, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24516, "top5_acc": 0.48516, "loss_cls": 4.39193, "loss": 4.39193, "time": 0.81893} +{"mode": "val", "epoch": 50, "iter": 309, "lr": 0.075, "top1_acc": 0.17657, "top5_acc": 0.39948, "mean_class_accuracy": 0.17635} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.07498, "memory": 15990, "data_time": 1.32385, "top1_acc": 0.24047, "top5_acc": 0.48484, "loss_cls": 4.38559, "loss": 4.38559, "time": 2.30943} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.07495, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25, "top5_acc": 0.50187, "loss_cls": 4.31392, "loss": 4.31392, "time": 0.81507} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.07493, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24, "top5_acc": 0.48594, "loss_cls": 4.39593, "loss": 4.39593, "time": 0.81376} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.0749, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24656, "top5_acc": 0.49203, "loss_cls": 4.34795, "loss": 4.34795, "time": 0.80836} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.07488, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24141, "top5_acc": 0.49562, "loss_cls": 4.34918, "loss": 4.34918, "time": 0.81129} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.07485, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24984, "top5_acc": 0.49547, "loss_cls": 4.32007, "loss": 4.32007, "time": 0.8137} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.07483, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24078, "top5_acc": 0.48812, "loss_cls": 4.38056, "loss": 4.38056, "time": 0.81638} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.07481, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25703, "top5_acc": 0.49953, "loss_cls": 4.30773, "loss": 4.30773, "time": 0.81988} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.07478, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23844, "top5_acc": 0.49812, "loss_cls": 4.34448, "loss": 4.34448, "time": 0.81596} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.07476, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24906, "top5_acc": 0.50172, "loss_cls": 4.3324, "loss": 4.3324, "time": 0.81044} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.07473, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24453, "top5_acc": 0.48594, "loss_cls": 4.37546, "loss": 4.37546, "time": 0.81171} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.07471, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23953, "top5_acc": 0.48547, "loss_cls": 4.39568, "loss": 4.39568, "time": 0.81204} +{"mode": "train", "epoch": 51, "iter": 1300, "lr": 0.07468, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25469, "top5_acc": 0.49734, "loss_cls": 4.35614, "loss": 4.35614, "time": 0.81932} +{"mode": "train", "epoch": 51, "iter": 1400, "lr": 0.07466, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24922, "top5_acc": 0.49156, "loss_cls": 4.35331, "loss": 4.35331, "time": 0.81162} +{"mode": "train", "epoch": 51, "iter": 1500, "lr": 0.07464, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24438, "top5_acc": 0.48531, "loss_cls": 4.37448, "loss": 4.37448, "time": 0.81443} +{"mode": "train", "epoch": 51, "iter": 1600, "lr": 0.07461, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25, "top5_acc": 0.49297, "loss_cls": 4.3618, "loss": 4.3618, "time": 0.81604} +{"mode": "train", "epoch": 51, "iter": 1700, "lr": 0.07459, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24641, "top5_acc": 0.47484, "loss_cls": 4.38361, "loss": 4.38361, "time": 0.80707} +{"mode": "train", "epoch": 51, "iter": 1800, "lr": 0.07456, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23891, "top5_acc": 0.49641, "loss_cls": 4.34205, "loss": 4.34205, "time": 0.81354} +{"mode": "train", "epoch": 51, "iter": 1900, "lr": 0.07454, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23594, "top5_acc": 0.47953, "loss_cls": 4.40574, "loss": 4.40574, "time": 0.8158} +{"mode": "train", "epoch": 51, "iter": 2000, "lr": 0.07451, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23938, "top5_acc": 0.48922, "loss_cls": 4.38491, "loss": 4.38491, "time": 0.81174} +{"mode": "train", "epoch": 51, "iter": 2100, "lr": 0.07449, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23516, "top5_acc": 0.48766, "loss_cls": 4.38362, "loss": 4.38362, "time": 0.81396} +{"mode": "train", "epoch": 51, "iter": 2200, "lr": 0.07447, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23922, "top5_acc": 0.49406, "loss_cls": 4.35679, "loss": 4.35679, "time": 0.81661} +{"mode": "train", "epoch": 51, "iter": 2300, "lr": 0.07444, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23844, "top5_acc": 0.47875, "loss_cls": 4.41466, "loss": 4.41466, "time": 0.82286} +{"mode": "train", "epoch": 51, "iter": 2400, "lr": 0.07442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24422, "top5_acc": 0.48938, "loss_cls": 4.36007, "loss": 4.36007, "time": 0.80973} +{"mode": "train", "epoch": 51, "iter": 2500, "lr": 0.07439, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23484, "top5_acc": 0.47844, "loss_cls": 4.39541, "loss": 4.39541, "time": 0.81212} +{"mode": "train", "epoch": 51, "iter": 2600, "lr": 0.07437, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25547, "top5_acc": 0.49547, "loss_cls": 4.34731, "loss": 4.34731, "time": 0.81514} +{"mode": "train", "epoch": 51, "iter": 2700, "lr": 0.07434, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23312, "top5_acc": 0.47828, "loss_cls": 4.38801, "loss": 4.38801, "time": 0.81309} +{"mode": "train", "epoch": 51, "iter": 2800, "lr": 0.07432, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23109, "top5_acc": 0.47547, "loss_cls": 4.44004, "loss": 4.44004, "time": 0.81393} +{"mode": "train", "epoch": 51, "iter": 2900, "lr": 0.07429, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23375, "top5_acc": 0.47516, "loss_cls": 4.4326, "loss": 4.4326, "time": 0.8105} +{"mode": "train", "epoch": 51, "iter": 3000, "lr": 0.07427, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2475, "top5_acc": 0.49094, "loss_cls": 4.37209, "loss": 4.37209, "time": 0.80952} +{"mode": "train", "epoch": 51, "iter": 3100, "lr": 0.07425, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24531, "top5_acc": 0.49125, "loss_cls": 4.38678, "loss": 4.38678, "time": 0.80802} +{"mode": "train", "epoch": 51, "iter": 3200, "lr": 0.07422, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23906, "top5_acc": 0.48141, "loss_cls": 4.41646, "loss": 4.41646, "time": 0.81275} +{"mode": "train", "epoch": 51, "iter": 3300, "lr": 0.0742, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23906, "top5_acc": 0.48516, "loss_cls": 4.39053, "loss": 4.39053, "time": 0.81781} +{"mode": "train", "epoch": 51, "iter": 3400, "lr": 0.07417, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23766, "top5_acc": 0.47859, "loss_cls": 4.40497, "loss": 4.40497, "time": 0.81558} +{"mode": "train", "epoch": 51, "iter": 3500, "lr": 0.07415, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24172, "top5_acc": 0.48172, "loss_cls": 4.41947, "loss": 4.41947, "time": 0.81723} +{"mode": "train", "epoch": 51, "iter": 3600, "lr": 0.07412, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24422, "top5_acc": 0.4875, "loss_cls": 4.38739, "loss": 4.38739, "time": 0.80848} +{"mode": "train", "epoch": 51, "iter": 3700, "lr": 0.0741, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24094, "top5_acc": 0.48047, "loss_cls": 4.39968, "loss": 4.39968, "time": 0.81335} +{"mode": "val", "epoch": 51, "iter": 309, "lr": 0.07409, "top1_acc": 0.17763, "top5_acc": 0.39467, "mean_class_accuracy": 0.17741} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.07406, "memory": 15990, "data_time": 1.34693, "top1_acc": 0.24188, "top5_acc": 0.49125, "loss_cls": 4.35101, "loss": 4.35101, "time": 2.32931} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.07404, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24453, "top5_acc": 0.49094, "loss_cls": 4.36015, "loss": 4.36015, "time": 0.81854} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.07401, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24922, "top5_acc": 0.49984, "loss_cls": 4.31731, "loss": 4.31731, "time": 0.80784} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.07399, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24359, "top5_acc": 0.49734, "loss_cls": 4.31166, "loss": 4.31166, "time": 0.81167} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.07397, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25, "top5_acc": 0.49219, "loss_cls": 4.36205, "loss": 4.36205, "time": 0.81411} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.07394, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25625, "top5_acc": 0.50109, "loss_cls": 4.29059, "loss": 4.29059, "time": 0.82357} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.07392, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23656, "top5_acc": 0.48531, "loss_cls": 4.38245, "loss": 4.38245, "time": 0.80884} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.07389, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24297, "top5_acc": 0.49594, "loss_cls": 4.33909, "loss": 4.33909, "time": 0.81891} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.07387, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24734, "top5_acc": 0.49312, "loss_cls": 4.33677, "loss": 4.33677, "time": 0.81338} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.07384, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2425, "top5_acc": 0.48359, "loss_cls": 4.37357, "loss": 4.37357, "time": 0.82044} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.07382, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24109, "top5_acc": 0.48641, "loss_cls": 4.37706, "loss": 4.37706, "time": 0.81453} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.07379, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23625, "top5_acc": 0.48844, "loss_cls": 4.40746, "loss": 4.40746, "time": 0.81053} +{"mode": "train", "epoch": 52, "iter": 1300, "lr": 0.07377, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24703, "top5_acc": 0.49453, "loss_cls": 4.33183, "loss": 4.33183, "time": 0.8081} +{"mode": "train", "epoch": 52, "iter": 1400, "lr": 0.07374, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24719, "top5_acc": 0.48875, "loss_cls": 4.38047, "loss": 4.38047, "time": 0.80864} +{"mode": "train", "epoch": 52, "iter": 1500, "lr": 0.07372, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23078, "top5_acc": 0.48297, "loss_cls": 4.40548, "loss": 4.40548, "time": 0.80983} +{"mode": "train", "epoch": 52, "iter": 1600, "lr": 0.0737, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24891, "top5_acc": 0.48688, "loss_cls": 4.38418, "loss": 4.38418, "time": 0.80923} +{"mode": "train", "epoch": 52, "iter": 1700, "lr": 0.07367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24422, "top5_acc": 0.49688, "loss_cls": 4.34711, "loss": 4.34711, "time": 0.81583} +{"mode": "train", "epoch": 52, "iter": 1800, "lr": 0.07365, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23703, "top5_acc": 0.48797, "loss_cls": 4.41597, "loss": 4.41597, "time": 0.80924} +{"mode": "train", "epoch": 52, "iter": 1900, "lr": 0.07362, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24219, "top5_acc": 0.49812, "loss_cls": 4.3424, "loss": 4.3424, "time": 0.81081} +{"mode": "train", "epoch": 52, "iter": 2000, "lr": 0.0736, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24312, "top5_acc": 0.48641, "loss_cls": 4.36542, "loss": 4.36542, "time": 0.81181} +{"mode": "train", "epoch": 52, "iter": 2100, "lr": 0.07357, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25109, "top5_acc": 0.49953, "loss_cls": 4.33536, "loss": 4.33536, "time": 0.81093} +{"mode": "train", "epoch": 52, "iter": 2200, "lr": 0.07355, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24281, "top5_acc": 0.49328, "loss_cls": 4.35976, "loss": 4.35976, "time": 0.81228} +{"mode": "train", "epoch": 52, "iter": 2300, "lr": 0.07352, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25047, "top5_acc": 0.49047, "loss_cls": 4.33072, "loss": 4.33072, "time": 0.81091} +{"mode": "train", "epoch": 52, "iter": 2400, "lr": 0.0735, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24781, "top5_acc": 0.49859, "loss_cls": 4.35073, "loss": 4.35073, "time": 0.8133} +{"mode": "train", "epoch": 52, "iter": 2500, "lr": 0.07347, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24062, "top5_acc": 0.4825, "loss_cls": 4.3947, "loss": 4.3947, "time": 0.82057} +{"mode": "train", "epoch": 52, "iter": 2600, "lr": 0.07345, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23969, "top5_acc": 0.49297, "loss_cls": 4.37088, "loss": 4.37088, "time": 0.81563} +{"mode": "train", "epoch": 52, "iter": 2700, "lr": 0.07342, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24234, "top5_acc": 0.48656, "loss_cls": 4.39075, "loss": 4.39075, "time": 0.81165} +{"mode": "train", "epoch": 52, "iter": 2800, "lr": 0.0734, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23125, "top5_acc": 0.48156, "loss_cls": 4.40769, "loss": 4.40769, "time": 0.81358} +{"mode": "train", "epoch": 52, "iter": 2900, "lr": 0.07337, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24484, "top5_acc": 0.49266, "loss_cls": 4.33807, "loss": 4.33807, "time": 0.80781} +{"mode": "train", "epoch": 52, "iter": 3000, "lr": 0.07335, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24359, "top5_acc": 0.48984, "loss_cls": 4.37119, "loss": 4.37119, "time": 0.81411} +{"mode": "train", "epoch": 52, "iter": 3100, "lr": 0.07332, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24547, "top5_acc": 0.48516, "loss_cls": 4.34761, "loss": 4.34761, "time": 0.81622} +{"mode": "train", "epoch": 52, "iter": 3200, "lr": 0.0733, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24984, "top5_acc": 0.49734, "loss_cls": 4.32985, "loss": 4.32985, "time": 0.81998} +{"mode": "train", "epoch": 52, "iter": 3300, "lr": 0.07328, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2375, "top5_acc": 0.49062, "loss_cls": 4.34389, "loss": 4.34389, "time": 0.81112} +{"mode": "train", "epoch": 52, "iter": 3400, "lr": 0.07325, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23828, "top5_acc": 0.49, "loss_cls": 4.37101, "loss": 4.37101, "time": 0.80964} +{"mode": "train", "epoch": 52, "iter": 3500, "lr": 0.07323, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24391, "top5_acc": 0.48922, "loss_cls": 4.35198, "loss": 4.35198, "time": 0.81855} +{"mode": "train", "epoch": 52, "iter": 3600, "lr": 0.0732, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24844, "top5_acc": 0.48172, "loss_cls": 4.39462, "loss": 4.39462, "time": 0.81073} +{"mode": "train", "epoch": 52, "iter": 3700, "lr": 0.07318, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23969, "top5_acc": 0.48203, "loss_cls": 4.39481, "loss": 4.39481, "time": 0.81898} +{"mode": "val", "epoch": 52, "iter": 309, "lr": 0.07317, "top1_acc": 0.17621, "top5_acc": 0.40328, "mean_class_accuracy": 0.17599} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.07314, "memory": 15990, "data_time": 1.28377, "top1_acc": 0.25312, "top5_acc": 0.50047, "loss_cls": 4.30932, "loss": 4.30932, "time": 2.27677} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.07312, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24609, "top5_acc": 0.485, "loss_cls": 4.37604, "loss": 4.37604, "time": 0.82018} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.07309, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24375, "top5_acc": 0.49016, "loss_cls": 4.34345, "loss": 4.34345, "time": 0.81255} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.07307, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24672, "top5_acc": 0.49172, "loss_cls": 4.37093, "loss": 4.37093, "time": 0.81665} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.07304, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24234, "top5_acc": 0.49109, "loss_cls": 4.35194, "loss": 4.35194, "time": 0.81249} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.07302, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24234, "top5_acc": 0.49578, "loss_cls": 4.34278, "loss": 4.34278, "time": 0.81762} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.07299, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23875, "top5_acc": 0.47031, "loss_cls": 4.41252, "loss": 4.41252, "time": 0.8129} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.07297, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23969, "top5_acc": 0.5025, "loss_cls": 4.33369, "loss": 4.33369, "time": 0.81037} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.07294, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23422, "top5_acc": 0.48375, "loss_cls": 4.39167, "loss": 4.39167, "time": 0.82271} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.07292, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25828, "top5_acc": 0.49812, "loss_cls": 4.3127, "loss": 4.3127, "time": 0.81144} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.07289, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24953, "top5_acc": 0.49234, "loss_cls": 4.32608, "loss": 4.32608, "time": 0.8084} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.07287, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23812, "top5_acc": 0.47906, "loss_cls": 4.40211, "loss": 4.40211, "time": 0.81261} +{"mode": "train", "epoch": 53, "iter": 1300, "lr": 0.07284, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24922, "top5_acc": 0.5025, "loss_cls": 4.34214, "loss": 4.34214, "time": 0.80729} +{"mode": "train", "epoch": 53, "iter": 1400, "lr": 0.07282, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25422, "top5_acc": 0.49031, "loss_cls": 4.34644, "loss": 4.34644, "time": 0.81124} +{"mode": "train", "epoch": 53, "iter": 1500, "lr": 0.07279, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24656, "top5_acc": 0.49469, "loss_cls": 4.3469, "loss": 4.3469, "time": 0.8114} +{"mode": "train", "epoch": 53, "iter": 1600, "lr": 0.07277, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24984, "top5_acc": 0.48875, "loss_cls": 4.36196, "loss": 4.36196, "time": 0.81171} +{"mode": "train", "epoch": 53, "iter": 1700, "lr": 0.07274, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25609, "top5_acc": 0.48938, "loss_cls": 4.32687, "loss": 4.32687, "time": 0.8173} +{"mode": "train", "epoch": 53, "iter": 1800, "lr": 0.07272, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25094, "top5_acc": 0.50094, "loss_cls": 4.29333, "loss": 4.29333, "time": 0.81118} +{"mode": "train", "epoch": 53, "iter": 1900, "lr": 0.07269, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24141, "top5_acc": 0.48719, "loss_cls": 4.39799, "loss": 4.39799, "time": 0.81496} +{"mode": "train", "epoch": 53, "iter": 2000, "lr": 0.07267, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25109, "top5_acc": 0.50359, "loss_cls": 4.32977, "loss": 4.32977, "time": 0.81142} +{"mode": "train", "epoch": 53, "iter": 2100, "lr": 0.07264, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24547, "top5_acc": 0.48562, "loss_cls": 4.34332, "loss": 4.34332, "time": 0.8096} +{"mode": "train", "epoch": 53, "iter": 2200, "lr": 0.07262, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25188, "top5_acc": 0.49359, "loss_cls": 4.33659, "loss": 4.33659, "time": 0.8091} +{"mode": "train", "epoch": 53, "iter": 2300, "lr": 0.07259, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24781, "top5_acc": 0.49438, "loss_cls": 4.34062, "loss": 4.34062, "time": 0.81066} +{"mode": "train", "epoch": 53, "iter": 2400, "lr": 0.07257, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2475, "top5_acc": 0.4975, "loss_cls": 4.32048, "loss": 4.32048, "time": 0.81905} +{"mode": "train", "epoch": 53, "iter": 2500, "lr": 0.07254, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24828, "top5_acc": 0.49656, "loss_cls": 4.33531, "loss": 4.33531, "time": 0.81373} +{"mode": "train", "epoch": 53, "iter": 2600, "lr": 0.07252, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24156, "top5_acc": 0.48672, "loss_cls": 4.35733, "loss": 4.35733, "time": 0.81544} +{"mode": "train", "epoch": 53, "iter": 2700, "lr": 0.07249, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24328, "top5_acc": 0.49531, "loss_cls": 4.35436, "loss": 4.35436, "time": 0.81487} +{"mode": "train", "epoch": 53, "iter": 2800, "lr": 0.07247, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24938, "top5_acc": 0.48672, "loss_cls": 4.35929, "loss": 4.35929, "time": 0.81567} +{"mode": "train", "epoch": 53, "iter": 2900, "lr": 0.07244, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24766, "top5_acc": 0.48938, "loss_cls": 4.34972, "loss": 4.34972, "time": 0.81026} +{"mode": "train", "epoch": 53, "iter": 3000, "lr": 0.07242, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24312, "top5_acc": 0.48891, "loss_cls": 4.35612, "loss": 4.35612, "time": 0.80827} +{"mode": "train", "epoch": 53, "iter": 3100, "lr": 0.07239, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25, "top5_acc": 0.49969, "loss_cls": 4.32698, "loss": 4.32698, "time": 0.81353} +{"mode": "train", "epoch": 53, "iter": 3200, "lr": 0.07237, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24641, "top5_acc": 0.49109, "loss_cls": 4.36269, "loss": 4.36269, "time": 0.81517} +{"mode": "train", "epoch": 53, "iter": 3300, "lr": 0.07234, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25203, "top5_acc": 0.48922, "loss_cls": 4.33362, "loss": 4.33362, "time": 0.81356} +{"mode": "train", "epoch": 53, "iter": 3400, "lr": 0.07232, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24109, "top5_acc": 0.49125, "loss_cls": 4.37772, "loss": 4.37772, "time": 0.81142} +{"mode": "train", "epoch": 53, "iter": 3500, "lr": 0.07229, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25125, "top5_acc": 0.50203, "loss_cls": 4.33198, "loss": 4.33198, "time": 0.81957} +{"mode": "train", "epoch": 53, "iter": 3600, "lr": 0.07227, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24125, "top5_acc": 0.49516, "loss_cls": 4.35477, "loss": 4.35477, "time": 0.82348} +{"mode": "train", "epoch": 53, "iter": 3700, "lr": 0.07224, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23641, "top5_acc": 0.47938, "loss_cls": 4.39002, "loss": 4.39002, "time": 0.81556} +{"mode": "val", "epoch": 53, "iter": 309, "lr": 0.07223, "top1_acc": 0.17388, "top5_acc": 0.39386, "mean_class_accuracy": 0.17377} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.07221, "memory": 15990, "data_time": 1.28237, "top1_acc": 0.25984, "top5_acc": 0.49828, "loss_cls": 4.3197, "loss": 4.3197, "time": 2.26467} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.07218, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25297, "top5_acc": 0.49625, "loss_cls": 4.31965, "loss": 4.31965, "time": 0.8235} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.07216, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24922, "top5_acc": 0.49828, "loss_cls": 4.31871, "loss": 4.31871, "time": 0.81054} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.07213, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23969, "top5_acc": 0.49469, "loss_cls": 4.31752, "loss": 4.31752, "time": 0.81215} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.07211, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24219, "top5_acc": 0.48875, "loss_cls": 4.3643, "loss": 4.3643, "time": 0.8091} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.07208, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25562, "top5_acc": 0.50281, "loss_cls": 4.31211, "loss": 4.31211, "time": 0.81019} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.07206, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24562, "top5_acc": 0.49609, "loss_cls": 4.31078, "loss": 4.31078, "time": 0.8162} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.07203, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25672, "top5_acc": 0.50234, "loss_cls": 4.32073, "loss": 4.32073, "time": 0.81309} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.07201, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24266, "top5_acc": 0.49172, "loss_cls": 4.34851, "loss": 4.34851, "time": 0.81706} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.07198, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25016, "top5_acc": 0.49188, "loss_cls": 4.35505, "loss": 4.35505, "time": 0.80837} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.07196, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25344, "top5_acc": 0.50516, "loss_cls": 4.34059, "loss": 4.34059, "time": 0.81471} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.07193, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24469, "top5_acc": 0.4925, "loss_cls": 4.31936, "loss": 4.31936, "time": 0.81227} +{"mode": "train", "epoch": 54, "iter": 1300, "lr": 0.07191, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24125, "top5_acc": 0.49578, "loss_cls": 4.34513, "loss": 4.34513, "time": 0.81043} +{"mode": "train", "epoch": 54, "iter": 1400, "lr": 0.07188, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24641, "top5_acc": 0.49828, "loss_cls": 4.32797, "loss": 4.32797, "time": 0.81216} +{"mode": "train", "epoch": 54, "iter": 1500, "lr": 0.07186, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24703, "top5_acc": 0.50219, "loss_cls": 4.31675, "loss": 4.31675, "time": 0.81395} +{"mode": "train", "epoch": 54, "iter": 1600, "lr": 0.07183, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24609, "top5_acc": 0.49484, "loss_cls": 4.35729, "loss": 4.35729, "time": 0.81162} +{"mode": "train", "epoch": 54, "iter": 1700, "lr": 0.07181, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24672, "top5_acc": 0.48469, "loss_cls": 4.35661, "loss": 4.35661, "time": 0.81904} +{"mode": "train", "epoch": 54, "iter": 1800, "lr": 0.07178, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24266, "top5_acc": 0.4975, "loss_cls": 4.35073, "loss": 4.35073, "time": 0.81007} +{"mode": "train", "epoch": 54, "iter": 1900, "lr": 0.07176, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23719, "top5_acc": 0.48438, "loss_cls": 4.39479, "loss": 4.39479, "time": 0.81275} +{"mode": "train", "epoch": 54, "iter": 2000, "lr": 0.07173, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25859, "top5_acc": 0.50266, "loss_cls": 4.31192, "loss": 4.31192, "time": 0.81327} +{"mode": "train", "epoch": 54, "iter": 2100, "lr": 0.0717, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24094, "top5_acc": 0.49156, "loss_cls": 4.35942, "loss": 4.35942, "time": 0.81729} +{"mode": "train", "epoch": 54, "iter": 2200, "lr": 0.07168, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24516, "top5_acc": 0.49594, "loss_cls": 4.35661, "loss": 4.35661, "time": 0.80989} +{"mode": "train", "epoch": 54, "iter": 2300, "lr": 0.07165, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25156, "top5_acc": 0.49219, "loss_cls": 4.3291, "loss": 4.3291, "time": 0.81311} +{"mode": "train", "epoch": 54, "iter": 2400, "lr": 0.07163, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25078, "top5_acc": 0.49172, "loss_cls": 4.35246, "loss": 4.35246, "time": 0.8171} +{"mode": "train", "epoch": 54, "iter": 2500, "lr": 0.0716, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24078, "top5_acc": 0.49406, "loss_cls": 4.34012, "loss": 4.34012, "time": 0.81013} +{"mode": "train", "epoch": 54, "iter": 2600, "lr": 0.07158, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25266, "top5_acc": 0.49859, "loss_cls": 4.28721, "loss": 4.28721, "time": 0.81299} +{"mode": "train", "epoch": 54, "iter": 2700, "lr": 0.07155, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24562, "top5_acc": 0.49344, "loss_cls": 4.34628, "loss": 4.34628, "time": 0.80862} +{"mode": "train", "epoch": 54, "iter": 2800, "lr": 0.07153, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24438, "top5_acc": 0.48938, "loss_cls": 4.3623, "loss": 4.3623, "time": 0.8091} +{"mode": "train", "epoch": 54, "iter": 2900, "lr": 0.0715, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25641, "top5_acc": 0.50344, "loss_cls": 4.31925, "loss": 4.31925, "time": 0.81504} +{"mode": "train", "epoch": 54, "iter": 3000, "lr": 0.07148, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24641, "top5_acc": 0.49094, "loss_cls": 4.36633, "loss": 4.36633, "time": 0.8123} +{"mode": "train", "epoch": 54, "iter": 3100, "lr": 0.07145, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24078, "top5_acc": 0.48438, "loss_cls": 4.41076, "loss": 4.41076, "time": 0.81312} +{"mode": "train", "epoch": 54, "iter": 3200, "lr": 0.07143, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24297, "top5_acc": 0.48938, "loss_cls": 4.37293, "loss": 4.37293, "time": 0.80927} +{"mode": "train", "epoch": 54, "iter": 3300, "lr": 0.0714, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24156, "top5_acc": 0.48938, "loss_cls": 4.35754, "loss": 4.35754, "time": 0.81115} +{"mode": "train", "epoch": 54, "iter": 3400, "lr": 0.07138, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2525, "top5_acc": 0.49891, "loss_cls": 4.32812, "loss": 4.32812, "time": 0.80979} +{"mode": "train", "epoch": 54, "iter": 3500, "lr": 0.07135, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25734, "top5_acc": 0.50328, "loss_cls": 4.31421, "loss": 4.31421, "time": 0.82209} +{"mode": "train", "epoch": 54, "iter": 3600, "lr": 0.07133, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23547, "top5_acc": 0.48844, "loss_cls": 4.35958, "loss": 4.35958, "time": 0.8129} +{"mode": "train", "epoch": 54, "iter": 3700, "lr": 0.0713, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24938, "top5_acc": 0.50469, "loss_cls": 4.29411, "loss": 4.29411, "time": 0.81623} +{"mode": "val", "epoch": 54, "iter": 309, "lr": 0.07129, "top1_acc": 0.15915, "top5_acc": 0.37016, "mean_class_accuracy": 0.15905} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.07126, "memory": 15990, "data_time": 1.32156, "top1_acc": 0.25391, "top5_acc": 0.50828, "loss_cls": 4.25073, "loss": 4.25073, "time": 2.29714} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.07124, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25359, "top5_acc": 0.50719, "loss_cls": 4.29522, "loss": 4.29522, "time": 0.81533} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.07121, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25047, "top5_acc": 0.50016, "loss_cls": 4.29518, "loss": 4.29518, "time": 0.81352} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.07119, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24484, "top5_acc": 0.48812, "loss_cls": 4.35887, "loss": 4.35887, "time": 0.81867} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.07116, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24594, "top5_acc": 0.50531, "loss_cls": 4.30164, "loss": 4.30164, "time": 0.8159} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.07114, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24297, "top5_acc": 0.49688, "loss_cls": 4.34043, "loss": 4.34043, "time": 0.80838} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.07111, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24141, "top5_acc": 0.48672, "loss_cls": 4.35788, "loss": 4.35788, "time": 0.81307} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.07109, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25641, "top5_acc": 0.50438, "loss_cls": 4.28206, "loss": 4.28206, "time": 0.80733} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.07106, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25438, "top5_acc": 0.49938, "loss_cls": 4.3259, "loss": 4.3259, "time": 0.81479} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.07104, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25641, "top5_acc": 0.5, "loss_cls": 4.27824, "loss": 4.27824, "time": 0.815} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.07101, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25578, "top5_acc": 0.50234, "loss_cls": 4.33527, "loss": 4.33527, "time": 0.8093} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.07099, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25453, "top5_acc": 0.50062, "loss_cls": 4.28912, "loss": 4.28912, "time": 0.81622} +{"mode": "train", "epoch": 55, "iter": 1300, "lr": 0.07096, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24578, "top5_acc": 0.49453, "loss_cls": 4.34548, "loss": 4.34548, "time": 0.81187} +{"mode": "train", "epoch": 55, "iter": 1400, "lr": 0.07093, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24828, "top5_acc": 0.49797, "loss_cls": 4.33639, "loss": 4.33639, "time": 0.80889} +{"mode": "train", "epoch": 55, "iter": 1500, "lr": 0.07091, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24422, "top5_acc": 0.49156, "loss_cls": 4.35984, "loss": 4.35984, "time": 0.81479} +{"mode": "train", "epoch": 55, "iter": 1600, "lr": 0.07088, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24703, "top5_acc": 0.50203, "loss_cls": 4.33902, "loss": 4.33902, "time": 0.80843} +{"mode": "train", "epoch": 55, "iter": 1700, "lr": 0.07086, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25828, "top5_acc": 0.49734, "loss_cls": 4.2824, "loss": 4.2824, "time": 0.81483} +{"mode": "train", "epoch": 55, "iter": 1800, "lr": 0.07083, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24984, "top5_acc": 0.50547, "loss_cls": 4.29203, "loss": 4.29203, "time": 0.80926} +{"mode": "train", "epoch": 55, "iter": 1900, "lr": 0.07081, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25672, "top5_acc": 0.49969, "loss_cls": 4.30674, "loss": 4.30674, "time": 0.80622} +{"mode": "train", "epoch": 55, "iter": 2000, "lr": 0.07078, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25125, "top5_acc": 0.50016, "loss_cls": 4.3321, "loss": 4.3321, "time": 0.81293} +{"mode": "train", "epoch": 55, "iter": 2100, "lr": 0.07076, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24031, "top5_acc": 0.48344, "loss_cls": 4.41137, "loss": 4.41137, "time": 0.80941} +{"mode": "train", "epoch": 55, "iter": 2200, "lr": 0.07073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25703, "top5_acc": 0.50187, "loss_cls": 4.28557, "loss": 4.28557, "time": 0.81711} +{"mode": "train", "epoch": 55, "iter": 2300, "lr": 0.07071, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23969, "top5_acc": 0.49359, "loss_cls": 4.37151, "loss": 4.37151, "time": 0.80795} +{"mode": "train", "epoch": 55, "iter": 2400, "lr": 0.07068, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24516, "top5_acc": 0.49781, "loss_cls": 4.33405, "loss": 4.33405, "time": 0.81069} +{"mode": "train", "epoch": 55, "iter": 2500, "lr": 0.07065, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24688, "top5_acc": 0.495, "loss_cls": 4.37581, "loss": 4.37581, "time": 0.81504} +{"mode": "train", "epoch": 55, "iter": 2600, "lr": 0.07063, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24562, "top5_acc": 0.49469, "loss_cls": 4.34652, "loss": 4.34652, "time": 0.81545} +{"mode": "train", "epoch": 55, "iter": 2700, "lr": 0.0706, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25, "top5_acc": 0.49766, "loss_cls": 4.31874, "loss": 4.31874, "time": 0.81661} +{"mode": "train", "epoch": 55, "iter": 2800, "lr": 0.07058, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24969, "top5_acc": 0.48953, "loss_cls": 4.34105, "loss": 4.34105, "time": 0.81679} +{"mode": "train", "epoch": 55, "iter": 2900, "lr": 0.07055, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24938, "top5_acc": 0.50359, "loss_cls": 4.31424, "loss": 4.31424, "time": 0.80913} +{"mode": "train", "epoch": 55, "iter": 3000, "lr": 0.07053, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24797, "top5_acc": 0.49641, "loss_cls": 4.33846, "loss": 4.33846, "time": 0.81489} +{"mode": "train", "epoch": 55, "iter": 3100, "lr": 0.0705, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24469, "top5_acc": 0.49094, "loss_cls": 4.36933, "loss": 4.36933, "time": 0.81108} +{"mode": "train", "epoch": 55, "iter": 3200, "lr": 0.07048, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24703, "top5_acc": 0.49828, "loss_cls": 4.31332, "loss": 4.31332, "time": 0.80911} +{"mode": "train", "epoch": 55, "iter": 3300, "lr": 0.07045, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25156, "top5_acc": 0.48828, "loss_cls": 4.33127, "loss": 4.33127, "time": 0.80994} +{"mode": "train", "epoch": 55, "iter": 3400, "lr": 0.07043, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25375, "top5_acc": 0.50109, "loss_cls": 4.29815, "loss": 4.29815, "time": 0.80802} +{"mode": "train", "epoch": 55, "iter": 3500, "lr": 0.0704, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24391, "top5_acc": 0.48859, "loss_cls": 4.38846, "loss": 4.38846, "time": 0.81451} +{"mode": "train", "epoch": 55, "iter": 3600, "lr": 0.07037, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25828, "top5_acc": 0.50438, "loss_cls": 4.28661, "loss": 4.28661, "time": 0.80739} +{"mode": "train", "epoch": 55, "iter": 3700, "lr": 0.07035, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24531, "top5_acc": 0.49297, "loss_cls": 4.33025, "loss": 4.33025, "time": 0.81544} +{"mode": "val", "epoch": 55, "iter": 309, "lr": 0.07034, "top1_acc": 0.18675, "top5_acc": 0.41301, "mean_class_accuracy": 0.18656} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.07031, "memory": 15990, "data_time": 1.31338, "top1_acc": 0.25031, "top5_acc": 0.50875, "loss_cls": 4.27081, "loss": 4.27081, "time": 2.2844} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.07029, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25859, "top5_acc": 0.50797, "loss_cls": 4.27999, "loss": 4.27999, "time": 0.81934} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.07026, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24734, "top5_acc": 0.49812, "loss_cls": 4.32986, "loss": 4.32986, "time": 0.81127} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.07023, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2575, "top5_acc": 0.50094, "loss_cls": 4.29195, "loss": 4.29195, "time": 0.81079} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.07021, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24594, "top5_acc": 0.49219, "loss_cls": 4.29598, "loss": 4.29598, "time": 0.81061} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.07018, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26297, "top5_acc": 0.51266, "loss_cls": 4.24355, "loss": 4.24355, "time": 0.81287} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.07016, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25766, "top5_acc": 0.49828, "loss_cls": 4.32825, "loss": 4.32825, "time": 0.81243} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.07013, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25156, "top5_acc": 0.50672, "loss_cls": 4.27333, "loss": 4.27333, "time": 0.81204} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.07011, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24109, "top5_acc": 0.50109, "loss_cls": 4.3164, "loss": 4.3164, "time": 0.81629} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.07008, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25875, "top5_acc": 0.49875, "loss_cls": 4.3034, "loss": 4.3034, "time": 0.81643} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.07006, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25422, "top5_acc": 0.50359, "loss_cls": 4.27893, "loss": 4.27893, "time": 0.81519} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.07003, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24844, "top5_acc": 0.49672, "loss_cls": 4.3334, "loss": 4.3334, "time": 0.8167} +{"mode": "train", "epoch": 56, "iter": 1300, "lr": 0.07, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25188, "top5_acc": 0.495, "loss_cls": 4.33805, "loss": 4.33805, "time": 0.80965} +{"mode": "train", "epoch": 56, "iter": 1400, "lr": 0.06998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25688, "top5_acc": 0.50703, "loss_cls": 4.29025, "loss": 4.29025, "time": 0.81274} +{"mode": "train", "epoch": 56, "iter": 1500, "lr": 0.06995, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24625, "top5_acc": 0.49672, "loss_cls": 4.32384, "loss": 4.32384, "time": 0.81184} +{"mode": "train", "epoch": 56, "iter": 1600, "lr": 0.06993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24781, "top5_acc": 0.50375, "loss_cls": 4.32014, "loss": 4.32014, "time": 0.81728} +{"mode": "train", "epoch": 56, "iter": 1700, "lr": 0.0699, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25438, "top5_acc": 0.50328, "loss_cls": 4.31327, "loss": 4.31327, "time": 0.81437} +{"mode": "train", "epoch": 56, "iter": 1800, "lr": 0.06988, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25312, "top5_acc": 0.49734, "loss_cls": 4.3029, "loss": 4.3029, "time": 0.82046} +{"mode": "train", "epoch": 56, "iter": 1900, "lr": 0.06985, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24516, "top5_acc": 0.49391, "loss_cls": 4.3539, "loss": 4.3539, "time": 0.81351} +{"mode": "train", "epoch": 56, "iter": 2000, "lr": 0.06983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24328, "top5_acc": 0.49906, "loss_cls": 4.33162, "loss": 4.33162, "time": 0.81135} +{"mode": "train", "epoch": 56, "iter": 2100, "lr": 0.0698, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25062, "top5_acc": 0.49469, "loss_cls": 4.3097, "loss": 4.3097, "time": 0.81011} +{"mode": "train", "epoch": 56, "iter": 2200, "lr": 0.06977, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25734, "top5_acc": 0.50297, "loss_cls": 4.29944, "loss": 4.29944, "time": 0.81017} +{"mode": "train", "epoch": 56, "iter": 2300, "lr": 0.06975, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24797, "top5_acc": 0.49906, "loss_cls": 4.33835, "loss": 4.33835, "time": 0.81415} +{"mode": "train", "epoch": 56, "iter": 2400, "lr": 0.06972, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2525, "top5_acc": 0.49328, "loss_cls": 4.36599, "loss": 4.36599, "time": 0.81483} +{"mode": "train", "epoch": 56, "iter": 2500, "lr": 0.0697, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24297, "top5_acc": 0.49516, "loss_cls": 4.37703, "loss": 4.37703, "time": 0.8148} +{"mode": "train", "epoch": 56, "iter": 2600, "lr": 0.06967, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25, "top5_acc": 0.49125, "loss_cls": 4.35954, "loss": 4.35954, "time": 0.81283} +{"mode": "train", "epoch": 56, "iter": 2700, "lr": 0.06965, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25203, "top5_acc": 0.49781, "loss_cls": 4.31448, "loss": 4.31448, "time": 0.81138} +{"mode": "train", "epoch": 56, "iter": 2800, "lr": 0.06962, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24375, "top5_acc": 0.49688, "loss_cls": 4.33014, "loss": 4.33014, "time": 0.81399} +{"mode": "train", "epoch": 56, "iter": 2900, "lr": 0.06959, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24922, "top5_acc": 0.49688, "loss_cls": 4.32547, "loss": 4.32547, "time": 0.81417} +{"mode": "train", "epoch": 56, "iter": 3000, "lr": 0.06957, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.255, "top5_acc": 0.49859, "loss_cls": 4.3065, "loss": 4.3065, "time": 0.8101} +{"mode": "train", "epoch": 56, "iter": 3100, "lr": 0.06954, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25734, "top5_acc": 0.50891, "loss_cls": 4.28911, "loss": 4.28911, "time": 0.8126} +{"mode": "train", "epoch": 56, "iter": 3200, "lr": 0.06952, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24281, "top5_acc": 0.48891, "loss_cls": 4.36067, "loss": 4.36067, "time": 0.81248} +{"mode": "train", "epoch": 56, "iter": 3300, "lr": 0.06949, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24953, "top5_acc": 0.50031, "loss_cls": 4.31607, "loss": 4.31607, "time": 0.81399} +{"mode": "train", "epoch": 56, "iter": 3400, "lr": 0.06947, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25156, "top5_acc": 0.4975, "loss_cls": 4.301, "loss": 4.301, "time": 0.81248} +{"mode": "train", "epoch": 56, "iter": 3500, "lr": 0.06944, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26172, "top5_acc": 0.50688, "loss_cls": 4.25978, "loss": 4.25978, "time": 0.81446} +{"mode": "train", "epoch": 56, "iter": 3600, "lr": 0.06941, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24641, "top5_acc": 0.49922, "loss_cls": 4.3348, "loss": 4.3348, "time": 0.8144} +{"mode": "train", "epoch": 56, "iter": 3700, "lr": 0.06939, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25047, "top5_acc": 0.49266, "loss_cls": 4.33233, "loss": 4.33233, "time": 0.81328} +{"mode": "val", "epoch": 56, "iter": 309, "lr": 0.06938, "top1_acc": 0.15778, "top5_acc": 0.3543, "mean_class_accuracy": 0.1578} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.06935, "memory": 15990, "data_time": 1.33643, "top1_acc": 0.25156, "top5_acc": 0.50562, "loss_cls": 4.2894, "loss": 4.2894, "time": 2.32349} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.06932, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25219, "top5_acc": 0.50984, "loss_cls": 4.24916, "loss": 4.24916, "time": 0.81655} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.0693, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26047, "top5_acc": 0.51031, "loss_cls": 4.25531, "loss": 4.25531, "time": 0.81737} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.06927, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25891, "top5_acc": 0.52203, "loss_cls": 4.23914, "loss": 4.23914, "time": 0.81627} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.06925, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25375, "top5_acc": 0.50219, "loss_cls": 4.29842, "loss": 4.29842, "time": 0.81076} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.06922, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25828, "top5_acc": 0.50922, "loss_cls": 4.26446, "loss": 4.26446, "time": 0.81072} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.0692, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25078, "top5_acc": 0.50516, "loss_cls": 4.30672, "loss": 4.30672, "time": 0.81486} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.06917, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24891, "top5_acc": 0.49844, "loss_cls": 4.31005, "loss": 4.31005, "time": 0.82484} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.06914, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25281, "top5_acc": 0.50156, "loss_cls": 4.31331, "loss": 4.31331, "time": 0.81708} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.06912, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25844, "top5_acc": 0.50922, "loss_cls": 4.2703, "loss": 4.2703, "time": 0.81775} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.06909, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25484, "top5_acc": 0.50734, "loss_cls": 4.28085, "loss": 4.28085, "time": 0.81462} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.06907, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25078, "top5_acc": 0.50516, "loss_cls": 4.29748, "loss": 4.29748, "time": 0.80896} +{"mode": "train", "epoch": 57, "iter": 1300, "lr": 0.06904, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25906, "top5_acc": 0.51484, "loss_cls": 4.26305, "loss": 4.26305, "time": 0.8073} +{"mode": "train", "epoch": 57, "iter": 1400, "lr": 0.06901, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24875, "top5_acc": 0.50906, "loss_cls": 4.30982, "loss": 4.30982, "time": 0.80972} +{"mode": "train", "epoch": 57, "iter": 1500, "lr": 0.06899, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25, "top5_acc": 0.50125, "loss_cls": 4.28382, "loss": 4.28382, "time": 0.81401} +{"mode": "train", "epoch": 57, "iter": 1600, "lr": 0.06896, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23875, "top5_acc": 0.48969, "loss_cls": 4.36697, "loss": 4.36697, "time": 0.81138} +{"mode": "train", "epoch": 57, "iter": 1700, "lr": 0.06894, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25016, "top5_acc": 0.50109, "loss_cls": 4.358, "loss": 4.358, "time": 0.81312} +{"mode": "train", "epoch": 57, "iter": 1800, "lr": 0.06891, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23766, "top5_acc": 0.49062, "loss_cls": 4.34936, "loss": 4.34936, "time": 0.81143} +{"mode": "train", "epoch": 57, "iter": 1900, "lr": 0.06889, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24578, "top5_acc": 0.4925, "loss_cls": 4.34021, "loss": 4.34021, "time": 0.81848} +{"mode": "train", "epoch": 57, "iter": 2000, "lr": 0.06886, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24781, "top5_acc": 0.4975, "loss_cls": 4.31282, "loss": 4.31282, "time": 0.81633} +{"mode": "train", "epoch": 57, "iter": 2100, "lr": 0.06883, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25172, "top5_acc": 0.50547, "loss_cls": 4.28125, "loss": 4.28125, "time": 0.81327} +{"mode": "train", "epoch": 57, "iter": 2200, "lr": 0.06881, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25312, "top5_acc": 0.50031, "loss_cls": 4.33596, "loss": 4.33596, "time": 0.81242} +{"mode": "train", "epoch": 57, "iter": 2300, "lr": 0.06878, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24562, "top5_acc": 0.49203, "loss_cls": 4.33942, "loss": 4.33942, "time": 0.81196} +{"mode": "train", "epoch": 57, "iter": 2400, "lr": 0.06876, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25203, "top5_acc": 0.49906, "loss_cls": 4.30304, "loss": 4.30304, "time": 0.81563} +{"mode": "train", "epoch": 57, "iter": 2500, "lr": 0.06873, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25578, "top5_acc": 0.49719, "loss_cls": 4.3252, "loss": 4.3252, "time": 0.81362} +{"mode": "train", "epoch": 57, "iter": 2600, "lr": 0.0687, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25828, "top5_acc": 0.50547, "loss_cls": 4.30324, "loss": 4.30324, "time": 0.81563} +{"mode": "train", "epoch": 57, "iter": 2700, "lr": 0.06868, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24891, "top5_acc": 0.49469, "loss_cls": 4.3396, "loss": 4.3396, "time": 0.80982} +{"mode": "train", "epoch": 57, "iter": 2800, "lr": 0.06865, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25688, "top5_acc": 0.50062, "loss_cls": 4.30168, "loss": 4.30168, "time": 0.81152} +{"mode": "train", "epoch": 57, "iter": 2900, "lr": 0.06863, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2575, "top5_acc": 0.51047, "loss_cls": 4.27255, "loss": 4.27255, "time": 0.8084} +{"mode": "train", "epoch": 57, "iter": 3000, "lr": 0.0686, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24219, "top5_acc": 0.48922, "loss_cls": 4.34719, "loss": 4.34719, "time": 0.8154} +{"mode": "train", "epoch": 57, "iter": 3100, "lr": 0.06857, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24906, "top5_acc": 0.49406, "loss_cls": 4.34813, "loss": 4.34813, "time": 0.8117} +{"mode": "train", "epoch": 57, "iter": 3200, "lr": 0.06855, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24984, "top5_acc": 0.49781, "loss_cls": 4.32688, "loss": 4.32688, "time": 0.80899} +{"mode": "train", "epoch": 57, "iter": 3300, "lr": 0.06852, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24969, "top5_acc": 0.49219, "loss_cls": 4.32447, "loss": 4.32447, "time": 0.81002} +{"mode": "train", "epoch": 57, "iter": 3400, "lr": 0.0685, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25125, "top5_acc": 0.49453, "loss_cls": 4.32061, "loss": 4.32061, "time": 0.81164} +{"mode": "train", "epoch": 57, "iter": 3500, "lr": 0.06847, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24797, "top5_acc": 0.49625, "loss_cls": 4.30125, "loss": 4.30125, "time": 0.81756} +{"mode": "train", "epoch": 57, "iter": 3600, "lr": 0.06844, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25781, "top5_acc": 0.50813, "loss_cls": 4.26189, "loss": 4.26189, "time": 0.81169} +{"mode": "train", "epoch": 57, "iter": 3700, "lr": 0.06842, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24938, "top5_acc": 0.50187, "loss_cls": 4.32429, "loss": 4.32429, "time": 0.82075} +{"mode": "val", "epoch": 57, "iter": 309, "lr": 0.06841, "top1_acc": 0.17854, "top5_acc": 0.40115, "mean_class_accuracy": 0.17846} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.06838, "memory": 15990, "data_time": 1.29085, "top1_acc": 0.25562, "top5_acc": 0.50438, "loss_cls": 4.27393, "loss": 4.27393, "time": 2.26901} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.06835, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25453, "top5_acc": 0.4975, "loss_cls": 4.30376, "loss": 4.30376, "time": 0.81287} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.06833, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25969, "top5_acc": 0.505, "loss_cls": 4.25272, "loss": 4.25272, "time": 0.81851} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.0683, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24094, "top5_acc": 0.49766, "loss_cls": 4.30819, "loss": 4.30819, "time": 0.81304} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.06828, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25922, "top5_acc": 0.50594, "loss_cls": 4.27635, "loss": 4.27635, "time": 0.81497} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.06825, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25859, "top5_acc": 0.51109, "loss_cls": 4.27671, "loss": 4.27671, "time": 0.81036} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.06822, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25656, "top5_acc": 0.50531, "loss_cls": 4.29428, "loss": 4.29428, "time": 0.81086} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.0682, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25156, "top5_acc": 0.50187, "loss_cls": 4.29881, "loss": 4.29881, "time": 0.8209} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.06817, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25703, "top5_acc": 0.50578, "loss_cls": 4.27634, "loss": 4.27634, "time": 0.81377} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.06815, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25672, "top5_acc": 0.50016, "loss_cls": 4.29076, "loss": 4.29076, "time": 0.81458} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.06812, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26859, "top5_acc": 0.52156, "loss_cls": 4.23202, "loss": 4.23202, "time": 0.81242} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.06809, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26672, "top5_acc": 0.50297, "loss_cls": 4.25324, "loss": 4.25324, "time": 0.80777} +{"mode": "train", "epoch": 58, "iter": 1300, "lr": 0.06807, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26594, "top5_acc": 0.50453, "loss_cls": 4.26065, "loss": 4.26065, "time": 0.80659} +{"mode": "train", "epoch": 58, "iter": 1400, "lr": 0.06804, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25609, "top5_acc": 0.50344, "loss_cls": 4.28399, "loss": 4.28399, "time": 0.80934} +{"mode": "train", "epoch": 58, "iter": 1500, "lr": 0.06802, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24922, "top5_acc": 0.49375, "loss_cls": 4.34626, "loss": 4.34626, "time": 0.81306} +{"mode": "train", "epoch": 58, "iter": 1600, "lr": 0.06799, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25953, "top5_acc": 0.50625, "loss_cls": 4.27736, "loss": 4.27736, "time": 0.81268} +{"mode": "train", "epoch": 58, "iter": 1700, "lr": 0.06796, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24938, "top5_acc": 0.51031, "loss_cls": 4.28866, "loss": 4.28866, "time": 0.81414} +{"mode": "train", "epoch": 58, "iter": 1800, "lr": 0.06794, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26031, "top5_acc": 0.51156, "loss_cls": 4.26829, "loss": 4.26829, "time": 0.81061} +{"mode": "train", "epoch": 58, "iter": 1900, "lr": 0.06791, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25781, "top5_acc": 0.51734, "loss_cls": 4.2751, "loss": 4.2751, "time": 0.81109} +{"mode": "train", "epoch": 58, "iter": 2000, "lr": 0.06789, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24266, "top5_acc": 0.49766, "loss_cls": 4.33264, "loss": 4.33264, "time": 0.81811} +{"mode": "train", "epoch": 58, "iter": 2100, "lr": 0.06786, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24422, "top5_acc": 0.49078, "loss_cls": 4.36693, "loss": 4.36693, "time": 0.81118} +{"mode": "train", "epoch": 58, "iter": 2200, "lr": 0.06783, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23938, "top5_acc": 0.49125, "loss_cls": 4.37106, "loss": 4.37106, "time": 0.81967} +{"mode": "train", "epoch": 58, "iter": 2300, "lr": 0.06781, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26406, "top5_acc": 0.51594, "loss_cls": 4.24243, "loss": 4.24243, "time": 0.81364} +{"mode": "train", "epoch": 58, "iter": 2400, "lr": 0.06778, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26391, "top5_acc": 0.51172, "loss_cls": 4.27801, "loss": 4.27801, "time": 0.81531} +{"mode": "train", "epoch": 58, "iter": 2500, "lr": 0.06775, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25969, "top5_acc": 0.51109, "loss_cls": 4.25224, "loss": 4.25224, "time": 0.81596} +{"mode": "train", "epoch": 58, "iter": 2600, "lr": 0.06773, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26031, "top5_acc": 0.50281, "loss_cls": 4.31243, "loss": 4.31243, "time": 0.81148} +{"mode": "train", "epoch": 58, "iter": 2700, "lr": 0.0677, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24984, "top5_acc": 0.50703, "loss_cls": 4.29851, "loss": 4.29851, "time": 0.81109} +{"mode": "train", "epoch": 58, "iter": 2800, "lr": 0.06768, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25234, "top5_acc": 0.51125, "loss_cls": 4.28324, "loss": 4.28324, "time": 0.81415} +{"mode": "train", "epoch": 58, "iter": 2900, "lr": 0.06765, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25578, "top5_acc": 0.50109, "loss_cls": 4.27668, "loss": 4.27668, "time": 0.81354} +{"mode": "train", "epoch": 58, "iter": 3000, "lr": 0.06762, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23406, "top5_acc": 0.49234, "loss_cls": 4.35392, "loss": 4.35392, "time": 0.81047} +{"mode": "train", "epoch": 58, "iter": 3100, "lr": 0.0676, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25594, "top5_acc": 0.505, "loss_cls": 4.27362, "loss": 4.27362, "time": 0.81738} +{"mode": "train", "epoch": 58, "iter": 3200, "lr": 0.06757, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25047, "top5_acc": 0.5075, "loss_cls": 4.27689, "loss": 4.27689, "time": 0.81005} +{"mode": "train", "epoch": 58, "iter": 3300, "lr": 0.06755, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25047, "top5_acc": 0.50406, "loss_cls": 4.33349, "loss": 4.33349, "time": 0.80949} +{"mode": "train", "epoch": 58, "iter": 3400, "lr": 0.06752, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24672, "top5_acc": 0.49531, "loss_cls": 4.307, "loss": 4.307, "time": 0.81118} +{"mode": "train", "epoch": 58, "iter": 3500, "lr": 0.06749, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25078, "top5_acc": 0.50187, "loss_cls": 4.30032, "loss": 4.30032, "time": 0.82235} +{"mode": "train", "epoch": 58, "iter": 3600, "lr": 0.06747, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24281, "top5_acc": 0.49062, "loss_cls": 4.35375, "loss": 4.35375, "time": 0.8133} +{"mode": "train", "epoch": 58, "iter": 3700, "lr": 0.06744, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24844, "top5_acc": 0.49688, "loss_cls": 4.32196, "loss": 4.32196, "time": 0.81983} +{"mode": "val", "epoch": 58, "iter": 309, "lr": 0.06743, "top1_acc": 0.16147, "top5_acc": 0.36681, "mean_class_accuracy": 0.16153} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.0674, "memory": 15990, "data_time": 1.31023, "top1_acc": 0.25359, "top5_acc": 0.51078, "loss_cls": 4.24292, "loss": 4.24292, "time": 2.28908} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.06738, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25109, "top5_acc": 0.49938, "loss_cls": 4.28432, "loss": 4.28432, "time": 0.81576} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.06735, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26328, "top5_acc": 0.51062, "loss_cls": 4.24196, "loss": 4.24196, "time": 0.81806} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.06732, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25703, "top5_acc": 0.50359, "loss_cls": 4.30875, "loss": 4.30875, "time": 0.81239} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.0673, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25719, "top5_acc": 0.50219, "loss_cls": 4.27868, "loss": 4.27868, "time": 0.82091} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.06727, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26875, "top5_acc": 0.50609, "loss_cls": 4.23824, "loss": 4.23824, "time": 0.81302} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.06725, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25359, "top5_acc": 0.50391, "loss_cls": 4.27886, "loss": 4.27886, "time": 0.80876} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.06722, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25734, "top5_acc": 0.49797, "loss_cls": 4.30637, "loss": 4.30637, "time": 0.80959} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.06719, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26016, "top5_acc": 0.51469, "loss_cls": 4.23402, "loss": 4.23402, "time": 0.81289} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.06717, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26078, "top5_acc": 0.51656, "loss_cls": 4.25333, "loss": 4.25333, "time": 0.81821} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.06714, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25891, "top5_acc": 0.51469, "loss_cls": 4.27327, "loss": 4.27327, "time": 0.81352} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.06711, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26094, "top5_acc": 0.50422, "loss_cls": 4.26945, "loss": 4.26945, "time": 0.81734} +{"mode": "train", "epoch": 59, "iter": 1300, "lr": 0.06709, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24688, "top5_acc": 0.49734, "loss_cls": 4.31618, "loss": 4.31618, "time": 0.81042} +{"mode": "train", "epoch": 59, "iter": 1400, "lr": 0.06706, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24984, "top5_acc": 0.50906, "loss_cls": 4.29539, "loss": 4.29539, "time": 0.80782} +{"mode": "train", "epoch": 59, "iter": 1500, "lr": 0.06704, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25516, "top5_acc": 0.49266, "loss_cls": 4.31519, "loss": 4.31519, "time": 0.80528} +{"mode": "train", "epoch": 59, "iter": 1600, "lr": 0.06701, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24719, "top5_acc": 0.49531, "loss_cls": 4.32579, "loss": 4.32579, "time": 0.81131} +{"mode": "train", "epoch": 59, "iter": 1700, "lr": 0.06698, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26234, "top5_acc": 0.51141, "loss_cls": 4.26708, "loss": 4.26708, "time": 0.8122} +{"mode": "train", "epoch": 59, "iter": 1800, "lr": 0.06696, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25031, "top5_acc": 0.49516, "loss_cls": 4.30615, "loss": 4.30615, "time": 0.81558} +{"mode": "train", "epoch": 59, "iter": 1900, "lr": 0.06693, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25219, "top5_acc": 0.50156, "loss_cls": 4.30294, "loss": 4.30294, "time": 0.80677} +{"mode": "train", "epoch": 59, "iter": 2000, "lr": 0.0669, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25734, "top5_acc": 0.50109, "loss_cls": 4.30431, "loss": 4.30431, "time": 0.80943} +{"mode": "train", "epoch": 59, "iter": 2100, "lr": 0.06688, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25766, "top5_acc": 0.51344, "loss_cls": 4.26175, "loss": 4.26175, "time": 0.80657} +{"mode": "train", "epoch": 59, "iter": 2200, "lr": 0.06685, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25328, "top5_acc": 0.49531, "loss_cls": 4.31264, "loss": 4.31264, "time": 0.81516} +{"mode": "train", "epoch": 59, "iter": 2300, "lr": 0.06682, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25047, "top5_acc": 0.49391, "loss_cls": 4.33001, "loss": 4.33001, "time": 0.81707} +{"mode": "train", "epoch": 59, "iter": 2400, "lr": 0.0668, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24953, "top5_acc": 0.49469, "loss_cls": 4.31664, "loss": 4.31664, "time": 0.81389} +{"mode": "train", "epoch": 59, "iter": 2500, "lr": 0.06677, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25469, "top5_acc": 0.50094, "loss_cls": 4.30552, "loss": 4.30552, "time": 0.81219} +{"mode": "train", "epoch": 59, "iter": 2600, "lr": 0.06675, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25656, "top5_acc": 0.50641, "loss_cls": 4.29809, "loss": 4.29809, "time": 0.81171} +{"mode": "train", "epoch": 59, "iter": 2700, "lr": 0.06672, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26344, "top5_acc": 0.50984, "loss_cls": 4.25052, "loss": 4.25052, "time": 0.81211} +{"mode": "train", "epoch": 59, "iter": 2800, "lr": 0.06669, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25922, "top5_acc": 0.50922, "loss_cls": 4.29207, "loss": 4.29207, "time": 0.8083} +{"mode": "train", "epoch": 59, "iter": 2900, "lr": 0.06667, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24984, "top5_acc": 0.49875, "loss_cls": 4.29304, "loss": 4.29304, "time": 0.81649} +{"mode": "train", "epoch": 59, "iter": 3000, "lr": 0.06664, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25234, "top5_acc": 0.50047, "loss_cls": 4.30729, "loss": 4.30729, "time": 0.80668} +{"mode": "train", "epoch": 59, "iter": 3100, "lr": 0.06661, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25297, "top5_acc": 0.49891, "loss_cls": 4.30171, "loss": 4.30171, "time": 0.80925} +{"mode": "train", "epoch": 59, "iter": 3200, "lr": 0.06659, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25078, "top5_acc": 0.49266, "loss_cls": 4.31213, "loss": 4.31213, "time": 0.81512} +{"mode": "train", "epoch": 59, "iter": 3300, "lr": 0.06656, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24781, "top5_acc": 0.49922, "loss_cls": 4.32612, "loss": 4.32612, "time": 0.81335} +{"mode": "train", "epoch": 59, "iter": 3400, "lr": 0.06653, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26641, "top5_acc": 0.51359, "loss_cls": 4.23644, "loss": 4.23644, "time": 0.80983} +{"mode": "train", "epoch": 59, "iter": 3500, "lr": 0.06651, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25422, "top5_acc": 0.50016, "loss_cls": 4.2785, "loss": 4.2785, "time": 0.81271} +{"mode": "train", "epoch": 59, "iter": 3600, "lr": 0.06648, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26016, "top5_acc": 0.5075, "loss_cls": 4.26509, "loss": 4.26509, "time": 0.81361} +{"mode": "train", "epoch": 59, "iter": 3700, "lr": 0.06646, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25078, "top5_acc": 0.49547, "loss_cls": 4.33261, "loss": 4.33261, "time": 0.81327} +{"mode": "val", "epoch": 59, "iter": 309, "lr": 0.06644, "top1_acc": 0.18721, "top5_acc": 0.41199, "mean_class_accuracy": 0.18716} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.06642, "memory": 15990, "data_time": 1.32779, "top1_acc": 0.26625, "top5_acc": 0.51859, "loss_cls": 4.21338, "loss": 4.21338, "time": 2.30445} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.06639, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2575, "top5_acc": 0.51672, "loss_cls": 4.21334, "loss": 4.21334, "time": 0.81732} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.06636, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25562, "top5_acc": 0.51109, "loss_cls": 4.23535, "loss": 4.23535, "time": 0.80715} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.06634, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25469, "top5_acc": 0.50469, "loss_cls": 4.2811, "loss": 4.2811, "time": 0.80838} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.06631, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24984, "top5_acc": 0.50187, "loss_cls": 4.29898, "loss": 4.29898, "time": 0.81224} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.06629, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25578, "top5_acc": 0.50984, "loss_cls": 4.27086, "loss": 4.27086, "time": 0.81206} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.06626, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25125, "top5_acc": 0.51281, "loss_cls": 4.26208, "loss": 4.26208, "time": 0.81126} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.06623, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26312, "top5_acc": 0.51438, "loss_cls": 4.22787, "loss": 4.22787, "time": 0.80839} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.06621, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25547, "top5_acc": 0.51016, "loss_cls": 4.26284, "loss": 4.26284, "time": 0.82351} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.06618, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25641, "top5_acc": 0.50906, "loss_cls": 4.28769, "loss": 4.28769, "time": 0.81086} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.06615, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25969, "top5_acc": 0.50219, "loss_cls": 4.26399, "loss": 4.26399, "time": 0.81202} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.06613, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25062, "top5_acc": 0.50016, "loss_cls": 4.28321, "loss": 4.28321, "time": 0.8224} +{"mode": "train", "epoch": 60, "iter": 1300, "lr": 0.0661, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24891, "top5_acc": 0.50266, "loss_cls": 4.31221, "loss": 4.31221, "time": 0.81202} +{"mode": "train", "epoch": 60, "iter": 1400, "lr": 0.06607, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25375, "top5_acc": 0.50078, "loss_cls": 4.29133, "loss": 4.29133, "time": 0.81469} +{"mode": "train", "epoch": 60, "iter": 1500, "lr": 0.06605, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24938, "top5_acc": 0.50438, "loss_cls": 4.32829, "loss": 4.32829, "time": 0.81064} +{"mode": "train", "epoch": 60, "iter": 1600, "lr": 0.06602, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25453, "top5_acc": 0.50547, "loss_cls": 4.30713, "loss": 4.30713, "time": 0.80904} +{"mode": "train", "epoch": 60, "iter": 1700, "lr": 0.06599, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25984, "top5_acc": 0.50453, "loss_cls": 4.26173, "loss": 4.26173, "time": 0.80893} +{"mode": "train", "epoch": 60, "iter": 1800, "lr": 0.06597, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25125, "top5_acc": 0.50438, "loss_cls": 4.28503, "loss": 4.28503, "time": 0.81587} +{"mode": "train", "epoch": 60, "iter": 1900, "lr": 0.06594, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26016, "top5_acc": 0.50516, "loss_cls": 4.26586, "loss": 4.26586, "time": 0.8091} +{"mode": "train", "epoch": 60, "iter": 2000, "lr": 0.06591, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26797, "top5_acc": 0.51844, "loss_cls": 4.23469, "loss": 4.23469, "time": 0.80951} +{"mode": "train", "epoch": 60, "iter": 2100, "lr": 0.06589, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26422, "top5_acc": 0.51766, "loss_cls": 4.24745, "loss": 4.24745, "time": 0.81236} +{"mode": "train", "epoch": 60, "iter": 2200, "lr": 0.06586, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24375, "top5_acc": 0.495, "loss_cls": 4.30477, "loss": 4.30477, "time": 0.81141} +{"mode": "train", "epoch": 60, "iter": 2300, "lr": 0.06584, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25906, "top5_acc": 0.49625, "loss_cls": 4.29259, "loss": 4.29259, "time": 0.81242} +{"mode": "train", "epoch": 60, "iter": 2400, "lr": 0.06581, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26234, "top5_acc": 0.51266, "loss_cls": 4.25681, "loss": 4.25681, "time": 0.80824} +{"mode": "train", "epoch": 60, "iter": 2500, "lr": 0.06578, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26781, "top5_acc": 0.51672, "loss_cls": 4.21418, "loss": 4.21418, "time": 0.8136} +{"mode": "train", "epoch": 60, "iter": 2600, "lr": 0.06576, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25812, "top5_acc": 0.50047, "loss_cls": 4.29091, "loss": 4.29091, "time": 0.81334} +{"mode": "train", "epoch": 60, "iter": 2700, "lr": 0.06573, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25391, "top5_acc": 0.50141, "loss_cls": 4.27796, "loss": 4.27796, "time": 0.81453} +{"mode": "train", "epoch": 60, "iter": 2800, "lr": 0.0657, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25922, "top5_acc": 0.50484, "loss_cls": 4.3128, "loss": 4.3128, "time": 0.81298} +{"mode": "train", "epoch": 60, "iter": 2900, "lr": 0.06568, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25016, "top5_acc": 0.49812, "loss_cls": 4.31156, "loss": 4.31156, "time": 0.8139} +{"mode": "train", "epoch": 60, "iter": 3000, "lr": 0.06565, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27047, "top5_acc": 0.51031, "loss_cls": 4.22683, "loss": 4.22683, "time": 0.81791} +{"mode": "train", "epoch": 60, "iter": 3100, "lr": 0.06562, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25047, "top5_acc": 0.49859, "loss_cls": 4.31671, "loss": 4.31671, "time": 0.81642} +{"mode": "train", "epoch": 60, "iter": 3200, "lr": 0.0656, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25734, "top5_acc": 0.50156, "loss_cls": 4.29747, "loss": 4.29747, "time": 0.81005} +{"mode": "train", "epoch": 60, "iter": 3300, "lr": 0.06557, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24578, "top5_acc": 0.49578, "loss_cls": 4.29681, "loss": 4.29681, "time": 0.8153} +{"mode": "train", "epoch": 60, "iter": 3400, "lr": 0.06554, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24766, "top5_acc": 0.495, "loss_cls": 4.35664, "loss": 4.35664, "time": 0.81586} +{"mode": "train", "epoch": 60, "iter": 3500, "lr": 0.06552, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2625, "top5_acc": 0.50297, "loss_cls": 4.26275, "loss": 4.26275, "time": 0.80831} +{"mode": "train", "epoch": 60, "iter": 3600, "lr": 0.06549, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26328, "top5_acc": 0.50672, "loss_cls": 4.28166, "loss": 4.28166, "time": 0.81696} +{"mode": "train", "epoch": 60, "iter": 3700, "lr": 0.06546, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26, "top5_acc": 0.50656, "loss_cls": 4.25847, "loss": 4.25847, "time": 0.81264} +{"mode": "val", "epoch": 60, "iter": 309, "lr": 0.06545, "top1_acc": 0.187, "top5_acc": 0.42142, "mean_class_accuracy": 0.18695} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.06542, "memory": 15990, "data_time": 1.28761, "top1_acc": 0.2675, "top5_acc": 0.51781, "loss_cls": 4.20766, "loss": 4.20766, "time": 2.26092} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.0654, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26469, "top5_acc": 0.51125, "loss_cls": 4.24649, "loss": 4.24649, "time": 0.81003} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.06537, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27172, "top5_acc": 0.51766, "loss_cls": 4.22864, "loss": 4.22864, "time": 0.81341} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.06534, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26516, "top5_acc": 0.51609, "loss_cls": 4.23181, "loss": 4.23181, "time": 0.81082} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.06532, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25766, "top5_acc": 0.51578, "loss_cls": 4.2707, "loss": 4.2707, "time": 0.80947} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.06529, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26609, "top5_acc": 0.50641, "loss_cls": 4.24657, "loss": 4.24657, "time": 0.81559} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.06526, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24969, "top5_acc": 0.50797, "loss_cls": 4.2918, "loss": 4.2918, "time": 0.80646} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.06524, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25953, "top5_acc": 0.51359, "loss_cls": 4.24752, "loss": 4.24752, "time": 0.8079} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.06521, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26156, "top5_acc": 0.50828, "loss_cls": 4.24134, "loss": 4.24134, "time": 0.81208} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.06519, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27078, "top5_acc": 0.52703, "loss_cls": 4.18024, "loss": 4.18024, "time": 0.81932} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.06516, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25656, "top5_acc": 0.50484, "loss_cls": 4.23994, "loss": 4.23994, "time": 0.81678} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.06513, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25656, "top5_acc": 0.51125, "loss_cls": 4.26219, "loss": 4.26219, "time": 0.81452} +{"mode": "train", "epoch": 61, "iter": 1300, "lr": 0.06511, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26453, "top5_acc": 0.50844, "loss_cls": 4.26526, "loss": 4.26526, "time": 0.81634} +{"mode": "train", "epoch": 61, "iter": 1400, "lr": 0.06508, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25469, "top5_acc": 0.49766, "loss_cls": 4.3082, "loss": 4.3082, "time": 0.81153} +{"mode": "train", "epoch": 61, "iter": 1500, "lr": 0.06505, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26281, "top5_acc": 0.51109, "loss_cls": 4.2387, "loss": 4.2387, "time": 0.81155} +{"mode": "train", "epoch": 61, "iter": 1600, "lr": 0.06503, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25188, "top5_acc": 0.49641, "loss_cls": 4.30326, "loss": 4.30326, "time": 0.80725} +{"mode": "train", "epoch": 61, "iter": 1700, "lr": 0.065, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24828, "top5_acc": 0.49938, "loss_cls": 4.32268, "loss": 4.32268, "time": 0.81004} +{"mode": "train", "epoch": 61, "iter": 1800, "lr": 0.06497, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25312, "top5_acc": 0.50297, "loss_cls": 4.28116, "loss": 4.28116, "time": 0.81026} +{"mode": "train", "epoch": 61, "iter": 1900, "lr": 0.06495, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24703, "top5_acc": 0.50781, "loss_cls": 4.28931, "loss": 4.28931, "time": 0.80694} +{"mode": "train", "epoch": 61, "iter": 2000, "lr": 0.06492, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25078, "top5_acc": 0.50641, "loss_cls": 4.27974, "loss": 4.27974, "time": 0.80918} +{"mode": "train", "epoch": 61, "iter": 2100, "lr": 0.06489, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24969, "top5_acc": 0.50375, "loss_cls": 4.31915, "loss": 4.31915, "time": 0.80985} +{"mode": "train", "epoch": 61, "iter": 2200, "lr": 0.06487, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26203, "top5_acc": 0.51359, "loss_cls": 4.24651, "loss": 4.24651, "time": 0.80744} +{"mode": "train", "epoch": 61, "iter": 2300, "lr": 0.06484, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25891, "top5_acc": 0.50844, "loss_cls": 4.28124, "loss": 4.28124, "time": 0.80515} +{"mode": "train", "epoch": 61, "iter": 2400, "lr": 0.06481, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26406, "top5_acc": 0.51625, "loss_cls": 4.22932, "loss": 4.22932, "time": 0.80964} +{"mode": "train", "epoch": 61, "iter": 2500, "lr": 0.06478, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25312, "top5_acc": 0.51062, "loss_cls": 4.24493, "loss": 4.24493, "time": 0.80859} +{"mode": "train", "epoch": 61, "iter": 2600, "lr": 0.06476, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26438, "top5_acc": 0.50875, "loss_cls": 4.25745, "loss": 4.25745, "time": 0.81279} +{"mode": "train", "epoch": 61, "iter": 2700, "lr": 0.06473, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26047, "top5_acc": 0.50609, "loss_cls": 4.27344, "loss": 4.27344, "time": 0.81846} +{"mode": "train", "epoch": 61, "iter": 2800, "lr": 0.0647, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25844, "top5_acc": 0.50766, "loss_cls": 4.26958, "loss": 4.26958, "time": 0.80916} +{"mode": "train", "epoch": 61, "iter": 2900, "lr": 0.06468, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26344, "top5_acc": 0.50984, "loss_cls": 4.23634, "loss": 4.23634, "time": 0.81399} +{"mode": "train", "epoch": 61, "iter": 3000, "lr": 0.06465, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25469, "top5_acc": 0.51562, "loss_cls": 4.25445, "loss": 4.25445, "time": 0.80853} +{"mode": "train", "epoch": 61, "iter": 3100, "lr": 0.06462, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26578, "top5_acc": 0.50969, "loss_cls": 4.26258, "loss": 4.26258, "time": 0.81979} +{"mode": "train", "epoch": 61, "iter": 3200, "lr": 0.0646, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25, "top5_acc": 0.49906, "loss_cls": 4.32515, "loss": 4.32515, "time": 0.80721} +{"mode": "train", "epoch": 61, "iter": 3300, "lr": 0.06457, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26531, "top5_acc": 0.515, "loss_cls": 4.23857, "loss": 4.23857, "time": 0.81424} +{"mode": "train", "epoch": 61, "iter": 3400, "lr": 0.06454, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26172, "top5_acc": 0.50734, "loss_cls": 4.24494, "loss": 4.24494, "time": 0.80696} +{"mode": "train", "epoch": 61, "iter": 3500, "lr": 0.06452, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25891, "top5_acc": 0.51188, "loss_cls": 4.24218, "loss": 4.24218, "time": 0.8085} +{"mode": "train", "epoch": 61, "iter": 3600, "lr": 0.06449, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2525, "top5_acc": 0.49422, "loss_cls": 4.32757, "loss": 4.32757, "time": 0.81152} +{"mode": "train", "epoch": 61, "iter": 3700, "lr": 0.06446, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25234, "top5_acc": 0.51203, "loss_cls": 4.27878, "loss": 4.27878, "time": 0.82033} +{"mode": "val", "epoch": 61, "iter": 309, "lr": 0.06445, "top1_acc": 0.1949, "top5_acc": 0.42876, "mean_class_accuracy": 0.19476} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.06443, "memory": 15990, "data_time": 1.30621, "top1_acc": 0.26781, "top5_acc": 0.50891, "loss_cls": 4.21431, "loss": 4.21431, "time": 2.27644} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.0644, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26438, "top5_acc": 0.52047, "loss_cls": 4.19674, "loss": 4.19674, "time": 0.81878} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.06437, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25938, "top5_acc": 0.50969, "loss_cls": 4.25175, "loss": 4.25175, "time": 0.81351} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.06434, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26094, "top5_acc": 0.50016, "loss_cls": 4.25834, "loss": 4.25834, "time": 0.80726} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.06432, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26391, "top5_acc": 0.51328, "loss_cls": 4.23932, "loss": 4.23932, "time": 0.81645} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.06429, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26344, "top5_acc": 0.50984, "loss_cls": 4.24657, "loss": 4.24657, "time": 0.81183} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.06426, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27125, "top5_acc": 0.51719, "loss_cls": 4.20928, "loss": 4.20928, "time": 0.81276} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.06424, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26656, "top5_acc": 0.51141, "loss_cls": 4.27925, "loss": 4.27925, "time": 0.81217} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.06421, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25781, "top5_acc": 0.51031, "loss_cls": 4.25331, "loss": 4.25331, "time": 0.81334} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.06418, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26016, "top5_acc": 0.51781, "loss_cls": 4.22408, "loss": 4.22408, "time": 0.82236} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.06416, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26828, "top5_acc": 0.51297, "loss_cls": 4.25236, "loss": 4.25236, "time": 0.81071} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.06413, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25422, "top5_acc": 0.51938, "loss_cls": 4.2285, "loss": 4.2285, "time": 0.81571} +{"mode": "train", "epoch": 62, "iter": 1300, "lr": 0.0641, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27578, "top5_acc": 0.52406, "loss_cls": 4.20658, "loss": 4.20658, "time": 0.80943} +{"mode": "train", "epoch": 62, "iter": 1400, "lr": 0.06408, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25719, "top5_acc": 0.51375, "loss_cls": 4.24354, "loss": 4.24354, "time": 0.81081} +{"mode": "train", "epoch": 62, "iter": 1500, "lr": 0.06405, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25547, "top5_acc": 0.50172, "loss_cls": 4.24098, "loss": 4.24098, "time": 0.8177} +{"mode": "train", "epoch": 62, "iter": 1600, "lr": 0.06402, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25297, "top5_acc": 0.50375, "loss_cls": 4.29171, "loss": 4.29171, "time": 0.80888} +{"mode": "train", "epoch": 62, "iter": 1700, "lr": 0.064, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25828, "top5_acc": 0.50516, "loss_cls": 4.2693, "loss": 4.2693, "time": 0.81347} +{"mode": "train", "epoch": 62, "iter": 1800, "lr": 0.06397, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2675, "top5_acc": 0.52125, "loss_cls": 4.23036, "loss": 4.23036, "time": 0.81167} +{"mode": "train", "epoch": 62, "iter": 1900, "lr": 0.06394, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26641, "top5_acc": 0.51141, "loss_cls": 4.2556, "loss": 4.2556, "time": 0.81072} +{"mode": "train", "epoch": 62, "iter": 2000, "lr": 0.06392, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25656, "top5_acc": 0.49859, "loss_cls": 4.30887, "loss": 4.30887, "time": 0.81057} +{"mode": "train", "epoch": 62, "iter": 2100, "lr": 0.06389, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2575, "top5_acc": 0.50453, "loss_cls": 4.2751, "loss": 4.2751, "time": 0.81494} +{"mode": "train", "epoch": 62, "iter": 2200, "lr": 0.06386, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26359, "top5_acc": 0.50641, "loss_cls": 4.25158, "loss": 4.25158, "time": 0.80977} +{"mode": "train", "epoch": 62, "iter": 2300, "lr": 0.06384, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25188, "top5_acc": 0.5, "loss_cls": 4.29979, "loss": 4.29979, "time": 0.81569} +{"mode": "train", "epoch": 62, "iter": 2400, "lr": 0.06381, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25797, "top5_acc": 0.50016, "loss_cls": 4.28616, "loss": 4.28616, "time": 0.81051} +{"mode": "train", "epoch": 62, "iter": 2500, "lr": 0.06378, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26062, "top5_acc": 0.50828, "loss_cls": 4.28623, "loss": 4.28623, "time": 0.81103} +{"mode": "train", "epoch": 62, "iter": 2600, "lr": 0.06375, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26172, "top5_acc": 0.50797, "loss_cls": 4.25605, "loss": 4.25605, "time": 0.81598} +{"mode": "train", "epoch": 62, "iter": 2700, "lr": 0.06373, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27062, "top5_acc": 0.50688, "loss_cls": 4.24363, "loss": 4.24363, "time": 0.81495} +{"mode": "train", "epoch": 62, "iter": 2800, "lr": 0.0637, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24406, "top5_acc": 0.49922, "loss_cls": 4.30765, "loss": 4.30765, "time": 0.81425} +{"mode": "train", "epoch": 62, "iter": 2900, "lr": 0.06367, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26625, "top5_acc": 0.51828, "loss_cls": 4.23687, "loss": 4.23687, "time": 0.81416} +{"mode": "train", "epoch": 62, "iter": 3000, "lr": 0.06365, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26281, "top5_acc": 0.52125, "loss_cls": 4.22592, "loss": 4.22592, "time": 0.80942} +{"mode": "train", "epoch": 62, "iter": 3100, "lr": 0.06362, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27078, "top5_acc": 0.51875, "loss_cls": 4.21432, "loss": 4.21432, "time": 0.81208} +{"mode": "train", "epoch": 62, "iter": 3200, "lr": 0.06359, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26203, "top5_acc": 0.51328, "loss_cls": 4.22227, "loss": 4.22227, "time": 0.80964} +{"mode": "train", "epoch": 62, "iter": 3300, "lr": 0.06357, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25469, "top5_acc": 0.50266, "loss_cls": 4.27773, "loss": 4.27773, "time": 0.82062} +{"mode": "train", "epoch": 62, "iter": 3400, "lr": 0.06354, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25516, "top5_acc": 0.5025, "loss_cls": 4.27314, "loss": 4.27314, "time": 0.81427} +{"mode": "train", "epoch": 62, "iter": 3500, "lr": 0.06351, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26297, "top5_acc": 0.51297, "loss_cls": 4.22951, "loss": 4.22951, "time": 0.81306} +{"mode": "train", "epoch": 62, "iter": 3600, "lr": 0.06349, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26844, "top5_acc": 0.51609, "loss_cls": 4.21624, "loss": 4.21624, "time": 0.81726} +{"mode": "train", "epoch": 62, "iter": 3700, "lr": 0.06346, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25406, "top5_acc": 0.51016, "loss_cls": 4.28805, "loss": 4.28805, "time": 0.81533} +{"mode": "val", "epoch": 62, "iter": 309, "lr": 0.06345, "top1_acc": 0.18391, "top5_acc": 0.4164, "mean_class_accuracy": 0.18382} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.06342, "memory": 15990, "data_time": 1.30359, "top1_acc": 0.26391, "top5_acc": 0.51391, "loss_cls": 4.23613, "loss": 4.23613, "time": 2.30572} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.06339, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26203, "top5_acc": 0.51484, "loss_cls": 4.242, "loss": 4.242, "time": 0.83013} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.06337, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26453, "top5_acc": 0.51688, "loss_cls": 4.2138, "loss": 4.2138, "time": 0.83254} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.06334, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26672, "top5_acc": 0.51391, "loss_cls": 4.23212, "loss": 4.23212, "time": 0.82705} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.06331, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26297, "top5_acc": 0.52141, "loss_cls": 4.22024, "loss": 4.22024, "time": 0.82979} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.06328, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26547, "top5_acc": 0.51922, "loss_cls": 4.21756, "loss": 4.21756, "time": 0.83185} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.06326, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26562, "top5_acc": 0.51156, "loss_cls": 4.23082, "loss": 4.23082, "time": 0.83388} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.06323, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25766, "top5_acc": 0.50672, "loss_cls": 4.29368, "loss": 4.29368, "time": 0.83232} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.0632, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25531, "top5_acc": 0.50359, "loss_cls": 4.27396, "loss": 4.27396, "time": 0.82577} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.06318, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26922, "top5_acc": 0.52109, "loss_cls": 4.21258, "loss": 4.21258, "time": 0.8221} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.06315, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25781, "top5_acc": 0.50391, "loss_cls": 4.25237, "loss": 4.25237, "time": 0.81157} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.06312, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25875, "top5_acc": 0.51297, "loss_cls": 4.22623, "loss": 4.22623, "time": 0.81283} +{"mode": "train", "epoch": 63, "iter": 1300, "lr": 0.0631, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25688, "top5_acc": 0.50609, "loss_cls": 4.26461, "loss": 4.26461, "time": 0.81918} +{"mode": "train", "epoch": 63, "iter": 1400, "lr": 0.06307, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27047, "top5_acc": 0.51547, "loss_cls": 4.24515, "loss": 4.24515, "time": 0.80843} +{"mode": "train", "epoch": 63, "iter": 1500, "lr": 0.06304, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26094, "top5_acc": 0.51016, "loss_cls": 4.2464, "loss": 4.2464, "time": 0.81345} +{"mode": "train", "epoch": 63, "iter": 1600, "lr": 0.06301, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26094, "top5_acc": 0.50359, "loss_cls": 4.26629, "loss": 4.26629, "time": 0.81173} +{"mode": "train", "epoch": 63, "iter": 1700, "lr": 0.06299, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25406, "top5_acc": 0.49906, "loss_cls": 4.27847, "loss": 4.27847, "time": 0.80984} +{"mode": "train", "epoch": 63, "iter": 1800, "lr": 0.06296, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26047, "top5_acc": 0.50219, "loss_cls": 4.2895, "loss": 4.2895, "time": 0.80728} +{"mode": "train", "epoch": 63, "iter": 1900, "lr": 0.06293, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26391, "top5_acc": 0.51953, "loss_cls": 4.19339, "loss": 4.19339, "time": 0.81216} +{"mode": "train", "epoch": 63, "iter": 2000, "lr": 0.06291, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25281, "top5_acc": 0.51484, "loss_cls": 4.22573, "loss": 4.22573, "time": 0.80875} +{"mode": "train", "epoch": 63, "iter": 2100, "lr": 0.06288, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25562, "top5_acc": 0.49656, "loss_cls": 4.28689, "loss": 4.28689, "time": 0.81722} +{"mode": "train", "epoch": 63, "iter": 2200, "lr": 0.06285, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24984, "top5_acc": 0.49156, "loss_cls": 4.32205, "loss": 4.32205, "time": 0.81852} +{"mode": "train", "epoch": 63, "iter": 2300, "lr": 0.06283, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26422, "top5_acc": 0.51641, "loss_cls": 4.22913, "loss": 4.22913, "time": 0.8109} +{"mode": "train", "epoch": 63, "iter": 2400, "lr": 0.0628, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25781, "top5_acc": 0.51859, "loss_cls": 4.22701, "loss": 4.22701, "time": 0.81} +{"mode": "train", "epoch": 63, "iter": 2500, "lr": 0.06277, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24891, "top5_acc": 0.50172, "loss_cls": 4.30236, "loss": 4.30236, "time": 0.81481} +{"mode": "train", "epoch": 63, "iter": 2600, "lr": 0.06274, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26297, "top5_acc": 0.51734, "loss_cls": 4.18984, "loss": 4.18984, "time": 0.81173} +{"mode": "train", "epoch": 63, "iter": 2700, "lr": 0.06272, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25906, "top5_acc": 0.50078, "loss_cls": 4.27713, "loss": 4.27713, "time": 0.81508} +{"mode": "train", "epoch": 63, "iter": 2800, "lr": 0.06269, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26641, "top5_acc": 0.51281, "loss_cls": 4.21339, "loss": 4.21339, "time": 0.8111} +{"mode": "train", "epoch": 63, "iter": 2900, "lr": 0.06266, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26906, "top5_acc": 0.51609, "loss_cls": 4.21632, "loss": 4.21632, "time": 0.80974} +{"mode": "train", "epoch": 63, "iter": 3000, "lr": 0.06264, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26422, "top5_acc": 0.51828, "loss_cls": 4.18523, "loss": 4.18523, "time": 0.8112} +{"mode": "train", "epoch": 63, "iter": 3100, "lr": 0.06261, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26859, "top5_acc": 0.52156, "loss_cls": 4.2019, "loss": 4.2019, "time": 0.81426} +{"mode": "train", "epoch": 63, "iter": 3200, "lr": 0.06258, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25547, "top5_acc": 0.51141, "loss_cls": 4.26617, "loss": 4.26617, "time": 0.80982} +{"mode": "train", "epoch": 63, "iter": 3300, "lr": 0.06256, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26484, "top5_acc": 0.50609, "loss_cls": 4.26884, "loss": 4.26884, "time": 0.8156} +{"mode": "train", "epoch": 63, "iter": 3400, "lr": 0.06253, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26156, "top5_acc": 0.51172, "loss_cls": 4.25082, "loss": 4.25082, "time": 0.81553} +{"mode": "train", "epoch": 63, "iter": 3500, "lr": 0.0625, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25906, "top5_acc": 0.51625, "loss_cls": 4.24488, "loss": 4.24488, "time": 0.81133} +{"mode": "train", "epoch": 63, "iter": 3600, "lr": 0.06247, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26547, "top5_acc": 0.51562, "loss_cls": 4.22219, "loss": 4.22219, "time": 0.81623} +{"mode": "train", "epoch": 63, "iter": 3700, "lr": 0.06245, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26859, "top5_acc": 0.51734, "loss_cls": 4.21711, "loss": 4.21711, "time": 0.81832} +{"mode": "val", "epoch": 63, "iter": 309, "lr": 0.06243, "top1_acc": 0.20078, "top5_acc": 0.42927, "mean_class_accuracy": 0.20063} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.06241, "memory": 15990, "data_time": 1.29199, "top1_acc": 0.26562, "top5_acc": 0.52172, "loss_cls": 4.19374, "loss": 4.19374, "time": 2.28618} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.06238, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27953, "top5_acc": 0.53016, "loss_cls": 4.13492, "loss": 4.13492, "time": 0.8256} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.06235, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26766, "top5_acc": 0.52828, "loss_cls": 4.18134, "loss": 4.18134, "time": 0.8231} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.06233, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26188, "top5_acc": 0.50625, "loss_cls": 4.24419, "loss": 4.24419, "time": 0.8172} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.0623, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25859, "top5_acc": 0.51141, "loss_cls": 4.25404, "loss": 4.25404, "time": 0.81913} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.06227, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26703, "top5_acc": 0.50703, "loss_cls": 4.23514, "loss": 4.23514, "time": 0.82313} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.06225, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26312, "top5_acc": 0.51828, "loss_cls": 4.25446, "loss": 4.25446, "time": 0.82192} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.06222, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25766, "top5_acc": 0.51172, "loss_cls": 4.24446, "loss": 4.24446, "time": 0.82261} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.06219, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24516, "top5_acc": 0.50016, "loss_cls": 4.30429, "loss": 4.30429, "time": 0.82343} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.06216, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26, "top5_acc": 0.50625, "loss_cls": 4.26103, "loss": 4.26103, "time": 0.81927} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.06214, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26422, "top5_acc": 0.50844, "loss_cls": 4.23196, "loss": 4.23196, "time": 0.82191} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.06211, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26797, "top5_acc": 0.52047, "loss_cls": 4.18886, "loss": 4.18886, "time": 0.81942} +{"mode": "train", "epoch": 64, "iter": 1300, "lr": 0.06208, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27031, "top5_acc": 0.51594, "loss_cls": 4.22689, "loss": 4.22689, "time": 0.82007} +{"mode": "train", "epoch": 64, "iter": 1400, "lr": 0.06206, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26766, "top5_acc": 0.51766, "loss_cls": 4.22348, "loss": 4.22348, "time": 0.81962} +{"mode": "train", "epoch": 64, "iter": 1500, "lr": 0.06203, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.2625, "top5_acc": 0.51234, "loss_cls": 4.24738, "loss": 4.24738, "time": 0.81403} +{"mode": "train", "epoch": 64, "iter": 1600, "lr": 0.062, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26453, "top5_acc": 0.51297, "loss_cls": 4.25625, "loss": 4.25625, "time": 0.82084} +{"mode": "train", "epoch": 64, "iter": 1700, "lr": 0.06197, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26625, "top5_acc": 0.52375, "loss_cls": 4.20746, "loss": 4.20746, "time": 0.81345} +{"mode": "train", "epoch": 64, "iter": 1800, "lr": 0.06195, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26266, "top5_acc": 0.52125, "loss_cls": 4.20712, "loss": 4.20712, "time": 0.81006} +{"mode": "train", "epoch": 64, "iter": 1900, "lr": 0.06192, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26672, "top5_acc": 0.51812, "loss_cls": 4.25202, "loss": 4.25202, "time": 0.81259} +{"mode": "train", "epoch": 64, "iter": 2000, "lr": 0.06189, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25844, "top5_acc": 0.51594, "loss_cls": 4.2202, "loss": 4.2202, "time": 0.80861} +{"mode": "train", "epoch": 64, "iter": 2100, "lr": 0.06187, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25578, "top5_acc": 0.52203, "loss_cls": 4.2108, "loss": 4.2108, "time": 0.80939} +{"mode": "train", "epoch": 64, "iter": 2200, "lr": 0.06184, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26641, "top5_acc": 0.51562, "loss_cls": 4.23898, "loss": 4.23898, "time": 0.81013} +{"mode": "train", "epoch": 64, "iter": 2300, "lr": 0.06181, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25797, "top5_acc": 0.50547, "loss_cls": 4.26119, "loss": 4.26119, "time": 0.81688} +{"mode": "train", "epoch": 64, "iter": 2400, "lr": 0.06178, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26687, "top5_acc": 0.51313, "loss_cls": 4.22773, "loss": 4.22773, "time": 0.80776} +{"mode": "train", "epoch": 64, "iter": 2500, "lr": 0.06176, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26047, "top5_acc": 0.51547, "loss_cls": 4.22597, "loss": 4.22597, "time": 0.81256} +{"mode": "train", "epoch": 64, "iter": 2600, "lr": 0.06173, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26797, "top5_acc": 0.51172, "loss_cls": 4.23975, "loss": 4.23975, "time": 0.81832} +{"mode": "train", "epoch": 64, "iter": 2700, "lr": 0.0617, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26281, "top5_acc": 0.50562, "loss_cls": 4.24623, "loss": 4.24623, "time": 0.80939} +{"mode": "train", "epoch": 64, "iter": 2800, "lr": 0.06168, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26703, "top5_acc": 0.51219, "loss_cls": 4.20522, "loss": 4.20522, "time": 0.81396} +{"mode": "train", "epoch": 64, "iter": 2900, "lr": 0.06165, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26188, "top5_acc": 0.51188, "loss_cls": 4.23229, "loss": 4.23229, "time": 0.81665} +{"mode": "train", "epoch": 64, "iter": 3000, "lr": 0.06162, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25938, "top5_acc": 0.51234, "loss_cls": 4.26335, "loss": 4.26335, "time": 0.81308} +{"mode": "train", "epoch": 64, "iter": 3100, "lr": 0.06159, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25703, "top5_acc": 0.50688, "loss_cls": 4.26524, "loss": 4.26524, "time": 0.81684} +{"mode": "train", "epoch": 64, "iter": 3200, "lr": 0.06157, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2525, "top5_acc": 0.51078, "loss_cls": 4.26331, "loss": 4.26331, "time": 0.81598} +{"mode": "train", "epoch": 64, "iter": 3300, "lr": 0.06154, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26719, "top5_acc": 0.51938, "loss_cls": 4.20717, "loss": 4.20717, "time": 0.80718} +{"mode": "train", "epoch": 64, "iter": 3400, "lr": 0.06151, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26344, "top5_acc": 0.5025, "loss_cls": 4.28671, "loss": 4.28671, "time": 0.80884} +{"mode": "train", "epoch": 64, "iter": 3500, "lr": 0.06148, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26687, "top5_acc": 0.51422, "loss_cls": 4.25698, "loss": 4.25698, "time": 0.81029} +{"mode": "train", "epoch": 64, "iter": 3600, "lr": 0.06146, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27141, "top5_acc": 0.52062, "loss_cls": 4.20359, "loss": 4.20359, "time": 0.81628} +{"mode": "train", "epoch": 64, "iter": 3700, "lr": 0.06143, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27156, "top5_acc": 0.51297, "loss_cls": 4.24246, "loss": 4.24246, "time": 0.8169} +{"mode": "val", "epoch": 64, "iter": 309, "lr": 0.06142, "top1_acc": 0.16912, "top5_acc": 0.38135, "mean_class_accuracy": 0.16882} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.06139, "memory": 15990, "data_time": 1.37325, "top1_acc": 0.27453, "top5_acc": 0.51953, "loss_cls": 4.18984, "loss": 4.18984, "time": 2.38658} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.06136, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26844, "top5_acc": 0.52281, "loss_cls": 4.2063, "loss": 4.2063, "time": 0.83866} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.06134, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26141, "top5_acc": 0.51641, "loss_cls": 4.21956, "loss": 4.21956, "time": 0.83368} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.06131, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27312, "top5_acc": 0.52266, "loss_cls": 4.16223, "loss": 4.16223, "time": 0.83066} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.06128, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26453, "top5_acc": 0.52062, "loss_cls": 4.21405, "loss": 4.21405, "time": 0.83085} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.06125, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26094, "top5_acc": 0.51812, "loss_cls": 4.20748, "loss": 4.20748, "time": 0.83576} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.06123, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26406, "top5_acc": 0.51281, "loss_cls": 4.22117, "loss": 4.22117, "time": 0.83012} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0612, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26438, "top5_acc": 0.51781, "loss_cls": 4.23132, "loss": 4.23132, "time": 0.82349} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.06117, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26219, "top5_acc": 0.52078, "loss_cls": 4.18955, "loss": 4.18955, "time": 0.81494} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.06115, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25766, "top5_acc": 0.51656, "loss_cls": 4.23808, "loss": 4.23808, "time": 0.81301} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.06112, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26641, "top5_acc": 0.51313, "loss_cls": 4.23172, "loss": 4.23172, "time": 0.81692} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.06109, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26797, "top5_acc": 0.51281, "loss_cls": 4.21517, "loss": 4.21517, "time": 0.81464} +{"mode": "train", "epoch": 65, "iter": 1300, "lr": 0.06106, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26406, "top5_acc": 0.52344, "loss_cls": 4.20255, "loss": 4.20255, "time": 0.81626} +{"mode": "train", "epoch": 65, "iter": 1400, "lr": 0.06104, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26687, "top5_acc": 0.52672, "loss_cls": 4.20315, "loss": 4.20315, "time": 0.81888} +{"mode": "train", "epoch": 65, "iter": 1500, "lr": 0.06101, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24797, "top5_acc": 0.50469, "loss_cls": 4.28183, "loss": 4.28183, "time": 0.8136} +{"mode": "train", "epoch": 65, "iter": 1600, "lr": 0.06098, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27172, "top5_acc": 0.52234, "loss_cls": 4.19426, "loss": 4.19426, "time": 0.81222} +{"mode": "train", "epoch": 65, "iter": 1700, "lr": 0.06095, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26547, "top5_acc": 0.51375, "loss_cls": 4.22806, "loss": 4.22806, "time": 0.81947} +{"mode": "train", "epoch": 65, "iter": 1800, "lr": 0.06093, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26031, "top5_acc": 0.50969, "loss_cls": 4.25216, "loss": 4.25216, "time": 0.81464} +{"mode": "train", "epoch": 65, "iter": 1900, "lr": 0.0609, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25109, "top5_acc": 0.51594, "loss_cls": 4.24533, "loss": 4.24533, "time": 0.81022} +{"mode": "train", "epoch": 65, "iter": 2000, "lr": 0.06087, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25312, "top5_acc": 0.50625, "loss_cls": 4.28545, "loss": 4.28545, "time": 0.81564} +{"mode": "train", "epoch": 65, "iter": 2100, "lr": 0.06085, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2625, "top5_acc": 0.51703, "loss_cls": 4.21493, "loss": 4.21493, "time": 0.81752} +{"mode": "train", "epoch": 65, "iter": 2200, "lr": 0.06082, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25844, "top5_acc": 0.51047, "loss_cls": 4.22741, "loss": 4.22741, "time": 0.80964} +{"mode": "train", "epoch": 65, "iter": 2300, "lr": 0.06079, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27281, "top5_acc": 0.52078, "loss_cls": 4.18653, "loss": 4.18653, "time": 0.81335} +{"mode": "train", "epoch": 65, "iter": 2400, "lr": 0.06076, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25328, "top5_acc": 0.51078, "loss_cls": 4.25864, "loss": 4.25864, "time": 0.81017} +{"mode": "train", "epoch": 65, "iter": 2500, "lr": 0.06074, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27187, "top5_acc": 0.51531, "loss_cls": 4.21838, "loss": 4.21838, "time": 0.8147} +{"mode": "train", "epoch": 65, "iter": 2600, "lr": 0.06071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25906, "top5_acc": 0.51391, "loss_cls": 4.22023, "loss": 4.22023, "time": 0.81165} +{"mode": "train", "epoch": 65, "iter": 2700, "lr": 0.06068, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26266, "top5_acc": 0.51578, "loss_cls": 4.238, "loss": 4.238, "time": 0.82142} +{"mode": "train", "epoch": 65, "iter": 2800, "lr": 0.06065, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27312, "top5_acc": 0.51516, "loss_cls": 4.20137, "loss": 4.20137, "time": 0.81139} +{"mode": "train", "epoch": 65, "iter": 2900, "lr": 0.06063, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25594, "top5_acc": 0.51031, "loss_cls": 4.26551, "loss": 4.26551, "time": 0.80946} +{"mode": "train", "epoch": 65, "iter": 3000, "lr": 0.0606, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26281, "top5_acc": 0.515, "loss_cls": 4.22081, "loss": 4.22081, "time": 0.81175} +{"mode": "train", "epoch": 65, "iter": 3100, "lr": 0.06057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26687, "top5_acc": 0.51938, "loss_cls": 4.22698, "loss": 4.22698, "time": 0.82062} +{"mode": "train", "epoch": 65, "iter": 3200, "lr": 0.06055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25797, "top5_acc": 0.51266, "loss_cls": 4.24132, "loss": 4.24132, "time": 0.81236} +{"mode": "train", "epoch": 65, "iter": 3300, "lr": 0.06052, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25922, "top5_acc": 0.50656, "loss_cls": 4.25785, "loss": 4.25785, "time": 0.81099} +{"mode": "train", "epoch": 65, "iter": 3400, "lr": 0.06049, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26531, "top5_acc": 0.51359, "loss_cls": 4.27154, "loss": 4.27154, "time": 0.81178} +{"mode": "train", "epoch": 65, "iter": 3500, "lr": 0.06046, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28094, "top5_acc": 0.52797, "loss_cls": 4.17017, "loss": 4.17017, "time": 0.81115} +{"mode": "train", "epoch": 65, "iter": 3600, "lr": 0.06044, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26562, "top5_acc": 0.51297, "loss_cls": 4.27277, "loss": 4.27277, "time": 0.81522} +{"mode": "train", "epoch": 65, "iter": 3700, "lr": 0.06041, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26797, "top5_acc": 0.50344, "loss_cls": 4.22353, "loss": 4.22353, "time": 0.81229} +{"mode": "val", "epoch": 65, "iter": 309, "lr": 0.0604, "top1_acc": 0.18812, "top5_acc": 0.41326, "mean_class_accuracy": 0.18777} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.06037, "memory": 15990, "data_time": 1.27268, "top1_acc": 0.26562, "top5_acc": 0.51438, "loss_cls": 4.22187, "loss": 4.22187, "time": 2.23198} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.06034, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26734, "top5_acc": 0.5275, "loss_cls": 4.17856, "loss": 4.17856, "time": 0.8211} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.06031, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26312, "top5_acc": 0.52047, "loss_cls": 4.18393, "loss": 4.18393, "time": 0.8127} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.06029, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26438, "top5_acc": 0.51641, "loss_cls": 4.21576, "loss": 4.21576, "time": 0.81008} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.06026, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25828, "top5_acc": 0.50391, "loss_cls": 4.25725, "loss": 4.25725, "time": 0.81094} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.06023, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27234, "top5_acc": 0.52484, "loss_cls": 4.18635, "loss": 4.18635, "time": 0.8113} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.0602, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26578, "top5_acc": 0.52359, "loss_cls": 4.22339, "loss": 4.22339, "time": 0.80826} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.06018, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26469, "top5_acc": 0.52828, "loss_cls": 4.20385, "loss": 4.20385, "time": 0.81162} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.06015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.265, "top5_acc": 0.52062, "loss_cls": 4.21997, "loss": 4.21997, "time": 0.81138} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.06012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27187, "top5_acc": 0.52422, "loss_cls": 4.17159, "loss": 4.17159, "time": 0.81154} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.06009, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26547, "top5_acc": 0.51766, "loss_cls": 4.20042, "loss": 4.20042, "time": 0.81569} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.06007, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25922, "top5_acc": 0.50797, "loss_cls": 4.26378, "loss": 4.26378, "time": 0.81667} +{"mode": "train", "epoch": 66, "iter": 1300, "lr": 0.06004, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27734, "top5_acc": 0.52453, "loss_cls": 4.18097, "loss": 4.18097, "time": 0.81425} +{"mode": "train", "epoch": 66, "iter": 1400, "lr": 0.06001, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26812, "top5_acc": 0.51953, "loss_cls": 4.20352, "loss": 4.20352, "time": 0.81738} +{"mode": "train", "epoch": 66, "iter": 1500, "lr": 0.05999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26266, "top5_acc": 0.51234, "loss_cls": 4.22118, "loss": 4.22118, "time": 0.81892} +{"mode": "train", "epoch": 66, "iter": 1600, "lr": 0.05996, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27062, "top5_acc": 0.5175, "loss_cls": 4.21508, "loss": 4.21508, "time": 0.81234} +{"mode": "train", "epoch": 66, "iter": 1700, "lr": 0.05993, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26312, "top5_acc": 0.51781, "loss_cls": 4.22065, "loss": 4.22065, "time": 0.80531} +{"mode": "train", "epoch": 66, "iter": 1800, "lr": 0.0599, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26297, "top5_acc": 0.50891, "loss_cls": 4.22269, "loss": 4.22269, "time": 0.81619} +{"mode": "train", "epoch": 66, "iter": 1900, "lr": 0.05988, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26141, "top5_acc": 0.52297, "loss_cls": 4.21095, "loss": 4.21095, "time": 0.82135} +{"mode": "train", "epoch": 66, "iter": 2000, "lr": 0.05985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25953, "top5_acc": 0.51031, "loss_cls": 4.25572, "loss": 4.25572, "time": 0.81314} +{"mode": "train", "epoch": 66, "iter": 2100, "lr": 0.05982, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26594, "top5_acc": 0.52266, "loss_cls": 4.19912, "loss": 4.19912, "time": 0.81119} +{"mode": "train", "epoch": 66, "iter": 2200, "lr": 0.05979, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27703, "top5_acc": 0.52375, "loss_cls": 4.17847, "loss": 4.17847, "time": 0.80984} +{"mode": "train", "epoch": 66, "iter": 2300, "lr": 0.05977, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25641, "top5_acc": 0.50438, "loss_cls": 4.26508, "loss": 4.26508, "time": 0.81145} +{"mode": "train", "epoch": 66, "iter": 2400, "lr": 0.05974, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26234, "top5_acc": 0.51422, "loss_cls": 4.23803, "loss": 4.23803, "time": 0.81067} +{"mode": "train", "epoch": 66, "iter": 2500, "lr": 0.05971, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26687, "top5_acc": 0.51203, "loss_cls": 4.21459, "loss": 4.21459, "time": 0.81093} +{"mode": "train", "epoch": 66, "iter": 2600, "lr": 0.05968, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26641, "top5_acc": 0.51797, "loss_cls": 4.19053, "loss": 4.19053, "time": 0.81862} +{"mode": "train", "epoch": 66, "iter": 2700, "lr": 0.05966, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27266, "top5_acc": 0.52734, "loss_cls": 4.17928, "loss": 4.17928, "time": 0.81314} +{"mode": "train", "epoch": 66, "iter": 2800, "lr": 0.05963, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26219, "top5_acc": 0.51188, "loss_cls": 4.21599, "loss": 4.21599, "time": 0.81832} +{"mode": "train", "epoch": 66, "iter": 2900, "lr": 0.0596, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26969, "top5_acc": 0.52297, "loss_cls": 4.20767, "loss": 4.20767, "time": 0.81114} +{"mode": "train", "epoch": 66, "iter": 3000, "lr": 0.05957, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26438, "top5_acc": 0.52156, "loss_cls": 4.22312, "loss": 4.22312, "time": 0.80912} +{"mode": "train", "epoch": 66, "iter": 3100, "lr": 0.05955, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26219, "top5_acc": 0.52406, "loss_cls": 4.2098, "loss": 4.2098, "time": 0.81863} +{"mode": "train", "epoch": 66, "iter": 3200, "lr": 0.05952, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27719, "top5_acc": 0.52891, "loss_cls": 4.16804, "loss": 4.16804, "time": 0.8106} +{"mode": "train", "epoch": 66, "iter": 3300, "lr": 0.05949, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26562, "top5_acc": 0.51281, "loss_cls": 4.24029, "loss": 4.24029, "time": 0.80647} +{"mode": "train", "epoch": 66, "iter": 3400, "lr": 0.05946, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26703, "top5_acc": 0.51516, "loss_cls": 4.2091, "loss": 4.2091, "time": 0.81151} +{"mode": "train", "epoch": 66, "iter": 3500, "lr": 0.05944, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27187, "top5_acc": 0.525, "loss_cls": 4.16952, "loss": 4.16952, "time": 0.81428} +{"mode": "train", "epoch": 66, "iter": 3600, "lr": 0.05941, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25703, "top5_acc": 0.51219, "loss_cls": 4.25289, "loss": 4.25289, "time": 0.81774} +{"mode": "train", "epoch": 66, "iter": 3700, "lr": 0.05938, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26687, "top5_acc": 0.50781, "loss_cls": 4.23389, "loss": 4.23389, "time": 0.81635} +{"mode": "val", "epoch": 66, "iter": 309, "lr": 0.05937, "top1_acc": 0.21116, "top5_acc": 0.4435, "mean_class_accuracy": 0.21083} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.05934, "memory": 15990, "data_time": 1.28616, "top1_acc": 0.27297, "top5_acc": 0.52703, "loss_cls": 4.14868, "loss": 4.14868, "time": 2.25232} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.05931, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27906, "top5_acc": 0.52906, "loss_cls": 4.13676, "loss": 4.13676, "time": 0.81558} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.05929, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26937, "top5_acc": 0.52641, "loss_cls": 4.20116, "loss": 4.20116, "time": 0.81721} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.05926, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27141, "top5_acc": 0.52953, "loss_cls": 4.15268, "loss": 4.15268, "time": 0.81008} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.05923, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27234, "top5_acc": 0.51734, "loss_cls": 4.17993, "loss": 4.17993, "time": 0.81207} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.0592, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27641, "top5_acc": 0.52297, "loss_cls": 4.19156, "loss": 4.19156, "time": 0.81326} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.05918, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27016, "top5_acc": 0.53609, "loss_cls": 4.15103, "loss": 4.15103, "time": 0.81662} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.05915, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27516, "top5_acc": 0.53109, "loss_cls": 4.13101, "loss": 4.13101, "time": 0.80982} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.05912, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26188, "top5_acc": 0.51828, "loss_cls": 4.22391, "loss": 4.22391, "time": 0.81397} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.05909, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26297, "top5_acc": 0.51375, "loss_cls": 4.21883, "loss": 4.21883, "time": 0.82062} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.05907, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27906, "top5_acc": 0.53281, "loss_cls": 4.15608, "loss": 4.15608, "time": 0.81533} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.05904, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26172, "top5_acc": 0.50688, "loss_cls": 4.25596, "loss": 4.25596, "time": 0.80856} +{"mode": "train", "epoch": 67, "iter": 1300, "lr": 0.05901, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27, "top5_acc": 0.51469, "loss_cls": 4.22108, "loss": 4.22108, "time": 0.81396} +{"mode": "train", "epoch": 67, "iter": 1400, "lr": 0.05898, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26812, "top5_acc": 0.51719, "loss_cls": 4.20328, "loss": 4.20328, "time": 0.81666} +{"mode": "train", "epoch": 67, "iter": 1500, "lr": 0.05896, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27062, "top5_acc": 0.51594, "loss_cls": 4.22535, "loss": 4.22535, "time": 0.80975} +{"mode": "train", "epoch": 67, "iter": 1600, "lr": 0.05893, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26359, "top5_acc": 0.50797, "loss_cls": 4.23336, "loss": 4.23336, "time": 0.81755} +{"mode": "train", "epoch": 67, "iter": 1700, "lr": 0.0589, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26812, "top5_acc": 0.51797, "loss_cls": 4.20037, "loss": 4.20037, "time": 0.81389} +{"mode": "train", "epoch": 67, "iter": 1800, "lr": 0.05887, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27078, "top5_acc": 0.51156, "loss_cls": 4.21378, "loss": 4.21378, "time": 0.81376} +{"mode": "train", "epoch": 67, "iter": 1900, "lr": 0.05885, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27422, "top5_acc": 0.53266, "loss_cls": 4.15864, "loss": 4.15864, "time": 0.81351} +{"mode": "train", "epoch": 67, "iter": 2000, "lr": 0.05882, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27391, "top5_acc": 0.52125, "loss_cls": 4.18749, "loss": 4.18749, "time": 0.80962} +{"mode": "train", "epoch": 67, "iter": 2100, "lr": 0.05879, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26266, "top5_acc": 0.53016, "loss_cls": 4.16534, "loss": 4.16534, "time": 0.81127} +{"mode": "train", "epoch": 67, "iter": 2200, "lr": 0.05876, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26906, "top5_acc": 0.52562, "loss_cls": 4.19212, "loss": 4.19212, "time": 0.8078} +{"mode": "train", "epoch": 67, "iter": 2300, "lr": 0.05874, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26469, "top5_acc": 0.51828, "loss_cls": 4.22091, "loss": 4.22091, "time": 0.8085} +{"mode": "train", "epoch": 67, "iter": 2400, "lr": 0.05871, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25734, "top5_acc": 0.50656, "loss_cls": 4.27122, "loss": 4.27122, "time": 0.81267} +{"mode": "train", "epoch": 67, "iter": 2500, "lr": 0.05868, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26359, "top5_acc": 0.51922, "loss_cls": 4.25712, "loss": 4.25712, "time": 0.81059} +{"mode": "train", "epoch": 67, "iter": 2600, "lr": 0.05865, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27094, "top5_acc": 0.52656, "loss_cls": 4.17173, "loss": 4.17173, "time": 0.81747} +{"mode": "train", "epoch": 67, "iter": 2700, "lr": 0.05863, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26375, "top5_acc": 0.51641, "loss_cls": 4.21376, "loss": 4.21376, "time": 0.80983} +{"mode": "train", "epoch": 67, "iter": 2800, "lr": 0.0586, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26969, "top5_acc": 0.52438, "loss_cls": 4.17669, "loss": 4.17669, "time": 0.81786} +{"mode": "train", "epoch": 67, "iter": 2900, "lr": 0.05857, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25531, "top5_acc": 0.51094, "loss_cls": 4.25887, "loss": 4.25887, "time": 0.81212} +{"mode": "train", "epoch": 67, "iter": 3000, "lr": 0.05854, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25625, "top5_acc": 0.51484, "loss_cls": 4.25555, "loss": 4.25555, "time": 0.81158} +{"mode": "train", "epoch": 67, "iter": 3100, "lr": 0.05852, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26, "top5_acc": 0.50859, "loss_cls": 4.28214, "loss": 4.28214, "time": 0.80806} +{"mode": "train", "epoch": 67, "iter": 3200, "lr": 0.05849, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26672, "top5_acc": 0.5275, "loss_cls": 4.20566, "loss": 4.20566, "time": 0.80898} +{"mode": "train", "epoch": 67, "iter": 3300, "lr": 0.05846, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26531, "top5_acc": 0.52859, "loss_cls": 4.17942, "loss": 4.17942, "time": 0.81442} +{"mode": "train", "epoch": 67, "iter": 3400, "lr": 0.05843, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26906, "top5_acc": 0.5225, "loss_cls": 4.18693, "loss": 4.18693, "time": 0.81201} +{"mode": "train", "epoch": 67, "iter": 3500, "lr": 0.05841, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27141, "top5_acc": 0.52547, "loss_cls": 4.18105, "loss": 4.18105, "time": 0.80785} +{"mode": "train", "epoch": 67, "iter": 3600, "lr": 0.05838, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26578, "top5_acc": 0.51688, "loss_cls": 4.21704, "loss": 4.21704, "time": 0.81211} +{"mode": "train", "epoch": 67, "iter": 3700, "lr": 0.05835, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26875, "top5_acc": 0.52688, "loss_cls": 4.19024, "loss": 4.19024, "time": 0.81248} +{"mode": "val", "epoch": 67, "iter": 309, "lr": 0.05834, "top1_acc": 0.1827, "top5_acc": 0.41179, "mean_class_accuracy": 0.18286} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.05831, "memory": 15990, "data_time": 1.26673, "top1_acc": 0.27266, "top5_acc": 0.53344, "loss_cls": 4.12557, "loss": 4.12557, "time": 2.23321} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.05828, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26875, "top5_acc": 0.52609, "loss_cls": 4.15578, "loss": 4.15578, "time": 0.81056} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.05826, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26844, "top5_acc": 0.52188, "loss_cls": 4.16941, "loss": 4.16941, "time": 0.80954} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.05823, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26188, "top5_acc": 0.52125, "loss_cls": 4.19229, "loss": 4.19229, "time": 0.80779} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.0582, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26297, "top5_acc": 0.51766, "loss_cls": 4.19665, "loss": 4.19665, "time": 0.80969} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.05817, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27422, "top5_acc": 0.52641, "loss_cls": 4.17376, "loss": 4.17376, "time": 0.8136} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.05815, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28375, "top5_acc": 0.52812, "loss_cls": 4.13351, "loss": 4.13351, "time": 0.81228} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.05812, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27609, "top5_acc": 0.52938, "loss_cls": 4.13371, "loss": 4.13371, "time": 0.80881} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.05809, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27141, "top5_acc": 0.51844, "loss_cls": 4.2083, "loss": 4.2083, "time": 0.81168} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.05806, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27609, "top5_acc": 0.52891, "loss_cls": 4.12625, "loss": 4.12625, "time": 0.8098} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.05804, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26953, "top5_acc": 0.50891, "loss_cls": 4.21787, "loss": 4.21787, "time": 0.81209} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.05801, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27219, "top5_acc": 0.52469, "loss_cls": 4.18643, "loss": 4.18643, "time": 0.80818} +{"mode": "train", "epoch": 68, "iter": 1300, "lr": 0.05798, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27078, "top5_acc": 0.52672, "loss_cls": 4.16285, "loss": 4.16285, "time": 0.82238} +{"mode": "train", "epoch": 68, "iter": 1400, "lr": 0.05795, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26859, "top5_acc": 0.52203, "loss_cls": 4.20104, "loss": 4.20104, "time": 0.81082} +{"mode": "train", "epoch": 68, "iter": 1500, "lr": 0.05792, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26422, "top5_acc": 0.52172, "loss_cls": 4.18375, "loss": 4.18375, "time": 0.81697} +{"mode": "train", "epoch": 68, "iter": 1600, "lr": 0.0579, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26766, "top5_acc": 0.51188, "loss_cls": 4.24209, "loss": 4.24209, "time": 0.81523} +{"mode": "train", "epoch": 68, "iter": 1700, "lr": 0.05787, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26438, "top5_acc": 0.52203, "loss_cls": 4.20081, "loss": 4.20081, "time": 0.81401} +{"mode": "train", "epoch": 68, "iter": 1800, "lr": 0.05784, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26438, "top5_acc": 0.52172, "loss_cls": 4.22064, "loss": 4.22064, "time": 0.81101} +{"mode": "train", "epoch": 68, "iter": 1900, "lr": 0.05781, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27, "top5_acc": 0.51703, "loss_cls": 4.22004, "loss": 4.22004, "time": 0.81098} +{"mode": "train", "epoch": 68, "iter": 2000, "lr": 0.05779, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26406, "top5_acc": 0.515, "loss_cls": 4.21029, "loss": 4.21029, "time": 0.81254} +{"mode": "train", "epoch": 68, "iter": 2100, "lr": 0.05776, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26687, "top5_acc": 0.50594, "loss_cls": 4.25521, "loss": 4.25521, "time": 0.8096} +{"mode": "train", "epoch": 68, "iter": 2200, "lr": 0.05773, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26438, "top5_acc": 0.51484, "loss_cls": 4.20135, "loss": 4.20135, "time": 0.81156} +{"mode": "train", "epoch": 68, "iter": 2300, "lr": 0.0577, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27203, "top5_acc": 0.52031, "loss_cls": 4.22566, "loss": 4.22566, "time": 0.81189} +{"mode": "train", "epoch": 68, "iter": 2400, "lr": 0.05768, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.275, "top5_acc": 0.52188, "loss_cls": 4.17784, "loss": 4.17784, "time": 0.80849} +{"mode": "train", "epoch": 68, "iter": 2500, "lr": 0.05765, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26562, "top5_acc": 0.52094, "loss_cls": 4.19762, "loss": 4.19762, "time": 0.80949} +{"mode": "train", "epoch": 68, "iter": 2600, "lr": 0.05762, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27047, "top5_acc": 0.52688, "loss_cls": 4.17663, "loss": 4.17663, "time": 0.81181} +{"mode": "train", "epoch": 68, "iter": 2700, "lr": 0.05759, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26891, "top5_acc": 0.52406, "loss_cls": 4.21375, "loss": 4.21375, "time": 0.81811} +{"mode": "train", "epoch": 68, "iter": 2800, "lr": 0.05757, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26953, "top5_acc": 0.52344, "loss_cls": 4.18948, "loss": 4.18948, "time": 0.81822} +{"mode": "train", "epoch": 68, "iter": 2900, "lr": 0.05754, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26516, "top5_acc": 0.52203, "loss_cls": 4.20622, "loss": 4.20622, "time": 0.81506} +{"mode": "train", "epoch": 68, "iter": 3000, "lr": 0.05751, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25953, "top5_acc": 0.51812, "loss_cls": 4.22839, "loss": 4.22839, "time": 0.81058} +{"mode": "train", "epoch": 68, "iter": 3100, "lr": 0.05748, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26047, "top5_acc": 0.51219, "loss_cls": 4.25872, "loss": 4.25872, "time": 0.80669} +{"mode": "train", "epoch": 68, "iter": 3200, "lr": 0.05746, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27219, "top5_acc": 0.51875, "loss_cls": 4.18714, "loss": 4.18714, "time": 0.80851} +{"mode": "train", "epoch": 68, "iter": 3300, "lr": 0.05743, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27812, "top5_acc": 0.53344, "loss_cls": 4.13629, "loss": 4.13629, "time": 0.80842} +{"mode": "train", "epoch": 68, "iter": 3400, "lr": 0.0574, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25969, "top5_acc": 0.51094, "loss_cls": 4.24255, "loss": 4.24255, "time": 0.81068} +{"mode": "train", "epoch": 68, "iter": 3500, "lr": 0.05737, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27094, "top5_acc": 0.52406, "loss_cls": 4.2049, "loss": 4.2049, "time": 0.80904} +{"mode": "train", "epoch": 68, "iter": 3600, "lr": 0.05734, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27453, "top5_acc": 0.51922, "loss_cls": 4.17941, "loss": 4.17941, "time": 0.81549} +{"mode": "train", "epoch": 68, "iter": 3700, "lr": 0.05732, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27062, "top5_acc": 0.525, "loss_cls": 4.18986, "loss": 4.18986, "time": 0.8176} +{"mode": "val", "epoch": 68, "iter": 309, "lr": 0.0573, "top1_acc": 0.21506, "top5_acc": 0.4472, "mean_class_accuracy": 0.21493} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.05728, "memory": 15990, "data_time": 1.35066, "top1_acc": 0.27672, "top5_acc": 0.53562, "loss_cls": 4.14678, "loss": 4.14678, "time": 2.36382} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.05725, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27016, "top5_acc": 0.52812, "loss_cls": 4.17004, "loss": 4.17004, "time": 0.82846} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.05722, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27, "top5_acc": 0.5175, "loss_cls": 4.22679, "loss": 4.22679, "time": 0.83476} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.05719, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26125, "top5_acc": 0.52188, "loss_cls": 4.18793, "loss": 4.18793, "time": 0.83587} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.05717, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27047, "top5_acc": 0.52938, "loss_cls": 4.17458, "loss": 4.17458, "time": 0.83295} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.05714, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28031, "top5_acc": 0.52766, "loss_cls": 4.14972, "loss": 4.14972, "time": 0.82756} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.05711, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27187, "top5_acc": 0.52812, "loss_cls": 4.19177, "loss": 4.19177, "time": 0.82741} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.05708, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27906, "top5_acc": 0.5325, "loss_cls": 4.14009, "loss": 4.14009, "time": 0.82683} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.05706, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28156, "top5_acc": 0.52422, "loss_cls": 4.14246, "loss": 4.14246, "time": 0.82581} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.05703, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27312, "top5_acc": 0.52188, "loss_cls": 4.18656, "loss": 4.18656, "time": 0.82329} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.057, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25906, "top5_acc": 0.50641, "loss_cls": 4.23798, "loss": 4.23798, "time": 0.8267} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.05697, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25781, "top5_acc": 0.51094, "loss_cls": 4.25558, "loss": 4.25558, "time": 0.81486} +{"mode": "train", "epoch": 69, "iter": 1300, "lr": 0.05694, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26844, "top5_acc": 0.52016, "loss_cls": 4.19079, "loss": 4.19079, "time": 0.82035} +{"mode": "train", "epoch": 69, "iter": 1400, "lr": 0.05692, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28484, "top5_acc": 0.53031, "loss_cls": 4.10939, "loss": 4.10939, "time": 0.8136} +{"mode": "train", "epoch": 69, "iter": 1500, "lr": 0.05689, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26906, "top5_acc": 0.51547, "loss_cls": 4.22103, "loss": 4.22103, "time": 0.81806} +{"mode": "train", "epoch": 69, "iter": 1600, "lr": 0.05686, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26687, "top5_acc": 0.51703, "loss_cls": 4.19734, "loss": 4.19734, "time": 0.81985} +{"mode": "train", "epoch": 69, "iter": 1700, "lr": 0.05683, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27875, "top5_acc": 0.52156, "loss_cls": 4.15013, "loss": 4.15013, "time": 0.81377} +{"mode": "train", "epoch": 69, "iter": 1800, "lr": 0.05681, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27219, "top5_acc": 0.52094, "loss_cls": 4.18158, "loss": 4.18158, "time": 0.81534} +{"mode": "train", "epoch": 69, "iter": 1900, "lr": 0.05678, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27437, "top5_acc": 0.52938, "loss_cls": 4.13059, "loss": 4.13059, "time": 0.81743} +{"mode": "train", "epoch": 69, "iter": 2000, "lr": 0.05675, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27328, "top5_acc": 0.51516, "loss_cls": 4.20961, "loss": 4.20961, "time": 0.81269} +{"mode": "train", "epoch": 69, "iter": 2100, "lr": 0.05672, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.275, "top5_acc": 0.51859, "loss_cls": 4.22133, "loss": 4.22133, "time": 0.81092} +{"mode": "train", "epoch": 69, "iter": 2200, "lr": 0.0567, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26281, "top5_acc": 0.51547, "loss_cls": 4.22609, "loss": 4.22609, "time": 0.80829} +{"mode": "train", "epoch": 69, "iter": 2300, "lr": 0.05667, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27047, "top5_acc": 0.52047, "loss_cls": 4.1697, "loss": 4.1697, "time": 0.81314} +{"mode": "train", "epoch": 69, "iter": 2400, "lr": 0.05664, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26453, "top5_acc": 0.51906, "loss_cls": 4.2059, "loss": 4.2059, "time": 0.81107} +{"mode": "train", "epoch": 69, "iter": 2500, "lr": 0.05661, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.265, "top5_acc": 0.5225, "loss_cls": 4.19954, "loss": 4.19954, "time": 0.81204} +{"mode": "train", "epoch": 69, "iter": 2600, "lr": 0.05658, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27906, "top5_acc": 0.53938, "loss_cls": 4.12757, "loss": 4.12757, "time": 0.81259} +{"mode": "train", "epoch": 69, "iter": 2700, "lr": 0.05656, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27391, "top5_acc": 0.53125, "loss_cls": 4.16042, "loss": 4.16042, "time": 0.81535} +{"mode": "train", "epoch": 69, "iter": 2800, "lr": 0.05653, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26391, "top5_acc": 0.51781, "loss_cls": 4.21996, "loss": 4.21996, "time": 0.81742} +{"mode": "train", "epoch": 69, "iter": 2900, "lr": 0.0565, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27562, "top5_acc": 0.52281, "loss_cls": 4.20415, "loss": 4.20415, "time": 0.81556} +{"mode": "train", "epoch": 69, "iter": 3000, "lr": 0.05647, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26281, "top5_acc": 0.51953, "loss_cls": 4.23352, "loss": 4.23352, "time": 0.81385} +{"mode": "train", "epoch": 69, "iter": 3100, "lr": 0.05645, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28078, "top5_acc": 0.52031, "loss_cls": 4.16413, "loss": 4.16413, "time": 0.80933} +{"mode": "train", "epoch": 69, "iter": 3200, "lr": 0.05642, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26234, "top5_acc": 0.51766, "loss_cls": 4.19136, "loss": 4.19136, "time": 0.80876} +{"mode": "train", "epoch": 69, "iter": 3300, "lr": 0.05639, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26844, "top5_acc": 0.51609, "loss_cls": 4.20747, "loss": 4.20747, "time": 0.80491} +{"mode": "train", "epoch": 69, "iter": 3400, "lr": 0.05636, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27359, "top5_acc": 0.52328, "loss_cls": 4.18844, "loss": 4.18844, "time": 0.81466} +{"mode": "train", "epoch": 69, "iter": 3500, "lr": 0.05634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26219, "top5_acc": 0.51453, "loss_cls": 4.21296, "loss": 4.21296, "time": 0.81349} +{"mode": "train", "epoch": 69, "iter": 3600, "lr": 0.05631, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27391, "top5_acc": 0.53078, "loss_cls": 4.15348, "loss": 4.15348, "time": 0.81618} +{"mode": "train", "epoch": 69, "iter": 3700, "lr": 0.05628, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2575, "top5_acc": 0.51625, "loss_cls": 4.21576, "loss": 4.21576, "time": 0.81338} +{"mode": "val", "epoch": 69, "iter": 309, "lr": 0.05627, "top1_acc": 0.21273, "top5_acc": 0.43848, "mean_class_accuracy": 0.21277} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.05624, "memory": 15990, "data_time": 1.32603, "top1_acc": 0.28422, "top5_acc": 0.53328, "loss_cls": 4.13117, "loss": 4.13117, "time": 2.32489} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.05621, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27516, "top5_acc": 0.52672, "loss_cls": 4.15591, "loss": 4.15591, "time": 0.83157} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.05618, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27094, "top5_acc": 0.51828, "loss_cls": 4.19667, "loss": 4.19667, "time": 0.84225} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.05616, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27187, "top5_acc": 0.52438, "loss_cls": 4.16101, "loss": 4.16101, "time": 0.83747} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.05613, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27266, "top5_acc": 0.52812, "loss_cls": 4.18607, "loss": 4.18607, "time": 0.83439} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.0561, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26953, "top5_acc": 0.525, "loss_cls": 4.17515, "loss": 4.17515, "time": 0.83458} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.05607, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28016, "top5_acc": 0.52766, "loss_cls": 4.15201, "loss": 4.15201, "time": 0.83699} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.05605, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27734, "top5_acc": 0.53594, "loss_cls": 4.13476, "loss": 4.13476, "time": 0.8314} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.05602, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27969, "top5_acc": 0.54328, "loss_cls": 4.1302, "loss": 4.1302, "time": 0.83797} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.05599, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27719, "top5_acc": 0.52484, "loss_cls": 4.17385, "loss": 4.17385, "time": 0.83746} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.05596, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26266, "top5_acc": 0.51531, "loss_cls": 4.20979, "loss": 4.20979, "time": 0.83508} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.05593, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27734, "top5_acc": 0.52547, "loss_cls": 4.16077, "loss": 4.16077, "time": 0.83475} +{"mode": "train", "epoch": 70, "iter": 1300, "lr": 0.05591, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27641, "top5_acc": 0.51844, "loss_cls": 4.16461, "loss": 4.16461, "time": 0.83566} +{"mode": "train", "epoch": 70, "iter": 1400, "lr": 0.05588, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27688, "top5_acc": 0.52297, "loss_cls": 4.15361, "loss": 4.15361, "time": 0.83308} +{"mode": "train", "epoch": 70, "iter": 1500, "lr": 0.05585, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26578, "top5_acc": 0.51516, "loss_cls": 4.19164, "loss": 4.19164, "time": 0.83797} +{"mode": "train", "epoch": 70, "iter": 1600, "lr": 0.05582, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27828, "top5_acc": 0.53297, "loss_cls": 4.15831, "loss": 4.15831, "time": 0.82279} +{"mode": "train", "epoch": 70, "iter": 1700, "lr": 0.0558, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27375, "top5_acc": 0.52516, "loss_cls": 4.18463, "loss": 4.18463, "time": 0.82585} +{"mode": "train", "epoch": 70, "iter": 1800, "lr": 0.05577, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26719, "top5_acc": 0.51688, "loss_cls": 4.22358, "loss": 4.22358, "time": 0.83457} +{"mode": "train", "epoch": 70, "iter": 1900, "lr": 0.05574, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27016, "top5_acc": 0.52578, "loss_cls": 4.15674, "loss": 4.15674, "time": 0.83622} +{"mode": "train", "epoch": 70, "iter": 2000, "lr": 0.05571, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26828, "top5_acc": 0.52266, "loss_cls": 4.19484, "loss": 4.19484, "time": 0.82568} +{"mode": "train", "epoch": 70, "iter": 2100, "lr": 0.05568, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26609, "top5_acc": 0.52391, "loss_cls": 4.17251, "loss": 4.17251, "time": 0.83187} +{"mode": "train", "epoch": 70, "iter": 2200, "lr": 0.05566, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27297, "top5_acc": 0.52906, "loss_cls": 4.1349, "loss": 4.1349, "time": 0.82623} +{"mode": "train", "epoch": 70, "iter": 2300, "lr": 0.05563, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28391, "top5_acc": 0.53953, "loss_cls": 4.12869, "loss": 4.12869, "time": 0.8247} +{"mode": "train", "epoch": 70, "iter": 2400, "lr": 0.0556, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27484, "top5_acc": 0.53344, "loss_cls": 4.15573, "loss": 4.15573, "time": 0.82359} +{"mode": "train", "epoch": 70, "iter": 2500, "lr": 0.05557, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26281, "top5_acc": 0.51953, "loss_cls": 4.17947, "loss": 4.17947, "time": 0.82008} +{"mode": "train", "epoch": 70, "iter": 2600, "lr": 0.05555, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26984, "top5_acc": 0.52031, "loss_cls": 4.20065, "loss": 4.20065, "time": 0.82364} +{"mode": "train", "epoch": 70, "iter": 2700, "lr": 0.05552, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27781, "top5_acc": 0.53609, "loss_cls": 4.12992, "loss": 4.12992, "time": 0.82995} +{"mode": "train", "epoch": 70, "iter": 2800, "lr": 0.05549, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26656, "top5_acc": 0.52266, "loss_cls": 4.19731, "loss": 4.19731, "time": 0.82248} +{"mode": "train", "epoch": 70, "iter": 2900, "lr": 0.05546, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27969, "top5_acc": 0.525, "loss_cls": 4.19772, "loss": 4.19772, "time": 0.82969} +{"mode": "train", "epoch": 70, "iter": 3000, "lr": 0.05543, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26609, "top5_acc": 0.52141, "loss_cls": 4.18675, "loss": 4.18675, "time": 0.83015} +{"mode": "train", "epoch": 70, "iter": 3100, "lr": 0.05541, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.275, "top5_acc": 0.52484, "loss_cls": 4.15999, "loss": 4.15999, "time": 0.81993} +{"mode": "train", "epoch": 70, "iter": 3200, "lr": 0.05538, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27359, "top5_acc": 0.53078, "loss_cls": 4.17281, "loss": 4.17281, "time": 0.81762} +{"mode": "train", "epoch": 70, "iter": 3300, "lr": 0.05535, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26703, "top5_acc": 0.51953, "loss_cls": 4.19761, "loss": 4.19761, "time": 0.81977} +{"mode": "train", "epoch": 70, "iter": 3400, "lr": 0.05532, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27016, "top5_acc": 0.52266, "loss_cls": 4.17463, "loss": 4.17463, "time": 0.82038} +{"mode": "train", "epoch": 70, "iter": 3500, "lr": 0.0553, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27391, "top5_acc": 0.5275, "loss_cls": 4.17, "loss": 4.17, "time": 0.82734} +{"mode": "train", "epoch": 70, "iter": 3600, "lr": 0.05527, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27266, "top5_acc": 0.52391, "loss_cls": 4.19959, "loss": 4.19959, "time": 0.83096} +{"mode": "train", "epoch": 70, "iter": 3700, "lr": 0.05524, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28891, "top5_acc": 0.53359, "loss_cls": 4.13547, "loss": 4.13547, "time": 0.82415} +{"mode": "val", "epoch": 70, "iter": 309, "lr": 0.05523, "top1_acc": 0.19323, "top5_acc": 0.4244, "mean_class_accuracy": 0.1931} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.0552, "memory": 15990, "data_time": 1.27077, "top1_acc": 0.28938, "top5_acc": 0.54016, "loss_cls": 4.08413, "loss": 4.08413, "time": 2.2534} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.05517, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27844, "top5_acc": 0.54344, "loss_cls": 4.09187, "loss": 4.09187, "time": 0.83043} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.05514, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29672, "top5_acc": 0.54812, "loss_cls": 4.04859, "loss": 4.04859, "time": 0.8292} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.05512, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27844, "top5_acc": 0.53297, "loss_cls": 4.13554, "loss": 4.13554, "time": 0.82759} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.05509, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27234, "top5_acc": 0.52672, "loss_cls": 4.18533, "loss": 4.18533, "time": 0.82017} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.05506, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27359, "top5_acc": 0.52094, "loss_cls": 4.17914, "loss": 4.17914, "time": 0.83116} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.05503, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27578, "top5_acc": 0.53047, "loss_cls": 4.13099, "loss": 4.13099, "time": 0.82647} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.055, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27219, "top5_acc": 0.52906, "loss_cls": 4.16645, "loss": 4.16645, "time": 0.82635} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.05498, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28344, "top5_acc": 0.54266, "loss_cls": 4.09078, "loss": 4.09078, "time": 0.82896} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.05495, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27156, "top5_acc": 0.51875, "loss_cls": 4.19005, "loss": 4.19005, "time": 0.82218} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.05492, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27359, "top5_acc": 0.52359, "loss_cls": 4.17099, "loss": 4.17099, "time": 0.8268} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.05489, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2825, "top5_acc": 0.51828, "loss_cls": 4.16469, "loss": 4.16469, "time": 0.83284} +{"mode": "train", "epoch": 71, "iter": 1300, "lr": 0.05487, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26547, "top5_acc": 0.52219, "loss_cls": 4.17918, "loss": 4.17918, "time": 0.82623} +{"mode": "train", "epoch": 71, "iter": 1400, "lr": 0.05484, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27391, "top5_acc": 0.52359, "loss_cls": 4.1662, "loss": 4.1662, "time": 0.83681} +{"mode": "train", "epoch": 71, "iter": 1500, "lr": 0.05481, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27609, "top5_acc": 0.53906, "loss_cls": 4.11783, "loss": 4.11783, "time": 0.83175} +{"mode": "train", "epoch": 71, "iter": 1600, "lr": 0.05478, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.27641, "top5_acc": 0.52203, "loss_cls": 4.1591, "loss": 4.1591, "time": 0.82573} +{"mode": "train", "epoch": 71, "iter": 1700, "lr": 0.05475, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26672, "top5_acc": 0.51891, "loss_cls": 4.1979, "loss": 4.1979, "time": 0.82612} +{"mode": "train", "epoch": 71, "iter": 1800, "lr": 0.05473, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27609, "top5_acc": 0.52797, "loss_cls": 4.17829, "loss": 4.17829, "time": 0.83627} +{"mode": "train", "epoch": 71, "iter": 1900, "lr": 0.0547, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28406, "top5_acc": 0.53562, "loss_cls": 4.10044, "loss": 4.10044, "time": 0.83339} +{"mode": "train", "epoch": 71, "iter": 2000, "lr": 0.05467, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26859, "top5_acc": 0.53391, "loss_cls": 4.15988, "loss": 4.15988, "time": 0.82856} +{"mode": "train", "epoch": 71, "iter": 2100, "lr": 0.05464, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27703, "top5_acc": 0.53266, "loss_cls": 4.13858, "loss": 4.13858, "time": 0.82896} +{"mode": "train", "epoch": 71, "iter": 2200, "lr": 0.05461, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27219, "top5_acc": 0.52828, "loss_cls": 4.16367, "loss": 4.16367, "time": 0.82232} +{"mode": "train", "epoch": 71, "iter": 2300, "lr": 0.05459, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26031, "top5_acc": 0.52438, "loss_cls": 4.18458, "loss": 4.18458, "time": 0.8242} +{"mode": "train", "epoch": 71, "iter": 2400, "lr": 0.05456, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27094, "top5_acc": 0.53016, "loss_cls": 4.13966, "loss": 4.13966, "time": 0.82812} +{"mode": "train", "epoch": 71, "iter": 2500, "lr": 0.05453, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27875, "top5_acc": 0.53297, "loss_cls": 4.11372, "loss": 4.11372, "time": 0.82546} +{"mode": "train", "epoch": 71, "iter": 2600, "lr": 0.0545, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27125, "top5_acc": 0.52844, "loss_cls": 4.15723, "loss": 4.15723, "time": 0.83459} +{"mode": "train", "epoch": 71, "iter": 2700, "lr": 0.05448, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26594, "top5_acc": 0.52125, "loss_cls": 4.18977, "loss": 4.18977, "time": 0.82246} +{"mode": "train", "epoch": 71, "iter": 2800, "lr": 0.05445, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26844, "top5_acc": 0.52078, "loss_cls": 4.21148, "loss": 4.21148, "time": 0.82549} +{"mode": "train", "epoch": 71, "iter": 2900, "lr": 0.05442, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25859, "top5_acc": 0.51469, "loss_cls": 4.23203, "loss": 4.23203, "time": 0.8346} +{"mode": "train", "epoch": 71, "iter": 3000, "lr": 0.05439, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26953, "top5_acc": 0.53047, "loss_cls": 4.14532, "loss": 4.14532, "time": 0.83132} +{"mode": "train", "epoch": 71, "iter": 3100, "lr": 0.05436, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27047, "top5_acc": 0.53719, "loss_cls": 4.14701, "loss": 4.14701, "time": 0.82505} +{"mode": "train", "epoch": 71, "iter": 3200, "lr": 0.05434, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27578, "top5_acc": 0.52938, "loss_cls": 4.15131, "loss": 4.15131, "time": 0.82863} +{"mode": "train", "epoch": 71, "iter": 3300, "lr": 0.05431, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26656, "top5_acc": 0.51812, "loss_cls": 4.18401, "loss": 4.18401, "time": 0.8256} +{"mode": "train", "epoch": 71, "iter": 3400, "lr": 0.05428, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27141, "top5_acc": 0.52609, "loss_cls": 4.19588, "loss": 4.19588, "time": 0.82838} +{"mode": "train", "epoch": 71, "iter": 3500, "lr": 0.05425, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28203, "top5_acc": 0.53484, "loss_cls": 4.15077, "loss": 4.15077, "time": 0.8254} +{"mode": "train", "epoch": 71, "iter": 3600, "lr": 0.05422, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27078, "top5_acc": 0.52391, "loss_cls": 4.16509, "loss": 4.16509, "time": 0.82465} +{"mode": "train", "epoch": 71, "iter": 3700, "lr": 0.0542, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26922, "top5_acc": 0.52641, "loss_cls": 4.17706, "loss": 4.17706, "time": 0.82499} +{"mode": "val", "epoch": 71, "iter": 309, "lr": 0.05418, "top1_acc": 0.18878, "top5_acc": 0.41225, "mean_class_accuracy": 0.18868} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.05416, "memory": 15990, "data_time": 1.30507, "top1_acc": 0.27812, "top5_acc": 0.53469, "loss_cls": 4.1083, "loss": 4.1083, "time": 2.30307} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.05413, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28797, "top5_acc": 0.54172, "loss_cls": 4.10773, "loss": 4.10773, "time": 0.83433} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.0541, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27922, "top5_acc": 0.52703, "loss_cls": 4.16082, "loss": 4.16082, "time": 0.83173} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.05407, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28359, "top5_acc": 0.53172, "loss_cls": 4.13007, "loss": 4.13007, "time": 0.83436} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.05404, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27516, "top5_acc": 0.53234, "loss_cls": 4.15, "loss": 4.15, "time": 0.83044} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.05402, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26719, "top5_acc": 0.52688, "loss_cls": 4.19335, "loss": 4.19335, "time": 0.82597} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.05399, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27469, "top5_acc": 0.52828, "loss_cls": 4.15174, "loss": 4.15174, "time": 0.8258} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.05396, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27484, "top5_acc": 0.52719, "loss_cls": 4.16464, "loss": 4.16464, "time": 0.82935} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.05393, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27953, "top5_acc": 0.53234, "loss_cls": 4.15839, "loss": 4.15839, "time": 0.82483} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.05391, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28312, "top5_acc": 0.5375, "loss_cls": 4.11871, "loss": 4.11871, "time": 0.82625} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.05388, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27531, "top5_acc": 0.53156, "loss_cls": 4.16159, "loss": 4.16159, "time": 0.82557} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.05385, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28062, "top5_acc": 0.52641, "loss_cls": 4.14305, "loss": 4.14305, "time": 0.8295} +{"mode": "train", "epoch": 72, "iter": 1300, "lr": 0.05382, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27656, "top5_acc": 0.52469, "loss_cls": 4.1515, "loss": 4.1515, "time": 0.82162} +{"mode": "train", "epoch": 72, "iter": 1400, "lr": 0.05379, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2725, "top5_acc": 0.52797, "loss_cls": 4.15546, "loss": 4.15546, "time": 0.82145} +{"mode": "train", "epoch": 72, "iter": 1500, "lr": 0.05377, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28078, "top5_acc": 0.53016, "loss_cls": 4.15392, "loss": 4.15392, "time": 0.8363} +{"mode": "train", "epoch": 72, "iter": 1600, "lr": 0.05374, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28422, "top5_acc": 0.53781, "loss_cls": 4.09776, "loss": 4.09776, "time": 0.82758} +{"mode": "train", "epoch": 72, "iter": 1700, "lr": 0.05371, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28141, "top5_acc": 0.52375, "loss_cls": 4.15087, "loss": 4.15087, "time": 0.82735} +{"mode": "train", "epoch": 72, "iter": 1800, "lr": 0.05368, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27578, "top5_acc": 0.51812, "loss_cls": 4.18351, "loss": 4.18351, "time": 0.83391} +{"mode": "train", "epoch": 72, "iter": 1900, "lr": 0.05365, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27953, "top5_acc": 0.53828, "loss_cls": 4.12972, "loss": 4.12972, "time": 0.83372} +{"mode": "train", "epoch": 72, "iter": 2000, "lr": 0.05363, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28094, "top5_acc": 0.53875, "loss_cls": 4.13025, "loss": 4.13025, "time": 0.83745} +{"mode": "train", "epoch": 72, "iter": 2100, "lr": 0.0536, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27328, "top5_acc": 0.52688, "loss_cls": 4.15944, "loss": 4.15944, "time": 0.83239} +{"mode": "train", "epoch": 72, "iter": 2200, "lr": 0.05357, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26703, "top5_acc": 0.51875, "loss_cls": 4.17853, "loss": 4.17853, "time": 0.83922} +{"mode": "train", "epoch": 72, "iter": 2300, "lr": 0.05354, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27516, "top5_acc": 0.52953, "loss_cls": 4.14922, "loss": 4.14922, "time": 0.83867} +{"mode": "train", "epoch": 72, "iter": 2400, "lr": 0.05352, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27891, "top5_acc": 0.53359, "loss_cls": 4.12563, "loss": 4.12563, "time": 0.83768} +{"mode": "train", "epoch": 72, "iter": 2500, "lr": 0.05349, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28406, "top5_acc": 0.53406, "loss_cls": 4.12786, "loss": 4.12786, "time": 0.83508} +{"mode": "train", "epoch": 72, "iter": 2600, "lr": 0.05346, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27391, "top5_acc": 0.53156, "loss_cls": 4.16439, "loss": 4.16439, "time": 0.83105} +{"mode": "train", "epoch": 72, "iter": 2700, "lr": 0.05343, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28766, "top5_acc": 0.53109, "loss_cls": 4.11895, "loss": 4.11895, "time": 0.82528} +{"mode": "train", "epoch": 72, "iter": 2800, "lr": 0.0534, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26875, "top5_acc": 0.52828, "loss_cls": 4.16934, "loss": 4.16934, "time": 0.83137} +{"mode": "train", "epoch": 72, "iter": 2900, "lr": 0.05338, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27609, "top5_acc": 0.53641, "loss_cls": 4.13399, "loss": 4.13399, "time": 0.83532} +{"mode": "train", "epoch": 72, "iter": 3000, "lr": 0.05335, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26812, "top5_acc": 0.52516, "loss_cls": 4.17452, "loss": 4.17452, "time": 0.82823} +{"mode": "train", "epoch": 72, "iter": 3100, "lr": 0.05332, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27906, "top5_acc": 0.53312, "loss_cls": 4.1372, "loss": 4.1372, "time": 0.82028} +{"mode": "train", "epoch": 72, "iter": 3200, "lr": 0.05329, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26859, "top5_acc": 0.52156, "loss_cls": 4.14828, "loss": 4.14828, "time": 0.82237} +{"mode": "train", "epoch": 72, "iter": 3300, "lr": 0.05326, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28344, "top5_acc": 0.53, "loss_cls": 4.13329, "loss": 4.13329, "time": 0.82394} +{"mode": "train", "epoch": 72, "iter": 3400, "lr": 0.05324, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27172, "top5_acc": 0.52688, "loss_cls": 4.1633, "loss": 4.1633, "time": 0.82301} +{"mode": "train", "epoch": 72, "iter": 3500, "lr": 0.05321, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27, "top5_acc": 0.51672, "loss_cls": 4.18782, "loss": 4.18782, "time": 0.83058} +{"mode": "train", "epoch": 72, "iter": 3600, "lr": 0.05318, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28203, "top5_acc": 0.53688, "loss_cls": 4.11595, "loss": 4.11595, "time": 0.83074} +{"mode": "train", "epoch": 72, "iter": 3700, "lr": 0.05315, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28047, "top5_acc": 0.52375, "loss_cls": 4.15979, "loss": 4.15979, "time": 0.82352} +{"mode": "val", "epoch": 72, "iter": 309, "lr": 0.05314, "top1_acc": 0.21061, "top5_acc": 0.44634, "mean_class_accuracy": 0.21042} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.05311, "memory": 15990, "data_time": 1.33596, "top1_acc": 0.29547, "top5_acc": 0.54672, "loss_cls": 4.06048, "loss": 4.06048, "time": 2.33372} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.05308, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28422, "top5_acc": 0.54422, "loss_cls": 4.0913, "loss": 4.0913, "time": 0.83755} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.05306, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28781, "top5_acc": 0.53109, "loss_cls": 4.10086, "loss": 4.10086, "time": 0.83796} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.05303, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28594, "top5_acc": 0.53484, "loss_cls": 4.1178, "loss": 4.1178, "time": 0.82659} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.053, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28688, "top5_acc": 0.53719, "loss_cls": 4.08789, "loss": 4.08789, "time": 0.82555} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.05297, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27016, "top5_acc": 0.53547, "loss_cls": 4.13335, "loss": 4.13335, "time": 0.82157} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.05294, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.285, "top5_acc": 0.53734, "loss_cls": 4.10007, "loss": 4.10007, "time": 0.82053} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.05292, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27156, "top5_acc": 0.51812, "loss_cls": 4.16579, "loss": 4.16579, "time": 0.82252} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.05289, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26297, "top5_acc": 0.53078, "loss_cls": 4.19435, "loss": 4.19435, "time": 0.82535} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.05286, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27828, "top5_acc": 0.52203, "loss_cls": 4.18265, "loss": 4.18265, "time": 0.82249} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.05283, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27375, "top5_acc": 0.53078, "loss_cls": 4.13274, "loss": 4.13274, "time": 0.82784} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.0528, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28484, "top5_acc": 0.53344, "loss_cls": 4.12557, "loss": 4.12557, "time": 0.8207} +{"mode": "train", "epoch": 73, "iter": 1300, "lr": 0.05278, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27281, "top5_acc": 0.51953, "loss_cls": 4.16574, "loss": 4.16574, "time": 0.81068} +{"mode": "train", "epoch": 73, "iter": 1400, "lr": 0.05275, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28188, "top5_acc": 0.53641, "loss_cls": 4.10365, "loss": 4.10365, "time": 0.81352} +{"mode": "train", "epoch": 73, "iter": 1500, "lr": 0.05272, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27984, "top5_acc": 0.53641, "loss_cls": 4.1039, "loss": 4.1039, "time": 0.81251} +{"mode": "train", "epoch": 73, "iter": 1600, "lr": 0.05269, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27406, "top5_acc": 0.52938, "loss_cls": 4.15943, "loss": 4.15943, "time": 0.82007} +{"mode": "train", "epoch": 73, "iter": 1700, "lr": 0.05267, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28453, "top5_acc": 0.53219, "loss_cls": 4.11375, "loss": 4.11375, "time": 0.81307} +{"mode": "train", "epoch": 73, "iter": 1800, "lr": 0.05264, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27141, "top5_acc": 0.52234, "loss_cls": 4.17467, "loss": 4.17467, "time": 0.81301} +{"mode": "train", "epoch": 73, "iter": 1900, "lr": 0.05261, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.275, "top5_acc": 0.52844, "loss_cls": 4.16092, "loss": 4.16092, "time": 0.81848} +{"mode": "train", "epoch": 73, "iter": 2000, "lr": 0.05258, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28312, "top5_acc": 0.53797, "loss_cls": 4.11792, "loss": 4.11792, "time": 0.81145} +{"mode": "train", "epoch": 73, "iter": 2100, "lr": 0.05255, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28438, "top5_acc": 0.53812, "loss_cls": 4.11219, "loss": 4.11219, "time": 0.81485} +{"mode": "train", "epoch": 73, "iter": 2200, "lr": 0.05253, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27594, "top5_acc": 0.52844, "loss_cls": 4.15702, "loss": 4.15702, "time": 0.81179} +{"mode": "train", "epoch": 73, "iter": 2300, "lr": 0.0525, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27734, "top5_acc": 0.53, "loss_cls": 4.12728, "loss": 4.12728, "time": 0.81463} +{"mode": "train", "epoch": 73, "iter": 2400, "lr": 0.05247, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2725, "top5_acc": 0.52844, "loss_cls": 4.15599, "loss": 4.15599, "time": 0.8135} +{"mode": "train", "epoch": 73, "iter": 2500, "lr": 0.05244, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26953, "top5_acc": 0.51406, "loss_cls": 4.20118, "loss": 4.20118, "time": 0.82232} +{"mode": "train", "epoch": 73, "iter": 2600, "lr": 0.05241, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28344, "top5_acc": 0.52938, "loss_cls": 4.12273, "loss": 4.12273, "time": 0.81494} +{"mode": "train", "epoch": 73, "iter": 2700, "lr": 0.05239, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28438, "top5_acc": 0.54141, "loss_cls": 4.114, "loss": 4.114, "time": 0.8169} +{"mode": "train", "epoch": 73, "iter": 2800, "lr": 0.05236, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27344, "top5_acc": 0.52859, "loss_cls": 4.15616, "loss": 4.15616, "time": 0.81484} +{"mode": "train", "epoch": 73, "iter": 2900, "lr": 0.05233, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27484, "top5_acc": 0.52328, "loss_cls": 4.16317, "loss": 4.16317, "time": 0.81381} +{"mode": "train", "epoch": 73, "iter": 3000, "lr": 0.0523, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27594, "top5_acc": 0.53094, "loss_cls": 4.16095, "loss": 4.16095, "time": 0.8137} +{"mode": "train", "epoch": 73, "iter": 3100, "lr": 0.05227, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28141, "top5_acc": 0.53188, "loss_cls": 4.14879, "loss": 4.14879, "time": 0.80887} +{"mode": "train", "epoch": 73, "iter": 3200, "lr": 0.05225, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27594, "top5_acc": 0.52422, "loss_cls": 4.13661, "loss": 4.13661, "time": 0.81144} +{"mode": "train", "epoch": 73, "iter": 3300, "lr": 0.05222, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28547, "top5_acc": 0.53391, "loss_cls": 4.12003, "loss": 4.12003, "time": 0.81086} +{"mode": "train", "epoch": 73, "iter": 3400, "lr": 0.05219, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27281, "top5_acc": 0.53328, "loss_cls": 4.14626, "loss": 4.14626, "time": 0.82009} +{"mode": "train", "epoch": 73, "iter": 3500, "lr": 0.05216, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27312, "top5_acc": 0.53031, "loss_cls": 4.14763, "loss": 4.14763, "time": 0.81508} +{"mode": "train", "epoch": 73, "iter": 3600, "lr": 0.05213, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28125, "top5_acc": 0.53781, "loss_cls": 4.13015, "loss": 4.13015, "time": 0.81105} +{"mode": "train", "epoch": 73, "iter": 3700, "lr": 0.05211, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27328, "top5_acc": 0.53828, "loss_cls": 4.12102, "loss": 4.12102, "time": 0.80908} +{"mode": "val", "epoch": 73, "iter": 309, "lr": 0.05209, "top1_acc": 0.19587, "top5_acc": 0.42339, "mean_class_accuracy": 0.19566} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.05207, "memory": 15990, "data_time": 1.33575, "top1_acc": 0.28516, "top5_acc": 0.54141, "loss_cls": 4.07296, "loss": 4.07296, "time": 2.32983} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.05204, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28797, "top5_acc": 0.54031, "loss_cls": 4.07145, "loss": 4.07145, "time": 0.83595} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.05201, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28578, "top5_acc": 0.54719, "loss_cls": 4.06238, "loss": 4.06238, "time": 0.83666} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.05198, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28078, "top5_acc": 0.54859, "loss_cls": 4.09329, "loss": 4.09329, "time": 0.82679} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.05195, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27422, "top5_acc": 0.53922, "loss_cls": 4.09319, "loss": 4.09319, "time": 0.83028} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.05193, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27672, "top5_acc": 0.53266, "loss_cls": 4.11388, "loss": 4.11388, "time": 0.82127} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.0519, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28188, "top5_acc": 0.54391, "loss_cls": 4.103, "loss": 4.103, "time": 0.82641} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.05187, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28547, "top5_acc": 0.54172, "loss_cls": 4.06846, "loss": 4.06846, "time": 0.82649} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.05184, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28516, "top5_acc": 0.54656, "loss_cls": 4.09198, "loss": 4.09198, "time": 0.82568} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.05181, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28328, "top5_acc": 0.53297, "loss_cls": 4.12164, "loss": 4.12164, "time": 0.82818} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.05179, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27797, "top5_acc": 0.53516, "loss_cls": 4.14233, "loss": 4.14233, "time": 0.82913} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.05176, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28625, "top5_acc": 0.53422, "loss_cls": 4.11357, "loss": 4.11357, "time": 0.82158} +{"mode": "train", "epoch": 74, "iter": 1300, "lr": 0.05173, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27594, "top5_acc": 0.52859, "loss_cls": 4.15423, "loss": 4.15423, "time": 0.82532} +{"mode": "train", "epoch": 74, "iter": 1400, "lr": 0.0517, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28219, "top5_acc": 0.5475, "loss_cls": 4.10379, "loss": 4.10379, "time": 0.83149} +{"mode": "train", "epoch": 74, "iter": 1500, "lr": 0.05168, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28203, "top5_acc": 0.53406, "loss_cls": 4.13661, "loss": 4.13661, "time": 0.82622} +{"mode": "train", "epoch": 74, "iter": 1600, "lr": 0.05165, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.28516, "top5_acc": 0.54328, "loss_cls": 4.09748, "loss": 4.09748, "time": 0.82746} +{"mode": "train", "epoch": 74, "iter": 1700, "lr": 0.05162, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26828, "top5_acc": 0.52547, "loss_cls": 4.16469, "loss": 4.16469, "time": 0.83294} +{"mode": "train", "epoch": 74, "iter": 1800, "lr": 0.05159, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27953, "top5_acc": 0.53531, "loss_cls": 4.12806, "loss": 4.12806, "time": 0.83631} +{"mode": "train", "epoch": 74, "iter": 1900, "lr": 0.05156, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26922, "top5_acc": 0.52781, "loss_cls": 4.1536, "loss": 4.1536, "time": 0.82535} +{"mode": "train", "epoch": 74, "iter": 2000, "lr": 0.05154, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27828, "top5_acc": 0.52375, "loss_cls": 4.16339, "loss": 4.16339, "time": 0.82857} +{"mode": "train", "epoch": 74, "iter": 2100, "lr": 0.05151, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28781, "top5_acc": 0.53984, "loss_cls": 4.06821, "loss": 4.06821, "time": 0.82382} +{"mode": "train", "epoch": 74, "iter": 2200, "lr": 0.05148, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28, "top5_acc": 0.52766, "loss_cls": 4.14114, "loss": 4.14114, "time": 0.82313} +{"mode": "train", "epoch": 74, "iter": 2300, "lr": 0.05145, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27375, "top5_acc": 0.52719, "loss_cls": 4.13794, "loss": 4.13794, "time": 0.82146} +{"mode": "train", "epoch": 74, "iter": 2400, "lr": 0.05142, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27641, "top5_acc": 0.53719, "loss_cls": 4.13594, "loss": 4.13594, "time": 0.83308} +{"mode": "train", "epoch": 74, "iter": 2500, "lr": 0.0514, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27469, "top5_acc": 0.52312, "loss_cls": 4.14513, "loss": 4.14513, "time": 0.83349} +{"mode": "train", "epoch": 74, "iter": 2600, "lr": 0.05137, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27547, "top5_acc": 0.52297, "loss_cls": 4.15395, "loss": 4.15395, "time": 0.82759} +{"mode": "train", "epoch": 74, "iter": 2700, "lr": 0.05134, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27, "top5_acc": 0.52781, "loss_cls": 4.1725, "loss": 4.1725, "time": 0.8326} +{"mode": "train", "epoch": 74, "iter": 2800, "lr": 0.05131, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28156, "top5_acc": 0.52688, "loss_cls": 4.15788, "loss": 4.15788, "time": 0.8409} +{"mode": "train", "epoch": 74, "iter": 2900, "lr": 0.05128, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29031, "top5_acc": 0.53688, "loss_cls": 4.09057, "loss": 4.09057, "time": 0.83678} +{"mode": "train", "epoch": 74, "iter": 3000, "lr": 0.05126, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28234, "top5_acc": 0.52156, "loss_cls": 4.14977, "loss": 4.14977, "time": 0.83028} +{"mode": "train", "epoch": 74, "iter": 3100, "lr": 0.05123, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27172, "top5_acc": 0.52547, "loss_cls": 4.16359, "loss": 4.16359, "time": 0.83315} +{"mode": "train", "epoch": 74, "iter": 3200, "lr": 0.0512, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28016, "top5_acc": 0.53234, "loss_cls": 4.12987, "loss": 4.12987, "time": 0.82524} +{"mode": "train", "epoch": 74, "iter": 3300, "lr": 0.05117, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2875, "top5_acc": 0.53062, "loss_cls": 4.13274, "loss": 4.13274, "time": 0.82476} +{"mode": "train", "epoch": 74, "iter": 3400, "lr": 0.05114, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27281, "top5_acc": 0.53031, "loss_cls": 4.14673, "loss": 4.14673, "time": 0.83289} +{"mode": "train", "epoch": 74, "iter": 3500, "lr": 0.05112, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28453, "top5_acc": 0.53703, "loss_cls": 4.12575, "loss": 4.12575, "time": 0.82391} +{"mode": "train", "epoch": 74, "iter": 3600, "lr": 0.05109, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27938, "top5_acc": 0.52719, "loss_cls": 4.15323, "loss": 4.15323, "time": 0.82601} +{"mode": "train", "epoch": 74, "iter": 3700, "lr": 0.05106, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28188, "top5_acc": 0.53328, "loss_cls": 4.12952, "loss": 4.12952, "time": 0.83906} +{"mode": "val", "epoch": 74, "iter": 309, "lr": 0.05105, "top1_acc": 0.22281, "top5_acc": 0.46518, "mean_class_accuracy": 0.22252} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.05102, "memory": 15990, "data_time": 1.31283, "top1_acc": 0.28766, "top5_acc": 0.54062, "loss_cls": 4.06507, "loss": 4.06507, "time": 2.31038} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.05099, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28438, "top5_acc": 0.54359, "loss_cls": 4.1052, "loss": 4.1052, "time": 0.83485} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.05096, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28562, "top5_acc": 0.54797, "loss_cls": 4.04345, "loss": 4.04345, "time": 0.83704} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.05094, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27938, "top5_acc": 0.52734, "loss_cls": 4.1497, "loss": 4.1497, "time": 0.83647} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.05091, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27688, "top5_acc": 0.54391, "loss_cls": 4.09205, "loss": 4.09205, "time": 0.83889} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.05088, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28234, "top5_acc": 0.52953, "loss_cls": 4.13116, "loss": 4.13116, "time": 0.83944} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.05085, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28359, "top5_acc": 0.53844, "loss_cls": 4.10363, "loss": 4.10363, "time": 0.83673} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.05082, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28688, "top5_acc": 0.54188, "loss_cls": 4.08796, "loss": 4.08796, "time": 0.8346} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.0508, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27641, "top5_acc": 0.53969, "loss_cls": 4.10241, "loss": 4.10241, "time": 0.84213} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.05077, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28344, "top5_acc": 0.53828, "loss_cls": 4.09282, "loss": 4.09282, "time": 0.84062} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.05074, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27984, "top5_acc": 0.53516, "loss_cls": 4.12245, "loss": 4.12245, "time": 0.83451} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.05071, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27859, "top5_acc": 0.54203, "loss_cls": 4.13456, "loss": 4.13456, "time": 0.83715} +{"mode": "train", "epoch": 75, "iter": 1300, "lr": 0.05068, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27047, "top5_acc": 0.51984, "loss_cls": 4.18769, "loss": 4.18769, "time": 0.83215} +{"mode": "train", "epoch": 75, "iter": 1400, "lr": 0.05066, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28531, "top5_acc": 0.53328, "loss_cls": 4.12906, "loss": 4.12906, "time": 0.83434} +{"mode": "train", "epoch": 75, "iter": 1500, "lr": 0.05063, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28562, "top5_acc": 0.53656, "loss_cls": 4.10166, "loss": 4.10166, "time": 0.82851} +{"mode": "train", "epoch": 75, "iter": 1600, "lr": 0.0506, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27484, "top5_acc": 0.53359, "loss_cls": 4.14815, "loss": 4.14815, "time": 0.83054} +{"mode": "train", "epoch": 75, "iter": 1700, "lr": 0.05057, "memory": 15990, "data_time": 0.00068, "top1_acc": 0.28047, "top5_acc": 0.53828, "loss_cls": 4.11436, "loss": 4.11436, "time": 0.83648} +{"mode": "train", "epoch": 75, "iter": 1800, "lr": 0.05054, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27891, "top5_acc": 0.53219, "loss_cls": 4.12473, "loss": 4.12473, "time": 0.82338} +{"mode": "train", "epoch": 75, "iter": 1900, "lr": 0.05052, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27359, "top5_acc": 0.52734, "loss_cls": 4.15053, "loss": 4.15053, "time": 0.82702} +{"mode": "train", "epoch": 75, "iter": 2000, "lr": 0.05049, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28953, "top5_acc": 0.53453, "loss_cls": 4.12431, "loss": 4.12431, "time": 0.823} +{"mode": "train", "epoch": 75, "iter": 2100, "lr": 0.05046, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27547, "top5_acc": 0.54844, "loss_cls": 4.08929, "loss": 4.08929, "time": 0.82194} +{"mode": "train", "epoch": 75, "iter": 2200, "lr": 0.05043, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29328, "top5_acc": 0.54375, "loss_cls": 4.05912, "loss": 4.05912, "time": 0.82058} +{"mode": "train", "epoch": 75, "iter": 2300, "lr": 0.0504, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27703, "top5_acc": 0.53219, "loss_cls": 4.13236, "loss": 4.13236, "time": 0.82032} +{"mode": "train", "epoch": 75, "iter": 2400, "lr": 0.05038, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28047, "top5_acc": 0.52453, "loss_cls": 4.13907, "loss": 4.13907, "time": 0.82857} +{"mode": "train", "epoch": 75, "iter": 2500, "lr": 0.05035, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27344, "top5_acc": 0.52891, "loss_cls": 4.13601, "loss": 4.13601, "time": 0.82524} +{"mode": "train", "epoch": 75, "iter": 2600, "lr": 0.05032, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28188, "top5_acc": 0.52578, "loss_cls": 4.16319, "loss": 4.16319, "time": 0.82716} +{"mode": "train", "epoch": 75, "iter": 2700, "lr": 0.05029, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28078, "top5_acc": 0.53812, "loss_cls": 4.10306, "loss": 4.10306, "time": 0.83688} +{"mode": "train", "epoch": 75, "iter": 2800, "lr": 0.05026, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27391, "top5_acc": 0.52875, "loss_cls": 4.14447, "loss": 4.14447, "time": 0.83263} +{"mode": "train", "epoch": 75, "iter": 2900, "lr": 0.05024, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27594, "top5_acc": 0.53484, "loss_cls": 4.12054, "loss": 4.12054, "time": 0.83141} +{"mode": "train", "epoch": 75, "iter": 3000, "lr": 0.05021, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26719, "top5_acc": 0.52297, "loss_cls": 4.1741, "loss": 4.1741, "time": 0.83577} +{"mode": "train", "epoch": 75, "iter": 3100, "lr": 0.05018, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27719, "top5_acc": 0.53234, "loss_cls": 4.13827, "loss": 4.13827, "time": 0.83854} +{"mode": "train", "epoch": 75, "iter": 3200, "lr": 0.05015, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2825, "top5_acc": 0.53812, "loss_cls": 4.11442, "loss": 4.11442, "time": 0.83527} +{"mode": "train", "epoch": 75, "iter": 3300, "lr": 0.05012, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2875, "top5_acc": 0.53625, "loss_cls": 4.09289, "loss": 4.09289, "time": 0.83459} +{"mode": "train", "epoch": 75, "iter": 3400, "lr": 0.0501, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28016, "top5_acc": 0.54328, "loss_cls": 4.10024, "loss": 4.10024, "time": 0.82312} +{"mode": "train", "epoch": 75, "iter": 3500, "lr": 0.05007, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28734, "top5_acc": 0.54344, "loss_cls": 4.10192, "loss": 4.10192, "time": 0.82967} +{"mode": "train", "epoch": 75, "iter": 3600, "lr": 0.05004, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27719, "top5_acc": 0.53516, "loss_cls": 4.12118, "loss": 4.12118, "time": 0.83441} +{"mode": "train", "epoch": 75, "iter": 3700, "lr": 0.05001, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28703, "top5_acc": 0.52828, "loss_cls": 4.12059, "loss": 4.12059, "time": 0.83449} +{"mode": "val", "epoch": 75, "iter": 309, "lr": 0.05, "top1_acc": 0.20291, "top5_acc": 0.42673, "mean_class_accuracy": 0.20295} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.04997, "memory": 15990, "data_time": 1.2965, "top1_acc": 0.28266, "top5_acc": 0.54438, "loss_cls": 4.08247, "loss": 4.08247, "time": 2.29714} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.04994, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27547, "top5_acc": 0.53047, "loss_cls": 4.13284, "loss": 4.13284, "time": 0.83566} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.04992, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28703, "top5_acc": 0.53734, "loss_cls": 4.10329, "loss": 4.10329, "time": 0.83873} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.04989, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28172, "top5_acc": 0.54375, "loss_cls": 4.0866, "loss": 4.0866, "time": 0.83985} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.04986, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28312, "top5_acc": 0.5425, "loss_cls": 4.09644, "loss": 4.09644, "time": 0.83867} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.04983, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28484, "top5_acc": 0.54125, "loss_cls": 4.06751, "loss": 4.06751, "time": 0.83617} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.0498, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28031, "top5_acc": 0.54141, "loss_cls": 4.09707, "loss": 4.09707, "time": 0.83945} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.04978, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29516, "top5_acc": 0.54719, "loss_cls": 4.07177, "loss": 4.07177, "time": 0.831} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.04975, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28281, "top5_acc": 0.54344, "loss_cls": 4.08653, "loss": 4.08653, "time": 0.83784} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.04972, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28469, "top5_acc": 0.53938, "loss_cls": 4.10812, "loss": 4.10812, "time": 0.83404} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.04969, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28422, "top5_acc": 0.53859, "loss_cls": 4.09396, "loss": 4.09396, "time": 0.83486} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.04966, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28438, "top5_acc": 0.53906, "loss_cls": 4.11046, "loss": 4.11046, "time": 0.8302} +{"mode": "train", "epoch": 76, "iter": 1300, "lr": 0.04964, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28203, "top5_acc": 0.53797, "loss_cls": 4.10027, "loss": 4.10027, "time": 0.83465} +{"mode": "train", "epoch": 76, "iter": 1400, "lr": 0.04961, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28719, "top5_acc": 0.54062, "loss_cls": 4.09884, "loss": 4.09884, "time": 0.82528} +{"mode": "train", "epoch": 76, "iter": 1500, "lr": 0.04958, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28359, "top5_acc": 0.54797, "loss_cls": 4.04867, "loss": 4.04867, "time": 0.8303} +{"mode": "train", "epoch": 76, "iter": 1600, "lr": 0.04955, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28656, "top5_acc": 0.53219, "loss_cls": 4.09958, "loss": 4.09958, "time": 0.83465} +{"mode": "train", "epoch": 76, "iter": 1700, "lr": 0.04953, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28516, "top5_acc": 0.54078, "loss_cls": 4.06559, "loss": 4.06559, "time": 0.83934} +{"mode": "train", "epoch": 76, "iter": 1800, "lr": 0.0495, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27719, "top5_acc": 0.53094, "loss_cls": 4.15108, "loss": 4.15108, "time": 0.82311} +{"mode": "train", "epoch": 76, "iter": 1900, "lr": 0.04947, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28141, "top5_acc": 0.53531, "loss_cls": 4.12316, "loss": 4.12316, "time": 0.8228} +{"mode": "train", "epoch": 76, "iter": 2000, "lr": 0.04944, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28797, "top5_acc": 0.53938, "loss_cls": 4.09952, "loss": 4.09952, "time": 0.82338} +{"mode": "train", "epoch": 76, "iter": 2100, "lr": 0.04941, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28203, "top5_acc": 0.53688, "loss_cls": 4.11297, "loss": 4.11297, "time": 0.82648} +{"mode": "train", "epoch": 76, "iter": 2200, "lr": 0.04939, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28828, "top5_acc": 0.54797, "loss_cls": 4.05179, "loss": 4.05179, "time": 0.81858} +{"mode": "train", "epoch": 76, "iter": 2300, "lr": 0.04936, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28422, "top5_acc": 0.52719, "loss_cls": 4.11665, "loss": 4.11665, "time": 0.8292} +{"mode": "train", "epoch": 76, "iter": 2400, "lr": 0.04933, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28203, "top5_acc": 0.52812, "loss_cls": 4.11319, "loss": 4.11319, "time": 0.82842} +{"mode": "train", "epoch": 76, "iter": 2500, "lr": 0.0493, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.28375, "top5_acc": 0.53906, "loss_cls": 4.08414, "loss": 4.08414, "time": 0.82511} +{"mode": "train", "epoch": 76, "iter": 2600, "lr": 0.04927, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28, "top5_acc": 0.52766, "loss_cls": 4.14377, "loss": 4.14377, "time": 0.82767} +{"mode": "train", "epoch": 76, "iter": 2700, "lr": 0.04925, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27875, "top5_acc": 0.53703, "loss_cls": 4.10168, "loss": 4.10168, "time": 0.83001} +{"mode": "train", "epoch": 76, "iter": 2800, "lr": 0.04922, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27859, "top5_acc": 0.53422, "loss_cls": 4.11642, "loss": 4.11642, "time": 0.82408} +{"mode": "train", "epoch": 76, "iter": 2900, "lr": 0.04919, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29203, "top5_acc": 0.53688, "loss_cls": 4.10483, "loss": 4.10483, "time": 0.8231} +{"mode": "train", "epoch": 76, "iter": 3000, "lr": 0.04916, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28828, "top5_acc": 0.53812, "loss_cls": 4.11898, "loss": 4.11898, "time": 0.82388} +{"mode": "train", "epoch": 76, "iter": 3100, "lr": 0.04913, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2825, "top5_acc": 0.54047, "loss_cls": 4.08091, "loss": 4.08091, "time": 0.82423} +{"mode": "train", "epoch": 76, "iter": 3200, "lr": 0.04911, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28078, "top5_acc": 0.53812, "loss_cls": 4.11936, "loss": 4.11936, "time": 0.83271} +{"mode": "train", "epoch": 76, "iter": 3300, "lr": 0.04908, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29031, "top5_acc": 0.54453, "loss_cls": 4.09252, "loss": 4.09252, "time": 0.82286} +{"mode": "train", "epoch": 76, "iter": 3400, "lr": 0.04905, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28047, "top5_acc": 0.53688, "loss_cls": 4.11641, "loss": 4.11641, "time": 0.81997} +{"mode": "train", "epoch": 76, "iter": 3500, "lr": 0.04902, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26625, "top5_acc": 0.52734, "loss_cls": 4.15084, "loss": 4.15084, "time": 0.83047} +{"mode": "train", "epoch": 76, "iter": 3600, "lr": 0.04899, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28297, "top5_acc": 0.53375, "loss_cls": 4.12024, "loss": 4.12024, "time": 0.82589} +{"mode": "train", "epoch": 76, "iter": 3700, "lr": 0.04897, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28453, "top5_acc": 0.53859, "loss_cls": 4.09389, "loss": 4.09389, "time": 0.83039} +{"mode": "val", "epoch": 76, "iter": 309, "lr": 0.04895, "top1_acc": 0.19308, "top5_acc": 0.41361, "mean_class_accuracy": 0.19321} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.04893, "memory": 15990, "data_time": 1.25071, "top1_acc": 0.28969, "top5_acc": 0.54937, "loss_cls": 4.04915, "loss": 4.04915, "time": 2.24648} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0489, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28766, "top5_acc": 0.54531, "loss_cls": 4.07519, "loss": 4.07519, "time": 0.82762} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.04887, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29812, "top5_acc": 0.54859, "loss_cls": 4.03753, "loss": 4.03753, "time": 0.82814} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.04884, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28719, "top5_acc": 0.54906, "loss_cls": 4.05326, "loss": 4.05326, "time": 0.82672} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.04881, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29141, "top5_acc": 0.53953, "loss_cls": 4.06711, "loss": 4.06711, "time": 0.82575} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.04879, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28797, "top5_acc": 0.5425, "loss_cls": 4.09314, "loss": 4.09314, "time": 0.8193} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.04876, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28328, "top5_acc": 0.53078, "loss_cls": 4.13412, "loss": 4.13412, "time": 0.82455} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.04873, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2825, "top5_acc": 0.54234, "loss_cls": 4.07928, "loss": 4.07928, "time": 0.82064} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.0487, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29172, "top5_acc": 0.55016, "loss_cls": 4.08245, "loss": 4.08245, "time": 0.82237} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.04867, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28391, "top5_acc": 0.54109, "loss_cls": 4.1207, "loss": 4.1207, "time": 0.82195} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.04865, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28422, "top5_acc": 0.54266, "loss_cls": 4.07503, "loss": 4.07503, "time": 0.82393} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.04862, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27453, "top5_acc": 0.54031, "loss_cls": 4.11157, "loss": 4.11157, "time": 0.81809} +{"mode": "train", "epoch": 77, "iter": 1300, "lr": 0.04859, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27688, "top5_acc": 0.54484, "loss_cls": 4.10138, "loss": 4.10138, "time": 0.83822} +{"mode": "train", "epoch": 77, "iter": 1400, "lr": 0.04856, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28562, "top5_acc": 0.53531, "loss_cls": 4.1114, "loss": 4.1114, "time": 0.82121} +{"mode": "train", "epoch": 77, "iter": 1500, "lr": 0.04853, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28688, "top5_acc": 0.54734, "loss_cls": 4.05738, "loss": 4.05738, "time": 0.82998} +{"mode": "train", "epoch": 77, "iter": 1600, "lr": 0.04851, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27641, "top5_acc": 0.53453, "loss_cls": 4.14093, "loss": 4.14093, "time": 0.82979} +{"mode": "train", "epoch": 77, "iter": 1700, "lr": 0.04848, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28203, "top5_acc": 0.53969, "loss_cls": 4.09658, "loss": 4.09658, "time": 0.82945} +{"mode": "train", "epoch": 77, "iter": 1800, "lr": 0.04845, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29125, "top5_acc": 0.54719, "loss_cls": 4.04835, "loss": 4.04835, "time": 0.82217} +{"mode": "train", "epoch": 77, "iter": 1900, "lr": 0.04842, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27812, "top5_acc": 0.52984, "loss_cls": 4.12551, "loss": 4.12551, "time": 0.82848} +{"mode": "train", "epoch": 77, "iter": 2000, "lr": 0.04839, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28703, "top5_acc": 0.53656, "loss_cls": 4.08565, "loss": 4.08565, "time": 0.82617} +{"mode": "train", "epoch": 77, "iter": 2100, "lr": 0.04837, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28141, "top5_acc": 0.54109, "loss_cls": 4.09274, "loss": 4.09274, "time": 0.82295} +{"mode": "train", "epoch": 77, "iter": 2200, "lr": 0.04834, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28547, "top5_acc": 0.54125, "loss_cls": 4.07051, "loss": 4.07051, "time": 0.81573} +{"mode": "train", "epoch": 77, "iter": 2300, "lr": 0.04831, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28172, "top5_acc": 0.53359, "loss_cls": 4.10559, "loss": 4.10559, "time": 0.82884} +{"mode": "train", "epoch": 77, "iter": 2400, "lr": 0.04828, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28344, "top5_acc": 0.53438, "loss_cls": 4.10952, "loss": 4.10952, "time": 0.82663} +{"mode": "train", "epoch": 77, "iter": 2500, "lr": 0.04825, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.29281, "top5_acc": 0.54781, "loss_cls": 4.03889, "loss": 4.03889, "time": 0.82765} +{"mode": "train", "epoch": 77, "iter": 2600, "lr": 0.04823, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29609, "top5_acc": 0.54203, "loss_cls": 4.07958, "loss": 4.07958, "time": 0.83193} +{"mode": "train", "epoch": 77, "iter": 2700, "lr": 0.0482, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28281, "top5_acc": 0.54266, "loss_cls": 4.09146, "loss": 4.09146, "time": 0.82899} +{"mode": "train", "epoch": 77, "iter": 2800, "lr": 0.04817, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28062, "top5_acc": 0.54438, "loss_cls": 4.08853, "loss": 4.08853, "time": 0.82271} +{"mode": "train", "epoch": 77, "iter": 2900, "lr": 0.04814, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29313, "top5_acc": 0.54531, "loss_cls": 4.06169, "loss": 4.06169, "time": 0.82764} +{"mode": "train", "epoch": 77, "iter": 3000, "lr": 0.04811, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29531, "top5_acc": 0.54922, "loss_cls": 4.04628, "loss": 4.04628, "time": 0.82885} +{"mode": "train", "epoch": 77, "iter": 3100, "lr": 0.04809, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28578, "top5_acc": 0.53562, "loss_cls": 4.10841, "loss": 4.10841, "time": 0.83241} +{"mode": "train", "epoch": 77, "iter": 3200, "lr": 0.04806, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28328, "top5_acc": 0.53875, "loss_cls": 4.08936, "loss": 4.08936, "time": 0.82518} +{"mode": "train", "epoch": 77, "iter": 3300, "lr": 0.04803, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27859, "top5_acc": 0.53078, "loss_cls": 4.15172, "loss": 4.15172, "time": 0.81827} +{"mode": "train", "epoch": 77, "iter": 3400, "lr": 0.048, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28562, "top5_acc": 0.54219, "loss_cls": 4.08029, "loss": 4.08029, "time": 0.82418} +{"mode": "train", "epoch": 77, "iter": 3500, "lr": 0.04798, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29078, "top5_acc": 0.53453, "loss_cls": 4.10851, "loss": 4.10851, "time": 0.83182} +{"mode": "train", "epoch": 77, "iter": 3600, "lr": 0.04795, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28453, "top5_acc": 0.53906, "loss_cls": 4.13517, "loss": 4.13517, "time": 0.82182} +{"mode": "train", "epoch": 77, "iter": 3700, "lr": 0.04792, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27953, "top5_acc": 0.53375, "loss_cls": 4.11855, "loss": 4.11855, "time": 0.82962} +{"mode": "val", "epoch": 77, "iter": 309, "lr": 0.04791, "top1_acc": 0.22499, "top5_acc": 0.46497, "mean_class_accuracy": 0.22488} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.04788, "memory": 15990, "data_time": 1.24129, "top1_acc": 0.29375, "top5_acc": 0.55656, "loss_cls": 4.00272, "loss": 4.00272, "time": 2.22952} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.04785, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29656, "top5_acc": 0.54672, "loss_cls": 4.02334, "loss": 4.02334, "time": 0.82176} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.04782, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29313, "top5_acc": 0.54688, "loss_cls": 4.04253, "loss": 4.04253, "time": 0.82812} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.04779, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29313, "top5_acc": 0.53797, "loss_cls": 4.08008, "loss": 4.08008, "time": 0.82368} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.04777, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28641, "top5_acc": 0.5475, "loss_cls": 4.05258, "loss": 4.05258, "time": 0.82083} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.04774, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28031, "top5_acc": 0.53672, "loss_cls": 4.08207, "loss": 4.08207, "time": 0.82889} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.04771, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29844, "top5_acc": 0.55391, "loss_cls": 4.00209, "loss": 4.00209, "time": 0.82229} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.04768, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28938, "top5_acc": 0.54875, "loss_cls": 4.068, "loss": 4.068, "time": 0.82423} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.04766, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29359, "top5_acc": 0.54891, "loss_cls": 4.02886, "loss": 4.02886, "time": 0.81707} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.04763, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29328, "top5_acc": 0.54531, "loss_cls": 4.06031, "loss": 4.06031, "time": 0.8179} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.0476, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28328, "top5_acc": 0.54281, "loss_cls": 4.10083, "loss": 4.10083, "time": 0.81898} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.04757, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28219, "top5_acc": 0.53531, "loss_cls": 4.11143, "loss": 4.11143, "time": 0.82389} +{"mode": "train", "epoch": 78, "iter": 1300, "lr": 0.04754, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28234, "top5_acc": 0.54406, "loss_cls": 4.0889, "loss": 4.0889, "time": 0.83469} +{"mode": "train", "epoch": 78, "iter": 1400, "lr": 0.04752, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27828, "top5_acc": 0.54281, "loss_cls": 4.11627, "loss": 4.11627, "time": 0.81769} +{"mode": "train", "epoch": 78, "iter": 1500, "lr": 0.04749, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.28453, "top5_acc": 0.55016, "loss_cls": 4.0484, "loss": 4.0484, "time": 0.8366} +{"mode": "train", "epoch": 78, "iter": 1600, "lr": 0.04746, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28625, "top5_acc": 0.53875, "loss_cls": 4.07219, "loss": 4.07219, "time": 0.83566} +{"mode": "train", "epoch": 78, "iter": 1700, "lr": 0.04743, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28781, "top5_acc": 0.55078, "loss_cls": 4.05623, "loss": 4.05623, "time": 0.82851} +{"mode": "train", "epoch": 78, "iter": 1800, "lr": 0.0474, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29484, "top5_acc": 0.54391, "loss_cls": 4.04779, "loss": 4.04779, "time": 0.83056} +{"mode": "train", "epoch": 78, "iter": 1900, "lr": 0.04738, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2875, "top5_acc": 0.54844, "loss_cls": 4.07977, "loss": 4.07977, "time": 0.82422} +{"mode": "train", "epoch": 78, "iter": 2000, "lr": 0.04735, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28328, "top5_acc": 0.53328, "loss_cls": 4.10135, "loss": 4.10135, "time": 0.82618} +{"mode": "train", "epoch": 78, "iter": 2100, "lr": 0.04732, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28781, "top5_acc": 0.53984, "loss_cls": 4.07787, "loss": 4.07787, "time": 0.82393} +{"mode": "train", "epoch": 78, "iter": 2200, "lr": 0.04729, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.28531, "top5_acc": 0.53688, "loss_cls": 4.09951, "loss": 4.09951, "time": 0.82546} +{"mode": "train", "epoch": 78, "iter": 2300, "lr": 0.04726, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28375, "top5_acc": 0.54234, "loss_cls": 4.0977, "loss": 4.0977, "time": 0.83225} +{"mode": "train", "epoch": 78, "iter": 2400, "lr": 0.04724, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28734, "top5_acc": 0.5425, "loss_cls": 4.05581, "loss": 4.05581, "time": 0.82456} +{"mode": "train", "epoch": 78, "iter": 2500, "lr": 0.04721, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28672, "top5_acc": 0.53656, "loss_cls": 4.08931, "loss": 4.08931, "time": 0.82512} +{"mode": "train", "epoch": 78, "iter": 2600, "lr": 0.04718, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26594, "top5_acc": 0.52812, "loss_cls": 4.18786, "loss": 4.18786, "time": 0.83159} +{"mode": "train", "epoch": 78, "iter": 2700, "lr": 0.04715, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28203, "top5_acc": 0.54281, "loss_cls": 4.08094, "loss": 4.08094, "time": 0.83618} +{"mode": "train", "epoch": 78, "iter": 2800, "lr": 0.04712, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29266, "top5_acc": 0.545, "loss_cls": 4.05857, "loss": 4.05857, "time": 0.82764} +{"mode": "train", "epoch": 78, "iter": 2900, "lr": 0.0471, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29109, "top5_acc": 0.54656, "loss_cls": 4.07519, "loss": 4.07519, "time": 0.82597} +{"mode": "train", "epoch": 78, "iter": 3000, "lr": 0.04707, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29063, "top5_acc": 0.54031, "loss_cls": 4.05098, "loss": 4.05098, "time": 0.82287} +{"mode": "train", "epoch": 78, "iter": 3100, "lr": 0.04704, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28797, "top5_acc": 0.54484, "loss_cls": 4.09201, "loss": 4.09201, "time": 0.83922} +{"mode": "train", "epoch": 78, "iter": 3200, "lr": 0.04701, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28094, "top5_acc": 0.53797, "loss_cls": 4.10767, "loss": 4.10767, "time": 0.82241} +{"mode": "train", "epoch": 78, "iter": 3300, "lr": 0.04699, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29078, "top5_acc": 0.53969, "loss_cls": 4.0897, "loss": 4.0897, "time": 0.8263} +{"mode": "train", "epoch": 78, "iter": 3400, "lr": 0.04696, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28641, "top5_acc": 0.53828, "loss_cls": 4.09957, "loss": 4.09957, "time": 0.83577} +{"mode": "train", "epoch": 78, "iter": 3500, "lr": 0.04693, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28172, "top5_acc": 0.53609, "loss_cls": 4.10297, "loss": 4.10297, "time": 0.82478} +{"mode": "train", "epoch": 78, "iter": 3600, "lr": 0.0469, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29375, "top5_acc": 0.54047, "loss_cls": 4.0844, "loss": 4.0844, "time": 0.81935} +{"mode": "train", "epoch": 78, "iter": 3700, "lr": 0.04687, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28281, "top5_acc": 0.54172, "loss_cls": 4.07845, "loss": 4.07845, "time": 0.82904} +{"mode": "val", "epoch": 78, "iter": 309, "lr": 0.04686, "top1_acc": 0.21334, "top5_acc": 0.45039, "mean_class_accuracy": 0.21306} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.04683, "memory": 15990, "data_time": 1.26747, "top1_acc": 0.28656, "top5_acc": 0.55703, "loss_cls": 4.02389, "loss": 4.02389, "time": 2.24818} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.0468, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29281, "top5_acc": 0.54891, "loss_cls": 4.02131, "loss": 4.02131, "time": 0.834} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.04678, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29297, "top5_acc": 0.54516, "loss_cls": 4.03175, "loss": 4.03175, "time": 0.82433} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.04675, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29156, "top5_acc": 0.55016, "loss_cls": 4.01653, "loss": 4.01653, "time": 0.82223} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.04672, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28625, "top5_acc": 0.55281, "loss_cls": 4.06062, "loss": 4.06062, "time": 0.8201} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.04669, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28813, "top5_acc": 0.54672, "loss_cls": 4.08173, "loss": 4.08173, "time": 0.82965} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.04667, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28469, "top5_acc": 0.54031, "loss_cls": 4.09734, "loss": 4.09734, "time": 0.82624} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.04664, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28594, "top5_acc": 0.54672, "loss_cls": 4.0559, "loss": 4.0559, "time": 0.82206} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.04661, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28531, "top5_acc": 0.54375, "loss_cls": 4.04768, "loss": 4.04768, "time": 0.82479} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.04658, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28859, "top5_acc": 0.53656, "loss_cls": 4.08129, "loss": 4.08129, "time": 0.82411} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.04655, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29453, "top5_acc": 0.54594, "loss_cls": 4.04055, "loss": 4.04055, "time": 0.82207} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.04653, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28422, "top5_acc": 0.54172, "loss_cls": 4.09315, "loss": 4.09315, "time": 0.83008} +{"mode": "train", "epoch": 79, "iter": 1300, "lr": 0.0465, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27969, "top5_acc": 0.54031, "loss_cls": 4.07686, "loss": 4.07686, "time": 0.8305} +{"mode": "train", "epoch": 79, "iter": 1400, "lr": 0.04647, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29016, "top5_acc": 0.54953, "loss_cls": 4.0606, "loss": 4.0606, "time": 0.82319} +{"mode": "train", "epoch": 79, "iter": 1500, "lr": 0.04644, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28391, "top5_acc": 0.53672, "loss_cls": 4.11428, "loss": 4.11428, "time": 0.83547} +{"mode": "train", "epoch": 79, "iter": 1600, "lr": 0.04641, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29078, "top5_acc": 0.54516, "loss_cls": 4.04651, "loss": 4.04651, "time": 0.83117} +{"mode": "train", "epoch": 79, "iter": 1700, "lr": 0.04639, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27875, "top5_acc": 0.54188, "loss_cls": 4.11852, "loss": 4.11852, "time": 0.8271} +{"mode": "train", "epoch": 79, "iter": 1800, "lr": 0.04636, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28219, "top5_acc": 0.54375, "loss_cls": 4.07582, "loss": 4.07582, "time": 0.82934} +{"mode": "train", "epoch": 79, "iter": 1900, "lr": 0.04633, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28906, "top5_acc": 0.53656, "loss_cls": 4.09507, "loss": 4.09507, "time": 0.82554} +{"mode": "train", "epoch": 79, "iter": 2000, "lr": 0.0463, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28719, "top5_acc": 0.54656, "loss_cls": 4.05356, "loss": 4.05356, "time": 0.81872} +{"mode": "train", "epoch": 79, "iter": 2100, "lr": 0.04628, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29969, "top5_acc": 0.54281, "loss_cls": 4.04023, "loss": 4.04023, "time": 0.82815} +{"mode": "train", "epoch": 79, "iter": 2200, "lr": 0.04625, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29359, "top5_acc": 0.54172, "loss_cls": 4.05625, "loss": 4.05625, "time": 0.82016} +{"mode": "train", "epoch": 79, "iter": 2300, "lr": 0.04622, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28813, "top5_acc": 0.54875, "loss_cls": 4.04362, "loss": 4.04362, "time": 0.82945} +{"mode": "train", "epoch": 79, "iter": 2400, "lr": 0.04619, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29, "top5_acc": 0.54578, "loss_cls": 4.05096, "loss": 4.05096, "time": 0.82375} +{"mode": "train", "epoch": 79, "iter": 2500, "lr": 0.04616, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28469, "top5_acc": 0.54234, "loss_cls": 4.0966, "loss": 4.0966, "time": 0.82428} +{"mode": "train", "epoch": 79, "iter": 2600, "lr": 0.04614, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29469, "top5_acc": 0.55422, "loss_cls": 4.03788, "loss": 4.03788, "time": 0.83047} +{"mode": "train", "epoch": 79, "iter": 2700, "lr": 0.04611, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28031, "top5_acc": 0.5425, "loss_cls": 4.09152, "loss": 4.09152, "time": 0.8221} +{"mode": "train", "epoch": 79, "iter": 2800, "lr": 0.04608, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29719, "top5_acc": 0.54656, "loss_cls": 4.03175, "loss": 4.03175, "time": 0.82004} +{"mode": "train", "epoch": 79, "iter": 2900, "lr": 0.04605, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29594, "top5_acc": 0.55391, "loss_cls": 4.04112, "loss": 4.04112, "time": 0.81893} +{"mode": "train", "epoch": 79, "iter": 3000, "lr": 0.04602, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28594, "top5_acc": 0.54344, "loss_cls": 4.0655, "loss": 4.0655, "time": 0.82773} +{"mode": "train", "epoch": 79, "iter": 3100, "lr": 0.046, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2975, "top5_acc": 0.55016, "loss_cls": 4.0251, "loss": 4.0251, "time": 0.82811} +{"mode": "train", "epoch": 79, "iter": 3200, "lr": 0.04597, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28453, "top5_acc": 0.54297, "loss_cls": 4.09999, "loss": 4.09999, "time": 0.8289} +{"mode": "train", "epoch": 79, "iter": 3300, "lr": 0.04594, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27922, "top5_acc": 0.53734, "loss_cls": 4.09587, "loss": 4.09587, "time": 0.82534} +{"mode": "train", "epoch": 79, "iter": 3400, "lr": 0.04591, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28891, "top5_acc": 0.54406, "loss_cls": 4.05923, "loss": 4.05923, "time": 0.82844} +{"mode": "train", "epoch": 79, "iter": 3500, "lr": 0.04588, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27953, "top5_acc": 0.54219, "loss_cls": 4.10649, "loss": 4.10649, "time": 0.81738} +{"mode": "train", "epoch": 79, "iter": 3600, "lr": 0.04586, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30188, "top5_acc": 0.55422, "loss_cls": 4.02967, "loss": 4.02967, "time": 0.81883} +{"mode": "train", "epoch": 79, "iter": 3700, "lr": 0.04583, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.29984, "top5_acc": 0.55719, "loss_cls": 4.00377, "loss": 4.00377, "time": 0.82787} +{"mode": "val", "epoch": 79, "iter": 309, "lr": 0.04582, "top1_acc": 0.22798, "top5_acc": 0.46786, "mean_class_accuracy": 0.2277} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.04579, "memory": 15990, "data_time": 1.24079, "top1_acc": 0.30109, "top5_acc": 0.56078, "loss_cls": 3.97128, "loss": 3.97128, "time": 2.23355} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.04576, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29359, "top5_acc": 0.54828, "loss_cls": 3.98979, "loss": 3.98979, "time": 0.82561} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.04573, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30297, "top5_acc": 0.55641, "loss_cls": 4.00416, "loss": 4.00416, "time": 0.82051} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.0457, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28531, "top5_acc": 0.54656, "loss_cls": 4.05528, "loss": 4.05528, "time": 0.82523} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.04568, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28703, "top5_acc": 0.55344, "loss_cls": 4.03855, "loss": 4.03855, "time": 0.8216} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.04565, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29828, "top5_acc": 0.5575, "loss_cls": 4.00776, "loss": 4.00776, "time": 0.8228} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.04562, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29703, "top5_acc": 0.55578, "loss_cls": 4.02965, "loss": 4.02965, "time": 0.82221} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.04559, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29656, "top5_acc": 0.54672, "loss_cls": 4.0199, "loss": 4.0199, "time": 0.81951} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.04557, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29766, "top5_acc": 0.54875, "loss_cls": 4.01504, "loss": 4.01504, "time": 0.81592} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.04554, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29281, "top5_acc": 0.54469, "loss_cls": 4.03965, "loss": 4.03965, "time": 0.81811} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.04551, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29578, "top5_acc": 0.55375, "loss_cls": 4.02273, "loss": 4.02273, "time": 0.82726} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.04548, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29094, "top5_acc": 0.54797, "loss_cls": 4.05422, "loss": 4.05422, "time": 0.82255} +{"mode": "train", "epoch": 80, "iter": 1300, "lr": 0.04545, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29688, "top5_acc": 0.55359, "loss_cls": 4.03295, "loss": 4.03295, "time": 0.82487} +{"mode": "train", "epoch": 80, "iter": 1400, "lr": 0.04543, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29172, "top5_acc": 0.55437, "loss_cls": 4.02428, "loss": 4.02428, "time": 0.81855} +{"mode": "train", "epoch": 80, "iter": 1500, "lr": 0.0454, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29297, "top5_acc": 0.54859, "loss_cls": 4.04649, "loss": 4.04649, "time": 0.831} +{"mode": "train", "epoch": 80, "iter": 1600, "lr": 0.04537, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28734, "top5_acc": 0.54328, "loss_cls": 4.09431, "loss": 4.09431, "time": 0.82775} +{"mode": "train", "epoch": 80, "iter": 1700, "lr": 0.04534, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28188, "top5_acc": 0.54688, "loss_cls": 4.05588, "loss": 4.05588, "time": 0.82157} +{"mode": "train", "epoch": 80, "iter": 1800, "lr": 0.04532, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28594, "top5_acc": 0.54422, "loss_cls": 4.0644, "loss": 4.0644, "time": 0.8377} +{"mode": "train", "epoch": 80, "iter": 1900, "lr": 0.04529, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29406, "top5_acc": 0.54859, "loss_cls": 4.0307, "loss": 4.0307, "time": 0.8282} +{"mode": "train", "epoch": 80, "iter": 2000, "lr": 0.04526, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28891, "top5_acc": 0.55, "loss_cls": 4.06546, "loss": 4.06546, "time": 0.82122} +{"mode": "train", "epoch": 80, "iter": 2100, "lr": 0.04523, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28688, "top5_acc": 0.54031, "loss_cls": 4.10272, "loss": 4.10272, "time": 0.8255} +{"mode": "train", "epoch": 80, "iter": 2200, "lr": 0.0452, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29094, "top5_acc": 0.54438, "loss_cls": 4.07941, "loss": 4.07941, "time": 0.8276} +{"mode": "train", "epoch": 80, "iter": 2300, "lr": 0.04518, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28922, "top5_acc": 0.54234, "loss_cls": 4.06774, "loss": 4.06774, "time": 0.83365} +{"mode": "train", "epoch": 80, "iter": 2400, "lr": 0.04515, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29828, "top5_acc": 0.55188, "loss_cls": 4.00954, "loss": 4.00954, "time": 0.82387} +{"mode": "train", "epoch": 80, "iter": 2500, "lr": 0.04512, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28797, "top5_acc": 0.55, "loss_cls": 4.05334, "loss": 4.05334, "time": 0.82867} +{"mode": "train", "epoch": 80, "iter": 2600, "lr": 0.04509, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28359, "top5_acc": 0.54156, "loss_cls": 4.09557, "loss": 4.09557, "time": 0.83224} +{"mode": "train", "epoch": 80, "iter": 2700, "lr": 0.04506, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28484, "top5_acc": 0.53828, "loss_cls": 4.1004, "loss": 4.1004, "time": 0.8239} +{"mode": "train", "epoch": 80, "iter": 2800, "lr": 0.04504, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29531, "top5_acc": 0.54797, "loss_cls": 4.02534, "loss": 4.02534, "time": 0.82176} +{"mode": "train", "epoch": 80, "iter": 2900, "lr": 0.04501, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28047, "top5_acc": 0.53609, "loss_cls": 4.10118, "loss": 4.10118, "time": 0.81859} +{"mode": "train", "epoch": 80, "iter": 3000, "lr": 0.04498, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.28562, "top5_acc": 0.53516, "loss_cls": 4.0786, "loss": 4.0786, "time": 0.82546} +{"mode": "train", "epoch": 80, "iter": 3100, "lr": 0.04495, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30047, "top5_acc": 0.55344, "loss_cls": 4.0325, "loss": 4.0325, "time": 0.82879} +{"mode": "train", "epoch": 80, "iter": 3200, "lr": 0.04493, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28766, "top5_acc": 0.54406, "loss_cls": 4.06649, "loss": 4.06649, "time": 0.82197} +{"mode": "train", "epoch": 80, "iter": 3300, "lr": 0.0449, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28656, "top5_acc": 0.53953, "loss_cls": 4.10769, "loss": 4.10769, "time": 0.83072} +{"mode": "train", "epoch": 80, "iter": 3400, "lr": 0.04487, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29203, "top5_acc": 0.55656, "loss_cls": 4.04241, "loss": 4.04241, "time": 0.82762} +{"mode": "train", "epoch": 80, "iter": 3500, "lr": 0.04484, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28188, "top5_acc": 0.54234, "loss_cls": 4.07713, "loss": 4.07713, "time": 0.8245} +{"mode": "train", "epoch": 80, "iter": 3600, "lr": 0.04481, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29156, "top5_acc": 0.54531, "loss_cls": 4.07312, "loss": 4.07312, "time": 0.82467} +{"mode": "train", "epoch": 80, "iter": 3700, "lr": 0.04479, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29359, "top5_acc": 0.54453, "loss_cls": 4.05885, "loss": 4.05885, "time": 0.83277} +{"mode": "val", "epoch": 80, "iter": 309, "lr": 0.04477, "top1_acc": 0.21557, "top5_acc": 0.45834, "mean_class_accuracy": 0.21528} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.04475, "memory": 15990, "data_time": 1.23524, "top1_acc": 0.30203, "top5_acc": 0.55562, "loss_cls": 4.0177, "loss": 4.0177, "time": 2.22788} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.04472, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30516, "top5_acc": 0.56219, "loss_cls": 3.96673, "loss": 3.96673, "time": 0.81951} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.04469, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29547, "top5_acc": 0.56531, "loss_cls": 3.98328, "loss": 3.98328, "time": 0.82329} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.04466, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2875, "top5_acc": 0.54516, "loss_cls": 4.07026, "loss": 4.07026, "time": 0.8226} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.04463, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.295, "top5_acc": 0.56344, "loss_cls": 4.00071, "loss": 4.00071, "time": 0.82323} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.04461, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28938, "top5_acc": 0.54375, "loss_cls": 4.06195, "loss": 4.06195, "time": 0.81981} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.04458, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29984, "top5_acc": 0.55312, "loss_cls": 4.02388, "loss": 4.02388, "time": 0.82414} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.04455, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29125, "top5_acc": 0.55266, "loss_cls": 4.0079, "loss": 4.0079, "time": 0.82048} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.04452, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28359, "top5_acc": 0.54547, "loss_cls": 4.09685, "loss": 4.09685, "time": 0.82577} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.0445, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2875, "top5_acc": 0.55141, "loss_cls": 4.05414, "loss": 4.05414, "time": 0.8236} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.04447, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29188, "top5_acc": 0.55062, "loss_cls": 4.0162, "loss": 4.0162, "time": 0.82107} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.04444, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29375, "top5_acc": 0.54953, "loss_cls": 4.04879, "loss": 4.04879, "time": 0.81866} +{"mode": "train", "epoch": 81, "iter": 1300, "lr": 0.04441, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29281, "top5_acc": 0.54641, "loss_cls": 4.05683, "loss": 4.05683, "time": 0.82755} +{"mode": "train", "epoch": 81, "iter": 1400, "lr": 0.04438, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29547, "top5_acc": 0.54172, "loss_cls": 4.06335, "loss": 4.06335, "time": 0.82073} +{"mode": "train", "epoch": 81, "iter": 1500, "lr": 0.04436, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29219, "top5_acc": 0.55797, "loss_cls": 4.01853, "loss": 4.01853, "time": 0.832} +{"mode": "train", "epoch": 81, "iter": 1600, "lr": 0.04433, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29109, "top5_acc": 0.54734, "loss_cls": 4.06183, "loss": 4.06183, "time": 0.82711} +{"mode": "train", "epoch": 81, "iter": 1700, "lr": 0.0443, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29125, "top5_acc": 0.53812, "loss_cls": 4.07182, "loss": 4.07182, "time": 0.82297} +{"mode": "train", "epoch": 81, "iter": 1800, "lr": 0.04427, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28891, "top5_acc": 0.54641, "loss_cls": 4.06632, "loss": 4.06632, "time": 0.82582} +{"mode": "train", "epoch": 81, "iter": 1900, "lr": 0.04425, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29453, "top5_acc": 0.55484, "loss_cls": 4.02036, "loss": 4.02036, "time": 0.82651} +{"mode": "train", "epoch": 81, "iter": 2000, "lr": 0.04422, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29047, "top5_acc": 0.54719, "loss_cls": 4.04077, "loss": 4.04077, "time": 0.82147} +{"mode": "train", "epoch": 81, "iter": 2100, "lr": 0.04419, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29812, "top5_acc": 0.54625, "loss_cls": 4.03566, "loss": 4.03566, "time": 0.81827} +{"mode": "train", "epoch": 81, "iter": 2200, "lr": 0.04416, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28922, "top5_acc": 0.55109, "loss_cls": 4.02735, "loss": 4.02735, "time": 0.82738} +{"mode": "train", "epoch": 81, "iter": 2300, "lr": 0.04413, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.28984, "top5_acc": 0.54891, "loss_cls": 4.05617, "loss": 4.05617, "time": 0.8214} +{"mode": "train", "epoch": 81, "iter": 2400, "lr": 0.04411, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28984, "top5_acc": 0.53734, "loss_cls": 4.08094, "loss": 4.08094, "time": 0.81478} +{"mode": "train", "epoch": 81, "iter": 2500, "lr": 0.04408, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30109, "top5_acc": 0.55297, "loss_cls": 3.98526, "loss": 3.98526, "time": 0.83046} +{"mode": "train", "epoch": 81, "iter": 2600, "lr": 0.04405, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28906, "top5_acc": 0.54141, "loss_cls": 4.05973, "loss": 4.05973, "time": 0.82562} +{"mode": "train", "epoch": 81, "iter": 2700, "lr": 0.04402, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28828, "top5_acc": 0.5375, "loss_cls": 4.08335, "loss": 4.08335, "time": 0.82013} +{"mode": "train", "epoch": 81, "iter": 2800, "lr": 0.044, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28828, "top5_acc": 0.54594, "loss_cls": 4.06359, "loss": 4.06359, "time": 0.81849} +{"mode": "train", "epoch": 81, "iter": 2900, "lr": 0.04397, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28516, "top5_acc": 0.55062, "loss_cls": 4.04631, "loss": 4.04631, "time": 0.81783} +{"mode": "train", "epoch": 81, "iter": 3000, "lr": 0.04394, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28609, "top5_acc": 0.5425, "loss_cls": 4.04937, "loss": 4.04937, "time": 0.82785} +{"mode": "train", "epoch": 81, "iter": 3100, "lr": 0.04391, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.29172, "top5_acc": 0.55578, "loss_cls": 4.03253, "loss": 4.03253, "time": 0.82107} +{"mode": "train", "epoch": 81, "iter": 3200, "lr": 0.04389, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29391, "top5_acc": 0.55078, "loss_cls": 4.03374, "loss": 4.03374, "time": 0.82297} +{"mode": "train", "epoch": 81, "iter": 3300, "lr": 0.04386, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28234, "top5_acc": 0.555, "loss_cls": 4.04515, "loss": 4.04515, "time": 0.83062} +{"mode": "train", "epoch": 81, "iter": 3400, "lr": 0.04383, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28453, "top5_acc": 0.54406, "loss_cls": 4.06555, "loss": 4.06555, "time": 0.82525} +{"mode": "train", "epoch": 81, "iter": 3500, "lr": 0.0438, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28047, "top5_acc": 0.53969, "loss_cls": 4.08187, "loss": 4.08187, "time": 0.82502} +{"mode": "train", "epoch": 81, "iter": 3600, "lr": 0.04377, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30656, "top5_acc": 0.56719, "loss_cls": 3.97116, "loss": 3.97116, "time": 0.81967} +{"mode": "train", "epoch": 81, "iter": 3700, "lr": 0.04375, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29938, "top5_acc": 0.55437, "loss_cls": 4.01911, "loss": 4.01911, "time": 0.83384} +{"mode": "val", "epoch": 81, "iter": 309, "lr": 0.04373, "top1_acc": 0.20427, "top5_acc": 0.42871, "mean_class_accuracy": 0.20425} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.04371, "memory": 15990, "data_time": 1.20874, "top1_acc": 0.29672, "top5_acc": 0.56297, "loss_cls": 3.98311, "loss": 3.98311, "time": 2.19386} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.04368, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30734, "top5_acc": 0.55219, "loss_cls": 4.00697, "loss": 4.00697, "time": 0.81784} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.04365, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30438, "top5_acc": 0.55188, "loss_cls": 4.02005, "loss": 4.02005, "time": 0.8248} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.04362, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29484, "top5_acc": 0.54891, "loss_cls": 4.03867, "loss": 4.03867, "time": 0.82021} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.04359, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29031, "top5_acc": 0.54688, "loss_cls": 4.05084, "loss": 4.05084, "time": 0.82485} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.04357, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29203, "top5_acc": 0.55422, "loss_cls": 4.01228, "loss": 4.01228, "time": 0.81981} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.04354, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28781, "top5_acc": 0.55078, "loss_cls": 4.04228, "loss": 4.04228, "time": 0.81959} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.04351, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29359, "top5_acc": 0.55781, "loss_cls": 4.00696, "loss": 4.00696, "time": 0.81654} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.04348, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29672, "top5_acc": 0.54875, "loss_cls": 4.01489, "loss": 4.01489, "time": 0.82134} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.04346, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29859, "top5_acc": 0.55766, "loss_cls": 4.02488, "loss": 4.02488, "time": 0.81961} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.04343, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29594, "top5_acc": 0.55625, "loss_cls": 3.99545, "loss": 3.99545, "time": 0.82275} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.0434, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29156, "top5_acc": 0.54797, "loss_cls": 4.0387, "loss": 4.0387, "time": 0.82366} +{"mode": "train", "epoch": 82, "iter": 1300, "lr": 0.04337, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29656, "top5_acc": 0.55812, "loss_cls": 3.99191, "loss": 3.99191, "time": 0.8292} +{"mode": "train", "epoch": 82, "iter": 1400, "lr": 0.04335, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28906, "top5_acc": 0.545, "loss_cls": 4.06216, "loss": 4.06216, "time": 0.82146} +{"mode": "train", "epoch": 82, "iter": 1500, "lr": 0.04332, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29469, "top5_acc": 0.55062, "loss_cls": 4.06467, "loss": 4.06467, "time": 0.83186} +{"mode": "train", "epoch": 82, "iter": 1600, "lr": 0.04329, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29594, "top5_acc": 0.55156, "loss_cls": 4.04105, "loss": 4.04105, "time": 0.82528} +{"mode": "train", "epoch": 82, "iter": 1700, "lr": 0.04326, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.28875, "top5_acc": 0.53766, "loss_cls": 4.07802, "loss": 4.07802, "time": 0.82973} +{"mode": "train", "epoch": 82, "iter": 1800, "lr": 0.04323, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29703, "top5_acc": 0.54859, "loss_cls": 4.03972, "loss": 4.03972, "time": 0.83282} +{"mode": "train", "epoch": 82, "iter": 1900, "lr": 0.04321, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30031, "top5_acc": 0.55562, "loss_cls": 3.98568, "loss": 3.98568, "time": 0.82497} +{"mode": "train", "epoch": 82, "iter": 2000, "lr": 0.04318, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29141, "top5_acc": 0.54375, "loss_cls": 4.06794, "loss": 4.06794, "time": 0.82135} +{"mode": "train", "epoch": 82, "iter": 2100, "lr": 0.04315, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30094, "top5_acc": 0.54688, "loss_cls": 4.0389, "loss": 4.0389, "time": 0.82285} +{"mode": "train", "epoch": 82, "iter": 2200, "lr": 0.04312, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27781, "top5_acc": 0.54297, "loss_cls": 4.08471, "loss": 4.08471, "time": 0.82875} +{"mode": "train", "epoch": 82, "iter": 2300, "lr": 0.0431, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29078, "top5_acc": 0.55062, "loss_cls": 4.01389, "loss": 4.01389, "time": 0.82193} +{"mode": "train", "epoch": 82, "iter": 2400, "lr": 0.04307, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29219, "top5_acc": 0.54781, "loss_cls": 4.01585, "loss": 4.01585, "time": 0.8199} +{"mode": "train", "epoch": 82, "iter": 2500, "lr": 0.04304, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28484, "top5_acc": 0.54688, "loss_cls": 4.068, "loss": 4.068, "time": 0.82772} +{"mode": "train", "epoch": 82, "iter": 2600, "lr": 0.04301, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29984, "top5_acc": 0.55609, "loss_cls": 4.01606, "loss": 4.01606, "time": 0.82794} +{"mode": "train", "epoch": 82, "iter": 2700, "lr": 0.04299, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30688, "top5_acc": 0.54953, "loss_cls": 4.00177, "loss": 4.00177, "time": 0.82474} +{"mode": "train", "epoch": 82, "iter": 2800, "lr": 0.04296, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29688, "top5_acc": 0.54516, "loss_cls": 4.03634, "loss": 4.03634, "time": 0.82104} +{"mode": "train", "epoch": 82, "iter": 2900, "lr": 0.04293, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29859, "top5_acc": 0.55047, "loss_cls": 4.02676, "loss": 4.02676, "time": 0.82173} +{"mode": "train", "epoch": 82, "iter": 3000, "lr": 0.0429, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30641, "top5_acc": 0.55219, "loss_cls": 3.99567, "loss": 3.99567, "time": 0.83073} +{"mode": "train", "epoch": 82, "iter": 3100, "lr": 0.04287, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30141, "top5_acc": 0.54828, "loss_cls": 4.00396, "loss": 4.00396, "time": 0.82245} +{"mode": "train", "epoch": 82, "iter": 3200, "lr": 0.04285, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29469, "top5_acc": 0.54719, "loss_cls": 4.02389, "loss": 4.02389, "time": 0.82381} +{"mode": "train", "epoch": 82, "iter": 3300, "lr": 0.04282, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28547, "top5_acc": 0.55, "loss_cls": 4.03446, "loss": 4.03446, "time": 0.83032} +{"mode": "train", "epoch": 82, "iter": 3400, "lr": 0.04279, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29719, "top5_acc": 0.54609, "loss_cls": 4.05803, "loss": 4.05803, "time": 0.82422} +{"mode": "train", "epoch": 82, "iter": 3500, "lr": 0.04276, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29734, "top5_acc": 0.54234, "loss_cls": 4.0354, "loss": 4.0354, "time": 0.82346} +{"mode": "train", "epoch": 82, "iter": 3600, "lr": 0.04274, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30016, "top5_acc": 0.55781, "loss_cls": 4.00483, "loss": 4.00483, "time": 0.82678} +{"mode": "train", "epoch": 82, "iter": 3700, "lr": 0.04271, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.28813, "top5_acc": 0.55141, "loss_cls": 4.04972, "loss": 4.04972, "time": 0.82841} +{"mode": "val", "epoch": 82, "iter": 309, "lr": 0.0427, "top1_acc": 0.22413, "top5_acc": 0.46077, "mean_class_accuracy": 0.22391} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.04267, "memory": 15990, "data_time": 1.23287, "top1_acc": 0.29844, "top5_acc": 0.56016, "loss_cls": 3.98188, "loss": 3.98188, "time": 2.21661} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.04264, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31141, "top5_acc": 0.56875, "loss_cls": 3.91128, "loss": 3.91128, "time": 0.8203} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.04261, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29812, "top5_acc": 0.55531, "loss_cls": 3.99174, "loss": 3.99174, "time": 0.82241} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.04259, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29484, "top5_acc": 0.54516, "loss_cls": 4.01469, "loss": 4.01469, "time": 0.81706} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.04256, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29875, "top5_acc": 0.55328, "loss_cls": 4.01537, "loss": 4.01537, "time": 0.82384} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.04253, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29203, "top5_acc": 0.55406, "loss_cls": 4.00454, "loss": 4.00454, "time": 0.82023} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.0425, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30422, "top5_acc": 0.55688, "loss_cls": 3.99757, "loss": 3.99757, "time": 0.81906} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.04247, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29063, "top5_acc": 0.55516, "loss_cls": 4.00235, "loss": 4.00235, "time": 0.82145} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.04245, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2925, "top5_acc": 0.5475, "loss_cls": 4.02442, "loss": 4.02442, "time": 0.82565} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.04242, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29906, "top5_acc": 0.55703, "loss_cls": 4.01, "loss": 4.01, "time": 0.82431} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.04239, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3, "top5_acc": 0.56281, "loss_cls": 4.00408, "loss": 4.00408, "time": 0.82567} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.04236, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29203, "top5_acc": 0.55453, "loss_cls": 4.02366, "loss": 4.02366, "time": 0.82518} +{"mode": "train", "epoch": 83, "iter": 1300, "lr": 0.04234, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29484, "top5_acc": 0.55406, "loss_cls": 3.99749, "loss": 3.99749, "time": 0.82925} +{"mode": "train", "epoch": 83, "iter": 1400, "lr": 0.04231, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29031, "top5_acc": 0.55437, "loss_cls": 4.00963, "loss": 4.00963, "time": 0.81945} +{"mode": "train", "epoch": 83, "iter": 1500, "lr": 0.04228, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29, "top5_acc": 0.54812, "loss_cls": 4.02591, "loss": 4.02591, "time": 0.83463} +{"mode": "train", "epoch": 83, "iter": 1600, "lr": 0.04225, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30344, "top5_acc": 0.55281, "loss_cls": 4.00176, "loss": 4.00176, "time": 0.82842} +{"mode": "train", "epoch": 83, "iter": 1700, "lr": 0.04223, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29609, "top5_acc": 0.54031, "loss_cls": 4.05034, "loss": 4.05034, "time": 0.82866} +{"mode": "train", "epoch": 83, "iter": 1800, "lr": 0.0422, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29563, "top5_acc": 0.5525, "loss_cls": 4.04674, "loss": 4.04674, "time": 0.82382} +{"mode": "train", "epoch": 83, "iter": 1900, "lr": 0.04217, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29297, "top5_acc": 0.55391, "loss_cls": 4.04019, "loss": 4.04019, "time": 0.82817} +{"mode": "train", "epoch": 83, "iter": 2000, "lr": 0.04214, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30047, "top5_acc": 0.55516, "loss_cls": 4.01527, "loss": 4.01527, "time": 0.82743} +{"mode": "train", "epoch": 83, "iter": 2100, "lr": 0.04212, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29703, "top5_acc": 0.54625, "loss_cls": 4.0485, "loss": 4.0485, "time": 0.82519} +{"mode": "train", "epoch": 83, "iter": 2200, "lr": 0.04209, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30125, "top5_acc": 0.55359, "loss_cls": 3.99879, "loss": 3.99879, "time": 0.83072} +{"mode": "train", "epoch": 83, "iter": 2300, "lr": 0.04206, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29422, "top5_acc": 0.56016, "loss_cls": 4.03039, "loss": 4.03039, "time": 0.81863} +{"mode": "train", "epoch": 83, "iter": 2400, "lr": 0.04203, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29297, "top5_acc": 0.53797, "loss_cls": 4.07955, "loss": 4.07955, "time": 0.82664} +{"mode": "train", "epoch": 83, "iter": 2500, "lr": 0.04201, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2925, "top5_acc": 0.54906, "loss_cls": 4.0251, "loss": 4.0251, "time": 0.83404} +{"mode": "train", "epoch": 83, "iter": 2600, "lr": 0.04198, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29281, "top5_acc": 0.55266, "loss_cls": 4.03528, "loss": 4.03528, "time": 0.82174} +{"mode": "train", "epoch": 83, "iter": 2700, "lr": 0.04195, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30031, "top5_acc": 0.56203, "loss_cls": 3.98834, "loss": 3.98834, "time": 0.82317} +{"mode": "train", "epoch": 83, "iter": 2800, "lr": 0.04192, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29188, "top5_acc": 0.54625, "loss_cls": 4.0498, "loss": 4.0498, "time": 0.81896} +{"mode": "train", "epoch": 83, "iter": 2900, "lr": 0.0419, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30688, "top5_acc": 0.56094, "loss_cls": 3.97783, "loss": 3.97783, "time": 0.82646} +{"mode": "train", "epoch": 83, "iter": 3000, "lr": 0.04187, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29594, "top5_acc": 0.55625, "loss_cls": 3.97374, "loss": 3.97374, "time": 0.82952} +{"mode": "train", "epoch": 83, "iter": 3100, "lr": 0.04184, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3, "top5_acc": 0.55, "loss_cls": 4.02349, "loss": 4.02349, "time": 0.82393} +{"mode": "train", "epoch": 83, "iter": 3200, "lr": 0.04181, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29594, "top5_acc": 0.54688, "loss_cls": 4.03198, "loss": 4.03198, "time": 0.82988} +{"mode": "train", "epoch": 83, "iter": 3300, "lr": 0.04178, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29266, "top5_acc": 0.5475, "loss_cls": 4.03864, "loss": 4.03864, "time": 0.82745} +{"mode": "train", "epoch": 83, "iter": 3400, "lr": 0.04176, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29766, "top5_acc": 0.56203, "loss_cls": 4.01717, "loss": 4.01717, "time": 0.81849} +{"mode": "train", "epoch": 83, "iter": 3500, "lr": 0.04173, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30859, "top5_acc": 0.56016, "loss_cls": 3.96064, "loss": 3.96064, "time": 0.81605} +{"mode": "train", "epoch": 83, "iter": 3600, "lr": 0.0417, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29391, "top5_acc": 0.54516, "loss_cls": 4.07025, "loss": 4.07025, "time": 0.82062} +{"mode": "train", "epoch": 83, "iter": 3700, "lr": 0.04167, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28578, "top5_acc": 0.55531, "loss_cls": 4.04293, "loss": 4.04293, "time": 0.82815} +{"mode": "val", "epoch": 83, "iter": 309, "lr": 0.04166, "top1_acc": 0.21, "top5_acc": 0.44304, "mean_class_accuracy": 0.20998} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.04163, "memory": 15990, "data_time": 1.26212, "top1_acc": 0.30516, "top5_acc": 0.55953, "loss_cls": 3.98154, "loss": 3.98154, "time": 2.25388} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.04161, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29969, "top5_acc": 0.54797, "loss_cls": 4.01954, "loss": 4.01954, "time": 0.82524} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.04158, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30375, "top5_acc": 0.55781, "loss_cls": 3.98225, "loss": 3.98225, "time": 0.82224} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.04155, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30359, "top5_acc": 0.56312, "loss_cls": 3.94783, "loss": 3.94783, "time": 0.82722} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.04152, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30672, "top5_acc": 0.57391, "loss_cls": 3.90729, "loss": 3.90729, "time": 0.83366} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.0415, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30281, "top5_acc": 0.55188, "loss_cls": 4.01529, "loss": 4.01529, "time": 0.82656} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.04147, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29906, "top5_acc": 0.56, "loss_cls": 3.99351, "loss": 3.99351, "time": 0.82175} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.04144, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29219, "top5_acc": 0.55547, "loss_cls": 4.03211, "loss": 4.03211, "time": 0.81837} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.04141, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29375, "top5_acc": 0.55437, "loss_cls": 4.00887, "loss": 4.00887, "time": 0.81908} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.04139, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3025, "top5_acc": 0.55344, "loss_cls": 4.00668, "loss": 4.00668, "time": 0.81994} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.04136, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29391, "top5_acc": 0.54875, "loss_cls": 3.99849, "loss": 3.99849, "time": 0.82234} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.04133, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29734, "top5_acc": 0.55344, "loss_cls": 4.02418, "loss": 4.02418, "time": 0.82636} +{"mode": "train", "epoch": 84, "iter": 1300, "lr": 0.0413, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29875, "top5_acc": 0.54656, "loss_cls": 4.00641, "loss": 4.00641, "time": 0.83218} +{"mode": "train", "epoch": 84, "iter": 1400, "lr": 0.04128, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30391, "top5_acc": 0.56641, "loss_cls": 3.96992, "loss": 3.96992, "time": 0.82449} +{"mode": "train", "epoch": 84, "iter": 1500, "lr": 0.04125, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29406, "top5_acc": 0.55969, "loss_cls": 3.98267, "loss": 3.98267, "time": 0.83332} +{"mode": "train", "epoch": 84, "iter": 1600, "lr": 0.04122, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.30797, "top5_acc": 0.5675, "loss_cls": 3.94591, "loss": 3.94591, "time": 0.82812} +{"mode": "train", "epoch": 84, "iter": 1700, "lr": 0.04119, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29406, "top5_acc": 0.53469, "loss_cls": 4.05431, "loss": 4.05431, "time": 0.83059} +{"mode": "train", "epoch": 84, "iter": 1800, "lr": 0.04117, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30391, "top5_acc": 0.56453, "loss_cls": 3.99641, "loss": 3.99641, "time": 0.82814} +{"mode": "train", "epoch": 84, "iter": 1900, "lr": 0.04114, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29234, "top5_acc": 0.55141, "loss_cls": 4.02774, "loss": 4.02774, "time": 0.82093} +{"mode": "train", "epoch": 84, "iter": 2000, "lr": 0.04111, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29453, "top5_acc": 0.555, "loss_cls": 4.04538, "loss": 4.04538, "time": 0.82381} +{"mode": "train", "epoch": 84, "iter": 2100, "lr": 0.04108, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.29516, "top5_acc": 0.54578, "loss_cls": 4.04917, "loss": 4.04917, "time": 0.82442} +{"mode": "train", "epoch": 84, "iter": 2200, "lr": 0.04106, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.29844, "top5_acc": 0.55594, "loss_cls": 3.99239, "loss": 3.99239, "time": 0.83223} +{"mode": "train", "epoch": 84, "iter": 2300, "lr": 0.04103, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30484, "top5_acc": 0.56484, "loss_cls": 3.9708, "loss": 3.9708, "time": 0.81881} +{"mode": "train", "epoch": 84, "iter": 2400, "lr": 0.041, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.295, "top5_acc": 0.55781, "loss_cls": 4.02244, "loss": 4.02244, "time": 0.82263} +{"mode": "train", "epoch": 84, "iter": 2500, "lr": 0.04097, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31234, "top5_acc": 0.56, "loss_cls": 3.98492, "loss": 3.98492, "time": 0.82667} +{"mode": "train", "epoch": 84, "iter": 2600, "lr": 0.04095, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2925, "top5_acc": 0.55172, "loss_cls": 4.02076, "loss": 4.02076, "time": 0.82016} +{"mode": "train", "epoch": 84, "iter": 2700, "lr": 0.04092, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29516, "top5_acc": 0.55844, "loss_cls": 4.02137, "loss": 4.02137, "time": 0.8244} +{"mode": "train", "epoch": 84, "iter": 2800, "lr": 0.04089, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3, "top5_acc": 0.55828, "loss_cls": 3.99605, "loss": 3.99605, "time": 0.82586} +{"mode": "train", "epoch": 84, "iter": 2900, "lr": 0.04086, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29531, "top5_acc": 0.5525, "loss_cls": 4.04221, "loss": 4.04221, "time": 0.82915} +{"mode": "train", "epoch": 84, "iter": 3000, "lr": 0.04084, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30031, "top5_acc": 0.56437, "loss_cls": 3.98376, "loss": 3.98376, "time": 0.82413} +{"mode": "train", "epoch": 84, "iter": 3100, "lr": 0.04081, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30938, "top5_acc": 0.56281, "loss_cls": 3.96014, "loss": 3.96014, "time": 0.82505} +{"mode": "train", "epoch": 84, "iter": 3200, "lr": 0.04078, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29484, "top5_acc": 0.54781, "loss_cls": 4.04185, "loss": 4.04185, "time": 0.82971} +{"mode": "train", "epoch": 84, "iter": 3300, "lr": 0.04075, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30734, "top5_acc": 0.56719, "loss_cls": 3.93317, "loss": 3.93317, "time": 0.81973} +{"mode": "train", "epoch": 84, "iter": 3400, "lr": 0.04073, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29906, "top5_acc": 0.55141, "loss_cls": 4.03534, "loss": 4.03534, "time": 0.82538} +{"mode": "train", "epoch": 84, "iter": 3500, "lr": 0.0407, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30375, "top5_acc": 0.55047, "loss_cls": 4.00611, "loss": 4.00611, "time": 0.83068} +{"mode": "train", "epoch": 84, "iter": 3600, "lr": 0.04067, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30219, "top5_acc": 0.55531, "loss_cls": 3.99068, "loss": 3.99068, "time": 0.82488} +{"mode": "train", "epoch": 84, "iter": 3700, "lr": 0.04064, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29609, "top5_acc": 0.55156, "loss_cls": 4.02469, "loss": 4.02469, "time": 0.83414} +{"mode": "val", "epoch": 84, "iter": 309, "lr": 0.04063, "top1_acc": 0.22454, "top5_acc": 0.46852, "mean_class_accuracy": 0.22426} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.0406, "memory": 15990, "data_time": 1.25516, "top1_acc": 0.31516, "top5_acc": 0.57719, "loss_cls": 3.9187, "loss": 3.9187, "time": 2.25273} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.04058, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29266, "top5_acc": 0.55906, "loss_cls": 4.00523, "loss": 4.00523, "time": 0.83189} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.04055, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30406, "top5_acc": 0.56016, "loss_cls": 3.98317, "loss": 3.98317, "time": 0.82971} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.04052, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30766, "top5_acc": 0.56578, "loss_cls": 3.94101, "loss": 3.94101, "time": 0.8329} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.04049, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30938, "top5_acc": 0.56844, "loss_cls": 3.93857, "loss": 3.93857, "time": 0.81844} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.04047, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29875, "top5_acc": 0.56656, "loss_cls": 3.93087, "loss": 3.93087, "time": 0.81759} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.04044, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31766, "top5_acc": 0.57656, "loss_cls": 3.87913, "loss": 3.87913, "time": 0.81612} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.04041, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30438, "top5_acc": 0.55625, "loss_cls": 4.00381, "loss": 4.00381, "time": 0.81197} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.04038, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30234, "top5_acc": 0.55875, "loss_cls": 3.9744, "loss": 3.9744, "time": 0.81484} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.04036, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29516, "top5_acc": 0.55281, "loss_cls": 4.01631, "loss": 4.01631, "time": 0.81175} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.04033, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29812, "top5_acc": 0.55437, "loss_cls": 3.99243, "loss": 3.99243, "time": 0.80905} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.0403, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30688, "top5_acc": 0.57172, "loss_cls": 3.92172, "loss": 3.92172, "time": 0.81693} +{"mode": "train", "epoch": 85, "iter": 1300, "lr": 0.04027, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30469, "top5_acc": 0.56203, "loss_cls": 3.95855, "loss": 3.95855, "time": 0.81142} +{"mode": "train", "epoch": 85, "iter": 1400, "lr": 0.04025, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29656, "top5_acc": 0.56828, "loss_cls": 3.97867, "loss": 3.97867, "time": 0.82278} +{"mode": "train", "epoch": 85, "iter": 1500, "lr": 0.04022, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29938, "top5_acc": 0.55594, "loss_cls": 3.9989, "loss": 3.9989, "time": 0.81686} +{"mode": "train", "epoch": 85, "iter": 1600, "lr": 0.04019, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2925, "top5_acc": 0.55641, "loss_cls": 4.007, "loss": 4.007, "time": 0.81494} +{"mode": "train", "epoch": 85, "iter": 1700, "lr": 0.04016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29719, "top5_acc": 0.55297, "loss_cls": 4.00384, "loss": 4.00384, "time": 0.81152} +{"mode": "train", "epoch": 85, "iter": 1800, "lr": 0.04014, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29812, "top5_acc": 0.55703, "loss_cls": 4.00451, "loss": 4.00451, "time": 0.81213} +{"mode": "train", "epoch": 85, "iter": 1900, "lr": 0.04011, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29281, "top5_acc": 0.5425, "loss_cls": 4.03989, "loss": 4.03989, "time": 0.81512} +{"mode": "train", "epoch": 85, "iter": 2000, "lr": 0.04008, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30453, "top5_acc": 0.56594, "loss_cls": 3.97554, "loss": 3.97554, "time": 0.82038} +{"mode": "train", "epoch": 85, "iter": 2100, "lr": 0.04006, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29891, "top5_acc": 0.54859, "loss_cls": 4.02068, "loss": 4.02068, "time": 0.81359} +{"mode": "train", "epoch": 85, "iter": 2200, "lr": 0.04003, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29422, "top5_acc": 0.55984, "loss_cls": 4.01128, "loss": 4.01128, "time": 0.82029} +{"mode": "train", "epoch": 85, "iter": 2300, "lr": 0.04, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30656, "top5_acc": 0.55531, "loss_cls": 3.97884, "loss": 3.97884, "time": 0.81126} +{"mode": "train", "epoch": 85, "iter": 2400, "lr": 0.03997, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30172, "top5_acc": 0.55297, "loss_cls": 3.96847, "loss": 3.96847, "time": 0.81548} +{"mode": "train", "epoch": 85, "iter": 2500, "lr": 0.03995, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30562, "top5_acc": 0.55625, "loss_cls": 3.99412, "loss": 3.99412, "time": 0.81787} +{"mode": "train", "epoch": 85, "iter": 2600, "lr": 0.03992, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30219, "top5_acc": 0.56531, "loss_cls": 3.95078, "loss": 3.95078, "time": 0.81704} +{"mode": "train", "epoch": 85, "iter": 2700, "lr": 0.03989, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29625, "top5_acc": 0.55922, "loss_cls": 3.9889, "loss": 3.9889, "time": 0.81677} +{"mode": "train", "epoch": 85, "iter": 2800, "lr": 0.03986, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29313, "top5_acc": 0.5525, "loss_cls": 4.0335, "loss": 4.0335, "time": 0.81792} +{"mode": "train", "epoch": 85, "iter": 2900, "lr": 0.03984, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30031, "top5_acc": 0.56203, "loss_cls": 3.9541, "loss": 3.9541, "time": 0.81068} +{"mode": "train", "epoch": 85, "iter": 3000, "lr": 0.03981, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30438, "top5_acc": 0.56938, "loss_cls": 3.94556, "loss": 3.94556, "time": 0.81564} +{"mode": "train", "epoch": 85, "iter": 3100, "lr": 0.03978, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29125, "top5_acc": 0.56281, "loss_cls": 3.97748, "loss": 3.97748, "time": 0.81857} +{"mode": "train", "epoch": 85, "iter": 3200, "lr": 0.03975, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29359, "top5_acc": 0.54797, "loss_cls": 4.03203, "loss": 4.03203, "time": 0.81441} +{"mode": "train", "epoch": 85, "iter": 3300, "lr": 0.03973, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30062, "top5_acc": 0.55141, "loss_cls": 4.02055, "loss": 4.02055, "time": 0.81597} +{"mode": "train", "epoch": 85, "iter": 3400, "lr": 0.0397, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30609, "top5_acc": 0.57719, "loss_cls": 3.9352, "loss": 3.9352, "time": 0.8153} +{"mode": "train", "epoch": 85, "iter": 3500, "lr": 0.03967, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30078, "top5_acc": 0.54953, "loss_cls": 4.03279, "loss": 4.03279, "time": 0.81392} +{"mode": "train", "epoch": 85, "iter": 3600, "lr": 0.03964, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28734, "top5_acc": 0.55078, "loss_cls": 4.0246, "loss": 4.0246, "time": 0.81811} +{"mode": "train", "epoch": 85, "iter": 3700, "lr": 0.03962, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2975, "top5_acc": 0.55641, "loss_cls": 3.97575, "loss": 3.97575, "time": 0.81773} +{"mode": "val", "epoch": 85, "iter": 309, "lr": 0.0396, "top1_acc": 0.21891, "top5_acc": 0.4588, "mean_class_accuracy": 0.21887} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.03958, "memory": 15990, "data_time": 1.41372, "top1_acc": 0.31125, "top5_acc": 0.56531, "loss_cls": 3.93057, "loss": 3.93057, "time": 2.39896} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.03955, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31766, "top5_acc": 0.57891, "loss_cls": 3.87118, "loss": 3.87118, "time": 0.82591} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.03952, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31312, "top5_acc": 0.58094, "loss_cls": 3.88955, "loss": 3.88955, "time": 0.82095} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.0395, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31016, "top5_acc": 0.56406, "loss_cls": 3.95637, "loss": 3.95637, "time": 0.82427} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.03947, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29031, "top5_acc": 0.55516, "loss_cls": 3.99943, "loss": 3.99943, "time": 0.81859} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.03944, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30781, "top5_acc": 0.56031, "loss_cls": 3.974, "loss": 3.974, "time": 0.81801} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.03941, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30859, "top5_acc": 0.55844, "loss_cls": 3.95098, "loss": 3.95098, "time": 0.81221} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.03939, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28953, "top5_acc": 0.56469, "loss_cls": 3.98741, "loss": 3.98741, "time": 0.81176} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.03936, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29891, "top5_acc": 0.56047, "loss_cls": 3.98965, "loss": 3.98965, "time": 0.81062} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.03933, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30766, "top5_acc": 0.5675, "loss_cls": 3.96162, "loss": 3.96162, "time": 0.8129} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.0393, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30391, "top5_acc": 0.56328, "loss_cls": 3.97441, "loss": 3.97441, "time": 0.81063} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.03928, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29672, "top5_acc": 0.56844, "loss_cls": 3.94948, "loss": 3.94948, "time": 0.81356} +{"mode": "train", "epoch": 86, "iter": 1300, "lr": 0.03925, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30188, "top5_acc": 0.5625, "loss_cls": 3.9488, "loss": 3.9488, "time": 0.81567} +{"mode": "train", "epoch": 86, "iter": 1400, "lr": 0.03922, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30516, "top5_acc": 0.56109, "loss_cls": 3.97982, "loss": 3.97982, "time": 0.81668} +{"mode": "train", "epoch": 86, "iter": 1500, "lr": 0.03919, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29891, "top5_acc": 0.56844, "loss_cls": 3.97586, "loss": 3.97586, "time": 0.81147} +{"mode": "train", "epoch": 86, "iter": 1600, "lr": 0.03917, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30047, "top5_acc": 0.55828, "loss_cls": 4.00842, "loss": 4.00842, "time": 0.81517} +{"mode": "train", "epoch": 86, "iter": 1700, "lr": 0.03914, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29812, "top5_acc": 0.55344, "loss_cls": 4.02186, "loss": 4.02186, "time": 0.81194} +{"mode": "train", "epoch": 86, "iter": 1800, "lr": 0.03911, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30688, "top5_acc": 0.55875, "loss_cls": 3.95197, "loss": 3.95197, "time": 0.81065} +{"mode": "train", "epoch": 86, "iter": 1900, "lr": 0.03909, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3, "top5_acc": 0.55734, "loss_cls": 4.00815, "loss": 4.00815, "time": 0.80798} +{"mode": "train", "epoch": 86, "iter": 2000, "lr": 0.03906, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29453, "top5_acc": 0.56297, "loss_cls": 3.98964, "loss": 3.98964, "time": 0.8137} +{"mode": "train", "epoch": 86, "iter": 2100, "lr": 0.03903, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28953, "top5_acc": 0.54531, "loss_cls": 4.05629, "loss": 4.05629, "time": 0.81137} +{"mode": "train", "epoch": 86, "iter": 2200, "lr": 0.039, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30312, "top5_acc": 0.55688, "loss_cls": 3.9724, "loss": 3.9724, "time": 0.81223} +{"mode": "train", "epoch": 86, "iter": 2300, "lr": 0.03898, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30844, "top5_acc": 0.56437, "loss_cls": 3.95276, "loss": 3.95276, "time": 0.81308} +{"mode": "train", "epoch": 86, "iter": 2400, "lr": 0.03895, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29953, "top5_acc": 0.56312, "loss_cls": 3.95033, "loss": 3.95033, "time": 0.81306} +{"mode": "train", "epoch": 86, "iter": 2500, "lr": 0.03892, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29672, "top5_acc": 0.56031, "loss_cls": 4.01263, "loss": 4.01263, "time": 0.81727} +{"mode": "train", "epoch": 86, "iter": 2600, "lr": 0.03889, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29766, "top5_acc": 0.54719, "loss_cls": 4.02911, "loss": 4.02911, "time": 0.81601} +{"mode": "train", "epoch": 86, "iter": 2700, "lr": 0.03887, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29156, "top5_acc": 0.55328, "loss_cls": 4.00867, "loss": 4.00867, "time": 0.81056} +{"mode": "train", "epoch": 86, "iter": 2800, "lr": 0.03884, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30188, "top5_acc": 0.56234, "loss_cls": 3.97146, "loss": 3.97146, "time": 0.81635} +{"mode": "train", "epoch": 86, "iter": 2900, "lr": 0.03881, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30391, "top5_acc": 0.555, "loss_cls": 3.99312, "loss": 3.99312, "time": 0.80771} +{"mode": "train", "epoch": 86, "iter": 3000, "lr": 0.03879, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29531, "top5_acc": 0.55484, "loss_cls": 3.99145, "loss": 3.99145, "time": 0.82369} +{"mode": "train", "epoch": 86, "iter": 3100, "lr": 0.03876, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31391, "top5_acc": 0.56828, "loss_cls": 3.90725, "loss": 3.90725, "time": 0.81598} +{"mode": "train", "epoch": 86, "iter": 3200, "lr": 0.03873, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30984, "top5_acc": 0.57703, "loss_cls": 3.90279, "loss": 3.90279, "time": 0.81692} +{"mode": "train", "epoch": 86, "iter": 3300, "lr": 0.0387, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31328, "top5_acc": 0.56172, "loss_cls": 3.95903, "loss": 3.95903, "time": 0.81929} +{"mode": "train", "epoch": 86, "iter": 3400, "lr": 0.03868, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30391, "top5_acc": 0.56047, "loss_cls": 3.99372, "loss": 3.99372, "time": 0.80692} +{"mode": "train", "epoch": 86, "iter": 3500, "lr": 0.03865, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29047, "top5_acc": 0.55078, "loss_cls": 4.01954, "loss": 4.01954, "time": 0.81339} +{"mode": "train", "epoch": 86, "iter": 3600, "lr": 0.03862, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30281, "top5_acc": 0.55828, "loss_cls": 3.98929, "loss": 3.98929, "time": 0.8178} +{"mode": "train", "epoch": 86, "iter": 3700, "lr": 0.0386, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30938, "top5_acc": 0.55547, "loss_cls": 3.9782, "loss": 3.9782, "time": 0.81271} +{"mode": "val", "epoch": 86, "iter": 309, "lr": 0.03858, "top1_acc": 0.23416, "top5_acc": 0.47308, "mean_class_accuracy": 0.23386} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.03856, "memory": 15990, "data_time": 1.31459, "top1_acc": 0.29438, "top5_acc": 0.55672, "loss_cls": 3.97173, "loss": 3.97173, "time": 2.29386} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.03853, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30953, "top5_acc": 0.56672, "loss_cls": 3.90723, "loss": 3.90723, "time": 0.81786} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.0385, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30625, "top5_acc": 0.57156, "loss_cls": 3.94627, "loss": 3.94627, "time": 0.80774} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.03847, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.31094, "top5_acc": 0.56656, "loss_cls": 3.91869, "loss": 3.91869, "time": 0.80757} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.03845, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.30641, "top5_acc": 0.56422, "loss_cls": 3.94141, "loss": 3.94141, "time": 0.80944} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.03842, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29922, "top5_acc": 0.56094, "loss_cls": 3.98875, "loss": 3.98875, "time": 0.81313} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.03839, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.29734, "top5_acc": 0.56547, "loss_cls": 3.94849, "loss": 3.94849, "time": 0.81282} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.03837, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31031, "top5_acc": 0.55937, "loss_cls": 3.98029, "loss": 3.98029, "time": 0.8167} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.03834, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3025, "top5_acc": 0.56563, "loss_cls": 3.94499, "loss": 3.94499, "time": 0.81307} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.03831, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.305, "top5_acc": 0.56781, "loss_cls": 3.94384, "loss": 3.94384, "time": 0.81127} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.03828, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30734, "top5_acc": 0.57328, "loss_cls": 3.94366, "loss": 3.94366, "time": 0.81066} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.03826, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29953, "top5_acc": 0.55781, "loss_cls": 3.99872, "loss": 3.99872, "time": 0.80837} +{"mode": "train", "epoch": 87, "iter": 1300, "lr": 0.03823, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29734, "top5_acc": 0.5575, "loss_cls": 3.99294, "loss": 3.99294, "time": 0.81465} +{"mode": "train", "epoch": 87, "iter": 1400, "lr": 0.0382, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31219, "top5_acc": 0.56969, "loss_cls": 3.94956, "loss": 3.94956, "time": 0.80747} +{"mode": "train", "epoch": 87, "iter": 1500, "lr": 0.03817, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31672, "top5_acc": 0.58109, "loss_cls": 3.89461, "loss": 3.89461, "time": 0.82043} +{"mode": "train", "epoch": 87, "iter": 1600, "lr": 0.03815, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30125, "top5_acc": 0.56063, "loss_cls": 3.98311, "loss": 3.98311, "time": 0.81125} +{"mode": "train", "epoch": 87, "iter": 1700, "lr": 0.03812, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30359, "top5_acc": 0.57016, "loss_cls": 3.93752, "loss": 3.93752, "time": 0.80774} +{"mode": "train", "epoch": 87, "iter": 1800, "lr": 0.03809, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31125, "top5_acc": 0.56109, "loss_cls": 3.94111, "loss": 3.94111, "time": 0.80685} +{"mode": "train", "epoch": 87, "iter": 1900, "lr": 0.03807, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31531, "top5_acc": 0.56578, "loss_cls": 3.92046, "loss": 3.92046, "time": 0.81539} +{"mode": "train", "epoch": 87, "iter": 2000, "lr": 0.03804, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29781, "top5_acc": 0.56109, "loss_cls": 3.97511, "loss": 3.97511, "time": 0.81379} +{"mode": "train", "epoch": 87, "iter": 2100, "lr": 0.03801, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29953, "top5_acc": 0.55172, "loss_cls": 4.00148, "loss": 4.00148, "time": 0.81228} +{"mode": "train", "epoch": 87, "iter": 2200, "lr": 0.03798, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31062, "top5_acc": 0.5775, "loss_cls": 3.9172, "loss": 3.9172, "time": 0.81295} +{"mode": "train", "epoch": 87, "iter": 2300, "lr": 0.03796, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30688, "top5_acc": 0.56203, "loss_cls": 3.96117, "loss": 3.96117, "time": 0.81374} +{"mode": "train", "epoch": 87, "iter": 2400, "lr": 0.03793, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29766, "top5_acc": 0.55859, "loss_cls": 3.9853, "loss": 3.9853, "time": 0.82298} +{"mode": "train", "epoch": 87, "iter": 2500, "lr": 0.0379, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30516, "top5_acc": 0.56375, "loss_cls": 3.95012, "loss": 3.95012, "time": 0.81217} +{"mode": "train", "epoch": 87, "iter": 2600, "lr": 0.03788, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29938, "top5_acc": 0.56141, "loss_cls": 3.94444, "loss": 3.94444, "time": 0.81585} +{"mode": "train", "epoch": 87, "iter": 2700, "lr": 0.03785, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30125, "top5_acc": 0.56141, "loss_cls": 3.98005, "loss": 3.98005, "time": 0.80979} +{"mode": "train", "epoch": 87, "iter": 2800, "lr": 0.03782, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29984, "top5_acc": 0.55828, "loss_cls": 3.96467, "loss": 3.96467, "time": 0.81489} +{"mode": "train", "epoch": 87, "iter": 2900, "lr": 0.03779, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30172, "top5_acc": 0.5525, "loss_cls": 3.97259, "loss": 3.97259, "time": 0.81203} +{"mode": "train", "epoch": 87, "iter": 3000, "lr": 0.03777, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28453, "top5_acc": 0.54578, "loss_cls": 4.02135, "loss": 4.02135, "time": 0.8113} +{"mode": "train", "epoch": 87, "iter": 3100, "lr": 0.03774, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30266, "top5_acc": 0.55078, "loss_cls": 3.98685, "loss": 3.98685, "time": 0.81282} +{"mode": "train", "epoch": 87, "iter": 3200, "lr": 0.03771, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29719, "top5_acc": 0.55547, "loss_cls": 3.98779, "loss": 3.98779, "time": 0.8107} +{"mode": "train", "epoch": 87, "iter": 3300, "lr": 0.03769, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30094, "top5_acc": 0.55281, "loss_cls": 3.98902, "loss": 3.98902, "time": 0.81104} +{"mode": "train", "epoch": 87, "iter": 3400, "lr": 0.03766, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30469, "top5_acc": 0.5625, "loss_cls": 3.96779, "loss": 3.96779, "time": 0.81254} +{"mode": "train", "epoch": 87, "iter": 3500, "lr": 0.03763, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29625, "top5_acc": 0.55453, "loss_cls": 3.99724, "loss": 3.99724, "time": 0.8096} +{"mode": "train", "epoch": 87, "iter": 3600, "lr": 0.03761, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31766, "top5_acc": 0.57094, "loss_cls": 3.91998, "loss": 3.91998, "time": 0.80764} +{"mode": "train", "epoch": 87, "iter": 3700, "lr": 0.03758, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31094, "top5_acc": 0.56375, "loss_cls": 3.93544, "loss": 3.93544, "time": 0.81687} +{"mode": "val", "epoch": 87, "iter": 309, "lr": 0.03757, "top1_acc": 0.23421, "top5_acc": 0.48169, "mean_class_accuracy": 0.23401} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.03754, "memory": 15990, "data_time": 1.30164, "top1_acc": 0.30875, "top5_acc": 0.57828, "loss_cls": 3.86894, "loss": 3.86894, "time": 2.28033} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.03751, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31438, "top5_acc": 0.57359, "loss_cls": 3.87763, "loss": 3.87763, "time": 0.82087} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.03748, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32781, "top5_acc": 0.57781, "loss_cls": 3.88289, "loss": 3.88289, "time": 0.81328} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.03746, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32438, "top5_acc": 0.57859, "loss_cls": 3.87198, "loss": 3.87198, "time": 0.81115} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.03743, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30688, "top5_acc": 0.57156, "loss_cls": 3.92668, "loss": 3.92668, "time": 0.81597} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.0374, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30625, "top5_acc": 0.57, "loss_cls": 3.89297, "loss": 3.89297, "time": 0.81455} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.03738, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31609, "top5_acc": 0.57406, "loss_cls": 3.91277, "loss": 3.91277, "time": 0.81467} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.03735, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30234, "top5_acc": 0.55969, "loss_cls": 3.97323, "loss": 3.97323, "time": 0.8147} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.03732, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30484, "top5_acc": 0.55859, "loss_cls": 3.94767, "loss": 3.94767, "time": 0.81158} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.0373, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32109, "top5_acc": 0.57609, "loss_cls": 3.88766, "loss": 3.88766, "time": 0.80844} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.03727, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29984, "top5_acc": 0.5575, "loss_cls": 4.00342, "loss": 4.00342, "time": 0.81452} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.03724, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30719, "top5_acc": 0.56297, "loss_cls": 3.9476, "loss": 3.9476, "time": 0.81251} +{"mode": "train", "epoch": 88, "iter": 1300, "lr": 0.03721, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31984, "top5_acc": 0.57, "loss_cls": 3.89551, "loss": 3.89551, "time": 0.81294} +{"mode": "train", "epoch": 88, "iter": 1400, "lr": 0.03719, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31328, "top5_acc": 0.57141, "loss_cls": 3.92202, "loss": 3.92202, "time": 0.80767} +{"mode": "train", "epoch": 88, "iter": 1500, "lr": 0.03716, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31688, "top5_acc": 0.575, "loss_cls": 3.90343, "loss": 3.90343, "time": 0.81404} +{"mode": "train", "epoch": 88, "iter": 1600, "lr": 0.03713, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30312, "top5_acc": 0.56812, "loss_cls": 3.94089, "loss": 3.94089, "time": 0.80897} +{"mode": "train", "epoch": 88, "iter": 1700, "lr": 0.03711, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31203, "top5_acc": 0.56016, "loss_cls": 3.9422, "loss": 3.9422, "time": 0.81534} +{"mode": "train", "epoch": 88, "iter": 1800, "lr": 0.03708, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29922, "top5_acc": 0.55719, "loss_cls": 3.98252, "loss": 3.98252, "time": 0.81712} +{"mode": "train", "epoch": 88, "iter": 1900, "lr": 0.03705, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30312, "top5_acc": 0.56484, "loss_cls": 3.9653, "loss": 3.9653, "time": 0.80917} +{"mode": "train", "epoch": 88, "iter": 2000, "lr": 0.03703, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30969, "top5_acc": 0.55453, "loss_cls": 3.9784, "loss": 3.9784, "time": 0.80806} +{"mode": "train", "epoch": 88, "iter": 2100, "lr": 0.037, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30578, "top5_acc": 0.56547, "loss_cls": 3.93475, "loss": 3.93475, "time": 0.80885} +{"mode": "train", "epoch": 88, "iter": 2200, "lr": 0.03697, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30219, "top5_acc": 0.56516, "loss_cls": 3.94392, "loss": 3.94392, "time": 0.82399} +{"mode": "train", "epoch": 88, "iter": 2300, "lr": 0.03694, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2975, "top5_acc": 0.56047, "loss_cls": 3.96437, "loss": 3.96437, "time": 0.8156} +{"mode": "train", "epoch": 88, "iter": 2400, "lr": 0.03692, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30469, "top5_acc": 0.56, "loss_cls": 3.95614, "loss": 3.95614, "time": 0.81467} +{"mode": "train", "epoch": 88, "iter": 2500, "lr": 0.03689, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.305, "top5_acc": 0.56078, "loss_cls": 3.96323, "loss": 3.96323, "time": 0.80895} +{"mode": "train", "epoch": 88, "iter": 2600, "lr": 0.03686, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30625, "top5_acc": 0.55922, "loss_cls": 3.95749, "loss": 3.95749, "time": 0.81025} +{"mode": "train", "epoch": 88, "iter": 2700, "lr": 0.03684, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30531, "top5_acc": 0.56453, "loss_cls": 3.93884, "loss": 3.93884, "time": 0.80875} +{"mode": "train", "epoch": 88, "iter": 2800, "lr": 0.03681, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30688, "top5_acc": 0.55719, "loss_cls": 3.9734, "loss": 3.9734, "time": 0.81588} +{"mode": "train", "epoch": 88, "iter": 2900, "lr": 0.03678, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28984, "top5_acc": 0.55562, "loss_cls": 3.98619, "loss": 3.98619, "time": 0.8167} +{"mode": "train", "epoch": 88, "iter": 3000, "lr": 0.03676, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29688, "top5_acc": 0.54937, "loss_cls": 4.03503, "loss": 4.03503, "time": 0.81585} +{"mode": "train", "epoch": 88, "iter": 3100, "lr": 0.03673, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30312, "top5_acc": 0.5675, "loss_cls": 3.91121, "loss": 3.91121, "time": 0.81781} +{"mode": "train", "epoch": 88, "iter": 3200, "lr": 0.0367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30297, "top5_acc": 0.56078, "loss_cls": 3.98592, "loss": 3.98592, "time": 0.80957} +{"mode": "train", "epoch": 88, "iter": 3300, "lr": 0.03667, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29906, "top5_acc": 0.555, "loss_cls": 4.00954, "loss": 4.00954, "time": 0.80873} +{"mode": "train", "epoch": 88, "iter": 3400, "lr": 0.03665, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30703, "top5_acc": 0.56437, "loss_cls": 3.94203, "loss": 3.94203, "time": 0.80834} +{"mode": "train", "epoch": 88, "iter": 3500, "lr": 0.03662, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30469, "top5_acc": 0.56141, "loss_cls": 3.97084, "loss": 3.97084, "time": 0.81222} +{"mode": "train", "epoch": 88, "iter": 3600, "lr": 0.03659, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30719, "top5_acc": 0.57516, "loss_cls": 3.90683, "loss": 3.90683, "time": 0.80967} +{"mode": "train", "epoch": 88, "iter": 3700, "lr": 0.03657, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30438, "top5_acc": 0.56406, "loss_cls": 3.95619, "loss": 3.95619, "time": 0.81779} +{"mode": "val", "epoch": 88, "iter": 309, "lr": 0.03655, "top1_acc": 0.24647, "top5_acc": 0.49081, "mean_class_accuracy": 0.24638} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.03653, "memory": 15990, "data_time": 1.30993, "top1_acc": 0.31531, "top5_acc": 0.58406, "loss_cls": 3.86335, "loss": 3.86335, "time": 2.28615} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0365, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32266, "top5_acc": 0.58172, "loss_cls": 3.88571, "loss": 3.88571, "time": 0.81983} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.03647, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30969, "top5_acc": 0.57953, "loss_cls": 3.89987, "loss": 3.89987, "time": 0.81578} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.03645, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30719, "top5_acc": 0.56875, "loss_cls": 3.90785, "loss": 3.90785, "time": 0.81128} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.03642, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.30516, "top5_acc": 0.57078, "loss_cls": 3.93937, "loss": 3.93937, "time": 0.80883} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.03639, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32125, "top5_acc": 0.5725, "loss_cls": 3.90066, "loss": 3.90066, "time": 0.811} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.03637, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30734, "top5_acc": 0.57203, "loss_cls": 3.90948, "loss": 3.90948, "time": 0.81009} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.03634, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31312, "top5_acc": 0.56984, "loss_cls": 3.93311, "loss": 3.93311, "time": 0.80847} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.03631, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29938, "top5_acc": 0.56688, "loss_cls": 3.93915, "loss": 3.93915, "time": 0.81333} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.03629, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32062, "top5_acc": 0.57219, "loss_cls": 3.85971, "loss": 3.85971, "time": 0.81962} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.03626, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30656, "top5_acc": 0.57172, "loss_cls": 3.94315, "loss": 3.94315, "time": 0.80951} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.03623, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30266, "top5_acc": 0.56922, "loss_cls": 3.94316, "loss": 3.94316, "time": 0.81379} +{"mode": "train", "epoch": 89, "iter": 1300, "lr": 0.0362, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31547, "top5_acc": 0.56609, "loss_cls": 3.92786, "loss": 3.92786, "time": 0.80818} +{"mode": "train", "epoch": 89, "iter": 1400, "lr": 0.03618, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29734, "top5_acc": 0.55828, "loss_cls": 3.96385, "loss": 3.96385, "time": 0.81144} +{"mode": "train", "epoch": 89, "iter": 1500, "lr": 0.03615, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31281, "top5_acc": 0.57063, "loss_cls": 3.92048, "loss": 3.92048, "time": 0.80531} +{"mode": "train", "epoch": 89, "iter": 1600, "lr": 0.03612, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31484, "top5_acc": 0.57391, "loss_cls": 3.90662, "loss": 3.90662, "time": 0.8168} +{"mode": "train", "epoch": 89, "iter": 1700, "lr": 0.0361, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31641, "top5_acc": 0.56625, "loss_cls": 3.90868, "loss": 3.90868, "time": 0.81499} +{"mode": "train", "epoch": 89, "iter": 1800, "lr": 0.03607, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31188, "top5_acc": 0.56563, "loss_cls": 3.93817, "loss": 3.93817, "time": 0.80959} +{"mode": "train", "epoch": 89, "iter": 1900, "lr": 0.03604, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31016, "top5_acc": 0.57016, "loss_cls": 3.93608, "loss": 3.93608, "time": 0.81326} +{"mode": "train", "epoch": 89, "iter": 2000, "lr": 0.03602, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30469, "top5_acc": 0.56453, "loss_cls": 3.96028, "loss": 3.96028, "time": 0.81287} +{"mode": "train", "epoch": 89, "iter": 2100, "lr": 0.03599, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31125, "top5_acc": 0.56891, "loss_cls": 3.94942, "loss": 3.94942, "time": 0.80972} +{"mode": "train", "epoch": 89, "iter": 2200, "lr": 0.03596, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.31156, "top5_acc": 0.57031, "loss_cls": 3.91937, "loss": 3.91937, "time": 0.82223} +{"mode": "train", "epoch": 89, "iter": 2300, "lr": 0.03594, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30203, "top5_acc": 0.55875, "loss_cls": 3.99511, "loss": 3.99511, "time": 0.81255} +{"mode": "train", "epoch": 89, "iter": 2400, "lr": 0.03591, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31391, "top5_acc": 0.56297, "loss_cls": 3.93325, "loss": 3.93325, "time": 0.81092} +{"mode": "train", "epoch": 89, "iter": 2500, "lr": 0.03588, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30703, "top5_acc": 0.55281, "loss_cls": 3.98695, "loss": 3.98695, "time": 0.80958} +{"mode": "train", "epoch": 89, "iter": 2600, "lr": 0.03586, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30812, "top5_acc": 0.55641, "loss_cls": 3.97418, "loss": 3.97418, "time": 0.80907} +{"mode": "train", "epoch": 89, "iter": 2700, "lr": 0.03583, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3075, "top5_acc": 0.56844, "loss_cls": 3.92808, "loss": 3.92808, "time": 0.81097} +{"mode": "train", "epoch": 89, "iter": 2800, "lr": 0.0358, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31469, "top5_acc": 0.57469, "loss_cls": 3.91874, "loss": 3.91874, "time": 0.81232} +{"mode": "train", "epoch": 89, "iter": 2900, "lr": 0.03578, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30438, "top5_acc": 0.56312, "loss_cls": 3.94871, "loss": 3.94871, "time": 0.81706} +{"mode": "train", "epoch": 89, "iter": 3000, "lr": 0.03575, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30359, "top5_acc": 0.56344, "loss_cls": 3.92954, "loss": 3.92954, "time": 0.81286} +{"mode": "train", "epoch": 89, "iter": 3100, "lr": 0.03572, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30547, "top5_acc": 0.565, "loss_cls": 3.94377, "loss": 3.94377, "time": 0.80867} +{"mode": "train", "epoch": 89, "iter": 3200, "lr": 0.03569, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31203, "top5_acc": 0.57594, "loss_cls": 3.90906, "loss": 3.90906, "time": 0.80809} +{"mode": "train", "epoch": 89, "iter": 3300, "lr": 0.03567, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31625, "top5_acc": 0.57703, "loss_cls": 3.88364, "loss": 3.88364, "time": 0.8123} +{"mode": "train", "epoch": 89, "iter": 3400, "lr": 0.03564, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31188, "top5_acc": 0.56656, "loss_cls": 3.92674, "loss": 3.92674, "time": 0.80825} +{"mode": "train", "epoch": 89, "iter": 3500, "lr": 0.03561, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31578, "top5_acc": 0.55891, "loss_cls": 3.93882, "loss": 3.93882, "time": 0.81434} +{"mode": "train", "epoch": 89, "iter": 3600, "lr": 0.03559, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29609, "top5_acc": 0.55906, "loss_cls": 3.96072, "loss": 3.96072, "time": 0.81583} +{"mode": "train", "epoch": 89, "iter": 3700, "lr": 0.03556, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31344, "top5_acc": 0.56812, "loss_cls": 3.90894, "loss": 3.90894, "time": 0.80998} +{"mode": "val", "epoch": 89, "iter": 309, "lr": 0.03555, "top1_acc": 0.24297, "top5_acc": 0.48331, "mean_class_accuracy": 0.24295} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.03552, "memory": 15990, "data_time": 1.33704, "top1_acc": 0.3175, "top5_acc": 0.57953, "loss_cls": 3.86523, "loss": 3.86523, "time": 2.31133} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.0355, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32516, "top5_acc": 0.58766, "loss_cls": 3.81966, "loss": 3.81966, "time": 0.82326} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.03547, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.325, "top5_acc": 0.57734, "loss_cls": 3.87468, "loss": 3.87468, "time": 0.81341} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.03544, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31703, "top5_acc": 0.57875, "loss_cls": 3.87811, "loss": 3.87811, "time": 0.81377} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.03541, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31344, "top5_acc": 0.56875, "loss_cls": 3.8859, "loss": 3.8859, "time": 0.80651} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.03539, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31828, "top5_acc": 0.57484, "loss_cls": 3.89193, "loss": 3.89193, "time": 0.81934} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.03536, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32562, "top5_acc": 0.57359, "loss_cls": 3.86631, "loss": 3.86631, "time": 0.8092} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.03533, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31266, "top5_acc": 0.57188, "loss_cls": 3.91113, "loss": 3.91113, "time": 0.8118} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.03531, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30641, "top5_acc": 0.56734, "loss_cls": 3.90998, "loss": 3.90998, "time": 0.81251} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.03528, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32031, "top5_acc": 0.57672, "loss_cls": 3.91313, "loss": 3.91313, "time": 0.80901} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.03525, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30797, "top5_acc": 0.56688, "loss_cls": 3.94115, "loss": 3.94115, "time": 0.81348} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.03523, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31281, "top5_acc": 0.57422, "loss_cls": 3.89557, "loss": 3.89557, "time": 0.81201} +{"mode": "train", "epoch": 90, "iter": 1300, "lr": 0.0352, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30859, "top5_acc": 0.57125, "loss_cls": 3.89718, "loss": 3.89718, "time": 0.80974} +{"mode": "train", "epoch": 90, "iter": 1400, "lr": 0.03517, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30891, "top5_acc": 0.56422, "loss_cls": 3.91485, "loss": 3.91485, "time": 0.81825} +{"mode": "train", "epoch": 90, "iter": 1500, "lr": 0.03515, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31109, "top5_acc": 0.57109, "loss_cls": 3.9003, "loss": 3.9003, "time": 0.81052} +{"mode": "train", "epoch": 90, "iter": 1600, "lr": 0.03512, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31, "top5_acc": 0.57312, "loss_cls": 3.896, "loss": 3.896, "time": 0.81968} +{"mode": "train", "epoch": 90, "iter": 1700, "lr": 0.03509, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31, "top5_acc": 0.56938, "loss_cls": 3.92393, "loss": 3.92393, "time": 0.81482} +{"mode": "train", "epoch": 90, "iter": 1800, "lr": 0.03507, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30562, "top5_acc": 0.56328, "loss_cls": 3.9601, "loss": 3.9601, "time": 0.81636} +{"mode": "train", "epoch": 90, "iter": 1900, "lr": 0.03504, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.31109, "top5_acc": 0.57563, "loss_cls": 3.90035, "loss": 3.90035, "time": 0.82035} +{"mode": "train", "epoch": 90, "iter": 2000, "lr": 0.03501, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30719, "top5_acc": 0.57, "loss_cls": 3.91169, "loss": 3.91169, "time": 0.81785} +{"mode": "train", "epoch": 90, "iter": 2100, "lr": 0.03499, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30922, "top5_acc": 0.57078, "loss_cls": 3.89854, "loss": 3.89854, "time": 0.80898} +{"mode": "train", "epoch": 90, "iter": 2200, "lr": 0.03496, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30922, "top5_acc": 0.57188, "loss_cls": 3.90393, "loss": 3.90393, "time": 0.81544} +{"mode": "train", "epoch": 90, "iter": 2300, "lr": 0.03493, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31016, "top5_acc": 0.56297, "loss_cls": 3.9512, "loss": 3.9512, "time": 0.80904} +{"mode": "train", "epoch": 90, "iter": 2400, "lr": 0.03491, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31156, "top5_acc": 0.57359, "loss_cls": 3.92032, "loss": 3.92032, "time": 0.81605} +{"mode": "train", "epoch": 90, "iter": 2500, "lr": 0.03488, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30844, "top5_acc": 0.56328, "loss_cls": 3.95917, "loss": 3.95917, "time": 0.81373} +{"mode": "train", "epoch": 90, "iter": 2600, "lr": 0.03485, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30312, "top5_acc": 0.57031, "loss_cls": 3.94089, "loss": 3.94089, "time": 0.81095} +{"mode": "train", "epoch": 90, "iter": 2700, "lr": 0.03483, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3175, "top5_acc": 0.57266, "loss_cls": 3.8855, "loss": 3.8855, "time": 0.81223} +{"mode": "train", "epoch": 90, "iter": 2800, "lr": 0.0348, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30562, "top5_acc": 0.56375, "loss_cls": 3.94579, "loss": 3.94579, "time": 0.81678} +{"mode": "train", "epoch": 90, "iter": 2900, "lr": 0.03477, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30953, "top5_acc": 0.56625, "loss_cls": 3.92485, "loss": 3.92485, "time": 0.81808} +{"mode": "train", "epoch": 90, "iter": 3000, "lr": 0.03475, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30938, "top5_acc": 0.565, "loss_cls": 3.9293, "loss": 3.9293, "time": 0.81809} +{"mode": "train", "epoch": 90, "iter": 3100, "lr": 0.03472, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31891, "top5_acc": 0.58344, "loss_cls": 3.86504, "loss": 3.86504, "time": 0.81559} +{"mode": "train", "epoch": 90, "iter": 3200, "lr": 0.03469, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30797, "top5_acc": 0.55906, "loss_cls": 3.96124, "loss": 3.96124, "time": 0.80915} +{"mode": "train", "epoch": 90, "iter": 3300, "lr": 0.03467, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31609, "top5_acc": 0.56906, "loss_cls": 3.92977, "loss": 3.92977, "time": 0.81059} +{"mode": "train", "epoch": 90, "iter": 3400, "lr": 0.03464, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30812, "top5_acc": 0.56359, "loss_cls": 3.93736, "loss": 3.93736, "time": 0.80848} +{"mode": "train", "epoch": 90, "iter": 3500, "lr": 0.03461, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30797, "top5_acc": 0.57469, "loss_cls": 3.89224, "loss": 3.89224, "time": 0.81143} +{"mode": "train", "epoch": 90, "iter": 3600, "lr": 0.03459, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31297, "top5_acc": 0.57469, "loss_cls": 3.88448, "loss": 3.88448, "time": 0.8154} +{"mode": "train", "epoch": 90, "iter": 3700, "lr": 0.03456, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31578, "top5_acc": 0.57703, "loss_cls": 3.87476, "loss": 3.87476, "time": 0.81426} +{"mode": "val", "epoch": 90, "iter": 309, "lr": 0.03455, "top1_acc": 0.24667, "top5_acc": 0.4978, "mean_class_accuracy": 0.24671} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.03452, "memory": 15990, "data_time": 1.38278, "top1_acc": 0.32109, "top5_acc": 0.58891, "loss_cls": 3.83408, "loss": 3.83408, "time": 2.37903} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0345, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31516, "top5_acc": 0.56859, "loss_cls": 3.88763, "loss": 3.88763, "time": 0.83761} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.03447, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32031, "top5_acc": 0.58328, "loss_cls": 3.84785, "loss": 3.84785, "time": 0.83716} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.03444, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32219, "top5_acc": 0.58312, "loss_cls": 3.85179, "loss": 3.85179, "time": 0.83817} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.03442, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31859, "top5_acc": 0.59109, "loss_cls": 3.85265, "loss": 3.85265, "time": 0.84223} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.03439, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31781, "top5_acc": 0.57094, "loss_cls": 3.90876, "loss": 3.90876, "time": 0.84383} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.03436, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31703, "top5_acc": 0.57469, "loss_cls": 3.9022, "loss": 3.9022, "time": 0.83468} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.03434, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32016, "top5_acc": 0.57609, "loss_cls": 3.86088, "loss": 3.86088, "time": 0.83323} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.03431, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31031, "top5_acc": 0.56891, "loss_cls": 3.93134, "loss": 3.93134, "time": 0.84104} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.03428, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30703, "top5_acc": 0.57547, "loss_cls": 3.89909, "loss": 3.89909, "time": 0.83606} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.03426, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32234, "top5_acc": 0.58109, "loss_cls": 3.85304, "loss": 3.85304, "time": 0.83839} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.03423, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30797, "top5_acc": 0.57031, "loss_cls": 3.89105, "loss": 3.89105, "time": 0.83533} +{"mode": "train", "epoch": 91, "iter": 1300, "lr": 0.0342, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31094, "top5_acc": 0.57078, "loss_cls": 3.88829, "loss": 3.88829, "time": 0.83262} +{"mode": "train", "epoch": 91, "iter": 1400, "lr": 0.03418, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.30109, "top5_acc": 0.57594, "loss_cls": 3.92341, "loss": 3.92341, "time": 0.83406} +{"mode": "train", "epoch": 91, "iter": 1500, "lr": 0.03415, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30938, "top5_acc": 0.57141, "loss_cls": 3.90954, "loss": 3.90954, "time": 0.8344} +{"mode": "train", "epoch": 91, "iter": 1600, "lr": 0.03412, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32406, "top5_acc": 0.57063, "loss_cls": 3.88213, "loss": 3.88213, "time": 0.82945} +{"mode": "train", "epoch": 91, "iter": 1700, "lr": 0.0341, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31219, "top5_acc": 0.57531, "loss_cls": 3.91269, "loss": 3.91269, "time": 0.83558} +{"mode": "train", "epoch": 91, "iter": 1800, "lr": 0.03407, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31969, "top5_acc": 0.57656, "loss_cls": 3.86989, "loss": 3.86989, "time": 0.83716} +{"mode": "train", "epoch": 91, "iter": 1900, "lr": 0.03405, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3175, "top5_acc": 0.57906, "loss_cls": 3.86786, "loss": 3.86786, "time": 0.83975} +{"mode": "train", "epoch": 91, "iter": 2000, "lr": 0.03402, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31734, "top5_acc": 0.57438, "loss_cls": 3.88458, "loss": 3.88458, "time": 0.83733} +{"mode": "train", "epoch": 91, "iter": 2100, "lr": 0.03399, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31969, "top5_acc": 0.57203, "loss_cls": 3.90374, "loss": 3.90374, "time": 0.83638} +{"mode": "train", "epoch": 91, "iter": 2200, "lr": 0.03397, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31562, "top5_acc": 0.57734, "loss_cls": 3.87528, "loss": 3.87528, "time": 0.84093} +{"mode": "train", "epoch": 91, "iter": 2300, "lr": 0.03394, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31359, "top5_acc": 0.56469, "loss_cls": 3.91984, "loss": 3.91984, "time": 0.8417} +{"mode": "train", "epoch": 91, "iter": 2400, "lr": 0.03391, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.31641, "top5_acc": 0.57344, "loss_cls": 3.90135, "loss": 3.90135, "time": 0.83349} +{"mode": "train", "epoch": 91, "iter": 2500, "lr": 0.03389, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31922, "top5_acc": 0.57688, "loss_cls": 3.90718, "loss": 3.90718, "time": 0.83446} +{"mode": "train", "epoch": 91, "iter": 2600, "lr": 0.03386, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32297, "top5_acc": 0.57328, "loss_cls": 3.87766, "loss": 3.87766, "time": 0.83748} +{"mode": "train", "epoch": 91, "iter": 2700, "lr": 0.03383, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31562, "top5_acc": 0.55859, "loss_cls": 3.94148, "loss": 3.94148, "time": 0.833} +{"mode": "train", "epoch": 91, "iter": 2800, "lr": 0.03381, "memory": 15990, "data_time": 0.00067, "top1_acc": 0.31062, "top5_acc": 0.57359, "loss_cls": 3.91319, "loss": 3.91319, "time": 0.83842} +{"mode": "train", "epoch": 91, "iter": 2900, "lr": 0.03378, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31531, "top5_acc": 0.56359, "loss_cls": 3.92848, "loss": 3.92848, "time": 0.83074} +{"mode": "train", "epoch": 91, "iter": 3000, "lr": 0.03375, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31891, "top5_acc": 0.57391, "loss_cls": 3.8813, "loss": 3.8813, "time": 0.83507} +{"mode": "train", "epoch": 91, "iter": 3100, "lr": 0.03373, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31203, "top5_acc": 0.57281, "loss_cls": 3.91908, "loss": 3.91908, "time": 0.8405} +{"mode": "train", "epoch": 91, "iter": 3200, "lr": 0.0337, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30672, "top5_acc": 0.56063, "loss_cls": 3.93952, "loss": 3.93952, "time": 0.83641} +{"mode": "train", "epoch": 91, "iter": 3300, "lr": 0.03367, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32219, "top5_acc": 0.57422, "loss_cls": 3.86511, "loss": 3.86511, "time": 0.83763} +{"mode": "train", "epoch": 91, "iter": 3400, "lr": 0.03365, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30828, "top5_acc": 0.57109, "loss_cls": 3.90968, "loss": 3.90968, "time": 0.84064} +{"mode": "train", "epoch": 91, "iter": 3500, "lr": 0.03362, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30391, "top5_acc": 0.56172, "loss_cls": 3.95501, "loss": 3.95501, "time": 0.83944} +{"mode": "train", "epoch": 91, "iter": 3600, "lr": 0.0336, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31453, "top5_acc": 0.58078, "loss_cls": 3.88314, "loss": 3.88314, "time": 0.84004} +{"mode": "train", "epoch": 91, "iter": 3700, "lr": 0.03357, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31125, "top5_acc": 0.575, "loss_cls": 3.91694, "loss": 3.91694, "time": 0.83393} +{"mode": "val", "epoch": 91, "iter": 309, "lr": 0.03356, "top1_acc": 0.23284, "top5_acc": 0.46969, "mean_class_accuracy": 0.23253} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.03353, "memory": 15990, "data_time": 1.38232, "top1_acc": 0.32266, "top5_acc": 0.57922, "loss_cls": 3.84206, "loss": 3.84206, "time": 2.37178} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.0335, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32203, "top5_acc": 0.58562, "loss_cls": 3.82766, "loss": 3.82766, "time": 0.81118} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.03348, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31844, "top5_acc": 0.58156, "loss_cls": 3.83512, "loss": 3.83512, "time": 0.81929} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.03345, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31547, "top5_acc": 0.575, "loss_cls": 3.88427, "loss": 3.88427, "time": 0.81623} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.03342, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31391, "top5_acc": 0.57938, "loss_cls": 3.86591, "loss": 3.86591, "time": 0.81319} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.0334, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31938, "top5_acc": 0.57234, "loss_cls": 3.8889, "loss": 3.8889, "time": 0.81086} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.03337, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32109, "top5_acc": 0.58891, "loss_cls": 3.82958, "loss": 3.82958, "time": 0.80999} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.03335, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32141, "top5_acc": 0.57453, "loss_cls": 3.90689, "loss": 3.90689, "time": 0.81599} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.03332, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32578, "top5_acc": 0.59125, "loss_cls": 3.81335, "loss": 3.81335, "time": 0.80514} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.03329, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31734, "top5_acc": 0.57031, "loss_cls": 3.89977, "loss": 3.89977, "time": 0.80651} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.03327, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30953, "top5_acc": 0.5675, "loss_cls": 3.92884, "loss": 3.92884, "time": 0.81523} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.03324, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31266, "top5_acc": 0.5675, "loss_cls": 3.87706, "loss": 3.87706, "time": 0.80932} +{"mode": "train", "epoch": 92, "iter": 1300, "lr": 0.03321, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32297, "top5_acc": 0.58297, "loss_cls": 3.8569, "loss": 3.8569, "time": 0.81827} +{"mode": "train", "epoch": 92, "iter": 1400, "lr": 0.03319, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32969, "top5_acc": 0.59219, "loss_cls": 3.81707, "loss": 3.81707, "time": 0.81389} +{"mode": "train", "epoch": 92, "iter": 1500, "lr": 0.03316, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32344, "top5_acc": 0.58016, "loss_cls": 3.8466, "loss": 3.8466, "time": 0.80865} +{"mode": "train", "epoch": 92, "iter": 1600, "lr": 0.03314, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31969, "top5_acc": 0.58234, "loss_cls": 3.86619, "loss": 3.86619, "time": 0.81592} +{"mode": "train", "epoch": 92, "iter": 1700, "lr": 0.03311, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31203, "top5_acc": 0.57875, "loss_cls": 3.88081, "loss": 3.88081, "time": 0.80964} +{"mode": "train", "epoch": 92, "iter": 1800, "lr": 0.03308, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32594, "top5_acc": 0.58406, "loss_cls": 3.84147, "loss": 3.84147, "time": 0.81922} +{"mode": "train", "epoch": 92, "iter": 1900, "lr": 0.03306, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32094, "top5_acc": 0.58062, "loss_cls": 3.87166, "loss": 3.87166, "time": 0.81274} +{"mode": "train", "epoch": 92, "iter": 2000, "lr": 0.03303, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30312, "top5_acc": 0.57703, "loss_cls": 3.87969, "loss": 3.87969, "time": 0.81725} +{"mode": "train", "epoch": 92, "iter": 2100, "lr": 0.033, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31375, "top5_acc": 0.57359, "loss_cls": 3.92342, "loss": 3.92342, "time": 0.81161} +{"mode": "train", "epoch": 92, "iter": 2200, "lr": 0.03298, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30594, "top5_acc": 0.57719, "loss_cls": 3.90945, "loss": 3.90945, "time": 0.81293} +{"mode": "train", "epoch": 92, "iter": 2300, "lr": 0.03295, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31016, "top5_acc": 0.57031, "loss_cls": 3.90637, "loss": 3.90637, "time": 0.80961} +{"mode": "train", "epoch": 92, "iter": 2400, "lr": 0.03292, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30938, "top5_acc": 0.56094, "loss_cls": 3.93849, "loss": 3.93849, "time": 0.81618} +{"mode": "train", "epoch": 92, "iter": 2500, "lr": 0.0329, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31109, "top5_acc": 0.56531, "loss_cls": 3.94435, "loss": 3.94435, "time": 0.81385} +{"mode": "train", "epoch": 92, "iter": 2600, "lr": 0.03287, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31219, "top5_acc": 0.57203, "loss_cls": 3.92243, "loss": 3.92243, "time": 0.8105} +{"mode": "train", "epoch": 92, "iter": 2700, "lr": 0.03285, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32625, "top5_acc": 0.58188, "loss_cls": 3.85921, "loss": 3.85921, "time": 0.81892} +{"mode": "train", "epoch": 92, "iter": 2800, "lr": 0.03282, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31672, "top5_acc": 0.57172, "loss_cls": 3.90505, "loss": 3.90505, "time": 0.81553} +{"mode": "train", "epoch": 92, "iter": 2900, "lr": 0.03279, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31203, "top5_acc": 0.57438, "loss_cls": 3.89141, "loss": 3.89141, "time": 0.81369} +{"mode": "train", "epoch": 92, "iter": 3000, "lr": 0.03277, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30891, "top5_acc": 0.56188, "loss_cls": 3.93134, "loss": 3.93134, "time": 0.81518} +{"mode": "train", "epoch": 92, "iter": 3100, "lr": 0.03274, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32344, "top5_acc": 0.57734, "loss_cls": 3.86208, "loss": 3.86208, "time": 0.81439} +{"mode": "train", "epoch": 92, "iter": 3200, "lr": 0.03271, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32109, "top5_acc": 0.57375, "loss_cls": 3.90231, "loss": 3.90231, "time": 0.81596} +{"mode": "train", "epoch": 92, "iter": 3300, "lr": 0.03269, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31453, "top5_acc": 0.57047, "loss_cls": 3.90197, "loss": 3.90197, "time": 0.81112} +{"mode": "train", "epoch": 92, "iter": 3400, "lr": 0.03266, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31938, "top5_acc": 0.58375, "loss_cls": 3.83629, "loss": 3.83629, "time": 0.81318} +{"mode": "train", "epoch": 92, "iter": 3500, "lr": 0.03264, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31703, "top5_acc": 0.56969, "loss_cls": 3.88287, "loss": 3.88287, "time": 0.81292} +{"mode": "train", "epoch": 92, "iter": 3600, "lr": 0.03261, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31453, "top5_acc": 0.57828, "loss_cls": 3.8481, "loss": 3.8481, "time": 0.81547} +{"mode": "train", "epoch": 92, "iter": 3700, "lr": 0.03258, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31062, "top5_acc": 0.56875, "loss_cls": 3.92403, "loss": 3.92403, "time": 0.81166} +{"mode": "val", "epoch": 92, "iter": 309, "lr": 0.03257, "top1_acc": 0.26171, "top5_acc": 0.50904, "mean_class_accuracy": 0.26151} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.03255, "memory": 15990, "data_time": 1.32171, "top1_acc": 0.33062, "top5_acc": 0.58719, "loss_cls": 3.7949, "loss": 3.7949, "time": 2.32432} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.03252, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32141, "top5_acc": 0.58016, "loss_cls": 3.83563, "loss": 3.83563, "time": 0.8378} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.03249, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33438, "top5_acc": 0.59531, "loss_cls": 3.77452, "loss": 3.77452, "time": 0.82746} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.03247, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32484, "top5_acc": 0.57875, "loss_cls": 3.84924, "loss": 3.84924, "time": 0.82984} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.03244, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31844, "top5_acc": 0.58312, "loss_cls": 3.83454, "loss": 3.83454, "time": 0.82454} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.03241, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32094, "top5_acc": 0.58469, "loss_cls": 3.84553, "loss": 3.84553, "time": 0.82348} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.03239, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31141, "top5_acc": 0.57672, "loss_cls": 3.90417, "loss": 3.90417, "time": 0.82656} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.03236, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31125, "top5_acc": 0.56859, "loss_cls": 3.92476, "loss": 3.92476, "time": 0.82325} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.03234, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31656, "top5_acc": 0.58031, "loss_cls": 3.86201, "loss": 3.86201, "time": 0.82487} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.03231, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32875, "top5_acc": 0.58781, "loss_cls": 3.81169, "loss": 3.81169, "time": 0.8263} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.03228, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31438, "top5_acc": 0.56812, "loss_cls": 3.89084, "loss": 3.89084, "time": 0.82668} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.03226, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32828, "top5_acc": 0.57719, "loss_cls": 3.86975, "loss": 3.86975, "time": 0.83089} +{"mode": "train", "epoch": 93, "iter": 1300, "lr": 0.03223, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31734, "top5_acc": 0.58203, "loss_cls": 3.8894, "loss": 3.8894, "time": 0.82276} +{"mode": "train", "epoch": 93, "iter": 1400, "lr": 0.03221, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31391, "top5_acc": 0.56953, "loss_cls": 3.91158, "loss": 3.91158, "time": 0.81605} +{"mode": "train", "epoch": 93, "iter": 1500, "lr": 0.03218, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31547, "top5_acc": 0.56531, "loss_cls": 3.92894, "loss": 3.92894, "time": 0.81297} +{"mode": "train", "epoch": 93, "iter": 1600, "lr": 0.03215, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32516, "top5_acc": 0.58156, "loss_cls": 3.82374, "loss": 3.82374, "time": 0.81735} +{"mode": "train", "epoch": 93, "iter": 1700, "lr": 0.03213, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31203, "top5_acc": 0.57969, "loss_cls": 3.8877, "loss": 3.8877, "time": 0.81821} +{"mode": "train", "epoch": 93, "iter": 1800, "lr": 0.0321, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.315, "top5_acc": 0.57812, "loss_cls": 3.87188, "loss": 3.87188, "time": 0.81251} +{"mode": "train", "epoch": 93, "iter": 1900, "lr": 0.03207, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31766, "top5_acc": 0.57781, "loss_cls": 3.88072, "loss": 3.88072, "time": 0.81475} +{"mode": "train", "epoch": 93, "iter": 2000, "lr": 0.03205, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31609, "top5_acc": 0.57016, "loss_cls": 3.87366, "loss": 3.87366, "time": 0.81191} +{"mode": "train", "epoch": 93, "iter": 2100, "lr": 0.03202, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33453, "top5_acc": 0.59438, "loss_cls": 3.77788, "loss": 3.77788, "time": 0.81172} +{"mode": "train", "epoch": 93, "iter": 2200, "lr": 0.032, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32016, "top5_acc": 0.58375, "loss_cls": 3.84668, "loss": 3.84668, "time": 0.81629} +{"mode": "train", "epoch": 93, "iter": 2300, "lr": 0.03197, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32047, "top5_acc": 0.58328, "loss_cls": 3.86813, "loss": 3.86813, "time": 0.81602} +{"mode": "train", "epoch": 93, "iter": 2400, "lr": 0.03194, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33, "top5_acc": 0.58516, "loss_cls": 3.82243, "loss": 3.82243, "time": 0.81725} +{"mode": "train", "epoch": 93, "iter": 2500, "lr": 0.03192, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31391, "top5_acc": 0.57125, "loss_cls": 3.90279, "loss": 3.90279, "time": 0.81234} +{"mode": "train", "epoch": 93, "iter": 2600, "lr": 0.03189, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32812, "top5_acc": 0.57641, "loss_cls": 3.86252, "loss": 3.86252, "time": 0.81047} +{"mode": "train", "epoch": 93, "iter": 2700, "lr": 0.03187, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32359, "top5_acc": 0.57516, "loss_cls": 3.88955, "loss": 3.88955, "time": 0.81442} +{"mode": "train", "epoch": 93, "iter": 2800, "lr": 0.03184, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31609, "top5_acc": 0.57359, "loss_cls": 3.88641, "loss": 3.88641, "time": 0.81557} +{"mode": "train", "epoch": 93, "iter": 2900, "lr": 0.03181, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31531, "top5_acc": 0.57812, "loss_cls": 3.91561, "loss": 3.91561, "time": 0.82153} +{"mode": "train", "epoch": 93, "iter": 3000, "lr": 0.03179, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32266, "top5_acc": 0.58844, "loss_cls": 3.83293, "loss": 3.83293, "time": 0.8127} +{"mode": "train", "epoch": 93, "iter": 3100, "lr": 0.03176, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32828, "top5_acc": 0.57922, "loss_cls": 3.86311, "loss": 3.86311, "time": 0.81344} +{"mode": "train", "epoch": 93, "iter": 3200, "lr": 0.03174, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31469, "top5_acc": 0.57438, "loss_cls": 3.89505, "loss": 3.89505, "time": 0.81256} +{"mode": "train", "epoch": 93, "iter": 3300, "lr": 0.03171, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31875, "top5_acc": 0.57891, "loss_cls": 3.85005, "loss": 3.85005, "time": 0.80737} +{"mode": "train", "epoch": 93, "iter": 3400, "lr": 0.03168, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31891, "top5_acc": 0.57266, "loss_cls": 3.85536, "loss": 3.85536, "time": 0.81228} +{"mode": "train", "epoch": 93, "iter": 3500, "lr": 0.03166, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31562, "top5_acc": 0.57312, "loss_cls": 3.90728, "loss": 3.90728, "time": 0.80756} +{"mode": "train", "epoch": 93, "iter": 3600, "lr": 0.03163, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31828, "top5_acc": 0.56859, "loss_cls": 3.88665, "loss": 3.88665, "time": 0.81008} +{"mode": "train", "epoch": 93, "iter": 3700, "lr": 0.03161, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31547, "top5_acc": 0.57656, "loss_cls": 3.90114, "loss": 3.90114, "time": 0.81069} +{"mode": "val", "epoch": 93, "iter": 309, "lr": 0.03159, "top1_acc": 0.24905, "top5_acc": 0.49445, "mean_class_accuracy": 0.24882} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.03157, "memory": 15990, "data_time": 1.34445, "top1_acc": 0.33609, "top5_acc": 0.59156, "loss_cls": 3.77904, "loss": 3.77904, "time": 2.34092} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.03154, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32984, "top5_acc": 0.59688, "loss_cls": 3.78951, "loss": 3.78951, "time": 0.83111} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.03152, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32672, "top5_acc": 0.58141, "loss_cls": 3.87195, "loss": 3.87195, "time": 0.82815} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.03149, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33031, "top5_acc": 0.58672, "loss_cls": 3.80295, "loss": 3.80295, "time": 0.82937} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.03146, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32875, "top5_acc": 0.59391, "loss_cls": 3.78889, "loss": 3.78889, "time": 0.83049} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.03144, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31906, "top5_acc": 0.58266, "loss_cls": 3.83808, "loss": 3.83808, "time": 0.83325} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.03141, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32594, "top5_acc": 0.57922, "loss_cls": 3.82188, "loss": 3.82188, "time": 0.83106} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.03139, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32141, "top5_acc": 0.58859, "loss_cls": 3.81584, "loss": 3.81584, "time": 0.83048} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.03136, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32312, "top5_acc": 0.57953, "loss_cls": 3.84632, "loss": 3.84632, "time": 0.82943} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.03133, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31203, "top5_acc": 0.58141, "loss_cls": 3.88018, "loss": 3.88018, "time": 0.83} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.03131, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.315, "top5_acc": 0.57797, "loss_cls": 3.89971, "loss": 3.89971, "time": 0.83451} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.03128, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32703, "top5_acc": 0.5825, "loss_cls": 3.85196, "loss": 3.85196, "time": 0.82894} +{"mode": "train", "epoch": 94, "iter": 1300, "lr": 0.03126, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31531, "top5_acc": 0.57688, "loss_cls": 3.88176, "loss": 3.88176, "time": 0.82762} +{"mode": "train", "epoch": 94, "iter": 1400, "lr": 0.03123, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31734, "top5_acc": 0.57703, "loss_cls": 3.8884, "loss": 3.8884, "time": 0.83548} +{"mode": "train", "epoch": 94, "iter": 1500, "lr": 0.0312, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31828, "top5_acc": 0.58, "loss_cls": 3.8599, "loss": 3.8599, "time": 0.82675} +{"mode": "train", "epoch": 94, "iter": 1600, "lr": 0.03118, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32547, "top5_acc": 0.58703, "loss_cls": 3.81624, "loss": 3.81624, "time": 0.82979} +{"mode": "train", "epoch": 94, "iter": 1700, "lr": 0.03115, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.30766, "top5_acc": 0.56828, "loss_cls": 3.89292, "loss": 3.89292, "time": 0.8304} +{"mode": "train", "epoch": 94, "iter": 1800, "lr": 0.03113, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32094, "top5_acc": 0.58109, "loss_cls": 3.86028, "loss": 3.86028, "time": 0.83902} +{"mode": "train", "epoch": 94, "iter": 1900, "lr": 0.0311, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32359, "top5_acc": 0.57422, "loss_cls": 3.85702, "loss": 3.85702, "time": 0.8301} +{"mode": "train", "epoch": 94, "iter": 2000, "lr": 0.03108, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32328, "top5_acc": 0.5875, "loss_cls": 3.82969, "loss": 3.82969, "time": 0.82394} +{"mode": "train", "epoch": 94, "iter": 2100, "lr": 0.03105, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32531, "top5_acc": 0.57594, "loss_cls": 3.84306, "loss": 3.84306, "time": 0.82383} +{"mode": "train", "epoch": 94, "iter": 2200, "lr": 0.03102, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32453, "top5_acc": 0.57625, "loss_cls": 3.85325, "loss": 3.85325, "time": 0.83502} +{"mode": "train", "epoch": 94, "iter": 2300, "lr": 0.031, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32672, "top5_acc": 0.58469, "loss_cls": 3.84843, "loss": 3.84843, "time": 0.829} +{"mode": "train", "epoch": 94, "iter": 2400, "lr": 0.03097, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33, "top5_acc": 0.59078, "loss_cls": 3.83311, "loss": 3.83311, "time": 0.82504} +{"mode": "train", "epoch": 94, "iter": 2500, "lr": 0.03095, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33109, "top5_acc": 0.58844, "loss_cls": 3.79852, "loss": 3.79852, "time": 0.83584} +{"mode": "train", "epoch": 94, "iter": 2600, "lr": 0.03092, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.31766, "top5_acc": 0.58172, "loss_cls": 3.87717, "loss": 3.87717, "time": 0.83297} +{"mode": "train", "epoch": 94, "iter": 2700, "lr": 0.03089, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31922, "top5_acc": 0.58328, "loss_cls": 3.87754, "loss": 3.87754, "time": 0.83591} +{"mode": "train", "epoch": 94, "iter": 2800, "lr": 0.03087, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32469, "top5_acc": 0.58109, "loss_cls": 3.83977, "loss": 3.83977, "time": 0.82315} +{"mode": "train", "epoch": 94, "iter": 2900, "lr": 0.03084, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31766, "top5_acc": 0.57266, "loss_cls": 3.87723, "loss": 3.87723, "time": 0.8213} +{"mode": "train", "epoch": 94, "iter": 3000, "lr": 0.03082, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3275, "top5_acc": 0.57344, "loss_cls": 3.85759, "loss": 3.85759, "time": 0.83474} +{"mode": "train", "epoch": 94, "iter": 3100, "lr": 0.03079, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31594, "top5_acc": 0.58438, "loss_cls": 3.84698, "loss": 3.84698, "time": 0.83369} +{"mode": "train", "epoch": 94, "iter": 3200, "lr": 0.03077, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32672, "top5_acc": 0.57984, "loss_cls": 3.8596, "loss": 3.8596, "time": 0.82514} +{"mode": "train", "epoch": 94, "iter": 3300, "lr": 0.03074, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31766, "top5_acc": 0.56938, "loss_cls": 3.90437, "loss": 3.90437, "time": 0.82878} +{"mode": "train", "epoch": 94, "iter": 3400, "lr": 0.03071, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32, "top5_acc": 0.58469, "loss_cls": 3.84344, "loss": 3.84344, "time": 0.82015} +{"mode": "train", "epoch": 94, "iter": 3500, "lr": 0.03069, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31922, "top5_acc": 0.57719, "loss_cls": 3.86807, "loss": 3.86807, "time": 0.82767} +{"mode": "train", "epoch": 94, "iter": 3600, "lr": 0.03066, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32406, "top5_acc": 0.58344, "loss_cls": 3.84566, "loss": 3.84566, "time": 0.81927} +{"mode": "train", "epoch": 94, "iter": 3700, "lr": 0.03064, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31281, "top5_acc": 0.57609, "loss_cls": 3.90249, "loss": 3.90249, "time": 0.82589} +{"mode": "val", "epoch": 94, "iter": 309, "lr": 0.03062, "top1_acc": 0.25695, "top5_acc": 0.51071, "mean_class_accuracy": 0.2568} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.0306, "memory": 15990, "data_time": 1.32787, "top1_acc": 0.33594, "top5_acc": 0.59969, "loss_cls": 3.72251, "loss": 3.72251, "time": 2.32461} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.03057, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32609, "top5_acc": 0.59047, "loss_cls": 3.82106, "loss": 3.82106, "time": 0.83091} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.03055, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32922, "top5_acc": 0.58578, "loss_cls": 3.79345, "loss": 3.79345, "time": 0.83092} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.03052, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32688, "top5_acc": 0.58594, "loss_cls": 3.83223, "loss": 3.83223, "time": 0.82391} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.0305, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33125, "top5_acc": 0.58703, "loss_cls": 3.82035, "loss": 3.82035, "time": 0.8255} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.03047, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32562, "top5_acc": 0.59062, "loss_cls": 3.80564, "loss": 3.80564, "time": 0.82592} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.03044, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33422, "top5_acc": 0.59688, "loss_cls": 3.76903, "loss": 3.76903, "time": 0.82105} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.03042, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32125, "top5_acc": 0.57859, "loss_cls": 3.85538, "loss": 3.85538, "time": 0.82584} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.03039, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33656, "top5_acc": 0.59906, "loss_cls": 3.7723, "loss": 3.7723, "time": 0.8281} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.03037, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33125, "top5_acc": 0.59328, "loss_cls": 3.79191, "loss": 3.79191, "time": 0.83103} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.03034, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32203, "top5_acc": 0.57578, "loss_cls": 3.85026, "loss": 3.85026, "time": 0.82601} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.03032, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32703, "top5_acc": 0.58656, "loss_cls": 3.82844, "loss": 3.82844, "time": 0.8253} +{"mode": "train", "epoch": 95, "iter": 1300, "lr": 0.03029, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3275, "top5_acc": 0.57672, "loss_cls": 3.85199, "loss": 3.85199, "time": 0.82325} +{"mode": "train", "epoch": 95, "iter": 1400, "lr": 0.03026, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31984, "top5_acc": 0.59531, "loss_cls": 3.79896, "loss": 3.79896, "time": 0.83099} +{"mode": "train", "epoch": 95, "iter": 1500, "lr": 0.03024, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31688, "top5_acc": 0.57609, "loss_cls": 3.87718, "loss": 3.87718, "time": 0.82045} +{"mode": "train", "epoch": 95, "iter": 1600, "lr": 0.03021, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32, "top5_acc": 0.57406, "loss_cls": 3.89234, "loss": 3.89234, "time": 0.81746} +{"mode": "train", "epoch": 95, "iter": 1700, "lr": 0.03019, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32969, "top5_acc": 0.58734, "loss_cls": 3.81735, "loss": 3.81735, "time": 0.83121} +{"mode": "train", "epoch": 95, "iter": 1800, "lr": 0.03016, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31266, "top5_acc": 0.57625, "loss_cls": 3.84639, "loss": 3.84639, "time": 0.837} +{"mode": "train", "epoch": 95, "iter": 1900, "lr": 0.03014, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32078, "top5_acc": 0.58391, "loss_cls": 3.85872, "loss": 3.85872, "time": 0.82109} +{"mode": "train", "epoch": 95, "iter": 2000, "lr": 0.03011, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31984, "top5_acc": 0.58297, "loss_cls": 3.86523, "loss": 3.86523, "time": 0.82972} +{"mode": "train", "epoch": 95, "iter": 2100, "lr": 0.03008, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32219, "top5_acc": 0.58312, "loss_cls": 3.85869, "loss": 3.85869, "time": 0.82916} +{"mode": "train", "epoch": 95, "iter": 2200, "lr": 0.03006, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33078, "top5_acc": 0.57938, "loss_cls": 3.81726, "loss": 3.81726, "time": 0.81864} +{"mode": "train", "epoch": 95, "iter": 2300, "lr": 0.03003, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33031, "top5_acc": 0.58531, "loss_cls": 3.85418, "loss": 3.85418, "time": 0.82353} +{"mode": "train", "epoch": 95, "iter": 2400, "lr": 0.03001, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31719, "top5_acc": 0.58609, "loss_cls": 3.869, "loss": 3.869, "time": 0.83396} +{"mode": "train", "epoch": 95, "iter": 2500, "lr": 0.02998, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32156, "top5_acc": 0.57344, "loss_cls": 3.85003, "loss": 3.85003, "time": 0.81752} +{"mode": "train", "epoch": 95, "iter": 2600, "lr": 0.02996, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31797, "top5_acc": 0.57266, "loss_cls": 3.88732, "loss": 3.88732, "time": 0.83132} +{"mode": "train", "epoch": 95, "iter": 2700, "lr": 0.02993, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33359, "top5_acc": 0.59359, "loss_cls": 3.80987, "loss": 3.80987, "time": 0.81695} +{"mode": "train", "epoch": 95, "iter": 2800, "lr": 0.02991, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32984, "top5_acc": 0.58969, "loss_cls": 3.82974, "loss": 3.82974, "time": 0.82414} +{"mode": "train", "epoch": 95, "iter": 2900, "lr": 0.02988, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33203, "top5_acc": 0.58578, "loss_cls": 3.81107, "loss": 3.81107, "time": 0.8251} +{"mode": "train", "epoch": 95, "iter": 3000, "lr": 0.02985, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32906, "top5_acc": 0.58797, "loss_cls": 3.83083, "loss": 3.83083, "time": 0.825} +{"mode": "train", "epoch": 95, "iter": 3100, "lr": 0.02983, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32172, "top5_acc": 0.58578, "loss_cls": 3.82189, "loss": 3.82189, "time": 0.82599} +{"mode": "train", "epoch": 95, "iter": 3200, "lr": 0.0298, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32984, "top5_acc": 0.57422, "loss_cls": 3.86728, "loss": 3.86728, "time": 0.82747} +{"mode": "train", "epoch": 95, "iter": 3300, "lr": 0.02978, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32547, "top5_acc": 0.58938, "loss_cls": 3.80397, "loss": 3.80397, "time": 0.82295} +{"mode": "train", "epoch": 95, "iter": 3400, "lr": 0.02975, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32594, "top5_acc": 0.57953, "loss_cls": 3.83784, "loss": 3.83784, "time": 0.82256} +{"mode": "train", "epoch": 95, "iter": 3500, "lr": 0.02973, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32453, "top5_acc": 0.58156, "loss_cls": 3.8863, "loss": 3.8863, "time": 0.819} +{"mode": "train", "epoch": 95, "iter": 3600, "lr": 0.0297, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33312, "top5_acc": 0.59203, "loss_cls": 3.80689, "loss": 3.80689, "time": 0.81858} +{"mode": "train", "epoch": 95, "iter": 3700, "lr": 0.02968, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32266, "top5_acc": 0.58125, "loss_cls": 3.85812, "loss": 3.85812, "time": 0.83416} +{"mode": "val", "epoch": 95, "iter": 309, "lr": 0.02966, "top1_acc": 0.25645, "top5_acc": 0.49511, "mean_class_accuracy": 0.25628} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.02964, "memory": 15990, "data_time": 1.31021, "top1_acc": 0.35234, "top5_acc": 0.60578, "loss_cls": 3.69689, "loss": 3.69689, "time": 2.30391} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.02961, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33375, "top5_acc": 0.59422, "loss_cls": 3.76704, "loss": 3.76704, "time": 0.83011} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.02959, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33562, "top5_acc": 0.59719, "loss_cls": 3.7708, "loss": 3.7708, "time": 0.82462} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.02956, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33125, "top5_acc": 0.59375, "loss_cls": 3.75948, "loss": 3.75948, "time": 0.82378} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.02954, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33281, "top5_acc": 0.58234, "loss_cls": 3.84672, "loss": 3.84672, "time": 0.8215} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.02951, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32625, "top5_acc": 0.59078, "loss_cls": 3.80965, "loss": 3.80965, "time": 0.81877} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.02948, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33594, "top5_acc": 0.59641, "loss_cls": 3.76693, "loss": 3.76693, "time": 0.8224} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.02946, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32938, "top5_acc": 0.59641, "loss_cls": 3.77732, "loss": 3.77732, "time": 0.81786} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.02943, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32812, "top5_acc": 0.59266, "loss_cls": 3.81099, "loss": 3.81099, "time": 0.81926} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.02941, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31969, "top5_acc": 0.58516, "loss_cls": 3.81881, "loss": 3.81881, "time": 0.82377} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.02938, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32172, "top5_acc": 0.58016, "loss_cls": 3.85885, "loss": 3.85885, "time": 0.82231} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.02936, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32984, "top5_acc": 0.5875, "loss_cls": 3.79835, "loss": 3.79835, "time": 0.8233} +{"mode": "train", "epoch": 96, "iter": 1300, "lr": 0.02933, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.3225, "top5_acc": 0.58172, "loss_cls": 3.84431, "loss": 3.84431, "time": 0.82482} +{"mode": "train", "epoch": 96, "iter": 1400, "lr": 0.02931, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32859, "top5_acc": 0.57969, "loss_cls": 3.82057, "loss": 3.82057, "time": 0.82484} +{"mode": "train", "epoch": 96, "iter": 1500, "lr": 0.02928, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33266, "top5_acc": 0.59, "loss_cls": 3.81054, "loss": 3.81054, "time": 0.81804} +{"mode": "train", "epoch": 96, "iter": 1600, "lr": 0.02926, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32062, "top5_acc": 0.58547, "loss_cls": 3.86642, "loss": 3.86642, "time": 0.82722} +{"mode": "train", "epoch": 96, "iter": 1700, "lr": 0.02923, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33109, "top5_acc": 0.59203, "loss_cls": 3.80357, "loss": 3.80357, "time": 0.83415} +{"mode": "train", "epoch": 96, "iter": 1800, "lr": 0.0292, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.31938, "top5_acc": 0.58656, "loss_cls": 3.83214, "loss": 3.83214, "time": 0.82842} +{"mode": "train", "epoch": 96, "iter": 1900, "lr": 0.02918, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32656, "top5_acc": 0.58203, "loss_cls": 3.83642, "loss": 3.83642, "time": 0.82085} +{"mode": "train", "epoch": 96, "iter": 2000, "lr": 0.02915, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32938, "top5_acc": 0.58594, "loss_cls": 3.80988, "loss": 3.80988, "time": 0.82471} +{"mode": "train", "epoch": 96, "iter": 2100, "lr": 0.02913, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33688, "top5_acc": 0.60281, "loss_cls": 3.75291, "loss": 3.75291, "time": 0.83696} +{"mode": "train", "epoch": 96, "iter": 2200, "lr": 0.0291, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32484, "top5_acc": 0.57578, "loss_cls": 3.85493, "loss": 3.85493, "time": 0.82554} +{"mode": "train", "epoch": 96, "iter": 2300, "lr": 0.02908, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32781, "top5_acc": 0.58906, "loss_cls": 3.79733, "loss": 3.79733, "time": 0.82554} +{"mode": "train", "epoch": 96, "iter": 2400, "lr": 0.02905, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32297, "top5_acc": 0.58141, "loss_cls": 3.84167, "loss": 3.84167, "time": 0.83422} +{"mode": "train", "epoch": 96, "iter": 2500, "lr": 0.02903, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.325, "top5_acc": 0.585, "loss_cls": 3.82087, "loss": 3.82087, "time": 0.82421} +{"mode": "train", "epoch": 96, "iter": 2600, "lr": 0.029, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32219, "top5_acc": 0.58719, "loss_cls": 3.81649, "loss": 3.81649, "time": 0.83156} +{"mode": "train", "epoch": 96, "iter": 2700, "lr": 0.02898, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32172, "top5_acc": 0.58359, "loss_cls": 3.85665, "loss": 3.85665, "time": 0.82222} +{"mode": "train", "epoch": 96, "iter": 2800, "lr": 0.02895, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31844, "top5_acc": 0.58234, "loss_cls": 3.83449, "loss": 3.83449, "time": 0.82495} +{"mode": "train", "epoch": 96, "iter": 2900, "lr": 0.02893, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33625, "top5_acc": 0.59734, "loss_cls": 3.77933, "loss": 3.77933, "time": 0.82439} +{"mode": "train", "epoch": 96, "iter": 3000, "lr": 0.0289, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32031, "top5_acc": 0.57453, "loss_cls": 3.88659, "loss": 3.88659, "time": 0.82013} +{"mode": "train", "epoch": 96, "iter": 3100, "lr": 0.02887, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32734, "top5_acc": 0.58359, "loss_cls": 3.8458, "loss": 3.8458, "time": 0.82199} +{"mode": "train", "epoch": 96, "iter": 3200, "lr": 0.02885, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32156, "top5_acc": 0.58188, "loss_cls": 3.82757, "loss": 3.82757, "time": 0.82514} +{"mode": "train", "epoch": 96, "iter": 3300, "lr": 0.02882, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32797, "top5_acc": 0.59047, "loss_cls": 3.81011, "loss": 3.81011, "time": 0.82546} +{"mode": "train", "epoch": 96, "iter": 3400, "lr": 0.0288, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31969, "top5_acc": 0.57297, "loss_cls": 3.85746, "loss": 3.85746, "time": 0.82199} +{"mode": "train", "epoch": 96, "iter": 3500, "lr": 0.02877, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32156, "top5_acc": 0.59688, "loss_cls": 3.82362, "loss": 3.82362, "time": 0.81545} +{"mode": "train", "epoch": 96, "iter": 3600, "lr": 0.02875, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32266, "top5_acc": 0.58609, "loss_cls": 3.83083, "loss": 3.83083, "time": 0.81998} +{"mode": "train", "epoch": 96, "iter": 3700, "lr": 0.02872, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31922, "top5_acc": 0.57734, "loss_cls": 3.85796, "loss": 3.85796, "time": 0.82963} +{"mode": "val", "epoch": 96, "iter": 309, "lr": 0.02871, "top1_acc": 0.27574, "top5_acc": 0.5293, "mean_class_accuracy": 0.27553} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.02869, "memory": 15990, "data_time": 1.2611, "top1_acc": 0.33484, "top5_acc": 0.59922, "loss_cls": 3.76243, "loss": 3.76243, "time": 2.25066} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.02866, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32438, "top5_acc": 0.59531, "loss_cls": 3.79312, "loss": 3.79312, "time": 0.83181} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.02864, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32391, "top5_acc": 0.59391, "loss_cls": 3.77346, "loss": 3.77346, "time": 0.83333} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.02861, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34172, "top5_acc": 0.60422, "loss_cls": 3.7259, "loss": 3.7259, "time": 0.83213} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.02858, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.3275, "top5_acc": 0.58891, "loss_cls": 3.81295, "loss": 3.81295, "time": 0.82712} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.02856, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32953, "top5_acc": 0.58672, "loss_cls": 3.83282, "loss": 3.83282, "time": 0.82746} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.02853, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33094, "top5_acc": 0.59281, "loss_cls": 3.79828, "loss": 3.79828, "time": 0.83134} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.02851, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32516, "top5_acc": 0.59078, "loss_cls": 3.80094, "loss": 3.80094, "time": 0.83002} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.02848, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33547, "top5_acc": 0.59891, "loss_cls": 3.74477, "loss": 3.74477, "time": 0.83371} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.02846, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34375, "top5_acc": 0.60094, "loss_cls": 3.73577, "loss": 3.73577, "time": 0.83247} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.02843, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33672, "top5_acc": 0.59609, "loss_cls": 3.76931, "loss": 3.76931, "time": 0.82669} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.02841, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31766, "top5_acc": 0.57625, "loss_cls": 3.87763, "loss": 3.87763, "time": 0.82968} +{"mode": "train", "epoch": 97, "iter": 1300, "lr": 0.02838, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32609, "top5_acc": 0.59453, "loss_cls": 3.81228, "loss": 3.81228, "time": 0.83649} +{"mode": "train", "epoch": 97, "iter": 1400, "lr": 0.02836, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32328, "top5_acc": 0.58547, "loss_cls": 3.85498, "loss": 3.85498, "time": 0.82625} +{"mode": "train", "epoch": 97, "iter": 1500, "lr": 0.02833, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32016, "top5_acc": 0.58406, "loss_cls": 3.84856, "loss": 3.84856, "time": 0.82425} +{"mode": "train", "epoch": 97, "iter": 1600, "lr": 0.02831, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33062, "top5_acc": 0.59344, "loss_cls": 3.81655, "loss": 3.81655, "time": 0.83228} +{"mode": "train", "epoch": 97, "iter": 1700, "lr": 0.02828, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32688, "top5_acc": 0.58, "loss_cls": 3.83424, "loss": 3.83424, "time": 0.84142} +{"mode": "train", "epoch": 97, "iter": 1800, "lr": 0.02826, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32891, "top5_acc": 0.58141, "loss_cls": 3.81803, "loss": 3.81803, "time": 0.82875} +{"mode": "train", "epoch": 97, "iter": 1900, "lr": 0.02823, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33016, "top5_acc": 0.58734, "loss_cls": 3.8359, "loss": 3.8359, "time": 0.82763} +{"mode": "train", "epoch": 97, "iter": 2000, "lr": 0.02821, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32938, "top5_acc": 0.59656, "loss_cls": 3.79253, "loss": 3.79253, "time": 0.8334} +{"mode": "train", "epoch": 97, "iter": 2100, "lr": 0.02818, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33359, "top5_acc": 0.59531, "loss_cls": 3.7873, "loss": 3.7873, "time": 0.8359} +{"mode": "train", "epoch": 97, "iter": 2200, "lr": 0.02816, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32422, "top5_acc": 0.57734, "loss_cls": 3.85449, "loss": 3.85449, "time": 0.82333} +{"mode": "train", "epoch": 97, "iter": 2300, "lr": 0.02813, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32906, "top5_acc": 0.58734, "loss_cls": 3.82526, "loss": 3.82526, "time": 0.82826} +{"mode": "train", "epoch": 97, "iter": 2400, "lr": 0.02811, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33219, "top5_acc": 0.585, "loss_cls": 3.80036, "loss": 3.80036, "time": 0.83385} +{"mode": "train", "epoch": 97, "iter": 2500, "lr": 0.02808, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.32391, "top5_acc": 0.5725, "loss_cls": 3.84009, "loss": 3.84009, "time": 0.83703} +{"mode": "train", "epoch": 97, "iter": 2600, "lr": 0.02806, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33547, "top5_acc": 0.59578, "loss_cls": 3.80066, "loss": 3.80066, "time": 0.83324} +{"mode": "train", "epoch": 97, "iter": 2700, "lr": 0.02803, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33656, "top5_acc": 0.60062, "loss_cls": 3.7748, "loss": 3.7748, "time": 0.82713} +{"mode": "train", "epoch": 97, "iter": 2800, "lr": 0.02801, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33797, "top5_acc": 0.59047, "loss_cls": 3.79207, "loss": 3.79207, "time": 0.83372} +{"mode": "train", "epoch": 97, "iter": 2900, "lr": 0.02798, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32859, "top5_acc": 0.59031, "loss_cls": 3.79428, "loss": 3.79428, "time": 0.82254} +{"mode": "train", "epoch": 97, "iter": 3000, "lr": 0.02796, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32938, "top5_acc": 0.58344, "loss_cls": 3.8131, "loss": 3.8131, "time": 0.81901} +{"mode": "train", "epoch": 97, "iter": 3100, "lr": 0.02793, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33141, "top5_acc": 0.59188, "loss_cls": 3.7906, "loss": 3.7906, "time": 0.82629} +{"mode": "train", "epoch": 97, "iter": 3200, "lr": 0.02791, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33531, "top5_acc": 0.59516, "loss_cls": 3.77244, "loss": 3.77244, "time": 0.82282} +{"mode": "train", "epoch": 97, "iter": 3300, "lr": 0.02788, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32219, "top5_acc": 0.57797, "loss_cls": 3.85685, "loss": 3.85685, "time": 0.82451} +{"mode": "train", "epoch": 97, "iter": 3400, "lr": 0.02786, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33516, "top5_acc": 0.58781, "loss_cls": 3.80366, "loss": 3.80366, "time": 0.82176} +{"mode": "train", "epoch": 97, "iter": 3500, "lr": 0.02783, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32719, "top5_acc": 0.58469, "loss_cls": 3.83263, "loss": 3.83263, "time": 0.81997} +{"mode": "train", "epoch": 97, "iter": 3600, "lr": 0.02781, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34016, "top5_acc": 0.59625, "loss_cls": 3.75777, "loss": 3.75777, "time": 0.82128} +{"mode": "train", "epoch": 97, "iter": 3700, "lr": 0.02778, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33547, "top5_acc": 0.59766, "loss_cls": 3.76795, "loss": 3.76795, "time": 0.83751} +{"mode": "val", "epoch": 97, "iter": 309, "lr": 0.02777, "top1_acc": 0.26095, "top5_acc": 0.49967, "mean_class_accuracy": 0.26082} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.02774, "memory": 15990, "data_time": 1.33306, "top1_acc": 0.33453, "top5_acc": 0.59719, "loss_cls": 3.74432, "loss": 3.74432, "time": 2.32388} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.02772, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32016, "top5_acc": 0.59188, "loss_cls": 3.78094, "loss": 3.78094, "time": 0.83418} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.02769, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33906, "top5_acc": 0.59969, "loss_cls": 3.74567, "loss": 3.74567, "time": 0.82682} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.02767, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.3475, "top5_acc": 0.59859, "loss_cls": 3.73764, "loss": 3.73764, "time": 0.82084} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.02764, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32, "top5_acc": 0.58297, "loss_cls": 3.82439, "loss": 3.82439, "time": 0.82934} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.02762, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33359, "top5_acc": 0.59312, "loss_cls": 3.76772, "loss": 3.76772, "time": 0.8292} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.02759, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33953, "top5_acc": 0.60219, "loss_cls": 3.75258, "loss": 3.75258, "time": 0.82443} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.02757, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33891, "top5_acc": 0.59875, "loss_cls": 3.76543, "loss": 3.76543, "time": 0.82407} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.02754, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33531, "top5_acc": 0.59297, "loss_cls": 3.76842, "loss": 3.76842, "time": 0.82778} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.02752, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.335, "top5_acc": 0.58547, "loss_cls": 3.78654, "loss": 3.78654, "time": 0.82699} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.02749, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33219, "top5_acc": 0.59406, "loss_cls": 3.7823, "loss": 3.7823, "time": 0.81803} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.02747, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33094, "top5_acc": 0.59047, "loss_cls": 3.7857, "loss": 3.7857, "time": 0.8222} +{"mode": "train", "epoch": 98, "iter": 1300, "lr": 0.02744, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31562, "top5_acc": 0.58203, "loss_cls": 3.83039, "loss": 3.83039, "time": 0.83274} +{"mode": "train", "epoch": 98, "iter": 1400, "lr": 0.02742, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33406, "top5_acc": 0.59125, "loss_cls": 3.79088, "loss": 3.79088, "time": 0.82272} +{"mode": "train", "epoch": 98, "iter": 1500, "lr": 0.02739, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33562, "top5_acc": 0.59031, "loss_cls": 3.80829, "loss": 3.80829, "time": 0.82263} +{"mode": "train", "epoch": 98, "iter": 1600, "lr": 0.02737, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.3375, "top5_acc": 0.58922, "loss_cls": 3.81424, "loss": 3.81424, "time": 0.8328} +{"mode": "train", "epoch": 98, "iter": 1700, "lr": 0.02734, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33188, "top5_acc": 0.6025, "loss_cls": 3.75129, "loss": 3.75129, "time": 0.83971} +{"mode": "train", "epoch": 98, "iter": 1800, "lr": 0.02732, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34188, "top5_acc": 0.59188, "loss_cls": 3.74171, "loss": 3.74171, "time": 0.82108} +{"mode": "train", "epoch": 98, "iter": 1900, "lr": 0.02729, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33094, "top5_acc": 0.60188, "loss_cls": 3.75974, "loss": 3.75974, "time": 0.8222} +{"mode": "train", "epoch": 98, "iter": 2000, "lr": 0.02727, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.33531, "top5_acc": 0.58625, "loss_cls": 3.83863, "loss": 3.83863, "time": 0.83333} +{"mode": "train", "epoch": 98, "iter": 2100, "lr": 0.02724, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33422, "top5_acc": 0.59234, "loss_cls": 3.77176, "loss": 3.77176, "time": 0.82486} +{"mode": "train", "epoch": 98, "iter": 2200, "lr": 0.02722, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32297, "top5_acc": 0.58906, "loss_cls": 3.81013, "loss": 3.81013, "time": 0.82183} +{"mode": "train", "epoch": 98, "iter": 2300, "lr": 0.02719, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33188, "top5_acc": 0.58641, "loss_cls": 3.79695, "loss": 3.79695, "time": 0.82805} +{"mode": "train", "epoch": 98, "iter": 2400, "lr": 0.02717, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.33562, "top5_acc": 0.59047, "loss_cls": 3.79942, "loss": 3.79942, "time": 0.83434} +{"mode": "train", "epoch": 98, "iter": 2500, "lr": 0.02714, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33266, "top5_acc": 0.5975, "loss_cls": 3.7822, "loss": 3.7822, "time": 0.8271} +{"mode": "train", "epoch": 98, "iter": 2600, "lr": 0.02712, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32578, "top5_acc": 0.58797, "loss_cls": 3.80244, "loss": 3.80244, "time": 0.82016} +{"mode": "train", "epoch": 98, "iter": 2700, "lr": 0.02709, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32953, "top5_acc": 0.58844, "loss_cls": 3.79718, "loss": 3.79718, "time": 0.82955} +{"mode": "train", "epoch": 98, "iter": 2800, "lr": 0.02707, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3325, "top5_acc": 0.59359, "loss_cls": 3.82206, "loss": 3.82206, "time": 0.82182} +{"mode": "train", "epoch": 98, "iter": 2900, "lr": 0.02705, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32406, "top5_acc": 0.5825, "loss_cls": 3.82623, "loss": 3.82623, "time": 0.82324} +{"mode": "train", "epoch": 98, "iter": 3000, "lr": 0.02702, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33266, "top5_acc": 0.59406, "loss_cls": 3.81179, "loss": 3.81179, "time": 0.81776} +{"mode": "train", "epoch": 98, "iter": 3100, "lr": 0.027, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32828, "top5_acc": 0.58969, "loss_cls": 3.81521, "loss": 3.81521, "time": 0.82214} +{"mode": "train", "epoch": 98, "iter": 3200, "lr": 0.02697, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33062, "top5_acc": 0.59141, "loss_cls": 3.78088, "loss": 3.78088, "time": 0.82463} +{"mode": "train", "epoch": 98, "iter": 3300, "lr": 0.02695, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34125, "top5_acc": 0.59109, "loss_cls": 3.78351, "loss": 3.78351, "time": 0.82739} +{"mode": "train", "epoch": 98, "iter": 3400, "lr": 0.02692, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33641, "top5_acc": 0.5975, "loss_cls": 3.7687, "loss": 3.7687, "time": 0.82138} +{"mode": "train", "epoch": 98, "iter": 3500, "lr": 0.0269, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33703, "top5_acc": 0.59484, "loss_cls": 3.78859, "loss": 3.78859, "time": 0.82136} +{"mode": "train", "epoch": 98, "iter": 3600, "lr": 0.02687, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32734, "top5_acc": 0.59188, "loss_cls": 3.82411, "loss": 3.82411, "time": 0.81931} +{"mode": "train", "epoch": 98, "iter": 3700, "lr": 0.02685, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3325, "top5_acc": 0.58844, "loss_cls": 3.78893, "loss": 3.78893, "time": 0.82949} +{"mode": "val", "epoch": 98, "iter": 309, "lr": 0.02684, "top1_acc": 0.24966, "top5_acc": 0.49223, "mean_class_accuracy": 0.24923} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.02681, "memory": 15990, "data_time": 1.24325, "top1_acc": 0.34672, "top5_acc": 0.59797, "loss_cls": 3.70913, "loss": 3.70913, "time": 2.2341} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.02679, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33906, "top5_acc": 0.6075, "loss_cls": 3.72732, "loss": 3.72732, "time": 0.8251} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.02676, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34266, "top5_acc": 0.59953, "loss_cls": 3.75717, "loss": 3.75717, "time": 0.82684} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.02674, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33922, "top5_acc": 0.58766, "loss_cls": 3.77674, "loss": 3.77674, "time": 0.82465} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.02671, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33828, "top5_acc": 0.60156, "loss_cls": 3.75586, "loss": 3.75586, "time": 0.8268} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.02669, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33766, "top5_acc": 0.605, "loss_cls": 3.75761, "loss": 3.75761, "time": 0.82506} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.02666, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33547, "top5_acc": 0.58891, "loss_cls": 3.79044, "loss": 3.79044, "time": 0.81763} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.02664, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33516, "top5_acc": 0.59578, "loss_cls": 3.7644, "loss": 3.7644, "time": 0.81865} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.02661, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34172, "top5_acc": 0.60625, "loss_cls": 3.72175, "loss": 3.72175, "time": 0.81565} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.02659, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34328, "top5_acc": 0.60375, "loss_cls": 3.71242, "loss": 3.71242, "time": 0.82364} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.02656, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31938, "top5_acc": 0.58266, "loss_cls": 3.8538, "loss": 3.8538, "time": 0.82206} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.02654, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34969, "top5_acc": 0.60609, "loss_cls": 3.68874, "loss": 3.68874, "time": 0.8172} +{"mode": "train", "epoch": 99, "iter": 1300, "lr": 0.02651, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33531, "top5_acc": 0.59609, "loss_cls": 3.75209, "loss": 3.75209, "time": 0.82485} +{"mode": "train", "epoch": 99, "iter": 1400, "lr": 0.02649, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34203, "top5_acc": 0.60312, "loss_cls": 3.7487, "loss": 3.7487, "time": 0.82242} +{"mode": "train", "epoch": 99, "iter": 1500, "lr": 0.02646, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32641, "top5_acc": 0.58453, "loss_cls": 3.79667, "loss": 3.79667, "time": 0.82602} +{"mode": "train", "epoch": 99, "iter": 1600, "lr": 0.02644, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32812, "top5_acc": 0.59234, "loss_cls": 3.79116, "loss": 3.79116, "time": 0.83226} +{"mode": "train", "epoch": 99, "iter": 1700, "lr": 0.02642, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.34062, "top5_acc": 0.59656, "loss_cls": 3.75722, "loss": 3.75722, "time": 0.83569} +{"mode": "train", "epoch": 99, "iter": 1800, "lr": 0.02639, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.32891, "top5_acc": 0.58406, "loss_cls": 3.79864, "loss": 3.79864, "time": 0.81919} +{"mode": "train", "epoch": 99, "iter": 1900, "lr": 0.02637, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34203, "top5_acc": 0.59594, "loss_cls": 3.77115, "loss": 3.77115, "time": 0.83239} +{"mode": "train", "epoch": 99, "iter": 2000, "lr": 0.02634, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33312, "top5_acc": 0.59203, "loss_cls": 3.7797, "loss": 3.7797, "time": 0.84079} +{"mode": "train", "epoch": 99, "iter": 2100, "lr": 0.02632, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33859, "top5_acc": 0.59641, "loss_cls": 3.77025, "loss": 3.77025, "time": 0.82612} +{"mode": "train", "epoch": 99, "iter": 2200, "lr": 0.02629, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33469, "top5_acc": 0.59812, "loss_cls": 3.77518, "loss": 3.77518, "time": 0.82557} +{"mode": "train", "epoch": 99, "iter": 2300, "lr": 0.02627, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32672, "top5_acc": 0.58406, "loss_cls": 3.81654, "loss": 3.81654, "time": 0.83316} +{"mode": "train", "epoch": 99, "iter": 2400, "lr": 0.02624, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32688, "top5_acc": 0.59031, "loss_cls": 3.81213, "loss": 3.81213, "time": 0.83109} +{"mode": "train", "epoch": 99, "iter": 2500, "lr": 0.02622, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34844, "top5_acc": 0.60812, "loss_cls": 3.72087, "loss": 3.72087, "time": 0.8264} +{"mode": "train", "epoch": 99, "iter": 2600, "lr": 0.02619, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33094, "top5_acc": 0.59125, "loss_cls": 3.78226, "loss": 3.78226, "time": 0.82407} +{"mode": "train", "epoch": 99, "iter": 2700, "lr": 0.02617, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33281, "top5_acc": 0.60047, "loss_cls": 3.77244, "loss": 3.77244, "time": 0.82982} +{"mode": "train", "epoch": 99, "iter": 2800, "lr": 0.02614, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33594, "top5_acc": 0.59062, "loss_cls": 3.7725, "loss": 3.7725, "time": 0.82714} +{"mode": "train", "epoch": 99, "iter": 2900, "lr": 0.02612, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34062, "top5_acc": 0.5975, "loss_cls": 3.72932, "loss": 3.72932, "time": 0.82264} +{"mode": "train", "epoch": 99, "iter": 3000, "lr": 0.0261, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33688, "top5_acc": 0.58969, "loss_cls": 3.7941, "loss": 3.7941, "time": 0.81787} +{"mode": "train", "epoch": 99, "iter": 3100, "lr": 0.02607, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33438, "top5_acc": 0.59781, "loss_cls": 3.78478, "loss": 3.78478, "time": 0.82264} +{"mode": "train", "epoch": 99, "iter": 3200, "lr": 0.02605, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32391, "top5_acc": 0.59094, "loss_cls": 3.80493, "loss": 3.80493, "time": 0.82117} +{"mode": "train", "epoch": 99, "iter": 3300, "lr": 0.02602, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32703, "top5_acc": 0.58734, "loss_cls": 3.81318, "loss": 3.81318, "time": 0.82451} +{"mode": "train", "epoch": 99, "iter": 3400, "lr": 0.026, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33688, "top5_acc": 0.59188, "loss_cls": 3.7778, "loss": 3.7778, "time": 0.82102} +{"mode": "train", "epoch": 99, "iter": 3500, "lr": 0.02597, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33375, "top5_acc": 0.59, "loss_cls": 3.80021, "loss": 3.80021, "time": 0.82047} +{"mode": "train", "epoch": 99, "iter": 3600, "lr": 0.02595, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34531, "top5_acc": 0.59953, "loss_cls": 3.74289, "loss": 3.74289, "time": 0.8252} +{"mode": "train", "epoch": 99, "iter": 3700, "lr": 0.02592, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33062, "top5_acc": 0.59391, "loss_cls": 3.76315, "loss": 3.76315, "time": 0.82981} +{"mode": "val", "epoch": 99, "iter": 309, "lr": 0.02591, "top1_acc": 0.25077, "top5_acc": 0.49688, "mean_class_accuracy": 0.25042} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.02589, "memory": 15990, "data_time": 1.24232, "top1_acc": 0.35188, "top5_acc": 0.61312, "loss_cls": 3.65777, "loss": 3.65777, "time": 2.222} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.02586, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34453, "top5_acc": 0.60938, "loss_cls": 3.71319, "loss": 3.71319, "time": 0.82018} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.02584, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34172, "top5_acc": 0.60453, "loss_cls": 3.69177, "loss": 3.69177, "time": 0.82389} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.02581, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33719, "top5_acc": 0.60688, "loss_cls": 3.70563, "loss": 3.70563, "time": 0.82257} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.02579, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34594, "top5_acc": 0.60797, "loss_cls": 3.697, "loss": 3.697, "time": 0.82104} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.02577, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33359, "top5_acc": 0.59969, "loss_cls": 3.7478, "loss": 3.7478, "time": 0.82148} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.02574, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33812, "top5_acc": 0.60281, "loss_cls": 3.73108, "loss": 3.73108, "time": 0.82665} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.02572, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34469, "top5_acc": 0.61141, "loss_cls": 3.71527, "loss": 3.71527, "time": 0.81744} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.02569, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33594, "top5_acc": 0.59672, "loss_cls": 3.74947, "loss": 3.74947, "time": 0.82202} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.02567, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34922, "top5_acc": 0.61297, "loss_cls": 3.70581, "loss": 3.70581, "time": 0.81848} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.02564, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32672, "top5_acc": 0.59469, "loss_cls": 3.76232, "loss": 3.76232, "time": 0.82442} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.02562, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33922, "top5_acc": 0.58891, "loss_cls": 3.77324, "loss": 3.77324, "time": 0.82099} +{"mode": "train", "epoch": 100, "iter": 1300, "lr": 0.02559, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.33328, "top5_acc": 0.60453, "loss_cls": 3.76394, "loss": 3.76394, "time": 0.8289} +{"mode": "train", "epoch": 100, "iter": 1400, "lr": 0.02557, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34031, "top5_acc": 0.59547, "loss_cls": 3.72192, "loss": 3.72192, "time": 0.82148} +{"mode": "train", "epoch": 100, "iter": 1500, "lr": 0.02555, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.3375, "top5_acc": 0.60656, "loss_cls": 3.73241, "loss": 3.73241, "time": 0.81947} +{"mode": "train", "epoch": 100, "iter": 1600, "lr": 0.02552, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3375, "top5_acc": 0.59766, "loss_cls": 3.7524, "loss": 3.7524, "time": 0.83372} +{"mode": "train", "epoch": 100, "iter": 1700, "lr": 0.0255, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.33281, "top5_acc": 0.59109, "loss_cls": 3.79951, "loss": 3.79951, "time": 0.83875} +{"mode": "train", "epoch": 100, "iter": 1800, "lr": 0.02547, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33375, "top5_acc": 0.59156, "loss_cls": 3.79456, "loss": 3.79456, "time": 0.82649} +{"mode": "train", "epoch": 100, "iter": 1900, "lr": 0.02545, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.34062, "top5_acc": 0.59531, "loss_cls": 3.74989, "loss": 3.74989, "time": 0.82708} +{"mode": "train", "epoch": 100, "iter": 2000, "lr": 0.02542, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32953, "top5_acc": 0.59562, "loss_cls": 3.75703, "loss": 3.75703, "time": 0.83875} +{"mode": "train", "epoch": 100, "iter": 2100, "lr": 0.0254, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34109, "top5_acc": 0.59906, "loss_cls": 3.72002, "loss": 3.72002, "time": 0.82747} +{"mode": "train", "epoch": 100, "iter": 2200, "lr": 0.02538, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33031, "top5_acc": 0.59453, "loss_cls": 3.78717, "loss": 3.78717, "time": 0.8222} +{"mode": "train", "epoch": 100, "iter": 2300, "lr": 0.02535, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33438, "top5_acc": 0.59297, "loss_cls": 3.7468, "loss": 3.7468, "time": 0.83263} +{"mode": "train", "epoch": 100, "iter": 2400, "lr": 0.02533, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33859, "top5_acc": 0.5975, "loss_cls": 3.75572, "loss": 3.75572, "time": 0.83813} +{"mode": "train", "epoch": 100, "iter": 2500, "lr": 0.0253, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33719, "top5_acc": 0.60828, "loss_cls": 3.73405, "loss": 3.73405, "time": 0.82086} +{"mode": "train", "epoch": 100, "iter": 2600, "lr": 0.02528, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33469, "top5_acc": 0.59797, "loss_cls": 3.8012, "loss": 3.8012, "time": 0.82547} +{"mode": "train", "epoch": 100, "iter": 2700, "lr": 0.02525, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33891, "top5_acc": 0.5975, "loss_cls": 3.73992, "loss": 3.73992, "time": 0.83005} +{"mode": "train", "epoch": 100, "iter": 2800, "lr": 0.02523, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34062, "top5_acc": 0.59156, "loss_cls": 3.77785, "loss": 3.77785, "time": 0.8281} +{"mode": "train", "epoch": 100, "iter": 2900, "lr": 0.02521, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33719, "top5_acc": 0.59828, "loss_cls": 3.75917, "loss": 3.75917, "time": 0.82279} +{"mode": "train", "epoch": 100, "iter": 3000, "lr": 0.02518, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34172, "top5_acc": 0.60516, "loss_cls": 3.70704, "loss": 3.70704, "time": 0.8255} +{"mode": "train", "epoch": 100, "iter": 3100, "lr": 0.02516, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33219, "top5_acc": 0.5875, "loss_cls": 3.80748, "loss": 3.80748, "time": 0.83108} +{"mode": "train", "epoch": 100, "iter": 3200, "lr": 0.02513, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33672, "top5_acc": 0.59656, "loss_cls": 3.76069, "loss": 3.76069, "time": 0.82619} +{"mode": "train", "epoch": 100, "iter": 3300, "lr": 0.02511, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33531, "top5_acc": 0.60047, "loss_cls": 3.77343, "loss": 3.77343, "time": 0.82374} +{"mode": "train", "epoch": 100, "iter": 3400, "lr": 0.02508, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34516, "top5_acc": 0.59469, "loss_cls": 3.76393, "loss": 3.76393, "time": 0.81984} +{"mode": "train", "epoch": 100, "iter": 3500, "lr": 0.02506, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34812, "top5_acc": 0.60453, "loss_cls": 3.72225, "loss": 3.72225, "time": 0.82116} +{"mode": "train", "epoch": 100, "iter": 3600, "lr": 0.02504, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33656, "top5_acc": 0.59375, "loss_cls": 3.77453, "loss": 3.77453, "time": 0.82738} +{"mode": "train", "epoch": 100, "iter": 3700, "lr": 0.02501, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32562, "top5_acc": 0.58453, "loss_cls": 3.83214, "loss": 3.83214, "time": 0.83044} +{"mode": "val", "epoch": 100, "iter": 309, "lr": 0.025, "top1_acc": 0.28547, "top5_acc": 0.53487, "mean_class_accuracy": 0.28521} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.02498, "memory": 15990, "data_time": 1.23948, "top1_acc": 0.34594, "top5_acc": 0.61516, "loss_cls": 3.67378, "loss": 3.67378, "time": 2.21941} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.02495, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33844, "top5_acc": 0.60281, "loss_cls": 3.72697, "loss": 3.72697, "time": 0.82015} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.02493, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34625, "top5_acc": 0.60891, "loss_cls": 3.6958, "loss": 3.6958, "time": 0.82199} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.0249, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35078, "top5_acc": 0.60438, "loss_cls": 3.72034, "loss": 3.72034, "time": 0.81655} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.02488, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34641, "top5_acc": 0.60016, "loss_cls": 3.70237, "loss": 3.70237, "time": 0.82085} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.02486, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34531, "top5_acc": 0.59641, "loss_cls": 3.72481, "loss": 3.72481, "time": 0.81724} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.02483, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34609, "top5_acc": 0.60844, "loss_cls": 3.70612, "loss": 3.70612, "time": 0.82478} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.02481, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34016, "top5_acc": 0.60141, "loss_cls": 3.74025, "loss": 3.74025, "time": 0.81682} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.02478, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34891, "top5_acc": 0.60547, "loss_cls": 3.72108, "loss": 3.72108, "time": 0.82774} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.02476, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34172, "top5_acc": 0.60219, "loss_cls": 3.72699, "loss": 3.72699, "time": 0.82305} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.02473, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34938, "top5_acc": 0.59734, "loss_cls": 3.70971, "loss": 3.70971, "time": 0.82019} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.02471, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33719, "top5_acc": 0.59828, "loss_cls": 3.74113, "loss": 3.74113, "time": 0.81554} +{"mode": "train", "epoch": 101, "iter": 1300, "lr": 0.02469, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34688, "top5_acc": 0.61328, "loss_cls": 3.69432, "loss": 3.69432, "time": 0.82843} +{"mode": "train", "epoch": 101, "iter": 1400, "lr": 0.02466, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32828, "top5_acc": 0.59766, "loss_cls": 3.76219, "loss": 3.76219, "time": 0.81929} +{"mode": "train", "epoch": 101, "iter": 1500, "lr": 0.02464, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32719, "top5_acc": 0.59609, "loss_cls": 3.74372, "loss": 3.74372, "time": 0.82224} +{"mode": "train", "epoch": 101, "iter": 1600, "lr": 0.02461, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.345, "top5_acc": 0.61187, "loss_cls": 3.69501, "loss": 3.69501, "time": 0.83513} +{"mode": "train", "epoch": 101, "iter": 1700, "lr": 0.02459, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33922, "top5_acc": 0.60031, "loss_cls": 3.73151, "loss": 3.73151, "time": 0.83535} +{"mode": "train", "epoch": 101, "iter": 1800, "lr": 0.02457, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34172, "top5_acc": 0.59797, "loss_cls": 3.74288, "loss": 3.74288, "time": 0.82571} +{"mode": "train", "epoch": 101, "iter": 1900, "lr": 0.02454, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34078, "top5_acc": 0.60562, "loss_cls": 3.70242, "loss": 3.70242, "time": 0.82652} +{"mode": "train", "epoch": 101, "iter": 2000, "lr": 0.02452, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33922, "top5_acc": 0.59891, "loss_cls": 3.7457, "loss": 3.7457, "time": 0.83879} +{"mode": "train", "epoch": 101, "iter": 2100, "lr": 0.02449, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33, "top5_acc": 0.59531, "loss_cls": 3.75019, "loss": 3.75019, "time": 0.81888} +{"mode": "train", "epoch": 101, "iter": 2200, "lr": 0.02447, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33406, "top5_acc": 0.60094, "loss_cls": 3.72579, "loss": 3.72579, "time": 0.82795} +{"mode": "train", "epoch": 101, "iter": 2300, "lr": 0.02445, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.34656, "top5_acc": 0.61281, "loss_cls": 3.67891, "loss": 3.67891, "time": 0.83616} +{"mode": "train", "epoch": 101, "iter": 2400, "lr": 0.02442, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34031, "top5_acc": 0.59906, "loss_cls": 3.73341, "loss": 3.73341, "time": 0.82495} +{"mode": "train", "epoch": 101, "iter": 2500, "lr": 0.0244, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34156, "top5_acc": 0.59906, "loss_cls": 3.73113, "loss": 3.73113, "time": 0.81839} +{"mode": "train", "epoch": 101, "iter": 2600, "lr": 0.02437, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33891, "top5_acc": 0.59828, "loss_cls": 3.77112, "loss": 3.77112, "time": 0.82873} +{"mode": "train", "epoch": 101, "iter": 2700, "lr": 0.02435, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33625, "top5_acc": 0.59391, "loss_cls": 3.78187, "loss": 3.78187, "time": 0.83191} +{"mode": "train", "epoch": 101, "iter": 2800, "lr": 0.02433, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34703, "top5_acc": 0.60438, "loss_cls": 3.71145, "loss": 3.71145, "time": 0.82301} +{"mode": "train", "epoch": 101, "iter": 2900, "lr": 0.0243, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34594, "top5_acc": 0.60547, "loss_cls": 3.69697, "loss": 3.69697, "time": 0.82492} +{"mode": "train", "epoch": 101, "iter": 3000, "lr": 0.02428, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33984, "top5_acc": 0.60062, "loss_cls": 3.74617, "loss": 3.74617, "time": 0.82436} +{"mode": "train", "epoch": 101, "iter": 3100, "lr": 0.02425, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35266, "top5_acc": 0.60109, "loss_cls": 3.72399, "loss": 3.72399, "time": 0.82366} +{"mode": "train", "epoch": 101, "iter": 3200, "lr": 0.02423, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31875, "top5_acc": 0.5825, "loss_cls": 3.80772, "loss": 3.80772, "time": 0.82729} +{"mode": "train", "epoch": 101, "iter": 3300, "lr": 0.02421, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33344, "top5_acc": 0.58938, "loss_cls": 3.78577, "loss": 3.78577, "time": 0.82396} +{"mode": "train", "epoch": 101, "iter": 3400, "lr": 0.02418, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34219, "top5_acc": 0.59922, "loss_cls": 3.72566, "loss": 3.72566, "time": 0.82135} +{"mode": "train", "epoch": 101, "iter": 3500, "lr": 0.02416, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32984, "top5_acc": 0.58719, "loss_cls": 3.80646, "loss": 3.80646, "time": 0.82699} +{"mode": "train", "epoch": 101, "iter": 3600, "lr": 0.02413, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33859, "top5_acc": 0.59688, "loss_cls": 3.74972, "loss": 3.74972, "time": 0.8287} +{"mode": "train", "epoch": 101, "iter": 3700, "lr": 0.02411, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33875, "top5_acc": 0.59219, "loss_cls": 3.76, "loss": 3.76, "time": 0.82931} +{"mode": "val", "epoch": 101, "iter": 309, "lr": 0.0241, "top1_acc": 0.28223, "top5_acc": 0.53042, "mean_class_accuracy": 0.28223} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.02407, "memory": 15990, "data_time": 1.25254, "top1_acc": 0.36516, "top5_acc": 0.61641, "loss_cls": 3.64331, "loss": 3.64331, "time": 2.24041} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.02405, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35609, "top5_acc": 0.62187, "loss_cls": 3.6229, "loss": 3.6229, "time": 0.82438} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.02403, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35031, "top5_acc": 0.6, "loss_cls": 3.72509, "loss": 3.72509, "time": 0.82457} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.024, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34875, "top5_acc": 0.61391, "loss_cls": 3.67609, "loss": 3.67609, "time": 0.81851} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.02398, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34797, "top5_acc": 0.60344, "loss_cls": 3.71157, "loss": 3.71157, "time": 0.81763} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.02396, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33859, "top5_acc": 0.59703, "loss_cls": 3.72972, "loss": 3.72972, "time": 0.82316} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.02393, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3425, "top5_acc": 0.60625, "loss_cls": 3.70584, "loss": 3.70584, "time": 0.81865} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.02391, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33562, "top5_acc": 0.60516, "loss_cls": 3.69585, "loss": 3.69585, "time": 0.82044} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.02388, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34312, "top5_acc": 0.61062, "loss_cls": 3.65894, "loss": 3.65894, "time": 0.82581} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.02386, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33641, "top5_acc": 0.605, "loss_cls": 3.72942, "loss": 3.72942, "time": 0.82731} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.02384, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35703, "top5_acc": 0.61656, "loss_cls": 3.64484, "loss": 3.64484, "time": 0.82259} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.02381, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34703, "top5_acc": 0.60969, "loss_cls": 3.67627, "loss": 3.67627, "time": 0.82406} +{"mode": "train", "epoch": 102, "iter": 1300, "lr": 0.02379, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.33891, "top5_acc": 0.60531, "loss_cls": 3.69688, "loss": 3.69688, "time": 0.83275} +{"mode": "train", "epoch": 102, "iter": 1400, "lr": 0.02376, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35, "top5_acc": 0.59922, "loss_cls": 3.73048, "loss": 3.73048, "time": 0.82084} +{"mode": "train", "epoch": 102, "iter": 1500, "lr": 0.02374, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3375, "top5_acc": 0.59703, "loss_cls": 3.73573, "loss": 3.73573, "time": 0.82339} +{"mode": "train", "epoch": 102, "iter": 1600, "lr": 0.02372, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34234, "top5_acc": 0.60828, "loss_cls": 3.70537, "loss": 3.70537, "time": 0.83174} +{"mode": "train", "epoch": 102, "iter": 1700, "lr": 0.02369, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33094, "top5_acc": 0.59547, "loss_cls": 3.76053, "loss": 3.76053, "time": 0.83582} +{"mode": "train", "epoch": 102, "iter": 1800, "lr": 0.02367, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34578, "top5_acc": 0.61031, "loss_cls": 3.6753, "loss": 3.6753, "time": 0.8202} +{"mode": "train", "epoch": 102, "iter": 1900, "lr": 0.02365, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35, "top5_acc": 0.61375, "loss_cls": 3.67016, "loss": 3.67016, "time": 0.83014} +{"mode": "train", "epoch": 102, "iter": 2000, "lr": 0.02362, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34578, "top5_acc": 0.59953, "loss_cls": 3.73438, "loss": 3.73438, "time": 0.83071} +{"mode": "train", "epoch": 102, "iter": 2100, "lr": 0.0236, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35125, "top5_acc": 0.60578, "loss_cls": 3.70152, "loss": 3.70152, "time": 0.82566} +{"mode": "train", "epoch": 102, "iter": 2200, "lr": 0.02357, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34906, "top5_acc": 0.60688, "loss_cls": 3.68128, "loss": 3.68128, "time": 0.82681} +{"mode": "train", "epoch": 102, "iter": 2300, "lr": 0.02355, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34219, "top5_acc": 0.59672, "loss_cls": 3.74154, "loss": 3.74154, "time": 0.83026} +{"mode": "train", "epoch": 102, "iter": 2400, "lr": 0.02353, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.34281, "top5_acc": 0.59781, "loss_cls": 3.73297, "loss": 3.73297, "time": 0.82045} +{"mode": "train", "epoch": 102, "iter": 2500, "lr": 0.0235, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33906, "top5_acc": 0.59344, "loss_cls": 3.78331, "loss": 3.78331, "time": 0.82137} +{"mode": "train", "epoch": 102, "iter": 2600, "lr": 0.02348, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35031, "top5_acc": 0.61625, "loss_cls": 3.66718, "loss": 3.66718, "time": 0.83037} +{"mode": "train", "epoch": 102, "iter": 2700, "lr": 0.02346, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34891, "top5_acc": 0.61687, "loss_cls": 3.66275, "loss": 3.66275, "time": 0.81519} +{"mode": "train", "epoch": 102, "iter": 2800, "lr": 0.02343, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34828, "top5_acc": 0.60328, "loss_cls": 3.71247, "loss": 3.71247, "time": 0.8179} +{"mode": "train", "epoch": 102, "iter": 2900, "lr": 0.02341, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33484, "top5_acc": 0.60219, "loss_cls": 3.75572, "loss": 3.75572, "time": 0.82275} +{"mode": "train", "epoch": 102, "iter": 3000, "lr": 0.02339, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32812, "top5_acc": 0.58891, "loss_cls": 3.78219, "loss": 3.78219, "time": 0.8207} +{"mode": "train", "epoch": 102, "iter": 3100, "lr": 0.02336, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34062, "top5_acc": 0.60312, "loss_cls": 3.72371, "loss": 3.72371, "time": 0.82152} +{"mode": "train", "epoch": 102, "iter": 3200, "lr": 0.02334, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34484, "top5_acc": 0.59953, "loss_cls": 3.7163, "loss": 3.7163, "time": 0.81868} +{"mode": "train", "epoch": 102, "iter": 3300, "lr": 0.02331, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34281, "top5_acc": 0.60094, "loss_cls": 3.74462, "loss": 3.74462, "time": 0.81589} +{"mode": "train", "epoch": 102, "iter": 3400, "lr": 0.02329, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33547, "top5_acc": 0.59391, "loss_cls": 3.77692, "loss": 3.77692, "time": 0.81233} +{"mode": "train", "epoch": 102, "iter": 3500, "lr": 0.02327, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34188, "top5_acc": 0.59109, "loss_cls": 3.75264, "loss": 3.75264, "time": 0.81949} +{"mode": "train", "epoch": 102, "iter": 3600, "lr": 0.02324, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.3375, "top5_acc": 0.60703, "loss_cls": 3.71491, "loss": 3.71491, "time": 0.83365} +{"mode": "train", "epoch": 102, "iter": 3700, "lr": 0.02322, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33641, "top5_acc": 0.59688, "loss_cls": 3.73259, "loss": 3.73259, "time": 0.81898} +{"mode": "val", "epoch": 102, "iter": 309, "lr": 0.02321, "top1_acc": 0.24966, "top5_acc": 0.49861, "mean_class_accuracy": 0.24959} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.02319, "memory": 15990, "data_time": 1.25597, "top1_acc": 0.35703, "top5_acc": 0.61938, "loss_cls": 3.62783, "loss": 3.62783, "time": 2.23605} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.02316, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36656, "top5_acc": 0.61859, "loss_cls": 3.60261, "loss": 3.60261, "time": 0.82748} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.02314, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34859, "top5_acc": 0.61297, "loss_cls": 3.65958, "loss": 3.65958, "time": 0.82016} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.02311, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35125, "top5_acc": 0.61281, "loss_cls": 3.65895, "loss": 3.65895, "time": 0.82246} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.02309, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35562, "top5_acc": 0.60734, "loss_cls": 3.676, "loss": 3.676, "time": 0.82404} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.02307, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33922, "top5_acc": 0.61219, "loss_cls": 3.7021, "loss": 3.7021, "time": 0.819} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.02304, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.355, "top5_acc": 0.61328, "loss_cls": 3.65763, "loss": 3.65763, "time": 0.82984} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.02302, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35094, "top5_acc": 0.60969, "loss_cls": 3.67934, "loss": 3.67934, "time": 0.82736} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.023, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34234, "top5_acc": 0.60578, "loss_cls": 3.70517, "loss": 3.70517, "time": 0.82307} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.02297, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32625, "top5_acc": 0.59578, "loss_cls": 3.77564, "loss": 3.77564, "time": 0.82434} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.02295, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34516, "top5_acc": 0.61672, "loss_cls": 3.66856, "loss": 3.66856, "time": 0.82244} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.02293, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34906, "top5_acc": 0.61156, "loss_cls": 3.68274, "loss": 3.68274, "time": 0.82676} +{"mode": "train", "epoch": 103, "iter": 1300, "lr": 0.0229, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35531, "top5_acc": 0.61328, "loss_cls": 3.66255, "loss": 3.66255, "time": 0.8261} +{"mode": "train", "epoch": 103, "iter": 1400, "lr": 0.02288, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.35109, "top5_acc": 0.60484, "loss_cls": 3.70166, "loss": 3.70166, "time": 0.82252} +{"mode": "train", "epoch": 103, "iter": 1500, "lr": 0.02286, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35547, "top5_acc": 0.60109, "loss_cls": 3.6831, "loss": 3.6831, "time": 0.82165} +{"mode": "train", "epoch": 103, "iter": 1600, "lr": 0.02283, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33656, "top5_acc": 0.60172, "loss_cls": 3.70996, "loss": 3.70996, "time": 0.83272} +{"mode": "train", "epoch": 103, "iter": 1700, "lr": 0.02281, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35125, "top5_acc": 0.60812, "loss_cls": 3.69872, "loss": 3.69872, "time": 0.83194} +{"mode": "train", "epoch": 103, "iter": 1800, "lr": 0.02279, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34297, "top5_acc": 0.60469, "loss_cls": 3.71095, "loss": 3.71095, "time": 0.82172} +{"mode": "train", "epoch": 103, "iter": 1900, "lr": 0.02276, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34719, "top5_acc": 0.60516, "loss_cls": 3.7168, "loss": 3.7168, "time": 0.83322} +{"mode": "train", "epoch": 103, "iter": 2000, "lr": 0.02274, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34188, "top5_acc": 0.61062, "loss_cls": 3.69954, "loss": 3.69954, "time": 0.8292} +{"mode": "train", "epoch": 103, "iter": 2100, "lr": 0.02272, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34797, "top5_acc": 0.61031, "loss_cls": 3.69801, "loss": 3.69801, "time": 0.82897} +{"mode": "train", "epoch": 103, "iter": 2200, "lr": 0.02269, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34547, "top5_acc": 0.6025, "loss_cls": 3.69983, "loss": 3.69983, "time": 0.8277} +{"mode": "train", "epoch": 103, "iter": 2300, "lr": 0.02267, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.34328, "top5_acc": 0.60297, "loss_cls": 3.71044, "loss": 3.71044, "time": 0.83602} +{"mode": "train", "epoch": 103, "iter": 2400, "lr": 0.02264, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34781, "top5_acc": 0.60875, "loss_cls": 3.71997, "loss": 3.71997, "time": 0.81719} +{"mode": "train", "epoch": 103, "iter": 2500, "lr": 0.02262, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34969, "top5_acc": 0.61844, "loss_cls": 3.64645, "loss": 3.64645, "time": 0.82908} +{"mode": "train", "epoch": 103, "iter": 2600, "lr": 0.0226, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34531, "top5_acc": 0.61094, "loss_cls": 3.69059, "loss": 3.69059, "time": 0.83062} +{"mode": "train", "epoch": 103, "iter": 2700, "lr": 0.02257, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34281, "top5_acc": 0.59688, "loss_cls": 3.74992, "loss": 3.74992, "time": 0.82621} +{"mode": "train", "epoch": 103, "iter": 2800, "lr": 0.02255, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34625, "top5_acc": 0.60359, "loss_cls": 3.69488, "loss": 3.69488, "time": 0.82152} +{"mode": "train", "epoch": 103, "iter": 2900, "lr": 0.02253, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33594, "top5_acc": 0.60422, "loss_cls": 3.73214, "loss": 3.73214, "time": 0.81839} +{"mode": "train", "epoch": 103, "iter": 3000, "lr": 0.0225, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34359, "top5_acc": 0.60109, "loss_cls": 3.72866, "loss": 3.72866, "time": 0.81791} +{"mode": "train", "epoch": 103, "iter": 3100, "lr": 0.02248, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35203, "top5_acc": 0.61078, "loss_cls": 3.66474, "loss": 3.66474, "time": 0.81851} +{"mode": "train", "epoch": 103, "iter": 3200, "lr": 0.02246, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35594, "top5_acc": 0.61156, "loss_cls": 3.65929, "loss": 3.65929, "time": 0.81772} +{"mode": "train", "epoch": 103, "iter": 3300, "lr": 0.02243, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32781, "top5_acc": 0.60328, "loss_cls": 3.73567, "loss": 3.73567, "time": 0.81805} +{"mode": "train", "epoch": 103, "iter": 3400, "lr": 0.02241, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34594, "top5_acc": 0.61578, "loss_cls": 3.6853, "loss": 3.6853, "time": 0.81928} +{"mode": "train", "epoch": 103, "iter": 3500, "lr": 0.02239, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34938, "top5_acc": 0.60578, "loss_cls": 3.6986, "loss": 3.6986, "time": 0.81847} +{"mode": "train", "epoch": 103, "iter": 3600, "lr": 0.02236, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.34656, "top5_acc": 0.6, "loss_cls": 3.71324, "loss": 3.71324, "time": 0.8309} +{"mode": "train", "epoch": 103, "iter": 3700, "lr": 0.02234, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33875, "top5_acc": 0.59656, "loss_cls": 3.73307, "loss": 3.73307, "time": 0.8246} +{"mode": "val", "epoch": 103, "iter": 309, "lr": 0.02233, "top1_acc": 0.27752, "top5_acc": 0.52652, "mean_class_accuracy": 0.27719} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.02231, "memory": 15990, "data_time": 1.25184, "top1_acc": 0.35094, "top5_acc": 0.62562, "loss_cls": 3.63138, "loss": 3.63138, "time": 2.23415} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.02228, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36109, "top5_acc": 0.62859, "loss_cls": 3.59252, "loss": 3.59252, "time": 0.82216} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.02226, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35562, "top5_acc": 0.61328, "loss_cls": 3.64351, "loss": 3.64351, "time": 0.8234} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.02224, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36141, "top5_acc": 0.62781, "loss_cls": 3.58267, "loss": 3.58267, "time": 0.8249} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.02221, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35344, "top5_acc": 0.61406, "loss_cls": 3.6346, "loss": 3.6346, "time": 0.81669} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.02219, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34797, "top5_acc": 0.61391, "loss_cls": 3.6928, "loss": 3.6928, "time": 0.82259} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.02217, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35047, "top5_acc": 0.61516, "loss_cls": 3.64212, "loss": 3.64212, "time": 0.82559} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.02214, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34781, "top5_acc": 0.60656, "loss_cls": 3.68256, "loss": 3.68256, "time": 0.82264} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.02212, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34828, "top5_acc": 0.61703, "loss_cls": 3.65893, "loss": 3.65893, "time": 0.81773} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.0221, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35375, "top5_acc": 0.61, "loss_cls": 3.64336, "loss": 3.64336, "time": 0.8224} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.02208, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34312, "top5_acc": 0.59891, "loss_cls": 3.72744, "loss": 3.72744, "time": 0.81713} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.02205, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34562, "top5_acc": 0.59516, "loss_cls": 3.7068, "loss": 3.7068, "time": 0.81991} +{"mode": "train", "epoch": 104, "iter": 1300, "lr": 0.02203, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34547, "top5_acc": 0.61391, "loss_cls": 3.67848, "loss": 3.67848, "time": 0.82615} +{"mode": "train", "epoch": 104, "iter": 1400, "lr": 0.02201, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34906, "top5_acc": 0.61562, "loss_cls": 3.64254, "loss": 3.64254, "time": 0.82684} +{"mode": "train", "epoch": 104, "iter": 1500, "lr": 0.02198, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34734, "top5_acc": 0.61406, "loss_cls": 3.67989, "loss": 3.67989, "time": 0.8266} +{"mode": "train", "epoch": 104, "iter": 1600, "lr": 0.02196, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34391, "top5_acc": 0.60453, "loss_cls": 3.70524, "loss": 3.70524, "time": 0.82748} +{"mode": "train", "epoch": 104, "iter": 1700, "lr": 0.02194, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35469, "top5_acc": 0.60891, "loss_cls": 3.68922, "loss": 3.68922, "time": 0.83498} +{"mode": "train", "epoch": 104, "iter": 1800, "lr": 0.02191, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34609, "top5_acc": 0.61312, "loss_cls": 3.68847, "loss": 3.68847, "time": 0.82593} +{"mode": "train", "epoch": 104, "iter": 1900, "lr": 0.02189, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35656, "top5_acc": 0.62344, "loss_cls": 3.64877, "loss": 3.64877, "time": 0.82887} +{"mode": "train", "epoch": 104, "iter": 2000, "lr": 0.02187, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33781, "top5_acc": 0.60453, "loss_cls": 3.7353, "loss": 3.7353, "time": 0.83564} +{"mode": "train", "epoch": 104, "iter": 2100, "lr": 0.02184, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35234, "top5_acc": 0.61016, "loss_cls": 3.67502, "loss": 3.67502, "time": 0.82611} +{"mode": "train", "epoch": 104, "iter": 2200, "lr": 0.02182, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34766, "top5_acc": 0.60734, "loss_cls": 3.70499, "loss": 3.70499, "time": 0.83822} +{"mode": "train", "epoch": 104, "iter": 2300, "lr": 0.0218, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35516, "top5_acc": 0.61281, "loss_cls": 3.66299, "loss": 3.66299, "time": 0.83147} +{"mode": "train", "epoch": 104, "iter": 2400, "lr": 0.02177, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34609, "top5_acc": 0.59875, "loss_cls": 3.71291, "loss": 3.71291, "time": 0.82442} +{"mode": "train", "epoch": 104, "iter": 2500, "lr": 0.02175, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34922, "top5_acc": 0.60641, "loss_cls": 3.70271, "loss": 3.70271, "time": 0.83363} +{"mode": "train", "epoch": 104, "iter": 2600, "lr": 0.02173, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33969, "top5_acc": 0.60703, "loss_cls": 3.68956, "loss": 3.68956, "time": 0.8295} +{"mode": "train", "epoch": 104, "iter": 2700, "lr": 0.02171, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34938, "top5_acc": 0.61234, "loss_cls": 3.65504, "loss": 3.65504, "time": 0.8271} +{"mode": "train", "epoch": 104, "iter": 2800, "lr": 0.02168, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35, "top5_acc": 0.60719, "loss_cls": 3.68892, "loss": 3.68892, "time": 0.82195} +{"mode": "train", "epoch": 104, "iter": 2900, "lr": 0.02166, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34828, "top5_acc": 0.61125, "loss_cls": 3.72624, "loss": 3.72624, "time": 0.82301} +{"mode": "train", "epoch": 104, "iter": 3000, "lr": 0.02164, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36375, "top5_acc": 0.61391, "loss_cls": 3.64144, "loss": 3.64144, "time": 0.82535} +{"mode": "train", "epoch": 104, "iter": 3100, "lr": 0.02161, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34188, "top5_acc": 0.60156, "loss_cls": 3.70288, "loss": 3.70288, "time": 0.8182} +{"mode": "train", "epoch": 104, "iter": 3200, "lr": 0.02159, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34953, "top5_acc": 0.61391, "loss_cls": 3.64345, "loss": 3.64345, "time": 0.82651} +{"mode": "train", "epoch": 104, "iter": 3300, "lr": 0.02157, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33656, "top5_acc": 0.60328, "loss_cls": 3.71679, "loss": 3.71679, "time": 0.82357} +{"mode": "train", "epoch": 104, "iter": 3400, "lr": 0.02154, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3525, "top5_acc": 0.61547, "loss_cls": 3.66446, "loss": 3.66446, "time": 0.82277} +{"mode": "train", "epoch": 104, "iter": 3500, "lr": 0.02152, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34891, "top5_acc": 0.61234, "loss_cls": 3.66835, "loss": 3.66835, "time": 0.82056} +{"mode": "train", "epoch": 104, "iter": 3600, "lr": 0.0215, "memory": 15990, "data_time": 0.00087, "top1_acc": 0.34094, "top5_acc": 0.59859, "loss_cls": 3.73372, "loss": 3.73372, "time": 0.83789} +{"mode": "train", "epoch": 104, "iter": 3700, "lr": 0.02148, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34391, "top5_acc": 0.6075, "loss_cls": 3.7193, "loss": 3.7193, "time": 0.8196} +{"mode": "val", "epoch": 104, "iter": 309, "lr": 0.02146, "top1_acc": 0.28461, "top5_acc": 0.53391, "mean_class_accuracy": 0.2844} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.02144, "memory": 15990, "data_time": 1.26095, "top1_acc": 0.35797, "top5_acc": 0.62, "loss_cls": 3.60957, "loss": 3.60957, "time": 2.24737} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.02142, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.365, "top5_acc": 0.62438, "loss_cls": 3.58739, "loss": 3.58739, "time": 0.82431} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.0214, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35406, "top5_acc": 0.61422, "loss_cls": 3.61719, "loss": 3.61719, "time": 0.8298} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.02137, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35625, "top5_acc": 0.61719, "loss_cls": 3.64555, "loss": 3.64555, "time": 0.82291} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.02135, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34656, "top5_acc": 0.60734, "loss_cls": 3.72801, "loss": 3.72801, "time": 0.82746} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.02133, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34719, "top5_acc": 0.61297, "loss_cls": 3.66031, "loss": 3.66031, "time": 0.82087} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.0213, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35719, "top5_acc": 0.61156, "loss_cls": 3.69275, "loss": 3.69275, "time": 0.82434} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.02128, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35406, "top5_acc": 0.61859, "loss_cls": 3.63022, "loss": 3.63022, "time": 0.82509} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.02126, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34797, "top5_acc": 0.61297, "loss_cls": 3.6755, "loss": 3.6755, "time": 0.82067} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.02124, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35562, "top5_acc": 0.61172, "loss_cls": 3.66577, "loss": 3.66577, "time": 0.82114} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.02121, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35328, "top5_acc": 0.62078, "loss_cls": 3.62021, "loss": 3.62021, "time": 0.82287} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.02119, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35031, "top5_acc": 0.60844, "loss_cls": 3.66037, "loss": 3.66037, "time": 0.82167} +{"mode": "train", "epoch": 105, "iter": 1300, "lr": 0.02117, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35578, "top5_acc": 0.62766, "loss_cls": 3.60772, "loss": 3.60772, "time": 0.83023} +{"mode": "train", "epoch": 105, "iter": 1400, "lr": 0.02114, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34938, "top5_acc": 0.61562, "loss_cls": 3.65077, "loss": 3.65077, "time": 0.82936} +{"mode": "train", "epoch": 105, "iter": 1500, "lr": 0.02112, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35281, "top5_acc": 0.61562, "loss_cls": 3.63142, "loss": 3.63142, "time": 0.82858} +{"mode": "train", "epoch": 105, "iter": 1600, "lr": 0.0211, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35312, "top5_acc": 0.61312, "loss_cls": 3.66384, "loss": 3.66384, "time": 0.83029} +{"mode": "train", "epoch": 105, "iter": 1700, "lr": 0.02108, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35328, "top5_acc": 0.61938, "loss_cls": 3.62628, "loss": 3.62628, "time": 0.82989} +{"mode": "train", "epoch": 105, "iter": 1800, "lr": 0.02105, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.3575, "top5_acc": 0.62141, "loss_cls": 3.62781, "loss": 3.62781, "time": 0.82348} +{"mode": "train", "epoch": 105, "iter": 1900, "lr": 0.02103, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35812, "top5_acc": 0.61969, "loss_cls": 3.63712, "loss": 3.63712, "time": 0.83216} +{"mode": "train", "epoch": 105, "iter": 2000, "lr": 0.02101, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34703, "top5_acc": 0.61156, "loss_cls": 3.655, "loss": 3.655, "time": 0.82075} +{"mode": "train", "epoch": 105, "iter": 2100, "lr": 0.02098, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35469, "top5_acc": 0.61906, "loss_cls": 3.63529, "loss": 3.63529, "time": 0.8237} +{"mode": "train", "epoch": 105, "iter": 2200, "lr": 0.02096, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.34344, "top5_acc": 0.61453, "loss_cls": 3.66994, "loss": 3.66994, "time": 0.83925} +{"mode": "train", "epoch": 105, "iter": 2300, "lr": 0.02094, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34422, "top5_acc": 0.61516, "loss_cls": 3.67273, "loss": 3.67273, "time": 0.82771} +{"mode": "train", "epoch": 105, "iter": 2400, "lr": 0.02092, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3525, "top5_acc": 0.61203, "loss_cls": 3.67868, "loss": 3.67868, "time": 0.82494} +{"mode": "train", "epoch": 105, "iter": 2500, "lr": 0.02089, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35578, "top5_acc": 0.62031, "loss_cls": 3.65352, "loss": 3.65352, "time": 0.83038} +{"mode": "train", "epoch": 105, "iter": 2600, "lr": 0.02087, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35188, "top5_acc": 0.60375, "loss_cls": 3.67595, "loss": 3.67595, "time": 0.82939} +{"mode": "train", "epoch": 105, "iter": 2700, "lr": 0.02085, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34844, "top5_acc": 0.60516, "loss_cls": 3.68693, "loss": 3.68693, "time": 0.82557} +{"mode": "train", "epoch": 105, "iter": 2800, "lr": 0.02083, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34719, "top5_acc": 0.60688, "loss_cls": 3.70476, "loss": 3.70476, "time": 0.81659} +{"mode": "train", "epoch": 105, "iter": 2900, "lr": 0.0208, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35094, "top5_acc": 0.6175, "loss_cls": 3.62249, "loss": 3.62249, "time": 0.82264} +{"mode": "train", "epoch": 105, "iter": 3000, "lr": 0.02078, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35062, "top5_acc": 0.61281, "loss_cls": 3.68334, "loss": 3.68334, "time": 0.82225} +{"mode": "train", "epoch": 105, "iter": 3100, "lr": 0.02076, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35719, "top5_acc": 0.60938, "loss_cls": 3.66901, "loss": 3.66901, "time": 0.82407} +{"mode": "train", "epoch": 105, "iter": 3200, "lr": 0.02073, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34906, "top5_acc": 0.61109, "loss_cls": 3.66931, "loss": 3.66931, "time": 0.82683} +{"mode": "train", "epoch": 105, "iter": 3300, "lr": 0.02071, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34344, "top5_acc": 0.60562, "loss_cls": 3.68334, "loss": 3.68334, "time": 0.83047} +{"mode": "train", "epoch": 105, "iter": 3400, "lr": 0.02069, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35625, "top5_acc": 0.61047, "loss_cls": 3.66638, "loss": 3.66638, "time": 0.82905} +{"mode": "train", "epoch": 105, "iter": 3500, "lr": 0.02067, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35453, "top5_acc": 0.60797, "loss_cls": 3.6444, "loss": 3.6444, "time": 0.82377} +{"mode": "train", "epoch": 105, "iter": 3600, "lr": 0.02064, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35312, "top5_acc": 0.61078, "loss_cls": 3.68061, "loss": 3.68061, "time": 0.83952} +{"mode": "train", "epoch": 105, "iter": 3700, "lr": 0.02062, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34656, "top5_acc": 0.60969, "loss_cls": 3.68178, "loss": 3.68178, "time": 0.82328} +{"mode": "val", "epoch": 105, "iter": 309, "lr": 0.02061, "top1_acc": 0.29459, "top5_acc": 0.54855, "mean_class_accuracy": 0.29441} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.02059, "memory": 15990, "data_time": 1.29717, "top1_acc": 0.35938, "top5_acc": 0.625, "loss_cls": 3.58641, "loss": 3.58641, "time": 2.27995} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.02057, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35938, "top5_acc": 0.62547, "loss_cls": 3.59116, "loss": 3.59116, "time": 0.82409} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.02054, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37391, "top5_acc": 0.62922, "loss_cls": 3.55788, "loss": 3.55788, "time": 0.82946} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.02052, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35891, "top5_acc": 0.62141, "loss_cls": 3.61765, "loss": 3.61765, "time": 0.82368} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.0205, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35516, "top5_acc": 0.61812, "loss_cls": 3.60841, "loss": 3.60841, "time": 0.82202} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.02048, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34938, "top5_acc": 0.62, "loss_cls": 3.64101, "loss": 3.64101, "time": 0.82442} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.02045, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35906, "top5_acc": 0.62641, "loss_cls": 3.61845, "loss": 3.61845, "time": 0.81922} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.02043, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35562, "top5_acc": 0.61047, "loss_cls": 3.64941, "loss": 3.64941, "time": 0.8213} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.02041, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36297, "top5_acc": 0.62187, "loss_cls": 3.60985, "loss": 3.60985, "time": 0.81992} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.02039, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34672, "top5_acc": 0.61797, "loss_cls": 3.65549, "loss": 3.65549, "time": 0.81945} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.02036, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34016, "top5_acc": 0.61641, "loss_cls": 3.68439, "loss": 3.68439, "time": 0.82141} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.02034, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35891, "top5_acc": 0.61047, "loss_cls": 3.63601, "loss": 3.63601, "time": 0.829} +{"mode": "train", "epoch": 106, "iter": 1300, "lr": 0.02032, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36578, "top5_acc": 0.63516, "loss_cls": 3.5488, "loss": 3.5488, "time": 0.8222} +{"mode": "train", "epoch": 106, "iter": 1400, "lr": 0.0203, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35281, "top5_acc": 0.61172, "loss_cls": 3.63678, "loss": 3.63678, "time": 0.82552} +{"mode": "train", "epoch": 106, "iter": 1500, "lr": 0.02027, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35609, "top5_acc": 0.61187, "loss_cls": 3.6595, "loss": 3.6595, "time": 0.82488} +{"mode": "train", "epoch": 106, "iter": 1600, "lr": 0.02025, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35344, "top5_acc": 0.61187, "loss_cls": 3.64836, "loss": 3.64836, "time": 0.82701} +{"mode": "train", "epoch": 106, "iter": 1700, "lr": 0.02023, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35734, "top5_acc": 0.61656, "loss_cls": 3.63733, "loss": 3.63733, "time": 0.82804} +{"mode": "train", "epoch": 106, "iter": 1800, "lr": 0.02021, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35438, "top5_acc": 0.615, "loss_cls": 3.64741, "loss": 3.64741, "time": 0.82376} +{"mode": "train", "epoch": 106, "iter": 1900, "lr": 0.02018, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35328, "top5_acc": 0.62031, "loss_cls": 3.63794, "loss": 3.63794, "time": 0.83212} +{"mode": "train", "epoch": 106, "iter": 2000, "lr": 0.02016, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35281, "top5_acc": 0.6175, "loss_cls": 3.65356, "loss": 3.65356, "time": 0.83483} +{"mode": "train", "epoch": 106, "iter": 2100, "lr": 0.02014, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35672, "top5_acc": 0.61375, "loss_cls": 3.62895, "loss": 3.62895, "time": 0.82753} +{"mode": "train", "epoch": 106, "iter": 2200, "lr": 0.02012, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34438, "top5_acc": 0.61906, "loss_cls": 3.64427, "loss": 3.64427, "time": 0.83028} +{"mode": "train", "epoch": 106, "iter": 2300, "lr": 0.02009, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.35938, "top5_acc": 0.61922, "loss_cls": 3.63066, "loss": 3.63066, "time": 0.82073} +{"mode": "train", "epoch": 106, "iter": 2400, "lr": 0.02007, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36078, "top5_acc": 0.62203, "loss_cls": 3.60632, "loss": 3.60632, "time": 0.83396} +{"mode": "train", "epoch": 106, "iter": 2500, "lr": 0.02005, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36062, "top5_acc": 0.62, "loss_cls": 3.62207, "loss": 3.62207, "time": 0.83076} +{"mode": "train", "epoch": 106, "iter": 2600, "lr": 0.02003, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35891, "top5_acc": 0.62625, "loss_cls": 3.61386, "loss": 3.61386, "time": 0.82406} +{"mode": "train", "epoch": 106, "iter": 2700, "lr": 0.02, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34422, "top5_acc": 0.60844, "loss_cls": 3.69325, "loss": 3.69325, "time": 0.82131} +{"mode": "train", "epoch": 106, "iter": 2800, "lr": 0.01998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34156, "top5_acc": 0.60922, "loss_cls": 3.69087, "loss": 3.69087, "time": 0.82157} +{"mode": "train", "epoch": 106, "iter": 2900, "lr": 0.01996, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35625, "top5_acc": 0.6125, "loss_cls": 3.64144, "loss": 3.64144, "time": 0.82471} +{"mode": "train", "epoch": 106, "iter": 3000, "lr": 0.01994, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35828, "top5_acc": 0.61547, "loss_cls": 3.65538, "loss": 3.65538, "time": 0.82248} +{"mode": "train", "epoch": 106, "iter": 3100, "lr": 0.01991, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35656, "top5_acc": 0.61891, "loss_cls": 3.64581, "loss": 3.64581, "time": 0.8288} +{"mode": "train", "epoch": 106, "iter": 3200, "lr": 0.01989, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35656, "top5_acc": 0.60891, "loss_cls": 3.65372, "loss": 3.65372, "time": 0.8231} +{"mode": "train", "epoch": 106, "iter": 3300, "lr": 0.01987, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34203, "top5_acc": 0.60438, "loss_cls": 3.65781, "loss": 3.65781, "time": 0.82286} +{"mode": "train", "epoch": 106, "iter": 3400, "lr": 0.01985, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36312, "top5_acc": 0.6175, "loss_cls": 3.59089, "loss": 3.59089, "time": 0.82389} +{"mode": "train", "epoch": 106, "iter": 3500, "lr": 0.01983, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35469, "top5_acc": 0.61078, "loss_cls": 3.65552, "loss": 3.65552, "time": 0.82787} +{"mode": "train", "epoch": 106, "iter": 3600, "lr": 0.0198, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34906, "top5_acc": 0.61422, "loss_cls": 3.66243, "loss": 3.66243, "time": 0.83075} +{"mode": "train", "epoch": 106, "iter": 3700, "lr": 0.01978, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35547, "top5_acc": 0.60781, "loss_cls": 3.64706, "loss": 3.64706, "time": 0.8204} +{"mode": "val", "epoch": 106, "iter": 309, "lr": 0.01977, "top1_acc": 0.29226, "top5_acc": 0.54586, "mean_class_accuracy": 0.2921} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.01975, "memory": 15990, "data_time": 1.28503, "top1_acc": 0.36797, "top5_acc": 0.62641, "loss_cls": 3.5744, "loss": 3.5744, "time": 2.26404} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.01973, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35938, "top5_acc": 0.62969, "loss_cls": 3.54988, "loss": 3.54988, "time": 0.82782} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.0197, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36328, "top5_acc": 0.62375, "loss_cls": 3.59696, "loss": 3.59696, "time": 0.82687} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.01968, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36609, "top5_acc": 0.62359, "loss_cls": 3.56438, "loss": 3.56438, "time": 0.82218} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.01966, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36672, "top5_acc": 0.62891, "loss_cls": 3.57451, "loss": 3.57451, "time": 0.82213} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.01964, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36688, "top5_acc": 0.62422, "loss_cls": 3.57538, "loss": 3.57538, "time": 0.82063} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.01961, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36031, "top5_acc": 0.62594, "loss_cls": 3.60885, "loss": 3.60885, "time": 0.82309} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.01959, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35875, "top5_acc": 0.62172, "loss_cls": 3.61041, "loss": 3.61041, "time": 0.82114} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.01957, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36344, "top5_acc": 0.61797, "loss_cls": 3.59247, "loss": 3.59247, "time": 0.82148} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.01955, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35719, "top5_acc": 0.62672, "loss_cls": 3.60271, "loss": 3.60271, "time": 0.82096} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.01953, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34984, "top5_acc": 0.625, "loss_cls": 3.60901, "loss": 3.60901, "time": 0.82308} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.0195, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.35844, "top5_acc": 0.62141, "loss_cls": 3.60016, "loss": 3.60016, "time": 0.82504} +{"mode": "train", "epoch": 107, "iter": 1300, "lr": 0.01948, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35109, "top5_acc": 0.61969, "loss_cls": 3.64422, "loss": 3.64422, "time": 0.82519} +{"mode": "train", "epoch": 107, "iter": 1400, "lr": 0.01946, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36672, "top5_acc": 0.61875, "loss_cls": 3.60513, "loss": 3.60513, "time": 0.82265} +{"mode": "train", "epoch": 107, "iter": 1500, "lr": 0.01944, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36484, "top5_acc": 0.62672, "loss_cls": 3.57862, "loss": 3.57862, "time": 0.82607} +{"mode": "train", "epoch": 107, "iter": 1600, "lr": 0.01942, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.365, "top5_acc": 0.61359, "loss_cls": 3.61683, "loss": 3.61683, "time": 0.83196} +{"mode": "train", "epoch": 107, "iter": 1700, "lr": 0.01939, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35531, "top5_acc": 0.62047, "loss_cls": 3.62775, "loss": 3.62775, "time": 0.82811} +{"mode": "train", "epoch": 107, "iter": 1800, "lr": 0.01937, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35094, "top5_acc": 0.61609, "loss_cls": 3.66465, "loss": 3.66465, "time": 0.8265} +{"mode": "train", "epoch": 107, "iter": 1900, "lr": 0.01935, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35953, "top5_acc": 0.61812, "loss_cls": 3.65258, "loss": 3.65258, "time": 0.83607} +{"mode": "train", "epoch": 107, "iter": 2000, "lr": 0.01933, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35625, "top5_acc": 0.61906, "loss_cls": 3.60452, "loss": 3.60452, "time": 0.82973} +{"mode": "train", "epoch": 107, "iter": 2100, "lr": 0.0193, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35562, "top5_acc": 0.61828, "loss_cls": 3.61939, "loss": 3.61939, "time": 0.84461} +{"mode": "train", "epoch": 107, "iter": 2200, "lr": 0.01928, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.355, "top5_acc": 0.6125, "loss_cls": 3.65859, "loss": 3.65859, "time": 0.83198} +{"mode": "train", "epoch": 107, "iter": 2300, "lr": 0.01926, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35297, "top5_acc": 0.60578, "loss_cls": 3.68117, "loss": 3.68117, "time": 0.83086} +{"mode": "train", "epoch": 107, "iter": 2400, "lr": 0.01924, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34953, "top5_acc": 0.61656, "loss_cls": 3.64792, "loss": 3.64792, "time": 0.83648} +{"mode": "train", "epoch": 107, "iter": 2500, "lr": 0.01922, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35984, "top5_acc": 0.61703, "loss_cls": 3.62123, "loss": 3.62123, "time": 0.84311} +{"mode": "train", "epoch": 107, "iter": 2600, "lr": 0.01919, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36078, "top5_acc": 0.63359, "loss_cls": 3.5679, "loss": 3.5679, "time": 0.83339} +{"mode": "train", "epoch": 107, "iter": 2700, "lr": 0.01917, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35484, "top5_acc": 0.62094, "loss_cls": 3.62703, "loss": 3.62703, "time": 0.83708} +{"mode": "train", "epoch": 107, "iter": 2800, "lr": 0.01915, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36625, "top5_acc": 0.6325, "loss_cls": 3.54747, "loss": 3.54747, "time": 0.83718} +{"mode": "train", "epoch": 107, "iter": 2900, "lr": 0.01913, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36547, "top5_acc": 0.61953, "loss_cls": 3.60291, "loss": 3.60291, "time": 0.83772} +{"mode": "train", "epoch": 107, "iter": 3000, "lr": 0.01911, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35359, "top5_acc": 0.62781, "loss_cls": 3.6292, "loss": 3.6292, "time": 0.83806} +{"mode": "train", "epoch": 107, "iter": 3100, "lr": 0.01908, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35812, "top5_acc": 0.61687, "loss_cls": 3.65365, "loss": 3.65365, "time": 0.83425} +{"mode": "train", "epoch": 107, "iter": 3200, "lr": 0.01906, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36031, "top5_acc": 0.61922, "loss_cls": 3.64494, "loss": 3.64494, "time": 0.83625} +{"mode": "train", "epoch": 107, "iter": 3300, "lr": 0.01904, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35703, "top5_acc": 0.60859, "loss_cls": 3.63871, "loss": 3.63871, "time": 0.83401} +{"mode": "train", "epoch": 107, "iter": 3400, "lr": 0.01902, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35625, "top5_acc": 0.61469, "loss_cls": 3.65215, "loss": 3.65215, "time": 0.83718} +{"mode": "train", "epoch": 107, "iter": 3500, "lr": 0.019, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34891, "top5_acc": 0.61672, "loss_cls": 3.63149, "loss": 3.63149, "time": 0.83858} +{"mode": "train", "epoch": 107, "iter": 3600, "lr": 0.01897, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35688, "top5_acc": 0.61469, "loss_cls": 3.6156, "loss": 3.6156, "time": 0.83008} +{"mode": "train", "epoch": 107, "iter": 3700, "lr": 0.01895, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35469, "top5_acc": 0.62047, "loss_cls": 3.62461, "loss": 3.62461, "time": 0.82519} +{"mode": "val", "epoch": 107, "iter": 309, "lr": 0.01894, "top1_acc": 0.29286, "top5_acc": 0.53903, "mean_class_accuracy": 0.29268} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.01892, "memory": 15990, "data_time": 1.33928, "top1_acc": 0.37578, "top5_acc": 0.63922, "loss_cls": 3.50023, "loss": 3.50023, "time": 2.34498} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0189, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36703, "top5_acc": 0.62953, "loss_cls": 3.54856, "loss": 3.54856, "time": 0.82832} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.01888, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36547, "top5_acc": 0.63172, "loss_cls": 3.53687, "loss": 3.53687, "time": 0.83483} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.01886, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36875, "top5_acc": 0.63, "loss_cls": 3.5677, "loss": 3.5677, "time": 0.82071} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.01883, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36531, "top5_acc": 0.62375, "loss_cls": 3.57862, "loss": 3.57862, "time": 0.81877} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.01881, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36516, "top5_acc": 0.63078, "loss_cls": 3.5665, "loss": 3.5665, "time": 0.82098} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.01879, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36062, "top5_acc": 0.62203, "loss_cls": 3.61085, "loss": 3.61085, "time": 0.81528} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.01877, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34922, "top5_acc": 0.61453, "loss_cls": 3.65336, "loss": 3.65336, "time": 0.81629} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.01875, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36297, "top5_acc": 0.63406, "loss_cls": 3.55707, "loss": 3.55707, "time": 0.81597} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.01872, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36172, "top5_acc": 0.62531, "loss_cls": 3.5903, "loss": 3.5903, "time": 0.81162} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.0187, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35812, "top5_acc": 0.62156, "loss_cls": 3.583, "loss": 3.583, "time": 0.81249} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.01868, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.365, "top5_acc": 0.61812, "loss_cls": 3.6138, "loss": 3.6138, "time": 0.81137} +{"mode": "train", "epoch": 108, "iter": 1300, "lr": 0.01866, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37062, "top5_acc": 0.62203, "loss_cls": 3.57007, "loss": 3.57007, "time": 0.80952} +{"mode": "train", "epoch": 108, "iter": 1400, "lr": 0.01864, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36484, "top5_acc": 0.62641, "loss_cls": 3.59291, "loss": 3.59291, "time": 0.81459} +{"mode": "train", "epoch": 108, "iter": 1500, "lr": 0.01862, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36719, "top5_acc": 0.62297, "loss_cls": 3.57719, "loss": 3.57719, "time": 0.81102} +{"mode": "train", "epoch": 108, "iter": 1600, "lr": 0.01859, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35469, "top5_acc": 0.61938, "loss_cls": 3.62839, "loss": 3.62839, "time": 0.81726} +{"mode": "train", "epoch": 108, "iter": 1700, "lr": 0.01857, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3675, "top5_acc": 0.62906, "loss_cls": 3.55741, "loss": 3.55741, "time": 0.81337} +{"mode": "train", "epoch": 108, "iter": 1800, "lr": 0.01855, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36641, "top5_acc": 0.63203, "loss_cls": 3.56122, "loss": 3.56122, "time": 0.81937} +{"mode": "train", "epoch": 108, "iter": 1900, "lr": 0.01853, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35031, "top5_acc": 0.62031, "loss_cls": 3.63725, "loss": 3.63725, "time": 0.81531} +{"mode": "train", "epoch": 108, "iter": 2000, "lr": 0.01851, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36484, "top5_acc": 0.62344, "loss_cls": 3.59065, "loss": 3.59065, "time": 0.81785} +{"mode": "train", "epoch": 108, "iter": 2100, "lr": 0.01848, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35812, "top5_acc": 0.61969, "loss_cls": 3.61304, "loss": 3.61304, "time": 0.81462} +{"mode": "train", "epoch": 108, "iter": 2200, "lr": 0.01846, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36594, "top5_acc": 0.61969, "loss_cls": 3.61772, "loss": 3.61772, "time": 0.81706} +{"mode": "train", "epoch": 108, "iter": 2300, "lr": 0.01844, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36656, "top5_acc": 0.62531, "loss_cls": 3.59509, "loss": 3.59509, "time": 0.81809} +{"mode": "train", "epoch": 108, "iter": 2400, "lr": 0.01842, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37, "top5_acc": 0.62516, "loss_cls": 3.57598, "loss": 3.57598, "time": 0.81503} +{"mode": "train", "epoch": 108, "iter": 2500, "lr": 0.0184, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36453, "top5_acc": 0.62047, "loss_cls": 3.59408, "loss": 3.59408, "time": 0.81634} +{"mode": "train", "epoch": 108, "iter": 2600, "lr": 0.01838, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36188, "top5_acc": 0.62297, "loss_cls": 3.5903, "loss": 3.5903, "time": 0.81404} +{"mode": "train", "epoch": 108, "iter": 2700, "lr": 0.01835, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35922, "top5_acc": 0.61781, "loss_cls": 3.60112, "loss": 3.60112, "time": 0.80992} +{"mode": "train", "epoch": 108, "iter": 2800, "lr": 0.01833, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36547, "top5_acc": 0.62234, "loss_cls": 3.60063, "loss": 3.60063, "time": 0.81765} +{"mode": "train", "epoch": 108, "iter": 2900, "lr": 0.01831, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3625, "top5_acc": 0.62672, "loss_cls": 3.60082, "loss": 3.60082, "time": 0.81065} +{"mode": "train", "epoch": 108, "iter": 3000, "lr": 0.01829, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36844, "top5_acc": 0.615, "loss_cls": 3.58498, "loss": 3.58498, "time": 0.81602} +{"mode": "train", "epoch": 108, "iter": 3100, "lr": 0.01827, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35359, "top5_acc": 0.61703, "loss_cls": 3.63851, "loss": 3.63851, "time": 0.81173} +{"mode": "train", "epoch": 108, "iter": 3200, "lr": 0.01825, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35938, "top5_acc": 0.61516, "loss_cls": 3.62935, "loss": 3.62935, "time": 0.81436} +{"mode": "train", "epoch": 108, "iter": 3300, "lr": 0.01823, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3625, "top5_acc": 0.61812, "loss_cls": 3.6287, "loss": 3.6287, "time": 0.81216} +{"mode": "train", "epoch": 108, "iter": 3400, "lr": 0.0182, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35672, "top5_acc": 0.61031, "loss_cls": 3.63285, "loss": 3.63285, "time": 0.81711} +{"mode": "train", "epoch": 108, "iter": 3500, "lr": 0.01818, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36422, "top5_acc": 0.63391, "loss_cls": 3.59291, "loss": 3.59291, "time": 0.81578} +{"mode": "train", "epoch": 108, "iter": 3600, "lr": 0.01816, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35812, "top5_acc": 0.61938, "loss_cls": 3.61588, "loss": 3.61588, "time": 0.81784} +{"mode": "train", "epoch": 108, "iter": 3700, "lr": 0.01814, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36547, "top5_acc": 0.62313, "loss_cls": 3.57754, "loss": 3.57754, "time": 0.81403} +{"mode": "val", "epoch": 108, "iter": 309, "lr": 0.01813, "top1_acc": 0.30497, "top5_acc": 0.55969, "mean_class_accuracy": 0.30466} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.01811, "memory": 15990, "data_time": 1.36255, "top1_acc": 0.37906, "top5_acc": 0.63906, "loss_cls": 3.5256, "loss": 3.5256, "time": 2.35462} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.01809, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36938, "top5_acc": 0.62797, "loss_cls": 3.56732, "loss": 3.56732, "time": 0.81654} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.01806, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36938, "top5_acc": 0.63188, "loss_cls": 3.54403, "loss": 3.54403, "time": 0.81879} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.01804, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37203, "top5_acc": 0.63422, "loss_cls": 3.52125, "loss": 3.52125, "time": 0.81864} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.01802, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36234, "top5_acc": 0.62344, "loss_cls": 3.56805, "loss": 3.56805, "time": 0.8151} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37219, "top5_acc": 0.64141, "loss_cls": 3.53559, "loss": 3.53559, "time": 0.8095} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.01798, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37219, "top5_acc": 0.63578, "loss_cls": 3.52501, "loss": 3.52501, "time": 0.80858} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.01796, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37062, "top5_acc": 0.62953, "loss_cls": 3.54675, "loss": 3.54675, "time": 0.81317} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.01794, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36016, "top5_acc": 0.61453, "loss_cls": 3.60713, "loss": 3.60713, "time": 0.80999} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.01791, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35562, "top5_acc": 0.62547, "loss_cls": 3.60447, "loss": 3.60447, "time": 0.80832} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.01789, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37, "top5_acc": 0.62734, "loss_cls": 3.583, "loss": 3.583, "time": 0.81182} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.01787, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36469, "top5_acc": 0.635, "loss_cls": 3.56565, "loss": 3.56565, "time": 0.81506} +{"mode": "train", "epoch": 109, "iter": 1300, "lr": 0.01785, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37422, "top5_acc": 0.62562, "loss_cls": 3.567, "loss": 3.567, "time": 0.8074} +{"mode": "train", "epoch": 109, "iter": 1400, "lr": 0.01783, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36844, "top5_acc": 0.63047, "loss_cls": 3.585, "loss": 3.585, "time": 0.812} +{"mode": "train", "epoch": 109, "iter": 1500, "lr": 0.01781, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36891, "top5_acc": 0.62922, "loss_cls": 3.5931, "loss": 3.5931, "time": 0.81668} +{"mode": "train", "epoch": 109, "iter": 1600, "lr": 0.01779, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36312, "top5_acc": 0.62844, "loss_cls": 3.5823, "loss": 3.5823, "time": 0.81439} +{"mode": "train", "epoch": 109, "iter": 1700, "lr": 0.01776, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37062, "top5_acc": 0.63922, "loss_cls": 3.51619, "loss": 3.51619, "time": 0.81042} +{"mode": "train", "epoch": 109, "iter": 1800, "lr": 0.01774, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36781, "top5_acc": 0.63516, "loss_cls": 3.51406, "loss": 3.51406, "time": 0.81815} +{"mode": "train", "epoch": 109, "iter": 1900, "lr": 0.01772, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37281, "top5_acc": 0.63422, "loss_cls": 3.53183, "loss": 3.53183, "time": 0.82557} +{"mode": "train", "epoch": 109, "iter": 2000, "lr": 0.0177, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36234, "top5_acc": 0.62859, "loss_cls": 3.59896, "loss": 3.59896, "time": 0.81368} +{"mode": "train", "epoch": 109, "iter": 2100, "lr": 0.01768, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35969, "top5_acc": 0.62344, "loss_cls": 3.62702, "loss": 3.62702, "time": 0.81269} +{"mode": "train", "epoch": 109, "iter": 2200, "lr": 0.01766, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35781, "top5_acc": 0.61828, "loss_cls": 3.6034, "loss": 3.6034, "time": 0.81508} +{"mode": "train", "epoch": 109, "iter": 2300, "lr": 0.01764, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36188, "top5_acc": 0.61906, "loss_cls": 3.61295, "loss": 3.61295, "time": 0.81615} +{"mode": "train", "epoch": 109, "iter": 2400, "lr": 0.01761, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37281, "top5_acc": 0.63375, "loss_cls": 3.51775, "loss": 3.51775, "time": 0.81661} +{"mode": "train", "epoch": 109, "iter": 2500, "lr": 0.01759, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37422, "top5_acc": 0.63969, "loss_cls": 3.51262, "loss": 3.51262, "time": 0.81742} +{"mode": "train", "epoch": 109, "iter": 2600, "lr": 0.01757, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36156, "top5_acc": 0.62703, "loss_cls": 3.59231, "loss": 3.59231, "time": 0.81422} +{"mode": "train", "epoch": 109, "iter": 2700, "lr": 0.01755, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36609, "top5_acc": 0.62328, "loss_cls": 3.60754, "loss": 3.60754, "time": 0.81539} +{"mode": "train", "epoch": 109, "iter": 2800, "lr": 0.01753, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36969, "top5_acc": 0.63, "loss_cls": 3.58191, "loss": 3.58191, "time": 0.81207} +{"mode": "train", "epoch": 109, "iter": 2900, "lr": 0.01751, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36703, "top5_acc": 0.62797, "loss_cls": 3.5703, "loss": 3.5703, "time": 0.81586} +{"mode": "train", "epoch": 109, "iter": 3000, "lr": 0.01749, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37047, "top5_acc": 0.6325, "loss_cls": 3.54734, "loss": 3.54734, "time": 0.81009} +{"mode": "train", "epoch": 109, "iter": 3100, "lr": 0.01747, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36328, "top5_acc": 0.61578, "loss_cls": 3.58563, "loss": 3.58563, "time": 0.81307} +{"mode": "train", "epoch": 109, "iter": 3200, "lr": 0.01744, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3475, "top5_acc": 0.60781, "loss_cls": 3.68758, "loss": 3.68758, "time": 0.80976} +{"mode": "train", "epoch": 109, "iter": 3300, "lr": 0.01742, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35766, "top5_acc": 0.61891, "loss_cls": 3.62999, "loss": 3.62999, "time": 0.81642} +{"mode": "train", "epoch": 109, "iter": 3400, "lr": 0.0174, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36078, "top5_acc": 0.61687, "loss_cls": 3.62615, "loss": 3.62615, "time": 0.81714} +{"mode": "train", "epoch": 109, "iter": 3500, "lr": 0.01738, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36234, "top5_acc": 0.62766, "loss_cls": 3.577, "loss": 3.577, "time": 0.81216} +{"mode": "train", "epoch": 109, "iter": 3600, "lr": 0.01736, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35844, "top5_acc": 0.62156, "loss_cls": 3.5979, "loss": 3.5979, "time": 0.81908} +{"mode": "train", "epoch": 109, "iter": 3700, "lr": 0.01734, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35078, "top5_acc": 0.60453, "loss_cls": 3.64737, "loss": 3.64737, "time": 0.81251} +{"mode": "val", "epoch": 109, "iter": 309, "lr": 0.01733, "top1_acc": 0.2957, "top5_acc": 0.5524, "mean_class_accuracy": 0.29552} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.01731, "memory": 15990, "data_time": 1.37797, "top1_acc": 0.38266, "top5_acc": 0.64047, "loss_cls": 3.46946, "loss": 3.46946, "time": 2.36921} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.01729, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.365, "top5_acc": 0.63453, "loss_cls": 3.54113, "loss": 3.54113, "time": 0.82001} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.01727, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37938, "top5_acc": 0.64688, "loss_cls": 3.48533, "loss": 3.48533, "time": 0.81643} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.01724, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37, "top5_acc": 0.64453, "loss_cls": 3.49812, "loss": 3.49812, "time": 0.81495} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.01722, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38469, "top5_acc": 0.6375, "loss_cls": 3.50445, "loss": 3.50445, "time": 0.8079} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.0172, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38141, "top5_acc": 0.64172, "loss_cls": 3.51162, "loss": 3.51162, "time": 0.81302} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.01718, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37578, "top5_acc": 0.63188, "loss_cls": 3.51717, "loss": 3.51717, "time": 0.81197} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.01716, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37328, "top5_acc": 0.62453, "loss_cls": 3.56512, "loss": 3.56512, "time": 0.81072} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.01714, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35734, "top5_acc": 0.61609, "loss_cls": 3.59845, "loss": 3.59845, "time": 0.8124} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.01712, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36031, "top5_acc": 0.62406, "loss_cls": 3.58324, "loss": 3.58324, "time": 0.8214} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.0171, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37359, "top5_acc": 0.63359, "loss_cls": 3.55208, "loss": 3.55208, "time": 0.81394} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.01708, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35047, "top5_acc": 0.61656, "loss_cls": 3.64259, "loss": 3.64259, "time": 0.81183} +{"mode": "train", "epoch": 110, "iter": 1300, "lr": 0.01705, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3625, "top5_acc": 0.62969, "loss_cls": 3.5729, "loss": 3.5729, "time": 0.80945} +{"mode": "train", "epoch": 110, "iter": 1400, "lr": 0.01703, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37875, "top5_acc": 0.63609, "loss_cls": 3.52746, "loss": 3.52746, "time": 0.81143} +{"mode": "train", "epoch": 110, "iter": 1500, "lr": 0.01701, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37125, "top5_acc": 0.63516, "loss_cls": 3.56667, "loss": 3.56667, "time": 0.81141} +{"mode": "train", "epoch": 110, "iter": 1600, "lr": 0.01699, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36719, "top5_acc": 0.63313, "loss_cls": 3.54134, "loss": 3.54134, "time": 0.8208} +{"mode": "train", "epoch": 110, "iter": 1700, "lr": 0.01697, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3725, "top5_acc": 0.62875, "loss_cls": 3.55322, "loss": 3.55322, "time": 0.80966} +{"mode": "train", "epoch": 110, "iter": 1800, "lr": 0.01695, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36984, "top5_acc": 0.63734, "loss_cls": 3.55326, "loss": 3.55326, "time": 0.81285} +{"mode": "train", "epoch": 110, "iter": 1900, "lr": 0.01693, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37531, "top5_acc": 0.62391, "loss_cls": 3.57946, "loss": 3.57946, "time": 0.81768} +{"mode": "train", "epoch": 110, "iter": 2000, "lr": 0.01691, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36438, "top5_acc": 0.62297, "loss_cls": 3.56901, "loss": 3.56901, "time": 0.81402} +{"mode": "train", "epoch": 110, "iter": 2100, "lr": 0.01689, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36969, "top5_acc": 0.63203, "loss_cls": 3.54814, "loss": 3.54814, "time": 0.81649} +{"mode": "train", "epoch": 110, "iter": 2200, "lr": 0.01687, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38078, "top5_acc": 0.63781, "loss_cls": 3.51051, "loss": 3.51051, "time": 0.81618} +{"mode": "train", "epoch": 110, "iter": 2300, "lr": 0.01685, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36922, "top5_acc": 0.62687, "loss_cls": 3.55476, "loss": 3.55476, "time": 0.8088} +{"mode": "train", "epoch": 110, "iter": 2400, "lr": 0.01682, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36984, "top5_acc": 0.61812, "loss_cls": 3.57124, "loss": 3.57124, "time": 0.80951} +{"mode": "train", "epoch": 110, "iter": 2500, "lr": 0.0168, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36344, "top5_acc": 0.63438, "loss_cls": 3.55615, "loss": 3.55615, "time": 0.81084} +{"mode": "train", "epoch": 110, "iter": 2600, "lr": 0.01678, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36344, "top5_acc": 0.62375, "loss_cls": 3.58852, "loss": 3.58852, "time": 0.81248} +{"mode": "train", "epoch": 110, "iter": 2700, "lr": 0.01676, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35797, "top5_acc": 0.61391, "loss_cls": 3.6175, "loss": 3.6175, "time": 0.81489} +{"mode": "train", "epoch": 110, "iter": 2800, "lr": 0.01674, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36219, "top5_acc": 0.62703, "loss_cls": 3.5844, "loss": 3.5844, "time": 0.81698} +{"mode": "train", "epoch": 110, "iter": 2900, "lr": 0.01672, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37688, "top5_acc": 0.63766, "loss_cls": 3.51986, "loss": 3.51986, "time": 0.81274} +{"mode": "train", "epoch": 110, "iter": 3000, "lr": 0.0167, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37875, "top5_acc": 0.63969, "loss_cls": 3.50931, "loss": 3.50931, "time": 0.81533} +{"mode": "train", "epoch": 110, "iter": 3100, "lr": 0.01668, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37109, "top5_acc": 0.6325, "loss_cls": 3.55335, "loss": 3.55335, "time": 0.80454} +{"mode": "train", "epoch": 110, "iter": 3200, "lr": 0.01666, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36141, "top5_acc": 0.62969, "loss_cls": 3.59575, "loss": 3.59575, "time": 0.80907} +{"mode": "train", "epoch": 110, "iter": 3300, "lr": 0.01664, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37422, "top5_acc": 0.63109, "loss_cls": 3.53915, "loss": 3.53915, "time": 0.80756} +{"mode": "train", "epoch": 110, "iter": 3400, "lr": 0.01662, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36719, "top5_acc": 0.61969, "loss_cls": 3.59068, "loss": 3.59068, "time": 0.81036} +{"mode": "train", "epoch": 110, "iter": 3500, "lr": 0.01659, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36547, "top5_acc": 0.62953, "loss_cls": 3.54678, "loss": 3.54678, "time": 0.80818} +{"mode": "train", "epoch": 110, "iter": 3600, "lr": 0.01657, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36734, "top5_acc": 0.62828, "loss_cls": 3.55599, "loss": 3.55599, "time": 0.8136} +{"mode": "train", "epoch": 110, "iter": 3700, "lr": 0.01655, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36547, "top5_acc": 0.62641, "loss_cls": 3.57938, "loss": 3.57938, "time": 0.811} +{"mode": "val", "epoch": 110, "iter": 309, "lr": 0.01654, "top1_acc": 0.30259, "top5_acc": 0.55959, "mean_class_accuracy": 0.30247} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.01652, "memory": 15990, "data_time": 1.27514, "top1_acc": 0.37594, "top5_acc": 0.63203, "loss_cls": 3.5324, "loss": 3.5324, "time": 2.24658} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.0165, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37859, "top5_acc": 0.6425, "loss_cls": 3.51889, "loss": 3.51889, "time": 0.81311} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.01648, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37672, "top5_acc": 0.64172, "loss_cls": 3.49008, "loss": 3.49008, "time": 0.81561} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.01646, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37703, "top5_acc": 0.63656, "loss_cls": 3.50738, "loss": 3.50738, "time": 0.8092} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.01644, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36578, "top5_acc": 0.63562, "loss_cls": 3.51952, "loss": 3.51952, "time": 0.81321} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.01642, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36969, "top5_acc": 0.63469, "loss_cls": 3.54501, "loss": 3.54501, "time": 0.80918} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.0164, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37094, "top5_acc": 0.63828, "loss_cls": 3.52999, "loss": 3.52999, "time": 0.81029} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.01638, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37406, "top5_acc": 0.63391, "loss_cls": 3.53978, "loss": 3.53978, "time": 0.81146} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.01636, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37516, "top5_acc": 0.64828, "loss_cls": 3.49133, "loss": 3.49133, "time": 0.81844} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.01634, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36875, "top5_acc": 0.6375, "loss_cls": 3.5347, "loss": 3.5347, "time": 0.80923} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.01632, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37734, "top5_acc": 0.64281, "loss_cls": 3.49973, "loss": 3.49973, "time": 0.81612} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.0163, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38266, "top5_acc": 0.63844, "loss_cls": 3.50737, "loss": 3.50737, "time": 0.81202} +{"mode": "train", "epoch": 111, "iter": 1300, "lr": 0.01627, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37797, "top5_acc": 0.63469, "loss_cls": 3.52288, "loss": 3.52288, "time": 0.81613} +{"mode": "train", "epoch": 111, "iter": 1400, "lr": 0.01625, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36656, "top5_acc": 0.62953, "loss_cls": 3.55153, "loss": 3.55153, "time": 0.8103} +{"mode": "train", "epoch": 111, "iter": 1500, "lr": 0.01623, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36406, "top5_acc": 0.62734, "loss_cls": 3.55024, "loss": 3.55024, "time": 0.80938} +{"mode": "train", "epoch": 111, "iter": 1600, "lr": 0.01621, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36531, "top5_acc": 0.63047, "loss_cls": 3.55927, "loss": 3.55927, "time": 0.80778} +{"mode": "train", "epoch": 111, "iter": 1700, "lr": 0.01619, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36672, "top5_acc": 0.62953, "loss_cls": 3.5545, "loss": 3.5545, "time": 0.82099} +{"mode": "train", "epoch": 111, "iter": 1800, "lr": 0.01617, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36969, "top5_acc": 0.635, "loss_cls": 3.56802, "loss": 3.56802, "time": 0.8146} +{"mode": "train", "epoch": 111, "iter": 1900, "lr": 0.01615, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36672, "top5_acc": 0.62703, "loss_cls": 3.55648, "loss": 3.55648, "time": 0.81744} +{"mode": "train", "epoch": 111, "iter": 2000, "lr": 0.01613, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36359, "top5_acc": 0.62953, "loss_cls": 3.56199, "loss": 3.56199, "time": 0.81796} +{"mode": "train", "epoch": 111, "iter": 2100, "lr": 0.01611, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36938, "top5_acc": 0.62313, "loss_cls": 3.5556, "loss": 3.5556, "time": 0.81319} +{"mode": "train", "epoch": 111, "iter": 2200, "lr": 0.01609, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36922, "top5_acc": 0.64078, "loss_cls": 3.52598, "loss": 3.52598, "time": 0.8156} +{"mode": "train", "epoch": 111, "iter": 2300, "lr": 0.01607, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.375, "top5_acc": 0.62984, "loss_cls": 3.54859, "loss": 3.54859, "time": 0.81572} +{"mode": "train", "epoch": 111, "iter": 2400, "lr": 0.01605, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37719, "top5_acc": 0.63297, "loss_cls": 3.51375, "loss": 3.51375, "time": 0.80607} +{"mode": "train", "epoch": 111, "iter": 2500, "lr": 0.01603, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37078, "top5_acc": 0.63203, "loss_cls": 3.57143, "loss": 3.57143, "time": 0.80811} +{"mode": "train", "epoch": 111, "iter": 2600, "lr": 0.01601, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38109, "top5_acc": 0.64297, "loss_cls": 3.48322, "loss": 3.48322, "time": 0.8136} +{"mode": "train", "epoch": 111, "iter": 2700, "lr": 0.01599, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36688, "top5_acc": 0.63641, "loss_cls": 3.52396, "loss": 3.52396, "time": 0.81233} +{"mode": "train", "epoch": 111, "iter": 2800, "lr": 0.01597, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37016, "top5_acc": 0.63141, "loss_cls": 3.53256, "loss": 3.53256, "time": 0.81399} +{"mode": "train", "epoch": 111, "iter": 2900, "lr": 0.01595, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36828, "top5_acc": 0.63734, "loss_cls": 3.50887, "loss": 3.50887, "time": 0.81301} +{"mode": "train", "epoch": 111, "iter": 3000, "lr": 0.01593, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37672, "top5_acc": 0.63656, "loss_cls": 3.52155, "loss": 3.52155, "time": 0.8117} +{"mode": "train", "epoch": 111, "iter": 3100, "lr": 0.0159, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36625, "top5_acc": 0.62031, "loss_cls": 3.58791, "loss": 3.58791, "time": 0.81626} +{"mode": "train", "epoch": 111, "iter": 3200, "lr": 0.01588, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37781, "top5_acc": 0.63172, "loss_cls": 3.51459, "loss": 3.51459, "time": 0.81103} +{"mode": "train", "epoch": 111, "iter": 3300, "lr": 0.01586, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3675, "top5_acc": 0.63625, "loss_cls": 3.54185, "loss": 3.54185, "time": 0.81178} +{"mode": "train", "epoch": 111, "iter": 3400, "lr": 0.01584, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37266, "top5_acc": 0.62953, "loss_cls": 3.57558, "loss": 3.57558, "time": 0.81123} +{"mode": "train", "epoch": 111, "iter": 3500, "lr": 0.01582, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37734, "top5_acc": 0.64125, "loss_cls": 3.51335, "loss": 3.51335, "time": 0.81038} +{"mode": "train", "epoch": 111, "iter": 3600, "lr": 0.0158, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37031, "top5_acc": 0.63078, "loss_cls": 3.55809, "loss": 3.55809, "time": 0.81408} +{"mode": "train", "epoch": 111, "iter": 3700, "lr": 0.01578, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36641, "top5_acc": 0.63031, "loss_cls": 3.55898, "loss": 3.55898, "time": 0.81475} +{"mode": "val", "epoch": 111, "iter": 309, "lr": 0.01577, "top1_acc": 0.28805, "top5_acc": 0.54181, "mean_class_accuracy": 0.28791} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.01575, "memory": 15990, "data_time": 1.3199, "top1_acc": 0.38531, "top5_acc": 0.64656, "loss_cls": 3.47345, "loss": 3.47345, "time": 2.30044} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.01573, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37734, "top5_acc": 0.64641, "loss_cls": 3.45912, "loss": 3.45912, "time": 0.81646} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.01571, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37406, "top5_acc": 0.63406, "loss_cls": 3.54421, "loss": 3.54421, "time": 0.81452} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.01569, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37266, "top5_acc": 0.64703, "loss_cls": 3.49342, "loss": 3.49342, "time": 0.80967} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.01567, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.375, "top5_acc": 0.63562, "loss_cls": 3.49467, "loss": 3.49467, "time": 0.81371} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.01565, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38406, "top5_acc": 0.64828, "loss_cls": 3.49127, "loss": 3.49127, "time": 0.81325} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.01563, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37891, "top5_acc": 0.64531, "loss_cls": 3.48942, "loss": 3.48942, "time": 0.81387} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.01561, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38109, "top5_acc": 0.64234, "loss_cls": 3.48326, "loss": 3.48326, "time": 0.80895} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.01559, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.375, "top5_acc": 0.63453, "loss_cls": 3.51546, "loss": 3.51546, "time": 0.81559} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.01557, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.38281, "top5_acc": 0.64109, "loss_cls": 3.4988, "loss": 3.4988, "time": 0.81564} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.01555, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38344, "top5_acc": 0.64297, "loss_cls": 3.4954, "loss": 3.4954, "time": 0.81098} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.01553, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.37641, "top5_acc": 0.63719, "loss_cls": 3.5123, "loss": 3.5123, "time": 0.81071} +{"mode": "train", "epoch": 112, "iter": 1300, "lr": 0.01551, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37906, "top5_acc": 0.64094, "loss_cls": 3.52027, "loss": 3.52027, "time": 0.81544} +{"mode": "train", "epoch": 112, "iter": 1400, "lr": 0.01549, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37094, "top5_acc": 0.63656, "loss_cls": 3.54266, "loss": 3.54266, "time": 0.81114} +{"mode": "train", "epoch": 112, "iter": 1500, "lr": 0.01547, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38078, "top5_acc": 0.64281, "loss_cls": 3.47811, "loss": 3.47811, "time": 0.81165} +{"mode": "train", "epoch": 112, "iter": 1600, "lr": 0.01545, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36766, "top5_acc": 0.63781, "loss_cls": 3.50991, "loss": 3.50991, "time": 0.80667} +{"mode": "train", "epoch": 112, "iter": 1700, "lr": 0.01543, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3925, "top5_acc": 0.65297, "loss_cls": 3.43479, "loss": 3.43479, "time": 0.81846} +{"mode": "train", "epoch": 112, "iter": 1800, "lr": 0.01541, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37359, "top5_acc": 0.62813, "loss_cls": 3.54474, "loss": 3.54474, "time": 0.81317} +{"mode": "train", "epoch": 112, "iter": 1900, "lr": 0.01539, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37422, "top5_acc": 0.63219, "loss_cls": 3.54713, "loss": 3.54713, "time": 0.8123} +{"mode": "train", "epoch": 112, "iter": 2000, "lr": 0.01537, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37609, "top5_acc": 0.64203, "loss_cls": 3.51589, "loss": 3.51589, "time": 0.81774} +{"mode": "train", "epoch": 112, "iter": 2100, "lr": 0.01535, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37891, "top5_acc": 0.64, "loss_cls": 3.5069, "loss": 3.5069, "time": 0.81998} +{"mode": "train", "epoch": 112, "iter": 2200, "lr": 0.01533, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37031, "top5_acc": 0.63578, "loss_cls": 3.53942, "loss": 3.53942, "time": 0.81263} +{"mode": "train", "epoch": 112, "iter": 2300, "lr": 0.01531, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36688, "top5_acc": 0.63188, "loss_cls": 3.55516, "loss": 3.55516, "time": 0.81057} +{"mode": "train", "epoch": 112, "iter": 2400, "lr": 0.01529, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37516, "top5_acc": 0.63984, "loss_cls": 3.49826, "loss": 3.49826, "time": 0.81705} +{"mode": "train", "epoch": 112, "iter": 2500, "lr": 0.01527, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36938, "top5_acc": 0.62969, "loss_cls": 3.55531, "loss": 3.55531, "time": 0.81218} +{"mode": "train", "epoch": 112, "iter": 2600, "lr": 0.01525, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38234, "top5_acc": 0.64188, "loss_cls": 3.47081, "loss": 3.47081, "time": 0.81305} +{"mode": "train", "epoch": 112, "iter": 2700, "lr": 0.01523, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38203, "top5_acc": 0.63156, "loss_cls": 3.51977, "loss": 3.51977, "time": 0.80861} +{"mode": "train", "epoch": 112, "iter": 2800, "lr": 0.01521, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37766, "top5_acc": 0.63266, "loss_cls": 3.49702, "loss": 3.49702, "time": 0.81108} +{"mode": "train", "epoch": 112, "iter": 2900, "lr": 0.01519, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36969, "top5_acc": 0.63031, "loss_cls": 3.55539, "loss": 3.55539, "time": 0.81196} +{"mode": "train", "epoch": 112, "iter": 3000, "lr": 0.01517, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.36828, "top5_acc": 0.63172, "loss_cls": 3.5467, "loss": 3.5467, "time": 0.81558} +{"mode": "train", "epoch": 112, "iter": 3100, "lr": 0.01515, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3825, "top5_acc": 0.63813, "loss_cls": 3.48624, "loss": 3.48624, "time": 0.80971} +{"mode": "train", "epoch": 112, "iter": 3200, "lr": 0.01513, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36422, "top5_acc": 0.63188, "loss_cls": 3.54945, "loss": 3.54945, "time": 0.80963} +{"mode": "train", "epoch": 112, "iter": 3300, "lr": 0.01511, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37516, "top5_acc": 0.62984, "loss_cls": 3.55221, "loss": 3.55221, "time": 0.8076} +{"mode": "train", "epoch": 112, "iter": 3400, "lr": 0.01509, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37219, "top5_acc": 0.63656, "loss_cls": 3.49546, "loss": 3.49546, "time": 0.8078} +{"mode": "train", "epoch": 112, "iter": 3500, "lr": 0.01507, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37953, "top5_acc": 0.63844, "loss_cls": 3.50138, "loss": 3.50138, "time": 0.81147} +{"mode": "train", "epoch": 112, "iter": 3600, "lr": 0.01505, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37469, "top5_acc": 0.64266, "loss_cls": 3.48587, "loss": 3.48587, "time": 0.81408} +{"mode": "train", "epoch": 112, "iter": 3700, "lr": 0.01503, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36812, "top5_acc": 0.64344, "loss_cls": 3.51508, "loss": 3.51508, "time": 0.82101} +{"mode": "val", "epoch": 112, "iter": 309, "lr": 0.01502, "top1_acc": 0.30857, "top5_acc": 0.56491, "mean_class_accuracy": 0.30838} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.015, "memory": 15990, "data_time": 1.27953, "top1_acc": 0.39562, "top5_acc": 0.65422, "loss_cls": 3.39364, "loss": 3.39364, "time": 2.25265} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.01498, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38422, "top5_acc": 0.64766, "loss_cls": 3.43151, "loss": 3.43151, "time": 0.81404} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.01496, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38875, "top5_acc": 0.65141, "loss_cls": 3.43331, "loss": 3.43331, "time": 0.82308} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.01494, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38266, "top5_acc": 0.64938, "loss_cls": 3.41663, "loss": 3.41663, "time": 0.81112} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.01492, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38422, "top5_acc": 0.64578, "loss_cls": 3.45908, "loss": 3.45908, "time": 0.82725} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.0149, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38875, "top5_acc": 0.64828, "loss_cls": 3.43405, "loss": 3.43405, "time": 0.81843} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.01488, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36766, "top5_acc": 0.63922, "loss_cls": 3.51137, "loss": 3.51137, "time": 0.817} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.01486, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.3875, "top5_acc": 0.64812, "loss_cls": 3.4638, "loss": 3.4638, "time": 0.81115} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.01484, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38891, "top5_acc": 0.64344, "loss_cls": 3.44452, "loss": 3.44452, "time": 0.80736} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.01482, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38547, "top5_acc": 0.65031, "loss_cls": 3.45258, "loss": 3.45258, "time": 0.81537} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0148, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38203, "top5_acc": 0.63813, "loss_cls": 3.4989, "loss": 3.4989, "time": 0.81696} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.01478, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39156, "top5_acc": 0.65328, "loss_cls": 3.4146, "loss": 3.4146, "time": 0.82362} +{"mode": "train", "epoch": 113, "iter": 1300, "lr": 0.01476, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37141, "top5_acc": 0.63953, "loss_cls": 3.5225, "loss": 3.5225, "time": 0.81573} +{"mode": "train", "epoch": 113, "iter": 1400, "lr": 0.01474, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38141, "top5_acc": 0.63641, "loss_cls": 3.49571, "loss": 3.49571, "time": 0.81179} +{"mode": "train", "epoch": 113, "iter": 1500, "lr": 0.01472, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36781, "top5_acc": 0.63594, "loss_cls": 3.55495, "loss": 3.55495, "time": 0.81466} +{"mode": "train", "epoch": 113, "iter": 1600, "lr": 0.0147, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38641, "top5_acc": 0.64891, "loss_cls": 3.45263, "loss": 3.45263, "time": 0.81469} +{"mode": "train", "epoch": 113, "iter": 1700, "lr": 0.01468, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37812, "top5_acc": 0.63844, "loss_cls": 3.4927, "loss": 3.4927, "time": 0.81273} +{"mode": "train", "epoch": 113, "iter": 1800, "lr": 0.01466, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38406, "top5_acc": 0.64703, "loss_cls": 3.46916, "loss": 3.46916, "time": 0.81709} +{"mode": "train", "epoch": 113, "iter": 1900, "lr": 0.01464, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38344, "top5_acc": 0.64172, "loss_cls": 3.45557, "loss": 3.45557, "time": 0.8134} +{"mode": "train", "epoch": 113, "iter": 2000, "lr": 0.01462, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38438, "top5_acc": 0.64016, "loss_cls": 3.49119, "loss": 3.49119, "time": 0.82296} +{"mode": "train", "epoch": 113, "iter": 2100, "lr": 0.0146, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37266, "top5_acc": 0.63625, "loss_cls": 3.51593, "loss": 3.51593, "time": 0.81185} +{"mode": "train", "epoch": 113, "iter": 2200, "lr": 0.01458, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37219, "top5_acc": 0.63422, "loss_cls": 3.50494, "loss": 3.50494, "time": 0.81594} +{"mode": "train", "epoch": 113, "iter": 2300, "lr": 0.01456, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38359, "top5_acc": 0.64625, "loss_cls": 3.47392, "loss": 3.47392, "time": 0.8135} +{"mode": "train", "epoch": 113, "iter": 2400, "lr": 0.01454, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37109, "top5_acc": 0.63266, "loss_cls": 3.54821, "loss": 3.54821, "time": 0.81101} +{"mode": "train", "epoch": 113, "iter": 2500, "lr": 0.01452, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38328, "top5_acc": 0.63625, "loss_cls": 3.52431, "loss": 3.52431, "time": 0.81391} +{"mode": "train", "epoch": 113, "iter": 2600, "lr": 0.0145, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37234, "top5_acc": 0.63641, "loss_cls": 3.52584, "loss": 3.52584, "time": 0.81415} +{"mode": "train", "epoch": 113, "iter": 2700, "lr": 0.01448, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38109, "top5_acc": 0.64656, "loss_cls": 3.48225, "loss": 3.48225, "time": 0.81019} +{"mode": "train", "epoch": 113, "iter": 2800, "lr": 0.01446, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37031, "top5_acc": 0.62953, "loss_cls": 3.54532, "loss": 3.54532, "time": 0.81249} +{"mode": "train", "epoch": 113, "iter": 2900, "lr": 0.01444, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38469, "top5_acc": 0.64875, "loss_cls": 3.46758, "loss": 3.46758, "time": 0.81469} +{"mode": "train", "epoch": 113, "iter": 3000, "lr": 0.01442, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37859, "top5_acc": 0.63047, "loss_cls": 3.51096, "loss": 3.51096, "time": 0.81499} +{"mode": "train", "epoch": 113, "iter": 3100, "lr": 0.0144, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37828, "top5_acc": 0.64281, "loss_cls": 3.48131, "loss": 3.48131, "time": 0.80832} +{"mode": "train", "epoch": 113, "iter": 3200, "lr": 0.01438, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37266, "top5_acc": 0.63297, "loss_cls": 3.53484, "loss": 3.53484, "time": 0.80979} +{"mode": "train", "epoch": 113, "iter": 3300, "lr": 0.01436, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37938, "top5_acc": 0.64016, "loss_cls": 3.50262, "loss": 3.50262, "time": 0.80984} +{"mode": "train", "epoch": 113, "iter": 3400, "lr": 0.01434, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38047, "top5_acc": 0.63438, "loss_cls": 3.51182, "loss": 3.51182, "time": 0.8139} +{"mode": "train", "epoch": 113, "iter": 3500, "lr": 0.01432, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3625, "top5_acc": 0.63188, "loss_cls": 3.54447, "loss": 3.54447, "time": 0.80992} +{"mode": "train", "epoch": 113, "iter": 3600, "lr": 0.01431, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37625, "top5_acc": 0.62766, "loss_cls": 3.53349, "loss": 3.53349, "time": 0.80793} +{"mode": "train", "epoch": 113, "iter": 3700, "lr": 0.01429, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.38016, "top5_acc": 0.63609, "loss_cls": 3.49724, "loss": 3.49724, "time": 0.8092} +{"mode": "val", "epoch": 113, "iter": 309, "lr": 0.01428, "top1_acc": 0.31616, "top5_acc": 0.57301, "mean_class_accuracy": 0.31594} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.01426, "memory": 15990, "data_time": 1.28935, "top1_acc": 0.38781, "top5_acc": 0.65062, "loss_cls": 3.41828, "loss": 3.41828, "time": 2.26575} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.01424, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39906, "top5_acc": 0.65812, "loss_cls": 3.38061, "loss": 3.38061, "time": 0.81508} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.01422, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38422, "top5_acc": 0.65047, "loss_cls": 3.4524, "loss": 3.4524, "time": 0.81012} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.0142, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38266, "top5_acc": 0.64953, "loss_cls": 3.4509, "loss": 3.4509, "time": 0.81189} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.01418, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38234, "top5_acc": 0.64594, "loss_cls": 3.45729, "loss": 3.45729, "time": 0.81454} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.01416, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38297, "top5_acc": 0.64688, "loss_cls": 3.45271, "loss": 3.45271, "time": 0.81695} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.01414, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39125, "top5_acc": 0.65359, "loss_cls": 3.43645, "loss": 3.43645, "time": 0.81323} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.01412, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37781, "top5_acc": 0.64859, "loss_cls": 3.49522, "loss": 3.49522, "time": 0.81195} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.0141, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38859, "top5_acc": 0.64281, "loss_cls": 3.44092, "loss": 3.44092, "time": 0.81315} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.01408, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39047, "top5_acc": 0.65281, "loss_cls": 3.47399, "loss": 3.47399, "time": 0.80803} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.01406, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38906, "top5_acc": 0.65828, "loss_cls": 3.45234, "loss": 3.45234, "time": 0.81251} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.01404, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.38375, "top5_acc": 0.64828, "loss_cls": 3.45479, "loss": 3.45479, "time": 0.81676} +{"mode": "train", "epoch": 114, "iter": 1300, "lr": 0.01402, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38422, "top5_acc": 0.64641, "loss_cls": 3.44675, "loss": 3.44675, "time": 0.81573} +{"mode": "train", "epoch": 114, "iter": 1400, "lr": 0.014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38078, "top5_acc": 0.6425, "loss_cls": 3.46188, "loss": 3.46188, "time": 0.81602} +{"mode": "train", "epoch": 114, "iter": 1500, "lr": 0.01398, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38188, "top5_acc": 0.64391, "loss_cls": 3.44672, "loss": 3.44672, "time": 0.81261} +{"mode": "train", "epoch": 114, "iter": 1600, "lr": 0.01397, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39016, "top5_acc": 0.64766, "loss_cls": 3.44719, "loss": 3.44719, "time": 0.8106} +{"mode": "train", "epoch": 114, "iter": 1700, "lr": 0.01395, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38266, "top5_acc": 0.64844, "loss_cls": 3.44769, "loss": 3.44769, "time": 0.80752} +{"mode": "train", "epoch": 114, "iter": 1800, "lr": 0.01393, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38875, "top5_acc": 0.63938, "loss_cls": 3.48398, "loss": 3.48398, "time": 0.8205} +{"mode": "train", "epoch": 114, "iter": 1900, "lr": 0.01391, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37828, "top5_acc": 0.64078, "loss_cls": 3.48204, "loss": 3.48204, "time": 0.81138} +{"mode": "train", "epoch": 114, "iter": 2000, "lr": 0.01389, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37328, "top5_acc": 0.63953, "loss_cls": 3.50992, "loss": 3.50992, "time": 0.82279} +{"mode": "train", "epoch": 114, "iter": 2100, "lr": 0.01387, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38609, "top5_acc": 0.65062, "loss_cls": 3.43531, "loss": 3.43531, "time": 0.81204} +{"mode": "train", "epoch": 114, "iter": 2200, "lr": 0.01385, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39141, "top5_acc": 0.64453, "loss_cls": 3.45544, "loss": 3.45544, "time": 0.81815} +{"mode": "train", "epoch": 114, "iter": 2300, "lr": 0.01383, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37594, "top5_acc": 0.63844, "loss_cls": 3.49053, "loss": 3.49053, "time": 0.8141} +{"mode": "train", "epoch": 114, "iter": 2400, "lr": 0.01381, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38297, "top5_acc": 0.64438, "loss_cls": 3.46841, "loss": 3.46841, "time": 0.81374} +{"mode": "train", "epoch": 114, "iter": 2500, "lr": 0.01379, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38812, "top5_acc": 0.655, "loss_cls": 3.4283, "loss": 3.4283, "time": 0.81517} +{"mode": "train", "epoch": 114, "iter": 2600, "lr": 0.01377, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38391, "top5_acc": 0.63406, "loss_cls": 3.49914, "loss": 3.49914, "time": 0.81324} +{"mode": "train", "epoch": 114, "iter": 2700, "lr": 0.01375, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38109, "top5_acc": 0.64266, "loss_cls": 3.48122, "loss": 3.48122, "time": 0.80951} +{"mode": "train", "epoch": 114, "iter": 2800, "lr": 0.01373, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38031, "top5_acc": 0.64547, "loss_cls": 3.46165, "loss": 3.46165, "time": 0.80609} +{"mode": "train", "epoch": 114, "iter": 2900, "lr": 0.01371, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37016, "top5_acc": 0.64266, "loss_cls": 3.50514, "loss": 3.50514, "time": 0.81795} +{"mode": "train", "epoch": 114, "iter": 3000, "lr": 0.01369, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38828, "top5_acc": 0.64547, "loss_cls": 3.432, "loss": 3.432, "time": 0.81127} +{"mode": "train", "epoch": 114, "iter": 3100, "lr": 0.01368, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38516, "top5_acc": 0.64094, "loss_cls": 3.466, "loss": 3.466, "time": 0.81458} +{"mode": "train", "epoch": 114, "iter": 3200, "lr": 0.01366, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37781, "top5_acc": 0.64344, "loss_cls": 3.50255, "loss": 3.50255, "time": 0.81145} +{"mode": "train", "epoch": 114, "iter": 3300, "lr": 0.01364, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37844, "top5_acc": 0.64484, "loss_cls": 3.49984, "loss": 3.49984, "time": 0.81343} +{"mode": "train", "epoch": 114, "iter": 3400, "lr": 0.01362, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38328, "top5_acc": 0.65375, "loss_cls": 3.43941, "loss": 3.43941, "time": 0.80898} +{"mode": "train", "epoch": 114, "iter": 3500, "lr": 0.0136, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38266, "top5_acc": 0.63391, "loss_cls": 3.49814, "loss": 3.49814, "time": 0.81159} +{"mode": "train", "epoch": 114, "iter": 3600, "lr": 0.01358, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38906, "top5_acc": 0.64578, "loss_cls": 3.46789, "loss": 3.46789, "time": 0.80967} +{"mode": "train", "epoch": 114, "iter": 3700, "lr": 0.01356, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38375, "top5_acc": 0.64578, "loss_cls": 3.45992, "loss": 3.45992, "time": 0.81435} +{"mode": "val", "epoch": 114, "iter": 309, "lr": 0.01355, "top1_acc": 0.31662, "top5_acc": 0.57529, "mean_class_accuracy": 0.31646} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.01353, "memory": 15990, "data_time": 1.28454, "top1_acc": 0.39984, "top5_acc": 0.66672, "loss_cls": 3.35847, "loss": 3.35847, "time": 2.25656} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.01351, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40125, "top5_acc": 0.65766, "loss_cls": 3.38911, "loss": 3.38911, "time": 0.81419} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.01349, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39594, "top5_acc": 0.65438, "loss_cls": 3.43149, "loss": 3.43149, "time": 0.81712} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.01348, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39969, "top5_acc": 0.65594, "loss_cls": 3.40263, "loss": 3.40263, "time": 0.81142} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.01346, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38, "top5_acc": 0.65188, "loss_cls": 3.47142, "loss": 3.47142, "time": 0.807} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.01344, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38344, "top5_acc": 0.65266, "loss_cls": 3.4124, "loss": 3.4124, "time": 0.81294} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.01342, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39391, "top5_acc": 0.65625, "loss_cls": 3.42338, "loss": 3.42338, "time": 0.81538} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.0134, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38375, "top5_acc": 0.65016, "loss_cls": 3.44324, "loss": 3.44324, "time": 0.81417} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.01338, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.39844, "top5_acc": 0.65016, "loss_cls": 3.41474, "loss": 3.41474, "time": 0.81347} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.01336, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39703, "top5_acc": 0.65969, "loss_cls": 3.41369, "loss": 3.41369, "time": 0.81442} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.01334, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38797, "top5_acc": 0.64953, "loss_cls": 3.45449, "loss": 3.45449, "time": 0.81214} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.01332, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.39078, "top5_acc": 0.64625, "loss_cls": 3.4424, "loss": 3.4424, "time": 0.8124} +{"mode": "train", "epoch": 115, "iter": 1300, "lr": 0.0133, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38156, "top5_acc": 0.65219, "loss_cls": 3.42901, "loss": 3.42901, "time": 0.8107} +{"mode": "train", "epoch": 115, "iter": 1400, "lr": 0.01328, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.40031, "top5_acc": 0.65969, "loss_cls": 3.3647, "loss": 3.3647, "time": 0.81329} +{"mode": "train", "epoch": 115, "iter": 1500, "lr": 0.01327, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38641, "top5_acc": 0.66359, "loss_cls": 3.39451, "loss": 3.39451, "time": 0.81107} +{"mode": "train", "epoch": 115, "iter": 1600, "lr": 0.01325, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38062, "top5_acc": 0.64797, "loss_cls": 3.46683, "loss": 3.46683, "time": 0.81225} +{"mode": "train", "epoch": 115, "iter": 1700, "lr": 0.01323, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38781, "top5_acc": 0.65609, "loss_cls": 3.42603, "loss": 3.42603, "time": 0.81465} +{"mode": "train", "epoch": 115, "iter": 1800, "lr": 0.01321, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38141, "top5_acc": 0.65109, "loss_cls": 3.44748, "loss": 3.44748, "time": 0.8166} +{"mode": "train", "epoch": 115, "iter": 1900, "lr": 0.01319, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37969, "top5_acc": 0.64062, "loss_cls": 3.47059, "loss": 3.47059, "time": 0.81514} +{"mode": "train", "epoch": 115, "iter": 2000, "lr": 0.01317, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38766, "top5_acc": 0.65266, "loss_cls": 3.42923, "loss": 3.42923, "time": 0.81718} +{"mode": "train", "epoch": 115, "iter": 2100, "lr": 0.01315, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37797, "top5_acc": 0.64953, "loss_cls": 3.46702, "loss": 3.46702, "time": 0.82035} +{"mode": "train", "epoch": 115, "iter": 2200, "lr": 0.01313, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38562, "top5_acc": 0.65297, "loss_cls": 3.42826, "loss": 3.42826, "time": 0.82237} +{"mode": "train", "epoch": 115, "iter": 2300, "lr": 0.01311, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39578, "top5_acc": 0.65781, "loss_cls": 3.40024, "loss": 3.40024, "time": 0.81391} +{"mode": "train", "epoch": 115, "iter": 2400, "lr": 0.0131, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37969, "top5_acc": 0.64438, "loss_cls": 3.48909, "loss": 3.48909, "time": 0.81264} +{"mode": "train", "epoch": 115, "iter": 2500, "lr": 0.01308, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38219, "top5_acc": 0.64766, "loss_cls": 3.45407, "loss": 3.45407, "time": 0.81426} +{"mode": "train", "epoch": 115, "iter": 2600, "lr": 0.01306, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38609, "top5_acc": 0.64078, "loss_cls": 3.48256, "loss": 3.48256, "time": 0.81288} +{"mode": "train", "epoch": 115, "iter": 2700, "lr": 0.01304, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3825, "top5_acc": 0.64547, "loss_cls": 3.43974, "loss": 3.43974, "time": 0.80844} +{"mode": "train", "epoch": 115, "iter": 2800, "lr": 0.01302, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.3875, "top5_acc": 0.65625, "loss_cls": 3.43017, "loss": 3.43017, "time": 0.81133} +{"mode": "train", "epoch": 115, "iter": 2900, "lr": 0.013, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39406, "top5_acc": 0.65297, "loss_cls": 3.43242, "loss": 3.43242, "time": 0.81369} +{"mode": "train", "epoch": 115, "iter": 3000, "lr": 0.01298, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38938, "top5_acc": 0.63828, "loss_cls": 3.46253, "loss": 3.46253, "time": 0.81279} +{"mode": "train", "epoch": 115, "iter": 3100, "lr": 0.01296, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38516, "top5_acc": 0.64641, "loss_cls": 3.44119, "loss": 3.44119, "time": 0.80755} +{"mode": "train", "epoch": 115, "iter": 3200, "lr": 0.01295, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37953, "top5_acc": 0.63844, "loss_cls": 3.48533, "loss": 3.48533, "time": 0.81549} +{"mode": "train", "epoch": 115, "iter": 3300, "lr": 0.01293, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37922, "top5_acc": 0.64766, "loss_cls": 3.45326, "loss": 3.45326, "time": 0.81409} +{"mode": "train", "epoch": 115, "iter": 3400, "lr": 0.01291, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38578, "top5_acc": 0.64766, "loss_cls": 3.4501, "loss": 3.4501, "time": 0.81251} +{"mode": "train", "epoch": 115, "iter": 3500, "lr": 0.01289, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38172, "top5_acc": 0.64469, "loss_cls": 3.47945, "loss": 3.47945, "time": 0.81211} +{"mode": "train", "epoch": 115, "iter": 3600, "lr": 0.01287, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38, "top5_acc": 0.64172, "loss_cls": 3.48139, "loss": 3.48139, "time": 0.80751} +{"mode": "train", "epoch": 115, "iter": 3700, "lr": 0.01285, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36797, "top5_acc": 0.64141, "loss_cls": 3.49427, "loss": 3.49427, "time": 0.80972} +{"mode": "val", "epoch": 115, "iter": 309, "lr": 0.01284, "top1_acc": 0.32386, "top5_acc": 0.57742, "mean_class_accuracy": 0.32369} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.01282, "memory": 15990, "data_time": 1.31027, "top1_acc": 0.39688, "top5_acc": 0.65953, "loss_cls": 3.35991, "loss": 3.35991, "time": 2.28573} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.01281, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40047, "top5_acc": 0.66422, "loss_cls": 3.34237, "loss": 3.34237, "time": 0.82426} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.01279, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39719, "top5_acc": 0.66047, "loss_cls": 3.37713, "loss": 3.37713, "time": 0.82294} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.01277, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39625, "top5_acc": 0.65766, "loss_cls": 3.40321, "loss": 3.40321, "time": 0.81897} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.01275, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39469, "top5_acc": 0.65891, "loss_cls": 3.37991, "loss": 3.37991, "time": 0.81643} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.01273, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39594, "top5_acc": 0.65625, "loss_cls": 3.40033, "loss": 3.40033, "time": 0.81272} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.01271, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.40703, "top5_acc": 0.67609, "loss_cls": 3.32, "loss": 3.32, "time": 0.8117} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.01269, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39797, "top5_acc": 0.65281, "loss_cls": 3.40792, "loss": 3.40792, "time": 0.81092} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.01268, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38656, "top5_acc": 0.65594, "loss_cls": 3.43127, "loss": 3.43127, "time": 0.81042} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.01266, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39219, "top5_acc": 0.65203, "loss_cls": 3.44064, "loss": 3.44064, "time": 0.8149} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.01264, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39188, "top5_acc": 0.64531, "loss_cls": 3.43045, "loss": 3.43045, "time": 0.8194} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.01262, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40188, "top5_acc": 0.65922, "loss_cls": 3.36165, "loss": 3.36165, "time": 0.82005} +{"mode": "train", "epoch": 116, "iter": 1300, "lr": 0.0126, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38734, "top5_acc": 0.65203, "loss_cls": 3.39397, "loss": 3.39397, "time": 0.81408} +{"mode": "train", "epoch": 116, "iter": 1400, "lr": 0.01258, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38812, "top5_acc": 0.64422, "loss_cls": 3.43872, "loss": 3.43872, "time": 0.81613} +{"mode": "train", "epoch": 116, "iter": 1500, "lr": 0.01256, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38578, "top5_acc": 0.64891, "loss_cls": 3.43985, "loss": 3.43985, "time": 0.81261} +{"mode": "train", "epoch": 116, "iter": 1600, "lr": 0.01255, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40266, "top5_acc": 0.66484, "loss_cls": 3.36235, "loss": 3.36235, "time": 0.81507} +{"mode": "train", "epoch": 116, "iter": 1700, "lr": 0.01253, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38344, "top5_acc": 0.64906, "loss_cls": 3.43864, "loss": 3.43864, "time": 0.81092} +{"mode": "train", "epoch": 116, "iter": 1800, "lr": 0.01251, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38922, "top5_acc": 0.65641, "loss_cls": 3.40715, "loss": 3.40715, "time": 0.81232} +{"mode": "train", "epoch": 116, "iter": 1900, "lr": 0.01249, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39344, "top5_acc": 0.64812, "loss_cls": 3.43499, "loss": 3.43499, "time": 0.81371} +{"mode": "train", "epoch": 116, "iter": 2000, "lr": 0.01247, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38891, "top5_acc": 0.65391, "loss_cls": 3.4188, "loss": 3.4188, "time": 0.81778} +{"mode": "train", "epoch": 116, "iter": 2100, "lr": 0.01245, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39875, "top5_acc": 0.65219, "loss_cls": 3.40735, "loss": 3.40735, "time": 0.81675} +{"mode": "train", "epoch": 116, "iter": 2200, "lr": 0.01243, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39172, "top5_acc": 0.64562, "loss_cls": 3.4271, "loss": 3.4271, "time": 0.81462} +{"mode": "train", "epoch": 116, "iter": 2300, "lr": 0.01242, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39, "top5_acc": 0.64438, "loss_cls": 3.46848, "loss": 3.46848, "time": 0.81437} +{"mode": "train", "epoch": 116, "iter": 2400, "lr": 0.0124, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38844, "top5_acc": 0.64844, "loss_cls": 3.42379, "loss": 3.42379, "time": 0.8156} +{"mode": "train", "epoch": 116, "iter": 2500, "lr": 0.01238, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39922, "top5_acc": 0.65781, "loss_cls": 3.39427, "loss": 3.39427, "time": 0.81074} +{"mode": "train", "epoch": 116, "iter": 2600, "lr": 0.01236, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39375, "top5_acc": 0.65203, "loss_cls": 3.39275, "loss": 3.39275, "time": 0.81636} +{"mode": "train", "epoch": 116, "iter": 2700, "lr": 0.01234, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38938, "top5_acc": 0.64938, "loss_cls": 3.42394, "loss": 3.42394, "time": 0.80736} +{"mode": "train", "epoch": 116, "iter": 2800, "lr": 0.01232, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38828, "top5_acc": 0.65422, "loss_cls": 3.40655, "loss": 3.40655, "time": 0.81592} +{"mode": "train", "epoch": 116, "iter": 2900, "lr": 0.01231, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39672, "top5_acc": 0.65125, "loss_cls": 3.43159, "loss": 3.43159, "time": 0.80924} +{"mode": "train", "epoch": 116, "iter": 3000, "lr": 0.01229, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38422, "top5_acc": 0.64547, "loss_cls": 3.43858, "loss": 3.43858, "time": 0.81507} +{"mode": "train", "epoch": 116, "iter": 3100, "lr": 0.01227, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38953, "top5_acc": 0.65047, "loss_cls": 3.42041, "loss": 3.42041, "time": 0.80793} +{"mode": "train", "epoch": 116, "iter": 3200, "lr": 0.01225, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39734, "top5_acc": 0.64625, "loss_cls": 3.41039, "loss": 3.41039, "time": 0.81435} +{"mode": "train", "epoch": 116, "iter": 3300, "lr": 0.01223, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38812, "top5_acc": 0.64938, "loss_cls": 3.44174, "loss": 3.44174, "time": 0.81246} +{"mode": "train", "epoch": 116, "iter": 3400, "lr": 0.01221, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.3825, "top5_acc": 0.64, "loss_cls": 3.47829, "loss": 3.47829, "time": 0.81125} +{"mode": "train", "epoch": 116, "iter": 3500, "lr": 0.0122, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39391, "top5_acc": 0.64688, "loss_cls": 3.42899, "loss": 3.42899, "time": 0.80966} +{"mode": "train", "epoch": 116, "iter": 3600, "lr": 0.01218, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.38031, "top5_acc": 0.63344, "loss_cls": 3.49048, "loss": 3.49048, "time": 0.80896} +{"mode": "train", "epoch": 116, "iter": 3700, "lr": 0.01216, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38516, "top5_acc": 0.63891, "loss_cls": 3.47575, "loss": 3.47575, "time": 0.80816} +{"mode": "val", "epoch": 116, "iter": 309, "lr": 0.01215, "top1_acc": 0.31596, "top5_acc": 0.57154, "mean_class_accuracy": 0.31585} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.01213, "memory": 15990, "data_time": 1.34538, "top1_acc": 0.40172, "top5_acc": 0.66609, "loss_cls": 3.34475, "loss": 3.34475, "time": 2.32806} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.01211, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40906, "top5_acc": 0.66641, "loss_cls": 3.35468, "loss": 3.35468, "time": 0.82476} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.0121, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39656, "top5_acc": 0.65406, "loss_cls": 3.40429, "loss": 3.40429, "time": 0.81869} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.01208, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41125, "top5_acc": 0.66938, "loss_cls": 3.29953, "loss": 3.29953, "time": 0.81777} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.01206, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40328, "top5_acc": 0.66453, "loss_cls": 3.34553, "loss": 3.34553, "time": 0.81358} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.01204, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39969, "top5_acc": 0.66328, "loss_cls": 3.37164, "loss": 3.37164, "time": 0.81378} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.01202, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39609, "top5_acc": 0.66734, "loss_cls": 3.35001, "loss": 3.35001, "time": 0.81557} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4025, "top5_acc": 0.66109, "loss_cls": 3.35031, "loss": 3.35031, "time": 0.81551} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.01199, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39516, "top5_acc": 0.65969, "loss_cls": 3.38739, "loss": 3.38739, "time": 0.81097} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.01197, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39156, "top5_acc": 0.65266, "loss_cls": 3.40701, "loss": 3.40701, "time": 0.81364} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.01195, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39875, "top5_acc": 0.655, "loss_cls": 3.38615, "loss": 3.38615, "time": 0.81039} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.01193, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38766, "top5_acc": 0.65766, "loss_cls": 3.38589, "loss": 3.38589, "time": 0.81074} +{"mode": "train", "epoch": 117, "iter": 1300, "lr": 0.01191, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40406, "top5_acc": 0.65953, "loss_cls": 3.37643, "loss": 3.37643, "time": 0.80734} +{"mode": "train", "epoch": 117, "iter": 1400, "lr": 0.0119, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39062, "top5_acc": 0.65, "loss_cls": 3.41857, "loss": 3.41857, "time": 0.81097} +{"mode": "train", "epoch": 117, "iter": 1500, "lr": 0.01188, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38844, "top5_acc": 0.64672, "loss_cls": 3.42695, "loss": 3.42695, "time": 0.81344} +{"mode": "train", "epoch": 117, "iter": 1600, "lr": 0.01186, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40531, "top5_acc": 0.6625, "loss_cls": 3.34289, "loss": 3.34289, "time": 0.8107} +{"mode": "train", "epoch": 117, "iter": 1700, "lr": 0.01184, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38531, "top5_acc": 0.65453, "loss_cls": 3.43433, "loss": 3.43433, "time": 0.80815} +{"mode": "train", "epoch": 117, "iter": 1800, "lr": 0.01182, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39969, "top5_acc": 0.66219, "loss_cls": 3.3667, "loss": 3.3667, "time": 0.80699} +{"mode": "train", "epoch": 117, "iter": 1900, "lr": 0.01181, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39844, "top5_acc": 0.65312, "loss_cls": 3.40378, "loss": 3.40378, "time": 0.81915} +{"mode": "train", "epoch": 117, "iter": 2000, "lr": 0.01179, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38844, "top5_acc": 0.65141, "loss_cls": 3.42267, "loss": 3.42267, "time": 0.81617} +{"mode": "train", "epoch": 117, "iter": 2100, "lr": 0.01177, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39609, "top5_acc": 0.65922, "loss_cls": 3.39608, "loss": 3.39608, "time": 0.81031} +{"mode": "train", "epoch": 117, "iter": 2200, "lr": 0.01175, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39172, "top5_acc": 0.6525, "loss_cls": 3.42096, "loss": 3.42096, "time": 0.81464} +{"mode": "train", "epoch": 117, "iter": 2300, "lr": 0.01173, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40094, "top5_acc": 0.66406, "loss_cls": 3.37636, "loss": 3.37636, "time": 0.81359} +{"mode": "train", "epoch": 117, "iter": 2400, "lr": 0.01172, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39375, "top5_acc": 0.64984, "loss_cls": 3.41566, "loss": 3.41566, "time": 0.81533} +{"mode": "train", "epoch": 117, "iter": 2500, "lr": 0.0117, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38906, "top5_acc": 0.65125, "loss_cls": 3.4343, "loss": 3.4343, "time": 0.81103} +{"mode": "train", "epoch": 117, "iter": 2600, "lr": 0.01168, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40094, "top5_acc": 0.65844, "loss_cls": 3.39207, "loss": 3.39207, "time": 0.8151} +{"mode": "train", "epoch": 117, "iter": 2700, "lr": 0.01166, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39812, "top5_acc": 0.65453, "loss_cls": 3.39832, "loss": 3.39832, "time": 0.80941} +{"mode": "train", "epoch": 117, "iter": 2800, "lr": 0.01164, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39344, "top5_acc": 0.66062, "loss_cls": 3.40503, "loss": 3.40503, "time": 0.80918} +{"mode": "train", "epoch": 117, "iter": 2900, "lr": 0.01163, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40734, "top5_acc": 0.66125, "loss_cls": 3.36085, "loss": 3.36085, "time": 0.80945} +{"mode": "train", "epoch": 117, "iter": 3000, "lr": 0.01161, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39219, "top5_acc": 0.65297, "loss_cls": 3.39626, "loss": 3.39626, "time": 0.81141} +{"mode": "train", "epoch": 117, "iter": 3100, "lr": 0.01159, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39391, "top5_acc": 0.64531, "loss_cls": 3.43271, "loss": 3.43271, "time": 0.81622} +{"mode": "train", "epoch": 117, "iter": 3200, "lr": 0.01157, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39016, "top5_acc": 0.64469, "loss_cls": 3.41106, "loss": 3.41106, "time": 0.81649} +{"mode": "train", "epoch": 117, "iter": 3300, "lr": 0.01155, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.38594, "top5_acc": 0.65047, "loss_cls": 3.43952, "loss": 3.43952, "time": 0.80641} +{"mode": "train", "epoch": 117, "iter": 3400, "lr": 0.01154, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3975, "top5_acc": 0.66, "loss_cls": 3.37876, "loss": 3.37876, "time": 0.81218} +{"mode": "train", "epoch": 117, "iter": 3500, "lr": 0.01152, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.38812, "top5_acc": 0.65031, "loss_cls": 3.41026, "loss": 3.41026, "time": 0.80784} +{"mode": "train", "epoch": 117, "iter": 3600, "lr": 0.0115, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4, "top5_acc": 0.65812, "loss_cls": 3.39136, "loss": 3.39136, "time": 0.80766} +{"mode": "train", "epoch": 117, "iter": 3700, "lr": 0.01148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39516, "top5_acc": 0.665, "loss_cls": 3.38351, "loss": 3.38351, "time": 0.81162} +{"mode": "val", "epoch": 117, "iter": 309, "lr": 0.01147, "top1_acc": 0.31378, "top5_acc": 0.57367, "mean_class_accuracy": 0.3136} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.01146, "memory": 15990, "data_time": 1.32537, "top1_acc": 0.40828, "top5_acc": 0.67375, "loss_cls": 3.28104, "loss": 3.28104, "time": 2.30105} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.01144, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41188, "top5_acc": 0.67297, "loss_cls": 3.30203, "loss": 3.30203, "time": 0.81896} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.01142, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41859, "top5_acc": 0.67375, "loss_cls": 3.29177, "loss": 3.29177, "time": 0.81466} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.0114, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40531, "top5_acc": 0.66844, "loss_cls": 3.32114, "loss": 3.32114, "time": 0.81084} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.01139, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40109, "top5_acc": 0.66438, "loss_cls": 3.3688, "loss": 3.3688, "time": 0.81721} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.01137, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40375, "top5_acc": 0.66734, "loss_cls": 3.33952, "loss": 3.33952, "time": 0.811} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.01135, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39281, "top5_acc": 0.66625, "loss_cls": 3.38274, "loss": 3.38274, "time": 0.80985} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.01133, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.38953, "top5_acc": 0.65203, "loss_cls": 3.39873, "loss": 3.39873, "time": 0.80717} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.01131, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41, "top5_acc": 0.66594, "loss_cls": 3.33466, "loss": 3.33466, "time": 0.81176} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.0113, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.39781, "top5_acc": 0.65734, "loss_cls": 3.37689, "loss": 3.37689, "time": 0.81079} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.01128, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39016, "top5_acc": 0.64734, "loss_cls": 3.38907, "loss": 3.38907, "time": 0.80879} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.01126, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40562, "top5_acc": 0.66797, "loss_cls": 3.33773, "loss": 3.33773, "time": 0.81874} +{"mode": "train", "epoch": 118, "iter": 1300, "lr": 0.01124, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3975, "top5_acc": 0.655, "loss_cls": 3.39358, "loss": 3.39358, "time": 0.81401} +{"mode": "train", "epoch": 118, "iter": 1400, "lr": 0.01123, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39953, "top5_acc": 0.66953, "loss_cls": 3.34736, "loss": 3.34736, "time": 0.81092} +{"mode": "train", "epoch": 118, "iter": 1500, "lr": 0.01121, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38688, "top5_acc": 0.64734, "loss_cls": 3.42754, "loss": 3.42754, "time": 0.81471} +{"mode": "train", "epoch": 118, "iter": 1600, "lr": 0.01119, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39531, "top5_acc": 0.65344, "loss_cls": 3.4098, "loss": 3.4098, "time": 0.81055} +{"mode": "train", "epoch": 118, "iter": 1700, "lr": 0.01117, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39562, "top5_acc": 0.66734, "loss_cls": 3.35964, "loss": 3.35964, "time": 0.81743} +{"mode": "train", "epoch": 118, "iter": 1800, "lr": 0.01116, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39734, "top5_acc": 0.65641, "loss_cls": 3.39654, "loss": 3.39654, "time": 0.80954} +{"mode": "train", "epoch": 118, "iter": 1900, "lr": 0.01114, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40656, "top5_acc": 0.65984, "loss_cls": 3.34071, "loss": 3.34071, "time": 0.8142} +{"mode": "train", "epoch": 118, "iter": 2000, "lr": 0.01112, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39875, "top5_acc": 0.66594, "loss_cls": 3.34834, "loss": 3.34834, "time": 0.81888} +{"mode": "train", "epoch": 118, "iter": 2100, "lr": 0.0111, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40109, "top5_acc": 0.65578, "loss_cls": 3.38378, "loss": 3.38378, "time": 0.8127} +{"mode": "train", "epoch": 118, "iter": 2200, "lr": 0.01109, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39938, "top5_acc": 0.6575, "loss_cls": 3.36044, "loss": 3.36044, "time": 0.81386} +{"mode": "train", "epoch": 118, "iter": 2300, "lr": 0.01107, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39906, "top5_acc": 0.66188, "loss_cls": 3.36482, "loss": 3.36482, "time": 0.81668} +{"mode": "train", "epoch": 118, "iter": 2400, "lr": 0.01105, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40328, "top5_acc": 0.66078, "loss_cls": 3.36116, "loss": 3.36116, "time": 0.8125} +{"mode": "train", "epoch": 118, "iter": 2500, "lr": 0.01103, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38969, "top5_acc": 0.64797, "loss_cls": 3.42498, "loss": 3.42498, "time": 0.81405} +{"mode": "train", "epoch": 118, "iter": 2600, "lr": 0.01102, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39781, "top5_acc": 0.65688, "loss_cls": 3.40414, "loss": 3.40414, "time": 0.81155} +{"mode": "train", "epoch": 118, "iter": 2700, "lr": 0.011, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39047, "top5_acc": 0.64953, "loss_cls": 3.42242, "loss": 3.42242, "time": 0.81151} +{"mode": "train", "epoch": 118, "iter": 2800, "lr": 0.01098, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40359, "top5_acc": 0.65906, "loss_cls": 3.36947, "loss": 3.36947, "time": 0.8184} +{"mode": "train", "epoch": 118, "iter": 2900, "lr": 0.01096, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39359, "top5_acc": 0.65781, "loss_cls": 3.37186, "loss": 3.37186, "time": 0.81075} +{"mode": "train", "epoch": 118, "iter": 3000, "lr": 0.01095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39734, "top5_acc": 0.66, "loss_cls": 3.36234, "loss": 3.36234, "time": 0.80894} +{"mode": "train", "epoch": 118, "iter": 3100, "lr": 0.01093, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40016, "top5_acc": 0.66156, "loss_cls": 3.35076, "loss": 3.35076, "time": 0.81192} +{"mode": "train", "epoch": 118, "iter": 3200, "lr": 0.01091, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39578, "top5_acc": 0.66531, "loss_cls": 3.36904, "loss": 3.36904, "time": 0.80707} +{"mode": "train", "epoch": 118, "iter": 3300, "lr": 0.01089, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.39734, "top5_acc": 0.65875, "loss_cls": 3.40342, "loss": 3.40342, "time": 0.81382} +{"mode": "train", "epoch": 118, "iter": 3400, "lr": 0.01088, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38531, "top5_acc": 0.65984, "loss_cls": 3.40296, "loss": 3.40296, "time": 0.81092} +{"mode": "train", "epoch": 118, "iter": 3500, "lr": 0.01086, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.40219, "top5_acc": 0.65344, "loss_cls": 3.3844, "loss": 3.3844, "time": 0.81083} +{"mode": "train", "epoch": 118, "iter": 3600, "lr": 0.01084, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.39141, "top5_acc": 0.64859, "loss_cls": 3.41753, "loss": 3.41753, "time": 0.80746} +{"mode": "train", "epoch": 118, "iter": 3700, "lr": 0.01082, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40562, "top5_acc": 0.66312, "loss_cls": 3.32008, "loss": 3.32008, "time": 0.80668} +{"mode": "val", "epoch": 118, "iter": 309, "lr": 0.01082, "top1_acc": 0.32862, "top5_acc": 0.58608, "mean_class_accuracy": 0.32845} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.0108, "memory": 15990, "data_time": 1.3064, "top1_acc": 0.42141, "top5_acc": 0.67906, "loss_cls": 3.24659, "loss": 3.24659, "time": 2.2838} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.01078, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41672, "top5_acc": 0.68641, "loss_cls": 3.2317, "loss": 3.2317, "time": 0.81944} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.01076, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41156, "top5_acc": 0.66672, "loss_cls": 3.28836, "loss": 3.28836, "time": 0.82283} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.01075, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4025, "top5_acc": 0.66125, "loss_cls": 3.35518, "loss": 3.35518, "time": 0.81453} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.01073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41562, "top5_acc": 0.67859, "loss_cls": 3.28499, "loss": 3.28499, "time": 0.81768} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.01071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39969, "top5_acc": 0.66625, "loss_cls": 3.33589, "loss": 3.33589, "time": 0.81331} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.01069, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40391, "top5_acc": 0.66797, "loss_cls": 3.32119, "loss": 3.32119, "time": 0.81351} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.01068, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39516, "top5_acc": 0.66609, "loss_cls": 3.33833, "loss": 3.33833, "time": 0.81298} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.01066, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40594, "top5_acc": 0.66016, "loss_cls": 3.34511, "loss": 3.34511, "time": 0.81044} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.01064, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40875, "top5_acc": 0.67094, "loss_cls": 3.28669, "loss": 3.28669, "time": 0.81326} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.01063, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39516, "top5_acc": 0.665, "loss_cls": 3.34705, "loss": 3.34705, "time": 0.80826} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.01061, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41281, "top5_acc": 0.67031, "loss_cls": 3.2999, "loss": 3.2999, "time": 0.81209} +{"mode": "train", "epoch": 119, "iter": 1300, "lr": 0.01059, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41047, "top5_acc": 0.6725, "loss_cls": 3.27836, "loss": 3.27836, "time": 0.81314} +{"mode": "train", "epoch": 119, "iter": 1400, "lr": 0.01057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40766, "top5_acc": 0.67156, "loss_cls": 3.31701, "loss": 3.31701, "time": 0.81254} +{"mode": "train", "epoch": 119, "iter": 1500, "lr": 0.01056, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40484, "top5_acc": 0.66719, "loss_cls": 3.31573, "loss": 3.31573, "time": 0.81238} +{"mode": "train", "epoch": 119, "iter": 1600, "lr": 0.01054, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40562, "top5_acc": 0.67219, "loss_cls": 3.32502, "loss": 3.32502, "time": 0.81337} +{"mode": "train", "epoch": 119, "iter": 1700, "lr": 0.01052, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41078, "top5_acc": 0.66656, "loss_cls": 3.33325, "loss": 3.33325, "time": 0.81897} +{"mode": "train", "epoch": 119, "iter": 1800, "lr": 0.0105, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41062, "top5_acc": 0.66406, "loss_cls": 3.3232, "loss": 3.3232, "time": 0.81763} +{"mode": "train", "epoch": 119, "iter": 1900, "lr": 0.01049, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39844, "top5_acc": 0.65703, "loss_cls": 3.37185, "loss": 3.37185, "time": 0.81278} +{"mode": "train", "epoch": 119, "iter": 2000, "lr": 0.01047, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39688, "top5_acc": 0.66422, "loss_cls": 3.33146, "loss": 3.33146, "time": 0.82228} +{"mode": "train", "epoch": 119, "iter": 2100, "lr": 0.01045, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38938, "top5_acc": 0.65375, "loss_cls": 3.41998, "loss": 3.41998, "time": 0.81468} +{"mode": "train", "epoch": 119, "iter": 2200, "lr": 0.01044, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40453, "top5_acc": 0.66312, "loss_cls": 3.34947, "loss": 3.34947, "time": 0.81093} +{"mode": "train", "epoch": 119, "iter": 2300, "lr": 0.01042, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40125, "top5_acc": 0.65016, "loss_cls": 3.39029, "loss": 3.39029, "time": 0.81631} +{"mode": "train", "epoch": 119, "iter": 2400, "lr": 0.0104, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39484, "top5_acc": 0.65734, "loss_cls": 3.39137, "loss": 3.39137, "time": 0.81384} +{"mode": "train", "epoch": 119, "iter": 2500, "lr": 0.01039, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38984, "top5_acc": 0.65766, "loss_cls": 3.38853, "loss": 3.38853, "time": 0.81418} +{"mode": "train", "epoch": 119, "iter": 2600, "lr": 0.01037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40172, "top5_acc": 0.67188, "loss_cls": 3.3507, "loss": 3.3507, "time": 0.81704} +{"mode": "train", "epoch": 119, "iter": 2700, "lr": 0.01035, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39797, "top5_acc": 0.65594, "loss_cls": 3.38129, "loss": 3.38129, "time": 0.8127} +{"mode": "train", "epoch": 119, "iter": 2800, "lr": 0.01033, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40953, "top5_acc": 0.67031, "loss_cls": 3.31892, "loss": 3.31892, "time": 0.81265} +{"mode": "train", "epoch": 119, "iter": 2900, "lr": 0.01032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40484, "top5_acc": 0.66188, "loss_cls": 3.36146, "loss": 3.36146, "time": 0.80871} +{"mode": "train", "epoch": 119, "iter": 3000, "lr": 0.0103, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40203, "top5_acc": 0.65688, "loss_cls": 3.39464, "loss": 3.39464, "time": 0.81475} +{"mode": "train", "epoch": 119, "iter": 3100, "lr": 0.01028, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40266, "top5_acc": 0.66922, "loss_cls": 3.30978, "loss": 3.30978, "time": 0.80594} +{"mode": "train", "epoch": 119, "iter": 3200, "lr": 0.01027, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40203, "top5_acc": 0.65734, "loss_cls": 3.36207, "loss": 3.36207, "time": 0.80916} +{"mode": "train", "epoch": 119, "iter": 3300, "lr": 0.01025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39641, "top5_acc": 0.65781, "loss_cls": 3.36918, "loss": 3.36918, "time": 0.81602} +{"mode": "train", "epoch": 119, "iter": 3400, "lr": 0.01023, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40516, "top5_acc": 0.66328, "loss_cls": 3.37359, "loss": 3.37359, "time": 0.8148} +{"mode": "train", "epoch": 119, "iter": 3500, "lr": 0.01022, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39391, "top5_acc": 0.65859, "loss_cls": 3.38849, "loss": 3.38849, "time": 0.8143} +{"mode": "train", "epoch": 119, "iter": 3600, "lr": 0.0102, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40328, "top5_acc": 0.66578, "loss_cls": 3.35377, "loss": 3.35377, "time": 0.81658} +{"mode": "train", "epoch": 119, "iter": 3700, "lr": 0.01018, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39125, "top5_acc": 0.65969, "loss_cls": 3.39645, "loss": 3.39645, "time": 0.80993} +{"mode": "val", "epoch": 119, "iter": 309, "lr": 0.01017, "top1_acc": 0.33085, "top5_acc": 0.58841, "mean_class_accuracy": 0.33063} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.01016, "memory": 15990, "data_time": 1.28307, "top1_acc": 0.41984, "top5_acc": 0.68859, "loss_cls": 3.20967, "loss": 3.20967, "time": 2.25683} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.01014, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41719, "top5_acc": 0.68141, "loss_cls": 3.24852, "loss": 3.24852, "time": 0.81932} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.01012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41547, "top5_acc": 0.6725, "loss_cls": 3.27838, "loss": 3.27838, "time": 0.81339} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.01011, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42109, "top5_acc": 0.68203, "loss_cls": 3.23454, "loss": 3.23454, "time": 0.81347} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.01009, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40781, "top5_acc": 0.67281, "loss_cls": 3.28566, "loss": 3.28566, "time": 0.80723} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.01007, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42297, "top5_acc": 0.67969, "loss_cls": 3.27769, "loss": 3.27769, "time": 0.80816} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.01006, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39828, "top5_acc": 0.66438, "loss_cls": 3.35386, "loss": 3.35386, "time": 0.81329} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.01004, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40453, "top5_acc": 0.665, "loss_cls": 3.33839, "loss": 3.33839, "time": 0.80833} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.01002, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4175, "top5_acc": 0.67, "loss_cls": 3.27515, "loss": 3.27515, "time": 0.81367} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.01001, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4125, "top5_acc": 0.67859, "loss_cls": 3.29833, "loss": 3.29833, "time": 0.80854} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41078, "top5_acc": 0.67312, "loss_cls": 3.29893, "loss": 3.29893, "time": 0.81079} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.00997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40766, "top5_acc": 0.66547, "loss_cls": 3.27686, "loss": 3.27686, "time": 0.81303} +{"mode": "train", "epoch": 120, "iter": 1300, "lr": 0.00996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39938, "top5_acc": 0.66594, "loss_cls": 3.34008, "loss": 3.34008, "time": 0.81053} +{"mode": "train", "epoch": 120, "iter": 1400, "lr": 0.00994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40031, "top5_acc": 0.66766, "loss_cls": 3.34138, "loss": 3.34138, "time": 0.81686} +{"mode": "train", "epoch": 120, "iter": 1500, "lr": 0.00992, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.41359, "top5_acc": 0.68266, "loss_cls": 3.27443, "loss": 3.27443, "time": 0.81259} +{"mode": "train", "epoch": 120, "iter": 1600, "lr": 0.0099, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40312, "top5_acc": 0.65953, "loss_cls": 3.37573, "loss": 3.37573, "time": 0.80915} +{"mode": "train", "epoch": 120, "iter": 1700, "lr": 0.00989, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40609, "top5_acc": 0.66906, "loss_cls": 3.31557, "loss": 3.31557, "time": 0.81146} +{"mode": "train", "epoch": 120, "iter": 1800, "lr": 0.00987, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.40469, "top5_acc": 0.66109, "loss_cls": 3.33959, "loss": 3.33959, "time": 0.81051} +{"mode": "train", "epoch": 120, "iter": 1900, "lr": 0.00985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39438, "top5_acc": 0.6575, "loss_cls": 3.37821, "loss": 3.37821, "time": 0.81199} +{"mode": "train", "epoch": 120, "iter": 2000, "lr": 0.00984, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40672, "top5_acc": 0.66656, "loss_cls": 3.33714, "loss": 3.33714, "time": 0.8104} +{"mode": "train", "epoch": 120, "iter": 2100, "lr": 0.00982, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.4075, "top5_acc": 0.67469, "loss_cls": 3.32101, "loss": 3.32101, "time": 0.81714} +{"mode": "train", "epoch": 120, "iter": 2200, "lr": 0.0098, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40828, "top5_acc": 0.66859, "loss_cls": 3.30262, "loss": 3.30262, "time": 0.81316} +{"mode": "train", "epoch": 120, "iter": 2300, "lr": 0.00979, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40312, "top5_acc": 0.66875, "loss_cls": 3.33383, "loss": 3.33383, "time": 0.81157} +{"mode": "train", "epoch": 120, "iter": 2400, "lr": 0.00977, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40844, "top5_acc": 0.67109, "loss_cls": 3.32592, "loss": 3.32592, "time": 0.81753} +{"mode": "train", "epoch": 120, "iter": 2500, "lr": 0.00976, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39844, "top5_acc": 0.65766, "loss_cls": 3.33818, "loss": 3.33818, "time": 0.81104} +{"mode": "train", "epoch": 120, "iter": 2600, "lr": 0.00974, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40516, "top5_acc": 0.66344, "loss_cls": 3.32804, "loss": 3.32804, "time": 0.81401} +{"mode": "train", "epoch": 120, "iter": 2700, "lr": 0.00972, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40672, "top5_acc": 0.65781, "loss_cls": 3.35276, "loss": 3.35276, "time": 0.81627} +{"mode": "train", "epoch": 120, "iter": 2800, "lr": 0.00971, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40656, "top5_acc": 0.65844, "loss_cls": 3.34372, "loss": 3.34372, "time": 0.80939} +{"mode": "train", "epoch": 120, "iter": 2900, "lr": 0.00969, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41047, "top5_acc": 0.67188, "loss_cls": 3.31682, "loss": 3.31682, "time": 0.81777} +{"mode": "train", "epoch": 120, "iter": 3000, "lr": 0.00967, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40688, "top5_acc": 0.66516, "loss_cls": 3.34479, "loss": 3.34479, "time": 0.81182} +{"mode": "train", "epoch": 120, "iter": 3100, "lr": 0.00966, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3975, "top5_acc": 0.65719, "loss_cls": 3.36754, "loss": 3.36754, "time": 0.8123} +{"mode": "train", "epoch": 120, "iter": 3200, "lr": 0.00964, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41078, "top5_acc": 0.66625, "loss_cls": 3.32863, "loss": 3.32863, "time": 0.81161} +{"mode": "train", "epoch": 120, "iter": 3300, "lr": 0.00962, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40672, "top5_acc": 0.66406, "loss_cls": 3.32568, "loss": 3.32568, "time": 0.81339} +{"mode": "train", "epoch": 120, "iter": 3400, "lr": 0.00961, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4125, "top5_acc": 0.66969, "loss_cls": 3.29133, "loss": 3.29133, "time": 0.81299} +{"mode": "train", "epoch": 120, "iter": 3500, "lr": 0.00959, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40516, "top5_acc": 0.67281, "loss_cls": 3.31941, "loss": 3.31941, "time": 0.81182} +{"mode": "train", "epoch": 120, "iter": 3600, "lr": 0.00957, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39609, "top5_acc": 0.66578, "loss_cls": 3.34219, "loss": 3.34219, "time": 0.80666} +{"mode": "train", "epoch": 120, "iter": 3700, "lr": 0.00956, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40172, "top5_acc": 0.66672, "loss_cls": 3.32117, "loss": 3.32117, "time": 0.81149} +{"mode": "val", "epoch": 120, "iter": 309, "lr": 0.00955, "top1_acc": 0.34392, "top5_acc": 0.59905, "mean_class_accuracy": 0.34382} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00953, "memory": 15990, "data_time": 1.30723, "top1_acc": 0.43594, "top5_acc": 0.68688, "loss_cls": 3.19273, "loss": 3.19273, "time": 2.28468} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00952, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42203, "top5_acc": 0.67703, "loss_cls": 3.2353, "loss": 3.2353, "time": 0.82289} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.0095, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41188, "top5_acc": 0.67859, "loss_cls": 3.27273, "loss": 3.27273, "time": 0.81561} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00948, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41203, "top5_acc": 0.67703, "loss_cls": 3.26114, "loss": 3.26114, "time": 0.81598} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00947, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41172, "top5_acc": 0.67391, "loss_cls": 3.30783, "loss": 3.30783, "time": 0.81634} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00945, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42094, "top5_acc": 0.68047, "loss_cls": 3.25559, "loss": 3.25559, "time": 0.81037} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.00943, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40828, "top5_acc": 0.68156, "loss_cls": 3.26474, "loss": 3.26474, "time": 0.81814} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00942, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42031, "top5_acc": 0.6825, "loss_cls": 3.25454, "loss": 3.25454, "time": 0.81417} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.0094, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40312, "top5_acc": 0.67266, "loss_cls": 3.30201, "loss": 3.30201, "time": 0.81408} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00939, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.41594, "top5_acc": 0.67484, "loss_cls": 3.28484, "loss": 3.28484, "time": 0.81152} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00937, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41484, "top5_acc": 0.66969, "loss_cls": 3.29338, "loss": 3.29338, "time": 0.80841} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00935, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41781, "top5_acc": 0.68109, "loss_cls": 3.24172, "loss": 3.24172, "time": 0.80818} +{"mode": "train", "epoch": 121, "iter": 1300, "lr": 0.00934, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42031, "top5_acc": 0.68141, "loss_cls": 3.2207, "loss": 3.2207, "time": 0.80759} +{"mode": "train", "epoch": 121, "iter": 1400, "lr": 0.00932, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41078, "top5_acc": 0.67125, "loss_cls": 3.28097, "loss": 3.28097, "time": 0.81421} +{"mode": "train", "epoch": 121, "iter": 1500, "lr": 0.0093, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40828, "top5_acc": 0.66766, "loss_cls": 3.29694, "loss": 3.29694, "time": 0.81211} +{"mode": "train", "epoch": 121, "iter": 1600, "lr": 0.00929, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41844, "top5_acc": 0.67469, "loss_cls": 3.26473, "loss": 3.26473, "time": 0.81497} +{"mode": "train", "epoch": 121, "iter": 1700, "lr": 0.00927, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40938, "top5_acc": 0.67641, "loss_cls": 3.27749, "loss": 3.27749, "time": 0.81126} +{"mode": "train", "epoch": 121, "iter": 1800, "lr": 0.00926, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41078, "top5_acc": 0.67141, "loss_cls": 3.29145, "loss": 3.29145, "time": 0.81281} +{"mode": "train", "epoch": 121, "iter": 1900, "lr": 0.00924, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41297, "top5_acc": 0.66719, "loss_cls": 3.31744, "loss": 3.31744, "time": 0.81299} +{"mode": "train", "epoch": 121, "iter": 2000, "lr": 0.00922, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4025, "top5_acc": 0.67203, "loss_cls": 3.30663, "loss": 3.30663, "time": 0.81576} +{"mode": "train", "epoch": 121, "iter": 2100, "lr": 0.00921, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40031, "top5_acc": 0.66297, "loss_cls": 3.36044, "loss": 3.36044, "time": 0.82147} +{"mode": "train", "epoch": 121, "iter": 2200, "lr": 0.00919, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40547, "top5_acc": 0.66609, "loss_cls": 3.29855, "loss": 3.29855, "time": 0.82303} +{"mode": "train", "epoch": 121, "iter": 2300, "lr": 0.00917, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40781, "top5_acc": 0.66391, "loss_cls": 3.32489, "loss": 3.32489, "time": 0.81611} +{"mode": "train", "epoch": 121, "iter": 2400, "lr": 0.00916, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.405, "top5_acc": 0.66109, "loss_cls": 3.35268, "loss": 3.35268, "time": 0.81664} +{"mode": "train", "epoch": 121, "iter": 2500, "lr": 0.00914, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41016, "top5_acc": 0.67156, "loss_cls": 3.29294, "loss": 3.29294, "time": 0.81468} +{"mode": "train", "epoch": 121, "iter": 2600, "lr": 0.00913, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40891, "top5_acc": 0.66641, "loss_cls": 3.30134, "loss": 3.30134, "time": 0.80801} +{"mode": "train", "epoch": 121, "iter": 2700, "lr": 0.00911, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40953, "top5_acc": 0.66734, "loss_cls": 3.29273, "loss": 3.29273, "time": 0.81203} +{"mode": "train", "epoch": 121, "iter": 2800, "lr": 0.00909, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42344, "top5_acc": 0.67172, "loss_cls": 3.28809, "loss": 3.28809, "time": 0.8086} +{"mode": "train", "epoch": 121, "iter": 2900, "lr": 0.00908, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41234, "top5_acc": 0.665, "loss_cls": 3.29589, "loss": 3.29589, "time": 0.8084} +{"mode": "train", "epoch": 121, "iter": 3000, "lr": 0.00906, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41062, "top5_acc": 0.66656, "loss_cls": 3.3238, "loss": 3.3238, "time": 0.80995} +{"mode": "train", "epoch": 121, "iter": 3100, "lr": 0.00905, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40453, "top5_acc": 0.66578, "loss_cls": 3.31816, "loss": 3.31816, "time": 0.80976} +{"mode": "train", "epoch": 121, "iter": 3200, "lr": 0.00903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41359, "top5_acc": 0.67391, "loss_cls": 3.28723, "loss": 3.28723, "time": 0.81109} +{"mode": "train", "epoch": 121, "iter": 3300, "lr": 0.00901, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.40812, "top5_acc": 0.67125, "loss_cls": 3.32644, "loss": 3.32644, "time": 0.81122} +{"mode": "train", "epoch": 121, "iter": 3400, "lr": 0.009, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41062, "top5_acc": 0.67531, "loss_cls": 3.28366, "loss": 3.28366, "time": 0.81695} +{"mode": "train", "epoch": 121, "iter": 3500, "lr": 0.00898, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40203, "top5_acc": 0.67359, "loss_cls": 3.30819, "loss": 3.30819, "time": 0.8088} +{"mode": "train", "epoch": 121, "iter": 3600, "lr": 0.00897, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40641, "top5_acc": 0.67016, "loss_cls": 3.32014, "loss": 3.32014, "time": 0.81188} +{"mode": "train", "epoch": 121, "iter": 3700, "lr": 0.00895, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.41016, "top5_acc": 0.66469, "loss_cls": 3.31349, "loss": 3.31349, "time": 0.81407} +{"mode": "val", "epoch": 121, "iter": 309, "lr": 0.00894, "top1_acc": 0.34848, "top5_acc": 0.6071, "mean_class_accuracy": 0.34827} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00893, "memory": 15990, "data_time": 1.30471, "top1_acc": 0.41344, "top5_acc": 0.67703, "loss_cls": 3.24238, "loss": 3.24238, "time": 2.27132} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00891, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42578, "top5_acc": 0.67891, "loss_cls": 3.2278, "loss": 3.2278, "time": 0.81181} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.00889, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43031, "top5_acc": 0.69, "loss_cls": 3.17909, "loss": 3.17909, "time": 0.81847} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00888, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43, "top5_acc": 0.69156, "loss_cls": 3.19046, "loss": 3.19046, "time": 0.81135} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00886, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42766, "top5_acc": 0.68453, "loss_cls": 3.20165, "loss": 3.20165, "time": 0.81041} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00885, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41594, "top5_acc": 0.67875, "loss_cls": 3.2424, "loss": 3.2424, "time": 0.81405} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00883, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40984, "top5_acc": 0.67766, "loss_cls": 3.26716, "loss": 3.26716, "time": 0.81248} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00882, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.42328, "top5_acc": 0.68312, "loss_cls": 3.21649, "loss": 3.21649, "time": 0.81143} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.0088, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41281, "top5_acc": 0.67828, "loss_cls": 3.25993, "loss": 3.25993, "time": 0.81105} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00878, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41766, "top5_acc": 0.68, "loss_cls": 3.25275, "loss": 3.25275, "time": 0.81033} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00877, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41484, "top5_acc": 0.67125, "loss_cls": 3.31951, "loss": 3.31951, "time": 0.80708} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.00875, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42156, "top5_acc": 0.6725, "loss_cls": 3.2687, "loss": 3.2687, "time": 0.81336} +{"mode": "train", "epoch": 122, "iter": 1300, "lr": 0.00874, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41875, "top5_acc": 0.68594, "loss_cls": 3.22499, "loss": 3.22499, "time": 0.81606} +{"mode": "train", "epoch": 122, "iter": 1400, "lr": 0.00872, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42328, "top5_acc": 0.68219, "loss_cls": 3.23465, "loss": 3.23465, "time": 0.81233} +{"mode": "train", "epoch": 122, "iter": 1500, "lr": 0.0087, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41203, "top5_acc": 0.67484, "loss_cls": 3.29431, "loss": 3.29431, "time": 0.81675} +{"mode": "train", "epoch": 122, "iter": 1600, "lr": 0.00869, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42016, "top5_acc": 0.67688, "loss_cls": 3.25984, "loss": 3.25984, "time": 0.81425} +{"mode": "train", "epoch": 122, "iter": 1700, "lr": 0.00867, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40766, "top5_acc": 0.66547, "loss_cls": 3.30139, "loss": 3.30139, "time": 0.80499} +{"mode": "train", "epoch": 122, "iter": 1800, "lr": 0.00866, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41812, "top5_acc": 0.67578, "loss_cls": 3.2901, "loss": 3.2901, "time": 0.81424} +{"mode": "train", "epoch": 122, "iter": 1900, "lr": 0.00864, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41516, "top5_acc": 0.67688, "loss_cls": 3.26907, "loss": 3.26907, "time": 0.81284} +{"mode": "train", "epoch": 122, "iter": 2000, "lr": 0.00863, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4225, "top5_acc": 0.68625, "loss_cls": 3.20915, "loss": 3.20915, "time": 0.80711} +{"mode": "train", "epoch": 122, "iter": 2100, "lr": 0.00861, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41422, "top5_acc": 0.67078, "loss_cls": 3.29817, "loss": 3.29817, "time": 0.81629} +{"mode": "train", "epoch": 122, "iter": 2200, "lr": 0.00859, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42938, "top5_acc": 0.68922, "loss_cls": 3.20105, "loss": 3.20105, "time": 0.81418} +{"mode": "train", "epoch": 122, "iter": 2300, "lr": 0.00858, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41656, "top5_acc": 0.67812, "loss_cls": 3.26581, "loss": 3.26581, "time": 0.82009} +{"mode": "train", "epoch": 122, "iter": 2400, "lr": 0.00856, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41344, "top5_acc": 0.66938, "loss_cls": 3.25767, "loss": 3.25767, "time": 0.8166} +{"mode": "train", "epoch": 122, "iter": 2500, "lr": 0.00855, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41938, "top5_acc": 0.67156, "loss_cls": 3.25832, "loss": 3.25832, "time": 0.81575} +{"mode": "train", "epoch": 122, "iter": 2600, "lr": 0.00853, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40906, "top5_acc": 0.66766, "loss_cls": 3.315, "loss": 3.315, "time": 0.81246} +{"mode": "train", "epoch": 122, "iter": 2700, "lr": 0.00852, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40953, "top5_acc": 0.6725, "loss_cls": 3.30519, "loss": 3.30519, "time": 0.82114} +{"mode": "train", "epoch": 122, "iter": 2800, "lr": 0.0085, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4175, "top5_acc": 0.67547, "loss_cls": 3.25567, "loss": 3.25567, "time": 0.81612} +{"mode": "train", "epoch": 122, "iter": 2900, "lr": 0.00849, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42125, "top5_acc": 0.67312, "loss_cls": 3.28254, "loss": 3.28254, "time": 0.80996} +{"mode": "train", "epoch": 122, "iter": 3000, "lr": 0.00847, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41328, "top5_acc": 0.68531, "loss_cls": 3.24474, "loss": 3.24474, "time": 0.809} +{"mode": "train", "epoch": 122, "iter": 3100, "lr": 0.00845, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39781, "top5_acc": 0.66766, "loss_cls": 3.34341, "loss": 3.34341, "time": 0.80806} +{"mode": "train", "epoch": 122, "iter": 3200, "lr": 0.00844, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41594, "top5_acc": 0.67672, "loss_cls": 3.2579, "loss": 3.2579, "time": 0.81235} +{"mode": "train", "epoch": 122, "iter": 3300, "lr": 0.00842, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41031, "top5_acc": 0.68, "loss_cls": 3.27295, "loss": 3.27295, "time": 0.80784} +{"mode": "train", "epoch": 122, "iter": 3400, "lr": 0.00841, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41266, "top5_acc": 0.67828, "loss_cls": 3.25582, "loss": 3.25582, "time": 0.809} +{"mode": "train", "epoch": 122, "iter": 3500, "lr": 0.00839, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40344, "top5_acc": 0.67969, "loss_cls": 3.28453, "loss": 3.28453, "time": 0.81062} +{"mode": "train", "epoch": 122, "iter": 3600, "lr": 0.00838, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.40406, "top5_acc": 0.67, "loss_cls": 3.28681, "loss": 3.28681, "time": 0.8176} +{"mode": "train", "epoch": 122, "iter": 3700, "lr": 0.00836, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42359, "top5_acc": 0.68109, "loss_cls": 3.24956, "loss": 3.24956, "time": 0.80953} +{"mode": "val", "epoch": 122, "iter": 309, "lr": 0.00835, "top1_acc": 0.33485, "top5_acc": 0.59408, "mean_class_accuracy": 0.33461} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00834, "memory": 15990, "data_time": 1.30623, "top1_acc": 0.42906, "top5_acc": 0.68859, "loss_cls": 3.17757, "loss": 3.17757, "time": 2.28032} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00832, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42141, "top5_acc": 0.68344, "loss_cls": 3.24367, "loss": 3.24367, "time": 0.81527} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00831, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43, "top5_acc": 0.69203, "loss_cls": 3.19358, "loss": 3.19358, "time": 0.8106} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00829, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42844, "top5_acc": 0.69219, "loss_cls": 3.19248, "loss": 3.19248, "time": 0.81542} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00828, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43297, "top5_acc": 0.69891, "loss_cls": 3.16779, "loss": 3.16779, "time": 0.81223} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00826, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42234, "top5_acc": 0.68, "loss_cls": 3.24069, "loss": 3.24069, "time": 0.81524} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00825, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42828, "top5_acc": 0.68594, "loss_cls": 3.20763, "loss": 3.20763, "time": 0.80872} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.00823, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43609, "top5_acc": 0.69734, "loss_cls": 3.18393, "loss": 3.18393, "time": 0.81424} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00822, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42906, "top5_acc": 0.68969, "loss_cls": 3.2001, "loss": 3.2001, "time": 0.80865} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.0082, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41031, "top5_acc": 0.67891, "loss_cls": 3.27209, "loss": 3.27209, "time": 0.80657} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00818, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41922, "top5_acc": 0.67969, "loss_cls": 3.23024, "loss": 3.23024, "time": 0.80801} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42203, "top5_acc": 0.68672, "loss_cls": 3.19059, "loss": 3.19059, "time": 0.81111} +{"mode": "train", "epoch": 123, "iter": 1300, "lr": 0.00815, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42344, "top5_acc": 0.685, "loss_cls": 3.21609, "loss": 3.21609, "time": 0.81198} +{"mode": "train", "epoch": 123, "iter": 1400, "lr": 0.00814, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41766, "top5_acc": 0.66609, "loss_cls": 3.26892, "loss": 3.26892, "time": 0.80867} +{"mode": "train", "epoch": 123, "iter": 1500, "lr": 0.00812, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42781, "top5_acc": 0.68469, "loss_cls": 3.20596, "loss": 3.20596, "time": 0.80825} +{"mode": "train", "epoch": 123, "iter": 1600, "lr": 0.00811, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42297, "top5_acc": 0.68328, "loss_cls": 3.22163, "loss": 3.22163, "time": 0.80859} +{"mode": "train", "epoch": 123, "iter": 1700, "lr": 0.00809, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42531, "top5_acc": 0.6825, "loss_cls": 3.2316, "loss": 3.2316, "time": 0.81669} +{"mode": "train", "epoch": 123, "iter": 1800, "lr": 0.00808, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40406, "top5_acc": 0.67594, "loss_cls": 3.26966, "loss": 3.26966, "time": 0.81016} +{"mode": "train", "epoch": 123, "iter": 1900, "lr": 0.00806, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42234, "top5_acc": 0.67672, "loss_cls": 3.22289, "loss": 3.22289, "time": 0.81448} +{"mode": "train", "epoch": 123, "iter": 2000, "lr": 0.00805, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42062, "top5_acc": 0.68016, "loss_cls": 3.24443, "loss": 3.24443, "time": 0.81503} +{"mode": "train", "epoch": 123, "iter": 2100, "lr": 0.00803, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42, "top5_acc": 0.68781, "loss_cls": 3.21182, "loss": 3.21182, "time": 0.82029} +{"mode": "train", "epoch": 123, "iter": 2200, "lr": 0.00802, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42094, "top5_acc": 0.67609, "loss_cls": 3.25066, "loss": 3.25066, "time": 0.81361} +{"mode": "train", "epoch": 123, "iter": 2300, "lr": 0.008, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42156, "top5_acc": 0.68188, "loss_cls": 3.23839, "loss": 3.23839, "time": 0.81473} +{"mode": "train", "epoch": 123, "iter": 2400, "lr": 0.00799, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41672, "top5_acc": 0.67797, "loss_cls": 3.23051, "loss": 3.23051, "time": 0.81688} +{"mode": "train", "epoch": 123, "iter": 2500, "lr": 0.00797, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41328, "top5_acc": 0.68188, "loss_cls": 3.24412, "loss": 3.24412, "time": 0.81533} +{"mode": "train", "epoch": 123, "iter": 2600, "lr": 0.00796, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41109, "top5_acc": 0.66734, "loss_cls": 3.3238, "loss": 3.3238, "time": 0.81658} +{"mode": "train", "epoch": 123, "iter": 2700, "lr": 0.00794, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41781, "top5_acc": 0.67844, "loss_cls": 3.24983, "loss": 3.24983, "time": 0.80916} +{"mode": "train", "epoch": 123, "iter": 2800, "lr": 0.00793, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.435, "top5_acc": 0.69328, "loss_cls": 3.18772, "loss": 3.18772, "time": 0.81459} +{"mode": "train", "epoch": 123, "iter": 2900, "lr": 0.00791, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41562, "top5_acc": 0.67172, "loss_cls": 3.28243, "loss": 3.28243, "time": 0.81181} +{"mode": "train", "epoch": 123, "iter": 3000, "lr": 0.0079, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.42094, "top5_acc": 0.68438, "loss_cls": 3.23277, "loss": 3.23277, "time": 0.80596} +{"mode": "train", "epoch": 123, "iter": 3100, "lr": 0.00788, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42188, "top5_acc": 0.67469, "loss_cls": 3.23679, "loss": 3.23679, "time": 0.81519} +{"mode": "train", "epoch": 123, "iter": 3200, "lr": 0.00787, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42078, "top5_acc": 0.68188, "loss_cls": 3.24134, "loss": 3.24134, "time": 0.81242} +{"mode": "train", "epoch": 123, "iter": 3300, "lr": 0.00785, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41234, "top5_acc": 0.67188, "loss_cls": 3.27647, "loss": 3.27647, "time": 0.81282} +{"mode": "train", "epoch": 123, "iter": 3400, "lr": 0.00784, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41938, "top5_acc": 0.69109, "loss_cls": 3.23062, "loss": 3.23062, "time": 0.811} +{"mode": "train", "epoch": 123, "iter": 3500, "lr": 0.00782, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41719, "top5_acc": 0.67625, "loss_cls": 3.26212, "loss": 3.26212, "time": 0.81217} +{"mode": "train", "epoch": 123, "iter": 3600, "lr": 0.00781, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.41406, "top5_acc": 0.67672, "loss_cls": 3.24887, "loss": 3.24887, "time": 0.81024} +{"mode": "train", "epoch": 123, "iter": 3700, "lr": 0.00779, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42656, "top5_acc": 0.68562, "loss_cls": 3.2115, "loss": 3.2115, "time": 0.80696} +{"mode": "val", "epoch": 123, "iter": 309, "lr": 0.00778, "top1_acc": 0.34382, "top5_acc": 0.60371, "mean_class_accuracy": 0.34368} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00777, "memory": 15990, "data_time": 1.25738, "top1_acc": 0.43828, "top5_acc": 0.70469, "loss_cls": 3.14006, "loss": 3.14006, "time": 2.23272} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00775, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43156, "top5_acc": 0.69984, "loss_cls": 3.13697, "loss": 3.13697, "time": 0.81063} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00774, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43969, "top5_acc": 0.69953, "loss_cls": 3.11767, "loss": 3.11767, "time": 0.81544} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.00772, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.43828, "top5_acc": 0.69969, "loss_cls": 3.14973, "loss": 3.14973, "time": 0.80558} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00771, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42469, "top5_acc": 0.69094, "loss_cls": 3.184, "loss": 3.184, "time": 0.81349} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00769, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.43641, "top5_acc": 0.70156, "loss_cls": 3.13985, "loss": 3.13985, "time": 0.80861} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00768, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42859, "top5_acc": 0.68531, "loss_cls": 3.20479, "loss": 3.20479, "time": 0.8086} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00766, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44094, "top5_acc": 0.70141, "loss_cls": 3.13169, "loss": 3.13169, "time": 0.81155} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00765, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43984, "top5_acc": 0.69344, "loss_cls": 3.14741, "loss": 3.14741, "time": 0.81226} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00763, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4275, "top5_acc": 0.69844, "loss_cls": 3.17034, "loss": 3.17034, "time": 0.81244} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00762, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43156, "top5_acc": 0.68156, "loss_cls": 3.22067, "loss": 3.22067, "time": 0.81237} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.0076, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42547, "top5_acc": 0.6825, "loss_cls": 3.21777, "loss": 3.21777, "time": 0.81356} +{"mode": "train", "epoch": 124, "iter": 1300, "lr": 0.00759, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42797, "top5_acc": 0.68344, "loss_cls": 3.21447, "loss": 3.21447, "time": 0.81441} +{"mode": "train", "epoch": 124, "iter": 1400, "lr": 0.00758, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42047, "top5_acc": 0.68141, "loss_cls": 3.23512, "loss": 3.23512, "time": 0.81554} +{"mode": "train", "epoch": 124, "iter": 1500, "lr": 0.00756, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42359, "top5_acc": 0.68891, "loss_cls": 3.18572, "loss": 3.18572, "time": 0.81123} +{"mode": "train", "epoch": 124, "iter": 1600, "lr": 0.00755, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42188, "top5_acc": 0.69234, "loss_cls": 3.19766, "loss": 3.19766, "time": 0.81253} +{"mode": "train", "epoch": 124, "iter": 1700, "lr": 0.00753, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42422, "top5_acc": 0.68797, "loss_cls": 3.21852, "loss": 3.21852, "time": 0.81829} +{"mode": "train", "epoch": 124, "iter": 1800, "lr": 0.00752, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42531, "top5_acc": 0.69062, "loss_cls": 3.2094, "loss": 3.2094, "time": 0.81089} +{"mode": "train", "epoch": 124, "iter": 1900, "lr": 0.0075, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42953, "top5_acc": 0.68766, "loss_cls": 3.18339, "loss": 3.18339, "time": 0.81062} +{"mode": "train", "epoch": 124, "iter": 2000, "lr": 0.00749, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41594, "top5_acc": 0.68266, "loss_cls": 3.23058, "loss": 3.23058, "time": 0.81544} +{"mode": "train", "epoch": 124, "iter": 2100, "lr": 0.00747, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43406, "top5_acc": 0.695, "loss_cls": 3.14896, "loss": 3.14896, "time": 0.81186} +{"mode": "train", "epoch": 124, "iter": 2200, "lr": 0.00746, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41375, "top5_acc": 0.67953, "loss_cls": 3.2702, "loss": 3.2702, "time": 0.81536} +{"mode": "train", "epoch": 124, "iter": 2300, "lr": 0.00744, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42578, "top5_acc": 0.69312, "loss_cls": 3.18373, "loss": 3.18373, "time": 0.81395} +{"mode": "train", "epoch": 124, "iter": 2400, "lr": 0.00743, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42031, "top5_acc": 0.68344, "loss_cls": 3.22074, "loss": 3.22074, "time": 0.81417} +{"mode": "train", "epoch": 124, "iter": 2500, "lr": 0.00741, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4075, "top5_acc": 0.67906, "loss_cls": 3.28855, "loss": 3.28855, "time": 0.81426} +{"mode": "train", "epoch": 124, "iter": 2600, "lr": 0.0074, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42281, "top5_acc": 0.68156, "loss_cls": 3.24741, "loss": 3.24741, "time": 0.81659} +{"mode": "train", "epoch": 124, "iter": 2700, "lr": 0.00738, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41844, "top5_acc": 0.67766, "loss_cls": 3.2499, "loss": 3.2499, "time": 0.81906} +{"mode": "train", "epoch": 124, "iter": 2800, "lr": 0.00737, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42516, "top5_acc": 0.69281, "loss_cls": 3.16908, "loss": 3.16908, "time": 0.81052} +{"mode": "train", "epoch": 124, "iter": 2900, "lr": 0.00735, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42641, "top5_acc": 0.67656, "loss_cls": 3.23625, "loss": 3.23625, "time": 0.81567} +{"mode": "train", "epoch": 124, "iter": 3000, "lr": 0.00734, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4225, "top5_acc": 0.68234, "loss_cls": 3.22244, "loss": 3.22244, "time": 0.81528} +{"mode": "train", "epoch": 124, "iter": 3100, "lr": 0.00733, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42266, "top5_acc": 0.68781, "loss_cls": 3.21248, "loss": 3.21248, "time": 0.81733} +{"mode": "train", "epoch": 124, "iter": 3200, "lr": 0.00731, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42125, "top5_acc": 0.68297, "loss_cls": 3.23366, "loss": 3.23366, "time": 0.80952} +{"mode": "train", "epoch": 124, "iter": 3300, "lr": 0.0073, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41719, "top5_acc": 0.67922, "loss_cls": 3.26563, "loss": 3.26563, "time": 0.8102} +{"mode": "train", "epoch": 124, "iter": 3400, "lr": 0.00728, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42234, "top5_acc": 0.68266, "loss_cls": 3.21786, "loss": 3.21786, "time": 0.81654} +{"mode": "train", "epoch": 124, "iter": 3500, "lr": 0.00727, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42359, "top5_acc": 0.68391, "loss_cls": 3.20154, "loss": 3.20154, "time": 0.81107} +{"mode": "train", "epoch": 124, "iter": 3600, "lr": 0.00725, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42562, "top5_acc": 0.68859, "loss_cls": 3.21927, "loss": 3.21927, "time": 0.815} +{"mode": "train", "epoch": 124, "iter": 3700, "lr": 0.00724, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42672, "top5_acc": 0.68594, "loss_cls": 3.19427, "loss": 3.19427, "time": 0.81235} +{"mode": "val", "epoch": 124, "iter": 309, "lr": 0.00723, "top1_acc": 0.34356, "top5_acc": 0.60021, "mean_class_accuracy": 0.34347} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.00722, "memory": 15990, "data_time": 1.24431, "top1_acc": 0.44391, "top5_acc": 0.69547, "loss_cls": 3.11159, "loss": 3.11159, "time": 2.21432} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.0072, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43781, "top5_acc": 0.70172, "loss_cls": 3.11976, "loss": 3.11976, "time": 0.81418} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00719, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45297, "top5_acc": 0.71, "loss_cls": 3.0579, "loss": 3.0579, "time": 0.81339} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00717, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44953, "top5_acc": 0.69453, "loss_cls": 3.1004, "loss": 3.1004, "time": 0.81526} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00716, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43656, "top5_acc": 0.69766, "loss_cls": 3.13766, "loss": 3.13766, "time": 0.8089} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00715, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42922, "top5_acc": 0.69781, "loss_cls": 3.15392, "loss": 3.15392, "time": 0.80814} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00713, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43531, "top5_acc": 0.70188, "loss_cls": 3.10936, "loss": 3.10936, "time": 0.81643} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00712, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43109, "top5_acc": 0.69359, "loss_cls": 3.16631, "loss": 3.16631, "time": 0.81054} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.0071, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43812, "top5_acc": 0.69406, "loss_cls": 3.15714, "loss": 3.15714, "time": 0.81211} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.00709, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4375, "top5_acc": 0.69328, "loss_cls": 3.16175, "loss": 3.16175, "time": 0.80943} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00707, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42516, "top5_acc": 0.68484, "loss_cls": 3.17483, "loss": 3.17483, "time": 0.81201} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00706, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.4375, "top5_acc": 0.69078, "loss_cls": 3.16879, "loss": 3.16879, "time": 0.81512} +{"mode": "train", "epoch": 125, "iter": 1300, "lr": 0.00704, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44297, "top5_acc": 0.69797, "loss_cls": 3.13191, "loss": 3.13191, "time": 0.81468} +{"mode": "train", "epoch": 125, "iter": 1400, "lr": 0.00703, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42719, "top5_acc": 0.68906, "loss_cls": 3.1876, "loss": 3.1876, "time": 0.80748} +{"mode": "train", "epoch": 125, "iter": 1500, "lr": 0.00702, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42297, "top5_acc": 0.68875, "loss_cls": 3.19438, "loss": 3.19438, "time": 0.81004} +{"mode": "train", "epoch": 125, "iter": 1600, "lr": 0.007, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43234, "top5_acc": 0.70453, "loss_cls": 3.13733, "loss": 3.13733, "time": 0.81133} +{"mode": "train", "epoch": 125, "iter": 1700, "lr": 0.00699, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42953, "top5_acc": 0.68828, "loss_cls": 3.20583, "loss": 3.20583, "time": 0.80758} +{"mode": "train", "epoch": 125, "iter": 1800, "lr": 0.00697, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42047, "top5_acc": 0.69406, "loss_cls": 3.18053, "loss": 3.18053, "time": 0.81554} +{"mode": "train", "epoch": 125, "iter": 1900, "lr": 0.00696, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43625, "top5_acc": 0.69141, "loss_cls": 3.1618, "loss": 3.1618, "time": 0.80774} +{"mode": "train", "epoch": 125, "iter": 2000, "lr": 0.00694, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43703, "top5_acc": 0.69375, "loss_cls": 3.14788, "loss": 3.14788, "time": 0.81154} +{"mode": "train", "epoch": 125, "iter": 2100, "lr": 0.00693, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43578, "top5_acc": 0.69859, "loss_cls": 3.15044, "loss": 3.15044, "time": 0.81124} +{"mode": "train", "epoch": 125, "iter": 2200, "lr": 0.00692, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.415, "top5_acc": 0.67828, "loss_cls": 3.25953, "loss": 3.25953, "time": 0.81294} +{"mode": "train", "epoch": 125, "iter": 2300, "lr": 0.0069, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43969, "top5_acc": 0.69797, "loss_cls": 3.13786, "loss": 3.13786, "time": 0.81861} +{"mode": "train", "epoch": 125, "iter": 2400, "lr": 0.00689, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43312, "top5_acc": 0.69, "loss_cls": 3.18602, "loss": 3.18602, "time": 0.81751} +{"mode": "train", "epoch": 125, "iter": 2500, "lr": 0.00687, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42203, "top5_acc": 0.68297, "loss_cls": 3.22171, "loss": 3.22171, "time": 0.81951} +{"mode": "train", "epoch": 125, "iter": 2600, "lr": 0.00686, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41719, "top5_acc": 0.67906, "loss_cls": 3.21193, "loss": 3.21193, "time": 0.81716} +{"mode": "train", "epoch": 125, "iter": 2700, "lr": 0.00685, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43359, "top5_acc": 0.69969, "loss_cls": 3.12723, "loss": 3.12723, "time": 0.82004} +{"mode": "train", "epoch": 125, "iter": 2800, "lr": 0.00683, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42906, "top5_acc": 0.67859, "loss_cls": 3.23552, "loss": 3.23552, "time": 0.81947} +{"mode": "train", "epoch": 125, "iter": 2900, "lr": 0.00682, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.42969, "top5_acc": 0.69266, "loss_cls": 3.18742, "loss": 3.18742, "time": 0.81516} +{"mode": "train", "epoch": 125, "iter": 3000, "lr": 0.0068, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43391, "top5_acc": 0.68422, "loss_cls": 3.18289, "loss": 3.18289, "time": 0.81466} +{"mode": "train", "epoch": 125, "iter": 3100, "lr": 0.00679, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42641, "top5_acc": 0.67641, "loss_cls": 3.22229, "loss": 3.22229, "time": 0.81461} +{"mode": "train", "epoch": 125, "iter": 3200, "lr": 0.00678, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43547, "top5_acc": 0.69125, "loss_cls": 3.14289, "loss": 3.14289, "time": 0.8117} +{"mode": "train", "epoch": 125, "iter": 3300, "lr": 0.00676, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41969, "top5_acc": 0.68281, "loss_cls": 3.20543, "loss": 3.20543, "time": 0.81194} +{"mode": "train", "epoch": 125, "iter": 3400, "lr": 0.00675, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43719, "top5_acc": 0.69453, "loss_cls": 3.17318, "loss": 3.17318, "time": 0.81062} +{"mode": "train", "epoch": 125, "iter": 3500, "lr": 0.00673, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.42969, "top5_acc": 0.68953, "loss_cls": 3.18385, "loss": 3.18385, "time": 0.80614} +{"mode": "train", "epoch": 125, "iter": 3600, "lr": 0.00672, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42797, "top5_acc": 0.68594, "loss_cls": 3.20559, "loss": 3.20559, "time": 0.81175} +{"mode": "train", "epoch": 125, "iter": 3700, "lr": 0.00671, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42297, "top5_acc": 0.69016, "loss_cls": 3.16836, "loss": 3.16836, "time": 0.81186} +{"mode": "val", "epoch": 125, "iter": 309, "lr": 0.0067, "top1_acc": 0.35187, "top5_acc": 0.60887, "mean_class_accuracy": 0.35169} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00668, "memory": 15990, "data_time": 1.26136, "top1_acc": 0.45656, "top5_acc": 0.71125, "loss_cls": 3.0473, "loss": 3.0473, "time": 2.23671} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00667, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44141, "top5_acc": 0.69578, "loss_cls": 3.13621, "loss": 3.13621, "time": 0.8181} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00666, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45156, "top5_acc": 0.69766, "loss_cls": 3.0947, "loss": 3.0947, "time": 0.80951} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00664, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43812, "top5_acc": 0.70578, "loss_cls": 3.12012, "loss": 3.12012, "time": 0.81432} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00663, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43438, "top5_acc": 0.69516, "loss_cls": 3.14514, "loss": 3.14514, "time": 0.80974} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00662, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43656, "top5_acc": 0.695, "loss_cls": 3.13442, "loss": 3.13442, "time": 0.81421} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0066, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44984, "top5_acc": 0.70109, "loss_cls": 3.07032, "loss": 3.07032, "time": 0.81358} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00659, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44609, "top5_acc": 0.70438, "loss_cls": 3.08877, "loss": 3.08877, "time": 0.80898} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00657, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43125, "top5_acc": 0.69781, "loss_cls": 3.13692, "loss": 3.13692, "time": 0.81494} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00656, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44016, "top5_acc": 0.70188, "loss_cls": 3.13033, "loss": 3.13033, "time": 0.81357} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00655, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43828, "top5_acc": 0.69531, "loss_cls": 3.13922, "loss": 3.13922, "time": 0.81148} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00653, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43703, "top5_acc": 0.69828, "loss_cls": 3.1552, "loss": 3.1552, "time": 0.8115} +{"mode": "train", "epoch": 126, "iter": 1300, "lr": 0.00652, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44, "top5_acc": 0.6925, "loss_cls": 3.1218, "loss": 3.1218, "time": 0.81269} +{"mode": "train", "epoch": 126, "iter": 1400, "lr": 0.0065, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44141, "top5_acc": 0.69844, "loss_cls": 3.11542, "loss": 3.11542, "time": 0.8156} +{"mode": "train", "epoch": 126, "iter": 1500, "lr": 0.00649, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4325, "top5_acc": 0.695, "loss_cls": 3.14503, "loss": 3.14503, "time": 0.80996} +{"mode": "train", "epoch": 126, "iter": 1600, "lr": 0.00648, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43516, "top5_acc": 0.69922, "loss_cls": 3.12091, "loss": 3.12091, "time": 0.81019} +{"mode": "train", "epoch": 126, "iter": 1700, "lr": 0.00646, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42391, "top5_acc": 0.68172, "loss_cls": 3.20079, "loss": 3.20079, "time": 0.81436} +{"mode": "train", "epoch": 126, "iter": 1800, "lr": 0.00645, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43875, "top5_acc": 0.69594, "loss_cls": 3.12703, "loss": 3.12703, "time": 0.81309} +{"mode": "train", "epoch": 126, "iter": 1900, "lr": 0.00644, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44234, "top5_acc": 0.69719, "loss_cls": 3.12638, "loss": 3.12638, "time": 0.80927} +{"mode": "train", "epoch": 126, "iter": 2000, "lr": 0.00642, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43625, "top5_acc": 0.69047, "loss_cls": 3.13952, "loss": 3.13952, "time": 0.80906} +{"mode": "train", "epoch": 126, "iter": 2100, "lr": 0.00641, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42984, "top5_acc": 0.69594, "loss_cls": 3.15825, "loss": 3.15825, "time": 0.81215} +{"mode": "train", "epoch": 126, "iter": 2200, "lr": 0.00639, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44078, "top5_acc": 0.69641, "loss_cls": 3.10932, "loss": 3.10932, "time": 0.81274} +{"mode": "train", "epoch": 126, "iter": 2300, "lr": 0.00638, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43281, "top5_acc": 0.70391, "loss_cls": 3.11596, "loss": 3.11596, "time": 0.81064} +{"mode": "train", "epoch": 126, "iter": 2400, "lr": 0.00637, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42609, "top5_acc": 0.68875, "loss_cls": 3.17201, "loss": 3.17201, "time": 0.82288} +{"mode": "train", "epoch": 126, "iter": 2500, "lr": 0.00635, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45094, "top5_acc": 0.70375, "loss_cls": 3.1079, "loss": 3.1079, "time": 0.82116} +{"mode": "train", "epoch": 126, "iter": 2600, "lr": 0.00634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43328, "top5_acc": 0.69031, "loss_cls": 3.17941, "loss": 3.17941, "time": 0.81909} +{"mode": "train", "epoch": 126, "iter": 2700, "lr": 0.00633, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43, "top5_acc": 0.68875, "loss_cls": 3.17684, "loss": 3.17684, "time": 0.81189} +{"mode": "train", "epoch": 126, "iter": 2800, "lr": 0.00631, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43469, "top5_acc": 0.68672, "loss_cls": 3.18487, "loss": 3.18487, "time": 0.81429} +{"mode": "train", "epoch": 126, "iter": 2900, "lr": 0.0063, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43031, "top5_acc": 0.69125, "loss_cls": 3.18587, "loss": 3.18587, "time": 0.8159} +{"mode": "train", "epoch": 126, "iter": 3000, "lr": 0.00629, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43625, "top5_acc": 0.70453, "loss_cls": 3.12881, "loss": 3.12881, "time": 0.81367} +{"mode": "train", "epoch": 126, "iter": 3100, "lr": 0.00627, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42922, "top5_acc": 0.68844, "loss_cls": 3.17585, "loss": 3.17585, "time": 0.8082} +{"mode": "train", "epoch": 126, "iter": 3200, "lr": 0.00626, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43484, "top5_acc": 0.69344, "loss_cls": 3.13395, "loss": 3.13395, "time": 0.80917} +{"mode": "train", "epoch": 126, "iter": 3300, "lr": 0.00625, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42781, "top5_acc": 0.68438, "loss_cls": 3.19312, "loss": 3.19312, "time": 0.80929} +{"mode": "train", "epoch": 126, "iter": 3400, "lr": 0.00623, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43906, "top5_acc": 0.70172, "loss_cls": 3.15077, "loss": 3.15077, "time": 0.81221} +{"mode": "train", "epoch": 126, "iter": 3500, "lr": 0.00622, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.43953, "top5_acc": 0.70188, "loss_cls": 3.1263, "loss": 3.1263, "time": 0.80946} +{"mode": "train", "epoch": 126, "iter": 3600, "lr": 0.0062, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43891, "top5_acc": 0.69312, "loss_cls": 3.13889, "loss": 3.13889, "time": 0.81071} +{"mode": "train", "epoch": 126, "iter": 3700, "lr": 0.00619, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43297, "top5_acc": 0.69188, "loss_cls": 3.16022, "loss": 3.16022, "time": 0.80995} +{"mode": "val", "epoch": 126, "iter": 309, "lr": 0.00618, "top1_acc": 0.3578, "top5_acc": 0.61647, "mean_class_accuracy": 0.35754} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00617, "memory": 15990, "data_time": 1.26755, "top1_acc": 0.45719, "top5_acc": 0.71703, "loss_cls": 3.0294, "loss": 3.0294, "time": 2.24478} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00616, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45, "top5_acc": 0.71547, "loss_cls": 3.04082, "loss": 3.04082, "time": 0.8183} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00614, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45453, "top5_acc": 0.70766, "loss_cls": 3.07714, "loss": 3.07714, "time": 0.81656} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00613, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44891, "top5_acc": 0.69938, "loss_cls": 3.07888, "loss": 3.07888, "time": 0.81871} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.00612, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44891, "top5_acc": 0.70234, "loss_cls": 3.07283, "loss": 3.07283, "time": 0.81509} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.0061, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43953, "top5_acc": 0.69953, "loss_cls": 3.11029, "loss": 3.11029, "time": 0.82052} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00609, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45438, "top5_acc": 0.70797, "loss_cls": 3.04478, "loss": 3.04478, "time": 0.80843} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00608, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43562, "top5_acc": 0.69906, "loss_cls": 3.13183, "loss": 3.13183, "time": 0.81034} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00606, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45109, "top5_acc": 0.70781, "loss_cls": 3.08916, "loss": 3.08916, "time": 0.81084} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00605, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44312, "top5_acc": 0.69438, "loss_cls": 3.12253, "loss": 3.12253, "time": 0.81408} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00604, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44234, "top5_acc": 0.70609, "loss_cls": 3.12238, "loss": 3.12238, "time": 0.80938} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00602, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44812, "top5_acc": 0.72156, "loss_cls": 3.04342, "loss": 3.04342, "time": 0.80976} +{"mode": "train", "epoch": 127, "iter": 1300, "lr": 0.00601, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43484, "top5_acc": 0.69953, "loss_cls": 3.12772, "loss": 3.12772, "time": 0.80853} +{"mode": "train", "epoch": 127, "iter": 1400, "lr": 0.006, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44609, "top5_acc": 0.70672, "loss_cls": 3.07188, "loss": 3.07188, "time": 0.81573} +{"mode": "train", "epoch": 127, "iter": 1500, "lr": 0.00598, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44906, "top5_acc": 0.70531, "loss_cls": 3.08899, "loss": 3.08899, "time": 0.80977} +{"mode": "train", "epoch": 127, "iter": 1600, "lr": 0.00597, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44141, "top5_acc": 0.69688, "loss_cls": 3.12339, "loss": 3.12339, "time": 0.81159} +{"mode": "train", "epoch": 127, "iter": 1700, "lr": 0.00596, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44672, "top5_acc": 0.70453, "loss_cls": 3.09951, "loss": 3.09951, "time": 0.80954} +{"mode": "train", "epoch": 127, "iter": 1800, "lr": 0.00594, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44719, "top5_acc": 0.71312, "loss_cls": 3.0595, "loss": 3.0595, "time": 0.81039} +{"mode": "train", "epoch": 127, "iter": 1900, "lr": 0.00593, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43609, "top5_acc": 0.70875, "loss_cls": 3.10029, "loss": 3.10029, "time": 0.81531} +{"mode": "train", "epoch": 127, "iter": 2000, "lr": 0.00592, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44094, "top5_acc": 0.6975, "loss_cls": 3.1198, "loss": 3.1198, "time": 0.80955} +{"mode": "train", "epoch": 127, "iter": 2100, "lr": 0.00591, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43891, "top5_acc": 0.70375, "loss_cls": 3.1024, "loss": 3.1024, "time": 0.81328} +{"mode": "train", "epoch": 127, "iter": 2200, "lr": 0.00589, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42562, "top5_acc": 0.69594, "loss_cls": 3.14007, "loss": 3.14007, "time": 0.81282} +{"mode": "train", "epoch": 127, "iter": 2300, "lr": 0.00588, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42438, "top5_acc": 0.69234, "loss_cls": 3.14802, "loss": 3.14802, "time": 0.81531} +{"mode": "train", "epoch": 127, "iter": 2400, "lr": 0.00587, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44312, "top5_acc": 0.70562, "loss_cls": 3.07482, "loss": 3.07482, "time": 0.81604} +{"mode": "train", "epoch": 127, "iter": 2500, "lr": 0.00585, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44609, "top5_acc": 0.70594, "loss_cls": 3.10113, "loss": 3.10113, "time": 0.82605} +{"mode": "train", "epoch": 127, "iter": 2600, "lr": 0.00584, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44344, "top5_acc": 0.69891, "loss_cls": 3.11933, "loss": 3.11933, "time": 0.81888} +{"mode": "train", "epoch": 127, "iter": 2700, "lr": 0.00583, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4425, "top5_acc": 0.69344, "loss_cls": 3.12618, "loss": 3.12618, "time": 0.80886} +{"mode": "train", "epoch": 127, "iter": 2800, "lr": 0.00581, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43984, "top5_acc": 0.69984, "loss_cls": 3.12531, "loss": 3.12531, "time": 0.82526} +{"mode": "train", "epoch": 127, "iter": 2900, "lr": 0.0058, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44297, "top5_acc": 0.68719, "loss_cls": 3.13048, "loss": 3.13048, "time": 0.81494} +{"mode": "train", "epoch": 127, "iter": 3000, "lr": 0.00579, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44734, "top5_acc": 0.70781, "loss_cls": 3.09031, "loss": 3.09031, "time": 0.81298} +{"mode": "train", "epoch": 127, "iter": 3100, "lr": 0.00577, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43906, "top5_acc": 0.69312, "loss_cls": 3.15283, "loss": 3.15283, "time": 0.81161} +{"mode": "train", "epoch": 127, "iter": 3200, "lr": 0.00576, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43328, "top5_acc": 0.68641, "loss_cls": 3.19505, "loss": 3.19505, "time": 0.80853} +{"mode": "train", "epoch": 127, "iter": 3300, "lr": 0.00575, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45266, "top5_acc": 0.70531, "loss_cls": 3.066, "loss": 3.066, "time": 0.80975} +{"mode": "train", "epoch": 127, "iter": 3400, "lr": 0.00573, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44484, "top5_acc": 0.70391, "loss_cls": 3.10504, "loss": 3.10504, "time": 0.81179} +{"mode": "train", "epoch": 127, "iter": 3500, "lr": 0.00572, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44266, "top5_acc": 0.69562, "loss_cls": 3.14626, "loss": 3.14626, "time": 0.80607} +{"mode": "train", "epoch": 127, "iter": 3600, "lr": 0.00571, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42859, "top5_acc": 0.68906, "loss_cls": 3.15619, "loss": 3.15619, "time": 0.8106} +{"mode": "train", "epoch": 127, "iter": 3700, "lr": 0.0057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44406, "top5_acc": 0.70422, "loss_cls": 3.11584, "loss": 3.11584, "time": 0.81303} +{"mode": "val", "epoch": 127, "iter": 309, "lr": 0.00569, "top1_acc": 0.3694, "top5_acc": 0.62584, "mean_class_accuracy": 0.36912} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00568, "memory": 15990, "data_time": 1.27938, "top1_acc": 0.45594, "top5_acc": 0.72469, "loss_cls": 2.98671, "loss": 2.98671, "time": 2.25281} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.00566, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46812, "top5_acc": 0.71938, "loss_cls": 2.98192, "loss": 2.98192, "time": 0.82439} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00565, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45531, "top5_acc": 0.72125, "loss_cls": 3.00849, "loss": 3.00849, "time": 0.81626} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00564, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45438, "top5_acc": 0.715, "loss_cls": 3.02853, "loss": 3.02853, "time": 0.81457} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00563, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47297, "top5_acc": 0.72781, "loss_cls": 2.95473, "loss": 2.95473, "time": 0.81578} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00561, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44719, "top5_acc": 0.71359, "loss_cls": 3.0343, "loss": 3.0343, "time": 0.81785} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.0056, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45547, "top5_acc": 0.71531, "loss_cls": 3.04183, "loss": 3.04183, "time": 0.81408} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00559, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44891, "top5_acc": 0.70531, "loss_cls": 3.07043, "loss": 3.07043, "time": 0.81494} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00557, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45781, "top5_acc": 0.71391, "loss_cls": 3.02899, "loss": 3.02899, "time": 0.80903} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00556, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44766, "top5_acc": 0.70938, "loss_cls": 3.02823, "loss": 3.02823, "time": 0.81045} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00555, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43609, "top5_acc": 0.70469, "loss_cls": 3.11069, "loss": 3.11069, "time": 0.80974} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00554, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44781, "top5_acc": 0.70281, "loss_cls": 3.04446, "loss": 3.04446, "time": 0.80995} +{"mode": "train", "epoch": 128, "iter": 1300, "lr": 0.00552, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44578, "top5_acc": 0.70203, "loss_cls": 3.12068, "loss": 3.12068, "time": 0.80894} +{"mode": "train", "epoch": 128, "iter": 1400, "lr": 0.00551, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44109, "top5_acc": 0.70047, "loss_cls": 3.10918, "loss": 3.10918, "time": 0.80931} +{"mode": "train", "epoch": 128, "iter": 1500, "lr": 0.0055, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46078, "top5_acc": 0.71094, "loss_cls": 3.03815, "loss": 3.03815, "time": 0.81105} +{"mode": "train", "epoch": 128, "iter": 1600, "lr": 0.00548, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44766, "top5_acc": 0.70516, "loss_cls": 3.0974, "loss": 3.0974, "time": 0.81462} +{"mode": "train", "epoch": 128, "iter": 1700, "lr": 0.00547, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44422, "top5_acc": 0.70297, "loss_cls": 3.08703, "loss": 3.08703, "time": 0.80718} +{"mode": "train", "epoch": 128, "iter": 1800, "lr": 0.00546, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44484, "top5_acc": 0.70594, "loss_cls": 3.09491, "loss": 3.09491, "time": 0.80941} +{"mode": "train", "epoch": 128, "iter": 1900, "lr": 0.00545, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44594, "top5_acc": 0.70109, "loss_cls": 3.08678, "loss": 3.08678, "time": 0.81853} +{"mode": "train", "epoch": 128, "iter": 2000, "lr": 0.00543, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43672, "top5_acc": 0.70016, "loss_cls": 3.11089, "loss": 3.11089, "time": 0.81654} +{"mode": "train", "epoch": 128, "iter": 2100, "lr": 0.00542, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44812, "top5_acc": 0.70797, "loss_cls": 3.07507, "loss": 3.07507, "time": 0.81141} +{"mode": "train", "epoch": 128, "iter": 2200, "lr": 0.00541, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45328, "top5_acc": 0.70734, "loss_cls": 3.05472, "loss": 3.05472, "time": 0.81498} +{"mode": "train", "epoch": 128, "iter": 2300, "lr": 0.0054, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44594, "top5_acc": 0.70781, "loss_cls": 3.06897, "loss": 3.06897, "time": 0.81841} +{"mode": "train", "epoch": 128, "iter": 2400, "lr": 0.00538, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43938, "top5_acc": 0.69422, "loss_cls": 3.15155, "loss": 3.15155, "time": 0.82357} +{"mode": "train", "epoch": 128, "iter": 2500, "lr": 0.00537, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44484, "top5_acc": 0.71109, "loss_cls": 3.08036, "loss": 3.08036, "time": 0.81488} +{"mode": "train", "epoch": 128, "iter": 2600, "lr": 0.00536, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44375, "top5_acc": 0.70406, "loss_cls": 3.09373, "loss": 3.09373, "time": 0.81823} +{"mode": "train", "epoch": 128, "iter": 2700, "lr": 0.00535, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45328, "top5_acc": 0.70641, "loss_cls": 3.0612, "loss": 3.0612, "time": 0.82036} +{"mode": "train", "epoch": 128, "iter": 2800, "lr": 0.00533, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44234, "top5_acc": 0.7, "loss_cls": 3.10513, "loss": 3.10513, "time": 0.81223} +{"mode": "train", "epoch": 128, "iter": 2900, "lr": 0.00532, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45172, "top5_acc": 0.71031, "loss_cls": 3.07575, "loss": 3.07575, "time": 0.81185} +{"mode": "train", "epoch": 128, "iter": 3000, "lr": 0.00531, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44125, "top5_acc": 0.69891, "loss_cls": 3.11739, "loss": 3.11739, "time": 0.81029} +{"mode": "train", "epoch": 128, "iter": 3100, "lr": 0.0053, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44375, "top5_acc": 0.70453, "loss_cls": 3.09176, "loss": 3.09176, "time": 0.80892} +{"mode": "train", "epoch": 128, "iter": 3200, "lr": 0.00528, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43594, "top5_acc": 0.70156, "loss_cls": 3.11598, "loss": 3.11598, "time": 0.80645} +{"mode": "train", "epoch": 128, "iter": 3300, "lr": 0.00527, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44719, "top5_acc": 0.71297, "loss_cls": 3.05922, "loss": 3.05922, "time": 0.81151} +{"mode": "train", "epoch": 128, "iter": 3400, "lr": 0.00526, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44422, "top5_acc": 0.70406, "loss_cls": 3.08641, "loss": 3.08641, "time": 0.80885} +{"mode": "train", "epoch": 128, "iter": 3500, "lr": 0.00525, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44312, "top5_acc": 0.70781, "loss_cls": 3.08022, "loss": 3.08022, "time": 0.81022} +{"mode": "train", "epoch": 128, "iter": 3600, "lr": 0.00523, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44047, "top5_acc": 0.69797, "loss_cls": 3.11011, "loss": 3.11011, "time": 0.8104} +{"mode": "train", "epoch": 128, "iter": 3700, "lr": 0.00522, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.44766, "top5_acc": 0.70766, "loss_cls": 3.09807, "loss": 3.09807, "time": 0.80845} +{"mode": "val", "epoch": 128, "iter": 309, "lr": 0.00521, "top1_acc": 0.36712, "top5_acc": 0.62564, "mean_class_accuracy": 0.36691} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.0052, "memory": 15990, "data_time": 1.30529, "top1_acc": 0.45891, "top5_acc": 0.72422, "loss_cls": 2.98389, "loss": 2.98389, "time": 2.31481} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00519, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46391, "top5_acc": 0.72422, "loss_cls": 2.96397, "loss": 2.96397, "time": 0.8222} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00518, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46516, "top5_acc": 0.71875, "loss_cls": 2.99116, "loss": 2.99116, "time": 0.82063} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00516, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45031, "top5_acc": 0.70922, "loss_cls": 3.04594, "loss": 3.04594, "time": 0.81537} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00515, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4575, "top5_acc": 0.715, "loss_cls": 3.02177, "loss": 3.02177, "time": 0.81728} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00514, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46156, "top5_acc": 0.71969, "loss_cls": 2.98859, "loss": 2.98859, "time": 0.81271} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00513, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45562, "top5_acc": 0.71547, "loss_cls": 3.04754, "loss": 3.04754, "time": 0.80929} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00512, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45312, "top5_acc": 0.70812, "loss_cls": 3.03651, "loss": 3.03651, "time": 0.80674} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.0051, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45531, "top5_acc": 0.71547, "loss_cls": 3.02895, "loss": 3.02895, "time": 0.81193} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00509, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45547, "top5_acc": 0.71359, "loss_cls": 3.03362, "loss": 3.03362, "time": 0.80441} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00508, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46484, "top5_acc": 0.71438, "loss_cls": 3.00105, "loss": 3.00105, "time": 0.81242} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.00507, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45078, "top5_acc": 0.70875, "loss_cls": 3.07109, "loss": 3.07109, "time": 0.81407} +{"mode": "train", "epoch": 129, "iter": 1300, "lr": 0.00505, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45719, "top5_acc": 0.71203, "loss_cls": 3.02165, "loss": 3.02165, "time": 0.80876} +{"mode": "train", "epoch": 129, "iter": 1400, "lr": 0.00504, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46688, "top5_acc": 0.71812, "loss_cls": 3.00269, "loss": 3.00269, "time": 0.80751} +{"mode": "train", "epoch": 129, "iter": 1500, "lr": 0.00503, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45203, "top5_acc": 0.71453, "loss_cls": 3.03095, "loss": 3.03095, "time": 0.81293} +{"mode": "train", "epoch": 129, "iter": 1600, "lr": 0.00502, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45484, "top5_acc": 0.71547, "loss_cls": 3.03367, "loss": 3.03367, "time": 0.8108} +{"mode": "train", "epoch": 129, "iter": 1700, "lr": 0.00501, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45328, "top5_acc": 0.71703, "loss_cls": 3.03107, "loss": 3.03107, "time": 0.81142} +{"mode": "train", "epoch": 129, "iter": 1800, "lr": 0.00499, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46156, "top5_acc": 0.71734, "loss_cls": 3.00604, "loss": 3.00604, "time": 0.81415} +{"mode": "train", "epoch": 129, "iter": 1900, "lr": 0.00498, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45062, "top5_acc": 0.70891, "loss_cls": 3.04867, "loss": 3.04867, "time": 0.81639} +{"mode": "train", "epoch": 129, "iter": 2000, "lr": 0.00497, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44453, "top5_acc": 0.70078, "loss_cls": 3.07825, "loss": 3.07825, "time": 0.81197} +{"mode": "train", "epoch": 129, "iter": 2100, "lr": 0.00496, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45641, "top5_acc": 0.70922, "loss_cls": 3.0443, "loss": 3.0443, "time": 0.81008} +{"mode": "train", "epoch": 129, "iter": 2200, "lr": 0.00494, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46219, "top5_acc": 0.72, "loss_cls": 2.98686, "loss": 2.98686, "time": 0.81116} +{"mode": "train", "epoch": 129, "iter": 2300, "lr": 0.00493, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45328, "top5_acc": 0.71141, "loss_cls": 3.05799, "loss": 3.05799, "time": 0.82467} +{"mode": "train", "epoch": 129, "iter": 2400, "lr": 0.00492, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46625, "top5_acc": 0.71188, "loss_cls": 3.04939, "loss": 3.04939, "time": 0.81332} +{"mode": "train", "epoch": 129, "iter": 2500, "lr": 0.00491, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45344, "top5_acc": 0.70953, "loss_cls": 3.06824, "loss": 3.06824, "time": 0.81689} +{"mode": "train", "epoch": 129, "iter": 2600, "lr": 0.0049, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44984, "top5_acc": 0.71328, "loss_cls": 3.0502, "loss": 3.0502, "time": 0.81849} +{"mode": "train", "epoch": 129, "iter": 2700, "lr": 0.00488, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45688, "top5_acc": 0.71109, "loss_cls": 3.0385, "loss": 3.0385, "time": 0.81445} +{"mode": "train", "epoch": 129, "iter": 2800, "lr": 0.00487, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44531, "top5_acc": 0.71328, "loss_cls": 3.05397, "loss": 3.05397, "time": 0.81662} +{"mode": "train", "epoch": 129, "iter": 2900, "lr": 0.00486, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45969, "top5_acc": 0.71469, "loss_cls": 3.01194, "loss": 3.01194, "time": 0.82052} +{"mode": "train", "epoch": 129, "iter": 3000, "lr": 0.00485, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45656, "top5_acc": 0.71266, "loss_cls": 3.03576, "loss": 3.03576, "time": 0.80739} +{"mode": "train", "epoch": 129, "iter": 3100, "lr": 0.00484, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43969, "top5_acc": 0.69922, "loss_cls": 3.12166, "loss": 3.12166, "time": 0.8107} +{"mode": "train", "epoch": 129, "iter": 3200, "lr": 0.00482, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45438, "top5_acc": 0.71125, "loss_cls": 3.05164, "loss": 3.05164, "time": 0.80815} +{"mode": "train", "epoch": 129, "iter": 3300, "lr": 0.00481, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45594, "top5_acc": 0.70969, "loss_cls": 3.04118, "loss": 3.04118, "time": 0.80943} +{"mode": "train", "epoch": 129, "iter": 3400, "lr": 0.0048, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46031, "top5_acc": 0.70734, "loss_cls": 3.0513, "loss": 3.0513, "time": 0.80808} +{"mode": "train", "epoch": 129, "iter": 3500, "lr": 0.00479, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45172, "top5_acc": 0.71484, "loss_cls": 3.04918, "loss": 3.04918, "time": 0.81052} +{"mode": "train", "epoch": 129, "iter": 3600, "lr": 0.00478, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44922, "top5_acc": 0.71359, "loss_cls": 3.05764, "loss": 3.05764, "time": 0.81223} +{"mode": "train", "epoch": 129, "iter": 3700, "lr": 0.00476, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45156, "top5_acc": 0.71328, "loss_cls": 3.05587, "loss": 3.05587, "time": 0.81981} +{"mode": "val", "epoch": 129, "iter": 309, "lr": 0.00476, "top1_acc": 0.37892, "top5_acc": 0.63319, "mean_class_accuracy": 0.37876} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00475, "memory": 15990, "data_time": 1.27686, "top1_acc": 0.47438, "top5_acc": 0.72578, "loss_cls": 2.92875, "loss": 2.92875, "time": 2.25814} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00473, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47297, "top5_acc": 0.73016, "loss_cls": 2.92843, "loss": 2.92843, "time": 0.82316} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00472, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47812, "top5_acc": 0.73172, "loss_cls": 2.934, "loss": 2.934, "time": 0.81959} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00471, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46859, "top5_acc": 0.72594, "loss_cls": 2.94935, "loss": 2.94935, "time": 0.81637} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.0047, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45812, "top5_acc": 0.72219, "loss_cls": 2.98916, "loss": 2.98916, "time": 0.81725} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00469, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47, "top5_acc": 0.72094, "loss_cls": 2.96889, "loss": 2.96889, "time": 0.81219} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00468, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46562, "top5_acc": 0.71875, "loss_cls": 2.97364, "loss": 2.97364, "time": 0.80826} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00466, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46688, "top5_acc": 0.71859, "loss_cls": 2.97433, "loss": 2.97433, "time": 0.81256} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00465, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46734, "top5_acc": 0.7225, "loss_cls": 2.98863, "loss": 2.98863, "time": 0.81126} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.00464, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45828, "top5_acc": 0.71781, "loss_cls": 3.00647, "loss": 3.00647, "time": 0.81282} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.00463, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45797, "top5_acc": 0.71609, "loss_cls": 3.02297, "loss": 3.02297, "time": 0.81475} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00462, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46016, "top5_acc": 0.71812, "loss_cls": 2.99863, "loss": 2.99863, "time": 0.80908} +{"mode": "train", "epoch": 130, "iter": 1300, "lr": 0.00461, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46844, "top5_acc": 0.72109, "loss_cls": 3.00022, "loss": 3.00022, "time": 0.81169} +{"mode": "train", "epoch": 130, "iter": 1400, "lr": 0.00459, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46125, "top5_acc": 0.71359, "loss_cls": 3.00232, "loss": 3.00232, "time": 0.81311} +{"mode": "train", "epoch": 130, "iter": 1500, "lr": 0.00458, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45719, "top5_acc": 0.71469, "loss_cls": 3.01367, "loss": 3.01367, "time": 0.81566} +{"mode": "train", "epoch": 130, "iter": 1600, "lr": 0.00457, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.445, "top5_acc": 0.71031, "loss_cls": 3.04107, "loss": 3.04107, "time": 0.81303} +{"mode": "train", "epoch": 130, "iter": 1700, "lr": 0.00456, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.45672, "top5_acc": 0.72172, "loss_cls": 2.99753, "loss": 2.99753, "time": 0.81527} +{"mode": "train", "epoch": 130, "iter": 1800, "lr": 0.00455, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45875, "top5_acc": 0.72219, "loss_cls": 2.97707, "loss": 2.97707, "time": 0.80768} +{"mode": "train", "epoch": 130, "iter": 1900, "lr": 0.00454, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47406, "top5_acc": 0.72547, "loss_cls": 2.94289, "loss": 2.94289, "time": 0.81125} +{"mode": "train", "epoch": 130, "iter": 2000, "lr": 0.00452, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45312, "top5_acc": 0.70688, "loss_cls": 3.06249, "loss": 3.06249, "time": 0.82135} +{"mode": "train", "epoch": 130, "iter": 2100, "lr": 0.00451, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45781, "top5_acc": 0.72188, "loss_cls": 2.99932, "loss": 2.99932, "time": 0.80885} +{"mode": "train", "epoch": 130, "iter": 2200, "lr": 0.0045, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47516, "top5_acc": 0.72938, "loss_cls": 2.93721, "loss": 2.93721, "time": 0.81724} +{"mode": "train", "epoch": 130, "iter": 2300, "lr": 0.00449, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46484, "top5_acc": 0.72062, "loss_cls": 3.01222, "loss": 3.01222, "time": 0.81637} +{"mode": "train", "epoch": 130, "iter": 2400, "lr": 0.00448, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45812, "top5_acc": 0.71219, "loss_cls": 3.02579, "loss": 3.02579, "time": 0.81859} +{"mode": "train", "epoch": 130, "iter": 2500, "lr": 0.00447, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45969, "top5_acc": 0.71797, "loss_cls": 2.9849, "loss": 2.9849, "time": 0.82142} +{"mode": "train", "epoch": 130, "iter": 2600, "lr": 0.00445, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45969, "top5_acc": 0.71719, "loss_cls": 3.00515, "loss": 3.00515, "time": 0.81619} +{"mode": "train", "epoch": 130, "iter": 2700, "lr": 0.00444, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45609, "top5_acc": 0.71031, "loss_cls": 3.04447, "loss": 3.04447, "time": 0.82298} +{"mode": "train", "epoch": 130, "iter": 2800, "lr": 0.00443, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45109, "top5_acc": 0.70938, "loss_cls": 3.03135, "loss": 3.03135, "time": 0.81211} +{"mode": "train", "epoch": 130, "iter": 2900, "lr": 0.00442, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45156, "top5_acc": 0.71016, "loss_cls": 3.04932, "loss": 3.04932, "time": 0.81505} +{"mode": "train", "epoch": 130, "iter": 3000, "lr": 0.00441, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45562, "top5_acc": 0.7175, "loss_cls": 2.98343, "loss": 2.98343, "time": 0.8126} +{"mode": "train", "epoch": 130, "iter": 3100, "lr": 0.0044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45531, "top5_acc": 0.71516, "loss_cls": 3.02499, "loss": 3.02499, "time": 0.80915} +{"mode": "train", "epoch": 130, "iter": 3200, "lr": 0.00439, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45219, "top5_acc": 0.70516, "loss_cls": 3.06052, "loss": 3.06052, "time": 0.81101} +{"mode": "train", "epoch": 130, "iter": 3300, "lr": 0.00437, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45125, "top5_acc": 0.71688, "loss_cls": 3.01641, "loss": 3.01641, "time": 0.81035} +{"mode": "train", "epoch": 130, "iter": 3400, "lr": 0.00436, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46625, "top5_acc": 0.71984, "loss_cls": 2.96608, "loss": 2.96608, "time": 0.80958} +{"mode": "train", "epoch": 130, "iter": 3500, "lr": 0.00435, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46125, "top5_acc": 0.71156, "loss_cls": 3.02551, "loss": 3.02551, "time": 0.80991} +{"mode": "train", "epoch": 130, "iter": 3600, "lr": 0.00434, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45047, "top5_acc": 0.71219, "loss_cls": 3.04492, "loss": 3.04492, "time": 0.81117} +{"mode": "train", "epoch": 130, "iter": 3700, "lr": 0.00433, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44625, "top5_acc": 0.70922, "loss_cls": 3.06847, "loss": 3.06847, "time": 0.81306} +{"mode": "val", "epoch": 130, "iter": 309, "lr": 0.00432, "top1_acc": 0.38282, "top5_acc": 0.6344, "mean_class_accuracy": 0.38257} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00431, "memory": 15990, "data_time": 1.29404, "top1_acc": 0.47891, "top5_acc": 0.73266, "loss_cls": 2.90596, "loss": 2.90596, "time": 2.27302} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.0043, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48125, "top5_acc": 0.73359, "loss_cls": 2.91321, "loss": 2.91321, "time": 0.82535} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00429, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47656, "top5_acc": 0.73406, "loss_cls": 2.90996, "loss": 2.90996, "time": 0.82052} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00428, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46875, "top5_acc": 0.72188, "loss_cls": 2.95168, "loss": 2.95168, "time": 0.82096} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00427, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47609, "top5_acc": 0.73109, "loss_cls": 2.90033, "loss": 2.90033, "time": 0.81341} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00425, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46547, "top5_acc": 0.72328, "loss_cls": 2.93387, "loss": 2.93387, "time": 0.8134} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00424, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47688, "top5_acc": 0.73734, "loss_cls": 2.90083, "loss": 2.90083, "time": 0.81053} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00423, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.48172, "top5_acc": 0.72906, "loss_cls": 2.92465, "loss": 2.92465, "time": 0.80746} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00422, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47688, "top5_acc": 0.73266, "loss_cls": 2.90375, "loss": 2.90375, "time": 0.8075} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.00421, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46016, "top5_acc": 0.72609, "loss_cls": 2.95656, "loss": 2.95656, "time": 0.81747} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.0042, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47859, "top5_acc": 0.73344, "loss_cls": 2.91763, "loss": 2.91763, "time": 0.81599} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00419, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46172, "top5_acc": 0.73031, "loss_cls": 2.94494, "loss": 2.94494, "time": 0.81576} +{"mode": "train", "epoch": 131, "iter": 1300, "lr": 0.00418, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46875, "top5_acc": 0.72109, "loss_cls": 2.95174, "loss": 2.95174, "time": 0.81041} +{"mode": "train", "epoch": 131, "iter": 1400, "lr": 0.00417, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47594, "top5_acc": 0.73078, "loss_cls": 2.93233, "loss": 2.93233, "time": 0.81152} +{"mode": "train", "epoch": 131, "iter": 1500, "lr": 0.00415, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46484, "top5_acc": 0.71781, "loss_cls": 2.97482, "loss": 2.97482, "time": 0.80586} +{"mode": "train", "epoch": 131, "iter": 1600, "lr": 0.00414, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46234, "top5_acc": 0.72516, "loss_cls": 3.0033, "loss": 3.0033, "time": 0.81973} +{"mode": "train", "epoch": 131, "iter": 1700, "lr": 0.00413, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45609, "top5_acc": 0.71656, "loss_cls": 2.99483, "loss": 2.99483, "time": 0.81607} +{"mode": "train", "epoch": 131, "iter": 1800, "lr": 0.00412, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46625, "top5_acc": 0.72297, "loss_cls": 2.95505, "loss": 2.95505, "time": 0.81102} +{"mode": "train", "epoch": 131, "iter": 1900, "lr": 0.00411, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46141, "top5_acc": 0.72703, "loss_cls": 2.96737, "loss": 2.96737, "time": 0.81217} +{"mode": "train", "epoch": 131, "iter": 2000, "lr": 0.0041, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45859, "top5_acc": 0.71688, "loss_cls": 3.00864, "loss": 3.00864, "time": 0.81316} +{"mode": "train", "epoch": 131, "iter": 2100, "lr": 0.00409, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45531, "top5_acc": 0.71562, "loss_cls": 3.01426, "loss": 3.01426, "time": 0.80895} +{"mode": "train", "epoch": 131, "iter": 2200, "lr": 0.00408, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46766, "top5_acc": 0.71922, "loss_cls": 2.99261, "loss": 2.99261, "time": 0.80959} +{"mode": "train", "epoch": 131, "iter": 2300, "lr": 0.00407, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46531, "top5_acc": 0.71609, "loss_cls": 2.99699, "loss": 2.99699, "time": 0.81433} +{"mode": "train", "epoch": 131, "iter": 2400, "lr": 0.00405, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46266, "top5_acc": 0.71625, "loss_cls": 3.01987, "loss": 3.01987, "time": 0.81291} +{"mode": "train", "epoch": 131, "iter": 2500, "lr": 0.00404, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.47047, "top5_acc": 0.71984, "loss_cls": 2.98021, "loss": 2.98021, "time": 0.81215} +{"mode": "train", "epoch": 131, "iter": 2600, "lr": 0.00403, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46953, "top5_acc": 0.72188, "loss_cls": 2.95195, "loss": 2.95195, "time": 0.81262} +{"mode": "train", "epoch": 131, "iter": 2700, "lr": 0.00402, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46734, "top5_acc": 0.72594, "loss_cls": 2.94577, "loss": 2.94577, "time": 0.81146} +{"mode": "train", "epoch": 131, "iter": 2800, "lr": 0.00401, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47172, "top5_acc": 0.73281, "loss_cls": 2.92958, "loss": 2.92958, "time": 0.81948} +{"mode": "train", "epoch": 131, "iter": 2900, "lr": 0.004, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.46766, "top5_acc": 0.72938, "loss_cls": 2.95236, "loss": 2.95236, "time": 0.81156} +{"mode": "train", "epoch": 131, "iter": 3000, "lr": 0.00399, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46734, "top5_acc": 0.71828, "loss_cls": 2.96225, "loss": 2.96225, "time": 0.82218} +{"mode": "train", "epoch": 131, "iter": 3100, "lr": 0.00398, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45797, "top5_acc": 0.71109, "loss_cls": 3.02567, "loss": 3.02567, "time": 0.81129} +{"mode": "train", "epoch": 131, "iter": 3200, "lr": 0.00397, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45656, "top5_acc": 0.71359, "loss_cls": 2.9954, "loss": 2.9954, "time": 0.80846} +{"mode": "train", "epoch": 131, "iter": 3300, "lr": 0.00396, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46031, "top5_acc": 0.71344, "loss_cls": 3.0096, "loss": 3.0096, "time": 0.819} +{"mode": "train", "epoch": 131, "iter": 3400, "lr": 0.00394, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45641, "top5_acc": 0.71406, "loss_cls": 3.01995, "loss": 3.01995, "time": 0.8149} +{"mode": "train", "epoch": 131, "iter": 3500, "lr": 0.00393, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46953, "top5_acc": 0.7275, "loss_cls": 2.95645, "loss": 2.95645, "time": 0.81101} +{"mode": "train", "epoch": 131, "iter": 3600, "lr": 0.00392, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46922, "top5_acc": 0.72641, "loss_cls": 2.96573, "loss": 2.96573, "time": 0.81215} +{"mode": "train", "epoch": 131, "iter": 3700, "lr": 0.00391, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46922, "top5_acc": 0.72469, "loss_cls": 2.94555, "loss": 2.94555, "time": 0.80913} +{"mode": "val", "epoch": 131, "iter": 309, "lr": 0.00391, "top1_acc": 0.37973, "top5_acc": 0.63511, "mean_class_accuracy": 0.37954} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.0039, "memory": 15990, "data_time": 1.31409, "top1_acc": 0.48297, "top5_acc": 0.73891, "loss_cls": 2.88971, "loss": 2.88971, "time": 2.28791} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00389, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48047, "top5_acc": 0.73953, "loss_cls": 2.86733, "loss": 2.86733, "time": 0.826} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00387, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49516, "top5_acc": 0.74359, "loss_cls": 2.80335, "loss": 2.80335, "time": 0.82627} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00386, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.48516, "top5_acc": 0.73406, "loss_cls": 2.88962, "loss": 2.88962, "time": 0.8245} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00385, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48484, "top5_acc": 0.73547, "loss_cls": 2.87942, "loss": 2.87942, "time": 0.81442} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00384, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46641, "top5_acc": 0.72906, "loss_cls": 2.92726, "loss": 2.92726, "time": 0.81315} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00383, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47656, "top5_acc": 0.73344, "loss_cls": 2.89326, "loss": 2.89326, "time": 0.81206} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00382, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47984, "top5_acc": 0.73234, "loss_cls": 2.88378, "loss": 2.88378, "time": 0.80957} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00381, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47266, "top5_acc": 0.73844, "loss_cls": 2.87589, "loss": 2.87589, "time": 0.81202} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0038, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48062, "top5_acc": 0.72859, "loss_cls": 2.90434, "loss": 2.90434, "time": 0.81038} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00379, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47594, "top5_acc": 0.73516, "loss_cls": 2.91756, "loss": 2.91756, "time": 0.81627} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00378, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48781, "top5_acc": 0.74953, "loss_cls": 2.82408, "loss": 2.82408, "time": 0.8139} +{"mode": "train", "epoch": 132, "iter": 1300, "lr": 0.00377, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48078, "top5_acc": 0.73844, "loss_cls": 2.87633, "loss": 2.87633, "time": 0.81307} +{"mode": "train", "epoch": 132, "iter": 1400, "lr": 0.00376, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47141, "top5_acc": 0.73219, "loss_cls": 2.94842, "loss": 2.94842, "time": 0.81366} +{"mode": "train", "epoch": 132, "iter": 1500, "lr": 0.00375, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47625, "top5_acc": 0.73031, "loss_cls": 2.93622, "loss": 2.93622, "time": 0.81878} +{"mode": "train", "epoch": 132, "iter": 1600, "lr": 0.00374, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46984, "top5_acc": 0.72688, "loss_cls": 2.93861, "loss": 2.93861, "time": 0.80718} +{"mode": "train", "epoch": 132, "iter": 1700, "lr": 0.00372, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47391, "top5_acc": 0.72922, "loss_cls": 2.92631, "loss": 2.92631, "time": 0.81487} +{"mode": "train", "epoch": 132, "iter": 1800, "lr": 0.00371, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47672, "top5_acc": 0.72719, "loss_cls": 2.90016, "loss": 2.90016, "time": 0.81262} +{"mode": "train", "epoch": 132, "iter": 1900, "lr": 0.0037, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47891, "top5_acc": 0.72828, "loss_cls": 2.90246, "loss": 2.90246, "time": 0.81198} +{"mode": "train", "epoch": 132, "iter": 2000, "lr": 0.00369, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46312, "top5_acc": 0.72109, "loss_cls": 2.95911, "loss": 2.95911, "time": 0.8132} +{"mode": "train", "epoch": 132, "iter": 2100, "lr": 0.00368, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48812, "top5_acc": 0.72875, "loss_cls": 2.89535, "loss": 2.89535, "time": 0.80843} +{"mode": "train", "epoch": 132, "iter": 2200, "lr": 0.00367, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47391, "top5_acc": 0.72938, "loss_cls": 2.90422, "loss": 2.90422, "time": 0.81085} +{"mode": "train", "epoch": 132, "iter": 2300, "lr": 0.00366, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46719, "top5_acc": 0.725, "loss_cls": 2.97269, "loss": 2.97269, "time": 0.81558} +{"mode": "train", "epoch": 132, "iter": 2400, "lr": 0.00365, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46719, "top5_acc": 0.72953, "loss_cls": 2.93291, "loss": 2.93291, "time": 0.80991} +{"mode": "train", "epoch": 132, "iter": 2500, "lr": 0.00364, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46578, "top5_acc": 0.72328, "loss_cls": 2.97693, "loss": 2.97693, "time": 0.81414} +{"mode": "train", "epoch": 132, "iter": 2600, "lr": 0.00363, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47203, "top5_acc": 0.73234, "loss_cls": 2.9448, "loss": 2.9448, "time": 0.81215} +{"mode": "train", "epoch": 132, "iter": 2700, "lr": 0.00362, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47469, "top5_acc": 0.73016, "loss_cls": 2.89041, "loss": 2.89041, "time": 0.80925} +{"mode": "train", "epoch": 132, "iter": 2800, "lr": 0.00361, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47156, "top5_acc": 0.73047, "loss_cls": 2.94744, "loss": 2.94744, "time": 0.81688} +{"mode": "train", "epoch": 132, "iter": 2900, "lr": 0.0036, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47625, "top5_acc": 0.73266, "loss_cls": 2.90078, "loss": 2.90078, "time": 0.81462} +{"mode": "train", "epoch": 132, "iter": 3000, "lr": 0.00359, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46375, "top5_acc": 0.71547, "loss_cls": 2.9755, "loss": 2.9755, "time": 0.8231} +{"mode": "train", "epoch": 132, "iter": 3100, "lr": 0.00358, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46266, "top5_acc": 0.72, "loss_cls": 2.96656, "loss": 2.96656, "time": 0.81561} +{"mode": "train", "epoch": 132, "iter": 3200, "lr": 0.00357, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46297, "top5_acc": 0.71984, "loss_cls": 2.98229, "loss": 2.98229, "time": 0.81078} +{"mode": "train", "epoch": 132, "iter": 3300, "lr": 0.00356, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46672, "top5_acc": 0.72594, "loss_cls": 2.94595, "loss": 2.94595, "time": 0.80976} +{"mode": "train", "epoch": 132, "iter": 3400, "lr": 0.00355, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46234, "top5_acc": 0.72234, "loss_cls": 2.98151, "loss": 2.98151, "time": 0.81099} +{"mode": "train", "epoch": 132, "iter": 3500, "lr": 0.00354, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46016, "top5_acc": 0.73016, "loss_cls": 2.9596, "loss": 2.9596, "time": 0.80964} +{"mode": "train", "epoch": 132, "iter": 3600, "lr": 0.00353, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46719, "top5_acc": 0.71375, "loss_cls": 2.99199, "loss": 2.99199, "time": 0.81041} +{"mode": "train", "epoch": 132, "iter": 3700, "lr": 0.00352, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46672, "top5_acc": 0.71719, "loss_cls": 2.97948, "loss": 2.97948, "time": 0.81268} +{"mode": "val", "epoch": 132, "iter": 309, "lr": 0.00351, "top1_acc": 0.38935, "top5_acc": 0.64494, "mean_class_accuracy": 0.3892} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.0035, "memory": 15990, "data_time": 1.31566, "top1_acc": 0.49188, "top5_acc": 0.74344, "loss_cls": 2.82817, "loss": 2.82817, "time": 2.29434} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00349, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48547, "top5_acc": 0.74797, "loss_cls": 2.83026, "loss": 2.83026, "time": 0.82214} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00348, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48969, "top5_acc": 0.74141, "loss_cls": 2.84807, "loss": 2.84807, "time": 0.81596} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00347, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48531, "top5_acc": 0.74281, "loss_cls": 2.85439, "loss": 2.85439, "time": 0.81857} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00346, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49094, "top5_acc": 0.74672, "loss_cls": 2.82467, "loss": 2.82467, "time": 0.82434} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00345, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48609, "top5_acc": 0.74094, "loss_cls": 2.84667, "loss": 2.84667, "time": 0.81353} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00344, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48078, "top5_acc": 0.74109, "loss_cls": 2.8803, "loss": 2.8803, "time": 0.81861} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00343, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48969, "top5_acc": 0.74516, "loss_cls": 2.81111, "loss": 2.81111, "time": 0.81882} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00342, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48188, "top5_acc": 0.73766, "loss_cls": 2.88123, "loss": 2.88123, "time": 0.81662} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.00341, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48984, "top5_acc": 0.74234, "loss_cls": 2.85059, "loss": 2.85059, "time": 0.8099} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0034, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47859, "top5_acc": 0.74156, "loss_cls": 2.86141, "loss": 2.86141, "time": 0.81109} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00339, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47766, "top5_acc": 0.72953, "loss_cls": 2.9258, "loss": 2.9258, "time": 0.81193} +{"mode": "train", "epoch": 133, "iter": 1300, "lr": 0.00338, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47422, "top5_acc": 0.73156, "loss_cls": 2.91446, "loss": 2.91446, "time": 0.80993} +{"mode": "train", "epoch": 133, "iter": 1400, "lr": 0.00337, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47656, "top5_acc": 0.72922, "loss_cls": 2.90071, "loss": 2.90071, "time": 0.80945} +{"mode": "train", "epoch": 133, "iter": 1500, "lr": 0.00336, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47312, "top5_acc": 0.73484, "loss_cls": 2.90493, "loss": 2.90493, "time": 0.81021} +{"mode": "train", "epoch": 133, "iter": 1600, "lr": 0.00335, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48828, "top5_acc": 0.73922, "loss_cls": 2.85355, "loss": 2.85355, "time": 0.81235} +{"mode": "train", "epoch": 133, "iter": 1700, "lr": 0.00334, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48094, "top5_acc": 0.73859, "loss_cls": 2.87313, "loss": 2.87313, "time": 0.80803} +{"mode": "train", "epoch": 133, "iter": 1800, "lr": 0.00333, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47297, "top5_acc": 0.72688, "loss_cls": 2.91707, "loss": 2.91707, "time": 0.8153} +{"mode": "train", "epoch": 133, "iter": 1900, "lr": 0.00332, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47766, "top5_acc": 0.73797, "loss_cls": 2.87913, "loss": 2.87913, "time": 0.81138} +{"mode": "train", "epoch": 133, "iter": 2000, "lr": 0.00331, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48125, "top5_acc": 0.73406, "loss_cls": 2.88942, "loss": 2.88942, "time": 0.81175} +{"mode": "train", "epoch": 133, "iter": 2100, "lr": 0.0033, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48453, "top5_acc": 0.73359, "loss_cls": 2.89775, "loss": 2.89775, "time": 0.81565} +{"mode": "train", "epoch": 133, "iter": 2200, "lr": 0.00329, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48469, "top5_acc": 0.73594, "loss_cls": 2.8553, "loss": 2.8553, "time": 0.80765} +{"mode": "train", "epoch": 133, "iter": 2300, "lr": 0.00328, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48672, "top5_acc": 0.74094, "loss_cls": 2.85444, "loss": 2.85444, "time": 0.81558} +{"mode": "train", "epoch": 133, "iter": 2400, "lr": 0.00327, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48078, "top5_acc": 0.73406, "loss_cls": 2.89592, "loss": 2.89592, "time": 0.81364} +{"mode": "train", "epoch": 133, "iter": 2500, "lr": 0.00326, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46297, "top5_acc": 0.72469, "loss_cls": 2.95459, "loss": 2.95459, "time": 0.82001} +{"mode": "train", "epoch": 133, "iter": 2600, "lr": 0.00325, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48641, "top5_acc": 0.73078, "loss_cls": 2.88051, "loss": 2.88051, "time": 0.80943} +{"mode": "train", "epoch": 133, "iter": 2700, "lr": 0.00324, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46891, "top5_acc": 0.73156, "loss_cls": 2.90685, "loss": 2.90685, "time": 0.80987} +{"mode": "train", "epoch": 133, "iter": 2800, "lr": 0.00323, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46984, "top5_acc": 0.72031, "loss_cls": 2.94047, "loss": 2.94047, "time": 0.81561} +{"mode": "train", "epoch": 133, "iter": 2900, "lr": 0.00322, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47406, "top5_acc": 0.73234, "loss_cls": 2.90065, "loss": 2.90065, "time": 0.81664} +{"mode": "train", "epoch": 133, "iter": 3000, "lr": 0.00321, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47703, "top5_acc": 0.73781, "loss_cls": 2.9018, "loss": 2.9018, "time": 0.81268} +{"mode": "train", "epoch": 133, "iter": 3100, "lr": 0.0032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47062, "top5_acc": 0.73516, "loss_cls": 2.91509, "loss": 2.91509, "time": 0.81639} +{"mode": "train", "epoch": 133, "iter": 3200, "lr": 0.00319, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47625, "top5_acc": 0.72984, "loss_cls": 2.90328, "loss": 2.90328, "time": 0.81246} +{"mode": "train", "epoch": 133, "iter": 3300, "lr": 0.00318, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47047, "top5_acc": 0.73203, "loss_cls": 2.88822, "loss": 2.88822, "time": 0.81434} +{"mode": "train", "epoch": 133, "iter": 3400, "lr": 0.00317, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46922, "top5_acc": 0.72969, "loss_cls": 2.91754, "loss": 2.91754, "time": 0.81783} +{"mode": "train", "epoch": 133, "iter": 3500, "lr": 0.00316, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49734, "top5_acc": 0.73703, "loss_cls": 2.85166, "loss": 2.85166, "time": 0.81442} +{"mode": "train", "epoch": 133, "iter": 3600, "lr": 0.00315, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48203, "top5_acc": 0.73391, "loss_cls": 2.8883, "loss": 2.8883, "time": 0.81094} +{"mode": "train", "epoch": 133, "iter": 3700, "lr": 0.00314, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47031, "top5_acc": 0.72266, "loss_cls": 2.9538, "loss": 2.9538, "time": 0.81061} +{"mode": "val", "epoch": 133, "iter": 309, "lr": 0.00314, "top1_acc": 0.38763, "top5_acc": 0.64428, "mean_class_accuracy": 0.38741} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00313, "memory": 15990, "data_time": 1.31873, "top1_acc": 0.51234, "top5_acc": 0.76312, "loss_cls": 2.73002, "loss": 2.73002, "time": 2.29258} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00312, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50078, "top5_acc": 0.75578, "loss_cls": 2.73533, "loss": 2.73533, "time": 0.81485} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00311, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49984, "top5_acc": 0.74969, "loss_cls": 2.79508, "loss": 2.79508, "time": 0.81268} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.0031, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49094, "top5_acc": 0.74844, "loss_cls": 2.81803, "loss": 2.81803, "time": 0.81856} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00309, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49547, "top5_acc": 0.74875, "loss_cls": 2.81443, "loss": 2.81443, "time": 0.8151} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00308, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49438, "top5_acc": 0.74609, "loss_cls": 2.77385, "loss": 2.77385, "time": 0.81607} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00307, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48469, "top5_acc": 0.74062, "loss_cls": 2.8348, "loss": 2.8348, "time": 0.81562} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00306, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49312, "top5_acc": 0.74406, "loss_cls": 2.82822, "loss": 2.82822, "time": 0.80963} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00305, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49078, "top5_acc": 0.75281, "loss_cls": 2.81251, "loss": 2.81251, "time": 0.8128} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00304, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49906, "top5_acc": 0.75484, "loss_cls": 2.78084, "loss": 2.78084, "time": 0.81952} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00303, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49469, "top5_acc": 0.74438, "loss_cls": 2.81617, "loss": 2.81617, "time": 0.8123} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.00302, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48359, "top5_acc": 0.73641, "loss_cls": 2.83729, "loss": 2.83729, "time": 0.81224} +{"mode": "train", "epoch": 134, "iter": 1300, "lr": 0.00301, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49188, "top5_acc": 0.73719, "loss_cls": 2.83952, "loss": 2.83952, "time": 0.81042} +{"mode": "train", "epoch": 134, "iter": 1400, "lr": 0.003, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49922, "top5_acc": 0.74562, "loss_cls": 2.80471, "loss": 2.80471, "time": 0.81855} +{"mode": "train", "epoch": 134, "iter": 1500, "lr": 0.00299, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49281, "top5_acc": 0.73969, "loss_cls": 2.82277, "loss": 2.82277, "time": 0.8137} +{"mode": "train", "epoch": 134, "iter": 1600, "lr": 0.00298, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48734, "top5_acc": 0.73844, "loss_cls": 2.85821, "loss": 2.85821, "time": 0.81325} +{"mode": "train", "epoch": 134, "iter": 1700, "lr": 0.00297, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.47859, "top5_acc": 0.74438, "loss_cls": 2.86487, "loss": 2.86487, "time": 0.80972} +{"mode": "train", "epoch": 134, "iter": 1800, "lr": 0.00296, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48031, "top5_acc": 0.74406, "loss_cls": 2.85579, "loss": 2.85579, "time": 0.80821} +{"mode": "train", "epoch": 134, "iter": 1900, "lr": 0.00295, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48078, "top5_acc": 0.73062, "loss_cls": 2.85404, "loss": 2.85404, "time": 0.8172} +{"mode": "train", "epoch": 134, "iter": 2000, "lr": 0.00294, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49531, "top5_acc": 0.74656, "loss_cls": 2.81422, "loss": 2.81422, "time": 0.80927} +{"mode": "train", "epoch": 134, "iter": 2100, "lr": 0.00293, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48328, "top5_acc": 0.73734, "loss_cls": 2.87786, "loss": 2.87786, "time": 0.81081} +{"mode": "train", "epoch": 134, "iter": 2200, "lr": 0.00293, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48969, "top5_acc": 0.7375, "loss_cls": 2.83745, "loss": 2.83745, "time": 0.81289} +{"mode": "train", "epoch": 134, "iter": 2300, "lr": 0.00292, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.48531, "top5_acc": 0.75188, "loss_cls": 2.81377, "loss": 2.81377, "time": 0.81711} +{"mode": "train", "epoch": 134, "iter": 2400, "lr": 0.00291, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47641, "top5_acc": 0.72609, "loss_cls": 2.89177, "loss": 2.89177, "time": 0.80992} +{"mode": "train", "epoch": 134, "iter": 2500, "lr": 0.0029, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48547, "top5_acc": 0.73859, "loss_cls": 2.86892, "loss": 2.86892, "time": 0.82134} +{"mode": "train", "epoch": 134, "iter": 2600, "lr": 0.00289, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48828, "top5_acc": 0.73766, "loss_cls": 2.85915, "loss": 2.85915, "time": 0.8135} +{"mode": "train", "epoch": 134, "iter": 2700, "lr": 0.00288, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48281, "top5_acc": 0.74359, "loss_cls": 2.83821, "loss": 2.83821, "time": 0.81424} +{"mode": "train", "epoch": 134, "iter": 2800, "lr": 0.00287, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48094, "top5_acc": 0.74609, "loss_cls": 2.85313, "loss": 2.85313, "time": 0.81219} +{"mode": "train", "epoch": 134, "iter": 2900, "lr": 0.00286, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48469, "top5_acc": 0.74203, "loss_cls": 2.86587, "loss": 2.86587, "time": 0.81183} +{"mode": "train", "epoch": 134, "iter": 3000, "lr": 0.00285, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48141, "top5_acc": 0.73578, "loss_cls": 2.90018, "loss": 2.90018, "time": 0.81391} +{"mode": "train", "epoch": 134, "iter": 3100, "lr": 0.00284, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48109, "top5_acc": 0.73828, "loss_cls": 2.8656, "loss": 2.8656, "time": 0.81266} +{"mode": "train", "epoch": 134, "iter": 3200, "lr": 0.00283, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48766, "top5_acc": 0.73078, "loss_cls": 2.89036, "loss": 2.89036, "time": 0.81303} +{"mode": "train", "epoch": 134, "iter": 3300, "lr": 0.00282, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.48828, "top5_acc": 0.74031, "loss_cls": 2.83559, "loss": 2.83559, "time": 0.8091} +{"mode": "train", "epoch": 134, "iter": 3400, "lr": 0.00281, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47219, "top5_acc": 0.73344, "loss_cls": 2.91099, "loss": 2.91099, "time": 0.80696} +{"mode": "train", "epoch": 134, "iter": 3500, "lr": 0.0028, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48656, "top5_acc": 0.74047, "loss_cls": 2.82631, "loss": 2.82631, "time": 0.81254} +{"mode": "train", "epoch": 134, "iter": 3600, "lr": 0.00279, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47953, "top5_acc": 0.73359, "loss_cls": 2.88724, "loss": 2.88724, "time": 0.81121} +{"mode": "train", "epoch": 134, "iter": 3700, "lr": 0.00279, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48172, "top5_acc": 0.73312, "loss_cls": 2.88814, "loss": 2.88814, "time": 0.81426} +{"mode": "val", "epoch": 134, "iter": 309, "lr": 0.00278, "top1_acc": 0.39143, "top5_acc": 0.65, "mean_class_accuracy": 0.39117} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00277, "memory": 15990, "data_time": 1.29624, "top1_acc": 0.5075, "top5_acc": 0.76156, "loss_cls": 2.72774, "loss": 2.72774, "time": 2.27153} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00276, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52062, "top5_acc": 0.77062, "loss_cls": 2.66365, "loss": 2.66365, "time": 0.81237} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00275, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51313, "top5_acc": 0.75703, "loss_cls": 2.73779, "loss": 2.73779, "time": 0.81391} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00274, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49531, "top5_acc": 0.74688, "loss_cls": 2.80726, "loss": 2.80726, "time": 0.81109} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00274, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51203, "top5_acc": 0.75797, "loss_cls": 2.73967, "loss": 2.73967, "time": 0.81901} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00273, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49578, "top5_acc": 0.75031, "loss_cls": 2.77576, "loss": 2.77576, "time": 0.81556} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00272, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4975, "top5_acc": 0.74969, "loss_cls": 2.77867, "loss": 2.77867, "time": 0.82461} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00271, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49703, "top5_acc": 0.75594, "loss_cls": 2.78491, "loss": 2.78491, "time": 0.81535} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.0027, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49422, "top5_acc": 0.75516, "loss_cls": 2.77691, "loss": 2.77691, "time": 0.81309} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00269, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49344, "top5_acc": 0.75125, "loss_cls": 2.7802, "loss": 2.7802, "time": 0.81443} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00268, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49766, "top5_acc": 0.74812, "loss_cls": 2.78504, "loss": 2.78504, "time": 0.81016} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00267, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49906, "top5_acc": 0.75172, "loss_cls": 2.77842, "loss": 2.77842, "time": 0.81224} +{"mode": "train", "epoch": 135, "iter": 1300, "lr": 0.00266, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48844, "top5_acc": 0.74672, "loss_cls": 2.81887, "loss": 2.81887, "time": 0.81184} +{"mode": "train", "epoch": 135, "iter": 1400, "lr": 0.00265, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50938, "top5_acc": 0.76562, "loss_cls": 2.71032, "loss": 2.71032, "time": 0.81473} +{"mode": "train", "epoch": 135, "iter": 1500, "lr": 0.00265, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49625, "top5_acc": 0.74625, "loss_cls": 2.81183, "loss": 2.81183, "time": 0.82704} +{"mode": "train", "epoch": 135, "iter": 1600, "lr": 0.00264, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49578, "top5_acc": 0.74672, "loss_cls": 2.80914, "loss": 2.80914, "time": 0.82341} +{"mode": "train", "epoch": 135, "iter": 1700, "lr": 0.00263, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49734, "top5_acc": 0.74578, "loss_cls": 2.80687, "loss": 2.80687, "time": 0.80801} +{"mode": "train", "epoch": 135, "iter": 1800, "lr": 0.00262, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48844, "top5_acc": 0.74656, "loss_cls": 2.82771, "loss": 2.82771, "time": 0.81501} +{"mode": "train", "epoch": 135, "iter": 1900, "lr": 0.00261, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.50266, "top5_acc": 0.74688, "loss_cls": 2.80206, "loss": 2.80206, "time": 0.81161} +{"mode": "train", "epoch": 135, "iter": 2000, "lr": 0.0026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48938, "top5_acc": 0.74688, "loss_cls": 2.82181, "loss": 2.82181, "time": 0.81265} +{"mode": "train", "epoch": 135, "iter": 2100, "lr": 0.00259, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49016, "top5_acc": 0.75844, "loss_cls": 2.78209, "loss": 2.78209, "time": 0.8132} +{"mode": "train", "epoch": 135, "iter": 2200, "lr": 0.00258, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47703, "top5_acc": 0.72984, "loss_cls": 2.9004, "loss": 2.9004, "time": 0.8157} +{"mode": "train", "epoch": 135, "iter": 2300, "lr": 0.00257, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49609, "top5_acc": 0.75109, "loss_cls": 2.77942, "loss": 2.77942, "time": 0.8156} +{"mode": "train", "epoch": 135, "iter": 2400, "lr": 0.00256, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.50172, "top5_acc": 0.74688, "loss_cls": 2.78648, "loss": 2.78648, "time": 0.81667} +{"mode": "train", "epoch": 135, "iter": 2500, "lr": 0.00256, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50281, "top5_acc": 0.74875, "loss_cls": 2.79913, "loss": 2.79913, "time": 0.81511} +{"mode": "train", "epoch": 135, "iter": 2600, "lr": 0.00255, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48609, "top5_acc": 0.74109, "loss_cls": 2.83301, "loss": 2.83301, "time": 0.80819} +{"mode": "train", "epoch": 135, "iter": 2700, "lr": 0.00254, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49203, "top5_acc": 0.74141, "loss_cls": 2.8426, "loss": 2.8426, "time": 0.80948} +{"mode": "train", "epoch": 135, "iter": 2800, "lr": 0.00253, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49109, "top5_acc": 0.74469, "loss_cls": 2.83638, "loss": 2.83638, "time": 0.81214} +{"mode": "train", "epoch": 135, "iter": 2900, "lr": 0.00252, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48422, "top5_acc": 0.74234, "loss_cls": 2.81124, "loss": 2.81124, "time": 0.81404} +{"mode": "train", "epoch": 135, "iter": 3000, "lr": 0.00251, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49016, "top5_acc": 0.74094, "loss_cls": 2.82926, "loss": 2.82926, "time": 0.81832} +{"mode": "train", "epoch": 135, "iter": 3100, "lr": 0.0025, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49281, "top5_acc": 0.74031, "loss_cls": 2.82066, "loss": 2.82066, "time": 0.81465} +{"mode": "train", "epoch": 135, "iter": 3200, "lr": 0.00249, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48859, "top5_acc": 0.73703, "loss_cls": 2.86202, "loss": 2.86202, "time": 0.81746} +{"mode": "train", "epoch": 135, "iter": 3300, "lr": 0.00249, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48531, "top5_acc": 0.74609, "loss_cls": 2.83352, "loss": 2.83352, "time": 0.82015} +{"mode": "train", "epoch": 135, "iter": 3400, "lr": 0.00248, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49344, "top5_acc": 0.74594, "loss_cls": 2.8344, "loss": 2.8344, "time": 0.81236} +{"mode": "train", "epoch": 135, "iter": 3500, "lr": 0.00247, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49609, "top5_acc": 0.74125, "loss_cls": 2.80789, "loss": 2.80789, "time": 0.81007} +{"mode": "train", "epoch": 135, "iter": 3600, "lr": 0.00246, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48562, "top5_acc": 0.74078, "loss_cls": 2.84217, "loss": 2.84217, "time": 0.80755} +{"mode": "train", "epoch": 135, "iter": 3700, "lr": 0.00245, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48766, "top5_acc": 0.74562, "loss_cls": 2.82568, "loss": 2.82568, "time": 0.81012} +{"mode": "val", "epoch": 135, "iter": 309, "lr": 0.00245, "top1_acc": 0.39817, "top5_acc": 0.65492, "mean_class_accuracy": 0.39799} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00244, "memory": 15990, "data_time": 1.26782, "top1_acc": 0.50422, "top5_acc": 0.75953, "loss_cls": 2.73149, "loss": 2.73149, "time": 2.23952} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.00243, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50781, "top5_acc": 0.76844, "loss_cls": 2.69522, "loss": 2.69522, "time": 0.81225} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00242, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50219, "top5_acc": 0.75812, "loss_cls": 2.75306, "loss": 2.75306, "time": 0.81393} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00241, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50906, "top5_acc": 0.76328, "loss_cls": 2.7212, "loss": 2.7212, "time": 0.80798} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.0024, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50969, "top5_acc": 0.76438, "loss_cls": 2.71551, "loss": 2.71551, "time": 0.8212} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.0024, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50484, "top5_acc": 0.76266, "loss_cls": 2.7251, "loss": 2.7251, "time": 0.81451} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00239, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.51016, "top5_acc": 0.76062, "loss_cls": 2.72073, "loss": 2.72073, "time": 0.81696} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00238, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49391, "top5_acc": 0.75734, "loss_cls": 2.75707, "loss": 2.75707, "time": 0.8175} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00237, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50703, "top5_acc": 0.75219, "loss_cls": 2.73623, "loss": 2.73623, "time": 0.81195} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00236, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51, "top5_acc": 0.75797, "loss_cls": 2.71255, "loss": 2.71255, "time": 0.80902} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00235, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50172, "top5_acc": 0.75094, "loss_cls": 2.75847, "loss": 2.75847, "time": 0.81504} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00234, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.49234, "top5_acc": 0.74906, "loss_cls": 2.7926, "loss": 2.7926, "time": 0.81188} +{"mode": "train", "epoch": 136, "iter": 1300, "lr": 0.00234, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50375, "top5_acc": 0.75531, "loss_cls": 2.71848, "loss": 2.71848, "time": 0.81547} +{"mode": "train", "epoch": 136, "iter": 1400, "lr": 0.00233, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50078, "top5_acc": 0.75578, "loss_cls": 2.78155, "loss": 2.78155, "time": 0.81617} +{"mode": "train", "epoch": 136, "iter": 1500, "lr": 0.00232, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50016, "top5_acc": 0.75406, "loss_cls": 2.75637, "loss": 2.75637, "time": 0.81111} +{"mode": "train", "epoch": 136, "iter": 1600, "lr": 0.00231, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50438, "top5_acc": 0.75609, "loss_cls": 2.76436, "loss": 2.76436, "time": 0.80952} +{"mode": "train", "epoch": 136, "iter": 1700, "lr": 0.0023, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.495, "top5_acc": 0.75688, "loss_cls": 2.76053, "loss": 2.76053, "time": 0.81399} +{"mode": "train", "epoch": 136, "iter": 1800, "lr": 0.00229, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50078, "top5_acc": 0.74188, "loss_cls": 2.79939, "loss": 2.79939, "time": 0.80991} +{"mode": "train", "epoch": 136, "iter": 1900, "lr": 0.00229, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49766, "top5_acc": 0.75078, "loss_cls": 2.77781, "loss": 2.77781, "time": 0.81525} +{"mode": "train", "epoch": 136, "iter": 2000, "lr": 0.00228, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50719, "top5_acc": 0.75234, "loss_cls": 2.74488, "loss": 2.74488, "time": 0.81337} +{"mode": "train", "epoch": 136, "iter": 2100, "lr": 0.00227, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4975, "top5_acc": 0.75891, "loss_cls": 2.74401, "loss": 2.74401, "time": 0.81457} +{"mode": "train", "epoch": 136, "iter": 2200, "lr": 0.00226, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49594, "top5_acc": 0.75359, "loss_cls": 2.77664, "loss": 2.77664, "time": 0.8193} +{"mode": "train", "epoch": 136, "iter": 2300, "lr": 0.00225, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50594, "top5_acc": 0.7575, "loss_cls": 2.75031, "loss": 2.75031, "time": 0.82251} +{"mode": "train", "epoch": 136, "iter": 2400, "lr": 0.00224, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50172, "top5_acc": 0.76266, "loss_cls": 2.72736, "loss": 2.72736, "time": 0.81518} +{"mode": "train", "epoch": 136, "iter": 2500, "lr": 0.00224, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49609, "top5_acc": 0.75266, "loss_cls": 2.78101, "loss": 2.78101, "time": 0.81458} +{"mode": "train", "epoch": 136, "iter": 2600, "lr": 0.00223, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49891, "top5_acc": 0.75516, "loss_cls": 2.78258, "loss": 2.78258, "time": 0.81184} +{"mode": "train", "epoch": 136, "iter": 2700, "lr": 0.00222, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.49938, "top5_acc": 0.74453, "loss_cls": 2.79381, "loss": 2.79381, "time": 0.81595} +{"mode": "train", "epoch": 136, "iter": 2800, "lr": 0.00221, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50594, "top5_acc": 0.75078, "loss_cls": 2.7707, "loss": 2.7707, "time": 0.80989} +{"mode": "train", "epoch": 136, "iter": 2900, "lr": 0.0022, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49812, "top5_acc": 0.75188, "loss_cls": 2.79085, "loss": 2.79085, "time": 0.81888} +{"mode": "train", "epoch": 136, "iter": 3000, "lr": 0.00219, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50422, "top5_acc": 0.74984, "loss_cls": 2.76041, "loss": 2.76041, "time": 0.81236} +{"mode": "train", "epoch": 136, "iter": 3100, "lr": 0.00219, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.505, "top5_acc": 0.75594, "loss_cls": 2.75223, "loss": 2.75223, "time": 0.82068} +{"mode": "train", "epoch": 136, "iter": 3200, "lr": 0.00218, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49641, "top5_acc": 0.75688, "loss_cls": 2.75883, "loss": 2.75883, "time": 0.8106} +{"mode": "train", "epoch": 136, "iter": 3300, "lr": 0.00217, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49953, "top5_acc": 0.75297, "loss_cls": 2.76377, "loss": 2.76377, "time": 0.81388} +{"mode": "train", "epoch": 136, "iter": 3400, "lr": 0.00216, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50656, "top5_acc": 0.75359, "loss_cls": 2.74971, "loss": 2.74971, "time": 0.81565} +{"mode": "train", "epoch": 136, "iter": 3500, "lr": 0.00215, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50141, "top5_acc": 0.74875, "loss_cls": 2.77979, "loss": 2.77979, "time": 0.8095} +{"mode": "train", "epoch": 136, "iter": 3600, "lr": 0.00215, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50313, "top5_acc": 0.75203, "loss_cls": 2.7578, "loss": 2.7578, "time": 0.81628} +{"mode": "train", "epoch": 136, "iter": 3700, "lr": 0.00214, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49266, "top5_acc": 0.74922, "loss_cls": 2.79301, "loss": 2.79301, "time": 0.80999} +{"mode": "val", "epoch": 136, "iter": 309, "lr": 0.00213, "top1_acc": 0.39518, "top5_acc": 0.65096, "mean_class_accuracy": 0.39496} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00213, "memory": 15990, "data_time": 1.26922, "top1_acc": 0.52125, "top5_acc": 0.76938, "loss_cls": 2.66085, "loss": 2.66085, "time": 2.23708} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00212, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51484, "top5_acc": 0.76062, "loss_cls": 2.70015, "loss": 2.70015, "time": 0.81844} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00211, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.525, "top5_acc": 0.77266, "loss_cls": 2.63018, "loss": 2.63018, "time": 0.81434} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.0021, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51328, "top5_acc": 0.76984, "loss_cls": 2.67081, "loss": 2.67081, "time": 0.81569} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.00209, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52906, "top5_acc": 0.77391, "loss_cls": 2.62181, "loss": 2.62181, "time": 0.82106} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.00209, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.51438, "top5_acc": 0.76578, "loss_cls": 2.68069, "loss": 2.68069, "time": 0.81888} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00208, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.51047, "top5_acc": 0.76594, "loss_cls": 2.69347, "loss": 2.69347, "time": 0.81152} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00207, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52453, "top5_acc": 0.76703, "loss_cls": 2.67124, "loss": 2.67124, "time": 0.81577} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00206, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.50203, "top5_acc": 0.76375, "loss_cls": 2.70115, "loss": 2.70115, "time": 0.81045} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00205, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50938, "top5_acc": 0.76766, "loss_cls": 2.67048, "loss": 2.67048, "time": 0.81632} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00205, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51391, "top5_acc": 0.7625, "loss_cls": 2.70717, "loss": 2.70717, "time": 0.81343} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00204, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51594, "top5_acc": 0.77078, "loss_cls": 2.65744, "loss": 2.65744, "time": 0.81565} +{"mode": "train", "epoch": 137, "iter": 1300, "lr": 0.00203, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51828, "top5_acc": 0.76672, "loss_cls": 2.6785, "loss": 2.6785, "time": 0.8105} +{"mode": "train", "epoch": 137, "iter": 1400, "lr": 0.00202, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50797, "top5_acc": 0.76219, "loss_cls": 2.71287, "loss": 2.71287, "time": 0.81553} +{"mode": "train", "epoch": 137, "iter": 1500, "lr": 0.00201, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52109, "top5_acc": 0.77328, "loss_cls": 2.64208, "loss": 2.64208, "time": 0.80833} +{"mode": "train", "epoch": 137, "iter": 1600, "lr": 0.00201, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50719, "top5_acc": 0.76109, "loss_cls": 2.73016, "loss": 2.73016, "time": 0.8152} +{"mode": "train", "epoch": 137, "iter": 1700, "lr": 0.002, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50734, "top5_acc": 0.75609, "loss_cls": 2.74858, "loss": 2.74858, "time": 0.80897} +{"mode": "train", "epoch": 137, "iter": 1800, "lr": 0.00199, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.51062, "top5_acc": 0.76109, "loss_cls": 2.74308, "loss": 2.74308, "time": 0.80925} +{"mode": "train", "epoch": 137, "iter": 1900, "lr": 0.00198, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51984, "top5_acc": 0.75984, "loss_cls": 2.69686, "loss": 2.69686, "time": 0.80877} +{"mode": "train", "epoch": 137, "iter": 2000, "lr": 0.00198, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51203, "top5_acc": 0.76391, "loss_cls": 2.6842, "loss": 2.6842, "time": 0.81273} +{"mode": "train", "epoch": 137, "iter": 2100, "lr": 0.00197, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51047, "top5_acc": 0.755, "loss_cls": 2.74522, "loss": 2.74522, "time": 0.81788} +{"mode": "train", "epoch": 137, "iter": 2200, "lr": 0.00196, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49859, "top5_acc": 0.75625, "loss_cls": 2.76715, "loss": 2.76715, "time": 0.81021} +{"mode": "train", "epoch": 137, "iter": 2300, "lr": 0.00195, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49922, "top5_acc": 0.7575, "loss_cls": 2.77819, "loss": 2.77819, "time": 0.81483} +{"mode": "train", "epoch": 137, "iter": 2400, "lr": 0.00194, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51313, "top5_acc": 0.76938, "loss_cls": 2.67533, "loss": 2.67533, "time": 0.81897} +{"mode": "train", "epoch": 137, "iter": 2500, "lr": 0.00194, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51172, "top5_acc": 0.76453, "loss_cls": 2.70331, "loss": 2.70331, "time": 0.81833} +{"mode": "train", "epoch": 137, "iter": 2600, "lr": 0.00193, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.50828, "top5_acc": 0.76406, "loss_cls": 2.73192, "loss": 2.73192, "time": 0.80925} +{"mode": "train", "epoch": 137, "iter": 2700, "lr": 0.00192, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50531, "top5_acc": 0.75578, "loss_cls": 2.74777, "loss": 2.74777, "time": 0.81554} +{"mode": "train", "epoch": 137, "iter": 2800, "lr": 0.00191, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50375, "top5_acc": 0.75984, "loss_cls": 2.72444, "loss": 2.72444, "time": 0.81474} +{"mode": "train", "epoch": 137, "iter": 2900, "lr": 0.00191, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49828, "top5_acc": 0.75047, "loss_cls": 2.7604, "loss": 2.7604, "time": 0.80929} +{"mode": "train", "epoch": 137, "iter": 3000, "lr": 0.0019, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51344, "top5_acc": 0.76391, "loss_cls": 2.68896, "loss": 2.68896, "time": 0.80482} +{"mode": "train", "epoch": 137, "iter": 3100, "lr": 0.00189, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49859, "top5_acc": 0.75922, "loss_cls": 2.72956, "loss": 2.72956, "time": 0.81622} +{"mode": "train", "epoch": 137, "iter": 3200, "lr": 0.00188, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49844, "top5_acc": 0.74891, "loss_cls": 2.76656, "loss": 2.76656, "time": 0.81767} +{"mode": "train", "epoch": 137, "iter": 3300, "lr": 0.00188, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50828, "top5_acc": 0.75391, "loss_cls": 2.73625, "loss": 2.73625, "time": 0.81899} +{"mode": "train", "epoch": 137, "iter": 3400, "lr": 0.00187, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.51, "top5_acc": 0.75234, "loss_cls": 2.7493, "loss": 2.7493, "time": 0.81052} +{"mode": "train", "epoch": 137, "iter": 3500, "lr": 0.00186, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51203, "top5_acc": 0.76547, "loss_cls": 2.70514, "loss": 2.70514, "time": 0.81429} +{"mode": "train", "epoch": 137, "iter": 3600, "lr": 0.00185, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.51219, "top5_acc": 0.75156, "loss_cls": 2.72258, "loss": 2.72258, "time": 0.80686} +{"mode": "train", "epoch": 137, "iter": 3700, "lr": 0.00185, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50703, "top5_acc": 0.75391, "loss_cls": 2.76208, "loss": 2.76208, "time": 0.80888} +{"mode": "val", "epoch": 137, "iter": 309, "lr": 0.00184, "top1_acc": 0.40242, "top5_acc": 0.65866, "mean_class_accuracy": 0.40222} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00183, "memory": 15990, "data_time": 1.25863, "top1_acc": 0.52875, "top5_acc": 0.77859, "loss_cls": 2.60872, "loss": 2.60872, "time": 2.231} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00183, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.52906, "top5_acc": 0.78375, "loss_cls": 2.58502, "loss": 2.58502, "time": 0.81096} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00182, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53109, "top5_acc": 0.77906, "loss_cls": 2.61806, "loss": 2.61806, "time": 0.81454} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00181, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52344, "top5_acc": 0.76547, "loss_cls": 2.67297, "loss": 2.67297, "time": 0.8169} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.0018, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53969, "top5_acc": 0.78422, "loss_cls": 2.55784, "loss": 2.55784, "time": 0.81506} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.0018, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51234, "top5_acc": 0.77141, "loss_cls": 2.68798, "loss": 2.68798, "time": 0.81906} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00179, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52641, "top5_acc": 0.77281, "loss_cls": 2.64555, "loss": 2.64555, "time": 0.81177} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00178, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52047, "top5_acc": 0.76781, "loss_cls": 2.66458, "loss": 2.66458, "time": 0.8238} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00177, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53406, "top5_acc": 0.78219, "loss_cls": 2.5731, "loss": 2.5731, "time": 0.82048} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00177, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51438, "top5_acc": 0.77172, "loss_cls": 2.66288, "loss": 2.66288, "time": 0.81792} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.00176, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52531, "top5_acc": 0.77031, "loss_cls": 2.64346, "loss": 2.64346, "time": 0.81508} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.00175, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51641, "top5_acc": 0.76984, "loss_cls": 2.63677, "loss": 2.63677, "time": 0.81323} +{"mode": "train", "epoch": 138, "iter": 1300, "lr": 0.00175, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50984, "top5_acc": 0.75766, "loss_cls": 2.70372, "loss": 2.70372, "time": 0.80974} +{"mode": "train", "epoch": 138, "iter": 1400, "lr": 0.00174, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51547, "top5_acc": 0.77391, "loss_cls": 2.65975, "loss": 2.65975, "time": 0.81929} +{"mode": "train", "epoch": 138, "iter": 1500, "lr": 0.00173, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51578, "top5_acc": 0.77172, "loss_cls": 2.67154, "loss": 2.67154, "time": 0.81464} +{"mode": "train", "epoch": 138, "iter": 1600, "lr": 0.00172, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53203, "top5_acc": 0.7775, "loss_cls": 2.60931, "loss": 2.60931, "time": 0.81046} +{"mode": "train", "epoch": 138, "iter": 1700, "lr": 0.00172, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52828, "top5_acc": 0.765, "loss_cls": 2.65302, "loss": 2.65302, "time": 0.81348} +{"mode": "train", "epoch": 138, "iter": 1800, "lr": 0.00171, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51922, "top5_acc": 0.76156, "loss_cls": 2.69561, "loss": 2.69561, "time": 0.81555} +{"mode": "train", "epoch": 138, "iter": 1900, "lr": 0.0017, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51969, "top5_acc": 0.76734, "loss_cls": 2.67436, "loss": 2.67436, "time": 0.81269} +{"mode": "train", "epoch": 138, "iter": 2000, "lr": 0.00169, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.52438, "top5_acc": 0.76781, "loss_cls": 2.65846, "loss": 2.65846, "time": 0.81326} +{"mode": "train", "epoch": 138, "iter": 2100, "lr": 0.00169, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52, "top5_acc": 0.77734, "loss_cls": 2.61059, "loss": 2.61059, "time": 0.81024} +{"mode": "train", "epoch": 138, "iter": 2200, "lr": 0.00168, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52453, "top5_acc": 0.75984, "loss_cls": 2.65661, "loss": 2.65661, "time": 0.81364} +{"mode": "train", "epoch": 138, "iter": 2300, "lr": 0.00167, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51234, "top5_acc": 0.76141, "loss_cls": 2.72449, "loss": 2.72449, "time": 0.81584} +{"mode": "train", "epoch": 138, "iter": 2400, "lr": 0.00167, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51297, "top5_acc": 0.76359, "loss_cls": 2.68678, "loss": 2.68678, "time": 0.80724} +{"mode": "train", "epoch": 138, "iter": 2500, "lr": 0.00166, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.52625, "top5_acc": 0.77078, "loss_cls": 2.62913, "loss": 2.62913, "time": 0.8201} +{"mode": "train", "epoch": 138, "iter": 2600, "lr": 0.00165, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52938, "top5_acc": 0.77125, "loss_cls": 2.64595, "loss": 2.64595, "time": 0.81336} +{"mode": "train", "epoch": 138, "iter": 2700, "lr": 0.00164, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51625, "top5_acc": 0.76297, "loss_cls": 2.6799, "loss": 2.6799, "time": 0.80895} +{"mode": "train", "epoch": 138, "iter": 2800, "lr": 0.00164, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50516, "top5_acc": 0.76594, "loss_cls": 2.70501, "loss": 2.70501, "time": 0.81643} +{"mode": "train", "epoch": 138, "iter": 2900, "lr": 0.00163, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51266, "top5_acc": 0.75953, "loss_cls": 2.71115, "loss": 2.71115, "time": 0.81161} +{"mode": "train", "epoch": 138, "iter": 3000, "lr": 0.00162, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52203, "top5_acc": 0.77281, "loss_cls": 2.63598, "loss": 2.63598, "time": 0.80917} +{"mode": "train", "epoch": 138, "iter": 3100, "lr": 0.00162, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51281, "top5_acc": 0.76547, "loss_cls": 2.68401, "loss": 2.68401, "time": 0.81851} +{"mode": "train", "epoch": 138, "iter": 3200, "lr": 0.00161, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51359, "top5_acc": 0.76469, "loss_cls": 2.68498, "loss": 2.68498, "time": 0.81312} +{"mode": "train", "epoch": 138, "iter": 3300, "lr": 0.0016, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52062, "top5_acc": 0.76641, "loss_cls": 2.66534, "loss": 2.66534, "time": 0.82184} +{"mode": "train", "epoch": 138, "iter": 3400, "lr": 0.0016, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50641, "top5_acc": 0.76375, "loss_cls": 2.69683, "loss": 2.69683, "time": 0.8137} +{"mode": "train", "epoch": 138, "iter": 3500, "lr": 0.00159, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51297, "top5_acc": 0.76641, "loss_cls": 2.68377, "loss": 2.68377, "time": 0.81782} +{"mode": "train", "epoch": 138, "iter": 3600, "lr": 0.00158, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52016, "top5_acc": 0.76469, "loss_cls": 2.66746, "loss": 2.66746, "time": 0.80783} +{"mode": "train", "epoch": 138, "iter": 3700, "lr": 0.00157, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.52219, "top5_acc": 0.76875, "loss_cls": 2.66836, "loss": 2.66836, "time": 0.81359} +{"mode": "val", "epoch": 138, "iter": 309, "lr": 0.00157, "top1_acc": 0.40865, "top5_acc": 0.66738, "mean_class_accuracy": 0.40843} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00156, "memory": 15990, "data_time": 1.25304, "top1_acc": 0.53875, "top5_acc": 0.79141, "loss_cls": 2.53988, "loss": 2.53988, "time": 2.21809} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00156, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53688, "top5_acc": 0.78688, "loss_cls": 2.56124, "loss": 2.56124, "time": 0.81192} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00155, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.54422, "top5_acc": 0.79, "loss_cls": 2.53093, "loss": 2.53093, "time": 0.81327} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00154, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53141, "top5_acc": 0.77656, "loss_cls": 2.59268, "loss": 2.59268, "time": 0.81502} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00154, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.52531, "top5_acc": 0.77578, "loss_cls": 2.6005, "loss": 2.6005, "time": 0.81995} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00153, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.53734, "top5_acc": 0.785, "loss_cls": 2.56091, "loss": 2.56091, "time": 0.81195} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00152, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52422, "top5_acc": 0.77359, "loss_cls": 2.62274, "loss": 2.62274, "time": 0.81717} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00152, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53094, "top5_acc": 0.77938, "loss_cls": 2.60149, "loss": 2.60149, "time": 0.8133} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00151, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53938, "top5_acc": 0.7775, "loss_cls": 2.56543, "loss": 2.56543, "time": 0.81205} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.0015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52562, "top5_acc": 0.78062, "loss_cls": 2.59444, "loss": 2.59444, "time": 0.81422} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.0015, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53828, "top5_acc": 0.77797, "loss_cls": 2.56371, "loss": 2.56371, "time": 0.81346} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00149, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53547, "top5_acc": 0.7725, "loss_cls": 2.59702, "loss": 2.59702, "time": 0.81962} +{"mode": "train", "epoch": 139, "iter": 1300, "lr": 0.00148, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.52141, "top5_acc": 0.7775, "loss_cls": 2.59651, "loss": 2.59651, "time": 0.81357} +{"mode": "train", "epoch": 139, "iter": 1400, "lr": 0.00148, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52828, "top5_acc": 0.78031, "loss_cls": 2.6082, "loss": 2.6082, "time": 0.81145} +{"mode": "train", "epoch": 139, "iter": 1500, "lr": 0.00147, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52875, "top5_acc": 0.77594, "loss_cls": 2.61505, "loss": 2.61505, "time": 0.81149} +{"mode": "train", "epoch": 139, "iter": 1600, "lr": 0.00146, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52031, "top5_acc": 0.77172, "loss_cls": 2.64799, "loss": 2.64799, "time": 0.81056} +{"mode": "train", "epoch": 139, "iter": 1700, "lr": 0.00145, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.53688, "top5_acc": 0.77781, "loss_cls": 2.60226, "loss": 2.60226, "time": 0.81359} +{"mode": "train", "epoch": 139, "iter": 1800, "lr": 0.00145, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53281, "top5_acc": 0.7775, "loss_cls": 2.58007, "loss": 2.58007, "time": 0.80856} +{"mode": "train", "epoch": 139, "iter": 1900, "lr": 0.00144, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52562, "top5_acc": 0.77625, "loss_cls": 2.60876, "loss": 2.60876, "time": 0.80732} +{"mode": "train", "epoch": 139, "iter": 2000, "lr": 0.00143, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51656, "top5_acc": 0.76828, "loss_cls": 2.66186, "loss": 2.66186, "time": 0.81265} +{"mode": "train", "epoch": 139, "iter": 2100, "lr": 0.00143, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52969, "top5_acc": 0.77406, "loss_cls": 2.62232, "loss": 2.62232, "time": 0.81636} +{"mode": "train", "epoch": 139, "iter": 2200, "lr": 0.00142, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.525, "top5_acc": 0.77516, "loss_cls": 2.61853, "loss": 2.61853, "time": 0.8131} +{"mode": "train", "epoch": 139, "iter": 2300, "lr": 0.00142, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52406, "top5_acc": 0.77016, "loss_cls": 2.6496, "loss": 2.6496, "time": 0.80908} +{"mode": "train", "epoch": 139, "iter": 2400, "lr": 0.00141, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52047, "top5_acc": 0.77203, "loss_cls": 2.60937, "loss": 2.60937, "time": 0.81314} +{"mode": "train", "epoch": 139, "iter": 2500, "lr": 0.0014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53797, "top5_acc": 0.77094, "loss_cls": 2.59682, "loss": 2.59682, "time": 0.80896} +{"mode": "train", "epoch": 139, "iter": 2600, "lr": 0.0014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52969, "top5_acc": 0.76906, "loss_cls": 2.63585, "loss": 2.63585, "time": 0.81232} +{"mode": "train", "epoch": 139, "iter": 2700, "lr": 0.00139, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.53, "top5_acc": 0.77344, "loss_cls": 2.61923, "loss": 2.61923, "time": 0.81853} +{"mode": "train", "epoch": 139, "iter": 2800, "lr": 0.00138, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52031, "top5_acc": 0.76703, "loss_cls": 2.65797, "loss": 2.65797, "time": 0.8145} +{"mode": "train", "epoch": 139, "iter": 2900, "lr": 0.00138, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52578, "top5_acc": 0.77, "loss_cls": 2.64512, "loss": 2.64512, "time": 0.8146} +{"mode": "train", "epoch": 139, "iter": 3000, "lr": 0.00137, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5225, "top5_acc": 0.76469, "loss_cls": 2.65937, "loss": 2.65937, "time": 0.81787} +{"mode": "train", "epoch": 139, "iter": 3100, "lr": 0.00136, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51953, "top5_acc": 0.77516, "loss_cls": 2.64431, "loss": 2.64431, "time": 0.81676} +{"mode": "train", "epoch": 139, "iter": 3200, "lr": 0.00136, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52484, "top5_acc": 0.77625, "loss_cls": 2.60294, "loss": 2.60294, "time": 0.81178} +{"mode": "train", "epoch": 139, "iter": 3300, "lr": 0.00135, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53547, "top5_acc": 0.78594, "loss_cls": 2.5726, "loss": 2.5726, "time": 0.81988} +{"mode": "train", "epoch": 139, "iter": 3400, "lr": 0.00134, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52531, "top5_acc": 0.76609, "loss_cls": 2.65793, "loss": 2.65793, "time": 0.82057} +{"mode": "train", "epoch": 139, "iter": 3500, "lr": 0.00134, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52, "top5_acc": 0.77109, "loss_cls": 2.6346, "loss": 2.6346, "time": 0.81013} +{"mode": "train", "epoch": 139, "iter": 3600, "lr": 0.00133, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52625, "top5_acc": 0.77016, "loss_cls": 2.63609, "loss": 2.63609, "time": 0.81884} +{"mode": "train", "epoch": 139, "iter": 3700, "lr": 0.00132, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52906, "top5_acc": 0.77703, "loss_cls": 2.63218, "loss": 2.63218, "time": 0.82199} +{"mode": "val", "epoch": 139, "iter": 309, "lr": 0.00132, "top1_acc": 0.41068, "top5_acc": 0.6689, "mean_class_accuracy": 0.41053} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00131, "memory": 15990, "data_time": 1.32381, "top1_acc": 0.54641, "top5_acc": 0.79719, "loss_cls": 2.49203, "loss": 2.49203, "time": 2.29959} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00131, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54969, "top5_acc": 0.78594, "loss_cls": 2.50801, "loss": 2.50801, "time": 0.82235} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.0013, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54891, "top5_acc": 0.78406, "loss_cls": 2.53799, "loss": 2.53799, "time": 0.81826} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.0013, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54844, "top5_acc": 0.79062, "loss_cls": 2.50519, "loss": 2.50519, "time": 0.81396} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00129, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.54203, "top5_acc": 0.77781, "loss_cls": 2.56277, "loss": 2.56277, "time": 0.81465} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.00128, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54312, "top5_acc": 0.78547, "loss_cls": 2.5457, "loss": 2.5457, "time": 0.81804} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.00128, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54984, "top5_acc": 0.78688, "loss_cls": 2.5443, "loss": 2.5443, "time": 0.81615} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00127, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53656, "top5_acc": 0.77875, "loss_cls": 2.55723, "loss": 2.55723, "time": 0.82321} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00126, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53797, "top5_acc": 0.77922, "loss_cls": 2.5564, "loss": 2.5564, "time": 0.81265} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00126, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54125, "top5_acc": 0.79125, "loss_cls": 2.53822, "loss": 2.53822, "time": 0.80651} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00125, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.535, "top5_acc": 0.78844, "loss_cls": 2.56571, "loss": 2.56571, "time": 0.81144} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00125, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54688, "top5_acc": 0.78594, "loss_cls": 2.52367, "loss": 2.52367, "time": 0.81249} +{"mode": "train", "epoch": 140, "iter": 1300, "lr": 0.00124, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.5275, "top5_acc": 0.77312, "loss_cls": 2.62352, "loss": 2.62352, "time": 0.81315} +{"mode": "train", "epoch": 140, "iter": 1400, "lr": 0.00123, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53734, "top5_acc": 0.77969, "loss_cls": 2.57496, "loss": 2.57496, "time": 0.81264} +{"mode": "train", "epoch": 140, "iter": 1500, "lr": 0.00123, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54109, "top5_acc": 0.77984, "loss_cls": 2.56866, "loss": 2.56866, "time": 0.81041} +{"mode": "train", "epoch": 140, "iter": 1600, "lr": 0.00122, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54672, "top5_acc": 0.78609, "loss_cls": 2.52137, "loss": 2.52137, "time": 0.813} +{"mode": "train", "epoch": 140, "iter": 1700, "lr": 0.00121, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54656, "top5_acc": 0.78719, "loss_cls": 2.51456, "loss": 2.51456, "time": 0.80978} +{"mode": "train", "epoch": 140, "iter": 1800, "lr": 0.00121, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53359, "top5_acc": 0.77812, "loss_cls": 2.59868, "loss": 2.59868, "time": 0.81049} +{"mode": "train", "epoch": 140, "iter": 1900, "lr": 0.0012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54578, "top5_acc": 0.78766, "loss_cls": 2.53641, "loss": 2.53641, "time": 0.81262} +{"mode": "train", "epoch": 140, "iter": 2000, "lr": 0.0012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53859, "top5_acc": 0.78219, "loss_cls": 2.57079, "loss": 2.57079, "time": 0.81014} +{"mode": "train", "epoch": 140, "iter": 2100, "lr": 0.00119, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.52578, "top5_acc": 0.77234, "loss_cls": 2.59426, "loss": 2.59426, "time": 0.81424} +{"mode": "train", "epoch": 140, "iter": 2200, "lr": 0.00118, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54078, "top5_acc": 0.77281, "loss_cls": 2.58887, "loss": 2.58887, "time": 0.81665} +{"mode": "train", "epoch": 140, "iter": 2300, "lr": 0.00118, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53938, "top5_acc": 0.79469, "loss_cls": 2.51813, "loss": 2.51813, "time": 0.81459} +{"mode": "train", "epoch": 140, "iter": 2400, "lr": 0.00117, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.53094, "top5_acc": 0.77906, "loss_cls": 2.58839, "loss": 2.58839, "time": 0.81963} +{"mode": "train", "epoch": 140, "iter": 2500, "lr": 0.00117, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53625, "top5_acc": 0.77719, "loss_cls": 2.58457, "loss": 2.58457, "time": 0.81883} +{"mode": "train", "epoch": 140, "iter": 2600, "lr": 0.00116, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.53469, "top5_acc": 0.78375, "loss_cls": 2.57148, "loss": 2.57148, "time": 0.81202} +{"mode": "train", "epoch": 140, "iter": 2700, "lr": 0.00115, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.52953, "top5_acc": 0.78109, "loss_cls": 2.57801, "loss": 2.57801, "time": 0.81732} +{"mode": "train", "epoch": 140, "iter": 2800, "lr": 0.00115, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54328, "top5_acc": 0.77766, "loss_cls": 2.56531, "loss": 2.56531, "time": 0.81362} +{"mode": "train", "epoch": 140, "iter": 2900, "lr": 0.00114, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53359, "top5_acc": 0.77812, "loss_cls": 2.56996, "loss": 2.56996, "time": 0.82076} +{"mode": "train", "epoch": 140, "iter": 3000, "lr": 0.00114, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53891, "top5_acc": 0.78359, "loss_cls": 2.55164, "loss": 2.55164, "time": 0.80856} +{"mode": "train", "epoch": 140, "iter": 3100, "lr": 0.00113, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52891, "top5_acc": 0.78406, "loss_cls": 2.57466, "loss": 2.57466, "time": 0.81721} +{"mode": "train", "epoch": 140, "iter": 3200, "lr": 0.00112, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53141, "top5_acc": 0.78078, "loss_cls": 2.58686, "loss": 2.58686, "time": 0.81554} +{"mode": "train", "epoch": 140, "iter": 3300, "lr": 0.00112, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53375, "top5_acc": 0.78484, "loss_cls": 2.5718, "loss": 2.5718, "time": 0.82179} +{"mode": "train", "epoch": 140, "iter": 3400, "lr": 0.00111, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.53297, "top5_acc": 0.77547, "loss_cls": 2.58905, "loss": 2.58905, "time": 0.81439} +{"mode": "train", "epoch": 140, "iter": 3500, "lr": 0.00111, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52625, "top5_acc": 0.77984, "loss_cls": 2.60396, "loss": 2.60396, "time": 0.81597} +{"mode": "train", "epoch": 140, "iter": 3600, "lr": 0.0011, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53609, "top5_acc": 0.78703, "loss_cls": 2.55206, "loss": 2.55206, "time": 0.81537} +{"mode": "train", "epoch": 140, "iter": 3700, "lr": 0.0011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53625, "top5_acc": 0.78797, "loss_cls": 2.53432, "loss": 2.53432, "time": 0.81295} +{"mode": "val", "epoch": 140, "iter": 309, "lr": 0.00109, "top1_acc": 0.41169, "top5_acc": 0.67148, "mean_class_accuracy": 0.41157} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00109, "memory": 15990, "data_time": 1.28225, "top1_acc": 0.55578, "top5_acc": 0.80141, "loss_cls": 2.45067, "loss": 2.45067, "time": 2.25722} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00108, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54812, "top5_acc": 0.79359, "loss_cls": 2.49526, "loss": 2.49526, "time": 0.81967} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00108, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54875, "top5_acc": 0.79484, "loss_cls": 2.49882, "loss": 2.49882, "time": 0.81846} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00107, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56016, "top5_acc": 0.78984, "loss_cls": 2.45899, "loss": 2.45899, "time": 0.81892} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00106, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54656, "top5_acc": 0.78391, "loss_cls": 2.53585, "loss": 2.53585, "time": 0.81365} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00106, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55031, "top5_acc": 0.79109, "loss_cls": 2.50473, "loss": 2.50473, "time": 0.81032} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00105, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54156, "top5_acc": 0.79422, "loss_cls": 2.51133, "loss": 2.51133, "time": 0.81319} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00105, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55078, "top5_acc": 0.79016, "loss_cls": 2.49909, "loss": 2.49909, "time": 0.81018} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00104, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55219, "top5_acc": 0.79359, "loss_cls": 2.50705, "loss": 2.50705, "time": 0.8139} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00104, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54906, "top5_acc": 0.78688, "loss_cls": 2.5136, "loss": 2.5136, "time": 0.81084} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00103, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54703, "top5_acc": 0.8, "loss_cls": 2.48464, "loss": 2.48464, "time": 0.8121} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00102, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54828, "top5_acc": 0.79875, "loss_cls": 2.4937, "loss": 2.4937, "time": 0.81534} +{"mode": "train", "epoch": 141, "iter": 1300, "lr": 0.00102, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54266, "top5_acc": 0.78484, "loss_cls": 2.5577, "loss": 2.5577, "time": 0.81533} +{"mode": "train", "epoch": 141, "iter": 1400, "lr": 0.00101, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55562, "top5_acc": 0.78969, "loss_cls": 2.50923, "loss": 2.50923, "time": 0.80806} +{"mode": "train", "epoch": 141, "iter": 1500, "lr": 0.00101, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55188, "top5_acc": 0.79422, "loss_cls": 2.48262, "loss": 2.48262, "time": 0.80987} +{"mode": "train", "epoch": 141, "iter": 1600, "lr": 0.001, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54594, "top5_acc": 0.79062, "loss_cls": 2.5185, "loss": 2.5185, "time": 0.81165} +{"mode": "train", "epoch": 141, "iter": 1700, "lr": 0.001, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.54875, "top5_acc": 0.79328, "loss_cls": 2.49119, "loss": 2.49119, "time": 0.80847} +{"mode": "train", "epoch": 141, "iter": 1800, "lr": 0.00099, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54531, "top5_acc": 0.78297, "loss_cls": 2.54822, "loss": 2.54822, "time": 0.81228} +{"mode": "train", "epoch": 141, "iter": 1900, "lr": 0.00099, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.5625, "top5_acc": 0.79422, "loss_cls": 2.4574, "loss": 2.4574, "time": 0.81291} +{"mode": "train", "epoch": 141, "iter": 2000, "lr": 0.00098, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54578, "top5_acc": 0.78859, "loss_cls": 2.52093, "loss": 2.52093, "time": 0.81055} +{"mode": "train", "epoch": 141, "iter": 2100, "lr": 0.00097, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55094, "top5_acc": 0.78922, "loss_cls": 2.50199, "loss": 2.50199, "time": 0.81124} +{"mode": "train", "epoch": 141, "iter": 2200, "lr": 0.00097, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55094, "top5_acc": 0.79516, "loss_cls": 2.48792, "loss": 2.48792, "time": 0.8119} +{"mode": "train", "epoch": 141, "iter": 2300, "lr": 0.00096, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54031, "top5_acc": 0.78969, "loss_cls": 2.51726, "loss": 2.51726, "time": 0.80994} +{"mode": "train", "epoch": 141, "iter": 2400, "lr": 0.00096, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55141, "top5_acc": 0.78922, "loss_cls": 2.49756, "loss": 2.49756, "time": 0.81125} +{"mode": "train", "epoch": 141, "iter": 2500, "lr": 0.00095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54937, "top5_acc": 0.7925, "loss_cls": 2.49924, "loss": 2.49924, "time": 0.81146} +{"mode": "train", "epoch": 141, "iter": 2600, "lr": 0.00095, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54344, "top5_acc": 0.78516, "loss_cls": 2.5243, "loss": 2.5243, "time": 0.81635} +{"mode": "train", "epoch": 141, "iter": 2700, "lr": 0.00094, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.54375, "top5_acc": 0.78844, "loss_cls": 2.54359, "loss": 2.54359, "time": 0.81682} +{"mode": "train", "epoch": 141, "iter": 2800, "lr": 0.00094, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55328, "top5_acc": 0.79672, "loss_cls": 2.46993, "loss": 2.46993, "time": 0.81707} +{"mode": "train", "epoch": 141, "iter": 2900, "lr": 0.00093, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54937, "top5_acc": 0.78875, "loss_cls": 2.51431, "loss": 2.51431, "time": 0.81433} +{"mode": "train", "epoch": 141, "iter": 3000, "lr": 0.00093, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55672, "top5_acc": 0.78625, "loss_cls": 2.49081, "loss": 2.49081, "time": 0.82004} +{"mode": "train", "epoch": 141, "iter": 3100, "lr": 0.00092, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53594, "top5_acc": 0.78141, "loss_cls": 2.53798, "loss": 2.53798, "time": 0.81559} +{"mode": "train", "epoch": 141, "iter": 3200, "lr": 0.00091, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54719, "top5_acc": 0.78953, "loss_cls": 2.51781, "loss": 2.51781, "time": 0.81429} +{"mode": "train", "epoch": 141, "iter": 3300, "lr": 0.00091, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54297, "top5_acc": 0.78812, "loss_cls": 2.5209, "loss": 2.5209, "time": 0.817} +{"mode": "train", "epoch": 141, "iter": 3400, "lr": 0.0009, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.5475, "top5_acc": 0.79078, "loss_cls": 2.52222, "loss": 2.52222, "time": 0.81564} +{"mode": "train", "epoch": 141, "iter": 3500, "lr": 0.0009, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53812, "top5_acc": 0.78844, "loss_cls": 2.57448, "loss": 2.57448, "time": 0.81717} +{"mode": "train", "epoch": 141, "iter": 3600, "lr": 0.00089, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.53359, "top5_acc": 0.79125, "loss_cls": 2.54039, "loss": 2.54039, "time": 0.81207} +{"mode": "train", "epoch": 141, "iter": 3700, "lr": 0.00089, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54188, "top5_acc": 0.78531, "loss_cls": 2.5454, "loss": 2.5454, "time": 0.81409} +{"mode": "val", "epoch": 141, "iter": 309, "lr": 0.00089, "top1_acc": 0.41736, "top5_acc": 0.67138, "mean_class_accuracy": 0.41722} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00088, "memory": 15990, "data_time": 1.28175, "top1_acc": 0.55031, "top5_acc": 0.79641, "loss_cls": 2.47021, "loss": 2.47021, "time": 2.25283} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00088, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56375, "top5_acc": 0.80203, "loss_cls": 2.44189, "loss": 2.44189, "time": 0.8151} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00087, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5675, "top5_acc": 0.80812, "loss_cls": 2.4077, "loss": 2.4077, "time": 0.81262} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00086, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56141, "top5_acc": 0.79656, "loss_cls": 2.44734, "loss": 2.44734, "time": 0.81281} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.00086, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57516, "top5_acc": 0.81531, "loss_cls": 2.34885, "loss": 2.34885, "time": 0.80975} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.00085, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56156, "top5_acc": 0.80469, "loss_cls": 2.42755, "loss": 2.42755, "time": 0.80684} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.00085, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56234, "top5_acc": 0.80047, "loss_cls": 2.42365, "loss": 2.42365, "time": 0.81437} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00084, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56172, "top5_acc": 0.80188, "loss_cls": 2.43287, "loss": 2.43287, "time": 0.81353} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00084, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56719, "top5_acc": 0.80359, "loss_cls": 2.41487, "loss": 2.41487, "time": 0.82146} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00083, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.565, "top5_acc": 0.80297, "loss_cls": 2.42241, "loss": 2.42241, "time": 0.8196} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00083, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56156, "top5_acc": 0.79625, "loss_cls": 2.44308, "loss": 2.44308, "time": 0.82244} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00082, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56672, "top5_acc": 0.79266, "loss_cls": 2.46116, "loss": 2.46116, "time": 0.81018} +{"mode": "train", "epoch": 142, "iter": 1300, "lr": 0.00082, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5625, "top5_acc": 0.80141, "loss_cls": 2.44404, "loss": 2.44404, "time": 0.80739} +{"mode": "train", "epoch": 142, "iter": 1400, "lr": 0.00081, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56063, "top5_acc": 0.80484, "loss_cls": 2.44435, "loss": 2.44435, "time": 0.80981} +{"mode": "train", "epoch": 142, "iter": 1500, "lr": 0.00081, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.55594, "top5_acc": 0.79969, "loss_cls": 2.44803, "loss": 2.44803, "time": 0.81261} +{"mode": "train", "epoch": 142, "iter": 1600, "lr": 0.0008, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56484, "top5_acc": 0.79938, "loss_cls": 2.43195, "loss": 2.43195, "time": 0.81325} +{"mode": "train", "epoch": 142, "iter": 1700, "lr": 0.0008, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55953, "top5_acc": 0.80234, "loss_cls": 2.44062, "loss": 2.44062, "time": 0.8075} +{"mode": "train", "epoch": 142, "iter": 1800, "lr": 0.00079, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55297, "top5_acc": 0.79281, "loss_cls": 2.47183, "loss": 2.47183, "time": 0.81042} +{"mode": "train", "epoch": 142, "iter": 1900, "lr": 0.00079, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54875, "top5_acc": 0.79328, "loss_cls": 2.49129, "loss": 2.49129, "time": 0.81463} +{"mode": "train", "epoch": 142, "iter": 2000, "lr": 0.00078, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55688, "top5_acc": 0.79328, "loss_cls": 2.44418, "loss": 2.44418, "time": 0.80922} +{"mode": "train", "epoch": 142, "iter": 2100, "lr": 0.00078, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.5475, "top5_acc": 0.79375, "loss_cls": 2.49197, "loss": 2.49197, "time": 0.80783} +{"mode": "train", "epoch": 142, "iter": 2200, "lr": 0.00077, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.55047, "top5_acc": 0.79234, "loss_cls": 2.48411, "loss": 2.48411, "time": 0.81198} +{"mode": "train", "epoch": 142, "iter": 2300, "lr": 0.00077, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55656, "top5_acc": 0.79688, "loss_cls": 2.45162, "loss": 2.45162, "time": 0.81149} +{"mode": "train", "epoch": 142, "iter": 2400, "lr": 0.00076, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5675, "top5_acc": 0.79766, "loss_cls": 2.42701, "loss": 2.42701, "time": 0.81177} +{"mode": "train", "epoch": 142, "iter": 2500, "lr": 0.00076, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.55453, "top5_acc": 0.79391, "loss_cls": 2.50032, "loss": 2.50032, "time": 0.80933} +{"mode": "train", "epoch": 142, "iter": 2600, "lr": 0.00075, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55391, "top5_acc": 0.79359, "loss_cls": 2.49289, "loss": 2.49289, "time": 0.80585} +{"mode": "train", "epoch": 142, "iter": 2700, "lr": 0.00075, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55969, "top5_acc": 0.79953, "loss_cls": 2.45637, "loss": 2.45637, "time": 0.82344} +{"mode": "train", "epoch": 142, "iter": 2800, "lr": 0.00075, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54062, "top5_acc": 0.78766, "loss_cls": 2.52685, "loss": 2.52685, "time": 0.81025} +{"mode": "train", "epoch": 142, "iter": 2900, "lr": 0.00074, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54719, "top5_acc": 0.79156, "loss_cls": 2.51501, "loss": 2.51501, "time": 0.81783} +{"mode": "train", "epoch": 142, "iter": 3000, "lr": 0.00074, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55297, "top5_acc": 0.79562, "loss_cls": 2.48158, "loss": 2.48158, "time": 0.81027} +{"mode": "train", "epoch": 142, "iter": 3100, "lr": 0.00073, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55672, "top5_acc": 0.8025, "loss_cls": 2.43474, "loss": 2.43474, "time": 0.81351} +{"mode": "train", "epoch": 142, "iter": 3200, "lr": 0.00073, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54859, "top5_acc": 0.79062, "loss_cls": 2.4872, "loss": 2.4872, "time": 0.8147} +{"mode": "train", "epoch": 142, "iter": 3300, "lr": 0.00072, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55406, "top5_acc": 0.79547, "loss_cls": 2.46663, "loss": 2.46663, "time": 0.81074} +{"mode": "train", "epoch": 142, "iter": 3400, "lr": 0.00072, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55672, "top5_acc": 0.78562, "loss_cls": 2.50753, "loss": 2.50753, "time": 0.81346} +{"mode": "train", "epoch": 142, "iter": 3500, "lr": 0.00071, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53875, "top5_acc": 0.79391, "loss_cls": 2.50497, "loss": 2.50497, "time": 0.8132} +{"mode": "train", "epoch": 142, "iter": 3600, "lr": 0.00071, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.555, "top5_acc": 0.79734, "loss_cls": 2.4615, "loss": 2.4615, "time": 0.82156} +{"mode": "train", "epoch": 142, "iter": 3700, "lr": 0.0007, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55672, "top5_acc": 0.79312, "loss_cls": 2.47593, "loss": 2.47593, "time": 0.81583} +{"mode": "val", "epoch": 142, "iter": 309, "lr": 0.0007, "top1_acc": 0.41549, "top5_acc": 0.67188, "mean_class_accuracy": 0.41535} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.0007, "memory": 15990, "data_time": 1.30491, "top1_acc": 0.57672, "top5_acc": 0.81109, "loss_cls": 2.36047, "loss": 2.36047, "time": 2.28148} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00069, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.57297, "top5_acc": 0.8125, "loss_cls": 2.36124, "loss": 2.36124, "time": 0.82457} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00069, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57609, "top5_acc": 0.81031, "loss_cls": 2.37758, "loss": 2.37758, "time": 0.81655} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00068, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57094, "top5_acc": 0.80812, "loss_cls": 2.37851, "loss": 2.37851, "time": 0.8095} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00068, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.56422, "top5_acc": 0.80422, "loss_cls": 2.43406, "loss": 2.43406, "time": 0.81301} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00067, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56484, "top5_acc": 0.80422, "loss_cls": 2.39419, "loss": 2.39419, "time": 0.80676} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00067, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57312, "top5_acc": 0.80938, "loss_cls": 2.4003, "loss": 2.4003, "time": 0.80867} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00066, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56703, "top5_acc": 0.80062, "loss_cls": 2.42312, "loss": 2.42312, "time": 0.81661} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00066, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55484, "top5_acc": 0.80203, "loss_cls": 2.42468, "loss": 2.42468, "time": 0.81233} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57359, "top5_acc": 0.80531, "loss_cls": 2.37923, "loss": 2.37923, "time": 0.81034} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57312, "top5_acc": 0.81234, "loss_cls": 2.35462, "loss": 2.35462, "time": 0.81064} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57422, "top5_acc": 0.80719, "loss_cls": 2.36703, "loss": 2.36703, "time": 0.81275} +{"mode": "train", "epoch": 143, "iter": 1300, "lr": 0.00064, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.57188, "top5_acc": 0.80703, "loss_cls": 2.38436, "loss": 2.38436, "time": 0.80959} +{"mode": "train", "epoch": 143, "iter": 1400, "lr": 0.00064, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.555, "top5_acc": 0.79625, "loss_cls": 2.46167, "loss": 2.46167, "time": 0.81064} +{"mode": "train", "epoch": 143, "iter": 1500, "lr": 0.00063, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56391, "top5_acc": 0.80656, "loss_cls": 2.40817, "loss": 2.40817, "time": 0.8134} +{"mode": "train", "epoch": 143, "iter": 1600, "lr": 0.00063, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56641, "top5_acc": 0.80594, "loss_cls": 2.40539, "loss": 2.40539, "time": 0.81642} +{"mode": "train", "epoch": 143, "iter": 1700, "lr": 0.00062, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56656, "top5_acc": 0.80234, "loss_cls": 2.42644, "loss": 2.42644, "time": 0.81573} +{"mode": "train", "epoch": 143, "iter": 1800, "lr": 0.00062, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57016, "top5_acc": 0.80391, "loss_cls": 2.39303, "loss": 2.39303, "time": 0.81173} +{"mode": "train", "epoch": 143, "iter": 1900, "lr": 0.00061, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57078, "top5_acc": 0.8025, "loss_cls": 2.39273, "loss": 2.39273, "time": 0.81314} +{"mode": "train", "epoch": 143, "iter": 2000, "lr": 0.00061, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57141, "top5_acc": 0.80219, "loss_cls": 2.38896, "loss": 2.38896, "time": 0.81321} +{"mode": "train", "epoch": 143, "iter": 2100, "lr": 0.00061, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56172, "top5_acc": 0.80172, "loss_cls": 2.42127, "loss": 2.42127, "time": 0.81354} +{"mode": "train", "epoch": 143, "iter": 2200, "lr": 0.0006, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56875, "top5_acc": 0.81188, "loss_cls": 2.3713, "loss": 2.3713, "time": 0.81661} +{"mode": "train", "epoch": 143, "iter": 2300, "lr": 0.0006, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57094, "top5_acc": 0.80625, "loss_cls": 2.40931, "loss": 2.40931, "time": 0.81269} +{"mode": "train", "epoch": 143, "iter": 2400, "lr": 0.00059, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56891, "top5_acc": 0.79969, "loss_cls": 2.41556, "loss": 2.41556, "time": 0.8124} +{"mode": "train", "epoch": 143, "iter": 2500, "lr": 0.00059, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55937, "top5_acc": 0.79844, "loss_cls": 2.43577, "loss": 2.43577, "time": 0.8143} +{"mode": "train", "epoch": 143, "iter": 2600, "lr": 0.00058, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56422, "top5_acc": 0.80359, "loss_cls": 2.43062, "loss": 2.43062, "time": 0.80891} +{"mode": "train", "epoch": 143, "iter": 2700, "lr": 0.00058, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.5625, "top5_acc": 0.80406, "loss_cls": 2.41127, "loss": 2.41127, "time": 0.81476} +{"mode": "train", "epoch": 143, "iter": 2800, "lr": 0.00058, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57469, "top5_acc": 0.80375, "loss_cls": 2.40989, "loss": 2.40989, "time": 0.80978} +{"mode": "train", "epoch": 143, "iter": 2900, "lr": 0.00057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56547, "top5_acc": 0.80281, "loss_cls": 2.42511, "loss": 2.42511, "time": 0.81602} +{"mode": "train", "epoch": 143, "iter": 3000, "lr": 0.00057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56203, "top5_acc": 0.805, "loss_cls": 2.41871, "loss": 2.41871, "time": 0.80937} +{"mode": "train", "epoch": 143, "iter": 3100, "lr": 0.00056, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55672, "top5_acc": 0.79969, "loss_cls": 2.42257, "loss": 2.42257, "time": 0.81497} +{"mode": "train", "epoch": 143, "iter": 3200, "lr": 0.00056, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57094, "top5_acc": 0.80672, "loss_cls": 2.39438, "loss": 2.39438, "time": 0.81396} +{"mode": "train", "epoch": 143, "iter": 3300, "lr": 0.00055, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55234, "top5_acc": 0.79469, "loss_cls": 2.47484, "loss": 2.47484, "time": 0.81656} +{"mode": "train", "epoch": 143, "iter": 3400, "lr": 0.00055, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56219, "top5_acc": 0.8, "loss_cls": 2.44216, "loss": 2.44216, "time": 0.81449} +{"mode": "train", "epoch": 143, "iter": 3500, "lr": 0.00055, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56641, "top5_acc": 0.80344, "loss_cls": 2.39915, "loss": 2.39915, "time": 0.81199} +{"mode": "train", "epoch": 143, "iter": 3600, "lr": 0.00054, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55922, "top5_acc": 0.79906, "loss_cls": 2.44918, "loss": 2.44918, "time": 0.81348} +{"mode": "train", "epoch": 143, "iter": 3700, "lr": 0.00054, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56297, "top5_acc": 0.80641, "loss_cls": 2.39468, "loss": 2.39468, "time": 0.81052} +{"mode": "val", "epoch": 143, "iter": 309, "lr": 0.00054, "top1_acc": 0.42005, "top5_acc": 0.67523, "mean_class_accuracy": 0.41991} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00053, "memory": 15990, "data_time": 1.26214, "top1_acc": 0.58016, "top5_acc": 0.81969, "loss_cls": 2.33195, "loss": 2.33195, "time": 2.22919} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00053, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57797, "top5_acc": 0.81219, "loss_cls": 2.34691, "loss": 2.34691, "time": 0.81172} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00052, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58562, "top5_acc": 0.81969, "loss_cls": 2.31526, "loss": 2.31526, "time": 0.80886} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58859, "top5_acc": 0.81641, "loss_cls": 2.31734, "loss": 2.31734, "time": 0.81713} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57438, "top5_acc": 0.81469, "loss_cls": 2.35811, "loss": 2.35811, "time": 0.8114} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00051, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57328, "top5_acc": 0.81359, "loss_cls": 2.36076, "loss": 2.36076, "time": 0.81654} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00051, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57359, "top5_acc": 0.80891, "loss_cls": 2.36927, "loss": 2.36927, "time": 0.8073} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.0005, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57734, "top5_acc": 0.80891, "loss_cls": 2.36841, "loss": 2.36841, "time": 0.8129} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.0005, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57734, "top5_acc": 0.81359, "loss_cls": 2.33246, "loss": 2.33246, "time": 0.8103} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.0005, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58531, "top5_acc": 0.81828, "loss_cls": 2.31434, "loss": 2.31434, "time": 0.80948} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.00049, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58391, "top5_acc": 0.81328, "loss_cls": 2.34372, "loss": 2.34372, "time": 0.81192} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.00049, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57453, "top5_acc": 0.80594, "loss_cls": 2.37026, "loss": 2.37026, "time": 0.81737} +{"mode": "train", "epoch": 144, "iter": 1300, "lr": 0.00048, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57719, "top5_acc": 0.81469, "loss_cls": 2.36124, "loss": 2.36124, "time": 0.81451} +{"mode": "train", "epoch": 144, "iter": 1400, "lr": 0.00048, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57391, "top5_acc": 0.80844, "loss_cls": 2.38578, "loss": 2.38578, "time": 0.8101} +{"mode": "train", "epoch": 144, "iter": 1500, "lr": 0.00048, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57641, "top5_acc": 0.82062, "loss_cls": 2.35216, "loss": 2.35216, "time": 0.81441} +{"mode": "train", "epoch": 144, "iter": 1600, "lr": 0.00047, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57484, "top5_acc": 0.81875, "loss_cls": 2.34629, "loss": 2.34629, "time": 0.81004} +{"mode": "train", "epoch": 144, "iter": 1700, "lr": 0.00047, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57172, "top5_acc": 0.80594, "loss_cls": 2.39654, "loss": 2.39654, "time": 0.80941} +{"mode": "train", "epoch": 144, "iter": 1800, "lr": 0.00047, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56734, "top5_acc": 0.80781, "loss_cls": 2.39499, "loss": 2.39499, "time": 0.81388} +{"mode": "train", "epoch": 144, "iter": 1900, "lr": 0.00046, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57281, "top5_acc": 0.81266, "loss_cls": 2.36793, "loss": 2.36793, "time": 0.81576} +{"mode": "train", "epoch": 144, "iter": 2000, "lr": 0.00046, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58016, "top5_acc": 0.81312, "loss_cls": 2.34444, "loss": 2.34444, "time": 0.81051} +{"mode": "train", "epoch": 144, "iter": 2100, "lr": 0.00045, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.585, "top5_acc": 0.81, "loss_cls": 2.35355, "loss": 2.35355, "time": 0.8155} +{"mode": "train", "epoch": 144, "iter": 2200, "lr": 0.00045, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58094, "top5_acc": 0.81297, "loss_cls": 2.35235, "loss": 2.35235, "time": 0.81319} +{"mode": "train", "epoch": 144, "iter": 2300, "lr": 0.00045, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57547, "top5_acc": 0.80953, "loss_cls": 2.37106, "loss": 2.37106, "time": 0.82157} +{"mode": "train", "epoch": 144, "iter": 2400, "lr": 0.00044, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.58094, "top5_acc": 0.81562, "loss_cls": 2.34493, "loss": 2.34493, "time": 0.81162} +{"mode": "train", "epoch": 144, "iter": 2500, "lr": 0.00044, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57672, "top5_acc": 0.81312, "loss_cls": 2.35085, "loss": 2.35085, "time": 0.80724} +{"mode": "train", "epoch": 144, "iter": 2600, "lr": 0.00044, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57781, "top5_acc": 0.80781, "loss_cls": 2.37318, "loss": 2.37318, "time": 0.81137} +{"mode": "train", "epoch": 144, "iter": 2700, "lr": 0.00043, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58125, "top5_acc": 0.81688, "loss_cls": 2.33118, "loss": 2.33118, "time": 0.80786} +{"mode": "train", "epoch": 144, "iter": 2800, "lr": 0.00043, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56391, "top5_acc": 0.80562, "loss_cls": 2.39025, "loss": 2.39025, "time": 0.81372} +{"mode": "train", "epoch": 144, "iter": 2900, "lr": 0.00042, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58047, "top5_acc": 0.81031, "loss_cls": 2.35238, "loss": 2.35238, "time": 0.81129} +{"mode": "train", "epoch": 144, "iter": 3000, "lr": 0.00042, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57375, "top5_acc": 0.80969, "loss_cls": 2.35471, "loss": 2.35471, "time": 0.82288} +{"mode": "train", "epoch": 144, "iter": 3100, "lr": 0.00042, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57578, "top5_acc": 0.81062, "loss_cls": 2.3579, "loss": 2.3579, "time": 0.80618} +{"mode": "train", "epoch": 144, "iter": 3200, "lr": 0.00041, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56984, "top5_acc": 0.80469, "loss_cls": 2.37857, "loss": 2.37857, "time": 0.81031} +{"mode": "train", "epoch": 144, "iter": 3300, "lr": 0.00041, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58203, "top5_acc": 0.81375, "loss_cls": 2.3435, "loss": 2.3435, "time": 0.81669} +{"mode": "train", "epoch": 144, "iter": 3400, "lr": 0.00041, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56516, "top5_acc": 0.80141, "loss_cls": 2.40083, "loss": 2.40083, "time": 0.81659} +{"mode": "train", "epoch": 144, "iter": 3500, "lr": 0.0004, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.57312, "top5_acc": 0.80547, "loss_cls": 2.39097, "loss": 2.39097, "time": 0.81222} +{"mode": "train", "epoch": 144, "iter": 3600, "lr": 0.0004, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58391, "top5_acc": 0.81719, "loss_cls": 2.31772, "loss": 2.31772, "time": 0.80825} +{"mode": "train", "epoch": 144, "iter": 3700, "lr": 0.0004, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57688, "top5_acc": 0.80906, "loss_cls": 2.34995, "loss": 2.34995, "time": 0.81461} +{"mode": "val", "epoch": 144, "iter": 309, "lr": 0.00039, "top1_acc": 0.4239, "top5_acc": 0.67695, "mean_class_accuracy": 0.42373} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.00039, "memory": 15990, "data_time": 1.34595, "top1_acc": 0.59016, "top5_acc": 0.8225, "loss_cls": 2.27104, "loss": 2.27104, "time": 2.32573} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 0.00039, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.58547, "top5_acc": 0.81938, "loss_cls": 2.29361, "loss": 2.29361, "time": 0.83057} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 0.00038, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.58359, "top5_acc": 0.81609, "loss_cls": 2.29603, "loss": 2.29603, "time": 0.82768} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 0.00038, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.59031, "top5_acc": 0.82234, "loss_cls": 2.26608, "loss": 2.26608, "time": 0.82707} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 0.00038, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.59328, "top5_acc": 0.82281, "loss_cls": 2.2729, "loss": 2.2729, "time": 0.82185} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 0.00037, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58406, "top5_acc": 0.82156, "loss_cls": 2.31983, "loss": 2.31983, "time": 0.82278} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 0.00037, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57641, "top5_acc": 0.81719, "loss_cls": 2.34019, "loss": 2.34019, "time": 0.82981} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 0.00037, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58344, "top5_acc": 0.81609, "loss_cls": 2.31397, "loss": 2.31397, "time": 0.81822} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 0.00036, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58078, "top5_acc": 0.82297, "loss_cls": 2.30941, "loss": 2.30941, "time": 0.81896} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 0.00036, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58969, "top5_acc": 0.82281, "loss_cls": 2.30305, "loss": 2.30305, "time": 0.81471} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 0.00036, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.59516, "top5_acc": 0.81891, "loss_cls": 2.29579, "loss": 2.29579, "time": 0.81774} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 0.00035, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58453, "top5_acc": 0.81828, "loss_cls": 2.31932, "loss": 2.31932, "time": 0.81162} +{"mode": "train", "epoch": 145, "iter": 1300, "lr": 0.00035, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59328, "top5_acc": 0.82281, "loss_cls": 2.27579, "loss": 2.27579, "time": 0.81604} +{"mode": "train", "epoch": 145, "iter": 1400, "lr": 0.00035, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59281, "top5_acc": 0.82, "loss_cls": 2.28556, "loss": 2.28556, "time": 0.81309} +{"mode": "train", "epoch": 145, "iter": 1500, "lr": 0.00034, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58859, "top5_acc": 0.81141, "loss_cls": 2.32737, "loss": 2.32737, "time": 0.81153} +{"mode": "train", "epoch": 145, "iter": 1600, "lr": 0.00034, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59688, "top5_acc": 0.82812, "loss_cls": 2.27183, "loss": 2.27183, "time": 0.81163} +{"mode": "train", "epoch": 145, "iter": 1700, "lr": 0.00034, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57984, "top5_acc": 0.81438, "loss_cls": 2.32971, "loss": 2.32971, "time": 0.80661} +{"mode": "train", "epoch": 145, "iter": 1800, "lr": 0.00033, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57875, "top5_acc": 0.81047, "loss_cls": 2.35975, "loss": 2.35975, "time": 0.80862} +{"mode": "train", "epoch": 145, "iter": 1900, "lr": 0.00033, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57578, "top5_acc": 0.81875, "loss_cls": 2.31593, "loss": 2.31593, "time": 0.80835} +{"mode": "train", "epoch": 145, "iter": 2000, "lr": 0.00033, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.595, "top5_acc": 0.82812, "loss_cls": 2.26067, "loss": 2.26067, "time": 0.80613} +{"mode": "train", "epoch": 145, "iter": 2100, "lr": 0.00032, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58516, "top5_acc": 0.82172, "loss_cls": 2.30463, "loss": 2.30463, "time": 0.80834} +{"mode": "train", "epoch": 145, "iter": 2200, "lr": 0.00032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58469, "top5_acc": 0.82, "loss_cls": 2.2977, "loss": 2.2977, "time": 0.81315} +{"mode": "train", "epoch": 145, "iter": 2300, "lr": 0.00032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58703, "top5_acc": 0.81438, "loss_cls": 2.3399, "loss": 2.3399, "time": 0.8134} +{"mode": "train", "epoch": 145, "iter": 2400, "lr": 0.00031, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58094, "top5_acc": 0.81234, "loss_cls": 2.33904, "loss": 2.33904, "time": 0.80998} +{"mode": "train", "epoch": 145, "iter": 2500, "lr": 0.00031, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57641, "top5_acc": 0.8175, "loss_cls": 2.32932, "loss": 2.32932, "time": 0.80725} +{"mode": "train", "epoch": 145, "iter": 2600, "lr": 0.00031, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.58688, "top5_acc": 0.81875, "loss_cls": 2.30876, "loss": 2.30876, "time": 0.81626} +{"mode": "train", "epoch": 145, "iter": 2700, "lr": 0.00031, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58719, "top5_acc": 0.82031, "loss_cls": 2.30591, "loss": 2.30591, "time": 0.80711} +{"mode": "train", "epoch": 145, "iter": 2800, "lr": 0.0003, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.58656, "top5_acc": 0.81469, "loss_cls": 2.31691, "loss": 2.31691, "time": 0.82221} +{"mode": "train", "epoch": 145, "iter": 2900, "lr": 0.0003, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57688, "top5_acc": 0.81531, "loss_cls": 2.33259, "loss": 2.33259, "time": 0.81684} +{"mode": "train", "epoch": 145, "iter": 3000, "lr": 0.0003, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56797, "top5_acc": 0.80453, "loss_cls": 2.37746, "loss": 2.37746, "time": 0.81131} +{"mode": "train", "epoch": 145, "iter": 3100, "lr": 0.00029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58547, "top5_acc": 0.82156, "loss_cls": 2.31467, "loss": 2.31467, "time": 0.81639} +{"mode": "train", "epoch": 145, "iter": 3200, "lr": 0.00029, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59047, "top5_acc": 0.82797, "loss_cls": 2.27279, "loss": 2.27279, "time": 0.81024} +{"mode": "train", "epoch": 145, "iter": 3300, "lr": 0.00029, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.59344, "top5_acc": 0.81766, "loss_cls": 2.3113, "loss": 2.3113, "time": 0.82081} +{"mode": "train", "epoch": 145, "iter": 3400, "lr": 0.00028, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58156, "top5_acc": 0.81672, "loss_cls": 2.32368, "loss": 2.32368, "time": 0.81375} +{"mode": "train", "epoch": 145, "iter": 3500, "lr": 0.00028, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.58781, "top5_acc": 0.82141, "loss_cls": 2.30397, "loss": 2.30397, "time": 0.81866} +{"mode": "train", "epoch": 145, "iter": 3600, "lr": 0.00028, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57578, "top5_acc": 0.81359, "loss_cls": 2.33533, "loss": 2.33533, "time": 0.81038} +{"mode": "train", "epoch": 145, "iter": 3700, "lr": 0.00028, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57672, "top5_acc": 0.81234, "loss_cls": 2.3386, "loss": 2.3386, "time": 0.81405} +{"mode": "val", "epoch": 145, "iter": 309, "lr": 0.00027, "top1_acc": 0.42405, "top5_acc": 0.67619, "mean_class_accuracy": 0.42394} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 0.00027, "memory": 15990, "data_time": 1.29474, "top1_acc": 0.60812, "top5_acc": 0.83422, "loss_cls": 2.21452, "loss": 2.21452, "time": 2.27086} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 0.00027, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59562, "top5_acc": 0.82906, "loss_cls": 2.25201, "loss": 2.25201, "time": 0.81151} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 0.00027, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60125, "top5_acc": 0.83141, "loss_cls": 2.22243, "loss": 2.22243, "time": 0.81221} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 0.00026, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60078, "top5_acc": 0.82875, "loss_cls": 2.24398, "loss": 2.24398, "time": 0.81066} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 0.00026, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59406, "top5_acc": 0.82188, "loss_cls": 2.28507, "loss": 2.28507, "time": 0.81161} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 0.00026, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60094, "top5_acc": 0.82547, "loss_cls": 2.23899, "loss": 2.23899, "time": 0.80877} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 0.00025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60375, "top5_acc": 0.82672, "loss_cls": 2.22941, "loss": 2.22941, "time": 0.809} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 0.00025, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58516, "top5_acc": 0.81812, "loss_cls": 2.30149, "loss": 2.30149, "time": 0.81027} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 0.00025, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.59688, "top5_acc": 0.82828, "loss_cls": 2.25027, "loss": 2.25027, "time": 0.81317} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 0.00025, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59359, "top5_acc": 0.81984, "loss_cls": 2.28056, "loss": 2.28056, "time": 0.81074} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 0.00024, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59562, "top5_acc": 0.82672, "loss_cls": 2.25154, "loss": 2.25154, "time": 0.81442} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 0.00024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59, "top5_acc": 0.81672, "loss_cls": 2.30551, "loss": 2.30551, "time": 0.8163} +{"mode": "train", "epoch": 146, "iter": 1300, "lr": 0.00024, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.59734, "top5_acc": 0.82516, "loss_cls": 2.2634, "loss": 2.2634, "time": 0.80905} +{"mode": "train", "epoch": 146, "iter": 1400, "lr": 0.00023, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.59141, "top5_acc": 0.82609, "loss_cls": 2.26804, "loss": 2.26804, "time": 0.81129} +{"mode": "train", "epoch": 146, "iter": 1500, "lr": 0.00023, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59625, "top5_acc": 0.81906, "loss_cls": 2.29654, "loss": 2.29654, "time": 0.81033} +{"mode": "train", "epoch": 146, "iter": 1600, "lr": 0.00023, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58625, "top5_acc": 0.82453, "loss_cls": 2.30394, "loss": 2.30394, "time": 0.8152} +{"mode": "train", "epoch": 146, "iter": 1700, "lr": 0.00023, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59844, "top5_acc": 0.82578, "loss_cls": 2.26615, "loss": 2.26615, "time": 0.81467} +{"mode": "train", "epoch": 146, "iter": 1800, "lr": 0.00022, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60297, "top5_acc": 0.82828, "loss_cls": 2.24267, "loss": 2.24267, "time": 0.8138} +{"mode": "train", "epoch": 146, "iter": 1900, "lr": 0.00022, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.59703, "top5_acc": 0.83469, "loss_cls": 2.24606, "loss": 2.24606, "time": 0.81388} +{"mode": "train", "epoch": 146, "iter": 2000, "lr": 0.00022, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58688, "top5_acc": 0.82297, "loss_cls": 2.28945, "loss": 2.28945, "time": 0.80857} +{"mode": "train", "epoch": 146, "iter": 2100, "lr": 0.00022, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.59016, "top5_acc": 0.825, "loss_cls": 2.28897, "loss": 2.28897, "time": 0.80555} +{"mode": "train", "epoch": 146, "iter": 2200, "lr": 0.00021, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.58062, "top5_acc": 0.82109, "loss_cls": 2.29987, "loss": 2.29987, "time": 0.80873} +{"mode": "train", "epoch": 146, "iter": 2300, "lr": 0.00021, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59234, "top5_acc": 0.83016, "loss_cls": 2.24643, "loss": 2.24643, "time": 0.81832} +{"mode": "train", "epoch": 146, "iter": 2400, "lr": 0.00021, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.60219, "top5_acc": 0.82812, "loss_cls": 2.23197, "loss": 2.23197, "time": 0.80671} +{"mode": "train", "epoch": 146, "iter": 2500, "lr": 0.00021, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5925, "top5_acc": 0.82219, "loss_cls": 2.26189, "loss": 2.26189, "time": 0.82427} +{"mode": "train", "epoch": 146, "iter": 2600, "lr": 0.0002, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59438, "top5_acc": 0.82156, "loss_cls": 2.2888, "loss": 2.2888, "time": 0.81188} +{"mode": "train", "epoch": 146, "iter": 2700, "lr": 0.0002, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58531, "top5_acc": 0.81844, "loss_cls": 2.30184, "loss": 2.30184, "time": 0.81184} +{"mode": "train", "epoch": 146, "iter": 2800, "lr": 0.0002, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59281, "top5_acc": 0.82016, "loss_cls": 2.26499, "loss": 2.26499, "time": 0.80862} +{"mode": "train", "epoch": 146, "iter": 2900, "lr": 0.0002, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58547, "top5_acc": 0.82469, "loss_cls": 2.26767, "loss": 2.26767, "time": 0.81741} +{"mode": "train", "epoch": 146, "iter": 3000, "lr": 0.00019, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58734, "top5_acc": 0.82062, "loss_cls": 2.27162, "loss": 2.27162, "time": 0.81645} +{"mode": "train", "epoch": 146, "iter": 3100, "lr": 0.00019, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59062, "top5_acc": 0.82938, "loss_cls": 2.2712, "loss": 2.2712, "time": 0.8134} +{"mode": "train", "epoch": 146, "iter": 3200, "lr": 0.00019, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59359, "top5_acc": 0.82422, "loss_cls": 2.26293, "loss": 2.26293, "time": 0.81992} +{"mode": "train", "epoch": 146, "iter": 3300, "lr": 0.00019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60906, "top5_acc": 0.83359, "loss_cls": 2.21323, "loss": 2.21323, "time": 0.82168} +{"mode": "train", "epoch": 146, "iter": 3400, "lr": 0.00018, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58312, "top5_acc": 0.81562, "loss_cls": 2.30897, "loss": 2.30897, "time": 0.81178} +{"mode": "train", "epoch": 146, "iter": 3500, "lr": 0.00018, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59984, "top5_acc": 0.82984, "loss_cls": 2.25887, "loss": 2.25887, "time": 0.81415} +{"mode": "train", "epoch": 146, "iter": 3600, "lr": 0.00018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59641, "top5_acc": 0.8225, "loss_cls": 2.24763, "loss": 2.24763, "time": 0.81401} +{"mode": "train", "epoch": 146, "iter": 3700, "lr": 0.00018, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.59469, "top5_acc": 0.81547, "loss_cls": 2.30602, "loss": 2.30602, "time": 0.80632} +{"mode": "val", "epoch": 146, "iter": 309, "lr": 0.00018, "top1_acc": 0.42628, "top5_acc": 0.67746, "mean_class_accuracy": 0.42612} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 0.00017, "memory": 15990, "data_time": 1.2823, "top1_acc": 0.60391, "top5_acc": 0.835, "loss_cls": 2.19762, "loss": 2.19762, "time": 2.25959} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 0.00017, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60359, "top5_acc": 0.82953, "loss_cls": 2.21987, "loss": 2.21987, "time": 0.81299} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 0.00017, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61, "top5_acc": 0.83469, "loss_cls": 2.18331, "loss": 2.18331, "time": 0.80742} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 0.00017, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60516, "top5_acc": 0.82891, "loss_cls": 2.23136, "loss": 2.23136, "time": 0.81366} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 0.00016, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60031, "top5_acc": 0.83438, "loss_cls": 2.21733, "loss": 2.21733, "time": 0.80841} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 0.00016, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60953, "top5_acc": 0.82641, "loss_cls": 2.23548, "loss": 2.23548, "time": 0.81104} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 0.00016, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.60656, "top5_acc": 0.82922, "loss_cls": 2.22508, "loss": 2.22508, "time": 0.81033} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 0.00016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61219, "top5_acc": 0.84234, "loss_cls": 2.16417, "loss": 2.16417, "time": 0.81088} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 0.00015, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.595, "top5_acc": 0.82609, "loss_cls": 2.255, "loss": 2.255, "time": 0.81667} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 0.00015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59953, "top5_acc": 0.82344, "loss_cls": 2.23516, "loss": 2.23516, "time": 0.81339} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 0.00015, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59578, "top5_acc": 0.83156, "loss_cls": 2.25049, "loss": 2.25049, "time": 0.81178} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 0.00015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59953, "top5_acc": 0.82875, "loss_cls": 2.22314, "loss": 2.22314, "time": 0.81184} +{"mode": "train", "epoch": 147, "iter": 1300, "lr": 0.00015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58547, "top5_acc": 0.82438, "loss_cls": 2.2706, "loss": 2.2706, "time": 0.8116} +{"mode": "train", "epoch": 147, "iter": 1400, "lr": 0.00014, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.60109, "top5_acc": 0.83234, "loss_cls": 2.22941, "loss": 2.22941, "time": 0.80809} +{"mode": "train", "epoch": 147, "iter": 1500, "lr": 0.00014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61406, "top5_acc": 0.83703, "loss_cls": 2.17805, "loss": 2.17805, "time": 0.8158} +{"mode": "train", "epoch": 147, "iter": 1600, "lr": 0.00014, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59656, "top5_acc": 0.83109, "loss_cls": 2.25429, "loss": 2.25429, "time": 0.80978} +{"mode": "train", "epoch": 147, "iter": 1700, "lr": 0.00014, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59828, "top5_acc": 0.82344, "loss_cls": 2.23911, "loss": 2.23911, "time": 0.80442} +{"mode": "train", "epoch": 147, "iter": 1800, "lr": 0.00014, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.60172, "top5_acc": 0.82531, "loss_cls": 2.23259, "loss": 2.23259, "time": 0.81021} +{"mode": "train", "epoch": 147, "iter": 1900, "lr": 0.00013, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59406, "top5_acc": 0.83062, "loss_cls": 2.24252, "loss": 2.24252, "time": 0.81308} +{"mode": "train", "epoch": 147, "iter": 2000, "lr": 0.00013, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59438, "top5_acc": 0.83188, "loss_cls": 2.23749, "loss": 2.23749, "time": 0.81274} +{"mode": "train", "epoch": 147, "iter": 2100, "lr": 0.00013, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59484, "top5_acc": 0.82359, "loss_cls": 2.26487, "loss": 2.26487, "time": 0.81322} +{"mode": "train", "epoch": 147, "iter": 2200, "lr": 0.00013, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60172, "top5_acc": 0.82797, "loss_cls": 2.22785, "loss": 2.22785, "time": 0.81161} +{"mode": "train", "epoch": 147, "iter": 2300, "lr": 0.00013, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59625, "top5_acc": 0.82703, "loss_cls": 2.24329, "loss": 2.24329, "time": 0.80769} +{"mode": "train", "epoch": 147, "iter": 2400, "lr": 0.00012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60812, "top5_acc": 0.83, "loss_cls": 2.21517, "loss": 2.21517, "time": 0.81074} +{"mode": "train", "epoch": 147, "iter": 2500, "lr": 0.00012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59438, "top5_acc": 0.82609, "loss_cls": 2.24386, "loss": 2.24386, "time": 0.81326} +{"mode": "train", "epoch": 147, "iter": 2600, "lr": 0.00012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59344, "top5_acc": 0.82125, "loss_cls": 2.27704, "loss": 2.27704, "time": 0.80642} +{"mode": "train", "epoch": 147, "iter": 2700, "lr": 0.00012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59812, "top5_acc": 0.82453, "loss_cls": 2.26713, "loss": 2.26713, "time": 0.80553} +{"mode": "train", "epoch": 147, "iter": 2800, "lr": 0.00012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57969, "top5_acc": 0.81156, "loss_cls": 2.29274, "loss": 2.29274, "time": 0.80974} +{"mode": "train", "epoch": 147, "iter": 2900, "lr": 0.00011, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60906, "top5_acc": 0.83609, "loss_cls": 2.1985, "loss": 2.1985, "time": 0.81712} +{"mode": "train", "epoch": 147, "iter": 3000, "lr": 0.00011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60062, "top5_acc": 0.82297, "loss_cls": 2.23189, "loss": 2.23189, "time": 0.8157} +{"mode": "train", "epoch": 147, "iter": 3100, "lr": 0.00011, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59703, "top5_acc": 0.82766, "loss_cls": 2.24676, "loss": 2.24676, "time": 0.80994} +{"mode": "train", "epoch": 147, "iter": 3200, "lr": 0.00011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61047, "top5_acc": 0.83875, "loss_cls": 2.18479, "loss": 2.18479, "time": 0.81269} +{"mode": "train", "epoch": 147, "iter": 3300, "lr": 0.00011, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59906, "top5_acc": 0.83141, "loss_cls": 2.25633, "loss": 2.25633, "time": 0.82111} +{"mode": "train", "epoch": 147, "iter": 3400, "lr": 0.0001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59547, "top5_acc": 0.82031, "loss_cls": 2.27087, "loss": 2.27087, "time": 0.81835} +{"mode": "train", "epoch": 147, "iter": 3500, "lr": 0.0001, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.60297, "top5_acc": 0.8225, "loss_cls": 2.24478, "loss": 2.24478, "time": 0.81302} +{"mode": "train", "epoch": 147, "iter": 3600, "lr": 0.0001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59656, "top5_acc": 0.82547, "loss_cls": 2.24805, "loss": 2.24805, "time": 0.81205} +{"mode": "train", "epoch": 147, "iter": 3700, "lr": 0.0001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60891, "top5_acc": 0.82984, "loss_cls": 2.22814, "loss": 2.22814, "time": 0.80936} +{"mode": "val", "epoch": 147, "iter": 309, "lr": 0.0001, "top1_acc": 0.42739, "top5_acc": 0.67897, "mean_class_accuracy": 0.42722} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 0.0001, "memory": 15990, "data_time": 1.32696, "top1_acc": 0.60422, "top5_acc": 0.8375, "loss_cls": 2.22103, "loss": 2.22103, "time": 2.30012} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 0.0001, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60797, "top5_acc": 0.8375, "loss_cls": 2.17305, "loss": 2.17305, "time": 0.81284} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 9e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.6075, "top5_acc": 0.83203, "loss_cls": 2.22228, "loss": 2.22228, "time": 0.81639} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 9e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60547, "top5_acc": 0.83344, "loss_cls": 2.21336, "loss": 2.21336, "time": 0.81127} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 9e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59703, "top5_acc": 0.82703, "loss_cls": 2.25328, "loss": 2.25328, "time": 0.81204} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 9e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60688, "top5_acc": 0.82531, "loss_cls": 2.22055, "loss": 2.22055, "time": 0.80935} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 9e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59875, "top5_acc": 0.82641, "loss_cls": 2.23602, "loss": 2.23602, "time": 0.81474} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 9e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61328, "top5_acc": 0.83422, "loss_cls": 2.1919, "loss": 2.1919, "time": 0.8131} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 8e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59625, "top5_acc": 0.82906, "loss_cls": 2.2328, "loss": 2.2328, "time": 0.82158} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 8e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61359, "top5_acc": 0.82641, "loss_cls": 2.21224, "loss": 2.21224, "time": 0.81472} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 8e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60609, "top5_acc": 0.82281, "loss_cls": 2.2361, "loss": 2.2361, "time": 0.81188} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 8e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58938, "top5_acc": 0.81828, "loss_cls": 2.26857, "loss": 2.26857, "time": 0.80926} +{"mode": "train", "epoch": 148, "iter": 1300, "lr": 8e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.6125, "top5_acc": 0.83266, "loss_cls": 2.18695, "loss": 2.18695, "time": 0.81052} +{"mode": "train", "epoch": 148, "iter": 1400, "lr": 8e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59812, "top5_acc": 0.83281, "loss_cls": 2.21915, "loss": 2.21915, "time": 0.81101} +{"mode": "train", "epoch": 148, "iter": 1500, "lr": 7e-05, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.5975, "top5_acc": 0.82641, "loss_cls": 2.23724, "loss": 2.23724, "time": 0.81449} +{"mode": "train", "epoch": 148, "iter": 1600, "lr": 7e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60234, "top5_acc": 0.83297, "loss_cls": 2.2268, "loss": 2.2268, "time": 0.81171} +{"mode": "train", "epoch": 148, "iter": 1700, "lr": 7e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60719, "top5_acc": 0.82844, "loss_cls": 2.2117, "loss": 2.2117, "time": 0.81487} +{"mode": "train", "epoch": 148, "iter": 1800, "lr": 7e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.6125, "top5_acc": 0.83531, "loss_cls": 2.20122, "loss": 2.20122, "time": 0.81011} +{"mode": "train", "epoch": 148, "iter": 1900, "lr": 7e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60562, "top5_acc": 0.835, "loss_cls": 2.20678, "loss": 2.20678, "time": 0.81101} +{"mode": "train", "epoch": 148, "iter": 2000, "lr": 7e-05, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.60234, "top5_acc": 0.83406, "loss_cls": 2.19861, "loss": 2.19861, "time": 0.80754} +{"mode": "train", "epoch": 148, "iter": 2100, "lr": 7e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60375, "top5_acc": 0.83438, "loss_cls": 2.21911, "loss": 2.21911, "time": 0.81225} +{"mode": "train", "epoch": 148, "iter": 2200, "lr": 6e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60453, "top5_acc": 0.83969, "loss_cls": 2.18182, "loss": 2.18182, "time": 0.81143} +{"mode": "train", "epoch": 148, "iter": 2300, "lr": 6e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60672, "top5_acc": 0.83922, "loss_cls": 2.19068, "loss": 2.19068, "time": 0.80909} +{"mode": "train", "epoch": 148, "iter": 2400, "lr": 6e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61312, "top5_acc": 0.83859, "loss_cls": 2.182, "loss": 2.182, "time": 0.81485} +{"mode": "train", "epoch": 148, "iter": 2500, "lr": 6e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60234, "top5_acc": 0.82484, "loss_cls": 2.23555, "loss": 2.23555, "time": 0.80928} +{"mode": "train", "epoch": 148, "iter": 2600, "lr": 6e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.5975, "top5_acc": 0.82781, "loss_cls": 2.22654, "loss": 2.22654, "time": 0.81197} +{"mode": "train", "epoch": 148, "iter": 2700, "lr": 6e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60328, "top5_acc": 0.83594, "loss_cls": 2.19042, "loss": 2.19042, "time": 0.81034} +{"mode": "train", "epoch": 148, "iter": 2800, "lr": 6e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60094, "top5_acc": 0.83281, "loss_cls": 2.22561, "loss": 2.22561, "time": 0.81091} +{"mode": "train", "epoch": 148, "iter": 2900, "lr": 5e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60375, "top5_acc": 0.82859, "loss_cls": 2.21926, "loss": 2.21926, "time": 0.80934} +{"mode": "train", "epoch": 148, "iter": 3000, "lr": 5e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.60719, "top5_acc": 0.83562, "loss_cls": 2.18795, "loss": 2.18795, "time": 0.81032} +{"mode": "train", "epoch": 148, "iter": 3100, "lr": 5e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.61, "top5_acc": 0.83094, "loss_cls": 2.20045, "loss": 2.20045, "time": 0.81149} +{"mode": "train", "epoch": 148, "iter": 3200, "lr": 5e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.61, "top5_acc": 0.83766, "loss_cls": 2.17224, "loss": 2.17224, "time": 0.81467} +{"mode": "train", "epoch": 148, "iter": 3300, "lr": 5e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59531, "top5_acc": 0.82078, "loss_cls": 2.27246, "loss": 2.27246, "time": 0.81003} +{"mode": "train", "epoch": 148, "iter": 3400, "lr": 5e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60656, "top5_acc": 0.83516, "loss_cls": 2.19415, "loss": 2.19415, "time": 0.81643} +{"mode": "train", "epoch": 148, "iter": 3500, "lr": 5e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.6075, "top5_acc": 0.83578, "loss_cls": 2.17848, "loss": 2.17848, "time": 0.81196} +{"mode": "train", "epoch": 148, "iter": 3600, "lr": 5e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60969, "top5_acc": 0.83688, "loss_cls": 2.18159, "loss": 2.18159, "time": 0.81731} +{"mode": "train", "epoch": 148, "iter": 3700, "lr": 4e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61047, "top5_acc": 0.83078, "loss_cls": 2.19124, "loss": 2.19124, "time": 0.81582} +{"mode": "val", "epoch": 148, "iter": 309, "lr": 4e-05, "top1_acc": 0.42815, "top5_acc": 0.67958, "mean_class_accuracy": 0.428} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 4e-05, "memory": 15990, "data_time": 1.27487, "top1_acc": 0.60672, "top5_acc": 0.83281, "loss_cls": 2.20924, "loss": 2.20924, "time": 2.24865} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 4e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61203, "top5_acc": 0.83422, "loss_cls": 2.1657, "loss": 2.1657, "time": 0.81784} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 4e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60281, "top5_acc": 0.84422, "loss_cls": 2.17848, "loss": 2.17848, "time": 0.81547} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 4e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61812, "top5_acc": 0.84141, "loss_cls": 2.1744, "loss": 2.1744, "time": 0.81721} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 4e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59781, "top5_acc": 0.82625, "loss_cls": 2.22741, "loss": 2.22741, "time": 0.80798} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 4e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60719, "top5_acc": 0.83312, "loss_cls": 2.21194, "loss": 2.21194, "time": 0.81321} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 4e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61969, "top5_acc": 0.83484, "loss_cls": 2.17043, "loss": 2.17043, "time": 0.81214} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 4e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60875, "top5_acc": 0.835, "loss_cls": 2.19758, "loss": 2.19758, "time": 0.80815} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60844, "top5_acc": 0.82953, "loss_cls": 2.23635, "loss": 2.23635, "time": 0.81618} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61531, "top5_acc": 0.83375, "loss_cls": 2.18006, "loss": 2.18006, "time": 0.81959} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61141, "top5_acc": 0.83453, "loss_cls": 2.17956, "loss": 2.17956, "time": 0.81455} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 3e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.60656, "top5_acc": 0.83266, "loss_cls": 2.20938, "loss": 2.20938, "time": 0.81355} +{"mode": "train", "epoch": 149, "iter": 1300, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59812, "top5_acc": 0.82891, "loss_cls": 2.21503, "loss": 2.21503, "time": 0.81818} +{"mode": "train", "epoch": 149, "iter": 1400, "lr": 3e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61359, "top5_acc": 0.83672, "loss_cls": 2.17153, "loss": 2.17153, "time": 0.81436} +{"mode": "train", "epoch": 149, "iter": 1500, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61312, "top5_acc": 0.83656, "loss_cls": 2.16462, "loss": 2.16462, "time": 0.81134} +{"mode": "train", "epoch": 149, "iter": 1600, "lr": 3e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61375, "top5_acc": 0.83672, "loss_cls": 2.19256, "loss": 2.19256, "time": 0.81313} +{"mode": "train", "epoch": 149, "iter": 1700, "lr": 3e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61391, "top5_acc": 0.83984, "loss_cls": 2.15941, "loss": 2.15941, "time": 0.8116} +{"mode": "train", "epoch": 149, "iter": 1800, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60969, "top5_acc": 0.83, "loss_cls": 2.17922, "loss": 2.17922, "time": 0.81173} +{"mode": "train", "epoch": 149, "iter": 1900, "lr": 2e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61297, "top5_acc": 0.83, "loss_cls": 2.21249, "loss": 2.21249, "time": 0.81111} +{"mode": "train", "epoch": 149, "iter": 2000, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60438, "top5_acc": 0.83766, "loss_cls": 2.19385, "loss": 2.19385, "time": 0.80744} +{"mode": "train", "epoch": 149, "iter": 2100, "lr": 2e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60156, "top5_acc": 0.83062, "loss_cls": 2.21002, "loss": 2.21002, "time": 0.81253} +{"mode": "train", "epoch": 149, "iter": 2200, "lr": 2e-05, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.60953, "top5_acc": 0.83453, "loss_cls": 2.18447, "loss": 2.18447, "time": 0.81371} +{"mode": "train", "epoch": 149, "iter": 2300, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60859, "top5_acc": 0.83969, "loss_cls": 2.16101, "loss": 2.16101, "time": 0.8152} +{"mode": "train", "epoch": 149, "iter": 2400, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62062, "top5_acc": 0.83906, "loss_cls": 2.16622, "loss": 2.16622, "time": 0.81218} +{"mode": "train", "epoch": 149, "iter": 2500, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61625, "top5_acc": 0.84219, "loss_cls": 2.16591, "loss": 2.16591, "time": 0.81743} +{"mode": "train", "epoch": 149, "iter": 2600, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60031, "top5_acc": 0.82516, "loss_cls": 2.23999, "loss": 2.23999, "time": 0.80912} +{"mode": "train", "epoch": 149, "iter": 2700, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60562, "top5_acc": 0.83438, "loss_cls": 2.18551, "loss": 2.18551, "time": 0.8086} +{"mode": "train", "epoch": 149, "iter": 2800, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60531, "top5_acc": 0.83094, "loss_cls": 2.23226, "loss": 2.23226, "time": 0.8121} +{"mode": "train", "epoch": 149, "iter": 2900, "lr": 2e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60922, "top5_acc": 0.83906, "loss_cls": 2.16879, "loss": 2.16879, "time": 0.81274} +{"mode": "train", "epoch": 149, "iter": 3000, "lr": 2e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61531, "top5_acc": 0.83422, "loss_cls": 2.18038, "loss": 2.18038, "time": 0.81882} +{"mode": "train", "epoch": 149, "iter": 3100, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61031, "top5_acc": 0.83875, "loss_cls": 2.17834, "loss": 2.17834, "time": 0.81281} +{"mode": "train", "epoch": 149, "iter": 3200, "lr": 1e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.60547, "top5_acc": 0.83094, "loss_cls": 2.20309, "loss": 2.20309, "time": 0.81502} +{"mode": "train", "epoch": 149, "iter": 3300, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60656, "top5_acc": 0.83906, "loss_cls": 2.19193, "loss": 2.19193, "time": 0.81375} +{"mode": "train", "epoch": 149, "iter": 3400, "lr": 1e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.60297, "top5_acc": 0.82641, "loss_cls": 2.23534, "loss": 2.23534, "time": 0.81701} +{"mode": "train", "epoch": 149, "iter": 3500, "lr": 1e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61516, "top5_acc": 0.83375, "loss_cls": 2.18792, "loss": 2.18792, "time": 0.80695} +{"mode": "train", "epoch": 149, "iter": 3600, "lr": 1e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61047, "top5_acc": 0.82953, "loss_cls": 2.18874, "loss": 2.18874, "time": 0.81601} +{"mode": "train", "epoch": 149, "iter": 3700, "lr": 1e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60906, "top5_acc": 0.83016, "loss_cls": 2.199, "loss": 2.199, "time": 0.81203} +{"mode": "val", "epoch": 149, "iter": 309, "lr": 1e-05, "top1_acc": 0.4277, "top5_acc": 0.68201, "mean_class_accuracy": 0.42757} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 1e-05, "memory": 15990, "data_time": 1.27379, "top1_acc": 0.61328, "top5_acc": 0.83766, "loss_cls": 2.1807, "loss": 2.1807, "time": 2.24842} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59297, "top5_acc": 0.82859, "loss_cls": 2.24, "loss": 2.24, "time": 0.81197} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 1e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.59906, "top5_acc": 0.83359, "loss_cls": 2.23267, "loss": 2.23267, "time": 0.81451} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 1e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60938, "top5_acc": 0.83359, "loss_cls": 2.20493, "loss": 2.20493, "time": 0.81062} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61281, "top5_acc": 0.83594, "loss_cls": 2.15638, "loss": 2.15638, "time": 0.81217} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 1e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61344, "top5_acc": 0.83969, "loss_cls": 2.17429, "loss": 2.17429, "time": 0.82142} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61156, "top5_acc": 0.83797, "loss_cls": 2.19634, "loss": 2.19634, "time": 0.81464} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61687, "top5_acc": 0.83812, "loss_cls": 2.16002, "loss": 2.16002, "time": 0.81309} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61984, "top5_acc": 0.83641, "loss_cls": 2.16218, "loss": 2.16218, "time": 0.80636} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60953, "top5_acc": 0.84031, "loss_cls": 2.16971, "loss": 2.16971, "time": 0.81954} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60391, "top5_acc": 0.83406, "loss_cls": 2.20878, "loss": 2.20878, "time": 0.81233} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 1e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.62172, "top5_acc": 0.84141, "loss_cls": 2.14629, "loss": 2.14629, "time": 0.81182} +{"mode": "train", "epoch": 150, "iter": 1300, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61281, "top5_acc": 0.84359, "loss_cls": 2.17641, "loss": 2.17641, "time": 0.81139} +{"mode": "train", "epoch": 150, "iter": 1400, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60391, "top5_acc": 0.84094, "loss_cls": 2.18074, "loss": 2.18074, "time": 0.81535} +{"mode": "train", "epoch": 150, "iter": 1500, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61609, "top5_acc": 0.83609, "loss_cls": 2.18419, "loss": 2.18419, "time": 0.81159} +{"mode": "train", "epoch": 150, "iter": 1600, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60766, "top5_acc": 0.8375, "loss_cls": 2.22801, "loss": 2.22801, "time": 0.81084} +{"mode": "train", "epoch": 150, "iter": 1700, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60922, "top5_acc": 0.83141, "loss_cls": 2.1745, "loss": 2.1745, "time": 0.8096} +{"mode": "train", "epoch": 150, "iter": 1800, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61047, "top5_acc": 0.83734, "loss_cls": 2.18494, "loss": 2.18494, "time": 0.81222} +{"mode": "train", "epoch": 150, "iter": 1900, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60672, "top5_acc": 0.83719, "loss_cls": 2.19049, "loss": 2.19049, "time": 0.81783} +{"mode": "train", "epoch": 150, "iter": 2000, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61359, "top5_acc": 0.84031, "loss_cls": 2.16091, "loss": 2.16091, "time": 0.80698} +{"mode": "train", "epoch": 150, "iter": 2100, "lr": 0.0, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.60969, "top5_acc": 0.83547, "loss_cls": 2.17762, "loss": 2.17762, "time": 0.80872} +{"mode": "train", "epoch": 150, "iter": 2200, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.615, "top5_acc": 0.83484, "loss_cls": 2.17252, "loss": 2.17252, "time": 0.8156} +{"mode": "train", "epoch": 150, "iter": 2300, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61062, "top5_acc": 0.82797, "loss_cls": 2.21162, "loss": 2.21162, "time": 0.81533} +{"mode": "train", "epoch": 150, "iter": 2400, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60688, "top5_acc": 0.84094, "loss_cls": 2.16214, "loss": 2.16214, "time": 0.81141} +{"mode": "train", "epoch": 150, "iter": 2500, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61391, "top5_acc": 0.8375, "loss_cls": 2.16444, "loss": 2.16444, "time": 0.81581} +{"mode": "train", "epoch": 150, "iter": 2600, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60547, "top5_acc": 0.82922, "loss_cls": 2.22528, "loss": 2.22528, "time": 0.81274} +{"mode": "train", "epoch": 150, "iter": 2700, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61281, "top5_acc": 0.83969, "loss_cls": 2.15647, "loss": 2.15647, "time": 0.80991} +{"mode": "train", "epoch": 150, "iter": 2800, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61266, "top5_acc": 0.84562, "loss_cls": 2.13976, "loss": 2.13976, "time": 0.8103} +{"mode": "train", "epoch": 150, "iter": 2900, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62203, "top5_acc": 0.83969, "loss_cls": 2.1361, "loss": 2.1361, "time": 0.81262} +{"mode": "train", "epoch": 150, "iter": 3000, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60297, "top5_acc": 0.83203, "loss_cls": 2.20863, "loss": 2.20863, "time": 0.81226} +{"mode": "train", "epoch": 150, "iter": 3100, "lr": 0.0, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.61344, "top5_acc": 0.84156, "loss_cls": 2.16295, "loss": 2.16295, "time": 0.818} +{"mode": "train", "epoch": 150, "iter": 3200, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60781, "top5_acc": 0.83094, "loss_cls": 2.18578, "loss": 2.18578, "time": 0.81075} +{"mode": "train", "epoch": 150, "iter": 3300, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60625, "top5_acc": 0.82484, "loss_cls": 2.23106, "loss": 2.23106, "time": 0.81229} +{"mode": "train", "epoch": 150, "iter": 3400, "lr": 0.0, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.60891, "top5_acc": 0.83469, "loss_cls": 2.18464, "loss": 2.18464, "time": 0.81632} +{"mode": "train", "epoch": 150, "iter": 3500, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60656, "top5_acc": 0.83453, "loss_cls": 2.20161, "loss": 2.20161, "time": 0.81208} +{"mode": "train", "epoch": 150, "iter": 3600, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61266, "top5_acc": 0.83547, "loss_cls": 2.18628, "loss": 2.18628, "time": 0.81257} +{"mode": "train", "epoch": 150, "iter": 3700, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60469, "top5_acc": 0.83031, "loss_cls": 2.18629, "loss": 2.18629, "time": 0.80996} +{"mode": "val", "epoch": 150, "iter": 309, "lr": 0.0, "top1_acc": 0.42825, "top5_acc": 0.68085, "mean_class_accuracy": 0.42812} diff --git a/k400/jm/best_pred.pkl b/k400/jm/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..5c174cea1464210dd49c8edb6be42b93f1ea0ca2 --- /dev/null +++ b/k400/jm/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b321a27a2bd3de2ddc3036c3ddde88515e99fdb1df9ac86b91dc9a3bc0c7830c +size 44885560 diff --git a/k400/jm/best_top1_acc_epoch_150.pth b/k400/jm/best_top1_acc_epoch_150.pth new file mode 100644 index 0000000000000000000000000000000000000000..4ed2ac4d9367520753a64835af0777f101ccf06c --- /dev/null +++ b/k400/jm/best_top1_acc_epoch_150.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:090378d2487b744e8ab43ef369e2a838b6b7e4de1e4c2e6ad9acff336d40c529 +size 32926705 diff --git a/k400/jm/jm.py b/k400/jm/jm.py new file mode 100644 index 0000000000000000000000000000000000000000..3de50ffc73c45d158f740e898cd02c432e70e6d5 --- /dev/null +++ b/k400/jm/jm.py @@ -0,0 +1,133 @@ +modality = 'jm' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/jm' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['jm']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/k400/k400_ensemble.py b/k400/k400_ensemble.py new file mode 100644 index 0000000000000000000000000000000000000000..e8b4bb886e06a2bbc04a1b36b1bf59849d5bf77e --- /dev/null +++ b/k400/k400_ensemble.py @@ -0,0 +1,74 @@ +from mmcv import load +import sys +# Note: please adjust the relative path according to the actual situation. +sys.path.append('../..') +from aclnet.smp import * + + +j_1 = load('j_1/best_pred.pkl') +b_1 = load('b_1/best_pred.pkl') +k_1 = load('k_1/best_pred.pkl') +jm = load('jm/best_pred.pkl') +bm = load('bm/best_pred.pkl') +km = load('km/best_pred.pkl') +j_2 = load('j_2/best_pred.pkl') +b_2 = load('b_2/best_pred.pkl') +k_2 = load('k_2/best_pred.pkl') +j_3 = load('j_3/best_pred.pkl') +b_3 = load('b_3/best_pred.pkl') +k_3 = load('k_3/best_pred.pkl') +label = load_label('/data/k400/k400_hrnet.pkl', 'val') + + +""" +*************** +InfoGCN v1: +j j b b k k +2S: 50.35 / 74.18 +4S: 51.33 / 75.36 +6S: 51.99 / 75.60 +*************** +""" +print('InfoGCN v1:') +print('j j b b k k') +print('2S') +fused = comb([j_1, b_1], [1, 1]) +print('Top-1', top1(fused, label)) +print('Top-5', topk(fused, label, 5)) + +print('4S') +fused = comb([j_1, b_1, j_2, b_2], [1, 1, 1, 1]) +print('Top-1', top1(fused, label)) +print('Top-5', topk(fused, label, 5)) + +print('6S') +fused = comb([j_1, j_2, b_1, b_2, k_1, k_2], [9, 9, 5, 5, 3, 3]) +print('Top-1', top1(fused, label)) +print('Top-5', topk(fused, label, 5)) + + +""" +*************** +HD-GCN v1: +j b j b j b +2S: 50.35 / 74.18 +4S: 51.50 / 75.36 +6S: 52.13 / 75.91 +*************** +""" +print('HD-GCN v1:') +print('j b j b j b') +print('2S') +fused = comb([j_1, b_1], [1, 1]) +print('Top-1', top1(fused, label)) +print('Top-5', topk(fused, label, 5)) + +print('4S') +fused = comb([j_1, b_1, j_2, b_2], [5, 3, 5, 3]) +print('Top-1', top1(fused, label)) +print('Top-5', topk(fused, label, 5)) + +print('6S') +fused = comb([j_1, b_1, j_2, b_2, j_3, b_3], [5, 3, 5, 3, 5, 3]) +print('Top-1', top1(fused, label)) +print('Top-5', topk(fused, label, 5)) diff --git a/k400/k_1/20240722_022901.log b/k400/k_1/20240722_022901.log new file mode 100644 index 0000000000000000000000000000000000000000..ed44f21bf5c2a595a68860684327175d3fdf5040 --- /dev/null +++ b/k400/k_1/20240722_022901.log @@ -0,0 +1,7304 @@ +2024-07-22 02:29:01,386 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2024-07-22 02:29:01,776 - pyskl - INFO - Config: modality = 'k' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/k_1' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2024-07-22 02:29:01,777 - pyskl - INFO - Set random seed to 233891380, deterministic: False +2024-07-22 02:29:12,714 - pyskl - INFO - 239737 videos remain after valid thresholding +2024-07-22 02:29:28,081 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-07-22 02:29:28,083 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1 +2024-07-22 02:29:28,091 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2024-07-22 02:29:28,114 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2024-07-22 02:29:28,119 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1 by HardDiskBackend. +2024-07-22 02:32:48,010 - pyskl - INFO - Epoch [1][100/3746] lr: 1.000e-01, eta: 12 days, 23:55:49, time: 1.999, data_time: 1.286, memory: 15990, top1_acc: 0.0081, top5_acc: 0.0345, loss_cls: 6.3626, loss: 6.3626 +2024-07-22 02:33:58,998 - pyskl - INFO - Epoch [1][200/3746] lr: 1.000e-01, eta: 8 days, 19:19:02, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.0127, top5_acc: 0.0533, loss_cls: 6.3380, loss: 6.3380 +2024-07-22 02:35:09,441 - pyskl - INFO - Epoch [1][300/3746] lr: 1.000e-01, eta: 7 days, 9:28:57, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0152, top5_acc: 0.0725, loss_cls: 6.1813, loss: 6.1813 +2024-07-22 02:36:19,944 - pyskl - INFO - Epoch [1][400/3746] lr: 1.000e-01, eta: 6 days, 16:34:45, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0239, top5_acc: 0.0855, loss_cls: 6.0461, loss: 6.0461 +2024-07-22 02:37:30,443 - pyskl - INFO - Epoch [1][500/3746] lr: 1.000e-01, eta: 6 days, 6:25:41, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0303, top5_acc: 0.1069, loss_cls: 5.9193, loss: 5.9193 +2024-07-22 02:38:41,011 - pyskl - INFO - Epoch [1][600/3746] lr: 1.000e-01, eta: 5 days, 23:40:19, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.0303, top5_acc: 0.1136, loss_cls: 5.8819, loss: 5.8819 +2024-07-22 02:39:51,436 - pyskl - INFO - Epoch [1][700/3746] lr: 1.000e-01, eta: 5 days, 18:48:32, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0336, top5_acc: 0.1289, loss_cls: 5.8023, loss: 5.8023 +2024-07-22 02:41:01,970 - pyskl - INFO - Epoch [1][800/3746] lr: 1.000e-01, eta: 5 days, 15:10:40, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0352, top5_acc: 0.1313, loss_cls: 5.7685, loss: 5.7685 +2024-07-22 02:42:12,107 - pyskl - INFO - Epoch [1][900/3746] lr: 1.000e-01, eta: 5 days, 12:16:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0395, top5_acc: 0.1355, loss_cls: 5.7035, loss: 5.7035 +2024-07-22 02:43:22,376 - pyskl - INFO - Epoch [1][1000/3746] lr: 1.000e-01, eta: 5 days, 9:58:46, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0428, top5_acc: 0.1447, loss_cls: 5.7176, loss: 5.7176 +2024-07-22 02:44:32,634 - pyskl - INFO - Epoch [1][1100/3746] lr: 1.000e-01, eta: 5 days, 8:05:30, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0398, top5_acc: 0.1423, loss_cls: 5.6881, loss: 5.6881 +2024-07-22 02:45:42,805 - pyskl - INFO - Epoch [1][1200/3746] lr: 1.000e-01, eta: 5 days, 6:30:14, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0434, top5_acc: 0.1625, loss_cls: 5.6265, loss: 5.6265 +2024-07-22 02:46:53,048 - pyskl - INFO - Epoch [1][1300/3746] lr: 1.000e-01, eta: 5 days, 5:09:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0517, top5_acc: 0.1630, loss_cls: 5.5949, loss: 5.5949 +2024-07-22 02:48:03,563 - pyskl - INFO - Epoch [1][1400/3746] lr: 1.000e-01, eta: 5 days, 4:02:48, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0498, top5_acc: 0.1747, loss_cls: 5.5668, loss: 5.5668 +2024-07-22 02:49:13,952 - pyskl - INFO - Epoch [1][1500/3746] lr: 1.000e-01, eta: 5 days, 3:03:39, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0573, top5_acc: 0.1816, loss_cls: 5.5348, loss: 5.5348 +2024-07-22 02:50:24,191 - pyskl - INFO - Epoch [1][1600/3746] lr: 1.000e-01, eta: 5 days, 2:10:53, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0616, top5_acc: 0.1927, loss_cls: 5.4946, loss: 5.4946 +2024-07-22 02:51:34,959 - pyskl - INFO - Epoch [1][1700/3746] lr: 1.000e-01, eta: 5 days, 1:27:05, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.0630, top5_acc: 0.1917, loss_cls: 5.5074, loss: 5.5074 +2024-07-22 02:52:45,502 - pyskl - INFO - Epoch [1][1800/3746] lr: 1.000e-01, eta: 5 days, 0:46:52, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0659, top5_acc: 0.2034, loss_cls: 5.4475, loss: 5.4475 +2024-07-22 02:53:55,587 - pyskl - INFO - Epoch [1][1900/3746] lr: 1.000e-01, eta: 5 days, 0:08:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0672, top5_acc: 0.2111, loss_cls: 5.4317, loss: 5.4317 +2024-07-22 02:55:07,883 - pyskl - INFO - Epoch [1][2000/3746] lr: 1.000e-01, eta: 4 days, 23:44:10, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.0719, top5_acc: 0.2128, loss_cls: 5.4156, loss: 5.4156 +2024-07-22 02:56:20,858 - pyskl - INFO - Epoch [1][2100/3746] lr: 1.000e-01, eta: 4 days, 23:25:03, time: 0.730, data_time: 0.000, memory: 15990, top1_acc: 0.0723, top5_acc: 0.2125, loss_cls: 5.3950, loss: 5.3950 +2024-07-22 02:57:30,889 - pyskl - INFO - Epoch [1][2200/3746] lr: 1.000e-01, eta: 4 days, 22:55:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0737, top5_acc: 0.2277, loss_cls: 5.3700, loss: 5.3700 +2024-07-22 02:58:41,137 - pyskl - INFO - Epoch [1][2300/3746] lr: 1.000e-01, eta: 4 days, 22:28:30, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0811, top5_acc: 0.2241, loss_cls: 5.3658, loss: 5.3658 +2024-07-22 02:59:51,590 - pyskl - INFO - Epoch [1][2400/3746] lr: 1.000e-01, eta: 4 days, 22:04:50, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0842, top5_acc: 0.2356, loss_cls: 5.3357, loss: 5.3357 +2024-07-22 03:01:01,680 - pyskl - INFO - Epoch [1][2500/3746] lr: 1.000e-01, eta: 4 days, 21:41:36, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0802, top5_acc: 0.2327, loss_cls: 5.3240, loss: 5.3240 +2024-07-22 03:02:11,824 - pyskl - INFO - Epoch [1][2600/3746] lr: 9.999e-02, eta: 4 days, 21:20:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0873, top5_acc: 0.2464, loss_cls: 5.2720, loss: 5.2720 +2024-07-22 03:03:21,879 - pyskl - INFO - Epoch [1][2700/3746] lr: 9.999e-02, eta: 4 days, 21:00:08, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0931, top5_acc: 0.2466, loss_cls: 5.2786, loss: 5.2786 +2024-07-22 03:04:31,967 - pyskl - INFO - Epoch [1][2800/3746] lr: 9.999e-02, eta: 4 days, 20:41:27, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0905, top5_acc: 0.2453, loss_cls: 5.2329, loss: 5.2329 +2024-07-22 03:05:42,228 - pyskl - INFO - Epoch [1][2900/3746] lr: 9.999e-02, eta: 4 days, 20:24:32, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0958, top5_acc: 0.2608, loss_cls: 5.2285, loss: 5.2285 +2024-07-22 03:06:52,646 - pyskl - INFO - Epoch [1][3000/3746] lr: 9.999e-02, eta: 4 days, 20:09:09, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0959, top5_acc: 0.2669, loss_cls: 5.2043, loss: 5.2043 +2024-07-22 03:08:02,783 - pyskl - INFO - Epoch [1][3100/3746] lr: 9.999e-02, eta: 4 days, 19:53:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0909, top5_acc: 0.2611, loss_cls: 5.2063, loss: 5.2063 +2024-07-22 03:09:12,875 - pyskl - INFO - Epoch [1][3200/3746] lr: 9.999e-02, eta: 4 days, 19:39:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1023, top5_acc: 0.2708, loss_cls: 5.2046, loss: 5.2046 +2024-07-22 03:10:22,993 - pyskl - INFO - Epoch [1][3300/3746] lr: 9.999e-02, eta: 4 days, 19:25:37, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0931, top5_acc: 0.2639, loss_cls: 5.2147, loss: 5.2147 +2024-07-22 03:11:33,726 - pyskl - INFO - Epoch [1][3400/3746] lr: 9.999e-02, eta: 4 days, 19:14:22, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.0953, top5_acc: 0.2614, loss_cls: 5.1898, loss: 5.1898 +2024-07-22 03:12:44,236 - pyskl - INFO - Epoch [1][3500/3746] lr: 9.999e-02, eta: 4 days, 19:03:06, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0980, top5_acc: 0.2720, loss_cls: 5.1731, loss: 5.1731 +2024-07-22 03:13:54,580 - pyskl - INFO - Epoch [1][3600/3746] lr: 9.999e-02, eta: 4 days, 18:51:58, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0988, top5_acc: 0.2748, loss_cls: 5.1442, loss: 5.1442 +2024-07-22 03:15:04,873 - pyskl - INFO - Epoch [1][3700/3746] lr: 9.999e-02, eta: 4 days, 18:41:14, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1105, top5_acc: 0.2888, loss_cls: 5.1237, loss: 5.1237 +2024-07-22 03:15:40,282 - pyskl - INFO - Saving checkpoint at 1 epochs +2024-07-22 03:17:33,114 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 03:17:33,800 - pyskl - INFO - +top1_acc 0.0769 +top5_acc 0.2210 +2024-07-22 03:17:33,800 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 03:17:33,841 - pyskl - INFO - +mean_acc 0.0769 +2024-07-22 03:17:34,082 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2024-07-22 03:17:34,083 - pyskl - INFO - Best top1_acc is 0.0769 at 1 epoch. +2024-07-22 03:17:34,097 - pyskl - INFO - Epoch(val) [1][309] top1_acc: 0.0769, top5_acc: 0.2210, mean_class_accuracy: 0.0769 +2024-07-22 03:20:54,337 - pyskl - INFO - Epoch [2][100/3746] lr: 9.999e-02, eta: 4 days, 22:22:30, time: 2.002, data_time: 1.295, memory: 15990, top1_acc: 0.1077, top5_acc: 0.2906, loss_cls: 5.0839, loss: 5.0839 +2024-07-22 03:22:05,030 - pyskl - INFO - Epoch [2][200/3746] lr: 9.999e-02, eta: 4 days, 22:07:52, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1122, top5_acc: 0.2925, loss_cls: 5.0760, loss: 5.0760 +2024-07-22 03:23:15,498 - pyskl - INFO - Epoch [2][300/3746] lr: 9.999e-02, eta: 4 days, 21:53:22, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1125, top5_acc: 0.2911, loss_cls: 5.0862, loss: 5.0862 +2024-07-22 03:24:25,563 - pyskl - INFO - Epoch [2][400/3746] lr: 9.999e-02, eta: 4 days, 21:38:37, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1087, top5_acc: 0.2898, loss_cls: 5.1061, loss: 5.1061 +2024-07-22 03:25:35,460 - pyskl - INFO - Epoch [2][500/3746] lr: 9.999e-02, eta: 4 days, 21:24:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1070, top5_acc: 0.2967, loss_cls: 5.0662, loss: 5.0662 +2024-07-22 03:26:45,459 - pyskl - INFO - Epoch [2][600/3746] lr: 9.999e-02, eta: 4 days, 21:10:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1044, top5_acc: 0.2942, loss_cls: 5.0757, loss: 5.0757 +2024-07-22 03:27:55,435 - pyskl - INFO - Epoch [2][700/3746] lr: 9.998e-02, eta: 4 days, 20:57:22, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1141, top5_acc: 0.2961, loss_cls: 5.0690, loss: 5.0690 +2024-07-22 03:29:05,649 - pyskl - INFO - Epoch [2][800/3746] lr: 9.998e-02, eta: 4 days, 20:45:14, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1181, top5_acc: 0.3078, loss_cls: 5.0189, loss: 5.0189 +2024-07-22 03:30:15,798 - pyskl - INFO - Epoch [2][900/3746] lr: 9.998e-02, eta: 4 days, 20:33:27, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1141, top5_acc: 0.2944, loss_cls: 5.0687, loss: 5.0687 +2024-07-22 03:31:25,991 - pyskl - INFO - Epoch [2][1000/3746] lr: 9.998e-02, eta: 4 days, 20:22:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1214, top5_acc: 0.3053, loss_cls: 5.0287, loss: 5.0287 +2024-07-22 03:32:36,196 - pyskl - INFO - Epoch [2][1100/3746] lr: 9.998e-02, eta: 4 days, 20:11:24, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1147, top5_acc: 0.3006, loss_cls: 5.0137, loss: 5.0137 +2024-07-22 03:33:46,326 - pyskl - INFO - Epoch [2][1200/3746] lr: 9.998e-02, eta: 4 days, 20:00:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1241, top5_acc: 0.3173, loss_cls: 4.9476, loss: 4.9476 +2024-07-22 03:34:56,286 - pyskl - INFO - Epoch [2][1300/3746] lr: 9.998e-02, eta: 4 days, 19:50:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1186, top5_acc: 0.3139, loss_cls: 4.9668, loss: 4.9668 +2024-07-22 03:36:06,409 - pyskl - INFO - Epoch [2][1400/3746] lr: 9.998e-02, eta: 4 days, 19:40:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1256, top5_acc: 0.3186, loss_cls: 4.9557, loss: 4.9557 +2024-07-22 03:37:16,328 - pyskl - INFO - Epoch [2][1500/3746] lr: 9.998e-02, eta: 4 days, 19:30:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1244, top5_acc: 0.3120, loss_cls: 4.9534, loss: 4.9534 +2024-07-22 03:38:26,457 - pyskl - INFO - Epoch [2][1600/3746] lr: 9.998e-02, eta: 4 days, 19:21:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1267, top5_acc: 0.3273, loss_cls: 4.9513, loss: 4.9513 +2024-07-22 03:39:36,331 - pyskl - INFO - Epoch [2][1700/3746] lr: 9.998e-02, eta: 4 days, 19:12:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1331, top5_acc: 0.3233, loss_cls: 4.9313, loss: 4.9313 +2024-07-22 03:40:46,401 - pyskl - INFO - Epoch [2][1800/3746] lr: 9.998e-02, eta: 4 days, 19:03:25, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1267, top5_acc: 0.3264, loss_cls: 4.9377, loss: 4.9377 +2024-07-22 03:41:56,538 - pyskl - INFO - Epoch [2][1900/3746] lr: 9.998e-02, eta: 4 days, 18:55:06, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1264, top5_acc: 0.3291, loss_cls: 4.8973, loss: 4.8973 +2024-07-22 03:43:06,434 - pyskl - INFO - Epoch [2][2000/3746] lr: 9.997e-02, eta: 4 days, 18:46:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1302, top5_acc: 0.3341, loss_cls: 4.9003, loss: 4.9003 +2024-07-22 03:44:16,452 - pyskl - INFO - Epoch [2][2100/3746] lr: 9.997e-02, eta: 4 days, 18:38:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1309, top5_acc: 0.3355, loss_cls: 4.9329, loss: 4.9329 +2024-07-22 03:45:26,531 - pyskl - INFO - Epoch [2][2200/3746] lr: 9.997e-02, eta: 4 days, 18:30:55, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1369, top5_acc: 0.3327, loss_cls: 4.9178, loss: 4.9178 +2024-07-22 03:46:36,912 - pyskl - INFO - Epoch [2][2300/3746] lr: 9.997e-02, eta: 4 days, 18:23:54, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1364, top5_acc: 0.3417, loss_cls: 4.8527, loss: 4.8527 +2024-07-22 03:47:46,827 - pyskl - INFO - Epoch [2][2400/3746] lr: 9.997e-02, eta: 4 days, 18:16:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1323, top5_acc: 0.3302, loss_cls: 4.9074, loss: 4.9074 +2024-07-22 03:48:56,663 - pyskl - INFO - Epoch [2][2500/3746] lr: 9.997e-02, eta: 4 days, 18:08:55, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1331, top5_acc: 0.3381, loss_cls: 4.8677, loss: 4.8677 +2024-07-22 03:50:06,573 - pyskl - INFO - Epoch [2][2600/3746] lr: 9.997e-02, eta: 4 days, 18:01:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1359, top5_acc: 0.3439, loss_cls: 4.8453, loss: 4.8453 +2024-07-22 03:51:16,838 - pyskl - INFO - Epoch [2][2700/3746] lr: 9.997e-02, eta: 4 days, 17:55:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1384, top5_acc: 0.3391, loss_cls: 4.9043, loss: 4.9043 +2024-07-22 03:52:26,817 - pyskl - INFO - Epoch [2][2800/3746] lr: 9.997e-02, eta: 4 days, 17:48:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1467, top5_acc: 0.3552, loss_cls: 4.8087, loss: 4.8087 +2024-07-22 03:53:36,898 - pyskl - INFO - Epoch [2][2900/3746] lr: 9.997e-02, eta: 4 days, 17:42:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1425, top5_acc: 0.3531, loss_cls: 4.8232, loss: 4.8232 +2024-07-22 03:54:46,991 - pyskl - INFO - Epoch [2][3000/3746] lr: 9.996e-02, eta: 4 days, 17:36:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1444, top5_acc: 0.3586, loss_cls: 4.7887, loss: 4.7887 +2024-07-22 03:55:56,999 - pyskl - INFO - Epoch [2][3100/3746] lr: 9.996e-02, eta: 4 days, 17:29:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1431, top5_acc: 0.3598, loss_cls: 4.7711, loss: 4.7711 +2024-07-22 03:57:07,070 - pyskl - INFO - Epoch [2][3200/3746] lr: 9.996e-02, eta: 4 days, 17:23:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1384, top5_acc: 0.3594, loss_cls: 4.7754, loss: 4.7754 +2024-07-22 03:58:17,460 - pyskl - INFO - Epoch [2][3300/3746] lr: 9.996e-02, eta: 4 days, 17:18:34, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1483, top5_acc: 0.3583, loss_cls: 4.7786, loss: 4.7786 +2024-07-22 03:59:27,720 - pyskl - INFO - Epoch [2][3400/3746] lr: 9.996e-02, eta: 4 days, 17:13:08, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1480, top5_acc: 0.3503, loss_cls: 4.8228, loss: 4.8228 +2024-07-22 04:00:38,051 - pyskl - INFO - Epoch [2][3500/3746] lr: 9.996e-02, eta: 4 days, 17:07:54, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1497, top5_acc: 0.3695, loss_cls: 4.7436, loss: 4.7436 +2024-07-22 04:01:48,390 - pyskl - INFO - Epoch [2][3600/3746] lr: 9.996e-02, eta: 4 days, 17:02:47, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1536, top5_acc: 0.3703, loss_cls: 4.7518, loss: 4.7518 +2024-07-22 04:02:59,424 - pyskl - INFO - Epoch [2][3700/3746] lr: 9.996e-02, eta: 4 days, 16:58:38, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1598, top5_acc: 0.3706, loss_cls: 4.7140, loss: 4.7140 +2024-07-22 04:03:35,074 - pyskl - INFO - Saving checkpoint at 2 epochs +2024-07-22 04:05:29,242 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 04:05:29,900 - pyskl - INFO - +top1_acc 0.1093 +top5_acc 0.2885 +2024-07-22 04:05:29,900 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 04:05:29,944 - pyskl - INFO - +mean_acc 0.1093 +2024-07-22 04:05:29,949 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_1.pth was removed +2024-07-22 04:05:30,262 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2024-07-22 04:05:30,263 - pyskl - INFO - Best top1_acc is 0.1093 at 2 epoch. +2024-07-22 04:05:30,276 - pyskl - INFO - Epoch(val) [2][309] top1_acc: 0.1093, top5_acc: 0.2885, mean_class_accuracy: 0.1093 +2024-07-22 04:08:53,606 - pyskl - INFO - Epoch [3][100/3746] lr: 9.995e-02, eta: 4 days, 18:53:57, time: 2.033, data_time: 1.326, memory: 15990, top1_acc: 0.1539, top5_acc: 0.3678, loss_cls: 4.7278, loss: 4.7278 +2024-07-22 04:10:03,990 - pyskl - INFO - Epoch [3][200/3746] lr: 9.995e-02, eta: 4 days, 18:47:37, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1586, top5_acc: 0.3667, loss_cls: 4.7288, loss: 4.7288 +2024-07-22 04:11:14,339 - pyskl - INFO - Epoch [3][300/3746] lr: 9.995e-02, eta: 4 days, 18:41:22, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1616, top5_acc: 0.3809, loss_cls: 4.6863, loss: 4.6863 +2024-07-22 04:12:24,635 - pyskl - INFO - Epoch [3][400/3746] lr: 9.995e-02, eta: 4 days, 18:35:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1572, top5_acc: 0.3606, loss_cls: 4.7362, loss: 4.7362 +2024-07-22 04:13:34,846 - pyskl - INFO - Epoch [3][500/3746] lr: 9.995e-02, eta: 4 days, 18:29:02, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1586, top5_acc: 0.3841, loss_cls: 4.6921, loss: 4.6921 +2024-07-22 04:14:45,201 - pyskl - INFO - Epoch [3][600/3746] lr: 9.995e-02, eta: 4 days, 18:23:11, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1663, top5_acc: 0.3803, loss_cls: 4.6521, loss: 4.6521 +2024-07-22 04:15:55,379 - pyskl - INFO - Epoch [3][700/3746] lr: 9.995e-02, eta: 4 days, 18:17:14, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1608, top5_acc: 0.3753, loss_cls: 4.7031, loss: 4.7031 +2024-07-22 04:17:05,609 - pyskl - INFO - Epoch [3][800/3746] lr: 9.995e-02, eta: 4 days, 18:11:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1589, top5_acc: 0.3758, loss_cls: 4.7040, loss: 4.7040 +2024-07-22 04:18:15,698 - pyskl - INFO - Epoch [3][900/3746] lr: 9.994e-02, eta: 4 days, 18:05:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1611, top5_acc: 0.3831, loss_cls: 4.6725, loss: 4.6725 +2024-07-22 04:19:25,739 - pyskl - INFO - Epoch [3][1000/3746] lr: 9.994e-02, eta: 4 days, 17:59:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1636, top5_acc: 0.3730, loss_cls: 4.6976, loss: 4.6976 +2024-07-22 04:20:36,025 - pyskl - INFO - Epoch [3][1100/3746] lr: 9.994e-02, eta: 4 days, 17:54:29, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1620, top5_acc: 0.3814, loss_cls: 4.6931, loss: 4.6931 +2024-07-22 04:21:46,356 - pyskl - INFO - Epoch [3][1200/3746] lr: 9.994e-02, eta: 4 days, 17:49:14, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1614, top5_acc: 0.3833, loss_cls: 4.6922, loss: 4.6922 +2024-07-22 04:22:56,279 - pyskl - INFO - Epoch [3][1300/3746] lr: 9.994e-02, eta: 4 days, 17:43:39, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1733, top5_acc: 0.3912, loss_cls: 4.6362, loss: 4.6362 +2024-07-22 04:24:06,445 - pyskl - INFO - Epoch [3][1400/3746] lr: 9.994e-02, eta: 4 days, 17:38:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1638, top5_acc: 0.3828, loss_cls: 4.6771, loss: 4.6771 +2024-07-22 04:25:16,686 - pyskl - INFO - Epoch [3][1500/3746] lr: 9.994e-02, eta: 4 days, 17:33:22, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1711, top5_acc: 0.3961, loss_cls: 4.6287, loss: 4.6287 +2024-07-22 04:26:26,698 - pyskl - INFO - Epoch [3][1600/3746] lr: 9.994e-02, eta: 4 days, 17:28:09, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1613, top5_acc: 0.3722, loss_cls: 4.7135, loss: 4.7135 +2024-07-22 04:27:36,678 - pyskl - INFO - Epoch [3][1700/3746] lr: 9.993e-02, eta: 4 days, 17:23:00, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1673, top5_acc: 0.3841, loss_cls: 4.6789, loss: 4.6789 +2024-07-22 04:28:46,839 - pyskl - INFO - Epoch [3][1800/3746] lr: 9.993e-02, eta: 4 days, 17:18:06, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1661, top5_acc: 0.3800, loss_cls: 4.6719, loss: 4.6719 +2024-07-22 04:29:57,024 - pyskl - INFO - Epoch [3][1900/3746] lr: 9.993e-02, eta: 4 days, 17:13:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1634, top5_acc: 0.3845, loss_cls: 4.6292, loss: 4.6292 +2024-07-22 04:31:07,030 - pyskl - INFO - Epoch [3][2000/3746] lr: 9.993e-02, eta: 4 days, 17:08:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1658, top5_acc: 0.3802, loss_cls: 4.6483, loss: 4.6483 +2024-07-22 04:32:17,041 - pyskl - INFO - Epoch [3][2100/3746] lr: 9.993e-02, eta: 4 days, 17:03:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1620, top5_acc: 0.3881, loss_cls: 4.6197, loss: 4.6197 +2024-07-22 04:33:27,253 - pyskl - INFO - Epoch [3][2200/3746] lr: 9.993e-02, eta: 4 days, 16:59:06, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1758, top5_acc: 0.3930, loss_cls: 4.6477, loss: 4.6477 +2024-07-22 04:34:37,382 - pyskl - INFO - Epoch [3][2300/3746] lr: 9.993e-02, eta: 4 days, 16:54:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1817, top5_acc: 0.4042, loss_cls: 4.5705, loss: 4.5705 +2024-07-22 04:35:47,318 - pyskl - INFO - Epoch [3][2400/3746] lr: 9.992e-02, eta: 4 days, 16:49:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1795, top5_acc: 0.3950, loss_cls: 4.6046, loss: 4.6046 +2024-07-22 04:36:57,465 - pyskl - INFO - Epoch [3][2500/3746] lr: 9.992e-02, eta: 4 days, 16:45:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1753, top5_acc: 0.3998, loss_cls: 4.6195, loss: 4.6195 +2024-07-22 04:38:07,318 - pyskl - INFO - Epoch [3][2600/3746] lr: 9.992e-02, eta: 4 days, 16:40:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1683, top5_acc: 0.3986, loss_cls: 4.6212, loss: 4.6212 +2024-07-22 04:39:17,391 - pyskl - INFO - Epoch [3][2700/3746] lr: 9.992e-02, eta: 4 days, 16:36:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1819, top5_acc: 0.3903, loss_cls: 4.5893, loss: 4.5893 +2024-07-22 04:40:27,280 - pyskl - INFO - Epoch [3][2800/3746] lr: 9.992e-02, eta: 4 days, 16:32:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1833, top5_acc: 0.3998, loss_cls: 4.5811, loss: 4.5811 +2024-07-22 04:41:37,143 - pyskl - INFO - Epoch [3][2900/3746] lr: 9.992e-02, eta: 4 days, 16:27:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1675, top5_acc: 0.4037, loss_cls: 4.6066, loss: 4.6066 +2024-07-22 04:42:47,247 - pyskl - INFO - Epoch [3][3000/3746] lr: 9.991e-02, eta: 4 days, 16:23:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1806, top5_acc: 0.3984, loss_cls: 4.5801, loss: 4.5801 +2024-07-22 04:43:57,389 - pyskl - INFO - Epoch [3][3100/3746] lr: 9.991e-02, eta: 4 days, 16:19:36, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1833, top5_acc: 0.4016, loss_cls: 4.5638, loss: 4.5638 +2024-07-22 04:45:07,571 - pyskl - INFO - Epoch [3][3200/3746] lr: 9.991e-02, eta: 4 days, 16:15:39, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1834, top5_acc: 0.4123, loss_cls: 4.5360, loss: 4.5360 +2024-07-22 04:46:18,073 - pyskl - INFO - Epoch [3][3300/3746] lr: 9.991e-02, eta: 4 days, 16:12:02, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1783, top5_acc: 0.3994, loss_cls: 4.6003, loss: 4.6003 +2024-07-22 04:47:28,482 - pyskl - INFO - Epoch [3][3400/3746] lr: 9.991e-02, eta: 4 days, 16:08:23, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1737, top5_acc: 0.4131, loss_cls: 4.5742, loss: 4.5742 +2024-07-22 04:48:38,949 - pyskl - INFO - Epoch [3][3500/3746] lr: 9.991e-02, eta: 4 days, 16:04:49, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1859, top5_acc: 0.4058, loss_cls: 4.5710, loss: 4.5710 +2024-07-22 04:49:49,064 - pyskl - INFO - Epoch [3][3600/3746] lr: 9.990e-02, eta: 4 days, 16:01:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1891, top5_acc: 0.4120, loss_cls: 4.5179, loss: 4.5179 +2024-07-22 04:50:59,939 - pyskl - INFO - Epoch [3][3700/3746] lr: 9.990e-02, eta: 4 days, 15:57:52, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1861, top5_acc: 0.4139, loss_cls: 4.5193, loss: 4.5193 +2024-07-22 04:51:35,415 - pyskl - INFO - Saving checkpoint at 3 epochs +2024-07-22 04:53:27,543 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 04:53:28,236 - pyskl - INFO - +top1_acc 0.1054 +top5_acc 0.2960 +2024-07-22 04:53:28,236 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 04:53:28,288 - pyskl - INFO - +mean_acc 0.1052 +2024-07-22 04:53:28,304 - pyskl - INFO - Epoch(val) [3][309] top1_acc: 0.1054, top5_acc: 0.2960, mean_class_accuracy: 0.1052 +2024-07-22 04:57:02,277 - pyskl - INFO - Epoch [4][100/3746] lr: 9.990e-02, eta: 4 days, 17:22:46, time: 2.140, data_time: 1.419, memory: 15990, top1_acc: 0.1903, top5_acc: 0.4233, loss_cls: 4.4794, loss: 4.4794 +2024-07-22 04:58:14,433 - pyskl - INFO - Epoch [4][200/3746] lr: 9.990e-02, eta: 4 days, 17:19:57, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1902, top5_acc: 0.4155, loss_cls: 4.5145, loss: 4.5145 +2024-07-22 04:59:26,477 - pyskl - INFO - Epoch [4][300/3746] lr: 9.990e-02, eta: 4 days, 17:17:04, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1853, top5_acc: 0.4153, loss_cls: 4.5224, loss: 4.5224 +2024-07-22 05:00:38,216 - pyskl - INFO - Epoch [4][400/3746] lr: 9.989e-02, eta: 4 days, 17:13:58, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1748, top5_acc: 0.4156, loss_cls: 4.5126, loss: 4.5126 +2024-07-22 05:01:50,136 - pyskl - INFO - Epoch [4][500/3746] lr: 9.989e-02, eta: 4 days, 17:11:02, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1934, top5_acc: 0.4242, loss_cls: 4.4899, loss: 4.4899 +2024-07-22 05:03:01,921 - pyskl - INFO - Epoch [4][600/3746] lr: 9.989e-02, eta: 4 days, 17:08:03, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1858, top5_acc: 0.4256, loss_cls: 4.5029, loss: 4.5029 +2024-07-22 05:04:13,830 - pyskl - INFO - Epoch [4][700/3746] lr: 9.989e-02, eta: 4 days, 17:05:10, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1852, top5_acc: 0.4202, loss_cls: 4.5073, loss: 4.5073 +2024-07-22 05:05:25,776 - pyskl - INFO - Epoch [4][800/3746] lr: 9.989e-02, eta: 4 days, 17:02:21, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1758, top5_acc: 0.4113, loss_cls: 4.5163, loss: 4.5163 +2024-07-22 05:06:37,827 - pyskl - INFO - Epoch [4][900/3746] lr: 9.988e-02, eta: 4 days, 16:59:38, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1864, top5_acc: 0.4175, loss_cls: 4.5117, loss: 4.5117 +2024-07-22 05:07:49,643 - pyskl - INFO - Epoch [4][1000/3746] lr: 9.988e-02, eta: 4 days, 16:56:47, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1947, top5_acc: 0.4286, loss_cls: 4.4619, loss: 4.4619 +2024-07-22 05:09:01,339 - pyskl - INFO - Epoch [4][1100/3746] lr: 9.988e-02, eta: 4 days, 16:53:51, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1900, top5_acc: 0.4169, loss_cls: 4.4995, loss: 4.4995 +2024-07-22 05:10:13,447 - pyskl - INFO - Epoch [4][1200/3746] lr: 9.988e-02, eta: 4 days, 16:51:16, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1900, top5_acc: 0.4205, loss_cls: 4.5046, loss: 4.5046 +2024-07-22 05:11:24,973 - pyskl - INFO - Epoch [4][1300/3746] lr: 9.988e-02, eta: 4 days, 16:48:16, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1984, top5_acc: 0.4255, loss_cls: 4.4679, loss: 4.4679 +2024-07-22 05:12:36,594 - pyskl - INFO - Epoch [4][1400/3746] lr: 9.988e-02, eta: 4 days, 16:45:22, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1859, top5_acc: 0.4227, loss_cls: 4.4987, loss: 4.4987 +2024-07-22 05:13:48,333 - pyskl - INFO - Epoch [4][1500/3746] lr: 9.987e-02, eta: 4 days, 16:42:35, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1998, top5_acc: 0.4219, loss_cls: 4.4707, loss: 4.4707 +2024-07-22 05:15:00,071 - pyskl - INFO - Epoch [4][1600/3746] lr: 9.987e-02, eta: 4 days, 16:39:49, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2005, top5_acc: 0.4311, loss_cls: 4.4479, loss: 4.4479 +2024-07-22 05:16:12,115 - pyskl - INFO - Epoch [4][1700/3746] lr: 9.987e-02, eta: 4 days, 16:37:17, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1992, top5_acc: 0.4336, loss_cls: 4.4515, loss: 4.4515 +2024-07-22 05:17:24,081 - pyskl - INFO - Epoch [4][1800/3746] lr: 9.987e-02, eta: 4 days, 16:34:44, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1961, top5_acc: 0.4284, loss_cls: 4.4683, loss: 4.4683 +2024-07-22 05:18:35,162 - pyskl - INFO - Epoch [4][1900/3746] lr: 9.987e-02, eta: 4 days, 16:31:35, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4319, loss_cls: 4.4341, loss: 4.4341 +2024-07-22 05:19:45,737 - pyskl - INFO - Epoch [4][2000/3746] lr: 9.986e-02, eta: 4 days, 16:28:06, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1963, top5_acc: 0.4238, loss_cls: 4.4812, loss: 4.4812 +2024-07-22 05:20:56,985 - pyskl - INFO - Epoch [4][2100/3746] lr: 9.986e-02, eta: 4 days, 16:25:08, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1948, top5_acc: 0.4327, loss_cls: 4.4426, loss: 4.4426 +2024-07-22 05:22:07,792 - pyskl - INFO - Epoch [4][2200/3746] lr: 9.986e-02, eta: 4 days, 16:21:53, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1992, top5_acc: 0.4364, loss_cls: 4.4164, loss: 4.4164 +2024-07-22 05:23:18,627 - pyskl - INFO - Epoch [4][2300/3746] lr: 9.986e-02, eta: 4 days, 16:18:41, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2000, top5_acc: 0.4289, loss_cls: 4.4464, loss: 4.4464 +2024-07-22 05:24:29,225 - pyskl - INFO - Epoch [4][2400/3746] lr: 9.985e-02, eta: 4 days, 16:15:21, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1866, top5_acc: 0.4297, loss_cls: 4.4641, loss: 4.4641 +2024-07-22 05:25:39,826 - pyskl - INFO - Epoch [4][2500/3746] lr: 9.985e-02, eta: 4 days, 16:12:03, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1905, top5_acc: 0.4309, loss_cls: 4.4521, loss: 4.4521 +2024-07-22 05:26:49,951 - pyskl - INFO - Epoch [4][2600/3746] lr: 9.985e-02, eta: 4 days, 16:08:28, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4394, loss_cls: 4.4301, loss: 4.4301 +2024-07-22 05:28:00,168 - pyskl - INFO - Epoch [4][2700/3746] lr: 9.985e-02, eta: 4 days, 16:04:59, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2006, top5_acc: 0.4297, loss_cls: 4.4332, loss: 4.4332 +2024-07-22 05:29:10,678 - pyskl - INFO - Epoch [4][2800/3746] lr: 9.985e-02, eta: 4 days, 16:01:44, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2000, top5_acc: 0.4398, loss_cls: 4.4155, loss: 4.4155 +2024-07-22 05:30:20,757 - pyskl - INFO - Epoch [4][2900/3746] lr: 9.984e-02, eta: 4 days, 15:58:13, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2008, top5_acc: 0.4353, loss_cls: 4.4270, loss: 4.4270 +2024-07-22 05:31:31,220 - pyskl - INFO - Epoch [4][3000/3746] lr: 9.984e-02, eta: 4 days, 15:54:59, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1997, top5_acc: 0.4339, loss_cls: 4.4234, loss: 4.4234 +2024-07-22 05:32:41,607 - pyskl - INFO - Epoch [4][3100/3746] lr: 9.984e-02, eta: 4 days, 15:51:44, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1995, top5_acc: 0.4297, loss_cls: 4.4327, loss: 4.4327 +2024-07-22 05:33:51,776 - pyskl - INFO - Epoch [4][3200/3746] lr: 9.984e-02, eta: 4 days, 15:48:22, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4350, loss_cls: 4.4060, loss: 4.4060 +2024-07-22 05:35:02,656 - pyskl - INFO - Epoch [4][3300/3746] lr: 9.983e-02, eta: 4 days, 15:45:29, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4397, loss_cls: 4.3761, loss: 4.3761 +2024-07-22 05:36:13,064 - pyskl - INFO - Epoch [4][3400/3746] lr: 9.983e-02, eta: 4 days, 15:42:20, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2025, top5_acc: 0.4339, loss_cls: 4.4345, loss: 4.4345 +2024-07-22 05:37:23,549 - pyskl - INFO - Epoch [4][3500/3746] lr: 9.983e-02, eta: 4 days, 15:39:15, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4430, loss_cls: 4.3975, loss: 4.3975 +2024-07-22 05:38:34,011 - pyskl - INFO - Epoch [4][3600/3746] lr: 9.983e-02, eta: 4 days, 15:36:11, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4402, loss_cls: 4.4160, loss: 4.4160 +2024-07-22 05:39:44,845 - pyskl - INFO - Epoch [4][3700/3746] lr: 9.983e-02, eta: 4 days, 15:33:22, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1952, top5_acc: 0.4378, loss_cls: 4.4156, loss: 4.4156 +2024-07-22 05:40:19,643 - pyskl - INFO - Saving checkpoint at 4 epochs +2024-07-22 05:42:11,599 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 05:42:12,272 - pyskl - INFO - +top1_acc 0.1370 +top5_acc 0.3294 +2024-07-22 05:42:12,272 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 05:42:12,312 - pyskl - INFO - +mean_acc 0.1369 +2024-07-22 05:42:12,316 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_2.pth was removed +2024-07-22 05:42:12,568 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2024-07-22 05:42:12,569 - pyskl - INFO - Best top1_acc is 0.1370 at 4 epoch. +2024-07-22 05:42:12,580 - pyskl - INFO - Epoch(val) [4][309] top1_acc: 0.1370, top5_acc: 0.3294, mean_class_accuracy: 0.1369 +2024-07-22 05:45:37,984 - pyskl - INFO - Epoch [5][100/3746] lr: 9.982e-02, eta: 4 days, 16:30:55, time: 2.054, data_time: 1.351, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4528, loss_cls: 4.3816, loss: 4.3816 +2024-07-22 05:46:48,502 - pyskl - INFO - Epoch [5][200/3746] lr: 9.982e-02, eta: 4 days, 16:27:33, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4364, loss_cls: 4.4406, loss: 4.4406 +2024-07-22 05:47:59,458 - pyskl - INFO - Epoch [5][300/3746] lr: 9.982e-02, eta: 4 days, 16:24:28, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4441, loss_cls: 4.3806, loss: 4.3806 +2024-07-22 05:49:09,915 - pyskl - INFO - Epoch [5][400/3746] lr: 9.982e-02, eta: 4 days, 16:21:07, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4478, loss_cls: 4.3624, loss: 4.3624 +2024-07-22 05:50:20,552 - pyskl - INFO - Epoch [5][500/3746] lr: 9.981e-02, eta: 4 days, 16:17:54, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4459, loss_cls: 4.3762, loss: 4.3762 +2024-07-22 05:51:31,174 - pyskl - INFO - Epoch [5][600/3746] lr: 9.981e-02, eta: 4 days, 16:14:42, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4506, loss_cls: 4.3601, loss: 4.3601 +2024-07-22 05:52:41,812 - pyskl - INFO - Epoch [5][700/3746] lr: 9.981e-02, eta: 4 days, 16:11:32, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2000, top5_acc: 0.4377, loss_cls: 4.3956, loss: 4.3956 +2024-07-22 05:53:52,233 - pyskl - INFO - Epoch [5][800/3746] lr: 9.981e-02, eta: 4 days, 16:08:16, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4511, loss_cls: 4.3891, loss: 4.3891 +2024-07-22 05:55:02,531 - pyskl - INFO - Epoch [5][900/3746] lr: 9.980e-02, eta: 4 days, 16:04:58, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4502, loss_cls: 4.3673, loss: 4.3673 +2024-07-22 05:56:12,760 - pyskl - INFO - Epoch [5][1000/3746] lr: 9.980e-02, eta: 4 days, 16:01:38, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4542, loss_cls: 4.3172, loss: 4.3172 +2024-07-22 05:57:22,993 - pyskl - INFO - Epoch [5][1100/3746] lr: 9.980e-02, eta: 4 days, 15:58:21, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4548, loss_cls: 4.3561, loss: 4.3561 +2024-07-22 05:58:33,342 - pyskl - INFO - Epoch [5][1200/3746] lr: 9.980e-02, eta: 4 days, 15:55:09, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4489, loss_cls: 4.3660, loss: 4.3660 +2024-07-22 05:59:44,073 - pyskl - INFO - Epoch [5][1300/3746] lr: 9.979e-02, eta: 4 days, 15:52:11, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4483, loss_cls: 4.3952, loss: 4.3952 +2024-07-22 06:00:54,533 - pyskl - INFO - Epoch [5][1400/3746] lr: 9.979e-02, eta: 4 days, 15:49:06, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4495, loss_cls: 4.3738, loss: 4.3738 +2024-07-22 06:02:04,940 - pyskl - INFO - Epoch [5][1500/3746] lr: 9.979e-02, eta: 4 days, 15:46:00, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4516, loss_cls: 4.3481, loss: 4.3481 +2024-07-22 06:03:15,519 - pyskl - INFO - Epoch [5][1600/3746] lr: 9.979e-02, eta: 4 days, 15:43:01, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4502, loss_cls: 4.3524, loss: 4.3524 +2024-07-22 06:04:25,732 - pyskl - INFO - Epoch [5][1700/3746] lr: 9.978e-02, eta: 4 days, 15:39:52, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4483, loss_cls: 4.3671, loss: 4.3671 +2024-07-22 06:05:36,171 - pyskl - INFO - Epoch [5][1800/3746] lr: 9.978e-02, eta: 4 days, 15:36:51, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4480, loss_cls: 4.3839, loss: 4.3839 +2024-07-22 06:06:46,322 - pyskl - INFO - Epoch [5][1900/3746] lr: 9.978e-02, eta: 4 days, 15:33:42, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4575, loss_cls: 4.3554, loss: 4.3554 +2024-07-22 06:07:56,692 - pyskl - INFO - Epoch [5][2000/3746] lr: 9.977e-02, eta: 4 days, 15:30:42, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4580, loss_cls: 4.3175, loss: 4.3175 +2024-07-22 06:09:07,064 - pyskl - INFO - Epoch [5][2100/3746] lr: 9.977e-02, eta: 4 days, 15:27:43, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4545, loss_cls: 4.3355, loss: 4.3355 +2024-07-22 06:10:17,493 - pyskl - INFO - Epoch [5][2200/3746] lr: 9.977e-02, eta: 4 days, 15:24:47, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4603, loss_cls: 4.3284, loss: 4.3284 +2024-07-22 06:11:28,378 - pyskl - INFO - Epoch [5][2300/3746] lr: 9.977e-02, eta: 4 days, 15:22:07, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4481, loss_cls: 4.3911, loss: 4.3911 +2024-07-22 06:12:38,526 - pyskl - INFO - Epoch [5][2400/3746] lr: 9.976e-02, eta: 4 days, 15:19:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4511, loss_cls: 4.3805, loss: 4.3805 +2024-07-22 06:13:48,713 - pyskl - INFO - Epoch [5][2500/3746] lr: 9.976e-02, eta: 4 days, 15:16:05, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4498, loss_cls: 4.3776, loss: 4.3776 +2024-07-22 06:14:58,961 - pyskl - INFO - Epoch [5][2600/3746] lr: 9.976e-02, eta: 4 days, 15:13:08, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2114, top5_acc: 0.4511, loss_cls: 4.3391, loss: 4.3391 +2024-07-22 06:16:09,296 - pyskl - INFO - Epoch [5][2700/3746] lr: 9.976e-02, eta: 4 days, 15:10:16, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4545, loss_cls: 4.3374, loss: 4.3374 +2024-07-22 06:17:19,254 - pyskl - INFO - Epoch [5][2800/3746] lr: 9.975e-02, eta: 4 days, 15:07:12, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4612, loss_cls: 4.2882, loss: 4.2882 +2024-07-22 06:18:29,590 - pyskl - INFO - Epoch [5][2900/3746] lr: 9.975e-02, eta: 4 days, 15:04:22, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4503, loss_cls: 4.3341, loss: 4.3341 +2024-07-22 06:19:39,697 - pyskl - INFO - Epoch [5][3000/3746] lr: 9.975e-02, eta: 4 days, 15:01:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4617, loss_cls: 4.3326, loss: 4.3326 +2024-07-22 06:20:49,825 - pyskl - INFO - Epoch [5][3100/3746] lr: 9.974e-02, eta: 4 days, 14:58:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4592, loss_cls: 4.3189, loss: 4.3189 +2024-07-22 06:22:00,418 - pyskl - INFO - Epoch [5][3200/3746] lr: 9.974e-02, eta: 4 days, 14:55:52, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4630, loss_cls: 4.3012, loss: 4.3012 +2024-07-22 06:23:11,323 - pyskl - INFO - Epoch [5][3300/3746] lr: 9.974e-02, eta: 4 days, 14:53:23, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4531, loss_cls: 4.3176, loss: 4.3176 +2024-07-22 06:24:21,955 - pyskl - INFO - Epoch [5][3400/3746] lr: 9.974e-02, eta: 4 days, 14:50:47, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4627, loss_cls: 4.3335, loss: 4.3335 +2024-07-22 06:25:32,656 - pyskl - INFO - Epoch [5][3500/3746] lr: 9.973e-02, eta: 4 days, 14:48:13, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4597, loss_cls: 4.2831, loss: 4.2831 +2024-07-22 06:26:43,900 - pyskl - INFO - Epoch [5][3600/3746] lr: 9.973e-02, eta: 4 days, 14:45:57, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4642, loss_cls: 4.2797, loss: 4.2797 +2024-07-22 06:27:54,860 - pyskl - INFO - Epoch [5][3700/3746] lr: 9.973e-02, eta: 4 days, 14:43:32, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4550, loss_cls: 4.3078, loss: 4.3078 +2024-07-22 06:28:29,238 - pyskl - INFO - Saving checkpoint at 5 epochs +2024-07-22 06:30:20,871 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 06:30:21,526 - pyskl - INFO - +top1_acc 0.1725 +top5_acc 0.3861 +2024-07-22 06:30:21,526 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 06:30:21,564 - pyskl - INFO - +mean_acc 0.1725 +2024-07-22 06:30:21,568 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_4.pth was removed +2024-07-22 06:30:21,792 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2024-07-22 06:30:21,792 - pyskl - INFO - Best top1_acc is 0.1725 at 5 epoch. +2024-07-22 06:30:21,803 - pyskl - INFO - Epoch(val) [5][309] top1_acc: 0.1725, top5_acc: 0.3861, mean_class_accuracy: 0.1725 +2024-07-22 06:33:39,611 - pyskl - INFO - Epoch [6][100/3746] lr: 9.972e-02, eta: 4 days, 15:25:20, time: 1.978, data_time: 1.271, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4633, loss_cls: 4.3062, loss: 4.3062 +2024-07-22 06:34:50,022 - pyskl - INFO - Epoch [6][200/3746] lr: 9.972e-02, eta: 4 days, 15:22:27, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4691, loss_cls: 4.2666, loss: 4.2666 +2024-07-22 06:36:00,344 - pyskl - INFO - Epoch [6][300/3746] lr: 9.972e-02, eta: 4 days, 15:19:33, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4595, loss_cls: 4.2994, loss: 4.2994 +2024-07-22 06:37:10,993 - pyskl - INFO - Epoch [6][400/3746] lr: 9.971e-02, eta: 4 days, 15:16:49, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4614, loss_cls: 4.2913, loss: 4.2913 +2024-07-22 06:38:21,502 - pyskl - INFO - Epoch [6][500/3746] lr: 9.971e-02, eta: 4 days, 15:14:02, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4634, loss_cls: 4.2957, loss: 4.2957 +2024-07-22 06:39:31,462 - pyskl - INFO - Epoch [6][600/3746] lr: 9.971e-02, eta: 4 days, 15:11:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4503, loss_cls: 4.3100, loss: 4.3100 +2024-07-22 06:40:41,635 - pyskl - INFO - Epoch [6][700/3746] lr: 9.971e-02, eta: 4 days, 15:08:06, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4627, loss_cls: 4.3161, loss: 4.3161 +2024-07-22 06:41:52,002 - pyskl - INFO - Epoch [6][800/3746] lr: 9.970e-02, eta: 4 days, 15:05:19, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4752, loss_cls: 4.2526, loss: 4.2526 +2024-07-22 06:43:02,247 - pyskl - INFO - Epoch [6][900/3746] lr: 9.970e-02, eta: 4 days, 15:02:28, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4652, loss_cls: 4.2765, loss: 4.2765 +2024-07-22 06:44:12,839 - pyskl - INFO - Epoch [6][1000/3746] lr: 9.970e-02, eta: 4 days, 14:59:49, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4684, loss_cls: 4.2597, loss: 4.2597 +2024-07-22 06:45:23,035 - pyskl - INFO - Epoch [6][1100/3746] lr: 9.969e-02, eta: 4 days, 14:56:59, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4545, loss_cls: 4.3078, loss: 4.3078 +2024-07-22 06:46:33,132 - pyskl - INFO - Epoch [6][1200/3746] lr: 9.969e-02, eta: 4 days, 14:54:08, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4650, loss_cls: 4.2920, loss: 4.2920 +2024-07-22 06:47:43,441 - pyskl - INFO - Epoch [6][1300/3746] lr: 9.969e-02, eta: 4 days, 14:51:24, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4627, loss_cls: 4.2986, loss: 4.2986 +2024-07-22 06:48:54,217 - pyskl - INFO - Epoch [6][1400/3746] lr: 9.968e-02, eta: 4 days, 14:48:53, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4614, loss_cls: 4.2964, loss: 4.2964 +2024-07-22 06:50:04,552 - pyskl - INFO - Epoch [6][1500/3746] lr: 9.968e-02, eta: 4 days, 14:46:10, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4608, loss_cls: 4.2905, loss: 4.2905 +2024-07-22 06:51:14,851 - pyskl - INFO - Epoch [6][1600/3746] lr: 9.968e-02, eta: 4 days, 14:43:28, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4694, loss_cls: 4.2897, loss: 4.2897 +2024-07-22 06:52:25,030 - pyskl - INFO - Epoch [6][1700/3746] lr: 9.967e-02, eta: 4 days, 14:40:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4664, loss_cls: 4.2922, loss: 4.2922 +2024-07-22 06:53:35,204 - pyskl - INFO - Epoch [6][1800/3746] lr: 9.967e-02, eta: 4 days, 14:38:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4686, loss_cls: 4.3138, loss: 4.3138 +2024-07-22 06:54:45,517 - pyskl - INFO - Epoch [6][1900/3746] lr: 9.967e-02, eta: 4 days, 14:35:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4695, loss_cls: 4.2588, loss: 4.2588 +2024-07-22 06:55:56,009 - pyskl - INFO - Epoch [6][2000/3746] lr: 9.966e-02, eta: 4 days, 14:32:48, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4659, loss_cls: 4.2917, loss: 4.2917 +2024-07-22 06:57:06,151 - pyskl - INFO - Epoch [6][2100/3746] lr: 9.966e-02, eta: 4 days, 14:30:06, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4583, loss_cls: 4.3216, loss: 4.3216 +2024-07-22 06:58:16,726 - pyskl - INFO - Epoch [6][2200/3746] lr: 9.966e-02, eta: 4 days, 14:27:36, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4639, loss_cls: 4.3005, loss: 4.3005 +2024-07-22 06:59:27,098 - pyskl - INFO - Epoch [6][2300/3746] lr: 9.965e-02, eta: 4 days, 14:25:02, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4656, loss_cls: 4.2732, loss: 4.2732 +2024-07-22 07:00:37,420 - pyskl - INFO - Epoch [6][2400/3746] lr: 9.965e-02, eta: 4 days, 14:22:28, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4759, loss_cls: 4.1945, loss: 4.1945 +2024-07-22 07:01:47,513 - pyskl - INFO - Epoch [6][2500/3746] lr: 9.965e-02, eta: 4 days, 14:19:48, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4736, loss_cls: 4.2527, loss: 4.2527 +2024-07-22 07:02:57,783 - pyskl - INFO - Epoch [6][2600/3746] lr: 9.964e-02, eta: 4 days, 14:17:13, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4686, loss_cls: 4.2772, loss: 4.2772 +2024-07-22 07:04:07,993 - pyskl - INFO - Epoch [6][2700/3746] lr: 9.964e-02, eta: 4 days, 14:14:38, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4698, loss_cls: 4.2738, loss: 4.2738 +2024-07-22 07:05:18,626 - pyskl - INFO - Epoch [6][2800/3746] lr: 9.964e-02, eta: 4 days, 14:12:15, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4706, loss_cls: 4.2545, loss: 4.2545 +2024-07-22 07:06:28,794 - pyskl - INFO - Epoch [6][2900/3746] lr: 9.963e-02, eta: 4 days, 14:09:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4695, loss_cls: 4.3054, loss: 4.3054 +2024-07-22 07:07:39,034 - pyskl - INFO - Epoch [6][3000/3746] lr: 9.963e-02, eta: 4 days, 14:07:08, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4614, loss_cls: 4.3127, loss: 4.3127 +2024-07-22 07:08:49,465 - pyskl - INFO - Epoch [6][3100/3746] lr: 9.963e-02, eta: 4 days, 14:04:41, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4763, loss_cls: 4.2420, loss: 4.2420 +2024-07-22 07:09:59,745 - pyskl - INFO - Epoch [6][3200/3746] lr: 9.962e-02, eta: 4 days, 14:02:12, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4694, loss_cls: 4.2366, loss: 4.2366 +2024-07-22 07:11:10,397 - pyskl - INFO - Epoch [6][3300/3746] lr: 9.962e-02, eta: 4 days, 13:59:52, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4608, loss_cls: 4.2827, loss: 4.2827 +2024-07-22 07:12:20,810 - pyskl - INFO - Epoch [6][3400/3746] lr: 9.962e-02, eta: 4 days, 13:57:27, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4736, loss_cls: 4.2514, loss: 4.2514 +2024-07-22 07:13:31,425 - pyskl - INFO - Epoch [6][3500/3746] lr: 9.961e-02, eta: 4 days, 13:55:07, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4672, loss_cls: 4.2792, loss: 4.2792 +2024-07-22 07:14:42,171 - pyskl - INFO - Epoch [6][3600/3746] lr: 9.961e-02, eta: 4 days, 13:52:52, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4802, loss_cls: 4.2438, loss: 4.2438 +2024-07-22 07:15:53,123 - pyskl - INFO - Epoch [6][3700/3746] lr: 9.961e-02, eta: 4 days, 13:50:42, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4736, loss_cls: 4.2500, loss: 4.2500 +2024-07-22 07:16:28,068 - pyskl - INFO - Saving checkpoint at 6 epochs +2024-07-22 07:18:19,857 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 07:18:20,522 - pyskl - INFO - +top1_acc 0.1631 +top5_acc 0.3762 +2024-07-22 07:18:20,522 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 07:18:20,562 - pyskl - INFO - +mean_acc 0.1629 +2024-07-22 07:18:20,572 - pyskl - INFO - Epoch(val) [6][309] top1_acc: 0.1631, top5_acc: 0.3762, mean_class_accuracy: 0.1629 +2024-07-22 07:21:38,258 - pyskl - INFO - Epoch [7][100/3746] lr: 9.960e-02, eta: 4 days, 14:25:00, time: 1.977, data_time: 1.270, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4667, loss_cls: 4.2521, loss: 4.2521 +2024-07-22 07:22:48,516 - pyskl - INFO - Epoch [7][200/3746] lr: 9.960e-02, eta: 4 days, 14:22:25, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4731, loss_cls: 4.2308, loss: 4.2308 +2024-07-22 07:23:59,132 - pyskl - INFO - Epoch [7][300/3746] lr: 9.960e-02, eta: 4 days, 14:19:58, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4744, loss_cls: 4.2339, loss: 4.2339 +2024-07-22 07:25:09,429 - pyskl - INFO - Epoch [7][400/3746] lr: 9.959e-02, eta: 4 days, 14:17:25, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4686, loss_cls: 4.2703, loss: 4.2703 +2024-07-22 07:26:19,910 - pyskl - INFO - Epoch [7][500/3746] lr: 9.959e-02, eta: 4 days, 14:14:57, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4772, loss_cls: 4.1875, loss: 4.1875 +2024-07-22 07:27:30,085 - pyskl - INFO - Epoch [7][600/3746] lr: 9.958e-02, eta: 4 days, 14:12:22, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4767, loss_cls: 4.2242, loss: 4.2242 +2024-07-22 07:28:40,485 - pyskl - INFO - Epoch [7][700/3746] lr: 9.958e-02, eta: 4 days, 14:09:53, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4725, loss_cls: 4.2357, loss: 4.2357 +2024-07-22 07:29:50,829 - pyskl - INFO - Epoch [7][800/3746] lr: 9.958e-02, eta: 4 days, 14:07:24, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4619, loss_cls: 4.2671, loss: 4.2671 +2024-07-22 07:31:00,998 - pyskl - INFO - Epoch [7][900/3746] lr: 9.957e-02, eta: 4 days, 14:04:51, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4681, loss_cls: 4.2662, loss: 4.2662 +2024-07-22 07:32:11,582 - pyskl - INFO - Epoch [7][1000/3746] lr: 9.957e-02, eta: 4 days, 14:02:29, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4728, loss_cls: 4.2421, loss: 4.2421 +2024-07-22 07:33:22,288 - pyskl - INFO - Epoch [7][1100/3746] lr: 9.957e-02, eta: 4 days, 14:00:10, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4673, loss_cls: 4.2697, loss: 4.2697 +2024-07-22 07:34:32,780 - pyskl - INFO - Epoch [7][1200/3746] lr: 9.956e-02, eta: 4 days, 13:57:46, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4838, loss_cls: 4.2330, loss: 4.2330 +2024-07-22 07:35:43,125 - pyskl - INFO - Epoch [7][1300/3746] lr: 9.956e-02, eta: 4 days, 13:55:20, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4725, loss_cls: 4.2577, loss: 4.2577 +2024-07-22 07:36:53,157 - pyskl - INFO - Epoch [7][1400/3746] lr: 9.956e-02, eta: 4 days, 13:52:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4719, loss_cls: 4.2319, loss: 4.2319 +2024-07-22 07:38:03,242 - pyskl - INFO - Epoch [7][1500/3746] lr: 9.955e-02, eta: 4 days, 13:50:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4770, loss_cls: 4.2159, loss: 4.2159 +2024-07-22 07:39:13,564 - pyskl - INFO - Epoch [7][1600/3746] lr: 9.955e-02, eta: 4 days, 13:47:52, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4683, loss_cls: 4.2929, loss: 4.2929 +2024-07-22 07:40:23,703 - pyskl - INFO - Epoch [7][1700/3746] lr: 9.954e-02, eta: 4 days, 13:45:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4695, loss_cls: 4.2491, loss: 4.2491 +2024-07-22 07:41:34,143 - pyskl - INFO - Epoch [7][1800/3746] lr: 9.954e-02, eta: 4 days, 13:43:03, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4725, loss_cls: 4.2340, loss: 4.2340 +2024-07-22 07:42:44,220 - pyskl - INFO - Epoch [7][1900/3746] lr: 9.954e-02, eta: 4 days, 13:40:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4587, loss_cls: 4.2690, loss: 4.2690 +2024-07-22 07:43:54,707 - pyskl - INFO - Epoch [7][2000/3746] lr: 9.953e-02, eta: 4 days, 13:38:16, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4680, loss_cls: 4.2893, loss: 4.2893 +2024-07-22 07:45:04,851 - pyskl - INFO - Epoch [7][2100/3746] lr: 9.953e-02, eta: 4 days, 13:35:51, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4867, loss_cls: 4.1879, loss: 4.1879 +2024-07-22 07:46:15,493 - pyskl - INFO - Epoch [7][2200/3746] lr: 9.952e-02, eta: 4 days, 13:33:37, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4866, loss_cls: 4.2195, loss: 4.2195 +2024-07-22 07:47:26,252 - pyskl - INFO - Epoch [7][2300/3746] lr: 9.952e-02, eta: 4 days, 13:31:25, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4628, loss_cls: 4.2720, loss: 4.2720 +2024-07-22 07:48:36,430 - pyskl - INFO - Epoch [7][2400/3746] lr: 9.952e-02, eta: 4 days, 13:29:02, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4767, loss_cls: 4.2423, loss: 4.2423 +2024-07-22 07:49:46,642 - pyskl - INFO - Epoch [7][2500/3746] lr: 9.951e-02, eta: 4 days, 13:26:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4786, loss_cls: 4.2371, loss: 4.2371 +2024-07-22 07:50:56,770 - pyskl - INFO - Epoch [7][2600/3746] lr: 9.951e-02, eta: 4 days, 13:24:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4723, loss_cls: 4.2424, loss: 4.2424 +2024-07-22 07:52:06,937 - pyskl - INFO - Epoch [7][2700/3746] lr: 9.951e-02, eta: 4 days, 13:21:56, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4750, loss_cls: 4.2440, loss: 4.2440 +2024-07-22 07:53:17,372 - pyskl - INFO - Epoch [7][2800/3746] lr: 9.950e-02, eta: 4 days, 13:19:40, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4778, loss_cls: 4.2586, loss: 4.2586 +2024-07-22 07:54:27,600 - pyskl - INFO - Epoch [7][2900/3746] lr: 9.950e-02, eta: 4 days, 13:17:21, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4711, loss_cls: 4.2536, loss: 4.2536 +2024-07-22 07:55:37,890 - pyskl - INFO - Epoch [7][3000/3746] lr: 9.949e-02, eta: 4 days, 13:15:04, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4716, loss_cls: 4.2498, loss: 4.2498 +2024-07-22 07:56:48,305 - pyskl - INFO - Epoch [7][3100/3746] lr: 9.949e-02, eta: 4 days, 13:12:50, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4855, loss_cls: 4.1952, loss: 4.1952 +2024-07-22 07:57:59,367 - pyskl - INFO - Epoch [7][3200/3746] lr: 9.949e-02, eta: 4 days, 13:10:49, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4809, loss_cls: 4.2131, loss: 4.2131 +2024-07-22 07:59:10,321 - pyskl - INFO - Epoch [7][3300/3746] lr: 9.948e-02, eta: 4 days, 13:08:47, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4622, loss_cls: 4.3084, loss: 4.3084 +2024-07-22 08:00:21,041 - pyskl - INFO - Epoch [7][3400/3746] lr: 9.948e-02, eta: 4 days, 13:06:41, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4836, loss_cls: 4.1844, loss: 4.1844 +2024-07-22 08:01:31,345 - pyskl - INFO - Epoch [7][3500/3746] lr: 9.947e-02, eta: 4 days, 13:04:26, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4767, loss_cls: 4.2265, loss: 4.2265 +2024-07-22 08:02:42,385 - pyskl - INFO - Epoch [7][3600/3746] lr: 9.947e-02, eta: 4 days, 13:02:27, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4772, loss_cls: 4.2302, loss: 4.2302 +2024-07-22 08:03:53,758 - pyskl - INFO - Epoch [7][3700/3746] lr: 9.947e-02, eta: 4 days, 13:00:35, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4803, loss_cls: 4.2032, loss: 4.2032 +2024-07-22 08:04:28,525 - pyskl - INFO - Saving checkpoint at 7 epochs +2024-07-22 08:06:20,899 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 08:06:21,568 - pyskl - INFO - +top1_acc 0.1808 +top5_acc 0.4123 +2024-07-22 08:06:21,568 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 08:06:21,607 - pyskl - INFO - +mean_acc 0.1806 +2024-07-22 08:06:21,612 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_5.pth was removed +2024-07-22 08:06:21,854 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2024-07-22 08:06:21,854 - pyskl - INFO - Best top1_acc is 0.1808 at 7 epoch. +2024-07-22 08:06:21,864 - pyskl - INFO - Epoch(val) [7][309] top1_acc: 0.1808, top5_acc: 0.4123, mean_class_accuracy: 0.1806 +2024-07-22 08:09:38,499 - pyskl - INFO - Epoch [8][100/3746] lr: 9.946e-02, eta: 4 days, 13:29:13, time: 1.966, data_time: 1.264, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4919, loss_cls: 4.1432, loss: 4.1432 +2024-07-22 08:10:48,824 - pyskl - INFO - Epoch [8][200/3746] lr: 9.946e-02, eta: 4 days, 13:26:53, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4908, loss_cls: 4.1709, loss: 4.1709 +2024-07-22 08:11:59,193 - pyskl - INFO - Epoch [8][300/3746] lr: 9.945e-02, eta: 4 days, 13:24:34, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4819, loss_cls: 4.1850, loss: 4.1850 +2024-07-22 08:13:09,930 - pyskl - INFO - Epoch [8][400/3746] lr: 9.945e-02, eta: 4 days, 13:22:24, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4700, loss_cls: 4.2339, loss: 4.2339 +2024-07-22 08:14:20,091 - pyskl - INFO - Epoch [8][500/3746] lr: 9.944e-02, eta: 4 days, 13:20:02, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4728, loss_cls: 4.2461, loss: 4.2461 +2024-07-22 08:15:30,378 - pyskl - INFO - Epoch [8][600/3746] lr: 9.944e-02, eta: 4 days, 13:17:44, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4734, loss_cls: 4.2517, loss: 4.2517 +2024-07-22 08:16:40,821 - pyskl - INFO - Epoch [8][700/3746] lr: 9.943e-02, eta: 4 days, 13:15:29, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4813, loss_cls: 4.1991, loss: 4.1991 +2024-07-22 08:17:51,391 - pyskl - INFO - Epoch [8][800/3746] lr: 9.943e-02, eta: 4 days, 13:13:17, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4822, loss_cls: 4.1969, loss: 4.1969 +2024-07-22 08:19:02,012 - pyskl - INFO - Epoch [8][900/3746] lr: 9.943e-02, eta: 4 days, 13:11:06, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4713, loss_cls: 4.2467, loss: 4.2467 +2024-07-22 08:20:12,520 - pyskl - INFO - Epoch [8][1000/3746] lr: 9.942e-02, eta: 4 days, 13:08:54, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4859, loss_cls: 4.1866, loss: 4.1866 +2024-07-22 08:21:22,728 - pyskl - INFO - Epoch [8][1100/3746] lr: 9.942e-02, eta: 4 days, 13:06:36, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4836, loss_cls: 4.2093, loss: 4.2093 +2024-07-22 08:22:33,358 - pyskl - INFO - Epoch [8][1200/3746] lr: 9.941e-02, eta: 4 days, 13:04:27, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4822, loss_cls: 4.2048, loss: 4.2048 +2024-07-22 08:23:43,590 - pyskl - INFO - Epoch [8][1300/3746] lr: 9.941e-02, eta: 4 days, 13:02:11, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4863, loss_cls: 4.1917, loss: 4.1917 +2024-07-22 08:24:53,686 - pyskl - INFO - Epoch [8][1400/3746] lr: 9.940e-02, eta: 4 days, 12:59:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4772, loss_cls: 4.2334, loss: 4.2334 +2024-07-22 08:26:04,054 - pyskl - INFO - Epoch [8][1500/3746] lr: 9.940e-02, eta: 4 days, 12:57:40, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4850, loss_cls: 4.2077, loss: 4.2077 +2024-07-22 08:27:14,110 - pyskl - INFO - Epoch [8][1600/3746] lr: 9.940e-02, eta: 4 days, 12:55:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4736, loss_cls: 4.2185, loss: 4.2185 +2024-07-22 08:28:25,087 - pyskl - INFO - Epoch [8][1700/3746] lr: 9.939e-02, eta: 4 days, 12:53:21, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4848, loss_cls: 4.1851, loss: 4.1851 +2024-07-22 08:29:35,288 - pyskl - INFO - Epoch [8][1800/3746] lr: 9.939e-02, eta: 4 days, 12:51:07, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4683, loss_cls: 4.2587, loss: 4.2587 +2024-07-22 08:30:45,178 - pyskl - INFO - Epoch [8][1900/3746] lr: 9.938e-02, eta: 4 days, 12:48:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4828, loss_cls: 4.2040, loss: 4.2040 +2024-07-22 08:31:55,475 - pyskl - INFO - Epoch [8][2000/3746] lr: 9.938e-02, eta: 4 days, 12:46:35, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4806, loss_cls: 4.1947, loss: 4.1947 +2024-07-22 08:33:05,807 - pyskl - INFO - Epoch [8][2100/3746] lr: 9.937e-02, eta: 4 days, 12:44:24, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4811, loss_cls: 4.1763, loss: 4.1763 +2024-07-22 08:34:15,857 - pyskl - INFO - Epoch [8][2200/3746] lr: 9.937e-02, eta: 4 days, 12:42:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4756, loss_cls: 4.2205, loss: 4.2205 +2024-07-22 08:35:26,202 - pyskl - INFO - Epoch [8][2300/3746] lr: 9.937e-02, eta: 4 days, 12:39:59, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4838, loss_cls: 4.1892, loss: 4.1892 +2024-07-22 08:36:36,929 - pyskl - INFO - Epoch [8][2400/3746] lr: 9.936e-02, eta: 4 days, 12:37:57, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4742, loss_cls: 4.2087, loss: 4.2087 +2024-07-22 08:37:47,041 - pyskl - INFO - Epoch [8][2500/3746] lr: 9.936e-02, eta: 4 days, 12:35:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4834, loss_cls: 4.1982, loss: 4.1982 +2024-07-22 08:38:57,216 - pyskl - INFO - Epoch [8][2600/3746] lr: 9.935e-02, eta: 4 days, 12:33:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4814, loss_cls: 4.2127, loss: 4.2127 +2024-07-22 08:40:07,803 - pyskl - INFO - Epoch [8][2700/3746] lr: 9.935e-02, eta: 4 days, 12:31:28, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4795, loss_cls: 4.1944, loss: 4.1944 +2024-07-22 08:41:18,005 - pyskl - INFO - Epoch [8][2800/3746] lr: 9.934e-02, eta: 4 days, 12:29:18, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4855, loss_cls: 4.2091, loss: 4.2091 +2024-07-22 08:42:28,512 - pyskl - INFO - Epoch [8][2900/3746] lr: 9.934e-02, eta: 4 days, 12:27:14, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4752, loss_cls: 4.2253, loss: 4.2253 +2024-07-22 08:43:39,233 - pyskl - INFO - Epoch [8][3000/3746] lr: 9.933e-02, eta: 4 days, 12:25:14, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4772, loss_cls: 4.2229, loss: 4.2229 +2024-07-22 08:44:49,565 - pyskl - INFO - Epoch [8][3100/3746] lr: 9.933e-02, eta: 4 days, 12:23:07, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4831, loss_cls: 4.1818, loss: 4.1818 +2024-07-22 08:46:00,176 - pyskl - INFO - Epoch [8][3200/3746] lr: 9.933e-02, eta: 4 days, 12:21:06, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4869, loss_cls: 4.1647, loss: 4.1647 +2024-07-22 08:47:10,623 - pyskl - INFO - Epoch [8][3300/3746] lr: 9.932e-02, eta: 4 days, 12:19:02, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4908, loss_cls: 4.1423, loss: 4.1423 +2024-07-22 08:48:21,035 - pyskl - INFO - Epoch [8][3400/3746] lr: 9.932e-02, eta: 4 days, 12:16:58, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4809, loss_cls: 4.1766, loss: 4.1766 +2024-07-22 08:49:31,451 - pyskl - INFO - Epoch [8][3500/3746] lr: 9.931e-02, eta: 4 days, 12:14:54, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4792, loss_cls: 4.2168, loss: 4.2168 +2024-07-22 08:50:42,994 - pyskl - INFO - Epoch [8][3600/3746] lr: 9.931e-02, eta: 4 days, 12:13:11, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4844, loss_cls: 4.2103, loss: 4.2103 +2024-07-22 08:51:54,452 - pyskl - INFO - Epoch [8][3700/3746] lr: 9.930e-02, eta: 4 days, 12:11:26, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4648, loss_cls: 4.2371, loss: 4.2371 +2024-07-22 08:52:28,918 - pyskl - INFO - Saving checkpoint at 8 epochs +2024-07-22 08:54:20,674 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 08:54:21,351 - pyskl - INFO - +top1_acc 0.1616 +top5_acc 0.3730 +2024-07-22 08:54:21,352 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 08:54:21,389 - pyskl - INFO - +mean_acc 0.1613 +2024-07-22 08:54:21,400 - pyskl - INFO - Epoch(val) [8][309] top1_acc: 0.1616, top5_acc: 0.3730, mean_class_accuracy: 0.1613 +2024-07-22 08:57:38,066 - pyskl - INFO - Epoch [9][100/3746] lr: 9.930e-02, eta: 4 days, 12:36:07, time: 1.967, data_time: 1.257, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4863, loss_cls: 4.1914, loss: 4.1914 +2024-07-22 08:58:48,990 - pyskl - INFO - Epoch [9][200/3746] lr: 9.929e-02, eta: 4 days, 12:34:08, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4941, loss_cls: 4.1409, loss: 4.1409 +2024-07-22 08:59:59,283 - pyskl - INFO - Epoch [9][300/3746] lr: 9.929e-02, eta: 4 days, 12:31:58, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4866, loss_cls: 4.1908, loss: 4.1908 +2024-07-22 09:01:09,747 - pyskl - INFO - Epoch [9][400/3746] lr: 9.928e-02, eta: 4 days, 12:29:51, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4880, loss_cls: 4.1663, loss: 4.1663 +2024-07-22 09:02:20,043 - pyskl - INFO - Epoch [9][500/3746] lr: 9.928e-02, eta: 4 days, 12:27:42, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4900, loss_cls: 4.1481, loss: 4.1481 +2024-07-22 09:03:30,341 - pyskl - INFO - Epoch [9][600/3746] lr: 9.927e-02, eta: 4 days, 12:25:33, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4850, loss_cls: 4.2060, loss: 4.2060 +2024-07-22 09:04:40,707 - pyskl - INFO - Epoch [9][700/3746] lr: 9.927e-02, eta: 4 days, 12:23:26, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5031, loss_cls: 4.1308, loss: 4.1308 +2024-07-22 09:05:50,833 - pyskl - INFO - Epoch [9][800/3746] lr: 9.926e-02, eta: 4 days, 12:21:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4856, loss_cls: 4.1797, loss: 4.1797 +2024-07-22 09:07:01,377 - pyskl - INFO - Epoch [9][900/3746] lr: 9.926e-02, eta: 4 days, 12:19:12, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4891, loss_cls: 4.1905, loss: 4.1905 +2024-07-22 09:08:11,962 - pyskl - INFO - Epoch [9][1000/3746] lr: 9.925e-02, eta: 4 days, 12:17:10, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4898, loss_cls: 4.1766, loss: 4.1766 +2024-07-22 09:09:22,213 - pyskl - INFO - Epoch [9][1100/3746] lr: 9.925e-02, eta: 4 days, 12:15:02, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4883, loss_cls: 4.1827, loss: 4.1827 +2024-07-22 09:10:32,736 - pyskl - INFO - Epoch [9][1200/3746] lr: 9.924e-02, eta: 4 days, 12:12:59, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4838, loss_cls: 4.1795, loss: 4.1795 +2024-07-22 09:11:42,895 - pyskl - INFO - Epoch [9][1300/3746] lr: 9.924e-02, eta: 4 days, 12:10:51, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4889, loss_cls: 4.1647, loss: 4.1647 +2024-07-22 09:12:53,446 - pyskl - INFO - Epoch [9][1400/3746] lr: 9.923e-02, eta: 4 days, 12:08:49, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4813, loss_cls: 4.1715, loss: 4.1715 +2024-07-22 09:14:04,150 - pyskl - INFO - Epoch [9][1500/3746] lr: 9.923e-02, eta: 4 days, 12:06:51, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4820, loss_cls: 4.2089, loss: 4.2089 +2024-07-22 09:15:14,443 - pyskl - INFO - Epoch [9][1600/3746] lr: 9.922e-02, eta: 4 days, 12:04:46, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4852, loss_cls: 4.1488, loss: 4.1488 +2024-07-22 09:16:24,882 - pyskl - INFO - Epoch [9][1700/3746] lr: 9.922e-02, eta: 4 days, 12:02:43, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4877, loss_cls: 4.1911, loss: 4.1911 +2024-07-22 09:17:35,138 - pyskl - INFO - Epoch [9][1800/3746] lr: 9.921e-02, eta: 4 days, 12:00:38, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4931, loss_cls: 4.1613, loss: 4.1613 +2024-07-22 09:18:45,315 - pyskl - INFO - Epoch [9][1900/3746] lr: 9.921e-02, eta: 4 days, 11:58:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4889, loss_cls: 4.1823, loss: 4.1823 +2024-07-22 09:19:55,423 - pyskl - INFO - Epoch [9][2000/3746] lr: 9.920e-02, eta: 4 days, 11:56:25, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4820, loss_cls: 4.1810, loss: 4.1810 +2024-07-22 09:21:05,506 - pyskl - INFO - Epoch [9][2100/3746] lr: 9.920e-02, eta: 4 days, 11:54:18, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4813, loss_cls: 4.1681, loss: 4.1681 +2024-07-22 09:22:16,177 - pyskl - INFO - Epoch [9][2200/3746] lr: 9.919e-02, eta: 4 days, 11:52:21, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4733, loss_cls: 4.2258, loss: 4.2258 +2024-07-22 09:23:26,371 - pyskl - INFO - Epoch [9][2300/3746] lr: 9.919e-02, eta: 4 days, 11:50:17, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4853, loss_cls: 4.1767, loss: 4.1767 +2024-07-22 09:24:36,864 - pyskl - INFO - Epoch [9][2400/3746] lr: 9.918e-02, eta: 4 days, 11:48:18, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4850, loss_cls: 4.1539, loss: 4.1539 +2024-07-22 09:25:47,109 - pyskl - INFO - Epoch [9][2500/3746] lr: 9.918e-02, eta: 4 days, 11:46:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4778, loss_cls: 4.2213, loss: 4.2213 +2024-07-22 09:26:57,358 - pyskl - INFO - Epoch [9][2600/3746] lr: 9.917e-02, eta: 4 days, 11:44:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4834, loss_cls: 4.1722, loss: 4.1722 +2024-07-22 09:28:07,596 - pyskl - INFO - Epoch [9][2700/3746] lr: 9.917e-02, eta: 4 days, 11:42:10, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4836, loss_cls: 4.2058, loss: 4.2058 +2024-07-22 09:29:18,103 - pyskl - INFO - Epoch [9][2800/3746] lr: 9.916e-02, eta: 4 days, 11:40:12, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4794, loss_cls: 4.2165, loss: 4.2165 +2024-07-22 09:30:28,488 - pyskl - INFO - Epoch [9][2900/3746] lr: 9.916e-02, eta: 4 days, 11:38:12, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4767, loss_cls: 4.1998, loss: 4.1998 +2024-07-22 09:31:38,613 - pyskl - INFO - Epoch [9][3000/3746] lr: 9.915e-02, eta: 4 days, 11:36:09, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4805, loss_cls: 4.2101, loss: 4.2101 +2024-07-22 09:32:48,844 - pyskl - INFO - Epoch [9][3100/3746] lr: 9.915e-02, eta: 4 days, 11:34:08, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4814, loss_cls: 4.2063, loss: 4.2063 +2024-07-22 09:34:00,114 - pyskl - INFO - Epoch [9][3200/3746] lr: 9.914e-02, eta: 4 days, 11:32:23, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4819, loss_cls: 4.1751, loss: 4.1751 +2024-07-22 09:35:10,447 - pyskl - INFO - Epoch [9][3300/3746] lr: 9.914e-02, eta: 4 days, 11:30:24, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4878, loss_cls: 4.1719, loss: 4.1719 +2024-07-22 09:36:21,328 - pyskl - INFO - Epoch [9][3400/3746] lr: 9.913e-02, eta: 4 days, 11:28:34, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4820, loss_cls: 4.2287, loss: 4.2287 +2024-07-22 09:37:32,024 - pyskl - INFO - Epoch [9][3500/3746] lr: 9.913e-02, eta: 4 days, 11:26:41, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4905, loss_cls: 4.1628, loss: 4.1628 +2024-07-22 09:38:42,851 - pyskl - INFO - Epoch [9][3600/3746] lr: 9.912e-02, eta: 4 days, 11:24:51, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4803, loss_cls: 4.1887, loss: 4.1887 +2024-07-22 09:39:53,929 - pyskl - INFO - Epoch [9][3700/3746] lr: 9.912e-02, eta: 4 days, 11:23:04, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4794, loss_cls: 4.2052, loss: 4.2052 +2024-07-22 09:40:28,871 - pyskl - INFO - Saving checkpoint at 9 epochs +2024-07-22 09:42:20,598 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 09:42:21,261 - pyskl - INFO - +top1_acc 0.1891 +top5_acc 0.4119 +2024-07-22 09:42:21,261 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 09:42:21,301 - pyskl - INFO - +mean_acc 0.1888 +2024-07-22 09:42:21,306 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_7.pth was removed +2024-07-22 09:42:21,549 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_9.pth. +2024-07-22 09:42:21,550 - pyskl - INFO - Best top1_acc is 0.1891 at 9 epoch. +2024-07-22 09:42:21,560 - pyskl - INFO - Epoch(val) [9][309] top1_acc: 0.1891, top5_acc: 0.4119, mean_class_accuracy: 0.1888 +2024-07-22 09:45:39,253 - pyskl - INFO - Epoch [10][100/3746] lr: 9.911e-02, eta: 4 days, 11:44:56, time: 1.977, data_time: 1.273, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4934, loss_cls: 4.1413, loss: 4.1413 +2024-07-22 09:46:49,495 - pyskl - INFO - Epoch [10][200/3746] lr: 9.910e-02, eta: 4 days, 11:42:53, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4977, loss_cls: 4.1321, loss: 4.1321 +2024-07-22 09:47:59,914 - pyskl - INFO - Epoch [10][300/3746] lr: 9.910e-02, eta: 4 days, 11:40:52, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4958, loss_cls: 4.1352, loss: 4.1352 +2024-07-22 09:49:10,239 - pyskl - INFO - Epoch [10][400/3746] lr: 9.909e-02, eta: 4 days, 11:38:51, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4684, loss_cls: 4.2402, loss: 4.2402 +2024-07-22 09:50:21,069 - pyskl - INFO - Epoch [10][500/3746] lr: 9.909e-02, eta: 4 days, 11:36:57, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4891, loss_cls: 4.1640, loss: 4.1640 +2024-07-22 09:51:31,505 - pyskl - INFO - Epoch [10][600/3746] lr: 9.908e-02, eta: 4 days, 11:34:58, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4902, loss_cls: 4.1733, loss: 4.1733 +2024-07-22 09:52:42,294 - pyskl - INFO - Epoch [10][700/3746] lr: 9.908e-02, eta: 4 days, 11:33:04, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4895, loss_cls: 4.1824, loss: 4.1824 +2024-07-22 09:53:52,766 - pyskl - INFO - Epoch [10][800/3746] lr: 9.907e-02, eta: 4 days, 11:31:06, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4916, loss_cls: 4.1887, loss: 4.1887 +2024-07-22 09:55:03,240 - pyskl - INFO - Epoch [10][900/3746] lr: 9.907e-02, eta: 4 days, 11:29:08, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4983, loss_cls: 4.1217, loss: 4.1217 +2024-07-22 09:56:13,439 - pyskl - INFO - Epoch [10][1000/3746] lr: 9.906e-02, eta: 4 days, 11:27:06, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4908, loss_cls: 4.1593, loss: 4.1593 +2024-07-22 09:57:23,623 - pyskl - INFO - Epoch [10][1100/3746] lr: 9.906e-02, eta: 4 days, 11:25:05, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4864, loss_cls: 4.1621, loss: 4.1621 +2024-07-22 09:58:34,228 - pyskl - INFO - Epoch [10][1200/3746] lr: 9.905e-02, eta: 4 days, 11:23:10, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4863, loss_cls: 4.1842, loss: 4.1842 +2024-07-22 09:59:45,022 - pyskl - INFO - Epoch [10][1300/3746] lr: 9.905e-02, eta: 4 days, 11:21:18, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4819, loss_cls: 4.2151, loss: 4.2151 +2024-07-22 10:00:55,308 - pyskl - INFO - Epoch [10][1400/3746] lr: 9.904e-02, eta: 4 days, 11:19:18, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4883, loss_cls: 4.1814, loss: 4.1814 +2024-07-22 10:02:05,434 - pyskl - INFO - Epoch [10][1500/3746] lr: 9.903e-02, eta: 4 days, 11:17:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4916, loss_cls: 4.1515, loss: 4.1515 +2024-07-22 10:03:15,788 - pyskl - INFO - Epoch [10][1600/3746] lr: 9.903e-02, eta: 4 days, 11:15:19, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4922, loss_cls: 4.1407, loss: 4.1407 +2024-07-22 10:04:26,166 - pyskl - INFO - Epoch [10][1700/3746] lr: 9.902e-02, eta: 4 days, 11:13:22, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4806, loss_cls: 4.2106, loss: 4.2106 +2024-07-22 10:05:36,274 - pyskl - INFO - Epoch [10][1800/3746] lr: 9.902e-02, eta: 4 days, 11:11:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4892, loss_cls: 4.1612, loss: 4.1612 +2024-07-22 10:06:46,478 - pyskl - INFO - Epoch [10][1900/3746] lr: 9.901e-02, eta: 4 days, 11:09:22, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4967, loss_cls: 4.1414, loss: 4.1414 +2024-07-22 10:07:56,636 - pyskl - INFO - Epoch [10][2000/3746] lr: 9.901e-02, eta: 4 days, 11:07:22, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4894, loss_cls: 4.1891, loss: 4.1891 +2024-07-22 10:09:07,248 - pyskl - INFO - Epoch [10][2100/3746] lr: 9.900e-02, eta: 4 days, 11:05:30, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4819, loss_cls: 4.1956, loss: 4.1956 +2024-07-22 10:10:17,616 - pyskl - INFO - Epoch [10][2200/3746] lr: 9.900e-02, eta: 4 days, 11:03:34, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4933, loss_cls: 4.1495, loss: 4.1495 +2024-07-22 10:11:28,301 - pyskl - INFO - Epoch [10][2300/3746] lr: 9.899e-02, eta: 4 days, 11:01:43, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4948, loss_cls: 4.1392, loss: 4.1392 +2024-07-22 10:12:38,434 - pyskl - INFO - Epoch [10][2400/3746] lr: 9.898e-02, eta: 4 days, 10:59:44, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4939, loss_cls: 4.1362, loss: 4.1362 +2024-07-22 10:13:49,001 - pyskl - INFO - Epoch [10][2500/3746] lr: 9.898e-02, eta: 4 days, 10:57:51, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4998, loss_cls: 4.1377, loss: 4.1377 +2024-07-22 10:14:59,394 - pyskl - INFO - Epoch [10][2600/3746] lr: 9.897e-02, eta: 4 days, 10:55:57, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4770, loss_cls: 4.2250, loss: 4.2250 +2024-07-22 10:16:09,615 - pyskl - INFO - Epoch [10][2700/3746] lr: 9.897e-02, eta: 4 days, 10:54:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4903, loss_cls: 4.1764, loss: 4.1764 +2024-07-22 10:17:19,960 - pyskl - INFO - Epoch [10][2800/3746] lr: 9.896e-02, eta: 4 days, 10:52:05, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4839, loss_cls: 4.1720, loss: 4.1720 +2024-07-22 10:18:29,896 - pyskl - INFO - Epoch [10][2900/3746] lr: 9.896e-02, eta: 4 days, 10:50:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4816, loss_cls: 4.2259, loss: 4.2259 +2024-07-22 10:19:40,606 - pyskl - INFO - Epoch [10][3000/3746] lr: 9.895e-02, eta: 4 days, 10:48:15, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4872, loss_cls: 4.1491, loss: 4.1491 +2024-07-22 10:20:50,577 - pyskl - INFO - Epoch [10][3100/3746] lr: 9.894e-02, eta: 4 days, 10:46:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4917, loss_cls: 4.1222, loss: 4.1222 +2024-07-22 10:22:01,492 - pyskl - INFO - Epoch [10][3200/3746] lr: 9.894e-02, eta: 4 days, 10:44:30, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4795, loss_cls: 4.2034, loss: 4.2034 +2024-07-22 10:23:11,686 - pyskl - INFO - Epoch [10][3300/3746] lr: 9.893e-02, eta: 4 days, 10:42:34, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4880, loss_cls: 4.1856, loss: 4.1856 +2024-07-22 10:24:22,250 - pyskl - INFO - Epoch [10][3400/3746] lr: 9.893e-02, eta: 4 days, 10:40:44, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4819, loss_cls: 4.2223, loss: 4.2223 +2024-07-22 10:25:32,920 - pyskl - INFO - Epoch [10][3500/3746] lr: 9.892e-02, eta: 4 days, 10:38:56, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4913, loss_cls: 4.1822, loss: 4.1822 +2024-07-22 10:26:43,771 - pyskl - INFO - Epoch [10][3600/3746] lr: 9.892e-02, eta: 4 days, 10:37:10, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4834, loss_cls: 4.1931, loss: 4.1931 +2024-07-22 10:27:54,462 - pyskl - INFO - Epoch [10][3700/3746] lr: 9.891e-02, eta: 4 days, 10:35:22, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5091, loss_cls: 4.0872, loss: 4.0872 +2024-07-22 10:28:29,390 - pyskl - INFO - Saving checkpoint at 10 epochs +2024-07-22 10:30:20,809 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 10:30:21,481 - pyskl - INFO - +top1_acc 0.1860 +top5_acc 0.4083 +2024-07-22 10:30:21,481 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 10:30:21,519 - pyskl - INFO - +mean_acc 0.1857 +2024-07-22 10:30:21,530 - pyskl - INFO - Epoch(val) [10][309] top1_acc: 0.1860, top5_acc: 0.4083, mean_class_accuracy: 0.1857 +2024-07-22 10:33:38,645 - pyskl - INFO - Epoch [11][100/3746] lr: 9.890e-02, eta: 4 days, 10:54:35, time: 1.971, data_time: 1.266, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4959, loss_cls: 4.1205, loss: 4.1205 +2024-07-22 10:34:49,804 - pyskl - INFO - Epoch [11][200/3746] lr: 9.890e-02, eta: 4 days, 10:52:51, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4975, loss_cls: 4.1092, loss: 4.1092 +2024-07-22 10:36:00,192 - pyskl - INFO - Epoch [11][300/3746] lr: 9.889e-02, eta: 4 days, 10:50:56, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4886, loss_cls: 4.1671, loss: 4.1671 +2024-07-22 10:37:10,931 - pyskl - INFO - Epoch [11][400/3746] lr: 9.888e-02, eta: 4 days, 10:49:06, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4838, loss_cls: 4.1765, loss: 4.1765 +2024-07-22 10:38:21,131 - pyskl - INFO - Epoch [11][500/3746] lr: 9.888e-02, eta: 4 days, 10:47:08, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5089, loss_cls: 4.0765, loss: 4.0765 +2024-07-22 10:39:31,900 - pyskl - INFO - Epoch [11][600/3746] lr: 9.887e-02, eta: 4 days, 10:45:19, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4784, loss_cls: 4.1844, loss: 4.1844 +2024-07-22 10:40:42,042 - pyskl - INFO - Epoch [11][700/3746] lr: 9.887e-02, eta: 4 days, 10:43:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4861, loss_cls: 4.1556, loss: 4.1556 +2024-07-22 10:41:52,541 - pyskl - INFO - Epoch [11][800/3746] lr: 9.886e-02, eta: 4 days, 10:41:29, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4952, loss_cls: 4.1575, loss: 4.1575 +2024-07-22 10:43:02,994 - pyskl - INFO - Epoch [11][900/3746] lr: 9.885e-02, eta: 4 days, 10:39:36, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5139, loss_cls: 4.0824, loss: 4.0824 +2024-07-22 10:44:13,172 - pyskl - INFO - Epoch [11][1000/3746] lr: 9.885e-02, eta: 4 days, 10:37:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4861, loss_cls: 4.2040, loss: 4.2040 +2024-07-22 10:45:23,369 - pyskl - INFO - Epoch [11][1100/3746] lr: 9.884e-02, eta: 4 days, 10:35:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4792, loss_cls: 4.1843, loss: 4.1843 +2024-07-22 10:46:33,421 - pyskl - INFO - Epoch [11][1200/3746] lr: 9.884e-02, eta: 4 days, 10:33:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4880, loss_cls: 4.1643, loss: 4.1643 +2024-07-22 10:47:43,533 - pyskl - INFO - Epoch [11][1300/3746] lr: 9.883e-02, eta: 4 days, 10:31:49, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4834, loss_cls: 4.1886, loss: 4.1886 +2024-07-22 10:48:53,453 - pyskl - INFO - Epoch [11][1400/3746] lr: 9.882e-02, eta: 4 days, 10:29:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4847, loss_cls: 4.1696, loss: 4.1696 +2024-07-22 10:50:03,696 - pyskl - INFO - Epoch [11][1500/3746] lr: 9.882e-02, eta: 4 days, 10:27:56, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4975, loss_cls: 4.1386, loss: 4.1386 +2024-07-22 10:51:13,834 - pyskl - INFO - Epoch [11][1600/3746] lr: 9.881e-02, eta: 4 days, 10:26:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4891, loss_cls: 4.1291, loss: 4.1291 +2024-07-22 10:52:24,337 - pyskl - INFO - Epoch [11][1700/3746] lr: 9.881e-02, eta: 4 days, 10:24:10, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4997, loss_cls: 4.1121, loss: 4.1121 +2024-07-22 10:53:34,393 - pyskl - INFO - Epoch [11][1800/3746] lr: 9.880e-02, eta: 4 days, 10:22:14, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4867, loss_cls: 4.1779, loss: 4.1779 +2024-07-22 10:54:44,844 - pyskl - INFO - Epoch [11][1900/3746] lr: 9.879e-02, eta: 4 days, 10:20:23, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4966, loss_cls: 4.1250, loss: 4.1250 +2024-07-22 10:55:55,133 - pyskl - INFO - Epoch [11][2000/3746] lr: 9.879e-02, eta: 4 days, 10:18:31, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4950, loss_cls: 4.1615, loss: 4.1615 +2024-07-22 10:57:05,653 - pyskl - INFO - Epoch [11][2100/3746] lr: 9.878e-02, eta: 4 days, 10:16:41, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4839, loss_cls: 4.1927, loss: 4.1927 +2024-07-22 10:58:15,580 - pyskl - INFO - Epoch [11][2200/3746] lr: 9.878e-02, eta: 4 days, 10:14:44, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4855, loss_cls: 4.1636, loss: 4.1636 +2024-07-22 10:59:26,218 - pyskl - INFO - Epoch [11][2300/3746] lr: 9.877e-02, eta: 4 days, 10:12:57, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4977, loss_cls: 4.1401, loss: 4.1401 +2024-07-22 11:00:36,231 - pyskl - INFO - Epoch [11][2400/3746] lr: 9.876e-02, eta: 4 days, 10:11:02, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4930, loss_cls: 4.1421, loss: 4.1421 +2024-07-22 11:01:46,053 - pyskl - INFO - Epoch [11][2500/3746] lr: 9.876e-02, eta: 4 days, 10:09:04, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4834, loss_cls: 4.2076, loss: 4.2076 +2024-07-22 11:02:56,320 - pyskl - INFO - Epoch [11][2600/3746] lr: 9.875e-02, eta: 4 days, 10:07:12, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4939, loss_cls: 4.1232, loss: 4.1232 +2024-07-22 11:04:06,422 - pyskl - INFO - Epoch [11][2700/3746] lr: 9.874e-02, eta: 4 days, 10:05:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5027, loss_cls: 4.1196, loss: 4.1196 +2024-07-22 11:05:16,292 - pyskl - INFO - Epoch [11][2800/3746] lr: 9.874e-02, eta: 4 days, 10:03:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4947, loss_cls: 4.1582, loss: 4.1582 +2024-07-22 11:06:26,374 - pyskl - INFO - Epoch [11][2900/3746] lr: 9.873e-02, eta: 4 days, 10:01:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4953, loss_cls: 4.1452, loss: 4.1452 +2024-07-22 11:07:36,166 - pyskl - INFO - Epoch [11][3000/3746] lr: 9.873e-02, eta: 4 days, 9:59:32, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4827, loss_cls: 4.1782, loss: 4.1782 +2024-07-22 11:08:46,070 - pyskl - INFO - Epoch [11][3100/3746] lr: 9.872e-02, eta: 4 days, 9:57:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4936, loss_cls: 4.1560, loss: 4.1560 +2024-07-22 11:09:57,121 - pyskl - INFO - Epoch [11][3200/3746] lr: 9.871e-02, eta: 4 days, 9:55:56, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4847, loss_cls: 4.1731, loss: 4.1731 +2024-07-22 11:11:07,558 - pyskl - INFO - Epoch [11][3300/3746] lr: 9.871e-02, eta: 4 days, 9:54:08, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4891, loss_cls: 4.1305, loss: 4.1305 +2024-07-22 11:12:17,905 - pyskl - INFO - Epoch [11][3400/3746] lr: 9.870e-02, eta: 4 days, 9:52:19, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4889, loss_cls: 4.1640, loss: 4.1640 +2024-07-22 11:13:28,647 - pyskl - INFO - Epoch [11][3500/3746] lr: 9.869e-02, eta: 4 days, 9:50:36, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4816, loss_cls: 4.1822, loss: 4.1822 +2024-07-22 11:14:39,822 - pyskl - INFO - Epoch [11][3600/3746] lr: 9.869e-02, eta: 4 days, 9:48:57, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4880, loss_cls: 4.1641, loss: 4.1641 +2024-07-22 11:15:51,013 - pyskl - INFO - Epoch [11][3700/3746] lr: 9.868e-02, eta: 4 days, 9:47:20, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4900, loss_cls: 4.1608, loss: 4.1608 +2024-07-22 11:16:25,891 - pyskl - INFO - Saving checkpoint at 11 epochs +2024-07-22 11:18:18,369 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 11:18:19,044 - pyskl - INFO - +top1_acc 0.1967 +top5_acc 0.4216 +2024-07-22 11:18:19,044 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 11:18:19,085 - pyskl - INFO - +mean_acc 0.1963 +2024-07-22 11:18:19,089 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_9.pth was removed +2024-07-22 11:18:19,337 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_11.pth. +2024-07-22 11:18:19,338 - pyskl - INFO - Best top1_acc is 0.1967 at 11 epoch. +2024-07-22 11:18:19,351 - pyskl - INFO - Epoch(val) [11][309] top1_acc: 0.1967, top5_acc: 0.4216, mean_class_accuracy: 0.1963 +2024-07-22 11:21:47,792 - pyskl - INFO - Epoch [12][100/3746] lr: 9.867e-02, eta: 4 days, 10:06:54, time: 2.084, data_time: 1.366, memory: 15990, top1_acc: 0.2469, top5_acc: 0.5006, loss_cls: 4.1363, loss: 4.1363 +2024-07-22 11:22:58,784 - pyskl - INFO - Epoch [12][200/3746] lr: 9.867e-02, eta: 4 days, 10:05:11, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4972, loss_cls: 4.0980, loss: 4.0980 +2024-07-22 11:24:10,637 - pyskl - INFO - Epoch [12][300/3746] lr: 9.866e-02, eta: 4 days, 10:03:38, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.5022, loss_cls: 4.1154, loss: 4.1154 +2024-07-22 11:25:21,773 - pyskl - INFO - Epoch [12][400/3746] lr: 9.865e-02, eta: 4 days, 10:01:57, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.4973, loss_cls: 4.1214, loss: 4.1214 +2024-07-22 11:26:33,450 - pyskl - INFO - Epoch [12][500/3746] lr: 9.865e-02, eta: 4 days, 10:00:22, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4919, loss_cls: 4.1264, loss: 4.1264 +2024-07-22 11:27:44,828 - pyskl - INFO - Epoch [12][600/3746] lr: 9.864e-02, eta: 4 days, 9:58:44, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4995, loss_cls: 4.1384, loss: 4.1384 +2024-07-22 11:28:56,262 - pyskl - INFO - Epoch [12][700/3746] lr: 9.863e-02, eta: 4 days, 9:57:07, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4884, loss_cls: 4.1522, loss: 4.1522 +2024-07-22 11:30:07,432 - pyskl - INFO - Epoch [12][800/3746] lr: 9.863e-02, eta: 4 days, 9:55:27, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.5100, loss_cls: 4.0863, loss: 4.0863 +2024-07-22 11:31:18,813 - pyskl - INFO - Epoch [12][900/3746] lr: 9.862e-02, eta: 4 days, 9:53:49, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4895, loss_cls: 4.1362, loss: 4.1362 +2024-07-22 11:32:30,257 - pyskl - INFO - Epoch [12][1000/3746] lr: 9.861e-02, eta: 4 days, 9:52:12, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4806, loss_cls: 4.1804, loss: 4.1804 +2024-07-22 11:33:41,741 - pyskl - INFO - Epoch [12][1100/3746] lr: 9.861e-02, eta: 4 days, 9:50:36, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4906, loss_cls: 4.1379, loss: 4.1379 +2024-07-22 11:34:53,041 - pyskl - INFO - Epoch [12][1200/3746] lr: 9.860e-02, eta: 4 days, 9:48:58, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4963, loss_cls: 4.1094, loss: 4.1094 +2024-07-22 11:36:04,528 - pyskl - INFO - Epoch [12][1300/3746] lr: 9.859e-02, eta: 4 days, 9:47:22, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.5038, loss_cls: 4.1109, loss: 4.1109 +2024-07-22 11:37:16,090 - pyskl - INFO - Epoch [12][1400/3746] lr: 9.859e-02, eta: 4 days, 9:45:47, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4931, loss_cls: 4.1191, loss: 4.1191 +2024-07-22 11:38:27,221 - pyskl - INFO - Epoch [12][1500/3746] lr: 9.858e-02, eta: 4 days, 9:44:07, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4864, loss_cls: 4.1454, loss: 4.1454 +2024-07-22 11:39:38,404 - pyskl - INFO - Epoch [12][1600/3746] lr: 9.857e-02, eta: 4 days, 9:42:28, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4858, loss_cls: 4.1597, loss: 4.1597 +2024-07-22 11:40:49,678 - pyskl - INFO - Epoch [12][1700/3746] lr: 9.857e-02, eta: 4 days, 9:40:50, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4867, loss_cls: 4.1428, loss: 4.1428 +2024-07-22 11:42:00,797 - pyskl - INFO - Epoch [12][1800/3746] lr: 9.856e-02, eta: 4 days, 9:39:11, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4889, loss_cls: 4.1854, loss: 4.1854 +2024-07-22 11:43:12,016 - pyskl - INFO - Epoch [12][1900/3746] lr: 9.855e-02, eta: 4 days, 9:37:32, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4942, loss_cls: 4.1399, loss: 4.1399 +2024-07-22 11:44:23,365 - pyskl - INFO - Epoch [12][2000/3746] lr: 9.855e-02, eta: 4 days, 9:35:55, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5008, loss_cls: 4.1337, loss: 4.1337 +2024-07-22 11:45:34,528 - pyskl - INFO - Epoch [12][2100/3746] lr: 9.854e-02, eta: 4 days, 9:34:17, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4992, loss_cls: 4.1683, loss: 4.1683 +2024-07-22 11:46:45,842 - pyskl - INFO - Epoch [12][2200/3746] lr: 9.853e-02, eta: 4 days, 9:32:40, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5027, loss_cls: 4.1088, loss: 4.1088 +2024-07-22 11:47:57,204 - pyskl - INFO - Epoch [12][2300/3746] lr: 9.853e-02, eta: 4 days, 9:31:04, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4923, loss_cls: 4.1469, loss: 4.1469 +2024-07-22 11:49:08,599 - pyskl - INFO - Epoch [12][2400/3746] lr: 9.852e-02, eta: 4 days, 9:29:28, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5050, loss_cls: 4.1119, loss: 4.1119 +2024-07-22 11:50:19,982 - pyskl - INFO - Epoch [12][2500/3746] lr: 9.851e-02, eta: 4 days, 9:27:52, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4908, loss_cls: 4.1565, loss: 4.1565 +2024-07-22 11:51:31,038 - pyskl - INFO - Epoch [12][2600/3746] lr: 9.851e-02, eta: 4 days, 9:26:13, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4827, loss_cls: 4.1802, loss: 4.1802 +2024-07-22 11:52:42,278 - pyskl - INFO - Epoch [12][2700/3746] lr: 9.850e-02, eta: 4 days, 9:24:36, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4931, loss_cls: 4.1679, loss: 4.1679 +2024-07-22 11:53:53,511 - pyskl - INFO - Epoch [12][2800/3746] lr: 9.849e-02, eta: 4 days, 9:22:59, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5048, loss_cls: 4.0995, loss: 4.0995 +2024-07-22 11:55:04,773 - pyskl - INFO - Epoch [12][2900/3746] lr: 9.849e-02, eta: 4 days, 9:21:22, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4950, loss_cls: 4.1219, loss: 4.1219 +2024-07-22 11:56:15,946 - pyskl - INFO - Epoch [12][3000/3746] lr: 9.848e-02, eta: 4 days, 9:19:44, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4938, loss_cls: 4.1435, loss: 4.1435 +2024-07-22 11:57:27,521 - pyskl - INFO - Epoch [12][3100/3746] lr: 9.847e-02, eta: 4 days, 9:18:12, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4916, loss_cls: 4.1472, loss: 4.1472 +2024-07-22 11:58:39,129 - pyskl - INFO - Epoch [12][3200/3746] lr: 9.847e-02, eta: 4 days, 9:16:39, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4983, loss_cls: 4.1386, loss: 4.1386 +2024-07-22 11:59:50,209 - pyskl - INFO - Epoch [12][3300/3746] lr: 9.846e-02, eta: 4 days, 9:15:01, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4795, loss_cls: 4.1630, loss: 4.1630 +2024-07-22 12:01:01,195 - pyskl - INFO - Epoch [12][3400/3746] lr: 9.845e-02, eta: 4 days, 9:13:22, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4792, loss_cls: 4.1884, loss: 4.1884 +2024-07-22 12:02:12,777 - pyskl - INFO - Epoch [12][3500/3746] lr: 9.845e-02, eta: 4 days, 9:11:49, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4867, loss_cls: 4.1859, loss: 4.1859 +2024-07-22 12:03:24,774 - pyskl - INFO - Epoch [12][3600/3746] lr: 9.844e-02, eta: 4 days, 9:10:22, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4916, loss_cls: 4.1564, loss: 4.1564 +2024-07-22 12:04:36,253 - pyskl - INFO - Epoch [12][3700/3746] lr: 9.843e-02, eta: 4 days, 9:08:49, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4884, loss_cls: 4.1752, loss: 4.1752 +2024-07-22 12:05:11,239 - pyskl - INFO - Saving checkpoint at 12 epochs +2024-07-22 12:07:04,027 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 12:07:04,697 - pyskl - INFO - +top1_acc 0.1846 +top5_acc 0.4082 +2024-07-22 12:07:04,697 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 12:07:04,742 - pyskl - INFO - +mean_acc 0.1842 +2024-07-22 12:07:04,757 - pyskl - INFO - Epoch(val) [12][309] top1_acc: 0.1846, top5_acc: 0.4082, mean_class_accuracy: 0.1842 +2024-07-22 12:10:29,901 - pyskl - INFO - Epoch [13][100/3746] lr: 9.842e-02, eta: 4 days, 9:25:49, time: 2.051, data_time: 1.336, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4998, loss_cls: 4.1047, loss: 4.1047 +2024-07-22 12:11:41,356 - pyskl - INFO - Epoch [13][200/3746] lr: 9.842e-02, eta: 4 days, 9:24:13, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4898, loss_cls: 4.1453, loss: 4.1453 +2024-07-22 12:12:53,117 - pyskl - INFO - Epoch [13][300/3746] lr: 9.841e-02, eta: 4 days, 9:22:40, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5078, loss_cls: 4.0558, loss: 4.0558 +2024-07-22 12:14:05,134 - pyskl - INFO - Epoch [13][400/3746] lr: 9.840e-02, eta: 4 days, 9:21:11, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5095, loss_cls: 4.0871, loss: 4.0871 +2024-07-22 12:15:16,058 - pyskl - INFO - Epoch [13][500/3746] lr: 9.839e-02, eta: 4 days, 9:19:29, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5003, loss_cls: 4.1359, loss: 4.1359 +2024-07-22 12:16:27,739 - pyskl - INFO - Epoch [13][600/3746] lr: 9.839e-02, eta: 4 days, 9:17:56, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5009, loss_cls: 4.0936, loss: 4.0936 +2024-07-22 12:17:39,480 - pyskl - INFO - Epoch [13][700/3746] lr: 9.838e-02, eta: 4 days, 9:16:23, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5020, loss_cls: 4.1229, loss: 4.1229 +2024-07-22 12:18:50,965 - pyskl - INFO - Epoch [13][800/3746] lr: 9.837e-02, eta: 4 days, 9:14:48, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.5083, loss_cls: 4.1166, loss: 4.1166 +2024-07-22 12:20:02,504 - pyskl - INFO - Epoch [13][900/3746] lr: 9.837e-02, eta: 4 days, 9:13:14, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4919, loss_cls: 4.1345, loss: 4.1345 +2024-07-22 12:21:14,130 - pyskl - INFO - Epoch [13][1000/3746] lr: 9.836e-02, eta: 4 days, 9:11:40, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5030, loss_cls: 4.0940, loss: 4.0940 +2024-07-22 12:22:25,309 - pyskl - INFO - Epoch [13][1100/3746] lr: 9.835e-02, eta: 4 days, 9:10:02, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4880, loss_cls: 4.1210, loss: 4.1210 +2024-07-22 12:23:36,851 - pyskl - INFO - Epoch [13][1200/3746] lr: 9.834e-02, eta: 4 days, 9:08:28, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4909, loss_cls: 4.1506, loss: 4.1506 +2024-07-22 12:24:48,896 - pyskl - INFO - Epoch [13][1300/3746] lr: 9.834e-02, eta: 4 days, 9:07:00, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5028, loss_cls: 4.0811, loss: 4.0811 +2024-07-22 12:26:00,076 - pyskl - INFO - Epoch [13][1400/3746] lr: 9.833e-02, eta: 4 days, 9:05:22, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5017, loss_cls: 4.1236, loss: 4.1236 +2024-07-22 12:27:11,471 - pyskl - INFO - Epoch [13][1500/3746] lr: 9.832e-02, eta: 4 days, 9:03:46, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4908, loss_cls: 4.1436, loss: 4.1436 +2024-07-22 12:28:23,051 - pyskl - INFO - Epoch [13][1600/3746] lr: 9.832e-02, eta: 4 days, 9:02:13, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4977, loss_cls: 4.1471, loss: 4.1471 +2024-07-22 12:29:34,691 - pyskl - INFO - Epoch [13][1700/3746] lr: 9.831e-02, eta: 4 days, 9:00:40, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4973, loss_cls: 4.1268, loss: 4.1268 +2024-07-22 12:30:46,189 - pyskl - INFO - Epoch [13][1800/3746] lr: 9.830e-02, eta: 4 days, 8:59:06, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5009, loss_cls: 4.1023, loss: 4.1023 +2024-07-22 12:31:57,611 - pyskl - INFO - Epoch [13][1900/3746] lr: 9.829e-02, eta: 4 days, 8:57:32, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4902, loss_cls: 4.1340, loss: 4.1340 +2024-07-22 12:33:09,516 - pyskl - INFO - Epoch [13][2000/3746] lr: 9.829e-02, eta: 4 days, 8:56:02, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4930, loss_cls: 4.1062, loss: 4.1062 +2024-07-22 12:34:21,035 - pyskl - INFO - Epoch [13][2100/3746] lr: 9.828e-02, eta: 4 days, 8:54:29, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4870, loss_cls: 4.1813, loss: 4.1813 +2024-07-22 12:35:32,453 - pyskl - INFO - Epoch [13][2200/3746] lr: 9.827e-02, eta: 4 days, 8:52:54, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4956, loss_cls: 4.1189, loss: 4.1189 +2024-07-22 12:36:43,917 - pyskl - INFO - Epoch [13][2300/3746] lr: 9.827e-02, eta: 4 days, 8:51:20, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.4938, loss_cls: 4.1040, loss: 4.1040 +2024-07-22 12:37:55,187 - pyskl - INFO - Epoch [13][2400/3746] lr: 9.826e-02, eta: 4 days, 8:49:44, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4828, loss_cls: 4.1815, loss: 4.1815 +2024-07-22 12:39:06,436 - pyskl - INFO - Epoch [13][2500/3746] lr: 9.825e-02, eta: 4 days, 8:48:08, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4973, loss_cls: 4.1405, loss: 4.1405 +2024-07-22 12:40:18,043 - pyskl - INFO - Epoch [13][2600/3746] lr: 9.824e-02, eta: 4 days, 8:46:36, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5003, loss_cls: 4.1029, loss: 4.1029 +2024-07-22 12:41:29,645 - pyskl - INFO - Epoch [13][2700/3746] lr: 9.824e-02, eta: 4 days, 8:45:04, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4956, loss_cls: 4.0866, loss: 4.0866 +2024-07-22 12:42:41,032 - pyskl - INFO - Epoch [13][2800/3746] lr: 9.823e-02, eta: 4 days, 8:43:30, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4942, loss_cls: 4.1169, loss: 4.1169 +2024-07-22 12:43:52,542 - pyskl - INFO - Epoch [13][2900/3746] lr: 9.822e-02, eta: 4 days, 8:41:57, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4964, loss_cls: 4.1205, loss: 4.1205 +2024-07-22 12:45:04,023 - pyskl - INFO - Epoch [13][3000/3746] lr: 9.821e-02, eta: 4 days, 8:40:24, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4842, loss_cls: 4.2041, loss: 4.2041 +2024-07-22 12:46:15,700 - pyskl - INFO - Epoch [13][3100/3746] lr: 9.821e-02, eta: 4 days, 8:38:53, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4852, loss_cls: 4.1492, loss: 4.1492 +2024-07-22 12:47:26,715 - pyskl - INFO - Epoch [13][3200/3746] lr: 9.820e-02, eta: 4 days, 8:37:15, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4870, loss_cls: 4.1762, loss: 4.1762 +2024-07-22 12:48:37,448 - pyskl - INFO - Epoch [13][3300/3746] lr: 9.819e-02, eta: 4 days, 8:35:34, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4961, loss_cls: 4.1507, loss: 4.1507 +2024-07-22 12:49:48,942 - pyskl - INFO - Epoch [13][3400/3746] lr: 9.818e-02, eta: 4 days, 8:34:02, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4891, loss_cls: 4.1722, loss: 4.1722 +2024-07-22 12:51:00,435 - pyskl - INFO - Epoch [13][3500/3746] lr: 9.818e-02, eta: 4 days, 8:32:29, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5162, loss_cls: 4.0541, loss: 4.0541 +2024-07-22 12:52:11,621 - pyskl - INFO - Epoch [13][3600/3746] lr: 9.817e-02, eta: 4 days, 8:30:54, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4820, loss_cls: 4.1752, loss: 4.1752 +2024-07-22 12:53:23,113 - pyskl - INFO - Epoch [13][3700/3746] lr: 9.816e-02, eta: 4 days, 8:29:21, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4973, loss_cls: 4.1355, loss: 4.1355 +2024-07-22 12:53:57,807 - pyskl - INFO - Saving checkpoint at 13 epochs +2024-07-22 12:55:50,124 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 12:55:50,824 - pyskl - INFO - +top1_acc 0.1632 +top5_acc 0.3860 +2024-07-22 12:55:50,824 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 12:55:50,867 - pyskl - INFO - +mean_acc 0.1630 +2024-07-22 12:55:50,879 - pyskl - INFO - Epoch(val) [13][309] top1_acc: 0.1632, top5_acc: 0.3860, mean_class_accuracy: 0.1630 +2024-07-22 12:59:19,111 - pyskl - INFO - Epoch [14][100/3746] lr: 9.815e-02, eta: 4 days, 8:45:19, time: 2.082, data_time: 1.370, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5056, loss_cls: 4.0505, loss: 4.0505 +2024-07-22 13:00:30,316 - pyskl - INFO - Epoch [14][200/3746] lr: 9.814e-02, eta: 4 days, 8:43:41, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4961, loss_cls: 4.0861, loss: 4.0861 +2024-07-22 13:01:41,720 - pyskl - INFO - Epoch [14][300/3746] lr: 9.814e-02, eta: 4 days, 8:42:06, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4958, loss_cls: 4.1291, loss: 4.1291 +2024-07-22 13:02:53,263 - pyskl - INFO - Epoch [14][400/3746] lr: 9.813e-02, eta: 4 days, 8:40:32, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5061, loss_cls: 4.0890, loss: 4.0890 +2024-07-22 13:04:04,686 - pyskl - INFO - Epoch [14][500/3746] lr: 9.812e-02, eta: 4 days, 8:38:57, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4988, loss_cls: 4.1236, loss: 4.1236 +2024-07-22 13:05:16,049 - pyskl - INFO - Epoch [14][600/3746] lr: 9.811e-02, eta: 4 days, 8:37:21, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4873, loss_cls: 4.1485, loss: 4.1485 +2024-07-22 13:06:27,425 - pyskl - INFO - Epoch [14][700/3746] lr: 9.811e-02, eta: 4 days, 8:35:46, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4897, loss_cls: 4.1438, loss: 4.1438 +2024-07-22 13:07:38,687 - pyskl - INFO - Epoch [14][800/3746] lr: 9.810e-02, eta: 4 days, 8:34:10, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.4978, loss_cls: 4.1265, loss: 4.1265 +2024-07-22 13:08:50,232 - pyskl - INFO - Epoch [14][900/3746] lr: 9.809e-02, eta: 4 days, 8:32:36, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5052, loss_cls: 4.0953, loss: 4.0953 +2024-07-22 13:10:01,275 - pyskl - INFO - Epoch [14][1000/3746] lr: 9.808e-02, eta: 4 days, 8:30:58, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4875, loss_cls: 4.1497, loss: 4.1497 +2024-07-22 13:11:12,597 - pyskl - INFO - Epoch [14][1100/3746] lr: 9.807e-02, eta: 4 days, 8:29:23, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5008, loss_cls: 4.0906, loss: 4.0906 +2024-07-22 13:12:23,746 - pyskl - INFO - Epoch [14][1200/3746] lr: 9.807e-02, eta: 4 days, 8:27:45, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5036, loss_cls: 4.1052, loss: 4.1052 +2024-07-22 13:13:34,910 - pyskl - INFO - Epoch [14][1300/3746] lr: 9.806e-02, eta: 4 days, 8:26:09, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4961, loss_cls: 4.1194, loss: 4.1194 +2024-07-22 13:14:46,405 - pyskl - INFO - Epoch [14][1400/3746] lr: 9.805e-02, eta: 4 days, 8:24:35, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5088, loss_cls: 4.0763, loss: 4.0763 +2024-07-22 13:15:58,174 - pyskl - INFO - Epoch [14][1500/3746] lr: 9.804e-02, eta: 4 days, 8:23:05, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5030, loss_cls: 4.0989, loss: 4.0989 +2024-07-22 13:17:09,390 - pyskl - INFO - Epoch [14][1600/3746] lr: 9.804e-02, eta: 4 days, 8:21:29, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4983, loss_cls: 4.1385, loss: 4.1385 +2024-07-22 13:18:20,840 - pyskl - INFO - Epoch [14][1700/3746] lr: 9.803e-02, eta: 4 days, 8:19:55, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4913, loss_cls: 4.1714, loss: 4.1714 +2024-07-22 13:19:32,422 - pyskl - INFO - Epoch [14][1800/3746] lr: 9.802e-02, eta: 4 days, 8:18:23, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4883, loss_cls: 4.1329, loss: 4.1329 +2024-07-22 13:20:43,649 - pyskl - INFO - Epoch [14][1900/3746] lr: 9.801e-02, eta: 4 days, 8:16:47, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4930, loss_cls: 4.1481, loss: 4.1481 +2024-07-22 13:21:54,875 - pyskl - INFO - Epoch [14][2000/3746] lr: 9.800e-02, eta: 4 days, 8:15:12, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5038, loss_cls: 4.0782, loss: 4.0782 +2024-07-22 13:23:06,089 - pyskl - INFO - Epoch [14][2100/3746] lr: 9.800e-02, eta: 4 days, 8:13:36, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4989, loss_cls: 4.1145, loss: 4.1145 +2024-07-22 13:24:17,447 - pyskl - INFO - Epoch [14][2200/3746] lr: 9.799e-02, eta: 4 days, 8:12:02, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5067, loss_cls: 4.0972, loss: 4.0972 +2024-07-22 13:25:28,570 - pyskl - INFO - Epoch [14][2300/3746] lr: 9.798e-02, eta: 4 days, 8:10:26, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4975, loss_cls: 4.1263, loss: 4.1263 +2024-07-22 13:26:39,683 - pyskl - INFO - Epoch [14][2400/3746] lr: 9.797e-02, eta: 4 days, 8:08:49, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4964, loss_cls: 4.1310, loss: 4.1310 +2024-07-22 13:27:51,167 - pyskl - INFO - Epoch [14][2500/3746] lr: 9.797e-02, eta: 4 days, 8:07:17, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4959, loss_cls: 4.1488, loss: 4.1488 +2024-07-22 13:29:02,438 - pyskl - INFO - Epoch [14][2600/3746] lr: 9.796e-02, eta: 4 days, 8:05:42, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4894, loss_cls: 4.1687, loss: 4.1687 +2024-07-22 13:30:13,432 - pyskl - INFO - Epoch [14][2700/3746] lr: 9.795e-02, eta: 4 days, 8:04:05, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5038, loss_cls: 4.0959, loss: 4.0959 +2024-07-22 13:31:24,505 - pyskl - INFO - Epoch [14][2800/3746] lr: 9.794e-02, eta: 4 days, 8:02:29, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4825, loss_cls: 4.1517, loss: 4.1517 +2024-07-22 13:32:35,539 - pyskl - INFO - Epoch [14][2900/3746] lr: 9.793e-02, eta: 4 days, 8:00:52, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4823, loss_cls: 4.1694, loss: 4.1694 +2024-07-22 13:33:47,656 - pyskl - INFO - Epoch [14][3000/3746] lr: 9.793e-02, eta: 4 days, 7:59:26, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5052, loss_cls: 4.0981, loss: 4.0981 +2024-07-22 13:34:58,738 - pyskl - INFO - Epoch [14][3100/3746] lr: 9.792e-02, eta: 4 days, 7:57:50, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4884, loss_cls: 4.1411, loss: 4.1411 +2024-07-22 13:36:09,276 - pyskl - INFO - Epoch [14][3200/3746] lr: 9.791e-02, eta: 4 days, 7:56:09, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5078, loss_cls: 4.1022, loss: 4.1022 +2024-07-22 13:37:21,046 - pyskl - INFO - Epoch [14][3300/3746] lr: 9.790e-02, eta: 4 days, 7:54:40, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4827, loss_cls: 4.1554, loss: 4.1554 +2024-07-22 13:38:33,073 - pyskl - INFO - Epoch [14][3400/3746] lr: 9.789e-02, eta: 4 days, 7:53:14, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4861, loss_cls: 4.1314, loss: 4.1314 +2024-07-22 13:39:44,653 - pyskl - INFO - Epoch [14][3500/3746] lr: 9.789e-02, eta: 4 days, 7:51:43, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4994, loss_cls: 4.1371, loss: 4.1371 +2024-07-22 13:40:56,421 - pyskl - INFO - Epoch [14][3600/3746] lr: 9.788e-02, eta: 4 days, 7:50:14, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.4978, loss_cls: 4.1124, loss: 4.1124 +2024-07-22 13:42:07,943 - pyskl - INFO - Epoch [14][3700/3746] lr: 9.787e-02, eta: 4 days, 7:48:43, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4900, loss_cls: 4.1462, loss: 4.1462 +2024-07-22 13:42:42,283 - pyskl - INFO - Saving checkpoint at 14 epochs +2024-07-22 13:44:34,894 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 13:44:35,673 - pyskl - INFO - +top1_acc 0.1869 +top5_acc 0.4163 +2024-07-22 13:44:35,673 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 13:44:35,733 - pyskl - INFO - +mean_acc 0.1867 +2024-07-22 13:44:35,756 - pyskl - INFO - Epoch(val) [14][309] top1_acc: 0.1869, top5_acc: 0.4163, mean_class_accuracy: 0.1867 +2024-07-22 13:48:02,106 - pyskl - INFO - Epoch [15][100/3746] lr: 9.786e-02, eta: 4 days, 8:02:58, time: 2.063, data_time: 1.351, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5092, loss_cls: 4.0565, loss: 4.0565 +2024-07-22 13:49:12,643 - pyskl - INFO - Epoch [15][200/3746] lr: 9.785e-02, eta: 4 days, 8:01:15, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4970, loss_cls: 4.1390, loss: 4.1390 +2024-07-22 13:50:22,774 - pyskl - INFO - Epoch [15][300/3746] lr: 9.784e-02, eta: 4 days, 7:59:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4995, loss_cls: 4.0767, loss: 4.0767 +2024-07-22 13:51:32,980 - pyskl - INFO - Epoch [15][400/3746] lr: 9.783e-02, eta: 4 days, 7:57:43, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4909, loss_cls: 4.1120, loss: 4.1120 +2024-07-22 13:52:42,911 - pyskl - INFO - Epoch [15][500/3746] lr: 9.783e-02, eta: 4 days, 7:55:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.5022, loss_cls: 4.1267, loss: 4.1267 +2024-07-22 13:53:52,709 - pyskl - INFO - Epoch [15][600/3746] lr: 9.782e-02, eta: 4 days, 7:54:06, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4964, loss_cls: 4.1216, loss: 4.1216 +2024-07-22 13:55:02,477 - pyskl - INFO - Epoch [15][700/3746] lr: 9.781e-02, eta: 4 days, 7:52:17, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4998, loss_cls: 4.0819, loss: 4.0819 +2024-07-22 13:56:12,521 - pyskl - INFO - Epoch [15][800/3746] lr: 9.780e-02, eta: 4 days, 7:50:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4911, loss_cls: 4.1259, loss: 4.1259 +2024-07-22 13:57:22,659 - pyskl - INFO - Epoch [15][900/3746] lr: 9.779e-02, eta: 4 days, 7:48:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5125, loss_cls: 4.0408, loss: 4.0408 +2024-07-22 13:58:32,683 - pyskl - INFO - Epoch [15][1000/3746] lr: 9.778e-02, eta: 4 days, 7:46:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4958, loss_cls: 4.0877, loss: 4.0877 +2024-07-22 13:59:42,888 - pyskl - INFO - Epoch [15][1100/3746] lr: 9.778e-02, eta: 4 days, 7:45:14, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.4988, loss_cls: 4.0971, loss: 4.0971 +2024-07-22 14:00:52,862 - pyskl - INFO - Epoch [15][1200/3746] lr: 9.777e-02, eta: 4 days, 7:43:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4981, loss_cls: 4.1353, loss: 4.1353 +2024-07-22 14:02:03,067 - pyskl - INFO - Epoch [15][1300/3746] lr: 9.776e-02, eta: 4 days, 7:41:43, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.4933, loss_cls: 4.1172, loss: 4.1172 +2024-07-22 14:03:13,436 - pyskl - INFO - Epoch [15][1400/3746] lr: 9.775e-02, eta: 4 days, 7:40:00, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5078, loss_cls: 4.1015, loss: 4.1015 +2024-07-22 14:04:23,452 - pyskl - INFO - Epoch [15][1500/3746] lr: 9.774e-02, eta: 4 days, 7:38:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4975, loss_cls: 4.1292, loss: 4.1292 +2024-07-22 14:05:33,630 - pyskl - INFO - Epoch [15][1600/3746] lr: 9.773e-02, eta: 4 days, 7:36:30, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5081, loss_cls: 4.0808, loss: 4.0808 +2024-07-22 14:06:43,553 - pyskl - INFO - Epoch [15][1700/3746] lr: 9.773e-02, eta: 4 days, 7:34:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.4986, loss_cls: 4.0823, loss: 4.0823 +2024-07-22 14:07:53,499 - pyskl - INFO - Epoch [15][1800/3746] lr: 9.772e-02, eta: 4 days, 7:32:57, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4913, loss_cls: 4.1233, loss: 4.1233 +2024-07-22 14:09:03,567 - pyskl - INFO - Epoch [15][1900/3746] lr: 9.771e-02, eta: 4 days, 7:31:12, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5005, loss_cls: 4.0940, loss: 4.0940 +2024-07-22 14:10:13,452 - pyskl - INFO - Epoch [15][2000/3746] lr: 9.770e-02, eta: 4 days, 7:29:26, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5070, loss_cls: 4.0672, loss: 4.0672 +2024-07-22 14:11:23,417 - pyskl - INFO - Epoch [15][2100/3746] lr: 9.769e-02, eta: 4 days, 7:27:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4927, loss_cls: 4.1297, loss: 4.1297 +2024-07-22 14:12:33,546 - pyskl - INFO - Epoch [15][2200/3746] lr: 9.768e-02, eta: 4 days, 7:25:57, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.4956, loss_cls: 4.1271, loss: 4.1271 +2024-07-22 14:13:43,492 - pyskl - INFO - Epoch [15][2300/3746] lr: 9.768e-02, eta: 4 days, 7:24:11, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4920, loss_cls: 4.1337, loss: 4.1337 +2024-07-22 14:14:53,645 - pyskl - INFO - Epoch [15][2400/3746] lr: 9.767e-02, eta: 4 days, 7:22:28, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4858, loss_cls: 4.1268, loss: 4.1268 +2024-07-22 14:16:03,748 - pyskl - INFO - Epoch [15][2500/3746] lr: 9.766e-02, eta: 4 days, 7:20:44, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4995, loss_cls: 4.1223, loss: 4.1223 +2024-07-22 14:17:13,818 - pyskl - INFO - Epoch [15][2600/3746] lr: 9.765e-02, eta: 4 days, 7:19:00, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4967, loss_cls: 4.1267, loss: 4.1267 +2024-07-22 14:18:23,923 - pyskl - INFO - Epoch [15][2700/3746] lr: 9.764e-02, eta: 4 days, 7:17:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5019, loss_cls: 4.1210, loss: 4.1210 +2024-07-22 14:19:34,022 - pyskl - INFO - Epoch [15][2800/3746] lr: 9.763e-02, eta: 4 days, 7:15:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.4964, loss_cls: 4.0845, loss: 4.0845 +2024-07-22 14:20:43,838 - pyskl - INFO - Epoch [15][2900/3746] lr: 9.763e-02, eta: 4 days, 7:13:47, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5067, loss_cls: 4.0795, loss: 4.0795 +2024-07-22 14:21:54,409 - pyskl - INFO - Epoch [15][3000/3746] lr: 9.762e-02, eta: 4 days, 7:12:08, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4922, loss_cls: 4.1398, loss: 4.1398 +2024-07-22 14:23:04,580 - pyskl - INFO - Epoch [15][3100/3746] lr: 9.761e-02, eta: 4 days, 7:10:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4850, loss_cls: 4.1848, loss: 4.1848 +2024-07-22 14:24:15,022 - pyskl - INFO - Epoch [15][3200/3746] lr: 9.760e-02, eta: 4 days, 7:08:46, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4856, loss_cls: 4.1621, loss: 4.1621 +2024-07-22 14:25:25,811 - pyskl - INFO - Epoch [15][3300/3746] lr: 9.759e-02, eta: 4 days, 7:07:09, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4986, loss_cls: 4.1323, loss: 4.1323 +2024-07-22 14:26:36,719 - pyskl - INFO - Epoch [15][3400/3746] lr: 9.758e-02, eta: 4 days, 7:05:34, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4873, loss_cls: 4.1446, loss: 4.1446 +2024-07-22 14:27:47,282 - pyskl - INFO - Epoch [15][3500/3746] lr: 9.757e-02, eta: 4 days, 7:03:56, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4953, loss_cls: 4.1080, loss: 4.1080 +2024-07-22 14:28:58,202 - pyskl - INFO - Epoch [15][3600/3746] lr: 9.757e-02, eta: 4 days, 7:02:20, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4873, loss_cls: 4.1754, loss: 4.1754 +2024-07-22 14:30:08,558 - pyskl - INFO - Epoch [15][3700/3746] lr: 9.756e-02, eta: 4 days, 7:00:40, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4866, loss_cls: 4.1728, loss: 4.1728 +2024-07-22 14:30:43,152 - pyskl - INFO - Saving checkpoint at 15 epochs +2024-07-22 14:32:36,064 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 14:32:36,727 - pyskl - INFO - +top1_acc 0.1684 +top5_acc 0.3871 +2024-07-22 14:32:36,727 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 14:32:36,767 - pyskl - INFO - +mean_acc 0.1681 +2024-07-22 14:32:36,778 - pyskl - INFO - Epoch(val) [15][309] top1_acc: 0.1684, top5_acc: 0.3871, mean_class_accuracy: 0.1681 +2024-07-22 14:35:55,676 - pyskl - INFO - Epoch [16][100/3746] lr: 9.754e-02, eta: 4 days, 7:12:38, time: 1.989, data_time: 1.286, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4983, loss_cls: 4.0841, loss: 4.0841 +2024-07-22 14:37:05,813 - pyskl - INFO - Epoch [16][200/3746] lr: 9.754e-02, eta: 4 days, 7:10:55, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4958, loss_cls: 4.1135, loss: 4.1135 +2024-07-22 14:38:16,075 - pyskl - INFO - Epoch [16][300/3746] lr: 9.753e-02, eta: 4 days, 7:09:12, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5036, loss_cls: 4.1076, loss: 4.1076 +2024-07-22 14:39:26,067 - pyskl - INFO - Epoch [16][400/3746] lr: 9.752e-02, eta: 4 days, 7:07:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5016, loss_cls: 4.1043, loss: 4.1043 +2024-07-22 14:40:36,204 - pyskl - INFO - Epoch [16][500/3746] lr: 9.751e-02, eta: 4 days, 7:05:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5002, loss_cls: 4.1056, loss: 4.1056 +2024-07-22 14:41:46,508 - pyskl - INFO - Epoch [16][600/3746] lr: 9.750e-02, eta: 4 days, 7:04:03, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4927, loss_cls: 4.1495, loss: 4.1495 +2024-07-22 14:42:56,811 - pyskl - INFO - Epoch [16][700/3746] lr: 9.749e-02, eta: 4 days, 7:02:22, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4981, loss_cls: 4.1216, loss: 4.1216 +2024-07-22 14:44:06,597 - pyskl - INFO - Epoch [16][800/3746] lr: 9.748e-02, eta: 4 days, 7:00:36, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4925, loss_cls: 4.1260, loss: 4.1260 +2024-07-22 14:45:16,633 - pyskl - INFO - Epoch [16][900/3746] lr: 9.747e-02, eta: 4 days, 6:58:52, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4950, loss_cls: 4.1150, loss: 4.1150 +2024-07-22 14:46:26,844 - pyskl - INFO - Epoch [16][1000/3746] lr: 9.747e-02, eta: 4 days, 6:57:10, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.5072, loss_cls: 4.0793, loss: 4.0793 +2024-07-22 14:47:36,710 - pyskl - INFO - Epoch [16][1100/3746] lr: 9.746e-02, eta: 4 days, 6:55:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5038, loss_cls: 4.0799, loss: 4.0799 +2024-07-22 14:48:46,748 - pyskl - INFO - Epoch [16][1200/3746] lr: 9.745e-02, eta: 4 days, 6:53:42, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.4958, loss_cls: 4.0986, loss: 4.0986 +2024-07-22 14:49:56,730 - pyskl - INFO - Epoch [16][1300/3746] lr: 9.744e-02, eta: 4 days, 6:51:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.5042, loss_cls: 4.0925, loss: 4.0925 +2024-07-22 14:51:06,831 - pyskl - INFO - Epoch [16][1400/3746] lr: 9.743e-02, eta: 4 days, 6:50:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4903, loss_cls: 4.1529, loss: 4.1529 +2024-07-22 14:52:16,817 - pyskl - INFO - Epoch [16][1500/3746] lr: 9.742e-02, eta: 4 days, 6:48:33, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4983, loss_cls: 4.1159, loss: 4.1159 +2024-07-22 14:53:26,652 - pyskl - INFO - Epoch [16][1600/3746] lr: 9.741e-02, eta: 4 days, 6:46:48, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5106, loss_cls: 4.0691, loss: 4.0691 +2024-07-22 14:54:36,477 - pyskl - INFO - Epoch [16][1700/3746] lr: 9.740e-02, eta: 4 days, 6:45:03, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5031, loss_cls: 4.0931, loss: 4.0931 +2024-07-22 14:55:46,396 - pyskl - INFO - Epoch [16][1800/3746] lr: 9.740e-02, eta: 4 days, 6:43:20, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.5059, loss_cls: 4.0981, loss: 4.0981 +2024-07-22 14:56:56,269 - pyskl - INFO - Epoch [16][1900/3746] lr: 9.739e-02, eta: 4 days, 6:41:36, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4986, loss_cls: 4.1251, loss: 4.1251 +2024-07-22 14:58:06,156 - pyskl - INFO - Epoch [16][2000/3746] lr: 9.738e-02, eta: 4 days, 6:39:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.5084, loss_cls: 4.0864, loss: 4.0864 +2024-07-22 14:59:16,474 - pyskl - INFO - Epoch [16][2100/3746] lr: 9.737e-02, eta: 4 days, 6:38:13, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.4942, loss_cls: 4.1092, loss: 4.1092 +2024-07-22 15:00:26,886 - pyskl - INFO - Epoch [16][2200/3746] lr: 9.736e-02, eta: 4 days, 6:36:34, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4900, loss_cls: 4.1393, loss: 4.1393 +2024-07-22 15:01:37,005 - pyskl - INFO - Epoch [16][2300/3746] lr: 9.735e-02, eta: 4 days, 6:34:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4969, loss_cls: 4.1212, loss: 4.1212 +2024-07-22 15:02:46,878 - pyskl - INFO - Epoch [16][2400/3746] lr: 9.734e-02, eta: 4 days, 6:33:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4923, loss_cls: 4.1126, loss: 4.1126 +2024-07-22 15:03:57,009 - pyskl - INFO - Epoch [16][2500/3746] lr: 9.733e-02, eta: 4 days, 6:31:28, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4934, loss_cls: 4.1277, loss: 4.1277 +2024-07-22 15:05:07,008 - pyskl - INFO - Epoch [16][2600/3746] lr: 9.732e-02, eta: 4 days, 6:29:46, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4989, loss_cls: 4.1297, loss: 4.1297 +2024-07-22 15:06:16,960 - pyskl - INFO - Epoch [16][2700/3746] lr: 9.731e-02, eta: 4 days, 6:28:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5031, loss_cls: 4.1071, loss: 4.1071 +2024-07-22 15:07:27,197 - pyskl - INFO - Epoch [16][2800/3746] lr: 9.731e-02, eta: 4 days, 6:26:24, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5019, loss_cls: 4.0992, loss: 4.0992 +2024-07-22 15:08:37,290 - pyskl - INFO - Epoch [16][2900/3746] lr: 9.730e-02, eta: 4 days, 6:24:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4881, loss_cls: 4.1705, loss: 4.1705 +2024-07-22 15:09:47,710 - pyskl - INFO - Epoch [16][3000/3746] lr: 9.729e-02, eta: 4 days, 6:23:05, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.5016, loss_cls: 4.1417, loss: 4.1417 +2024-07-22 15:10:58,353 - pyskl - INFO - Epoch [16][3100/3746] lr: 9.728e-02, eta: 4 days, 6:21:29, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5042, loss_cls: 4.0731, loss: 4.0731 +2024-07-22 15:12:09,057 - pyskl - INFO - Epoch [16][3200/3746] lr: 9.727e-02, eta: 4 days, 6:19:54, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.4948, loss_cls: 4.0874, loss: 4.0874 +2024-07-22 15:13:19,366 - pyskl - INFO - Epoch [16][3300/3746] lr: 9.726e-02, eta: 4 days, 6:18:15, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4994, loss_cls: 4.1026, loss: 4.1026 +2024-07-22 15:14:30,319 - pyskl - INFO - Epoch [16][3400/3746] lr: 9.725e-02, eta: 4 days, 6:16:42, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5077, loss_cls: 4.0839, loss: 4.0839 +2024-07-22 15:15:40,607 - pyskl - INFO - Epoch [16][3500/3746] lr: 9.724e-02, eta: 4 days, 6:15:03, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.5084, loss_cls: 4.1091, loss: 4.1091 +2024-07-22 15:16:51,576 - pyskl - INFO - Epoch [16][3600/3746] lr: 9.723e-02, eta: 4 days, 6:13:30, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.4881, loss_cls: 4.1473, loss: 4.1473 +2024-07-22 15:18:01,949 - pyskl - INFO - Epoch [16][3700/3746] lr: 9.722e-02, eta: 4 days, 6:11:53, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5023, loss_cls: 4.1031, loss: 4.1031 +2024-07-22 15:18:36,661 - pyskl - INFO - Saving checkpoint at 16 epochs +2024-07-22 15:20:28,850 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 15:20:29,507 - pyskl - INFO - +top1_acc 0.1935 +top5_acc 0.4236 +2024-07-22 15:20:29,508 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 15:20:29,546 - pyskl - INFO - +mean_acc 0.1932 +2024-07-22 15:20:29,556 - pyskl - INFO - Epoch(val) [16][309] top1_acc: 0.1935, top5_acc: 0.4236, mean_class_accuracy: 0.1932 +2024-07-22 15:23:47,821 - pyskl - INFO - Epoch [17][100/3746] lr: 9.721e-02, eta: 4 days, 6:22:48, time: 1.983, data_time: 1.279, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5041, loss_cls: 4.0937, loss: 4.0937 +2024-07-22 15:24:58,165 - pyskl - INFO - Epoch [17][200/3746] lr: 9.720e-02, eta: 4 days, 6:21:09, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5066, loss_cls: 4.0822, loss: 4.0822 +2024-07-22 15:26:08,119 - pyskl - INFO - Epoch [17][300/3746] lr: 9.719e-02, eta: 4 days, 6:19:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5052, loss_cls: 4.0832, loss: 4.0832 +2024-07-22 15:27:18,250 - pyskl - INFO - Epoch [17][400/3746] lr: 9.718e-02, eta: 4 days, 6:17:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5128, loss_cls: 4.0577, loss: 4.0577 +2024-07-22 15:28:28,923 - pyskl - INFO - Epoch [17][500/3746] lr: 9.717e-02, eta: 4 days, 6:16:10, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5128, loss_cls: 4.0568, loss: 4.0568 +2024-07-22 15:29:38,707 - pyskl - INFO - Epoch [17][600/3746] lr: 9.716e-02, eta: 4 days, 6:14:26, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5012, loss_cls: 4.0904, loss: 4.0904 +2024-07-22 15:30:48,669 - pyskl - INFO - Epoch [17][700/3746] lr: 9.715e-02, eta: 4 days, 6:12:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5112, loss_cls: 4.0521, loss: 4.0521 +2024-07-22 15:31:58,831 - pyskl - INFO - Epoch [17][800/3746] lr: 9.714e-02, eta: 4 days, 6:11:04, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5008, loss_cls: 4.1104, loss: 4.1104 +2024-07-22 15:33:08,866 - pyskl - INFO - Epoch [17][900/3746] lr: 9.714e-02, eta: 4 days, 6:09:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4988, loss_cls: 4.1014, loss: 4.1014 +2024-07-22 15:34:18,719 - pyskl - INFO - Epoch [17][1000/3746] lr: 9.713e-02, eta: 4 days, 6:07:40, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5067, loss_cls: 4.0843, loss: 4.0843 +2024-07-22 15:35:28,587 - pyskl - INFO - Epoch [17][1100/3746] lr: 9.712e-02, eta: 4 days, 6:05:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5016, loss_cls: 4.1202, loss: 4.1202 +2024-07-22 15:36:38,633 - pyskl - INFO - Epoch [17][1200/3746] lr: 9.711e-02, eta: 4 days, 6:04:17, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5072, loss_cls: 4.1066, loss: 4.1066 +2024-07-22 15:37:48,524 - pyskl - INFO - Epoch [17][1300/3746] lr: 9.710e-02, eta: 4 days, 6:02:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5011, loss_cls: 4.0843, loss: 4.0843 +2024-07-22 15:38:58,461 - pyskl - INFO - Epoch [17][1400/3746] lr: 9.709e-02, eta: 4 days, 6:00:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4964, loss_cls: 4.1287, loss: 4.1287 +2024-07-22 15:40:08,297 - pyskl - INFO - Epoch [17][1500/3746] lr: 9.708e-02, eta: 4 days, 5:59:12, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5000, loss_cls: 4.0965, loss: 4.0965 +2024-07-22 15:41:18,381 - pyskl - INFO - Epoch [17][1600/3746] lr: 9.707e-02, eta: 4 days, 5:57:32, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4975, loss_cls: 4.1038, loss: 4.1038 +2024-07-22 15:42:28,518 - pyskl - INFO - Epoch [17][1700/3746] lr: 9.706e-02, eta: 4 days, 5:55:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.5042, loss_cls: 4.0977, loss: 4.0977 +2024-07-22 15:43:38,535 - pyskl - INFO - Epoch [17][1800/3746] lr: 9.705e-02, eta: 4 days, 5:54:12, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.4981, loss_cls: 4.1055, loss: 4.1055 +2024-07-22 15:44:48,359 - pyskl - INFO - Epoch [17][1900/3746] lr: 9.704e-02, eta: 4 days, 5:52:30, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4992, loss_cls: 4.1046, loss: 4.1046 +2024-07-22 15:45:58,213 - pyskl - INFO - Epoch [17][2000/3746] lr: 9.703e-02, eta: 4 days, 5:50:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5045, loss_cls: 4.0811, loss: 4.0811 +2024-07-22 15:47:08,242 - pyskl - INFO - Epoch [17][2100/3746] lr: 9.702e-02, eta: 4 days, 5:49:09, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5025, loss_cls: 4.0603, loss: 4.0603 +2024-07-22 15:48:18,449 - pyskl - INFO - Epoch [17][2200/3746] lr: 9.701e-02, eta: 4 days, 5:47:30, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4969, loss_cls: 4.1175, loss: 4.1175 +2024-07-22 15:49:28,476 - pyskl - INFO - Epoch [17][2300/3746] lr: 9.700e-02, eta: 4 days, 5:45:50, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.4995, loss_cls: 4.0694, loss: 4.0694 +2024-07-22 15:50:38,753 - pyskl - INFO - Epoch [17][2400/3746] lr: 9.699e-02, eta: 4 days, 5:44:13, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4945, loss_cls: 4.1048, loss: 4.1048 +2024-07-22 15:51:48,579 - pyskl - INFO - Epoch [17][2500/3746] lr: 9.698e-02, eta: 4 days, 5:42:31, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4998, loss_cls: 4.0848, loss: 4.0848 +2024-07-22 15:52:58,463 - pyskl - INFO - Epoch [17][2600/3746] lr: 9.697e-02, eta: 4 days, 5:40:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5005, loss_cls: 4.0868, loss: 4.0868 +2024-07-22 15:54:08,572 - pyskl - INFO - Epoch [17][2700/3746] lr: 9.697e-02, eta: 4 days, 5:39:12, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.5039, loss_cls: 4.0868, loss: 4.0868 +2024-07-22 15:55:18,589 - pyskl - INFO - Epoch [17][2800/3746] lr: 9.696e-02, eta: 4 days, 5:37:33, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5038, loss_cls: 4.0881, loss: 4.0881 +2024-07-22 15:56:29,105 - pyskl - INFO - Epoch [17][2900/3746] lr: 9.695e-02, eta: 4 days, 5:35:57, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.4894, loss_cls: 4.1045, loss: 4.1045 +2024-07-22 15:57:39,677 - pyskl - INFO - Epoch [17][3000/3746] lr: 9.694e-02, eta: 4 days, 5:34:22, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5106, loss_cls: 4.0968, loss: 4.0968 +2024-07-22 15:58:49,706 - pyskl - INFO - Epoch [17][3100/3746] lr: 9.693e-02, eta: 4 days, 5:32:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5044, loss_cls: 4.0834, loss: 4.0834 +2024-07-22 15:59:59,717 - pyskl - INFO - Epoch [17][3200/3746] lr: 9.692e-02, eta: 4 days, 5:31:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4966, loss_cls: 4.1224, loss: 4.1224 +2024-07-22 16:01:10,330 - pyskl - INFO - Epoch [17][3300/3746] lr: 9.691e-02, eta: 4 days, 5:29:30, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4952, loss_cls: 4.1661, loss: 4.1661 +2024-07-22 16:02:21,066 - pyskl - INFO - Epoch [17][3400/3746] lr: 9.690e-02, eta: 4 days, 5:27:57, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5020, loss_cls: 4.1028, loss: 4.1028 +2024-07-22 16:03:32,275 - pyskl - INFO - Epoch [17][3500/3746] lr: 9.689e-02, eta: 4 days, 5:26:27, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5009, loss_cls: 4.0844, loss: 4.0844 +2024-07-22 16:04:43,032 - pyskl - INFO - Epoch [17][3600/3746] lr: 9.688e-02, eta: 4 days, 5:24:54, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4939, loss_cls: 4.1488, loss: 4.1488 +2024-07-22 16:05:53,724 - pyskl - INFO - Epoch [17][3700/3746] lr: 9.687e-02, eta: 4 days, 5:23:21, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.5033, loss_cls: 4.1042, loss: 4.1042 +2024-07-22 16:06:28,164 - pyskl - INFO - Saving checkpoint at 17 epochs +2024-07-22 16:08:21,013 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 16:08:21,679 - pyskl - INFO - +top1_acc 0.1742 +top5_acc 0.3931 +2024-07-22 16:08:21,679 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 16:08:21,719 - pyskl - INFO - +mean_acc 0.1739 +2024-07-22 16:08:21,729 - pyskl - INFO - Epoch(val) [17][309] top1_acc: 0.1742, top5_acc: 0.3931, mean_class_accuracy: 0.1739 +2024-07-22 16:11:43,529 - pyskl - INFO - Epoch [18][100/3746] lr: 9.685e-02, eta: 4 days, 5:33:55, time: 2.018, data_time: 1.313, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5169, loss_cls: 4.0709, loss: 4.0709 +2024-07-22 16:12:53,686 - pyskl - INFO - Epoch [18][200/3746] lr: 9.684e-02, eta: 4 days, 5:32:16, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5150, loss_cls: 4.0347, loss: 4.0347 +2024-07-22 16:14:03,754 - pyskl - INFO - Epoch [18][300/3746] lr: 9.683e-02, eta: 4 days, 5:30:37, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5088, loss_cls: 4.0574, loss: 4.0574 +2024-07-22 16:15:13,609 - pyskl - INFO - Epoch [18][400/3746] lr: 9.683e-02, eta: 4 days, 5:28:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5095, loss_cls: 4.0564, loss: 4.0564 +2024-07-22 16:16:23,618 - pyskl - INFO - Epoch [18][500/3746] lr: 9.682e-02, eta: 4 days, 5:27:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4986, loss_cls: 4.0975, loss: 4.0975 +2024-07-22 16:17:33,656 - pyskl - INFO - Epoch [18][600/3746] lr: 9.681e-02, eta: 4 days, 5:25:37, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5080, loss_cls: 4.0758, loss: 4.0758 +2024-07-22 16:18:43,705 - pyskl - INFO - Epoch [18][700/3746] lr: 9.680e-02, eta: 4 days, 5:23:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4975, loss_cls: 4.1200, loss: 4.1200 +2024-07-22 16:19:53,743 - pyskl - INFO - Epoch [18][800/3746] lr: 9.679e-02, eta: 4 days, 5:22:19, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.4975, loss_cls: 4.0896, loss: 4.0896 +2024-07-22 16:21:03,746 - pyskl - INFO - Epoch [18][900/3746] lr: 9.678e-02, eta: 4 days, 5:20:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5002, loss_cls: 4.1186, loss: 4.1186 +2024-07-22 16:22:13,689 - pyskl - INFO - Epoch [18][1000/3746] lr: 9.677e-02, eta: 4 days, 5:19:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.4997, loss_cls: 4.1038, loss: 4.1038 +2024-07-22 16:23:23,811 - pyskl - INFO - Epoch [18][1100/3746] lr: 9.676e-02, eta: 4 days, 5:17:22, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5078, loss_cls: 4.0734, loss: 4.0734 +2024-07-22 16:24:33,751 - pyskl - INFO - Epoch [18][1200/3746] lr: 9.675e-02, eta: 4 days, 5:15:42, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5059, loss_cls: 4.1103, loss: 4.1103 +2024-07-22 16:25:43,642 - pyskl - INFO - Epoch [18][1300/3746] lr: 9.674e-02, eta: 4 days, 5:14:02, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5127, loss_cls: 4.0530, loss: 4.0530 +2024-07-22 16:26:53,424 - pyskl - INFO - Epoch [18][1400/3746] lr: 9.673e-02, eta: 4 days, 5:12:22, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.4981, loss_cls: 4.0904, loss: 4.0904 +2024-07-22 16:28:03,471 - pyskl - INFO - Epoch [18][1500/3746] lr: 9.672e-02, eta: 4 days, 5:10:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5027, loss_cls: 4.1155, loss: 4.1155 +2024-07-22 16:29:13,641 - pyskl - INFO - Epoch [18][1600/3746] lr: 9.671e-02, eta: 4 days, 5:09:06, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5019, loss_cls: 4.1140, loss: 4.1140 +2024-07-22 16:30:23,586 - pyskl - INFO - Epoch [18][1700/3746] lr: 9.670e-02, eta: 4 days, 5:07:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5020, loss_cls: 4.0820, loss: 4.0820 +2024-07-22 16:31:33,592 - pyskl - INFO - Epoch [18][1800/3746] lr: 9.669e-02, eta: 4 days, 5:05:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5091, loss_cls: 4.0783, loss: 4.0783 +2024-07-22 16:32:43,668 - pyskl - INFO - Epoch [18][1900/3746] lr: 9.668e-02, eta: 4 days, 5:04:11, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5105, loss_cls: 4.0864, loss: 4.0864 +2024-07-22 16:33:53,510 - pyskl - INFO - Epoch [18][2000/3746] lr: 9.667e-02, eta: 4 days, 5:02:31, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5011, loss_cls: 4.0564, loss: 4.0564 +2024-07-22 16:35:03,406 - pyskl - INFO - Epoch [18][2100/3746] lr: 9.666e-02, eta: 4 days, 5:00:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5078, loss_cls: 4.0862, loss: 4.0862 +2024-07-22 16:36:13,574 - pyskl - INFO - Epoch [18][2200/3746] lr: 9.665e-02, eta: 4 days, 4:59:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4986, loss_cls: 4.1051, loss: 4.1051 +2024-07-22 16:37:23,524 - pyskl - INFO - Epoch [18][2300/3746] lr: 9.664e-02, eta: 4 days, 4:57:37, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5081, loss_cls: 4.0932, loss: 4.0932 +2024-07-22 16:38:33,398 - pyskl - INFO - Epoch [18][2400/3746] lr: 9.663e-02, eta: 4 days, 4:55:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5020, loss_cls: 4.0853, loss: 4.0853 +2024-07-22 16:39:43,285 - pyskl - INFO - Epoch [18][2500/3746] lr: 9.662e-02, eta: 4 days, 4:54:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5053, loss_cls: 4.1106, loss: 4.1106 +2024-07-22 16:40:53,286 - pyskl - INFO - Epoch [18][2600/3746] lr: 9.661e-02, eta: 4 days, 4:52:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5086, loss_cls: 4.0649, loss: 4.0649 +2024-07-22 16:42:03,383 - pyskl - INFO - Epoch [18][2700/3746] lr: 9.660e-02, eta: 4 days, 4:51:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.4995, loss_cls: 4.0884, loss: 4.0884 +2024-07-22 16:43:13,538 - pyskl - INFO - Epoch [18][2800/3746] lr: 9.659e-02, eta: 4 days, 4:49:28, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4914, loss_cls: 4.1219, loss: 4.1219 +2024-07-22 16:44:24,403 - pyskl - INFO - Epoch [18][2900/3746] lr: 9.658e-02, eta: 4 days, 4:47:57, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4950, loss_cls: 4.1616, loss: 4.1616 +2024-07-22 16:45:34,639 - pyskl - INFO - Epoch [18][3000/3746] lr: 9.657e-02, eta: 4 days, 4:46:21, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5080, loss_cls: 4.0652, loss: 4.0652 +2024-07-22 16:46:45,318 - pyskl - INFO - Epoch [18][3100/3746] lr: 9.656e-02, eta: 4 days, 4:44:49, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5053, loss_cls: 4.0967, loss: 4.0967 +2024-07-22 16:47:55,768 - pyskl - INFO - Epoch [18][3200/3746] lr: 9.654e-02, eta: 4 days, 4:43:15, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4922, loss_cls: 4.1390, loss: 4.1390 +2024-07-22 16:49:06,422 - pyskl - INFO - Epoch [18][3300/3746] lr: 9.653e-02, eta: 4 days, 4:41:42, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4894, loss_cls: 4.1384, loss: 4.1384 +2024-07-22 16:50:17,747 - pyskl - INFO - Epoch [18][3400/3746] lr: 9.652e-02, eta: 4 days, 4:40:15, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5042, loss_cls: 4.0782, loss: 4.0782 +2024-07-22 16:51:28,547 - pyskl - INFO - Epoch [18][3500/3746] lr: 9.651e-02, eta: 4 days, 4:38:44, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.4977, loss_cls: 4.1102, loss: 4.1102 +2024-07-22 16:52:39,588 - pyskl - INFO - Epoch [18][3600/3746] lr: 9.650e-02, eta: 4 days, 4:37:14, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5100, loss_cls: 4.0741, loss: 4.0741 +2024-07-22 16:53:49,912 - pyskl - INFO - Epoch [18][3700/3746] lr: 9.649e-02, eta: 4 days, 4:35:40, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5062, loss_cls: 4.0956, loss: 4.0956 +2024-07-22 16:54:24,420 - pyskl - INFO - Saving checkpoint at 18 epochs +2024-07-22 16:56:15,961 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 16:56:16,640 - pyskl - INFO - +top1_acc 0.1964 +top5_acc 0.4231 +2024-07-22 16:56:16,641 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 16:56:16,678 - pyskl - INFO - +mean_acc 0.1961 +2024-07-22 16:56:16,688 - pyskl - INFO - Epoch(val) [18][309] top1_acc: 0.1964, top5_acc: 0.4231, mean_class_accuracy: 0.1961 +2024-07-22 16:59:35,952 - pyskl - INFO - Epoch [19][100/3746] lr: 9.648e-02, eta: 4 days, 4:45:09, time: 1.993, data_time: 1.288, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5142, loss_cls: 4.0270, loss: 4.0270 +2024-07-22 17:00:45,941 - pyskl - INFO - Epoch [19][200/3746] lr: 9.647e-02, eta: 4 days, 4:43:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5008, loss_cls: 4.0946, loss: 4.0946 +2024-07-22 17:01:56,052 - pyskl - INFO - Epoch [19][300/3746] lr: 9.646e-02, eta: 4 days, 4:41:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5125, loss_cls: 4.0729, loss: 4.0729 +2024-07-22 17:03:06,423 - pyskl - INFO - Epoch [19][400/3746] lr: 9.645e-02, eta: 4 days, 4:40:18, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5105, loss_cls: 4.0528, loss: 4.0528 +2024-07-22 17:04:16,442 - pyskl - INFO - Epoch [19][500/3746] lr: 9.644e-02, eta: 4 days, 4:38:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5097, loss_cls: 4.0855, loss: 4.0855 +2024-07-22 17:05:26,420 - pyskl - INFO - Epoch [19][600/3746] lr: 9.643e-02, eta: 4 days, 4:37:03, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5078, loss_cls: 4.0413, loss: 4.0413 +2024-07-22 17:06:36,683 - pyskl - INFO - Epoch [19][700/3746] lr: 9.642e-02, eta: 4 days, 4:35:27, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5039, loss_cls: 4.0818, loss: 4.0818 +2024-07-22 17:07:46,753 - pyskl - INFO - Epoch [19][800/3746] lr: 9.641e-02, eta: 4 days, 4:33:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4930, loss_cls: 4.1460, loss: 4.1460 +2024-07-22 17:08:56,585 - pyskl - INFO - Epoch [19][900/3746] lr: 9.640e-02, eta: 4 days, 4:32:12, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5109, loss_cls: 4.0621, loss: 4.0621 +2024-07-22 17:10:06,419 - pyskl - INFO - Epoch [19][1000/3746] lr: 9.639e-02, eta: 4 days, 4:30:33, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5016, loss_cls: 4.1355, loss: 4.1355 +2024-07-22 17:11:16,364 - pyskl - INFO - Epoch [19][1100/3746] lr: 9.637e-02, eta: 4 days, 4:28:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5158, loss_cls: 4.0284, loss: 4.0284 +2024-07-22 17:12:26,511 - pyskl - INFO - Epoch [19][1200/3746] lr: 9.636e-02, eta: 4 days, 4:27:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4945, loss_cls: 4.1398, loss: 4.1398 +2024-07-22 17:13:36,772 - pyskl - INFO - Epoch [19][1300/3746] lr: 9.635e-02, eta: 4 days, 4:25:44, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5056, loss_cls: 4.0583, loss: 4.0583 +2024-07-22 17:14:46,676 - pyskl - INFO - Epoch [19][1400/3746] lr: 9.634e-02, eta: 4 days, 4:24:06, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5138, loss_cls: 4.0503, loss: 4.0503 +2024-07-22 17:15:56,463 - pyskl - INFO - Epoch [19][1500/3746] lr: 9.633e-02, eta: 4 days, 4:22:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5088, loss_cls: 4.0860, loss: 4.0860 +2024-07-22 17:17:06,407 - pyskl - INFO - Epoch [19][1600/3746] lr: 9.632e-02, eta: 4 days, 4:20:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5128, loss_cls: 4.0414, loss: 4.0414 +2024-07-22 17:18:16,258 - pyskl - INFO - Epoch [19][1700/3746] lr: 9.631e-02, eta: 4 days, 4:19:13, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5052, loss_cls: 4.0669, loss: 4.0669 +2024-07-22 17:19:26,210 - pyskl - INFO - Epoch [19][1800/3746] lr: 9.630e-02, eta: 4 days, 4:17:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4972, loss_cls: 4.1157, loss: 4.1157 +2024-07-22 17:20:36,254 - pyskl - INFO - Epoch [19][1900/3746] lr: 9.629e-02, eta: 4 days, 4:15:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5034, loss_cls: 4.1079, loss: 4.1079 +2024-07-22 17:21:46,127 - pyskl - INFO - Epoch [19][2000/3746] lr: 9.628e-02, eta: 4 days, 4:14:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5048, loss_cls: 4.0938, loss: 4.0938 +2024-07-22 17:22:56,036 - pyskl - INFO - Epoch [19][2100/3746] lr: 9.627e-02, eta: 4 days, 4:12:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.4991, loss_cls: 4.0936, loss: 4.0936 +2024-07-22 17:24:06,001 - pyskl - INFO - Epoch [19][2200/3746] lr: 9.626e-02, eta: 4 days, 4:11:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5017, loss_cls: 4.0777, loss: 4.0777 +2024-07-22 17:25:16,067 - pyskl - INFO - Epoch [19][2300/3746] lr: 9.625e-02, eta: 4 days, 4:09:32, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5008, loss_cls: 4.1120, loss: 4.1120 +2024-07-22 17:26:25,956 - pyskl - INFO - Epoch [19][2400/3746] lr: 9.624e-02, eta: 4 days, 4:07:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5072, loss_cls: 4.0651, loss: 4.0651 +2024-07-22 17:27:36,228 - pyskl - INFO - Epoch [19][2500/3746] lr: 9.623e-02, eta: 4 days, 4:06:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4977, loss_cls: 4.1492, loss: 4.1492 +2024-07-22 17:28:46,044 - pyskl - INFO - Epoch [19][2600/3746] lr: 9.622e-02, eta: 4 days, 4:04:44, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5106, loss_cls: 4.0549, loss: 4.0549 +2024-07-22 17:29:56,131 - pyskl - INFO - Epoch [19][2700/3746] lr: 9.621e-02, eta: 4 days, 4:03:08, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.4983, loss_cls: 4.0914, loss: 4.0914 +2024-07-22 17:31:06,261 - pyskl - INFO - Epoch [19][2800/3746] lr: 9.620e-02, eta: 4 days, 4:01:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4970, loss_cls: 4.0971, loss: 4.0971 +2024-07-22 17:32:16,912 - pyskl - INFO - Epoch [19][2900/3746] lr: 9.618e-02, eta: 4 days, 4:00:02, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.4998, loss_cls: 4.1063, loss: 4.1063 +2024-07-22 17:33:27,251 - pyskl - INFO - Epoch [19][3000/3746] lr: 9.617e-02, eta: 4 days, 3:58:28, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5069, loss_cls: 4.0718, loss: 4.0718 +2024-07-22 17:34:37,604 - pyskl - INFO - Epoch [19][3100/3746] lr: 9.616e-02, eta: 4 days, 3:56:55, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5077, loss_cls: 4.1008, loss: 4.1008 +2024-07-22 17:35:48,652 - pyskl - INFO - Epoch [19][3200/3746] lr: 9.615e-02, eta: 4 days, 3:55:27, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5044, loss_cls: 4.1179, loss: 4.1179 +2024-07-22 17:36:59,300 - pyskl - INFO - Epoch [19][3300/3746] lr: 9.614e-02, eta: 4 days, 3:53:56, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5042, loss_cls: 4.0786, loss: 4.0786 +2024-07-22 17:38:09,757 - pyskl - INFO - Epoch [19][3400/3746] lr: 9.613e-02, eta: 4 days, 3:52:23, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5014, loss_cls: 4.0914, loss: 4.0914 +2024-07-22 17:39:20,696 - pyskl - INFO - Epoch [19][3500/3746] lr: 9.612e-02, eta: 4 days, 3:50:54, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4992, loss_cls: 4.1050, loss: 4.1050 +2024-07-22 17:40:31,767 - pyskl - INFO - Epoch [19][3600/3746] lr: 9.611e-02, eta: 4 days, 3:49:26, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5052, loss_cls: 4.0921, loss: 4.0921 +2024-07-22 17:41:42,198 - pyskl - INFO - Epoch [19][3700/3746] lr: 9.610e-02, eta: 4 days, 3:47:54, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5030, loss_cls: 4.0779, loss: 4.0779 +2024-07-22 17:42:17,093 - pyskl - INFO - Saving checkpoint at 19 epochs +2024-07-22 17:44:09,789 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 17:44:10,451 - pyskl - INFO - +top1_acc 0.1969 +top5_acc 0.4199 +2024-07-22 17:44:10,452 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 17:44:10,491 - pyskl - INFO - +mean_acc 0.1967 +2024-07-22 17:44:10,496 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_11.pth was removed +2024-07-22 17:44:10,741 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_19.pth. +2024-07-22 17:44:10,742 - pyskl - INFO - Best top1_acc is 0.1969 at 19 epoch. +2024-07-22 17:44:10,753 - pyskl - INFO - Epoch(val) [19][309] top1_acc: 0.1969, top5_acc: 0.4199, mean_class_accuracy: 0.1967 +2024-07-22 17:47:29,368 - pyskl - INFO - Epoch [20][100/3746] lr: 9.608e-02, eta: 4 days, 3:56:38, time: 1.986, data_time: 1.283, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5142, loss_cls: 4.0371, loss: 4.0371 +2024-07-22 17:48:39,755 - pyskl - INFO - Epoch [20][200/3746] lr: 9.607e-02, eta: 4 days, 3:55:05, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5073, loss_cls: 4.0786, loss: 4.0786 +2024-07-22 17:49:49,741 - pyskl - INFO - Epoch [20][300/3746] lr: 9.606e-02, eta: 4 days, 3:53:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5067, loss_cls: 4.0578, loss: 4.0578 +2024-07-22 17:50:59,901 - pyskl - INFO - Epoch [20][400/3746] lr: 9.605e-02, eta: 4 days, 3:51:53, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5041, loss_cls: 4.0436, loss: 4.0436 +2024-07-22 17:52:09,991 - pyskl - INFO - Epoch [20][500/3746] lr: 9.604e-02, eta: 4 days, 3:50:18, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4964, loss_cls: 4.1121, loss: 4.1121 +2024-07-22 17:53:20,007 - pyskl - INFO - Epoch [20][600/3746] lr: 9.603e-02, eta: 4 days, 3:48:42, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5080, loss_cls: 4.0438, loss: 4.0438 +2024-07-22 17:54:29,989 - pyskl - INFO - Epoch [20][700/3746] lr: 9.602e-02, eta: 4 days, 3:47:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.5075, loss_cls: 4.0809, loss: 4.0809 +2024-07-22 17:55:40,094 - pyskl - INFO - Epoch [20][800/3746] lr: 9.601e-02, eta: 4 days, 3:45:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4939, loss_cls: 4.0923, loss: 4.0923 +2024-07-22 17:56:50,013 - pyskl - INFO - Epoch [20][900/3746] lr: 9.600e-02, eta: 4 days, 3:43:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5034, loss_cls: 4.0708, loss: 4.0708 +2024-07-22 17:58:00,049 - pyskl - INFO - Epoch [20][1000/3746] lr: 9.598e-02, eta: 4 days, 3:42:19, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.5009, loss_cls: 4.0962, loss: 4.0962 +2024-07-22 17:59:10,223 - pyskl - INFO - Epoch [20][1100/3746] lr: 9.597e-02, eta: 4 days, 3:40:45, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5000, loss_cls: 4.0932, loss: 4.0932 +2024-07-22 18:00:20,337 - pyskl - INFO - Epoch [20][1200/3746] lr: 9.596e-02, eta: 4 days, 3:39:10, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5048, loss_cls: 4.0701, loss: 4.0701 +2024-07-22 18:01:30,082 - pyskl - INFO - Epoch [20][1300/3746] lr: 9.595e-02, eta: 4 days, 3:37:33, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5041, loss_cls: 4.0735, loss: 4.0735 +2024-07-22 18:02:40,044 - pyskl - INFO - Epoch [20][1400/3746] lr: 9.594e-02, eta: 4 days, 3:35:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5002, loss_cls: 4.0836, loss: 4.0836 +2024-07-22 18:03:50,272 - pyskl - INFO - Epoch [20][1500/3746] lr: 9.593e-02, eta: 4 days, 3:34:23, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5083, loss_cls: 4.0651, loss: 4.0651 +2024-07-22 18:05:00,214 - pyskl - INFO - Epoch [20][1600/3746] lr: 9.592e-02, eta: 4 days, 3:32:48, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5028, loss_cls: 4.0871, loss: 4.0871 +2024-07-22 18:06:10,292 - pyskl - INFO - Epoch [20][1700/3746] lr: 9.591e-02, eta: 4 days, 3:31:13, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5131, loss_cls: 4.0335, loss: 4.0335 +2024-07-22 18:07:20,361 - pyskl - INFO - Epoch [20][1800/3746] lr: 9.590e-02, eta: 4 days, 3:29:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5089, loss_cls: 4.0700, loss: 4.0700 +2024-07-22 18:08:30,600 - pyskl - INFO - Epoch [20][1900/3746] lr: 9.588e-02, eta: 4 days, 3:28:05, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5061, loss_cls: 4.0649, loss: 4.0649 +2024-07-22 18:09:40,863 - pyskl - INFO - Epoch [20][2000/3746] lr: 9.587e-02, eta: 4 days, 3:26:32, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5009, loss_cls: 4.1145, loss: 4.1145 +2024-07-22 18:10:51,150 - pyskl - INFO - Epoch [20][2100/3746] lr: 9.586e-02, eta: 4 days, 3:24:59, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4977, loss_cls: 4.1198, loss: 4.1198 +2024-07-22 18:12:01,499 - pyskl - INFO - Epoch [20][2200/3746] lr: 9.585e-02, eta: 4 days, 3:23:26, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5045, loss_cls: 4.0915, loss: 4.0915 +2024-07-22 18:13:11,757 - pyskl - INFO - Epoch [20][2300/3746] lr: 9.584e-02, eta: 4 days, 3:21:53, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.5020, loss_cls: 4.0894, loss: 4.0894 +2024-07-22 18:14:21,925 - pyskl - INFO - Epoch [20][2400/3746] lr: 9.583e-02, eta: 4 days, 3:20:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5034, loss_cls: 4.0954, loss: 4.0954 +2024-07-22 18:15:32,327 - pyskl - INFO - Epoch [20][2500/3746] lr: 9.582e-02, eta: 4 days, 3:18:47, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5009, loss_cls: 4.1271, loss: 4.1271 +2024-07-22 18:16:42,356 - pyskl - INFO - Epoch [20][2600/3746] lr: 9.581e-02, eta: 4 days, 3:17:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5011, loss_cls: 4.0718, loss: 4.0718 +2024-07-22 18:17:52,491 - pyskl - INFO - Epoch [20][2700/3746] lr: 9.580e-02, eta: 4 days, 3:15:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5112, loss_cls: 4.0820, loss: 4.0820 +2024-07-22 18:19:02,399 - pyskl - INFO - Epoch [20][2800/3746] lr: 9.578e-02, eta: 4 days, 3:14:04, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5009, loss_cls: 4.0789, loss: 4.0789 +2024-07-22 18:20:13,101 - pyskl - INFO - Epoch [20][2900/3746] lr: 9.577e-02, eta: 4 days, 3:12:34, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5064, loss_cls: 4.0685, loss: 4.0685 +2024-07-22 18:21:23,467 - pyskl - INFO - Epoch [20][3000/3746] lr: 9.576e-02, eta: 4 days, 3:11:03, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5089, loss_cls: 4.0647, loss: 4.0647 +2024-07-22 18:22:33,715 - pyskl - INFO - Epoch [20][3100/3746] lr: 9.575e-02, eta: 4 days, 3:09:30, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5028, loss_cls: 4.0741, loss: 4.0741 +2024-07-22 18:23:44,298 - pyskl - INFO - Epoch [20][3200/3746] lr: 9.574e-02, eta: 4 days, 3:07:59, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5120, loss_cls: 4.0635, loss: 4.0635 +2024-07-22 18:24:55,075 - pyskl - INFO - Epoch [20][3300/3746] lr: 9.573e-02, eta: 4 days, 3:06:30, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4914, loss_cls: 4.1332, loss: 4.1332 +2024-07-22 18:26:05,564 - pyskl - INFO - Epoch [20][3400/3746] lr: 9.572e-02, eta: 4 days, 3:04:59, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4989, loss_cls: 4.1241, loss: 4.1241 +2024-07-22 18:27:16,244 - pyskl - INFO - Epoch [20][3500/3746] lr: 9.571e-02, eta: 4 days, 3:03:30, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4959, loss_cls: 4.1024, loss: 4.1024 +2024-07-22 18:28:27,021 - pyskl - INFO - Epoch [20][3600/3746] lr: 9.569e-02, eta: 4 days, 3:02:01, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5136, loss_cls: 4.0396, loss: 4.0396 +2024-07-22 18:29:38,120 - pyskl - INFO - Epoch [20][3700/3746] lr: 9.568e-02, eta: 4 days, 3:00:34, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5080, loss_cls: 4.0716, loss: 4.0716 +2024-07-22 18:30:12,794 - pyskl - INFO - Saving checkpoint at 20 epochs +2024-07-22 18:32:05,138 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 18:32:05,799 - pyskl - INFO - +top1_acc 0.2034 +top5_acc 0.4368 +2024-07-22 18:32:05,799 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 18:32:05,838 - pyskl - INFO - +mean_acc 0.2032 +2024-07-22 18:32:05,842 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_19.pth was removed +2024-07-22 18:32:06,084 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_20.pth. +2024-07-22 18:32:06,084 - pyskl - INFO - Best top1_acc is 0.2034 at 20 epoch. +2024-07-22 18:32:06,095 - pyskl - INFO - Epoch(val) [20][309] top1_acc: 0.2034, top5_acc: 0.4368, mean_class_accuracy: 0.2032 +2024-07-22 18:35:26,029 - pyskl - INFO - Epoch [21][100/3746] lr: 9.567e-02, eta: 4 days, 3:08:51, time: 1.999, data_time: 1.294, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5064, loss_cls: 4.0310, loss: 4.0310 +2024-07-22 18:36:36,116 - pyskl - INFO - Epoch [21][200/3746] lr: 9.565e-02, eta: 4 days, 3:07:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5031, loss_cls: 4.0713, loss: 4.0713 +2024-07-22 18:37:46,136 - pyskl - INFO - Epoch [21][300/3746] lr: 9.564e-02, eta: 4 days, 3:05:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4989, loss_cls: 4.0957, loss: 4.0957 +2024-07-22 18:38:56,050 - pyskl - INFO - Epoch [21][400/3746] lr: 9.563e-02, eta: 4 days, 3:04:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5216, loss_cls: 4.0133, loss: 4.0133 +2024-07-22 18:40:06,098 - pyskl - INFO - Epoch [21][500/3746] lr: 9.562e-02, eta: 4 days, 3:02:33, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5011, loss_cls: 4.1002, loss: 4.1002 +2024-07-22 18:41:16,262 - pyskl - INFO - Epoch [21][600/3746] lr: 9.561e-02, eta: 4 days, 3:01:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5011, loss_cls: 4.0570, loss: 4.0570 +2024-07-22 18:42:26,341 - pyskl - INFO - Epoch [21][700/3746] lr: 9.560e-02, eta: 4 days, 2:59:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5084, loss_cls: 4.0853, loss: 4.0853 +2024-07-22 18:43:36,466 - pyskl - INFO - Epoch [21][800/3746] lr: 9.559e-02, eta: 4 days, 2:57:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5022, loss_cls: 4.0813, loss: 4.0813 +2024-07-22 18:44:46,636 - pyskl - INFO - Epoch [21][900/3746] lr: 9.557e-02, eta: 4 days, 2:56:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5089, loss_cls: 4.0609, loss: 4.0609 +2024-07-22 18:45:56,670 - pyskl - INFO - Epoch [21][1000/3746] lr: 9.556e-02, eta: 4 days, 2:54:45, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5061, loss_cls: 4.0655, loss: 4.0655 +2024-07-22 18:47:06,541 - pyskl - INFO - Epoch [21][1100/3746] lr: 9.555e-02, eta: 4 days, 2:53:10, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5148, loss_cls: 3.9963, loss: 3.9963 +2024-07-22 18:48:16,435 - pyskl - INFO - Epoch [21][1200/3746] lr: 9.554e-02, eta: 4 days, 2:51:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5072, loss_cls: 4.0485, loss: 4.0485 +2024-07-22 18:49:26,729 - pyskl - INFO - Epoch [21][1300/3746] lr: 9.553e-02, eta: 4 days, 2:50:03, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5066, loss_cls: 4.0586, loss: 4.0586 +2024-07-22 18:50:36,586 - pyskl - INFO - Epoch [21][1400/3746] lr: 9.552e-02, eta: 4 days, 2:48:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5127, loss_cls: 4.0285, loss: 4.0285 +2024-07-22 18:51:46,845 - pyskl - INFO - Epoch [21][1500/3746] lr: 9.551e-02, eta: 4 days, 2:46:56, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.4995, loss_cls: 4.0883, loss: 4.0883 +2024-07-22 18:52:56,889 - pyskl - INFO - Epoch [21][1600/3746] lr: 9.549e-02, eta: 4 days, 2:45:22, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5023, loss_cls: 4.0999, loss: 4.0999 +2024-07-22 18:54:06,869 - pyskl - INFO - Epoch [21][1700/3746] lr: 9.548e-02, eta: 4 days, 2:43:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4970, loss_cls: 4.1159, loss: 4.1159 +2024-07-22 18:55:16,934 - pyskl - INFO - Epoch [21][1800/3746] lr: 9.547e-02, eta: 4 days, 2:42:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.4945, loss_cls: 4.1063, loss: 4.1063 +2024-07-22 18:56:26,975 - pyskl - INFO - Epoch [21][1900/3746] lr: 9.546e-02, eta: 4 days, 2:40:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5088, loss_cls: 4.0677, loss: 4.0677 +2024-07-22 18:57:36,951 - pyskl - INFO - Epoch [21][2000/3746] lr: 9.545e-02, eta: 4 days, 2:39:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4964, loss_cls: 4.1196, loss: 4.1196 +2024-07-22 18:58:46,915 - pyskl - INFO - Epoch [21][2100/3746] lr: 9.544e-02, eta: 4 days, 2:37:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4998, loss_cls: 4.1192, loss: 4.1192 +2024-07-22 18:59:57,109 - pyskl - INFO - Epoch [21][2200/3746] lr: 9.542e-02, eta: 4 days, 2:36:02, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5103, loss_cls: 4.0683, loss: 4.0683 +2024-07-22 19:01:07,272 - pyskl - INFO - Epoch [21][2300/3746] lr: 9.541e-02, eta: 4 days, 2:34:29, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5125, loss_cls: 4.0556, loss: 4.0556 +2024-07-22 19:02:17,591 - pyskl - INFO - Epoch [21][2400/3746] lr: 9.540e-02, eta: 4 days, 2:32:58, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4889, loss_cls: 4.1404, loss: 4.1404 +2024-07-22 19:03:27,610 - pyskl - INFO - Epoch [21][2500/3746] lr: 9.539e-02, eta: 4 days, 2:31:25, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.5028, loss_cls: 4.1003, loss: 4.1003 +2024-07-22 19:04:37,495 - pyskl - INFO - Epoch [21][2600/3746] lr: 9.538e-02, eta: 4 days, 2:29:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5086, loss_cls: 4.0559, loss: 4.0559 +2024-07-22 19:05:47,413 - pyskl - INFO - Epoch [21][2700/3746] lr: 9.537e-02, eta: 4 days, 2:28:17, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5108, loss_cls: 4.0390, loss: 4.0390 +2024-07-22 19:06:57,765 - pyskl - INFO - Epoch [21][2800/3746] lr: 9.535e-02, eta: 4 days, 2:26:46, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5002, loss_cls: 4.0803, loss: 4.0803 +2024-07-22 19:08:08,263 - pyskl - INFO - Epoch [21][2900/3746] lr: 9.534e-02, eta: 4 days, 2:25:16, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5100, loss_cls: 4.0710, loss: 4.0710 +2024-07-22 19:09:18,155 - pyskl - INFO - Epoch [21][3000/3746] lr: 9.533e-02, eta: 4 days, 2:23:42, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5077, loss_cls: 4.0537, loss: 4.0537 +2024-07-22 19:10:28,343 - pyskl - INFO - Epoch [21][3100/3746] lr: 9.532e-02, eta: 4 days, 2:22:10, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5102, loss_cls: 4.0916, loss: 4.0916 +2024-07-22 19:11:38,726 - pyskl - INFO - Epoch [21][3200/3746] lr: 9.531e-02, eta: 4 days, 2:20:40, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5016, loss_cls: 4.1145, loss: 4.1145 +2024-07-22 19:12:49,638 - pyskl - INFO - Epoch [21][3300/3746] lr: 9.529e-02, eta: 4 days, 2:19:13, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5016, loss_cls: 4.0887, loss: 4.0887 +2024-07-22 19:14:00,198 - pyskl - INFO - Epoch [21][3400/3746] lr: 9.528e-02, eta: 4 days, 2:17:43, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5003, loss_cls: 4.1311, loss: 4.1311 +2024-07-22 19:15:10,763 - pyskl - INFO - Epoch [21][3500/3746] lr: 9.527e-02, eta: 4 days, 2:16:14, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.4986, loss_cls: 4.1080, loss: 4.1080 +2024-07-22 19:16:21,135 - pyskl - INFO - Epoch [21][3600/3746] lr: 9.526e-02, eta: 4 days, 2:14:44, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.4975, loss_cls: 4.0854, loss: 4.0854 +2024-07-22 19:17:31,620 - pyskl - INFO - Epoch [21][3700/3746] lr: 9.525e-02, eta: 4 days, 2:13:14, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5058, loss_cls: 4.0888, loss: 4.0888 +2024-07-22 19:18:06,244 - pyskl - INFO - Saving checkpoint at 21 epochs +2024-07-22 19:19:58,943 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 19:19:59,627 - pyskl - INFO - +top1_acc 0.1823 +top5_acc 0.4104 +2024-07-22 19:19:59,627 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 19:19:59,669 - pyskl - INFO - +mean_acc 0.1820 +2024-07-22 19:19:59,680 - pyskl - INFO - Epoch(val) [21][309] top1_acc: 0.1823, top5_acc: 0.4104, mean_class_accuracy: 0.1820 +2024-07-22 19:23:17,945 - pyskl - INFO - Epoch [22][100/3746] lr: 9.523e-02, eta: 4 days, 2:20:48, time: 1.983, data_time: 1.278, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5181, loss_cls: 4.0102, loss: 4.0102 +2024-07-22 19:24:28,300 - pyskl - INFO - Epoch [22][200/3746] lr: 9.522e-02, eta: 4 days, 2:19:17, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5138, loss_cls: 4.0405, loss: 4.0405 +2024-07-22 19:25:38,219 - pyskl - INFO - Epoch [22][300/3746] lr: 9.521e-02, eta: 4 days, 2:17:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5036, loss_cls: 4.0669, loss: 4.0669 +2024-07-22 19:26:48,608 - pyskl - INFO - Epoch [22][400/3746] lr: 9.519e-02, eta: 4 days, 2:16:12, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5052, loss_cls: 4.0792, loss: 4.0792 +2024-07-22 19:27:58,495 - pyskl - INFO - Epoch [22][500/3746] lr: 9.518e-02, eta: 4 days, 2:14:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5091, loss_cls: 4.0464, loss: 4.0464 +2024-07-22 19:29:08,542 - pyskl - INFO - Epoch [22][600/3746] lr: 9.517e-02, eta: 4 days, 2:13:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5080, loss_cls: 4.0669, loss: 4.0669 +2024-07-22 19:30:18,522 - pyskl - INFO - Epoch [22][700/3746] lr: 9.516e-02, eta: 4 days, 2:11:32, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5134, loss_cls: 4.0496, loss: 4.0496 +2024-07-22 19:31:28,559 - pyskl - INFO - Epoch [22][800/3746] lr: 9.515e-02, eta: 4 days, 2:09:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5023, loss_cls: 4.1190, loss: 4.1190 +2024-07-22 19:32:38,711 - pyskl - INFO - Epoch [22][900/3746] lr: 9.513e-02, eta: 4 days, 2:08:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5014, loss_cls: 4.0721, loss: 4.0721 +2024-07-22 19:33:48,451 - pyskl - INFO - Epoch [22][1000/3746] lr: 9.512e-02, eta: 4 days, 2:06:53, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5119, loss_cls: 4.0393, loss: 4.0393 +2024-07-22 19:34:58,585 - pyskl - INFO - Epoch [22][1100/3746] lr: 9.511e-02, eta: 4 days, 2:05:21, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5127, loss_cls: 4.0308, loss: 4.0308 +2024-07-22 19:36:08,461 - pyskl - INFO - Epoch [22][1200/3746] lr: 9.510e-02, eta: 4 days, 2:03:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4902, loss_cls: 4.1091, loss: 4.1091 +2024-07-22 19:37:18,347 - pyskl - INFO - Epoch [22][1300/3746] lr: 9.509e-02, eta: 4 days, 2:02:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5106, loss_cls: 4.0682, loss: 4.0682 +2024-07-22 19:38:28,315 - pyskl - INFO - Epoch [22][1400/3746] lr: 9.507e-02, eta: 4 days, 2:00:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5023, loss_cls: 4.0699, loss: 4.0699 +2024-07-22 19:39:38,215 - pyskl - INFO - Epoch [22][1500/3746] lr: 9.506e-02, eta: 4 days, 1:59:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5098, loss_cls: 4.0689, loss: 4.0689 +2024-07-22 19:40:48,198 - pyskl - INFO - Epoch [22][1600/3746] lr: 9.505e-02, eta: 4 days, 1:57:35, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5009, loss_cls: 4.1047, loss: 4.1047 +2024-07-22 19:41:58,286 - pyskl - INFO - Epoch [22][1700/3746] lr: 9.504e-02, eta: 4 days, 1:56:03, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5159, loss_cls: 4.0475, loss: 4.0475 +2024-07-22 19:43:08,511 - pyskl - INFO - Epoch [22][1800/3746] lr: 9.502e-02, eta: 4 days, 1:54:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5084, loss_cls: 4.0731, loss: 4.0731 +2024-07-22 19:44:18,842 - pyskl - INFO - Epoch [22][1900/3746] lr: 9.501e-02, eta: 4 days, 1:53:01, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5088, loss_cls: 4.1012, loss: 4.1012 +2024-07-22 19:45:28,742 - pyskl - INFO - Epoch [22][2000/3746] lr: 9.500e-02, eta: 4 days, 1:51:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5031, loss_cls: 4.0780, loss: 4.0780 +2024-07-22 19:46:38,753 - pyskl - INFO - Epoch [22][2100/3746] lr: 9.499e-02, eta: 4 days, 1:49:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.4995, loss_cls: 4.1009, loss: 4.1009 +2024-07-22 19:47:48,855 - pyskl - INFO - Epoch [22][2200/3746] lr: 9.498e-02, eta: 4 days, 1:48:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5112, loss_cls: 4.0352, loss: 4.0352 +2024-07-22 19:48:58,929 - pyskl - INFO - Epoch [22][2300/3746] lr: 9.496e-02, eta: 4 days, 1:46:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5014, loss_cls: 4.1006, loss: 4.1006 +2024-07-22 19:50:09,097 - pyskl - INFO - Epoch [22][2400/3746] lr: 9.495e-02, eta: 4 days, 1:45:21, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5012, loss_cls: 4.0796, loss: 4.0796 +2024-07-22 19:51:19,067 - pyskl - INFO - Epoch [22][2500/3746] lr: 9.494e-02, eta: 4 days, 1:43:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5031, loss_cls: 4.0910, loss: 4.0910 +2024-07-22 19:52:29,170 - pyskl - INFO - Epoch [22][2600/3746] lr: 9.493e-02, eta: 4 days, 1:42:18, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5003, loss_cls: 4.0954, loss: 4.0954 +2024-07-22 19:53:39,114 - pyskl - INFO - Epoch [22][2700/3746] lr: 9.491e-02, eta: 4 days, 1:40:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5050, loss_cls: 4.0706, loss: 4.0706 +2024-07-22 19:54:49,305 - pyskl - INFO - Epoch [22][2800/3746] lr: 9.490e-02, eta: 4 days, 1:39:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5058, loss_cls: 4.0672, loss: 4.0672 +2024-07-22 19:55:59,763 - pyskl - INFO - Epoch [22][2900/3746] lr: 9.489e-02, eta: 4 days, 1:37:45, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5206, loss_cls: 4.0277, loss: 4.0277 +2024-07-22 19:57:10,355 - pyskl - INFO - Epoch [22][3000/3746] lr: 9.488e-02, eta: 4 days, 1:36:17, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5014, loss_cls: 4.0698, loss: 4.0698 +2024-07-22 19:58:20,640 - pyskl - INFO - Epoch [22][3100/3746] lr: 9.487e-02, eta: 4 days, 1:34:47, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5075, loss_cls: 4.0572, loss: 4.0572 +2024-07-22 19:59:30,940 - pyskl - INFO - Epoch [22][3200/3746] lr: 9.485e-02, eta: 4 days, 1:33:17, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5031, loss_cls: 4.0715, loss: 4.0715 +2024-07-22 20:00:41,610 - pyskl - INFO - Epoch [22][3300/3746] lr: 9.484e-02, eta: 4 days, 1:31:49, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5102, loss_cls: 4.0325, loss: 4.0325 +2024-07-22 20:01:52,668 - pyskl - INFO - Epoch [22][3400/3746] lr: 9.483e-02, eta: 4 days, 1:30:24, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.5034, loss_cls: 4.0951, loss: 4.0951 +2024-07-22 20:03:03,671 - pyskl - INFO - Epoch [22][3500/3746] lr: 9.482e-02, eta: 4 days, 1:28:58, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4891, loss_cls: 4.1185, loss: 4.1185 +2024-07-22 20:04:14,183 - pyskl - INFO - Epoch [22][3600/3746] lr: 9.480e-02, eta: 4 days, 1:27:30, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4958, loss_cls: 4.1465, loss: 4.1465 +2024-07-22 20:05:24,557 - pyskl - INFO - Epoch [22][3700/3746] lr: 9.479e-02, eta: 4 days, 1:26:00, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5041, loss_cls: 4.1010, loss: 4.1010 +2024-07-22 20:05:59,217 - pyskl - INFO - Saving checkpoint at 22 epochs +2024-07-22 20:07:50,808 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 20:07:51,474 - pyskl - INFO - +top1_acc 0.1810 +top5_acc 0.4031 +2024-07-22 20:07:51,474 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 20:07:51,515 - pyskl - INFO - +mean_acc 0.1806 +2024-07-22 20:07:51,527 - pyskl - INFO - Epoch(val) [22][309] top1_acc: 0.1810, top5_acc: 0.4031, mean_class_accuracy: 0.1806 +2024-07-22 20:11:08,946 - pyskl - INFO - Epoch [23][100/3746] lr: 9.477e-02, eta: 4 days, 1:33:00, time: 1.974, data_time: 1.271, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5050, loss_cls: 4.0313, loss: 4.0313 +2024-07-22 20:12:18,997 - pyskl - INFO - Epoch [23][200/3746] lr: 9.476e-02, eta: 4 days, 1:31:28, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5230, loss_cls: 4.0200, loss: 4.0200 +2024-07-22 20:13:29,021 - pyskl - INFO - Epoch [23][300/3746] lr: 9.475e-02, eta: 4 days, 1:29:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5100, loss_cls: 4.0549, loss: 4.0549 +2024-07-22 20:14:39,329 - pyskl - INFO - Epoch [23][400/3746] lr: 9.474e-02, eta: 4 days, 1:28:26, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5153, loss_cls: 4.0313, loss: 4.0313 +2024-07-22 20:15:49,419 - pyskl - INFO - Epoch [23][500/3746] lr: 9.472e-02, eta: 4 days, 1:26:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5059, loss_cls: 4.0500, loss: 4.0500 +2024-07-22 20:16:59,556 - pyskl - INFO - Epoch [23][600/3746] lr: 9.471e-02, eta: 4 days, 1:25:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5170, loss_cls: 4.0253, loss: 4.0253 +2024-07-22 20:18:09,808 - pyskl - INFO - Epoch [23][700/3746] lr: 9.470e-02, eta: 4 days, 1:23:53, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5138, loss_cls: 4.0195, loss: 4.0195 +2024-07-22 20:19:19,687 - pyskl - INFO - Epoch [23][800/3746] lr: 9.469e-02, eta: 4 days, 1:22:20, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5136, loss_cls: 4.0303, loss: 4.0303 +2024-07-22 20:20:30,048 - pyskl - INFO - Epoch [23][900/3746] lr: 9.467e-02, eta: 4 days, 1:20:51, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4986, loss_cls: 4.1065, loss: 4.1065 +2024-07-22 20:21:40,016 - pyskl - INFO - Epoch [23][1000/3746] lr: 9.466e-02, eta: 4 days, 1:19:19, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5059, loss_cls: 4.0876, loss: 4.0876 +2024-07-22 20:22:49,831 - pyskl - INFO - Epoch [23][1100/3746] lr: 9.465e-02, eta: 4 days, 1:17:46, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5078, loss_cls: 4.0372, loss: 4.0372 +2024-07-22 20:23:59,985 - pyskl - INFO - Epoch [23][1200/3746] lr: 9.464e-02, eta: 4 days, 1:16:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5075, loss_cls: 4.0675, loss: 4.0675 +2024-07-22 20:25:10,295 - pyskl - INFO - Epoch [23][1300/3746] lr: 9.462e-02, eta: 4 days, 1:14:45, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5022, loss_cls: 4.0828, loss: 4.0828 +2024-07-22 20:26:20,544 - pyskl - INFO - Epoch [23][1400/3746] lr: 9.461e-02, eta: 4 days, 1:13:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5073, loss_cls: 4.0668, loss: 4.0668 +2024-07-22 20:27:30,502 - pyskl - INFO - Epoch [23][1500/3746] lr: 9.460e-02, eta: 4 days, 1:11:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5070, loss_cls: 4.0420, loss: 4.0420 +2024-07-22 20:28:40,411 - pyskl - INFO - Epoch [23][1600/3746] lr: 9.459e-02, eta: 4 days, 1:10:12, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.5019, loss_cls: 4.1147, loss: 4.1147 +2024-07-22 20:29:50,545 - pyskl - INFO - Epoch [23][1700/3746] lr: 9.457e-02, eta: 4 days, 1:08:41, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5114, loss_cls: 4.0659, loss: 4.0659 +2024-07-22 20:31:00,828 - pyskl - INFO - Epoch [23][1800/3746] lr: 9.456e-02, eta: 4 days, 1:07:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5003, loss_cls: 4.1051, loss: 4.1051 +2024-07-22 20:32:10,836 - pyskl - INFO - Epoch [23][1900/3746] lr: 9.455e-02, eta: 4 days, 1:05:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5086, loss_cls: 4.0340, loss: 4.0340 +2024-07-22 20:33:20,820 - pyskl - INFO - Epoch [23][2000/3746] lr: 9.453e-02, eta: 4 days, 1:04:09, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4986, loss_cls: 4.1048, loss: 4.1048 +2024-07-22 20:34:30,806 - pyskl - INFO - Epoch [23][2100/3746] lr: 9.452e-02, eta: 4 days, 1:02:37, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4995, loss_cls: 4.1449, loss: 4.1449 +2024-07-22 20:35:40,622 - pyskl - INFO - Epoch [23][2200/3746] lr: 9.451e-02, eta: 4 days, 1:01:05, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5094, loss_cls: 4.0681, loss: 4.0681 +2024-07-22 20:36:50,529 - pyskl - INFO - Epoch [23][2300/3746] lr: 9.450e-02, eta: 4 days, 0:59:34, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4944, loss_cls: 4.1094, loss: 4.1094 +2024-07-22 20:38:00,854 - pyskl - INFO - Epoch [23][2400/3746] lr: 9.448e-02, eta: 4 days, 0:58:04, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5083, loss_cls: 4.0643, loss: 4.0643 +2024-07-22 20:39:10,717 - pyskl - INFO - Epoch [23][2500/3746] lr: 9.447e-02, eta: 4 days, 0:56:33, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.4956, loss_cls: 4.1045, loss: 4.1045 +2024-07-22 20:40:20,580 - pyskl - INFO - Epoch [23][2600/3746] lr: 9.446e-02, eta: 4 days, 0:55:01, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5094, loss_cls: 4.0413, loss: 4.0413 +2024-07-22 20:41:30,817 - pyskl - INFO - Epoch [23][2700/3746] lr: 9.445e-02, eta: 4 days, 0:53:31, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4992, loss_cls: 4.1262, loss: 4.1262 +2024-07-22 20:42:40,779 - pyskl - INFO - Epoch [23][2800/3746] lr: 9.443e-02, eta: 4 days, 0:52:00, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5012, loss_cls: 4.0852, loss: 4.0852 +2024-07-22 20:43:51,425 - pyskl - INFO - Epoch [23][2900/3746] lr: 9.442e-02, eta: 4 days, 0:50:33, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5053, loss_cls: 4.0745, loss: 4.0745 +2024-07-22 20:45:01,643 - pyskl - INFO - Epoch [23][3000/3746] lr: 9.441e-02, eta: 4 days, 0:49:04, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5142, loss_cls: 4.0412, loss: 4.0412 +2024-07-22 20:46:12,207 - pyskl - INFO - Epoch [23][3100/3746] lr: 9.439e-02, eta: 4 days, 0:47:36, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5172, loss_cls: 4.0013, loss: 4.0013 +2024-07-22 20:47:22,661 - pyskl - INFO - Epoch [23][3200/3746] lr: 9.438e-02, eta: 4 days, 0:46:08, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5033, loss_cls: 4.0643, loss: 4.0643 +2024-07-22 20:48:33,157 - pyskl - INFO - Epoch [23][3300/3746] lr: 9.437e-02, eta: 4 days, 0:44:40, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5067, loss_cls: 4.0520, loss: 4.0520 +2024-07-22 20:49:43,886 - pyskl - INFO - Epoch [23][3400/3746] lr: 9.436e-02, eta: 4 days, 0:43:14, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5022, loss_cls: 4.0884, loss: 4.0884 +2024-07-22 20:50:55,150 - pyskl - INFO - Epoch [23][3500/3746] lr: 9.434e-02, eta: 4 days, 0:41:50, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4983, loss_cls: 4.1086, loss: 4.1086 +2024-07-22 20:52:05,714 - pyskl - INFO - Epoch [23][3600/3746] lr: 9.433e-02, eta: 4 days, 0:40:23, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5042, loss_cls: 4.0860, loss: 4.0860 +2024-07-22 20:53:16,034 - pyskl - INFO - Epoch [23][3700/3746] lr: 9.432e-02, eta: 4 days, 0:38:54, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5172, loss_cls: 4.0273, loss: 4.0273 +2024-07-22 20:53:50,478 - pyskl - INFO - Saving checkpoint at 23 epochs +2024-07-22 20:55:42,450 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 20:55:43,119 - pyskl - INFO - +top1_acc 0.1976 +top5_acc 0.4229 +2024-07-22 20:55:43,119 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 20:55:43,157 - pyskl - INFO - +mean_acc 0.1972 +2024-07-22 20:55:43,167 - pyskl - INFO - Epoch(val) [23][309] top1_acc: 0.1976, top5_acc: 0.4229, mean_class_accuracy: 0.1972 +2024-07-22 20:59:01,522 - pyskl - INFO - Epoch [24][100/3746] lr: 9.430e-02, eta: 4 days, 0:45:32, time: 1.983, data_time: 1.278, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5206, loss_cls: 4.0059, loss: 4.0059 +2024-07-22 21:00:12,115 - pyskl - INFO - Epoch [24][200/3746] lr: 9.428e-02, eta: 4 days, 0:44:05, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5233, loss_cls: 4.0064, loss: 4.0064 +2024-07-22 21:01:22,182 - pyskl - INFO - Epoch [24][300/3746] lr: 9.427e-02, eta: 4 days, 0:42:34, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5119, loss_cls: 4.0547, loss: 4.0547 +2024-07-22 21:02:32,464 - pyskl - INFO - Epoch [24][400/3746] lr: 9.426e-02, eta: 4 days, 0:41:04, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5162, loss_cls: 4.0480, loss: 4.0480 +2024-07-22 21:03:42,689 - pyskl - INFO - Epoch [24][500/3746] lr: 9.425e-02, eta: 4 days, 0:39:35, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5094, loss_cls: 4.0411, loss: 4.0411 +2024-07-22 21:04:52,440 - pyskl - INFO - Epoch [24][600/3746] lr: 9.423e-02, eta: 4 days, 0:38:03, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5127, loss_cls: 4.0242, loss: 4.0242 +2024-07-22 21:06:02,543 - pyskl - INFO - Epoch [24][700/3746] lr: 9.422e-02, eta: 4 days, 0:36:32, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5075, loss_cls: 4.0610, loss: 4.0610 +2024-07-22 21:07:12,625 - pyskl - INFO - Epoch [24][800/3746] lr: 9.421e-02, eta: 4 days, 0:35:02, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5108, loss_cls: 4.0271, loss: 4.0271 +2024-07-22 21:08:22,503 - pyskl - INFO - Epoch [24][900/3746] lr: 9.419e-02, eta: 4 days, 0:33:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5112, loss_cls: 4.0313, loss: 4.0313 +2024-07-22 21:09:32,428 - pyskl - INFO - Epoch [24][1000/3746] lr: 9.418e-02, eta: 4 days, 0:32:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5055, loss_cls: 4.0650, loss: 4.0650 +2024-07-22 21:10:42,499 - pyskl - INFO - Epoch [24][1100/3746] lr: 9.417e-02, eta: 4 days, 0:30:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.4944, loss_cls: 4.1080, loss: 4.1080 +2024-07-22 21:11:52,636 - pyskl - INFO - Epoch [24][1200/3746] lr: 9.415e-02, eta: 4 days, 0:28:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5066, loss_cls: 4.0425, loss: 4.0425 +2024-07-22 21:13:02,465 - pyskl - INFO - Epoch [24][1300/3746] lr: 9.414e-02, eta: 4 days, 0:27:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5139, loss_cls: 4.0745, loss: 4.0745 +2024-07-22 21:14:12,449 - pyskl - INFO - Epoch [24][1400/3746] lr: 9.413e-02, eta: 4 days, 0:25:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5069, loss_cls: 4.0433, loss: 4.0433 +2024-07-22 21:15:22,528 - pyskl - INFO - Epoch [24][1500/3746] lr: 9.411e-02, eta: 4 days, 0:24:27, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4989, loss_cls: 4.1196, loss: 4.1196 +2024-07-22 21:16:32,859 - pyskl - INFO - Epoch [24][1600/3746] lr: 9.410e-02, eta: 4 days, 0:22:59, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5166, loss_cls: 4.0223, loss: 4.0223 +2024-07-22 21:17:42,719 - pyskl - INFO - Epoch [24][1700/3746] lr: 9.409e-02, eta: 4 days, 0:21:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5170, loss_cls: 4.0403, loss: 4.0403 +2024-07-22 21:18:52,926 - pyskl - INFO - Epoch [24][1800/3746] lr: 9.407e-02, eta: 4 days, 0:19:59, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5103, loss_cls: 4.0348, loss: 4.0348 +2024-07-22 21:20:03,103 - pyskl - INFO - Epoch [24][1900/3746] lr: 9.406e-02, eta: 4 days, 0:18:29, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5077, loss_cls: 4.0601, loss: 4.0601 +2024-07-22 21:21:13,351 - pyskl - INFO - Epoch [24][2000/3746] lr: 9.405e-02, eta: 4 days, 0:17:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5077, loss_cls: 4.0755, loss: 4.0755 +2024-07-22 21:22:23,454 - pyskl - INFO - Epoch [24][2100/3746] lr: 9.404e-02, eta: 4 days, 0:15:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5073, loss_cls: 4.0638, loss: 4.0638 +2024-07-22 21:23:33,595 - pyskl - INFO - Epoch [24][2200/3746] lr: 9.402e-02, eta: 4 days, 0:14:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5138, loss_cls: 4.0658, loss: 4.0658 +2024-07-22 21:24:43,573 - pyskl - INFO - Epoch [24][2300/3746] lr: 9.401e-02, eta: 4 days, 0:12:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4903, loss_cls: 4.1429, loss: 4.1429 +2024-07-22 21:25:53,634 - pyskl - INFO - Epoch [24][2400/3746] lr: 9.400e-02, eta: 4 days, 0:11:02, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5053, loss_cls: 4.1012, loss: 4.1012 +2024-07-22 21:27:03,698 - pyskl - INFO - Epoch [24][2500/3746] lr: 9.398e-02, eta: 4 days, 0:09:32, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5078, loss_cls: 4.0752, loss: 4.0752 +2024-07-22 21:28:13,842 - pyskl - INFO - Epoch [24][2600/3746] lr: 9.397e-02, eta: 4 days, 0:08:03, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5014, loss_cls: 4.1117, loss: 4.1117 +2024-07-22 21:29:24,092 - pyskl - INFO - Epoch [24][2700/3746] lr: 9.396e-02, eta: 4 days, 0:06:34, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5022, loss_cls: 4.0707, loss: 4.0707 +2024-07-22 21:30:34,248 - pyskl - INFO - Epoch [24][2800/3746] lr: 9.394e-02, eta: 4 days, 0:05:05, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5003, loss_cls: 4.0922, loss: 4.0922 +2024-07-22 21:31:44,561 - pyskl - INFO - Epoch [24][2900/3746] lr: 9.393e-02, eta: 4 days, 0:03:37, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5219, loss_cls: 4.0185, loss: 4.0185 +2024-07-22 21:32:54,738 - pyskl - INFO - Epoch [24][3000/3746] lr: 9.392e-02, eta: 4 days, 0:02:08, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5038, loss_cls: 4.0605, loss: 4.0605 +2024-07-22 21:34:05,214 - pyskl - INFO - Epoch [24][3100/3746] lr: 9.390e-02, eta: 4 days, 0:00:41, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5202, loss_cls: 4.0194, loss: 4.0194 +2024-07-22 21:35:15,690 - pyskl - INFO - Epoch [24][3200/3746] lr: 9.389e-02, eta: 3 days, 23:59:14, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5083, loss_cls: 4.0433, loss: 4.0433 +2024-07-22 21:36:26,425 - pyskl - INFO - Epoch [24][3300/3746] lr: 9.388e-02, eta: 3 days, 23:57:48, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5006, loss_cls: 4.0864, loss: 4.0864 +2024-07-22 21:37:37,245 - pyskl - INFO - Epoch [24][3400/3746] lr: 9.386e-02, eta: 3 days, 23:56:23, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4933, loss_cls: 4.1210, loss: 4.1210 +2024-07-22 21:38:48,219 - pyskl - INFO - Epoch [24][3500/3746] lr: 9.385e-02, eta: 3 days, 23:54:58, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5152, loss_cls: 4.0453, loss: 4.0453 +2024-07-22 21:39:58,660 - pyskl - INFO - Epoch [24][3600/3746] lr: 9.384e-02, eta: 3 days, 23:53:31, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4936, loss_cls: 4.1320, loss: 4.1320 +2024-07-22 21:41:09,282 - pyskl - INFO - Epoch [24][3700/3746] lr: 9.382e-02, eta: 3 days, 23:52:05, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5098, loss_cls: 4.0654, loss: 4.0654 +2024-07-22 21:41:43,716 - pyskl - INFO - Saving checkpoint at 24 epochs +2024-07-22 21:43:35,758 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 21:43:36,423 - pyskl - INFO - +top1_acc 0.1992 +top5_acc 0.4218 +2024-07-22 21:43:36,423 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 21:43:36,464 - pyskl - INFO - +mean_acc 0.1989 +2024-07-22 21:43:36,474 - pyskl - INFO - Epoch(val) [24][309] top1_acc: 0.1992, top5_acc: 0.4218, mean_class_accuracy: 0.1989 +2024-07-22 21:46:54,578 - pyskl - INFO - Epoch [25][100/3746] lr: 9.380e-02, eta: 3 days, 23:58:17, time: 1.981, data_time: 1.276, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5111, loss_cls: 4.0527, loss: 4.0527 +2024-07-22 21:48:04,721 - pyskl - INFO - Epoch [25][200/3746] lr: 9.379e-02, eta: 3 days, 23:56:48, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5123, loss_cls: 4.0319, loss: 4.0319 +2024-07-22 21:49:14,708 - pyskl - INFO - Epoch [25][300/3746] lr: 9.378e-02, eta: 3 days, 23:55:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5048, loss_cls: 4.0478, loss: 4.0478 +2024-07-22 21:50:24,897 - pyskl - INFO - Epoch [25][400/3746] lr: 9.376e-02, eta: 3 days, 23:53:49, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5147, loss_cls: 4.0190, loss: 4.0190 +2024-07-22 21:51:34,947 - pyskl - INFO - Epoch [25][500/3746] lr: 9.375e-02, eta: 3 days, 23:52:19, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5058, loss_cls: 4.0331, loss: 4.0331 +2024-07-22 21:52:45,354 - pyskl - INFO - Epoch [25][600/3746] lr: 9.373e-02, eta: 3 days, 23:50:51, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5127, loss_cls: 4.0507, loss: 4.0507 +2024-07-22 21:53:55,387 - pyskl - INFO - Epoch [25][700/3746] lr: 9.372e-02, eta: 3 days, 23:49:22, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5086, loss_cls: 4.0624, loss: 4.0624 +2024-07-22 21:55:05,496 - pyskl - INFO - Epoch [25][800/3746] lr: 9.371e-02, eta: 3 days, 23:47:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5072, loss_cls: 4.0632, loss: 4.0632 +2024-07-22 21:56:15,736 - pyskl - INFO - Epoch [25][900/3746] lr: 9.369e-02, eta: 3 days, 23:46:24, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5116, loss_cls: 4.0354, loss: 4.0354 +2024-07-22 21:57:25,494 - pyskl - INFO - Epoch [25][1000/3746] lr: 9.368e-02, eta: 3 days, 23:44:53, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5053, loss_cls: 4.0712, loss: 4.0712 +2024-07-22 21:58:35,426 - pyskl - INFO - Epoch [25][1100/3746] lr: 9.367e-02, eta: 3 days, 23:43:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5045, loss_cls: 4.0440, loss: 4.0440 +2024-07-22 21:59:45,483 - pyskl - INFO - Epoch [25][1200/3746] lr: 9.365e-02, eta: 3 days, 23:41:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5091, loss_cls: 4.0468, loss: 4.0468 +2024-07-22 22:00:55,263 - pyskl - INFO - Epoch [25][1300/3746] lr: 9.364e-02, eta: 3 days, 23:40:23, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5009, loss_cls: 4.0770, loss: 4.0770 +2024-07-22 22:02:05,285 - pyskl - INFO - Epoch [25][1400/3746] lr: 9.363e-02, eta: 3 days, 23:38:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5123, loss_cls: 4.0558, loss: 4.0558 +2024-07-22 22:03:15,271 - pyskl - INFO - Epoch [25][1500/3746] lr: 9.361e-02, eta: 3 days, 23:37:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5111, loss_cls: 4.0647, loss: 4.0647 +2024-07-22 22:04:25,244 - pyskl - INFO - Epoch [25][1600/3746] lr: 9.360e-02, eta: 3 days, 23:35:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5209, loss_cls: 4.0184, loss: 4.0184 +2024-07-22 22:05:35,432 - pyskl - INFO - Epoch [25][1700/3746] lr: 9.358e-02, eta: 3 days, 23:34:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5134, loss_cls: 4.0609, loss: 4.0609 +2024-07-22 22:06:45,312 - pyskl - INFO - Epoch [25][1800/3746] lr: 9.357e-02, eta: 3 days, 23:32:56, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5009, loss_cls: 4.0929, loss: 4.0929 +2024-07-22 22:07:55,514 - pyskl - INFO - Epoch [25][1900/3746] lr: 9.356e-02, eta: 3 days, 23:31:28, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4931, loss_cls: 4.1140, loss: 4.1140 +2024-07-22 22:09:05,570 - pyskl - INFO - Epoch [25][2000/3746] lr: 9.354e-02, eta: 3 days, 23:29:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5075, loss_cls: 4.0762, loss: 4.0762 +2024-07-22 22:10:15,711 - pyskl - INFO - Epoch [25][2100/3746] lr: 9.353e-02, eta: 3 days, 23:28:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5172, loss_cls: 4.0432, loss: 4.0432 +2024-07-22 22:11:25,703 - pyskl - INFO - Epoch [25][2200/3746] lr: 9.352e-02, eta: 3 days, 23:27:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5112, loss_cls: 4.0397, loss: 4.0397 +2024-07-22 22:12:35,764 - pyskl - INFO - Epoch [25][2300/3746] lr: 9.350e-02, eta: 3 days, 23:25:32, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5088, loss_cls: 4.0299, loss: 4.0299 +2024-07-22 22:13:45,695 - pyskl - INFO - Epoch [25][2400/3746] lr: 9.349e-02, eta: 3 days, 23:24:03, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5102, loss_cls: 4.0916, loss: 4.0916 +2024-07-22 22:14:55,671 - pyskl - INFO - Epoch [25][2500/3746] lr: 9.347e-02, eta: 3 days, 23:22:33, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5084, loss_cls: 4.0568, loss: 4.0568 +2024-07-22 22:16:05,750 - pyskl - INFO - Epoch [25][2600/3746] lr: 9.346e-02, eta: 3 days, 23:21:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5059, loss_cls: 4.1067, loss: 4.1067 +2024-07-22 22:17:15,711 - pyskl - INFO - Epoch [25][2700/3746] lr: 9.345e-02, eta: 3 days, 23:19:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5052, loss_cls: 4.1060, loss: 4.1060 +2024-07-22 22:18:26,018 - pyskl - INFO - Epoch [25][2800/3746] lr: 9.343e-02, eta: 3 days, 23:18:08, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5142, loss_cls: 4.0389, loss: 4.0389 +2024-07-22 22:19:36,512 - pyskl - INFO - Epoch [25][2900/3746] lr: 9.342e-02, eta: 3 days, 23:16:42, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5131, loss_cls: 4.0639, loss: 4.0639 +2024-07-22 22:20:46,864 - pyskl - INFO - Epoch [25][3000/3746] lr: 9.341e-02, eta: 3 days, 23:15:15, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5153, loss_cls: 4.0494, loss: 4.0494 +2024-07-22 22:21:57,544 - pyskl - INFO - Epoch [25][3100/3746] lr: 9.339e-02, eta: 3 days, 23:13:49, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.4989, loss_cls: 4.0597, loss: 4.0597 +2024-07-22 22:23:07,994 - pyskl - INFO - Epoch [25][3200/3746] lr: 9.338e-02, eta: 3 days, 23:12:23, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4972, loss_cls: 4.1214, loss: 4.1214 +2024-07-22 22:24:19,114 - pyskl - INFO - Epoch [25][3300/3746] lr: 9.336e-02, eta: 3 days, 23:11:00, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5039, loss_cls: 4.0592, loss: 4.0592 +2024-07-22 22:25:29,786 - pyskl - INFO - Epoch [25][3400/3746] lr: 9.335e-02, eta: 3 days, 23:09:34, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5206, loss_cls: 4.0167, loss: 4.0167 +2024-07-22 22:26:40,281 - pyskl - INFO - Epoch [25][3500/3746] lr: 9.334e-02, eta: 3 days, 23:08:08, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5048, loss_cls: 4.0659, loss: 4.0659 +2024-07-22 22:27:50,806 - pyskl - INFO - Epoch [25][3600/3746] lr: 9.332e-02, eta: 3 days, 23:06:42, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4972, loss_cls: 4.1055, loss: 4.1055 +2024-07-22 22:29:01,257 - pyskl - INFO - Epoch [25][3700/3746] lr: 9.331e-02, eta: 3 days, 23:05:16, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5127, loss_cls: 4.0311, loss: 4.0311 +2024-07-22 22:29:36,042 - pyskl - INFO - Saving checkpoint at 25 epochs +2024-07-22 22:31:27,825 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 22:31:28,490 - pyskl - INFO - +top1_acc 0.1965 +top5_acc 0.4223 +2024-07-22 22:31:28,491 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 22:31:28,530 - pyskl - INFO - +mean_acc 0.1963 +2024-07-22 22:31:28,540 - pyskl - INFO - Epoch(val) [25][309] top1_acc: 0.1965, top5_acc: 0.4223, mean_class_accuracy: 0.1963 +2024-07-22 22:34:50,985 - pyskl - INFO - Epoch [26][100/3746] lr: 9.329e-02, eta: 3 days, 23:11:27, time: 2.024, data_time: 1.318, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5233, loss_cls: 3.9738, loss: 3.9738 +2024-07-22 22:36:01,300 - pyskl - INFO - Epoch [26][200/3746] lr: 9.327e-02, eta: 3 days, 23:09:59, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5142, loss_cls: 4.0222, loss: 4.0222 +2024-07-22 22:37:11,386 - pyskl - INFO - Epoch [26][300/3746] lr: 9.326e-02, eta: 3 days, 23:08:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5089, loss_cls: 4.0277, loss: 4.0277 +2024-07-22 22:38:21,125 - pyskl - INFO - Epoch [26][400/3746] lr: 9.325e-02, eta: 3 days, 23:07:00, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5122, loss_cls: 4.0394, loss: 4.0394 +2024-07-22 22:39:31,416 - pyskl - INFO - Epoch [26][500/3746] lr: 9.323e-02, eta: 3 days, 23:05:33, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5075, loss_cls: 4.0659, loss: 4.0659 +2024-07-22 22:40:41,513 - pyskl - INFO - Epoch [26][600/3746] lr: 9.322e-02, eta: 3 days, 23:04:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5202, loss_cls: 4.0243, loss: 4.0243 +2024-07-22 22:41:51,830 - pyskl - INFO - Epoch [26][700/3746] lr: 9.320e-02, eta: 3 days, 23:02:37, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5145, loss_cls: 4.0400, loss: 4.0400 +2024-07-22 22:43:01,742 - pyskl - INFO - Epoch [26][800/3746] lr: 9.319e-02, eta: 3 days, 23:01:07, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5112, loss_cls: 4.0391, loss: 4.0391 +2024-07-22 22:44:11,542 - pyskl - INFO - Epoch [26][900/3746] lr: 9.318e-02, eta: 3 days, 22:59:37, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5178, loss_cls: 4.0131, loss: 4.0131 +2024-07-22 22:45:21,236 - pyskl - INFO - Epoch [26][1000/3746] lr: 9.316e-02, eta: 3 days, 22:58:07, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5102, loss_cls: 4.0515, loss: 4.0515 +2024-07-22 22:46:31,082 - pyskl - INFO - Epoch [26][1100/3746] lr: 9.315e-02, eta: 3 days, 22:56:37, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5105, loss_cls: 4.0461, loss: 4.0461 +2024-07-22 22:47:41,142 - pyskl - INFO - Epoch [26][1200/3746] lr: 9.313e-02, eta: 3 days, 22:55:09, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5203, loss_cls: 4.0402, loss: 4.0402 +2024-07-22 22:48:51,106 - pyskl - INFO - Epoch [26][1300/3746] lr: 9.312e-02, eta: 3 days, 22:53:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5034, loss_cls: 4.0654, loss: 4.0654 +2024-07-22 22:50:01,112 - pyskl - INFO - Epoch [26][1400/3746] lr: 9.310e-02, eta: 3 days, 22:52:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5045, loss_cls: 4.0905, loss: 4.0905 +2024-07-22 22:51:11,479 - pyskl - INFO - Epoch [26][1500/3746] lr: 9.309e-02, eta: 3 days, 22:50:45, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5097, loss_cls: 4.0462, loss: 4.0462 +2024-07-22 22:52:21,437 - pyskl - INFO - Epoch [26][1600/3746] lr: 9.308e-02, eta: 3 days, 22:49:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5047, loss_cls: 4.0707, loss: 4.0707 +2024-07-22 22:53:31,686 - pyskl - INFO - Epoch [26][1700/3746] lr: 9.306e-02, eta: 3 days, 22:47:48, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5053, loss_cls: 4.0442, loss: 4.0442 +2024-07-22 22:54:41,760 - pyskl - INFO - Epoch [26][1800/3746] lr: 9.305e-02, eta: 3 days, 22:46:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5092, loss_cls: 4.0743, loss: 4.0743 +2024-07-22 22:55:51,685 - pyskl - INFO - Epoch [26][1900/3746] lr: 9.303e-02, eta: 3 days, 22:44:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5108, loss_cls: 4.0694, loss: 4.0694 +2024-07-22 22:57:01,740 - pyskl - INFO - Epoch [26][2000/3746] lr: 9.302e-02, eta: 3 days, 22:43:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5086, loss_cls: 4.0702, loss: 4.0702 +2024-07-22 22:58:11,748 - pyskl - INFO - Epoch [26][2100/3746] lr: 9.300e-02, eta: 3 days, 22:41:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5086, loss_cls: 4.0716, loss: 4.0716 +2024-07-22 22:59:21,787 - pyskl - INFO - Epoch [26][2200/3746] lr: 9.299e-02, eta: 3 days, 22:40:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5103, loss_cls: 4.0473, loss: 4.0473 +2024-07-22 23:00:31,662 - pyskl - INFO - Epoch [26][2300/3746] lr: 9.298e-02, eta: 3 days, 22:38:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5156, loss_cls: 4.0253, loss: 4.0253 +2024-07-22 23:01:41,735 - pyskl - INFO - Epoch [26][2400/3746] lr: 9.296e-02, eta: 3 days, 22:37:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5089, loss_cls: 4.0461, loss: 4.0461 +2024-07-22 23:02:51,915 - pyskl - INFO - Epoch [26][2500/3746] lr: 9.295e-02, eta: 3 days, 22:36:02, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5120, loss_cls: 4.0139, loss: 4.0139 +2024-07-22 23:04:02,141 - pyskl - INFO - Epoch [26][2600/3746] lr: 9.293e-02, eta: 3 days, 22:34:35, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5084, loss_cls: 4.0316, loss: 4.0316 +2024-07-22 23:05:12,404 - pyskl - INFO - Epoch [26][2700/3746] lr: 9.292e-02, eta: 3 days, 22:33:08, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5030, loss_cls: 4.1024, loss: 4.1024 +2024-07-22 23:06:23,081 - pyskl - INFO - Epoch [26][2800/3746] lr: 9.290e-02, eta: 3 days, 22:31:43, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5120, loss_cls: 4.0524, loss: 4.0524 +2024-07-22 23:07:33,635 - pyskl - INFO - Epoch [26][2900/3746] lr: 9.289e-02, eta: 3 days, 22:30:18, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5056, loss_cls: 4.0713, loss: 4.0713 +2024-07-22 23:08:43,970 - pyskl - INFO - Epoch [26][3000/3746] lr: 9.288e-02, eta: 3 days, 22:28:51, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5023, loss_cls: 4.0782, loss: 4.0782 +2024-07-22 23:09:54,598 - pyskl - INFO - Epoch [26][3100/3746] lr: 9.286e-02, eta: 3 days, 22:27:26, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5078, loss_cls: 4.0435, loss: 4.0435 +2024-07-22 23:11:05,257 - pyskl - INFO - Epoch [26][3200/3746] lr: 9.285e-02, eta: 3 days, 22:26:02, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5005, loss_cls: 4.0882, loss: 4.0882 +2024-07-22 23:12:16,267 - pyskl - INFO - Epoch [26][3300/3746] lr: 9.283e-02, eta: 3 days, 22:24:38, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5000, loss_cls: 4.0852, loss: 4.0852 +2024-07-22 23:13:27,755 - pyskl - INFO - Epoch [26][3400/3746] lr: 9.282e-02, eta: 3 days, 22:23:18, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5116, loss_cls: 4.0651, loss: 4.0651 +2024-07-22 23:14:38,380 - pyskl - INFO - Epoch [26][3500/3746] lr: 9.280e-02, eta: 3 days, 22:21:53, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5023, loss_cls: 4.1071, loss: 4.1071 +2024-07-22 23:15:49,014 - pyskl - INFO - Epoch [26][3600/3746] lr: 9.279e-02, eta: 3 days, 22:20:28, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5028, loss_cls: 4.0893, loss: 4.0893 +2024-07-22 23:16:59,834 - pyskl - INFO - Epoch [26][3700/3746] lr: 9.278e-02, eta: 3 days, 22:19:04, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5117, loss_cls: 4.0497, loss: 4.0497 +2024-07-22 23:17:34,371 - pyskl - INFO - Saving checkpoint at 26 epochs +2024-07-22 23:19:26,591 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-22 23:19:27,256 - pyskl - INFO - +top1_acc 0.1950 +top5_acc 0.4205 +2024-07-22 23:19:27,257 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-22 23:19:27,298 - pyskl - INFO - +mean_acc 0.1948 +2024-07-22 23:19:27,309 - pyskl - INFO - Epoch(val) [26][309] top1_acc: 0.1950, top5_acc: 0.4205, mean_class_accuracy: 0.1948 +2024-07-22 23:22:43,921 - pyskl - INFO - Epoch [27][100/3746] lr: 9.275e-02, eta: 3 days, 22:24:25, time: 1.966, data_time: 1.259, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5092, loss_cls: 4.0520, loss: 4.0520 +2024-07-22 23:23:54,077 - pyskl - INFO - Epoch [27][200/3746] lr: 9.274e-02, eta: 3 days, 22:22:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5095, loss_cls: 4.0596, loss: 4.0596 +2024-07-22 23:25:04,170 - pyskl - INFO - Epoch [27][300/3746] lr: 9.272e-02, eta: 3 days, 22:21:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5234, loss_cls: 3.9751, loss: 3.9751 +2024-07-22 23:26:14,168 - pyskl - INFO - Epoch [27][400/3746] lr: 9.271e-02, eta: 3 days, 22:20:02, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5048, loss_cls: 4.0653, loss: 4.0653 +2024-07-22 23:27:24,324 - pyskl - INFO - Epoch [27][500/3746] lr: 9.270e-02, eta: 3 days, 22:18:34, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5080, loss_cls: 4.0483, loss: 4.0483 +2024-07-22 23:28:34,392 - pyskl - INFO - Epoch [27][600/3746] lr: 9.268e-02, eta: 3 days, 22:17:06, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5091, loss_cls: 4.0810, loss: 4.0810 +2024-07-22 23:29:44,430 - pyskl - INFO - Epoch [27][700/3746] lr: 9.267e-02, eta: 3 days, 22:15:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5148, loss_cls: 4.0049, loss: 4.0049 +2024-07-22 23:30:54,442 - pyskl - INFO - Epoch [27][800/3746] lr: 9.265e-02, eta: 3 days, 22:14:10, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5164, loss_cls: 4.0466, loss: 4.0466 +2024-07-22 23:32:04,297 - pyskl - INFO - Epoch [27][900/3746] lr: 9.264e-02, eta: 3 days, 22:12:42, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5200, loss_cls: 3.9918, loss: 3.9918 +2024-07-22 23:33:14,334 - pyskl - INFO - Epoch [27][1000/3746] lr: 9.262e-02, eta: 3 days, 22:11:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5072, loss_cls: 4.0327, loss: 4.0327 +2024-07-22 23:34:24,326 - pyskl - INFO - Epoch [27][1100/3746] lr: 9.261e-02, eta: 3 days, 22:09:46, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5064, loss_cls: 4.0512, loss: 4.0512 +2024-07-22 23:35:34,650 - pyskl - INFO - Epoch [27][1200/3746] lr: 9.259e-02, eta: 3 days, 22:08:19, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5070, loss_cls: 4.0354, loss: 4.0354 +2024-07-22 23:36:44,467 - pyskl - INFO - Epoch [27][1300/3746] lr: 9.258e-02, eta: 3 days, 22:06:50, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5227, loss_cls: 3.9930, loss: 3.9930 +2024-07-22 23:37:54,540 - pyskl - INFO - Epoch [27][1400/3746] lr: 9.256e-02, eta: 3 days, 22:05:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5066, loss_cls: 4.0535, loss: 4.0535 +2024-07-22 23:39:04,397 - pyskl - INFO - Epoch [27][1500/3746] lr: 9.255e-02, eta: 3 days, 22:03:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5138, loss_cls: 4.0004, loss: 4.0004 +2024-07-22 23:40:14,303 - pyskl - INFO - Epoch [27][1600/3746] lr: 9.253e-02, eta: 3 days, 22:02:26, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5098, loss_cls: 4.0394, loss: 4.0394 +2024-07-22 23:41:24,684 - pyskl - INFO - Epoch [27][1700/3746] lr: 9.252e-02, eta: 3 days, 22:01:00, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5102, loss_cls: 4.0373, loss: 4.0373 +2024-07-22 23:42:34,849 - pyskl - INFO - Epoch [27][1800/3746] lr: 9.251e-02, eta: 3 days, 21:59:33, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5097, loss_cls: 4.0708, loss: 4.0708 +2024-07-22 23:43:44,642 - pyskl - INFO - Epoch [27][1900/3746] lr: 9.249e-02, eta: 3 days, 21:58:04, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5116, loss_cls: 4.0259, loss: 4.0259 +2024-07-22 23:44:54,663 - pyskl - INFO - Epoch [27][2000/3746] lr: 9.248e-02, eta: 3 days, 21:56:37, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5091, loss_cls: 4.0522, loss: 4.0522 +2024-07-22 23:46:04,862 - pyskl - INFO - Epoch [27][2100/3746] lr: 9.246e-02, eta: 3 days, 21:55:10, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5050, loss_cls: 4.0649, loss: 4.0649 +2024-07-22 23:47:14,669 - pyskl - INFO - Epoch [27][2200/3746] lr: 9.245e-02, eta: 3 days, 21:53:42, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5098, loss_cls: 4.0691, loss: 4.0691 +2024-07-22 23:48:24,624 - pyskl - INFO - Epoch [27][2300/3746] lr: 9.243e-02, eta: 3 days, 21:52:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5083, loss_cls: 4.0528, loss: 4.0528 +2024-07-22 23:49:34,874 - pyskl - INFO - Epoch [27][2400/3746] lr: 9.242e-02, eta: 3 days, 21:50:47, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5017, loss_cls: 4.0725, loss: 4.0725 +2024-07-22 23:50:44,875 - pyskl - INFO - Epoch [27][2500/3746] lr: 9.240e-02, eta: 3 days, 21:49:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5133, loss_cls: 4.0161, loss: 4.0161 +2024-07-22 23:51:54,803 - pyskl - INFO - Epoch [27][2600/3746] lr: 9.239e-02, eta: 3 days, 21:47:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5050, loss_cls: 4.0728, loss: 4.0728 +2024-07-22 23:53:04,537 - pyskl - INFO - Epoch [27][2700/3746] lr: 9.237e-02, eta: 3 days, 21:46:23, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5075, loss_cls: 4.0911, loss: 4.0911 +2024-07-22 23:54:14,887 - pyskl - INFO - Epoch [27][2800/3746] lr: 9.236e-02, eta: 3 days, 21:44:58, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5156, loss_cls: 4.0268, loss: 4.0268 +2024-07-22 23:55:25,163 - pyskl - INFO - Epoch [27][2900/3746] lr: 9.234e-02, eta: 3 days, 21:43:32, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5216, loss_cls: 4.0104, loss: 4.0104 +2024-07-22 23:56:35,485 - pyskl - INFO - Epoch [27][3000/3746] lr: 9.233e-02, eta: 3 days, 21:42:06, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5162, loss_cls: 4.0318, loss: 4.0318 +2024-07-22 23:57:45,960 - pyskl - INFO - Epoch [27][3100/3746] lr: 9.231e-02, eta: 3 days, 21:40:41, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5095, loss_cls: 4.0666, loss: 4.0666 +2024-07-22 23:58:56,316 - pyskl - INFO - Epoch [27][3200/3746] lr: 9.230e-02, eta: 3 days, 21:39:15, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.4983, loss_cls: 4.0838, loss: 4.0838 +2024-07-23 00:00:07,474 - pyskl - INFO - Epoch [27][3300/3746] lr: 9.228e-02, eta: 3 days, 21:37:53, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5162, loss_cls: 4.0129, loss: 4.0129 +2024-07-23 00:01:18,166 - pyskl - INFO - Epoch [27][3400/3746] lr: 9.227e-02, eta: 3 days, 21:36:29, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5030, loss_cls: 4.0880, loss: 4.0880 +2024-07-23 00:02:29,286 - pyskl - INFO - Epoch [27][3500/3746] lr: 9.225e-02, eta: 3 days, 21:35:07, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5008, loss_cls: 4.0826, loss: 4.0826 +2024-07-23 00:03:40,089 - pyskl - INFO - Epoch [27][3600/3746] lr: 9.224e-02, eta: 3 days, 21:33:43, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4991, loss_cls: 4.1164, loss: 4.1164 +2024-07-23 00:04:50,852 - pyskl - INFO - Epoch [27][3700/3746] lr: 9.222e-02, eta: 3 days, 21:32:20, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5066, loss_cls: 4.0769, loss: 4.0769 +2024-07-23 00:05:25,325 - pyskl - INFO - Saving checkpoint at 27 epochs +2024-07-23 00:07:16,244 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 00:07:16,906 - pyskl - INFO - +top1_acc 0.1657 +top5_acc 0.3800 +2024-07-23 00:07:16,906 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 00:07:16,944 - pyskl - INFO - +mean_acc 0.1655 +2024-07-23 00:07:16,954 - pyskl - INFO - Epoch(val) [27][309] top1_acc: 0.1657, top5_acc: 0.3800, mean_class_accuracy: 0.1655 +2024-07-23 00:10:35,470 - pyskl - INFO - Epoch [28][100/3746] lr: 9.220e-02, eta: 3 days, 21:37:31, time: 1.985, data_time: 1.282, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5194, loss_cls: 3.9973, loss: 3.9973 +2024-07-23 00:11:45,555 - pyskl - INFO - Epoch [28][200/3746] lr: 9.219e-02, eta: 3 days, 21:36:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5148, loss_cls: 3.9902, loss: 3.9902 +2024-07-23 00:12:55,684 - pyskl - INFO - Epoch [28][300/3746] lr: 9.217e-02, eta: 3 days, 21:34:37, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5219, loss_cls: 3.9971, loss: 3.9971 +2024-07-23 00:14:06,004 - pyskl - INFO - Epoch [28][400/3746] lr: 9.216e-02, eta: 3 days, 21:33:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5227, loss_cls: 4.0144, loss: 4.0144 +2024-07-23 00:15:16,140 - pyskl - INFO - Epoch [28][500/3746] lr: 9.214e-02, eta: 3 days, 21:31:44, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5191, loss_cls: 3.9993, loss: 3.9993 +2024-07-23 00:16:26,495 - pyskl - INFO - Epoch [28][600/3746] lr: 9.213e-02, eta: 3 days, 21:30:18, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5080, loss_cls: 4.0561, loss: 4.0561 +2024-07-23 00:17:36,464 - pyskl - INFO - Epoch [28][700/3746] lr: 9.211e-02, eta: 3 days, 21:28:51, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5162, loss_cls: 4.0351, loss: 4.0351 +2024-07-23 00:18:46,420 - pyskl - INFO - Epoch [28][800/3746] lr: 9.210e-02, eta: 3 days, 21:27:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5162, loss_cls: 3.9918, loss: 3.9918 +2024-07-23 00:19:56,394 - pyskl - INFO - Epoch [28][900/3746] lr: 9.208e-02, eta: 3 days, 21:25:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5097, loss_cls: 4.0292, loss: 4.0292 +2024-07-23 00:21:06,669 - pyskl - INFO - Epoch [28][1000/3746] lr: 9.207e-02, eta: 3 days, 21:24:29, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5092, loss_cls: 4.0368, loss: 4.0368 +2024-07-23 00:22:16,720 - pyskl - INFO - Epoch [28][1100/3746] lr: 9.205e-02, eta: 3 days, 21:23:02, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5103, loss_cls: 4.0559, loss: 4.0559 +2024-07-23 00:23:26,660 - pyskl - INFO - Epoch [28][1200/3746] lr: 9.204e-02, eta: 3 days, 21:21:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5134, loss_cls: 4.0089, loss: 4.0089 +2024-07-23 00:24:36,739 - pyskl - INFO - Epoch [28][1300/3746] lr: 9.202e-02, eta: 3 days, 21:20:08, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5138, loss_cls: 4.0243, loss: 4.0243 +2024-07-23 00:25:46,912 - pyskl - INFO - Epoch [28][1400/3746] lr: 9.201e-02, eta: 3 days, 21:18:42, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5133, loss_cls: 4.0386, loss: 4.0386 +2024-07-23 00:26:56,788 - pyskl - INFO - Epoch [28][1500/3746] lr: 9.199e-02, eta: 3 days, 21:17:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5134, loss_cls: 4.0122, loss: 4.0122 +2024-07-23 00:28:06,783 - pyskl - INFO - Epoch [28][1600/3746] lr: 9.198e-02, eta: 3 days, 21:15:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.4998, loss_cls: 4.0971, loss: 4.0971 +2024-07-23 00:29:16,943 - pyskl - INFO - Epoch [28][1700/3746] lr: 9.196e-02, eta: 3 days, 21:14:21, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5033, loss_cls: 4.0813, loss: 4.0813 +2024-07-23 00:30:27,229 - pyskl - INFO - Epoch [28][1800/3746] lr: 9.194e-02, eta: 3 days, 21:12:55, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5094, loss_cls: 4.0497, loss: 4.0497 +2024-07-23 00:31:36,973 - pyskl - INFO - Epoch [28][1900/3746] lr: 9.193e-02, eta: 3 days, 21:11:27, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5073, loss_cls: 4.0730, loss: 4.0730 +2024-07-23 00:32:46,987 - pyskl - INFO - Epoch [28][2000/3746] lr: 9.191e-02, eta: 3 days, 21:10:00, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5111, loss_cls: 4.0624, loss: 4.0624 +2024-07-23 00:33:56,898 - pyskl - INFO - Epoch [28][2100/3746] lr: 9.190e-02, eta: 3 days, 21:08:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5109, loss_cls: 4.0482, loss: 4.0482 +2024-07-23 00:35:06,901 - pyskl - INFO - Epoch [28][2200/3746] lr: 9.188e-02, eta: 3 days, 21:07:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5020, loss_cls: 4.0471, loss: 4.0471 +2024-07-23 00:36:16,697 - pyskl - INFO - Epoch [28][2300/3746] lr: 9.187e-02, eta: 3 days, 21:05:38, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5069, loss_cls: 4.0837, loss: 4.0837 +2024-07-23 00:37:26,584 - pyskl - INFO - Epoch [28][2400/3746] lr: 9.185e-02, eta: 3 days, 21:04:10, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5155, loss_cls: 4.0551, loss: 4.0551 +2024-07-23 00:38:36,481 - pyskl - INFO - Epoch [28][2500/3746] lr: 9.184e-02, eta: 3 days, 21:02:43, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5103, loss_cls: 4.0446, loss: 4.0446 +2024-07-23 00:39:46,606 - pyskl - INFO - Epoch [28][2600/3746] lr: 9.182e-02, eta: 3 days, 21:01:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.4980, loss_cls: 4.0610, loss: 4.0610 +2024-07-23 00:40:56,790 - pyskl - INFO - Epoch [28][2700/3746] lr: 9.181e-02, eta: 3 days, 20:59:51, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5058, loss_cls: 4.0481, loss: 4.0481 +2024-07-23 00:42:07,412 - pyskl - INFO - Epoch [28][2800/3746] lr: 9.179e-02, eta: 3 days, 20:58:27, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5078, loss_cls: 4.0399, loss: 4.0399 +2024-07-23 00:43:17,781 - pyskl - INFO - Epoch [28][2900/3746] lr: 9.178e-02, eta: 3 days, 20:57:02, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4997, loss_cls: 4.0704, loss: 4.0704 +2024-07-23 00:44:28,201 - pyskl - INFO - Epoch [28][3000/3746] lr: 9.176e-02, eta: 3 days, 20:55:37, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5100, loss_cls: 4.0465, loss: 4.0465 +2024-07-23 00:45:38,485 - pyskl - INFO - Epoch [28][3100/3746] lr: 9.175e-02, eta: 3 days, 20:54:12, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5155, loss_cls: 4.0504, loss: 4.0504 +2024-07-23 00:46:49,204 - pyskl - INFO - Epoch [28][3200/3746] lr: 9.173e-02, eta: 3 days, 20:52:48, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5086, loss_cls: 4.0685, loss: 4.0685 +2024-07-23 00:48:00,347 - pyskl - INFO - Epoch [28][3300/3746] lr: 9.172e-02, eta: 3 days, 20:51:27, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5189, loss_cls: 4.0122, loss: 4.0122 +2024-07-23 00:49:11,073 - pyskl - INFO - Epoch [28][3400/3746] lr: 9.170e-02, eta: 3 days, 20:50:04, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5027, loss_cls: 4.0623, loss: 4.0623 +2024-07-23 00:50:21,745 - pyskl - INFO - Epoch [28][3500/3746] lr: 9.168e-02, eta: 3 days, 20:48:40, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5056, loss_cls: 4.0810, loss: 4.0810 +2024-07-23 00:51:32,483 - pyskl - INFO - Epoch [28][3600/3746] lr: 9.167e-02, eta: 3 days, 20:47:17, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5123, loss_cls: 4.0335, loss: 4.0335 +2024-07-23 00:52:42,799 - pyskl - INFO - Epoch [28][3700/3746] lr: 9.165e-02, eta: 3 days, 20:45:52, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.5009, loss_cls: 4.1016, loss: 4.1016 +2024-07-23 00:53:17,053 - pyskl - INFO - Saving checkpoint at 28 epochs +2024-07-23 00:55:08,171 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 00:55:08,846 - pyskl - INFO - +top1_acc 0.1824 +top5_acc 0.4082 +2024-07-23 00:55:08,846 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 00:55:08,888 - pyskl - INFO - +mean_acc 0.1822 +2024-07-23 00:55:08,900 - pyskl - INFO - Epoch(val) [28][309] top1_acc: 0.1824, top5_acc: 0.4082, mean_class_accuracy: 0.1822 +2024-07-23 00:58:24,784 - pyskl - INFO - Epoch [29][100/3746] lr: 9.163e-02, eta: 3 days, 20:50:33, time: 1.959, data_time: 1.256, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5264, loss_cls: 3.9861, loss: 3.9861 +2024-07-23 00:59:35,019 - pyskl - INFO - Epoch [29][200/3746] lr: 9.162e-02, eta: 3 days, 20:49:07, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5284, loss_cls: 3.9628, loss: 3.9628 +2024-07-23 01:00:45,114 - pyskl - INFO - Epoch [29][300/3746] lr: 9.160e-02, eta: 3 days, 20:47:41, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5078, loss_cls: 4.0361, loss: 4.0361 +2024-07-23 01:01:55,188 - pyskl - INFO - Epoch [29][400/3746] lr: 9.158e-02, eta: 3 days, 20:46:14, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5006, loss_cls: 4.0544, loss: 4.0544 +2024-07-23 01:03:05,174 - pyskl - INFO - Epoch [29][500/3746] lr: 9.157e-02, eta: 3 days, 20:44:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5194, loss_cls: 4.0068, loss: 4.0068 +2024-07-23 01:04:15,098 - pyskl - INFO - Epoch [29][600/3746] lr: 9.155e-02, eta: 3 days, 20:43:21, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5075, loss_cls: 4.0402, loss: 4.0402 +2024-07-23 01:05:25,371 - pyskl - INFO - Epoch [29][700/3746] lr: 9.154e-02, eta: 3 days, 20:41:55, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5159, loss_cls: 4.0254, loss: 4.0254 +2024-07-23 01:06:35,303 - pyskl - INFO - Epoch [29][800/3746] lr: 9.152e-02, eta: 3 days, 20:40:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5155, loss_cls: 4.0268, loss: 4.0268 +2024-07-23 01:07:45,367 - pyskl - INFO - Epoch [29][900/3746] lr: 9.151e-02, eta: 3 days, 20:39:02, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5086, loss_cls: 4.0531, loss: 4.0531 +2024-07-23 01:08:55,483 - pyskl - INFO - Epoch [29][1000/3746] lr: 9.149e-02, eta: 3 days, 20:37:36, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5238, loss_cls: 3.9847, loss: 3.9847 +2024-07-23 01:10:05,467 - pyskl - INFO - Epoch [29][1100/3746] lr: 9.148e-02, eta: 3 days, 20:36:09, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5080, loss_cls: 4.0753, loss: 4.0753 +2024-07-23 01:11:15,793 - pyskl - INFO - Epoch [29][1200/3746] lr: 9.146e-02, eta: 3 days, 20:34:44, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5025, loss_cls: 4.0650, loss: 4.0650 +2024-07-23 01:12:25,879 - pyskl - INFO - Epoch [29][1300/3746] lr: 9.144e-02, eta: 3 days, 20:33:18, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5141, loss_cls: 4.0225, loss: 4.0225 +2024-07-23 01:13:36,032 - pyskl - INFO - Epoch [29][1400/3746] lr: 9.143e-02, eta: 3 days, 20:31:52, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5006, loss_cls: 4.0739, loss: 4.0739 +2024-07-23 01:14:46,330 - pyskl - INFO - Epoch [29][1500/3746] lr: 9.141e-02, eta: 3 days, 20:30:27, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5252, loss_cls: 3.9776, loss: 3.9776 +2024-07-23 01:15:56,314 - pyskl - INFO - Epoch [29][1600/3746] lr: 9.140e-02, eta: 3 days, 20:29:00, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5100, loss_cls: 4.0338, loss: 4.0338 +2024-07-23 01:17:06,444 - pyskl - INFO - Epoch [29][1700/3746] lr: 9.138e-02, eta: 3 days, 20:27:34, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5064, loss_cls: 4.0500, loss: 4.0500 +2024-07-23 01:18:16,450 - pyskl - INFO - Epoch [29][1800/3746] lr: 9.137e-02, eta: 3 days, 20:26:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5130, loss_cls: 4.0604, loss: 4.0604 +2024-07-23 01:19:26,493 - pyskl - INFO - Epoch [29][1900/3746] lr: 9.135e-02, eta: 3 days, 20:24:42, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5145, loss_cls: 4.0452, loss: 4.0452 +2024-07-23 01:20:36,413 - pyskl - INFO - Epoch [29][2000/3746] lr: 9.133e-02, eta: 3 days, 20:23:15, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5083, loss_cls: 4.0570, loss: 4.0570 +2024-07-23 01:21:46,608 - pyskl - INFO - Epoch [29][2100/3746] lr: 9.132e-02, eta: 3 days, 20:21:50, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4961, loss_cls: 4.1036, loss: 4.1036 +2024-07-23 01:22:56,644 - pyskl - INFO - Epoch [29][2200/3746] lr: 9.130e-02, eta: 3 days, 20:20:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5123, loss_cls: 4.0327, loss: 4.0327 +2024-07-23 01:24:06,722 - pyskl - INFO - Epoch [29][2300/3746] lr: 9.129e-02, eta: 3 days, 20:18:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5108, loss_cls: 3.9982, loss: 3.9982 +2024-07-23 01:25:16,568 - pyskl - INFO - Epoch [29][2400/3746] lr: 9.127e-02, eta: 3 days, 20:17:31, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5248, loss_cls: 4.0069, loss: 4.0069 +2024-07-23 01:26:26,871 - pyskl - INFO - Epoch [29][2500/3746] lr: 9.126e-02, eta: 3 days, 20:16:06, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5152, loss_cls: 4.0370, loss: 4.0370 +2024-07-23 01:27:36,897 - pyskl - INFO - Epoch [29][2600/3746] lr: 9.124e-02, eta: 3 days, 20:14:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5156, loss_cls: 4.0446, loss: 4.0446 +2024-07-23 01:28:47,034 - pyskl - INFO - Epoch [29][2700/3746] lr: 9.122e-02, eta: 3 days, 20:13:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5109, loss_cls: 4.0475, loss: 4.0475 +2024-07-23 01:29:57,995 - pyskl - INFO - Epoch [29][2800/3746] lr: 9.121e-02, eta: 3 days, 20:11:53, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5209, loss_cls: 3.9950, loss: 3.9950 +2024-07-23 01:31:08,276 - pyskl - INFO - Epoch [29][2900/3746] lr: 9.119e-02, eta: 3 days, 20:10:28, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4992, loss_cls: 4.0754, loss: 4.0754 +2024-07-23 01:32:18,711 - pyskl - INFO - Epoch [29][3000/3746] lr: 9.118e-02, eta: 3 days, 20:09:04, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5097, loss_cls: 4.0645, loss: 4.0645 +2024-07-23 01:33:29,088 - pyskl - INFO - Epoch [29][3100/3746] lr: 9.116e-02, eta: 3 days, 20:07:39, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5170, loss_cls: 4.0148, loss: 4.0148 +2024-07-23 01:34:39,385 - pyskl - INFO - Epoch [29][3200/3746] lr: 9.114e-02, eta: 3 days, 20:06:15, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5256, loss_cls: 4.0089, loss: 4.0089 +2024-07-23 01:35:50,343 - pyskl - INFO - Epoch [29][3300/3746] lr: 9.113e-02, eta: 3 days, 20:04:53, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5067, loss_cls: 4.0498, loss: 4.0498 +2024-07-23 01:37:01,443 - pyskl - INFO - Epoch [29][3400/3746] lr: 9.111e-02, eta: 3 days, 20:03:31, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5134, loss_cls: 4.0385, loss: 4.0385 +2024-07-23 01:38:12,078 - pyskl - INFO - Epoch [29][3500/3746] lr: 9.110e-02, eta: 3 days, 20:02:08, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5000, loss_cls: 4.1136, loss: 4.1136 +2024-07-23 01:39:22,238 - pyskl - INFO - Epoch [29][3600/3746] lr: 9.108e-02, eta: 3 days, 20:00:43, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5106, loss_cls: 4.0535, loss: 4.0535 +2024-07-23 01:40:32,438 - pyskl - INFO - Epoch [29][3700/3746] lr: 9.106e-02, eta: 3 days, 19:59:18, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5158, loss_cls: 4.0545, loss: 4.0545 +2024-07-23 01:41:06,785 - pyskl - INFO - Saving checkpoint at 29 epochs +2024-07-23 01:42:57,958 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 01:42:58,614 - pyskl - INFO - +top1_acc 0.1974 +top5_acc 0.4285 +2024-07-23 01:42:58,615 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 01:42:58,655 - pyskl - INFO - +mean_acc 0.1972 +2024-07-23 01:42:58,666 - pyskl - INFO - Epoch(val) [29][309] top1_acc: 0.1974, top5_acc: 0.4285, mean_class_accuracy: 0.1972 +2024-07-23 01:46:24,364 - pyskl - INFO - Epoch [30][100/3746] lr: 9.104e-02, eta: 3 days, 20:04:24, time: 2.057, data_time: 1.249, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5337, loss_cls: 3.9655, loss: 3.9655 +2024-07-23 01:47:44,924 - pyskl - INFO - Epoch [30][200/3746] lr: 9.103e-02, eta: 3 days, 20:03:42, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5192, loss_cls: 4.0023, loss: 4.0023 +2024-07-23 01:49:05,047 - pyskl - INFO - Epoch [30][300/3746] lr: 9.101e-02, eta: 3 days, 20:02:58, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.4988, loss_cls: 4.0867, loss: 4.0867 +2024-07-23 01:50:25,497 - pyskl - INFO - Epoch [30][400/3746] lr: 9.099e-02, eta: 3 days, 20:02:15, time: 0.804, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5112, loss_cls: 4.0368, loss: 4.0368 +2024-07-23 01:51:45,619 - pyskl - INFO - Epoch [30][500/3746] lr: 9.098e-02, eta: 3 days, 20:01:31, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5241, loss_cls: 4.0055, loss: 4.0055 +2024-07-23 01:53:06,066 - pyskl - INFO - Epoch [30][600/3746] lr: 9.096e-02, eta: 3 days, 20:00:48, time: 0.804, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5120, loss_cls: 4.0319, loss: 4.0319 +2024-07-23 01:54:26,728 - pyskl - INFO - Epoch [30][700/3746] lr: 9.095e-02, eta: 3 days, 20:00:05, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5144, loss_cls: 4.0208, loss: 4.0208 +2024-07-23 01:55:46,941 - pyskl - INFO - Epoch [30][800/3746] lr: 9.093e-02, eta: 3 days, 19:59:21, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5159, loss_cls: 3.9853, loss: 3.9853 +2024-07-23 01:57:06,839 - pyskl - INFO - Epoch [30][900/3746] lr: 9.091e-02, eta: 3 days, 19:58:36, time: 0.799, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5167, loss_cls: 4.0328, loss: 4.0328 +2024-07-23 01:58:26,922 - pyskl - INFO - Epoch [30][1000/3746] lr: 9.090e-02, eta: 3 days, 19:57:51, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5078, loss_cls: 4.0645, loss: 4.0645 +2024-07-23 01:59:47,004 - pyskl - INFO - Epoch [30][1100/3746] lr: 9.088e-02, eta: 3 days, 19:57:06, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5172, loss_cls: 4.0387, loss: 4.0387 +2024-07-23 02:01:07,707 - pyskl - INFO - Epoch [30][1200/3746] lr: 9.087e-02, eta: 3 days, 19:56:24, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5139, loss_cls: 4.0387, loss: 4.0387 +2024-07-23 02:02:28,069 - pyskl - INFO - Epoch [30][1300/3746] lr: 9.085e-02, eta: 3 days, 19:55:40, time: 0.804, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5119, loss_cls: 4.0784, loss: 4.0784 +2024-07-23 02:03:48,158 - pyskl - INFO - Epoch [30][1400/3746] lr: 9.083e-02, eta: 3 days, 19:54:55, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5184, loss_cls: 4.0034, loss: 4.0034 +2024-07-23 02:05:08,568 - pyskl - INFO - Epoch [30][1500/3746] lr: 9.082e-02, eta: 3 days, 19:54:11, time: 0.804, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5228, loss_cls: 4.0219, loss: 4.0219 +2024-07-23 02:06:28,625 - pyskl - INFO - Epoch [30][1600/3746] lr: 9.080e-02, eta: 3 days, 19:53:26, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5158, loss_cls: 4.0278, loss: 4.0278 +2024-07-23 02:07:49,001 - pyskl - INFO - Epoch [30][1700/3746] lr: 9.078e-02, eta: 3 days, 19:52:42, time: 0.804, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5131, loss_cls: 4.0531, loss: 4.0531 +2024-07-23 02:09:09,307 - pyskl - INFO - Epoch [30][1800/3746] lr: 9.077e-02, eta: 3 days, 19:51:57, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5281, loss_cls: 3.9688, loss: 3.9688 +2024-07-23 02:10:29,835 - pyskl - INFO - Epoch [30][1900/3746] lr: 9.075e-02, eta: 3 days, 19:51:14, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5189, loss_cls: 4.0136, loss: 4.0136 +2024-07-23 02:11:50,473 - pyskl - INFO - Epoch [30][2000/3746] lr: 9.074e-02, eta: 3 days, 19:50:31, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5112, loss_cls: 4.0732, loss: 4.0732 +2024-07-23 02:13:10,740 - pyskl - INFO - Epoch [30][2100/3746] lr: 9.072e-02, eta: 3 days, 19:49:46, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5078, loss_cls: 4.0684, loss: 4.0684 +2024-07-23 02:14:30,699 - pyskl - INFO - Epoch [30][2200/3746] lr: 9.070e-02, eta: 3 days, 19:49:00, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5236, loss_cls: 4.0079, loss: 4.0079 +2024-07-23 02:15:50,500 - pyskl - INFO - Epoch [30][2300/3746] lr: 9.069e-02, eta: 3 days, 19:48:13, time: 0.798, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5170, loss_cls: 4.0411, loss: 4.0411 +2024-07-23 02:17:10,438 - pyskl - INFO - Epoch [30][2400/3746] lr: 9.067e-02, eta: 3 days, 19:47:27, time: 0.799, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5083, loss_cls: 4.0564, loss: 4.0564 +2024-07-23 02:18:30,712 - pyskl - INFO - Epoch [30][2500/3746] lr: 9.065e-02, eta: 3 days, 19:46:42, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5066, loss_cls: 4.0441, loss: 4.0441 +2024-07-23 02:19:50,979 - pyskl - INFO - Epoch [30][2600/3746] lr: 9.064e-02, eta: 3 days, 19:45:57, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5061, loss_cls: 4.0527, loss: 4.0527 +2024-07-23 02:21:11,003 - pyskl - INFO - Epoch [30][2700/3746] lr: 9.062e-02, eta: 3 days, 19:45:11, time: 0.800, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5142, loss_cls: 4.0422, loss: 4.0422 +2024-07-23 02:22:31,540 - pyskl - INFO - Epoch [30][2800/3746] lr: 9.061e-02, eta: 3 days, 19:44:26, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5167, loss_cls: 4.0013, loss: 4.0013 +2024-07-23 02:23:51,739 - pyskl - INFO - Epoch [30][2900/3746] lr: 9.059e-02, eta: 3 days, 19:43:41, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5095, loss_cls: 4.0339, loss: 4.0339 +2024-07-23 02:25:11,988 - pyskl - INFO - Epoch [30][3000/3746] lr: 9.057e-02, eta: 3 days, 19:42:56, time: 0.802, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5123, loss_cls: 4.0264, loss: 4.0264 +2024-07-23 02:26:32,720 - pyskl - INFO - Epoch [30][3100/3746] lr: 9.056e-02, eta: 3 days, 19:42:12, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5070, loss_cls: 4.0723, loss: 4.0723 +2024-07-23 02:27:53,000 - pyskl - INFO - Epoch [30][3200/3746] lr: 9.054e-02, eta: 3 days, 19:41:27, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5059, loss_cls: 4.0329, loss: 4.0329 +2024-07-23 02:29:14,273 - pyskl - INFO - Epoch [30][3300/3746] lr: 9.052e-02, eta: 3 days, 19:40:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5109, loss_cls: 4.0610, loss: 4.0610 +2024-07-23 02:30:35,730 - pyskl - INFO - Epoch [30][3400/3746] lr: 9.051e-02, eta: 3 days, 19:40:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5095, loss_cls: 4.0628, loss: 4.0628 +2024-07-23 02:31:56,761 - pyskl - INFO - Epoch [30][3500/3746] lr: 9.049e-02, eta: 3 days, 19:39:22, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5227, loss_cls: 4.0010, loss: 4.0010 +2024-07-23 02:33:17,222 - pyskl - INFO - Epoch [30][3600/3746] lr: 9.047e-02, eta: 3 days, 19:38:37, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5244, loss_cls: 4.0022, loss: 4.0022 +2024-07-23 02:34:37,718 - pyskl - INFO - Epoch [30][3700/3746] lr: 9.046e-02, eta: 3 days, 19:37:52, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5156, loss_cls: 4.0377, loss: 4.0377 +2024-07-23 02:35:16,816 - pyskl - INFO - Saving checkpoint at 30 epochs +2024-07-23 02:37:07,766 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 02:37:08,429 - pyskl - INFO - +top1_acc 0.2009 +top5_acc 0.4320 +2024-07-23 02:37:08,429 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 02:37:08,470 - pyskl - INFO - +mean_acc 0.2005 +2024-07-23 02:37:08,481 - pyskl - INFO - Epoch(val) [30][309] top1_acc: 0.2009, top5_acc: 0.4320, mean_class_accuracy: 0.2005 +2024-07-23 02:40:52,284 - pyskl - INFO - Epoch [31][100/3746] lr: 9.043e-02, eta: 3 days, 19:43:51, time: 2.238, data_time: 1.263, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5164, loss_cls: 4.2289, loss: 4.2289 +2024-07-23 02:42:14,088 - pyskl - INFO - Epoch [31][200/3746] lr: 9.042e-02, eta: 3 days, 19:43:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5092, loss_cls: 4.2529, loss: 4.2529 +2024-07-23 02:43:35,637 - pyskl - INFO - Epoch [31][300/3746] lr: 9.040e-02, eta: 3 days, 19:42:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5284, loss_cls: 4.1754, loss: 4.1754 +2024-07-23 02:44:57,125 - pyskl - INFO - Epoch [31][400/3746] lr: 9.039e-02, eta: 3 days, 19:41:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5275, loss_cls: 4.1819, loss: 4.1819 +2024-07-23 02:46:18,795 - pyskl - INFO - Epoch [31][500/3746] lr: 9.037e-02, eta: 3 days, 19:41:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5159, loss_cls: 4.2257, loss: 4.2257 +2024-07-23 02:47:40,783 - pyskl - INFO - Epoch [31][600/3746] lr: 9.035e-02, eta: 3 days, 19:40:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5109, loss_cls: 4.2609, loss: 4.2609 +2024-07-23 02:49:03,023 - pyskl - INFO - Epoch [31][700/3746] lr: 9.034e-02, eta: 3 days, 19:39:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5195, loss_cls: 4.2139, loss: 4.2139 +2024-07-23 02:50:24,620 - pyskl - INFO - Epoch [31][800/3746] lr: 9.032e-02, eta: 3 days, 19:39:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5084, loss_cls: 4.2616, loss: 4.2616 +2024-07-23 02:51:45,845 - pyskl - INFO - Epoch [31][900/3746] lr: 9.030e-02, eta: 3 days, 19:38:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5238, loss_cls: 4.2221, loss: 4.2221 +2024-07-23 02:53:07,510 - pyskl - INFO - Epoch [31][1000/3746] lr: 9.029e-02, eta: 3 days, 19:37:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5103, loss_cls: 4.2581, loss: 4.2581 +2024-07-23 02:54:29,374 - pyskl - INFO - Epoch [31][1100/3746] lr: 9.027e-02, eta: 3 days, 19:37:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5211, loss_cls: 4.2400, loss: 4.2400 +2024-07-23 02:55:50,733 - pyskl - INFO - Epoch [31][1200/3746] lr: 9.025e-02, eta: 3 days, 19:36:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5128, loss_cls: 4.2607, loss: 4.2607 +2024-07-23 02:57:12,492 - pyskl - INFO - Epoch [31][1300/3746] lr: 9.024e-02, eta: 3 days, 19:35:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5216, loss_cls: 4.2426, loss: 4.2426 +2024-07-23 02:58:34,177 - pyskl - INFO - Epoch [31][1400/3746] lr: 9.022e-02, eta: 3 days, 19:34:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5091, loss_cls: 4.2636, loss: 4.2636 +2024-07-23 02:59:56,785 - pyskl - INFO - Epoch [31][1500/3746] lr: 9.020e-02, eta: 3 days, 19:34:18, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5178, loss_cls: 4.2051, loss: 4.2051 +2024-07-23 03:01:18,435 - pyskl - INFO - Epoch [31][1600/3746] lr: 9.019e-02, eta: 3 days, 19:33:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5144, loss_cls: 4.2389, loss: 4.2389 +2024-07-23 03:02:40,899 - pyskl - INFO - Epoch [31][1700/3746] lr: 9.017e-02, eta: 3 days, 19:32:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5138, loss_cls: 4.2737, loss: 4.2737 +2024-07-23 03:04:02,756 - pyskl - INFO - Epoch [31][1800/3746] lr: 9.015e-02, eta: 3 days, 19:32:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5073, loss_cls: 4.2571, loss: 4.2571 +2024-07-23 03:05:24,740 - pyskl - INFO - Epoch [31][1900/3746] lr: 9.014e-02, eta: 3 days, 19:31:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5188, loss_cls: 4.2247, loss: 4.2247 +2024-07-23 03:06:46,558 - pyskl - INFO - Epoch [31][2000/3746] lr: 9.012e-02, eta: 3 days, 19:30:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5236, loss_cls: 4.2085, loss: 4.2085 +2024-07-23 03:08:07,665 - pyskl - INFO - Epoch [31][2100/3746] lr: 9.010e-02, eta: 3 days, 19:30:09, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5247, loss_cls: 4.2069, loss: 4.2069 +2024-07-23 03:09:28,835 - pyskl - INFO - Epoch [31][2200/3746] lr: 9.009e-02, eta: 3 days, 19:29:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.4970, loss_cls: 4.2792, loss: 4.2792 +2024-07-23 03:10:50,427 - pyskl - INFO - Epoch [31][2300/3746] lr: 9.007e-02, eta: 3 days, 19:28:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5088, loss_cls: 4.2471, loss: 4.2471 +2024-07-23 03:12:12,033 - pyskl - INFO - Epoch [31][2400/3746] lr: 9.005e-02, eta: 3 days, 19:28:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5100, loss_cls: 4.2554, loss: 4.2554 +2024-07-23 03:13:33,935 - pyskl - INFO - Epoch [31][2500/3746] lr: 9.004e-02, eta: 3 days, 19:27:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5206, loss_cls: 4.2330, loss: 4.2330 +2024-07-23 03:14:55,504 - pyskl - INFO - Epoch [31][2600/3746] lr: 9.002e-02, eta: 3 days, 19:26:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5166, loss_cls: 4.2444, loss: 4.2444 +2024-07-23 03:16:17,367 - pyskl - INFO - Epoch [31][2700/3746] lr: 9.000e-02, eta: 3 days, 19:25:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5089, loss_cls: 4.2452, loss: 4.2452 +2024-07-23 03:17:39,645 - pyskl - INFO - Epoch [31][2800/3746] lr: 8.999e-02, eta: 3 days, 19:25:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5181, loss_cls: 4.2129, loss: 4.2129 +2024-07-23 03:19:01,387 - pyskl - INFO - Epoch [31][2900/3746] lr: 8.997e-02, eta: 3 days, 19:24:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5042, loss_cls: 4.2706, loss: 4.2706 +2024-07-23 03:20:23,641 - pyskl - INFO - Epoch [31][3000/3746] lr: 8.995e-02, eta: 3 days, 19:23:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5097, loss_cls: 4.2522, loss: 4.2522 +2024-07-23 03:21:45,851 - pyskl - INFO - Epoch [31][3100/3746] lr: 8.994e-02, eta: 3 days, 19:23:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5081, loss_cls: 4.2725, loss: 4.2725 +2024-07-23 03:23:08,118 - pyskl - INFO - Epoch [31][3200/3746] lr: 8.992e-02, eta: 3 days, 19:22:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5170, loss_cls: 4.1995, loss: 4.1995 +2024-07-23 03:24:30,777 - pyskl - INFO - Epoch [31][3300/3746] lr: 8.990e-02, eta: 3 days, 19:21:50, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5167, loss_cls: 4.2510, loss: 4.2510 +2024-07-23 03:25:53,929 - pyskl - INFO - Epoch [31][3400/3746] lr: 8.989e-02, eta: 3 days, 19:21:13, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5105, loss_cls: 4.2742, loss: 4.2742 +2024-07-23 03:27:16,120 - pyskl - INFO - Epoch [31][3500/3746] lr: 8.987e-02, eta: 3 days, 19:20:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5189, loss_cls: 4.2006, loss: 4.2006 +2024-07-23 03:28:38,193 - pyskl - INFO - Epoch [31][3600/3746] lr: 8.985e-02, eta: 3 days, 19:19:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5205, loss_cls: 4.2277, loss: 4.2277 +2024-07-23 03:29:59,614 - pyskl - INFO - Epoch [31][3700/3746] lr: 8.983e-02, eta: 3 days, 19:19:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5052, loss_cls: 4.2597, loss: 4.2597 +2024-07-23 03:30:39,828 - pyskl - INFO - Saving checkpoint at 31 epochs +2024-07-23 03:32:31,979 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 03:32:32,642 - pyskl - INFO - +top1_acc 0.2027 +top5_acc 0.4487 +2024-07-23 03:32:32,642 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 03:32:32,680 - pyskl - INFO - +mean_acc 0.2024 +2024-07-23 03:32:32,690 - pyskl - INFO - Epoch(val) [31][309] top1_acc: 0.2027, top5_acc: 0.4487, mean_class_accuracy: 0.2024 +2024-07-23 03:36:16,428 - pyskl - INFO - Epoch [32][100/3746] lr: 8.981e-02, eta: 3 days, 19:24:43, time: 2.237, data_time: 1.264, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5212, loss_cls: 4.2062, loss: 4.2062 +2024-07-23 03:37:38,076 - pyskl - INFO - Epoch [32][200/3746] lr: 8.979e-02, eta: 3 days, 19:23:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5161, loss_cls: 4.2339, loss: 4.2339 +2024-07-23 03:38:59,868 - pyskl - INFO - Epoch [32][300/3746] lr: 8.978e-02, eta: 3 days, 19:23:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5147, loss_cls: 4.2299, loss: 4.2299 +2024-07-23 03:40:21,496 - pyskl - INFO - Epoch [32][400/3746] lr: 8.976e-02, eta: 3 days, 19:22:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5178, loss_cls: 4.2501, loss: 4.2501 +2024-07-23 03:41:43,317 - pyskl - INFO - Epoch [32][500/3746] lr: 8.974e-02, eta: 3 days, 19:21:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5075, loss_cls: 4.2938, loss: 4.2938 +2024-07-23 03:43:05,212 - pyskl - INFO - Epoch [32][600/3746] lr: 8.973e-02, eta: 3 days, 19:21:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5066, loss_cls: 4.2476, loss: 4.2476 +2024-07-23 03:44:27,125 - pyskl - INFO - Epoch [32][700/3746] lr: 8.971e-02, eta: 3 days, 19:20:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5092, loss_cls: 4.2788, loss: 4.2788 +2024-07-23 03:45:48,778 - pyskl - INFO - Epoch [32][800/3746] lr: 8.969e-02, eta: 3 days, 19:19:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5109, loss_cls: 4.2650, loss: 4.2650 +2024-07-23 03:47:10,524 - pyskl - INFO - Epoch [32][900/3746] lr: 8.967e-02, eta: 3 days, 19:18:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5148, loss_cls: 4.2334, loss: 4.2334 +2024-07-23 03:48:32,382 - pyskl - INFO - Epoch [32][1000/3746] lr: 8.966e-02, eta: 3 days, 19:18:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5267, loss_cls: 4.2181, loss: 4.2181 +2024-07-23 03:49:54,138 - pyskl - INFO - Epoch [32][1100/3746] lr: 8.964e-02, eta: 3 days, 19:17:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5134, loss_cls: 4.2081, loss: 4.2081 +2024-07-23 03:51:16,196 - pyskl - INFO - Epoch [32][1200/3746] lr: 8.962e-02, eta: 3 days, 19:16:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5127, loss_cls: 4.2577, loss: 4.2577 +2024-07-23 03:52:38,022 - pyskl - INFO - Epoch [32][1300/3746] lr: 8.961e-02, eta: 3 days, 19:15:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5234, loss_cls: 4.2052, loss: 4.2052 +2024-07-23 03:53:59,532 - pyskl - INFO - Epoch [32][1400/3746] lr: 8.959e-02, eta: 3 days, 19:15:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5292, loss_cls: 4.1750, loss: 4.1750 +2024-07-23 03:55:21,144 - pyskl - INFO - Epoch [32][1500/3746] lr: 8.957e-02, eta: 3 days, 19:14:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5294, loss_cls: 4.1901, loss: 4.1901 +2024-07-23 03:56:43,152 - pyskl - INFO - Epoch [32][1600/3746] lr: 8.955e-02, eta: 3 days, 19:13:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5084, loss_cls: 4.2276, loss: 4.2276 +2024-07-23 03:58:04,437 - pyskl - INFO - Epoch [32][1700/3746] lr: 8.954e-02, eta: 3 days, 19:12:59, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5131, loss_cls: 4.2683, loss: 4.2683 +2024-07-23 03:59:26,308 - pyskl - INFO - Epoch [32][1800/3746] lr: 8.952e-02, eta: 3 days, 19:12:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5164, loss_cls: 4.2338, loss: 4.2338 +2024-07-23 04:00:48,242 - pyskl - INFO - Epoch [32][1900/3746] lr: 8.950e-02, eta: 3 days, 19:11:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5281, loss_cls: 4.1744, loss: 4.1744 +2024-07-23 04:02:09,984 - pyskl - INFO - Epoch [32][2000/3746] lr: 8.949e-02, eta: 3 days, 19:10:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5181, loss_cls: 4.2129, loss: 4.2129 +2024-07-23 04:03:31,604 - pyskl - INFO - Epoch [32][2100/3746] lr: 8.947e-02, eta: 3 days, 19:10:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5103, loss_cls: 4.2675, loss: 4.2675 +2024-07-23 04:04:53,018 - pyskl - INFO - Epoch [32][2200/3746] lr: 8.945e-02, eta: 3 days, 19:09:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5197, loss_cls: 4.2570, loss: 4.2570 +2024-07-23 04:06:14,814 - pyskl - INFO - Epoch [32][2300/3746] lr: 8.943e-02, eta: 3 days, 19:08:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5145, loss_cls: 4.2141, loss: 4.2141 +2024-07-23 04:07:35,933 - pyskl - INFO - Epoch [32][2400/3746] lr: 8.942e-02, eta: 3 days, 19:07:43, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5203, loss_cls: 4.2415, loss: 4.2415 +2024-07-23 04:08:57,389 - pyskl - INFO - Epoch [32][2500/3746] lr: 8.940e-02, eta: 3 days, 19:06:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5084, loss_cls: 4.2650, loss: 4.2650 +2024-07-23 04:10:19,278 - pyskl - INFO - Epoch [32][2600/3746] lr: 8.938e-02, eta: 3 days, 19:06:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5062, loss_cls: 4.2591, loss: 4.2591 +2024-07-23 04:11:40,999 - pyskl - INFO - Epoch [32][2700/3746] lr: 8.937e-02, eta: 3 days, 19:05:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5125, loss_cls: 4.2394, loss: 4.2394 +2024-07-23 04:13:02,592 - pyskl - INFO - Epoch [32][2800/3746] lr: 8.935e-02, eta: 3 days, 19:04:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5144, loss_cls: 4.2647, loss: 4.2647 +2024-07-23 04:14:24,693 - pyskl - INFO - Epoch [32][2900/3746] lr: 8.933e-02, eta: 3 days, 19:03:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5119, loss_cls: 4.2559, loss: 4.2559 +2024-07-23 04:15:46,209 - pyskl - INFO - Epoch [32][3000/3746] lr: 8.931e-02, eta: 3 days, 19:03:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5152, loss_cls: 4.2189, loss: 4.2189 +2024-07-23 04:17:08,778 - pyskl - INFO - Epoch [32][3100/3746] lr: 8.930e-02, eta: 3 days, 19:02:28, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5073, loss_cls: 4.2541, loss: 4.2541 +2024-07-23 04:18:30,626 - pyskl - INFO - Epoch [32][3200/3746] lr: 8.928e-02, eta: 3 days, 19:01:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5195, loss_cls: 4.2125, loss: 4.2125 +2024-07-23 04:19:52,882 - pyskl - INFO - Epoch [32][3300/3746] lr: 8.926e-02, eta: 3 days, 19:01:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5181, loss_cls: 4.2392, loss: 4.2392 +2024-07-23 04:21:14,281 - pyskl - INFO - Epoch [32][3400/3746] lr: 8.924e-02, eta: 3 days, 19:00:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5122, loss_cls: 4.2507, loss: 4.2507 +2024-07-23 04:22:36,897 - pyskl - INFO - Epoch [32][3500/3746] lr: 8.923e-02, eta: 3 days, 18:59:30, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5075, loss_cls: 4.2834, loss: 4.2834 +2024-07-23 04:23:58,917 - pyskl - INFO - Epoch [32][3600/3746] lr: 8.921e-02, eta: 3 days, 18:58:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5278, loss_cls: 4.1971, loss: 4.1971 +2024-07-23 04:25:21,009 - pyskl - INFO - Epoch [32][3700/3746] lr: 8.919e-02, eta: 3 days, 18:58:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5172, loss_cls: 4.2083, loss: 4.2083 +2024-07-23 04:26:00,628 - pyskl - INFO - Saving checkpoint at 32 epochs +2024-07-23 04:27:50,933 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 04:27:51,591 - pyskl - INFO - +top1_acc 0.1959 +top5_acc 0.4240 +2024-07-23 04:27:51,591 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 04:27:51,629 - pyskl - INFO - +mean_acc 0.1957 +2024-07-23 04:27:51,639 - pyskl - INFO - Epoch(val) [32][309] top1_acc: 0.1959, top5_acc: 0.4240, mean_class_accuracy: 0.1957 +2024-07-23 04:31:40,066 - pyskl - INFO - Epoch [33][100/3746] lr: 8.917e-02, eta: 3 days, 19:03:36, time: 2.284, data_time: 1.268, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5259, loss_cls: 4.1791, loss: 4.1791 +2024-07-23 04:33:02,163 - pyskl - INFO - Epoch [33][200/3746] lr: 8.915e-02, eta: 3 days, 19:02:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5216, loss_cls: 4.1568, loss: 4.1568 +2024-07-23 04:34:24,240 - pyskl - INFO - Epoch [33][300/3746] lr: 8.913e-02, eta: 3 days, 19:02:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5219, loss_cls: 4.2421, loss: 4.2421 +2024-07-23 04:35:45,948 - pyskl - INFO - Epoch [33][400/3746] lr: 8.912e-02, eta: 3 days, 19:01:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5262, loss_cls: 4.2084, loss: 4.2084 +2024-07-23 04:37:07,638 - pyskl - INFO - Epoch [33][500/3746] lr: 8.910e-02, eta: 3 days, 19:00:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5062, loss_cls: 4.2875, loss: 4.2875 +2024-07-23 04:38:29,090 - pyskl - INFO - Epoch [33][600/3746] lr: 8.908e-02, eta: 3 days, 18:59:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5203, loss_cls: 4.2196, loss: 4.2196 +2024-07-23 04:39:50,687 - pyskl - INFO - Epoch [33][700/3746] lr: 8.906e-02, eta: 3 days, 18:58:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5117, loss_cls: 4.2579, loss: 4.2579 +2024-07-23 04:41:12,110 - pyskl - INFO - Epoch [33][800/3746] lr: 8.905e-02, eta: 3 days, 18:58:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5145, loss_cls: 4.2331, loss: 4.2331 +2024-07-23 04:42:33,373 - pyskl - INFO - Epoch [33][900/3746] lr: 8.903e-02, eta: 3 days, 18:57:21, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5181, loss_cls: 4.2051, loss: 4.2051 +2024-07-23 04:43:55,180 - pyskl - INFO - Epoch [33][1000/3746] lr: 8.901e-02, eta: 3 days, 18:56:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5112, loss_cls: 4.2351, loss: 4.2351 +2024-07-23 04:45:16,748 - pyskl - INFO - Epoch [33][1100/3746] lr: 8.899e-02, eta: 3 days, 18:55:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5250, loss_cls: 4.1687, loss: 4.1687 +2024-07-23 04:46:38,499 - pyskl - INFO - Epoch [33][1200/3746] lr: 8.898e-02, eta: 3 days, 18:55:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5253, loss_cls: 4.1905, loss: 4.1905 +2024-07-23 04:48:00,357 - pyskl - INFO - Epoch [33][1300/3746] lr: 8.896e-02, eta: 3 days, 18:54:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5183, loss_cls: 4.2216, loss: 4.2216 +2024-07-23 04:49:22,623 - pyskl - INFO - Epoch [33][1400/3746] lr: 8.894e-02, eta: 3 days, 18:53:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5192, loss_cls: 4.2388, loss: 4.2388 +2024-07-23 04:50:44,408 - pyskl - INFO - Epoch [33][1500/3746] lr: 8.892e-02, eta: 3 days, 18:52:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5102, loss_cls: 4.2944, loss: 4.2944 +2024-07-23 04:52:06,289 - pyskl - INFO - Epoch [33][1600/3746] lr: 8.891e-02, eta: 3 days, 18:51:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5266, loss_cls: 4.2057, loss: 4.2057 +2024-07-23 04:53:28,116 - pyskl - INFO - Epoch [33][1700/3746] lr: 8.889e-02, eta: 3 days, 18:51:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5122, loss_cls: 4.2603, loss: 4.2603 +2024-07-23 04:54:49,665 - pyskl - INFO - Epoch [33][1800/3746] lr: 8.887e-02, eta: 3 days, 18:50:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5191, loss_cls: 4.2243, loss: 4.2243 +2024-07-23 04:56:11,352 - pyskl - INFO - Epoch [33][1900/3746] lr: 8.885e-02, eta: 3 days, 18:49:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5077, loss_cls: 4.2394, loss: 4.2394 +2024-07-23 04:57:32,802 - pyskl - INFO - Epoch [33][2000/3746] lr: 8.884e-02, eta: 3 days, 18:48:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5095, loss_cls: 4.2445, loss: 4.2445 +2024-07-23 04:58:54,437 - pyskl - INFO - Epoch [33][2100/3746] lr: 8.882e-02, eta: 3 days, 18:47:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5269, loss_cls: 4.1732, loss: 4.1732 +2024-07-23 05:00:15,855 - pyskl - INFO - Epoch [33][2200/3746] lr: 8.880e-02, eta: 3 days, 18:47:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5216, loss_cls: 4.2504, loss: 4.2504 +2024-07-23 05:01:37,618 - pyskl - INFO - Epoch [33][2300/3746] lr: 8.878e-02, eta: 3 days, 18:46:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5136, loss_cls: 4.2595, loss: 4.2595 +2024-07-23 05:02:59,013 - pyskl - INFO - Epoch [33][2400/3746] lr: 8.876e-02, eta: 3 days, 18:45:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5302, loss_cls: 4.1856, loss: 4.1856 +2024-07-23 05:04:20,478 - pyskl - INFO - Epoch [33][2500/3746] lr: 8.875e-02, eta: 3 days, 18:44:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5261, loss_cls: 4.2205, loss: 4.2205 +2024-07-23 05:05:42,376 - pyskl - INFO - Epoch [33][2600/3746] lr: 8.873e-02, eta: 3 days, 18:43:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5134, loss_cls: 4.2470, loss: 4.2470 +2024-07-23 05:07:04,773 - pyskl - INFO - Epoch [33][2700/3746] lr: 8.871e-02, eta: 3 days, 18:43:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5183, loss_cls: 4.2435, loss: 4.2435 +2024-07-23 05:08:26,341 - pyskl - INFO - Epoch [33][2800/3746] lr: 8.869e-02, eta: 3 days, 18:42:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5048, loss_cls: 4.2681, loss: 4.2681 +2024-07-23 05:09:49,005 - pyskl - INFO - Epoch [33][2900/3746] lr: 8.868e-02, eta: 3 days, 18:41:37, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5145, loss_cls: 4.2780, loss: 4.2780 +2024-07-23 05:11:11,684 - pyskl - INFO - Epoch [33][3000/3746] lr: 8.866e-02, eta: 3 days, 18:40:52, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5170, loss_cls: 4.2303, loss: 4.2303 +2024-07-23 05:12:33,265 - pyskl - INFO - Epoch [33][3100/3746] lr: 8.864e-02, eta: 3 days, 18:40:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5117, loss_cls: 4.2378, loss: 4.2378 +2024-07-23 05:13:54,847 - pyskl - INFO - Epoch [33][3200/3746] lr: 8.862e-02, eta: 3 days, 18:39:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5206, loss_cls: 4.2228, loss: 4.2228 +2024-07-23 05:15:17,646 - pyskl - INFO - Epoch [33][3300/3746] lr: 8.861e-02, eta: 3 days, 18:38:31, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5158, loss_cls: 4.2491, loss: 4.2491 +2024-07-23 05:16:39,509 - pyskl - INFO - Epoch [33][3400/3746] lr: 8.859e-02, eta: 3 days, 18:37:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5222, loss_cls: 4.2361, loss: 4.2361 +2024-07-23 05:18:02,120 - pyskl - INFO - Epoch [33][3500/3746] lr: 8.857e-02, eta: 3 days, 18:36:58, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5227, loss_cls: 4.2265, loss: 4.2265 +2024-07-23 05:19:23,632 - pyskl - INFO - Epoch [33][3600/3746] lr: 8.855e-02, eta: 3 days, 18:36:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5208, loss_cls: 4.2146, loss: 4.2146 +2024-07-23 05:20:45,528 - pyskl - INFO - Epoch [33][3700/3746] lr: 8.853e-02, eta: 3 days, 18:35:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5177, loss_cls: 4.2412, loss: 4.2412 +2024-07-23 05:21:25,288 - pyskl - INFO - Saving checkpoint at 33 epochs +2024-07-23 05:23:17,252 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 05:23:17,908 - pyskl - INFO - +top1_acc 0.2014 +top5_acc 0.4375 +2024-07-23 05:23:17,908 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 05:23:17,946 - pyskl - INFO - +mean_acc 0.2010 +2024-07-23 05:23:17,957 - pyskl - INFO - Epoch(val) [33][309] top1_acc: 0.2014, top5_acc: 0.4375, mean_class_accuracy: 0.2010 +2024-07-23 05:27:03,388 - pyskl - INFO - Epoch [34][100/3746] lr: 8.851e-02, eta: 3 days, 18:40:26, time: 2.254, data_time: 1.279, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5381, loss_cls: 4.1199, loss: 4.1199 +2024-07-23 05:28:25,060 - pyskl - INFO - Epoch [34][200/3746] lr: 8.849e-02, eta: 3 days, 18:39:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5172, loss_cls: 4.2154, loss: 4.2154 +2024-07-23 05:29:46,993 - pyskl - INFO - Epoch [34][300/3746] lr: 8.847e-02, eta: 3 days, 18:38:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5264, loss_cls: 4.1617, loss: 4.1617 +2024-07-23 05:31:08,328 - pyskl - INFO - Epoch [34][400/3746] lr: 8.845e-02, eta: 3 days, 18:37:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5222, loss_cls: 4.1895, loss: 4.1895 +2024-07-23 05:32:29,945 - pyskl - INFO - Epoch [34][500/3746] lr: 8.844e-02, eta: 3 days, 18:37:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5197, loss_cls: 4.1981, loss: 4.1981 +2024-07-23 05:33:51,322 - pyskl - INFO - Epoch [34][600/3746] lr: 8.842e-02, eta: 3 days, 18:36:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5139, loss_cls: 4.2489, loss: 4.2489 +2024-07-23 05:35:13,411 - pyskl - INFO - Epoch [34][700/3746] lr: 8.840e-02, eta: 3 days, 18:35:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5206, loss_cls: 4.1727, loss: 4.1727 +2024-07-23 05:36:34,625 - pyskl - INFO - Epoch [34][800/3746] lr: 8.838e-02, eta: 3 days, 18:34:40, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5189, loss_cls: 4.2322, loss: 4.2322 +2024-07-23 05:37:55,728 - pyskl - INFO - Epoch [34][900/3746] lr: 8.836e-02, eta: 3 days, 18:33:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5141, loss_cls: 4.2327, loss: 4.2327 +2024-07-23 05:39:17,800 - pyskl - INFO - Epoch [34][1000/3746] lr: 8.835e-02, eta: 3 days, 18:33:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5180, loss_cls: 4.2248, loss: 4.2248 +2024-07-23 05:40:39,572 - pyskl - INFO - Epoch [34][1100/3746] lr: 8.833e-02, eta: 3 days, 18:32:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5102, loss_cls: 4.2456, loss: 4.2456 +2024-07-23 05:42:00,850 - pyskl - INFO - Epoch [34][1200/3746] lr: 8.831e-02, eta: 3 days, 18:31:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5145, loss_cls: 4.2377, loss: 4.2377 +2024-07-23 05:43:22,491 - pyskl - INFO - Epoch [34][1300/3746] lr: 8.829e-02, eta: 3 days, 18:30:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5162, loss_cls: 4.1900, loss: 4.1900 +2024-07-23 05:44:44,482 - pyskl - INFO - Epoch [34][1400/3746] lr: 8.828e-02, eta: 3 days, 18:29:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5158, loss_cls: 4.2435, loss: 4.2435 +2024-07-23 05:46:06,015 - pyskl - INFO - Epoch [34][1500/3746] lr: 8.826e-02, eta: 3 days, 18:28:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5205, loss_cls: 4.2098, loss: 4.2098 +2024-07-23 05:47:27,532 - pyskl - INFO - Epoch [34][1600/3746] lr: 8.824e-02, eta: 3 days, 18:28:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5192, loss_cls: 4.2334, loss: 4.2334 +2024-07-23 05:48:48,617 - pyskl - INFO - Epoch [34][1700/3746] lr: 8.822e-02, eta: 3 days, 18:27:09, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5222, loss_cls: 4.1919, loss: 4.1919 +2024-07-23 05:50:09,974 - pyskl - INFO - Epoch [34][1800/3746] lr: 8.820e-02, eta: 3 days, 18:26:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5262, loss_cls: 4.1702, loss: 4.1702 +2024-07-23 05:51:31,222 - pyskl - INFO - Epoch [34][1900/3746] lr: 8.819e-02, eta: 3 days, 18:25:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5127, loss_cls: 4.2395, loss: 4.2395 +2024-07-23 05:52:53,999 - pyskl - INFO - Epoch [34][2000/3746] lr: 8.817e-02, eta: 3 days, 18:24:40, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5206, loss_cls: 4.1956, loss: 4.1956 +2024-07-23 05:54:15,063 - pyskl - INFO - Epoch [34][2100/3746] lr: 8.815e-02, eta: 3 days, 18:23:48, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5162, loss_cls: 4.2431, loss: 4.2431 +2024-07-23 05:55:36,630 - pyskl - INFO - Epoch [34][2200/3746] lr: 8.813e-02, eta: 3 days, 18:22:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5239, loss_cls: 4.2177, loss: 4.2177 +2024-07-23 05:56:57,814 - pyskl - INFO - Epoch [34][2300/3746] lr: 8.811e-02, eta: 3 days, 18:22:06, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5192, loss_cls: 4.2178, loss: 4.2178 +2024-07-23 05:58:19,845 - pyskl - INFO - Epoch [34][2400/3746] lr: 8.809e-02, eta: 3 days, 18:21:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5155, loss_cls: 4.2115, loss: 4.2115 +2024-07-23 05:59:41,458 - pyskl - INFO - Epoch [34][2500/3746] lr: 8.808e-02, eta: 3 days, 18:20:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5248, loss_cls: 4.1788, loss: 4.1788 +2024-07-23 06:01:02,608 - pyskl - INFO - Epoch [34][2600/3746] lr: 8.806e-02, eta: 3 days, 18:19:34, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5253, loss_cls: 4.2109, loss: 4.2109 +2024-07-23 06:02:23,898 - pyskl - INFO - Epoch [34][2700/3746] lr: 8.804e-02, eta: 3 days, 18:18:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5103, loss_cls: 4.2401, loss: 4.2401 +2024-07-23 06:03:45,395 - pyskl - INFO - Epoch [34][2800/3746] lr: 8.802e-02, eta: 3 days, 18:17:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5133, loss_cls: 4.2578, loss: 4.2578 +2024-07-23 06:05:07,162 - pyskl - INFO - Epoch [34][2900/3746] lr: 8.800e-02, eta: 3 days, 18:17:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5108, loss_cls: 4.2618, loss: 4.2618 +2024-07-23 06:06:29,184 - pyskl - INFO - Epoch [34][3000/3746] lr: 8.799e-02, eta: 3 days, 18:16:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5155, loss_cls: 4.2185, loss: 4.2185 +2024-07-23 06:07:51,215 - pyskl - INFO - Epoch [34][3100/3746] lr: 8.797e-02, eta: 3 days, 18:15:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5255, loss_cls: 4.2335, loss: 4.2335 +2024-07-23 06:09:12,682 - pyskl - INFO - Epoch [34][3200/3746] lr: 8.795e-02, eta: 3 days, 18:14:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4988, loss_cls: 4.3197, loss: 4.3197 +2024-07-23 06:10:35,166 - pyskl - INFO - Epoch [34][3300/3746] lr: 8.793e-02, eta: 3 days, 18:13:43, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5164, loss_cls: 4.2264, loss: 4.2264 +2024-07-23 06:11:56,790 - pyskl - INFO - Epoch [34][3400/3746] lr: 8.791e-02, eta: 3 days, 18:12:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5108, loss_cls: 4.2764, loss: 4.2764 +2024-07-23 06:13:19,566 - pyskl - INFO - Epoch [34][3500/3746] lr: 8.789e-02, eta: 3 days, 18:12:05, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5216, loss_cls: 4.2318, loss: 4.2318 +2024-07-23 06:14:41,619 - pyskl - INFO - Epoch [34][3600/3746] lr: 8.788e-02, eta: 3 days, 18:11:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5075, loss_cls: 4.2690, loss: 4.2690 +2024-07-23 06:16:03,406 - pyskl - INFO - Epoch [34][3700/3746] lr: 8.786e-02, eta: 3 days, 18:10:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5262, loss_cls: 4.1881, loss: 4.1881 +2024-07-23 06:16:43,382 - pyskl - INFO - Saving checkpoint at 34 epochs +2024-07-23 06:18:34,581 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 06:18:35,239 - pyskl - INFO - +top1_acc 0.1746 +top5_acc 0.3858 +2024-07-23 06:18:35,239 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 06:18:35,282 - pyskl - INFO - +mean_acc 0.1745 +2024-07-23 06:18:35,293 - pyskl - INFO - Epoch(val) [34][309] top1_acc: 0.1746, top5_acc: 0.3858, mean_class_accuracy: 0.1745 +2024-07-23 06:22:19,494 - pyskl - INFO - Epoch [35][100/3746] lr: 8.783e-02, eta: 3 days, 18:15:09, time: 2.242, data_time: 1.260, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5288, loss_cls: 4.1721, loss: 4.1721 +2024-07-23 06:23:41,483 - pyskl - INFO - Epoch [35][200/3746] lr: 8.781e-02, eta: 3 days, 18:14:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5206, loss_cls: 4.2229, loss: 4.2229 +2024-07-23 06:25:03,612 - pyskl - INFO - Epoch [35][300/3746] lr: 8.780e-02, eta: 3 days, 18:13:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5188, loss_cls: 4.2342, loss: 4.2342 +2024-07-23 06:26:25,661 - pyskl - INFO - Epoch [35][400/3746] lr: 8.778e-02, eta: 3 days, 18:12:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5177, loss_cls: 4.2345, loss: 4.2345 +2024-07-23 06:27:47,268 - pyskl - INFO - Epoch [35][500/3746] lr: 8.776e-02, eta: 3 days, 18:11:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5116, loss_cls: 4.2392, loss: 4.2392 +2024-07-23 06:29:09,258 - pyskl - INFO - Epoch [35][600/3746] lr: 8.774e-02, eta: 3 days, 18:10:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5225, loss_cls: 4.2176, loss: 4.2176 +2024-07-23 06:30:31,030 - pyskl - INFO - Epoch [35][700/3746] lr: 8.772e-02, eta: 3 days, 18:10:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5291, loss_cls: 4.1584, loss: 4.1584 +2024-07-23 06:31:53,258 - pyskl - INFO - Epoch [35][800/3746] lr: 8.770e-02, eta: 3 days, 18:09:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5119, loss_cls: 4.2140, loss: 4.2140 +2024-07-23 06:33:15,525 - pyskl - INFO - Epoch [35][900/3746] lr: 8.769e-02, eta: 3 days, 18:08:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5141, loss_cls: 4.2127, loss: 4.2127 +2024-07-23 06:34:37,098 - pyskl - INFO - Epoch [35][1000/3746] lr: 8.767e-02, eta: 3 days, 18:07:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5172, loss_cls: 4.2237, loss: 4.2237 +2024-07-23 06:35:58,958 - pyskl - INFO - Epoch [35][1100/3746] lr: 8.765e-02, eta: 3 days, 18:06:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5244, loss_cls: 4.1796, loss: 4.1796 +2024-07-23 06:37:20,555 - pyskl - INFO - Epoch [35][1200/3746] lr: 8.763e-02, eta: 3 days, 18:05:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5180, loss_cls: 4.2008, loss: 4.2008 +2024-07-23 06:38:42,775 - pyskl - INFO - Epoch [35][1300/3746] lr: 8.761e-02, eta: 3 days, 18:05:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5267, loss_cls: 4.1578, loss: 4.1578 +2024-07-23 06:40:04,252 - pyskl - INFO - Epoch [35][1400/3746] lr: 8.759e-02, eta: 3 days, 18:04:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5198, loss_cls: 4.1965, loss: 4.1965 +2024-07-23 06:41:26,336 - pyskl - INFO - Epoch [35][1500/3746] lr: 8.757e-02, eta: 3 days, 18:03:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5223, loss_cls: 4.1850, loss: 4.1850 +2024-07-23 06:42:47,691 - pyskl - INFO - Epoch [35][1600/3746] lr: 8.756e-02, eta: 3 days, 18:02:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5152, loss_cls: 4.2277, loss: 4.2277 +2024-07-23 06:44:09,840 - pyskl - INFO - Epoch [35][1700/3746] lr: 8.754e-02, eta: 3 days, 18:01:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5227, loss_cls: 4.2009, loss: 4.2009 +2024-07-23 06:45:31,783 - pyskl - INFO - Epoch [35][1800/3746] lr: 8.752e-02, eta: 3 days, 18:00:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5153, loss_cls: 4.2500, loss: 4.2500 +2024-07-23 06:46:53,945 - pyskl - INFO - Epoch [35][1900/3746] lr: 8.750e-02, eta: 3 days, 17:59:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5267, loss_cls: 4.1849, loss: 4.1849 +2024-07-23 06:48:15,398 - pyskl - INFO - Epoch [35][2000/3746] lr: 8.748e-02, eta: 3 days, 17:58:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5095, loss_cls: 4.2716, loss: 4.2716 +2024-07-23 06:49:36,858 - pyskl - INFO - Epoch [35][2100/3746] lr: 8.746e-02, eta: 3 days, 17:58:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5172, loss_cls: 4.2209, loss: 4.2209 +2024-07-23 06:50:58,415 - pyskl - INFO - Epoch [35][2200/3746] lr: 8.745e-02, eta: 3 days, 17:57:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5188, loss_cls: 4.2214, loss: 4.2214 +2024-07-23 06:52:20,115 - pyskl - INFO - Epoch [35][2300/3746] lr: 8.743e-02, eta: 3 days, 17:56:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5191, loss_cls: 4.2238, loss: 4.2238 +2024-07-23 06:53:41,855 - pyskl - INFO - Epoch [35][2400/3746] lr: 8.741e-02, eta: 3 days, 17:55:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5227, loss_cls: 4.2271, loss: 4.2271 +2024-07-23 06:55:03,584 - pyskl - INFO - Epoch [35][2500/3746] lr: 8.739e-02, eta: 3 days, 17:54:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5186, loss_cls: 4.2276, loss: 4.2276 +2024-07-23 06:56:25,832 - pyskl - INFO - Epoch [35][2600/3746] lr: 8.737e-02, eta: 3 days, 17:53:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5162, loss_cls: 4.2511, loss: 4.2511 +2024-07-23 06:57:48,001 - pyskl - INFO - Epoch [35][2700/3746] lr: 8.735e-02, eta: 3 days, 17:52:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5212, loss_cls: 4.2126, loss: 4.2126 +2024-07-23 06:59:09,573 - pyskl - INFO - Epoch [35][2800/3746] lr: 8.733e-02, eta: 3 days, 17:52:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5212, loss_cls: 4.2069, loss: 4.2069 +2024-07-23 07:00:32,271 - pyskl - INFO - Epoch [35][2900/3746] lr: 8.732e-02, eta: 3 days, 17:51:13, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5067, loss_cls: 4.2939, loss: 4.2939 +2024-07-23 07:01:54,224 - pyskl - INFO - Epoch [35][3000/3746] lr: 8.730e-02, eta: 3 days, 17:50:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5214, loss_cls: 4.2189, loss: 4.2189 +2024-07-23 07:03:15,758 - pyskl - INFO - Epoch [35][3100/3746] lr: 8.728e-02, eta: 3 days, 17:49:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5202, loss_cls: 4.2459, loss: 4.2459 +2024-07-23 07:04:37,470 - pyskl - INFO - Epoch [35][3200/3746] lr: 8.726e-02, eta: 3 days, 17:48:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5184, loss_cls: 4.2062, loss: 4.2062 +2024-07-23 07:05:59,681 - pyskl - INFO - Epoch [35][3300/3746] lr: 8.724e-02, eta: 3 days, 17:47:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5234, loss_cls: 4.2051, loss: 4.2051 +2024-07-23 07:07:21,405 - pyskl - INFO - Epoch [35][3400/3746] lr: 8.722e-02, eta: 3 days, 17:46:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5153, loss_cls: 4.2310, loss: 4.2310 +2024-07-23 07:08:43,449 - pyskl - INFO - Epoch [35][3500/3746] lr: 8.720e-02, eta: 3 days, 17:46:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5222, loss_cls: 4.2059, loss: 4.2059 +2024-07-23 07:10:05,346 - pyskl - INFO - Epoch [35][3600/3746] lr: 8.718e-02, eta: 3 days, 17:45:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5175, loss_cls: 4.2409, loss: 4.2409 +2024-07-23 07:11:26,869 - pyskl - INFO - Epoch [35][3700/3746] lr: 8.717e-02, eta: 3 days, 17:44:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5188, loss_cls: 4.2082, loss: 4.2082 +2024-07-23 07:12:06,904 - pyskl - INFO - Saving checkpoint at 35 epochs +2024-07-23 07:13:58,400 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 07:13:59,054 - pyskl - INFO - +top1_acc 0.2056 +top5_acc 0.4383 +2024-07-23 07:13:59,055 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 07:13:59,092 - pyskl - INFO - +mean_acc 0.2055 +2024-07-23 07:13:59,097 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_20.pth was removed +2024-07-23 07:13:59,350 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_35.pth. +2024-07-23 07:13:59,351 - pyskl - INFO - Best top1_acc is 0.2056 at 35 epoch. +2024-07-23 07:13:59,363 - pyskl - INFO - Epoch(val) [35][309] top1_acc: 0.2056, top5_acc: 0.4383, mean_class_accuracy: 0.2055 +2024-07-23 07:17:44,909 - pyskl - INFO - Epoch [36][100/3746] lr: 8.714e-02, eta: 3 days, 17:48:46, time: 2.255, data_time: 1.270, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5311, loss_cls: 4.1452, loss: 4.1452 +2024-07-23 07:19:06,828 - pyskl - INFO - Epoch [36][200/3746] lr: 8.712e-02, eta: 3 days, 17:47:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5377, loss_cls: 4.1599, loss: 4.1599 +2024-07-23 07:20:28,297 - pyskl - INFO - Epoch [36][300/3746] lr: 8.710e-02, eta: 3 days, 17:46:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5233, loss_cls: 4.1675, loss: 4.1675 +2024-07-23 07:21:50,023 - pyskl - INFO - Epoch [36][400/3746] lr: 8.708e-02, eta: 3 days, 17:46:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5202, loss_cls: 4.2206, loss: 4.2206 +2024-07-23 07:23:11,658 - pyskl - INFO - Epoch [36][500/3746] lr: 8.706e-02, eta: 3 days, 17:45:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5264, loss_cls: 4.1868, loss: 4.1868 +2024-07-23 07:24:33,344 - pyskl - INFO - Epoch [36][600/3746] lr: 8.704e-02, eta: 3 days, 17:44:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5114, loss_cls: 4.2334, loss: 4.2334 +2024-07-23 07:25:54,891 - pyskl - INFO - Epoch [36][700/3746] lr: 8.703e-02, eta: 3 days, 17:43:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5311, loss_cls: 4.1948, loss: 4.1948 +2024-07-23 07:27:16,801 - pyskl - INFO - Epoch [36][800/3746] lr: 8.701e-02, eta: 3 days, 17:42:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5261, loss_cls: 4.1709, loss: 4.1709 +2024-07-23 07:28:38,577 - pyskl - INFO - Epoch [36][900/3746] lr: 8.699e-02, eta: 3 days, 17:41:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5250, loss_cls: 4.1861, loss: 4.1861 +2024-07-23 07:30:00,837 - pyskl - INFO - Epoch [36][1000/3746] lr: 8.697e-02, eta: 3 days, 17:40:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5231, loss_cls: 4.2289, loss: 4.2289 +2024-07-23 07:31:22,895 - pyskl - INFO - Epoch [36][1100/3746] lr: 8.695e-02, eta: 3 days, 17:39:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5175, loss_cls: 4.2053, loss: 4.2053 +2024-07-23 07:32:44,937 - pyskl - INFO - Epoch [36][1200/3746] lr: 8.693e-02, eta: 3 days, 17:39:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5266, loss_cls: 4.1841, loss: 4.1841 +2024-07-23 07:34:06,819 - pyskl - INFO - Epoch [36][1300/3746] lr: 8.691e-02, eta: 3 days, 17:38:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5186, loss_cls: 4.2342, loss: 4.2342 +2024-07-23 07:35:29,287 - pyskl - INFO - Epoch [36][1400/3746] lr: 8.689e-02, eta: 3 days, 17:37:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5231, loss_cls: 4.2152, loss: 4.2152 +2024-07-23 07:36:51,590 - pyskl - INFO - Epoch [36][1500/3746] lr: 8.688e-02, eta: 3 days, 17:36:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5192, loss_cls: 4.1984, loss: 4.1984 +2024-07-23 07:38:13,157 - pyskl - INFO - Epoch [36][1600/3746] lr: 8.686e-02, eta: 3 days, 17:35:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5252, loss_cls: 4.2117, loss: 4.2117 +2024-07-23 07:39:34,985 - pyskl - INFO - Epoch [36][1700/3746] lr: 8.684e-02, eta: 3 days, 17:34:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5264, loss_cls: 4.1837, loss: 4.1837 +2024-07-23 07:40:56,524 - pyskl - INFO - Epoch [36][1800/3746] lr: 8.682e-02, eta: 3 days, 17:33:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5170, loss_cls: 4.2323, loss: 4.2323 +2024-07-23 07:42:18,688 - pyskl - INFO - Epoch [36][1900/3746] lr: 8.680e-02, eta: 3 days, 17:32:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5148, loss_cls: 4.2265, loss: 4.2265 +2024-07-23 07:43:40,410 - pyskl - INFO - Epoch [36][2000/3746] lr: 8.678e-02, eta: 3 days, 17:31:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5180, loss_cls: 4.2393, loss: 4.2393 +2024-07-23 07:45:01,779 - pyskl - INFO - Epoch [36][2100/3746] lr: 8.676e-02, eta: 3 days, 17:31:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5205, loss_cls: 4.2047, loss: 4.2047 +2024-07-23 07:46:23,752 - pyskl - INFO - Epoch [36][2200/3746] lr: 8.674e-02, eta: 3 days, 17:30:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5166, loss_cls: 4.2446, loss: 4.2446 +2024-07-23 07:47:45,488 - pyskl - INFO - Epoch [36][2300/3746] lr: 8.672e-02, eta: 3 days, 17:29:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5209, loss_cls: 4.2100, loss: 4.2100 +2024-07-23 07:49:07,538 - pyskl - INFO - Epoch [36][2400/3746] lr: 8.671e-02, eta: 3 days, 17:28:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5230, loss_cls: 4.2304, loss: 4.2304 +2024-07-23 07:50:29,454 - pyskl - INFO - Epoch [36][2500/3746] lr: 8.669e-02, eta: 3 days, 17:27:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5378, loss_cls: 4.1351, loss: 4.1351 +2024-07-23 07:51:51,147 - pyskl - INFO - Epoch [36][2600/3746] lr: 8.667e-02, eta: 3 days, 17:26:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5270, loss_cls: 4.1764, loss: 4.1764 +2024-07-23 07:53:13,723 - pyskl - INFO - Epoch [36][2700/3746] lr: 8.665e-02, eta: 3 days, 17:25:40, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5361, loss_cls: 4.1926, loss: 4.1926 +2024-07-23 07:54:35,589 - pyskl - INFO - Epoch [36][2800/3746] lr: 8.663e-02, eta: 3 days, 17:24:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5181, loss_cls: 4.2263, loss: 4.2263 +2024-07-23 07:55:57,545 - pyskl - INFO - Epoch [36][2900/3746] lr: 8.661e-02, eta: 3 days, 17:23:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5252, loss_cls: 4.1768, loss: 4.1768 +2024-07-23 07:57:20,055 - pyskl - INFO - Epoch [36][3000/3746] lr: 8.659e-02, eta: 3 days, 17:23:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5234, loss_cls: 4.2108, loss: 4.2108 +2024-07-23 07:58:42,034 - pyskl - INFO - Epoch [36][3100/3746] lr: 8.657e-02, eta: 3 days, 17:22:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5211, loss_cls: 4.2435, loss: 4.2435 +2024-07-23 08:00:03,801 - pyskl - INFO - Epoch [36][3200/3746] lr: 8.655e-02, eta: 3 days, 17:21:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5170, loss_cls: 4.2484, loss: 4.2484 +2024-07-23 08:01:26,307 - pyskl - INFO - Epoch [36][3300/3746] lr: 8.653e-02, eta: 3 days, 17:20:20, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5162, loss_cls: 4.2487, loss: 4.2487 +2024-07-23 08:02:47,829 - pyskl - INFO - Epoch [36][3400/3746] lr: 8.651e-02, eta: 3 days, 17:19:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5094, loss_cls: 4.2755, loss: 4.2755 +2024-07-23 08:04:09,882 - pyskl - INFO - Epoch [36][3500/3746] lr: 8.650e-02, eta: 3 days, 17:18:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5198, loss_cls: 4.2512, loss: 4.2512 +2024-07-23 08:05:31,776 - pyskl - INFO - Epoch [36][3600/3746] lr: 8.648e-02, eta: 3 days, 17:17:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5067, loss_cls: 4.2845, loss: 4.2845 +2024-07-23 08:06:54,028 - pyskl - INFO - Epoch [36][3700/3746] lr: 8.646e-02, eta: 3 days, 17:16:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5089, loss_cls: 4.2731, loss: 4.2731 +2024-07-23 08:07:33,566 - pyskl - INFO - Saving checkpoint at 36 epochs +2024-07-23 08:09:25,622 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 08:09:26,280 - pyskl - INFO - +top1_acc 0.2012 +top5_acc 0.4274 +2024-07-23 08:09:26,280 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 08:09:26,318 - pyskl - INFO - +mean_acc 0.2010 +2024-07-23 08:09:26,328 - pyskl - INFO - Epoch(val) [36][309] top1_acc: 0.2012, top5_acc: 0.4274, mean_class_accuracy: 0.2010 +2024-07-23 08:13:09,232 - pyskl - INFO - Epoch [37][100/3746] lr: 8.643e-02, eta: 3 days, 17:20:52, time: 2.229, data_time: 1.259, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5319, loss_cls: 4.1700, loss: 4.1700 +2024-07-23 08:14:30,925 - pyskl - INFO - Epoch [37][200/3746] lr: 8.641e-02, eta: 3 days, 17:19:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5381, loss_cls: 4.1452, loss: 4.1452 +2024-07-23 08:15:52,326 - pyskl - INFO - Epoch [37][300/3746] lr: 8.639e-02, eta: 3 days, 17:19:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5234, loss_cls: 4.1867, loss: 4.1867 +2024-07-23 08:17:13,857 - pyskl - INFO - Epoch [37][400/3746] lr: 8.637e-02, eta: 3 days, 17:18:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5389, loss_cls: 4.1554, loss: 4.1554 +2024-07-23 08:18:36,437 - pyskl - INFO - Epoch [37][500/3746] lr: 8.635e-02, eta: 3 days, 17:17:12, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5311, loss_cls: 4.1764, loss: 4.1764 +2024-07-23 08:19:57,882 - pyskl - INFO - Epoch [37][600/3746] lr: 8.633e-02, eta: 3 days, 17:16:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5241, loss_cls: 4.2110, loss: 4.2110 +2024-07-23 08:21:19,726 - pyskl - INFO - Epoch [37][700/3746] lr: 8.631e-02, eta: 3 days, 17:15:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5164, loss_cls: 4.2134, loss: 4.2134 +2024-07-23 08:22:41,156 - pyskl - INFO - Epoch [37][800/3746] lr: 8.630e-02, eta: 3 days, 17:14:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5205, loss_cls: 4.1766, loss: 4.1766 +2024-07-23 08:24:03,143 - pyskl - INFO - Epoch [37][900/3746] lr: 8.628e-02, eta: 3 days, 17:13:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5238, loss_cls: 4.2293, loss: 4.2293 +2024-07-23 08:25:25,439 - pyskl - INFO - Epoch [37][1000/3746] lr: 8.626e-02, eta: 3 days, 17:12:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5256, loss_cls: 4.1962, loss: 4.1962 +2024-07-23 08:26:47,607 - pyskl - INFO - Epoch [37][1100/3746] lr: 8.624e-02, eta: 3 days, 17:11:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5127, loss_cls: 4.2503, loss: 4.2503 +2024-07-23 08:28:09,472 - pyskl - INFO - Epoch [37][1200/3746] lr: 8.622e-02, eta: 3 days, 17:10:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5216, loss_cls: 4.2137, loss: 4.2137 +2024-07-23 08:29:31,622 - pyskl - INFO - Epoch [37][1300/3746] lr: 8.620e-02, eta: 3 days, 17:09:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5281, loss_cls: 4.1828, loss: 4.1828 +2024-07-23 08:30:53,064 - pyskl - INFO - Epoch [37][1400/3746] lr: 8.618e-02, eta: 3 days, 17:08:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5238, loss_cls: 4.1765, loss: 4.1765 +2024-07-23 08:32:14,623 - pyskl - INFO - Epoch [37][1500/3746] lr: 8.616e-02, eta: 3 days, 17:08:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5217, loss_cls: 4.2058, loss: 4.2058 +2024-07-23 08:33:36,142 - pyskl - INFO - Epoch [37][1600/3746] lr: 8.614e-02, eta: 3 days, 17:07:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5255, loss_cls: 4.1956, loss: 4.1956 +2024-07-23 08:34:57,760 - pyskl - INFO - Epoch [37][1700/3746] lr: 8.612e-02, eta: 3 days, 17:06:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5152, loss_cls: 4.1988, loss: 4.1988 +2024-07-23 08:36:19,029 - pyskl - INFO - Epoch [37][1800/3746] lr: 8.610e-02, eta: 3 days, 17:05:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5242, loss_cls: 4.1905, loss: 4.1905 +2024-07-23 08:37:40,522 - pyskl - INFO - Epoch [37][1900/3746] lr: 8.608e-02, eta: 3 days, 17:04:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5366, loss_cls: 4.1589, loss: 4.1589 +2024-07-23 08:39:02,424 - pyskl - INFO - Epoch [37][2000/3746] lr: 8.606e-02, eta: 3 days, 17:03:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5225, loss_cls: 4.1612, loss: 4.1612 +2024-07-23 08:40:24,553 - pyskl - INFO - Epoch [37][2100/3746] lr: 8.604e-02, eta: 3 days, 17:02:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5264, loss_cls: 4.2104, loss: 4.2104 +2024-07-23 08:41:45,857 - pyskl - INFO - Epoch [37][2200/3746] lr: 8.602e-02, eta: 3 days, 17:01:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5092, loss_cls: 4.2374, loss: 4.2374 +2024-07-23 08:43:07,671 - pyskl - INFO - Epoch [37][2300/3746] lr: 8.601e-02, eta: 3 days, 17:00:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5173, loss_cls: 4.2113, loss: 4.2113 +2024-07-23 08:44:29,614 - pyskl - INFO - Epoch [37][2400/3746] lr: 8.599e-02, eta: 3 days, 16:59:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5212, loss_cls: 4.2380, loss: 4.2380 +2024-07-23 08:45:51,121 - pyskl - INFO - Epoch [37][2500/3746] lr: 8.597e-02, eta: 3 days, 16:58:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5073, loss_cls: 4.2762, loss: 4.2762 +2024-07-23 08:47:12,545 - pyskl - INFO - Epoch [37][2600/3746] lr: 8.595e-02, eta: 3 days, 16:57:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5366, loss_cls: 4.1729, loss: 4.1729 +2024-07-23 08:48:34,500 - pyskl - INFO - Epoch [37][2700/3746] lr: 8.593e-02, eta: 3 days, 16:56:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5239, loss_cls: 4.2040, loss: 4.2040 +2024-07-23 08:49:56,994 - pyskl - INFO - Epoch [37][2800/3746] lr: 8.591e-02, eta: 3 days, 16:55:53, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5136, loss_cls: 4.2388, loss: 4.2388 +2024-07-23 08:51:18,632 - pyskl - INFO - Epoch [37][2900/3746] lr: 8.589e-02, eta: 3 days, 16:54:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5353, loss_cls: 4.1775, loss: 4.1775 +2024-07-23 08:52:40,494 - pyskl - INFO - Epoch [37][3000/3746] lr: 8.587e-02, eta: 3 days, 16:54:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5247, loss_cls: 4.1984, loss: 4.1984 +2024-07-23 08:54:02,325 - pyskl - INFO - Epoch [37][3100/3746] lr: 8.585e-02, eta: 3 days, 16:53:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5280, loss_cls: 4.1552, loss: 4.1552 +2024-07-23 08:55:23,971 - pyskl - INFO - Epoch [37][3200/3746] lr: 8.583e-02, eta: 3 days, 16:52:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5153, loss_cls: 4.2169, loss: 4.2169 +2024-07-23 08:56:46,623 - pyskl - INFO - Epoch [37][3300/3746] lr: 8.581e-02, eta: 3 days, 16:51:15, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5131, loss_cls: 4.2859, loss: 4.2859 +2024-07-23 08:58:08,767 - pyskl - INFO - Epoch [37][3400/3746] lr: 8.579e-02, eta: 3 days, 16:50:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5097, loss_cls: 4.2527, loss: 4.2527 +2024-07-23 08:59:30,558 - pyskl - INFO - Epoch [37][3500/3746] lr: 8.577e-02, eta: 3 days, 16:49:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5277, loss_cls: 4.1939, loss: 4.1939 +2024-07-23 09:00:52,581 - pyskl - INFO - Epoch [37][3600/3746] lr: 8.575e-02, eta: 3 days, 16:48:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5106, loss_cls: 4.2644, loss: 4.2644 +2024-07-23 09:02:13,958 - pyskl - INFO - Epoch [37][3700/3746] lr: 8.573e-02, eta: 3 days, 16:47:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5223, loss_cls: 4.1973, loss: 4.1973 +2024-07-23 09:02:54,793 - pyskl - INFO - Saving checkpoint at 37 epochs +2024-07-23 09:04:46,576 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 09:04:47,235 - pyskl - INFO - +top1_acc 0.1963 +top5_acc 0.4240 +2024-07-23 09:04:47,235 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 09:04:47,273 - pyskl - INFO - +mean_acc 0.1959 +2024-07-23 09:04:47,283 - pyskl - INFO - Epoch(val) [37][309] top1_acc: 0.1963, top5_acc: 0.4240, mean_class_accuracy: 0.1959 +2024-07-23 09:08:32,864 - pyskl - INFO - Epoch [38][100/3746] lr: 8.570e-02, eta: 3 days, 16:51:32, time: 2.256, data_time: 1.283, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5331, loss_cls: 4.1527, loss: 4.1527 +2024-07-23 09:09:54,345 - pyskl - INFO - Epoch [38][200/3746] lr: 8.568e-02, eta: 3 days, 16:50:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5147, loss_cls: 4.2383, loss: 4.2383 +2024-07-23 09:11:16,424 - pyskl - INFO - Epoch [38][300/3746] lr: 8.567e-02, eta: 3 days, 16:49:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5292, loss_cls: 4.1849, loss: 4.1849 +2024-07-23 09:12:38,190 - pyskl - INFO - Epoch [38][400/3746] lr: 8.565e-02, eta: 3 days, 16:48:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5294, loss_cls: 4.1602, loss: 4.1602 +2024-07-23 09:13:59,759 - pyskl - INFO - Epoch [38][500/3746] lr: 8.563e-02, eta: 3 days, 16:47:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5236, loss_cls: 4.2013, loss: 4.2013 +2024-07-23 09:15:21,465 - pyskl - INFO - Epoch [38][600/3746] lr: 8.561e-02, eta: 3 days, 16:46:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5309, loss_cls: 4.1650, loss: 4.1650 +2024-07-23 09:16:42,897 - pyskl - INFO - Epoch [38][700/3746] lr: 8.559e-02, eta: 3 days, 16:45:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5212, loss_cls: 4.2255, loss: 4.2255 +2024-07-23 09:18:04,691 - pyskl - INFO - Epoch [38][800/3746] lr: 8.557e-02, eta: 3 days, 16:44:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5158, loss_cls: 4.2112, loss: 4.2112 +2024-07-23 09:19:26,907 - pyskl - INFO - Epoch [38][900/3746] lr: 8.555e-02, eta: 3 days, 16:43:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5303, loss_cls: 4.1440, loss: 4.1440 +2024-07-23 09:20:48,821 - pyskl - INFO - Epoch [38][1000/3746] lr: 8.553e-02, eta: 3 days, 16:43:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5230, loss_cls: 4.1771, loss: 4.1771 +2024-07-23 09:22:10,251 - pyskl - INFO - Epoch [38][1100/3746] lr: 8.551e-02, eta: 3 days, 16:42:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5247, loss_cls: 4.2046, loss: 4.2046 +2024-07-23 09:23:32,049 - pyskl - INFO - Epoch [38][1200/3746] lr: 8.549e-02, eta: 3 days, 16:41:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5244, loss_cls: 4.2172, loss: 4.2172 +2024-07-23 09:24:54,078 - pyskl - INFO - Epoch [38][1300/3746] lr: 8.547e-02, eta: 3 days, 16:40:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5272, loss_cls: 4.1486, loss: 4.1486 +2024-07-23 09:26:15,827 - pyskl - INFO - Epoch [38][1400/3746] lr: 8.545e-02, eta: 3 days, 16:39:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5247, loss_cls: 4.2090, loss: 4.2090 +2024-07-23 09:27:37,755 - pyskl - INFO - Epoch [38][1500/3746] lr: 8.543e-02, eta: 3 days, 16:38:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5192, loss_cls: 4.2086, loss: 4.2086 +2024-07-23 09:28:59,719 - pyskl - INFO - Epoch [38][1600/3746] lr: 8.541e-02, eta: 3 days, 16:37:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5250, loss_cls: 4.2063, loss: 4.2063 +2024-07-23 09:30:21,450 - pyskl - INFO - Epoch [38][1700/3746] lr: 8.539e-02, eta: 3 days, 16:36:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5216, loss_cls: 4.2198, loss: 4.2198 +2024-07-23 09:31:42,613 - pyskl - INFO - Epoch [38][1800/3746] lr: 8.537e-02, eta: 3 days, 16:35:21, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5222, loss_cls: 4.2069, loss: 4.2069 +2024-07-23 09:33:04,032 - pyskl - INFO - Epoch [38][1900/3746] lr: 8.535e-02, eta: 3 days, 16:34:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5267, loss_cls: 4.2022, loss: 4.2022 +2024-07-23 09:34:26,059 - pyskl - INFO - Epoch [38][2000/3746] lr: 8.533e-02, eta: 3 days, 16:33:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5300, loss_cls: 4.1637, loss: 4.1637 +2024-07-23 09:35:47,762 - pyskl - INFO - Epoch [38][2100/3746] lr: 8.531e-02, eta: 3 days, 16:32:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5144, loss_cls: 4.2257, loss: 4.2257 +2024-07-23 09:37:09,321 - pyskl - INFO - Epoch [38][2200/3746] lr: 8.529e-02, eta: 3 days, 16:31:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5186, loss_cls: 4.2135, loss: 4.2135 +2024-07-23 09:38:30,728 - pyskl - INFO - Epoch [38][2300/3746] lr: 8.527e-02, eta: 3 days, 16:30:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5250, loss_cls: 4.1555, loss: 4.1555 +2024-07-23 09:39:52,504 - pyskl - INFO - Epoch [38][2400/3746] lr: 8.525e-02, eta: 3 days, 16:29:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5252, loss_cls: 4.2069, loss: 4.2069 +2024-07-23 09:41:14,660 - pyskl - INFO - Epoch [38][2500/3746] lr: 8.523e-02, eta: 3 days, 16:28:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5144, loss_cls: 4.2309, loss: 4.2309 +2024-07-23 09:42:36,037 - pyskl - INFO - Epoch [38][2600/3746] lr: 8.521e-02, eta: 3 days, 16:27:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5316, loss_cls: 4.1634, loss: 4.1634 +2024-07-23 09:43:57,497 - pyskl - INFO - Epoch [38][2700/3746] lr: 8.519e-02, eta: 3 days, 16:26:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5156, loss_cls: 4.2462, loss: 4.2462 +2024-07-23 09:45:18,845 - pyskl - INFO - Epoch [38][2800/3746] lr: 8.517e-02, eta: 3 days, 16:25:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5159, loss_cls: 4.2168, loss: 4.2168 +2024-07-23 09:46:41,525 - pyskl - INFO - Epoch [38][2900/3746] lr: 8.515e-02, eta: 3 days, 16:24:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5208, loss_cls: 4.2286, loss: 4.2286 +2024-07-23 09:48:03,816 - pyskl - INFO - Epoch [38][3000/3746] lr: 8.513e-02, eta: 3 days, 16:23:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5197, loss_cls: 4.1989, loss: 4.1989 +2024-07-23 09:49:25,694 - pyskl - INFO - Epoch [38][3100/3746] lr: 8.511e-02, eta: 3 days, 16:22:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5122, loss_cls: 4.2196, loss: 4.2196 +2024-07-23 09:50:47,876 - pyskl - INFO - Epoch [38][3200/3746] lr: 8.509e-02, eta: 3 days, 16:21:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5272, loss_cls: 4.1821, loss: 4.1821 +2024-07-23 09:52:10,371 - pyskl - INFO - Epoch [38][3300/3746] lr: 8.507e-02, eta: 3 days, 16:21:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5123, loss_cls: 4.2244, loss: 4.2244 +2024-07-23 09:53:32,363 - pyskl - INFO - Epoch [38][3400/3746] lr: 8.505e-02, eta: 3 days, 16:20:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5194, loss_cls: 4.2274, loss: 4.2274 +2024-07-23 09:54:54,378 - pyskl - INFO - Epoch [38][3500/3746] lr: 8.503e-02, eta: 3 days, 16:19:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5223, loss_cls: 4.1986, loss: 4.1986 +2024-07-23 09:56:16,379 - pyskl - INFO - Epoch [38][3600/3746] lr: 8.501e-02, eta: 3 days, 16:18:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5195, loss_cls: 4.2430, loss: 4.2430 +2024-07-23 09:57:37,872 - pyskl - INFO - Epoch [38][3700/3746] lr: 8.499e-02, eta: 3 days, 16:17:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5242, loss_cls: 4.2011, loss: 4.2011 +2024-07-23 09:58:18,030 - pyskl - INFO - Saving checkpoint at 38 epochs +2024-07-23 10:00:11,377 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 10:00:12,032 - pyskl - INFO - +top1_acc 0.2121 +top5_acc 0.4542 +2024-07-23 10:00:12,032 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 10:00:12,070 - pyskl - INFO - +mean_acc 0.2118 +2024-07-23 10:00:12,075 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_35.pth was removed +2024-07-23 10:00:12,345 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_38.pth. +2024-07-23 10:00:12,346 - pyskl - INFO - Best top1_acc is 0.2121 at 38 epoch. +2024-07-23 10:00:12,356 - pyskl - INFO - Epoch(val) [38][309] top1_acc: 0.2121, top5_acc: 0.4542, mean_class_accuracy: 0.2118 +2024-07-23 10:04:13,375 - pyskl - INFO - Epoch [39][100/3746] lr: 8.496e-02, eta: 3 days, 16:21:44, time: 2.410, data_time: 1.386, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5289, loss_cls: 4.2007, loss: 4.2007 +2024-07-23 10:05:38,096 - pyskl - INFO - Epoch [39][200/3746] lr: 8.494e-02, eta: 3 days, 16:20:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5342, loss_cls: 4.1101, loss: 4.1101 +2024-07-23 10:07:02,706 - pyskl - INFO - Epoch [39][300/3746] lr: 8.492e-02, eta: 3 days, 16:20:04, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5234, loss_cls: 4.1919, loss: 4.1919 +2024-07-23 10:08:27,775 - pyskl - INFO - Epoch [39][400/3746] lr: 8.490e-02, eta: 3 days, 16:19:15, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5223, loss_cls: 4.2123, loss: 4.2123 +2024-07-23 10:09:52,856 - pyskl - INFO - Epoch [39][500/3746] lr: 8.488e-02, eta: 3 days, 16:18:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5252, loss_cls: 4.1754, loss: 4.1754 +2024-07-23 10:11:17,907 - pyskl - INFO - Epoch [39][600/3746] lr: 8.486e-02, eta: 3 days, 16:17:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5242, loss_cls: 4.2101, loss: 4.2101 +2024-07-23 10:12:43,046 - pyskl - INFO - Epoch [39][700/3746] lr: 8.484e-02, eta: 3 days, 16:16:49, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5192, loss_cls: 4.2305, loss: 4.2305 +2024-07-23 10:14:08,245 - pyskl - INFO - Epoch [39][800/3746] lr: 8.482e-02, eta: 3 days, 16:16:00, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5275, loss_cls: 4.1510, loss: 4.1510 +2024-07-23 10:15:33,347 - pyskl - INFO - Epoch [39][900/3746] lr: 8.480e-02, eta: 3 days, 16:15:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5319, loss_cls: 4.1743, loss: 4.1743 +2024-07-23 10:16:58,341 - pyskl - INFO - Epoch [39][1000/3746] lr: 8.478e-02, eta: 3 days, 16:14:22, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5172, loss_cls: 4.2088, loss: 4.2088 +2024-07-23 10:18:23,346 - pyskl - INFO - Epoch [39][1100/3746] lr: 8.476e-02, eta: 3 days, 16:13:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5225, loss_cls: 4.2294, loss: 4.2294 +2024-07-23 10:19:48,136 - pyskl - INFO - Epoch [39][1200/3746] lr: 8.474e-02, eta: 3 days, 16:12:43, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5159, loss_cls: 4.1889, loss: 4.1889 +2024-07-23 10:21:12,853 - pyskl - INFO - Epoch [39][1300/3746] lr: 8.472e-02, eta: 3 days, 16:11:52, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5184, loss_cls: 4.1976, loss: 4.1976 +2024-07-23 10:22:37,016 - pyskl - INFO - Epoch [39][1400/3746] lr: 8.470e-02, eta: 3 days, 16:11:01, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5387, loss_cls: 4.1665, loss: 4.1665 +2024-07-23 10:24:01,333 - pyskl - INFO - Epoch [39][1500/3746] lr: 8.468e-02, eta: 3 days, 16:10:09, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5253, loss_cls: 4.2060, loss: 4.2060 +2024-07-23 10:25:24,574 - pyskl - INFO - Epoch [39][1600/3746] lr: 8.466e-02, eta: 3 days, 16:09:14, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5222, loss_cls: 4.2130, loss: 4.2130 +2024-07-23 10:26:47,755 - pyskl - INFO - Epoch [39][1700/3746] lr: 8.464e-02, eta: 3 days, 16:08:19, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5228, loss_cls: 4.2190, loss: 4.2190 +2024-07-23 10:28:10,566 - pyskl - INFO - Epoch [39][1800/3746] lr: 8.462e-02, eta: 3 days, 16:07:23, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5277, loss_cls: 4.1872, loss: 4.1872 +2024-07-23 10:29:33,853 - pyskl - INFO - Epoch [39][1900/3746] lr: 8.460e-02, eta: 3 days, 16:06:29, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5284, loss_cls: 4.1958, loss: 4.1958 +2024-07-23 10:30:57,190 - pyskl - INFO - Epoch [39][2000/3746] lr: 8.458e-02, eta: 3 days, 16:05:34, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5275, loss_cls: 4.2188, loss: 4.2188 +2024-07-23 10:32:19,845 - pyskl - INFO - Epoch [39][2100/3746] lr: 8.456e-02, eta: 3 days, 16:04:38, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5291, loss_cls: 4.1542, loss: 4.1542 +2024-07-23 10:33:41,119 - pyskl - INFO - Epoch [39][2200/3746] lr: 8.454e-02, eta: 3 days, 16:03:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5244, loss_cls: 4.1930, loss: 4.1930 +2024-07-23 10:35:02,718 - pyskl - INFO - Epoch [39][2300/3746] lr: 8.452e-02, eta: 3 days, 16:02:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5189, loss_cls: 4.2011, loss: 4.2011 +2024-07-23 10:36:24,777 - pyskl - INFO - Epoch [39][2400/3746] lr: 8.450e-02, eta: 3 days, 16:01:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5283, loss_cls: 4.1776, loss: 4.1776 +2024-07-23 10:37:46,506 - pyskl - INFO - Epoch [39][2500/3746] lr: 8.448e-02, eta: 3 days, 16:00:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5253, loss_cls: 4.2010, loss: 4.2010 +2024-07-23 10:39:08,246 - pyskl - INFO - Epoch [39][2600/3746] lr: 8.446e-02, eta: 3 days, 15:59:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5234, loss_cls: 4.2279, loss: 4.2279 +2024-07-23 10:40:29,505 - pyskl - INFO - Epoch [39][2700/3746] lr: 8.444e-02, eta: 3 days, 15:58:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5233, loss_cls: 4.2153, loss: 4.2153 +2024-07-23 10:41:51,576 - pyskl - INFO - Epoch [39][2800/3746] lr: 8.442e-02, eta: 3 days, 15:57:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5303, loss_cls: 4.1794, loss: 4.1794 +2024-07-23 10:43:14,420 - pyskl - INFO - Epoch [39][2900/3746] lr: 8.440e-02, eta: 3 days, 15:56:45, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5283, loss_cls: 4.1854, loss: 4.1854 +2024-07-23 10:44:36,484 - pyskl - INFO - Epoch [39][3000/3746] lr: 8.438e-02, eta: 3 days, 15:55:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5286, loss_cls: 4.1828, loss: 4.1828 +2024-07-23 10:45:57,967 - pyskl - INFO - Epoch [39][3100/3746] lr: 8.436e-02, eta: 3 days, 15:54:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5175, loss_cls: 4.2231, loss: 4.2231 +2024-07-23 10:47:20,115 - pyskl - INFO - Epoch [39][3200/3746] lr: 8.434e-02, eta: 3 days, 15:53:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5230, loss_cls: 4.2065, loss: 4.2065 +2024-07-23 10:48:42,342 - pyskl - INFO - Epoch [39][3300/3746] lr: 8.432e-02, eta: 3 days, 15:52:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5292, loss_cls: 4.1869, loss: 4.1869 +2024-07-23 10:50:04,616 - pyskl - INFO - Epoch [39][3400/3746] lr: 8.430e-02, eta: 3 days, 15:51:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5139, loss_cls: 4.2271, loss: 4.2271 +2024-07-23 10:51:26,624 - pyskl - INFO - Epoch [39][3500/3746] lr: 8.428e-02, eta: 3 days, 15:50:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5203, loss_cls: 4.2058, loss: 4.2058 +2024-07-23 10:52:48,064 - pyskl - INFO - Epoch [39][3600/3746] lr: 8.426e-02, eta: 3 days, 15:49:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5294, loss_cls: 4.1621, loss: 4.1621 +2024-07-23 10:54:09,687 - pyskl - INFO - Epoch [39][3700/3746] lr: 8.424e-02, eta: 3 days, 15:48:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5277, loss_cls: 4.1905, loss: 4.1905 +2024-07-23 10:54:49,811 - pyskl - INFO - Saving checkpoint at 39 epochs +2024-07-23 10:56:43,304 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 10:56:43,991 - pyskl - INFO - +top1_acc 0.2122 +top5_acc 0.4484 +2024-07-23 10:56:43,991 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 10:56:44,032 - pyskl - INFO - +mean_acc 0.2120 +2024-07-23 10:56:44,037 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_38.pth was removed +2024-07-23 10:56:44,300 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_39.pth. +2024-07-23 10:56:44,301 - pyskl - INFO - Best top1_acc is 0.2122 at 39 epoch. +2024-07-23 10:56:44,317 - pyskl - INFO - Epoch(val) [39][309] top1_acc: 0.2122, top5_acc: 0.4484, mean_class_accuracy: 0.2120 +2024-07-23 11:00:43,310 - pyskl - INFO - Epoch [40][100/3746] lr: 8.421e-02, eta: 3 days, 15:53:04, time: 2.390, data_time: 1.373, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5298, loss_cls: 4.1516, loss: 4.1516 +2024-07-23 11:02:08,606 - pyskl - INFO - Epoch [40][200/3746] lr: 8.419e-02, eta: 3 days, 15:52:14, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5178, loss_cls: 4.2022, loss: 4.2022 +2024-07-23 11:03:33,504 - pyskl - INFO - Epoch [40][300/3746] lr: 8.417e-02, eta: 3 days, 15:51:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5273, loss_cls: 4.1450, loss: 4.1450 +2024-07-23 11:04:58,475 - pyskl - INFO - Epoch [40][400/3746] lr: 8.415e-02, eta: 3 days, 15:50:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5278, loss_cls: 4.1482, loss: 4.1482 +2024-07-23 11:06:23,256 - pyskl - INFO - Epoch [40][500/3746] lr: 8.413e-02, eta: 3 days, 15:49:41, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5355, loss_cls: 4.1593, loss: 4.1593 +2024-07-23 11:07:47,934 - pyskl - INFO - Epoch [40][600/3746] lr: 8.411e-02, eta: 3 days, 15:48:49, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5281, loss_cls: 4.1871, loss: 4.1871 +2024-07-23 11:09:12,599 - pyskl - INFO - Epoch [40][700/3746] lr: 8.408e-02, eta: 3 days, 15:47:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5259, loss_cls: 4.1624, loss: 4.1624 +2024-07-23 11:10:37,738 - pyskl - INFO - Epoch [40][800/3746] lr: 8.406e-02, eta: 3 days, 15:47:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5100, loss_cls: 4.2368, loss: 4.2368 +2024-07-23 11:12:02,557 - pyskl - INFO - Epoch [40][900/3746] lr: 8.404e-02, eta: 3 days, 15:46:14, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5208, loss_cls: 4.2035, loss: 4.2035 +2024-07-23 11:13:27,271 - pyskl - INFO - Epoch [40][1000/3746] lr: 8.402e-02, eta: 3 days, 15:45:22, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5317, loss_cls: 4.1580, loss: 4.1580 +2024-07-23 11:14:52,049 - pyskl - INFO - Epoch [40][1100/3746] lr: 8.400e-02, eta: 3 days, 15:44:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5255, loss_cls: 4.1973, loss: 4.1973 +2024-07-23 11:16:16,918 - pyskl - INFO - Epoch [40][1200/3746] lr: 8.398e-02, eta: 3 days, 15:43:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5197, loss_cls: 4.2036, loss: 4.2036 +2024-07-23 11:17:41,462 - pyskl - INFO - Epoch [40][1300/3746] lr: 8.396e-02, eta: 3 days, 15:42:46, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5231, loss_cls: 4.2043, loss: 4.2043 +2024-07-23 11:19:06,336 - pyskl - INFO - Epoch [40][1400/3746] lr: 8.394e-02, eta: 3 days, 15:41:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5255, loss_cls: 4.1634, loss: 4.1634 +2024-07-23 11:20:30,573 - pyskl - INFO - Epoch [40][1500/3746] lr: 8.392e-02, eta: 3 days, 15:41:01, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5188, loss_cls: 4.1925, loss: 4.1925 +2024-07-23 11:21:55,502 - pyskl - INFO - Epoch [40][1600/3746] lr: 8.390e-02, eta: 3 days, 15:40:09, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5242, loss_cls: 4.1905, loss: 4.1905 +2024-07-23 11:23:20,550 - pyskl - INFO - Epoch [40][1700/3746] lr: 8.388e-02, eta: 3 days, 15:39:18, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5252, loss_cls: 4.1767, loss: 4.1767 +2024-07-23 11:24:45,318 - pyskl - INFO - Epoch [40][1800/3746] lr: 8.386e-02, eta: 3 days, 15:38:25, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5153, loss_cls: 4.2094, loss: 4.2094 +2024-07-23 11:26:10,036 - pyskl - INFO - Epoch [40][1900/3746] lr: 8.384e-02, eta: 3 days, 15:37:33, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5294, loss_cls: 4.1603, loss: 4.1603 +2024-07-23 11:27:34,811 - pyskl - INFO - Epoch [40][2000/3746] lr: 8.382e-02, eta: 3 days, 15:36:41, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5314, loss_cls: 4.1948, loss: 4.1948 +2024-07-23 11:28:59,800 - pyskl - INFO - Epoch [40][2100/3746] lr: 8.380e-02, eta: 3 days, 15:35:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5167, loss_cls: 4.2168, loss: 4.2168 +2024-07-23 11:30:24,783 - pyskl - INFO - Epoch [40][2200/3746] lr: 8.378e-02, eta: 3 days, 15:34:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5230, loss_cls: 4.2154, loss: 4.2154 +2024-07-23 11:31:49,710 - pyskl - INFO - Epoch [40][2300/3746] lr: 8.376e-02, eta: 3 days, 15:34:05, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5119, loss_cls: 4.2340, loss: 4.2340 +2024-07-23 11:33:14,450 - pyskl - INFO - Epoch [40][2400/3746] lr: 8.374e-02, eta: 3 days, 15:33:13, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5188, loss_cls: 4.2111, loss: 4.2111 +2024-07-23 11:34:39,166 - pyskl - INFO - Epoch [40][2500/3746] lr: 8.371e-02, eta: 3 days, 15:32:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5241, loss_cls: 4.1865, loss: 4.1865 +2024-07-23 11:36:04,134 - pyskl - INFO - Epoch [40][2600/3746] lr: 8.369e-02, eta: 3 days, 15:31:28, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5133, loss_cls: 4.2194, loss: 4.2194 +2024-07-23 11:37:29,455 - pyskl - INFO - Epoch [40][2700/3746] lr: 8.367e-02, eta: 3 days, 15:30:37, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5392, loss_cls: 4.1268, loss: 4.1268 +2024-07-23 11:38:54,461 - pyskl - INFO - Epoch [40][2800/3746] lr: 8.365e-02, eta: 3 days, 15:29:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5328, loss_cls: 4.1603, loss: 4.1603 +2024-07-23 11:40:19,323 - pyskl - INFO - Epoch [40][2900/3746] lr: 8.363e-02, eta: 3 days, 15:28:52, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5278, loss_cls: 4.1694, loss: 4.1694 +2024-07-23 11:41:44,006 - pyskl - INFO - Epoch [40][3000/3746] lr: 8.361e-02, eta: 3 days, 15:27:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5198, loss_cls: 4.1914, loss: 4.1914 +2024-07-23 11:43:08,665 - pyskl - INFO - Epoch [40][3100/3746] lr: 8.359e-02, eta: 3 days, 15:27:06, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5273, loss_cls: 4.1880, loss: 4.1880 +2024-07-23 11:44:33,282 - pyskl - INFO - Epoch [40][3200/3746] lr: 8.357e-02, eta: 3 days, 15:26:13, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5292, loss_cls: 4.1773, loss: 4.1773 +2024-07-23 11:45:57,179 - pyskl - INFO - Epoch [40][3300/3746] lr: 8.355e-02, eta: 3 days, 15:25:18, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5314, loss_cls: 4.1745, loss: 4.1745 +2024-07-23 11:47:21,953 - pyskl - INFO - Epoch [40][3400/3746] lr: 8.353e-02, eta: 3 days, 15:24:25, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5331, loss_cls: 4.1419, loss: 4.1419 +2024-07-23 11:48:46,453 - pyskl - INFO - Epoch [40][3500/3746] lr: 8.351e-02, eta: 3 days, 15:23:31, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5122, loss_cls: 4.2720, loss: 4.2720 +2024-07-23 11:50:11,182 - pyskl - INFO - Epoch [40][3600/3746] lr: 8.349e-02, eta: 3 days, 15:22:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5177, loss_cls: 4.2428, loss: 4.2428 +2024-07-23 11:51:35,806 - pyskl - INFO - Epoch [40][3700/3746] lr: 8.347e-02, eta: 3 days, 15:21:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5159, loss_cls: 4.2048, loss: 4.2048 +2024-07-23 11:52:16,714 - pyskl - INFO - Saving checkpoint at 40 epochs +2024-07-23 11:54:10,832 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 11:54:11,504 - pyskl - INFO - +top1_acc 0.2053 +top5_acc 0.4345 +2024-07-23 11:54:11,504 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 11:54:11,547 - pyskl - INFO - +mean_acc 0.2053 +2024-07-23 11:54:11,561 - pyskl - INFO - Epoch(val) [40][309] top1_acc: 0.2053, top5_acc: 0.4345, mean_class_accuracy: 0.2053 +2024-07-23 11:58:10,898 - pyskl - INFO - Epoch [41][100/3746] lr: 8.344e-02, eta: 3 days, 15:25:44, time: 2.393, data_time: 1.373, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5397, loss_cls: 4.1175, loss: 4.1175 +2024-07-23 11:59:35,711 - pyskl - INFO - Epoch [41][200/3746] lr: 8.342e-02, eta: 3 days, 15:24:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5270, loss_cls: 4.1627, loss: 4.1627 +2024-07-23 12:01:00,422 - pyskl - INFO - Epoch [41][300/3746] lr: 8.339e-02, eta: 3 days, 15:23:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5239, loss_cls: 4.1762, loss: 4.1762 +2024-07-23 12:02:24,695 - pyskl - INFO - Epoch [41][400/3746] lr: 8.337e-02, eta: 3 days, 15:23:03, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5225, loss_cls: 4.1740, loss: 4.1740 +2024-07-23 12:03:49,307 - pyskl - INFO - Epoch [41][500/3746] lr: 8.335e-02, eta: 3 days, 15:22:09, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5264, loss_cls: 4.1662, loss: 4.1662 +2024-07-23 12:05:14,028 - pyskl - INFO - Epoch [41][600/3746] lr: 8.333e-02, eta: 3 days, 15:21:15, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5277, loss_cls: 4.1649, loss: 4.1649 +2024-07-23 12:06:39,017 - pyskl - INFO - Epoch [41][700/3746] lr: 8.331e-02, eta: 3 days, 15:20:22, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5330, loss_cls: 4.1830, loss: 4.1830 +2024-07-23 12:08:04,058 - pyskl - INFO - Epoch [41][800/3746] lr: 8.329e-02, eta: 3 days, 15:19:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5339, loss_cls: 4.1253, loss: 4.1253 +2024-07-23 12:09:28,960 - pyskl - INFO - Epoch [41][900/3746] lr: 8.327e-02, eta: 3 days, 15:18:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5106, loss_cls: 4.2107, loss: 4.2107 +2024-07-23 12:10:53,434 - pyskl - INFO - Epoch [41][1000/3746] lr: 8.325e-02, eta: 3 days, 15:17:41, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5352, loss_cls: 4.1283, loss: 4.1283 +2024-07-23 12:12:18,281 - pyskl - INFO - Epoch [41][1100/3746] lr: 8.323e-02, eta: 3 days, 15:16:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5161, loss_cls: 4.2471, loss: 4.2471 +2024-07-23 12:13:42,428 - pyskl - INFO - Epoch [41][1200/3746] lr: 8.321e-02, eta: 3 days, 15:15:52, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5400, loss_cls: 4.1407, loss: 4.1407 +2024-07-23 12:15:07,106 - pyskl - INFO - Epoch [41][1300/3746] lr: 8.319e-02, eta: 3 days, 15:14:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5144, loss_cls: 4.2223, loss: 4.2223 +2024-07-23 12:16:32,214 - pyskl - INFO - Epoch [41][1400/3746] lr: 8.316e-02, eta: 3 days, 15:14:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5264, loss_cls: 4.2106, loss: 4.2106 +2024-07-23 12:17:57,246 - pyskl - INFO - Epoch [41][1500/3746] lr: 8.314e-02, eta: 3 days, 15:13:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5280, loss_cls: 4.1808, loss: 4.1808 +2024-07-23 12:19:22,047 - pyskl - INFO - Epoch [41][1600/3746] lr: 8.312e-02, eta: 3 days, 15:12:18, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5212, loss_cls: 4.1726, loss: 4.1726 +2024-07-23 12:20:47,451 - pyskl - INFO - Epoch [41][1700/3746] lr: 8.310e-02, eta: 3 days, 15:11:25, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5239, loss_cls: 4.2057, loss: 4.2057 +2024-07-23 12:22:12,450 - pyskl - INFO - Epoch [41][1800/3746] lr: 8.308e-02, eta: 3 days, 15:10:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5381, loss_cls: 4.1444, loss: 4.1444 +2024-07-23 12:23:37,439 - pyskl - INFO - Epoch [41][1900/3746] lr: 8.306e-02, eta: 3 days, 15:09:38, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5269, loss_cls: 4.1922, loss: 4.1922 +2024-07-23 12:25:02,433 - pyskl - INFO - Epoch [41][2000/3746] lr: 8.304e-02, eta: 3 days, 15:08:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5253, loss_cls: 4.2016, loss: 4.2016 +2024-07-23 12:26:27,039 - pyskl - INFO - Epoch [41][2100/3746] lr: 8.302e-02, eta: 3 days, 15:07:50, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5156, loss_cls: 4.2471, loss: 4.2471 +2024-07-23 12:27:51,895 - pyskl - INFO - Epoch [41][2200/3746] lr: 8.300e-02, eta: 3 days, 15:06:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5152, loss_cls: 4.2103, loss: 4.2103 +2024-07-23 12:29:16,123 - pyskl - INFO - Epoch [41][2300/3746] lr: 8.298e-02, eta: 3 days, 15:06:00, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5264, loss_cls: 4.1936, loss: 4.1936 +2024-07-23 12:30:39,019 - pyskl - INFO - Epoch [41][2400/3746] lr: 8.296e-02, eta: 3 days, 15:05:01, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5220, loss_cls: 4.1919, loss: 4.1919 +2024-07-23 12:32:01,526 - pyskl - INFO - Epoch [41][2500/3746] lr: 8.293e-02, eta: 3 days, 15:04:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5227, loss_cls: 4.2154, loss: 4.2154 +2024-07-23 12:33:23,652 - pyskl - INFO - Epoch [41][2600/3746] lr: 8.291e-02, eta: 3 days, 15:02:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5216, loss_cls: 4.2133, loss: 4.2133 +2024-07-23 12:34:46,642 - pyskl - INFO - Epoch [41][2700/3746] lr: 8.289e-02, eta: 3 days, 15:02:00, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5205, loss_cls: 4.1893, loss: 4.1893 +2024-07-23 12:36:08,166 - pyskl - INFO - Epoch [41][2800/3746] lr: 8.287e-02, eta: 3 days, 15:00:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5295, loss_cls: 4.1538, loss: 4.1538 +2024-07-23 12:37:30,136 - pyskl - INFO - Epoch [41][2900/3746] lr: 8.285e-02, eta: 3 days, 14:59:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5139, loss_cls: 4.2081, loss: 4.2081 +2024-07-23 12:38:52,291 - pyskl - INFO - Epoch [41][3000/3746] lr: 8.283e-02, eta: 3 days, 14:58:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5130, loss_cls: 4.2193, loss: 4.2193 +2024-07-23 12:40:13,989 - pyskl - INFO - Epoch [41][3100/3746] lr: 8.281e-02, eta: 3 days, 14:57:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5345, loss_cls: 4.1230, loss: 4.1230 +2024-07-23 12:41:36,264 - pyskl - INFO - Epoch [41][3200/3746] lr: 8.279e-02, eta: 3 days, 14:56:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5248, loss_cls: 4.1911, loss: 4.1911 +2024-07-23 12:42:57,789 - pyskl - INFO - Epoch [41][3300/3746] lr: 8.277e-02, eta: 3 days, 14:55:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5284, loss_cls: 4.1539, loss: 4.1539 +2024-07-23 12:44:20,885 - pyskl - INFO - Epoch [41][3400/3746] lr: 8.274e-02, eta: 3 days, 14:54:46, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5327, loss_cls: 4.1596, loss: 4.1596 +2024-07-23 12:45:43,099 - pyskl - INFO - Epoch [41][3500/3746] lr: 8.272e-02, eta: 3 days, 14:53:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5247, loss_cls: 4.1558, loss: 4.1558 +2024-07-23 12:47:05,625 - pyskl - INFO - Epoch [41][3600/3746] lr: 8.270e-02, eta: 3 days, 14:52:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5195, loss_cls: 4.2191, loss: 4.2191 +2024-07-23 12:48:28,350 - pyskl - INFO - Epoch [41][3700/3746] lr: 8.268e-02, eta: 3 days, 14:51:44, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5267, loss_cls: 4.2086, loss: 4.2086 +2024-07-23 12:49:08,915 - pyskl - INFO - Saving checkpoint at 41 epochs +2024-07-23 12:51:01,438 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 12:51:02,127 - pyskl - INFO - +top1_acc 0.1976 +top5_acc 0.4228 +2024-07-23 12:51:02,128 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 12:51:02,175 - pyskl - INFO - +mean_acc 0.1974 +2024-07-23 12:51:02,188 - pyskl - INFO - Epoch(val) [41][309] top1_acc: 0.1976, top5_acc: 0.4228, mean_class_accuracy: 0.1974 +2024-07-23 12:54:54,510 - pyskl - INFO - Epoch [42][100/3746] lr: 8.265e-02, eta: 3 days, 14:55:12, time: 2.323, data_time: 1.311, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5275, loss_cls: 4.1665, loss: 4.1665 +2024-07-23 12:56:17,385 - pyskl - INFO - Epoch [42][200/3746] lr: 8.263e-02, eta: 3 days, 14:54:12, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5323, loss_cls: 4.1423, loss: 4.1423 +2024-07-23 12:57:39,358 - pyskl - INFO - Epoch [42][300/3746] lr: 8.261e-02, eta: 3 days, 14:53:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5330, loss_cls: 4.1702, loss: 4.1702 +2024-07-23 12:59:01,057 - pyskl - INFO - Epoch [42][400/3746] lr: 8.259e-02, eta: 3 days, 14:52:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5278, loss_cls: 4.1982, loss: 4.1982 +2024-07-23 13:00:23,162 - pyskl - INFO - Epoch [42][500/3746] lr: 8.257e-02, eta: 3 days, 14:51:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5153, loss_cls: 4.1806, loss: 4.1806 +2024-07-23 13:01:44,587 - pyskl - INFO - Epoch [42][600/3746] lr: 8.254e-02, eta: 3 days, 14:50:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5288, loss_cls: 4.1454, loss: 4.1454 +2024-07-23 13:03:06,589 - pyskl - INFO - Epoch [42][700/3746] lr: 8.252e-02, eta: 3 days, 14:48:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5194, loss_cls: 4.2408, loss: 4.2408 +2024-07-23 13:04:28,645 - pyskl - INFO - Epoch [42][800/3746] lr: 8.250e-02, eta: 3 days, 14:47:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5294, loss_cls: 4.1521, loss: 4.1521 +2024-07-23 13:05:50,676 - pyskl - INFO - Epoch [42][900/3746] lr: 8.248e-02, eta: 3 days, 14:46:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5309, loss_cls: 4.1641, loss: 4.1641 +2024-07-23 13:07:12,488 - pyskl - INFO - Epoch [42][1000/3746] lr: 8.246e-02, eta: 3 days, 14:45:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5441, loss_cls: 4.1253, loss: 4.1253 +2024-07-23 13:08:34,180 - pyskl - INFO - Epoch [42][1100/3746] lr: 8.244e-02, eta: 3 days, 14:44:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5258, loss_cls: 4.1722, loss: 4.1722 +2024-07-23 13:09:55,785 - pyskl - INFO - Epoch [42][1200/3746] lr: 8.242e-02, eta: 3 days, 14:43:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5197, loss_cls: 4.2042, loss: 4.2042 +2024-07-23 13:11:17,999 - pyskl - INFO - Epoch [42][1300/3746] lr: 8.240e-02, eta: 3 days, 14:42:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5406, loss_cls: 4.1109, loss: 4.1109 +2024-07-23 13:12:39,737 - pyskl - INFO - Epoch [42][1400/3746] lr: 8.237e-02, eta: 3 days, 14:41:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5248, loss_cls: 4.1864, loss: 4.1864 +2024-07-23 13:14:01,393 - pyskl - INFO - Epoch [42][1500/3746] lr: 8.235e-02, eta: 3 days, 14:40:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5348, loss_cls: 4.1714, loss: 4.1714 +2024-07-23 13:15:23,134 - pyskl - INFO - Epoch [42][1600/3746] lr: 8.233e-02, eta: 3 days, 14:39:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5381, loss_cls: 4.1304, loss: 4.1304 +2024-07-23 13:16:44,596 - pyskl - INFO - Epoch [42][1700/3746] lr: 8.231e-02, eta: 3 days, 14:38:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5381, loss_cls: 4.1626, loss: 4.1626 +2024-07-23 13:18:06,942 - pyskl - INFO - Epoch [42][1800/3746] lr: 8.229e-02, eta: 3 days, 14:37:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5297, loss_cls: 4.1772, loss: 4.1772 +2024-07-23 13:19:28,609 - pyskl - INFO - Epoch [42][1900/3746] lr: 8.227e-02, eta: 3 days, 14:36:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5130, loss_cls: 4.2324, loss: 4.2324 +2024-07-23 13:20:50,411 - pyskl - INFO - Epoch [42][2000/3746] lr: 8.225e-02, eta: 3 days, 14:35:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5228, loss_cls: 4.1717, loss: 4.1717 +2024-07-23 13:22:11,972 - pyskl - INFO - Epoch [42][2100/3746] lr: 8.222e-02, eta: 3 days, 14:34:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5131, loss_cls: 4.2499, loss: 4.2499 +2024-07-23 13:23:33,501 - pyskl - INFO - Epoch [42][2200/3746] lr: 8.220e-02, eta: 3 days, 14:33:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5363, loss_cls: 4.1300, loss: 4.1300 +2024-07-23 13:24:55,749 - pyskl - INFO - Epoch [42][2300/3746] lr: 8.218e-02, eta: 3 days, 14:32:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5178, loss_cls: 4.2299, loss: 4.2299 +2024-07-23 13:26:17,711 - pyskl - INFO - Epoch [42][2400/3746] lr: 8.216e-02, eta: 3 days, 14:31:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5291, loss_cls: 4.1568, loss: 4.1568 +2024-07-23 13:27:39,374 - pyskl - INFO - Epoch [42][2500/3746] lr: 8.214e-02, eta: 3 days, 14:29:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5259, loss_cls: 4.1847, loss: 4.1847 +2024-07-23 13:29:01,817 - pyskl - INFO - Epoch [42][2600/3746] lr: 8.212e-02, eta: 3 days, 14:28:56, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5267, loss_cls: 4.1821, loss: 4.1821 +2024-07-23 13:30:23,599 - pyskl - INFO - Epoch [42][2700/3746] lr: 8.210e-02, eta: 3 days, 14:27:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5253, loss_cls: 4.1643, loss: 4.1643 +2024-07-23 13:31:45,451 - pyskl - INFO - Epoch [42][2800/3746] lr: 8.207e-02, eta: 3 days, 14:26:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5227, loss_cls: 4.1969, loss: 4.1969 +2024-07-23 13:33:08,085 - pyskl - INFO - Epoch [42][2900/3746] lr: 8.205e-02, eta: 3 days, 14:25:47, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5303, loss_cls: 4.1678, loss: 4.1678 +2024-07-23 13:34:30,020 - pyskl - INFO - Epoch [42][3000/3746] lr: 8.203e-02, eta: 3 days, 14:24:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5200, loss_cls: 4.1997, loss: 4.1997 +2024-07-23 13:35:52,506 - pyskl - INFO - Epoch [42][3100/3746] lr: 8.201e-02, eta: 3 days, 14:23:42, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5320, loss_cls: 4.1781, loss: 4.1781 +2024-07-23 13:37:13,870 - pyskl - INFO - Epoch [42][3200/3746] lr: 8.199e-02, eta: 3 days, 14:22:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5392, loss_cls: 4.1250, loss: 4.1250 +2024-07-23 13:38:35,757 - pyskl - INFO - Epoch [42][3300/3746] lr: 8.197e-02, eta: 3 days, 14:21:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5238, loss_cls: 4.2381, loss: 4.2381 +2024-07-23 13:39:57,610 - pyskl - INFO - Epoch [42][3400/3746] lr: 8.195e-02, eta: 3 days, 14:20:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5322, loss_cls: 4.1527, loss: 4.1527 +2024-07-23 13:41:20,229 - pyskl - INFO - Epoch [42][3500/3746] lr: 8.192e-02, eta: 3 days, 14:19:28, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5067, loss_cls: 4.2433, loss: 4.2433 +2024-07-23 13:42:41,806 - pyskl - INFO - Epoch [42][3600/3746] lr: 8.190e-02, eta: 3 days, 14:18:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5159, loss_cls: 4.2155, loss: 4.2155 +2024-07-23 13:44:03,373 - pyskl - INFO - Epoch [42][3700/3746] lr: 8.188e-02, eta: 3 days, 14:17:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5348, loss_cls: 4.1467, loss: 4.1467 +2024-07-23 13:44:43,768 - pyskl - INFO - Saving checkpoint at 42 epochs +2024-07-23 13:46:34,979 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 13:46:35,645 - pyskl - INFO - +top1_acc 0.2016 +top5_acc 0.4373 +2024-07-23 13:46:35,645 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 13:46:35,684 - pyskl - INFO - +mean_acc 0.2015 +2024-07-23 13:46:35,696 - pyskl - INFO - Epoch(val) [42][309] top1_acc: 0.2016, top5_acc: 0.4373, mean_class_accuracy: 0.2015 +2024-07-23 13:50:23,297 - pyskl - INFO - Epoch [43][100/3746] lr: 8.185e-02, eta: 3 days, 14:20:24, time: 2.276, data_time: 1.301, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5309, loss_cls: 4.1324, loss: 4.1324 +2024-07-23 13:51:45,576 - pyskl - INFO - Epoch [43][200/3746] lr: 8.183e-02, eta: 3 days, 14:19:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5312, loss_cls: 4.1573, loss: 4.1573 +2024-07-23 13:53:07,307 - pyskl - INFO - Epoch [43][300/3746] lr: 8.181e-02, eta: 3 days, 14:18:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5377, loss_cls: 4.1744, loss: 4.1744 +2024-07-23 13:54:29,145 - pyskl - INFO - Epoch [43][400/3746] lr: 8.179e-02, eta: 3 days, 14:17:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5344, loss_cls: 4.1349, loss: 4.1349 +2024-07-23 13:55:50,704 - pyskl - INFO - Epoch [43][500/3746] lr: 8.176e-02, eta: 3 days, 14:16:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5219, loss_cls: 4.2103, loss: 4.2103 +2024-07-23 13:57:12,256 - pyskl - INFO - Epoch [43][600/3746] lr: 8.174e-02, eta: 3 days, 14:15:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5302, loss_cls: 4.1248, loss: 4.1248 +2024-07-23 13:58:34,028 - pyskl - INFO - Epoch [43][700/3746] lr: 8.172e-02, eta: 3 days, 14:13:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5278, loss_cls: 4.1722, loss: 4.1722 +2024-07-23 13:59:56,043 - pyskl - INFO - Epoch [43][800/3746] lr: 8.170e-02, eta: 3 days, 14:12:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5311, loss_cls: 4.1499, loss: 4.1499 +2024-07-23 14:01:17,624 - pyskl - INFO - Epoch [43][900/3746] lr: 8.168e-02, eta: 3 days, 14:11:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5341, loss_cls: 4.1317, loss: 4.1317 +2024-07-23 14:02:39,123 - pyskl - INFO - Epoch [43][1000/3746] lr: 8.166e-02, eta: 3 days, 14:10:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5183, loss_cls: 4.1989, loss: 4.1989 +2024-07-23 14:04:00,996 - pyskl - INFO - Epoch [43][1100/3746] lr: 8.163e-02, eta: 3 days, 14:09:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5264, loss_cls: 4.1325, loss: 4.1325 +2024-07-23 14:05:22,446 - pyskl - INFO - Epoch [43][1200/3746] lr: 8.161e-02, eta: 3 days, 14:08:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5316, loss_cls: 4.1864, loss: 4.1864 +2024-07-23 14:06:43,984 - pyskl - INFO - Epoch [43][1300/3746] lr: 8.159e-02, eta: 3 days, 14:07:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5184, loss_cls: 4.2118, loss: 4.2118 +2024-07-23 14:08:05,656 - pyskl - INFO - Epoch [43][1400/3746] lr: 8.157e-02, eta: 3 days, 14:06:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5281, loss_cls: 4.1643, loss: 4.1643 +2024-07-23 14:09:27,185 - pyskl - INFO - Epoch [43][1500/3746] lr: 8.155e-02, eta: 3 days, 14:05:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5275, loss_cls: 4.1645, loss: 4.1645 +2024-07-23 14:10:49,071 - pyskl - INFO - Epoch [43][1600/3746] lr: 8.153e-02, eta: 3 days, 14:04:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5309, loss_cls: 4.1825, loss: 4.1825 +2024-07-23 14:12:10,494 - pyskl - INFO - Epoch [43][1700/3746] lr: 8.150e-02, eta: 3 days, 14:03:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5277, loss_cls: 4.1789, loss: 4.1789 +2024-07-23 14:13:32,175 - pyskl - INFO - Epoch [43][1800/3746] lr: 8.148e-02, eta: 3 days, 14:02:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5211, loss_cls: 4.1799, loss: 4.1799 +2024-07-23 14:14:54,418 - pyskl - INFO - Epoch [43][1900/3746] lr: 8.146e-02, eta: 3 days, 14:01:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5341, loss_cls: 4.1447, loss: 4.1447 +2024-07-23 14:16:16,391 - pyskl - INFO - Epoch [43][2000/3746] lr: 8.144e-02, eta: 3 days, 13:59:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5198, loss_cls: 4.1823, loss: 4.1823 +2024-07-23 14:17:37,953 - pyskl - INFO - Epoch [43][2100/3746] lr: 8.142e-02, eta: 3 days, 13:58:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5214, loss_cls: 4.1791, loss: 4.1791 +2024-07-23 14:18:59,786 - pyskl - INFO - Epoch [43][2200/3746] lr: 8.140e-02, eta: 3 days, 13:57:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5225, loss_cls: 4.1862, loss: 4.1862 +2024-07-23 14:20:21,727 - pyskl - INFO - Epoch [43][2300/3746] lr: 8.137e-02, eta: 3 days, 13:56:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5272, loss_cls: 4.1841, loss: 4.1841 +2024-07-23 14:21:43,783 - pyskl - INFO - Epoch [43][2400/3746] lr: 8.135e-02, eta: 3 days, 13:55:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5284, loss_cls: 4.1735, loss: 4.1735 +2024-07-23 14:23:05,568 - pyskl - INFO - Epoch [43][2500/3746] lr: 8.133e-02, eta: 3 days, 13:54:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5258, loss_cls: 4.1891, loss: 4.1891 +2024-07-23 14:24:27,487 - pyskl - INFO - Epoch [43][2600/3746] lr: 8.131e-02, eta: 3 days, 13:53:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5252, loss_cls: 4.1401, loss: 4.1401 +2024-07-23 14:25:49,419 - pyskl - INFO - Epoch [43][2700/3746] lr: 8.129e-02, eta: 3 days, 13:52:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5250, loss_cls: 4.2049, loss: 4.2049 +2024-07-23 14:27:11,008 - pyskl - INFO - Epoch [43][2800/3746] lr: 8.126e-02, eta: 3 days, 13:51:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5238, loss_cls: 4.1573, loss: 4.1573 +2024-07-23 14:28:32,844 - pyskl - INFO - Epoch [43][2900/3746] lr: 8.124e-02, eta: 3 days, 13:50:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5244, loss_cls: 4.1827, loss: 4.1827 +2024-07-23 14:29:54,942 - pyskl - INFO - Epoch [43][3000/3746] lr: 8.122e-02, eta: 3 days, 13:49:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5303, loss_cls: 4.1639, loss: 4.1639 +2024-07-23 14:31:17,058 - pyskl - INFO - Epoch [43][3100/3746] lr: 8.120e-02, eta: 3 days, 13:48:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5320, loss_cls: 4.1545, loss: 4.1545 +2024-07-23 14:32:38,882 - pyskl - INFO - Epoch [43][3200/3746] lr: 8.118e-02, eta: 3 days, 13:47:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5283, loss_cls: 4.1594, loss: 4.1594 +2024-07-23 14:34:00,728 - pyskl - INFO - Epoch [43][3300/3746] lr: 8.116e-02, eta: 3 days, 13:46:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5384, loss_cls: 4.1536, loss: 4.1536 +2024-07-23 14:35:22,793 - pyskl - INFO - Epoch [43][3400/3746] lr: 8.113e-02, eta: 3 days, 13:44:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5320, loss_cls: 4.1734, loss: 4.1734 +2024-07-23 14:36:45,183 - pyskl - INFO - Epoch [43][3500/3746] lr: 8.111e-02, eta: 3 days, 13:43:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5289, loss_cls: 4.2004, loss: 4.2004 +2024-07-23 14:38:06,735 - pyskl - INFO - Epoch [43][3600/3746] lr: 8.109e-02, eta: 3 days, 13:42:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5164, loss_cls: 4.2305, loss: 4.2305 +2024-07-23 14:39:28,585 - pyskl - INFO - Epoch [43][3700/3746] lr: 8.107e-02, eta: 3 days, 13:41:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5267, loss_cls: 4.1925, loss: 4.1925 +2024-07-23 14:40:08,166 - pyskl - INFO - Saving checkpoint at 43 epochs +2024-07-23 14:42:00,980 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 14:42:01,639 - pyskl - INFO - +top1_acc 0.2259 +top5_acc 0.4677 +2024-07-23 14:42:01,639 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 14:42:01,678 - pyskl - INFO - +mean_acc 0.2257 +2024-07-23 14:42:01,684 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_39.pth was removed +2024-07-23 14:42:01,943 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_43.pth. +2024-07-23 14:42:01,944 - pyskl - INFO - Best top1_acc is 0.2259 at 43 epoch. +2024-07-23 14:42:01,959 - pyskl - INFO - Epoch(val) [43][309] top1_acc: 0.2259, top5_acc: 0.4677, mean_class_accuracy: 0.2257 +2024-07-23 14:45:47,974 - pyskl - INFO - Epoch [44][100/3746] lr: 8.104e-02, eta: 3 days, 13:44:33, time: 2.260, data_time: 1.287, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5481, loss_cls: 4.0590, loss: 4.0590 +2024-07-23 14:47:10,022 - pyskl - INFO - Epoch [44][200/3746] lr: 8.101e-02, eta: 3 days, 13:43:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5320, loss_cls: 4.1482, loss: 4.1482 +2024-07-23 14:48:31,775 - pyskl - INFO - Epoch [44][300/3746] lr: 8.099e-02, eta: 3 days, 13:42:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5361, loss_cls: 4.1453, loss: 4.1453 +2024-07-23 14:49:53,222 - pyskl - INFO - Epoch [44][400/3746] lr: 8.097e-02, eta: 3 days, 13:41:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5323, loss_cls: 4.1595, loss: 4.1595 +2024-07-23 14:51:15,752 - pyskl - INFO - Epoch [44][500/3746] lr: 8.095e-02, eta: 3 days, 13:40:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5267, loss_cls: 4.1350, loss: 4.1350 +2024-07-23 14:52:37,550 - pyskl - INFO - Epoch [44][600/3746] lr: 8.093e-02, eta: 3 days, 13:39:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5381, loss_cls: 4.1446, loss: 4.1446 +2024-07-23 14:53:59,331 - pyskl - INFO - Epoch [44][700/3746] lr: 8.090e-02, eta: 3 days, 13:38:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5378, loss_cls: 4.1285, loss: 4.1285 +2024-07-23 14:55:21,648 - pyskl - INFO - Epoch [44][800/3746] lr: 8.088e-02, eta: 3 days, 13:36:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5300, loss_cls: 4.1511, loss: 4.1511 +2024-07-23 14:56:43,138 - pyskl - INFO - Epoch [44][900/3746] lr: 8.086e-02, eta: 3 days, 13:35:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5467, loss_cls: 4.1139, loss: 4.1139 +2024-07-23 14:58:04,633 - pyskl - INFO - Epoch [44][1000/3746] lr: 8.084e-02, eta: 3 days, 13:34:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5059, loss_cls: 4.2666, loss: 4.2666 +2024-07-23 14:59:27,084 - pyskl - INFO - Epoch [44][1100/3746] lr: 8.082e-02, eta: 3 days, 13:33:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5428, loss_cls: 4.1050, loss: 4.1050 +2024-07-23 15:00:49,518 - pyskl - INFO - Epoch [44][1200/3746] lr: 8.079e-02, eta: 3 days, 13:32:39, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5394, loss_cls: 4.1289, loss: 4.1289 +2024-07-23 15:02:11,406 - pyskl - INFO - Epoch [44][1300/3746] lr: 8.077e-02, eta: 3 days, 13:31:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5370, loss_cls: 4.1478, loss: 4.1478 +2024-07-23 15:03:33,065 - pyskl - INFO - Epoch [44][1400/3746] lr: 8.075e-02, eta: 3 days, 13:30:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5372, loss_cls: 4.1311, loss: 4.1311 +2024-07-23 15:04:55,063 - pyskl - INFO - Epoch [44][1500/3746] lr: 8.073e-02, eta: 3 days, 13:29:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5344, loss_cls: 4.1468, loss: 4.1468 +2024-07-23 15:06:16,617 - pyskl - INFO - Epoch [44][1600/3746] lr: 8.071e-02, eta: 3 days, 13:28:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5245, loss_cls: 4.1696, loss: 4.1696 +2024-07-23 15:07:38,055 - pyskl - INFO - Epoch [44][1700/3746] lr: 8.068e-02, eta: 3 days, 13:27:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5361, loss_cls: 4.1366, loss: 4.1366 +2024-07-23 15:08:59,658 - pyskl - INFO - Epoch [44][1800/3746] lr: 8.066e-02, eta: 3 days, 13:26:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5297, loss_cls: 4.1534, loss: 4.1534 +2024-07-23 15:10:21,147 - pyskl - INFO - Epoch [44][1900/3746] lr: 8.064e-02, eta: 3 days, 13:24:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5339, loss_cls: 4.1306, loss: 4.1306 +2024-07-23 15:11:43,129 - pyskl - INFO - Epoch [44][2000/3746] lr: 8.062e-02, eta: 3 days, 13:23:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5316, loss_cls: 4.1374, loss: 4.1374 +2024-07-23 15:13:04,650 - pyskl - INFO - Epoch [44][2100/3746] lr: 8.060e-02, eta: 3 days, 13:22:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5336, loss_cls: 4.1826, loss: 4.1826 +2024-07-23 15:14:26,830 - pyskl - INFO - Epoch [44][2200/3746] lr: 8.057e-02, eta: 3 days, 13:21:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5231, loss_cls: 4.2077, loss: 4.2077 +2024-07-23 15:15:48,035 - pyskl - INFO - Epoch [44][2300/3746] lr: 8.055e-02, eta: 3 days, 13:20:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5266, loss_cls: 4.1865, loss: 4.1865 +2024-07-23 15:17:09,632 - pyskl - INFO - Epoch [44][2400/3746] lr: 8.053e-02, eta: 3 days, 13:19:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5325, loss_cls: 4.1737, loss: 4.1737 +2024-07-23 15:18:31,565 - pyskl - INFO - Epoch [44][2500/3746] lr: 8.051e-02, eta: 3 days, 13:18:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5194, loss_cls: 4.2184, loss: 4.2184 +2024-07-23 15:19:53,603 - pyskl - INFO - Epoch [44][2600/3746] lr: 8.048e-02, eta: 3 days, 13:17:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5288, loss_cls: 4.1798, loss: 4.1798 +2024-07-23 15:21:15,217 - pyskl - INFO - Epoch [44][2700/3746] lr: 8.046e-02, eta: 3 days, 13:16:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5166, loss_cls: 4.2174, loss: 4.2174 +2024-07-23 15:22:36,972 - pyskl - INFO - Epoch [44][2800/3746] lr: 8.044e-02, eta: 3 days, 13:15:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5342, loss_cls: 4.1525, loss: 4.1525 +2024-07-23 15:23:59,791 - pyskl - INFO - Epoch [44][2900/3746] lr: 8.042e-02, eta: 3 days, 13:14:04, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5244, loss_cls: 4.1614, loss: 4.1614 +2024-07-23 15:25:21,305 - pyskl - INFO - Epoch [44][3000/3746] lr: 8.040e-02, eta: 3 days, 13:12:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5211, loss_cls: 4.2104, loss: 4.2104 +2024-07-23 15:26:43,619 - pyskl - INFO - Epoch [44][3100/3746] lr: 8.037e-02, eta: 3 days, 13:11:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5242, loss_cls: 4.2077, loss: 4.2077 +2024-07-23 15:28:05,709 - pyskl - INFO - Epoch [44][3200/3746] lr: 8.035e-02, eta: 3 days, 13:10:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5336, loss_cls: 4.1846, loss: 4.1846 +2024-07-23 15:29:27,216 - pyskl - INFO - Epoch [44][3300/3746] lr: 8.033e-02, eta: 3 days, 13:09:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5303, loss_cls: 4.1465, loss: 4.1465 +2024-07-23 15:30:49,033 - pyskl - INFO - Epoch [44][3400/3746] lr: 8.031e-02, eta: 3 days, 13:08:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5227, loss_cls: 4.2143, loss: 4.2143 +2024-07-23 15:32:11,469 - pyskl - INFO - Epoch [44][3500/3746] lr: 8.028e-02, eta: 3 days, 13:07:31, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5198, loss_cls: 4.2128, loss: 4.2128 +2024-07-23 15:33:33,572 - pyskl - INFO - Epoch [44][3600/3746] lr: 8.026e-02, eta: 3 days, 13:06:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5331, loss_cls: 4.1362, loss: 4.1362 +2024-07-23 15:34:56,331 - pyskl - INFO - Epoch [44][3700/3746] lr: 8.024e-02, eta: 3 days, 13:05:22, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5267, loss_cls: 4.1835, loss: 4.1835 +2024-07-23 15:35:36,348 - pyskl - INFO - Saving checkpoint at 44 epochs +2024-07-23 15:37:28,602 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 15:37:29,270 - pyskl - INFO - +top1_acc 0.2076 +top5_acc 0.4414 +2024-07-23 15:37:29,271 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 15:37:29,311 - pyskl - INFO - +mean_acc 0.2073 +2024-07-23 15:37:29,322 - pyskl - INFO - Epoch(val) [44][309] top1_acc: 0.2076, top5_acc: 0.4414, mean_class_accuracy: 0.2073 +2024-07-23 15:41:13,684 - pyskl - INFO - Epoch [45][100/3746] lr: 8.021e-02, eta: 3 days, 13:07:59, time: 2.244, data_time: 1.273, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5303, loss_cls: 4.1307, loss: 4.1307 +2024-07-23 15:42:35,438 - pyskl - INFO - Epoch [45][200/3746] lr: 8.019e-02, eta: 3 days, 13:06:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5366, loss_cls: 4.1354, loss: 4.1354 +2024-07-23 15:43:56,850 - pyskl - INFO - Epoch [45][300/3746] lr: 8.016e-02, eta: 3 days, 13:05:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5370, loss_cls: 4.1254, loss: 4.1254 +2024-07-23 15:45:18,835 - pyskl - INFO - Epoch [45][400/3746] lr: 8.014e-02, eta: 3 days, 13:04:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5355, loss_cls: 4.1730, loss: 4.1730 +2024-07-23 15:46:40,570 - pyskl - INFO - Epoch [45][500/3746] lr: 8.012e-02, eta: 3 days, 13:03:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5366, loss_cls: 4.1083, loss: 4.1083 +2024-07-23 15:48:02,405 - pyskl - INFO - Epoch [45][600/3746] lr: 8.010e-02, eta: 3 days, 13:02:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5339, loss_cls: 4.1437, loss: 4.1437 +2024-07-23 15:49:24,214 - pyskl - INFO - Epoch [45][700/3746] lr: 8.007e-02, eta: 3 days, 13:01:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5344, loss_cls: 4.1430, loss: 4.1430 +2024-07-23 15:50:46,021 - pyskl - INFO - Epoch [45][800/3746] lr: 8.005e-02, eta: 3 days, 13:00:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5286, loss_cls: 4.1655, loss: 4.1655 +2024-07-23 15:52:07,692 - pyskl - INFO - Epoch [45][900/3746] lr: 8.003e-02, eta: 3 days, 12:59:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5322, loss_cls: 4.1612, loss: 4.1612 +2024-07-23 15:53:29,615 - pyskl - INFO - Epoch [45][1000/3746] lr: 8.001e-02, eta: 3 days, 12:58:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5358, loss_cls: 4.1316, loss: 4.1316 +2024-07-23 15:54:51,937 - pyskl - INFO - Epoch [45][1100/3746] lr: 7.998e-02, eta: 3 days, 12:56:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5291, loss_cls: 4.2011, loss: 4.2011 +2024-07-23 15:56:13,683 - pyskl - INFO - Epoch [45][1200/3746] lr: 7.996e-02, eta: 3 days, 12:55:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5314, loss_cls: 4.1459, loss: 4.1459 +2024-07-23 15:57:35,138 - pyskl - INFO - Epoch [45][1300/3746] lr: 7.994e-02, eta: 3 days, 12:54:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5270, loss_cls: 4.1584, loss: 4.1584 +2024-07-23 15:58:56,711 - pyskl - INFO - Epoch [45][1400/3746] lr: 7.992e-02, eta: 3 days, 12:53:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5291, loss_cls: 4.1525, loss: 4.1525 +2024-07-23 16:00:18,225 - pyskl - INFO - Epoch [45][1500/3746] lr: 7.990e-02, eta: 3 days, 12:52:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5248, loss_cls: 4.2084, loss: 4.2084 +2024-07-23 16:01:40,664 - pyskl - INFO - Epoch [45][1600/3746] lr: 7.987e-02, eta: 3 days, 12:51:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5345, loss_cls: 4.1526, loss: 4.1526 +2024-07-23 16:03:02,167 - pyskl - INFO - Epoch [45][1700/3746] lr: 7.985e-02, eta: 3 days, 12:50:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5325, loss_cls: 4.1257, loss: 4.1257 +2024-07-23 16:04:24,204 - pyskl - INFO - Epoch [45][1800/3746] lr: 7.983e-02, eta: 3 days, 12:49:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5347, loss_cls: 4.1426, loss: 4.1426 +2024-07-23 16:05:45,841 - pyskl - INFO - Epoch [45][1900/3746] lr: 7.981e-02, eta: 3 days, 12:48:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5206, loss_cls: 4.1716, loss: 4.1716 +2024-07-23 16:07:07,121 - pyskl - INFO - Epoch [45][2000/3746] lr: 7.978e-02, eta: 3 days, 12:46:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5283, loss_cls: 4.1481, loss: 4.1481 +2024-07-23 16:08:28,760 - pyskl - INFO - Epoch [45][2100/3746] lr: 7.976e-02, eta: 3 days, 12:45:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5364, loss_cls: 4.1320, loss: 4.1320 +2024-07-23 16:09:50,753 - pyskl - INFO - Epoch [45][2200/3746] lr: 7.974e-02, eta: 3 days, 12:44:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5312, loss_cls: 4.1457, loss: 4.1457 +2024-07-23 16:11:12,307 - pyskl - INFO - Epoch [45][2300/3746] lr: 7.972e-02, eta: 3 days, 12:43:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5358, loss_cls: 4.1305, loss: 4.1305 +2024-07-23 16:12:34,199 - pyskl - INFO - Epoch [45][2400/3746] lr: 7.969e-02, eta: 3 days, 12:42:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5294, loss_cls: 4.1796, loss: 4.1796 +2024-07-23 16:13:56,284 - pyskl - INFO - Epoch [45][2500/3746] lr: 7.967e-02, eta: 3 days, 12:41:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5256, loss_cls: 4.1876, loss: 4.1876 +2024-07-23 16:15:18,193 - pyskl - INFO - Epoch [45][2600/3746] lr: 7.965e-02, eta: 3 days, 12:40:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5258, loss_cls: 4.1702, loss: 4.1702 +2024-07-23 16:16:40,162 - pyskl - INFO - Epoch [45][2700/3746] lr: 7.963e-02, eta: 3 days, 12:39:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5341, loss_cls: 4.1833, loss: 4.1833 +2024-07-23 16:18:02,067 - pyskl - INFO - Epoch [45][2800/3746] lr: 7.960e-02, eta: 3 days, 12:38:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5297, loss_cls: 4.1579, loss: 4.1579 +2024-07-23 16:19:23,858 - pyskl - INFO - Epoch [45][2900/3746] lr: 7.958e-02, eta: 3 days, 12:36:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5205, loss_cls: 4.1761, loss: 4.1761 +2024-07-23 16:20:45,837 - pyskl - INFO - Epoch [45][3000/3746] lr: 7.956e-02, eta: 3 days, 12:35:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5189, loss_cls: 4.1977, loss: 4.1977 +2024-07-23 16:22:07,793 - pyskl - INFO - Epoch [45][3100/3746] lr: 7.954e-02, eta: 3 days, 12:34:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5383, loss_cls: 4.1448, loss: 4.1448 +2024-07-23 16:23:29,751 - pyskl - INFO - Epoch [45][3200/3746] lr: 7.951e-02, eta: 3 days, 12:33:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5356, loss_cls: 4.1162, loss: 4.1162 +2024-07-23 16:24:51,398 - pyskl - INFO - Epoch [45][3300/3746] lr: 7.949e-02, eta: 3 days, 12:32:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5297, loss_cls: 4.1612, loss: 4.1612 +2024-07-23 16:26:13,005 - pyskl - INFO - Epoch [45][3400/3746] lr: 7.947e-02, eta: 3 days, 12:31:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5298, loss_cls: 4.1345, loss: 4.1345 +2024-07-23 16:27:35,267 - pyskl - INFO - Epoch [45][3500/3746] lr: 7.945e-02, eta: 3 days, 12:30:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5273, loss_cls: 4.1743, loss: 4.1743 +2024-07-23 16:28:57,036 - pyskl - INFO - Epoch [45][3600/3746] lr: 7.942e-02, eta: 3 days, 12:29:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5294, loss_cls: 4.1616, loss: 4.1616 +2024-07-23 16:30:18,979 - pyskl - INFO - Epoch [45][3700/3746] lr: 7.940e-02, eta: 3 days, 12:28:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5353, loss_cls: 4.1655, loss: 4.1655 +2024-07-23 16:30:59,124 - pyskl - INFO - Saving checkpoint at 45 epochs +2024-07-23 16:32:51,124 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 16:32:51,781 - pyskl - INFO - +top1_acc 0.2055 +top5_acc 0.4463 +2024-07-23 16:32:51,781 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 16:32:51,819 - pyskl - INFO - +mean_acc 0.2054 +2024-07-23 16:32:51,830 - pyskl - INFO - Epoch(val) [45][309] top1_acc: 0.2055, top5_acc: 0.4463, mean_class_accuracy: 0.2054 +2024-07-23 16:36:38,372 - pyskl - INFO - Epoch [46][100/3746] lr: 7.937e-02, eta: 3 days, 12:30:39, time: 2.265, data_time: 1.274, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5311, loss_cls: 4.1350, loss: 4.1350 +2024-07-23 16:37:59,920 - pyskl - INFO - Epoch [46][200/3746] lr: 7.934e-02, eta: 3 days, 12:29:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5352, loss_cls: 4.1099, loss: 4.1099 +2024-07-23 16:39:22,024 - pyskl - INFO - Epoch [46][300/3746] lr: 7.932e-02, eta: 3 days, 12:28:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5444, loss_cls: 4.1148, loss: 4.1148 +2024-07-23 16:40:43,702 - pyskl - INFO - Epoch [46][400/3746] lr: 7.930e-02, eta: 3 days, 12:27:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5350, loss_cls: 4.1266, loss: 4.1266 +2024-07-23 16:42:05,656 - pyskl - INFO - Epoch [46][500/3746] lr: 7.928e-02, eta: 3 days, 12:26:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5420, loss_cls: 4.1038, loss: 4.1038 +2024-07-23 16:43:27,496 - pyskl - INFO - Epoch [46][600/3746] lr: 7.925e-02, eta: 3 days, 12:25:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5339, loss_cls: 4.1463, loss: 4.1463 +2024-07-23 16:44:49,200 - pyskl - INFO - Epoch [46][700/3746] lr: 7.923e-02, eta: 3 days, 12:23:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5459, loss_cls: 4.1268, loss: 4.1268 +2024-07-23 16:46:10,481 - pyskl - INFO - Epoch [46][800/3746] lr: 7.921e-02, eta: 3 days, 12:22:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5372, loss_cls: 4.1299, loss: 4.1299 +2024-07-23 16:47:32,156 - pyskl - INFO - Epoch [46][900/3746] lr: 7.919e-02, eta: 3 days, 12:21:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5320, loss_cls: 4.1650, loss: 4.1650 +2024-07-23 16:48:54,128 - pyskl - INFO - Epoch [46][1000/3746] lr: 7.916e-02, eta: 3 days, 12:20:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5425, loss_cls: 4.1206, loss: 4.1206 +2024-07-23 16:50:16,311 - pyskl - INFO - Epoch [46][1100/3746] lr: 7.914e-02, eta: 3 days, 12:19:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5333, loss_cls: 4.1528, loss: 4.1528 +2024-07-23 16:51:37,858 - pyskl - INFO - Epoch [46][1200/3746] lr: 7.912e-02, eta: 3 days, 12:18:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5241, loss_cls: 4.1803, loss: 4.1803 +2024-07-23 16:53:00,275 - pyskl - INFO - Epoch [46][1300/3746] lr: 7.909e-02, eta: 3 days, 12:17:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5398, loss_cls: 4.1263, loss: 4.1263 +2024-07-23 16:54:21,916 - pyskl - INFO - Epoch [46][1400/3746] lr: 7.907e-02, eta: 3 days, 12:16:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5325, loss_cls: 4.1534, loss: 4.1534 +2024-07-23 16:55:43,863 - pyskl - INFO - Epoch [46][1500/3746] lr: 7.905e-02, eta: 3 days, 12:14:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5408, loss_cls: 4.0919, loss: 4.0919 +2024-07-23 16:57:05,512 - pyskl - INFO - Epoch [46][1600/3746] lr: 7.903e-02, eta: 3 days, 12:13:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5288, loss_cls: 4.1455, loss: 4.1455 +2024-07-23 16:58:27,116 - pyskl - INFO - Epoch [46][1700/3746] lr: 7.900e-02, eta: 3 days, 12:12:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5434, loss_cls: 4.1240, loss: 4.1240 +2024-07-23 16:59:49,531 - pyskl - INFO - Epoch [46][1800/3746] lr: 7.898e-02, eta: 3 days, 12:11:38, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5367, loss_cls: 4.1223, loss: 4.1223 +2024-07-23 17:01:11,606 - pyskl - INFO - Epoch [46][1900/3746] lr: 7.896e-02, eta: 3 days, 12:10:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5169, loss_cls: 4.1877, loss: 4.1877 +2024-07-23 17:02:32,985 - pyskl - INFO - Epoch [46][2000/3746] lr: 7.894e-02, eta: 3 days, 12:09:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5320, loss_cls: 4.1670, loss: 4.1670 +2024-07-23 17:03:54,343 - pyskl - INFO - Epoch [46][2100/3746] lr: 7.891e-02, eta: 3 days, 12:08:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5320, loss_cls: 4.1581, loss: 4.1581 +2024-07-23 17:05:15,821 - pyskl - INFO - Epoch [46][2200/3746] lr: 7.889e-02, eta: 3 days, 12:07:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5322, loss_cls: 4.1696, loss: 4.1696 +2024-07-23 17:06:37,359 - pyskl - INFO - Epoch [46][2300/3746] lr: 7.887e-02, eta: 3 days, 12:05:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5275, loss_cls: 4.1588, loss: 4.1588 +2024-07-23 17:07:58,878 - pyskl - INFO - Epoch [46][2400/3746] lr: 7.884e-02, eta: 3 days, 12:04:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5363, loss_cls: 4.1254, loss: 4.1254 +2024-07-23 17:09:20,292 - pyskl - INFO - Epoch [46][2500/3746] lr: 7.882e-02, eta: 3 days, 12:03:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5261, loss_cls: 4.1833, loss: 4.1833 +2024-07-23 17:10:42,969 - pyskl - INFO - Epoch [46][2600/3746] lr: 7.880e-02, eta: 3 days, 12:02:36, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5367, loss_cls: 4.1437, loss: 4.1437 +2024-07-23 17:12:04,994 - pyskl - INFO - Epoch [46][2700/3746] lr: 7.878e-02, eta: 3 days, 12:01:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5356, loss_cls: 4.1172, loss: 4.1172 +2024-07-23 17:13:27,260 - pyskl - INFO - Epoch [46][2800/3746] lr: 7.875e-02, eta: 3 days, 12:00:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5380, loss_cls: 4.1271, loss: 4.1271 +2024-07-23 17:14:49,610 - pyskl - INFO - Epoch [46][2900/3746] lr: 7.873e-02, eta: 3 days, 11:59:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5236, loss_cls: 4.1655, loss: 4.1655 +2024-07-23 17:16:11,261 - pyskl - INFO - Epoch [46][3000/3746] lr: 7.871e-02, eta: 3 days, 11:58:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5356, loss_cls: 4.1343, loss: 4.1343 +2024-07-23 17:17:33,378 - pyskl - INFO - Epoch [46][3100/3746] lr: 7.868e-02, eta: 3 days, 11:57:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5206, loss_cls: 4.1929, loss: 4.1929 +2024-07-23 17:18:55,251 - pyskl - INFO - Epoch [46][3200/3746] lr: 7.866e-02, eta: 3 days, 11:55:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5317, loss_cls: 4.1656, loss: 4.1656 +2024-07-23 17:20:17,208 - pyskl - INFO - Epoch [46][3300/3746] lr: 7.864e-02, eta: 3 days, 11:54:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5267, loss_cls: 4.1836, loss: 4.1836 +2024-07-23 17:21:38,812 - pyskl - INFO - Epoch [46][3400/3746] lr: 7.862e-02, eta: 3 days, 11:53:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5292, loss_cls: 4.1718, loss: 4.1718 +2024-07-23 17:23:01,505 - pyskl - INFO - Epoch [46][3500/3746] lr: 7.859e-02, eta: 3 days, 11:52:34, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5433, loss_cls: 4.1246, loss: 4.1246 +2024-07-23 17:24:23,564 - pyskl - INFO - Epoch [46][3600/3746] lr: 7.857e-02, eta: 3 days, 11:51:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5308, loss_cls: 4.1639, loss: 4.1639 +2024-07-23 17:25:45,950 - pyskl - INFO - Epoch [46][3700/3746] lr: 7.855e-02, eta: 3 days, 11:50:20, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5312, loss_cls: 4.1353, loss: 4.1353 +2024-07-23 17:26:25,912 - pyskl - INFO - Saving checkpoint at 46 epochs +2024-07-23 17:28:18,218 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 17:28:18,941 - pyskl - INFO - +top1_acc 0.2132 +top5_acc 0.4486 +2024-07-23 17:28:18,941 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 17:28:18,985 - pyskl - INFO - +mean_acc 0.2129 +2024-07-23 17:28:18,995 - pyskl - INFO - Epoch(val) [46][309] top1_acc: 0.2132, top5_acc: 0.4486, mean_class_accuracy: 0.2129 +2024-07-23 17:32:10,247 - pyskl - INFO - Epoch [47][100/3746] lr: 7.851e-02, eta: 3 days, 11:52:54, time: 2.312, data_time: 1.329, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5408, loss_cls: 4.0902, loss: 4.0902 +2024-07-23 17:33:32,833 - pyskl - INFO - Epoch [47][200/3746] lr: 7.849e-02, eta: 3 days, 11:51:48, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5414, loss_cls: 4.0783, loss: 4.0783 +2024-07-23 17:34:55,211 - pyskl - INFO - Epoch [47][300/3746] lr: 7.847e-02, eta: 3 days, 11:50:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5314, loss_cls: 4.1241, loss: 4.1241 +2024-07-23 17:36:16,783 - pyskl - INFO - Epoch [47][400/3746] lr: 7.844e-02, eta: 3 days, 11:49:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5395, loss_cls: 4.0930, loss: 4.0930 +2024-07-23 17:37:38,665 - pyskl - INFO - Epoch [47][500/3746] lr: 7.842e-02, eta: 3 days, 11:48:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5413, loss_cls: 4.1031, loss: 4.1031 +2024-07-23 17:39:00,852 - pyskl - INFO - Epoch [47][600/3746] lr: 7.840e-02, eta: 3 days, 11:47:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5309, loss_cls: 4.1569, loss: 4.1569 +2024-07-23 17:40:22,212 - pyskl - INFO - Epoch [47][700/3746] lr: 7.838e-02, eta: 3 days, 11:46:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5361, loss_cls: 4.1321, loss: 4.1321 +2024-07-23 17:41:43,432 - pyskl - INFO - Epoch [47][800/3746] lr: 7.835e-02, eta: 3 days, 11:45:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5342, loss_cls: 4.1476, loss: 4.1476 +2024-07-23 17:43:04,725 - pyskl - INFO - Epoch [47][900/3746] lr: 7.833e-02, eta: 3 days, 11:43:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5383, loss_cls: 4.1381, loss: 4.1381 +2024-07-23 17:44:26,304 - pyskl - INFO - Epoch [47][1000/3746] lr: 7.831e-02, eta: 3 days, 11:42:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5333, loss_cls: 4.1087, loss: 4.1087 +2024-07-23 17:45:47,913 - pyskl - INFO - Epoch [47][1100/3746] lr: 7.828e-02, eta: 3 days, 11:41:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5264, loss_cls: 4.1946, loss: 4.1946 +2024-07-23 17:47:09,482 - pyskl - INFO - Epoch [47][1200/3746] lr: 7.826e-02, eta: 3 days, 11:40:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5297, loss_cls: 4.1637, loss: 4.1637 +2024-07-23 17:48:31,109 - pyskl - INFO - Epoch [47][1300/3746] lr: 7.824e-02, eta: 3 days, 11:39:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5284, loss_cls: 4.1556, loss: 4.1556 +2024-07-23 17:49:52,237 - pyskl - INFO - Epoch [47][1400/3746] lr: 7.821e-02, eta: 3 days, 11:38:07, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5347, loss_cls: 4.1244, loss: 4.1244 +2024-07-23 17:51:13,839 - pyskl - INFO - Epoch [47][1500/3746] lr: 7.819e-02, eta: 3 days, 11:36:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5217, loss_cls: 4.2133, loss: 4.2133 +2024-07-23 17:52:35,260 - pyskl - INFO - Epoch [47][1600/3746] lr: 7.817e-02, eta: 3 days, 11:35:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5314, loss_cls: 4.1504, loss: 4.1504 +2024-07-23 17:53:56,735 - pyskl - INFO - Epoch [47][1700/3746] lr: 7.814e-02, eta: 3 days, 11:34:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5361, loss_cls: 4.1423, loss: 4.1423 +2024-07-23 17:55:18,430 - pyskl - INFO - Epoch [47][1800/3746] lr: 7.812e-02, eta: 3 days, 11:33:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5423, loss_cls: 4.0982, loss: 4.0982 +2024-07-23 17:56:39,868 - pyskl - INFO - Epoch [47][1900/3746] lr: 7.810e-02, eta: 3 days, 11:32:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5280, loss_cls: 4.1893, loss: 4.1893 +2024-07-23 17:58:01,817 - pyskl - INFO - Epoch [47][2000/3746] lr: 7.808e-02, eta: 3 days, 11:31:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5289, loss_cls: 4.1075, loss: 4.1075 +2024-07-23 17:59:23,361 - pyskl - INFO - Epoch [47][2100/3746] lr: 7.805e-02, eta: 3 days, 11:30:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5363, loss_cls: 4.1304, loss: 4.1304 +2024-07-23 18:00:44,964 - pyskl - INFO - Epoch [47][2200/3746] lr: 7.803e-02, eta: 3 days, 11:28:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5258, loss_cls: 4.1855, loss: 4.1855 +2024-07-23 18:02:07,159 - pyskl - INFO - Epoch [47][2300/3746] lr: 7.801e-02, eta: 3 days, 11:27:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5291, loss_cls: 4.1597, loss: 4.1597 +2024-07-23 18:03:28,380 - pyskl - INFO - Epoch [47][2400/3746] lr: 7.798e-02, eta: 3 days, 11:26:41, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5423, loss_cls: 4.1325, loss: 4.1325 +2024-07-23 18:04:50,141 - pyskl - INFO - Epoch [47][2500/3746] lr: 7.796e-02, eta: 3 days, 11:25:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5288, loss_cls: 4.1677, loss: 4.1677 +2024-07-23 18:06:12,369 - pyskl - INFO - Epoch [47][2600/3746] lr: 7.794e-02, eta: 3 days, 11:24:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5206, loss_cls: 4.2302, loss: 4.2302 +2024-07-23 18:07:34,095 - pyskl - INFO - Epoch [47][2700/3746] lr: 7.791e-02, eta: 3 days, 11:23:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5175, loss_cls: 4.2131, loss: 4.2131 +2024-07-23 18:08:55,836 - pyskl - INFO - Epoch [47][2800/3746] lr: 7.789e-02, eta: 3 days, 11:22:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5177, loss_cls: 4.2265, loss: 4.2265 +2024-07-23 18:10:18,285 - pyskl - INFO - Epoch [47][2900/3746] lr: 7.787e-02, eta: 3 days, 11:21:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5392, loss_cls: 4.0927, loss: 4.0927 +2024-07-23 18:11:40,291 - pyskl - INFO - Epoch [47][3000/3746] lr: 7.784e-02, eta: 3 days, 11:19:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5284, loss_cls: 4.1335, loss: 4.1335 +2024-07-23 18:13:02,425 - pyskl - INFO - Epoch [47][3100/3746] lr: 7.782e-02, eta: 3 days, 11:18:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5328, loss_cls: 4.1241, loss: 4.1241 +2024-07-23 18:14:24,229 - pyskl - INFO - Epoch [47][3200/3746] lr: 7.780e-02, eta: 3 days, 11:17:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5400, loss_cls: 4.1234, loss: 4.1234 +2024-07-23 18:15:45,703 - pyskl - INFO - Epoch [47][3300/3746] lr: 7.777e-02, eta: 3 days, 11:16:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5347, loss_cls: 4.1090, loss: 4.1090 +2024-07-23 18:17:07,710 - pyskl - INFO - Epoch [47][3400/3746] lr: 7.775e-02, eta: 3 days, 11:15:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5316, loss_cls: 4.1725, loss: 4.1725 +2024-07-23 18:18:30,001 - pyskl - INFO - Epoch [47][3500/3746] lr: 7.773e-02, eta: 3 days, 11:14:14, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5316, loss_cls: 4.1551, loss: 4.1551 +2024-07-23 18:19:53,070 - pyskl - INFO - Epoch [47][3600/3746] lr: 7.770e-02, eta: 3 days, 11:13:08, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5255, loss_cls: 4.1912, loss: 4.1912 +2024-07-23 18:21:15,453 - pyskl - INFO - Epoch [47][3700/3746] lr: 7.768e-02, eta: 3 days, 11:12:01, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5345, loss_cls: 4.0924, loss: 4.0924 +2024-07-23 18:21:55,044 - pyskl - INFO - Saving checkpoint at 47 epochs +2024-07-23 18:23:47,584 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 18:23:48,430 - pyskl - INFO - +top1_acc 0.2267 +top5_acc 0.4686 +2024-07-23 18:23:48,430 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 18:23:48,477 - pyskl - INFO - +mean_acc 0.2263 +2024-07-23 18:23:48,483 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_43.pth was removed +2024-07-23 18:23:48,842 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_47.pth. +2024-07-23 18:23:48,843 - pyskl - INFO - Best top1_acc is 0.2267 at 47 epoch. +2024-07-23 18:23:48,858 - pyskl - INFO - Epoch(val) [47][309] top1_acc: 0.2267, top5_acc: 0.4686, mean_class_accuracy: 0.2263 +2024-07-23 18:27:44,388 - pyskl - INFO - Epoch [48][100/3746] lr: 7.765e-02, eta: 3 days, 11:14:35, time: 2.355, data_time: 1.379, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5480, loss_cls: 4.0945, loss: 4.0945 +2024-07-23 18:29:07,096 - pyskl - INFO - Epoch [48][200/3746] lr: 7.762e-02, eta: 3 days, 11:13:28, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5377, loss_cls: 4.1010, loss: 4.1010 +2024-07-23 18:30:28,938 - pyskl - INFO - Epoch [48][300/3746] lr: 7.760e-02, eta: 3 days, 11:12:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5297, loss_cls: 4.1462, loss: 4.1462 +2024-07-23 18:31:50,597 - pyskl - INFO - Epoch [48][400/3746] lr: 7.758e-02, eta: 3 days, 11:11:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5212, loss_cls: 4.1555, loss: 4.1555 +2024-07-23 18:33:12,213 - pyskl - INFO - Epoch [48][500/3746] lr: 7.755e-02, eta: 3 days, 11:10:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5294, loss_cls: 4.1572, loss: 4.1572 +2024-07-23 18:34:33,978 - pyskl - INFO - Epoch [48][600/3746] lr: 7.753e-02, eta: 3 days, 11:08:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5433, loss_cls: 4.0587, loss: 4.0587 +2024-07-23 18:35:55,440 - pyskl - INFO - Epoch [48][700/3746] lr: 7.751e-02, eta: 3 days, 11:07:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5453, loss_cls: 4.0753, loss: 4.0753 +2024-07-23 18:37:17,950 - pyskl - INFO - Epoch [48][800/3746] lr: 7.748e-02, eta: 3 days, 11:06:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5423, loss_cls: 4.0545, loss: 4.0545 +2024-07-23 18:38:39,775 - pyskl - INFO - Epoch [48][900/3746] lr: 7.746e-02, eta: 3 days, 11:05:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5334, loss_cls: 4.1517, loss: 4.1517 +2024-07-23 18:40:01,276 - pyskl - INFO - Epoch [48][1000/3746] lr: 7.744e-02, eta: 3 days, 11:04:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5281, loss_cls: 4.1738, loss: 4.1738 +2024-07-23 18:41:23,167 - pyskl - INFO - Epoch [48][1100/3746] lr: 7.741e-02, eta: 3 days, 11:03:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5283, loss_cls: 4.1687, loss: 4.1687 +2024-07-23 18:42:45,656 - pyskl - INFO - Epoch [48][1200/3746] lr: 7.739e-02, eta: 3 days, 11:02:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5311, loss_cls: 4.1915, loss: 4.1915 +2024-07-23 18:44:07,032 - pyskl - INFO - Epoch [48][1300/3746] lr: 7.737e-02, eta: 3 days, 11:00:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5334, loss_cls: 4.1621, loss: 4.1621 +2024-07-23 18:45:28,944 - pyskl - INFO - Epoch [48][1400/3746] lr: 7.734e-02, eta: 3 days, 10:59:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5312, loss_cls: 4.1154, loss: 4.1154 +2024-07-23 18:46:50,742 - pyskl - INFO - Epoch [48][1500/3746] lr: 7.732e-02, eta: 3 days, 10:58:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5348, loss_cls: 4.1492, loss: 4.1492 +2024-07-23 18:48:13,046 - pyskl - INFO - Epoch [48][1600/3746] lr: 7.730e-02, eta: 3 days, 10:57:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5408, loss_cls: 4.0996, loss: 4.0996 +2024-07-23 18:49:34,545 - pyskl - INFO - Epoch [48][1700/3746] lr: 7.727e-02, eta: 3 days, 10:56:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5322, loss_cls: 4.1645, loss: 4.1645 +2024-07-23 18:50:56,082 - pyskl - INFO - Epoch [48][1800/3746] lr: 7.725e-02, eta: 3 days, 10:55:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5348, loss_cls: 4.1283, loss: 4.1283 +2024-07-23 18:52:17,550 - pyskl - INFO - Epoch [48][1900/3746] lr: 7.723e-02, eta: 3 days, 10:53:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5241, loss_cls: 4.1768, loss: 4.1768 +2024-07-23 18:53:39,154 - pyskl - INFO - Epoch [48][2000/3746] lr: 7.720e-02, eta: 3 days, 10:52:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5248, loss_cls: 4.1672, loss: 4.1672 +2024-07-23 18:55:01,166 - pyskl - INFO - Epoch [48][2100/3746] lr: 7.718e-02, eta: 3 days, 10:51:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5356, loss_cls: 4.1256, loss: 4.1256 +2024-07-23 18:56:23,052 - pyskl - INFO - Epoch [48][2200/3746] lr: 7.716e-02, eta: 3 days, 10:50:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5341, loss_cls: 4.1230, loss: 4.1230 +2024-07-23 18:57:44,626 - pyskl - INFO - Epoch [48][2300/3746] lr: 7.713e-02, eta: 3 days, 10:49:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5288, loss_cls: 4.1869, loss: 4.1869 +2024-07-23 18:59:06,397 - pyskl - INFO - Epoch [48][2400/3746] lr: 7.711e-02, eta: 3 days, 10:48:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5391, loss_cls: 4.1324, loss: 4.1324 +2024-07-23 19:00:27,985 - pyskl - INFO - Epoch [48][2500/3746] lr: 7.709e-02, eta: 3 days, 10:47:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5336, loss_cls: 4.1346, loss: 4.1346 +2024-07-23 19:01:50,203 - pyskl - INFO - Epoch [48][2600/3746] lr: 7.706e-02, eta: 3 days, 10:45:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5255, loss_cls: 4.1888, loss: 4.1888 +2024-07-23 19:03:12,301 - pyskl - INFO - Epoch [48][2700/3746] lr: 7.704e-02, eta: 3 days, 10:44:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5475, loss_cls: 4.1000, loss: 4.1000 +2024-07-23 19:04:34,322 - pyskl - INFO - Epoch [48][2800/3746] lr: 7.701e-02, eta: 3 days, 10:43:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5280, loss_cls: 4.1960, loss: 4.1960 +2024-07-23 19:05:56,743 - pyskl - INFO - Epoch [48][2900/3746] lr: 7.699e-02, eta: 3 days, 10:42:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5442, loss_cls: 4.1127, loss: 4.1127 +2024-07-23 19:07:18,575 - pyskl - INFO - Epoch [48][3000/3746] lr: 7.697e-02, eta: 3 days, 10:41:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5380, loss_cls: 4.1166, loss: 4.1166 +2024-07-23 19:08:40,252 - pyskl - INFO - Epoch [48][3100/3746] lr: 7.694e-02, eta: 3 days, 10:40:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5355, loss_cls: 4.1361, loss: 4.1361 +2024-07-23 19:10:01,787 - pyskl - INFO - Epoch [48][3200/3746] lr: 7.692e-02, eta: 3 days, 10:39:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5436, loss_cls: 4.0891, loss: 4.0891 +2024-07-23 19:11:22,940 - pyskl - INFO - Epoch [48][3300/3746] lr: 7.690e-02, eta: 3 days, 10:37:51, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5227, loss_cls: 4.1796, loss: 4.1796 +2024-07-23 19:12:45,782 - pyskl - INFO - Epoch [48][3400/3746] lr: 7.687e-02, eta: 3 days, 10:36:45, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5366, loss_cls: 4.1225, loss: 4.1225 +2024-07-23 19:14:08,726 - pyskl - INFO - Epoch [48][3500/3746] lr: 7.685e-02, eta: 3 days, 10:35:38, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5308, loss_cls: 4.1495, loss: 4.1495 +2024-07-23 19:15:30,707 - pyskl - INFO - Epoch [48][3600/3746] lr: 7.683e-02, eta: 3 days, 10:34:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5344, loss_cls: 4.1341, loss: 4.1341 +2024-07-23 19:16:52,727 - pyskl - INFO - Epoch [48][3700/3746] lr: 7.680e-02, eta: 3 days, 10:33:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5361, loss_cls: 4.1473, loss: 4.1473 +2024-07-23 19:17:32,868 - pyskl - INFO - Saving checkpoint at 48 epochs +2024-07-23 19:19:25,939 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 19:19:26,605 - pyskl - INFO - +top1_acc 0.2070 +top5_acc 0.4413 +2024-07-23 19:19:26,605 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 19:19:26,646 - pyskl - INFO - +mean_acc 0.2068 +2024-07-23 19:19:26,658 - pyskl - INFO - Epoch(val) [48][309] top1_acc: 0.2070, top5_acc: 0.4413, mean_class_accuracy: 0.2068 +2024-07-23 19:23:20,104 - pyskl - INFO - Epoch [49][100/3746] lr: 7.677e-02, eta: 3 days, 10:35:41, time: 2.334, data_time: 1.353, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5378, loss_cls: 4.0950, loss: 4.0950 +2024-07-23 19:24:42,620 - pyskl - INFO - Epoch [49][200/3746] lr: 7.674e-02, eta: 3 days, 10:34:33, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5425, loss_cls: 4.0983, loss: 4.0983 +2024-07-23 19:26:04,577 - pyskl - INFO - Epoch [49][300/3746] lr: 7.672e-02, eta: 3 days, 10:33:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5502, loss_cls: 4.0852, loss: 4.0852 +2024-07-23 19:27:26,532 - pyskl - INFO - Epoch [49][400/3746] lr: 7.670e-02, eta: 3 days, 10:32:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5341, loss_cls: 4.1501, loss: 4.1501 +2024-07-23 19:28:48,081 - pyskl - INFO - Epoch [49][500/3746] lr: 7.667e-02, eta: 3 days, 10:31:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5433, loss_cls: 4.0972, loss: 4.0972 +2024-07-23 19:30:09,761 - pyskl - INFO - Epoch [49][600/3746] lr: 7.665e-02, eta: 3 days, 10:29:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5352, loss_cls: 4.1351, loss: 4.1351 +2024-07-23 19:31:31,377 - pyskl - INFO - Epoch [49][700/3746] lr: 7.663e-02, eta: 3 days, 10:28:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5252, loss_cls: 4.1603, loss: 4.1603 +2024-07-23 19:32:52,817 - pyskl - INFO - Epoch [49][800/3746] lr: 7.660e-02, eta: 3 days, 10:27:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5348, loss_cls: 4.1426, loss: 4.1426 +2024-07-23 19:34:14,457 - pyskl - INFO - Epoch [49][900/3746] lr: 7.658e-02, eta: 3 days, 10:26:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5370, loss_cls: 4.1388, loss: 4.1388 +2024-07-23 19:35:36,067 - pyskl - INFO - Epoch [49][1000/3746] lr: 7.656e-02, eta: 3 days, 10:25:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5311, loss_cls: 4.1447, loss: 4.1447 +2024-07-23 19:36:58,368 - pyskl - INFO - Epoch [49][1100/3746] lr: 7.653e-02, eta: 3 days, 10:24:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5423, loss_cls: 4.1162, loss: 4.1162 +2024-07-23 19:38:19,926 - pyskl - INFO - Epoch [49][1200/3746] lr: 7.651e-02, eta: 3 days, 10:22:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5345, loss_cls: 4.1321, loss: 4.1321 +2024-07-23 19:39:41,853 - pyskl - INFO - Epoch [49][1300/3746] lr: 7.648e-02, eta: 3 days, 10:21:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5369, loss_cls: 4.1370, loss: 4.1370 +2024-07-23 19:41:03,818 - pyskl - INFO - Epoch [49][1400/3746] lr: 7.646e-02, eta: 3 days, 10:20:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5308, loss_cls: 4.1350, loss: 4.1350 +2024-07-23 19:42:25,315 - pyskl - INFO - Epoch [49][1500/3746] lr: 7.644e-02, eta: 3 days, 10:19:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5309, loss_cls: 4.1564, loss: 4.1564 +2024-07-23 19:43:47,129 - pyskl - INFO - Epoch [49][1600/3746] lr: 7.641e-02, eta: 3 days, 10:18:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5264, loss_cls: 4.1719, loss: 4.1719 +2024-07-23 19:45:08,500 - pyskl - INFO - Epoch [49][1700/3746] lr: 7.639e-02, eta: 3 days, 10:17:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5428, loss_cls: 4.1067, loss: 4.1067 +2024-07-23 19:46:29,726 - pyskl - INFO - Epoch [49][1800/3746] lr: 7.637e-02, eta: 3 days, 10:15:57, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5370, loss_cls: 4.1532, loss: 4.1532 +2024-07-23 19:47:51,266 - pyskl - INFO - Epoch [49][1900/3746] lr: 7.634e-02, eta: 3 days, 10:14:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5317, loss_cls: 4.1526, loss: 4.1526 +2024-07-23 19:49:12,826 - pyskl - INFO - Epoch [49][2000/3746] lr: 7.632e-02, eta: 3 days, 10:13:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5487, loss_cls: 4.0618, loss: 4.0618 +2024-07-23 19:50:34,345 - pyskl - INFO - Epoch [49][2100/3746] lr: 7.629e-02, eta: 3 days, 10:12:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5483, loss_cls: 4.0734, loss: 4.0734 +2024-07-23 19:51:55,705 - pyskl - INFO - Epoch [49][2200/3746] lr: 7.627e-02, eta: 3 days, 10:11:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5255, loss_cls: 4.1718, loss: 4.1718 +2024-07-23 19:53:17,410 - pyskl - INFO - Epoch [49][2300/3746] lr: 7.625e-02, eta: 3 days, 10:10:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5408, loss_cls: 4.0879, loss: 4.0879 +2024-07-23 19:54:39,030 - pyskl - INFO - Epoch [49][2400/3746] lr: 7.622e-02, eta: 3 days, 10:08:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5292, loss_cls: 4.1246, loss: 4.1246 +2024-07-23 19:56:00,800 - pyskl - INFO - Epoch [49][2500/3746] lr: 7.620e-02, eta: 3 days, 10:07:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5464, loss_cls: 4.0812, loss: 4.0812 +2024-07-23 19:57:22,872 - pyskl - INFO - Epoch [49][2600/3746] lr: 7.618e-02, eta: 3 days, 10:06:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5283, loss_cls: 4.1593, loss: 4.1593 +2024-07-23 19:58:44,851 - pyskl - INFO - Epoch [49][2700/3746] lr: 7.615e-02, eta: 3 days, 10:05:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5381, loss_cls: 4.1060, loss: 4.1060 +2024-07-23 20:00:06,785 - pyskl - INFO - Epoch [49][2800/3746] lr: 7.613e-02, eta: 3 days, 10:04:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5333, loss_cls: 4.1470, loss: 4.1470 +2024-07-23 20:01:29,162 - pyskl - INFO - Epoch [49][2900/3746] lr: 7.610e-02, eta: 3 days, 10:03:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5437, loss_cls: 4.1061, loss: 4.1061 +2024-07-23 20:02:51,570 - pyskl - INFO - Epoch [49][3000/3746] lr: 7.608e-02, eta: 3 days, 10:02:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5372, loss_cls: 4.1253, loss: 4.1253 +2024-07-23 20:04:13,963 - pyskl - INFO - Epoch [49][3100/3746] lr: 7.606e-02, eta: 3 days, 10:00:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5436, loss_cls: 4.1119, loss: 4.1119 +2024-07-23 20:05:36,275 - pyskl - INFO - Epoch [49][3200/3746] lr: 7.603e-02, eta: 3 days, 9:59:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5508, loss_cls: 4.0857, loss: 4.0857 +2024-07-23 20:06:57,804 - pyskl - INFO - Epoch [49][3300/3746] lr: 7.601e-02, eta: 3 days, 9:58:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5316, loss_cls: 4.1476, loss: 4.1476 +2024-07-23 20:08:19,437 - pyskl - INFO - Epoch [49][3400/3746] lr: 7.598e-02, eta: 3 days, 9:57:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5306, loss_cls: 4.1319, loss: 4.1319 +2024-07-23 20:09:41,265 - pyskl - INFO - Epoch [49][3500/3746] lr: 7.596e-02, eta: 3 days, 9:56:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5308, loss_cls: 4.1687, loss: 4.1687 +2024-07-23 20:11:02,764 - pyskl - INFO - Epoch [49][3600/3746] lr: 7.594e-02, eta: 3 days, 9:55:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5373, loss_cls: 4.1305, loss: 4.1305 +2024-07-23 20:12:24,499 - pyskl - INFO - Epoch [49][3700/3746] lr: 7.591e-02, eta: 3 days, 9:53:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5377, loss_cls: 4.1189, loss: 4.1189 +2024-07-23 20:13:04,195 - pyskl - INFO - Saving checkpoint at 49 epochs +2024-07-23 20:14:56,832 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 20:14:57,490 - pyskl - INFO - +top1_acc 0.2309 +top5_acc 0.4687 +2024-07-23 20:14:57,491 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 20:14:57,530 - pyskl - INFO - +mean_acc 0.2306 +2024-07-23 20:14:57,535 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_47.pth was removed +2024-07-23 20:14:57,786 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_49.pth. +2024-07-23 20:14:57,787 - pyskl - INFO - Best top1_acc is 0.2309 at 49 epoch. +2024-07-23 20:14:57,797 - pyskl - INFO - Epoch(val) [49][309] top1_acc: 0.2309, top5_acc: 0.4687, mean_class_accuracy: 0.2306 +2024-07-23 20:18:50,066 - pyskl - INFO - Epoch [50][100/3746] lr: 7.588e-02, eta: 3 days, 9:56:02, time: 2.323, data_time: 1.340, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5508, loss_cls: 4.0518, loss: 4.0518 +2024-07-23 20:20:11,869 - pyskl - INFO - Epoch [50][200/3746] lr: 7.585e-02, eta: 3 days, 9:54:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5331, loss_cls: 4.1571, loss: 4.1571 +2024-07-23 20:21:34,253 - pyskl - INFO - Epoch [50][300/3746] lr: 7.583e-02, eta: 3 days, 9:53:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5503, loss_cls: 4.0862, loss: 4.0862 +2024-07-23 20:22:55,402 - pyskl - INFO - Epoch [50][400/3746] lr: 7.581e-02, eta: 3 days, 9:52:32, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5297, loss_cls: 4.1299, loss: 4.1299 +2024-07-23 20:24:17,483 - pyskl - INFO - Epoch [50][500/3746] lr: 7.578e-02, eta: 3 days, 9:51:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5380, loss_cls: 4.0948, loss: 4.0948 +2024-07-23 20:25:38,755 - pyskl - INFO - Epoch [50][600/3746] lr: 7.576e-02, eta: 3 days, 9:50:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5387, loss_cls: 4.1243, loss: 4.1243 +2024-07-23 20:27:00,197 - pyskl - INFO - Epoch [50][700/3746] lr: 7.573e-02, eta: 3 days, 9:49:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5292, loss_cls: 4.1884, loss: 4.1884 +2024-07-23 20:28:21,745 - pyskl - INFO - Epoch [50][800/3746] lr: 7.571e-02, eta: 3 days, 9:47:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5377, loss_cls: 4.1108, loss: 4.1108 +2024-07-23 20:29:43,140 - pyskl - INFO - Epoch [50][900/3746] lr: 7.569e-02, eta: 3 days, 9:46:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5408, loss_cls: 4.0946, loss: 4.0946 +2024-07-23 20:31:04,457 - pyskl - INFO - Epoch [50][1000/3746] lr: 7.566e-02, eta: 3 days, 9:45:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5309, loss_cls: 4.1131, loss: 4.1131 +2024-07-23 20:32:26,109 - pyskl - INFO - Epoch [50][1100/3746] lr: 7.564e-02, eta: 3 days, 9:44:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5381, loss_cls: 4.0934, loss: 4.0934 +2024-07-23 20:33:47,884 - pyskl - INFO - Epoch [50][1200/3746] lr: 7.561e-02, eta: 3 days, 9:43:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5298, loss_cls: 4.1617, loss: 4.1617 +2024-07-23 20:35:09,886 - pyskl - INFO - Epoch [50][1300/3746] lr: 7.559e-02, eta: 3 days, 9:41:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5269, loss_cls: 4.1603, loss: 4.1603 +2024-07-23 20:36:31,377 - pyskl - INFO - Epoch [50][1400/3746] lr: 7.557e-02, eta: 3 days, 9:40:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5328, loss_cls: 4.1482, loss: 4.1482 +2024-07-23 20:37:53,237 - pyskl - INFO - Epoch [50][1500/3746] lr: 7.554e-02, eta: 3 days, 9:39:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5420, loss_cls: 4.0859, loss: 4.0859 +2024-07-23 20:39:14,939 - pyskl - INFO - Epoch [50][1600/3746] lr: 7.552e-02, eta: 3 days, 9:38:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5352, loss_cls: 4.1114, loss: 4.1114 +2024-07-23 20:40:36,912 - pyskl - INFO - Epoch [50][1700/3746] lr: 7.549e-02, eta: 3 days, 9:37:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5436, loss_cls: 4.0884, loss: 4.0884 +2024-07-23 20:41:58,583 - pyskl - INFO - Epoch [50][1800/3746] lr: 7.547e-02, eta: 3 days, 9:36:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5356, loss_cls: 4.1334, loss: 4.1334 +2024-07-23 20:43:20,349 - pyskl - INFO - Epoch [50][1900/3746] lr: 7.545e-02, eta: 3 days, 9:34:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5297, loss_cls: 4.1261, loss: 4.1261 +2024-07-23 20:44:41,756 - pyskl - INFO - Epoch [50][2000/3746] lr: 7.542e-02, eta: 3 days, 9:33:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5392, loss_cls: 4.1070, loss: 4.1070 +2024-07-23 20:46:03,645 - pyskl - INFO - Epoch [50][2100/3746] lr: 7.540e-02, eta: 3 days, 9:32:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5306, loss_cls: 4.1227, loss: 4.1227 +2024-07-23 20:47:25,517 - pyskl - INFO - Epoch [50][2200/3746] lr: 7.537e-02, eta: 3 days, 9:31:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5312, loss_cls: 4.1549, loss: 4.1549 +2024-07-23 20:48:47,350 - pyskl - INFO - Epoch [50][2300/3746] lr: 7.535e-02, eta: 3 days, 9:30:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5409, loss_cls: 4.0798, loss: 4.0798 +2024-07-23 20:50:09,495 - pyskl - INFO - Epoch [50][2400/3746] lr: 7.533e-02, eta: 3 days, 9:29:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5352, loss_cls: 4.1546, loss: 4.1546 +2024-07-23 20:51:31,024 - pyskl - INFO - Epoch [50][2500/3746] lr: 7.530e-02, eta: 3 days, 9:27:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5361, loss_cls: 4.1044, loss: 4.1044 +2024-07-23 20:52:53,494 - pyskl - INFO - Epoch [50][2600/3746] lr: 7.528e-02, eta: 3 days, 9:26:42, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5414, loss_cls: 4.1281, loss: 4.1281 +2024-07-23 20:54:15,255 - pyskl - INFO - Epoch [50][2700/3746] lr: 7.525e-02, eta: 3 days, 9:25:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5273, loss_cls: 4.1714, loss: 4.1714 +2024-07-23 20:55:37,629 - pyskl - INFO - Epoch [50][2800/3746] lr: 7.523e-02, eta: 3 days, 9:24:23, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5359, loss_cls: 4.1209, loss: 4.1209 +2024-07-23 20:56:59,651 - pyskl - INFO - Epoch [50][2900/3746] lr: 7.520e-02, eta: 3 days, 9:23:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5366, loss_cls: 4.1327, loss: 4.1327 +2024-07-23 20:58:21,818 - pyskl - INFO - Epoch [50][3000/3746] lr: 7.518e-02, eta: 3 days, 9:22:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5342, loss_cls: 4.1293, loss: 4.1293 +2024-07-23 20:59:44,246 - pyskl - INFO - Epoch [50][3100/3746] lr: 7.516e-02, eta: 3 days, 9:20:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5384, loss_cls: 4.1048, loss: 4.1048 +2024-07-23 21:01:06,123 - pyskl - INFO - Epoch [50][3200/3746] lr: 7.513e-02, eta: 3 days, 9:19:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5350, loss_cls: 4.1358, loss: 4.1358 +2024-07-23 21:02:27,633 - pyskl - INFO - Epoch [50][3300/3746] lr: 7.511e-02, eta: 3 days, 9:18:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5302, loss_cls: 4.1575, loss: 4.1575 +2024-07-23 21:03:49,656 - pyskl - INFO - Epoch [50][3400/3746] lr: 7.508e-02, eta: 3 days, 9:17:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5405, loss_cls: 4.1225, loss: 4.1225 +2024-07-23 21:05:11,892 - pyskl - INFO - Epoch [50][3500/3746] lr: 7.506e-02, eta: 3 days, 9:16:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5405, loss_cls: 4.1070, loss: 4.1070 +2024-07-23 21:06:34,029 - pyskl - INFO - Epoch [50][3600/3746] lr: 7.504e-02, eta: 3 days, 9:15:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5406, loss_cls: 4.1025, loss: 4.1025 +2024-07-23 21:07:55,811 - pyskl - INFO - Epoch [50][3700/3746] lr: 7.501e-02, eta: 3 days, 9:13:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5409, loss_cls: 4.0824, loss: 4.0824 +2024-07-23 21:08:35,688 - pyskl - INFO - Saving checkpoint at 50 epochs +2024-07-23 21:10:28,807 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 21:10:29,468 - pyskl - INFO - +top1_acc 0.1979 +top5_acc 0.4228 +2024-07-23 21:10:29,468 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 21:10:29,508 - pyskl - INFO - +mean_acc 0.1977 +2024-07-23 21:10:29,520 - pyskl - INFO - Epoch(val) [50][309] top1_acc: 0.1979, top5_acc: 0.4228, mean_class_accuracy: 0.1977 +2024-07-23 21:14:22,318 - pyskl - INFO - Epoch [51][100/3746] lr: 7.498e-02, eta: 3 days, 9:15:55, time: 2.328, data_time: 1.360, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5506, loss_cls: 4.0517, loss: 4.0517 +2024-07-23 21:15:43,610 - pyskl - INFO - Epoch [51][200/3746] lr: 7.495e-02, eta: 3 days, 9:14:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5392, loss_cls: 4.0809, loss: 4.0809 +2024-07-23 21:17:05,105 - pyskl - INFO - Epoch [51][300/3746] lr: 7.493e-02, eta: 3 days, 9:13:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5436, loss_cls: 4.0737, loss: 4.0737 +2024-07-23 21:18:26,562 - pyskl - INFO - Epoch [51][400/3746] lr: 7.490e-02, eta: 3 days, 9:12:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5402, loss_cls: 4.1162, loss: 4.1162 +2024-07-23 21:19:48,170 - pyskl - INFO - Epoch [51][500/3746] lr: 7.488e-02, eta: 3 days, 9:11:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5417, loss_cls: 4.0943, loss: 4.0943 +2024-07-23 21:21:09,782 - pyskl - INFO - Epoch [51][600/3746] lr: 7.485e-02, eta: 3 days, 9:09:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5398, loss_cls: 4.1011, loss: 4.1011 +2024-07-23 21:22:31,983 - pyskl - INFO - Epoch [51][700/3746] lr: 7.483e-02, eta: 3 days, 9:08:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5395, loss_cls: 4.1146, loss: 4.1146 +2024-07-23 21:23:53,840 - pyskl - INFO - Epoch [51][800/3746] lr: 7.481e-02, eta: 3 days, 9:07:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5475, loss_cls: 4.0640, loss: 4.0640 +2024-07-23 21:25:15,592 - pyskl - INFO - Epoch [51][900/3746] lr: 7.478e-02, eta: 3 days, 9:06:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5364, loss_cls: 4.1336, loss: 4.1336 +2024-07-23 21:26:37,468 - pyskl - INFO - Epoch [51][1000/3746] lr: 7.476e-02, eta: 3 days, 9:05:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5397, loss_cls: 4.1051, loss: 4.1051 +2024-07-23 21:27:59,288 - pyskl - INFO - Epoch [51][1100/3746] lr: 7.473e-02, eta: 3 days, 9:04:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5353, loss_cls: 4.1363, loss: 4.1363 +2024-07-23 21:29:20,737 - pyskl - INFO - Epoch [51][1200/3746] lr: 7.471e-02, eta: 3 days, 9:02:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5350, loss_cls: 4.1320, loss: 4.1320 +2024-07-23 21:30:42,572 - pyskl - INFO - Epoch [51][1300/3746] lr: 7.468e-02, eta: 3 days, 9:01:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5309, loss_cls: 4.1285, loss: 4.1285 +2024-07-23 21:32:04,061 - pyskl - INFO - Epoch [51][1400/3746] lr: 7.466e-02, eta: 3 days, 9:00:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5292, loss_cls: 4.1567, loss: 4.1567 +2024-07-23 21:33:26,149 - pyskl - INFO - Epoch [51][1500/3746] lr: 7.464e-02, eta: 3 days, 8:59:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5394, loss_cls: 4.1186, loss: 4.1186 +2024-07-23 21:34:48,033 - pyskl - INFO - Epoch [51][1600/3746] lr: 7.461e-02, eta: 3 days, 8:58:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5437, loss_cls: 4.0715, loss: 4.0715 +2024-07-23 21:36:09,289 - pyskl - INFO - Epoch [51][1700/3746] lr: 7.459e-02, eta: 3 days, 8:56:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5269, loss_cls: 4.1907, loss: 4.1907 +2024-07-23 21:37:30,708 - pyskl - INFO - Epoch [51][1800/3746] lr: 7.456e-02, eta: 3 days, 8:55:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5327, loss_cls: 4.1460, loss: 4.1460 +2024-07-23 21:38:52,624 - pyskl - INFO - Epoch [51][1900/3746] lr: 7.454e-02, eta: 3 days, 8:54:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5439, loss_cls: 4.1197, loss: 4.1197 +2024-07-23 21:40:14,404 - pyskl - INFO - Epoch [51][2000/3746] lr: 7.451e-02, eta: 3 days, 8:53:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5387, loss_cls: 4.1164, loss: 4.1164 +2024-07-23 21:41:36,016 - pyskl - INFO - Epoch [51][2100/3746] lr: 7.449e-02, eta: 3 days, 8:52:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5309, loss_cls: 4.1564, loss: 4.1564 +2024-07-23 21:42:58,621 - pyskl - INFO - Epoch [51][2200/3746] lr: 7.447e-02, eta: 3 days, 8:51:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5331, loss_cls: 4.1121, loss: 4.1121 +2024-07-23 21:44:20,512 - pyskl - INFO - Epoch [51][2300/3746] lr: 7.444e-02, eta: 3 days, 8:49:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5342, loss_cls: 4.1600, loss: 4.1600 +2024-07-23 21:45:41,698 - pyskl - INFO - Epoch [51][2400/3746] lr: 7.442e-02, eta: 3 days, 8:48:41, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5381, loss_cls: 4.1207, loss: 4.1207 +2024-07-23 21:47:03,463 - pyskl - INFO - Epoch [51][2500/3746] lr: 7.439e-02, eta: 3 days, 8:47:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5383, loss_cls: 4.1396, loss: 4.1396 +2024-07-23 21:48:25,548 - pyskl - INFO - Epoch [51][2600/3746] lr: 7.437e-02, eta: 3 days, 8:46:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5305, loss_cls: 4.1266, loss: 4.1266 +2024-07-23 21:49:47,269 - pyskl - INFO - Epoch [51][2700/3746] lr: 7.434e-02, eta: 3 days, 8:45:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5466, loss_cls: 4.0847, loss: 4.0847 +2024-07-23 21:51:09,195 - pyskl - INFO - Epoch [51][2800/3746] lr: 7.432e-02, eta: 3 days, 8:43:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5395, loss_cls: 4.1119, loss: 4.1119 +2024-07-23 21:52:31,579 - pyskl - INFO - Epoch [51][2900/3746] lr: 7.429e-02, eta: 3 days, 8:42:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5448, loss_cls: 4.0776, loss: 4.0776 +2024-07-23 21:53:53,095 - pyskl - INFO - Epoch [51][3000/3746] lr: 7.427e-02, eta: 3 days, 8:41:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5298, loss_cls: 4.1245, loss: 4.1245 +2024-07-23 21:55:15,664 - pyskl - INFO - Epoch [51][3100/3746] lr: 7.425e-02, eta: 3 days, 8:40:26, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5384, loss_cls: 4.1178, loss: 4.1178 +2024-07-23 21:56:37,828 - pyskl - INFO - Epoch [51][3200/3746] lr: 7.422e-02, eta: 3 days, 8:39:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5414, loss_cls: 4.1046, loss: 4.1046 +2024-07-23 21:57:59,737 - pyskl - INFO - Epoch [51][3300/3746] lr: 7.420e-02, eta: 3 days, 8:38:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5417, loss_cls: 4.0995, loss: 4.0995 +2024-07-23 21:59:21,627 - pyskl - INFO - Epoch [51][3400/3746] lr: 7.417e-02, eta: 3 days, 8:36:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5536, loss_cls: 4.0608, loss: 4.0608 +2024-07-23 22:00:44,064 - pyskl - INFO - Epoch [51][3500/3746] lr: 7.415e-02, eta: 3 days, 8:35:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5363, loss_cls: 4.1160, loss: 4.1160 +2024-07-23 22:02:05,936 - pyskl - INFO - Epoch [51][3600/3746] lr: 7.412e-02, eta: 3 days, 8:34:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5394, loss_cls: 4.1098, loss: 4.1098 +2024-07-23 22:03:27,998 - pyskl - INFO - Epoch [51][3700/3746] lr: 7.410e-02, eta: 3 days, 8:33:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5355, loss_cls: 4.1399, loss: 4.1399 +2024-07-23 22:04:07,717 - pyskl - INFO - Saving checkpoint at 51 epochs +2024-07-23 22:06:00,273 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 22:06:00,934 - pyskl - INFO - +top1_acc 0.2183 +top5_acc 0.4558 +2024-07-23 22:06:00,934 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 22:06:00,973 - pyskl - INFO - +mean_acc 0.2180 +2024-07-23 22:06:00,983 - pyskl - INFO - Epoch(val) [51][309] top1_acc: 0.2183, top5_acc: 0.4558, mean_class_accuracy: 0.2180 +2024-07-23 22:09:53,096 - pyskl - INFO - Epoch [52][100/3746] lr: 7.406e-02, eta: 3 days, 8:35:17, time: 2.321, data_time: 1.341, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5480, loss_cls: 4.0821, loss: 4.0821 +2024-07-23 22:11:15,338 - pyskl - INFO - Epoch [52][200/3746] lr: 7.404e-02, eta: 3 days, 8:34:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5409, loss_cls: 4.0697, loss: 4.0697 +2024-07-23 22:12:36,593 - pyskl - INFO - Epoch [52][300/3746] lr: 7.401e-02, eta: 3 days, 8:32:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5430, loss_cls: 4.0863, loss: 4.0863 +2024-07-23 22:13:58,091 - pyskl - INFO - Epoch [52][400/3746] lr: 7.399e-02, eta: 3 days, 8:31:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5466, loss_cls: 4.1021, loss: 4.1021 +2024-07-23 22:15:19,889 - pyskl - INFO - Epoch [52][500/3746] lr: 7.397e-02, eta: 3 days, 8:30:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5331, loss_cls: 4.0768, loss: 4.0768 +2024-07-23 22:16:41,891 - pyskl - INFO - Epoch [52][600/3746] lr: 7.394e-02, eta: 3 days, 8:29:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5372, loss_cls: 4.0947, loss: 4.0947 +2024-07-23 22:18:03,435 - pyskl - INFO - Epoch [52][700/3746] lr: 7.392e-02, eta: 3 days, 8:28:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5378, loss_cls: 4.1132, loss: 4.1132 +2024-07-23 22:19:25,555 - pyskl - INFO - Epoch [52][800/3746] lr: 7.389e-02, eta: 3 days, 8:26:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5334, loss_cls: 4.1522, loss: 4.1522 +2024-07-23 22:20:47,411 - pyskl - INFO - Epoch [52][900/3746] lr: 7.387e-02, eta: 3 days, 8:25:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5394, loss_cls: 4.1228, loss: 4.1228 +2024-07-23 22:22:09,099 - pyskl - INFO - Epoch [52][1000/3746] lr: 7.384e-02, eta: 3 days, 8:24:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5427, loss_cls: 4.0904, loss: 4.0904 +2024-07-23 22:23:30,682 - pyskl - INFO - Epoch [52][1100/3746] lr: 7.382e-02, eta: 3 days, 8:23:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5314, loss_cls: 4.1478, loss: 4.1478 +2024-07-23 22:24:52,453 - pyskl - INFO - Epoch [52][1200/3746] lr: 7.379e-02, eta: 3 days, 8:22:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5402, loss_cls: 4.1014, loss: 4.1014 +2024-07-23 22:26:14,022 - pyskl - INFO - Epoch [52][1300/3746] lr: 7.377e-02, eta: 3 days, 8:20:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5314, loss_cls: 4.1526, loss: 4.1526 +2024-07-23 22:27:35,695 - pyskl - INFO - Epoch [52][1400/3746] lr: 7.374e-02, eta: 3 days, 8:19:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5406, loss_cls: 4.1128, loss: 4.1128 +2024-07-23 22:28:57,301 - pyskl - INFO - Epoch [52][1500/3746] lr: 7.372e-02, eta: 3 days, 8:18:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5367, loss_cls: 4.1027, loss: 4.1027 +2024-07-23 22:30:18,649 - pyskl - INFO - Epoch [52][1600/3746] lr: 7.370e-02, eta: 3 days, 8:17:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5394, loss_cls: 4.1144, loss: 4.1144 +2024-07-23 22:31:40,593 - pyskl - INFO - Epoch [52][1700/3746] lr: 7.367e-02, eta: 3 days, 8:16:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5436, loss_cls: 4.1304, loss: 4.1304 +2024-07-23 22:33:02,423 - pyskl - INFO - Epoch [52][1800/3746] lr: 7.365e-02, eta: 3 days, 8:14:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5416, loss_cls: 4.0929, loss: 4.0929 +2024-07-23 22:34:23,929 - pyskl - INFO - Epoch [52][1900/3746] lr: 7.362e-02, eta: 3 days, 8:13:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5363, loss_cls: 4.1133, loss: 4.1133 +2024-07-23 22:35:45,458 - pyskl - INFO - Epoch [52][2000/3746] lr: 7.360e-02, eta: 3 days, 8:12:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5455, loss_cls: 4.0823, loss: 4.0823 +2024-07-23 22:37:07,279 - pyskl - INFO - Epoch [52][2100/3746] lr: 7.357e-02, eta: 3 days, 8:11:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5403, loss_cls: 4.1363, loss: 4.1363 +2024-07-23 22:38:28,767 - pyskl - INFO - Epoch [52][2200/3746] lr: 7.355e-02, eta: 3 days, 8:10:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5345, loss_cls: 4.1324, loss: 4.1324 +2024-07-23 22:39:50,333 - pyskl - INFO - Epoch [52][2300/3746] lr: 7.352e-02, eta: 3 days, 8:09:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5373, loss_cls: 4.1174, loss: 4.1174 +2024-07-23 22:41:11,396 - pyskl - INFO - Epoch [52][2400/3746] lr: 7.350e-02, eta: 3 days, 8:07:46, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5298, loss_cls: 4.1907, loss: 4.1907 +2024-07-23 22:42:33,397 - pyskl - INFO - Epoch [52][2500/3746] lr: 7.347e-02, eta: 3 days, 8:06:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5495, loss_cls: 4.0611, loss: 4.0611 +2024-07-23 22:43:55,392 - pyskl - INFO - Epoch [52][2600/3746] lr: 7.345e-02, eta: 3 days, 8:05:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5525, loss_cls: 4.0891, loss: 4.0891 +2024-07-23 22:45:17,270 - pyskl - INFO - Epoch [52][2700/3746] lr: 7.342e-02, eta: 3 days, 8:04:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5445, loss_cls: 4.0755, loss: 4.0755 +2024-07-23 22:46:39,797 - pyskl - INFO - Epoch [52][2800/3746] lr: 7.340e-02, eta: 3 days, 8:03:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5387, loss_cls: 4.1071, loss: 4.1071 +2024-07-23 22:48:02,048 - pyskl - INFO - Epoch [52][2900/3746] lr: 7.337e-02, eta: 3 days, 8:01:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5464, loss_cls: 4.0624, loss: 4.0624 +2024-07-23 22:49:23,628 - pyskl - INFO - Epoch [52][3000/3746] lr: 7.335e-02, eta: 3 days, 8:00:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5377, loss_cls: 4.0987, loss: 4.0987 +2024-07-23 22:50:45,648 - pyskl - INFO - Epoch [52][3100/3746] lr: 7.332e-02, eta: 3 days, 7:59:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5433, loss_cls: 4.1069, loss: 4.1069 +2024-07-23 22:52:06,841 - pyskl - INFO - Epoch [52][3200/3746] lr: 7.330e-02, eta: 3 days, 7:58:15, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5466, loss_cls: 4.0598, loss: 4.0598 +2024-07-23 22:53:28,307 - pyskl - INFO - Epoch [52][3300/3746] lr: 7.328e-02, eta: 3 days, 7:57:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5380, loss_cls: 4.1013, loss: 4.1013 +2024-07-23 22:54:49,784 - pyskl - INFO - Epoch [52][3400/3746] lr: 7.325e-02, eta: 3 days, 7:55:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5425, loss_cls: 4.1104, loss: 4.1104 +2024-07-23 22:56:11,756 - pyskl - INFO - Epoch [52][3500/3746] lr: 7.323e-02, eta: 3 days, 7:54:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5400, loss_cls: 4.1066, loss: 4.1066 +2024-07-23 22:57:34,022 - pyskl - INFO - Epoch [52][3600/3746] lr: 7.320e-02, eta: 3 days, 7:53:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5375, loss_cls: 4.0892, loss: 4.0892 +2024-07-23 22:58:56,025 - pyskl - INFO - Epoch [52][3700/3746] lr: 7.318e-02, eta: 3 days, 7:52:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5366, loss_cls: 4.1209, loss: 4.1209 +2024-07-23 22:59:35,908 - pyskl - INFO - Saving checkpoint at 52 epochs +2024-07-23 23:01:28,757 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 23:01:29,420 - pyskl - INFO - +top1_acc 0.2174 +top5_acc 0.4642 +2024-07-23 23:01:29,420 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 23:01:29,461 - pyskl - INFO - +mean_acc 0.2171 +2024-07-23 23:01:29,472 - pyskl - INFO - Epoch(val) [52][309] top1_acc: 0.2174, top5_acc: 0.4642, mean_class_accuracy: 0.2171 +2024-07-23 23:05:21,943 - pyskl - INFO - Epoch [53][100/3746] lr: 7.314e-02, eta: 3 days, 7:54:05, time: 2.325, data_time: 1.350, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5409, loss_cls: 4.0674, loss: 4.0674 +2024-07-23 23:06:44,142 - pyskl - INFO - Epoch [53][200/3746] lr: 7.312e-02, eta: 3 days, 7:52:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5384, loss_cls: 4.0811, loss: 4.0811 +2024-07-23 23:08:05,925 - pyskl - INFO - Epoch [53][300/3746] lr: 7.309e-02, eta: 3 days, 7:51:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5403, loss_cls: 4.1244, loss: 4.1244 +2024-07-23 23:09:27,862 - pyskl - INFO - Epoch [53][400/3746] lr: 7.307e-02, eta: 3 days, 7:50:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5525, loss_cls: 4.0302, loss: 4.0302 +2024-07-23 23:10:49,774 - pyskl - INFO - Epoch [53][500/3746] lr: 7.304e-02, eta: 3 days, 7:49:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5391, loss_cls: 4.1038, loss: 4.1038 +2024-07-23 23:12:11,722 - pyskl - INFO - Epoch [53][600/3746] lr: 7.302e-02, eta: 3 days, 7:48:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5503, loss_cls: 4.0747, loss: 4.0747 +2024-07-23 23:13:33,663 - pyskl - INFO - Epoch [53][700/3746] lr: 7.299e-02, eta: 3 days, 7:46:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5467, loss_cls: 4.0862, loss: 4.0862 +2024-07-23 23:14:54,920 - pyskl - INFO - Epoch [53][800/3746] lr: 7.297e-02, eta: 3 days, 7:45:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5455, loss_cls: 4.0892, loss: 4.0892 +2024-07-23 23:16:16,633 - pyskl - INFO - Epoch [53][900/3746] lr: 7.294e-02, eta: 3 days, 7:44:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5419, loss_cls: 4.0854, loss: 4.0854 +2024-07-23 23:17:38,589 - pyskl - INFO - Epoch [53][1000/3746] lr: 7.292e-02, eta: 3 days, 7:43:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5302, loss_cls: 4.1349, loss: 4.1349 +2024-07-23 23:18:59,960 - pyskl - INFO - Epoch [53][1100/3746] lr: 7.289e-02, eta: 3 days, 7:42:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5477, loss_cls: 4.0824, loss: 4.0824 +2024-07-23 23:20:21,717 - pyskl - INFO - Epoch [53][1200/3746] lr: 7.287e-02, eta: 3 days, 7:40:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5514, loss_cls: 4.0463, loss: 4.0463 +2024-07-23 23:21:43,680 - pyskl - INFO - Epoch [53][1300/3746] lr: 7.284e-02, eta: 3 days, 7:39:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5323, loss_cls: 4.1427, loss: 4.1427 +2024-07-23 23:23:05,132 - pyskl - INFO - Epoch [53][1400/3746] lr: 7.282e-02, eta: 3 days, 7:38:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5430, loss_cls: 4.1241, loss: 4.1241 +2024-07-23 23:24:26,703 - pyskl - INFO - Epoch [53][1500/3746] lr: 7.279e-02, eta: 3 days, 7:37:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5386, loss_cls: 4.0954, loss: 4.0954 +2024-07-23 23:25:48,392 - pyskl - INFO - Epoch [53][1600/3746] lr: 7.277e-02, eta: 3 days, 7:36:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5495, loss_cls: 4.0731, loss: 4.0731 +2024-07-23 23:27:10,380 - pyskl - INFO - Epoch [53][1700/3746] lr: 7.274e-02, eta: 3 days, 7:34:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5323, loss_cls: 4.1130, loss: 4.1130 +2024-07-23 23:28:31,896 - pyskl - INFO - Epoch [53][1800/3746] lr: 7.272e-02, eta: 3 days, 7:33:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5431, loss_cls: 4.0678, loss: 4.0678 +2024-07-23 23:29:54,270 - pyskl - INFO - Epoch [53][1900/3746] lr: 7.269e-02, eta: 3 days, 7:32:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5298, loss_cls: 4.1513, loss: 4.1513 +2024-07-23 23:31:15,661 - pyskl - INFO - Epoch [53][2000/3746] lr: 7.267e-02, eta: 3 days, 7:31:16, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5425, loss_cls: 4.0889, loss: 4.0889 +2024-07-23 23:32:37,593 - pyskl - INFO - Epoch [53][2100/3746] lr: 7.264e-02, eta: 3 days, 7:30:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5344, loss_cls: 4.1062, loss: 4.1062 +2024-07-23 23:33:59,190 - pyskl - INFO - Epoch [53][2200/3746] lr: 7.262e-02, eta: 3 days, 7:28:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5480, loss_cls: 4.0415, loss: 4.0415 +2024-07-23 23:35:21,067 - pyskl - INFO - Epoch [53][2300/3746] lr: 7.259e-02, eta: 3 days, 7:27:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5453, loss_cls: 4.0902, loss: 4.0902 +2024-07-23 23:36:42,805 - pyskl - INFO - Epoch [53][2400/3746] lr: 7.257e-02, eta: 3 days, 7:26:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5375, loss_cls: 4.0910, loss: 4.0910 +2024-07-23 23:38:05,024 - pyskl - INFO - Epoch [53][2500/3746] lr: 7.254e-02, eta: 3 days, 7:25:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5445, loss_cls: 4.1107, loss: 4.1107 +2024-07-23 23:39:27,069 - pyskl - INFO - Epoch [53][2600/3746] lr: 7.252e-02, eta: 3 days, 7:24:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5386, loss_cls: 4.1219, loss: 4.1219 +2024-07-23 23:40:48,982 - pyskl - INFO - Epoch [53][2700/3746] lr: 7.249e-02, eta: 3 days, 7:22:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5411, loss_cls: 4.1114, loss: 4.1114 +2024-07-23 23:42:11,421 - pyskl - INFO - Epoch [53][2800/3746] lr: 7.247e-02, eta: 3 days, 7:21:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5466, loss_cls: 4.0861, loss: 4.0861 +2024-07-23 23:43:33,478 - pyskl - INFO - Epoch [53][2900/3746] lr: 7.244e-02, eta: 3 days, 7:20:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5391, loss_cls: 4.1021, loss: 4.1021 +2024-07-23 23:44:55,214 - pyskl - INFO - Epoch [53][3000/3746] lr: 7.242e-02, eta: 3 days, 7:19:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5391, loss_cls: 4.1117, loss: 4.1117 +2024-07-23 23:46:17,160 - pyskl - INFO - Epoch [53][3100/3746] lr: 7.239e-02, eta: 3 days, 7:18:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5344, loss_cls: 4.1254, loss: 4.1254 +2024-07-23 23:47:39,066 - pyskl - INFO - Epoch [53][3200/3746] lr: 7.237e-02, eta: 3 days, 7:16:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5334, loss_cls: 4.1335, loss: 4.1335 +2024-07-23 23:49:00,515 - pyskl - INFO - Epoch [53][3300/3746] lr: 7.234e-02, eta: 3 days, 7:15:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5442, loss_cls: 4.0953, loss: 4.0953 +2024-07-23 23:50:22,526 - pyskl - INFO - Epoch [53][3400/3746] lr: 7.232e-02, eta: 3 days, 7:14:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5428, loss_cls: 4.0952, loss: 4.0952 +2024-07-23 23:51:45,059 - pyskl - INFO - Epoch [53][3500/3746] lr: 7.229e-02, eta: 3 days, 7:13:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5302, loss_cls: 4.1312, loss: 4.1312 +2024-07-23 23:53:06,387 - pyskl - INFO - Epoch [53][3600/3746] lr: 7.227e-02, eta: 3 days, 7:12:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5333, loss_cls: 4.1239, loss: 4.1239 +2024-07-23 23:54:28,479 - pyskl - INFO - Epoch [53][3700/3746] lr: 7.224e-02, eta: 3 days, 7:10:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5420, loss_cls: 4.1276, loss: 4.1276 +2024-07-23 23:55:07,951 - pyskl - INFO - Saving checkpoint at 53 epochs +2024-07-23 23:57:01,297 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-23 23:57:01,962 - pyskl - INFO - +top1_acc 0.2293 +top5_acc 0.4735 +2024-07-23 23:57:01,963 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-23 23:57:02,004 - pyskl - INFO - +mean_acc 0.2291 +2024-07-23 23:57:02,015 - pyskl - INFO - Epoch(val) [53][309] top1_acc: 0.2293, top5_acc: 0.4735, mean_class_accuracy: 0.2291 +2024-07-24 00:00:55,876 - pyskl - INFO - Epoch [54][100/3746] lr: 7.221e-02, eta: 3 days, 7:12:36, time: 2.339, data_time: 1.367, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5575, loss_cls: 4.0186, loss: 4.0186 +2024-07-24 00:02:18,021 - pyskl - INFO - Epoch [54][200/3746] lr: 7.218e-02, eta: 3 days, 7:11:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5466, loss_cls: 4.0547, loss: 4.0547 +2024-07-24 00:03:39,564 - pyskl - INFO - Epoch [54][300/3746] lr: 7.216e-02, eta: 3 days, 7:10:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5530, loss_cls: 4.0548, loss: 4.0548 +2024-07-24 00:05:00,989 - pyskl - INFO - Epoch [54][400/3746] lr: 7.213e-02, eta: 3 days, 7:08:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5452, loss_cls: 4.1009, loss: 4.1009 +2024-07-24 00:06:22,432 - pyskl - INFO - Epoch [54][500/3746] lr: 7.211e-02, eta: 3 days, 7:07:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5572, loss_cls: 4.0494, loss: 4.0494 +2024-07-24 00:07:44,045 - pyskl - INFO - Epoch [54][600/3746] lr: 7.208e-02, eta: 3 days, 7:06:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5402, loss_cls: 4.1082, loss: 4.1082 +2024-07-24 00:09:06,127 - pyskl - INFO - Epoch [54][700/3746] lr: 7.206e-02, eta: 3 days, 7:05:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5369, loss_cls: 4.1242, loss: 4.1242 +2024-07-24 00:10:27,840 - pyskl - INFO - Epoch [54][800/3746] lr: 7.203e-02, eta: 3 days, 7:04:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5477, loss_cls: 4.0710, loss: 4.0710 +2024-07-24 00:11:49,574 - pyskl - INFO - Epoch [54][900/3746] lr: 7.201e-02, eta: 3 days, 7:02:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5391, loss_cls: 4.1199, loss: 4.1199 +2024-07-24 00:13:11,276 - pyskl - INFO - Epoch [54][1000/3746] lr: 7.198e-02, eta: 3 days, 7:01:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5486, loss_cls: 4.0549, loss: 4.0549 +2024-07-24 00:14:33,087 - pyskl - INFO - Epoch [54][1100/3746] lr: 7.196e-02, eta: 3 days, 7:00:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5383, loss_cls: 4.0968, loss: 4.0968 +2024-07-24 00:15:54,817 - pyskl - INFO - Epoch [54][1200/3746] lr: 7.193e-02, eta: 3 days, 6:59:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5450, loss_cls: 4.0656, loss: 4.0656 +2024-07-24 00:17:16,913 - pyskl - INFO - Epoch [54][1300/3746] lr: 7.191e-02, eta: 3 days, 6:58:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5480, loss_cls: 4.0695, loss: 4.0695 +2024-07-24 00:18:38,701 - pyskl - INFO - Epoch [54][1400/3746] lr: 7.188e-02, eta: 3 days, 6:56:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5323, loss_cls: 4.1068, loss: 4.1068 +2024-07-24 00:20:00,530 - pyskl - INFO - Epoch [54][1500/3746] lr: 7.186e-02, eta: 3 days, 6:55:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5464, loss_cls: 4.0369, loss: 4.0369 +2024-07-24 00:21:22,094 - pyskl - INFO - Epoch [54][1600/3746] lr: 7.183e-02, eta: 3 days, 6:54:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5342, loss_cls: 4.1335, loss: 4.1335 +2024-07-24 00:22:43,410 - pyskl - INFO - Epoch [54][1700/3746] lr: 7.181e-02, eta: 3 days, 6:53:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5444, loss_cls: 4.0903, loss: 4.0903 +2024-07-24 00:24:05,162 - pyskl - INFO - Epoch [54][1800/3746] lr: 7.178e-02, eta: 3 days, 6:52:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5489, loss_cls: 4.0917, loss: 4.0917 +2024-07-24 00:25:26,544 - pyskl - INFO - Epoch [54][1900/3746] lr: 7.176e-02, eta: 3 days, 6:50:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5367, loss_cls: 4.1253, loss: 4.1253 +2024-07-24 00:26:48,280 - pyskl - INFO - Epoch [54][2000/3746] lr: 7.173e-02, eta: 3 days, 6:49:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5395, loss_cls: 4.0983, loss: 4.0983 +2024-07-24 00:28:09,840 - pyskl - INFO - Epoch [54][2100/3746] lr: 7.170e-02, eta: 3 days, 6:48:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5470, loss_cls: 4.0991, loss: 4.0991 +2024-07-24 00:29:32,228 - pyskl - INFO - Epoch [54][2200/3746] lr: 7.168e-02, eta: 3 days, 6:47:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5359, loss_cls: 4.1199, loss: 4.1199 +2024-07-24 00:30:54,000 - pyskl - INFO - Epoch [54][2300/3746] lr: 7.165e-02, eta: 3 days, 6:45:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5380, loss_cls: 4.1319, loss: 4.1319 +2024-07-24 00:32:16,159 - pyskl - INFO - Epoch [54][2400/3746] lr: 7.163e-02, eta: 3 days, 6:44:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5236, loss_cls: 4.1546, loss: 4.1546 +2024-07-24 00:33:38,860 - pyskl - INFO - Epoch [54][2500/3746] lr: 7.160e-02, eta: 3 days, 6:43:34, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5581, loss_cls: 4.0413, loss: 4.0413 +2024-07-24 00:35:01,171 - pyskl - INFO - Epoch [54][2600/3746] lr: 7.158e-02, eta: 3 days, 6:42:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5423, loss_cls: 4.1130, loss: 4.1130 +2024-07-24 00:36:23,482 - pyskl - INFO - Epoch [54][2700/3746] lr: 7.155e-02, eta: 3 days, 6:41:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5523, loss_cls: 4.0354, loss: 4.0354 +2024-07-24 00:37:45,417 - pyskl - INFO - Epoch [54][2800/3746] lr: 7.153e-02, eta: 3 days, 6:39:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5373, loss_cls: 4.1215, loss: 4.1215 +2024-07-24 00:39:07,332 - pyskl - INFO - Epoch [54][2900/3746] lr: 7.150e-02, eta: 3 days, 6:38:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5448, loss_cls: 4.0674, loss: 4.0674 +2024-07-24 00:40:29,719 - pyskl - INFO - Epoch [54][3000/3746] lr: 7.148e-02, eta: 3 days, 6:37:34, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5270, loss_cls: 4.1467, loss: 4.1467 +2024-07-24 00:41:52,476 - pyskl - INFO - Epoch [54][3100/3746] lr: 7.145e-02, eta: 3 days, 6:36:23, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5361, loss_cls: 4.1139, loss: 4.1139 +2024-07-24 00:43:14,014 - pyskl - INFO - Epoch [54][3200/3746] lr: 7.143e-02, eta: 3 days, 6:35:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5492, loss_cls: 4.0667, loss: 4.0667 +2024-07-24 00:44:35,477 - pyskl - INFO - Epoch [54][3300/3746] lr: 7.140e-02, eta: 3 days, 6:33:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5391, loss_cls: 4.1258, loss: 4.1258 +2024-07-24 00:45:58,015 - pyskl - INFO - Epoch [54][3400/3746] lr: 7.138e-02, eta: 3 days, 6:32:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5363, loss_cls: 4.1192, loss: 4.1192 +2024-07-24 00:47:20,069 - pyskl - INFO - Epoch [54][3500/3746] lr: 7.135e-02, eta: 3 days, 6:31:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5484, loss_cls: 4.0612, loss: 4.0612 +2024-07-24 00:48:42,286 - pyskl - INFO - Epoch [54][3600/3746] lr: 7.133e-02, eta: 3 days, 6:30:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5370, loss_cls: 4.1386, loss: 4.1386 +2024-07-24 00:50:04,097 - pyskl - INFO - Epoch [54][3700/3746] lr: 7.130e-02, eta: 3 days, 6:29:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5428, loss_cls: 4.0983, loss: 4.0983 +2024-07-24 00:50:43,792 - pyskl - INFO - Saving checkpoint at 54 epochs +2024-07-24 00:52:35,992 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 00:52:36,654 - pyskl - INFO - +top1_acc 0.2292 +top5_acc 0.4768 +2024-07-24 00:52:36,654 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 00:52:36,694 - pyskl - INFO - +mean_acc 0.2291 +2024-07-24 00:52:36,705 - pyskl - INFO - Epoch(val) [54][309] top1_acc: 0.2292, top5_acc: 0.4768, mean_class_accuracy: 0.2291 +2024-07-24 00:56:27,628 - pyskl - INFO - Epoch [55][100/3746] lr: 7.126e-02, eta: 3 days, 6:30:39, time: 2.309, data_time: 1.325, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5614, loss_cls: 3.9864, loss: 3.9864 +2024-07-24 00:57:50,223 - pyskl - INFO - Epoch [55][200/3746] lr: 7.124e-02, eta: 3 days, 6:29:28, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5375, loss_cls: 4.0796, loss: 4.0796 +2024-07-24 00:59:12,231 - pyskl - INFO - Epoch [55][300/3746] lr: 7.121e-02, eta: 3 days, 6:28:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5433, loss_cls: 4.0769, loss: 4.0769 +2024-07-24 01:00:34,043 - pyskl - INFO - Epoch [55][400/3746] lr: 7.119e-02, eta: 3 days, 6:27:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5366, loss_cls: 4.1122, loss: 4.1122 +2024-07-24 01:01:56,197 - pyskl - INFO - Epoch [55][500/3746] lr: 7.116e-02, eta: 3 days, 6:25:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5503, loss_cls: 4.0525, loss: 4.0525 +2024-07-24 01:03:18,064 - pyskl - INFO - Epoch [55][600/3746] lr: 7.114e-02, eta: 3 days, 6:24:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5420, loss_cls: 4.0772, loss: 4.0772 +2024-07-24 01:04:39,684 - pyskl - INFO - Epoch [55][700/3746] lr: 7.111e-02, eta: 3 days, 6:23:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5356, loss_cls: 4.1329, loss: 4.1329 +2024-07-24 01:06:02,042 - pyskl - INFO - Epoch [55][800/3746] lr: 7.109e-02, eta: 3 days, 6:22:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5378, loss_cls: 4.1115, loss: 4.1115 +2024-07-24 01:07:23,805 - pyskl - INFO - Epoch [55][900/3746] lr: 7.106e-02, eta: 3 days, 6:20:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5489, loss_cls: 4.0408, loss: 4.0408 +2024-07-24 01:08:45,938 - pyskl - INFO - Epoch [55][1000/3746] lr: 7.104e-02, eta: 3 days, 6:19:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5484, loss_cls: 4.0689, loss: 4.0689 +2024-07-24 01:10:07,262 - pyskl - INFO - Epoch [55][1100/3746] lr: 7.101e-02, eta: 3 days, 6:18:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5425, loss_cls: 4.0668, loss: 4.0668 +2024-07-24 01:11:29,066 - pyskl - INFO - Epoch [55][1200/3746] lr: 7.099e-02, eta: 3 days, 6:17:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5431, loss_cls: 4.0663, loss: 4.0663 +2024-07-24 01:12:51,030 - pyskl - INFO - Epoch [55][1300/3746] lr: 7.096e-02, eta: 3 days, 6:16:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5492, loss_cls: 4.0644, loss: 4.0644 +2024-07-24 01:14:12,731 - pyskl - INFO - Epoch [55][1400/3746] lr: 7.093e-02, eta: 3 days, 6:14:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5417, loss_cls: 4.0913, loss: 4.0913 +2024-07-24 01:15:33,908 - pyskl - INFO - Epoch [55][1500/3746] lr: 7.091e-02, eta: 3 days, 6:13:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5361, loss_cls: 4.1075, loss: 4.1075 +2024-07-24 01:16:55,073 - pyskl - INFO - Epoch [55][1600/3746] lr: 7.088e-02, eta: 3 days, 6:12:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5472, loss_cls: 4.0748, loss: 4.0748 +2024-07-24 01:18:17,382 - pyskl - INFO - Epoch [55][1700/3746] lr: 7.086e-02, eta: 3 days, 6:11:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5400, loss_cls: 4.1363, loss: 4.1363 +2024-07-24 01:19:39,659 - pyskl - INFO - Epoch [55][1800/3746] lr: 7.083e-02, eta: 3 days, 6:10:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5402, loss_cls: 4.0973, loss: 4.0973 +2024-07-24 01:21:01,053 - pyskl - INFO - Epoch [55][1900/3746] lr: 7.081e-02, eta: 3 days, 6:08:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5461, loss_cls: 4.0423, loss: 4.0423 +2024-07-24 01:22:22,763 - pyskl - INFO - Epoch [55][2000/3746] lr: 7.078e-02, eta: 3 days, 6:07:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5414, loss_cls: 4.0540, loss: 4.0540 +2024-07-24 01:23:44,459 - pyskl - INFO - Epoch [55][2100/3746] lr: 7.076e-02, eta: 3 days, 6:06:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5370, loss_cls: 4.0997, loss: 4.0997 +2024-07-24 01:25:06,274 - pyskl - INFO - Epoch [55][2200/3746] lr: 7.073e-02, eta: 3 days, 6:05:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5359, loss_cls: 4.1255, loss: 4.1255 +2024-07-24 01:26:27,921 - pyskl - INFO - Epoch [55][2300/3746] lr: 7.071e-02, eta: 3 days, 6:03:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5481, loss_cls: 4.0652, loss: 4.0652 +2024-07-24 01:27:49,365 - pyskl - INFO - Epoch [55][2400/3746] lr: 7.068e-02, eta: 3 days, 6:02:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5411, loss_cls: 4.0913, loss: 4.0913 +2024-07-24 01:29:11,558 - pyskl - INFO - Epoch [55][2500/3746] lr: 7.065e-02, eta: 3 days, 6:01:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5380, loss_cls: 4.1068, loss: 4.1068 +2024-07-24 01:30:33,814 - pyskl - INFO - Epoch [55][2600/3746] lr: 7.063e-02, eta: 3 days, 6:00:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5417, loss_cls: 4.0981, loss: 4.0981 +2024-07-24 01:31:55,932 - pyskl - INFO - Epoch [55][2700/3746] lr: 7.060e-02, eta: 3 days, 5:59:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5511, loss_cls: 4.0666, loss: 4.0666 +2024-07-24 01:33:18,837 - pyskl - INFO - Epoch [55][2800/3746] lr: 7.058e-02, eta: 3 days, 5:57:51, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5367, loss_cls: 4.0833, loss: 4.0833 +2024-07-24 01:34:40,679 - pyskl - INFO - Epoch [55][2900/3746] lr: 7.055e-02, eta: 3 days, 5:56:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5331, loss_cls: 4.1546, loss: 4.1546 +2024-07-24 01:36:02,358 - pyskl - INFO - Epoch [55][3000/3746] lr: 7.053e-02, eta: 3 days, 5:55:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5372, loss_cls: 4.1106, loss: 4.1106 +2024-07-24 01:37:24,354 - pyskl - INFO - Epoch [55][3100/3746] lr: 7.050e-02, eta: 3 days, 5:54:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5356, loss_cls: 4.1284, loss: 4.1284 +2024-07-24 01:38:45,955 - pyskl - INFO - Epoch [55][3200/3746] lr: 7.048e-02, eta: 3 days, 5:52:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5422, loss_cls: 4.0814, loss: 4.0814 +2024-07-24 01:40:07,649 - pyskl - INFO - Epoch [55][3300/3746] lr: 7.045e-02, eta: 3 days, 5:51:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5531, loss_cls: 4.0267, loss: 4.0267 +2024-07-24 01:41:29,806 - pyskl - INFO - Epoch [55][3400/3746] lr: 7.043e-02, eta: 3 days, 5:50:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5534, loss_cls: 4.0454, loss: 4.0454 +2024-07-24 01:42:52,145 - pyskl - INFO - Epoch [55][3500/3746] lr: 7.040e-02, eta: 3 days, 5:49:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5448, loss_cls: 4.0994, loss: 4.0994 +2024-07-24 01:44:14,153 - pyskl - INFO - Epoch [55][3600/3746] lr: 7.037e-02, eta: 3 days, 5:48:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5392, loss_cls: 4.0917, loss: 4.0917 +2024-07-24 01:45:35,906 - pyskl - INFO - Epoch [55][3700/3746] lr: 7.035e-02, eta: 3 days, 5:46:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5447, loss_cls: 4.0826, loss: 4.0826 +2024-07-24 01:46:15,571 - pyskl - INFO - Saving checkpoint at 55 epochs +2024-07-24 01:48:07,931 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 01:48:08,598 - pyskl - INFO - +top1_acc 0.2286 +top5_acc 0.4678 +2024-07-24 01:48:08,598 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 01:48:08,637 - pyskl - INFO - +mean_acc 0.2285 +2024-07-24 01:48:08,648 - pyskl - INFO - Epoch(val) [55][309] top1_acc: 0.2286, top5_acc: 0.4678, mean_class_accuracy: 0.2285 +2024-07-24 01:52:00,940 - pyskl - INFO - Epoch [56][100/3746] lr: 7.031e-02, eta: 3 days, 5:48:21, time: 2.323, data_time: 1.340, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5470, loss_cls: 4.0597, loss: 4.0597 +2024-07-24 01:53:22,621 - pyskl - INFO - Epoch [56][200/3746] lr: 7.029e-02, eta: 3 days, 5:47:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5537, loss_cls: 4.0549, loss: 4.0549 +2024-07-24 01:54:44,409 - pyskl - INFO - Epoch [56][300/3746] lr: 7.026e-02, eta: 3 days, 5:45:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5528, loss_cls: 4.0693, loss: 4.0693 +2024-07-24 01:56:06,341 - pyskl - INFO - Epoch [56][400/3746] lr: 7.023e-02, eta: 3 days, 5:44:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5430, loss_cls: 4.0668, loss: 4.0668 +2024-07-24 01:57:27,917 - pyskl - INFO - Epoch [56][500/3746] lr: 7.021e-02, eta: 3 days, 5:43:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5423, loss_cls: 4.0894, loss: 4.0894 +2024-07-24 01:58:49,379 - pyskl - INFO - Epoch [56][600/3746] lr: 7.018e-02, eta: 3 days, 5:42:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5552, loss_cls: 4.0239, loss: 4.0239 +2024-07-24 02:00:11,365 - pyskl - INFO - Epoch [56][700/3746] lr: 7.016e-02, eta: 3 days, 5:40:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5450, loss_cls: 4.0449, loss: 4.0449 +2024-07-24 02:01:32,823 - pyskl - INFO - Epoch [56][800/3746] lr: 7.013e-02, eta: 3 days, 5:39:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5448, loss_cls: 4.0610, loss: 4.0610 +2024-07-24 02:02:54,657 - pyskl - INFO - Epoch [56][900/3746] lr: 7.011e-02, eta: 3 days, 5:38:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5413, loss_cls: 4.0668, loss: 4.0668 +2024-07-24 02:04:16,496 - pyskl - INFO - Epoch [56][1000/3746] lr: 7.008e-02, eta: 3 days, 5:37:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5369, loss_cls: 4.1102, loss: 4.1102 +2024-07-24 02:05:38,214 - pyskl - INFO - Epoch [56][1100/3746] lr: 7.006e-02, eta: 3 days, 5:36:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5495, loss_cls: 4.0686, loss: 4.0686 +2024-07-24 02:06:59,760 - pyskl - INFO - Epoch [56][1200/3746] lr: 7.003e-02, eta: 3 days, 5:34:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5441, loss_cls: 4.0874, loss: 4.0874 +2024-07-24 02:08:21,335 - pyskl - INFO - Epoch [56][1300/3746] lr: 7.000e-02, eta: 3 days, 5:33:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5536, loss_cls: 4.0652, loss: 4.0652 +2024-07-24 02:09:42,582 - pyskl - INFO - Epoch [56][1400/3746] lr: 6.998e-02, eta: 3 days, 5:32:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5427, loss_cls: 4.1026, loss: 4.1026 +2024-07-24 02:11:04,497 - pyskl - INFO - Epoch [56][1500/3746] lr: 6.995e-02, eta: 3 days, 5:31:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5428, loss_cls: 4.1096, loss: 4.1096 +2024-07-24 02:12:26,179 - pyskl - INFO - Epoch [56][1600/3746] lr: 6.993e-02, eta: 3 days, 5:29:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5363, loss_cls: 4.1184, loss: 4.1184 +2024-07-24 02:13:47,944 - pyskl - INFO - Epoch [56][1700/3746] lr: 6.990e-02, eta: 3 days, 5:28:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5433, loss_cls: 4.0770, loss: 4.0770 +2024-07-24 02:15:09,584 - pyskl - INFO - Epoch [56][1800/3746] lr: 6.988e-02, eta: 3 days, 5:27:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5516, loss_cls: 4.0453, loss: 4.0453 +2024-07-24 02:16:30,983 - pyskl - INFO - Epoch [56][1900/3746] lr: 6.985e-02, eta: 3 days, 5:26:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5403, loss_cls: 4.0975, loss: 4.0975 +2024-07-24 02:17:52,680 - pyskl - INFO - Epoch [56][2000/3746] lr: 6.983e-02, eta: 3 days, 5:25:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5536, loss_cls: 4.0257, loss: 4.0257 +2024-07-24 02:19:14,446 - pyskl - INFO - Epoch [56][2100/3746] lr: 6.980e-02, eta: 3 days, 5:23:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5411, loss_cls: 4.0790, loss: 4.0790 +2024-07-24 02:20:36,969 - pyskl - INFO - Epoch [56][2200/3746] lr: 6.977e-02, eta: 3 days, 5:22:34, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5422, loss_cls: 4.0776, loss: 4.0776 +2024-07-24 02:21:59,406 - pyskl - INFO - Epoch [56][2300/3746] lr: 6.975e-02, eta: 3 days, 5:21:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5494, loss_cls: 4.0607, loss: 4.0607 +2024-07-24 02:23:20,860 - pyskl - INFO - Epoch [56][2400/3746] lr: 6.972e-02, eta: 3 days, 5:20:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5377, loss_cls: 4.0801, loss: 4.0801 +2024-07-24 02:24:42,710 - pyskl - INFO - Epoch [56][2500/3746] lr: 6.970e-02, eta: 3 days, 5:18:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5437, loss_cls: 4.0643, loss: 4.0643 +2024-07-24 02:26:04,223 - pyskl - INFO - Epoch [56][2600/3746] lr: 6.967e-02, eta: 3 days, 5:17:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5423, loss_cls: 4.0884, loss: 4.0884 +2024-07-24 02:27:26,423 - pyskl - INFO - Epoch [56][2700/3746] lr: 6.965e-02, eta: 3 days, 5:16:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5353, loss_cls: 4.1184, loss: 4.1184 +2024-07-24 02:28:49,563 - pyskl - INFO - Epoch [56][2800/3746] lr: 6.962e-02, eta: 3 days, 5:15:15, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5516, loss_cls: 4.0463, loss: 4.0463 +2024-07-24 02:30:11,587 - pyskl - INFO - Epoch [56][2900/3746] lr: 6.959e-02, eta: 3 days, 5:14:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5383, loss_cls: 4.0998, loss: 4.0998 +2024-07-24 02:31:33,444 - pyskl - INFO - Epoch [56][3000/3746] lr: 6.957e-02, eta: 3 days, 5:12:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5380, loss_cls: 4.0969, loss: 4.0969 +2024-07-24 02:32:55,381 - pyskl - INFO - Epoch [56][3100/3746] lr: 6.954e-02, eta: 3 days, 5:11:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5463, loss_cls: 4.0726, loss: 4.0726 +2024-07-24 02:34:17,332 - pyskl - INFO - Epoch [56][3200/3746] lr: 6.952e-02, eta: 3 days, 5:10:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5428, loss_cls: 4.0860, loss: 4.0860 +2024-07-24 02:35:38,740 - pyskl - INFO - Epoch [56][3300/3746] lr: 6.949e-02, eta: 3 days, 5:09:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5391, loss_cls: 4.1200, loss: 4.1200 +2024-07-24 02:37:00,550 - pyskl - INFO - Epoch [56][3400/3746] lr: 6.947e-02, eta: 3 days, 5:07:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5481, loss_cls: 4.0819, loss: 4.0819 +2024-07-24 02:38:22,752 - pyskl - INFO - Epoch [56][3500/3746] lr: 6.944e-02, eta: 3 days, 5:06:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5384, loss_cls: 4.1054, loss: 4.1054 +2024-07-24 02:39:45,336 - pyskl - INFO - Epoch [56][3600/3746] lr: 6.941e-02, eta: 3 days, 5:05:28, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5425, loss_cls: 4.0895, loss: 4.0895 +2024-07-24 02:41:07,523 - pyskl - INFO - Epoch [56][3700/3746] lr: 6.939e-02, eta: 3 days, 5:04:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5522, loss_cls: 4.0129, loss: 4.0129 +2024-07-24 02:41:47,334 - pyskl - INFO - Saving checkpoint at 56 epochs +2024-07-24 02:43:39,914 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 02:43:40,577 - pyskl - INFO - +top1_acc 0.2332 +top5_acc 0.4772 +2024-07-24 02:43:40,577 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 02:43:40,618 - pyskl - INFO - +mean_acc 0.2331 +2024-07-24 02:43:40,623 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_49.pth was removed +2024-07-24 02:43:40,890 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_56.pth. +2024-07-24 02:43:40,890 - pyskl - INFO - Best top1_acc is 0.2332 at 56 epoch. +2024-07-24 02:43:40,904 - pyskl - INFO - Epoch(val) [56][309] top1_acc: 0.2332, top5_acc: 0.4772, mean_class_accuracy: 0.2331 +2024-07-24 02:47:36,436 - pyskl - INFO - Epoch [57][100/3746] lr: 6.935e-02, eta: 3 days, 5:05:42, time: 2.355, data_time: 1.349, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5505, loss_cls: 4.0555, loss: 4.0555 +2024-07-24 02:48:58,463 - pyskl - INFO - Epoch [57][200/3746] lr: 6.932e-02, eta: 3 days, 5:04:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5587, loss_cls: 3.9882, loss: 3.9882 +2024-07-24 02:50:20,671 - pyskl - INFO - Epoch [57][300/3746] lr: 6.930e-02, eta: 3 days, 5:03:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5486, loss_cls: 4.0589, loss: 4.0589 +2024-07-24 02:51:42,106 - pyskl - INFO - Epoch [57][400/3746] lr: 6.927e-02, eta: 3 days, 5:02:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5555, loss_cls: 4.0099, loss: 4.0099 +2024-07-24 02:53:03,802 - pyskl - INFO - Epoch [57][500/3746] lr: 6.925e-02, eta: 3 days, 5:00:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5517, loss_cls: 4.0503, loss: 4.0503 +2024-07-24 02:54:25,863 - pyskl - INFO - Epoch [57][600/3746] lr: 6.922e-02, eta: 3 days, 4:59:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5503, loss_cls: 4.0335, loss: 4.0335 +2024-07-24 02:55:47,376 - pyskl - INFO - Epoch [57][700/3746] lr: 6.920e-02, eta: 3 days, 4:58:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5548, loss_cls: 4.0352, loss: 4.0352 +2024-07-24 02:57:08,949 - pyskl - INFO - Epoch [57][800/3746] lr: 6.917e-02, eta: 3 days, 4:57:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5409, loss_cls: 4.1078, loss: 4.1078 +2024-07-24 02:58:31,006 - pyskl - INFO - Epoch [57][900/3746] lr: 6.914e-02, eta: 3 days, 4:55:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5386, loss_cls: 4.0985, loss: 4.0985 +2024-07-24 02:59:52,791 - pyskl - INFO - Epoch [57][1000/3746] lr: 6.912e-02, eta: 3 days, 4:54:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5536, loss_cls: 4.0503, loss: 4.0503 +2024-07-24 03:01:14,218 - pyskl - INFO - Epoch [57][1100/3746] lr: 6.909e-02, eta: 3 days, 4:53:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5430, loss_cls: 4.0866, loss: 4.0866 +2024-07-24 03:02:35,838 - pyskl - INFO - Epoch [57][1200/3746] lr: 6.907e-02, eta: 3 days, 4:52:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5414, loss_cls: 4.0895, loss: 4.0895 +2024-07-24 03:03:57,397 - pyskl - INFO - Epoch [57][1300/3746] lr: 6.904e-02, eta: 3 days, 4:50:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5537, loss_cls: 4.0595, loss: 4.0595 +2024-07-24 03:05:19,270 - pyskl - INFO - Epoch [57][1400/3746] lr: 6.901e-02, eta: 3 days, 4:49:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5455, loss_cls: 4.0647, loss: 4.0647 +2024-07-24 03:06:40,929 - pyskl - INFO - Epoch [57][1500/3746] lr: 6.899e-02, eta: 3 days, 4:48:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5386, loss_cls: 4.1000, loss: 4.1000 +2024-07-24 03:08:02,965 - pyskl - INFO - Epoch [57][1600/3746] lr: 6.896e-02, eta: 3 days, 4:47:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5413, loss_cls: 4.0867, loss: 4.0867 +2024-07-24 03:09:25,077 - pyskl - INFO - Epoch [57][1700/3746] lr: 6.894e-02, eta: 3 days, 4:45:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5528, loss_cls: 4.0524, loss: 4.0524 +2024-07-24 03:10:46,820 - pyskl - INFO - Epoch [57][1800/3746] lr: 6.891e-02, eta: 3 days, 4:44:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5481, loss_cls: 4.0629, loss: 4.0629 +2024-07-24 03:12:08,638 - pyskl - INFO - Epoch [57][1900/3746] lr: 6.889e-02, eta: 3 days, 4:43:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5442, loss_cls: 4.0656, loss: 4.0656 +2024-07-24 03:13:30,333 - pyskl - INFO - Epoch [57][2000/3746] lr: 6.886e-02, eta: 3 days, 4:42:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5444, loss_cls: 4.0797, loss: 4.0797 +2024-07-24 03:14:52,363 - pyskl - INFO - Epoch [57][2100/3746] lr: 6.883e-02, eta: 3 days, 4:41:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5359, loss_cls: 4.1140, loss: 4.1140 +2024-07-24 03:16:13,661 - pyskl - INFO - Epoch [57][2200/3746] lr: 6.881e-02, eta: 3 days, 4:39:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5450, loss_cls: 4.0851, loss: 4.0851 +2024-07-24 03:17:35,500 - pyskl - INFO - Epoch [57][2300/3746] lr: 6.878e-02, eta: 3 days, 4:38:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5530, loss_cls: 4.0553, loss: 4.0553 +2024-07-24 03:18:57,669 - pyskl - INFO - Epoch [57][2400/3746] lr: 6.876e-02, eta: 3 days, 4:37:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5478, loss_cls: 4.0257, loss: 4.0257 +2024-07-24 03:20:19,461 - pyskl - INFO - Epoch [57][2500/3746] lr: 6.873e-02, eta: 3 days, 4:36:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5342, loss_cls: 4.1044, loss: 4.1044 +2024-07-24 03:21:40,990 - pyskl - INFO - Epoch [57][2600/3746] lr: 6.870e-02, eta: 3 days, 4:34:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5437, loss_cls: 4.0958, loss: 4.0958 +2024-07-24 03:23:03,469 - pyskl - INFO - Epoch [57][2700/3746] lr: 6.868e-02, eta: 3 days, 4:33:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5367, loss_cls: 4.1167, loss: 4.1167 +2024-07-24 03:24:26,257 - pyskl - INFO - Epoch [57][2800/3746] lr: 6.865e-02, eta: 3 days, 4:32:27, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5483, loss_cls: 4.0416, loss: 4.0416 +2024-07-24 03:25:48,121 - pyskl - INFO - Epoch [57][2900/3746] lr: 6.863e-02, eta: 3 days, 4:31:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5475, loss_cls: 4.0652, loss: 4.0652 +2024-07-24 03:27:09,977 - pyskl - INFO - Epoch [57][3000/3746] lr: 6.860e-02, eta: 3 days, 4:29:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5481, loss_cls: 4.0530, loss: 4.0530 +2024-07-24 03:28:31,811 - pyskl - INFO - Epoch [57][3100/3746] lr: 6.857e-02, eta: 3 days, 4:28:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5452, loss_cls: 4.0637, loss: 4.0637 +2024-07-24 03:29:53,468 - pyskl - INFO - Epoch [57][3200/3746] lr: 6.855e-02, eta: 3 days, 4:27:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5514, loss_cls: 4.0297, loss: 4.0297 +2024-07-24 03:31:15,522 - pyskl - INFO - Epoch [57][3300/3746] lr: 6.852e-02, eta: 3 days, 4:26:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5519, loss_cls: 4.0310, loss: 4.0310 +2024-07-24 03:32:37,192 - pyskl - INFO - Epoch [57][3400/3746] lr: 6.850e-02, eta: 3 days, 4:25:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5375, loss_cls: 4.0817, loss: 4.0817 +2024-07-24 03:33:59,019 - pyskl - INFO - Epoch [57][3500/3746] lr: 6.847e-02, eta: 3 days, 4:23:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5434, loss_cls: 4.0720, loss: 4.0720 +2024-07-24 03:35:21,052 - pyskl - INFO - Epoch [57][3600/3746] lr: 6.844e-02, eta: 3 days, 4:22:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5469, loss_cls: 4.0896, loss: 4.0896 +2024-07-24 03:36:43,370 - pyskl - INFO - Epoch [57][3700/3746] lr: 6.842e-02, eta: 3 days, 4:21:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5461, loss_cls: 4.0579, loss: 4.0579 +2024-07-24 03:37:23,174 - pyskl - INFO - Saving checkpoint at 57 epochs +2024-07-24 03:39:15,515 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 03:39:16,173 - pyskl - INFO - +top1_acc 0.2238 +top5_acc 0.4615 +2024-07-24 03:39:16,173 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 03:39:16,215 - pyskl - INFO - +mean_acc 0.2237 +2024-07-24 03:39:16,227 - pyskl - INFO - Epoch(val) [57][309] top1_acc: 0.2238, top5_acc: 0.4615, mean_class_accuracy: 0.2237 +2024-07-24 03:43:15,948 - pyskl - INFO - Epoch [58][100/3746] lr: 6.838e-02, eta: 3 days, 4:22:49, time: 2.397, data_time: 1.375, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5709, loss_cls: 3.9252, loss: 3.9252 +2024-07-24 03:44:39,614 - pyskl - INFO - Epoch [58][200/3746] lr: 6.835e-02, eta: 3 days, 4:21:38, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5614, loss_cls: 4.0007, loss: 4.0007 +2024-07-24 03:46:02,936 - pyskl - INFO - Epoch [58][300/3746] lr: 6.833e-02, eta: 3 days, 4:20:26, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5520, loss_cls: 4.0090, loss: 4.0090 +2024-07-24 03:47:27,749 - pyskl - INFO - Epoch [58][400/3746] lr: 6.830e-02, eta: 3 days, 4:19:16, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5486, loss_cls: 4.0419, loss: 4.0419 +2024-07-24 03:48:51,822 - pyskl - INFO - Epoch [58][500/3746] lr: 6.828e-02, eta: 3 days, 4:18:06, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5519, loss_cls: 4.0218, loss: 4.0218 +2024-07-24 03:50:15,927 - pyskl - INFO - Epoch [58][600/3746] lr: 6.825e-02, eta: 3 days, 4:16:55, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5573, loss_cls: 3.9901, loss: 3.9901 +2024-07-24 03:51:40,178 - pyskl - INFO - Epoch [58][700/3746] lr: 6.822e-02, eta: 3 days, 4:15:45, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5500, loss_cls: 4.0306, loss: 4.0306 +2024-07-24 03:53:04,132 - pyskl - INFO - Epoch [58][800/3746] lr: 6.820e-02, eta: 3 days, 4:14:34, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5370, loss_cls: 4.1112, loss: 4.1112 +2024-07-24 03:54:28,655 - pyskl - INFO - Epoch [58][900/3746] lr: 6.817e-02, eta: 3 days, 4:13:24, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5495, loss_cls: 4.0992, loss: 4.0992 +2024-07-24 03:55:53,014 - pyskl - INFO - Epoch [58][1000/3746] lr: 6.815e-02, eta: 3 days, 4:12:14, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5547, loss_cls: 4.0499, loss: 4.0499 +2024-07-24 03:57:17,091 - pyskl - INFO - Epoch [58][1100/3746] lr: 6.812e-02, eta: 3 days, 4:11:03, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5486, loss_cls: 4.0480, loss: 4.0480 +2024-07-24 03:58:40,901 - pyskl - INFO - Epoch [58][1200/3746] lr: 6.809e-02, eta: 3 days, 4:09:52, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5436, loss_cls: 4.0816, loss: 4.0816 +2024-07-24 04:00:04,473 - pyskl - INFO - Epoch [58][1300/3746] lr: 6.807e-02, eta: 3 days, 4:08:40, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5461, loss_cls: 4.0652, loss: 4.0652 +2024-07-24 04:01:28,139 - pyskl - INFO - Epoch [58][1400/3746] lr: 6.804e-02, eta: 3 days, 4:07:29, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5542, loss_cls: 4.0326, loss: 4.0326 +2024-07-24 04:02:51,924 - pyskl - INFO - Epoch [58][1500/3746] lr: 6.802e-02, eta: 3 days, 4:06:17, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5437, loss_cls: 4.0714, loss: 4.0714 +2024-07-24 04:04:16,597 - pyskl - INFO - Epoch [58][1600/3746] lr: 6.799e-02, eta: 3 days, 4:05:08, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5469, loss_cls: 4.0656, loss: 4.0656 +2024-07-24 04:05:40,287 - pyskl - INFO - Epoch [58][1700/3746] lr: 6.796e-02, eta: 3 days, 4:03:56, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5416, loss_cls: 4.0805, loss: 4.0805 +2024-07-24 04:07:04,073 - pyskl - INFO - Epoch [58][1800/3746] lr: 6.794e-02, eta: 3 days, 4:02:45, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5477, loss_cls: 4.0768, loss: 4.0768 +2024-07-24 04:08:28,177 - pyskl - INFO - Epoch [58][1900/3746] lr: 6.791e-02, eta: 3 days, 4:01:34, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5380, loss_cls: 4.0727, loss: 4.0727 +2024-07-24 04:09:52,627 - pyskl - INFO - Epoch [58][2000/3746] lr: 6.789e-02, eta: 3 days, 4:00:24, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5445, loss_cls: 4.1072, loss: 4.1072 +2024-07-24 04:11:16,923 - pyskl - INFO - Epoch [58][2100/3746] lr: 6.786e-02, eta: 3 days, 3:59:13, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5544, loss_cls: 4.0342, loss: 4.0342 +2024-07-24 04:12:40,953 - pyskl - INFO - Epoch [58][2200/3746] lr: 6.783e-02, eta: 3 days, 3:58:03, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5372, loss_cls: 4.0958, loss: 4.0958 +2024-07-24 04:14:04,800 - pyskl - INFO - Epoch [58][2300/3746] lr: 6.781e-02, eta: 3 days, 3:56:51, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5500, loss_cls: 4.0500, loss: 4.0500 +2024-07-24 04:15:28,508 - pyskl - INFO - Epoch [58][2400/3746] lr: 6.778e-02, eta: 3 days, 3:55:40, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5530, loss_cls: 4.0342, loss: 4.0342 +2024-07-24 04:16:53,119 - pyskl - INFO - Epoch [58][2500/3746] lr: 6.775e-02, eta: 3 days, 3:54:30, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5378, loss_cls: 4.1054, loss: 4.1054 +2024-07-24 04:18:16,835 - pyskl - INFO - Epoch [58][2600/3746] lr: 6.773e-02, eta: 3 days, 3:53:18, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5463, loss_cls: 4.0697, loss: 4.0697 +2024-07-24 04:19:40,811 - pyskl - INFO - Epoch [58][2700/3746] lr: 6.770e-02, eta: 3 days, 3:52:07, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5470, loss_cls: 4.0888, loss: 4.0888 +2024-07-24 04:21:04,894 - pyskl - INFO - Epoch [58][2800/3746] lr: 6.768e-02, eta: 3 days, 3:50:56, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5481, loss_cls: 4.0707, loss: 4.0707 +2024-07-24 04:22:28,913 - pyskl - INFO - Epoch [58][2900/3746] lr: 6.765e-02, eta: 3 days, 3:49:45, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5405, loss_cls: 4.0826, loss: 4.0826 +2024-07-24 04:23:52,600 - pyskl - INFO - Epoch [58][3000/3746] lr: 6.762e-02, eta: 3 days, 3:48:34, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5370, loss_cls: 4.1039, loss: 4.1039 +2024-07-24 04:25:16,778 - pyskl - INFO - Epoch [58][3100/3746] lr: 6.760e-02, eta: 3 days, 3:47:23, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5434, loss_cls: 4.0784, loss: 4.0784 +2024-07-24 04:26:40,023 - pyskl - INFO - Epoch [58][3200/3746] lr: 6.757e-02, eta: 3 days, 3:46:11, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5353, loss_cls: 4.1096, loss: 4.1096 +2024-07-24 04:28:03,204 - pyskl - INFO - Epoch [58][3300/3746] lr: 6.755e-02, eta: 3 days, 3:44:58, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5464, loss_cls: 4.0608, loss: 4.0608 +2024-07-24 04:29:27,374 - pyskl - INFO - Epoch [58][3400/3746] lr: 6.752e-02, eta: 3 days, 3:43:47, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5416, loss_cls: 4.0996, loss: 4.0996 +2024-07-24 04:30:50,545 - pyskl - INFO - Epoch [58][3500/3746] lr: 6.749e-02, eta: 3 days, 3:42:35, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5461, loss_cls: 4.0440, loss: 4.0440 +2024-07-24 04:32:14,196 - pyskl - INFO - Epoch [58][3600/3746] lr: 6.747e-02, eta: 3 days, 3:41:23, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5409, loss_cls: 4.1176, loss: 4.1176 +2024-07-24 04:33:37,990 - pyskl - INFO - Epoch [58][3700/3746] lr: 6.744e-02, eta: 3 days, 3:40:12, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5461, loss_cls: 4.0335, loss: 4.0335 +2024-07-24 04:34:18,865 - pyskl - INFO - Saving checkpoint at 58 epochs +2024-07-24 04:36:11,828 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 04:36:12,506 - pyskl - INFO - +top1_acc 0.2323 +top5_acc 0.4681 +2024-07-24 04:36:12,506 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 04:36:12,551 - pyskl - INFO - +mean_acc 0.2322 +2024-07-24 04:36:12,563 - pyskl - INFO - Epoch(val) [58][309] top1_acc: 0.2323, top5_acc: 0.4681, mean_class_accuracy: 0.2322 +2024-07-24 04:40:08,736 - pyskl - INFO - Epoch [59][100/3746] lr: 6.740e-02, eta: 3 days, 3:41:28, time: 2.362, data_time: 1.362, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5608, loss_cls: 3.9965, loss: 3.9965 +2024-07-24 04:41:32,294 - pyskl - INFO - Epoch [59][200/3746] lr: 6.738e-02, eta: 3 days, 3:40:16, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5566, loss_cls: 4.0102, loss: 4.0102 +2024-07-24 04:42:56,701 - pyskl - INFO - Epoch [59][300/3746] lr: 6.735e-02, eta: 3 days, 3:39:05, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5605, loss_cls: 3.9926, loss: 3.9926 +2024-07-24 04:44:20,589 - pyskl - INFO - Epoch [59][400/3746] lr: 6.732e-02, eta: 3 days, 3:37:54, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5495, loss_cls: 4.0118, loss: 4.0118 +2024-07-24 04:45:43,500 - pyskl - INFO - Epoch [59][500/3746] lr: 6.730e-02, eta: 3 days, 3:36:41, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5445, loss_cls: 4.0767, loss: 4.0767 +2024-07-24 04:47:06,729 - pyskl - INFO - Epoch [59][600/3746] lr: 6.727e-02, eta: 3 days, 3:35:28, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5564, loss_cls: 4.0128, loss: 4.0128 +2024-07-24 04:48:30,480 - pyskl - INFO - Epoch [59][700/3746] lr: 6.725e-02, eta: 3 days, 3:34:16, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5613, loss_cls: 3.9930, loss: 3.9930 +2024-07-24 04:49:54,446 - pyskl - INFO - Epoch [59][800/3746] lr: 6.722e-02, eta: 3 days, 3:33:05, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5448, loss_cls: 4.0820, loss: 4.0820 +2024-07-24 04:51:18,031 - pyskl - INFO - Epoch [59][900/3746] lr: 6.719e-02, eta: 3 days, 3:31:53, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5466, loss_cls: 4.0635, loss: 4.0635 +2024-07-24 04:52:41,141 - pyskl - INFO - Epoch [59][1000/3746] lr: 6.717e-02, eta: 3 days, 3:30:40, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5437, loss_cls: 4.0634, loss: 4.0634 +2024-07-24 04:54:04,259 - pyskl - INFO - Epoch [59][1100/3746] lr: 6.714e-02, eta: 3 days, 3:29:27, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5495, loss_cls: 4.0541, loss: 4.0541 +2024-07-24 04:55:27,618 - pyskl - INFO - Epoch [59][1200/3746] lr: 6.711e-02, eta: 3 days, 3:28:15, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5453, loss_cls: 4.0480, loss: 4.0480 +2024-07-24 04:56:51,031 - pyskl - INFO - Epoch [59][1300/3746] lr: 6.709e-02, eta: 3 days, 3:27:02, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5328, loss_cls: 4.1089, loss: 4.1089 +2024-07-24 04:58:14,879 - pyskl - INFO - Epoch [59][1400/3746] lr: 6.706e-02, eta: 3 days, 3:25:51, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5427, loss_cls: 4.0382, loss: 4.0382 +2024-07-24 04:59:38,527 - pyskl - INFO - Epoch [59][1500/3746] lr: 6.704e-02, eta: 3 days, 3:24:39, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5492, loss_cls: 4.0370, loss: 4.0370 +2024-07-24 05:01:02,197 - pyskl - INFO - Epoch [59][1600/3746] lr: 6.701e-02, eta: 3 days, 3:23:27, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5516, loss_cls: 4.0310, loss: 4.0310 +2024-07-24 05:02:25,976 - pyskl - INFO - Epoch [59][1700/3746] lr: 6.698e-02, eta: 3 days, 3:22:15, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5466, loss_cls: 4.0819, loss: 4.0819 +2024-07-24 05:03:49,991 - pyskl - INFO - Epoch [59][1800/3746] lr: 6.696e-02, eta: 3 days, 3:21:03, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5444, loss_cls: 4.0864, loss: 4.0864 +2024-07-24 05:05:13,298 - pyskl - INFO - Epoch [59][1900/3746] lr: 6.693e-02, eta: 3 days, 3:19:51, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5567, loss_cls: 4.0569, loss: 4.0569 +2024-07-24 05:06:36,294 - pyskl - INFO - Epoch [59][2000/3746] lr: 6.690e-02, eta: 3 days, 3:18:38, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5522, loss_cls: 4.0399, loss: 4.0399 +2024-07-24 05:07:58,092 - pyskl - INFO - Epoch [59][2100/3746] lr: 6.688e-02, eta: 3 days, 3:17:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5445, loss_cls: 4.0673, loss: 4.0673 +2024-07-24 05:09:19,738 - pyskl - INFO - Epoch [59][2200/3746] lr: 6.685e-02, eta: 3 days, 3:16:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5434, loss_cls: 4.0966, loss: 4.0966 +2024-07-24 05:10:41,477 - pyskl - INFO - Epoch [59][2300/3746] lr: 6.682e-02, eta: 3 days, 3:14:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5406, loss_cls: 4.0583, loss: 4.0583 +2024-07-24 05:12:03,278 - pyskl - INFO - Epoch [59][2400/3746] lr: 6.680e-02, eta: 3 days, 3:13:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5364, loss_cls: 4.1058, loss: 4.1058 +2024-07-24 05:13:25,010 - pyskl - INFO - Epoch [59][2500/3746] lr: 6.677e-02, eta: 3 days, 3:12:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5469, loss_cls: 4.0450, loss: 4.0450 +2024-07-24 05:14:47,542 - pyskl - INFO - Epoch [59][2600/3746] lr: 6.675e-02, eta: 3 days, 3:11:09, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5539, loss_cls: 4.0266, loss: 4.0266 +2024-07-24 05:16:09,083 - pyskl - INFO - Epoch [59][2700/3746] lr: 6.672e-02, eta: 3 days, 3:09:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5409, loss_cls: 4.1184, loss: 4.1184 +2024-07-24 05:17:31,792 - pyskl - INFO - Epoch [59][2800/3746] lr: 6.669e-02, eta: 3 days, 3:08:40, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5514, loss_cls: 4.0749, loss: 4.0749 +2024-07-24 05:18:53,202 - pyskl - INFO - Epoch [59][2900/3746] lr: 6.667e-02, eta: 3 days, 3:07:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5425, loss_cls: 4.0511, loss: 4.0511 +2024-07-24 05:20:15,177 - pyskl - INFO - Epoch [59][3000/3746] lr: 6.664e-02, eta: 3 days, 3:06:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5494, loss_cls: 4.0617, loss: 4.0617 +2024-07-24 05:21:37,224 - pyskl - INFO - Epoch [59][3100/3746] lr: 6.661e-02, eta: 3 days, 3:04:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5297, loss_cls: 4.1297, loss: 4.1297 +2024-07-24 05:22:59,778 - pyskl - INFO - Epoch [59][3200/3746] lr: 6.659e-02, eta: 3 days, 3:03:41, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5428, loss_cls: 4.0677, loss: 4.0677 +2024-07-24 05:24:21,718 - pyskl - INFO - Epoch [59][3300/3746] lr: 6.656e-02, eta: 3 days, 3:02:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5563, loss_cls: 4.0567, loss: 4.0567 +2024-07-24 05:25:43,583 - pyskl - INFO - Epoch [59][3400/3746] lr: 6.653e-02, eta: 3 days, 3:01:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5569, loss_cls: 4.0214, loss: 4.0214 +2024-07-24 05:27:06,330 - pyskl - INFO - Epoch [59][3500/3746] lr: 6.651e-02, eta: 3 days, 2:59:57, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5555, loss_cls: 4.0137, loss: 4.0137 +2024-07-24 05:28:28,779 - pyskl - INFO - Epoch [59][3600/3746] lr: 6.648e-02, eta: 3 days, 2:58:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5542, loss_cls: 4.0212, loss: 4.0212 +2024-07-24 05:29:50,302 - pyskl - INFO - Epoch [59][3700/3746] lr: 6.646e-02, eta: 3 days, 2:57:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5341, loss_cls: 4.1293, loss: 4.1293 +2024-07-24 05:30:29,855 - pyskl - INFO - Saving checkpoint at 59 epochs +2024-07-24 05:32:22,677 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 05:32:23,393 - pyskl - INFO - +top1_acc 0.2380 +top5_acc 0.4882 +2024-07-24 05:32:23,393 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 05:32:23,434 - pyskl - INFO - +mean_acc 0.2380 +2024-07-24 05:32:23,439 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_56.pth was removed +2024-07-24 05:32:23,709 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_59.pth. +2024-07-24 05:32:23,710 - pyskl - INFO - Best top1_acc is 0.2380 at 59 epoch. +2024-07-24 05:32:23,722 - pyskl - INFO - Epoch(val) [59][309] top1_acc: 0.2380, top5_acc: 0.4882, mean_class_accuracy: 0.2380 +2024-07-24 05:36:22,082 - pyskl - INFO - Epoch [60][100/3746] lr: 6.642e-02, eta: 3 days, 2:58:42, time: 2.383, data_time: 1.385, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5611, loss_cls: 3.9920, loss: 3.9920 +2024-07-24 05:37:44,980 - pyskl - INFO - Epoch [60][200/3746] lr: 6.639e-02, eta: 3 days, 2:57:28, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5522, loss_cls: 4.0205, loss: 4.0205 +2024-07-24 05:39:06,799 - pyskl - INFO - Epoch [60][300/3746] lr: 6.636e-02, eta: 3 days, 2:56:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5664, loss_cls: 3.9954, loss: 3.9954 +2024-07-24 05:40:28,675 - pyskl - INFO - Epoch [60][400/3746] lr: 6.634e-02, eta: 3 days, 2:54:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5477, loss_cls: 4.0236, loss: 4.0236 +2024-07-24 05:41:50,485 - pyskl - INFO - Epoch [60][500/3746] lr: 6.631e-02, eta: 3 days, 2:53:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5569, loss_cls: 4.0173, loss: 4.0173 +2024-07-24 05:43:12,534 - pyskl - INFO - Epoch [60][600/3746] lr: 6.629e-02, eta: 3 days, 2:52:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5519, loss_cls: 4.0384, loss: 4.0384 +2024-07-24 05:44:34,108 - pyskl - INFO - Epoch [60][700/3746] lr: 6.626e-02, eta: 3 days, 2:51:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5656, loss_cls: 4.0020, loss: 4.0020 +2024-07-24 05:45:55,286 - pyskl - INFO - Epoch [60][800/3746] lr: 6.623e-02, eta: 3 days, 2:49:56, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5533, loss_cls: 4.0185, loss: 4.0185 +2024-07-24 05:47:17,261 - pyskl - INFO - Epoch [60][900/3746] lr: 6.621e-02, eta: 3 days, 2:48:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5550, loss_cls: 4.0211, loss: 4.0211 +2024-07-24 05:48:38,296 - pyskl - INFO - Epoch [60][1000/3746] lr: 6.618e-02, eta: 3 days, 2:47:24, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5497, loss_cls: 4.0292, loss: 4.0292 +2024-07-24 05:49:59,888 - pyskl - INFO - Epoch [60][1100/3746] lr: 6.615e-02, eta: 3 days, 2:46:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5467, loss_cls: 4.0545, loss: 4.0545 +2024-07-24 05:51:21,265 - pyskl - INFO - Epoch [60][1200/3746] lr: 6.613e-02, eta: 3 days, 2:44:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5547, loss_cls: 4.0099, loss: 4.0099 +2024-07-24 05:52:42,879 - pyskl - INFO - Epoch [60][1300/3746] lr: 6.610e-02, eta: 3 days, 2:43:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5519, loss_cls: 4.0667, loss: 4.0667 +2024-07-24 05:54:04,500 - pyskl - INFO - Epoch [60][1400/3746] lr: 6.607e-02, eta: 3 days, 2:42:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5489, loss_cls: 4.0597, loss: 4.0597 +2024-07-24 05:55:25,852 - pyskl - INFO - Epoch [60][1500/3746] lr: 6.605e-02, eta: 3 days, 2:41:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5430, loss_cls: 4.0633, loss: 4.0633 +2024-07-24 05:56:47,566 - pyskl - INFO - Epoch [60][1600/3746] lr: 6.602e-02, eta: 3 days, 2:39:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5364, loss_cls: 4.1014, loss: 4.1014 +2024-07-24 05:58:10,277 - pyskl - INFO - Epoch [60][1700/3746] lr: 6.599e-02, eta: 3 days, 2:38:37, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5366, loss_cls: 4.0940, loss: 4.0940 +2024-07-24 05:59:32,601 - pyskl - INFO - Epoch [60][1800/3746] lr: 6.597e-02, eta: 3 days, 2:37:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5448, loss_cls: 4.0936, loss: 4.0936 +2024-07-24 06:00:54,474 - pyskl - INFO - Epoch [60][1900/3746] lr: 6.594e-02, eta: 3 days, 2:36:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5448, loss_cls: 4.0534, loss: 4.0534 +2024-07-24 06:02:16,023 - pyskl - INFO - Epoch [60][2000/3746] lr: 6.591e-02, eta: 3 days, 2:34:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5555, loss_cls: 4.0198, loss: 4.0198 +2024-07-24 06:03:37,532 - pyskl - INFO - Epoch [60][2100/3746] lr: 6.589e-02, eta: 3 days, 2:33:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5563, loss_cls: 4.0303, loss: 4.0303 +2024-07-24 06:04:59,266 - pyskl - INFO - Epoch [60][2200/3746] lr: 6.586e-02, eta: 3 days, 2:32:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5363, loss_cls: 4.1068, loss: 4.1068 +2024-07-24 06:06:20,705 - pyskl - INFO - Epoch [60][2300/3746] lr: 6.584e-02, eta: 3 days, 2:31:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5487, loss_cls: 4.0218, loss: 4.0218 +2024-07-24 06:07:42,896 - pyskl - INFO - Epoch [60][2400/3746] lr: 6.581e-02, eta: 3 days, 2:29:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5458, loss_cls: 4.0702, loss: 4.0702 +2024-07-24 06:09:05,287 - pyskl - INFO - Epoch [60][2500/3746] lr: 6.578e-02, eta: 3 days, 2:28:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5484, loss_cls: 4.0256, loss: 4.0256 +2024-07-24 06:10:27,924 - pyskl - INFO - Epoch [60][2600/3746] lr: 6.576e-02, eta: 3 days, 2:27:21, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5528, loss_cls: 4.0414, loss: 4.0414 +2024-07-24 06:11:49,542 - pyskl - INFO - Epoch [60][2700/3746] lr: 6.573e-02, eta: 3 days, 2:26:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5427, loss_cls: 4.0689, loss: 4.0689 +2024-07-24 06:13:11,811 - pyskl - INFO - Epoch [60][2800/3746] lr: 6.570e-02, eta: 3 days, 2:24:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5517, loss_cls: 4.0475, loss: 4.0475 +2024-07-24 06:14:33,229 - pyskl - INFO - Epoch [60][2900/3746] lr: 6.568e-02, eta: 3 days, 2:23:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5527, loss_cls: 4.0090, loss: 4.0090 +2024-07-24 06:15:54,817 - pyskl - INFO - Epoch [60][3000/3746] lr: 6.565e-02, eta: 3 days, 2:22:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5494, loss_cls: 4.0523, loss: 4.0523 +2024-07-24 06:17:16,312 - pyskl - INFO - Epoch [60][3100/3746] lr: 6.562e-02, eta: 3 days, 2:21:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5464, loss_cls: 4.0308, loss: 4.0308 +2024-07-24 06:18:39,264 - pyskl - INFO - Epoch [60][3200/3746] lr: 6.560e-02, eta: 3 days, 2:19:49, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5481, loss_cls: 4.0718, loss: 4.0718 +2024-07-24 06:20:02,303 - pyskl - INFO - Epoch [60][3300/3746] lr: 6.557e-02, eta: 3 days, 2:18:36, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5397, loss_cls: 4.0787, loss: 4.0787 +2024-07-24 06:21:24,316 - pyskl - INFO - Epoch [60][3400/3746] lr: 6.554e-02, eta: 3 days, 2:17:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5516, loss_cls: 4.0382, loss: 4.0382 +2024-07-24 06:22:46,688 - pyskl - INFO - Epoch [60][3500/3746] lr: 6.552e-02, eta: 3 days, 2:16:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5567, loss_cls: 4.0343, loss: 4.0343 +2024-07-24 06:24:08,763 - pyskl - INFO - Epoch [60][3600/3746] lr: 6.549e-02, eta: 3 days, 2:14:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5525, loss_cls: 4.0548, loss: 4.0548 +2024-07-24 06:25:30,567 - pyskl - INFO - Epoch [60][3700/3746] lr: 6.546e-02, eta: 3 days, 2:13:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5544, loss_cls: 4.0523, loss: 4.0523 +2024-07-24 06:26:10,274 - pyskl - INFO - Saving checkpoint at 60 epochs +2024-07-24 06:28:03,167 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 06:28:04,004 - pyskl - INFO - +top1_acc 0.2172 +top5_acc 0.4562 +2024-07-24 06:28:04,004 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 06:28:04,054 - pyskl - INFO - +mean_acc 0.2169 +2024-07-24 06:28:04,067 - pyskl - INFO - Epoch(val) [60][309] top1_acc: 0.2172, top5_acc: 0.4562, mean_class_accuracy: 0.2169 +2024-07-24 06:32:01,377 - pyskl - INFO - Epoch [61][100/3746] lr: 6.542e-02, eta: 3 days, 2:14:42, time: 2.373, data_time: 1.382, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5497, loss_cls: 4.0415, loss: 4.0415 +2024-07-24 06:33:25,231 - pyskl - INFO - Epoch [61][200/3746] lr: 6.540e-02, eta: 3 days, 2:13:30, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5480, loss_cls: 4.0464, loss: 4.0464 +2024-07-24 06:34:49,451 - pyskl - INFO - Epoch [61][300/3746] lr: 6.537e-02, eta: 3 days, 2:12:18, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5531, loss_cls: 4.0018, loss: 4.0018 +2024-07-24 06:36:13,519 - pyskl - INFO - Epoch [61][400/3746] lr: 6.534e-02, eta: 3 days, 2:11:06, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5494, loss_cls: 4.0492, loss: 4.0492 +2024-07-24 06:37:37,778 - pyskl - INFO - Epoch [61][500/3746] lr: 6.532e-02, eta: 3 days, 2:09:54, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5677, loss_cls: 3.9799, loss: 3.9799 +2024-07-24 06:39:02,203 - pyskl - INFO - Epoch [61][600/3746] lr: 6.529e-02, eta: 3 days, 2:08:42, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5620, loss_cls: 3.9953, loss: 3.9953 +2024-07-24 06:40:26,480 - pyskl - INFO - Epoch [61][700/3746] lr: 6.526e-02, eta: 3 days, 2:07:30, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5539, loss_cls: 4.0452, loss: 4.0452 +2024-07-24 06:41:50,352 - pyskl - INFO - Epoch [61][800/3746] lr: 6.524e-02, eta: 3 days, 2:06:18, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5569, loss_cls: 4.0147, loss: 4.0147 +2024-07-24 06:43:14,413 - pyskl - INFO - Epoch [61][900/3746] lr: 6.521e-02, eta: 3 days, 2:05:05, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5542, loss_cls: 3.9889, loss: 3.9889 +2024-07-24 06:44:38,362 - pyskl - INFO - Epoch [61][1000/3746] lr: 6.519e-02, eta: 3 days, 2:03:53, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5484, loss_cls: 4.0652, loss: 4.0652 +2024-07-24 06:46:02,734 - pyskl - INFO - Epoch [61][1100/3746] lr: 6.516e-02, eta: 3 days, 2:02:41, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5437, loss_cls: 4.0765, loss: 4.0765 +2024-07-24 06:47:26,577 - pyskl - INFO - Epoch [61][1200/3746] lr: 6.513e-02, eta: 3 days, 2:01:29, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5509, loss_cls: 4.0342, loss: 4.0342 +2024-07-24 06:48:50,704 - pyskl - INFO - Epoch [61][1300/3746] lr: 6.511e-02, eta: 3 days, 2:00:16, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5544, loss_cls: 4.0273, loss: 4.0273 +2024-07-24 06:50:14,730 - pyskl - INFO - Epoch [61][1400/3746] lr: 6.508e-02, eta: 3 days, 1:59:04, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5505, loss_cls: 4.0290, loss: 4.0290 +2024-07-24 06:51:39,183 - pyskl - INFO - Epoch [61][1500/3746] lr: 6.505e-02, eta: 3 days, 1:57:52, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5442, loss_cls: 4.0744, loss: 4.0744 +2024-07-24 06:53:03,861 - pyskl - INFO - Epoch [61][1600/3746] lr: 6.503e-02, eta: 3 days, 1:56:41, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5573, loss_cls: 4.0209, loss: 4.0209 +2024-07-24 06:54:28,322 - pyskl - INFO - Epoch [61][1700/3746] lr: 6.500e-02, eta: 3 days, 1:55:29, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5528, loss_cls: 4.0331, loss: 4.0331 +2024-07-24 06:55:52,802 - pyskl - INFO - Epoch [61][1800/3746] lr: 6.497e-02, eta: 3 days, 1:54:17, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5472, loss_cls: 4.0584, loss: 4.0584 +2024-07-24 06:57:17,101 - pyskl - INFO - Epoch [61][1900/3746] lr: 6.495e-02, eta: 3 days, 1:53:05, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5539, loss_cls: 4.0104, loss: 4.0104 +2024-07-24 06:58:41,589 - pyskl - INFO - Epoch [61][2000/3746] lr: 6.492e-02, eta: 3 days, 1:51:54, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5467, loss_cls: 4.0735, loss: 4.0735 +2024-07-24 07:00:06,131 - pyskl - INFO - Epoch [61][2100/3746] lr: 6.489e-02, eta: 3 days, 1:50:42, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5400, loss_cls: 4.0910, loss: 4.0910 +2024-07-24 07:01:30,016 - pyskl - INFO - Epoch [61][2200/3746] lr: 6.487e-02, eta: 3 days, 1:49:29, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5505, loss_cls: 4.0674, loss: 4.0674 +2024-07-24 07:02:54,441 - pyskl - INFO - Epoch [61][2300/3746] lr: 6.484e-02, eta: 3 days, 1:48:17, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5566, loss_cls: 3.9841, loss: 3.9841 +2024-07-24 07:04:18,164 - pyskl - INFO - Epoch [61][2400/3746] lr: 6.481e-02, eta: 3 days, 1:47:05, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5587, loss_cls: 4.0140, loss: 4.0140 +2024-07-24 07:05:41,398 - pyskl - INFO - Epoch [61][2500/3746] lr: 6.478e-02, eta: 3 days, 1:45:51, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5520, loss_cls: 4.0130, loss: 4.0130 +2024-07-24 07:07:06,056 - pyskl - INFO - Epoch [61][2600/3746] lr: 6.476e-02, eta: 3 days, 1:44:39, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5634, loss_cls: 3.9875, loss: 3.9875 +2024-07-24 07:08:29,861 - pyskl - INFO - Epoch [61][2700/3746] lr: 6.473e-02, eta: 3 days, 1:43:27, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5486, loss_cls: 4.0472, loss: 4.0472 +2024-07-24 07:09:54,064 - pyskl - INFO - Epoch [61][2800/3746] lr: 6.470e-02, eta: 3 days, 1:42:14, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5473, loss_cls: 4.0551, loss: 4.0551 +2024-07-24 07:11:18,199 - pyskl - INFO - Epoch [61][2900/3746] lr: 6.468e-02, eta: 3 days, 1:41:02, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5484, loss_cls: 4.0407, loss: 4.0407 +2024-07-24 07:12:41,918 - pyskl - INFO - Epoch [61][3000/3746] lr: 6.465e-02, eta: 3 days, 1:39:49, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5581, loss_cls: 4.0081, loss: 4.0081 +2024-07-24 07:14:05,484 - pyskl - INFO - Epoch [61][3100/3746] lr: 6.462e-02, eta: 3 days, 1:38:36, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5564, loss_cls: 4.0293, loss: 4.0293 +2024-07-24 07:15:29,732 - pyskl - INFO - Epoch [61][3200/3746] lr: 6.460e-02, eta: 3 days, 1:37:24, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5522, loss_cls: 4.0623, loss: 4.0623 +2024-07-24 07:16:52,760 - pyskl - INFO - Epoch [61][3300/3746] lr: 6.457e-02, eta: 3 days, 1:36:10, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5536, loss_cls: 3.9988, loss: 3.9988 +2024-07-24 07:18:16,123 - pyskl - INFO - Epoch [61][3400/3746] lr: 6.454e-02, eta: 3 days, 1:34:56, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5406, loss_cls: 4.1096, loss: 4.1096 +2024-07-24 07:19:40,060 - pyskl - INFO - Epoch [61][3500/3746] lr: 6.452e-02, eta: 3 days, 1:33:43, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5513, loss_cls: 4.0416, loss: 4.0416 +2024-07-24 07:21:03,887 - pyskl - INFO - Epoch [61][3600/3746] lr: 6.449e-02, eta: 3 days, 1:32:30, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5473, loss_cls: 4.0723, loss: 4.0723 +2024-07-24 07:22:28,150 - pyskl - INFO - Epoch [61][3700/3746] lr: 6.446e-02, eta: 3 days, 1:31:18, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5437, loss_cls: 4.0461, loss: 4.0461 +2024-07-24 07:23:07,870 - pyskl - INFO - Saving checkpoint at 61 epochs +2024-07-24 07:25:01,336 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 07:25:02,026 - pyskl - INFO - +top1_acc 0.2248 +top5_acc 0.4609 +2024-07-24 07:25:02,027 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 07:25:02,082 - pyskl - INFO - +mean_acc 0.2245 +2024-07-24 07:25:02,107 - pyskl - INFO - Epoch(val) [61][309] top1_acc: 0.2248, top5_acc: 0.4609, mean_class_accuracy: 0.2245 +2024-07-24 07:28:59,864 - pyskl - INFO - Epoch [62][100/3746] lr: 6.443e-02, eta: 3 days, 1:32:20, time: 2.377, data_time: 1.373, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5719, loss_cls: 3.9412, loss: 3.9412 +2024-07-24 07:30:24,058 - pyskl - INFO - Epoch [62][200/3746] lr: 6.440e-02, eta: 3 days, 1:31:07, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5642, loss_cls: 3.9791, loss: 3.9791 +2024-07-24 07:31:47,982 - pyskl - INFO - Epoch [62][300/3746] lr: 6.437e-02, eta: 3 days, 1:29:55, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5631, loss_cls: 3.9992, loss: 3.9992 +2024-07-24 07:33:11,406 - pyskl - INFO - Epoch [62][400/3746] lr: 6.434e-02, eta: 3 days, 1:28:41, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5505, loss_cls: 4.0258, loss: 4.0258 +2024-07-24 07:34:34,717 - pyskl - INFO - Epoch [62][500/3746] lr: 6.432e-02, eta: 3 days, 1:27:27, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5514, loss_cls: 4.0273, loss: 4.0273 +2024-07-24 07:35:57,763 - pyskl - INFO - Epoch [62][600/3746] lr: 6.429e-02, eta: 3 days, 1:26:13, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5553, loss_cls: 4.0033, loss: 4.0033 +2024-07-24 07:37:20,840 - pyskl - INFO - Epoch [62][700/3746] lr: 6.426e-02, eta: 3 days, 1:24:59, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5558, loss_cls: 4.0227, loss: 4.0227 +2024-07-24 07:38:44,268 - pyskl - INFO - Epoch [62][800/3746] lr: 6.424e-02, eta: 3 days, 1:23:45, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5608, loss_cls: 4.0394, loss: 4.0394 +2024-07-24 07:40:07,276 - pyskl - INFO - Epoch [62][900/3746] lr: 6.421e-02, eta: 3 days, 1:22:31, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5623, loss_cls: 3.9971, loss: 3.9971 +2024-07-24 07:41:30,370 - pyskl - INFO - Epoch [62][1000/3746] lr: 6.418e-02, eta: 3 days, 1:21:17, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5620, loss_cls: 4.0157, loss: 4.0157 +2024-07-24 07:42:53,532 - pyskl - INFO - Epoch [62][1100/3746] lr: 6.416e-02, eta: 3 days, 1:20:03, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5505, loss_cls: 4.0270, loss: 4.0270 +2024-07-24 07:44:16,395 - pyskl - INFO - Epoch [62][1200/3746] lr: 6.413e-02, eta: 3 days, 1:18:48, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5587, loss_cls: 3.9923, loss: 3.9923 +2024-07-24 07:45:39,530 - pyskl - INFO - Epoch [62][1300/3746] lr: 6.410e-02, eta: 3 days, 1:17:34, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5578, loss_cls: 4.0115, loss: 4.0115 +2024-07-24 07:47:01,892 - pyskl - INFO - Epoch [62][1400/3746] lr: 6.408e-02, eta: 3 days, 1:16:19, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5523, loss_cls: 4.0385, loss: 4.0385 +2024-07-24 07:48:23,486 - pyskl - INFO - Epoch [62][1500/3746] lr: 6.405e-02, eta: 3 days, 1:15:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5550, loss_cls: 3.9984, loss: 3.9984 +2024-07-24 07:49:45,174 - pyskl - INFO - Epoch [62][1600/3746] lr: 6.402e-02, eta: 3 days, 1:13:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5444, loss_cls: 4.0681, loss: 4.0681 +2024-07-24 07:51:06,931 - pyskl - INFO - Epoch [62][1700/3746] lr: 6.400e-02, eta: 3 days, 1:12:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5584, loss_cls: 4.0134, loss: 4.0134 +2024-07-24 07:52:28,964 - pyskl - INFO - Epoch [62][1800/3746] lr: 6.397e-02, eta: 3 days, 1:11:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5505, loss_cls: 4.0170, loss: 4.0170 +2024-07-24 07:53:50,869 - pyskl - INFO - Epoch [62][1900/3746] lr: 6.394e-02, eta: 3 days, 1:09:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5564, loss_cls: 4.0126, loss: 4.0126 +2024-07-24 07:55:12,741 - pyskl - INFO - Epoch [62][2000/3746] lr: 6.392e-02, eta: 3 days, 1:08:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5566, loss_cls: 4.0149, loss: 4.0149 +2024-07-24 07:56:33,992 - pyskl - INFO - Epoch [62][2100/3746] lr: 6.389e-02, eta: 3 days, 1:07:25, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5567, loss_cls: 4.0080, loss: 4.0080 +2024-07-24 07:57:57,013 - pyskl - INFO - Epoch [62][2200/3746] lr: 6.386e-02, eta: 3 days, 1:06:11, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5378, loss_cls: 4.0930, loss: 4.0930 +2024-07-24 07:59:19,218 - pyskl - INFO - Epoch [62][2300/3746] lr: 6.384e-02, eta: 3 days, 1:04:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5547, loss_cls: 4.0268, loss: 4.0268 +2024-07-24 08:00:41,192 - pyskl - INFO - Epoch [62][2400/3746] lr: 6.381e-02, eta: 3 days, 1:03:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5566, loss_cls: 4.0042, loss: 4.0042 +2024-07-24 08:02:03,703 - pyskl - INFO - Epoch [62][2500/3746] lr: 6.378e-02, eta: 3 days, 1:02:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5506, loss_cls: 4.0634, loss: 4.0634 +2024-07-24 08:03:25,145 - pyskl - INFO - Epoch [62][2600/3746] lr: 6.375e-02, eta: 3 days, 1:01:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5455, loss_cls: 4.0386, loss: 4.0386 +2024-07-24 08:04:47,922 - pyskl - INFO - Epoch [62][2700/3746] lr: 6.373e-02, eta: 3 days, 0:59:53, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5489, loss_cls: 4.0334, loss: 4.0334 +2024-07-24 08:06:10,062 - pyskl - INFO - Epoch [62][2800/3746] lr: 6.370e-02, eta: 3 days, 0:58:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5489, loss_cls: 4.0474, loss: 4.0474 +2024-07-24 08:07:31,619 - pyskl - INFO - Epoch [62][2900/3746] lr: 6.367e-02, eta: 3 days, 0:57:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5573, loss_cls: 4.0093, loss: 4.0093 +2024-07-24 08:08:53,394 - pyskl - INFO - Epoch [62][3000/3746] lr: 6.365e-02, eta: 3 days, 0:56:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5511, loss_cls: 4.0603, loss: 4.0603 +2024-07-24 08:10:14,984 - pyskl - INFO - Epoch [62][3100/3746] lr: 6.362e-02, eta: 3 days, 0:54:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5411, loss_cls: 4.0518, loss: 4.0518 +2024-07-24 08:11:36,884 - pyskl - INFO - Epoch [62][3200/3746] lr: 6.359e-02, eta: 3 days, 0:53:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5567, loss_cls: 4.0455, loss: 4.0455 +2024-07-24 08:12:58,630 - pyskl - INFO - Epoch [62][3300/3746] lr: 6.357e-02, eta: 3 days, 0:52:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5450, loss_cls: 4.0337, loss: 4.0337 +2024-07-24 08:14:20,614 - pyskl - INFO - Epoch [62][3400/3746] lr: 6.354e-02, eta: 3 days, 0:51:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5516, loss_cls: 4.0322, loss: 4.0322 +2024-07-24 08:15:42,706 - pyskl - INFO - Epoch [62][3500/3746] lr: 6.351e-02, eta: 3 days, 0:49:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5486, loss_cls: 4.0348, loss: 4.0348 +2024-07-24 08:17:04,855 - pyskl - INFO - Epoch [62][3600/3746] lr: 6.349e-02, eta: 3 days, 0:48:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5630, loss_cls: 3.9808, loss: 3.9808 +2024-07-24 08:18:26,812 - pyskl - INFO - Epoch [62][3700/3746] lr: 6.346e-02, eta: 3 days, 0:47:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5452, loss_cls: 4.0500, loss: 4.0500 +2024-07-24 08:19:06,214 - pyskl - INFO - Saving checkpoint at 62 epochs +2024-07-24 08:21:00,092 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 08:21:00,847 - pyskl - INFO - +top1_acc 0.2404 +top5_acc 0.4909 +2024-07-24 08:21:00,847 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 08:21:00,896 - pyskl - INFO - +mean_acc 0.2402 +2024-07-24 08:21:00,900 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_59.pth was removed +2024-07-24 08:21:01,191 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_62.pth. +2024-07-24 08:21:01,192 - pyskl - INFO - Best top1_acc is 0.2404 at 62 epoch. +2024-07-24 08:21:01,211 - pyskl - INFO - Epoch(val) [62][309] top1_acc: 0.2404, top5_acc: 0.4909, mean_class_accuracy: 0.2402 +2024-07-24 08:24:55,869 - pyskl - INFO - Epoch [63][100/3746] lr: 6.342e-02, eta: 3 days, 0:48:05, time: 2.346, data_time: 1.339, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5583, loss_cls: 4.0034, loss: 4.0034 +2024-07-24 08:26:20,273 - pyskl - INFO - Epoch [63][200/3746] lr: 6.339e-02, eta: 3 days, 0:46:52, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5645, loss_cls: 3.9719, loss: 3.9719 +2024-07-24 08:27:44,293 - pyskl - INFO - Epoch [63][300/3746] lr: 6.337e-02, eta: 3 days, 0:45:39, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5661, loss_cls: 3.9438, loss: 3.9438 +2024-07-24 08:29:08,403 - pyskl - INFO - Epoch [63][400/3746] lr: 6.334e-02, eta: 3 days, 0:44:26, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5670, loss_cls: 3.9591, loss: 3.9591 +2024-07-24 08:30:33,123 - pyskl - INFO - Epoch [63][500/3746] lr: 6.331e-02, eta: 3 days, 0:43:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5628, loss_cls: 3.9759, loss: 3.9759 +2024-07-24 08:31:57,586 - pyskl - INFO - Epoch [63][600/3746] lr: 6.328e-02, eta: 3 days, 0:42:01, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5395, loss_cls: 4.0898, loss: 4.0898 +2024-07-24 08:33:21,369 - pyskl - INFO - Epoch [63][700/3746] lr: 6.326e-02, eta: 3 days, 0:40:48, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5519, loss_cls: 4.0260, loss: 4.0260 +2024-07-24 08:34:45,686 - pyskl - INFO - Epoch [63][800/3746] lr: 6.323e-02, eta: 3 days, 0:39:35, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5581, loss_cls: 3.9835, loss: 3.9835 +2024-07-24 08:36:09,837 - pyskl - INFO - Epoch [63][900/3746] lr: 6.320e-02, eta: 3 days, 0:38:22, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5622, loss_cls: 3.9785, loss: 3.9785 +2024-07-24 08:37:33,781 - pyskl - INFO - Epoch [63][1000/3746] lr: 6.318e-02, eta: 3 days, 0:37:09, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5547, loss_cls: 4.0181, loss: 4.0181 +2024-07-24 08:38:58,147 - pyskl - INFO - Epoch [63][1100/3746] lr: 6.315e-02, eta: 3 days, 0:35:56, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5550, loss_cls: 4.0183, loss: 4.0183 +2024-07-24 08:40:22,672 - pyskl - INFO - Epoch [63][1200/3746] lr: 6.312e-02, eta: 3 days, 0:34:43, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5409, loss_cls: 4.0437, loss: 4.0437 +2024-07-24 08:41:47,172 - pyskl - INFO - Epoch [63][1300/3746] lr: 6.310e-02, eta: 3 days, 0:33:31, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5600, loss_cls: 4.0322, loss: 4.0322 +2024-07-24 08:43:11,749 - pyskl - INFO - Epoch [63][1400/3746] lr: 6.307e-02, eta: 3 days, 0:32:18, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5572, loss_cls: 4.0214, loss: 4.0214 +2024-07-24 08:44:35,688 - pyskl - INFO - Epoch [63][1500/3746] lr: 6.304e-02, eta: 3 days, 0:31:05, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5545, loss_cls: 4.0240, loss: 4.0240 +2024-07-24 08:46:00,150 - pyskl - INFO - Epoch [63][1600/3746] lr: 6.301e-02, eta: 3 days, 0:29:52, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5527, loss_cls: 4.0386, loss: 4.0386 +2024-07-24 08:47:24,354 - pyskl - INFO - Epoch [63][1700/3746] lr: 6.299e-02, eta: 3 days, 0:28:39, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5413, loss_cls: 4.0483, loss: 4.0483 +2024-07-24 08:48:48,949 - pyskl - INFO - Epoch [63][1800/3746] lr: 6.296e-02, eta: 3 days, 0:27:27, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5584, loss_cls: 3.9977, loss: 3.9977 +2024-07-24 08:50:12,967 - pyskl - INFO - Epoch [63][1900/3746] lr: 6.293e-02, eta: 3 days, 0:26:13, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5467, loss_cls: 4.0642, loss: 4.0642 +2024-07-24 08:51:37,066 - pyskl - INFO - Epoch [63][2000/3746] lr: 6.291e-02, eta: 3 days, 0:25:00, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5563, loss_cls: 4.0134, loss: 4.0134 +2024-07-24 08:53:00,636 - pyskl - INFO - Epoch [63][2100/3746] lr: 6.288e-02, eta: 3 days, 0:23:46, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5473, loss_cls: 4.0503, loss: 4.0503 +2024-07-24 08:54:24,857 - pyskl - INFO - Epoch [63][2200/3746] lr: 6.285e-02, eta: 3 days, 0:22:33, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5487, loss_cls: 4.0625, loss: 4.0625 +2024-07-24 08:55:47,861 - pyskl - INFO - Epoch [63][2300/3746] lr: 6.283e-02, eta: 3 days, 0:21:18, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5583, loss_cls: 4.0074, loss: 4.0074 +2024-07-24 08:57:11,147 - pyskl - INFO - Epoch [63][2400/3746] lr: 6.280e-02, eta: 3 days, 0:20:04, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5600, loss_cls: 3.9726, loss: 3.9726 +2024-07-24 08:58:35,098 - pyskl - INFO - Epoch [63][2500/3746] lr: 6.277e-02, eta: 3 days, 0:18:50, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5477, loss_cls: 4.0305, loss: 4.0305 +2024-07-24 08:59:58,412 - pyskl - INFO - Epoch [63][2600/3746] lr: 6.274e-02, eta: 3 days, 0:17:36, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5589, loss_cls: 4.0455, loss: 4.0455 +2024-07-24 09:01:22,357 - pyskl - INFO - Epoch [63][2700/3746] lr: 6.272e-02, eta: 3 days, 0:16:22, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5631, loss_cls: 3.9809, loss: 3.9809 +2024-07-24 09:02:46,377 - pyskl - INFO - Epoch [63][2800/3746] lr: 6.269e-02, eta: 3 days, 0:15:09, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5502, loss_cls: 4.0431, loss: 4.0431 +2024-07-24 09:04:09,602 - pyskl - INFO - Epoch [63][2900/3746] lr: 6.266e-02, eta: 3 days, 0:13:55, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5608, loss_cls: 4.0001, loss: 4.0001 +2024-07-24 09:05:32,815 - pyskl - INFO - Epoch [63][3000/3746] lr: 6.264e-02, eta: 3 days, 0:12:40, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5480, loss_cls: 4.0577, loss: 4.0577 +2024-07-24 09:06:56,445 - pyskl - INFO - Epoch [63][3100/3746] lr: 6.261e-02, eta: 3 days, 0:11:26, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5587, loss_cls: 4.0231, loss: 4.0231 +2024-07-24 09:08:19,379 - pyskl - INFO - Epoch [63][3200/3746] lr: 6.258e-02, eta: 3 days, 0:10:11, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5534, loss_cls: 4.0007, loss: 4.0007 +2024-07-24 09:09:42,570 - pyskl - INFO - Epoch [63][3300/3746] lr: 6.256e-02, eta: 3 days, 0:08:57, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5583, loss_cls: 3.9967, loss: 3.9967 +2024-07-24 09:11:06,458 - pyskl - INFO - Epoch [63][3400/3746] lr: 6.253e-02, eta: 3 days, 0:07:43, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5673, loss_cls: 3.9946, loss: 3.9946 +2024-07-24 09:12:30,720 - pyskl - INFO - Epoch [63][3500/3746] lr: 6.250e-02, eta: 3 days, 0:06:30, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5544, loss_cls: 3.9956, loss: 3.9956 +2024-07-24 09:13:54,590 - pyskl - INFO - Epoch [63][3600/3746] lr: 6.247e-02, eta: 3 days, 0:05:16, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5495, loss_cls: 4.0724, loss: 4.0724 +2024-07-24 09:15:17,769 - pyskl - INFO - Epoch [63][3700/3746] lr: 6.245e-02, eta: 3 days, 0:04:01, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5530, loss_cls: 4.0471, loss: 4.0471 +2024-07-24 09:15:58,480 - pyskl - INFO - Saving checkpoint at 63 epochs +2024-07-24 09:17:52,637 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 09:17:53,370 - pyskl - INFO - +top1_acc 0.2330 +top5_acc 0.4719 +2024-07-24 09:17:53,370 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 09:17:53,414 - pyskl - INFO - +mean_acc 0.2328 +2024-07-24 09:17:53,428 - pyskl - INFO - Epoch(val) [63][309] top1_acc: 0.2330, top5_acc: 0.4719, mean_class_accuracy: 0.2328 +2024-07-24 09:21:41,649 - pyskl - INFO - Epoch [64][100/3746] lr: 6.241e-02, eta: 3 days, 0:04:40, time: 2.282, data_time: 1.281, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5611, loss_cls: 3.9922, loss: 3.9922 +2024-07-24 09:23:05,501 - pyskl - INFO - Epoch [64][200/3746] lr: 6.238e-02, eta: 3 days, 0:03:26, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5663, loss_cls: 3.9337, loss: 3.9337 +2024-07-24 09:24:29,345 - pyskl - INFO - Epoch [64][300/3746] lr: 6.235e-02, eta: 3 days, 0:02:12, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5550, loss_cls: 4.0010, loss: 4.0010 +2024-07-24 09:25:52,903 - pyskl - INFO - Epoch [64][400/3746] lr: 6.233e-02, eta: 3 days, 0:00:58, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5564, loss_cls: 4.0199, loss: 4.0199 +2024-07-24 09:27:16,861 - pyskl - INFO - Epoch [64][500/3746] lr: 6.230e-02, eta: 2 days, 23:59:44, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5600, loss_cls: 3.9871, loss: 3.9871 +2024-07-24 09:28:41,076 - pyskl - INFO - Epoch [64][600/3746] lr: 6.227e-02, eta: 2 days, 23:58:31, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5581, loss_cls: 3.9930, loss: 3.9930 +2024-07-24 09:30:05,293 - pyskl - INFO - Epoch [64][700/3746] lr: 6.225e-02, eta: 2 days, 23:57:17, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5667, loss_cls: 3.9664, loss: 3.9664 +2024-07-24 09:31:29,289 - pyskl - INFO - Epoch [64][800/3746] lr: 6.222e-02, eta: 2 days, 23:56:04, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5602, loss_cls: 3.9970, loss: 3.9970 +2024-07-24 09:32:53,838 - pyskl - INFO - Epoch [64][900/3746] lr: 6.219e-02, eta: 2 days, 23:54:51, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5516, loss_cls: 4.0221, loss: 4.0221 +2024-07-24 09:34:18,247 - pyskl - INFO - Epoch [64][1000/3746] lr: 6.216e-02, eta: 2 days, 23:53:38, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5420, loss_cls: 4.0655, loss: 4.0655 +2024-07-24 09:35:42,043 - pyskl - INFO - Epoch [64][1100/3746] lr: 6.214e-02, eta: 2 days, 23:52:24, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5541, loss_cls: 4.0294, loss: 4.0294 +2024-07-24 09:37:06,119 - pyskl - INFO - Epoch [64][1200/3746] lr: 6.211e-02, eta: 2 days, 23:51:10, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5652, loss_cls: 3.9513, loss: 3.9513 +2024-07-24 09:38:30,567 - pyskl - INFO - Epoch [64][1300/3746] lr: 6.208e-02, eta: 2 days, 23:49:57, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5545, loss_cls: 4.0010, loss: 4.0010 +2024-07-24 09:39:54,629 - pyskl - INFO - Epoch [64][1400/3746] lr: 6.206e-02, eta: 2 days, 23:48:43, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5561, loss_cls: 4.0086, loss: 4.0086 +2024-07-24 09:41:18,509 - pyskl - INFO - Epoch [64][1500/3746] lr: 6.203e-02, eta: 2 days, 23:47:29, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5603, loss_cls: 3.9657, loss: 3.9657 +2024-07-24 09:42:42,371 - pyskl - INFO - Epoch [64][1600/3746] lr: 6.200e-02, eta: 2 days, 23:46:15, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5484, loss_cls: 4.0548, loss: 4.0548 +2024-07-24 09:44:06,530 - pyskl - INFO - Epoch [64][1700/3746] lr: 6.197e-02, eta: 2 days, 23:45:02, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5589, loss_cls: 4.0144, loss: 4.0144 +2024-07-24 09:45:30,954 - pyskl - INFO - Epoch [64][1800/3746] lr: 6.195e-02, eta: 2 days, 23:43:49, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5581, loss_cls: 4.0077, loss: 4.0077 +2024-07-24 09:46:55,429 - pyskl - INFO - Epoch [64][1900/3746] lr: 6.192e-02, eta: 2 days, 23:42:35, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5548, loss_cls: 4.0327, loss: 4.0327 +2024-07-24 09:48:19,265 - pyskl - INFO - Epoch [64][2000/3746] lr: 6.189e-02, eta: 2 days, 23:41:21, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5556, loss_cls: 4.0069, loss: 4.0069 +2024-07-24 09:49:43,097 - pyskl - INFO - Epoch [64][2100/3746] lr: 6.187e-02, eta: 2 days, 23:40:07, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5575, loss_cls: 3.9771, loss: 3.9771 +2024-07-24 09:51:06,616 - pyskl - INFO - Epoch [64][2200/3746] lr: 6.184e-02, eta: 2 days, 23:38:53, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5600, loss_cls: 3.9901, loss: 3.9901 +2024-07-24 09:52:29,987 - pyskl - INFO - Epoch [64][2300/3746] lr: 6.181e-02, eta: 2 days, 23:37:38, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5508, loss_cls: 4.0286, loss: 4.0286 +2024-07-24 09:53:54,181 - pyskl - INFO - Epoch [64][2400/3746] lr: 6.178e-02, eta: 2 days, 23:36:25, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5653, loss_cls: 3.9838, loss: 3.9838 +2024-07-24 09:55:17,280 - pyskl - INFO - Epoch [64][2500/3746] lr: 6.176e-02, eta: 2 days, 23:35:10, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5603, loss_cls: 3.9979, loss: 3.9979 +2024-07-24 09:56:40,792 - pyskl - INFO - Epoch [64][2600/3746] lr: 6.173e-02, eta: 2 days, 23:33:55, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5545, loss_cls: 4.0015, loss: 4.0015 +2024-07-24 09:58:04,813 - pyskl - INFO - Epoch [64][2700/3746] lr: 6.170e-02, eta: 2 days, 23:32:41, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5559, loss_cls: 4.0354, loss: 4.0354 +2024-07-24 09:59:28,595 - pyskl - INFO - Epoch [64][2800/3746] lr: 6.168e-02, eta: 2 days, 23:31:27, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5555, loss_cls: 4.0128, loss: 4.0128 +2024-07-24 10:00:52,423 - pyskl - INFO - Epoch [64][2900/3746] lr: 6.165e-02, eta: 2 days, 23:30:13, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5567, loss_cls: 4.0166, loss: 4.0166 +2024-07-24 10:02:16,596 - pyskl - INFO - Epoch [64][3000/3746] lr: 6.162e-02, eta: 2 days, 23:28:59, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5595, loss_cls: 4.0012, loss: 4.0012 +2024-07-24 10:03:39,853 - pyskl - INFO - Epoch [64][3100/3746] lr: 6.159e-02, eta: 2 days, 23:27:44, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5584, loss_cls: 3.9873, loss: 3.9873 +2024-07-24 10:05:03,722 - pyskl - INFO - Epoch [64][3200/3746] lr: 6.157e-02, eta: 2 days, 23:26:30, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5631, loss_cls: 3.9760, loss: 3.9760 +2024-07-24 10:06:28,369 - pyskl - INFO - Epoch [64][3300/3746] lr: 6.154e-02, eta: 2 days, 23:25:17, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5534, loss_cls: 4.0311, loss: 4.0311 +2024-07-24 10:07:52,387 - pyskl - INFO - Epoch [64][3400/3746] lr: 6.151e-02, eta: 2 days, 23:24:03, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5509, loss_cls: 4.0291, loss: 4.0291 +2024-07-24 10:09:16,213 - pyskl - INFO - Epoch [64][3500/3746] lr: 6.148e-02, eta: 2 days, 23:22:49, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5584, loss_cls: 3.9987, loss: 3.9987 +2024-07-24 10:10:40,158 - pyskl - INFO - Epoch [64][3600/3746] lr: 6.146e-02, eta: 2 days, 23:21:35, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5561, loss_cls: 4.0112, loss: 4.0112 +2024-07-24 10:12:03,994 - pyskl - INFO - Epoch [64][3700/3746] lr: 6.143e-02, eta: 2 days, 23:20:21, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5491, loss_cls: 4.0363, loss: 4.0363 +2024-07-24 10:12:44,014 - pyskl - INFO - Saving checkpoint at 64 epochs +2024-07-24 10:14:37,561 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 10:14:38,290 - pyskl - INFO - +top1_acc 0.2396 +top5_acc 0.4798 +2024-07-24 10:14:38,290 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 10:14:38,330 - pyskl - INFO - +mean_acc 0.2394 +2024-07-24 10:14:38,343 - pyskl - INFO - Epoch(val) [64][309] top1_acc: 0.2396, top5_acc: 0.4798, mean_class_accuracy: 0.2394 +2024-07-24 10:18:28,427 - pyskl - INFO - Epoch [65][100/3746] lr: 6.139e-02, eta: 2 days, 23:20:57, time: 2.301, data_time: 1.297, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5659, loss_cls: 3.9679, loss: 3.9679 +2024-07-24 10:19:51,722 - pyskl - INFO - Epoch [65][200/3746] lr: 6.136e-02, eta: 2 days, 23:19:42, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5664, loss_cls: 3.9449, loss: 3.9449 +2024-07-24 10:21:15,616 - pyskl - INFO - Epoch [65][300/3746] lr: 6.134e-02, eta: 2 days, 23:18:28, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5728, loss_cls: 3.9210, loss: 3.9210 +2024-07-24 10:22:38,885 - pyskl - INFO - Epoch [65][400/3746] lr: 6.131e-02, eta: 2 days, 23:17:13, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5613, loss_cls: 3.9831, loss: 3.9831 +2024-07-24 10:24:00,762 - pyskl - INFO - Epoch [65][500/3746] lr: 6.128e-02, eta: 2 days, 23:15:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5639, loss_cls: 3.9544, loss: 3.9544 +2024-07-24 10:25:22,192 - pyskl - INFO - Epoch [65][600/3746] lr: 6.125e-02, eta: 2 days, 23:14:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5648, loss_cls: 3.9640, loss: 3.9640 +2024-07-24 10:26:43,770 - pyskl - INFO - Epoch [65][700/3746] lr: 6.123e-02, eta: 2 days, 23:13:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5652, loss_cls: 3.9591, loss: 3.9591 +2024-07-24 10:28:06,161 - pyskl - INFO - Epoch [65][800/3746] lr: 6.120e-02, eta: 2 days, 23:12:05, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5583, loss_cls: 4.0213, loss: 4.0213 +2024-07-24 10:29:28,046 - pyskl - INFO - Epoch [65][900/3746] lr: 6.117e-02, eta: 2 days, 23:10:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5536, loss_cls: 3.9835, loss: 3.9835 +2024-07-24 10:30:49,878 - pyskl - INFO - Epoch [65][1000/3746] lr: 6.115e-02, eta: 2 days, 23:09:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5570, loss_cls: 3.9872, loss: 3.9872 +2024-07-24 10:32:11,609 - pyskl - INFO - Epoch [65][1100/3746] lr: 6.112e-02, eta: 2 days, 23:08:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5602, loss_cls: 3.9673, loss: 3.9673 +2024-07-24 10:33:33,421 - pyskl - INFO - Epoch [65][1200/3746] lr: 6.109e-02, eta: 2 days, 23:06:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5605, loss_cls: 3.9936, loss: 3.9936 +2024-07-24 10:34:54,952 - pyskl - INFO - Epoch [65][1300/3746] lr: 6.106e-02, eta: 2 days, 23:05:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5463, loss_cls: 4.0071, loss: 4.0071 +2024-07-24 10:36:16,657 - pyskl - INFO - Epoch [65][1400/3746] lr: 6.104e-02, eta: 2 days, 23:04:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5586, loss_cls: 4.0014, loss: 4.0014 +2024-07-24 10:37:38,498 - pyskl - INFO - Epoch [65][1500/3746] lr: 6.101e-02, eta: 2 days, 23:03:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5537, loss_cls: 4.0027, loss: 4.0027 +2024-07-24 10:39:00,538 - pyskl - INFO - Epoch [65][1600/3746] lr: 6.098e-02, eta: 2 days, 23:01:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5495, loss_cls: 4.0623, loss: 4.0623 +2024-07-24 10:40:23,692 - pyskl - INFO - Epoch [65][1700/3746] lr: 6.095e-02, eta: 2 days, 23:00:33, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5581, loss_cls: 3.9996, loss: 3.9996 +2024-07-24 10:41:45,611 - pyskl - INFO - Epoch [65][1800/3746] lr: 6.093e-02, eta: 2 days, 22:59:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5519, loss_cls: 4.0484, loss: 4.0484 +2024-07-24 10:43:07,054 - pyskl - INFO - Epoch [65][1900/3746] lr: 6.090e-02, eta: 2 days, 22:57:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5530, loss_cls: 4.0276, loss: 4.0276 +2024-07-24 10:44:29,932 - pyskl - INFO - Epoch [65][2000/3746] lr: 6.087e-02, eta: 2 days, 22:56:43, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5486, loss_cls: 4.0500, loss: 4.0500 +2024-07-24 10:45:51,976 - pyskl - INFO - Epoch [65][2100/3746] lr: 6.085e-02, eta: 2 days, 22:55:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5539, loss_cls: 4.0280, loss: 4.0280 +2024-07-24 10:47:14,459 - pyskl - INFO - Epoch [65][2200/3746] lr: 6.082e-02, eta: 2 days, 22:54:10, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5517, loss_cls: 4.0248, loss: 4.0248 +2024-07-24 10:48:37,194 - pyskl - INFO - Epoch [65][2300/3746] lr: 6.079e-02, eta: 2 days, 22:52:54, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5594, loss_cls: 4.0157, loss: 4.0157 +2024-07-24 10:49:59,332 - pyskl - INFO - Epoch [65][2400/3746] lr: 6.076e-02, eta: 2 days, 22:51:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5520, loss_cls: 4.0001, loss: 4.0001 +2024-07-24 10:51:21,485 - pyskl - INFO - Epoch [65][2500/3746] lr: 6.074e-02, eta: 2 days, 22:50:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5563, loss_cls: 4.0470, loss: 4.0470 +2024-07-24 10:52:43,904 - pyskl - INFO - Epoch [65][2600/3746] lr: 6.071e-02, eta: 2 days, 22:49:05, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5681, loss_cls: 3.9537, loss: 3.9537 +2024-07-24 10:54:05,953 - pyskl - INFO - Epoch [65][2700/3746] lr: 6.068e-02, eta: 2 days, 22:47:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5609, loss_cls: 3.9699, loss: 3.9699 +2024-07-24 10:55:27,365 - pyskl - INFO - Epoch [65][2800/3746] lr: 6.065e-02, eta: 2 days, 22:46:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5602, loss_cls: 4.0075, loss: 4.0075 +2024-07-24 10:56:49,460 - pyskl - INFO - Epoch [65][2900/3746] lr: 6.063e-02, eta: 2 days, 22:45:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5489, loss_cls: 4.0367, loss: 4.0367 +2024-07-24 10:58:11,490 - pyskl - INFO - Epoch [65][3000/3746] lr: 6.060e-02, eta: 2 days, 22:43:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5516, loss_cls: 4.0452, loss: 4.0452 +2024-07-24 10:59:34,053 - pyskl - INFO - Epoch [65][3100/3746] lr: 6.057e-02, eta: 2 days, 22:42:41, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5606, loss_cls: 3.9485, loss: 3.9485 +2024-07-24 11:00:56,178 - pyskl - INFO - Epoch [65][3200/3746] lr: 6.055e-02, eta: 2 days, 22:41:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5659, loss_cls: 3.9632, loss: 3.9632 +2024-07-24 11:02:17,891 - pyskl - INFO - Epoch [65][3300/3746] lr: 6.052e-02, eta: 2 days, 22:40:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5636, loss_cls: 3.9836, loss: 3.9836 +2024-07-24 11:03:39,713 - pyskl - INFO - Epoch [65][3400/3746] lr: 6.049e-02, eta: 2 days, 22:38:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5616, loss_cls: 3.9804, loss: 3.9804 +2024-07-24 11:05:01,273 - pyskl - INFO - Epoch [65][3500/3746] lr: 6.046e-02, eta: 2 days, 22:37:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5514, loss_cls: 4.0088, loss: 4.0088 +2024-07-24 11:06:23,304 - pyskl - INFO - Epoch [65][3600/3746] lr: 6.044e-02, eta: 2 days, 22:36:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5556, loss_cls: 3.9792, loss: 3.9792 +2024-07-24 11:07:44,643 - pyskl - INFO - Epoch [65][3700/3746] lr: 6.041e-02, eta: 2 days, 22:34:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5653, loss_cls: 3.9609, loss: 3.9609 +2024-07-24 11:08:24,423 - pyskl - INFO - Saving checkpoint at 65 epochs +2024-07-24 11:10:16,894 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 11:10:17,561 - pyskl - INFO - +top1_acc 0.2488 +top5_acc 0.4938 +2024-07-24 11:10:17,561 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 11:10:17,602 - pyskl - INFO - +mean_acc 0.2487 +2024-07-24 11:10:17,607 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_62.pth was removed +2024-07-24 11:10:17,886 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_65.pth. +2024-07-24 11:10:17,887 - pyskl - INFO - Best top1_acc is 0.2488 at 65 epoch. +2024-07-24 11:10:17,903 - pyskl - INFO - Epoch(val) [65][309] top1_acc: 0.2488, top5_acc: 0.4938, mean_class_accuracy: 0.2487 +2024-07-24 11:14:14,773 - pyskl - INFO - Epoch [66][100/3746] lr: 6.037e-02, eta: 2 days, 22:35:38, time: 2.369, data_time: 1.359, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5702, loss_cls: 3.9403, loss: 3.9403 +2024-07-24 11:15:37,222 - pyskl - INFO - Epoch [66][200/3746] lr: 6.034e-02, eta: 2 days, 22:34:22, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5653, loss_cls: 3.9768, loss: 3.9768 +2024-07-24 11:16:59,747 - pyskl - INFO - Epoch [66][300/3746] lr: 6.031e-02, eta: 2 days, 22:33:05, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5631, loss_cls: 3.9613, loss: 3.9613 +2024-07-24 11:18:21,978 - pyskl - INFO - Epoch [66][400/3746] lr: 6.029e-02, eta: 2 days, 22:31:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5541, loss_cls: 3.9804, loss: 3.9804 +2024-07-24 11:19:43,668 - pyskl - INFO - Epoch [66][500/3746] lr: 6.026e-02, eta: 2 days, 22:30:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5581, loss_cls: 3.9771, loss: 3.9771 +2024-07-24 11:21:05,242 - pyskl - INFO - Epoch [66][600/3746] lr: 6.023e-02, eta: 2 days, 22:29:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5648, loss_cls: 3.9665, loss: 3.9665 +2024-07-24 11:22:27,153 - pyskl - INFO - Epoch [66][700/3746] lr: 6.020e-02, eta: 2 days, 22:27:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5545, loss_cls: 4.0176, loss: 4.0176 +2024-07-24 11:23:48,809 - pyskl - INFO - Epoch [66][800/3746] lr: 6.018e-02, eta: 2 days, 22:26:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5522, loss_cls: 3.9972, loss: 3.9972 +2024-07-24 11:25:10,531 - pyskl - INFO - Epoch [66][900/3746] lr: 6.015e-02, eta: 2 days, 22:25:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5427, loss_cls: 4.0463, loss: 4.0463 +2024-07-24 11:26:31,976 - pyskl - INFO - Epoch [66][1000/3746] lr: 6.012e-02, eta: 2 days, 22:24:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5736, loss_cls: 3.9681, loss: 3.9681 +2024-07-24 11:27:53,043 - pyskl - INFO - Epoch [66][1100/3746] lr: 6.009e-02, eta: 2 days, 22:22:46, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5656, loss_cls: 3.9156, loss: 3.9156 +2024-07-24 11:29:14,716 - pyskl - INFO - Epoch [66][1200/3746] lr: 6.007e-02, eta: 2 days, 22:21:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5581, loss_cls: 3.9818, loss: 3.9818 +2024-07-24 11:30:35,802 - pyskl - INFO - Epoch [66][1300/3746] lr: 6.004e-02, eta: 2 days, 22:20:10, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5722, loss_cls: 3.9443, loss: 3.9443 +2024-07-24 11:31:57,457 - pyskl - INFO - Epoch [66][1400/3746] lr: 6.001e-02, eta: 2 days, 22:18:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5537, loss_cls: 4.0045, loss: 4.0045 +2024-07-24 11:33:19,471 - pyskl - INFO - Epoch [66][1500/3746] lr: 5.999e-02, eta: 2 days, 22:17:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5567, loss_cls: 4.0356, loss: 4.0356 +2024-07-24 11:34:41,135 - pyskl - INFO - Epoch [66][1600/3746] lr: 5.996e-02, eta: 2 days, 22:16:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5578, loss_cls: 3.9767, loss: 3.9767 +2024-07-24 11:36:03,200 - pyskl - INFO - Epoch [66][1700/3746] lr: 5.993e-02, eta: 2 days, 22:15:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5641, loss_cls: 3.9844, loss: 3.9844 +2024-07-24 11:37:25,982 - pyskl - INFO - Epoch [66][1800/3746] lr: 5.990e-02, eta: 2 days, 22:13:45, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5577, loss_cls: 4.0015, loss: 4.0015 +2024-07-24 11:38:48,296 - pyskl - INFO - Epoch [66][1900/3746] lr: 5.988e-02, eta: 2 days, 22:12:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5444, loss_cls: 4.0794, loss: 4.0794 +2024-07-24 11:40:11,026 - pyskl - INFO - Epoch [66][2000/3746] lr: 5.985e-02, eta: 2 days, 22:11:12, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5600, loss_cls: 4.0276, loss: 4.0276 +2024-07-24 11:41:33,149 - pyskl - INFO - Epoch [66][2100/3746] lr: 5.982e-02, eta: 2 days, 22:09:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5702, loss_cls: 3.9407, loss: 3.9407 +2024-07-24 11:42:55,846 - pyskl - INFO - Epoch [66][2200/3746] lr: 5.979e-02, eta: 2 days, 22:08:39, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5663, loss_cls: 3.9771, loss: 3.9771 +2024-07-24 11:44:17,896 - pyskl - INFO - Epoch [66][2300/3746] lr: 5.977e-02, eta: 2 days, 22:07:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5600, loss_cls: 3.9927, loss: 3.9927 +2024-07-24 11:45:40,460 - pyskl - INFO - Epoch [66][2400/3746] lr: 5.974e-02, eta: 2 days, 22:06:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5633, loss_cls: 3.9817, loss: 3.9817 +2024-07-24 11:47:02,194 - pyskl - INFO - Epoch [66][2500/3746] lr: 5.971e-02, eta: 2 days, 22:04:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5548, loss_cls: 3.9961, loss: 3.9961 +2024-07-24 11:48:24,078 - pyskl - INFO - Epoch [66][2600/3746] lr: 5.968e-02, eta: 2 days, 22:03:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5547, loss_cls: 3.9999, loss: 3.9999 +2024-07-24 11:49:45,621 - pyskl - INFO - Epoch [66][2700/3746] lr: 5.966e-02, eta: 2 days, 22:02:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5567, loss_cls: 4.0100, loss: 4.0100 +2024-07-24 11:51:07,154 - pyskl - INFO - Epoch [66][2800/3746] lr: 5.963e-02, eta: 2 days, 22:00:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5644, loss_cls: 3.9849, loss: 3.9849 +2024-07-24 11:52:29,153 - pyskl - INFO - Epoch [66][2900/3746] lr: 5.960e-02, eta: 2 days, 21:59:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5563, loss_cls: 3.9976, loss: 3.9976 +2024-07-24 11:53:50,939 - pyskl - INFO - Epoch [66][3000/3746] lr: 5.957e-02, eta: 2 days, 21:58:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5678, loss_cls: 3.9393, loss: 3.9393 +2024-07-24 11:55:12,832 - pyskl - INFO - Epoch [66][3100/3746] lr: 5.955e-02, eta: 2 days, 21:57:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5687, loss_cls: 3.9525, loss: 3.9525 +2024-07-24 11:56:34,278 - pyskl - INFO - Epoch [66][3200/3746] lr: 5.952e-02, eta: 2 days, 21:55:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5644, loss_cls: 3.9655, loss: 3.9655 +2024-07-24 11:57:55,918 - pyskl - INFO - Epoch [66][3300/3746] lr: 5.949e-02, eta: 2 days, 21:54:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5580, loss_cls: 3.9989, loss: 3.9989 +2024-07-24 11:59:17,558 - pyskl - INFO - Epoch [66][3400/3746] lr: 5.946e-02, eta: 2 days, 21:53:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5606, loss_cls: 4.0076, loss: 4.0076 +2024-07-24 12:00:38,912 - pyskl - INFO - Epoch [66][3500/3746] lr: 5.944e-02, eta: 2 days, 21:51:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5587, loss_cls: 4.0116, loss: 4.0116 +2024-07-24 12:02:01,461 - pyskl - INFO - Epoch [66][3600/3746] lr: 5.941e-02, eta: 2 days, 21:50:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5505, loss_cls: 4.0167, loss: 4.0167 +2024-07-24 12:03:23,454 - pyskl - INFO - Epoch [66][3700/3746] lr: 5.938e-02, eta: 2 days, 21:49:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5611, loss_cls: 4.0190, loss: 4.0190 +2024-07-24 12:04:03,447 - pyskl - INFO - Saving checkpoint at 66 epochs +2024-07-24 12:05:55,920 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 12:05:56,604 - pyskl - INFO - +top1_acc 0.2472 +top5_acc 0.4955 +2024-07-24 12:05:56,604 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 12:05:56,650 - pyskl - INFO - +mean_acc 0.2468 +2024-07-24 12:05:56,665 - pyskl - INFO - Epoch(val) [66][309] top1_acc: 0.2472, top5_acc: 0.4955, mean_class_accuracy: 0.2468 +2024-07-24 12:09:46,567 - pyskl - INFO - Epoch [67][100/3746] lr: 5.934e-02, eta: 2 days, 21:49:46, time: 2.299, data_time: 1.326, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5709, loss_cls: 3.8933, loss: 3.8933 +2024-07-24 12:11:07,924 - pyskl - INFO - Epoch [67][200/3746] lr: 5.931e-02, eta: 2 days, 21:48:28, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5627, loss_cls: 3.9848, loss: 3.9848 +2024-07-24 12:12:29,810 - pyskl - INFO - Epoch [67][300/3746] lr: 5.929e-02, eta: 2 days, 21:47:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5559, loss_cls: 4.0170, loss: 4.0170 +2024-07-24 12:13:51,533 - pyskl - INFO - Epoch [67][400/3746] lr: 5.926e-02, eta: 2 days, 21:45:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5708, loss_cls: 3.9641, loss: 3.9641 +2024-07-24 12:15:12,901 - pyskl - INFO - Epoch [67][500/3746] lr: 5.923e-02, eta: 2 days, 21:44:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5675, loss_cls: 3.9624, loss: 3.9624 +2024-07-24 12:16:35,046 - pyskl - INFO - Epoch [67][600/3746] lr: 5.920e-02, eta: 2 days, 21:43:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5680, loss_cls: 3.9567, loss: 3.9567 +2024-07-24 12:17:56,874 - pyskl - INFO - Epoch [67][700/3746] lr: 5.918e-02, eta: 2 days, 21:42:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5641, loss_cls: 3.9825, loss: 3.9825 +2024-07-24 12:19:18,209 - pyskl - INFO - Epoch [67][800/3746] lr: 5.915e-02, eta: 2 days, 21:40:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5627, loss_cls: 3.9673, loss: 3.9673 +2024-07-24 12:20:40,106 - pyskl - INFO - Epoch [67][900/3746] lr: 5.912e-02, eta: 2 days, 21:39:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5639, loss_cls: 3.9608, loss: 3.9608 +2024-07-24 12:22:01,403 - pyskl - INFO - Epoch [67][1000/3746] lr: 5.909e-02, eta: 2 days, 21:38:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5705, loss_cls: 3.9356, loss: 3.9356 +2024-07-24 12:23:22,756 - pyskl - INFO - Epoch [67][1100/3746] lr: 5.907e-02, eta: 2 days, 21:36:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5550, loss_cls: 3.9985, loss: 3.9985 +2024-07-24 12:24:44,541 - pyskl - INFO - Epoch [67][1200/3746] lr: 5.904e-02, eta: 2 days, 21:35:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5631, loss_cls: 3.9843, loss: 3.9843 +2024-07-24 12:26:06,515 - pyskl - INFO - Epoch [67][1300/3746] lr: 5.901e-02, eta: 2 days, 21:34:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5634, loss_cls: 3.9427, loss: 3.9427 +2024-07-24 12:27:28,117 - pyskl - INFO - Epoch [67][1400/3746] lr: 5.898e-02, eta: 2 days, 21:32:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5519, loss_cls: 4.0319, loss: 4.0319 +2024-07-24 12:28:49,815 - pyskl - INFO - Epoch [67][1500/3746] lr: 5.896e-02, eta: 2 days, 21:31:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5591, loss_cls: 3.9774, loss: 3.9774 +2024-07-24 12:30:11,037 - pyskl - INFO - Epoch [67][1600/3746] lr: 5.893e-02, eta: 2 days, 21:30:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5744, loss_cls: 3.9375, loss: 3.9375 +2024-07-24 12:31:33,498 - pyskl - INFO - Epoch [67][1700/3746] lr: 5.890e-02, eta: 2 days, 21:29:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5753, loss_cls: 3.9398, loss: 3.9398 +2024-07-24 12:32:54,988 - pyskl - INFO - Epoch [67][1800/3746] lr: 5.887e-02, eta: 2 days, 21:27:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5637, loss_cls: 3.9756, loss: 3.9756 +2024-07-24 12:34:16,379 - pyskl - INFO - Epoch [67][1900/3746] lr: 5.885e-02, eta: 2 days, 21:26:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5713, loss_cls: 3.9529, loss: 3.9529 +2024-07-24 12:35:38,736 - pyskl - INFO - Epoch [67][2000/3746] lr: 5.882e-02, eta: 2 days, 21:25:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5559, loss_cls: 3.9977, loss: 3.9977 +2024-07-24 12:37:00,419 - pyskl - INFO - Epoch [67][2100/3746] lr: 5.879e-02, eta: 2 days, 21:23:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5625, loss_cls: 3.9754, loss: 3.9754 +2024-07-24 12:38:22,442 - pyskl - INFO - Epoch [67][2200/3746] lr: 5.876e-02, eta: 2 days, 21:22:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5702, loss_cls: 3.9376, loss: 3.9376 +2024-07-24 12:39:44,998 - pyskl - INFO - Epoch [67][2300/3746] lr: 5.874e-02, eta: 2 days, 21:21:18, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5572, loss_cls: 3.9925, loss: 3.9925 +2024-07-24 12:41:06,817 - pyskl - INFO - Epoch [67][2400/3746] lr: 5.871e-02, eta: 2 days, 21:20:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5653, loss_cls: 3.9772, loss: 3.9772 +2024-07-24 12:42:28,591 - pyskl - INFO - Epoch [67][2500/3746] lr: 5.868e-02, eta: 2 days, 21:18:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5630, loss_cls: 3.9745, loss: 3.9745 +2024-07-24 12:43:50,091 - pyskl - INFO - Epoch [67][2600/3746] lr: 5.865e-02, eta: 2 days, 21:17:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5642, loss_cls: 4.0093, loss: 4.0093 +2024-07-24 12:45:11,529 - pyskl - INFO - Epoch [67][2700/3746] lr: 5.863e-02, eta: 2 days, 21:16:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5598, loss_cls: 4.0000, loss: 4.0000 +2024-07-24 12:46:33,518 - pyskl - INFO - Epoch [67][2800/3746] lr: 5.860e-02, eta: 2 days, 21:14:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5436, loss_cls: 4.0314, loss: 4.0314 +2024-07-24 12:47:55,667 - pyskl - INFO - Epoch [67][2900/3746] lr: 5.857e-02, eta: 2 days, 21:13:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5631, loss_cls: 3.9795, loss: 3.9795 +2024-07-24 12:49:17,304 - pyskl - INFO - Epoch [67][3000/3746] lr: 5.854e-02, eta: 2 days, 21:12:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5631, loss_cls: 3.9919, loss: 3.9919 +2024-07-24 12:50:39,109 - pyskl - INFO - Epoch [67][3100/3746] lr: 5.852e-02, eta: 2 days, 21:10:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5617, loss_cls: 3.9689, loss: 3.9689 +2024-07-24 12:52:00,856 - pyskl - INFO - Epoch [67][3200/3746] lr: 5.849e-02, eta: 2 days, 21:09:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5637, loss_cls: 3.9452, loss: 3.9452 +2024-07-24 12:53:22,293 - pyskl - INFO - Epoch [67][3300/3746] lr: 5.846e-02, eta: 2 days, 21:08:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5558, loss_cls: 4.0170, loss: 4.0170 +2024-07-24 12:54:43,601 - pyskl - INFO - Epoch [67][3400/3746] lr: 5.843e-02, eta: 2 days, 21:07:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5569, loss_cls: 3.9939, loss: 3.9939 +2024-07-24 12:56:05,272 - pyskl - INFO - Epoch [67][3500/3746] lr: 5.841e-02, eta: 2 days, 21:05:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5616, loss_cls: 4.0016, loss: 4.0016 +2024-07-24 12:57:26,986 - pyskl - INFO - Epoch [67][3600/3746] lr: 5.838e-02, eta: 2 days, 21:04:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5498, loss_cls: 4.0461, loss: 4.0461 +2024-07-24 12:58:48,469 - pyskl - INFO - Epoch [67][3700/3746] lr: 5.835e-02, eta: 2 days, 21:03:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5667, loss_cls: 3.9601, loss: 3.9601 +2024-07-24 12:59:27,794 - pyskl - INFO - Saving checkpoint at 67 epochs +2024-07-24 13:01:19,679 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 13:01:20,455 - pyskl - INFO - +top1_acc 0.2463 +top5_acc 0.4889 +2024-07-24 13:01:20,456 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 13:01:20,494 - pyskl - INFO - +mean_acc 0.2460 +2024-07-24 13:01:20,505 - pyskl - INFO - Epoch(val) [67][309] top1_acc: 0.2463, top5_acc: 0.4889, mean_class_accuracy: 0.2460 +2024-07-24 13:05:08,102 - pyskl - INFO - Epoch [68][100/3746] lr: 5.831e-02, eta: 2 days, 21:03:30, time: 2.276, data_time: 1.291, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5644, loss_cls: 3.9488, loss: 3.9488 +2024-07-24 13:06:30,041 - pyskl - INFO - Epoch [68][200/3746] lr: 5.828e-02, eta: 2 days, 21:02:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5695, loss_cls: 3.9400, loss: 3.9400 +2024-07-24 13:07:51,801 - pyskl - INFO - Epoch [68][300/3746] lr: 5.826e-02, eta: 2 days, 21:00:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5716, loss_cls: 3.9268, loss: 3.9268 +2024-07-24 13:09:13,525 - pyskl - INFO - Epoch [68][400/3746] lr: 5.823e-02, eta: 2 days, 20:59:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5747, loss_cls: 3.8945, loss: 3.8945 +2024-07-24 13:10:35,392 - pyskl - INFO - Epoch [68][500/3746] lr: 5.820e-02, eta: 2 days, 20:58:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5594, loss_cls: 4.0010, loss: 4.0010 +2024-07-24 13:11:56,876 - pyskl - INFO - Epoch [68][600/3746] lr: 5.817e-02, eta: 2 days, 20:57:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5780, loss_cls: 3.8952, loss: 3.8952 +2024-07-24 13:13:18,383 - pyskl - INFO - Epoch [68][700/3746] lr: 5.815e-02, eta: 2 days, 20:55:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5655, loss_cls: 3.9456, loss: 3.9456 +2024-07-24 13:14:40,045 - pyskl - INFO - Epoch [68][800/3746] lr: 5.812e-02, eta: 2 days, 20:54:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5630, loss_cls: 3.9757, loss: 3.9757 +2024-07-24 13:16:01,683 - pyskl - INFO - Epoch [68][900/3746] lr: 5.809e-02, eta: 2 days, 20:53:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5625, loss_cls: 3.9756, loss: 3.9756 +2024-07-24 13:17:23,304 - pyskl - INFO - Epoch [68][1000/3746] lr: 5.806e-02, eta: 2 days, 20:51:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5617, loss_cls: 3.9904, loss: 3.9904 +2024-07-24 13:18:45,170 - pyskl - INFO - Epoch [68][1100/3746] lr: 5.804e-02, eta: 2 days, 20:50:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5664, loss_cls: 3.9711, loss: 3.9711 +2024-07-24 13:20:07,443 - pyskl - INFO - Epoch [68][1200/3746] lr: 5.801e-02, eta: 2 days, 20:49:14, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5709, loss_cls: 3.9406, loss: 3.9406 +2024-07-24 13:21:29,328 - pyskl - INFO - Epoch [68][1300/3746] lr: 5.798e-02, eta: 2 days, 20:47:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5569, loss_cls: 3.9943, loss: 3.9943 +2024-07-24 13:22:50,944 - pyskl - INFO - Epoch [68][1400/3746] lr: 5.795e-02, eta: 2 days, 20:46:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5506, loss_cls: 4.0351, loss: 4.0351 +2024-07-24 13:24:12,026 - pyskl - INFO - Epoch [68][1500/3746] lr: 5.792e-02, eta: 2 days, 20:45:20, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5647, loss_cls: 3.9732, loss: 3.9732 +2024-07-24 13:25:33,853 - pyskl - INFO - Epoch [68][1600/3746] lr: 5.790e-02, eta: 2 days, 20:44:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5623, loss_cls: 3.9479, loss: 3.9479 +2024-07-24 13:26:54,858 - pyskl - INFO - Epoch [68][1700/3746] lr: 5.787e-02, eta: 2 days, 20:42:43, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5553, loss_cls: 4.0069, loss: 4.0069 +2024-07-24 13:28:16,479 - pyskl - INFO - Epoch [68][1800/3746] lr: 5.784e-02, eta: 2 days, 20:41:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5642, loss_cls: 3.9776, loss: 3.9776 +2024-07-24 13:29:38,473 - pyskl - INFO - Epoch [68][1900/3746] lr: 5.781e-02, eta: 2 days, 20:40:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5695, loss_cls: 3.9526, loss: 3.9526 +2024-07-24 13:31:00,492 - pyskl - INFO - Epoch [68][2000/3746] lr: 5.779e-02, eta: 2 days, 20:38:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5625, loss_cls: 3.9790, loss: 3.9790 +2024-07-24 13:32:22,283 - pyskl - INFO - Epoch [68][2100/3746] lr: 5.776e-02, eta: 2 days, 20:37:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5631, loss_cls: 3.9893, loss: 3.9893 +2024-07-24 13:33:44,455 - pyskl - INFO - Epoch [68][2200/3746] lr: 5.773e-02, eta: 2 days, 20:36:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5648, loss_cls: 3.9780, loss: 3.9780 +2024-07-24 13:35:06,290 - pyskl - INFO - Epoch [68][2300/3746] lr: 5.770e-02, eta: 2 days, 20:34:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5617, loss_cls: 3.9867, loss: 3.9867 +2024-07-24 13:36:28,569 - pyskl - INFO - Epoch [68][2400/3746] lr: 5.768e-02, eta: 2 days, 20:33:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5678, loss_cls: 3.9461, loss: 3.9461 +2024-07-24 13:37:50,700 - pyskl - INFO - Epoch [68][2500/3746] lr: 5.765e-02, eta: 2 days, 20:32:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5575, loss_cls: 4.0096, loss: 4.0096 +2024-07-24 13:39:12,322 - pyskl - INFO - Epoch [68][2600/3746] lr: 5.762e-02, eta: 2 days, 20:31:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5633, loss_cls: 3.9642, loss: 3.9642 +2024-07-24 13:40:34,019 - pyskl - INFO - Epoch [68][2700/3746] lr: 5.759e-02, eta: 2 days, 20:29:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5603, loss_cls: 3.9920, loss: 3.9920 +2024-07-24 13:41:55,638 - pyskl - INFO - Epoch [68][2800/3746] lr: 5.757e-02, eta: 2 days, 20:28:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5672, loss_cls: 3.9437, loss: 3.9437 +2024-07-24 13:43:17,402 - pyskl - INFO - Epoch [68][2900/3746] lr: 5.754e-02, eta: 2 days, 20:27:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5639, loss_cls: 3.9832, loss: 3.9832 +2024-07-24 13:44:39,848 - pyskl - INFO - Epoch [68][3000/3746] lr: 5.751e-02, eta: 2 days, 20:25:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5581, loss_cls: 3.9868, loss: 3.9868 +2024-07-24 13:46:01,739 - pyskl - INFO - Epoch [68][3100/3746] lr: 5.748e-02, eta: 2 days, 20:24:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5650, loss_cls: 3.9799, loss: 3.9799 +2024-07-24 13:47:23,860 - pyskl - INFO - Epoch [68][3200/3746] lr: 5.746e-02, eta: 2 days, 20:23:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5713, loss_cls: 3.9182, loss: 3.9182 +2024-07-24 13:48:45,785 - pyskl - INFO - Epoch [68][3300/3746] lr: 5.743e-02, eta: 2 days, 20:22:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5659, loss_cls: 3.9564, loss: 3.9564 +2024-07-24 13:50:07,354 - pyskl - INFO - Epoch [68][3400/3746] lr: 5.740e-02, eta: 2 days, 20:20:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5577, loss_cls: 4.0067, loss: 4.0067 +2024-07-24 13:51:29,026 - pyskl - INFO - Epoch [68][3500/3746] lr: 5.737e-02, eta: 2 days, 20:19:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5530, loss_cls: 4.0082, loss: 4.0082 +2024-07-24 13:52:51,237 - pyskl - INFO - Epoch [68][3600/3746] lr: 5.734e-02, eta: 2 days, 20:18:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5641, loss_cls: 3.9408, loss: 3.9408 +2024-07-24 13:54:13,195 - pyskl - INFO - Epoch [68][3700/3746] lr: 5.732e-02, eta: 2 days, 20:16:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5573, loss_cls: 3.9694, loss: 3.9694 +2024-07-24 13:54:52,494 - pyskl - INFO - Saving checkpoint at 68 epochs +2024-07-24 13:56:44,358 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 13:56:45,008 - pyskl - INFO - +top1_acc 0.2458 +top5_acc 0.4949 +2024-07-24 13:56:45,008 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 13:56:45,048 - pyskl - INFO - +mean_acc 0.2455 +2024-07-24 13:56:45,059 - pyskl - INFO - Epoch(val) [68][309] top1_acc: 0.2458, top5_acc: 0.4949, mean_class_accuracy: 0.2455 +2024-07-24 14:00:31,988 - pyskl - INFO - Epoch [69][100/3746] lr: 5.728e-02, eta: 2 days, 20:17:06, time: 2.269, data_time: 1.299, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5802, loss_cls: 3.8889, loss: 3.8889 +2024-07-24 14:01:54,076 - pyskl - INFO - Epoch [69][200/3746] lr: 5.725e-02, eta: 2 days, 20:15:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5797, loss_cls: 3.8955, loss: 3.8955 +2024-07-24 14:03:15,860 - pyskl - INFO - Epoch [69][300/3746] lr: 5.722e-02, eta: 2 days, 20:14:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5641, loss_cls: 3.9479, loss: 3.9479 +2024-07-24 14:04:37,530 - pyskl - INFO - Epoch [69][400/3746] lr: 5.719e-02, eta: 2 days, 20:13:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5633, loss_cls: 3.9573, loss: 3.9573 +2024-07-24 14:05:59,146 - pyskl - INFO - Epoch [69][500/3746] lr: 5.717e-02, eta: 2 days, 20:11:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5748, loss_cls: 3.9370, loss: 3.9370 +2024-07-24 14:07:20,729 - pyskl - INFO - Epoch [69][600/3746] lr: 5.714e-02, eta: 2 days, 20:10:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5750, loss_cls: 3.8769, loss: 3.8769 +2024-07-24 14:08:42,301 - pyskl - INFO - Epoch [69][700/3746] lr: 5.711e-02, eta: 2 days, 20:09:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5661, loss_cls: 3.9453, loss: 3.9453 +2024-07-24 14:10:04,077 - pyskl - INFO - Epoch [69][800/3746] lr: 5.708e-02, eta: 2 days, 20:07:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5591, loss_cls: 3.9836, loss: 3.9836 +2024-07-24 14:11:25,776 - pyskl - INFO - Epoch [69][900/3746] lr: 5.706e-02, eta: 2 days, 20:06:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5697, loss_cls: 3.9189, loss: 3.9189 +2024-07-24 14:12:47,331 - pyskl - INFO - Epoch [69][1000/3746] lr: 5.703e-02, eta: 2 days, 20:05:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5713, loss_cls: 3.9283, loss: 3.9283 +2024-07-24 14:14:09,105 - pyskl - INFO - Epoch [69][1100/3746] lr: 5.700e-02, eta: 2 days, 20:04:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5627, loss_cls: 3.9585, loss: 3.9585 +2024-07-24 14:15:30,440 - pyskl - INFO - Epoch [69][1200/3746] lr: 5.697e-02, eta: 2 days, 20:02:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5739, loss_cls: 3.9269, loss: 3.9269 +2024-07-24 14:16:52,193 - pyskl - INFO - Epoch [69][1300/3746] lr: 5.694e-02, eta: 2 days, 20:01:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5678, loss_cls: 3.9386, loss: 3.9386 +2024-07-24 14:18:14,502 - pyskl - INFO - Epoch [69][1400/3746] lr: 5.692e-02, eta: 2 days, 20:00:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5564, loss_cls: 4.0106, loss: 4.0106 +2024-07-24 14:19:36,107 - pyskl - INFO - Epoch [69][1500/3746] lr: 5.689e-02, eta: 2 days, 19:58:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5609, loss_cls: 3.9519, loss: 3.9519 +2024-07-24 14:20:57,688 - pyskl - INFO - Epoch [69][1600/3746] lr: 5.686e-02, eta: 2 days, 19:57:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5608, loss_cls: 3.9547, loss: 3.9547 +2024-07-24 14:22:19,552 - pyskl - INFO - Epoch [69][1700/3746] lr: 5.683e-02, eta: 2 days, 19:56:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5692, loss_cls: 3.9603, loss: 3.9603 +2024-07-24 14:23:41,065 - pyskl - INFO - Epoch [69][1800/3746] lr: 5.681e-02, eta: 2 days, 19:54:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5642, loss_cls: 3.9378, loss: 3.9378 +2024-07-24 14:25:02,387 - pyskl - INFO - Epoch [69][1900/3746] lr: 5.678e-02, eta: 2 days, 19:53:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5589, loss_cls: 3.9452, loss: 3.9452 +2024-07-24 14:26:24,784 - pyskl - INFO - Epoch [69][2000/3746] lr: 5.675e-02, eta: 2 days, 19:52:22, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5531, loss_cls: 4.0215, loss: 4.0215 +2024-07-24 14:27:46,257 - pyskl - INFO - Epoch [69][2100/3746] lr: 5.672e-02, eta: 2 days, 19:51:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5669, loss_cls: 3.9861, loss: 3.9861 +2024-07-24 14:29:08,672 - pyskl - INFO - Epoch [69][2200/3746] lr: 5.670e-02, eta: 2 days, 19:49:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5609, loss_cls: 3.9697, loss: 3.9697 +2024-07-24 14:30:30,893 - pyskl - INFO - Epoch [69][2300/3746] lr: 5.667e-02, eta: 2 days, 19:48:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5617, loss_cls: 3.9841, loss: 3.9841 +2024-07-24 14:31:53,099 - pyskl - INFO - Epoch [69][2400/3746] lr: 5.664e-02, eta: 2 days, 19:47:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5713, loss_cls: 3.9435, loss: 3.9435 +2024-07-24 14:33:14,947 - pyskl - INFO - Epoch [69][2500/3746] lr: 5.661e-02, eta: 2 days, 19:45:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5539, loss_cls: 4.0072, loss: 4.0072 +2024-07-24 14:34:36,452 - pyskl - INFO - Epoch [69][2600/3746] lr: 5.658e-02, eta: 2 days, 19:44:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5642, loss_cls: 3.9668, loss: 3.9668 +2024-07-24 14:35:58,162 - pyskl - INFO - Epoch [69][2700/3746] lr: 5.656e-02, eta: 2 days, 19:43:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5611, loss_cls: 3.9812, loss: 3.9812 +2024-07-24 14:37:19,772 - pyskl - INFO - Epoch [69][2800/3746] lr: 5.653e-02, eta: 2 days, 19:41:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5695, loss_cls: 3.9491, loss: 3.9491 +2024-07-24 14:38:41,823 - pyskl - INFO - Epoch [69][2900/3746] lr: 5.650e-02, eta: 2 days, 19:40:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5578, loss_cls: 3.9950, loss: 3.9950 +2024-07-24 14:40:04,178 - pyskl - INFO - Epoch [69][3000/3746] lr: 5.647e-02, eta: 2 days, 19:39:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5633, loss_cls: 3.9537, loss: 3.9537 +2024-07-24 14:41:26,016 - pyskl - INFO - Epoch [69][3100/3746] lr: 5.645e-02, eta: 2 days, 19:38:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5659, loss_cls: 3.9604, loss: 3.9604 +2024-07-24 14:42:47,992 - pyskl - INFO - Epoch [69][3200/3746] lr: 5.642e-02, eta: 2 days, 19:36:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5567, loss_cls: 3.9820, loss: 3.9820 +2024-07-24 14:44:09,226 - pyskl - INFO - Epoch [69][3300/3746] lr: 5.639e-02, eta: 2 days, 19:35:29, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5683, loss_cls: 3.9286, loss: 3.9286 +2024-07-24 14:45:31,132 - pyskl - INFO - Epoch [69][3400/3746] lr: 5.636e-02, eta: 2 days, 19:34:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5528, loss_cls: 4.0157, loss: 4.0157 +2024-07-24 14:46:52,761 - pyskl - INFO - Epoch [69][3500/3746] lr: 5.634e-02, eta: 2 days, 19:32:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5620, loss_cls: 3.9707, loss: 3.9707 +2024-07-24 14:48:15,309 - pyskl - INFO - Epoch [69][3600/3746] lr: 5.631e-02, eta: 2 days, 19:31:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5677, loss_cls: 3.9658, loss: 3.9658 +2024-07-24 14:49:37,003 - pyskl - INFO - Epoch [69][3700/3746] lr: 5.628e-02, eta: 2 days, 19:30:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5667, loss_cls: 3.9507, loss: 3.9507 +2024-07-24 14:50:16,856 - pyskl - INFO - Saving checkpoint at 69 epochs +2024-07-24 14:52:08,159 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 14:52:08,818 - pyskl - INFO - +top1_acc 0.2514 +top5_acc 0.4944 +2024-07-24 14:52:08,818 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 14:52:08,857 - pyskl - INFO - +mean_acc 0.2514 +2024-07-24 14:52:08,862 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_65.pth was removed +2024-07-24 14:52:09,124 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_69.pth. +2024-07-24 14:52:09,125 - pyskl - INFO - Best top1_acc is 0.2514 at 69 epoch. +2024-07-24 14:52:09,137 - pyskl - INFO - Epoch(val) [69][309] top1_acc: 0.2514, top5_acc: 0.4944, mean_class_accuracy: 0.2514 +2024-07-24 14:55:59,142 - pyskl - INFO - Epoch [70][100/3746] lr: 5.624e-02, eta: 2 days, 19:30:33, time: 2.300, data_time: 1.308, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5763, loss_cls: 3.9273, loss: 3.9273 +2024-07-24 14:57:22,263 - pyskl - INFO - Epoch [70][200/3746] lr: 5.621e-02, eta: 2 days, 19:29:17, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5698, loss_cls: 3.9195, loss: 3.9195 +2024-07-24 14:58:45,362 - pyskl - INFO - Epoch [70][300/3746] lr: 5.618e-02, eta: 2 days, 19:28:00, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5731, loss_cls: 3.8842, loss: 3.8842 +2024-07-24 15:00:07,553 - pyskl - INFO - Epoch [70][400/3746] lr: 5.616e-02, eta: 2 days, 19:26:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5689, loss_cls: 3.9046, loss: 3.9046 +2024-07-24 15:01:29,272 - pyskl - INFO - Epoch [70][500/3746] lr: 5.613e-02, eta: 2 days, 19:25:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5720, loss_cls: 3.9124, loss: 3.9124 +2024-07-24 15:02:50,600 - pyskl - INFO - Epoch [70][600/3746] lr: 5.610e-02, eta: 2 days, 19:24:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5723, loss_cls: 3.9269, loss: 3.9269 +2024-07-24 15:04:11,962 - pyskl - INFO - Epoch [70][700/3746] lr: 5.607e-02, eta: 2 days, 19:22:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5630, loss_cls: 3.9894, loss: 3.9894 +2024-07-24 15:05:33,877 - pyskl - INFO - Epoch [70][800/3746] lr: 5.605e-02, eta: 2 days, 19:21:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5742, loss_cls: 3.9002, loss: 3.9002 +2024-07-24 15:06:55,655 - pyskl - INFO - Epoch [70][900/3746] lr: 5.602e-02, eta: 2 days, 19:20:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5620, loss_cls: 3.9600, loss: 3.9600 +2024-07-24 15:08:16,993 - pyskl - INFO - Epoch [70][1000/3746] lr: 5.599e-02, eta: 2 days, 19:18:52, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5530, loss_cls: 3.9901, loss: 3.9901 +2024-07-24 15:09:38,743 - pyskl - INFO - Epoch [70][1100/3746] lr: 5.596e-02, eta: 2 days, 19:17:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5720, loss_cls: 3.8963, loss: 3.8963 +2024-07-24 15:11:00,643 - pyskl - INFO - Epoch [70][1200/3746] lr: 5.593e-02, eta: 2 days, 19:16:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5564, loss_cls: 3.9882, loss: 3.9882 +2024-07-24 15:12:22,325 - pyskl - INFO - Epoch [70][1300/3746] lr: 5.591e-02, eta: 2 days, 19:14:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5633, loss_cls: 3.9503, loss: 3.9503 +2024-07-24 15:13:43,912 - pyskl - INFO - Epoch [70][1400/3746] lr: 5.588e-02, eta: 2 days, 19:13:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5723, loss_cls: 3.9334, loss: 3.9334 +2024-07-24 15:15:05,068 - pyskl - INFO - Epoch [70][1500/3746] lr: 5.585e-02, eta: 2 days, 19:12:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5770, loss_cls: 3.8899, loss: 3.8899 +2024-07-24 15:16:26,541 - pyskl - INFO - Epoch [70][1600/3746] lr: 5.582e-02, eta: 2 days, 19:11:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5767, loss_cls: 3.9193, loss: 3.9193 +2024-07-24 15:17:48,173 - pyskl - INFO - Epoch [70][1700/3746] lr: 5.580e-02, eta: 2 days, 19:09:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5687, loss_cls: 3.9503, loss: 3.9503 +2024-07-24 15:19:09,920 - pyskl - INFO - Epoch [70][1800/3746] lr: 5.577e-02, eta: 2 days, 19:08:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5650, loss_cls: 3.9397, loss: 3.9397 +2024-07-24 15:20:31,293 - pyskl - INFO - Epoch [70][1900/3746] lr: 5.574e-02, eta: 2 days, 19:07:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5717, loss_cls: 3.9384, loss: 3.9384 +2024-07-24 15:21:53,535 - pyskl - INFO - Epoch [70][2000/3746] lr: 5.571e-02, eta: 2 days, 19:05:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5766, loss_cls: 3.9266, loss: 3.9266 +2024-07-24 15:23:14,878 - pyskl - INFO - Epoch [70][2100/3746] lr: 5.568e-02, eta: 2 days, 19:04:29, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5706, loss_cls: 3.9638, loss: 3.9638 +2024-07-24 15:24:37,006 - pyskl - INFO - Epoch [70][2200/3746] lr: 5.566e-02, eta: 2 days, 19:03:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5778, loss_cls: 3.9260, loss: 3.9260 +2024-07-24 15:25:58,784 - pyskl - INFO - Epoch [70][2300/3746] lr: 5.563e-02, eta: 2 days, 19:01:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5620, loss_cls: 3.9363, loss: 3.9363 +2024-07-24 15:27:20,939 - pyskl - INFO - Epoch [70][2400/3746] lr: 5.560e-02, eta: 2 days, 19:00:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5627, loss_cls: 3.9976, loss: 3.9976 +2024-07-24 15:28:42,474 - pyskl - INFO - Epoch [70][2500/3746] lr: 5.557e-02, eta: 2 days, 18:59:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5578, loss_cls: 3.9954, loss: 3.9954 +2024-07-24 15:30:04,866 - pyskl - INFO - Epoch [70][2600/3746] lr: 5.555e-02, eta: 2 days, 18:57:59, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5739, loss_cls: 3.9466, loss: 3.9466 +2024-07-24 15:31:26,677 - pyskl - INFO - Epoch [70][2700/3746] lr: 5.552e-02, eta: 2 days, 18:56:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5600, loss_cls: 3.9476, loss: 3.9476 +2024-07-24 15:32:48,869 - pyskl - INFO - Epoch [70][2800/3746] lr: 5.549e-02, eta: 2 days, 18:55:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5708, loss_cls: 3.9513, loss: 3.9513 +2024-07-24 15:34:10,740 - pyskl - INFO - Epoch [70][2900/3746] lr: 5.546e-02, eta: 2 days, 18:54:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5500, loss_cls: 4.0296, loss: 4.0296 +2024-07-24 15:35:33,176 - pyskl - INFO - Epoch [70][3000/3746] lr: 5.543e-02, eta: 2 days, 18:52:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5698, loss_cls: 3.9169, loss: 3.9169 +2024-07-24 15:36:55,331 - pyskl - INFO - Epoch [70][3100/3746] lr: 5.541e-02, eta: 2 days, 18:51:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5580, loss_cls: 3.9892, loss: 3.9892 +2024-07-24 15:38:17,825 - pyskl - INFO - Epoch [70][3200/3746] lr: 5.538e-02, eta: 2 days, 18:50:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5578, loss_cls: 3.9711, loss: 3.9711 +2024-07-24 15:39:39,402 - pyskl - INFO - Epoch [70][3300/3746] lr: 5.535e-02, eta: 2 days, 18:48:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5630, loss_cls: 3.9605, loss: 3.9605 +2024-07-24 15:41:00,593 - pyskl - INFO - Epoch [70][3400/3746] lr: 5.532e-02, eta: 2 days, 18:47:35, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5742, loss_cls: 3.9122, loss: 3.9122 +2024-07-24 15:42:22,282 - pyskl - INFO - Epoch [70][3500/3746] lr: 5.530e-02, eta: 2 days, 18:46:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5645, loss_cls: 3.9937, loss: 3.9937 +2024-07-24 15:43:44,216 - pyskl - INFO - Epoch [70][3600/3746] lr: 5.527e-02, eta: 2 days, 18:44:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5609, loss_cls: 3.9717, loss: 3.9717 +2024-07-24 15:45:05,527 - pyskl - INFO - Epoch [70][3700/3746] lr: 5.524e-02, eta: 2 days, 18:43:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5586, loss_cls: 3.9581, loss: 3.9581 +2024-07-24 15:45:45,157 - pyskl - INFO - Saving checkpoint at 70 epochs +2024-07-24 15:47:37,671 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 15:47:38,453 - pyskl - INFO - +top1_acc 0.2432 +top5_acc 0.4858 +2024-07-24 15:47:38,454 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 15:47:38,498 - pyskl - INFO - +mean_acc 0.2429 +2024-07-24 15:47:38,511 - pyskl - INFO - Epoch(val) [70][309] top1_acc: 0.2432, top5_acc: 0.4858, mean_class_accuracy: 0.2429 +2024-07-24 15:51:33,331 - pyskl - INFO - Epoch [71][100/3746] lr: 5.520e-02, eta: 2 days, 18:43:57, time: 2.348, data_time: 1.359, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5716, loss_cls: 3.9073, loss: 3.9073 +2024-07-24 15:52:55,210 - pyskl - INFO - Epoch [71][200/3746] lr: 5.517e-02, eta: 2 days, 18:42:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5775, loss_cls: 3.9097, loss: 3.9097 +2024-07-24 15:54:17,491 - pyskl - INFO - Epoch [71][300/3746] lr: 5.514e-02, eta: 2 days, 18:41:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5725, loss_cls: 3.9289, loss: 3.9289 +2024-07-24 15:55:39,900 - pyskl - INFO - Epoch [71][400/3746] lr: 5.512e-02, eta: 2 days, 18:40:03, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5719, loss_cls: 3.8971, loss: 3.8971 +2024-07-24 15:57:02,022 - pyskl - INFO - Epoch [71][500/3746] lr: 5.509e-02, eta: 2 days, 18:38:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5780, loss_cls: 3.8850, loss: 3.8850 +2024-07-24 15:58:23,612 - pyskl - INFO - Epoch [71][600/3746] lr: 5.506e-02, eta: 2 days, 18:37:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5717, loss_cls: 3.9129, loss: 3.9129 +2024-07-24 15:59:44,954 - pyskl - INFO - Epoch [71][700/3746] lr: 5.503e-02, eta: 2 days, 18:36:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5698, loss_cls: 3.9404, loss: 3.9404 +2024-07-24 16:01:06,494 - pyskl - INFO - Epoch [71][800/3746] lr: 5.500e-02, eta: 2 days, 18:34:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5720, loss_cls: 3.9090, loss: 3.9090 +2024-07-24 16:02:28,059 - pyskl - INFO - Epoch [71][900/3746] lr: 5.498e-02, eta: 2 days, 18:33:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5723, loss_cls: 3.9359, loss: 3.9359 +2024-07-24 16:03:49,478 - pyskl - INFO - Epoch [71][1000/3746] lr: 5.495e-02, eta: 2 days, 18:32:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5637, loss_cls: 3.9177, loss: 3.9177 +2024-07-24 16:05:11,497 - pyskl - INFO - Epoch [71][1100/3746] lr: 5.492e-02, eta: 2 days, 18:30:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5670, loss_cls: 3.9530, loss: 3.9530 +2024-07-24 16:06:33,394 - pyskl - INFO - Epoch [71][1200/3746] lr: 5.489e-02, eta: 2 days, 18:29:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5667, loss_cls: 3.9305, loss: 3.9305 +2024-07-24 16:07:54,861 - pyskl - INFO - Epoch [71][1300/3746] lr: 5.487e-02, eta: 2 days, 18:28:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5648, loss_cls: 3.9742, loss: 3.9742 +2024-07-24 16:09:16,733 - pyskl - INFO - Epoch [71][1400/3746] lr: 5.484e-02, eta: 2 days, 18:26:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5691, loss_cls: 3.9620, loss: 3.9620 +2024-07-24 16:10:38,323 - pyskl - INFO - Epoch [71][1500/3746] lr: 5.481e-02, eta: 2 days, 18:25:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5716, loss_cls: 3.9503, loss: 3.9503 +2024-07-24 16:11:59,916 - pyskl - INFO - Epoch [71][1600/3746] lr: 5.478e-02, eta: 2 days, 18:24:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5642, loss_cls: 3.9641, loss: 3.9641 +2024-07-24 16:13:21,393 - pyskl - INFO - Epoch [71][1700/3746] lr: 5.475e-02, eta: 2 days, 18:23:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5692, loss_cls: 3.9488, loss: 3.9488 +2024-07-24 16:14:42,809 - pyskl - INFO - Epoch [71][1800/3746] lr: 5.473e-02, eta: 2 days, 18:21:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5744, loss_cls: 3.9045, loss: 3.9045 +2024-07-24 16:16:04,189 - pyskl - INFO - Epoch [71][1900/3746] lr: 5.470e-02, eta: 2 days, 18:20:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5694, loss_cls: 3.9637, loss: 3.9637 +2024-07-24 16:17:27,184 - pyskl - INFO - Epoch [71][2000/3746] lr: 5.467e-02, eta: 2 days, 18:19:08, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5641, loss_cls: 3.9444, loss: 3.9444 +2024-07-24 16:18:48,527 - pyskl - INFO - Epoch [71][2100/3746] lr: 5.464e-02, eta: 2 days, 18:17:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5720, loss_cls: 3.9260, loss: 3.9260 +2024-07-24 16:20:11,358 - pyskl - INFO - Epoch [71][2200/3746] lr: 5.461e-02, eta: 2 days, 18:16:31, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5655, loss_cls: 3.9429, loss: 3.9429 +2024-07-24 16:21:33,286 - pyskl - INFO - Epoch [71][2300/3746] lr: 5.459e-02, eta: 2 days, 18:15:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5628, loss_cls: 3.9381, loss: 3.9381 +2024-07-24 16:22:54,861 - pyskl - INFO - Epoch [71][2400/3746] lr: 5.456e-02, eta: 2 days, 18:13:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5684, loss_cls: 3.9639, loss: 3.9639 +2024-07-24 16:24:17,424 - pyskl - INFO - Epoch [71][2500/3746] lr: 5.453e-02, eta: 2 days, 18:12:37, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5714, loss_cls: 3.9301, loss: 3.9301 +2024-07-24 16:25:38,898 - pyskl - INFO - Epoch [71][2600/3746] lr: 5.450e-02, eta: 2 days, 18:11:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5636, loss_cls: 3.9455, loss: 3.9455 +2024-07-24 16:27:01,114 - pyskl - INFO - Epoch [71][2700/3746] lr: 5.448e-02, eta: 2 days, 18:10:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5573, loss_cls: 3.9867, loss: 3.9867 +2024-07-24 16:28:22,979 - pyskl - INFO - Epoch [71][2800/3746] lr: 5.445e-02, eta: 2 days, 18:08:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5689, loss_cls: 3.9502, loss: 3.9502 +2024-07-24 16:29:44,811 - pyskl - INFO - Epoch [71][2900/3746] lr: 5.442e-02, eta: 2 days, 18:07:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5716, loss_cls: 3.9407, loss: 3.9407 +2024-07-24 16:31:07,065 - pyskl - INFO - Epoch [71][3000/3746] lr: 5.439e-02, eta: 2 days, 18:06:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5713, loss_cls: 3.9275, loss: 3.9275 +2024-07-24 16:32:28,825 - pyskl - INFO - Epoch [71][3100/3746] lr: 5.436e-02, eta: 2 days, 18:04:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5636, loss_cls: 3.9286, loss: 3.9286 +2024-07-24 16:33:50,524 - pyskl - INFO - Epoch [71][3200/3746] lr: 5.434e-02, eta: 2 days, 18:03:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5663, loss_cls: 3.9291, loss: 3.9291 +2024-07-24 16:35:12,208 - pyskl - INFO - Epoch [71][3300/3746] lr: 5.431e-02, eta: 2 days, 18:02:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5758, loss_cls: 3.9039, loss: 3.9039 +2024-07-24 16:36:33,857 - pyskl - INFO - Epoch [71][3400/3746] lr: 5.428e-02, eta: 2 days, 18:00:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5614, loss_cls: 3.9477, loss: 3.9477 +2024-07-24 16:37:55,536 - pyskl - INFO - Epoch [71][3500/3746] lr: 5.425e-02, eta: 2 days, 17:59:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5781, loss_cls: 3.8888, loss: 3.8888 +2024-07-24 16:39:17,407 - pyskl - INFO - Epoch [71][3600/3746] lr: 5.422e-02, eta: 2 days, 17:58:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5717, loss_cls: 3.9424, loss: 3.9424 +2024-07-24 16:40:38,729 - pyskl - INFO - Epoch [71][3700/3746] lr: 5.420e-02, eta: 2 days, 17:56:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5795, loss_cls: 3.9199, loss: 3.9199 +2024-07-24 16:41:18,230 - pyskl - INFO - Saving checkpoint at 71 epochs +2024-07-24 16:43:11,158 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 16:43:11,982 - pyskl - INFO - +top1_acc 0.2364 +top5_acc 0.4822 +2024-07-24 16:43:11,982 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 16:43:12,039 - pyskl - INFO - +mean_acc 0.2363 +2024-07-24 16:43:12,053 - pyskl - INFO - Epoch(val) [71][309] top1_acc: 0.2364, top5_acc: 0.4822, mean_class_accuracy: 0.2363 +2024-07-24 16:47:04,721 - pyskl - INFO - Epoch [72][100/3746] lr: 5.416e-02, eta: 2 days, 17:57:07, time: 2.327, data_time: 1.347, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5687, loss_cls: 3.9050, loss: 3.9050 +2024-07-24 16:48:26,377 - pyskl - INFO - Epoch [72][200/3746] lr: 5.413e-02, eta: 2 days, 17:55:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5802, loss_cls: 3.8964, loss: 3.8964 +2024-07-24 16:49:47,744 - pyskl - INFO - Epoch [72][300/3746] lr: 5.410e-02, eta: 2 days, 17:54:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5777, loss_cls: 3.8657, loss: 3.8657 +2024-07-24 16:51:09,154 - pyskl - INFO - Epoch [72][400/3746] lr: 5.407e-02, eta: 2 days, 17:53:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5766, loss_cls: 3.9167, loss: 3.9167 +2024-07-24 16:52:31,060 - pyskl - INFO - Epoch [72][500/3746] lr: 5.404e-02, eta: 2 days, 17:51:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5702, loss_cls: 3.9239, loss: 3.9239 +2024-07-24 16:53:53,013 - pyskl - INFO - Epoch [72][600/3746] lr: 5.402e-02, eta: 2 days, 17:50:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5678, loss_cls: 3.9384, loss: 3.9384 +2024-07-24 16:55:14,896 - pyskl - INFO - Epoch [72][700/3746] lr: 5.399e-02, eta: 2 days, 17:49:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5644, loss_cls: 3.9365, loss: 3.9365 +2024-07-24 16:56:36,528 - pyskl - INFO - Epoch [72][800/3746] lr: 5.396e-02, eta: 2 days, 17:47:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5739, loss_cls: 3.8718, loss: 3.8718 +2024-07-24 16:57:58,349 - pyskl - INFO - Epoch [72][900/3746] lr: 5.393e-02, eta: 2 days, 17:46:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5739, loss_cls: 3.9324, loss: 3.9324 +2024-07-24 16:59:19,632 - pyskl - INFO - Epoch [72][1000/3746] lr: 5.391e-02, eta: 2 days, 17:45:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5772, loss_cls: 3.9180, loss: 3.9180 +2024-07-24 17:00:41,256 - pyskl - INFO - Epoch [72][1100/3746] lr: 5.388e-02, eta: 2 days, 17:44:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5816, loss_cls: 3.9040, loss: 3.9040 +2024-07-24 17:02:02,688 - pyskl - INFO - Epoch [72][1200/3746] lr: 5.385e-02, eta: 2 days, 17:42:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5656, loss_cls: 3.9238, loss: 3.9238 +2024-07-24 17:03:24,618 - pyskl - INFO - Epoch [72][1300/3746] lr: 5.382e-02, eta: 2 days, 17:41:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5669, loss_cls: 3.9355, loss: 3.9355 +2024-07-24 17:04:46,379 - pyskl - INFO - Epoch [72][1400/3746] lr: 5.379e-02, eta: 2 days, 17:40:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5698, loss_cls: 3.9166, loss: 3.9166 +2024-07-24 17:06:07,811 - pyskl - INFO - Epoch [72][1500/3746] lr: 5.377e-02, eta: 2 days, 17:38:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5742, loss_cls: 3.9299, loss: 3.9299 +2024-07-24 17:07:29,159 - pyskl - INFO - Epoch [72][1600/3746] lr: 5.374e-02, eta: 2 days, 17:37:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5781, loss_cls: 3.9122, loss: 3.9122 +2024-07-24 17:08:50,943 - pyskl - INFO - Epoch [72][1700/3746] lr: 5.371e-02, eta: 2 days, 17:36:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5733, loss_cls: 3.9133, loss: 3.9133 +2024-07-24 17:10:12,903 - pyskl - INFO - Epoch [72][1800/3746] lr: 5.368e-02, eta: 2 days, 17:34:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5814, loss_cls: 3.9077, loss: 3.9077 +2024-07-24 17:11:34,970 - pyskl - INFO - Epoch [72][1900/3746] lr: 5.365e-02, eta: 2 days, 17:33:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5730, loss_cls: 3.9050, loss: 3.9050 +2024-07-24 17:12:57,721 - pyskl - INFO - Epoch [72][2000/3746] lr: 5.363e-02, eta: 2 days, 17:32:13, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5705, loss_cls: 3.9387, loss: 3.9387 +2024-07-24 17:14:18,931 - pyskl - INFO - Epoch [72][2100/3746] lr: 5.360e-02, eta: 2 days, 17:30:53, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5653, loss_cls: 3.9499, loss: 3.9499 +2024-07-24 17:15:41,309 - pyskl - INFO - Epoch [72][2200/3746] lr: 5.357e-02, eta: 2 days, 17:29:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5698, loss_cls: 3.9123, loss: 3.9123 +2024-07-24 17:17:03,555 - pyskl - INFO - Epoch [72][2300/3746] lr: 5.354e-02, eta: 2 days, 17:28:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5658, loss_cls: 3.9352, loss: 3.9352 +2024-07-24 17:18:25,452 - pyskl - INFO - Epoch [72][2400/3746] lr: 5.352e-02, eta: 2 days, 17:26:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5681, loss_cls: 3.9355, loss: 3.9355 +2024-07-24 17:19:47,558 - pyskl - INFO - Epoch [72][2500/3746] lr: 5.349e-02, eta: 2 days, 17:25:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5667, loss_cls: 3.9431, loss: 3.9431 +2024-07-24 17:21:09,271 - pyskl - INFO - Epoch [72][2600/3746] lr: 5.346e-02, eta: 2 days, 17:24:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5723, loss_cls: 3.9026, loss: 3.9026 +2024-07-24 17:22:31,298 - pyskl - INFO - Epoch [72][2700/3746] lr: 5.343e-02, eta: 2 days, 17:23:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5625, loss_cls: 3.9503, loss: 3.9503 +2024-07-24 17:23:53,268 - pyskl - INFO - Epoch [72][2800/3746] lr: 5.340e-02, eta: 2 days, 17:21:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5609, loss_cls: 3.9560, loss: 3.9560 +2024-07-24 17:25:14,852 - pyskl - INFO - Epoch [72][2900/3746] lr: 5.338e-02, eta: 2 days, 17:20:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5681, loss_cls: 3.9235, loss: 3.9235 +2024-07-24 17:26:36,821 - pyskl - INFO - Epoch [72][3000/3746] lr: 5.335e-02, eta: 2 days, 17:19:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5714, loss_cls: 3.9347, loss: 3.9347 +2024-07-24 17:27:59,423 - pyskl - INFO - Epoch [72][3100/3746] lr: 5.332e-02, eta: 2 days, 17:17:50, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5630, loss_cls: 3.9417, loss: 3.9417 +2024-07-24 17:29:21,291 - pyskl - INFO - Epoch [72][3200/3746] lr: 5.329e-02, eta: 2 days, 17:16:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5698, loss_cls: 3.9230, loss: 3.9230 +2024-07-24 17:30:43,084 - pyskl - INFO - Epoch [72][3300/3746] lr: 5.326e-02, eta: 2 days, 17:15:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5775, loss_cls: 3.8905, loss: 3.8905 +2024-07-24 17:32:05,009 - pyskl - INFO - Epoch [72][3400/3746] lr: 5.324e-02, eta: 2 days, 17:13:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5702, loss_cls: 3.9322, loss: 3.9322 +2024-07-24 17:33:27,250 - pyskl - INFO - Epoch [72][3500/3746] lr: 5.321e-02, eta: 2 days, 17:12:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5689, loss_cls: 3.9553, loss: 3.9553 +2024-07-24 17:34:48,698 - pyskl - INFO - Epoch [72][3600/3746] lr: 5.318e-02, eta: 2 days, 17:11:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5716, loss_cls: 3.9328, loss: 3.9328 +2024-07-24 17:36:10,396 - pyskl - INFO - Epoch [72][3700/3746] lr: 5.315e-02, eta: 2 days, 17:09:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5606, loss_cls: 3.9777, loss: 3.9777 +2024-07-24 17:36:50,016 - pyskl - INFO - Saving checkpoint at 72 epochs +2024-07-24 17:38:41,753 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 17:38:42,433 - pyskl - INFO - +top1_acc 0.2621 +top5_acc 0.5008 +2024-07-24 17:38:42,433 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 17:38:42,477 - pyskl - INFO - +mean_acc 0.2620 +2024-07-24 17:38:42,483 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_69.pth was removed +2024-07-24 17:38:42,751 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_72.pth. +2024-07-24 17:38:42,752 - pyskl - INFO - Best top1_acc is 0.2621 at 72 epoch. +2024-07-24 17:38:42,770 - pyskl - INFO - Epoch(val) [72][309] top1_acc: 0.2621, top5_acc: 0.5008, mean_class_accuracy: 0.2620 +2024-07-24 17:42:34,184 - pyskl - INFO - Epoch [73][100/3746] lr: 5.311e-02, eta: 2 days, 17:10:05, time: 2.314, data_time: 1.329, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5866, loss_cls: 3.8348, loss: 3.8348 +2024-07-24 17:43:56,065 - pyskl - INFO - Epoch [73][200/3746] lr: 5.308e-02, eta: 2 days, 17:08:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5770, loss_cls: 3.8997, loss: 3.8997 +2024-07-24 17:45:18,480 - pyskl - INFO - Epoch [73][300/3746] lr: 5.306e-02, eta: 2 days, 17:07:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5847, loss_cls: 3.8552, loss: 3.8552 +2024-07-24 17:46:40,689 - pyskl - INFO - Epoch [73][400/3746] lr: 5.303e-02, eta: 2 days, 17:06:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5802, loss_cls: 3.9265, loss: 3.9265 +2024-07-24 17:48:02,474 - pyskl - INFO - Epoch [73][500/3746] lr: 5.300e-02, eta: 2 days, 17:04:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5783, loss_cls: 3.8615, loss: 3.8615 +2024-07-24 17:49:23,897 - pyskl - INFO - Epoch [73][600/3746] lr: 5.297e-02, eta: 2 days, 17:03:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5894, loss_cls: 3.8427, loss: 3.8427 +2024-07-24 17:50:46,102 - pyskl - INFO - Epoch [73][700/3746] lr: 5.294e-02, eta: 2 days, 17:02:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5831, loss_cls: 3.8960, loss: 3.8960 +2024-07-24 17:52:08,385 - pyskl - INFO - Epoch [73][800/3746] lr: 5.292e-02, eta: 2 days, 17:00:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5689, loss_cls: 3.9158, loss: 3.9158 +2024-07-24 17:53:30,058 - pyskl - INFO - Epoch [73][900/3746] lr: 5.289e-02, eta: 2 days, 16:59:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5720, loss_cls: 3.9221, loss: 3.9221 +2024-07-24 17:54:51,651 - pyskl - INFO - Epoch [73][1000/3746] lr: 5.286e-02, eta: 2 days, 16:58:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5755, loss_cls: 3.8894, loss: 3.8894 +2024-07-24 17:56:12,953 - pyskl - INFO - Epoch [73][1100/3746] lr: 5.283e-02, eta: 2 days, 16:56:59, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5703, loss_cls: 3.9416, loss: 3.9416 +2024-07-24 17:57:34,378 - pyskl - INFO - Epoch [73][1200/3746] lr: 5.280e-02, eta: 2 days, 16:55:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5752, loss_cls: 3.9115, loss: 3.9115 +2024-07-24 17:58:55,491 - pyskl - INFO - Epoch [73][1300/3746] lr: 5.278e-02, eta: 2 days, 16:54:20, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5731, loss_cls: 3.9019, loss: 3.9019 +2024-07-24 18:00:17,192 - pyskl - INFO - Epoch [73][1400/3746] lr: 5.275e-02, eta: 2 days, 16:53:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5730, loss_cls: 3.9219, loss: 3.9219 +2024-07-24 18:01:39,117 - pyskl - INFO - Epoch [73][1500/3746] lr: 5.272e-02, eta: 2 days, 16:51:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5834, loss_cls: 3.8769, loss: 3.8769 +2024-07-24 18:03:00,875 - pyskl - INFO - Epoch [73][1600/3746] lr: 5.269e-02, eta: 2 days, 16:50:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5603, loss_cls: 3.9801, loss: 3.9801 +2024-07-24 18:04:22,089 - pyskl - INFO - Epoch [73][1700/3746] lr: 5.267e-02, eta: 2 days, 16:49:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5645, loss_cls: 3.9419, loss: 3.9419 +2024-07-24 18:05:44,384 - pyskl - INFO - Epoch [73][1800/3746] lr: 5.264e-02, eta: 2 days, 16:47:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5697, loss_cls: 3.9225, loss: 3.9225 +2024-07-24 18:07:06,487 - pyskl - INFO - Epoch [73][1900/3746] lr: 5.261e-02, eta: 2 days, 16:46:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5681, loss_cls: 3.9219, loss: 3.9219 +2024-07-24 18:08:29,170 - pyskl - INFO - Epoch [73][2000/3746] lr: 5.258e-02, eta: 2 days, 16:45:10, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5723, loss_cls: 3.9053, loss: 3.9053 +2024-07-24 18:09:51,226 - pyskl - INFO - Epoch [73][2100/3746] lr: 5.255e-02, eta: 2 days, 16:43:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5781, loss_cls: 3.8781, loss: 3.8781 +2024-07-24 18:11:13,463 - pyskl - INFO - Epoch [73][2200/3746] lr: 5.253e-02, eta: 2 days, 16:42:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5667, loss_cls: 3.9436, loss: 3.9436 +2024-07-24 18:12:36,429 - pyskl - INFO - Epoch [73][2300/3746] lr: 5.250e-02, eta: 2 days, 16:41:15, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5675, loss_cls: 3.9170, loss: 3.9170 +2024-07-24 18:13:58,462 - pyskl - INFO - Epoch [73][2400/3746] lr: 5.247e-02, eta: 2 days, 16:39:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5820, loss_cls: 3.8840, loss: 3.8840 +2024-07-24 18:15:20,198 - pyskl - INFO - Epoch [73][2500/3746] lr: 5.244e-02, eta: 2 days, 16:38:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5791, loss_cls: 3.8751, loss: 3.8751 +2024-07-24 18:16:41,691 - pyskl - INFO - Epoch [73][2600/3746] lr: 5.241e-02, eta: 2 days, 16:37:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5795, loss_cls: 3.8845, loss: 3.8845 +2024-07-24 18:18:02,920 - pyskl - INFO - Epoch [73][2700/3746] lr: 5.239e-02, eta: 2 days, 16:36:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5669, loss_cls: 3.9470, loss: 3.9470 +2024-07-24 18:19:24,729 - pyskl - INFO - Epoch [73][2800/3746] lr: 5.236e-02, eta: 2 days, 16:34:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5737, loss_cls: 3.9070, loss: 3.9070 +2024-07-24 18:20:46,322 - pyskl - INFO - Epoch [73][2900/3746] lr: 5.233e-02, eta: 2 days, 16:33:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5713, loss_cls: 3.9625, loss: 3.9625 +2024-07-24 18:22:08,878 - pyskl - INFO - Epoch [73][3000/3746] lr: 5.230e-02, eta: 2 days, 16:32:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5684, loss_cls: 3.9391, loss: 3.9391 +2024-07-24 18:23:31,334 - pyskl - INFO - Epoch [73][3100/3746] lr: 5.227e-02, eta: 2 days, 16:30:46, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5748, loss_cls: 3.9370, loss: 3.9370 +2024-07-24 18:24:53,599 - pyskl - INFO - Epoch [73][3200/3746] lr: 5.225e-02, eta: 2 days, 16:29:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5611, loss_cls: 3.9622, loss: 3.9622 +2024-07-24 18:26:16,446 - pyskl - INFO - Epoch [73][3300/3746] lr: 5.222e-02, eta: 2 days, 16:28:10, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5756, loss_cls: 3.8787, loss: 3.8787 +2024-07-24 18:27:38,245 - pyskl - INFO - Epoch [73][3400/3746] lr: 5.219e-02, eta: 2 days, 16:26:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5666, loss_cls: 3.9338, loss: 3.9338 +2024-07-24 18:28:59,641 - pyskl - INFO - Epoch [73][3500/3746] lr: 5.216e-02, eta: 2 days, 16:25:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5689, loss_cls: 3.9469, loss: 3.9469 +2024-07-24 18:30:21,916 - pyskl - INFO - Epoch [73][3600/3746] lr: 5.213e-02, eta: 2 days, 16:24:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5695, loss_cls: 3.9168, loss: 3.9168 +2024-07-24 18:31:44,007 - pyskl - INFO - Epoch [73][3700/3746] lr: 5.211e-02, eta: 2 days, 16:22:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5695, loss_cls: 3.9427, loss: 3.9427 +2024-07-24 18:32:23,328 - pyskl - INFO - Saving checkpoint at 73 epochs +2024-07-24 18:34:14,975 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 18:34:15,714 - pyskl - INFO - +top1_acc 0.2409 +top5_acc 0.4834 +2024-07-24 18:34:15,714 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 18:34:15,757 - pyskl - INFO - +mean_acc 0.2406 +2024-07-24 18:34:15,771 - pyskl - INFO - Epoch(val) [73][309] top1_acc: 0.2409, top5_acc: 0.4834, mean_class_accuracy: 0.2406 +2024-07-24 18:38:14,868 - pyskl - INFO - Epoch [74][100/3746] lr: 5.207e-02, eta: 2 days, 16:23:06, time: 2.391, data_time: 1.403, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5786, loss_cls: 3.8679, loss: 3.8679 +2024-07-24 18:39:37,962 - pyskl - INFO - Epoch [74][200/3746] lr: 5.204e-02, eta: 2 days, 16:21:49, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5803, loss_cls: 3.8874, loss: 3.8874 +2024-07-24 18:41:01,287 - pyskl - INFO - Epoch [74][300/3746] lr: 5.201e-02, eta: 2 days, 16:20:31, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5808, loss_cls: 3.8734, loss: 3.8734 +2024-07-24 18:42:24,108 - pyskl - INFO - Epoch [74][400/3746] lr: 5.198e-02, eta: 2 days, 16:19:13, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5884, loss_cls: 3.8588, loss: 3.8588 +2024-07-24 18:43:46,633 - pyskl - INFO - Epoch [74][500/3746] lr: 5.195e-02, eta: 2 days, 16:17:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5702, loss_cls: 3.9089, loss: 3.9089 +2024-07-24 18:45:09,059 - pyskl - INFO - Epoch [74][600/3746] lr: 5.193e-02, eta: 2 days, 16:16:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5831, loss_cls: 3.8664, loss: 3.8664 +2024-07-24 18:46:30,677 - pyskl - INFO - Epoch [74][700/3746] lr: 5.190e-02, eta: 2 days, 16:15:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5836, loss_cls: 3.8735, loss: 3.8735 +2024-07-24 18:47:52,443 - pyskl - INFO - Epoch [74][800/3746] lr: 5.187e-02, eta: 2 days, 16:13:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5747, loss_cls: 3.8878, loss: 3.8878 +2024-07-24 18:49:14,332 - pyskl - INFO - Epoch [74][900/3746] lr: 5.184e-02, eta: 2 days, 16:12:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5730, loss_cls: 3.9147, loss: 3.9147 +2024-07-24 18:50:35,734 - pyskl - INFO - Epoch [74][1000/3746] lr: 5.181e-02, eta: 2 days, 16:11:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5755, loss_cls: 3.8900, loss: 3.8900 +2024-07-24 18:51:57,645 - pyskl - INFO - Epoch [74][1100/3746] lr: 5.179e-02, eta: 2 days, 16:10:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5733, loss_cls: 3.9089, loss: 3.9089 +2024-07-24 18:53:19,887 - pyskl - INFO - Epoch [74][1200/3746] lr: 5.176e-02, eta: 2 days, 16:08:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5783, loss_cls: 3.8923, loss: 3.8923 +2024-07-24 18:54:41,612 - pyskl - INFO - Epoch [74][1300/3746] lr: 5.173e-02, eta: 2 days, 16:07:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5764, loss_cls: 3.9018, loss: 3.9018 +2024-07-24 18:56:03,361 - pyskl - INFO - Epoch [74][1400/3746] lr: 5.170e-02, eta: 2 days, 16:06:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5678, loss_cls: 3.9357, loss: 3.9357 +2024-07-24 18:57:24,467 - pyskl - INFO - Epoch [74][1500/3746] lr: 5.168e-02, eta: 2 days, 16:04:46, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5795, loss_cls: 3.8988, loss: 3.8988 +2024-07-24 18:58:46,308 - pyskl - INFO - Epoch [74][1600/3746] lr: 5.165e-02, eta: 2 days, 16:03:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5756, loss_cls: 3.8588, loss: 3.8588 +2024-07-24 19:00:07,467 - pyskl - INFO - Epoch [74][1700/3746] lr: 5.162e-02, eta: 2 days, 16:02:07, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5739, loss_cls: 3.9155, loss: 3.9155 +2024-07-24 19:01:28,869 - pyskl - INFO - Epoch [74][1800/3746] lr: 5.159e-02, eta: 2 days, 16:00:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5722, loss_cls: 3.9088, loss: 3.9088 +2024-07-24 19:02:50,152 - pyskl - INFO - Epoch [74][1900/3746] lr: 5.156e-02, eta: 2 days, 15:59:29, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5728, loss_cls: 3.8992, loss: 3.8992 +2024-07-24 19:04:12,780 - pyskl - INFO - Epoch [74][2000/3746] lr: 5.154e-02, eta: 2 days, 15:58:10, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5666, loss_cls: 3.9446, loss: 3.9446 +2024-07-24 19:05:34,286 - pyskl - INFO - Epoch [74][2100/3746] lr: 5.151e-02, eta: 2 days, 15:56:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5736, loss_cls: 3.9118, loss: 3.9118 +2024-07-24 19:06:56,834 - pyskl - INFO - Epoch [74][2200/3746] lr: 5.148e-02, eta: 2 days, 15:55:33, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5684, loss_cls: 3.9172, loss: 3.9172 +2024-07-24 19:08:18,551 - pyskl - INFO - Epoch [74][2300/3746] lr: 5.145e-02, eta: 2 days, 15:54:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5703, loss_cls: 3.9031, loss: 3.9031 +2024-07-24 19:09:40,303 - pyskl - INFO - Epoch [74][2400/3746] lr: 5.142e-02, eta: 2 days, 15:52:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5617, loss_cls: 3.9564, loss: 3.9564 +2024-07-24 19:11:02,073 - pyskl - INFO - Epoch [74][2500/3746] lr: 5.140e-02, eta: 2 days, 15:51:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5720, loss_cls: 3.9284, loss: 3.9284 +2024-07-24 19:12:23,762 - pyskl - INFO - Epoch [74][2600/3746] lr: 5.137e-02, eta: 2 days, 15:50:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5653, loss_cls: 3.9235, loss: 3.9235 +2024-07-24 19:13:45,341 - pyskl - INFO - Epoch [74][2700/3746] lr: 5.134e-02, eta: 2 days, 15:48:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5739, loss_cls: 3.8887, loss: 3.8887 +2024-07-24 19:15:07,468 - pyskl - INFO - Epoch [74][2800/3746] lr: 5.131e-02, eta: 2 days, 15:47:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5694, loss_cls: 3.8890, loss: 3.8890 +2024-07-24 19:16:28,982 - pyskl - INFO - Epoch [74][2900/3746] lr: 5.128e-02, eta: 2 days, 15:46:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5767, loss_cls: 3.8935, loss: 3.8935 +2024-07-24 19:17:51,692 - pyskl - INFO - Epoch [74][3000/3746] lr: 5.126e-02, eta: 2 days, 15:45:02, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5703, loss_cls: 3.9118, loss: 3.9118 +2024-07-24 19:19:13,938 - pyskl - INFO - Epoch [74][3100/3746] lr: 5.123e-02, eta: 2 days, 15:43:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5708, loss_cls: 3.9118, loss: 3.9118 +2024-07-24 19:20:36,221 - pyskl - INFO - Epoch [74][3200/3746] lr: 5.120e-02, eta: 2 days, 15:42:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5816, loss_cls: 3.8581, loss: 3.8581 +2024-07-24 19:21:57,956 - pyskl - INFO - Epoch [74][3300/3746] lr: 5.117e-02, eta: 2 days, 15:41:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5772, loss_cls: 3.8996, loss: 3.8996 +2024-07-24 19:23:19,344 - pyskl - INFO - Epoch [74][3400/3746] lr: 5.114e-02, eta: 2 days, 15:39:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5731, loss_cls: 3.9202, loss: 3.9202 +2024-07-24 19:24:40,801 - pyskl - INFO - Epoch [74][3500/3746] lr: 5.112e-02, eta: 2 days, 15:38:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5709, loss_cls: 3.9105, loss: 3.9105 +2024-07-24 19:26:02,311 - pyskl - INFO - Epoch [74][3600/3746] lr: 5.109e-02, eta: 2 days, 15:37:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5797, loss_cls: 3.9121, loss: 3.9121 +2024-07-24 19:27:24,234 - pyskl - INFO - Epoch [74][3700/3746] lr: 5.106e-02, eta: 2 days, 15:35:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5661, loss_cls: 3.9542, loss: 3.9542 +2024-07-24 19:28:03,657 - pyskl - INFO - Saving checkpoint at 74 epochs +2024-07-24 19:29:56,258 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 19:29:56,951 - pyskl - INFO - +top1_acc 0.2370 +top5_acc 0.4839 +2024-07-24 19:29:56,951 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 19:29:56,996 - pyskl - INFO - +mean_acc 0.2372 +2024-07-24 19:29:57,009 - pyskl - INFO - Epoch(val) [74][309] top1_acc: 0.2370, top5_acc: 0.4839, mean_class_accuracy: 0.2372 +2024-07-24 19:33:53,239 - pyskl - INFO - Epoch [75][100/3746] lr: 5.102e-02, eta: 2 days, 15:35:54, time: 2.362, data_time: 1.355, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5895, loss_cls: 3.8179, loss: 3.8179 +2024-07-24 19:35:17,091 - pyskl - INFO - Epoch [75][200/3746] lr: 5.099e-02, eta: 2 days, 15:34:37, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5834, loss_cls: 3.8511, loss: 3.8511 +2024-07-24 19:36:41,155 - pyskl - INFO - Epoch [75][300/3746] lr: 5.096e-02, eta: 2 days, 15:33:20, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5706, loss_cls: 3.8899, loss: 3.8899 +2024-07-24 19:38:04,520 - pyskl - INFO - Epoch [75][400/3746] lr: 5.094e-02, eta: 2 days, 15:32:02, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5880, loss_cls: 3.8295, loss: 3.8295 +2024-07-24 19:39:27,512 - pyskl - INFO - Epoch [75][500/3746] lr: 5.091e-02, eta: 2 days, 15:30:45, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5822, loss_cls: 3.8693, loss: 3.8693 +2024-07-24 19:40:51,048 - pyskl - INFO - Epoch [75][600/3746] lr: 5.088e-02, eta: 2 days, 15:29:27, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5827, loss_cls: 3.8717, loss: 3.8717 +2024-07-24 19:42:14,645 - pyskl - INFO - Epoch [75][700/3746] lr: 5.085e-02, eta: 2 days, 15:28:10, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5755, loss_cls: 3.8999, loss: 3.8999 +2024-07-24 19:43:37,783 - pyskl - INFO - Epoch [75][800/3746] lr: 5.082e-02, eta: 2 days, 15:26:52, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5802, loss_cls: 3.9038, loss: 3.9038 +2024-07-24 19:45:01,051 - pyskl - INFO - Epoch [75][900/3746] lr: 5.080e-02, eta: 2 days, 15:25:35, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5891, loss_cls: 3.8229, loss: 3.8229 +2024-07-24 19:46:24,339 - pyskl - INFO - Epoch [75][1000/3746] lr: 5.077e-02, eta: 2 days, 15:24:17, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5681, loss_cls: 3.9196, loss: 3.9196 +2024-07-24 19:47:47,546 - pyskl - INFO - Epoch [75][1100/3746] lr: 5.074e-02, eta: 2 days, 15:23:00, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5763, loss_cls: 3.8959, loss: 3.8959 +2024-07-24 19:49:10,846 - pyskl - INFO - Epoch [75][1200/3746] lr: 5.071e-02, eta: 2 days, 15:21:42, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5694, loss_cls: 3.9088, loss: 3.9088 +2024-07-24 19:50:33,626 - pyskl - INFO - Epoch [75][1300/3746] lr: 5.068e-02, eta: 2 days, 15:20:24, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5680, loss_cls: 3.9065, loss: 3.9065 +2024-07-24 19:51:55,945 - pyskl - INFO - Epoch [75][1400/3746] lr: 5.066e-02, eta: 2 days, 15:19:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5737, loss_cls: 3.9147, loss: 3.9147 +2024-07-24 19:53:17,975 - pyskl - INFO - Epoch [75][1500/3746] lr: 5.063e-02, eta: 2 days, 15:17:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5787, loss_cls: 3.9217, loss: 3.9217 +2024-07-24 19:54:39,453 - pyskl - INFO - Epoch [75][1600/3746] lr: 5.060e-02, eta: 2 days, 15:16:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5795, loss_cls: 3.8630, loss: 3.8630 +2024-07-24 19:56:01,383 - pyskl - INFO - Epoch [75][1700/3746] lr: 5.057e-02, eta: 2 days, 15:15:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5778, loss_cls: 3.8795, loss: 3.8795 +2024-07-24 19:57:22,921 - pyskl - INFO - Epoch [75][1800/3746] lr: 5.054e-02, eta: 2 days, 15:13:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5761, loss_cls: 3.9290, loss: 3.9290 +2024-07-24 19:58:44,148 - pyskl - INFO - Epoch [75][1900/3746] lr: 5.052e-02, eta: 2 days, 15:12:29, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5731, loss_cls: 3.9036, loss: 3.9036 +2024-07-24 20:00:06,119 - pyskl - INFO - Epoch [75][2000/3746] lr: 5.049e-02, eta: 2 days, 15:11:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5713, loss_cls: 3.8869, loss: 3.8869 +2024-07-24 20:01:28,013 - pyskl - INFO - Epoch [75][2100/3746] lr: 5.046e-02, eta: 2 days, 15:09:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5866, loss_cls: 3.8417, loss: 3.8417 +2024-07-24 20:02:50,285 - pyskl - INFO - Epoch [75][2200/3746] lr: 5.043e-02, eta: 2 days, 15:08:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5773, loss_cls: 3.9331, loss: 3.9331 +2024-07-24 20:04:12,643 - pyskl - INFO - Epoch [75][2300/3746] lr: 5.040e-02, eta: 2 days, 15:07:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5689, loss_cls: 3.9357, loss: 3.9357 +2024-07-24 20:05:34,550 - pyskl - INFO - Epoch [75][2400/3746] lr: 5.038e-02, eta: 2 days, 15:05:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5692, loss_cls: 3.9305, loss: 3.9305 +2024-07-24 20:06:56,024 - pyskl - INFO - Epoch [75][2500/3746] lr: 5.035e-02, eta: 2 days, 15:04:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5792, loss_cls: 3.9124, loss: 3.9124 +2024-07-24 20:08:17,271 - pyskl - INFO - Epoch [75][2600/3746] lr: 5.032e-02, eta: 2 days, 15:03:16, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5755, loss_cls: 3.9196, loss: 3.9196 +2024-07-24 20:09:38,436 - pyskl - INFO - Epoch [75][2700/3746] lr: 5.029e-02, eta: 2 days, 15:01:56, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5839, loss_cls: 3.8447, loss: 3.8447 +2024-07-24 20:10:59,800 - pyskl - INFO - Epoch [75][2800/3746] lr: 5.026e-02, eta: 2 days, 15:00:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5825, loss_cls: 3.8629, loss: 3.8629 +2024-07-24 20:12:21,223 - pyskl - INFO - Epoch [75][2900/3746] lr: 5.024e-02, eta: 2 days, 14:59:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5761, loss_cls: 3.9054, loss: 3.9054 +2024-07-24 20:13:43,275 - pyskl - INFO - Epoch [75][3000/3746] lr: 5.021e-02, eta: 2 days, 14:57:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5783, loss_cls: 3.9214, loss: 3.9214 +2024-07-24 20:15:04,969 - pyskl - INFO - Epoch [75][3100/3746] lr: 5.018e-02, eta: 2 days, 14:56:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5702, loss_cls: 3.9002, loss: 3.9002 +2024-07-24 20:16:26,914 - pyskl - INFO - Epoch [75][3200/3746] lr: 5.015e-02, eta: 2 days, 14:55:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5625, loss_cls: 3.9321, loss: 3.9321 +2024-07-24 20:17:48,919 - pyskl - INFO - Epoch [75][3300/3746] lr: 5.012e-02, eta: 2 days, 14:54:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5806, loss_cls: 3.8780, loss: 3.8780 +2024-07-24 20:19:10,223 - pyskl - INFO - Epoch [75][3400/3746] lr: 5.010e-02, eta: 2 days, 14:52:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5836, loss_cls: 3.8760, loss: 3.8760 +2024-07-24 20:20:31,964 - pyskl - INFO - Epoch [75][3500/3746] lr: 5.007e-02, eta: 2 days, 14:51:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5706, loss_cls: 3.8892, loss: 3.8892 +2024-07-24 20:21:53,429 - pyskl - INFO - Epoch [75][3600/3746] lr: 5.004e-02, eta: 2 days, 14:50:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5769, loss_cls: 3.8975, loss: 3.8975 +2024-07-24 20:23:15,362 - pyskl - INFO - Epoch [75][3700/3746] lr: 5.001e-02, eta: 2 days, 14:48:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5789, loss_cls: 3.8733, loss: 3.8733 +2024-07-24 20:23:54,739 - pyskl - INFO - Saving checkpoint at 75 epochs +2024-07-24 20:25:48,286 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 20:25:49,005 - pyskl - INFO - +top1_acc 0.2684 +top5_acc 0.5191 +2024-07-24 20:25:49,006 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 20:25:49,049 - pyskl - INFO - +mean_acc 0.2681 +2024-07-24 20:25:49,054 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_72.pth was removed +2024-07-24 20:25:49,321 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_75.pth. +2024-07-24 20:25:49,322 - pyskl - INFO - Best top1_acc is 0.2684 at 75 epoch. +2024-07-24 20:25:49,335 - pyskl - INFO - Epoch(val) [75][309] top1_acc: 0.2684, top5_acc: 0.5191, mean_class_accuracy: 0.2681 +2024-07-24 20:29:43,366 - pyskl - INFO - Epoch [76][100/3746] lr: 4.997e-02, eta: 2 days, 14:48:42, time: 2.340, data_time: 1.341, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5839, loss_cls: 3.8815, loss: 3.8815 +2024-07-24 20:31:05,070 - pyskl - INFO - Epoch [76][200/3746] lr: 4.994e-02, eta: 2 days, 14:47:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5800, loss_cls: 3.9032, loss: 3.9032 +2024-07-24 20:32:26,605 - pyskl - INFO - Epoch [76][300/3746] lr: 4.992e-02, eta: 2 days, 14:46:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5811, loss_cls: 3.8732, loss: 3.8732 +2024-07-24 20:33:48,564 - pyskl - INFO - Epoch [76][400/3746] lr: 4.989e-02, eta: 2 days, 14:44:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5863, loss_cls: 3.8467, loss: 3.8467 +2024-07-24 20:35:10,368 - pyskl - INFO - Epoch [76][500/3746] lr: 4.986e-02, eta: 2 days, 14:43:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5828, loss_cls: 3.8408, loss: 3.8408 +2024-07-24 20:36:32,289 - pyskl - INFO - Epoch [76][600/3746] lr: 4.983e-02, eta: 2 days, 14:42:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5822, loss_cls: 3.8275, loss: 3.8275 +2024-07-24 20:37:54,509 - pyskl - INFO - Epoch [76][700/3746] lr: 4.980e-02, eta: 2 days, 14:40:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5797, loss_cls: 3.8822, loss: 3.8822 +2024-07-24 20:39:16,295 - pyskl - INFO - Epoch [76][800/3746] lr: 4.978e-02, eta: 2 days, 14:39:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5752, loss_cls: 3.8736, loss: 3.8736 +2024-07-24 20:40:38,498 - pyskl - INFO - Epoch [76][900/3746] lr: 4.975e-02, eta: 2 days, 14:38:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5772, loss_cls: 3.8705, loss: 3.8705 +2024-07-24 20:42:00,121 - pyskl - INFO - Epoch [76][1000/3746] lr: 4.972e-02, eta: 2 days, 14:36:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5733, loss_cls: 3.8716, loss: 3.8716 +2024-07-24 20:43:21,726 - pyskl - INFO - Epoch [76][1100/3746] lr: 4.969e-02, eta: 2 days, 14:35:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5736, loss_cls: 3.8937, loss: 3.8937 +2024-07-24 20:44:43,346 - pyskl - INFO - Epoch [76][1200/3746] lr: 4.966e-02, eta: 2 days, 14:34:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5753, loss_cls: 3.8925, loss: 3.8925 +2024-07-24 20:46:05,353 - pyskl - INFO - Epoch [76][1300/3746] lr: 4.964e-02, eta: 2 days, 14:32:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5839, loss_cls: 3.8803, loss: 3.8803 +2024-07-24 20:47:26,664 - pyskl - INFO - Epoch [76][1400/3746] lr: 4.961e-02, eta: 2 days, 14:31:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5845, loss_cls: 3.8651, loss: 3.8651 +2024-07-24 20:48:48,539 - pyskl - INFO - Epoch [76][1500/3746] lr: 4.958e-02, eta: 2 days, 14:30:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5784, loss_cls: 3.8539, loss: 3.8539 +2024-07-24 20:50:10,937 - pyskl - INFO - Epoch [76][1600/3746] lr: 4.955e-02, eta: 2 days, 14:28:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5802, loss_cls: 3.8599, loss: 3.8599 +2024-07-24 20:51:32,840 - pyskl - INFO - Epoch [76][1700/3746] lr: 4.953e-02, eta: 2 days, 14:27:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5834, loss_cls: 3.8680, loss: 3.8680 +2024-07-24 20:52:55,026 - pyskl - INFO - Epoch [76][1800/3746] lr: 4.950e-02, eta: 2 days, 14:26:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5805, loss_cls: 3.8616, loss: 3.8616 +2024-07-24 20:54:16,714 - pyskl - INFO - Epoch [76][1900/3746] lr: 4.947e-02, eta: 2 days, 14:24:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5705, loss_cls: 3.9552, loss: 3.9552 +2024-07-24 20:55:38,513 - pyskl - INFO - Epoch [76][2000/3746] lr: 4.944e-02, eta: 2 days, 14:23:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5841, loss_cls: 3.8555, loss: 3.8555 +2024-07-24 20:57:00,294 - pyskl - INFO - Epoch [76][2100/3746] lr: 4.941e-02, eta: 2 days, 14:22:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5820, loss_cls: 3.8511, loss: 3.8511 +2024-07-24 20:58:22,971 - pyskl - INFO - Epoch [76][2200/3746] lr: 4.939e-02, eta: 2 days, 14:21:00, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5689, loss_cls: 3.9491, loss: 3.9491 +2024-07-24 20:59:44,694 - pyskl - INFO - Epoch [76][2300/3746] lr: 4.936e-02, eta: 2 days, 14:19:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5769, loss_cls: 3.9067, loss: 3.9067 +2024-07-24 21:01:06,792 - pyskl - INFO - Epoch [76][2400/3746] lr: 4.933e-02, eta: 2 days, 14:18:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5797, loss_cls: 3.9106, loss: 3.9106 +2024-07-24 21:02:28,690 - pyskl - INFO - Epoch [76][2500/3746] lr: 4.930e-02, eta: 2 days, 14:17:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5775, loss_cls: 3.8896, loss: 3.8896 +2024-07-24 21:03:50,197 - pyskl - INFO - Epoch [76][2600/3746] lr: 4.927e-02, eta: 2 days, 14:15:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5713, loss_cls: 3.9154, loss: 3.9154 +2024-07-24 21:05:11,947 - pyskl - INFO - Epoch [76][2700/3746] lr: 4.925e-02, eta: 2 days, 14:14:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5831, loss_cls: 3.8478, loss: 3.8478 +2024-07-24 21:06:33,554 - pyskl - INFO - Epoch [76][2800/3746] lr: 4.922e-02, eta: 2 days, 14:13:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5723, loss_cls: 3.8822, loss: 3.8822 +2024-07-24 21:07:55,514 - pyskl - INFO - Epoch [76][2900/3746] lr: 4.919e-02, eta: 2 days, 14:11:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5870, loss_cls: 3.8594, loss: 3.8594 +2024-07-24 21:09:17,379 - pyskl - INFO - Epoch [76][3000/3746] lr: 4.916e-02, eta: 2 days, 14:10:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5800, loss_cls: 3.8983, loss: 3.8983 +2024-07-24 21:10:38,859 - pyskl - INFO - Epoch [76][3100/3746] lr: 4.913e-02, eta: 2 days, 14:09:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5806, loss_cls: 3.8626, loss: 3.8626 +2024-07-24 21:12:01,258 - pyskl - INFO - Epoch [76][3200/3746] lr: 4.911e-02, eta: 2 days, 14:07:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5778, loss_cls: 3.8898, loss: 3.8898 +2024-07-24 21:13:23,046 - pyskl - INFO - Epoch [76][3300/3746] lr: 4.908e-02, eta: 2 days, 14:06:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5758, loss_cls: 3.8817, loss: 3.8817 +2024-07-24 21:14:44,705 - pyskl - INFO - Epoch [76][3400/3746] lr: 4.905e-02, eta: 2 days, 14:05:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5961, loss_cls: 3.7986, loss: 3.7986 +2024-07-24 21:16:06,380 - pyskl - INFO - Epoch [76][3500/3746] lr: 4.902e-02, eta: 2 days, 14:03:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5748, loss_cls: 3.8733, loss: 3.8733 +2024-07-24 21:17:27,765 - pyskl - INFO - Epoch [76][3600/3746] lr: 4.899e-02, eta: 2 days, 14:02:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5744, loss_cls: 3.8715, loss: 3.8715 +2024-07-24 21:18:49,673 - pyskl - INFO - Epoch [76][3700/3746] lr: 4.897e-02, eta: 2 days, 14:01:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5772, loss_cls: 3.8957, loss: 3.8957 +2024-07-24 21:19:28,908 - pyskl - INFO - Saving checkpoint at 76 epochs +2024-07-24 21:21:21,266 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 21:21:21,982 - pyskl - INFO - +top1_acc 0.2582 +top5_acc 0.5098 +2024-07-24 21:21:21,982 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 21:21:22,033 - pyskl - INFO - +mean_acc 0.2581 +2024-07-24 21:21:22,048 - pyskl - INFO - Epoch(val) [76][309] top1_acc: 0.2582, top5_acc: 0.5098, mean_class_accuracy: 0.2581 +2024-07-24 21:25:14,748 - pyskl - INFO - Epoch [77][100/3746] lr: 4.893e-02, eta: 2 days, 14:01:05, time: 2.327, data_time: 1.343, memory: 15990, top1_acc: 0.3400, top5_acc: 0.5945, loss_cls: 3.7670, loss: 3.7670 +2024-07-24 21:26:37,361 - pyskl - INFO - Epoch [77][200/3746] lr: 4.890e-02, eta: 2 days, 13:59:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5983, loss_cls: 3.7699, loss: 3.7699 +2024-07-24 21:28:00,383 - pyskl - INFO - Epoch [77][300/3746] lr: 4.887e-02, eta: 2 days, 13:58:28, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5778, loss_cls: 3.8861, loss: 3.8861 +2024-07-24 21:29:22,031 - pyskl - INFO - Epoch [77][400/3746] lr: 4.884e-02, eta: 2 days, 13:57:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5753, loss_cls: 3.8808, loss: 3.8808 +2024-07-24 21:30:43,934 - pyskl - INFO - Epoch [77][500/3746] lr: 4.881e-02, eta: 2 days, 13:55:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5773, loss_cls: 3.8964, loss: 3.8964 +2024-07-24 21:32:06,547 - pyskl - INFO - Epoch [77][600/3746] lr: 4.879e-02, eta: 2 days, 13:54:31, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5845, loss_cls: 3.8417, loss: 3.8417 +2024-07-24 21:33:28,022 - pyskl - INFO - Epoch [77][700/3746] lr: 4.876e-02, eta: 2 days, 13:53:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5847, loss_cls: 3.8753, loss: 3.8753 +2024-07-24 21:34:49,329 - pyskl - INFO - Epoch [77][800/3746] lr: 4.873e-02, eta: 2 days, 13:51:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5741, loss_cls: 3.8782, loss: 3.8782 +2024-07-24 21:36:11,459 - pyskl - INFO - Epoch [77][900/3746] lr: 4.870e-02, eta: 2 days, 13:50:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5744, loss_cls: 3.8943, loss: 3.8943 +2024-07-24 21:37:33,130 - pyskl - INFO - Epoch [77][1000/3746] lr: 4.867e-02, eta: 2 days, 13:49:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5798, loss_cls: 3.8320, loss: 3.8320 +2024-07-24 21:38:55,125 - pyskl - INFO - Epoch [77][1100/3746] lr: 4.865e-02, eta: 2 days, 13:47:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5820, loss_cls: 3.8577, loss: 3.8577 +2024-07-24 21:40:16,237 - pyskl - INFO - Epoch [77][1200/3746] lr: 4.862e-02, eta: 2 days, 13:46:33, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5678, loss_cls: 3.9416, loss: 3.9416 +2024-07-24 21:41:37,500 - pyskl - INFO - Epoch [77][1300/3746] lr: 4.859e-02, eta: 2 days, 13:45:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5791, loss_cls: 3.8872, loss: 3.8872 +2024-07-24 21:42:59,238 - pyskl - INFO - Epoch [77][1400/3746] lr: 4.856e-02, eta: 2 days, 13:43:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5967, loss_cls: 3.7851, loss: 3.7851 +2024-07-24 21:44:21,324 - pyskl - INFO - Epoch [77][1500/3746] lr: 4.853e-02, eta: 2 days, 13:42:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5736, loss_cls: 3.8942, loss: 3.8942 +2024-07-24 21:45:43,282 - pyskl - INFO - Epoch [77][1600/3746] lr: 4.851e-02, eta: 2 days, 13:41:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5770, loss_cls: 3.8688, loss: 3.8688 +2024-07-24 21:47:05,294 - pyskl - INFO - Epoch [77][1700/3746] lr: 4.848e-02, eta: 2 days, 13:39:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5808, loss_cls: 3.8641, loss: 3.8641 +2024-07-24 21:48:27,037 - pyskl - INFO - Epoch [77][1800/3746] lr: 4.845e-02, eta: 2 days, 13:38:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5767, loss_cls: 3.9087, loss: 3.9087 +2024-07-24 21:49:48,655 - pyskl - INFO - Epoch [77][1900/3746] lr: 4.842e-02, eta: 2 days, 13:37:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5811, loss_cls: 3.8626, loss: 3.8626 +2024-07-24 21:51:10,516 - pyskl - INFO - Epoch [77][2000/3746] lr: 4.839e-02, eta: 2 days, 13:35:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5813, loss_cls: 3.8440, loss: 3.8440 +2024-07-24 21:52:32,575 - pyskl - INFO - Epoch [77][2100/3746] lr: 4.837e-02, eta: 2 days, 13:34:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5736, loss_cls: 3.8971, loss: 3.8971 +2024-07-24 21:53:55,550 - pyskl - INFO - Epoch [77][2200/3746] lr: 4.834e-02, eta: 2 days, 13:33:20, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5792, loss_cls: 3.8708, loss: 3.8708 +2024-07-24 21:55:17,014 - pyskl - INFO - Epoch [77][2300/3746] lr: 4.831e-02, eta: 2 days, 13:32:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5873, loss_cls: 3.8651, loss: 3.8651 +2024-07-24 21:56:38,321 - pyskl - INFO - Epoch [77][2400/3746] lr: 4.828e-02, eta: 2 days, 13:30:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5814, loss_cls: 3.8603, loss: 3.8603 +2024-07-24 21:57:59,847 - pyskl - INFO - Epoch [77][2500/3746] lr: 4.825e-02, eta: 2 days, 13:29:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5845, loss_cls: 3.8402, loss: 3.8402 +2024-07-24 21:59:20,931 - pyskl - INFO - Epoch [77][2600/3746] lr: 4.823e-02, eta: 2 days, 13:28:01, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5730, loss_cls: 3.9148, loss: 3.9148 +2024-07-24 22:00:42,848 - pyskl - INFO - Epoch [77][2700/3746] lr: 4.820e-02, eta: 2 days, 13:26:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5758, loss_cls: 3.8673, loss: 3.8673 +2024-07-24 22:02:04,579 - pyskl - INFO - Epoch [77][2800/3746] lr: 4.817e-02, eta: 2 days, 13:25:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5769, loss_cls: 3.9136, loss: 3.9136 +2024-07-24 22:03:26,032 - pyskl - INFO - Epoch [77][2900/3746] lr: 4.814e-02, eta: 2 days, 13:24:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5825, loss_cls: 3.8599, loss: 3.8599 +2024-07-24 22:04:48,306 - pyskl - INFO - Epoch [77][3000/3746] lr: 4.811e-02, eta: 2 days, 13:22:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5775, loss_cls: 3.8827, loss: 3.8827 +2024-07-24 22:06:09,937 - pyskl - INFO - Epoch [77][3100/3746] lr: 4.809e-02, eta: 2 days, 13:21:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5827, loss_cls: 3.8495, loss: 3.8495 +2024-07-24 22:07:31,932 - pyskl - INFO - Epoch [77][3200/3746] lr: 4.806e-02, eta: 2 days, 13:20:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5686, loss_cls: 3.9332, loss: 3.9332 +2024-07-24 22:08:53,187 - pyskl - INFO - Epoch [77][3300/3746] lr: 4.803e-02, eta: 2 days, 13:18:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5730, loss_cls: 3.9019, loss: 3.9019 +2024-07-24 22:10:14,897 - pyskl - INFO - Epoch [77][3400/3746] lr: 4.800e-02, eta: 2 days, 13:17:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5841, loss_cls: 3.8583, loss: 3.8583 +2024-07-24 22:11:36,312 - pyskl - INFO - Epoch [77][3500/3746] lr: 4.798e-02, eta: 2 days, 13:16:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5747, loss_cls: 3.8864, loss: 3.8864 +2024-07-24 22:12:58,376 - pyskl - INFO - Epoch [77][3600/3746] lr: 4.795e-02, eta: 2 days, 13:14:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5839, loss_cls: 3.8662, loss: 3.8662 +2024-07-24 22:14:20,106 - pyskl - INFO - Epoch [77][3700/3746] lr: 4.792e-02, eta: 2 days, 13:13:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5847, loss_cls: 3.8190, loss: 3.8190 +2024-07-24 22:15:00,606 - pyskl - INFO - Saving checkpoint at 77 epochs +2024-07-24 22:16:51,540 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 22:16:52,219 - pyskl - INFO - +top1_acc 0.2635 +top5_acc 0.5148 +2024-07-24 22:16:52,219 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 22:16:52,264 - pyskl - INFO - +mean_acc 0.2633 +2024-07-24 22:16:52,276 - pyskl - INFO - Epoch(val) [77][309] top1_acc: 0.2635, top5_acc: 0.5148, mean_class_accuracy: 0.2633 +2024-07-24 22:20:46,132 - pyskl - INFO - Epoch [78][100/3746] lr: 4.788e-02, eta: 2 days, 13:13:19, time: 2.338, data_time: 1.339, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5945, loss_cls: 3.7562, loss: 3.7562 +2024-07-24 22:22:09,649 - pyskl - INFO - Epoch [78][200/3746] lr: 4.785e-02, eta: 2 days, 13:12:01, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5866, loss_cls: 3.8199, loss: 3.8199 +2024-07-24 22:23:33,268 - pyskl - INFO - Epoch [78][300/3746] lr: 4.782e-02, eta: 2 days, 13:10:44, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5855, loss_cls: 3.8254, loss: 3.8254 +2024-07-24 22:24:57,116 - pyskl - INFO - Epoch [78][400/3746] lr: 4.779e-02, eta: 2 days, 13:09:26, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5814, loss_cls: 3.8417, loss: 3.8417 +2024-07-24 22:26:21,023 - pyskl - INFO - Epoch [78][500/3746] lr: 4.777e-02, eta: 2 days, 13:08:08, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5720, loss_cls: 3.8623, loss: 3.8623 +2024-07-24 22:27:44,852 - pyskl - INFO - Epoch [78][600/3746] lr: 4.774e-02, eta: 2 days, 13:06:51, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5898, loss_cls: 3.8219, loss: 3.8219 +2024-07-24 22:29:08,216 - pyskl - INFO - Epoch [78][700/3746] lr: 4.771e-02, eta: 2 days, 13:05:33, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5834, loss_cls: 3.8440, loss: 3.8440 +2024-07-24 22:30:31,779 - pyskl - INFO - Epoch [78][800/3746] lr: 4.768e-02, eta: 2 days, 13:04:15, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5883, loss_cls: 3.8262, loss: 3.8262 +2024-07-24 22:31:55,274 - pyskl - INFO - Epoch [78][900/3746] lr: 4.766e-02, eta: 2 days, 13:02:57, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5777, loss_cls: 3.8762, loss: 3.8762 +2024-07-24 22:33:18,362 - pyskl - INFO - Epoch [78][1000/3746] lr: 4.763e-02, eta: 2 days, 13:01:39, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5767, loss_cls: 3.8750, loss: 3.8750 +2024-07-24 22:34:41,382 - pyskl - INFO - Epoch [78][1100/3746] lr: 4.760e-02, eta: 2 days, 13:00:20, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5764, loss_cls: 3.8758, loss: 3.8758 +2024-07-24 22:36:03,920 - pyskl - INFO - Epoch [78][1200/3746] lr: 4.757e-02, eta: 2 days, 12:59:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5764, loss_cls: 3.9170, loss: 3.9170 +2024-07-24 22:37:27,268 - pyskl - INFO - Epoch [78][1300/3746] lr: 4.754e-02, eta: 2 days, 12:57:43, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5919, loss_cls: 3.8134, loss: 3.8134 +2024-07-24 22:38:50,427 - pyskl - INFO - Epoch [78][1400/3746] lr: 4.752e-02, eta: 2 days, 12:56:25, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5914, loss_cls: 3.8371, loss: 3.8371 +2024-07-24 22:40:13,358 - pyskl - INFO - Epoch [78][1500/3746] lr: 4.749e-02, eta: 2 days, 12:55:06, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5822, loss_cls: 3.8648, loss: 3.8648 +2024-07-24 22:41:36,759 - pyskl - INFO - Epoch [78][1600/3746] lr: 4.746e-02, eta: 2 days, 12:53:48, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5763, loss_cls: 3.8872, loss: 3.8872 +2024-07-24 22:42:59,812 - pyskl - INFO - Epoch [78][1700/3746] lr: 4.743e-02, eta: 2 days, 12:52:30, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5855, loss_cls: 3.8412, loss: 3.8412 +2024-07-24 22:44:22,966 - pyskl - INFO - Epoch [78][1800/3746] lr: 4.740e-02, eta: 2 days, 12:51:12, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5900, loss_cls: 3.8139, loss: 3.8139 +2024-07-24 22:45:46,317 - pyskl - INFO - Epoch [78][1900/3746] lr: 4.738e-02, eta: 2 days, 12:49:54, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5895, loss_cls: 3.8147, loss: 3.8147 +2024-07-24 22:47:09,979 - pyskl - INFO - Epoch [78][2000/3746] lr: 4.735e-02, eta: 2 days, 12:48:36, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5839, loss_cls: 3.8559, loss: 3.8559 +2024-07-24 22:48:32,481 - pyskl - INFO - Epoch [78][2100/3746] lr: 4.732e-02, eta: 2 days, 12:47:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5789, loss_cls: 3.8612, loss: 3.8612 +2024-07-24 22:49:56,719 - pyskl - INFO - Epoch [78][2200/3746] lr: 4.729e-02, eta: 2 days, 12:46:00, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5769, loss_cls: 3.8861, loss: 3.8861 +2024-07-24 22:51:19,521 - pyskl - INFO - Epoch [78][2300/3746] lr: 4.726e-02, eta: 2 days, 12:44:41, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5789, loss_cls: 3.8660, loss: 3.8660 +2024-07-24 22:52:42,184 - pyskl - INFO - Epoch [78][2400/3746] lr: 4.724e-02, eta: 2 days, 12:43:22, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5847, loss_cls: 3.8525, loss: 3.8525 +2024-07-24 22:54:05,404 - pyskl - INFO - Epoch [78][2500/3746] lr: 4.721e-02, eta: 2 days, 12:42:04, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5842, loss_cls: 3.8776, loss: 3.8776 +2024-07-24 22:55:28,666 - pyskl - INFO - Epoch [78][2600/3746] lr: 4.718e-02, eta: 2 days, 12:40:46, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5806, loss_cls: 3.8550, loss: 3.8550 +2024-07-24 22:56:51,719 - pyskl - INFO - Epoch [78][2700/3746] lr: 4.715e-02, eta: 2 days, 12:39:27, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5792, loss_cls: 3.8751, loss: 3.8751 +2024-07-24 22:58:14,566 - pyskl - INFO - Epoch [78][2800/3746] lr: 4.712e-02, eta: 2 days, 12:38:09, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5784, loss_cls: 3.8904, loss: 3.8904 +2024-07-24 22:59:37,251 - pyskl - INFO - Epoch [78][2900/3746] lr: 4.710e-02, eta: 2 days, 12:36:50, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5837, loss_cls: 3.8701, loss: 3.8701 +2024-07-24 23:01:00,638 - pyskl - INFO - Epoch [78][3000/3746] lr: 4.707e-02, eta: 2 days, 12:35:32, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5914, loss_cls: 3.8245, loss: 3.8245 +2024-07-24 23:02:23,493 - pyskl - INFO - Epoch [78][3100/3746] lr: 4.704e-02, eta: 2 days, 12:34:13, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5827, loss_cls: 3.8605, loss: 3.8605 +2024-07-24 23:03:47,229 - pyskl - INFO - Epoch [78][3200/3746] lr: 4.701e-02, eta: 2 days, 12:32:55, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5897, loss_cls: 3.8258, loss: 3.8258 +2024-07-24 23:05:10,607 - pyskl - INFO - Epoch [78][3300/3746] lr: 4.699e-02, eta: 2 days, 12:31:37, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5852, loss_cls: 3.8826, loss: 3.8826 +2024-07-24 23:06:33,979 - pyskl - INFO - Epoch [78][3400/3746] lr: 4.696e-02, eta: 2 days, 12:30:19, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5805, loss_cls: 3.8503, loss: 3.8503 +2024-07-24 23:07:57,066 - pyskl - INFO - Epoch [78][3500/3746] lr: 4.693e-02, eta: 2 days, 12:29:01, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5903, loss_cls: 3.8414, loss: 3.8414 +2024-07-24 23:09:20,364 - pyskl - INFO - Epoch [78][3600/3746] lr: 4.690e-02, eta: 2 days, 12:27:43, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5819, loss_cls: 3.8602, loss: 3.8602 +2024-07-24 23:10:43,961 - pyskl - INFO - Epoch [78][3700/3746] lr: 4.687e-02, eta: 2 days, 12:26:25, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5870, loss_cls: 3.8368, loss: 3.8368 +2024-07-24 23:11:24,102 - pyskl - INFO - Saving checkpoint at 78 epochs +2024-07-24 23:13:16,554 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-24 23:13:17,236 - pyskl - INFO - +top1_acc 0.2574 +top5_acc 0.5032 +2024-07-24 23:13:17,236 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-24 23:13:17,277 - pyskl - INFO - +mean_acc 0.2571 +2024-07-24 23:13:17,290 - pyskl - INFO - Epoch(val) [78][309] top1_acc: 0.2574, top5_acc: 0.5032, mean_class_accuracy: 0.2571 +2024-07-24 23:17:05,525 - pyskl - INFO - Epoch [79][100/3746] lr: 4.683e-02, eta: 2 days, 12:26:09, time: 2.282, data_time: 1.283, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5880, loss_cls: 3.8163, loss: 3.8163 +2024-07-24 23:18:28,927 - pyskl - INFO - Epoch [79][200/3746] lr: 4.680e-02, eta: 2 days, 12:24:51, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6012, loss_cls: 3.7536, loss: 3.7536 +2024-07-24 23:19:52,218 - pyskl - INFO - Epoch [79][300/3746] lr: 4.678e-02, eta: 2 days, 12:23:32, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5984, loss_cls: 3.7678, loss: 3.7678 +2024-07-24 23:21:15,371 - pyskl - INFO - Epoch [79][400/3746] lr: 4.675e-02, eta: 2 days, 12:22:14, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5925, loss_cls: 3.7798, loss: 3.7798 +2024-07-24 23:22:38,814 - pyskl - INFO - Epoch [79][500/3746] lr: 4.672e-02, eta: 2 days, 12:20:56, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5836, loss_cls: 3.8204, loss: 3.8204 +2024-07-24 23:24:02,052 - pyskl - INFO - Epoch [79][600/3746] lr: 4.669e-02, eta: 2 days, 12:19:37, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5933, loss_cls: 3.8006, loss: 3.8006 +2024-07-24 23:25:25,146 - pyskl - INFO - Epoch [79][700/3746] lr: 4.667e-02, eta: 2 days, 12:18:19, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5842, loss_cls: 3.8130, loss: 3.8130 +2024-07-24 23:26:48,471 - pyskl - INFO - Epoch [79][800/3746] lr: 4.664e-02, eta: 2 days, 12:17:00, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5822, loss_cls: 3.8645, loss: 3.8645 +2024-07-24 23:28:11,126 - pyskl - INFO - Epoch [79][900/3746] lr: 4.661e-02, eta: 2 days, 12:15:41, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5886, loss_cls: 3.8492, loss: 3.8492 +2024-07-24 23:29:34,293 - pyskl - INFO - Epoch [79][1000/3746] lr: 4.658e-02, eta: 2 days, 12:14:23, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5817, loss_cls: 3.8728, loss: 3.8728 +2024-07-24 23:30:57,456 - pyskl - INFO - Epoch [79][1100/3746] lr: 4.655e-02, eta: 2 days, 12:13:05, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5819, loss_cls: 3.8679, loss: 3.8679 +2024-07-24 23:32:20,602 - pyskl - INFO - Epoch [79][1200/3746] lr: 4.653e-02, eta: 2 days, 12:11:46, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5752, loss_cls: 3.8974, loss: 3.8974 +2024-07-24 23:33:43,743 - pyskl - INFO - Epoch [79][1300/3746] lr: 4.650e-02, eta: 2 days, 12:10:28, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5806, loss_cls: 3.8759, loss: 3.8759 +2024-07-24 23:35:06,927 - pyskl - INFO - Epoch [79][1400/3746] lr: 4.647e-02, eta: 2 days, 12:09:09, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5908, loss_cls: 3.8053, loss: 3.8053 +2024-07-24 23:36:30,177 - pyskl - INFO - Epoch [79][1500/3746] lr: 4.644e-02, eta: 2 days, 12:07:51, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5817, loss_cls: 3.8232, loss: 3.8232 +2024-07-24 23:37:53,259 - pyskl - INFO - Epoch [79][1600/3746] lr: 4.641e-02, eta: 2 days, 12:06:32, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5850, loss_cls: 3.8329, loss: 3.8329 +2024-07-24 23:39:15,919 - pyskl - INFO - Epoch [79][1700/3746] lr: 4.639e-02, eta: 2 days, 12:05:13, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5917, loss_cls: 3.8215, loss: 3.8215 +2024-07-24 23:40:39,232 - pyskl - INFO - Epoch [79][1800/3746] lr: 4.636e-02, eta: 2 days, 12:03:55, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5869, loss_cls: 3.8403, loss: 3.8403 +2024-07-24 23:42:03,049 - pyskl - INFO - Epoch [79][1900/3746] lr: 4.633e-02, eta: 2 days, 12:02:37, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5817, loss_cls: 3.8641, loss: 3.8641 +2024-07-24 23:43:26,279 - pyskl - INFO - Epoch [79][2000/3746] lr: 4.630e-02, eta: 2 days, 12:01:19, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5820, loss_cls: 3.8331, loss: 3.8331 +2024-07-24 23:44:49,668 - pyskl - INFO - Epoch [79][2100/3746] lr: 4.628e-02, eta: 2 days, 12:00:00, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5845, loss_cls: 3.8726, loss: 3.8726 +2024-07-24 23:46:13,357 - pyskl - INFO - Epoch [79][2200/3746] lr: 4.625e-02, eta: 2 days, 11:58:42, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5786, loss_cls: 3.8865, loss: 3.8865 +2024-07-24 23:47:36,126 - pyskl - INFO - Epoch [79][2300/3746] lr: 4.622e-02, eta: 2 days, 11:57:24, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5814, loss_cls: 3.8401, loss: 3.8401 +2024-07-24 23:48:59,857 - pyskl - INFO - Epoch [79][2400/3746] lr: 4.619e-02, eta: 2 days, 11:56:06, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5767, loss_cls: 3.8766, loss: 3.8766 +2024-07-24 23:50:22,921 - pyskl - INFO - Epoch [79][2500/3746] lr: 4.616e-02, eta: 2 days, 11:54:47, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5816, loss_cls: 3.8702, loss: 3.8702 +2024-07-24 23:51:45,392 - pyskl - INFO - Epoch [79][2600/3746] lr: 4.614e-02, eta: 2 days, 11:53:28, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5802, loss_cls: 3.8724, loss: 3.8724 +2024-07-24 23:53:07,878 - pyskl - INFO - Epoch [79][2700/3746] lr: 4.611e-02, eta: 2 days, 11:52:09, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5798, loss_cls: 3.8495, loss: 3.8495 +2024-07-24 23:54:30,283 - pyskl - INFO - Epoch [79][2800/3746] lr: 4.608e-02, eta: 2 days, 11:50:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5837, loss_cls: 3.8411, loss: 3.8411 +2024-07-24 23:55:54,136 - pyskl - INFO - Epoch [79][2900/3746] lr: 4.605e-02, eta: 2 days, 11:49:32, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5877, loss_cls: 3.8193, loss: 3.8193 +2024-07-24 23:57:17,230 - pyskl - INFO - Epoch [79][3000/3746] lr: 4.602e-02, eta: 2 days, 11:48:13, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5727, loss_cls: 3.8984, loss: 3.8984 +2024-07-24 23:58:40,202 - pyskl - INFO - Epoch [79][3100/3746] lr: 4.600e-02, eta: 2 days, 11:46:54, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5792, loss_cls: 3.8486, loss: 3.8486 +2024-07-25 00:00:04,189 - pyskl - INFO - Epoch [79][3200/3746] lr: 4.597e-02, eta: 2 days, 11:45:37, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5886, loss_cls: 3.8180, loss: 3.8180 +2024-07-25 00:01:27,730 - pyskl - INFO - Epoch [79][3300/3746] lr: 4.594e-02, eta: 2 days, 11:44:18, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5805, loss_cls: 3.8413, loss: 3.8413 +2024-07-25 00:02:51,043 - pyskl - INFO - Epoch [79][3400/3746] lr: 4.591e-02, eta: 2 days, 11:43:00, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5827, loss_cls: 3.8637, loss: 3.8637 +2024-07-25 00:04:14,213 - pyskl - INFO - Epoch [79][3500/3746] lr: 4.588e-02, eta: 2 days, 11:41:41, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5859, loss_cls: 3.8417, loss: 3.8417 +2024-07-25 00:05:37,510 - pyskl - INFO - Epoch [79][3600/3746] lr: 4.586e-02, eta: 2 days, 11:40:23, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5881, loss_cls: 3.8433, loss: 3.8433 +2024-07-25 00:07:01,034 - pyskl - INFO - Epoch [79][3700/3746] lr: 4.583e-02, eta: 2 days, 11:39:05, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5756, loss_cls: 3.8654, loss: 3.8654 +2024-07-25 00:07:40,443 - pyskl - INFO - Saving checkpoint at 79 epochs +2024-07-25 00:09:33,183 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 00:09:33,846 - pyskl - INFO - +top1_acc 0.2754 +top5_acc 0.5180 +2024-07-25 00:09:33,846 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 00:09:33,886 - pyskl - INFO - +mean_acc 0.2753 +2024-07-25 00:09:33,891 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_75.pth was removed +2024-07-25 00:09:34,205 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_79.pth. +2024-07-25 00:09:34,206 - pyskl - INFO - Best top1_acc is 0.2754 at 79 epoch. +2024-07-25 00:09:34,218 - pyskl - INFO - Epoch(val) [79][309] top1_acc: 0.2754, top5_acc: 0.5180, mean_class_accuracy: 0.2753 +2024-07-25 00:13:21,584 - pyskl - INFO - Epoch [80][100/3746] lr: 4.579e-02, eta: 2 days, 11:38:45, time: 2.274, data_time: 1.276, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6025, loss_cls: 3.7493, loss: 3.7493 +2024-07-25 00:14:44,866 - pyskl - INFO - Epoch [80][200/3746] lr: 4.576e-02, eta: 2 days, 11:37:27, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5958, loss_cls: 3.7833, loss: 3.7833 +2024-07-25 00:16:08,035 - pyskl - INFO - Epoch [80][300/3746] lr: 4.573e-02, eta: 2 days, 11:36:08, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5844, loss_cls: 3.8272, loss: 3.8272 +2024-07-25 00:17:30,797 - pyskl - INFO - Epoch [80][400/3746] lr: 4.570e-02, eta: 2 days, 11:34:49, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5913, loss_cls: 3.8100, loss: 3.8100 +2024-07-25 00:18:53,876 - pyskl - INFO - Epoch [80][500/3746] lr: 4.568e-02, eta: 2 days, 11:33:31, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3425, top5_acc: 0.6033, loss_cls: 3.7441, loss: 3.7441 +2024-07-25 00:20:16,866 - pyskl - INFO - Epoch [80][600/3746] lr: 4.565e-02, eta: 2 days, 11:32:12, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5753, loss_cls: 3.8805, loss: 3.8805 +2024-07-25 00:21:39,718 - pyskl - INFO - Epoch [80][700/3746] lr: 4.562e-02, eta: 2 days, 11:30:53, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5833, loss_cls: 3.8218, loss: 3.8218 +2024-07-25 00:23:02,835 - pyskl - INFO - Epoch [80][800/3746] lr: 4.559e-02, eta: 2 days, 11:29:34, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5859, loss_cls: 3.8244, loss: 3.8244 +2024-07-25 00:24:25,952 - pyskl - INFO - Epoch [80][900/3746] lr: 4.557e-02, eta: 2 days, 11:28:16, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5781, loss_cls: 3.8451, loss: 3.8451 +2024-07-25 00:25:48,675 - pyskl - INFO - Epoch [80][1000/3746] lr: 4.554e-02, eta: 2 days, 11:26:56, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5881, loss_cls: 3.8269, loss: 3.8269 +2024-07-25 00:27:11,297 - pyskl - INFO - Epoch [80][1100/3746] lr: 4.551e-02, eta: 2 days, 11:25:37, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5861, loss_cls: 3.8540, loss: 3.8540 +2024-07-25 00:28:34,714 - pyskl - INFO - Epoch [80][1200/3746] lr: 4.548e-02, eta: 2 days, 11:24:19, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5858, loss_cls: 3.8461, loss: 3.8461 +2024-07-25 00:29:57,916 - pyskl - INFO - Epoch [80][1300/3746] lr: 4.545e-02, eta: 2 days, 11:23:00, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.5934, loss_cls: 3.7649, loss: 3.7649 +2024-07-25 00:31:20,918 - pyskl - INFO - Epoch [80][1400/3746] lr: 4.543e-02, eta: 2 days, 11:21:42, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5933, loss_cls: 3.8219, loss: 3.8219 +2024-07-25 00:32:43,641 - pyskl - INFO - Epoch [80][1500/3746] lr: 4.540e-02, eta: 2 days, 11:20:22, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5895, loss_cls: 3.8245, loss: 3.8245 +2024-07-25 00:34:06,484 - pyskl - INFO - Epoch [80][1600/3746] lr: 4.537e-02, eta: 2 days, 11:19:04, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5825, loss_cls: 3.8273, loss: 3.8273 +2024-07-25 00:35:29,779 - pyskl - INFO - Epoch [80][1700/3746] lr: 4.534e-02, eta: 2 days, 11:17:45, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5920, loss_cls: 3.8177, loss: 3.8177 +2024-07-25 00:36:52,872 - pyskl - INFO - Epoch [80][1800/3746] lr: 4.532e-02, eta: 2 days, 11:16:26, time: 0.831, data_time: 0.001, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5853, loss_cls: 3.8616, loss: 3.8616 +2024-07-25 00:38:16,434 - pyskl - INFO - Epoch [80][1900/3746] lr: 4.529e-02, eta: 2 days, 11:15:08, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5745, loss_cls: 3.8879, loss: 3.8879 +2024-07-25 00:39:39,931 - pyskl - INFO - Epoch [80][2000/3746] lr: 4.526e-02, eta: 2 days, 11:13:50, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5869, loss_cls: 3.7859, loss: 3.7859 +2024-07-25 00:41:03,669 - pyskl - INFO - Epoch [80][2100/3746] lr: 4.523e-02, eta: 2 days, 11:12:31, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5814, loss_cls: 3.8421, loss: 3.8421 +2024-07-25 00:42:26,655 - pyskl - INFO - Epoch [80][2200/3746] lr: 4.520e-02, eta: 2 days, 11:11:13, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5820, loss_cls: 3.8606, loss: 3.8606 +2024-07-25 00:43:50,307 - pyskl - INFO - Epoch [80][2300/3746] lr: 4.518e-02, eta: 2 days, 11:09:54, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5797, loss_cls: 3.8910, loss: 3.8910 +2024-07-25 00:45:14,182 - pyskl - INFO - Epoch [80][2400/3746] lr: 4.515e-02, eta: 2 days, 11:08:36, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5941, loss_cls: 3.7968, loss: 3.7968 +2024-07-25 00:46:36,956 - pyskl - INFO - Epoch [80][2500/3746] lr: 4.512e-02, eta: 2 days, 11:07:17, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5991, loss_cls: 3.7646, loss: 3.7646 +2024-07-25 00:48:00,022 - pyskl - INFO - Epoch [80][2600/3746] lr: 4.509e-02, eta: 2 days, 11:05:59, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5795, loss_cls: 3.8951, loss: 3.8951 +2024-07-25 00:49:22,405 - pyskl - INFO - Epoch [80][2700/3746] lr: 4.506e-02, eta: 2 days, 11:04:39, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5883, loss_cls: 3.7938, loss: 3.7938 +2024-07-25 00:50:45,493 - pyskl - INFO - Epoch [80][2800/3746] lr: 4.504e-02, eta: 2 days, 11:03:20, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5887, loss_cls: 3.8387, loss: 3.8387 +2024-07-25 00:52:09,040 - pyskl - INFO - Epoch [80][2900/3746] lr: 4.501e-02, eta: 2 days, 11:02:02, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5978, loss_cls: 3.7678, loss: 3.7678 +2024-07-25 00:53:31,231 - pyskl - INFO - Epoch [80][3000/3746] lr: 4.498e-02, eta: 2 days, 11:00:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5831, loss_cls: 3.8444, loss: 3.8444 +2024-07-25 00:54:54,688 - pyskl - INFO - Epoch [80][3100/3746] lr: 4.495e-02, eta: 2 days, 10:59:24, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5872, loss_cls: 3.8332, loss: 3.8332 +2024-07-25 00:56:17,941 - pyskl - INFO - Epoch [80][3200/3746] lr: 4.493e-02, eta: 2 days, 10:58:05, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5747, loss_cls: 3.9046, loss: 3.9046 +2024-07-25 00:57:40,998 - pyskl - INFO - Epoch [80][3300/3746] lr: 4.490e-02, eta: 2 days, 10:56:47, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5858, loss_cls: 3.8588, loss: 3.8588 +2024-07-25 00:59:03,753 - pyskl - INFO - Epoch [80][3400/3746] lr: 4.487e-02, eta: 2 days, 10:55:28, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5792, loss_cls: 3.8744, loss: 3.8744 +2024-07-25 01:00:26,402 - pyskl - INFO - Epoch [80][3500/3746] lr: 4.484e-02, eta: 2 days, 10:54:08, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5834, loss_cls: 3.8483, loss: 3.8483 +2024-07-25 01:01:49,859 - pyskl - INFO - Epoch [80][3600/3746] lr: 4.481e-02, eta: 2 days, 10:52:50, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5770, loss_cls: 3.8641, loss: 3.8641 +2024-07-25 01:03:12,918 - pyskl - INFO - Epoch [80][3700/3746] lr: 4.479e-02, eta: 2 days, 10:51:31, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5797, loss_cls: 3.8347, loss: 3.8347 +2024-07-25 01:03:52,879 - pyskl - INFO - Saving checkpoint at 80 epochs +2024-07-25 01:05:46,495 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 01:05:47,186 - pyskl - INFO - +top1_acc 0.2888 +top5_acc 0.5319 +2024-07-25 01:05:47,186 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 01:05:47,233 - pyskl - INFO - +mean_acc 0.2885 +2024-07-25 01:05:47,240 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_79.pth was removed +2024-07-25 01:05:47,520 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_80.pth. +2024-07-25 01:05:47,521 - pyskl - INFO - Best top1_acc is 0.2888 at 80 epoch. +2024-07-25 01:05:47,533 - pyskl - INFO - Epoch(val) [80][309] top1_acc: 0.2888, top5_acc: 0.5319, mean_class_accuracy: 0.2885 +2024-07-25 01:09:36,069 - pyskl - INFO - Epoch [81][100/3746] lr: 4.475e-02, eta: 2 days, 10:51:10, time: 2.285, data_time: 1.284, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6073, loss_cls: 3.7266, loss: 3.7266 +2024-07-25 01:10:59,612 - pyskl - INFO - Epoch [81][200/3746] lr: 4.472e-02, eta: 2 days, 10:49:51, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5962, loss_cls: 3.7707, loss: 3.7707 +2024-07-25 01:12:23,346 - pyskl - INFO - Epoch [81][300/3746] lr: 4.469e-02, eta: 2 days, 10:48:33, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.5981, loss_cls: 3.7765, loss: 3.7765 +2024-07-25 01:13:46,743 - pyskl - INFO - Epoch [81][400/3746] lr: 4.466e-02, eta: 2 days, 10:47:14, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5939, loss_cls: 3.7815, loss: 3.7815 +2024-07-25 01:15:09,245 - pyskl - INFO - Epoch [81][500/3746] lr: 4.463e-02, eta: 2 days, 10:45:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5891, loss_cls: 3.8107, loss: 3.8107 +2024-07-25 01:16:32,996 - pyskl - INFO - Epoch [81][600/3746] lr: 4.461e-02, eta: 2 days, 10:44:37, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5989, loss_cls: 3.7886, loss: 3.7886 +2024-07-25 01:17:56,469 - pyskl - INFO - Epoch [81][700/3746] lr: 4.458e-02, eta: 2 days, 10:43:18, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5891, loss_cls: 3.8042, loss: 3.8042 +2024-07-25 01:19:19,242 - pyskl - INFO - Epoch [81][800/3746] lr: 4.455e-02, eta: 2 days, 10:41:59, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5855, loss_cls: 3.8235, loss: 3.8235 +2024-07-25 01:20:41,966 - pyskl - INFO - Epoch [81][900/3746] lr: 4.452e-02, eta: 2 days, 10:40:40, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.5909, loss_cls: 3.7982, loss: 3.7982 +2024-07-25 01:22:05,283 - pyskl - INFO - Epoch [81][1000/3746] lr: 4.450e-02, eta: 2 days, 10:39:21, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5920, loss_cls: 3.8143, loss: 3.8143 +2024-07-25 01:23:28,440 - pyskl - INFO - Epoch [81][1100/3746] lr: 4.447e-02, eta: 2 days, 10:38:02, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5859, loss_cls: 3.8329, loss: 3.8329 +2024-07-25 01:24:51,403 - pyskl - INFO - Epoch [81][1200/3746] lr: 4.444e-02, eta: 2 days, 10:36:43, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.5948, loss_cls: 3.7880, loss: 3.7880 +2024-07-25 01:26:14,439 - pyskl - INFO - Epoch [81][1300/3746] lr: 4.441e-02, eta: 2 days, 10:35:25, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5930, loss_cls: 3.7862, loss: 3.7862 +2024-07-25 01:27:37,308 - pyskl - INFO - Epoch [81][1400/3746] lr: 4.438e-02, eta: 2 days, 10:34:05, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5813, loss_cls: 3.8569, loss: 3.8569 +2024-07-25 01:29:00,641 - pyskl - INFO - Epoch [81][1500/3746] lr: 4.436e-02, eta: 2 days, 10:32:47, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5778, loss_cls: 3.8530, loss: 3.8530 +2024-07-25 01:30:23,672 - pyskl - INFO - Epoch [81][1600/3746] lr: 4.433e-02, eta: 2 days, 10:31:28, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5889, loss_cls: 3.7990, loss: 3.7990 +2024-07-25 01:31:47,225 - pyskl - INFO - Epoch [81][1700/3746] lr: 4.430e-02, eta: 2 days, 10:30:09, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5836, loss_cls: 3.8345, loss: 3.8345 +2024-07-25 01:33:10,807 - pyskl - INFO - Epoch [81][1800/3746] lr: 4.427e-02, eta: 2 days, 10:28:51, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5869, loss_cls: 3.8326, loss: 3.8326 +2024-07-25 01:34:33,758 - pyskl - INFO - Epoch [81][1900/3746] lr: 4.425e-02, eta: 2 days, 10:27:32, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5863, loss_cls: 3.8248, loss: 3.8248 +2024-07-25 01:35:57,303 - pyskl - INFO - Epoch [81][2000/3746] lr: 4.422e-02, eta: 2 days, 10:26:13, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5916, loss_cls: 3.7719, loss: 3.7719 +2024-07-25 01:37:21,228 - pyskl - INFO - Epoch [81][2100/3746] lr: 4.419e-02, eta: 2 days, 10:24:55, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5805, loss_cls: 3.8284, loss: 3.8284 +2024-07-25 01:38:44,500 - pyskl - INFO - Epoch [81][2200/3746] lr: 4.416e-02, eta: 2 days, 10:23:36, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5727, loss_cls: 3.8968, loss: 3.8968 +2024-07-25 01:40:09,381 - pyskl - INFO - Epoch [81][2300/3746] lr: 4.413e-02, eta: 2 days, 10:22:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5870, loss_cls: 3.8451, loss: 3.8451 +2024-07-25 01:41:33,464 - pyskl - INFO - Epoch [81][2400/3746] lr: 4.411e-02, eta: 2 days, 10:21:01, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5794, loss_cls: 3.8512, loss: 3.8512 +2024-07-25 01:42:57,527 - pyskl - INFO - Epoch [81][2500/3746] lr: 4.408e-02, eta: 2 days, 10:19:43, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5883, loss_cls: 3.8293, loss: 3.8293 +2024-07-25 01:44:21,237 - pyskl - INFO - Epoch [81][2600/3746] lr: 4.405e-02, eta: 2 days, 10:18:24, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5808, loss_cls: 3.8449, loss: 3.8449 +2024-07-25 01:45:44,122 - pyskl - INFO - Epoch [81][2700/3746] lr: 4.402e-02, eta: 2 days, 10:17:05, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5881, loss_cls: 3.8269, loss: 3.8269 +2024-07-25 01:47:07,336 - pyskl - INFO - Epoch [81][2800/3746] lr: 4.400e-02, eta: 2 days, 10:15:46, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5727, loss_cls: 3.8807, loss: 3.8807 +2024-07-25 01:48:29,841 - pyskl - INFO - Epoch [81][2900/3746] lr: 4.397e-02, eta: 2 days, 10:14:27, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5945, loss_cls: 3.8018, loss: 3.8018 +2024-07-25 01:49:52,722 - pyskl - INFO - Epoch [81][3000/3746] lr: 4.394e-02, eta: 2 days, 10:13:08, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5878, loss_cls: 3.8289, loss: 3.8289 +2024-07-25 01:51:16,767 - pyskl - INFO - Epoch [81][3100/3746] lr: 4.391e-02, eta: 2 days, 10:11:50, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5808, loss_cls: 3.8387, loss: 3.8387 +2024-07-25 01:52:40,626 - pyskl - INFO - Epoch [81][3200/3746] lr: 4.389e-02, eta: 2 days, 10:10:32, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5938, loss_cls: 3.7852, loss: 3.7852 +2024-07-25 01:54:04,348 - pyskl - INFO - Epoch [81][3300/3746] lr: 4.386e-02, eta: 2 days, 10:09:13, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5889, loss_cls: 3.8211, loss: 3.8211 +2024-07-25 01:55:27,463 - pyskl - INFO - Epoch [81][3400/3746] lr: 4.383e-02, eta: 2 days, 10:07:54, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5859, loss_cls: 3.7959, loss: 3.7959 +2024-07-25 01:56:51,259 - pyskl - INFO - Epoch [81][3500/3746] lr: 4.380e-02, eta: 2 days, 10:06:36, time: 0.838, data_time: 0.001, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5855, loss_cls: 3.8288, loss: 3.8288 +2024-07-25 01:58:14,822 - pyskl - INFO - Epoch [81][3600/3746] lr: 4.377e-02, eta: 2 days, 10:05:17, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5914, loss_cls: 3.8192, loss: 3.8192 +2024-07-25 01:59:37,463 - pyskl - INFO - Epoch [81][3700/3746] lr: 4.375e-02, eta: 2 days, 10:03:58, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5819, loss_cls: 3.8585, loss: 3.8585 +2024-07-25 02:00:17,954 - pyskl - INFO - Saving checkpoint at 81 epochs +2024-07-25 02:02:10,664 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 02:02:11,362 - pyskl - INFO - +top1_acc 0.2748 +top5_acc 0.5206 +2024-07-25 02:02:11,362 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 02:02:11,412 - pyskl - INFO - +mean_acc 0.2747 +2024-07-25 02:02:11,429 - pyskl - INFO - Epoch(val) [81][309] top1_acc: 0.2748, top5_acc: 0.5206, mean_class_accuracy: 0.2747 +2024-07-25 02:06:04,981 - pyskl - INFO - Epoch [82][100/3746] lr: 4.371e-02, eta: 2 days, 10:03:38, time: 2.335, data_time: 1.328, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6005, loss_cls: 3.7116, loss: 3.7116 +2024-07-25 02:07:28,172 - pyskl - INFO - Epoch [82][200/3746] lr: 4.368e-02, eta: 2 days, 10:02:19, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6009, loss_cls: 3.7639, loss: 3.7639 +2024-07-25 02:08:50,307 - pyskl - INFO - Epoch [82][300/3746] lr: 4.365e-02, eta: 2 days, 10:00:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5861, loss_cls: 3.8193, loss: 3.8193 +2024-07-25 02:10:11,728 - pyskl - INFO - Epoch [82][400/3746] lr: 4.362e-02, eta: 2 days, 9:59:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.6017, loss_cls: 3.7490, loss: 3.7490 +2024-07-25 02:11:33,823 - pyskl - INFO - Epoch [82][500/3746] lr: 4.359e-02, eta: 2 days, 9:58:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5927, loss_cls: 3.8008, loss: 3.8008 +2024-07-25 02:12:55,693 - pyskl - INFO - Epoch [82][600/3746] lr: 4.357e-02, eta: 2 days, 9:56:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5945, loss_cls: 3.8063, loss: 3.8063 +2024-07-25 02:14:17,285 - pyskl - INFO - Epoch [82][700/3746] lr: 4.354e-02, eta: 2 days, 9:55:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5973, loss_cls: 3.7856, loss: 3.7856 +2024-07-25 02:15:39,336 - pyskl - INFO - Epoch [82][800/3746] lr: 4.351e-02, eta: 2 days, 9:54:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5866, loss_cls: 3.7993, loss: 3.7993 +2024-07-25 02:17:00,900 - pyskl - INFO - Epoch [82][900/3746] lr: 4.348e-02, eta: 2 days, 9:52:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5847, loss_cls: 3.7961, loss: 3.7961 +2024-07-25 02:18:23,121 - pyskl - INFO - Epoch [82][1000/3746] lr: 4.346e-02, eta: 2 days, 9:51:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5969, loss_cls: 3.8068, loss: 3.8068 +2024-07-25 02:19:44,665 - pyskl - INFO - Epoch [82][1100/3746] lr: 4.343e-02, eta: 2 days, 9:50:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5866, loss_cls: 3.8046, loss: 3.8046 +2024-07-25 02:21:06,337 - pyskl - INFO - Epoch [82][1200/3746] lr: 4.340e-02, eta: 2 days, 9:48:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5881, loss_cls: 3.8304, loss: 3.8304 +2024-07-25 02:22:28,232 - pyskl - INFO - Epoch [82][1300/3746] lr: 4.337e-02, eta: 2 days, 9:47:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5891, loss_cls: 3.8118, loss: 3.8118 +2024-07-25 02:23:49,973 - pyskl - INFO - Epoch [82][1400/3746] lr: 4.335e-02, eta: 2 days, 9:46:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5834, loss_cls: 3.8331, loss: 3.8331 +2024-07-25 02:25:12,079 - pyskl - INFO - Epoch [82][1500/3746] lr: 4.332e-02, eta: 2 days, 9:44:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5978, loss_cls: 3.7883, loss: 3.7883 +2024-07-25 02:26:33,761 - pyskl - INFO - Epoch [82][1600/3746] lr: 4.329e-02, eta: 2 days, 9:43:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.6003, loss_cls: 3.7617, loss: 3.7617 +2024-07-25 02:27:55,237 - pyskl - INFO - Epoch [82][1700/3746] lr: 4.326e-02, eta: 2 days, 9:42:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.5997, loss_cls: 3.7883, loss: 3.7883 +2024-07-25 02:29:16,947 - pyskl - INFO - Epoch [82][1800/3746] lr: 4.323e-02, eta: 2 days, 9:40:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5884, loss_cls: 3.8125, loss: 3.8125 +2024-07-25 02:30:39,579 - pyskl - INFO - Epoch [82][1900/3746] lr: 4.321e-02, eta: 2 days, 9:39:38, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5944, loss_cls: 3.7826, loss: 3.7826 +2024-07-25 02:32:01,367 - pyskl - INFO - Epoch [82][2000/3746] lr: 4.318e-02, eta: 2 days, 9:38:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5906, loss_cls: 3.7935, loss: 3.7935 +2024-07-25 02:33:23,183 - pyskl - INFO - Epoch [82][2100/3746] lr: 4.315e-02, eta: 2 days, 9:36:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5870, loss_cls: 3.8096, loss: 3.8096 +2024-07-25 02:34:46,325 - pyskl - INFO - Epoch [82][2200/3746] lr: 4.312e-02, eta: 2 days, 9:35:38, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5891, loss_cls: 3.8374, loss: 3.8374 +2024-07-25 02:36:08,046 - pyskl - INFO - Epoch [82][2300/3746] lr: 4.310e-02, eta: 2 days, 9:34:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5887, loss_cls: 3.8142, loss: 3.8142 +2024-07-25 02:37:30,288 - pyskl - INFO - Epoch [82][2400/3746] lr: 4.307e-02, eta: 2 days, 9:32:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5973, loss_cls: 3.7946, loss: 3.7946 +2024-07-25 02:38:51,767 - pyskl - INFO - Epoch [82][2500/3746] lr: 4.304e-02, eta: 2 days, 9:31:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5853, loss_cls: 3.8217, loss: 3.8217 +2024-07-25 02:40:13,751 - pyskl - INFO - Epoch [82][2600/3746] lr: 4.301e-02, eta: 2 days, 9:30:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5877, loss_cls: 3.8230, loss: 3.8230 +2024-07-25 02:41:35,801 - pyskl - INFO - Epoch [82][2700/3746] lr: 4.299e-02, eta: 2 days, 9:28:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5864, loss_cls: 3.7919, loss: 3.7919 +2024-07-25 02:42:58,261 - pyskl - INFO - Epoch [82][2800/3746] lr: 4.296e-02, eta: 2 days, 9:27:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5859, loss_cls: 3.8624, loss: 3.8624 +2024-07-25 02:44:20,088 - pyskl - INFO - Epoch [82][2900/3746] lr: 4.293e-02, eta: 2 days, 9:26:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5967, loss_cls: 3.7751, loss: 3.7751 +2024-07-25 02:45:42,284 - pyskl - INFO - Epoch [82][3000/3746] lr: 4.290e-02, eta: 2 days, 9:24:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5808, loss_cls: 3.8644, loss: 3.8644 +2024-07-25 02:47:04,488 - pyskl - INFO - Epoch [82][3100/3746] lr: 4.287e-02, eta: 2 days, 9:23:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5919, loss_cls: 3.8290, loss: 3.8290 +2024-07-25 02:48:26,597 - pyskl - INFO - Epoch [82][3200/3746] lr: 4.285e-02, eta: 2 days, 9:22:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5867, loss_cls: 3.8415, loss: 3.8415 +2024-07-25 02:49:48,282 - pyskl - INFO - Epoch [82][3300/3746] lr: 4.282e-02, eta: 2 days, 9:20:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5928, loss_cls: 3.7977, loss: 3.7977 +2024-07-25 02:51:09,772 - pyskl - INFO - Epoch [82][3400/3746] lr: 4.279e-02, eta: 2 days, 9:19:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5859, loss_cls: 3.8248, loss: 3.8248 +2024-07-25 02:52:31,832 - pyskl - INFO - Epoch [82][3500/3746] lr: 4.276e-02, eta: 2 days, 9:18:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5931, loss_cls: 3.8272, loss: 3.8272 +2024-07-25 02:53:53,620 - pyskl - INFO - Epoch [82][3600/3746] lr: 4.274e-02, eta: 2 days, 9:16:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5953, loss_cls: 3.7803, loss: 3.7803 +2024-07-25 02:55:15,046 - pyskl - INFO - Epoch [82][3700/3746] lr: 4.271e-02, eta: 2 days, 9:15:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5916, loss_cls: 3.7953, loss: 3.7953 +2024-07-25 02:55:55,032 - pyskl - INFO - Saving checkpoint at 82 epochs +2024-07-25 02:57:47,462 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 02:57:48,117 - pyskl - INFO - +top1_acc 0.2694 +top5_acc 0.5225 +2024-07-25 02:57:48,117 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 02:57:48,157 - pyskl - INFO - +mean_acc 0.2692 +2024-07-25 02:57:48,169 - pyskl - INFO - Epoch(val) [82][309] top1_acc: 0.2694, top5_acc: 0.5225, mean_class_accuracy: 0.2692 +2024-07-25 03:01:35,696 - pyskl - INFO - Epoch [83][100/3746] lr: 4.267e-02, eta: 2 days, 9:15:10, time: 2.275, data_time: 1.296, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5959, loss_cls: 3.7563, loss: 3.7563 +2024-07-25 03:02:57,799 - pyskl - INFO - Epoch [83][200/3746] lr: 4.264e-02, eta: 2 days, 9:13:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6139, loss_cls: 3.6845, loss: 3.6845 +2024-07-25 03:04:20,006 - pyskl - INFO - Epoch [83][300/3746] lr: 4.261e-02, eta: 2 days, 9:12:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.6020, loss_cls: 3.7424, loss: 3.7424 +2024-07-25 03:05:41,486 - pyskl - INFO - Epoch [83][400/3746] lr: 4.259e-02, eta: 2 days, 9:11:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6059, loss_cls: 3.7182, loss: 3.7182 +2024-07-25 03:07:03,192 - pyskl - INFO - Epoch [83][500/3746] lr: 4.256e-02, eta: 2 days, 9:09:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5916, loss_cls: 3.7669, loss: 3.7669 +2024-07-25 03:08:24,862 - pyskl - INFO - Epoch [83][600/3746] lr: 4.253e-02, eta: 2 days, 9:08:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.6012, loss_cls: 3.7498, loss: 3.7498 +2024-07-25 03:09:45,954 - pyskl - INFO - Epoch [83][700/3746] lr: 4.250e-02, eta: 2 days, 9:07:08, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5942, loss_cls: 3.7662, loss: 3.7662 +2024-07-25 03:11:07,553 - pyskl - INFO - Epoch [83][800/3746] lr: 4.247e-02, eta: 2 days, 9:05:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.6092, loss_cls: 3.7146, loss: 3.7146 +2024-07-25 03:12:28,980 - pyskl - INFO - Epoch [83][900/3746] lr: 4.245e-02, eta: 2 days, 9:04:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5908, loss_cls: 3.8062, loss: 3.8062 +2024-07-25 03:13:50,424 - pyskl - INFO - Epoch [83][1000/3746] lr: 4.242e-02, eta: 2 days, 9:03:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5920, loss_cls: 3.8215, loss: 3.8215 +2024-07-25 03:15:12,095 - pyskl - INFO - Epoch [83][1100/3746] lr: 4.239e-02, eta: 2 days, 9:01:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.5948, loss_cls: 3.8007, loss: 3.8007 +2024-07-25 03:16:33,651 - pyskl - INFO - Epoch [83][1200/3746] lr: 4.236e-02, eta: 2 days, 9:00:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5930, loss_cls: 3.7930, loss: 3.7930 +2024-07-25 03:17:54,913 - pyskl - INFO - Epoch [83][1300/3746] lr: 4.234e-02, eta: 2 days, 8:59:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5909, loss_cls: 3.8131, loss: 3.8131 +2024-07-25 03:19:16,783 - pyskl - INFO - Epoch [83][1400/3746] lr: 4.231e-02, eta: 2 days, 8:57:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.5984, loss_cls: 3.7568, loss: 3.7568 +2024-07-25 03:20:38,025 - pyskl - INFO - Epoch [83][1500/3746] lr: 4.228e-02, eta: 2 days, 8:56:24, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.6056, loss_cls: 3.7315, loss: 3.7315 +2024-07-25 03:21:59,486 - pyskl - INFO - Epoch [83][1600/3746] lr: 4.225e-02, eta: 2 days, 8:55:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5913, loss_cls: 3.7829, loss: 3.7829 +2024-07-25 03:23:20,712 - pyskl - INFO - Epoch [83][1700/3746] lr: 4.223e-02, eta: 2 days, 8:53:43, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5950, loss_cls: 3.7853, loss: 3.7853 +2024-07-25 03:24:43,074 - pyskl - INFO - Epoch [83][1800/3746] lr: 4.220e-02, eta: 2 days, 8:52:23, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5980, loss_cls: 3.7994, loss: 3.7994 +2024-07-25 03:26:05,269 - pyskl - INFO - Epoch [83][1900/3746] lr: 4.217e-02, eta: 2 days, 8:51:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5972, loss_cls: 3.7807, loss: 3.7807 +2024-07-25 03:27:27,641 - pyskl - INFO - Epoch [83][2000/3746] lr: 4.214e-02, eta: 2 days, 8:49:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5891, loss_cls: 3.8198, loss: 3.8198 +2024-07-25 03:28:49,900 - pyskl - INFO - Epoch [83][2100/3746] lr: 4.212e-02, eta: 2 days, 8:48:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5867, loss_cls: 3.8267, loss: 3.8267 +2024-07-25 03:30:12,220 - pyskl - INFO - Epoch [83][2200/3746] lr: 4.209e-02, eta: 2 days, 8:47:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5920, loss_cls: 3.7868, loss: 3.7868 +2024-07-25 03:31:34,074 - pyskl - INFO - Epoch [83][2300/3746] lr: 4.206e-02, eta: 2 days, 8:45:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5852, loss_cls: 3.8468, loss: 3.8468 +2024-07-25 03:32:55,613 - pyskl - INFO - Epoch [83][2400/3746] lr: 4.203e-02, eta: 2 days, 8:44:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5875, loss_cls: 3.7735, loss: 3.7735 +2024-07-25 03:34:17,192 - pyskl - INFO - Epoch [83][2500/3746] lr: 4.201e-02, eta: 2 days, 8:43:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5820, loss_cls: 3.8451, loss: 3.8451 +2024-07-25 03:35:39,325 - pyskl - INFO - Epoch [83][2600/3746] lr: 4.198e-02, eta: 2 days, 8:41:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5927, loss_cls: 3.7983, loss: 3.7983 +2024-07-25 03:37:01,279 - pyskl - INFO - Epoch [83][2700/3746] lr: 4.195e-02, eta: 2 days, 8:40:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5948, loss_cls: 3.8007, loss: 3.8007 +2024-07-25 03:38:22,891 - pyskl - INFO - Epoch [83][2800/3746] lr: 4.192e-02, eta: 2 days, 8:39:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.5922, loss_cls: 3.7830, loss: 3.7830 +2024-07-25 03:39:44,986 - pyskl - INFO - Epoch [83][2900/3746] lr: 4.190e-02, eta: 2 days, 8:37:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5872, loss_cls: 3.8085, loss: 3.8085 +2024-07-25 03:41:07,125 - pyskl - INFO - Epoch [83][3000/3746] lr: 4.187e-02, eta: 2 days, 8:36:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5867, loss_cls: 3.8116, loss: 3.8116 +2024-07-25 03:42:29,016 - pyskl - INFO - Epoch [83][3100/3746] lr: 4.184e-02, eta: 2 days, 8:35:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.5988, loss_cls: 3.7640, loss: 3.7640 +2024-07-25 03:43:50,149 - pyskl - INFO - Epoch [83][3200/3746] lr: 4.181e-02, eta: 2 days, 8:33:41, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5867, loss_cls: 3.8424, loss: 3.8424 +2024-07-25 03:45:11,623 - pyskl - INFO - Epoch [83][3300/3746] lr: 4.178e-02, eta: 2 days, 8:32:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5864, loss_cls: 3.8246, loss: 3.8246 +2024-07-25 03:46:33,532 - pyskl - INFO - Epoch [83][3400/3746] lr: 4.176e-02, eta: 2 days, 8:31:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5870, loss_cls: 3.8151, loss: 3.8151 +2024-07-25 03:47:55,500 - pyskl - INFO - Epoch [83][3500/3746] lr: 4.173e-02, eta: 2 days, 8:29:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.5922, loss_cls: 3.7907, loss: 3.7907 +2024-07-25 03:49:17,157 - pyskl - INFO - Epoch [83][3600/3746] lr: 4.170e-02, eta: 2 days, 8:28:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5852, loss_cls: 3.8241, loss: 3.8241 +2024-07-25 03:50:39,111 - pyskl - INFO - Epoch [83][3700/3746] lr: 4.167e-02, eta: 2 days, 8:26:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.5975, loss_cls: 3.7794, loss: 3.7794 +2024-07-25 03:51:18,897 - pyskl - INFO - Saving checkpoint at 83 epochs +2024-07-25 03:53:11,865 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 03:53:12,527 - pyskl - INFO - +top1_acc 0.2779 +top5_acc 0.5256 +2024-07-25 03:53:12,527 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 03:53:12,567 - pyskl - INFO - +mean_acc 0.2776 +2024-07-25 03:53:12,579 - pyskl - INFO - Epoch(val) [83][309] top1_acc: 0.2779, top5_acc: 0.5256, mean_class_accuracy: 0.2776 +2024-07-25 03:57:09,884 - pyskl - INFO - Epoch [84][100/3746] lr: 4.163e-02, eta: 2 days, 8:26:37, time: 2.373, data_time: 1.369, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6036, loss_cls: 3.7219, loss: 3.7219 +2024-07-25 03:58:32,082 - pyskl - INFO - Epoch [84][200/3746] lr: 4.161e-02, eta: 2 days, 8:25:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5969, loss_cls: 3.7776, loss: 3.7776 +2024-07-25 03:59:54,610 - pyskl - INFO - Epoch [84][300/3746] lr: 4.158e-02, eta: 2 days, 8:23:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.5992, loss_cls: 3.7520, loss: 3.7520 +2024-07-25 04:01:16,662 - pyskl - INFO - Epoch [84][400/3746] lr: 4.155e-02, eta: 2 days, 8:22:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6027, loss_cls: 3.7375, loss: 3.7375 +2024-07-25 04:02:38,138 - pyskl - INFO - Epoch [84][500/3746] lr: 4.152e-02, eta: 2 days, 8:21:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5852, loss_cls: 3.8248, loss: 3.8248 +2024-07-25 04:03:59,899 - pyskl - INFO - Epoch [84][600/3746] lr: 4.150e-02, eta: 2 days, 8:19:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5989, loss_cls: 3.7725, loss: 3.7725 +2024-07-25 04:05:21,666 - pyskl - INFO - Epoch [84][700/3746] lr: 4.147e-02, eta: 2 days, 8:18:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5991, loss_cls: 3.7741, loss: 3.7741 +2024-07-25 04:06:43,306 - pyskl - INFO - Epoch [84][800/3746] lr: 4.144e-02, eta: 2 days, 8:17:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5939, loss_cls: 3.7820, loss: 3.7820 +2024-07-25 04:08:05,270 - pyskl - INFO - Epoch [84][900/3746] lr: 4.141e-02, eta: 2 days, 8:15:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6061, loss_cls: 3.7268, loss: 3.7268 +2024-07-25 04:09:26,818 - pyskl - INFO - Epoch [84][1000/3746] lr: 4.139e-02, eta: 2 days, 8:14:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.5991, loss_cls: 3.7448, loss: 3.7448 +2024-07-25 04:10:48,751 - pyskl - INFO - Epoch [84][1100/3746] lr: 4.136e-02, eta: 2 days, 8:13:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.5944, loss_cls: 3.7516, loss: 3.7516 +2024-07-25 04:12:10,534 - pyskl - INFO - Epoch [84][1200/3746] lr: 4.133e-02, eta: 2 days, 8:11:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.5948, loss_cls: 3.7765, loss: 3.7765 +2024-07-25 04:13:32,165 - pyskl - INFO - Epoch [84][1300/3746] lr: 4.130e-02, eta: 2 days, 8:10:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6002, loss_cls: 3.7464, loss: 3.7464 +2024-07-25 04:14:53,973 - pyskl - INFO - Epoch [84][1400/3746] lr: 4.128e-02, eta: 2 days, 8:09:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5913, loss_cls: 3.7794, loss: 3.7794 +2024-07-25 04:16:15,139 - pyskl - INFO - Epoch [84][1500/3746] lr: 4.125e-02, eta: 2 days, 8:07:52, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.5983, loss_cls: 3.7649, loss: 3.7649 +2024-07-25 04:17:37,069 - pyskl - INFO - Epoch [84][1600/3746] lr: 4.122e-02, eta: 2 days, 8:06:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5878, loss_cls: 3.8226, loss: 3.8226 +2024-07-25 04:18:59,056 - pyskl - INFO - Epoch [84][1700/3746] lr: 4.119e-02, eta: 2 days, 8:05:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5902, loss_cls: 3.7783, loss: 3.7783 +2024-07-25 04:20:21,283 - pyskl - INFO - Epoch [84][1800/3746] lr: 4.117e-02, eta: 2 days, 8:03:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6002, loss_cls: 3.7647, loss: 3.7647 +2024-07-25 04:21:43,405 - pyskl - INFO - Epoch [84][1900/3746] lr: 4.114e-02, eta: 2 days, 8:02:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5947, loss_cls: 3.7888, loss: 3.7888 +2024-07-25 04:23:06,628 - pyskl - INFO - Epoch [84][2000/3746] lr: 4.111e-02, eta: 2 days, 8:01:13, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5920, loss_cls: 3.7901, loss: 3.7901 +2024-07-25 04:24:29,015 - pyskl - INFO - Epoch [84][2100/3746] lr: 4.108e-02, eta: 2 days, 7:59:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5861, loss_cls: 3.8241, loss: 3.8241 +2024-07-25 04:25:51,209 - pyskl - INFO - Epoch [84][2200/3746] lr: 4.106e-02, eta: 2 days, 7:58:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5916, loss_cls: 3.7999, loss: 3.7999 +2024-07-25 04:27:13,125 - pyskl - INFO - Epoch [84][2300/3746] lr: 4.103e-02, eta: 2 days, 7:57:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.6016, loss_cls: 3.7763, loss: 3.7763 +2024-07-25 04:28:35,054 - pyskl - INFO - Epoch [84][2400/3746] lr: 4.100e-02, eta: 2 days, 7:55:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6045, loss_cls: 3.7555, loss: 3.7555 +2024-07-25 04:29:57,254 - pyskl - INFO - Epoch [84][2500/3746] lr: 4.097e-02, eta: 2 days, 7:54:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6095, loss_cls: 3.7091, loss: 3.7091 +2024-07-25 04:31:18,751 - pyskl - INFO - Epoch [84][2600/3746] lr: 4.095e-02, eta: 2 days, 7:53:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5803, loss_cls: 3.8515, loss: 3.8515 +2024-07-25 04:32:40,943 - pyskl - INFO - Epoch [84][2700/3746] lr: 4.092e-02, eta: 2 days, 7:51:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5867, loss_cls: 3.8236, loss: 3.8236 +2024-07-25 04:34:02,679 - pyskl - INFO - Epoch [84][2800/3746] lr: 4.089e-02, eta: 2 days, 7:50:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5841, loss_cls: 3.8411, loss: 3.8411 +2024-07-25 04:35:25,575 - pyskl - INFO - Epoch [84][2900/3746] lr: 4.086e-02, eta: 2 days, 7:49:11, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.6005, loss_cls: 3.7651, loss: 3.7651 +2024-07-25 04:36:47,763 - pyskl - INFO - Epoch [84][3000/3746] lr: 4.084e-02, eta: 2 days, 7:47:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.5978, loss_cls: 3.7390, loss: 3.7390 +2024-07-25 04:38:09,133 - pyskl - INFO - Epoch [84][3100/3746] lr: 4.081e-02, eta: 2 days, 7:46:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5930, loss_cls: 3.8011, loss: 3.8011 +2024-07-25 04:39:30,461 - pyskl - INFO - Epoch [84][3200/3746] lr: 4.078e-02, eta: 2 days, 7:45:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6048, loss_cls: 3.7339, loss: 3.7339 +2024-07-25 04:40:52,244 - pyskl - INFO - Epoch [84][3300/3746] lr: 4.075e-02, eta: 2 days, 7:43:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5998, loss_cls: 3.7894, loss: 3.7894 +2024-07-25 04:42:14,602 - pyskl - INFO - Epoch [84][3400/3746] lr: 4.073e-02, eta: 2 days, 7:42:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5863, loss_cls: 3.8269, loss: 3.8269 +2024-07-25 04:43:36,458 - pyskl - INFO - Epoch [84][3500/3746] lr: 4.070e-02, eta: 2 days, 7:41:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5913, loss_cls: 3.8062, loss: 3.8062 +2024-07-25 04:44:58,448 - pyskl - INFO - Epoch [84][3600/3746] lr: 4.067e-02, eta: 2 days, 7:39:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5920, loss_cls: 3.7828, loss: 3.7828 +2024-07-25 04:46:19,876 - pyskl - INFO - Epoch [84][3700/3746] lr: 4.064e-02, eta: 2 days, 7:38:28, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5967, loss_cls: 3.8036, loss: 3.8036 +2024-07-25 04:46:59,878 - pyskl - INFO - Saving checkpoint at 84 epochs +2024-07-25 04:48:52,676 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 04:48:53,351 - pyskl - INFO - +top1_acc 0.2653 +top5_acc 0.5133 +2024-07-25 04:48:53,351 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 04:48:53,401 - pyskl - INFO - +mean_acc 0.2651 +2024-07-25 04:48:53,418 - pyskl - INFO - Epoch(val) [84][309] top1_acc: 0.2653, top5_acc: 0.5133, mean_class_accuracy: 0.2651 +2024-07-25 04:52:52,236 - pyskl - INFO - Epoch [85][100/3746] lr: 4.060e-02, eta: 2 days, 7:38:05, time: 2.388, data_time: 1.397, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6045, loss_cls: 3.7122, loss: 3.7122 +2024-07-25 04:54:14,737 - pyskl - INFO - Epoch [85][200/3746] lr: 4.058e-02, eta: 2 days, 7:36:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.5998, loss_cls: 3.7197, loss: 3.7197 +2024-07-25 04:55:36,299 - pyskl - INFO - Epoch [85][300/3746] lr: 4.055e-02, eta: 2 days, 7:35:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.5948, loss_cls: 3.7375, loss: 3.7375 +2024-07-25 04:56:58,115 - pyskl - INFO - Epoch [85][400/3746] lr: 4.052e-02, eta: 2 days, 7:34:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5975, loss_cls: 3.7755, loss: 3.7755 +2024-07-25 04:58:20,000 - pyskl - INFO - Epoch [85][500/3746] lr: 4.049e-02, eta: 2 days, 7:32:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.5986, loss_cls: 3.7643, loss: 3.7643 +2024-07-25 04:59:41,743 - pyskl - INFO - Epoch [85][600/3746] lr: 4.047e-02, eta: 2 days, 7:31:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5972, loss_cls: 3.7549, loss: 3.7549 +2024-07-25 05:01:03,647 - pyskl - INFO - Epoch [85][700/3746] lr: 4.044e-02, eta: 2 days, 7:30:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.5938, loss_cls: 3.7589, loss: 3.7589 +2024-07-25 05:02:25,713 - pyskl - INFO - Epoch [85][800/3746] lr: 4.041e-02, eta: 2 days, 7:28:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5989, loss_cls: 3.7839, loss: 3.7839 +2024-07-25 05:03:47,437 - pyskl - INFO - Epoch [85][900/3746] lr: 4.038e-02, eta: 2 days, 7:27:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.6061, loss_cls: 3.7182, loss: 3.7182 +2024-07-25 05:05:09,077 - pyskl - INFO - Epoch [85][1000/3746] lr: 4.036e-02, eta: 2 days, 7:26:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.6012, loss_cls: 3.7592, loss: 3.7592 +2024-07-25 05:06:30,953 - pyskl - INFO - Epoch [85][1100/3746] lr: 4.033e-02, eta: 2 days, 7:24:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.6011, loss_cls: 3.7559, loss: 3.7559 +2024-07-25 05:07:53,749 - pyskl - INFO - Epoch [85][1200/3746] lr: 4.030e-02, eta: 2 days, 7:23:21, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.6017, loss_cls: 3.7463, loss: 3.7463 +2024-07-25 05:09:15,542 - pyskl - INFO - Epoch [85][1300/3746] lr: 4.027e-02, eta: 2 days, 7:22:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5948, loss_cls: 3.7834, loss: 3.7834 +2024-07-25 05:10:38,388 - pyskl - INFO - Epoch [85][1400/3746] lr: 4.025e-02, eta: 2 days, 7:20:41, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5997, loss_cls: 3.7812, loss: 3.7812 +2024-07-25 05:11:59,870 - pyskl - INFO - Epoch [85][1500/3746] lr: 4.022e-02, eta: 2 days, 7:19:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.6002, loss_cls: 3.7648, loss: 3.7648 +2024-07-25 05:13:21,905 - pyskl - INFO - Epoch [85][1600/3746] lr: 4.019e-02, eta: 2 days, 7:18:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5903, loss_cls: 3.8107, loss: 3.8107 +2024-07-25 05:14:43,622 - pyskl - INFO - Epoch [85][1700/3746] lr: 4.016e-02, eta: 2 days, 7:16:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5884, loss_cls: 3.7848, loss: 3.7848 +2024-07-25 05:16:06,256 - pyskl - INFO - Epoch [85][1800/3746] lr: 4.014e-02, eta: 2 days, 7:15:20, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.5977, loss_cls: 3.7699, loss: 3.7699 +2024-07-25 05:17:28,580 - pyskl - INFO - Epoch [85][1900/3746] lr: 4.011e-02, eta: 2 days, 7:14:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.5869, loss_cls: 3.7766, loss: 3.7766 +2024-07-25 05:18:50,216 - pyskl - INFO - Epoch [85][2000/3746] lr: 4.008e-02, eta: 2 days, 7:12:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5927, loss_cls: 3.7492, loss: 3.7492 +2024-07-25 05:20:12,286 - pyskl - INFO - Epoch [85][2100/3746] lr: 4.006e-02, eta: 2 days, 7:11:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6008, loss_cls: 3.7487, loss: 3.7487 +2024-07-25 05:21:34,044 - pyskl - INFO - Epoch [85][2200/3746] lr: 4.003e-02, eta: 2 days, 7:09:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5994, loss_cls: 3.7453, loss: 3.7453 +2024-07-25 05:22:55,926 - pyskl - INFO - Epoch [85][2300/3746] lr: 4.000e-02, eta: 2 days, 7:08:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.5955, loss_cls: 3.7637, loss: 3.7637 +2024-07-25 05:24:17,594 - pyskl - INFO - Epoch [85][2400/3746] lr: 3.997e-02, eta: 2 days, 7:07:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.5962, loss_cls: 3.7672, loss: 3.7672 +2024-07-25 05:25:39,706 - pyskl - INFO - Epoch [85][2500/3746] lr: 3.995e-02, eta: 2 days, 7:05:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5934, loss_cls: 3.7682, loss: 3.7682 +2024-07-25 05:27:01,329 - pyskl - INFO - Epoch [85][2600/3746] lr: 3.992e-02, eta: 2 days, 7:04:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.5966, loss_cls: 3.7687, loss: 3.7687 +2024-07-25 05:28:23,290 - pyskl - INFO - Epoch [85][2700/3746] lr: 3.989e-02, eta: 2 days, 7:03:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5983, loss_cls: 3.7647, loss: 3.7647 +2024-07-25 05:29:44,876 - pyskl - INFO - Epoch [85][2800/3746] lr: 3.986e-02, eta: 2 days, 7:01:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.6031, loss_cls: 3.7452, loss: 3.7452 +2024-07-25 05:31:06,808 - pyskl - INFO - Epoch [85][2900/3746] lr: 3.984e-02, eta: 2 days, 7:00:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.5953, loss_cls: 3.7834, loss: 3.7834 +2024-07-25 05:32:28,823 - pyskl - INFO - Epoch [85][3000/3746] lr: 3.981e-02, eta: 2 days, 6:59:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5903, loss_cls: 3.7939, loss: 3.7939 +2024-07-25 05:33:50,556 - pyskl - INFO - Epoch [85][3100/3746] lr: 3.978e-02, eta: 2 days, 6:57:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.5969, loss_cls: 3.7600, loss: 3.7600 +2024-07-25 05:35:12,766 - pyskl - INFO - Epoch [85][3200/3746] lr: 3.975e-02, eta: 2 days, 6:56:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.6045, loss_cls: 3.7632, loss: 3.7632 +2024-07-25 05:36:34,763 - pyskl - INFO - Epoch [85][3300/3746] lr: 3.973e-02, eta: 2 days, 6:55:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5991, loss_cls: 3.7487, loss: 3.7487 +2024-07-25 05:37:56,051 - pyskl - INFO - Epoch [85][3400/3746] lr: 3.970e-02, eta: 2 days, 6:53:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6106, loss_cls: 3.6912, loss: 3.6912 +2024-07-25 05:39:18,180 - pyskl - INFO - Epoch [85][3500/3746] lr: 3.967e-02, eta: 2 days, 6:52:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.5948, loss_cls: 3.7799, loss: 3.7799 +2024-07-25 05:40:40,010 - pyskl - INFO - Epoch [85][3600/3746] lr: 3.964e-02, eta: 2 days, 6:51:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5795, loss_cls: 3.8273, loss: 3.8273 +2024-07-25 05:42:01,944 - pyskl - INFO - Epoch [85][3700/3746] lr: 3.962e-02, eta: 2 days, 6:49:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5931, loss_cls: 3.7994, loss: 3.7994 +2024-07-25 05:42:42,210 - pyskl - INFO - Saving checkpoint at 85 epochs +2024-07-25 05:44:34,737 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 05:44:35,419 - pyskl - INFO - +top1_acc 0.2907 +top5_acc 0.5399 +2024-07-25 05:44:35,419 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 05:44:35,465 - pyskl - INFO - +mean_acc 0.2905 +2024-07-25 05:44:35,470 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_80.pth was removed +2024-07-25 05:44:35,747 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_85.pth. +2024-07-25 05:44:35,747 - pyskl - INFO - Best top1_acc is 0.2907 at 85 epoch. +2024-07-25 05:44:35,764 - pyskl - INFO - Epoch(val) [85][309] top1_acc: 0.2907, top5_acc: 0.5399, mean_class_accuracy: 0.2905 +2024-07-25 05:48:32,018 - pyskl - INFO - Epoch [86][100/3746] lr: 3.958e-02, eta: 2 days, 6:49:24, time: 2.362, data_time: 1.351, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6091, loss_cls: 3.7045, loss: 3.7045 +2024-07-25 05:49:56,061 - pyskl - INFO - Epoch [86][200/3746] lr: 3.955e-02, eta: 2 days, 6:48:05, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6184, loss_cls: 3.6799, loss: 3.6799 +2024-07-25 05:51:20,040 - pyskl - INFO - Epoch [86][300/3746] lr: 3.952e-02, eta: 2 days, 6:46:46, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6067, loss_cls: 3.7128, loss: 3.7128 +2024-07-25 05:52:43,412 - pyskl - INFO - Epoch [86][400/3746] lr: 3.950e-02, eta: 2 days, 6:45:26, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.5997, loss_cls: 3.7292, loss: 3.7292 +2024-07-25 05:54:06,694 - pyskl - INFO - Epoch [86][500/3746] lr: 3.947e-02, eta: 2 days, 6:44:07, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6000, loss_cls: 3.7085, loss: 3.7085 +2024-07-25 05:55:30,245 - pyskl - INFO - Epoch [86][600/3746] lr: 3.944e-02, eta: 2 days, 6:42:48, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5992, loss_cls: 3.7691, loss: 3.7691 +2024-07-25 05:56:52,986 - pyskl - INFO - Epoch [86][700/3746] lr: 3.941e-02, eta: 2 days, 6:41:28, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6034, loss_cls: 3.7256, loss: 3.7256 +2024-07-25 05:58:16,178 - pyskl - INFO - Epoch [86][800/3746] lr: 3.939e-02, eta: 2 days, 6:40:08, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.5902, loss_cls: 3.7764, loss: 3.7764 +2024-07-25 05:59:39,328 - pyskl - INFO - Epoch [86][900/3746] lr: 3.936e-02, eta: 2 days, 6:38:49, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5978, loss_cls: 3.7635, loss: 3.7635 +2024-07-25 06:01:02,278 - pyskl - INFO - Epoch [86][1000/3746] lr: 3.933e-02, eta: 2 days, 6:37:29, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.5961, loss_cls: 3.7432, loss: 3.7432 +2024-07-25 06:02:25,532 - pyskl - INFO - Epoch [86][1100/3746] lr: 3.930e-02, eta: 2 days, 6:36:10, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5981, loss_cls: 3.7709, loss: 3.7709 +2024-07-25 06:03:48,623 - pyskl - INFO - Epoch [86][1200/3746] lr: 3.928e-02, eta: 2 days, 6:34:50, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6034, loss_cls: 3.7198, loss: 3.7198 +2024-07-25 06:05:12,147 - pyskl - INFO - Epoch [86][1300/3746] lr: 3.925e-02, eta: 2 days, 6:33:31, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.6022, loss_cls: 3.7418, loss: 3.7418 +2024-07-25 06:06:34,510 - pyskl - INFO - Epoch [86][1400/3746] lr: 3.922e-02, eta: 2 days, 6:32:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.6019, loss_cls: 3.7568, loss: 3.7568 +2024-07-25 06:07:56,425 - pyskl - INFO - Epoch [86][1500/3746] lr: 3.919e-02, eta: 2 days, 6:30:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5944, loss_cls: 3.7887, loss: 3.7887 +2024-07-25 06:09:18,367 - pyskl - INFO - Epoch [86][1600/3746] lr: 3.917e-02, eta: 2 days, 6:29:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5955, loss_cls: 3.7761, loss: 3.7761 +2024-07-25 06:10:40,539 - pyskl - INFO - Epoch [86][1700/3746] lr: 3.914e-02, eta: 2 days, 6:28:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.6033, loss_cls: 3.7112, loss: 3.7112 +2024-07-25 06:12:02,513 - pyskl - INFO - Epoch [86][1800/3746] lr: 3.911e-02, eta: 2 days, 6:26:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.5984, loss_cls: 3.7563, loss: 3.7563 +2024-07-25 06:13:24,901 - pyskl - INFO - Epoch [86][1900/3746] lr: 3.909e-02, eta: 2 days, 6:25:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6025, loss_cls: 3.7411, loss: 3.7411 +2024-07-25 06:14:46,231 - pyskl - INFO - Epoch [86][2000/3746] lr: 3.906e-02, eta: 2 days, 6:24:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.5952, loss_cls: 3.7746, loss: 3.7746 +2024-07-25 06:16:08,830 - pyskl - INFO - Epoch [86][2100/3746] lr: 3.903e-02, eta: 2 days, 6:22:48, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6072, loss_cls: 3.7361, loss: 3.7361 +2024-07-25 06:17:30,738 - pyskl - INFO - Epoch [86][2200/3746] lr: 3.900e-02, eta: 2 days, 6:21:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6034, loss_cls: 3.7058, loss: 3.7058 +2024-07-25 06:18:52,283 - pyskl - INFO - Epoch [86][2300/3746] lr: 3.898e-02, eta: 2 days, 6:20:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6042, loss_cls: 3.7444, loss: 3.7444 +2024-07-25 06:20:14,470 - pyskl - INFO - Epoch [86][2400/3746] lr: 3.895e-02, eta: 2 days, 6:18:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5952, loss_cls: 3.7731, loss: 3.7731 +2024-07-25 06:21:36,310 - pyskl - INFO - Epoch [86][2500/3746] lr: 3.892e-02, eta: 2 days, 6:17:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6016, loss_cls: 3.7721, loss: 3.7721 +2024-07-25 06:22:58,923 - pyskl - INFO - Epoch [86][2600/3746] lr: 3.889e-02, eta: 2 days, 6:16:06, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5905, loss_cls: 3.7713, loss: 3.7713 +2024-07-25 06:24:20,919 - pyskl - INFO - Epoch [86][2700/3746] lr: 3.887e-02, eta: 2 days, 6:14:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6014, loss_cls: 3.7453, loss: 3.7453 +2024-07-25 06:25:43,270 - pyskl - INFO - Epoch [86][2800/3746] lr: 3.884e-02, eta: 2 days, 6:13:25, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.5970, loss_cls: 3.7719, loss: 3.7719 +2024-07-25 06:27:05,271 - pyskl - INFO - Epoch [86][2900/3746] lr: 3.881e-02, eta: 2 days, 6:12:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.6066, loss_cls: 3.7507, loss: 3.7507 +2024-07-25 06:28:27,242 - pyskl - INFO - Epoch [86][3000/3746] lr: 3.879e-02, eta: 2 days, 6:10:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.5958, loss_cls: 3.7656, loss: 3.7656 +2024-07-25 06:29:48,810 - pyskl - INFO - Epoch [86][3100/3746] lr: 3.876e-02, eta: 2 days, 6:09:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6002, loss_cls: 3.7570, loss: 3.7570 +2024-07-25 06:31:10,876 - pyskl - INFO - Epoch [86][3200/3746] lr: 3.873e-02, eta: 2 days, 6:08:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5977, loss_cls: 3.7707, loss: 3.7707 +2024-07-25 06:32:32,698 - pyskl - INFO - Epoch [86][3300/3746] lr: 3.870e-02, eta: 2 days, 6:06:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.6073, loss_cls: 3.7467, loss: 3.7467 +2024-07-25 06:33:54,081 - pyskl - INFO - Epoch [86][3400/3746] lr: 3.868e-02, eta: 2 days, 6:05:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5805, loss_cls: 3.8629, loss: 3.8629 +2024-07-25 06:35:16,111 - pyskl - INFO - Epoch [86][3500/3746] lr: 3.865e-02, eta: 2 days, 6:04:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5948, loss_cls: 3.7968, loss: 3.7968 +2024-07-25 06:36:37,524 - pyskl - INFO - Epoch [86][3600/3746] lr: 3.862e-02, eta: 2 days, 6:02:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6022, loss_cls: 3.7418, loss: 3.7418 +2024-07-25 06:37:59,797 - pyskl - INFO - Epoch [86][3700/3746] lr: 3.860e-02, eta: 2 days, 6:01:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.6036, loss_cls: 3.7959, loss: 3.7959 +2024-07-25 06:38:39,638 - pyskl - INFO - Saving checkpoint at 86 epochs +2024-07-25 06:40:32,623 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 06:40:33,310 - pyskl - INFO - +top1_acc 0.2951 +top5_acc 0.5495 +2024-07-25 06:40:33,310 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 06:40:33,366 - pyskl - INFO - +mean_acc 0.2948 +2024-07-25 06:40:33,372 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_85.pth was removed +2024-07-25 06:40:33,692 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_86.pth. +2024-07-25 06:40:33,693 - pyskl - INFO - Best top1_acc is 0.2951 at 86 epoch. +2024-07-25 06:40:33,717 - pyskl - INFO - Epoch(val) [86][309] top1_acc: 0.2951, top5_acc: 0.5495, mean_class_accuracy: 0.2948 +2024-07-25 06:44:31,693 - pyskl - INFO - Epoch [87][100/3746] lr: 3.856e-02, eta: 2 days, 6:00:51, time: 2.380, data_time: 1.375, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6109, loss_cls: 3.6766, loss: 3.6766 +2024-07-25 06:45:54,605 - pyskl - INFO - Epoch [87][200/3746] lr: 3.853e-02, eta: 2 days, 5:59:31, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6141, loss_cls: 3.6631, loss: 3.6631 +2024-07-25 06:47:17,435 - pyskl - INFO - Epoch [87][300/3746] lr: 3.850e-02, eta: 2 days, 5:58:11, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.6041, loss_cls: 3.7285, loss: 3.7285 +2024-07-25 06:48:39,897 - pyskl - INFO - Epoch [87][400/3746] lr: 3.847e-02, eta: 2 days, 5:56:51, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6075, loss_cls: 3.6636, loss: 3.6636 +2024-07-25 06:50:01,494 - pyskl - INFO - Epoch [87][500/3746] lr: 3.845e-02, eta: 2 days, 5:55:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.6031, loss_cls: 3.7380, loss: 3.7380 +2024-07-25 06:51:23,236 - pyskl - INFO - Epoch [87][600/3746] lr: 3.842e-02, eta: 2 days, 5:54:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6003, loss_cls: 3.7526, loss: 3.7526 +2024-07-25 06:52:45,325 - pyskl - INFO - Epoch [87][700/3746] lr: 3.839e-02, eta: 2 days, 5:52:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6034, loss_cls: 3.7425, loss: 3.7425 +2024-07-25 06:54:06,829 - pyskl - INFO - Epoch [87][800/3746] lr: 3.837e-02, eta: 2 days, 5:51:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5958, loss_cls: 3.7780, loss: 3.7780 +2024-07-25 06:55:28,464 - pyskl - INFO - Epoch [87][900/3746] lr: 3.834e-02, eta: 2 days, 5:50:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6020, loss_cls: 3.7737, loss: 3.7737 +2024-07-25 06:56:49,930 - pyskl - INFO - Epoch [87][1000/3746] lr: 3.831e-02, eta: 2 days, 5:48:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.6022, loss_cls: 3.7459, loss: 3.7459 +2024-07-25 06:58:11,710 - pyskl - INFO - Epoch [87][1100/3746] lr: 3.828e-02, eta: 2 days, 5:47:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6055, loss_cls: 3.7157, loss: 3.7157 +2024-07-25 06:59:33,085 - pyskl - INFO - Epoch [87][1200/3746] lr: 3.826e-02, eta: 2 days, 5:46:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.6020, loss_cls: 3.7461, loss: 3.7461 +2024-07-25 07:00:54,802 - pyskl - INFO - Epoch [87][1300/3746] lr: 3.823e-02, eta: 2 days, 5:44:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5922, loss_cls: 3.7509, loss: 3.7509 +2024-07-25 07:02:16,029 - pyskl - INFO - Epoch [87][1400/3746] lr: 3.820e-02, eta: 2 days, 5:43:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6061, loss_cls: 3.7393, loss: 3.7393 +2024-07-25 07:03:37,563 - pyskl - INFO - Epoch [87][1500/3746] lr: 3.817e-02, eta: 2 days, 5:42:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.5952, loss_cls: 3.7457, loss: 3.7457 +2024-07-25 07:04:59,139 - pyskl - INFO - Epoch [87][1600/3746] lr: 3.815e-02, eta: 2 days, 5:40:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6062, loss_cls: 3.7414, loss: 3.7414 +2024-07-25 07:06:21,258 - pyskl - INFO - Epoch [87][1700/3746] lr: 3.812e-02, eta: 2 days, 5:39:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6036, loss_cls: 3.7109, loss: 3.7109 +2024-07-25 07:07:43,322 - pyskl - INFO - Epoch [87][1800/3746] lr: 3.809e-02, eta: 2 days, 5:38:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.5992, loss_cls: 3.7304, loss: 3.7304 +2024-07-25 07:09:05,736 - pyskl - INFO - Epoch [87][1900/3746] lr: 3.807e-02, eta: 2 days, 5:36:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6066, loss_cls: 3.7177, loss: 3.7177 +2024-07-25 07:10:27,556 - pyskl - INFO - Epoch [87][2000/3746] lr: 3.804e-02, eta: 2 days, 5:35:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5978, loss_cls: 3.7606, loss: 3.7606 +2024-07-25 07:11:49,189 - pyskl - INFO - Epoch [87][2100/3746] lr: 3.801e-02, eta: 2 days, 5:33:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6030, loss_cls: 3.7429, loss: 3.7429 +2024-07-25 07:13:10,237 - pyskl - INFO - Epoch [87][2200/3746] lr: 3.798e-02, eta: 2 days, 5:32:38, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6127, loss_cls: 3.6898, loss: 3.6898 +2024-07-25 07:14:31,668 - pyskl - INFO - Epoch [87][2300/3746] lr: 3.796e-02, eta: 2 days, 5:31:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.5973, loss_cls: 3.7501, loss: 3.7501 +2024-07-25 07:15:54,156 - pyskl - INFO - Epoch [87][2400/3746] lr: 3.793e-02, eta: 2 days, 5:29:56, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5967, loss_cls: 3.8025, loss: 3.8025 +2024-07-25 07:17:16,263 - pyskl - INFO - Epoch [87][2500/3746] lr: 3.790e-02, eta: 2 days, 5:28:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5927, loss_cls: 3.8188, loss: 3.8188 +2024-07-25 07:18:38,415 - pyskl - INFO - Epoch [87][2600/3746] lr: 3.788e-02, eta: 2 days, 5:27:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6048, loss_cls: 3.7363, loss: 3.7363 +2024-07-25 07:20:00,500 - pyskl - INFO - Epoch [87][2700/3746] lr: 3.785e-02, eta: 2 days, 5:25:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6033, loss_cls: 3.7194, loss: 3.7194 +2024-07-25 07:21:22,630 - pyskl - INFO - Epoch [87][2800/3746] lr: 3.782e-02, eta: 2 days, 5:24:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6091, loss_cls: 3.7234, loss: 3.7234 +2024-07-25 07:22:44,853 - pyskl - INFO - Epoch [87][2900/3746] lr: 3.779e-02, eta: 2 days, 5:23:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6103, loss_cls: 3.6963, loss: 3.6963 +2024-07-25 07:24:06,320 - pyskl - INFO - Epoch [87][3000/3746] lr: 3.777e-02, eta: 2 days, 5:21:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.5975, loss_cls: 3.7371, loss: 3.7371 +2024-07-25 07:25:28,089 - pyskl - INFO - Epoch [87][3100/3746] lr: 3.774e-02, eta: 2 days, 5:20:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6005, loss_cls: 3.7474, loss: 3.7474 +2024-07-25 07:26:49,677 - pyskl - INFO - Epoch [87][3200/3746] lr: 3.771e-02, eta: 2 days, 5:19:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6028, loss_cls: 3.7237, loss: 3.7237 +2024-07-25 07:28:11,678 - pyskl - INFO - Epoch [87][3300/3746] lr: 3.769e-02, eta: 2 days, 5:17:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.5966, loss_cls: 3.7496, loss: 3.7496 +2024-07-25 07:29:33,130 - pyskl - INFO - Epoch [87][3400/3746] lr: 3.766e-02, eta: 2 days, 5:16:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6016, loss_cls: 3.7373, loss: 3.7373 +2024-07-25 07:30:54,652 - pyskl - INFO - Epoch [87][3500/3746] lr: 3.763e-02, eta: 2 days, 5:15:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5908, loss_cls: 3.7719, loss: 3.7719 +2024-07-25 07:32:15,899 - pyskl - INFO - Epoch [87][3600/3746] lr: 3.761e-02, eta: 2 days, 5:13:48, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.6044, loss_cls: 3.7511, loss: 3.7511 +2024-07-25 07:33:37,809 - pyskl - INFO - Epoch [87][3700/3746] lr: 3.758e-02, eta: 2 days, 5:12:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5905, loss_cls: 3.7758, loss: 3.7758 +2024-07-25 07:34:17,280 - pyskl - INFO - Saving checkpoint at 87 epochs +2024-07-25 07:36:10,041 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 07:36:10,709 - pyskl - INFO - +top1_acc 0.2907 +top5_acc 0.5477 +2024-07-25 07:36:10,709 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 07:36:10,750 - pyskl - INFO - +mean_acc 0.2905 +2024-07-25 07:36:10,763 - pyskl - INFO - Epoch(val) [87][309] top1_acc: 0.2907, top5_acc: 0.5477, mean_class_accuracy: 0.2905 +2024-07-25 07:40:07,098 - pyskl - INFO - Epoch [88][100/3746] lr: 3.754e-02, eta: 2 days, 5:11:55, time: 2.363, data_time: 1.354, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6133, loss_cls: 3.6809, loss: 3.6809 +2024-07-25 07:41:31,469 - pyskl - INFO - Epoch [88][200/3746] lr: 3.751e-02, eta: 2 days, 5:10:36, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6102, loss_cls: 3.7033, loss: 3.7033 +2024-07-25 07:42:55,875 - pyskl - INFO - Epoch [88][300/3746] lr: 3.748e-02, eta: 2 days, 5:09:17, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6088, loss_cls: 3.7057, loss: 3.7057 +2024-07-25 07:44:20,403 - pyskl - INFO - Epoch [88][400/3746] lr: 3.746e-02, eta: 2 days, 5:07:58, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6111, loss_cls: 3.6952, loss: 3.6952 +2024-07-25 07:45:45,280 - pyskl - INFO - Epoch [88][500/3746] lr: 3.743e-02, eta: 2 days, 5:06:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6142, loss_cls: 3.6246, loss: 3.6246 +2024-07-25 07:47:09,792 - pyskl - INFO - Epoch [88][600/3746] lr: 3.740e-02, eta: 2 days, 5:05:21, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6127, loss_cls: 3.6941, loss: 3.6941 +2024-07-25 07:48:34,423 - pyskl - INFO - Epoch [88][700/3746] lr: 3.738e-02, eta: 2 days, 5:04:02, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6148, loss_cls: 3.6590, loss: 3.6590 +2024-07-25 07:49:58,701 - pyskl - INFO - Epoch [88][800/3746] lr: 3.735e-02, eta: 2 days, 5:02:43, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6053, loss_cls: 3.7351, loss: 3.7351 +2024-07-25 07:51:23,250 - pyskl - INFO - Epoch [88][900/3746] lr: 3.732e-02, eta: 2 days, 5:01:24, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6056, loss_cls: 3.7083, loss: 3.7083 +2024-07-25 07:52:47,715 - pyskl - INFO - Epoch [88][1000/3746] lr: 3.730e-02, eta: 2 days, 5:00:06, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.6006, loss_cls: 3.7396, loss: 3.7396 +2024-07-25 07:54:12,174 - pyskl - INFO - Epoch [88][1100/3746] lr: 3.727e-02, eta: 2 days, 4:58:47, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6008, loss_cls: 3.7355, loss: 3.7355 +2024-07-25 07:55:36,717 - pyskl - INFO - Epoch [88][1200/3746] lr: 3.724e-02, eta: 2 days, 4:57:28, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6047, loss_cls: 3.7142, loss: 3.7142 +2024-07-25 07:57:00,964 - pyskl - INFO - Epoch [88][1300/3746] lr: 3.721e-02, eta: 2 days, 4:56:09, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.6016, loss_cls: 3.7171, loss: 3.7171 +2024-07-25 07:58:25,518 - pyskl - INFO - Epoch [88][1400/3746] lr: 3.719e-02, eta: 2 days, 4:54:50, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.6084, loss_cls: 3.7375, loss: 3.7375 +2024-07-25 07:59:50,045 - pyskl - INFO - Epoch [88][1500/3746] lr: 3.716e-02, eta: 2 days, 4:53:31, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6064, loss_cls: 3.7100, loss: 3.7100 +2024-07-25 08:01:14,575 - pyskl - INFO - Epoch [88][1600/3746] lr: 3.713e-02, eta: 2 days, 4:52:12, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6014, loss_cls: 3.7306, loss: 3.7306 +2024-07-25 08:02:39,096 - pyskl - INFO - Epoch [88][1700/3746] lr: 3.711e-02, eta: 2 days, 4:50:54, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.5986, loss_cls: 3.7568, loss: 3.7568 +2024-07-25 08:04:02,357 - pyskl - INFO - Epoch [88][1800/3746] lr: 3.708e-02, eta: 2 days, 4:49:34, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6036, loss_cls: 3.6894, loss: 3.6894 +2024-07-25 08:05:26,153 - pyskl - INFO - Epoch [88][1900/3746] lr: 3.705e-02, eta: 2 days, 4:48:15, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.5986, loss_cls: 3.7198, loss: 3.7198 +2024-07-25 08:06:50,080 - pyskl - INFO - Epoch [88][2000/3746] lr: 3.703e-02, eta: 2 days, 4:46:55, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6011, loss_cls: 3.7142, loss: 3.7142 +2024-07-25 08:08:13,956 - pyskl - INFO - Epoch [88][2100/3746] lr: 3.700e-02, eta: 2 days, 4:45:36, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6014, loss_cls: 3.7419, loss: 3.7419 +2024-07-25 08:09:38,202 - pyskl - INFO - Epoch [88][2200/3746] lr: 3.697e-02, eta: 2 days, 4:44:17, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6056, loss_cls: 3.7277, loss: 3.7277 +2024-07-25 08:11:02,512 - pyskl - INFO - Epoch [88][2300/3746] lr: 3.694e-02, eta: 2 days, 4:42:58, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6019, loss_cls: 3.7140, loss: 3.7140 +2024-07-25 08:12:26,338 - pyskl - INFO - Epoch [88][2400/3746] lr: 3.692e-02, eta: 2 days, 4:41:39, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5933, loss_cls: 3.7909, loss: 3.7909 +2024-07-25 08:13:49,338 - pyskl - INFO - Epoch [88][2500/3746] lr: 3.689e-02, eta: 2 days, 4:40:19, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6009, loss_cls: 3.7420, loss: 3.7420 +2024-07-25 08:15:13,661 - pyskl - INFO - Epoch [88][2600/3746] lr: 3.686e-02, eta: 2 days, 4:39:00, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.5988, loss_cls: 3.7640, loss: 3.7640 +2024-07-25 08:16:36,964 - pyskl - INFO - Epoch [88][2700/3746] lr: 3.684e-02, eta: 2 days, 4:37:40, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5969, loss_cls: 3.7563, loss: 3.7563 +2024-07-25 08:18:00,535 - pyskl - INFO - Epoch [88][2800/3746] lr: 3.681e-02, eta: 2 days, 4:36:20, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6036, loss_cls: 3.7086, loss: 3.7086 +2024-07-25 08:19:25,006 - pyskl - INFO - Epoch [88][2900/3746] lr: 3.678e-02, eta: 2 days, 4:35:02, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.5981, loss_cls: 3.7660, loss: 3.7660 +2024-07-25 08:20:48,957 - pyskl - INFO - Epoch [88][3000/3746] lr: 3.676e-02, eta: 2 days, 4:33:42, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.6138, loss_cls: 3.7307, loss: 3.7307 +2024-07-25 08:22:12,046 - pyskl - INFO - Epoch [88][3100/3746] lr: 3.673e-02, eta: 2 days, 4:32:22, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5970, loss_cls: 3.7711, loss: 3.7711 +2024-07-25 08:23:35,472 - pyskl - INFO - Epoch [88][3200/3746] lr: 3.670e-02, eta: 2 days, 4:31:03, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.5931, loss_cls: 3.7822, loss: 3.7822 +2024-07-25 08:24:58,765 - pyskl - INFO - Epoch [88][3300/3746] lr: 3.667e-02, eta: 2 days, 4:29:43, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6012, loss_cls: 3.7178, loss: 3.7178 +2024-07-25 08:26:21,973 - pyskl - INFO - Epoch [88][3400/3746] lr: 3.665e-02, eta: 2 days, 4:28:23, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6062, loss_cls: 3.7283, loss: 3.7283 +2024-07-25 08:27:45,001 - pyskl - INFO - Epoch [88][3500/3746] lr: 3.662e-02, eta: 2 days, 4:27:03, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5952, loss_cls: 3.7274, loss: 3.7274 +2024-07-25 08:29:08,315 - pyskl - INFO - Epoch [88][3600/3746] lr: 3.659e-02, eta: 2 days, 4:25:44, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6081, loss_cls: 3.7253, loss: 3.7253 +2024-07-25 08:30:32,269 - pyskl - INFO - Epoch [88][3700/3746] lr: 3.657e-02, eta: 2 days, 4:24:24, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3425, top5_acc: 0.6092, loss_cls: 3.7206, loss: 3.7206 +2024-07-25 08:31:12,775 - pyskl - INFO - Saving checkpoint at 88 epochs +2024-07-25 08:33:05,080 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 08:33:05,746 - pyskl - INFO - +top1_acc 0.2903 +top5_acc 0.5423 +2024-07-25 08:33:05,746 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 08:33:05,787 - pyskl - INFO - +mean_acc 0.2900 +2024-07-25 08:33:05,799 - pyskl - INFO - Epoch(val) [88][309] top1_acc: 0.2903, top5_acc: 0.5423, mean_class_accuracy: 0.2900 +2024-07-25 08:36:58,395 - pyskl - INFO - Epoch [89][100/3746] lr: 3.653e-02, eta: 2 days, 4:23:46, time: 2.326, data_time: 1.321, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6100, loss_cls: 3.6673, loss: 3.6673 +2024-07-25 08:38:22,240 - pyskl - INFO - Epoch [89][200/3746] lr: 3.650e-02, eta: 2 days, 4:22:26, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6147, loss_cls: 3.6809, loss: 3.6809 +2024-07-25 08:39:46,102 - pyskl - INFO - Epoch [89][300/3746] lr: 3.647e-02, eta: 2 days, 4:21:07, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6111, loss_cls: 3.6743, loss: 3.6743 +2024-07-25 08:41:10,534 - pyskl - INFO - Epoch [89][400/3746] lr: 3.645e-02, eta: 2 days, 4:19:48, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6173, loss_cls: 3.6698, loss: 3.6698 +2024-07-25 08:42:34,351 - pyskl - INFO - Epoch [89][500/3746] lr: 3.642e-02, eta: 2 days, 4:18:29, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.5981, loss_cls: 3.7104, loss: 3.7104 +2024-07-25 08:43:58,546 - pyskl - INFO - Epoch [89][600/3746] lr: 3.639e-02, eta: 2 days, 4:17:09, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6112, loss_cls: 3.6790, loss: 3.6790 +2024-07-25 08:45:22,171 - pyskl - INFO - Epoch [89][700/3746] lr: 3.637e-02, eta: 2 days, 4:15:50, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6106, loss_cls: 3.6634, loss: 3.6634 +2024-07-25 08:46:46,340 - pyskl - INFO - Epoch [89][800/3746] lr: 3.634e-02, eta: 2 days, 4:14:31, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6027, loss_cls: 3.7345, loss: 3.7345 +2024-07-25 08:48:09,997 - pyskl - INFO - Epoch [89][900/3746] lr: 3.631e-02, eta: 2 days, 4:13:11, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.6023, loss_cls: 3.7423, loss: 3.7423 +2024-07-25 08:49:33,789 - pyskl - INFO - Epoch [89][1000/3746] lr: 3.629e-02, eta: 2 days, 4:11:52, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6073, loss_cls: 3.7091, loss: 3.7091 +2024-07-25 08:50:57,648 - pyskl - INFO - Epoch [89][1100/3746] lr: 3.626e-02, eta: 2 days, 4:10:32, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6255, loss_cls: 3.6265, loss: 3.6265 +2024-07-25 08:52:21,168 - pyskl - INFO - Epoch [89][1200/3746] lr: 3.623e-02, eta: 2 days, 4:09:12, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6058, loss_cls: 3.7155, loss: 3.7155 +2024-07-25 08:53:43,400 - pyskl - INFO - Epoch [89][1300/3746] lr: 3.620e-02, eta: 2 days, 4:07:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6123, loss_cls: 3.7033, loss: 3.7033 +2024-07-25 08:55:05,578 - pyskl - INFO - Epoch [89][1400/3746] lr: 3.618e-02, eta: 2 days, 4:06:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6048, loss_cls: 3.7152, loss: 3.7152 +2024-07-25 08:56:27,347 - pyskl - INFO - Epoch [89][1500/3746] lr: 3.615e-02, eta: 2 days, 4:05:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6041, loss_cls: 3.7492, loss: 3.7492 +2024-07-25 08:57:49,143 - pyskl - INFO - Epoch [89][1600/3746] lr: 3.612e-02, eta: 2 days, 4:03:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6100, loss_cls: 3.7124, loss: 3.7124 +2024-07-25 08:59:11,305 - pyskl - INFO - Epoch [89][1700/3746] lr: 3.610e-02, eta: 2 days, 4:02:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6094, loss_cls: 3.7179, loss: 3.7179 +2024-07-25 09:00:34,252 - pyskl - INFO - Epoch [89][1800/3746] lr: 3.607e-02, eta: 2 days, 4:01:09, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5984, loss_cls: 3.7604, loss: 3.7604 +2024-07-25 09:01:57,783 - pyskl - INFO - Epoch [89][1900/3746] lr: 3.604e-02, eta: 2 days, 3:59:49, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6042, loss_cls: 3.7218, loss: 3.7218 +2024-07-25 09:03:19,974 - pyskl - INFO - Epoch [89][2000/3746] lr: 3.602e-02, eta: 2 days, 3:58:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6084, loss_cls: 3.7143, loss: 3.7143 +2024-07-25 09:04:42,001 - pyskl - INFO - Epoch [89][2100/3746] lr: 3.599e-02, eta: 2 days, 3:57:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.6036, loss_cls: 3.7402, loss: 3.7402 +2024-07-25 09:06:03,716 - pyskl - INFO - Epoch [89][2200/3746] lr: 3.596e-02, eta: 2 days, 3:55:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6005, loss_cls: 3.7587, loss: 3.7587 +2024-07-25 09:07:25,166 - pyskl - INFO - Epoch [89][2300/3746] lr: 3.594e-02, eta: 2 days, 3:54:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.5991, loss_cls: 3.7353, loss: 3.7353 +2024-07-25 09:08:46,509 - pyskl - INFO - Epoch [89][2400/3746] lr: 3.591e-02, eta: 2 days, 3:53:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6058, loss_cls: 3.6971, loss: 3.6971 +2024-07-25 09:10:09,001 - pyskl - INFO - Epoch [89][2500/3746] lr: 3.588e-02, eta: 2 days, 3:51:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6070, loss_cls: 3.7087, loss: 3.7087 +2024-07-25 09:11:30,626 - pyskl - INFO - Epoch [89][2600/3746] lr: 3.586e-02, eta: 2 days, 3:50:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6100, loss_cls: 3.6795, loss: 3.6795 +2024-07-25 09:12:52,830 - pyskl - INFO - Epoch [89][2700/3746] lr: 3.583e-02, eta: 2 days, 3:49:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6045, loss_cls: 3.7260, loss: 3.7260 +2024-07-25 09:14:14,932 - pyskl - INFO - Epoch [89][2800/3746] lr: 3.580e-02, eta: 2 days, 3:47:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6059, loss_cls: 3.7091, loss: 3.7091 +2024-07-25 09:15:36,875 - pyskl - INFO - Epoch [89][2900/3746] lr: 3.578e-02, eta: 2 days, 3:46:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3452, top5_acc: 0.6002, loss_cls: 3.7408, loss: 3.7408 +2024-07-25 09:16:59,200 - pyskl - INFO - Epoch [89][3000/3746] lr: 3.575e-02, eta: 2 days, 3:45:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6047, loss_cls: 3.7122, loss: 3.7122 +2024-07-25 09:18:20,690 - pyskl - INFO - Epoch [89][3100/3746] lr: 3.572e-02, eta: 2 days, 3:43:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6067, loss_cls: 3.7095, loss: 3.7095 +2024-07-25 09:19:42,554 - pyskl - INFO - Epoch [89][3200/3746] lr: 3.569e-02, eta: 2 days, 3:42:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6078, loss_cls: 3.7050, loss: 3.7050 +2024-07-25 09:21:03,821 - pyskl - INFO - Epoch [89][3300/3746] lr: 3.567e-02, eta: 2 days, 3:40:57, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6058, loss_cls: 3.7164, loss: 3.7164 +2024-07-25 09:22:25,807 - pyskl - INFO - Epoch [89][3400/3746] lr: 3.564e-02, eta: 2 days, 3:39:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6042, loss_cls: 3.7008, loss: 3.7008 +2024-07-25 09:23:48,184 - pyskl - INFO - Epoch [89][3500/3746] lr: 3.561e-02, eta: 2 days, 3:38:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5978, loss_cls: 3.7796, loss: 3.7796 +2024-07-25 09:25:10,246 - pyskl - INFO - Epoch [89][3600/3746] lr: 3.559e-02, eta: 2 days, 3:36:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6081, loss_cls: 3.7061, loss: 3.7061 +2024-07-25 09:26:32,403 - pyskl - INFO - Epoch [89][3700/3746] lr: 3.556e-02, eta: 2 days, 3:35:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.5936, loss_cls: 3.7522, loss: 3.7522 +2024-07-25 09:27:12,062 - pyskl - INFO - Saving checkpoint at 89 epochs +2024-07-25 09:29:04,340 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 09:29:05,055 - pyskl - INFO - +top1_acc 0.2948 +top5_acc 0.5449 +2024-07-25 09:29:05,055 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 09:29:05,098 - pyskl - INFO - +mean_acc 0.2946 +2024-07-25 09:29:05,111 - pyskl - INFO - Epoch(val) [89][309] top1_acc: 0.2948, top5_acc: 0.5449, mean_class_accuracy: 0.2946 +2024-07-25 09:33:02,799 - pyskl - INFO - Epoch [90][100/3746] lr: 3.552e-02, eta: 2 days, 3:34:57, time: 2.377, data_time: 1.378, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6156, loss_cls: 3.6702, loss: 3.6702 +2024-07-25 09:34:25,823 - pyskl - INFO - Epoch [90][200/3746] lr: 3.550e-02, eta: 2 days, 3:33:37, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6117, loss_cls: 3.6738, loss: 3.6738 +2024-07-25 09:35:47,757 - pyskl - INFO - Epoch [90][300/3746] lr: 3.547e-02, eta: 2 days, 3:32:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.6027, loss_cls: 3.7249, loss: 3.7249 +2024-07-25 09:37:09,351 - pyskl - INFO - Epoch [90][400/3746] lr: 3.544e-02, eta: 2 days, 3:30:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6112, loss_cls: 3.6830, loss: 3.6830 +2024-07-25 09:38:30,480 - pyskl - INFO - Epoch [90][500/3746] lr: 3.541e-02, eta: 2 days, 3:29:34, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6078, loss_cls: 3.7098, loss: 3.7098 +2024-07-25 09:39:52,035 - pyskl - INFO - Epoch [90][600/3746] lr: 3.539e-02, eta: 2 days, 3:28:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6098, loss_cls: 3.6620, loss: 3.6620 +2024-07-25 09:41:13,817 - pyskl - INFO - Epoch [90][700/3746] lr: 3.536e-02, eta: 2 days, 3:26:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6086, loss_cls: 3.6971, loss: 3.6971 +2024-07-25 09:42:35,826 - pyskl - INFO - Epoch [90][800/3746] lr: 3.533e-02, eta: 2 days, 3:25:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6125, loss_cls: 3.6794, loss: 3.6794 +2024-07-25 09:43:57,824 - pyskl - INFO - Epoch [90][900/3746] lr: 3.531e-02, eta: 2 days, 3:24:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6142, loss_cls: 3.6948, loss: 3.6948 +2024-07-25 09:45:19,210 - pyskl - INFO - Epoch [90][1000/3746] lr: 3.528e-02, eta: 2 days, 3:22:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6102, loss_cls: 3.6751, loss: 3.6751 +2024-07-25 09:46:40,720 - pyskl - INFO - Epoch [90][1100/3746] lr: 3.525e-02, eta: 2 days, 3:21:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6167, loss_cls: 3.6778, loss: 3.6778 +2024-07-25 09:48:03,213 - pyskl - INFO - Epoch [90][1200/3746] lr: 3.523e-02, eta: 2 days, 3:20:07, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.5984, loss_cls: 3.7134, loss: 3.7134 +2024-07-25 09:49:24,908 - pyskl - INFO - Epoch [90][1300/3746] lr: 3.520e-02, eta: 2 days, 3:18:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6036, loss_cls: 3.7079, loss: 3.7079 +2024-07-25 09:50:46,280 - pyskl - INFO - Epoch [90][1400/3746] lr: 3.517e-02, eta: 2 days, 3:17:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6039, loss_cls: 3.7086, loss: 3.7086 +2024-07-25 09:52:08,248 - pyskl - INFO - Epoch [90][1500/3746] lr: 3.515e-02, eta: 2 days, 3:16:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6064, loss_cls: 3.7267, loss: 3.7267 +2024-07-25 09:53:30,254 - pyskl - INFO - Epoch [90][1600/3746] lr: 3.512e-02, eta: 2 days, 3:14:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6088, loss_cls: 3.7078, loss: 3.7078 +2024-07-25 09:54:52,748 - pyskl - INFO - Epoch [90][1700/3746] lr: 3.509e-02, eta: 2 days, 3:13:23, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6083, loss_cls: 3.6778, loss: 3.6778 +2024-07-25 09:56:14,759 - pyskl - INFO - Epoch [90][1800/3746] lr: 3.507e-02, eta: 2 days, 3:12:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6122, loss_cls: 3.6701, loss: 3.6701 +2024-07-25 09:57:36,674 - pyskl - INFO - Epoch [90][1900/3746] lr: 3.504e-02, eta: 2 days, 3:10:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6059, loss_cls: 3.6963, loss: 3.6963 +2024-07-25 09:58:58,327 - pyskl - INFO - Epoch [90][2000/3746] lr: 3.501e-02, eta: 2 days, 3:09:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.6019, loss_cls: 3.7329, loss: 3.7329 +2024-07-25 10:00:19,934 - pyskl - INFO - Epoch [90][2100/3746] lr: 3.499e-02, eta: 2 days, 3:07:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6031, loss_cls: 3.7074, loss: 3.7074 +2024-07-25 10:01:41,541 - pyskl - INFO - Epoch [90][2200/3746] lr: 3.496e-02, eta: 2 days, 3:06:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.6081, loss_cls: 3.7065, loss: 3.7065 +2024-07-25 10:03:03,134 - pyskl - INFO - Epoch [90][2300/3746] lr: 3.493e-02, eta: 2 days, 3:05:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6070, loss_cls: 3.7182, loss: 3.7182 +2024-07-25 10:04:25,037 - pyskl - INFO - Epoch [90][2400/3746] lr: 3.491e-02, eta: 2 days, 3:03:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6100, loss_cls: 3.6964, loss: 3.6964 +2024-07-25 10:05:47,295 - pyskl - INFO - Epoch [90][2500/3746] lr: 3.488e-02, eta: 2 days, 3:02:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6155, loss_cls: 3.6879, loss: 3.6879 +2024-07-25 10:07:09,362 - pyskl - INFO - Epoch [90][2600/3746] lr: 3.485e-02, eta: 2 days, 3:01:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.5920, loss_cls: 3.7545, loss: 3.7545 +2024-07-25 10:08:31,038 - pyskl - INFO - Epoch [90][2700/3746] lr: 3.483e-02, eta: 2 days, 2:59:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6103, loss_cls: 3.7019, loss: 3.7019 +2024-07-25 10:09:53,097 - pyskl - INFO - Epoch [90][2800/3746] lr: 3.480e-02, eta: 2 days, 2:58:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6167, loss_cls: 3.6477, loss: 3.6477 +2024-07-25 10:11:14,895 - pyskl - INFO - Epoch [90][2900/3746] lr: 3.477e-02, eta: 2 days, 2:57:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6191, loss_cls: 3.6277, loss: 3.6277 +2024-07-25 10:12:36,288 - pyskl - INFO - Epoch [90][3000/3746] lr: 3.475e-02, eta: 2 days, 2:55:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6073, loss_cls: 3.6854, loss: 3.6854 +2024-07-25 10:13:58,388 - pyskl - INFO - Epoch [90][3100/3746] lr: 3.472e-02, eta: 2 days, 2:54:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6023, loss_cls: 3.7260, loss: 3.7260 +2024-07-25 10:15:20,670 - pyskl - INFO - Epoch [90][3200/3746] lr: 3.469e-02, eta: 2 days, 2:53:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6148, loss_cls: 3.6730, loss: 3.6730 +2024-07-25 10:16:42,503 - pyskl - INFO - Epoch [90][3300/3746] lr: 3.467e-02, eta: 2 days, 2:51:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6030, loss_cls: 3.7131, loss: 3.7131 +2024-07-25 10:18:04,219 - pyskl - INFO - Epoch [90][3400/3746] lr: 3.464e-02, eta: 2 days, 2:50:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6014, loss_cls: 3.7336, loss: 3.7336 +2024-07-25 10:19:26,557 - pyskl - INFO - Epoch [90][3500/3746] lr: 3.461e-02, eta: 2 days, 2:49:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6070, loss_cls: 3.7559, loss: 3.7559 +2024-07-25 10:20:48,931 - pyskl - INFO - Epoch [90][3600/3746] lr: 3.459e-02, eta: 2 days, 2:47:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6116, loss_cls: 3.6682, loss: 3.6682 +2024-07-25 10:22:11,195 - pyskl - INFO - Epoch [90][3700/3746] lr: 3.456e-02, eta: 2 days, 2:46:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6170, loss_cls: 3.6484, loss: 3.6484 +2024-07-25 10:22:51,207 - pyskl - INFO - Saving checkpoint at 90 epochs +2024-07-25 10:24:44,414 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 10:24:45,246 - pyskl - INFO - +top1_acc 0.2841 +top5_acc 0.5337 +2024-07-25 10:24:45,246 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 10:24:45,290 - pyskl - INFO - +mean_acc 0.2839 +2024-07-25 10:24:45,303 - pyskl - INFO - Epoch(val) [90][309] top1_acc: 0.2841, top5_acc: 0.5337, mean_class_accuracy: 0.2839 +2024-07-25 10:28:42,965 - pyskl - INFO - Epoch [91][100/3746] lr: 3.452e-02, eta: 2 days, 2:45:45, time: 2.377, data_time: 1.390, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6172, loss_cls: 3.6341, loss: 3.6341 +2024-07-25 10:30:05,351 - pyskl - INFO - Epoch [91][200/3746] lr: 3.450e-02, eta: 2 days, 2:44:25, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6203, loss_cls: 3.6064, loss: 3.6064 +2024-07-25 10:31:27,302 - pyskl - INFO - Epoch [91][300/3746] lr: 3.447e-02, eta: 2 days, 2:43:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6225, loss_cls: 3.6119, loss: 3.6119 +2024-07-25 10:32:49,197 - pyskl - INFO - Epoch [91][400/3746] lr: 3.444e-02, eta: 2 days, 2:41:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6178, loss_cls: 3.6840, loss: 3.6840 +2024-07-25 10:34:10,952 - pyskl - INFO - Epoch [91][500/3746] lr: 3.442e-02, eta: 2 days, 2:40:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6206, loss_cls: 3.5993, loss: 3.5993 +2024-07-25 10:35:32,725 - pyskl - INFO - Epoch [91][600/3746] lr: 3.439e-02, eta: 2 days, 2:39:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6223, loss_cls: 3.6389, loss: 3.6389 +2024-07-25 10:36:54,044 - pyskl - INFO - Epoch [91][700/3746] lr: 3.436e-02, eta: 2 days, 2:37:39, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3541, top5_acc: 0.6094, loss_cls: 3.6746, loss: 3.6746 +2024-07-25 10:38:15,852 - pyskl - INFO - Epoch [91][800/3746] lr: 3.434e-02, eta: 2 days, 2:36:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6150, loss_cls: 3.6645, loss: 3.6645 +2024-07-25 10:39:37,708 - pyskl - INFO - Epoch [91][900/3746] lr: 3.431e-02, eta: 2 days, 2:34:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6089, loss_cls: 3.6884, loss: 3.6884 +2024-07-25 10:40:59,073 - pyskl - INFO - Epoch [91][1000/3746] lr: 3.428e-02, eta: 2 days, 2:33:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6145, loss_cls: 3.6810, loss: 3.6810 +2024-07-25 10:42:21,460 - pyskl - INFO - Epoch [91][1100/3746] lr: 3.426e-02, eta: 2 days, 2:32:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6108, loss_cls: 3.6678, loss: 3.6678 +2024-07-25 10:43:42,935 - pyskl - INFO - Epoch [91][1200/3746] lr: 3.423e-02, eta: 2 days, 2:30:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6134, loss_cls: 3.6779, loss: 3.6779 +2024-07-25 10:45:04,838 - pyskl - INFO - Epoch [91][1300/3746] lr: 3.420e-02, eta: 2 days, 2:29:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6109, loss_cls: 3.7022, loss: 3.7022 +2024-07-25 10:46:26,320 - pyskl - INFO - Epoch [91][1400/3746] lr: 3.418e-02, eta: 2 days, 2:28:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6159, loss_cls: 3.6702, loss: 3.6702 +2024-07-25 10:47:48,119 - pyskl - INFO - Epoch [91][1500/3746] lr: 3.415e-02, eta: 2 days, 2:26:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6094, loss_cls: 3.6920, loss: 3.6920 +2024-07-25 10:49:09,786 - pyskl - INFO - Epoch [91][1600/3746] lr: 3.412e-02, eta: 2 days, 2:25:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6136, loss_cls: 3.6790, loss: 3.6790 +2024-07-25 10:50:32,653 - pyskl - INFO - Epoch [91][1700/3746] lr: 3.410e-02, eta: 2 days, 2:24:09, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.5952, loss_cls: 3.7485, loss: 3.7485 +2024-07-25 10:51:55,025 - pyskl - INFO - Epoch [91][1800/3746] lr: 3.407e-02, eta: 2 days, 2:22:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6083, loss_cls: 3.6721, loss: 3.6721 +2024-07-25 10:53:16,813 - pyskl - INFO - Epoch [91][1900/3746] lr: 3.405e-02, eta: 2 days, 2:21:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6183, loss_cls: 3.6637, loss: 3.6637 +2024-07-25 10:54:38,603 - pyskl - INFO - Epoch [91][2000/3746] lr: 3.402e-02, eta: 2 days, 2:20:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6144, loss_cls: 3.6806, loss: 3.6806 +2024-07-25 10:56:00,087 - pyskl - INFO - Epoch [91][2100/3746] lr: 3.399e-02, eta: 2 days, 2:18:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6102, loss_cls: 3.6958, loss: 3.6958 +2024-07-25 10:57:21,404 - pyskl - INFO - Epoch [91][2200/3746] lr: 3.397e-02, eta: 2 days, 2:17:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6116, loss_cls: 3.6618, loss: 3.6618 +2024-07-25 10:58:42,921 - pyskl - INFO - Epoch [91][2300/3746] lr: 3.394e-02, eta: 2 days, 2:16:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6089, loss_cls: 3.6873, loss: 3.6873 +2024-07-25 11:00:04,221 - pyskl - INFO - Epoch [91][2400/3746] lr: 3.391e-02, eta: 2 days, 2:14:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6072, loss_cls: 3.6800, loss: 3.6800 +2024-07-25 11:01:26,572 - pyskl - INFO - Epoch [91][2500/3746] lr: 3.389e-02, eta: 2 days, 2:13:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.6017, loss_cls: 3.7635, loss: 3.7635 +2024-07-25 11:02:48,836 - pyskl - INFO - Epoch [91][2600/3746] lr: 3.386e-02, eta: 2 days, 2:12:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6047, loss_cls: 3.7029, loss: 3.7029 +2024-07-25 11:04:11,060 - pyskl - INFO - Epoch [91][2700/3746] lr: 3.383e-02, eta: 2 days, 2:10:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6106, loss_cls: 3.6811, loss: 3.6811 +2024-07-25 11:05:33,601 - pyskl - INFO - Epoch [91][2800/3746] lr: 3.381e-02, eta: 2 days, 2:09:18, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6102, loss_cls: 3.6680, loss: 3.6680 +2024-07-25 11:06:55,424 - pyskl - INFO - Epoch [91][2900/3746] lr: 3.378e-02, eta: 2 days, 2:07:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6091, loss_cls: 3.6712, loss: 3.6712 +2024-07-25 11:08:17,485 - pyskl - INFO - Epoch [91][3000/3746] lr: 3.375e-02, eta: 2 days, 2:06:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6150, loss_cls: 3.6856, loss: 3.6856 +2024-07-25 11:09:38,767 - pyskl - INFO - Epoch [91][3100/3746] lr: 3.373e-02, eta: 2 days, 2:05:15, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6066, loss_cls: 3.7241, loss: 3.7241 +2024-07-25 11:11:00,282 - pyskl - INFO - Epoch [91][3200/3746] lr: 3.370e-02, eta: 2 days, 2:03:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6019, loss_cls: 3.7102, loss: 3.7102 +2024-07-25 11:12:21,590 - pyskl - INFO - Epoch [91][3300/3746] lr: 3.367e-02, eta: 2 days, 2:02:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6048, loss_cls: 3.7189, loss: 3.7189 +2024-07-25 11:13:43,720 - pyskl - INFO - Epoch [91][3400/3746] lr: 3.365e-02, eta: 2 days, 2:01:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3588, top5_acc: 0.6159, loss_cls: 3.6679, loss: 3.6679 +2024-07-25 11:15:05,840 - pyskl - INFO - Epoch [91][3500/3746] lr: 3.362e-02, eta: 2 days, 1:59:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6045, loss_cls: 3.7182, loss: 3.7182 +2024-07-25 11:16:27,979 - pyskl - INFO - Epoch [91][3600/3746] lr: 3.360e-02, eta: 2 days, 1:58:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6120, loss_cls: 3.6837, loss: 3.6837 +2024-07-25 11:17:50,788 - pyskl - INFO - Epoch [91][3700/3746] lr: 3.357e-02, eta: 2 days, 1:57:10, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.6053, loss_cls: 3.7330, loss: 3.7330 +2024-07-25 11:18:31,038 - pyskl - INFO - Saving checkpoint at 91 epochs +2024-07-25 11:20:23,180 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 11:20:23,860 - pyskl - INFO - +top1_acc 0.2971 +top5_acc 0.5461 +2024-07-25 11:20:23,860 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 11:20:23,904 - pyskl - INFO - +mean_acc 0.2969 +2024-07-25 11:20:23,909 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_86.pth was removed +2024-07-25 11:20:24,173 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_91.pth. +2024-07-25 11:20:24,173 - pyskl - INFO - Best top1_acc is 0.2971 at 91 epoch. +2024-07-25 11:20:24,188 - pyskl - INFO - Epoch(val) [91][309] top1_acc: 0.2971, top5_acc: 0.5461, mean_class_accuracy: 0.2969 +2024-07-25 11:24:19,273 - pyskl - INFO - Epoch [92][100/3746] lr: 3.353e-02, eta: 2 days, 1:56:26, time: 2.351, data_time: 1.364, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6345, loss_cls: 3.5674, loss: 3.5674 +2024-07-25 11:25:41,380 - pyskl - INFO - Epoch [92][200/3746] lr: 3.350e-02, eta: 2 days, 1:55:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6139, loss_cls: 3.6449, loss: 3.6449 +2024-07-25 11:27:03,045 - pyskl - INFO - Epoch [92][300/3746] lr: 3.348e-02, eta: 2 days, 1:53:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6203, loss_cls: 3.6236, loss: 3.6236 +2024-07-25 11:28:24,505 - pyskl - INFO - Epoch [92][400/3746] lr: 3.345e-02, eta: 2 days, 1:52:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6222, loss_cls: 3.5973, loss: 3.5973 +2024-07-25 11:29:45,953 - pyskl - INFO - Epoch [92][500/3746] lr: 3.342e-02, eta: 2 days, 1:51:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6266, loss_cls: 3.6029, loss: 3.6029 +2024-07-25 11:31:07,709 - pyskl - INFO - Epoch [92][600/3746] lr: 3.340e-02, eta: 2 days, 1:49:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6283, loss_cls: 3.6142, loss: 3.6142 +2024-07-25 11:32:29,176 - pyskl - INFO - Epoch [92][700/3746] lr: 3.337e-02, eta: 2 days, 1:48:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6162, loss_cls: 3.6522, loss: 3.6522 +2024-07-25 11:33:50,910 - pyskl - INFO - Epoch [92][800/3746] lr: 3.335e-02, eta: 2 days, 1:46:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6156, loss_cls: 3.6348, loss: 3.6348 +2024-07-25 11:35:12,753 - pyskl - INFO - Epoch [92][900/3746] lr: 3.332e-02, eta: 2 days, 1:45:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6033, loss_cls: 3.7093, loss: 3.7093 +2024-07-25 11:36:34,838 - pyskl - INFO - Epoch [92][1000/3746] lr: 3.329e-02, eta: 2 days, 1:44:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6142, loss_cls: 3.6473, loss: 3.6473 +2024-07-25 11:37:56,278 - pyskl - INFO - Epoch [92][1100/3746] lr: 3.327e-02, eta: 2 days, 1:42:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6206, loss_cls: 3.6614, loss: 3.6614 +2024-07-25 11:39:17,362 - pyskl - INFO - Epoch [92][1200/3746] lr: 3.324e-02, eta: 2 days, 1:41:33, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6066, loss_cls: 3.6871, loss: 3.6871 +2024-07-25 11:40:39,216 - pyskl - INFO - Epoch [92][1300/3746] lr: 3.321e-02, eta: 2 days, 1:40:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6141, loss_cls: 3.6546, loss: 3.6546 +2024-07-25 11:42:01,031 - pyskl - INFO - Epoch [92][1400/3746] lr: 3.319e-02, eta: 2 days, 1:38:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6169, loss_cls: 3.6640, loss: 3.6640 +2024-07-25 11:43:23,126 - pyskl - INFO - Epoch [92][1500/3746] lr: 3.316e-02, eta: 2 days, 1:37:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6202, loss_cls: 3.6327, loss: 3.6327 +2024-07-25 11:44:44,828 - pyskl - INFO - Epoch [92][1600/3746] lr: 3.314e-02, eta: 2 days, 1:36:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6092, loss_cls: 3.6850, loss: 3.6850 +2024-07-25 11:46:07,344 - pyskl - INFO - Epoch [92][1700/3746] lr: 3.311e-02, eta: 2 days, 1:34:48, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6183, loss_cls: 3.6345, loss: 3.6345 +2024-07-25 11:47:29,647 - pyskl - INFO - Epoch [92][1800/3746] lr: 3.308e-02, eta: 2 days, 1:33:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6156, loss_cls: 3.6475, loss: 3.6475 +2024-07-25 11:48:51,669 - pyskl - INFO - Epoch [92][1900/3746] lr: 3.306e-02, eta: 2 days, 1:32:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6084, loss_cls: 3.6601, loss: 3.6601 +2024-07-25 11:50:13,529 - pyskl - INFO - Epoch [92][2000/3746] lr: 3.303e-02, eta: 2 days, 1:30:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6120, loss_cls: 3.6996, loss: 3.6996 +2024-07-25 11:51:35,072 - pyskl - INFO - Epoch [92][2100/3746] lr: 3.300e-02, eta: 2 days, 1:29:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6089, loss_cls: 3.6788, loss: 3.6788 +2024-07-25 11:52:57,155 - pyskl - INFO - Epoch [92][2200/3746] lr: 3.298e-02, eta: 2 days, 1:28:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6081, loss_cls: 3.6869, loss: 3.6869 +2024-07-25 11:54:18,530 - pyskl - INFO - Epoch [92][2300/3746] lr: 3.295e-02, eta: 2 days, 1:26:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6081, loss_cls: 3.6961, loss: 3.6961 +2024-07-25 11:55:40,110 - pyskl - INFO - Epoch [92][2400/3746] lr: 3.292e-02, eta: 2 days, 1:25:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6030, loss_cls: 3.7459, loss: 3.7459 +2024-07-25 11:57:02,964 - pyskl - INFO - Epoch [92][2500/3746] lr: 3.290e-02, eta: 2 days, 1:24:00, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6127, loss_cls: 3.6802, loss: 3.6802 +2024-07-25 11:58:24,616 - pyskl - INFO - Epoch [92][2600/3746] lr: 3.287e-02, eta: 2 days, 1:22:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3541, top5_acc: 0.6089, loss_cls: 3.6698, loss: 3.6698 +2024-07-25 11:59:46,598 - pyskl - INFO - Epoch [92][2700/3746] lr: 3.285e-02, eta: 2 days, 1:21:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6116, loss_cls: 3.6967, loss: 3.6967 +2024-07-25 12:01:08,971 - pyskl - INFO - Epoch [92][2800/3746] lr: 3.282e-02, eta: 2 days, 1:19:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6077, loss_cls: 3.6967, loss: 3.6967 +2024-07-25 12:02:30,617 - pyskl - INFO - Epoch [92][2900/3746] lr: 3.279e-02, eta: 2 days, 1:18:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6083, loss_cls: 3.6927, loss: 3.6927 +2024-07-25 12:03:51,955 - pyskl - INFO - Epoch [92][3000/3746] lr: 3.277e-02, eta: 2 days, 1:17:14, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6183, loss_cls: 3.6393, loss: 3.6393 +2024-07-25 12:05:13,793 - pyskl - INFO - Epoch [92][3100/3746] lr: 3.274e-02, eta: 2 days, 1:15:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6052, loss_cls: 3.7318, loss: 3.7318 +2024-07-25 12:06:35,305 - pyskl - INFO - Epoch [92][3200/3746] lr: 3.271e-02, eta: 2 days, 1:14:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6130, loss_cls: 3.6653, loss: 3.6653 +2024-07-25 12:07:57,542 - pyskl - INFO - Epoch [92][3300/3746] lr: 3.269e-02, eta: 2 days, 1:13:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6056, loss_cls: 3.7149, loss: 3.7149 +2024-07-25 12:09:19,360 - pyskl - INFO - Epoch [92][3400/3746] lr: 3.266e-02, eta: 2 days, 1:11:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6059, loss_cls: 3.6873, loss: 3.6873 +2024-07-25 12:10:42,000 - pyskl - INFO - Epoch [92][3500/3746] lr: 3.264e-02, eta: 2 days, 1:10:29, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6150, loss_cls: 3.6545, loss: 3.6545 +2024-07-25 12:12:03,937 - pyskl - INFO - Epoch [92][3600/3746] lr: 3.261e-02, eta: 2 days, 1:09:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6059, loss_cls: 3.7242, loss: 3.7242 +2024-07-25 12:13:25,630 - pyskl - INFO - Epoch [92][3700/3746] lr: 3.258e-02, eta: 2 days, 1:07:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6108, loss_cls: 3.7107, loss: 3.7107 +2024-07-25 12:14:05,229 - pyskl - INFO - Saving checkpoint at 92 epochs +2024-07-25 12:15:57,400 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 12:15:58,062 - pyskl - INFO - +top1_acc 0.2941 +top5_acc 0.5546 +2024-07-25 12:15:58,062 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 12:15:58,104 - pyskl - INFO - +mean_acc 0.2937 +2024-07-25 12:15:58,116 - pyskl - INFO - Epoch(val) [92][309] top1_acc: 0.2941, top5_acc: 0.5546, mean_class_accuracy: 0.2937 +2024-07-25 12:19:49,502 - pyskl - INFO - Epoch [93][100/3746] lr: 3.255e-02, eta: 2 days, 1:06:59, time: 2.314, data_time: 1.322, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6344, loss_cls: 3.5589, loss: 3.5589 +2024-07-25 12:21:11,357 - pyskl - INFO - Epoch [93][200/3746] lr: 3.252e-02, eta: 2 days, 1:05:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6212, loss_cls: 3.6050, loss: 3.6050 +2024-07-25 12:22:32,961 - pyskl - INFO - Epoch [93][300/3746] lr: 3.249e-02, eta: 2 days, 1:04:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6312, loss_cls: 3.5296, loss: 3.5296 +2024-07-25 12:23:54,849 - pyskl - INFO - Epoch [93][400/3746] lr: 3.247e-02, eta: 2 days, 1:02:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6253, loss_cls: 3.6005, loss: 3.6005 +2024-07-25 12:25:16,993 - pyskl - INFO - Epoch [93][500/3746] lr: 3.244e-02, eta: 2 days, 1:01:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6164, loss_cls: 3.6192, loss: 3.6192 +2024-07-25 12:26:38,533 - pyskl - INFO - Epoch [93][600/3746] lr: 3.241e-02, eta: 2 days, 1:00:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6239, loss_cls: 3.6184, loss: 3.6184 +2024-07-25 12:27:59,989 - pyskl - INFO - Epoch [93][700/3746] lr: 3.239e-02, eta: 2 days, 0:58:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6130, loss_cls: 3.6300, loss: 3.6300 +2024-07-25 12:29:21,369 - pyskl - INFO - Epoch [93][800/3746] lr: 3.236e-02, eta: 2 days, 0:57:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6245, loss_cls: 3.5943, loss: 3.5943 +2024-07-25 12:30:43,106 - pyskl - INFO - Epoch [93][900/3746] lr: 3.234e-02, eta: 2 days, 0:56:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6138, loss_cls: 3.6580, loss: 3.6580 +2024-07-25 12:32:04,625 - pyskl - INFO - Epoch [93][1000/3746] lr: 3.231e-02, eta: 2 days, 0:54:47, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6234, loss_cls: 3.6220, loss: 3.6220 +2024-07-25 12:33:26,753 - pyskl - INFO - Epoch [93][1100/3746] lr: 3.228e-02, eta: 2 days, 0:53:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6295, loss_cls: 3.6172, loss: 3.6172 +2024-07-25 12:34:48,417 - pyskl - INFO - Epoch [93][1200/3746] lr: 3.226e-02, eta: 2 days, 0:52:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6109, loss_cls: 3.6972, loss: 3.6972 +2024-07-25 12:36:09,820 - pyskl - INFO - Epoch [93][1300/3746] lr: 3.223e-02, eta: 2 days, 0:50:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6198, loss_cls: 3.6609, loss: 3.6609 +2024-07-25 12:37:31,871 - pyskl - INFO - Epoch [93][1400/3746] lr: 3.221e-02, eta: 2 days, 0:49:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6125, loss_cls: 3.6998, loss: 3.6998 +2024-07-25 12:38:53,962 - pyskl - INFO - Epoch [93][1500/3746] lr: 3.218e-02, eta: 2 days, 0:48:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6173, loss_cls: 3.6494, loss: 3.6494 +2024-07-25 12:40:15,524 - pyskl - INFO - Epoch [93][1600/3746] lr: 3.215e-02, eta: 2 days, 0:46:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6086, loss_cls: 3.6909, loss: 3.6909 +2024-07-25 12:41:37,923 - pyskl - INFO - Epoch [93][1700/3746] lr: 3.213e-02, eta: 2 days, 0:45:20, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6145, loss_cls: 3.6716, loss: 3.6716 +2024-07-25 12:43:00,303 - pyskl - INFO - Epoch [93][1800/3746] lr: 3.210e-02, eta: 2 days, 0:43:59, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6223, loss_cls: 3.6509, loss: 3.6509 +2024-07-25 12:44:21,783 - pyskl - INFO - Epoch [93][1900/3746] lr: 3.207e-02, eta: 2 days, 0:42:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6253, loss_cls: 3.6251, loss: 3.6251 +2024-07-25 12:45:43,390 - pyskl - INFO - Epoch [93][2000/3746] lr: 3.205e-02, eta: 2 days, 0:41:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6147, loss_cls: 3.6480, loss: 3.6480 +2024-07-25 12:47:04,994 - pyskl - INFO - Epoch [93][2100/3746] lr: 3.202e-02, eta: 2 days, 0:39:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6242, loss_cls: 3.6827, loss: 3.6827 +2024-07-25 12:48:26,926 - pyskl - INFO - Epoch [93][2200/3746] lr: 3.200e-02, eta: 2 days, 0:38:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6167, loss_cls: 3.6711, loss: 3.6711 +2024-07-25 12:49:48,646 - pyskl - INFO - Epoch [93][2300/3746] lr: 3.197e-02, eta: 2 days, 0:37:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6125, loss_cls: 3.6659, loss: 3.6659 +2024-07-25 12:51:10,209 - pyskl - INFO - Epoch [93][2400/3746] lr: 3.194e-02, eta: 2 days, 0:35:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6119, loss_cls: 3.6730, loss: 3.6730 +2024-07-25 12:52:31,947 - pyskl - INFO - Epoch [93][2500/3746] lr: 3.192e-02, eta: 2 days, 0:34:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6069, loss_cls: 3.7024, loss: 3.7024 +2024-07-25 12:53:53,936 - pyskl - INFO - Epoch [93][2600/3746] lr: 3.189e-02, eta: 2 days, 0:33:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6145, loss_cls: 3.6637, loss: 3.6637 +2024-07-25 12:55:15,804 - pyskl - INFO - Epoch [93][2700/3746] lr: 3.187e-02, eta: 2 days, 0:31:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6103, loss_cls: 3.7144, loss: 3.7144 +2024-07-25 12:56:37,990 - pyskl - INFO - Epoch [93][2800/3746] lr: 3.184e-02, eta: 2 days, 0:30:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6197, loss_cls: 3.6283, loss: 3.6283 +2024-07-25 12:57:59,929 - pyskl - INFO - Epoch [93][2900/3746] lr: 3.181e-02, eta: 2 days, 0:29:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6245, loss_cls: 3.6026, loss: 3.6026 +2024-07-25 12:59:21,516 - pyskl - INFO - Epoch [93][3000/3746] lr: 3.179e-02, eta: 2 days, 0:27:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6147, loss_cls: 3.6778, loss: 3.6778 +2024-07-25 13:00:42,804 - pyskl - INFO - Epoch [93][3100/3746] lr: 3.176e-02, eta: 2 days, 0:26:23, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6194, loss_cls: 3.6677, loss: 3.6677 +2024-07-25 13:02:04,107 - pyskl - INFO - Epoch [93][3200/3746] lr: 3.174e-02, eta: 2 days, 0:25:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6209, loss_cls: 3.6247, loss: 3.6247 +2024-07-25 13:03:26,220 - pyskl - INFO - Epoch [93][3300/3746] lr: 3.171e-02, eta: 2 days, 0:23:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6202, loss_cls: 3.6610, loss: 3.6610 +2024-07-25 13:04:47,825 - pyskl - INFO - Epoch [93][3400/3746] lr: 3.168e-02, eta: 2 days, 0:22:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6136, loss_cls: 3.6920, loss: 3.6920 +2024-07-25 13:06:10,377 - pyskl - INFO - Epoch [93][3500/3746] lr: 3.166e-02, eta: 2 days, 0:20:58, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6153, loss_cls: 3.6719, loss: 3.6719 +2024-07-25 13:07:32,771 - pyskl - INFO - Epoch [93][3600/3746] lr: 3.163e-02, eta: 2 days, 0:19:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6019, loss_cls: 3.7156, loss: 3.7156 +2024-07-25 13:08:54,731 - pyskl - INFO - Epoch [93][3700/3746] lr: 3.161e-02, eta: 2 days, 0:18:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6166, loss_cls: 3.6480, loss: 3.6480 +2024-07-25 13:09:34,692 - pyskl - INFO - Saving checkpoint at 93 epochs +2024-07-25 13:11:26,087 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 13:11:26,764 - pyskl - INFO - +top1_acc 0.2928 +top5_acc 0.5481 +2024-07-25 13:11:26,765 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 13:11:26,808 - pyskl - INFO - +mean_acc 0.2924 +2024-07-25 13:11:26,822 - pyskl - INFO - Epoch(val) [93][309] top1_acc: 0.2928, top5_acc: 0.5481, mean_class_accuracy: 0.2924 +2024-07-25 13:15:17,448 - pyskl - INFO - Epoch [94][100/3746] lr: 3.157e-02, eta: 2 days, 0:17:26, time: 2.306, data_time: 1.312, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6294, loss_cls: 3.5541, loss: 3.5541 +2024-07-25 13:16:40,569 - pyskl - INFO - Epoch [94][200/3746] lr: 3.154e-02, eta: 2 days, 0:16:05, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6327, loss_cls: 3.5671, loss: 3.5671 +2024-07-25 13:18:02,164 - pyskl - INFO - Epoch [94][300/3746] lr: 3.152e-02, eta: 2 days, 0:14:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6370, loss_cls: 3.5773, loss: 3.5773 +2024-07-25 13:19:23,469 - pyskl - INFO - Epoch [94][400/3746] lr: 3.149e-02, eta: 2 days, 0:13:22, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6278, loss_cls: 3.6027, loss: 3.6027 +2024-07-25 13:20:44,889 - pyskl - INFO - Epoch [94][500/3746] lr: 3.146e-02, eta: 2 days, 0:12:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6247, loss_cls: 3.5742, loss: 3.5742 +2024-07-25 13:22:06,330 - pyskl - INFO - Epoch [94][600/3746] lr: 3.144e-02, eta: 2 days, 0:10:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6202, loss_cls: 3.6343, loss: 3.6343 +2024-07-25 13:23:28,584 - pyskl - INFO - Epoch [94][700/3746] lr: 3.141e-02, eta: 2 days, 0:09:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6228, loss_cls: 3.6223, loss: 3.6223 +2024-07-25 13:24:50,146 - pyskl - INFO - Epoch [94][800/3746] lr: 3.139e-02, eta: 2 days, 0:07:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6212, loss_cls: 3.5889, loss: 3.5889 +2024-07-25 13:26:11,691 - pyskl - INFO - Epoch [94][900/3746] lr: 3.136e-02, eta: 2 days, 0:06:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6156, loss_cls: 3.6622, loss: 3.6622 +2024-07-25 13:27:33,581 - pyskl - INFO - Epoch [94][1000/3746] lr: 3.133e-02, eta: 2 days, 0:05:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6164, loss_cls: 3.6740, loss: 3.6740 +2024-07-25 13:28:55,462 - pyskl - INFO - Epoch [94][1100/3746] lr: 3.131e-02, eta: 2 days, 0:03:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6183, loss_cls: 3.6433, loss: 3.6433 +2024-07-25 13:30:17,152 - pyskl - INFO - Epoch [94][1200/3746] lr: 3.128e-02, eta: 2 days, 0:02:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6131, loss_cls: 3.6547, loss: 3.6547 +2024-07-25 13:31:38,617 - pyskl - INFO - Epoch [94][1300/3746] lr: 3.126e-02, eta: 2 days, 0:01:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6064, loss_cls: 3.7003, loss: 3.7003 +2024-07-25 13:33:00,097 - pyskl - INFO - Epoch [94][1400/3746] lr: 3.123e-02, eta: 1 day, 23:59:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6314, loss_cls: 3.5773, loss: 3.5773 +2024-07-25 13:34:21,903 - pyskl - INFO - Epoch [94][1500/3746] lr: 3.120e-02, eta: 1 day, 23:58:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6198, loss_cls: 3.6229, loss: 3.6229 +2024-07-25 13:35:43,229 - pyskl - INFO - Epoch [94][1600/3746] lr: 3.118e-02, eta: 1 day, 23:57:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6209, loss_cls: 3.6133, loss: 3.6133 +2024-07-25 13:37:05,539 - pyskl - INFO - Epoch [94][1700/3746] lr: 3.115e-02, eta: 1 day, 23:55:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6055, loss_cls: 3.6909, loss: 3.6909 +2024-07-25 13:38:27,612 - pyskl - INFO - Epoch [94][1800/3746] lr: 3.113e-02, eta: 1 day, 23:54:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6036, loss_cls: 3.6998, loss: 3.6998 +2024-07-25 13:39:50,039 - pyskl - INFO - Epoch [94][1900/3746] lr: 3.110e-02, eta: 1 day, 23:53:03, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6220, loss_cls: 3.5936, loss: 3.5936 +2024-07-25 13:41:12,272 - pyskl - INFO - Epoch [94][2000/3746] lr: 3.108e-02, eta: 1 day, 23:51:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6170, loss_cls: 3.6474, loss: 3.6474 +2024-07-25 13:42:33,694 - pyskl - INFO - Epoch [94][2100/3746] lr: 3.105e-02, eta: 1 day, 23:50:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6097, loss_cls: 3.6596, loss: 3.6596 +2024-07-25 13:43:55,655 - pyskl - INFO - Epoch [94][2200/3746] lr: 3.102e-02, eta: 1 day, 23:49:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6094, loss_cls: 3.6786, loss: 3.6786 +2024-07-25 13:45:16,741 - pyskl - INFO - Epoch [94][2300/3746] lr: 3.100e-02, eta: 1 day, 23:47:38, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6250, loss_cls: 3.6163, loss: 3.6163 +2024-07-25 13:46:37,998 - pyskl - INFO - Epoch [94][2400/3746] lr: 3.097e-02, eta: 1 day, 23:46:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6192, loss_cls: 3.6810, loss: 3.6810 +2024-07-25 13:48:00,023 - pyskl - INFO - Epoch [94][2500/3746] lr: 3.095e-02, eta: 1 day, 23:44:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6191, loss_cls: 3.6190, loss: 3.6190 +2024-07-25 13:49:21,879 - pyskl - INFO - Epoch [94][2600/3746] lr: 3.092e-02, eta: 1 day, 23:43:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6205, loss_cls: 3.6446, loss: 3.6446 +2024-07-25 13:50:43,944 - pyskl - INFO - Epoch [94][2700/3746] lr: 3.089e-02, eta: 1 day, 23:42:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6169, loss_cls: 3.6663, loss: 3.6663 +2024-07-25 13:52:05,429 - pyskl - INFO - Epoch [94][2800/3746] lr: 3.087e-02, eta: 1 day, 23:40:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6156, loss_cls: 3.6754, loss: 3.6754 +2024-07-25 13:53:26,803 - pyskl - INFO - Epoch [94][2900/3746] lr: 3.084e-02, eta: 1 day, 23:39:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6231, loss_cls: 3.6072, loss: 3.6072 +2024-07-25 13:54:48,542 - pyskl - INFO - Epoch [94][3000/3746] lr: 3.082e-02, eta: 1 day, 23:38:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6192, loss_cls: 3.6630, loss: 3.6630 +2024-07-25 13:56:09,973 - pyskl - INFO - Epoch [94][3100/3746] lr: 3.079e-02, eta: 1 day, 23:36:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6080, loss_cls: 3.6839, loss: 3.6839 +2024-07-25 13:57:31,122 - pyskl - INFO - Epoch [94][3200/3746] lr: 3.077e-02, eta: 1 day, 23:35:26, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6194, loss_cls: 3.6327, loss: 3.6327 +2024-07-25 13:58:53,695 - pyskl - INFO - Epoch [94][3300/3746] lr: 3.074e-02, eta: 1 day, 23:34:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6102, loss_cls: 3.6773, loss: 3.6773 +2024-07-25 14:00:15,193 - pyskl - INFO - Epoch [94][3400/3746] lr: 3.071e-02, eta: 1 day, 23:32:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6192, loss_cls: 3.6661, loss: 3.6661 +2024-07-25 14:01:36,832 - pyskl - INFO - Epoch [94][3500/3746] lr: 3.069e-02, eta: 1 day, 23:31:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6130, loss_cls: 3.6469, loss: 3.6469 +2024-07-25 14:02:59,009 - pyskl - INFO - Epoch [94][3600/3746] lr: 3.066e-02, eta: 1 day, 23:30:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6086, loss_cls: 3.6616, loss: 3.6616 +2024-07-25 14:04:21,161 - pyskl - INFO - Epoch [94][3700/3746] lr: 3.064e-02, eta: 1 day, 23:28:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6156, loss_cls: 3.6405, loss: 3.6405 +2024-07-25 14:05:01,608 - pyskl - INFO - Saving checkpoint at 94 epochs +2024-07-25 14:06:53,035 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 14:06:53,696 - pyskl - INFO - +top1_acc 0.2911 +top5_acc 0.5454 +2024-07-25 14:06:53,697 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 14:06:53,737 - pyskl - INFO - +mean_acc 0.2910 +2024-07-25 14:06:53,748 - pyskl - INFO - Epoch(val) [94][309] top1_acc: 0.2911, top5_acc: 0.5454, mean_class_accuracy: 0.2910 +2024-07-25 14:10:40,655 - pyskl - INFO - Epoch [95][100/3746] lr: 3.060e-02, eta: 1 day, 23:27:45, time: 2.269, data_time: 1.289, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6266, loss_cls: 3.5606, loss: 3.5606 +2024-07-25 14:12:03,054 - pyskl - INFO - Epoch [95][200/3746] lr: 3.057e-02, eta: 1 day, 23:26:25, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6345, loss_cls: 3.5657, loss: 3.5657 +2024-07-25 14:13:24,801 - pyskl - INFO - Epoch [95][300/3746] lr: 3.055e-02, eta: 1 day, 23:25:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6277, loss_cls: 3.6108, loss: 3.6108 +2024-07-25 14:14:46,747 - pyskl - INFO - Epoch [95][400/3746] lr: 3.052e-02, eta: 1 day, 23:23:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6289, loss_cls: 3.5653, loss: 3.5653 +2024-07-25 14:16:08,221 - pyskl - INFO - Epoch [95][500/3746] lr: 3.050e-02, eta: 1 day, 23:22:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6334, loss_cls: 3.5575, loss: 3.5575 +2024-07-25 14:17:30,153 - pyskl - INFO - Epoch [95][600/3746] lr: 3.047e-02, eta: 1 day, 23:20:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6138, loss_cls: 3.6220, loss: 3.6220 +2024-07-25 14:18:51,985 - pyskl - INFO - Epoch [95][700/3746] lr: 3.044e-02, eta: 1 day, 23:19:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6222, loss_cls: 3.5985, loss: 3.5985 +2024-07-25 14:20:13,294 - pyskl - INFO - Epoch [95][800/3746] lr: 3.042e-02, eta: 1 day, 23:18:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6325, loss_cls: 3.5805, loss: 3.5805 +2024-07-25 14:21:35,061 - pyskl - INFO - Epoch [95][900/3746] lr: 3.039e-02, eta: 1 day, 23:16:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6183, loss_cls: 3.6283, loss: 3.6283 +2024-07-25 14:22:56,988 - pyskl - INFO - Epoch [95][1000/3746] lr: 3.037e-02, eta: 1 day, 23:15:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6250, loss_cls: 3.5974, loss: 3.5974 +2024-07-25 14:24:18,799 - pyskl - INFO - Epoch [95][1100/3746] lr: 3.034e-02, eta: 1 day, 23:14:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6136, loss_cls: 3.6636, loss: 3.6636 +2024-07-25 14:25:40,371 - pyskl - INFO - Epoch [95][1200/3746] lr: 3.032e-02, eta: 1 day, 23:12:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6181, loss_cls: 3.6477, loss: 3.6477 +2024-07-25 14:27:02,474 - pyskl - INFO - Epoch [95][1300/3746] lr: 3.029e-02, eta: 1 day, 23:11:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6298, loss_cls: 3.5605, loss: 3.5605 +2024-07-25 14:28:24,916 - pyskl - INFO - Epoch [95][1400/3746] lr: 3.026e-02, eta: 1 day, 23:10:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6192, loss_cls: 3.6285, loss: 3.6285 +2024-07-25 14:29:47,532 - pyskl - INFO - Epoch [95][1500/3746] lr: 3.024e-02, eta: 1 day, 23:08:48, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6269, loss_cls: 3.5747, loss: 3.5747 +2024-07-25 14:31:09,248 - pyskl - INFO - Epoch [95][1600/3746] lr: 3.021e-02, eta: 1 day, 23:07:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6209, loss_cls: 3.6344, loss: 3.6344 +2024-07-25 14:32:31,262 - pyskl - INFO - Epoch [95][1700/3746] lr: 3.019e-02, eta: 1 day, 23:06:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6212, loss_cls: 3.6324, loss: 3.6324 +2024-07-25 14:33:53,605 - pyskl - INFO - Epoch [95][1800/3746] lr: 3.016e-02, eta: 1 day, 23:04:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6280, loss_cls: 3.6072, loss: 3.6072 +2024-07-25 14:35:15,559 - pyskl - INFO - Epoch [95][1900/3746] lr: 3.014e-02, eta: 1 day, 23:03:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6261, loss_cls: 3.5895, loss: 3.5895 +2024-07-25 14:36:37,397 - pyskl - INFO - Epoch [95][2000/3746] lr: 3.011e-02, eta: 1 day, 23:02:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6205, loss_cls: 3.6685, loss: 3.6685 +2024-07-25 14:37:58,942 - pyskl - INFO - Epoch [95][2100/3746] lr: 3.008e-02, eta: 1 day, 23:00:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6208, loss_cls: 3.6141, loss: 3.6141 +2024-07-25 14:39:20,271 - pyskl - INFO - Epoch [95][2200/3746] lr: 3.006e-02, eta: 1 day, 22:59:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6119, loss_cls: 3.6686, loss: 3.6686 +2024-07-25 14:40:41,848 - pyskl - INFO - Epoch [95][2300/3746] lr: 3.003e-02, eta: 1 day, 22:57:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6266, loss_cls: 3.6144, loss: 3.6144 +2024-07-25 14:42:03,511 - pyskl - INFO - Epoch [95][2400/3746] lr: 3.001e-02, eta: 1 day, 22:56:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6191, loss_cls: 3.6515, loss: 3.6515 +2024-07-25 14:43:25,234 - pyskl - INFO - Epoch [95][2500/3746] lr: 2.998e-02, eta: 1 day, 22:55:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6234, loss_cls: 3.6060, loss: 3.6060 +2024-07-25 14:44:47,264 - pyskl - INFO - Epoch [95][2600/3746] lr: 2.996e-02, eta: 1 day, 22:53:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6078, loss_cls: 3.6673, loss: 3.6673 +2024-07-25 14:46:09,089 - pyskl - INFO - Epoch [95][2700/3746] lr: 2.993e-02, eta: 1 day, 22:52:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6256, loss_cls: 3.6044, loss: 3.6044 +2024-07-25 14:47:30,890 - pyskl - INFO - Epoch [95][2800/3746] lr: 2.991e-02, eta: 1 day, 22:51:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6092, loss_cls: 3.6736, loss: 3.6736 +2024-07-25 14:48:52,487 - pyskl - INFO - Epoch [95][2900/3746] lr: 2.988e-02, eta: 1 day, 22:49:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6292, loss_cls: 3.5944, loss: 3.5944 +2024-07-25 14:50:14,597 - pyskl - INFO - Epoch [95][3000/3746] lr: 2.985e-02, eta: 1 day, 22:48:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6102, loss_cls: 3.6914, loss: 3.6914 +2024-07-25 14:51:36,368 - pyskl - INFO - Epoch [95][3100/3746] lr: 2.983e-02, eta: 1 day, 22:47:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6158, loss_cls: 3.6538, loss: 3.6538 +2024-07-25 14:52:58,635 - pyskl - INFO - Epoch [95][3200/3746] lr: 2.980e-02, eta: 1 day, 22:45:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6183, loss_cls: 3.6526, loss: 3.6526 +2024-07-25 14:54:20,072 - pyskl - INFO - Epoch [95][3300/3746] lr: 2.978e-02, eta: 1 day, 22:44:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6197, loss_cls: 3.6302, loss: 3.6302 +2024-07-25 14:55:41,768 - pyskl - INFO - Epoch [95][3400/3746] lr: 2.975e-02, eta: 1 day, 22:43:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6305, loss_cls: 3.5620, loss: 3.5620 +2024-07-25 14:57:03,804 - pyskl - INFO - Epoch [95][3500/3746] lr: 2.973e-02, eta: 1 day, 22:41:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6138, loss_cls: 3.6782, loss: 3.6782 +2024-07-25 14:58:26,101 - pyskl - INFO - Epoch [95][3600/3746] lr: 2.970e-02, eta: 1 day, 22:40:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6291, loss_cls: 3.6139, loss: 3.6139 +2024-07-25 14:59:48,039 - pyskl - INFO - Epoch [95][3700/3746] lr: 2.968e-02, eta: 1 day, 22:39:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6180, loss_cls: 3.6347, loss: 3.6347 +2024-07-25 15:00:27,888 - pyskl - INFO - Saving checkpoint at 95 epochs +2024-07-25 15:02:18,472 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 15:02:19,185 - pyskl - INFO - +top1_acc 0.3019 +top5_acc 0.5526 +2024-07-25 15:02:19,185 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 15:02:19,224 - pyskl - INFO - +mean_acc 0.3017 +2024-07-25 15:02:19,228 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_91.pth was removed +2024-07-25 15:02:19,488 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_95.pth. +2024-07-25 15:02:19,489 - pyskl - INFO - Best top1_acc is 0.3019 at 95 epoch. +2024-07-25 15:02:19,504 - pyskl - INFO - Epoch(val) [95][309] top1_acc: 0.3019, top5_acc: 0.5526, mean_class_accuracy: 0.3017 +2024-07-25 15:06:04,165 - pyskl - INFO - Epoch [96][100/3746] lr: 2.964e-02, eta: 1 day, 22:38:02, time: 2.247, data_time: 1.262, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6359, loss_cls: 3.5425, loss: 3.5425 +2024-07-25 15:07:27,037 - pyskl - INFO - Epoch [96][200/3746] lr: 2.961e-02, eta: 1 day, 22:36:41, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6309, loss_cls: 3.5429, loss: 3.5429 +2024-07-25 15:08:48,659 - pyskl - INFO - Epoch [96][300/3746] lr: 2.959e-02, eta: 1 day, 22:35:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6292, loss_cls: 3.5395, loss: 3.5395 +2024-07-25 15:10:10,529 - pyskl - INFO - Epoch [96][400/3746] lr: 2.956e-02, eta: 1 day, 22:33:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6344, loss_cls: 3.5477, loss: 3.5477 +2024-07-25 15:11:32,879 - pyskl - INFO - Epoch [96][500/3746] lr: 2.954e-02, eta: 1 day, 22:32:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6325, loss_cls: 3.5767, loss: 3.5767 +2024-07-25 15:12:54,558 - pyskl - INFO - Epoch [96][600/3746] lr: 2.951e-02, eta: 1 day, 22:31:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6192, loss_cls: 3.5864, loss: 3.5864 +2024-07-25 15:14:17,015 - pyskl - INFO - Epoch [96][700/3746] lr: 2.948e-02, eta: 1 day, 22:29:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6233, loss_cls: 3.6061, loss: 3.6061 +2024-07-25 15:15:38,172 - pyskl - INFO - Epoch [96][800/3746] lr: 2.946e-02, eta: 1 day, 22:28:33, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6178, loss_cls: 3.6356, loss: 3.6356 +2024-07-25 15:16:59,495 - pyskl - INFO - Epoch [96][900/3746] lr: 2.943e-02, eta: 1 day, 22:27:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6236, loss_cls: 3.6193, loss: 3.6193 +2024-07-25 15:18:22,001 - pyskl - INFO - Epoch [96][1000/3746] lr: 2.941e-02, eta: 1 day, 22:25:51, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6303, loss_cls: 3.5555, loss: 3.5555 +2024-07-25 15:19:44,215 - pyskl - INFO - Epoch [96][1100/3746] lr: 2.938e-02, eta: 1 day, 22:24:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6359, loss_cls: 3.5601, loss: 3.5601 +2024-07-25 15:21:05,992 - pyskl - INFO - Epoch [96][1200/3746] lr: 2.936e-02, eta: 1 day, 22:23:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6366, loss_cls: 3.5412, loss: 3.5412 +2024-07-25 15:22:27,510 - pyskl - INFO - Epoch [96][1300/3746] lr: 2.933e-02, eta: 1 day, 22:21:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6192, loss_cls: 3.6159, loss: 3.6159 +2024-07-25 15:23:49,200 - pyskl - INFO - Epoch [96][1400/3746] lr: 2.931e-02, eta: 1 day, 22:20:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6284, loss_cls: 3.5893, loss: 3.5893 +2024-07-25 15:25:11,719 - pyskl - INFO - Epoch [96][1500/3746] lr: 2.928e-02, eta: 1 day, 22:19:04, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6267, loss_cls: 3.6091, loss: 3.6091 +2024-07-25 15:26:33,639 - pyskl - INFO - Epoch [96][1600/3746] lr: 2.926e-02, eta: 1 day, 22:17:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6234, loss_cls: 3.6036, loss: 3.6036 +2024-07-25 15:27:55,426 - pyskl - INFO - Epoch [96][1700/3746] lr: 2.923e-02, eta: 1 day, 22:16:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6281, loss_cls: 3.5767, loss: 3.5767 +2024-07-25 15:29:18,101 - pyskl - INFO - Epoch [96][1800/3746] lr: 2.920e-02, eta: 1 day, 22:15:01, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6234, loss_cls: 3.6242, loss: 3.6242 +2024-07-25 15:30:39,989 - pyskl - INFO - Epoch [96][1900/3746] lr: 2.918e-02, eta: 1 day, 22:13:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6223, loss_cls: 3.6517, loss: 3.6517 +2024-07-25 15:32:01,886 - pyskl - INFO - Epoch [96][2000/3746] lr: 2.915e-02, eta: 1 day, 22:12:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6314, loss_cls: 3.6149, loss: 3.6149 +2024-07-25 15:33:24,210 - pyskl - INFO - Epoch [96][2100/3746] lr: 2.913e-02, eta: 1 day, 22:10:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6242, loss_cls: 3.5974, loss: 3.5974 +2024-07-25 15:34:46,366 - pyskl - INFO - Epoch [96][2200/3746] lr: 2.910e-02, eta: 1 day, 22:09:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6200, loss_cls: 3.5992, loss: 3.5992 +2024-07-25 15:36:07,849 - pyskl - INFO - Epoch [96][2300/3746] lr: 2.908e-02, eta: 1 day, 22:08:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6319, loss_cls: 3.6007, loss: 3.6007 +2024-07-25 15:37:29,381 - pyskl - INFO - Epoch [96][2400/3746] lr: 2.905e-02, eta: 1 day, 22:06:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6155, loss_cls: 3.6381, loss: 3.6381 +2024-07-25 15:38:51,789 - pyskl - INFO - Epoch [96][2500/3746] lr: 2.903e-02, eta: 1 day, 22:05:32, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6109, loss_cls: 3.6571, loss: 3.6571 +2024-07-25 15:40:13,216 - pyskl - INFO - Epoch [96][2600/3746] lr: 2.900e-02, eta: 1 day, 22:04:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6127, loss_cls: 3.6420, loss: 3.6420 +2024-07-25 15:41:35,089 - pyskl - INFO - Epoch [96][2700/3746] lr: 2.898e-02, eta: 1 day, 22:02:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6200, loss_cls: 3.6262, loss: 3.6262 +2024-07-25 15:42:56,376 - pyskl - INFO - Epoch [96][2800/3746] lr: 2.895e-02, eta: 1 day, 22:01:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6197, loss_cls: 3.6082, loss: 3.6082 +2024-07-25 15:44:17,538 - pyskl - INFO - Epoch [96][2900/3746] lr: 2.893e-02, eta: 1 day, 22:00:05, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6230, loss_cls: 3.6172, loss: 3.6172 +2024-07-25 15:45:39,333 - pyskl - INFO - Epoch [96][3000/3746] lr: 2.890e-02, eta: 1 day, 21:58:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6289, loss_cls: 3.5861, loss: 3.5861 +2024-07-25 15:47:00,969 - pyskl - INFO - Epoch [96][3100/3746] lr: 2.887e-02, eta: 1 day, 21:57:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6222, loss_cls: 3.6035, loss: 3.6035 +2024-07-25 15:48:23,902 - pyskl - INFO - Epoch [96][3200/3746] lr: 2.885e-02, eta: 1 day, 21:56:02, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6225, loss_cls: 3.6447, loss: 3.6447 +2024-07-25 15:49:45,098 - pyskl - INFO - Epoch [96][3300/3746] lr: 2.882e-02, eta: 1 day, 21:54:40, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6152, loss_cls: 3.6519, loss: 3.6519 +2024-07-25 15:51:06,861 - pyskl - INFO - Epoch [96][3400/3746] lr: 2.880e-02, eta: 1 day, 21:53:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6142, loss_cls: 3.6046, loss: 3.6046 +2024-07-25 15:52:28,261 - pyskl - INFO - Epoch [96][3500/3746] lr: 2.877e-02, eta: 1 day, 21:51:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6142, loss_cls: 3.6287, loss: 3.6287 +2024-07-25 15:53:50,225 - pyskl - INFO - Epoch [96][3600/3746] lr: 2.875e-02, eta: 1 day, 21:50:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6262, loss_cls: 3.6054, loss: 3.6054 +2024-07-25 15:55:12,461 - pyskl - INFO - Epoch [96][3700/3746] lr: 2.872e-02, eta: 1 day, 21:49:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6245, loss_cls: 3.6270, loss: 3.6270 +2024-07-25 15:55:52,178 - pyskl - INFO - Saving checkpoint at 96 epochs +2024-07-25 15:57:43,534 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 15:57:44,256 - pyskl - INFO - +top1_acc 0.3044 +top5_acc 0.5556 +2024-07-25 15:57:44,257 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 15:57:44,308 - pyskl - INFO - +mean_acc 0.3042 +2024-07-25 15:57:44,314 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_95.pth was removed +2024-07-25 15:57:44,602 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_96.pth. +2024-07-25 15:57:44,603 - pyskl - INFO - Best top1_acc is 0.3044 at 96 epoch. +2024-07-25 15:57:44,615 - pyskl - INFO - Epoch(val) [96][309] top1_acc: 0.3044, top5_acc: 0.5556, mean_class_accuracy: 0.3042 +2024-07-25 16:01:33,124 - pyskl - INFO - Epoch [97][100/3746] lr: 2.869e-02, eta: 1 day, 21:48:17, time: 2.285, data_time: 1.304, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6322, loss_cls: 3.5261, loss: 3.5261 +2024-07-25 16:02:55,335 - pyskl - INFO - Epoch [97][200/3746] lr: 2.866e-02, eta: 1 day, 21:46:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6314, loss_cls: 3.5214, loss: 3.5214 +2024-07-25 16:04:17,055 - pyskl - INFO - Epoch [97][300/3746] lr: 2.864e-02, eta: 1 day, 21:45:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6395, loss_cls: 3.5541, loss: 3.5541 +2024-07-25 16:05:38,487 - pyskl - INFO - Epoch [97][400/3746] lr: 2.861e-02, eta: 1 day, 21:44:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6319, loss_cls: 3.5739, loss: 3.5739 +2024-07-25 16:07:00,425 - pyskl - INFO - Epoch [97][500/3746] lr: 2.858e-02, eta: 1 day, 21:42:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6325, loss_cls: 3.5897, loss: 3.5897 +2024-07-25 16:08:22,208 - pyskl - INFO - Epoch [97][600/3746] lr: 2.856e-02, eta: 1 day, 21:41:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6253, loss_cls: 3.5864, loss: 3.5864 +2024-07-25 16:09:43,909 - pyskl - INFO - Epoch [97][700/3746] lr: 2.853e-02, eta: 1 day, 21:40:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6300, loss_cls: 3.5812, loss: 3.5812 +2024-07-25 16:11:06,661 - pyskl - INFO - Epoch [97][800/3746] lr: 2.851e-02, eta: 1 day, 21:38:48, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6261, loss_cls: 3.5760, loss: 3.5760 +2024-07-25 16:12:28,453 - pyskl - INFO - Epoch [97][900/3746] lr: 2.848e-02, eta: 1 day, 21:37:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6370, loss_cls: 3.5195, loss: 3.5195 +2024-07-25 16:13:50,014 - pyskl - INFO - Epoch [97][1000/3746] lr: 2.846e-02, eta: 1 day, 21:36:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6323, loss_cls: 3.5683, loss: 3.5683 +2024-07-25 16:15:11,328 - pyskl - INFO - Epoch [97][1100/3746] lr: 2.843e-02, eta: 1 day, 21:34:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6319, loss_cls: 3.5655, loss: 3.5655 +2024-07-25 16:16:32,999 - pyskl - INFO - Epoch [97][1200/3746] lr: 2.841e-02, eta: 1 day, 21:33:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6267, loss_cls: 3.5616, loss: 3.5616 +2024-07-25 16:17:54,384 - pyskl - INFO - Epoch [97][1300/3746] lr: 2.838e-02, eta: 1 day, 21:32:00, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6316, loss_cls: 3.5611, loss: 3.5611 +2024-07-25 16:19:15,940 - pyskl - INFO - Epoch [97][1400/3746] lr: 2.836e-02, eta: 1 day, 21:30:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6216, loss_cls: 3.5789, loss: 3.5789 +2024-07-25 16:20:38,056 - pyskl - INFO - Epoch [97][1500/3746] lr: 2.833e-02, eta: 1 day, 21:29:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6142, loss_cls: 3.6310, loss: 3.6310 +2024-07-25 16:21:59,992 - pyskl - INFO - Epoch [97][1600/3746] lr: 2.831e-02, eta: 1 day, 21:27:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6327, loss_cls: 3.5608, loss: 3.5608 +2024-07-25 16:23:22,008 - pyskl - INFO - Epoch [97][1700/3746] lr: 2.828e-02, eta: 1 day, 21:26:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6261, loss_cls: 3.6022, loss: 3.6022 +2024-07-25 16:24:43,966 - pyskl - INFO - Epoch [97][1800/3746] lr: 2.826e-02, eta: 1 day, 21:25:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6244, loss_cls: 3.6197, loss: 3.6197 +2024-07-25 16:26:05,465 - pyskl - INFO - Epoch [97][1900/3746] lr: 2.823e-02, eta: 1 day, 21:23:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6280, loss_cls: 3.5743, loss: 3.5743 +2024-07-25 16:27:27,254 - pyskl - INFO - Epoch [97][2000/3746] lr: 2.821e-02, eta: 1 day, 21:22:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6172, loss_cls: 3.6103, loss: 3.6103 +2024-07-25 16:28:48,506 - pyskl - INFO - Epoch [97][2100/3746] lr: 2.818e-02, eta: 1 day, 21:21:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6253, loss_cls: 3.5781, loss: 3.5781 +2024-07-25 16:30:10,135 - pyskl - INFO - Epoch [97][2200/3746] lr: 2.816e-02, eta: 1 day, 21:19:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6220, loss_cls: 3.6170, loss: 3.6170 +2024-07-25 16:31:31,190 - pyskl - INFO - Epoch [97][2300/3746] lr: 2.813e-02, eta: 1 day, 21:18:25, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6162, loss_cls: 3.6434, loss: 3.6434 +2024-07-25 16:32:53,945 - pyskl - INFO - Epoch [97][2400/3746] lr: 2.811e-02, eta: 1 day, 21:17:04, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6205, loss_cls: 3.6317, loss: 3.6317 +2024-07-25 16:34:16,280 - pyskl - INFO - Epoch [97][2500/3746] lr: 2.808e-02, eta: 1 day, 21:15:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6308, loss_cls: 3.5740, loss: 3.5740 +2024-07-25 16:35:38,431 - pyskl - INFO - Epoch [97][2600/3746] lr: 2.806e-02, eta: 1 day, 21:14:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6281, loss_cls: 3.5833, loss: 3.5833 +2024-07-25 16:37:00,894 - pyskl - INFO - Epoch [97][2700/3746] lr: 2.803e-02, eta: 1 day, 21:13:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6206, loss_cls: 3.6230, loss: 3.6230 +2024-07-25 16:38:22,620 - pyskl - INFO - Epoch [97][2800/3746] lr: 2.801e-02, eta: 1 day, 21:11:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6173, loss_cls: 3.6294, loss: 3.6294 +2024-07-25 16:39:44,096 - pyskl - INFO - Epoch [97][2900/3746] lr: 2.798e-02, eta: 1 day, 21:10:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6341, loss_cls: 3.5445, loss: 3.5445 +2024-07-25 16:41:05,226 - pyskl - INFO - Epoch [97][3000/3746] lr: 2.796e-02, eta: 1 day, 21:08:56, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6303, loss_cls: 3.5901, loss: 3.5901 +2024-07-25 16:42:27,093 - pyskl - INFO - Epoch [97][3100/3746] lr: 2.793e-02, eta: 1 day, 21:07:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6158, loss_cls: 3.6538, loss: 3.6538 +2024-07-25 16:43:49,289 - pyskl - INFO - Epoch [97][3200/3746] lr: 2.791e-02, eta: 1 day, 21:06:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6172, loss_cls: 3.6147, loss: 3.6147 +2024-07-25 16:45:11,292 - pyskl - INFO - Epoch [97][3300/3746] lr: 2.788e-02, eta: 1 day, 21:04:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6338, loss_cls: 3.5517, loss: 3.5517 +2024-07-25 16:46:32,660 - pyskl - INFO - Epoch [97][3400/3746] lr: 2.786e-02, eta: 1 day, 21:03:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6175, loss_cls: 3.6563, loss: 3.6563 +2024-07-25 16:47:55,157 - pyskl - INFO - Epoch [97][3500/3746] lr: 2.783e-02, eta: 1 day, 21:02:09, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3622, top5_acc: 0.6227, loss_cls: 3.6209, loss: 3.6209 +2024-07-25 16:49:17,170 - pyskl - INFO - Epoch [97][3600/3746] lr: 2.781e-02, eta: 1 day, 21:00:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6278, loss_cls: 3.5767, loss: 3.5767 +2024-07-25 16:50:39,558 - pyskl - INFO - Epoch [97][3700/3746] lr: 2.778e-02, eta: 1 day, 20:59:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6284, loss_cls: 3.6021, loss: 3.6021 +2024-07-25 16:51:19,072 - pyskl - INFO - Saving checkpoint at 97 epochs +2024-07-25 16:53:11,455 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 16:53:12,144 - pyskl - INFO - +top1_acc 0.3134 +top5_acc 0.5713 +2024-07-25 16:53:12,144 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 16:53:12,194 - pyskl - INFO - +mean_acc 0.3131 +2024-07-25 16:53:12,199 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_96.pth was removed +2024-07-25 16:53:12,531 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_97.pth. +2024-07-25 16:53:12,532 - pyskl - INFO - Best top1_acc is 0.3134 at 97 epoch. +2024-07-25 16:53:12,548 - pyskl - INFO - Epoch(val) [97][309] top1_acc: 0.3134, top5_acc: 0.5713, mean_class_accuracy: 0.3131 +2024-07-25 16:57:10,027 - pyskl - INFO - Epoch [98][100/3746] lr: 2.774e-02, eta: 1 day, 20:58:32, time: 2.375, data_time: 1.367, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6408, loss_cls: 3.4927, loss: 3.4927 +2024-07-25 16:58:34,357 - pyskl - INFO - Epoch [98][200/3746] lr: 2.772e-02, eta: 1 day, 20:57:12, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6377, loss_cls: 3.5177, loss: 3.5177 +2024-07-25 16:59:58,736 - pyskl - INFO - Epoch [98][300/3746] lr: 2.769e-02, eta: 1 day, 20:55:52, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6389, loss_cls: 3.5382, loss: 3.5382 +2024-07-25 17:01:22,861 - pyskl - INFO - Epoch [98][400/3746] lr: 2.767e-02, eta: 1 day, 20:54:32, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6336, loss_cls: 3.5450, loss: 3.5450 +2024-07-25 17:02:47,424 - pyskl - INFO - Epoch [98][500/3746] lr: 2.764e-02, eta: 1 day, 20:53:12, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6256, loss_cls: 3.5991, loss: 3.5991 +2024-07-25 17:04:11,572 - pyskl - INFO - Epoch [98][600/3746] lr: 2.762e-02, eta: 1 day, 20:51:52, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6347, loss_cls: 3.5480, loss: 3.5480 +2024-07-25 17:05:35,895 - pyskl - INFO - Epoch [98][700/3746] lr: 2.759e-02, eta: 1 day, 20:50:31, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6352, loss_cls: 3.5490, loss: 3.5490 +2024-07-25 17:06:59,939 - pyskl - INFO - Epoch [98][800/3746] lr: 2.757e-02, eta: 1 day, 20:49:11, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6395, loss_cls: 3.5553, loss: 3.5553 +2024-07-25 17:08:24,808 - pyskl - INFO - Epoch [98][900/3746] lr: 2.754e-02, eta: 1 day, 20:47:51, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6333, loss_cls: 3.5658, loss: 3.5658 +2024-07-25 17:09:49,199 - pyskl - INFO - Epoch [98][1000/3746] lr: 2.752e-02, eta: 1 day, 20:46:31, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6323, loss_cls: 3.5621, loss: 3.5621 +2024-07-25 17:11:13,672 - pyskl - INFO - Epoch [98][1100/3746] lr: 2.749e-02, eta: 1 day, 20:45:11, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6389, loss_cls: 3.5398, loss: 3.5398 +2024-07-25 17:12:38,065 - pyskl - INFO - Epoch [98][1200/3746] lr: 2.747e-02, eta: 1 day, 20:43:51, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6278, loss_cls: 3.5697, loss: 3.5697 +2024-07-25 17:14:01,904 - pyskl - INFO - Epoch [98][1300/3746] lr: 2.744e-02, eta: 1 day, 20:42:31, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6328, loss_cls: 3.5736, loss: 3.5736 +2024-07-25 17:15:24,744 - pyskl - INFO - Epoch [98][1400/3746] lr: 2.742e-02, eta: 1 day, 20:41:10, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6183, loss_cls: 3.6110, loss: 3.6110 +2024-07-25 17:16:47,624 - pyskl - INFO - Epoch [98][1500/3746] lr: 2.739e-02, eta: 1 day, 20:39:49, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6394, loss_cls: 3.5080, loss: 3.5080 +2024-07-25 17:18:09,318 - pyskl - INFO - Epoch [98][1600/3746] lr: 2.737e-02, eta: 1 day, 20:38:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6433, loss_cls: 3.4951, loss: 3.4951 +2024-07-25 17:19:32,564 - pyskl - INFO - Epoch [98][1700/3746] lr: 2.734e-02, eta: 1 day, 20:37:07, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6314, loss_cls: 3.5301, loss: 3.5301 +2024-07-25 17:20:54,717 - pyskl - INFO - Epoch [98][1800/3746] lr: 2.732e-02, eta: 1 day, 20:35:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6286, loss_cls: 3.5709, loss: 3.5709 +2024-07-25 17:22:16,566 - pyskl - INFO - Epoch [98][1900/3746] lr: 2.729e-02, eta: 1 day, 20:34:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6277, loss_cls: 3.5949, loss: 3.5949 +2024-07-25 17:23:38,254 - pyskl - INFO - Epoch [98][2000/3746] lr: 2.727e-02, eta: 1 day, 20:33:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6189, loss_cls: 3.6354, loss: 3.6354 +2024-07-25 17:24:59,594 - pyskl - INFO - Epoch [98][2100/3746] lr: 2.724e-02, eta: 1 day, 20:31:41, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6261, loss_cls: 3.5926, loss: 3.5926 +2024-07-25 17:26:21,132 - pyskl - INFO - Epoch [98][2200/3746] lr: 2.722e-02, eta: 1 day, 20:30:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6178, loss_cls: 3.6483, loss: 3.6483 +2024-07-25 17:27:42,373 - pyskl - INFO - Epoch [98][2300/3746] lr: 2.719e-02, eta: 1 day, 20:28:57, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3723, top5_acc: 0.6227, loss_cls: 3.5916, loss: 3.5916 +2024-07-25 17:29:04,441 - pyskl - INFO - Epoch [98][2400/3746] lr: 2.717e-02, eta: 1 day, 20:27:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6370, loss_cls: 3.5263, loss: 3.5263 +2024-07-25 17:30:25,615 - pyskl - INFO - Epoch [98][2500/3746] lr: 2.714e-02, eta: 1 day, 20:26:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3708, top5_acc: 0.6344, loss_cls: 3.5346, loss: 3.5346 +2024-07-25 17:31:47,689 - pyskl - INFO - Epoch [98][2600/3746] lr: 2.712e-02, eta: 1 day, 20:24:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6303, loss_cls: 3.5746, loss: 3.5746 +2024-07-25 17:33:09,329 - pyskl - INFO - Epoch [98][2700/3746] lr: 2.709e-02, eta: 1 day, 20:23:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6261, loss_cls: 3.6013, loss: 3.6013 +2024-07-25 17:34:31,262 - pyskl - INFO - Epoch [98][2800/3746] lr: 2.707e-02, eta: 1 day, 20:22:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6248, loss_cls: 3.6206, loss: 3.6206 +2024-07-25 17:35:53,412 - pyskl - INFO - Epoch [98][2900/3746] lr: 2.705e-02, eta: 1 day, 20:20:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3731, top5_acc: 0.6253, loss_cls: 3.5753, loss: 3.5753 +2024-07-25 17:37:14,605 - pyskl - INFO - Epoch [98][3000/3746] lr: 2.702e-02, eta: 1 day, 20:19:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6300, loss_cls: 3.5984, loss: 3.5984 +2024-07-25 17:38:35,724 - pyskl - INFO - Epoch [98][3100/3746] lr: 2.700e-02, eta: 1 day, 20:18:05, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6309, loss_cls: 3.5694, loss: 3.5694 +2024-07-25 17:39:57,195 - pyskl - INFO - Epoch [98][3200/3746] lr: 2.697e-02, eta: 1 day, 20:16:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6355, loss_cls: 3.5732, loss: 3.5732 +2024-07-25 17:41:19,019 - pyskl - INFO - Epoch [98][3300/3746] lr: 2.695e-02, eta: 1 day, 20:15:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6219, loss_cls: 3.5963, loss: 3.5963 +2024-07-25 17:42:40,538 - pyskl - INFO - Epoch [98][3400/3746] lr: 2.692e-02, eta: 1 day, 20:14:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6312, loss_cls: 3.5854, loss: 3.5854 +2024-07-25 17:44:02,027 - pyskl - INFO - Epoch [98][3500/3746] lr: 2.690e-02, eta: 1 day, 20:12:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6239, loss_cls: 3.6153, loss: 3.6153 +2024-07-25 17:45:24,492 - pyskl - INFO - Epoch [98][3600/3746] lr: 2.687e-02, eta: 1 day, 20:11:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6356, loss_cls: 3.5753, loss: 3.5753 +2024-07-25 17:46:46,564 - pyskl - INFO - Epoch [98][3700/3746] lr: 2.685e-02, eta: 1 day, 20:09:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6203, loss_cls: 3.6124, loss: 3.6124 +2024-07-25 17:47:26,042 - pyskl - INFO - Saving checkpoint at 98 epochs +2024-07-25 17:49:17,759 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 17:49:18,414 - pyskl - INFO - +top1_acc 0.3163 +top5_acc 0.5655 +2024-07-25 17:49:18,414 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 17:49:18,456 - pyskl - INFO - +mean_acc 0.3160 +2024-07-25 17:49:18,460 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_97.pth was removed +2024-07-25 17:49:18,721 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_98.pth. +2024-07-25 17:49:18,721 - pyskl - INFO - Best top1_acc is 0.3163 at 98 epoch. +2024-07-25 17:49:18,735 - pyskl - INFO - Epoch(val) [98][309] top1_acc: 0.3163, top5_acc: 0.5655, mean_class_accuracy: 0.3160 +2024-07-25 17:53:10,407 - pyskl - INFO - Epoch [99][100/3746] lr: 2.681e-02, eta: 1 day, 20:08:56, time: 2.317, data_time: 1.325, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6420, loss_cls: 3.5202, loss: 3.5202 +2024-07-25 17:54:32,314 - pyskl - INFO - Epoch [99][200/3746] lr: 2.679e-02, eta: 1 day, 20:07:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6483, loss_cls: 3.4540, loss: 3.4540 +2024-07-25 17:55:54,027 - pyskl - INFO - Epoch [99][300/3746] lr: 2.676e-02, eta: 1 day, 20:06:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6388, loss_cls: 3.5197, loss: 3.5197 +2024-07-25 17:57:15,969 - pyskl - INFO - Epoch [99][400/3746] lr: 2.674e-02, eta: 1 day, 20:04:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6378, loss_cls: 3.5162, loss: 3.5162 +2024-07-25 17:58:37,939 - pyskl - INFO - Epoch [99][500/3746] lr: 2.671e-02, eta: 1 day, 20:03:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6358, loss_cls: 3.5561, loss: 3.5561 +2024-07-25 17:59:59,784 - pyskl - INFO - Epoch [99][600/3746] lr: 2.669e-02, eta: 1 day, 20:02:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6431, loss_cls: 3.5243, loss: 3.5243 +2024-07-25 18:01:21,636 - pyskl - INFO - Epoch [99][700/3746] lr: 2.666e-02, eta: 1 day, 20:00:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3741, top5_acc: 0.6297, loss_cls: 3.5571, loss: 3.5571 +2024-07-25 18:02:42,965 - pyskl - INFO - Epoch [99][800/3746] lr: 2.664e-02, eta: 1 day, 19:59:25, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3708, top5_acc: 0.6398, loss_cls: 3.5418, loss: 3.5418 +2024-07-25 18:04:04,222 - pyskl - INFO - Epoch [99][900/3746] lr: 2.661e-02, eta: 1 day, 19:58:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6330, loss_cls: 3.5560, loss: 3.5560 +2024-07-25 18:05:26,855 - pyskl - INFO - Epoch [99][1000/3746] lr: 2.659e-02, eta: 1 day, 19:56:42, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6200, loss_cls: 3.6159, loss: 3.6159 +2024-07-25 18:06:49,253 - pyskl - INFO - Epoch [99][1100/3746] lr: 2.656e-02, eta: 1 day, 19:55:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6364, loss_cls: 3.5415, loss: 3.5415 +2024-07-25 18:08:12,085 - pyskl - INFO - Epoch [99][1200/3746] lr: 2.654e-02, eta: 1 day, 19:54:00, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6383, loss_cls: 3.5115, loss: 3.5115 +2024-07-25 18:09:33,404 - pyskl - INFO - Epoch [99][1300/3746] lr: 2.651e-02, eta: 1 day, 19:52:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3708, top5_acc: 0.6266, loss_cls: 3.5531, loss: 3.5531 +2024-07-25 18:10:54,682 - pyskl - INFO - Epoch [99][1400/3746] lr: 2.649e-02, eta: 1 day, 19:51:16, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6336, loss_cls: 3.5585, loss: 3.5585 +2024-07-25 18:12:16,768 - pyskl - INFO - Epoch [99][1500/3746] lr: 2.646e-02, eta: 1 day, 19:49:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3622, top5_acc: 0.6255, loss_cls: 3.5876, loss: 3.5876 +2024-07-25 18:13:38,933 - pyskl - INFO - Epoch [99][1600/3746] lr: 2.644e-02, eta: 1 day, 19:48:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3720, top5_acc: 0.6272, loss_cls: 3.5650, loss: 3.5650 +2024-07-25 18:15:01,668 - pyskl - INFO - Epoch [99][1700/3746] lr: 2.642e-02, eta: 1 day, 19:47:13, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6402, loss_cls: 3.5277, loss: 3.5277 +2024-07-25 18:16:23,323 - pyskl - INFO - Epoch [99][1800/3746] lr: 2.639e-02, eta: 1 day, 19:45:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6355, loss_cls: 3.5577, loss: 3.5577 +2024-07-25 18:17:44,976 - pyskl - INFO - Epoch [99][1900/3746] lr: 2.637e-02, eta: 1 day, 19:44:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6256, loss_cls: 3.5525, loss: 3.5525 +2024-07-25 18:19:06,939 - pyskl - INFO - Epoch [99][2000/3746] lr: 2.634e-02, eta: 1 day, 19:43:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6316, loss_cls: 3.5854, loss: 3.5854 +2024-07-25 18:20:28,284 - pyskl - INFO - Epoch [99][2100/3746] lr: 2.632e-02, eta: 1 day, 19:41:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6314, loss_cls: 3.5789, loss: 3.5789 +2024-07-25 18:21:49,705 - pyskl - INFO - Epoch [99][2200/3746] lr: 2.629e-02, eta: 1 day, 19:40:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6281, loss_cls: 3.5758, loss: 3.5758 +2024-07-25 18:23:10,854 - pyskl - INFO - Epoch [99][2300/3746] lr: 2.627e-02, eta: 1 day, 19:39:02, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3686, top5_acc: 0.6239, loss_cls: 3.5827, loss: 3.5827 +2024-07-25 18:24:32,812 - pyskl - INFO - Epoch [99][2400/3746] lr: 2.624e-02, eta: 1 day, 19:37:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6358, loss_cls: 3.5241, loss: 3.5241 +2024-07-25 18:25:54,569 - pyskl - INFO - Epoch [99][2500/3746] lr: 2.622e-02, eta: 1 day, 19:36:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6361, loss_cls: 3.5535, loss: 3.5535 +2024-07-25 18:27:16,331 - pyskl - INFO - Epoch [99][2600/3746] lr: 2.619e-02, eta: 1 day, 19:34:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6302, loss_cls: 3.5491, loss: 3.5491 +2024-07-25 18:28:37,773 - pyskl - INFO - Epoch [99][2700/3746] lr: 2.617e-02, eta: 1 day, 19:33:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6328, loss_cls: 3.5689, loss: 3.5689 +2024-07-25 18:29:59,174 - pyskl - INFO - Epoch [99][2800/3746] lr: 2.614e-02, eta: 1 day, 19:32:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6255, loss_cls: 3.5805, loss: 3.5805 +2024-07-25 18:31:21,046 - pyskl - INFO - Epoch [99][2900/3746] lr: 2.612e-02, eta: 1 day, 19:30:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6159, loss_cls: 3.6193, loss: 3.6193 +2024-07-25 18:32:42,589 - pyskl - INFO - Epoch [99][3000/3746] lr: 2.610e-02, eta: 1 day, 19:29:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6344, loss_cls: 3.5268, loss: 3.5268 +2024-07-25 18:34:04,113 - pyskl - INFO - Epoch [99][3100/3746] lr: 2.607e-02, eta: 1 day, 19:28:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6330, loss_cls: 3.5622, loss: 3.5622 +2024-07-25 18:35:25,629 - pyskl - INFO - Epoch [99][3200/3746] lr: 2.605e-02, eta: 1 day, 19:26:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6259, loss_cls: 3.6028, loss: 3.6028 +2024-07-25 18:36:47,533 - pyskl - INFO - Epoch [99][3300/3746] lr: 2.602e-02, eta: 1 day, 19:25:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6408, loss_cls: 3.5177, loss: 3.5177 +2024-07-25 18:38:09,063 - pyskl - INFO - Epoch [99][3400/3746] lr: 2.600e-02, eta: 1 day, 19:24:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6380, loss_cls: 3.5312, loss: 3.5312 +2024-07-25 18:39:30,836 - pyskl - INFO - Epoch [99][3500/3746] lr: 2.597e-02, eta: 1 day, 19:22:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3686, top5_acc: 0.6247, loss_cls: 3.5783, loss: 3.5783 +2024-07-25 18:40:52,834 - pyskl - INFO - Epoch [99][3600/3746] lr: 2.595e-02, eta: 1 day, 19:21:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6297, loss_cls: 3.5810, loss: 3.5810 +2024-07-25 18:42:15,758 - pyskl - INFO - Epoch [99][3700/3746] lr: 2.592e-02, eta: 1 day, 19:20:01, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6294, loss_cls: 3.5973, loss: 3.5973 +2024-07-25 18:42:55,229 - pyskl - INFO - Saving checkpoint at 99 epochs +2024-07-25 18:44:47,567 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 18:44:48,247 - pyskl - INFO - +top1_acc 0.3127 +top5_acc 0.5682 +2024-07-25 18:44:48,247 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 18:44:48,293 - pyskl - INFO - +mean_acc 0.3125 +2024-07-25 18:44:48,305 - pyskl - INFO - Epoch(val) [99][309] top1_acc: 0.3127, top5_acc: 0.5682, mean_class_accuracy: 0.3125 +2024-07-25 18:48:47,447 - pyskl - INFO - Epoch [100][100/3746] lr: 2.589e-02, eta: 1 day, 19:19:03, time: 2.391, data_time: 1.392, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6506, loss_cls: 3.4657, loss: 3.4657 +2024-07-25 18:50:10,133 - pyskl - INFO - Epoch [100][200/3746] lr: 2.586e-02, eta: 1 day, 19:17:42, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6420, loss_cls: 3.4838, loss: 3.4838 +2024-07-25 18:51:32,324 - pyskl - INFO - Epoch [100][300/3746] lr: 2.584e-02, eta: 1 day, 19:16:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6416, loss_cls: 3.4677, loss: 3.4677 +2024-07-25 18:52:54,344 - pyskl - INFO - Epoch [100][400/3746] lr: 2.581e-02, eta: 1 day, 19:14:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6461, loss_cls: 3.4819, loss: 3.4819 +2024-07-25 18:54:16,837 - pyskl - INFO - Epoch [100][500/3746] lr: 2.579e-02, eta: 1 day, 19:13:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6439, loss_cls: 3.4980, loss: 3.4980 +2024-07-25 18:55:38,854 - pyskl - INFO - Epoch [100][600/3746] lr: 2.577e-02, eta: 1 day, 19:12:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6403, loss_cls: 3.5324, loss: 3.5324 +2024-07-25 18:57:00,308 - pyskl - INFO - Epoch [100][700/3746] lr: 2.574e-02, eta: 1 day, 19:10:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6358, loss_cls: 3.5541, loss: 3.5541 +2024-07-25 18:58:22,517 - pyskl - INFO - Epoch [100][800/3746] lr: 2.572e-02, eta: 1 day, 19:09:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6317, loss_cls: 3.5272, loss: 3.5272 +2024-07-25 18:59:44,230 - pyskl - INFO - Epoch [100][900/3746] lr: 2.569e-02, eta: 1 day, 19:08:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6439, loss_cls: 3.4841, loss: 3.4841 +2024-07-25 19:01:05,869 - pyskl - INFO - Epoch [100][1000/3746] lr: 2.567e-02, eta: 1 day, 19:06:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6478, loss_cls: 3.5025, loss: 3.5025 +2024-07-25 19:02:27,157 - pyskl - INFO - Epoch [100][1100/3746] lr: 2.564e-02, eta: 1 day, 19:05:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6339, loss_cls: 3.5439, loss: 3.5439 +2024-07-25 19:03:48,595 - pyskl - INFO - Epoch [100][1200/3746] lr: 2.562e-02, eta: 1 day, 19:04:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6259, loss_cls: 3.5443, loss: 3.5443 +2024-07-25 19:05:10,215 - pyskl - INFO - Epoch [100][1300/3746] lr: 2.559e-02, eta: 1 day, 19:02:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6341, loss_cls: 3.5437, loss: 3.5437 +2024-07-25 19:06:31,917 - pyskl - INFO - Epoch [100][1400/3746] lr: 2.557e-02, eta: 1 day, 19:01:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3767, top5_acc: 0.6375, loss_cls: 3.5562, loss: 3.5562 +2024-07-25 19:07:54,966 - pyskl - INFO - Epoch [100][1500/3746] lr: 2.555e-02, eta: 1 day, 19:00:02, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6375, loss_cls: 3.5120, loss: 3.5120 +2024-07-25 19:09:17,356 - pyskl - INFO - Epoch [100][1600/3746] lr: 2.552e-02, eta: 1 day, 18:58:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6253, loss_cls: 3.5539, loss: 3.5539 +2024-07-25 19:10:39,810 - pyskl - INFO - Epoch [100][1700/3746] lr: 2.550e-02, eta: 1 day, 18:57:19, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6252, loss_cls: 3.5702, loss: 3.5702 +2024-07-25 19:12:01,640 - pyskl - INFO - Epoch [100][1800/3746] lr: 2.547e-02, eta: 1 day, 18:55:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6389, loss_cls: 3.5237, loss: 3.5237 +2024-07-25 19:13:23,626 - pyskl - INFO - Epoch [100][1900/3746] lr: 2.545e-02, eta: 1 day, 18:54:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6191, loss_cls: 3.5936, loss: 3.5936 +2024-07-25 19:14:45,839 - pyskl - INFO - Epoch [100][2000/3746] lr: 2.542e-02, eta: 1 day, 18:53:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6345, loss_cls: 3.5085, loss: 3.5085 +2024-07-25 19:16:07,714 - pyskl - INFO - Epoch [100][2100/3746] lr: 2.540e-02, eta: 1 day, 18:51:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6314, loss_cls: 3.5637, loss: 3.5637 +2024-07-25 19:17:29,021 - pyskl - INFO - Epoch [100][2200/3746] lr: 2.538e-02, eta: 1 day, 18:50:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6439, loss_cls: 3.5031, loss: 3.5031 +2024-07-25 19:18:50,643 - pyskl - INFO - Epoch [100][2300/3746] lr: 2.535e-02, eta: 1 day, 18:49:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6314, loss_cls: 3.5607, loss: 3.5607 +2024-07-25 19:20:13,411 - pyskl - INFO - Epoch [100][2400/3746] lr: 2.533e-02, eta: 1 day, 18:47:49, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6311, loss_cls: 3.5668, loss: 3.5668 +2024-07-25 19:21:36,256 - pyskl - INFO - Epoch [100][2500/3746] lr: 2.530e-02, eta: 1 day, 18:46:27, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3822, top5_acc: 0.6442, loss_cls: 3.5218, loss: 3.5218 +2024-07-25 19:22:57,940 - pyskl - INFO - Epoch [100][2600/3746] lr: 2.528e-02, eta: 1 day, 18:45:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6391, loss_cls: 3.5201, loss: 3.5201 +2024-07-25 19:24:19,525 - pyskl - INFO - Epoch [100][2700/3746] lr: 2.525e-02, eta: 1 day, 18:43:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3720, top5_acc: 0.6353, loss_cls: 3.5521, loss: 3.5521 +2024-07-25 19:25:41,571 - pyskl - INFO - Epoch [100][2800/3746] lr: 2.523e-02, eta: 1 day, 18:42:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6397, loss_cls: 3.5503, loss: 3.5503 +2024-07-25 19:27:03,165 - pyskl - INFO - Epoch [100][2900/3746] lr: 2.521e-02, eta: 1 day, 18:41:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6202, loss_cls: 3.5890, loss: 3.5890 +2024-07-25 19:28:24,926 - pyskl - INFO - Epoch [100][3000/3746] lr: 2.518e-02, eta: 1 day, 18:39:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6288, loss_cls: 3.5645, loss: 3.5645 +2024-07-25 19:29:46,764 - pyskl - INFO - Epoch [100][3100/3746] lr: 2.516e-02, eta: 1 day, 18:38:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6358, loss_cls: 3.5314, loss: 3.5314 +2024-07-25 19:31:08,444 - pyskl - INFO - Epoch [100][3200/3746] lr: 2.513e-02, eta: 1 day, 18:36:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6338, loss_cls: 3.5458, loss: 3.5458 +2024-07-25 19:32:30,273 - pyskl - INFO - Epoch [100][3300/3746] lr: 2.511e-02, eta: 1 day, 18:35:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6272, loss_cls: 3.5894, loss: 3.5894 +2024-07-25 19:33:52,344 - pyskl - INFO - Epoch [100][3400/3746] lr: 2.508e-02, eta: 1 day, 18:34:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6320, loss_cls: 3.5506, loss: 3.5506 +2024-07-25 19:35:13,829 - pyskl - INFO - Epoch [100][3500/3746] lr: 2.506e-02, eta: 1 day, 18:32:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3748, top5_acc: 0.6217, loss_cls: 3.5591, loss: 3.5591 +2024-07-25 19:36:35,815 - pyskl - INFO - Epoch [100][3600/3746] lr: 2.504e-02, eta: 1 day, 18:31:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6198, loss_cls: 3.6089, loss: 3.6089 +2024-07-25 19:37:58,013 - pyskl - INFO - Epoch [100][3700/3746] lr: 2.501e-02, eta: 1 day, 18:30:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6306, loss_cls: 3.5409, loss: 3.5409 +2024-07-25 19:38:37,206 - pyskl - INFO - Saving checkpoint at 100 epochs +2024-07-25 19:40:30,292 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 19:40:30,961 - pyskl - INFO - +top1_acc 0.3130 +top5_acc 0.5649 +2024-07-25 19:40:30,961 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 19:40:31,050 - pyskl - INFO - +mean_acc 0.3127 +2024-07-25 19:40:31,065 - pyskl - INFO - Epoch(val) [100][309] top1_acc: 0.3130, top5_acc: 0.5649, mean_class_accuracy: 0.3127 +2024-07-25 19:44:20,054 - pyskl - INFO - Epoch [101][100/3746] lr: 2.498e-02, eta: 1 day, 18:29:04, time: 2.290, data_time: 1.293, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6441, loss_cls: 3.4637, loss: 3.4637 +2024-07-25 19:45:42,241 - pyskl - INFO - Epoch [101][200/3746] lr: 2.495e-02, eta: 1 day, 18:27:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6520, loss_cls: 3.4496, loss: 3.4496 +2024-07-25 19:47:03,652 - pyskl - INFO - Epoch [101][300/3746] lr: 2.493e-02, eta: 1 day, 18:26:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6559, loss_cls: 3.4395, loss: 3.4395 +2024-07-25 19:48:25,213 - pyskl - INFO - Epoch [101][400/3746] lr: 2.490e-02, eta: 1 day, 18:24:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6334, loss_cls: 3.5374, loss: 3.5374 +2024-07-25 19:49:47,132 - pyskl - INFO - Epoch [101][500/3746] lr: 2.488e-02, eta: 1 day, 18:23:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6331, loss_cls: 3.5240, loss: 3.5240 +2024-07-25 19:51:08,826 - pyskl - INFO - Epoch [101][600/3746] lr: 2.486e-02, eta: 1 day, 18:22:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6328, loss_cls: 3.5403, loss: 3.5403 +2024-07-25 19:52:30,814 - pyskl - INFO - Epoch [101][700/3746] lr: 2.483e-02, eta: 1 day, 18:20:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3819, top5_acc: 0.6438, loss_cls: 3.4790, loss: 3.4790 +2024-07-25 19:53:52,628 - pyskl - INFO - Epoch [101][800/3746] lr: 2.481e-02, eta: 1 day, 18:19:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6403, loss_cls: 3.4938, loss: 3.4938 +2024-07-25 19:55:14,097 - pyskl - INFO - Epoch [101][900/3746] lr: 2.478e-02, eta: 1 day, 18:18:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6445, loss_cls: 3.4931, loss: 3.4931 +2024-07-25 19:56:35,744 - pyskl - INFO - Epoch [101][1000/3746] lr: 2.476e-02, eta: 1 day, 18:16:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3762, top5_acc: 0.6402, loss_cls: 3.5008, loss: 3.5008 +2024-07-25 19:57:57,157 - pyskl - INFO - Epoch [101][1100/3746] lr: 2.473e-02, eta: 1 day, 18:15:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6277, loss_cls: 3.5684, loss: 3.5684 +2024-07-25 19:59:18,609 - pyskl - INFO - Epoch [101][1200/3746] lr: 2.471e-02, eta: 1 day, 18:14:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6370, loss_cls: 3.5041, loss: 3.5041 +2024-07-25 20:00:40,028 - pyskl - INFO - Epoch [101][1300/3746] lr: 2.469e-02, eta: 1 day, 18:12:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6403, loss_cls: 3.4765, loss: 3.4765 +2024-07-25 20:02:01,794 - pyskl - INFO - Epoch [101][1400/3746] lr: 2.466e-02, eta: 1 day, 18:11:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6316, loss_cls: 3.5370, loss: 3.5370 +2024-07-25 20:03:23,968 - pyskl - INFO - Epoch [101][1500/3746] lr: 2.464e-02, eta: 1 day, 18:10:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6414, loss_cls: 3.4986, loss: 3.4986 +2024-07-25 20:04:46,042 - pyskl - INFO - Epoch [101][1600/3746] lr: 2.461e-02, eta: 1 day, 18:08:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6317, loss_cls: 3.5358, loss: 3.5358 +2024-07-25 20:06:09,101 - pyskl - INFO - Epoch [101][1700/3746] lr: 2.459e-02, eta: 1 day, 18:07:17, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6428, loss_cls: 3.5227, loss: 3.5227 +2024-07-25 20:07:31,301 - pyskl - INFO - Epoch [101][1800/3746] lr: 2.457e-02, eta: 1 day, 18:05:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6375, loss_cls: 3.5231, loss: 3.5231 +2024-07-25 20:08:53,236 - pyskl - INFO - Epoch [101][1900/3746] lr: 2.454e-02, eta: 1 day, 18:04:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6411, loss_cls: 3.5231, loss: 3.5231 +2024-07-25 20:10:15,106 - pyskl - INFO - Epoch [101][2000/3746] lr: 2.452e-02, eta: 1 day, 18:03:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6389, loss_cls: 3.5148, loss: 3.5148 +2024-07-25 20:11:37,098 - pyskl - INFO - Epoch [101][2100/3746] lr: 2.449e-02, eta: 1 day, 18:01:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6359, loss_cls: 3.5476, loss: 3.5476 +2024-07-25 20:12:58,397 - pyskl - INFO - Epoch [101][2200/3746] lr: 2.447e-02, eta: 1 day, 18:00:29, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6303, loss_cls: 3.5702, loss: 3.5702 +2024-07-25 20:14:20,444 - pyskl - INFO - Epoch [101][2300/3746] lr: 2.445e-02, eta: 1 day, 17:59:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6434, loss_cls: 3.4737, loss: 3.4737 +2024-07-25 20:15:42,700 - pyskl - INFO - Epoch [101][2400/3746] lr: 2.442e-02, eta: 1 day, 17:57:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3739, top5_acc: 0.6364, loss_cls: 3.5617, loss: 3.5617 +2024-07-25 20:17:05,343 - pyskl - INFO - Epoch [101][2500/3746] lr: 2.440e-02, eta: 1 day, 17:56:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6366, loss_cls: 3.5387, loss: 3.5387 +2024-07-25 20:18:27,338 - pyskl - INFO - Epoch [101][2600/3746] lr: 2.437e-02, eta: 1 day, 17:55:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6298, loss_cls: 3.5692, loss: 3.5692 +2024-07-25 20:19:49,308 - pyskl - INFO - Epoch [101][2700/3746] lr: 2.435e-02, eta: 1 day, 17:53:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6398, loss_cls: 3.5379, loss: 3.5379 +2024-07-25 20:21:10,964 - pyskl - INFO - Epoch [101][2800/3746] lr: 2.433e-02, eta: 1 day, 17:52:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3822, top5_acc: 0.6388, loss_cls: 3.5135, loss: 3.5135 +2024-07-25 20:22:33,249 - pyskl - INFO - Epoch [101][2900/3746] lr: 2.430e-02, eta: 1 day, 17:50:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6405, loss_cls: 3.5179, loss: 3.5179 +2024-07-25 20:23:54,834 - pyskl - INFO - Epoch [101][3000/3746] lr: 2.428e-02, eta: 1 day, 17:49:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6470, loss_cls: 3.4626, loss: 3.4626 +2024-07-25 20:25:16,331 - pyskl - INFO - Epoch [101][3100/3746] lr: 2.425e-02, eta: 1 day, 17:48:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6306, loss_cls: 3.5790, loss: 3.5790 +2024-07-25 20:26:38,294 - pyskl - INFO - Epoch [101][3200/3746] lr: 2.423e-02, eta: 1 day, 17:46:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6362, loss_cls: 3.5480, loss: 3.5480 +2024-07-25 20:27:59,631 - pyskl - INFO - Epoch [101][3300/3746] lr: 2.421e-02, eta: 1 day, 17:45:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6495, loss_cls: 3.4757, loss: 3.4757 +2024-07-25 20:29:21,368 - pyskl - INFO - Epoch [101][3400/3746] lr: 2.418e-02, eta: 1 day, 17:44:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6339, loss_cls: 3.5613, loss: 3.5613 +2024-07-25 20:30:42,769 - pyskl - INFO - Epoch [101][3500/3746] lr: 2.416e-02, eta: 1 day, 17:42:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6352, loss_cls: 3.5314, loss: 3.5314 +2024-07-25 20:32:04,502 - pyskl - INFO - Epoch [101][3600/3746] lr: 2.413e-02, eta: 1 day, 17:41:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6258, loss_cls: 3.5847, loss: 3.5847 +2024-07-25 20:33:26,077 - pyskl - INFO - Epoch [101][3700/3746] lr: 2.411e-02, eta: 1 day, 17:40:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6258, loss_cls: 3.5419, loss: 3.5419 +2024-07-25 20:34:06,765 - pyskl - INFO - Saving checkpoint at 101 epochs +2024-07-25 20:35:57,339 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 20:35:58,003 - pyskl - INFO - +top1_acc 0.3147 +top5_acc 0.5739 +2024-07-25 20:35:58,003 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 20:35:58,045 - pyskl - INFO - +mean_acc 0.3144 +2024-07-25 20:35:58,056 - pyskl - INFO - Epoch(val) [101][309] top1_acc: 0.3147, top5_acc: 0.5739, mean_class_accuracy: 0.3144 +2024-07-25 20:39:46,061 - pyskl - INFO - Epoch [102][100/3746] lr: 2.407e-02, eta: 1 day, 17:38:58, time: 2.280, data_time: 1.302, memory: 15990, top1_acc: 0.4005, top5_acc: 0.6600, loss_cls: 3.4119, loss: 3.4119 +2024-07-25 20:41:07,660 - pyskl - INFO - Epoch [102][200/3746] lr: 2.405e-02, eta: 1 day, 17:37:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6528, loss_cls: 3.4313, loss: 3.4313 +2024-07-25 20:42:29,461 - pyskl - INFO - Epoch [102][300/3746] lr: 2.403e-02, eta: 1 day, 17:36:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6522, loss_cls: 3.4518, loss: 3.4518 +2024-07-25 20:43:51,313 - pyskl - INFO - Epoch [102][400/3746] lr: 2.400e-02, eta: 1 day, 17:34:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6292, loss_cls: 3.5373, loss: 3.5373 +2024-07-25 20:45:12,660 - pyskl - INFO - Epoch [102][500/3746] lr: 2.398e-02, eta: 1 day, 17:33:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6547, loss_cls: 3.4387, loss: 3.4387 +2024-07-25 20:46:34,357 - pyskl - INFO - Epoch [102][600/3746] lr: 2.396e-02, eta: 1 day, 17:32:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3822, top5_acc: 0.6478, loss_cls: 3.4704, loss: 3.4704 +2024-07-25 20:47:56,163 - pyskl - INFO - Epoch [102][700/3746] lr: 2.393e-02, eta: 1 day, 17:30:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6292, loss_cls: 3.5368, loss: 3.5368 +2024-07-25 20:49:17,765 - pyskl - INFO - Epoch [102][800/3746] lr: 2.391e-02, eta: 1 day, 17:29:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6495, loss_cls: 3.4415, loss: 3.4415 +2024-07-25 20:50:39,443 - pyskl - INFO - Epoch [102][900/3746] lr: 2.388e-02, eta: 1 day, 17:28:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6350, loss_cls: 3.5111, loss: 3.5111 +2024-07-25 20:52:00,995 - pyskl - INFO - Epoch [102][1000/3746] lr: 2.386e-02, eta: 1 day, 17:26:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3908, top5_acc: 0.6488, loss_cls: 3.4861, loss: 3.4861 +2024-07-25 20:53:23,140 - pyskl - INFO - Epoch [102][1100/3746] lr: 2.384e-02, eta: 1 day, 17:25:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6434, loss_cls: 3.4968, loss: 3.4968 +2024-07-25 20:54:44,591 - pyskl - INFO - Epoch [102][1200/3746] lr: 2.381e-02, eta: 1 day, 17:23:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6433, loss_cls: 3.4808, loss: 3.4808 +2024-07-25 20:56:05,853 - pyskl - INFO - Epoch [102][1300/3746] lr: 2.379e-02, eta: 1 day, 17:22:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6341, loss_cls: 3.5370, loss: 3.5370 +2024-07-25 20:57:27,834 - pyskl - INFO - Epoch [102][1400/3746] lr: 2.376e-02, eta: 1 day, 17:21:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6325, loss_cls: 3.5213, loss: 3.5213 +2024-07-25 20:58:49,942 - pyskl - INFO - Epoch [102][1500/3746] lr: 2.374e-02, eta: 1 day, 17:19:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6419, loss_cls: 3.4572, loss: 3.4572 +2024-07-25 21:00:11,734 - pyskl - INFO - Epoch [102][1600/3746] lr: 2.372e-02, eta: 1 day, 17:18:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6431, loss_cls: 3.5065, loss: 3.5065 +2024-07-25 21:01:33,917 - pyskl - INFO - Epoch [102][1700/3746] lr: 2.369e-02, eta: 1 day, 17:17:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6344, loss_cls: 3.5530, loss: 3.5530 +2024-07-25 21:02:56,324 - pyskl - INFO - Epoch [102][1800/3746] lr: 2.367e-02, eta: 1 day, 17:15:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6358, loss_cls: 3.5045, loss: 3.5045 +2024-07-25 21:04:18,073 - pyskl - INFO - Epoch [102][1900/3746] lr: 2.365e-02, eta: 1 day, 17:14:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6462, loss_cls: 3.4780, loss: 3.4780 +2024-07-25 21:05:39,941 - pyskl - INFO - Epoch [102][2000/3746] lr: 2.362e-02, eta: 1 day, 17:13:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6358, loss_cls: 3.5236, loss: 3.5236 +2024-07-25 21:07:01,614 - pyskl - INFO - Epoch [102][2100/3746] lr: 2.360e-02, eta: 1 day, 17:11:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6442, loss_cls: 3.5115, loss: 3.5115 +2024-07-25 21:08:23,373 - pyskl - INFO - Epoch [102][2200/3746] lr: 2.357e-02, eta: 1 day, 17:10:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6362, loss_cls: 3.5200, loss: 3.5200 +2024-07-25 21:09:44,518 - pyskl - INFO - Epoch [102][2300/3746] lr: 2.355e-02, eta: 1 day, 17:09:00, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3748, top5_acc: 0.6394, loss_cls: 3.5359, loss: 3.5359 +2024-07-25 21:11:06,201 - pyskl - INFO - Epoch [102][2400/3746] lr: 2.353e-02, eta: 1 day, 17:07:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6388, loss_cls: 3.5315, loss: 3.5315 +2024-07-25 21:12:27,870 - pyskl - INFO - Epoch [102][2500/3746] lr: 2.350e-02, eta: 1 day, 17:06:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6459, loss_cls: 3.4507, loss: 3.4507 +2024-07-25 21:13:50,308 - pyskl - INFO - Epoch [102][2600/3746] lr: 2.348e-02, eta: 1 day, 17:04:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6402, loss_cls: 3.5218, loss: 3.5218 +2024-07-25 21:15:11,960 - pyskl - INFO - Epoch [102][2700/3746] lr: 2.346e-02, eta: 1 day, 17:03:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6320, loss_cls: 3.5683, loss: 3.5683 +2024-07-25 21:16:33,789 - pyskl - INFO - Epoch [102][2800/3746] lr: 2.343e-02, eta: 1 day, 17:02:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6380, loss_cls: 3.5079, loss: 3.5079 +2024-07-25 21:17:55,288 - pyskl - INFO - Epoch [102][2900/3746] lr: 2.341e-02, eta: 1 day, 17:00:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6425, loss_cls: 3.5049, loss: 3.5049 +2024-07-25 21:19:16,503 - pyskl - INFO - Epoch [102][3000/3746] lr: 2.339e-02, eta: 1 day, 16:59:28, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6366, loss_cls: 3.5024, loss: 3.5024 +2024-07-25 21:20:38,173 - pyskl - INFO - Epoch [102][3100/3746] lr: 2.336e-02, eta: 1 day, 16:58:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6319, loss_cls: 3.5507, loss: 3.5507 +2024-07-25 21:21:59,493 - pyskl - INFO - Epoch [102][3200/3746] lr: 2.334e-02, eta: 1 day, 16:56:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6356, loss_cls: 3.5393, loss: 3.5393 +2024-07-25 21:23:20,813 - pyskl - INFO - Epoch [102][3300/3746] lr: 2.331e-02, eta: 1 day, 16:55:23, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6348, loss_cls: 3.5277, loss: 3.5277 +2024-07-25 21:24:42,214 - pyskl - INFO - Epoch [102][3400/3746] lr: 2.329e-02, eta: 1 day, 16:54:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6414, loss_cls: 3.5083, loss: 3.5083 +2024-07-25 21:26:04,128 - pyskl - INFO - Epoch [102][3500/3746] lr: 2.327e-02, eta: 1 day, 16:52:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6391, loss_cls: 3.5202, loss: 3.5202 +2024-07-25 21:27:25,782 - pyskl - INFO - Epoch [102][3600/3746] lr: 2.324e-02, eta: 1 day, 16:51:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6436, loss_cls: 3.4921, loss: 3.4921 +2024-07-25 21:28:47,361 - pyskl - INFO - Epoch [102][3700/3746] lr: 2.322e-02, eta: 1 day, 16:49:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6336, loss_cls: 3.5172, loss: 3.5172 +2024-07-25 21:29:27,317 - pyskl - INFO - Saving checkpoint at 102 epochs +2024-07-25 21:31:19,810 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 21:31:20,479 - pyskl - INFO - +top1_acc 0.3208 +top5_acc 0.5784 +2024-07-25 21:31:20,479 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 21:31:20,520 - pyskl - INFO - +mean_acc 0.3206 +2024-07-25 21:31:20,525 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_98.pth was removed +2024-07-25 21:31:20,778 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_102.pth. +2024-07-25 21:31:20,779 - pyskl - INFO - Best top1_acc is 0.3208 at 102 epoch. +2024-07-25 21:31:20,791 - pyskl - INFO - Epoch(val) [102][309] top1_acc: 0.3208, top5_acc: 0.5784, mean_class_accuracy: 0.3206 +2024-07-25 21:35:10,509 - pyskl - INFO - Epoch [103][100/3746] lr: 2.319e-02, eta: 1 day, 16:48:48, time: 2.297, data_time: 1.320, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6556, loss_cls: 3.4370, loss: 3.4370 +2024-07-25 21:36:32,051 - pyskl - INFO - Epoch [103][200/3746] lr: 2.316e-02, eta: 1 day, 16:47:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6350, loss_cls: 3.5091, loss: 3.5091 +2024-07-25 21:37:53,909 - pyskl - INFO - Epoch [103][300/3746] lr: 2.314e-02, eta: 1 day, 16:46:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6455, loss_cls: 3.4348, loss: 3.4348 +2024-07-25 21:39:15,296 - pyskl - INFO - Epoch [103][400/3746] lr: 2.311e-02, eta: 1 day, 16:44:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6530, loss_cls: 3.4160, loss: 3.4160 +2024-07-25 21:40:37,243 - pyskl - INFO - Epoch [103][500/3746] lr: 2.309e-02, eta: 1 day, 16:43:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6492, loss_cls: 3.4771, loss: 3.4771 +2024-07-25 21:41:58,761 - pyskl - INFO - Epoch [103][600/3746] lr: 2.307e-02, eta: 1 day, 16:41:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6489, loss_cls: 3.4694, loss: 3.4694 +2024-07-25 21:43:20,383 - pyskl - INFO - Epoch [103][700/3746] lr: 2.304e-02, eta: 1 day, 16:40:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6491, loss_cls: 3.4541, loss: 3.4541 +2024-07-25 21:44:41,749 - pyskl - INFO - Epoch [103][800/3746] lr: 2.302e-02, eta: 1 day, 16:39:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3898, top5_acc: 0.6562, loss_cls: 3.4483, loss: 3.4483 +2024-07-25 21:46:03,892 - pyskl - INFO - Epoch [103][900/3746] lr: 2.300e-02, eta: 1 day, 16:37:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6506, loss_cls: 3.4745, loss: 3.4745 +2024-07-25 21:47:25,277 - pyskl - INFO - Epoch [103][1000/3746] lr: 2.297e-02, eta: 1 day, 16:36:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6462, loss_cls: 3.5017, loss: 3.5017 +2024-07-25 21:48:46,852 - pyskl - INFO - Epoch [103][1100/3746] lr: 2.295e-02, eta: 1 day, 16:35:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6486, loss_cls: 3.4651, loss: 3.4651 +2024-07-25 21:50:08,426 - pyskl - INFO - Epoch [103][1200/3746] lr: 2.293e-02, eta: 1 day, 16:33:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6497, loss_cls: 3.4722, loss: 3.4722 +2024-07-25 21:51:29,826 - pyskl - INFO - Epoch [103][1300/3746] lr: 2.290e-02, eta: 1 day, 16:32:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6433, loss_cls: 3.4604, loss: 3.4604 +2024-07-25 21:52:51,261 - pyskl - INFO - Epoch [103][1400/3746] lr: 2.288e-02, eta: 1 day, 16:31:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6477, loss_cls: 3.4839, loss: 3.4839 +2024-07-25 21:54:13,750 - pyskl - INFO - Epoch [103][1500/3746] lr: 2.286e-02, eta: 1 day, 16:29:43, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6498, loss_cls: 3.4511, loss: 3.4511 +2024-07-25 21:55:35,195 - pyskl - INFO - Epoch [103][1600/3746] lr: 2.283e-02, eta: 1 day, 16:28:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3872, top5_acc: 0.6414, loss_cls: 3.4857, loss: 3.4857 +2024-07-25 21:56:57,353 - pyskl - INFO - Epoch [103][1700/3746] lr: 2.281e-02, eta: 1 day, 16:26:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6395, loss_cls: 3.4907, loss: 3.4907 +2024-07-25 21:58:18,861 - pyskl - INFO - Epoch [103][1800/3746] lr: 2.279e-02, eta: 1 day, 16:25:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6494, loss_cls: 3.4673, loss: 3.4673 +2024-07-25 21:59:41,268 - pyskl - INFO - Epoch [103][1900/3746] lr: 2.276e-02, eta: 1 day, 16:24:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3739, top5_acc: 0.6397, loss_cls: 3.5513, loss: 3.5513 +2024-07-25 22:01:02,972 - pyskl - INFO - Epoch [103][2000/3746] lr: 2.274e-02, eta: 1 day, 16:22:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6403, loss_cls: 3.4868, loss: 3.4868 +2024-07-25 22:02:24,314 - pyskl - INFO - Epoch [103][2100/3746] lr: 2.272e-02, eta: 1 day, 16:21:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6409, loss_cls: 3.4863, loss: 3.4863 +2024-07-25 22:03:45,691 - pyskl - INFO - Epoch [103][2200/3746] lr: 2.269e-02, eta: 1 day, 16:20:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6445, loss_cls: 3.4401, loss: 3.4401 +2024-07-25 22:05:07,505 - pyskl - INFO - Epoch [103][2300/3746] lr: 2.267e-02, eta: 1 day, 16:18:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6502, loss_cls: 3.4359, loss: 3.4359 +2024-07-25 22:06:29,393 - pyskl - INFO - Epoch [103][2400/3746] lr: 2.264e-02, eta: 1 day, 16:17:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6488, loss_cls: 3.4658, loss: 3.4658 +2024-07-25 22:07:51,230 - pyskl - INFO - Epoch [103][2500/3746] lr: 2.262e-02, eta: 1 day, 16:16:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6419, loss_cls: 3.5018, loss: 3.5018 +2024-07-25 22:09:13,613 - pyskl - INFO - Epoch [103][2600/3746] lr: 2.260e-02, eta: 1 day, 16:14:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6491, loss_cls: 3.4567, loss: 3.4567 +2024-07-25 22:10:35,384 - pyskl - INFO - Epoch [103][2700/3746] lr: 2.257e-02, eta: 1 day, 16:13:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6391, loss_cls: 3.5156, loss: 3.5156 +2024-07-25 22:11:57,121 - pyskl - INFO - Epoch [103][2800/3746] lr: 2.255e-02, eta: 1 day, 16:12:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6372, loss_cls: 3.5080, loss: 3.5080 +2024-07-25 22:13:19,602 - pyskl - INFO - Epoch [103][2900/3746] lr: 2.253e-02, eta: 1 day, 16:10:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6420, loss_cls: 3.4856, loss: 3.4856 +2024-07-25 22:14:41,735 - pyskl - INFO - Epoch [103][3000/3746] lr: 2.250e-02, eta: 1 day, 16:09:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6280, loss_cls: 3.5269, loss: 3.5269 +2024-07-25 22:16:03,249 - pyskl - INFO - Epoch [103][3100/3746] lr: 2.248e-02, eta: 1 day, 16:07:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6488, loss_cls: 3.4449, loss: 3.4449 +2024-07-25 22:17:24,630 - pyskl - INFO - Epoch [103][3200/3746] lr: 2.246e-02, eta: 1 day, 16:06:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3708, top5_acc: 0.6312, loss_cls: 3.5576, loss: 3.5576 +2024-07-25 22:18:45,966 - pyskl - INFO - Epoch [103][3300/3746] lr: 2.243e-02, eta: 1 day, 16:05:11, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6478, loss_cls: 3.4851, loss: 3.4851 +2024-07-25 22:20:07,604 - pyskl - INFO - Epoch [103][3400/3746] lr: 2.241e-02, eta: 1 day, 16:03:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6364, loss_cls: 3.5190, loss: 3.5190 +2024-07-25 22:21:28,861 - pyskl - INFO - Epoch [103][3500/3746] lr: 2.239e-02, eta: 1 day, 16:02:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6325, loss_cls: 3.5475, loss: 3.5475 +2024-07-25 22:22:51,191 - pyskl - INFO - Epoch [103][3600/3746] lr: 2.236e-02, eta: 1 day, 16:01:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6389, loss_cls: 3.5167, loss: 3.5167 +2024-07-25 22:24:12,480 - pyskl - INFO - Epoch [103][3700/3746] lr: 2.234e-02, eta: 1 day, 15:59:44, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6328, loss_cls: 3.5207, loss: 3.5207 +2024-07-25 22:24:52,479 - pyskl - INFO - Saving checkpoint at 103 epochs +2024-07-25 22:26:44,010 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 22:26:44,669 - pyskl - INFO - +top1_acc 0.3290 +top5_acc 0.5820 +2024-07-25 22:26:44,669 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 22:26:44,709 - pyskl - INFO - +mean_acc 0.3287 +2024-07-25 22:26:44,714 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_102.pth was removed +2024-07-25 22:26:45,137 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_103.pth. +2024-07-25 22:26:45,138 - pyskl - INFO - Best top1_acc is 0.3290 at 103 epoch. +2024-07-25 22:26:45,149 - pyskl - INFO - Epoch(val) [103][309] top1_acc: 0.3290, top5_acc: 0.5820, mean_class_accuracy: 0.3287 +2024-07-25 22:30:33,874 - pyskl - INFO - Epoch [104][100/3746] lr: 2.231e-02, eta: 1 day, 15:58:35, time: 2.287, data_time: 1.309, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6509, loss_cls: 3.4177, loss: 3.4177 +2024-07-25 22:31:55,581 - pyskl - INFO - Epoch [104][200/3746] lr: 2.228e-02, eta: 1 day, 15:57:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6630, loss_cls: 3.3877, loss: 3.3877 +2024-07-25 22:33:17,207 - pyskl - INFO - Epoch [104][300/3746] lr: 2.226e-02, eta: 1 day, 15:55:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6486, loss_cls: 3.4583, loss: 3.4583 +2024-07-25 22:34:38,747 - pyskl - INFO - Epoch [104][400/3746] lr: 2.224e-02, eta: 1 day, 15:54:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3903, top5_acc: 0.6442, loss_cls: 3.4729, loss: 3.4729 +2024-07-25 22:36:00,843 - pyskl - INFO - Epoch [104][500/3746] lr: 2.221e-02, eta: 1 day, 15:53:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3927, top5_acc: 0.6553, loss_cls: 3.3813, loss: 3.3813 +2024-07-25 22:37:22,490 - pyskl - INFO - Epoch [104][600/3746] lr: 2.219e-02, eta: 1 day, 15:51:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6488, loss_cls: 3.4320, loss: 3.4320 +2024-07-25 22:38:43,628 - pyskl - INFO - Epoch [104][700/3746] lr: 2.217e-02, eta: 1 day, 15:50:23, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3945, top5_acc: 0.6491, loss_cls: 3.4580, loss: 3.4580 +2024-07-25 22:40:05,187 - pyskl - INFO - Epoch [104][800/3746] lr: 2.214e-02, eta: 1 day, 15:49:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6405, loss_cls: 3.4937, loss: 3.4937 +2024-07-25 22:41:27,063 - pyskl - INFO - Epoch [104][900/3746] lr: 2.212e-02, eta: 1 day, 15:47:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6486, loss_cls: 3.4459, loss: 3.4459 +2024-07-25 22:42:48,674 - pyskl - INFO - Epoch [104][1000/3746] lr: 2.210e-02, eta: 1 day, 15:46:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6458, loss_cls: 3.4822, loss: 3.4822 +2024-07-25 22:44:10,391 - pyskl - INFO - Epoch [104][1100/3746] lr: 2.208e-02, eta: 1 day, 15:44:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3819, top5_acc: 0.6466, loss_cls: 3.4752, loss: 3.4752 +2024-07-25 22:45:31,990 - pyskl - INFO - Epoch [104][1200/3746] lr: 2.205e-02, eta: 1 day, 15:43:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6411, loss_cls: 3.4567, loss: 3.4567 +2024-07-25 22:46:53,344 - pyskl - INFO - Epoch [104][1300/3746] lr: 2.203e-02, eta: 1 day, 15:42:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6447, loss_cls: 3.4872, loss: 3.4872 +2024-07-25 22:48:14,749 - pyskl - INFO - Epoch [104][1400/3746] lr: 2.201e-02, eta: 1 day, 15:40:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6444, loss_cls: 3.5074, loss: 3.5074 +2024-07-25 22:49:37,000 - pyskl - INFO - Epoch [104][1500/3746] lr: 2.198e-02, eta: 1 day, 15:39:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6433, loss_cls: 3.4527, loss: 3.4527 +2024-07-25 22:50:59,016 - pyskl - INFO - Epoch [104][1600/3746] lr: 2.196e-02, eta: 1 day, 15:38:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6347, loss_cls: 3.5190, loss: 3.5190 +2024-07-25 22:52:21,586 - pyskl - INFO - Epoch [104][1700/3746] lr: 2.194e-02, eta: 1 day, 15:36:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3923, top5_acc: 0.6430, loss_cls: 3.4538, loss: 3.4538 +2024-07-25 22:53:43,609 - pyskl - INFO - Epoch [104][1800/3746] lr: 2.191e-02, eta: 1 day, 15:35:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6550, loss_cls: 3.4498, loss: 3.4498 +2024-07-25 22:55:05,609 - pyskl - INFO - Epoch [104][1900/3746] lr: 2.189e-02, eta: 1 day, 15:34:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6527, loss_cls: 3.4289, loss: 3.4289 +2024-07-25 22:56:27,674 - pyskl - INFO - Epoch [104][2000/3746] lr: 2.187e-02, eta: 1 day, 15:32:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6484, loss_cls: 3.4715, loss: 3.4715 +2024-07-25 22:57:49,874 - pyskl - INFO - Epoch [104][2100/3746] lr: 2.184e-02, eta: 1 day, 15:31:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6436, loss_cls: 3.5150, loss: 3.5150 +2024-07-25 22:59:11,505 - pyskl - INFO - Epoch [104][2200/3746] lr: 2.182e-02, eta: 1 day, 15:29:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6544, loss_cls: 3.4276, loss: 3.4276 +2024-07-25 23:00:32,418 - pyskl - INFO - Epoch [104][2300/3746] lr: 2.180e-02, eta: 1 day, 15:28:35, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.3898, top5_acc: 0.6406, loss_cls: 3.4622, loss: 3.4622 +2024-07-25 23:01:54,962 - pyskl - INFO - Epoch [104][2400/3746] lr: 2.177e-02, eta: 1 day, 15:27:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6542, loss_cls: 3.4171, loss: 3.4171 +2024-07-25 23:03:17,112 - pyskl - INFO - Epoch [104][2500/3746] lr: 2.175e-02, eta: 1 day, 15:25:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6403, loss_cls: 3.5097, loss: 3.5097 +2024-07-25 23:04:38,937 - pyskl - INFO - Epoch [104][2600/3746] lr: 2.173e-02, eta: 1 day, 15:24:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6458, loss_cls: 3.4824, loss: 3.4824 +2024-07-25 23:06:00,346 - pyskl - INFO - Epoch [104][2700/3746] lr: 2.171e-02, eta: 1 day, 15:23:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6462, loss_cls: 3.4964, loss: 3.4964 +2024-07-25 23:07:22,544 - pyskl - INFO - Epoch [104][2800/3746] lr: 2.168e-02, eta: 1 day, 15:21:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6502, loss_cls: 3.4230, loss: 3.4230 +2024-07-25 23:08:44,229 - pyskl - INFO - Epoch [104][2900/3746] lr: 2.166e-02, eta: 1 day, 15:20:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6552, loss_cls: 3.4491, loss: 3.4491 +2024-07-25 23:10:05,769 - pyskl - INFO - Epoch [104][3000/3746] lr: 2.164e-02, eta: 1 day, 15:19:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6447, loss_cls: 3.4858, loss: 3.4858 +2024-07-25 23:11:27,326 - pyskl - INFO - Epoch [104][3100/3746] lr: 2.161e-02, eta: 1 day, 15:17:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6453, loss_cls: 3.4571, loss: 3.4571 +2024-07-25 23:12:48,462 - pyskl - INFO - Epoch [104][3200/3746] lr: 2.159e-02, eta: 1 day, 15:16:19, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6506, loss_cls: 3.4639, loss: 3.4639 +2024-07-25 23:14:09,848 - pyskl - INFO - Epoch [104][3300/3746] lr: 2.157e-02, eta: 1 day, 15:14:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6366, loss_cls: 3.5525, loss: 3.5525 +2024-07-25 23:15:31,128 - pyskl - INFO - Epoch [104][3400/3746] lr: 2.154e-02, eta: 1 day, 15:13:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6375, loss_cls: 3.5042, loss: 3.5042 +2024-07-25 23:16:53,139 - pyskl - INFO - Epoch [104][3500/3746] lr: 2.152e-02, eta: 1 day, 15:12:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6483, loss_cls: 3.4725, loss: 3.4725 +2024-07-25 23:18:14,566 - pyskl - INFO - Epoch [104][3600/3746] lr: 2.150e-02, eta: 1 day, 15:10:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6398, loss_cls: 3.5037, loss: 3.5037 +2024-07-25 23:19:36,213 - pyskl - INFO - Epoch [104][3700/3746] lr: 2.148e-02, eta: 1 day, 15:09:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6483, loss_cls: 3.4517, loss: 3.4517 +2024-07-25 23:20:15,627 - pyskl - INFO - Saving checkpoint at 104 epochs +2024-07-25 23:22:07,338 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-25 23:22:08,019 - pyskl - INFO - +top1_acc 0.3243 +top5_acc 0.5805 +2024-07-25 23:22:08,019 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-25 23:22:08,061 - pyskl - INFO - +mean_acc 0.3242 +2024-07-25 23:22:08,072 - pyskl - INFO - Epoch(val) [104][309] top1_acc: 0.3243, top5_acc: 0.5805, mean_class_accuracy: 0.3242 +2024-07-25 23:25:57,141 - pyskl - INFO - Epoch [105][100/3746] lr: 2.144e-02, eta: 1 day, 15:08:18, time: 2.291, data_time: 1.305, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6598, loss_cls: 3.3444, loss: 3.3444 +2024-07-25 23:27:18,686 - pyskl - INFO - Epoch [105][200/3746] lr: 2.142e-02, eta: 1 day, 15:06:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3970, top5_acc: 0.6602, loss_cls: 3.4141, loss: 3.4141 +2024-07-25 23:28:40,580 - pyskl - INFO - Epoch [105][300/3746] lr: 2.140e-02, eta: 1 day, 15:05:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6636, loss_cls: 3.3816, loss: 3.3816 +2024-07-25 23:30:02,553 - pyskl - INFO - Epoch [105][400/3746] lr: 2.137e-02, eta: 1 day, 15:04:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6591, loss_cls: 3.3865, loss: 3.3865 +2024-07-25 23:31:24,309 - pyskl - INFO - Epoch [105][500/3746] lr: 2.135e-02, eta: 1 day, 15:02:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6548, loss_cls: 3.3980, loss: 3.3980 +2024-07-25 23:32:46,400 - pyskl - INFO - Epoch [105][600/3746] lr: 2.133e-02, eta: 1 day, 15:01:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6528, loss_cls: 3.4243, loss: 3.4243 +2024-07-25 23:34:08,454 - pyskl - INFO - Epoch [105][700/3746] lr: 2.130e-02, eta: 1 day, 15:00:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6613, loss_cls: 3.4244, loss: 3.4244 +2024-07-25 23:35:30,164 - pyskl - INFO - Epoch [105][800/3746] lr: 2.128e-02, eta: 1 day, 14:58:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3972, top5_acc: 0.6587, loss_cls: 3.3765, loss: 3.3765 +2024-07-25 23:36:51,804 - pyskl - INFO - Epoch [105][900/3746] lr: 2.126e-02, eta: 1 day, 14:57:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6569, loss_cls: 3.4204, loss: 3.4204 +2024-07-25 23:38:13,032 - pyskl - INFO - Epoch [105][1000/3746] lr: 2.124e-02, eta: 1 day, 14:56:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6531, loss_cls: 3.4428, loss: 3.4428 +2024-07-25 23:39:34,329 - pyskl - INFO - Epoch [105][1100/3746] lr: 2.121e-02, eta: 1 day, 14:54:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6514, loss_cls: 3.4328, loss: 3.4328 +2024-07-25 23:40:55,874 - pyskl - INFO - Epoch [105][1200/3746] lr: 2.119e-02, eta: 1 day, 14:53:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3923, top5_acc: 0.6430, loss_cls: 3.4751, loss: 3.4751 +2024-07-25 23:42:17,592 - pyskl - INFO - Epoch [105][1300/3746] lr: 2.117e-02, eta: 1 day, 14:51:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6475, loss_cls: 3.4151, loss: 3.4151 +2024-07-25 23:43:39,533 - pyskl - INFO - Epoch [105][1400/3746] lr: 2.114e-02, eta: 1 day, 14:50:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6456, loss_cls: 3.4814, loss: 3.4814 +2024-07-25 23:45:01,638 - pyskl - INFO - Epoch [105][1500/3746] lr: 2.112e-02, eta: 1 day, 14:49:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6442, loss_cls: 3.4121, loss: 3.4121 +2024-07-25 23:46:23,632 - pyskl - INFO - Epoch [105][1600/3746] lr: 2.110e-02, eta: 1 day, 14:47:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6469, loss_cls: 3.4542, loss: 3.4542 +2024-07-25 23:47:46,390 - pyskl - INFO - Epoch [105][1700/3746] lr: 2.108e-02, eta: 1 day, 14:46:29, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6498, loss_cls: 3.4618, loss: 3.4618 +2024-07-25 23:49:08,429 - pyskl - INFO - Epoch [105][1800/3746] lr: 2.105e-02, eta: 1 day, 14:45:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6427, loss_cls: 3.4698, loss: 3.4698 +2024-07-25 23:50:30,414 - pyskl - INFO - Epoch [105][1900/3746] lr: 2.103e-02, eta: 1 day, 14:43:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6577, loss_cls: 3.4491, loss: 3.4491 +2024-07-25 23:51:52,615 - pyskl - INFO - Epoch [105][2000/3746] lr: 2.101e-02, eta: 1 day, 14:42:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6531, loss_cls: 3.4283, loss: 3.4283 +2024-07-25 23:53:14,191 - pyskl - INFO - Epoch [105][2100/3746] lr: 2.098e-02, eta: 1 day, 14:41:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6470, loss_cls: 3.4563, loss: 3.4563 +2024-07-25 23:54:35,543 - pyskl - INFO - Epoch [105][2200/3746] lr: 2.096e-02, eta: 1 day, 14:39:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6467, loss_cls: 3.4961, loss: 3.4961 +2024-07-25 23:55:57,770 - pyskl - INFO - Epoch [105][2300/3746] lr: 2.094e-02, eta: 1 day, 14:38:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6419, loss_cls: 3.4912, loss: 3.4912 +2024-07-25 23:57:19,902 - pyskl - INFO - Epoch [105][2400/3746] lr: 2.092e-02, eta: 1 day, 14:36:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6377, loss_cls: 3.5177, loss: 3.5177 +2024-07-25 23:58:41,978 - pyskl - INFO - Epoch [105][2500/3746] lr: 2.089e-02, eta: 1 day, 14:35:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6452, loss_cls: 3.4723, loss: 3.4723 +2024-07-26 00:00:03,817 - pyskl - INFO - Epoch [105][2600/3746] lr: 2.087e-02, eta: 1 day, 14:34:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6506, loss_cls: 3.4576, loss: 3.4576 +2024-07-26 00:01:25,373 - pyskl - INFO - Epoch [105][2700/3746] lr: 2.085e-02, eta: 1 day, 14:32:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6489, loss_cls: 3.4596, loss: 3.4596 +2024-07-26 00:02:47,617 - pyskl - INFO - Epoch [105][2800/3746] lr: 2.083e-02, eta: 1 day, 14:31:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6495, loss_cls: 3.4740, loss: 3.4740 +2024-07-26 00:04:09,219 - pyskl - INFO - Epoch [105][2900/3746] lr: 2.080e-02, eta: 1 day, 14:30:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3909, top5_acc: 0.6506, loss_cls: 3.4516, loss: 3.4516 +2024-07-26 00:05:30,733 - pyskl - INFO - Epoch [105][3000/3746] lr: 2.078e-02, eta: 1 day, 14:28:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3822, top5_acc: 0.6383, loss_cls: 3.4987, loss: 3.4987 +2024-07-26 00:06:52,295 - pyskl - INFO - Epoch [105][3100/3746] lr: 2.076e-02, eta: 1 day, 14:27:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6441, loss_cls: 3.4805, loss: 3.4805 +2024-07-26 00:08:14,099 - pyskl - INFO - Epoch [105][3200/3746] lr: 2.073e-02, eta: 1 day, 14:26:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3819, top5_acc: 0.6439, loss_cls: 3.4731, loss: 3.4731 +2024-07-26 00:09:35,969 - pyskl - INFO - Epoch [105][3300/3746] lr: 2.071e-02, eta: 1 day, 14:24:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6366, loss_cls: 3.5123, loss: 3.5123 +2024-07-26 00:10:57,381 - pyskl - INFO - Epoch [105][3400/3746] lr: 2.069e-02, eta: 1 day, 14:23:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6545, loss_cls: 3.4185, loss: 3.4185 +2024-07-26 00:12:19,473 - pyskl - INFO - Epoch [105][3500/3746] lr: 2.067e-02, eta: 1 day, 14:21:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6414, loss_cls: 3.4682, loss: 3.4682 +2024-07-26 00:13:40,989 - pyskl - INFO - Epoch [105][3600/3746] lr: 2.064e-02, eta: 1 day, 14:20:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6436, loss_cls: 3.4560, loss: 3.4560 +2024-07-26 00:15:02,259 - pyskl - INFO - Epoch [105][3700/3746] lr: 2.062e-02, eta: 1 day, 14:19:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6453, loss_cls: 3.4563, loss: 3.4563 +2024-07-26 00:15:41,765 - pyskl - INFO - Saving checkpoint at 105 epochs +2024-07-26 00:17:33,743 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 00:17:34,416 - pyskl - INFO - +top1_acc 0.3285 +top5_acc 0.5891 +2024-07-26 00:17:34,416 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 00:17:34,464 - pyskl - INFO - +mean_acc 0.3283 +2024-07-26 00:17:34,477 - pyskl - INFO - Epoch(val) [105][309] top1_acc: 0.3285, top5_acc: 0.5891, mean_class_accuracy: 0.3283 +2024-07-26 00:21:25,159 - pyskl - INFO - Epoch [106][100/3746] lr: 2.059e-02, eta: 1 day, 14:18:01, time: 2.307, data_time: 1.327, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6525, loss_cls: 3.4056, loss: 3.4056 +2024-07-26 00:22:47,623 - pyskl - INFO - Epoch [106][200/3746] lr: 2.057e-02, eta: 1 day, 14:16:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6630, loss_cls: 3.3574, loss: 3.3574 +2024-07-26 00:24:09,509 - pyskl - INFO - Epoch [106][300/3746] lr: 2.054e-02, eta: 1 day, 14:15:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3994, top5_acc: 0.6681, loss_cls: 3.3714, loss: 3.3714 +2024-07-26 00:25:31,067 - pyskl - INFO - Epoch [106][400/3746] lr: 2.052e-02, eta: 1 day, 14:13:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3914, top5_acc: 0.6581, loss_cls: 3.4080, loss: 3.4080 +2024-07-26 00:26:52,256 - pyskl - INFO - Epoch [106][500/3746] lr: 2.050e-02, eta: 1 day, 14:12:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6616, loss_cls: 3.3737, loss: 3.3737 +2024-07-26 00:28:14,210 - pyskl - INFO - Epoch [106][600/3746] lr: 2.048e-02, eta: 1 day, 14:11:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3972, top5_acc: 0.6489, loss_cls: 3.3983, loss: 3.3983 +2024-07-26 00:29:35,725 - pyskl - INFO - Epoch [106][700/3746] lr: 2.045e-02, eta: 1 day, 14:09:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3908, top5_acc: 0.6494, loss_cls: 3.4467, loss: 3.4467 +2024-07-26 00:30:57,741 - pyskl - INFO - Epoch [106][800/3746] lr: 2.043e-02, eta: 1 day, 14:08:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6611, loss_cls: 3.3746, loss: 3.3746 +2024-07-26 00:32:19,346 - pyskl - INFO - Epoch [106][900/3746] lr: 2.041e-02, eta: 1 day, 14:07:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3908, top5_acc: 0.6528, loss_cls: 3.4429, loss: 3.4429 +2024-07-26 00:33:40,822 - pyskl - INFO - Epoch [106][1000/3746] lr: 2.039e-02, eta: 1 day, 14:05:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6503, loss_cls: 3.4122, loss: 3.4122 +2024-07-26 00:35:02,628 - pyskl - INFO - Epoch [106][1100/3746] lr: 2.036e-02, eta: 1 day, 14:04:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6633, loss_cls: 3.3691, loss: 3.3691 +2024-07-26 00:36:24,627 - pyskl - INFO - Epoch [106][1200/3746] lr: 2.034e-02, eta: 1 day, 14:03:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6503, loss_cls: 3.4386, loss: 3.4386 +2024-07-26 00:37:46,148 - pyskl - INFO - Epoch [106][1300/3746] lr: 2.032e-02, eta: 1 day, 14:01:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6570, loss_cls: 3.3790, loss: 3.3790 +2024-07-26 00:39:07,979 - pyskl - INFO - Epoch [106][1400/3746] lr: 2.030e-02, eta: 1 day, 14:00:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6483, loss_cls: 3.4670, loss: 3.4670 +2024-07-26 00:40:30,532 - pyskl - INFO - Epoch [106][1500/3746] lr: 2.027e-02, eta: 1 day, 13:58:55, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6538, loss_cls: 3.4389, loss: 3.4389 +2024-07-26 00:41:52,295 - pyskl - INFO - Epoch [106][1600/3746] lr: 2.025e-02, eta: 1 day, 13:57:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6525, loss_cls: 3.4279, loss: 3.4279 +2024-07-26 00:43:15,233 - pyskl - INFO - Epoch [106][1700/3746] lr: 2.023e-02, eta: 1 day, 13:56:12, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6530, loss_cls: 3.4411, loss: 3.4411 +2024-07-26 00:44:37,204 - pyskl - INFO - Epoch [106][1800/3746] lr: 2.021e-02, eta: 1 day, 13:54:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6558, loss_cls: 3.4153, loss: 3.4153 +2024-07-26 00:45:58,898 - pyskl - INFO - Epoch [106][1900/3746] lr: 2.018e-02, eta: 1 day, 13:53:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3941, top5_acc: 0.6528, loss_cls: 3.4150, loss: 3.4150 +2024-07-26 00:47:20,778 - pyskl - INFO - Epoch [106][2000/3746] lr: 2.016e-02, eta: 1 day, 13:52:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6514, loss_cls: 3.4649, loss: 3.4649 +2024-07-26 00:48:42,939 - pyskl - INFO - Epoch [106][2100/3746] lr: 2.014e-02, eta: 1 day, 13:50:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6459, loss_cls: 3.4437, loss: 3.4437 +2024-07-26 00:50:04,808 - pyskl - INFO - Epoch [106][2200/3746] lr: 2.012e-02, eta: 1 day, 13:49:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3914, top5_acc: 0.6553, loss_cls: 3.4402, loss: 3.4402 +2024-07-26 00:51:26,357 - pyskl - INFO - Epoch [106][2300/3746] lr: 2.009e-02, eta: 1 day, 13:48:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6531, loss_cls: 3.4071, loss: 3.4071 +2024-07-26 00:52:48,555 - pyskl - INFO - Epoch [106][2400/3746] lr: 2.007e-02, eta: 1 day, 13:46:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6506, loss_cls: 3.4739, loss: 3.4739 +2024-07-26 00:54:10,510 - pyskl - INFO - Epoch [106][2500/3746] lr: 2.005e-02, eta: 1 day, 13:45:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6427, loss_cls: 3.4678, loss: 3.4678 +2024-07-26 00:55:32,657 - pyskl - INFO - Epoch [106][2600/3746] lr: 2.003e-02, eta: 1 day, 13:43:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6494, loss_cls: 3.4584, loss: 3.4584 +2024-07-26 00:56:55,090 - pyskl - INFO - Epoch [106][2700/3746] lr: 2.000e-02, eta: 1 day, 13:42:34, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6369, loss_cls: 3.4964, loss: 3.4964 +2024-07-26 00:58:17,409 - pyskl - INFO - Epoch [106][2800/3746] lr: 1.998e-02, eta: 1 day, 13:41:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6516, loss_cls: 3.4047, loss: 3.4047 +2024-07-26 00:59:38,991 - pyskl - INFO - Epoch [106][2900/3746] lr: 1.996e-02, eta: 1 day, 13:39:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6556, loss_cls: 3.4007, loss: 3.4007 +2024-07-26 01:01:01,012 - pyskl - INFO - Epoch [106][3000/3746] lr: 1.994e-02, eta: 1 day, 13:38:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6434, loss_cls: 3.4595, loss: 3.4595 +2024-07-26 01:02:22,824 - pyskl - INFO - Epoch [106][3100/3746] lr: 1.991e-02, eta: 1 day, 13:37:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6486, loss_cls: 3.4718, loss: 3.4718 +2024-07-26 01:03:44,276 - pyskl - INFO - Epoch [106][3200/3746] lr: 1.989e-02, eta: 1 day, 13:35:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6481, loss_cls: 3.4254, loss: 3.4254 +2024-07-26 01:05:05,808 - pyskl - INFO - Epoch [106][3300/3746] lr: 1.987e-02, eta: 1 day, 13:34:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3945, top5_acc: 0.6488, loss_cls: 3.4652, loss: 3.4652 +2024-07-26 01:06:27,129 - pyskl - INFO - Epoch [106][3400/3746] lr: 1.985e-02, eta: 1 day, 13:33:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6445, loss_cls: 3.4700, loss: 3.4700 +2024-07-26 01:07:49,129 - pyskl - INFO - Epoch [106][3500/3746] lr: 1.983e-02, eta: 1 day, 13:31:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6473, loss_cls: 3.4191, loss: 3.4191 +2024-07-26 01:09:10,226 - pyskl - INFO - Epoch [106][3600/3746] lr: 1.980e-02, eta: 1 day, 13:30:17, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3858, top5_acc: 0.6400, loss_cls: 3.4749, loss: 3.4749 +2024-07-26 01:10:32,141 - pyskl - INFO - Epoch [106][3700/3746] lr: 1.978e-02, eta: 1 day, 13:28:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6508, loss_cls: 3.4245, loss: 3.4245 +2024-07-26 01:11:11,895 - pyskl - INFO - Saving checkpoint at 106 epochs +2024-07-26 01:13:04,228 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 01:13:04,948 - pyskl - INFO - +top1_acc 0.3291 +top5_acc 0.5856 +2024-07-26 01:13:04,948 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 01:13:04,989 - pyskl - INFO - +mean_acc 0.3289 +2024-07-26 01:13:04,993 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_103.pth was removed +2024-07-26 01:13:05,253 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_106.pth. +2024-07-26 01:13:05,254 - pyskl - INFO - Best top1_acc is 0.3291 at 106 epoch. +2024-07-26 01:13:05,265 - pyskl - INFO - Epoch(val) [106][309] top1_acc: 0.3291, top5_acc: 0.5856, mean_class_accuracy: 0.3289 +2024-07-26 01:16:59,304 - pyskl - INFO - Epoch [107][100/3746] lr: 1.975e-02, eta: 1 day, 13:27:43, time: 2.340, data_time: 1.357, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6600, loss_cls: 3.3292, loss: 3.3292 +2024-07-26 01:18:21,487 - pyskl - INFO - Epoch [107][200/3746] lr: 1.973e-02, eta: 1 day, 13:26:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4106, top5_acc: 0.6691, loss_cls: 3.3283, loss: 3.3283 +2024-07-26 01:19:43,233 - pyskl - INFO - Epoch [107][300/3746] lr: 1.970e-02, eta: 1 day, 13:24:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6669, loss_cls: 3.3499, loss: 3.3499 +2024-07-26 01:21:04,466 - pyskl - INFO - Epoch [107][400/3746] lr: 1.968e-02, eta: 1 day, 13:23:37, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4009, top5_acc: 0.6614, loss_cls: 3.3789, loss: 3.3789 +2024-07-26 01:22:25,533 - pyskl - INFO - Epoch [107][500/3746] lr: 1.966e-02, eta: 1 day, 13:22:15, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6577, loss_cls: 3.3662, loss: 3.3662 +2024-07-26 01:23:47,098 - pyskl - INFO - Epoch [107][600/3746] lr: 1.964e-02, eta: 1 day, 13:20:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6622, loss_cls: 3.4104, loss: 3.4104 +2024-07-26 01:25:09,062 - pyskl - INFO - Epoch [107][700/3746] lr: 1.961e-02, eta: 1 day, 13:19:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4025, top5_acc: 0.6605, loss_cls: 3.3665, loss: 3.3665 +2024-07-26 01:26:30,538 - pyskl - INFO - Epoch [107][800/3746] lr: 1.959e-02, eta: 1 day, 13:18:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6650, loss_cls: 3.3607, loss: 3.3607 +2024-07-26 01:27:52,595 - pyskl - INFO - Epoch [107][900/3746] lr: 1.957e-02, eta: 1 day, 13:16:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3986, top5_acc: 0.6623, loss_cls: 3.3751, loss: 3.3751 +2024-07-26 01:29:14,012 - pyskl - INFO - Epoch [107][1000/3746] lr: 1.955e-02, eta: 1 day, 13:15:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3966, top5_acc: 0.6633, loss_cls: 3.3940, loss: 3.3940 +2024-07-26 01:30:35,386 - pyskl - INFO - Epoch [107][1100/3746] lr: 1.953e-02, eta: 1 day, 13:14:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6608, loss_cls: 3.4027, loss: 3.4027 +2024-07-26 01:31:57,455 - pyskl - INFO - Epoch [107][1200/3746] lr: 1.950e-02, eta: 1 day, 13:12:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6536, loss_cls: 3.4325, loss: 3.4325 +2024-07-26 01:33:19,738 - pyskl - INFO - Epoch [107][1300/3746] lr: 1.948e-02, eta: 1 day, 13:11:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6470, loss_cls: 3.4450, loss: 3.4450 +2024-07-26 01:34:41,450 - pyskl - INFO - Epoch [107][1400/3746] lr: 1.946e-02, eta: 1 day, 13:09:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6614, loss_cls: 3.4084, loss: 3.4084 +2024-07-26 01:36:02,997 - pyskl - INFO - Epoch [107][1500/3746] lr: 1.944e-02, eta: 1 day, 13:08:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6536, loss_cls: 3.4290, loss: 3.4290 +2024-07-26 01:37:25,003 - pyskl - INFO - Epoch [107][1600/3746] lr: 1.942e-02, eta: 1 day, 13:07:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4062, top5_acc: 0.6584, loss_cls: 3.3881, loss: 3.3881 +2024-07-26 01:38:47,339 - pyskl - INFO - Epoch [107][1700/3746] lr: 1.939e-02, eta: 1 day, 13:05:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6587, loss_cls: 3.3806, loss: 3.3806 +2024-07-26 01:40:09,240 - pyskl - INFO - Epoch [107][1800/3746] lr: 1.937e-02, eta: 1 day, 13:04:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6458, loss_cls: 3.4367, loss: 3.4367 +2024-07-26 01:41:30,587 - pyskl - INFO - Epoch [107][1900/3746] lr: 1.935e-02, eta: 1 day, 13:03:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6492, loss_cls: 3.4524, loss: 3.4524 +2024-07-26 01:42:52,152 - pyskl - INFO - Epoch [107][2000/3746] lr: 1.933e-02, eta: 1 day, 13:01:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6395, loss_cls: 3.4879, loss: 3.4879 +2024-07-26 01:44:13,705 - pyskl - INFO - Epoch [107][2100/3746] lr: 1.930e-02, eta: 1 day, 13:00:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6667, loss_cls: 3.3635, loss: 3.3635 +2024-07-26 01:45:35,400 - pyskl - INFO - Epoch [107][2200/3746] lr: 1.928e-02, eta: 1 day, 12:59:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6539, loss_cls: 3.4034, loss: 3.4034 +2024-07-26 01:46:57,039 - pyskl - INFO - Epoch [107][2300/3746] lr: 1.926e-02, eta: 1 day, 12:57:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6438, loss_cls: 3.4509, loss: 3.4509 +2024-07-26 01:48:19,058 - pyskl - INFO - Epoch [107][2400/3746] lr: 1.924e-02, eta: 1 day, 12:56:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3941, top5_acc: 0.6578, loss_cls: 3.4028, loss: 3.4028 +2024-07-26 01:49:41,380 - pyskl - INFO - Epoch [107][2500/3746] lr: 1.922e-02, eta: 1 day, 12:54:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6461, loss_cls: 3.4660, loss: 3.4660 +2024-07-26 01:51:03,720 - pyskl - INFO - Epoch [107][2600/3746] lr: 1.919e-02, eta: 1 day, 12:53:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6538, loss_cls: 3.4400, loss: 3.4400 +2024-07-26 01:52:25,510 - pyskl - INFO - Epoch [107][2700/3746] lr: 1.917e-02, eta: 1 day, 12:52:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6608, loss_cls: 3.3889, loss: 3.3889 +2024-07-26 01:53:46,977 - pyskl - INFO - Epoch [107][2800/3746] lr: 1.915e-02, eta: 1 day, 12:50:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3914, top5_acc: 0.6505, loss_cls: 3.4625, loss: 3.4625 +2024-07-26 01:55:08,190 - pyskl - INFO - Epoch [107][2900/3746] lr: 1.913e-02, eta: 1 day, 12:49:29, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6511, loss_cls: 3.3949, loss: 3.3949 +2024-07-26 01:56:30,245 - pyskl - INFO - Epoch [107][3000/3746] lr: 1.911e-02, eta: 1 day, 12:48:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6617, loss_cls: 3.3715, loss: 3.3715 +2024-07-26 01:57:52,814 - pyskl - INFO - Epoch [107][3100/3746] lr: 1.908e-02, eta: 1 day, 12:46:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6559, loss_cls: 3.4162, loss: 3.4162 +2024-07-26 01:59:14,366 - pyskl - INFO - Epoch [107][3200/3746] lr: 1.906e-02, eta: 1 day, 12:45:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6497, loss_cls: 3.4514, loss: 3.4514 +2024-07-26 02:00:36,064 - pyskl - INFO - Epoch [107][3300/3746] lr: 1.904e-02, eta: 1 day, 12:44:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6530, loss_cls: 3.4120, loss: 3.4120 +2024-07-26 02:01:57,309 - pyskl - INFO - Epoch [107][3400/3746] lr: 1.902e-02, eta: 1 day, 12:42:40, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6531, loss_cls: 3.4220, loss: 3.4220 +2024-07-26 02:03:19,175 - pyskl - INFO - Epoch [107][3500/3746] lr: 1.900e-02, eta: 1 day, 12:41:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6438, loss_cls: 3.4896, loss: 3.4896 +2024-07-26 02:04:40,386 - pyskl - INFO - Epoch [107][3600/3746] lr: 1.897e-02, eta: 1 day, 12:39:56, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4088, top5_acc: 0.6634, loss_cls: 3.3570, loss: 3.3570 +2024-07-26 02:06:02,473 - pyskl - INFO - Epoch [107][3700/3746] lr: 1.895e-02, eta: 1 day, 12:38:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6497, loss_cls: 3.4279, loss: 3.4279 +2024-07-26 02:06:42,234 - pyskl - INFO - Saving checkpoint at 107 epochs +2024-07-26 02:08:33,443 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 02:08:34,117 - pyskl - INFO - +top1_acc 0.3273 +top5_acc 0.5874 +2024-07-26 02:08:34,117 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 02:08:34,157 - pyskl - INFO - +mean_acc 0.3271 +2024-07-26 02:08:34,169 - pyskl - INFO - Epoch(val) [107][309] top1_acc: 0.3273, top5_acc: 0.5874, mean_class_accuracy: 0.3271 +2024-07-26 02:12:25,699 - pyskl - INFO - Epoch [108][100/3746] lr: 1.892e-02, eta: 1 day, 12:37:19, time: 2.315, data_time: 1.330, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6713, loss_cls: 3.3244, loss: 3.3244 +2024-07-26 02:13:47,475 - pyskl - INFO - Epoch [108][200/3746] lr: 1.890e-02, eta: 1 day, 12:35:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6681, loss_cls: 3.3233, loss: 3.3233 +2024-07-26 02:15:09,195 - pyskl - INFO - Epoch [108][300/3746] lr: 1.888e-02, eta: 1 day, 12:34:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4033, top5_acc: 0.6677, loss_cls: 3.3387, loss: 3.3387 +2024-07-26 02:16:30,455 - pyskl - INFO - Epoch [108][400/3746] lr: 1.886e-02, eta: 1 day, 12:33:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6600, loss_cls: 3.3653, loss: 3.3653 +2024-07-26 02:17:52,130 - pyskl - INFO - Epoch [108][500/3746] lr: 1.883e-02, eta: 1 day, 12:31:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6752, loss_cls: 3.3043, loss: 3.3043 +2024-07-26 02:19:14,039 - pyskl - INFO - Epoch [108][600/3746] lr: 1.881e-02, eta: 1 day, 12:30:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6658, loss_cls: 3.3420, loss: 3.3420 +2024-07-26 02:20:35,829 - pyskl - INFO - Epoch [108][700/3746] lr: 1.879e-02, eta: 1 day, 12:29:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4062, top5_acc: 0.6645, loss_cls: 3.3496, loss: 3.3496 +2024-07-26 02:21:57,316 - pyskl - INFO - Epoch [108][800/3746] lr: 1.877e-02, eta: 1 day, 12:27:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6583, loss_cls: 3.3679, loss: 3.3679 +2024-07-26 02:23:19,726 - pyskl - INFO - Epoch [108][900/3746] lr: 1.875e-02, eta: 1 day, 12:26:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6589, loss_cls: 3.4115, loss: 3.4115 +2024-07-26 02:24:41,586 - pyskl - INFO - Epoch [108][1000/3746] lr: 1.872e-02, eta: 1 day, 12:25:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6506, loss_cls: 3.4261, loss: 3.4261 +2024-07-26 02:26:03,028 - pyskl - INFO - Epoch [108][1100/3746] lr: 1.870e-02, eta: 1 day, 12:23:40, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4033, top5_acc: 0.6641, loss_cls: 3.3687, loss: 3.3687 +2024-07-26 02:27:24,950 - pyskl - INFO - Epoch [108][1200/3746] lr: 1.868e-02, eta: 1 day, 12:22:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6531, loss_cls: 3.4121, loss: 3.4121 +2024-07-26 02:28:46,596 - pyskl - INFO - Epoch [108][1300/3746] lr: 1.866e-02, eta: 1 day, 12:20:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6584, loss_cls: 3.3919, loss: 3.3919 +2024-07-26 02:30:08,906 - pyskl - INFO - Epoch [108][1400/3746] lr: 1.864e-02, eta: 1 day, 12:19:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6664, loss_cls: 3.3554, loss: 3.3554 +2024-07-26 02:31:31,026 - pyskl - INFO - Epoch [108][1500/3746] lr: 1.862e-02, eta: 1 day, 12:18:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6520, loss_cls: 3.4200, loss: 3.4200 +2024-07-26 02:32:52,381 - pyskl - INFO - Epoch [108][1600/3746] lr: 1.859e-02, eta: 1 day, 12:16:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6584, loss_cls: 3.3939, loss: 3.3939 +2024-07-26 02:34:14,584 - pyskl - INFO - Epoch [108][1700/3746] lr: 1.857e-02, eta: 1 day, 12:15:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6559, loss_cls: 3.4123, loss: 3.4123 +2024-07-26 02:35:36,410 - pyskl - INFO - Epoch [108][1800/3746] lr: 1.855e-02, eta: 1 day, 12:14:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6530, loss_cls: 3.4260, loss: 3.4260 +2024-07-26 02:36:58,500 - pyskl - INFO - Epoch [108][1900/3746] lr: 1.853e-02, eta: 1 day, 12:12:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6645, loss_cls: 3.3613, loss: 3.3613 +2024-07-26 02:38:20,287 - pyskl - INFO - Epoch [108][2000/3746] lr: 1.851e-02, eta: 1 day, 12:11:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6405, loss_cls: 3.4587, loss: 3.4587 +2024-07-26 02:39:41,807 - pyskl - INFO - Epoch [108][2100/3746] lr: 1.848e-02, eta: 1 day, 12:10:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6587, loss_cls: 3.3992, loss: 3.3992 +2024-07-26 02:41:03,527 - pyskl - INFO - Epoch [108][2200/3746] lr: 1.846e-02, eta: 1 day, 12:08:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6669, loss_cls: 3.3651, loss: 3.3651 +2024-07-26 02:42:25,147 - pyskl - INFO - Epoch [108][2300/3746] lr: 1.844e-02, eta: 1 day, 12:07:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6575, loss_cls: 3.3710, loss: 3.3710 +2024-07-26 02:43:47,641 - pyskl - INFO - Epoch [108][2400/3746] lr: 1.842e-02, eta: 1 day, 12:05:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6542, loss_cls: 3.4251, loss: 3.4251 +2024-07-26 02:45:09,426 - pyskl - INFO - Epoch [108][2500/3746] lr: 1.840e-02, eta: 1 day, 12:04:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6584, loss_cls: 3.3945, loss: 3.3945 +2024-07-26 02:46:31,805 - pyskl - INFO - Epoch [108][2600/3746] lr: 1.838e-02, eta: 1 day, 12:03:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6587, loss_cls: 3.3961, loss: 3.3961 +2024-07-26 02:47:53,384 - pyskl - INFO - Epoch [108][2700/3746] lr: 1.835e-02, eta: 1 day, 12:01:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4152, top5_acc: 0.6609, loss_cls: 3.3618, loss: 3.3618 +2024-07-26 02:49:14,818 - pyskl - INFO - Epoch [108][2800/3746] lr: 1.833e-02, eta: 1 day, 12:00:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3989, top5_acc: 0.6520, loss_cls: 3.4144, loss: 3.4144 +2024-07-26 02:50:36,525 - pyskl - INFO - Epoch [108][2900/3746] lr: 1.831e-02, eta: 1 day, 11:59:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6533, loss_cls: 3.3947, loss: 3.3947 +2024-07-26 02:51:57,864 - pyskl - INFO - Epoch [108][3000/3746] lr: 1.829e-02, eta: 1 day, 11:57:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6450, loss_cls: 3.4615, loss: 3.4615 +2024-07-26 02:53:19,334 - pyskl - INFO - Epoch [108][3100/3746] lr: 1.827e-02, eta: 1 day, 11:56:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3970, top5_acc: 0.6486, loss_cls: 3.4349, loss: 3.4349 +2024-07-26 02:54:41,302 - pyskl - INFO - Epoch [108][3200/3746] lr: 1.825e-02, eta: 1 day, 11:54:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3923, top5_acc: 0.6525, loss_cls: 3.4261, loss: 3.4261 +2024-07-26 02:56:03,149 - pyskl - INFO - Epoch [108][3300/3746] lr: 1.823e-02, eta: 1 day, 11:53:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6473, loss_cls: 3.4370, loss: 3.4370 +2024-07-26 02:57:24,585 - pyskl - INFO - Epoch [108][3400/3746] lr: 1.820e-02, eta: 1 day, 11:52:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6645, loss_cls: 3.3587, loss: 3.3587 +2024-07-26 02:58:46,203 - pyskl - INFO - Epoch [108][3500/3746] lr: 1.818e-02, eta: 1 day, 11:50:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6472, loss_cls: 3.4404, loss: 3.4404 +2024-07-26 03:00:07,805 - pyskl - INFO - Epoch [108][3600/3746] lr: 1.816e-02, eta: 1 day, 11:49:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6602, loss_cls: 3.3740, loss: 3.3740 +2024-07-26 03:01:29,482 - pyskl - INFO - Epoch [108][3700/3746] lr: 1.814e-02, eta: 1 day, 11:48:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3970, top5_acc: 0.6578, loss_cls: 3.4073, loss: 3.4073 +2024-07-26 03:02:09,279 - pyskl - INFO - Saving checkpoint at 108 epochs +2024-07-26 03:04:00,213 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 03:04:00,878 - pyskl - INFO - +top1_acc 0.3497 +top5_acc 0.6052 +2024-07-26 03:04:00,878 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 03:04:00,918 - pyskl - INFO - +mean_acc 0.3496 +2024-07-26 03:04:00,924 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_106.pth was removed +2024-07-26 03:04:01,189 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_108.pth. +2024-07-26 03:04:01,190 - pyskl - INFO - Best top1_acc is 0.3497 at 108 epoch. +2024-07-26 03:04:01,202 - pyskl - INFO - Epoch(val) [108][309] top1_acc: 0.3497, top5_acc: 0.6052, mean_class_accuracy: 0.3496 +2024-07-26 03:07:51,414 - pyskl - INFO - Epoch [109][100/3746] lr: 1.811e-02, eta: 1 day, 11:46:53, time: 2.302, data_time: 1.319, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6811, loss_cls: 3.2674, loss: 3.2674 +2024-07-26 03:09:13,019 - pyskl - INFO - Epoch [109][200/3746] lr: 1.809e-02, eta: 1 day, 11:45:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6756, loss_cls: 3.3060, loss: 3.3060 +2024-07-26 03:10:34,317 - pyskl - INFO - Epoch [109][300/3746] lr: 1.806e-02, eta: 1 day, 11:44:08, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6680, loss_cls: 3.3503, loss: 3.3503 +2024-07-26 03:11:55,454 - pyskl - INFO - Epoch [109][400/3746] lr: 1.804e-02, eta: 1 day, 11:42:46, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6730, loss_cls: 3.3552, loss: 3.3552 +2024-07-26 03:13:17,136 - pyskl - INFO - Epoch [109][500/3746] lr: 1.802e-02, eta: 1 day, 11:41:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4119, top5_acc: 0.6736, loss_cls: 3.2966, loss: 3.2966 +2024-07-26 03:14:38,686 - pyskl - INFO - Epoch [109][600/3746] lr: 1.800e-02, eta: 1 day, 11:40:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6658, loss_cls: 3.3410, loss: 3.3410 +2024-07-26 03:16:00,545 - pyskl - INFO - Epoch [109][700/3746] lr: 1.798e-02, eta: 1 day, 11:38:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6594, loss_cls: 3.3560, loss: 3.3560 +2024-07-26 03:17:21,955 - pyskl - INFO - Epoch [109][800/3746] lr: 1.796e-02, eta: 1 day, 11:37:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4158, top5_acc: 0.6645, loss_cls: 3.3163, loss: 3.3163 +2024-07-26 03:18:43,821 - pyskl - INFO - Epoch [109][900/3746] lr: 1.794e-02, eta: 1 day, 11:35:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6677, loss_cls: 3.3551, loss: 3.3551 +2024-07-26 03:20:05,226 - pyskl - INFO - Epoch [109][1000/3746] lr: 1.791e-02, eta: 1 day, 11:34:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6597, loss_cls: 3.3666, loss: 3.3666 +2024-07-26 03:21:26,606 - pyskl - INFO - Epoch [109][1100/3746] lr: 1.789e-02, eta: 1 day, 11:33:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6600, loss_cls: 3.3557, loss: 3.3557 +2024-07-26 03:22:48,166 - pyskl - INFO - Epoch [109][1200/3746] lr: 1.787e-02, eta: 1 day, 11:31:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6548, loss_cls: 3.3763, loss: 3.3763 +2024-07-26 03:24:10,303 - pyskl - INFO - Epoch [109][1300/3746] lr: 1.785e-02, eta: 1 day, 11:30:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6627, loss_cls: 3.3745, loss: 3.3745 +2024-07-26 03:25:31,908 - pyskl - INFO - Epoch [109][1400/3746] lr: 1.783e-02, eta: 1 day, 11:29:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6566, loss_cls: 3.3686, loss: 3.3686 +2024-07-26 03:26:53,864 - pyskl - INFO - Epoch [109][1500/3746] lr: 1.781e-02, eta: 1 day, 11:27:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6627, loss_cls: 3.3576, loss: 3.3576 +2024-07-26 03:28:15,983 - pyskl - INFO - Epoch [109][1600/3746] lr: 1.779e-02, eta: 1 day, 11:26:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6655, loss_cls: 3.3741, loss: 3.3741 +2024-07-26 03:29:38,708 - pyskl - INFO - Epoch [109][1700/3746] lr: 1.776e-02, eta: 1 day, 11:25:00, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6623, loss_cls: 3.3798, loss: 3.3798 +2024-07-26 03:31:00,602 - pyskl - INFO - Epoch [109][1800/3746] lr: 1.774e-02, eta: 1 day, 11:23:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6619, loss_cls: 3.3670, loss: 3.3670 +2024-07-26 03:32:22,816 - pyskl - INFO - Epoch [109][1900/3746] lr: 1.772e-02, eta: 1 day, 11:22:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3966, top5_acc: 0.6534, loss_cls: 3.3897, loss: 3.3897 +2024-07-26 03:33:44,787 - pyskl - INFO - Epoch [109][2000/3746] lr: 1.770e-02, eta: 1 day, 11:20:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3992, top5_acc: 0.6609, loss_cls: 3.3863, loss: 3.3863 +2024-07-26 03:35:06,054 - pyskl - INFO - Epoch [109][2100/3746] lr: 1.768e-02, eta: 1 day, 11:19:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6687, loss_cls: 3.3246, loss: 3.3246 +2024-07-26 03:36:27,628 - pyskl - INFO - Epoch [109][2200/3746] lr: 1.766e-02, eta: 1 day, 11:18:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4095, top5_acc: 0.6670, loss_cls: 3.3454, loss: 3.3454 +2024-07-26 03:37:49,580 - pyskl - INFO - Epoch [109][2300/3746] lr: 1.764e-02, eta: 1 day, 11:16:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6598, loss_cls: 3.3701, loss: 3.3701 +2024-07-26 03:39:11,343 - pyskl - INFO - Epoch [109][2400/3746] lr: 1.761e-02, eta: 1 day, 11:15:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4141, top5_acc: 0.6656, loss_cls: 3.3637, loss: 3.3637 +2024-07-26 03:40:33,464 - pyskl - INFO - Epoch [109][2500/3746] lr: 1.759e-02, eta: 1 day, 11:14:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6580, loss_cls: 3.3825, loss: 3.3825 +2024-07-26 03:41:55,225 - pyskl - INFO - Epoch [109][2600/3746] lr: 1.757e-02, eta: 1 day, 11:12:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6464, loss_cls: 3.4206, loss: 3.4206 +2024-07-26 03:43:16,891 - pyskl - INFO - Epoch [109][2700/3746] lr: 1.755e-02, eta: 1 day, 11:11:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6639, loss_cls: 3.3479, loss: 3.3479 +2024-07-26 03:44:38,758 - pyskl - INFO - Epoch [109][2800/3746] lr: 1.753e-02, eta: 1 day, 11:09:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6709, loss_cls: 3.3396, loss: 3.3396 +2024-07-26 03:46:00,466 - pyskl - INFO - Epoch [109][2900/3746] lr: 1.751e-02, eta: 1 day, 11:08:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6655, loss_cls: 3.3524, loss: 3.3524 +2024-07-26 03:47:22,392 - pyskl - INFO - Epoch [109][3000/3746] lr: 1.749e-02, eta: 1 day, 11:07:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6587, loss_cls: 3.3690, loss: 3.3690 +2024-07-26 03:48:44,159 - pyskl - INFO - Epoch [109][3100/3746] lr: 1.747e-02, eta: 1 day, 11:05:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6528, loss_cls: 3.4109, loss: 3.4109 +2024-07-26 03:50:05,531 - pyskl - INFO - Epoch [109][3200/3746] lr: 1.744e-02, eta: 1 day, 11:04:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6584, loss_cls: 3.3705, loss: 3.3705 +2024-07-26 03:51:27,564 - pyskl - INFO - Epoch [109][3300/3746] lr: 1.742e-02, eta: 1 day, 11:03:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3923, top5_acc: 0.6589, loss_cls: 3.3960, loss: 3.3960 +2024-07-26 03:52:49,471 - pyskl - INFO - Epoch [109][3400/3746] lr: 1.740e-02, eta: 1 day, 11:01:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6541, loss_cls: 3.4064, loss: 3.4064 +2024-07-26 03:54:11,319 - pyskl - INFO - Epoch [109][3500/3746] lr: 1.738e-02, eta: 1 day, 11:00:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6528, loss_cls: 3.4050, loss: 3.4050 +2024-07-26 03:55:33,007 - pyskl - INFO - Epoch [109][3600/3746] lr: 1.736e-02, eta: 1 day, 10:59:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3989, top5_acc: 0.6591, loss_cls: 3.3933, loss: 3.3933 +2024-07-26 03:56:54,898 - pyskl - INFO - Epoch [109][3700/3746] lr: 1.734e-02, eta: 1 day, 10:57:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4008, top5_acc: 0.6617, loss_cls: 3.3826, loss: 3.3826 +2024-07-26 03:57:34,371 - pyskl - INFO - Saving checkpoint at 109 epochs +2024-07-26 03:59:26,635 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 03:59:27,372 - pyskl - INFO - +top1_acc 0.3453 +top5_acc 0.6017 +2024-07-26 03:59:27,372 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 03:59:27,421 - pyskl - INFO - +mean_acc 0.3451 +2024-07-26 03:59:27,435 - pyskl - INFO - Epoch(val) [109][309] top1_acc: 0.3453, top5_acc: 0.6017, mean_class_accuracy: 0.3451 +2024-07-26 04:03:28,835 - pyskl - INFO - Epoch [110][100/3746] lr: 1.731e-02, eta: 1 day, 10:56:27, time: 2.414, data_time: 1.417, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6723, loss_cls: 3.2833, loss: 3.2833 +2024-07-26 04:04:52,590 - pyskl - INFO - Epoch [110][200/3746] lr: 1.729e-02, eta: 1 day, 10:55:06, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4088, top5_acc: 0.6672, loss_cls: 3.3543, loss: 3.3543 +2024-07-26 04:06:16,268 - pyskl - INFO - Epoch [110][300/3746] lr: 1.727e-02, eta: 1 day, 10:53:45, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6745, loss_cls: 3.2870, loss: 3.2870 +2024-07-26 04:07:39,183 - pyskl - INFO - Epoch [110][400/3746] lr: 1.724e-02, eta: 1 day, 10:52:23, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6687, loss_cls: 3.3055, loss: 3.3055 +2024-07-26 04:09:02,192 - pyskl - INFO - Epoch [110][500/3746] lr: 1.722e-02, eta: 1 day, 10:51:01, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4202, top5_acc: 0.6767, loss_cls: 3.2786, loss: 3.2786 +2024-07-26 04:10:25,243 - pyskl - INFO - Epoch [110][600/3746] lr: 1.720e-02, eta: 1 day, 10:49:40, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6577, loss_cls: 3.3598, loss: 3.3598 +2024-07-26 04:11:48,022 - pyskl - INFO - Epoch [110][700/3746] lr: 1.718e-02, eta: 1 day, 10:48:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6677, loss_cls: 3.3401, loss: 3.3401 +2024-07-26 04:13:11,268 - pyskl - INFO - Epoch [110][800/3746] lr: 1.716e-02, eta: 1 day, 10:46:57, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6681, loss_cls: 3.3279, loss: 3.3279 +2024-07-26 04:14:34,572 - pyskl - INFO - Epoch [110][900/3746] lr: 1.714e-02, eta: 1 day, 10:45:35, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6719, loss_cls: 3.3137, loss: 3.3137 +2024-07-26 04:15:57,574 - pyskl - INFO - Epoch [110][1000/3746] lr: 1.712e-02, eta: 1 day, 10:44:14, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4017, top5_acc: 0.6623, loss_cls: 3.3688, loss: 3.3688 +2024-07-26 04:17:19,959 - pyskl - INFO - Epoch [110][1100/3746] lr: 1.710e-02, eta: 1 day, 10:42:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6680, loss_cls: 3.3287, loss: 3.3287 +2024-07-26 04:18:42,028 - pyskl - INFO - Epoch [110][1200/3746] lr: 1.708e-02, eta: 1 day, 10:41:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6655, loss_cls: 3.3070, loss: 3.3070 +2024-07-26 04:20:04,181 - pyskl - INFO - Epoch [110][1300/3746] lr: 1.705e-02, eta: 1 day, 10:40:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6623, loss_cls: 3.3546, loss: 3.3546 +2024-07-26 04:21:25,832 - pyskl - INFO - Epoch [110][1400/3746] lr: 1.703e-02, eta: 1 day, 10:38:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6602, loss_cls: 3.3864, loss: 3.3864 +2024-07-26 04:22:47,254 - pyskl - INFO - Epoch [110][1500/3746] lr: 1.701e-02, eta: 1 day, 10:37:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4169, top5_acc: 0.6686, loss_cls: 3.3235, loss: 3.3235 +2024-07-26 04:24:09,744 - pyskl - INFO - Epoch [110][1600/3746] lr: 1.699e-02, eta: 1 day, 10:36:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6752, loss_cls: 3.3103, loss: 3.3103 +2024-07-26 04:25:31,978 - pyskl - INFO - Epoch [110][1700/3746] lr: 1.697e-02, eta: 1 day, 10:34:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4042, top5_acc: 0.6595, loss_cls: 3.3931, loss: 3.3931 +2024-07-26 04:26:54,415 - pyskl - INFO - Epoch [110][1800/3746] lr: 1.695e-02, eta: 1 day, 10:33:19, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4091, top5_acc: 0.6586, loss_cls: 3.3713, loss: 3.3713 +2024-07-26 04:28:16,610 - pyskl - INFO - Epoch [110][1900/3746] lr: 1.693e-02, eta: 1 day, 10:31:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4016, top5_acc: 0.6622, loss_cls: 3.3930, loss: 3.3930 +2024-07-26 04:29:38,519 - pyskl - INFO - Epoch [110][2000/3746] lr: 1.691e-02, eta: 1 day, 10:30:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6641, loss_cls: 3.3477, loss: 3.3477 +2024-07-26 04:30:59,884 - pyskl - INFO - Epoch [110][2100/3746] lr: 1.689e-02, eta: 1 day, 10:29:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6634, loss_cls: 3.3559, loss: 3.3559 +2024-07-26 04:32:21,257 - pyskl - INFO - Epoch [110][2200/3746] lr: 1.687e-02, eta: 1 day, 10:27:50, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6687, loss_cls: 3.3388, loss: 3.3388 +2024-07-26 04:33:43,028 - pyskl - INFO - Epoch [110][2300/3746] lr: 1.685e-02, eta: 1 day, 10:26:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4072, top5_acc: 0.6569, loss_cls: 3.3709, loss: 3.3709 +2024-07-26 04:35:05,248 - pyskl - INFO - Epoch [110][2400/3746] lr: 1.682e-02, eta: 1 day, 10:25:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6637, loss_cls: 3.3396, loss: 3.3396 +2024-07-26 04:36:26,495 - pyskl - INFO - Epoch [110][2500/3746] lr: 1.680e-02, eta: 1 day, 10:23:44, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4062, top5_acc: 0.6677, loss_cls: 3.3470, loss: 3.3470 +2024-07-26 04:37:48,581 - pyskl - INFO - Epoch [110][2600/3746] lr: 1.678e-02, eta: 1 day, 10:22:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6566, loss_cls: 3.3591, loss: 3.3591 +2024-07-26 04:39:10,038 - pyskl - INFO - Epoch [110][2700/3746] lr: 1.676e-02, eta: 1 day, 10:21:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6692, loss_cls: 3.3080, loss: 3.3080 +2024-07-26 04:40:31,780 - pyskl - INFO - Epoch [110][2800/3746] lr: 1.674e-02, eta: 1 day, 10:19:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6727, loss_cls: 3.3300, loss: 3.3300 +2024-07-26 04:41:53,237 - pyskl - INFO - Epoch [110][2900/3746] lr: 1.672e-02, eta: 1 day, 10:18:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6547, loss_cls: 3.4037, loss: 3.4037 +2024-07-26 04:43:14,971 - pyskl - INFO - Epoch [110][3000/3746] lr: 1.670e-02, eta: 1 day, 10:16:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4006, top5_acc: 0.6536, loss_cls: 3.3956, loss: 3.3956 +2024-07-26 04:44:36,436 - pyskl - INFO - Epoch [110][3100/3746] lr: 1.668e-02, eta: 1 day, 10:15:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6617, loss_cls: 3.3794, loss: 3.3794 +2024-07-26 04:45:58,138 - pyskl - INFO - Epoch [110][3200/3746] lr: 1.666e-02, eta: 1 day, 10:14:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4141, top5_acc: 0.6589, loss_cls: 3.3430, loss: 3.3430 +2024-07-26 04:47:19,523 - pyskl - INFO - Epoch [110][3300/3746] lr: 1.664e-02, eta: 1 day, 10:12:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6641, loss_cls: 3.3493, loss: 3.3493 +2024-07-26 04:48:41,129 - pyskl - INFO - Epoch [110][3400/3746] lr: 1.662e-02, eta: 1 day, 10:11:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4102, top5_acc: 0.6598, loss_cls: 3.3722, loss: 3.3722 +2024-07-26 04:50:03,296 - pyskl - INFO - Epoch [110][3500/3746] lr: 1.659e-02, eta: 1 day, 10:10:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6597, loss_cls: 3.3649, loss: 3.3649 +2024-07-26 04:51:24,449 - pyskl - INFO - Epoch [110][3600/3746] lr: 1.657e-02, eta: 1 day, 10:08:41, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6592, loss_cls: 3.3706, loss: 3.3706 +2024-07-26 04:52:46,075 - pyskl - INFO - Epoch [110][3700/3746] lr: 1.655e-02, eta: 1 day, 10:07:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4078, top5_acc: 0.6730, loss_cls: 3.3292, loss: 3.3292 +2024-07-26 04:53:25,465 - pyskl - INFO - Saving checkpoint at 110 epochs +2024-07-26 04:55:18,518 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 04:55:19,189 - pyskl - INFO - +top1_acc 0.3503 +top5_acc 0.6017 +2024-07-26 04:55:19,189 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 04:55:19,233 - pyskl - INFO - +mean_acc 0.3500 +2024-07-26 04:55:19,239 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_108.pth was removed +2024-07-26 04:55:19,523 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_110.pth. +2024-07-26 04:55:19,524 - pyskl - INFO - Best top1_acc is 0.3503 at 110 epoch. +2024-07-26 04:55:19,539 - pyskl - INFO - Epoch(val) [110][309] top1_acc: 0.3503, top5_acc: 0.6017, mean_class_accuracy: 0.3500 +2024-07-26 04:59:13,882 - pyskl - INFO - Epoch [111][100/3746] lr: 1.652e-02, eta: 1 day, 10:06:01, time: 2.343, data_time: 1.356, memory: 15990, top1_acc: 0.4188, top5_acc: 0.6833, loss_cls: 3.2765, loss: 3.2765 +2024-07-26 05:00:36,986 - pyskl - INFO - Epoch [111][200/3746] lr: 1.650e-02, eta: 1 day, 10:04:40, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4291, top5_acc: 0.6845, loss_cls: 3.2328, loss: 3.2328 +2024-07-26 05:01:58,973 - pyskl - INFO - Epoch [111][300/3746] lr: 1.648e-02, eta: 1 day, 10:03:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6750, loss_cls: 3.2893, loss: 3.2893 +2024-07-26 05:03:20,721 - pyskl - INFO - Epoch [111][400/3746] lr: 1.646e-02, eta: 1 day, 10:01:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6723, loss_cls: 3.3218, loss: 3.3218 +2024-07-26 05:04:42,075 - pyskl - INFO - Epoch [111][500/3746] lr: 1.644e-02, eta: 1 day, 10:00:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6767, loss_cls: 3.2458, loss: 3.2458 +2024-07-26 05:06:04,426 - pyskl - INFO - Epoch [111][600/3746] lr: 1.642e-02, eta: 1 day, 9:59:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4086, top5_acc: 0.6777, loss_cls: 3.3134, loss: 3.3134 +2024-07-26 05:07:25,919 - pyskl - INFO - Epoch [111][700/3746] lr: 1.640e-02, eta: 1 day, 9:57:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6767, loss_cls: 3.3078, loss: 3.3078 +2024-07-26 05:08:47,625 - pyskl - INFO - Epoch [111][800/3746] lr: 1.638e-02, eta: 1 day, 9:56:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6875, loss_cls: 3.2408, loss: 3.2408 +2024-07-26 05:10:08,888 - pyskl - INFO - Epoch [111][900/3746] lr: 1.636e-02, eta: 1 day, 9:55:05, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6731, loss_cls: 3.3008, loss: 3.3008 +2024-07-26 05:11:30,811 - pyskl - INFO - Epoch [111][1000/3746] lr: 1.634e-02, eta: 1 day, 9:53:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4119, top5_acc: 0.6664, loss_cls: 3.3218, loss: 3.3218 +2024-07-26 05:12:52,545 - pyskl - INFO - Epoch [111][1100/3746] lr: 1.632e-02, eta: 1 day, 9:52:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6661, loss_cls: 3.3384, loss: 3.3384 +2024-07-26 05:14:14,047 - pyskl - INFO - Epoch [111][1200/3746] lr: 1.630e-02, eta: 1 day, 9:50:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6755, loss_cls: 3.2615, loss: 3.2615 +2024-07-26 05:15:36,090 - pyskl - INFO - Epoch [111][1300/3746] lr: 1.627e-02, eta: 1 day, 9:49:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6656, loss_cls: 3.3386, loss: 3.3386 +2024-07-26 05:16:58,315 - pyskl - INFO - Epoch [111][1400/3746] lr: 1.625e-02, eta: 1 day, 9:48:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6583, loss_cls: 3.3732, loss: 3.3732 +2024-07-26 05:18:20,105 - pyskl - INFO - Epoch [111][1500/3746] lr: 1.623e-02, eta: 1 day, 9:46:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6737, loss_cls: 3.2512, loss: 3.2512 +2024-07-26 05:19:42,793 - pyskl - INFO - Epoch [111][1600/3746] lr: 1.621e-02, eta: 1 day, 9:45:31, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4109, top5_acc: 0.6677, loss_cls: 3.3311, loss: 3.3311 +2024-07-26 05:21:05,191 - pyskl - INFO - Epoch [111][1700/3746] lr: 1.619e-02, eta: 1 day, 9:44:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6561, loss_cls: 3.4062, loss: 3.4062 +2024-07-26 05:22:27,575 - pyskl - INFO - Epoch [111][1800/3746] lr: 1.617e-02, eta: 1 day, 9:42:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6786, loss_cls: 3.2868, loss: 3.2868 +2024-07-26 05:23:49,416 - pyskl - INFO - Epoch [111][1900/3746] lr: 1.615e-02, eta: 1 day, 9:41:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4136, top5_acc: 0.6603, loss_cls: 3.3518, loss: 3.3518 +2024-07-26 05:25:10,996 - pyskl - INFO - Epoch [111][2000/3746] lr: 1.613e-02, eta: 1 day, 9:40:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6642, loss_cls: 3.3575, loss: 3.3575 +2024-07-26 05:26:32,702 - pyskl - INFO - Epoch [111][2100/3746] lr: 1.611e-02, eta: 1 day, 9:38:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6608, loss_cls: 3.3879, loss: 3.3879 +2024-07-26 05:27:54,508 - pyskl - INFO - Epoch [111][2200/3746] lr: 1.609e-02, eta: 1 day, 9:37:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4113, top5_acc: 0.6648, loss_cls: 3.3263, loss: 3.3263 +2024-07-26 05:29:16,088 - pyskl - INFO - Epoch [111][2300/3746] lr: 1.607e-02, eta: 1 day, 9:35:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6620, loss_cls: 3.3599, loss: 3.3599 +2024-07-26 05:30:38,802 - pyskl - INFO - Epoch [111][2400/3746] lr: 1.605e-02, eta: 1 day, 9:34:35, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6644, loss_cls: 3.3706, loss: 3.3706 +2024-07-26 05:32:00,422 - pyskl - INFO - Epoch [111][2500/3746] lr: 1.603e-02, eta: 1 day, 9:33:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6652, loss_cls: 3.3504, loss: 3.3504 +2024-07-26 05:33:22,659 - pyskl - INFO - Epoch [111][2600/3746] lr: 1.601e-02, eta: 1 day, 9:31:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4066, top5_acc: 0.6673, loss_cls: 3.3468, loss: 3.3468 +2024-07-26 05:34:44,595 - pyskl - INFO - Epoch [111][2700/3746] lr: 1.599e-02, eta: 1 day, 9:30:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6620, loss_cls: 3.3425, loss: 3.3425 +2024-07-26 05:36:06,252 - pyskl - INFO - Epoch [111][2800/3746] lr: 1.597e-02, eta: 1 day, 9:29:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6759, loss_cls: 3.3274, loss: 3.3274 +2024-07-26 05:37:27,480 - pyskl - INFO - Epoch [111][2900/3746] lr: 1.595e-02, eta: 1 day, 9:27:45, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4086, top5_acc: 0.6673, loss_cls: 3.3500, loss: 3.3500 +2024-07-26 05:38:49,256 - pyskl - INFO - Epoch [111][3000/3746] lr: 1.593e-02, eta: 1 day, 9:26:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6686, loss_cls: 3.3112, loss: 3.3112 +2024-07-26 05:40:11,055 - pyskl - INFO - Epoch [111][3100/3746] lr: 1.590e-02, eta: 1 day, 9:25:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6641, loss_cls: 3.3230, loss: 3.3230 +2024-07-26 05:41:32,867 - pyskl - INFO - Epoch [111][3200/3746] lr: 1.588e-02, eta: 1 day, 9:23:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6633, loss_cls: 3.3527, loss: 3.3527 +2024-07-26 05:42:54,095 - pyskl - INFO - Epoch [111][3300/3746] lr: 1.586e-02, eta: 1 day, 9:22:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4091, top5_acc: 0.6734, loss_cls: 3.3086, loss: 3.3086 +2024-07-26 05:44:15,331 - pyskl - INFO - Epoch [111][3400/3746] lr: 1.584e-02, eta: 1 day, 9:20:54, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6630, loss_cls: 3.3559, loss: 3.3559 +2024-07-26 05:45:37,246 - pyskl - INFO - Epoch [111][3500/3746] lr: 1.582e-02, eta: 1 day, 9:19:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4102, top5_acc: 0.6636, loss_cls: 3.3367, loss: 3.3367 +2024-07-26 05:46:58,664 - pyskl - INFO - Epoch [111][3600/3746] lr: 1.580e-02, eta: 1 day, 9:18:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4125, top5_acc: 0.6648, loss_cls: 3.3111, loss: 3.3111 +2024-07-26 05:48:20,776 - pyskl - INFO - Epoch [111][3700/3746] lr: 1.578e-02, eta: 1 day, 9:16:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6594, loss_cls: 3.3732, loss: 3.3732 +2024-07-26 05:49:00,413 - pyskl - INFO - Saving checkpoint at 111 epochs +2024-07-26 05:50:53,283 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 05:50:53,968 - pyskl - INFO - +top1_acc 0.3432 +top5_acc 0.6033 +2024-07-26 05:50:53,968 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 05:50:54,017 - pyskl - INFO - +mean_acc 0.3429 +2024-07-26 05:50:54,033 - pyskl - INFO - Epoch(val) [111][309] top1_acc: 0.3432, top5_acc: 0.6033, mean_class_accuracy: 0.3429 +2024-07-26 05:54:48,563 - pyskl - INFO - Epoch [112][100/3746] lr: 1.575e-02, eta: 1 day, 9:15:29, time: 2.345, data_time: 1.337, memory: 15990, top1_acc: 0.4178, top5_acc: 0.6748, loss_cls: 3.2588, loss: 3.2588 +2024-07-26 05:56:10,366 - pyskl - INFO - Epoch [112][200/3746] lr: 1.573e-02, eta: 1 day, 9:14:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4155, top5_acc: 0.6777, loss_cls: 3.2852, loss: 3.2852 +2024-07-26 05:57:32,977 - pyskl - INFO - Epoch [112][300/3746] lr: 1.571e-02, eta: 1 day, 9:12:45, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4138, top5_acc: 0.6787, loss_cls: 3.2652, loss: 3.2652 +2024-07-26 05:58:54,569 - pyskl - INFO - Epoch [112][400/3746] lr: 1.569e-02, eta: 1 day, 9:11:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6892, loss_cls: 3.2303, loss: 3.2303 +2024-07-26 06:00:15,976 - pyskl - INFO - Epoch [112][500/3746] lr: 1.567e-02, eta: 1 day, 9:10:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4156, top5_acc: 0.6722, loss_cls: 3.2849, loss: 3.2849 +2024-07-26 06:01:38,025 - pyskl - INFO - Epoch [112][600/3746] lr: 1.565e-02, eta: 1 day, 9:08:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4139, top5_acc: 0.6795, loss_cls: 3.3065, loss: 3.3065 +2024-07-26 06:02:59,507 - pyskl - INFO - Epoch [112][700/3746] lr: 1.563e-02, eta: 1 day, 9:07:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4245, top5_acc: 0.6756, loss_cls: 3.2918, loss: 3.2918 +2024-07-26 06:04:20,781 - pyskl - INFO - Epoch [112][800/3746] lr: 1.561e-02, eta: 1 day, 9:05:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4166, top5_acc: 0.6758, loss_cls: 3.2647, loss: 3.2647 +2024-07-26 06:05:42,997 - pyskl - INFO - Epoch [112][900/3746] lr: 1.559e-02, eta: 1 day, 9:04:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4202, top5_acc: 0.6791, loss_cls: 3.2561, loss: 3.2561 +2024-07-26 06:07:04,952 - pyskl - INFO - Epoch [112][1000/3746] lr: 1.557e-02, eta: 1 day, 9:03:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6791, loss_cls: 3.2709, loss: 3.2709 +2024-07-26 06:08:26,370 - pyskl - INFO - Epoch [112][1100/3746] lr: 1.555e-02, eta: 1 day, 9:01:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6697, loss_cls: 3.3240, loss: 3.3240 +2024-07-26 06:09:47,946 - pyskl - INFO - Epoch [112][1200/3746] lr: 1.553e-02, eta: 1 day, 9:00:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4227, top5_acc: 0.6772, loss_cls: 3.2905, loss: 3.2905 +2024-07-26 06:11:09,144 - pyskl - INFO - Epoch [112][1300/3746] lr: 1.551e-02, eta: 1 day, 8:59:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6766, loss_cls: 3.2558, loss: 3.2558 +2024-07-26 06:12:31,175 - pyskl - INFO - Epoch [112][1400/3746] lr: 1.549e-02, eta: 1 day, 8:57:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6669, loss_cls: 3.3004, loss: 3.3004 +2024-07-26 06:13:53,289 - pyskl - INFO - Epoch [112][1500/3746] lr: 1.547e-02, eta: 1 day, 8:56:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6723, loss_cls: 3.2803, loss: 3.2803 +2024-07-26 06:15:15,364 - pyskl - INFO - Epoch [112][1600/3746] lr: 1.545e-02, eta: 1 day, 8:54:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6683, loss_cls: 3.3103, loss: 3.3103 +2024-07-26 06:16:37,579 - pyskl - INFO - Epoch [112][1700/3746] lr: 1.543e-02, eta: 1 day, 8:53:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6756, loss_cls: 3.2884, loss: 3.2884 +2024-07-26 06:18:00,493 - pyskl - INFO - Epoch [112][1800/3746] lr: 1.541e-02, eta: 1 day, 8:52:14, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6766, loss_cls: 3.2872, loss: 3.2872 +2024-07-26 06:19:22,123 - pyskl - INFO - Epoch [112][1900/3746] lr: 1.539e-02, eta: 1 day, 8:50:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6681, loss_cls: 3.3384, loss: 3.3384 +2024-07-26 06:20:45,000 - pyskl - INFO - Epoch [112][2000/3746] lr: 1.537e-02, eta: 1 day, 8:49:30, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4059, top5_acc: 0.6658, loss_cls: 3.3353, loss: 3.3353 +2024-07-26 06:22:07,220 - pyskl - INFO - Epoch [112][2100/3746] lr: 1.535e-02, eta: 1 day, 8:48:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6761, loss_cls: 3.3260, loss: 3.3260 +2024-07-26 06:23:28,761 - pyskl - INFO - Epoch [112][2200/3746] lr: 1.533e-02, eta: 1 day, 8:46:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4211, top5_acc: 0.6756, loss_cls: 3.2811, loss: 3.2811 +2024-07-26 06:24:49,973 - pyskl - INFO - Epoch [112][2300/3746] lr: 1.531e-02, eta: 1 day, 8:45:24, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4177, top5_acc: 0.6744, loss_cls: 3.2720, loss: 3.2720 +2024-07-26 06:26:12,334 - pyskl - INFO - Epoch [112][2400/3746] lr: 1.529e-02, eta: 1 day, 8:44:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6711, loss_cls: 3.3259, loss: 3.3259 +2024-07-26 06:27:33,942 - pyskl - INFO - Epoch [112][2500/3746] lr: 1.527e-02, eta: 1 day, 8:42:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4136, top5_acc: 0.6706, loss_cls: 3.3153, loss: 3.3153 +2024-07-26 06:28:55,885 - pyskl - INFO - Epoch [112][2600/3746] lr: 1.525e-02, eta: 1 day, 8:41:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6717, loss_cls: 3.2926, loss: 3.2926 +2024-07-26 06:30:17,370 - pyskl - INFO - Epoch [112][2700/3746] lr: 1.523e-02, eta: 1 day, 8:39:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4119, top5_acc: 0.6666, loss_cls: 3.3099, loss: 3.3099 +2024-07-26 06:31:38,845 - pyskl - INFO - Epoch [112][2800/3746] lr: 1.521e-02, eta: 1 day, 8:38:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4205, top5_acc: 0.6656, loss_cls: 3.3083, loss: 3.3083 +2024-07-26 06:33:00,237 - pyskl - INFO - Epoch [112][2900/3746] lr: 1.519e-02, eta: 1 day, 8:37:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6631, loss_cls: 3.3615, loss: 3.3615 +2024-07-26 06:34:21,559 - pyskl - INFO - Epoch [112][3000/3746] lr: 1.517e-02, eta: 1 day, 8:35:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6631, loss_cls: 3.3749, loss: 3.3749 +2024-07-26 06:35:43,586 - pyskl - INFO - Epoch [112][3100/3746] lr: 1.515e-02, eta: 1 day, 8:34:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6595, loss_cls: 3.3784, loss: 3.3784 +2024-07-26 06:37:04,946 - pyskl - INFO - Epoch [112][3200/3746] lr: 1.513e-02, eta: 1 day, 8:33:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4102, top5_acc: 0.6723, loss_cls: 3.3197, loss: 3.3197 +2024-07-26 06:38:26,325 - pyskl - INFO - Epoch [112][3300/3746] lr: 1.511e-02, eta: 1 day, 8:31:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4059, top5_acc: 0.6661, loss_cls: 3.3339, loss: 3.3339 +2024-07-26 06:39:47,628 - pyskl - INFO - Epoch [112][3400/3746] lr: 1.509e-02, eta: 1 day, 8:30:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6705, loss_cls: 3.3362, loss: 3.3362 +2024-07-26 06:41:09,182 - pyskl - INFO - Epoch [112][3500/3746] lr: 1.507e-02, eta: 1 day, 8:28:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4113, top5_acc: 0.6681, loss_cls: 3.3061, loss: 3.3061 +2024-07-26 06:42:31,599 - pyskl - INFO - Epoch [112][3600/3746] lr: 1.505e-02, eta: 1 day, 8:27:36, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6659, loss_cls: 3.3173, loss: 3.3173 +2024-07-26 06:43:53,459 - pyskl - INFO - Epoch [112][3700/3746] lr: 1.503e-02, eta: 1 day, 8:26:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4145, top5_acc: 0.6661, loss_cls: 3.3208, loss: 3.3208 +2024-07-26 06:44:33,128 - pyskl - INFO - Saving checkpoint at 112 epochs +2024-07-26 06:46:25,449 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 06:46:26,106 - pyskl - INFO - +top1_acc 0.3571 +top5_acc 0.6145 +2024-07-26 06:46:26,106 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 06:46:26,146 - pyskl - INFO - +mean_acc 0.3569 +2024-07-26 06:46:26,150 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_110.pth was removed +2024-07-26 06:46:26,413 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_112.pth. +2024-07-26 06:46:26,414 - pyskl - INFO - Best top1_acc is 0.3571 at 112 epoch. +2024-07-26 06:46:26,425 - pyskl - INFO - Epoch(val) [112][309] top1_acc: 0.3571, top5_acc: 0.6145, mean_class_accuracy: 0.3569 +2024-07-26 06:50:15,316 - pyskl - INFO - Epoch [113][100/3746] lr: 1.500e-02, eta: 1 day, 8:24:51, time: 2.289, data_time: 1.307, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6914, loss_cls: 3.1850, loss: 3.1850 +2024-07-26 06:51:38,260 - pyskl - INFO - Epoch [113][200/3746] lr: 1.498e-02, eta: 1 day, 8:23:30, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6877, loss_cls: 3.1964, loss: 3.1964 +2024-07-26 06:53:00,021 - pyskl - INFO - Epoch [113][300/3746] lr: 1.496e-02, eta: 1 day, 8:22:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6925, loss_cls: 3.2153, loss: 3.2153 +2024-07-26 06:54:21,757 - pyskl - INFO - Epoch [113][400/3746] lr: 1.494e-02, eta: 1 day, 8:20:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6806, loss_cls: 3.2486, loss: 3.2486 +2024-07-26 06:55:43,348 - pyskl - INFO - Epoch [113][500/3746] lr: 1.492e-02, eta: 1 day, 8:19:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4323, top5_acc: 0.6941, loss_cls: 3.1964, loss: 3.1964 +2024-07-26 06:57:04,616 - pyskl - INFO - Epoch [113][600/3746] lr: 1.490e-02, eta: 1 day, 8:18:01, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4256, top5_acc: 0.6814, loss_cls: 3.2410, loss: 3.2410 +2024-07-26 06:58:26,223 - pyskl - INFO - Epoch [113][700/3746] lr: 1.488e-02, eta: 1 day, 8:16:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4177, top5_acc: 0.6784, loss_cls: 3.2588, loss: 3.2588 +2024-07-26 06:59:48,050 - pyskl - INFO - Epoch [113][800/3746] lr: 1.486e-02, eta: 1 day, 8:15:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4197, top5_acc: 0.6791, loss_cls: 3.2375, loss: 3.2375 +2024-07-26 07:01:09,732 - pyskl - INFO - Epoch [113][900/3746] lr: 1.484e-02, eta: 1 day, 8:13:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6641, loss_cls: 3.3505, loss: 3.3505 +2024-07-26 07:02:30,959 - pyskl - INFO - Epoch [113][1000/3746] lr: 1.482e-02, eta: 1 day, 8:12:32, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4145, top5_acc: 0.6759, loss_cls: 3.2635, loss: 3.2635 +2024-07-26 07:03:52,588 - pyskl - INFO - Epoch [113][1100/3746] lr: 1.480e-02, eta: 1 day, 8:11:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6669, loss_cls: 3.2911, loss: 3.2911 +2024-07-26 07:05:13,834 - pyskl - INFO - Epoch [113][1200/3746] lr: 1.478e-02, eta: 1 day, 8:09:48, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6711, loss_cls: 3.3121, loss: 3.3121 +2024-07-26 07:06:35,354 - pyskl - INFO - Epoch [113][1300/3746] lr: 1.476e-02, eta: 1 day, 8:08:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4086, top5_acc: 0.6709, loss_cls: 3.2873, loss: 3.2873 +2024-07-26 07:07:56,614 - pyskl - INFO - Epoch [113][1400/3746] lr: 1.474e-02, eta: 1 day, 8:07:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4189, top5_acc: 0.6797, loss_cls: 3.2592, loss: 3.2592 +2024-07-26 07:09:18,182 - pyskl - INFO - Epoch [113][1500/3746] lr: 1.472e-02, eta: 1 day, 8:05:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4130, top5_acc: 0.6681, loss_cls: 3.3152, loss: 3.3152 +2024-07-26 07:10:39,802 - pyskl - INFO - Epoch [113][1600/3746] lr: 1.470e-02, eta: 1 day, 8:04:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4178, top5_acc: 0.6841, loss_cls: 3.2367, loss: 3.2367 +2024-07-26 07:12:01,703 - pyskl - INFO - Epoch [113][1700/3746] lr: 1.468e-02, eta: 1 day, 8:02:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4134, top5_acc: 0.6763, loss_cls: 3.2924, loss: 3.2924 +2024-07-26 07:13:23,533 - pyskl - INFO - Epoch [113][1800/3746] lr: 1.466e-02, eta: 1 day, 8:01:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4114, top5_acc: 0.6669, loss_cls: 3.3225, loss: 3.3225 +2024-07-26 07:14:45,466 - pyskl - INFO - Epoch [113][1900/3746] lr: 1.464e-02, eta: 1 day, 8:00:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6725, loss_cls: 3.2933, loss: 3.2933 +2024-07-26 07:16:07,553 - pyskl - INFO - Epoch [113][2000/3746] lr: 1.462e-02, eta: 1 day, 7:58:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6817, loss_cls: 3.2583, loss: 3.2583 +2024-07-26 07:17:29,545 - pyskl - INFO - Epoch [113][2100/3746] lr: 1.460e-02, eta: 1 day, 7:57:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4155, top5_acc: 0.6731, loss_cls: 3.3188, loss: 3.3188 +2024-07-26 07:18:51,046 - pyskl - INFO - Epoch [113][2200/3746] lr: 1.458e-02, eta: 1 day, 7:56:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6628, loss_cls: 3.3616, loss: 3.3616 +2024-07-26 07:20:12,754 - pyskl - INFO - Epoch [113][2300/3746] lr: 1.456e-02, eta: 1 day, 7:54:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6720, loss_cls: 3.3216, loss: 3.3216 +2024-07-26 07:21:35,304 - pyskl - INFO - Epoch [113][2400/3746] lr: 1.454e-02, eta: 1 day, 7:53:22, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4178, top5_acc: 0.6825, loss_cls: 3.2592, loss: 3.2592 +2024-07-26 07:22:57,592 - pyskl - INFO - Epoch [113][2500/3746] lr: 1.452e-02, eta: 1 day, 7:52:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6694, loss_cls: 3.3116, loss: 3.3116 +2024-07-26 07:24:19,937 - pyskl - INFO - Epoch [113][2600/3746] lr: 1.450e-02, eta: 1 day, 7:50:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6708, loss_cls: 3.3060, loss: 3.3060 +2024-07-26 07:25:41,604 - pyskl - INFO - Epoch [113][2700/3746] lr: 1.448e-02, eta: 1 day, 7:49:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6775, loss_cls: 3.2934, loss: 3.2934 +2024-07-26 07:27:03,032 - pyskl - INFO - Epoch [113][2800/3746] lr: 1.446e-02, eta: 1 day, 7:47:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6794, loss_cls: 3.2803, loss: 3.2803 +2024-07-26 07:28:24,683 - pyskl - INFO - Epoch [113][2900/3746] lr: 1.444e-02, eta: 1 day, 7:46:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4138, top5_acc: 0.6694, loss_cls: 3.2877, loss: 3.2877 +2024-07-26 07:29:47,230 - pyskl - INFO - Epoch [113][3000/3746] lr: 1.442e-02, eta: 1 day, 7:45:10, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4138, top5_acc: 0.6741, loss_cls: 3.2892, loss: 3.2892 +2024-07-26 07:31:08,620 - pyskl - INFO - Epoch [113][3100/3746] lr: 1.440e-02, eta: 1 day, 7:43:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4109, top5_acc: 0.6637, loss_cls: 3.3301, loss: 3.3301 +2024-07-26 07:32:30,519 - pyskl - INFO - Epoch [113][3200/3746] lr: 1.438e-02, eta: 1 day, 7:42:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4214, top5_acc: 0.6800, loss_cls: 3.2454, loss: 3.2454 +2024-07-26 07:33:51,578 - pyskl - INFO - Epoch [113][3300/3746] lr: 1.436e-02, eta: 1 day, 7:41:03, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4228, top5_acc: 0.6716, loss_cls: 3.2928, loss: 3.2928 +2024-07-26 07:35:13,118 - pyskl - INFO - Epoch [113][3400/3746] lr: 1.434e-02, eta: 1 day, 7:39:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6758, loss_cls: 3.3063, loss: 3.3063 +2024-07-26 07:36:34,436 - pyskl - INFO - Epoch [113][3500/3746] lr: 1.432e-02, eta: 1 day, 7:38:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6677, loss_cls: 3.3347, loss: 3.3347 +2024-07-26 07:37:56,422 - pyskl - INFO - Epoch [113][3600/3746] lr: 1.431e-02, eta: 1 day, 7:36:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6777, loss_cls: 3.2815, loss: 3.2815 +2024-07-26 07:39:18,305 - pyskl - INFO - Epoch [113][3700/3746] lr: 1.429e-02, eta: 1 day, 7:35:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4248, top5_acc: 0.6819, loss_cls: 3.2684, loss: 3.2684 +2024-07-26 07:39:57,880 - pyskl - INFO - Saving checkpoint at 113 epochs +2024-07-26 07:41:48,705 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 07:41:49,367 - pyskl - INFO - +top1_acc 0.3532 +top5_acc 0.6052 +2024-07-26 07:41:49,368 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 07:41:49,411 - pyskl - INFO - +mean_acc 0.3531 +2024-07-26 07:41:49,423 - pyskl - INFO - Epoch(val) [113][309] top1_acc: 0.3532, top5_acc: 0.6052, mean_class_accuracy: 0.3531 +2024-07-26 07:45:42,235 - pyskl - INFO - Epoch [114][100/3746] lr: 1.426e-02, eta: 1 day, 7:34:12, time: 2.328, data_time: 1.335, memory: 15990, top1_acc: 0.4339, top5_acc: 0.6950, loss_cls: 3.1876, loss: 3.1876 +2024-07-26 07:47:04,240 - pyskl - INFO - Epoch [114][200/3746] lr: 1.424e-02, eta: 1 day, 7:32:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4350, top5_acc: 0.6909, loss_cls: 3.1777, loss: 3.1777 +2024-07-26 07:48:25,987 - pyskl - INFO - Epoch [114][300/3746] lr: 1.422e-02, eta: 1 day, 7:31:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6950, loss_cls: 3.2060, loss: 3.2060 +2024-07-26 07:49:47,470 - pyskl - INFO - Epoch [114][400/3746] lr: 1.420e-02, eta: 1 day, 7:30:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6763, loss_cls: 3.2446, loss: 3.2446 +2024-07-26 07:51:09,528 - pyskl - INFO - Epoch [114][500/3746] lr: 1.418e-02, eta: 1 day, 7:28:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6784, loss_cls: 3.2400, loss: 3.2400 +2024-07-26 07:52:31,053 - pyskl - INFO - Epoch [114][600/3746] lr: 1.416e-02, eta: 1 day, 7:27:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6806, loss_cls: 3.2541, loss: 3.2541 +2024-07-26 07:53:52,968 - pyskl - INFO - Epoch [114][700/3746] lr: 1.414e-02, eta: 1 day, 7:25:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4273, top5_acc: 0.6817, loss_cls: 3.2327, loss: 3.2327 +2024-07-26 07:55:14,879 - pyskl - INFO - Epoch [114][800/3746] lr: 1.412e-02, eta: 1 day, 7:24:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6842, loss_cls: 3.2730, loss: 3.2730 +2024-07-26 07:56:36,719 - pyskl - INFO - Epoch [114][900/3746] lr: 1.410e-02, eta: 1 day, 7:23:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4258, top5_acc: 0.6863, loss_cls: 3.2144, loss: 3.2144 +2024-07-26 07:57:58,165 - pyskl - INFO - Epoch [114][1000/3746] lr: 1.408e-02, eta: 1 day, 7:21:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4189, top5_acc: 0.6761, loss_cls: 3.2904, loss: 3.2904 +2024-07-26 07:59:20,121 - pyskl - INFO - Epoch [114][1100/3746] lr: 1.406e-02, eta: 1 day, 7:20:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6758, loss_cls: 3.2810, loss: 3.2810 +2024-07-26 08:00:42,076 - pyskl - INFO - Epoch [114][1200/3746] lr: 1.404e-02, eta: 1 day, 7:19:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4197, top5_acc: 0.6820, loss_cls: 3.2176, loss: 3.2176 +2024-07-26 08:02:03,708 - pyskl - INFO - Epoch [114][1300/3746] lr: 1.402e-02, eta: 1 day, 7:17:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6772, loss_cls: 3.2506, loss: 3.2506 +2024-07-26 08:03:25,974 - pyskl - INFO - Epoch [114][1400/3746] lr: 1.400e-02, eta: 1 day, 7:16:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4270, top5_acc: 0.6905, loss_cls: 3.2115, loss: 3.2115 +2024-07-26 08:04:47,964 - pyskl - INFO - Epoch [114][1500/3746] lr: 1.398e-02, eta: 1 day, 7:15:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6844, loss_cls: 3.2079, loss: 3.2079 +2024-07-26 08:06:10,763 - pyskl - INFO - Epoch [114][1600/3746] lr: 1.397e-02, eta: 1 day, 7:13:40, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4066, top5_acc: 0.6697, loss_cls: 3.2960, loss: 3.2960 +2024-07-26 08:07:32,600 - pyskl - INFO - Epoch [114][1700/3746] lr: 1.395e-02, eta: 1 day, 7:12:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4338, top5_acc: 0.6847, loss_cls: 3.2223, loss: 3.2223 +2024-07-26 08:08:54,797 - pyskl - INFO - Epoch [114][1800/3746] lr: 1.393e-02, eta: 1 day, 7:10:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6894, loss_cls: 3.2293, loss: 3.2293 +2024-07-26 08:10:16,505 - pyskl - INFO - Epoch [114][1900/3746] lr: 1.391e-02, eta: 1 day, 7:09:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6806, loss_cls: 3.2439, loss: 3.2439 +2024-07-26 08:11:38,008 - pyskl - INFO - Epoch [114][2000/3746] lr: 1.389e-02, eta: 1 day, 7:08:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4256, top5_acc: 0.6800, loss_cls: 3.2569, loss: 3.2569 +2024-07-26 08:12:59,384 - pyskl - INFO - Epoch [114][2100/3746] lr: 1.387e-02, eta: 1 day, 7:06:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6755, loss_cls: 3.2992, loss: 3.2992 +2024-07-26 08:14:20,595 - pyskl - INFO - Epoch [114][2200/3746] lr: 1.385e-02, eta: 1 day, 7:05:27, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6748, loss_cls: 3.2652, loss: 3.2652 +2024-07-26 08:15:42,175 - pyskl - INFO - Epoch [114][2300/3746] lr: 1.383e-02, eta: 1 day, 7:04:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6702, loss_cls: 3.2803, loss: 3.2803 +2024-07-26 08:17:03,885 - pyskl - INFO - Epoch [114][2400/3746] lr: 1.381e-02, eta: 1 day, 7:02:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6689, loss_cls: 3.2889, loss: 3.2889 +2024-07-26 08:18:26,023 - pyskl - INFO - Epoch [114][2500/3746] lr: 1.379e-02, eta: 1 day, 7:01:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6773, loss_cls: 3.2390, loss: 3.2390 +2024-07-26 08:19:47,843 - pyskl - INFO - Epoch [114][2600/3746] lr: 1.377e-02, eta: 1 day, 6:59:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4189, top5_acc: 0.6703, loss_cls: 3.2879, loss: 3.2879 +2024-07-26 08:21:09,833 - pyskl - INFO - Epoch [114][2700/3746] lr: 1.375e-02, eta: 1 day, 6:58:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6770, loss_cls: 3.2618, loss: 3.2618 +2024-07-26 08:22:31,143 - pyskl - INFO - Epoch [114][2800/3746] lr: 1.373e-02, eta: 1 day, 6:57:14, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6672, loss_cls: 3.3236, loss: 3.3236 +2024-07-26 08:23:52,561 - pyskl - INFO - Epoch [114][2900/3746] lr: 1.371e-02, eta: 1 day, 6:55:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6680, loss_cls: 3.2992, loss: 3.2992 +2024-07-26 08:25:14,447 - pyskl - INFO - Epoch [114][3000/3746] lr: 1.369e-02, eta: 1 day, 6:54:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6745, loss_cls: 3.2634, loss: 3.2634 +2024-07-26 08:26:35,858 - pyskl - INFO - Epoch [114][3100/3746] lr: 1.368e-02, eta: 1 day, 6:53:08, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6637, loss_cls: 3.3042, loss: 3.3042 +2024-07-26 08:27:57,691 - pyskl - INFO - Epoch [114][3200/3746] lr: 1.366e-02, eta: 1 day, 6:51:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4098, top5_acc: 0.6733, loss_cls: 3.2976, loss: 3.2976 +2024-07-26 08:29:19,460 - pyskl - INFO - Epoch [114][3300/3746] lr: 1.364e-02, eta: 1 day, 6:50:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4153, top5_acc: 0.6664, loss_cls: 3.2966, loss: 3.2966 +2024-07-26 08:30:41,303 - pyskl - INFO - Epoch [114][3400/3746] lr: 1.362e-02, eta: 1 day, 6:49:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4147, top5_acc: 0.6739, loss_cls: 3.3056, loss: 3.3056 +2024-07-26 08:32:02,939 - pyskl - INFO - Epoch [114][3500/3746] lr: 1.360e-02, eta: 1 day, 6:47:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4219, top5_acc: 0.6836, loss_cls: 3.2553, loss: 3.2553 +2024-07-26 08:33:24,483 - pyskl - INFO - Epoch [114][3600/3746] lr: 1.358e-02, eta: 1 day, 6:46:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4292, top5_acc: 0.6855, loss_cls: 3.2310, loss: 3.2310 +2024-07-26 08:34:46,653 - pyskl - INFO - Epoch [114][3700/3746] lr: 1.356e-02, eta: 1 day, 6:44:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6642, loss_cls: 3.2951, loss: 3.2951 +2024-07-26 08:35:25,781 - pyskl - INFO - Saving checkpoint at 114 epochs +2024-07-26 08:37:17,033 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 08:37:17,694 - pyskl - INFO - +top1_acc 0.3641 +top5_acc 0.6161 +2024-07-26 08:37:17,694 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 08:37:17,736 - pyskl - INFO - +mean_acc 0.3638 +2024-07-26 08:37:17,740 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_112.pth was removed +2024-07-26 08:37:18,007 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_114.pth. +2024-07-26 08:37:18,008 - pyskl - INFO - Best top1_acc is 0.3641 at 114 epoch. +2024-07-26 08:37:18,022 - pyskl - INFO - Epoch(val) [114][309] top1_acc: 0.3641, top5_acc: 0.6161, mean_class_accuracy: 0.3638 +2024-07-26 08:41:06,115 - pyskl - INFO - Epoch [115][100/3746] lr: 1.353e-02, eta: 1 day, 6:43:29, time: 2.281, data_time: 1.306, memory: 15990, top1_acc: 0.4386, top5_acc: 0.7006, loss_cls: 3.1557, loss: 3.1557 +2024-07-26 08:42:28,327 - pyskl - INFO - Epoch [115][200/3746] lr: 1.351e-02, eta: 1 day, 6:42:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4273, top5_acc: 0.6867, loss_cls: 3.2164, loss: 3.2164 +2024-07-26 08:43:49,663 - pyskl - INFO - Epoch [115][300/3746] lr: 1.349e-02, eta: 1 day, 6:40:45, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4238, top5_acc: 0.6881, loss_cls: 3.1800, loss: 3.1800 +2024-07-26 08:45:11,082 - pyskl - INFO - Epoch [115][400/3746] lr: 1.348e-02, eta: 1 day, 6:39:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4228, top5_acc: 0.6833, loss_cls: 3.2204, loss: 3.2204 +2024-07-26 08:46:32,579 - pyskl - INFO - Epoch [115][500/3746] lr: 1.346e-02, eta: 1 day, 6:38:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6903, loss_cls: 3.1970, loss: 3.1970 +2024-07-26 08:47:53,918 - pyskl - INFO - Epoch [115][600/3746] lr: 1.344e-02, eta: 1 day, 6:36:38, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.6977, loss_cls: 3.1694, loss: 3.1694 +2024-07-26 08:49:15,831 - pyskl - INFO - Epoch [115][700/3746] lr: 1.342e-02, eta: 1 day, 6:35:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6753, loss_cls: 3.2545, loss: 3.2545 +2024-07-26 08:50:38,011 - pyskl - INFO - Epoch [115][800/3746] lr: 1.340e-02, eta: 1 day, 6:33:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6842, loss_cls: 3.2286, loss: 3.2286 +2024-07-26 08:51:59,524 - pyskl - INFO - Epoch [115][900/3746] lr: 1.338e-02, eta: 1 day, 6:32:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6909, loss_cls: 3.1972, loss: 3.1972 +2024-07-26 08:53:21,069 - pyskl - INFO - Epoch [115][1000/3746] lr: 1.336e-02, eta: 1 day, 6:31:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6844, loss_cls: 3.2349, loss: 3.2349 +2024-07-26 08:54:42,351 - pyskl - INFO - Epoch [115][1100/3746] lr: 1.334e-02, eta: 1 day, 6:29:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6844, loss_cls: 3.2391, loss: 3.2391 +2024-07-26 08:56:03,841 - pyskl - INFO - Epoch [115][1200/3746] lr: 1.332e-02, eta: 1 day, 6:28:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4295, top5_acc: 0.6800, loss_cls: 3.2369, loss: 3.2369 +2024-07-26 08:57:25,411 - pyskl - INFO - Epoch [115][1300/3746] lr: 1.330e-02, eta: 1 day, 6:27:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6719, loss_cls: 3.2790, loss: 3.2790 +2024-07-26 08:58:46,896 - pyskl - INFO - Epoch [115][1400/3746] lr: 1.328e-02, eta: 1 day, 6:25:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6856, loss_cls: 3.2270, loss: 3.2270 +2024-07-26 09:00:08,661 - pyskl - INFO - Epoch [115][1500/3746] lr: 1.327e-02, eta: 1 day, 6:24:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6880, loss_cls: 3.2354, loss: 3.2354 +2024-07-26 09:01:31,029 - pyskl - INFO - Epoch [115][1600/3746] lr: 1.325e-02, eta: 1 day, 6:22:56, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4241, top5_acc: 0.6853, loss_cls: 3.2334, loss: 3.2334 +2024-07-26 09:02:52,774 - pyskl - INFO - Epoch [115][1700/3746] lr: 1.323e-02, eta: 1 day, 6:21:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4256, top5_acc: 0.6859, loss_cls: 3.2360, loss: 3.2360 +2024-07-26 09:04:14,286 - pyskl - INFO - Epoch [115][1800/3746] lr: 1.321e-02, eta: 1 day, 6:20:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4152, top5_acc: 0.6823, loss_cls: 3.2468, loss: 3.2468 +2024-07-26 09:05:35,828 - pyskl - INFO - Epoch [115][1900/3746] lr: 1.319e-02, eta: 1 day, 6:18:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4328, top5_acc: 0.6784, loss_cls: 3.2374, loss: 3.2374 +2024-07-26 09:06:57,528 - pyskl - INFO - Epoch [115][2000/3746] lr: 1.317e-02, eta: 1 day, 6:17:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6809, loss_cls: 3.2148, loss: 3.2148 +2024-07-26 09:08:19,094 - pyskl - INFO - Epoch [115][2100/3746] lr: 1.315e-02, eta: 1 day, 6:16:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4245, top5_acc: 0.6773, loss_cls: 3.2821, loss: 3.2821 +2024-07-26 09:09:40,829 - pyskl - INFO - Epoch [115][2200/3746] lr: 1.313e-02, eta: 1 day, 6:14:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4347, top5_acc: 0.6845, loss_cls: 3.1984, loss: 3.1984 +2024-07-26 09:11:02,689 - pyskl - INFO - Epoch [115][2300/3746] lr: 1.311e-02, eta: 1 day, 6:13:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6733, loss_cls: 3.2759, loss: 3.2759 +2024-07-26 09:12:25,640 - pyskl - INFO - Epoch [115][2400/3746] lr: 1.310e-02, eta: 1 day, 6:11:59, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6725, loss_cls: 3.2585, loss: 3.2585 +2024-07-26 09:13:47,538 - pyskl - INFO - Epoch [115][2500/3746] lr: 1.308e-02, eta: 1 day, 6:10:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4197, top5_acc: 0.6719, loss_cls: 3.2856, loss: 3.2856 +2024-07-26 09:15:09,445 - pyskl - INFO - Epoch [115][2600/3746] lr: 1.306e-02, eta: 1 day, 6:09:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4316, top5_acc: 0.6869, loss_cls: 3.1986, loss: 3.1986 +2024-07-26 09:16:31,696 - pyskl - INFO - Epoch [115][2700/3746] lr: 1.304e-02, eta: 1 day, 6:07:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4272, top5_acc: 0.6811, loss_cls: 3.2463, loss: 3.2463 +2024-07-26 09:17:53,020 - pyskl - INFO - Epoch [115][2800/3746] lr: 1.302e-02, eta: 1 day, 6:06:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4167, top5_acc: 0.6913, loss_cls: 3.2414, loss: 3.2414 +2024-07-26 09:19:14,908 - pyskl - INFO - Epoch [115][2900/3746] lr: 1.300e-02, eta: 1 day, 6:05:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6820, loss_cls: 3.2490, loss: 3.2490 +2024-07-26 09:20:36,406 - pyskl - INFO - Epoch [115][3000/3746] lr: 1.298e-02, eta: 1 day, 6:03:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4227, top5_acc: 0.6775, loss_cls: 3.2265, loss: 3.2265 +2024-07-26 09:21:58,137 - pyskl - INFO - Epoch [115][3100/3746] lr: 1.296e-02, eta: 1 day, 6:02:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4177, top5_acc: 0.6744, loss_cls: 3.2634, loss: 3.2634 +2024-07-26 09:23:19,767 - pyskl - INFO - Epoch [115][3200/3746] lr: 1.295e-02, eta: 1 day, 6:01:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4238, top5_acc: 0.6666, loss_cls: 3.2609, loss: 3.2609 +2024-07-26 09:24:41,720 - pyskl - INFO - Epoch [115][3300/3746] lr: 1.293e-02, eta: 1 day, 5:59:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6813, loss_cls: 3.2279, loss: 3.2279 +2024-07-26 09:26:03,471 - pyskl - INFO - Epoch [115][3400/3746] lr: 1.291e-02, eta: 1 day, 5:58:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4167, top5_acc: 0.6677, loss_cls: 3.2903, loss: 3.2903 +2024-07-26 09:27:25,039 - pyskl - INFO - Epoch [115][3500/3746] lr: 1.289e-02, eta: 1 day, 5:56:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4156, top5_acc: 0.6775, loss_cls: 3.2770, loss: 3.2770 +2024-07-26 09:28:46,718 - pyskl - INFO - Epoch [115][3600/3746] lr: 1.287e-02, eta: 1 day, 5:55:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6748, loss_cls: 3.2626, loss: 3.2626 +2024-07-26 09:30:08,003 - pyskl - INFO - Epoch [115][3700/3746] lr: 1.285e-02, eta: 1 day, 5:54:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6850, loss_cls: 3.2206, loss: 3.2206 +2024-07-26 09:30:47,640 - pyskl - INFO - Saving checkpoint at 115 epochs +2024-07-26 09:32:39,980 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 09:32:40,643 - pyskl - INFO - +top1_acc 0.3629 +top5_acc 0.6209 +2024-07-26 09:32:40,644 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 09:32:40,683 - pyskl - INFO - +mean_acc 0.3626 +2024-07-26 09:32:40,695 - pyskl - INFO - Epoch(val) [115][309] top1_acc: 0.3629, top5_acc: 0.6209, mean_class_accuracy: 0.3626 +2024-07-26 09:36:40,333 - pyskl - INFO - Epoch [116][100/3746] lr: 1.282e-02, eta: 1 day, 5:52:47, time: 2.396, data_time: 1.373, memory: 15990, top1_acc: 0.4370, top5_acc: 0.6925, loss_cls: 3.1551, loss: 3.1551 +2024-07-26 09:38:04,475 - pyskl - INFO - Epoch [116][200/3746] lr: 1.281e-02, eta: 1 day, 5:51:25, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.7000, loss_cls: 3.1076, loss: 3.1076 +2024-07-26 09:39:28,706 - pyskl - INFO - Epoch [116][300/3746] lr: 1.279e-02, eta: 1 day, 5:50:04, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4313, top5_acc: 0.6920, loss_cls: 3.1642, loss: 3.1642 +2024-07-26 09:40:52,304 - pyskl - INFO - Epoch [116][400/3746] lr: 1.277e-02, eta: 1 day, 5:48:42, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6800, loss_cls: 3.2116, loss: 3.2116 +2024-07-26 09:42:16,314 - pyskl - INFO - Epoch [116][500/3746] lr: 1.275e-02, eta: 1 day, 5:47:21, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.6970, loss_cls: 3.1388, loss: 3.1388 +2024-07-26 09:43:40,031 - pyskl - INFO - Epoch [116][600/3746] lr: 1.273e-02, eta: 1 day, 5:45:59, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4359, top5_acc: 0.6917, loss_cls: 3.1903, loss: 3.1903 +2024-07-26 09:45:03,729 - pyskl - INFO - Epoch [116][700/3746] lr: 1.271e-02, eta: 1 day, 5:44:37, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6898, loss_cls: 3.2245, loss: 3.2245 +2024-07-26 09:46:27,462 - pyskl - INFO - Epoch [116][800/3746] lr: 1.269e-02, eta: 1 day, 5:43:16, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4272, top5_acc: 0.6883, loss_cls: 3.2185, loss: 3.2185 +2024-07-26 09:47:50,959 - pyskl - INFO - Epoch [116][900/3746] lr: 1.268e-02, eta: 1 day, 5:41:54, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.6947, loss_cls: 3.1643, loss: 3.1643 +2024-07-26 09:49:13,876 - pyskl - INFO - Epoch [116][1000/3746] lr: 1.266e-02, eta: 1 day, 5:40:32, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4314, top5_acc: 0.6889, loss_cls: 3.1934, loss: 3.1934 +2024-07-26 09:50:37,629 - pyskl - INFO - Epoch [116][1100/3746] lr: 1.264e-02, eta: 1 day, 5:39:11, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.6963, loss_cls: 3.1672, loss: 3.1672 +2024-07-26 09:52:00,980 - pyskl - INFO - Epoch [116][1200/3746] lr: 1.262e-02, eta: 1 day, 5:37:49, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4322, top5_acc: 0.6823, loss_cls: 3.1941, loss: 3.1941 +2024-07-26 09:53:24,135 - pyskl - INFO - Epoch [116][1300/3746] lr: 1.260e-02, eta: 1 day, 5:36:27, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4302, top5_acc: 0.6825, loss_cls: 3.2314, loss: 3.2314 +2024-07-26 09:54:47,849 - pyskl - INFO - Epoch [116][1400/3746] lr: 1.258e-02, eta: 1 day, 5:35:05, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4263, top5_acc: 0.6886, loss_cls: 3.2132, loss: 3.2132 +2024-07-26 09:56:11,310 - pyskl - INFO - Epoch [116][1500/3746] lr: 1.256e-02, eta: 1 day, 5:33:44, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4369, top5_acc: 0.6908, loss_cls: 3.1905, loss: 3.1905 +2024-07-26 09:57:34,543 - pyskl - INFO - Epoch [116][1600/3746] lr: 1.255e-02, eta: 1 day, 5:32:22, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4370, top5_acc: 0.6844, loss_cls: 3.1992, loss: 3.1992 +2024-07-26 09:58:58,848 - pyskl - INFO - Epoch [116][1700/3746] lr: 1.253e-02, eta: 1 day, 5:31:01, time: 0.843, data_time: 0.001, memory: 15990, top1_acc: 0.4228, top5_acc: 0.6767, loss_cls: 3.2616, loss: 3.2616 +2024-07-26 10:00:22,891 - pyskl - INFO - Epoch [116][1800/3746] lr: 1.251e-02, eta: 1 day, 5:29:39, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.4213, top5_acc: 0.6819, loss_cls: 3.2397, loss: 3.2397 +2024-07-26 10:01:46,498 - pyskl - INFO - Epoch [116][1900/3746] lr: 1.249e-02, eta: 1 day, 5:28:17, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6830, loss_cls: 3.2238, loss: 3.2238 +2024-07-26 10:03:09,806 - pyskl - INFO - Epoch [116][2000/3746] lr: 1.247e-02, eta: 1 day, 5:26:56, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6942, loss_cls: 3.1746, loss: 3.1746 +2024-07-26 10:04:33,521 - pyskl - INFO - Epoch [116][2100/3746] lr: 1.245e-02, eta: 1 day, 5:25:34, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4313, top5_acc: 0.6855, loss_cls: 3.2419, loss: 3.2419 +2024-07-26 10:05:56,127 - pyskl - INFO - Epoch [116][2200/3746] lr: 1.243e-02, eta: 1 day, 5:24:12, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4245, top5_acc: 0.6863, loss_cls: 3.2149, loss: 3.2149 +2024-07-26 10:07:19,593 - pyskl - INFO - Epoch [116][2300/3746] lr: 1.242e-02, eta: 1 day, 5:22:50, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4292, top5_acc: 0.6875, loss_cls: 3.2036, loss: 3.2036 +2024-07-26 10:08:42,842 - pyskl - INFO - Epoch [116][2400/3746] lr: 1.240e-02, eta: 1 day, 5:21:28, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4308, top5_acc: 0.6825, loss_cls: 3.2000, loss: 3.2000 +2024-07-26 10:10:05,742 - pyskl - INFO - Epoch [116][2500/3746] lr: 1.238e-02, eta: 1 day, 5:20:07, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4248, top5_acc: 0.6863, loss_cls: 3.2165, loss: 3.2165 +2024-07-26 10:11:29,593 - pyskl - INFO - Epoch [116][2600/3746] lr: 1.236e-02, eta: 1 day, 5:18:45, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6809, loss_cls: 3.2423, loss: 3.2423 +2024-07-26 10:12:52,488 - pyskl - INFO - Epoch [116][2700/3746] lr: 1.234e-02, eta: 1 day, 5:17:23, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6880, loss_cls: 3.2184, loss: 3.2184 +2024-07-26 10:14:15,203 - pyskl - INFO - Epoch [116][2800/3746] lr: 1.232e-02, eta: 1 day, 5:16:01, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6773, loss_cls: 3.2428, loss: 3.2428 +2024-07-26 10:15:37,838 - pyskl - INFO - Epoch [116][2900/3746] lr: 1.231e-02, eta: 1 day, 5:14:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4300, top5_acc: 0.6837, loss_cls: 3.2230, loss: 3.2230 +2024-07-26 10:17:00,609 - pyskl - INFO - Epoch [116][3000/3746] lr: 1.229e-02, eta: 1 day, 5:13:17, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6866, loss_cls: 3.1999, loss: 3.1999 +2024-07-26 10:18:23,852 - pyskl - INFO - Epoch [116][3100/3746] lr: 1.227e-02, eta: 1 day, 5:11:56, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6786, loss_cls: 3.2176, loss: 3.2176 +2024-07-26 10:19:46,563 - pyskl - INFO - Epoch [116][3200/3746] lr: 1.225e-02, eta: 1 day, 5:10:34, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6811, loss_cls: 3.2328, loss: 3.2328 +2024-07-26 10:21:09,307 - pyskl - INFO - Epoch [116][3300/3746] lr: 1.223e-02, eta: 1 day, 5:09:12, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.6808, loss_cls: 3.2423, loss: 3.2423 +2024-07-26 10:22:32,074 - pyskl - INFO - Epoch [116][3400/3746] lr: 1.221e-02, eta: 1 day, 5:07:50, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6778, loss_cls: 3.2381, loss: 3.2381 +2024-07-26 10:23:54,844 - pyskl - INFO - Epoch [116][3500/3746] lr: 1.220e-02, eta: 1 day, 5:06:28, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4170, top5_acc: 0.6791, loss_cls: 3.2548, loss: 3.2548 +2024-07-26 10:25:18,020 - pyskl - INFO - Epoch [116][3600/3746] lr: 1.218e-02, eta: 1 day, 5:05:06, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4205, top5_acc: 0.6777, loss_cls: 3.2645, loss: 3.2645 +2024-07-26 10:26:40,517 - pyskl - INFO - Epoch [116][3700/3746] lr: 1.216e-02, eta: 1 day, 5:03:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4248, top5_acc: 0.6822, loss_cls: 3.2192, loss: 3.2192 +2024-07-26 10:27:20,189 - pyskl - INFO - Saving checkpoint at 116 epochs +2024-07-26 10:29:12,010 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 10:29:12,818 - pyskl - INFO - +top1_acc 0.3630 +top5_acc 0.6174 +2024-07-26 10:29:12,818 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 10:29:12,861 - pyskl - INFO - +mean_acc 0.3628 +2024-07-26 10:29:12,873 - pyskl - INFO - Epoch(val) [116][309] top1_acc: 0.3630, top5_acc: 0.6174, mean_class_accuracy: 0.3628 +2024-07-26 10:33:07,202 - pyskl - INFO - Epoch [117][100/3746] lr: 1.213e-02, eta: 1 day, 5:02:18, time: 2.343, data_time: 1.336, memory: 15990, top1_acc: 0.4502, top5_acc: 0.7031, loss_cls: 3.1354, loss: 3.1354 +2024-07-26 10:34:31,048 - pyskl - INFO - Epoch [117][200/3746] lr: 1.211e-02, eta: 1 day, 5:00:56, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.7066, loss_cls: 3.1216, loss: 3.1216 +2024-07-26 10:35:54,965 - pyskl - INFO - Epoch [117][300/3746] lr: 1.210e-02, eta: 1 day, 4:59:34, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4466, top5_acc: 0.7037, loss_cls: 3.1198, loss: 3.1198 +2024-07-26 10:37:18,718 - pyskl - INFO - Epoch [117][400/3746] lr: 1.208e-02, eta: 1 day, 4:58:13, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.6998, loss_cls: 3.1338, loss: 3.1338 +2024-07-26 10:38:41,839 - pyskl - INFO - Epoch [117][500/3746] lr: 1.206e-02, eta: 1 day, 4:56:51, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4328, top5_acc: 0.6937, loss_cls: 3.1755, loss: 3.1755 +2024-07-26 10:40:04,203 - pyskl - INFO - Epoch [117][600/3746] lr: 1.204e-02, eta: 1 day, 4:55:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4334, top5_acc: 0.6877, loss_cls: 3.2058, loss: 3.2058 +2024-07-26 10:41:27,155 - pyskl - INFO - Epoch [117][700/3746] lr: 1.202e-02, eta: 1 day, 4:54:07, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4297, top5_acc: 0.6870, loss_cls: 3.2191, loss: 3.2191 +2024-07-26 10:42:49,597 - pyskl - INFO - Epoch [117][800/3746] lr: 1.200e-02, eta: 1 day, 4:52:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4373, top5_acc: 0.6828, loss_cls: 3.1932, loss: 3.1932 +2024-07-26 10:44:12,278 - pyskl - INFO - Epoch [117][900/3746] lr: 1.199e-02, eta: 1 day, 4:51:23, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4283, top5_acc: 0.6877, loss_cls: 3.1921, loss: 3.1921 +2024-07-26 10:45:34,740 - pyskl - INFO - Epoch [117][1000/3746] lr: 1.197e-02, eta: 1 day, 4:50:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4331, top5_acc: 0.6964, loss_cls: 3.1632, loss: 3.1632 +2024-07-26 10:46:57,530 - pyskl - INFO - Epoch [117][1100/3746] lr: 1.195e-02, eta: 1 day, 4:48:39, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4281, top5_acc: 0.6863, loss_cls: 3.2082, loss: 3.2082 +2024-07-26 10:48:19,870 - pyskl - INFO - Epoch [117][1200/3746] lr: 1.193e-02, eta: 1 day, 4:47:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6847, loss_cls: 3.1981, loss: 3.1981 +2024-07-26 10:49:41,527 - pyskl - INFO - Epoch [117][1300/3746] lr: 1.191e-02, eta: 1 day, 4:45:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4314, top5_acc: 0.6784, loss_cls: 3.2072, loss: 3.2072 +2024-07-26 10:51:03,785 - pyskl - INFO - Epoch [117][1400/3746] lr: 1.190e-02, eta: 1 day, 4:44:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4345, top5_acc: 0.6870, loss_cls: 3.1731, loss: 3.1731 +2024-07-26 10:52:26,467 - pyskl - INFO - Epoch [117][1500/3746] lr: 1.188e-02, eta: 1 day, 4:43:10, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4359, top5_acc: 0.6913, loss_cls: 3.1603, loss: 3.1603 +2024-07-26 10:53:48,605 - pyskl - INFO - Epoch [117][1600/3746] lr: 1.186e-02, eta: 1 day, 4:41:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6897, loss_cls: 3.2004, loss: 3.2004 +2024-07-26 10:55:10,564 - pyskl - INFO - Epoch [117][1700/3746] lr: 1.184e-02, eta: 1 day, 4:40:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6933, loss_cls: 3.1629, loss: 3.1629 +2024-07-26 10:56:32,913 - pyskl - INFO - Epoch [117][1800/3746] lr: 1.182e-02, eta: 1 day, 4:39:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4306, top5_acc: 0.6902, loss_cls: 3.1793, loss: 3.1793 +2024-07-26 10:57:55,377 - pyskl - INFO - Epoch [117][1900/3746] lr: 1.181e-02, eta: 1 day, 4:37:42, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6877, loss_cls: 3.2027, loss: 3.2027 +2024-07-26 10:59:17,271 - pyskl - INFO - Epoch [117][2000/3746] lr: 1.179e-02, eta: 1 day, 4:36:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4298, top5_acc: 0.6945, loss_cls: 3.1750, loss: 3.1750 +2024-07-26 11:00:38,679 - pyskl - INFO - Epoch [117][2100/3746] lr: 1.177e-02, eta: 1 day, 4:34:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4269, top5_acc: 0.6891, loss_cls: 3.2030, loss: 3.2030 +2024-07-26 11:02:00,399 - pyskl - INFO - Epoch [117][2200/3746] lr: 1.175e-02, eta: 1 day, 4:33:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4408, top5_acc: 0.6911, loss_cls: 3.1792, loss: 3.1792 +2024-07-26 11:03:22,898 - pyskl - INFO - Epoch [117][2300/3746] lr: 1.173e-02, eta: 1 day, 4:32:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4462, top5_acc: 0.7023, loss_cls: 3.1140, loss: 3.1140 +2024-07-26 11:04:44,826 - pyskl - INFO - Epoch [117][2400/3746] lr: 1.172e-02, eta: 1 day, 4:30:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4306, top5_acc: 0.6861, loss_cls: 3.1972, loss: 3.1972 +2024-07-26 11:06:06,523 - pyskl - INFO - Epoch [117][2500/3746] lr: 1.170e-02, eta: 1 day, 4:29:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6805, loss_cls: 3.1969, loss: 3.1969 +2024-07-26 11:07:28,517 - pyskl - INFO - Epoch [117][2600/3746] lr: 1.168e-02, eta: 1 day, 4:28:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6880, loss_cls: 3.2065, loss: 3.2065 +2024-07-26 11:08:50,141 - pyskl - INFO - Epoch [117][2700/3746] lr: 1.166e-02, eta: 1 day, 4:26:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4394, top5_acc: 0.6895, loss_cls: 3.1830, loss: 3.1830 +2024-07-26 11:10:12,635 - pyskl - INFO - Epoch [117][2800/3746] lr: 1.164e-02, eta: 1 day, 4:25:22, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.6972, loss_cls: 3.1568, loss: 3.1568 +2024-07-26 11:11:33,702 - pyskl - INFO - Epoch [117][2900/3746] lr: 1.163e-02, eta: 1 day, 4:23:59, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4356, top5_acc: 0.6859, loss_cls: 3.1915, loss: 3.1915 +2024-07-26 11:12:55,347 - pyskl - INFO - Epoch [117][3000/3746] lr: 1.161e-02, eta: 1 day, 4:22:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6852, loss_cls: 3.2163, loss: 3.2163 +2024-07-26 11:14:16,855 - pyskl - INFO - Epoch [117][3100/3746] lr: 1.159e-02, eta: 1 day, 4:21:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6844, loss_cls: 3.2186, loss: 3.2186 +2024-07-26 11:15:39,076 - pyskl - INFO - Epoch [117][3200/3746] lr: 1.157e-02, eta: 1 day, 4:19:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.6850, loss_cls: 3.1883, loss: 3.1883 +2024-07-26 11:17:00,866 - pyskl - INFO - Epoch [117][3300/3746] lr: 1.155e-02, eta: 1 day, 4:18:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4277, top5_acc: 0.6825, loss_cls: 3.1971, loss: 3.1971 +2024-07-26 11:18:23,013 - pyskl - INFO - Epoch [117][3400/3746] lr: 1.154e-02, eta: 1 day, 4:17:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4273, top5_acc: 0.6863, loss_cls: 3.2161, loss: 3.2161 +2024-07-26 11:19:45,009 - pyskl - INFO - Epoch [117][3500/3746] lr: 1.152e-02, eta: 1 day, 4:15:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4277, top5_acc: 0.6787, loss_cls: 3.2462, loss: 3.2462 +2024-07-26 11:21:06,641 - pyskl - INFO - Epoch [117][3600/3746] lr: 1.150e-02, eta: 1 day, 4:14:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4300, top5_acc: 0.6817, loss_cls: 3.2053, loss: 3.2053 +2024-07-26 11:22:28,611 - pyskl - INFO - Epoch [117][3700/3746] lr: 1.148e-02, eta: 1 day, 4:13:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4317, top5_acc: 0.6816, loss_cls: 3.2247, loss: 3.2247 +2024-07-26 11:23:08,181 - pyskl - INFO - Saving checkpoint at 117 epochs +2024-07-26 11:25:00,166 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 11:25:00,864 - pyskl - INFO - +top1_acc 0.3671 +top5_acc 0.6221 +2024-07-26 11:25:00,865 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 11:25:00,905 - pyskl - INFO - +mean_acc 0.3669 +2024-07-26 11:25:00,910 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_114.pth was removed +2024-07-26 11:25:01,235 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_117.pth. +2024-07-26 11:25:01,236 - pyskl - INFO - Best top1_acc is 0.3671 at 117 epoch. +2024-07-26 11:25:01,253 - pyskl - INFO - Epoch(val) [117][309] top1_acc: 0.3671, top5_acc: 0.6221, mean_class_accuracy: 0.3669 +2024-07-26 11:28:51,512 - pyskl - INFO - Epoch [118][100/3746] lr: 1.146e-02, eta: 1 day, 4:11:33, time: 2.302, data_time: 1.325, memory: 15990, top1_acc: 0.4447, top5_acc: 0.6978, loss_cls: 3.1344, loss: 3.1344 +2024-07-26 11:30:12,812 - pyskl - INFO - Epoch [118][200/3746] lr: 1.144e-02, eta: 1 day, 4:10:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4458, top5_acc: 0.6983, loss_cls: 3.0947, loss: 3.0947 +2024-07-26 11:31:34,414 - pyskl - INFO - Epoch [118][300/3746] lr: 1.142e-02, eta: 1 day, 4:08:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4495, top5_acc: 0.7083, loss_cls: 3.0804, loss: 3.0804 +2024-07-26 11:32:56,695 - pyskl - INFO - Epoch [118][400/3746] lr: 1.140e-02, eta: 1 day, 4:07:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4412, top5_acc: 0.7073, loss_cls: 3.0902, loss: 3.0902 +2024-07-26 11:34:18,698 - pyskl - INFO - Epoch [118][500/3746] lr: 1.139e-02, eta: 1 day, 4:06:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4584, top5_acc: 0.7044, loss_cls: 3.0725, loss: 3.0725 +2024-07-26 11:35:40,506 - pyskl - INFO - Epoch [118][600/3746] lr: 1.137e-02, eta: 1 day, 4:04:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4544, top5_acc: 0.7041, loss_cls: 3.0805, loss: 3.0805 +2024-07-26 11:37:01,959 - pyskl - INFO - Epoch [118][700/3746] lr: 1.135e-02, eta: 1 day, 4:03:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4545, top5_acc: 0.7119, loss_cls: 3.0757, loss: 3.0757 +2024-07-26 11:38:23,426 - pyskl - INFO - Epoch [118][800/3746] lr: 1.133e-02, eta: 1 day, 4:01:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.6895, loss_cls: 3.1895, loss: 3.1895 +2024-07-26 11:39:44,992 - pyskl - INFO - Epoch [118][900/3746] lr: 1.131e-02, eta: 1 day, 4:00:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4431, top5_acc: 0.6955, loss_cls: 3.1547, loss: 3.1547 +2024-07-26 11:41:06,569 - pyskl - INFO - Epoch [118][1000/3746] lr: 1.130e-02, eta: 1 day, 3:59:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.7020, loss_cls: 3.1505, loss: 3.1505 +2024-07-26 11:42:28,268 - pyskl - INFO - Epoch [118][1100/3746] lr: 1.128e-02, eta: 1 day, 3:57:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4458, top5_acc: 0.7008, loss_cls: 3.1051, loss: 3.1051 +2024-07-26 11:43:50,261 - pyskl - INFO - Epoch [118][1200/3746] lr: 1.126e-02, eta: 1 day, 3:56:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4286, top5_acc: 0.6820, loss_cls: 3.2059, loss: 3.2059 +2024-07-26 11:45:11,324 - pyskl - INFO - Epoch [118][1300/3746] lr: 1.124e-02, eta: 1 day, 3:55:05, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4280, top5_acc: 0.6887, loss_cls: 3.1886, loss: 3.1886 +2024-07-26 11:46:34,010 - pyskl - INFO - Epoch [118][1400/3746] lr: 1.123e-02, eta: 1 day, 3:53:43, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4434, top5_acc: 0.6936, loss_cls: 3.1556, loss: 3.1556 +2024-07-26 11:47:55,978 - pyskl - INFO - Epoch [118][1500/3746] lr: 1.121e-02, eta: 1 day, 3:52:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4252, top5_acc: 0.6858, loss_cls: 3.2218, loss: 3.2218 +2024-07-26 11:49:18,433 - pyskl - INFO - Epoch [118][1600/3746] lr: 1.119e-02, eta: 1 day, 3:50:59, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4489, top5_acc: 0.6947, loss_cls: 3.1634, loss: 3.1634 +2024-07-26 11:50:40,398 - pyskl - INFO - Epoch [118][1700/3746] lr: 1.117e-02, eta: 1 day, 3:49:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4308, top5_acc: 0.6834, loss_cls: 3.2251, loss: 3.2251 +2024-07-26 11:52:01,945 - pyskl - INFO - Epoch [118][1800/3746] lr: 1.116e-02, eta: 1 day, 3:48:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4291, top5_acc: 0.6792, loss_cls: 3.2169, loss: 3.2169 +2024-07-26 11:53:23,733 - pyskl - INFO - Epoch [118][1900/3746] lr: 1.114e-02, eta: 1 day, 3:46:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.7023, loss_cls: 3.0960, loss: 3.0960 +2024-07-26 11:54:45,867 - pyskl - INFO - Epoch [118][2000/3746] lr: 1.112e-02, eta: 1 day, 3:45:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4389, top5_acc: 0.6969, loss_cls: 3.1315, loss: 3.1315 +2024-07-26 11:56:07,740 - pyskl - INFO - Epoch [118][2100/3746] lr: 1.110e-02, eta: 1 day, 3:44:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6805, loss_cls: 3.2257, loss: 3.2257 +2024-07-26 11:57:30,175 - pyskl - INFO - Epoch [118][2200/3746] lr: 1.109e-02, eta: 1 day, 3:42:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6844, loss_cls: 3.2123, loss: 3.2123 +2024-07-26 11:58:52,462 - pyskl - INFO - Epoch [118][2300/3746] lr: 1.107e-02, eta: 1 day, 3:41:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4366, top5_acc: 0.6961, loss_cls: 3.1555, loss: 3.1555 +2024-07-26 12:00:14,317 - pyskl - INFO - Epoch [118][2400/3746] lr: 1.105e-02, eta: 1 day, 3:40:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4289, top5_acc: 0.6877, loss_cls: 3.2337, loss: 3.2337 +2024-07-26 12:01:36,242 - pyskl - INFO - Epoch [118][2500/3746] lr: 1.103e-02, eta: 1 day, 3:38:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6995, loss_cls: 3.1739, loss: 3.1739 +2024-07-26 12:02:57,816 - pyskl - INFO - Epoch [118][2600/3746] lr: 1.102e-02, eta: 1 day, 3:37:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.6941, loss_cls: 3.1573, loss: 3.1573 +2024-07-26 12:04:19,737 - pyskl - INFO - Epoch [118][2700/3746] lr: 1.100e-02, eta: 1 day, 3:35:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4345, top5_acc: 0.6970, loss_cls: 3.1653, loss: 3.1653 +2024-07-26 12:05:41,156 - pyskl - INFO - Epoch [118][2800/3746] lr: 1.098e-02, eta: 1 day, 3:34:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4402, top5_acc: 0.6978, loss_cls: 3.1603, loss: 3.1603 +2024-07-26 12:07:02,746 - pyskl - INFO - Epoch [118][2900/3746] lr: 1.096e-02, eta: 1 day, 3:33:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.6983, loss_cls: 3.1320, loss: 3.1320 +2024-07-26 12:08:23,934 - pyskl - INFO - Epoch [118][3000/3746] lr: 1.095e-02, eta: 1 day, 3:31:47, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4266, top5_acc: 0.6884, loss_cls: 3.1871, loss: 3.1871 +2024-07-26 12:09:45,326 - pyskl - INFO - Epoch [118][3100/3746] lr: 1.093e-02, eta: 1 day, 3:30:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4527, top5_acc: 0.7036, loss_cls: 3.1054, loss: 3.1054 +2024-07-26 12:11:07,186 - pyskl - INFO - Epoch [118][3200/3746] lr: 1.091e-02, eta: 1 day, 3:29:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6834, loss_cls: 3.2057, loss: 3.2057 +2024-07-26 12:12:28,781 - pyskl - INFO - Epoch [118][3300/3746] lr: 1.089e-02, eta: 1 day, 3:27:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6872, loss_cls: 3.2418, loss: 3.2418 +2024-07-26 12:13:50,667 - pyskl - INFO - Epoch [118][3400/3746] lr: 1.088e-02, eta: 1 day, 3:26:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4384, top5_acc: 0.6930, loss_cls: 3.1732, loss: 3.1732 +2024-07-26 12:15:12,295 - pyskl - INFO - Epoch [118][3500/3746] lr: 1.086e-02, eta: 1 day, 3:24:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4295, top5_acc: 0.6828, loss_cls: 3.2000, loss: 3.2000 +2024-07-26 12:16:34,154 - pyskl - INFO - Epoch [118][3600/3746] lr: 1.084e-02, eta: 1 day, 3:23:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4347, top5_acc: 0.6952, loss_cls: 3.1829, loss: 3.1829 +2024-07-26 12:17:56,383 - pyskl - INFO - Epoch [118][3700/3746] lr: 1.082e-02, eta: 1 day, 3:22:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.6970, loss_cls: 3.1545, loss: 3.1545 +2024-07-26 12:18:35,905 - pyskl - INFO - Saving checkpoint at 118 epochs +2024-07-26 12:20:27,568 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 12:20:28,260 - pyskl - INFO - +top1_acc 0.3640 +top5_acc 0.6211 +2024-07-26 12:20:28,260 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 12:20:28,301 - pyskl - INFO - +mean_acc 0.3637 +2024-07-26 12:20:28,311 - pyskl - INFO - Epoch(val) [118][309] top1_acc: 0.3640, top5_acc: 0.6211, mean_class_accuracy: 0.3637 +2024-07-26 12:24:22,498 - pyskl - INFO - Epoch [119][100/3746] lr: 1.080e-02, eta: 1 day, 3:20:42, time: 2.342, data_time: 1.352, memory: 15990, top1_acc: 0.4436, top5_acc: 0.7023, loss_cls: 3.1110, loss: 3.1110 +2024-07-26 12:25:46,642 - pyskl - INFO - Epoch [119][200/3746] lr: 1.078e-02, eta: 1 day, 3:19:20, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4583, top5_acc: 0.7142, loss_cls: 3.0418, loss: 3.0418 +2024-07-26 12:27:10,867 - pyskl - INFO - Epoch [119][300/3746] lr: 1.076e-02, eta: 1 day, 3:17:59, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4544, top5_acc: 0.7059, loss_cls: 3.0645, loss: 3.0645 +2024-07-26 12:28:35,017 - pyskl - INFO - Epoch [119][400/3746] lr: 1.075e-02, eta: 1 day, 3:16:37, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4512, top5_acc: 0.7108, loss_cls: 3.0811, loss: 3.0811 +2024-07-26 12:29:58,580 - pyskl - INFO - Epoch [119][500/3746] lr: 1.073e-02, eta: 1 day, 3:15:15, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4542, top5_acc: 0.7023, loss_cls: 3.0754, loss: 3.0754 +2024-07-26 12:31:21,710 - pyskl - INFO - Epoch [119][600/3746] lr: 1.071e-02, eta: 1 day, 3:13:53, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7034, loss_cls: 3.1026, loss: 3.1026 +2024-07-26 12:32:45,207 - pyskl - INFO - Epoch [119][700/3746] lr: 1.069e-02, eta: 1 day, 3:12:32, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4400, top5_acc: 0.6992, loss_cls: 3.1516, loss: 3.1516 +2024-07-26 12:34:07,236 - pyskl - INFO - Epoch [119][800/3746] lr: 1.068e-02, eta: 1 day, 3:11:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4355, top5_acc: 0.6884, loss_cls: 3.1777, loss: 3.1777 +2024-07-26 12:35:29,547 - pyskl - INFO - Epoch [119][900/3746] lr: 1.066e-02, eta: 1 day, 3:09:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4439, top5_acc: 0.6995, loss_cls: 3.1299, loss: 3.1299 +2024-07-26 12:36:51,065 - pyskl - INFO - Epoch [119][1000/3746] lr: 1.064e-02, eta: 1 day, 3:08:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4533, top5_acc: 0.6963, loss_cls: 3.0963, loss: 3.0963 +2024-07-26 12:38:12,268 - pyskl - INFO - Epoch [119][1100/3746] lr: 1.063e-02, eta: 1 day, 3:07:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4448, top5_acc: 0.6989, loss_cls: 3.0927, loss: 3.0927 +2024-07-26 12:39:34,052 - pyskl - INFO - Epoch [119][1200/3746] lr: 1.061e-02, eta: 1 day, 3:05:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.7069, loss_cls: 3.1027, loss: 3.1027 +2024-07-26 12:40:56,027 - pyskl - INFO - Epoch [119][1300/3746] lr: 1.059e-02, eta: 1 day, 3:04:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.6980, loss_cls: 3.1387, loss: 3.1387 +2024-07-26 12:42:18,734 - pyskl - INFO - Epoch [119][1400/3746] lr: 1.057e-02, eta: 1 day, 3:02:56, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4405, top5_acc: 0.6898, loss_cls: 3.1605, loss: 3.1605 +2024-07-26 12:43:40,985 - pyskl - INFO - Epoch [119][1500/3746] lr: 1.056e-02, eta: 1 day, 3:01:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4406, top5_acc: 0.6994, loss_cls: 3.0882, loss: 3.0882 +2024-07-26 12:45:03,231 - pyskl - INFO - Epoch [119][1600/3746] lr: 1.054e-02, eta: 1 day, 3:00:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4477, top5_acc: 0.6939, loss_cls: 3.1308, loss: 3.1308 +2024-07-26 12:46:24,864 - pyskl - INFO - Epoch [119][1700/3746] lr: 1.052e-02, eta: 1 day, 2:58:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4361, top5_acc: 0.6889, loss_cls: 3.1481, loss: 3.1481 +2024-07-26 12:47:46,477 - pyskl - INFO - Epoch [119][1800/3746] lr: 1.050e-02, eta: 1 day, 2:57:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4461, top5_acc: 0.6989, loss_cls: 3.1192, loss: 3.1192 +2024-07-26 12:49:08,246 - pyskl - INFO - Epoch [119][1900/3746] lr: 1.049e-02, eta: 1 day, 2:56:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4369, top5_acc: 0.6903, loss_cls: 3.1518, loss: 3.1518 +2024-07-26 12:50:29,553 - pyskl - INFO - Epoch [119][2000/3746] lr: 1.047e-02, eta: 1 day, 2:54:42, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4422, top5_acc: 0.7023, loss_cls: 3.1253, loss: 3.1253 +2024-07-26 12:51:50,934 - pyskl - INFO - Epoch [119][2100/3746] lr: 1.045e-02, eta: 1 day, 2:53:19, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4317, top5_acc: 0.6909, loss_cls: 3.1816, loss: 3.1816 +2024-07-26 12:53:13,144 - pyskl - INFO - Epoch [119][2200/3746] lr: 1.044e-02, eta: 1 day, 2:51:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4445, top5_acc: 0.7006, loss_cls: 3.1435, loss: 3.1435 +2024-07-26 12:54:35,087 - pyskl - INFO - Epoch [119][2300/3746] lr: 1.042e-02, eta: 1 day, 2:50:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4302, top5_acc: 0.6895, loss_cls: 3.1951, loss: 3.1951 +2024-07-26 12:55:56,888 - pyskl - INFO - Epoch [119][2400/3746] lr: 1.040e-02, eta: 1 day, 2:49:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4425, top5_acc: 0.6928, loss_cls: 3.1209, loss: 3.1209 +2024-07-26 12:57:18,768 - pyskl - INFO - Epoch [119][2500/3746] lr: 1.039e-02, eta: 1 day, 2:47:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4428, top5_acc: 0.6966, loss_cls: 3.1447, loss: 3.1447 +2024-07-26 12:58:40,028 - pyskl - INFO - Epoch [119][2600/3746] lr: 1.037e-02, eta: 1 day, 2:46:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4547, top5_acc: 0.7092, loss_cls: 3.0914, loss: 3.0914 +2024-07-26 13:00:02,227 - pyskl - INFO - Epoch [119][2700/3746] lr: 1.035e-02, eta: 1 day, 2:45:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4478, top5_acc: 0.6917, loss_cls: 3.1318, loss: 3.1318 +2024-07-26 13:01:24,004 - pyskl - INFO - Epoch [119][2800/3746] lr: 1.033e-02, eta: 1 day, 2:43:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4311, top5_acc: 0.6956, loss_cls: 3.1340, loss: 3.1340 +2024-07-26 13:02:45,794 - pyskl - INFO - Epoch [119][2900/3746] lr: 1.032e-02, eta: 1 day, 2:42:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6884, loss_cls: 3.1999, loss: 3.1999 +2024-07-26 13:04:07,029 - pyskl - INFO - Epoch [119][3000/3746] lr: 1.030e-02, eta: 1 day, 2:40:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4392, top5_acc: 0.6953, loss_cls: 3.1208, loss: 3.1208 +2024-07-26 13:05:28,349 - pyskl - INFO - Epoch [119][3100/3746] lr: 1.028e-02, eta: 1 day, 2:39:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.6991, loss_cls: 3.1532, loss: 3.1532 +2024-07-26 13:06:49,737 - pyskl - INFO - Epoch [119][3200/3746] lr: 1.027e-02, eta: 1 day, 2:38:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4339, top5_acc: 0.6931, loss_cls: 3.1661, loss: 3.1661 +2024-07-26 13:08:11,273 - pyskl - INFO - Epoch [119][3300/3746] lr: 1.025e-02, eta: 1 day, 2:36:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4338, top5_acc: 0.6820, loss_cls: 3.2201, loss: 3.2201 +2024-07-26 13:09:33,130 - pyskl - INFO - Epoch [119][3400/3746] lr: 1.023e-02, eta: 1 day, 2:35:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4425, top5_acc: 0.6941, loss_cls: 3.1572, loss: 3.1572 +2024-07-26 13:10:54,148 - pyskl - INFO - Epoch [119][3500/3746] lr: 1.022e-02, eta: 1 day, 2:34:07, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.4302, top5_acc: 0.6902, loss_cls: 3.1913, loss: 3.1913 +2024-07-26 13:12:15,742 - pyskl - INFO - Epoch [119][3600/3746] lr: 1.020e-02, eta: 1 day, 2:32:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.7031, loss_cls: 3.1368, loss: 3.1368 +2024-07-26 13:13:36,832 - pyskl - INFO - Epoch [119][3700/3746] lr: 1.018e-02, eta: 1 day, 2:31:22, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4431, top5_acc: 0.6941, loss_cls: 3.1582, loss: 3.1582 +2024-07-26 13:14:16,515 - pyskl - INFO - Saving checkpoint at 119 epochs +2024-07-26 13:16:07,800 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 13:16:08,452 - pyskl - INFO - +top1_acc 0.3749 +top5_acc 0.6269 +2024-07-26 13:16:08,452 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 13:16:08,492 - pyskl - INFO - +mean_acc 0.3746 +2024-07-26 13:16:08,497 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_117.pth was removed +2024-07-26 13:16:08,756 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_119.pth. +2024-07-26 13:16:08,757 - pyskl - INFO - Best top1_acc is 0.3749 at 119 epoch. +2024-07-26 13:16:08,769 - pyskl - INFO - Epoch(val) [119][309] top1_acc: 0.3749, top5_acc: 0.6269, mean_class_accuracy: 0.3746 +2024-07-26 13:19:56,022 - pyskl - INFO - Epoch [120][100/3746] lr: 1.016e-02, eta: 1 day, 2:29:50, time: 2.272, data_time: 1.291, memory: 15990, top1_acc: 0.4608, top5_acc: 0.7161, loss_cls: 3.0061, loss: 3.0061 +2024-07-26 13:21:18,173 - pyskl - INFO - Epoch [120][200/3746] lr: 1.014e-02, eta: 1 day, 2:28:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4572, top5_acc: 0.7114, loss_cls: 3.0504, loss: 3.0504 +2024-07-26 13:22:39,871 - pyskl - INFO - Epoch [120][300/3746] lr: 1.012e-02, eta: 1 day, 2:27:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4494, top5_acc: 0.7033, loss_cls: 3.0844, loss: 3.0844 +2024-07-26 13:24:01,292 - pyskl - INFO - Epoch [120][400/3746] lr: 1.011e-02, eta: 1 day, 2:25:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4591, top5_acc: 0.7134, loss_cls: 3.0375, loss: 3.0375 +2024-07-26 13:25:23,315 - pyskl - INFO - Epoch [120][500/3746] lr: 1.009e-02, eta: 1 day, 2:24:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4541, top5_acc: 0.7037, loss_cls: 3.0494, loss: 3.0494 +2024-07-26 13:26:44,750 - pyskl - INFO - Epoch [120][600/3746] lr: 1.007e-02, eta: 1 day, 2:22:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4630, top5_acc: 0.7131, loss_cls: 3.0412, loss: 3.0412 +2024-07-26 13:28:06,187 - pyskl - INFO - Epoch [120][700/3746] lr: 1.006e-02, eta: 1 day, 2:21:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4480, top5_acc: 0.7069, loss_cls: 3.0752, loss: 3.0752 +2024-07-26 13:29:27,560 - pyskl - INFO - Epoch [120][800/3746] lr: 1.004e-02, eta: 1 day, 2:20:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.7013, loss_cls: 3.1159, loss: 3.1159 +2024-07-26 13:30:49,115 - pyskl - INFO - Epoch [120][900/3746] lr: 1.002e-02, eta: 1 day, 2:18:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4456, top5_acc: 0.7117, loss_cls: 3.0771, loss: 3.0771 +2024-07-26 13:32:11,217 - pyskl - INFO - Epoch [120][1000/3746] lr: 1.001e-02, eta: 1 day, 2:17:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4437, top5_acc: 0.7061, loss_cls: 3.0718, loss: 3.0718 +2024-07-26 13:33:32,748 - pyskl - INFO - Epoch [120][1100/3746] lr: 9.989e-03, eta: 1 day, 2:16:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4542, top5_acc: 0.7117, loss_cls: 3.0638, loss: 3.0638 +2024-07-26 13:34:54,778 - pyskl - INFO - Epoch [120][1200/3746] lr: 9.972e-03, eta: 1 day, 2:14:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4475, top5_acc: 0.7027, loss_cls: 3.1243, loss: 3.1243 +2024-07-26 13:36:16,473 - pyskl - INFO - Epoch [120][1300/3746] lr: 9.955e-03, eta: 1 day, 2:13:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4481, top5_acc: 0.7064, loss_cls: 3.0738, loss: 3.0738 +2024-07-26 13:37:38,197 - pyskl - INFO - Epoch [120][1400/3746] lr: 9.938e-03, eta: 1 day, 2:11:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4456, top5_acc: 0.7031, loss_cls: 3.1136, loss: 3.1136 +2024-07-26 13:39:00,208 - pyskl - INFO - Epoch [120][1500/3746] lr: 9.922e-03, eta: 1 day, 2:10:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.6969, loss_cls: 3.1329, loss: 3.1329 +2024-07-26 13:40:22,872 - pyskl - INFO - Epoch [120][1600/3746] lr: 9.905e-03, eta: 1 day, 2:09:15, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4441, top5_acc: 0.7078, loss_cls: 3.0895, loss: 3.0895 +2024-07-26 13:41:44,361 - pyskl - INFO - Epoch [120][1700/3746] lr: 9.888e-03, eta: 1 day, 2:07:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4375, top5_acc: 0.7027, loss_cls: 3.1096, loss: 3.1096 +2024-07-26 13:43:05,665 - pyskl - INFO - Epoch [120][1800/3746] lr: 9.871e-03, eta: 1 day, 2:06:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4506, top5_acc: 0.7036, loss_cls: 3.0856, loss: 3.0856 +2024-07-26 13:44:27,429 - pyskl - INFO - Epoch [120][1900/3746] lr: 9.855e-03, eta: 1 day, 2:05:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4442, top5_acc: 0.7003, loss_cls: 3.1111, loss: 3.1111 +2024-07-26 13:45:48,903 - pyskl - INFO - Epoch [120][2000/3746] lr: 9.838e-03, eta: 1 day, 2:03:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4447, top5_acc: 0.6955, loss_cls: 3.1413, loss: 3.1413 +2024-07-26 13:47:10,831 - pyskl - INFO - Epoch [120][2100/3746] lr: 9.821e-03, eta: 1 day, 2:02:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6891, loss_cls: 3.1867, loss: 3.1867 +2024-07-26 13:48:32,545 - pyskl - INFO - Epoch [120][2200/3746] lr: 9.805e-03, eta: 1 day, 2:01:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4512, top5_acc: 0.6914, loss_cls: 3.1541, loss: 3.1541 +2024-07-26 13:49:54,752 - pyskl - INFO - Epoch [120][2300/3746] lr: 9.788e-03, eta: 1 day, 1:59:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4455, top5_acc: 0.6952, loss_cls: 3.1398, loss: 3.1398 +2024-07-26 13:51:16,836 - pyskl - INFO - Epoch [120][2400/3746] lr: 9.772e-03, eta: 1 day, 1:58:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4445, top5_acc: 0.7036, loss_cls: 3.0984, loss: 3.0984 +2024-07-26 13:52:38,805 - pyskl - INFO - Epoch [120][2500/3746] lr: 9.755e-03, eta: 1 day, 1:56:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4425, top5_acc: 0.6984, loss_cls: 3.1293, loss: 3.1293 +2024-07-26 13:54:00,646 - pyskl - INFO - Epoch [120][2600/3746] lr: 9.738e-03, eta: 1 day, 1:55:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4427, top5_acc: 0.6964, loss_cls: 3.0981, loss: 3.0981 +2024-07-26 13:55:22,156 - pyskl - INFO - Epoch [120][2700/3746] lr: 9.722e-03, eta: 1 day, 1:54:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.6952, loss_cls: 3.1582, loss: 3.1582 +2024-07-26 13:56:43,776 - pyskl - INFO - Epoch [120][2800/3746] lr: 9.705e-03, eta: 1 day, 1:52:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.7080, loss_cls: 3.1118, loss: 3.1118 +2024-07-26 13:58:05,393 - pyskl - INFO - Epoch [120][2900/3746] lr: 9.689e-03, eta: 1 day, 1:51:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4408, top5_acc: 0.6948, loss_cls: 3.1820, loss: 3.1820 +2024-07-26 13:59:26,624 - pyskl - INFO - Epoch [120][3000/3746] lr: 9.672e-03, eta: 1 day, 1:50:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7100, loss_cls: 3.0662, loss: 3.0662 +2024-07-26 14:00:48,644 - pyskl - INFO - Epoch [120][3100/3746] lr: 9.656e-03, eta: 1 day, 1:48:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4453, top5_acc: 0.6941, loss_cls: 3.1444, loss: 3.1444 +2024-07-26 14:02:09,740 - pyskl - INFO - Epoch [120][3200/3746] lr: 9.639e-03, eta: 1 day, 1:47:17, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.6994, loss_cls: 3.1157, loss: 3.1157 +2024-07-26 14:03:30,925 - pyskl - INFO - Epoch [120][3300/3746] lr: 9.623e-03, eta: 1 day, 1:45:55, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.6891, loss_cls: 3.1680, loss: 3.1680 +2024-07-26 14:04:52,284 - pyskl - INFO - Epoch [120][3400/3746] lr: 9.606e-03, eta: 1 day, 1:44:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4338, top5_acc: 0.6909, loss_cls: 3.1630, loss: 3.1630 +2024-07-26 14:06:13,629 - pyskl - INFO - Epoch [120][3500/3746] lr: 9.590e-03, eta: 1 day, 1:43:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4458, top5_acc: 0.6950, loss_cls: 3.1225, loss: 3.1225 +2024-07-26 14:07:35,286 - pyskl - INFO - Epoch [120][3600/3746] lr: 9.573e-03, eta: 1 day, 1:41:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4442, top5_acc: 0.7009, loss_cls: 3.1231, loss: 3.1231 +2024-07-26 14:08:57,066 - pyskl - INFO - Epoch [120][3700/3746] lr: 9.557e-03, eta: 1 day, 1:40:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6803, loss_cls: 3.2152, loss: 3.2152 +2024-07-26 14:09:36,390 - pyskl - INFO - Saving checkpoint at 120 epochs +2024-07-26 14:11:26,759 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 14:11:27,442 - pyskl - INFO - +top1_acc 0.3885 +top5_acc 0.6409 +2024-07-26 14:11:27,442 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 14:11:27,481 - pyskl - INFO - +mean_acc 0.3883 +2024-07-26 14:11:27,485 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_119.pth was removed +2024-07-26 14:11:27,751 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_120.pth. +2024-07-26 14:11:27,751 - pyskl - INFO - Best top1_acc is 0.3885 at 120 epoch. +2024-07-26 14:11:27,762 - pyskl - INFO - Epoch(val) [120][309] top1_acc: 0.3885, top5_acc: 0.6409, mean_class_accuracy: 0.3883 +2024-07-26 14:15:15,221 - pyskl - INFO - Epoch [121][100/3746] lr: 9.533e-03, eta: 1 day, 1:38:52, time: 2.274, data_time: 1.296, memory: 15990, top1_acc: 0.4719, top5_acc: 0.7222, loss_cls: 2.9636, loss: 2.9636 +2024-07-26 14:16:36,870 - pyskl - INFO - Epoch [121][200/3746] lr: 9.516e-03, eta: 1 day, 1:37:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4697, top5_acc: 0.7142, loss_cls: 3.0033, loss: 3.0033 +2024-07-26 14:17:58,604 - pyskl - INFO - Epoch [121][300/3746] lr: 9.500e-03, eta: 1 day, 1:36:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4614, top5_acc: 0.7130, loss_cls: 3.0209, loss: 3.0209 +2024-07-26 14:19:20,854 - pyskl - INFO - Epoch [121][400/3746] lr: 9.484e-03, eta: 1 day, 1:34:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4542, top5_acc: 0.7172, loss_cls: 3.0326, loss: 3.0326 +2024-07-26 14:20:42,232 - pyskl - INFO - Epoch [121][500/3746] lr: 9.467e-03, eta: 1 day, 1:33:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4567, top5_acc: 0.7097, loss_cls: 3.0551, loss: 3.0551 +2024-07-26 14:22:03,434 - pyskl - INFO - Epoch [121][600/3746] lr: 9.451e-03, eta: 1 day, 1:32:00, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4542, top5_acc: 0.7073, loss_cls: 3.0600, loss: 3.0600 +2024-07-26 14:23:24,894 - pyskl - INFO - Epoch [121][700/3746] lr: 9.435e-03, eta: 1 day, 1:30:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4539, top5_acc: 0.7097, loss_cls: 3.0615, loss: 3.0615 +2024-07-26 14:24:46,772 - pyskl - INFO - Epoch [121][800/3746] lr: 9.418e-03, eta: 1 day, 1:29:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4500, top5_acc: 0.7141, loss_cls: 3.0475, loss: 3.0475 +2024-07-26 14:26:08,933 - pyskl - INFO - Epoch [121][900/3746] lr: 9.402e-03, eta: 1 day, 1:27:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4578, top5_acc: 0.7030, loss_cls: 3.0617, loss: 3.0617 +2024-07-26 14:27:30,834 - pyskl - INFO - Epoch [121][1000/3746] lr: 9.386e-03, eta: 1 day, 1:26:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4459, top5_acc: 0.7022, loss_cls: 3.1152, loss: 3.1152 +2024-07-26 14:28:52,038 - pyskl - INFO - Epoch [121][1100/3746] lr: 9.369e-03, eta: 1 day, 1:25:09, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4573, top5_acc: 0.7120, loss_cls: 3.0537, loss: 3.0537 +2024-07-26 14:30:13,687 - pyskl - INFO - Epoch [121][1200/3746] lr: 9.353e-03, eta: 1 day, 1:23:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.7058, loss_cls: 3.1009, loss: 3.1009 +2024-07-26 14:31:35,395 - pyskl - INFO - Epoch [121][1300/3746] lr: 9.337e-03, eta: 1 day, 1:22:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4452, top5_acc: 0.7000, loss_cls: 3.1115, loss: 3.1115 +2024-07-26 14:32:57,317 - pyskl - INFO - Epoch [121][1400/3746] lr: 9.321e-03, eta: 1 day, 1:21:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.6983, loss_cls: 3.1000, loss: 3.1000 +2024-07-26 14:34:19,073 - pyskl - INFO - Epoch [121][1500/3746] lr: 9.304e-03, eta: 1 day, 1:19:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4547, top5_acc: 0.7100, loss_cls: 3.0779, loss: 3.0779 +2024-07-26 14:35:41,097 - pyskl - INFO - Epoch [121][1600/3746] lr: 9.288e-03, eta: 1 day, 1:18:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4536, top5_acc: 0.7086, loss_cls: 3.0721, loss: 3.0721 +2024-07-26 14:37:02,679 - pyskl - INFO - Epoch [121][1700/3746] lr: 9.272e-03, eta: 1 day, 1:16:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4452, top5_acc: 0.7063, loss_cls: 3.0806, loss: 3.0806 +2024-07-26 14:38:24,523 - pyskl - INFO - Epoch [121][1800/3746] lr: 9.256e-03, eta: 1 day, 1:15:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4459, top5_acc: 0.6959, loss_cls: 3.1460, loss: 3.1460 +2024-07-26 14:39:46,810 - pyskl - INFO - Epoch [121][1900/3746] lr: 9.239e-03, eta: 1 day, 1:14:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4458, top5_acc: 0.7014, loss_cls: 3.1134, loss: 3.1134 +2024-07-26 14:41:08,269 - pyskl - INFO - Epoch [121][2000/3746] lr: 9.223e-03, eta: 1 day, 1:12:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4523, top5_acc: 0.7078, loss_cls: 3.0500, loss: 3.0500 +2024-07-26 14:42:30,445 - pyskl - INFO - Epoch [121][2100/3746] lr: 9.207e-03, eta: 1 day, 1:11:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4475, top5_acc: 0.7058, loss_cls: 3.0845, loss: 3.0845 +2024-07-26 14:43:52,596 - pyskl - INFO - Epoch [121][2200/3746] lr: 9.191e-03, eta: 1 day, 1:10:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4503, top5_acc: 0.7023, loss_cls: 3.0768, loss: 3.0768 +2024-07-26 14:45:14,131 - pyskl - INFO - Epoch [121][2300/3746] lr: 9.175e-03, eta: 1 day, 1:08:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4584, top5_acc: 0.7153, loss_cls: 3.0177, loss: 3.0177 +2024-07-26 14:46:35,924 - pyskl - INFO - Epoch [121][2400/3746] lr: 9.159e-03, eta: 1 day, 1:07:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4558, top5_acc: 0.7059, loss_cls: 3.0670, loss: 3.0670 +2024-07-26 14:47:57,886 - pyskl - INFO - Epoch [121][2500/3746] lr: 9.142e-03, eta: 1 day, 1:05:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4455, top5_acc: 0.7070, loss_cls: 3.0781, loss: 3.0781 +2024-07-26 14:49:19,475 - pyskl - INFO - Epoch [121][2600/3746] lr: 9.126e-03, eta: 1 day, 1:04:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4417, top5_acc: 0.6945, loss_cls: 3.1323, loss: 3.1323 +2024-07-26 14:50:41,329 - pyskl - INFO - Epoch [121][2700/3746] lr: 9.110e-03, eta: 1 day, 1:03:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4473, top5_acc: 0.7059, loss_cls: 3.1076, loss: 3.1076 +2024-07-26 14:52:02,525 - pyskl - INFO - Epoch [121][2800/3746] lr: 9.094e-03, eta: 1 day, 1:01:49, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.7011, loss_cls: 3.1224, loss: 3.1224 +2024-07-26 14:53:23,799 - pyskl - INFO - Epoch [121][2900/3746] lr: 9.078e-03, eta: 1 day, 1:00:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4412, top5_acc: 0.6959, loss_cls: 3.1477, loss: 3.1477 +2024-07-26 14:54:45,050 - pyskl - INFO - Epoch [121][3000/3746] lr: 9.062e-03, eta: 1 day, 0:59:04, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4387, top5_acc: 0.7028, loss_cls: 3.0999, loss: 3.0999 +2024-07-26 14:56:06,276 - pyskl - INFO - Epoch [121][3100/3746] lr: 9.046e-03, eta: 1 day, 0:57:41, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.6992, loss_cls: 3.1033, loss: 3.1033 +2024-07-26 14:57:27,464 - pyskl - INFO - Epoch [121][3200/3746] lr: 9.030e-03, eta: 1 day, 0:56:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4506, top5_acc: 0.7044, loss_cls: 3.0911, loss: 3.0911 +2024-07-26 14:58:48,705 - pyskl - INFO - Epoch [121][3300/3746] lr: 9.014e-03, eta: 1 day, 0:54:56, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4444, top5_acc: 0.6987, loss_cls: 3.1215, loss: 3.1215 +2024-07-26 15:00:09,995 - pyskl - INFO - Epoch [121][3400/3746] lr: 8.998e-03, eta: 1 day, 0:53:34, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4475, top5_acc: 0.7080, loss_cls: 3.0665, loss: 3.0665 +2024-07-26 15:01:31,723 - pyskl - INFO - Epoch [121][3500/3746] lr: 8.982e-03, eta: 1 day, 0:52:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4534, top5_acc: 0.7053, loss_cls: 3.0911, loss: 3.0911 +2024-07-26 15:02:53,286 - pyskl - INFO - Epoch [121][3600/3746] lr: 8.966e-03, eta: 1 day, 0:50:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4372, top5_acc: 0.6953, loss_cls: 3.1353, loss: 3.1353 +2024-07-26 15:04:14,719 - pyskl - INFO - Epoch [121][3700/3746] lr: 8.950e-03, eta: 1 day, 0:49:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4514, top5_acc: 0.7072, loss_cls: 3.0895, loss: 3.0895 +2024-07-26 15:04:54,485 - pyskl - INFO - Saving checkpoint at 121 epochs +2024-07-26 15:06:45,895 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 15:06:46,565 - pyskl - INFO - +top1_acc 0.3865 +top5_acc 0.6403 +2024-07-26 15:06:46,565 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 15:06:46,606 - pyskl - INFO - +mean_acc 0.3862 +2024-07-26 15:06:46,618 - pyskl - INFO - Epoch(val) [121][309] top1_acc: 0.3865, top5_acc: 0.6403, mean_class_accuracy: 0.3862 +2024-07-26 15:10:37,065 - pyskl - INFO - Epoch [122][100/3746] lr: 8.927e-03, eta: 1 day, 0:47:53, time: 2.304, data_time: 1.332, memory: 15990, top1_acc: 0.4653, top5_acc: 0.7216, loss_cls: 3.0031, loss: 3.0031 +2024-07-26 15:11:59,013 - pyskl - INFO - Epoch [122][200/3746] lr: 8.911e-03, eta: 1 day, 0:46:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4587, top5_acc: 0.7128, loss_cls: 3.0093, loss: 3.0093 +2024-07-26 15:13:20,591 - pyskl - INFO - Epoch [122][300/3746] lr: 8.895e-03, eta: 1 day, 0:45:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4672, top5_acc: 0.7264, loss_cls: 2.9690, loss: 2.9690 +2024-07-26 15:14:42,422 - pyskl - INFO - Epoch [122][400/3746] lr: 8.879e-03, eta: 1 day, 0:43:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4584, top5_acc: 0.7072, loss_cls: 3.0509, loss: 3.0509 +2024-07-26 15:16:04,324 - pyskl - INFO - Epoch [122][500/3746] lr: 8.863e-03, eta: 1 day, 0:42:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4558, top5_acc: 0.7081, loss_cls: 3.0599, loss: 3.0599 +2024-07-26 15:17:26,127 - pyskl - INFO - Epoch [122][600/3746] lr: 8.847e-03, eta: 1 day, 0:41:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4637, top5_acc: 0.7123, loss_cls: 3.0351, loss: 3.0351 +2024-07-26 15:18:48,165 - pyskl - INFO - Epoch [122][700/3746] lr: 8.831e-03, eta: 1 day, 0:39:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4620, top5_acc: 0.7136, loss_cls: 3.0181, loss: 3.0181 +2024-07-26 15:20:09,662 - pyskl - INFO - Epoch [122][800/3746] lr: 8.815e-03, eta: 1 day, 0:38:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7127, loss_cls: 3.0164, loss: 3.0164 +2024-07-26 15:21:31,744 - pyskl - INFO - Epoch [122][900/3746] lr: 8.800e-03, eta: 1 day, 0:36:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4553, top5_acc: 0.7148, loss_cls: 3.0128, loss: 3.0128 +2024-07-26 15:22:53,432 - pyskl - INFO - Epoch [122][1000/3746] lr: 8.784e-03, eta: 1 day, 0:35:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4473, top5_acc: 0.7041, loss_cls: 3.0985, loss: 3.0985 +2024-07-26 15:24:14,775 - pyskl - INFO - Epoch [122][1100/3746] lr: 8.768e-03, eta: 1 day, 0:34:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4544, top5_acc: 0.7072, loss_cls: 3.0462, loss: 3.0462 +2024-07-26 15:25:36,565 - pyskl - INFO - Epoch [122][1200/3746] lr: 8.752e-03, eta: 1 day, 0:32:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4517, top5_acc: 0.7147, loss_cls: 3.0312, loss: 3.0312 +2024-07-26 15:26:58,037 - pyskl - INFO - Epoch [122][1300/3746] lr: 8.736e-03, eta: 1 day, 0:31:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4545, top5_acc: 0.7100, loss_cls: 3.0596, loss: 3.0596 +2024-07-26 15:28:19,693 - pyskl - INFO - Epoch [122][1400/3746] lr: 8.721e-03, eta: 1 day, 0:30:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4520, top5_acc: 0.7098, loss_cls: 3.0682, loss: 3.0682 +2024-07-26 15:29:41,373 - pyskl - INFO - Epoch [122][1500/3746] lr: 8.705e-03, eta: 1 day, 0:28:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4644, top5_acc: 0.7123, loss_cls: 3.0201, loss: 3.0201 +2024-07-26 15:31:03,400 - pyskl - INFO - Epoch [122][1600/3746] lr: 8.689e-03, eta: 1 day, 0:27:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4477, top5_acc: 0.7098, loss_cls: 3.0652, loss: 3.0652 +2024-07-26 15:32:25,525 - pyskl - INFO - Epoch [122][1700/3746] lr: 8.673e-03, eta: 1 day, 0:25:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4625, top5_acc: 0.7114, loss_cls: 3.0339, loss: 3.0339 +2024-07-26 15:33:48,158 - pyskl - INFO - Epoch [122][1800/3746] lr: 8.658e-03, eta: 1 day, 0:24:33, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4561, top5_acc: 0.7058, loss_cls: 3.0619, loss: 3.0619 +2024-07-26 15:35:10,251 - pyskl - INFO - Epoch [122][1900/3746] lr: 8.642e-03, eta: 1 day, 0:23:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4559, top5_acc: 0.7083, loss_cls: 3.0351, loss: 3.0351 +2024-07-26 15:36:32,453 - pyskl - INFO - Epoch [122][2000/3746] lr: 8.626e-03, eta: 1 day, 0:21:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4505, top5_acc: 0.7047, loss_cls: 3.0579, loss: 3.0579 +2024-07-26 15:37:53,947 - pyskl - INFO - Epoch [122][2100/3746] lr: 8.610e-03, eta: 1 day, 0:20:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7080, loss_cls: 3.0935, loss: 3.0935 +2024-07-26 15:39:15,876 - pyskl - INFO - Epoch [122][2200/3746] lr: 8.595e-03, eta: 1 day, 0:19:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4505, top5_acc: 0.7067, loss_cls: 3.0792, loss: 3.0792 +2024-07-26 15:40:37,740 - pyskl - INFO - Epoch [122][2300/3746] lr: 8.579e-03, eta: 1 day, 0:17:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4484, top5_acc: 0.7000, loss_cls: 3.0854, loss: 3.0854 +2024-07-26 15:41:59,507 - pyskl - INFO - Epoch [122][2400/3746] lr: 8.563e-03, eta: 1 day, 0:16:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4392, top5_acc: 0.6981, loss_cls: 3.1228, loss: 3.1228 +2024-07-26 15:43:21,734 - pyskl - INFO - Epoch [122][2500/3746] lr: 8.548e-03, eta: 1 day, 0:14:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4489, top5_acc: 0.7081, loss_cls: 3.0727, loss: 3.0727 +2024-07-26 15:44:43,578 - pyskl - INFO - Epoch [122][2600/3746] lr: 8.532e-03, eta: 1 day, 0:13:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7166, loss_cls: 3.0586, loss: 3.0586 +2024-07-26 15:46:05,567 - pyskl - INFO - Epoch [122][2700/3746] lr: 8.517e-03, eta: 1 day, 0:12:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4505, top5_acc: 0.7106, loss_cls: 3.0376, loss: 3.0376 +2024-07-26 15:47:27,254 - pyskl - INFO - Epoch [122][2800/3746] lr: 8.501e-03, eta: 1 day, 0:10:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4567, top5_acc: 0.7131, loss_cls: 3.0313, loss: 3.0313 +2024-07-26 15:48:48,329 - pyskl - INFO - Epoch [122][2900/3746] lr: 8.485e-03, eta: 1 day, 0:09:27, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4461, top5_acc: 0.7089, loss_cls: 3.0944, loss: 3.0944 +2024-07-26 15:50:09,895 - pyskl - INFO - Epoch [122][3000/3746] lr: 8.470e-03, eta: 1 day, 0:08:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4502, top5_acc: 0.7059, loss_cls: 3.0855, loss: 3.0855 +2024-07-26 15:51:31,465 - pyskl - INFO - Epoch [122][3100/3746] lr: 8.454e-03, eta: 1 day, 0:06:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4503, top5_acc: 0.7073, loss_cls: 3.0997, loss: 3.0997 +2024-07-26 15:52:52,781 - pyskl - INFO - Epoch [122][3200/3746] lr: 8.439e-03, eta: 1 day, 0:05:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4558, top5_acc: 0.7141, loss_cls: 3.0272, loss: 3.0272 +2024-07-26 15:54:14,212 - pyskl - INFO - Epoch [122][3300/3746] lr: 8.423e-03, eta: 1 day, 0:03:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4547, top5_acc: 0.7098, loss_cls: 3.0576, loss: 3.0576 +2024-07-26 15:55:35,601 - pyskl - INFO - Epoch [122][3400/3746] lr: 8.408e-03, eta: 1 day, 0:02:35, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4484, top5_acc: 0.7095, loss_cls: 3.0645, loss: 3.0645 +2024-07-26 15:56:57,127 - pyskl - INFO - Epoch [122][3500/3746] lr: 8.392e-03, eta: 1 day, 0:01:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4494, top5_acc: 0.7027, loss_cls: 3.1146, loss: 3.1146 +2024-07-26 15:58:18,703 - pyskl - INFO - Epoch [122][3600/3746] lr: 8.377e-03, eta: 23:59:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7178, loss_cls: 3.0063, loss: 3.0063 +2024-07-26 15:59:39,821 - pyskl - INFO - Epoch [122][3700/3746] lr: 8.361e-03, eta: 23:58:28, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4561, top5_acc: 0.7033, loss_cls: 3.0548, loss: 3.0548 +2024-07-26 16:00:19,357 - pyskl - INFO - Saving checkpoint at 122 epochs +2024-07-26 16:02:10,863 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 16:02:11,603 - pyskl - INFO - +top1_acc 0.3835 +top5_acc 0.6395 +2024-07-26 16:02:11,604 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 16:02:11,656 - pyskl - INFO - +mean_acc 0.3833 +2024-07-26 16:02:11,670 - pyskl - INFO - Epoch(val) [122][309] top1_acc: 0.3835, top5_acc: 0.6395, mean_class_accuracy: 0.3833 +2024-07-26 16:06:07,692 - pyskl - INFO - Epoch [123][100/3746] lr: 8.339e-03, eta: 23:56:54, time: 2.360, data_time: 1.381, memory: 15990, top1_acc: 0.4745, top5_acc: 0.7277, loss_cls: 2.9248, loss: 2.9248 +2024-07-26 16:07:29,785 - pyskl - INFO - Epoch [123][200/3746] lr: 8.323e-03, eta: 23:55:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4653, top5_acc: 0.7217, loss_cls: 2.9828, loss: 2.9828 +2024-07-26 16:08:51,273 - pyskl - INFO - Epoch [123][300/3746] lr: 8.308e-03, eta: 23:54:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4728, top5_acc: 0.7194, loss_cls: 2.9591, loss: 2.9591 +2024-07-26 16:10:12,493 - pyskl - INFO - Epoch [123][400/3746] lr: 8.292e-03, eta: 23:52:47, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4691, top5_acc: 0.7164, loss_cls: 2.9882, loss: 2.9882 +2024-07-26 16:11:34,392 - pyskl - INFO - Epoch [123][500/3746] lr: 8.277e-03, eta: 23:51:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4598, top5_acc: 0.7222, loss_cls: 2.9961, loss: 2.9961 +2024-07-26 16:12:56,259 - pyskl - INFO - Epoch [123][600/3746] lr: 8.262e-03, eta: 23:50:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4673, top5_acc: 0.7152, loss_cls: 2.9792, loss: 2.9792 +2024-07-26 16:14:18,263 - pyskl - INFO - Epoch [123][700/3746] lr: 8.246e-03, eta: 23:48:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7067, loss_cls: 3.0520, loss: 3.0520 +2024-07-26 16:15:40,084 - pyskl - INFO - Epoch [123][800/3746] lr: 8.231e-03, eta: 23:47:18, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4677, top5_acc: 0.7241, loss_cls: 3.0059, loss: 3.0059 +2024-07-26 16:17:01,768 - pyskl - INFO - Epoch [123][900/3746] lr: 8.215e-03, eta: 23:45:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4633, top5_acc: 0.7244, loss_cls: 2.9760, loss: 2.9760 +2024-07-26 16:18:23,258 - pyskl - INFO - Epoch [123][1000/3746] lr: 8.200e-03, eta: 23:44:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4553, top5_acc: 0.7033, loss_cls: 3.0400, loss: 3.0400 +2024-07-26 16:19:44,730 - pyskl - INFO - Epoch [123][1100/3746] lr: 8.185e-03, eta: 23:43:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4736, top5_acc: 0.7216, loss_cls: 2.9858, loss: 2.9858 +2024-07-26 16:21:06,653 - pyskl - INFO - Epoch [123][1200/3746] lr: 8.169e-03, eta: 23:41:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4458, top5_acc: 0.7083, loss_cls: 3.0578, loss: 3.0578 +2024-07-26 16:22:28,741 - pyskl - INFO - Epoch [123][1300/3746] lr: 8.154e-03, eta: 23:40:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7130, loss_cls: 3.0222, loss: 3.0222 +2024-07-26 16:23:51,479 - pyskl - INFO - Epoch [123][1400/3746] lr: 8.139e-03, eta: 23:39:03, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4534, top5_acc: 0.7066, loss_cls: 3.0447, loss: 3.0447 +2024-07-26 16:25:13,335 - pyskl - INFO - Epoch [123][1500/3746] lr: 8.124e-03, eta: 23:37:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4686, top5_acc: 0.7214, loss_cls: 2.9928, loss: 2.9928 +2024-07-26 16:26:35,574 - pyskl - INFO - Epoch [123][1600/3746] lr: 8.108e-03, eta: 23:36:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7122, loss_cls: 3.0531, loss: 3.0531 +2024-07-26 16:27:57,927 - pyskl - INFO - Epoch [123][1700/3746] lr: 8.093e-03, eta: 23:34:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4511, top5_acc: 0.7064, loss_cls: 3.0427, loss: 3.0427 +2024-07-26 16:29:19,759 - pyskl - INFO - Epoch [123][1800/3746] lr: 8.078e-03, eta: 23:33:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4595, top5_acc: 0.7159, loss_cls: 3.0274, loss: 3.0274 +2024-07-26 16:30:41,420 - pyskl - INFO - Epoch [123][1900/3746] lr: 8.063e-03, eta: 23:32:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4541, top5_acc: 0.7078, loss_cls: 3.0638, loss: 3.0638 +2024-07-26 16:32:02,873 - pyskl - INFO - Epoch [123][2000/3746] lr: 8.047e-03, eta: 23:30:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4575, top5_acc: 0.7136, loss_cls: 3.0480, loss: 3.0480 +2024-07-26 16:33:25,051 - pyskl - INFO - Epoch [123][2100/3746] lr: 8.032e-03, eta: 23:29:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4481, top5_acc: 0.7053, loss_cls: 3.0630, loss: 3.0630 +2024-07-26 16:34:47,137 - pyskl - INFO - Epoch [123][2200/3746] lr: 8.017e-03, eta: 23:28:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.7066, loss_cls: 3.0623, loss: 3.0623 +2024-07-26 16:36:08,740 - pyskl - INFO - Epoch [123][2300/3746] lr: 8.002e-03, eta: 23:26:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4655, top5_acc: 0.7234, loss_cls: 3.0058, loss: 3.0058 +2024-07-26 16:37:30,416 - pyskl - INFO - Epoch [123][2400/3746] lr: 7.987e-03, eta: 23:25:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4614, top5_acc: 0.7036, loss_cls: 3.0625, loss: 3.0625 +2024-07-26 16:38:52,362 - pyskl - INFO - Epoch [123][2500/3746] lr: 7.971e-03, eta: 23:23:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4539, top5_acc: 0.7109, loss_cls: 3.0635, loss: 3.0635 +2024-07-26 16:40:14,330 - pyskl - INFO - Epoch [123][2600/3746] lr: 7.956e-03, eta: 23:22:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7087, loss_cls: 2.9920, loss: 2.9920 +2024-07-26 16:41:35,648 - pyskl - INFO - Epoch [123][2700/3746] lr: 7.941e-03, eta: 23:21:13, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4494, top5_acc: 0.7042, loss_cls: 3.0573, loss: 3.0573 +2024-07-26 16:42:56,991 - pyskl - INFO - Epoch [123][2800/3746] lr: 7.926e-03, eta: 23:19:50, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4587, top5_acc: 0.7170, loss_cls: 3.0258, loss: 3.0258 +2024-07-26 16:44:18,269 - pyskl - INFO - Epoch [123][2900/3746] lr: 7.911e-03, eta: 23:18:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4608, top5_acc: 0.7219, loss_cls: 2.9983, loss: 2.9983 +2024-07-26 16:45:39,813 - pyskl - INFO - Epoch [123][3000/3746] lr: 7.896e-03, eta: 23:17:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4633, top5_acc: 0.7186, loss_cls: 3.0349, loss: 3.0349 +2024-07-26 16:47:01,440 - pyskl - INFO - Epoch [123][3100/3746] lr: 7.881e-03, eta: 23:15:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4619, top5_acc: 0.7106, loss_cls: 3.0354, loss: 3.0354 +2024-07-26 16:48:23,035 - pyskl - INFO - Epoch [123][3200/3746] lr: 7.866e-03, eta: 23:14:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4672, top5_acc: 0.7256, loss_cls: 2.9780, loss: 2.9780 +2024-07-26 16:49:44,862 - pyskl - INFO - Epoch [123][3300/3746] lr: 7.851e-03, eta: 23:12:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4473, top5_acc: 0.7045, loss_cls: 3.1081, loss: 3.1081 +2024-07-26 16:51:05,954 - pyskl - INFO - Epoch [123][3400/3746] lr: 7.836e-03, eta: 23:11:36, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4489, top5_acc: 0.7056, loss_cls: 3.0670, loss: 3.0670 +2024-07-26 16:52:27,418 - pyskl - INFO - Epoch [123][3500/3746] lr: 7.821e-03, eta: 23:10:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7116, loss_cls: 3.0277, loss: 3.0277 +2024-07-26 16:53:48,862 - pyskl - INFO - Epoch [123][3600/3746] lr: 7.806e-03, eta: 23:08:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4619, top5_acc: 0.7030, loss_cls: 3.0422, loss: 3.0422 +2024-07-26 16:55:10,399 - pyskl - INFO - Epoch [123][3700/3746] lr: 7.791e-03, eta: 23:07:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4716, top5_acc: 0.7192, loss_cls: 2.9766, loss: 2.9766 +2024-07-26 16:55:49,927 - pyskl - INFO - Saving checkpoint at 123 epochs +2024-07-26 16:57:42,339 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 16:57:43,154 - pyskl - INFO - +top1_acc 0.3961 +top5_acc 0.6500 +2024-07-26 16:57:43,155 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 16:57:43,205 - pyskl - INFO - +mean_acc 0.3959 +2024-07-26 16:57:43,210 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_120.pth was removed +2024-07-26 16:57:43,521 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_123.pth. +2024-07-26 16:57:43,521 - pyskl - INFO - Best top1_acc is 0.3961 at 123 epoch. +2024-07-26 16:57:43,534 - pyskl - INFO - Epoch(val) [123][309] top1_acc: 0.3961, top5_acc: 0.6500, mean_class_accuracy: 0.3959 +2024-07-26 17:01:37,374 - pyskl - INFO - Epoch [124][100/3746] lr: 7.769e-03, eta: 23:05:53, time: 2.338, data_time: 1.356, memory: 15990, top1_acc: 0.4677, top5_acc: 0.7320, loss_cls: 2.9353, loss: 2.9353 +2024-07-26 17:02:59,202 - pyskl - INFO - Epoch [124][200/3746] lr: 7.754e-03, eta: 23:04:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4731, top5_acc: 0.7227, loss_cls: 2.9418, loss: 2.9418 +2024-07-26 17:04:21,205 - pyskl - INFO - Epoch [124][300/3746] lr: 7.739e-03, eta: 23:03:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4592, top5_acc: 0.7188, loss_cls: 2.9640, loss: 2.9640 +2024-07-26 17:05:42,930 - pyskl - INFO - Epoch [124][400/3746] lr: 7.724e-03, eta: 23:01:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4830, top5_acc: 0.7295, loss_cls: 2.8916, loss: 2.8916 +2024-07-26 17:07:04,435 - pyskl - INFO - Epoch [124][500/3746] lr: 7.709e-03, eta: 23:00:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4702, top5_acc: 0.7231, loss_cls: 2.9914, loss: 2.9914 +2024-07-26 17:08:26,327 - pyskl - INFO - Epoch [124][600/3746] lr: 7.694e-03, eta: 22:59:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4572, top5_acc: 0.7205, loss_cls: 3.0146, loss: 3.0146 +2024-07-26 17:09:48,120 - pyskl - INFO - Epoch [124][700/3746] lr: 7.679e-03, eta: 22:57:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7173, loss_cls: 3.0022, loss: 3.0022 +2024-07-26 17:11:09,652 - pyskl - INFO - Epoch [124][800/3746] lr: 7.664e-03, eta: 22:56:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4795, top5_acc: 0.7259, loss_cls: 2.9319, loss: 2.9319 +2024-07-26 17:12:31,003 - pyskl - INFO - Epoch [124][900/3746] lr: 7.649e-03, eta: 22:54:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7258, loss_cls: 2.9662, loss: 2.9662 +2024-07-26 17:13:52,326 - pyskl - INFO - Epoch [124][1000/3746] lr: 7.635e-03, eta: 22:53:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4713, top5_acc: 0.7342, loss_cls: 2.9164, loss: 2.9164 +2024-07-26 17:15:14,225 - pyskl - INFO - Epoch [124][1100/3746] lr: 7.620e-03, eta: 22:52:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4778, top5_acc: 0.7214, loss_cls: 2.9481, loss: 2.9481 +2024-07-26 17:16:36,372 - pyskl - INFO - Epoch [124][1200/3746] lr: 7.605e-03, eta: 22:50:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4666, top5_acc: 0.7152, loss_cls: 2.9999, loss: 2.9999 +2024-07-26 17:17:58,041 - pyskl - INFO - Epoch [124][1300/3746] lr: 7.590e-03, eta: 22:49:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4748, top5_acc: 0.7233, loss_cls: 2.9669, loss: 2.9669 +2024-07-26 17:19:20,520 - pyskl - INFO - Epoch [124][1400/3746] lr: 7.575e-03, eta: 22:48:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4602, top5_acc: 0.7169, loss_cls: 3.0072, loss: 3.0072 +2024-07-26 17:20:41,942 - pyskl - INFO - Epoch [124][1500/3746] lr: 7.561e-03, eta: 22:46:39, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7172, loss_cls: 3.0212, loss: 3.0212 +2024-07-26 17:22:03,846 - pyskl - INFO - Epoch [124][1600/3746] lr: 7.546e-03, eta: 22:45:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4598, top5_acc: 0.7206, loss_cls: 3.0042, loss: 3.0042 +2024-07-26 17:23:25,642 - pyskl - INFO - Epoch [124][1700/3746] lr: 7.531e-03, eta: 22:43:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4659, top5_acc: 0.7205, loss_cls: 3.0136, loss: 3.0136 +2024-07-26 17:24:47,669 - pyskl - INFO - Epoch [124][1800/3746] lr: 7.516e-03, eta: 22:42:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4670, top5_acc: 0.7175, loss_cls: 2.9951, loss: 2.9951 +2024-07-26 17:26:09,579 - pyskl - INFO - Epoch [124][1900/3746] lr: 7.502e-03, eta: 22:41:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4664, top5_acc: 0.7141, loss_cls: 3.0185, loss: 3.0185 +2024-07-26 17:27:31,527 - pyskl - INFO - Epoch [124][2000/3746] lr: 7.487e-03, eta: 22:39:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4536, top5_acc: 0.7173, loss_cls: 3.0253, loss: 3.0253 +2024-07-26 17:28:53,298 - pyskl - INFO - Epoch [124][2100/3746] lr: 7.472e-03, eta: 22:38:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4612, top5_acc: 0.7200, loss_cls: 3.0027, loss: 3.0027 +2024-07-26 17:30:15,145 - pyskl - INFO - Epoch [124][2200/3746] lr: 7.457e-03, eta: 22:37:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4533, top5_acc: 0.7144, loss_cls: 3.0439, loss: 3.0439 +2024-07-26 17:31:36,727 - pyskl - INFO - Epoch [124][2300/3746] lr: 7.443e-03, eta: 22:35:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4650, top5_acc: 0.7225, loss_cls: 2.9975, loss: 2.9975 +2024-07-26 17:32:58,936 - pyskl - INFO - Epoch [124][2400/3746] lr: 7.428e-03, eta: 22:34:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4656, top5_acc: 0.7177, loss_cls: 2.9945, loss: 2.9945 +2024-07-26 17:34:20,525 - pyskl - INFO - Epoch [124][2500/3746] lr: 7.413e-03, eta: 22:32:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4645, top5_acc: 0.7264, loss_cls: 2.9760, loss: 2.9760 +2024-07-26 17:35:41,686 - pyskl - INFO - Epoch [124][2600/3746] lr: 7.399e-03, eta: 22:31:33, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4622, top5_acc: 0.7137, loss_cls: 3.0372, loss: 3.0372 +2024-07-26 17:37:02,962 - pyskl - INFO - Epoch [124][2700/3746] lr: 7.384e-03, eta: 22:30:10, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4747, top5_acc: 0.7238, loss_cls: 2.9380, loss: 2.9380 +2024-07-26 17:38:24,122 - pyskl - INFO - Epoch [124][2800/3746] lr: 7.370e-03, eta: 22:28:48, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4608, top5_acc: 0.7216, loss_cls: 2.9838, loss: 2.9838 +2024-07-26 17:39:45,576 - pyskl - INFO - Epoch [124][2900/3746] lr: 7.355e-03, eta: 22:27:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4583, top5_acc: 0.7091, loss_cls: 3.0629, loss: 3.0629 +2024-07-26 17:41:07,555 - pyskl - INFO - Epoch [124][3000/3746] lr: 7.340e-03, eta: 22:26:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4580, top5_acc: 0.7153, loss_cls: 3.0271, loss: 3.0271 +2024-07-26 17:42:29,323 - pyskl - INFO - Epoch [124][3100/3746] lr: 7.326e-03, eta: 22:24:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4677, top5_acc: 0.7116, loss_cls: 2.9712, loss: 2.9712 +2024-07-26 17:43:50,637 - pyskl - INFO - Epoch [124][3200/3746] lr: 7.311e-03, eta: 22:23:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4634, top5_acc: 0.7109, loss_cls: 3.0253, loss: 3.0253 +2024-07-26 17:45:12,210 - pyskl - INFO - Epoch [124][3300/3746] lr: 7.297e-03, eta: 22:21:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4586, top5_acc: 0.7130, loss_cls: 3.0238, loss: 3.0238 +2024-07-26 17:46:33,886 - pyskl - INFO - Epoch [124][3400/3746] lr: 7.282e-03, eta: 22:20:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7084, loss_cls: 3.0348, loss: 3.0348 +2024-07-26 17:47:55,810 - pyskl - INFO - Epoch [124][3500/3746] lr: 7.268e-03, eta: 22:19:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4552, top5_acc: 0.7098, loss_cls: 3.0225, loss: 3.0225 +2024-07-26 17:49:17,018 - pyskl - INFO - Epoch [124][3600/3746] lr: 7.253e-03, eta: 22:17:48, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4605, top5_acc: 0.7148, loss_cls: 3.0179, loss: 3.0179 +2024-07-26 17:50:38,465 - pyskl - INFO - Epoch [124][3700/3746] lr: 7.239e-03, eta: 22:16:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4586, top5_acc: 0.7169, loss_cls: 3.0232, loss: 3.0232 +2024-07-26 17:51:18,106 - pyskl - INFO - Saving checkpoint at 124 epochs +2024-07-26 17:53:09,730 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 17:53:10,406 - pyskl - INFO - +top1_acc 0.3995 +top5_acc 0.6492 +2024-07-26 17:53:10,406 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 17:53:10,447 - pyskl - INFO - +mean_acc 0.3992 +2024-07-26 17:53:10,453 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_123.pth was removed +2024-07-26 17:53:10,727 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_124.pth. +2024-07-26 17:53:10,728 - pyskl - INFO - Best top1_acc is 0.3995 at 124 epoch. +2024-07-26 17:53:10,741 - pyskl - INFO - Epoch(val) [124][309] top1_acc: 0.3995, top5_acc: 0.6492, mean_class_accuracy: 0.3992 +2024-07-26 17:57:09,856 - pyskl - INFO - Epoch [125][100/3746] lr: 7.217e-03, eta: 22:14:51, time: 2.391, data_time: 1.376, memory: 15990, top1_acc: 0.4880, top5_acc: 0.7402, loss_cls: 2.8725, loss: 2.8725 +2024-07-26 17:58:33,965 - pyskl - INFO - Epoch [125][200/3746] lr: 7.203e-03, eta: 22:13:29, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4756, top5_acc: 0.7305, loss_cls: 2.8923, loss: 2.8923 +2024-07-26 17:59:58,422 - pyskl - INFO - Epoch [125][300/3746] lr: 7.189e-03, eta: 22:12:07, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4752, top5_acc: 0.7306, loss_cls: 2.9395, loss: 2.9395 +2024-07-26 18:01:22,173 - pyskl - INFO - Epoch [125][400/3746] lr: 7.174e-03, eta: 22:10:45, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4806, top5_acc: 0.7242, loss_cls: 2.9357, loss: 2.9357 +2024-07-26 18:02:45,872 - pyskl - INFO - Epoch [125][500/3746] lr: 7.160e-03, eta: 22:09:23, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4725, top5_acc: 0.7344, loss_cls: 2.9033, loss: 2.9033 +2024-07-26 18:04:08,116 - pyskl - INFO - Epoch [125][600/3746] lr: 7.145e-03, eta: 22:08:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4636, top5_acc: 0.7205, loss_cls: 2.9766, loss: 2.9766 +2024-07-26 18:05:30,858 - pyskl - INFO - Epoch [125][700/3746] lr: 7.131e-03, eta: 22:06:38, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4673, top5_acc: 0.7247, loss_cls: 2.9693, loss: 2.9693 +2024-07-26 18:06:54,079 - pyskl - INFO - Epoch [125][800/3746] lr: 7.117e-03, eta: 22:05:16, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4689, top5_acc: 0.7175, loss_cls: 2.9887, loss: 2.9887 +2024-07-26 18:08:17,627 - pyskl - INFO - Epoch [125][900/3746] lr: 7.102e-03, eta: 22:03:54, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4695, top5_acc: 0.7191, loss_cls: 2.9728, loss: 2.9728 +2024-07-26 18:09:40,118 - pyskl - INFO - Epoch [125][1000/3746] lr: 7.088e-03, eta: 22:02:32, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7206, loss_cls: 2.9529, loss: 2.9529 +2024-07-26 18:11:02,642 - pyskl - INFO - Epoch [125][1100/3746] lr: 7.073e-03, eta: 22:01:09, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4758, top5_acc: 0.7325, loss_cls: 2.9236, loss: 2.9236 +2024-07-26 18:12:25,842 - pyskl - INFO - Epoch [125][1200/3746] lr: 7.059e-03, eta: 21:59:47, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4675, top5_acc: 0.7259, loss_cls: 2.9460, loss: 2.9460 +2024-07-26 18:13:48,466 - pyskl - INFO - Epoch [125][1300/3746] lr: 7.045e-03, eta: 21:58:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4684, top5_acc: 0.7270, loss_cls: 2.9730, loss: 2.9730 +2024-07-26 18:15:13,017 - pyskl - INFO - Epoch [125][1400/3746] lr: 7.031e-03, eta: 21:57:03, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4769, top5_acc: 0.7236, loss_cls: 2.9597, loss: 2.9597 +2024-07-26 18:16:35,735 - pyskl - INFO - Epoch [125][1500/3746] lr: 7.016e-03, eta: 21:55:41, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4720, top5_acc: 0.7253, loss_cls: 2.9401, loss: 2.9401 +2024-07-26 18:17:59,308 - pyskl - INFO - Epoch [125][1600/3746] lr: 7.002e-03, eta: 21:54:19, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4528, top5_acc: 0.7147, loss_cls: 3.0214, loss: 3.0214 +2024-07-26 18:19:23,069 - pyskl - INFO - Epoch [125][1700/3746] lr: 6.988e-03, eta: 21:52:57, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4698, top5_acc: 0.7280, loss_cls: 2.9540, loss: 2.9540 +2024-07-26 18:20:46,474 - pyskl - INFO - Epoch [125][1800/3746] lr: 6.973e-03, eta: 21:51:35, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7180, loss_cls: 3.0042, loss: 3.0042 +2024-07-26 18:22:10,235 - pyskl - INFO - Epoch [125][1900/3746] lr: 6.959e-03, eta: 21:50:13, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4605, top5_acc: 0.7180, loss_cls: 3.0013, loss: 3.0013 +2024-07-26 18:23:33,334 - pyskl - INFO - Epoch [125][2000/3746] lr: 6.945e-03, eta: 21:48:51, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4769, top5_acc: 0.7194, loss_cls: 2.9487, loss: 2.9487 +2024-07-26 18:24:56,178 - pyskl - INFO - Epoch [125][2100/3746] lr: 6.931e-03, eta: 21:47:28, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4789, top5_acc: 0.7306, loss_cls: 2.9176, loss: 2.9176 +2024-07-26 18:26:19,284 - pyskl - INFO - Epoch [125][2200/3746] lr: 6.917e-03, eta: 21:46:06, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4633, top5_acc: 0.7184, loss_cls: 3.0022, loss: 3.0022 +2024-07-26 18:27:42,684 - pyskl - INFO - Epoch [125][2300/3746] lr: 6.902e-03, eta: 21:44:44, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4713, top5_acc: 0.7214, loss_cls: 2.9927, loss: 2.9927 +2024-07-26 18:29:05,215 - pyskl - INFO - Epoch [125][2400/3746] lr: 6.888e-03, eta: 21:43:22, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4592, top5_acc: 0.7158, loss_cls: 2.9944, loss: 2.9944 +2024-07-26 18:30:29,253 - pyskl - INFO - Epoch [125][2500/3746] lr: 6.874e-03, eta: 21:42:00, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.4612, top5_acc: 0.7083, loss_cls: 3.0248, loss: 3.0248 +2024-07-26 18:31:53,318 - pyskl - INFO - Epoch [125][2600/3746] lr: 6.860e-03, eta: 21:40:38, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4694, top5_acc: 0.7278, loss_cls: 2.9552, loss: 2.9552 +2024-07-26 18:33:16,278 - pyskl - INFO - Epoch [125][2700/3746] lr: 6.846e-03, eta: 21:39:16, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4594, top5_acc: 0.7208, loss_cls: 3.0174, loss: 3.0174 +2024-07-26 18:34:38,783 - pyskl - INFO - Epoch [125][2800/3746] lr: 6.832e-03, eta: 21:37:53, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4656, top5_acc: 0.7266, loss_cls: 2.9690, loss: 2.9690 +2024-07-26 18:36:01,399 - pyskl - INFO - Epoch [125][2900/3746] lr: 6.818e-03, eta: 21:36:31, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4720, top5_acc: 0.7200, loss_cls: 2.9563, loss: 2.9563 +2024-07-26 18:37:23,823 - pyskl - INFO - Epoch [125][3000/3746] lr: 6.804e-03, eta: 21:35:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7217, loss_cls: 2.9479, loss: 2.9479 +2024-07-26 18:38:47,197 - pyskl - INFO - Epoch [125][3100/3746] lr: 6.789e-03, eta: 21:33:47, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4766, top5_acc: 0.7214, loss_cls: 2.9748, loss: 2.9748 +2024-07-26 18:40:09,763 - pyskl - INFO - Epoch [125][3200/3746] lr: 6.775e-03, eta: 21:32:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4648, top5_acc: 0.7206, loss_cls: 2.9854, loss: 2.9854 +2024-07-26 18:41:32,491 - pyskl - INFO - Epoch [125][3300/3746] lr: 6.761e-03, eta: 21:31:02, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4758, top5_acc: 0.7200, loss_cls: 3.0117, loss: 3.0117 +2024-07-26 18:42:55,542 - pyskl - INFO - Epoch [125][3400/3746] lr: 6.747e-03, eta: 21:29:40, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4717, top5_acc: 0.7322, loss_cls: 2.9301, loss: 2.9301 +2024-07-26 18:44:18,067 - pyskl - INFO - Epoch [125][3500/3746] lr: 6.733e-03, eta: 21:28:18, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4564, top5_acc: 0.7238, loss_cls: 3.0007, loss: 3.0007 +2024-07-26 18:45:40,757 - pyskl - INFO - Epoch [125][3600/3746] lr: 6.719e-03, eta: 21:26:55, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7195, loss_cls: 2.9736, loss: 2.9736 +2024-07-26 18:47:03,675 - pyskl - INFO - Epoch [125][3700/3746] lr: 6.705e-03, eta: 21:25:33, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4623, top5_acc: 0.7175, loss_cls: 3.0208, loss: 3.0208 +2024-07-26 18:47:43,418 - pyskl - INFO - Saving checkpoint at 125 epochs +2024-07-26 18:49:35,113 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 18:49:35,787 - pyskl - INFO - +top1_acc 0.3849 +top5_acc 0.6467 +2024-07-26 18:49:35,787 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 18:49:35,828 - pyskl - INFO - +mean_acc 0.3846 +2024-07-26 18:49:35,840 - pyskl - INFO - Epoch(val) [125][309] top1_acc: 0.3849, top5_acc: 0.6467, mean_class_accuracy: 0.3846 +2024-07-26 18:53:26,045 - pyskl - INFO - Epoch [126][100/3746] lr: 6.685e-03, eta: 21:23:55, time: 2.302, data_time: 1.288, memory: 15990, top1_acc: 0.4891, top5_acc: 0.7473, loss_cls: 2.8262, loss: 2.8262 +2024-07-26 18:54:49,566 - pyskl - INFO - Epoch [126][200/3746] lr: 6.671e-03, eta: 21:22:33, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4792, top5_acc: 0.7331, loss_cls: 2.8783, loss: 2.8783 +2024-07-26 18:56:13,889 - pyskl - INFO - Epoch [126][300/3746] lr: 6.657e-03, eta: 21:21:11, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4836, top5_acc: 0.7419, loss_cls: 2.8875, loss: 2.8875 +2024-07-26 18:57:38,196 - pyskl - INFO - Epoch [126][400/3746] lr: 6.643e-03, eta: 21:19:49, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4745, top5_acc: 0.7386, loss_cls: 2.8870, loss: 2.8870 +2024-07-26 18:59:01,832 - pyskl - INFO - Epoch [126][500/3746] lr: 6.629e-03, eta: 21:18:27, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7391, loss_cls: 2.9006, loss: 2.9006 +2024-07-26 19:00:24,822 - pyskl - INFO - Epoch [126][600/3746] lr: 6.615e-03, eta: 21:17:05, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4858, top5_acc: 0.7317, loss_cls: 2.9165, loss: 2.9165 +2024-07-26 19:01:47,464 - pyskl - INFO - Epoch [126][700/3746] lr: 6.601e-03, eta: 21:15:42, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4705, top5_acc: 0.7231, loss_cls: 2.9544, loss: 2.9544 +2024-07-26 19:03:09,971 - pyskl - INFO - Epoch [126][800/3746] lr: 6.587e-03, eta: 21:14:20, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4758, top5_acc: 0.7294, loss_cls: 2.9102, loss: 2.9102 +2024-07-26 19:04:33,681 - pyskl - INFO - Epoch [126][900/3746] lr: 6.574e-03, eta: 21:12:58, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4744, top5_acc: 0.7259, loss_cls: 2.9272, loss: 2.9272 +2024-07-26 19:05:56,683 - pyskl - INFO - Epoch [126][1000/3746] lr: 6.560e-03, eta: 21:11:36, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4641, top5_acc: 0.7219, loss_cls: 2.9504, loss: 2.9504 +2024-07-26 19:07:18,996 - pyskl - INFO - Epoch [126][1100/3746] lr: 6.546e-03, eta: 21:10:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4758, top5_acc: 0.7220, loss_cls: 2.9425, loss: 2.9425 +2024-07-26 19:08:41,990 - pyskl - INFO - Epoch [126][1200/3746] lr: 6.532e-03, eta: 21:08:51, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4866, top5_acc: 0.7364, loss_cls: 2.8722, loss: 2.8722 +2024-07-26 19:10:04,768 - pyskl - INFO - Epoch [126][1300/3746] lr: 6.518e-03, eta: 21:07:29, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4805, top5_acc: 0.7409, loss_cls: 2.8619, loss: 2.8619 +2024-07-26 19:11:28,195 - pyskl - INFO - Epoch [126][1400/3746] lr: 6.505e-03, eta: 21:06:07, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4867, top5_acc: 0.7245, loss_cls: 2.9128, loss: 2.9128 +2024-07-26 19:12:51,003 - pyskl - INFO - Epoch [126][1500/3746] lr: 6.491e-03, eta: 21:04:45, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7252, loss_cls: 2.9465, loss: 2.9465 +2024-07-26 19:14:14,594 - pyskl - INFO - Epoch [126][1600/3746] lr: 6.477e-03, eta: 21:03:22, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4819, top5_acc: 0.7323, loss_cls: 2.8913, loss: 2.8913 +2024-07-26 19:15:38,532 - pyskl - INFO - Epoch [126][1700/3746] lr: 6.463e-03, eta: 21:02:00, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4631, top5_acc: 0.7156, loss_cls: 2.9929, loss: 2.9929 +2024-07-26 19:17:01,930 - pyskl - INFO - Epoch [126][1800/3746] lr: 6.449e-03, eta: 21:00:38, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4711, top5_acc: 0.7227, loss_cls: 2.9479, loss: 2.9479 +2024-07-26 19:18:25,207 - pyskl - INFO - Epoch [126][1900/3746] lr: 6.436e-03, eta: 20:59:16, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4802, top5_acc: 0.7341, loss_cls: 2.8850, loss: 2.8850 +2024-07-26 19:19:48,071 - pyskl - INFO - Epoch [126][2000/3746] lr: 6.422e-03, eta: 20:57:54, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4692, top5_acc: 0.7277, loss_cls: 2.9526, loss: 2.9526 +2024-07-26 19:21:11,528 - pyskl - INFO - Epoch [126][2100/3746] lr: 6.408e-03, eta: 20:56:32, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4698, top5_acc: 0.7291, loss_cls: 2.9326, loss: 2.9326 +2024-07-26 19:22:35,887 - pyskl - INFO - Epoch [126][2200/3746] lr: 6.395e-03, eta: 20:55:10, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4750, top5_acc: 0.7261, loss_cls: 2.9373, loss: 2.9373 +2024-07-26 19:23:59,297 - pyskl - INFO - Epoch [126][2300/3746] lr: 6.381e-03, eta: 20:53:48, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4644, top5_acc: 0.7247, loss_cls: 2.9930, loss: 2.9930 +2024-07-26 19:25:23,141 - pyskl - INFO - Epoch [126][2400/3746] lr: 6.367e-03, eta: 20:52:26, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7189, loss_cls: 2.9430, loss: 2.9430 +2024-07-26 19:26:47,245 - pyskl - INFO - Epoch [126][2500/3746] lr: 6.354e-03, eta: 20:51:04, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7203, loss_cls: 2.9570, loss: 2.9570 +2024-07-26 19:28:11,620 - pyskl - INFO - Epoch [126][2600/3746] lr: 6.340e-03, eta: 20:49:42, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4788, top5_acc: 0.7286, loss_cls: 2.9186, loss: 2.9186 +2024-07-26 19:29:35,417 - pyskl - INFO - Epoch [126][2700/3746] lr: 6.326e-03, eta: 20:48:19, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4748, top5_acc: 0.7234, loss_cls: 2.9677, loss: 2.9677 +2024-07-26 19:30:58,161 - pyskl - INFO - Epoch [126][2800/3746] lr: 6.313e-03, eta: 20:46:57, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4655, top5_acc: 0.7186, loss_cls: 3.0127, loss: 3.0127 +2024-07-26 19:32:21,373 - pyskl - INFO - Epoch [126][2900/3746] lr: 6.299e-03, eta: 20:45:35, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4719, top5_acc: 0.7189, loss_cls: 2.9645, loss: 2.9645 +2024-07-26 19:33:44,210 - pyskl - INFO - Epoch [126][3000/3746] lr: 6.286e-03, eta: 20:44:13, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4714, top5_acc: 0.7258, loss_cls: 2.9265, loss: 2.9265 +2024-07-26 19:35:07,867 - pyskl - INFO - Epoch [126][3100/3746] lr: 6.272e-03, eta: 20:42:51, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4845, top5_acc: 0.7244, loss_cls: 2.9144, loss: 2.9144 +2024-07-26 19:36:31,134 - pyskl - INFO - Epoch [126][3200/3746] lr: 6.259e-03, eta: 20:41:28, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7195, loss_cls: 2.9889, loss: 2.9889 +2024-07-26 19:37:53,302 - pyskl - INFO - Epoch [126][3300/3746] lr: 6.245e-03, eta: 20:40:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4716, top5_acc: 0.7255, loss_cls: 2.9422, loss: 2.9422 +2024-07-26 19:39:16,452 - pyskl - INFO - Epoch [126][3400/3746] lr: 6.231e-03, eta: 20:38:44, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4720, top5_acc: 0.7231, loss_cls: 2.9600, loss: 2.9600 +2024-07-26 19:40:39,001 - pyskl - INFO - Epoch [126][3500/3746] lr: 6.218e-03, eta: 20:37:22, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4720, top5_acc: 0.7247, loss_cls: 2.9291, loss: 2.9291 +2024-07-26 19:42:01,551 - pyskl - INFO - Epoch [126][3600/3746] lr: 6.204e-03, eta: 20:35:59, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4761, top5_acc: 0.7281, loss_cls: 2.9130, loss: 2.9130 +2024-07-26 19:43:24,045 - pyskl - INFO - Epoch [126][3700/3746] lr: 6.191e-03, eta: 20:34:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7181, loss_cls: 2.9934, loss: 2.9934 +2024-07-26 19:44:03,447 - pyskl - INFO - Saving checkpoint at 126 epochs +2024-07-26 19:45:55,256 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 19:45:55,931 - pyskl - INFO - +top1_acc 0.3932 +top5_acc 0.6510 +2024-07-26 19:45:55,931 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 19:45:55,971 - pyskl - INFO - +mean_acc 0.3931 +2024-07-26 19:45:55,983 - pyskl - INFO - Epoch(val) [126][309] top1_acc: 0.3932, top5_acc: 0.6510, mean_class_accuracy: 0.3931 +2024-07-26 19:49:44,696 - pyskl - INFO - Epoch [127][100/3746] lr: 6.171e-03, eta: 20:32:57, time: 2.287, data_time: 1.298, memory: 15990, top1_acc: 0.4952, top5_acc: 0.7427, loss_cls: 2.8437, loss: 2.8437 +2024-07-26 19:51:08,986 - pyskl - INFO - Epoch [127][200/3746] lr: 6.158e-03, eta: 20:31:35, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4931, top5_acc: 0.7441, loss_cls: 2.8583, loss: 2.8583 +2024-07-26 19:52:33,257 - pyskl - INFO - Epoch [127][300/3746] lr: 6.144e-03, eta: 20:30:13, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4838, top5_acc: 0.7289, loss_cls: 2.9122, loss: 2.9122 +2024-07-26 19:53:57,648 - pyskl - INFO - Epoch [127][400/3746] lr: 6.131e-03, eta: 20:28:51, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4783, top5_acc: 0.7362, loss_cls: 2.8793, loss: 2.8793 +2024-07-26 19:55:21,494 - pyskl - INFO - Epoch [127][500/3746] lr: 6.118e-03, eta: 20:27:29, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4925, top5_acc: 0.7402, loss_cls: 2.8345, loss: 2.8345 +2024-07-26 19:56:45,175 - pyskl - INFO - Epoch [127][600/3746] lr: 6.104e-03, eta: 20:26:07, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4944, top5_acc: 0.7480, loss_cls: 2.8257, loss: 2.8257 +2024-07-26 19:58:08,267 - pyskl - INFO - Epoch [127][700/3746] lr: 6.091e-03, eta: 20:24:45, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4975, top5_acc: 0.7417, loss_cls: 2.8383, loss: 2.8383 +2024-07-26 19:59:31,322 - pyskl - INFO - Epoch [127][800/3746] lr: 6.077e-03, eta: 20:23:23, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4880, top5_acc: 0.7477, loss_cls: 2.8393, loss: 2.8393 +2024-07-26 20:00:54,756 - pyskl - INFO - Epoch [127][900/3746] lr: 6.064e-03, eta: 20:22:00, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4852, top5_acc: 0.7300, loss_cls: 2.8988, loss: 2.8988 +2024-07-26 20:02:17,619 - pyskl - INFO - Epoch [127][1000/3746] lr: 6.051e-03, eta: 20:20:38, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4834, top5_acc: 0.7334, loss_cls: 2.8897, loss: 2.8897 +2024-07-26 20:03:40,674 - pyskl - INFO - Epoch [127][1100/3746] lr: 6.037e-03, eta: 20:19:16, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4825, top5_acc: 0.7362, loss_cls: 2.8795, loss: 2.8795 +2024-07-26 20:05:04,004 - pyskl - INFO - Epoch [127][1200/3746] lr: 6.024e-03, eta: 20:17:54, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4723, top5_acc: 0.7297, loss_cls: 2.9331, loss: 2.9331 +2024-07-26 20:06:28,220 - pyskl - INFO - Epoch [127][1300/3746] lr: 6.011e-03, eta: 20:16:32, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4816, top5_acc: 0.7288, loss_cls: 2.9116, loss: 2.9116 +2024-07-26 20:07:51,365 - pyskl - INFO - Epoch [127][1400/3746] lr: 5.998e-03, eta: 20:15:09, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4881, top5_acc: 0.7423, loss_cls: 2.8843, loss: 2.8843 +2024-07-26 20:09:15,741 - pyskl - INFO - Epoch [127][1500/3746] lr: 5.984e-03, eta: 20:13:47, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4822, top5_acc: 0.7417, loss_cls: 2.8622, loss: 2.8622 +2024-07-26 20:10:39,632 - pyskl - INFO - Epoch [127][1600/3746] lr: 5.971e-03, eta: 20:12:25, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.4800, top5_acc: 0.7302, loss_cls: 2.9106, loss: 2.9106 +2024-07-26 20:12:03,889 - pyskl - INFO - Epoch [127][1700/3746] lr: 5.958e-03, eta: 20:11:03, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4902, top5_acc: 0.7422, loss_cls: 2.8308, loss: 2.8308 +2024-07-26 20:13:28,051 - pyskl - INFO - Epoch [127][1800/3746] lr: 5.945e-03, eta: 20:09:41, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4870, top5_acc: 0.7302, loss_cls: 2.9088, loss: 2.9088 +2024-07-26 20:14:51,753 - pyskl - INFO - Epoch [127][1900/3746] lr: 5.931e-03, eta: 20:08:19, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.4791, top5_acc: 0.7327, loss_cls: 2.9187, loss: 2.9187 +2024-07-26 20:16:15,205 - pyskl - INFO - Epoch [127][2000/3746] lr: 5.918e-03, eta: 20:06:57, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.4842, top5_acc: 0.7361, loss_cls: 2.8743, loss: 2.8743 +2024-07-26 20:17:39,191 - pyskl - INFO - Epoch [127][2100/3746] lr: 5.905e-03, eta: 20:05:35, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.4775, top5_acc: 0.7323, loss_cls: 2.9086, loss: 2.9086 +2024-07-26 20:19:02,253 - pyskl - INFO - Epoch [127][2200/3746] lr: 5.892e-03, eta: 20:04:12, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4841, top5_acc: 0.7381, loss_cls: 2.8670, loss: 2.8670 +2024-07-26 20:20:25,251 - pyskl - INFO - Epoch [127][2300/3746] lr: 5.879e-03, eta: 20:02:50, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4695, top5_acc: 0.7206, loss_cls: 2.9435, loss: 2.9435 +2024-07-26 20:21:49,629 - pyskl - INFO - Epoch [127][2400/3746] lr: 5.866e-03, eta: 20:01:28, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4767, top5_acc: 0.7319, loss_cls: 2.9521, loss: 2.9521 +2024-07-26 20:23:13,803 - pyskl - INFO - Epoch [127][2500/3746] lr: 5.852e-03, eta: 20:00:06, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4827, top5_acc: 0.7247, loss_cls: 2.9046, loss: 2.9046 +2024-07-26 20:24:37,164 - pyskl - INFO - Epoch [127][2600/3746] lr: 5.839e-03, eta: 19:58:44, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4711, top5_acc: 0.7225, loss_cls: 2.9452, loss: 2.9452 +2024-07-26 20:26:00,051 - pyskl - INFO - Epoch [127][2700/3746] lr: 5.826e-03, eta: 19:57:22, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4786, top5_acc: 0.7283, loss_cls: 2.9137, loss: 2.9137 +2024-07-26 20:27:23,161 - pyskl - INFO - Epoch [127][2800/3746] lr: 5.813e-03, eta: 19:55:59, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4727, top5_acc: 0.7216, loss_cls: 2.9683, loss: 2.9683 +2024-07-26 20:28:46,481 - pyskl - INFO - Epoch [127][2900/3746] lr: 5.800e-03, eta: 19:54:37, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4809, top5_acc: 0.7308, loss_cls: 2.8998, loss: 2.8998 +2024-07-26 20:30:09,226 - pyskl - INFO - Epoch [127][3000/3746] lr: 5.787e-03, eta: 19:53:15, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4770, top5_acc: 0.7272, loss_cls: 2.9399, loss: 2.9399 +2024-07-26 20:31:31,446 - pyskl - INFO - Epoch [127][3100/3746] lr: 5.774e-03, eta: 19:51:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4677, top5_acc: 0.7175, loss_cls: 2.9779, loss: 2.9779 +2024-07-26 20:32:54,386 - pyskl - INFO - Epoch [127][3200/3746] lr: 5.761e-03, eta: 19:50:30, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4794, top5_acc: 0.7362, loss_cls: 2.8715, loss: 2.8715 +2024-07-26 20:34:17,704 - pyskl - INFO - Epoch [127][3300/3746] lr: 5.748e-03, eta: 19:49:08, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.4800, top5_acc: 0.7267, loss_cls: 2.9420, loss: 2.9420 +2024-07-26 20:35:40,813 - pyskl - INFO - Epoch [127][3400/3746] lr: 5.735e-03, eta: 19:47:46, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4741, top5_acc: 0.7256, loss_cls: 2.9238, loss: 2.9238 +2024-07-26 20:37:04,039 - pyskl - INFO - Epoch [127][3500/3746] lr: 5.722e-03, eta: 19:46:23, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4775, top5_acc: 0.7267, loss_cls: 2.9469, loss: 2.9469 +2024-07-26 20:38:27,257 - pyskl - INFO - Epoch [127][3600/3746] lr: 5.709e-03, eta: 19:45:01, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4845, top5_acc: 0.7323, loss_cls: 2.8955, loss: 2.8955 +2024-07-26 20:39:50,259 - pyskl - INFO - Epoch [127][3700/3746] lr: 5.696e-03, eta: 19:43:39, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4694, top5_acc: 0.7270, loss_cls: 2.9246, loss: 2.9246 +2024-07-26 20:40:30,131 - pyskl - INFO - Saving checkpoint at 127 epochs +2024-07-26 20:42:23,269 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 20:42:23,939 - pyskl - INFO - +top1_acc 0.3985 +top5_acc 0.6547 +2024-07-26 20:42:23,939 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 20:42:23,981 - pyskl - INFO - +mean_acc 0.3982 +2024-07-26 20:42:23,993 - pyskl - INFO - Epoch(val) [127][309] top1_acc: 0.3985, top5_acc: 0.6547, mean_class_accuracy: 0.3982 +2024-07-26 20:46:16,858 - pyskl - INFO - Epoch [128][100/3746] lr: 5.677e-03, eta: 19:41:59, time: 2.329, data_time: 1.349, memory: 15990, top1_acc: 0.5027, top5_acc: 0.7542, loss_cls: 2.7829, loss: 2.7829 +2024-07-26 20:47:39,153 - pyskl - INFO - Epoch [128][200/3746] lr: 5.664e-03, eta: 19:40:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4947, top5_acc: 0.7445, loss_cls: 2.8124, loss: 2.8124 +2024-07-26 20:49:01,632 - pyskl - INFO - Epoch [128][300/3746] lr: 5.651e-03, eta: 19:39:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5003, top5_acc: 0.7461, loss_cls: 2.7788, loss: 2.7788 +2024-07-26 20:50:23,331 - pyskl - INFO - Epoch [128][400/3746] lr: 5.638e-03, eta: 19:37:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4883, top5_acc: 0.7411, loss_cls: 2.8294, loss: 2.8294 +2024-07-26 20:51:45,293 - pyskl - INFO - Epoch [128][500/3746] lr: 5.625e-03, eta: 19:36:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4898, top5_acc: 0.7444, loss_cls: 2.8482, loss: 2.8482 +2024-07-26 20:53:06,718 - pyskl - INFO - Epoch [128][600/3746] lr: 5.612e-03, eta: 19:35:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4811, top5_acc: 0.7323, loss_cls: 2.8875, loss: 2.8875 +2024-07-26 20:54:28,225 - pyskl - INFO - Epoch [128][700/3746] lr: 5.600e-03, eta: 19:33:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4838, top5_acc: 0.7388, loss_cls: 2.8848, loss: 2.8848 +2024-07-26 20:55:49,736 - pyskl - INFO - Epoch [128][800/3746] lr: 5.587e-03, eta: 19:32:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4847, top5_acc: 0.7388, loss_cls: 2.8588, loss: 2.8588 +2024-07-26 20:57:11,879 - pyskl - INFO - Epoch [128][900/3746] lr: 5.574e-03, eta: 19:30:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4914, top5_acc: 0.7459, loss_cls: 2.8201, loss: 2.8201 +2024-07-26 20:58:33,687 - pyskl - INFO - Epoch [128][1000/3746] lr: 5.561e-03, eta: 19:29:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4859, top5_acc: 0.7381, loss_cls: 2.8956, loss: 2.8956 +2024-07-26 20:59:55,679 - pyskl - INFO - Epoch [128][1100/3746] lr: 5.548e-03, eta: 19:28:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4809, top5_acc: 0.7319, loss_cls: 2.8880, loss: 2.8880 +2024-07-26 21:01:17,871 - pyskl - INFO - Epoch [128][1200/3746] lr: 5.536e-03, eta: 19:26:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4795, top5_acc: 0.7355, loss_cls: 2.8782, loss: 2.8782 +2024-07-26 21:02:39,280 - pyskl - INFO - Epoch [128][1300/3746] lr: 5.523e-03, eta: 19:25:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4864, top5_acc: 0.7339, loss_cls: 2.8727, loss: 2.8727 +2024-07-26 21:04:01,958 - pyskl - INFO - Epoch [128][1400/3746] lr: 5.510e-03, eta: 19:24:07, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4919, top5_acc: 0.7417, loss_cls: 2.8253, loss: 2.8253 +2024-07-26 21:05:24,049 - pyskl - INFO - Epoch [128][1500/3746] lr: 5.497e-03, eta: 19:22:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4872, top5_acc: 0.7414, loss_cls: 2.8595, loss: 2.8595 +2024-07-26 21:06:45,681 - pyskl - INFO - Epoch [128][1600/3746] lr: 5.485e-03, eta: 19:21:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4938, top5_acc: 0.7442, loss_cls: 2.8492, loss: 2.8492 +2024-07-26 21:08:07,690 - pyskl - INFO - Epoch [128][1700/3746] lr: 5.472e-03, eta: 19:19:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4888, top5_acc: 0.7364, loss_cls: 2.8573, loss: 2.8573 +2024-07-26 21:09:28,976 - pyskl - INFO - Epoch [128][1800/3746] lr: 5.459e-03, eta: 19:18:37, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4788, top5_acc: 0.7383, loss_cls: 2.8981, loss: 2.8981 +2024-07-26 21:10:51,049 - pyskl - INFO - Epoch [128][1900/3746] lr: 5.446e-03, eta: 19:17:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4783, top5_acc: 0.7405, loss_cls: 2.8864, loss: 2.8864 +2024-07-26 21:12:13,222 - pyskl - INFO - Epoch [128][2000/3746] lr: 5.434e-03, eta: 19:15:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4806, top5_acc: 0.7380, loss_cls: 2.8964, loss: 2.8964 +2024-07-26 21:13:34,921 - pyskl - INFO - Epoch [128][2100/3746] lr: 5.421e-03, eta: 19:14:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4930, top5_acc: 0.7383, loss_cls: 2.8502, loss: 2.8502 +2024-07-26 21:14:57,044 - pyskl - INFO - Epoch [128][2200/3746] lr: 5.408e-03, eta: 19:13:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4933, top5_acc: 0.7347, loss_cls: 2.8654, loss: 2.8654 +2024-07-26 21:16:19,049 - pyskl - INFO - Epoch [128][2300/3746] lr: 5.396e-03, eta: 19:11:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4711, top5_acc: 0.7186, loss_cls: 2.9621, loss: 2.9621 +2024-07-26 21:17:40,681 - pyskl - INFO - Epoch [128][2400/3746] lr: 5.383e-03, eta: 19:10:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4863, top5_acc: 0.7350, loss_cls: 2.8835, loss: 2.8835 +2024-07-26 21:19:02,683 - pyskl - INFO - Epoch [128][2500/3746] lr: 5.370e-03, eta: 19:08:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4891, top5_acc: 0.7358, loss_cls: 2.8664, loss: 2.8664 +2024-07-26 21:20:24,093 - pyskl - INFO - Epoch [128][2600/3746] lr: 5.358e-03, eta: 19:07:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4941, top5_acc: 0.7361, loss_cls: 2.8693, loss: 2.8693 +2024-07-26 21:21:45,916 - pyskl - INFO - Epoch [128][2700/3746] lr: 5.345e-03, eta: 19:06:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7352, loss_cls: 2.8427, loss: 2.8427 +2024-07-26 21:23:07,326 - pyskl - INFO - Epoch [128][2800/3746] lr: 5.333e-03, eta: 19:04:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7344, loss_cls: 2.8955, loss: 2.8955 +2024-07-26 21:24:28,801 - pyskl - INFO - Epoch [128][2900/3746] lr: 5.320e-03, eta: 19:03:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4831, top5_acc: 0.7319, loss_cls: 2.8996, loss: 2.8996 +2024-07-26 21:25:50,130 - pyskl - INFO - Epoch [128][3000/3746] lr: 5.308e-03, eta: 19:02:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7383, loss_cls: 2.8927, loss: 2.8927 +2024-07-26 21:27:11,699 - pyskl - INFO - Epoch [128][3100/3746] lr: 5.295e-03, eta: 19:00:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4908, top5_acc: 0.7367, loss_cls: 2.8610, loss: 2.8610 +2024-07-26 21:28:33,249 - pyskl - INFO - Epoch [128][3200/3746] lr: 5.283e-03, eta: 18:59:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4806, top5_acc: 0.7316, loss_cls: 2.8989, loss: 2.8989 +2024-07-26 21:29:54,665 - pyskl - INFO - Epoch [128][3300/3746] lr: 5.270e-03, eta: 18:57:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4844, top5_acc: 0.7294, loss_cls: 2.8956, loss: 2.8956 +2024-07-26 21:31:16,450 - pyskl - INFO - Epoch [128][3400/3746] lr: 5.258e-03, eta: 18:56:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4781, top5_acc: 0.7355, loss_cls: 2.9040, loss: 2.9040 +2024-07-26 21:32:38,151 - pyskl - INFO - Epoch [128][3500/3746] lr: 5.245e-03, eta: 18:55:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4883, top5_acc: 0.7316, loss_cls: 2.8642, loss: 2.8642 +2024-07-26 21:34:00,473 - pyskl - INFO - Epoch [128][3600/3746] lr: 5.233e-03, eta: 18:53:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4858, top5_acc: 0.7377, loss_cls: 2.8901, loss: 2.8901 +2024-07-26 21:35:22,006 - pyskl - INFO - Epoch [128][3700/3746] lr: 5.220e-03, eta: 18:52:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4869, top5_acc: 0.7367, loss_cls: 2.8673, loss: 2.8673 +2024-07-26 21:36:01,370 - pyskl - INFO - Saving checkpoint at 128 epochs +2024-07-26 21:37:53,750 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 21:37:54,526 - pyskl - INFO - +top1_acc 0.4083 +top5_acc 0.6606 +2024-07-26 21:37:54,526 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 21:37:54,577 - pyskl - INFO - +mean_acc 0.4082 +2024-07-26 21:37:54,582 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_124.pth was removed +2024-07-26 21:37:54,896 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_128.pth. +2024-07-26 21:37:54,897 - pyskl - INFO - Best top1_acc is 0.4083 at 128 epoch. +2024-07-26 21:37:54,911 - pyskl - INFO - Epoch(val) [128][309] top1_acc: 0.4083, top5_acc: 0.6606, mean_class_accuracy: 0.4082 +2024-07-26 21:41:49,362 - pyskl - INFO - Epoch [129][100/3746] lr: 5.202e-03, eta: 18:50:48, time: 2.344, data_time: 1.365, memory: 15990, top1_acc: 0.5012, top5_acc: 0.7561, loss_cls: 2.7751, loss: 2.7751 +2024-07-26 21:43:10,906 - pyskl - INFO - Epoch [129][200/3746] lr: 5.190e-03, eta: 18:49:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5016, top5_acc: 0.7509, loss_cls: 2.7670, loss: 2.7670 +2024-07-26 21:44:32,354 - pyskl - INFO - Epoch [129][300/3746] lr: 5.177e-03, eta: 18:48:03, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5083, top5_acc: 0.7505, loss_cls: 2.7577, loss: 2.7577 +2024-07-26 21:45:54,253 - pyskl - INFO - Epoch [129][400/3746] lr: 5.165e-03, eta: 18:46:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5136, top5_acc: 0.7547, loss_cls: 2.7353, loss: 2.7353 +2024-07-26 21:47:15,634 - pyskl - INFO - Epoch [129][500/3746] lr: 5.153e-03, eta: 18:45:18, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4977, top5_acc: 0.7502, loss_cls: 2.8121, loss: 2.8121 +2024-07-26 21:48:37,247 - pyskl - INFO - Epoch [129][600/3746] lr: 5.140e-03, eta: 18:43:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5042, top5_acc: 0.7486, loss_cls: 2.7902, loss: 2.7902 +2024-07-26 21:49:58,852 - pyskl - INFO - Epoch [129][700/3746] lr: 5.128e-03, eta: 18:42:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4897, top5_acc: 0.7409, loss_cls: 2.8306, loss: 2.8306 +2024-07-26 21:51:20,248 - pyskl - INFO - Epoch [129][800/3746] lr: 5.116e-03, eta: 18:41:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4928, top5_acc: 0.7494, loss_cls: 2.8032, loss: 2.8032 +2024-07-26 21:52:41,910 - pyskl - INFO - Epoch [129][900/3746] lr: 5.103e-03, eta: 18:39:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5030, top5_acc: 0.7477, loss_cls: 2.7825, loss: 2.7825 +2024-07-26 21:54:03,448 - pyskl - INFO - Epoch [129][1000/3746] lr: 5.091e-03, eta: 18:38:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4809, top5_acc: 0.7412, loss_cls: 2.8596, loss: 2.8596 +2024-07-26 21:55:25,246 - pyskl - INFO - Epoch [129][1100/3746] lr: 5.079e-03, eta: 18:37:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4995, top5_acc: 0.7461, loss_cls: 2.7938, loss: 2.7938 +2024-07-26 21:56:47,358 - pyskl - INFO - Epoch [129][1200/3746] lr: 5.066e-03, eta: 18:35:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5033, top5_acc: 0.7480, loss_cls: 2.7957, loss: 2.7957 +2024-07-26 21:58:09,446 - pyskl - INFO - Epoch [129][1300/3746] lr: 5.054e-03, eta: 18:34:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4852, top5_acc: 0.7311, loss_cls: 2.8753, loss: 2.8753 +2024-07-26 21:59:31,479 - pyskl - INFO - Epoch [129][1400/3746] lr: 5.042e-03, eta: 18:32:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4875, top5_acc: 0.7319, loss_cls: 2.8593, loss: 2.8593 +2024-07-26 22:00:53,349 - pyskl - INFO - Epoch [129][1500/3746] lr: 5.030e-03, eta: 18:31:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4927, top5_acc: 0.7428, loss_cls: 2.8556, loss: 2.8556 +2024-07-26 22:02:15,372 - pyskl - INFO - Epoch [129][1600/3746] lr: 5.017e-03, eta: 18:30:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4959, top5_acc: 0.7408, loss_cls: 2.8283, loss: 2.8283 +2024-07-26 22:03:37,362 - pyskl - INFO - Epoch [129][1700/3746] lr: 5.005e-03, eta: 18:28:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4930, top5_acc: 0.7472, loss_cls: 2.8060, loss: 2.8060 +2024-07-26 22:04:59,146 - pyskl - INFO - Epoch [129][1800/3746] lr: 4.993e-03, eta: 18:27:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4941, top5_acc: 0.7466, loss_cls: 2.8343, loss: 2.8343 +2024-07-26 22:06:21,090 - pyskl - INFO - Epoch [129][1900/3746] lr: 4.981e-03, eta: 18:26:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5070, top5_acc: 0.7528, loss_cls: 2.7799, loss: 2.7799 +2024-07-26 22:07:43,242 - pyskl - INFO - Epoch [129][2000/3746] lr: 4.969e-03, eta: 18:24:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5011, top5_acc: 0.7450, loss_cls: 2.8110, loss: 2.8110 +2024-07-26 22:09:05,153 - pyskl - INFO - Epoch [129][2100/3746] lr: 4.957e-03, eta: 18:23:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4898, top5_acc: 0.7430, loss_cls: 2.8447, loss: 2.8447 +2024-07-26 22:10:27,108 - pyskl - INFO - Epoch [129][2200/3746] lr: 4.944e-03, eta: 18:21:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4800, top5_acc: 0.7377, loss_cls: 2.8833, loss: 2.8833 +2024-07-26 22:11:49,328 - pyskl - INFO - Epoch [129][2300/3746] lr: 4.932e-03, eta: 18:20:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4850, top5_acc: 0.7297, loss_cls: 2.8884, loss: 2.8884 +2024-07-26 22:13:11,136 - pyskl - INFO - Epoch [129][2400/3746] lr: 4.920e-03, eta: 18:19:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4750, top5_acc: 0.7384, loss_cls: 2.8805, loss: 2.8805 +2024-07-26 22:14:32,837 - pyskl - INFO - Epoch [129][2500/3746] lr: 4.908e-03, eta: 18:17:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4961, top5_acc: 0.7344, loss_cls: 2.8313, loss: 2.8313 +2024-07-26 22:15:54,434 - pyskl - INFO - Epoch [129][2600/3746] lr: 4.896e-03, eta: 18:16:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4938, top5_acc: 0.7481, loss_cls: 2.8156, loss: 2.8156 +2024-07-26 22:17:16,253 - pyskl - INFO - Epoch [129][2700/3746] lr: 4.884e-03, eta: 18:15:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4891, top5_acc: 0.7416, loss_cls: 2.8637, loss: 2.8637 +2024-07-26 22:18:38,488 - pyskl - INFO - Epoch [129][2800/3746] lr: 4.872e-03, eta: 18:13:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4830, top5_acc: 0.7392, loss_cls: 2.8693, loss: 2.8693 +2024-07-26 22:20:00,330 - pyskl - INFO - Epoch [129][2900/3746] lr: 4.860e-03, eta: 18:12:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4814, top5_acc: 0.7278, loss_cls: 2.9260, loss: 2.9260 +2024-07-26 22:21:21,744 - pyskl - INFO - Epoch [129][3000/3746] lr: 4.848e-03, eta: 18:10:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4825, top5_acc: 0.7377, loss_cls: 2.8858, loss: 2.8858 +2024-07-26 22:22:43,220 - pyskl - INFO - Epoch [129][3100/3746] lr: 4.836e-03, eta: 18:09:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4744, top5_acc: 0.7403, loss_cls: 2.8746, loss: 2.8746 +2024-07-26 22:24:04,742 - pyskl - INFO - Epoch [129][3200/3746] lr: 4.824e-03, eta: 18:08:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4973, top5_acc: 0.7562, loss_cls: 2.7913, loss: 2.7913 +2024-07-26 22:25:26,385 - pyskl - INFO - Epoch [129][3300/3746] lr: 4.812e-03, eta: 18:06:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4939, top5_acc: 0.7422, loss_cls: 2.8303, loss: 2.8303 +2024-07-26 22:26:47,809 - pyskl - INFO - Epoch [129][3400/3746] lr: 4.800e-03, eta: 18:05:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4859, top5_acc: 0.7378, loss_cls: 2.8476, loss: 2.8476 +2024-07-26 22:28:09,049 - pyskl - INFO - Epoch [129][3500/3746] lr: 4.788e-03, eta: 18:04:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4917, top5_acc: 0.7319, loss_cls: 2.8666, loss: 2.8666 +2024-07-26 22:29:30,532 - pyskl - INFO - Epoch [129][3600/3746] lr: 4.776e-03, eta: 18:02:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4823, top5_acc: 0.7372, loss_cls: 2.8671, loss: 2.8671 +2024-07-26 22:30:51,630 - pyskl - INFO - Epoch [129][3700/3746] lr: 4.764e-03, eta: 18:01:16, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7467, loss_cls: 2.8199, loss: 2.8199 +2024-07-26 22:31:31,341 - pyskl - INFO - Saving checkpoint at 129 epochs +2024-07-26 22:33:23,230 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 22:33:23,953 - pyskl - INFO - +top1_acc 0.4078 +top5_acc 0.6591 +2024-07-26 22:33:23,953 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 22:33:23,994 - pyskl - INFO - +mean_acc 0.4077 +2024-07-26 22:33:24,005 - pyskl - INFO - Epoch(val) [129][309] top1_acc: 0.4078, top5_acc: 0.6591, mean_class_accuracy: 0.4077 +2024-07-26 22:37:15,170 - pyskl - INFO - Epoch [130][100/3746] lr: 4.747e-03, eta: 17:59:34, time: 2.312, data_time: 1.329, memory: 15990, top1_acc: 0.5044, top5_acc: 0.7547, loss_cls: 2.7605, loss: 2.7605 +2024-07-26 22:38:37,484 - pyskl - INFO - Epoch [130][200/3746] lr: 4.735e-03, eta: 17:58:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5084, top5_acc: 0.7561, loss_cls: 2.7343, loss: 2.7343 +2024-07-26 22:40:00,331 - pyskl - INFO - Epoch [130][300/3746] lr: 4.723e-03, eta: 17:56:49, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5050, top5_acc: 0.7503, loss_cls: 2.7644, loss: 2.7644 +2024-07-26 22:41:21,725 - pyskl - INFO - Epoch [130][400/3746] lr: 4.711e-03, eta: 17:55:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5066, top5_acc: 0.7562, loss_cls: 2.7411, loss: 2.7411 +2024-07-26 22:42:43,125 - pyskl - INFO - Epoch [130][500/3746] lr: 4.699e-03, eta: 17:54:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5008, top5_acc: 0.7455, loss_cls: 2.8084, loss: 2.8084 +2024-07-26 22:44:04,498 - pyskl - INFO - Epoch [130][600/3746] lr: 4.688e-03, eta: 17:52:41, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5033, top5_acc: 0.7516, loss_cls: 2.7672, loss: 2.7672 +2024-07-26 22:45:26,185 - pyskl - INFO - Epoch [130][700/3746] lr: 4.676e-03, eta: 17:51:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5055, top5_acc: 0.7552, loss_cls: 2.7689, loss: 2.7689 +2024-07-26 22:46:48,078 - pyskl - INFO - Epoch [130][800/3746] lr: 4.664e-03, eta: 17:49:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5184, top5_acc: 0.7586, loss_cls: 2.7356, loss: 2.7356 +2024-07-26 22:48:09,665 - pyskl - INFO - Epoch [130][900/3746] lr: 4.652e-03, eta: 17:48:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4983, top5_acc: 0.7441, loss_cls: 2.8159, loss: 2.8159 +2024-07-26 22:49:31,293 - pyskl - INFO - Epoch [130][1000/3746] lr: 4.640e-03, eta: 17:47:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5048, top5_acc: 0.7514, loss_cls: 2.7531, loss: 2.7531 +2024-07-26 22:50:52,601 - pyskl - INFO - Epoch [130][1100/3746] lr: 4.629e-03, eta: 17:45:48, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4997, top5_acc: 0.7480, loss_cls: 2.7885, loss: 2.7885 +2024-07-26 22:52:14,749 - pyskl - INFO - Epoch [130][1200/3746] lr: 4.617e-03, eta: 17:44:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5094, top5_acc: 0.7480, loss_cls: 2.7596, loss: 2.7596 +2024-07-26 22:53:36,351 - pyskl - INFO - Epoch [130][1300/3746] lr: 4.605e-03, eta: 17:43:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4995, top5_acc: 0.7464, loss_cls: 2.7993, loss: 2.7993 +2024-07-26 22:54:58,283 - pyskl - INFO - Epoch [130][1400/3746] lr: 4.594e-03, eta: 17:41:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4966, top5_acc: 0.7497, loss_cls: 2.7913, loss: 2.7913 +2024-07-26 22:56:19,896 - pyskl - INFO - Epoch [130][1500/3746] lr: 4.582e-03, eta: 17:40:18, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5059, top5_acc: 0.7514, loss_cls: 2.7660, loss: 2.7660 +2024-07-26 22:57:41,780 - pyskl - INFO - Epoch [130][1600/3746] lr: 4.570e-03, eta: 17:38:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4964, top5_acc: 0.7462, loss_cls: 2.7954, loss: 2.7954 +2024-07-26 22:59:03,651 - pyskl - INFO - Epoch [130][1700/3746] lr: 4.558e-03, eta: 17:37:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5038, top5_acc: 0.7498, loss_cls: 2.7603, loss: 2.7603 +2024-07-26 23:00:25,314 - pyskl - INFO - Epoch [130][1800/3746] lr: 4.547e-03, eta: 17:36:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5003, top5_acc: 0.7538, loss_cls: 2.7925, loss: 2.7925 +2024-07-26 23:01:46,982 - pyskl - INFO - Epoch [130][1900/3746] lr: 4.535e-03, eta: 17:34:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4967, top5_acc: 0.7436, loss_cls: 2.8198, loss: 2.8198 +2024-07-26 23:03:08,904 - pyskl - INFO - Epoch [130][2000/3746] lr: 4.524e-03, eta: 17:33:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4973, top5_acc: 0.7473, loss_cls: 2.8070, loss: 2.8070 +2024-07-26 23:04:30,887 - pyskl - INFO - Epoch [130][2100/3746] lr: 4.512e-03, eta: 17:32:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4992, top5_acc: 0.7428, loss_cls: 2.8218, loss: 2.8218 +2024-07-26 23:05:52,691 - pyskl - INFO - Epoch [130][2200/3746] lr: 4.500e-03, eta: 17:30:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5047, top5_acc: 0.7447, loss_cls: 2.8036, loss: 2.8036 +2024-07-26 23:07:14,199 - pyskl - INFO - Epoch [130][2300/3746] lr: 4.489e-03, eta: 17:29:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4888, top5_acc: 0.7452, loss_cls: 2.8217, loss: 2.8217 +2024-07-26 23:08:35,858 - pyskl - INFO - Epoch [130][2400/3746] lr: 4.477e-03, eta: 17:27:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4838, top5_acc: 0.7412, loss_cls: 2.8650, loss: 2.8650 +2024-07-26 23:09:58,187 - pyskl - INFO - Epoch [130][2500/3746] lr: 4.466e-03, eta: 17:26:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4991, top5_acc: 0.7459, loss_cls: 2.7950, loss: 2.7950 +2024-07-26 23:11:19,746 - pyskl - INFO - Epoch [130][2600/3746] lr: 4.454e-03, eta: 17:25:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4942, top5_acc: 0.7422, loss_cls: 2.8293, loss: 2.8293 +2024-07-26 23:12:40,976 - pyskl - INFO - Epoch [130][2700/3746] lr: 4.443e-03, eta: 17:23:48, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.4939, top5_acc: 0.7464, loss_cls: 2.8138, loss: 2.8138 +2024-07-26 23:14:02,343 - pyskl - INFO - Epoch [130][2800/3746] lr: 4.431e-03, eta: 17:22:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4961, top5_acc: 0.7441, loss_cls: 2.7881, loss: 2.7881 +2024-07-26 23:15:24,328 - pyskl - INFO - Epoch [130][2900/3746] lr: 4.420e-03, eta: 17:21:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4934, top5_acc: 0.7530, loss_cls: 2.7673, loss: 2.7673 +2024-07-26 23:16:45,628 - pyskl - INFO - Epoch [130][3000/3746] lr: 4.408e-03, eta: 17:19:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4972, top5_acc: 0.7516, loss_cls: 2.7966, loss: 2.7966 +2024-07-26 23:18:07,017 - pyskl - INFO - Epoch [130][3100/3746] lr: 4.397e-03, eta: 17:18:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4952, top5_acc: 0.7436, loss_cls: 2.8071, loss: 2.8071 +2024-07-26 23:19:28,811 - pyskl - INFO - Epoch [130][3200/3746] lr: 4.385e-03, eta: 17:16:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4950, top5_acc: 0.7378, loss_cls: 2.8488, loss: 2.8488 +2024-07-26 23:20:50,488 - pyskl - INFO - Epoch [130][3300/3746] lr: 4.374e-03, eta: 17:15:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4897, top5_acc: 0.7498, loss_cls: 2.8284, loss: 2.8284 +2024-07-26 23:22:12,226 - pyskl - INFO - Epoch [130][3400/3746] lr: 4.362e-03, eta: 17:14:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5020, top5_acc: 0.7503, loss_cls: 2.7847, loss: 2.7847 +2024-07-26 23:23:33,816 - pyskl - INFO - Epoch [130][3500/3746] lr: 4.351e-03, eta: 17:12:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4902, top5_acc: 0.7439, loss_cls: 2.8344, loss: 2.8344 +2024-07-26 23:24:55,762 - pyskl - INFO - Epoch [130][3600/3746] lr: 4.339e-03, eta: 17:11:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4888, top5_acc: 0.7503, loss_cls: 2.8033, loss: 2.8033 +2024-07-26 23:26:17,596 - pyskl - INFO - Epoch [130][3700/3746] lr: 4.328e-03, eta: 17:10:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5005, top5_acc: 0.7438, loss_cls: 2.8161, loss: 2.8161 +2024-07-26 23:26:57,698 - pyskl - INFO - Saving checkpoint at 130 epochs +2024-07-26 23:28:48,485 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-26 23:28:49,140 - pyskl - INFO - +top1_acc 0.4129 +top5_acc 0.6646 +2024-07-26 23:28:49,140 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-26 23:28:49,180 - pyskl - INFO - +mean_acc 0.4127 +2024-07-26 23:28:49,185 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_128.pth was removed +2024-07-26 23:28:49,453 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_130.pth. +2024-07-26 23:28:49,454 - pyskl - INFO - Best top1_acc is 0.4129 at 130 epoch. +2024-07-26 23:28:49,466 - pyskl - INFO - Epoch(val) [130][309] top1_acc: 0.4129, top5_acc: 0.6646, mean_class_accuracy: 0.4127 +2024-07-26 23:32:39,382 - pyskl - INFO - Epoch [131][100/3746] lr: 4.311e-03, eta: 17:08:18, time: 2.299, data_time: 1.319, memory: 15990, top1_acc: 0.5186, top5_acc: 0.7681, loss_cls: 2.6714, loss: 2.6714 +2024-07-26 23:34:01,181 - pyskl - INFO - Epoch [131][200/3746] lr: 4.300e-03, eta: 17:06:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5127, top5_acc: 0.7691, loss_cls: 2.6716, loss: 2.6716 +2024-07-26 23:35:23,310 - pyskl - INFO - Epoch [131][300/3746] lr: 4.289e-03, eta: 17:05:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5170, top5_acc: 0.7700, loss_cls: 2.6695, loss: 2.6695 +2024-07-26 23:36:44,963 - pyskl - INFO - Epoch [131][400/3746] lr: 4.277e-03, eta: 17:04:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5147, top5_acc: 0.7642, loss_cls: 2.7037, loss: 2.7037 +2024-07-26 23:38:06,603 - pyskl - INFO - Epoch [131][500/3746] lr: 4.266e-03, eta: 17:02:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5050, top5_acc: 0.7509, loss_cls: 2.7602, loss: 2.7602 +2024-07-26 23:39:27,816 - pyskl - INFO - Epoch [131][600/3746] lr: 4.255e-03, eta: 17:01:26, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5053, top5_acc: 0.7578, loss_cls: 2.7378, loss: 2.7378 +2024-07-26 23:40:49,291 - pyskl - INFO - Epoch [131][700/3746] lr: 4.244e-03, eta: 17:00:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5236, top5_acc: 0.7738, loss_cls: 2.6603, loss: 2.6603 +2024-07-26 23:42:11,120 - pyskl - INFO - Epoch [131][800/3746] lr: 4.232e-03, eta: 16:58:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5036, top5_acc: 0.7538, loss_cls: 2.7794, loss: 2.7794 +2024-07-26 23:43:32,650 - pyskl - INFO - Epoch [131][900/3746] lr: 4.221e-03, eta: 16:57:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4995, top5_acc: 0.7539, loss_cls: 2.7765, loss: 2.7765 +2024-07-26 23:44:54,655 - pyskl - INFO - Epoch [131][1000/3746] lr: 4.210e-03, eta: 16:55:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5156, top5_acc: 0.7578, loss_cls: 2.7182, loss: 2.7182 +2024-07-26 23:46:16,067 - pyskl - INFO - Epoch [131][1100/3746] lr: 4.199e-03, eta: 16:54:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5094, top5_acc: 0.7603, loss_cls: 2.7350, loss: 2.7350 +2024-07-26 23:47:38,958 - pyskl - INFO - Epoch [131][1200/3746] lr: 4.187e-03, eta: 16:53:10, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5128, top5_acc: 0.7530, loss_cls: 2.7497, loss: 2.7497 +2024-07-26 23:49:00,943 - pyskl - INFO - Epoch [131][1300/3746] lr: 4.176e-03, eta: 16:51:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5059, top5_acc: 0.7538, loss_cls: 2.7589, loss: 2.7589 +2024-07-26 23:50:24,098 - pyskl - INFO - Epoch [131][1400/3746] lr: 4.165e-03, eta: 16:50:25, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5072, top5_acc: 0.7514, loss_cls: 2.7758, loss: 2.7758 +2024-07-26 23:51:45,767 - pyskl - INFO - Epoch [131][1500/3746] lr: 4.154e-03, eta: 16:49:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5100, top5_acc: 0.7572, loss_cls: 2.7406, loss: 2.7406 +2024-07-26 23:53:07,503 - pyskl - INFO - Epoch [131][1600/3746] lr: 4.143e-03, eta: 16:47:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5158, top5_acc: 0.7548, loss_cls: 2.7213, loss: 2.7213 +2024-07-26 23:54:29,897 - pyskl - INFO - Epoch [131][1700/3746] lr: 4.132e-03, eta: 16:46:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5016, top5_acc: 0.7528, loss_cls: 2.7840, loss: 2.7840 +2024-07-26 23:55:51,610 - pyskl - INFO - Epoch [131][1800/3746] lr: 4.120e-03, eta: 16:44:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5106, top5_acc: 0.7534, loss_cls: 2.7535, loss: 2.7535 +2024-07-26 23:57:13,253 - pyskl - INFO - Epoch [131][1900/3746] lr: 4.109e-03, eta: 16:43:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5022, top5_acc: 0.7492, loss_cls: 2.7587, loss: 2.7587 +2024-07-26 23:58:35,667 - pyskl - INFO - Epoch [131][2000/3746] lr: 4.098e-03, eta: 16:42:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5089, top5_acc: 0.7552, loss_cls: 2.7147, loss: 2.7147 +2024-07-26 23:59:57,688 - pyskl - INFO - Epoch [131][2100/3746] lr: 4.087e-03, eta: 16:40:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5042, top5_acc: 0.7519, loss_cls: 2.7572, loss: 2.7572 +2024-07-27 00:01:20,416 - pyskl - INFO - Epoch [131][2200/3746] lr: 4.076e-03, eta: 16:39:25, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4998, top5_acc: 0.7453, loss_cls: 2.8070, loss: 2.8070 +2024-07-27 00:02:42,153 - pyskl - INFO - Epoch [131][2300/3746] lr: 4.065e-03, eta: 16:38:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4984, top5_acc: 0.7570, loss_cls: 2.7661, loss: 2.7661 +2024-07-27 00:04:03,430 - pyskl - INFO - Epoch [131][2400/3746] lr: 4.054e-03, eta: 16:36:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.4939, top5_acc: 0.7523, loss_cls: 2.7727, loss: 2.7727 +2024-07-27 00:05:24,790 - pyskl - INFO - Epoch [131][2500/3746] lr: 4.043e-03, eta: 16:35:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5053, top5_acc: 0.7559, loss_cls: 2.7451, loss: 2.7451 +2024-07-27 00:06:46,622 - pyskl - INFO - Epoch [131][2600/3746] lr: 4.032e-03, eta: 16:33:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4963, top5_acc: 0.7441, loss_cls: 2.7783, loss: 2.7783 +2024-07-27 00:08:08,398 - pyskl - INFO - Epoch [131][2700/3746] lr: 4.021e-03, eta: 16:32:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5008, top5_acc: 0.7564, loss_cls: 2.7603, loss: 2.7603 +2024-07-27 00:09:30,003 - pyskl - INFO - Epoch [131][2800/3746] lr: 4.010e-03, eta: 16:31:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5070, top5_acc: 0.7602, loss_cls: 2.7428, loss: 2.7428 +2024-07-27 00:10:51,228 - pyskl - INFO - Epoch [131][2900/3746] lr: 3.999e-03, eta: 16:29:47, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5067, top5_acc: 0.7545, loss_cls: 2.7670, loss: 2.7670 +2024-07-27 00:12:12,996 - pyskl - INFO - Epoch [131][3000/3746] lr: 3.988e-03, eta: 16:28:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5034, top5_acc: 0.7542, loss_cls: 2.7726, loss: 2.7726 +2024-07-27 00:13:34,289 - pyskl - INFO - Epoch [131][3100/3746] lr: 3.977e-03, eta: 16:27:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5016, top5_acc: 0.7452, loss_cls: 2.8137, loss: 2.8137 +2024-07-27 00:14:55,975 - pyskl - INFO - Epoch [131][3200/3746] lr: 3.966e-03, eta: 16:25:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4984, top5_acc: 0.7441, loss_cls: 2.8118, loss: 2.8118 +2024-07-27 00:16:17,186 - pyskl - INFO - Epoch [131][3300/3746] lr: 3.955e-03, eta: 16:24:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5055, top5_acc: 0.7520, loss_cls: 2.7718, loss: 2.7718 +2024-07-27 00:17:38,694 - pyskl - INFO - Epoch [131][3400/3746] lr: 3.945e-03, eta: 16:22:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4994, top5_acc: 0.7481, loss_cls: 2.7890, loss: 2.7890 +2024-07-27 00:19:00,414 - pyskl - INFO - Epoch [131][3500/3746] lr: 3.934e-03, eta: 16:21:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4977, top5_acc: 0.7423, loss_cls: 2.7757, loss: 2.7757 +2024-07-27 00:20:21,523 - pyskl - INFO - Epoch [131][3600/3746] lr: 3.923e-03, eta: 16:20:09, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4988, top5_acc: 0.7450, loss_cls: 2.8028, loss: 2.8028 +2024-07-27 00:21:43,367 - pyskl - INFO - Epoch [131][3700/3746] lr: 3.912e-03, eta: 16:18:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4980, top5_acc: 0.7462, loss_cls: 2.7983, loss: 2.7983 +2024-07-27 00:22:22,565 - pyskl - INFO - Saving checkpoint at 131 epochs +2024-07-27 00:24:12,518 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 00:24:13,179 - pyskl - INFO - +top1_acc 0.4130 +top5_acc 0.6624 +2024-07-27 00:24:13,180 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 00:24:13,221 - pyskl - INFO - +mean_acc 0.4127 +2024-07-27 00:24:13,225 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_130.pth was removed +2024-07-27 00:24:13,483 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_131.pth. +2024-07-27 00:24:13,484 - pyskl - INFO - Best top1_acc is 0.4130 at 131 epoch. +2024-07-27 00:24:13,496 - pyskl - INFO - Epoch(val) [131][309] top1_acc: 0.4130, top5_acc: 0.6624, mean_class_accuracy: 0.4127 +2024-07-27 00:28:03,386 - pyskl - INFO - Epoch [132][100/3746] lr: 3.896e-03, eta: 16:17:02, time: 2.299, data_time: 1.316, memory: 15990, top1_acc: 0.5284, top5_acc: 0.7667, loss_cls: 2.6745, loss: 2.6745 +2024-07-27 00:29:25,442 - pyskl - INFO - Epoch [132][200/3746] lr: 3.885e-03, eta: 16:15:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5097, top5_acc: 0.7611, loss_cls: 2.7196, loss: 2.7196 +2024-07-27 00:30:47,118 - pyskl - INFO - Epoch [132][300/3746] lr: 3.875e-03, eta: 16:14:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5153, top5_acc: 0.7691, loss_cls: 2.6918, loss: 2.6918 +2024-07-27 00:32:09,685 - pyskl - INFO - Epoch [132][400/3746] lr: 3.864e-03, eta: 16:12:54, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5134, top5_acc: 0.7697, loss_cls: 2.7010, loss: 2.7010 +2024-07-27 00:33:31,253 - pyskl - INFO - Epoch [132][500/3746] lr: 3.853e-03, eta: 16:11:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5117, top5_acc: 0.7644, loss_cls: 2.6766, loss: 2.6766 +2024-07-27 00:34:52,860 - pyskl - INFO - Epoch [132][600/3746] lr: 3.842e-03, eta: 16:10:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5156, top5_acc: 0.7569, loss_cls: 2.7165, loss: 2.7165 +2024-07-27 00:36:14,828 - pyskl - INFO - Epoch [132][700/3746] lr: 3.831e-03, eta: 16:08:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5156, top5_acc: 0.7670, loss_cls: 2.6842, loss: 2.6842 +2024-07-27 00:37:36,069 - pyskl - INFO - Epoch [132][800/3746] lr: 3.821e-03, eta: 16:07:24, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5188, top5_acc: 0.7584, loss_cls: 2.7019, loss: 2.7019 +2024-07-27 00:38:57,544 - pyskl - INFO - Epoch [132][900/3746] lr: 3.810e-03, eta: 16:06:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5158, top5_acc: 0.7706, loss_cls: 2.6661, loss: 2.6661 +2024-07-27 00:40:19,806 - pyskl - INFO - Epoch [132][1000/3746] lr: 3.799e-03, eta: 16:04:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5111, top5_acc: 0.7623, loss_cls: 2.7194, loss: 2.7194 +2024-07-27 00:41:41,438 - pyskl - INFO - Epoch [132][1100/3746] lr: 3.789e-03, eta: 16:03:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5130, top5_acc: 0.7577, loss_cls: 2.7291, loss: 2.7291 +2024-07-27 00:43:03,129 - pyskl - INFO - Epoch [132][1200/3746] lr: 3.778e-03, eta: 16:01:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5148, top5_acc: 0.7569, loss_cls: 2.7028, loss: 2.7028 +2024-07-27 00:44:24,764 - pyskl - INFO - Epoch [132][1300/3746] lr: 3.767e-03, eta: 16:00:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5255, top5_acc: 0.7722, loss_cls: 2.6457, loss: 2.6457 +2024-07-27 00:45:47,776 - pyskl - INFO - Epoch [132][1400/3746] lr: 3.757e-03, eta: 15:59:08, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5167, top5_acc: 0.7584, loss_cls: 2.7029, loss: 2.7029 +2024-07-27 00:47:09,126 - pyskl - INFO - Epoch [132][1500/3746] lr: 3.746e-03, eta: 15:57:46, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5161, top5_acc: 0.7639, loss_cls: 2.7010, loss: 2.7010 +2024-07-27 00:48:31,352 - pyskl - INFO - Epoch [132][1600/3746] lr: 3.735e-03, eta: 15:56:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5144, top5_acc: 0.7584, loss_cls: 2.7362, loss: 2.7362 +2024-07-27 00:49:53,363 - pyskl - INFO - Epoch [132][1700/3746] lr: 3.725e-03, eta: 15:55:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5023, top5_acc: 0.7516, loss_cls: 2.7454, loss: 2.7454 +2024-07-27 00:51:15,133 - pyskl - INFO - Epoch [132][1800/3746] lr: 3.714e-03, eta: 15:53:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5128, top5_acc: 0.7531, loss_cls: 2.7438, loss: 2.7438 +2024-07-27 00:52:36,557 - pyskl - INFO - Epoch [132][1900/3746] lr: 3.704e-03, eta: 15:52:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5145, top5_acc: 0.7578, loss_cls: 2.7294, loss: 2.7294 +2024-07-27 00:53:58,060 - pyskl - INFO - Epoch [132][2000/3746] lr: 3.693e-03, eta: 15:50:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5088, top5_acc: 0.7642, loss_cls: 2.7033, loss: 2.7033 +2024-07-27 00:55:20,181 - pyskl - INFO - Epoch [132][2100/3746] lr: 3.683e-03, eta: 15:49:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5055, top5_acc: 0.7606, loss_cls: 2.7519, loss: 2.7519 +2024-07-27 00:56:43,043 - pyskl - INFO - Epoch [132][2200/3746] lr: 3.672e-03, eta: 15:48:08, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5092, top5_acc: 0.7670, loss_cls: 2.6963, loss: 2.6963 +2024-07-27 00:58:05,281 - pyskl - INFO - Epoch [132][2300/3746] lr: 3.662e-03, eta: 15:46:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5111, top5_acc: 0.7602, loss_cls: 2.7284, loss: 2.7284 +2024-07-27 00:59:28,050 - pyskl - INFO - Epoch [132][2400/3746] lr: 3.651e-03, eta: 15:45:23, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5045, top5_acc: 0.7503, loss_cls: 2.7746, loss: 2.7746 +2024-07-27 01:00:49,885 - pyskl - INFO - Epoch [132][2500/3746] lr: 3.641e-03, eta: 15:44:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4977, top5_acc: 0.7441, loss_cls: 2.7785, loss: 2.7785 +2024-07-27 01:02:11,602 - pyskl - INFO - Epoch [132][2600/3746] lr: 3.630e-03, eta: 15:42:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5162, top5_acc: 0.7575, loss_cls: 2.7290, loss: 2.7290 +2024-07-27 01:03:32,605 - pyskl - INFO - Epoch [132][2700/3746] lr: 3.620e-03, eta: 15:41:15, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5172, top5_acc: 0.7661, loss_cls: 2.6854, loss: 2.6854 +2024-07-27 01:04:54,059 - pyskl - INFO - Epoch [132][2800/3746] lr: 3.609e-03, eta: 15:39:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5064, top5_acc: 0.7662, loss_cls: 2.7096, loss: 2.7096 +2024-07-27 01:06:15,561 - pyskl - INFO - Epoch [132][2900/3746] lr: 3.599e-03, eta: 15:38:30, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5061, top5_acc: 0.7495, loss_cls: 2.7355, loss: 2.7355 +2024-07-27 01:07:36,662 - pyskl - INFO - Epoch [132][3000/3746] lr: 3.588e-03, eta: 15:37:07, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5077, top5_acc: 0.7527, loss_cls: 2.7479, loss: 2.7479 +2024-07-27 01:08:58,668 - pyskl - INFO - Epoch [132][3100/3746] lr: 3.578e-03, eta: 15:35:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5170, top5_acc: 0.7616, loss_cls: 2.6946, loss: 2.6946 +2024-07-27 01:10:20,024 - pyskl - INFO - Epoch [132][3200/3746] lr: 3.568e-03, eta: 15:34:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5170, top5_acc: 0.7608, loss_cls: 2.7114, loss: 2.7114 +2024-07-27 01:11:41,572 - pyskl - INFO - Epoch [132][3300/3746] lr: 3.557e-03, eta: 15:32:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5156, top5_acc: 0.7580, loss_cls: 2.7238, loss: 2.7238 +2024-07-27 01:13:03,289 - pyskl - INFO - Epoch [132][3400/3746] lr: 3.547e-03, eta: 15:31:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5070, top5_acc: 0.7542, loss_cls: 2.7653, loss: 2.7653 +2024-07-27 01:14:24,981 - pyskl - INFO - Epoch [132][3500/3746] lr: 3.537e-03, eta: 15:30:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5050, top5_acc: 0.7619, loss_cls: 2.7228, loss: 2.7228 +2024-07-27 01:15:46,102 - pyskl - INFO - Epoch [132][3600/3746] lr: 3.526e-03, eta: 15:28:52, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.4995, top5_acc: 0.7569, loss_cls: 2.7446, loss: 2.7446 +2024-07-27 01:17:08,098 - pyskl - INFO - Epoch [132][3700/3746] lr: 3.516e-03, eta: 15:27:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4952, top5_acc: 0.7527, loss_cls: 2.7738, loss: 2.7738 +2024-07-27 01:17:47,609 - pyskl - INFO - Saving checkpoint at 132 epochs +2024-07-27 01:19:38,718 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 01:19:39,394 - pyskl - INFO - +top1_acc 0.4188 +top5_acc 0.6691 +2024-07-27 01:19:39,395 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 01:19:39,440 - pyskl - INFO - +mean_acc 0.4185 +2024-07-27 01:19:39,446 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_131.pth was removed +2024-07-27 01:19:39,716 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_132.pth. +2024-07-27 01:19:39,717 - pyskl - INFO - Best top1_acc is 0.4188 at 132 epoch. +2024-07-27 01:19:39,733 - pyskl - INFO - Epoch(val) [132][309] top1_acc: 0.4188, top5_acc: 0.6691, mean_class_accuracy: 0.4185 +2024-07-27 01:23:28,570 - pyskl - INFO - Epoch [133][100/3746] lr: 3.501e-03, eta: 15:25:43, time: 2.288, data_time: 1.310, memory: 15990, top1_acc: 0.5334, top5_acc: 0.7842, loss_cls: 2.5720, loss: 2.5720 +2024-07-27 01:24:50,532 - pyskl - INFO - Epoch [133][200/3746] lr: 3.491e-03, eta: 15:24:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5377, top5_acc: 0.7769, loss_cls: 2.6032, loss: 2.6032 +2024-07-27 01:26:12,188 - pyskl - INFO - Epoch [133][300/3746] lr: 3.480e-03, eta: 15:22:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5245, top5_acc: 0.7662, loss_cls: 2.6521, loss: 2.6521 +2024-07-27 01:27:33,533 - pyskl - INFO - Epoch [133][400/3746] lr: 3.470e-03, eta: 15:21:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5245, top5_acc: 0.7662, loss_cls: 2.6416, loss: 2.6416 +2024-07-27 01:28:55,096 - pyskl - INFO - Epoch [133][500/3746] lr: 3.460e-03, eta: 15:20:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5270, top5_acc: 0.7723, loss_cls: 2.6291, loss: 2.6291 +2024-07-27 01:30:16,896 - pyskl - INFO - Epoch [133][600/3746] lr: 3.450e-03, eta: 15:18:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5294, top5_acc: 0.7753, loss_cls: 2.6274, loss: 2.6274 +2024-07-27 01:31:38,151 - pyskl - INFO - Epoch [133][700/3746] lr: 3.440e-03, eta: 15:17:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5275, top5_acc: 0.7755, loss_cls: 2.6348, loss: 2.6348 +2024-07-27 01:32:59,838 - pyskl - INFO - Epoch [133][800/3746] lr: 3.429e-03, eta: 15:16:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5173, top5_acc: 0.7691, loss_cls: 2.6539, loss: 2.6539 +2024-07-27 01:34:21,716 - pyskl - INFO - Epoch [133][900/3746] lr: 3.419e-03, eta: 15:14:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5319, top5_acc: 0.7672, loss_cls: 2.6444, loss: 2.6444 +2024-07-27 01:35:43,982 - pyskl - INFO - Epoch [133][1000/3746] lr: 3.409e-03, eta: 15:13:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5170, top5_acc: 0.7688, loss_cls: 2.6983, loss: 2.6983 +2024-07-27 01:37:05,747 - pyskl - INFO - Epoch [133][1100/3746] lr: 3.399e-03, eta: 15:11:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5288, top5_acc: 0.7744, loss_cls: 2.6418, loss: 2.6418 +2024-07-27 01:38:27,956 - pyskl - INFO - Epoch [133][1200/3746] lr: 3.389e-03, eta: 15:10:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5308, top5_acc: 0.7744, loss_cls: 2.6352, loss: 2.6352 +2024-07-27 01:39:50,173 - pyskl - INFO - Epoch [133][1300/3746] lr: 3.379e-03, eta: 15:09:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5211, top5_acc: 0.7675, loss_cls: 2.6722, loss: 2.6722 +2024-07-27 01:41:12,806 - pyskl - INFO - Epoch [133][1400/3746] lr: 3.369e-03, eta: 15:07:50, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5211, top5_acc: 0.7669, loss_cls: 2.6629, loss: 2.6629 +2024-07-27 01:42:34,436 - pyskl - INFO - Epoch [133][1500/3746] lr: 3.359e-03, eta: 15:06:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5159, top5_acc: 0.7641, loss_cls: 2.6765, loss: 2.6765 +2024-07-27 01:43:56,292 - pyskl - INFO - Epoch [133][1600/3746] lr: 3.348e-03, eta: 15:05:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5170, top5_acc: 0.7689, loss_cls: 2.6671, loss: 2.6671 +2024-07-27 01:45:18,207 - pyskl - INFO - Epoch [133][1700/3746] lr: 3.338e-03, eta: 15:03:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5041, top5_acc: 0.7633, loss_cls: 2.7252, loss: 2.7252 +2024-07-27 01:46:39,360 - pyskl - INFO - Epoch [133][1800/3746] lr: 3.328e-03, eta: 15:02:19, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5236, top5_acc: 0.7636, loss_cls: 2.6517, loss: 2.6517 +2024-07-27 01:48:00,941 - pyskl - INFO - Epoch [133][1900/3746] lr: 3.318e-03, eta: 15:00:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5214, top5_acc: 0.7669, loss_cls: 2.6545, loss: 2.6545 +2024-07-27 01:49:22,413 - pyskl - INFO - Epoch [133][2000/3746] lr: 3.308e-03, eta: 14:59:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7753, loss_cls: 2.6466, loss: 2.6466 +2024-07-27 01:50:44,510 - pyskl - INFO - Epoch [133][2100/3746] lr: 3.298e-03, eta: 14:58:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5195, top5_acc: 0.7609, loss_cls: 2.6943, loss: 2.6943 +2024-07-27 01:52:06,774 - pyskl - INFO - Epoch [133][2200/3746] lr: 3.288e-03, eta: 14:56:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5192, top5_acc: 0.7622, loss_cls: 2.6919, loss: 2.6919 +2024-07-27 01:53:28,391 - pyskl - INFO - Epoch [133][2300/3746] lr: 3.278e-03, eta: 14:55:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5181, top5_acc: 0.7617, loss_cls: 2.6846, loss: 2.6846 +2024-07-27 01:54:50,050 - pyskl - INFO - Epoch [133][2400/3746] lr: 3.268e-03, eta: 14:54:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5248, top5_acc: 0.7716, loss_cls: 2.6360, loss: 2.6360 +2024-07-27 01:56:11,918 - pyskl - INFO - Epoch [133][2500/3746] lr: 3.259e-03, eta: 14:52:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5292, top5_acc: 0.7661, loss_cls: 2.6438, loss: 2.6438 +2024-07-27 01:57:33,433 - pyskl - INFO - Epoch [133][2600/3746] lr: 3.249e-03, eta: 14:51:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5108, top5_acc: 0.7622, loss_cls: 2.7120, loss: 2.7120 +2024-07-27 01:58:55,224 - pyskl - INFO - Epoch [133][2700/3746] lr: 3.239e-03, eta: 14:49:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5158, top5_acc: 0.7577, loss_cls: 2.7037, loss: 2.7037 +2024-07-27 02:00:16,642 - pyskl - INFO - Epoch [133][2800/3746] lr: 3.229e-03, eta: 14:48:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5211, top5_acc: 0.7606, loss_cls: 2.6863, loss: 2.6863 +2024-07-27 02:01:38,563 - pyskl - INFO - Epoch [133][2900/3746] lr: 3.219e-03, eta: 14:47:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5089, top5_acc: 0.7575, loss_cls: 2.7268, loss: 2.7268 +2024-07-27 02:03:00,065 - pyskl - INFO - Epoch [133][3000/3746] lr: 3.209e-03, eta: 14:45:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5044, top5_acc: 0.7539, loss_cls: 2.7414, loss: 2.7414 +2024-07-27 02:04:21,598 - pyskl - INFO - Epoch [133][3100/3746] lr: 3.199e-03, eta: 14:44:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5131, top5_acc: 0.7484, loss_cls: 2.7332, loss: 2.7332 +2024-07-27 02:05:43,305 - pyskl - INFO - Epoch [133][3200/3746] lr: 3.189e-03, eta: 14:43:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5100, top5_acc: 0.7566, loss_cls: 2.7172, loss: 2.7172 +2024-07-27 02:07:05,139 - pyskl - INFO - Epoch [133][3300/3746] lr: 3.180e-03, eta: 14:41:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5083, top5_acc: 0.7538, loss_cls: 2.7295, loss: 2.7295 +2024-07-27 02:08:27,164 - pyskl - INFO - Epoch [133][3400/3746] lr: 3.170e-03, eta: 14:40:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5153, top5_acc: 0.7606, loss_cls: 2.7136, loss: 2.7136 +2024-07-27 02:09:48,527 - pyskl - INFO - Epoch [133][3500/3746] lr: 3.160e-03, eta: 14:38:55, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5164, top5_acc: 0.7622, loss_cls: 2.7045, loss: 2.7045 +2024-07-27 02:11:10,228 - pyskl - INFO - Epoch [133][3600/3746] lr: 3.150e-03, eta: 14:37:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5155, top5_acc: 0.7500, loss_cls: 2.7262, loss: 2.7262 +2024-07-27 02:12:31,681 - pyskl - INFO - Epoch [133][3700/3746] lr: 3.140e-03, eta: 14:36:10, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5117, top5_acc: 0.7617, loss_cls: 2.7177, loss: 2.7177 +2024-07-27 02:13:11,373 - pyskl - INFO - Saving checkpoint at 133 epochs +2024-07-27 02:15:01,886 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 02:15:02,546 - pyskl - INFO - +top1_acc 0.4262 +top5_acc 0.6730 +2024-07-27 02:15:02,547 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 02:15:02,587 - pyskl - INFO - +mean_acc 0.4260 +2024-07-27 02:15:02,591 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_132.pth was removed +2024-07-27 02:15:02,854 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_133.pth. +2024-07-27 02:15:02,855 - pyskl - INFO - Best top1_acc is 0.4262 at 133 epoch. +2024-07-27 02:15:02,867 - pyskl - INFO - Epoch(val) [133][309] top1_acc: 0.4262, top5_acc: 0.6730, mean_class_accuracy: 0.4260 +2024-07-27 02:18:50,567 - pyskl - INFO - Epoch [134][100/3746] lr: 3.126e-03, eta: 14:34:23, time: 2.277, data_time: 1.299, memory: 15990, top1_acc: 0.5420, top5_acc: 0.7858, loss_cls: 2.5535, loss: 2.5535 +2024-07-27 02:20:12,579 - pyskl - INFO - Epoch [134][200/3746] lr: 3.117e-03, eta: 14:33:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5344, top5_acc: 0.7767, loss_cls: 2.5999, loss: 2.5999 +2024-07-27 02:21:34,062 - pyskl - INFO - Epoch [134][300/3746] lr: 3.107e-03, eta: 14:31:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5411, top5_acc: 0.7788, loss_cls: 2.5623, loss: 2.5623 +2024-07-27 02:22:55,759 - pyskl - INFO - Epoch [134][400/3746] lr: 3.097e-03, eta: 14:30:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5422, top5_acc: 0.7817, loss_cls: 2.5772, loss: 2.5772 +2024-07-27 02:24:17,087 - pyskl - INFO - Epoch [134][500/3746] lr: 3.087e-03, eta: 14:28:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5344, top5_acc: 0.7725, loss_cls: 2.6147, loss: 2.6147 +2024-07-27 02:25:38,888 - pyskl - INFO - Epoch [134][600/3746] lr: 3.078e-03, eta: 14:27:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5283, top5_acc: 0.7744, loss_cls: 2.6178, loss: 2.6178 +2024-07-27 02:27:00,362 - pyskl - INFO - Epoch [134][700/3746] lr: 3.068e-03, eta: 14:26:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5317, top5_acc: 0.7770, loss_cls: 2.6167, loss: 2.6167 +2024-07-27 02:28:21,928 - pyskl - INFO - Epoch [134][800/3746] lr: 3.059e-03, eta: 14:24:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5327, top5_acc: 0.7675, loss_cls: 2.6374, loss: 2.6374 +2024-07-27 02:29:43,136 - pyskl - INFO - Epoch [134][900/3746] lr: 3.049e-03, eta: 14:23:22, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5302, top5_acc: 0.7731, loss_cls: 2.6016, loss: 2.6016 +2024-07-27 02:31:04,591 - pyskl - INFO - Epoch [134][1000/3746] lr: 3.039e-03, eta: 14:22:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5328, top5_acc: 0.7775, loss_cls: 2.6187, loss: 2.6187 +2024-07-27 02:32:25,999 - pyskl - INFO - Epoch [134][1100/3746] lr: 3.030e-03, eta: 14:20:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5300, top5_acc: 0.7727, loss_cls: 2.5981, loss: 2.5981 +2024-07-27 02:33:48,587 - pyskl - INFO - Epoch [134][1200/3746] lr: 3.020e-03, eta: 14:19:14, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5395, top5_acc: 0.7820, loss_cls: 2.5639, loss: 2.5639 +2024-07-27 02:35:10,525 - pyskl - INFO - Epoch [134][1300/3746] lr: 3.011e-03, eta: 14:17:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5281, top5_acc: 0.7686, loss_cls: 2.6368, loss: 2.6368 +2024-07-27 02:36:32,643 - pyskl - INFO - Epoch [134][1400/3746] lr: 3.001e-03, eta: 14:16:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5414, top5_acc: 0.7780, loss_cls: 2.5885, loss: 2.5885 +2024-07-27 02:37:55,005 - pyskl - INFO - Epoch [134][1500/3746] lr: 2.991e-03, eta: 14:15:07, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5295, top5_acc: 0.7731, loss_cls: 2.6099, loss: 2.6099 +2024-07-27 02:39:16,622 - pyskl - INFO - Epoch [134][1600/3746] lr: 2.982e-03, eta: 14:13:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5272, top5_acc: 0.7778, loss_cls: 2.6251, loss: 2.6251 +2024-07-27 02:40:39,344 - pyskl - INFO - Epoch [134][1700/3746] lr: 2.972e-03, eta: 14:12:22, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5417, top5_acc: 0.7850, loss_cls: 2.5623, loss: 2.5623 +2024-07-27 02:42:00,987 - pyskl - INFO - Epoch [134][1800/3746] lr: 2.963e-03, eta: 14:10:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5270, top5_acc: 0.7652, loss_cls: 2.6382, loss: 2.6382 +2024-07-27 02:43:22,664 - pyskl - INFO - Epoch [134][1900/3746] lr: 2.953e-03, eta: 14:09:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5272, top5_acc: 0.7702, loss_cls: 2.6500, loss: 2.6500 +2024-07-27 02:44:44,546 - pyskl - INFO - Epoch [134][2000/3746] lr: 2.944e-03, eta: 14:08:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5325, top5_acc: 0.7792, loss_cls: 2.6020, loss: 2.6020 +2024-07-27 02:46:06,562 - pyskl - INFO - Epoch [134][2100/3746] lr: 2.935e-03, eta: 14:06:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5227, top5_acc: 0.7631, loss_cls: 2.6781, loss: 2.6781 +2024-07-27 02:47:28,513 - pyskl - INFO - Epoch [134][2200/3746] lr: 2.925e-03, eta: 14:05:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5156, top5_acc: 0.7616, loss_cls: 2.6912, loss: 2.6912 +2024-07-27 02:48:50,189 - pyskl - INFO - Epoch [134][2300/3746] lr: 2.916e-03, eta: 14:04:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5239, top5_acc: 0.7603, loss_cls: 2.6599, loss: 2.6599 +2024-07-27 02:50:11,433 - pyskl - INFO - Epoch [134][2400/3746] lr: 2.906e-03, eta: 14:02:43, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5241, top5_acc: 0.7666, loss_cls: 2.6617, loss: 2.6617 +2024-07-27 02:51:32,789 - pyskl - INFO - Epoch [134][2500/3746] lr: 2.897e-03, eta: 14:01:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7653, loss_cls: 2.6745, loss: 2.6745 +2024-07-27 02:52:54,309 - pyskl - INFO - Epoch [134][2600/3746] lr: 2.888e-03, eta: 13:59:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5294, top5_acc: 0.7702, loss_cls: 2.6471, loss: 2.6471 +2024-07-27 02:54:16,061 - pyskl - INFO - Epoch [134][2700/3746] lr: 2.878e-03, eta: 13:58:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5223, top5_acc: 0.7711, loss_cls: 2.6623, loss: 2.6623 +2024-07-27 02:55:37,650 - pyskl - INFO - Epoch [134][2800/3746] lr: 2.869e-03, eta: 13:57:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5353, top5_acc: 0.7792, loss_cls: 2.5838, loss: 2.5838 +2024-07-27 02:56:59,112 - pyskl - INFO - Epoch [134][2900/3746] lr: 2.860e-03, eta: 13:55:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5186, top5_acc: 0.7634, loss_cls: 2.7061, loss: 2.7061 +2024-07-27 02:58:20,714 - pyskl - INFO - Epoch [134][3000/3746] lr: 2.850e-03, eta: 13:54:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5238, top5_acc: 0.7623, loss_cls: 2.6806, loss: 2.6806 +2024-07-27 02:59:42,148 - pyskl - INFO - Epoch [134][3100/3746] lr: 2.841e-03, eta: 13:53:05, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5225, top5_acc: 0.7744, loss_cls: 2.6341, loss: 2.6341 +2024-07-27 03:01:03,859 - pyskl - INFO - Epoch [134][3200/3746] lr: 2.832e-03, eta: 13:51:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5192, top5_acc: 0.7692, loss_cls: 2.6575, loss: 2.6575 +2024-07-27 03:02:24,952 - pyskl - INFO - Epoch [134][3300/3746] lr: 2.822e-03, eta: 13:50:20, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5147, top5_acc: 0.7656, loss_cls: 2.6614, loss: 2.6614 +2024-07-27 03:03:46,927 - pyskl - INFO - Epoch [134][3400/3746] lr: 2.813e-03, eta: 13:48:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5170, top5_acc: 0.7578, loss_cls: 2.6964, loss: 2.6964 +2024-07-27 03:05:08,683 - pyskl - INFO - Epoch [134][3500/3746] lr: 2.804e-03, eta: 13:47:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5198, top5_acc: 0.7641, loss_cls: 2.6808, loss: 2.6808 +2024-07-27 03:06:30,617 - pyskl - INFO - Epoch [134][3600/3746] lr: 2.795e-03, eta: 13:46:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5272, top5_acc: 0.7698, loss_cls: 2.6499, loss: 2.6499 +2024-07-27 03:07:52,029 - pyskl - INFO - Epoch [134][3700/3746] lr: 2.786e-03, eta: 13:44:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5144, top5_acc: 0.7703, loss_cls: 2.6737, loss: 2.6737 +2024-07-27 03:08:31,433 - pyskl - INFO - Saving checkpoint at 134 epochs +2024-07-27 03:10:22,324 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 03:10:22,991 - pyskl - INFO - +top1_acc 0.4276 +top5_acc 0.6756 +2024-07-27 03:10:22,991 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 03:10:23,030 - pyskl - INFO - +mean_acc 0.4275 +2024-07-27 03:10:23,034 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_133.pth was removed +2024-07-27 03:10:23,281 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_134.pth. +2024-07-27 03:10:23,282 - pyskl - INFO - Best top1_acc is 0.4276 at 134 epoch. +2024-07-27 03:10:23,294 - pyskl - INFO - Epoch(val) [134][309] top1_acc: 0.4276, top5_acc: 0.6756, mean_class_accuracy: 0.4275 +2024-07-27 03:14:10,452 - pyskl - INFO - Epoch [135][100/3746] lr: 2.772e-03, eta: 13:43:02, time: 2.271, data_time: 1.284, memory: 15990, top1_acc: 0.5397, top5_acc: 0.7883, loss_cls: 2.5517, loss: 2.5517 +2024-07-27 03:15:32,844 - pyskl - INFO - Epoch [135][200/3746] lr: 2.763e-03, eta: 13:41:39, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5516, top5_acc: 0.7863, loss_cls: 2.5096, loss: 2.5096 +2024-07-27 03:16:55,356 - pyskl - INFO - Epoch [135][300/3746] lr: 2.754e-03, eta: 13:40:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5439, top5_acc: 0.7866, loss_cls: 2.5475, loss: 2.5475 +2024-07-27 03:18:17,124 - pyskl - INFO - Epoch [135][400/3746] lr: 2.745e-03, eta: 13:38:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5545, top5_acc: 0.7925, loss_cls: 2.4899, loss: 2.4899 +2024-07-27 03:19:38,387 - pyskl - INFO - Epoch [135][500/3746] lr: 2.735e-03, eta: 13:37:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5466, top5_acc: 0.7845, loss_cls: 2.5453, loss: 2.5453 +2024-07-27 03:20:59,962 - pyskl - INFO - Epoch [135][600/3746] lr: 2.726e-03, eta: 13:36:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5439, top5_acc: 0.7878, loss_cls: 2.5291, loss: 2.5291 +2024-07-27 03:22:21,213 - pyskl - INFO - Epoch [135][700/3746] lr: 2.717e-03, eta: 13:34:46, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5420, top5_acc: 0.7802, loss_cls: 2.5659, loss: 2.5659 +2024-07-27 03:23:42,386 - pyskl - INFO - Epoch [135][800/3746] lr: 2.708e-03, eta: 13:33:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5348, top5_acc: 0.7806, loss_cls: 2.5504, loss: 2.5504 +2024-07-27 03:25:03,617 - pyskl - INFO - Epoch [135][900/3746] lr: 2.699e-03, eta: 13:32:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5288, top5_acc: 0.7703, loss_cls: 2.6192, loss: 2.6192 +2024-07-27 03:26:25,745 - pyskl - INFO - Epoch [135][1000/3746] lr: 2.690e-03, eta: 13:30:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5361, top5_acc: 0.7814, loss_cls: 2.5770, loss: 2.5770 +2024-07-27 03:27:47,218 - pyskl - INFO - Epoch [135][1100/3746] lr: 2.681e-03, eta: 13:29:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5392, top5_acc: 0.7755, loss_cls: 2.5942, loss: 2.5942 +2024-07-27 03:29:09,225 - pyskl - INFO - Epoch [135][1200/3746] lr: 2.672e-03, eta: 13:27:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5363, top5_acc: 0.7819, loss_cls: 2.5513, loss: 2.5513 +2024-07-27 03:30:31,238 - pyskl - INFO - Epoch [135][1300/3746] lr: 2.663e-03, eta: 13:26:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5258, top5_acc: 0.7744, loss_cls: 2.6357, loss: 2.6357 +2024-07-27 03:31:53,629 - pyskl - INFO - Epoch [135][1400/3746] lr: 2.654e-03, eta: 13:25:08, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5427, top5_acc: 0.7856, loss_cls: 2.5594, loss: 2.5594 +2024-07-27 03:33:15,314 - pyskl - INFO - Epoch [135][1500/3746] lr: 2.645e-03, eta: 13:23:45, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5416, top5_acc: 0.7698, loss_cls: 2.5800, loss: 2.5800 +2024-07-27 03:34:37,396 - pyskl - INFO - Epoch [135][1600/3746] lr: 2.636e-03, eta: 13:22:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5359, top5_acc: 0.7875, loss_cls: 2.5610, loss: 2.5610 +2024-07-27 03:35:59,084 - pyskl - INFO - Epoch [135][1700/3746] lr: 2.627e-03, eta: 13:21:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5444, top5_acc: 0.7878, loss_cls: 2.5384, loss: 2.5384 +2024-07-27 03:37:20,588 - pyskl - INFO - Epoch [135][1800/3746] lr: 2.618e-03, eta: 13:19:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5387, top5_acc: 0.7761, loss_cls: 2.5893, loss: 2.5893 +2024-07-27 03:38:42,236 - pyskl - INFO - Epoch [135][1900/3746] lr: 2.609e-03, eta: 13:18:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5337, top5_acc: 0.7814, loss_cls: 2.5731, loss: 2.5731 +2024-07-27 03:40:03,677 - pyskl - INFO - Epoch [135][2000/3746] lr: 2.600e-03, eta: 13:16:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5344, top5_acc: 0.7758, loss_cls: 2.6056, loss: 2.6056 +2024-07-27 03:41:25,804 - pyskl - INFO - Epoch [135][2100/3746] lr: 2.591e-03, eta: 13:15:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5406, top5_acc: 0.7828, loss_cls: 2.5768, loss: 2.5768 +2024-07-27 03:42:48,268 - pyskl - INFO - Epoch [135][2200/3746] lr: 2.583e-03, eta: 13:14:07, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5413, top5_acc: 0.7706, loss_cls: 2.5937, loss: 2.5937 +2024-07-27 03:44:10,360 - pyskl - INFO - Epoch [135][2300/3746] lr: 2.574e-03, eta: 13:12:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5288, top5_acc: 0.7728, loss_cls: 2.6214, loss: 2.6214 +2024-07-27 03:45:32,548 - pyskl - INFO - Epoch [135][2400/3746] lr: 2.565e-03, eta: 13:11:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5242, top5_acc: 0.7738, loss_cls: 2.6207, loss: 2.6207 +2024-07-27 03:46:54,553 - pyskl - INFO - Epoch [135][2500/3746] lr: 2.556e-03, eta: 13:09:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5300, top5_acc: 0.7781, loss_cls: 2.6014, loss: 2.6014 +2024-07-27 03:48:16,122 - pyskl - INFO - Epoch [135][2600/3746] lr: 2.547e-03, eta: 13:08:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5173, top5_acc: 0.7677, loss_cls: 2.6561, loss: 2.6561 +2024-07-27 03:49:37,584 - pyskl - INFO - Epoch [135][2700/3746] lr: 2.538e-03, eta: 13:07:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5369, top5_acc: 0.7755, loss_cls: 2.5853, loss: 2.5853 +2024-07-27 03:50:59,297 - pyskl - INFO - Epoch [135][2800/3746] lr: 2.530e-03, eta: 13:05:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5358, top5_acc: 0.7755, loss_cls: 2.5927, loss: 2.5927 +2024-07-27 03:52:20,922 - pyskl - INFO - Epoch [135][2900/3746] lr: 2.521e-03, eta: 13:04:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5297, top5_acc: 0.7705, loss_cls: 2.6126, loss: 2.6126 +2024-07-27 03:53:42,639 - pyskl - INFO - Epoch [135][3000/3746] lr: 2.512e-03, eta: 13:03:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5292, top5_acc: 0.7712, loss_cls: 2.6152, loss: 2.6152 +2024-07-27 03:55:04,012 - pyskl - INFO - Epoch [135][3100/3746] lr: 2.503e-03, eta: 13:01:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5252, top5_acc: 0.7697, loss_cls: 2.6081, loss: 2.6081 +2024-07-27 03:56:25,637 - pyskl - INFO - Epoch [135][3200/3746] lr: 2.495e-03, eta: 13:00:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5305, top5_acc: 0.7739, loss_cls: 2.6113, loss: 2.6113 +2024-07-27 03:57:46,721 - pyskl - INFO - Epoch [135][3300/3746] lr: 2.486e-03, eta: 12:58:58, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5262, top5_acc: 0.7837, loss_cls: 2.6009, loss: 2.6009 +2024-07-27 03:59:08,194 - pyskl - INFO - Epoch [135][3400/3746] lr: 2.477e-03, eta: 12:57:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5319, top5_acc: 0.7750, loss_cls: 2.6270, loss: 2.6270 +2024-07-27 04:00:30,045 - pyskl - INFO - Epoch [135][3500/3746] lr: 2.469e-03, eta: 12:56:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5344, top5_acc: 0.7770, loss_cls: 2.5946, loss: 2.5946 +2024-07-27 04:01:51,949 - pyskl - INFO - Epoch [135][3600/3746] lr: 2.460e-03, eta: 12:54:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5327, top5_acc: 0.7775, loss_cls: 2.5912, loss: 2.5912 +2024-07-27 04:03:13,480 - pyskl - INFO - Epoch [135][3700/3746] lr: 2.451e-03, eta: 12:53:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5295, top5_acc: 0.7708, loss_cls: 2.6332, loss: 2.6332 +2024-07-27 04:03:53,078 - pyskl - INFO - Saving checkpoint at 135 epochs +2024-07-27 04:05:43,925 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 04:05:44,589 - pyskl - INFO - +top1_acc 0.4318 +top5_acc 0.6824 +2024-07-27 04:05:44,589 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 04:05:44,628 - pyskl - INFO - +mean_acc 0.4316 +2024-07-27 04:05:44,633 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_134.pth was removed +2024-07-27 04:05:44,897 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_135.pth. +2024-07-27 04:05:44,898 - pyskl - INFO - Best top1_acc is 0.4318 at 135 epoch. +2024-07-27 04:05:44,909 - pyskl - INFO - Epoch(val) [135][309] top1_acc: 0.4318, top5_acc: 0.6824, mean_class_accuracy: 0.4316 +2024-07-27 04:09:33,135 - pyskl - INFO - Epoch [136][100/3746] lr: 2.439e-03, eta: 12:51:39, time: 2.282, data_time: 1.293, memory: 15990, top1_acc: 0.5617, top5_acc: 0.8000, loss_cls: 2.4622, loss: 2.4622 +2024-07-27 04:10:55,561 - pyskl - INFO - Epoch [136][200/3746] lr: 2.430e-03, eta: 12:50:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5670, top5_acc: 0.7991, loss_cls: 2.4404, loss: 2.4404 +2024-07-27 04:12:18,009 - pyskl - INFO - Epoch [136][300/3746] lr: 2.421e-03, eta: 12:48:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5489, top5_acc: 0.7947, loss_cls: 2.4947, loss: 2.4947 +2024-07-27 04:13:39,620 - pyskl - INFO - Epoch [136][400/3746] lr: 2.413e-03, eta: 12:47:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5561, top5_acc: 0.7992, loss_cls: 2.4855, loss: 2.4855 +2024-07-27 04:15:01,241 - pyskl - INFO - Epoch [136][500/3746] lr: 2.404e-03, eta: 12:46:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5531, top5_acc: 0.7933, loss_cls: 2.4952, loss: 2.4952 +2024-07-27 04:16:22,289 - pyskl - INFO - Epoch [136][600/3746] lr: 2.396e-03, eta: 12:44:46, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5533, top5_acc: 0.7952, loss_cls: 2.4804, loss: 2.4804 +2024-07-27 04:17:43,186 - pyskl - INFO - Epoch [136][700/3746] lr: 2.387e-03, eta: 12:43:23, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.5553, top5_acc: 0.7908, loss_cls: 2.4834, loss: 2.4834 +2024-07-27 04:19:05,237 - pyskl - INFO - Epoch [136][800/3746] lr: 2.379e-03, eta: 12:42:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5464, top5_acc: 0.7841, loss_cls: 2.5214, loss: 2.5214 +2024-07-27 04:20:26,886 - pyskl - INFO - Epoch [136][900/3746] lr: 2.370e-03, eta: 12:40:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5473, top5_acc: 0.7870, loss_cls: 2.5086, loss: 2.5086 +2024-07-27 04:21:48,112 - pyskl - INFO - Epoch [136][1000/3746] lr: 2.362e-03, eta: 12:39:15, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5528, top5_acc: 0.7822, loss_cls: 2.5354, loss: 2.5354 +2024-07-27 04:23:09,957 - pyskl - INFO - Epoch [136][1100/3746] lr: 2.353e-03, eta: 12:37:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5444, top5_acc: 0.7830, loss_cls: 2.5308, loss: 2.5308 +2024-07-27 04:24:31,837 - pyskl - INFO - Epoch [136][1200/3746] lr: 2.345e-03, eta: 12:36:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5430, top5_acc: 0.7831, loss_cls: 2.5550, loss: 2.5550 +2024-07-27 04:25:53,670 - pyskl - INFO - Epoch [136][1300/3746] lr: 2.336e-03, eta: 12:35:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5487, top5_acc: 0.7866, loss_cls: 2.5378, loss: 2.5378 +2024-07-27 04:27:16,093 - pyskl - INFO - Epoch [136][1400/3746] lr: 2.328e-03, eta: 12:33:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5566, top5_acc: 0.7887, loss_cls: 2.4916, loss: 2.4916 +2024-07-27 04:28:38,189 - pyskl - INFO - Epoch [136][1500/3746] lr: 2.319e-03, eta: 12:32:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5389, top5_acc: 0.7755, loss_cls: 2.5599, loss: 2.5599 +2024-07-27 04:30:00,982 - pyskl - INFO - Epoch [136][1600/3746] lr: 2.311e-03, eta: 12:31:00, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5452, top5_acc: 0.7812, loss_cls: 2.5607, loss: 2.5607 +2024-07-27 04:31:22,666 - pyskl - INFO - Epoch [136][1700/3746] lr: 2.303e-03, eta: 12:29:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5430, top5_acc: 0.7864, loss_cls: 2.5149, loss: 2.5149 +2024-07-27 04:32:44,740 - pyskl - INFO - Epoch [136][1800/3746] lr: 2.294e-03, eta: 12:28:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5452, top5_acc: 0.7791, loss_cls: 2.5789, loss: 2.5789 +2024-07-27 04:34:06,012 - pyskl - INFO - Epoch [136][1900/3746] lr: 2.286e-03, eta: 12:26:52, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5309, top5_acc: 0.7759, loss_cls: 2.5925, loss: 2.5925 +2024-07-27 04:35:27,691 - pyskl - INFO - Epoch [136][2000/3746] lr: 2.277e-03, eta: 12:25:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5370, top5_acc: 0.7805, loss_cls: 2.5829, loss: 2.5829 +2024-07-27 04:36:49,363 - pyskl - INFO - Epoch [136][2100/3746] lr: 2.269e-03, eta: 12:24:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5363, top5_acc: 0.7891, loss_cls: 2.5514, loss: 2.5514 +2024-07-27 04:38:11,392 - pyskl - INFO - Epoch [136][2200/3746] lr: 2.261e-03, eta: 12:22:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5419, top5_acc: 0.7911, loss_cls: 2.5293, loss: 2.5293 +2024-07-27 04:39:32,876 - pyskl - INFO - Epoch [136][2300/3746] lr: 2.253e-03, eta: 12:21:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5278, top5_acc: 0.7748, loss_cls: 2.5987, loss: 2.5987 +2024-07-27 04:40:54,715 - pyskl - INFO - Epoch [136][2400/3746] lr: 2.244e-03, eta: 12:19:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5502, top5_acc: 0.7864, loss_cls: 2.5259, loss: 2.5259 +2024-07-27 04:42:16,115 - pyskl - INFO - Epoch [136][2500/3746] lr: 2.236e-03, eta: 12:18:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5453, top5_acc: 0.7823, loss_cls: 2.5433, loss: 2.5433 +2024-07-27 04:43:37,962 - pyskl - INFO - Epoch [136][2600/3746] lr: 2.228e-03, eta: 12:17:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5502, top5_acc: 0.7812, loss_cls: 2.5531, loss: 2.5531 +2024-07-27 04:44:59,476 - pyskl - INFO - Epoch [136][2700/3746] lr: 2.219e-03, eta: 12:15:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5378, top5_acc: 0.7780, loss_cls: 2.5663, loss: 2.5663 +2024-07-27 04:46:21,232 - pyskl - INFO - Epoch [136][2800/3746] lr: 2.211e-03, eta: 12:14:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5347, top5_acc: 0.7767, loss_cls: 2.5918, loss: 2.5918 +2024-07-27 04:47:42,872 - pyskl - INFO - Epoch [136][2900/3746] lr: 2.203e-03, eta: 12:13:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5330, top5_acc: 0.7811, loss_cls: 2.5822, loss: 2.5822 +2024-07-27 04:49:04,441 - pyskl - INFO - Epoch [136][3000/3746] lr: 2.195e-03, eta: 12:11:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5420, top5_acc: 0.7812, loss_cls: 2.5719, loss: 2.5719 +2024-07-27 04:50:25,810 - pyskl - INFO - Epoch [136][3100/3746] lr: 2.187e-03, eta: 12:10:20, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5445, top5_acc: 0.7780, loss_cls: 2.5645, loss: 2.5645 +2024-07-27 04:51:47,337 - pyskl - INFO - Epoch [136][3200/3746] lr: 2.178e-03, eta: 12:08:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5453, top5_acc: 0.7834, loss_cls: 2.5383, loss: 2.5383 +2024-07-27 04:53:09,576 - pyskl - INFO - Epoch [136][3300/3746] lr: 2.170e-03, eta: 12:07:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5389, top5_acc: 0.7873, loss_cls: 2.5263, loss: 2.5263 +2024-07-27 04:54:31,388 - pyskl - INFO - Epoch [136][3400/3746] lr: 2.162e-03, eta: 12:06:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5283, top5_acc: 0.7728, loss_cls: 2.5956, loss: 2.5956 +2024-07-27 04:55:52,981 - pyskl - INFO - Epoch [136][3500/3746] lr: 2.154e-03, eta: 12:04:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5381, top5_acc: 0.7750, loss_cls: 2.5810, loss: 2.5810 +2024-07-27 04:57:14,285 - pyskl - INFO - Epoch [136][3600/3746] lr: 2.146e-03, eta: 12:03:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5347, top5_acc: 0.7827, loss_cls: 2.5647, loss: 2.5647 +2024-07-27 04:58:35,930 - pyskl - INFO - Epoch [136][3700/3746] lr: 2.138e-03, eta: 12:02:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5320, top5_acc: 0.7812, loss_cls: 2.5882, loss: 2.5882 +2024-07-27 04:59:15,337 - pyskl - INFO - Saving checkpoint at 136 epochs +2024-07-27 05:01:06,247 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 05:01:06,955 - pyskl - INFO - +top1_acc 0.4313 +top5_acc 0.6803 +2024-07-27 05:01:06,955 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 05:01:07,000 - pyskl - INFO - +mean_acc 0.4310 +2024-07-27 05:01:07,012 - pyskl - INFO - Epoch(val) [136][309] top1_acc: 0.4313, top5_acc: 0.6803, mean_class_accuracy: 0.4310 +2024-07-27 05:04:57,052 - pyskl - INFO - Epoch [137][100/3746] lr: 2.126e-03, eta: 12:00:15, time: 2.300, data_time: 1.310, memory: 15990, top1_acc: 0.5678, top5_acc: 0.7941, loss_cls: 2.4726, loss: 2.4726 +2024-07-27 05:06:19,192 - pyskl - INFO - Epoch [137][200/3746] lr: 2.118e-03, eta: 11:58:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5687, top5_acc: 0.7972, loss_cls: 2.4160, loss: 2.4160 +2024-07-27 05:07:40,968 - pyskl - INFO - Epoch [137][300/3746] lr: 2.110e-03, eta: 11:57:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5341, top5_acc: 0.7852, loss_cls: 2.5325, loss: 2.5325 +2024-07-27 05:09:02,227 - pyskl - INFO - Epoch [137][400/3746] lr: 2.102e-03, eta: 11:56:07, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5572, top5_acc: 0.7884, loss_cls: 2.4917, loss: 2.4917 +2024-07-27 05:10:23,663 - pyskl - INFO - Epoch [137][500/3746] lr: 2.094e-03, eta: 11:54:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5620, top5_acc: 0.7987, loss_cls: 2.4528, loss: 2.4528 +2024-07-27 05:11:45,638 - pyskl - INFO - Epoch [137][600/3746] lr: 2.086e-03, eta: 11:53:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5647, top5_acc: 0.7967, loss_cls: 2.4560, loss: 2.4560 +2024-07-27 05:13:07,359 - pyskl - INFO - Epoch [137][700/3746] lr: 2.078e-03, eta: 11:51:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5619, top5_acc: 0.8045, loss_cls: 2.4430, loss: 2.4430 +2024-07-27 05:14:28,940 - pyskl - INFO - Epoch [137][800/3746] lr: 2.070e-03, eta: 11:50:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5637, top5_acc: 0.7973, loss_cls: 2.4448, loss: 2.4448 +2024-07-27 05:15:50,587 - pyskl - INFO - Epoch [137][900/3746] lr: 2.062e-03, eta: 11:49:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5516, top5_acc: 0.8005, loss_cls: 2.4789, loss: 2.4789 +2024-07-27 05:17:12,690 - pyskl - INFO - Epoch [137][1000/3746] lr: 2.054e-03, eta: 11:47:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5480, top5_acc: 0.7878, loss_cls: 2.5258, loss: 2.5258 +2024-07-27 05:18:34,144 - pyskl - INFO - Epoch [137][1100/3746] lr: 2.046e-03, eta: 11:46:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5569, top5_acc: 0.7906, loss_cls: 2.4811, loss: 2.4811 +2024-07-27 05:19:56,459 - pyskl - INFO - Epoch [137][1200/3746] lr: 2.038e-03, eta: 11:45:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5572, top5_acc: 0.7945, loss_cls: 2.4605, loss: 2.4605 +2024-07-27 05:21:18,206 - pyskl - INFO - Epoch [137][1300/3746] lr: 2.030e-03, eta: 11:43:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5523, top5_acc: 0.7880, loss_cls: 2.4980, loss: 2.4980 +2024-07-27 05:22:40,993 - pyskl - INFO - Epoch [137][1400/3746] lr: 2.022e-03, eta: 11:42:21, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5486, top5_acc: 0.7914, loss_cls: 2.5193, loss: 2.5193 +2024-07-27 05:24:02,882 - pyskl - INFO - Epoch [137][1500/3746] lr: 2.015e-03, eta: 11:40:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5563, top5_acc: 0.7955, loss_cls: 2.4599, loss: 2.4599 +2024-07-27 05:25:24,878 - pyskl - INFO - Epoch [137][1600/3746] lr: 2.007e-03, eta: 11:39:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5527, top5_acc: 0.8061, loss_cls: 2.4326, loss: 2.4326 +2024-07-27 05:26:46,637 - pyskl - INFO - Epoch [137][1700/3746] lr: 1.999e-03, eta: 11:38:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5464, top5_acc: 0.7883, loss_cls: 2.5220, loss: 2.5220 +2024-07-27 05:28:08,668 - pyskl - INFO - Epoch [137][1800/3746] lr: 1.991e-03, eta: 11:36:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5506, top5_acc: 0.7878, loss_cls: 2.4760, loss: 2.4760 +2024-07-27 05:29:30,188 - pyskl - INFO - Epoch [137][1900/3746] lr: 1.983e-03, eta: 11:35:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5581, top5_acc: 0.7939, loss_cls: 2.4545, loss: 2.4545 +2024-07-27 05:30:52,232 - pyskl - INFO - Epoch [137][2000/3746] lr: 1.976e-03, eta: 11:34:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5575, top5_acc: 0.7909, loss_cls: 2.4755, loss: 2.4755 +2024-07-27 05:32:14,198 - pyskl - INFO - Epoch [137][2100/3746] lr: 1.968e-03, eta: 11:32:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5408, top5_acc: 0.7805, loss_cls: 2.5599, loss: 2.5599 +2024-07-27 05:33:35,994 - pyskl - INFO - Epoch [137][2200/3746] lr: 1.960e-03, eta: 11:31:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5422, top5_acc: 0.7850, loss_cls: 2.5426, loss: 2.5426 +2024-07-27 05:34:57,482 - pyskl - INFO - Epoch [137][2300/3746] lr: 1.952e-03, eta: 11:29:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5441, top5_acc: 0.7802, loss_cls: 2.5389, loss: 2.5389 +2024-07-27 05:36:18,661 - pyskl - INFO - Epoch [137][2400/3746] lr: 1.944e-03, eta: 11:28:34, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5498, top5_acc: 0.7825, loss_cls: 2.5142, loss: 2.5142 +2024-07-27 05:37:40,120 - pyskl - INFO - Epoch [137][2500/3746] lr: 1.937e-03, eta: 11:27:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5500, top5_acc: 0.7895, loss_cls: 2.5243, loss: 2.5243 +2024-07-27 05:39:01,352 - pyskl - INFO - Epoch [137][2600/3746] lr: 1.929e-03, eta: 11:25:49, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5537, top5_acc: 0.7913, loss_cls: 2.4953, loss: 2.4953 +2024-07-27 05:40:22,383 - pyskl - INFO - Epoch [137][2700/3746] lr: 1.921e-03, eta: 11:24:26, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5658, top5_acc: 0.7992, loss_cls: 2.4247, loss: 2.4247 +2024-07-27 05:41:43,906 - pyskl - INFO - Epoch [137][2800/3746] lr: 1.914e-03, eta: 11:23:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5514, top5_acc: 0.7891, loss_cls: 2.5109, loss: 2.5109 +2024-07-27 05:43:04,996 - pyskl - INFO - Epoch [137][2900/3746] lr: 1.906e-03, eta: 11:21:41, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5598, top5_acc: 0.7966, loss_cls: 2.4634, loss: 2.4634 +2024-07-27 05:44:26,536 - pyskl - INFO - Epoch [137][3000/3746] lr: 1.898e-03, eta: 11:20:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5484, top5_acc: 0.7814, loss_cls: 2.5362, loss: 2.5362 +2024-07-27 05:45:48,035 - pyskl - INFO - Epoch [137][3100/3746] lr: 1.891e-03, eta: 11:18:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5463, top5_acc: 0.7817, loss_cls: 2.5318, loss: 2.5318 +2024-07-27 05:47:09,706 - pyskl - INFO - Epoch [137][3200/3746] lr: 1.883e-03, eta: 11:17:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5484, top5_acc: 0.7950, loss_cls: 2.4788, loss: 2.4788 +2024-07-27 05:48:31,070 - pyskl - INFO - Epoch [137][3300/3746] lr: 1.876e-03, eta: 11:16:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5369, top5_acc: 0.7833, loss_cls: 2.5553, loss: 2.5553 +2024-07-27 05:49:52,460 - pyskl - INFO - Epoch [137][3400/3746] lr: 1.868e-03, eta: 11:14:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5545, top5_acc: 0.7877, loss_cls: 2.5149, loss: 2.5149 +2024-07-27 05:51:13,750 - pyskl - INFO - Epoch [137][3500/3746] lr: 1.860e-03, eta: 11:13:25, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5409, top5_acc: 0.7856, loss_cls: 2.5301, loss: 2.5301 +2024-07-27 05:52:35,050 - pyskl - INFO - Epoch [137][3600/3746] lr: 1.853e-03, eta: 11:12:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5444, top5_acc: 0.7889, loss_cls: 2.5383, loss: 2.5383 +2024-07-27 05:53:56,541 - pyskl - INFO - Epoch [137][3700/3746] lr: 1.845e-03, eta: 11:10:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5439, top5_acc: 0.7877, loss_cls: 2.5160, loss: 2.5160 +2024-07-27 05:54:36,136 - pyskl - INFO - Saving checkpoint at 137 epochs +2024-07-27 05:56:26,614 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 05:56:27,384 - pyskl - INFO - +top1_acc 0.4363 +top5_acc 0.6852 +2024-07-27 05:56:27,385 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 05:56:27,431 - pyskl - INFO - +mean_acc 0.4361 +2024-07-27 05:56:27,435 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_135.pth was removed +2024-07-27 05:56:27,705 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_137.pth. +2024-07-27 05:56:27,705 - pyskl - INFO - Best top1_acc is 0.4363 at 137 epoch. +2024-07-27 05:56:27,722 - pyskl - INFO - Epoch(val) [137][309] top1_acc: 0.4363, top5_acc: 0.6852, mean_class_accuracy: 0.4361 +2024-07-27 06:00:15,739 - pyskl - INFO - Epoch [138][100/3746] lr: 1.834e-03, eta: 11:08:49, time: 2.280, data_time: 1.296, memory: 15990, top1_acc: 0.5769, top5_acc: 0.8092, loss_cls: 2.3779, loss: 2.3779 +2024-07-27 06:01:38,436 - pyskl - INFO - Epoch [138][200/3746] lr: 1.827e-03, eta: 11:07:27, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5731, top5_acc: 0.8066, loss_cls: 2.3955, loss: 2.3955 +2024-07-27 06:03:00,671 - pyskl - INFO - Epoch [138][300/3746] lr: 1.819e-03, eta: 11:06:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5742, top5_acc: 0.8150, loss_cls: 2.3755, loss: 2.3755 +2024-07-27 06:04:22,695 - pyskl - INFO - Epoch [138][400/3746] lr: 1.812e-03, eta: 11:04:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5700, top5_acc: 0.8066, loss_cls: 2.4003, loss: 2.4003 +2024-07-27 06:05:43,994 - pyskl - INFO - Epoch [138][500/3746] lr: 1.805e-03, eta: 11:03:19, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5669, top5_acc: 0.8009, loss_cls: 2.4039, loss: 2.4039 +2024-07-27 06:07:05,384 - pyskl - INFO - Epoch [138][600/3746] lr: 1.797e-03, eta: 11:01:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5587, top5_acc: 0.7992, loss_cls: 2.4449, loss: 2.4449 +2024-07-27 06:08:26,944 - pyskl - INFO - Epoch [138][700/3746] lr: 1.790e-03, eta: 11:00:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5673, top5_acc: 0.8020, loss_cls: 2.4222, loss: 2.4222 +2024-07-27 06:09:48,746 - pyskl - INFO - Epoch [138][800/3746] lr: 1.782e-03, eta: 10:59:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5664, top5_acc: 0.8056, loss_cls: 2.4220, loss: 2.4220 +2024-07-27 06:11:09,922 - pyskl - INFO - Epoch [138][900/3746] lr: 1.775e-03, eta: 10:57:48, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5672, top5_acc: 0.7981, loss_cls: 2.4292, loss: 2.4292 +2024-07-27 06:12:31,810 - pyskl - INFO - Epoch [138][1000/3746] lr: 1.768e-03, eta: 10:56:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5697, top5_acc: 0.8052, loss_cls: 2.4174, loss: 2.4174 +2024-07-27 06:13:53,580 - pyskl - INFO - Epoch [138][1100/3746] lr: 1.760e-03, eta: 10:55:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5577, top5_acc: 0.8025, loss_cls: 2.4567, loss: 2.4567 +2024-07-27 06:15:15,503 - pyskl - INFO - Epoch [138][1200/3746] lr: 1.753e-03, eta: 10:53:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5637, top5_acc: 0.7989, loss_cls: 2.4279, loss: 2.4279 +2024-07-27 06:16:37,932 - pyskl - INFO - Epoch [138][1300/3746] lr: 1.745e-03, eta: 10:52:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5678, top5_acc: 0.8033, loss_cls: 2.4012, loss: 2.4012 +2024-07-27 06:18:00,050 - pyskl - INFO - Epoch [138][1400/3746] lr: 1.738e-03, eta: 10:50:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5639, top5_acc: 0.8009, loss_cls: 2.4260, loss: 2.4260 +2024-07-27 06:19:21,476 - pyskl - INFO - Epoch [138][1500/3746] lr: 1.731e-03, eta: 10:49:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5620, top5_acc: 0.7911, loss_cls: 2.4600, loss: 2.4600 +2024-07-27 06:20:43,278 - pyskl - INFO - Epoch [138][1600/3746] lr: 1.724e-03, eta: 10:48:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5487, top5_acc: 0.7950, loss_cls: 2.4914, loss: 2.4914 +2024-07-27 06:22:04,563 - pyskl - INFO - Epoch [138][1700/3746] lr: 1.716e-03, eta: 10:46:47, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5637, top5_acc: 0.7908, loss_cls: 2.4409, loss: 2.4409 +2024-07-27 06:23:26,054 - pyskl - INFO - Epoch [138][1800/3746] lr: 1.709e-03, eta: 10:45:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5620, top5_acc: 0.7961, loss_cls: 2.4511, loss: 2.4511 +2024-07-27 06:24:47,719 - pyskl - INFO - Epoch [138][1900/3746] lr: 1.702e-03, eta: 10:44:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5767, top5_acc: 0.8005, loss_cls: 2.4189, loss: 2.4189 +2024-07-27 06:26:09,333 - pyskl - INFO - Epoch [138][2000/3746] lr: 1.695e-03, eta: 10:42:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5536, top5_acc: 0.7933, loss_cls: 2.4547, loss: 2.4547 +2024-07-27 06:27:31,329 - pyskl - INFO - Epoch [138][2100/3746] lr: 1.687e-03, eta: 10:41:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5608, top5_acc: 0.7963, loss_cls: 2.4469, loss: 2.4469 +2024-07-27 06:28:53,193 - pyskl - INFO - Epoch [138][2200/3746] lr: 1.680e-03, eta: 10:39:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5681, top5_acc: 0.8067, loss_cls: 2.4133, loss: 2.4133 +2024-07-27 06:30:14,855 - pyskl - INFO - Epoch [138][2300/3746] lr: 1.673e-03, eta: 10:38:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5642, top5_acc: 0.8055, loss_cls: 2.4192, loss: 2.4192 +2024-07-27 06:31:36,785 - pyskl - INFO - Epoch [138][2400/3746] lr: 1.666e-03, eta: 10:37:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5652, top5_acc: 0.8028, loss_cls: 2.4187, loss: 2.4187 +2024-07-27 06:32:58,461 - pyskl - INFO - Epoch [138][2500/3746] lr: 1.659e-03, eta: 10:35:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5509, top5_acc: 0.7856, loss_cls: 2.5066, loss: 2.5066 +2024-07-27 06:34:20,827 - pyskl - INFO - Epoch [138][2600/3746] lr: 1.652e-03, eta: 10:34:23, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5617, top5_acc: 0.7945, loss_cls: 2.4651, loss: 2.4651 +2024-07-27 06:35:42,366 - pyskl - INFO - Epoch [138][2700/3746] lr: 1.644e-03, eta: 10:33:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5637, top5_acc: 0.7919, loss_cls: 2.4372, loss: 2.4372 +2024-07-27 06:37:04,290 - pyskl - INFO - Epoch [138][2800/3746] lr: 1.637e-03, eta: 10:31:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5497, top5_acc: 0.7967, loss_cls: 2.4601, loss: 2.4601 +2024-07-27 06:38:25,702 - pyskl - INFO - Epoch [138][2900/3746] lr: 1.630e-03, eta: 10:30:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5578, top5_acc: 0.7961, loss_cls: 2.4513, loss: 2.4513 +2024-07-27 06:39:47,068 - pyskl - INFO - Epoch [138][3000/3746] lr: 1.623e-03, eta: 10:28:53, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5587, top5_acc: 0.7964, loss_cls: 2.4472, loss: 2.4472 +2024-07-27 06:41:08,286 - pyskl - INFO - Epoch [138][3100/3746] lr: 1.616e-03, eta: 10:27:30, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5548, top5_acc: 0.8052, loss_cls: 2.4378, loss: 2.4378 +2024-07-27 06:42:30,104 - pyskl - INFO - Epoch [138][3200/3746] lr: 1.609e-03, eta: 10:26:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5627, top5_acc: 0.7947, loss_cls: 2.4741, loss: 2.4741 +2024-07-27 06:43:51,655 - pyskl - INFO - Epoch [138][3300/3746] lr: 1.602e-03, eta: 10:24:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5600, top5_acc: 0.7937, loss_cls: 2.4303, loss: 2.4303 +2024-07-27 06:45:13,495 - pyskl - INFO - Epoch [138][3400/3746] lr: 1.595e-03, eta: 10:23:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5519, top5_acc: 0.7883, loss_cls: 2.4899, loss: 2.4899 +2024-07-27 06:46:34,672 - pyskl - INFO - Epoch [138][3500/3746] lr: 1.588e-03, eta: 10:21:59, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5631, top5_acc: 0.7931, loss_cls: 2.4661, loss: 2.4661 +2024-07-27 06:47:56,454 - pyskl - INFO - Epoch [138][3600/3746] lr: 1.581e-03, eta: 10:20:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5547, top5_acc: 0.7919, loss_cls: 2.4819, loss: 2.4819 +2024-07-27 06:49:17,666 - pyskl - INFO - Epoch [138][3700/3746] lr: 1.574e-03, eta: 10:19:14, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5587, top5_acc: 0.7930, loss_cls: 2.4603, loss: 2.4603 +2024-07-27 06:49:57,725 - pyskl - INFO - Saving checkpoint at 138 epochs +2024-07-27 06:51:47,765 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 06:51:48,423 - pyskl - INFO - +top1_acc 0.4380 +top5_acc 0.6847 +2024-07-27 06:51:48,424 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 06:51:48,463 - pyskl - INFO - +mean_acc 0.4378 +2024-07-27 06:51:48,468 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_137.pth was removed +2024-07-27 06:51:48,729 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_138.pth. +2024-07-27 06:51:48,729 - pyskl - INFO - Best top1_acc is 0.4380 at 138 epoch. +2024-07-27 06:51:48,741 - pyskl - INFO - Epoch(val) [138][309] top1_acc: 0.4380, top5_acc: 0.6847, mean_class_accuracy: 0.4378 +2024-07-27 06:55:33,059 - pyskl - INFO - Epoch [139][100/3746] lr: 1.564e-03, eta: 10:17:22, time: 2.243, data_time: 1.267, memory: 15990, top1_acc: 0.5820, top5_acc: 0.8078, loss_cls: 2.3318, loss: 2.3318 +2024-07-27 06:56:55,169 - pyskl - INFO - Epoch [139][200/3746] lr: 1.557e-03, eta: 10:16:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5894, top5_acc: 0.8172, loss_cls: 2.3212, loss: 2.3212 +2024-07-27 06:58:16,913 - pyskl - INFO - Epoch [139][300/3746] lr: 1.550e-03, eta: 10:14:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5777, top5_acc: 0.8137, loss_cls: 2.3492, loss: 2.3492 +2024-07-27 06:59:39,009 - pyskl - INFO - Epoch [139][400/3746] lr: 1.543e-03, eta: 10:13:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5714, top5_acc: 0.8041, loss_cls: 2.3911, loss: 2.3911 +2024-07-27 07:01:00,453 - pyskl - INFO - Epoch [139][500/3746] lr: 1.536e-03, eta: 10:11:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5631, top5_acc: 0.8023, loss_cls: 2.3952, loss: 2.3952 +2024-07-27 07:02:22,352 - pyskl - INFO - Epoch [139][600/3746] lr: 1.529e-03, eta: 10:10:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5787, top5_acc: 0.8130, loss_cls: 2.3543, loss: 2.3543 +2024-07-27 07:03:44,176 - pyskl - INFO - Epoch [139][700/3746] lr: 1.523e-03, eta: 10:09:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5766, top5_acc: 0.8086, loss_cls: 2.3486, loss: 2.3486 +2024-07-27 07:05:05,636 - pyskl - INFO - Epoch [139][800/3746] lr: 1.516e-03, eta: 10:07:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5759, top5_acc: 0.8142, loss_cls: 2.3538, loss: 2.3538 +2024-07-27 07:06:27,775 - pyskl - INFO - Epoch [139][900/3746] lr: 1.509e-03, eta: 10:06:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5722, top5_acc: 0.8091, loss_cls: 2.3782, loss: 2.3782 +2024-07-27 07:07:48,753 - pyskl - INFO - Epoch [139][1000/3746] lr: 1.502e-03, eta: 10:04:59, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5772, top5_acc: 0.8123, loss_cls: 2.3632, loss: 2.3632 +2024-07-27 07:09:10,219 - pyskl - INFO - Epoch [139][1100/3746] lr: 1.495e-03, eta: 10:03:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5733, top5_acc: 0.8097, loss_cls: 2.3797, loss: 2.3797 +2024-07-27 07:10:32,394 - pyskl - INFO - Epoch [139][1200/3746] lr: 1.489e-03, eta: 10:02:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5713, top5_acc: 0.8047, loss_cls: 2.3974, loss: 2.3974 +2024-07-27 07:11:54,178 - pyskl - INFO - Epoch [139][1300/3746] lr: 1.482e-03, eta: 10:00:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5659, top5_acc: 0.8037, loss_cls: 2.3950, loss: 2.3950 +2024-07-27 07:13:16,490 - pyskl - INFO - Epoch [139][1400/3746] lr: 1.475e-03, eta: 9:59:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5780, top5_acc: 0.8083, loss_cls: 2.3707, loss: 2.3707 +2024-07-27 07:14:38,978 - pyskl - INFO - Epoch [139][1500/3746] lr: 1.468e-03, eta: 9:58:05, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5866, top5_acc: 0.8125, loss_cls: 2.3401, loss: 2.3401 +2024-07-27 07:16:01,244 - pyskl - INFO - Epoch [139][1600/3746] lr: 1.462e-03, eta: 9:56:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5705, top5_acc: 0.8069, loss_cls: 2.3920, loss: 2.3920 +2024-07-27 07:17:23,196 - pyskl - INFO - Epoch [139][1700/3746] lr: 1.455e-03, eta: 9:55:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5753, top5_acc: 0.8072, loss_cls: 2.3551, loss: 2.3551 +2024-07-27 07:18:44,634 - pyskl - INFO - Epoch [139][1800/3746] lr: 1.448e-03, eta: 9:53:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5695, top5_acc: 0.8023, loss_cls: 2.4017, loss: 2.4017 +2024-07-27 07:20:06,971 - pyskl - INFO - Epoch [139][1900/3746] lr: 1.442e-03, eta: 9:52:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5708, top5_acc: 0.8025, loss_cls: 2.4068, loss: 2.4068 +2024-07-27 07:21:28,661 - pyskl - INFO - Epoch [139][2000/3746] lr: 1.435e-03, eta: 9:51:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5741, top5_acc: 0.8075, loss_cls: 2.3850, loss: 2.3850 +2024-07-27 07:22:50,860 - pyskl - INFO - Epoch [139][2100/3746] lr: 1.428e-03, eta: 9:49:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5555, top5_acc: 0.7944, loss_cls: 2.4449, loss: 2.4449 +2024-07-27 07:24:12,723 - pyskl - INFO - Epoch [139][2200/3746] lr: 1.422e-03, eta: 9:48:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5684, top5_acc: 0.8006, loss_cls: 2.4199, loss: 2.4199 +2024-07-27 07:25:34,069 - pyskl - INFO - Epoch [139][2300/3746] lr: 1.415e-03, eta: 9:47:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5708, top5_acc: 0.8078, loss_cls: 2.3962, loss: 2.3962 +2024-07-27 07:26:55,879 - pyskl - INFO - Epoch [139][2400/3746] lr: 1.408e-03, eta: 9:45:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5758, top5_acc: 0.8102, loss_cls: 2.3587, loss: 2.3587 +2024-07-27 07:28:17,334 - pyskl - INFO - Epoch [139][2500/3746] lr: 1.402e-03, eta: 9:44:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5697, top5_acc: 0.8066, loss_cls: 2.3996, loss: 2.3996 +2024-07-27 07:29:38,832 - pyskl - INFO - Epoch [139][2600/3746] lr: 1.395e-03, eta: 9:42:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5672, top5_acc: 0.8091, loss_cls: 2.3985, loss: 2.3985 +2024-07-27 07:31:00,319 - pyskl - INFO - Epoch [139][2700/3746] lr: 1.389e-03, eta: 9:41:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5683, top5_acc: 0.8045, loss_cls: 2.3853, loss: 2.3853 +2024-07-27 07:32:21,497 - pyskl - INFO - Epoch [139][2800/3746] lr: 1.382e-03, eta: 9:40:11, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5658, top5_acc: 0.8027, loss_cls: 2.4105, loss: 2.4105 +2024-07-27 07:33:42,950 - pyskl - INFO - Epoch [139][2900/3746] lr: 1.376e-03, eta: 9:38:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5644, top5_acc: 0.8033, loss_cls: 2.4024, loss: 2.4024 +2024-07-27 07:35:04,897 - pyskl - INFO - Epoch [139][3000/3746] lr: 1.369e-03, eta: 9:37:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5605, top5_acc: 0.8014, loss_cls: 2.4408, loss: 2.4408 +2024-07-27 07:36:26,216 - pyskl - INFO - Epoch [139][3100/3746] lr: 1.363e-03, eta: 9:36:03, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5617, top5_acc: 0.8052, loss_cls: 2.4081, loss: 2.4081 +2024-07-27 07:37:47,860 - pyskl - INFO - Epoch [139][3200/3746] lr: 1.356e-03, eta: 9:34:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5705, top5_acc: 0.8025, loss_cls: 2.3890, loss: 2.3890 +2024-07-27 07:39:09,294 - pyskl - INFO - Epoch [139][3300/3746] lr: 1.350e-03, eta: 9:33:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5678, top5_acc: 0.8091, loss_cls: 2.3871, loss: 2.3871 +2024-07-27 07:40:30,616 - pyskl - INFO - Epoch [139][3400/3746] lr: 1.343e-03, eta: 9:31:55, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5622, top5_acc: 0.7984, loss_cls: 2.4299, loss: 2.4299 +2024-07-27 07:41:52,078 - pyskl - INFO - Epoch [139][3500/3746] lr: 1.337e-03, eta: 9:30:32, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5606, top5_acc: 0.7966, loss_cls: 2.4431, loss: 2.4431 +2024-07-27 07:43:13,636 - pyskl - INFO - Epoch [139][3600/3746] lr: 1.330e-03, eta: 9:29:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5700, top5_acc: 0.8022, loss_cls: 2.4194, loss: 2.4194 +2024-07-27 07:44:35,587 - pyskl - INFO - Epoch [139][3700/3746] lr: 1.324e-03, eta: 9:27:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5667, top5_acc: 0.7952, loss_cls: 2.4490, loss: 2.4490 +2024-07-27 07:45:15,030 - pyskl - INFO - Saving checkpoint at 139 epochs +2024-07-27 07:47:06,178 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 07:47:06,847 - pyskl - INFO - +top1_acc 0.4425 +top5_acc 0.6860 +2024-07-27 07:47:06,848 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 07:47:06,888 - pyskl - INFO - +mean_acc 0.4423 +2024-07-27 07:47:06,892 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_138.pth was removed +2024-07-27 07:47:07,157 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_139.pth. +2024-07-27 07:47:07,157 - pyskl - INFO - Best top1_acc is 0.4425 at 139 epoch. +2024-07-27 07:47:07,173 - pyskl - INFO - Epoch(val) [139][309] top1_acc: 0.4425, top5_acc: 0.6860, mean_class_accuracy: 0.4423 +2024-07-27 07:50:55,605 - pyskl - INFO - Epoch [140][100/3746] lr: 1.315e-03, eta: 9:25:55, time: 2.284, data_time: 1.301, memory: 15990, top1_acc: 0.5884, top5_acc: 0.8192, loss_cls: 2.3023, loss: 2.3023 +2024-07-27 07:52:17,935 - pyskl - INFO - Epoch [140][200/3746] lr: 1.308e-03, eta: 9:24:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5984, top5_acc: 0.8194, loss_cls: 2.2659, loss: 2.2659 +2024-07-27 07:53:40,346 - pyskl - INFO - Epoch [140][300/3746] lr: 1.302e-03, eta: 9:23:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5920, top5_acc: 0.8298, loss_cls: 2.2608, loss: 2.2608 +2024-07-27 07:55:02,229 - pyskl - INFO - Epoch [140][400/3746] lr: 1.296e-03, eta: 9:21:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5895, top5_acc: 0.8133, loss_cls: 2.3060, loss: 2.3060 +2024-07-27 07:56:24,336 - pyskl - INFO - Epoch [140][500/3746] lr: 1.289e-03, eta: 9:20:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5887, top5_acc: 0.8175, loss_cls: 2.3057, loss: 2.3057 +2024-07-27 07:57:46,137 - pyskl - INFO - Epoch [140][600/3746] lr: 1.283e-03, eta: 9:19:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5870, top5_acc: 0.8170, loss_cls: 2.2824, loss: 2.2824 +2024-07-27 07:59:08,138 - pyskl - INFO - Epoch [140][700/3746] lr: 1.277e-03, eta: 9:17:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5909, top5_acc: 0.8269, loss_cls: 2.2634, loss: 2.2634 +2024-07-27 08:00:29,922 - pyskl - INFO - Epoch [140][800/3746] lr: 1.271e-03, eta: 9:16:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5736, top5_acc: 0.8089, loss_cls: 2.3537, loss: 2.3537 +2024-07-27 08:01:51,450 - pyskl - INFO - Epoch [140][900/3746] lr: 1.264e-03, eta: 9:14:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5897, top5_acc: 0.8163, loss_cls: 2.2883, loss: 2.2883 +2024-07-27 08:03:13,165 - pyskl - INFO - Epoch [140][1000/3746] lr: 1.258e-03, eta: 9:13:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5834, top5_acc: 0.8169, loss_cls: 2.3085, loss: 2.3085 +2024-07-27 08:04:34,752 - pyskl - INFO - Epoch [140][1100/3746] lr: 1.252e-03, eta: 9:12:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5967, top5_acc: 0.8198, loss_cls: 2.2813, loss: 2.2813 +2024-07-27 08:05:57,265 - pyskl - INFO - Epoch [140][1200/3746] lr: 1.246e-03, eta: 9:10:46, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5728, top5_acc: 0.8145, loss_cls: 2.3461, loss: 2.3461 +2024-07-27 08:07:19,040 - pyskl - INFO - Epoch [140][1300/3746] lr: 1.239e-03, eta: 9:09:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5823, top5_acc: 0.8153, loss_cls: 2.3253, loss: 2.3253 +2024-07-27 08:08:40,847 - pyskl - INFO - Epoch [140][1400/3746] lr: 1.233e-03, eta: 9:08:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5719, top5_acc: 0.8037, loss_cls: 2.3997, loss: 2.3997 +2024-07-27 08:10:03,517 - pyskl - INFO - Epoch [140][1500/3746] lr: 1.227e-03, eta: 9:06:38, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5831, top5_acc: 0.8103, loss_cls: 2.3277, loss: 2.3277 +2024-07-27 08:11:25,151 - pyskl - INFO - Epoch [140][1600/3746] lr: 1.221e-03, eta: 9:05:15, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5813, top5_acc: 0.8086, loss_cls: 2.3558, loss: 2.3558 +2024-07-27 08:12:47,069 - pyskl - INFO - Epoch [140][1700/3746] lr: 1.215e-03, eta: 9:03:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5916, top5_acc: 0.8242, loss_cls: 2.3021, loss: 2.3021 +2024-07-27 08:14:08,433 - pyskl - INFO - Epoch [140][1800/3746] lr: 1.209e-03, eta: 9:02:30, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5767, top5_acc: 0.8125, loss_cls: 2.3444, loss: 2.3444 +2024-07-27 08:15:29,791 - pyskl - INFO - Epoch [140][1900/3746] lr: 1.203e-03, eta: 9:01:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5703, top5_acc: 0.8034, loss_cls: 2.3951, loss: 2.3951 +2024-07-27 08:16:52,021 - pyskl - INFO - Epoch [140][2000/3746] lr: 1.196e-03, eta: 8:59:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5766, top5_acc: 0.7945, loss_cls: 2.3860, loss: 2.3860 +2024-07-27 08:18:14,016 - pyskl - INFO - Epoch [140][2100/3746] lr: 1.190e-03, eta: 8:58:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5802, top5_acc: 0.8173, loss_cls: 2.3279, loss: 2.3279 +2024-07-27 08:19:36,020 - pyskl - INFO - Epoch [140][2200/3746] lr: 1.184e-03, eta: 8:56:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5808, top5_acc: 0.8148, loss_cls: 2.3504, loss: 2.3504 +2024-07-27 08:20:58,077 - pyskl - INFO - Epoch [140][2300/3746] lr: 1.178e-03, eta: 8:55:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5819, top5_acc: 0.8086, loss_cls: 2.3546, loss: 2.3546 +2024-07-27 08:22:19,877 - pyskl - INFO - Epoch [140][2400/3746] lr: 1.172e-03, eta: 8:54:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5750, top5_acc: 0.8069, loss_cls: 2.3640, loss: 2.3640 +2024-07-27 08:23:41,156 - pyskl - INFO - Epoch [140][2500/3746] lr: 1.166e-03, eta: 8:52:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5791, top5_acc: 0.8133, loss_cls: 2.3377, loss: 2.3377 +2024-07-27 08:25:02,227 - pyskl - INFO - Epoch [140][2600/3746] lr: 1.160e-03, eta: 8:51:28, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5742, top5_acc: 0.8095, loss_cls: 2.3638, loss: 2.3638 +2024-07-27 08:26:23,626 - pyskl - INFO - Epoch [140][2700/3746] lr: 1.154e-03, eta: 8:50:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5773, top5_acc: 0.8081, loss_cls: 2.3765, loss: 2.3765 +2024-07-27 08:27:45,289 - pyskl - INFO - Epoch [140][2800/3746] lr: 1.148e-03, eta: 8:48:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5741, top5_acc: 0.8081, loss_cls: 2.3470, loss: 2.3470 +2024-07-27 08:29:06,803 - pyskl - INFO - Epoch [140][2900/3746] lr: 1.142e-03, eta: 8:47:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5792, top5_acc: 0.8036, loss_cls: 2.3702, loss: 2.3702 +2024-07-27 08:30:28,497 - pyskl - INFO - Epoch [140][3000/3746] lr: 1.136e-03, eta: 8:45:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5759, top5_acc: 0.8048, loss_cls: 2.3745, loss: 2.3745 +2024-07-27 08:31:50,597 - pyskl - INFO - Epoch [140][3100/3746] lr: 1.131e-03, eta: 8:44:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5956, top5_acc: 0.8228, loss_cls: 2.2995, loss: 2.2995 +2024-07-27 08:33:12,462 - pyskl - INFO - Epoch [140][3200/3746] lr: 1.125e-03, eta: 8:43:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5695, top5_acc: 0.8114, loss_cls: 2.3470, loss: 2.3470 +2024-07-27 08:34:33,628 - pyskl - INFO - Epoch [140][3300/3746] lr: 1.119e-03, eta: 8:41:50, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5814, top5_acc: 0.8169, loss_cls: 2.3256, loss: 2.3256 +2024-07-27 08:35:54,704 - pyskl - INFO - Epoch [140][3400/3746] lr: 1.113e-03, eta: 8:40:27, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5786, top5_acc: 0.8130, loss_cls: 2.3377, loss: 2.3377 +2024-07-27 08:37:16,230 - pyskl - INFO - Epoch [140][3500/3746] lr: 1.107e-03, eta: 8:39:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5795, top5_acc: 0.8114, loss_cls: 2.3401, loss: 2.3401 +2024-07-27 08:38:37,789 - pyskl - INFO - Epoch [140][3600/3746] lr: 1.101e-03, eta: 8:37:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5883, top5_acc: 0.8177, loss_cls: 2.3267, loss: 2.3267 +2024-07-27 08:39:59,729 - pyskl - INFO - Epoch [140][3700/3746] lr: 1.095e-03, eta: 8:36:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5795, top5_acc: 0.8103, loss_cls: 2.3565, loss: 2.3565 +2024-07-27 08:40:39,181 - pyskl - INFO - Saving checkpoint at 140 epochs +2024-07-27 08:42:29,685 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 08:42:30,341 - pyskl - INFO - +top1_acc 0.4431 +top5_acc 0.6905 +2024-07-27 08:42:30,341 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 08:42:30,380 - pyskl - INFO - +mean_acc 0.4429 +2024-07-27 08:42:30,385 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_139.pth was removed +2024-07-27 08:42:30,633 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_140.pth. +2024-07-27 08:42:30,633 - pyskl - INFO - Best top1_acc is 0.4431 at 140 epoch. +2024-07-27 08:42:30,645 - pyskl - INFO - Epoch(val) [140][309] top1_acc: 0.4431, top5_acc: 0.6905, mean_class_accuracy: 0.4429 +2024-07-27 08:46:18,637 - pyskl - INFO - Epoch [141][100/3746] lr: 1.087e-03, eta: 8:34:26, time: 2.280, data_time: 1.297, memory: 15990, top1_acc: 0.6097, top5_acc: 0.8309, loss_cls: 2.2063, loss: 2.2063 +2024-07-27 08:47:40,215 - pyskl - INFO - Epoch [141][200/3746] lr: 1.081e-03, eta: 8:33:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6061, top5_acc: 0.8319, loss_cls: 2.2068, loss: 2.2068 +2024-07-27 08:49:02,804 - pyskl - INFO - Epoch [141][300/3746] lr: 1.075e-03, eta: 8:31:41, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.6153, top5_acc: 0.8398, loss_cls: 2.1721, loss: 2.1721 +2024-07-27 08:50:23,792 - pyskl - INFO - Epoch [141][400/3746] lr: 1.070e-03, eta: 8:30:18, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5936, top5_acc: 0.8244, loss_cls: 2.2581, loss: 2.2581 +2024-07-27 08:51:45,314 - pyskl - INFO - Epoch [141][500/3746] lr: 1.064e-03, eta: 8:28:55, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5969, top5_acc: 0.8291, loss_cls: 2.2617, loss: 2.2617 +2024-07-27 08:53:06,686 - pyskl - INFO - Epoch [141][600/3746] lr: 1.058e-03, eta: 8:27:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6034, top5_acc: 0.8328, loss_cls: 2.2107, loss: 2.2107 +2024-07-27 08:54:28,379 - pyskl - INFO - Epoch [141][700/3746] lr: 1.052e-03, eta: 8:26:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5977, top5_acc: 0.8181, loss_cls: 2.2803, loss: 2.2803 +2024-07-27 08:55:50,063 - pyskl - INFO - Epoch [141][800/3746] lr: 1.047e-03, eta: 8:24:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5961, top5_acc: 0.8242, loss_cls: 2.2575, loss: 2.2575 +2024-07-27 08:57:11,166 - pyskl - INFO - Epoch [141][900/3746] lr: 1.041e-03, eta: 8:23:24, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5991, top5_acc: 0.8309, loss_cls: 2.2413, loss: 2.2413 +2024-07-27 08:58:32,672 - pyskl - INFO - Epoch [141][1000/3746] lr: 1.035e-03, eta: 8:22:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5847, top5_acc: 0.8167, loss_cls: 2.3031, loss: 2.3031 +2024-07-27 08:59:54,146 - pyskl - INFO - Epoch [141][1100/3746] lr: 1.030e-03, eta: 8:20:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5839, top5_acc: 0.8175, loss_cls: 2.3142, loss: 2.3142 +2024-07-27 09:01:16,495 - pyskl - INFO - Epoch [141][1200/3746] lr: 1.024e-03, eta: 8:19:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5986, top5_acc: 0.8247, loss_cls: 2.2558, loss: 2.2558 +2024-07-27 09:02:38,655 - pyskl - INFO - Epoch [141][1300/3746] lr: 1.018e-03, eta: 8:17:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5881, top5_acc: 0.8144, loss_cls: 2.3107, loss: 2.3107 +2024-07-27 09:04:01,318 - pyskl - INFO - Epoch [141][1400/3746] lr: 1.013e-03, eta: 8:16:31, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5939, top5_acc: 0.8191, loss_cls: 2.3176, loss: 2.3176 +2024-07-27 09:05:23,083 - pyskl - INFO - Epoch [141][1500/3746] lr: 1.007e-03, eta: 8:15:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5852, top5_acc: 0.8163, loss_cls: 2.3170, loss: 2.3170 +2024-07-27 09:06:45,241 - pyskl - INFO - Epoch [141][1600/3746] lr: 1.002e-03, eta: 8:13:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5820, top5_acc: 0.8106, loss_cls: 2.3450, loss: 2.3450 +2024-07-27 09:08:07,308 - pyskl - INFO - Epoch [141][1700/3746] lr: 9.961e-04, eta: 8:12:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5923, top5_acc: 0.8236, loss_cls: 2.2964, loss: 2.2964 +2024-07-27 09:09:29,590 - pyskl - INFO - Epoch [141][1800/3746] lr: 9.905e-04, eta: 8:11:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5873, top5_acc: 0.8192, loss_cls: 2.2918, loss: 2.2918 +2024-07-27 09:10:51,081 - pyskl - INFO - Epoch [141][1900/3746] lr: 9.850e-04, eta: 8:09:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5961, top5_acc: 0.8264, loss_cls: 2.2569, loss: 2.2569 +2024-07-27 09:12:13,566 - pyskl - INFO - Epoch [141][2000/3746] lr: 9.795e-04, eta: 8:08:15, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5905, top5_acc: 0.8209, loss_cls: 2.2843, loss: 2.2843 +2024-07-27 09:13:35,132 - pyskl - INFO - Epoch [141][2100/3746] lr: 9.740e-04, eta: 8:06:52, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5961, top5_acc: 0.8252, loss_cls: 2.2631, loss: 2.2631 +2024-07-27 09:14:57,243 - pyskl - INFO - Epoch [141][2200/3746] lr: 9.685e-04, eta: 8:05:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5847, top5_acc: 0.8148, loss_cls: 2.3076, loss: 2.3076 +2024-07-27 09:16:18,821 - pyskl - INFO - Epoch [141][2300/3746] lr: 9.630e-04, eta: 8:04:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5884, top5_acc: 0.8155, loss_cls: 2.2794, loss: 2.2794 +2024-07-27 09:17:40,989 - pyskl - INFO - Epoch [141][2400/3746] lr: 9.576e-04, eta: 8:02:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5919, top5_acc: 0.8247, loss_cls: 2.2737, loss: 2.2737 +2024-07-27 09:19:02,611 - pyskl - INFO - Epoch [141][2500/3746] lr: 9.522e-04, eta: 8:01:22, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5916, top5_acc: 0.8203, loss_cls: 2.2801, loss: 2.2801 +2024-07-27 09:20:24,372 - pyskl - INFO - Epoch [141][2600/3746] lr: 9.467e-04, eta: 7:59:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6027, top5_acc: 0.8302, loss_cls: 2.2470, loss: 2.2470 +2024-07-27 09:21:45,645 - pyskl - INFO - Epoch [141][2700/3746] lr: 9.413e-04, eta: 7:58:36, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6008, top5_acc: 0.8280, loss_cls: 2.2349, loss: 2.2349 +2024-07-27 09:23:07,076 - pyskl - INFO - Epoch [141][2800/3746] lr: 9.359e-04, eta: 7:57:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5975, top5_acc: 0.8216, loss_cls: 2.2626, loss: 2.2626 +2024-07-27 09:24:28,834 - pyskl - INFO - Epoch [141][2900/3746] lr: 9.306e-04, eta: 7:55:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5848, top5_acc: 0.8156, loss_cls: 2.3008, loss: 2.3008 +2024-07-27 09:25:50,169 - pyskl - INFO - Epoch [141][3000/3746] lr: 9.252e-04, eta: 7:54:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5819, top5_acc: 0.8114, loss_cls: 2.3321, loss: 2.3321 +2024-07-27 09:27:11,724 - pyskl - INFO - Epoch [141][3100/3746] lr: 9.199e-04, eta: 7:53:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5791, top5_acc: 0.8014, loss_cls: 2.3563, loss: 2.3563 +2024-07-27 09:28:33,073 - pyskl - INFO - Epoch [141][3200/3746] lr: 9.145e-04, eta: 7:51:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5945, top5_acc: 0.8239, loss_cls: 2.2626, loss: 2.2626 +2024-07-27 09:29:54,817 - pyskl - INFO - Epoch [141][3300/3746] lr: 9.092e-04, eta: 7:50:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5887, top5_acc: 0.8200, loss_cls: 2.2674, loss: 2.2674 +2024-07-27 09:31:17,081 - pyskl - INFO - Epoch [141][3400/3746] lr: 9.039e-04, eta: 7:48:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5886, top5_acc: 0.8141, loss_cls: 2.2905, loss: 2.2905 +2024-07-27 09:32:38,950 - pyskl - INFO - Epoch [141][3500/3746] lr: 8.986e-04, eta: 7:47:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5972, top5_acc: 0.8158, loss_cls: 2.2996, loss: 2.2996 +2024-07-27 09:34:00,466 - pyskl - INFO - Epoch [141][3600/3746] lr: 8.934e-04, eta: 7:46:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5900, top5_acc: 0.8163, loss_cls: 2.2968, loss: 2.2968 +2024-07-27 09:35:22,006 - pyskl - INFO - Epoch [141][3700/3746] lr: 8.881e-04, eta: 7:44:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5881, top5_acc: 0.8125, loss_cls: 2.3227, loss: 2.3227 +2024-07-27 09:36:01,332 - pyskl - INFO - Saving checkpoint at 141 epochs +2024-07-27 09:37:52,328 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 09:37:52,991 - pyskl - INFO - +top1_acc 0.4475 +top5_acc 0.6922 +2024-07-27 09:37:52,992 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 09:37:53,031 - pyskl - INFO - +mean_acc 0.4473 +2024-07-27 09:37:53,035 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_140.pth was removed +2024-07-27 09:37:53,302 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_141.pth. +2024-07-27 09:37:53,303 - pyskl - INFO - Best top1_acc is 0.4475 at 141 epoch. +2024-07-27 09:37:53,314 - pyskl - INFO - Epoch(val) [141][309] top1_acc: 0.4475, top5_acc: 0.6922, mean_class_accuracy: 0.4473 +2024-07-27 09:41:46,685 - pyskl - INFO - Epoch [142][100/3746] lr: 8.805e-04, eta: 7:42:56, time: 2.334, data_time: 1.336, memory: 15990, top1_acc: 0.6142, top5_acc: 0.8364, loss_cls: 2.1565, loss: 2.1565 +2024-07-27 09:43:08,589 - pyskl - INFO - Epoch [142][200/3746] lr: 8.752e-04, eta: 7:41:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6078, top5_acc: 0.8323, loss_cls: 2.2141, loss: 2.2141 +2024-07-27 09:44:31,610 - pyskl - INFO - Epoch [142][300/3746] lr: 8.700e-04, eta: 7:40:11, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.6141, top5_acc: 0.8383, loss_cls: 2.1658, loss: 2.1658 +2024-07-27 09:45:53,152 - pyskl - INFO - Epoch [142][400/3746] lr: 8.649e-04, eta: 7:38:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6042, top5_acc: 0.8272, loss_cls: 2.2273, loss: 2.2273 +2024-07-27 09:47:15,214 - pyskl - INFO - Epoch [142][500/3746] lr: 8.597e-04, eta: 7:37:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6050, top5_acc: 0.8356, loss_cls: 2.2188, loss: 2.2188 +2024-07-27 09:48:36,921 - pyskl - INFO - Epoch [142][600/3746] lr: 8.545e-04, eta: 7:36:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6073, top5_acc: 0.8345, loss_cls: 2.1913, loss: 2.1913 +2024-07-27 09:49:58,247 - pyskl - INFO - Epoch [142][700/3746] lr: 8.494e-04, eta: 7:34:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6125, top5_acc: 0.8267, loss_cls: 2.2032, loss: 2.2032 +2024-07-27 09:51:19,814 - pyskl - INFO - Epoch [142][800/3746] lr: 8.443e-04, eta: 7:33:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6086, top5_acc: 0.8305, loss_cls: 2.2058, loss: 2.2058 +2024-07-27 09:52:40,832 - pyskl - INFO - Epoch [142][900/3746] lr: 8.392e-04, eta: 7:31:55, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.6012, top5_acc: 0.8273, loss_cls: 2.2351, loss: 2.2351 +2024-07-27 09:54:02,541 - pyskl - INFO - Epoch [142][1000/3746] lr: 8.341e-04, eta: 7:30:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6062, top5_acc: 0.8311, loss_cls: 2.2110, loss: 2.2110 +2024-07-27 09:55:24,393 - pyskl - INFO - Epoch [142][1100/3746] lr: 8.290e-04, eta: 7:29:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6020, top5_acc: 0.8292, loss_cls: 2.2195, loss: 2.2195 +2024-07-27 09:56:46,351 - pyskl - INFO - Epoch [142][1200/3746] lr: 8.239e-04, eta: 7:27:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6003, top5_acc: 0.8313, loss_cls: 2.2187, loss: 2.2187 +2024-07-27 09:58:08,284 - pyskl - INFO - Epoch [142][1300/3746] lr: 8.189e-04, eta: 7:26:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6031, top5_acc: 0.8330, loss_cls: 2.2094, loss: 2.2094 +2024-07-27 09:59:30,668 - pyskl - INFO - Epoch [142][1400/3746] lr: 8.139e-04, eta: 7:25:01, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6094, top5_acc: 0.8375, loss_cls: 2.2096, loss: 2.2096 +2024-07-27 10:00:52,206 - pyskl - INFO - Epoch [142][1500/3746] lr: 8.088e-04, eta: 7:23:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6014, top5_acc: 0.8294, loss_cls: 2.2155, loss: 2.2155 +2024-07-27 10:02:14,709 - pyskl - INFO - Epoch [142][1600/3746] lr: 8.038e-04, eta: 7:22:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5977, top5_acc: 0.8230, loss_cls: 2.2505, loss: 2.2505 +2024-07-27 10:03:36,966 - pyskl - INFO - Epoch [142][1700/3746] lr: 7.989e-04, eta: 7:20:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6083, top5_acc: 0.8347, loss_cls: 2.1846, loss: 2.1846 +2024-07-27 10:04:58,801 - pyskl - INFO - Epoch [142][1800/3746] lr: 7.939e-04, eta: 7:19:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6062, top5_acc: 0.8323, loss_cls: 2.1892, loss: 2.1892 +2024-07-27 10:06:19,921 - pyskl - INFO - Epoch [142][1900/3746] lr: 7.889e-04, eta: 7:18:08, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6127, top5_acc: 0.8325, loss_cls: 2.1981, loss: 2.1981 +2024-07-27 10:07:41,537 - pyskl - INFO - Epoch [142][2000/3746] lr: 7.840e-04, eta: 7:16:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5973, top5_acc: 0.8244, loss_cls: 2.2530, loss: 2.2530 +2024-07-27 10:09:02,848 - pyskl - INFO - Epoch [142][2100/3746] lr: 7.791e-04, eta: 7:15:23, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5967, top5_acc: 0.8233, loss_cls: 2.2483, loss: 2.2483 +2024-07-27 10:10:24,360 - pyskl - INFO - Epoch [142][2200/3746] lr: 7.742e-04, eta: 7:14:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6045, top5_acc: 0.8248, loss_cls: 2.2326, loss: 2.2326 +2024-07-27 10:11:46,397 - pyskl - INFO - Epoch [142][2300/3746] lr: 7.693e-04, eta: 7:12:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5977, top5_acc: 0.8281, loss_cls: 2.2301, loss: 2.2301 +2024-07-27 10:13:08,739 - pyskl - INFO - Epoch [142][2400/3746] lr: 7.644e-04, eta: 7:11:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6019, top5_acc: 0.8320, loss_cls: 2.2035, loss: 2.2035 +2024-07-27 10:14:31,044 - pyskl - INFO - Epoch [142][2500/3746] lr: 7.595e-04, eta: 7:09:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6078, top5_acc: 0.8341, loss_cls: 2.2101, loss: 2.2101 +2024-07-27 10:15:52,578 - pyskl - INFO - Epoch [142][2600/3746] lr: 7.547e-04, eta: 7:08:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6034, top5_acc: 0.8275, loss_cls: 2.2214, loss: 2.2214 +2024-07-27 10:17:13,904 - pyskl - INFO - Epoch [142][2700/3746] lr: 7.499e-04, eta: 7:07:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5978, top5_acc: 0.8192, loss_cls: 2.2733, loss: 2.2733 +2024-07-27 10:18:35,796 - pyskl - INFO - Epoch [142][2800/3746] lr: 7.450e-04, eta: 7:05:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6050, top5_acc: 0.8328, loss_cls: 2.2017, loss: 2.2017 +2024-07-27 10:19:57,177 - pyskl - INFO - Epoch [142][2900/3746] lr: 7.402e-04, eta: 7:04:21, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6055, top5_acc: 0.8370, loss_cls: 2.1895, loss: 2.1895 +2024-07-27 10:21:18,669 - pyskl - INFO - Epoch [142][3000/3746] lr: 7.355e-04, eta: 7:02:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5952, top5_acc: 0.8245, loss_cls: 2.2436, loss: 2.2436 +2024-07-27 10:22:40,074 - pyskl - INFO - Epoch [142][3100/3746] lr: 7.307e-04, eta: 7:01:36, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6108, top5_acc: 0.8334, loss_cls: 2.1791, loss: 2.1791 +2024-07-27 10:24:01,519 - pyskl - INFO - Epoch [142][3200/3746] lr: 7.259e-04, eta: 7:00:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5969, top5_acc: 0.8225, loss_cls: 2.2592, loss: 2.2592 +2024-07-27 10:25:23,200 - pyskl - INFO - Epoch [142][3300/3746] lr: 7.212e-04, eta: 6:58:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6072, top5_acc: 0.8319, loss_cls: 2.2056, loss: 2.2056 +2024-07-27 10:26:44,540 - pyskl - INFO - Epoch [142][3400/3746] lr: 7.165e-04, eta: 6:57:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5942, top5_acc: 0.8192, loss_cls: 2.2455, loss: 2.2455 +2024-07-27 10:28:06,228 - pyskl - INFO - Epoch [142][3500/3746] lr: 7.118e-04, eta: 6:56:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5925, top5_acc: 0.8223, loss_cls: 2.2763, loss: 2.2763 +2024-07-27 10:29:27,304 - pyskl - INFO - Epoch [142][3600/3746] lr: 7.071e-04, eta: 6:54:42, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5989, top5_acc: 0.8220, loss_cls: 2.2503, loss: 2.2503 +2024-07-27 10:30:48,568 - pyskl - INFO - Epoch [142][3700/3746] lr: 7.024e-04, eta: 6:53:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5945, top5_acc: 0.8250, loss_cls: 2.2606, loss: 2.2606 +2024-07-27 10:31:27,825 - pyskl - INFO - Saving checkpoint at 142 epochs +2024-07-27 10:33:18,665 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 10:33:19,333 - pyskl - INFO - +top1_acc 0.4459 +top5_acc 0.6888 +2024-07-27 10:33:19,333 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 10:33:19,378 - pyskl - INFO - +mean_acc 0.4456 +2024-07-27 10:33:19,391 - pyskl - INFO - Epoch(val) [142][309] top1_acc: 0.4459, top5_acc: 0.6888, mean_class_accuracy: 0.4456 +2024-07-27 10:37:06,588 - pyskl - INFO - Epoch [143][100/3746] lr: 6.956e-04, eta: 6:51:25, time: 2.272, data_time: 1.294, memory: 15990, top1_acc: 0.6202, top5_acc: 0.8386, loss_cls: 2.1168, loss: 2.1168 +2024-07-27 10:38:28,696 - pyskl - INFO - Epoch [143][200/3746] lr: 6.910e-04, eta: 6:50:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6209, top5_acc: 0.8381, loss_cls: 2.1427, loss: 2.1427 +2024-07-27 10:39:50,646 - pyskl - INFO - Epoch [143][300/3746] lr: 6.863e-04, eta: 6:48:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6252, top5_acc: 0.8411, loss_cls: 2.1124, loss: 2.1124 +2024-07-27 10:41:12,523 - pyskl - INFO - Epoch [143][400/3746] lr: 6.817e-04, eta: 6:47:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6212, top5_acc: 0.8384, loss_cls: 2.1452, loss: 2.1452 +2024-07-27 10:42:34,074 - pyskl - INFO - Epoch [143][500/3746] lr: 6.771e-04, eta: 6:45:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6264, top5_acc: 0.8419, loss_cls: 2.1143, loss: 2.1143 +2024-07-27 10:43:56,605 - pyskl - INFO - Epoch [143][600/3746] lr: 6.725e-04, eta: 6:44:31, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6242, top5_acc: 0.8477, loss_cls: 2.1122, loss: 2.1122 +2024-07-27 10:45:18,376 - pyskl - INFO - Epoch [143][700/3746] lr: 6.680e-04, eta: 6:43:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6134, top5_acc: 0.8356, loss_cls: 2.1601, loss: 2.1601 +2024-07-27 10:46:39,748 - pyskl - INFO - Epoch [143][800/3746] lr: 6.634e-04, eta: 6:41:46, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6058, top5_acc: 0.8402, loss_cls: 2.1662, loss: 2.1662 +2024-07-27 10:48:01,036 - pyskl - INFO - Epoch [143][900/3746] lr: 6.589e-04, eta: 6:40:23, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6211, top5_acc: 0.8370, loss_cls: 2.1588, loss: 2.1588 +2024-07-27 10:49:22,622 - pyskl - INFO - Epoch [143][1000/3746] lr: 6.544e-04, eta: 6:39:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6120, top5_acc: 0.8411, loss_cls: 2.1759, loss: 2.1759 +2024-07-27 10:50:44,263 - pyskl - INFO - Epoch [143][1100/3746] lr: 6.499e-04, eta: 6:37:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6333, top5_acc: 0.8459, loss_cls: 2.0989, loss: 2.0989 +2024-07-27 10:52:06,314 - pyskl - INFO - Epoch [143][1200/3746] lr: 6.454e-04, eta: 6:36:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6152, top5_acc: 0.8366, loss_cls: 2.1477, loss: 2.1477 +2024-07-27 10:53:28,348 - pyskl - INFO - Epoch [143][1300/3746] lr: 6.409e-04, eta: 6:34:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6150, top5_acc: 0.8425, loss_cls: 2.1464, loss: 2.1464 +2024-07-27 10:54:51,064 - pyskl - INFO - Epoch [143][1400/3746] lr: 6.365e-04, eta: 6:33:30, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6112, top5_acc: 0.8406, loss_cls: 2.1610, loss: 2.1610 +2024-07-27 10:56:12,865 - pyskl - INFO - Epoch [143][1500/3746] lr: 6.320e-04, eta: 6:32:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6108, top5_acc: 0.8334, loss_cls: 2.1875, loss: 2.1875 +2024-07-27 10:57:34,312 - pyskl - INFO - Epoch [143][1600/3746] lr: 6.276e-04, eta: 6:30:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6073, top5_acc: 0.8356, loss_cls: 2.1829, loss: 2.1829 +2024-07-27 10:58:55,990 - pyskl - INFO - Epoch [143][1700/3746] lr: 6.232e-04, eta: 6:29:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6042, top5_acc: 0.8344, loss_cls: 2.1894, loss: 2.1894 +2024-07-27 11:00:18,195 - pyskl - INFO - Epoch [143][1800/3746] lr: 6.188e-04, eta: 6:27:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6197, top5_acc: 0.8359, loss_cls: 2.1741, loss: 2.1741 +2024-07-27 11:01:40,086 - pyskl - INFO - Epoch [143][1900/3746] lr: 6.144e-04, eta: 6:26:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6112, top5_acc: 0.8394, loss_cls: 2.1726, loss: 2.1726 +2024-07-27 11:03:02,177 - pyskl - INFO - Epoch [143][2000/3746] lr: 6.101e-04, eta: 6:25:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6123, top5_acc: 0.8289, loss_cls: 2.1839, loss: 2.1839 +2024-07-27 11:04:23,544 - pyskl - INFO - Epoch [143][2100/3746] lr: 6.057e-04, eta: 6:23:51, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6095, top5_acc: 0.8370, loss_cls: 2.1484, loss: 2.1484 +2024-07-27 11:05:45,347 - pyskl - INFO - Epoch [143][2200/3746] lr: 6.014e-04, eta: 6:22:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6225, top5_acc: 0.8359, loss_cls: 2.1376, loss: 2.1376 +2024-07-27 11:07:06,883 - pyskl - INFO - Epoch [143][2300/3746] lr: 5.971e-04, eta: 6:21:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5955, top5_acc: 0.8317, loss_cls: 2.2064, loss: 2.2064 +2024-07-27 11:08:28,010 - pyskl - INFO - Epoch [143][2400/3746] lr: 5.928e-04, eta: 6:19:43, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6094, top5_acc: 0.8372, loss_cls: 2.1664, loss: 2.1664 +2024-07-27 11:09:49,496 - pyskl - INFO - Epoch [143][2500/3746] lr: 5.885e-04, eta: 6:18:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6109, top5_acc: 0.8336, loss_cls: 2.1950, loss: 2.1950 +2024-07-27 11:11:10,933 - pyskl - INFO - Epoch [143][2600/3746] lr: 5.842e-04, eta: 6:16:58, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6164, top5_acc: 0.8372, loss_cls: 2.1563, loss: 2.1563 +2024-07-27 11:12:32,218 - pyskl - INFO - Epoch [143][2700/3746] lr: 5.800e-04, eta: 6:15:35, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6178, top5_acc: 0.8364, loss_cls: 2.1497, loss: 2.1497 +2024-07-27 11:13:54,033 - pyskl - INFO - Epoch [143][2800/3746] lr: 5.757e-04, eta: 6:14:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6059, top5_acc: 0.8294, loss_cls: 2.2048, loss: 2.2048 +2024-07-27 11:15:15,294 - pyskl - INFO - Epoch [143][2900/3746] lr: 5.715e-04, eta: 6:12:49, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6138, top5_acc: 0.8325, loss_cls: 2.1784, loss: 2.1784 +2024-07-27 11:16:36,930 - pyskl - INFO - Epoch [143][3000/3746] lr: 5.673e-04, eta: 6:11:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6183, top5_acc: 0.8284, loss_cls: 2.1875, loss: 2.1875 +2024-07-27 11:17:58,655 - pyskl - INFO - Epoch [143][3100/3746] lr: 5.631e-04, eta: 6:10:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6120, top5_acc: 0.8325, loss_cls: 2.2045, loss: 2.2045 +2024-07-27 11:19:20,477 - pyskl - INFO - Epoch [143][3200/3746] lr: 5.590e-04, eta: 6:08:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6069, top5_acc: 0.8322, loss_cls: 2.1893, loss: 2.1893 +2024-07-27 11:20:41,993 - pyskl - INFO - Epoch [143][3300/3746] lr: 5.548e-04, eta: 6:07:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5981, top5_acc: 0.8300, loss_cls: 2.2261, loss: 2.2261 +2024-07-27 11:22:03,398 - pyskl - INFO - Epoch [143][3400/3746] lr: 5.506e-04, eta: 6:05:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6150, top5_acc: 0.8302, loss_cls: 2.1632, loss: 2.1632 +2024-07-27 11:23:24,871 - pyskl - INFO - Epoch [143][3500/3746] lr: 5.465e-04, eta: 6:04:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6134, top5_acc: 0.8341, loss_cls: 2.1580, loss: 2.1580 +2024-07-27 11:24:46,273 - pyskl - INFO - Epoch [143][3600/3746] lr: 5.424e-04, eta: 6:03:11, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6148, top5_acc: 0.8337, loss_cls: 2.1754, loss: 2.1754 +2024-07-27 11:26:07,643 - pyskl - INFO - Epoch [143][3700/3746] lr: 5.383e-04, eta: 6:01:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6169, top5_acc: 0.8394, loss_cls: 2.1668, loss: 2.1668 +2024-07-27 11:26:47,235 - pyskl - INFO - Saving checkpoint at 143 epochs +2024-07-27 11:28:38,534 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 11:28:39,268 - pyskl - INFO - +top1_acc 0.4477 +top5_acc 0.6907 +2024-07-27 11:28:39,269 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 11:28:39,310 - pyskl - INFO - +mean_acc 0.4475 +2024-07-27 11:28:39,316 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_141.pth was removed +2024-07-27 11:28:39,577 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_143.pth. +2024-07-27 11:28:39,578 - pyskl - INFO - Best top1_acc is 0.4477 at 143 epoch. +2024-07-27 11:28:39,594 - pyskl - INFO - Epoch(val) [143][309] top1_acc: 0.4477, top5_acc: 0.6907, mean_class_accuracy: 0.4475 +2024-07-27 11:32:25,478 - pyskl - INFO - Epoch [144][100/3746] lr: 5.323e-04, eta: 5:59:52, time: 2.259, data_time: 1.286, memory: 15990, top1_acc: 0.6267, top5_acc: 0.8481, loss_cls: 2.0880, loss: 2.0880 +2024-07-27 11:33:46,775 - pyskl - INFO - Epoch [144][200/3746] lr: 5.283e-04, eta: 5:58:30, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6483, top5_acc: 0.8522, loss_cls: 2.0224, loss: 2.0224 +2024-07-27 11:35:08,353 - pyskl - INFO - Epoch [144][300/3746] lr: 5.242e-04, eta: 5:57:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6417, top5_acc: 0.8550, loss_cls: 2.0434, loss: 2.0434 +2024-07-27 11:36:31,418 - pyskl - INFO - Epoch [144][400/3746] lr: 5.202e-04, eta: 5:55:44, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.6239, top5_acc: 0.8531, loss_cls: 2.0881, loss: 2.0881 +2024-07-27 11:37:52,897 - pyskl - INFO - Epoch [144][500/3746] lr: 5.162e-04, eta: 5:54:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6334, top5_acc: 0.8512, loss_cls: 2.0518, loss: 2.0518 +2024-07-27 11:39:14,256 - pyskl - INFO - Epoch [144][600/3746] lr: 5.122e-04, eta: 5:52:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6200, top5_acc: 0.8469, loss_cls: 2.1199, loss: 2.1199 +2024-07-27 11:40:36,051 - pyskl - INFO - Epoch [144][700/3746] lr: 5.082e-04, eta: 5:51:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6316, top5_acc: 0.8498, loss_cls: 2.0793, loss: 2.0793 +2024-07-27 11:41:57,484 - pyskl - INFO - Epoch [144][800/3746] lr: 5.042e-04, eta: 5:50:13, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6344, top5_acc: 0.8500, loss_cls: 2.0391, loss: 2.0391 +2024-07-27 11:43:18,729 - pyskl - INFO - Epoch [144][900/3746] lr: 5.003e-04, eta: 5:48:51, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6355, top5_acc: 0.8483, loss_cls: 2.0594, loss: 2.0594 +2024-07-27 11:44:40,072 - pyskl - INFO - Epoch [144][1000/3746] lr: 4.964e-04, eta: 5:47:28, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6272, top5_acc: 0.8456, loss_cls: 2.1073, loss: 2.1073 +2024-07-27 11:46:02,003 - pyskl - INFO - Epoch [144][1100/3746] lr: 4.924e-04, eta: 5:46:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6256, top5_acc: 0.8472, loss_cls: 2.1052, loss: 2.1052 +2024-07-27 11:47:24,120 - pyskl - INFO - Epoch [144][1200/3746] lr: 4.885e-04, eta: 5:44:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6192, top5_acc: 0.8413, loss_cls: 2.1302, loss: 2.1302 +2024-07-27 11:48:46,069 - pyskl - INFO - Epoch [144][1300/3746] lr: 4.846e-04, eta: 5:43:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6195, top5_acc: 0.8419, loss_cls: 2.1452, loss: 2.1452 +2024-07-27 11:50:08,338 - pyskl - INFO - Epoch [144][1400/3746] lr: 4.808e-04, eta: 5:41:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6184, top5_acc: 0.8441, loss_cls: 2.1361, loss: 2.1361 +2024-07-27 11:51:30,073 - pyskl - INFO - Epoch [144][1500/3746] lr: 4.769e-04, eta: 5:40:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6234, top5_acc: 0.8356, loss_cls: 2.1308, loss: 2.1308 +2024-07-27 11:52:52,182 - pyskl - INFO - Epoch [144][1600/3746] lr: 4.731e-04, eta: 5:39:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6231, top5_acc: 0.8469, loss_cls: 2.1183, loss: 2.1183 +2024-07-27 11:54:13,970 - pyskl - INFO - Epoch [144][1700/3746] lr: 4.692e-04, eta: 5:37:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6208, top5_acc: 0.8462, loss_cls: 2.1219, loss: 2.1219 +2024-07-27 11:55:35,467 - pyskl - INFO - Epoch [144][1800/3746] lr: 4.654e-04, eta: 5:36:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6238, top5_acc: 0.8466, loss_cls: 2.1006, loss: 2.1006 +2024-07-27 11:56:56,859 - pyskl - INFO - Epoch [144][1900/3746] lr: 4.616e-04, eta: 5:35:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6177, top5_acc: 0.8395, loss_cls: 2.1548, loss: 2.1548 +2024-07-27 11:58:19,117 - pyskl - INFO - Epoch [144][2000/3746] lr: 4.578e-04, eta: 5:33:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6072, top5_acc: 0.8350, loss_cls: 2.1839, loss: 2.1839 +2024-07-27 11:59:40,854 - pyskl - INFO - Epoch [144][2100/3746] lr: 4.541e-04, eta: 5:32:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6214, top5_acc: 0.8409, loss_cls: 2.1123, loss: 2.1123 +2024-07-27 12:01:02,614 - pyskl - INFO - Epoch [144][2200/3746] lr: 4.503e-04, eta: 5:30:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6283, top5_acc: 0.8383, loss_cls: 2.1238, loss: 2.1238 +2024-07-27 12:02:23,894 - pyskl - INFO - Epoch [144][2300/3746] lr: 4.466e-04, eta: 5:29:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6305, top5_acc: 0.8467, loss_cls: 2.0963, loss: 2.0963 +2024-07-27 12:03:45,552 - pyskl - INFO - Epoch [144][2400/3746] lr: 4.429e-04, eta: 5:28:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6228, top5_acc: 0.8398, loss_cls: 2.1359, loss: 2.1359 +2024-07-27 12:05:06,917 - pyskl - INFO - Epoch [144][2500/3746] lr: 4.392e-04, eta: 5:26:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6255, top5_acc: 0.8392, loss_cls: 2.1164, loss: 2.1164 +2024-07-27 12:06:28,657 - pyskl - INFO - Epoch [144][2600/3746] lr: 4.355e-04, eta: 5:25:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6345, top5_acc: 0.8431, loss_cls: 2.0782, loss: 2.0782 +2024-07-27 12:07:50,013 - pyskl - INFO - Epoch [144][2700/3746] lr: 4.318e-04, eta: 5:24:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6233, top5_acc: 0.8459, loss_cls: 2.1267, loss: 2.1267 +2024-07-27 12:09:11,220 - pyskl - INFO - Epoch [144][2800/3746] lr: 4.281e-04, eta: 5:22:39, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6344, top5_acc: 0.8492, loss_cls: 2.0872, loss: 2.0872 +2024-07-27 12:10:32,942 - pyskl - INFO - Epoch [144][2900/3746] lr: 4.245e-04, eta: 5:21:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6183, top5_acc: 0.8398, loss_cls: 2.1491, loss: 2.1491 +2024-07-27 12:11:54,511 - pyskl - INFO - Epoch [144][3000/3746] lr: 4.209e-04, eta: 5:19:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6225, top5_acc: 0.8381, loss_cls: 2.1396, loss: 2.1396 +2024-07-27 12:13:15,953 - pyskl - INFO - Epoch [144][3100/3746] lr: 4.173e-04, eta: 5:18:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6230, top5_acc: 0.8392, loss_cls: 2.1312, loss: 2.1312 +2024-07-27 12:14:37,902 - pyskl - INFO - Epoch [144][3200/3746] lr: 4.137e-04, eta: 5:17:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6216, top5_acc: 0.8411, loss_cls: 2.1259, loss: 2.1259 +2024-07-27 12:15:59,564 - pyskl - INFO - Epoch [144][3300/3746] lr: 4.101e-04, eta: 5:15:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6281, top5_acc: 0.8458, loss_cls: 2.1004, loss: 2.1004 +2024-07-27 12:17:20,975 - pyskl - INFO - Epoch [144][3400/3746] lr: 4.065e-04, eta: 5:14:23, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6234, top5_acc: 0.8384, loss_cls: 2.1364, loss: 2.1364 +2024-07-27 12:18:41,880 - pyskl - INFO - Epoch [144][3500/3746] lr: 4.030e-04, eta: 5:13:00, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6195, top5_acc: 0.8414, loss_cls: 2.1083, loss: 2.1083 +2024-07-27 12:20:03,614 - pyskl - INFO - Epoch [144][3600/3746] lr: 3.994e-04, eta: 5:11:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6209, top5_acc: 0.8364, loss_cls: 2.1332, loss: 2.1332 +2024-07-27 12:21:25,836 - pyskl - INFO - Epoch [144][3700/3746] lr: 3.959e-04, eta: 5:10:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6244, top5_acc: 0.8455, loss_cls: 2.1299, loss: 2.1299 +2024-07-27 12:22:05,192 - pyskl - INFO - Saving checkpoint at 144 epochs +2024-07-27 12:23:55,734 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 12:23:56,408 - pyskl - INFO - +top1_acc 0.4502 +top5_acc 0.6941 +2024-07-27 12:23:56,409 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 12:23:56,449 - pyskl - INFO - +mean_acc 0.4500 +2024-07-27 12:23:56,454 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_143.pth was removed +2024-07-27 12:23:56,709 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_144.pth. +2024-07-27 12:23:56,710 - pyskl - INFO - Best top1_acc is 0.4502 at 144 epoch. +2024-07-27 12:23:56,722 - pyskl - INFO - Epoch(val) [144][309] top1_acc: 0.4502, top5_acc: 0.6941, mean_class_accuracy: 0.4500 +2024-07-27 12:27:41,639 - pyskl - INFO - Epoch [145][100/3746] lr: 3.908e-04, eta: 5:08:19, time: 2.249, data_time: 1.272, memory: 15990, top1_acc: 0.6522, top5_acc: 0.8639, loss_cls: 1.9775, loss: 1.9775 +2024-07-27 12:29:03,429 - pyskl - INFO - Epoch [145][200/3746] lr: 3.873e-04, eta: 5:06:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6433, top5_acc: 0.8597, loss_cls: 2.0084, loss: 2.0084 +2024-07-27 12:30:24,986 - pyskl - INFO - Epoch [145][300/3746] lr: 3.839e-04, eta: 5:05:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6405, top5_acc: 0.8519, loss_cls: 2.0453, loss: 2.0453 +2024-07-27 12:31:46,679 - pyskl - INFO - Epoch [145][400/3746] lr: 3.804e-04, eta: 5:04:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6328, top5_acc: 0.8542, loss_cls: 2.0529, loss: 2.0529 +2024-07-27 12:33:08,696 - pyskl - INFO - Epoch [145][500/3746] lr: 3.770e-04, eta: 5:02:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6338, top5_acc: 0.8519, loss_cls: 2.0691, loss: 2.0691 +2024-07-27 12:34:30,256 - pyskl - INFO - Epoch [145][600/3746] lr: 3.736e-04, eta: 5:01:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6375, top5_acc: 0.8511, loss_cls: 2.0586, loss: 2.0586 +2024-07-27 12:35:51,569 - pyskl - INFO - Epoch [145][700/3746] lr: 3.702e-04, eta: 5:00:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6362, top5_acc: 0.8530, loss_cls: 2.0573, loss: 2.0573 +2024-07-27 12:37:12,820 - pyskl - INFO - Epoch [145][800/3746] lr: 3.668e-04, eta: 4:58:40, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6359, top5_acc: 0.8484, loss_cls: 2.0706, loss: 2.0706 +2024-07-27 12:38:34,155 - pyskl - INFO - Epoch [145][900/3746] lr: 3.634e-04, eta: 4:57:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6314, top5_acc: 0.8503, loss_cls: 2.0651, loss: 2.0651 +2024-07-27 12:39:55,926 - pyskl - INFO - Epoch [145][1000/3746] lr: 3.600e-04, eta: 4:55:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6384, top5_acc: 0.8478, loss_cls: 2.0632, loss: 2.0632 +2024-07-27 12:41:17,755 - pyskl - INFO - Epoch [145][1100/3746] lr: 3.567e-04, eta: 4:54:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6325, top5_acc: 0.8475, loss_cls: 2.0666, loss: 2.0666 +2024-07-27 12:42:40,043 - pyskl - INFO - Epoch [145][1200/3746] lr: 3.534e-04, eta: 4:53:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6327, top5_acc: 0.8461, loss_cls: 2.0850, loss: 2.0850 +2024-07-27 12:44:01,819 - pyskl - INFO - Epoch [145][1300/3746] lr: 3.501e-04, eta: 4:51:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6408, top5_acc: 0.8575, loss_cls: 2.0439, loss: 2.0439 +2024-07-27 12:45:24,248 - pyskl - INFO - Epoch [145][1400/3746] lr: 3.468e-04, eta: 4:50:23, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6239, top5_acc: 0.8498, loss_cls: 2.0900, loss: 2.0900 +2024-07-27 12:46:46,590 - pyskl - INFO - Epoch [145][1500/3746] lr: 3.435e-04, eta: 4:49:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6391, top5_acc: 0.8538, loss_cls: 2.0575, loss: 2.0575 +2024-07-27 12:48:08,458 - pyskl - INFO - Epoch [145][1600/3746] lr: 3.402e-04, eta: 4:47:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6422, top5_acc: 0.8491, loss_cls: 2.0356, loss: 2.0356 +2024-07-27 12:49:29,812 - pyskl - INFO - Epoch [145][1700/3746] lr: 3.370e-04, eta: 4:46:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6339, top5_acc: 0.8538, loss_cls: 2.0510, loss: 2.0510 +2024-07-27 12:50:51,436 - pyskl - INFO - Epoch [145][1800/3746] lr: 3.337e-04, eta: 4:44:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6291, top5_acc: 0.8411, loss_cls: 2.1059, loss: 2.1059 +2024-07-27 12:52:13,097 - pyskl - INFO - Epoch [145][1900/3746] lr: 3.305e-04, eta: 4:43:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6286, top5_acc: 0.8453, loss_cls: 2.1003, loss: 2.1003 +2024-07-27 12:53:34,948 - pyskl - INFO - Epoch [145][2000/3746] lr: 3.273e-04, eta: 4:42:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6388, top5_acc: 0.8539, loss_cls: 2.0486, loss: 2.0486 +2024-07-27 12:54:56,937 - pyskl - INFO - Epoch [145][2100/3746] lr: 3.241e-04, eta: 4:40:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6392, top5_acc: 0.8522, loss_cls: 2.0463, loss: 2.0463 +2024-07-27 12:56:19,098 - pyskl - INFO - Epoch [145][2200/3746] lr: 3.210e-04, eta: 4:39:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6455, top5_acc: 0.8539, loss_cls: 2.0148, loss: 2.0148 +2024-07-27 12:57:40,622 - pyskl - INFO - Epoch [145][2300/3746] lr: 3.178e-04, eta: 4:37:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6395, top5_acc: 0.8548, loss_cls: 2.0428, loss: 2.0428 +2024-07-27 12:59:02,925 - pyskl - INFO - Epoch [145][2400/3746] lr: 3.147e-04, eta: 4:36:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6355, top5_acc: 0.8517, loss_cls: 2.0354, loss: 2.0354 +2024-07-27 13:00:24,478 - pyskl - INFO - Epoch [145][2500/3746] lr: 3.116e-04, eta: 4:35:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6408, top5_acc: 0.8509, loss_cls: 2.0484, loss: 2.0484 +2024-07-27 13:01:46,564 - pyskl - INFO - Epoch [145][2600/3746] lr: 3.084e-04, eta: 4:33:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6250, top5_acc: 0.8394, loss_cls: 2.1382, loss: 2.1382 +2024-07-27 13:03:08,051 - pyskl - INFO - Epoch [145][2700/3746] lr: 3.054e-04, eta: 4:32:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6347, top5_acc: 0.8509, loss_cls: 2.0562, loss: 2.0562 +2024-07-27 13:04:29,799 - pyskl - INFO - Epoch [145][2800/3746] lr: 3.023e-04, eta: 4:31:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6398, top5_acc: 0.8562, loss_cls: 2.0329, loss: 2.0329 +2024-07-27 13:05:51,380 - pyskl - INFO - Epoch [145][2900/3746] lr: 2.992e-04, eta: 4:29:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6314, top5_acc: 0.8498, loss_cls: 2.0777, loss: 2.0777 +2024-07-27 13:07:12,664 - pyskl - INFO - Epoch [145][3000/3746] lr: 2.962e-04, eta: 4:28:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6356, top5_acc: 0.8555, loss_cls: 2.0380, loss: 2.0380 +2024-07-27 13:08:34,624 - pyskl - INFO - Epoch [145][3100/3746] lr: 2.931e-04, eta: 4:26:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6362, top5_acc: 0.8492, loss_cls: 2.0886, loss: 2.0886 +2024-07-27 13:09:56,265 - pyskl - INFO - Epoch [145][3200/3746] lr: 2.901e-04, eta: 4:25:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6383, top5_acc: 0.8508, loss_cls: 2.0599, loss: 2.0599 +2024-07-27 13:11:18,229 - pyskl - INFO - Epoch [145][3300/3746] lr: 2.871e-04, eta: 4:24:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6456, top5_acc: 0.8617, loss_cls: 2.0000, loss: 2.0000 +2024-07-27 13:12:39,791 - pyskl - INFO - Epoch [145][3400/3746] lr: 2.841e-04, eta: 4:22:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6323, top5_acc: 0.8433, loss_cls: 2.0645, loss: 2.0645 +2024-07-27 13:14:01,255 - pyskl - INFO - Epoch [145][3500/3746] lr: 2.812e-04, eta: 4:21:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6403, top5_acc: 0.8491, loss_cls: 2.0403, loss: 2.0403 +2024-07-27 13:15:22,916 - pyskl - INFO - Epoch [145][3600/3746] lr: 2.782e-04, eta: 4:20:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6295, top5_acc: 0.8405, loss_cls: 2.0946, loss: 2.0946 +2024-07-27 13:16:44,972 - pyskl - INFO - Epoch [145][3700/3746] lr: 2.753e-04, eta: 4:18:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6278, top5_acc: 0.8452, loss_cls: 2.0735, loss: 2.0735 +2024-07-27 13:17:24,396 - pyskl - INFO - Saving checkpoint at 145 epochs +2024-07-27 13:19:16,213 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 13:19:17,007 - pyskl - INFO - +top1_acc 0.4524 +top5_acc 0.6947 +2024-07-27 13:19:17,007 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 13:19:17,082 - pyskl - INFO - +mean_acc 0.4521 +2024-07-27 13:19:17,087 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_144.pth was removed +2024-07-27 13:19:17,406 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_145.pth. +2024-07-27 13:19:17,407 - pyskl - INFO - Best top1_acc is 0.4524 at 145 epoch. +2024-07-27 13:19:17,429 - pyskl - INFO - Epoch(val) [145][309] top1_acc: 0.4524, top5_acc: 0.6947, mean_class_accuracy: 0.4521 +2024-07-27 13:23:10,099 - pyskl - INFO - Epoch [146][100/3746] lr: 2.710e-04, eta: 4:16:44, time: 2.327, data_time: 1.336, memory: 15990, top1_acc: 0.6447, top5_acc: 0.8580, loss_cls: 1.9904, loss: 1.9904 +2024-07-27 13:24:32,433 - pyskl - INFO - Epoch [146][200/3746] lr: 2.681e-04, eta: 4:15:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6416, top5_acc: 0.8555, loss_cls: 2.0310, loss: 2.0310 +2024-07-27 13:25:54,533 - pyskl - INFO - Epoch [146][300/3746] lr: 2.652e-04, eta: 4:13:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6514, top5_acc: 0.8595, loss_cls: 2.0051, loss: 2.0051 +2024-07-27 13:27:16,581 - pyskl - INFO - Epoch [146][400/3746] lr: 2.624e-04, eta: 4:12:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6544, top5_acc: 0.8644, loss_cls: 1.9543, loss: 1.9543 +2024-07-27 13:28:39,059 - pyskl - INFO - Epoch [146][500/3746] lr: 2.595e-04, eta: 4:11:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6309, top5_acc: 0.8514, loss_cls: 2.0594, loss: 2.0594 +2024-07-27 13:30:01,409 - pyskl - INFO - Epoch [146][600/3746] lr: 2.567e-04, eta: 4:09:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6455, top5_acc: 0.8505, loss_cls: 2.0338, loss: 2.0338 +2024-07-27 13:31:23,458 - pyskl - INFO - Epoch [146][700/3746] lr: 2.539e-04, eta: 4:08:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6380, top5_acc: 0.8489, loss_cls: 2.0687, loss: 2.0687 +2024-07-27 13:32:45,262 - pyskl - INFO - Epoch [146][800/3746] lr: 2.511e-04, eta: 4:07:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6488, top5_acc: 0.8603, loss_cls: 1.9981, loss: 1.9981 +2024-07-27 13:34:06,665 - pyskl - INFO - Epoch [146][900/3746] lr: 2.483e-04, eta: 4:05:43, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6528, top5_acc: 0.8608, loss_cls: 1.9799, loss: 1.9799 +2024-07-27 13:35:27,875 - pyskl - INFO - Epoch [146][1000/3746] lr: 2.455e-04, eta: 4:04:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6500, top5_acc: 0.8555, loss_cls: 1.9903, loss: 1.9903 +2024-07-27 13:36:49,928 - pyskl - INFO - Epoch [146][1100/3746] lr: 2.427e-04, eta: 4:02:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6455, top5_acc: 0.8631, loss_cls: 1.9790, loss: 1.9790 +2024-07-27 13:38:11,998 - pyskl - INFO - Epoch [146][1200/3746] lr: 2.400e-04, eta: 4:01:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6395, top5_acc: 0.8517, loss_cls: 2.0423, loss: 2.0423 +2024-07-27 13:39:34,497 - pyskl - INFO - Epoch [146][1300/3746] lr: 2.373e-04, eta: 4:00:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6567, top5_acc: 0.8638, loss_cls: 1.9642, loss: 1.9642 +2024-07-27 13:40:56,674 - pyskl - INFO - Epoch [146][1400/3746] lr: 2.345e-04, eta: 3:58:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6375, top5_acc: 0.8503, loss_cls: 2.0582, loss: 2.0582 +2024-07-27 13:42:18,439 - pyskl - INFO - Epoch [146][1500/3746] lr: 2.318e-04, eta: 3:57:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6505, top5_acc: 0.8486, loss_cls: 2.0170, loss: 2.0170 +2024-07-27 13:43:39,887 - pyskl - INFO - Epoch [146][1600/3746] lr: 2.292e-04, eta: 3:56:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6442, top5_acc: 0.8567, loss_cls: 2.0358, loss: 2.0358 +2024-07-27 13:45:01,878 - pyskl - INFO - Epoch [146][1700/3746] lr: 2.265e-04, eta: 3:54:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6509, top5_acc: 0.8583, loss_cls: 2.0107, loss: 2.0107 +2024-07-27 13:46:23,588 - pyskl - INFO - Epoch [146][1800/3746] lr: 2.239e-04, eta: 3:53:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6359, top5_acc: 0.8600, loss_cls: 2.0169, loss: 2.0169 +2024-07-27 13:47:44,952 - pyskl - INFO - Epoch [146][1900/3746] lr: 2.212e-04, eta: 3:51:56, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6441, top5_acc: 0.8556, loss_cls: 2.0019, loss: 2.0019 +2024-07-27 13:49:07,608 - pyskl - INFO - Epoch [146][2000/3746] lr: 2.186e-04, eta: 3:50:33, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6498, top5_acc: 0.8548, loss_cls: 2.0270, loss: 2.0270 +2024-07-27 13:50:29,642 - pyskl - INFO - Epoch [146][2100/3746] lr: 2.160e-04, eta: 3:49:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6427, top5_acc: 0.8539, loss_cls: 2.0466, loss: 2.0466 +2024-07-27 13:51:52,075 - pyskl - INFO - Epoch [146][2200/3746] lr: 2.134e-04, eta: 3:47:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6505, top5_acc: 0.8531, loss_cls: 2.0145, loss: 2.0145 +2024-07-27 13:53:13,588 - pyskl - INFO - Epoch [146][2300/3746] lr: 2.108e-04, eta: 3:46:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6448, top5_acc: 0.8591, loss_cls: 2.0120, loss: 2.0120 +2024-07-27 13:54:35,116 - pyskl - INFO - Epoch [146][2400/3746] lr: 2.083e-04, eta: 3:45:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6561, top5_acc: 0.8614, loss_cls: 1.9733, loss: 1.9733 +2024-07-27 13:55:56,813 - pyskl - INFO - Epoch [146][2500/3746] lr: 2.057e-04, eta: 3:43:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6450, top5_acc: 0.8592, loss_cls: 2.0102, loss: 2.0102 +2024-07-27 13:57:18,424 - pyskl - INFO - Epoch [146][2600/3746] lr: 2.032e-04, eta: 3:42:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6386, top5_acc: 0.8522, loss_cls: 2.0089, loss: 2.0089 +2024-07-27 13:58:40,177 - pyskl - INFO - Epoch [146][2700/3746] lr: 2.007e-04, eta: 3:40:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6439, top5_acc: 0.8642, loss_cls: 1.9813, loss: 1.9813 +2024-07-27 14:00:01,821 - pyskl - INFO - Epoch [146][2800/3746] lr: 1.982e-04, eta: 3:39:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6491, top5_acc: 0.8594, loss_cls: 1.9889, loss: 1.9889 +2024-07-27 14:01:23,293 - pyskl - INFO - Epoch [146][2900/3746] lr: 1.957e-04, eta: 3:38:08, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6386, top5_acc: 0.8489, loss_cls: 2.0426, loss: 2.0426 +2024-07-27 14:02:45,072 - pyskl - INFO - Epoch [146][3000/3746] lr: 1.933e-04, eta: 3:36:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6306, top5_acc: 0.8489, loss_cls: 2.0551, loss: 2.0551 +2024-07-27 14:04:06,313 - pyskl - INFO - Epoch [146][3100/3746] lr: 1.908e-04, eta: 3:35:23, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6438, top5_acc: 0.8608, loss_cls: 2.0029, loss: 2.0029 +2024-07-27 14:05:28,109 - pyskl - INFO - Epoch [146][3200/3746] lr: 1.884e-04, eta: 3:34:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6428, top5_acc: 0.8539, loss_cls: 2.0398, loss: 2.0398 +2024-07-27 14:06:50,211 - pyskl - INFO - Epoch [146][3300/3746] lr: 1.860e-04, eta: 3:32:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6422, top5_acc: 0.8528, loss_cls: 2.0121, loss: 2.0121 +2024-07-27 14:08:11,643 - pyskl - INFO - Epoch [146][3400/3746] lr: 1.836e-04, eta: 3:31:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6464, top5_acc: 0.8520, loss_cls: 2.0385, loss: 2.0385 +2024-07-27 14:09:33,664 - pyskl - INFO - Epoch [146][3500/3746] lr: 1.812e-04, eta: 3:29:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6484, top5_acc: 0.8531, loss_cls: 2.0050, loss: 2.0050 +2024-07-27 14:10:55,254 - pyskl - INFO - Epoch [146][3600/3746] lr: 1.788e-04, eta: 3:28:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6433, top5_acc: 0.8533, loss_cls: 2.0121, loss: 2.0121 +2024-07-27 14:12:17,138 - pyskl - INFO - Epoch [146][3700/3746] lr: 1.765e-04, eta: 3:27:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6544, top5_acc: 0.8541, loss_cls: 2.0045, loss: 2.0045 +2024-07-27 14:12:56,716 - pyskl - INFO - Saving checkpoint at 146 epochs +2024-07-27 14:14:48,696 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 14:14:49,362 - pyskl - INFO - +top1_acc 0.4499 +top5_acc 0.6928 +2024-07-27 14:14:49,362 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 14:14:49,403 - pyskl - INFO - +mean_acc 0.4497 +2024-07-27 14:14:49,415 - pyskl - INFO - Epoch(val) [146][309] top1_acc: 0.4499, top5_acc: 0.6928, mean_class_accuracy: 0.4497 +2024-07-27 14:18:39,109 - pyskl - INFO - Epoch [147][100/3746] lr: 1.730e-04, eta: 3:25:09, time: 2.297, data_time: 1.314, memory: 15990, top1_acc: 0.6584, top5_acc: 0.8617, loss_cls: 1.9536, loss: 1.9536 +2024-07-27 14:20:00,894 - pyskl - INFO - Epoch [147][200/3746] lr: 1.707e-04, eta: 3:23:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6544, top5_acc: 0.8642, loss_cls: 1.9740, loss: 1.9740 +2024-07-27 14:21:22,438 - pyskl - INFO - Epoch [147][300/3746] lr: 1.684e-04, eta: 3:22:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6495, top5_acc: 0.8562, loss_cls: 1.9918, loss: 1.9918 +2024-07-27 14:22:43,636 - pyskl - INFO - Epoch [147][400/3746] lr: 1.661e-04, eta: 3:21:01, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6583, top5_acc: 0.8659, loss_cls: 1.9563, loss: 1.9563 +2024-07-27 14:24:06,406 - pyskl - INFO - Epoch [147][500/3746] lr: 1.639e-04, eta: 3:19:38, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6484, top5_acc: 0.8647, loss_cls: 1.9890, loss: 1.9890 +2024-07-27 14:25:28,213 - pyskl - INFO - Epoch [147][600/3746] lr: 1.616e-04, eta: 3:18:15, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6506, top5_acc: 0.8605, loss_cls: 1.9694, loss: 1.9694 +2024-07-27 14:26:50,433 - pyskl - INFO - Epoch [147][700/3746] lr: 1.594e-04, eta: 3:16:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6470, top5_acc: 0.8581, loss_cls: 1.9991, loss: 1.9991 +2024-07-27 14:28:12,438 - pyskl - INFO - Epoch [147][800/3746] lr: 1.572e-04, eta: 3:15:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6619, top5_acc: 0.8594, loss_cls: 1.9626, loss: 1.9626 +2024-07-27 14:29:34,009 - pyskl - INFO - Epoch [147][900/3746] lr: 1.550e-04, eta: 3:14:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6441, top5_acc: 0.8570, loss_cls: 2.0018, loss: 2.0018 +2024-07-27 14:30:55,434 - pyskl - INFO - Epoch [147][1000/3746] lr: 1.528e-04, eta: 3:12:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6470, top5_acc: 0.8620, loss_cls: 1.9775, loss: 1.9775 +2024-07-27 14:32:16,838 - pyskl - INFO - Epoch [147][1100/3746] lr: 1.506e-04, eta: 3:11:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6577, top5_acc: 0.8672, loss_cls: 1.9475, loss: 1.9475 +2024-07-27 14:33:38,187 - pyskl - INFO - Epoch [147][1200/3746] lr: 1.484e-04, eta: 3:09:59, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6444, top5_acc: 0.8588, loss_cls: 2.0022, loss: 2.0022 +2024-07-27 14:35:00,425 - pyskl - INFO - Epoch [147][1300/3746] lr: 1.463e-04, eta: 3:08:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6567, top5_acc: 0.8627, loss_cls: 1.9568, loss: 1.9568 +2024-07-27 14:36:22,675 - pyskl - INFO - Epoch [147][1400/3746] lr: 1.442e-04, eta: 3:07:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6567, top5_acc: 0.8719, loss_cls: 1.9305, loss: 1.9305 +2024-07-27 14:37:44,427 - pyskl - INFO - Epoch [147][1500/3746] lr: 1.420e-04, eta: 3:05:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6491, top5_acc: 0.8570, loss_cls: 2.0136, loss: 2.0136 +2024-07-27 14:39:06,318 - pyskl - INFO - Epoch [147][1600/3746] lr: 1.399e-04, eta: 3:04:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6555, top5_acc: 0.8656, loss_cls: 1.9410, loss: 1.9410 +2024-07-27 14:40:29,000 - pyskl - INFO - Epoch [147][1700/3746] lr: 1.379e-04, eta: 3:03:05, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.6441, top5_acc: 0.8556, loss_cls: 2.0257, loss: 2.0257 +2024-07-27 14:41:50,328 - pyskl - INFO - Epoch [147][1800/3746] lr: 1.358e-04, eta: 3:01:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6439, top5_acc: 0.8561, loss_cls: 2.0076, loss: 2.0076 +2024-07-27 14:43:11,564 - pyskl - INFO - Epoch [147][1900/3746] lr: 1.337e-04, eta: 3:00:20, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6595, top5_acc: 0.8653, loss_cls: 1.9456, loss: 1.9456 +2024-07-27 14:44:33,486 - pyskl - INFO - Epoch [147][2000/3746] lr: 1.317e-04, eta: 2:58:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6655, top5_acc: 0.8686, loss_cls: 1.9264, loss: 1.9264 +2024-07-27 14:45:55,521 - pyskl - INFO - Epoch [147][2100/3746] lr: 1.297e-04, eta: 2:57:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6477, top5_acc: 0.8598, loss_cls: 1.9914, loss: 1.9914 +2024-07-27 14:47:16,724 - pyskl - INFO - Epoch [147][2200/3746] lr: 1.277e-04, eta: 2:56:12, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6553, top5_acc: 0.8548, loss_cls: 1.9978, loss: 1.9978 +2024-07-27 14:48:38,701 - pyskl - INFO - Epoch [147][2300/3746] lr: 1.257e-04, eta: 2:54:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6458, top5_acc: 0.8531, loss_cls: 2.0234, loss: 2.0234 +2024-07-27 14:50:00,325 - pyskl - INFO - Epoch [147][2400/3746] lr: 1.237e-04, eta: 2:53:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6633, top5_acc: 0.8591, loss_cls: 1.9729, loss: 1.9729 +2024-07-27 14:51:21,644 - pyskl - INFO - Epoch [147][2500/3746] lr: 1.218e-04, eta: 2:52:04, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6475, top5_acc: 0.8639, loss_cls: 1.9849, loss: 1.9849 +2024-07-27 14:52:43,368 - pyskl - INFO - Epoch [147][2600/3746] lr: 1.198e-04, eta: 2:50:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6506, top5_acc: 0.8639, loss_cls: 1.9702, loss: 1.9702 +2024-07-27 14:54:05,109 - pyskl - INFO - Epoch [147][2700/3746] lr: 1.179e-04, eta: 2:49:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6525, top5_acc: 0.8653, loss_cls: 1.9755, loss: 1.9755 +2024-07-27 14:55:26,824 - pyskl - INFO - Epoch [147][2800/3746] lr: 1.160e-04, eta: 2:47:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6561, top5_acc: 0.8639, loss_cls: 1.9697, loss: 1.9697 +2024-07-27 14:56:48,230 - pyskl - INFO - Epoch [147][2900/3746] lr: 1.141e-04, eta: 2:46:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6531, top5_acc: 0.8614, loss_cls: 1.9655, loss: 1.9655 +2024-07-27 14:58:09,171 - pyskl - INFO - Epoch [147][3000/3746] lr: 1.122e-04, eta: 2:45:10, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6575, top5_acc: 0.8666, loss_cls: 1.9431, loss: 1.9431 +2024-07-27 14:59:30,560 - pyskl - INFO - Epoch [147][3100/3746] lr: 1.103e-04, eta: 2:43:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6511, top5_acc: 0.8589, loss_cls: 1.9762, loss: 1.9762 +2024-07-27 15:00:52,468 - pyskl - INFO - Epoch [147][3200/3746] lr: 1.085e-04, eta: 2:42:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6500, top5_acc: 0.8572, loss_cls: 1.9818, loss: 1.9818 +2024-07-27 15:02:13,860 - pyskl - INFO - Epoch [147][3300/3746] lr: 1.067e-04, eta: 2:41:02, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6545, top5_acc: 0.8620, loss_cls: 1.9900, loss: 1.9900 +2024-07-27 15:03:35,609 - pyskl - INFO - Epoch [147][3400/3746] lr: 1.048e-04, eta: 2:39:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6587, top5_acc: 0.8641, loss_cls: 1.9662, loss: 1.9662 +2024-07-27 15:04:57,397 - pyskl - INFO - Epoch [147][3500/3746] lr: 1.030e-04, eta: 2:38:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6477, top5_acc: 0.8627, loss_cls: 2.0041, loss: 2.0041 +2024-07-27 15:06:18,911 - pyskl - INFO - Epoch [147][3600/3746] lr: 1.013e-04, eta: 2:36:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6547, top5_acc: 0.8612, loss_cls: 1.9741, loss: 1.9741 +2024-07-27 15:07:40,798 - pyskl - INFO - Epoch [147][3700/3746] lr: 9.949e-05, eta: 2:35:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6455, top5_acc: 0.8562, loss_cls: 2.0054, loss: 2.0054 +2024-07-27 15:08:20,035 - pyskl - INFO - Saving checkpoint at 147 epochs +2024-07-27 15:10:11,323 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 15:10:11,990 - pyskl - INFO - +top1_acc 0.4508 +top5_acc 0.6935 +2024-07-27 15:10:11,990 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 15:10:12,031 - pyskl - INFO - +mean_acc 0.4506 +2024-07-27 15:10:12,043 - pyskl - INFO - Epoch(val) [147][309] top1_acc: 0.4508, top5_acc: 0.6935, mean_class_accuracy: 0.4506 +2024-07-27 15:14:02,231 - pyskl - INFO - Epoch [148][100/3746] lr: 9.693e-05, eta: 2:33:32, time: 2.302, data_time: 1.314, memory: 15990, top1_acc: 0.6598, top5_acc: 0.8658, loss_cls: 1.9591, loss: 1.9591 +2024-07-27 15:15:24,398 - pyskl - INFO - Epoch [148][200/3746] lr: 9.520e-05, eta: 2:32:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6691, top5_acc: 0.8723, loss_cls: 1.8977, loss: 1.8977 +2024-07-27 15:16:46,128 - pyskl - INFO - Epoch [148][300/3746] lr: 9.348e-05, eta: 2:30:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6587, top5_acc: 0.8681, loss_cls: 1.9359, loss: 1.9359 +2024-07-27 15:18:08,063 - pyskl - INFO - Epoch [148][400/3746] lr: 9.178e-05, eta: 2:29:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6478, top5_acc: 0.8672, loss_cls: 1.9563, loss: 1.9563 +2024-07-27 15:19:30,584 - pyskl - INFO - Epoch [148][500/3746] lr: 9.010e-05, eta: 2:28:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6627, top5_acc: 0.8658, loss_cls: 1.9513, loss: 1.9513 +2024-07-27 15:20:52,391 - pyskl - INFO - Epoch [148][600/3746] lr: 8.843e-05, eta: 2:26:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6589, top5_acc: 0.8653, loss_cls: 1.9416, loss: 1.9416 +2024-07-27 15:22:14,435 - pyskl - INFO - Epoch [148][700/3746] lr: 8.678e-05, eta: 2:25:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6589, top5_acc: 0.8642, loss_cls: 1.9530, loss: 1.9530 +2024-07-27 15:23:36,200 - pyskl - INFO - Epoch [148][800/3746] lr: 8.514e-05, eta: 2:23:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6622, top5_acc: 0.8617, loss_cls: 1.9443, loss: 1.9443 +2024-07-27 15:24:57,741 - pyskl - INFO - Epoch [148][900/3746] lr: 8.351e-05, eta: 2:22:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6686, top5_acc: 0.8711, loss_cls: 1.9183, loss: 1.9183 +2024-07-27 15:26:19,360 - pyskl - INFO - Epoch [148][1000/3746] lr: 8.191e-05, eta: 2:21:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6623, top5_acc: 0.8641, loss_cls: 1.9305, loss: 1.9305 +2024-07-27 15:27:40,722 - pyskl - INFO - Epoch [148][1100/3746] lr: 8.031e-05, eta: 2:19:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6566, top5_acc: 0.8634, loss_cls: 1.9545, loss: 1.9545 +2024-07-27 15:29:02,640 - pyskl - INFO - Epoch [148][1200/3746] lr: 7.874e-05, eta: 2:18:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6641, top5_acc: 0.8683, loss_cls: 1.9080, loss: 1.9080 +2024-07-27 15:30:24,288 - pyskl - INFO - Epoch [148][1300/3746] lr: 7.718e-05, eta: 2:17:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6583, top5_acc: 0.8638, loss_cls: 1.9429, loss: 1.9429 +2024-07-27 15:31:46,354 - pyskl - INFO - Epoch [148][1400/3746] lr: 7.563e-05, eta: 2:15:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6636, top5_acc: 0.8662, loss_cls: 1.9239, loss: 1.9239 +2024-07-27 15:33:08,076 - pyskl - INFO - Epoch [148][1500/3746] lr: 7.410e-05, eta: 2:14:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6761, top5_acc: 0.8766, loss_cls: 1.8810, loss: 1.8810 +2024-07-27 15:34:29,927 - pyskl - INFO - Epoch [148][1600/3746] lr: 7.259e-05, eta: 2:12:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6623, top5_acc: 0.8647, loss_cls: 1.9419, loss: 1.9419 +2024-07-27 15:35:52,122 - pyskl - INFO - Epoch [148][1700/3746] lr: 7.109e-05, eta: 2:11:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6633, top5_acc: 0.8677, loss_cls: 1.9091, loss: 1.9091 +2024-07-27 15:37:13,458 - pyskl - INFO - Epoch [148][1800/3746] lr: 6.961e-05, eta: 2:10:06, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6620, top5_acc: 0.8597, loss_cls: 1.9607, loss: 1.9607 +2024-07-27 15:38:35,121 - pyskl - INFO - Epoch [148][1900/3746] lr: 6.814e-05, eta: 2:08:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6555, top5_acc: 0.8589, loss_cls: 1.9816, loss: 1.9816 +2024-07-27 15:39:57,280 - pyskl - INFO - Epoch [148][2000/3746] lr: 6.669e-05, eta: 2:07:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6520, top5_acc: 0.8614, loss_cls: 1.9611, loss: 1.9611 +2024-07-27 15:41:18,934 - pyskl - INFO - Epoch [148][2100/3746] lr: 6.526e-05, eta: 2:05:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6491, top5_acc: 0.8617, loss_cls: 1.9679, loss: 1.9679 +2024-07-27 15:42:40,675 - pyskl - INFO - Epoch [148][2200/3746] lr: 6.384e-05, eta: 2:04:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6562, top5_acc: 0.8664, loss_cls: 1.9687, loss: 1.9687 +2024-07-27 15:44:02,534 - pyskl - INFO - Epoch [148][2300/3746] lr: 6.243e-05, eta: 2:03:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6519, top5_acc: 0.8552, loss_cls: 2.0054, loss: 2.0054 +2024-07-27 15:45:24,291 - pyskl - INFO - Epoch [148][2400/3746] lr: 6.104e-05, eta: 2:01:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6580, top5_acc: 0.8678, loss_cls: 1.9400, loss: 1.9400 +2024-07-27 15:46:46,205 - pyskl - INFO - Epoch [148][2500/3746] lr: 5.967e-05, eta: 2:00:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6580, top5_acc: 0.8694, loss_cls: 1.9293, loss: 1.9293 +2024-07-27 15:48:07,961 - pyskl - INFO - Epoch [148][2600/3746] lr: 5.831e-05, eta: 1:59:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6672, top5_acc: 0.8734, loss_cls: 1.9004, loss: 1.9004 +2024-07-27 15:49:29,026 - pyskl - INFO - Epoch [148][2700/3746] lr: 5.697e-05, eta: 1:57:41, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6584, top5_acc: 0.8670, loss_cls: 1.9468, loss: 1.9468 +2024-07-27 15:50:50,680 - pyskl - INFO - Epoch [148][2800/3746] lr: 5.564e-05, eta: 1:56:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6581, top5_acc: 0.8655, loss_cls: 1.9494, loss: 1.9494 +2024-07-27 15:52:12,156 - pyskl - INFO - Epoch [148][2900/3746] lr: 5.433e-05, eta: 1:54:56, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6544, top5_acc: 0.8681, loss_cls: 1.9669, loss: 1.9669 +2024-07-27 15:53:33,480 - pyskl - INFO - Epoch [148][3000/3746] lr: 5.304e-05, eta: 1:53:33, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6506, top5_acc: 0.8673, loss_cls: 1.9796, loss: 1.9796 +2024-07-27 15:54:55,179 - pyskl - INFO - Epoch [148][3100/3746] lr: 5.176e-05, eta: 1:52:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6603, top5_acc: 0.8605, loss_cls: 1.9620, loss: 1.9620 +2024-07-27 15:56:16,652 - pyskl - INFO - Epoch [148][3200/3746] lr: 5.050e-05, eta: 1:50:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6528, top5_acc: 0.8605, loss_cls: 1.9865, loss: 1.9865 +2024-07-27 15:57:37,914 - pyskl - INFO - Epoch [148][3300/3746] lr: 4.925e-05, eta: 1:49:25, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6517, top5_acc: 0.8666, loss_cls: 1.9674, loss: 1.9674 +2024-07-27 15:58:59,164 - pyskl - INFO - Epoch [148][3400/3746] lr: 4.801e-05, eta: 1:48:02, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6678, top5_acc: 0.8681, loss_cls: 1.9747, loss: 1.9747 +2024-07-27 16:00:20,653 - pyskl - INFO - Epoch [148][3500/3746] lr: 4.680e-05, eta: 1:46:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6683, top5_acc: 0.8694, loss_cls: 1.9283, loss: 1.9283 +2024-07-27 16:01:41,840 - pyskl - INFO - Epoch [148][3600/3746] lr: 4.560e-05, eta: 1:45:17, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6645, top5_acc: 0.8645, loss_cls: 1.9399, loss: 1.9399 +2024-07-27 16:03:03,157 - pyskl - INFO - Epoch [148][3700/3746] lr: 4.441e-05, eta: 1:43:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6541, top5_acc: 0.8620, loss_cls: 1.9629, loss: 1.9629 +2024-07-27 16:03:42,855 - pyskl - INFO - Saving checkpoint at 148 epochs +2024-07-27 16:05:33,527 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 16:05:34,196 - pyskl - INFO - +top1_acc 0.4513 +top5_acc 0.6928 +2024-07-27 16:05:34,197 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 16:05:34,237 - pyskl - INFO - +mean_acc 0.4512 +2024-07-27 16:05:34,248 - pyskl - INFO - Epoch(val) [148][309] top1_acc: 0.4513, top5_acc: 0.6928, mean_class_accuracy: 0.4512 +2024-07-27 16:09:20,215 - pyskl - INFO - Epoch [149][100/3746] lr: 4.271e-05, eta: 1:41:55, time: 2.260, data_time: 1.278, memory: 15990, top1_acc: 0.6648, top5_acc: 0.8619, loss_cls: 1.9419, loss: 1.9419 +2024-07-27 16:10:41,794 - pyskl - INFO - Epoch [149][200/3746] lr: 4.156e-05, eta: 1:40:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6627, top5_acc: 0.8697, loss_cls: 1.9050, loss: 1.9050 +2024-07-27 16:12:02,658 - pyskl - INFO - Epoch [149][300/3746] lr: 4.043e-05, eta: 1:39:09, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6716, top5_acc: 0.8711, loss_cls: 1.9238, loss: 1.9238 +2024-07-27 16:13:23,769 - pyskl - INFO - Epoch [149][400/3746] lr: 3.931e-05, eta: 1:37:47, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6566, top5_acc: 0.8695, loss_cls: 1.9304, loss: 1.9304 +2024-07-27 16:14:45,429 - pyskl - INFO - Epoch [149][500/3746] lr: 3.821e-05, eta: 1:36:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6600, top5_acc: 0.8619, loss_cls: 1.9542, loss: 1.9542 +2024-07-27 16:16:08,342 - pyskl - INFO - Epoch [149][600/3746] lr: 3.713e-05, eta: 1:35:01, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.6672, top5_acc: 0.8661, loss_cls: 1.9328, loss: 1.9328 +2024-07-27 16:17:29,769 - pyskl - INFO - Epoch [149][700/3746] lr: 3.606e-05, eta: 1:33:38, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6692, top5_acc: 0.8656, loss_cls: 1.9075, loss: 1.9075 +2024-07-27 16:18:51,715 - pyskl - INFO - Epoch [149][800/3746] lr: 3.500e-05, eta: 1:32:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6742, top5_acc: 0.8728, loss_cls: 1.8793, loss: 1.8793 +2024-07-27 16:20:13,053 - pyskl - INFO - Epoch [149][900/3746] lr: 3.397e-05, eta: 1:30:53, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6623, top5_acc: 0.8614, loss_cls: 1.9557, loss: 1.9557 +2024-07-27 16:21:34,771 - pyskl - INFO - Epoch [149][1000/3746] lr: 3.294e-05, eta: 1:29:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6698, top5_acc: 0.8703, loss_cls: 1.9050, loss: 1.9050 +2024-07-27 16:22:56,956 - pyskl - INFO - Epoch [149][1100/3746] lr: 3.194e-05, eta: 1:28:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6664, top5_acc: 0.8678, loss_cls: 1.9151, loss: 1.9151 +2024-07-27 16:24:19,476 - pyskl - INFO - Epoch [149][1200/3746] lr: 3.095e-05, eta: 1:26:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6553, top5_acc: 0.8597, loss_cls: 1.9952, loss: 1.9952 +2024-07-27 16:25:41,262 - pyskl - INFO - Epoch [149][1300/3746] lr: 2.997e-05, eta: 1:25:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6700, top5_acc: 0.8662, loss_cls: 1.9133, loss: 1.9133 +2024-07-27 16:27:03,480 - pyskl - INFO - Epoch [149][1400/3746] lr: 2.901e-05, eta: 1:23:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6631, top5_acc: 0.8622, loss_cls: 1.9513, loss: 1.9513 +2024-07-27 16:28:26,264 - pyskl - INFO - Epoch [149][1500/3746] lr: 2.807e-05, eta: 1:22:36, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.6734, top5_acc: 0.8709, loss_cls: 1.9050, loss: 1.9050 +2024-07-27 16:29:48,150 - pyskl - INFO - Epoch [149][1600/3746] lr: 2.714e-05, eta: 1:21:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6562, top5_acc: 0.8675, loss_cls: 1.9487, loss: 1.9487 +2024-07-27 16:31:09,948 - pyskl - INFO - Epoch [149][1700/3746] lr: 2.622e-05, eta: 1:19:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6700, top5_acc: 0.8677, loss_cls: 1.9304, loss: 1.9304 +2024-07-27 16:32:31,610 - pyskl - INFO - Epoch [149][1800/3746] lr: 2.533e-05, eta: 1:18:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6705, top5_acc: 0.8708, loss_cls: 1.9139, loss: 1.9139 +2024-07-27 16:33:53,340 - pyskl - INFO - Epoch [149][1900/3746] lr: 2.444e-05, eta: 1:17:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6553, top5_acc: 0.8738, loss_cls: 1.9346, loss: 1.9346 +2024-07-27 16:35:15,484 - pyskl - INFO - Epoch [149][2000/3746] lr: 2.358e-05, eta: 1:15:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6578, top5_acc: 0.8666, loss_cls: 1.9561, loss: 1.9561 +2024-07-27 16:36:37,501 - pyskl - INFO - Epoch [149][2100/3746] lr: 2.273e-05, eta: 1:14:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6659, top5_acc: 0.8753, loss_cls: 1.8885, loss: 1.8885 +2024-07-27 16:37:59,659 - pyskl - INFO - Epoch [149][2200/3746] lr: 2.189e-05, eta: 1:12:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6680, top5_acc: 0.8708, loss_cls: 1.8948, loss: 1.8948 +2024-07-27 16:39:20,806 - pyskl - INFO - Epoch [149][2300/3746] lr: 2.107e-05, eta: 1:11:35, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.6530, top5_acc: 0.8628, loss_cls: 1.9568, loss: 1.9568 +2024-07-27 16:40:42,013 - pyskl - INFO - Epoch [149][2400/3746] lr: 2.027e-05, eta: 1:10:12, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6652, top5_acc: 0.8777, loss_cls: 1.9029, loss: 1.9029 +2024-07-27 16:42:03,994 - pyskl - INFO - Epoch [149][2500/3746] lr: 1.948e-05, eta: 1:08:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6714, top5_acc: 0.8692, loss_cls: 1.9067, loss: 1.9067 +2024-07-27 16:43:25,632 - pyskl - INFO - Epoch [149][2600/3746] lr: 1.871e-05, eta: 1:07:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6716, top5_acc: 0.8719, loss_cls: 1.8898, loss: 1.8898 +2024-07-27 16:44:47,149 - pyskl - INFO - Epoch [149][2700/3746] lr: 1.795e-05, eta: 1:06:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6623, top5_acc: 0.8652, loss_cls: 1.9245, loss: 1.9245 +2024-07-27 16:46:09,150 - pyskl - INFO - Epoch [149][2800/3746] lr: 1.721e-05, eta: 1:04:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6631, top5_acc: 0.8698, loss_cls: 1.9248, loss: 1.9248 +2024-07-27 16:47:30,485 - pyskl - INFO - Epoch [149][2900/3746] lr: 1.649e-05, eta: 1:03:18, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6619, top5_acc: 0.8659, loss_cls: 1.9155, loss: 1.9155 +2024-07-27 16:48:52,039 - pyskl - INFO - Epoch [149][3000/3746] lr: 1.578e-05, eta: 1:01:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6580, top5_acc: 0.8619, loss_cls: 1.9582, loss: 1.9582 +2024-07-27 16:50:13,753 - pyskl - INFO - Epoch [149][3100/3746] lr: 1.508e-05, eta: 1:00:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6611, top5_acc: 0.8734, loss_cls: 1.9073, loss: 1.9073 +2024-07-27 16:51:35,461 - pyskl - INFO - Epoch [149][3200/3746] lr: 1.440e-05, eta: 0:59:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6653, top5_acc: 0.8684, loss_cls: 1.9160, loss: 1.9160 +2024-07-27 16:52:56,910 - pyskl - INFO - Epoch [149][3300/3746] lr: 1.374e-05, eta: 0:57:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6559, top5_acc: 0.8681, loss_cls: 1.9351, loss: 1.9351 +2024-07-27 16:54:18,628 - pyskl - INFO - Epoch [149][3400/3746] lr: 1.309e-05, eta: 0:56:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6608, top5_acc: 0.8681, loss_cls: 1.9329, loss: 1.9329 +2024-07-27 16:55:40,613 - pyskl - INFO - Epoch [149][3500/3746] lr: 1.246e-05, eta: 0:55:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6617, top5_acc: 0.8614, loss_cls: 1.9621, loss: 1.9621 +2024-07-27 16:57:02,113 - pyskl - INFO - Epoch [149][3600/3746] lr: 1.184e-05, eta: 0:53:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6545, top5_acc: 0.8666, loss_cls: 1.9534, loss: 1.9534 +2024-07-27 16:58:23,326 - pyskl - INFO - Epoch [149][3700/3746] lr: 1.124e-05, eta: 0:52:16, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6623, top5_acc: 0.8666, loss_cls: 1.9414, loss: 1.9414 +2024-07-27 16:59:02,659 - pyskl - INFO - Saving checkpoint at 149 epochs +2024-07-27 17:00:52,719 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 17:00:53,411 - pyskl - INFO - +top1_acc 0.4509 +top5_acc 0.6923 +2024-07-27 17:00:53,411 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 17:00:53,451 - pyskl - INFO - +mean_acc 0.4507 +2024-07-27 17:00:53,462 - pyskl - INFO - Epoch(val) [149][309] top1_acc: 0.4509, top5_acc: 0.6923, mean_class_accuracy: 0.4507 +2024-07-27 17:04:39,190 - pyskl - INFO - Epoch [150][100/3746] lr: 1.039e-05, eta: 0:50:16, time: 2.257, data_time: 1.282, memory: 15990, top1_acc: 0.6639, top5_acc: 0.8728, loss_cls: 1.9165, loss: 1.9165 +2024-07-27 17:06:01,015 - pyskl - INFO - Epoch [150][200/3746] lr: 9.832e-06, eta: 0:48:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6705, top5_acc: 0.8731, loss_cls: 1.9009, loss: 1.9009 +2024-07-27 17:07:22,394 - pyskl - INFO - Epoch [150][300/3746] lr: 9.285e-06, eta: 0:47:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6659, top5_acc: 0.8750, loss_cls: 1.9184, loss: 1.9184 +2024-07-27 17:08:44,044 - pyskl - INFO - Epoch [150][400/3746] lr: 8.754e-06, eta: 0:46:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6659, top5_acc: 0.8728, loss_cls: 1.9033, loss: 1.9033 +2024-07-27 17:10:05,519 - pyskl - INFO - Epoch [150][500/3746] lr: 8.239e-06, eta: 0:44:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6637, top5_acc: 0.8684, loss_cls: 1.9126, loss: 1.9126 +2024-07-27 17:11:27,896 - pyskl - INFO - Epoch [150][600/3746] lr: 7.739e-06, eta: 0:43:23, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.6648, top5_acc: 0.8656, loss_cls: 1.9286, loss: 1.9286 +2024-07-27 17:12:49,521 - pyskl - INFO - Epoch [150][700/3746] lr: 7.255e-06, eta: 0:42:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6653, top5_acc: 0.8706, loss_cls: 1.9167, loss: 1.9167 +2024-07-27 17:14:11,262 - pyskl - INFO - Epoch [150][800/3746] lr: 6.787e-06, eta: 0:40:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6752, top5_acc: 0.8658, loss_cls: 1.9066, loss: 1.9066 +2024-07-27 17:15:32,826 - pyskl - INFO - Epoch [150][900/3746] lr: 6.334e-06, eta: 0:39:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6659, top5_acc: 0.8684, loss_cls: 1.9047, loss: 1.9047 +2024-07-27 17:16:54,240 - pyskl - INFO - Epoch [150][1000/3746] lr: 5.897e-06, eta: 0:37:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6659, top5_acc: 0.8658, loss_cls: 1.9252, loss: 1.9252 +2024-07-27 17:18:16,337 - pyskl - INFO - Epoch [150][1100/3746] lr: 5.475e-06, eta: 0:36:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6691, top5_acc: 0.8689, loss_cls: 1.9289, loss: 1.9289 +2024-07-27 17:19:38,133 - pyskl - INFO - Epoch [150][1200/3746] lr: 5.070e-06, eta: 0:35:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.6687, top5_acc: 0.8809, loss_cls: 1.8742, loss: 1.8742 +2024-07-27 17:21:00,153 - pyskl - INFO - Epoch [150][1300/3746] lr: 4.679e-06, eta: 0:33:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6725, top5_acc: 0.8695, loss_cls: 1.9021, loss: 1.9021 +2024-07-27 17:22:22,322 - pyskl - INFO - Epoch [150][1400/3746] lr: 4.305e-06, eta: 0:32:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6728, top5_acc: 0.8681, loss_cls: 1.8902, loss: 1.8902 +2024-07-27 17:23:43,885 - pyskl - INFO - Epoch [150][1500/3746] lr: 3.946e-06, eta: 0:30:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.6633, top5_acc: 0.8653, loss_cls: 1.9472, loss: 1.9472 +2024-07-27 17:25:06,063 - pyskl - INFO - Epoch [150][1600/3746] lr: 3.602e-06, eta: 0:29:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.6670, top5_acc: 0.8653, loss_cls: 1.9449, loss: 1.9449 +2024-07-27 17:26:27,477 - pyskl - INFO - Epoch [150][1700/3746] lr: 3.275e-06, eta: 0:28:12, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.6708, top5_acc: 0.8680, loss_cls: 1.9272, loss: 1.9272 +2024-07-27 17:27:49,005 - pyskl - INFO - Epoch [150][1800/3746] lr: 2.962e-06, eta: 0:26:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6678, top5_acc: 0.8712, loss_cls: 1.9001, loss: 1.9001 +2024-07-27 17:29:11,071 - pyskl - INFO - Epoch [150][1900/3746] lr: 2.666e-06, eta: 0:25:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.6677, top5_acc: 0.8730, loss_cls: 1.9146, loss: 1.9146 +2024-07-27 17:30:33,080 - pyskl - INFO - Epoch [150][2000/3746] lr: 2.385e-06, eta: 0:24:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.6634, top5_acc: 0.8602, loss_cls: 1.9521, loss: 1.9521 +2024-07-27 17:31:55,406 - pyskl - INFO - Epoch [150][2100/3746] lr: 2.120e-06, eta: 0:22:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.6672, top5_acc: 0.8745, loss_cls: 1.8858, loss: 1.8858 +2024-07-27 17:33:17,074 - pyskl - INFO - Epoch [150][2200/3746] lr: 1.870e-06, eta: 0:21:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6655, top5_acc: 0.8708, loss_cls: 1.9295, loss: 1.9295 +2024-07-27 17:34:38,754 - pyskl - INFO - Epoch [150][2300/3746] lr: 1.636e-06, eta: 0:19:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.6577, top5_acc: 0.8688, loss_cls: 1.9404, loss: 1.9404 +2024-07-27 17:36:01,750 - pyskl - INFO - Epoch [150][2400/3746] lr: 1.418e-06, eta: 0:18:33, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.6606, top5_acc: 0.8614, loss_cls: 1.9490, loss: 1.9490 +2024-07-27 17:37:23,697 - pyskl - INFO - Epoch [150][2500/3746] lr: 1.215e-06, eta: 0:17:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.6670, top5_acc: 0.8716, loss_cls: 1.9090, loss: 1.9090 +2024-07-27 17:38:44,914 - pyskl - INFO - Epoch [150][2600/3746] lr: 1.028e-06, eta: 0:15:48, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.6656, top5_acc: 0.8694, loss_cls: 1.9391, loss: 1.9391 +2024-07-27 17:40:06,427 - pyskl - INFO - Epoch [150][2700/3746] lr: 8.567e-07, eta: 0:14:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6641, top5_acc: 0.8644, loss_cls: 1.9188, loss: 1.9188 +2024-07-27 17:41:27,763 - pyskl - INFO - Epoch [150][2800/3746] lr: 7.008e-07, eta: 0:13:02, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6691, top5_acc: 0.8684, loss_cls: 1.9136, loss: 1.9136 +2024-07-27 17:42:50,281 - pyskl - INFO - Epoch [150][2900/3746] lr: 5.606e-07, eta: 0:11:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.6720, top5_acc: 0.8702, loss_cls: 1.8921, loss: 1.8921 +2024-07-27 17:44:11,823 - pyskl - INFO - Epoch [150][3000/3746] lr: 4.361e-07, eta: 0:10:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6623, top5_acc: 0.8639, loss_cls: 1.9287, loss: 1.9287 +2024-07-27 17:45:33,127 - pyskl - INFO - Epoch [150][3100/3746] lr: 3.271e-07, eta: 0:08:54, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.6695, top5_acc: 0.8717, loss_cls: 1.9179, loss: 1.9179 +2024-07-27 17:46:53,979 - pyskl - INFO - Epoch [150][3200/3746] lr: 2.338e-07, eta: 0:07:31, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6555, top5_acc: 0.8686, loss_cls: 1.9638, loss: 1.9638 +2024-07-27 17:48:15,444 - pyskl - INFO - Epoch [150][3300/3746] lr: 1.561e-07, eta: 0:06:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6691, top5_acc: 0.8748, loss_cls: 1.8889, loss: 1.8889 +2024-07-27 17:49:36,978 - pyskl - INFO - Epoch [150][3400/3746] lr: 9.410e-08, eta: 0:04:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.6723, top5_acc: 0.8700, loss_cls: 1.9025, loss: 1.9025 +2024-07-27 17:50:57,556 - pyskl - INFO - Epoch [150][3500/3746] lr: 4.768e-08, eta: 0:03:23, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.6597, top5_acc: 0.8686, loss_cls: 1.9439, loss: 1.9439 +2024-07-27 17:52:18,458 - pyskl - INFO - Epoch [150][3600/3746] lr: 1.689e-08, eta: 0:02:00, time: 0.809, data_time: 0.000, memory: 15990, top1_acc: 0.6653, top5_acc: 0.8656, loss_cls: 1.9246, loss: 1.9246 +2024-07-27 17:53:39,119 - pyskl - INFO - Epoch [150][3700/3746] lr: 1.726e-09, eta: 0:00:38, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.6691, top5_acc: 0.8731, loss_cls: 1.8945, loss: 1.8945 +2024-07-27 17:54:17,958 - pyskl - INFO - Saving checkpoint at 150 epochs +2024-07-27 17:56:05,920 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-27 17:56:06,561 - pyskl - INFO - +top1_acc 0.4512 +top5_acc 0.6929 +2024-07-27 17:56:06,561 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-27 17:56:06,598 - pyskl - INFO - +mean_acc 0.4510 +2024-07-27 17:56:06,610 - pyskl - INFO - Epoch(val) [150][309] top1_acc: 0.4512, top5_acc: 0.6929, mean_class_accuracy: 0.4510 +2024-07-27 17:56:20,011 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-07-27 18:08:01,469 - pyskl - INFO - Testing results of the last checkpoint +2024-07-27 18:08:01,469 - pyskl - INFO - top1_acc: 0.4618 +2024-07-27 18:08:01,469 - pyskl - INFO - top5_acc: 0.7050 +2024-07-27 18:08:01,469 - pyskl - INFO - mean_class_accuracy: 0.4616 +2024-07-27 18:08:01,470 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/k400/k_1/best_top1_acc_epoch_145.pth +2024-07-27 18:19:43,003 - pyskl - INFO - Testing results of the best checkpoint +2024-07-27 18:19:43,003 - pyskl - INFO - top1_acc: 0.4625 +2024-07-27 18:19:43,003 - pyskl - INFO - top5_acc: 0.7066 +2024-07-27 18:19:43,004 - pyskl - INFO - mean_class_accuracy: 0.4623 diff --git a/k400/k_1/20240722_022901.log.json b/k400/k_1/20240722_022901.log.json new file mode 100644 index 0000000000000000000000000000000000000000..e400661e63f989efac7e4e02acd66984ba5ffad7 --- /dev/null +++ b/k400/k_1/20240722_022901.log.json @@ -0,0 +1,5701 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 233891380, "config_name": "k_1.py", "work_dir": "k_1", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.1, "memory": 15990, "data_time": 1.28598, "top1_acc": 0.00812, "top5_acc": 0.03453, "loss_cls": 6.36264, "loss": 6.36264, "time": 1.99884} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.01266, "top5_acc": 0.05328, "loss_cls": 6.33801, "loss": 6.33801, "time": 0.70987} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.01516, "top5_acc": 0.0725, "loss_cls": 6.18129, "loss": 6.18129, "time": 0.70441} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.02391, "top5_acc": 0.08547, "loss_cls": 6.04607, "loss": 6.04607, "time": 0.70502} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.03031, "top5_acc": 0.10688, "loss_cls": 5.91933, "loss": 5.91933, "time": 0.70498} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.03031, "top5_acc": 0.11359, "loss_cls": 5.88194, "loss": 5.88194, "time": 0.70567} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.03359, "top5_acc": 0.12891, "loss_cls": 5.80231, "loss": 5.80231, "time": 0.70425} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.03516, "top5_acc": 0.13125, "loss_cls": 5.76851, "loss": 5.76851, "time": 0.70533} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.03953, "top5_acc": 0.13547, "loss_cls": 5.70349, "loss": 5.70349, "time": 0.70137} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.04281, "top5_acc": 0.14469, "loss_cls": 5.71763, "loss": 5.71763, "time": 0.70267} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.03984, "top5_acc": 0.14234, "loss_cls": 5.68809, "loss": 5.68809, "time": 0.70257} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.04344, "top5_acc": 0.1625, "loss_cls": 5.62647, "loss": 5.62647, "time": 0.7017} +{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.05172, "top5_acc": 0.16297, "loss_cls": 5.59488, "loss": 5.59488, "time": 0.70243} +{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.04984, "top5_acc": 0.17469, "loss_cls": 5.56678, "loss": 5.56678, "time": 0.70514} +{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.1, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.05734, "top5_acc": 0.18156, "loss_cls": 5.53477, "loss": 5.53477, "time": 0.70388} +{"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.06156, "top5_acc": 0.19266, "loss_cls": 5.49458, "loss": 5.49458, "time": 0.70239} +{"mode": "train", "epoch": 1, "iter": 1700, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.06297, "top5_acc": 0.19172, "loss_cls": 5.50736, "loss": 5.50736, "time": 0.70767} +{"mode": "train", "epoch": 1, "iter": 1800, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.06594, "top5_acc": 0.20344, "loss_cls": 5.44751, "loss": 5.44751, "time": 0.70542} +{"mode": "train", "epoch": 1, "iter": 1900, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.06719, "top5_acc": 0.21109, "loss_cls": 5.43172, "loss": 5.43172, "time": 0.70084} +{"mode": "train", "epoch": 1, "iter": 2000, "lr": 0.1, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.07187, "top5_acc": 0.21281, "loss_cls": 5.41557, "loss": 5.41557, "time": 0.72295} +{"mode": "train", "epoch": 1, "iter": 2100, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.07234, "top5_acc": 0.2125, "loss_cls": 5.39496, "loss": 5.39496, "time": 0.72975} +{"mode": "train", "epoch": 1, "iter": 2200, "lr": 0.1, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.07375, "top5_acc": 0.22766, "loss_cls": 5.37003, "loss": 5.37003, "time": 0.70029} +{"mode": "train", "epoch": 1, "iter": 2300, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.08109, "top5_acc": 0.22406, "loss_cls": 5.36581, "loss": 5.36581, "time": 0.70248} +{"mode": "train", "epoch": 1, "iter": 2400, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.08422, "top5_acc": 0.23562, "loss_cls": 5.33573, "loss": 5.33573, "time": 0.70452} +{"mode": "train", "epoch": 1, "iter": 2500, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.08016, "top5_acc": 0.23266, "loss_cls": 5.32396, "loss": 5.32396, "time": 0.70088} +{"mode": "train", "epoch": 1, "iter": 2600, "lr": 0.09999, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.08734, "top5_acc": 0.24641, "loss_cls": 5.27201, "loss": 5.27201, "time": 0.70143} +{"mode": "train", "epoch": 1, "iter": 2700, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.09312, "top5_acc": 0.24656, "loss_cls": 5.27856, "loss": 5.27856, "time": 0.70054} +{"mode": "train", "epoch": 1, "iter": 2800, "lr": 0.09999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.09047, "top5_acc": 0.24531, "loss_cls": 5.2329, "loss": 5.2329, "time": 0.70087} +{"mode": "train", "epoch": 1, "iter": 2900, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.09578, "top5_acc": 0.26078, "loss_cls": 5.22846, "loss": 5.22846, "time": 0.70261} +{"mode": "train", "epoch": 1, "iter": 3000, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.09594, "top5_acc": 0.26687, "loss_cls": 5.20427, "loss": 5.20427, "time": 0.70417} +{"mode": "train", "epoch": 1, "iter": 3100, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.09094, "top5_acc": 0.26109, "loss_cls": 5.20626, "loss": 5.20626, "time": 0.70136} +{"mode": "train", "epoch": 1, "iter": 3200, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.10234, "top5_acc": 0.27078, "loss_cls": 5.20462, "loss": 5.20462, "time": 0.70092} +{"mode": "train", "epoch": 1, "iter": 3300, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.09312, "top5_acc": 0.26391, "loss_cls": 5.21474, "loss": 5.21474, "time": 0.70117} +{"mode": "train", "epoch": 1, "iter": 3400, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.09531, "top5_acc": 0.26141, "loss_cls": 5.18978, "loss": 5.18978, "time": 0.70732} +{"mode": "train", "epoch": 1, "iter": 3500, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.09797, "top5_acc": 0.27203, "loss_cls": 5.17305, "loss": 5.17305, "time": 0.7051} +{"mode": "train", "epoch": 1, "iter": 3600, "lr": 0.09999, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.09875, "top5_acc": 0.27484, "loss_cls": 5.1442, "loss": 5.1442, "time": 0.70343} +{"mode": "train", "epoch": 1, "iter": 3700, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.11047, "top5_acc": 0.28875, "loss_cls": 5.12373, "loss": 5.12373, "time": 0.70292} +{"mode": "val", "epoch": 1, "iter": 309, "lr": 0.09999, "top1_acc": 0.07694, "top5_acc": 0.22099, "mean_class_accuracy": 0.07692} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.09999, "memory": 15990, "data_time": 1.29483, "top1_acc": 0.10766, "top5_acc": 0.29063, "loss_cls": 5.08395, "loss": 5.08395, "time": 2.00232} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.11219, "top5_acc": 0.2925, "loss_cls": 5.07601, "loss": 5.07601, "time": 0.70691} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.09999, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.1125, "top5_acc": 0.29109, "loss_cls": 5.08623, "loss": 5.08623, "time": 0.70466} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.10875, "top5_acc": 0.28984, "loss_cls": 5.1061, "loss": 5.1061, "time": 0.70064} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.09999, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.10703, "top5_acc": 0.29672, "loss_cls": 5.06619, "loss": 5.06619, "time": 0.69896} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.09999, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.10438, "top5_acc": 0.29422, "loss_cls": 5.0757, "loss": 5.0757, "time": 0.69998} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.11406, "top5_acc": 0.29609, "loss_cls": 5.06896, "loss": 5.06896, "time": 0.69975} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.11812, "top5_acc": 0.30781, "loss_cls": 5.01892, "loss": 5.01892, "time": 0.70213} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.09998, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.11406, "top5_acc": 0.29438, "loss_cls": 5.06872, "loss": 5.06872, "time": 0.70148} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.12141, "top5_acc": 0.30531, "loss_cls": 5.0287, "loss": 5.0287, "time": 0.70192} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.11469, "top5_acc": 0.30062, "loss_cls": 5.01371, "loss": 5.01371, "time": 0.70205} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.09998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.12406, "top5_acc": 0.31734, "loss_cls": 4.94755, "loss": 4.94755, "time": 0.70129} +{"mode": "train", "epoch": 2, "iter": 1300, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.11859, "top5_acc": 0.31391, "loss_cls": 4.96683, "loss": 4.96683, "time": 0.69959} +{"mode": "train", "epoch": 2, "iter": 1400, "lr": 0.09998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.12562, "top5_acc": 0.31859, "loss_cls": 4.95572, "loss": 4.95572, "time": 0.70122} +{"mode": "train", "epoch": 2, "iter": 1500, "lr": 0.09998, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.12438, "top5_acc": 0.31203, "loss_cls": 4.95341, "loss": 4.95341, "time": 0.69918} +{"mode": "train", "epoch": 2, "iter": 1600, "lr": 0.09998, "memory": 15990, "data_time": 0.0002, "top1_acc": 0.12672, "top5_acc": 0.32734, "loss_cls": 4.95126, "loss": 4.95126, "time": 0.70128} +{"mode": "train", "epoch": 2, "iter": 1700, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.13312, "top5_acc": 0.32328, "loss_cls": 4.93128, "loss": 4.93128, "time": 0.69873} +{"mode": "train", "epoch": 2, "iter": 1800, "lr": 0.09998, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.12672, "top5_acc": 0.32641, "loss_cls": 4.93775, "loss": 4.93775, "time": 0.70069} +{"mode": "train", "epoch": 2, "iter": 1900, "lr": 0.09998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.12641, "top5_acc": 0.32906, "loss_cls": 4.89727, "loss": 4.89727, "time": 0.70136} +{"mode": "train", "epoch": 2, "iter": 2000, "lr": 0.09997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.13016, "top5_acc": 0.33406, "loss_cls": 4.90032, "loss": 4.90032, "time": 0.69895} +{"mode": "train", "epoch": 2, "iter": 2100, "lr": 0.09997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.13094, "top5_acc": 0.33547, "loss_cls": 4.93288, "loss": 4.93288, "time": 0.70017} +{"mode": "train", "epoch": 2, "iter": 2200, "lr": 0.09997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.13688, "top5_acc": 0.33266, "loss_cls": 4.9178, "loss": 4.9178, "time": 0.70079} +{"mode": "train", "epoch": 2, "iter": 2300, "lr": 0.09997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.13641, "top5_acc": 0.34172, "loss_cls": 4.85269, "loss": 4.85269, "time": 0.7038} +{"mode": "train", "epoch": 2, "iter": 2400, "lr": 0.09997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.13234, "top5_acc": 0.33016, "loss_cls": 4.90741, "loss": 4.90741, "time": 0.69914} +{"mode": "train", "epoch": 2, "iter": 2500, "lr": 0.09997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.13312, "top5_acc": 0.33812, "loss_cls": 4.86767, "loss": 4.86767, "time": 0.69835} +{"mode": "train", "epoch": 2, "iter": 2600, "lr": 0.09997, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.13594, "top5_acc": 0.34391, "loss_cls": 4.84533, "loss": 4.84533, "time": 0.6991} +{"mode": "train", "epoch": 2, "iter": 2700, "lr": 0.09997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.13844, "top5_acc": 0.33906, "loss_cls": 4.90432, "loss": 4.90432, "time": 0.70264} +{"mode": "train", "epoch": 2, "iter": 2800, "lr": 0.09997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.14672, "top5_acc": 0.35516, "loss_cls": 4.80872, "loss": 4.80872, "time": 0.69978} +{"mode": "train", "epoch": 2, "iter": 2900, "lr": 0.09997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.1425, "top5_acc": 0.35312, "loss_cls": 4.82319, "loss": 4.82319, "time": 0.7008} +{"mode": "train", "epoch": 2, "iter": 3000, "lr": 0.09996, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.14438, "top5_acc": 0.35859, "loss_cls": 4.78873, "loss": 4.78873, "time": 0.70092} +{"mode": "train", "epoch": 2, "iter": 3100, "lr": 0.09996, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.14312, "top5_acc": 0.35984, "loss_cls": 4.77115, "loss": 4.77115, "time": 0.70008} +{"mode": "train", "epoch": 2, "iter": 3200, "lr": 0.09996, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.13844, "top5_acc": 0.35938, "loss_cls": 4.77543, "loss": 4.77543, "time": 0.7007} +{"mode": "train", "epoch": 2, "iter": 3300, "lr": 0.09996, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.14828, "top5_acc": 0.35828, "loss_cls": 4.77861, "loss": 4.77861, "time": 0.7039} +{"mode": "train", "epoch": 2, "iter": 3400, "lr": 0.09996, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.14797, "top5_acc": 0.35031, "loss_cls": 4.82279, "loss": 4.82279, "time": 0.70258} +{"mode": "train", "epoch": 2, "iter": 3500, "lr": 0.09996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.14969, "top5_acc": 0.36953, "loss_cls": 4.74357, "loss": 4.74357, "time": 0.7033} +{"mode": "train", "epoch": 2, "iter": 3600, "lr": 0.09996, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.15359, "top5_acc": 0.37031, "loss_cls": 4.75183, "loss": 4.75183, "time": 0.70339} +{"mode": "train", "epoch": 2, "iter": 3700, "lr": 0.09996, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.15984, "top5_acc": 0.37062, "loss_cls": 4.71404, "loss": 4.71404, "time": 0.71032} +{"mode": "val", "epoch": 2, "iter": 309, "lr": 0.09996, "top1_acc": 0.1093, "top5_acc": 0.28846, "mean_class_accuracy": 0.10932} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.09995, "memory": 15990, "data_time": 1.32563, "top1_acc": 0.15391, "top5_acc": 0.36781, "loss_cls": 4.72782, "loss": 4.72782, "time": 2.0332} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.09995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15859, "top5_acc": 0.36672, "loss_cls": 4.72884, "loss": 4.72884, "time": 0.70383} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.09995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16156, "top5_acc": 0.38094, "loss_cls": 4.68631, "loss": 4.68631, "time": 0.70348} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.09995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15719, "top5_acc": 0.36062, "loss_cls": 4.73623, "loss": 4.73623, "time": 0.70295} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.09995, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.15859, "top5_acc": 0.38406, "loss_cls": 4.69214, "loss": 4.69214, "time": 0.7021} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.09995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16625, "top5_acc": 0.38031, "loss_cls": 4.6521, "loss": 4.6521, "time": 0.70354} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.09995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16078, "top5_acc": 0.37531, "loss_cls": 4.70305, "loss": 4.70305, "time": 0.70177} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.09995, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.15891, "top5_acc": 0.37578, "loss_cls": 4.70396, "loss": 4.70396, "time": 0.70229} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.09994, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16109, "top5_acc": 0.38312, "loss_cls": 4.67249, "loss": 4.67249, "time": 0.70088} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.09994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16359, "top5_acc": 0.37297, "loss_cls": 4.69757, "loss": 4.69757, "time": 0.7004} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.09994, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.16203, "top5_acc": 0.38141, "loss_cls": 4.69312, "loss": 4.69312, "time": 0.70286} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.09994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16141, "top5_acc": 0.38328, "loss_cls": 4.69219, "loss": 4.69219, "time": 0.7033} +{"mode": "train", "epoch": 3, "iter": 1300, "lr": 0.09994, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17328, "top5_acc": 0.39125, "loss_cls": 4.63618, "loss": 4.63618, "time": 0.69922} +{"mode": "train", "epoch": 3, "iter": 1400, "lr": 0.09994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16375, "top5_acc": 0.38281, "loss_cls": 4.67712, "loss": 4.67712, "time": 0.70165} +{"mode": "train", "epoch": 3, "iter": 1500, "lr": 0.09994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17109, "top5_acc": 0.39609, "loss_cls": 4.62868, "loss": 4.62868, "time": 0.7024} +{"mode": "train", "epoch": 3, "iter": 1600, "lr": 0.09994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16125, "top5_acc": 0.37219, "loss_cls": 4.7135, "loss": 4.7135, "time": 0.70011} +{"mode": "train", "epoch": 3, "iter": 1700, "lr": 0.09993, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16734, "top5_acc": 0.38406, "loss_cls": 4.67886, "loss": 4.67886, "time": 0.69979} +{"mode": "train", "epoch": 3, "iter": 1800, "lr": 0.09993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16609, "top5_acc": 0.38, "loss_cls": 4.67193, "loss": 4.67193, "time": 0.7016} +{"mode": "train", "epoch": 3, "iter": 1900, "lr": 0.09993, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16344, "top5_acc": 0.38453, "loss_cls": 4.6292, "loss": 4.6292, "time": 0.70184} +{"mode": "train", "epoch": 3, "iter": 2000, "lr": 0.09993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16578, "top5_acc": 0.38016, "loss_cls": 4.64828, "loss": 4.64828, "time": 0.70006} +{"mode": "train", "epoch": 3, "iter": 2100, "lr": 0.09993, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16203, "top5_acc": 0.38812, "loss_cls": 4.61974, "loss": 4.61974, "time": 0.7001} +{"mode": "train", "epoch": 3, "iter": 2200, "lr": 0.09993, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17578, "top5_acc": 0.39297, "loss_cls": 4.64775, "loss": 4.64775, "time": 0.70211} +{"mode": "train", "epoch": 3, "iter": 2300, "lr": 0.09993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18172, "top5_acc": 0.40422, "loss_cls": 4.57046, "loss": 4.57046, "time": 0.70129} +{"mode": "train", "epoch": 3, "iter": 2400, "lr": 0.09992, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17953, "top5_acc": 0.395, "loss_cls": 4.60464, "loss": 4.60464, "time": 0.69935} +{"mode": "train", "epoch": 3, "iter": 2500, "lr": 0.09992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17531, "top5_acc": 0.39984, "loss_cls": 4.61945, "loss": 4.61945, "time": 0.70146} +{"mode": "train", "epoch": 3, "iter": 2600, "lr": 0.09992, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16828, "top5_acc": 0.39859, "loss_cls": 4.62116, "loss": 4.62116, "time": 0.69852} +{"mode": "train", "epoch": 3, "iter": 2700, "lr": 0.09992, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18188, "top5_acc": 0.39031, "loss_cls": 4.58931, "loss": 4.58931, "time": 0.70072} +{"mode": "train", "epoch": 3, "iter": 2800, "lr": 0.09992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18328, "top5_acc": 0.39984, "loss_cls": 4.58111, "loss": 4.58111, "time": 0.69887} +{"mode": "train", "epoch": 3, "iter": 2900, "lr": 0.09992, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.1675, "top5_acc": 0.40375, "loss_cls": 4.60662, "loss": 4.60662, "time": 0.69864} +{"mode": "train", "epoch": 3, "iter": 3000, "lr": 0.09991, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.18062, "top5_acc": 0.39844, "loss_cls": 4.58008, "loss": 4.58008, "time": 0.70102} +{"mode": "train", "epoch": 3, "iter": 3100, "lr": 0.09991, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18328, "top5_acc": 0.40156, "loss_cls": 4.5638, "loss": 4.5638, "time": 0.70142} +{"mode": "train", "epoch": 3, "iter": 3200, "lr": 0.09991, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18344, "top5_acc": 0.41234, "loss_cls": 4.53597, "loss": 4.53597, "time": 0.70181} +{"mode": "train", "epoch": 3, "iter": 3300, "lr": 0.09991, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.17828, "top5_acc": 0.39938, "loss_cls": 4.60028, "loss": 4.60028, "time": 0.70501} +{"mode": "train", "epoch": 3, "iter": 3400, "lr": 0.09991, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17375, "top5_acc": 0.41312, "loss_cls": 4.5742, "loss": 4.5742, "time": 0.70409} +{"mode": "train", "epoch": 3, "iter": 3500, "lr": 0.09991, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.18594, "top5_acc": 0.40578, "loss_cls": 4.57102, "loss": 4.57102, "time": 0.70466} +{"mode": "train", "epoch": 3, "iter": 3600, "lr": 0.0999, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.18906, "top5_acc": 0.41203, "loss_cls": 4.51788, "loss": 4.51788, "time": 0.70114} +{"mode": "train", "epoch": 3, "iter": 3700, "lr": 0.0999, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.18609, "top5_acc": 0.41391, "loss_cls": 4.51935, "loss": 4.51935, "time": 0.70873} +{"mode": "val", "epoch": 3, "iter": 309, "lr": 0.0999, "top1_acc": 0.1054, "top5_acc": 0.296, "mean_class_accuracy": 0.10521} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.0999, "memory": 15990, "data_time": 1.41863, "top1_acc": 0.19031, "top5_acc": 0.42328, "loss_cls": 4.47937, "loss": 4.47937, "time": 2.13961} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.0999, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.19016, "top5_acc": 0.41547, "loss_cls": 4.51447, "loss": 4.51447, "time": 0.72155} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.0999, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.18531, "top5_acc": 0.41531, "loss_cls": 4.5224, "loss": 4.5224, "time": 0.72043} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.09989, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.17484, "top5_acc": 0.41562, "loss_cls": 4.51258, "loss": 4.51258, "time": 0.71738} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.09989, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19344, "top5_acc": 0.42422, "loss_cls": 4.48995, "loss": 4.48995, "time": 0.71919} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.09989, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.18578, "top5_acc": 0.42562, "loss_cls": 4.50287, "loss": 4.50287, "time": 0.71783} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.09989, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.18516, "top5_acc": 0.42016, "loss_cls": 4.50732, "loss": 4.50732, "time": 0.71909} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.09989, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.17578, "top5_acc": 0.41125, "loss_cls": 4.51629, "loss": 4.51629, "time": 0.71944} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.09988, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.18641, "top5_acc": 0.4175, "loss_cls": 4.51172, "loss": 4.51172, "time": 0.7205} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.09988, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19469, "top5_acc": 0.42859, "loss_cls": 4.4619, "loss": 4.4619, "time": 0.71815} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.09988, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.19, "top5_acc": 0.41688, "loss_cls": 4.49946, "loss": 4.49946, "time": 0.71695} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.09988, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.19, "top5_acc": 0.42047, "loss_cls": 4.50461, "loss": 4.50461, "time": 0.72107} +{"mode": "train", "epoch": 4, "iter": 1300, "lr": 0.09988, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.19844, "top5_acc": 0.42547, "loss_cls": 4.46793, "loss": 4.46793, "time": 0.71525} +{"mode": "train", "epoch": 4, "iter": 1400, "lr": 0.09988, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.18594, "top5_acc": 0.42266, "loss_cls": 4.49867, "loss": 4.49867, "time": 0.7162} +{"mode": "train", "epoch": 4, "iter": 1500, "lr": 0.09987, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.19984, "top5_acc": 0.42188, "loss_cls": 4.47069, "loss": 4.47069, "time": 0.71738} +{"mode": "train", "epoch": 4, "iter": 1600, "lr": 0.09987, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20047, "top5_acc": 0.43109, "loss_cls": 4.44791, "loss": 4.44791, "time": 0.71737} +{"mode": "train", "epoch": 4, "iter": 1700, "lr": 0.09987, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.19922, "top5_acc": 0.43359, "loss_cls": 4.4515, "loss": 4.4515, "time": 0.72042} +{"mode": "train", "epoch": 4, "iter": 1800, "lr": 0.09987, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.19609, "top5_acc": 0.42844, "loss_cls": 4.46833, "loss": 4.46833, "time": 0.71966} +{"mode": "train", "epoch": 4, "iter": 1900, "lr": 0.09987, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20609, "top5_acc": 0.43188, "loss_cls": 4.43411, "loss": 4.43411, "time": 0.71079} +{"mode": "train", "epoch": 4, "iter": 2000, "lr": 0.09986, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.19625, "top5_acc": 0.42375, "loss_cls": 4.48118, "loss": 4.48118, "time": 0.70575} +{"mode": "train", "epoch": 4, "iter": 2100, "lr": 0.09986, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19484, "top5_acc": 0.43266, "loss_cls": 4.44261, "loss": 4.44261, "time": 0.71246} +{"mode": "train", "epoch": 4, "iter": 2200, "lr": 0.09986, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.19922, "top5_acc": 0.43641, "loss_cls": 4.41644, "loss": 4.41644, "time": 0.70807} +{"mode": "train", "epoch": 4, "iter": 2300, "lr": 0.09986, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2, "top5_acc": 0.42891, "loss_cls": 4.4464, "loss": 4.4464, "time": 0.70833} +{"mode": "train", "epoch": 4, "iter": 2400, "lr": 0.09985, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.18656, "top5_acc": 0.42969, "loss_cls": 4.46409, "loss": 4.46409, "time": 0.70598} +{"mode": "train", "epoch": 4, "iter": 2500, "lr": 0.09985, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19047, "top5_acc": 0.43094, "loss_cls": 4.4521, "loss": 4.4521, "time": 0.706} +{"mode": "train", "epoch": 4, "iter": 2600, "lr": 0.09985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19938, "top5_acc": 0.43938, "loss_cls": 4.43011, "loss": 4.43011, "time": 0.70124} +{"mode": "train", "epoch": 4, "iter": 2700, "lr": 0.09985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20062, "top5_acc": 0.42969, "loss_cls": 4.43315, "loss": 4.43315, "time": 0.70216} +{"mode": "train", "epoch": 4, "iter": 2800, "lr": 0.09985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2, "top5_acc": 0.43984, "loss_cls": 4.4155, "loss": 4.4155, "time": 0.7051} +{"mode": "train", "epoch": 4, "iter": 2900, "lr": 0.09984, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20078, "top5_acc": 0.43531, "loss_cls": 4.42702, "loss": 4.42702, "time": 0.70078} +{"mode": "train", "epoch": 4, "iter": 3000, "lr": 0.09984, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.19969, "top5_acc": 0.43391, "loss_cls": 4.42342, "loss": 4.42342, "time": 0.70462} +{"mode": "train", "epoch": 4, "iter": 3100, "lr": 0.09984, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19953, "top5_acc": 0.42969, "loss_cls": 4.43269, "loss": 4.43269, "time": 0.70386} +{"mode": "train", "epoch": 4, "iter": 3200, "lr": 0.09984, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21188, "top5_acc": 0.435, "loss_cls": 4.40597, "loss": 4.40597, "time": 0.70168} +{"mode": "train", "epoch": 4, "iter": 3300, "lr": 0.09983, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21219, "top5_acc": 0.43969, "loss_cls": 4.37611, "loss": 4.37611, "time": 0.7088} +{"mode": "train", "epoch": 4, "iter": 3400, "lr": 0.09983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2025, "top5_acc": 0.43391, "loss_cls": 4.43453, "loss": 4.43453, "time": 0.70406} +{"mode": "train", "epoch": 4, "iter": 3500, "lr": 0.09983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20312, "top5_acc": 0.44297, "loss_cls": 4.39754, "loss": 4.39754, "time": 0.70484} +{"mode": "train", "epoch": 4, "iter": 3600, "lr": 0.09983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20422, "top5_acc": 0.44016, "loss_cls": 4.41598, "loss": 4.41598, "time": 0.70461} +{"mode": "train", "epoch": 4, "iter": 3700, "lr": 0.09983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19516, "top5_acc": 0.43781, "loss_cls": 4.41563, "loss": 4.41563, "time": 0.70833} +{"mode": "val", "epoch": 4, "iter": 309, "lr": 0.09982, "top1_acc": 0.13701, "top5_acc": 0.32938, "mean_class_accuracy": 0.13687} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.09982, "memory": 15990, "data_time": 1.35119, "top1_acc": 0.20953, "top5_acc": 0.45281, "loss_cls": 4.38158, "loss": 4.38158, "time": 2.05396} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.09982, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20156, "top5_acc": 0.43641, "loss_cls": 4.44063, "loss": 4.44063, "time": 0.70517} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.09982, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20531, "top5_acc": 0.44406, "loss_cls": 4.38062, "loss": 4.38062, "time": 0.70955} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.09982, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20875, "top5_acc": 0.44781, "loss_cls": 4.36238, "loss": 4.36238, "time": 0.70456} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.09981, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20641, "top5_acc": 0.44594, "loss_cls": 4.37619, "loss": 4.37619, "time": 0.70637} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.09981, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2075, "top5_acc": 0.45062, "loss_cls": 4.36014, "loss": 4.36014, "time": 0.70621} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.09981, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2, "top5_acc": 0.43766, "loss_cls": 4.39556, "loss": 4.39556, "time": 0.70637} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.09981, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20953, "top5_acc": 0.45109, "loss_cls": 4.38906, "loss": 4.38906, "time": 0.7042} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.0998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21469, "top5_acc": 0.45016, "loss_cls": 4.36732, "loss": 4.36732, "time": 0.70296} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.0998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21328, "top5_acc": 0.45422, "loss_cls": 4.31722, "loss": 4.31722, "time": 0.70229} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.0998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20984, "top5_acc": 0.45484, "loss_cls": 4.35605, "loss": 4.35605, "time": 0.70231} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.0998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21375, "top5_acc": 0.44891, "loss_cls": 4.36602, "loss": 4.36602, "time": 0.70349} +{"mode": "train", "epoch": 5, "iter": 1300, "lr": 0.09979, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21266, "top5_acc": 0.44828, "loss_cls": 4.39516, "loss": 4.39516, "time": 0.7073} +{"mode": "train", "epoch": 5, "iter": 1400, "lr": 0.09979, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21188, "top5_acc": 0.44953, "loss_cls": 4.3738, "loss": 4.3738, "time": 0.70459} +{"mode": "train", "epoch": 5, "iter": 1500, "lr": 0.09979, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21359, "top5_acc": 0.45156, "loss_cls": 4.34808, "loss": 4.34808, "time": 0.70406} +{"mode": "train", "epoch": 5, "iter": 1600, "lr": 0.09979, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21266, "top5_acc": 0.45016, "loss_cls": 4.35241, "loss": 4.35241, "time": 0.70578} +{"mode": "train", "epoch": 5, "iter": 1700, "lr": 0.09978, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21344, "top5_acc": 0.44828, "loss_cls": 4.36713, "loss": 4.36713, "time": 0.70212} +{"mode": "train", "epoch": 5, "iter": 1800, "lr": 0.09978, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20844, "top5_acc": 0.44797, "loss_cls": 4.38394, "loss": 4.38394, "time": 0.70439} +{"mode": "train", "epoch": 5, "iter": 1900, "lr": 0.09978, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21156, "top5_acc": 0.4575, "loss_cls": 4.35537, "loss": 4.35537, "time": 0.7015} +{"mode": "train", "epoch": 5, "iter": 2000, "lr": 0.09977, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22, "top5_acc": 0.45797, "loss_cls": 4.31754, "loss": 4.31754, "time": 0.70369} +{"mode": "train", "epoch": 5, "iter": 2100, "lr": 0.09977, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22219, "top5_acc": 0.45453, "loss_cls": 4.33547, "loss": 4.33547, "time": 0.70371} +{"mode": "train", "epoch": 5, "iter": 2200, "lr": 0.09977, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22578, "top5_acc": 0.46031, "loss_cls": 4.32842, "loss": 4.32842, "time": 0.70428} +{"mode": "train", "epoch": 5, "iter": 2300, "lr": 0.09977, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20953, "top5_acc": 0.44812, "loss_cls": 4.3911, "loss": 4.3911, "time": 0.70884} +{"mode": "train", "epoch": 5, "iter": 2400, "lr": 0.09976, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21188, "top5_acc": 0.45109, "loss_cls": 4.38053, "loss": 4.38053, "time": 0.70147} +{"mode": "train", "epoch": 5, "iter": 2500, "lr": 0.09976, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21031, "top5_acc": 0.44984, "loss_cls": 4.37761, "loss": 4.37761, "time": 0.70185} +{"mode": "train", "epoch": 5, "iter": 2600, "lr": 0.09976, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21141, "top5_acc": 0.45109, "loss_cls": 4.33914, "loss": 4.33914, "time": 0.70248} +{"mode": "train", "epoch": 5, "iter": 2700, "lr": 0.09976, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21188, "top5_acc": 0.45453, "loss_cls": 4.33743, "loss": 4.33743, "time": 0.70334} +{"mode": "train", "epoch": 5, "iter": 2800, "lr": 0.09975, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22703, "top5_acc": 0.46125, "loss_cls": 4.28818, "loss": 4.28818, "time": 0.69956} +{"mode": "train", "epoch": 5, "iter": 2900, "lr": 0.09975, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21812, "top5_acc": 0.45031, "loss_cls": 4.33413, "loss": 4.33413, "time": 0.70336} +{"mode": "train", "epoch": 5, "iter": 3000, "lr": 0.09975, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21094, "top5_acc": 0.46172, "loss_cls": 4.33262, "loss": 4.33262, "time": 0.70106} +{"mode": "train", "epoch": 5, "iter": 3100, "lr": 0.09974, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21375, "top5_acc": 0.45922, "loss_cls": 4.31892, "loss": 4.31892, "time": 0.70127} +{"mode": "train", "epoch": 5, "iter": 3200, "lr": 0.09974, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22969, "top5_acc": 0.46297, "loss_cls": 4.30119, "loss": 4.30119, "time": 0.70593} +{"mode": "train", "epoch": 5, "iter": 3300, "lr": 0.09974, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22094, "top5_acc": 0.45312, "loss_cls": 4.31765, "loss": 4.31765, "time": 0.70904} +{"mode": "train", "epoch": 5, "iter": 3400, "lr": 0.09974, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20891, "top5_acc": 0.46266, "loss_cls": 4.33347, "loss": 4.33347, "time": 0.70631} +{"mode": "train", "epoch": 5, "iter": 3500, "lr": 0.09973, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22469, "top5_acc": 0.45969, "loss_cls": 4.28307, "loss": 4.28307, "time": 0.707} +{"mode": "train", "epoch": 5, "iter": 3600, "lr": 0.09973, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22516, "top5_acc": 0.46422, "loss_cls": 4.27972, "loss": 4.27972, "time": 0.71243} +{"mode": "train", "epoch": 5, "iter": 3700, "lr": 0.09973, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21688, "top5_acc": 0.455, "loss_cls": 4.30779, "loss": 4.30779, "time": 0.70959} +{"mode": "val", "epoch": 5, "iter": 309, "lr": 0.09973, "top1_acc": 0.17252, "top5_acc": 0.38606, "mean_class_accuracy": 0.17249} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.09972, "memory": 15990, "data_time": 1.27119, "top1_acc": 0.21984, "top5_acc": 0.46328, "loss_cls": 4.30625, "loss": 4.30625, "time": 1.97801} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.09972, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23047, "top5_acc": 0.46906, "loss_cls": 4.26658, "loss": 4.26658, "time": 0.7041} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.09972, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22266, "top5_acc": 0.45953, "loss_cls": 4.2994, "loss": 4.2994, "time": 0.70321} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.09971, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22453, "top5_acc": 0.46141, "loss_cls": 4.2913, "loss": 4.2913, "time": 0.70648} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.09971, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22125, "top5_acc": 0.46344, "loss_cls": 4.29574, "loss": 4.29574, "time": 0.70508} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.09971, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21422, "top5_acc": 0.45031, "loss_cls": 4.31, "loss": 4.31, "time": 0.69959} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.09971, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22312, "top5_acc": 0.46266, "loss_cls": 4.31608, "loss": 4.31608, "time": 0.70172} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.0997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22406, "top5_acc": 0.47516, "loss_cls": 4.25264, "loss": 4.25264, "time": 0.70366} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.0997, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22422, "top5_acc": 0.46516, "loss_cls": 4.27653, "loss": 4.27653, "time": 0.70244} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.0997, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2225, "top5_acc": 0.46844, "loss_cls": 4.25972, "loss": 4.25972, "time": 0.70591} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.09969, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21969, "top5_acc": 0.45453, "loss_cls": 4.30776, "loss": 4.30776, "time": 0.70196} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.09969, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.22516, "top5_acc": 0.465, "loss_cls": 4.29195, "loss": 4.29195, "time": 0.70096} +{"mode": "train", "epoch": 6, "iter": 1300, "lr": 0.09969, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22422, "top5_acc": 0.46266, "loss_cls": 4.29863, "loss": 4.29863, "time": 0.70308} +{"mode": "train", "epoch": 6, "iter": 1400, "lr": 0.09968, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22109, "top5_acc": 0.46141, "loss_cls": 4.29643, "loss": 4.29643, "time": 0.70775} +{"mode": "train", "epoch": 6, "iter": 1500, "lr": 0.09968, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2175, "top5_acc": 0.46078, "loss_cls": 4.29052, "loss": 4.29052, "time": 0.70334} +{"mode": "train", "epoch": 6, "iter": 1600, "lr": 0.09968, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22625, "top5_acc": 0.46938, "loss_cls": 4.28973, "loss": 4.28973, "time": 0.70298} +{"mode": "train", "epoch": 6, "iter": 1700, "lr": 0.09967, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21969, "top5_acc": 0.46641, "loss_cls": 4.2922, "loss": 4.2922, "time": 0.70178} +{"mode": "train", "epoch": 6, "iter": 1800, "lr": 0.09967, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22, "top5_acc": 0.46859, "loss_cls": 4.31383, "loss": 4.31383, "time": 0.70173} +{"mode": "train", "epoch": 6, "iter": 1900, "lr": 0.09967, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22641, "top5_acc": 0.46953, "loss_cls": 4.25885, "loss": 4.25885, "time": 0.70312} +{"mode": "train", "epoch": 6, "iter": 2000, "lr": 0.09966, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22781, "top5_acc": 0.46594, "loss_cls": 4.2917, "loss": 4.2917, "time": 0.70492} +{"mode": "train", "epoch": 6, "iter": 2100, "lr": 0.09966, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21734, "top5_acc": 0.45828, "loss_cls": 4.3216, "loss": 4.3216, "time": 0.70142} +{"mode": "train", "epoch": 6, "iter": 2200, "lr": 0.09966, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22484, "top5_acc": 0.46391, "loss_cls": 4.30051, "loss": 4.30051, "time": 0.70573} +{"mode": "train", "epoch": 6, "iter": 2300, "lr": 0.09965, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22688, "top5_acc": 0.46562, "loss_cls": 4.27322, "loss": 4.27322, "time": 0.70372} +{"mode": "train", "epoch": 6, "iter": 2400, "lr": 0.09965, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24109, "top5_acc": 0.47594, "loss_cls": 4.1945, "loss": 4.1945, "time": 0.70321} +{"mode": "train", "epoch": 6, "iter": 2500, "lr": 0.09965, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22562, "top5_acc": 0.47359, "loss_cls": 4.25274, "loss": 4.25274, "time": 0.70093} +{"mode": "train", "epoch": 6, "iter": 2600, "lr": 0.09964, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22219, "top5_acc": 0.46859, "loss_cls": 4.27716, "loss": 4.27716, "time": 0.70269} +{"mode": "train", "epoch": 6, "iter": 2700, "lr": 0.09964, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22938, "top5_acc": 0.46984, "loss_cls": 4.2738, "loss": 4.2738, "time": 0.70209} +{"mode": "train", "epoch": 6, "iter": 2800, "lr": 0.09964, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22859, "top5_acc": 0.47062, "loss_cls": 4.25455, "loss": 4.25455, "time": 0.70633} +{"mode": "train", "epoch": 6, "iter": 2900, "lr": 0.09963, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22438, "top5_acc": 0.46953, "loss_cls": 4.30536, "loss": 4.30536, "time": 0.70167} +{"mode": "train", "epoch": 6, "iter": 3000, "lr": 0.09963, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22328, "top5_acc": 0.46141, "loss_cls": 4.31265, "loss": 4.31265, "time": 0.70239} +{"mode": "train", "epoch": 6, "iter": 3100, "lr": 0.09963, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23172, "top5_acc": 0.47625, "loss_cls": 4.24203, "loss": 4.24203, "time": 0.7043} +{"mode": "train", "epoch": 6, "iter": 3200, "lr": 0.09962, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22797, "top5_acc": 0.46938, "loss_cls": 4.23664, "loss": 4.23664, "time": 0.70279} +{"mode": "train", "epoch": 6, "iter": 3300, "lr": 0.09962, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22891, "top5_acc": 0.46078, "loss_cls": 4.28274, "loss": 4.28274, "time": 0.70651} +{"mode": "train", "epoch": 6, "iter": 3400, "lr": 0.09962, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23047, "top5_acc": 0.47359, "loss_cls": 4.25145, "loss": 4.25145, "time": 0.70412} +{"mode": "train", "epoch": 6, "iter": 3500, "lr": 0.09961, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22484, "top5_acc": 0.46719, "loss_cls": 4.2792, "loss": 4.2792, "time": 0.70615} +{"mode": "train", "epoch": 6, "iter": 3600, "lr": 0.09961, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22922, "top5_acc": 0.48016, "loss_cls": 4.24384, "loss": 4.24384, "time": 0.70745} +{"mode": "train", "epoch": 6, "iter": 3700, "lr": 0.09961, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22688, "top5_acc": 0.47359, "loss_cls": 4.25005, "loss": 4.25005, "time": 0.70952} +{"mode": "val", "epoch": 6, "iter": 309, "lr": 0.09961, "top1_acc": 0.1631, "top5_acc": 0.37618, "mean_class_accuracy": 0.16289} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0996, "memory": 15990, "data_time": 1.26959, "top1_acc": 0.22547, "top5_acc": 0.46672, "loss_cls": 4.25213, "loss": 4.25213, "time": 1.97677} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0996, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23156, "top5_acc": 0.47312, "loss_cls": 4.23084, "loss": 4.23084, "time": 0.70257} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.0996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22828, "top5_acc": 0.47438, "loss_cls": 4.23392, "loss": 4.23392, "time": 0.70615} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.09959, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2225, "top5_acc": 0.46859, "loss_cls": 4.27032, "loss": 4.27032, "time": 0.70296} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.09959, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23625, "top5_acc": 0.47719, "loss_cls": 4.18754, "loss": 4.18754, "time": 0.7048} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.09958, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23594, "top5_acc": 0.47672, "loss_cls": 4.22419, "loss": 4.22419, "time": 0.70173} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.09958, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22562, "top5_acc": 0.4725, "loss_cls": 4.2357, "loss": 4.2357, "time": 0.704} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.09958, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22641, "top5_acc": 0.46188, "loss_cls": 4.26709, "loss": 4.26709, "time": 0.70343} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.09957, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22047, "top5_acc": 0.46812, "loss_cls": 4.2662, "loss": 4.2662, "time": 0.70168} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.09957, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22719, "top5_acc": 0.47281, "loss_cls": 4.24213, "loss": 4.24213, "time": 0.70583} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.09957, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22875, "top5_acc": 0.46734, "loss_cls": 4.26974, "loss": 4.26974, "time": 0.70705} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.09956, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23453, "top5_acc": 0.48375, "loss_cls": 4.23295, "loss": 4.23295, "time": 0.70491} +{"mode": "train", "epoch": 7, "iter": 1300, "lr": 0.09956, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22969, "top5_acc": 0.4725, "loss_cls": 4.25772, "loss": 4.25772, "time": 0.70344} +{"mode": "train", "epoch": 7, "iter": 1400, "lr": 0.09956, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23234, "top5_acc": 0.47188, "loss_cls": 4.23186, "loss": 4.23186, "time": 0.70031} +{"mode": "train", "epoch": 7, "iter": 1500, "lr": 0.09955, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23656, "top5_acc": 0.47703, "loss_cls": 4.21589, "loss": 4.21589, "time": 0.70084} +{"mode": "train", "epoch": 7, "iter": 1600, "lr": 0.09955, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22922, "top5_acc": 0.46828, "loss_cls": 4.29291, "loss": 4.29291, "time": 0.70321} +{"mode": "train", "epoch": 7, "iter": 1700, "lr": 0.09954, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22969, "top5_acc": 0.46953, "loss_cls": 4.24912, "loss": 4.24912, "time": 0.70138} +{"mode": "train", "epoch": 7, "iter": 1800, "lr": 0.09954, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22766, "top5_acc": 0.4725, "loss_cls": 4.23399, "loss": 4.23399, "time": 0.70439} +{"mode": "train", "epoch": 7, "iter": 1900, "lr": 0.09954, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22438, "top5_acc": 0.45875, "loss_cls": 4.26905, "loss": 4.26905, "time": 0.70077} +{"mode": "train", "epoch": 7, "iter": 2000, "lr": 0.09953, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22562, "top5_acc": 0.46797, "loss_cls": 4.28931, "loss": 4.28931, "time": 0.70486} +{"mode": "train", "epoch": 7, "iter": 2100, "lr": 0.09953, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.245, "top5_acc": 0.48672, "loss_cls": 4.1879, "loss": 4.1879, "time": 0.70143} +{"mode": "train", "epoch": 7, "iter": 2200, "lr": 0.09952, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23734, "top5_acc": 0.48656, "loss_cls": 4.21948, "loss": 4.21948, "time": 0.70641} +{"mode": "train", "epoch": 7, "iter": 2300, "lr": 0.09952, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21828, "top5_acc": 0.46281, "loss_cls": 4.272, "loss": 4.272, "time": 0.70759} +{"mode": "train", "epoch": 7, "iter": 2400, "lr": 0.09952, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23141, "top5_acc": 0.47672, "loss_cls": 4.2423, "loss": 4.2423, "time": 0.70176} +{"mode": "train", "epoch": 7, "iter": 2500, "lr": 0.09951, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23031, "top5_acc": 0.47859, "loss_cls": 4.23711, "loss": 4.23711, "time": 0.70211} +{"mode": "train", "epoch": 7, "iter": 2600, "lr": 0.09951, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23297, "top5_acc": 0.47234, "loss_cls": 4.24241, "loss": 4.24241, "time": 0.70127} +{"mode": "train", "epoch": 7, "iter": 2700, "lr": 0.09951, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22812, "top5_acc": 0.475, "loss_cls": 4.24396, "loss": 4.24396, "time": 0.70167} +{"mode": "train", "epoch": 7, "iter": 2800, "lr": 0.0995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2325, "top5_acc": 0.47781, "loss_cls": 4.25862, "loss": 4.25862, "time": 0.70434} +{"mode": "train", "epoch": 7, "iter": 2900, "lr": 0.0995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23703, "top5_acc": 0.47109, "loss_cls": 4.25359, "loss": 4.25359, "time": 0.70227} +{"mode": "train", "epoch": 7, "iter": 3000, "lr": 0.09949, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22969, "top5_acc": 0.47156, "loss_cls": 4.2498, "loss": 4.2498, "time": 0.70289} +{"mode": "train", "epoch": 7, "iter": 3100, "lr": 0.09949, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24188, "top5_acc": 0.48547, "loss_cls": 4.1952, "loss": 4.1952, "time": 0.70414} +{"mode": "train", "epoch": 7, "iter": 3200, "lr": 0.09949, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23781, "top5_acc": 0.48094, "loss_cls": 4.21311, "loss": 4.21311, "time": 0.71062} +{"mode": "train", "epoch": 7, "iter": 3300, "lr": 0.09948, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22219, "top5_acc": 0.46219, "loss_cls": 4.30842, "loss": 4.30842, "time": 0.70952} +{"mode": "train", "epoch": 7, "iter": 3400, "lr": 0.09948, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23875, "top5_acc": 0.48359, "loss_cls": 4.18441, "loss": 4.18441, "time": 0.70719} +{"mode": "train", "epoch": 7, "iter": 3500, "lr": 0.09947, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23469, "top5_acc": 0.47672, "loss_cls": 4.22649, "loss": 4.22649, "time": 0.70304} +{"mode": "train", "epoch": 7, "iter": 3600, "lr": 0.09947, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23391, "top5_acc": 0.47719, "loss_cls": 4.23018, "loss": 4.23018, "time": 0.71039} +{"mode": "train", "epoch": 7, "iter": 3700, "lr": 0.09947, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23109, "top5_acc": 0.48031, "loss_cls": 4.20318, "loss": 4.20318, "time": 0.71372} +{"mode": "val", "epoch": 7, "iter": 309, "lr": 0.09946, "top1_acc": 0.18082, "top5_acc": 0.4123, "mean_class_accuracy": 0.18056} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.09946, "memory": 15990, "data_time": 1.26358, "top1_acc": 0.24672, "top5_acc": 0.49188, "loss_cls": 4.14323, "loss": 4.14323, "time": 1.96626} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.09946, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24375, "top5_acc": 0.49078, "loss_cls": 4.17092, "loss": 4.17092, "time": 0.70324} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.09945, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23922, "top5_acc": 0.48188, "loss_cls": 4.18504, "loss": 4.18504, "time": 0.70368} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.09945, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22609, "top5_acc": 0.47, "loss_cls": 4.23389, "loss": 4.23389, "time": 0.70736} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.09944, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23219, "top5_acc": 0.47281, "loss_cls": 4.24614, "loss": 4.24614, "time": 0.7016} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.09944, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23359, "top5_acc": 0.47344, "loss_cls": 4.25171, "loss": 4.25171, "time": 0.70286} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.09943, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23984, "top5_acc": 0.48125, "loss_cls": 4.19907, "loss": 4.19907, "time": 0.70442} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.09943, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23422, "top5_acc": 0.48219, "loss_cls": 4.19688, "loss": 4.19688, "time": 0.70569} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.09943, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23531, "top5_acc": 0.47125, "loss_cls": 4.24673, "loss": 4.24673, "time": 0.7062} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.09942, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2425, "top5_acc": 0.48594, "loss_cls": 4.18664, "loss": 4.18664, "time": 0.70507} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.09942, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23734, "top5_acc": 0.48359, "loss_cls": 4.2093, "loss": 4.2093, "time": 0.70207} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.09941, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23328, "top5_acc": 0.48219, "loss_cls": 4.20481, "loss": 4.20481, "time": 0.70629} +{"mode": "train", "epoch": 8, "iter": 1300, "lr": 0.09941, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24328, "top5_acc": 0.48625, "loss_cls": 4.19167, "loss": 4.19167, "time": 0.70231} +{"mode": "train", "epoch": 8, "iter": 1400, "lr": 0.0994, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23094, "top5_acc": 0.47719, "loss_cls": 4.23342, "loss": 4.23342, "time": 0.70094} +{"mode": "train", "epoch": 8, "iter": 1500, "lr": 0.0994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23297, "top5_acc": 0.485, "loss_cls": 4.20773, "loss": 4.20773, "time": 0.70367} +{"mode": "train", "epoch": 8, "iter": 1600, "lr": 0.0994, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23, "top5_acc": 0.47359, "loss_cls": 4.21849, "loss": 4.21849, "time": 0.70055} +{"mode": "train", "epoch": 8, "iter": 1700, "lr": 0.09939, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23547, "top5_acc": 0.48484, "loss_cls": 4.18507, "loss": 4.18507, "time": 0.70976} +{"mode": "train", "epoch": 8, "iter": 1800, "lr": 0.09939, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23516, "top5_acc": 0.46828, "loss_cls": 4.25866, "loss": 4.25866, "time": 0.702} +{"mode": "train", "epoch": 8, "iter": 1900, "lr": 0.09938, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23891, "top5_acc": 0.48281, "loss_cls": 4.20398, "loss": 4.20398, "time": 0.6989} +{"mode": "train", "epoch": 8, "iter": 2000, "lr": 0.09938, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24047, "top5_acc": 0.48062, "loss_cls": 4.19469, "loss": 4.19469, "time": 0.70296} +{"mode": "train", "epoch": 8, "iter": 2100, "lr": 0.09937, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23938, "top5_acc": 0.48109, "loss_cls": 4.17629, "loss": 4.17629, "time": 0.70331} +{"mode": "train", "epoch": 8, "iter": 2200, "lr": 0.09937, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23516, "top5_acc": 0.47562, "loss_cls": 4.22053, "loss": 4.22053, "time": 0.70049} +{"mode": "train", "epoch": 8, "iter": 2300, "lr": 0.09937, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24016, "top5_acc": 0.48375, "loss_cls": 4.18923, "loss": 4.18923, "time": 0.70344} +{"mode": "train", "epoch": 8, "iter": 2400, "lr": 0.09936, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23375, "top5_acc": 0.47422, "loss_cls": 4.20873, "loss": 4.20873, "time": 0.70726} +{"mode": "train", "epoch": 8, "iter": 2500, "lr": 0.09936, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24156, "top5_acc": 0.48344, "loss_cls": 4.19817, "loss": 4.19817, "time": 0.70111} +{"mode": "train", "epoch": 8, "iter": 2600, "lr": 0.09935, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23688, "top5_acc": 0.48141, "loss_cls": 4.21269, "loss": 4.21269, "time": 0.70175} +{"mode": "train", "epoch": 8, "iter": 2700, "lr": 0.09935, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23953, "top5_acc": 0.47953, "loss_cls": 4.1944, "loss": 4.1944, "time": 0.70586} +{"mode": "train", "epoch": 8, "iter": 2800, "lr": 0.09934, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23312, "top5_acc": 0.48547, "loss_cls": 4.20912, "loss": 4.20912, "time": 0.70201} +{"mode": "train", "epoch": 8, "iter": 2900, "lr": 0.09934, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23297, "top5_acc": 0.47516, "loss_cls": 4.22525, "loss": 4.22525, "time": 0.70507} +{"mode": "train", "epoch": 8, "iter": 3000, "lr": 0.09933, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23328, "top5_acc": 0.47719, "loss_cls": 4.22288, "loss": 4.22288, "time": 0.7072} +{"mode": "train", "epoch": 8, "iter": 3100, "lr": 0.09933, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23969, "top5_acc": 0.48312, "loss_cls": 4.18178, "loss": 4.18178, "time": 0.70331} +{"mode": "train", "epoch": 8, "iter": 3200, "lr": 0.09933, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24531, "top5_acc": 0.48688, "loss_cls": 4.1647, "loss": 4.1647, "time": 0.7061} +{"mode": "train", "epoch": 8, "iter": 3300, "lr": 0.09932, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24344, "top5_acc": 0.49078, "loss_cls": 4.1423, "loss": 4.1423, "time": 0.70446} +{"mode": "train", "epoch": 8, "iter": 3400, "lr": 0.09932, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23547, "top5_acc": 0.48094, "loss_cls": 4.17661, "loss": 4.17661, "time": 0.70411} +{"mode": "train", "epoch": 8, "iter": 3500, "lr": 0.09931, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23281, "top5_acc": 0.47922, "loss_cls": 4.21677, "loss": 4.21677, "time": 0.70416} +{"mode": "train", "epoch": 8, "iter": 3600, "lr": 0.09931, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24094, "top5_acc": 0.48438, "loss_cls": 4.21032, "loss": 4.21032, "time": 0.71542} +{"mode": "train", "epoch": 8, "iter": 3700, "lr": 0.0993, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23359, "top5_acc": 0.46484, "loss_cls": 4.23711, "loss": 4.23711, "time": 0.71457} +{"mode": "val", "epoch": 8, "iter": 309, "lr": 0.0993, "top1_acc": 0.16163, "top5_acc": 0.37299, "mean_class_accuracy": 0.16134} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.0993, "memory": 15990, "data_time": 1.25682, "top1_acc": 0.23875, "top5_acc": 0.48625, "loss_cls": 4.19141, "loss": 4.19141, "time": 1.96657} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.09929, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24734, "top5_acc": 0.49406, "loss_cls": 4.14092, "loss": 4.14092, "time": 0.70922} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.09929, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23453, "top5_acc": 0.48656, "loss_cls": 4.19085, "loss": 4.19085, "time": 0.70292} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.09928, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23891, "top5_acc": 0.48797, "loss_cls": 4.16629, "loss": 4.16629, "time": 0.70463} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.09928, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24359, "top5_acc": 0.49, "loss_cls": 4.14808, "loss": 4.14808, "time": 0.70295} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.09927, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24391, "top5_acc": 0.485, "loss_cls": 4.20601, "loss": 4.20601, "time": 0.70296} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.09927, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24984, "top5_acc": 0.50313, "loss_cls": 4.13083, "loss": 4.13083, "time": 0.70366} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.09926, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23781, "top5_acc": 0.48562, "loss_cls": 4.17972, "loss": 4.17972, "time": 0.70124} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.09926, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23484, "top5_acc": 0.48906, "loss_cls": 4.19053, "loss": 4.19053, "time": 0.70544} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.09925, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24078, "top5_acc": 0.48984, "loss_cls": 4.17663, "loss": 4.17663, "time": 0.70584} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.09925, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23906, "top5_acc": 0.48828, "loss_cls": 4.18271, "loss": 4.18271, "time": 0.7025} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.09924, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23969, "top5_acc": 0.48375, "loss_cls": 4.17948, "loss": 4.17948, "time": 0.70522} +{"mode": "train", "epoch": 9, "iter": 1300, "lr": 0.09924, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24672, "top5_acc": 0.48891, "loss_cls": 4.16469, "loss": 4.16469, "time": 0.70158} +{"mode": "train", "epoch": 9, "iter": 1400, "lr": 0.09923, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24359, "top5_acc": 0.48125, "loss_cls": 4.17151, "loss": 4.17151, "time": 0.70551} +{"mode": "train", "epoch": 9, "iter": 1500, "lr": 0.09923, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24125, "top5_acc": 0.48203, "loss_cls": 4.20887, "loss": 4.20887, "time": 0.70703} +{"mode": "train", "epoch": 9, "iter": 1600, "lr": 0.09922, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24172, "top5_acc": 0.48516, "loss_cls": 4.14881, "loss": 4.14881, "time": 0.70293} +{"mode": "train", "epoch": 9, "iter": 1700, "lr": 0.09922, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23719, "top5_acc": 0.48766, "loss_cls": 4.19109, "loss": 4.19109, "time": 0.70437} +{"mode": "train", "epoch": 9, "iter": 1800, "lr": 0.09921, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24312, "top5_acc": 0.49312, "loss_cls": 4.16126, "loss": 4.16126, "time": 0.70255} +{"mode": "train", "epoch": 9, "iter": 1900, "lr": 0.09921, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2375, "top5_acc": 0.48891, "loss_cls": 4.18226, "loss": 4.18226, "time": 0.70177} +{"mode": "train", "epoch": 9, "iter": 2000, "lr": 0.0992, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23859, "top5_acc": 0.48203, "loss_cls": 4.18101, "loss": 4.18101, "time": 0.70107} +{"mode": "train", "epoch": 9, "iter": 2100, "lr": 0.0992, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24297, "top5_acc": 0.48125, "loss_cls": 4.16813, "loss": 4.16813, "time": 0.70082} +{"mode": "train", "epoch": 9, "iter": 2200, "lr": 0.09919, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23219, "top5_acc": 0.47328, "loss_cls": 4.22583, "loss": 4.22583, "time": 0.7067} +{"mode": "train", "epoch": 9, "iter": 2300, "lr": 0.09919, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23734, "top5_acc": 0.48531, "loss_cls": 4.1767, "loss": 4.1767, "time": 0.70193} +{"mode": "train", "epoch": 9, "iter": 2400, "lr": 0.09918, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24547, "top5_acc": 0.485, "loss_cls": 4.15392, "loss": 4.15392, "time": 0.70493} +{"mode": "train", "epoch": 9, "iter": 2500, "lr": 0.09918, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23828, "top5_acc": 0.47781, "loss_cls": 4.2213, "loss": 4.2213, "time": 0.70243} +{"mode": "train", "epoch": 9, "iter": 2600, "lr": 0.09917, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24531, "top5_acc": 0.48344, "loss_cls": 4.17222, "loss": 4.17222, "time": 0.70249} +{"mode": "train", "epoch": 9, "iter": 2700, "lr": 0.09917, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24484, "top5_acc": 0.48359, "loss_cls": 4.20577, "loss": 4.20577, "time": 0.70236} +{"mode": "train", "epoch": 9, "iter": 2800, "lr": 0.09916, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23391, "top5_acc": 0.47938, "loss_cls": 4.21651, "loss": 4.21651, "time": 0.70506} +{"mode": "train", "epoch": 9, "iter": 2900, "lr": 0.09916, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23391, "top5_acc": 0.47672, "loss_cls": 4.19982, "loss": 4.19982, "time": 0.70384} +{"mode": "train", "epoch": 9, "iter": 3000, "lr": 0.09915, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24531, "top5_acc": 0.48047, "loss_cls": 4.21013, "loss": 4.21013, "time": 0.70124} +{"mode": "train", "epoch": 9, "iter": 3100, "lr": 0.09915, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24875, "top5_acc": 0.48141, "loss_cls": 4.20626, "loss": 4.20626, "time": 0.70231} +{"mode": "train", "epoch": 9, "iter": 3200, "lr": 0.09914, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24922, "top5_acc": 0.48188, "loss_cls": 4.17512, "loss": 4.17512, "time": 0.71269} +{"mode": "train", "epoch": 9, "iter": 3300, "lr": 0.09914, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24531, "top5_acc": 0.48781, "loss_cls": 4.17192, "loss": 4.17192, "time": 0.70332} +{"mode": "train", "epoch": 9, "iter": 3400, "lr": 0.09913, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23578, "top5_acc": 0.48203, "loss_cls": 4.22869, "loss": 4.22869, "time": 0.7088} +{"mode": "train", "epoch": 9, "iter": 3500, "lr": 0.09913, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24312, "top5_acc": 0.49047, "loss_cls": 4.16282, "loss": 4.16282, "time": 0.70695} +{"mode": "train", "epoch": 9, "iter": 3600, "lr": 0.09912, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23922, "top5_acc": 0.48031, "loss_cls": 4.18865, "loss": 4.18865, "time": 0.70826} +{"mode": "train", "epoch": 9, "iter": 3700, "lr": 0.09912, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23594, "top5_acc": 0.47938, "loss_cls": 4.20524, "loss": 4.20524, "time": 0.71077} +{"mode": "val", "epoch": 9, "iter": 309, "lr": 0.09911, "top1_acc": 0.18908, "top5_acc": 0.41189, "mean_class_accuracy": 0.18884} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.09911, "memory": 15990, "data_time": 1.27334, "top1_acc": 0.24766, "top5_acc": 0.49344, "loss_cls": 4.14129, "loss": 4.14129, "time": 1.97685} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.0991, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24188, "top5_acc": 0.49766, "loss_cls": 4.13215, "loss": 4.13215, "time": 0.7024} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.0991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24891, "top5_acc": 0.49578, "loss_cls": 4.13522, "loss": 4.13522, "time": 0.70418} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.09909, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23531, "top5_acc": 0.46844, "loss_cls": 4.24019, "loss": 4.24019, "time": 0.70324} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.09909, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23891, "top5_acc": 0.48906, "loss_cls": 4.164, "loss": 4.164, "time": 0.7083} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.09908, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24219, "top5_acc": 0.49016, "loss_cls": 4.17332, "loss": 4.17332, "time": 0.70435} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.09908, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23656, "top5_acc": 0.48953, "loss_cls": 4.18236, "loss": 4.18236, "time": 0.70788} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.09907, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24188, "top5_acc": 0.49156, "loss_cls": 4.18873, "loss": 4.18873, "time": 0.70471} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.09907, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25094, "top5_acc": 0.49828, "loss_cls": 4.12172, "loss": 4.12172, "time": 0.70473} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.09906, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24625, "top5_acc": 0.49078, "loss_cls": 4.15932, "loss": 4.15932, "time": 0.70199} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.09906, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24516, "top5_acc": 0.48641, "loss_cls": 4.16208, "loss": 4.16208, "time": 0.70183} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.09905, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24266, "top5_acc": 0.48625, "loss_cls": 4.18421, "loss": 4.18421, "time": 0.70605} +{"mode": "train", "epoch": 10, "iter": 1300, "lr": 0.09905, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22922, "top5_acc": 0.48188, "loss_cls": 4.21509, "loss": 4.21509, "time": 0.70793} +{"mode": "train", "epoch": 10, "iter": 1400, "lr": 0.09904, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24812, "top5_acc": 0.48828, "loss_cls": 4.18139, "loss": 4.18139, "time": 0.70285} +{"mode": "train", "epoch": 10, "iter": 1500, "lr": 0.09903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24234, "top5_acc": 0.49156, "loss_cls": 4.15146, "loss": 4.15146, "time": 0.70125} +{"mode": "train", "epoch": 10, "iter": 1600, "lr": 0.09903, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24312, "top5_acc": 0.49219, "loss_cls": 4.14074, "loss": 4.14074, "time": 0.70353} +{"mode": "train", "epoch": 10, "iter": 1700, "lr": 0.09902, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23109, "top5_acc": 0.48062, "loss_cls": 4.21062, "loss": 4.21062, "time": 0.70378} +{"mode": "train", "epoch": 10, "iter": 1800, "lr": 0.09902, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24906, "top5_acc": 0.48922, "loss_cls": 4.1612, "loss": 4.1612, "time": 0.70107} +{"mode": "train", "epoch": 10, "iter": 1900, "lr": 0.09901, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24328, "top5_acc": 0.49672, "loss_cls": 4.14141, "loss": 4.14141, "time": 0.70203} +{"mode": "train", "epoch": 10, "iter": 2000, "lr": 0.09901, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24281, "top5_acc": 0.48938, "loss_cls": 4.18906, "loss": 4.18906, "time": 0.70158} +{"mode": "train", "epoch": 10, "iter": 2100, "lr": 0.099, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24062, "top5_acc": 0.48188, "loss_cls": 4.19559, "loss": 4.19559, "time": 0.70611} +{"mode": "train", "epoch": 10, "iter": 2200, "lr": 0.099, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24922, "top5_acc": 0.49328, "loss_cls": 4.14952, "loss": 4.14952, "time": 0.70367} +{"mode": "train", "epoch": 10, "iter": 2300, "lr": 0.09899, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24125, "top5_acc": 0.49484, "loss_cls": 4.13917, "loss": 4.13917, "time": 0.70684} +{"mode": "train", "epoch": 10, "iter": 2400, "lr": 0.09898, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24578, "top5_acc": 0.49391, "loss_cls": 4.1362, "loss": 4.1362, "time": 0.70132} +{"mode": "train", "epoch": 10, "iter": 2500, "lr": 0.09898, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24781, "top5_acc": 0.49984, "loss_cls": 4.13773, "loss": 4.13773, "time": 0.70566} +{"mode": "train", "epoch": 10, "iter": 2600, "lr": 0.09897, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23766, "top5_acc": 0.47703, "loss_cls": 4.22497, "loss": 4.22497, "time": 0.70392} +{"mode": "train", "epoch": 10, "iter": 2700, "lr": 0.09897, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24016, "top5_acc": 0.49031, "loss_cls": 4.1764, "loss": 4.1764, "time": 0.70221} +{"mode": "train", "epoch": 10, "iter": 2800, "lr": 0.09896, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24547, "top5_acc": 0.48391, "loss_cls": 4.17203, "loss": 4.17203, "time": 0.70344} +{"mode": "train", "epoch": 10, "iter": 2900, "lr": 0.09896, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23375, "top5_acc": 0.48156, "loss_cls": 4.22586, "loss": 4.22586, "time": 0.69935} +{"mode": "train", "epoch": 10, "iter": 3000, "lr": 0.09895, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24016, "top5_acc": 0.48719, "loss_cls": 4.14905, "loss": 4.14905, "time": 0.7071} +{"mode": "train", "epoch": 10, "iter": 3100, "lr": 0.09894, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24703, "top5_acc": 0.49172, "loss_cls": 4.12217, "loss": 4.12217, "time": 0.6997} +{"mode": "train", "epoch": 10, "iter": 3200, "lr": 0.09894, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23094, "top5_acc": 0.47953, "loss_cls": 4.20337, "loss": 4.20337, "time": 0.70914} +{"mode": "train", "epoch": 10, "iter": 3300, "lr": 0.09893, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23766, "top5_acc": 0.48797, "loss_cls": 4.18561, "loss": 4.18561, "time": 0.70194} +{"mode": "train", "epoch": 10, "iter": 3400, "lr": 0.09893, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23516, "top5_acc": 0.48188, "loss_cls": 4.2223, "loss": 4.2223, "time": 0.70563} +{"mode": "train", "epoch": 10, "iter": 3500, "lr": 0.09892, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24547, "top5_acc": 0.49125, "loss_cls": 4.18219, "loss": 4.18219, "time": 0.7067} +{"mode": "train", "epoch": 10, "iter": 3600, "lr": 0.09892, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24, "top5_acc": 0.48344, "loss_cls": 4.19309, "loss": 4.19309, "time": 0.7085} +{"mode": "train", "epoch": 10, "iter": 3700, "lr": 0.09891, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25734, "top5_acc": 0.50906, "loss_cls": 4.08718, "loss": 4.08718, "time": 0.7069} +{"mode": "val", "epoch": 10, "iter": 309, "lr": 0.09891, "top1_acc": 0.18599, "top5_acc": 0.4083, "mean_class_accuracy": 0.18567} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.0989, "memory": 15990, "data_time": 1.26602, "top1_acc": 0.245, "top5_acc": 0.49594, "loss_cls": 4.12047, "loss": 4.12047, "time": 1.97107} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.0989, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24922, "top5_acc": 0.4975, "loss_cls": 4.10921, "loss": 4.10921, "time": 0.71158} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.09889, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24297, "top5_acc": 0.48859, "loss_cls": 4.16713, "loss": 4.16713, "time": 0.70388} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.09888, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24078, "top5_acc": 0.48375, "loss_cls": 4.17652, "loss": 4.17652, "time": 0.70737} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.09888, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25891, "top5_acc": 0.50891, "loss_cls": 4.0765, "loss": 4.0765, "time": 0.702} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.09887, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24625, "top5_acc": 0.47844, "loss_cls": 4.18444, "loss": 4.18444, "time": 0.70768} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.09887, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24703, "top5_acc": 0.48609, "loss_cls": 4.15561, "loss": 4.15561, "time": 0.70142} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.09886, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24328, "top5_acc": 0.49516, "loss_cls": 4.15753, "loss": 4.15753, "time": 0.70497} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.09885, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24984, "top5_acc": 0.51391, "loss_cls": 4.08242, "loss": 4.08242, "time": 0.70453} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.09885, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24, "top5_acc": 0.48609, "loss_cls": 4.20396, "loss": 4.20396, "time": 0.70177} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.09884, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24422, "top5_acc": 0.47922, "loss_cls": 4.18426, "loss": 4.18426, "time": 0.70196} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.09884, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24234, "top5_acc": 0.48797, "loss_cls": 4.16426, "loss": 4.16426, "time": 0.70051} +{"mode": "train", "epoch": 11, "iter": 1300, "lr": 0.09883, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23891, "top5_acc": 0.48344, "loss_cls": 4.18857, "loss": 4.18857, "time": 0.70111} +{"mode": "train", "epoch": 11, "iter": 1400, "lr": 0.09882, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23375, "top5_acc": 0.48469, "loss_cls": 4.16959, "loss": 4.16959, "time": 0.6992} +{"mode": "train", "epoch": 11, "iter": 1500, "lr": 0.09882, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24688, "top5_acc": 0.4975, "loss_cls": 4.13861, "loss": 4.13861, "time": 0.70242} +{"mode": "train", "epoch": 11, "iter": 1600, "lr": 0.09881, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24672, "top5_acc": 0.48906, "loss_cls": 4.12914, "loss": 4.12914, "time": 0.70137} +{"mode": "train", "epoch": 11, "iter": 1700, "lr": 0.09881, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24719, "top5_acc": 0.49969, "loss_cls": 4.11213, "loss": 4.11213, "time": 0.70503} +{"mode": "train", "epoch": 11, "iter": 1800, "lr": 0.0988, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24875, "top5_acc": 0.48672, "loss_cls": 4.1779, "loss": 4.1779, "time": 0.70055} +{"mode": "train", "epoch": 11, "iter": 1900, "lr": 0.09879, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25375, "top5_acc": 0.49656, "loss_cls": 4.125, "loss": 4.125, "time": 0.70451} +{"mode": "train", "epoch": 11, "iter": 2000, "lr": 0.09879, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23938, "top5_acc": 0.495, "loss_cls": 4.16148, "loss": 4.16148, "time": 0.70288} +{"mode": "train", "epoch": 11, "iter": 2100, "lr": 0.09878, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24266, "top5_acc": 0.48391, "loss_cls": 4.19274, "loss": 4.19274, "time": 0.70519} +{"mode": "train", "epoch": 11, "iter": 2200, "lr": 0.09878, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.23875, "top5_acc": 0.48547, "loss_cls": 4.16363, "loss": 4.16363, "time": 0.69926} +{"mode": "train", "epoch": 11, "iter": 2300, "lr": 0.09877, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25141, "top5_acc": 0.49766, "loss_cls": 4.14009, "loss": 4.14009, "time": 0.70637} +{"mode": "train", "epoch": 11, "iter": 2400, "lr": 0.09876, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24938, "top5_acc": 0.49297, "loss_cls": 4.14206, "loss": 4.14206, "time": 0.70012} +{"mode": "train", "epoch": 11, "iter": 2500, "lr": 0.09876, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24109, "top5_acc": 0.48344, "loss_cls": 4.20756, "loss": 4.20756, "time": 0.69821} +{"mode": "train", "epoch": 11, "iter": 2600, "lr": 0.09875, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24953, "top5_acc": 0.49391, "loss_cls": 4.12319, "loss": 4.12319, "time": 0.70267} +{"mode": "train", "epoch": 11, "iter": 2700, "lr": 0.09874, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24781, "top5_acc": 0.50266, "loss_cls": 4.1196, "loss": 4.1196, "time": 0.70101} +{"mode": "train", "epoch": 11, "iter": 2800, "lr": 0.09874, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24234, "top5_acc": 0.49469, "loss_cls": 4.15825, "loss": 4.15825, "time": 0.6987} +{"mode": "train", "epoch": 11, "iter": 2900, "lr": 0.09873, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24984, "top5_acc": 0.49531, "loss_cls": 4.14522, "loss": 4.14522, "time": 0.70081} +{"mode": "train", "epoch": 11, "iter": 3000, "lr": 0.09873, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24234, "top5_acc": 0.48266, "loss_cls": 4.17823, "loss": 4.17823, "time": 0.69791} +{"mode": "train", "epoch": 11, "iter": 3100, "lr": 0.09872, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.2425, "top5_acc": 0.49359, "loss_cls": 4.15598, "loss": 4.15598, "time": 0.69903} +{"mode": "train", "epoch": 11, "iter": 3200, "lr": 0.09871, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24031, "top5_acc": 0.48469, "loss_cls": 4.17312, "loss": 4.17312, "time": 0.71051} +{"mode": "train", "epoch": 11, "iter": 3300, "lr": 0.09871, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25328, "top5_acc": 0.48906, "loss_cls": 4.13047, "loss": 4.13047, "time": 0.70436} +{"mode": "train", "epoch": 11, "iter": 3400, "lr": 0.0987, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24312, "top5_acc": 0.48891, "loss_cls": 4.16399, "loss": 4.16399, "time": 0.70347} +{"mode": "train", "epoch": 11, "iter": 3500, "lr": 0.09869, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2475, "top5_acc": 0.48156, "loss_cls": 4.18224, "loss": 4.18224, "time": 0.70741} +{"mode": "train", "epoch": 11, "iter": 3600, "lr": 0.09869, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24516, "top5_acc": 0.48797, "loss_cls": 4.16411, "loss": 4.16411, "time": 0.71174} +{"mode": "train", "epoch": 11, "iter": 3700, "lr": 0.09868, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24734, "top5_acc": 0.49, "loss_cls": 4.16084, "loss": 4.16084, "time": 0.7119} +{"mode": "val", "epoch": 11, "iter": 309, "lr": 0.09868, "top1_acc": 0.19668, "top5_acc": 0.42157, "mean_class_accuracy": 0.19631} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.09867, "memory": 15990, "data_time": 1.36636, "top1_acc": 0.24688, "top5_acc": 0.50062, "loss_cls": 4.13635, "loss": 4.13635, "time": 2.08432} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.09867, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24656, "top5_acc": 0.49719, "loss_cls": 4.09797, "loss": 4.09797, "time": 0.7099} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.09866, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24938, "top5_acc": 0.50219, "loss_cls": 4.11542, "loss": 4.11542, "time": 0.71852} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.09865, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25906, "top5_acc": 0.49734, "loss_cls": 4.1214, "loss": 4.1214, "time": 0.71134} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.09865, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24375, "top5_acc": 0.49188, "loss_cls": 4.12643, "loss": 4.12643, "time": 0.71676} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.09864, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24797, "top5_acc": 0.49953, "loss_cls": 4.1384, "loss": 4.1384, "time": 0.71377} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.09863, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24391, "top5_acc": 0.48844, "loss_cls": 4.15215, "loss": 4.15215, "time": 0.71433} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.09863, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24828, "top5_acc": 0.51, "loss_cls": 4.08629, "loss": 4.08629, "time": 0.7117} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.09862, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25109, "top5_acc": 0.48953, "loss_cls": 4.1362, "loss": 4.1362, "time": 0.71379} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.09861, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24266, "top5_acc": 0.48062, "loss_cls": 4.18042, "loss": 4.18042, "time": 0.71443} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.09861, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25391, "top5_acc": 0.49062, "loss_cls": 4.13788, "loss": 4.13788, "time": 0.71483} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.0986, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24859, "top5_acc": 0.49625, "loss_cls": 4.10939, "loss": 4.10939, "time": 0.71299} +{"mode": "train", "epoch": 12, "iter": 1300, "lr": 0.09859, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24766, "top5_acc": 0.50375, "loss_cls": 4.1109, "loss": 4.1109, "time": 0.71486} +{"mode": "train", "epoch": 12, "iter": 1400, "lr": 0.09859, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25391, "top5_acc": 0.49312, "loss_cls": 4.11912, "loss": 4.11912, "time": 0.71561} +{"mode": "train", "epoch": 12, "iter": 1500, "lr": 0.09858, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25109, "top5_acc": 0.48641, "loss_cls": 4.14542, "loss": 4.14542, "time": 0.7113} +{"mode": "train", "epoch": 12, "iter": 1600, "lr": 0.09857, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24578, "top5_acc": 0.48578, "loss_cls": 4.15973, "loss": 4.15973, "time": 0.71182} +{"mode": "train", "epoch": 12, "iter": 1700, "lr": 0.09857, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24766, "top5_acc": 0.48672, "loss_cls": 4.14278, "loss": 4.14278, "time": 0.71273} +{"mode": "train", "epoch": 12, "iter": 1800, "lr": 0.09856, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24438, "top5_acc": 0.48891, "loss_cls": 4.18539, "loss": 4.18539, "time": 0.71118} +{"mode": "train", "epoch": 12, "iter": 1900, "lr": 0.09855, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25094, "top5_acc": 0.49422, "loss_cls": 4.13987, "loss": 4.13987, "time": 0.71218} +{"mode": "train", "epoch": 12, "iter": 2000, "lr": 0.09855, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25109, "top5_acc": 0.50078, "loss_cls": 4.1337, "loss": 4.1337, "time": 0.71348} +{"mode": "train", "epoch": 12, "iter": 2100, "lr": 0.09854, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25297, "top5_acc": 0.49922, "loss_cls": 4.16826, "loss": 4.16826, "time": 0.71161} +{"mode": "train", "epoch": 12, "iter": 2200, "lr": 0.09853, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25469, "top5_acc": 0.50266, "loss_cls": 4.1088, "loss": 4.1088, "time": 0.71314} +{"mode": "train", "epoch": 12, "iter": 2300, "lr": 0.09853, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24531, "top5_acc": 0.49234, "loss_cls": 4.14691, "loss": 4.14691, "time": 0.71361} +{"mode": "train", "epoch": 12, "iter": 2400, "lr": 0.09852, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25719, "top5_acc": 0.505, "loss_cls": 4.11195, "loss": 4.11195, "time": 0.71394} +{"mode": "train", "epoch": 12, "iter": 2500, "lr": 0.09851, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23422, "top5_acc": 0.49078, "loss_cls": 4.15654, "loss": 4.15654, "time": 0.71382} +{"mode": "train", "epoch": 12, "iter": 2600, "lr": 0.09851, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24172, "top5_acc": 0.48266, "loss_cls": 4.18016, "loss": 4.18016, "time": 0.71054} +{"mode": "train", "epoch": 12, "iter": 2700, "lr": 0.0985, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23922, "top5_acc": 0.49312, "loss_cls": 4.16788, "loss": 4.16788, "time": 0.71239} +{"mode": "train", "epoch": 12, "iter": 2800, "lr": 0.09849, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25344, "top5_acc": 0.50484, "loss_cls": 4.09952, "loss": 4.09952, "time": 0.71232} +{"mode": "train", "epoch": 12, "iter": 2900, "lr": 0.09849, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25328, "top5_acc": 0.495, "loss_cls": 4.12194, "loss": 4.12194, "time": 0.71262} +{"mode": "train", "epoch": 12, "iter": 3000, "lr": 0.09848, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24328, "top5_acc": 0.49375, "loss_cls": 4.14346, "loss": 4.14346, "time": 0.71172} +{"mode": "train", "epoch": 12, "iter": 3100, "lr": 0.09847, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24547, "top5_acc": 0.49156, "loss_cls": 4.14718, "loss": 4.14718, "time": 0.71574} +{"mode": "train", "epoch": 12, "iter": 3200, "lr": 0.09847, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24062, "top5_acc": 0.49828, "loss_cls": 4.13856, "loss": 4.13856, "time": 0.71607} +{"mode": "train", "epoch": 12, "iter": 3300, "lr": 0.09846, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24062, "top5_acc": 0.47953, "loss_cls": 4.16298, "loss": 4.16298, "time": 0.71079} +{"mode": "train", "epoch": 12, "iter": 3400, "lr": 0.09845, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24078, "top5_acc": 0.47922, "loss_cls": 4.18836, "loss": 4.18836, "time": 0.70985} +{"mode": "train", "epoch": 12, "iter": 3500, "lr": 0.09845, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23969, "top5_acc": 0.48672, "loss_cls": 4.18587, "loss": 4.18587, "time": 0.7158} +{"mode": "train", "epoch": 12, "iter": 3600, "lr": 0.09844, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24906, "top5_acc": 0.49156, "loss_cls": 4.1564, "loss": 4.1564, "time": 0.71996} +{"mode": "train", "epoch": 12, "iter": 3700, "lr": 0.09843, "memory": 15990, "data_time": 0.00087, "top1_acc": 0.23953, "top5_acc": 0.48844, "loss_cls": 4.17515, "loss": 4.17515, "time": 0.71478} +{"mode": "val", "epoch": 12, "iter": 309, "lr": 0.09843, "top1_acc": 0.18457, "top5_acc": 0.4082, "mean_class_accuracy": 0.18421} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.09842, "memory": 15990, "data_time": 1.33619, "top1_acc": 0.25078, "top5_acc": 0.49984, "loss_cls": 4.10467, "loss": 4.10467, "time": 2.05134} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.09842, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23969, "top5_acc": 0.48984, "loss_cls": 4.14534, "loss": 4.14534, "time": 0.71454} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.09841, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25, "top5_acc": 0.50781, "loss_cls": 4.05581, "loss": 4.05581, "time": 0.7176} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.0984, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25641, "top5_acc": 0.50953, "loss_cls": 4.08707, "loss": 4.08707, "time": 0.72016} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.09839, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25156, "top5_acc": 0.50031, "loss_cls": 4.13594, "loss": 4.13594, "time": 0.70924} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.09839, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25297, "top5_acc": 0.50094, "loss_cls": 4.09359, "loss": 4.09359, "time": 0.71679} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.09838, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25656, "top5_acc": 0.50203, "loss_cls": 4.12291, "loss": 4.12291, "time": 0.7174} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.09837, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24938, "top5_acc": 0.50828, "loss_cls": 4.11659, "loss": 4.11659, "time": 0.71484} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.09837, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24234, "top5_acc": 0.49188, "loss_cls": 4.13454, "loss": 4.13454, "time": 0.71538} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.09836, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25328, "top5_acc": 0.50297, "loss_cls": 4.09397, "loss": 4.09397, "time": 0.71625} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.09835, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24656, "top5_acc": 0.48797, "loss_cls": 4.12104, "loss": 4.12104, "time": 0.71178} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.09834, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24828, "top5_acc": 0.49094, "loss_cls": 4.15055, "loss": 4.15055, "time": 0.7154} +{"mode": "train", "epoch": 13, "iter": 1300, "lr": 0.09834, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2575, "top5_acc": 0.50281, "loss_cls": 4.08106, "loss": 4.08106, "time": 0.72045} +{"mode": "train", "epoch": 13, "iter": 1400, "lr": 0.09833, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25047, "top5_acc": 0.50172, "loss_cls": 4.12356, "loss": 4.12356, "time": 0.71179} +{"mode": "train", "epoch": 13, "iter": 1500, "lr": 0.09832, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24672, "top5_acc": 0.49078, "loss_cls": 4.14357, "loss": 4.14357, "time": 0.71394} +{"mode": "train", "epoch": 13, "iter": 1600, "lr": 0.09832, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25031, "top5_acc": 0.49766, "loss_cls": 4.14708, "loss": 4.14708, "time": 0.71578} +{"mode": "train", "epoch": 13, "iter": 1700, "lr": 0.09831, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25141, "top5_acc": 0.49734, "loss_cls": 4.12684, "loss": 4.12684, "time": 0.71639} +{"mode": "train", "epoch": 13, "iter": 1800, "lr": 0.0983, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26, "top5_acc": 0.50094, "loss_cls": 4.10225, "loss": 4.10225, "time": 0.71497} +{"mode": "train", "epoch": 13, "iter": 1900, "lr": 0.09829, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25047, "top5_acc": 0.49016, "loss_cls": 4.13398, "loss": 4.13398, "time": 0.71421} +{"mode": "train", "epoch": 13, "iter": 2000, "lr": 0.09829, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24922, "top5_acc": 0.49297, "loss_cls": 4.10618, "loss": 4.10618, "time": 0.71904} +{"mode": "train", "epoch": 13, "iter": 2100, "lr": 0.09828, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24547, "top5_acc": 0.48703, "loss_cls": 4.18125, "loss": 4.18125, "time": 0.71518} +{"mode": "train", "epoch": 13, "iter": 2200, "lr": 0.09827, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24922, "top5_acc": 0.49562, "loss_cls": 4.11894, "loss": 4.11894, "time": 0.71417} +{"mode": "train", "epoch": 13, "iter": 2300, "lr": 0.09827, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25625, "top5_acc": 0.49375, "loss_cls": 4.10398, "loss": 4.10398, "time": 0.71463} +{"mode": "train", "epoch": 13, "iter": 2400, "lr": 0.09826, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24062, "top5_acc": 0.48281, "loss_cls": 4.18147, "loss": 4.18147, "time": 0.71268} +{"mode": "train", "epoch": 13, "iter": 2500, "lr": 0.09825, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24781, "top5_acc": 0.49734, "loss_cls": 4.14049, "loss": 4.14049, "time": 0.71249} +{"mode": "train", "epoch": 13, "iter": 2600, "lr": 0.09824, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25203, "top5_acc": 0.50031, "loss_cls": 4.10293, "loss": 4.10293, "time": 0.71606} +{"mode": "train", "epoch": 13, "iter": 2700, "lr": 0.09824, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25078, "top5_acc": 0.49562, "loss_cls": 4.0866, "loss": 4.0866, "time": 0.71601} +{"mode": "train", "epoch": 13, "iter": 2800, "lr": 0.09823, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25609, "top5_acc": 0.49422, "loss_cls": 4.11689, "loss": 4.11689, "time": 0.71386} +{"mode": "train", "epoch": 13, "iter": 2900, "lr": 0.09822, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25422, "top5_acc": 0.49641, "loss_cls": 4.12048, "loss": 4.12048, "time": 0.71509} +{"mode": "train", "epoch": 13, "iter": 3000, "lr": 0.09821, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23578, "top5_acc": 0.48422, "loss_cls": 4.20408, "loss": 4.20408, "time": 0.7148} +{"mode": "train", "epoch": 13, "iter": 3100, "lr": 0.09821, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24375, "top5_acc": 0.48516, "loss_cls": 4.14924, "loss": 4.14924, "time": 0.71677} +{"mode": "train", "epoch": 13, "iter": 3200, "lr": 0.0982, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23828, "top5_acc": 0.48703, "loss_cls": 4.17622, "loss": 4.17622, "time": 0.71013} +{"mode": "train", "epoch": 13, "iter": 3300, "lr": 0.09819, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25016, "top5_acc": 0.49609, "loss_cls": 4.1507, "loss": 4.1507, "time": 0.70733} +{"mode": "train", "epoch": 13, "iter": 3400, "lr": 0.09818, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.24406, "top5_acc": 0.48906, "loss_cls": 4.17222, "loss": 4.17222, "time": 0.71493} +{"mode": "train", "epoch": 13, "iter": 3500, "lr": 0.09818, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.2625, "top5_acc": 0.51625, "loss_cls": 4.05409, "loss": 4.05409, "time": 0.71492} +{"mode": "train", "epoch": 13, "iter": 3600, "lr": 0.09817, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24281, "top5_acc": 0.48203, "loss_cls": 4.17517, "loss": 4.17517, "time": 0.71185} +{"mode": "train", "epoch": 13, "iter": 3700, "lr": 0.09816, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25297, "top5_acc": 0.49734, "loss_cls": 4.13553, "loss": 4.13553, "time": 0.71491} +{"mode": "val", "epoch": 13, "iter": 309, "lr": 0.09816, "top1_acc": 0.1632, "top5_acc": 0.38601, "mean_class_accuracy": 0.16304} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.09815, "memory": 15990, "data_time": 1.36983, "top1_acc": 0.26281, "top5_acc": 0.50562, "loss_cls": 4.05048, "loss": 4.05048, "time": 2.08222} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.09814, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25141, "top5_acc": 0.49609, "loss_cls": 4.08614, "loss": 4.08614, "time": 0.71203} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.09814, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25188, "top5_acc": 0.49578, "loss_cls": 4.12911, "loss": 4.12911, "time": 0.71403} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.09813, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25531, "top5_acc": 0.50609, "loss_cls": 4.08898, "loss": 4.08898, "time": 0.71542} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.09812, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25062, "top5_acc": 0.49875, "loss_cls": 4.12362, "loss": 4.12362, "time": 0.71422} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.09811, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24062, "top5_acc": 0.48734, "loss_cls": 4.1485, "loss": 4.1485, "time": 0.71362} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.09811, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25125, "top5_acc": 0.48969, "loss_cls": 4.14381, "loss": 4.14381, "time": 0.71375} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.0981, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25234, "top5_acc": 0.49781, "loss_cls": 4.12646, "loss": 4.12646, "time": 0.71261} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.09809, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25359, "top5_acc": 0.50516, "loss_cls": 4.09534, "loss": 4.09534, "time": 0.71544} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.09808, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24547, "top5_acc": 0.4875, "loss_cls": 4.14966, "loss": 4.14966, "time": 0.71042} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.09807, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2575, "top5_acc": 0.50078, "loss_cls": 4.09057, "loss": 4.09057, "time": 0.71321} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.09807, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25203, "top5_acc": 0.50359, "loss_cls": 4.10517, "loss": 4.10517, "time": 0.71147} +{"mode": "train", "epoch": 14, "iter": 1300, "lr": 0.09806, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25516, "top5_acc": 0.49609, "loss_cls": 4.1194, "loss": 4.1194, "time": 0.71163} +{"mode": "train", "epoch": 14, "iter": 1400, "lr": 0.09805, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25656, "top5_acc": 0.50875, "loss_cls": 4.07626, "loss": 4.07626, "time": 0.71494} +{"mode": "train", "epoch": 14, "iter": 1500, "lr": 0.09804, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25094, "top5_acc": 0.50297, "loss_cls": 4.0989, "loss": 4.0989, "time": 0.71767} +{"mode": "train", "epoch": 14, "iter": 1600, "lr": 0.09804, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25391, "top5_acc": 0.49828, "loss_cls": 4.13853, "loss": 4.13853, "time": 0.71216} +{"mode": "train", "epoch": 14, "iter": 1700, "lr": 0.09803, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24688, "top5_acc": 0.49125, "loss_cls": 4.17137, "loss": 4.17137, "time": 0.71449} +{"mode": "train", "epoch": 14, "iter": 1800, "lr": 0.09802, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25094, "top5_acc": 0.48828, "loss_cls": 4.13288, "loss": 4.13288, "time": 0.71581} +{"mode": "train", "epoch": 14, "iter": 1900, "lr": 0.09801, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24312, "top5_acc": 0.49297, "loss_cls": 4.14812, "loss": 4.14812, "time": 0.71227} +{"mode": "train", "epoch": 14, "iter": 2000, "lr": 0.098, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25797, "top5_acc": 0.50375, "loss_cls": 4.07823, "loss": 4.07823, "time": 0.71225} +{"mode": "train", "epoch": 14, "iter": 2100, "lr": 0.098, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24969, "top5_acc": 0.49891, "loss_cls": 4.11446, "loss": 4.11446, "time": 0.71213} +{"mode": "train", "epoch": 14, "iter": 2200, "lr": 0.09799, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25156, "top5_acc": 0.50672, "loss_cls": 4.09719, "loss": 4.09719, "time": 0.71357} +{"mode": "train", "epoch": 14, "iter": 2300, "lr": 0.09798, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25406, "top5_acc": 0.4975, "loss_cls": 4.12625, "loss": 4.12625, "time": 0.71121} +{"mode": "train", "epoch": 14, "iter": 2400, "lr": 0.09797, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24891, "top5_acc": 0.49641, "loss_cls": 4.13098, "loss": 4.13098, "time": 0.71112} +{"mode": "train", "epoch": 14, "iter": 2500, "lr": 0.09797, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24875, "top5_acc": 0.49594, "loss_cls": 4.14876, "loss": 4.14876, "time": 0.71483} +{"mode": "train", "epoch": 14, "iter": 2600, "lr": 0.09796, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24453, "top5_acc": 0.48938, "loss_cls": 4.16875, "loss": 4.16875, "time": 0.71271} +{"mode": "train", "epoch": 14, "iter": 2700, "lr": 0.09795, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25359, "top5_acc": 0.50375, "loss_cls": 4.09587, "loss": 4.09587, "time": 0.70992} +{"mode": "train", "epoch": 14, "iter": 2800, "lr": 0.09794, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25094, "top5_acc": 0.4825, "loss_cls": 4.15167, "loss": 4.15167, "time": 0.71072} +{"mode": "train", "epoch": 14, "iter": 2900, "lr": 0.09793, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23609, "top5_acc": 0.48234, "loss_cls": 4.16944, "loss": 4.16944, "time": 0.71033} +{"mode": "train", "epoch": 14, "iter": 3000, "lr": 0.09793, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.25328, "top5_acc": 0.50516, "loss_cls": 4.09811, "loss": 4.09811, "time": 0.72117} +{"mode": "train", "epoch": 14, "iter": 3100, "lr": 0.09792, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24484, "top5_acc": 0.48844, "loss_cls": 4.14108, "loss": 4.14108, "time": 0.71081} +{"mode": "train", "epoch": 14, "iter": 3200, "lr": 0.09791, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25719, "top5_acc": 0.50781, "loss_cls": 4.10216, "loss": 4.10216, "time": 0.70537} +{"mode": "train", "epoch": 14, "iter": 3300, "lr": 0.0979, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24141, "top5_acc": 0.48266, "loss_cls": 4.1554, "loss": 4.1554, "time": 0.71769} +{"mode": "train", "epoch": 14, "iter": 3400, "lr": 0.09789, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24344, "top5_acc": 0.48609, "loss_cls": 4.13139, "loss": 4.13139, "time": 0.72026} +{"mode": "train", "epoch": 14, "iter": 3500, "lr": 0.09789, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25172, "top5_acc": 0.49938, "loss_cls": 4.13713, "loss": 4.13713, "time": 0.71579} +{"mode": "train", "epoch": 14, "iter": 3600, "lr": 0.09788, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25547, "top5_acc": 0.49781, "loss_cls": 4.11235, "loss": 4.11235, "time": 0.71766} +{"mode": "train", "epoch": 14, "iter": 3700, "lr": 0.09787, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24906, "top5_acc": 0.49, "loss_cls": 4.14625, "loss": 4.14625, "time": 0.71521} +{"mode": "val", "epoch": 14, "iter": 309, "lr": 0.09787, "top1_acc": 0.18685, "top5_acc": 0.4163, "mean_class_accuracy": 0.18671} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.09786, "memory": 15990, "data_time": 1.35146, "top1_acc": 0.26125, "top5_acc": 0.50922, "loss_cls": 4.05654, "loss": 4.05654, "time": 2.06339} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.09785, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25047, "top5_acc": 0.49703, "loss_cls": 4.13896, "loss": 4.13896, "time": 0.70535} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.09784, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25188, "top5_acc": 0.49953, "loss_cls": 4.07671, "loss": 4.07671, "time": 0.7013} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.09783, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24578, "top5_acc": 0.49094, "loss_cls": 4.11199, "loss": 4.11199, "time": 0.70205} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.09783, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24594, "top5_acc": 0.50219, "loss_cls": 4.12667, "loss": 4.12667, "time": 0.6993} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.09782, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25016, "top5_acc": 0.49641, "loss_cls": 4.12155, "loss": 4.12155, "time": 0.69797} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.09781, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25062, "top5_acc": 0.49984, "loss_cls": 4.08187, "loss": 4.08187, "time": 0.69767} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.0978, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24891, "top5_acc": 0.49109, "loss_cls": 4.12587, "loss": 4.12587, "time": 0.70043} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.09779, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25859, "top5_acc": 0.5125, "loss_cls": 4.04079, "loss": 4.04079, "time": 0.70138} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.09778, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25047, "top5_acc": 0.49578, "loss_cls": 4.08769, "loss": 4.08769, "time": 0.70023} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.09778, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25234, "top5_acc": 0.49875, "loss_cls": 4.09709, "loss": 4.09709, "time": 0.70204} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.09777, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24938, "top5_acc": 0.49812, "loss_cls": 4.13527, "loss": 4.13527, "time": 0.69973} +{"mode": "train", "epoch": 15, "iter": 1300, "lr": 0.09776, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25484, "top5_acc": 0.49328, "loss_cls": 4.11716, "loss": 4.11716, "time": 0.70204} +{"mode": "train", "epoch": 15, "iter": 1400, "lr": 0.09775, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25453, "top5_acc": 0.50781, "loss_cls": 4.10151, "loss": 4.10151, "time": 0.70368} +{"mode": "train", "epoch": 15, "iter": 1500, "lr": 0.09774, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25078, "top5_acc": 0.4975, "loss_cls": 4.12923, "loss": 4.12923, "time": 0.70015} +{"mode": "train", "epoch": 15, "iter": 1600, "lr": 0.09773, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26047, "top5_acc": 0.50813, "loss_cls": 4.08077, "loss": 4.08077, "time": 0.70177} +{"mode": "train", "epoch": 15, "iter": 1700, "lr": 0.09773, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25469, "top5_acc": 0.49859, "loss_cls": 4.08233, "loss": 4.08233, "time": 0.69922} +{"mode": "train", "epoch": 15, "iter": 1800, "lr": 0.09772, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25391, "top5_acc": 0.49125, "loss_cls": 4.12331, "loss": 4.12331, "time": 0.69946} +{"mode": "train", "epoch": 15, "iter": 1900, "lr": 0.09771, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25047, "top5_acc": 0.50047, "loss_cls": 4.09404, "loss": 4.09404, "time": 0.70066} +{"mode": "train", "epoch": 15, "iter": 2000, "lr": 0.0977, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25969, "top5_acc": 0.50703, "loss_cls": 4.06719, "loss": 4.06719, "time": 0.69885} +{"mode": "train", "epoch": 15, "iter": 2100, "lr": 0.09769, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24766, "top5_acc": 0.49266, "loss_cls": 4.12973, "loss": 4.12973, "time": 0.69964} +{"mode": "train", "epoch": 15, "iter": 2200, "lr": 0.09768, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25562, "top5_acc": 0.49562, "loss_cls": 4.1271, "loss": 4.1271, "time": 0.70129} +{"mode": "train", "epoch": 15, "iter": 2300, "lr": 0.09768, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24672, "top5_acc": 0.49203, "loss_cls": 4.13369, "loss": 4.13369, "time": 0.69945} +{"mode": "train", "epoch": 15, "iter": 2400, "lr": 0.09767, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25516, "top5_acc": 0.48578, "loss_cls": 4.12677, "loss": 4.12677, "time": 0.70152} +{"mode": "train", "epoch": 15, "iter": 2500, "lr": 0.09766, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24906, "top5_acc": 0.49953, "loss_cls": 4.12233, "loss": 4.12233, "time": 0.70103} +{"mode": "train", "epoch": 15, "iter": 2600, "lr": 0.09765, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25516, "top5_acc": 0.49672, "loss_cls": 4.12667, "loss": 4.12667, "time": 0.70069} +{"mode": "train", "epoch": 15, "iter": 2700, "lr": 0.09764, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25094, "top5_acc": 0.50187, "loss_cls": 4.12102, "loss": 4.12102, "time": 0.70104} +{"mode": "train", "epoch": 15, "iter": 2800, "lr": 0.09763, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25734, "top5_acc": 0.49641, "loss_cls": 4.08453, "loss": 4.08453, "time": 0.70098} +{"mode": "train", "epoch": 15, "iter": 2900, "lr": 0.09763, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25844, "top5_acc": 0.50672, "loss_cls": 4.07953, "loss": 4.07953, "time": 0.69815} +{"mode": "train", "epoch": 15, "iter": 3000, "lr": 0.09762, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24344, "top5_acc": 0.49219, "loss_cls": 4.13978, "loss": 4.13978, "time": 0.7057} +{"mode": "train", "epoch": 15, "iter": 3100, "lr": 0.09761, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24031, "top5_acc": 0.485, "loss_cls": 4.18484, "loss": 4.18484, "time": 0.7017} +{"mode": "train", "epoch": 15, "iter": 3200, "lr": 0.0976, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24703, "top5_acc": 0.48562, "loss_cls": 4.16211, "loss": 4.16211, "time": 0.70442} +{"mode": "train", "epoch": 15, "iter": 3300, "lr": 0.09759, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25406, "top5_acc": 0.49859, "loss_cls": 4.13232, "loss": 4.13232, "time": 0.70787} +{"mode": "train", "epoch": 15, "iter": 3400, "lr": 0.09758, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24766, "top5_acc": 0.48734, "loss_cls": 4.14458, "loss": 4.14458, "time": 0.70907} +{"mode": "train", "epoch": 15, "iter": 3500, "lr": 0.09757, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25609, "top5_acc": 0.49531, "loss_cls": 4.10802, "loss": 4.10802, "time": 0.70563} +{"mode": "train", "epoch": 15, "iter": 3600, "lr": 0.09757, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24406, "top5_acc": 0.48734, "loss_cls": 4.17541, "loss": 4.17541, "time": 0.70919} +{"mode": "train", "epoch": 15, "iter": 3700, "lr": 0.09756, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24422, "top5_acc": 0.48656, "loss_cls": 4.17281, "loss": 4.17281, "time": 0.70355} +{"mode": "val", "epoch": 15, "iter": 309, "lr": 0.09755, "top1_acc": 0.16836, "top5_acc": 0.38712, "mean_class_accuracy": 0.16805} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.09754, "memory": 15990, "data_time": 1.28559, "top1_acc": 0.25516, "top5_acc": 0.49828, "loss_cls": 4.08406, "loss": 4.08406, "time": 1.9889} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.09754, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25406, "top5_acc": 0.49578, "loss_cls": 4.11345, "loss": 4.11345, "time": 0.70136} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.09753, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25109, "top5_acc": 0.50359, "loss_cls": 4.10757, "loss": 4.10757, "time": 0.70261} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.09752, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25438, "top5_acc": 0.50156, "loss_cls": 4.10433, "loss": 4.10433, "time": 0.6999} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.09751, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25141, "top5_acc": 0.50016, "loss_cls": 4.10559, "loss": 4.10559, "time": 0.70136} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.0975, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24984, "top5_acc": 0.49266, "loss_cls": 4.14947, "loss": 4.14947, "time": 0.70303} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.09749, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25078, "top5_acc": 0.49812, "loss_cls": 4.12158, "loss": 4.12158, "time": 0.70302} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.09748, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25016, "top5_acc": 0.4925, "loss_cls": 4.12601, "loss": 4.12601, "time": 0.69786} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.09747, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24844, "top5_acc": 0.495, "loss_cls": 4.11497, "loss": 4.11497, "time": 0.70035} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.09747, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24938, "top5_acc": 0.50719, "loss_cls": 4.07925, "loss": 4.07925, "time": 0.7021} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.09746, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25656, "top5_acc": 0.50375, "loss_cls": 4.07989, "loss": 4.07989, "time": 0.69865} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.09745, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25797, "top5_acc": 0.49578, "loss_cls": 4.09859, "loss": 4.09859, "time": 0.70037} +{"mode": "train", "epoch": 16, "iter": 1300, "lr": 0.09744, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24703, "top5_acc": 0.50422, "loss_cls": 4.09251, "loss": 4.09251, "time": 0.69981} +{"mode": "train", "epoch": 16, "iter": 1400, "lr": 0.09743, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24422, "top5_acc": 0.49031, "loss_cls": 4.15289, "loss": 4.15289, "time": 0.701} +{"mode": "train", "epoch": 16, "iter": 1500, "lr": 0.09742, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24844, "top5_acc": 0.49828, "loss_cls": 4.11592, "loss": 4.11592, "time": 0.69986} +{"mode": "train", "epoch": 16, "iter": 1600, "lr": 0.09741, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25156, "top5_acc": 0.51062, "loss_cls": 4.06913, "loss": 4.06913, "time": 0.69834} +{"mode": "train", "epoch": 16, "iter": 1700, "lr": 0.0974, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25188, "top5_acc": 0.50313, "loss_cls": 4.09305, "loss": 4.09305, "time": 0.69824} +{"mode": "train", "epoch": 16, "iter": 1800, "lr": 0.0974, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24719, "top5_acc": 0.50594, "loss_cls": 4.09812, "loss": 4.09812, "time": 0.69919} +{"mode": "train", "epoch": 16, "iter": 1900, "lr": 0.09739, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24781, "top5_acc": 0.49859, "loss_cls": 4.12507, "loss": 4.12507, "time": 0.69872} +{"mode": "train", "epoch": 16, "iter": 2000, "lr": 0.09738, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24812, "top5_acc": 0.50844, "loss_cls": 4.08639, "loss": 4.08639, "time": 0.69886} +{"mode": "train", "epoch": 16, "iter": 2100, "lr": 0.09737, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25359, "top5_acc": 0.49422, "loss_cls": 4.10919, "loss": 4.10919, "time": 0.70317} +{"mode": "train", "epoch": 16, "iter": 2200, "lr": 0.09736, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24891, "top5_acc": 0.49, "loss_cls": 4.13927, "loss": 4.13927, "time": 0.70412} +{"mode": "train", "epoch": 16, "iter": 2300, "lr": 0.09735, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25062, "top5_acc": 0.49688, "loss_cls": 4.12118, "loss": 4.12118, "time": 0.70118} +{"mode": "train", "epoch": 16, "iter": 2400, "lr": 0.09734, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25344, "top5_acc": 0.49234, "loss_cls": 4.11263, "loss": 4.11263, "time": 0.69872} +{"mode": "train", "epoch": 16, "iter": 2500, "lr": 0.09733, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25344, "top5_acc": 0.49344, "loss_cls": 4.12768, "loss": 4.12768, "time": 0.70131} +{"mode": "train", "epoch": 16, "iter": 2600, "lr": 0.09732, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24344, "top5_acc": 0.49891, "loss_cls": 4.12966, "loss": 4.12966, "time": 0.69998} +{"mode": "train", "epoch": 16, "iter": 2700, "lr": 0.09731, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25219, "top5_acc": 0.50313, "loss_cls": 4.10705, "loss": 4.10705, "time": 0.69951} +{"mode": "train", "epoch": 16, "iter": 2800, "lr": 0.09731, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25328, "top5_acc": 0.50187, "loss_cls": 4.09916, "loss": 4.09916, "time": 0.70236} +{"mode": "train", "epoch": 16, "iter": 2900, "lr": 0.0973, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24781, "top5_acc": 0.48812, "loss_cls": 4.17053, "loss": 4.17053, "time": 0.70092} +{"mode": "train", "epoch": 16, "iter": 3000, "lr": 0.09729, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24797, "top5_acc": 0.50156, "loss_cls": 4.14173, "loss": 4.14173, "time": 0.7042} +{"mode": "train", "epoch": 16, "iter": 3100, "lr": 0.09728, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25781, "top5_acc": 0.50422, "loss_cls": 4.07305, "loss": 4.07305, "time": 0.70642} +{"mode": "train", "epoch": 16, "iter": 3200, "lr": 0.09727, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26125, "top5_acc": 0.49484, "loss_cls": 4.08744, "loss": 4.08744, "time": 0.70703} +{"mode": "train", "epoch": 16, "iter": 3300, "lr": 0.09726, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25078, "top5_acc": 0.49938, "loss_cls": 4.10265, "loss": 4.10265, "time": 0.70309} +{"mode": "train", "epoch": 16, "iter": 3400, "lr": 0.09725, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25156, "top5_acc": 0.50766, "loss_cls": 4.08387, "loss": 4.08387, "time": 0.70952} +{"mode": "train", "epoch": 16, "iter": 3500, "lr": 0.09724, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24719, "top5_acc": 0.50844, "loss_cls": 4.10908, "loss": 4.10908, "time": 0.70287} +{"mode": "train", "epoch": 16, "iter": 3600, "lr": 0.09723, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25938, "top5_acc": 0.48812, "loss_cls": 4.14727, "loss": 4.14727, "time": 0.70969} +{"mode": "train", "epoch": 16, "iter": 3700, "lr": 0.09722, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25734, "top5_acc": 0.50234, "loss_cls": 4.10313, "loss": 4.10313, "time": 0.70372} +{"mode": "val", "epoch": 16, "iter": 309, "lr": 0.09722, "top1_acc": 0.19349, "top5_acc": 0.42364, "mean_class_accuracy": 0.19317} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.09721, "memory": 15990, "data_time": 1.27944, "top1_acc": 0.25594, "top5_acc": 0.50406, "loss_cls": 4.09369, "loss": 4.09369, "time": 1.98257} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.0972, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25234, "top5_acc": 0.50656, "loss_cls": 4.08222, "loss": 4.08222, "time": 0.70343} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.09719, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25281, "top5_acc": 0.50516, "loss_cls": 4.08323, "loss": 4.08323, "time": 0.69953} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.09718, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25984, "top5_acc": 0.51281, "loss_cls": 4.05766, "loss": 4.05766, "time": 0.7013} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.09717, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26047, "top5_acc": 0.51281, "loss_cls": 4.05678, "loss": 4.05678, "time": 0.70672} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.09716, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2575, "top5_acc": 0.50125, "loss_cls": 4.09041, "loss": 4.09041, "time": 0.69784} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.09715, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26516, "top5_acc": 0.51125, "loss_cls": 4.05207, "loss": 4.05207, "time": 0.6996} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.09714, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25203, "top5_acc": 0.50078, "loss_cls": 4.11043, "loss": 4.11043, "time": 0.70162} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.09714, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24594, "top5_acc": 0.49875, "loss_cls": 4.1014, "loss": 4.1014, "time": 0.70034} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.09713, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25906, "top5_acc": 0.50672, "loss_cls": 4.08431, "loss": 4.08431, "time": 0.69852} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.09712, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25344, "top5_acc": 0.50156, "loss_cls": 4.1202, "loss": 4.1202, "time": 0.69867} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.09711, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25109, "top5_acc": 0.50719, "loss_cls": 4.10656, "loss": 4.10656, "time": 0.70046} +{"mode": "train", "epoch": 17, "iter": 1300, "lr": 0.0971, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26312, "top5_acc": 0.50109, "loss_cls": 4.08434, "loss": 4.08434, "time": 0.6989} +{"mode": "train", "epoch": 17, "iter": 1400, "lr": 0.09709, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.245, "top5_acc": 0.49641, "loss_cls": 4.12866, "loss": 4.12866, "time": 0.69937} +{"mode": "train", "epoch": 17, "iter": 1500, "lr": 0.09708, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25047, "top5_acc": 0.5, "loss_cls": 4.09651, "loss": 4.09651, "time": 0.69834} +{"mode": "train", "epoch": 17, "iter": 1600, "lr": 0.09707, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25062, "top5_acc": 0.4975, "loss_cls": 4.10378, "loss": 4.10378, "time": 0.70084} +{"mode": "train", "epoch": 17, "iter": 1700, "lr": 0.09706, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24828, "top5_acc": 0.50422, "loss_cls": 4.09769, "loss": 4.09769, "time": 0.70136} +{"mode": "train", "epoch": 17, "iter": 1800, "lr": 0.09705, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25641, "top5_acc": 0.49812, "loss_cls": 4.10554, "loss": 4.10554, "time": 0.70016} +{"mode": "train", "epoch": 17, "iter": 1900, "lr": 0.09704, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25125, "top5_acc": 0.49922, "loss_cls": 4.1046, "loss": 4.1046, "time": 0.69823} +{"mode": "train", "epoch": 17, "iter": 2000, "lr": 0.09703, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25672, "top5_acc": 0.50453, "loss_cls": 4.08114, "loss": 4.08114, "time": 0.69854} +{"mode": "train", "epoch": 17, "iter": 2100, "lr": 0.09702, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2575, "top5_acc": 0.5025, "loss_cls": 4.06032, "loss": 4.06032, "time": 0.70028} +{"mode": "train", "epoch": 17, "iter": 2200, "lr": 0.09701, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25406, "top5_acc": 0.49688, "loss_cls": 4.11747, "loss": 4.11747, "time": 0.70207} +{"mode": "train", "epoch": 17, "iter": 2300, "lr": 0.097, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25562, "top5_acc": 0.49953, "loss_cls": 4.06937, "loss": 4.06937, "time": 0.70026} +{"mode": "train", "epoch": 17, "iter": 2400, "lr": 0.09699, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23969, "top5_acc": 0.49453, "loss_cls": 4.10479, "loss": 4.10479, "time": 0.70276} +{"mode": "train", "epoch": 17, "iter": 2500, "lr": 0.09698, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25203, "top5_acc": 0.49984, "loss_cls": 4.08475, "loss": 4.08475, "time": 0.69825} +{"mode": "train", "epoch": 17, "iter": 2600, "lr": 0.09697, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25266, "top5_acc": 0.50047, "loss_cls": 4.0868, "loss": 4.0868, "time": 0.69884} +{"mode": "train", "epoch": 17, "iter": 2700, "lr": 0.09697, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24766, "top5_acc": 0.50391, "loss_cls": 4.08685, "loss": 4.08685, "time": 0.70108} +{"mode": "train", "epoch": 17, "iter": 2800, "lr": 0.09696, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26047, "top5_acc": 0.50375, "loss_cls": 4.0881, "loss": 4.0881, "time": 0.70016} +{"mode": "train", "epoch": 17, "iter": 2900, "lr": 0.09695, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25469, "top5_acc": 0.48938, "loss_cls": 4.10447, "loss": 4.10447, "time": 0.70515} +{"mode": "train", "epoch": 17, "iter": 3000, "lr": 0.09694, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25719, "top5_acc": 0.51062, "loss_cls": 4.09684, "loss": 4.09684, "time": 0.70571} +{"mode": "train", "epoch": 17, "iter": 3100, "lr": 0.09693, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25156, "top5_acc": 0.50438, "loss_cls": 4.08342, "loss": 4.08342, "time": 0.70029} +{"mode": "train", "epoch": 17, "iter": 3200, "lr": 0.09692, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25609, "top5_acc": 0.49656, "loss_cls": 4.12237, "loss": 4.12237, "time": 0.7001} +{"mode": "train", "epoch": 17, "iter": 3300, "lr": 0.09691, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24469, "top5_acc": 0.49516, "loss_cls": 4.16611, "loss": 4.16611, "time": 0.70613} +{"mode": "train", "epoch": 17, "iter": 3400, "lr": 0.0969, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25188, "top5_acc": 0.50203, "loss_cls": 4.10283, "loss": 4.10283, "time": 0.70734} +{"mode": "train", "epoch": 17, "iter": 3500, "lr": 0.09689, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25422, "top5_acc": 0.50094, "loss_cls": 4.08445, "loss": 4.08445, "time": 0.71208} +{"mode": "train", "epoch": 17, "iter": 3600, "lr": 0.09688, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25422, "top5_acc": 0.49391, "loss_cls": 4.14878, "loss": 4.14878, "time": 0.70757} +{"mode": "train", "epoch": 17, "iter": 3700, "lr": 0.09687, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24938, "top5_acc": 0.50328, "loss_cls": 4.10417, "loss": 4.10417, "time": 0.70691} +{"mode": "val", "epoch": 17, "iter": 309, "lr": 0.09686, "top1_acc": 0.17424, "top5_acc": 0.39305, "mean_class_accuracy": 0.17388} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.09685, "memory": 15990, "data_time": 1.31311, "top1_acc": 0.25719, "top5_acc": 0.51688, "loss_cls": 4.07088, "loss": 4.07088, "time": 2.01791} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.09684, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26406, "top5_acc": 0.515, "loss_cls": 4.03466, "loss": 4.03466, "time": 0.70156} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.09683, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26438, "top5_acc": 0.50875, "loss_cls": 4.05739, "loss": 4.05739, "time": 0.70067} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.09683, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25656, "top5_acc": 0.50953, "loss_cls": 4.05643, "loss": 4.05643, "time": 0.69855} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.09682, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24828, "top5_acc": 0.49859, "loss_cls": 4.09754, "loss": 4.09754, "time": 0.70008} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.09681, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25438, "top5_acc": 0.50797, "loss_cls": 4.07583, "loss": 4.07583, "time": 0.70037} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.0968, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24859, "top5_acc": 0.4975, "loss_cls": 4.11999, "loss": 4.11999, "time": 0.70048} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.09679, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25938, "top5_acc": 0.4975, "loss_cls": 4.08958, "loss": 4.08958, "time": 0.70038} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.09678, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25188, "top5_acc": 0.50016, "loss_cls": 4.11859, "loss": 4.11859, "time": 0.70002} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.09677, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25531, "top5_acc": 0.49969, "loss_cls": 4.10383, "loss": 4.10383, "time": 0.69943} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.09676, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25375, "top5_acc": 0.50781, "loss_cls": 4.07337, "loss": 4.07337, "time": 0.7012} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.09675, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25688, "top5_acc": 0.50594, "loss_cls": 4.11025, "loss": 4.11025, "time": 0.69939} +{"mode": "train", "epoch": 18, "iter": 1300, "lr": 0.09674, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25703, "top5_acc": 0.51266, "loss_cls": 4.05301, "loss": 4.05301, "time": 0.6989} +{"mode": "train", "epoch": 18, "iter": 1400, "lr": 0.09673, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25531, "top5_acc": 0.49812, "loss_cls": 4.09041, "loss": 4.09041, "time": 0.69781} +{"mode": "train", "epoch": 18, "iter": 1500, "lr": 0.09672, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25703, "top5_acc": 0.50266, "loss_cls": 4.11551, "loss": 4.11551, "time": 0.70046} +{"mode": "train", "epoch": 18, "iter": 1600, "lr": 0.09671, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25359, "top5_acc": 0.50187, "loss_cls": 4.11402, "loss": 4.11402, "time": 0.7017} +{"mode": "train", "epoch": 18, "iter": 1700, "lr": 0.0967, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25812, "top5_acc": 0.50203, "loss_cls": 4.08203, "loss": 4.08203, "time": 0.69943} +{"mode": "train", "epoch": 18, "iter": 1800, "lr": 0.09669, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26219, "top5_acc": 0.50906, "loss_cls": 4.07832, "loss": 4.07832, "time": 0.70006} +{"mode": "train", "epoch": 18, "iter": 1900, "lr": 0.09668, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26391, "top5_acc": 0.51047, "loss_cls": 4.08643, "loss": 4.08643, "time": 0.70075} +{"mode": "train", "epoch": 18, "iter": 2000, "lr": 0.09667, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26094, "top5_acc": 0.50109, "loss_cls": 4.05642, "loss": 4.05642, "time": 0.6984} +{"mode": "train", "epoch": 18, "iter": 2100, "lr": 0.09666, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25828, "top5_acc": 0.50781, "loss_cls": 4.08615, "loss": 4.08615, "time": 0.69896} +{"mode": "train", "epoch": 18, "iter": 2200, "lr": 0.09665, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25328, "top5_acc": 0.49859, "loss_cls": 4.10512, "loss": 4.10512, "time": 0.70167} +{"mode": "train", "epoch": 18, "iter": 2300, "lr": 0.09664, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26, "top5_acc": 0.50813, "loss_cls": 4.09322, "loss": 4.09322, "time": 0.69949} +{"mode": "train", "epoch": 18, "iter": 2400, "lr": 0.09663, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24781, "top5_acc": 0.50203, "loss_cls": 4.08527, "loss": 4.08527, "time": 0.69873} +{"mode": "train", "epoch": 18, "iter": 2500, "lr": 0.09662, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25359, "top5_acc": 0.50531, "loss_cls": 4.1106, "loss": 4.1106, "time": 0.69886} +{"mode": "train", "epoch": 18, "iter": 2600, "lr": 0.09661, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2625, "top5_acc": 0.50859, "loss_cls": 4.06489, "loss": 4.06489, "time": 0.7} +{"mode": "train", "epoch": 18, "iter": 2700, "lr": 0.0966, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25844, "top5_acc": 0.49953, "loss_cls": 4.08839, "loss": 4.08839, "time": 0.70096} +{"mode": "train", "epoch": 18, "iter": 2800, "lr": 0.09659, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25, "top5_acc": 0.49141, "loss_cls": 4.12187, "loss": 4.12187, "time": 0.70154} +{"mode": "train", "epoch": 18, "iter": 2900, "lr": 0.09658, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24547, "top5_acc": 0.495, "loss_cls": 4.16157, "loss": 4.16157, "time": 0.70865} +{"mode": "train", "epoch": 18, "iter": 3000, "lr": 0.09657, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25719, "top5_acc": 0.50797, "loss_cls": 4.06519, "loss": 4.06519, "time": 0.70235} +{"mode": "train", "epoch": 18, "iter": 3100, "lr": 0.09656, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25094, "top5_acc": 0.50531, "loss_cls": 4.09667, "loss": 4.09667, "time": 0.70679} +{"mode": "train", "epoch": 18, "iter": 3200, "lr": 0.09654, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24766, "top5_acc": 0.49219, "loss_cls": 4.13901, "loss": 4.13901, "time": 0.70448} +{"mode": "train", "epoch": 18, "iter": 3300, "lr": 0.09653, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24719, "top5_acc": 0.48938, "loss_cls": 4.13836, "loss": 4.13836, "time": 0.70654} +{"mode": "train", "epoch": 18, "iter": 3400, "lr": 0.09652, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25516, "top5_acc": 0.50422, "loss_cls": 4.07816, "loss": 4.07816, "time": 0.71323} +{"mode": "train", "epoch": 18, "iter": 3500, "lr": 0.09651, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25922, "top5_acc": 0.49766, "loss_cls": 4.11016, "loss": 4.11016, "time": 0.70799} +{"mode": "train", "epoch": 18, "iter": 3600, "lr": 0.0965, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25656, "top5_acc": 0.51, "loss_cls": 4.07414, "loss": 4.07414, "time": 0.71041} +{"mode": "train", "epoch": 18, "iter": 3700, "lr": 0.09649, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26109, "top5_acc": 0.50625, "loss_cls": 4.0956, "loss": 4.0956, "time": 0.70323} +{"mode": "val", "epoch": 18, "iter": 309, "lr": 0.09649, "top1_acc": 0.19637, "top5_acc": 0.42309, "mean_class_accuracy": 0.19613} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.09648, "memory": 15990, "data_time": 1.28842, "top1_acc": 0.26578, "top5_acc": 0.51422, "loss_cls": 4.02697, "loss": 4.02697, "time": 1.99256} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.09647, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25859, "top5_acc": 0.50078, "loss_cls": 4.09456, "loss": 4.09456, "time": 0.69987} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.09646, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26062, "top5_acc": 0.5125, "loss_cls": 4.07285, "loss": 4.07285, "time": 0.7011} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.09645, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25703, "top5_acc": 0.51047, "loss_cls": 4.0528, "loss": 4.0528, "time": 0.7037} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.09644, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25344, "top5_acc": 0.50969, "loss_cls": 4.08546, "loss": 4.08546, "time": 0.70018} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.09643, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25297, "top5_acc": 0.50781, "loss_cls": 4.04129, "loss": 4.04129, "time": 0.69977} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.09642, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25, "top5_acc": 0.50391, "loss_cls": 4.08176, "loss": 4.08176, "time": 0.70262} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.09641, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24578, "top5_acc": 0.49297, "loss_cls": 4.14598, "loss": 4.14598, "time": 0.70069} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.0964, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26, "top5_acc": 0.51094, "loss_cls": 4.06214, "loss": 4.06214, "time": 0.69831} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.09639, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25, "top5_acc": 0.50156, "loss_cls": 4.13546, "loss": 4.13546, "time": 0.69834} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.09637, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26406, "top5_acc": 0.51578, "loss_cls": 4.02842, "loss": 4.02842, "time": 0.69944} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.09636, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25141, "top5_acc": 0.49453, "loss_cls": 4.13978, "loss": 4.13978, "time": 0.70146} +{"mode": "train", "epoch": 19, "iter": 1300, "lr": 0.09635, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26203, "top5_acc": 0.50562, "loss_cls": 4.05829, "loss": 4.05829, "time": 0.7026} +{"mode": "train", "epoch": 19, "iter": 1400, "lr": 0.09634, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26469, "top5_acc": 0.51375, "loss_cls": 4.05029, "loss": 4.05029, "time": 0.69904} +{"mode": "train", "epoch": 19, "iter": 1500, "lr": 0.09633, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25078, "top5_acc": 0.50875, "loss_cls": 4.08602, "loss": 4.08602, "time": 0.69786} +{"mode": "train", "epoch": 19, "iter": 1600, "lr": 0.09632, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26562, "top5_acc": 0.51281, "loss_cls": 4.04136, "loss": 4.04136, "time": 0.69943} +{"mode": "train", "epoch": 19, "iter": 1700, "lr": 0.09631, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25969, "top5_acc": 0.50516, "loss_cls": 4.06686, "loss": 4.06686, "time": 0.6985} +{"mode": "train", "epoch": 19, "iter": 1800, "lr": 0.0963, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25297, "top5_acc": 0.49719, "loss_cls": 4.11568, "loss": 4.11568, "time": 0.69952} +{"mode": "train", "epoch": 19, "iter": 1900, "lr": 0.09629, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25469, "top5_acc": 0.50344, "loss_cls": 4.10791, "loss": 4.10791, "time": 0.70043} +{"mode": "train", "epoch": 19, "iter": 2000, "lr": 0.09628, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25562, "top5_acc": 0.50484, "loss_cls": 4.09377, "loss": 4.09377, "time": 0.69872} +{"mode": "train", "epoch": 19, "iter": 2100, "lr": 0.09627, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25812, "top5_acc": 0.49906, "loss_cls": 4.09359, "loss": 4.09359, "time": 0.69908} +{"mode": "train", "epoch": 19, "iter": 2200, "lr": 0.09626, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25953, "top5_acc": 0.50172, "loss_cls": 4.07773, "loss": 4.07773, "time": 0.69964} +{"mode": "train", "epoch": 19, "iter": 2300, "lr": 0.09625, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25188, "top5_acc": 0.50078, "loss_cls": 4.112, "loss": 4.112, "time": 0.70065} +{"mode": "train", "epoch": 19, "iter": 2400, "lr": 0.09624, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25812, "top5_acc": 0.50719, "loss_cls": 4.06505, "loss": 4.06505, "time": 0.69888} +{"mode": "train", "epoch": 19, "iter": 2500, "lr": 0.09623, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24578, "top5_acc": 0.49766, "loss_cls": 4.14918, "loss": 4.14918, "time": 0.70272} +{"mode": "train", "epoch": 19, "iter": 2600, "lr": 0.09622, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26188, "top5_acc": 0.51062, "loss_cls": 4.05491, "loss": 4.05491, "time": 0.69815} +{"mode": "train", "epoch": 19, "iter": 2700, "lr": 0.09621, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25484, "top5_acc": 0.49828, "loss_cls": 4.09139, "loss": 4.09139, "time": 0.70086} +{"mode": "train", "epoch": 19, "iter": 2800, "lr": 0.0962, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24906, "top5_acc": 0.49703, "loss_cls": 4.09712, "loss": 4.09712, "time": 0.70129} +{"mode": "train", "epoch": 19, "iter": 2900, "lr": 0.09618, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25594, "top5_acc": 0.49984, "loss_cls": 4.1063, "loss": 4.1063, "time": 0.7065} +{"mode": "train", "epoch": 19, "iter": 3000, "lr": 0.09617, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25016, "top5_acc": 0.50688, "loss_cls": 4.07175, "loss": 4.07175, "time": 0.70338} +{"mode": "train", "epoch": 19, "iter": 3100, "lr": 0.09616, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25531, "top5_acc": 0.50766, "loss_cls": 4.10083, "loss": 4.10083, "time": 0.70352} +{"mode": "train", "epoch": 19, "iter": 3200, "lr": 0.09615, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25078, "top5_acc": 0.50438, "loss_cls": 4.11793, "loss": 4.11793, "time": 0.71047} +{"mode": "train", "epoch": 19, "iter": 3300, "lr": 0.09614, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25938, "top5_acc": 0.50422, "loss_cls": 4.0786, "loss": 4.0786, "time": 0.70647} +{"mode": "train", "epoch": 19, "iter": 3400, "lr": 0.09613, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25375, "top5_acc": 0.50141, "loss_cls": 4.0914, "loss": 4.0914, "time": 0.70455} +{"mode": "train", "epoch": 19, "iter": 3500, "lr": 0.09612, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.255, "top5_acc": 0.49922, "loss_cls": 4.10497, "loss": 4.10497, "time": 0.70938} +{"mode": "train", "epoch": 19, "iter": 3600, "lr": 0.09611, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2525, "top5_acc": 0.50516, "loss_cls": 4.09208, "loss": 4.09208, "time": 0.71071} +{"mode": "train", "epoch": 19, "iter": 3700, "lr": 0.0961, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26094, "top5_acc": 0.50297, "loss_cls": 4.07786, "loss": 4.07786, "time": 0.7043} +{"mode": "val", "epoch": 19, "iter": 309, "lr": 0.09609, "top1_acc": 0.19688, "top5_acc": 0.4199, "mean_class_accuracy": 0.19667} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.09608, "memory": 15990, "data_time": 1.28326, "top1_acc": 0.25812, "top5_acc": 0.51422, "loss_cls": 4.03711, "loss": 4.03711, "time": 1.98607} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.09607, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25297, "top5_acc": 0.50734, "loss_cls": 4.07859, "loss": 4.07859, "time": 0.70386} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.09606, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26281, "top5_acc": 0.50672, "loss_cls": 4.05781, "loss": 4.05781, "time": 0.69985} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.09605, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26016, "top5_acc": 0.50406, "loss_cls": 4.04363, "loss": 4.04363, "time": 0.70159} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.09604, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24703, "top5_acc": 0.49641, "loss_cls": 4.11206, "loss": 4.11206, "time": 0.70089} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.09603, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26078, "top5_acc": 0.50797, "loss_cls": 4.04383, "loss": 4.04383, "time": 0.70015} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.09602, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24641, "top5_acc": 0.5075, "loss_cls": 4.08092, "loss": 4.08092, "time": 0.69981} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.09601, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25141, "top5_acc": 0.49391, "loss_cls": 4.09231, "loss": 4.09231, "time": 0.70105} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.096, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26094, "top5_acc": 0.50344, "loss_cls": 4.07083, "loss": 4.07083, "time": 0.69918} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.09598, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24719, "top5_acc": 0.50094, "loss_cls": 4.09621, "loss": 4.09621, "time": 0.70034} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.09597, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26109, "top5_acc": 0.5, "loss_cls": 4.09321, "loss": 4.09321, "time": 0.70173} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.09596, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25344, "top5_acc": 0.50484, "loss_cls": 4.07008, "loss": 4.07008, "time": 0.70113} +{"mode": "train", "epoch": 20, "iter": 1300, "lr": 0.09595, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25938, "top5_acc": 0.50406, "loss_cls": 4.07349, "loss": 4.07349, "time": 0.69744} +{"mode": "train", "epoch": 20, "iter": 1400, "lr": 0.09594, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25812, "top5_acc": 0.50016, "loss_cls": 4.08362, "loss": 4.08362, "time": 0.69961} +{"mode": "train", "epoch": 20, "iter": 1500, "lr": 0.09593, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26156, "top5_acc": 0.50828, "loss_cls": 4.06513, "loss": 4.06513, "time": 0.70227} +{"mode": "train", "epoch": 20, "iter": 1600, "lr": 0.09592, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25156, "top5_acc": 0.50281, "loss_cls": 4.08714, "loss": 4.08714, "time": 0.69941} +{"mode": "train", "epoch": 20, "iter": 1700, "lr": 0.09591, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26594, "top5_acc": 0.51313, "loss_cls": 4.03355, "loss": 4.03355, "time": 0.70077} +{"mode": "train", "epoch": 20, "iter": 1800, "lr": 0.0959, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25766, "top5_acc": 0.50891, "loss_cls": 4.06995, "loss": 4.06995, "time": 0.70068} +{"mode": "train", "epoch": 20, "iter": 1900, "lr": 0.09588, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25906, "top5_acc": 0.50609, "loss_cls": 4.06486, "loss": 4.06486, "time": 0.70239} +{"mode": "train", "epoch": 20, "iter": 2000, "lr": 0.09587, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25266, "top5_acc": 0.50094, "loss_cls": 4.11445, "loss": 4.11445, "time": 0.70262} +{"mode": "train", "epoch": 20, "iter": 2100, "lr": 0.09586, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24422, "top5_acc": 0.49766, "loss_cls": 4.11983, "loss": 4.11983, "time": 0.70287} +{"mode": "train", "epoch": 20, "iter": 2200, "lr": 0.09585, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25562, "top5_acc": 0.50453, "loss_cls": 4.09153, "loss": 4.09153, "time": 0.70348} +{"mode": "train", "epoch": 20, "iter": 2300, "lr": 0.09584, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24906, "top5_acc": 0.50203, "loss_cls": 4.08937, "loss": 4.08937, "time": 0.70257} +{"mode": "train", "epoch": 20, "iter": 2400, "lr": 0.09583, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26109, "top5_acc": 0.50344, "loss_cls": 4.09538, "loss": 4.09538, "time": 0.70167} +{"mode": "train", "epoch": 20, "iter": 2500, "lr": 0.09582, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.255, "top5_acc": 0.50094, "loss_cls": 4.12709, "loss": 4.12709, "time": 0.70401} +{"mode": "train", "epoch": 20, "iter": 2600, "lr": 0.09581, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2525, "top5_acc": 0.50109, "loss_cls": 4.07178, "loss": 4.07178, "time": 0.70028} +{"mode": "train", "epoch": 20, "iter": 2700, "lr": 0.0958, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.255, "top5_acc": 0.51125, "loss_cls": 4.08196, "loss": 4.08196, "time": 0.70134} +{"mode": "train", "epoch": 20, "iter": 2800, "lr": 0.09578, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25547, "top5_acc": 0.50094, "loss_cls": 4.07893, "loss": 4.07893, "time": 0.69907} +{"mode": "train", "epoch": 20, "iter": 2900, "lr": 0.09577, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26484, "top5_acc": 0.50641, "loss_cls": 4.06847, "loss": 4.06847, "time": 0.70702} +{"mode": "train", "epoch": 20, "iter": 3000, "lr": 0.09576, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25734, "top5_acc": 0.50891, "loss_cls": 4.0647, "loss": 4.0647, "time": 0.70365} +{"mode": "train", "epoch": 20, "iter": 3100, "lr": 0.09575, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25297, "top5_acc": 0.50281, "loss_cls": 4.07414, "loss": 4.07414, "time": 0.70247} +{"mode": "train", "epoch": 20, "iter": 3200, "lr": 0.09574, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25766, "top5_acc": 0.51203, "loss_cls": 4.06346, "loss": 4.06346, "time": 0.70582} +{"mode": "train", "epoch": 20, "iter": 3300, "lr": 0.09573, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24125, "top5_acc": 0.49141, "loss_cls": 4.13316, "loss": 4.13316, "time": 0.70777} +{"mode": "train", "epoch": 20, "iter": 3400, "lr": 0.09572, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25141, "top5_acc": 0.49891, "loss_cls": 4.12412, "loss": 4.12412, "time": 0.70488} +{"mode": "train", "epoch": 20, "iter": 3500, "lr": 0.09571, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25047, "top5_acc": 0.49594, "loss_cls": 4.10235, "loss": 4.10235, "time": 0.70679} +{"mode": "train", "epoch": 20, "iter": 3600, "lr": 0.09569, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26656, "top5_acc": 0.51359, "loss_cls": 4.03962, "loss": 4.03962, "time": 0.70776} +{"mode": "train", "epoch": 20, "iter": 3700, "lr": 0.09568, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25922, "top5_acc": 0.50797, "loss_cls": 4.07165, "loss": 4.07165, "time": 0.71097} +{"mode": "val", "epoch": 20, "iter": 309, "lr": 0.09568, "top1_acc": 0.20336, "top5_acc": 0.43676, "mean_class_accuracy": 0.20317} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.09567, "memory": 15990, "data_time": 1.29421, "top1_acc": 0.26219, "top5_acc": 0.50641, "loss_cls": 4.03095, "loss": 4.03095, "time": 1.99926} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.09565, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25953, "top5_acc": 0.50313, "loss_cls": 4.07132, "loss": 4.07132, "time": 0.70085} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.09564, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25, "top5_acc": 0.49891, "loss_cls": 4.09573, "loss": 4.09573, "time": 0.70019} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.09563, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26625, "top5_acc": 0.52156, "loss_cls": 4.01325, "loss": 4.01325, "time": 0.69913} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.09562, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25031, "top5_acc": 0.50109, "loss_cls": 4.10017, "loss": 4.10017, "time": 0.70047} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.09561, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26141, "top5_acc": 0.50109, "loss_cls": 4.05705, "loss": 4.05705, "time": 0.70163} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.0956, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26125, "top5_acc": 0.50844, "loss_cls": 4.08526, "loss": 4.08526, "time": 0.70077} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.09559, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25859, "top5_acc": 0.50219, "loss_cls": 4.08127, "loss": 4.08127, "time": 0.70124} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.09557, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25703, "top5_acc": 0.50891, "loss_cls": 4.0609, "loss": 4.0609, "time": 0.70169} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.09556, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25797, "top5_acc": 0.50609, "loss_cls": 4.06549, "loss": 4.06549, "time": 0.70033} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.09555, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26484, "top5_acc": 0.51484, "loss_cls": 3.99631, "loss": 3.99631, "time": 0.6987} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.09554, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25656, "top5_acc": 0.50719, "loss_cls": 4.04849, "loss": 4.04849, "time": 0.69894} +{"mode": "train", "epoch": 21, "iter": 1300, "lr": 0.09553, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25906, "top5_acc": 0.50656, "loss_cls": 4.05859, "loss": 4.05859, "time": 0.70292} +{"mode": "train", "epoch": 21, "iter": 1400, "lr": 0.09552, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26406, "top5_acc": 0.51266, "loss_cls": 4.0285, "loss": 4.0285, "time": 0.69856} +{"mode": "train", "epoch": 21, "iter": 1500, "lr": 0.09551, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25531, "top5_acc": 0.49953, "loss_cls": 4.08828, "loss": 4.08828, "time": 0.70259} +{"mode": "train", "epoch": 21, "iter": 1600, "lr": 0.09549, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25109, "top5_acc": 0.50234, "loss_cls": 4.0999, "loss": 4.0999, "time": 0.70043} +{"mode": "train", "epoch": 21, "iter": 1700, "lr": 0.09548, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24156, "top5_acc": 0.49703, "loss_cls": 4.11593, "loss": 4.11593, "time": 0.69979} +{"mode": "train", "epoch": 21, "iter": 1800, "lr": 0.09547, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25891, "top5_acc": 0.49453, "loss_cls": 4.10628, "loss": 4.10628, "time": 0.70065} +{"mode": "train", "epoch": 21, "iter": 1900, "lr": 0.09546, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2575, "top5_acc": 0.50875, "loss_cls": 4.06771, "loss": 4.06771, "time": 0.70039} +{"mode": "train", "epoch": 21, "iter": 2000, "lr": 0.09545, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24641, "top5_acc": 0.49641, "loss_cls": 4.11957, "loss": 4.11957, "time": 0.69975} +{"mode": "train", "epoch": 21, "iter": 2100, "lr": 0.09544, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25188, "top5_acc": 0.49984, "loss_cls": 4.11919, "loss": 4.11919, "time": 0.69964} +{"mode": "train", "epoch": 21, "iter": 2200, "lr": 0.09542, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26141, "top5_acc": 0.51031, "loss_cls": 4.06834, "loss": 4.06834, "time": 0.70193} +{"mode": "train", "epoch": 21, "iter": 2300, "lr": 0.09541, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25875, "top5_acc": 0.5125, "loss_cls": 4.05562, "loss": 4.05562, "time": 0.70163} +{"mode": "train", "epoch": 21, "iter": 2400, "lr": 0.0954, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24734, "top5_acc": 0.48891, "loss_cls": 4.14037, "loss": 4.14037, "time": 0.70318} +{"mode": "train", "epoch": 21, "iter": 2500, "lr": 0.09539, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24906, "top5_acc": 0.50281, "loss_cls": 4.10027, "loss": 4.10027, "time": 0.70019} +{"mode": "train", "epoch": 21, "iter": 2600, "lr": 0.09538, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26328, "top5_acc": 0.50859, "loss_cls": 4.05587, "loss": 4.05587, "time": 0.69883} +{"mode": "train", "epoch": 21, "iter": 2700, "lr": 0.09537, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26234, "top5_acc": 0.51078, "loss_cls": 4.03903, "loss": 4.03903, "time": 0.69918} +{"mode": "train", "epoch": 21, "iter": 2800, "lr": 0.09535, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25781, "top5_acc": 0.50016, "loss_cls": 4.08025, "loss": 4.08025, "time": 0.70351} +{"mode": "train", "epoch": 21, "iter": 2900, "lr": 0.09534, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25859, "top5_acc": 0.51, "loss_cls": 4.07096, "loss": 4.07096, "time": 0.70497} +{"mode": "train", "epoch": 21, "iter": 3000, "lr": 0.09533, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26469, "top5_acc": 0.50766, "loss_cls": 4.05373, "loss": 4.05373, "time": 0.69891} +{"mode": "train", "epoch": 21, "iter": 3100, "lr": 0.09532, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26219, "top5_acc": 0.51016, "loss_cls": 4.09157, "loss": 4.09157, "time": 0.70187} +{"mode": "train", "epoch": 21, "iter": 3200, "lr": 0.09531, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25766, "top5_acc": 0.50156, "loss_cls": 4.11454, "loss": 4.11454, "time": 0.70382} +{"mode": "train", "epoch": 21, "iter": 3300, "lr": 0.09529, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25172, "top5_acc": 0.50156, "loss_cls": 4.08867, "loss": 4.08867, "time": 0.70911} +{"mode": "train", "epoch": 21, "iter": 3400, "lr": 0.09528, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2525, "top5_acc": 0.50031, "loss_cls": 4.13107, "loss": 4.13107, "time": 0.7056} +{"mode": "train", "epoch": 21, "iter": 3500, "lr": 0.09527, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25562, "top5_acc": 0.49859, "loss_cls": 4.10801, "loss": 4.10801, "time": 0.70563} +{"mode": "train", "epoch": 21, "iter": 3600, "lr": 0.09526, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26141, "top5_acc": 0.4975, "loss_cls": 4.0854, "loss": 4.0854, "time": 0.70371} +{"mode": "train", "epoch": 21, "iter": 3700, "lr": 0.09525, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25234, "top5_acc": 0.50578, "loss_cls": 4.08882, "loss": 4.08882, "time": 0.70485} +{"mode": "val", "epoch": 21, "iter": 309, "lr": 0.09524, "top1_acc": 0.18229, "top5_acc": 0.41037, "mean_class_accuracy": 0.18204} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.09523, "memory": 15990, "data_time": 1.27816, "top1_acc": 0.26797, "top5_acc": 0.51812, "loss_cls": 4.01017, "loss": 4.01017, "time": 1.98256} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.09522, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26188, "top5_acc": 0.51375, "loss_cls": 4.04046, "loss": 4.04046, "time": 0.70354} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.09521, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26031, "top5_acc": 0.50359, "loss_cls": 4.06689, "loss": 4.06689, "time": 0.69918} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.09519, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26625, "top5_acc": 0.50516, "loss_cls": 4.07923, "loss": 4.07923, "time": 0.70387} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.09518, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25828, "top5_acc": 0.50906, "loss_cls": 4.04645, "loss": 4.04645, "time": 0.69886} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.09517, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25969, "top5_acc": 0.50797, "loss_cls": 4.06688, "loss": 4.06688, "time": 0.70047} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.09516, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26312, "top5_acc": 0.51344, "loss_cls": 4.04959, "loss": 4.04959, "time": 0.69979} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.09515, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25203, "top5_acc": 0.50234, "loss_cls": 4.11898, "loss": 4.11898, "time": 0.70036} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.09513, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25531, "top5_acc": 0.50141, "loss_cls": 4.07206, "loss": 4.07206, "time": 0.70151} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.09512, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26562, "top5_acc": 0.51188, "loss_cls": 4.03935, "loss": 4.03935, "time": 0.69739} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.09511, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26562, "top5_acc": 0.51266, "loss_cls": 4.03083, "loss": 4.03083, "time": 0.70134} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0951, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24969, "top5_acc": 0.49016, "loss_cls": 4.10914, "loss": 4.10914, "time": 0.69875} +{"mode": "train", "epoch": 22, "iter": 1300, "lr": 0.09509, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26234, "top5_acc": 0.51062, "loss_cls": 4.06816, "loss": 4.06816, "time": 0.69885} +{"mode": "train", "epoch": 22, "iter": 1400, "lr": 0.09507, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25719, "top5_acc": 0.50234, "loss_cls": 4.06992, "loss": 4.06992, "time": 0.69968} +{"mode": "train", "epoch": 22, "iter": 1500, "lr": 0.09506, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25453, "top5_acc": 0.50984, "loss_cls": 4.06894, "loss": 4.06894, "time": 0.69899} +{"mode": "train", "epoch": 22, "iter": 1600, "lr": 0.09505, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25781, "top5_acc": 0.50094, "loss_cls": 4.10466, "loss": 4.10466, "time": 0.69982} +{"mode": "train", "epoch": 22, "iter": 1700, "lr": 0.09504, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26078, "top5_acc": 0.51594, "loss_cls": 4.04754, "loss": 4.04754, "time": 0.70088} +{"mode": "train", "epoch": 22, "iter": 1800, "lr": 0.09502, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25703, "top5_acc": 0.50844, "loss_cls": 4.07314, "loss": 4.07314, "time": 0.70223} +{"mode": "train", "epoch": 22, "iter": 1900, "lr": 0.09501, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25922, "top5_acc": 0.50875, "loss_cls": 4.10121, "loss": 4.10121, "time": 0.7033} +{"mode": "train", "epoch": 22, "iter": 2000, "lr": 0.095, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25422, "top5_acc": 0.50313, "loss_cls": 4.07805, "loss": 4.07805, "time": 0.69899} +{"mode": "train", "epoch": 22, "iter": 2100, "lr": 0.09499, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25547, "top5_acc": 0.49953, "loss_cls": 4.1009, "loss": 4.1009, "time": 0.70011} +{"mode": "train", "epoch": 22, "iter": 2200, "lr": 0.09498, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26516, "top5_acc": 0.51125, "loss_cls": 4.03517, "loss": 4.03517, "time": 0.70101} +{"mode": "train", "epoch": 22, "iter": 2300, "lr": 0.09496, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25594, "top5_acc": 0.50141, "loss_cls": 4.10058, "loss": 4.10058, "time": 0.70074} +{"mode": "train", "epoch": 22, "iter": 2400, "lr": 0.09495, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25359, "top5_acc": 0.50125, "loss_cls": 4.07961, "loss": 4.07961, "time": 0.70167} +{"mode": "train", "epoch": 22, "iter": 2500, "lr": 0.09494, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25328, "top5_acc": 0.50313, "loss_cls": 4.09098, "loss": 4.09098, "time": 0.69969} +{"mode": "train", "epoch": 22, "iter": 2600, "lr": 0.09493, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25547, "top5_acc": 0.50031, "loss_cls": 4.09544, "loss": 4.09544, "time": 0.70103} +{"mode": "train", "epoch": 22, "iter": 2700, "lr": 0.09491, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26375, "top5_acc": 0.505, "loss_cls": 4.07062, "loss": 4.07062, "time": 0.69942} +{"mode": "train", "epoch": 22, "iter": 2800, "lr": 0.0949, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25531, "top5_acc": 0.50578, "loss_cls": 4.06724, "loss": 4.06724, "time": 0.7019} +{"mode": "train", "epoch": 22, "iter": 2900, "lr": 0.09489, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26547, "top5_acc": 0.52062, "loss_cls": 4.02765, "loss": 4.02765, "time": 0.70457} +{"mode": "train", "epoch": 22, "iter": 3000, "lr": 0.09488, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25672, "top5_acc": 0.50141, "loss_cls": 4.06984, "loss": 4.06984, "time": 0.70591} +{"mode": "train", "epoch": 22, "iter": 3100, "lr": 0.09487, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26141, "top5_acc": 0.5075, "loss_cls": 4.05725, "loss": 4.05725, "time": 0.70285} +{"mode": "train", "epoch": 22, "iter": 3200, "lr": 0.09485, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25719, "top5_acc": 0.50313, "loss_cls": 4.07151, "loss": 4.07151, "time": 0.70298} +{"mode": "train", "epoch": 22, "iter": 3300, "lr": 0.09484, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26703, "top5_acc": 0.51016, "loss_cls": 4.03247, "loss": 4.03247, "time": 0.70669} +{"mode": "train", "epoch": 22, "iter": 3400, "lr": 0.09483, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24672, "top5_acc": 0.50344, "loss_cls": 4.09505, "loss": 4.09505, "time": 0.71057} +{"mode": "train", "epoch": 22, "iter": 3500, "lr": 0.09482, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24938, "top5_acc": 0.48906, "loss_cls": 4.11846, "loss": 4.11846, "time": 0.71002} +{"mode": "train", "epoch": 22, "iter": 3600, "lr": 0.0948, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25109, "top5_acc": 0.49578, "loss_cls": 4.14647, "loss": 4.14647, "time": 0.70511} +{"mode": "train", "epoch": 22, "iter": 3700, "lr": 0.09479, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25359, "top5_acc": 0.50406, "loss_cls": 4.10097, "loss": 4.10097, "time": 0.70373} +{"mode": "val", "epoch": 22, "iter": 309, "lr": 0.09479, "top1_acc": 0.18103, "top5_acc": 0.40308, "mean_class_accuracy": 0.1806} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.09477, "memory": 15990, "data_time": 1.27066, "top1_acc": 0.26453, "top5_acc": 0.505, "loss_cls": 4.03134, "loss": 4.03134, "time": 1.9741} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.09476, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26562, "top5_acc": 0.52297, "loss_cls": 4.01999, "loss": 4.01999, "time": 0.70051} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.09475, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25672, "top5_acc": 0.51, "loss_cls": 4.05491, "loss": 4.05491, "time": 0.70023} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.09474, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26937, "top5_acc": 0.51531, "loss_cls": 4.03127, "loss": 4.03127, "time": 0.70307} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.09472, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2525, "top5_acc": 0.50594, "loss_cls": 4.05003, "loss": 4.05003, "time": 0.70089} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.09471, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27203, "top5_acc": 0.51703, "loss_cls": 4.02526, "loss": 4.02526, "time": 0.70136} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.0947, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26203, "top5_acc": 0.51375, "loss_cls": 4.01946, "loss": 4.01946, "time": 0.70251} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.09469, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26422, "top5_acc": 0.51359, "loss_cls": 4.03031, "loss": 4.03031, "time": 0.69878} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.09467, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25016, "top5_acc": 0.49859, "loss_cls": 4.10653, "loss": 4.10653, "time": 0.70359} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.09466, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25422, "top5_acc": 0.50594, "loss_cls": 4.08756, "loss": 4.08756, "time": 0.69968} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.09465, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26875, "top5_acc": 0.50781, "loss_cls": 4.03721, "loss": 4.03721, "time": 0.69814} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.09464, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.265, "top5_acc": 0.5075, "loss_cls": 4.06749, "loss": 4.06749, "time": 0.70153} +{"mode": "train", "epoch": 23, "iter": 1300, "lr": 0.09462, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25375, "top5_acc": 0.50219, "loss_cls": 4.08284, "loss": 4.08284, "time": 0.70309} +{"mode": "train", "epoch": 23, "iter": 1400, "lr": 0.09461, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25469, "top5_acc": 0.50734, "loss_cls": 4.06676, "loss": 4.06676, "time": 0.70248} +{"mode": "train", "epoch": 23, "iter": 1500, "lr": 0.0946, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25281, "top5_acc": 0.50703, "loss_cls": 4.04197, "loss": 4.04197, "time": 0.69957} +{"mode": "train", "epoch": 23, "iter": 1600, "lr": 0.09459, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24875, "top5_acc": 0.50187, "loss_cls": 4.11474, "loss": 4.11474, "time": 0.69908} +{"mode": "train", "epoch": 23, "iter": 1700, "lr": 0.09457, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26297, "top5_acc": 0.51141, "loss_cls": 4.06594, "loss": 4.06594, "time": 0.70133} +{"mode": "train", "epoch": 23, "iter": 1800, "lr": 0.09456, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25094, "top5_acc": 0.50031, "loss_cls": 4.10514, "loss": 4.10514, "time": 0.70283} +{"mode": "train", "epoch": 23, "iter": 1900, "lr": 0.09455, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26188, "top5_acc": 0.50859, "loss_cls": 4.03403, "loss": 4.03403, "time": 0.70007} +{"mode": "train", "epoch": 23, "iter": 2000, "lr": 0.09453, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25266, "top5_acc": 0.49859, "loss_cls": 4.10479, "loss": 4.10479, "time": 0.69983} +{"mode": "train", "epoch": 23, "iter": 2100, "lr": 0.09452, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.24672, "top5_acc": 0.49953, "loss_cls": 4.14492, "loss": 4.14492, "time": 0.69985} +{"mode": "train", "epoch": 23, "iter": 2200, "lr": 0.09451, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26125, "top5_acc": 0.50938, "loss_cls": 4.06808, "loss": 4.06808, "time": 0.69816} +{"mode": "train", "epoch": 23, "iter": 2300, "lr": 0.0945, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25203, "top5_acc": 0.49438, "loss_cls": 4.10936, "loss": 4.10936, "time": 0.69906} +{"mode": "train", "epoch": 23, "iter": 2400, "lr": 0.09448, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26047, "top5_acc": 0.50828, "loss_cls": 4.06429, "loss": 4.06429, "time": 0.70325} +{"mode": "train", "epoch": 23, "iter": 2500, "lr": 0.09447, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25234, "top5_acc": 0.49562, "loss_cls": 4.10452, "loss": 4.10452, "time": 0.69862} +{"mode": "train", "epoch": 23, "iter": 2600, "lr": 0.09446, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26016, "top5_acc": 0.50938, "loss_cls": 4.04125, "loss": 4.04125, "time": 0.69863} +{"mode": "train", "epoch": 23, "iter": 2700, "lr": 0.09445, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25609, "top5_acc": 0.49922, "loss_cls": 4.12616, "loss": 4.12616, "time": 0.70236} +{"mode": "train", "epoch": 23, "iter": 2800, "lr": 0.09443, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25984, "top5_acc": 0.50125, "loss_cls": 4.08522, "loss": 4.08522, "time": 0.69961} +{"mode": "train", "epoch": 23, "iter": 2900, "lr": 0.09442, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25641, "top5_acc": 0.50531, "loss_cls": 4.07452, "loss": 4.07452, "time": 0.70645} +{"mode": "train", "epoch": 23, "iter": 3000, "lr": 0.09441, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26703, "top5_acc": 0.51422, "loss_cls": 4.04121, "loss": 4.04121, "time": 0.70216} +{"mode": "train", "epoch": 23, "iter": 3100, "lr": 0.09439, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26625, "top5_acc": 0.51719, "loss_cls": 4.00133, "loss": 4.00133, "time": 0.70564} +{"mode": "train", "epoch": 23, "iter": 3200, "lr": 0.09438, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25594, "top5_acc": 0.50328, "loss_cls": 4.06435, "loss": 4.06435, "time": 0.70453} +{"mode": "train", "epoch": 23, "iter": 3300, "lr": 0.09437, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25953, "top5_acc": 0.50672, "loss_cls": 4.05201, "loss": 4.05201, "time": 0.70496} +{"mode": "train", "epoch": 23, "iter": 3400, "lr": 0.09436, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26172, "top5_acc": 0.50219, "loss_cls": 4.08845, "loss": 4.08845, "time": 0.70727} +{"mode": "train", "epoch": 23, "iter": 3500, "lr": 0.09434, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24688, "top5_acc": 0.49828, "loss_cls": 4.10855, "loss": 4.10855, "time": 0.71264} +{"mode": "train", "epoch": 23, "iter": 3600, "lr": 0.09433, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25375, "top5_acc": 0.50422, "loss_cls": 4.08601, "loss": 4.08601, "time": 0.70563} +{"mode": "train", "epoch": 23, "iter": 3700, "lr": 0.09432, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27, "top5_acc": 0.51719, "loss_cls": 4.02732, "loss": 4.02732, "time": 0.70319} +{"mode": "val", "epoch": 23, "iter": 309, "lr": 0.09431, "top1_acc": 0.19759, "top5_acc": 0.42293, "mean_class_accuracy": 0.19725} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.0943, "memory": 15990, "data_time": 1.27835, "top1_acc": 0.25656, "top5_acc": 0.52062, "loss_cls": 4.00592, "loss": 4.00592, "time": 1.98347} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.09428, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26687, "top5_acc": 0.52328, "loss_cls": 4.00639, "loss": 4.00639, "time": 0.70592} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.09427, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26016, "top5_acc": 0.51188, "loss_cls": 4.05472, "loss": 4.05472, "time": 0.70067} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.09426, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26391, "top5_acc": 0.51625, "loss_cls": 4.04803, "loss": 4.04803, "time": 0.7028} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.09425, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26203, "top5_acc": 0.50938, "loss_cls": 4.04107, "loss": 4.04107, "time": 0.70224} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.09423, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25828, "top5_acc": 0.51266, "loss_cls": 4.02417, "loss": 4.02417, "time": 0.6975} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.09422, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26094, "top5_acc": 0.5075, "loss_cls": 4.06102, "loss": 4.06102, "time": 0.70101} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.09421, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25953, "top5_acc": 0.51078, "loss_cls": 4.02714, "loss": 4.02714, "time": 0.70082} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.09419, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26312, "top5_acc": 0.51125, "loss_cls": 4.03133, "loss": 4.03133, "time": 0.69877} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.09418, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26156, "top5_acc": 0.50547, "loss_cls": 4.06504, "loss": 4.06504, "time": 0.69924} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.09417, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26219, "top5_acc": 0.49438, "loss_cls": 4.108, "loss": 4.108, "time": 0.7007} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.09415, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26656, "top5_acc": 0.50656, "loss_cls": 4.04247, "loss": 4.04247, "time": 0.70137} +{"mode": "train", "epoch": 24, "iter": 1300, "lr": 0.09414, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26141, "top5_acc": 0.51391, "loss_cls": 4.07453, "loss": 4.07453, "time": 0.69827} +{"mode": "train", "epoch": 24, "iter": 1400, "lr": 0.09413, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2625, "top5_acc": 0.50688, "loss_cls": 4.04335, "loss": 4.04335, "time": 0.69984} +{"mode": "train", "epoch": 24, "iter": 1500, "lr": 0.09411, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25516, "top5_acc": 0.49891, "loss_cls": 4.11957, "loss": 4.11957, "time": 0.70078} +{"mode": "train", "epoch": 24, "iter": 1600, "lr": 0.0941, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26422, "top5_acc": 0.51656, "loss_cls": 4.02228, "loss": 4.02228, "time": 0.7033} +{"mode": "train", "epoch": 24, "iter": 1700, "lr": 0.09409, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26766, "top5_acc": 0.51703, "loss_cls": 4.04033, "loss": 4.04033, "time": 0.6986} +{"mode": "train", "epoch": 24, "iter": 1800, "lr": 0.09407, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26734, "top5_acc": 0.51031, "loss_cls": 4.03475, "loss": 4.03475, "time": 0.70206} +{"mode": "train", "epoch": 24, "iter": 1900, "lr": 0.09406, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25891, "top5_acc": 0.50766, "loss_cls": 4.06008, "loss": 4.06008, "time": 0.70176} +{"mode": "train", "epoch": 24, "iter": 2000, "lr": 0.09405, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26125, "top5_acc": 0.50766, "loss_cls": 4.07545, "loss": 4.07545, "time": 0.70248} +{"mode": "train", "epoch": 24, "iter": 2100, "lr": 0.09404, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25516, "top5_acc": 0.50734, "loss_cls": 4.06377, "loss": 4.06377, "time": 0.70102} +{"mode": "train", "epoch": 24, "iter": 2200, "lr": 0.09402, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26062, "top5_acc": 0.51375, "loss_cls": 4.0658, "loss": 4.0658, "time": 0.7014} +{"mode": "train", "epoch": 24, "iter": 2300, "lr": 0.09401, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24672, "top5_acc": 0.49031, "loss_cls": 4.14293, "loss": 4.14293, "time": 0.69978} +{"mode": "train", "epoch": 24, "iter": 2400, "lr": 0.094, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25547, "top5_acc": 0.50531, "loss_cls": 4.10123, "loss": 4.10123, "time": 0.7006} +{"mode": "train", "epoch": 24, "iter": 2500, "lr": 0.09398, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25453, "top5_acc": 0.50781, "loss_cls": 4.0752, "loss": 4.0752, "time": 0.70064} +{"mode": "train", "epoch": 24, "iter": 2600, "lr": 0.09397, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25484, "top5_acc": 0.50141, "loss_cls": 4.11175, "loss": 4.11175, "time": 0.70142} +{"mode": "train", "epoch": 24, "iter": 2700, "lr": 0.09396, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2575, "top5_acc": 0.50219, "loss_cls": 4.07074, "loss": 4.07074, "time": 0.7025} +{"mode": "train", "epoch": 24, "iter": 2800, "lr": 0.09394, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25641, "top5_acc": 0.50031, "loss_cls": 4.09221, "loss": 4.09221, "time": 0.70155} +{"mode": "train", "epoch": 24, "iter": 2900, "lr": 0.09393, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26859, "top5_acc": 0.52188, "loss_cls": 4.01854, "loss": 4.01854, "time": 0.70313} +{"mode": "train", "epoch": 24, "iter": 3000, "lr": 0.09392, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2625, "top5_acc": 0.50375, "loss_cls": 4.06046, "loss": 4.06046, "time": 0.70176} +{"mode": "train", "epoch": 24, "iter": 3100, "lr": 0.0939, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26859, "top5_acc": 0.52016, "loss_cls": 4.01938, "loss": 4.01938, "time": 0.70476} +{"mode": "train", "epoch": 24, "iter": 3200, "lr": 0.09389, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26188, "top5_acc": 0.50828, "loss_cls": 4.04333, "loss": 4.04333, "time": 0.70474} +{"mode": "train", "epoch": 24, "iter": 3300, "lr": 0.09388, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26156, "top5_acc": 0.50062, "loss_cls": 4.08636, "loss": 4.08636, "time": 0.70735} +{"mode": "train", "epoch": 24, "iter": 3400, "lr": 0.09386, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25141, "top5_acc": 0.49328, "loss_cls": 4.12099, "loss": 4.12099, "time": 0.70819} +{"mode": "train", "epoch": 24, "iter": 3500, "lr": 0.09385, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25922, "top5_acc": 0.51516, "loss_cls": 4.04531, "loss": 4.04531, "time": 0.70973} +{"mode": "train", "epoch": 24, "iter": 3600, "lr": 0.09384, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24828, "top5_acc": 0.49359, "loss_cls": 4.13198, "loss": 4.13198, "time": 0.70435} +{"mode": "train", "epoch": 24, "iter": 3700, "lr": 0.09382, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25344, "top5_acc": 0.50984, "loss_cls": 4.06542, "loss": 4.06542, "time": 0.70626} +{"mode": "val", "epoch": 24, "iter": 309, "lr": 0.09382, "top1_acc": 0.19921, "top5_acc": 0.42182, "mean_class_accuracy": 0.19886} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.0938, "memory": 15990, "data_time": 1.27583, "top1_acc": 0.2575, "top5_acc": 0.51109, "loss_cls": 4.05271, "loss": 4.05271, "time": 1.98096} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.09379, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2625, "top5_acc": 0.51234, "loss_cls": 4.03188, "loss": 4.03188, "time": 0.70142} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.09378, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25828, "top5_acc": 0.50484, "loss_cls": 4.04781, "loss": 4.04781, "time": 0.69985} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.09376, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26266, "top5_acc": 0.51469, "loss_cls": 4.01896, "loss": 4.01896, "time": 0.70188} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.09375, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2675, "top5_acc": 0.50578, "loss_cls": 4.03313, "loss": 4.03313, "time": 0.70049} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.09373, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26172, "top5_acc": 0.51266, "loss_cls": 4.05071, "loss": 4.05071, "time": 0.70407} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.09372, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25797, "top5_acc": 0.50859, "loss_cls": 4.06239, "loss": 4.06239, "time": 0.70032} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.09371, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25234, "top5_acc": 0.50719, "loss_cls": 4.06323, "loss": 4.06323, "time": 0.70108} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.09369, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25734, "top5_acc": 0.51156, "loss_cls": 4.03536, "loss": 4.03536, "time": 0.70239} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.09368, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26609, "top5_acc": 0.50531, "loss_cls": 4.07123, "loss": 4.07123, "time": 0.69757} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.09367, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26734, "top5_acc": 0.50453, "loss_cls": 4.04399, "loss": 4.04399, "time": 0.69931} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.09365, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25719, "top5_acc": 0.50906, "loss_cls": 4.04678, "loss": 4.04678, "time": 0.70057} +{"mode": "train", "epoch": 25, "iter": 1300, "lr": 0.09364, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25625, "top5_acc": 0.50094, "loss_cls": 4.07697, "loss": 4.07697, "time": 0.69779} +{"mode": "train", "epoch": 25, "iter": 1400, "lr": 0.09363, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26219, "top5_acc": 0.51234, "loss_cls": 4.05579, "loss": 4.05579, "time": 0.70021} +{"mode": "train", "epoch": 25, "iter": 1500, "lr": 0.09361, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25797, "top5_acc": 0.51109, "loss_cls": 4.0647, "loss": 4.0647, "time": 0.69985} +{"mode": "train", "epoch": 25, "iter": 1600, "lr": 0.0936, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26203, "top5_acc": 0.52094, "loss_cls": 4.01836, "loss": 4.01836, "time": 0.69972} +{"mode": "train", "epoch": 25, "iter": 1700, "lr": 0.09358, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25594, "top5_acc": 0.51344, "loss_cls": 4.06089, "loss": 4.06089, "time": 0.70187} +{"mode": "train", "epoch": 25, "iter": 1800, "lr": 0.09357, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25188, "top5_acc": 0.50094, "loss_cls": 4.09293, "loss": 4.09293, "time": 0.69879} +{"mode": "train", "epoch": 25, "iter": 1900, "lr": 0.09356, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25203, "top5_acc": 0.49312, "loss_cls": 4.11399, "loss": 4.11399, "time": 0.70201} +{"mode": "train", "epoch": 25, "iter": 2000, "lr": 0.09354, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25797, "top5_acc": 0.5075, "loss_cls": 4.07618, "loss": 4.07618, "time": 0.70056} +{"mode": "train", "epoch": 25, "iter": 2100, "lr": 0.09353, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26859, "top5_acc": 0.51719, "loss_cls": 4.04321, "loss": 4.04321, "time": 0.70139} +{"mode": "train", "epoch": 25, "iter": 2200, "lr": 0.09352, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26141, "top5_acc": 0.51125, "loss_cls": 4.03973, "loss": 4.03973, "time": 0.69991} +{"mode": "train", "epoch": 25, "iter": 2300, "lr": 0.0935, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26344, "top5_acc": 0.50875, "loss_cls": 4.02985, "loss": 4.02985, "time": 0.7006} +{"mode": "train", "epoch": 25, "iter": 2400, "lr": 0.09349, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25234, "top5_acc": 0.51016, "loss_cls": 4.09163, "loss": 4.09163, "time": 0.6993} +{"mode": "train", "epoch": 25, "iter": 2500, "lr": 0.09347, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26703, "top5_acc": 0.50844, "loss_cls": 4.05679, "loss": 4.05679, "time": 0.69975} +{"mode": "train", "epoch": 25, "iter": 2600, "lr": 0.09346, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2525, "top5_acc": 0.50594, "loss_cls": 4.1067, "loss": 4.1067, "time": 0.70079} +{"mode": "train", "epoch": 25, "iter": 2700, "lr": 0.09345, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25297, "top5_acc": 0.50516, "loss_cls": 4.10602, "loss": 4.10602, "time": 0.6996} +{"mode": "train", "epoch": 25, "iter": 2800, "lr": 0.09343, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26375, "top5_acc": 0.51422, "loss_cls": 4.03894, "loss": 4.03894, "time": 0.70306} +{"mode": "train", "epoch": 25, "iter": 2900, "lr": 0.09342, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26687, "top5_acc": 0.51313, "loss_cls": 4.06385, "loss": 4.06385, "time": 0.70493} +{"mode": "train", "epoch": 25, "iter": 3000, "lr": 0.09341, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26391, "top5_acc": 0.51531, "loss_cls": 4.04943, "loss": 4.04943, "time": 0.70351} +{"mode": "train", "epoch": 25, "iter": 3100, "lr": 0.09339, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26234, "top5_acc": 0.49891, "loss_cls": 4.05966, "loss": 4.05966, "time": 0.70679} +{"mode": "train", "epoch": 25, "iter": 3200, "lr": 0.09338, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24859, "top5_acc": 0.49719, "loss_cls": 4.1214, "loss": 4.1214, "time": 0.70449} +{"mode": "train", "epoch": 25, "iter": 3300, "lr": 0.09336, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26734, "top5_acc": 0.50391, "loss_cls": 4.0592, "loss": 4.0592, "time": 0.71119} +{"mode": "train", "epoch": 25, "iter": 3400, "lr": 0.09335, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2625, "top5_acc": 0.52062, "loss_cls": 4.01673, "loss": 4.01673, "time": 0.7067} +{"mode": "train", "epoch": 25, "iter": 3500, "lr": 0.09334, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26438, "top5_acc": 0.50484, "loss_cls": 4.06595, "loss": 4.06595, "time": 0.70495} +{"mode": "train", "epoch": 25, "iter": 3600, "lr": 0.09332, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25156, "top5_acc": 0.49719, "loss_cls": 4.10546, "loss": 4.10546, "time": 0.70524} +{"mode": "train", "epoch": 25, "iter": 3700, "lr": 0.09331, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26609, "top5_acc": 0.51266, "loss_cls": 4.03107, "loss": 4.03107, "time": 0.70451} +{"mode": "val", "epoch": 25, "iter": 309, "lr": 0.0933, "top1_acc": 0.19647, "top5_acc": 0.42228, "mean_class_accuracy": 0.19626} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.09329, "memory": 15990, "data_time": 1.31754, "top1_acc": 0.26844, "top5_acc": 0.52328, "loss_cls": 3.97378, "loss": 3.97378, "time": 2.02436} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.09327, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26406, "top5_acc": 0.51422, "loss_cls": 4.02224, "loss": 4.02224, "time": 0.70314} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.09326, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26203, "top5_acc": 0.50891, "loss_cls": 4.02769, "loss": 4.02769, "time": 0.70085} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.09325, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25938, "top5_acc": 0.51219, "loss_cls": 4.03941, "loss": 4.03941, "time": 0.69739} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.09323, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25594, "top5_acc": 0.5075, "loss_cls": 4.06589, "loss": 4.06589, "time": 0.7029} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.09322, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26031, "top5_acc": 0.52016, "loss_cls": 4.02432, "loss": 4.02432, "time": 0.70096} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.0932, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25812, "top5_acc": 0.51453, "loss_cls": 4.03998, "loss": 4.03998, "time": 0.70317} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.09319, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26312, "top5_acc": 0.51125, "loss_cls": 4.03912, "loss": 4.03912, "time": 0.69911} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.09318, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26594, "top5_acc": 0.51781, "loss_cls": 4.01305, "loss": 4.01305, "time": 0.69799} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.09316, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26266, "top5_acc": 0.51016, "loss_cls": 4.05153, "loss": 4.05153, "time": 0.69693} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.09315, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26062, "top5_acc": 0.51047, "loss_cls": 4.04612, "loss": 4.04612, "time": 0.69845} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.09313, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25766, "top5_acc": 0.52031, "loss_cls": 4.04022, "loss": 4.04022, "time": 0.70058} +{"mode": "train", "epoch": 26, "iter": 1300, "lr": 0.09312, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25578, "top5_acc": 0.50344, "loss_cls": 4.06541, "loss": 4.06541, "time": 0.69964} +{"mode": "train", "epoch": 26, "iter": 1400, "lr": 0.0931, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26281, "top5_acc": 0.50453, "loss_cls": 4.09052, "loss": 4.09052, "time": 0.70005} +{"mode": "train", "epoch": 26, "iter": 1500, "lr": 0.09309, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26687, "top5_acc": 0.50969, "loss_cls": 4.0462, "loss": 4.0462, "time": 0.70366} +{"mode": "train", "epoch": 26, "iter": 1600, "lr": 0.09308, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25797, "top5_acc": 0.50469, "loss_cls": 4.07067, "loss": 4.07067, "time": 0.69958} +{"mode": "train", "epoch": 26, "iter": 1700, "lr": 0.09306, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26344, "top5_acc": 0.50531, "loss_cls": 4.04425, "loss": 4.04425, "time": 0.70248} +{"mode": "train", "epoch": 26, "iter": 1800, "lr": 0.09305, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25844, "top5_acc": 0.50922, "loss_cls": 4.07432, "loss": 4.07432, "time": 0.70074} +{"mode": "train", "epoch": 26, "iter": 1900, "lr": 0.09303, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25703, "top5_acc": 0.51078, "loss_cls": 4.06944, "loss": 4.06944, "time": 0.69924} +{"mode": "train", "epoch": 26, "iter": 2000, "lr": 0.09302, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25359, "top5_acc": 0.50859, "loss_cls": 4.07022, "loss": 4.07022, "time": 0.70055} +{"mode": "train", "epoch": 26, "iter": 2100, "lr": 0.093, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26141, "top5_acc": 0.50859, "loss_cls": 4.07155, "loss": 4.07155, "time": 0.70008} +{"mode": "train", "epoch": 26, "iter": 2200, "lr": 0.09299, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26109, "top5_acc": 0.51031, "loss_cls": 4.04735, "loss": 4.04735, "time": 0.70038} +{"mode": "train", "epoch": 26, "iter": 2300, "lr": 0.09298, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26656, "top5_acc": 0.51562, "loss_cls": 4.02525, "loss": 4.02525, "time": 0.69874} +{"mode": "train", "epoch": 26, "iter": 2400, "lr": 0.09296, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26031, "top5_acc": 0.50891, "loss_cls": 4.04608, "loss": 4.04608, "time": 0.70072} +{"mode": "train", "epoch": 26, "iter": 2500, "lr": 0.09295, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26922, "top5_acc": 0.51203, "loss_cls": 4.01388, "loss": 4.01388, "time": 0.7018} +{"mode": "train", "epoch": 26, "iter": 2600, "lr": 0.09293, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26, "top5_acc": 0.50844, "loss_cls": 4.0316, "loss": 4.0316, "time": 0.70225} +{"mode": "train", "epoch": 26, "iter": 2700, "lr": 0.09292, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25578, "top5_acc": 0.50297, "loss_cls": 4.10236, "loss": 4.10236, "time": 0.70262} +{"mode": "train", "epoch": 26, "iter": 2800, "lr": 0.0929, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26625, "top5_acc": 0.51203, "loss_cls": 4.05238, "loss": 4.05238, "time": 0.70676} +{"mode": "train", "epoch": 26, "iter": 2900, "lr": 0.09289, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25984, "top5_acc": 0.50562, "loss_cls": 4.07125, "loss": 4.07125, "time": 0.70553} +{"mode": "train", "epoch": 26, "iter": 3000, "lr": 0.09288, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26156, "top5_acc": 0.50234, "loss_cls": 4.07818, "loss": 4.07818, "time": 0.70334} +{"mode": "train", "epoch": 26, "iter": 3100, "lr": 0.09286, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25922, "top5_acc": 0.50781, "loss_cls": 4.04346, "loss": 4.04346, "time": 0.70627} +{"mode": "train", "epoch": 26, "iter": 3200, "lr": 0.09285, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26156, "top5_acc": 0.50047, "loss_cls": 4.08817, "loss": 4.08817, "time": 0.70658} +{"mode": "train", "epoch": 26, "iter": 3300, "lr": 0.09283, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25359, "top5_acc": 0.5, "loss_cls": 4.08516, "loss": 4.08516, "time": 0.71009} +{"mode": "train", "epoch": 26, "iter": 3400, "lr": 0.09282, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25766, "top5_acc": 0.51156, "loss_cls": 4.06514, "loss": 4.06514, "time": 0.71487} +{"mode": "train", "epoch": 26, "iter": 3500, "lr": 0.0928, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25719, "top5_acc": 0.50234, "loss_cls": 4.10714, "loss": 4.10714, "time": 0.70625} +{"mode": "train", "epoch": 26, "iter": 3600, "lr": 0.09279, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25047, "top5_acc": 0.50281, "loss_cls": 4.08934, "loss": 4.08934, "time": 0.70633} +{"mode": "train", "epoch": 26, "iter": 3700, "lr": 0.09278, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26031, "top5_acc": 0.51172, "loss_cls": 4.04973, "loss": 4.04973, "time": 0.70819} +{"mode": "val", "epoch": 26, "iter": 309, "lr": 0.09277, "top1_acc": 0.19501, "top5_acc": 0.42045, "mean_class_accuracy": 0.19478} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.09275, "memory": 15990, "data_time": 1.25878, "top1_acc": 0.25719, "top5_acc": 0.50922, "loss_cls": 4.05196, "loss": 4.05196, "time": 1.96604} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.09274, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25906, "top5_acc": 0.50953, "loss_cls": 4.05961, "loss": 4.05961, "time": 0.70155} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.09272, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27281, "top5_acc": 0.52344, "loss_cls": 3.97511, "loss": 3.97511, "time": 0.70091} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.09271, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26484, "top5_acc": 0.50484, "loss_cls": 4.06528, "loss": 4.06528, "time": 0.69998} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.0927, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26141, "top5_acc": 0.50797, "loss_cls": 4.04832, "loss": 4.04832, "time": 0.70155} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.09268, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26016, "top5_acc": 0.50906, "loss_cls": 4.08097, "loss": 4.08097, "time": 0.70066} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.09267, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26547, "top5_acc": 0.51484, "loss_cls": 4.00489, "loss": 4.00489, "time": 0.70038} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.09265, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25922, "top5_acc": 0.51641, "loss_cls": 4.04662, "loss": 4.04662, "time": 0.70012} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.09264, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27016, "top5_acc": 0.52, "loss_cls": 3.99183, "loss": 3.99183, "time": 0.69854} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.09262, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25938, "top5_acc": 0.50719, "loss_cls": 4.03269, "loss": 4.03269, "time": 0.70035} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.09261, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25781, "top5_acc": 0.50641, "loss_cls": 4.05122, "loss": 4.05122, "time": 0.69991} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.09259, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26125, "top5_acc": 0.50703, "loss_cls": 4.03537, "loss": 4.03537, "time": 0.70324} +{"mode": "train", "epoch": 27, "iter": 1300, "lr": 0.09258, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27094, "top5_acc": 0.52266, "loss_cls": 3.99302, "loss": 3.99302, "time": 0.69817} +{"mode": "train", "epoch": 27, "iter": 1400, "lr": 0.09256, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26422, "top5_acc": 0.50656, "loss_cls": 4.05349, "loss": 4.05349, "time": 0.70071} +{"mode": "train", "epoch": 27, "iter": 1500, "lr": 0.09255, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27234, "top5_acc": 0.51375, "loss_cls": 4.0004, "loss": 4.0004, "time": 0.69857} +{"mode": "train", "epoch": 27, "iter": 1600, "lr": 0.09253, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27031, "top5_acc": 0.50984, "loss_cls": 4.03938, "loss": 4.03938, "time": 0.69905} +{"mode": "train", "epoch": 27, "iter": 1700, "lr": 0.09252, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25812, "top5_acc": 0.51016, "loss_cls": 4.03733, "loss": 4.03733, "time": 0.7038} +{"mode": "train", "epoch": 27, "iter": 1800, "lr": 0.09251, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26062, "top5_acc": 0.50969, "loss_cls": 4.07079, "loss": 4.07079, "time": 0.70164} +{"mode": "train", "epoch": 27, "iter": 1900, "lr": 0.09249, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25781, "top5_acc": 0.51156, "loss_cls": 4.02589, "loss": 4.02589, "time": 0.69792} +{"mode": "train", "epoch": 27, "iter": 2000, "lr": 0.09248, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25938, "top5_acc": 0.50906, "loss_cls": 4.05218, "loss": 4.05218, "time": 0.7002} +{"mode": "train", "epoch": 27, "iter": 2100, "lr": 0.09246, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25703, "top5_acc": 0.505, "loss_cls": 4.06487, "loss": 4.06487, "time": 0.70198} +{"mode": "train", "epoch": 27, "iter": 2200, "lr": 0.09245, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.25734, "top5_acc": 0.50984, "loss_cls": 4.06907, "loss": 4.06907, "time": 0.69806} +{"mode": "train", "epoch": 27, "iter": 2300, "lr": 0.09243, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25703, "top5_acc": 0.50828, "loss_cls": 4.05277, "loss": 4.05277, "time": 0.69955} +{"mode": "train", "epoch": 27, "iter": 2400, "lr": 0.09242, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25719, "top5_acc": 0.50172, "loss_cls": 4.07252, "loss": 4.07252, "time": 0.70249} +{"mode": "train", "epoch": 27, "iter": 2500, "lr": 0.0924, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25844, "top5_acc": 0.51328, "loss_cls": 4.01609, "loss": 4.01609, "time": 0.7} +{"mode": "train", "epoch": 27, "iter": 2600, "lr": 0.09239, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26297, "top5_acc": 0.505, "loss_cls": 4.07275, "loss": 4.07275, "time": 0.69928} +{"mode": "train", "epoch": 27, "iter": 2700, "lr": 0.09237, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25078, "top5_acc": 0.5075, "loss_cls": 4.09107, "loss": 4.09107, "time": 0.69733} +{"mode": "train", "epoch": 27, "iter": 2800, "lr": 0.09236, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25828, "top5_acc": 0.51562, "loss_cls": 4.02682, "loss": 4.02682, "time": 0.70349} +{"mode": "train", "epoch": 27, "iter": 2900, "lr": 0.09234, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26312, "top5_acc": 0.52156, "loss_cls": 4.01044, "loss": 4.01044, "time": 0.70275} +{"mode": "train", "epoch": 27, "iter": 3000, "lr": 0.09233, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26359, "top5_acc": 0.51625, "loss_cls": 4.03177, "loss": 4.03177, "time": 0.70321} +{"mode": "train", "epoch": 27, "iter": 3100, "lr": 0.09231, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25891, "top5_acc": 0.50953, "loss_cls": 4.06659, "loss": 4.06659, "time": 0.70474} +{"mode": "train", "epoch": 27, "iter": 3200, "lr": 0.0923, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25656, "top5_acc": 0.49828, "loss_cls": 4.08375, "loss": 4.08375, "time": 0.70356} +{"mode": "train", "epoch": 27, "iter": 3300, "lr": 0.09228, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26812, "top5_acc": 0.51625, "loss_cls": 4.01293, "loss": 4.01293, "time": 0.71158} +{"mode": "train", "epoch": 27, "iter": 3400, "lr": 0.09227, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26031, "top5_acc": 0.50297, "loss_cls": 4.08797, "loss": 4.08797, "time": 0.70691} +{"mode": "train", "epoch": 27, "iter": 3500, "lr": 0.09225, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25938, "top5_acc": 0.50078, "loss_cls": 4.08262, "loss": 4.08262, "time": 0.71119} +{"mode": "train", "epoch": 27, "iter": 3600, "lr": 0.09224, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2525, "top5_acc": 0.49906, "loss_cls": 4.11642, "loss": 4.11642, "time": 0.70802} +{"mode": "train", "epoch": 27, "iter": 3700, "lr": 0.09222, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25547, "top5_acc": 0.50656, "loss_cls": 4.07686, "loss": 4.07686, "time": 0.70763} +{"mode": "val", "epoch": 27, "iter": 309, "lr": 0.09222, "top1_acc": 0.16573, "top5_acc": 0.38003, "mean_class_accuracy": 0.1655} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.0922, "memory": 15990, "data_time": 1.28207, "top1_acc": 0.27016, "top5_acc": 0.51938, "loss_cls": 3.99733, "loss": 3.99733, "time": 1.98507} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.09219, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27125, "top5_acc": 0.51484, "loss_cls": 3.99023, "loss": 3.99023, "time": 0.70084} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.09217, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26312, "top5_acc": 0.52188, "loss_cls": 3.99705, "loss": 3.99705, "time": 0.70128} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.09216, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26578, "top5_acc": 0.52266, "loss_cls": 4.01441, "loss": 4.01441, "time": 0.70319} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.09214, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27016, "top5_acc": 0.51906, "loss_cls": 3.99926, "loss": 3.99926, "time": 0.70134} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.09213, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25328, "top5_acc": 0.50797, "loss_cls": 4.05614, "loss": 4.05614, "time": 0.70355} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.09211, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26844, "top5_acc": 0.51625, "loss_cls": 4.03511, "loss": 4.03511, "time": 0.69968} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.0921, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27375, "top5_acc": 0.51625, "loss_cls": 3.99176, "loss": 3.99176, "time": 0.69955} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.09208, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26438, "top5_acc": 0.50969, "loss_cls": 4.02922, "loss": 4.02922, "time": 0.69973} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.09207, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26266, "top5_acc": 0.50922, "loss_cls": 4.03683, "loss": 4.03683, "time": 0.70274} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.09205, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26469, "top5_acc": 0.51031, "loss_cls": 4.0559, "loss": 4.0559, "time": 0.7005} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.09204, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26234, "top5_acc": 0.51344, "loss_cls": 4.0089, "loss": 4.0089, "time": 0.69939} +{"mode": "train", "epoch": 28, "iter": 1300, "lr": 0.09202, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26516, "top5_acc": 0.51375, "loss_cls": 4.02427, "loss": 4.02427, "time": 0.70078} +{"mode": "train", "epoch": 28, "iter": 1400, "lr": 0.09201, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26609, "top5_acc": 0.51328, "loss_cls": 4.03864, "loss": 4.03864, "time": 0.70173} +{"mode": "train", "epoch": 28, "iter": 1500, "lr": 0.09199, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.27078, "top5_acc": 0.51344, "loss_cls": 4.01225, "loss": 4.01225, "time": 0.69875} +{"mode": "train", "epoch": 28, "iter": 1600, "lr": 0.09198, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25594, "top5_acc": 0.49984, "loss_cls": 4.09707, "loss": 4.09707, "time": 0.69995} +{"mode": "train", "epoch": 28, "iter": 1700, "lr": 0.09196, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25953, "top5_acc": 0.50328, "loss_cls": 4.08134, "loss": 4.08134, "time": 0.70159} +{"mode": "train", "epoch": 28, "iter": 1800, "lr": 0.09194, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26641, "top5_acc": 0.50938, "loss_cls": 4.04975, "loss": 4.04975, "time": 0.70285} +{"mode": "train", "epoch": 28, "iter": 1900, "lr": 0.09193, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25391, "top5_acc": 0.50734, "loss_cls": 4.07296, "loss": 4.07296, "time": 0.69744} +{"mode": "train", "epoch": 28, "iter": 2000, "lr": 0.09191, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26188, "top5_acc": 0.51109, "loss_cls": 4.0624, "loss": 4.0624, "time": 0.70013} +{"mode": "train", "epoch": 28, "iter": 2100, "lr": 0.0919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25922, "top5_acc": 0.51094, "loss_cls": 4.04817, "loss": 4.04817, "time": 0.6991} +{"mode": "train", "epoch": 28, "iter": 2200, "lr": 0.09188, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.265, "top5_acc": 0.50203, "loss_cls": 4.04714, "loss": 4.04714, "time": 0.70002} +{"mode": "train", "epoch": 28, "iter": 2300, "lr": 0.09187, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25172, "top5_acc": 0.50688, "loss_cls": 4.08367, "loss": 4.08367, "time": 0.69795} +{"mode": "train", "epoch": 28, "iter": 2400, "lr": 0.09185, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25844, "top5_acc": 0.51547, "loss_cls": 4.05506, "loss": 4.05506, "time": 0.69887} +{"mode": "train", "epoch": 28, "iter": 2500, "lr": 0.09184, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25625, "top5_acc": 0.51031, "loss_cls": 4.04462, "loss": 4.04462, "time": 0.69896} +{"mode": "train", "epoch": 28, "iter": 2600, "lr": 0.09182, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25984, "top5_acc": 0.49797, "loss_cls": 4.06095, "loss": 4.06095, "time": 0.70124} +{"mode": "train", "epoch": 28, "iter": 2700, "lr": 0.09181, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26562, "top5_acc": 0.50578, "loss_cls": 4.04809, "loss": 4.04809, "time": 0.70183} +{"mode": "train", "epoch": 28, "iter": 2800, "lr": 0.09179, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26531, "top5_acc": 0.50781, "loss_cls": 4.03989, "loss": 4.03989, "time": 0.70621} +{"mode": "train", "epoch": 28, "iter": 2900, "lr": 0.09178, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25203, "top5_acc": 0.49969, "loss_cls": 4.07042, "loss": 4.07042, "time": 0.70369} +{"mode": "train", "epoch": 28, "iter": 3000, "lr": 0.09176, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26672, "top5_acc": 0.51, "loss_cls": 4.04649, "loss": 4.04649, "time": 0.70419} +{"mode": "train", "epoch": 28, "iter": 3100, "lr": 0.09175, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26234, "top5_acc": 0.51547, "loss_cls": 4.05045, "loss": 4.05045, "time": 0.70283} +{"mode": "train", "epoch": 28, "iter": 3200, "lr": 0.09173, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25656, "top5_acc": 0.50859, "loss_cls": 4.06854, "loss": 4.06854, "time": 0.70717} +{"mode": "train", "epoch": 28, "iter": 3300, "lr": 0.09172, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26672, "top5_acc": 0.51891, "loss_cls": 4.01216, "loss": 4.01216, "time": 0.71142} +{"mode": "train", "epoch": 28, "iter": 3400, "lr": 0.0917, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26188, "top5_acc": 0.50266, "loss_cls": 4.06234, "loss": 4.06234, "time": 0.70725} +{"mode": "train", "epoch": 28, "iter": 3500, "lr": 0.09168, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25234, "top5_acc": 0.50562, "loss_cls": 4.08105, "loss": 4.08105, "time": 0.70671} +{"mode": "train", "epoch": 28, "iter": 3600, "lr": 0.09167, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26609, "top5_acc": 0.51234, "loss_cls": 4.03352, "loss": 4.03352, "time": 0.70737} +{"mode": "train", "epoch": 28, "iter": 3700, "lr": 0.09165, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.24859, "top5_acc": 0.50094, "loss_cls": 4.10164, "loss": 4.10164, "time": 0.70315} +{"mode": "val", "epoch": 28, "iter": 309, "lr": 0.09165, "top1_acc": 0.18239, "top5_acc": 0.4082, "mean_class_accuracy": 0.18216} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.09163, "memory": 15990, "data_time": 1.25578, "top1_acc": 0.26641, "top5_acc": 0.52641, "loss_cls": 3.98613, "loss": 3.98613, "time": 1.95876} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.09162, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27781, "top5_acc": 0.52844, "loss_cls": 3.96276, "loss": 3.96276, "time": 0.70234} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.0916, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26812, "top5_acc": 0.50781, "loss_cls": 4.03607, "loss": 4.03607, "time": 0.70094} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.09158, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25984, "top5_acc": 0.50062, "loss_cls": 4.0544, "loss": 4.0544, "time": 0.70073} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.09157, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26406, "top5_acc": 0.51938, "loss_cls": 4.00677, "loss": 4.00677, "time": 0.69986} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.09155, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26766, "top5_acc": 0.5075, "loss_cls": 4.04021, "loss": 4.04021, "time": 0.69922} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.09154, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27484, "top5_acc": 0.51594, "loss_cls": 4.02539, "loss": 4.02539, "time": 0.70272} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.09152, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26234, "top5_acc": 0.51547, "loss_cls": 4.02684, "loss": 4.02684, "time": 0.69931} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.09151, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25781, "top5_acc": 0.50859, "loss_cls": 4.05308, "loss": 4.05308, "time": 0.70064} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.09149, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27453, "top5_acc": 0.52375, "loss_cls": 3.98471, "loss": 3.98471, "time": 0.70115} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.09148, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26266, "top5_acc": 0.50797, "loss_cls": 4.07528, "loss": 4.07528, "time": 0.69983} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.09146, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25844, "top5_acc": 0.5025, "loss_cls": 4.06501, "loss": 4.06501, "time": 0.70326} +{"mode": "train", "epoch": 29, "iter": 1300, "lr": 0.09144, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26297, "top5_acc": 0.51406, "loss_cls": 4.02254, "loss": 4.02254, "time": 0.70085} +{"mode": "train", "epoch": 29, "iter": 1400, "lr": 0.09143, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2575, "top5_acc": 0.50062, "loss_cls": 4.07389, "loss": 4.07389, "time": 0.70152} +{"mode": "train", "epoch": 29, "iter": 1500, "lr": 0.09141, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27594, "top5_acc": 0.52516, "loss_cls": 3.97763, "loss": 3.97763, "time": 0.70297} +{"mode": "train", "epoch": 29, "iter": 1600, "lr": 0.0914, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27484, "top5_acc": 0.51, "loss_cls": 4.03378, "loss": 4.03378, "time": 0.69983} +{"mode": "train", "epoch": 29, "iter": 1700, "lr": 0.09138, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.26078, "top5_acc": 0.50641, "loss_cls": 4.05004, "loss": 4.05004, "time": 0.70129} +{"mode": "train", "epoch": 29, "iter": 1800, "lr": 0.09137, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25984, "top5_acc": 0.51297, "loss_cls": 4.06044, "loss": 4.06044, "time": 0.70006} +{"mode": "train", "epoch": 29, "iter": 1900, "lr": 0.09135, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26047, "top5_acc": 0.51453, "loss_cls": 4.04519, "loss": 4.04519, "time": 0.70041} +{"mode": "train", "epoch": 29, "iter": 2000, "lr": 0.09133, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2625, "top5_acc": 0.50828, "loss_cls": 4.05699, "loss": 4.05699, "time": 0.6992} +{"mode": "train", "epoch": 29, "iter": 2100, "lr": 0.09132, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.25219, "top5_acc": 0.49609, "loss_cls": 4.10362, "loss": 4.10362, "time": 0.70194} +{"mode": "train", "epoch": 29, "iter": 2200, "lr": 0.0913, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.265, "top5_acc": 0.51234, "loss_cls": 4.03272, "loss": 4.03272, "time": 0.70035} +{"mode": "train", "epoch": 29, "iter": 2300, "lr": 0.09129, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26766, "top5_acc": 0.51078, "loss_cls": 3.99821, "loss": 3.99821, "time": 0.70077} +{"mode": "train", "epoch": 29, "iter": 2400, "lr": 0.09127, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26031, "top5_acc": 0.52484, "loss_cls": 4.00687, "loss": 4.00687, "time": 0.69845} +{"mode": "train", "epoch": 29, "iter": 2500, "lr": 0.09126, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26531, "top5_acc": 0.51516, "loss_cls": 4.03704, "loss": 4.03704, "time": 0.70302} +{"mode": "train", "epoch": 29, "iter": 2600, "lr": 0.09124, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26937, "top5_acc": 0.51562, "loss_cls": 4.04465, "loss": 4.04465, "time": 0.70026} +{"mode": "train", "epoch": 29, "iter": 2700, "lr": 0.09122, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26484, "top5_acc": 0.51094, "loss_cls": 4.04747, "loss": 4.04747, "time": 0.70136} +{"mode": "train", "epoch": 29, "iter": 2800, "lr": 0.09121, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26984, "top5_acc": 0.52094, "loss_cls": 3.99501, "loss": 3.99501, "time": 0.7096} +{"mode": "train", "epoch": 29, "iter": 2900, "lr": 0.09119, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25391, "top5_acc": 0.49922, "loss_cls": 4.07545, "loss": 4.07545, "time": 0.7028} +{"mode": "train", "epoch": 29, "iter": 3000, "lr": 0.09118, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25672, "top5_acc": 0.50969, "loss_cls": 4.06452, "loss": 4.06452, "time": 0.70433} +{"mode": "train", "epoch": 29, "iter": 3100, "lr": 0.09116, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26547, "top5_acc": 0.51703, "loss_cls": 4.01481, "loss": 4.01481, "time": 0.70377} +{"mode": "train", "epoch": 29, "iter": 3200, "lr": 0.09114, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26469, "top5_acc": 0.52562, "loss_cls": 4.00891, "loss": 4.00891, "time": 0.70296} +{"mode": "train", "epoch": 29, "iter": 3300, "lr": 0.09113, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26359, "top5_acc": 0.50672, "loss_cls": 4.04979, "loss": 4.04979, "time": 0.70957} +{"mode": "train", "epoch": 29, "iter": 3400, "lr": 0.09111, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26125, "top5_acc": 0.51344, "loss_cls": 4.03849, "loss": 4.03849, "time": 0.71099} +{"mode": "train", "epoch": 29, "iter": 3500, "lr": 0.0911, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25719, "top5_acc": 0.5, "loss_cls": 4.1136, "loss": 4.1136, "time": 0.70635} +{"mode": "train", "epoch": 29, "iter": 3600, "lr": 0.09108, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25844, "top5_acc": 0.51062, "loss_cls": 4.0535, "loss": 4.0535, "time": 0.70159} +{"mode": "train", "epoch": 29, "iter": 3700, "lr": 0.09106, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26328, "top5_acc": 0.51578, "loss_cls": 4.05453, "loss": 4.05453, "time": 0.70199} +{"mode": "val", "epoch": 29, "iter": 309, "lr": 0.09106, "top1_acc": 0.19744, "top5_acc": 0.42846, "mean_class_accuracy": 0.19723} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.09104, "memory": 15990, "data_time": 1.24937, "top1_acc": 0.26891, "top5_acc": 0.53375, "loss_cls": 3.96549, "loss": 3.96549, "time": 2.0569} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.09103, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26438, "top5_acc": 0.51922, "loss_cls": 4.00233, "loss": 4.00233, "time": 0.80559} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.09101, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25469, "top5_acc": 0.49875, "loss_cls": 4.08667, "loss": 4.08667, "time": 0.80122} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.09099, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25922, "top5_acc": 0.51125, "loss_cls": 4.03684, "loss": 4.03684, "time": 0.80449} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.09098, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26203, "top5_acc": 0.52406, "loss_cls": 4.00551, "loss": 4.00551, "time": 0.8012} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.09096, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27719, "top5_acc": 0.51203, "loss_cls": 4.0319, "loss": 4.0319, "time": 0.80446} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.09095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26281, "top5_acc": 0.51438, "loss_cls": 4.02078, "loss": 4.02078, "time": 0.80661} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.09093, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27516, "top5_acc": 0.51594, "loss_cls": 3.98533, "loss": 3.98533, "time": 0.80212} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.09091, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27016, "top5_acc": 0.51672, "loss_cls": 4.03279, "loss": 4.03279, "time": 0.79897} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.0909, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25672, "top5_acc": 0.50781, "loss_cls": 4.06445, "loss": 4.06445, "time": 0.80082} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.09088, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25562, "top5_acc": 0.51719, "loss_cls": 4.0387, "loss": 4.0387, "time": 0.80082} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.09087, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26, "top5_acc": 0.51391, "loss_cls": 4.03872, "loss": 4.03872, "time": 0.80701} +{"mode": "train", "epoch": 30, "iter": 1300, "lr": 0.09085, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25688, "top5_acc": 0.51188, "loss_cls": 4.07845, "loss": 4.07845, "time": 0.80361} +{"mode": "train", "epoch": 30, "iter": 1400, "lr": 0.09083, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26359, "top5_acc": 0.51844, "loss_cls": 4.00342, "loss": 4.00342, "time": 0.80088} +{"mode": "train", "epoch": 30, "iter": 1500, "lr": 0.09082, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26562, "top5_acc": 0.52281, "loss_cls": 4.02192, "loss": 4.02192, "time": 0.80409} +{"mode": "train", "epoch": 30, "iter": 1600, "lr": 0.0908, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26516, "top5_acc": 0.51578, "loss_cls": 4.02776, "loss": 4.02776, "time": 0.80057} +{"mode": "train", "epoch": 30, "iter": 1700, "lr": 0.09078, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26078, "top5_acc": 0.51313, "loss_cls": 4.05309, "loss": 4.05309, "time": 0.80375} +{"mode": "train", "epoch": 30, "iter": 1800, "lr": 0.09077, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27547, "top5_acc": 0.52812, "loss_cls": 3.96882, "loss": 3.96882, "time": 0.80305} +{"mode": "train", "epoch": 30, "iter": 1900, "lr": 0.09075, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26734, "top5_acc": 0.51891, "loss_cls": 4.01355, "loss": 4.01355, "time": 0.80527} +{"mode": "train", "epoch": 30, "iter": 2000, "lr": 0.09074, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25469, "top5_acc": 0.51125, "loss_cls": 4.07324, "loss": 4.07324, "time": 0.80637} +{"mode": "train", "epoch": 30, "iter": 2100, "lr": 0.09072, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25562, "top5_acc": 0.50781, "loss_cls": 4.06843, "loss": 4.06843, "time": 0.80266} +{"mode": "train", "epoch": 30, "iter": 2200, "lr": 0.0907, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27016, "top5_acc": 0.52359, "loss_cls": 4.0079, "loss": 4.0079, "time": 0.79959} +{"mode": "train", "epoch": 30, "iter": 2300, "lr": 0.09069, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26594, "top5_acc": 0.51703, "loss_cls": 4.04113, "loss": 4.04113, "time": 0.798} +{"mode": "train", "epoch": 30, "iter": 2400, "lr": 0.09067, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25594, "top5_acc": 0.50828, "loss_cls": 4.05644, "loss": 4.05644, "time": 0.79937} +{"mode": "train", "epoch": 30, "iter": 2500, "lr": 0.09065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26578, "top5_acc": 0.50656, "loss_cls": 4.04409, "loss": 4.04409, "time": 0.80273} +{"mode": "train", "epoch": 30, "iter": 2600, "lr": 0.09064, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25812, "top5_acc": 0.50609, "loss_cls": 4.0527, "loss": 4.0527, "time": 0.80266} +{"mode": "train", "epoch": 30, "iter": 2700, "lr": 0.09062, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26109, "top5_acc": 0.51422, "loss_cls": 4.0422, "loss": 4.0422, "time": 0.80023} +{"mode": "train", "epoch": 30, "iter": 2800, "lr": 0.09061, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26594, "top5_acc": 0.51672, "loss_cls": 4.00135, "loss": 4.00135, "time": 0.80537} +{"mode": "train", "epoch": 30, "iter": 2900, "lr": 0.09059, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26609, "top5_acc": 0.50953, "loss_cls": 4.03388, "loss": 4.03388, "time": 0.80198} +{"mode": "train", "epoch": 30, "iter": 3000, "lr": 0.09057, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27016, "top5_acc": 0.51234, "loss_cls": 4.02639, "loss": 4.02639, "time": 0.80248} +{"mode": "train", "epoch": 30, "iter": 3100, "lr": 0.09056, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25672, "top5_acc": 0.50703, "loss_cls": 4.07229, "loss": 4.07229, "time": 0.80731} +{"mode": "train", "epoch": 30, "iter": 3200, "lr": 0.09054, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25875, "top5_acc": 0.50594, "loss_cls": 4.03293, "loss": 4.03293, "time": 0.80279} +{"mode": "train", "epoch": 30, "iter": 3300, "lr": 0.09052, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25609, "top5_acc": 0.51094, "loss_cls": 4.06097, "loss": 4.06097, "time": 0.81273} +{"mode": "train", "epoch": 30, "iter": 3400, "lr": 0.09051, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26062, "top5_acc": 0.50953, "loss_cls": 4.06279, "loss": 4.06279, "time": 0.81456} +{"mode": "train", "epoch": 30, "iter": 3500, "lr": 0.09049, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27125, "top5_acc": 0.52266, "loss_cls": 4.00095, "loss": 4.00095, "time": 0.8103} +{"mode": "train", "epoch": 30, "iter": 3600, "lr": 0.09047, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26906, "top5_acc": 0.52438, "loss_cls": 4.00219, "loss": 4.00219, "time": 0.8046} +{"mode": "train", "epoch": 30, "iter": 3700, "lr": 0.09046, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26594, "top5_acc": 0.51562, "loss_cls": 4.03769, "loss": 4.03769, "time": 0.80496} +{"mode": "val", "epoch": 30, "iter": 309, "lr": 0.09045, "top1_acc": 0.20088, "top5_acc": 0.432, "mean_class_accuracy": 0.20055} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.09043, "memory": 15990, "data_time": 1.26334, "top1_acc": 0.27531, "top5_acc": 0.51641, "loss_cls": 4.22888, "loss": 4.22888, "time": 2.23794} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.09042, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26375, "top5_acc": 0.50922, "loss_cls": 4.25291, "loss": 4.25291, "time": 0.81803} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.0904, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27375, "top5_acc": 0.52844, "loss_cls": 4.17537, "loss": 4.17537, "time": 0.81549} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.09039, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27187, "top5_acc": 0.5275, "loss_cls": 4.18187, "loss": 4.18187, "time": 0.81487} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.09037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26141, "top5_acc": 0.51594, "loss_cls": 4.22566, "loss": 4.22566, "time": 0.81668} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.09035, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26344, "top5_acc": 0.51094, "loss_cls": 4.26086, "loss": 4.26086, "time": 0.81988} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.09034, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27391, "top5_acc": 0.51953, "loss_cls": 4.21386, "loss": 4.21386, "time": 0.82239} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.09032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26312, "top5_acc": 0.50844, "loss_cls": 4.26155, "loss": 4.26155, "time": 0.81596} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27, "top5_acc": 0.52375, "loss_cls": 4.22213, "loss": 4.22213, "time": 0.81224} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.09029, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2625, "top5_acc": 0.51031, "loss_cls": 4.25814, "loss": 4.25814, "time": 0.81664} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.09027, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26594, "top5_acc": 0.52109, "loss_cls": 4.23999, "loss": 4.23999, "time": 0.81863} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.09025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26266, "top5_acc": 0.51281, "loss_cls": 4.26074, "loss": 4.26074, "time": 0.81358} +{"mode": "train", "epoch": 31, "iter": 1300, "lr": 0.09024, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26484, "top5_acc": 0.52156, "loss_cls": 4.24256, "loss": 4.24256, "time": 0.81758} +{"mode": "train", "epoch": 31, "iter": 1400, "lr": 0.09022, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.265, "top5_acc": 0.50906, "loss_cls": 4.26361, "loss": 4.26361, "time": 0.81684} +{"mode": "train", "epoch": 31, "iter": 1500, "lr": 0.0902, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2725, "top5_acc": 0.51781, "loss_cls": 4.20512, "loss": 4.20512, "time": 0.82607} +{"mode": "train", "epoch": 31, "iter": 1600, "lr": 0.09019, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26953, "top5_acc": 0.51438, "loss_cls": 4.23893, "loss": 4.23893, "time": 0.81649} +{"mode": "train", "epoch": 31, "iter": 1700, "lr": 0.09017, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26422, "top5_acc": 0.51375, "loss_cls": 4.2737, "loss": 4.2737, "time": 0.82463} +{"mode": "train", "epoch": 31, "iter": 1800, "lr": 0.09015, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26547, "top5_acc": 0.50734, "loss_cls": 4.25709, "loss": 4.25709, "time": 0.81856} +{"mode": "train", "epoch": 31, "iter": 1900, "lr": 0.09014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26859, "top5_acc": 0.51875, "loss_cls": 4.22468, "loss": 4.22468, "time": 0.81983} +{"mode": "train", "epoch": 31, "iter": 2000, "lr": 0.09012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26875, "top5_acc": 0.52359, "loss_cls": 4.20853, "loss": 4.20853, "time": 0.81818} +{"mode": "train", "epoch": 31, "iter": 2100, "lr": 0.0901, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27344, "top5_acc": 0.52469, "loss_cls": 4.20692, "loss": 4.20692, "time": 0.81106} +{"mode": "train", "epoch": 31, "iter": 2200, "lr": 0.09009, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26734, "top5_acc": 0.49703, "loss_cls": 4.27919, "loss": 4.27919, "time": 0.81169} +{"mode": "train", "epoch": 31, "iter": 2300, "lr": 0.09007, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26984, "top5_acc": 0.50875, "loss_cls": 4.24713, "loss": 4.24713, "time": 0.81591} +{"mode": "train", "epoch": 31, "iter": 2400, "lr": 0.09005, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26203, "top5_acc": 0.51, "loss_cls": 4.2554, "loss": 4.2554, "time": 0.81606} +{"mode": "train", "epoch": 31, "iter": 2500, "lr": 0.09004, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26609, "top5_acc": 0.52062, "loss_cls": 4.23298, "loss": 4.23298, "time": 0.81901} +{"mode": "train", "epoch": 31, "iter": 2600, "lr": 0.09002, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26406, "top5_acc": 0.51656, "loss_cls": 4.24443, "loss": 4.24443, "time": 0.81568} +{"mode": "train", "epoch": 31, "iter": 2700, "lr": 0.09, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26828, "top5_acc": 0.50891, "loss_cls": 4.24522, "loss": 4.24522, "time": 0.81862} +{"mode": "train", "epoch": 31, "iter": 2800, "lr": 0.08999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26969, "top5_acc": 0.51812, "loss_cls": 4.21289, "loss": 4.21289, "time": 0.82278} +{"mode": "train", "epoch": 31, "iter": 2900, "lr": 0.08997, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26172, "top5_acc": 0.50422, "loss_cls": 4.27065, "loss": 4.27065, "time": 0.81741} +{"mode": "train", "epoch": 31, "iter": 3000, "lr": 0.08995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26344, "top5_acc": 0.50969, "loss_cls": 4.25223, "loss": 4.25223, "time": 0.82253} +{"mode": "train", "epoch": 31, "iter": 3100, "lr": 0.08994, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25797, "top5_acc": 0.50813, "loss_cls": 4.27251, "loss": 4.27251, "time": 0.82209} +{"mode": "train", "epoch": 31, "iter": 3200, "lr": 0.08992, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27562, "top5_acc": 0.51703, "loss_cls": 4.19953, "loss": 4.19953, "time": 0.82266} +{"mode": "train", "epoch": 31, "iter": 3300, "lr": 0.0899, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.265, "top5_acc": 0.51672, "loss_cls": 4.25099, "loss": 4.25099, "time": 0.82658} +{"mode": "train", "epoch": 31, "iter": 3400, "lr": 0.08989, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25938, "top5_acc": 0.51047, "loss_cls": 4.27416, "loss": 4.27416, "time": 0.83151} +{"mode": "train", "epoch": 31, "iter": 3500, "lr": 0.08987, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27703, "top5_acc": 0.51891, "loss_cls": 4.20063, "loss": 4.20063, "time": 0.82191} +{"mode": "train", "epoch": 31, "iter": 3600, "lr": 0.08985, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27047, "top5_acc": 0.52047, "loss_cls": 4.22765, "loss": 4.22765, "time": 0.82072} +{"mode": "train", "epoch": 31, "iter": 3700, "lr": 0.08983, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26781, "top5_acc": 0.50516, "loss_cls": 4.25968, "loss": 4.25968, "time": 0.81421} +{"mode": "val", "epoch": 31, "iter": 309, "lr": 0.08983, "top1_acc": 0.2027, "top5_acc": 0.44867, "mean_class_accuracy": 0.20245} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.08981, "memory": 15990, "data_time": 1.26444, "top1_acc": 0.26797, "top5_acc": 0.52125, "loss_cls": 4.20625, "loss": 4.20625, "time": 2.2373} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.08979, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26531, "top5_acc": 0.51609, "loss_cls": 4.2339, "loss": 4.2339, "time": 0.81646} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.08978, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27359, "top5_acc": 0.51469, "loss_cls": 4.2299, "loss": 4.2299, "time": 0.81791} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.08976, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26188, "top5_acc": 0.51781, "loss_cls": 4.25009, "loss": 4.25009, "time": 0.81627} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.08974, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25562, "top5_acc": 0.5075, "loss_cls": 4.29382, "loss": 4.29382, "time": 0.8182} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.08973, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26203, "top5_acc": 0.50656, "loss_cls": 4.24761, "loss": 4.24761, "time": 0.81894} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.08971, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26172, "top5_acc": 0.50922, "loss_cls": 4.27885, "loss": 4.27885, "time": 0.81912} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.08969, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.265, "top5_acc": 0.51094, "loss_cls": 4.26497, "loss": 4.26497, "time": 0.81652} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.08967, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26625, "top5_acc": 0.51484, "loss_cls": 4.23343, "loss": 4.23343, "time": 0.81745} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.08966, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26672, "top5_acc": 0.52672, "loss_cls": 4.21806, "loss": 4.21806, "time": 0.81857} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.08964, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26812, "top5_acc": 0.51344, "loss_cls": 4.20807, "loss": 4.20807, "time": 0.81756} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.08962, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25578, "top5_acc": 0.51266, "loss_cls": 4.25772, "loss": 4.25772, "time": 0.82057} +{"mode": "train", "epoch": 32, "iter": 1300, "lr": 0.08961, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27453, "top5_acc": 0.52344, "loss_cls": 4.20521, "loss": 4.20521, "time": 0.81824} +{"mode": "train", "epoch": 32, "iter": 1400, "lr": 0.08959, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27375, "top5_acc": 0.52922, "loss_cls": 4.17496, "loss": 4.17496, "time": 0.8151} +{"mode": "train", "epoch": 32, "iter": 1500, "lr": 0.08957, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27078, "top5_acc": 0.52938, "loss_cls": 4.19006, "loss": 4.19006, "time": 0.81611} +{"mode": "train", "epoch": 32, "iter": 1600, "lr": 0.08955, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26578, "top5_acc": 0.50844, "loss_cls": 4.22764, "loss": 4.22764, "time": 0.82007} +{"mode": "train", "epoch": 32, "iter": 1700, "lr": 0.08954, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26141, "top5_acc": 0.51313, "loss_cls": 4.26825, "loss": 4.26825, "time": 0.81284} +{"mode": "train", "epoch": 32, "iter": 1800, "lr": 0.08952, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26625, "top5_acc": 0.51641, "loss_cls": 4.23375, "loss": 4.23375, "time": 0.8187} +{"mode": "train", "epoch": 32, "iter": 1900, "lr": 0.0895, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26984, "top5_acc": 0.52812, "loss_cls": 4.17435, "loss": 4.17435, "time": 0.81933} +{"mode": "train", "epoch": 32, "iter": 2000, "lr": 0.08949, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27266, "top5_acc": 0.51812, "loss_cls": 4.21294, "loss": 4.21294, "time": 0.81741} +{"mode": "train", "epoch": 32, "iter": 2100, "lr": 0.08947, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26609, "top5_acc": 0.51031, "loss_cls": 4.26751, "loss": 4.26751, "time": 0.81619} +{"mode": "train", "epoch": 32, "iter": 2200, "lr": 0.08945, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26234, "top5_acc": 0.51969, "loss_cls": 4.25699, "loss": 4.25699, "time": 0.81414} +{"mode": "train", "epoch": 32, "iter": 2300, "lr": 0.08943, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26844, "top5_acc": 0.51453, "loss_cls": 4.21408, "loss": 4.21408, "time": 0.81795} +{"mode": "train", "epoch": 32, "iter": 2400, "lr": 0.08942, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.26781, "top5_acc": 0.52031, "loss_cls": 4.24155, "loss": 4.24155, "time": 0.81118} +{"mode": "train", "epoch": 32, "iter": 2500, "lr": 0.0894, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26109, "top5_acc": 0.50844, "loss_cls": 4.26495, "loss": 4.26495, "time": 0.81455} +{"mode": "train", "epoch": 32, "iter": 2600, "lr": 0.08938, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26094, "top5_acc": 0.50625, "loss_cls": 4.25914, "loss": 4.25914, "time": 0.81888} +{"mode": "train", "epoch": 32, "iter": 2700, "lr": 0.08937, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26484, "top5_acc": 0.5125, "loss_cls": 4.23943, "loss": 4.23943, "time": 0.8172} +{"mode": "train", "epoch": 32, "iter": 2800, "lr": 0.08935, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26328, "top5_acc": 0.51438, "loss_cls": 4.26466, "loss": 4.26466, "time": 0.81592} +{"mode": "train", "epoch": 32, "iter": 2900, "lr": 0.08933, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25938, "top5_acc": 0.51188, "loss_cls": 4.25589, "loss": 4.25589, "time": 0.821} +{"mode": "train", "epoch": 32, "iter": 3000, "lr": 0.08931, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25969, "top5_acc": 0.51516, "loss_cls": 4.21885, "loss": 4.21885, "time": 0.81515} +{"mode": "train", "epoch": 32, "iter": 3100, "lr": 0.0893, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26953, "top5_acc": 0.50734, "loss_cls": 4.2541, "loss": 4.2541, "time": 0.82568} +{"mode": "train", "epoch": 32, "iter": 3200, "lr": 0.08928, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26953, "top5_acc": 0.51953, "loss_cls": 4.21246, "loss": 4.21246, "time": 0.81847} +{"mode": "train", "epoch": 32, "iter": 3300, "lr": 0.08926, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26297, "top5_acc": 0.51812, "loss_cls": 4.23923, "loss": 4.23923, "time": 0.82255} +{"mode": "train", "epoch": 32, "iter": 3400, "lr": 0.08924, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26312, "top5_acc": 0.51219, "loss_cls": 4.25069, "loss": 4.25069, "time": 0.81398} +{"mode": "train", "epoch": 32, "iter": 3500, "lr": 0.08923, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26547, "top5_acc": 0.5075, "loss_cls": 4.28336, "loss": 4.28336, "time": 0.82615} +{"mode": "train", "epoch": 32, "iter": 3600, "lr": 0.08921, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27516, "top5_acc": 0.52781, "loss_cls": 4.19714, "loss": 4.19714, "time": 0.82019} +{"mode": "train", "epoch": 32, "iter": 3700, "lr": 0.08919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27016, "top5_acc": 0.51719, "loss_cls": 4.20835, "loss": 4.20835, "time": 0.82091} +{"mode": "val", "epoch": 32, "iter": 309, "lr": 0.08918, "top1_acc": 0.19587, "top5_acc": 0.424, "mean_class_accuracy": 0.1957} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.08917, "memory": 15990, "data_time": 1.26809, "top1_acc": 0.27734, "top5_acc": 0.52594, "loss_cls": 4.17908, "loss": 4.17908, "time": 2.28418} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.08915, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27938, "top5_acc": 0.52156, "loss_cls": 4.15682, "loss": 4.15682, "time": 0.82096} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.08913, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26562, "top5_acc": 0.52188, "loss_cls": 4.24206, "loss": 4.24206, "time": 0.82076} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.08912, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26297, "top5_acc": 0.52625, "loss_cls": 4.20844, "loss": 4.20844, "time": 0.81706} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.0891, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25875, "top5_acc": 0.50625, "loss_cls": 4.28749, "loss": 4.28749, "time": 0.81689} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.08908, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26359, "top5_acc": 0.52031, "loss_cls": 4.2196, "loss": 4.2196, "time": 0.81452} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.08906, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26672, "top5_acc": 0.51172, "loss_cls": 4.2579, "loss": 4.2579, "time": 0.81595} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.08905, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26797, "top5_acc": 0.51453, "loss_cls": 4.2331, "loss": 4.2331, "time": 0.81422} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.08903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26312, "top5_acc": 0.51812, "loss_cls": 4.20511, "loss": 4.20511, "time": 0.81261} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.08901, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26875, "top5_acc": 0.51125, "loss_cls": 4.23509, "loss": 4.23509, "time": 0.81806} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.08899, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27688, "top5_acc": 0.525, "loss_cls": 4.16872, "loss": 4.16872, "time": 0.81568} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.08898, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26641, "top5_acc": 0.52531, "loss_cls": 4.19049, "loss": 4.19049, "time": 0.8175} +{"mode": "train", "epoch": 33, "iter": 1300, "lr": 0.08896, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26453, "top5_acc": 0.51828, "loss_cls": 4.22156, "loss": 4.22156, "time": 0.81857} +{"mode": "train", "epoch": 33, "iter": 1400, "lr": 0.08894, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26766, "top5_acc": 0.51922, "loss_cls": 4.23879, "loss": 4.23879, "time": 0.82265} +{"mode": "train", "epoch": 33, "iter": 1500, "lr": 0.08892, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25828, "top5_acc": 0.51016, "loss_cls": 4.29443, "loss": 4.29443, "time": 0.81785} +{"mode": "train", "epoch": 33, "iter": 1600, "lr": 0.08891, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.275, "top5_acc": 0.52656, "loss_cls": 4.2057, "loss": 4.2057, "time": 0.8188} +{"mode": "train", "epoch": 33, "iter": 1700, "lr": 0.08889, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27031, "top5_acc": 0.51219, "loss_cls": 4.26025, "loss": 4.26025, "time": 0.81826} +{"mode": "train", "epoch": 33, "iter": 1800, "lr": 0.08887, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27234, "top5_acc": 0.51906, "loss_cls": 4.22428, "loss": 4.22428, "time": 0.81548} +{"mode": "train", "epoch": 33, "iter": 1900, "lr": 0.08885, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26453, "top5_acc": 0.50766, "loss_cls": 4.23939, "loss": 4.23939, "time": 0.81686} +{"mode": "train", "epoch": 33, "iter": 2000, "lr": 0.08884, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26031, "top5_acc": 0.50953, "loss_cls": 4.24453, "loss": 4.24453, "time": 0.81449} +{"mode": "train", "epoch": 33, "iter": 2100, "lr": 0.08882, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27156, "top5_acc": 0.52688, "loss_cls": 4.17321, "loss": 4.17321, "time": 0.81634} +{"mode": "train", "epoch": 33, "iter": 2200, "lr": 0.0888, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25844, "top5_acc": 0.52156, "loss_cls": 4.25041, "loss": 4.25041, "time": 0.81418} +{"mode": "train", "epoch": 33, "iter": 2300, "lr": 0.08878, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25656, "top5_acc": 0.51359, "loss_cls": 4.25947, "loss": 4.25947, "time": 0.81762} +{"mode": "train", "epoch": 33, "iter": 2400, "lr": 0.08876, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27219, "top5_acc": 0.53016, "loss_cls": 4.18555, "loss": 4.18555, "time": 0.81395} +{"mode": "train", "epoch": 33, "iter": 2500, "lr": 0.08875, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27, "top5_acc": 0.52609, "loss_cls": 4.22046, "loss": 4.22046, "time": 0.81464} +{"mode": "train", "epoch": 33, "iter": 2600, "lr": 0.08873, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26375, "top5_acc": 0.51344, "loss_cls": 4.24696, "loss": 4.24696, "time": 0.81897} +{"mode": "train", "epoch": 33, "iter": 2700, "lr": 0.08871, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26328, "top5_acc": 0.51828, "loss_cls": 4.24351, "loss": 4.24351, "time": 0.82396} +{"mode": "train", "epoch": 33, "iter": 2800, "lr": 0.08869, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26828, "top5_acc": 0.50484, "loss_cls": 4.26806, "loss": 4.26806, "time": 0.81567} +{"mode": "train", "epoch": 33, "iter": 2900, "lr": 0.08868, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26312, "top5_acc": 0.51453, "loss_cls": 4.27796, "loss": 4.27796, "time": 0.82663} +{"mode": "train", "epoch": 33, "iter": 3000, "lr": 0.08866, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27312, "top5_acc": 0.51703, "loss_cls": 4.23027, "loss": 4.23027, "time": 0.82678} +{"mode": "train", "epoch": 33, "iter": 3100, "lr": 0.08864, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27453, "top5_acc": 0.51172, "loss_cls": 4.23782, "loss": 4.23782, "time": 0.8158} +{"mode": "train", "epoch": 33, "iter": 3200, "lr": 0.08862, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26438, "top5_acc": 0.52062, "loss_cls": 4.22282, "loss": 4.22282, "time": 0.81582} +{"mode": "train", "epoch": 33, "iter": 3300, "lr": 0.08861, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26531, "top5_acc": 0.51578, "loss_cls": 4.24905, "loss": 4.24905, "time": 0.82798} +{"mode": "train", "epoch": 33, "iter": 3400, "lr": 0.08859, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26687, "top5_acc": 0.52219, "loss_cls": 4.23613, "loss": 4.23613, "time": 0.81861} +{"mode": "train", "epoch": 33, "iter": 3500, "lr": 0.08857, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26781, "top5_acc": 0.52266, "loss_cls": 4.22646, "loss": 4.22646, "time": 0.82611} +{"mode": "train", "epoch": 33, "iter": 3600, "lr": 0.08855, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27156, "top5_acc": 0.52078, "loss_cls": 4.21463, "loss": 4.21463, "time": 0.81511} +{"mode": "train", "epoch": 33, "iter": 3700, "lr": 0.08853, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26859, "top5_acc": 0.51766, "loss_cls": 4.24125, "loss": 4.24125, "time": 0.81895} +{"mode": "val", "epoch": 33, "iter": 309, "lr": 0.08853, "top1_acc": 0.20139, "top5_acc": 0.43747, "mean_class_accuracy": 0.20099} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.08851, "memory": 15990, "data_time": 1.27883, "top1_acc": 0.28172, "top5_acc": 0.53812, "loss_cls": 4.11993, "loss": 4.11993, "time": 2.25423} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.08849, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26984, "top5_acc": 0.51719, "loss_cls": 4.21536, "loss": 4.21536, "time": 0.8167} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.08847, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26625, "top5_acc": 0.52641, "loss_cls": 4.16168, "loss": 4.16168, "time": 0.81932} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.08845, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27516, "top5_acc": 0.52219, "loss_cls": 4.18947, "loss": 4.18947, "time": 0.81334} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.08844, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27484, "top5_acc": 0.51969, "loss_cls": 4.19811, "loss": 4.19811, "time": 0.81616} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.08842, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26453, "top5_acc": 0.51391, "loss_cls": 4.24892, "loss": 4.24892, "time": 0.81376} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.0884, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27469, "top5_acc": 0.52062, "loss_cls": 4.17274, "loss": 4.17274, "time": 0.82089} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.08838, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26484, "top5_acc": 0.51891, "loss_cls": 4.23221, "loss": 4.23221, "time": 0.81213} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.08836, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2675, "top5_acc": 0.51406, "loss_cls": 4.23267, "loss": 4.23267, "time": 0.81102} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.08835, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27469, "top5_acc": 0.51797, "loss_cls": 4.22476, "loss": 4.22476, "time": 0.82071} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.08833, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26438, "top5_acc": 0.51016, "loss_cls": 4.2456, "loss": 4.2456, "time": 0.81771} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.08831, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26406, "top5_acc": 0.51453, "loss_cls": 4.2377, "loss": 4.2377, "time": 0.81277} +{"mode": "train", "epoch": 34, "iter": 1300, "lr": 0.08829, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27859, "top5_acc": 0.51625, "loss_cls": 4.19004, "loss": 4.19004, "time": 0.8164} +{"mode": "train", "epoch": 34, "iter": 1400, "lr": 0.08828, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26672, "top5_acc": 0.51578, "loss_cls": 4.24349, "loss": 4.24349, "time": 0.8199} +{"mode": "train", "epoch": 34, "iter": 1500, "lr": 0.08826, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26609, "top5_acc": 0.52047, "loss_cls": 4.20978, "loss": 4.20978, "time": 0.81532} +{"mode": "train", "epoch": 34, "iter": 1600, "lr": 0.08824, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27, "top5_acc": 0.51922, "loss_cls": 4.23337, "loss": 4.23337, "time": 0.81517} +{"mode": "train", "epoch": 34, "iter": 1700, "lr": 0.08822, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27609, "top5_acc": 0.52219, "loss_cls": 4.19193, "loss": 4.19193, "time": 0.81084} +{"mode": "train", "epoch": 34, "iter": 1800, "lr": 0.0882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27344, "top5_acc": 0.52625, "loss_cls": 4.1702, "loss": 4.1702, "time": 0.81356} +{"mode": "train", "epoch": 34, "iter": 1900, "lr": 0.08819, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26594, "top5_acc": 0.51266, "loss_cls": 4.23951, "loss": 4.23951, "time": 0.81247} +{"mode": "train", "epoch": 34, "iter": 2000, "lr": 0.08817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27266, "top5_acc": 0.52062, "loss_cls": 4.19564, "loss": 4.19564, "time": 0.82776} +{"mode": "train", "epoch": 34, "iter": 2100, "lr": 0.08815, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26469, "top5_acc": 0.51625, "loss_cls": 4.24308, "loss": 4.24308, "time": 0.81064} +{"mode": "train", "epoch": 34, "iter": 2200, "lr": 0.08813, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26391, "top5_acc": 0.52391, "loss_cls": 4.21769, "loss": 4.21769, "time": 0.81566} +{"mode": "train", "epoch": 34, "iter": 2300, "lr": 0.08811, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26328, "top5_acc": 0.51922, "loss_cls": 4.21779, "loss": 4.21779, "time": 0.81184} +{"mode": "train", "epoch": 34, "iter": 2400, "lr": 0.08809, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27172, "top5_acc": 0.51547, "loss_cls": 4.21145, "loss": 4.21145, "time": 0.82029} +{"mode": "train", "epoch": 34, "iter": 2500, "lr": 0.08808, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27, "top5_acc": 0.52484, "loss_cls": 4.17885, "loss": 4.17885, "time": 0.81613} +{"mode": "train", "epoch": 34, "iter": 2600, "lr": 0.08806, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26703, "top5_acc": 0.52531, "loss_cls": 4.21094, "loss": 4.21094, "time": 0.81149} +{"mode": "train", "epoch": 34, "iter": 2700, "lr": 0.08804, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26875, "top5_acc": 0.51031, "loss_cls": 4.24007, "loss": 4.24007, "time": 0.81289} +{"mode": "train", "epoch": 34, "iter": 2800, "lr": 0.08802, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27031, "top5_acc": 0.51328, "loss_cls": 4.25782, "loss": 4.25782, "time": 0.81496} +{"mode": "train", "epoch": 34, "iter": 2900, "lr": 0.088, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2625, "top5_acc": 0.51078, "loss_cls": 4.26181, "loss": 4.26181, "time": 0.81766} +{"mode": "train", "epoch": 34, "iter": 3000, "lr": 0.08799, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26453, "top5_acc": 0.51547, "loss_cls": 4.21846, "loss": 4.21846, "time": 0.82021} +{"mode": "train", "epoch": 34, "iter": 3100, "lr": 0.08797, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26828, "top5_acc": 0.52547, "loss_cls": 4.23352, "loss": 4.23352, "time": 0.8203} +{"mode": "train", "epoch": 34, "iter": 3200, "lr": 0.08795, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25047, "top5_acc": 0.49875, "loss_cls": 4.31969, "loss": 4.31969, "time": 0.81467} +{"mode": "train", "epoch": 34, "iter": 3300, "lr": 0.08793, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26078, "top5_acc": 0.51641, "loss_cls": 4.22638, "loss": 4.22638, "time": 0.82483} +{"mode": "train", "epoch": 34, "iter": 3400, "lr": 0.08791, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26531, "top5_acc": 0.51078, "loss_cls": 4.27645, "loss": 4.27645, "time": 0.81623} +{"mode": "train", "epoch": 34, "iter": 3500, "lr": 0.08789, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27047, "top5_acc": 0.52156, "loss_cls": 4.23176, "loss": 4.23176, "time": 0.82774} +{"mode": "train", "epoch": 34, "iter": 3600, "lr": 0.08788, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26, "top5_acc": 0.5075, "loss_cls": 4.26897, "loss": 4.26897, "time": 0.82052} +{"mode": "train", "epoch": 34, "iter": 3700, "lr": 0.08786, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26953, "top5_acc": 0.52625, "loss_cls": 4.18812, "loss": 4.18812, "time": 0.81787} +{"mode": "val", "epoch": 34, "iter": 309, "lr": 0.08785, "top1_acc": 0.17464, "top5_acc": 0.38576, "mean_class_accuracy": 0.17447} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.08783, "memory": 15990, "data_time": 1.26012, "top1_acc": 0.27594, "top5_acc": 0.52875, "loss_cls": 4.17207, "loss": 4.17207, "time": 2.24193} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.08781, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26469, "top5_acc": 0.52062, "loss_cls": 4.22292, "loss": 4.22292, "time": 0.81988} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.0878, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26922, "top5_acc": 0.51875, "loss_cls": 4.23418, "loss": 4.23418, "time": 0.82128} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.08778, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26953, "top5_acc": 0.51766, "loss_cls": 4.23446, "loss": 4.23446, "time": 0.82049} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.08776, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26859, "top5_acc": 0.51156, "loss_cls": 4.23924, "loss": 4.23924, "time": 0.81605} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.08774, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26469, "top5_acc": 0.5225, "loss_cls": 4.21765, "loss": 4.21765, "time": 0.81989} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.08772, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27547, "top5_acc": 0.52906, "loss_cls": 4.15835, "loss": 4.15835, "time": 0.81772} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.0877, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2625, "top5_acc": 0.51188, "loss_cls": 4.21396, "loss": 4.21396, "time": 0.82227} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.08769, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27141, "top5_acc": 0.51406, "loss_cls": 4.21272, "loss": 4.21272, "time": 0.82266} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.08767, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2675, "top5_acc": 0.51719, "loss_cls": 4.22371, "loss": 4.22371, "time": 0.81572} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.08765, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27109, "top5_acc": 0.52438, "loss_cls": 4.17957, "loss": 4.17957, "time": 0.81859} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.08763, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27203, "top5_acc": 0.51797, "loss_cls": 4.20085, "loss": 4.20085, "time": 0.81595} +{"mode": "train", "epoch": 35, "iter": 1300, "lr": 0.08761, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27672, "top5_acc": 0.52672, "loss_cls": 4.15781, "loss": 4.15781, "time": 0.82221} +{"mode": "train", "epoch": 35, "iter": 1400, "lr": 0.08759, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27203, "top5_acc": 0.51984, "loss_cls": 4.19648, "loss": 4.19648, "time": 0.81476} +{"mode": "train", "epoch": 35, "iter": 1500, "lr": 0.08757, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27688, "top5_acc": 0.52234, "loss_cls": 4.18502, "loss": 4.18502, "time": 0.82082} +{"mode": "train", "epoch": 35, "iter": 1600, "lr": 0.08756, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26078, "top5_acc": 0.51516, "loss_cls": 4.22767, "loss": 4.22767, "time": 0.81354} +{"mode": "train", "epoch": 35, "iter": 1700, "lr": 0.08754, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27109, "top5_acc": 0.52266, "loss_cls": 4.20091, "loss": 4.20091, "time": 0.82148} +{"mode": "train", "epoch": 35, "iter": 1800, "lr": 0.08752, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26297, "top5_acc": 0.51531, "loss_cls": 4.24998, "loss": 4.24998, "time": 0.81943} +{"mode": "train", "epoch": 35, "iter": 1900, "lr": 0.0875, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27125, "top5_acc": 0.52672, "loss_cls": 4.18487, "loss": 4.18487, "time": 0.82161} +{"mode": "train", "epoch": 35, "iter": 2000, "lr": 0.08748, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26422, "top5_acc": 0.50953, "loss_cls": 4.27157, "loss": 4.27157, "time": 0.81452} +{"mode": "train", "epoch": 35, "iter": 2100, "lr": 0.08746, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26375, "top5_acc": 0.51719, "loss_cls": 4.22086, "loss": 4.22086, "time": 0.81459} +{"mode": "train", "epoch": 35, "iter": 2200, "lr": 0.08745, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26672, "top5_acc": 0.51875, "loss_cls": 4.22142, "loss": 4.22142, "time": 0.81557} +{"mode": "train", "epoch": 35, "iter": 2300, "lr": 0.08743, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26734, "top5_acc": 0.51906, "loss_cls": 4.22376, "loss": 4.22376, "time": 0.81699} +{"mode": "train", "epoch": 35, "iter": 2400, "lr": 0.08741, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26422, "top5_acc": 0.52266, "loss_cls": 4.22709, "loss": 4.22709, "time": 0.81739} +{"mode": "train", "epoch": 35, "iter": 2500, "lr": 0.08739, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26937, "top5_acc": 0.51859, "loss_cls": 4.2276, "loss": 4.2276, "time": 0.81729} +{"mode": "train", "epoch": 35, "iter": 2600, "lr": 0.08737, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.265, "top5_acc": 0.51625, "loss_cls": 4.25106, "loss": 4.25106, "time": 0.82247} +{"mode": "train", "epoch": 35, "iter": 2700, "lr": 0.08735, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27734, "top5_acc": 0.52125, "loss_cls": 4.21264, "loss": 4.21264, "time": 0.82168} +{"mode": "train", "epoch": 35, "iter": 2800, "lr": 0.08733, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26984, "top5_acc": 0.52125, "loss_cls": 4.20691, "loss": 4.20691, "time": 0.81571} +{"mode": "train", "epoch": 35, "iter": 2900, "lr": 0.08732, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25609, "top5_acc": 0.50672, "loss_cls": 4.29389, "loss": 4.29389, "time": 0.82697} +{"mode": "train", "epoch": 35, "iter": 3000, "lr": 0.0873, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26203, "top5_acc": 0.52141, "loss_cls": 4.21887, "loss": 4.21887, "time": 0.81951} +{"mode": "train", "epoch": 35, "iter": 3100, "lr": 0.08728, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26891, "top5_acc": 0.52016, "loss_cls": 4.24594, "loss": 4.24594, "time": 0.81534} +{"mode": "train", "epoch": 35, "iter": 3200, "lr": 0.08726, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26937, "top5_acc": 0.51844, "loss_cls": 4.20615, "loss": 4.20615, "time": 0.81711} +{"mode": "train", "epoch": 35, "iter": 3300, "lr": 0.08724, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27797, "top5_acc": 0.52344, "loss_cls": 4.20507, "loss": 4.20507, "time": 0.8221} +{"mode": "train", "epoch": 35, "iter": 3400, "lr": 0.08722, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27328, "top5_acc": 0.51531, "loss_cls": 4.23098, "loss": 4.23098, "time": 0.81723} +{"mode": "train", "epoch": 35, "iter": 3500, "lr": 0.0872, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27375, "top5_acc": 0.52219, "loss_cls": 4.20588, "loss": 4.20588, "time": 0.82043} +{"mode": "train", "epoch": 35, "iter": 3600, "lr": 0.08718, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26797, "top5_acc": 0.5175, "loss_cls": 4.24094, "loss": 4.24094, "time": 0.81896} +{"mode": "train", "epoch": 35, "iter": 3700, "lr": 0.08717, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27484, "top5_acc": 0.51875, "loss_cls": 4.20825, "loss": 4.20825, "time": 0.81522} +{"mode": "val", "epoch": 35, "iter": 309, "lr": 0.08716, "top1_acc": 0.20564, "top5_acc": 0.43833, "mean_class_accuracy": 0.20546} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.08714, "memory": 15990, "data_time": 1.27, "top1_acc": 0.27422, "top5_acc": 0.53109, "loss_cls": 4.14521, "loss": 4.14521, "time": 2.25536} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.08712, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28047, "top5_acc": 0.53766, "loss_cls": 4.15985, "loss": 4.15985, "time": 0.81917} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.0871, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27703, "top5_acc": 0.52328, "loss_cls": 4.16755, "loss": 4.16755, "time": 0.81468} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.08708, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26797, "top5_acc": 0.52016, "loss_cls": 4.22056, "loss": 4.22056, "time": 0.81725} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.08706, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27, "top5_acc": 0.52641, "loss_cls": 4.18677, "loss": 4.18677, "time": 0.81634} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.08704, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2575, "top5_acc": 0.51141, "loss_cls": 4.23337, "loss": 4.23337, "time": 0.81685} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.08703, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26359, "top5_acc": 0.53109, "loss_cls": 4.19482, "loss": 4.19482, "time": 0.81546} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.08701, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27141, "top5_acc": 0.52609, "loss_cls": 4.17089, "loss": 4.17089, "time": 0.81909} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.08699, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26891, "top5_acc": 0.525, "loss_cls": 4.18612, "loss": 4.18612, "time": 0.81775} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.08697, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26266, "top5_acc": 0.52312, "loss_cls": 4.22886, "loss": 4.22886, "time": 0.82259} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.08695, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27016, "top5_acc": 0.5175, "loss_cls": 4.2053, "loss": 4.2053, "time": 0.82057} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.08693, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27719, "top5_acc": 0.52656, "loss_cls": 4.18405, "loss": 4.18405, "time": 0.82041} +{"mode": "train", "epoch": 36, "iter": 1300, "lr": 0.08691, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26297, "top5_acc": 0.51859, "loss_cls": 4.23416, "loss": 4.23416, "time": 0.81881} +{"mode": "train", "epoch": 36, "iter": 1400, "lr": 0.08689, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27203, "top5_acc": 0.52312, "loss_cls": 4.21515, "loss": 4.21515, "time": 0.82467} +{"mode": "train", "epoch": 36, "iter": 1500, "lr": 0.08688, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27625, "top5_acc": 0.51922, "loss_cls": 4.19844, "loss": 4.19844, "time": 0.82302} +{"mode": "train", "epoch": 36, "iter": 1600, "lr": 0.08686, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26859, "top5_acc": 0.52516, "loss_cls": 4.21175, "loss": 4.21175, "time": 0.81567} +{"mode": "train", "epoch": 36, "iter": 1700, "lr": 0.08684, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27547, "top5_acc": 0.52641, "loss_cls": 4.18374, "loss": 4.18374, "time": 0.81827} +{"mode": "train", "epoch": 36, "iter": 1800, "lr": 0.08682, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26453, "top5_acc": 0.51703, "loss_cls": 4.23229, "loss": 4.23229, "time": 0.81539} +{"mode": "train", "epoch": 36, "iter": 1900, "lr": 0.0868, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27094, "top5_acc": 0.51484, "loss_cls": 4.22651, "loss": 4.22651, "time": 0.82163} +{"mode": "train", "epoch": 36, "iter": 2000, "lr": 0.08678, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26453, "top5_acc": 0.51797, "loss_cls": 4.23927, "loss": 4.23927, "time": 0.81722} +{"mode": "train", "epoch": 36, "iter": 2100, "lr": 0.08676, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27484, "top5_acc": 0.52047, "loss_cls": 4.20467, "loss": 4.20467, "time": 0.81368} +{"mode": "train", "epoch": 36, "iter": 2200, "lr": 0.08674, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26906, "top5_acc": 0.51656, "loss_cls": 4.2446, "loss": 4.2446, "time": 0.81972} +{"mode": "train", "epoch": 36, "iter": 2300, "lr": 0.08672, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27094, "top5_acc": 0.52094, "loss_cls": 4.20997, "loss": 4.20997, "time": 0.81735} +{"mode": "train", "epoch": 36, "iter": 2400, "lr": 0.08671, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26719, "top5_acc": 0.52297, "loss_cls": 4.23036, "loss": 4.23036, "time": 0.82049} +{"mode": "train", "epoch": 36, "iter": 2500, "lr": 0.08669, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28484, "top5_acc": 0.53781, "loss_cls": 4.13507, "loss": 4.13507, "time": 0.81915} +{"mode": "train", "epoch": 36, "iter": 2600, "lr": 0.08667, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27437, "top5_acc": 0.52703, "loss_cls": 4.17642, "loss": 4.17642, "time": 0.81692} +{"mode": "train", "epoch": 36, "iter": 2700, "lr": 0.08665, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26531, "top5_acc": 0.53609, "loss_cls": 4.1926, "loss": 4.1926, "time": 0.82575} +{"mode": "train", "epoch": 36, "iter": 2800, "lr": 0.08663, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26859, "top5_acc": 0.51812, "loss_cls": 4.22625, "loss": 4.22625, "time": 0.81865} +{"mode": "train", "epoch": 36, "iter": 2900, "lr": 0.08661, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27656, "top5_acc": 0.52516, "loss_cls": 4.17682, "loss": 4.17682, "time": 0.81956} +{"mode": "train", "epoch": 36, "iter": 3000, "lr": 0.08659, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27078, "top5_acc": 0.52344, "loss_cls": 4.21075, "loss": 4.21075, "time": 0.82508} +{"mode": "train", "epoch": 36, "iter": 3100, "lr": 0.08657, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26516, "top5_acc": 0.52109, "loss_cls": 4.24346, "loss": 4.24346, "time": 0.81978} +{"mode": "train", "epoch": 36, "iter": 3200, "lr": 0.08655, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26953, "top5_acc": 0.51703, "loss_cls": 4.24839, "loss": 4.24839, "time": 0.81766} +{"mode": "train", "epoch": 36, "iter": 3300, "lr": 0.08653, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26266, "top5_acc": 0.51625, "loss_cls": 4.2487, "loss": 4.2487, "time": 0.82504} +{"mode": "train", "epoch": 36, "iter": 3400, "lr": 0.08651, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25938, "top5_acc": 0.50938, "loss_cls": 4.2755, "loss": 4.2755, "time": 0.81521} +{"mode": "train", "epoch": 36, "iter": 3500, "lr": 0.0865, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26344, "top5_acc": 0.51984, "loss_cls": 4.25123, "loss": 4.25123, "time": 0.82053} +{"mode": "train", "epoch": 36, "iter": 3600, "lr": 0.08648, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2625, "top5_acc": 0.50672, "loss_cls": 4.28452, "loss": 4.28452, "time": 0.81893} +{"mode": "train", "epoch": 36, "iter": 3700, "lr": 0.08646, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25969, "top5_acc": 0.50891, "loss_cls": 4.2731, "loss": 4.2731, "time": 0.82251} +{"mode": "val", "epoch": 36, "iter": 309, "lr": 0.08645, "top1_acc": 0.20119, "top5_acc": 0.42739, "mean_class_accuracy": 0.20098} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.08643, "memory": 15990, "data_time": 1.25925, "top1_acc": 0.27547, "top5_acc": 0.53188, "loss_cls": 4.16997, "loss": 4.16997, "time": 2.22896} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.08641, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28203, "top5_acc": 0.53812, "loss_cls": 4.14517, "loss": 4.14517, "time": 0.81692} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.08639, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28234, "top5_acc": 0.52344, "loss_cls": 4.18672, "loss": 4.18672, "time": 0.81399} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.08637, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28297, "top5_acc": 0.53891, "loss_cls": 4.15536, "loss": 4.15536, "time": 0.81531} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.08635, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27203, "top5_acc": 0.53109, "loss_cls": 4.17642, "loss": 4.17642, "time": 0.82579} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.08633, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26406, "top5_acc": 0.52406, "loss_cls": 4.21101, "loss": 4.21101, "time": 0.81444} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.08631, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26672, "top5_acc": 0.51641, "loss_cls": 4.21344, "loss": 4.21344, "time": 0.81843} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0863, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27359, "top5_acc": 0.52047, "loss_cls": 4.17656, "loss": 4.17656, "time": 0.81429} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.08628, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26516, "top5_acc": 0.52375, "loss_cls": 4.22926, "loss": 4.22926, "time": 0.81986} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.08626, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26531, "top5_acc": 0.52562, "loss_cls": 4.19621, "loss": 4.19621, "time": 0.82295} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.08624, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25859, "top5_acc": 0.51266, "loss_cls": 4.25027, "loss": 4.25027, "time": 0.82167} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.08622, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26781, "top5_acc": 0.52156, "loss_cls": 4.21373, "loss": 4.21373, "time": 0.81864} +{"mode": "train", "epoch": 37, "iter": 1300, "lr": 0.0862, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27844, "top5_acc": 0.52812, "loss_cls": 4.18276, "loss": 4.18276, "time": 0.82148} +{"mode": "train", "epoch": 37, "iter": 1400, "lr": 0.08618, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27859, "top5_acc": 0.52375, "loss_cls": 4.17649, "loss": 4.17649, "time": 0.81442} +{"mode": "train", "epoch": 37, "iter": 1500, "lr": 0.08616, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27109, "top5_acc": 0.52172, "loss_cls": 4.20584, "loss": 4.20584, "time": 0.81558} +{"mode": "train", "epoch": 37, "iter": 1600, "lr": 0.08614, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27328, "top5_acc": 0.52547, "loss_cls": 4.19555, "loss": 4.19555, "time": 0.81518} +{"mode": "train", "epoch": 37, "iter": 1700, "lr": 0.08612, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26781, "top5_acc": 0.51516, "loss_cls": 4.1988, "loss": 4.1988, "time": 0.81617} +{"mode": "train", "epoch": 37, "iter": 1800, "lr": 0.0861, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27031, "top5_acc": 0.52422, "loss_cls": 4.19047, "loss": 4.19047, "time": 0.81268} +{"mode": "train", "epoch": 37, "iter": 1900, "lr": 0.08608, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27797, "top5_acc": 0.53656, "loss_cls": 4.15888, "loss": 4.15888, "time": 0.81492} +{"mode": "train", "epoch": 37, "iter": 2000, "lr": 0.08606, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27938, "top5_acc": 0.5225, "loss_cls": 4.16119, "loss": 4.16119, "time": 0.81901} +{"mode": "train", "epoch": 37, "iter": 2100, "lr": 0.08604, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2725, "top5_acc": 0.52641, "loss_cls": 4.21036, "loss": 4.21036, "time": 0.82129} +{"mode": "train", "epoch": 37, "iter": 2200, "lr": 0.08602, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26656, "top5_acc": 0.50922, "loss_cls": 4.23742, "loss": 4.23742, "time": 0.81303} +{"mode": "train", "epoch": 37, "iter": 2300, "lr": 0.08601, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26531, "top5_acc": 0.51734, "loss_cls": 4.21134, "loss": 4.21134, "time": 0.81813} +{"mode": "train", "epoch": 37, "iter": 2400, "lr": 0.08599, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26969, "top5_acc": 0.52125, "loss_cls": 4.23804, "loss": 4.23804, "time": 0.81942} +{"mode": "train", "epoch": 37, "iter": 2500, "lr": 0.08597, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26375, "top5_acc": 0.50734, "loss_cls": 4.27622, "loss": 4.27622, "time": 0.81506} +{"mode": "train", "epoch": 37, "iter": 2600, "lr": 0.08595, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27437, "top5_acc": 0.53656, "loss_cls": 4.17295, "loss": 4.17295, "time": 0.81423} +{"mode": "train", "epoch": 37, "iter": 2700, "lr": 0.08593, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26719, "top5_acc": 0.52391, "loss_cls": 4.20397, "loss": 4.20397, "time": 0.81954} +{"mode": "train", "epoch": 37, "iter": 2800, "lr": 0.08591, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26297, "top5_acc": 0.51359, "loss_cls": 4.23878, "loss": 4.23878, "time": 0.82493} +{"mode": "train", "epoch": 37, "iter": 2900, "lr": 0.08589, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27156, "top5_acc": 0.53531, "loss_cls": 4.17748, "loss": 4.17748, "time": 0.81636} +{"mode": "train", "epoch": 37, "iter": 3000, "lr": 0.08587, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27016, "top5_acc": 0.52469, "loss_cls": 4.1984, "loss": 4.1984, "time": 0.81862} +{"mode": "train", "epoch": 37, "iter": 3100, "lr": 0.08585, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28031, "top5_acc": 0.52797, "loss_cls": 4.15519, "loss": 4.15519, "time": 0.8183} +{"mode": "train", "epoch": 37, "iter": 3200, "lr": 0.08583, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26812, "top5_acc": 0.51531, "loss_cls": 4.21687, "loss": 4.21687, "time": 0.81645} +{"mode": "train", "epoch": 37, "iter": 3300, "lr": 0.08581, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25547, "top5_acc": 0.51313, "loss_cls": 4.2859, "loss": 4.2859, "time": 0.82652} +{"mode": "train", "epoch": 37, "iter": 3400, "lr": 0.08579, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26297, "top5_acc": 0.50969, "loss_cls": 4.25272, "loss": 4.25272, "time": 0.82143} +{"mode": "train", "epoch": 37, "iter": 3500, "lr": 0.08577, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26609, "top5_acc": 0.52766, "loss_cls": 4.19392, "loss": 4.19392, "time": 0.8179} +{"mode": "train", "epoch": 37, "iter": 3600, "lr": 0.08575, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26312, "top5_acc": 0.51062, "loss_cls": 4.26435, "loss": 4.26435, "time": 0.82022} +{"mode": "train", "epoch": 37, "iter": 3700, "lr": 0.08573, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27422, "top5_acc": 0.52234, "loss_cls": 4.19725, "loss": 4.19725, "time": 0.81376} +{"mode": "val", "epoch": 37, "iter": 309, "lr": 0.08572, "top1_acc": 0.19632, "top5_acc": 0.424, "mean_class_accuracy": 0.19595} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.0857, "memory": 15990, "data_time": 1.2827, "top1_acc": 0.27172, "top5_acc": 0.53312, "loss_cls": 4.15273, "loss": 4.15273, "time": 2.25573} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.08568, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27, "top5_acc": 0.51469, "loss_cls": 4.23832, "loss": 4.23832, "time": 0.8148} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.08567, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27234, "top5_acc": 0.52922, "loss_cls": 4.18491, "loss": 4.18491, "time": 0.82078} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.08565, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27969, "top5_acc": 0.52938, "loss_cls": 4.1602, "loss": 4.1602, "time": 0.81765} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.08563, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27359, "top5_acc": 0.52359, "loss_cls": 4.20134, "loss": 4.20134, "time": 0.81568} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.08561, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27406, "top5_acc": 0.53094, "loss_cls": 4.165, "loss": 4.165, "time": 0.81706} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.08559, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26953, "top5_acc": 0.52125, "loss_cls": 4.22547, "loss": 4.22547, "time": 0.8143} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.08557, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26984, "top5_acc": 0.51578, "loss_cls": 4.21122, "loss": 4.21122, "time": 0.81794} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.08555, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27969, "top5_acc": 0.53031, "loss_cls": 4.14405, "loss": 4.14405, "time": 0.82215} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.08553, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27453, "top5_acc": 0.52297, "loss_cls": 4.17707, "loss": 4.17707, "time": 0.81913} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.08551, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26953, "top5_acc": 0.52469, "loss_cls": 4.20455, "loss": 4.20455, "time": 0.81429} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.08549, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26656, "top5_acc": 0.52438, "loss_cls": 4.21721, "loss": 4.21721, "time": 0.81798} +{"mode": "train", "epoch": 38, "iter": 1300, "lr": 0.08547, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2775, "top5_acc": 0.52719, "loss_cls": 4.14865, "loss": 4.14865, "time": 0.82028} +{"mode": "train", "epoch": 38, "iter": 1400, "lr": 0.08545, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26984, "top5_acc": 0.52469, "loss_cls": 4.20902, "loss": 4.20902, "time": 0.81748} +{"mode": "train", "epoch": 38, "iter": 1500, "lr": 0.08543, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26453, "top5_acc": 0.51922, "loss_cls": 4.2086, "loss": 4.2086, "time": 0.81927} +{"mode": "train", "epoch": 38, "iter": 1600, "lr": 0.08541, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26922, "top5_acc": 0.525, "loss_cls": 4.20629, "loss": 4.20629, "time": 0.81964} +{"mode": "train", "epoch": 38, "iter": 1700, "lr": 0.08539, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26641, "top5_acc": 0.52156, "loss_cls": 4.21985, "loss": 4.21985, "time": 0.8173} +{"mode": "train", "epoch": 38, "iter": 1800, "lr": 0.08537, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.27297, "top5_acc": 0.52219, "loss_cls": 4.2069, "loss": 4.2069, "time": 0.81162} +{"mode": "train", "epoch": 38, "iter": 1900, "lr": 0.08535, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26984, "top5_acc": 0.52672, "loss_cls": 4.20225, "loss": 4.20225, "time": 0.81418} +{"mode": "train", "epoch": 38, "iter": 2000, "lr": 0.08533, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2825, "top5_acc": 0.53, "loss_cls": 4.16368, "loss": 4.16368, "time": 0.82026} +{"mode": "train", "epoch": 38, "iter": 2100, "lr": 0.08531, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26859, "top5_acc": 0.51438, "loss_cls": 4.2257, "loss": 4.2257, "time": 0.81702} +{"mode": "train", "epoch": 38, "iter": 2200, "lr": 0.08529, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26969, "top5_acc": 0.51859, "loss_cls": 4.2135, "loss": 4.2135, "time": 0.81559} +{"mode": "train", "epoch": 38, "iter": 2300, "lr": 0.08527, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27234, "top5_acc": 0.525, "loss_cls": 4.15552, "loss": 4.15552, "time": 0.81406} +{"mode": "train", "epoch": 38, "iter": 2400, "lr": 0.08525, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27734, "top5_acc": 0.52516, "loss_cls": 4.20694, "loss": 4.20694, "time": 0.81776} +{"mode": "train", "epoch": 38, "iter": 2500, "lr": 0.08523, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25891, "top5_acc": 0.51438, "loss_cls": 4.23094, "loss": 4.23094, "time": 0.82155} +{"mode": "train", "epoch": 38, "iter": 2600, "lr": 0.08521, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27672, "top5_acc": 0.53156, "loss_cls": 4.16336, "loss": 4.16336, "time": 0.81375} +{"mode": "train", "epoch": 38, "iter": 2700, "lr": 0.08519, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26344, "top5_acc": 0.51562, "loss_cls": 4.24618, "loss": 4.24618, "time": 0.81459} +{"mode": "train", "epoch": 38, "iter": 2800, "lr": 0.08517, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26719, "top5_acc": 0.51594, "loss_cls": 4.2168, "loss": 4.2168, "time": 0.81348} +{"mode": "train", "epoch": 38, "iter": 2900, "lr": 0.08515, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26516, "top5_acc": 0.52078, "loss_cls": 4.22865, "loss": 4.22865, "time": 0.82679} +{"mode": "train", "epoch": 38, "iter": 3000, "lr": 0.08513, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27578, "top5_acc": 0.51969, "loss_cls": 4.19893, "loss": 4.19893, "time": 0.8229} +{"mode": "train", "epoch": 38, "iter": 3100, "lr": 0.08511, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26734, "top5_acc": 0.51219, "loss_cls": 4.21961, "loss": 4.21961, "time": 0.81877} +{"mode": "train", "epoch": 38, "iter": 3200, "lr": 0.08509, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27609, "top5_acc": 0.52719, "loss_cls": 4.18211, "loss": 4.18211, "time": 0.82181} +{"mode": "train", "epoch": 38, "iter": 3300, "lr": 0.08507, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26953, "top5_acc": 0.51234, "loss_cls": 4.22443, "loss": 4.22443, "time": 0.82494} +{"mode": "train", "epoch": 38, "iter": 3400, "lr": 0.08505, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26781, "top5_acc": 0.51938, "loss_cls": 4.22737, "loss": 4.22737, "time": 0.8199} +{"mode": "train", "epoch": 38, "iter": 3500, "lr": 0.08503, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26828, "top5_acc": 0.52234, "loss_cls": 4.19861, "loss": 4.19861, "time": 0.82015} +{"mode": "train", "epoch": 38, "iter": 3600, "lr": 0.08501, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27375, "top5_acc": 0.51953, "loss_cls": 4.24295, "loss": 4.24295, "time": 0.82} +{"mode": "train", "epoch": 38, "iter": 3700, "lr": 0.08499, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26797, "top5_acc": 0.52422, "loss_cls": 4.20105, "loss": 4.20105, "time": 0.81492} +{"mode": "val", "epoch": 38, "iter": 309, "lr": 0.08498, "top1_acc": 0.21208, "top5_acc": 0.45419, "mean_class_accuracy": 0.21181} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.08496, "memory": 15990, "data_time": 1.38642, "top1_acc": 0.26719, "top5_acc": 0.52891, "loss_cls": 4.2007, "loss": 4.2007, "time": 2.4101} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.08494, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28672, "top5_acc": 0.53422, "loss_cls": 4.11014, "loss": 4.11014, "time": 0.8472} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.08492, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27031, "top5_acc": 0.52344, "loss_cls": 4.1919, "loss": 4.1919, "time": 0.84609} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.0849, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26344, "top5_acc": 0.52234, "loss_cls": 4.21228, "loss": 4.21228, "time": 0.85067} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.08488, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27469, "top5_acc": 0.52516, "loss_cls": 4.17536, "loss": 4.17536, "time": 0.8508} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.08486, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26828, "top5_acc": 0.52422, "loss_cls": 4.21011, "loss": 4.21011, "time": 0.8505} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.08484, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26922, "top5_acc": 0.51922, "loss_cls": 4.23052, "loss": 4.23052, "time": 0.85137} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.08482, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28141, "top5_acc": 0.5275, "loss_cls": 4.15101, "loss": 4.15101, "time": 0.85198} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.0848, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28141, "top5_acc": 0.53188, "loss_cls": 4.17434, "loss": 4.17434, "time": 0.851} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.08478, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26828, "top5_acc": 0.51719, "loss_cls": 4.20878, "loss": 4.20878, "time": 0.84992} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.08476, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26859, "top5_acc": 0.5225, "loss_cls": 4.22942, "loss": 4.22942, "time": 0.85004} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.08474, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26766, "top5_acc": 0.51594, "loss_cls": 4.18887, "loss": 4.18887, "time": 0.84789} +{"mode": "train", "epoch": 39, "iter": 1300, "lr": 0.08472, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26875, "top5_acc": 0.51844, "loss_cls": 4.19755, "loss": 4.19755, "time": 0.84715} +{"mode": "train", "epoch": 39, "iter": 1400, "lr": 0.0847, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27828, "top5_acc": 0.53875, "loss_cls": 4.16645, "loss": 4.16645, "time": 0.84162} +{"mode": "train", "epoch": 39, "iter": 1500, "lr": 0.08468, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26969, "top5_acc": 0.52531, "loss_cls": 4.20598, "loss": 4.20598, "time": 0.84316} +{"mode": "train", "epoch": 39, "iter": 1600, "lr": 0.08466, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26969, "top5_acc": 0.52219, "loss_cls": 4.21298, "loss": 4.21298, "time": 0.8324} +{"mode": "train", "epoch": 39, "iter": 1700, "lr": 0.08464, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26828, "top5_acc": 0.52281, "loss_cls": 4.21904, "loss": 4.21904, "time": 0.83179} +{"mode": "train", "epoch": 39, "iter": 1800, "lr": 0.08462, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26734, "top5_acc": 0.52766, "loss_cls": 4.18721, "loss": 4.18721, "time": 0.82811} +{"mode": "train", "epoch": 39, "iter": 1900, "lr": 0.0846, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27047, "top5_acc": 0.52844, "loss_cls": 4.19582, "loss": 4.19582, "time": 0.83285} +{"mode": "train", "epoch": 39, "iter": 2000, "lr": 0.08458, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27391, "top5_acc": 0.5275, "loss_cls": 4.21884, "loss": 4.21884, "time": 0.83336} +{"mode": "train", "epoch": 39, "iter": 2100, "lr": 0.08456, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27281, "top5_acc": 0.52906, "loss_cls": 4.15417, "loss": 4.15417, "time": 0.82654} +{"mode": "train", "epoch": 39, "iter": 2200, "lr": 0.08454, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27578, "top5_acc": 0.52438, "loss_cls": 4.19305, "loss": 4.19305, "time": 0.81272} +{"mode": "train", "epoch": 39, "iter": 2300, "lr": 0.08452, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27062, "top5_acc": 0.51891, "loss_cls": 4.20113, "loss": 4.20113, "time": 0.81599} +{"mode": "train", "epoch": 39, "iter": 2400, "lr": 0.0845, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27125, "top5_acc": 0.52828, "loss_cls": 4.17761, "loss": 4.17761, "time": 0.82058} +{"mode": "train", "epoch": 39, "iter": 2500, "lr": 0.08448, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27234, "top5_acc": 0.52531, "loss_cls": 4.20104, "loss": 4.20104, "time": 0.81728} +{"mode": "train", "epoch": 39, "iter": 2600, "lr": 0.08446, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27891, "top5_acc": 0.52344, "loss_cls": 4.22793, "loss": 4.22793, "time": 0.81739} +{"mode": "train", "epoch": 39, "iter": 2700, "lr": 0.08444, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27328, "top5_acc": 0.52328, "loss_cls": 4.21529, "loss": 4.21529, "time": 0.81258} +{"mode": "train", "epoch": 39, "iter": 2800, "lr": 0.08442, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27672, "top5_acc": 0.53031, "loss_cls": 4.17937, "loss": 4.17937, "time": 0.8207} +{"mode": "train", "epoch": 39, "iter": 2900, "lr": 0.0844, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27281, "top5_acc": 0.52828, "loss_cls": 4.18539, "loss": 4.18539, "time": 0.82843} +{"mode": "train", "epoch": 39, "iter": 3000, "lr": 0.08438, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27734, "top5_acc": 0.52859, "loss_cls": 4.18279, "loss": 4.18279, "time": 0.82063} +{"mode": "train", "epoch": 39, "iter": 3100, "lr": 0.08436, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27078, "top5_acc": 0.5175, "loss_cls": 4.22315, "loss": 4.22315, "time": 0.81482} +{"mode": "train", "epoch": 39, "iter": 3200, "lr": 0.08434, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27641, "top5_acc": 0.52297, "loss_cls": 4.20648, "loss": 4.20648, "time": 0.82147} +{"mode": "train", "epoch": 39, "iter": 3300, "lr": 0.08432, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27281, "top5_acc": 0.52922, "loss_cls": 4.18691, "loss": 4.18691, "time": 0.82225} +{"mode": "train", "epoch": 39, "iter": 3400, "lr": 0.0843, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26875, "top5_acc": 0.51391, "loss_cls": 4.22708, "loss": 4.22708, "time": 0.82273} +{"mode": "train", "epoch": 39, "iter": 3500, "lr": 0.08428, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27062, "top5_acc": 0.52031, "loss_cls": 4.20582, "loss": 4.20582, "time": 0.82008} +{"mode": "train", "epoch": 39, "iter": 3600, "lr": 0.08426, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27734, "top5_acc": 0.52938, "loss_cls": 4.16209, "loss": 4.16209, "time": 0.81439} +{"mode": "train", "epoch": 39, "iter": 3700, "lr": 0.08424, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27406, "top5_acc": 0.52766, "loss_cls": 4.19047, "loss": 4.19047, "time": 0.81621} +{"mode": "val", "epoch": 39, "iter": 309, "lr": 0.08423, "top1_acc": 0.21218, "top5_acc": 0.44841, "mean_class_accuracy": 0.21203} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.08421, "memory": 15990, "data_time": 1.37303, "top1_acc": 0.285, "top5_acc": 0.52984, "loss_cls": 4.15158, "loss": 4.15158, "time": 2.38983} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.08419, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26953, "top5_acc": 0.51781, "loss_cls": 4.20215, "loss": 4.20215, "time": 0.85296} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.08417, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28516, "top5_acc": 0.52734, "loss_cls": 4.14502, "loss": 4.14502, "time": 0.84897} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.08415, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27812, "top5_acc": 0.52781, "loss_cls": 4.14825, "loss": 4.14825, "time": 0.84969} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.08413, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27828, "top5_acc": 0.53547, "loss_cls": 4.15927, "loss": 4.15927, "time": 0.8478} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.08411, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27734, "top5_acc": 0.52812, "loss_cls": 4.18707, "loss": 4.18707, "time": 0.84676} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.08408, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27469, "top5_acc": 0.52594, "loss_cls": 4.16236, "loss": 4.16236, "time": 0.84664} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.08406, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27219, "top5_acc": 0.51, "loss_cls": 4.23676, "loss": 4.23676, "time": 0.85138} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.08404, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26906, "top5_acc": 0.52078, "loss_cls": 4.20349, "loss": 4.20349, "time": 0.84818} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.08402, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27656, "top5_acc": 0.53172, "loss_cls": 4.158, "loss": 4.158, "time": 0.84712} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.084, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27047, "top5_acc": 0.52547, "loss_cls": 4.19731, "loss": 4.19731, "time": 0.84777} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.08398, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27, "top5_acc": 0.51969, "loss_cls": 4.20362, "loss": 4.20362, "time": 0.84869} +{"mode": "train", "epoch": 40, "iter": 1300, "lr": 0.08396, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26328, "top5_acc": 0.52312, "loss_cls": 4.20431, "loss": 4.20431, "time": 0.84542} +{"mode": "train", "epoch": 40, "iter": 1400, "lr": 0.08394, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27875, "top5_acc": 0.52547, "loss_cls": 4.16345, "loss": 4.16345, "time": 0.84873} +{"mode": "train", "epoch": 40, "iter": 1500, "lr": 0.08392, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27734, "top5_acc": 0.51875, "loss_cls": 4.19246, "loss": 4.19246, "time": 0.84236} +{"mode": "train", "epoch": 40, "iter": 1600, "lr": 0.0839, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27234, "top5_acc": 0.52422, "loss_cls": 4.19045, "loss": 4.19045, "time": 0.84928} +{"mode": "train", "epoch": 40, "iter": 1700, "lr": 0.08388, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27062, "top5_acc": 0.52516, "loss_cls": 4.17675, "loss": 4.17675, "time": 0.85046} +{"mode": "train", "epoch": 40, "iter": 1800, "lr": 0.08386, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27031, "top5_acc": 0.51531, "loss_cls": 4.20939, "loss": 4.20939, "time": 0.84767} +{"mode": "train", "epoch": 40, "iter": 1900, "lr": 0.08384, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27625, "top5_acc": 0.52938, "loss_cls": 4.16033, "loss": 4.16033, "time": 0.84717} +{"mode": "train", "epoch": 40, "iter": 2000, "lr": 0.08382, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27125, "top5_acc": 0.53141, "loss_cls": 4.19483, "loss": 4.19483, "time": 0.84774} +{"mode": "train", "epoch": 40, "iter": 2100, "lr": 0.0838, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27125, "top5_acc": 0.51672, "loss_cls": 4.21678, "loss": 4.21678, "time": 0.84988} +{"mode": "train", "epoch": 40, "iter": 2200, "lr": 0.08378, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27047, "top5_acc": 0.52297, "loss_cls": 4.21541, "loss": 4.21541, "time": 0.84982} +{"mode": "train", "epoch": 40, "iter": 2300, "lr": 0.08376, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26656, "top5_acc": 0.51188, "loss_cls": 4.23402, "loss": 4.23402, "time": 0.84926} +{"mode": "train", "epoch": 40, "iter": 2400, "lr": 0.08374, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26375, "top5_acc": 0.51875, "loss_cls": 4.21105, "loss": 4.21105, "time": 0.84739} +{"mode": "train", "epoch": 40, "iter": 2500, "lr": 0.08371, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28047, "top5_acc": 0.52406, "loss_cls": 4.18646, "loss": 4.18646, "time": 0.84715} +{"mode": "train", "epoch": 40, "iter": 2600, "lr": 0.08369, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26781, "top5_acc": 0.51328, "loss_cls": 4.21939, "loss": 4.21939, "time": 0.84967} +{"mode": "train", "epoch": 40, "iter": 2700, "lr": 0.08367, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28578, "top5_acc": 0.53922, "loss_cls": 4.12683, "loss": 4.12683, "time": 0.8532} +{"mode": "train", "epoch": 40, "iter": 2800, "lr": 0.08365, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27844, "top5_acc": 0.53281, "loss_cls": 4.16025, "loss": 4.16025, "time": 0.85004} +{"mode": "train", "epoch": 40, "iter": 2900, "lr": 0.08363, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28219, "top5_acc": 0.52781, "loss_cls": 4.16942, "loss": 4.16942, "time": 0.84862} +{"mode": "train", "epoch": 40, "iter": 3000, "lr": 0.08361, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27281, "top5_acc": 0.51984, "loss_cls": 4.19141, "loss": 4.19141, "time": 0.84681} +{"mode": "train", "epoch": 40, "iter": 3100, "lr": 0.08359, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27266, "top5_acc": 0.52734, "loss_cls": 4.18804, "loss": 4.18804, "time": 0.84659} +{"mode": "train", "epoch": 40, "iter": 3200, "lr": 0.08357, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28312, "top5_acc": 0.52922, "loss_cls": 4.1773, "loss": 4.1773, "time": 0.84616} +{"mode": "train", "epoch": 40, "iter": 3300, "lr": 0.08355, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27266, "top5_acc": 0.53141, "loss_cls": 4.17454, "loss": 4.17454, "time": 0.83895} +{"mode": "train", "epoch": 40, "iter": 3400, "lr": 0.08353, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28719, "top5_acc": 0.53312, "loss_cls": 4.14191, "loss": 4.14191, "time": 0.84774} +{"mode": "train", "epoch": 40, "iter": 3500, "lr": 0.08351, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26094, "top5_acc": 0.51219, "loss_cls": 4.27198, "loss": 4.27198, "time": 0.84499} +{"mode": "train", "epoch": 40, "iter": 3600, "lr": 0.08349, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26359, "top5_acc": 0.51766, "loss_cls": 4.24278, "loss": 4.24278, "time": 0.84728} +{"mode": "train", "epoch": 40, "iter": 3700, "lr": 0.08347, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26656, "top5_acc": 0.51594, "loss_cls": 4.20484, "loss": 4.20484, "time": 0.84623} +{"mode": "val", "epoch": 40, "iter": 309, "lr": 0.08346, "top1_acc": 0.20534, "top5_acc": 0.43448, "mean_class_accuracy": 0.20531} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.08344, "memory": 15990, "data_time": 1.37323, "top1_acc": 0.28375, "top5_acc": 0.53969, "loss_cls": 4.11752, "loss": 4.11752, "time": 2.39326} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.08342, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2675, "top5_acc": 0.52703, "loss_cls": 4.16271, "loss": 4.16271, "time": 0.84812} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.08339, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27094, "top5_acc": 0.52391, "loss_cls": 4.17621, "loss": 4.17621, "time": 0.84709} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.08337, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27609, "top5_acc": 0.5225, "loss_cls": 4.17402, "loss": 4.17402, "time": 0.84271} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.08335, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27641, "top5_acc": 0.52641, "loss_cls": 4.16616, "loss": 4.16616, "time": 0.84611} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.08333, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28, "top5_acc": 0.52766, "loss_cls": 4.16494, "loss": 4.16494, "time": 0.84719} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.08331, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27047, "top5_acc": 0.53297, "loss_cls": 4.18296, "loss": 4.18296, "time": 0.84988} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.08329, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28453, "top5_acc": 0.53391, "loss_cls": 4.1253, "loss": 4.1253, "time": 0.8504} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.08327, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26766, "top5_acc": 0.51062, "loss_cls": 4.21075, "loss": 4.21075, "time": 0.84901} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.08325, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27828, "top5_acc": 0.53516, "loss_cls": 4.12829, "loss": 4.12829, "time": 0.84473} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.08323, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26547, "top5_acc": 0.51609, "loss_cls": 4.24712, "loss": 4.24712, "time": 0.84845} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.08321, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28516, "top5_acc": 0.54, "loss_cls": 4.14066, "loss": 4.14066, "time": 0.84146} +{"mode": "train", "epoch": 41, "iter": 1300, "lr": 0.08319, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27359, "top5_acc": 0.51438, "loss_cls": 4.22233, "loss": 4.22233, "time": 0.84677} +{"mode": "train", "epoch": 41, "iter": 1400, "lr": 0.08316, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27594, "top5_acc": 0.52641, "loss_cls": 4.21063, "loss": 4.21063, "time": 0.85107} +{"mode": "train", "epoch": 41, "iter": 1500, "lr": 0.08314, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27187, "top5_acc": 0.52797, "loss_cls": 4.18077, "loss": 4.18077, "time": 0.8503} +{"mode": "train", "epoch": 41, "iter": 1600, "lr": 0.08312, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27547, "top5_acc": 0.52125, "loss_cls": 4.17259, "loss": 4.17259, "time": 0.84801} +{"mode": "train", "epoch": 41, "iter": 1700, "lr": 0.0831, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27437, "top5_acc": 0.52391, "loss_cls": 4.20566, "loss": 4.20566, "time": 0.85402} +{"mode": "train", "epoch": 41, "iter": 1800, "lr": 0.08308, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28359, "top5_acc": 0.53812, "loss_cls": 4.14444, "loss": 4.14444, "time": 0.84998} +{"mode": "train", "epoch": 41, "iter": 1900, "lr": 0.08306, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27938, "top5_acc": 0.52688, "loss_cls": 4.19216, "loss": 4.19216, "time": 0.84988} +{"mode": "train", "epoch": 41, "iter": 2000, "lr": 0.08304, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27281, "top5_acc": 0.52531, "loss_cls": 4.20162, "loss": 4.20162, "time": 0.84993} +{"mode": "train", "epoch": 41, "iter": 2100, "lr": 0.08302, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26391, "top5_acc": 0.51562, "loss_cls": 4.24712, "loss": 4.24712, "time": 0.84605} +{"mode": "train", "epoch": 41, "iter": 2200, "lr": 0.083, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26703, "top5_acc": 0.51516, "loss_cls": 4.21027, "loss": 4.21027, "time": 0.84854} +{"mode": "train", "epoch": 41, "iter": 2300, "lr": 0.08298, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28047, "top5_acc": 0.52641, "loss_cls": 4.19359, "loss": 4.19359, "time": 0.84227} +{"mode": "train", "epoch": 41, "iter": 2400, "lr": 0.08296, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26875, "top5_acc": 0.52203, "loss_cls": 4.19187, "loss": 4.19187, "time": 0.82895} +{"mode": "train", "epoch": 41, "iter": 2500, "lr": 0.08293, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26469, "top5_acc": 0.52266, "loss_cls": 4.21538, "loss": 4.21538, "time": 0.82506} +{"mode": "train", "epoch": 41, "iter": 2600, "lr": 0.08291, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2725, "top5_acc": 0.52156, "loss_cls": 4.21326, "loss": 4.21326, "time": 0.82126} +{"mode": "train", "epoch": 41, "iter": 2700, "lr": 0.08289, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26687, "top5_acc": 0.52047, "loss_cls": 4.18935, "loss": 4.18935, "time": 0.82988} +{"mode": "train", "epoch": 41, "iter": 2800, "lr": 0.08287, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28047, "top5_acc": 0.52953, "loss_cls": 4.15383, "loss": 4.15383, "time": 0.81523} +{"mode": "train", "epoch": 41, "iter": 2900, "lr": 0.08285, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26969, "top5_acc": 0.51391, "loss_cls": 4.20805, "loss": 4.20805, "time": 0.8197} +{"mode": "train", "epoch": 41, "iter": 3000, "lr": 0.08283, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26547, "top5_acc": 0.51297, "loss_cls": 4.21931, "loss": 4.21931, "time": 0.82154} +{"mode": "train", "epoch": 41, "iter": 3100, "lr": 0.08281, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27688, "top5_acc": 0.53453, "loss_cls": 4.12295, "loss": 4.12295, "time": 0.81697} +{"mode": "train", "epoch": 41, "iter": 3200, "lr": 0.08279, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27781, "top5_acc": 0.52484, "loss_cls": 4.19113, "loss": 4.19113, "time": 0.82274} +{"mode": "train", "epoch": 41, "iter": 3300, "lr": 0.08277, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26672, "top5_acc": 0.52844, "loss_cls": 4.15389, "loss": 4.15389, "time": 0.81523} +{"mode": "train", "epoch": 41, "iter": 3400, "lr": 0.08274, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27891, "top5_acc": 0.53266, "loss_cls": 4.15955, "loss": 4.15955, "time": 0.83096} +{"mode": "train", "epoch": 41, "iter": 3500, "lr": 0.08272, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27812, "top5_acc": 0.52469, "loss_cls": 4.15577, "loss": 4.15577, "time": 0.82212} +{"mode": "train", "epoch": 41, "iter": 3600, "lr": 0.0827, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26406, "top5_acc": 0.51953, "loss_cls": 4.21907, "loss": 4.21907, "time": 0.82525} +{"mode": "train", "epoch": 41, "iter": 3700, "lr": 0.08268, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26547, "top5_acc": 0.52672, "loss_cls": 4.20855, "loss": 4.20855, "time": 0.82725} +{"mode": "val", "epoch": 41, "iter": 309, "lr": 0.08267, "top1_acc": 0.19764, "top5_acc": 0.42283, "mean_class_accuracy": 0.19744} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.08265, "memory": 15990, "data_time": 1.31086, "top1_acc": 0.27156, "top5_acc": 0.5275, "loss_cls": 4.16652, "loss": 4.16652, "time": 2.32311} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.08263, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28438, "top5_acc": 0.53234, "loss_cls": 4.14226, "loss": 4.14226, "time": 0.82874} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.08261, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26922, "top5_acc": 0.53297, "loss_cls": 4.17025, "loss": 4.17025, "time": 0.81973} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.08259, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27453, "top5_acc": 0.52781, "loss_cls": 4.19823, "loss": 4.19823, "time": 0.81697} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.08257, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27312, "top5_acc": 0.51531, "loss_cls": 4.1806, "loss": 4.1806, "time": 0.82104} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.08254, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27516, "top5_acc": 0.52875, "loss_cls": 4.14545, "loss": 4.14545, "time": 0.81424} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.08252, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26531, "top5_acc": 0.51938, "loss_cls": 4.24078, "loss": 4.24078, "time": 0.82001} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.0825, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28312, "top5_acc": 0.52938, "loss_cls": 4.15212, "loss": 4.15212, "time": 0.82055} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.08248, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27625, "top5_acc": 0.53094, "loss_cls": 4.16414, "loss": 4.16414, "time": 0.8203} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.08246, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27766, "top5_acc": 0.54406, "loss_cls": 4.12534, "loss": 4.12534, "time": 0.81811} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.08244, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2775, "top5_acc": 0.52578, "loss_cls": 4.17223, "loss": 4.17223, "time": 0.8169} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.08242, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27344, "top5_acc": 0.51969, "loss_cls": 4.20419, "loss": 4.20419, "time": 0.81604} +{"mode": "train", "epoch": 42, "iter": 1300, "lr": 0.0824, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28922, "top5_acc": 0.54062, "loss_cls": 4.11087, "loss": 4.11087, "time": 0.82214} +{"mode": "train", "epoch": 42, "iter": 1400, "lr": 0.08237, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27594, "top5_acc": 0.52484, "loss_cls": 4.1864, "loss": 4.1864, "time": 0.81737} +{"mode": "train", "epoch": 42, "iter": 1500, "lr": 0.08235, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28125, "top5_acc": 0.53484, "loss_cls": 4.1714, "loss": 4.1714, "time": 0.81654} +{"mode": "train", "epoch": 42, "iter": 1600, "lr": 0.08233, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27969, "top5_acc": 0.53812, "loss_cls": 4.13036, "loss": 4.13036, "time": 0.8174} +{"mode": "train", "epoch": 42, "iter": 1700, "lr": 0.08231, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27609, "top5_acc": 0.53812, "loss_cls": 4.16259, "loss": 4.16259, "time": 0.81461} +{"mode": "train", "epoch": 42, "iter": 1800, "lr": 0.08229, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27812, "top5_acc": 0.52969, "loss_cls": 4.17715, "loss": 4.17715, "time": 0.82345} +{"mode": "train", "epoch": 42, "iter": 1900, "lr": 0.08227, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26094, "top5_acc": 0.51297, "loss_cls": 4.23244, "loss": 4.23244, "time": 0.81666} +{"mode": "train", "epoch": 42, "iter": 2000, "lr": 0.08225, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26875, "top5_acc": 0.52281, "loss_cls": 4.17173, "loss": 4.17173, "time": 0.81801} +{"mode": "train", "epoch": 42, "iter": 2100, "lr": 0.08222, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26953, "top5_acc": 0.51313, "loss_cls": 4.24995, "loss": 4.24995, "time": 0.81561} +{"mode": "train", "epoch": 42, "iter": 2200, "lr": 0.0822, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28438, "top5_acc": 0.53625, "loss_cls": 4.12998, "loss": 4.12998, "time": 0.81528} +{"mode": "train", "epoch": 42, "iter": 2300, "lr": 0.08218, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26578, "top5_acc": 0.51781, "loss_cls": 4.22988, "loss": 4.22988, "time": 0.82247} +{"mode": "train", "epoch": 42, "iter": 2400, "lr": 0.08216, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28281, "top5_acc": 0.52906, "loss_cls": 4.1568, "loss": 4.1568, "time": 0.81961} +{"mode": "train", "epoch": 42, "iter": 2500, "lr": 0.08214, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2775, "top5_acc": 0.52594, "loss_cls": 4.18473, "loss": 4.18473, "time": 0.81662} +{"mode": "train", "epoch": 42, "iter": 2600, "lr": 0.08212, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27359, "top5_acc": 0.52672, "loss_cls": 4.18206, "loss": 4.18206, "time": 0.82442} +{"mode": "train", "epoch": 42, "iter": 2700, "lr": 0.0821, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27688, "top5_acc": 0.52531, "loss_cls": 4.16429, "loss": 4.16429, "time": 0.81781} +{"mode": "train", "epoch": 42, "iter": 2800, "lr": 0.08207, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27156, "top5_acc": 0.52266, "loss_cls": 4.1969, "loss": 4.1969, "time": 0.81851} +{"mode": "train", "epoch": 42, "iter": 2900, "lr": 0.08205, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27422, "top5_acc": 0.53031, "loss_cls": 4.1678, "loss": 4.1678, "time": 0.82633} +{"mode": "train", "epoch": 42, "iter": 3000, "lr": 0.08203, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27625, "top5_acc": 0.52, "loss_cls": 4.19971, "loss": 4.19971, "time": 0.81935} +{"mode": "train", "epoch": 42, "iter": 3100, "lr": 0.08201, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28188, "top5_acc": 0.53203, "loss_cls": 4.17814, "loss": 4.17814, "time": 0.82485} +{"mode": "train", "epoch": 42, "iter": 3200, "lr": 0.08199, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28797, "top5_acc": 0.53922, "loss_cls": 4.12499, "loss": 4.12499, "time": 0.81363} +{"mode": "train", "epoch": 42, "iter": 3300, "lr": 0.08197, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27031, "top5_acc": 0.52375, "loss_cls": 4.23814, "loss": 4.23814, "time": 0.81887} +{"mode": "train", "epoch": 42, "iter": 3400, "lr": 0.08195, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.53219, "loss_cls": 4.15272, "loss": 4.15272, "time": 0.81852} +{"mode": "train", "epoch": 42, "iter": 3500, "lr": 0.08192, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26828, "top5_acc": 0.50672, "loss_cls": 4.24327, "loss": 4.24327, "time": 0.82617} +{"mode": "train", "epoch": 42, "iter": 3600, "lr": 0.0819, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27234, "top5_acc": 0.51594, "loss_cls": 4.21547, "loss": 4.21547, "time": 0.81576} +{"mode": "train", "epoch": 42, "iter": 3700, "lr": 0.08188, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27812, "top5_acc": 0.53484, "loss_cls": 4.14666, "loss": 4.14666, "time": 0.81566} +{"mode": "val", "epoch": 42, "iter": 309, "lr": 0.08187, "top1_acc": 0.20159, "top5_acc": 0.43732, "mean_class_accuracy": 0.20148} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.08185, "memory": 15990, "data_time": 1.3007, "top1_acc": 0.28094, "top5_acc": 0.53094, "loss_cls": 4.13243, "loss": 4.13243, "time": 2.27592} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.08183, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27328, "top5_acc": 0.53125, "loss_cls": 4.15729, "loss": 4.15729, "time": 0.82279} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.08181, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28016, "top5_acc": 0.53766, "loss_cls": 4.17435, "loss": 4.17435, "time": 0.8173} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.08179, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28281, "top5_acc": 0.53438, "loss_cls": 4.13488, "loss": 4.13488, "time": 0.81835} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.08176, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26672, "top5_acc": 0.52188, "loss_cls": 4.21032, "loss": 4.21032, "time": 0.81559} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.08174, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28609, "top5_acc": 0.53016, "loss_cls": 4.12482, "loss": 4.12482, "time": 0.81551} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.08172, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27984, "top5_acc": 0.52781, "loss_cls": 4.17224, "loss": 4.17224, "time": 0.81771} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.0817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28172, "top5_acc": 0.53109, "loss_cls": 4.14987, "loss": 4.14987, "time": 0.82015} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.08168, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28828, "top5_acc": 0.53406, "loss_cls": 4.13173, "loss": 4.13173, "time": 0.8158} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.08166, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26984, "top5_acc": 0.51828, "loss_cls": 4.19891, "loss": 4.19891, "time": 0.81498} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.08163, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27656, "top5_acc": 0.52641, "loss_cls": 4.1325, "loss": 4.1325, "time": 0.81872} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.08161, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27297, "top5_acc": 0.53156, "loss_cls": 4.18638, "loss": 4.18638, "time": 0.81449} +{"mode": "train", "epoch": 43, "iter": 1300, "lr": 0.08159, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26984, "top5_acc": 0.51844, "loss_cls": 4.21178, "loss": 4.21178, "time": 0.81537} +{"mode": "train", "epoch": 43, "iter": 1400, "lr": 0.08157, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27469, "top5_acc": 0.52812, "loss_cls": 4.16434, "loss": 4.16434, "time": 0.81671} +{"mode": "train", "epoch": 43, "iter": 1500, "lr": 0.08155, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27469, "top5_acc": 0.5275, "loss_cls": 4.16446, "loss": 4.16446, "time": 0.81528} +{"mode": "train", "epoch": 43, "iter": 1600, "lr": 0.08153, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27609, "top5_acc": 0.53094, "loss_cls": 4.18252, "loss": 4.18252, "time": 0.81886} +{"mode": "train", "epoch": 43, "iter": 1700, "lr": 0.0815, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2725, "top5_acc": 0.52766, "loss_cls": 4.17892, "loss": 4.17892, "time": 0.81422} +{"mode": "train", "epoch": 43, "iter": 1800, "lr": 0.08148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27234, "top5_acc": 0.52109, "loss_cls": 4.17991, "loss": 4.17991, "time": 0.8168} +{"mode": "train", "epoch": 43, "iter": 1900, "lr": 0.08146, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27984, "top5_acc": 0.53406, "loss_cls": 4.14467, "loss": 4.14467, "time": 0.82242} +{"mode": "train", "epoch": 43, "iter": 2000, "lr": 0.08144, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27953, "top5_acc": 0.51984, "loss_cls": 4.18228, "loss": 4.18228, "time": 0.81973} +{"mode": "train", "epoch": 43, "iter": 2100, "lr": 0.08142, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26172, "top5_acc": 0.52141, "loss_cls": 4.17913, "loss": 4.17913, "time": 0.81561} +{"mode": "train", "epoch": 43, "iter": 2200, "lr": 0.0814, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26953, "top5_acc": 0.5225, "loss_cls": 4.18623, "loss": 4.18623, "time": 0.81832} +{"mode": "train", "epoch": 43, "iter": 2300, "lr": 0.08137, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27453, "top5_acc": 0.52719, "loss_cls": 4.1841, "loss": 4.1841, "time": 0.8194} +{"mode": "train", "epoch": 43, "iter": 2400, "lr": 0.08135, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27516, "top5_acc": 0.52844, "loss_cls": 4.17353, "loss": 4.17353, "time": 0.82055} +{"mode": "train", "epoch": 43, "iter": 2500, "lr": 0.08133, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27906, "top5_acc": 0.52578, "loss_cls": 4.18915, "loss": 4.18915, "time": 0.81785} +{"mode": "train", "epoch": 43, "iter": 2600, "lr": 0.08131, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27672, "top5_acc": 0.52516, "loss_cls": 4.1401, "loss": 4.1401, "time": 0.81918} +{"mode": "train", "epoch": 43, "iter": 2700, "lr": 0.08129, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27562, "top5_acc": 0.525, "loss_cls": 4.20488, "loss": 4.20488, "time": 0.81931} +{"mode": "train", "epoch": 43, "iter": 2800, "lr": 0.08126, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27906, "top5_acc": 0.52375, "loss_cls": 4.15733, "loss": 4.15733, "time": 0.81588} +{"mode": "train", "epoch": 43, "iter": 2900, "lr": 0.08124, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27484, "top5_acc": 0.52438, "loss_cls": 4.18266, "loss": 4.18266, "time": 0.81836} +{"mode": "train", "epoch": 43, "iter": 3000, "lr": 0.08122, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28109, "top5_acc": 0.53031, "loss_cls": 4.16389, "loss": 4.16389, "time": 0.82097} +{"mode": "train", "epoch": 43, "iter": 3100, "lr": 0.0812, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28031, "top5_acc": 0.53203, "loss_cls": 4.15448, "loss": 4.15448, "time": 0.82115} +{"mode": "train", "epoch": 43, "iter": 3200, "lr": 0.08118, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27656, "top5_acc": 0.52828, "loss_cls": 4.15937, "loss": 4.15937, "time": 0.81824} +{"mode": "train", "epoch": 43, "iter": 3300, "lr": 0.08116, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27078, "top5_acc": 0.53844, "loss_cls": 4.15363, "loss": 4.15363, "time": 0.81844} +{"mode": "train", "epoch": 43, "iter": 3400, "lr": 0.08113, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27719, "top5_acc": 0.53203, "loss_cls": 4.17342, "loss": 4.17342, "time": 0.82065} +{"mode": "train", "epoch": 43, "iter": 3500, "lr": 0.08111, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27359, "top5_acc": 0.52891, "loss_cls": 4.20041, "loss": 4.20041, "time": 0.82389} +{"mode": "train", "epoch": 43, "iter": 3600, "lr": 0.08109, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27359, "top5_acc": 0.51641, "loss_cls": 4.23055, "loss": 4.23055, "time": 0.81551} +{"mode": "train", "epoch": 43, "iter": 3700, "lr": 0.08107, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27172, "top5_acc": 0.52672, "loss_cls": 4.19249, "loss": 4.19249, "time": 0.8185} +{"mode": "val", "epoch": 43, "iter": 309, "lr": 0.08106, "top1_acc": 0.2259, "top5_acc": 0.46771, "mean_class_accuracy": 0.22574} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.08104, "memory": 15990, "data_time": 1.28692, "top1_acc": 0.29563, "top5_acc": 0.54812, "loss_cls": 4.05904, "loss": 4.05904, "time": 2.26007} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.08101, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2825, "top5_acc": 0.53203, "loss_cls": 4.14822, "loss": 4.14822, "time": 0.82047} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.08099, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28078, "top5_acc": 0.53609, "loss_cls": 4.1453, "loss": 4.1453, "time": 0.81752} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.08097, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28094, "top5_acc": 0.53234, "loss_cls": 4.15948, "loss": 4.15948, "time": 0.81446} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.08095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2775, "top5_acc": 0.52672, "loss_cls": 4.13495, "loss": 4.13495, "time": 0.82529} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.08093, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28109, "top5_acc": 0.53812, "loss_cls": 4.14464, "loss": 4.14464, "time": 0.81797} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.0809, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28266, "top5_acc": 0.53781, "loss_cls": 4.12853, "loss": 4.12853, "time": 0.8178} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.08088, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27875, "top5_acc": 0.53, "loss_cls": 4.15111, "loss": 4.15111, "time": 0.82317} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.08086, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28828, "top5_acc": 0.54672, "loss_cls": 4.11388, "loss": 4.11388, "time": 0.81488} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.08084, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26219, "top5_acc": 0.50594, "loss_cls": 4.26658, "loss": 4.26658, "time": 0.81494} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.08082, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28156, "top5_acc": 0.54281, "loss_cls": 4.10501, "loss": 4.10501, "time": 0.8245} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.08079, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28328, "top5_acc": 0.53938, "loss_cls": 4.1289, "loss": 4.1289, "time": 0.82434} +{"mode": "train", "epoch": 44, "iter": 1300, "lr": 0.08077, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27531, "top5_acc": 0.53703, "loss_cls": 4.14783, "loss": 4.14783, "time": 0.81887} +{"mode": "train", "epoch": 44, "iter": 1400, "lr": 0.08075, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27234, "top5_acc": 0.53719, "loss_cls": 4.13105, "loss": 4.13105, "time": 0.81659} +{"mode": "train", "epoch": 44, "iter": 1500, "lr": 0.08073, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28203, "top5_acc": 0.53438, "loss_cls": 4.14676, "loss": 4.14676, "time": 0.81997} +{"mode": "train", "epoch": 44, "iter": 1600, "lr": 0.08071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27516, "top5_acc": 0.52453, "loss_cls": 4.16959, "loss": 4.16959, "time": 0.81553} +{"mode": "train", "epoch": 44, "iter": 1700, "lr": 0.08068, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28359, "top5_acc": 0.53609, "loss_cls": 4.13664, "loss": 4.13664, "time": 0.81437} +{"mode": "train", "epoch": 44, "iter": 1800, "lr": 0.08066, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27562, "top5_acc": 0.52969, "loss_cls": 4.15335, "loss": 4.15335, "time": 0.81603} +{"mode": "train", "epoch": 44, "iter": 1900, "lr": 0.08064, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28297, "top5_acc": 0.53391, "loss_cls": 4.13064, "loss": 4.13064, "time": 0.81488} +{"mode": "train", "epoch": 44, "iter": 2000, "lr": 0.08062, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28047, "top5_acc": 0.53156, "loss_cls": 4.13741, "loss": 4.13741, "time": 0.81982} +{"mode": "train", "epoch": 44, "iter": 2100, "lr": 0.0806, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27406, "top5_acc": 0.53359, "loss_cls": 4.18255, "loss": 4.18255, "time": 0.8152} +{"mode": "train", "epoch": 44, "iter": 2200, "lr": 0.08057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26922, "top5_acc": 0.52312, "loss_cls": 4.20773, "loss": 4.20773, "time": 0.82179} +{"mode": "train", "epoch": 44, "iter": 2300, "lr": 0.08055, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27297, "top5_acc": 0.52656, "loss_cls": 4.1865, "loss": 4.1865, "time": 0.81205} +{"mode": "train", "epoch": 44, "iter": 2400, "lr": 0.08053, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28016, "top5_acc": 0.5325, "loss_cls": 4.17369, "loss": 4.17369, "time": 0.81596} +{"mode": "train", "epoch": 44, "iter": 2500, "lr": 0.08051, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27109, "top5_acc": 0.51938, "loss_cls": 4.21838, "loss": 4.21838, "time": 0.81932} +{"mode": "train", "epoch": 44, "iter": 2600, "lr": 0.08048, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27484, "top5_acc": 0.52875, "loss_cls": 4.17982, "loss": 4.17982, "time": 0.82037} +{"mode": "train", "epoch": 44, "iter": 2700, "lr": 0.08046, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27844, "top5_acc": 0.51656, "loss_cls": 4.21742, "loss": 4.21742, "time": 0.81613} +{"mode": "train", "epoch": 44, "iter": 2800, "lr": 0.08044, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.275, "top5_acc": 0.53422, "loss_cls": 4.15246, "loss": 4.15246, "time": 0.81754} +{"mode": "train", "epoch": 44, "iter": 2900, "lr": 0.08042, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27422, "top5_acc": 0.52438, "loss_cls": 4.16137, "loss": 4.16137, "time": 0.82819} +{"mode": "train", "epoch": 44, "iter": 3000, "lr": 0.0804, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26875, "top5_acc": 0.52109, "loss_cls": 4.21036, "loss": 4.21036, "time": 0.81512} +{"mode": "train", "epoch": 44, "iter": 3100, "lr": 0.08037, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27375, "top5_acc": 0.52422, "loss_cls": 4.20773, "loss": 4.20773, "time": 0.82313} +{"mode": "train", "epoch": 44, "iter": 3200, "lr": 0.08035, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27547, "top5_acc": 0.53359, "loss_cls": 4.1846, "loss": 4.1846, "time": 0.8209} +{"mode": "train", "epoch": 44, "iter": 3300, "lr": 0.08033, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27453, "top5_acc": 0.53031, "loss_cls": 4.14651, "loss": 4.14651, "time": 0.81506} +{"mode": "train", "epoch": 44, "iter": 3400, "lr": 0.08031, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27516, "top5_acc": 0.52266, "loss_cls": 4.21432, "loss": 4.21432, "time": 0.81817} +{"mode": "train", "epoch": 44, "iter": 3500, "lr": 0.08028, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27266, "top5_acc": 0.51984, "loss_cls": 4.21279, "loss": 4.21279, "time": 0.82435} +{"mode": "train", "epoch": 44, "iter": 3600, "lr": 0.08026, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2775, "top5_acc": 0.53312, "loss_cls": 4.1362, "loss": 4.1362, "time": 0.82102} +{"mode": "train", "epoch": 44, "iter": 3700, "lr": 0.08024, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27297, "top5_acc": 0.52672, "loss_cls": 4.18353, "loss": 4.18353, "time": 0.82758} +{"mode": "val", "epoch": 44, "iter": 309, "lr": 0.08023, "top1_acc": 0.20762, "top5_acc": 0.44142, "mean_class_accuracy": 0.20732} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.08021, "memory": 15990, "data_time": 1.27301, "top1_acc": 0.28172, "top5_acc": 0.53031, "loss_cls": 4.13075, "loss": 4.13075, "time": 2.24352} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.08019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27578, "top5_acc": 0.53656, "loss_cls": 4.13535, "loss": 4.13535, "time": 0.81753} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.08016, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28172, "top5_acc": 0.53703, "loss_cls": 4.12536, "loss": 4.12536, "time": 0.81411} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.08014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27578, "top5_acc": 0.53547, "loss_cls": 4.17297, "loss": 4.17297, "time": 0.81984} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.08012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2875, "top5_acc": 0.53656, "loss_cls": 4.10835, "loss": 4.10835, "time": 0.81734} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.0801, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28281, "top5_acc": 0.53391, "loss_cls": 4.14373, "loss": 4.14373, "time": 0.81835} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.08007, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28094, "top5_acc": 0.53438, "loss_cls": 4.14303, "loss": 4.14303, "time": 0.81808} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.08005, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27594, "top5_acc": 0.52859, "loss_cls": 4.16554, "loss": 4.16554, "time": 0.81806} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.08003, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27828, "top5_acc": 0.53219, "loss_cls": 4.16118, "loss": 4.16118, "time": 0.8167} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.08001, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27891, "top5_acc": 0.53578, "loss_cls": 4.13161, "loss": 4.13161, "time": 0.81923} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.07998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27812, "top5_acc": 0.52906, "loss_cls": 4.20114, "loss": 4.20114, "time": 0.82321} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.07996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28578, "top5_acc": 0.53141, "loss_cls": 4.14586, "loss": 4.14586, "time": 0.81745} +{"mode": "train", "epoch": 45, "iter": 1300, "lr": 0.07994, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27375, "top5_acc": 0.52703, "loss_cls": 4.15838, "loss": 4.15838, "time": 0.81454} +{"mode": "train", "epoch": 45, "iter": 1400, "lr": 0.07992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28047, "top5_acc": 0.52906, "loss_cls": 4.15254, "loss": 4.15254, "time": 0.81572} +{"mode": "train", "epoch": 45, "iter": 1500, "lr": 0.0799, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27344, "top5_acc": 0.52484, "loss_cls": 4.20841, "loss": 4.20841, "time": 0.81513} +{"mode": "train", "epoch": 45, "iter": 1600, "lr": 0.07987, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27625, "top5_acc": 0.53453, "loss_cls": 4.15259, "loss": 4.15259, "time": 0.82438} +{"mode": "train", "epoch": 45, "iter": 1700, "lr": 0.07985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28531, "top5_acc": 0.5325, "loss_cls": 4.12567, "loss": 4.12567, "time": 0.81502} +{"mode": "train", "epoch": 45, "iter": 1800, "lr": 0.07983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28234, "top5_acc": 0.53469, "loss_cls": 4.14264, "loss": 4.14264, "time": 0.82036} +{"mode": "train", "epoch": 45, "iter": 1900, "lr": 0.07981, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28094, "top5_acc": 0.52062, "loss_cls": 4.17158, "loss": 4.17158, "time": 0.81636} +{"mode": "train", "epoch": 45, "iter": 2000, "lr": 0.07978, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28094, "top5_acc": 0.52828, "loss_cls": 4.14814, "loss": 4.14814, "time": 0.81279} +{"mode": "train", "epoch": 45, "iter": 2100, "lr": 0.07976, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.53641, "loss_cls": 4.13199, "loss": 4.13199, "time": 0.81638} +{"mode": "train", "epoch": 45, "iter": 2200, "lr": 0.07974, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28469, "top5_acc": 0.53125, "loss_cls": 4.14569, "loss": 4.14569, "time": 0.81992} +{"mode": "train", "epoch": 45, "iter": 2300, "lr": 0.07972, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28344, "top5_acc": 0.53578, "loss_cls": 4.1305, "loss": 4.1305, "time": 0.81553} +{"mode": "train", "epoch": 45, "iter": 2400, "lr": 0.07969, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27703, "top5_acc": 0.52938, "loss_cls": 4.17957, "loss": 4.17957, "time": 0.81891} +{"mode": "train", "epoch": 45, "iter": 2500, "lr": 0.07967, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27656, "top5_acc": 0.52562, "loss_cls": 4.18763, "loss": 4.18763, "time": 0.82084} +{"mode": "train", "epoch": 45, "iter": 2600, "lr": 0.07965, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27984, "top5_acc": 0.52578, "loss_cls": 4.17023, "loss": 4.17023, "time": 0.81908} +{"mode": "train", "epoch": 45, "iter": 2700, "lr": 0.07963, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27625, "top5_acc": 0.53406, "loss_cls": 4.18326, "loss": 4.18326, "time": 0.81968} +{"mode": "train", "epoch": 45, "iter": 2800, "lr": 0.0796, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27469, "top5_acc": 0.52969, "loss_cls": 4.15787, "loss": 4.15787, "time": 0.81905} +{"mode": "train", "epoch": 45, "iter": 2900, "lr": 0.07958, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27297, "top5_acc": 0.52047, "loss_cls": 4.17609, "loss": 4.17609, "time": 0.8179} +{"mode": "train", "epoch": 45, "iter": 3000, "lr": 0.07956, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27406, "top5_acc": 0.51891, "loss_cls": 4.19773, "loss": 4.19773, "time": 0.81978} +{"mode": "train", "epoch": 45, "iter": 3100, "lr": 0.07954, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28016, "top5_acc": 0.53828, "loss_cls": 4.14485, "loss": 4.14485, "time": 0.81955} +{"mode": "train", "epoch": 45, "iter": 3200, "lr": 0.07951, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28344, "top5_acc": 0.53562, "loss_cls": 4.11618, "loss": 4.11618, "time": 0.81957} +{"mode": "train", "epoch": 45, "iter": 3300, "lr": 0.07949, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27781, "top5_acc": 0.52969, "loss_cls": 4.16118, "loss": 4.16118, "time": 0.81646} +{"mode": "train", "epoch": 45, "iter": 3400, "lr": 0.07947, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27812, "top5_acc": 0.52984, "loss_cls": 4.13455, "loss": 4.13455, "time": 0.81607} +{"mode": "train", "epoch": 45, "iter": 3500, "lr": 0.07945, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27172, "top5_acc": 0.52734, "loss_cls": 4.17435, "loss": 4.17435, "time": 0.82261} +{"mode": "train", "epoch": 45, "iter": 3600, "lr": 0.07942, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27734, "top5_acc": 0.52938, "loss_cls": 4.16156, "loss": 4.16156, "time": 0.81768} +{"mode": "train", "epoch": 45, "iter": 3700, "lr": 0.0794, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27359, "top5_acc": 0.53531, "loss_cls": 4.16554, "loss": 4.16554, "time": 0.81942} +{"mode": "val", "epoch": 45, "iter": 309, "lr": 0.07939, "top1_acc": 0.20554, "top5_acc": 0.44628, "mean_class_accuracy": 0.20538} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.07937, "memory": 15990, "data_time": 1.27389, "top1_acc": 0.28734, "top5_acc": 0.53109, "loss_cls": 4.13504, "loss": 4.13504, "time": 2.26533} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.07934, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28891, "top5_acc": 0.53516, "loss_cls": 4.10988, "loss": 4.10988, "time": 0.81547} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.07932, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29094, "top5_acc": 0.54438, "loss_cls": 4.11482, "loss": 4.11482, "time": 0.82103} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.0793, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28234, "top5_acc": 0.535, "loss_cls": 4.12664, "loss": 4.12664, "time": 0.81676} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.07928, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28328, "top5_acc": 0.54203, "loss_cls": 4.10382, "loss": 4.10382, "time": 0.81954} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.07925, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27422, "top5_acc": 0.53391, "loss_cls": 4.14631, "loss": 4.14631, "time": 0.81838} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.07923, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27375, "top5_acc": 0.54594, "loss_cls": 4.12685, "loss": 4.12685, "time": 0.81704} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.07921, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.285, "top5_acc": 0.53719, "loss_cls": 4.12993, "loss": 4.12993, "time": 0.8128} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.07919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27422, "top5_acc": 0.53203, "loss_cls": 4.16497, "loss": 4.16497, "time": 0.81674} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.07916, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28938, "top5_acc": 0.5425, "loss_cls": 4.12063, "loss": 4.12063, "time": 0.81971} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.07914, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27391, "top5_acc": 0.53328, "loss_cls": 4.15282, "loss": 4.15282, "time": 0.82182} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.07912, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27375, "top5_acc": 0.52406, "loss_cls": 4.18034, "loss": 4.18034, "time": 0.81546} +{"mode": "train", "epoch": 46, "iter": 1300, "lr": 0.07909, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28266, "top5_acc": 0.53984, "loss_cls": 4.12633, "loss": 4.12633, "time": 0.82416} +{"mode": "train", "epoch": 46, "iter": 1400, "lr": 0.07907, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27844, "top5_acc": 0.5325, "loss_cls": 4.15344, "loss": 4.15344, "time": 0.81641} +{"mode": "train", "epoch": 46, "iter": 1500, "lr": 0.07905, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29266, "top5_acc": 0.54078, "loss_cls": 4.09188, "loss": 4.09188, "time": 0.81946} +{"mode": "train", "epoch": 46, "iter": 1600, "lr": 0.07903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27469, "top5_acc": 0.52875, "loss_cls": 4.14555, "loss": 4.14555, "time": 0.81648} +{"mode": "train", "epoch": 46, "iter": 1700, "lr": 0.079, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.285, "top5_acc": 0.54344, "loss_cls": 4.12397, "loss": 4.12397, "time": 0.81603} +{"mode": "train", "epoch": 46, "iter": 1800, "lr": 0.07898, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28125, "top5_acc": 0.53672, "loss_cls": 4.12227, "loss": 4.12227, "time": 0.82414} +{"mode": "train", "epoch": 46, "iter": 1900, "lr": 0.07896, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27891, "top5_acc": 0.51688, "loss_cls": 4.18765, "loss": 4.18765, "time": 0.82074} +{"mode": "train", "epoch": 46, "iter": 2000, "lr": 0.07894, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2725, "top5_acc": 0.53203, "loss_cls": 4.16702, "loss": 4.16702, "time": 0.81378} +{"mode": "train", "epoch": 46, "iter": 2100, "lr": 0.07891, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27625, "top5_acc": 0.53203, "loss_cls": 4.15805, "loss": 4.15805, "time": 0.81357} +{"mode": "train", "epoch": 46, "iter": 2200, "lr": 0.07889, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27641, "top5_acc": 0.53219, "loss_cls": 4.16965, "loss": 4.16965, "time": 0.81477} +{"mode": "train", "epoch": 46, "iter": 2300, "lr": 0.07887, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28203, "top5_acc": 0.5275, "loss_cls": 4.15878, "loss": 4.15878, "time": 0.81537} +{"mode": "train", "epoch": 46, "iter": 2400, "lr": 0.07884, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27703, "top5_acc": 0.53625, "loss_cls": 4.12545, "loss": 4.12545, "time": 0.81519} +{"mode": "train", "epoch": 46, "iter": 2500, "lr": 0.07882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27938, "top5_acc": 0.52609, "loss_cls": 4.18334, "loss": 4.18334, "time": 0.81414} +{"mode": "train", "epoch": 46, "iter": 2600, "lr": 0.0788, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27391, "top5_acc": 0.53672, "loss_cls": 4.14371, "loss": 4.14371, "time": 0.82675} +{"mode": "train", "epoch": 46, "iter": 2700, "lr": 0.07878, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28109, "top5_acc": 0.53562, "loss_cls": 4.11718, "loss": 4.11718, "time": 0.82024} +{"mode": "train", "epoch": 46, "iter": 2800, "lr": 0.07875, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28703, "top5_acc": 0.53797, "loss_cls": 4.12707, "loss": 4.12707, "time": 0.82266} +{"mode": "train", "epoch": 46, "iter": 2900, "lr": 0.07873, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27734, "top5_acc": 0.52359, "loss_cls": 4.16546, "loss": 4.16546, "time": 0.82349} +{"mode": "train", "epoch": 46, "iter": 3000, "lr": 0.07871, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28141, "top5_acc": 0.53562, "loss_cls": 4.13428, "loss": 4.13428, "time": 0.8165} +{"mode": "train", "epoch": 46, "iter": 3100, "lr": 0.07868, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27547, "top5_acc": 0.52062, "loss_cls": 4.1929, "loss": 4.1929, "time": 0.82116} +{"mode": "train", "epoch": 46, "iter": 3200, "lr": 0.07866, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27969, "top5_acc": 0.53172, "loss_cls": 4.16556, "loss": 4.16556, "time": 0.81873} +{"mode": "train", "epoch": 46, "iter": 3300, "lr": 0.07864, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27719, "top5_acc": 0.52672, "loss_cls": 4.18357, "loss": 4.18357, "time": 0.81956} +{"mode": "train", "epoch": 46, "iter": 3400, "lr": 0.07862, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27797, "top5_acc": 0.52922, "loss_cls": 4.17183, "loss": 4.17183, "time": 0.81604} +{"mode": "train", "epoch": 46, "iter": 3500, "lr": 0.07859, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28688, "top5_acc": 0.54328, "loss_cls": 4.12456, "loss": 4.12456, "time": 0.82692} +{"mode": "train", "epoch": 46, "iter": 3600, "lr": 0.07857, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27812, "top5_acc": 0.53078, "loss_cls": 4.16385, "loss": 4.16385, "time": 0.82058} +{"mode": "train", "epoch": 46, "iter": 3700, "lr": 0.07855, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27891, "top5_acc": 0.53125, "loss_cls": 4.13534, "loss": 4.13534, "time": 0.82385} +{"mode": "val", "epoch": 46, "iter": 309, "lr": 0.07854, "top1_acc": 0.21319, "top5_acc": 0.44861, "mean_class_accuracy": 0.2129} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.07851, "memory": 15990, "data_time": 1.32881, "top1_acc": 0.28734, "top5_acc": 0.54078, "loss_cls": 4.09017, "loss": 4.09017, "time": 2.31244} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.07849, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28719, "top5_acc": 0.54141, "loss_cls": 4.07827, "loss": 4.07827, "time": 0.82585} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.07847, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28391, "top5_acc": 0.53141, "loss_cls": 4.1241, "loss": 4.1241, "time": 0.82378} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.07844, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28734, "top5_acc": 0.53953, "loss_cls": 4.09296, "loss": 4.09296, "time": 0.81571} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.07842, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28453, "top5_acc": 0.54125, "loss_cls": 4.10313, "loss": 4.10313, "time": 0.81881} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.0784, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27922, "top5_acc": 0.53094, "loss_cls": 4.15694, "loss": 4.15694, "time": 0.82186} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.07838, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27562, "top5_acc": 0.53609, "loss_cls": 4.13205, "loss": 4.13205, "time": 0.81359} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.07835, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2775, "top5_acc": 0.53422, "loss_cls": 4.14763, "loss": 4.14763, "time": 0.81219} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.07833, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27422, "top5_acc": 0.53828, "loss_cls": 4.13813, "loss": 4.13813, "time": 0.81291} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.07831, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28719, "top5_acc": 0.53328, "loss_cls": 4.10868, "loss": 4.10868, "time": 0.81578} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.07828, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26766, "top5_acc": 0.52641, "loss_cls": 4.19456, "loss": 4.19456, "time": 0.81609} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.07826, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27031, "top5_acc": 0.52969, "loss_cls": 4.1637, "loss": 4.1637, "time": 0.81567} +{"mode": "train", "epoch": 47, "iter": 1300, "lr": 0.07824, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28359, "top5_acc": 0.52844, "loss_cls": 4.15563, "loss": 4.15563, "time": 0.81627} +{"mode": "train", "epoch": 47, "iter": 1400, "lr": 0.07821, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28062, "top5_acc": 0.53469, "loss_cls": 4.1244, "loss": 4.1244, "time": 0.81127} +{"mode": "train", "epoch": 47, "iter": 1500, "lr": 0.07819, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26859, "top5_acc": 0.52172, "loss_cls": 4.21333, "loss": 4.21333, "time": 0.816} +{"mode": "train", "epoch": 47, "iter": 1600, "lr": 0.07817, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28516, "top5_acc": 0.53141, "loss_cls": 4.15035, "loss": 4.15035, "time": 0.81421} +{"mode": "train", "epoch": 47, "iter": 1700, "lr": 0.07814, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27938, "top5_acc": 0.53609, "loss_cls": 4.1423, "loss": 4.1423, "time": 0.81474} +{"mode": "train", "epoch": 47, "iter": 1800, "lr": 0.07812, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28281, "top5_acc": 0.54234, "loss_cls": 4.09824, "loss": 4.09824, "time": 0.81694} +{"mode": "train", "epoch": 47, "iter": 1900, "lr": 0.0781, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27594, "top5_acc": 0.52797, "loss_cls": 4.18933, "loss": 4.18933, "time": 0.81437} +{"mode": "train", "epoch": 47, "iter": 2000, "lr": 0.07808, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28375, "top5_acc": 0.52891, "loss_cls": 4.10754, "loss": 4.10754, "time": 0.81948} +{"mode": "train", "epoch": 47, "iter": 2100, "lr": 0.07805, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28391, "top5_acc": 0.53625, "loss_cls": 4.13038, "loss": 4.13038, "time": 0.81544} +{"mode": "train", "epoch": 47, "iter": 2200, "lr": 0.07803, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27859, "top5_acc": 0.52578, "loss_cls": 4.18547, "loss": 4.18547, "time": 0.81601} +{"mode": "train", "epoch": 47, "iter": 2300, "lr": 0.07801, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27781, "top5_acc": 0.52906, "loss_cls": 4.15974, "loss": 4.15974, "time": 0.82195} +{"mode": "train", "epoch": 47, "iter": 2400, "lr": 0.07798, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28578, "top5_acc": 0.54234, "loss_cls": 4.13248, "loss": 4.13248, "time": 0.8122} +{"mode": "train", "epoch": 47, "iter": 2500, "lr": 0.07796, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28172, "top5_acc": 0.52875, "loss_cls": 4.16773, "loss": 4.16773, "time": 0.8176} +{"mode": "train", "epoch": 47, "iter": 2600, "lr": 0.07794, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26609, "top5_acc": 0.52062, "loss_cls": 4.23018, "loss": 4.23018, "time": 0.82228} +{"mode": "train", "epoch": 47, "iter": 2700, "lr": 0.07791, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27, "top5_acc": 0.5175, "loss_cls": 4.21308, "loss": 4.21308, "time": 0.81725} +{"mode": "train", "epoch": 47, "iter": 2800, "lr": 0.07789, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27109, "top5_acc": 0.51766, "loss_cls": 4.22651, "loss": 4.22651, "time": 0.8174} +{"mode": "train", "epoch": 47, "iter": 2900, "lr": 0.07787, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28734, "top5_acc": 0.53922, "loss_cls": 4.09267, "loss": 4.09267, "time": 0.82449} +{"mode": "train", "epoch": 47, "iter": 3000, "lr": 0.07784, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27641, "top5_acc": 0.52844, "loss_cls": 4.13352, "loss": 4.13352, "time": 0.82005} +{"mode": "train", "epoch": 47, "iter": 3100, "lr": 0.07782, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28125, "top5_acc": 0.53281, "loss_cls": 4.12408, "loss": 4.12408, "time": 0.82133} +{"mode": "train", "epoch": 47, "iter": 3200, "lr": 0.0778, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27953, "top5_acc": 0.54, "loss_cls": 4.12336, "loss": 4.12336, "time": 0.81802} +{"mode": "train", "epoch": 47, "iter": 3300, "lr": 0.07777, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28406, "top5_acc": 0.53469, "loss_cls": 4.10897, "loss": 4.10897, "time": 0.81474} +{"mode": "train", "epoch": 47, "iter": 3400, "lr": 0.07775, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27844, "top5_acc": 0.53156, "loss_cls": 4.17253, "loss": 4.17253, "time": 0.82006} +{"mode": "train", "epoch": 47, "iter": 3500, "lr": 0.07773, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28141, "top5_acc": 0.53156, "loss_cls": 4.15512, "loss": 4.15512, "time": 0.8229} +{"mode": "train", "epoch": 47, "iter": 3600, "lr": 0.0777, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26609, "top5_acc": 0.52547, "loss_cls": 4.19124, "loss": 4.19124, "time": 0.83069} +{"mode": "train", "epoch": 47, "iter": 3700, "lr": 0.07768, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28156, "top5_acc": 0.53453, "loss_cls": 4.09238, "loss": 4.09238, "time": 0.82382} +{"mode": "val", "epoch": 47, "iter": 309, "lr": 0.07767, "top1_acc": 0.22666, "top5_acc": 0.46862, "mean_class_accuracy": 0.22632} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.07765, "memory": 15990, "data_time": 1.37929, "top1_acc": 0.28625, "top5_acc": 0.54797, "loss_cls": 4.09452, "loss": 4.09452, "time": 2.3552} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.07762, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28422, "top5_acc": 0.53766, "loss_cls": 4.10101, "loss": 4.10101, "time": 0.82707} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.0776, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27672, "top5_acc": 0.52969, "loss_cls": 4.1462, "loss": 4.1462, "time": 0.81841} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.07758, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28062, "top5_acc": 0.52125, "loss_cls": 4.15546, "loss": 4.15546, "time": 0.81658} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.07755, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28359, "top5_acc": 0.52938, "loss_cls": 4.15716, "loss": 4.15716, "time": 0.81615} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.07753, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28688, "top5_acc": 0.54328, "loss_cls": 4.05865, "loss": 4.05865, "time": 0.81764} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.07751, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28594, "top5_acc": 0.54531, "loss_cls": 4.07529, "loss": 4.07529, "time": 0.81461} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.07748, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29063, "top5_acc": 0.54234, "loss_cls": 4.05446, "loss": 4.05446, "time": 0.82509} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.07746, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28531, "top5_acc": 0.53344, "loss_cls": 4.15174, "loss": 4.15174, "time": 0.81824} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.07744, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27703, "top5_acc": 0.52812, "loss_cls": 4.17383, "loss": 4.17383, "time": 0.815} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.07741, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28, "top5_acc": 0.52828, "loss_cls": 4.16873, "loss": 4.16873, "time": 0.8189} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.07739, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27672, "top5_acc": 0.53109, "loss_cls": 4.19154, "loss": 4.19154, "time": 0.82488} +{"mode": "train", "epoch": 48, "iter": 1300, "lr": 0.07737, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27828, "top5_acc": 0.53344, "loss_cls": 4.16214, "loss": 4.16214, "time": 0.81374} +{"mode": "train", "epoch": 48, "iter": 1400, "lr": 0.07734, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28641, "top5_acc": 0.53125, "loss_cls": 4.11544, "loss": 4.11544, "time": 0.81912} +{"mode": "train", "epoch": 48, "iter": 1500, "lr": 0.07732, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28953, "top5_acc": 0.53484, "loss_cls": 4.14924, "loss": 4.14924, "time": 0.81797} +{"mode": "train", "epoch": 48, "iter": 1600, "lr": 0.0773, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28609, "top5_acc": 0.54078, "loss_cls": 4.09957, "loss": 4.09957, "time": 0.82303} +{"mode": "train", "epoch": 48, "iter": 1700, "lr": 0.07727, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27594, "top5_acc": 0.53219, "loss_cls": 4.1645, "loss": 4.1645, "time": 0.81498} +{"mode": "train", "epoch": 48, "iter": 1800, "lr": 0.07725, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28453, "top5_acc": 0.53484, "loss_cls": 4.12828, "loss": 4.12828, "time": 0.81536} +{"mode": "train", "epoch": 48, "iter": 1900, "lr": 0.07723, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28172, "top5_acc": 0.52406, "loss_cls": 4.17683, "loss": 4.17683, "time": 0.81467} +{"mode": "train", "epoch": 48, "iter": 2000, "lr": 0.0772, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27672, "top5_acc": 0.52484, "loss_cls": 4.16725, "loss": 4.16725, "time": 0.81603} +{"mode": "train", "epoch": 48, "iter": 2100, "lr": 0.07718, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28203, "top5_acc": 0.53562, "loss_cls": 4.12563, "loss": 4.12563, "time": 0.82011} +{"mode": "train", "epoch": 48, "iter": 2200, "lr": 0.07716, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28938, "top5_acc": 0.53406, "loss_cls": 4.12301, "loss": 4.12301, "time": 0.81885} +{"mode": "train", "epoch": 48, "iter": 2300, "lr": 0.07713, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27469, "top5_acc": 0.52875, "loss_cls": 4.18687, "loss": 4.18687, "time": 0.81574} +{"mode": "train", "epoch": 48, "iter": 2400, "lr": 0.07711, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28578, "top5_acc": 0.53906, "loss_cls": 4.13236, "loss": 4.13236, "time": 0.8177} +{"mode": "train", "epoch": 48, "iter": 2500, "lr": 0.07709, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.285, "top5_acc": 0.53359, "loss_cls": 4.13457, "loss": 4.13457, "time": 0.81587} +{"mode": "train", "epoch": 48, "iter": 2600, "lr": 0.07706, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27703, "top5_acc": 0.52547, "loss_cls": 4.18883, "loss": 4.18883, "time": 0.82217} +{"mode": "train", "epoch": 48, "iter": 2700, "lr": 0.07704, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28953, "top5_acc": 0.5475, "loss_cls": 4.10001, "loss": 4.10001, "time": 0.82097} +{"mode": "train", "epoch": 48, "iter": 2800, "lr": 0.07701, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26922, "top5_acc": 0.52797, "loss_cls": 4.19604, "loss": 4.19604, "time": 0.8202} +{"mode": "train", "epoch": 48, "iter": 2900, "lr": 0.07699, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28344, "top5_acc": 0.54422, "loss_cls": 4.11274, "loss": 4.11274, "time": 0.82421} +{"mode": "train", "epoch": 48, "iter": 3000, "lr": 0.07697, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28641, "top5_acc": 0.53797, "loss_cls": 4.11656, "loss": 4.11656, "time": 0.81831} +{"mode": "train", "epoch": 48, "iter": 3100, "lr": 0.07694, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28062, "top5_acc": 0.53547, "loss_cls": 4.13612, "loss": 4.13612, "time": 0.81676} +{"mode": "train", "epoch": 48, "iter": 3200, "lr": 0.07692, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28875, "top5_acc": 0.54359, "loss_cls": 4.08912, "loss": 4.08912, "time": 0.81534} +{"mode": "train", "epoch": 48, "iter": 3300, "lr": 0.0769, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26734, "top5_acc": 0.52266, "loss_cls": 4.17959, "loss": 4.17959, "time": 0.81153} +{"mode": "train", "epoch": 48, "iter": 3400, "lr": 0.07687, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27781, "top5_acc": 0.53656, "loss_cls": 4.12249, "loss": 4.12249, "time": 0.8284} +{"mode": "train", "epoch": 48, "iter": 3500, "lr": 0.07685, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27922, "top5_acc": 0.53078, "loss_cls": 4.14951, "loss": 4.14951, "time": 0.82944} +{"mode": "train", "epoch": 48, "iter": 3600, "lr": 0.07683, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27906, "top5_acc": 0.53438, "loss_cls": 4.13411, "loss": 4.13411, "time": 0.8198} +{"mode": "train", "epoch": 48, "iter": 3700, "lr": 0.0768, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28891, "top5_acc": 0.53609, "loss_cls": 4.14734, "loss": 4.14734, "time": 0.8202} +{"mode": "val", "epoch": 48, "iter": 309, "lr": 0.07679, "top1_acc": 0.20701, "top5_acc": 0.44132, "mean_class_accuracy": 0.20682} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.07677, "memory": 15990, "data_time": 1.35316, "top1_acc": 0.29063, "top5_acc": 0.53781, "loss_cls": 4.09503, "loss": 4.09503, "time": 2.33437} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.07674, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27672, "top5_acc": 0.5425, "loss_cls": 4.09827, "loss": 4.09827, "time": 0.82514} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.07672, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29016, "top5_acc": 0.55016, "loss_cls": 4.0852, "loss": 4.0852, "time": 0.81957} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.0767, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28312, "top5_acc": 0.53406, "loss_cls": 4.15006, "loss": 4.15006, "time": 0.81954} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.07667, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28188, "top5_acc": 0.54328, "loss_cls": 4.09717, "loss": 4.09717, "time": 0.81548} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.07665, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28719, "top5_acc": 0.53516, "loss_cls": 4.13507, "loss": 4.13507, "time": 0.81679} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.07663, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2775, "top5_acc": 0.52516, "loss_cls": 4.16032, "loss": 4.16032, "time": 0.81615} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.0766, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28344, "top5_acc": 0.53484, "loss_cls": 4.14258, "loss": 4.14258, "time": 0.81439} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.07658, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28562, "top5_acc": 0.53703, "loss_cls": 4.13878, "loss": 4.13878, "time": 0.81639} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.07656, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28656, "top5_acc": 0.53109, "loss_cls": 4.14472, "loss": 4.14472, "time": 0.81609} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.07653, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29063, "top5_acc": 0.54234, "loss_cls": 4.11624, "loss": 4.11624, "time": 0.823} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.07651, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28406, "top5_acc": 0.53453, "loss_cls": 4.13209, "loss": 4.13209, "time": 0.81557} +{"mode": "train", "epoch": 49, "iter": 1300, "lr": 0.07648, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27953, "top5_acc": 0.53688, "loss_cls": 4.13701, "loss": 4.13701, "time": 0.81926} +{"mode": "train", "epoch": 49, "iter": 1400, "lr": 0.07646, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28047, "top5_acc": 0.53078, "loss_cls": 4.13496, "loss": 4.13496, "time": 0.81964} +{"mode": "train", "epoch": 49, "iter": 1500, "lr": 0.07644, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27703, "top5_acc": 0.53094, "loss_cls": 4.15643, "loss": 4.15643, "time": 0.81497} +{"mode": "train", "epoch": 49, "iter": 1600, "lr": 0.07641, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27703, "top5_acc": 0.52641, "loss_cls": 4.17192, "loss": 4.17192, "time": 0.81813} +{"mode": "train", "epoch": 49, "iter": 1700, "lr": 0.07639, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28766, "top5_acc": 0.54281, "loss_cls": 4.1067, "loss": 4.1067, "time": 0.81371} +{"mode": "train", "epoch": 49, "iter": 1800, "lr": 0.07637, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27734, "top5_acc": 0.53703, "loss_cls": 4.15319, "loss": 4.15319, "time": 0.81224} +{"mode": "train", "epoch": 49, "iter": 1900, "lr": 0.07634, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28375, "top5_acc": 0.53172, "loss_cls": 4.15264, "loss": 4.15264, "time": 0.8154} +{"mode": "train", "epoch": 49, "iter": 2000, "lr": 0.07632, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.295, "top5_acc": 0.54875, "loss_cls": 4.06183, "loss": 4.06183, "time": 0.81559} +{"mode": "train", "epoch": 49, "iter": 2100, "lr": 0.07629, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28672, "top5_acc": 0.54828, "loss_cls": 4.07337, "loss": 4.07337, "time": 0.81518} +{"mode": "train", "epoch": 49, "iter": 2200, "lr": 0.07627, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2775, "top5_acc": 0.52547, "loss_cls": 4.17178, "loss": 4.17178, "time": 0.81359} +{"mode": "train", "epoch": 49, "iter": 2300, "lr": 0.07625, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28438, "top5_acc": 0.54078, "loss_cls": 4.08795, "loss": 4.08795, "time": 0.81704} +{"mode": "train", "epoch": 49, "iter": 2400, "lr": 0.07622, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28984, "top5_acc": 0.52922, "loss_cls": 4.12463, "loss": 4.12463, "time": 0.8162} +{"mode": "train", "epoch": 49, "iter": 2500, "lr": 0.0762, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28813, "top5_acc": 0.54641, "loss_cls": 4.08122, "loss": 4.08122, "time": 0.81769} +{"mode": "train", "epoch": 49, "iter": 2600, "lr": 0.07618, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27531, "top5_acc": 0.52828, "loss_cls": 4.15934, "loss": 4.15934, "time": 0.82071} +{"mode": "train", "epoch": 49, "iter": 2700, "lr": 0.07615, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28625, "top5_acc": 0.53812, "loss_cls": 4.10596, "loss": 4.10596, "time": 0.81978} +{"mode": "train", "epoch": 49, "iter": 2800, "lr": 0.07613, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27969, "top5_acc": 0.53328, "loss_cls": 4.14698, "loss": 4.14698, "time": 0.81933} +{"mode": "train", "epoch": 49, "iter": 2900, "lr": 0.0761, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28234, "top5_acc": 0.54375, "loss_cls": 4.10614, "loss": 4.10614, "time": 0.82376} +{"mode": "train", "epoch": 49, "iter": 3000, "lr": 0.07608, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28562, "top5_acc": 0.53719, "loss_cls": 4.12531, "loss": 4.12531, "time": 0.82407} +{"mode": "train", "epoch": 49, "iter": 3100, "lr": 0.07606, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28422, "top5_acc": 0.54359, "loss_cls": 4.11189, "loss": 4.11189, "time": 0.82392} +{"mode": "train", "epoch": 49, "iter": 3200, "lr": 0.07603, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29156, "top5_acc": 0.55078, "loss_cls": 4.08575, "loss": 4.08575, "time": 0.82312} +{"mode": "train", "epoch": 49, "iter": 3300, "lr": 0.07601, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28859, "top5_acc": 0.53156, "loss_cls": 4.14757, "loss": 4.14757, "time": 0.81527} +{"mode": "train", "epoch": 49, "iter": 3400, "lr": 0.07598, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28031, "top5_acc": 0.53062, "loss_cls": 4.13188, "loss": 4.13188, "time": 0.81633} +{"mode": "train", "epoch": 49, "iter": 3500, "lr": 0.07596, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28266, "top5_acc": 0.53078, "loss_cls": 4.16875, "loss": 4.16875, "time": 0.81827} +{"mode": "train", "epoch": 49, "iter": 3600, "lr": 0.07594, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28688, "top5_acc": 0.53734, "loss_cls": 4.1305, "loss": 4.1305, "time": 0.81498} +{"mode": "train", "epoch": 49, "iter": 3700, "lr": 0.07591, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28141, "top5_acc": 0.53766, "loss_cls": 4.11886, "loss": 4.11886, "time": 0.81735} +{"mode": "val", "epoch": 49, "iter": 309, "lr": 0.0759, "top1_acc": 0.23087, "top5_acc": 0.46872, "mean_class_accuracy": 0.23061} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.07588, "memory": 15990, "data_time": 1.33999, "top1_acc": 0.29625, "top5_acc": 0.55078, "loss_cls": 4.05181, "loss": 4.05181, "time": 2.3226} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.07585, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2775, "top5_acc": 0.53312, "loss_cls": 4.15711, "loss": 4.15711, "time": 0.81802} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.07583, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27812, "top5_acc": 0.55031, "loss_cls": 4.08621, "loss": 4.08621, "time": 0.82383} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.07581, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28531, "top5_acc": 0.52969, "loss_cls": 4.12987, "loss": 4.12987, "time": 0.81148} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.07578, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28703, "top5_acc": 0.53797, "loss_cls": 4.09482, "loss": 4.09482, "time": 0.8208} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.07576, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28141, "top5_acc": 0.53875, "loss_cls": 4.12427, "loss": 4.12427, "time": 0.8127} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.07573, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27547, "top5_acc": 0.52922, "loss_cls": 4.1884, "loss": 4.1884, "time": 0.81441} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.07571, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28297, "top5_acc": 0.53766, "loss_cls": 4.11083, "loss": 4.11083, "time": 0.81548} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.07569, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28922, "top5_acc": 0.54078, "loss_cls": 4.09456, "loss": 4.09456, "time": 0.81394} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.07566, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29313, "top5_acc": 0.53094, "loss_cls": 4.11305, "loss": 4.11305, "time": 0.81315} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.07564, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29469, "top5_acc": 0.53812, "loss_cls": 4.09335, "loss": 4.09335, "time": 0.81652} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.07561, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27953, "top5_acc": 0.52984, "loss_cls": 4.16172, "loss": 4.16172, "time": 0.81774} +{"mode": "train", "epoch": 50, "iter": 1300, "lr": 0.07559, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27531, "top5_acc": 0.52688, "loss_cls": 4.16033, "loss": 4.16033, "time": 0.82001} +{"mode": "train", "epoch": 50, "iter": 1400, "lr": 0.07557, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28375, "top5_acc": 0.53281, "loss_cls": 4.14825, "loss": 4.14825, "time": 0.81489} +{"mode": "train", "epoch": 50, "iter": 1500, "lr": 0.07554, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28438, "top5_acc": 0.54203, "loss_cls": 4.08592, "loss": 4.08592, "time": 0.81859} +{"mode": "train", "epoch": 50, "iter": 1600, "lr": 0.07552, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28531, "top5_acc": 0.53516, "loss_cls": 4.1114, "loss": 4.1114, "time": 0.81701} +{"mode": "train", "epoch": 50, "iter": 1700, "lr": 0.07549, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28609, "top5_acc": 0.54359, "loss_cls": 4.08837, "loss": 4.08837, "time": 0.81972} +{"mode": "train", "epoch": 50, "iter": 1800, "lr": 0.07547, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2775, "top5_acc": 0.53562, "loss_cls": 4.1334, "loss": 4.1334, "time": 0.8167} +{"mode": "train", "epoch": 50, "iter": 1900, "lr": 0.07545, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28422, "top5_acc": 0.52969, "loss_cls": 4.12606, "loss": 4.12606, "time": 0.81766} +{"mode": "train", "epoch": 50, "iter": 2000, "lr": 0.07542, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29047, "top5_acc": 0.53922, "loss_cls": 4.10704, "loss": 4.10704, "time": 0.81406} +{"mode": "train", "epoch": 50, "iter": 2100, "lr": 0.0754, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.285, "top5_acc": 0.53062, "loss_cls": 4.12265, "loss": 4.12265, "time": 0.81888} +{"mode": "train", "epoch": 50, "iter": 2200, "lr": 0.07537, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27594, "top5_acc": 0.53125, "loss_cls": 4.15486, "loss": 4.15486, "time": 0.81872} +{"mode": "train", "epoch": 50, "iter": 2300, "lr": 0.07535, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28453, "top5_acc": 0.54094, "loss_cls": 4.07977, "loss": 4.07977, "time": 0.81832} +{"mode": "train", "epoch": 50, "iter": 2400, "lr": 0.07533, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28516, "top5_acc": 0.53516, "loss_cls": 4.1546, "loss": 4.1546, "time": 0.82144} +{"mode": "train", "epoch": 50, "iter": 2500, "lr": 0.0753, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28641, "top5_acc": 0.53609, "loss_cls": 4.10439, "loss": 4.10439, "time": 0.81528} +{"mode": "train", "epoch": 50, "iter": 2600, "lr": 0.07528, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28891, "top5_acc": 0.54141, "loss_cls": 4.12811, "loss": 4.12811, "time": 0.82469} +{"mode": "train", "epoch": 50, "iter": 2700, "lr": 0.07525, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27453, "top5_acc": 0.52734, "loss_cls": 4.17143, "loss": 4.17143, "time": 0.8176} +{"mode": "train", "epoch": 50, "iter": 2800, "lr": 0.07523, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28344, "top5_acc": 0.53594, "loss_cls": 4.12095, "loss": 4.12095, "time": 0.82373} +{"mode": "train", "epoch": 50, "iter": 2900, "lr": 0.0752, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27453, "top5_acc": 0.53656, "loss_cls": 4.13275, "loss": 4.13275, "time": 0.82021} +{"mode": "train", "epoch": 50, "iter": 3000, "lr": 0.07518, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28266, "top5_acc": 0.53422, "loss_cls": 4.12927, "loss": 4.12927, "time": 0.82166} +{"mode": "train", "epoch": 50, "iter": 3100, "lr": 0.07516, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28922, "top5_acc": 0.53844, "loss_cls": 4.10476, "loss": 4.10476, "time": 0.82427} +{"mode": "train", "epoch": 50, "iter": 3200, "lr": 0.07513, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28391, "top5_acc": 0.535, "loss_cls": 4.1358, "loss": 4.1358, "time": 0.81876} +{"mode": "train", "epoch": 50, "iter": 3300, "lr": 0.07511, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27234, "top5_acc": 0.53016, "loss_cls": 4.15748, "loss": 4.15748, "time": 0.81509} +{"mode": "train", "epoch": 50, "iter": 3400, "lr": 0.07508, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28266, "top5_acc": 0.54047, "loss_cls": 4.12252, "loss": 4.12252, "time": 0.82023} +{"mode": "train", "epoch": 50, "iter": 3500, "lr": 0.07506, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28781, "top5_acc": 0.54047, "loss_cls": 4.10699, "loss": 4.10699, "time": 0.82235} +{"mode": "train", "epoch": 50, "iter": 3600, "lr": 0.07504, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29172, "top5_acc": 0.54062, "loss_cls": 4.10253, "loss": 4.10253, "time": 0.82137} +{"mode": "train", "epoch": 50, "iter": 3700, "lr": 0.07501, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29203, "top5_acc": 0.54094, "loss_cls": 4.08237, "loss": 4.08237, "time": 0.81781} +{"mode": "val", "epoch": 50, "iter": 309, "lr": 0.075, "top1_acc": 0.19794, "top5_acc": 0.42283, "mean_class_accuracy": 0.19773} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.07498, "memory": 15990, "data_time": 1.35976, "top1_acc": 0.29609, "top5_acc": 0.55062, "loss_cls": 4.05173, "loss": 4.05173, "time": 2.32789} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.07495, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28953, "top5_acc": 0.53922, "loss_cls": 4.08092, "loss": 4.08092, "time": 0.81291} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.07493, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29203, "top5_acc": 0.54359, "loss_cls": 4.07369, "loss": 4.07369, "time": 0.81494} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.0749, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28484, "top5_acc": 0.54016, "loss_cls": 4.11623, "loss": 4.11623, "time": 0.81456} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.07488, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28766, "top5_acc": 0.54172, "loss_cls": 4.09429, "loss": 4.09429, "time": 0.81606} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.07485, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28188, "top5_acc": 0.53984, "loss_cls": 4.10113, "loss": 4.10113, "time": 0.81611} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.07483, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28328, "top5_acc": 0.53953, "loss_cls": 4.11464, "loss": 4.11464, "time": 0.822} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.07481, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28922, "top5_acc": 0.5475, "loss_cls": 4.06401, "loss": 4.06401, "time": 0.81855} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.07478, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27688, "top5_acc": 0.53641, "loss_cls": 4.13364, "loss": 4.13364, "time": 0.81752} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.07476, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28969, "top5_acc": 0.53969, "loss_cls": 4.10506, "loss": 4.10506, "time": 0.81875} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.07473, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28078, "top5_acc": 0.53531, "loss_cls": 4.13628, "loss": 4.13628, "time": 0.81819} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.07471, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28125, "top5_acc": 0.535, "loss_cls": 4.13196, "loss": 4.13196, "time": 0.81448} +{"mode": "train", "epoch": 51, "iter": 1300, "lr": 0.07468, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27812, "top5_acc": 0.53094, "loss_cls": 4.12852, "loss": 4.12852, "time": 0.81835} +{"mode": "train", "epoch": 51, "iter": 1400, "lr": 0.07466, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28109, "top5_acc": 0.52922, "loss_cls": 4.15675, "loss": 4.15675, "time": 0.81487} +{"mode": "train", "epoch": 51, "iter": 1500, "lr": 0.07464, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28766, "top5_acc": 0.53938, "loss_cls": 4.1186, "loss": 4.1186, "time": 0.82087} +{"mode": "train", "epoch": 51, "iter": 1600, "lr": 0.07461, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28719, "top5_acc": 0.54375, "loss_cls": 4.07152, "loss": 4.07152, "time": 0.81883} +{"mode": "train", "epoch": 51, "iter": 1700, "lr": 0.07459, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27484, "top5_acc": 0.52688, "loss_cls": 4.19066, "loss": 4.19066, "time": 0.81255} +{"mode": "train", "epoch": 51, "iter": 1800, "lr": 0.07456, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27828, "top5_acc": 0.53266, "loss_cls": 4.146, "loss": 4.146, "time": 0.81418} +{"mode": "train", "epoch": 51, "iter": 1900, "lr": 0.07454, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29047, "top5_acc": 0.54391, "loss_cls": 4.11971, "loss": 4.11971, "time": 0.81914} +{"mode": "train", "epoch": 51, "iter": 2000, "lr": 0.07451, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28609, "top5_acc": 0.53875, "loss_cls": 4.11643, "loss": 4.11643, "time": 0.8178} +{"mode": "train", "epoch": 51, "iter": 2100, "lr": 0.07449, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27672, "top5_acc": 0.53094, "loss_cls": 4.15642, "loss": 4.15642, "time": 0.81611} +{"mode": "train", "epoch": 51, "iter": 2200, "lr": 0.07447, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28375, "top5_acc": 0.53312, "loss_cls": 4.11208, "loss": 4.11208, "time": 0.82604} +{"mode": "train", "epoch": 51, "iter": 2300, "lr": 0.07444, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28062, "top5_acc": 0.53422, "loss_cls": 4.15998, "loss": 4.15998, "time": 0.8189} +{"mode": "train", "epoch": 51, "iter": 2400, "lr": 0.07442, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28312, "top5_acc": 0.53812, "loss_cls": 4.12065, "loss": 4.12065, "time": 0.81184} +{"mode": "train", "epoch": 51, "iter": 2500, "lr": 0.07439, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.53828, "loss_cls": 4.13963, "loss": 4.13963, "time": 0.81765} +{"mode": "train", "epoch": 51, "iter": 2600, "lr": 0.07437, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28141, "top5_acc": 0.53047, "loss_cls": 4.1266, "loss": 4.1266, "time": 0.82084} +{"mode": "train", "epoch": 51, "iter": 2700, "lr": 0.07434, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28547, "top5_acc": 0.54656, "loss_cls": 4.08468, "loss": 4.08468, "time": 0.8172} +{"mode": "train", "epoch": 51, "iter": 2800, "lr": 0.07432, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28125, "top5_acc": 0.53953, "loss_cls": 4.11187, "loss": 4.11187, "time": 0.81925} +{"mode": "train", "epoch": 51, "iter": 2900, "lr": 0.07429, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29812, "top5_acc": 0.54484, "loss_cls": 4.07762, "loss": 4.07762, "time": 0.82384} +{"mode": "train", "epoch": 51, "iter": 3000, "lr": 0.07427, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28141, "top5_acc": 0.52984, "loss_cls": 4.12449, "loss": 4.12449, "time": 0.81514} +{"mode": "train", "epoch": 51, "iter": 3100, "lr": 0.07425, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28703, "top5_acc": 0.53844, "loss_cls": 4.1178, "loss": 4.1178, "time": 0.82569} +{"mode": "train", "epoch": 51, "iter": 3200, "lr": 0.07422, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28453, "top5_acc": 0.54141, "loss_cls": 4.10461, "loss": 4.10461, "time": 0.82163} +{"mode": "train", "epoch": 51, "iter": 3300, "lr": 0.0742, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2825, "top5_acc": 0.54172, "loss_cls": 4.0995, "loss": 4.0995, "time": 0.81908} +{"mode": "train", "epoch": 51, "iter": 3400, "lr": 0.07417, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29031, "top5_acc": 0.55359, "loss_cls": 4.06079, "loss": 4.06079, "time": 0.81889} +{"mode": "train", "epoch": 51, "iter": 3500, "lr": 0.07415, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27938, "top5_acc": 0.53625, "loss_cls": 4.11596, "loss": 4.11596, "time": 0.82436} +{"mode": "train", "epoch": 51, "iter": 3600, "lr": 0.07412, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28625, "top5_acc": 0.53938, "loss_cls": 4.10979, "loss": 4.10979, "time": 0.81871} +{"mode": "train", "epoch": 51, "iter": 3700, "lr": 0.0741, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27938, "top5_acc": 0.53547, "loss_cls": 4.13992, "loss": 4.13992, "time": 0.82061} +{"mode": "val", "epoch": 51, "iter": 309, "lr": 0.07409, "top1_acc": 0.21831, "top5_acc": 0.45576, "mean_class_accuracy": 0.21805} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.07406, "memory": 15990, "data_time": 1.3412, "top1_acc": 0.2925, "top5_acc": 0.54797, "loss_cls": 4.08214, "loss": 4.08214, "time": 2.32104} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.07404, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29047, "top5_acc": 0.54094, "loss_cls": 4.06974, "loss": 4.06974, "time": 0.82241} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.07401, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28875, "top5_acc": 0.54297, "loss_cls": 4.08632, "loss": 4.08632, "time": 0.81254} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.07399, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29047, "top5_acc": 0.54656, "loss_cls": 4.10205, "loss": 4.10205, "time": 0.81497} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.07397, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29281, "top5_acc": 0.53312, "loss_cls": 4.0768, "loss": 4.0768, "time": 0.81797} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.07394, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29, "top5_acc": 0.53719, "loss_cls": 4.09473, "loss": 4.09473, "time": 0.82001} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.07392, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27641, "top5_acc": 0.53781, "loss_cls": 4.11318, "loss": 4.11318, "time": 0.81543} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.07389, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26953, "top5_acc": 0.53344, "loss_cls": 4.15219, "loss": 4.15219, "time": 0.82118} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.07387, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27828, "top5_acc": 0.53938, "loss_cls": 4.12278, "loss": 4.12278, "time": 0.81856} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.07384, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2925, "top5_acc": 0.54266, "loss_cls": 4.09039, "loss": 4.09039, "time": 0.81687} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.07382, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27859, "top5_acc": 0.53141, "loss_cls": 4.14779, "loss": 4.14779, "time": 0.81582} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.07379, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29109, "top5_acc": 0.54016, "loss_cls": 4.10137, "loss": 4.10137, "time": 0.8177} +{"mode": "train", "epoch": 52, "iter": 1300, "lr": 0.07377, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27891, "top5_acc": 0.53141, "loss_cls": 4.15264, "loss": 4.15264, "time": 0.81568} +{"mode": "train", "epoch": 52, "iter": 1400, "lr": 0.07374, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28281, "top5_acc": 0.54062, "loss_cls": 4.11283, "loss": 4.11283, "time": 0.81672} +{"mode": "train", "epoch": 52, "iter": 1500, "lr": 0.07372, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28766, "top5_acc": 0.53672, "loss_cls": 4.10271, "loss": 4.10271, "time": 0.81605} +{"mode": "train", "epoch": 52, "iter": 1600, "lr": 0.0737, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28766, "top5_acc": 0.53938, "loss_cls": 4.11439, "loss": 4.11439, "time": 0.81347} +{"mode": "train", "epoch": 52, "iter": 1700, "lr": 0.07367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29609, "top5_acc": 0.54359, "loss_cls": 4.13039, "loss": 4.13039, "time": 0.81943} +{"mode": "train", "epoch": 52, "iter": 1800, "lr": 0.07365, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28438, "top5_acc": 0.54156, "loss_cls": 4.0929, "loss": 4.0929, "time": 0.81828} +{"mode": "train", "epoch": 52, "iter": 1900, "lr": 0.07362, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28797, "top5_acc": 0.53625, "loss_cls": 4.11332, "loss": 4.11332, "time": 0.81506} +{"mode": "train", "epoch": 52, "iter": 2000, "lr": 0.0736, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29125, "top5_acc": 0.54547, "loss_cls": 4.08226, "loss": 4.08226, "time": 0.81528} +{"mode": "train", "epoch": 52, "iter": 2100, "lr": 0.07357, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28094, "top5_acc": 0.54031, "loss_cls": 4.1363, "loss": 4.1363, "time": 0.8182} +{"mode": "train", "epoch": 52, "iter": 2200, "lr": 0.07355, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2825, "top5_acc": 0.53453, "loss_cls": 4.1324, "loss": 4.1324, "time": 0.81488} +{"mode": "train", "epoch": 52, "iter": 2300, "lr": 0.07352, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28297, "top5_acc": 0.53734, "loss_cls": 4.11737, "loss": 4.11737, "time": 0.81565} +{"mode": "train", "epoch": 52, "iter": 2400, "lr": 0.0735, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27547, "top5_acc": 0.52984, "loss_cls": 4.1907, "loss": 4.1907, "time": 0.81062} +{"mode": "train", "epoch": 52, "iter": 2500, "lr": 0.07347, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29078, "top5_acc": 0.54953, "loss_cls": 4.06111, "loss": 4.06111, "time": 0.81999} +{"mode": "train", "epoch": 52, "iter": 2600, "lr": 0.07345, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28641, "top5_acc": 0.5525, "loss_cls": 4.08913, "loss": 4.08913, "time": 0.81994} +{"mode": "train", "epoch": 52, "iter": 2700, "lr": 0.07342, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28609, "top5_acc": 0.54453, "loss_cls": 4.07551, "loss": 4.07551, "time": 0.81877} +{"mode": "train", "epoch": 52, "iter": 2800, "lr": 0.0734, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29531, "top5_acc": 0.53875, "loss_cls": 4.10707, "loss": 4.10707, "time": 0.82526} +{"mode": "train", "epoch": 52, "iter": 2900, "lr": 0.07337, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28578, "top5_acc": 0.54641, "loss_cls": 4.06242, "loss": 4.06242, "time": 0.82251} +{"mode": "train", "epoch": 52, "iter": 3000, "lr": 0.07335, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28672, "top5_acc": 0.53766, "loss_cls": 4.09874, "loss": 4.09874, "time": 0.81579} +{"mode": "train", "epoch": 52, "iter": 3100, "lr": 0.07332, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28516, "top5_acc": 0.54328, "loss_cls": 4.10693, "loss": 4.10693, "time": 0.82019} +{"mode": "train", "epoch": 52, "iter": 3200, "lr": 0.0733, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29563, "top5_acc": 0.54656, "loss_cls": 4.05979, "loss": 4.05979, "time": 0.81193} +{"mode": "train", "epoch": 52, "iter": 3300, "lr": 0.07328, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29141, "top5_acc": 0.53797, "loss_cls": 4.10134, "loss": 4.10134, "time": 0.81465} +{"mode": "train", "epoch": 52, "iter": 3400, "lr": 0.07325, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28906, "top5_acc": 0.5425, "loss_cls": 4.11042, "loss": 4.11042, "time": 0.81476} +{"mode": "train", "epoch": 52, "iter": 3500, "lr": 0.07323, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28344, "top5_acc": 0.54, "loss_cls": 4.10663, "loss": 4.10663, "time": 0.81972} +{"mode": "train", "epoch": 52, "iter": 3600, "lr": 0.0732, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29328, "top5_acc": 0.5375, "loss_cls": 4.08918, "loss": 4.08918, "time": 0.82265} +{"mode": "train", "epoch": 52, "iter": 3700, "lr": 0.07318, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28172, "top5_acc": 0.53656, "loss_cls": 4.12092, "loss": 4.12092, "time": 0.82002} +{"mode": "val", "epoch": 52, "iter": 309, "lr": 0.07317, "top1_acc": 0.21739, "top5_acc": 0.46422, "mean_class_accuracy": 0.21707} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.07314, "memory": 15990, "data_time": 1.35025, "top1_acc": 0.29266, "top5_acc": 0.54094, "loss_cls": 4.06737, "loss": 4.06737, "time": 2.32461} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.07312, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28719, "top5_acc": 0.53844, "loss_cls": 4.08112, "loss": 4.08112, "time": 0.82198} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.07309, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28531, "top5_acc": 0.54031, "loss_cls": 4.12437, "loss": 4.12437, "time": 0.81782} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.07307, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30047, "top5_acc": 0.5525, "loss_cls": 4.03018, "loss": 4.03018, "time": 0.81936} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.07304, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28078, "top5_acc": 0.53906, "loss_cls": 4.1038, "loss": 4.1038, "time": 0.81912} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.07302, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2925, "top5_acc": 0.55031, "loss_cls": 4.07474, "loss": 4.07474, "time": 0.81947} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.07299, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28578, "top5_acc": 0.54672, "loss_cls": 4.08615, "loss": 4.08615, "time": 0.81939} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.07297, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28891, "top5_acc": 0.54547, "loss_cls": 4.08919, "loss": 4.08919, "time": 0.81257} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.07294, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28953, "top5_acc": 0.54188, "loss_cls": 4.0854, "loss": 4.0854, "time": 0.81712} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.07292, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28578, "top5_acc": 0.53016, "loss_cls": 4.13491, "loss": 4.13491, "time": 0.81955} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.07289, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29063, "top5_acc": 0.54766, "loss_cls": 4.0824, "loss": 4.0824, "time": 0.8137} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.07287, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29484, "top5_acc": 0.55141, "loss_cls": 4.04633, "loss": 4.04633, "time": 0.81757} +{"mode": "train", "epoch": 53, "iter": 1300, "lr": 0.07284, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27891, "top5_acc": 0.53234, "loss_cls": 4.1427, "loss": 4.1427, "time": 0.81961} +{"mode": "train", "epoch": 53, "iter": 1400, "lr": 0.07282, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29313, "top5_acc": 0.54297, "loss_cls": 4.12413, "loss": 4.12413, "time": 0.81451} +{"mode": "train", "epoch": 53, "iter": 1500, "lr": 0.07279, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28391, "top5_acc": 0.53859, "loss_cls": 4.09537, "loss": 4.09537, "time": 0.8157} +{"mode": "train", "epoch": 53, "iter": 1600, "lr": 0.07277, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2975, "top5_acc": 0.54953, "loss_cls": 4.07312, "loss": 4.07312, "time": 0.81688} +{"mode": "train", "epoch": 53, "iter": 1700, "lr": 0.07274, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28766, "top5_acc": 0.53234, "loss_cls": 4.11299, "loss": 4.11299, "time": 0.81987} +{"mode": "train", "epoch": 53, "iter": 1800, "lr": 0.07272, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29359, "top5_acc": 0.54312, "loss_cls": 4.06784, "loss": 4.06784, "time": 0.81515} +{"mode": "train", "epoch": 53, "iter": 1900, "lr": 0.07269, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27516, "top5_acc": 0.52984, "loss_cls": 4.15132, "loss": 4.15132, "time": 0.82373} +{"mode": "train", "epoch": 53, "iter": 2000, "lr": 0.07267, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28359, "top5_acc": 0.5425, "loss_cls": 4.08891, "loss": 4.08891, "time": 0.8139} +{"mode": "train", "epoch": 53, "iter": 2100, "lr": 0.07264, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28516, "top5_acc": 0.53438, "loss_cls": 4.10619, "loss": 4.10619, "time": 0.8193} +{"mode": "train", "epoch": 53, "iter": 2200, "lr": 0.07262, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29672, "top5_acc": 0.54797, "loss_cls": 4.04152, "loss": 4.04152, "time": 0.81597} +{"mode": "train", "epoch": 53, "iter": 2300, "lr": 0.07259, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29156, "top5_acc": 0.54531, "loss_cls": 4.0902, "loss": 4.0902, "time": 0.81876} +{"mode": "train", "epoch": 53, "iter": 2400, "lr": 0.07257, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29375, "top5_acc": 0.5375, "loss_cls": 4.09103, "loss": 4.09103, "time": 0.81738} +{"mode": "train", "epoch": 53, "iter": 2500, "lr": 0.07254, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28734, "top5_acc": 0.54453, "loss_cls": 4.11068, "loss": 4.11068, "time": 0.82218} +{"mode": "train", "epoch": 53, "iter": 2600, "lr": 0.07252, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27953, "top5_acc": 0.53859, "loss_cls": 4.12193, "loss": 4.12193, "time": 0.82044} +{"mode": "train", "epoch": 53, "iter": 2700, "lr": 0.07249, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28875, "top5_acc": 0.54109, "loss_cls": 4.11136, "loss": 4.11136, "time": 0.81912} +{"mode": "train", "epoch": 53, "iter": 2800, "lr": 0.07247, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29703, "top5_acc": 0.54656, "loss_cls": 4.08608, "loss": 4.08608, "time": 0.82438} +{"mode": "train", "epoch": 53, "iter": 2900, "lr": 0.07244, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29016, "top5_acc": 0.53906, "loss_cls": 4.10209, "loss": 4.10209, "time": 0.82057} +{"mode": "train", "epoch": 53, "iter": 3000, "lr": 0.07242, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28656, "top5_acc": 0.53906, "loss_cls": 4.11167, "loss": 4.11167, "time": 0.81734} +{"mode": "train", "epoch": 53, "iter": 3100, "lr": 0.07239, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28516, "top5_acc": 0.53438, "loss_cls": 4.12542, "loss": 4.12542, "time": 0.81946} +{"mode": "train", "epoch": 53, "iter": 3200, "lr": 0.07237, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28484, "top5_acc": 0.53344, "loss_cls": 4.13346, "loss": 4.13346, "time": 0.81905} +{"mode": "train", "epoch": 53, "iter": 3300, "lr": 0.07234, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28609, "top5_acc": 0.54422, "loss_cls": 4.09528, "loss": 4.09528, "time": 0.81448} +{"mode": "train", "epoch": 53, "iter": 3400, "lr": 0.07232, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29031, "top5_acc": 0.54281, "loss_cls": 4.09515, "loss": 4.09515, "time": 0.8201} +{"mode": "train", "epoch": 53, "iter": 3500, "lr": 0.07229, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28672, "top5_acc": 0.53016, "loss_cls": 4.13122, "loss": 4.13122, "time": 0.82532} +{"mode": "train", "epoch": 53, "iter": 3600, "lr": 0.07227, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27969, "top5_acc": 0.53328, "loss_cls": 4.12385, "loss": 4.12385, "time": 0.81327} +{"mode": "train", "epoch": 53, "iter": 3700, "lr": 0.07224, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28453, "top5_acc": 0.54203, "loss_cls": 4.12765, "loss": 4.12765, "time": 0.82091} +{"mode": "val", "epoch": 53, "iter": 309, "lr": 0.07223, "top1_acc": 0.22935, "top5_acc": 0.47348, "mean_class_accuracy": 0.22907} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.07221, "memory": 15990, "data_time": 1.36747, "top1_acc": 0.29328, "top5_acc": 0.5575, "loss_cls": 4.01862, "loss": 4.01862, "time": 2.33852} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.07218, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29047, "top5_acc": 0.54656, "loss_cls": 4.05474, "loss": 4.05474, "time": 0.82144} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.07216, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29922, "top5_acc": 0.55297, "loss_cls": 4.0548, "loss": 4.0548, "time": 0.81542} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.07213, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28438, "top5_acc": 0.54516, "loss_cls": 4.10092, "loss": 4.10092, "time": 0.81424} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.07211, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29828, "top5_acc": 0.55719, "loss_cls": 4.04936, "loss": 4.04936, "time": 0.81442} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.07208, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27641, "top5_acc": 0.54016, "loss_cls": 4.10825, "loss": 4.10825, "time": 0.81612} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.07206, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.285, "top5_acc": 0.53688, "loss_cls": 4.12421, "loss": 4.12421, "time": 0.82081} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.07203, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28359, "top5_acc": 0.54766, "loss_cls": 4.07102, "loss": 4.07102, "time": 0.81712} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.07201, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2875, "top5_acc": 0.53906, "loss_cls": 4.1199, "loss": 4.1199, "time": 0.81733} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.07198, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29375, "top5_acc": 0.54859, "loss_cls": 4.0549, "loss": 4.0549, "time": 0.81701} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.07196, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29047, "top5_acc": 0.53828, "loss_cls": 4.09682, "loss": 4.09682, "time": 0.8181} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.07193, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29422, "top5_acc": 0.545, "loss_cls": 4.06564, "loss": 4.06564, "time": 0.81728} +{"mode": "train", "epoch": 54, "iter": 1300, "lr": 0.07191, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29969, "top5_acc": 0.54797, "loss_cls": 4.06946, "loss": 4.06946, "time": 0.82095} +{"mode": "train", "epoch": 54, "iter": 1400, "lr": 0.07188, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28938, "top5_acc": 0.53234, "loss_cls": 4.10677, "loss": 4.10677, "time": 0.81787} +{"mode": "train", "epoch": 54, "iter": 1500, "lr": 0.07186, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30016, "top5_acc": 0.54641, "loss_cls": 4.03694, "loss": 4.03694, "time": 0.81828} +{"mode": "train", "epoch": 54, "iter": 1600, "lr": 0.07183, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27953, "top5_acc": 0.53422, "loss_cls": 4.13352, "loss": 4.13352, "time": 0.81564} +{"mode": "train", "epoch": 54, "iter": 1700, "lr": 0.07181, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28984, "top5_acc": 0.54438, "loss_cls": 4.09034, "loss": 4.09034, "time": 0.81315} +{"mode": "train", "epoch": 54, "iter": 1800, "lr": 0.07178, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29203, "top5_acc": 0.54891, "loss_cls": 4.09167, "loss": 4.09167, "time": 0.81751} +{"mode": "train", "epoch": 54, "iter": 1900, "lr": 0.07176, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.53672, "loss_cls": 4.12529, "loss": 4.12529, "time": 0.81381} +{"mode": "train", "epoch": 54, "iter": 2000, "lr": 0.07173, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28578, "top5_acc": 0.53953, "loss_cls": 4.09828, "loss": 4.09828, "time": 0.81735} +{"mode": "train", "epoch": 54, "iter": 2100, "lr": 0.0717, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29453, "top5_acc": 0.54703, "loss_cls": 4.09912, "loss": 4.09912, "time": 0.81559} +{"mode": "train", "epoch": 54, "iter": 2200, "lr": 0.07168, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28594, "top5_acc": 0.53594, "loss_cls": 4.11991, "loss": 4.11991, "time": 0.82387} +{"mode": "train", "epoch": 54, "iter": 2300, "lr": 0.07165, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28813, "top5_acc": 0.53797, "loss_cls": 4.13191, "loss": 4.13191, "time": 0.81771} +{"mode": "train", "epoch": 54, "iter": 2400, "lr": 0.07163, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28172, "top5_acc": 0.52359, "loss_cls": 4.15459, "loss": 4.15459, "time": 0.82158} +{"mode": "train", "epoch": 54, "iter": 2500, "lr": 0.0716, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29125, "top5_acc": 0.55812, "loss_cls": 4.04132, "loss": 4.04132, "time": 0.827} +{"mode": "train", "epoch": 54, "iter": 2600, "lr": 0.07158, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29109, "top5_acc": 0.54234, "loss_cls": 4.11304, "loss": 4.11304, "time": 0.8231} +{"mode": "train", "epoch": 54, "iter": 2700, "lr": 0.07155, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29281, "top5_acc": 0.55234, "loss_cls": 4.03538, "loss": 4.03538, "time": 0.8231} +{"mode": "train", "epoch": 54, "iter": 2800, "lr": 0.07153, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28594, "top5_acc": 0.53734, "loss_cls": 4.12147, "loss": 4.12147, "time": 0.81934} +{"mode": "train", "epoch": 54, "iter": 2900, "lr": 0.0715, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29328, "top5_acc": 0.54484, "loss_cls": 4.06741, "loss": 4.06741, "time": 0.81915} +{"mode": "train", "epoch": 54, "iter": 3000, "lr": 0.07148, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27156, "top5_acc": 0.52703, "loss_cls": 4.14669, "loss": 4.14669, "time": 0.82385} +{"mode": "train", "epoch": 54, "iter": 3100, "lr": 0.07145, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29328, "top5_acc": 0.53609, "loss_cls": 4.11393, "loss": 4.11393, "time": 0.82756} +{"mode": "train", "epoch": 54, "iter": 3200, "lr": 0.07143, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29375, "top5_acc": 0.54922, "loss_cls": 4.06673, "loss": 4.06673, "time": 0.81537} +{"mode": "train", "epoch": 54, "iter": 3300, "lr": 0.0714, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28656, "top5_acc": 0.53906, "loss_cls": 4.12575, "loss": 4.12575, "time": 0.81463} +{"mode": "train", "epoch": 54, "iter": 3400, "lr": 0.07138, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28484, "top5_acc": 0.53625, "loss_cls": 4.11916, "loss": 4.11916, "time": 0.82537} +{"mode": "train", "epoch": 54, "iter": 3500, "lr": 0.07135, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29859, "top5_acc": 0.54844, "loss_cls": 4.0612, "loss": 4.0612, "time": 0.82053} +{"mode": "train", "epoch": 54, "iter": 3600, "lr": 0.07133, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28156, "top5_acc": 0.53703, "loss_cls": 4.13865, "loss": 4.13865, "time": 0.82216} +{"mode": "train", "epoch": 54, "iter": 3700, "lr": 0.0713, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27625, "top5_acc": 0.54281, "loss_cls": 4.09827, "loss": 4.09827, "time": 0.81811} +{"mode": "val", "epoch": 54, "iter": 309, "lr": 0.07129, "top1_acc": 0.2292, "top5_acc": 0.47683, "mean_class_accuracy": 0.22907} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.07126, "memory": 15990, "data_time": 1.32456, "top1_acc": 0.29844, "top5_acc": 0.56141, "loss_cls": 3.98641, "loss": 3.98641, "time": 2.30914} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.07124, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29016, "top5_acc": 0.5375, "loss_cls": 4.07964, "loss": 4.07964, "time": 0.82594} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.07121, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28813, "top5_acc": 0.54328, "loss_cls": 4.07692, "loss": 4.07692, "time": 0.82008} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.07119, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28906, "top5_acc": 0.53656, "loss_cls": 4.11217, "loss": 4.11217, "time": 0.8181} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.07116, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29234, "top5_acc": 0.55031, "loss_cls": 4.05252, "loss": 4.05252, "time": 0.82153} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.07114, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28984, "top5_acc": 0.54203, "loss_cls": 4.07717, "loss": 4.07717, "time": 0.81866} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.07111, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28859, "top5_acc": 0.53562, "loss_cls": 4.13291, "loss": 4.13291, "time": 0.81619} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.07109, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28984, "top5_acc": 0.53781, "loss_cls": 4.11147, "loss": 4.11147, "time": 0.82357} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.07106, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.295, "top5_acc": 0.54891, "loss_cls": 4.04075, "loss": 4.04075, "time": 0.81763} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.07104, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28703, "top5_acc": 0.54844, "loss_cls": 4.06887, "loss": 4.06887, "time": 0.82132} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.07101, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28719, "top5_acc": 0.5425, "loss_cls": 4.06676, "loss": 4.06676, "time": 0.81323} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.07099, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28828, "top5_acc": 0.54312, "loss_cls": 4.06631, "loss": 4.06631, "time": 0.81803} +{"mode": "train", "epoch": 55, "iter": 1300, "lr": 0.07096, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29406, "top5_acc": 0.54922, "loss_cls": 4.06439, "loss": 4.06439, "time": 0.81962} +{"mode": "train", "epoch": 55, "iter": 1400, "lr": 0.07093, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28813, "top5_acc": 0.54172, "loss_cls": 4.09126, "loss": 4.09126, "time": 0.817} +{"mode": "train", "epoch": 55, "iter": 1500, "lr": 0.07091, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28891, "top5_acc": 0.53609, "loss_cls": 4.10752, "loss": 4.10752, "time": 0.81177} +{"mode": "train", "epoch": 55, "iter": 1600, "lr": 0.07088, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28969, "top5_acc": 0.54719, "loss_cls": 4.07479, "loss": 4.07479, "time": 0.81163} +{"mode": "train", "epoch": 55, "iter": 1700, "lr": 0.07086, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28672, "top5_acc": 0.54, "loss_cls": 4.13629, "loss": 4.13629, "time": 0.82308} +{"mode": "train", "epoch": 55, "iter": 1800, "lr": 0.07083, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28625, "top5_acc": 0.54016, "loss_cls": 4.09729, "loss": 4.09729, "time": 0.82277} +{"mode": "train", "epoch": 55, "iter": 1900, "lr": 0.07081, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30234, "top5_acc": 0.54609, "loss_cls": 4.04233, "loss": 4.04233, "time": 0.81393} +{"mode": "train", "epoch": 55, "iter": 2000, "lr": 0.07078, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29375, "top5_acc": 0.54141, "loss_cls": 4.05399, "loss": 4.05399, "time": 0.81709} +{"mode": "train", "epoch": 55, "iter": 2100, "lr": 0.07076, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28938, "top5_acc": 0.53703, "loss_cls": 4.09969, "loss": 4.09969, "time": 0.81696} +{"mode": "train", "epoch": 55, "iter": 2200, "lr": 0.07073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28406, "top5_acc": 0.53594, "loss_cls": 4.12553, "loss": 4.12553, "time": 0.81814} +{"mode": "train", "epoch": 55, "iter": 2300, "lr": 0.07071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29172, "top5_acc": 0.54812, "loss_cls": 4.06516, "loss": 4.06516, "time": 0.81647} +{"mode": "train", "epoch": 55, "iter": 2400, "lr": 0.07068, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28734, "top5_acc": 0.54109, "loss_cls": 4.09127, "loss": 4.09127, "time": 0.81443} +{"mode": "train", "epoch": 55, "iter": 2500, "lr": 0.07065, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.285, "top5_acc": 0.53797, "loss_cls": 4.10677, "loss": 4.10677, "time": 0.82192} +{"mode": "train", "epoch": 55, "iter": 2600, "lr": 0.07063, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.54172, "loss_cls": 4.09813, "loss": 4.09813, "time": 0.82255} +{"mode": "train", "epoch": 55, "iter": 2700, "lr": 0.0706, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29594, "top5_acc": 0.55109, "loss_cls": 4.06659, "loss": 4.06659, "time": 0.82117} +{"mode": "train", "epoch": 55, "iter": 2800, "lr": 0.07058, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28625, "top5_acc": 0.53672, "loss_cls": 4.08326, "loss": 4.08326, "time": 0.82904} +{"mode": "train", "epoch": 55, "iter": 2900, "lr": 0.07055, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27797, "top5_acc": 0.53312, "loss_cls": 4.15462, "loss": 4.15462, "time": 0.81842} +{"mode": "train", "epoch": 55, "iter": 3000, "lr": 0.07053, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28375, "top5_acc": 0.53719, "loss_cls": 4.11064, "loss": 4.11064, "time": 0.81677} +{"mode": "train", "epoch": 55, "iter": 3100, "lr": 0.0705, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27984, "top5_acc": 0.53562, "loss_cls": 4.12844, "loss": 4.12844, "time": 0.81995} +{"mode": "train", "epoch": 55, "iter": 3200, "lr": 0.07048, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29812, "top5_acc": 0.54219, "loss_cls": 4.08141, "loss": 4.08141, "time": 0.816} +{"mode": "train", "epoch": 55, "iter": 3300, "lr": 0.07045, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30609, "top5_acc": 0.55312, "loss_cls": 4.02666, "loss": 4.02666, "time": 0.81693} +{"mode": "train", "epoch": 55, "iter": 3400, "lr": 0.07043, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28844, "top5_acc": 0.55344, "loss_cls": 4.04544, "loss": 4.04544, "time": 0.82156} +{"mode": "train", "epoch": 55, "iter": 3500, "lr": 0.0704, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29219, "top5_acc": 0.54484, "loss_cls": 4.0994, "loss": 4.0994, "time": 0.82339} +{"mode": "train", "epoch": 55, "iter": 3600, "lr": 0.07037, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2875, "top5_acc": 0.53922, "loss_cls": 4.09167, "loss": 4.09167, "time": 0.82007} +{"mode": "train", "epoch": 55, "iter": 3700, "lr": 0.07035, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28578, "top5_acc": 0.54469, "loss_cls": 4.08262, "loss": 4.08262, "time": 0.81753} +{"mode": "val", "epoch": 55, "iter": 309, "lr": 0.07034, "top1_acc": 0.22864, "top5_acc": 0.46781, "mean_class_accuracy": 0.22849} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.07031, "memory": 15990, "data_time": 1.34017, "top1_acc": 0.28906, "top5_acc": 0.54703, "loss_cls": 4.05967, "loss": 4.05967, "time": 2.32283} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.07029, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28969, "top5_acc": 0.55375, "loss_cls": 4.05485, "loss": 4.05485, "time": 0.8168} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.07026, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29406, "top5_acc": 0.55281, "loss_cls": 4.06927, "loss": 4.06927, "time": 0.81787} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.07023, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29313, "top5_acc": 0.54297, "loss_cls": 4.06684, "loss": 4.06684, "time": 0.81931} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.07021, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28828, "top5_acc": 0.54234, "loss_cls": 4.08941, "loss": 4.08941, "time": 0.81575} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.07018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29891, "top5_acc": 0.55516, "loss_cls": 4.02389, "loss": 4.02389, "time": 0.81461} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.07016, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29453, "top5_acc": 0.545, "loss_cls": 4.0449, "loss": 4.0449, "time": 0.81985} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.07013, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29078, "top5_acc": 0.54484, "loss_cls": 4.06097, "loss": 4.06097, "time": 0.81457} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.07011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28969, "top5_acc": 0.54125, "loss_cls": 4.06684, "loss": 4.06684, "time": 0.81832} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.07008, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28672, "top5_acc": 0.53688, "loss_cls": 4.11024, "loss": 4.11024, "time": 0.81838} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.07006, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29688, "top5_acc": 0.54953, "loss_cls": 4.06858, "loss": 4.06858, "time": 0.81716} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.07003, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29141, "top5_acc": 0.54406, "loss_cls": 4.08735, "loss": 4.08735, "time": 0.81546} +{"mode": "train", "epoch": 56, "iter": 1300, "lr": 0.07, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29313, "top5_acc": 0.55359, "loss_cls": 4.06519, "loss": 4.06519, "time": 0.81575} +{"mode": "train", "epoch": 56, "iter": 1400, "lr": 0.06998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28875, "top5_acc": 0.54266, "loss_cls": 4.10261, "loss": 4.10261, "time": 0.81246} +{"mode": "train", "epoch": 56, "iter": 1500, "lr": 0.06995, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28422, "top5_acc": 0.54281, "loss_cls": 4.10964, "loss": 4.10964, "time": 0.81913} +{"mode": "train", "epoch": 56, "iter": 1600, "lr": 0.06993, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29047, "top5_acc": 0.53625, "loss_cls": 4.11837, "loss": 4.11837, "time": 0.81681} +{"mode": "train", "epoch": 56, "iter": 1700, "lr": 0.0699, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28594, "top5_acc": 0.54328, "loss_cls": 4.07702, "loss": 4.07702, "time": 0.81764} +{"mode": "train", "epoch": 56, "iter": 1800, "lr": 0.06988, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29859, "top5_acc": 0.55156, "loss_cls": 4.04533, "loss": 4.04533, "time": 0.8164} +{"mode": "train", "epoch": 56, "iter": 1900, "lr": 0.06985, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27938, "top5_acc": 0.54031, "loss_cls": 4.09746, "loss": 4.09746, "time": 0.81397} +{"mode": "train", "epoch": 56, "iter": 2000, "lr": 0.06983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29609, "top5_acc": 0.55359, "loss_cls": 4.02571, "loss": 4.02571, "time": 0.81697} +{"mode": "train", "epoch": 56, "iter": 2100, "lr": 0.0698, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29125, "top5_acc": 0.54109, "loss_cls": 4.07898, "loss": 4.07898, "time": 0.81765} +{"mode": "train", "epoch": 56, "iter": 2200, "lr": 0.06977, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29734, "top5_acc": 0.54219, "loss_cls": 4.0776, "loss": 4.0776, "time": 0.82522} +{"mode": "train", "epoch": 56, "iter": 2300, "lr": 0.06975, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29063, "top5_acc": 0.54937, "loss_cls": 4.06074, "loss": 4.06074, "time": 0.82436} +{"mode": "train", "epoch": 56, "iter": 2400, "lr": 0.06972, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29422, "top5_acc": 0.53766, "loss_cls": 4.0801, "loss": 4.0801, "time": 0.81453} +{"mode": "train", "epoch": 56, "iter": 2500, "lr": 0.0697, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2925, "top5_acc": 0.54375, "loss_cls": 4.06426, "loss": 4.06426, "time": 0.81849} +{"mode": "train", "epoch": 56, "iter": 2600, "lr": 0.06967, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29094, "top5_acc": 0.54234, "loss_cls": 4.08844, "loss": 4.08844, "time": 0.81513} +{"mode": "train", "epoch": 56, "iter": 2700, "lr": 0.06965, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27859, "top5_acc": 0.53531, "loss_cls": 4.11844, "loss": 4.11844, "time": 0.82199} +{"mode": "train", "epoch": 56, "iter": 2800, "lr": 0.06962, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29188, "top5_acc": 0.55156, "loss_cls": 4.04627, "loss": 4.04627, "time": 0.83138} +{"mode": "train", "epoch": 56, "iter": 2900, "lr": 0.06959, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28734, "top5_acc": 0.53828, "loss_cls": 4.09982, "loss": 4.09982, "time": 0.82023} +{"mode": "train", "epoch": 56, "iter": 3000, "lr": 0.06957, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28906, "top5_acc": 0.53797, "loss_cls": 4.09691, "loss": 4.09691, "time": 0.81856} +{"mode": "train", "epoch": 56, "iter": 3100, "lr": 0.06954, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29, "top5_acc": 0.54625, "loss_cls": 4.07256, "loss": 4.07256, "time": 0.81936} +{"mode": "train", "epoch": 56, "iter": 3200, "lr": 0.06952, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29047, "top5_acc": 0.54281, "loss_cls": 4.08601, "loss": 4.08601, "time": 0.8195} +{"mode": "train", "epoch": 56, "iter": 3300, "lr": 0.06949, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28906, "top5_acc": 0.53906, "loss_cls": 4.12003, "loss": 4.12003, "time": 0.81407} +{"mode": "train", "epoch": 56, "iter": 3400, "lr": 0.06947, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28875, "top5_acc": 0.54812, "loss_cls": 4.08189, "loss": 4.08189, "time": 0.81808} +{"mode": "train", "epoch": 56, "iter": 3500, "lr": 0.06944, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28688, "top5_acc": 0.53844, "loss_cls": 4.10538, "loss": 4.10538, "time": 0.82202} +{"mode": "train", "epoch": 56, "iter": 3600, "lr": 0.06941, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29219, "top5_acc": 0.5425, "loss_cls": 4.08948, "loss": 4.08948, "time": 0.82583} +{"mode": "train", "epoch": 56, "iter": 3700, "lr": 0.06939, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29844, "top5_acc": 0.55219, "loss_cls": 4.01293, "loss": 4.01293, "time": 0.82187} +{"mode": "val", "epoch": 56, "iter": 309, "lr": 0.06938, "top1_acc": 0.2332, "top5_acc": 0.47718, "mean_class_accuracy": 0.23309} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.06935, "memory": 15990, "data_time": 1.34889, "top1_acc": 0.29375, "top5_acc": 0.55047, "loss_cls": 4.05546, "loss": 4.05546, "time": 2.35522} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.06932, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29406, "top5_acc": 0.55875, "loss_cls": 3.98816, "loss": 3.98816, "time": 0.82026} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.0693, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29516, "top5_acc": 0.54859, "loss_cls": 4.05889, "loss": 4.05889, "time": 0.82207} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.06927, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29984, "top5_acc": 0.55547, "loss_cls": 4.0099, "loss": 4.0099, "time": 0.81434} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.06925, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29656, "top5_acc": 0.55172, "loss_cls": 4.05029, "loss": 4.05029, "time": 0.81695} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.06922, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29266, "top5_acc": 0.55031, "loss_cls": 4.0335, "loss": 4.0335, "time": 0.8206} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.0692, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29906, "top5_acc": 0.55484, "loss_cls": 4.03525, "loss": 4.03525, "time": 0.81511} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.06917, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28125, "top5_acc": 0.54094, "loss_cls": 4.10777, "loss": 4.10777, "time": 0.81572} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.06914, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28359, "top5_acc": 0.53859, "loss_cls": 4.09855, "loss": 4.09855, "time": 0.82056} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.06912, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29875, "top5_acc": 0.55359, "loss_cls": 4.05031, "loss": 4.05031, "time": 0.81784} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.06909, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29219, "top5_acc": 0.54297, "loss_cls": 4.08658, "loss": 4.08658, "time": 0.81426} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.06907, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2925, "top5_acc": 0.54141, "loss_cls": 4.08946, "loss": 4.08946, "time": 0.81619} +{"mode": "train", "epoch": 57, "iter": 1300, "lr": 0.06904, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29406, "top5_acc": 0.55375, "loss_cls": 4.05951, "loss": 4.05951, "time": 0.81557} +{"mode": "train", "epoch": 57, "iter": 1400, "lr": 0.06901, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29641, "top5_acc": 0.54547, "loss_cls": 4.06473, "loss": 4.06473, "time": 0.81873} +{"mode": "train", "epoch": 57, "iter": 1500, "lr": 0.06899, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28391, "top5_acc": 0.53859, "loss_cls": 4.09996, "loss": 4.09996, "time": 0.81658} +{"mode": "train", "epoch": 57, "iter": 1600, "lr": 0.06896, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29547, "top5_acc": 0.54125, "loss_cls": 4.08669, "loss": 4.08669, "time": 0.82036} +{"mode": "train", "epoch": 57, "iter": 1700, "lr": 0.06894, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28891, "top5_acc": 0.55281, "loss_cls": 4.05236, "loss": 4.05236, "time": 0.82111} +{"mode": "train", "epoch": 57, "iter": 1800, "lr": 0.06891, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29219, "top5_acc": 0.54812, "loss_cls": 4.06286, "loss": 4.06286, "time": 0.81742} +{"mode": "train", "epoch": 57, "iter": 1900, "lr": 0.06889, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29875, "top5_acc": 0.54422, "loss_cls": 4.06561, "loss": 4.06561, "time": 0.81817} +{"mode": "train", "epoch": 57, "iter": 2000, "lr": 0.06886, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29313, "top5_acc": 0.54438, "loss_cls": 4.07965, "loss": 4.07965, "time": 0.81694} +{"mode": "train", "epoch": 57, "iter": 2100, "lr": 0.06883, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28375, "top5_acc": 0.53594, "loss_cls": 4.114, "loss": 4.114, "time": 0.82029} +{"mode": "train", "epoch": 57, "iter": 2200, "lr": 0.06881, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29406, "top5_acc": 0.545, "loss_cls": 4.08513, "loss": 4.08513, "time": 0.81297} +{"mode": "train", "epoch": 57, "iter": 2300, "lr": 0.06878, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28953, "top5_acc": 0.55297, "loss_cls": 4.05534, "loss": 4.05534, "time": 0.81838} +{"mode": "train", "epoch": 57, "iter": 2400, "lr": 0.06876, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29812, "top5_acc": 0.54781, "loss_cls": 4.02567, "loss": 4.02567, "time": 0.82167} +{"mode": "train", "epoch": 57, "iter": 2500, "lr": 0.06873, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27641, "top5_acc": 0.53422, "loss_cls": 4.10444, "loss": 4.10444, "time": 0.81792} +{"mode": "train", "epoch": 57, "iter": 2600, "lr": 0.0687, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29109, "top5_acc": 0.54375, "loss_cls": 4.09584, "loss": 4.09584, "time": 0.81528} +{"mode": "train", "epoch": 57, "iter": 2700, "lr": 0.06868, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28438, "top5_acc": 0.53672, "loss_cls": 4.11669, "loss": 4.11669, "time": 0.82478} +{"mode": "train", "epoch": 57, "iter": 2800, "lr": 0.06865, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29844, "top5_acc": 0.54828, "loss_cls": 4.04164, "loss": 4.04164, "time": 0.82787} +{"mode": "train", "epoch": 57, "iter": 2900, "lr": 0.06863, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28297, "top5_acc": 0.5475, "loss_cls": 4.06523, "loss": 4.06523, "time": 0.81863} +{"mode": "train", "epoch": 57, "iter": 3000, "lr": 0.0686, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28859, "top5_acc": 0.54812, "loss_cls": 4.05301, "loss": 4.05301, "time": 0.81854} +{"mode": "train", "epoch": 57, "iter": 3100, "lr": 0.06857, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29422, "top5_acc": 0.54516, "loss_cls": 4.06375, "loss": 4.06375, "time": 0.81833} +{"mode": "train", "epoch": 57, "iter": 3200, "lr": 0.06855, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30141, "top5_acc": 0.55141, "loss_cls": 4.02975, "loss": 4.02975, "time": 0.81656} +{"mode": "train", "epoch": 57, "iter": 3300, "lr": 0.06852, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29391, "top5_acc": 0.55188, "loss_cls": 4.03103, "loss": 4.03103, "time": 0.82054} +{"mode": "train", "epoch": 57, "iter": 3400, "lr": 0.0685, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28859, "top5_acc": 0.5375, "loss_cls": 4.08166, "loss": 4.08166, "time": 0.81669} +{"mode": "train", "epoch": 57, "iter": 3500, "lr": 0.06847, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.295, "top5_acc": 0.54344, "loss_cls": 4.07199, "loss": 4.07199, "time": 0.81824} +{"mode": "train", "epoch": 57, "iter": 3600, "lr": 0.06844, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29234, "top5_acc": 0.54688, "loss_cls": 4.08957, "loss": 4.08957, "time": 0.82033} +{"mode": "train", "epoch": 57, "iter": 3700, "lr": 0.06842, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28766, "top5_acc": 0.54609, "loss_cls": 4.05789, "loss": 4.05789, "time": 0.82317} +{"mode": "val", "epoch": 57, "iter": 309, "lr": 0.06841, "top1_acc": 0.22383, "top5_acc": 0.46153, "mean_class_accuracy": 0.22373} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.06838, "memory": 15990, "data_time": 1.37464, "top1_acc": 0.31141, "top5_acc": 0.57094, "loss_cls": 3.92517, "loss": 3.92517, "time": 2.39712} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.06835, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30703, "top5_acc": 0.56141, "loss_cls": 4.00071, "loss": 4.00071, "time": 0.83665} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.06833, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29906, "top5_acc": 0.55203, "loss_cls": 4.00899, "loss": 4.00899, "time": 0.83321} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.0683, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30016, "top5_acc": 0.54859, "loss_cls": 4.04187, "loss": 4.04187, "time": 0.84811} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.06828, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29594, "top5_acc": 0.55188, "loss_cls": 4.02179, "loss": 4.02179, "time": 0.84073} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.06825, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30766, "top5_acc": 0.55734, "loss_cls": 3.99011, "loss": 3.99011, "time": 0.84103} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.06822, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29203, "top5_acc": 0.55, "loss_cls": 4.03063, "loss": 4.03063, "time": 0.8425} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.0682, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27859, "top5_acc": 0.53703, "loss_cls": 4.11121, "loss": 4.11121, "time": 0.83953} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.06817, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28141, "top5_acc": 0.54953, "loss_cls": 4.09916, "loss": 4.09916, "time": 0.84522} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.06815, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29, "top5_acc": 0.55469, "loss_cls": 4.04985, "loss": 4.04985, "time": 0.84357} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.06812, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29844, "top5_acc": 0.54859, "loss_cls": 4.04799, "loss": 4.04799, "time": 0.84076} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.06809, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29125, "top5_acc": 0.54359, "loss_cls": 4.08158, "loss": 4.08158, "time": 0.83808} +{"mode": "train", "epoch": 58, "iter": 1300, "lr": 0.06807, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29047, "top5_acc": 0.54609, "loss_cls": 4.06522, "loss": 4.06522, "time": 0.83571} +{"mode": "train", "epoch": 58, "iter": 1400, "lr": 0.06804, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29891, "top5_acc": 0.55422, "loss_cls": 4.0326, "loss": 4.0326, "time": 0.83664} +{"mode": "train", "epoch": 58, "iter": 1500, "lr": 0.06802, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29, "top5_acc": 0.54375, "loss_cls": 4.07141, "loss": 4.07141, "time": 0.83784} +{"mode": "train", "epoch": 58, "iter": 1600, "lr": 0.06799, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29812, "top5_acc": 0.54688, "loss_cls": 4.06557, "loss": 4.06557, "time": 0.84672} +{"mode": "train", "epoch": 58, "iter": 1700, "lr": 0.06796, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2925, "top5_acc": 0.54156, "loss_cls": 4.08045, "loss": 4.08045, "time": 0.83689} +{"mode": "train", "epoch": 58, "iter": 1800, "lr": 0.06794, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.295, "top5_acc": 0.54766, "loss_cls": 4.07679, "loss": 4.07679, "time": 0.83785} +{"mode": "train", "epoch": 58, "iter": 1900, "lr": 0.06791, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29703, "top5_acc": 0.53797, "loss_cls": 4.0727, "loss": 4.0727, "time": 0.84102} +{"mode": "train", "epoch": 58, "iter": 2000, "lr": 0.06789, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29234, "top5_acc": 0.54453, "loss_cls": 4.10717, "loss": 4.10717, "time": 0.8445} +{"mode": "train", "epoch": 58, "iter": 2100, "lr": 0.06786, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29078, "top5_acc": 0.55437, "loss_cls": 4.03421, "loss": 4.03421, "time": 0.84294} +{"mode": "train", "epoch": 58, "iter": 2200, "lr": 0.06783, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28797, "top5_acc": 0.53719, "loss_cls": 4.09582, "loss": 4.09582, "time": 0.8403} +{"mode": "train", "epoch": 58, "iter": 2300, "lr": 0.06781, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29812, "top5_acc": 0.55, "loss_cls": 4.05001, "loss": 4.05001, "time": 0.83846} +{"mode": "train", "epoch": 58, "iter": 2400, "lr": 0.06778, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29969, "top5_acc": 0.55297, "loss_cls": 4.03422, "loss": 4.03422, "time": 0.83706} +{"mode": "train", "epoch": 58, "iter": 2500, "lr": 0.06775, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28234, "top5_acc": 0.53781, "loss_cls": 4.10536, "loss": 4.10536, "time": 0.8461} +{"mode": "train", "epoch": 58, "iter": 2600, "lr": 0.06773, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28922, "top5_acc": 0.54625, "loss_cls": 4.06972, "loss": 4.06972, "time": 0.83714} +{"mode": "train", "epoch": 58, "iter": 2700, "lr": 0.0677, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28656, "top5_acc": 0.54703, "loss_cls": 4.08883, "loss": 4.08883, "time": 0.83976} +{"mode": "train", "epoch": 58, "iter": 2800, "lr": 0.06768, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29641, "top5_acc": 0.54812, "loss_cls": 4.07072, "loss": 4.07072, "time": 0.84081} +{"mode": "train", "epoch": 58, "iter": 2900, "lr": 0.06765, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28391, "top5_acc": 0.54047, "loss_cls": 4.08259, "loss": 4.08259, "time": 0.84018} +{"mode": "train", "epoch": 58, "iter": 3000, "lr": 0.06762, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28469, "top5_acc": 0.53703, "loss_cls": 4.10387, "loss": 4.10387, "time": 0.83686} +{"mode": "train", "epoch": 58, "iter": 3100, "lr": 0.0676, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28562, "top5_acc": 0.54344, "loss_cls": 4.07836, "loss": 4.07836, "time": 0.84177} +{"mode": "train", "epoch": 58, "iter": 3200, "lr": 0.06757, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28578, "top5_acc": 0.53531, "loss_cls": 4.10957, "loss": 4.10957, "time": 0.83244} +{"mode": "train", "epoch": 58, "iter": 3300, "lr": 0.06755, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29063, "top5_acc": 0.54641, "loss_cls": 4.06084, "loss": 4.06084, "time": 0.8318} +{"mode": "train", "epoch": 58, "iter": 3400, "lr": 0.06752, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28703, "top5_acc": 0.54156, "loss_cls": 4.09956, "loss": 4.09956, "time": 0.84168} +{"mode": "train", "epoch": 58, "iter": 3500, "lr": 0.06749, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29422, "top5_acc": 0.54609, "loss_cls": 4.04403, "loss": 4.04403, "time": 0.83171} +{"mode": "train", "epoch": 58, "iter": 3600, "lr": 0.06747, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28891, "top5_acc": 0.54094, "loss_cls": 4.11763, "loss": 4.11763, "time": 0.8365} +{"mode": "train", "epoch": 58, "iter": 3700, "lr": 0.06744, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29891, "top5_acc": 0.54609, "loss_cls": 4.03353, "loss": 4.03353, "time": 0.83793} +{"mode": "val", "epoch": 58, "iter": 309, "lr": 0.06743, "top1_acc": 0.23234, "top5_acc": 0.46812, "mean_class_accuracy": 0.23216} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.0674, "memory": 15990, "data_time": 1.36241, "top1_acc": 0.29516, "top5_acc": 0.56078, "loss_cls": 3.99646, "loss": 3.99646, "time": 2.36163} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.06738, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30609, "top5_acc": 0.55656, "loss_cls": 4.01021, "loss": 4.01021, "time": 0.83557} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.06735, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29266, "top5_acc": 0.56047, "loss_cls": 3.99261, "loss": 3.99261, "time": 0.84406} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.06732, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29906, "top5_acc": 0.54953, "loss_cls": 4.01179, "loss": 4.01179, "time": 0.83887} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.0673, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28922, "top5_acc": 0.54453, "loss_cls": 4.07667, "loss": 4.07667, "time": 0.8291} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.06727, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30453, "top5_acc": 0.55641, "loss_cls": 4.01284, "loss": 4.01284, "time": 0.83228} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.06725, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30375, "top5_acc": 0.56125, "loss_cls": 3.99298, "loss": 3.99298, "time": 0.8375} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.06722, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.285, "top5_acc": 0.54484, "loss_cls": 4.082, "loss": 4.082, "time": 0.83965} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.06719, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29031, "top5_acc": 0.54656, "loss_cls": 4.06354, "loss": 4.06354, "time": 0.83584} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.06717, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29281, "top5_acc": 0.54375, "loss_cls": 4.06338, "loss": 4.06338, "time": 0.83108} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.06714, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30078, "top5_acc": 0.54953, "loss_cls": 4.05414, "loss": 4.05414, "time": 0.83117} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.06711, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29766, "top5_acc": 0.54531, "loss_cls": 4.04795, "loss": 4.04795, "time": 0.83358} +{"mode": "train", "epoch": 59, "iter": 1300, "lr": 0.06709, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27906, "top5_acc": 0.53281, "loss_cls": 4.10886, "loss": 4.10886, "time": 0.83411} +{"mode": "train", "epoch": 59, "iter": 1400, "lr": 0.06706, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29906, "top5_acc": 0.54266, "loss_cls": 4.03821, "loss": 4.03821, "time": 0.83847} +{"mode": "train", "epoch": 59, "iter": 1500, "lr": 0.06704, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30172, "top5_acc": 0.54922, "loss_cls": 4.03699, "loss": 4.03699, "time": 0.83647} +{"mode": "train", "epoch": 59, "iter": 1600, "lr": 0.06701, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29875, "top5_acc": 0.55156, "loss_cls": 4.031, "loss": 4.031, "time": 0.83669} +{"mode": "train", "epoch": 59, "iter": 1700, "lr": 0.06698, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29031, "top5_acc": 0.54656, "loss_cls": 4.0819, "loss": 4.0819, "time": 0.83778} +{"mode": "train", "epoch": 59, "iter": 1800, "lr": 0.06696, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28359, "top5_acc": 0.54438, "loss_cls": 4.08637, "loss": 4.08637, "time": 0.84013} +{"mode": "train", "epoch": 59, "iter": 1900, "lr": 0.06693, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29391, "top5_acc": 0.55672, "loss_cls": 4.0569, "loss": 4.0569, "time": 0.83306} +{"mode": "train", "epoch": 59, "iter": 2000, "lr": 0.0669, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29531, "top5_acc": 0.55219, "loss_cls": 4.03988, "loss": 4.03988, "time": 0.82995} +{"mode": "train", "epoch": 59, "iter": 2100, "lr": 0.06688, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29688, "top5_acc": 0.54453, "loss_cls": 4.06732, "loss": 4.06732, "time": 0.81798} +{"mode": "train", "epoch": 59, "iter": 2200, "lr": 0.06685, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28938, "top5_acc": 0.54344, "loss_cls": 4.09665, "loss": 4.09665, "time": 0.81645} +{"mode": "train", "epoch": 59, "iter": 2300, "lr": 0.06682, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29578, "top5_acc": 0.54062, "loss_cls": 4.05829, "loss": 4.05829, "time": 0.81738} +{"mode": "train", "epoch": 59, "iter": 2400, "lr": 0.0668, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28562, "top5_acc": 0.53641, "loss_cls": 4.10581, "loss": 4.10581, "time": 0.818} +{"mode": "train", "epoch": 59, "iter": 2500, "lr": 0.06677, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29281, "top5_acc": 0.54688, "loss_cls": 4.04496, "loss": 4.04496, "time": 0.81731} +{"mode": "train", "epoch": 59, "iter": 2600, "lr": 0.06675, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30469, "top5_acc": 0.55391, "loss_cls": 4.02664, "loss": 4.02664, "time": 0.82532} +{"mode": "train", "epoch": 59, "iter": 2700, "lr": 0.06672, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28453, "top5_acc": 0.54094, "loss_cls": 4.11842, "loss": 4.11842, "time": 0.8154} +{"mode": "train", "epoch": 59, "iter": 2800, "lr": 0.06669, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28844, "top5_acc": 0.55141, "loss_cls": 4.07489, "loss": 4.07489, "time": 0.82708} +{"mode": "train", "epoch": 59, "iter": 2900, "lr": 0.06667, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29703, "top5_acc": 0.5425, "loss_cls": 4.05106, "loss": 4.05106, "time": 0.8141} +{"mode": "train", "epoch": 59, "iter": 3000, "lr": 0.06664, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29297, "top5_acc": 0.54937, "loss_cls": 4.06167, "loss": 4.06167, "time": 0.81974} +{"mode": "train", "epoch": 59, "iter": 3100, "lr": 0.06661, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28312, "top5_acc": 0.52969, "loss_cls": 4.12974, "loss": 4.12974, "time": 0.82045} +{"mode": "train", "epoch": 59, "iter": 3200, "lr": 0.06659, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29172, "top5_acc": 0.54281, "loss_cls": 4.06766, "loss": 4.06766, "time": 0.82553} +{"mode": "train", "epoch": 59, "iter": 3300, "lr": 0.06656, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29547, "top5_acc": 0.55625, "loss_cls": 4.05668, "loss": 4.05668, "time": 0.81939} +{"mode": "train", "epoch": 59, "iter": 3400, "lr": 0.06653, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29875, "top5_acc": 0.55688, "loss_cls": 4.02138, "loss": 4.02138, "time": 0.81864} +{"mode": "train", "epoch": 59, "iter": 3500, "lr": 0.06651, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30297, "top5_acc": 0.55547, "loss_cls": 4.01368, "loss": 4.01368, "time": 0.82746} +{"mode": "train", "epoch": 59, "iter": 3600, "lr": 0.06648, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30031, "top5_acc": 0.55422, "loss_cls": 4.02123, "loss": 4.02123, "time": 0.82448} +{"mode": "train", "epoch": 59, "iter": 3700, "lr": 0.06646, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28922, "top5_acc": 0.53406, "loss_cls": 4.12927, "loss": 4.12927, "time": 0.81522} +{"mode": "val", "epoch": 59, "iter": 309, "lr": 0.06644, "top1_acc": 0.23801, "top5_acc": 0.48817, "mean_class_accuracy": 0.23799} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.06642, "memory": 15990, "data_time": 1.3849, "top1_acc": 0.3025, "top5_acc": 0.56109, "loss_cls": 3.99202, "loss": 3.99202, "time": 2.38349} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.06639, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29672, "top5_acc": 0.55219, "loss_cls": 4.0205, "loss": 4.0205, "time": 0.82897} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.06636, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29203, "top5_acc": 0.56641, "loss_cls": 3.9954, "loss": 3.9954, "time": 0.81818} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.06634, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29484, "top5_acc": 0.54766, "loss_cls": 4.02362, "loss": 4.02362, "time": 0.81875} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.06631, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30516, "top5_acc": 0.55688, "loss_cls": 4.01728, "loss": 4.01728, "time": 0.81809} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.06629, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30188, "top5_acc": 0.55188, "loss_cls": 4.03836, "loss": 4.03836, "time": 0.82048} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.06626, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30641, "top5_acc": 0.56563, "loss_cls": 4.00202, "loss": 4.00202, "time": 0.81574} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.06623, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30438, "top5_acc": 0.55328, "loss_cls": 4.01852, "loss": 4.01852, "time": 0.81176} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.06621, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30234, "top5_acc": 0.555, "loss_cls": 4.02108, "loss": 4.02108, "time": 0.81974} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.06618, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30297, "top5_acc": 0.54969, "loss_cls": 4.02918, "loss": 4.02918, "time": 0.81035} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.06615, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29313, "top5_acc": 0.54672, "loss_cls": 4.05451, "loss": 4.05451, "time": 0.81591} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.06613, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29563, "top5_acc": 0.55469, "loss_cls": 4.00989, "loss": 4.00989, "time": 0.81376} +{"mode": "train", "epoch": 60, "iter": 1300, "lr": 0.0661, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28875, "top5_acc": 0.55188, "loss_cls": 4.06672, "loss": 4.06672, "time": 0.81613} +{"mode": "train", "epoch": 60, "iter": 1400, "lr": 0.06607, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30188, "top5_acc": 0.54891, "loss_cls": 4.05965, "loss": 4.05965, "time": 0.8162} +{"mode": "train", "epoch": 60, "iter": 1500, "lr": 0.06605, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29953, "top5_acc": 0.54297, "loss_cls": 4.06326, "loss": 4.06326, "time": 0.81351} +{"mode": "train", "epoch": 60, "iter": 1600, "lr": 0.06602, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28641, "top5_acc": 0.53641, "loss_cls": 4.10141, "loss": 4.10141, "time": 0.81713} +{"mode": "train", "epoch": 60, "iter": 1700, "lr": 0.06599, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28938, "top5_acc": 0.53656, "loss_cls": 4.09402, "loss": 4.09402, "time": 0.8271} +{"mode": "train", "epoch": 60, "iter": 1800, "lr": 0.06597, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29281, "top5_acc": 0.54484, "loss_cls": 4.09362, "loss": 4.09362, "time": 0.82323} +{"mode": "train", "epoch": 60, "iter": 1900, "lr": 0.06594, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29438, "top5_acc": 0.54484, "loss_cls": 4.05342, "loss": 4.05342, "time": 0.81873} +{"mode": "train", "epoch": 60, "iter": 2000, "lr": 0.06591, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30156, "top5_acc": 0.55547, "loss_cls": 4.01976, "loss": 4.01976, "time": 0.81548} +{"mode": "train", "epoch": 60, "iter": 2100, "lr": 0.06589, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29906, "top5_acc": 0.55625, "loss_cls": 4.0303, "loss": 4.0303, "time": 0.81508} +{"mode": "train", "epoch": 60, "iter": 2200, "lr": 0.06586, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28828, "top5_acc": 0.53625, "loss_cls": 4.10678, "loss": 4.10678, "time": 0.81733} +{"mode": "train", "epoch": 60, "iter": 2300, "lr": 0.06584, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29578, "top5_acc": 0.54875, "loss_cls": 4.02176, "loss": 4.02176, "time": 0.81438} +{"mode": "train", "epoch": 60, "iter": 2400, "lr": 0.06581, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28656, "top5_acc": 0.54578, "loss_cls": 4.07021, "loss": 4.07021, "time": 0.8219} +{"mode": "train", "epoch": 60, "iter": 2500, "lr": 0.06578, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30359, "top5_acc": 0.54844, "loss_cls": 4.02559, "loss": 4.02559, "time": 0.8239} +{"mode": "train", "epoch": 60, "iter": 2600, "lr": 0.06576, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29672, "top5_acc": 0.55281, "loss_cls": 4.04142, "loss": 4.04142, "time": 0.82637} +{"mode": "train", "epoch": 60, "iter": 2700, "lr": 0.06573, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2875, "top5_acc": 0.54266, "loss_cls": 4.06889, "loss": 4.06889, "time": 0.81617} +{"mode": "train", "epoch": 60, "iter": 2800, "lr": 0.0657, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30031, "top5_acc": 0.55172, "loss_cls": 4.04751, "loss": 4.04751, "time": 0.82268} +{"mode": "train", "epoch": 60, "iter": 2900, "lr": 0.06568, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30062, "top5_acc": 0.55266, "loss_cls": 4.00904, "loss": 4.00904, "time": 0.81417} +{"mode": "train", "epoch": 60, "iter": 3000, "lr": 0.06565, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30016, "top5_acc": 0.54937, "loss_cls": 4.0523, "loss": 4.0523, "time": 0.81587} +{"mode": "train", "epoch": 60, "iter": 3100, "lr": 0.06562, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29281, "top5_acc": 0.54641, "loss_cls": 4.03079, "loss": 4.03079, "time": 0.81495} +{"mode": "train", "epoch": 60, "iter": 3200, "lr": 0.0656, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28734, "top5_acc": 0.54812, "loss_cls": 4.07176, "loss": 4.07176, "time": 0.8295} +{"mode": "train", "epoch": 60, "iter": 3300, "lr": 0.06557, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29219, "top5_acc": 0.53969, "loss_cls": 4.07874, "loss": 4.07874, "time": 0.83039} +{"mode": "train", "epoch": 60, "iter": 3400, "lr": 0.06554, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30078, "top5_acc": 0.55156, "loss_cls": 4.03817, "loss": 4.03817, "time": 0.82012} +{"mode": "train", "epoch": 60, "iter": 3500, "lr": 0.06552, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29844, "top5_acc": 0.55672, "loss_cls": 4.03432, "loss": 4.03432, "time": 0.82371} +{"mode": "train", "epoch": 60, "iter": 3600, "lr": 0.06549, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28703, "top5_acc": 0.5525, "loss_cls": 4.05478, "loss": 4.05478, "time": 0.82074} +{"mode": "train", "epoch": 60, "iter": 3700, "lr": 0.06546, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29563, "top5_acc": 0.55437, "loss_cls": 4.05229, "loss": 4.05229, "time": 0.81803} +{"mode": "val", "epoch": 60, "iter": 309, "lr": 0.06545, "top1_acc": 0.21719, "top5_acc": 0.45621, "mean_class_accuracy": 0.21687} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.06542, "memory": 15990, "data_time": 1.3819, "top1_acc": 0.29625, "top5_acc": 0.54969, "loss_cls": 4.04149, "loss": 4.04149, "time": 2.37299} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.0654, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29766, "top5_acc": 0.54797, "loss_cls": 4.04644, "loss": 4.04644, "time": 0.83852} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.06537, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30359, "top5_acc": 0.55312, "loss_cls": 4.00181, "loss": 4.00181, "time": 0.84219} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.06534, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29375, "top5_acc": 0.54937, "loss_cls": 4.0492, "loss": 4.0492, "time": 0.84067} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.06532, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30438, "top5_acc": 0.56766, "loss_cls": 3.97991, "loss": 3.97991, "time": 0.84258} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.06529, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30688, "top5_acc": 0.56203, "loss_cls": 3.99534, "loss": 3.99534, "time": 0.84423} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.06526, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29375, "top5_acc": 0.55391, "loss_cls": 4.04523, "loss": 4.04523, "time": 0.84277} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.06524, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30406, "top5_acc": 0.55688, "loss_cls": 4.01468, "loss": 4.01468, "time": 0.8387} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.06521, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30672, "top5_acc": 0.55422, "loss_cls": 3.98894, "loss": 3.98894, "time": 0.8406} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.06519, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28609, "top5_acc": 0.54844, "loss_cls": 4.06515, "loss": 4.06515, "time": 0.83948} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.06516, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29453, "top5_acc": 0.54375, "loss_cls": 4.07646, "loss": 4.07646, "time": 0.84371} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.06513, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30219, "top5_acc": 0.55094, "loss_cls": 4.03418, "loss": 4.03418, "time": 0.83842} +{"mode": "train", "epoch": 61, "iter": 1300, "lr": 0.06511, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29516, "top5_acc": 0.55437, "loss_cls": 4.02728, "loss": 4.02728, "time": 0.84126} +{"mode": "train", "epoch": 61, "iter": 1400, "lr": 0.06508, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29063, "top5_acc": 0.55047, "loss_cls": 4.02901, "loss": 4.02901, "time": 0.84025} +{"mode": "train", "epoch": 61, "iter": 1500, "lr": 0.06505, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29484, "top5_acc": 0.54422, "loss_cls": 4.07437, "loss": 4.07437, "time": 0.84452} +{"mode": "train", "epoch": 61, "iter": 1600, "lr": 0.06503, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29938, "top5_acc": 0.55734, "loss_cls": 4.02091, "loss": 4.02091, "time": 0.84677} +{"mode": "train", "epoch": 61, "iter": 1700, "lr": 0.065, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29234, "top5_acc": 0.55281, "loss_cls": 4.03306, "loss": 4.03306, "time": 0.8446} +{"mode": "train", "epoch": 61, "iter": 1800, "lr": 0.06497, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29625, "top5_acc": 0.54719, "loss_cls": 4.05837, "loss": 4.05837, "time": 0.84479} +{"mode": "train", "epoch": 61, "iter": 1900, "lr": 0.06495, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29938, "top5_acc": 0.55391, "loss_cls": 4.01038, "loss": 4.01038, "time": 0.84298} +{"mode": "train", "epoch": 61, "iter": 2000, "lr": 0.06492, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29359, "top5_acc": 0.54672, "loss_cls": 4.07346, "loss": 4.07346, "time": 0.84487} +{"mode": "train", "epoch": 61, "iter": 2100, "lr": 0.06489, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28375, "top5_acc": 0.54, "loss_cls": 4.09096, "loss": 4.09096, "time": 0.84541} +{"mode": "train", "epoch": 61, "iter": 2200, "lr": 0.06487, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29453, "top5_acc": 0.55047, "loss_cls": 4.06744, "loss": 4.06744, "time": 0.83884} +{"mode": "train", "epoch": 61, "iter": 2300, "lr": 0.06484, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.30453, "top5_acc": 0.55656, "loss_cls": 3.98413, "loss": 3.98413, "time": 0.84424} +{"mode": "train", "epoch": 61, "iter": 2400, "lr": 0.06481, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29969, "top5_acc": 0.55875, "loss_cls": 4.014, "loss": 4.014, "time": 0.83721} +{"mode": "train", "epoch": 61, "iter": 2500, "lr": 0.06478, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29734, "top5_acc": 0.55203, "loss_cls": 4.01304, "loss": 4.01304, "time": 0.83234} +{"mode": "train", "epoch": 61, "iter": 2600, "lr": 0.06476, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.30688, "top5_acc": 0.56344, "loss_cls": 3.98746, "loss": 3.98746, "time": 0.84656} +{"mode": "train", "epoch": 61, "iter": 2700, "lr": 0.06473, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29375, "top5_acc": 0.54859, "loss_cls": 4.04719, "loss": 4.04719, "time": 0.83803} +{"mode": "train", "epoch": 61, "iter": 2800, "lr": 0.0647, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29547, "top5_acc": 0.54734, "loss_cls": 4.05514, "loss": 4.05514, "time": 0.84202} +{"mode": "train", "epoch": 61, "iter": 2900, "lr": 0.06468, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30438, "top5_acc": 0.54844, "loss_cls": 4.04065, "loss": 4.04065, "time": 0.84134} +{"mode": "train", "epoch": 61, "iter": 3000, "lr": 0.06465, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29875, "top5_acc": 0.55812, "loss_cls": 4.00808, "loss": 4.00808, "time": 0.83718} +{"mode": "train", "epoch": 61, "iter": 3100, "lr": 0.06462, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29672, "top5_acc": 0.55641, "loss_cls": 4.02925, "loss": 4.02925, "time": 0.83565} +{"mode": "train", "epoch": 61, "iter": 3200, "lr": 0.0646, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30031, "top5_acc": 0.55219, "loss_cls": 4.06234, "loss": 4.06234, "time": 0.84247} +{"mode": "train", "epoch": 61, "iter": 3300, "lr": 0.06457, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30422, "top5_acc": 0.55359, "loss_cls": 3.99877, "loss": 3.99877, "time": 0.83028} +{"mode": "train", "epoch": 61, "iter": 3400, "lr": 0.06454, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28047, "top5_acc": 0.54062, "loss_cls": 4.10962, "loss": 4.10962, "time": 0.83362} +{"mode": "train", "epoch": 61, "iter": 3500, "lr": 0.06452, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29406, "top5_acc": 0.55125, "loss_cls": 4.04156, "loss": 4.04156, "time": 0.83936} +{"mode": "train", "epoch": 61, "iter": 3600, "lr": 0.06449, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28734, "top5_acc": 0.54734, "loss_cls": 4.07229, "loss": 4.07229, "time": 0.83826} +{"mode": "train", "epoch": 61, "iter": 3700, "lr": 0.06446, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29359, "top5_acc": 0.54375, "loss_cls": 4.04615, "loss": 4.04615, "time": 0.84261} +{"mode": "val", "epoch": 61, "iter": 309, "lr": 0.06445, "top1_acc": 0.22484, "top5_acc": 0.46087, "mean_class_accuracy": 0.22453} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.06443, "memory": 15990, "data_time": 1.37338, "top1_acc": 0.305, "top5_acc": 0.57188, "loss_cls": 3.94121, "loss": 3.94121, "time": 2.37747} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.0644, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30828, "top5_acc": 0.56422, "loss_cls": 3.97906, "loss": 3.97906, "time": 0.84193} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.06437, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29906, "top5_acc": 0.56312, "loss_cls": 3.99916, "loss": 3.99916, "time": 0.83922} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.06434, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30031, "top5_acc": 0.55047, "loss_cls": 4.02579, "loss": 4.02579, "time": 0.83419} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.06432, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30484, "top5_acc": 0.55141, "loss_cls": 4.0273, "loss": 4.0273, "time": 0.83314} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.06429, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30375, "top5_acc": 0.55531, "loss_cls": 4.00333, "loss": 4.00333, "time": 0.83045} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.06426, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30609, "top5_acc": 0.55578, "loss_cls": 4.02267, "loss": 4.02267, "time": 0.83076} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.06424, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29156, "top5_acc": 0.56078, "loss_cls": 4.03945, "loss": 4.03945, "time": 0.83427} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.06421, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30234, "top5_acc": 0.56234, "loss_cls": 3.99715, "loss": 3.99715, "time": 0.83007} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.06418, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29797, "top5_acc": 0.56203, "loss_cls": 4.01571, "loss": 4.01571, "time": 0.83092} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.06416, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30438, "top5_acc": 0.55047, "loss_cls": 4.02702, "loss": 4.02702, "time": 0.83162} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.06413, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30047, "top5_acc": 0.55875, "loss_cls": 3.99226, "loss": 3.99226, "time": 0.82861} +{"mode": "train", "epoch": 62, "iter": 1300, "lr": 0.0641, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30766, "top5_acc": 0.55781, "loss_cls": 4.01146, "loss": 4.01146, "time": 0.83134} +{"mode": "train", "epoch": 62, "iter": 1400, "lr": 0.06408, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29516, "top5_acc": 0.55234, "loss_cls": 4.03851, "loss": 4.03851, "time": 0.82361} +{"mode": "train", "epoch": 62, "iter": 1500, "lr": 0.06405, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30203, "top5_acc": 0.555, "loss_cls": 3.99841, "loss": 3.99841, "time": 0.81593} +{"mode": "train", "epoch": 62, "iter": 1600, "lr": 0.06402, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29625, "top5_acc": 0.54438, "loss_cls": 4.06814, "loss": 4.06814, "time": 0.81687} +{"mode": "train", "epoch": 62, "iter": 1700, "lr": 0.064, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29563, "top5_acc": 0.55844, "loss_cls": 4.01343, "loss": 4.01343, "time": 0.81756} +{"mode": "train", "epoch": 62, "iter": 1800, "lr": 0.06397, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30031, "top5_acc": 0.55047, "loss_cls": 4.01697, "loss": 4.01697, "time": 0.82032} +{"mode": "train", "epoch": 62, "iter": 1900, "lr": 0.06394, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30328, "top5_acc": 0.55641, "loss_cls": 4.01264, "loss": 4.01264, "time": 0.81904} +{"mode": "train", "epoch": 62, "iter": 2000, "lr": 0.06392, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29656, "top5_acc": 0.55656, "loss_cls": 4.01493, "loss": 4.01493, "time": 0.81871} +{"mode": "train", "epoch": 62, "iter": 2100, "lr": 0.06389, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30609, "top5_acc": 0.55672, "loss_cls": 4.00802, "loss": 4.00802, "time": 0.8125} +{"mode": "train", "epoch": 62, "iter": 2200, "lr": 0.06386, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28344, "top5_acc": 0.53781, "loss_cls": 4.09303, "loss": 4.09303, "time": 0.8302} +{"mode": "train", "epoch": 62, "iter": 2300, "lr": 0.06384, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29984, "top5_acc": 0.55469, "loss_cls": 4.02678, "loss": 4.02678, "time": 0.82204} +{"mode": "train", "epoch": 62, "iter": 2400, "lr": 0.06381, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30016, "top5_acc": 0.55656, "loss_cls": 4.00422, "loss": 4.00422, "time": 0.81973} +{"mode": "train", "epoch": 62, "iter": 2500, "lr": 0.06378, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29563, "top5_acc": 0.55062, "loss_cls": 4.06343, "loss": 4.06343, "time": 0.8251} +{"mode": "train", "epoch": 62, "iter": 2600, "lr": 0.06375, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29906, "top5_acc": 0.54547, "loss_cls": 4.03857, "loss": 4.03857, "time": 0.81442} +{"mode": "train", "epoch": 62, "iter": 2700, "lr": 0.06373, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29516, "top5_acc": 0.54891, "loss_cls": 4.03336, "loss": 4.03336, "time": 0.82775} +{"mode": "train", "epoch": 62, "iter": 2800, "lr": 0.0637, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30344, "top5_acc": 0.54891, "loss_cls": 4.04744, "loss": 4.04744, "time": 0.82139} +{"mode": "train", "epoch": 62, "iter": 2900, "lr": 0.06367, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29812, "top5_acc": 0.55734, "loss_cls": 4.00931, "loss": 4.00931, "time": 0.81556} +{"mode": "train", "epoch": 62, "iter": 3000, "lr": 0.06365, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29828, "top5_acc": 0.55109, "loss_cls": 4.06034, "loss": 4.06034, "time": 0.81774} +{"mode": "train", "epoch": 62, "iter": 3100, "lr": 0.06362, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29297, "top5_acc": 0.54109, "loss_cls": 4.05175, "loss": 4.05175, "time": 0.81589} +{"mode": "train", "epoch": 62, "iter": 3200, "lr": 0.06359, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29266, "top5_acc": 0.55672, "loss_cls": 4.04548, "loss": 4.04548, "time": 0.81899} +{"mode": "train", "epoch": 62, "iter": 3300, "lr": 0.06357, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29891, "top5_acc": 0.545, "loss_cls": 4.03373, "loss": 4.03373, "time": 0.81745} +{"mode": "train", "epoch": 62, "iter": 3400, "lr": 0.06354, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29422, "top5_acc": 0.55156, "loss_cls": 4.03216, "loss": 4.03216, "time": 0.81983} +{"mode": "train", "epoch": 62, "iter": 3500, "lr": 0.06351, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29781, "top5_acc": 0.54859, "loss_cls": 4.0348, "loss": 4.0348, "time": 0.8209} +{"mode": "train", "epoch": 62, "iter": 3600, "lr": 0.06349, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30203, "top5_acc": 0.56297, "loss_cls": 3.9808, "loss": 3.9808, "time": 0.82148} +{"mode": "train", "epoch": 62, "iter": 3700, "lr": 0.06346, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29563, "top5_acc": 0.54516, "loss_cls": 4.04998, "loss": 4.04998, "time": 0.81957} +{"mode": "val", "epoch": 62, "iter": 309, "lr": 0.06345, "top1_acc": 0.24039, "top5_acc": 0.49086, "mean_class_accuracy": 0.24018} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.06342, "memory": 15990, "data_time": 1.33851, "top1_acc": 0.29547, "top5_acc": 0.55828, "loss_cls": 4.00338, "loss": 4.00338, "time": 2.34648} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.06339, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30828, "top5_acc": 0.56453, "loss_cls": 3.97192, "loss": 3.97192, "time": 0.84403} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.06337, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30672, "top5_acc": 0.56609, "loss_cls": 3.94376, "loss": 3.94376, "time": 0.84018} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.06334, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31453, "top5_acc": 0.56703, "loss_cls": 3.95909, "loss": 3.95909, "time": 0.84109} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.06331, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30219, "top5_acc": 0.56281, "loss_cls": 3.97593, "loss": 3.97593, "time": 0.84719} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.06328, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28016, "top5_acc": 0.53953, "loss_cls": 4.08978, "loss": 4.08978, "time": 0.84461} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.06326, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30297, "top5_acc": 0.55188, "loss_cls": 4.02603, "loss": 4.02603, "time": 0.83782} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.06323, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30547, "top5_acc": 0.55812, "loss_cls": 3.98347, "loss": 3.98347, "time": 0.84316} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.0632, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30297, "top5_acc": 0.56219, "loss_cls": 3.97847, "loss": 3.97847, "time": 0.8415} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.06318, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30156, "top5_acc": 0.55469, "loss_cls": 4.01806, "loss": 4.01806, "time": 0.83943} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.06315, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30609, "top5_acc": 0.555, "loss_cls": 4.0183, "loss": 4.0183, "time": 0.84365} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.06312, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29625, "top5_acc": 0.54094, "loss_cls": 4.04373, "loss": 4.04373, "time": 0.84523} +{"mode": "train", "epoch": 63, "iter": 1300, "lr": 0.0631, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29531, "top5_acc": 0.56, "loss_cls": 4.03219, "loss": 4.03219, "time": 0.84499} +{"mode": "train", "epoch": 63, "iter": 1400, "lr": 0.06307, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30547, "top5_acc": 0.55719, "loss_cls": 4.02139, "loss": 4.02139, "time": 0.84575} +{"mode": "train", "epoch": 63, "iter": 1500, "lr": 0.06304, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30766, "top5_acc": 0.55453, "loss_cls": 4.02397, "loss": 4.02397, "time": 0.83938} +{"mode": "train", "epoch": 63, "iter": 1600, "lr": 0.06301, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2925, "top5_acc": 0.55266, "loss_cls": 4.03864, "loss": 4.03864, "time": 0.84462} +{"mode": "train", "epoch": 63, "iter": 1700, "lr": 0.06299, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29172, "top5_acc": 0.54125, "loss_cls": 4.04832, "loss": 4.04832, "time": 0.84203} +{"mode": "train", "epoch": 63, "iter": 1800, "lr": 0.06296, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30078, "top5_acc": 0.55844, "loss_cls": 3.99771, "loss": 3.99771, "time": 0.84593} +{"mode": "train", "epoch": 63, "iter": 1900, "lr": 0.06293, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29344, "top5_acc": 0.54672, "loss_cls": 4.06421, "loss": 4.06421, "time": 0.84018} +{"mode": "train", "epoch": 63, "iter": 2000, "lr": 0.06291, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29641, "top5_acc": 0.55625, "loss_cls": 4.01339, "loss": 4.01339, "time": 0.84097} +{"mode": "train", "epoch": 63, "iter": 2100, "lr": 0.06288, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29703, "top5_acc": 0.54734, "loss_cls": 4.05032, "loss": 4.05032, "time": 0.83569} +{"mode": "train", "epoch": 63, "iter": 2200, "lr": 0.06285, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.295, "top5_acc": 0.54875, "loss_cls": 4.06249, "loss": 4.06249, "time": 0.8422} +{"mode": "train", "epoch": 63, "iter": 2300, "lr": 0.06283, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30328, "top5_acc": 0.55828, "loss_cls": 4.00738, "loss": 4.00738, "time": 0.83004} +{"mode": "train", "epoch": 63, "iter": 2400, "lr": 0.0628, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30562, "top5_acc": 0.56, "loss_cls": 3.97262, "loss": 3.97262, "time": 0.83284} +{"mode": "train", "epoch": 63, "iter": 2500, "lr": 0.06277, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29641, "top5_acc": 0.54766, "loss_cls": 4.03054, "loss": 4.03054, "time": 0.83951} +{"mode": "train", "epoch": 63, "iter": 2600, "lr": 0.06274, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29469, "top5_acc": 0.55891, "loss_cls": 4.04551, "loss": 4.04551, "time": 0.83312} +{"mode": "train", "epoch": 63, "iter": 2700, "lr": 0.06272, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30156, "top5_acc": 0.56312, "loss_cls": 3.98094, "loss": 3.98094, "time": 0.83944} +{"mode": "train", "epoch": 63, "iter": 2800, "lr": 0.06269, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29156, "top5_acc": 0.55016, "loss_cls": 4.0431, "loss": 4.0431, "time": 0.84019} +{"mode": "train", "epoch": 63, "iter": 2900, "lr": 0.06266, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30094, "top5_acc": 0.56078, "loss_cls": 4.00014, "loss": 4.00014, "time": 0.83225} +{"mode": "train", "epoch": 63, "iter": 3000, "lr": 0.06264, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29406, "top5_acc": 0.54797, "loss_cls": 4.05772, "loss": 4.05772, "time": 0.83211} +{"mode": "train", "epoch": 63, "iter": 3100, "lr": 0.06261, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29594, "top5_acc": 0.55875, "loss_cls": 4.02309, "loss": 4.02309, "time": 0.83629} +{"mode": "train", "epoch": 63, "iter": 3200, "lr": 0.06258, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30312, "top5_acc": 0.55344, "loss_cls": 4.0007, "loss": 4.0007, "time": 0.82933} +{"mode": "train", "epoch": 63, "iter": 3300, "lr": 0.06256, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30672, "top5_acc": 0.55828, "loss_cls": 3.99667, "loss": 3.99667, "time": 0.8319} +{"mode": "train", "epoch": 63, "iter": 3400, "lr": 0.06253, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30359, "top5_acc": 0.56734, "loss_cls": 3.99457, "loss": 3.99457, "time": 0.83887} +{"mode": "train", "epoch": 63, "iter": 3500, "lr": 0.0625, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29828, "top5_acc": 0.55437, "loss_cls": 3.99557, "loss": 3.99557, "time": 0.84261} +{"mode": "train", "epoch": 63, "iter": 3600, "lr": 0.06247, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29219, "top5_acc": 0.54953, "loss_cls": 4.07239, "loss": 4.07239, "time": 0.83869} +{"mode": "train", "epoch": 63, "iter": 3700, "lr": 0.06245, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30281, "top5_acc": 0.55297, "loss_cls": 4.04712, "loss": 4.04712, "time": 0.83178} +{"mode": "val", "epoch": 63, "iter": 309, "lr": 0.06243, "top1_acc": 0.23304, "top5_acc": 0.47186, "mean_class_accuracy": 0.23284} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.06241, "memory": 15990, "data_time": 1.28081, "top1_acc": 0.30359, "top5_acc": 0.56109, "loss_cls": 3.99218, "loss": 3.99218, "time": 2.28211} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.06238, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31172, "top5_acc": 0.56625, "loss_cls": 3.93366, "loss": 3.93366, "time": 0.83851} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.06235, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29656, "top5_acc": 0.555, "loss_cls": 4.00098, "loss": 4.00098, "time": 0.83843} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.06233, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29656, "top5_acc": 0.55641, "loss_cls": 4.01993, "loss": 4.01993, "time": 0.83556} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.0623, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30531, "top5_acc": 0.56, "loss_cls": 3.98706, "loss": 3.98706, "time": 0.83957} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.06227, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30734, "top5_acc": 0.55812, "loss_cls": 3.99304, "loss": 3.99304, "time": 0.84214} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.06225, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30922, "top5_acc": 0.56672, "loss_cls": 3.96636, "loss": 3.96636, "time": 0.84215} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.06222, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30188, "top5_acc": 0.56016, "loss_cls": 3.997, "loss": 3.997, "time": 0.83995} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.06219, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30531, "top5_acc": 0.55156, "loss_cls": 4.02213, "loss": 4.02213, "time": 0.84548} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.06216, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29672, "top5_acc": 0.54203, "loss_cls": 4.06555, "loss": 4.06555, "time": 0.84409} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.06214, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29938, "top5_acc": 0.55406, "loss_cls": 4.02942, "loss": 4.02942, "time": 0.83795} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.06211, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30969, "top5_acc": 0.56516, "loss_cls": 3.95129, "loss": 3.95129, "time": 0.84074} +{"mode": "train", "epoch": 64, "iter": 1300, "lr": 0.06208, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30766, "top5_acc": 0.55453, "loss_cls": 4.00096, "loss": 4.00096, "time": 0.84447} +{"mode": "train", "epoch": 64, "iter": 1400, "lr": 0.06206, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29953, "top5_acc": 0.55609, "loss_cls": 4.00857, "loss": 4.00857, "time": 0.84061} +{"mode": "train", "epoch": 64, "iter": 1500, "lr": 0.06203, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30844, "top5_acc": 0.56031, "loss_cls": 3.96575, "loss": 3.96575, "time": 0.83879} +{"mode": "train", "epoch": 64, "iter": 1600, "lr": 0.062, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29391, "top5_acc": 0.54844, "loss_cls": 4.05475, "loss": 4.05475, "time": 0.83861} +{"mode": "train", "epoch": 64, "iter": 1700, "lr": 0.06197, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30094, "top5_acc": 0.55891, "loss_cls": 4.01443, "loss": 4.01443, "time": 0.84157} +{"mode": "train", "epoch": 64, "iter": 1800, "lr": 0.06195, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29875, "top5_acc": 0.55812, "loss_cls": 4.00767, "loss": 4.00767, "time": 0.84424} +{"mode": "train", "epoch": 64, "iter": 1900, "lr": 0.06192, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29859, "top5_acc": 0.55484, "loss_cls": 4.03268, "loss": 4.03268, "time": 0.84473} +{"mode": "train", "epoch": 64, "iter": 2000, "lr": 0.06189, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29516, "top5_acc": 0.55562, "loss_cls": 4.00691, "loss": 4.00691, "time": 0.83834} +{"mode": "train", "epoch": 64, "iter": 2100, "lr": 0.06187, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30297, "top5_acc": 0.5575, "loss_cls": 3.97708, "loss": 3.97708, "time": 0.83832} +{"mode": "train", "epoch": 64, "iter": 2200, "lr": 0.06184, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3125, "top5_acc": 0.56, "loss_cls": 3.99006, "loss": 3.99006, "time": 0.83518} +{"mode": "train", "epoch": 64, "iter": 2300, "lr": 0.06181, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29531, "top5_acc": 0.55078, "loss_cls": 4.0286, "loss": 4.0286, "time": 0.8337} +{"mode": "train", "epoch": 64, "iter": 2400, "lr": 0.06178, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30516, "top5_acc": 0.56531, "loss_cls": 3.98376, "loss": 3.98376, "time": 0.84192} +{"mode": "train", "epoch": 64, "iter": 2500, "lr": 0.06176, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30391, "top5_acc": 0.56031, "loss_cls": 3.99788, "loss": 3.99788, "time": 0.83098} +{"mode": "train", "epoch": 64, "iter": 2600, "lr": 0.06173, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30609, "top5_acc": 0.55453, "loss_cls": 4.00148, "loss": 4.00148, "time": 0.83511} +{"mode": "train", "epoch": 64, "iter": 2700, "lr": 0.0617, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29906, "top5_acc": 0.55594, "loss_cls": 4.03544, "loss": 4.03544, "time": 0.8402} +{"mode": "train", "epoch": 64, "iter": 2800, "lr": 0.06168, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30656, "top5_acc": 0.55547, "loss_cls": 4.01281, "loss": 4.01281, "time": 0.83781} +{"mode": "train", "epoch": 64, "iter": 2900, "lr": 0.06165, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29969, "top5_acc": 0.55672, "loss_cls": 4.01665, "loss": 4.01665, "time": 0.83827} +{"mode": "train", "epoch": 64, "iter": 3000, "lr": 0.06162, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30047, "top5_acc": 0.55953, "loss_cls": 4.0012, "loss": 4.0012, "time": 0.84172} +{"mode": "train", "epoch": 64, "iter": 3100, "lr": 0.06159, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30125, "top5_acc": 0.55844, "loss_cls": 3.98728, "loss": 3.98728, "time": 0.83256} +{"mode": "train", "epoch": 64, "iter": 3200, "lr": 0.06157, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31078, "top5_acc": 0.56312, "loss_cls": 3.97604, "loss": 3.97604, "time": 0.83869} +{"mode": "train", "epoch": 64, "iter": 3300, "lr": 0.06154, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29563, "top5_acc": 0.55344, "loss_cls": 4.03113, "loss": 4.03113, "time": 0.84646} +{"mode": "train", "epoch": 64, "iter": 3400, "lr": 0.06151, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29313, "top5_acc": 0.55094, "loss_cls": 4.0291, "loss": 4.0291, "time": 0.84017} +{"mode": "train", "epoch": 64, "iter": 3500, "lr": 0.06148, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29469, "top5_acc": 0.55844, "loss_cls": 3.99869, "loss": 3.99869, "time": 0.83824} +{"mode": "train", "epoch": 64, "iter": 3600, "lr": 0.06146, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30156, "top5_acc": 0.55609, "loss_cls": 4.01116, "loss": 4.01116, "time": 0.83945} +{"mode": "train", "epoch": 64, "iter": 3700, "lr": 0.06143, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29422, "top5_acc": 0.54906, "loss_cls": 4.03633, "loss": 4.03633, "time": 0.83835} +{"mode": "val", "epoch": 64, "iter": 309, "lr": 0.06142, "top1_acc": 0.23963, "top5_acc": 0.47976, "mean_class_accuracy": 0.23944} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.06139, "memory": 15990, "data_time": 1.29688, "top1_acc": 0.30781, "top5_acc": 0.56594, "loss_cls": 3.9679, "loss": 3.9679, "time": 2.30074} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.06136, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31188, "top5_acc": 0.56641, "loss_cls": 3.94494, "loss": 3.94494, "time": 0.83293} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.06134, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30484, "top5_acc": 0.57281, "loss_cls": 3.92103, "loss": 3.92103, "time": 0.83893} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.06131, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30391, "top5_acc": 0.56125, "loss_cls": 3.98309, "loss": 3.98309, "time": 0.83268} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.06128, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30906, "top5_acc": 0.56391, "loss_cls": 3.95439, "loss": 3.95439, "time": 0.81875} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.06125, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30609, "top5_acc": 0.56484, "loss_cls": 3.96405, "loss": 3.96405, "time": 0.81429} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.06123, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31469, "top5_acc": 0.56516, "loss_cls": 3.95909, "loss": 3.95909, "time": 0.81577} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0612, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30125, "top5_acc": 0.55828, "loss_cls": 4.02125, "loss": 4.02125, "time": 0.82389} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.06117, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30094, "top5_acc": 0.55359, "loss_cls": 3.98351, "loss": 3.98351, "time": 0.81885} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.06115, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30516, "top5_acc": 0.55703, "loss_cls": 3.98715, "loss": 3.98715, "time": 0.8183} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.06112, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30641, "top5_acc": 0.56016, "loss_cls": 3.96727, "loss": 3.96727, "time": 0.8173} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.06109, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30406, "top5_acc": 0.56047, "loss_cls": 3.99356, "loss": 3.99356, "time": 0.81811} +{"mode": "train", "epoch": 65, "iter": 1300, "lr": 0.06106, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29984, "top5_acc": 0.54625, "loss_cls": 4.00708, "loss": 4.00708, "time": 0.8153} +{"mode": "train", "epoch": 65, "iter": 1400, "lr": 0.06104, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29734, "top5_acc": 0.55859, "loss_cls": 4.00142, "loss": 4.00142, "time": 0.81704} +{"mode": "train", "epoch": 65, "iter": 1500, "lr": 0.06101, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30578, "top5_acc": 0.55375, "loss_cls": 4.00266, "loss": 4.00266, "time": 0.81841} +{"mode": "train", "epoch": 65, "iter": 1600, "lr": 0.06098, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29609, "top5_acc": 0.54953, "loss_cls": 4.06229, "loss": 4.06229, "time": 0.82039} +{"mode": "train", "epoch": 65, "iter": 1700, "lr": 0.06095, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30094, "top5_acc": 0.55812, "loss_cls": 3.99963, "loss": 3.99963, "time": 0.83152} +{"mode": "train", "epoch": 65, "iter": 1800, "lr": 0.06093, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29766, "top5_acc": 0.55188, "loss_cls": 4.04839, "loss": 4.04839, "time": 0.81918} +{"mode": "train", "epoch": 65, "iter": 1900, "lr": 0.0609, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30312, "top5_acc": 0.55297, "loss_cls": 4.02764, "loss": 4.02764, "time": 0.81442} +{"mode": "train", "epoch": 65, "iter": 2000, "lr": 0.06087, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29688, "top5_acc": 0.54859, "loss_cls": 4.05002, "loss": 4.05002, "time": 0.82877} +{"mode": "train", "epoch": 65, "iter": 2100, "lr": 0.06085, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30719, "top5_acc": 0.55391, "loss_cls": 4.02805, "loss": 4.02805, "time": 0.82043} +{"mode": "train", "epoch": 65, "iter": 2200, "lr": 0.06082, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30219, "top5_acc": 0.55172, "loss_cls": 4.02477, "loss": 4.02477, "time": 0.82482} +{"mode": "train", "epoch": 65, "iter": 2300, "lr": 0.06079, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30484, "top5_acc": 0.55937, "loss_cls": 4.0157, "loss": 4.0157, "time": 0.82734} +{"mode": "train", "epoch": 65, "iter": 2400, "lr": 0.06076, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30641, "top5_acc": 0.55203, "loss_cls": 4.00012, "loss": 4.00012, "time": 0.82137} +{"mode": "train", "epoch": 65, "iter": 2500, "lr": 0.06074, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29594, "top5_acc": 0.55625, "loss_cls": 4.04702, "loss": 4.04702, "time": 0.82152} +{"mode": "train", "epoch": 65, "iter": 2600, "lr": 0.06071, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30625, "top5_acc": 0.56812, "loss_cls": 3.95374, "loss": 3.95374, "time": 0.82418} +{"mode": "train", "epoch": 65, "iter": 2700, "lr": 0.06068, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30438, "top5_acc": 0.56094, "loss_cls": 3.96987, "loss": 3.96987, "time": 0.82047} +{"mode": "train", "epoch": 65, "iter": 2800, "lr": 0.06065, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29656, "top5_acc": 0.56016, "loss_cls": 4.00751, "loss": 4.00751, "time": 0.81412} +{"mode": "train", "epoch": 65, "iter": 2900, "lr": 0.06063, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28578, "top5_acc": 0.54891, "loss_cls": 4.03672, "loss": 4.03672, "time": 0.82093} +{"mode": "train", "epoch": 65, "iter": 3000, "lr": 0.0606, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28594, "top5_acc": 0.55156, "loss_cls": 4.04523, "loss": 4.04523, "time": 0.82029} +{"mode": "train", "epoch": 65, "iter": 3100, "lr": 0.06057, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31047, "top5_acc": 0.56063, "loss_cls": 3.94853, "loss": 3.94853, "time": 0.82563} +{"mode": "train", "epoch": 65, "iter": 3200, "lr": 0.06055, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30672, "top5_acc": 0.56594, "loss_cls": 3.96317, "loss": 3.96317, "time": 0.82124} +{"mode": "train", "epoch": 65, "iter": 3300, "lr": 0.06052, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29547, "top5_acc": 0.56359, "loss_cls": 3.98357, "loss": 3.98357, "time": 0.81712} +{"mode": "train", "epoch": 65, "iter": 3400, "lr": 0.06049, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31141, "top5_acc": 0.56156, "loss_cls": 3.9804, "loss": 3.9804, "time": 0.8182} +{"mode": "train", "epoch": 65, "iter": 3500, "lr": 0.06046, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30047, "top5_acc": 0.55141, "loss_cls": 4.0088, "loss": 4.0088, "time": 0.81559} +{"mode": "train", "epoch": 65, "iter": 3600, "lr": 0.06044, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29984, "top5_acc": 0.55562, "loss_cls": 3.97918, "loss": 3.97918, "time": 0.8203} +{"mode": "train", "epoch": 65, "iter": 3700, "lr": 0.06041, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30547, "top5_acc": 0.56531, "loss_cls": 3.96091, "loss": 3.96091, "time": 0.81339} +{"mode": "val", "epoch": 65, "iter": 309, "lr": 0.0604, "top1_acc": 0.24885, "top5_acc": 0.49385, "mean_class_accuracy": 0.24866} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.06037, "memory": 15990, "data_time": 1.35872, "top1_acc": 0.31062, "top5_acc": 0.57016, "loss_cls": 3.94032, "loss": 3.94032, "time": 2.3686} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.06034, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30422, "top5_acc": 0.56531, "loss_cls": 3.97678, "loss": 3.97678, "time": 0.82447} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.06031, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3025, "top5_acc": 0.56312, "loss_cls": 3.96134, "loss": 3.96134, "time": 0.82524} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.06029, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29516, "top5_acc": 0.55406, "loss_cls": 3.98044, "loss": 3.98044, "time": 0.82229} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.06026, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30219, "top5_acc": 0.55812, "loss_cls": 3.97709, "loss": 3.97709, "time": 0.81689} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.06023, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30719, "top5_acc": 0.56484, "loss_cls": 3.96654, "loss": 3.96654, "time": 0.81573} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.0602, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30234, "top5_acc": 0.55453, "loss_cls": 4.01756, "loss": 4.01756, "time": 0.8191} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.06018, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3075, "top5_acc": 0.55219, "loss_cls": 3.99719, "loss": 3.99719, "time": 0.81656} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.06015, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29609, "top5_acc": 0.54266, "loss_cls": 4.04625, "loss": 4.04625, "time": 0.81721} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.06012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30891, "top5_acc": 0.57359, "loss_cls": 3.96812, "loss": 3.96812, "time": 0.81444} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.06009, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30891, "top5_acc": 0.56563, "loss_cls": 3.9156, "loss": 3.9156, "time": 0.81065} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.06007, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30812, "top5_acc": 0.55812, "loss_cls": 3.98181, "loss": 3.98181, "time": 0.81673} +{"mode": "train", "epoch": 66, "iter": 1300, "lr": 0.06004, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30875, "top5_acc": 0.57219, "loss_cls": 3.94435, "loss": 3.94435, "time": 0.81085} +{"mode": "train", "epoch": 66, "iter": 1400, "lr": 0.06001, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30656, "top5_acc": 0.55375, "loss_cls": 4.00449, "loss": 4.00449, "time": 0.81654} +{"mode": "train", "epoch": 66, "iter": 1500, "lr": 0.05999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29969, "top5_acc": 0.55672, "loss_cls": 4.03561, "loss": 4.03561, "time": 0.82014} +{"mode": "train", "epoch": 66, "iter": 1600, "lr": 0.05996, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.305, "top5_acc": 0.55781, "loss_cls": 3.97674, "loss": 3.97674, "time": 0.81662} +{"mode": "train", "epoch": 66, "iter": 1700, "lr": 0.05993, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29891, "top5_acc": 0.56406, "loss_cls": 3.98435, "loss": 3.98435, "time": 0.82064} +{"mode": "train", "epoch": 66, "iter": 1800, "lr": 0.0599, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29578, "top5_acc": 0.55766, "loss_cls": 4.0015, "loss": 4.0015, "time": 0.82782} +{"mode": "train", "epoch": 66, "iter": 1900, "lr": 0.05988, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28734, "top5_acc": 0.54438, "loss_cls": 4.07939, "loss": 4.07939, "time": 0.82312} +{"mode": "train", "epoch": 66, "iter": 2000, "lr": 0.05985, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29906, "top5_acc": 0.56, "loss_cls": 4.0276, "loss": 4.0276, "time": 0.82726} +{"mode": "train", "epoch": 66, "iter": 2100, "lr": 0.05982, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31219, "top5_acc": 0.57016, "loss_cls": 3.94069, "loss": 3.94069, "time": 0.82121} +{"mode": "train", "epoch": 66, "iter": 2200, "lr": 0.05979, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30672, "top5_acc": 0.56625, "loss_cls": 3.97707, "loss": 3.97707, "time": 0.82696} +{"mode": "train", "epoch": 66, "iter": 2300, "lr": 0.05977, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30625, "top5_acc": 0.56, "loss_cls": 3.99266, "loss": 3.99266, "time": 0.82049} +{"mode": "train", "epoch": 66, "iter": 2400, "lr": 0.05974, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30812, "top5_acc": 0.56328, "loss_cls": 3.98174, "loss": 3.98174, "time": 0.82562} +{"mode": "train", "epoch": 66, "iter": 2500, "lr": 0.05971, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30891, "top5_acc": 0.55484, "loss_cls": 3.99612, "loss": 3.99612, "time": 0.81735} +{"mode": "train", "epoch": 66, "iter": 2600, "lr": 0.05968, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29563, "top5_acc": 0.55469, "loss_cls": 3.99991, "loss": 3.99991, "time": 0.81883} +{"mode": "train", "epoch": 66, "iter": 2700, "lr": 0.05966, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30109, "top5_acc": 0.55672, "loss_cls": 4.00997, "loss": 4.00997, "time": 0.81542} +{"mode": "train", "epoch": 66, "iter": 2800, "lr": 0.05963, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30922, "top5_acc": 0.56437, "loss_cls": 3.98486, "loss": 3.98486, "time": 0.81532} +{"mode": "train", "epoch": 66, "iter": 2900, "lr": 0.0596, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30734, "top5_acc": 0.55625, "loss_cls": 3.99759, "loss": 3.99759, "time": 0.81998} +{"mode": "train", "epoch": 66, "iter": 3000, "lr": 0.05957, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31266, "top5_acc": 0.56781, "loss_cls": 3.93926, "loss": 3.93926, "time": 0.81785} +{"mode": "train", "epoch": 66, "iter": 3100, "lr": 0.05955, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30719, "top5_acc": 0.56875, "loss_cls": 3.95253, "loss": 3.95253, "time": 0.81893} +{"mode": "train", "epoch": 66, "iter": 3200, "lr": 0.05952, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30875, "top5_acc": 0.56437, "loss_cls": 3.9655, "loss": 3.9655, "time": 0.81445} +{"mode": "train", "epoch": 66, "iter": 3300, "lr": 0.05949, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30469, "top5_acc": 0.55797, "loss_cls": 3.99889, "loss": 3.99889, "time": 0.81639} +{"mode": "train", "epoch": 66, "iter": 3400, "lr": 0.05946, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30375, "top5_acc": 0.56063, "loss_cls": 4.00757, "loss": 4.00757, "time": 0.81639} +{"mode": "train", "epoch": 66, "iter": 3500, "lr": 0.05944, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30656, "top5_acc": 0.55875, "loss_cls": 4.01157, "loss": 4.01157, "time": 0.81354} +{"mode": "train", "epoch": 66, "iter": 3600, "lr": 0.05941, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30734, "top5_acc": 0.55047, "loss_cls": 4.01672, "loss": 4.01672, "time": 0.82548} +{"mode": "train", "epoch": 66, "iter": 3700, "lr": 0.05938, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29828, "top5_acc": 0.56109, "loss_cls": 4.01902, "loss": 4.01902, "time": 0.81992} +{"mode": "val", "epoch": 66, "iter": 309, "lr": 0.05937, "top1_acc": 0.24718, "top5_acc": 0.49547, "mean_class_accuracy": 0.24678} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.05934, "memory": 15990, "data_time": 1.32615, "top1_acc": 0.31781, "top5_acc": 0.57094, "loss_cls": 3.89326, "loss": 3.89326, "time": 2.29889} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.05931, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30781, "top5_acc": 0.56266, "loss_cls": 3.98479, "loss": 3.98479, "time": 0.81357} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.05929, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29172, "top5_acc": 0.55594, "loss_cls": 4.017, "loss": 4.017, "time": 0.81885} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.05926, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31047, "top5_acc": 0.57078, "loss_cls": 3.96412, "loss": 3.96412, "time": 0.81723} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.05923, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30875, "top5_acc": 0.5675, "loss_cls": 3.96238, "loss": 3.96238, "time": 0.81367} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.0592, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30422, "top5_acc": 0.56797, "loss_cls": 3.95665, "loss": 3.95665, "time": 0.82144} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.05918, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29922, "top5_acc": 0.56406, "loss_cls": 3.98245, "loss": 3.98245, "time": 0.81827} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.05915, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31172, "top5_acc": 0.56266, "loss_cls": 3.96731, "loss": 3.96731, "time": 0.81335} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.05912, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30359, "top5_acc": 0.56391, "loss_cls": 3.96082, "loss": 3.96082, "time": 0.81896} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.05909, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31047, "top5_acc": 0.57047, "loss_cls": 3.9356, "loss": 3.9356, "time": 0.81296} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.05907, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30312, "top5_acc": 0.555, "loss_cls": 3.99855, "loss": 3.99855, "time": 0.81352} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.05904, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31406, "top5_acc": 0.56312, "loss_cls": 3.98427, "loss": 3.98427, "time": 0.81784} +{"mode": "train", "epoch": 67, "iter": 1300, "lr": 0.05901, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31297, "top5_acc": 0.56344, "loss_cls": 3.94268, "loss": 3.94268, "time": 0.81973} +{"mode": "train", "epoch": 67, "iter": 1400, "lr": 0.05898, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30016, "top5_acc": 0.55188, "loss_cls": 4.03189, "loss": 4.03189, "time": 0.81601} +{"mode": "train", "epoch": 67, "iter": 1500, "lr": 0.05896, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29797, "top5_acc": 0.55906, "loss_cls": 3.97742, "loss": 3.97742, "time": 0.81697} +{"mode": "train", "epoch": 67, "iter": 1600, "lr": 0.05893, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30438, "top5_acc": 0.57438, "loss_cls": 3.93754, "loss": 3.93754, "time": 0.81221} +{"mode": "train", "epoch": 67, "iter": 1700, "lr": 0.0589, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32125, "top5_acc": 0.57531, "loss_cls": 3.93983, "loss": 3.93983, "time": 0.8246} +{"mode": "train", "epoch": 67, "iter": 1800, "lr": 0.05887, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30188, "top5_acc": 0.56375, "loss_cls": 3.97565, "loss": 3.97565, "time": 0.81489} +{"mode": "train", "epoch": 67, "iter": 1900, "lr": 0.05885, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31031, "top5_acc": 0.57125, "loss_cls": 3.95292, "loss": 3.95292, "time": 0.81391} +{"mode": "train", "epoch": 67, "iter": 2000, "lr": 0.05882, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30016, "top5_acc": 0.55594, "loss_cls": 3.99769, "loss": 3.99769, "time": 0.82355} +{"mode": "train", "epoch": 67, "iter": 2100, "lr": 0.05879, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30641, "top5_acc": 0.5625, "loss_cls": 3.97544, "loss": 3.97544, "time": 0.81683} +{"mode": "train", "epoch": 67, "iter": 2200, "lr": 0.05876, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30578, "top5_acc": 0.57016, "loss_cls": 3.93759, "loss": 3.93759, "time": 0.82022} +{"mode": "train", "epoch": 67, "iter": 2300, "lr": 0.05874, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31047, "top5_acc": 0.55719, "loss_cls": 3.99253, "loss": 3.99253, "time": 0.82556} +{"mode": "train", "epoch": 67, "iter": 2400, "lr": 0.05871, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30609, "top5_acc": 0.56531, "loss_cls": 3.97725, "loss": 3.97725, "time": 0.81818} +{"mode": "train", "epoch": 67, "iter": 2500, "lr": 0.05868, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31188, "top5_acc": 0.56297, "loss_cls": 3.97453, "loss": 3.97453, "time": 0.81773} +{"mode": "train", "epoch": 67, "iter": 2600, "lr": 0.05865, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30781, "top5_acc": 0.56422, "loss_cls": 4.00927, "loss": 4.00927, "time": 0.81499} +{"mode": "train", "epoch": 67, "iter": 2700, "lr": 0.05863, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29859, "top5_acc": 0.55984, "loss_cls": 4.00002, "loss": 4.00002, "time": 0.81437} +{"mode": "train", "epoch": 67, "iter": 2800, "lr": 0.0586, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29875, "top5_acc": 0.54359, "loss_cls": 4.03139, "loss": 4.03139, "time": 0.81988} +{"mode": "train", "epoch": 67, "iter": 2900, "lr": 0.05857, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31109, "top5_acc": 0.56312, "loss_cls": 3.97949, "loss": 3.97949, "time": 0.82147} +{"mode": "train", "epoch": 67, "iter": 3000, "lr": 0.05854, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30672, "top5_acc": 0.56312, "loss_cls": 3.99187, "loss": 3.99187, "time": 0.81637} +{"mode": "train", "epoch": 67, "iter": 3100, "lr": 0.05852, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31234, "top5_acc": 0.56172, "loss_cls": 3.96885, "loss": 3.96885, "time": 0.81804} +{"mode": "train", "epoch": 67, "iter": 3200, "lr": 0.05849, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30766, "top5_acc": 0.56375, "loss_cls": 3.94522, "loss": 3.94522, "time": 0.81746} +{"mode": "train", "epoch": 67, "iter": 3300, "lr": 0.05846, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30844, "top5_acc": 0.55578, "loss_cls": 4.01698, "loss": 4.01698, "time": 0.81436} +{"mode": "train", "epoch": 67, "iter": 3400, "lr": 0.05843, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29844, "top5_acc": 0.55688, "loss_cls": 3.99394, "loss": 3.99394, "time": 0.81307} +{"mode": "train", "epoch": 67, "iter": 3500, "lr": 0.05841, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30391, "top5_acc": 0.56156, "loss_cls": 4.00162, "loss": 4.00162, "time": 0.8167} +{"mode": "train", "epoch": 67, "iter": 3600, "lr": 0.05838, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29578, "top5_acc": 0.54984, "loss_cls": 4.04611, "loss": 4.04611, "time": 0.81713} +{"mode": "train", "epoch": 67, "iter": 3700, "lr": 0.05835, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30547, "top5_acc": 0.56672, "loss_cls": 3.96013, "loss": 3.96013, "time": 0.81483} +{"mode": "val", "epoch": 67, "iter": 309, "lr": 0.05834, "top1_acc": 0.24632, "top5_acc": 0.48893, "mean_class_accuracy": 0.24602} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.05831, "memory": 15990, "data_time": 1.29091, "top1_acc": 0.30734, "top5_acc": 0.56437, "loss_cls": 3.94882, "loss": 3.94882, "time": 2.27589} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.05828, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31594, "top5_acc": 0.56953, "loss_cls": 3.94, "loss": 3.94, "time": 0.81937} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.05826, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31312, "top5_acc": 0.57156, "loss_cls": 3.92682, "loss": 3.92682, "time": 0.81759} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.05823, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32516, "top5_acc": 0.57469, "loss_cls": 3.89452, "loss": 3.89452, "time": 0.81724} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.0582, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30109, "top5_acc": 0.55937, "loss_cls": 4.00098, "loss": 4.00098, "time": 0.81866} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.05817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30828, "top5_acc": 0.57797, "loss_cls": 3.89522, "loss": 3.89522, "time": 0.81483} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.05815, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31516, "top5_acc": 0.56547, "loss_cls": 3.9456, "loss": 3.9456, "time": 0.81506} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.05812, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31375, "top5_acc": 0.56297, "loss_cls": 3.97573, "loss": 3.97573, "time": 0.81661} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.05809, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30562, "top5_acc": 0.5625, "loss_cls": 3.97557, "loss": 3.97557, "time": 0.81636} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.05806, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30984, "top5_acc": 0.56172, "loss_cls": 3.99042, "loss": 3.99042, "time": 0.8162} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.05804, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31156, "top5_acc": 0.56641, "loss_cls": 3.97107, "loss": 3.97107, "time": 0.81865} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.05801, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30656, "top5_acc": 0.57094, "loss_cls": 3.94064, "loss": 3.94064, "time": 0.82273} +{"mode": "train", "epoch": 68, "iter": 1300, "lr": 0.05798, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30297, "top5_acc": 0.55688, "loss_cls": 3.99427, "loss": 3.99427, "time": 0.81883} +{"mode": "train", "epoch": 68, "iter": 1400, "lr": 0.05795, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29938, "top5_acc": 0.55062, "loss_cls": 4.03509, "loss": 4.03509, "time": 0.81616} +{"mode": "train", "epoch": 68, "iter": 1500, "lr": 0.05792, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30734, "top5_acc": 0.56469, "loss_cls": 3.97317, "loss": 3.97317, "time": 0.81081} +{"mode": "train", "epoch": 68, "iter": 1600, "lr": 0.0579, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30469, "top5_acc": 0.56234, "loss_cls": 3.94795, "loss": 3.94795, "time": 0.81827} +{"mode": "train", "epoch": 68, "iter": 1700, "lr": 0.05787, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30484, "top5_acc": 0.55531, "loss_cls": 4.00689, "loss": 4.00689, "time": 0.81004} +{"mode": "train", "epoch": 68, "iter": 1800, "lr": 0.05784, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29922, "top5_acc": 0.56422, "loss_cls": 3.97759, "loss": 3.97759, "time": 0.8162} +{"mode": "train", "epoch": 68, "iter": 1900, "lr": 0.05781, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30688, "top5_acc": 0.56953, "loss_cls": 3.95261, "loss": 3.95261, "time": 0.81994} +{"mode": "train", "epoch": 68, "iter": 2000, "lr": 0.05779, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30438, "top5_acc": 0.5625, "loss_cls": 3.97899, "loss": 3.97899, "time": 0.82018} +{"mode": "train", "epoch": 68, "iter": 2100, "lr": 0.05776, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30766, "top5_acc": 0.56312, "loss_cls": 3.98929, "loss": 3.98929, "time": 0.8179} +{"mode": "train", "epoch": 68, "iter": 2200, "lr": 0.05773, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30859, "top5_acc": 0.56484, "loss_cls": 3.97802, "loss": 3.97802, "time": 0.82171} +{"mode": "train", "epoch": 68, "iter": 2300, "lr": 0.0577, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31125, "top5_acc": 0.56172, "loss_cls": 3.98667, "loss": 3.98667, "time": 0.81834} +{"mode": "train", "epoch": 68, "iter": 2400, "lr": 0.05768, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31938, "top5_acc": 0.56781, "loss_cls": 3.94613, "loss": 3.94613, "time": 0.82278} +{"mode": "train", "epoch": 68, "iter": 2500, "lr": 0.05765, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29938, "top5_acc": 0.5575, "loss_cls": 4.0096, "loss": 4.0096, "time": 0.8213} +{"mode": "train", "epoch": 68, "iter": 2600, "lr": 0.05762, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31188, "top5_acc": 0.56328, "loss_cls": 3.9642, "loss": 3.9642, "time": 0.81621} +{"mode": "train", "epoch": 68, "iter": 2700, "lr": 0.05759, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30188, "top5_acc": 0.56031, "loss_cls": 3.99197, "loss": 3.99197, "time": 0.81696} +{"mode": "train", "epoch": 68, "iter": 2800, "lr": 0.05757, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30422, "top5_acc": 0.56719, "loss_cls": 3.94367, "loss": 3.94367, "time": 0.81618} +{"mode": "train", "epoch": 68, "iter": 2900, "lr": 0.05754, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31062, "top5_acc": 0.56391, "loss_cls": 3.98317, "loss": 3.98317, "time": 0.81763} +{"mode": "train", "epoch": 68, "iter": 3000, "lr": 0.05751, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30188, "top5_acc": 0.55812, "loss_cls": 3.98676, "loss": 3.98676, "time": 0.82445} +{"mode": "train", "epoch": 68, "iter": 3100, "lr": 0.05748, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30547, "top5_acc": 0.565, "loss_cls": 3.97989, "loss": 3.97989, "time": 0.81891} +{"mode": "train", "epoch": 68, "iter": 3200, "lr": 0.05746, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31234, "top5_acc": 0.57125, "loss_cls": 3.91816, "loss": 3.91816, "time": 0.8212} +{"mode": "train", "epoch": 68, "iter": 3300, "lr": 0.05743, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3125, "top5_acc": 0.56594, "loss_cls": 3.95636, "loss": 3.95636, "time": 0.81924} +{"mode": "train", "epoch": 68, "iter": 3400, "lr": 0.0574, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30125, "top5_acc": 0.55766, "loss_cls": 4.00673, "loss": 4.00673, "time": 0.81568} +{"mode": "train", "epoch": 68, "iter": 3500, "lr": 0.05737, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29875, "top5_acc": 0.55297, "loss_cls": 4.0082, "loss": 4.0082, "time": 0.81672} +{"mode": "train", "epoch": 68, "iter": 3600, "lr": 0.05734, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31328, "top5_acc": 0.56406, "loss_cls": 3.94084, "loss": 3.94084, "time": 0.8221} +{"mode": "train", "epoch": 68, "iter": 3700, "lr": 0.05732, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30172, "top5_acc": 0.55734, "loss_cls": 3.96945, "loss": 3.96945, "time": 0.81958} +{"mode": "val", "epoch": 68, "iter": 309, "lr": 0.0573, "top1_acc": 0.24581, "top5_acc": 0.49491, "mean_class_accuracy": 0.24552} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.05728, "memory": 15990, "data_time": 1.29858, "top1_acc": 0.3225, "top5_acc": 0.58016, "loss_cls": 3.88886, "loss": 3.88886, "time": 2.26921} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.05725, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32047, "top5_acc": 0.57969, "loss_cls": 3.89554, "loss": 3.89554, "time": 0.82087} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.05722, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30781, "top5_acc": 0.56406, "loss_cls": 3.94792, "loss": 3.94792, "time": 0.81783} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.05719, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31219, "top5_acc": 0.56328, "loss_cls": 3.95732, "loss": 3.95732, "time": 0.81668} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.05717, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30969, "top5_acc": 0.57484, "loss_cls": 3.93697, "loss": 3.93697, "time": 0.81615} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.05714, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31438, "top5_acc": 0.575, "loss_cls": 3.87686, "loss": 3.87686, "time": 0.81582} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.05711, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30625, "top5_acc": 0.56609, "loss_cls": 3.94531, "loss": 3.94531, "time": 0.81572} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.05708, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30234, "top5_acc": 0.55906, "loss_cls": 3.98359, "loss": 3.98359, "time": 0.81774} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.05706, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31219, "top5_acc": 0.56969, "loss_cls": 3.91893, "loss": 3.91893, "time": 0.81699} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.05703, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31297, "top5_acc": 0.57125, "loss_cls": 3.92826, "loss": 3.92826, "time": 0.81554} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31094, "top5_acc": 0.56266, "loss_cls": 3.95847, "loss": 3.95847, "time": 0.81773} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.05697, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31891, "top5_acc": 0.57391, "loss_cls": 3.92687, "loss": 3.92687, "time": 0.81334} +{"mode": "train", "epoch": 69, "iter": 1300, "lr": 0.05694, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31328, "top5_acc": 0.56781, "loss_cls": 3.93864, "loss": 3.93864, "time": 0.81752} +{"mode": "train", "epoch": 69, "iter": 1400, "lr": 0.05692, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30297, "top5_acc": 0.55641, "loss_cls": 4.01056, "loss": 4.01056, "time": 0.82308} +{"mode": "train", "epoch": 69, "iter": 1500, "lr": 0.05689, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31203, "top5_acc": 0.56094, "loss_cls": 3.95193, "loss": 3.95193, "time": 0.81604} +{"mode": "train", "epoch": 69, "iter": 1600, "lr": 0.05686, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30312, "top5_acc": 0.56078, "loss_cls": 3.95469, "loss": 3.95469, "time": 0.8158} +{"mode": "train", "epoch": 69, "iter": 1700, "lr": 0.05683, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30938, "top5_acc": 0.56922, "loss_cls": 3.9603, "loss": 3.9603, "time": 0.81863} +{"mode": "train", "epoch": 69, "iter": 1800, "lr": 0.05681, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31266, "top5_acc": 0.56422, "loss_cls": 3.93776, "loss": 3.93776, "time": 0.81512} +{"mode": "train", "epoch": 69, "iter": 1900, "lr": 0.05678, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31109, "top5_acc": 0.55891, "loss_cls": 3.94516, "loss": 3.94516, "time": 0.81322} +{"mode": "train", "epoch": 69, "iter": 2000, "lr": 0.05675, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29781, "top5_acc": 0.55312, "loss_cls": 4.02147, "loss": 4.02147, "time": 0.82396} +{"mode": "train", "epoch": 69, "iter": 2100, "lr": 0.05672, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30422, "top5_acc": 0.56688, "loss_cls": 3.98615, "loss": 3.98615, "time": 0.81472} +{"mode": "train", "epoch": 69, "iter": 2200, "lr": 0.0567, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31, "top5_acc": 0.56094, "loss_cls": 3.9697, "loss": 3.9697, "time": 0.82414} +{"mode": "train", "epoch": 69, "iter": 2300, "lr": 0.05667, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30734, "top5_acc": 0.56172, "loss_cls": 3.98414, "loss": 3.98414, "time": 0.8222} +{"mode": "train", "epoch": 69, "iter": 2400, "lr": 0.05664, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30859, "top5_acc": 0.57125, "loss_cls": 3.94349, "loss": 3.94349, "time": 0.82204} +{"mode": "train", "epoch": 69, "iter": 2500, "lr": 0.05661, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29172, "top5_acc": 0.55391, "loss_cls": 4.00724, "loss": 4.00724, "time": 0.81848} +{"mode": "train", "epoch": 69, "iter": 2600, "lr": 0.05658, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30844, "top5_acc": 0.56422, "loss_cls": 3.96685, "loss": 3.96685, "time": 0.81504} +{"mode": "train", "epoch": 69, "iter": 2700, "lr": 0.05656, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30734, "top5_acc": 0.56109, "loss_cls": 3.98121, "loss": 3.98121, "time": 0.81709} +{"mode": "train", "epoch": 69, "iter": 2800, "lr": 0.05653, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31266, "top5_acc": 0.56953, "loss_cls": 3.94906, "loss": 3.94906, "time": 0.8161} +{"mode": "train", "epoch": 69, "iter": 2900, "lr": 0.0565, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29828, "top5_acc": 0.55781, "loss_cls": 3.99503, "loss": 3.99503, "time": 0.8205} +{"mode": "train", "epoch": 69, "iter": 3000, "lr": 0.05647, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31062, "top5_acc": 0.56328, "loss_cls": 3.95369, "loss": 3.95369, "time": 0.82354} +{"mode": "train", "epoch": 69, "iter": 3100, "lr": 0.05645, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29969, "top5_acc": 0.56594, "loss_cls": 3.96044, "loss": 3.96044, "time": 0.81837} +{"mode": "train", "epoch": 69, "iter": 3200, "lr": 0.05642, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30641, "top5_acc": 0.55672, "loss_cls": 3.98196, "loss": 3.98196, "time": 0.81975} +{"mode": "train", "epoch": 69, "iter": 3300, "lr": 0.05639, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30812, "top5_acc": 0.56828, "loss_cls": 3.92864, "loss": 3.92864, "time": 0.81233} +{"mode": "train", "epoch": 69, "iter": 3400, "lr": 0.05636, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29922, "top5_acc": 0.55281, "loss_cls": 4.01573, "loss": 4.01573, "time": 0.81905} +{"mode": "train", "epoch": 69, "iter": 3500, "lr": 0.05634, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31375, "top5_acc": 0.56203, "loss_cls": 3.97065, "loss": 3.97065, "time": 0.81628} +{"mode": "train", "epoch": 69, "iter": 3600, "lr": 0.05631, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31422, "top5_acc": 0.56766, "loss_cls": 3.96583, "loss": 3.96583, "time": 0.82547} +{"mode": "train", "epoch": 69, "iter": 3700, "lr": 0.05628, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31734, "top5_acc": 0.56672, "loss_cls": 3.95073, "loss": 3.95073, "time": 0.81693} +{"mode": "val", "epoch": 69, "iter": 309, "lr": 0.05627, "top1_acc": 0.25143, "top5_acc": 0.49435, "mean_class_accuracy": 0.25138} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.05624, "memory": 15990, "data_time": 1.30811, "top1_acc": 0.31734, "top5_acc": 0.57625, "loss_cls": 3.92726, "loss": 3.92726, "time": 2.29996} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.05621, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30875, "top5_acc": 0.56984, "loss_cls": 3.91949, "loss": 3.91949, "time": 0.83119} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.05618, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32281, "top5_acc": 0.57312, "loss_cls": 3.88419, "loss": 3.88419, "time": 0.83098} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.05616, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31531, "top5_acc": 0.56891, "loss_cls": 3.9046, "loss": 3.9046, "time": 0.8219} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.05613, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31234, "top5_acc": 0.57203, "loss_cls": 3.9124, "loss": 3.9124, "time": 0.81718} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.0561, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31141, "top5_acc": 0.57234, "loss_cls": 3.92688, "loss": 3.92688, "time": 0.81327} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.05607, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31219, "top5_acc": 0.56297, "loss_cls": 3.98945, "loss": 3.98945, "time": 0.81361} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.05605, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31547, "top5_acc": 0.57422, "loss_cls": 3.90024, "loss": 3.90024, "time": 0.81913} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.05602, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30359, "top5_acc": 0.56203, "loss_cls": 3.96004, "loss": 3.96004, "time": 0.81777} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.05599, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30812, "top5_acc": 0.55297, "loss_cls": 3.9901, "loss": 3.9901, "time": 0.81337} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.05596, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31594, "top5_acc": 0.57203, "loss_cls": 3.89628, "loss": 3.89628, "time": 0.81749} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.05593, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30078, "top5_acc": 0.55641, "loss_cls": 3.98818, "loss": 3.98818, "time": 0.81899} +{"mode": "train", "epoch": 70, "iter": 1300, "lr": 0.05591, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31359, "top5_acc": 0.56328, "loss_cls": 3.95035, "loss": 3.95035, "time": 0.81681} +{"mode": "train", "epoch": 70, "iter": 1400, "lr": 0.05588, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30969, "top5_acc": 0.57234, "loss_cls": 3.93345, "loss": 3.93345, "time": 0.81587} +{"mode": "train", "epoch": 70, "iter": 1500, "lr": 0.05585, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31734, "top5_acc": 0.57703, "loss_cls": 3.88989, "loss": 3.88989, "time": 0.81155} +{"mode": "train", "epoch": 70, "iter": 1600, "lr": 0.05582, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32031, "top5_acc": 0.57672, "loss_cls": 3.91927, "loss": 3.91927, "time": 0.81472} +{"mode": "train", "epoch": 70, "iter": 1700, "lr": 0.0558, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30453, "top5_acc": 0.56875, "loss_cls": 3.95034, "loss": 3.95034, "time": 0.81632} +{"mode": "train", "epoch": 70, "iter": 1800, "lr": 0.05577, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30672, "top5_acc": 0.565, "loss_cls": 3.93968, "loss": 3.93968, "time": 0.81746} +{"mode": "train", "epoch": 70, "iter": 1900, "lr": 0.05574, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31672, "top5_acc": 0.57172, "loss_cls": 3.93841, "loss": 3.93841, "time": 0.81372} +{"mode": "train", "epoch": 70, "iter": 2000, "lr": 0.05571, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31844, "top5_acc": 0.57656, "loss_cls": 3.92657, "loss": 3.92657, "time": 0.82241} +{"mode": "train", "epoch": 70, "iter": 2100, "lr": 0.05568, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31109, "top5_acc": 0.57063, "loss_cls": 3.96384, "loss": 3.96384, "time": 0.81343} +{"mode": "train", "epoch": 70, "iter": 2200, "lr": 0.05566, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30891, "top5_acc": 0.57781, "loss_cls": 3.92596, "loss": 3.92596, "time": 0.82128} +{"mode": "train", "epoch": 70, "iter": 2300, "lr": 0.05563, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31125, "top5_acc": 0.56203, "loss_cls": 3.93635, "loss": 3.93635, "time": 0.81777} +{"mode": "train", "epoch": 70, "iter": 2400, "lr": 0.0556, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30719, "top5_acc": 0.56266, "loss_cls": 3.99761, "loss": 3.99761, "time": 0.82154} +{"mode": "train", "epoch": 70, "iter": 2500, "lr": 0.05557, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30484, "top5_acc": 0.55781, "loss_cls": 3.9954, "loss": 3.9954, "time": 0.81534} +{"mode": "train", "epoch": 70, "iter": 2600, "lr": 0.05555, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30672, "top5_acc": 0.57391, "loss_cls": 3.94655, "loss": 3.94655, "time": 0.82391} +{"mode": "train", "epoch": 70, "iter": 2700, "lr": 0.05552, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30219, "top5_acc": 0.56, "loss_cls": 3.94755, "loss": 3.94755, "time": 0.8181} +{"mode": "train", "epoch": 70, "iter": 2800, "lr": 0.05549, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31203, "top5_acc": 0.57078, "loss_cls": 3.95131, "loss": 3.95131, "time": 0.82191} +{"mode": "train", "epoch": 70, "iter": 2900, "lr": 0.05546, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29797, "top5_acc": 0.55, "loss_cls": 4.02961, "loss": 4.02961, "time": 0.8187} +{"mode": "train", "epoch": 70, "iter": 3000, "lr": 0.05543, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3125, "top5_acc": 0.56984, "loss_cls": 3.91691, "loss": 3.91691, "time": 0.82435} +{"mode": "train", "epoch": 70, "iter": 3100, "lr": 0.05541, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30109, "top5_acc": 0.55797, "loss_cls": 3.98925, "loss": 3.98925, "time": 0.82154} +{"mode": "train", "epoch": 70, "iter": 3200, "lr": 0.05538, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3075, "top5_acc": 0.55781, "loss_cls": 3.97107, "loss": 3.97107, "time": 0.82493} +{"mode": "train", "epoch": 70, "iter": 3300, "lr": 0.05535, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31406, "top5_acc": 0.56297, "loss_cls": 3.9605, "loss": 3.9605, "time": 0.81577} +{"mode": "train", "epoch": 70, "iter": 3400, "lr": 0.05532, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31406, "top5_acc": 0.57422, "loss_cls": 3.91224, "loss": 3.91224, "time": 0.8119} +{"mode": "train", "epoch": 70, "iter": 3500, "lr": 0.0553, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30562, "top5_acc": 0.56453, "loss_cls": 3.99373, "loss": 3.99373, "time": 0.81688} +{"mode": "train", "epoch": 70, "iter": 3600, "lr": 0.05527, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30516, "top5_acc": 0.56094, "loss_cls": 3.97172, "loss": 3.97172, "time": 0.81932} +{"mode": "train", "epoch": 70, "iter": 3700, "lr": 0.05524, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31219, "top5_acc": 0.55859, "loss_cls": 3.95813, "loss": 3.95813, "time": 0.8131} +{"mode": "val", "epoch": 70, "iter": 309, "lr": 0.05523, "top1_acc": 0.24317, "top5_acc": 0.48579, "mean_class_accuracy": 0.24286} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.0552, "memory": 15990, "data_time": 1.35854, "top1_acc": 0.31188, "top5_acc": 0.57156, "loss_cls": 3.90728, "loss": 3.90728, "time": 2.34809} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.05517, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31781, "top5_acc": 0.5775, "loss_cls": 3.90967, "loss": 3.90967, "time": 0.81878} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.05514, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31797, "top5_acc": 0.5725, "loss_cls": 3.92888, "loss": 3.92888, "time": 0.82279} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.05512, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31453, "top5_acc": 0.57188, "loss_cls": 3.89705, "loss": 3.89705, "time": 0.82408} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.05509, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31406, "top5_acc": 0.57797, "loss_cls": 3.88501, "loss": 3.88501, "time": 0.82121} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.05506, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32109, "top5_acc": 0.57172, "loss_cls": 3.91288, "loss": 3.91288, "time": 0.81589} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.05503, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30969, "top5_acc": 0.56984, "loss_cls": 3.94043, "loss": 3.94043, "time": 0.81342} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.055, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31625, "top5_acc": 0.57203, "loss_cls": 3.90905, "loss": 3.90905, "time": 0.81538} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.05498, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31797, "top5_acc": 0.57234, "loss_cls": 3.9359, "loss": 3.9359, "time": 0.81564} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.05495, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31188, "top5_acc": 0.56375, "loss_cls": 3.91775, "loss": 3.91775, "time": 0.81418} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.05492, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3075, "top5_acc": 0.56703, "loss_cls": 3.95305, "loss": 3.95305, "time": 0.82019} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.05489, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31781, "top5_acc": 0.56672, "loss_cls": 3.93055, "loss": 3.93055, "time": 0.81895} +{"mode": "train", "epoch": 71, "iter": 1300, "lr": 0.05487, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30844, "top5_acc": 0.56484, "loss_cls": 3.97423, "loss": 3.97423, "time": 0.81466} +{"mode": "train", "epoch": 71, "iter": 1400, "lr": 0.05484, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31344, "top5_acc": 0.56906, "loss_cls": 3.96198, "loss": 3.96198, "time": 0.81871} +{"mode": "train", "epoch": 71, "iter": 1500, "lr": 0.05481, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31375, "top5_acc": 0.57156, "loss_cls": 3.95032, "loss": 3.95032, "time": 0.81589} +{"mode": "train", "epoch": 71, "iter": 1600, "lr": 0.05478, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30156, "top5_acc": 0.56422, "loss_cls": 3.96406, "loss": 3.96406, "time": 0.81592} +{"mode": "train", "epoch": 71, "iter": 1700, "lr": 0.05475, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30719, "top5_acc": 0.56922, "loss_cls": 3.94883, "loss": 3.94883, "time": 0.81476} +{"mode": "train", "epoch": 71, "iter": 1800, "lr": 0.05473, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3125, "top5_acc": 0.57438, "loss_cls": 3.90451, "loss": 3.90451, "time": 0.81415} +{"mode": "train", "epoch": 71, "iter": 1900, "lr": 0.0547, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31547, "top5_acc": 0.56938, "loss_cls": 3.96372, "loss": 3.96372, "time": 0.8138} +{"mode": "train", "epoch": 71, "iter": 2000, "lr": 0.05467, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.3125, "top5_acc": 0.56406, "loss_cls": 3.94444, "loss": 3.94444, "time": 0.82994} +{"mode": "train", "epoch": 71, "iter": 2100, "lr": 0.05464, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30969, "top5_acc": 0.57203, "loss_cls": 3.92602, "loss": 3.92602, "time": 0.81342} +{"mode": "train", "epoch": 71, "iter": 2200, "lr": 0.05461, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31266, "top5_acc": 0.56547, "loss_cls": 3.94294, "loss": 3.94294, "time": 0.8283} +{"mode": "train", "epoch": 71, "iter": 2300, "lr": 0.05459, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31312, "top5_acc": 0.56281, "loss_cls": 3.9381, "loss": 3.9381, "time": 0.81927} +{"mode": "train", "epoch": 71, "iter": 2400, "lr": 0.05456, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30375, "top5_acc": 0.56844, "loss_cls": 3.96394, "loss": 3.96394, "time": 0.81574} +{"mode": "train", "epoch": 71, "iter": 2500, "lr": 0.05453, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31188, "top5_acc": 0.57141, "loss_cls": 3.93007, "loss": 3.93007, "time": 0.82561} +{"mode": "train", "epoch": 71, "iter": 2600, "lr": 0.0545, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31328, "top5_acc": 0.56359, "loss_cls": 3.94553, "loss": 3.94553, "time": 0.81474} +{"mode": "train", "epoch": 71, "iter": 2700, "lr": 0.05448, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30797, "top5_acc": 0.55734, "loss_cls": 3.98669, "loss": 3.98669, "time": 0.82214} +{"mode": "train", "epoch": 71, "iter": 2800, "lr": 0.05445, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30828, "top5_acc": 0.56891, "loss_cls": 3.95024, "loss": 3.95024, "time": 0.81864} +{"mode": "train", "epoch": 71, "iter": 2900, "lr": 0.05442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30844, "top5_acc": 0.57156, "loss_cls": 3.94071, "loss": 3.94071, "time": 0.81831} +{"mode": "train", "epoch": 71, "iter": 3000, "lr": 0.05439, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31594, "top5_acc": 0.57125, "loss_cls": 3.92751, "loss": 3.92751, "time": 0.82254} +{"mode": "train", "epoch": 71, "iter": 3100, "lr": 0.05436, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31688, "top5_acc": 0.56359, "loss_cls": 3.92857, "loss": 3.92857, "time": 0.81759} +{"mode": "train", "epoch": 71, "iter": 3200, "lr": 0.05434, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31797, "top5_acc": 0.56625, "loss_cls": 3.92907, "loss": 3.92907, "time": 0.81698} +{"mode": "train", "epoch": 71, "iter": 3300, "lr": 0.05431, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30922, "top5_acc": 0.57578, "loss_cls": 3.90392, "loss": 3.90392, "time": 0.81684} +{"mode": "train", "epoch": 71, "iter": 3400, "lr": 0.05428, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31469, "top5_acc": 0.56141, "loss_cls": 3.94766, "loss": 3.94766, "time": 0.81648} +{"mode": "train", "epoch": 71, "iter": 3500, "lr": 0.05425, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32344, "top5_acc": 0.57812, "loss_cls": 3.88883, "loss": 3.88883, "time": 0.81679} +{"mode": "train", "epoch": 71, "iter": 3600, "lr": 0.05422, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30562, "top5_acc": 0.57172, "loss_cls": 3.94238, "loss": 3.94238, "time": 0.8187} +{"mode": "train", "epoch": 71, "iter": 3700, "lr": 0.0542, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31453, "top5_acc": 0.57953, "loss_cls": 3.91985, "loss": 3.91985, "time": 0.81321} +{"mode": "val", "epoch": 71, "iter": 309, "lr": 0.05418, "top1_acc": 0.23639, "top5_acc": 0.48225, "mean_class_accuracy": 0.2363} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.05416, "memory": 15990, "data_time": 1.3465, "top1_acc": 0.31328, "top5_acc": 0.56875, "loss_cls": 3.90497, "loss": 3.90497, "time": 2.32656} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.05413, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3175, "top5_acc": 0.58016, "loss_cls": 3.89642, "loss": 3.89642, "time": 0.81654} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.0541, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31422, "top5_acc": 0.57766, "loss_cls": 3.86573, "loss": 3.86573, "time": 0.81366} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.05407, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31625, "top5_acc": 0.57656, "loss_cls": 3.91667, "loss": 3.91667, "time": 0.8141} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.05404, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30891, "top5_acc": 0.57016, "loss_cls": 3.92388, "loss": 3.92388, "time": 0.81905} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.05402, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31141, "top5_acc": 0.56781, "loss_cls": 3.93845, "loss": 3.93845, "time": 0.81952} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.05399, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31156, "top5_acc": 0.56437, "loss_cls": 3.9365, "loss": 3.9365, "time": 0.81882} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.05396, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31953, "top5_acc": 0.57391, "loss_cls": 3.87178, "loss": 3.87178, "time": 0.81632} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.05393, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31281, "top5_acc": 0.57391, "loss_cls": 3.93244, "loss": 3.93244, "time": 0.8182} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.05391, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31297, "top5_acc": 0.57719, "loss_cls": 3.91801, "loss": 3.91801, "time": 0.81282} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.05388, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31859, "top5_acc": 0.58156, "loss_cls": 3.90396, "loss": 3.90396, "time": 0.81623} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.05385, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30953, "top5_acc": 0.56563, "loss_cls": 3.92384, "loss": 3.92384, "time": 0.81432} +{"mode": "train", "epoch": 72, "iter": 1300, "lr": 0.05382, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3075, "top5_acc": 0.56688, "loss_cls": 3.93552, "loss": 3.93552, "time": 0.81928} +{"mode": "train", "epoch": 72, "iter": 1400, "lr": 0.05379, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31406, "top5_acc": 0.56984, "loss_cls": 3.91655, "loss": 3.91655, "time": 0.81761} +{"mode": "train", "epoch": 72, "iter": 1500, "lr": 0.05377, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30922, "top5_acc": 0.57422, "loss_cls": 3.9299, "loss": 3.9299, "time": 0.81431} +{"mode": "train", "epoch": 72, "iter": 1600, "lr": 0.05374, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31938, "top5_acc": 0.57812, "loss_cls": 3.91215, "loss": 3.91215, "time": 0.81347} +{"mode": "train", "epoch": 72, "iter": 1700, "lr": 0.05371, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31359, "top5_acc": 0.57328, "loss_cls": 3.91334, "loss": 3.91334, "time": 0.81783} +{"mode": "train", "epoch": 72, "iter": 1800, "lr": 0.05368, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31672, "top5_acc": 0.58141, "loss_cls": 3.90774, "loss": 3.90774, "time": 0.81959} +{"mode": "train", "epoch": 72, "iter": 1900, "lr": 0.05365, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.315, "top5_acc": 0.57297, "loss_cls": 3.90499, "loss": 3.90499, "time": 0.82067} +{"mode": "train", "epoch": 72, "iter": 2000, "lr": 0.05363, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31203, "top5_acc": 0.57047, "loss_cls": 3.93868, "loss": 3.93868, "time": 0.8275} +{"mode": "train", "epoch": 72, "iter": 2100, "lr": 0.0536, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31172, "top5_acc": 0.56531, "loss_cls": 3.94991, "loss": 3.94991, "time": 0.81208} +{"mode": "train", "epoch": 72, "iter": 2200, "lr": 0.05357, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31797, "top5_acc": 0.56984, "loss_cls": 3.91229, "loss": 3.91229, "time": 0.82378} +{"mode": "train", "epoch": 72, "iter": 2300, "lr": 0.05354, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31422, "top5_acc": 0.56578, "loss_cls": 3.93517, "loss": 3.93517, "time": 0.82245} +{"mode": "train", "epoch": 72, "iter": 2400, "lr": 0.05352, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31719, "top5_acc": 0.56812, "loss_cls": 3.93553, "loss": 3.93553, "time": 0.81896} +{"mode": "train", "epoch": 72, "iter": 2500, "lr": 0.05349, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31328, "top5_acc": 0.56672, "loss_cls": 3.9431, "loss": 3.9431, "time": 0.82106} +{"mode": "train", "epoch": 72, "iter": 2600, "lr": 0.05346, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31672, "top5_acc": 0.57234, "loss_cls": 3.90265, "loss": 3.90265, "time": 0.81712} +{"mode": "train", "epoch": 72, "iter": 2700, "lr": 0.05343, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30812, "top5_acc": 0.5625, "loss_cls": 3.95034, "loss": 3.95034, "time": 0.82026} +{"mode": "train", "epoch": 72, "iter": 2800, "lr": 0.0534, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30953, "top5_acc": 0.56094, "loss_cls": 3.95603, "loss": 3.95603, "time": 0.81969} +{"mode": "train", "epoch": 72, "iter": 2900, "lr": 0.05338, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.315, "top5_acc": 0.56812, "loss_cls": 3.92353, "loss": 3.92353, "time": 0.81583} +{"mode": "train", "epoch": 72, "iter": 3000, "lr": 0.05335, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31203, "top5_acc": 0.57141, "loss_cls": 3.93473, "loss": 3.93473, "time": 0.81968} +{"mode": "train", "epoch": 72, "iter": 3100, "lr": 0.05332, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30766, "top5_acc": 0.56297, "loss_cls": 3.94165, "loss": 3.94165, "time": 0.82602} +{"mode": "train", "epoch": 72, "iter": 3200, "lr": 0.05329, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31438, "top5_acc": 0.56984, "loss_cls": 3.92303, "loss": 3.92303, "time": 0.81866} +{"mode": "train", "epoch": 72, "iter": 3300, "lr": 0.05326, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31969, "top5_acc": 0.5775, "loss_cls": 3.8905, "loss": 3.8905, "time": 0.81793} +{"mode": "train", "epoch": 72, "iter": 3400, "lr": 0.05324, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31328, "top5_acc": 0.57016, "loss_cls": 3.93221, "loss": 3.93221, "time": 0.81924} +{"mode": "train", "epoch": 72, "iter": 3500, "lr": 0.05321, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30516, "top5_acc": 0.56891, "loss_cls": 3.9553, "loss": 3.9553, "time": 0.8224} +{"mode": "train", "epoch": 72, "iter": 3600, "lr": 0.05318, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32016, "top5_acc": 0.57156, "loss_cls": 3.93277, "loss": 3.93277, "time": 0.81448} +{"mode": "train", "epoch": 72, "iter": 3700, "lr": 0.05315, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30547, "top5_acc": 0.56063, "loss_cls": 3.9777, "loss": 3.9777, "time": 0.81697} +{"mode": "val", "epoch": 72, "iter": 309, "lr": 0.05314, "top1_acc": 0.26212, "top5_acc": 0.50084, "mean_class_accuracy": 0.26204} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.05311, "memory": 15990, "data_time": 1.32868, "top1_acc": 0.31594, "top5_acc": 0.58656, "loss_cls": 3.83477, "loss": 3.83477, "time": 2.31405} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.05308, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31594, "top5_acc": 0.57703, "loss_cls": 3.89974, "loss": 3.89974, "time": 0.8188} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.05306, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32297, "top5_acc": 0.58469, "loss_cls": 3.8552, "loss": 3.8552, "time": 0.82414} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.05303, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31188, "top5_acc": 0.58016, "loss_cls": 3.92648, "loss": 3.92648, "time": 0.82207} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.053, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32281, "top5_acc": 0.57828, "loss_cls": 3.86151, "loss": 3.86151, "time": 0.81785} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.05297, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33375, "top5_acc": 0.58938, "loss_cls": 3.84265, "loss": 3.84265, "time": 0.81421} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.05294, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32188, "top5_acc": 0.58312, "loss_cls": 3.89599, "loss": 3.89599, "time": 0.82205} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.05292, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31844, "top5_acc": 0.56891, "loss_cls": 3.91584, "loss": 3.91584, "time": 0.82282} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.05289, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31281, "top5_acc": 0.57203, "loss_cls": 3.9221, "loss": 3.9221, "time": 0.81671} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.05286, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31625, "top5_acc": 0.57547, "loss_cls": 3.88939, "loss": 3.88939, "time": 0.81592} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.05283, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30688, "top5_acc": 0.57031, "loss_cls": 3.94163, "loss": 3.94163, "time": 0.81302} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.0528, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31219, "top5_acc": 0.57516, "loss_cls": 3.91152, "loss": 3.91152, "time": 0.81424} +{"mode": "train", "epoch": 73, "iter": 1300, "lr": 0.05278, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32141, "top5_acc": 0.57312, "loss_cls": 3.90185, "loss": 3.90185, "time": 0.81112} +{"mode": "train", "epoch": 73, "iter": 1400, "lr": 0.05275, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31359, "top5_acc": 0.57297, "loss_cls": 3.92185, "loss": 3.92185, "time": 0.817} +{"mode": "train", "epoch": 73, "iter": 1500, "lr": 0.05272, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.32531, "top5_acc": 0.58344, "loss_cls": 3.87693, "loss": 3.87693, "time": 0.81925} +{"mode": "train", "epoch": 73, "iter": 1600, "lr": 0.05269, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31422, "top5_acc": 0.56031, "loss_cls": 3.98007, "loss": 3.98007, "time": 0.81756} +{"mode": "train", "epoch": 73, "iter": 1700, "lr": 0.05267, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31094, "top5_acc": 0.56453, "loss_cls": 3.94188, "loss": 3.94188, "time": 0.81213} +{"mode": "train", "epoch": 73, "iter": 1800, "lr": 0.05264, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31531, "top5_acc": 0.56969, "loss_cls": 3.92251, "loss": 3.92251, "time": 0.82294} +{"mode": "train", "epoch": 73, "iter": 1900, "lr": 0.05261, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31531, "top5_acc": 0.56812, "loss_cls": 3.92195, "loss": 3.92195, "time": 0.82102} +{"mode": "train", "epoch": 73, "iter": 2000, "lr": 0.05258, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31406, "top5_acc": 0.57234, "loss_cls": 3.9053, "loss": 3.9053, "time": 0.82682} +{"mode": "train", "epoch": 73, "iter": 2100, "lr": 0.05255, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31781, "top5_acc": 0.57812, "loss_cls": 3.8781, "loss": 3.8781, "time": 0.82054} +{"mode": "train", "epoch": 73, "iter": 2200, "lr": 0.05253, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31594, "top5_acc": 0.56672, "loss_cls": 3.9436, "loss": 3.9436, "time": 0.82236} +{"mode": "train", "epoch": 73, "iter": 2300, "lr": 0.0525, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.315, "top5_acc": 0.5675, "loss_cls": 3.91697, "loss": 3.91697, "time": 0.82966} +{"mode": "train", "epoch": 73, "iter": 2400, "lr": 0.05247, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31891, "top5_acc": 0.58203, "loss_cls": 3.88402, "loss": 3.88402, "time": 0.82032} +{"mode": "train", "epoch": 73, "iter": 2500, "lr": 0.05244, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31984, "top5_acc": 0.57906, "loss_cls": 3.87508, "loss": 3.87508, "time": 0.81735} +{"mode": "train", "epoch": 73, "iter": 2600, "lr": 0.05241, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31828, "top5_acc": 0.57953, "loss_cls": 3.88454, "loss": 3.88454, "time": 0.81492} +{"mode": "train", "epoch": 73, "iter": 2700, "lr": 0.05239, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30438, "top5_acc": 0.56688, "loss_cls": 3.94704, "loss": 3.94704, "time": 0.81229} +{"mode": "train", "epoch": 73, "iter": 2800, "lr": 0.05236, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32141, "top5_acc": 0.57375, "loss_cls": 3.90698, "loss": 3.90698, "time": 0.81808} +{"mode": "train", "epoch": 73, "iter": 2900, "lr": 0.05233, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31453, "top5_acc": 0.57125, "loss_cls": 3.96247, "loss": 3.96247, "time": 0.81592} +{"mode": "train", "epoch": 73, "iter": 3000, "lr": 0.0523, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31188, "top5_acc": 0.56844, "loss_cls": 3.9391, "loss": 3.9391, "time": 0.82555} +{"mode": "train", "epoch": 73, "iter": 3100, "lr": 0.05227, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31203, "top5_acc": 0.57484, "loss_cls": 3.93696, "loss": 3.93696, "time": 0.82455} +{"mode": "train", "epoch": 73, "iter": 3200, "lr": 0.05225, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30547, "top5_acc": 0.56109, "loss_cls": 3.96225, "loss": 3.96225, "time": 0.82264} +{"mode": "train", "epoch": 73, "iter": 3300, "lr": 0.05222, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32766, "top5_acc": 0.57563, "loss_cls": 3.87867, "loss": 3.87867, "time": 0.82846} +{"mode": "train", "epoch": 73, "iter": 3400, "lr": 0.05219, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31375, "top5_acc": 0.56656, "loss_cls": 3.93379, "loss": 3.93379, "time": 0.81798} +{"mode": "train", "epoch": 73, "iter": 3500, "lr": 0.05216, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31422, "top5_acc": 0.56891, "loss_cls": 3.94693, "loss": 3.94693, "time": 0.81395} +{"mode": "train", "epoch": 73, "iter": 3600, "lr": 0.05213, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31203, "top5_acc": 0.56953, "loss_cls": 3.91676, "loss": 3.91676, "time": 0.82274} +{"mode": "train", "epoch": 73, "iter": 3700, "lr": 0.05211, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31594, "top5_acc": 0.56953, "loss_cls": 3.94269, "loss": 3.94269, "time": 0.82091} +{"mode": "val", "epoch": 73, "iter": 309, "lr": 0.05209, "top1_acc": 0.2409, "top5_acc": 0.48341, "mean_class_accuracy": 0.24059} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.05207, "memory": 15990, "data_time": 1.40324, "top1_acc": 0.32047, "top5_acc": 0.57859, "loss_cls": 3.86791, "loss": 3.86791, "time": 2.39087} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.05204, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31891, "top5_acc": 0.58031, "loss_cls": 3.88745, "loss": 3.88745, "time": 0.83093} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.05201, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32016, "top5_acc": 0.58078, "loss_cls": 3.87344, "loss": 3.87344, "time": 0.83324} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.05198, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31672, "top5_acc": 0.58844, "loss_cls": 3.8588, "loss": 3.8588, "time": 0.82819} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.05195, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31203, "top5_acc": 0.57016, "loss_cls": 3.90888, "loss": 3.90888, "time": 0.82524} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.05193, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32109, "top5_acc": 0.58312, "loss_cls": 3.86639, "loss": 3.86639, "time": 0.82425} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.0519, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33188, "top5_acc": 0.58359, "loss_cls": 3.8735, "loss": 3.8735, "time": 0.81617} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.05187, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31984, "top5_acc": 0.57469, "loss_cls": 3.88783, "loss": 3.88783, "time": 0.81765} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.05184, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31812, "top5_acc": 0.57297, "loss_cls": 3.91468, "loss": 3.91468, "time": 0.81888} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.05181, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32312, "top5_acc": 0.57547, "loss_cls": 3.88998, "loss": 3.88998, "time": 0.81402} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.05179, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31969, "top5_acc": 0.57328, "loss_cls": 3.90889, "loss": 3.90889, "time": 0.81909} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.05176, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31688, "top5_acc": 0.57828, "loss_cls": 3.89226, "loss": 3.89226, "time": 0.82242} +{"mode": "train", "epoch": 74, "iter": 1300, "lr": 0.05173, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31812, "top5_acc": 0.57641, "loss_cls": 3.90181, "loss": 3.90181, "time": 0.81724} +{"mode": "train", "epoch": 74, "iter": 1400, "lr": 0.0517, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31359, "top5_acc": 0.56781, "loss_cls": 3.93573, "loss": 3.93573, "time": 0.81749} +{"mode": "train", "epoch": 74, "iter": 1500, "lr": 0.05168, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31078, "top5_acc": 0.57953, "loss_cls": 3.89883, "loss": 3.89883, "time": 0.81105} +{"mode": "train", "epoch": 74, "iter": 1600, "lr": 0.05165, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32266, "top5_acc": 0.57563, "loss_cls": 3.85877, "loss": 3.85877, "time": 0.8184} +{"mode": "train", "epoch": 74, "iter": 1700, "lr": 0.05162, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31828, "top5_acc": 0.57391, "loss_cls": 3.91554, "loss": 3.91554, "time": 0.81157} +{"mode": "train", "epoch": 74, "iter": 1800, "lr": 0.05159, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31875, "top5_acc": 0.57219, "loss_cls": 3.90876, "loss": 3.90876, "time": 0.81401} +{"mode": "train", "epoch": 74, "iter": 1900, "lr": 0.05156, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32281, "top5_acc": 0.57281, "loss_cls": 3.89924, "loss": 3.89924, "time": 0.81283} +{"mode": "train", "epoch": 74, "iter": 2000, "lr": 0.05154, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30906, "top5_acc": 0.56656, "loss_cls": 3.94455, "loss": 3.94455, "time": 0.82627} +{"mode": "train", "epoch": 74, "iter": 2100, "lr": 0.05151, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31047, "top5_acc": 0.57359, "loss_cls": 3.91179, "loss": 3.91179, "time": 0.81505} +{"mode": "train", "epoch": 74, "iter": 2200, "lr": 0.05148, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30984, "top5_acc": 0.56844, "loss_cls": 3.91723, "loss": 3.91723, "time": 0.82547} +{"mode": "train", "epoch": 74, "iter": 2300, "lr": 0.05145, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3175, "top5_acc": 0.57031, "loss_cls": 3.90307, "loss": 3.90307, "time": 0.81717} +{"mode": "train", "epoch": 74, "iter": 2400, "lr": 0.05142, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30938, "top5_acc": 0.56172, "loss_cls": 3.95645, "loss": 3.95645, "time": 0.81751} +{"mode": "train", "epoch": 74, "iter": 2500, "lr": 0.0514, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31266, "top5_acc": 0.57203, "loss_cls": 3.92836, "loss": 3.92836, "time": 0.81769} +{"mode": "train", "epoch": 74, "iter": 2600, "lr": 0.05137, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31562, "top5_acc": 0.56531, "loss_cls": 3.92349, "loss": 3.92349, "time": 0.81688} +{"mode": "train", "epoch": 74, "iter": 2700, "lr": 0.05134, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31156, "top5_acc": 0.57391, "loss_cls": 3.88871, "loss": 3.88871, "time": 0.81578} +{"mode": "train", "epoch": 74, "iter": 2800, "lr": 0.05131, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32094, "top5_acc": 0.56938, "loss_cls": 3.88896, "loss": 3.88896, "time": 0.82126} +{"mode": "train", "epoch": 74, "iter": 2900, "lr": 0.05128, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31922, "top5_acc": 0.57672, "loss_cls": 3.89355, "loss": 3.89355, "time": 0.81513} +{"mode": "train", "epoch": 74, "iter": 3000, "lr": 0.05126, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31375, "top5_acc": 0.57031, "loss_cls": 3.91182, "loss": 3.91182, "time": 0.82709} +{"mode": "train", "epoch": 74, "iter": 3100, "lr": 0.05123, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31953, "top5_acc": 0.57078, "loss_cls": 3.91182, "loss": 3.91182, "time": 0.82246} +{"mode": "train", "epoch": 74, "iter": 3200, "lr": 0.0512, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32297, "top5_acc": 0.58156, "loss_cls": 3.85808, "loss": 3.85808, "time": 0.82281} +{"mode": "train", "epoch": 74, "iter": 3300, "lr": 0.05117, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31844, "top5_acc": 0.57719, "loss_cls": 3.8996, "loss": 3.8996, "time": 0.81734} +{"mode": "train", "epoch": 74, "iter": 3400, "lr": 0.05114, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3175, "top5_acc": 0.57312, "loss_cls": 3.92022, "loss": 3.92022, "time": 0.81387} +{"mode": "train", "epoch": 74, "iter": 3500, "lr": 0.05112, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32188, "top5_acc": 0.57094, "loss_cls": 3.9105, "loss": 3.9105, "time": 0.81457} +{"mode": "train", "epoch": 74, "iter": 3600, "lr": 0.05109, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31984, "top5_acc": 0.57969, "loss_cls": 3.91214, "loss": 3.91214, "time": 0.81509} +{"mode": "train", "epoch": 74, "iter": 3700, "lr": 0.05106, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31797, "top5_acc": 0.56609, "loss_cls": 3.95416, "loss": 3.95416, "time": 0.81922} +{"mode": "val", "epoch": 74, "iter": 309, "lr": 0.05105, "top1_acc": 0.23705, "top5_acc": 0.48392, "mean_class_accuracy": 0.23718} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.05102, "memory": 15990, "data_time": 1.35544, "top1_acc": 0.32516, "top5_acc": 0.58953, "loss_cls": 3.81788, "loss": 3.81788, "time": 2.3622} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.05099, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32672, "top5_acc": 0.58344, "loss_cls": 3.85113, "loss": 3.85113, "time": 0.83851} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.05096, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32562, "top5_acc": 0.57063, "loss_cls": 3.88993, "loss": 3.88993, "time": 0.84062} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.05094, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32703, "top5_acc": 0.58797, "loss_cls": 3.82946, "loss": 3.82946, "time": 0.83364} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.05091, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32219, "top5_acc": 0.58219, "loss_cls": 3.86935, "loss": 3.86935, "time": 0.82991} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.05088, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32625, "top5_acc": 0.58266, "loss_cls": 3.87166, "loss": 3.87166, "time": 0.83535} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.05085, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31984, "top5_acc": 0.57547, "loss_cls": 3.89986, "loss": 3.89986, "time": 0.83596} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.05082, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3225, "top5_acc": 0.58016, "loss_cls": 3.90384, "loss": 3.90384, "time": 0.83137} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.0508, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32734, "top5_acc": 0.58906, "loss_cls": 3.82293, "loss": 3.82293, "time": 0.83266} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.05077, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31062, "top5_acc": 0.56812, "loss_cls": 3.91961, "loss": 3.91961, "time": 0.83287} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.05074, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32344, "top5_acc": 0.57625, "loss_cls": 3.89593, "loss": 3.89593, "time": 0.83207} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.05071, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31047, "top5_acc": 0.56938, "loss_cls": 3.90884, "loss": 3.90884, "time": 0.83298} +{"mode": "train", "epoch": 75, "iter": 1300, "lr": 0.05068, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31531, "top5_acc": 0.56797, "loss_cls": 3.9065, "loss": 3.9065, "time": 0.8278} +{"mode": "train", "epoch": 75, "iter": 1400, "lr": 0.05066, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31016, "top5_acc": 0.57375, "loss_cls": 3.91465, "loss": 3.91465, "time": 0.82318} +{"mode": "train", "epoch": 75, "iter": 1500, "lr": 0.05063, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31359, "top5_acc": 0.57875, "loss_cls": 3.92171, "loss": 3.92171, "time": 0.82028} +{"mode": "train", "epoch": 75, "iter": 1600, "lr": 0.0506, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32703, "top5_acc": 0.57953, "loss_cls": 3.86296, "loss": 3.86296, "time": 0.81478} +{"mode": "train", "epoch": 75, "iter": 1700, "lr": 0.05057, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32469, "top5_acc": 0.57781, "loss_cls": 3.87952, "loss": 3.87952, "time": 0.81929} +{"mode": "train", "epoch": 75, "iter": 1800, "lr": 0.05054, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31438, "top5_acc": 0.57609, "loss_cls": 3.92903, "loss": 3.92903, "time": 0.81538} +{"mode": "train", "epoch": 75, "iter": 1900, "lr": 0.05052, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32484, "top5_acc": 0.57312, "loss_cls": 3.90358, "loss": 3.90358, "time": 0.81226} +{"mode": "train", "epoch": 75, "iter": 2000, "lr": 0.05049, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32578, "top5_acc": 0.57125, "loss_cls": 3.88691, "loss": 3.88691, "time": 0.8197} +{"mode": "train", "epoch": 75, "iter": 2100, "lr": 0.05046, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33281, "top5_acc": 0.58656, "loss_cls": 3.84168, "loss": 3.84168, "time": 0.81893} +{"mode": "train", "epoch": 75, "iter": 2200, "lr": 0.05043, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31859, "top5_acc": 0.57734, "loss_cls": 3.93306, "loss": 3.93306, "time": 0.82271} +{"mode": "train", "epoch": 75, "iter": 2300, "lr": 0.0504, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31047, "top5_acc": 0.56891, "loss_cls": 3.93569, "loss": 3.93569, "time": 0.82357} +{"mode": "train", "epoch": 75, "iter": 2400, "lr": 0.05038, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30469, "top5_acc": 0.56922, "loss_cls": 3.93054, "loss": 3.93054, "time": 0.81906} +{"mode": "train", "epoch": 75, "iter": 2500, "lr": 0.05035, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31219, "top5_acc": 0.57922, "loss_cls": 3.91238, "loss": 3.91238, "time": 0.81473} +{"mode": "train", "epoch": 75, "iter": 2600, "lr": 0.05032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30703, "top5_acc": 0.57547, "loss_cls": 3.91962, "loss": 3.91962, "time": 0.81246} +{"mode": "train", "epoch": 75, "iter": 2700, "lr": 0.05029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32797, "top5_acc": 0.58391, "loss_cls": 3.84467, "loss": 3.84467, "time": 0.81164} +{"mode": "train", "epoch": 75, "iter": 2800, "lr": 0.05026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31953, "top5_acc": 0.5825, "loss_cls": 3.86293, "loss": 3.86293, "time": 0.81362} +{"mode": "train", "epoch": 75, "iter": 2900, "lr": 0.05024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31906, "top5_acc": 0.57609, "loss_cls": 3.90535, "loss": 3.90535, "time": 0.81422} +{"mode": "train", "epoch": 75, "iter": 3000, "lr": 0.05021, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32547, "top5_acc": 0.57828, "loss_cls": 3.9214, "loss": 3.9214, "time": 0.82051} +{"mode": "train", "epoch": 75, "iter": 3100, "lr": 0.05018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32484, "top5_acc": 0.57016, "loss_cls": 3.90023, "loss": 3.90023, "time": 0.81693} +{"mode": "train", "epoch": 75, "iter": 3200, "lr": 0.05015, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31422, "top5_acc": 0.5625, "loss_cls": 3.93211, "loss": 3.93211, "time": 0.81944} +{"mode": "train", "epoch": 75, "iter": 3300, "lr": 0.05012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32203, "top5_acc": 0.58062, "loss_cls": 3.87795, "loss": 3.87795, "time": 0.82004} +{"mode": "train", "epoch": 75, "iter": 3400, "lr": 0.0501, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31922, "top5_acc": 0.58359, "loss_cls": 3.87602, "loss": 3.87602, "time": 0.81303} +{"mode": "train", "epoch": 75, "iter": 3500, "lr": 0.05007, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31469, "top5_acc": 0.57063, "loss_cls": 3.88919, "loss": 3.88919, "time": 0.8174} +{"mode": "train", "epoch": 75, "iter": 3600, "lr": 0.05004, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31188, "top5_acc": 0.57688, "loss_cls": 3.89754, "loss": 3.89754, "time": 0.81464} +{"mode": "train", "epoch": 75, "iter": 3700, "lr": 0.05001, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31953, "top5_acc": 0.57891, "loss_cls": 3.87328, "loss": 3.87328, "time": 0.81933} +{"mode": "val", "epoch": 75, "iter": 309, "lr": 0.05, "top1_acc": 0.26845, "top5_acc": 0.51912, "mean_class_accuracy": 0.26813} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.04997, "memory": 15990, "data_time": 1.3413, "top1_acc": 0.32312, "top5_acc": 0.58391, "loss_cls": 3.88149, "loss": 3.88149, "time": 2.3402} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.04994, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31234, "top5_acc": 0.58, "loss_cls": 3.90321, "loss": 3.90321, "time": 0.81702} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.04992, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32531, "top5_acc": 0.58109, "loss_cls": 3.87322, "loss": 3.87322, "time": 0.81535} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.04989, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33609, "top5_acc": 0.58625, "loss_cls": 3.84666, "loss": 3.84666, "time": 0.81957} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.04986, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32031, "top5_acc": 0.58281, "loss_cls": 3.84081, "loss": 3.84081, "time": 0.81804} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.04983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33375, "top5_acc": 0.58219, "loss_cls": 3.82755, "loss": 3.82755, "time": 0.81919} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.0498, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31656, "top5_acc": 0.57969, "loss_cls": 3.88216, "loss": 3.88216, "time": 0.8222} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.04978, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32125, "top5_acc": 0.57516, "loss_cls": 3.87365, "loss": 3.87365, "time": 0.81785} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.04975, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31875, "top5_acc": 0.57719, "loss_cls": 3.87047, "loss": 3.87047, "time": 0.82202} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.04972, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32203, "top5_acc": 0.57328, "loss_cls": 3.87164, "loss": 3.87164, "time": 0.81623} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.04969, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31719, "top5_acc": 0.57359, "loss_cls": 3.89373, "loss": 3.89373, "time": 0.81604} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.04966, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31578, "top5_acc": 0.57531, "loss_cls": 3.8925, "loss": 3.8925, "time": 0.81618} +{"mode": "train", "epoch": 76, "iter": 1300, "lr": 0.04964, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32062, "top5_acc": 0.58391, "loss_cls": 3.88026, "loss": 3.88026, "time": 0.82007} +{"mode": "train", "epoch": 76, "iter": 1400, "lr": 0.04961, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31703, "top5_acc": 0.58453, "loss_cls": 3.86514, "loss": 3.86514, "time": 0.8131} +{"mode": "train", "epoch": 76, "iter": 1500, "lr": 0.04958, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32422, "top5_acc": 0.57844, "loss_cls": 3.85394, "loss": 3.85394, "time": 0.81874} +{"mode": "train", "epoch": 76, "iter": 1600, "lr": 0.04955, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32, "top5_acc": 0.58016, "loss_cls": 3.8599, "loss": 3.8599, "time": 0.82397} +{"mode": "train", "epoch": 76, "iter": 1700, "lr": 0.04953, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32922, "top5_acc": 0.58344, "loss_cls": 3.86804, "loss": 3.86804, "time": 0.81903} +{"mode": "train", "epoch": 76, "iter": 1800, "lr": 0.0495, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32688, "top5_acc": 0.58047, "loss_cls": 3.86158, "loss": 3.86158, "time": 0.82185} +{"mode": "train", "epoch": 76, "iter": 1900, "lr": 0.04947, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31297, "top5_acc": 0.57047, "loss_cls": 3.95519, "loss": 3.95519, "time": 0.81675} +{"mode": "train", "epoch": 76, "iter": 2000, "lr": 0.04944, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31766, "top5_acc": 0.58406, "loss_cls": 3.85547, "loss": 3.85547, "time": 0.81809} +{"mode": "train", "epoch": 76, "iter": 2100, "lr": 0.04941, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32156, "top5_acc": 0.58203, "loss_cls": 3.85112, "loss": 3.85112, "time": 0.81781} +{"mode": "train", "epoch": 76, "iter": 2200, "lr": 0.04939, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31375, "top5_acc": 0.56891, "loss_cls": 3.94912, "loss": 3.94912, "time": 0.82676} +{"mode": "train", "epoch": 76, "iter": 2300, "lr": 0.04936, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31359, "top5_acc": 0.57688, "loss_cls": 3.90673, "loss": 3.90673, "time": 0.81722} +{"mode": "train", "epoch": 76, "iter": 2400, "lr": 0.04933, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31422, "top5_acc": 0.57969, "loss_cls": 3.91057, "loss": 3.91057, "time": 0.82097} +{"mode": "train", "epoch": 76, "iter": 2500, "lr": 0.0493, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31375, "top5_acc": 0.5775, "loss_cls": 3.88962, "loss": 3.88962, "time": 0.81898} +{"mode": "train", "epoch": 76, "iter": 2600, "lr": 0.04927, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31562, "top5_acc": 0.57125, "loss_cls": 3.91539, "loss": 3.91539, "time": 0.81506} +{"mode": "train", "epoch": 76, "iter": 2700, "lr": 0.04925, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33016, "top5_acc": 0.58312, "loss_cls": 3.84778, "loss": 3.84778, "time": 0.81749} +{"mode": "train", "epoch": 76, "iter": 2800, "lr": 0.04922, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32062, "top5_acc": 0.57234, "loss_cls": 3.88217, "loss": 3.88217, "time": 0.81607} +{"mode": "train", "epoch": 76, "iter": 2900, "lr": 0.04919, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32547, "top5_acc": 0.58703, "loss_cls": 3.8594, "loss": 3.8594, "time": 0.81959} +{"mode": "train", "epoch": 76, "iter": 3000, "lr": 0.04916, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31188, "top5_acc": 0.58, "loss_cls": 3.89833, "loss": 3.89833, "time": 0.81864} +{"mode": "train", "epoch": 76, "iter": 3100, "lr": 0.04913, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32844, "top5_acc": 0.58062, "loss_cls": 3.86258, "loss": 3.86258, "time": 0.81479} +{"mode": "train", "epoch": 76, "iter": 3200, "lr": 0.04911, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31969, "top5_acc": 0.57781, "loss_cls": 3.88981, "loss": 3.88981, "time": 0.82398} +{"mode": "train", "epoch": 76, "iter": 3300, "lr": 0.04908, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32031, "top5_acc": 0.57578, "loss_cls": 3.88173, "loss": 3.88173, "time": 0.81787} +{"mode": "train", "epoch": 76, "iter": 3400, "lr": 0.04905, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32641, "top5_acc": 0.59609, "loss_cls": 3.79861, "loss": 3.79861, "time": 0.81658} +{"mode": "train", "epoch": 76, "iter": 3500, "lr": 0.04902, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32312, "top5_acc": 0.57484, "loss_cls": 3.87334, "loss": 3.87334, "time": 0.81674} +{"mode": "train", "epoch": 76, "iter": 3600, "lr": 0.04899, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32156, "top5_acc": 0.57438, "loss_cls": 3.87148, "loss": 3.87148, "time": 0.81384} +{"mode": "train", "epoch": 76, "iter": 3700, "lr": 0.04897, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31578, "top5_acc": 0.57719, "loss_cls": 3.89565, "loss": 3.89565, "time": 0.81907} +{"mode": "val", "epoch": 76, "iter": 309, "lr": 0.04895, "top1_acc": 0.25817, "top5_acc": 0.5098, "mean_class_accuracy": 0.25806} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.04893, "memory": 15990, "data_time": 1.34323, "top1_acc": 0.34, "top5_acc": 0.59453, "loss_cls": 3.76695, "loss": 3.76695, "time": 2.32688} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0489, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33562, "top5_acc": 0.59828, "loss_cls": 3.76992, "loss": 3.76992, "time": 0.82611} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.04887, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31891, "top5_acc": 0.57781, "loss_cls": 3.88606, "loss": 3.88606, "time": 0.83021} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.04884, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31609, "top5_acc": 0.57531, "loss_cls": 3.88085, "loss": 3.88085, "time": 0.81647} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.04881, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31781, "top5_acc": 0.57734, "loss_cls": 3.89637, "loss": 3.89637, "time": 0.81902} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.04879, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31844, "top5_acc": 0.58453, "loss_cls": 3.8417, "loss": 3.8417, "time": 0.82611} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.04876, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32125, "top5_acc": 0.58469, "loss_cls": 3.87534, "loss": 3.87534, "time": 0.81474} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.04873, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32562, "top5_acc": 0.57406, "loss_cls": 3.87819, "loss": 3.87819, "time": 0.81306} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.0487, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31688, "top5_acc": 0.57438, "loss_cls": 3.8943, "loss": 3.8943, "time": 0.82129} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.04867, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32969, "top5_acc": 0.57984, "loss_cls": 3.83197, "loss": 3.83197, "time": 0.81671} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.04865, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32484, "top5_acc": 0.58203, "loss_cls": 3.85775, "loss": 3.85775, "time": 0.81994} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.04862, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31328, "top5_acc": 0.56781, "loss_cls": 3.94157, "loss": 3.94157, "time": 0.81111} +{"mode": "train", "epoch": 77, "iter": 1300, "lr": 0.04859, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3175, "top5_acc": 0.57906, "loss_cls": 3.8872, "loss": 3.8872, "time": 0.81262} +{"mode": "train", "epoch": 77, "iter": 1400, "lr": 0.04856, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33344, "top5_acc": 0.59672, "loss_cls": 3.78511, "loss": 3.78511, "time": 0.81738} +{"mode": "train", "epoch": 77, "iter": 1500, "lr": 0.04853, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32078, "top5_acc": 0.57359, "loss_cls": 3.89422, "loss": 3.89422, "time": 0.82084} +{"mode": "train", "epoch": 77, "iter": 1600, "lr": 0.04851, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32484, "top5_acc": 0.57703, "loss_cls": 3.86879, "loss": 3.86879, "time": 0.81957} +{"mode": "train", "epoch": 77, "iter": 1700, "lr": 0.04848, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32281, "top5_acc": 0.58078, "loss_cls": 3.86409, "loss": 3.86409, "time": 0.82011} +{"mode": "train", "epoch": 77, "iter": 1800, "lr": 0.04845, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32266, "top5_acc": 0.57672, "loss_cls": 3.90869, "loss": 3.90869, "time": 0.81742} +{"mode": "train", "epoch": 77, "iter": 1900, "lr": 0.04842, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32812, "top5_acc": 0.58109, "loss_cls": 3.86259, "loss": 3.86259, "time": 0.81617} +{"mode": "train", "epoch": 77, "iter": 2000, "lr": 0.04839, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33016, "top5_acc": 0.58125, "loss_cls": 3.844, "loss": 3.844, "time": 0.81861} +{"mode": "train", "epoch": 77, "iter": 2100, "lr": 0.04837, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32078, "top5_acc": 0.57359, "loss_cls": 3.89707, "loss": 3.89707, "time": 0.82058} +{"mode": "train", "epoch": 77, "iter": 2200, "lr": 0.04834, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32891, "top5_acc": 0.57922, "loss_cls": 3.87083, "loss": 3.87083, "time": 0.82974} +{"mode": "train", "epoch": 77, "iter": 2300, "lr": 0.04831, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32016, "top5_acc": 0.58734, "loss_cls": 3.86512, "loss": 3.86512, "time": 0.81463} +{"mode": "train", "epoch": 77, "iter": 2400, "lr": 0.04828, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32422, "top5_acc": 0.58141, "loss_cls": 3.86028, "loss": 3.86028, "time": 0.81306} +{"mode": "train", "epoch": 77, "iter": 2500, "lr": 0.04825, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32922, "top5_acc": 0.58453, "loss_cls": 3.84015, "loss": 3.84015, "time": 0.81525} +{"mode": "train", "epoch": 77, "iter": 2600, "lr": 0.04823, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31469, "top5_acc": 0.57297, "loss_cls": 3.91481, "loss": 3.91481, "time": 0.81084} +{"mode": "train", "epoch": 77, "iter": 2700, "lr": 0.0482, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32359, "top5_acc": 0.57578, "loss_cls": 3.8673, "loss": 3.8673, "time": 0.81915} +{"mode": "train", "epoch": 77, "iter": 2800, "lr": 0.04817, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31766, "top5_acc": 0.57688, "loss_cls": 3.91356, "loss": 3.91356, "time": 0.81731} +{"mode": "train", "epoch": 77, "iter": 2900, "lr": 0.04814, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32531, "top5_acc": 0.5825, "loss_cls": 3.8599, "loss": 3.8599, "time": 0.81451} +{"mode": "train", "epoch": 77, "iter": 3000, "lr": 0.04811, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31328, "top5_acc": 0.5775, "loss_cls": 3.88272, "loss": 3.88272, "time": 0.82274} +{"mode": "train", "epoch": 77, "iter": 3100, "lr": 0.04809, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31688, "top5_acc": 0.58266, "loss_cls": 3.84955, "loss": 3.84955, "time": 0.8163} +{"mode": "train", "epoch": 77, "iter": 3200, "lr": 0.04806, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31734, "top5_acc": 0.56859, "loss_cls": 3.93315, "loss": 3.93315, "time": 0.81994} +{"mode": "train", "epoch": 77, "iter": 3300, "lr": 0.04803, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31875, "top5_acc": 0.57297, "loss_cls": 3.9019, "loss": 3.9019, "time": 0.81254} +{"mode": "train", "epoch": 77, "iter": 3400, "lr": 0.048, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32047, "top5_acc": 0.58406, "loss_cls": 3.85828, "loss": 3.85828, "time": 0.8171} +{"mode": "train", "epoch": 77, "iter": 3500, "lr": 0.04798, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32062, "top5_acc": 0.57469, "loss_cls": 3.88637, "loss": 3.88637, "time": 0.81414} +{"mode": "train", "epoch": 77, "iter": 3600, "lr": 0.04795, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32859, "top5_acc": 0.58391, "loss_cls": 3.86616, "loss": 3.86616, "time": 0.82063} +{"mode": "train", "epoch": 77, "iter": 3700, "lr": 0.04792, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32531, "top5_acc": 0.58469, "loss_cls": 3.81899, "loss": 3.81899, "time": 0.81729} +{"mode": "val", "epoch": 77, "iter": 309, "lr": 0.04791, "top1_acc": 0.26354, "top5_acc": 0.51476, "mean_class_accuracy": 0.26332} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.04788, "memory": 15990, "data_time": 1.33938, "top1_acc": 0.33891, "top5_acc": 0.59453, "loss_cls": 3.75618, "loss": 3.75618, "time": 2.33845} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.04785, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32609, "top5_acc": 0.58656, "loss_cls": 3.81985, "loss": 3.81985, "time": 0.83515} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.04782, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33281, "top5_acc": 0.58547, "loss_cls": 3.82538, "loss": 3.82538, "time": 0.83618} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.04779, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33047, "top5_acc": 0.58141, "loss_cls": 3.84167, "loss": 3.84167, "time": 0.83847} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.04777, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31828, "top5_acc": 0.57203, "loss_cls": 3.86234, "loss": 3.86234, "time": 0.83905} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.04774, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34031, "top5_acc": 0.58984, "loss_cls": 3.82188, "loss": 3.82188, "time": 0.83828} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.04771, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32484, "top5_acc": 0.58344, "loss_cls": 3.84397, "loss": 3.84397, "time": 0.83363} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.04768, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33375, "top5_acc": 0.58828, "loss_cls": 3.8262, "loss": 3.8262, "time": 0.83562} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.04766, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32281, "top5_acc": 0.57766, "loss_cls": 3.87618, "loss": 3.87618, "time": 0.83494} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.04763, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32266, "top5_acc": 0.57672, "loss_cls": 3.87499, "loss": 3.87499, "time": 0.83087} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.0476, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31359, "top5_acc": 0.57641, "loss_cls": 3.87578, "loss": 3.87578, "time": 0.83019} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.04757, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32062, "top5_acc": 0.57641, "loss_cls": 3.917, "loss": 3.917, "time": 0.82536} +{"mode": "train", "epoch": 78, "iter": 1300, "lr": 0.04754, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33453, "top5_acc": 0.59188, "loss_cls": 3.81337, "loss": 3.81337, "time": 0.83347} +{"mode": "train", "epoch": 78, "iter": 1400, "lr": 0.04752, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32891, "top5_acc": 0.59141, "loss_cls": 3.83711, "loss": 3.83711, "time": 0.83157} +{"mode": "train", "epoch": 78, "iter": 1500, "lr": 0.04749, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32484, "top5_acc": 0.58219, "loss_cls": 3.8648, "loss": 3.8648, "time": 0.8293} +{"mode": "train", "epoch": 78, "iter": 1600, "lr": 0.04746, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31625, "top5_acc": 0.57625, "loss_cls": 3.88719, "loss": 3.88719, "time": 0.834} +{"mode": "train", "epoch": 78, "iter": 1700, "lr": 0.04743, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32875, "top5_acc": 0.58547, "loss_cls": 3.84122, "loss": 3.84122, "time": 0.83052} +{"mode": "train", "epoch": 78, "iter": 1800, "lr": 0.0474, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32891, "top5_acc": 0.59, "loss_cls": 3.8139, "loss": 3.8139, "time": 0.83154} +{"mode": "train", "epoch": 78, "iter": 1900, "lr": 0.04738, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.32703, "top5_acc": 0.58953, "loss_cls": 3.81469, "loss": 3.81469, "time": 0.8335} +{"mode": "train", "epoch": 78, "iter": 2000, "lr": 0.04735, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.32797, "top5_acc": 0.58391, "loss_cls": 3.8559, "loss": 3.8559, "time": 0.83661} +{"mode": "train", "epoch": 78, "iter": 2100, "lr": 0.04732, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32969, "top5_acc": 0.57891, "loss_cls": 3.8612, "loss": 3.8612, "time": 0.82501} +{"mode": "train", "epoch": 78, "iter": 2200, "lr": 0.04729, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32234, "top5_acc": 0.57688, "loss_cls": 3.88612, "loss": 3.88612, "time": 0.84237} +{"mode": "train", "epoch": 78, "iter": 2300, "lr": 0.04726, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32219, "top5_acc": 0.57891, "loss_cls": 3.866, "loss": 3.866, "time": 0.82802} +{"mode": "train", "epoch": 78, "iter": 2400, "lr": 0.04724, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31875, "top5_acc": 0.58469, "loss_cls": 3.85255, "loss": 3.85255, "time": 0.82662} +{"mode": "train", "epoch": 78, "iter": 2500, "lr": 0.04721, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32969, "top5_acc": 0.58422, "loss_cls": 3.8776, "loss": 3.8776, "time": 0.8322} +{"mode": "train", "epoch": 78, "iter": 2600, "lr": 0.04718, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32875, "top5_acc": 0.58062, "loss_cls": 3.85496, "loss": 3.85496, "time": 0.83261} +{"mode": "train", "epoch": 78, "iter": 2700, "lr": 0.04715, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32422, "top5_acc": 0.57922, "loss_cls": 3.87511, "loss": 3.87511, "time": 0.83052} +{"mode": "train", "epoch": 78, "iter": 2800, "lr": 0.04712, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32812, "top5_acc": 0.57844, "loss_cls": 3.89038, "loss": 3.89038, "time": 0.82845} +{"mode": "train", "epoch": 78, "iter": 2900, "lr": 0.0471, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32375, "top5_acc": 0.58375, "loss_cls": 3.87005, "loss": 3.87005, "time": 0.82685} +{"mode": "train", "epoch": 78, "iter": 3000, "lr": 0.04707, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33359, "top5_acc": 0.59141, "loss_cls": 3.82452, "loss": 3.82452, "time": 0.83385} +{"mode": "train", "epoch": 78, "iter": 3100, "lr": 0.04704, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32094, "top5_acc": 0.58266, "loss_cls": 3.86049, "loss": 3.86049, "time": 0.82855} +{"mode": "train", "epoch": 78, "iter": 3200, "lr": 0.04701, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32375, "top5_acc": 0.58969, "loss_cls": 3.82577, "loss": 3.82577, "time": 0.83735} +{"mode": "train", "epoch": 78, "iter": 3300, "lr": 0.04699, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32344, "top5_acc": 0.58516, "loss_cls": 3.88262, "loss": 3.88262, "time": 0.83377} +{"mode": "train", "epoch": 78, "iter": 3400, "lr": 0.04696, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32438, "top5_acc": 0.58047, "loss_cls": 3.85035, "loss": 3.85035, "time": 0.83371} +{"mode": "train", "epoch": 78, "iter": 3500, "lr": 0.04693, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32797, "top5_acc": 0.59031, "loss_cls": 3.84144, "loss": 3.84144, "time": 0.83086} +{"mode": "train", "epoch": 78, "iter": 3600, "lr": 0.0469, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32812, "top5_acc": 0.58188, "loss_cls": 3.86021, "loss": 3.86021, "time": 0.83297} +{"mode": "train", "epoch": 78, "iter": 3700, "lr": 0.04687, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.32797, "top5_acc": 0.58703, "loss_cls": 3.83675, "loss": 3.83675, "time": 0.83596} +{"mode": "val", "epoch": 78, "iter": 309, "lr": 0.04686, "top1_acc": 0.25736, "top5_acc": 0.50322, "mean_class_accuracy": 0.25713} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.04683, "memory": 15990, "data_time": 1.28341, "top1_acc": 0.33359, "top5_acc": 0.58797, "loss_cls": 3.81632, "loss": 3.81632, "time": 2.28224} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.0468, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34172, "top5_acc": 0.60125, "loss_cls": 3.75357, "loss": 3.75357, "time": 0.83401} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.04678, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33125, "top5_acc": 0.59844, "loss_cls": 3.76778, "loss": 3.76778, "time": 0.8329} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.04675, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33359, "top5_acc": 0.5925, "loss_cls": 3.77981, "loss": 3.77981, "time": 0.83151} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.04672, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33125, "top5_acc": 0.58359, "loss_cls": 3.82035, "loss": 3.82035, "time": 0.83441} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.04669, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33078, "top5_acc": 0.59328, "loss_cls": 3.80064, "loss": 3.80064, "time": 0.83237} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.04667, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32891, "top5_acc": 0.58422, "loss_cls": 3.81298, "loss": 3.81298, "time": 0.83093} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.04664, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32078, "top5_acc": 0.58219, "loss_cls": 3.8645, "loss": 3.8645, "time": 0.83323} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.04661, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33406, "top5_acc": 0.58859, "loss_cls": 3.84918, "loss": 3.84918, "time": 0.82654} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.04658, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32391, "top5_acc": 0.58172, "loss_cls": 3.87282, "loss": 3.87282, "time": 0.83166} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.04655, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32266, "top5_acc": 0.58188, "loss_cls": 3.86789, "loss": 3.86789, "time": 0.83162} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.04653, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31375, "top5_acc": 0.57516, "loss_cls": 3.89741, "loss": 3.89741, "time": 0.83145} +{"mode": "train", "epoch": 79, "iter": 1300, "lr": 0.0465, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31547, "top5_acc": 0.58062, "loss_cls": 3.87586, "loss": 3.87586, "time": 0.8314} +{"mode": "train", "epoch": 79, "iter": 1400, "lr": 0.04647, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32844, "top5_acc": 0.59078, "loss_cls": 3.8053, "loss": 3.8053, "time": 0.83184} +{"mode": "train", "epoch": 79, "iter": 1500, "lr": 0.04644, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33375, "top5_acc": 0.58172, "loss_cls": 3.82317, "loss": 3.82317, "time": 0.83248} +{"mode": "train", "epoch": 79, "iter": 1600, "lr": 0.04641, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33, "top5_acc": 0.585, "loss_cls": 3.8329, "loss": 3.8329, "time": 0.83081} +{"mode": "train", "epoch": 79, "iter": 1700, "lr": 0.04639, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33172, "top5_acc": 0.59172, "loss_cls": 3.8215, "loss": 3.8215, "time": 0.82659} +{"mode": "train", "epoch": 79, "iter": 1800, "lr": 0.04636, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33078, "top5_acc": 0.58688, "loss_cls": 3.84028, "loss": 3.84028, "time": 0.83312} +{"mode": "train", "epoch": 79, "iter": 1900, "lr": 0.04633, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32297, "top5_acc": 0.58172, "loss_cls": 3.86409, "loss": 3.86409, "time": 0.83816} +{"mode": "train", "epoch": 79, "iter": 2000, "lr": 0.0463, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32875, "top5_acc": 0.58203, "loss_cls": 3.83314, "loss": 3.83314, "time": 0.83229} +{"mode": "train", "epoch": 79, "iter": 2100, "lr": 0.04628, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.32531, "top5_acc": 0.58453, "loss_cls": 3.87256, "loss": 3.87256, "time": 0.83388} +{"mode": "train", "epoch": 79, "iter": 2200, "lr": 0.04625, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31438, "top5_acc": 0.57859, "loss_cls": 3.88651, "loss": 3.88651, "time": 0.83689} +{"mode": "train", "epoch": 79, "iter": 2300, "lr": 0.04622, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33125, "top5_acc": 0.58141, "loss_cls": 3.84005, "loss": 3.84005, "time": 0.82768} +{"mode": "train", "epoch": 79, "iter": 2400, "lr": 0.04619, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31406, "top5_acc": 0.57672, "loss_cls": 3.8766, "loss": 3.8766, "time": 0.8373} +{"mode": "train", "epoch": 79, "iter": 2500, "lr": 0.04616, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32312, "top5_acc": 0.58156, "loss_cls": 3.87015, "loss": 3.87015, "time": 0.83063} +{"mode": "train", "epoch": 79, "iter": 2600, "lr": 0.04614, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31891, "top5_acc": 0.58016, "loss_cls": 3.87243, "loss": 3.87243, "time": 0.8247} +{"mode": "train", "epoch": 79, "iter": 2700, "lr": 0.04611, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32672, "top5_acc": 0.57984, "loss_cls": 3.84955, "loss": 3.84955, "time": 0.82485} +{"mode": "train", "epoch": 79, "iter": 2800, "lr": 0.04608, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32094, "top5_acc": 0.58375, "loss_cls": 3.8411, "loss": 3.8411, "time": 0.82404} +{"mode": "train", "epoch": 79, "iter": 2900, "lr": 0.04605, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33578, "top5_acc": 0.58766, "loss_cls": 3.81931, "loss": 3.81931, "time": 0.83852} +{"mode": "train", "epoch": 79, "iter": 3000, "lr": 0.04602, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32234, "top5_acc": 0.57266, "loss_cls": 3.89844, "loss": 3.89844, "time": 0.83093} +{"mode": "train", "epoch": 79, "iter": 3100, "lr": 0.046, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.32953, "top5_acc": 0.57922, "loss_cls": 3.84857, "loss": 3.84857, "time": 0.82971} +{"mode": "train", "epoch": 79, "iter": 3200, "lr": 0.04597, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32656, "top5_acc": 0.58859, "loss_cls": 3.81797, "loss": 3.81797, "time": 0.83986} +{"mode": "train", "epoch": 79, "iter": 3300, "lr": 0.04594, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3325, "top5_acc": 0.58047, "loss_cls": 3.84127, "loss": 3.84127, "time": 0.8354} +{"mode": "train", "epoch": 79, "iter": 3400, "lr": 0.04591, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31828, "top5_acc": 0.58266, "loss_cls": 3.86367, "loss": 3.86367, "time": 0.83312} +{"mode": "train", "epoch": 79, "iter": 3500, "lr": 0.04588, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33, "top5_acc": 0.58594, "loss_cls": 3.8417, "loss": 3.8417, "time": 0.83169} +{"mode": "train", "epoch": 79, "iter": 3600, "lr": 0.04586, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32469, "top5_acc": 0.58812, "loss_cls": 3.84326, "loss": 3.84326, "time": 0.83296} +{"mode": "train", "epoch": 79, "iter": 3700, "lr": 0.04583, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32297, "top5_acc": 0.57563, "loss_cls": 3.86537, "loss": 3.86537, "time": 0.83523} +{"mode": "val", "epoch": 79, "iter": 309, "lr": 0.04582, "top1_acc": 0.27544, "top5_acc": 0.51801, "mean_class_accuracy": 0.2753} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.04579, "memory": 15990, "data_time": 1.27585, "top1_acc": 0.33906, "top5_acc": 0.6025, "loss_cls": 3.74932, "loss": 3.74932, "time": 2.27355} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.04576, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32594, "top5_acc": 0.59578, "loss_cls": 3.78325, "loss": 3.78325, "time": 0.83282} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.04573, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32641, "top5_acc": 0.58438, "loss_cls": 3.82723, "loss": 3.82723, "time": 0.83168} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.0457, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32719, "top5_acc": 0.59125, "loss_cls": 3.81005, "loss": 3.81005, "time": 0.82761} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.04568, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3425, "top5_acc": 0.60328, "loss_cls": 3.7441, "loss": 3.7441, "time": 0.83078} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.04565, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3175, "top5_acc": 0.57531, "loss_cls": 3.88046, "loss": 3.88046, "time": 0.82989} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.04562, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33047, "top5_acc": 0.58328, "loss_cls": 3.82183, "loss": 3.82183, "time": 0.82851} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.04559, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32109, "top5_acc": 0.58594, "loss_cls": 3.82439, "loss": 3.82439, "time": 0.83116} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.04557, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32969, "top5_acc": 0.57812, "loss_cls": 3.84515, "loss": 3.84515, "time": 0.83116} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.04554, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33016, "top5_acc": 0.58812, "loss_cls": 3.82694, "loss": 3.82694, "time": 0.82722} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.04551, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32953, "top5_acc": 0.58609, "loss_cls": 3.85404, "loss": 3.85404, "time": 0.82622} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.04548, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32328, "top5_acc": 0.58578, "loss_cls": 3.84611, "loss": 3.84611, "time": 0.83415} +{"mode": "train", "epoch": 80, "iter": 1300, "lr": 0.04545, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34625, "top5_acc": 0.59344, "loss_cls": 3.76489, "loss": 3.76489, "time": 0.83201} +{"mode": "train", "epoch": 80, "iter": 1400, "lr": 0.04543, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33156, "top5_acc": 0.59328, "loss_cls": 3.82187, "loss": 3.82187, "time": 0.83001} +{"mode": "train", "epoch": 80, "iter": 1500, "lr": 0.0454, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33031, "top5_acc": 0.58953, "loss_cls": 3.82448, "loss": 3.82448, "time": 0.82722} +{"mode": "train", "epoch": 80, "iter": 1600, "lr": 0.04537, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33062, "top5_acc": 0.5825, "loss_cls": 3.82731, "loss": 3.82731, "time": 0.82841} +{"mode": "train", "epoch": 80, "iter": 1700, "lr": 0.04534, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33062, "top5_acc": 0.59203, "loss_cls": 3.8177, "loss": 3.8177, "time": 0.83293} +{"mode": "train", "epoch": 80, "iter": 1800, "lr": 0.04532, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.32609, "top5_acc": 0.58531, "loss_cls": 3.86161, "loss": 3.86161, "time": 0.83093} +{"mode": "train", "epoch": 80, "iter": 1900, "lr": 0.04529, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32312, "top5_acc": 0.57453, "loss_cls": 3.88794, "loss": 3.88794, "time": 0.83561} +{"mode": "train", "epoch": 80, "iter": 2000, "lr": 0.04526, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.32953, "top5_acc": 0.58688, "loss_cls": 3.78595, "loss": 3.78595, "time": 0.83496} +{"mode": "train", "epoch": 80, "iter": 2100, "lr": 0.04523, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32234, "top5_acc": 0.58141, "loss_cls": 3.84215, "loss": 3.84215, "time": 0.83737} +{"mode": "train", "epoch": 80, "iter": 2200, "lr": 0.0452, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32516, "top5_acc": 0.58203, "loss_cls": 3.86055, "loss": 3.86055, "time": 0.82985} +{"mode": "train", "epoch": 80, "iter": 2300, "lr": 0.04518, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32156, "top5_acc": 0.57969, "loss_cls": 3.89099, "loss": 3.89099, "time": 0.83651} +{"mode": "train", "epoch": 80, "iter": 2400, "lr": 0.04515, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33328, "top5_acc": 0.59406, "loss_cls": 3.79685, "loss": 3.79685, "time": 0.83874} +{"mode": "train", "epoch": 80, "iter": 2500, "lr": 0.04512, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34109, "top5_acc": 0.59906, "loss_cls": 3.76457, "loss": 3.76457, "time": 0.82773} +{"mode": "train", "epoch": 80, "iter": 2600, "lr": 0.04509, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32172, "top5_acc": 0.57953, "loss_cls": 3.89511, "loss": 3.89511, "time": 0.83065} +{"mode": "train", "epoch": 80, "iter": 2700, "lr": 0.04506, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33828, "top5_acc": 0.58828, "loss_cls": 3.79384, "loss": 3.79384, "time": 0.82382} +{"mode": "train", "epoch": 80, "iter": 2800, "lr": 0.04504, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32203, "top5_acc": 0.58875, "loss_cls": 3.83865, "loss": 3.83865, "time": 0.83087} +{"mode": "train", "epoch": 80, "iter": 2900, "lr": 0.04501, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34047, "top5_acc": 0.59781, "loss_cls": 3.7678, "loss": 3.7678, "time": 0.83546} +{"mode": "train", "epoch": 80, "iter": 3000, "lr": 0.04498, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32594, "top5_acc": 0.58312, "loss_cls": 3.84443, "loss": 3.84443, "time": 0.8219} +{"mode": "train", "epoch": 80, "iter": 3100, "lr": 0.04495, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32875, "top5_acc": 0.58719, "loss_cls": 3.83315, "loss": 3.83315, "time": 0.83456} +{"mode": "train", "epoch": 80, "iter": 3200, "lr": 0.04493, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32688, "top5_acc": 0.57469, "loss_cls": 3.9046, "loss": 3.9046, "time": 0.83252} +{"mode": "train", "epoch": 80, "iter": 3300, "lr": 0.0449, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31953, "top5_acc": 0.58578, "loss_cls": 3.8588, "loss": 3.8588, "time": 0.83057} +{"mode": "train", "epoch": 80, "iter": 3400, "lr": 0.04487, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32672, "top5_acc": 0.57922, "loss_cls": 3.87439, "loss": 3.87439, "time": 0.82753} +{"mode": "train", "epoch": 80, "iter": 3500, "lr": 0.04484, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32797, "top5_acc": 0.58344, "loss_cls": 3.84833, "loss": 3.84833, "time": 0.82649} +{"mode": "train", "epoch": 80, "iter": 3600, "lr": 0.04481, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32594, "top5_acc": 0.57703, "loss_cls": 3.86406, "loss": 3.86406, "time": 0.83456} +{"mode": "train", "epoch": 80, "iter": 3700, "lr": 0.04479, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32531, "top5_acc": 0.57969, "loss_cls": 3.83473, "loss": 3.83473, "time": 0.83058} +{"mode": "val", "epoch": 80, "iter": 309, "lr": 0.04477, "top1_acc": 0.28876, "top5_acc": 0.53188, "mean_class_accuracy": 0.28847} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.04475, "memory": 15990, "data_time": 1.28374, "top1_acc": 0.34672, "top5_acc": 0.60734, "loss_cls": 3.72664, "loss": 3.72664, "time": 2.28527} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.04472, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33609, "top5_acc": 0.59625, "loss_cls": 3.77075, "loss": 3.77075, "time": 0.83541} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.04469, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33953, "top5_acc": 0.59812, "loss_cls": 3.77649, "loss": 3.77649, "time": 0.83733} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.04466, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33234, "top5_acc": 0.59391, "loss_cls": 3.78154, "loss": 3.78154, "time": 0.83396} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.04463, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33, "top5_acc": 0.58906, "loss_cls": 3.81067, "loss": 3.81067, "time": 0.82501} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.04461, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33078, "top5_acc": 0.59891, "loss_cls": 3.78862, "loss": 3.78862, "time": 0.83749} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.04458, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32547, "top5_acc": 0.58906, "loss_cls": 3.80415, "loss": 3.80415, "time": 0.83472} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.04455, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32875, "top5_acc": 0.58547, "loss_cls": 3.82355, "loss": 3.82355, "time": 0.82772} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.04452, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34156, "top5_acc": 0.59094, "loss_cls": 3.79815, "loss": 3.79815, "time": 0.82723} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.0445, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33109, "top5_acc": 0.59203, "loss_cls": 3.8143, "loss": 3.8143, "time": 0.83316} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.04447, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32625, "top5_acc": 0.58594, "loss_cls": 3.83293, "loss": 3.83293, "time": 0.83156} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.04444, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34062, "top5_acc": 0.59484, "loss_cls": 3.78805, "loss": 3.78805, "time": 0.82962} +{"mode": "train", "epoch": 81, "iter": 1300, "lr": 0.04441, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33125, "top5_acc": 0.59297, "loss_cls": 3.78625, "loss": 3.78625, "time": 0.83034} +{"mode": "train", "epoch": 81, "iter": 1400, "lr": 0.04438, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32969, "top5_acc": 0.58125, "loss_cls": 3.85692, "loss": 3.85692, "time": 0.82869} +{"mode": "train", "epoch": 81, "iter": 1500, "lr": 0.04436, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32922, "top5_acc": 0.57781, "loss_cls": 3.85299, "loss": 3.85299, "time": 0.83331} +{"mode": "train", "epoch": 81, "iter": 1600, "lr": 0.04433, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33609, "top5_acc": 0.58891, "loss_cls": 3.79905, "loss": 3.79905, "time": 0.8303} +{"mode": "train", "epoch": 81, "iter": 1700, "lr": 0.0443, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33234, "top5_acc": 0.58359, "loss_cls": 3.83448, "loss": 3.83448, "time": 0.83552} +{"mode": "train", "epoch": 81, "iter": 1800, "lr": 0.04427, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32328, "top5_acc": 0.58688, "loss_cls": 3.83257, "loss": 3.83257, "time": 0.83581} +{"mode": "train", "epoch": 81, "iter": 1900, "lr": 0.04425, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.3325, "top5_acc": 0.58625, "loss_cls": 3.82476, "loss": 3.82476, "time": 0.8295} +{"mode": "train", "epoch": 81, "iter": 2000, "lr": 0.04422, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.33688, "top5_acc": 0.59156, "loss_cls": 3.77189, "loss": 3.77189, "time": 0.83544} +{"mode": "train", "epoch": 81, "iter": 2100, "lr": 0.04419, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33219, "top5_acc": 0.58047, "loss_cls": 3.82841, "loss": 3.82841, "time": 0.83924} +{"mode": "train", "epoch": 81, "iter": 2200, "lr": 0.04416, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31812, "top5_acc": 0.57266, "loss_cls": 3.89676, "loss": 3.89676, "time": 0.83271} +{"mode": "train", "epoch": 81, "iter": 2300, "lr": 0.04413, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32375, "top5_acc": 0.58703, "loss_cls": 3.84508, "loss": 3.84508, "time": 0.8488} +{"mode": "train", "epoch": 81, "iter": 2400, "lr": 0.04411, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32375, "top5_acc": 0.57938, "loss_cls": 3.85122, "loss": 3.85122, "time": 0.84082} +{"mode": "train", "epoch": 81, "iter": 2500, "lr": 0.04408, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32688, "top5_acc": 0.58828, "loss_cls": 3.82932, "loss": 3.82932, "time": 0.84061} +{"mode": "train", "epoch": 81, "iter": 2600, "lr": 0.04405, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32688, "top5_acc": 0.58078, "loss_cls": 3.84486, "loss": 3.84486, "time": 0.83709} +{"mode": "train", "epoch": 81, "iter": 2700, "lr": 0.04402, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32891, "top5_acc": 0.58812, "loss_cls": 3.82695, "loss": 3.82695, "time": 0.82884} +{"mode": "train", "epoch": 81, "iter": 2800, "lr": 0.044, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31609, "top5_acc": 0.57266, "loss_cls": 3.8807, "loss": 3.8807, "time": 0.83213} +{"mode": "train", "epoch": 81, "iter": 2900, "lr": 0.04397, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33594, "top5_acc": 0.59453, "loss_cls": 3.80184, "loss": 3.80184, "time": 0.82503} +{"mode": "train", "epoch": 81, "iter": 3000, "lr": 0.04394, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32812, "top5_acc": 0.58781, "loss_cls": 3.82889, "loss": 3.82889, "time": 0.8288} +{"mode": "train", "epoch": 81, "iter": 3100, "lr": 0.04391, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31969, "top5_acc": 0.58078, "loss_cls": 3.83868, "loss": 3.83868, "time": 0.84044} +{"mode": "train", "epoch": 81, "iter": 3200, "lr": 0.04389, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32344, "top5_acc": 0.59375, "loss_cls": 3.78518, "loss": 3.78518, "time": 0.83859} +{"mode": "train", "epoch": 81, "iter": 3300, "lr": 0.04386, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32938, "top5_acc": 0.58891, "loss_cls": 3.82114, "loss": 3.82114, "time": 0.8372} +{"mode": "train", "epoch": 81, "iter": 3400, "lr": 0.04383, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33891, "top5_acc": 0.58594, "loss_cls": 3.79587, "loss": 3.79587, "time": 0.83114} +{"mode": "train", "epoch": 81, "iter": 3500, "lr": 0.0438, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.32938, "top5_acc": 0.58547, "loss_cls": 3.82884, "loss": 3.82884, "time": 0.83796} +{"mode": "train", "epoch": 81, "iter": 3600, "lr": 0.04377, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32469, "top5_acc": 0.59141, "loss_cls": 3.81923, "loss": 3.81923, "time": 0.83562} +{"mode": "train", "epoch": 81, "iter": 3700, "lr": 0.04375, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32156, "top5_acc": 0.58188, "loss_cls": 3.85851, "loss": 3.85851, "time": 0.8264} +{"mode": "val", "epoch": 81, "iter": 309, "lr": 0.04373, "top1_acc": 0.27483, "top5_acc": 0.52059, "mean_class_accuracy": 0.27467} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.04371, "memory": 15990, "data_time": 1.32814, "top1_acc": 0.34109, "top5_acc": 0.60047, "loss_cls": 3.71164, "loss": 3.71164, "time": 2.33541} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.04368, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34, "top5_acc": 0.60094, "loss_cls": 3.76391, "loss": 3.76391, "time": 0.83189} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.04365, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33094, "top5_acc": 0.58609, "loss_cls": 3.8193, "loss": 3.8193, "time": 0.82134} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.04362, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3375, "top5_acc": 0.60172, "loss_cls": 3.74904, "loss": 3.74904, "time": 0.8142} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.04359, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33438, "top5_acc": 0.59266, "loss_cls": 3.80077, "loss": 3.80077, "time": 0.82094} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.04357, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32906, "top5_acc": 0.59453, "loss_cls": 3.80627, "loss": 3.80627, "time": 0.81869} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.04354, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33484, "top5_acc": 0.59734, "loss_cls": 3.78562, "loss": 3.78562, "time": 0.81591} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.04351, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.335, "top5_acc": 0.58656, "loss_cls": 3.79932, "loss": 3.79932, "time": 0.82049} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.04348, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33609, "top5_acc": 0.58469, "loss_cls": 3.79614, "loss": 3.79614, "time": 0.81564} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.04346, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33406, "top5_acc": 0.59688, "loss_cls": 3.80684, "loss": 3.80684, "time": 0.8222} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.04343, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32406, "top5_acc": 0.58656, "loss_cls": 3.80459, "loss": 3.80459, "time": 0.81543} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.0434, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32016, "top5_acc": 0.58812, "loss_cls": 3.8304, "loss": 3.8304, "time": 0.81671} +{"mode": "train", "epoch": 82, "iter": 1300, "lr": 0.04337, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32688, "top5_acc": 0.58906, "loss_cls": 3.81179, "loss": 3.81179, "time": 0.81894} +{"mode": "train", "epoch": 82, "iter": 1400, "lr": 0.04335, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32859, "top5_acc": 0.58344, "loss_cls": 3.83312, "loss": 3.83312, "time": 0.81741} +{"mode": "train", "epoch": 82, "iter": 1500, "lr": 0.04332, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33203, "top5_acc": 0.59781, "loss_cls": 3.78826, "loss": 3.78826, "time": 0.82105} +{"mode": "train", "epoch": 82, "iter": 1600, "lr": 0.04329, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33828, "top5_acc": 0.60031, "loss_cls": 3.76173, "loss": 3.76173, "time": 0.8168} +{"mode": "train", "epoch": 82, "iter": 1700, "lr": 0.04326, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34016, "top5_acc": 0.59969, "loss_cls": 3.78831, "loss": 3.78831, "time": 0.81476} +{"mode": "train", "epoch": 82, "iter": 1800, "lr": 0.04323, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32797, "top5_acc": 0.58844, "loss_cls": 3.8125, "loss": 3.8125, "time": 0.81709} +{"mode": "train", "epoch": 82, "iter": 1900, "lr": 0.04321, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.335, "top5_acc": 0.59438, "loss_cls": 3.78259, "loss": 3.78259, "time": 0.82631} +{"mode": "train", "epoch": 82, "iter": 2000, "lr": 0.04318, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33016, "top5_acc": 0.59062, "loss_cls": 3.79349, "loss": 3.79349, "time": 0.81786} +{"mode": "train", "epoch": 82, "iter": 2100, "lr": 0.04315, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33797, "top5_acc": 0.58703, "loss_cls": 3.80963, "loss": 3.80963, "time": 0.81816} +{"mode": "train", "epoch": 82, "iter": 2200, "lr": 0.04312, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33297, "top5_acc": 0.58906, "loss_cls": 3.83745, "loss": 3.83745, "time": 0.83141} +{"mode": "train", "epoch": 82, "iter": 2300, "lr": 0.0431, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32469, "top5_acc": 0.58875, "loss_cls": 3.81423, "loss": 3.81423, "time": 0.81721} +{"mode": "train", "epoch": 82, "iter": 2400, "lr": 0.04307, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32938, "top5_acc": 0.59734, "loss_cls": 3.79458, "loss": 3.79458, "time": 0.82241} +{"mode": "train", "epoch": 82, "iter": 2500, "lr": 0.04304, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32828, "top5_acc": 0.58531, "loss_cls": 3.82173, "loss": 3.82173, "time": 0.81478} +{"mode": "train", "epoch": 82, "iter": 2600, "lr": 0.04301, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33172, "top5_acc": 0.58766, "loss_cls": 3.82304, "loss": 3.82304, "time": 0.81982} +{"mode": "train", "epoch": 82, "iter": 2700, "lr": 0.04299, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33141, "top5_acc": 0.58641, "loss_cls": 3.79191, "loss": 3.79191, "time": 0.82049} +{"mode": "train", "epoch": 82, "iter": 2800, "lr": 0.04296, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32625, "top5_acc": 0.58594, "loss_cls": 3.86244, "loss": 3.86244, "time": 0.8246} +{"mode": "train", "epoch": 82, "iter": 2900, "lr": 0.04293, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33562, "top5_acc": 0.59672, "loss_cls": 3.77507, "loss": 3.77507, "time": 0.81826} +{"mode": "train", "epoch": 82, "iter": 3000, "lr": 0.0429, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32297, "top5_acc": 0.58078, "loss_cls": 3.86439, "loss": 3.86439, "time": 0.82195} +{"mode": "train", "epoch": 82, "iter": 3100, "lr": 0.04287, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32578, "top5_acc": 0.59188, "loss_cls": 3.82897, "loss": 3.82897, "time": 0.82203} +{"mode": "train", "epoch": 82, "iter": 3200, "lr": 0.04285, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32719, "top5_acc": 0.58672, "loss_cls": 3.84149, "loss": 3.84149, "time": 0.82108} +{"mode": "train", "epoch": 82, "iter": 3300, "lr": 0.04282, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32578, "top5_acc": 0.59281, "loss_cls": 3.79769, "loss": 3.79769, "time": 0.81684} +{"mode": "train", "epoch": 82, "iter": 3400, "lr": 0.04279, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33094, "top5_acc": 0.58594, "loss_cls": 3.82485, "loss": 3.82485, "time": 0.81489} +{"mode": "train", "epoch": 82, "iter": 3500, "lr": 0.04276, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33125, "top5_acc": 0.59312, "loss_cls": 3.82716, "loss": 3.82716, "time": 0.82059} +{"mode": "train", "epoch": 82, "iter": 3600, "lr": 0.04274, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33078, "top5_acc": 0.59531, "loss_cls": 3.7803, "loss": 3.7803, "time": 0.81786} +{"mode": "train", "epoch": 82, "iter": 3700, "lr": 0.04271, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33484, "top5_acc": 0.59156, "loss_cls": 3.79534, "loss": 3.79534, "time": 0.81425} +{"mode": "val", "epoch": 82, "iter": 309, "lr": 0.0427, "top1_acc": 0.26936, "top5_acc": 0.52251, "mean_class_accuracy": 0.26919} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.04267, "memory": 15990, "data_time": 1.29554, "top1_acc": 0.33812, "top5_acc": 0.59594, "loss_cls": 3.75625, "loss": 3.75625, "time": 2.27517} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.04264, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34797, "top5_acc": 0.61391, "loss_cls": 3.68449, "loss": 3.68449, "time": 0.82102} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.04261, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33984, "top5_acc": 0.60203, "loss_cls": 3.74237, "loss": 3.74237, "time": 0.82207} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.04259, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34016, "top5_acc": 0.60594, "loss_cls": 3.71824, "loss": 3.71824, "time": 0.81479} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.04256, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33672, "top5_acc": 0.59156, "loss_cls": 3.76693, "loss": 3.76693, "time": 0.81704} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.04253, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34062, "top5_acc": 0.60125, "loss_cls": 3.74977, "loss": 3.74977, "time": 0.8167} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.0425, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33344, "top5_acc": 0.59422, "loss_cls": 3.76621, "loss": 3.76621, "time": 0.81091} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.04247, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34031, "top5_acc": 0.60922, "loss_cls": 3.71463, "loss": 3.71463, "time": 0.81598} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.04245, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33391, "top5_acc": 0.59078, "loss_cls": 3.80618, "loss": 3.80618, "time": 0.81426} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.04242, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33078, "top5_acc": 0.59203, "loss_cls": 3.82151, "loss": 3.82151, "time": 0.81443} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.04239, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33547, "top5_acc": 0.59484, "loss_cls": 3.8007, "loss": 3.8007, "time": 0.8167} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.04236, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33578, "top5_acc": 0.59297, "loss_cls": 3.79297, "loss": 3.79297, "time": 0.81555} +{"mode": "train", "epoch": 83, "iter": 1300, "lr": 0.04234, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33312, "top5_acc": 0.59094, "loss_cls": 3.81307, "loss": 3.81307, "time": 0.81262} +{"mode": "train", "epoch": 83, "iter": 1400, "lr": 0.04231, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34172, "top5_acc": 0.59844, "loss_cls": 3.7568, "loss": 3.7568, "time": 0.81869} +{"mode": "train", "epoch": 83, "iter": 1500, "lr": 0.04228, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34156, "top5_acc": 0.60562, "loss_cls": 3.73154, "loss": 3.73154, "time": 0.81241} +{"mode": "train", "epoch": 83, "iter": 1600, "lr": 0.04225, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33359, "top5_acc": 0.59125, "loss_cls": 3.78287, "loss": 3.78287, "time": 0.8146} +{"mode": "train", "epoch": 83, "iter": 1700, "lr": 0.04223, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33609, "top5_acc": 0.595, "loss_cls": 3.78534, "loss": 3.78534, "time": 0.81225} +{"mode": "train", "epoch": 83, "iter": 1800, "lr": 0.0422, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33797, "top5_acc": 0.59797, "loss_cls": 3.79944, "loss": 3.79944, "time": 0.82361} +{"mode": "train", "epoch": 83, "iter": 1900, "lr": 0.04217, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33703, "top5_acc": 0.59719, "loss_cls": 3.78069, "loss": 3.78069, "time": 0.82194} +{"mode": "train", "epoch": 83, "iter": 2000, "lr": 0.04214, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33266, "top5_acc": 0.58906, "loss_cls": 3.81979, "loss": 3.81979, "time": 0.82371} +{"mode": "train", "epoch": 83, "iter": 2100, "lr": 0.04212, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33297, "top5_acc": 0.58672, "loss_cls": 3.82668, "loss": 3.82668, "time": 0.82259} +{"mode": "train", "epoch": 83, "iter": 2200, "lr": 0.04209, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32969, "top5_acc": 0.59203, "loss_cls": 3.78676, "loss": 3.78676, "time": 0.82319} +{"mode": "train", "epoch": 83, "iter": 2300, "lr": 0.04206, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33125, "top5_acc": 0.58516, "loss_cls": 3.84685, "loss": 3.84685, "time": 0.81853} +{"mode": "train", "epoch": 83, "iter": 2400, "lr": 0.04203, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33531, "top5_acc": 0.5875, "loss_cls": 3.77349, "loss": 3.77349, "time": 0.81538} +{"mode": "train", "epoch": 83, "iter": 2500, "lr": 0.04201, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.325, "top5_acc": 0.58203, "loss_cls": 3.84514, "loss": 3.84514, "time": 0.81578} +{"mode": "train", "epoch": 83, "iter": 2600, "lr": 0.04198, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33672, "top5_acc": 0.59266, "loss_cls": 3.79835, "loss": 3.79835, "time": 0.82132} +{"mode": "train", "epoch": 83, "iter": 2700, "lr": 0.04195, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33453, "top5_acc": 0.59484, "loss_cls": 3.80071, "loss": 3.80071, "time": 0.81953} +{"mode": "train", "epoch": 83, "iter": 2800, "lr": 0.04192, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33781, "top5_acc": 0.59219, "loss_cls": 3.78301, "loss": 3.78301, "time": 0.81611} +{"mode": "train", "epoch": 83, "iter": 2900, "lr": 0.0419, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33125, "top5_acc": 0.58719, "loss_cls": 3.80852, "loss": 3.80852, "time": 0.82094} +{"mode": "train", "epoch": 83, "iter": 3000, "lr": 0.04187, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33453, "top5_acc": 0.58672, "loss_cls": 3.8116, "loss": 3.8116, "time": 0.82138} +{"mode": "train", "epoch": 83, "iter": 3100, "lr": 0.04184, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34016, "top5_acc": 0.59875, "loss_cls": 3.76397, "loss": 3.76397, "time": 0.8189} +{"mode": "train", "epoch": 83, "iter": 3200, "lr": 0.04181, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32922, "top5_acc": 0.58672, "loss_cls": 3.84238, "loss": 3.84238, "time": 0.81132} +{"mode": "train", "epoch": 83, "iter": 3300, "lr": 0.04178, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33266, "top5_acc": 0.58641, "loss_cls": 3.82462, "loss": 3.82462, "time": 0.81473} +{"mode": "train", "epoch": 83, "iter": 3400, "lr": 0.04176, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33109, "top5_acc": 0.58703, "loss_cls": 3.81508, "loss": 3.81508, "time": 0.81908} +{"mode": "train", "epoch": 83, "iter": 3500, "lr": 0.04173, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33844, "top5_acc": 0.59219, "loss_cls": 3.79071, "loss": 3.79071, "time": 0.81967} +{"mode": "train", "epoch": 83, "iter": 3600, "lr": 0.0417, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32656, "top5_acc": 0.58516, "loss_cls": 3.82406, "loss": 3.82406, "time": 0.81656} +{"mode": "train", "epoch": 83, "iter": 3700, "lr": 0.04167, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33984, "top5_acc": 0.5975, "loss_cls": 3.77941, "loss": 3.77941, "time": 0.81953} +{"mode": "val", "epoch": 83, "iter": 309, "lr": 0.04166, "top1_acc": 0.27787, "top5_acc": 0.5256, "mean_class_accuracy": 0.27761} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.04163, "memory": 15990, "data_time": 1.36858, "top1_acc": 0.34859, "top5_acc": 0.60359, "loss_cls": 3.72188, "loss": 3.72188, "time": 2.37296} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.04161, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33453, "top5_acc": 0.59688, "loss_cls": 3.77757, "loss": 3.77757, "time": 0.82197} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.04158, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34078, "top5_acc": 0.59922, "loss_cls": 3.75202, "loss": 3.75202, "time": 0.82526} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.04155, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34328, "top5_acc": 0.60266, "loss_cls": 3.73754, "loss": 3.73754, "time": 0.82051} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.04152, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3325, "top5_acc": 0.58516, "loss_cls": 3.82478, "loss": 3.82478, "time": 0.81475} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.0415, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33047, "top5_acc": 0.59891, "loss_cls": 3.77249, "loss": 3.77249, "time": 0.81759} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.04147, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33375, "top5_acc": 0.59906, "loss_cls": 3.77415, "loss": 3.77415, "time": 0.81766} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.04144, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33453, "top5_acc": 0.59391, "loss_cls": 3.78201, "loss": 3.78201, "time": 0.81639} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.04141, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34297, "top5_acc": 0.60609, "loss_cls": 3.72677, "loss": 3.72677, "time": 0.81963} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.04139, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34344, "top5_acc": 0.59906, "loss_cls": 3.7448, "loss": 3.7448, "time": 0.81547} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.04136, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34734, "top5_acc": 0.59438, "loss_cls": 3.75155, "loss": 3.75155, "time": 0.81932} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.04133, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34172, "top5_acc": 0.59484, "loss_cls": 3.77651, "loss": 3.77651, "time": 0.81783} +{"mode": "train", "epoch": 84, "iter": 1300, "lr": 0.0413, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34391, "top5_acc": 0.60016, "loss_cls": 3.7464, "loss": 3.7464, "time": 0.81629} +{"mode": "train", "epoch": 84, "iter": 1400, "lr": 0.04128, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33594, "top5_acc": 0.59125, "loss_cls": 3.77944, "loss": 3.77944, "time": 0.81807} +{"mode": "train", "epoch": 84, "iter": 1500, "lr": 0.04125, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34062, "top5_acc": 0.59828, "loss_cls": 3.76495, "loss": 3.76495, "time": 0.81165} +{"mode": "train", "epoch": 84, "iter": 1600, "lr": 0.04122, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.335, "top5_acc": 0.58781, "loss_cls": 3.82265, "loss": 3.82265, "time": 0.81929} +{"mode": "train", "epoch": 84, "iter": 1700, "lr": 0.04119, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33672, "top5_acc": 0.59016, "loss_cls": 3.77828, "loss": 3.77828, "time": 0.81986} +{"mode": "train", "epoch": 84, "iter": 1800, "lr": 0.04117, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34375, "top5_acc": 0.60016, "loss_cls": 3.76471, "loss": 3.76471, "time": 0.82226} +{"mode": "train", "epoch": 84, "iter": 1900, "lr": 0.04114, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33453, "top5_acc": 0.59469, "loss_cls": 3.78877, "loss": 3.78877, "time": 0.82121} +{"mode": "train", "epoch": 84, "iter": 2000, "lr": 0.04111, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33594, "top5_acc": 0.59203, "loss_cls": 3.79013, "loss": 3.79013, "time": 0.83221} +{"mode": "train", "epoch": 84, "iter": 2100, "lr": 0.04108, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33219, "top5_acc": 0.58609, "loss_cls": 3.82406, "loss": 3.82406, "time": 0.82386} +{"mode": "train", "epoch": 84, "iter": 2200, "lr": 0.04106, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33062, "top5_acc": 0.59156, "loss_cls": 3.79989, "loss": 3.79989, "time": 0.82194} +{"mode": "train", "epoch": 84, "iter": 2300, "lr": 0.04103, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33656, "top5_acc": 0.60156, "loss_cls": 3.77627, "loss": 3.77627, "time": 0.81915} +{"mode": "train", "epoch": 84, "iter": 2400, "lr": 0.041, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34438, "top5_acc": 0.60453, "loss_cls": 3.75548, "loss": 3.75548, "time": 0.81928} +{"mode": "train", "epoch": 84, "iter": 2500, "lr": 0.04097, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34562, "top5_acc": 0.60953, "loss_cls": 3.70911, "loss": 3.70911, "time": 0.82199} +{"mode": "train", "epoch": 84, "iter": 2600, "lr": 0.04095, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32953, "top5_acc": 0.58031, "loss_cls": 3.85149, "loss": 3.85149, "time": 0.81496} +{"mode": "train", "epoch": 84, "iter": 2700, "lr": 0.04092, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33734, "top5_acc": 0.58672, "loss_cls": 3.82357, "loss": 3.82357, "time": 0.82191} +{"mode": "train", "epoch": 84, "iter": 2800, "lr": 0.04089, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32484, "top5_acc": 0.58406, "loss_cls": 3.8411, "loss": 3.8411, "time": 0.81735} +{"mode": "train", "epoch": 84, "iter": 2900, "lr": 0.04086, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33234, "top5_acc": 0.60047, "loss_cls": 3.76508, "loss": 3.76508, "time": 0.82894} +{"mode": "train", "epoch": 84, "iter": 3000, "lr": 0.04084, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34266, "top5_acc": 0.59781, "loss_cls": 3.73897, "loss": 3.73897, "time": 0.82188} +{"mode": "train", "epoch": 84, "iter": 3100, "lr": 0.04081, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33828, "top5_acc": 0.59297, "loss_cls": 3.80106, "loss": 3.80106, "time": 0.81369} +{"mode": "train", "epoch": 84, "iter": 3200, "lr": 0.04078, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34406, "top5_acc": 0.60484, "loss_cls": 3.73395, "loss": 3.73395, "time": 0.81327} +{"mode": "train", "epoch": 84, "iter": 3300, "lr": 0.04075, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33406, "top5_acc": 0.59984, "loss_cls": 3.78942, "loss": 3.78942, "time": 0.81783} +{"mode": "train", "epoch": 84, "iter": 3400, "lr": 0.04073, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33375, "top5_acc": 0.58625, "loss_cls": 3.82693, "loss": 3.82693, "time": 0.82357} +{"mode": "train", "epoch": 84, "iter": 3500, "lr": 0.0407, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33453, "top5_acc": 0.59125, "loss_cls": 3.80615, "loss": 3.80615, "time": 0.81855} +{"mode": "train", "epoch": 84, "iter": 3600, "lr": 0.04067, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33469, "top5_acc": 0.59203, "loss_cls": 3.78284, "loss": 3.78284, "time": 0.81989} +{"mode": "train", "epoch": 84, "iter": 3700, "lr": 0.04064, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33594, "top5_acc": 0.59672, "loss_cls": 3.80362, "loss": 3.80362, "time": 0.81427} +{"mode": "val", "epoch": 84, "iter": 309, "lr": 0.04063, "top1_acc": 0.26526, "top5_acc": 0.51335, "mean_class_accuracy": 0.26514} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.0406, "memory": 15990, "data_time": 1.3972, "top1_acc": 0.34172, "top5_acc": 0.60453, "loss_cls": 3.71222, "loss": 3.71222, "time": 2.38808} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.04058, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35, "top5_acc": 0.59984, "loss_cls": 3.71973, "loss": 3.71973, "time": 0.82499} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.04055, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34062, "top5_acc": 0.59484, "loss_cls": 3.73751, "loss": 3.73751, "time": 0.81561} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.04052, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33469, "top5_acc": 0.5975, "loss_cls": 3.77553, "loss": 3.77553, "time": 0.81815} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.04049, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33781, "top5_acc": 0.59859, "loss_cls": 3.76432, "loss": 3.76432, "time": 0.81884} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.04047, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34188, "top5_acc": 0.59719, "loss_cls": 3.75491, "loss": 3.75491, "time": 0.81742} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.04044, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34125, "top5_acc": 0.59375, "loss_cls": 3.75887, "loss": 3.75887, "time": 0.81903} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.04041, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33453, "top5_acc": 0.59891, "loss_cls": 3.78395, "loss": 3.78395, "time": 0.82065} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.04038, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33938, "top5_acc": 0.60609, "loss_cls": 3.71817, "loss": 3.71817, "time": 0.81723} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.04036, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33938, "top5_acc": 0.60125, "loss_cls": 3.75922, "loss": 3.75922, "time": 0.81639} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.04033, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33953, "top5_acc": 0.60109, "loss_cls": 3.75594, "loss": 3.75594, "time": 0.81875} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.0403, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34219, "top5_acc": 0.60172, "loss_cls": 3.74625, "loss": 3.74625, "time": 0.82794} +{"mode": "train", "epoch": 85, "iter": 1300, "lr": 0.04027, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33641, "top5_acc": 0.59484, "loss_cls": 3.78337, "loss": 3.78337, "time": 0.81793} +{"mode": "train", "epoch": 85, "iter": 1400, "lr": 0.04025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33516, "top5_acc": 0.59969, "loss_cls": 3.78122, "loss": 3.78122, "time": 0.82845} +{"mode": "train", "epoch": 85, "iter": 1500, "lr": 0.04022, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33641, "top5_acc": 0.60016, "loss_cls": 3.7648, "loss": 3.7648, "time": 0.81481} +{"mode": "train", "epoch": 85, "iter": 1600, "lr": 0.04019, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32672, "top5_acc": 0.59031, "loss_cls": 3.81074, "loss": 3.81074, "time": 0.82033} +{"mode": "train", "epoch": 85, "iter": 1700, "lr": 0.04016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33719, "top5_acc": 0.58844, "loss_cls": 3.78478, "loss": 3.78478, "time": 0.81716} +{"mode": "train", "epoch": 85, "iter": 1800, "lr": 0.04014, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34281, "top5_acc": 0.59766, "loss_cls": 3.76989, "loss": 3.76989, "time": 0.82634} +{"mode": "train", "epoch": 85, "iter": 1900, "lr": 0.04011, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33656, "top5_acc": 0.58688, "loss_cls": 3.77659, "loss": 3.77659, "time": 0.82322} +{"mode": "train", "epoch": 85, "iter": 2000, "lr": 0.04008, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34188, "top5_acc": 0.59266, "loss_cls": 3.74917, "loss": 3.74917, "time": 0.81636} +{"mode": "train", "epoch": 85, "iter": 2100, "lr": 0.04006, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34469, "top5_acc": 0.60078, "loss_cls": 3.74872, "loss": 3.74872, "time": 0.82068} +{"mode": "train", "epoch": 85, "iter": 2200, "lr": 0.04003, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34047, "top5_acc": 0.59938, "loss_cls": 3.74534, "loss": 3.74534, "time": 0.81758} +{"mode": "train", "epoch": 85, "iter": 2300, "lr": 0.04, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34, "top5_acc": 0.59547, "loss_cls": 3.76366, "loss": 3.76366, "time": 0.8188} +{"mode": "train", "epoch": 85, "iter": 2400, "lr": 0.03997, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33859, "top5_acc": 0.59625, "loss_cls": 3.76723, "loss": 3.76723, "time": 0.81667} +{"mode": "train", "epoch": 85, "iter": 2500, "lr": 0.03995, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33531, "top5_acc": 0.59344, "loss_cls": 3.76818, "loss": 3.76818, "time": 0.82111} +{"mode": "train", "epoch": 85, "iter": 2600, "lr": 0.03992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33766, "top5_acc": 0.59656, "loss_cls": 3.76868, "loss": 3.76868, "time": 0.81622} +{"mode": "train", "epoch": 85, "iter": 2700, "lr": 0.03989, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34109, "top5_acc": 0.59828, "loss_cls": 3.76474, "loss": 3.76474, "time": 0.81961} +{"mode": "train", "epoch": 85, "iter": 2800, "lr": 0.03986, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33641, "top5_acc": 0.60312, "loss_cls": 3.74516, "loss": 3.74516, "time": 0.81584} +{"mode": "train", "epoch": 85, "iter": 2900, "lr": 0.03984, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33766, "top5_acc": 0.59531, "loss_cls": 3.78336, "loss": 3.78336, "time": 0.81932} +{"mode": "train", "epoch": 85, "iter": 3000, "lr": 0.03981, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33641, "top5_acc": 0.59031, "loss_cls": 3.79392, "loss": 3.79392, "time": 0.82014} +{"mode": "train", "epoch": 85, "iter": 3100, "lr": 0.03978, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34469, "top5_acc": 0.59688, "loss_cls": 3.76003, "loss": 3.76003, "time": 0.81732} +{"mode": "train", "epoch": 85, "iter": 3200, "lr": 0.03975, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34062, "top5_acc": 0.60453, "loss_cls": 3.76319, "loss": 3.76319, "time": 0.82209} +{"mode": "train", "epoch": 85, "iter": 3300, "lr": 0.03973, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34109, "top5_acc": 0.59906, "loss_cls": 3.74865, "loss": 3.74865, "time": 0.81997} +{"mode": "train", "epoch": 85, "iter": 3400, "lr": 0.0397, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35859, "top5_acc": 0.61062, "loss_cls": 3.69124, "loss": 3.69124, "time": 0.81287} +{"mode": "train", "epoch": 85, "iter": 3500, "lr": 0.03967, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34531, "top5_acc": 0.59484, "loss_cls": 3.77993, "loss": 3.77993, "time": 0.82128} +{"mode": "train", "epoch": 85, "iter": 3600, "lr": 0.03964, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33406, "top5_acc": 0.57953, "loss_cls": 3.82726, "loss": 3.82726, "time": 0.81829} +{"mode": "train", "epoch": 85, "iter": 3700, "lr": 0.03962, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.33031, "top5_acc": 0.59312, "loss_cls": 3.7994, "loss": 3.7994, "time": 0.81933} +{"mode": "val", "epoch": 85, "iter": 309, "lr": 0.0396, "top1_acc": 0.29074, "top5_acc": 0.53994, "mean_class_accuracy": 0.29052} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.03958, "memory": 15990, "data_time": 1.35146, "top1_acc": 0.34406, "top5_acc": 0.60906, "loss_cls": 3.70451, "loss": 3.70451, "time": 2.36245} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.03955, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35609, "top5_acc": 0.61844, "loss_cls": 3.67992, "loss": 3.67992, "time": 0.84042} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.03952, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34188, "top5_acc": 0.60672, "loss_cls": 3.71281, "loss": 3.71281, "time": 0.83978} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.0395, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34812, "top5_acc": 0.59969, "loss_cls": 3.72922, "loss": 3.72922, "time": 0.8337} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.03947, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35203, "top5_acc": 0.6, "loss_cls": 3.70852, "loss": 3.70852, "time": 0.83282} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.03944, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33594, "top5_acc": 0.59922, "loss_cls": 3.76915, "loss": 3.76915, "time": 0.83549} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.03941, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34016, "top5_acc": 0.60344, "loss_cls": 3.72557, "loss": 3.72557, "time": 0.82739} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.03939, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33844, "top5_acc": 0.59016, "loss_cls": 3.77643, "loss": 3.77643, "time": 0.83192} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.03936, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33703, "top5_acc": 0.59781, "loss_cls": 3.76355, "loss": 3.76355, "time": 0.83148} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.03933, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33844, "top5_acc": 0.59609, "loss_cls": 3.74319, "loss": 3.74319, "time": 0.82949} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.0393, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33359, "top5_acc": 0.59812, "loss_cls": 3.77088, "loss": 3.77088, "time": 0.83253} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.03928, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34766, "top5_acc": 0.60344, "loss_cls": 3.71984, "loss": 3.71984, "time": 0.8309} +{"mode": "train", "epoch": 86, "iter": 1300, "lr": 0.03925, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34125, "top5_acc": 0.60219, "loss_cls": 3.74176, "loss": 3.74176, "time": 0.83524} +{"mode": "train", "epoch": 86, "iter": 1400, "lr": 0.03922, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33469, "top5_acc": 0.60188, "loss_cls": 3.75677, "loss": 3.75677, "time": 0.82362} +{"mode": "train", "epoch": 86, "iter": 1500, "lr": 0.03919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33203, "top5_acc": 0.59438, "loss_cls": 3.7887, "loss": 3.7887, "time": 0.81914} +{"mode": "train", "epoch": 86, "iter": 1600, "lr": 0.03917, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33375, "top5_acc": 0.59547, "loss_cls": 3.77606, "loss": 3.77606, "time": 0.81941} +{"mode": "train", "epoch": 86, "iter": 1700, "lr": 0.03914, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3375, "top5_acc": 0.60328, "loss_cls": 3.71122, "loss": 3.71122, "time": 0.82171} +{"mode": "train", "epoch": 86, "iter": 1800, "lr": 0.03911, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34812, "top5_acc": 0.59844, "loss_cls": 3.75633, "loss": 3.75633, "time": 0.81973} +{"mode": "train", "epoch": 86, "iter": 1900, "lr": 0.03909, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34438, "top5_acc": 0.6025, "loss_cls": 3.7411, "loss": 3.7411, "time": 0.82387} +{"mode": "train", "epoch": 86, "iter": 2000, "lr": 0.03906, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33859, "top5_acc": 0.59516, "loss_cls": 3.77457, "loss": 3.77457, "time": 0.81329} +{"mode": "train", "epoch": 86, "iter": 2100, "lr": 0.03903, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34047, "top5_acc": 0.60719, "loss_cls": 3.73613, "loss": 3.73613, "time": 0.82598} +{"mode": "train", "epoch": 86, "iter": 2200, "lr": 0.039, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34891, "top5_acc": 0.60344, "loss_cls": 3.7058, "loss": 3.7058, "time": 0.81906} +{"mode": "train", "epoch": 86, "iter": 2300, "lr": 0.03898, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34922, "top5_acc": 0.60422, "loss_cls": 3.74439, "loss": 3.74439, "time": 0.81544} +{"mode": "train", "epoch": 86, "iter": 2400, "lr": 0.03895, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33891, "top5_acc": 0.59516, "loss_cls": 3.77311, "loss": 3.77311, "time": 0.82186} +{"mode": "train", "epoch": 86, "iter": 2500, "lr": 0.03892, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34734, "top5_acc": 0.60156, "loss_cls": 3.77211, "loss": 3.77211, "time": 0.8184} +{"mode": "train", "epoch": 86, "iter": 2600, "lr": 0.03889, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33453, "top5_acc": 0.59047, "loss_cls": 3.77125, "loss": 3.77125, "time": 0.82612} +{"mode": "train", "epoch": 86, "iter": 2700, "lr": 0.03887, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34688, "top5_acc": 0.60141, "loss_cls": 3.7453, "loss": 3.7453, "time": 0.81994} +{"mode": "train", "epoch": 86, "iter": 2800, "lr": 0.03884, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33969, "top5_acc": 0.59703, "loss_cls": 3.77195, "loss": 3.77195, "time": 0.82351} +{"mode": "train", "epoch": 86, "iter": 2900, "lr": 0.03881, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34141, "top5_acc": 0.60656, "loss_cls": 3.75071, "loss": 3.75071, "time": 0.82} +{"mode": "train", "epoch": 86, "iter": 3000, "lr": 0.03879, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34531, "top5_acc": 0.59578, "loss_cls": 3.76562, "loss": 3.76562, "time": 0.8197} +{"mode": "train", "epoch": 86, "iter": 3100, "lr": 0.03876, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34625, "top5_acc": 0.60016, "loss_cls": 3.75696, "loss": 3.75696, "time": 0.81567} +{"mode": "train", "epoch": 86, "iter": 3200, "lr": 0.03873, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33625, "top5_acc": 0.59766, "loss_cls": 3.77066, "loss": 3.77066, "time": 0.82065} +{"mode": "train", "epoch": 86, "iter": 3300, "lr": 0.0387, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33938, "top5_acc": 0.60734, "loss_cls": 3.74666, "loss": 3.74666, "time": 0.81821} +{"mode": "train", "epoch": 86, "iter": 3400, "lr": 0.03868, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32609, "top5_acc": 0.58047, "loss_cls": 3.86288, "loss": 3.86288, "time": 0.81382} +{"mode": "train", "epoch": 86, "iter": 3500, "lr": 0.03865, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33594, "top5_acc": 0.59484, "loss_cls": 3.79679, "loss": 3.79679, "time": 0.82029} +{"mode": "train", "epoch": 86, "iter": 3600, "lr": 0.03862, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34719, "top5_acc": 0.60219, "loss_cls": 3.74177, "loss": 3.74177, "time": 0.81413} +{"mode": "train", "epoch": 86, "iter": 3700, "lr": 0.0386, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33641, "top5_acc": 0.60359, "loss_cls": 3.79591, "loss": 3.79591, "time": 0.82272} +{"mode": "val", "epoch": 86, "iter": 309, "lr": 0.03858, "top1_acc": 0.29509, "top5_acc": 0.54946, "mean_class_accuracy": 0.29479} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.03856, "memory": 15990, "data_time": 1.37507, "top1_acc": 0.34578, "top5_acc": 0.61094, "loss_cls": 3.67656, "loss": 3.67656, "time": 2.37964} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.03853, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35719, "top5_acc": 0.61406, "loss_cls": 3.66312, "loss": 3.66312, "time": 0.82911} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.0385, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33266, "top5_acc": 0.60406, "loss_cls": 3.72852, "loss": 3.72852, "time": 0.82829} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.03847, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35172, "top5_acc": 0.6075, "loss_cls": 3.66357, "loss": 3.66357, "time": 0.82461} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.03845, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34156, "top5_acc": 0.60312, "loss_cls": 3.73798, "loss": 3.73798, "time": 0.81596} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.03842, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34766, "top5_acc": 0.60031, "loss_cls": 3.7526, "loss": 3.7526, "time": 0.8174} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.03839, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34891, "top5_acc": 0.60344, "loss_cls": 3.74254, "loss": 3.74254, "time": 0.82089} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.03837, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3375, "top5_acc": 0.59578, "loss_cls": 3.77801, "loss": 3.77801, "time": 0.81502} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.03834, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33906, "top5_acc": 0.60203, "loss_cls": 3.77373, "loss": 3.77373, "time": 0.81635} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.03831, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34062, "top5_acc": 0.60219, "loss_cls": 3.74589, "loss": 3.74589, "time": 0.81465} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.03828, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3525, "top5_acc": 0.60547, "loss_cls": 3.7157, "loss": 3.7157, "time": 0.81779} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.03826, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33484, "top5_acc": 0.60203, "loss_cls": 3.74615, "loss": 3.74615, "time": 0.81374} +{"mode": "train", "epoch": 87, "iter": 1300, "lr": 0.03823, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34047, "top5_acc": 0.59219, "loss_cls": 3.75094, "loss": 3.75094, "time": 0.81716} +{"mode": "train", "epoch": 87, "iter": 1400, "lr": 0.0382, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34172, "top5_acc": 0.60609, "loss_cls": 3.73928, "loss": 3.73928, "time": 0.81226} +{"mode": "train", "epoch": 87, "iter": 1500, "lr": 0.03817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34016, "top5_acc": 0.59516, "loss_cls": 3.74568, "loss": 3.74568, "time": 0.81533} +{"mode": "train", "epoch": 87, "iter": 1600, "lr": 0.03815, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34109, "top5_acc": 0.60625, "loss_cls": 3.74138, "loss": 3.74138, "time": 0.81575} +{"mode": "train", "epoch": 87, "iter": 1700, "lr": 0.03812, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35656, "top5_acc": 0.60359, "loss_cls": 3.71094, "loss": 3.71094, "time": 0.82119} +{"mode": "train", "epoch": 87, "iter": 1800, "lr": 0.03809, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34078, "top5_acc": 0.59922, "loss_cls": 3.73038, "loss": 3.73038, "time": 0.82063} +{"mode": "train", "epoch": 87, "iter": 1900, "lr": 0.03807, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34422, "top5_acc": 0.60656, "loss_cls": 3.71767, "loss": 3.71767, "time": 0.82412} +{"mode": "train", "epoch": 87, "iter": 2000, "lr": 0.03804, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33359, "top5_acc": 0.59781, "loss_cls": 3.76055, "loss": 3.76055, "time": 0.8182} +{"mode": "train", "epoch": 87, "iter": 2100, "lr": 0.03801, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34422, "top5_acc": 0.60297, "loss_cls": 3.74287, "loss": 3.74287, "time": 0.81632} +{"mode": "train", "epoch": 87, "iter": 2200, "lr": 0.03798, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35, "top5_acc": 0.61266, "loss_cls": 3.68978, "loss": 3.68978, "time": 0.81047} +{"mode": "train", "epoch": 87, "iter": 2300, "lr": 0.03796, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34, "top5_acc": 0.59734, "loss_cls": 3.75007, "loss": 3.75007, "time": 0.8143} +{"mode": "train", "epoch": 87, "iter": 2400, "lr": 0.03793, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32516, "top5_acc": 0.59672, "loss_cls": 3.80253, "loss": 3.80253, "time": 0.82487} +{"mode": "train", "epoch": 87, "iter": 2500, "lr": 0.0379, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33438, "top5_acc": 0.59266, "loss_cls": 3.81884, "loss": 3.81884, "time": 0.82106} +{"mode": "train", "epoch": 87, "iter": 2600, "lr": 0.03788, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34266, "top5_acc": 0.60484, "loss_cls": 3.73633, "loss": 3.73633, "time": 0.82151} +{"mode": "train", "epoch": 87, "iter": 2700, "lr": 0.03785, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35172, "top5_acc": 0.60328, "loss_cls": 3.71936, "loss": 3.71936, "time": 0.82084} +{"mode": "train", "epoch": 87, "iter": 2800, "lr": 0.03782, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35125, "top5_acc": 0.60906, "loss_cls": 3.72345, "loss": 3.72345, "time": 0.82129} +{"mode": "train", "epoch": 87, "iter": 2900, "lr": 0.03779, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34953, "top5_acc": 0.61031, "loss_cls": 3.69627, "loss": 3.69627, "time": 0.82222} +{"mode": "train", "epoch": 87, "iter": 3000, "lr": 0.03777, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34422, "top5_acc": 0.5975, "loss_cls": 3.73712, "loss": 3.73712, "time": 0.81465} +{"mode": "train", "epoch": 87, "iter": 3100, "lr": 0.03774, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34688, "top5_acc": 0.60047, "loss_cls": 3.74736, "loss": 3.74736, "time": 0.81769} +{"mode": "train", "epoch": 87, "iter": 3200, "lr": 0.03771, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34672, "top5_acc": 0.60281, "loss_cls": 3.72372, "loss": 3.72372, "time": 0.81587} +{"mode": "train", "epoch": 87, "iter": 3300, "lr": 0.03769, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34281, "top5_acc": 0.59656, "loss_cls": 3.74959, "loss": 3.74959, "time": 0.82001} +{"mode": "train", "epoch": 87, "iter": 3400, "lr": 0.03766, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34906, "top5_acc": 0.60156, "loss_cls": 3.73729, "loss": 3.73729, "time": 0.81451} +{"mode": "train", "epoch": 87, "iter": 3500, "lr": 0.03763, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33625, "top5_acc": 0.59078, "loss_cls": 3.77192, "loss": 3.77192, "time": 0.81521} +{"mode": "train", "epoch": 87, "iter": 3600, "lr": 0.03761, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.335, "top5_acc": 0.60438, "loss_cls": 3.75111, "loss": 3.75111, "time": 0.81247} +{"mode": "train", "epoch": 87, "iter": 3700, "lr": 0.03758, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33719, "top5_acc": 0.59047, "loss_cls": 3.77583, "loss": 3.77583, "time": 0.81908} +{"mode": "val", "epoch": 87, "iter": 309, "lr": 0.03757, "top1_acc": 0.29074, "top5_acc": 0.54774, "mean_class_accuracy": 0.29051} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.03754, "memory": 15990, "data_time": 1.35407, "top1_acc": 0.34375, "top5_acc": 0.61328, "loss_cls": 3.68094, "loss": 3.68094, "time": 2.36325} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.03751, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34766, "top5_acc": 0.61016, "loss_cls": 3.70326, "loss": 3.70326, "time": 0.8437} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.03748, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34688, "top5_acc": 0.60875, "loss_cls": 3.70571, "loss": 3.70571, "time": 0.84404} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.03746, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34328, "top5_acc": 0.61109, "loss_cls": 3.69522, "loss": 3.69522, "time": 0.84527} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.03743, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36297, "top5_acc": 0.61422, "loss_cls": 3.62455, "loss": 3.62455, "time": 0.84876} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.0374, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34438, "top5_acc": 0.61266, "loss_cls": 3.69414, "loss": 3.69414, "time": 0.84511} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.03738, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35094, "top5_acc": 0.61484, "loss_cls": 3.65897, "loss": 3.65897, "time": 0.8463} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.03735, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33891, "top5_acc": 0.60531, "loss_cls": 3.73513, "loss": 3.73513, "time": 0.84276} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.03732, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34625, "top5_acc": 0.60562, "loss_cls": 3.70829, "loss": 3.70829, "time": 0.84548} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.0373, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34234, "top5_acc": 0.60062, "loss_cls": 3.73959, "loss": 3.73959, "time": 0.84464} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.03727, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34375, "top5_acc": 0.60078, "loss_cls": 3.73549, "loss": 3.73549, "time": 0.84458} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.03724, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34562, "top5_acc": 0.60469, "loss_cls": 3.7142, "loss": 3.7142, "time": 0.84542} +{"mode": "train", "epoch": 88, "iter": 1300, "lr": 0.03721, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34125, "top5_acc": 0.60156, "loss_cls": 3.71706, "loss": 3.71706, "time": 0.84245} +{"mode": "train", "epoch": 88, "iter": 1400, "lr": 0.03719, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33703, "top5_acc": 0.60844, "loss_cls": 3.73748, "loss": 3.73748, "time": 0.84552} +{"mode": "train", "epoch": 88, "iter": 1500, "lr": 0.03716, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35047, "top5_acc": 0.60641, "loss_cls": 3.70998, "loss": 3.70998, "time": 0.84527} +{"mode": "train", "epoch": 88, "iter": 1600, "lr": 0.03713, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34188, "top5_acc": 0.60141, "loss_cls": 3.73056, "loss": 3.73056, "time": 0.84528} +{"mode": "train", "epoch": 88, "iter": 1700, "lr": 0.03711, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34234, "top5_acc": 0.59859, "loss_cls": 3.75678, "loss": 3.75678, "time": 0.8452} +{"mode": "train", "epoch": 88, "iter": 1800, "lr": 0.03708, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34938, "top5_acc": 0.60359, "loss_cls": 3.68941, "loss": 3.68941, "time": 0.8326} +{"mode": "train", "epoch": 88, "iter": 1900, "lr": 0.03705, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34781, "top5_acc": 0.59859, "loss_cls": 3.7198, "loss": 3.7198, "time": 0.83795} +{"mode": "train", "epoch": 88, "iter": 2000, "lr": 0.03703, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34562, "top5_acc": 0.60109, "loss_cls": 3.71422, "loss": 3.71422, "time": 0.83926} +{"mode": "train", "epoch": 88, "iter": 2100, "lr": 0.037, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34078, "top5_acc": 0.60141, "loss_cls": 3.74194, "loss": 3.74194, "time": 0.83876} +{"mode": "train", "epoch": 88, "iter": 2200, "lr": 0.03697, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35094, "top5_acc": 0.60562, "loss_cls": 3.72775, "loss": 3.72775, "time": 0.84245} +{"mode": "train", "epoch": 88, "iter": 2300, "lr": 0.03694, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34797, "top5_acc": 0.60188, "loss_cls": 3.71401, "loss": 3.71401, "time": 0.84309} +{"mode": "train", "epoch": 88, "iter": 2400, "lr": 0.03692, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33734, "top5_acc": 0.59328, "loss_cls": 3.79086, "loss": 3.79086, "time": 0.83825} +{"mode": "train", "epoch": 88, "iter": 2500, "lr": 0.03689, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34, "top5_acc": 0.60094, "loss_cls": 3.74204, "loss": 3.74204, "time": 0.82999} +{"mode": "train", "epoch": 88, "iter": 2600, "lr": 0.03686, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34781, "top5_acc": 0.59875, "loss_cls": 3.76396, "loss": 3.76396, "time": 0.84321} +{"mode": "train", "epoch": 88, "iter": 2700, "lr": 0.03684, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33234, "top5_acc": 0.59688, "loss_cls": 3.75628, "loss": 3.75628, "time": 0.83303} +{"mode": "train", "epoch": 88, "iter": 2800, "lr": 0.03681, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35234, "top5_acc": 0.60359, "loss_cls": 3.70863, "loss": 3.70863, "time": 0.83569} +{"mode": "train", "epoch": 88, "iter": 2900, "lr": 0.03678, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34156, "top5_acc": 0.59812, "loss_cls": 3.76603, "loss": 3.76603, "time": 0.8447} +{"mode": "train", "epoch": 88, "iter": 3000, "lr": 0.03676, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33734, "top5_acc": 0.61375, "loss_cls": 3.73067, "loss": 3.73067, "time": 0.8395} +{"mode": "train", "epoch": 88, "iter": 3100, "lr": 0.03673, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34188, "top5_acc": 0.59703, "loss_cls": 3.77106, "loss": 3.77106, "time": 0.83088} +{"mode": "train", "epoch": 88, "iter": 3200, "lr": 0.0367, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34078, "top5_acc": 0.59312, "loss_cls": 3.78222, "loss": 3.78222, "time": 0.83425} +{"mode": "train", "epoch": 88, "iter": 3300, "lr": 0.03667, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34406, "top5_acc": 0.60125, "loss_cls": 3.71776, "loss": 3.71776, "time": 0.83291} +{"mode": "train", "epoch": 88, "iter": 3400, "lr": 0.03665, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35062, "top5_acc": 0.60625, "loss_cls": 3.72829, "loss": 3.72829, "time": 0.83207} +{"mode": "train", "epoch": 88, "iter": 3500, "lr": 0.03662, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33797, "top5_acc": 0.59516, "loss_cls": 3.72738, "loss": 3.72738, "time": 0.83026} +{"mode": "train", "epoch": 88, "iter": 3600, "lr": 0.03659, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34469, "top5_acc": 0.60812, "loss_cls": 3.72532, "loss": 3.72532, "time": 0.83314} +{"mode": "train", "epoch": 88, "iter": 3700, "lr": 0.03657, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.3425, "top5_acc": 0.60922, "loss_cls": 3.72062, "loss": 3.72062, "time": 0.83953} +{"mode": "val", "epoch": 88, "iter": 309, "lr": 0.03655, "top1_acc": 0.29028, "top5_acc": 0.54227, "mean_class_accuracy": 0.28997} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.03653, "memory": 15990, "data_time": 1.32086, "top1_acc": 0.35859, "top5_acc": 0.61, "loss_cls": 3.66732, "loss": 3.66732, "time": 2.32586} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0365, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.3525, "top5_acc": 0.61469, "loss_cls": 3.68091, "loss": 3.68091, "time": 0.83843} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.03647, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34922, "top5_acc": 0.61109, "loss_cls": 3.67428, "loss": 3.67428, "time": 0.83861} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.03645, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35453, "top5_acc": 0.61734, "loss_cls": 3.66983, "loss": 3.66983, "time": 0.8443} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.03642, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.3475, "top5_acc": 0.59812, "loss_cls": 3.71035, "loss": 3.71035, "time": 0.83816} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.03639, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35188, "top5_acc": 0.61125, "loss_cls": 3.67895, "loss": 3.67895, "time": 0.84194} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.03637, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.355, "top5_acc": 0.61062, "loss_cls": 3.66341, "loss": 3.66341, "time": 0.83623} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.03634, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34391, "top5_acc": 0.60266, "loss_cls": 3.7345, "loss": 3.7345, "time": 0.84168} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.03631, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33562, "top5_acc": 0.60234, "loss_cls": 3.74228, "loss": 3.74228, "time": 0.83657} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.03629, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34281, "top5_acc": 0.60734, "loss_cls": 3.70913, "loss": 3.70913, "time": 0.8379} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.03626, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36453, "top5_acc": 0.62547, "loss_cls": 3.62647, "loss": 3.62647, "time": 0.83858} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.03623, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34906, "top5_acc": 0.60578, "loss_cls": 3.71546, "loss": 3.71546, "time": 0.83519} +{"mode": "train", "epoch": 89, "iter": 1300, "lr": 0.0362, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34609, "top5_acc": 0.61234, "loss_cls": 3.70331, "loss": 3.70331, "time": 0.82231} +{"mode": "train", "epoch": 89, "iter": 1400, "lr": 0.03618, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34297, "top5_acc": 0.60484, "loss_cls": 3.71521, "loss": 3.71521, "time": 0.82177} +{"mode": "train", "epoch": 89, "iter": 1500, "lr": 0.03615, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35094, "top5_acc": 0.60406, "loss_cls": 3.74919, "loss": 3.74919, "time": 0.81768} +{"mode": "train", "epoch": 89, "iter": 1600, "lr": 0.03612, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35125, "top5_acc": 0.61, "loss_cls": 3.71242, "loss": 3.71242, "time": 0.81795} +{"mode": "train", "epoch": 89, "iter": 1700, "lr": 0.0361, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34328, "top5_acc": 0.60938, "loss_cls": 3.71788, "loss": 3.71788, "time": 0.8216} +{"mode": "train", "epoch": 89, "iter": 1800, "lr": 0.03607, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34141, "top5_acc": 0.59844, "loss_cls": 3.76043, "loss": 3.76043, "time": 0.82947} +{"mode": "train", "epoch": 89, "iter": 1900, "lr": 0.03604, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34375, "top5_acc": 0.60422, "loss_cls": 3.72175, "loss": 3.72175, "time": 0.83529} +{"mode": "train", "epoch": 89, "iter": 2000, "lr": 0.03602, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34875, "top5_acc": 0.60844, "loss_cls": 3.71433, "loss": 3.71433, "time": 0.8219} +{"mode": "train", "epoch": 89, "iter": 2100, "lr": 0.03599, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33453, "top5_acc": 0.60359, "loss_cls": 3.74021, "loss": 3.74021, "time": 0.82026} +{"mode": "train", "epoch": 89, "iter": 2200, "lr": 0.03596, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34328, "top5_acc": 0.60047, "loss_cls": 3.7587, "loss": 3.7587, "time": 0.81714} +{"mode": "train", "epoch": 89, "iter": 2300, "lr": 0.03594, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34531, "top5_acc": 0.59906, "loss_cls": 3.73533, "loss": 3.73533, "time": 0.81449} +{"mode": "train", "epoch": 89, "iter": 2400, "lr": 0.03591, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34719, "top5_acc": 0.60578, "loss_cls": 3.69711, "loss": 3.69711, "time": 0.81343} +{"mode": "train", "epoch": 89, "iter": 2500, "lr": 0.03588, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35078, "top5_acc": 0.60703, "loss_cls": 3.70872, "loss": 3.70872, "time": 0.82491} +{"mode": "train", "epoch": 89, "iter": 2600, "lr": 0.03586, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35312, "top5_acc": 0.61, "loss_cls": 3.67952, "loss": 3.67952, "time": 0.81624} +{"mode": "train", "epoch": 89, "iter": 2700, "lr": 0.03583, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34531, "top5_acc": 0.60453, "loss_cls": 3.726, "loss": 3.726, "time": 0.82203} +{"mode": "train", "epoch": 89, "iter": 2800, "lr": 0.0358, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34781, "top5_acc": 0.60594, "loss_cls": 3.70914, "loss": 3.70914, "time": 0.82101} +{"mode": "train", "epoch": 89, "iter": 2900, "lr": 0.03578, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34516, "top5_acc": 0.60016, "loss_cls": 3.74085, "loss": 3.74085, "time": 0.81942} +{"mode": "train", "epoch": 89, "iter": 3000, "lr": 0.03575, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34594, "top5_acc": 0.60469, "loss_cls": 3.71224, "loss": 3.71224, "time": 0.82323} +{"mode": "train", "epoch": 89, "iter": 3100, "lr": 0.03572, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34422, "top5_acc": 0.60672, "loss_cls": 3.70953, "loss": 3.70953, "time": 0.8149} +{"mode": "train", "epoch": 89, "iter": 3200, "lr": 0.03569, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34734, "top5_acc": 0.60781, "loss_cls": 3.70503, "loss": 3.70503, "time": 0.81863} +{"mode": "train", "epoch": 89, "iter": 3300, "lr": 0.03567, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3525, "top5_acc": 0.60578, "loss_cls": 3.71635, "loss": 3.71635, "time": 0.81266} +{"mode": "train", "epoch": 89, "iter": 3400, "lr": 0.03564, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34172, "top5_acc": 0.60422, "loss_cls": 3.70078, "loss": 3.70078, "time": 0.81985} +{"mode": "train", "epoch": 89, "iter": 3500, "lr": 0.03561, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33578, "top5_acc": 0.59781, "loss_cls": 3.77957, "loss": 3.77957, "time": 0.82376} +{"mode": "train", "epoch": 89, "iter": 3600, "lr": 0.03559, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34844, "top5_acc": 0.60812, "loss_cls": 3.70605, "loss": 3.70605, "time": 0.8206} +{"mode": "train", "epoch": 89, "iter": 3700, "lr": 0.03556, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34875, "top5_acc": 0.59359, "loss_cls": 3.75217, "loss": 3.75217, "time": 0.82157} +{"mode": "val", "epoch": 89, "iter": 309, "lr": 0.03555, "top1_acc": 0.29479, "top5_acc": 0.5449, "mean_class_accuracy": 0.29462} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.03552, "memory": 15990, "data_time": 1.37766, "top1_acc": 0.34875, "top5_acc": 0.61562, "loss_cls": 3.67017, "loss": 3.67017, "time": 2.37679} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.0355, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34953, "top5_acc": 0.61172, "loss_cls": 3.67383, "loss": 3.67383, "time": 0.83023} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.03547, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34031, "top5_acc": 0.60266, "loss_cls": 3.72494, "loss": 3.72494, "time": 0.81932} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.03544, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35125, "top5_acc": 0.61125, "loss_cls": 3.68304, "loss": 3.68304, "time": 0.81593} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.03541, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34422, "top5_acc": 0.60781, "loss_cls": 3.70982, "loss": 3.70982, "time": 0.81128} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.03539, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35047, "top5_acc": 0.60984, "loss_cls": 3.66196, "loss": 3.66196, "time": 0.81554} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.03536, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34625, "top5_acc": 0.60859, "loss_cls": 3.69707, "loss": 3.69707, "time": 0.8178} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.03533, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3575, "top5_acc": 0.6125, "loss_cls": 3.67943, "loss": 3.67943, "time": 0.82008} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.03531, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34922, "top5_acc": 0.61422, "loss_cls": 3.69476, "loss": 3.69476, "time": 0.81997} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.03528, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35297, "top5_acc": 0.61016, "loss_cls": 3.67511, "loss": 3.67511, "time": 0.81385} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.03525, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35891, "top5_acc": 0.61672, "loss_cls": 3.67778, "loss": 3.67778, "time": 0.8151} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.03523, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34719, "top5_acc": 0.59844, "loss_cls": 3.71345, "loss": 3.71345, "time": 0.82492} +{"mode": "train", "epoch": 90, "iter": 1300, "lr": 0.0352, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34953, "top5_acc": 0.60359, "loss_cls": 3.70791, "loss": 3.70791, "time": 0.81694} +{"mode": "train", "epoch": 90, "iter": 1400, "lr": 0.03517, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34906, "top5_acc": 0.60391, "loss_cls": 3.70859, "loss": 3.70859, "time": 0.8137} +{"mode": "train", "epoch": 90, "iter": 1500, "lr": 0.03515, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34359, "top5_acc": 0.60641, "loss_cls": 3.72666, "loss": 3.72666, "time": 0.81967} +{"mode": "train", "epoch": 90, "iter": 1600, "lr": 0.03512, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34797, "top5_acc": 0.60875, "loss_cls": 3.70779, "loss": 3.70779, "time": 0.82006} +{"mode": "train", "epoch": 90, "iter": 1700, "lr": 0.03509, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35047, "top5_acc": 0.60828, "loss_cls": 3.67775, "loss": 3.67775, "time": 0.82492} +{"mode": "train", "epoch": 90, "iter": 1800, "lr": 0.03507, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35297, "top5_acc": 0.61219, "loss_cls": 3.67008, "loss": 3.67008, "time": 0.8201} +{"mode": "train", "epoch": 90, "iter": 1900, "lr": 0.03504, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35172, "top5_acc": 0.60594, "loss_cls": 3.69628, "loss": 3.69628, "time": 0.81914} +{"mode": "train", "epoch": 90, "iter": 2000, "lr": 0.03501, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3325, "top5_acc": 0.60188, "loss_cls": 3.73292, "loss": 3.73292, "time": 0.81652} +{"mode": "train", "epoch": 90, "iter": 2100, "lr": 0.03499, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34484, "top5_acc": 0.60312, "loss_cls": 3.70738, "loss": 3.70738, "time": 0.81606} +{"mode": "train", "epoch": 90, "iter": 2200, "lr": 0.03496, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34219, "top5_acc": 0.60812, "loss_cls": 3.7065, "loss": 3.7065, "time": 0.81606} +{"mode": "train", "epoch": 90, "iter": 2300, "lr": 0.03493, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34766, "top5_acc": 0.60703, "loss_cls": 3.71818, "loss": 3.71818, "time": 0.81592} +{"mode": "train", "epoch": 90, "iter": 2400, "lr": 0.03491, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3525, "top5_acc": 0.61, "loss_cls": 3.69642, "loss": 3.69642, "time": 0.81902} +{"mode": "train", "epoch": 90, "iter": 2500, "lr": 0.03488, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35062, "top5_acc": 0.61547, "loss_cls": 3.68794, "loss": 3.68794, "time": 0.82258} +{"mode": "train", "epoch": 90, "iter": 2600, "lr": 0.03485, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35156, "top5_acc": 0.59203, "loss_cls": 3.75453, "loss": 3.75453, "time": 0.82066} +{"mode": "train", "epoch": 90, "iter": 2700, "lr": 0.03483, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35297, "top5_acc": 0.61031, "loss_cls": 3.70187, "loss": 3.70187, "time": 0.81675} +{"mode": "train", "epoch": 90, "iter": 2800, "lr": 0.0348, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3625, "top5_acc": 0.61672, "loss_cls": 3.64766, "loss": 3.64766, "time": 0.82058} +{"mode": "train", "epoch": 90, "iter": 2900, "lr": 0.03477, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35766, "top5_acc": 0.61906, "loss_cls": 3.62772, "loss": 3.62772, "time": 0.81797} +{"mode": "train", "epoch": 90, "iter": 3000, "lr": 0.03475, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34906, "top5_acc": 0.60734, "loss_cls": 3.68538, "loss": 3.68538, "time": 0.81392} +{"mode": "train", "epoch": 90, "iter": 3100, "lr": 0.03472, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34422, "top5_acc": 0.60234, "loss_cls": 3.72602, "loss": 3.72602, "time": 0.82099} +{"mode": "train", "epoch": 90, "iter": 3200, "lr": 0.03469, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35125, "top5_acc": 0.61484, "loss_cls": 3.67298, "loss": 3.67298, "time": 0.82282} +{"mode": "train", "epoch": 90, "iter": 3300, "lr": 0.03467, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35547, "top5_acc": 0.60297, "loss_cls": 3.71309, "loss": 3.71309, "time": 0.81832} +{"mode": "train", "epoch": 90, "iter": 3400, "lr": 0.03464, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34406, "top5_acc": 0.60141, "loss_cls": 3.73355, "loss": 3.73355, "time": 0.81715} +{"mode": "train", "epoch": 90, "iter": 3500, "lr": 0.03461, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34422, "top5_acc": 0.60703, "loss_cls": 3.75592, "loss": 3.75592, "time": 0.82337} +{"mode": "train", "epoch": 90, "iter": 3600, "lr": 0.03459, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35578, "top5_acc": 0.61156, "loss_cls": 3.66823, "loss": 3.66823, "time": 0.82373} +{"mode": "train", "epoch": 90, "iter": 3700, "lr": 0.03456, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35297, "top5_acc": 0.61703, "loss_cls": 3.64841, "loss": 3.64841, "time": 0.82263} +{"mode": "val", "epoch": 90, "iter": 309, "lr": 0.03455, "top1_acc": 0.2841, "top5_acc": 0.53371, "mean_class_accuracy": 0.28391} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.03452, "memory": 15990, "data_time": 1.39042, "top1_acc": 0.35984, "top5_acc": 0.61719, "loss_cls": 3.63409, "loss": 3.63409, "time": 2.37651} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0345, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36531, "top5_acc": 0.62031, "loss_cls": 3.60638, "loss": 3.60638, "time": 0.82385} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.03447, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36453, "top5_acc": 0.6225, "loss_cls": 3.61193, "loss": 3.61193, "time": 0.81951} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.03444, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35344, "top5_acc": 0.61781, "loss_cls": 3.68396, "loss": 3.68396, "time": 0.81893} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.03442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36438, "top5_acc": 0.62062, "loss_cls": 3.59933, "loss": 3.59933, "time": 0.81754} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.03439, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35484, "top5_acc": 0.62234, "loss_cls": 3.63892, "loss": 3.63892, "time": 0.81772} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.03436, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35406, "top5_acc": 0.60938, "loss_cls": 3.67464, "loss": 3.67464, "time": 0.81318} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.03434, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34984, "top5_acc": 0.615, "loss_cls": 3.66451, "loss": 3.66451, "time": 0.81807} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.03431, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3525, "top5_acc": 0.60891, "loss_cls": 3.6884, "loss": 3.6884, "time": 0.81855} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.03428, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35281, "top5_acc": 0.61453, "loss_cls": 3.68097, "loss": 3.68097, "time": 0.81363} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.03426, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35156, "top5_acc": 0.61078, "loss_cls": 3.66779, "loss": 3.66779, "time": 0.82386} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.03423, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35375, "top5_acc": 0.61344, "loss_cls": 3.6779, "loss": 3.6779, "time": 0.81475} +{"mode": "train", "epoch": 91, "iter": 1300, "lr": 0.0342, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34719, "top5_acc": 0.61094, "loss_cls": 3.70217, "loss": 3.70217, "time": 0.81902} +{"mode": "train", "epoch": 91, "iter": 1400, "lr": 0.03418, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35203, "top5_acc": 0.61594, "loss_cls": 3.67023, "loss": 3.67023, "time": 0.8148} +{"mode": "train", "epoch": 91, "iter": 1500, "lr": 0.03415, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35469, "top5_acc": 0.60938, "loss_cls": 3.69199, "loss": 3.69199, "time": 0.81798} +{"mode": "train", "epoch": 91, "iter": 1600, "lr": 0.03412, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35078, "top5_acc": 0.61359, "loss_cls": 3.679, "loss": 3.679, "time": 0.81666} +{"mode": "train", "epoch": 91, "iter": 1700, "lr": 0.0341, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34094, "top5_acc": 0.59516, "loss_cls": 3.74852, "loss": 3.74852, "time": 0.82866} +{"mode": "train", "epoch": 91, "iter": 1800, "lr": 0.03407, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34656, "top5_acc": 0.60828, "loss_cls": 3.6721, "loss": 3.6721, "time": 0.82371} +{"mode": "train", "epoch": 91, "iter": 1900, "lr": 0.03405, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35688, "top5_acc": 0.61828, "loss_cls": 3.66369, "loss": 3.66369, "time": 0.81787} +{"mode": "train", "epoch": 91, "iter": 2000, "lr": 0.03402, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35922, "top5_acc": 0.61438, "loss_cls": 3.68064, "loss": 3.68064, "time": 0.8179} +{"mode": "train", "epoch": 91, "iter": 2100, "lr": 0.03399, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35062, "top5_acc": 0.61016, "loss_cls": 3.6958, "loss": 3.6958, "time": 0.81483} +{"mode": "train", "epoch": 91, "iter": 2200, "lr": 0.03397, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35578, "top5_acc": 0.61156, "loss_cls": 3.66183, "loss": 3.66183, "time": 0.81316} +{"mode": "train", "epoch": 91, "iter": 2300, "lr": 0.03394, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35766, "top5_acc": 0.60891, "loss_cls": 3.68726, "loss": 3.68726, "time": 0.81516} +{"mode": "train", "epoch": 91, "iter": 2400, "lr": 0.03391, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35734, "top5_acc": 0.60719, "loss_cls": 3.67999, "loss": 3.67999, "time": 0.81299} +{"mode": "train", "epoch": 91, "iter": 2500, "lr": 0.03389, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33797, "top5_acc": 0.60172, "loss_cls": 3.7635, "loss": 3.7635, "time": 0.82349} +{"mode": "train", "epoch": 91, "iter": 2600, "lr": 0.03386, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34969, "top5_acc": 0.60469, "loss_cls": 3.70291, "loss": 3.70291, "time": 0.82263} +{"mode": "train", "epoch": 91, "iter": 2700, "lr": 0.03383, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34859, "top5_acc": 0.61062, "loss_cls": 3.68112, "loss": 3.68112, "time": 0.82223} +{"mode": "train", "epoch": 91, "iter": 2800, "lr": 0.03381, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35828, "top5_acc": 0.61016, "loss_cls": 3.66798, "loss": 3.66798, "time": 0.8254} +{"mode": "train", "epoch": 91, "iter": 2900, "lr": 0.03378, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35859, "top5_acc": 0.60906, "loss_cls": 3.67118, "loss": 3.67118, "time": 0.81822} +{"mode": "train", "epoch": 91, "iter": 3000, "lr": 0.03375, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35375, "top5_acc": 0.615, "loss_cls": 3.68559, "loss": 3.68559, "time": 0.8206} +{"mode": "train", "epoch": 91, "iter": 3100, "lr": 0.03373, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34422, "top5_acc": 0.60656, "loss_cls": 3.72415, "loss": 3.72415, "time": 0.81281} +{"mode": "train", "epoch": 91, "iter": 3200, "lr": 0.0337, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34656, "top5_acc": 0.60188, "loss_cls": 3.71023, "loss": 3.71023, "time": 0.81514} +{"mode": "train", "epoch": 91, "iter": 3300, "lr": 0.03367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34453, "top5_acc": 0.60484, "loss_cls": 3.71894, "loss": 3.71894, "time": 0.81308} +{"mode": "train", "epoch": 91, "iter": 3400, "lr": 0.03365, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35875, "top5_acc": 0.61594, "loss_cls": 3.66787, "loss": 3.66787, "time": 0.82128} +{"mode": "train", "epoch": 91, "iter": 3500, "lr": 0.03362, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35297, "top5_acc": 0.60453, "loss_cls": 3.71816, "loss": 3.71816, "time": 0.82119} +{"mode": "train", "epoch": 91, "iter": 3600, "lr": 0.0336, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35641, "top5_acc": 0.61203, "loss_cls": 3.68373, "loss": 3.68373, "time": 0.82139} +{"mode": "train", "epoch": 91, "iter": 3700, "lr": 0.03357, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33984, "top5_acc": 0.60531, "loss_cls": 3.733, "loss": 3.733, "time": 0.82808} +{"mode": "val", "epoch": 91, "iter": 309, "lr": 0.03356, "top1_acc": 0.29712, "top5_acc": 0.54612, "mean_class_accuracy": 0.29693} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.03353, "memory": 15990, "data_time": 1.36447, "top1_acc": 0.36953, "top5_acc": 0.63453, "loss_cls": 3.56739, "loss": 3.56739, "time": 2.35076} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.0335, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36312, "top5_acc": 0.61391, "loss_cls": 3.64494, "loss": 3.64494, "time": 0.82105} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.03348, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35906, "top5_acc": 0.62031, "loss_cls": 3.6236, "loss": 3.6236, "time": 0.81665} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.03345, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36797, "top5_acc": 0.62219, "loss_cls": 3.5973, "loss": 3.5973, "time": 0.81459} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.03342, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36281, "top5_acc": 0.62656, "loss_cls": 3.6029, "loss": 3.6029, "time": 0.81447} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.0334, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36438, "top5_acc": 0.62828, "loss_cls": 3.61424, "loss": 3.61424, "time": 0.81755} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.03337, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35672, "top5_acc": 0.61625, "loss_cls": 3.65215, "loss": 3.65215, "time": 0.81465} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.03335, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36609, "top5_acc": 0.61562, "loss_cls": 3.63485, "loss": 3.63485, "time": 0.81733} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.03332, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34766, "top5_acc": 0.60328, "loss_cls": 3.70934, "loss": 3.70934, "time": 0.81842} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.03329, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35906, "top5_acc": 0.61422, "loss_cls": 3.64731, "loss": 3.64731, "time": 0.82084} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.03327, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35719, "top5_acc": 0.62062, "loss_cls": 3.66143, "loss": 3.66143, "time": 0.81439} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.03324, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34844, "top5_acc": 0.60656, "loss_cls": 3.68714, "loss": 3.68714, "time": 0.81083} +{"mode": "train", "epoch": 92, "iter": 1300, "lr": 0.03321, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36172, "top5_acc": 0.61406, "loss_cls": 3.65463, "loss": 3.65463, "time": 0.81852} +{"mode": "train", "epoch": 92, "iter": 1400, "lr": 0.03319, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35062, "top5_acc": 0.61687, "loss_cls": 3.66402, "loss": 3.66402, "time": 0.81815} +{"mode": "train", "epoch": 92, "iter": 1500, "lr": 0.03316, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35984, "top5_acc": 0.62016, "loss_cls": 3.63275, "loss": 3.63275, "time": 0.82094} +{"mode": "train", "epoch": 92, "iter": 1600, "lr": 0.03314, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35141, "top5_acc": 0.60922, "loss_cls": 3.68502, "loss": 3.68502, "time": 0.81702} +{"mode": "train", "epoch": 92, "iter": 1700, "lr": 0.03311, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35797, "top5_acc": 0.61828, "loss_cls": 3.63447, "loss": 3.63447, "time": 0.82515} +{"mode": "train", "epoch": 92, "iter": 1800, "lr": 0.03308, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35781, "top5_acc": 0.61562, "loss_cls": 3.64747, "loss": 3.64747, "time": 0.82302} +{"mode": "train", "epoch": 92, "iter": 1900, "lr": 0.03306, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34641, "top5_acc": 0.60844, "loss_cls": 3.66005, "loss": 3.66005, "time": 0.82022} +{"mode": "train", "epoch": 92, "iter": 2000, "lr": 0.03303, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34375, "top5_acc": 0.61203, "loss_cls": 3.69964, "loss": 3.69964, "time": 0.81858} +{"mode": "train", "epoch": 92, "iter": 2100, "lr": 0.033, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35391, "top5_acc": 0.60891, "loss_cls": 3.6788, "loss": 3.6788, "time": 0.81543} +{"mode": "train", "epoch": 92, "iter": 2200, "lr": 0.03298, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35156, "top5_acc": 0.60812, "loss_cls": 3.6869, "loss": 3.6869, "time": 0.82082} +{"mode": "train", "epoch": 92, "iter": 2300, "lr": 0.03295, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35234, "top5_acc": 0.60812, "loss_cls": 3.6961, "loss": 3.6961, "time": 0.81374} +{"mode": "train", "epoch": 92, "iter": 2400, "lr": 0.03292, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34172, "top5_acc": 0.60297, "loss_cls": 3.74594, "loss": 3.74594, "time": 0.81578} +{"mode": "train", "epoch": 92, "iter": 2500, "lr": 0.0329, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35, "top5_acc": 0.61266, "loss_cls": 3.68024, "loss": 3.68024, "time": 0.82853} +{"mode": "train", "epoch": 92, "iter": 2600, "lr": 0.03287, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35406, "top5_acc": 0.60891, "loss_cls": 3.66979, "loss": 3.66979, "time": 0.81652} +{"mode": "train", "epoch": 92, "iter": 2700, "lr": 0.03285, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35297, "top5_acc": 0.61156, "loss_cls": 3.69666, "loss": 3.69666, "time": 0.8198} +{"mode": "train", "epoch": 92, "iter": 2800, "lr": 0.03282, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34547, "top5_acc": 0.60766, "loss_cls": 3.69667, "loss": 3.69667, "time": 0.82373} +{"mode": "train", "epoch": 92, "iter": 2900, "lr": 0.03279, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35422, "top5_acc": 0.60828, "loss_cls": 3.69272, "loss": 3.69272, "time": 0.81645} +{"mode": "train", "epoch": 92, "iter": 3000, "lr": 0.03277, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35734, "top5_acc": 0.61828, "loss_cls": 3.63934, "loss": 3.63934, "time": 0.81337} +{"mode": "train", "epoch": 92, "iter": 3100, "lr": 0.03274, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34578, "top5_acc": 0.60516, "loss_cls": 3.73178, "loss": 3.73178, "time": 0.81838} +{"mode": "train", "epoch": 92, "iter": 3200, "lr": 0.03271, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35297, "top5_acc": 0.61297, "loss_cls": 3.6653, "loss": 3.6653, "time": 0.81511} +{"mode": "train", "epoch": 92, "iter": 3300, "lr": 0.03269, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35062, "top5_acc": 0.60562, "loss_cls": 3.71494, "loss": 3.71494, "time": 0.82236} +{"mode": "train", "epoch": 92, "iter": 3400, "lr": 0.03266, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35031, "top5_acc": 0.60594, "loss_cls": 3.68726, "loss": 3.68726, "time": 0.81818} +{"mode": "train", "epoch": 92, "iter": 3500, "lr": 0.03264, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35859, "top5_acc": 0.615, "loss_cls": 3.6545, "loss": 3.6545, "time": 0.82639} +{"mode": "train", "epoch": 92, "iter": 3600, "lr": 0.03261, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34688, "top5_acc": 0.60594, "loss_cls": 3.72416, "loss": 3.72416, "time": 0.81936} +{"mode": "train", "epoch": 92, "iter": 3700, "lr": 0.03258, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35344, "top5_acc": 0.61078, "loss_cls": 3.7107, "loss": 3.7107, "time": 0.81692} +{"mode": "val", "epoch": 92, "iter": 309, "lr": 0.03257, "top1_acc": 0.29413, "top5_acc": 0.55463, "mean_class_accuracy": 0.29373} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.03255, "memory": 15990, "data_time": 1.32168, "top1_acc": 0.36922, "top5_acc": 0.63438, "loss_cls": 3.55893, "loss": 3.55893, "time": 2.31377} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.03252, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36188, "top5_acc": 0.62125, "loss_cls": 3.60505, "loss": 3.60505, "time": 0.81854} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.03249, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37922, "top5_acc": 0.63125, "loss_cls": 3.52963, "loss": 3.52963, "time": 0.81603} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.03247, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36406, "top5_acc": 0.62531, "loss_cls": 3.60051, "loss": 3.60051, "time": 0.81887} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.03244, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35516, "top5_acc": 0.61641, "loss_cls": 3.61923, "loss": 3.61923, "time": 0.82143} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.03241, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36672, "top5_acc": 0.62391, "loss_cls": 3.61837, "loss": 3.61837, "time": 0.81538} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.03239, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36641, "top5_acc": 0.61297, "loss_cls": 3.62997, "loss": 3.62997, "time": 0.81455} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.03236, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36734, "top5_acc": 0.62453, "loss_cls": 3.59431, "loss": 3.59431, "time": 0.81379} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.03234, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35562, "top5_acc": 0.61375, "loss_cls": 3.65802, "loss": 3.65802, "time": 0.81737} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.03231, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35641, "top5_acc": 0.62344, "loss_cls": 3.622, "loss": 3.622, "time": 0.81518} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.03228, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36484, "top5_acc": 0.62953, "loss_cls": 3.6172, "loss": 3.6172, "time": 0.82127} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.03226, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34953, "top5_acc": 0.61094, "loss_cls": 3.69718, "loss": 3.69718, "time": 0.81663} +{"mode": "train", "epoch": 93, "iter": 1300, "lr": 0.03223, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34812, "top5_acc": 0.61984, "loss_cls": 3.66086, "loss": 3.66086, "time": 0.81402} +{"mode": "train", "epoch": 93, "iter": 1400, "lr": 0.03221, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35531, "top5_acc": 0.6125, "loss_cls": 3.69976, "loss": 3.69976, "time": 0.8205} +{"mode": "train", "epoch": 93, "iter": 1500, "lr": 0.03218, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36625, "top5_acc": 0.61734, "loss_cls": 3.64937, "loss": 3.64937, "time": 0.8209} +{"mode": "train", "epoch": 93, "iter": 1600, "lr": 0.03215, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34656, "top5_acc": 0.60859, "loss_cls": 3.69086, "loss": 3.69086, "time": 0.81561} +{"mode": "train", "epoch": 93, "iter": 1700, "lr": 0.03213, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34891, "top5_acc": 0.61453, "loss_cls": 3.6716, "loss": 3.6716, "time": 0.82398} +{"mode": "train", "epoch": 93, "iter": 1800, "lr": 0.0321, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35781, "top5_acc": 0.62234, "loss_cls": 3.65092, "loss": 3.65092, "time": 0.82379} +{"mode": "train", "epoch": 93, "iter": 1900, "lr": 0.03207, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35953, "top5_acc": 0.62531, "loss_cls": 3.62512, "loss": 3.62512, "time": 0.81479} +{"mode": "train", "epoch": 93, "iter": 2000, "lr": 0.03205, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35078, "top5_acc": 0.61469, "loss_cls": 3.64801, "loss": 3.64801, "time": 0.81607} +{"mode": "train", "epoch": 93, "iter": 2100, "lr": 0.03202, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35453, "top5_acc": 0.62422, "loss_cls": 3.68274, "loss": 3.68274, "time": 0.81603} +{"mode": "train", "epoch": 93, "iter": 2200, "lr": 0.032, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3525, "top5_acc": 0.61672, "loss_cls": 3.67114, "loss": 3.67114, "time": 0.81931} +{"mode": "train", "epoch": 93, "iter": 2300, "lr": 0.03197, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35578, "top5_acc": 0.6125, "loss_cls": 3.6659, "loss": 3.6659, "time": 0.81719} +{"mode": "train", "epoch": 93, "iter": 2400, "lr": 0.03194, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35359, "top5_acc": 0.61187, "loss_cls": 3.673, "loss": 3.673, "time": 0.81562} +{"mode": "train", "epoch": 93, "iter": 2500, "lr": 0.03192, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35312, "top5_acc": 0.60688, "loss_cls": 3.70239, "loss": 3.70239, "time": 0.81737} +{"mode": "train", "epoch": 93, "iter": 2600, "lr": 0.03189, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35156, "top5_acc": 0.61453, "loss_cls": 3.66367, "loss": 3.66367, "time": 0.81988} +{"mode": "train", "epoch": 93, "iter": 2700, "lr": 0.03187, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3475, "top5_acc": 0.61031, "loss_cls": 3.71442, "loss": 3.71442, "time": 0.81866} +{"mode": "train", "epoch": 93, "iter": 2800, "lr": 0.03184, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36453, "top5_acc": 0.61969, "loss_cls": 3.62829, "loss": 3.62829, "time": 0.82185} +{"mode": "train", "epoch": 93, "iter": 2900, "lr": 0.03181, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36266, "top5_acc": 0.62453, "loss_cls": 3.60262, "loss": 3.60262, "time": 0.81939} +{"mode": "train", "epoch": 93, "iter": 3000, "lr": 0.03179, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35062, "top5_acc": 0.61469, "loss_cls": 3.6778, "loss": 3.6778, "time": 0.81585} +{"mode": "train", "epoch": 93, "iter": 3100, "lr": 0.03176, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35484, "top5_acc": 0.61938, "loss_cls": 3.66766, "loss": 3.66766, "time": 0.81288} +{"mode": "train", "epoch": 93, "iter": 3200, "lr": 0.03174, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35609, "top5_acc": 0.62094, "loss_cls": 3.62475, "loss": 3.62475, "time": 0.81302} +{"mode": "train", "epoch": 93, "iter": 3300, "lr": 0.03171, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35625, "top5_acc": 0.62016, "loss_cls": 3.66099, "loss": 3.66099, "time": 0.82112} +{"mode": "train", "epoch": 93, "iter": 3400, "lr": 0.03168, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35516, "top5_acc": 0.61359, "loss_cls": 3.69197, "loss": 3.69197, "time": 0.81604} +{"mode": "train", "epoch": 93, "iter": 3500, "lr": 0.03166, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3575, "top5_acc": 0.61531, "loss_cls": 3.67193, "loss": 3.67193, "time": 0.82551} +{"mode": "train", "epoch": 93, "iter": 3600, "lr": 0.03163, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34797, "top5_acc": 0.60188, "loss_cls": 3.71556, "loss": 3.71556, "time": 0.82393} +{"mode": "train", "epoch": 93, "iter": 3700, "lr": 0.03161, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36234, "top5_acc": 0.61656, "loss_cls": 3.64798, "loss": 3.64798, "time": 0.8196} +{"mode": "val", "epoch": 93, "iter": 309, "lr": 0.03159, "top1_acc": 0.29276, "top5_acc": 0.54809, "mean_class_accuracy": 0.29243} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.03157, "memory": 15990, "data_time": 1.31216, "top1_acc": 0.36812, "top5_acc": 0.62938, "loss_cls": 3.55407, "loss": 3.55407, "time": 2.30617} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.03154, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36781, "top5_acc": 0.63266, "loss_cls": 3.56711, "loss": 3.56711, "time": 0.83121} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.03152, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36688, "top5_acc": 0.63703, "loss_cls": 3.57733, "loss": 3.57733, "time": 0.81594} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.03149, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36656, "top5_acc": 0.62781, "loss_cls": 3.60269, "loss": 3.60269, "time": 0.81304} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.03146, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36828, "top5_acc": 0.62469, "loss_cls": 3.57421, "loss": 3.57421, "time": 0.81419} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.03144, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36344, "top5_acc": 0.62016, "loss_cls": 3.63428, "loss": 3.63428, "time": 0.81439} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.03141, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36344, "top5_acc": 0.62281, "loss_cls": 3.62228, "loss": 3.62228, "time": 0.82254} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.03139, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37141, "top5_acc": 0.62125, "loss_cls": 3.58886, "loss": 3.58886, "time": 0.81561} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.03136, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35672, "top5_acc": 0.61562, "loss_cls": 3.66222, "loss": 3.66222, "time": 0.81545} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.03133, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34844, "top5_acc": 0.61641, "loss_cls": 3.67396, "loss": 3.67396, "time": 0.81889} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.03131, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36016, "top5_acc": 0.61828, "loss_cls": 3.64327, "loss": 3.64327, "time": 0.81879} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.03128, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35266, "top5_acc": 0.61312, "loss_cls": 3.65474, "loss": 3.65474, "time": 0.81689} +{"mode": "train", "epoch": 94, "iter": 1300, "lr": 0.03126, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35125, "top5_acc": 0.60641, "loss_cls": 3.70026, "loss": 3.70026, "time": 0.81464} +{"mode": "train", "epoch": 94, "iter": 1400, "lr": 0.03123, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37062, "top5_acc": 0.63141, "loss_cls": 3.57729, "loss": 3.57729, "time": 0.81478} +{"mode": "train", "epoch": 94, "iter": 1500, "lr": 0.0312, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36594, "top5_acc": 0.61984, "loss_cls": 3.62285, "loss": 3.62285, "time": 0.81806} +{"mode": "train", "epoch": 94, "iter": 1600, "lr": 0.03118, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36578, "top5_acc": 0.62094, "loss_cls": 3.61332, "loss": 3.61332, "time": 0.81325} +{"mode": "train", "epoch": 94, "iter": 1700, "lr": 0.03115, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35453, "top5_acc": 0.60547, "loss_cls": 3.69088, "loss": 3.69088, "time": 0.82309} +{"mode": "train", "epoch": 94, "iter": 1800, "lr": 0.03113, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35109, "top5_acc": 0.60359, "loss_cls": 3.69981, "loss": 3.69981, "time": 0.82073} +{"mode": "train", "epoch": 94, "iter": 1900, "lr": 0.0311, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37141, "top5_acc": 0.62203, "loss_cls": 3.59364, "loss": 3.59364, "time": 0.82426} +{"mode": "train", "epoch": 94, "iter": 2000, "lr": 0.03108, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35906, "top5_acc": 0.61703, "loss_cls": 3.64741, "loss": 3.64741, "time": 0.82232} +{"mode": "train", "epoch": 94, "iter": 2100, "lr": 0.03105, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35703, "top5_acc": 0.60969, "loss_cls": 3.6596, "loss": 3.6596, "time": 0.81421} +{"mode": "train", "epoch": 94, "iter": 2200, "lr": 0.03102, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36312, "top5_acc": 0.60938, "loss_cls": 3.67857, "loss": 3.67857, "time": 0.81961} +{"mode": "train", "epoch": 94, "iter": 2300, "lr": 0.031, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36031, "top5_acc": 0.625, "loss_cls": 3.61633, "loss": 3.61633, "time": 0.81085} +{"mode": "train", "epoch": 94, "iter": 2400, "lr": 0.03097, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35188, "top5_acc": 0.61922, "loss_cls": 3.68101, "loss": 3.68101, "time": 0.81256} +{"mode": "train", "epoch": 94, "iter": 2500, "lr": 0.03095, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36391, "top5_acc": 0.61906, "loss_cls": 3.61903, "loss": 3.61903, "time": 0.82025} +{"mode": "train", "epoch": 94, "iter": 2600, "lr": 0.03092, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35969, "top5_acc": 0.62047, "loss_cls": 3.64461, "loss": 3.64461, "time": 0.81854} +{"mode": "train", "epoch": 94, "iter": 2700, "lr": 0.03089, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35328, "top5_acc": 0.61687, "loss_cls": 3.66631, "loss": 3.66631, "time": 0.82064} +{"mode": "train", "epoch": 94, "iter": 2800, "lr": 0.03087, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35312, "top5_acc": 0.61562, "loss_cls": 3.67543, "loss": 3.67543, "time": 0.81484} +{"mode": "train", "epoch": 94, "iter": 2900, "lr": 0.03084, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37219, "top5_acc": 0.62313, "loss_cls": 3.60719, "loss": 3.60719, "time": 0.81374} +{"mode": "train", "epoch": 94, "iter": 3000, "lr": 0.03082, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35922, "top5_acc": 0.61922, "loss_cls": 3.66295, "loss": 3.66295, "time": 0.81739} +{"mode": "train", "epoch": 94, "iter": 3100, "lr": 0.03079, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35156, "top5_acc": 0.60797, "loss_cls": 3.68388, "loss": 3.68388, "time": 0.81429} +{"mode": "train", "epoch": 94, "iter": 3200, "lr": 0.03077, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35672, "top5_acc": 0.61938, "loss_cls": 3.63265, "loss": 3.63265, "time": 0.81148} +{"mode": "train", "epoch": 94, "iter": 3300, "lr": 0.03074, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36281, "top5_acc": 0.61016, "loss_cls": 3.67728, "loss": 3.67728, "time": 0.82572} +{"mode": "train", "epoch": 94, "iter": 3400, "lr": 0.03071, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36188, "top5_acc": 0.61922, "loss_cls": 3.66611, "loss": 3.66611, "time": 0.81498} +{"mode": "train", "epoch": 94, "iter": 3500, "lr": 0.03069, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35906, "top5_acc": 0.61297, "loss_cls": 3.64688, "loss": 3.64688, "time": 0.81637} +{"mode": "train", "epoch": 94, "iter": 3600, "lr": 0.03066, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35328, "top5_acc": 0.60859, "loss_cls": 3.66159, "loss": 3.66159, "time": 0.82176} +{"mode": "train", "epoch": 94, "iter": 3700, "lr": 0.03064, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36125, "top5_acc": 0.61562, "loss_cls": 3.64053, "loss": 3.64053, "time": 0.82152} +{"mode": "val", "epoch": 94, "iter": 309, "lr": 0.03062, "top1_acc": 0.29114, "top5_acc": 0.54536, "mean_class_accuracy": 0.29096} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.0306, "memory": 15990, "data_time": 1.28891, "top1_acc": 0.36609, "top5_acc": 0.62656, "loss_cls": 3.56063, "loss": 3.56063, "time": 2.26897} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.03057, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37172, "top5_acc": 0.63453, "loss_cls": 3.56571, "loss": 3.56571, "time": 0.82399} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.03055, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36875, "top5_acc": 0.62766, "loss_cls": 3.6108, "loss": 3.6108, "time": 0.81745} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.03052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36953, "top5_acc": 0.62891, "loss_cls": 3.56531, "loss": 3.56531, "time": 0.81946} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.0305, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37188, "top5_acc": 0.63344, "loss_cls": 3.55749, "loss": 3.55749, "time": 0.81473} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.03047, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35453, "top5_acc": 0.61375, "loss_cls": 3.62205, "loss": 3.62205, "time": 0.81929} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.03044, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.355, "top5_acc": 0.62219, "loss_cls": 3.59852, "loss": 3.59852, "time": 0.81833} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.03042, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36797, "top5_acc": 0.6325, "loss_cls": 3.58052, "loss": 3.58052, "time": 0.81307} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.03039, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36453, "top5_acc": 0.61828, "loss_cls": 3.62827, "loss": 3.62827, "time": 0.81766} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.03037, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36734, "top5_acc": 0.625, "loss_cls": 3.59738, "loss": 3.59738, "time": 0.81926} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.03034, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36172, "top5_acc": 0.61359, "loss_cls": 3.66361, "loss": 3.66361, "time": 0.81811} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.03032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35953, "top5_acc": 0.61812, "loss_cls": 3.64774, "loss": 3.64774, "time": 0.81571} +{"mode": "train", "epoch": 95, "iter": 1300, "lr": 0.03029, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37156, "top5_acc": 0.62984, "loss_cls": 3.56055, "loss": 3.56055, "time": 0.82102} +{"mode": "train", "epoch": 95, "iter": 1400, "lr": 0.03026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36719, "top5_acc": 0.61922, "loss_cls": 3.62855, "loss": 3.62855, "time": 0.82441} +{"mode": "train", "epoch": 95, "iter": 1500, "lr": 0.03024, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36781, "top5_acc": 0.62687, "loss_cls": 3.57473, "loss": 3.57473, "time": 0.82615} +{"mode": "train", "epoch": 95, "iter": 1600, "lr": 0.03021, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36266, "top5_acc": 0.62094, "loss_cls": 3.63442, "loss": 3.63442, "time": 0.81716} +{"mode": "train", "epoch": 95, "iter": 1700, "lr": 0.03019, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36344, "top5_acc": 0.62125, "loss_cls": 3.63235, "loss": 3.63235, "time": 0.82007} +{"mode": "train", "epoch": 95, "iter": 1800, "lr": 0.03016, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37, "top5_acc": 0.62797, "loss_cls": 3.60724, "loss": 3.60724, "time": 0.82343} +{"mode": "train", "epoch": 95, "iter": 1900, "lr": 0.03014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36984, "top5_acc": 0.62609, "loss_cls": 3.58954, "loss": 3.58954, "time": 0.81953} +{"mode": "train", "epoch": 95, "iter": 2000, "lr": 0.03011, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36016, "top5_acc": 0.62047, "loss_cls": 3.66854, "loss": 3.66854, "time": 0.81837} +{"mode": "train", "epoch": 95, "iter": 2100, "lr": 0.03008, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36188, "top5_acc": 0.62078, "loss_cls": 3.61409, "loss": 3.61409, "time": 0.81544} +{"mode": "train", "epoch": 95, "iter": 2200, "lr": 0.03006, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34391, "top5_acc": 0.61187, "loss_cls": 3.66857, "loss": 3.66857, "time": 0.81327} +{"mode": "train", "epoch": 95, "iter": 2300, "lr": 0.03003, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36406, "top5_acc": 0.62656, "loss_cls": 3.61445, "loss": 3.61445, "time": 0.81576} +{"mode": "train", "epoch": 95, "iter": 2400, "lr": 0.03001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36047, "top5_acc": 0.61906, "loss_cls": 3.65151, "loss": 3.65151, "time": 0.81663} +{"mode": "train", "epoch": 95, "iter": 2500, "lr": 0.02998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36156, "top5_acc": 0.62344, "loss_cls": 3.60604, "loss": 3.60604, "time": 0.81722} +{"mode": "train", "epoch": 95, "iter": 2600, "lr": 0.02996, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34859, "top5_acc": 0.60781, "loss_cls": 3.66728, "loss": 3.66728, "time": 0.82028} +{"mode": "train", "epoch": 95, "iter": 2700, "lr": 0.02993, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36516, "top5_acc": 0.62562, "loss_cls": 3.60443, "loss": 3.60443, "time": 0.81824} +{"mode": "train", "epoch": 95, "iter": 2800, "lr": 0.02991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36031, "top5_acc": 0.60922, "loss_cls": 3.67359, "loss": 3.67359, "time": 0.818} +{"mode": "train", "epoch": 95, "iter": 2900, "lr": 0.02988, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36656, "top5_acc": 0.62922, "loss_cls": 3.59443, "loss": 3.59443, "time": 0.81596} +{"mode": "train", "epoch": 95, "iter": 3000, "lr": 0.02985, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35453, "top5_acc": 0.61016, "loss_cls": 3.69136, "loss": 3.69136, "time": 0.82109} +{"mode": "train", "epoch": 95, "iter": 3100, "lr": 0.02983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36875, "top5_acc": 0.61578, "loss_cls": 3.65381, "loss": 3.65381, "time": 0.8177} +{"mode": "train", "epoch": 95, "iter": 3200, "lr": 0.0298, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35375, "top5_acc": 0.61828, "loss_cls": 3.65255, "loss": 3.65255, "time": 0.82267} +{"mode": "train", "epoch": 95, "iter": 3300, "lr": 0.02978, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36469, "top5_acc": 0.61969, "loss_cls": 3.63018, "loss": 3.63018, "time": 0.81436} +{"mode": "train", "epoch": 95, "iter": 3400, "lr": 0.02975, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37469, "top5_acc": 0.63047, "loss_cls": 3.56202, "loss": 3.56202, "time": 0.81695} +{"mode": "train", "epoch": 95, "iter": 3500, "lr": 0.02973, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35828, "top5_acc": 0.61375, "loss_cls": 3.67817, "loss": 3.67817, "time": 0.82035} +{"mode": "train", "epoch": 95, "iter": 3600, "lr": 0.0297, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36547, "top5_acc": 0.62906, "loss_cls": 3.61387, "loss": 3.61387, "time": 0.82297} +{"mode": "train", "epoch": 95, "iter": 3700, "lr": 0.02968, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.355, "top5_acc": 0.61797, "loss_cls": 3.63474, "loss": 3.63474, "time": 0.81936} +{"mode": "val", "epoch": 95, "iter": 309, "lr": 0.02966, "top1_acc": 0.30193, "top5_acc": 0.5526, "mean_class_accuracy": 0.30169} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.02964, "memory": 15990, "data_time": 1.26201, "top1_acc": 0.37344, "top5_acc": 0.63594, "loss_cls": 3.54252, "loss": 3.54252, "time": 2.24652} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.02961, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3825, "top5_acc": 0.63094, "loss_cls": 3.54292, "loss": 3.54292, "time": 0.82871} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.02959, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37578, "top5_acc": 0.62922, "loss_cls": 3.53948, "loss": 3.53948, "time": 0.81621} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.02956, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37266, "top5_acc": 0.63438, "loss_cls": 3.54766, "loss": 3.54766, "time": 0.81869} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.02954, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36359, "top5_acc": 0.6325, "loss_cls": 3.57671, "loss": 3.57671, "time": 0.82349} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.02951, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37094, "top5_acc": 0.61922, "loss_cls": 3.58637, "loss": 3.58637, "time": 0.81677} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.02948, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36344, "top5_acc": 0.62328, "loss_cls": 3.60608, "loss": 3.60608, "time": 0.82456} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.02946, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36469, "top5_acc": 0.61781, "loss_cls": 3.63565, "loss": 3.63565, "time": 0.81156} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.02943, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36875, "top5_acc": 0.62359, "loss_cls": 3.61933, "loss": 3.61933, "time": 0.81322} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.02941, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3625, "top5_acc": 0.63031, "loss_cls": 3.55552, "loss": 3.55552, "time": 0.82505} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.02938, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36656, "top5_acc": 0.63594, "loss_cls": 3.56008, "loss": 3.56008, "time": 0.82214} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.02936, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37031, "top5_acc": 0.63656, "loss_cls": 3.5412, "loss": 3.5412, "time": 0.81776} +{"mode": "train", "epoch": 96, "iter": 1300, "lr": 0.02933, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35609, "top5_acc": 0.61922, "loss_cls": 3.61589, "loss": 3.61589, "time": 0.81517} +{"mode": "train", "epoch": 96, "iter": 1400, "lr": 0.02931, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36516, "top5_acc": 0.62844, "loss_cls": 3.58929, "loss": 3.58929, "time": 0.81689} +{"mode": "train", "epoch": 96, "iter": 1500, "lr": 0.02928, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35766, "top5_acc": 0.62672, "loss_cls": 3.60908, "loss": 3.60908, "time": 0.82518} +{"mode": "train", "epoch": 96, "iter": 1600, "lr": 0.02926, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37297, "top5_acc": 0.62344, "loss_cls": 3.6036, "loss": 3.6036, "time": 0.81918} +{"mode": "train", "epoch": 96, "iter": 1700, "lr": 0.02923, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36312, "top5_acc": 0.62813, "loss_cls": 3.57667, "loss": 3.57667, "time": 0.81786} +{"mode": "train", "epoch": 96, "iter": 1800, "lr": 0.0292, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36594, "top5_acc": 0.62344, "loss_cls": 3.62423, "loss": 3.62423, "time": 0.82674} +{"mode": "train", "epoch": 96, "iter": 1900, "lr": 0.02918, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.355, "top5_acc": 0.62234, "loss_cls": 3.65172, "loss": 3.65172, "time": 0.81888} +{"mode": "train", "epoch": 96, "iter": 2000, "lr": 0.02915, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35719, "top5_acc": 0.63141, "loss_cls": 3.61488, "loss": 3.61488, "time": 0.81896} +{"mode": "train", "epoch": 96, "iter": 2100, "lr": 0.02913, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36938, "top5_acc": 0.62422, "loss_cls": 3.59739, "loss": 3.59739, "time": 0.82323} +{"mode": "train", "epoch": 96, "iter": 2200, "lr": 0.0291, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36703, "top5_acc": 0.62, "loss_cls": 3.59917, "loss": 3.59917, "time": 0.82155} +{"mode": "train", "epoch": 96, "iter": 2300, "lr": 0.02908, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36594, "top5_acc": 0.63188, "loss_cls": 3.60066, "loss": 3.60066, "time": 0.81482} +{"mode": "train", "epoch": 96, "iter": 2400, "lr": 0.02905, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35453, "top5_acc": 0.61547, "loss_cls": 3.6381, "loss": 3.6381, "time": 0.81531} +{"mode": "train", "epoch": 96, "iter": 2500, "lr": 0.02903, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34938, "top5_acc": 0.61094, "loss_cls": 3.6571, "loss": 3.6571, "time": 0.82408} +{"mode": "train", "epoch": 96, "iter": 2600, "lr": 0.029, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36078, "top5_acc": 0.61266, "loss_cls": 3.64198, "loss": 3.64198, "time": 0.81425} +{"mode": "train", "epoch": 96, "iter": 2700, "lr": 0.02898, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36641, "top5_acc": 0.62, "loss_cls": 3.62616, "loss": 3.62616, "time": 0.81873} +{"mode": "train", "epoch": 96, "iter": 2800, "lr": 0.02895, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36594, "top5_acc": 0.61969, "loss_cls": 3.60823, "loss": 3.60823, "time": 0.81286} +{"mode": "train", "epoch": 96, "iter": 2900, "lr": 0.02893, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35969, "top5_acc": 0.62297, "loss_cls": 3.61718, "loss": 3.61718, "time": 0.81161} +{"mode": "train", "epoch": 96, "iter": 3000, "lr": 0.0289, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37047, "top5_acc": 0.62891, "loss_cls": 3.58607, "loss": 3.58607, "time": 0.81794} +{"mode": "train", "epoch": 96, "iter": 3100, "lr": 0.02887, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36453, "top5_acc": 0.62219, "loss_cls": 3.60351, "loss": 3.60351, "time": 0.81635} +{"mode": "train", "epoch": 96, "iter": 3200, "lr": 0.02885, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35719, "top5_acc": 0.6225, "loss_cls": 3.64474, "loss": 3.64474, "time": 0.82932} +{"mode": "train", "epoch": 96, "iter": 3300, "lr": 0.02882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35844, "top5_acc": 0.61516, "loss_cls": 3.65192, "loss": 3.65192, "time": 0.81194} +{"mode": "train", "epoch": 96, "iter": 3400, "lr": 0.0288, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36703, "top5_acc": 0.61422, "loss_cls": 3.60456, "loss": 3.60456, "time": 0.81763} +{"mode": "train", "epoch": 96, "iter": 3500, "lr": 0.02877, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36344, "top5_acc": 0.61422, "loss_cls": 3.62866, "loss": 3.62866, "time": 0.81399} +{"mode": "train", "epoch": 96, "iter": 3600, "lr": 0.02875, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36375, "top5_acc": 0.62625, "loss_cls": 3.6054, "loss": 3.6054, "time": 0.81964} +{"mode": "train", "epoch": 96, "iter": 3700, "lr": 0.02872, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36406, "top5_acc": 0.62453, "loss_cls": 3.62699, "loss": 3.62699, "time": 0.82235} +{"mode": "val", "epoch": 96, "iter": 309, "lr": 0.02871, "top1_acc": 0.30441, "top5_acc": 0.55564, "mean_class_accuracy": 0.3042} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.02869, "memory": 15990, "data_time": 1.30355, "top1_acc": 0.37609, "top5_acc": 0.63219, "loss_cls": 3.52605, "loss": 3.52605, "time": 2.285} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.02866, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38172, "top5_acc": 0.63141, "loss_cls": 3.52137, "loss": 3.52137, "time": 0.8221} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.02864, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37297, "top5_acc": 0.63953, "loss_cls": 3.55412, "loss": 3.55412, "time": 0.81719} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.02861, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38, "top5_acc": 0.63188, "loss_cls": 3.57392, "loss": 3.57392, "time": 0.81432} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.02858, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36531, "top5_acc": 0.6325, "loss_cls": 3.58972, "loss": 3.58972, "time": 0.81936} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.02856, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37, "top5_acc": 0.62531, "loss_cls": 3.58641, "loss": 3.58641, "time": 0.81782} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.02853, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36141, "top5_acc": 0.63, "loss_cls": 3.58115, "loss": 3.58115, "time": 0.81701} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.02851, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36812, "top5_acc": 0.62609, "loss_cls": 3.57605, "loss": 3.57605, "time": 0.82751} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.02848, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37844, "top5_acc": 0.63703, "loss_cls": 3.51949, "loss": 3.51949, "time": 0.8179} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.02846, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37141, "top5_acc": 0.63234, "loss_cls": 3.56833, "loss": 3.56833, "time": 0.8156} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.02843, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37359, "top5_acc": 0.63188, "loss_cls": 3.56545, "loss": 3.56545, "time": 0.81313} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.02841, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36797, "top5_acc": 0.62672, "loss_cls": 3.56162, "loss": 3.56162, "time": 0.81671} +{"mode": "train", "epoch": 97, "iter": 1300, "lr": 0.02838, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37578, "top5_acc": 0.63156, "loss_cls": 3.56113, "loss": 3.56113, "time": 0.81384} +{"mode": "train", "epoch": 97, "iter": 1400, "lr": 0.02836, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36781, "top5_acc": 0.62156, "loss_cls": 3.57892, "loss": 3.57892, "time": 0.81554} +{"mode": "train", "epoch": 97, "iter": 1500, "lr": 0.02833, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35719, "top5_acc": 0.61422, "loss_cls": 3.63098, "loss": 3.63098, "time": 0.82115} +{"mode": "train", "epoch": 97, "iter": 1600, "lr": 0.02831, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37688, "top5_acc": 0.63266, "loss_cls": 3.56076, "loss": 3.56076, "time": 0.81935} +{"mode": "train", "epoch": 97, "iter": 1700, "lr": 0.02828, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36344, "top5_acc": 0.62609, "loss_cls": 3.60218, "loss": 3.60218, "time": 0.82015} +{"mode": "train", "epoch": 97, "iter": 1800, "lr": 0.02826, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36312, "top5_acc": 0.62438, "loss_cls": 3.61969, "loss": 3.61969, "time": 0.81957} +{"mode": "train", "epoch": 97, "iter": 1900, "lr": 0.02823, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37047, "top5_acc": 0.62797, "loss_cls": 3.57434, "loss": 3.57434, "time": 0.81498} +{"mode": "train", "epoch": 97, "iter": 2000, "lr": 0.02821, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36672, "top5_acc": 0.61719, "loss_cls": 3.61034, "loss": 3.61034, "time": 0.81788} +{"mode": "train", "epoch": 97, "iter": 2100, "lr": 0.02818, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36641, "top5_acc": 0.62531, "loss_cls": 3.57806, "loss": 3.57806, "time": 0.81251} +{"mode": "train", "epoch": 97, "iter": 2200, "lr": 0.02816, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35953, "top5_acc": 0.62203, "loss_cls": 3.61698, "loss": 3.61698, "time": 0.81628} +{"mode": "train", "epoch": 97, "iter": 2300, "lr": 0.02813, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35641, "top5_acc": 0.61625, "loss_cls": 3.64341, "loss": 3.64341, "time": 0.81054} +{"mode": "train", "epoch": 97, "iter": 2400, "lr": 0.02811, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36516, "top5_acc": 0.62047, "loss_cls": 3.63172, "loss": 3.63172, "time": 0.82754} +{"mode": "train", "epoch": 97, "iter": 2500, "lr": 0.02808, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.36812, "top5_acc": 0.63078, "loss_cls": 3.57401, "loss": 3.57401, "time": 0.82335} +{"mode": "train", "epoch": 97, "iter": 2600, "lr": 0.02806, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36781, "top5_acc": 0.62813, "loss_cls": 3.58335, "loss": 3.58335, "time": 0.8215} +{"mode": "train", "epoch": 97, "iter": 2700, "lr": 0.02803, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36, "top5_acc": 0.62062, "loss_cls": 3.62297, "loss": 3.62297, "time": 0.82461} +{"mode": "train", "epoch": 97, "iter": 2800, "lr": 0.02801, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36234, "top5_acc": 0.61734, "loss_cls": 3.6294, "loss": 3.6294, "time": 0.81725} +{"mode": "train", "epoch": 97, "iter": 2900, "lr": 0.02798, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37266, "top5_acc": 0.63406, "loss_cls": 3.54447, "loss": 3.54447, "time": 0.81475} +{"mode": "train", "epoch": 97, "iter": 3000, "lr": 0.02796, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36984, "top5_acc": 0.63031, "loss_cls": 3.59011, "loss": 3.59011, "time": 0.81129} +{"mode": "train", "epoch": 97, "iter": 3100, "lr": 0.02793, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35906, "top5_acc": 0.61578, "loss_cls": 3.65377, "loss": 3.65377, "time": 0.81866} +{"mode": "train", "epoch": 97, "iter": 3200, "lr": 0.02791, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36281, "top5_acc": 0.61719, "loss_cls": 3.61471, "loss": 3.61471, "time": 0.82195} +{"mode": "train", "epoch": 97, "iter": 3300, "lr": 0.02788, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37984, "top5_acc": 0.63375, "loss_cls": 3.55167, "loss": 3.55167, "time": 0.82002} +{"mode": "train", "epoch": 97, "iter": 3400, "lr": 0.02786, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35734, "top5_acc": 0.6175, "loss_cls": 3.65629, "loss": 3.65629, "time": 0.81367} +{"mode": "train", "epoch": 97, "iter": 3500, "lr": 0.02783, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36219, "top5_acc": 0.62266, "loss_cls": 3.62092, "loss": 3.62092, "time": 0.82496} +{"mode": "train", "epoch": 97, "iter": 3600, "lr": 0.02781, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36656, "top5_acc": 0.62781, "loss_cls": 3.57668, "loss": 3.57668, "time": 0.82011} +{"mode": "train", "epoch": 97, "iter": 3700, "lr": 0.02778, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35328, "top5_acc": 0.62844, "loss_cls": 3.60206, "loss": 3.60206, "time": 0.82388} +{"mode": "val", "epoch": 97, "iter": 309, "lr": 0.02777, "top1_acc": 0.31338, "top5_acc": 0.57129, "mean_class_accuracy": 0.3131} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.02774, "memory": 15990, "data_time": 1.36658, "top1_acc": 0.38516, "top5_acc": 0.64078, "loss_cls": 3.49271, "loss": 3.49271, "time": 2.37468} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.02772, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38344, "top5_acc": 0.63766, "loss_cls": 3.51772, "loss": 3.51772, "time": 0.84329} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.02769, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37281, "top5_acc": 0.63891, "loss_cls": 3.53821, "loss": 3.53821, "time": 0.84377} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.02767, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38109, "top5_acc": 0.63359, "loss_cls": 3.54499, "loss": 3.54499, "time": 0.84124} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.02764, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36641, "top5_acc": 0.62562, "loss_cls": 3.59913, "loss": 3.59913, "time": 0.84562} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.02762, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36594, "top5_acc": 0.63469, "loss_cls": 3.54802, "loss": 3.54802, "time": 0.84146} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.02759, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37984, "top5_acc": 0.63516, "loss_cls": 3.54899, "loss": 3.54899, "time": 0.84322} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.02757, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36516, "top5_acc": 0.63953, "loss_cls": 3.55528, "loss": 3.55528, "time": 0.84039} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.02754, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37016, "top5_acc": 0.63328, "loss_cls": 3.56576, "loss": 3.56576, "time": 0.84868} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.02752, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36828, "top5_acc": 0.63234, "loss_cls": 3.56211, "loss": 3.56211, "time": 0.8439} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.02749, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36703, "top5_acc": 0.63891, "loss_cls": 3.53981, "loss": 3.53981, "time": 0.84472} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.02747, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37453, "top5_acc": 0.62781, "loss_cls": 3.56965, "loss": 3.56965, "time": 0.84392} +{"mode": "train", "epoch": 98, "iter": 1300, "lr": 0.02744, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36438, "top5_acc": 0.63281, "loss_cls": 3.57361, "loss": 3.57361, "time": 0.83838} +{"mode": "train", "epoch": 98, "iter": 1400, "lr": 0.02742, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36031, "top5_acc": 0.61828, "loss_cls": 3.61099, "loss": 3.61099, "time": 0.82839} +{"mode": "train", "epoch": 98, "iter": 1500, "lr": 0.02739, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37859, "top5_acc": 0.63938, "loss_cls": 3.50796, "loss": 3.50796, "time": 0.82877} +{"mode": "train", "epoch": 98, "iter": 1600, "lr": 0.02737, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37953, "top5_acc": 0.64328, "loss_cls": 3.49513, "loss": 3.49513, "time": 0.81694} +{"mode": "train", "epoch": 98, "iter": 1700, "lr": 0.02734, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37609, "top5_acc": 0.63141, "loss_cls": 3.53005, "loss": 3.53005, "time": 0.83245} +{"mode": "train", "epoch": 98, "iter": 1800, "lr": 0.02732, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36969, "top5_acc": 0.62859, "loss_cls": 3.57094, "loss": 3.57094, "time": 0.82152} +{"mode": "train", "epoch": 98, "iter": 1900, "lr": 0.02729, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36688, "top5_acc": 0.62766, "loss_cls": 3.59492, "loss": 3.59492, "time": 0.81848} +{"mode": "train", "epoch": 98, "iter": 2000, "lr": 0.02727, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35266, "top5_acc": 0.61891, "loss_cls": 3.63543, "loss": 3.63543, "time": 0.81688} +{"mode": "train", "epoch": 98, "iter": 2100, "lr": 0.02724, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36531, "top5_acc": 0.62609, "loss_cls": 3.59264, "loss": 3.59264, "time": 0.81339} +{"mode": "train", "epoch": 98, "iter": 2200, "lr": 0.02722, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34969, "top5_acc": 0.61781, "loss_cls": 3.64827, "loss": 3.64827, "time": 0.81538} +{"mode": "train", "epoch": 98, "iter": 2300, "lr": 0.02719, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37234, "top5_acc": 0.62266, "loss_cls": 3.59157, "loss": 3.59157, "time": 0.8124} +{"mode": "train", "epoch": 98, "iter": 2400, "lr": 0.02717, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37766, "top5_acc": 0.63703, "loss_cls": 3.52629, "loss": 3.52629, "time": 0.82067} +{"mode": "train", "epoch": 98, "iter": 2500, "lr": 0.02714, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37078, "top5_acc": 0.63438, "loss_cls": 3.53463, "loss": 3.53463, "time": 0.81173} +{"mode": "train", "epoch": 98, "iter": 2600, "lr": 0.02712, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37188, "top5_acc": 0.63031, "loss_cls": 3.57462, "loss": 3.57462, "time": 0.82073} +{"mode": "train", "epoch": 98, "iter": 2700, "lr": 0.02709, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36547, "top5_acc": 0.62609, "loss_cls": 3.6013, "loss": 3.6013, "time": 0.81639} +{"mode": "train", "epoch": 98, "iter": 2800, "lr": 0.02707, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36469, "top5_acc": 0.62484, "loss_cls": 3.62058, "loss": 3.62058, "time": 0.81932} +{"mode": "train", "epoch": 98, "iter": 2900, "lr": 0.02705, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37312, "top5_acc": 0.62531, "loss_cls": 3.57535, "loss": 3.57535, "time": 0.82149} +{"mode": "train", "epoch": 98, "iter": 3000, "lr": 0.02702, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36766, "top5_acc": 0.63, "loss_cls": 3.5984, "loss": 3.5984, "time": 0.81191} +{"mode": "train", "epoch": 98, "iter": 3100, "lr": 0.027, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36547, "top5_acc": 0.63094, "loss_cls": 3.56937, "loss": 3.56937, "time": 0.81119} +{"mode": "train", "epoch": 98, "iter": 3200, "lr": 0.02697, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36422, "top5_acc": 0.63547, "loss_cls": 3.57319, "loss": 3.57319, "time": 0.8147} +{"mode": "train", "epoch": 98, "iter": 3300, "lr": 0.02695, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36281, "top5_acc": 0.62187, "loss_cls": 3.59628, "loss": 3.59628, "time": 0.81823} +{"mode": "train", "epoch": 98, "iter": 3400, "lr": 0.02692, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36797, "top5_acc": 0.63125, "loss_cls": 3.58543, "loss": 3.58543, "time": 0.81518} +{"mode": "train", "epoch": 98, "iter": 3500, "lr": 0.0269, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36594, "top5_acc": 0.62391, "loss_cls": 3.61534, "loss": 3.61534, "time": 0.81488} +{"mode": "train", "epoch": 98, "iter": 3600, "lr": 0.02687, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37094, "top5_acc": 0.63562, "loss_cls": 3.57533, "loss": 3.57533, "time": 0.82464} +{"mode": "train", "epoch": 98, "iter": 3700, "lr": 0.02685, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36828, "top5_acc": 0.62031, "loss_cls": 3.6124, "loss": 3.6124, "time": 0.82071} +{"mode": "val", "epoch": 98, "iter": 309, "lr": 0.02684, "top1_acc": 0.31626, "top5_acc": 0.56552, "mean_class_accuracy": 0.31604} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.02681, "memory": 15990, "data_time": 1.32466, "top1_acc": 0.375, "top5_acc": 0.64203, "loss_cls": 3.52022, "loss": 3.52022, "time": 2.31663} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.02679, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3875, "top5_acc": 0.64828, "loss_cls": 3.45396, "loss": 3.45396, "time": 0.81907} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.02676, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37328, "top5_acc": 0.63875, "loss_cls": 3.51975, "loss": 3.51975, "time": 0.81711} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.02674, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38156, "top5_acc": 0.63781, "loss_cls": 3.51617, "loss": 3.51617, "time": 0.81941} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.02671, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37344, "top5_acc": 0.63578, "loss_cls": 3.5561, "loss": 3.5561, "time": 0.81969} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.02669, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37, "top5_acc": 0.64312, "loss_cls": 3.52431, "loss": 3.52431, "time": 0.81844} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.02666, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37406, "top5_acc": 0.62969, "loss_cls": 3.55714, "loss": 3.55714, "time": 0.81851} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.02664, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37078, "top5_acc": 0.63984, "loss_cls": 3.5418, "loss": 3.5418, "time": 0.81328} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.02661, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36672, "top5_acc": 0.63297, "loss_cls": 3.55599, "loss": 3.55599, "time": 0.81256} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.02659, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36484, "top5_acc": 0.62, "loss_cls": 3.6159, "loss": 3.6159, "time": 0.82632} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.02656, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37859, "top5_acc": 0.63641, "loss_cls": 3.54152, "loss": 3.54152, "time": 0.82397} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.02654, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37688, "top5_acc": 0.63828, "loss_cls": 3.51153, "loss": 3.51153, "time": 0.82831} +{"mode": "train", "epoch": 99, "iter": 1300, "lr": 0.02651, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37078, "top5_acc": 0.62656, "loss_cls": 3.55309, "loss": 3.55309, "time": 0.81319} +{"mode": "train", "epoch": 99, "iter": 1400, "lr": 0.02649, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37359, "top5_acc": 0.63359, "loss_cls": 3.55855, "loss": 3.55855, "time": 0.81277} +{"mode": "train", "epoch": 99, "iter": 1500, "lr": 0.02646, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36219, "top5_acc": 0.62547, "loss_cls": 3.58757, "loss": 3.58757, "time": 0.82086} +{"mode": "train", "epoch": 99, "iter": 1600, "lr": 0.02644, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37203, "top5_acc": 0.62719, "loss_cls": 3.56497, "loss": 3.56497, "time": 0.82164} +{"mode": "train", "epoch": 99, "iter": 1700, "lr": 0.02642, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37656, "top5_acc": 0.64016, "loss_cls": 3.52768, "loss": 3.52768, "time": 0.82734} +{"mode": "train", "epoch": 99, "iter": 1800, "lr": 0.02639, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37344, "top5_acc": 0.63547, "loss_cls": 3.55771, "loss": 3.55771, "time": 0.81654} +{"mode": "train", "epoch": 99, "iter": 1900, "lr": 0.02637, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36688, "top5_acc": 0.62562, "loss_cls": 3.55251, "loss": 3.55251, "time": 0.81652} +{"mode": "train", "epoch": 99, "iter": 2000, "lr": 0.02634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37047, "top5_acc": 0.63156, "loss_cls": 3.58544, "loss": 3.58544, "time": 0.81962} +{"mode": "train", "epoch": 99, "iter": 2100, "lr": 0.02632, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37109, "top5_acc": 0.63141, "loss_cls": 3.57894, "loss": 3.57894, "time": 0.81345} +{"mode": "train", "epoch": 99, "iter": 2200, "lr": 0.02629, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37359, "top5_acc": 0.62813, "loss_cls": 3.57577, "loss": 3.57577, "time": 0.8142} +{"mode": "train", "epoch": 99, "iter": 2300, "lr": 0.02627, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36859, "top5_acc": 0.62391, "loss_cls": 3.58269, "loss": 3.58269, "time": 0.81148} +{"mode": "train", "epoch": 99, "iter": 2400, "lr": 0.02624, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38203, "top5_acc": 0.63578, "loss_cls": 3.52411, "loss": 3.52411, "time": 0.81957} +{"mode": "train", "epoch": 99, "iter": 2500, "lr": 0.02622, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3775, "top5_acc": 0.63609, "loss_cls": 3.55345, "loss": 3.55345, "time": 0.81756} +{"mode": "train", "epoch": 99, "iter": 2600, "lr": 0.02619, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37594, "top5_acc": 0.63016, "loss_cls": 3.54913, "loss": 3.54913, "time": 0.81762} +{"mode": "train", "epoch": 99, "iter": 2700, "lr": 0.02617, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37094, "top5_acc": 0.63281, "loss_cls": 3.56893, "loss": 3.56893, "time": 0.81441} +{"mode": "train", "epoch": 99, "iter": 2800, "lr": 0.02614, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36828, "top5_acc": 0.62547, "loss_cls": 3.58051, "loss": 3.58051, "time": 0.814} +{"mode": "train", "epoch": 99, "iter": 2900, "lr": 0.02612, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36516, "top5_acc": 0.61594, "loss_cls": 3.61934, "loss": 3.61934, "time": 0.81872} +{"mode": "train", "epoch": 99, "iter": 3000, "lr": 0.0261, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37359, "top5_acc": 0.63438, "loss_cls": 3.5268, "loss": 3.5268, "time": 0.81542} +{"mode": "train", "epoch": 99, "iter": 3100, "lr": 0.02607, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37891, "top5_acc": 0.63297, "loss_cls": 3.56224, "loss": 3.56224, "time": 0.81523} +{"mode": "train", "epoch": 99, "iter": 3200, "lr": 0.02605, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36203, "top5_acc": 0.62594, "loss_cls": 3.60279, "loss": 3.60279, "time": 0.81515} +{"mode": "train", "epoch": 99, "iter": 3300, "lr": 0.02602, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38078, "top5_acc": 0.64078, "loss_cls": 3.51769, "loss": 3.51769, "time": 0.81903} +{"mode": "train", "epoch": 99, "iter": 3400, "lr": 0.026, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37578, "top5_acc": 0.63797, "loss_cls": 3.53119, "loss": 3.53119, "time": 0.81529} +{"mode": "train", "epoch": 99, "iter": 3500, "lr": 0.02597, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36859, "top5_acc": 0.62469, "loss_cls": 3.57832, "loss": 3.57832, "time": 0.81772} +{"mode": "train", "epoch": 99, "iter": 3600, "lr": 0.02595, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36094, "top5_acc": 0.62969, "loss_cls": 3.581, "loss": 3.581, "time": 0.81998} +{"mode": "train", "epoch": 99, "iter": 3700, "lr": 0.02592, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36797, "top5_acc": 0.62938, "loss_cls": 3.59727, "loss": 3.59727, "time": 0.82923} +{"mode": "val", "epoch": 99, "iter": 309, "lr": 0.02591, "top1_acc": 0.31267, "top5_acc": 0.5682, "mean_class_accuracy": 0.31245} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.02589, "memory": 15990, "data_time": 1.39155, "top1_acc": 0.38047, "top5_acc": 0.65062, "loss_cls": 3.46569, "loss": 3.46569, "time": 2.39132} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.02586, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37844, "top5_acc": 0.64203, "loss_cls": 3.48377, "loss": 3.48377, "time": 0.82685} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.02584, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38562, "top5_acc": 0.64156, "loss_cls": 3.46766, "loss": 3.46766, "time": 0.8219} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.02581, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38203, "top5_acc": 0.64609, "loss_cls": 3.48186, "loss": 3.48186, "time": 0.82019} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.02579, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38172, "top5_acc": 0.64391, "loss_cls": 3.49795, "loss": 3.49795, "time": 0.82492} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.02577, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37562, "top5_acc": 0.64031, "loss_cls": 3.53238, "loss": 3.53238, "time": 0.82016} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.02574, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36766, "top5_acc": 0.63578, "loss_cls": 3.55406, "loss": 3.55406, "time": 0.81453} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.02572, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37672, "top5_acc": 0.63172, "loss_cls": 3.5272, "loss": 3.5272, "time": 0.82208} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.02569, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38297, "top5_acc": 0.64391, "loss_cls": 3.48407, "loss": 3.48407, "time": 0.81711} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.02567, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37672, "top5_acc": 0.64781, "loss_cls": 3.50252, "loss": 3.50252, "time": 0.81638} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.02564, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37281, "top5_acc": 0.63391, "loss_cls": 3.54386, "loss": 3.54386, "time": 0.81288} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.02562, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37781, "top5_acc": 0.62594, "loss_cls": 3.54435, "loss": 3.54435, "time": 0.81436} +{"mode": "train", "epoch": 100, "iter": 1300, "lr": 0.02559, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37594, "top5_acc": 0.63406, "loss_cls": 3.54375, "loss": 3.54375, "time": 0.81619} +{"mode": "train", "epoch": 100, "iter": 1400, "lr": 0.02557, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37672, "top5_acc": 0.6375, "loss_cls": 3.55616, "loss": 3.55616, "time": 0.81701} +{"mode": "train", "epoch": 100, "iter": 1500, "lr": 0.02555, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38172, "top5_acc": 0.6375, "loss_cls": 3.512, "loss": 3.512, "time": 0.83049} +{"mode": "train", "epoch": 100, "iter": 1600, "lr": 0.02552, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38062, "top5_acc": 0.62531, "loss_cls": 3.5539, "loss": 3.5539, "time": 0.82389} +{"mode": "train", "epoch": 100, "iter": 1700, "lr": 0.0255, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37453, "top5_acc": 0.62516, "loss_cls": 3.57017, "loss": 3.57017, "time": 0.82452} +{"mode": "train", "epoch": 100, "iter": 1800, "lr": 0.02547, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38172, "top5_acc": 0.63891, "loss_cls": 3.5237, "loss": 3.5237, "time": 0.81829} +{"mode": "train", "epoch": 100, "iter": 1900, "lr": 0.02545, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35719, "top5_acc": 0.61906, "loss_cls": 3.59363, "loss": 3.59363, "time": 0.81985} +{"mode": "train", "epoch": 100, "iter": 2000, "lr": 0.02542, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37703, "top5_acc": 0.63453, "loss_cls": 3.50848, "loss": 3.50848, "time": 0.82213} +{"mode": "train", "epoch": 100, "iter": 2100, "lr": 0.0254, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37438, "top5_acc": 0.63141, "loss_cls": 3.56367, "loss": 3.56367, "time": 0.81874} +{"mode": "train", "epoch": 100, "iter": 2200, "lr": 0.02538, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37422, "top5_acc": 0.64391, "loss_cls": 3.50306, "loss": 3.50306, "time": 0.81306} +{"mode": "train", "epoch": 100, "iter": 2300, "lr": 0.02535, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37641, "top5_acc": 0.63141, "loss_cls": 3.56068, "loss": 3.56068, "time": 0.81621} +{"mode": "train", "epoch": 100, "iter": 2400, "lr": 0.02533, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36875, "top5_acc": 0.63109, "loss_cls": 3.56679, "loss": 3.56679, "time": 0.82766} +{"mode": "train", "epoch": 100, "iter": 2500, "lr": 0.0253, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38219, "top5_acc": 0.64422, "loss_cls": 3.52182, "loss": 3.52182, "time": 0.82844} +{"mode": "train", "epoch": 100, "iter": 2600, "lr": 0.02528, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37344, "top5_acc": 0.63906, "loss_cls": 3.52006, "loss": 3.52006, "time": 0.81683} +{"mode": "train", "epoch": 100, "iter": 2700, "lr": 0.02525, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37203, "top5_acc": 0.63531, "loss_cls": 3.55211, "loss": 3.55211, "time": 0.81584} +{"mode": "train", "epoch": 100, "iter": 2800, "lr": 0.02523, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36984, "top5_acc": 0.63969, "loss_cls": 3.55026, "loss": 3.55026, "time": 0.82045} +{"mode": "train", "epoch": 100, "iter": 2900, "lr": 0.02521, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36891, "top5_acc": 0.62016, "loss_cls": 3.58903, "loss": 3.58903, "time": 0.81592} +{"mode": "train", "epoch": 100, "iter": 3000, "lr": 0.02518, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37219, "top5_acc": 0.62875, "loss_cls": 3.56449, "loss": 3.56449, "time": 0.8176} +{"mode": "train", "epoch": 100, "iter": 3100, "lr": 0.02516, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37453, "top5_acc": 0.63578, "loss_cls": 3.53135, "loss": 3.53135, "time": 0.81838} +{"mode": "train", "epoch": 100, "iter": 3200, "lr": 0.02513, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37703, "top5_acc": 0.63375, "loss_cls": 3.54583, "loss": 3.54583, "time": 0.81679} +{"mode": "train", "epoch": 100, "iter": 3300, "lr": 0.02511, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36797, "top5_acc": 0.62719, "loss_cls": 3.58938, "loss": 3.58938, "time": 0.81828} +{"mode": "train", "epoch": 100, "iter": 3400, "lr": 0.02508, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37656, "top5_acc": 0.63203, "loss_cls": 3.55059, "loss": 3.55059, "time": 0.82069} +{"mode": "train", "epoch": 100, "iter": 3500, "lr": 0.02506, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37484, "top5_acc": 0.62172, "loss_cls": 3.55914, "loss": 3.55914, "time": 0.81485} +{"mode": "train", "epoch": 100, "iter": 3600, "lr": 0.02504, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36922, "top5_acc": 0.61984, "loss_cls": 3.6089, "loss": 3.6089, "time": 0.81985} +{"mode": "train", "epoch": 100, "iter": 3700, "lr": 0.02501, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37719, "top5_acc": 0.63062, "loss_cls": 3.5409, "loss": 3.5409, "time": 0.82196} +{"mode": "val", "epoch": 100, "iter": 309, "lr": 0.025, "top1_acc": 0.31302, "top5_acc": 0.56486, "mean_class_accuracy": 0.31273} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.02498, "memory": 15990, "data_time": 1.29268, "top1_acc": 0.38156, "top5_acc": 0.64406, "loss_cls": 3.46374, "loss": 3.46374, "time": 2.28979} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.02495, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38953, "top5_acc": 0.65203, "loss_cls": 3.44957, "loss": 3.44957, "time": 0.82186} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.02493, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38922, "top5_acc": 0.65594, "loss_cls": 3.4395, "loss": 3.4395, "time": 0.8141} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.0249, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3825, "top5_acc": 0.63344, "loss_cls": 3.53744, "loss": 3.53744, "time": 0.81561} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.02488, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37547, "top5_acc": 0.63313, "loss_cls": 3.52397, "loss": 3.52397, "time": 0.81918} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.02486, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38047, "top5_acc": 0.63281, "loss_cls": 3.54028, "loss": 3.54028, "time": 0.81692} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.02483, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38188, "top5_acc": 0.64375, "loss_cls": 3.47898, "loss": 3.47898, "time": 0.81987} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.02481, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37719, "top5_acc": 0.64031, "loss_cls": 3.49381, "loss": 3.49381, "time": 0.81813} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.02478, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38531, "top5_acc": 0.64453, "loss_cls": 3.49314, "loss": 3.49314, "time": 0.81468} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.02476, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37625, "top5_acc": 0.64016, "loss_cls": 3.50081, "loss": 3.50081, "time": 0.81647} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.02473, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36891, "top5_acc": 0.62766, "loss_cls": 3.5684, "loss": 3.5684, "time": 0.81412} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.02471, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37812, "top5_acc": 0.63703, "loss_cls": 3.50414, "loss": 3.50414, "time": 0.81451} +{"mode": "train", "epoch": 101, "iter": 1300, "lr": 0.02469, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38422, "top5_acc": 0.64031, "loss_cls": 3.47648, "loss": 3.47648, "time": 0.81418} +{"mode": "train", "epoch": 101, "iter": 1400, "lr": 0.02466, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37531, "top5_acc": 0.63156, "loss_cls": 3.53699, "loss": 3.53699, "time": 0.81765} +{"mode": "train", "epoch": 101, "iter": 1500, "lr": 0.02464, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37812, "top5_acc": 0.64141, "loss_cls": 3.49865, "loss": 3.49865, "time": 0.82174} +{"mode": "train", "epoch": 101, "iter": 1600, "lr": 0.02461, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37359, "top5_acc": 0.63172, "loss_cls": 3.53576, "loss": 3.53576, "time": 0.82073} +{"mode": "train", "epoch": 101, "iter": 1700, "lr": 0.02459, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.38141, "top5_acc": 0.64281, "loss_cls": 3.52273, "loss": 3.52273, "time": 0.83057} +{"mode": "train", "epoch": 101, "iter": 1800, "lr": 0.02457, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37562, "top5_acc": 0.6375, "loss_cls": 3.52314, "loss": 3.52314, "time": 0.82199} +{"mode": "train", "epoch": 101, "iter": 1900, "lr": 0.02454, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37812, "top5_acc": 0.64109, "loss_cls": 3.52305, "loss": 3.52305, "time": 0.81935} +{"mode": "train", "epoch": 101, "iter": 2000, "lr": 0.02452, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38062, "top5_acc": 0.63891, "loss_cls": 3.51477, "loss": 3.51477, "time": 0.81869} +{"mode": "train", "epoch": 101, "iter": 2100, "lr": 0.02449, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37172, "top5_acc": 0.63594, "loss_cls": 3.54761, "loss": 3.54761, "time": 0.81991} +{"mode": "train", "epoch": 101, "iter": 2200, "lr": 0.02447, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36922, "top5_acc": 0.63031, "loss_cls": 3.57018, "loss": 3.57018, "time": 0.81299} +{"mode": "train", "epoch": 101, "iter": 2300, "lr": 0.02445, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38422, "top5_acc": 0.64344, "loss_cls": 3.47372, "loss": 3.47372, "time": 0.82046} +{"mode": "train", "epoch": 101, "iter": 2400, "lr": 0.02442, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37391, "top5_acc": 0.63641, "loss_cls": 3.56172, "loss": 3.56172, "time": 0.82255} +{"mode": "train", "epoch": 101, "iter": 2500, "lr": 0.0244, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37781, "top5_acc": 0.63656, "loss_cls": 3.53873, "loss": 3.53873, "time": 0.82642} +{"mode": "train", "epoch": 101, "iter": 2600, "lr": 0.02437, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36719, "top5_acc": 0.62984, "loss_cls": 3.56924, "loss": 3.56924, "time": 0.81994} +{"mode": "train", "epoch": 101, "iter": 2700, "lr": 0.02435, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37, "top5_acc": 0.63984, "loss_cls": 3.53793, "loss": 3.53793, "time": 0.8197} +{"mode": "train", "epoch": 101, "iter": 2800, "lr": 0.02433, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38219, "top5_acc": 0.63875, "loss_cls": 3.51349, "loss": 3.51349, "time": 0.81655} +{"mode": "train", "epoch": 101, "iter": 2900, "lr": 0.0243, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37219, "top5_acc": 0.64047, "loss_cls": 3.51795, "loss": 3.51795, "time": 0.82284} +{"mode": "train", "epoch": 101, "iter": 3000, "lr": 0.02428, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38781, "top5_acc": 0.64703, "loss_cls": 3.46263, "loss": 3.46263, "time": 0.81584} +{"mode": "train", "epoch": 101, "iter": 3100, "lr": 0.02425, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37562, "top5_acc": 0.63062, "loss_cls": 3.57896, "loss": 3.57896, "time": 0.81497} +{"mode": "train", "epoch": 101, "iter": 3200, "lr": 0.02423, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37109, "top5_acc": 0.63625, "loss_cls": 3.54802, "loss": 3.54802, "time": 0.81962} +{"mode": "train", "epoch": 101, "iter": 3300, "lr": 0.02421, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38734, "top5_acc": 0.64953, "loss_cls": 3.47567, "loss": 3.47567, "time": 0.81336} +{"mode": "train", "epoch": 101, "iter": 3400, "lr": 0.02418, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37109, "top5_acc": 0.63391, "loss_cls": 3.56127, "loss": 3.56127, "time": 0.81736} +{"mode": "train", "epoch": 101, "iter": 3500, "lr": 0.02416, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37766, "top5_acc": 0.63516, "loss_cls": 3.53137, "loss": 3.53137, "time": 0.81401} +{"mode": "train", "epoch": 101, "iter": 3600, "lr": 0.02413, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3675, "top5_acc": 0.62578, "loss_cls": 3.58475, "loss": 3.58475, "time": 0.81732} +{"mode": "train", "epoch": 101, "iter": 3700, "lr": 0.02411, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.375, "top5_acc": 0.62578, "loss_cls": 3.54193, "loss": 3.54193, "time": 0.81573} +{"mode": "val", "epoch": 101, "iter": 309, "lr": 0.0241, "top1_acc": 0.31474, "top5_acc": 0.57392, "mean_class_accuracy": 0.31441} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.02407, "memory": 15990, "data_time": 1.30195, "top1_acc": 0.40047, "top5_acc": 0.66, "loss_cls": 3.41191, "loss": 3.41191, "time": 2.27996} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.02405, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38828, "top5_acc": 0.65281, "loss_cls": 3.43125, "loss": 3.43125, "time": 0.81598} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.02403, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39547, "top5_acc": 0.65219, "loss_cls": 3.45183, "loss": 3.45183, "time": 0.818} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.024, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38203, "top5_acc": 0.62922, "loss_cls": 3.53734, "loss": 3.53734, "time": 0.81851} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.02398, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39172, "top5_acc": 0.65469, "loss_cls": 3.4387, "loss": 3.4387, "time": 0.81346} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.02396, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38219, "top5_acc": 0.64781, "loss_cls": 3.47044, "loss": 3.47044, "time": 0.81695} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.02393, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37547, "top5_acc": 0.62922, "loss_cls": 3.53682, "loss": 3.53682, "time": 0.81806} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.02391, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38609, "top5_acc": 0.64953, "loss_cls": 3.44153, "loss": 3.44153, "time": 0.81601} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.02388, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37594, "top5_acc": 0.635, "loss_cls": 3.51112, "loss": 3.51112, "time": 0.81677} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.02386, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39078, "top5_acc": 0.64875, "loss_cls": 3.4861, "loss": 3.4861, "time": 0.81551} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.02384, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38391, "top5_acc": 0.64344, "loss_cls": 3.49678, "loss": 3.49678, "time": 0.82144} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.02381, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38906, "top5_acc": 0.64328, "loss_cls": 3.48075, "loss": 3.48075, "time": 0.8145} +{"mode": "train", "epoch": 102, "iter": 1300, "lr": 0.02379, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37875, "top5_acc": 0.63406, "loss_cls": 3.53704, "loss": 3.53704, "time": 0.81262} +{"mode": "train", "epoch": 102, "iter": 1400, "lr": 0.02376, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38078, "top5_acc": 0.6325, "loss_cls": 3.52132, "loss": 3.52132, "time": 0.8198} +{"mode": "train", "epoch": 102, "iter": 1500, "lr": 0.02374, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3925, "top5_acc": 0.64188, "loss_cls": 3.45716, "loss": 3.45716, "time": 0.82107} +{"mode": "train", "epoch": 102, "iter": 1600, "lr": 0.02372, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37688, "top5_acc": 0.64312, "loss_cls": 3.50648, "loss": 3.50648, "time": 0.81791} +{"mode": "train", "epoch": 102, "iter": 1700, "lr": 0.02369, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37609, "top5_acc": 0.63438, "loss_cls": 3.55302, "loss": 3.55302, "time": 0.82182} +{"mode": "train", "epoch": 102, "iter": 1800, "lr": 0.02367, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37922, "top5_acc": 0.63578, "loss_cls": 3.50449, "loss": 3.50449, "time": 0.82406} +{"mode": "train", "epoch": 102, "iter": 1900, "lr": 0.02365, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38328, "top5_acc": 0.64625, "loss_cls": 3.47803, "loss": 3.47803, "time": 0.81748} +{"mode": "train", "epoch": 102, "iter": 2000, "lr": 0.02362, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37844, "top5_acc": 0.63578, "loss_cls": 3.52363, "loss": 3.52363, "time": 0.81867} +{"mode": "train", "epoch": 102, "iter": 2100, "lr": 0.0236, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38094, "top5_acc": 0.64422, "loss_cls": 3.51151, "loss": 3.51151, "time": 0.81672} +{"mode": "train", "epoch": 102, "iter": 2200, "lr": 0.02357, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3725, "top5_acc": 0.63625, "loss_cls": 3.52003, "loss": 3.52003, "time": 0.81759} +{"mode": "train", "epoch": 102, "iter": 2300, "lr": 0.02355, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37484, "top5_acc": 0.63938, "loss_cls": 3.53587, "loss": 3.53587, "time": 0.81143} +{"mode": "train", "epoch": 102, "iter": 2400, "lr": 0.02353, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37734, "top5_acc": 0.63875, "loss_cls": 3.5315, "loss": 3.5315, "time": 0.81683} +{"mode": "train", "epoch": 102, "iter": 2500, "lr": 0.0235, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39016, "top5_acc": 0.64594, "loss_cls": 3.45066, "loss": 3.45066, "time": 0.81668} +{"mode": "train", "epoch": 102, "iter": 2600, "lr": 0.02348, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37812, "top5_acc": 0.64016, "loss_cls": 3.52184, "loss": 3.52184, "time": 0.82437} +{"mode": "train", "epoch": 102, "iter": 2700, "lr": 0.02346, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36344, "top5_acc": 0.63203, "loss_cls": 3.56834, "loss": 3.56834, "time": 0.8165} +{"mode": "train", "epoch": 102, "iter": 2800, "lr": 0.02343, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37734, "top5_acc": 0.63797, "loss_cls": 3.50793, "loss": 3.50793, "time": 0.81829} +{"mode": "train", "epoch": 102, "iter": 2900, "lr": 0.02341, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38078, "top5_acc": 0.6425, "loss_cls": 3.50489, "loss": 3.50489, "time": 0.81498} +{"mode": "train", "epoch": 102, "iter": 3000, "lr": 0.02339, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38234, "top5_acc": 0.63656, "loss_cls": 3.50239, "loss": 3.50239, "time": 0.81214} +{"mode": "train", "epoch": 102, "iter": 3100, "lr": 0.02336, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37953, "top5_acc": 0.63188, "loss_cls": 3.55067, "loss": 3.55067, "time": 0.81669} +{"mode": "train", "epoch": 102, "iter": 3200, "lr": 0.02334, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37547, "top5_acc": 0.63562, "loss_cls": 3.53929, "loss": 3.53929, "time": 0.8132} +{"mode": "train", "epoch": 102, "iter": 3300, "lr": 0.02331, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37969, "top5_acc": 0.63484, "loss_cls": 3.52772, "loss": 3.52772, "time": 0.81319} +{"mode": "train", "epoch": 102, "iter": 3400, "lr": 0.02329, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3775, "top5_acc": 0.64141, "loss_cls": 3.50827, "loss": 3.50827, "time": 0.814} +{"mode": "train", "epoch": 102, "iter": 3500, "lr": 0.02327, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37844, "top5_acc": 0.63906, "loss_cls": 3.52023, "loss": 3.52023, "time": 0.81914} +{"mode": "train", "epoch": 102, "iter": 3600, "lr": 0.02324, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38141, "top5_acc": 0.64359, "loss_cls": 3.49207, "loss": 3.49207, "time": 0.81653} +{"mode": "train", "epoch": 102, "iter": 3700, "lr": 0.02322, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37453, "top5_acc": 0.63359, "loss_cls": 3.5172, "loss": 3.5172, "time": 0.81578} +{"mode": "val", "epoch": 102, "iter": 309, "lr": 0.02321, "top1_acc": 0.32077, "top5_acc": 0.57838, "mean_class_accuracy": 0.32063} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.02319, "memory": 15990, "data_time": 1.32024, "top1_acc": 0.38906, "top5_acc": 0.65562, "loss_cls": 3.437, "loss": 3.437, "time": 2.29709} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.02316, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37594, "top5_acc": 0.635, "loss_cls": 3.50914, "loss": 3.50914, "time": 0.81541} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.02314, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38766, "top5_acc": 0.64547, "loss_cls": 3.43477, "loss": 3.43477, "time": 0.81857} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.02311, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39531, "top5_acc": 0.65297, "loss_cls": 3.41605, "loss": 3.41605, "time": 0.81386} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.02309, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37969, "top5_acc": 0.64922, "loss_cls": 3.47705, "loss": 3.47705, "time": 0.81946} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.02307, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38109, "top5_acc": 0.64891, "loss_cls": 3.46944, "loss": 3.46944, "time": 0.81517} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.02304, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38734, "top5_acc": 0.64906, "loss_cls": 3.4541, "loss": 3.4541, "time": 0.81621} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.02302, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38984, "top5_acc": 0.65625, "loss_cls": 3.44833, "loss": 3.44833, "time": 0.81365} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.023, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39016, "top5_acc": 0.65062, "loss_cls": 3.47451, "loss": 3.47451, "time": 0.82142} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.02297, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38031, "top5_acc": 0.64625, "loss_cls": 3.50171, "loss": 3.50171, "time": 0.81384} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.02295, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38047, "top5_acc": 0.64859, "loss_cls": 3.46506, "loss": 3.46506, "time": 0.81573} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.02293, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38453, "top5_acc": 0.64969, "loss_cls": 3.47224, "loss": 3.47224, "time": 0.81574} +{"mode": "train", "epoch": 103, "iter": 1300, "lr": 0.0229, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38469, "top5_acc": 0.64328, "loss_cls": 3.46039, "loss": 3.46039, "time": 0.81399} +{"mode": "train", "epoch": 103, "iter": 1400, "lr": 0.02288, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38047, "top5_acc": 0.64766, "loss_cls": 3.48387, "loss": 3.48387, "time": 0.81434} +{"mode": "train", "epoch": 103, "iter": 1500, "lr": 0.02286, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38734, "top5_acc": 0.64984, "loss_cls": 3.45113, "loss": 3.45113, "time": 0.82488} +{"mode": "train", "epoch": 103, "iter": 1600, "lr": 0.02283, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38719, "top5_acc": 0.64141, "loss_cls": 3.48569, "loss": 3.48569, "time": 0.81444} +{"mode": "train", "epoch": 103, "iter": 1700, "lr": 0.02281, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3825, "top5_acc": 0.63953, "loss_cls": 3.49065, "loss": 3.49065, "time": 0.82158} +{"mode": "train", "epoch": 103, "iter": 1800, "lr": 0.02279, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39219, "top5_acc": 0.64938, "loss_cls": 3.46731, "loss": 3.46731, "time": 0.81507} +{"mode": "train", "epoch": 103, "iter": 1900, "lr": 0.02276, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37391, "top5_acc": 0.63969, "loss_cls": 3.55134, "loss": 3.55134, "time": 0.82406} +{"mode": "train", "epoch": 103, "iter": 2000, "lr": 0.02274, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37766, "top5_acc": 0.64031, "loss_cls": 3.48679, "loss": 3.48679, "time": 0.81703} +{"mode": "train", "epoch": 103, "iter": 2100, "lr": 0.02272, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38547, "top5_acc": 0.64094, "loss_cls": 3.48629, "loss": 3.48629, "time": 0.81342} +{"mode": "train", "epoch": 103, "iter": 2200, "lr": 0.02269, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39281, "top5_acc": 0.64453, "loss_cls": 3.44007, "loss": 3.44007, "time": 0.81376} +{"mode": "train", "epoch": 103, "iter": 2300, "lr": 0.02267, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39531, "top5_acc": 0.65016, "loss_cls": 3.4359, "loss": 3.4359, "time": 0.81813} +{"mode": "train", "epoch": 103, "iter": 2400, "lr": 0.02264, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38781, "top5_acc": 0.64875, "loss_cls": 3.46578, "loss": 3.46578, "time": 0.81888} +{"mode": "train", "epoch": 103, "iter": 2500, "lr": 0.02262, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37688, "top5_acc": 0.64188, "loss_cls": 3.50176, "loss": 3.50176, "time": 0.81836} +{"mode": "train", "epoch": 103, "iter": 2600, "lr": 0.0226, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38547, "top5_acc": 0.64906, "loss_cls": 3.45667, "loss": 3.45667, "time": 0.82382} +{"mode": "train", "epoch": 103, "iter": 2700, "lr": 0.02257, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38391, "top5_acc": 0.63906, "loss_cls": 3.51565, "loss": 3.51565, "time": 0.8177} +{"mode": "train", "epoch": 103, "iter": 2800, "lr": 0.02255, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37812, "top5_acc": 0.63719, "loss_cls": 3.50797, "loss": 3.50797, "time": 0.81736} +{"mode": "train", "epoch": 103, "iter": 2900, "lr": 0.02253, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38906, "top5_acc": 0.64203, "loss_cls": 3.48562, "loss": 3.48562, "time": 0.82479} +{"mode": "train", "epoch": 103, "iter": 3000, "lr": 0.0225, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37375, "top5_acc": 0.62797, "loss_cls": 3.52687, "loss": 3.52687, "time": 0.82132} +{"mode": "train", "epoch": 103, "iter": 3100, "lr": 0.02248, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39, "top5_acc": 0.64875, "loss_cls": 3.44488, "loss": 3.44488, "time": 0.81514} +{"mode": "train", "epoch": 103, "iter": 3200, "lr": 0.02246, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37078, "top5_acc": 0.63125, "loss_cls": 3.5576, "loss": 3.5576, "time": 0.8138} +{"mode": "train", "epoch": 103, "iter": 3300, "lr": 0.02243, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38562, "top5_acc": 0.64781, "loss_cls": 3.48506, "loss": 3.48506, "time": 0.81336} +{"mode": "train", "epoch": 103, "iter": 3400, "lr": 0.02241, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38891, "top5_acc": 0.63641, "loss_cls": 3.51902, "loss": 3.51902, "time": 0.81637} +{"mode": "train", "epoch": 103, "iter": 3500, "lr": 0.02239, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37531, "top5_acc": 0.6325, "loss_cls": 3.54749, "loss": 3.54749, "time": 0.81256} +{"mode": "train", "epoch": 103, "iter": 3600, "lr": 0.02236, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38641, "top5_acc": 0.63891, "loss_cls": 3.51671, "loss": 3.51671, "time": 0.8233} +{"mode": "train", "epoch": 103, "iter": 3700, "lr": 0.02234, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37812, "top5_acc": 0.63281, "loss_cls": 3.52069, "loss": 3.52069, "time": 0.81288} +{"mode": "val", "epoch": 103, "iter": 309, "lr": 0.02233, "top1_acc": 0.32898, "top5_acc": 0.58198, "mean_class_accuracy": 0.32874} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.02231, "memory": 15990, "data_time": 1.30851, "top1_acc": 0.39203, "top5_acc": 0.65094, "loss_cls": 3.41768, "loss": 3.41768, "time": 2.28716} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.02228, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39688, "top5_acc": 0.66297, "loss_cls": 3.38771, "loss": 3.38771, "time": 0.81706} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.02226, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38062, "top5_acc": 0.64859, "loss_cls": 3.45828, "loss": 3.45828, "time": 0.81625} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.02224, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39031, "top5_acc": 0.64422, "loss_cls": 3.47287, "loss": 3.47287, "time": 0.81539} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.02221, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39266, "top5_acc": 0.65531, "loss_cls": 3.38127, "loss": 3.38127, "time": 0.82095} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.02219, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39062, "top5_acc": 0.64875, "loss_cls": 3.43196, "loss": 3.43196, "time": 0.81646} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.02217, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39453, "top5_acc": 0.64906, "loss_cls": 3.458, "loss": 3.458, "time": 0.81137} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.02214, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38328, "top5_acc": 0.64047, "loss_cls": 3.49369, "loss": 3.49369, "time": 0.81559} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.02212, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39375, "top5_acc": 0.64859, "loss_cls": 3.44591, "loss": 3.44591, "time": 0.81875} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.0221, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38562, "top5_acc": 0.64578, "loss_cls": 3.48217, "loss": 3.48217, "time": 0.8161} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.02208, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38188, "top5_acc": 0.64656, "loss_cls": 3.47521, "loss": 3.47521, "time": 0.81716} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.02205, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38047, "top5_acc": 0.64109, "loss_cls": 3.45669, "loss": 3.45669, "time": 0.81598} +{"mode": "train", "epoch": 104, "iter": 1300, "lr": 0.02203, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37953, "top5_acc": 0.64469, "loss_cls": 3.48724, "loss": 3.48724, "time": 0.81353} +{"mode": "train", "epoch": 104, "iter": 1400, "lr": 0.02201, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38094, "top5_acc": 0.64438, "loss_cls": 3.50742, "loss": 3.50742, "time": 0.81404} +{"mode": "train", "epoch": 104, "iter": 1500, "lr": 0.02198, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38922, "top5_acc": 0.64328, "loss_cls": 3.45272, "loss": 3.45272, "time": 0.8225} +{"mode": "train", "epoch": 104, "iter": 1600, "lr": 0.02196, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38031, "top5_acc": 0.63469, "loss_cls": 3.51904, "loss": 3.51904, "time": 0.82015} +{"mode": "train", "epoch": 104, "iter": 1700, "lr": 0.02194, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39234, "top5_acc": 0.64297, "loss_cls": 3.4538, "loss": 3.4538, "time": 0.82569} +{"mode": "train", "epoch": 104, "iter": 1800, "lr": 0.02191, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38672, "top5_acc": 0.655, "loss_cls": 3.44976, "loss": 3.44976, "time": 0.82023} +{"mode": "train", "epoch": 104, "iter": 1900, "lr": 0.02189, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39516, "top5_acc": 0.65266, "loss_cls": 3.42885, "loss": 3.42885, "time": 0.81998} +{"mode": "train", "epoch": 104, "iter": 2000, "lr": 0.02187, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38938, "top5_acc": 0.64844, "loss_cls": 3.47154, "loss": 3.47154, "time": 0.82065} +{"mode": "train", "epoch": 104, "iter": 2100, "lr": 0.02184, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37656, "top5_acc": 0.64359, "loss_cls": 3.51502, "loss": 3.51502, "time": 0.82199} +{"mode": "train", "epoch": 104, "iter": 2200, "lr": 0.02182, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39484, "top5_acc": 0.65438, "loss_cls": 3.42761, "loss": 3.42761, "time": 0.8163} +{"mode": "train", "epoch": 104, "iter": 2300, "lr": 0.0218, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38984, "top5_acc": 0.64062, "loss_cls": 3.46222, "loss": 3.46222, "time": 0.80912} +{"mode": "train", "epoch": 104, "iter": 2400, "lr": 0.02177, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39484, "top5_acc": 0.65422, "loss_cls": 3.41711, "loss": 3.41711, "time": 0.82543} +{"mode": "train", "epoch": 104, "iter": 2500, "lr": 0.02175, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37688, "top5_acc": 0.64031, "loss_cls": 3.50968, "loss": 3.50968, "time": 0.8215} +{"mode": "train", "epoch": 104, "iter": 2600, "lr": 0.02173, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38172, "top5_acc": 0.64578, "loss_cls": 3.48244, "loss": 3.48244, "time": 0.81824} +{"mode": "train", "epoch": 104, "iter": 2700, "lr": 0.02171, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38266, "top5_acc": 0.64625, "loss_cls": 3.49636, "loss": 3.49636, "time": 0.81408} +{"mode": "train", "epoch": 104, "iter": 2800, "lr": 0.02168, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38922, "top5_acc": 0.65016, "loss_cls": 3.42298, "loss": 3.42298, "time": 0.82197} +{"mode": "train", "epoch": 104, "iter": 2900, "lr": 0.02166, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38062, "top5_acc": 0.65516, "loss_cls": 3.44913, "loss": 3.44913, "time": 0.81684} +{"mode": "train", "epoch": 104, "iter": 3000, "lr": 0.02164, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38266, "top5_acc": 0.64469, "loss_cls": 3.48583, "loss": 3.48583, "time": 0.81539} +{"mode": "train", "epoch": 104, "iter": 3100, "lr": 0.02161, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39, "top5_acc": 0.64531, "loss_cls": 3.45713, "loss": 3.45713, "time": 0.81556} +{"mode": "train", "epoch": 104, "iter": 3200, "lr": 0.02159, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38359, "top5_acc": 0.65062, "loss_cls": 3.46387, "loss": 3.46387, "time": 0.81135} +{"mode": "train", "epoch": 104, "iter": 3300, "lr": 0.02157, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36969, "top5_acc": 0.63656, "loss_cls": 3.55253, "loss": 3.55253, "time": 0.81386} +{"mode": "train", "epoch": 104, "iter": 3400, "lr": 0.02154, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38422, "top5_acc": 0.6375, "loss_cls": 3.50421, "loss": 3.50421, "time": 0.81279} +{"mode": "train", "epoch": 104, "iter": 3500, "lr": 0.02152, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38453, "top5_acc": 0.64828, "loss_cls": 3.47249, "loss": 3.47249, "time": 0.82011} +{"mode": "train", "epoch": 104, "iter": 3600, "lr": 0.0215, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37766, "top5_acc": 0.63984, "loss_cls": 3.50372, "loss": 3.50372, "time": 0.81426} +{"mode": "train", "epoch": 104, "iter": 3700, "lr": 0.02148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39359, "top5_acc": 0.64828, "loss_cls": 3.45169, "loss": 3.45169, "time": 0.81646} +{"mode": "val", "epoch": 104, "iter": 309, "lr": 0.02146, "top1_acc": 0.32427, "top5_acc": 0.58046, "mean_class_accuracy": 0.32419} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.02144, "memory": 15990, "data_time": 1.30463, "top1_acc": 0.40766, "top5_acc": 0.65984, "loss_cls": 3.34438, "loss": 3.34438, "time": 2.29059} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.02142, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39703, "top5_acc": 0.66016, "loss_cls": 3.41407, "loss": 3.41407, "time": 0.81544} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.0214, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40562, "top5_acc": 0.66359, "loss_cls": 3.38157, "loss": 3.38157, "time": 0.81893} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.02137, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40391, "top5_acc": 0.65906, "loss_cls": 3.3865, "loss": 3.3865, "time": 0.81972} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.02135, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39844, "top5_acc": 0.65484, "loss_cls": 3.39796, "loss": 3.39796, "time": 0.81755} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.02133, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.395, "top5_acc": 0.65281, "loss_cls": 3.4243, "loss": 3.4243, "time": 0.8209} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.0213, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39984, "top5_acc": 0.66125, "loss_cls": 3.42442, "loss": 3.42442, "time": 0.82053} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.02128, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39719, "top5_acc": 0.65875, "loss_cls": 3.37653, "loss": 3.37653, "time": 0.81709} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.02126, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38828, "top5_acc": 0.65688, "loss_cls": 3.42043, "loss": 3.42043, "time": 0.81639} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.02124, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39484, "top5_acc": 0.65312, "loss_cls": 3.44281, "loss": 3.44281, "time": 0.81227} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.02121, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3875, "top5_acc": 0.65141, "loss_cls": 3.43282, "loss": 3.43282, "time": 0.81297} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.02119, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39234, "top5_acc": 0.64297, "loss_cls": 3.47508, "loss": 3.47508, "time": 0.81544} +{"mode": "train", "epoch": 105, "iter": 1300, "lr": 0.02117, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39547, "top5_acc": 0.6475, "loss_cls": 3.41511, "loss": 3.41511, "time": 0.81717} +{"mode": "train", "epoch": 105, "iter": 1400, "lr": 0.02114, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37875, "top5_acc": 0.64562, "loss_cls": 3.48142, "loss": 3.48142, "time": 0.81941} +{"mode": "train", "epoch": 105, "iter": 1500, "lr": 0.02112, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40203, "top5_acc": 0.64422, "loss_cls": 3.41207, "loss": 3.41207, "time": 0.82104} +{"mode": "train", "epoch": 105, "iter": 1600, "lr": 0.0211, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39641, "top5_acc": 0.64688, "loss_cls": 3.45424, "loss": 3.45424, "time": 0.81993} +{"mode": "train", "epoch": 105, "iter": 1700, "lr": 0.02108, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38422, "top5_acc": 0.64984, "loss_cls": 3.46182, "loss": 3.46182, "time": 0.82757} +{"mode": "train", "epoch": 105, "iter": 1800, "lr": 0.02105, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39312, "top5_acc": 0.64266, "loss_cls": 3.46983, "loss": 3.46983, "time": 0.82038} +{"mode": "train", "epoch": 105, "iter": 1900, "lr": 0.02103, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39281, "top5_acc": 0.65766, "loss_cls": 3.44915, "loss": 3.44915, "time": 0.81984} +{"mode": "train", "epoch": 105, "iter": 2000, "lr": 0.02101, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38844, "top5_acc": 0.65312, "loss_cls": 3.4283, "loss": 3.4283, "time": 0.822} +{"mode": "train", "epoch": 105, "iter": 2100, "lr": 0.02098, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38938, "top5_acc": 0.64703, "loss_cls": 3.4563, "loss": 3.4563, "time": 0.81575} +{"mode": "train", "epoch": 105, "iter": 2200, "lr": 0.02096, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38344, "top5_acc": 0.64672, "loss_cls": 3.49608, "loss": 3.49608, "time": 0.81352} +{"mode": "train", "epoch": 105, "iter": 2300, "lr": 0.02094, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38531, "top5_acc": 0.64188, "loss_cls": 3.4912, "loss": 3.4912, "time": 0.82226} +{"mode": "train", "epoch": 105, "iter": 2400, "lr": 0.02092, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37984, "top5_acc": 0.63766, "loss_cls": 3.51774, "loss": 3.51774, "time": 0.82131} +{"mode": "train", "epoch": 105, "iter": 2500, "lr": 0.02089, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38359, "top5_acc": 0.64516, "loss_cls": 3.4723, "loss": 3.4723, "time": 0.82075} +{"mode": "train", "epoch": 105, "iter": 2600, "lr": 0.02087, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37969, "top5_acc": 0.65062, "loss_cls": 3.45759, "loss": 3.45759, "time": 0.81838} +{"mode": "train", "epoch": 105, "iter": 2700, "lr": 0.02085, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39203, "top5_acc": 0.64891, "loss_cls": 3.45958, "loss": 3.45958, "time": 0.81554} +{"mode": "train", "epoch": 105, "iter": 2800, "lr": 0.02083, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37766, "top5_acc": 0.64953, "loss_cls": 3.47401, "loss": 3.47401, "time": 0.82243} +{"mode": "train", "epoch": 105, "iter": 2900, "lr": 0.0208, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39094, "top5_acc": 0.65062, "loss_cls": 3.4516, "loss": 3.4516, "time": 0.81602} +{"mode": "train", "epoch": 105, "iter": 3000, "lr": 0.02078, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38219, "top5_acc": 0.63828, "loss_cls": 3.49872, "loss": 3.49872, "time": 0.81512} +{"mode": "train", "epoch": 105, "iter": 3100, "lr": 0.02076, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38891, "top5_acc": 0.64406, "loss_cls": 3.48048, "loss": 3.48048, "time": 0.81561} +{"mode": "train", "epoch": 105, "iter": 3200, "lr": 0.02073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38188, "top5_acc": 0.64391, "loss_cls": 3.47312, "loss": 3.47312, "time": 0.81803} +{"mode": "train", "epoch": 105, "iter": 3300, "lr": 0.02071, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38203, "top5_acc": 0.63656, "loss_cls": 3.51233, "loss": 3.51233, "time": 0.8187} +{"mode": "train", "epoch": 105, "iter": 3400, "lr": 0.02069, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39625, "top5_acc": 0.65453, "loss_cls": 3.41846, "loss": 3.41846, "time": 0.81411} +{"mode": "train", "epoch": 105, "iter": 3500, "lr": 0.02067, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38688, "top5_acc": 0.64141, "loss_cls": 3.46816, "loss": 3.46816, "time": 0.82091} +{"mode": "train", "epoch": 105, "iter": 3600, "lr": 0.02064, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39688, "top5_acc": 0.64359, "loss_cls": 3.45595, "loss": 3.45595, "time": 0.81516} +{"mode": "train", "epoch": 105, "iter": 3700, "lr": 0.02062, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38953, "top5_acc": 0.64531, "loss_cls": 3.45629, "loss": 3.45629, "time": 0.81269} +{"mode": "val", "epoch": 105, "iter": 309, "lr": 0.02061, "top1_acc": 0.32852, "top5_acc": 0.58907, "mean_class_accuracy": 0.32828} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.02059, "memory": 15990, "data_time": 1.32729, "top1_acc": 0.39484, "top5_acc": 0.6525, "loss_cls": 3.40557, "loss": 3.40557, "time": 2.30673} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.02057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40359, "top5_acc": 0.66297, "loss_cls": 3.35736, "loss": 3.35736, "time": 0.82463} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.02054, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39938, "top5_acc": 0.66812, "loss_cls": 3.37137, "loss": 3.37137, "time": 0.81886} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.02052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39141, "top5_acc": 0.65812, "loss_cls": 3.40804, "loss": 3.40804, "time": 0.81556} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.0205, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40266, "top5_acc": 0.66156, "loss_cls": 3.37369, "loss": 3.37369, "time": 0.81188} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.02048, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39719, "top5_acc": 0.64891, "loss_cls": 3.39832, "loss": 3.39832, "time": 0.81952} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.02045, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39078, "top5_acc": 0.64938, "loss_cls": 3.44673, "loss": 3.44673, "time": 0.81515} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.02043, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39812, "top5_acc": 0.66109, "loss_cls": 3.37459, "loss": 3.37459, "time": 0.82015} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.02041, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39078, "top5_acc": 0.65281, "loss_cls": 3.44287, "loss": 3.44287, "time": 0.81604} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.02039, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39828, "top5_acc": 0.65031, "loss_cls": 3.41224, "loss": 3.41224, "time": 0.81475} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.02036, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40297, "top5_acc": 0.66328, "loss_cls": 3.36911, "loss": 3.36911, "time": 0.81806} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.02034, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39422, "top5_acc": 0.65031, "loss_cls": 3.43865, "loss": 3.43865, "time": 0.81997} +{"mode": "train", "epoch": 106, "iter": 1300, "lr": 0.02032, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40141, "top5_acc": 0.65703, "loss_cls": 3.37902, "loss": 3.37902, "time": 0.81521} +{"mode": "train", "epoch": 106, "iter": 1400, "lr": 0.0203, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38531, "top5_acc": 0.64828, "loss_cls": 3.46703, "loss": 3.46703, "time": 0.8183} +{"mode": "train", "epoch": 106, "iter": 1500, "lr": 0.02027, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38688, "top5_acc": 0.65375, "loss_cls": 3.43891, "loss": 3.43891, "time": 0.82551} +{"mode": "train", "epoch": 106, "iter": 1600, "lr": 0.02025, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39547, "top5_acc": 0.6525, "loss_cls": 3.4279, "loss": 3.4279, "time": 0.81763} +{"mode": "train", "epoch": 106, "iter": 1700, "lr": 0.02023, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39203, "top5_acc": 0.65297, "loss_cls": 3.44112, "loss": 3.44112, "time": 0.82937} +{"mode": "train", "epoch": 106, "iter": 1800, "lr": 0.02021, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39547, "top5_acc": 0.65578, "loss_cls": 3.41534, "loss": 3.41534, "time": 0.81969} +{"mode": "train", "epoch": 106, "iter": 1900, "lr": 0.02018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39406, "top5_acc": 0.65281, "loss_cls": 3.41501, "loss": 3.41501, "time": 0.81693} +{"mode": "train", "epoch": 106, "iter": 2000, "lr": 0.02016, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38922, "top5_acc": 0.65141, "loss_cls": 3.46491, "loss": 3.46491, "time": 0.81879} +{"mode": "train", "epoch": 106, "iter": 2100, "lr": 0.02014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.395, "top5_acc": 0.64594, "loss_cls": 3.44372, "loss": 3.44372, "time": 0.8216} +{"mode": "train", "epoch": 106, "iter": 2200, "lr": 0.02012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39141, "top5_acc": 0.65531, "loss_cls": 3.44017, "loss": 3.44017, "time": 0.81868} +{"mode": "train", "epoch": 106, "iter": 2300, "lr": 0.02009, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39906, "top5_acc": 0.65312, "loss_cls": 3.40711, "loss": 3.40711, "time": 0.81548} +{"mode": "train", "epoch": 106, "iter": 2400, "lr": 0.02007, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38516, "top5_acc": 0.65062, "loss_cls": 3.47392, "loss": 3.47392, "time": 0.82196} +{"mode": "train", "epoch": 106, "iter": 2500, "lr": 0.02005, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39125, "top5_acc": 0.64266, "loss_cls": 3.46778, "loss": 3.46778, "time": 0.81955} +{"mode": "train", "epoch": 106, "iter": 2600, "lr": 0.02003, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3875, "top5_acc": 0.64938, "loss_cls": 3.45841, "loss": 3.45841, "time": 0.82145} +{"mode": "train", "epoch": 106, "iter": 2700, "lr": 0.02, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38453, "top5_acc": 0.63688, "loss_cls": 3.49642, "loss": 3.49642, "time": 0.82433} +{"mode": "train", "epoch": 106, "iter": 2800, "lr": 0.01998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39516, "top5_acc": 0.65156, "loss_cls": 3.40471, "loss": 3.40471, "time": 0.82318} +{"mode": "train", "epoch": 106, "iter": 2900, "lr": 0.01996, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40297, "top5_acc": 0.65562, "loss_cls": 3.40072, "loss": 3.40072, "time": 0.81581} +{"mode": "train", "epoch": 106, "iter": 3000, "lr": 0.01994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38922, "top5_acc": 0.64344, "loss_cls": 3.4595, "loss": 3.4595, "time": 0.8202} +{"mode": "train", "epoch": 106, "iter": 3100, "lr": 0.01991, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3825, "top5_acc": 0.64859, "loss_cls": 3.47184, "loss": 3.47184, "time": 0.81811} +{"mode": "train", "epoch": 106, "iter": 3200, "lr": 0.01989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40109, "top5_acc": 0.64812, "loss_cls": 3.42539, "loss": 3.42539, "time": 0.81452} +{"mode": "train", "epoch": 106, "iter": 3300, "lr": 0.01987, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39453, "top5_acc": 0.64875, "loss_cls": 3.46524, "loss": 3.46524, "time": 0.81532} +{"mode": "train", "epoch": 106, "iter": 3400, "lr": 0.01985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38875, "top5_acc": 0.64453, "loss_cls": 3.47, "loss": 3.47, "time": 0.81319} +{"mode": "train", "epoch": 106, "iter": 3500, "lr": 0.01983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3925, "top5_acc": 0.64734, "loss_cls": 3.41914, "loss": 3.41914, "time": 0.81999} +{"mode": "train", "epoch": 106, "iter": 3600, "lr": 0.0198, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38578, "top5_acc": 0.64, "loss_cls": 3.47493, "loss": 3.47493, "time": 0.81097} +{"mode": "train", "epoch": 106, "iter": 3700, "lr": 0.01978, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39391, "top5_acc": 0.65078, "loss_cls": 3.4245, "loss": 3.4245, "time": 0.81914} +{"mode": "val", "epoch": 106, "iter": 309, "lr": 0.01977, "top1_acc": 0.32908, "top5_acc": 0.58557, "mean_class_accuracy": 0.32895} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.01975, "memory": 15990, "data_time": 1.3574, "top1_acc": 0.4075, "top5_acc": 0.66, "loss_cls": 3.32921, "loss": 3.32921, "time": 2.3403} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.01973, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41062, "top5_acc": 0.66906, "loss_cls": 3.3283, "loss": 3.3283, "time": 0.82181} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.0197, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40844, "top5_acc": 0.66688, "loss_cls": 3.34994, "loss": 3.34994, "time": 0.81745} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.01968, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40094, "top5_acc": 0.66141, "loss_cls": 3.37889, "loss": 3.37889, "time": 0.81233} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.01966, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39984, "top5_acc": 0.65766, "loss_cls": 3.36623, "loss": 3.36623, "time": 0.81066} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.01964, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39203, "top5_acc": 0.66219, "loss_cls": 3.41043, "loss": 3.41043, "time": 0.81563} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.01961, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4025, "top5_acc": 0.66047, "loss_cls": 3.36654, "loss": 3.36654, "time": 0.81964} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.01959, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39828, "top5_acc": 0.665, "loss_cls": 3.36069, "loss": 3.36069, "time": 0.81474} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.01957, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39859, "top5_acc": 0.66234, "loss_cls": 3.37513, "loss": 3.37513, "time": 0.82056} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.01955, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39656, "top5_acc": 0.66328, "loss_cls": 3.39398, "loss": 3.39398, "time": 0.81416} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.01953, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39531, "top5_acc": 0.66078, "loss_cls": 3.40265, "loss": 3.40265, "time": 0.81373} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.0195, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38953, "top5_acc": 0.65359, "loss_cls": 3.43246, "loss": 3.43246, "time": 0.82069} +{"mode": "train", "epoch": 107, "iter": 1300, "lr": 0.01948, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39016, "top5_acc": 0.64703, "loss_cls": 3.44495, "loss": 3.44495, "time": 0.82281} +{"mode": "train", "epoch": 107, "iter": 1400, "lr": 0.01946, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39438, "top5_acc": 0.66141, "loss_cls": 3.40842, "loss": 3.40842, "time": 0.81711} +{"mode": "train", "epoch": 107, "iter": 1500, "lr": 0.01944, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39562, "top5_acc": 0.65359, "loss_cls": 3.42898, "loss": 3.42898, "time": 0.81546} +{"mode": "train", "epoch": 107, "iter": 1600, "lr": 0.01942, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40625, "top5_acc": 0.65844, "loss_cls": 3.38809, "loss": 3.38809, "time": 0.82005} +{"mode": "train", "epoch": 107, "iter": 1700, "lr": 0.01939, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40109, "top5_acc": 0.65875, "loss_cls": 3.38057, "loss": 3.38057, "time": 0.82335} +{"mode": "train", "epoch": 107, "iter": 1800, "lr": 0.01937, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39219, "top5_acc": 0.64578, "loss_cls": 3.43669, "loss": 3.43669, "time": 0.819} +{"mode": "train", "epoch": 107, "iter": 1900, "lr": 0.01935, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39016, "top5_acc": 0.64922, "loss_cls": 3.45243, "loss": 3.45243, "time": 0.81346} +{"mode": "train", "epoch": 107, "iter": 2000, "lr": 0.01933, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38703, "top5_acc": 0.63953, "loss_cls": 3.48789, "loss": 3.48789, "time": 0.81564} +{"mode": "train", "epoch": 107, "iter": 2100, "lr": 0.0193, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40406, "top5_acc": 0.66672, "loss_cls": 3.36349, "loss": 3.36349, "time": 0.81552} +{"mode": "train", "epoch": 107, "iter": 2200, "lr": 0.01928, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39438, "top5_acc": 0.65391, "loss_cls": 3.40336, "loss": 3.40336, "time": 0.81694} +{"mode": "train", "epoch": 107, "iter": 2300, "lr": 0.01926, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38969, "top5_acc": 0.64375, "loss_cls": 3.4509, "loss": 3.4509, "time": 0.81638} +{"mode": "train", "epoch": 107, "iter": 2400, "lr": 0.01924, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39406, "top5_acc": 0.65781, "loss_cls": 3.40279, "loss": 3.40279, "time": 0.82018} +{"mode": "train", "epoch": 107, "iter": 2500, "lr": 0.01922, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38547, "top5_acc": 0.64609, "loss_cls": 3.46605, "loss": 3.46605, "time": 0.82321} +{"mode": "train", "epoch": 107, "iter": 2600, "lr": 0.01919, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38953, "top5_acc": 0.65375, "loss_cls": 3.43997, "loss": 3.43997, "time": 0.82339} +{"mode": "train", "epoch": 107, "iter": 2700, "lr": 0.01917, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.395, "top5_acc": 0.66078, "loss_cls": 3.38894, "loss": 3.38894, "time": 0.8179} +{"mode": "train", "epoch": 107, "iter": 2800, "lr": 0.01915, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39141, "top5_acc": 0.65047, "loss_cls": 3.46245, "loss": 3.46245, "time": 0.81466} +{"mode": "train", "epoch": 107, "iter": 2900, "lr": 0.01913, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40141, "top5_acc": 0.65109, "loss_cls": 3.39487, "loss": 3.39487, "time": 0.81213} +{"mode": "train", "epoch": 107, "iter": 3000, "lr": 0.01911, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40312, "top5_acc": 0.66172, "loss_cls": 3.37151, "loss": 3.37151, "time": 0.82053} +{"mode": "train", "epoch": 107, "iter": 3100, "lr": 0.01908, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39781, "top5_acc": 0.65594, "loss_cls": 3.41624, "loss": 3.41624, "time": 0.82568} +{"mode": "train", "epoch": 107, "iter": 3200, "lr": 0.01906, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38812, "top5_acc": 0.64969, "loss_cls": 3.45137, "loss": 3.45137, "time": 0.81552} +{"mode": "train", "epoch": 107, "iter": 3300, "lr": 0.01904, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40031, "top5_acc": 0.65297, "loss_cls": 3.41201, "loss": 3.41201, "time": 0.81697} +{"mode": "train", "epoch": 107, "iter": 3400, "lr": 0.01902, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3875, "top5_acc": 0.65312, "loss_cls": 3.422, "loss": 3.422, "time": 0.81244} +{"mode": "train", "epoch": 107, "iter": 3500, "lr": 0.019, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38109, "top5_acc": 0.64375, "loss_cls": 3.48955, "loss": 3.48955, "time": 0.81866} +{"mode": "train", "epoch": 107, "iter": 3600, "lr": 0.01897, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40875, "top5_acc": 0.66344, "loss_cls": 3.35696, "loss": 3.35696, "time": 0.8121} +{"mode": "train", "epoch": 107, "iter": 3700, "lr": 0.01895, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39219, "top5_acc": 0.64969, "loss_cls": 3.42786, "loss": 3.42786, "time": 0.82087} +{"mode": "val", "epoch": 107, "iter": 309, "lr": 0.01894, "top1_acc": 0.32731, "top5_acc": 0.58745, "mean_class_accuracy": 0.32707} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.01892, "memory": 15990, "data_time": 1.33043, "top1_acc": 0.42031, "top5_acc": 0.67125, "loss_cls": 3.32438, "loss": 3.32438, "time": 2.31521} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0189, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40812, "top5_acc": 0.66812, "loss_cls": 3.32328, "loss": 3.32328, "time": 0.81775} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.01888, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40328, "top5_acc": 0.66766, "loss_cls": 3.33871, "loss": 3.33871, "time": 0.81719} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.01886, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40453, "top5_acc": 0.66, "loss_cls": 3.36525, "loss": 3.36525, "time": 0.81259} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.01883, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40484, "top5_acc": 0.67516, "loss_cls": 3.30431, "loss": 3.30431, "time": 0.81674} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.01881, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40297, "top5_acc": 0.66578, "loss_cls": 3.34197, "loss": 3.34197, "time": 0.81908} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.01879, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40625, "top5_acc": 0.66453, "loss_cls": 3.34963, "loss": 3.34963, "time": 0.8179} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.01877, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40266, "top5_acc": 0.65828, "loss_cls": 3.36787, "loss": 3.36787, "time": 0.81486} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.01875, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3975, "top5_acc": 0.65891, "loss_cls": 3.41147, "loss": 3.41147, "time": 0.82409} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.01872, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39344, "top5_acc": 0.65062, "loss_cls": 3.4261, "loss": 3.4261, "time": 0.81859} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.0187, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40328, "top5_acc": 0.66406, "loss_cls": 3.36867, "loss": 3.36867, "time": 0.81442} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.01868, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39016, "top5_acc": 0.65312, "loss_cls": 3.41213, "loss": 3.41213, "time": 0.8192} +{"mode": "train", "epoch": 108, "iter": 1300, "lr": 0.01866, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40375, "top5_acc": 0.65844, "loss_cls": 3.39194, "loss": 3.39194, "time": 0.81645} +{"mode": "train", "epoch": 108, "iter": 1400, "lr": 0.01864, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41031, "top5_acc": 0.66641, "loss_cls": 3.35544, "loss": 3.35544, "time": 0.82309} +{"mode": "train", "epoch": 108, "iter": 1500, "lr": 0.01862, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38641, "top5_acc": 0.65203, "loss_cls": 3.41995, "loss": 3.41995, "time": 0.82119} +{"mode": "train", "epoch": 108, "iter": 1600, "lr": 0.01859, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40125, "top5_acc": 0.65844, "loss_cls": 3.39386, "loss": 3.39386, "time": 0.81354} +{"mode": "train", "epoch": 108, "iter": 1700, "lr": 0.01857, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39828, "top5_acc": 0.65594, "loss_cls": 3.41234, "loss": 3.41234, "time": 0.82202} +{"mode": "train", "epoch": 108, "iter": 1800, "lr": 0.01855, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39219, "top5_acc": 0.65297, "loss_cls": 3.426, "loss": 3.426, "time": 0.81826} +{"mode": "train", "epoch": 108, "iter": 1900, "lr": 0.01853, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40188, "top5_acc": 0.66453, "loss_cls": 3.36132, "loss": 3.36132, "time": 0.82089} +{"mode": "train", "epoch": 108, "iter": 2000, "lr": 0.01851, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39203, "top5_acc": 0.64047, "loss_cls": 3.45867, "loss": 3.45867, "time": 0.81787} +{"mode": "train", "epoch": 108, "iter": 2100, "lr": 0.01848, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39688, "top5_acc": 0.65875, "loss_cls": 3.39924, "loss": 3.39924, "time": 0.81519} +{"mode": "train", "epoch": 108, "iter": 2200, "lr": 0.01846, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40469, "top5_acc": 0.66688, "loss_cls": 3.36513, "loss": 3.36513, "time": 0.81719} +{"mode": "train", "epoch": 108, "iter": 2300, "lr": 0.01844, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3975, "top5_acc": 0.6575, "loss_cls": 3.37104, "loss": 3.37104, "time": 0.81618} +{"mode": "train", "epoch": 108, "iter": 2400, "lr": 0.01842, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39734, "top5_acc": 0.65422, "loss_cls": 3.42515, "loss": 3.42515, "time": 0.82493} +{"mode": "train", "epoch": 108, "iter": 2500, "lr": 0.0184, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39312, "top5_acc": 0.65844, "loss_cls": 3.39446, "loss": 3.39446, "time": 0.81784} +{"mode": "train", "epoch": 108, "iter": 2600, "lr": 0.01838, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40203, "top5_acc": 0.65875, "loss_cls": 3.39609, "loss": 3.39609, "time": 0.82379} +{"mode": "train", "epoch": 108, "iter": 2700, "lr": 0.01835, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41516, "top5_acc": 0.66094, "loss_cls": 3.36182, "loss": 3.36182, "time": 0.81578} +{"mode": "train", "epoch": 108, "iter": 2800, "lr": 0.01833, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39891, "top5_acc": 0.65203, "loss_cls": 3.41436, "loss": 3.41436, "time": 0.81433} +{"mode": "train", "epoch": 108, "iter": 2900, "lr": 0.01831, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40406, "top5_acc": 0.65328, "loss_cls": 3.39468, "loss": 3.39468, "time": 0.81706} +{"mode": "train", "epoch": 108, "iter": 3000, "lr": 0.01829, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37969, "top5_acc": 0.645, "loss_cls": 3.46155, "loss": 3.46155, "time": 0.81338} +{"mode": "train", "epoch": 108, "iter": 3100, "lr": 0.01827, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39703, "top5_acc": 0.64859, "loss_cls": 3.43494, "loss": 3.43494, "time": 0.81469} +{"mode": "train", "epoch": 108, "iter": 3200, "lr": 0.01825, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39234, "top5_acc": 0.6525, "loss_cls": 3.42608, "loss": 3.42608, "time": 0.81968} +{"mode": "train", "epoch": 108, "iter": 3300, "lr": 0.01823, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39047, "top5_acc": 0.64734, "loss_cls": 3.43697, "loss": 3.43697, "time": 0.81846} +{"mode": "train", "epoch": 108, "iter": 3400, "lr": 0.0182, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40516, "top5_acc": 0.66453, "loss_cls": 3.35865, "loss": 3.35865, "time": 0.81435} +{"mode": "train", "epoch": 108, "iter": 3500, "lr": 0.01818, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40125, "top5_acc": 0.64719, "loss_cls": 3.44037, "loss": 3.44037, "time": 0.81617} +{"mode": "train", "epoch": 108, "iter": 3600, "lr": 0.01816, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39531, "top5_acc": 0.66016, "loss_cls": 3.37401, "loss": 3.37401, "time": 0.81602} +{"mode": "train", "epoch": 108, "iter": 3700, "lr": 0.01814, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39703, "top5_acc": 0.65781, "loss_cls": 3.40726, "loss": 3.40726, "time": 0.81676} +{"mode": "val", "epoch": 108, "iter": 309, "lr": 0.01813, "top1_acc": 0.34974, "top5_acc": 0.60518, "mean_class_accuracy": 0.34965} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.01811, "memory": 15990, "data_time": 1.31887, "top1_acc": 0.42609, "top5_acc": 0.68109, "loss_cls": 3.26736, "loss": 3.26736, "time": 2.30203} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.01809, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41078, "top5_acc": 0.67562, "loss_cls": 3.30596, "loss": 3.30596, "time": 0.81604} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.01806, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40609, "top5_acc": 0.66797, "loss_cls": 3.35029, "loss": 3.35029, "time": 0.81297} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.01804, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40031, "top5_acc": 0.67297, "loss_cls": 3.35519, "loss": 3.35519, "time": 0.81135} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.01802, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41188, "top5_acc": 0.67359, "loss_cls": 3.2966, "loss": 3.2966, "time": 0.81681} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4, "top5_acc": 0.66578, "loss_cls": 3.341, "loss": 3.341, "time": 0.8155} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.01798, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40406, "top5_acc": 0.65938, "loss_cls": 3.35595, "loss": 3.35595, "time": 0.81857} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.01796, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41578, "top5_acc": 0.66453, "loss_cls": 3.31634, "loss": 3.31634, "time": 0.8141} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.01794, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40469, "top5_acc": 0.66766, "loss_cls": 3.35514, "loss": 3.35514, "time": 0.81865} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.01791, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40406, "top5_acc": 0.65969, "loss_cls": 3.36658, "loss": 3.36658, "time": 0.81404} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.01789, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40797, "top5_acc": 0.66, "loss_cls": 3.35568, "loss": 3.35568, "time": 0.81379} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.01787, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4075, "top5_acc": 0.65484, "loss_cls": 3.37625, "loss": 3.37625, "time": 0.81559} +{"mode": "train", "epoch": 109, "iter": 1300, "lr": 0.01785, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40469, "top5_acc": 0.66266, "loss_cls": 3.3745, "loss": 3.3745, "time": 0.82137} +{"mode": "train", "epoch": 109, "iter": 1400, "lr": 0.01783, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40609, "top5_acc": 0.65656, "loss_cls": 3.36855, "loss": 3.36855, "time": 0.81604} +{"mode": "train", "epoch": 109, "iter": 1500, "lr": 0.01781, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39812, "top5_acc": 0.66266, "loss_cls": 3.35757, "loss": 3.35757, "time": 0.81955} +{"mode": "train", "epoch": 109, "iter": 1600, "lr": 0.01779, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40141, "top5_acc": 0.66547, "loss_cls": 3.37412, "loss": 3.37412, "time": 0.82118} +{"mode": "train", "epoch": 109, "iter": 1700, "lr": 0.01776, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.40391, "top5_acc": 0.66234, "loss_cls": 3.3798, "loss": 3.3798, "time": 0.82724} +{"mode": "train", "epoch": 109, "iter": 1800, "lr": 0.01774, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39531, "top5_acc": 0.66188, "loss_cls": 3.367, "loss": 3.367, "time": 0.81893} +{"mode": "train", "epoch": 109, "iter": 1900, "lr": 0.01772, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39656, "top5_acc": 0.65344, "loss_cls": 3.38966, "loss": 3.38966, "time": 0.82213} +{"mode": "train", "epoch": 109, "iter": 2000, "lr": 0.0177, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39922, "top5_acc": 0.66094, "loss_cls": 3.38631, "loss": 3.38631, "time": 0.8197} +{"mode": "train", "epoch": 109, "iter": 2100, "lr": 0.01768, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42094, "top5_acc": 0.66875, "loss_cls": 3.32462, "loss": 3.32462, "time": 0.81266} +{"mode": "train", "epoch": 109, "iter": 2200, "lr": 0.01766, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40953, "top5_acc": 0.66703, "loss_cls": 3.34544, "loss": 3.34544, "time": 0.81573} +{"mode": "train", "epoch": 109, "iter": 2300, "lr": 0.01764, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40297, "top5_acc": 0.65984, "loss_cls": 3.37007, "loss": 3.37007, "time": 0.81951} +{"mode": "train", "epoch": 109, "iter": 2400, "lr": 0.01761, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41406, "top5_acc": 0.66562, "loss_cls": 3.36373, "loss": 3.36373, "time": 0.81762} +{"mode": "train", "epoch": 109, "iter": 2500, "lr": 0.01759, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40109, "top5_acc": 0.65797, "loss_cls": 3.38248, "loss": 3.38248, "time": 0.8212} +{"mode": "train", "epoch": 109, "iter": 2600, "lr": 0.01757, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39422, "top5_acc": 0.64641, "loss_cls": 3.42061, "loss": 3.42061, "time": 0.8176} +{"mode": "train", "epoch": 109, "iter": 2700, "lr": 0.01755, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40672, "top5_acc": 0.66391, "loss_cls": 3.34793, "loss": 3.34793, "time": 0.81665} +{"mode": "train", "epoch": 109, "iter": 2800, "lr": 0.01753, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40922, "top5_acc": 0.67094, "loss_cls": 3.33964, "loss": 3.33964, "time": 0.81866} +{"mode": "train", "epoch": 109, "iter": 2900, "lr": 0.01751, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40938, "top5_acc": 0.66547, "loss_cls": 3.35242, "loss": 3.35242, "time": 0.81707} +{"mode": "train", "epoch": 109, "iter": 3000, "lr": 0.01749, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40375, "top5_acc": 0.65875, "loss_cls": 3.36897, "loss": 3.36897, "time": 0.81925} +{"mode": "train", "epoch": 109, "iter": 3100, "lr": 0.01747, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41, "top5_acc": 0.65281, "loss_cls": 3.41089, "loss": 3.41089, "time": 0.81765} +{"mode": "train", "epoch": 109, "iter": 3200, "lr": 0.01744, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39984, "top5_acc": 0.65844, "loss_cls": 3.37049, "loss": 3.37049, "time": 0.81372} +{"mode": "train", "epoch": 109, "iter": 3300, "lr": 0.01742, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39234, "top5_acc": 0.65891, "loss_cls": 3.396, "loss": 3.396, "time": 0.82032} +{"mode": "train", "epoch": 109, "iter": 3400, "lr": 0.0174, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39844, "top5_acc": 0.65406, "loss_cls": 3.40638, "loss": 3.40638, "time": 0.81906} +{"mode": "train", "epoch": 109, "iter": 3500, "lr": 0.01738, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39828, "top5_acc": 0.65281, "loss_cls": 3.40499, "loss": 3.40499, "time": 0.81846} +{"mode": "train", "epoch": 109, "iter": 3600, "lr": 0.01736, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39891, "top5_acc": 0.65906, "loss_cls": 3.39331, "loss": 3.39331, "time": 0.81687} +{"mode": "train", "epoch": 109, "iter": 3700, "lr": 0.01734, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40078, "top5_acc": 0.66172, "loss_cls": 3.38257, "loss": 3.38257, "time": 0.81891} +{"mode": "val", "epoch": 109, "iter": 309, "lr": 0.01733, "top1_acc": 0.34534, "top5_acc": 0.60168, "mean_class_accuracy": 0.34508} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.01731, "memory": 15990, "data_time": 1.41716, "top1_acc": 0.42156, "top5_acc": 0.67234, "loss_cls": 3.28333, "loss": 3.28333, "time": 2.41389} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.01729, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40875, "top5_acc": 0.66719, "loss_cls": 3.35434, "loss": 3.35434, "time": 0.83753} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.01727, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41859, "top5_acc": 0.67453, "loss_cls": 3.28696, "loss": 3.28696, "time": 0.83677} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.01724, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.4, "top5_acc": 0.66875, "loss_cls": 3.30552, "loss": 3.30552, "time": 0.82913} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.01722, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42016, "top5_acc": 0.67672, "loss_cls": 3.27863, "loss": 3.27863, "time": 0.83008} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.0172, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40125, "top5_acc": 0.65766, "loss_cls": 3.35984, "loss": 3.35984, "time": 0.83051} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.01718, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40359, "top5_acc": 0.66766, "loss_cls": 3.34006, "loss": 3.34006, "time": 0.82777} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.01716, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40203, "top5_acc": 0.66812, "loss_cls": 3.32789, "loss": 3.32789, "time": 0.83246} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.01714, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41109, "top5_acc": 0.67188, "loss_cls": 3.31373, "loss": 3.31373, "time": 0.83302} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.01712, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.40172, "top5_acc": 0.66234, "loss_cls": 3.36879, "loss": 3.36879, "time": 0.83002} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.0171, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40812, "top5_acc": 0.66797, "loss_cls": 3.32874, "loss": 3.32874, "time": 0.82383} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.01708, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41922, "top5_acc": 0.66547, "loss_cls": 3.30697, "loss": 3.30697, "time": 0.82069} +{"mode": "train", "epoch": 110, "iter": 1300, "lr": 0.01705, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40578, "top5_acc": 0.66234, "loss_cls": 3.35458, "loss": 3.35458, "time": 0.82152} +{"mode": "train", "epoch": 110, "iter": 1400, "lr": 0.01703, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40609, "top5_acc": 0.66016, "loss_cls": 3.38644, "loss": 3.38644, "time": 0.81649} +{"mode": "train", "epoch": 110, "iter": 1500, "lr": 0.01701, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41688, "top5_acc": 0.66859, "loss_cls": 3.32349, "loss": 3.32349, "time": 0.81421} +{"mode": "train", "epoch": 110, "iter": 1600, "lr": 0.01699, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40922, "top5_acc": 0.67516, "loss_cls": 3.31028, "loss": 3.31028, "time": 0.82489} +{"mode": "train", "epoch": 110, "iter": 1700, "lr": 0.01697, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40422, "top5_acc": 0.65953, "loss_cls": 3.39312, "loss": 3.39312, "time": 0.82233} +{"mode": "train", "epoch": 110, "iter": 1800, "lr": 0.01695, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40906, "top5_acc": 0.65859, "loss_cls": 3.37133, "loss": 3.37133, "time": 0.82436} +{"mode": "train", "epoch": 110, "iter": 1900, "lr": 0.01693, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40156, "top5_acc": 0.66219, "loss_cls": 3.39298, "loss": 3.39298, "time": 0.82194} +{"mode": "train", "epoch": 110, "iter": 2000, "lr": 0.01691, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40578, "top5_acc": 0.66406, "loss_cls": 3.34766, "loss": 3.34766, "time": 0.81909} +{"mode": "train", "epoch": 110, "iter": 2100, "lr": 0.01689, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40484, "top5_acc": 0.66344, "loss_cls": 3.35587, "loss": 3.35587, "time": 0.81364} +{"mode": "train", "epoch": 110, "iter": 2200, "lr": 0.01687, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40531, "top5_acc": 0.66875, "loss_cls": 3.33883, "loss": 3.33883, "time": 0.81372} +{"mode": "train", "epoch": 110, "iter": 2300, "lr": 0.01685, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40719, "top5_acc": 0.65688, "loss_cls": 3.37087, "loss": 3.37087, "time": 0.81771} +{"mode": "train", "epoch": 110, "iter": 2400, "lr": 0.01682, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40469, "top5_acc": 0.66375, "loss_cls": 3.33962, "loss": 3.33962, "time": 0.82218} +{"mode": "train", "epoch": 110, "iter": 2500, "lr": 0.0168, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40625, "top5_acc": 0.66766, "loss_cls": 3.347, "loss": 3.347, "time": 0.81246} +{"mode": "train", "epoch": 110, "iter": 2600, "lr": 0.01678, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40312, "top5_acc": 0.65656, "loss_cls": 3.35907, "loss": 3.35907, "time": 0.82086} +{"mode": "train", "epoch": 110, "iter": 2700, "lr": 0.01676, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41281, "top5_acc": 0.66922, "loss_cls": 3.30802, "loss": 3.30802, "time": 0.81455} +{"mode": "train", "epoch": 110, "iter": 2800, "lr": 0.01674, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41484, "top5_acc": 0.67266, "loss_cls": 3.32997, "loss": 3.32997, "time": 0.81741} +{"mode": "train", "epoch": 110, "iter": 2900, "lr": 0.01672, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39875, "top5_acc": 0.65469, "loss_cls": 3.40369, "loss": 3.40369, "time": 0.81457} +{"mode": "train", "epoch": 110, "iter": 3000, "lr": 0.0167, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40062, "top5_acc": 0.65359, "loss_cls": 3.3956, "loss": 3.3956, "time": 0.81733} +{"mode": "train", "epoch": 110, "iter": 3100, "lr": 0.01668, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40391, "top5_acc": 0.66172, "loss_cls": 3.37942, "loss": 3.37942, "time": 0.81464} +{"mode": "train", "epoch": 110, "iter": 3200, "lr": 0.01666, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41406, "top5_acc": 0.65891, "loss_cls": 3.34295, "loss": 3.34295, "time": 0.81701} +{"mode": "train", "epoch": 110, "iter": 3300, "lr": 0.01664, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.405, "top5_acc": 0.66406, "loss_cls": 3.34926, "loss": 3.34926, "time": 0.81384} +{"mode": "train", "epoch": 110, "iter": 3400, "lr": 0.01662, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41016, "top5_acc": 0.65984, "loss_cls": 3.37224, "loss": 3.37224, "time": 0.81605} +{"mode": "train", "epoch": 110, "iter": 3500, "lr": 0.01659, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39984, "top5_acc": 0.65969, "loss_cls": 3.36487, "loss": 3.36487, "time": 0.82166} +{"mode": "train", "epoch": 110, "iter": 3600, "lr": 0.01657, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3975, "top5_acc": 0.65922, "loss_cls": 3.37057, "loss": 3.37057, "time": 0.81152} +{"mode": "train", "epoch": 110, "iter": 3700, "lr": 0.01655, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40781, "top5_acc": 0.67297, "loss_cls": 3.32922, "loss": 3.32922, "time": 0.81625} +{"mode": "val", "epoch": 110, "iter": 309, "lr": 0.01654, "top1_acc": 0.3503, "top5_acc": 0.60173, "mean_class_accuracy": 0.34999} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.01652, "memory": 15990, "data_time": 1.35558, "top1_acc": 0.41875, "top5_acc": 0.68328, "loss_cls": 3.27652, "loss": 3.27652, "time": 2.34332} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.0165, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42906, "top5_acc": 0.68453, "loss_cls": 3.23278, "loss": 3.23278, "time": 0.83103} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.01648, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41047, "top5_acc": 0.675, "loss_cls": 3.28927, "loss": 3.28927, "time": 0.81986} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.01646, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40578, "top5_acc": 0.67234, "loss_cls": 3.32177, "loss": 3.32177, "time": 0.81747} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.01644, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42875, "top5_acc": 0.67672, "loss_cls": 3.24579, "loss": 3.24579, "time": 0.81352} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.01642, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40859, "top5_acc": 0.67766, "loss_cls": 3.31337, "loss": 3.31337, "time": 0.8235} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.0164, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41078, "top5_acc": 0.67672, "loss_cls": 3.30781, "loss": 3.30781, "time": 0.81492} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.01638, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42031, "top5_acc": 0.6875, "loss_cls": 3.24083, "loss": 3.24083, "time": 0.81705} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.01636, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41484, "top5_acc": 0.67312, "loss_cls": 3.30076, "loss": 3.30076, "time": 0.81262} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.01634, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41188, "top5_acc": 0.66641, "loss_cls": 3.32184, "loss": 3.32184, "time": 0.81923} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.01632, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41, "top5_acc": 0.66609, "loss_cls": 3.33838, "loss": 3.33838, "time": 0.81733} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.0163, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41906, "top5_acc": 0.67547, "loss_cls": 3.26146, "loss": 3.26146, "time": 0.81501} +{"mode": "train", "epoch": 111, "iter": 1300, "lr": 0.01627, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41266, "top5_acc": 0.66562, "loss_cls": 3.33856, "loss": 3.33856, "time": 0.82042} +{"mode": "train", "epoch": 111, "iter": 1400, "lr": 0.01625, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39344, "top5_acc": 0.65828, "loss_cls": 3.37322, "loss": 3.37322, "time": 0.82225} +{"mode": "train", "epoch": 111, "iter": 1500, "lr": 0.01623, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41281, "top5_acc": 0.67375, "loss_cls": 3.2512, "loss": 3.2512, "time": 0.81788} +{"mode": "train", "epoch": 111, "iter": 1600, "lr": 0.01621, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41094, "top5_acc": 0.66766, "loss_cls": 3.33107, "loss": 3.33107, "time": 0.82688} +{"mode": "train", "epoch": 111, "iter": 1700, "lr": 0.01619, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.4, "top5_acc": 0.65609, "loss_cls": 3.40616, "loss": 3.40616, "time": 0.82396} +{"mode": "train", "epoch": 111, "iter": 1800, "lr": 0.01617, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41719, "top5_acc": 0.67859, "loss_cls": 3.28676, "loss": 3.28676, "time": 0.82382} +{"mode": "train", "epoch": 111, "iter": 1900, "lr": 0.01615, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41359, "top5_acc": 0.66031, "loss_cls": 3.35176, "loss": 3.35176, "time": 0.81841} +{"mode": "train", "epoch": 111, "iter": 2000, "lr": 0.01613, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40109, "top5_acc": 0.66422, "loss_cls": 3.35747, "loss": 3.35747, "time": 0.8158} +{"mode": "train", "epoch": 111, "iter": 2100, "lr": 0.01611, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40109, "top5_acc": 0.66078, "loss_cls": 3.38795, "loss": 3.38795, "time": 0.81705} +{"mode": "train", "epoch": 111, "iter": 2200, "lr": 0.01609, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41125, "top5_acc": 0.66484, "loss_cls": 3.32632, "loss": 3.32632, "time": 0.81805} +{"mode": "train", "epoch": 111, "iter": 2300, "lr": 0.01607, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41078, "top5_acc": 0.66203, "loss_cls": 3.3599, "loss": 3.3599, "time": 0.81579} +{"mode": "train", "epoch": 111, "iter": 2400, "lr": 0.01605, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39188, "top5_acc": 0.66438, "loss_cls": 3.37059, "loss": 3.37059, "time": 0.82714} +{"mode": "train", "epoch": 111, "iter": 2500, "lr": 0.01603, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40672, "top5_acc": 0.66516, "loss_cls": 3.35044, "loss": 3.35044, "time": 0.81612} +{"mode": "train", "epoch": 111, "iter": 2600, "lr": 0.01601, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40656, "top5_acc": 0.66734, "loss_cls": 3.34678, "loss": 3.34678, "time": 0.82243} +{"mode": "train", "epoch": 111, "iter": 2700, "lr": 0.01599, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41109, "top5_acc": 0.66203, "loss_cls": 3.34248, "loss": 3.34248, "time": 0.81935} +{"mode": "train", "epoch": 111, "iter": 2800, "lr": 0.01597, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40672, "top5_acc": 0.67594, "loss_cls": 3.32737, "loss": 3.32737, "time": 0.81657} +{"mode": "train", "epoch": 111, "iter": 2900, "lr": 0.01595, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40859, "top5_acc": 0.66734, "loss_cls": 3.35004, "loss": 3.35004, "time": 0.81227} +{"mode": "train", "epoch": 111, "iter": 3000, "lr": 0.01593, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41844, "top5_acc": 0.66859, "loss_cls": 3.31116, "loss": 3.31116, "time": 0.81776} +{"mode": "train", "epoch": 111, "iter": 3100, "lr": 0.0159, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41234, "top5_acc": 0.66406, "loss_cls": 3.323, "loss": 3.323, "time": 0.81797} +{"mode": "train", "epoch": 111, "iter": 3200, "lr": 0.01588, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40375, "top5_acc": 0.66328, "loss_cls": 3.35269, "loss": 3.35269, "time": 0.81811} +{"mode": "train", "epoch": 111, "iter": 3300, "lr": 0.01586, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40906, "top5_acc": 0.67344, "loss_cls": 3.30857, "loss": 3.30857, "time": 0.81228} +{"mode": "train", "epoch": 111, "iter": 3400, "lr": 0.01584, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40922, "top5_acc": 0.66297, "loss_cls": 3.35587, "loss": 3.35587, "time": 0.81234} +{"mode": "train", "epoch": 111, "iter": 3500, "lr": 0.01582, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41016, "top5_acc": 0.66359, "loss_cls": 3.33673, "loss": 3.33673, "time": 0.81915} +{"mode": "train", "epoch": 111, "iter": 3600, "lr": 0.0158, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4125, "top5_acc": 0.66484, "loss_cls": 3.3111, "loss": 3.3111, "time": 0.81418} +{"mode": "train", "epoch": 111, "iter": 3700, "lr": 0.01578, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40109, "top5_acc": 0.65938, "loss_cls": 3.37316, "loss": 3.37316, "time": 0.82111} +{"mode": "val", "epoch": 111, "iter": 309, "lr": 0.01577, "top1_acc": 0.34316, "top5_acc": 0.6033, "mean_class_accuracy": 0.34288} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.01575, "memory": 15990, "data_time": 1.33727, "top1_acc": 0.41781, "top5_acc": 0.67484, "loss_cls": 3.2588, "loss": 3.2588, "time": 2.34518} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.01573, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41547, "top5_acc": 0.67766, "loss_cls": 3.28525, "loss": 3.28525, "time": 0.81802} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.01571, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41375, "top5_acc": 0.67875, "loss_cls": 3.26524, "loss": 3.26524, "time": 0.8261} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.01569, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42672, "top5_acc": 0.68922, "loss_cls": 3.23031, "loss": 3.23031, "time": 0.81591} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.01567, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41562, "top5_acc": 0.67219, "loss_cls": 3.28492, "loss": 3.28492, "time": 0.81406} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.01565, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41391, "top5_acc": 0.67953, "loss_cls": 3.3065, "loss": 3.3065, "time": 0.82047} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.01563, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42453, "top5_acc": 0.67562, "loss_cls": 3.29175, "loss": 3.29175, "time": 0.81482} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.01561, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41656, "top5_acc": 0.67578, "loss_cls": 3.2647, "loss": 3.2647, "time": 0.81272} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.01559, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42016, "top5_acc": 0.67906, "loss_cls": 3.25612, "loss": 3.25612, "time": 0.82215} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.01557, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42031, "top5_acc": 0.67906, "loss_cls": 3.27093, "loss": 3.27093, "time": 0.81954} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.01555, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40812, "top5_acc": 0.66969, "loss_cls": 3.32398, "loss": 3.32398, "time": 0.81418} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.01553, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42266, "top5_acc": 0.67719, "loss_cls": 3.29053, "loss": 3.29053, "time": 0.81575} +{"mode": "train", "epoch": 112, "iter": 1300, "lr": 0.01551, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41922, "top5_acc": 0.67656, "loss_cls": 3.25579, "loss": 3.25579, "time": 0.81197} +{"mode": "train", "epoch": 112, "iter": 1400, "lr": 0.01549, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42031, "top5_acc": 0.66688, "loss_cls": 3.30043, "loss": 3.30043, "time": 0.8203} +{"mode": "train", "epoch": 112, "iter": 1500, "lr": 0.01547, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41109, "top5_acc": 0.67234, "loss_cls": 3.28027, "loss": 3.28027, "time": 0.82113} +{"mode": "train", "epoch": 112, "iter": 1600, "lr": 0.01545, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40766, "top5_acc": 0.66828, "loss_cls": 3.31035, "loss": 3.31035, "time": 0.82074} +{"mode": "train", "epoch": 112, "iter": 1700, "lr": 0.01543, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41438, "top5_acc": 0.67562, "loss_cls": 3.28835, "loss": 3.28835, "time": 0.82214} +{"mode": "train", "epoch": 112, "iter": 1800, "lr": 0.01541, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4175, "top5_acc": 0.67656, "loss_cls": 3.28724, "loss": 3.28724, "time": 0.82913} +{"mode": "train", "epoch": 112, "iter": 1900, "lr": 0.01539, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40266, "top5_acc": 0.66812, "loss_cls": 3.33839, "loss": 3.33839, "time": 0.81629} +{"mode": "train", "epoch": 112, "iter": 2000, "lr": 0.01537, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40594, "top5_acc": 0.66578, "loss_cls": 3.33535, "loss": 3.33535, "time": 0.82876} +{"mode": "train", "epoch": 112, "iter": 2100, "lr": 0.01535, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40562, "top5_acc": 0.67609, "loss_cls": 3.32596, "loss": 3.32596, "time": 0.8222} +{"mode": "train", "epoch": 112, "iter": 2200, "lr": 0.01533, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42109, "top5_acc": 0.67562, "loss_cls": 3.28114, "loss": 3.28114, "time": 0.8154} +{"mode": "train", "epoch": 112, "iter": 2300, "lr": 0.01531, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41766, "top5_acc": 0.67438, "loss_cls": 3.27199, "loss": 3.27199, "time": 0.81211} +{"mode": "train", "epoch": 112, "iter": 2400, "lr": 0.01529, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.40578, "top5_acc": 0.67109, "loss_cls": 3.32592, "loss": 3.32592, "time": 0.8236} +{"mode": "train", "epoch": 112, "iter": 2500, "lr": 0.01527, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41359, "top5_acc": 0.67062, "loss_cls": 3.3153, "loss": 3.3153, "time": 0.81607} +{"mode": "train", "epoch": 112, "iter": 2600, "lr": 0.01525, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41031, "top5_acc": 0.67172, "loss_cls": 3.2926, "loss": 3.2926, "time": 0.81942} +{"mode": "train", "epoch": 112, "iter": 2700, "lr": 0.01523, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41188, "top5_acc": 0.66656, "loss_cls": 3.30992, "loss": 3.30992, "time": 0.81483} +{"mode": "train", "epoch": 112, "iter": 2800, "lr": 0.01521, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42047, "top5_acc": 0.66562, "loss_cls": 3.30834, "loss": 3.30834, "time": 0.81475} +{"mode": "train", "epoch": 112, "iter": 2900, "lr": 0.01519, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39672, "top5_acc": 0.66312, "loss_cls": 3.36153, "loss": 3.36153, "time": 0.81391} +{"mode": "train", "epoch": 112, "iter": 3000, "lr": 0.01517, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40109, "top5_acc": 0.66312, "loss_cls": 3.37492, "loss": 3.37492, "time": 0.8132} +{"mode": "train", "epoch": 112, "iter": 3100, "lr": 0.01515, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40016, "top5_acc": 0.65953, "loss_cls": 3.37839, "loss": 3.37839, "time": 0.82026} +{"mode": "train", "epoch": 112, "iter": 3200, "lr": 0.01513, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41016, "top5_acc": 0.67234, "loss_cls": 3.31968, "loss": 3.31968, "time": 0.8136} +{"mode": "train", "epoch": 112, "iter": 3300, "lr": 0.01511, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40594, "top5_acc": 0.66609, "loss_cls": 3.33394, "loss": 3.33394, "time": 0.81378} +{"mode": "train", "epoch": 112, "iter": 3400, "lr": 0.01509, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40828, "top5_acc": 0.67047, "loss_cls": 3.3362, "loss": 3.3362, "time": 0.81302} +{"mode": "train", "epoch": 112, "iter": 3500, "lr": 0.01507, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41125, "top5_acc": 0.66812, "loss_cls": 3.30611, "loss": 3.30611, "time": 0.81553} +{"mode": "train", "epoch": 112, "iter": 3600, "lr": 0.01505, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40453, "top5_acc": 0.66594, "loss_cls": 3.3173, "loss": 3.3173, "time": 0.82416} +{"mode": "train", "epoch": 112, "iter": 3700, "lr": 0.01503, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41453, "top5_acc": 0.66609, "loss_cls": 3.32078, "loss": 3.32078, "time": 0.8186} +{"mode": "val", "epoch": 112, "iter": 309, "lr": 0.01502, "top1_acc": 0.35714, "top5_acc": 0.6145, "mean_class_accuracy": 0.3569} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.015, "memory": 15990, "data_time": 1.3066, "top1_acc": 0.42875, "top5_acc": 0.69141, "loss_cls": 3.18502, "loss": 3.18502, "time": 2.28881} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.01498, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42875, "top5_acc": 0.68766, "loss_cls": 3.19635, "loss": 3.19635, "time": 0.82943} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.01496, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42328, "top5_acc": 0.6925, "loss_cls": 3.21529, "loss": 3.21529, "time": 0.8176} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.01494, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41859, "top5_acc": 0.68062, "loss_cls": 3.2486, "loss": 3.2486, "time": 0.81735} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.01492, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43234, "top5_acc": 0.69406, "loss_cls": 3.19644, "loss": 3.19644, "time": 0.8159} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.0149, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42562, "top5_acc": 0.68141, "loss_cls": 3.241, "loss": 3.241, "time": 0.81267} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.01488, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41766, "top5_acc": 0.67844, "loss_cls": 3.25878, "loss": 3.25878, "time": 0.81606} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.01486, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41969, "top5_acc": 0.67906, "loss_cls": 3.23747, "loss": 3.23747, "time": 0.81826} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.01484, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40531, "top5_acc": 0.66406, "loss_cls": 3.35048, "loss": 3.35048, "time": 0.81681} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.01482, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41453, "top5_acc": 0.67594, "loss_cls": 3.26353, "loss": 3.26353, "time": 0.81226} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0148, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40812, "top5_acc": 0.66688, "loss_cls": 3.29114, "loss": 3.29114, "time": 0.81629} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.01478, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40359, "top5_acc": 0.67109, "loss_cls": 3.3121, "loss": 3.3121, "time": 0.81245} +{"mode": "train", "epoch": 113, "iter": 1300, "lr": 0.01476, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40859, "top5_acc": 0.67094, "loss_cls": 3.28727, "loss": 3.28727, "time": 0.81519} +{"mode": "train", "epoch": 113, "iter": 1400, "lr": 0.01474, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41891, "top5_acc": 0.67969, "loss_cls": 3.25922, "loss": 3.25922, "time": 0.81259} +{"mode": "train", "epoch": 113, "iter": 1500, "lr": 0.01472, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41297, "top5_acc": 0.66812, "loss_cls": 3.31515, "loss": 3.31515, "time": 0.81567} +{"mode": "train", "epoch": 113, "iter": 1600, "lr": 0.0147, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41781, "top5_acc": 0.68406, "loss_cls": 3.23673, "loss": 3.23673, "time": 0.81618} +{"mode": "train", "epoch": 113, "iter": 1700, "lr": 0.01468, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41344, "top5_acc": 0.67625, "loss_cls": 3.2924, "loss": 3.2924, "time": 0.81901} +{"mode": "train", "epoch": 113, "iter": 1800, "lr": 0.01466, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41141, "top5_acc": 0.66688, "loss_cls": 3.32247, "loss": 3.32247, "time": 0.81829} +{"mode": "train", "epoch": 113, "iter": 1900, "lr": 0.01464, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4175, "top5_acc": 0.6725, "loss_cls": 3.29331, "loss": 3.29331, "time": 0.81932} +{"mode": "train", "epoch": 113, "iter": 2000, "lr": 0.01462, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42062, "top5_acc": 0.68172, "loss_cls": 3.25827, "loss": 3.25827, "time": 0.82086} +{"mode": "train", "epoch": 113, "iter": 2100, "lr": 0.0146, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41547, "top5_acc": 0.67312, "loss_cls": 3.31884, "loss": 3.31884, "time": 0.81991} +{"mode": "train", "epoch": 113, "iter": 2200, "lr": 0.01458, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40484, "top5_acc": 0.66281, "loss_cls": 3.36157, "loss": 3.36157, "time": 0.81501} +{"mode": "train", "epoch": 113, "iter": 2300, "lr": 0.01456, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41219, "top5_acc": 0.67203, "loss_cls": 3.32162, "loss": 3.32162, "time": 0.81707} +{"mode": "train", "epoch": 113, "iter": 2400, "lr": 0.01454, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41781, "top5_acc": 0.6825, "loss_cls": 3.25925, "loss": 3.25925, "time": 0.8255} +{"mode": "train", "epoch": 113, "iter": 2500, "lr": 0.01452, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41047, "top5_acc": 0.66938, "loss_cls": 3.31157, "loss": 3.31157, "time": 0.82287} +{"mode": "train", "epoch": 113, "iter": 2600, "lr": 0.0145, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40812, "top5_acc": 0.67078, "loss_cls": 3.30598, "loss": 3.30598, "time": 0.82344} +{"mode": "train", "epoch": 113, "iter": 2700, "lr": 0.01448, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42438, "top5_acc": 0.6775, "loss_cls": 3.29339, "loss": 3.29339, "time": 0.81666} +{"mode": "train", "epoch": 113, "iter": 2800, "lr": 0.01446, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42094, "top5_acc": 0.67938, "loss_cls": 3.28026, "loss": 3.28026, "time": 0.81427} +{"mode": "train", "epoch": 113, "iter": 2900, "lr": 0.01444, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41375, "top5_acc": 0.66938, "loss_cls": 3.28775, "loss": 3.28775, "time": 0.81651} +{"mode": "train", "epoch": 113, "iter": 3000, "lr": 0.01442, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41375, "top5_acc": 0.67406, "loss_cls": 3.28918, "loss": 3.28918, "time": 0.82546} +{"mode": "train", "epoch": 113, "iter": 3100, "lr": 0.0144, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41094, "top5_acc": 0.66375, "loss_cls": 3.33011, "loss": 3.33011, "time": 0.81389} +{"mode": "train", "epoch": 113, "iter": 3200, "lr": 0.01438, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42141, "top5_acc": 0.68, "loss_cls": 3.24541, "loss": 3.24541, "time": 0.81898} +{"mode": "train", "epoch": 113, "iter": 3300, "lr": 0.01436, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42281, "top5_acc": 0.67156, "loss_cls": 3.29279, "loss": 3.29279, "time": 0.81058} +{"mode": "train", "epoch": 113, "iter": 3400, "lr": 0.01434, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40938, "top5_acc": 0.67578, "loss_cls": 3.30632, "loss": 3.30632, "time": 0.8154} +{"mode": "train", "epoch": 113, "iter": 3500, "lr": 0.01432, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41984, "top5_acc": 0.66766, "loss_cls": 3.33469, "loss": 3.33469, "time": 0.81317} +{"mode": "train", "epoch": 113, "iter": 3600, "lr": 0.01431, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42297, "top5_acc": 0.67766, "loss_cls": 3.28154, "loss": 3.28154, "time": 0.81985} +{"mode": "train", "epoch": 113, "iter": 3700, "lr": 0.01429, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42484, "top5_acc": 0.68188, "loss_cls": 3.26839, "loss": 3.26839, "time": 0.81883} +{"mode": "val", "epoch": 113, "iter": 309, "lr": 0.01428, "top1_acc": 0.35319, "top5_acc": 0.60523, "mean_class_accuracy": 0.35306} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.01426, "memory": 15990, "data_time": 1.33544, "top1_acc": 0.43391, "top5_acc": 0.695, "loss_cls": 3.18762, "loss": 3.18762, "time": 2.32803} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.01424, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.435, "top5_acc": 0.69094, "loss_cls": 3.17767, "loss": 3.17767, "time": 0.82004} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.01422, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42672, "top5_acc": 0.695, "loss_cls": 3.20602, "loss": 3.20602, "time": 0.81746} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.0142, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42234, "top5_acc": 0.67625, "loss_cls": 3.24456, "loss": 3.24456, "time": 0.81482} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.01418, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42312, "top5_acc": 0.67844, "loss_cls": 3.24004, "loss": 3.24004, "time": 0.82056} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.01416, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42, "top5_acc": 0.68062, "loss_cls": 3.25407, "loss": 3.25407, "time": 0.81525} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.01414, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42734, "top5_acc": 0.68172, "loss_cls": 3.23267, "loss": 3.23267, "time": 0.81914} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.01412, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41312, "top5_acc": 0.68422, "loss_cls": 3.273, "loss": 3.273, "time": 0.81909} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.0141, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42578, "top5_acc": 0.68625, "loss_cls": 3.21444, "loss": 3.21444, "time": 0.81839} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.01408, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41891, "top5_acc": 0.67609, "loss_cls": 3.29042, "loss": 3.29042, "time": 0.81445} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.01406, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41266, "top5_acc": 0.67578, "loss_cls": 3.28099, "loss": 3.28099, "time": 0.81956} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.01404, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41969, "top5_acc": 0.68203, "loss_cls": 3.21762, "loss": 3.21762, "time": 0.81954} +{"mode": "train", "epoch": 114, "iter": 1300, "lr": 0.01402, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.425, "top5_acc": 0.67719, "loss_cls": 3.25062, "loss": 3.25062, "time": 0.81631} +{"mode": "train", "epoch": 114, "iter": 1400, "lr": 0.014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42703, "top5_acc": 0.69047, "loss_cls": 3.21151, "loss": 3.21151, "time": 0.82265} +{"mode": "train", "epoch": 114, "iter": 1500, "lr": 0.01398, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42438, "top5_acc": 0.68438, "loss_cls": 3.20789, "loss": 3.20789, "time": 0.81988} +{"mode": "train", "epoch": 114, "iter": 1600, "lr": 0.01397, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40656, "top5_acc": 0.66969, "loss_cls": 3.29605, "loss": 3.29605, "time": 0.82798} +{"mode": "train", "epoch": 114, "iter": 1700, "lr": 0.01395, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43375, "top5_acc": 0.68469, "loss_cls": 3.22227, "loss": 3.22227, "time": 0.81836} +{"mode": "train", "epoch": 114, "iter": 1800, "lr": 0.01393, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42312, "top5_acc": 0.68938, "loss_cls": 3.22934, "loss": 3.22934, "time": 0.82196} +{"mode": "train", "epoch": 114, "iter": 1900, "lr": 0.01391, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42234, "top5_acc": 0.68062, "loss_cls": 3.24393, "loss": 3.24393, "time": 0.81707} +{"mode": "train", "epoch": 114, "iter": 2000, "lr": 0.01389, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42562, "top5_acc": 0.68, "loss_cls": 3.25695, "loss": 3.25695, "time": 0.81503} +{"mode": "train", "epoch": 114, "iter": 2100, "lr": 0.01387, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41797, "top5_acc": 0.67547, "loss_cls": 3.29918, "loss": 3.29918, "time": 0.81375} +{"mode": "train", "epoch": 114, "iter": 2200, "lr": 0.01385, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42547, "top5_acc": 0.67484, "loss_cls": 3.26521, "loss": 3.26521, "time": 0.8121} +{"mode": "train", "epoch": 114, "iter": 2300, "lr": 0.01383, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42234, "top5_acc": 0.67016, "loss_cls": 3.28033, "loss": 3.28033, "time": 0.81579} +{"mode": "train", "epoch": 114, "iter": 2400, "lr": 0.01381, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41984, "top5_acc": 0.66891, "loss_cls": 3.28888, "loss": 3.28888, "time": 0.8171} +{"mode": "train", "epoch": 114, "iter": 2500, "lr": 0.01379, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42391, "top5_acc": 0.67734, "loss_cls": 3.23897, "loss": 3.23897, "time": 0.82136} +{"mode": "train", "epoch": 114, "iter": 2600, "lr": 0.01377, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41891, "top5_acc": 0.67031, "loss_cls": 3.28787, "loss": 3.28787, "time": 0.8182} +{"mode": "train", "epoch": 114, "iter": 2700, "lr": 0.01375, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42172, "top5_acc": 0.67703, "loss_cls": 3.26177, "loss": 3.26177, "time": 0.81988} +{"mode": "train", "epoch": 114, "iter": 2800, "lr": 0.01373, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41203, "top5_acc": 0.66719, "loss_cls": 3.32363, "loss": 3.32363, "time": 0.8131} +{"mode": "train", "epoch": 114, "iter": 2900, "lr": 0.01371, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40922, "top5_acc": 0.66797, "loss_cls": 3.29925, "loss": 3.29925, "time": 0.81416} +{"mode": "train", "epoch": 114, "iter": 3000, "lr": 0.01369, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42219, "top5_acc": 0.67453, "loss_cls": 3.26345, "loss": 3.26345, "time": 0.81885} +{"mode": "train", "epoch": 114, "iter": 3100, "lr": 0.01368, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40812, "top5_acc": 0.66375, "loss_cls": 3.30416, "loss": 3.30416, "time": 0.81411} +{"mode": "train", "epoch": 114, "iter": 3200, "lr": 0.01366, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40984, "top5_acc": 0.67328, "loss_cls": 3.29759, "loss": 3.29759, "time": 0.81832} +{"mode": "train", "epoch": 114, "iter": 3300, "lr": 0.01364, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41531, "top5_acc": 0.66641, "loss_cls": 3.2966, "loss": 3.2966, "time": 0.81768} +{"mode": "train", "epoch": 114, "iter": 3400, "lr": 0.01362, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41469, "top5_acc": 0.67391, "loss_cls": 3.30564, "loss": 3.30564, "time": 0.81842} +{"mode": "train", "epoch": 114, "iter": 3500, "lr": 0.0136, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42188, "top5_acc": 0.68359, "loss_cls": 3.25528, "loss": 3.25528, "time": 0.81635} +{"mode": "train", "epoch": 114, "iter": 3600, "lr": 0.01358, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42922, "top5_acc": 0.68547, "loss_cls": 3.23095, "loss": 3.23095, "time": 0.81542} +{"mode": "train", "epoch": 114, "iter": 3700, "lr": 0.01356, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41859, "top5_acc": 0.66422, "loss_cls": 3.29507, "loss": 3.29507, "time": 0.8217} +{"mode": "val", "epoch": 114, "iter": 309, "lr": 0.01355, "top1_acc": 0.36413, "top5_acc": 0.61612, "mean_class_accuracy": 0.36381} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.01353, "memory": 15990, "data_time": 1.30598, "top1_acc": 0.43859, "top5_acc": 0.70062, "loss_cls": 3.1557, "loss": 3.1557, "time": 2.28083} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.01351, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42734, "top5_acc": 0.68672, "loss_cls": 3.21641, "loss": 3.21641, "time": 0.82211} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.01349, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42375, "top5_acc": 0.68812, "loss_cls": 3.17998, "loss": 3.17998, "time": 0.81335} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.01348, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42281, "top5_acc": 0.68328, "loss_cls": 3.22045, "loss": 3.22045, "time": 0.81418} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.01346, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42359, "top5_acc": 0.69031, "loss_cls": 3.19702, "loss": 3.19702, "time": 0.81496} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.01344, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43047, "top5_acc": 0.69766, "loss_cls": 3.16935, "loss": 3.16935, "time": 0.81338} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.01342, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42297, "top5_acc": 0.67531, "loss_cls": 3.25448, "loss": 3.25448, "time": 0.81912} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.0134, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42422, "top5_acc": 0.68422, "loss_cls": 3.22855, "loss": 3.22855, "time": 0.82178} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.01338, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43188, "top5_acc": 0.69094, "loss_cls": 3.19722, "loss": 3.19722, "time": 0.81513} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.01336, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41938, "top5_acc": 0.68438, "loss_cls": 3.23488, "loss": 3.23488, "time": 0.81544} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.01334, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41844, "top5_acc": 0.68438, "loss_cls": 3.23913, "loss": 3.23913, "time": 0.81281} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.01332, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42953, "top5_acc": 0.68, "loss_cls": 3.23687, "loss": 3.23687, "time": 0.81489} +{"mode": "train", "epoch": 115, "iter": 1300, "lr": 0.0133, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4175, "top5_acc": 0.67188, "loss_cls": 3.27899, "loss": 3.27899, "time": 0.81569} +{"mode": "train", "epoch": 115, "iter": 1400, "lr": 0.01328, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41938, "top5_acc": 0.68562, "loss_cls": 3.22704, "loss": 3.22704, "time": 0.81484} +{"mode": "train", "epoch": 115, "iter": 1500, "lr": 0.01327, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42172, "top5_acc": 0.68797, "loss_cls": 3.23543, "loss": 3.23543, "time": 0.81764} +{"mode": "train", "epoch": 115, "iter": 1600, "lr": 0.01325, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42406, "top5_acc": 0.68531, "loss_cls": 3.23336, "loss": 3.23336, "time": 0.82367} +{"mode": "train", "epoch": 115, "iter": 1700, "lr": 0.01323, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42562, "top5_acc": 0.68594, "loss_cls": 3.23595, "loss": 3.23595, "time": 0.81745} +{"mode": "train", "epoch": 115, "iter": 1800, "lr": 0.01321, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41516, "top5_acc": 0.68234, "loss_cls": 3.24679, "loss": 3.24679, "time": 0.81511} +{"mode": "train", "epoch": 115, "iter": 1900, "lr": 0.01319, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43281, "top5_acc": 0.67844, "loss_cls": 3.23737, "loss": 3.23737, "time": 0.81541} +{"mode": "train", "epoch": 115, "iter": 2000, "lr": 0.01317, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42391, "top5_acc": 0.68094, "loss_cls": 3.21476, "loss": 3.21476, "time": 0.81699} +{"mode": "train", "epoch": 115, "iter": 2100, "lr": 0.01315, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42453, "top5_acc": 0.67734, "loss_cls": 3.28211, "loss": 3.28211, "time": 0.81565} +{"mode": "train", "epoch": 115, "iter": 2200, "lr": 0.01313, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43469, "top5_acc": 0.68453, "loss_cls": 3.19844, "loss": 3.19844, "time": 0.81734} +{"mode": "train", "epoch": 115, "iter": 2300, "lr": 0.01311, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41906, "top5_acc": 0.67328, "loss_cls": 3.27593, "loss": 3.27593, "time": 0.8186} +{"mode": "train", "epoch": 115, "iter": 2400, "lr": 0.0131, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42391, "top5_acc": 0.6725, "loss_cls": 3.25852, "loss": 3.25852, "time": 0.8295} +{"mode": "train", "epoch": 115, "iter": 2500, "lr": 0.01308, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41969, "top5_acc": 0.67188, "loss_cls": 3.28557, "loss": 3.28557, "time": 0.81897} +{"mode": "train", "epoch": 115, "iter": 2600, "lr": 0.01306, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43156, "top5_acc": 0.68688, "loss_cls": 3.19858, "loss": 3.19858, "time": 0.81906} +{"mode": "train", "epoch": 115, "iter": 2700, "lr": 0.01304, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42719, "top5_acc": 0.68109, "loss_cls": 3.24626, "loss": 3.24626, "time": 0.8225} +{"mode": "train", "epoch": 115, "iter": 2800, "lr": 0.01302, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41672, "top5_acc": 0.69125, "loss_cls": 3.24141, "loss": 3.24141, "time": 0.81323} +{"mode": "train", "epoch": 115, "iter": 2900, "lr": 0.013, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41984, "top5_acc": 0.68203, "loss_cls": 3.24904, "loss": 3.24904, "time": 0.81888} +{"mode": "train", "epoch": 115, "iter": 3000, "lr": 0.01298, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42266, "top5_acc": 0.6775, "loss_cls": 3.22646, "loss": 3.22646, "time": 0.81497} +{"mode": "train", "epoch": 115, "iter": 3100, "lr": 0.01296, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41766, "top5_acc": 0.67438, "loss_cls": 3.26344, "loss": 3.26344, "time": 0.8173} +{"mode": "train", "epoch": 115, "iter": 3200, "lr": 0.01295, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42375, "top5_acc": 0.66656, "loss_cls": 3.26089, "loss": 3.26089, "time": 0.8163} +{"mode": "train", "epoch": 115, "iter": 3300, "lr": 0.01293, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42156, "top5_acc": 0.68125, "loss_cls": 3.22794, "loss": 3.22794, "time": 0.81952} +{"mode": "train", "epoch": 115, "iter": 3400, "lr": 0.01291, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41672, "top5_acc": 0.66766, "loss_cls": 3.29026, "loss": 3.29026, "time": 0.8175} +{"mode": "train", "epoch": 115, "iter": 3500, "lr": 0.01289, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41562, "top5_acc": 0.6775, "loss_cls": 3.27698, "loss": 3.27698, "time": 0.81567} +{"mode": "train", "epoch": 115, "iter": 3600, "lr": 0.01287, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41797, "top5_acc": 0.67484, "loss_cls": 3.26265, "loss": 3.26265, "time": 0.81678} +{"mode": "train", "epoch": 115, "iter": 3700, "lr": 0.01285, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43188, "top5_acc": 0.685, "loss_cls": 3.22062, "loss": 3.22062, "time": 0.81284} +{"mode": "val", "epoch": 115, "iter": 309, "lr": 0.01284, "top1_acc": 0.36286, "top5_acc": 0.62088, "mean_class_accuracy": 0.36257} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.01282, "memory": 15990, "data_time": 1.3732, "top1_acc": 0.43703, "top5_acc": 0.6925, "loss_cls": 3.15509, "loss": 3.15509, "time": 2.39627} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.01281, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44094, "top5_acc": 0.7, "loss_cls": 3.1076, "loss": 3.1076, "time": 0.84142} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.01279, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43125, "top5_acc": 0.69203, "loss_cls": 3.16419, "loss": 3.16419, "time": 0.84231} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.01277, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42438, "top5_acc": 0.68, "loss_cls": 3.21157, "loss": 3.21157, "time": 0.83596} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.01275, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43969, "top5_acc": 0.69703, "loss_cls": 3.13879, "loss": 3.13879, "time": 0.84009} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.01273, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43594, "top5_acc": 0.69172, "loss_cls": 3.19033, "loss": 3.19033, "time": 0.83715} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.01271, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.425, "top5_acc": 0.68984, "loss_cls": 3.22447, "loss": 3.22447, "time": 0.83697} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.01269, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42719, "top5_acc": 0.68828, "loss_cls": 3.21847, "loss": 3.21847, "time": 0.83732} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.01268, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44203, "top5_acc": 0.69469, "loss_cls": 3.16428, "loss": 3.16428, "time": 0.83496} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.01266, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43141, "top5_acc": 0.68891, "loss_cls": 3.19344, "loss": 3.19344, "time": 0.82916} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.01264, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44094, "top5_acc": 0.69625, "loss_cls": 3.16721, "loss": 3.16721, "time": 0.83752} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.01262, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43219, "top5_acc": 0.68234, "loss_cls": 3.1941, "loss": 3.1941, "time": 0.8335} +{"mode": "train", "epoch": 116, "iter": 1300, "lr": 0.0126, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43016, "top5_acc": 0.6825, "loss_cls": 3.23138, "loss": 3.23138, "time": 0.83154} +{"mode": "train", "epoch": 116, "iter": 1400, "lr": 0.01258, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.42625, "top5_acc": 0.68859, "loss_cls": 3.21324, "loss": 3.21324, "time": 0.83713} +{"mode": "train", "epoch": 116, "iter": 1500, "lr": 0.01256, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43688, "top5_acc": 0.69078, "loss_cls": 3.19045, "loss": 3.19045, "time": 0.8346} +{"mode": "train", "epoch": 116, "iter": 1600, "lr": 0.01255, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43703, "top5_acc": 0.68438, "loss_cls": 3.19924, "loss": 3.19924, "time": 0.83232} +{"mode": "train", "epoch": 116, "iter": 1700, "lr": 0.01253, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.42281, "top5_acc": 0.67672, "loss_cls": 3.26156, "loss": 3.26156, "time": 0.84303} +{"mode": "train", "epoch": 116, "iter": 1800, "lr": 0.01251, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42125, "top5_acc": 0.68188, "loss_cls": 3.23966, "loss": 3.23966, "time": 0.84042} +{"mode": "train", "epoch": 116, "iter": 1900, "lr": 0.01249, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42234, "top5_acc": 0.68297, "loss_cls": 3.22375, "loss": 3.22375, "time": 0.83606} +{"mode": "train", "epoch": 116, "iter": 2000, "lr": 0.01247, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43516, "top5_acc": 0.69422, "loss_cls": 3.17455, "loss": 3.17455, "time": 0.83307} +{"mode": "train", "epoch": 116, "iter": 2100, "lr": 0.01245, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43125, "top5_acc": 0.68547, "loss_cls": 3.2419, "loss": 3.2419, "time": 0.83714} +{"mode": "train", "epoch": 116, "iter": 2200, "lr": 0.01243, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42453, "top5_acc": 0.68625, "loss_cls": 3.21487, "loss": 3.21487, "time": 0.82606} +{"mode": "train", "epoch": 116, "iter": 2300, "lr": 0.01242, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42922, "top5_acc": 0.6875, "loss_cls": 3.20363, "loss": 3.20363, "time": 0.83464} +{"mode": "train", "epoch": 116, "iter": 2400, "lr": 0.0124, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43078, "top5_acc": 0.6825, "loss_cls": 3.19999, "loss": 3.19999, "time": 0.83248} +{"mode": "train", "epoch": 116, "iter": 2500, "lr": 0.01238, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42484, "top5_acc": 0.68625, "loss_cls": 3.21654, "loss": 3.21654, "time": 0.829} +{"mode": "train", "epoch": 116, "iter": 2600, "lr": 0.01236, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4225, "top5_acc": 0.68094, "loss_cls": 3.24233, "loss": 3.24233, "time": 0.8385} +{"mode": "train", "epoch": 116, "iter": 2700, "lr": 0.01234, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43359, "top5_acc": 0.68797, "loss_cls": 3.21839, "loss": 3.21839, "time": 0.82894} +{"mode": "train", "epoch": 116, "iter": 2800, "lr": 0.01232, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.4175, "top5_acc": 0.67734, "loss_cls": 3.24285, "loss": 3.24285, "time": 0.82714} +{"mode": "train", "epoch": 116, "iter": 2900, "lr": 0.01231, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43, "top5_acc": 0.68375, "loss_cls": 3.22303, "loss": 3.22303, "time": 0.82635} +{"mode": "train", "epoch": 116, "iter": 3000, "lr": 0.01229, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42844, "top5_acc": 0.68656, "loss_cls": 3.19989, "loss": 3.19989, "time": 0.8277} +{"mode": "train", "epoch": 116, "iter": 3100, "lr": 0.01227, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.4325, "top5_acc": 0.67859, "loss_cls": 3.21757, "loss": 3.21757, "time": 0.83242} +{"mode": "train", "epoch": 116, "iter": 3200, "lr": 0.01225, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42391, "top5_acc": 0.68109, "loss_cls": 3.23285, "loss": 3.23285, "time": 0.8271} +{"mode": "train", "epoch": 116, "iter": 3300, "lr": 0.01223, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43047, "top5_acc": 0.68078, "loss_cls": 3.24225, "loss": 3.24225, "time": 0.82743} +{"mode": "train", "epoch": 116, "iter": 3400, "lr": 0.01221, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42547, "top5_acc": 0.67781, "loss_cls": 3.23815, "loss": 3.23815, "time": 0.82767} +{"mode": "train", "epoch": 116, "iter": 3500, "lr": 0.0122, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41703, "top5_acc": 0.67906, "loss_cls": 3.25484, "loss": 3.25484, "time": 0.82768} +{"mode": "train", "epoch": 116, "iter": 3600, "lr": 0.01218, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42047, "top5_acc": 0.67766, "loss_cls": 3.2645, "loss": 3.2645, "time": 0.83175} +{"mode": "train", "epoch": 116, "iter": 3700, "lr": 0.01216, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42484, "top5_acc": 0.68219, "loss_cls": 3.21925, "loss": 3.21925, "time": 0.82496} +{"mode": "val", "epoch": 116, "iter": 309, "lr": 0.01215, "top1_acc": 0.36296, "top5_acc": 0.61738, "mean_class_accuracy": 0.36282} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.01213, "memory": 15990, "data_time": 1.33568, "top1_acc": 0.45016, "top5_acc": 0.70312, "loss_cls": 3.13545, "loss": 3.13545, "time": 2.34319} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.01211, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43906, "top5_acc": 0.70656, "loss_cls": 3.12161, "loss": 3.12161, "time": 0.83844} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.0121, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44656, "top5_acc": 0.70375, "loss_cls": 3.11983, "loss": 3.11983, "time": 0.83916} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.01208, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44234, "top5_acc": 0.69984, "loss_cls": 3.13384, "loss": 3.13384, "time": 0.83751} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.01206, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43281, "top5_acc": 0.69375, "loss_cls": 3.17554, "loss": 3.17554, "time": 0.83119} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.01204, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43344, "top5_acc": 0.68766, "loss_cls": 3.20584, "loss": 3.20584, "time": 0.82364} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.01202, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42969, "top5_acc": 0.68703, "loss_cls": 3.21907, "loss": 3.21907, "time": 0.8295} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.012, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43734, "top5_acc": 0.68281, "loss_cls": 3.19316, "loss": 3.19316, "time": 0.82441} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.01199, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42828, "top5_acc": 0.68766, "loss_cls": 3.19215, "loss": 3.19215, "time": 0.8268} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.01197, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43312, "top5_acc": 0.69641, "loss_cls": 3.16316, "loss": 3.16316, "time": 0.82461} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.01195, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42812, "top5_acc": 0.68625, "loss_cls": 3.20825, "loss": 3.20825, "time": 0.82789} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.01193, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.425, "top5_acc": 0.68469, "loss_cls": 3.19809, "loss": 3.19809, "time": 0.82339} +{"mode": "train", "epoch": 117, "iter": 1300, "lr": 0.01191, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43141, "top5_acc": 0.67844, "loss_cls": 3.20725, "loss": 3.20725, "time": 0.81656} +{"mode": "train", "epoch": 117, "iter": 1400, "lr": 0.0119, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43453, "top5_acc": 0.68703, "loss_cls": 3.17309, "loss": 3.17309, "time": 0.82258} +{"mode": "train", "epoch": 117, "iter": 1500, "lr": 0.01188, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43594, "top5_acc": 0.69125, "loss_cls": 3.16029, "loss": 3.16029, "time": 0.82681} +{"mode": "train", "epoch": 117, "iter": 1600, "lr": 0.01186, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43516, "top5_acc": 0.68969, "loss_cls": 3.20037, "loss": 3.20037, "time": 0.82138} +{"mode": "train", "epoch": 117, "iter": 1700, "lr": 0.01184, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42875, "top5_acc": 0.69328, "loss_cls": 3.16293, "loss": 3.16293, "time": 0.81958} +{"mode": "train", "epoch": 117, "iter": 1800, "lr": 0.01182, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43062, "top5_acc": 0.69016, "loss_cls": 3.17932, "loss": 3.17932, "time": 0.82347} +{"mode": "train", "epoch": 117, "iter": 1900, "lr": 0.01181, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42781, "top5_acc": 0.68766, "loss_cls": 3.20272, "loss": 3.20272, "time": 0.82464} +{"mode": "train", "epoch": 117, "iter": 2000, "lr": 0.01179, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42984, "top5_acc": 0.69453, "loss_cls": 3.17502, "loss": 3.17502, "time": 0.81893} +{"mode": "train", "epoch": 117, "iter": 2100, "lr": 0.01177, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42688, "top5_acc": 0.68906, "loss_cls": 3.20299, "loss": 3.20299, "time": 0.81407} +{"mode": "train", "epoch": 117, "iter": 2200, "lr": 0.01175, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44078, "top5_acc": 0.69109, "loss_cls": 3.17916, "loss": 3.17916, "time": 0.81719} +{"mode": "train", "epoch": 117, "iter": 2300, "lr": 0.01173, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44625, "top5_acc": 0.70234, "loss_cls": 3.11401, "loss": 3.11401, "time": 0.82498} +{"mode": "train", "epoch": 117, "iter": 2400, "lr": 0.01172, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43062, "top5_acc": 0.68609, "loss_cls": 3.19721, "loss": 3.19721, "time": 0.81927} +{"mode": "train", "epoch": 117, "iter": 2500, "lr": 0.0117, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43359, "top5_acc": 0.68047, "loss_cls": 3.19688, "loss": 3.19688, "time": 0.81696} +{"mode": "train", "epoch": 117, "iter": 2600, "lr": 0.01168, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41984, "top5_acc": 0.68797, "loss_cls": 3.20646, "loss": 3.20646, "time": 0.81994} +{"mode": "train", "epoch": 117, "iter": 2700, "lr": 0.01166, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43938, "top5_acc": 0.68953, "loss_cls": 3.18296, "loss": 3.18296, "time": 0.81623} +{"mode": "train", "epoch": 117, "iter": 2800, "lr": 0.01164, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44234, "top5_acc": 0.69719, "loss_cls": 3.15676, "loss": 3.15676, "time": 0.82493} +{"mode": "train", "epoch": 117, "iter": 2900, "lr": 0.01163, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43562, "top5_acc": 0.68594, "loss_cls": 3.19154, "loss": 3.19154, "time": 0.81066} +{"mode": "train", "epoch": 117, "iter": 3000, "lr": 0.01161, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42359, "top5_acc": 0.68516, "loss_cls": 3.2163, "loss": 3.2163, "time": 0.81644} +{"mode": "train", "epoch": 117, "iter": 3100, "lr": 0.01159, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4225, "top5_acc": 0.68438, "loss_cls": 3.21857, "loss": 3.21857, "time": 0.81507} +{"mode": "train", "epoch": 117, "iter": 3200, "lr": 0.01157, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43953, "top5_acc": 0.685, "loss_cls": 3.18834, "loss": 3.18834, "time": 0.82221} +{"mode": "train", "epoch": 117, "iter": 3300, "lr": 0.01155, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42766, "top5_acc": 0.6825, "loss_cls": 3.19709, "loss": 3.19709, "time": 0.81789} +{"mode": "train", "epoch": 117, "iter": 3400, "lr": 0.01154, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42734, "top5_acc": 0.68625, "loss_cls": 3.21611, "loss": 3.21611, "time": 0.82146} +{"mode": "train", "epoch": 117, "iter": 3500, "lr": 0.01152, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42766, "top5_acc": 0.67875, "loss_cls": 3.24616, "loss": 3.24616, "time": 0.81996} +{"mode": "train", "epoch": 117, "iter": 3600, "lr": 0.0115, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43, "top5_acc": 0.68172, "loss_cls": 3.20533, "loss": 3.20533, "time": 0.81631} +{"mode": "train", "epoch": 117, "iter": 3700, "lr": 0.01148, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43172, "top5_acc": 0.68156, "loss_cls": 3.2247, "loss": 3.2247, "time": 0.8197} +{"mode": "val", "epoch": 117, "iter": 309, "lr": 0.01147, "top1_acc": 0.36712, "top5_acc": 0.62209, "mean_class_accuracy": 0.3669} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.01146, "memory": 15990, "data_time": 1.3249, "top1_acc": 0.44469, "top5_acc": 0.69781, "loss_cls": 3.13439, "loss": 3.13439, "time": 2.30246} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.01144, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44578, "top5_acc": 0.69828, "loss_cls": 3.09467, "loss": 3.09467, "time": 0.81301} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.01142, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44953, "top5_acc": 0.70828, "loss_cls": 3.08044, "loss": 3.08044, "time": 0.81601} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.0114, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44125, "top5_acc": 0.70734, "loss_cls": 3.09017, "loss": 3.09017, "time": 0.8228} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.01139, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45844, "top5_acc": 0.70438, "loss_cls": 3.07251, "loss": 3.07251, "time": 0.82002} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.01137, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45438, "top5_acc": 0.70406, "loss_cls": 3.08047, "loss": 3.08047, "time": 0.81806} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.01135, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45453, "top5_acc": 0.71188, "loss_cls": 3.07568, "loss": 3.07568, "time": 0.81453} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.01133, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43047, "top5_acc": 0.68953, "loss_cls": 3.18949, "loss": 3.18949, "time": 0.81465} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.01131, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44312, "top5_acc": 0.69547, "loss_cls": 3.15473, "loss": 3.15473, "time": 0.81566} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.0113, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43953, "top5_acc": 0.70203, "loss_cls": 3.15051, "loss": 3.15051, "time": 0.81576} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.01128, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44578, "top5_acc": 0.70078, "loss_cls": 3.10507, "loss": 3.10507, "time": 0.81698} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.01126, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42859, "top5_acc": 0.68203, "loss_cls": 3.20591, "loss": 3.20591, "time": 0.81992} +{"mode": "train", "epoch": 118, "iter": 1300, "lr": 0.01124, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.42797, "top5_acc": 0.68875, "loss_cls": 3.18863, "loss": 3.18863, "time": 0.81062} +{"mode": "train", "epoch": 118, "iter": 1400, "lr": 0.01123, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44344, "top5_acc": 0.69359, "loss_cls": 3.15564, "loss": 3.15564, "time": 0.82685} +{"mode": "train", "epoch": 118, "iter": 1500, "lr": 0.01121, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42516, "top5_acc": 0.68578, "loss_cls": 3.22182, "loss": 3.22182, "time": 0.81967} +{"mode": "train", "epoch": 118, "iter": 1600, "lr": 0.01119, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44891, "top5_acc": 0.69469, "loss_cls": 3.16339, "loss": 3.16339, "time": 0.82454} +{"mode": "train", "epoch": 118, "iter": 1700, "lr": 0.01117, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43078, "top5_acc": 0.68344, "loss_cls": 3.22509, "loss": 3.22509, "time": 0.81964} +{"mode": "train", "epoch": 118, "iter": 1800, "lr": 0.01116, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42906, "top5_acc": 0.67922, "loss_cls": 3.2169, "loss": 3.2169, "time": 0.81546} +{"mode": "train", "epoch": 118, "iter": 1900, "lr": 0.01114, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44719, "top5_acc": 0.70234, "loss_cls": 3.09604, "loss": 3.09604, "time": 0.81787} +{"mode": "train", "epoch": 118, "iter": 2000, "lr": 0.01112, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43891, "top5_acc": 0.69688, "loss_cls": 3.13147, "loss": 3.13147, "time": 0.82133} +{"mode": "train", "epoch": 118, "iter": 2100, "lr": 0.0111, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42344, "top5_acc": 0.68047, "loss_cls": 3.22567, "loss": 3.22567, "time": 0.81873} +{"mode": "train", "epoch": 118, "iter": 2200, "lr": 0.01109, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42875, "top5_acc": 0.68438, "loss_cls": 3.21228, "loss": 3.21228, "time": 0.82433} +{"mode": "train", "epoch": 118, "iter": 2300, "lr": 0.01107, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43656, "top5_acc": 0.69609, "loss_cls": 3.15552, "loss": 3.15552, "time": 0.82287} +{"mode": "train", "epoch": 118, "iter": 2400, "lr": 0.01105, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42891, "top5_acc": 0.68766, "loss_cls": 3.23372, "loss": 3.23372, "time": 0.81854} +{"mode": "train", "epoch": 118, "iter": 2500, "lr": 0.01103, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43328, "top5_acc": 0.69953, "loss_cls": 3.17388, "loss": 3.17388, "time": 0.81924} +{"mode": "train", "epoch": 118, "iter": 2600, "lr": 0.01102, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44094, "top5_acc": 0.69406, "loss_cls": 3.15731, "loss": 3.15731, "time": 0.81572} +{"mode": "train", "epoch": 118, "iter": 2700, "lr": 0.011, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43453, "top5_acc": 0.69703, "loss_cls": 3.16526, "loss": 3.16526, "time": 0.8192} +{"mode": "train", "epoch": 118, "iter": 2800, "lr": 0.01098, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44016, "top5_acc": 0.69781, "loss_cls": 3.1603, "loss": 3.1603, "time": 0.81418} +{"mode": "train", "epoch": 118, "iter": 2900, "lr": 0.01096, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44094, "top5_acc": 0.69828, "loss_cls": 3.13203, "loss": 3.13203, "time": 0.8159} +{"mode": "train", "epoch": 118, "iter": 3000, "lr": 0.01095, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42656, "top5_acc": 0.68844, "loss_cls": 3.18708, "loss": 3.18708, "time": 0.81187} +{"mode": "train", "epoch": 118, "iter": 3100, "lr": 0.01093, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45266, "top5_acc": 0.70359, "loss_cls": 3.10541, "loss": 3.10541, "time": 0.81391} +{"mode": "train", "epoch": 118, "iter": 3200, "lr": 0.01091, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4325, "top5_acc": 0.68344, "loss_cls": 3.20569, "loss": 3.20569, "time": 0.81859} +{"mode": "train", "epoch": 118, "iter": 3300, "lr": 0.01089, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41906, "top5_acc": 0.68719, "loss_cls": 3.24179, "loss": 3.24179, "time": 0.81594} +{"mode": "train", "epoch": 118, "iter": 3400, "lr": 0.01088, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43844, "top5_acc": 0.69297, "loss_cls": 3.17318, "loss": 3.17318, "time": 0.81885} +{"mode": "train", "epoch": 118, "iter": 3500, "lr": 0.01086, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42953, "top5_acc": 0.68281, "loss_cls": 3.19995, "loss": 3.19995, "time": 0.81628} +{"mode": "train", "epoch": 118, "iter": 3600, "lr": 0.01084, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43469, "top5_acc": 0.69516, "loss_cls": 3.18285, "loss": 3.18285, "time": 0.81857} +{"mode": "train", "epoch": 118, "iter": 3700, "lr": 0.01082, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43641, "top5_acc": 0.69703, "loss_cls": 3.15448, "loss": 3.15448, "time": 0.82228} +{"mode": "val", "epoch": 118, "iter": 309, "lr": 0.01082, "top1_acc": 0.36398, "top5_acc": 0.62108, "mean_class_accuracy": 0.36371} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.0108, "memory": 15990, "data_time": 1.3517, "top1_acc": 0.44359, "top5_acc": 0.70234, "loss_cls": 3.11102, "loss": 3.11102, "time": 2.34178} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.01078, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45828, "top5_acc": 0.71422, "loss_cls": 3.04178, "loss": 3.04178, "time": 0.84142} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.01076, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45438, "top5_acc": 0.70594, "loss_cls": 3.06454, "loss": 3.06454, "time": 0.84224} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.01075, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45125, "top5_acc": 0.71078, "loss_cls": 3.08113, "loss": 3.08113, "time": 0.84149} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.01073, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45422, "top5_acc": 0.70234, "loss_cls": 3.0754, "loss": 3.0754, "time": 0.8356} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.01071, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45219, "top5_acc": 0.70344, "loss_cls": 3.1026, "loss": 3.1026, "time": 0.8313} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.01069, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44, "top5_acc": 0.69922, "loss_cls": 3.15156, "loss": 3.15156, "time": 0.83495} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.01068, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43547, "top5_acc": 0.68844, "loss_cls": 3.17773, "loss": 3.17773, "time": 0.82029} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.01066, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44391, "top5_acc": 0.69953, "loss_cls": 3.12985, "loss": 3.12985, "time": 0.8231} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.01064, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45328, "top5_acc": 0.69625, "loss_cls": 3.09627, "loss": 3.09627, "time": 0.81518} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.01063, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44484, "top5_acc": 0.69891, "loss_cls": 3.09273, "loss": 3.09273, "time": 0.81201} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.01061, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44109, "top5_acc": 0.70688, "loss_cls": 3.10273, "loss": 3.10273, "time": 0.81783} +{"mode": "train", "epoch": 119, "iter": 1300, "lr": 0.01059, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44969, "top5_acc": 0.69797, "loss_cls": 3.13871, "loss": 3.13871, "time": 0.81974} +{"mode": "train", "epoch": 119, "iter": 1400, "lr": 0.01057, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44047, "top5_acc": 0.68984, "loss_cls": 3.1605, "loss": 3.1605, "time": 0.82706} +{"mode": "train", "epoch": 119, "iter": 1500, "lr": 0.01056, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44062, "top5_acc": 0.69938, "loss_cls": 3.08818, "loss": 3.08818, "time": 0.8225} +{"mode": "train", "epoch": 119, "iter": 1600, "lr": 0.01054, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44766, "top5_acc": 0.69391, "loss_cls": 3.13076, "loss": 3.13076, "time": 0.82236} +{"mode": "train", "epoch": 119, "iter": 1700, "lr": 0.01052, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43609, "top5_acc": 0.68891, "loss_cls": 3.14806, "loss": 3.14806, "time": 0.81642} +{"mode": "train", "epoch": 119, "iter": 1800, "lr": 0.0105, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44609, "top5_acc": 0.69891, "loss_cls": 3.11917, "loss": 3.11917, "time": 0.81612} +{"mode": "train", "epoch": 119, "iter": 1900, "lr": 0.01049, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43688, "top5_acc": 0.69031, "loss_cls": 3.15184, "loss": 3.15184, "time": 0.81768} +{"mode": "train", "epoch": 119, "iter": 2000, "lr": 0.01047, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44219, "top5_acc": 0.70234, "loss_cls": 3.12533, "loss": 3.12533, "time": 0.81306} +{"mode": "train", "epoch": 119, "iter": 2100, "lr": 0.01045, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43172, "top5_acc": 0.69094, "loss_cls": 3.18163, "loss": 3.18163, "time": 0.8138} +{"mode": "train", "epoch": 119, "iter": 2200, "lr": 0.01044, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44453, "top5_acc": 0.70062, "loss_cls": 3.14346, "loss": 3.14346, "time": 0.82209} +{"mode": "train", "epoch": 119, "iter": 2300, "lr": 0.01042, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43016, "top5_acc": 0.68953, "loss_cls": 3.19505, "loss": 3.19505, "time": 0.81942} +{"mode": "train", "epoch": 119, "iter": 2400, "lr": 0.0104, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.4425, "top5_acc": 0.69281, "loss_cls": 3.1209, "loss": 3.1209, "time": 0.81799} +{"mode": "train", "epoch": 119, "iter": 2500, "lr": 0.01039, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44281, "top5_acc": 0.69656, "loss_cls": 3.14468, "loss": 3.14468, "time": 0.8188} +{"mode": "train", "epoch": 119, "iter": 2600, "lr": 0.01037, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45469, "top5_acc": 0.70922, "loss_cls": 3.09135, "loss": 3.09135, "time": 0.81259} +{"mode": "train", "epoch": 119, "iter": 2700, "lr": 0.01035, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44781, "top5_acc": 0.69172, "loss_cls": 3.13183, "loss": 3.13183, "time": 0.82198} +{"mode": "train", "epoch": 119, "iter": 2800, "lr": 0.01033, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43109, "top5_acc": 0.69562, "loss_cls": 3.13396, "loss": 3.13396, "time": 0.81777} +{"mode": "train", "epoch": 119, "iter": 2900, "lr": 0.01032, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43516, "top5_acc": 0.68844, "loss_cls": 3.19987, "loss": 3.19987, "time": 0.81789} +{"mode": "train", "epoch": 119, "iter": 3000, "lr": 0.0103, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43922, "top5_acc": 0.69531, "loss_cls": 3.12084, "loss": 3.12084, "time": 0.81234} +{"mode": "train", "epoch": 119, "iter": 3100, "lr": 0.01028, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43906, "top5_acc": 0.69906, "loss_cls": 3.15315, "loss": 3.15315, "time": 0.81319} +{"mode": "train", "epoch": 119, "iter": 3200, "lr": 0.01027, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43391, "top5_acc": 0.69312, "loss_cls": 3.16614, "loss": 3.16614, "time": 0.81386} +{"mode": "train", "epoch": 119, "iter": 3300, "lr": 0.01025, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43375, "top5_acc": 0.68203, "loss_cls": 3.22006, "loss": 3.22006, "time": 0.81535} +{"mode": "train", "epoch": 119, "iter": 3400, "lr": 0.01023, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4425, "top5_acc": 0.69406, "loss_cls": 3.15718, "loss": 3.15718, "time": 0.81856} +{"mode": "train", "epoch": 119, "iter": 3500, "lr": 0.01022, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43016, "top5_acc": 0.69016, "loss_cls": 3.19129, "loss": 3.19129, "time": 0.81017} +{"mode": "train", "epoch": 119, "iter": 3600, "lr": 0.0102, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43672, "top5_acc": 0.70312, "loss_cls": 3.13676, "loss": 3.13676, "time": 0.81594} +{"mode": "train", "epoch": 119, "iter": 3700, "lr": 0.01018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44312, "top5_acc": 0.69406, "loss_cls": 3.15816, "loss": 3.15816, "time": 0.81089} +{"mode": "val", "epoch": 119, "iter": 309, "lr": 0.01017, "top1_acc": 0.37487, "top5_acc": 0.62686, "mean_class_accuracy": 0.37462} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.01016, "memory": 15990, "data_time": 1.2912, "top1_acc": 0.46078, "top5_acc": 0.71609, "loss_cls": 3.0061, "loss": 3.0061, "time": 2.27243} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.01014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45719, "top5_acc": 0.71141, "loss_cls": 3.0504, "loss": 3.0504, "time": 0.82151} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.01012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44938, "top5_acc": 0.70328, "loss_cls": 3.08443, "loss": 3.08443, "time": 0.81697} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.01011, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45906, "top5_acc": 0.71344, "loss_cls": 3.03748, "loss": 3.03748, "time": 0.81419} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.01009, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45406, "top5_acc": 0.70375, "loss_cls": 3.04944, "loss": 3.04944, "time": 0.82022} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.01007, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46297, "top5_acc": 0.71312, "loss_cls": 3.04125, "loss": 3.04125, "time": 0.81434} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.01006, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44797, "top5_acc": 0.70688, "loss_cls": 3.07517, "loss": 3.07517, "time": 0.81436} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.01004, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44719, "top5_acc": 0.70125, "loss_cls": 3.11593, "loss": 3.11593, "time": 0.81372} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.01002, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44562, "top5_acc": 0.71172, "loss_cls": 3.07714, "loss": 3.07714, "time": 0.81554} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.01001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44375, "top5_acc": 0.70609, "loss_cls": 3.07183, "loss": 3.07183, "time": 0.82102} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45422, "top5_acc": 0.71172, "loss_cls": 3.06376, "loss": 3.06376, "time": 0.8153} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.00997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4475, "top5_acc": 0.70266, "loss_cls": 3.12434, "loss": 3.12434, "time": 0.8203} +{"mode": "train", "epoch": 120, "iter": 1300, "lr": 0.00996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44812, "top5_acc": 0.70641, "loss_cls": 3.07382, "loss": 3.07382, "time": 0.81694} +{"mode": "train", "epoch": 120, "iter": 1400, "lr": 0.00994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44562, "top5_acc": 0.70312, "loss_cls": 3.11358, "loss": 3.11358, "time": 0.81723} +{"mode": "train", "epoch": 120, "iter": 1500, "lr": 0.00992, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44203, "top5_acc": 0.69688, "loss_cls": 3.13287, "loss": 3.13287, "time": 0.82009} +{"mode": "train", "epoch": 120, "iter": 1600, "lr": 0.0099, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44406, "top5_acc": 0.70781, "loss_cls": 3.08947, "loss": 3.08947, "time": 0.82664} +{"mode": "train", "epoch": 120, "iter": 1700, "lr": 0.00989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4375, "top5_acc": 0.70266, "loss_cls": 3.10959, "loss": 3.10959, "time": 0.81488} +{"mode": "train", "epoch": 120, "iter": 1800, "lr": 0.00987, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45062, "top5_acc": 0.70359, "loss_cls": 3.0856, "loss": 3.0856, "time": 0.81304} +{"mode": "train", "epoch": 120, "iter": 1900, "lr": 0.00985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44422, "top5_acc": 0.70031, "loss_cls": 3.11112, "loss": 3.11112, "time": 0.81763} +{"mode": "train", "epoch": 120, "iter": 2000, "lr": 0.00984, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44469, "top5_acc": 0.69547, "loss_cls": 3.14128, "loss": 3.14128, "time": 0.81473} +{"mode": "train", "epoch": 120, "iter": 2100, "lr": 0.00982, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43359, "top5_acc": 0.68906, "loss_cls": 3.18667, "loss": 3.18667, "time": 0.81927} +{"mode": "train", "epoch": 120, "iter": 2200, "lr": 0.0098, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45125, "top5_acc": 0.69141, "loss_cls": 3.15413, "loss": 3.15413, "time": 0.81714} +{"mode": "train", "epoch": 120, "iter": 2300, "lr": 0.00979, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44547, "top5_acc": 0.69516, "loss_cls": 3.13984, "loss": 3.13984, "time": 0.82206} +{"mode": "train", "epoch": 120, "iter": 2400, "lr": 0.00977, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44453, "top5_acc": 0.70359, "loss_cls": 3.09836, "loss": 3.09836, "time": 0.82083} +{"mode": "train", "epoch": 120, "iter": 2500, "lr": 0.00976, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4425, "top5_acc": 0.69844, "loss_cls": 3.12932, "loss": 3.12932, "time": 0.81968} +{"mode": "train", "epoch": 120, "iter": 2600, "lr": 0.00974, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44266, "top5_acc": 0.69641, "loss_cls": 3.09812, "loss": 3.09812, "time": 0.8184} +{"mode": "train", "epoch": 120, "iter": 2700, "lr": 0.00972, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43953, "top5_acc": 0.69516, "loss_cls": 3.15823, "loss": 3.15823, "time": 0.8151} +{"mode": "train", "epoch": 120, "iter": 2800, "lr": 0.00971, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43906, "top5_acc": 0.70797, "loss_cls": 3.11178, "loss": 3.11178, "time": 0.81619} +{"mode": "train", "epoch": 120, "iter": 2900, "lr": 0.00969, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44078, "top5_acc": 0.69484, "loss_cls": 3.18199, "loss": 3.18199, "time": 0.81616} +{"mode": "train", "epoch": 120, "iter": 3000, "lr": 0.00967, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45219, "top5_acc": 0.71, "loss_cls": 3.06618, "loss": 3.06618, "time": 0.8123} +{"mode": "train", "epoch": 120, "iter": 3100, "lr": 0.00966, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44531, "top5_acc": 0.69406, "loss_cls": 3.14445, "loss": 3.14445, "time": 0.8202} +{"mode": "train", "epoch": 120, "iter": 3200, "lr": 0.00964, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44297, "top5_acc": 0.69938, "loss_cls": 3.11572, "loss": 3.11572, "time": 0.81095} +{"mode": "train", "epoch": 120, "iter": 3300, "lr": 0.00962, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43672, "top5_acc": 0.68906, "loss_cls": 3.16796, "loss": 3.16796, "time": 0.81185} +{"mode": "train", "epoch": 120, "iter": 3400, "lr": 0.00961, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43375, "top5_acc": 0.69094, "loss_cls": 3.16304, "loss": 3.16304, "time": 0.81358} +{"mode": "train", "epoch": 120, "iter": 3500, "lr": 0.00959, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44578, "top5_acc": 0.695, "loss_cls": 3.12248, "loss": 3.12248, "time": 0.81344} +{"mode": "train", "epoch": 120, "iter": 3600, "lr": 0.00957, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44422, "top5_acc": 0.70094, "loss_cls": 3.12312, "loss": 3.12312, "time": 0.81656} +{"mode": "train", "epoch": 120, "iter": 3700, "lr": 0.00956, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41906, "top5_acc": 0.68031, "loss_cls": 3.21521, "loss": 3.21521, "time": 0.81779} +{"mode": "val", "epoch": 120, "iter": 309, "lr": 0.00955, "top1_acc": 0.38854, "top5_acc": 0.64089, "mean_class_accuracy": 0.38828} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00953, "memory": 15990, "data_time": 1.29602, "top1_acc": 0.47188, "top5_acc": 0.72219, "loss_cls": 2.96359, "loss": 2.96359, "time": 2.2745} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00952, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46969, "top5_acc": 0.71422, "loss_cls": 3.00329, "loss": 3.00329, "time": 0.81648} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.0095, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46141, "top5_acc": 0.71297, "loss_cls": 3.02086, "loss": 3.02086, "time": 0.81732} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00948, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45422, "top5_acc": 0.71719, "loss_cls": 3.03256, "loss": 3.03256, "time": 0.82249} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00947, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45672, "top5_acc": 0.70969, "loss_cls": 3.05512, "loss": 3.05512, "time": 0.81377} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00945, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45422, "top5_acc": 0.70734, "loss_cls": 3.05996, "loss": 3.05996, "time": 0.81201} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.00943, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45391, "top5_acc": 0.70969, "loss_cls": 3.0615, "loss": 3.0615, "time": 0.81459} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00942, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45, "top5_acc": 0.71406, "loss_cls": 3.04751, "loss": 3.04751, "time": 0.81877} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.0094, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45781, "top5_acc": 0.70297, "loss_cls": 3.06173, "loss": 3.06173, "time": 0.8216} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00939, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44594, "top5_acc": 0.70219, "loss_cls": 3.11519, "loss": 3.11519, "time": 0.819} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00937, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45734, "top5_acc": 0.71203, "loss_cls": 3.05366, "loss": 3.05366, "time": 0.81203} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00935, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44875, "top5_acc": 0.70578, "loss_cls": 3.10087, "loss": 3.10087, "time": 0.81649} +{"mode": "train", "epoch": 121, "iter": 1300, "lr": 0.00934, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44516, "top5_acc": 0.7, "loss_cls": 3.11155, "loss": 3.11155, "time": 0.81707} +{"mode": "train", "epoch": 121, "iter": 1400, "lr": 0.00932, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44234, "top5_acc": 0.69828, "loss_cls": 3.10003, "loss": 3.10003, "time": 0.81921} +{"mode": "train", "epoch": 121, "iter": 1500, "lr": 0.0093, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45469, "top5_acc": 0.71, "loss_cls": 3.07795, "loss": 3.07795, "time": 0.81755} +{"mode": "train", "epoch": 121, "iter": 1600, "lr": 0.00929, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45359, "top5_acc": 0.70859, "loss_cls": 3.0721, "loss": 3.0721, "time": 0.82024} +{"mode": "train", "epoch": 121, "iter": 1700, "lr": 0.00927, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44516, "top5_acc": 0.70625, "loss_cls": 3.08058, "loss": 3.08058, "time": 0.81581} +{"mode": "train", "epoch": 121, "iter": 1800, "lr": 0.00926, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44594, "top5_acc": 0.69594, "loss_cls": 3.14604, "loss": 3.14604, "time": 0.81843} +{"mode": "train", "epoch": 121, "iter": 1900, "lr": 0.00924, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44578, "top5_acc": 0.70141, "loss_cls": 3.11345, "loss": 3.11345, "time": 0.82286} +{"mode": "train", "epoch": 121, "iter": 2000, "lr": 0.00922, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45234, "top5_acc": 0.70781, "loss_cls": 3.04998, "loss": 3.04998, "time": 0.81458} +{"mode": "train", "epoch": 121, "iter": 2100, "lr": 0.00921, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.4475, "top5_acc": 0.70578, "loss_cls": 3.08446, "loss": 3.08446, "time": 0.82175} +{"mode": "train", "epoch": 121, "iter": 2200, "lr": 0.00919, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45031, "top5_acc": 0.70234, "loss_cls": 3.07681, "loss": 3.07681, "time": 0.82149} +{"mode": "train", "epoch": 121, "iter": 2300, "lr": 0.00917, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45844, "top5_acc": 0.71531, "loss_cls": 3.01768, "loss": 3.01768, "time": 0.81535} +{"mode": "train", "epoch": 121, "iter": 2400, "lr": 0.00916, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45578, "top5_acc": 0.70594, "loss_cls": 3.06703, "loss": 3.06703, "time": 0.81792} +{"mode": "train", "epoch": 121, "iter": 2500, "lr": 0.00914, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44547, "top5_acc": 0.70703, "loss_cls": 3.07811, "loss": 3.07811, "time": 0.81961} +{"mode": "train", "epoch": 121, "iter": 2600, "lr": 0.00913, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44172, "top5_acc": 0.69453, "loss_cls": 3.13227, "loss": 3.13227, "time": 0.81589} +{"mode": "train", "epoch": 121, "iter": 2700, "lr": 0.00911, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44734, "top5_acc": 0.70594, "loss_cls": 3.10756, "loss": 3.10756, "time": 0.81852} +{"mode": "train", "epoch": 121, "iter": 2800, "lr": 0.00909, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44719, "top5_acc": 0.70109, "loss_cls": 3.12236, "loss": 3.12236, "time": 0.81196} +{"mode": "train", "epoch": 121, "iter": 2900, "lr": 0.00908, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44125, "top5_acc": 0.69594, "loss_cls": 3.14767, "loss": 3.14767, "time": 0.81273} +{"mode": "train", "epoch": 121, "iter": 3000, "lr": 0.00906, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43875, "top5_acc": 0.70281, "loss_cls": 3.09987, "loss": 3.09987, "time": 0.8125} +{"mode": "train", "epoch": 121, "iter": 3100, "lr": 0.00905, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43953, "top5_acc": 0.69922, "loss_cls": 3.10331, "loss": 3.10331, "time": 0.81225} +{"mode": "train", "epoch": 121, "iter": 3200, "lr": 0.00903, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45062, "top5_acc": 0.70438, "loss_cls": 3.09115, "loss": 3.09115, "time": 0.81188} +{"mode": "train", "epoch": 121, "iter": 3300, "lr": 0.00901, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44438, "top5_acc": 0.69875, "loss_cls": 3.12151, "loss": 3.12151, "time": 0.8124} +{"mode": "train", "epoch": 121, "iter": 3400, "lr": 0.009, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4475, "top5_acc": 0.70797, "loss_cls": 3.06649, "loss": 3.06649, "time": 0.81288} +{"mode": "train", "epoch": 121, "iter": 3500, "lr": 0.00898, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45344, "top5_acc": 0.70531, "loss_cls": 3.09112, "loss": 3.09112, "time": 0.81728} +{"mode": "train", "epoch": 121, "iter": 3600, "lr": 0.00897, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43719, "top5_acc": 0.69531, "loss_cls": 3.1353, "loss": 3.1353, "time": 0.81562} +{"mode": "train", "epoch": 121, "iter": 3700, "lr": 0.00895, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45141, "top5_acc": 0.70719, "loss_cls": 3.08955, "loss": 3.08955, "time": 0.81432} +{"mode": "val", "epoch": 121, "iter": 309, "lr": 0.00894, "top1_acc": 0.38652, "top5_acc": 0.64028, "mean_class_accuracy": 0.3862} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00893, "memory": 15990, "data_time": 1.33215, "top1_acc": 0.46531, "top5_acc": 0.72156, "loss_cls": 3.00308, "loss": 3.00308, "time": 2.30438} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00891, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45875, "top5_acc": 0.71281, "loss_cls": 3.00926, "loss": 3.00926, "time": 0.81946} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.00889, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46719, "top5_acc": 0.72641, "loss_cls": 2.96896, "loss": 2.96896, "time": 0.81577} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00888, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45844, "top5_acc": 0.70719, "loss_cls": 3.05088, "loss": 3.05088, "time": 0.81831} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00886, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45578, "top5_acc": 0.70812, "loss_cls": 3.05987, "loss": 3.05987, "time": 0.819} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00885, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46375, "top5_acc": 0.71234, "loss_cls": 3.03511, "loss": 3.03511, "time": 0.81803} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00883, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46203, "top5_acc": 0.71359, "loss_cls": 3.0181, "loss": 3.0181, "time": 0.82036} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46672, "top5_acc": 0.71266, "loss_cls": 3.01642, "loss": 3.01642, "time": 0.81496} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.0088, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45531, "top5_acc": 0.71484, "loss_cls": 3.01277, "loss": 3.01277, "time": 0.82081} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00878, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44734, "top5_acc": 0.70406, "loss_cls": 3.09848, "loss": 3.09848, "time": 0.81688} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00877, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45438, "top5_acc": 0.70719, "loss_cls": 3.04625, "loss": 3.04625, "time": 0.81341} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.00875, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45172, "top5_acc": 0.71469, "loss_cls": 3.03121, "loss": 3.03121, "time": 0.8179} +{"mode": "train", "epoch": 122, "iter": 1300, "lr": 0.00874, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45453, "top5_acc": 0.71, "loss_cls": 3.05964, "loss": 3.05964, "time": 0.8147} +{"mode": "train", "epoch": 122, "iter": 1400, "lr": 0.00872, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45203, "top5_acc": 0.70984, "loss_cls": 3.06816, "loss": 3.06816, "time": 0.81655} +{"mode": "train", "epoch": 122, "iter": 1500, "lr": 0.0087, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46438, "top5_acc": 0.71234, "loss_cls": 3.02014, "loss": 3.02014, "time": 0.81679} +{"mode": "train", "epoch": 122, "iter": 1600, "lr": 0.00869, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44766, "top5_acc": 0.70984, "loss_cls": 3.06519, "loss": 3.06519, "time": 0.82026} +{"mode": "train", "epoch": 122, "iter": 1700, "lr": 0.00867, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.4625, "top5_acc": 0.71141, "loss_cls": 3.0339, "loss": 3.0339, "time": 0.82124} +{"mode": "train", "epoch": 122, "iter": 1800, "lr": 0.00866, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45609, "top5_acc": 0.70578, "loss_cls": 3.06194, "loss": 3.06194, "time": 0.82632} +{"mode": "train", "epoch": 122, "iter": 1900, "lr": 0.00864, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45594, "top5_acc": 0.70828, "loss_cls": 3.03507, "loss": 3.03507, "time": 0.82092} +{"mode": "train", "epoch": 122, "iter": 2000, "lr": 0.00863, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45047, "top5_acc": 0.70469, "loss_cls": 3.05793, "loss": 3.05793, "time": 0.82202} +{"mode": "train", "epoch": 122, "iter": 2100, "lr": 0.00861, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45156, "top5_acc": 0.70797, "loss_cls": 3.09348, "loss": 3.09348, "time": 0.81492} +{"mode": "train", "epoch": 122, "iter": 2200, "lr": 0.00859, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45047, "top5_acc": 0.70672, "loss_cls": 3.07923, "loss": 3.07923, "time": 0.81928} +{"mode": "train", "epoch": 122, "iter": 2300, "lr": 0.00858, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44844, "top5_acc": 0.7, "loss_cls": 3.08542, "loss": 3.08542, "time": 0.81864} +{"mode": "train", "epoch": 122, "iter": 2400, "lr": 0.00856, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43922, "top5_acc": 0.69812, "loss_cls": 3.12283, "loss": 3.12283, "time": 0.81765} +{"mode": "train", "epoch": 122, "iter": 2500, "lr": 0.00855, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44891, "top5_acc": 0.70812, "loss_cls": 3.07269, "loss": 3.07269, "time": 0.82225} +{"mode": "train", "epoch": 122, "iter": 2600, "lr": 0.00853, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45656, "top5_acc": 0.71656, "loss_cls": 3.05858, "loss": 3.05858, "time": 0.81844} +{"mode": "train", "epoch": 122, "iter": 2700, "lr": 0.00852, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45047, "top5_acc": 0.71062, "loss_cls": 3.03756, "loss": 3.03756, "time": 0.81987} +{"mode": "train", "epoch": 122, "iter": 2800, "lr": 0.0085, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45672, "top5_acc": 0.71312, "loss_cls": 3.03127, "loss": 3.03127, "time": 0.81687} +{"mode": "train", "epoch": 122, "iter": 2900, "lr": 0.00849, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44609, "top5_acc": 0.70891, "loss_cls": 3.09444, "loss": 3.09444, "time": 0.81074} +{"mode": "train", "epoch": 122, "iter": 3000, "lr": 0.00847, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45016, "top5_acc": 0.70594, "loss_cls": 3.0855, "loss": 3.0855, "time": 0.81565} +{"mode": "train", "epoch": 122, "iter": 3100, "lr": 0.00845, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45031, "top5_acc": 0.70734, "loss_cls": 3.0997, "loss": 3.0997, "time": 0.8157} +{"mode": "train", "epoch": 122, "iter": 3200, "lr": 0.00844, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45578, "top5_acc": 0.71406, "loss_cls": 3.02724, "loss": 3.02724, "time": 0.81315} +{"mode": "train", "epoch": 122, "iter": 3300, "lr": 0.00842, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45469, "top5_acc": 0.70984, "loss_cls": 3.05756, "loss": 3.05756, "time": 0.8143} +{"mode": "train", "epoch": 122, "iter": 3400, "lr": 0.00841, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44844, "top5_acc": 0.70953, "loss_cls": 3.06449, "loss": 3.06449, "time": 0.81389} +{"mode": "train", "epoch": 122, "iter": 3500, "lr": 0.00839, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44938, "top5_acc": 0.70266, "loss_cls": 3.11455, "loss": 3.11455, "time": 0.81525} +{"mode": "train", "epoch": 122, "iter": 3600, "lr": 0.00838, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46, "top5_acc": 0.71781, "loss_cls": 3.00625, "loss": 3.00625, "time": 0.81575} +{"mode": "train", "epoch": 122, "iter": 3700, "lr": 0.00836, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45609, "top5_acc": 0.70328, "loss_cls": 3.05483, "loss": 3.05483, "time": 0.81117} +{"mode": "val", "epoch": 122, "iter": 309, "lr": 0.00835, "top1_acc": 0.38348, "top5_acc": 0.63952, "mean_class_accuracy": 0.38332} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00834, "memory": 15990, "data_time": 1.38073, "top1_acc": 0.47453, "top5_acc": 0.72766, "loss_cls": 2.92482, "loss": 2.92482, "time": 2.36012} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00832, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46531, "top5_acc": 0.72172, "loss_cls": 2.98283, "loss": 2.98283, "time": 0.82092} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00831, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47281, "top5_acc": 0.71938, "loss_cls": 2.95915, "loss": 2.95915, "time": 0.81487} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00829, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46906, "top5_acc": 0.71641, "loss_cls": 2.98815, "loss": 2.98815, "time": 0.81219} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00828, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45984, "top5_acc": 0.72219, "loss_cls": 2.99612, "loss": 2.99612, "time": 0.81899} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00826, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46734, "top5_acc": 0.71516, "loss_cls": 2.97917, "loss": 2.97917, "time": 0.81866} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00825, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45656, "top5_acc": 0.70672, "loss_cls": 3.05199, "loss": 3.05199, "time": 0.82002} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.00823, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46766, "top5_acc": 0.72406, "loss_cls": 3.00588, "loss": 3.00588, "time": 0.81821} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00822, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46328, "top5_acc": 0.72438, "loss_cls": 2.97599, "loss": 2.97599, "time": 0.81683} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.0082, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45531, "top5_acc": 0.70328, "loss_cls": 3.03995, "loss": 3.03995, "time": 0.81489} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00818, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47359, "top5_acc": 0.72156, "loss_cls": 2.98583, "loss": 2.98583, "time": 0.81471} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00817, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44578, "top5_acc": 0.70828, "loss_cls": 3.05784, "loss": 3.05784, "time": 0.81923} +{"mode": "train", "epoch": 123, "iter": 1300, "lr": 0.00815, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45219, "top5_acc": 0.71297, "loss_cls": 3.02223, "loss": 3.02223, "time": 0.82087} +{"mode": "train", "epoch": 123, "iter": 1400, "lr": 0.00814, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45344, "top5_acc": 0.70656, "loss_cls": 3.04467, "loss": 3.04467, "time": 0.82737} +{"mode": "train", "epoch": 123, "iter": 1500, "lr": 0.00812, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46859, "top5_acc": 0.72141, "loss_cls": 2.99278, "loss": 2.99278, "time": 0.81855} +{"mode": "train", "epoch": 123, "iter": 1600, "lr": 0.00811, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45156, "top5_acc": 0.71219, "loss_cls": 3.05306, "loss": 3.05306, "time": 0.82239} +{"mode": "train", "epoch": 123, "iter": 1700, "lr": 0.00809, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45109, "top5_acc": 0.70641, "loss_cls": 3.0427, "loss": 3.0427, "time": 0.82352} +{"mode": "train", "epoch": 123, "iter": 1800, "lr": 0.00808, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45953, "top5_acc": 0.71594, "loss_cls": 3.02744, "loss": 3.02744, "time": 0.81832} +{"mode": "train", "epoch": 123, "iter": 1900, "lr": 0.00806, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45406, "top5_acc": 0.70781, "loss_cls": 3.06379, "loss": 3.06379, "time": 0.8166} +{"mode": "train", "epoch": 123, "iter": 2000, "lr": 0.00805, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4575, "top5_acc": 0.71359, "loss_cls": 3.04805, "loss": 3.04805, "time": 0.81452} +{"mode": "train", "epoch": 123, "iter": 2100, "lr": 0.00803, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44812, "top5_acc": 0.70531, "loss_cls": 3.06303, "loss": 3.06303, "time": 0.82177} +{"mode": "train", "epoch": 123, "iter": 2200, "lr": 0.00802, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44969, "top5_acc": 0.70656, "loss_cls": 3.06227, "loss": 3.06227, "time": 0.82085} +{"mode": "train", "epoch": 123, "iter": 2300, "lr": 0.008, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46547, "top5_acc": 0.72344, "loss_cls": 3.00585, "loss": 3.00585, "time": 0.81602} +{"mode": "train", "epoch": 123, "iter": 2400, "lr": 0.00799, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46141, "top5_acc": 0.70359, "loss_cls": 3.06247, "loss": 3.06247, "time": 0.81675} +{"mode": "train", "epoch": 123, "iter": 2500, "lr": 0.00797, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45391, "top5_acc": 0.71094, "loss_cls": 3.06349, "loss": 3.06349, "time": 0.81945} +{"mode": "train", "epoch": 123, "iter": 2600, "lr": 0.00796, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46, "top5_acc": 0.70875, "loss_cls": 2.99196, "loss": 2.99196, "time": 0.81968} +{"mode": "train", "epoch": 123, "iter": 2700, "lr": 0.00794, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44938, "top5_acc": 0.70422, "loss_cls": 3.05733, "loss": 3.05733, "time": 0.81316} +{"mode": "train", "epoch": 123, "iter": 2800, "lr": 0.00793, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45875, "top5_acc": 0.71703, "loss_cls": 3.02585, "loss": 3.02585, "time": 0.81342} +{"mode": "train", "epoch": 123, "iter": 2900, "lr": 0.00791, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46078, "top5_acc": 0.72188, "loss_cls": 2.99835, "loss": 2.99835, "time": 0.81277} +{"mode": "train", "epoch": 123, "iter": 3000, "lr": 0.0079, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46328, "top5_acc": 0.71859, "loss_cls": 3.03485, "loss": 3.03485, "time": 0.81543} +{"mode": "train", "epoch": 123, "iter": 3100, "lr": 0.00788, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46188, "top5_acc": 0.71062, "loss_cls": 3.03537, "loss": 3.03537, "time": 0.81627} +{"mode": "train", "epoch": 123, "iter": 3200, "lr": 0.00787, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46719, "top5_acc": 0.72562, "loss_cls": 2.978, "loss": 2.978, "time": 0.81594} +{"mode": "train", "epoch": 123, "iter": 3300, "lr": 0.00785, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44734, "top5_acc": 0.70453, "loss_cls": 3.10814, "loss": 3.10814, "time": 0.81826} +{"mode": "train", "epoch": 123, "iter": 3400, "lr": 0.00784, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44891, "top5_acc": 0.70562, "loss_cls": 3.06696, "loss": 3.06696, "time": 0.81091} +{"mode": "train", "epoch": 123, "iter": 3500, "lr": 0.00782, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46109, "top5_acc": 0.71156, "loss_cls": 3.02774, "loss": 3.02774, "time": 0.81464} +{"mode": "train", "epoch": 123, "iter": 3600, "lr": 0.00781, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46188, "top5_acc": 0.70297, "loss_cls": 3.04225, "loss": 3.04225, "time": 0.81442} +{"mode": "train", "epoch": 123, "iter": 3700, "lr": 0.00779, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47156, "top5_acc": 0.71922, "loss_cls": 2.97664, "loss": 2.97664, "time": 0.81537} +{"mode": "val", "epoch": 123, "iter": 309, "lr": 0.00778, "top1_acc": 0.39609, "top5_acc": 0.64995, "mean_class_accuracy": 0.39587} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00777, "memory": 15990, "data_time": 1.3559, "top1_acc": 0.46766, "top5_acc": 0.73203, "loss_cls": 2.93529, "loss": 2.93529, "time": 2.3383} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00775, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47312, "top5_acc": 0.72266, "loss_cls": 2.94182, "loss": 2.94182, "time": 0.81827} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00774, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45922, "top5_acc": 0.71875, "loss_cls": 2.96404, "loss": 2.96404, "time": 0.82001} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.00772, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48297, "top5_acc": 0.72953, "loss_cls": 2.8916, "loss": 2.8916, "time": 0.81724} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00771, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47016, "top5_acc": 0.72312, "loss_cls": 2.99144, "loss": 2.99144, "time": 0.81505} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00769, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45719, "top5_acc": 0.72047, "loss_cls": 3.01457, "loss": 3.01457, "time": 0.81891} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00768, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47, "top5_acc": 0.71734, "loss_cls": 3.0022, "loss": 3.0022, "time": 0.81792} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00766, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47953, "top5_acc": 0.72594, "loss_cls": 2.93194, "loss": 2.93194, "time": 0.8153} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00765, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46172, "top5_acc": 0.72578, "loss_cls": 2.96623, "loss": 2.96623, "time": 0.8135} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00763, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47125, "top5_acc": 0.73422, "loss_cls": 2.91638, "loss": 2.91638, "time": 0.81322} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00762, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47781, "top5_acc": 0.72141, "loss_cls": 2.9481, "loss": 2.9481, "time": 0.81898} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.0076, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46656, "top5_acc": 0.71516, "loss_cls": 2.99991, "loss": 2.99991, "time": 0.82146} +{"mode": "train", "epoch": 124, "iter": 1300, "lr": 0.00759, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47484, "top5_acc": 0.72328, "loss_cls": 2.96688, "loss": 2.96688, "time": 0.81669} +{"mode": "train", "epoch": 124, "iter": 1400, "lr": 0.00758, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46016, "top5_acc": 0.71688, "loss_cls": 3.00724, "loss": 3.00724, "time": 0.82478} +{"mode": "train", "epoch": 124, "iter": 1500, "lr": 0.00756, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46109, "top5_acc": 0.71719, "loss_cls": 3.02121, "loss": 3.02121, "time": 0.8142} +{"mode": "train", "epoch": 124, "iter": 1600, "lr": 0.00755, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45984, "top5_acc": 0.72062, "loss_cls": 3.00421, "loss": 3.00421, "time": 0.81904} +{"mode": "train", "epoch": 124, "iter": 1700, "lr": 0.00753, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46594, "top5_acc": 0.72047, "loss_cls": 3.01364, "loss": 3.01364, "time": 0.81795} +{"mode": "train", "epoch": 124, "iter": 1800, "lr": 0.00752, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46703, "top5_acc": 0.7175, "loss_cls": 2.99508, "loss": 2.99508, "time": 0.82026} +{"mode": "train", "epoch": 124, "iter": 1900, "lr": 0.0075, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46641, "top5_acc": 0.71406, "loss_cls": 3.01851, "loss": 3.01851, "time": 0.81909} +{"mode": "train", "epoch": 124, "iter": 2000, "lr": 0.00749, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45359, "top5_acc": 0.71734, "loss_cls": 3.02525, "loss": 3.02525, "time": 0.81947} +{"mode": "train", "epoch": 124, "iter": 2100, "lr": 0.00747, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46125, "top5_acc": 0.72, "loss_cls": 3.0027, "loss": 3.0027, "time": 0.8177} +{"mode": "train", "epoch": 124, "iter": 2200, "lr": 0.00746, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45328, "top5_acc": 0.71438, "loss_cls": 3.0439, "loss": 3.0439, "time": 0.81846} +{"mode": "train", "epoch": 124, "iter": 2300, "lr": 0.00744, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.465, "top5_acc": 0.7225, "loss_cls": 2.99752, "loss": 2.99752, "time": 0.81581} +{"mode": "train", "epoch": 124, "iter": 2400, "lr": 0.00743, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.46562, "top5_acc": 0.71766, "loss_cls": 2.99453, "loss": 2.99453, "time": 0.82209} +{"mode": "train", "epoch": 124, "iter": 2500, "lr": 0.00741, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46453, "top5_acc": 0.72641, "loss_cls": 2.97599, "loss": 2.97599, "time": 0.81588} +{"mode": "train", "epoch": 124, "iter": 2600, "lr": 0.0074, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.46219, "top5_acc": 0.71375, "loss_cls": 3.03724, "loss": 3.03724, "time": 0.81161} +{"mode": "train", "epoch": 124, "iter": 2700, "lr": 0.00738, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47469, "top5_acc": 0.72375, "loss_cls": 2.93797, "loss": 2.93797, "time": 0.81274} +{"mode": "train", "epoch": 124, "iter": 2800, "lr": 0.00737, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46078, "top5_acc": 0.72156, "loss_cls": 2.9838, "loss": 2.9838, "time": 0.8116} +{"mode": "train", "epoch": 124, "iter": 2900, "lr": 0.00735, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.45828, "top5_acc": 0.70906, "loss_cls": 3.06294, "loss": 3.06294, "time": 0.81453} +{"mode": "train", "epoch": 124, "iter": 3000, "lr": 0.00734, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45797, "top5_acc": 0.71531, "loss_cls": 3.02714, "loss": 3.02714, "time": 0.81978} +{"mode": "train", "epoch": 124, "iter": 3100, "lr": 0.00733, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46766, "top5_acc": 0.71156, "loss_cls": 2.97121, "loss": 2.97121, "time": 0.81767} +{"mode": "train", "epoch": 124, "iter": 3200, "lr": 0.00731, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46344, "top5_acc": 0.71094, "loss_cls": 3.02527, "loss": 3.02527, "time": 0.81313} +{"mode": "train", "epoch": 124, "iter": 3300, "lr": 0.0073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45859, "top5_acc": 0.71297, "loss_cls": 3.02375, "loss": 3.02375, "time": 0.81572} +{"mode": "train", "epoch": 124, "iter": 3400, "lr": 0.00728, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46422, "top5_acc": 0.70844, "loss_cls": 3.03484, "loss": 3.03484, "time": 0.81675} +{"mode": "train", "epoch": 124, "iter": 3500, "lr": 0.00727, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45516, "top5_acc": 0.70984, "loss_cls": 3.02249, "loss": 3.02249, "time": 0.81923} +{"mode": "train", "epoch": 124, "iter": 3600, "lr": 0.00725, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46047, "top5_acc": 0.71484, "loss_cls": 3.01786, "loss": 3.01786, "time": 0.81207} +{"mode": "train", "epoch": 124, "iter": 3700, "lr": 0.00724, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45859, "top5_acc": 0.71688, "loss_cls": 3.02318, "loss": 3.02318, "time": 0.81446} +{"mode": "val", "epoch": 124, "iter": 309, "lr": 0.00723, "top1_acc": 0.39953, "top5_acc": 0.64919, "mean_class_accuracy": 0.39917} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.00722, "memory": 15990, "data_time": 1.37556, "top1_acc": 0.48797, "top5_acc": 0.74016, "loss_cls": 2.87245, "loss": 2.87245, "time": 2.39106} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.0072, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.47562, "top5_acc": 0.73047, "loss_cls": 2.89234, "loss": 2.89234, "time": 0.84108} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00719, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47516, "top5_acc": 0.73062, "loss_cls": 2.93952, "loss": 2.93952, "time": 0.84454} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00717, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.48062, "top5_acc": 0.72422, "loss_cls": 2.9357, "loss": 2.9357, "time": 0.8375} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00716, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.4725, "top5_acc": 0.73438, "loss_cls": 2.90327, "loss": 2.90327, "time": 0.83698} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00715, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46359, "top5_acc": 0.72047, "loss_cls": 2.97657, "loss": 2.97657, "time": 0.82242} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00713, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46734, "top5_acc": 0.72469, "loss_cls": 2.96927, "loss": 2.96927, "time": 0.82741} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00712, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46891, "top5_acc": 0.7175, "loss_cls": 2.98869, "loss": 2.98869, "time": 0.83219} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.0071, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46953, "top5_acc": 0.71906, "loss_cls": 2.97275, "loss": 2.97275, "time": 0.83547} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.00709, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47219, "top5_acc": 0.72062, "loss_cls": 2.95289, "loss": 2.95289, "time": 0.8249} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00707, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47578, "top5_acc": 0.7325, "loss_cls": 2.92365, "loss": 2.92365, "time": 0.82523} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00706, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.4675, "top5_acc": 0.72594, "loss_cls": 2.94604, "loss": 2.94604, "time": 0.83198} +{"mode": "train", "epoch": 125, "iter": 1300, "lr": 0.00704, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46844, "top5_acc": 0.72703, "loss_cls": 2.97299, "loss": 2.97299, "time": 0.82624} +{"mode": "train", "epoch": 125, "iter": 1400, "lr": 0.00703, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47688, "top5_acc": 0.72359, "loss_cls": 2.95967, "loss": 2.95967, "time": 0.8455} +{"mode": "train", "epoch": 125, "iter": 1500, "lr": 0.00702, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.47203, "top5_acc": 0.72531, "loss_cls": 2.94012, "loss": 2.94012, "time": 0.82717} +{"mode": "train", "epoch": 125, "iter": 1600, "lr": 0.007, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.45281, "top5_acc": 0.71469, "loss_cls": 3.02142, "loss": 3.02142, "time": 0.83572} +{"mode": "train", "epoch": 125, "iter": 1700, "lr": 0.00699, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46984, "top5_acc": 0.72797, "loss_cls": 2.95402, "loss": 2.95402, "time": 0.8376} +{"mode": "train", "epoch": 125, "iter": 1800, "lr": 0.00697, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46109, "top5_acc": 0.71797, "loss_cls": 3.00417, "loss": 3.00417, "time": 0.83404} +{"mode": "train", "epoch": 125, "iter": 1900, "lr": 0.00696, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46047, "top5_acc": 0.71797, "loss_cls": 3.00132, "loss": 3.00132, "time": 0.8376} +{"mode": "train", "epoch": 125, "iter": 2000, "lr": 0.00694, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47688, "top5_acc": 0.71938, "loss_cls": 2.94868, "loss": 2.94868, "time": 0.83098} +{"mode": "train", "epoch": 125, "iter": 2100, "lr": 0.00693, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47891, "top5_acc": 0.73062, "loss_cls": 2.91764, "loss": 2.91764, "time": 0.82843} +{"mode": "train", "epoch": 125, "iter": 2200, "lr": 0.00692, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.46328, "top5_acc": 0.71844, "loss_cls": 3.00223, "loss": 3.00223, "time": 0.83105} +{"mode": "train", "epoch": 125, "iter": 2300, "lr": 0.0069, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47125, "top5_acc": 0.72141, "loss_cls": 2.99271, "loss": 2.99271, "time": 0.83399} +{"mode": "train", "epoch": 125, "iter": 2400, "lr": 0.00689, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45922, "top5_acc": 0.71578, "loss_cls": 2.99438, "loss": 2.99438, "time": 0.8253} +{"mode": "train", "epoch": 125, "iter": 2500, "lr": 0.00687, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46125, "top5_acc": 0.70828, "loss_cls": 3.02476, "loss": 3.02476, "time": 0.84036} +{"mode": "train", "epoch": 125, "iter": 2600, "lr": 0.00686, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46938, "top5_acc": 0.72781, "loss_cls": 2.95522, "loss": 2.95522, "time": 0.84064} +{"mode": "train", "epoch": 125, "iter": 2700, "lr": 0.00685, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45938, "top5_acc": 0.72078, "loss_cls": 3.01742, "loss": 3.01742, "time": 0.82959} +{"mode": "train", "epoch": 125, "iter": 2800, "lr": 0.00683, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46562, "top5_acc": 0.72656, "loss_cls": 2.96902, "loss": 2.96902, "time": 0.82503} +{"mode": "train", "epoch": 125, "iter": 2900, "lr": 0.00682, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47203, "top5_acc": 0.72, "loss_cls": 2.95629, "loss": 2.95629, "time": 0.82616} +{"mode": "train", "epoch": 125, "iter": 3000, "lr": 0.0068, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47, "top5_acc": 0.72172, "loss_cls": 2.94794, "loss": 2.94794, "time": 0.82422} +{"mode": "train", "epoch": 125, "iter": 3100, "lr": 0.00679, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47656, "top5_acc": 0.72141, "loss_cls": 2.97484, "loss": 2.97484, "time": 0.83373} +{"mode": "train", "epoch": 125, "iter": 3200, "lr": 0.00678, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46484, "top5_acc": 0.72062, "loss_cls": 2.98542, "loss": 2.98542, "time": 0.82565} +{"mode": "train", "epoch": 125, "iter": 3300, "lr": 0.00676, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47578, "top5_acc": 0.72, "loss_cls": 3.01166, "loss": 3.01166, "time": 0.82727} +{"mode": "train", "epoch": 125, "iter": 3400, "lr": 0.00675, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47172, "top5_acc": 0.73219, "loss_cls": 2.93006, "loss": 2.93006, "time": 0.8305} +{"mode": "train", "epoch": 125, "iter": 3500, "lr": 0.00673, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45641, "top5_acc": 0.72375, "loss_cls": 3.00072, "loss": 3.00072, "time": 0.82524} +{"mode": "train", "epoch": 125, "iter": 3600, "lr": 0.00672, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46422, "top5_acc": 0.71953, "loss_cls": 2.97361, "loss": 2.97361, "time": 0.82689} +{"mode": "train", "epoch": 125, "iter": 3700, "lr": 0.00671, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46234, "top5_acc": 0.7175, "loss_cls": 3.02078, "loss": 3.02078, "time": 0.82918} +{"mode": "val", "epoch": 125, "iter": 309, "lr": 0.0067, "top1_acc": 0.38495, "top5_acc": 0.64671, "mean_class_accuracy": 0.38463} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00668, "memory": 15990, "data_time": 1.2884, "top1_acc": 0.48906, "top5_acc": 0.74734, "loss_cls": 2.82619, "loss": 2.82619, "time": 2.30195} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00667, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47922, "top5_acc": 0.73312, "loss_cls": 2.87826, "loss": 2.87826, "time": 0.8352} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00666, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48359, "top5_acc": 0.74188, "loss_cls": 2.88754, "loss": 2.88754, "time": 0.84321} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00664, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47453, "top5_acc": 0.73859, "loss_cls": 2.88697, "loss": 2.88697, "time": 0.84307} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00663, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48078, "top5_acc": 0.73906, "loss_cls": 2.90059, "loss": 2.90059, "time": 0.83634} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00662, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48578, "top5_acc": 0.73172, "loss_cls": 2.91653, "loss": 2.91653, "time": 0.82989} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0066, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47047, "top5_acc": 0.72312, "loss_cls": 2.95436, "loss": 2.95436, "time": 0.8264} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00659, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47578, "top5_acc": 0.72938, "loss_cls": 2.91016, "loss": 2.91016, "time": 0.82506} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00657, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47438, "top5_acc": 0.72594, "loss_cls": 2.92723, "loss": 2.92723, "time": 0.8371} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00656, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46406, "top5_acc": 0.72188, "loss_cls": 2.95036, "loss": 2.95036, "time": 0.83} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00655, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47578, "top5_acc": 0.72203, "loss_cls": 2.94251, "loss": 2.94251, "time": 0.82313} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00653, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48656, "top5_acc": 0.73641, "loss_cls": 2.87219, "loss": 2.87219, "time": 0.82993} +{"mode": "train", "epoch": 126, "iter": 1300, "lr": 0.00652, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.48047, "top5_acc": 0.74094, "loss_cls": 2.8619, "loss": 2.8619, "time": 0.82776} +{"mode": "train", "epoch": 126, "iter": 1400, "lr": 0.0065, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48672, "top5_acc": 0.72453, "loss_cls": 2.9128, "loss": 2.9128, "time": 0.83427} +{"mode": "train", "epoch": 126, "iter": 1500, "lr": 0.00649, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47, "top5_acc": 0.72516, "loss_cls": 2.94654, "loss": 2.94654, "time": 0.82807} +{"mode": "train", "epoch": 126, "iter": 1600, "lr": 0.00648, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48188, "top5_acc": 0.73234, "loss_cls": 2.89135, "loss": 2.89135, "time": 0.83589} +{"mode": "train", "epoch": 126, "iter": 1700, "lr": 0.00646, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46312, "top5_acc": 0.71562, "loss_cls": 2.9929, "loss": 2.9929, "time": 0.83937} +{"mode": "train", "epoch": 126, "iter": 1800, "lr": 0.00645, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47109, "top5_acc": 0.72266, "loss_cls": 2.94791, "loss": 2.94791, "time": 0.83397} +{"mode": "train", "epoch": 126, "iter": 1900, "lr": 0.00644, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48016, "top5_acc": 0.73406, "loss_cls": 2.88503, "loss": 2.88503, "time": 0.83277} +{"mode": "train", "epoch": 126, "iter": 2000, "lr": 0.00642, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46922, "top5_acc": 0.72766, "loss_cls": 2.9526, "loss": 2.9526, "time": 0.82863} +{"mode": "train", "epoch": 126, "iter": 2100, "lr": 0.00641, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46984, "top5_acc": 0.72906, "loss_cls": 2.93263, "loss": 2.93263, "time": 0.83455} +{"mode": "train", "epoch": 126, "iter": 2200, "lr": 0.00639, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.475, "top5_acc": 0.72609, "loss_cls": 2.93727, "loss": 2.93727, "time": 0.84359} +{"mode": "train", "epoch": 126, "iter": 2300, "lr": 0.00638, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46438, "top5_acc": 0.72469, "loss_cls": 2.99298, "loss": 2.99298, "time": 0.83409} +{"mode": "train", "epoch": 126, "iter": 2400, "lr": 0.00637, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48078, "top5_acc": 0.71891, "loss_cls": 2.94301, "loss": 2.94301, "time": 0.83842} +{"mode": "train", "epoch": 126, "iter": 2500, "lr": 0.00635, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47219, "top5_acc": 0.72031, "loss_cls": 2.95704, "loss": 2.95704, "time": 0.84104} +{"mode": "train", "epoch": 126, "iter": 2600, "lr": 0.00634, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47875, "top5_acc": 0.72859, "loss_cls": 2.91857, "loss": 2.91857, "time": 0.84374} +{"mode": "train", "epoch": 126, "iter": 2700, "lr": 0.00633, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47484, "top5_acc": 0.72344, "loss_cls": 2.96772, "loss": 2.96772, "time": 0.83795} +{"mode": "train", "epoch": 126, "iter": 2800, "lr": 0.00631, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46547, "top5_acc": 0.71859, "loss_cls": 3.01272, "loss": 3.01272, "time": 0.82743} +{"mode": "train", "epoch": 126, "iter": 2900, "lr": 0.0063, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47188, "top5_acc": 0.71891, "loss_cls": 2.96448, "loss": 2.96448, "time": 0.83212} +{"mode": "train", "epoch": 126, "iter": 3000, "lr": 0.00629, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47141, "top5_acc": 0.72578, "loss_cls": 2.9265, "loss": 2.9265, "time": 0.82835} +{"mode": "train", "epoch": 126, "iter": 3100, "lr": 0.00627, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48453, "top5_acc": 0.72438, "loss_cls": 2.91445, "loss": 2.91445, "time": 0.83656} +{"mode": "train", "epoch": 126, "iter": 3200, "lr": 0.00626, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46422, "top5_acc": 0.71953, "loss_cls": 2.98891, "loss": 2.98891, "time": 0.83266} +{"mode": "train", "epoch": 126, "iter": 3300, "lr": 0.00625, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47156, "top5_acc": 0.72547, "loss_cls": 2.94218, "loss": 2.94218, "time": 0.82167} +{"mode": "train", "epoch": 126, "iter": 3400, "lr": 0.00623, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47203, "top5_acc": 0.72312, "loss_cls": 2.96, "loss": 2.96, "time": 0.83149} +{"mode": "train", "epoch": 126, "iter": 3500, "lr": 0.00622, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47203, "top5_acc": 0.72469, "loss_cls": 2.92906, "loss": 2.92906, "time": 0.82548} +{"mode": "train", "epoch": 126, "iter": 3600, "lr": 0.0062, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47609, "top5_acc": 0.72812, "loss_cls": 2.91298, "loss": 2.91298, "time": 0.8255} +{"mode": "train", "epoch": 126, "iter": 3700, "lr": 0.00619, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47, "top5_acc": 0.71812, "loss_cls": 2.99344, "loss": 2.99344, "time": 0.82493} +{"mode": "val", "epoch": 126, "iter": 309, "lr": 0.00618, "top1_acc": 0.3932, "top5_acc": 0.65096, "mean_class_accuracy": 0.39309} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00617, "memory": 15990, "data_time": 1.29838, "top1_acc": 0.49516, "top5_acc": 0.74266, "loss_cls": 2.8437, "loss": 2.8437, "time": 2.28702} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00616, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49312, "top5_acc": 0.74406, "loss_cls": 2.8583, "loss": 2.8583, "time": 0.84289} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00614, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48375, "top5_acc": 0.72891, "loss_cls": 2.91219, "loss": 2.91219, "time": 0.8427} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00613, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47828, "top5_acc": 0.73625, "loss_cls": 2.8793, "loss": 2.8793, "time": 0.8439} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.00612, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.4925, "top5_acc": 0.74016, "loss_cls": 2.83455, "loss": 2.83455, "time": 0.83844} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.0061, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49438, "top5_acc": 0.74797, "loss_cls": 2.82574, "loss": 2.82574, "time": 0.8368} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00609, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.4975, "top5_acc": 0.74172, "loss_cls": 2.83832, "loss": 2.83832, "time": 0.83092} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00608, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48797, "top5_acc": 0.74766, "loss_cls": 2.8393, "loss": 2.8393, "time": 0.83053} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00606, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48516, "top5_acc": 0.73, "loss_cls": 2.89885, "loss": 2.89885, "time": 0.83434} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00605, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48344, "top5_acc": 0.73344, "loss_cls": 2.88966, "loss": 2.88966, "time": 0.82862} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00604, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.4825, "top5_acc": 0.73625, "loss_cls": 2.87949, "loss": 2.87949, "time": 0.83054} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00602, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47234, "top5_acc": 0.72969, "loss_cls": 2.93307, "loss": 2.93307, "time": 0.83329} +{"mode": "train", "epoch": 127, "iter": 1300, "lr": 0.00601, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48156, "top5_acc": 0.72875, "loss_cls": 2.91157, "loss": 2.91157, "time": 0.84215} +{"mode": "train", "epoch": 127, "iter": 1400, "lr": 0.006, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48812, "top5_acc": 0.74234, "loss_cls": 2.88434, "loss": 2.88434, "time": 0.83144} +{"mode": "train", "epoch": 127, "iter": 1500, "lr": 0.00598, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.48219, "top5_acc": 0.74172, "loss_cls": 2.86223, "loss": 2.86223, "time": 0.84374} +{"mode": "train", "epoch": 127, "iter": 1600, "lr": 0.00597, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48, "top5_acc": 0.73016, "loss_cls": 2.9106, "loss": 2.9106, "time": 0.83891} +{"mode": "train", "epoch": 127, "iter": 1700, "lr": 0.00596, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49016, "top5_acc": 0.74219, "loss_cls": 2.8308, "loss": 2.8308, "time": 0.84256} +{"mode": "train", "epoch": 127, "iter": 1800, "lr": 0.00594, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48703, "top5_acc": 0.73016, "loss_cls": 2.90877, "loss": 2.90877, "time": 0.84161} +{"mode": "train", "epoch": 127, "iter": 1900, "lr": 0.00593, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47906, "top5_acc": 0.73266, "loss_cls": 2.9187, "loss": 2.9187, "time": 0.83701} +{"mode": "train", "epoch": 127, "iter": 2000, "lr": 0.00592, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48422, "top5_acc": 0.73609, "loss_cls": 2.8743, "loss": 2.8743, "time": 0.83451} +{"mode": "train", "epoch": 127, "iter": 2100, "lr": 0.00591, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.4775, "top5_acc": 0.73234, "loss_cls": 2.90865, "loss": 2.90865, "time": 0.83985} +{"mode": "train", "epoch": 127, "iter": 2200, "lr": 0.00589, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48406, "top5_acc": 0.73812, "loss_cls": 2.86695, "loss": 2.86695, "time": 0.8306} +{"mode": "train", "epoch": 127, "iter": 2300, "lr": 0.00588, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46953, "top5_acc": 0.72062, "loss_cls": 2.94353, "loss": 2.94353, "time": 0.82998} +{"mode": "train", "epoch": 127, "iter": 2400, "lr": 0.00587, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47672, "top5_acc": 0.73188, "loss_cls": 2.95207, "loss": 2.95207, "time": 0.84376} +{"mode": "train", "epoch": 127, "iter": 2500, "lr": 0.00585, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48266, "top5_acc": 0.72469, "loss_cls": 2.90465, "loss": 2.90465, "time": 0.84173} +{"mode": "train", "epoch": 127, "iter": 2600, "lr": 0.00584, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47109, "top5_acc": 0.7225, "loss_cls": 2.9452, "loss": 2.9452, "time": 0.8336} +{"mode": "train", "epoch": 127, "iter": 2700, "lr": 0.00583, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47859, "top5_acc": 0.72828, "loss_cls": 2.9137, "loss": 2.9137, "time": 0.82886} +{"mode": "train", "epoch": 127, "iter": 2800, "lr": 0.00581, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47266, "top5_acc": 0.72156, "loss_cls": 2.96835, "loss": 2.96835, "time": 0.83108} +{"mode": "train", "epoch": 127, "iter": 2900, "lr": 0.0058, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48094, "top5_acc": 0.73078, "loss_cls": 2.89979, "loss": 2.89979, "time": 0.83319} +{"mode": "train", "epoch": 127, "iter": 3000, "lr": 0.00579, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47703, "top5_acc": 0.72719, "loss_cls": 2.93988, "loss": 2.93988, "time": 0.82744} +{"mode": "train", "epoch": 127, "iter": 3100, "lr": 0.00577, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46766, "top5_acc": 0.7175, "loss_cls": 2.97791, "loss": 2.97791, "time": 0.82219} +{"mode": "train", "epoch": 127, "iter": 3200, "lr": 0.00576, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47938, "top5_acc": 0.73625, "loss_cls": 2.8715, "loss": 2.8715, "time": 0.82939} +{"mode": "train", "epoch": 127, "iter": 3300, "lr": 0.00575, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48, "top5_acc": 0.72672, "loss_cls": 2.942, "loss": 2.942, "time": 0.83318} +{"mode": "train", "epoch": 127, "iter": 3400, "lr": 0.00573, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47406, "top5_acc": 0.72562, "loss_cls": 2.92382, "loss": 2.92382, "time": 0.83108} +{"mode": "train", "epoch": 127, "iter": 3500, "lr": 0.00572, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.4775, "top5_acc": 0.72672, "loss_cls": 2.94686, "loss": 2.94686, "time": 0.83225} +{"mode": "train", "epoch": 127, "iter": 3600, "lr": 0.00571, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48453, "top5_acc": 0.73234, "loss_cls": 2.89555, "loss": 2.89555, "time": 0.83217} +{"mode": "train", "epoch": 127, "iter": 3700, "lr": 0.0057, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46938, "top5_acc": 0.72703, "loss_cls": 2.92459, "loss": 2.92459, "time": 0.83001} +{"mode": "val", "epoch": 127, "iter": 309, "lr": 0.00569, "top1_acc": 0.39847, "top5_acc": 0.65466, "mean_class_accuracy": 0.39823} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00568, "memory": 15990, "data_time": 1.34925, "top1_acc": 0.50266, "top5_acc": 0.75422, "loss_cls": 2.78287, "loss": 2.78287, "time": 2.32855} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.00566, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49469, "top5_acc": 0.74453, "loss_cls": 2.81238, "loss": 2.81238, "time": 0.82295} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00565, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50031, "top5_acc": 0.74609, "loss_cls": 2.77883, "loss": 2.77883, "time": 0.82478} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00564, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48828, "top5_acc": 0.74109, "loss_cls": 2.82939, "loss": 2.82939, "time": 0.81697} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00563, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48984, "top5_acc": 0.74438, "loss_cls": 2.84819, "loss": 2.84819, "time": 0.81962} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00561, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48109, "top5_acc": 0.73234, "loss_cls": 2.88747, "loss": 2.88747, "time": 0.81423} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.0056, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.48375, "top5_acc": 0.73875, "loss_cls": 2.88482, "loss": 2.88482, "time": 0.81506} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00559, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48469, "top5_acc": 0.73875, "loss_cls": 2.85876, "loss": 2.85876, "time": 0.8151} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00557, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49141, "top5_acc": 0.74594, "loss_cls": 2.82014, "loss": 2.82014, "time": 0.82142} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00556, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48594, "top5_acc": 0.73812, "loss_cls": 2.8956, "loss": 2.8956, "time": 0.81807} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00555, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48094, "top5_acc": 0.73188, "loss_cls": 2.88805, "loss": 2.88805, "time": 0.81991} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00554, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.47953, "top5_acc": 0.73547, "loss_cls": 2.87816, "loss": 2.87816, "time": 0.82191} +{"mode": "train", "epoch": 128, "iter": 1300, "lr": 0.00552, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48641, "top5_acc": 0.73391, "loss_cls": 2.87273, "loss": 2.87273, "time": 0.81408} +{"mode": "train", "epoch": 128, "iter": 1400, "lr": 0.00551, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49188, "top5_acc": 0.74172, "loss_cls": 2.82531, "loss": 2.82531, "time": 0.82676} +{"mode": "train", "epoch": 128, "iter": 1500, "lr": 0.0055, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48719, "top5_acc": 0.74141, "loss_cls": 2.8595, "loss": 2.8595, "time": 0.82091} +{"mode": "train", "epoch": 128, "iter": 1600, "lr": 0.00548, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49375, "top5_acc": 0.74422, "loss_cls": 2.84922, "loss": 2.84922, "time": 0.81631} +{"mode": "train", "epoch": 128, "iter": 1700, "lr": 0.00547, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48875, "top5_acc": 0.73641, "loss_cls": 2.85725, "loss": 2.85725, "time": 0.82008} +{"mode": "train", "epoch": 128, "iter": 1800, "lr": 0.00546, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47875, "top5_acc": 0.73828, "loss_cls": 2.89807, "loss": 2.89807, "time": 0.81286} +{"mode": "train", "epoch": 128, "iter": 1900, "lr": 0.00545, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47828, "top5_acc": 0.74047, "loss_cls": 2.88644, "loss": 2.88644, "time": 0.82072} +{"mode": "train", "epoch": 128, "iter": 2000, "lr": 0.00543, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48062, "top5_acc": 0.73797, "loss_cls": 2.8964, "loss": 2.8964, "time": 0.82172} +{"mode": "train", "epoch": 128, "iter": 2100, "lr": 0.00542, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49297, "top5_acc": 0.73828, "loss_cls": 2.85022, "loss": 2.85022, "time": 0.81698} +{"mode": "train", "epoch": 128, "iter": 2200, "lr": 0.00541, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49328, "top5_acc": 0.73469, "loss_cls": 2.86535, "loss": 2.86535, "time": 0.82121} +{"mode": "train", "epoch": 128, "iter": 2300, "lr": 0.0054, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47109, "top5_acc": 0.71859, "loss_cls": 2.96206, "loss": 2.96206, "time": 0.82005} +{"mode": "train", "epoch": 128, "iter": 2400, "lr": 0.00538, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48625, "top5_acc": 0.735, "loss_cls": 2.88352, "loss": 2.88352, "time": 0.81631} +{"mode": "train", "epoch": 128, "iter": 2500, "lr": 0.00537, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48906, "top5_acc": 0.73578, "loss_cls": 2.86636, "loss": 2.86636, "time": 0.82001} +{"mode": "train", "epoch": 128, "iter": 2600, "lr": 0.00536, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49406, "top5_acc": 0.73609, "loss_cls": 2.86929, "loss": 2.86929, "time": 0.8141} +{"mode": "train", "epoch": 128, "iter": 2700, "lr": 0.00535, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49578, "top5_acc": 0.73516, "loss_cls": 2.84274, "loss": 2.84274, "time": 0.81821} +{"mode": "train", "epoch": 128, "iter": 2800, "lr": 0.00533, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49109, "top5_acc": 0.73438, "loss_cls": 2.8955, "loss": 2.8955, "time": 0.81409} +{"mode": "train", "epoch": 128, "iter": 2900, "lr": 0.00532, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48312, "top5_acc": 0.73188, "loss_cls": 2.89964, "loss": 2.89964, "time": 0.81475} +{"mode": "train", "epoch": 128, "iter": 3000, "lr": 0.00531, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48078, "top5_acc": 0.73828, "loss_cls": 2.89272, "loss": 2.89272, "time": 0.81328} +{"mode": "train", "epoch": 128, "iter": 3100, "lr": 0.0053, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49078, "top5_acc": 0.73672, "loss_cls": 2.86098, "loss": 2.86098, "time": 0.81568} +{"mode": "train", "epoch": 128, "iter": 3200, "lr": 0.00528, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48062, "top5_acc": 0.73156, "loss_cls": 2.89894, "loss": 2.89894, "time": 0.81549} +{"mode": "train", "epoch": 128, "iter": 3300, "lr": 0.00527, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48438, "top5_acc": 0.72938, "loss_cls": 2.89558, "loss": 2.89558, "time": 0.81415} +{"mode": "train", "epoch": 128, "iter": 3400, "lr": 0.00526, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47812, "top5_acc": 0.73547, "loss_cls": 2.90397, "loss": 2.90397, "time": 0.81784} +{"mode": "train", "epoch": 128, "iter": 3500, "lr": 0.00525, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48828, "top5_acc": 0.73156, "loss_cls": 2.86423, "loss": 2.86423, "time": 0.817} +{"mode": "train", "epoch": 128, "iter": 3600, "lr": 0.00523, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48578, "top5_acc": 0.73766, "loss_cls": 2.8901, "loss": 2.8901, "time": 0.82321} +{"mode": "train", "epoch": 128, "iter": 3700, "lr": 0.00522, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48688, "top5_acc": 0.73672, "loss_cls": 2.86726, "loss": 2.86726, "time": 0.81532} +{"mode": "val", "epoch": 128, "iter": 309, "lr": 0.00521, "top1_acc": 0.40835, "top5_acc": 0.66064, "mean_class_accuracy": 0.40817} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.0052, "memory": 15990, "data_time": 1.36535, "top1_acc": 0.50125, "top5_acc": 0.75609, "loss_cls": 2.77509, "loss": 2.77509, "time": 2.34441} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00519, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50156, "top5_acc": 0.75094, "loss_cls": 2.76695, "loss": 2.76695, "time": 0.81543} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00518, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50828, "top5_acc": 0.75047, "loss_cls": 2.75766, "loss": 2.75766, "time": 0.81447} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00516, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51359, "top5_acc": 0.75469, "loss_cls": 2.73532, "loss": 2.73532, "time": 0.81899} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00515, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49766, "top5_acc": 0.75016, "loss_cls": 2.81213, "loss": 2.81213, "time": 0.8138} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00514, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50422, "top5_acc": 0.74859, "loss_cls": 2.79016, "loss": 2.79016, "time": 0.81611} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00513, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48969, "top5_acc": 0.74094, "loss_cls": 2.83057, "loss": 2.83057, "time": 0.81604} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00512, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49281, "top5_acc": 0.74938, "loss_cls": 2.80321, "loss": 2.80321, "time": 0.81395} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.0051, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50297, "top5_acc": 0.74766, "loss_cls": 2.78251, "loss": 2.78251, "time": 0.81661} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00509, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48094, "top5_acc": 0.74125, "loss_cls": 2.85958, "loss": 2.85958, "time": 0.81538} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00508, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49953, "top5_acc": 0.74609, "loss_cls": 2.79377, "loss": 2.79377, "time": 0.81797} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.00507, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50328, "top5_acc": 0.74797, "loss_cls": 2.79571, "loss": 2.79571, "time": 0.82111} +{"mode": "train", "epoch": 129, "iter": 1300, "lr": 0.00505, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48516, "top5_acc": 0.73109, "loss_cls": 2.87531, "loss": 2.87531, "time": 0.82087} +{"mode": "train", "epoch": 129, "iter": 1400, "lr": 0.00504, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.4875, "top5_acc": 0.73188, "loss_cls": 2.85928, "loss": 2.85928, "time": 0.82031} +{"mode": "train", "epoch": 129, "iter": 1500, "lr": 0.00503, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49266, "top5_acc": 0.74281, "loss_cls": 2.8556, "loss": 2.8556, "time": 0.81869} +{"mode": "train", "epoch": 129, "iter": 1600, "lr": 0.00502, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49594, "top5_acc": 0.74078, "loss_cls": 2.82826, "loss": 2.82826, "time": 0.82022} +{"mode": "train", "epoch": 129, "iter": 1700, "lr": 0.00501, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49297, "top5_acc": 0.74719, "loss_cls": 2.80597, "loss": 2.80597, "time": 0.81989} +{"mode": "train", "epoch": 129, "iter": 1800, "lr": 0.00499, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49406, "top5_acc": 0.74656, "loss_cls": 2.83429, "loss": 2.83429, "time": 0.81783} +{"mode": "train", "epoch": 129, "iter": 1900, "lr": 0.00498, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50703, "top5_acc": 0.75281, "loss_cls": 2.7799, "loss": 2.7799, "time": 0.81943} +{"mode": "train", "epoch": 129, "iter": 2000, "lr": 0.00497, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50109, "top5_acc": 0.745, "loss_cls": 2.811, "loss": 2.811, "time": 0.82151} +{"mode": "train", "epoch": 129, "iter": 2100, "lr": 0.00496, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48984, "top5_acc": 0.74297, "loss_cls": 2.84471, "loss": 2.84471, "time": 0.81909} +{"mode": "train", "epoch": 129, "iter": 2200, "lr": 0.00494, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48, "top5_acc": 0.73766, "loss_cls": 2.88334, "loss": 2.88334, "time": 0.81954} +{"mode": "train", "epoch": 129, "iter": 2300, "lr": 0.00493, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.485, "top5_acc": 0.72969, "loss_cls": 2.88837, "loss": 2.88837, "time": 0.8222} +{"mode": "train", "epoch": 129, "iter": 2400, "lr": 0.00492, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.475, "top5_acc": 0.73844, "loss_cls": 2.88055, "loss": 2.88055, "time": 0.81806} +{"mode": "train", "epoch": 129, "iter": 2500, "lr": 0.00491, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49609, "top5_acc": 0.73438, "loss_cls": 2.83131, "loss": 2.83131, "time": 0.81701} +{"mode": "train", "epoch": 129, "iter": 2600, "lr": 0.0049, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49375, "top5_acc": 0.74812, "loss_cls": 2.81564, "loss": 2.81564, "time": 0.81596} +{"mode": "train", "epoch": 129, "iter": 2700, "lr": 0.00488, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48906, "top5_acc": 0.74156, "loss_cls": 2.86374, "loss": 2.86374, "time": 0.81817} +{"mode": "train", "epoch": 129, "iter": 2800, "lr": 0.00487, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48297, "top5_acc": 0.73922, "loss_cls": 2.86926, "loss": 2.86926, "time": 0.82234} +{"mode": "train", "epoch": 129, "iter": 2900, "lr": 0.00486, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48141, "top5_acc": 0.72781, "loss_cls": 2.92598, "loss": 2.92598, "time": 0.81842} +{"mode": "train", "epoch": 129, "iter": 3000, "lr": 0.00485, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4825, "top5_acc": 0.73766, "loss_cls": 2.88579, "loss": 2.88579, "time": 0.81412} +{"mode": "train", "epoch": 129, "iter": 3100, "lr": 0.00484, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47438, "top5_acc": 0.74031, "loss_cls": 2.87459, "loss": 2.87459, "time": 0.81476} +{"mode": "train", "epoch": 129, "iter": 3200, "lr": 0.00482, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49734, "top5_acc": 0.75625, "loss_cls": 2.79132, "loss": 2.79132, "time": 0.81521} +{"mode": "train", "epoch": 129, "iter": 3300, "lr": 0.00481, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49391, "top5_acc": 0.74219, "loss_cls": 2.83028, "loss": 2.83028, "time": 0.81642} +{"mode": "train", "epoch": 129, "iter": 3400, "lr": 0.0048, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48594, "top5_acc": 0.73781, "loss_cls": 2.84759, "loss": 2.84759, "time": 0.81423} +{"mode": "train", "epoch": 129, "iter": 3500, "lr": 0.00479, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49172, "top5_acc": 0.73188, "loss_cls": 2.86663, "loss": 2.86663, "time": 0.81239} +{"mode": "train", "epoch": 129, "iter": 3600, "lr": 0.00478, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48234, "top5_acc": 0.73719, "loss_cls": 2.86714, "loss": 2.86714, "time": 0.81482} +{"mode": "train", "epoch": 129, "iter": 3700, "lr": 0.00476, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49109, "top5_acc": 0.74672, "loss_cls": 2.81988, "loss": 2.81988, "time": 0.81097} +{"mode": "val", "epoch": 129, "iter": 309, "lr": 0.00476, "top1_acc": 0.40779, "top5_acc": 0.65912, "mean_class_accuracy": 0.40766} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00475, "memory": 15990, "data_time": 1.3289, "top1_acc": 0.50438, "top5_acc": 0.75469, "loss_cls": 2.76047, "loss": 2.76047, "time": 2.31154} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00473, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50844, "top5_acc": 0.75609, "loss_cls": 2.73426, "loss": 2.73426, "time": 0.82315} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00472, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.505, "top5_acc": 0.75031, "loss_cls": 2.76435, "loss": 2.76435, "time": 0.82846} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00471, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50656, "top5_acc": 0.75625, "loss_cls": 2.74114, "loss": 2.74114, "time": 0.81393} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.0047, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50078, "top5_acc": 0.74547, "loss_cls": 2.80837, "loss": 2.80837, "time": 0.81399} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00469, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50328, "top5_acc": 0.75156, "loss_cls": 2.76721, "loss": 2.76721, "time": 0.81372} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00468, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50547, "top5_acc": 0.75516, "loss_cls": 2.76889, "loss": 2.76889, "time": 0.81686} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00466, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51844, "top5_acc": 0.75859, "loss_cls": 2.73564, "loss": 2.73564, "time": 0.81893} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00465, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49828, "top5_acc": 0.74406, "loss_cls": 2.81586, "loss": 2.81586, "time": 0.81586} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.00464, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50484, "top5_acc": 0.75141, "loss_cls": 2.75308, "loss": 2.75308, "time": 0.81627} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.00463, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49969, "top5_acc": 0.74797, "loss_cls": 2.7885, "loss": 2.7885, "time": 0.81308} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00462, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50938, "top5_acc": 0.74797, "loss_cls": 2.75962, "loss": 2.75962, "time": 0.82147} +{"mode": "train", "epoch": 130, "iter": 1300, "lr": 0.00461, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49953, "top5_acc": 0.74641, "loss_cls": 2.79931, "loss": 2.79931, "time": 0.816} +{"mode": "train", "epoch": 130, "iter": 1400, "lr": 0.00459, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49656, "top5_acc": 0.74969, "loss_cls": 2.79133, "loss": 2.79133, "time": 0.81932} +{"mode": "train", "epoch": 130, "iter": 1500, "lr": 0.00458, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50594, "top5_acc": 0.75141, "loss_cls": 2.76596, "loss": 2.76596, "time": 0.81612} +{"mode": "train", "epoch": 130, "iter": 1600, "lr": 0.00457, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49641, "top5_acc": 0.74625, "loss_cls": 2.79543, "loss": 2.79543, "time": 0.81883} +{"mode": "train", "epoch": 130, "iter": 1700, "lr": 0.00456, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50375, "top5_acc": 0.74984, "loss_cls": 2.76025, "loss": 2.76025, "time": 0.8187} +{"mode": "train", "epoch": 130, "iter": 1800, "lr": 0.00455, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50031, "top5_acc": 0.75375, "loss_cls": 2.7925, "loss": 2.7925, "time": 0.81662} +{"mode": "train", "epoch": 130, "iter": 1900, "lr": 0.00454, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49672, "top5_acc": 0.74359, "loss_cls": 2.81982, "loss": 2.81982, "time": 0.81667} +{"mode": "train", "epoch": 130, "iter": 2000, "lr": 0.00452, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49734, "top5_acc": 0.74734, "loss_cls": 2.80701, "loss": 2.80701, "time": 0.81921} +{"mode": "train", "epoch": 130, "iter": 2100, "lr": 0.00451, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49922, "top5_acc": 0.74281, "loss_cls": 2.82182, "loss": 2.82182, "time": 0.81981} +{"mode": "train", "epoch": 130, "iter": 2200, "lr": 0.0045, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50469, "top5_acc": 0.74469, "loss_cls": 2.80362, "loss": 2.80362, "time": 0.81804} +{"mode": "train", "epoch": 130, "iter": 2300, "lr": 0.00449, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48875, "top5_acc": 0.74516, "loss_cls": 2.82173, "loss": 2.82173, "time": 0.81507} +{"mode": "train", "epoch": 130, "iter": 2400, "lr": 0.00448, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.48375, "top5_acc": 0.74125, "loss_cls": 2.86497, "loss": 2.86497, "time": 0.81658} +{"mode": "train", "epoch": 130, "iter": 2500, "lr": 0.00447, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49906, "top5_acc": 0.74594, "loss_cls": 2.79498, "loss": 2.79498, "time": 0.82328} +{"mode": "train", "epoch": 130, "iter": 2600, "lr": 0.00445, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49422, "top5_acc": 0.74219, "loss_cls": 2.82935, "loss": 2.82935, "time": 0.81559} +{"mode": "train", "epoch": 130, "iter": 2700, "lr": 0.00444, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49391, "top5_acc": 0.74641, "loss_cls": 2.81385, "loss": 2.81385, "time": 0.81229} +{"mode": "train", "epoch": 130, "iter": 2800, "lr": 0.00443, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49609, "top5_acc": 0.74406, "loss_cls": 2.78807, "loss": 2.78807, "time": 0.81366} +{"mode": "train", "epoch": 130, "iter": 2900, "lr": 0.00442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49344, "top5_acc": 0.75297, "loss_cls": 2.76734, "loss": 2.76734, "time": 0.81984} +{"mode": "train", "epoch": 130, "iter": 3000, "lr": 0.00441, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49719, "top5_acc": 0.75156, "loss_cls": 2.79659, "loss": 2.79659, "time": 0.81299} +{"mode": "train", "epoch": 130, "iter": 3100, "lr": 0.0044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49516, "top5_acc": 0.74359, "loss_cls": 2.80708, "loss": 2.80708, "time": 0.81388} +{"mode": "train", "epoch": 130, "iter": 3200, "lr": 0.00439, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.495, "top5_acc": 0.73781, "loss_cls": 2.84876, "loss": 2.84876, "time": 0.81794} +{"mode": "train", "epoch": 130, "iter": 3300, "lr": 0.00437, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48969, "top5_acc": 0.74984, "loss_cls": 2.82842, "loss": 2.82842, "time": 0.81676} +{"mode": "train", "epoch": 130, "iter": 3400, "lr": 0.00436, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50203, "top5_acc": 0.75031, "loss_cls": 2.78472, "loss": 2.78472, "time": 0.81737} +{"mode": "train", "epoch": 130, "iter": 3500, "lr": 0.00435, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49016, "top5_acc": 0.74391, "loss_cls": 2.83441, "loss": 2.83441, "time": 0.8159} +{"mode": "train", "epoch": 130, "iter": 3600, "lr": 0.00434, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48875, "top5_acc": 0.75031, "loss_cls": 2.80332, "loss": 2.80332, "time": 0.81945} +{"mode": "train", "epoch": 130, "iter": 3700, "lr": 0.00433, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50047, "top5_acc": 0.74375, "loss_cls": 2.81611, "loss": 2.81611, "time": 0.81833} +{"mode": "val", "epoch": 130, "iter": 309, "lr": 0.00432, "top1_acc": 0.41286, "top5_acc": 0.66459, "mean_class_accuracy": 0.41269} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00431, "memory": 15990, "data_time": 1.31931, "top1_acc": 0.51859, "top5_acc": 0.76812, "loss_cls": 2.67139, "loss": 2.67139, "time": 2.29906} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.0043, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51266, "top5_acc": 0.76906, "loss_cls": 2.67159, "loss": 2.67159, "time": 0.81798} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00429, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51703, "top5_acc": 0.77, "loss_cls": 2.6695, "loss": 2.6695, "time": 0.82128} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00428, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51469, "top5_acc": 0.76422, "loss_cls": 2.70374, "loss": 2.70374, "time": 0.81652} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00427, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.505, "top5_acc": 0.75094, "loss_cls": 2.76017, "loss": 2.76017, "time": 0.81639} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00425, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50531, "top5_acc": 0.75781, "loss_cls": 2.73779, "loss": 2.73779, "time": 0.81212} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00424, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52359, "top5_acc": 0.77375, "loss_cls": 2.66033, "loss": 2.66033, "time": 0.81474} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00423, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50359, "top5_acc": 0.75375, "loss_cls": 2.77941, "loss": 2.77941, "time": 0.81828} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00422, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49953, "top5_acc": 0.75391, "loss_cls": 2.77649, "loss": 2.77649, "time": 0.81529} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.00421, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.51562, "top5_acc": 0.75781, "loss_cls": 2.71822, "loss": 2.71822, "time": 0.82004} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.0042, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50938, "top5_acc": 0.76031, "loss_cls": 2.735, "loss": 2.735, "time": 0.81411} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00419, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51281, "top5_acc": 0.75297, "loss_cls": 2.74966, "loss": 2.74966, "time": 0.8289} +{"mode": "train", "epoch": 131, "iter": 1300, "lr": 0.00418, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50594, "top5_acc": 0.75375, "loss_cls": 2.75887, "loss": 2.75887, "time": 0.81984} +{"mode": "train", "epoch": 131, "iter": 1400, "lr": 0.00417, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.50719, "top5_acc": 0.75141, "loss_cls": 2.77576, "loss": 2.77576, "time": 0.83153} +{"mode": "train", "epoch": 131, "iter": 1500, "lr": 0.00415, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51, "top5_acc": 0.75719, "loss_cls": 2.74062, "loss": 2.74062, "time": 0.81669} +{"mode": "train", "epoch": 131, "iter": 1600, "lr": 0.00414, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.51578, "top5_acc": 0.75484, "loss_cls": 2.72125, "loss": 2.72125, "time": 0.81735} +{"mode": "train", "epoch": 131, "iter": 1700, "lr": 0.00413, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.50156, "top5_acc": 0.75281, "loss_cls": 2.78399, "loss": 2.78399, "time": 0.82393} +{"mode": "train", "epoch": 131, "iter": 1800, "lr": 0.00412, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51062, "top5_acc": 0.75344, "loss_cls": 2.7535, "loss": 2.7535, "time": 0.81712} +{"mode": "train", "epoch": 131, "iter": 1900, "lr": 0.00411, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50219, "top5_acc": 0.74922, "loss_cls": 2.75866, "loss": 2.75866, "time": 0.81642} +{"mode": "train", "epoch": 131, "iter": 2000, "lr": 0.0041, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50891, "top5_acc": 0.75516, "loss_cls": 2.71466, "loss": 2.71466, "time": 0.82414} +{"mode": "train", "epoch": 131, "iter": 2100, "lr": 0.00409, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50422, "top5_acc": 0.75188, "loss_cls": 2.75723, "loss": 2.75723, "time": 0.8202} +{"mode": "train", "epoch": 131, "iter": 2200, "lr": 0.00408, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49984, "top5_acc": 0.74531, "loss_cls": 2.80699, "loss": 2.80699, "time": 0.82727} +{"mode": "train", "epoch": 131, "iter": 2300, "lr": 0.00407, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49844, "top5_acc": 0.75703, "loss_cls": 2.76614, "loss": 2.76614, "time": 0.81736} +{"mode": "train", "epoch": 131, "iter": 2400, "lr": 0.00405, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49391, "top5_acc": 0.75234, "loss_cls": 2.77273, "loss": 2.77273, "time": 0.81276} +{"mode": "train", "epoch": 131, "iter": 2500, "lr": 0.00404, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50531, "top5_acc": 0.75594, "loss_cls": 2.74511, "loss": 2.74511, "time": 0.8136} +{"mode": "train", "epoch": 131, "iter": 2600, "lr": 0.00403, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49625, "top5_acc": 0.74406, "loss_cls": 2.77831, "loss": 2.77831, "time": 0.81831} +{"mode": "train", "epoch": 131, "iter": 2700, "lr": 0.00402, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50078, "top5_acc": 0.75641, "loss_cls": 2.76029, "loss": 2.76029, "time": 0.81776} +{"mode": "train", "epoch": 131, "iter": 2800, "lr": 0.00401, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50703, "top5_acc": 0.76016, "loss_cls": 2.7428, "loss": 2.7428, "time": 0.81604} +{"mode": "train", "epoch": 131, "iter": 2900, "lr": 0.004, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50672, "top5_acc": 0.75453, "loss_cls": 2.76696, "loss": 2.76696, "time": 0.81224} +{"mode": "train", "epoch": 131, "iter": 3000, "lr": 0.00399, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50344, "top5_acc": 0.75422, "loss_cls": 2.77264, "loss": 2.77264, "time": 0.81767} +{"mode": "train", "epoch": 131, "iter": 3100, "lr": 0.00398, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50156, "top5_acc": 0.74516, "loss_cls": 2.81367, "loss": 2.81367, "time": 0.81292} +{"mode": "train", "epoch": 131, "iter": 3200, "lr": 0.00397, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49844, "top5_acc": 0.74406, "loss_cls": 2.81179, "loss": 2.81179, "time": 0.81686} +{"mode": "train", "epoch": 131, "iter": 3300, "lr": 0.00396, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50547, "top5_acc": 0.75203, "loss_cls": 2.7718, "loss": 2.7718, "time": 0.8121} +{"mode": "train", "epoch": 131, "iter": 3400, "lr": 0.00394, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49938, "top5_acc": 0.74812, "loss_cls": 2.78898, "loss": 2.78898, "time": 0.81508} +{"mode": "train", "epoch": 131, "iter": 3500, "lr": 0.00393, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49766, "top5_acc": 0.74234, "loss_cls": 2.77569, "loss": 2.77569, "time": 0.81719} +{"mode": "train", "epoch": 131, "iter": 3600, "lr": 0.00392, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49875, "top5_acc": 0.745, "loss_cls": 2.80281, "loss": 2.80281, "time": 0.81108} +{"mode": "train", "epoch": 131, "iter": 3700, "lr": 0.00391, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49797, "top5_acc": 0.74625, "loss_cls": 2.79826, "loss": 2.79826, "time": 0.81843} +{"mode": "val", "epoch": 131, "iter": 309, "lr": 0.00391, "top1_acc": 0.41296, "top5_acc": 0.66236, "mean_class_accuracy": 0.41267} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.0039, "memory": 15990, "data_time": 1.31621, "top1_acc": 0.52844, "top5_acc": 0.76672, "loss_cls": 2.67454, "loss": 2.67454, "time": 2.29882} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00389, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50969, "top5_acc": 0.76109, "loss_cls": 2.71963, "loss": 2.71963, "time": 0.82054} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00387, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51531, "top5_acc": 0.76906, "loss_cls": 2.69176, "loss": 2.69176, "time": 0.81675} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00386, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51344, "top5_acc": 0.76969, "loss_cls": 2.70104, "loss": 2.70104, "time": 0.82566} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00385, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51172, "top5_acc": 0.76438, "loss_cls": 2.67661, "loss": 2.67661, "time": 0.81568} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00384, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51562, "top5_acc": 0.75688, "loss_cls": 2.7165, "loss": 2.7165, "time": 0.81606} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00383, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51562, "top5_acc": 0.76703, "loss_cls": 2.68418, "loss": 2.68418, "time": 0.81967} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00382, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51875, "top5_acc": 0.75844, "loss_cls": 2.70191, "loss": 2.70191, "time": 0.81241} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00381, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51578, "top5_acc": 0.77062, "loss_cls": 2.66613, "loss": 2.66613, "time": 0.81474} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0038, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51109, "top5_acc": 0.76234, "loss_cls": 2.71936, "loss": 2.71936, "time": 0.82261} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00379, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51297, "top5_acc": 0.75766, "loss_cls": 2.72906, "loss": 2.72906, "time": 0.8163} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00378, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51484, "top5_acc": 0.75688, "loss_cls": 2.70277, "loss": 2.70277, "time": 0.8169} +{"mode": "train", "epoch": 132, "iter": 1300, "lr": 0.00377, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52547, "top5_acc": 0.77219, "loss_cls": 2.64574, "loss": 2.64574, "time": 0.81634} +{"mode": "train", "epoch": 132, "iter": 1400, "lr": 0.00376, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.51672, "top5_acc": 0.75844, "loss_cls": 2.70287, "loss": 2.70287, "time": 0.83012} +{"mode": "train", "epoch": 132, "iter": 1500, "lr": 0.00375, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51609, "top5_acc": 0.76391, "loss_cls": 2.70105, "loss": 2.70105, "time": 0.81349} +{"mode": "train", "epoch": 132, "iter": 1600, "lr": 0.00374, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51438, "top5_acc": 0.75844, "loss_cls": 2.73615, "loss": 2.73615, "time": 0.82225} +{"mode": "train", "epoch": 132, "iter": 1700, "lr": 0.00372, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50234, "top5_acc": 0.75156, "loss_cls": 2.7454, "loss": 2.7454, "time": 0.82011} +{"mode": "train", "epoch": 132, "iter": 1800, "lr": 0.00371, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51281, "top5_acc": 0.75312, "loss_cls": 2.74383, "loss": 2.74383, "time": 0.81769} +{"mode": "train", "epoch": 132, "iter": 1900, "lr": 0.0037, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51453, "top5_acc": 0.75781, "loss_cls": 2.72936, "loss": 2.72936, "time": 0.81423} +{"mode": "train", "epoch": 132, "iter": 2000, "lr": 0.00369, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50875, "top5_acc": 0.76422, "loss_cls": 2.70331, "loss": 2.70331, "time": 0.81502} +{"mode": "train", "epoch": 132, "iter": 2100, "lr": 0.00368, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50547, "top5_acc": 0.76062, "loss_cls": 2.75186, "loss": 2.75186, "time": 0.82121} +{"mode": "train", "epoch": 132, "iter": 2200, "lr": 0.00367, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50922, "top5_acc": 0.76703, "loss_cls": 2.69631, "loss": 2.69631, "time": 0.82861} +{"mode": "train", "epoch": 132, "iter": 2300, "lr": 0.00366, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51109, "top5_acc": 0.76016, "loss_cls": 2.72835, "loss": 2.72835, "time": 0.82237} +{"mode": "train", "epoch": 132, "iter": 2400, "lr": 0.00365, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50453, "top5_acc": 0.75031, "loss_cls": 2.77456, "loss": 2.77456, "time": 0.82768} +{"mode": "train", "epoch": 132, "iter": 2500, "lr": 0.00364, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49766, "top5_acc": 0.74406, "loss_cls": 2.77853, "loss": 2.77853, "time": 0.81834} +{"mode": "train", "epoch": 132, "iter": 2600, "lr": 0.00363, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51625, "top5_acc": 0.7575, "loss_cls": 2.72896, "loss": 2.72896, "time": 0.81716} +{"mode": "train", "epoch": 132, "iter": 2700, "lr": 0.00362, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51719, "top5_acc": 0.76609, "loss_cls": 2.6854, "loss": 2.6854, "time": 0.81002} +{"mode": "train", "epoch": 132, "iter": 2800, "lr": 0.00361, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50641, "top5_acc": 0.76625, "loss_cls": 2.70958, "loss": 2.70958, "time": 0.81453} +{"mode": "train", "epoch": 132, "iter": 2900, "lr": 0.0036, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.50609, "top5_acc": 0.74953, "loss_cls": 2.73548, "loss": 2.73548, "time": 0.81501} +{"mode": "train", "epoch": 132, "iter": 3000, "lr": 0.00359, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50766, "top5_acc": 0.75266, "loss_cls": 2.7479, "loss": 2.7479, "time": 0.811} +{"mode": "train", "epoch": 132, "iter": 3100, "lr": 0.00358, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51703, "top5_acc": 0.76156, "loss_cls": 2.69463, "loss": 2.69463, "time": 0.82005} +{"mode": "train", "epoch": 132, "iter": 3200, "lr": 0.00357, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51703, "top5_acc": 0.76078, "loss_cls": 2.71138, "loss": 2.71138, "time": 0.81355} +{"mode": "train", "epoch": 132, "iter": 3300, "lr": 0.00356, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51562, "top5_acc": 0.75797, "loss_cls": 2.72382, "loss": 2.72382, "time": 0.81547} +{"mode": "train", "epoch": 132, "iter": 3400, "lr": 0.00355, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50703, "top5_acc": 0.75422, "loss_cls": 2.7653, "loss": 2.7653, "time": 0.81716} +{"mode": "train", "epoch": 132, "iter": 3500, "lr": 0.00354, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.505, "top5_acc": 0.76188, "loss_cls": 2.72284, "loss": 2.72284, "time": 0.81691} +{"mode": "train", "epoch": 132, "iter": 3600, "lr": 0.00353, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49953, "top5_acc": 0.75688, "loss_cls": 2.74465, "loss": 2.74465, "time": 0.8112} +{"mode": "train", "epoch": 132, "iter": 3700, "lr": 0.00352, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49516, "top5_acc": 0.75266, "loss_cls": 2.77381, "loss": 2.77381, "time": 0.81995} +{"mode": "val", "epoch": 132, "iter": 309, "lr": 0.00351, "top1_acc": 0.41878, "top5_acc": 0.6691, "mean_class_accuracy": 0.41853} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.0035, "memory": 15990, "data_time": 1.31017, "top1_acc": 0.53344, "top5_acc": 0.78422, "loss_cls": 2.57203, "loss": 2.57203, "time": 2.28827} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00349, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53766, "top5_acc": 0.77688, "loss_cls": 2.60322, "loss": 2.60322, "time": 0.81962} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00348, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52453, "top5_acc": 0.76625, "loss_cls": 2.65211, "loss": 2.65211, "time": 0.81655} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00347, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52453, "top5_acc": 0.76625, "loss_cls": 2.64165, "loss": 2.64165, "time": 0.81342} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00346, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52703, "top5_acc": 0.77234, "loss_cls": 2.62908, "loss": 2.62908, "time": 0.81563} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00345, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52938, "top5_acc": 0.77531, "loss_cls": 2.62743, "loss": 2.62743, "time": 0.81799} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00344, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5275, "top5_acc": 0.77547, "loss_cls": 2.63478, "loss": 2.63478, "time": 0.81254} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00343, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51734, "top5_acc": 0.76906, "loss_cls": 2.65393, "loss": 2.65393, "time": 0.81686} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00342, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53188, "top5_acc": 0.76719, "loss_cls": 2.6444, "loss": 2.6444, "time": 0.81877} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.00341, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51703, "top5_acc": 0.76875, "loss_cls": 2.69833, "loss": 2.69833, "time": 0.82265} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0034, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52875, "top5_acc": 0.77438, "loss_cls": 2.6418, "loss": 2.6418, "time": 0.81764} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00339, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53078, "top5_acc": 0.77438, "loss_cls": 2.63524, "loss": 2.63524, "time": 0.82207} +{"mode": "train", "epoch": 133, "iter": 1300, "lr": 0.00338, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52109, "top5_acc": 0.7675, "loss_cls": 2.67217, "loss": 2.67217, "time": 0.82217} +{"mode": "train", "epoch": 133, "iter": 1400, "lr": 0.00337, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.52109, "top5_acc": 0.76688, "loss_cls": 2.66291, "loss": 2.66291, "time": 0.82632} +{"mode": "train", "epoch": 133, "iter": 1500, "lr": 0.00336, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51594, "top5_acc": 0.76406, "loss_cls": 2.67655, "loss": 2.67655, "time": 0.81629} +{"mode": "train", "epoch": 133, "iter": 1600, "lr": 0.00335, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51703, "top5_acc": 0.76891, "loss_cls": 2.66708, "loss": 2.66708, "time": 0.81856} +{"mode": "train", "epoch": 133, "iter": 1700, "lr": 0.00334, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50406, "top5_acc": 0.76328, "loss_cls": 2.72518, "loss": 2.72518, "time": 0.81914} +{"mode": "train", "epoch": 133, "iter": 1800, "lr": 0.00333, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52359, "top5_acc": 0.76359, "loss_cls": 2.65168, "loss": 2.65168, "time": 0.81151} +{"mode": "train", "epoch": 133, "iter": 1900, "lr": 0.00332, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52141, "top5_acc": 0.76688, "loss_cls": 2.65449, "loss": 2.65449, "time": 0.8158} +{"mode": "train", "epoch": 133, "iter": 2000, "lr": 0.00331, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52281, "top5_acc": 0.77531, "loss_cls": 2.64661, "loss": 2.64661, "time": 0.81472} +{"mode": "train", "epoch": 133, "iter": 2100, "lr": 0.0033, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51953, "top5_acc": 0.76094, "loss_cls": 2.69427, "loss": 2.69427, "time": 0.82095} +{"mode": "train", "epoch": 133, "iter": 2200, "lr": 0.00329, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51922, "top5_acc": 0.76219, "loss_cls": 2.69193, "loss": 2.69193, "time": 0.82263} +{"mode": "train", "epoch": 133, "iter": 2300, "lr": 0.00328, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51812, "top5_acc": 0.76172, "loss_cls": 2.68459, "loss": 2.68459, "time": 0.81617} +{"mode": "train", "epoch": 133, "iter": 2400, "lr": 0.00327, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52484, "top5_acc": 0.77156, "loss_cls": 2.63596, "loss": 2.63596, "time": 0.81658} +{"mode": "train", "epoch": 133, "iter": 2500, "lr": 0.00326, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52922, "top5_acc": 0.76609, "loss_cls": 2.64382, "loss": 2.64382, "time": 0.81867} +{"mode": "train", "epoch": 133, "iter": 2600, "lr": 0.00325, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51078, "top5_acc": 0.76219, "loss_cls": 2.71202, "loss": 2.71202, "time": 0.81514} +{"mode": "train", "epoch": 133, "iter": 2700, "lr": 0.00324, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51578, "top5_acc": 0.75766, "loss_cls": 2.7037, "loss": 2.7037, "time": 0.8179} +{"mode": "train", "epoch": 133, "iter": 2800, "lr": 0.00323, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52109, "top5_acc": 0.76062, "loss_cls": 2.68631, "loss": 2.68631, "time": 0.81418} +{"mode": "train", "epoch": 133, "iter": 2900, "lr": 0.00322, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50891, "top5_acc": 0.7575, "loss_cls": 2.7268, "loss": 2.7268, "time": 0.8192} +{"mode": "train", "epoch": 133, "iter": 3000, "lr": 0.00321, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50438, "top5_acc": 0.75391, "loss_cls": 2.74144, "loss": 2.74144, "time": 0.81501} +{"mode": "train", "epoch": 133, "iter": 3100, "lr": 0.0032, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51313, "top5_acc": 0.74844, "loss_cls": 2.73323, "loss": 2.73323, "time": 0.81533} +{"mode": "train", "epoch": 133, "iter": 3200, "lr": 0.00319, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51, "top5_acc": 0.75656, "loss_cls": 2.71724, "loss": 2.71724, "time": 0.81705} +{"mode": "train", "epoch": 133, "iter": 3300, "lr": 0.00318, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50828, "top5_acc": 0.75375, "loss_cls": 2.72951, "loss": 2.72951, "time": 0.81833} +{"mode": "train", "epoch": 133, "iter": 3400, "lr": 0.00317, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51531, "top5_acc": 0.76062, "loss_cls": 2.71362, "loss": 2.71362, "time": 0.82025} +{"mode": "train", "epoch": 133, "iter": 3500, "lr": 0.00316, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51641, "top5_acc": 0.76219, "loss_cls": 2.70448, "loss": 2.70448, "time": 0.81362} +{"mode": "train", "epoch": 133, "iter": 3600, "lr": 0.00315, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51547, "top5_acc": 0.75, "loss_cls": 2.72623, "loss": 2.72623, "time": 0.81701} +{"mode": "train", "epoch": 133, "iter": 3700, "lr": 0.00314, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51172, "top5_acc": 0.76172, "loss_cls": 2.71772, "loss": 2.71772, "time": 0.81452} +{"mode": "val", "epoch": 133, "iter": 309, "lr": 0.00314, "top1_acc": 0.42618, "top5_acc": 0.67305, "mean_class_accuracy": 0.42595} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00313, "memory": 15990, "data_time": 1.29874, "top1_acc": 0.54203, "top5_acc": 0.78578, "loss_cls": 2.55346, "loss": 2.55346, "time": 2.27692} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00312, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53438, "top5_acc": 0.77672, "loss_cls": 2.59986, "loss": 2.59986, "time": 0.82011} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00311, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54109, "top5_acc": 0.77875, "loss_cls": 2.56234, "loss": 2.56234, "time": 0.81482} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.0031, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54219, "top5_acc": 0.78172, "loss_cls": 2.57716, "loss": 2.57716, "time": 0.81696} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00309, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53438, "top5_acc": 0.7725, "loss_cls": 2.61473, "loss": 2.61473, "time": 0.81327} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00308, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52828, "top5_acc": 0.77438, "loss_cls": 2.61785, "loss": 2.61785, "time": 0.81799} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00307, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53172, "top5_acc": 0.77703, "loss_cls": 2.61673, "loss": 2.61673, "time": 0.81473} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00306, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53266, "top5_acc": 0.7675, "loss_cls": 2.63744, "loss": 2.63744, "time": 0.81565} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00305, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53016, "top5_acc": 0.77312, "loss_cls": 2.60158, "loss": 2.60158, "time": 0.81207} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00304, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53281, "top5_acc": 0.7775, "loss_cls": 2.61866, "loss": 2.61866, "time": 0.81454} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00303, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53, "top5_acc": 0.77266, "loss_cls": 2.59813, "loss": 2.59813, "time": 0.81407} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.00302, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.53953, "top5_acc": 0.78203, "loss_cls": 2.56388, "loss": 2.56388, "time": 0.82588} +{"mode": "train", "epoch": 134, "iter": 1300, "lr": 0.00301, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52812, "top5_acc": 0.76859, "loss_cls": 2.63676, "loss": 2.63676, "time": 0.81936} +{"mode": "train", "epoch": 134, "iter": 1400, "lr": 0.003, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.54141, "top5_acc": 0.77797, "loss_cls": 2.58853, "loss": 2.58853, "time": 0.82117} +{"mode": "train", "epoch": 134, "iter": 1500, "lr": 0.00299, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.52953, "top5_acc": 0.77312, "loss_cls": 2.6099, "loss": 2.6099, "time": 0.82362} +{"mode": "train", "epoch": 134, "iter": 1600, "lr": 0.00298, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.52719, "top5_acc": 0.77781, "loss_cls": 2.62508, "loss": 2.62508, "time": 0.81615} +{"mode": "train", "epoch": 134, "iter": 1700, "lr": 0.00297, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54172, "top5_acc": 0.785, "loss_cls": 2.5623, "loss": 2.5623, "time": 0.82722} +{"mode": "train", "epoch": 134, "iter": 1800, "lr": 0.00296, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52703, "top5_acc": 0.76516, "loss_cls": 2.63823, "loss": 2.63823, "time": 0.81642} +{"mode": "train", "epoch": 134, "iter": 1900, "lr": 0.00295, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52719, "top5_acc": 0.77016, "loss_cls": 2.65, "loss": 2.65, "time": 0.81676} +{"mode": "train", "epoch": 134, "iter": 2000, "lr": 0.00294, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5325, "top5_acc": 0.77922, "loss_cls": 2.60196, "loss": 2.60196, "time": 0.81881} +{"mode": "train", "epoch": 134, "iter": 2100, "lr": 0.00293, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52266, "top5_acc": 0.76312, "loss_cls": 2.67814, "loss": 2.67814, "time": 0.82014} +{"mode": "train", "epoch": 134, "iter": 2200, "lr": 0.00293, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.51562, "top5_acc": 0.76156, "loss_cls": 2.69118, "loss": 2.69118, "time": 0.8195} +{"mode": "train", "epoch": 134, "iter": 2300, "lr": 0.00292, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52391, "top5_acc": 0.76031, "loss_cls": 2.65985, "loss": 2.65985, "time": 0.81676} +{"mode": "train", "epoch": 134, "iter": 2400, "lr": 0.00291, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52406, "top5_acc": 0.76656, "loss_cls": 2.6617, "loss": 2.6617, "time": 0.81242} +{"mode": "train", "epoch": 134, "iter": 2500, "lr": 0.0029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52281, "top5_acc": 0.76531, "loss_cls": 2.67453, "loss": 2.67453, "time": 0.81356} +{"mode": "train", "epoch": 134, "iter": 2600, "lr": 0.00289, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52938, "top5_acc": 0.77016, "loss_cls": 2.64711, "loss": 2.64711, "time": 0.8152} +{"mode": "train", "epoch": 134, "iter": 2700, "lr": 0.00288, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52234, "top5_acc": 0.77109, "loss_cls": 2.6623, "loss": 2.6623, "time": 0.81751} +{"mode": "train", "epoch": 134, "iter": 2800, "lr": 0.00287, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53531, "top5_acc": 0.77922, "loss_cls": 2.58379, "loss": 2.58379, "time": 0.81588} +{"mode": "train", "epoch": 134, "iter": 2900, "lr": 0.00286, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51859, "top5_acc": 0.76344, "loss_cls": 2.70614, "loss": 2.70614, "time": 0.81461} +{"mode": "train", "epoch": 134, "iter": 3000, "lr": 0.00285, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52375, "top5_acc": 0.76234, "loss_cls": 2.68058, "loss": 2.68058, "time": 0.81601} +{"mode": "train", "epoch": 134, "iter": 3100, "lr": 0.00284, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5225, "top5_acc": 0.77438, "loss_cls": 2.63413, "loss": 2.63413, "time": 0.81433} +{"mode": "train", "epoch": 134, "iter": 3200, "lr": 0.00283, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51922, "top5_acc": 0.76922, "loss_cls": 2.65752, "loss": 2.65752, "time": 0.8171} +{"mode": "train", "epoch": 134, "iter": 3300, "lr": 0.00282, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51469, "top5_acc": 0.76562, "loss_cls": 2.6614, "loss": 2.6614, "time": 0.81093} +{"mode": "train", "epoch": 134, "iter": 3400, "lr": 0.00281, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51703, "top5_acc": 0.75781, "loss_cls": 2.69643, "loss": 2.69643, "time": 0.81974} +{"mode": "train", "epoch": 134, "iter": 3500, "lr": 0.0028, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51984, "top5_acc": 0.76406, "loss_cls": 2.68076, "loss": 2.68076, "time": 0.81755} +{"mode": "train", "epoch": 134, "iter": 3600, "lr": 0.00279, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52719, "top5_acc": 0.76984, "loss_cls": 2.64987, "loss": 2.64987, "time": 0.81933} +{"mode": "train", "epoch": 134, "iter": 3700, "lr": 0.00279, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51438, "top5_acc": 0.77031, "loss_cls": 2.67369, "loss": 2.67369, "time": 0.81411} +{"mode": "val", "epoch": 134, "iter": 309, "lr": 0.00278, "top1_acc": 0.42759, "top5_acc": 0.67563, "mean_class_accuracy": 0.42747} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00277, "memory": 15990, "data_time": 1.28427, "top1_acc": 0.53969, "top5_acc": 0.78828, "loss_cls": 2.55175, "loss": 2.55175, "time": 2.27149} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00276, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55156, "top5_acc": 0.78625, "loss_cls": 2.50963, "loss": 2.50963, "time": 0.82391} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00275, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54391, "top5_acc": 0.78656, "loss_cls": 2.54751, "loss": 2.54751, "time": 0.82511} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00274, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55453, "top5_acc": 0.7925, "loss_cls": 2.48992, "loss": 2.48992, "time": 0.81767} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00274, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54656, "top5_acc": 0.78453, "loss_cls": 2.54527, "loss": 2.54527, "time": 0.81262} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00273, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54391, "top5_acc": 0.78781, "loss_cls": 2.52912, "loss": 2.52912, "time": 0.81573} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00272, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54203, "top5_acc": 0.78016, "loss_cls": 2.56588, "loss": 2.56588, "time": 0.8125} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00271, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53484, "top5_acc": 0.78062, "loss_cls": 2.5504, "loss": 2.5504, "time": 0.81172} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.0027, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52875, "top5_acc": 0.77031, "loss_cls": 2.61918, "loss": 2.61918, "time": 0.81231} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00269, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53609, "top5_acc": 0.78141, "loss_cls": 2.57703, "loss": 2.57703, "time": 0.82127} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00268, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53922, "top5_acc": 0.77547, "loss_cls": 2.59415, "loss": 2.59415, "time": 0.81472} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00267, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.53625, "top5_acc": 0.78188, "loss_cls": 2.55129, "loss": 2.55129, "time": 0.82006} +{"mode": "train", "epoch": 135, "iter": 1300, "lr": 0.00266, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52578, "top5_acc": 0.77438, "loss_cls": 2.63572, "loss": 2.63572, "time": 0.82013} +{"mode": "train", "epoch": 135, "iter": 1400, "lr": 0.00265, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.54266, "top5_acc": 0.78562, "loss_cls": 2.55936, "loss": 2.55936, "time": 0.8239} +{"mode": "train", "epoch": 135, "iter": 1500, "lr": 0.00265, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54156, "top5_acc": 0.76984, "loss_cls": 2.58002, "loss": 2.58002, "time": 0.81684} +{"mode": "train", "epoch": 135, "iter": 1600, "lr": 0.00264, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53594, "top5_acc": 0.7875, "loss_cls": 2.561, "loss": 2.561, "time": 0.82081} +{"mode": "train", "epoch": 135, "iter": 1700, "lr": 0.00263, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54438, "top5_acc": 0.78781, "loss_cls": 2.5384, "loss": 2.5384, "time": 0.81688} +{"mode": "train", "epoch": 135, "iter": 1800, "lr": 0.00262, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53875, "top5_acc": 0.77609, "loss_cls": 2.58926, "loss": 2.58926, "time": 0.81502} +{"mode": "train", "epoch": 135, "iter": 1900, "lr": 0.00261, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53375, "top5_acc": 0.78141, "loss_cls": 2.57307, "loss": 2.57307, "time": 0.81647} +{"mode": "train", "epoch": 135, "iter": 2000, "lr": 0.0026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53438, "top5_acc": 0.77578, "loss_cls": 2.60563, "loss": 2.60563, "time": 0.8144} +{"mode": "train", "epoch": 135, "iter": 2100, "lr": 0.00259, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.54062, "top5_acc": 0.78281, "loss_cls": 2.57684, "loss": 2.57684, "time": 0.82126} +{"mode": "train", "epoch": 135, "iter": 2200, "lr": 0.00258, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54125, "top5_acc": 0.77062, "loss_cls": 2.59374, "loss": 2.59374, "time": 0.82463} +{"mode": "train", "epoch": 135, "iter": 2300, "lr": 0.00257, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.52875, "top5_acc": 0.77281, "loss_cls": 2.62135, "loss": 2.62135, "time": 0.82091} +{"mode": "train", "epoch": 135, "iter": 2400, "lr": 0.00256, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52422, "top5_acc": 0.77375, "loss_cls": 2.62069, "loss": 2.62069, "time": 0.82187} +{"mode": "train", "epoch": 135, "iter": 2500, "lr": 0.00256, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53, "top5_acc": 0.77812, "loss_cls": 2.60139, "loss": 2.60139, "time": 0.82005} +{"mode": "train", "epoch": 135, "iter": 2600, "lr": 0.00255, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51734, "top5_acc": 0.76766, "loss_cls": 2.65605, "loss": 2.65605, "time": 0.81567} +{"mode": "train", "epoch": 135, "iter": 2700, "lr": 0.00254, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53688, "top5_acc": 0.77547, "loss_cls": 2.58529, "loss": 2.58529, "time": 0.81461} +{"mode": "train", "epoch": 135, "iter": 2800, "lr": 0.00253, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53578, "top5_acc": 0.77547, "loss_cls": 2.59269, "loss": 2.59269, "time": 0.81713} +{"mode": "train", "epoch": 135, "iter": 2900, "lr": 0.00252, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52969, "top5_acc": 0.77047, "loss_cls": 2.61264, "loss": 2.61264, "time": 0.81624} +{"mode": "train", "epoch": 135, "iter": 3000, "lr": 0.00251, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52922, "top5_acc": 0.77125, "loss_cls": 2.61516, "loss": 2.61516, "time": 0.81716} +{"mode": "train", "epoch": 135, "iter": 3100, "lr": 0.0025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52516, "top5_acc": 0.76969, "loss_cls": 2.60812, "loss": 2.60812, "time": 0.81372} +{"mode": "train", "epoch": 135, "iter": 3200, "lr": 0.00249, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53047, "top5_acc": 0.77391, "loss_cls": 2.6113, "loss": 2.6113, "time": 0.81624} +{"mode": "train", "epoch": 135, "iter": 3300, "lr": 0.00249, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52625, "top5_acc": 0.78375, "loss_cls": 2.60094, "loss": 2.60094, "time": 0.81083} +{"mode": "train", "epoch": 135, "iter": 3400, "lr": 0.00248, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53188, "top5_acc": 0.775, "loss_cls": 2.627, "loss": 2.627, "time": 0.81472} +{"mode": "train", "epoch": 135, "iter": 3500, "lr": 0.00247, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53438, "top5_acc": 0.77703, "loss_cls": 2.59457, "loss": 2.59457, "time": 0.8185} +{"mode": "train", "epoch": 135, "iter": 3600, "lr": 0.00246, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53266, "top5_acc": 0.7775, "loss_cls": 2.59124, "loss": 2.59124, "time": 0.81904} +{"mode": "train", "epoch": 135, "iter": 3700, "lr": 0.00245, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.52953, "top5_acc": 0.77078, "loss_cls": 2.63324, "loss": 2.63324, "time": 0.8153} +{"mode": "val", "epoch": 135, "iter": 309, "lr": 0.00245, "top1_acc": 0.4318, "top5_acc": 0.68237, "mean_class_accuracy": 0.43162} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00244, "memory": 15990, "data_time": 1.29296, "top1_acc": 0.56172, "top5_acc": 0.8, "loss_cls": 2.4622, "loss": 2.4622, "time": 2.28217} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.00243, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.56703, "top5_acc": 0.79906, "loss_cls": 2.44043, "loss": 2.44043, "time": 0.82425} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00242, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54891, "top5_acc": 0.79469, "loss_cls": 2.49473, "loss": 2.49473, "time": 0.82447} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00241, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55609, "top5_acc": 0.79922, "loss_cls": 2.48553, "loss": 2.48553, "time": 0.8161} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.0024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55312, "top5_acc": 0.79328, "loss_cls": 2.49517, "loss": 2.49517, "time": 0.81619} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.0024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55328, "top5_acc": 0.79516, "loss_cls": 2.48045, "loss": 2.48045, "time": 0.81048} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00239, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55531, "top5_acc": 0.79078, "loss_cls": 2.48339, "loss": 2.48339, "time": 0.80896} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00238, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54641, "top5_acc": 0.78406, "loss_cls": 2.52138, "loss": 2.52138, "time": 0.82049} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00237, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54734, "top5_acc": 0.78703, "loss_cls": 2.50857, "loss": 2.50857, "time": 0.81647} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00236, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55281, "top5_acc": 0.78219, "loss_cls": 2.5354, "loss": 2.5354, "time": 0.81226} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00235, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54438, "top5_acc": 0.78297, "loss_cls": 2.53082, "loss": 2.53082, "time": 0.81844} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00234, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54297, "top5_acc": 0.78312, "loss_cls": 2.555, "loss": 2.555, "time": 0.81878} +{"mode": "train", "epoch": 136, "iter": 1300, "lr": 0.00234, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54875, "top5_acc": 0.78656, "loss_cls": 2.53784, "loss": 2.53784, "time": 0.81832} +{"mode": "train", "epoch": 136, "iter": 1400, "lr": 0.00233, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55656, "top5_acc": 0.78875, "loss_cls": 2.49158, "loss": 2.49158, "time": 0.82422} +{"mode": "train", "epoch": 136, "iter": 1500, "lr": 0.00232, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53891, "top5_acc": 0.77547, "loss_cls": 2.55986, "loss": 2.55986, "time": 0.82095} +{"mode": "train", "epoch": 136, "iter": 1600, "lr": 0.00231, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54516, "top5_acc": 0.78125, "loss_cls": 2.56072, "loss": 2.56072, "time": 0.82792} +{"mode": "train", "epoch": 136, "iter": 1700, "lr": 0.0023, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54297, "top5_acc": 0.78641, "loss_cls": 2.51485, "loss": 2.51485, "time": 0.81682} +{"mode": "train", "epoch": 136, "iter": 1800, "lr": 0.00229, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54516, "top5_acc": 0.77906, "loss_cls": 2.57888, "loss": 2.57888, "time": 0.82073} +{"mode": "train", "epoch": 136, "iter": 1900, "lr": 0.00229, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53094, "top5_acc": 0.77594, "loss_cls": 2.59254, "loss": 2.59254, "time": 0.81271} +{"mode": "train", "epoch": 136, "iter": 2000, "lr": 0.00228, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53703, "top5_acc": 0.78047, "loss_cls": 2.58286, "loss": 2.58286, "time": 0.81678} +{"mode": "train", "epoch": 136, "iter": 2100, "lr": 0.00227, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.53625, "top5_acc": 0.78906, "loss_cls": 2.55141, "loss": 2.55141, "time": 0.81671} +{"mode": "train", "epoch": 136, "iter": 2200, "lr": 0.00226, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.54188, "top5_acc": 0.79109, "loss_cls": 2.52928, "loss": 2.52928, "time": 0.82029} +{"mode": "train", "epoch": 136, "iter": 2300, "lr": 0.00225, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52781, "top5_acc": 0.77484, "loss_cls": 2.59875, "loss": 2.59875, "time": 0.81484} +{"mode": "train", "epoch": 136, "iter": 2400, "lr": 0.00224, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55016, "top5_acc": 0.78641, "loss_cls": 2.52588, "loss": 2.52588, "time": 0.81837} +{"mode": "train", "epoch": 136, "iter": 2500, "lr": 0.00224, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54531, "top5_acc": 0.78234, "loss_cls": 2.54329, "loss": 2.54329, "time": 0.814} +{"mode": "train", "epoch": 136, "iter": 2600, "lr": 0.00223, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55016, "top5_acc": 0.78125, "loss_cls": 2.55309, "loss": 2.55309, "time": 0.81847} +{"mode": "train", "epoch": 136, "iter": 2700, "lr": 0.00222, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53781, "top5_acc": 0.77797, "loss_cls": 2.56631, "loss": 2.56631, "time": 0.81513} +{"mode": "train", "epoch": 136, "iter": 2800, "lr": 0.00221, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53469, "top5_acc": 0.77672, "loss_cls": 2.59175, "loss": 2.59175, "time": 0.81756} +{"mode": "train", "epoch": 136, "iter": 2900, "lr": 0.0022, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53297, "top5_acc": 0.78109, "loss_cls": 2.58215, "loss": 2.58215, "time": 0.81639} +{"mode": "train", "epoch": 136, "iter": 3000, "lr": 0.00219, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54203, "top5_acc": 0.78125, "loss_cls": 2.57192, "loss": 2.57192, "time": 0.81568} +{"mode": "train", "epoch": 136, "iter": 3100, "lr": 0.00219, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54453, "top5_acc": 0.77797, "loss_cls": 2.56448, "loss": 2.56448, "time": 0.81368} +{"mode": "train", "epoch": 136, "iter": 3200, "lr": 0.00218, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54531, "top5_acc": 0.78344, "loss_cls": 2.53826, "loss": 2.53826, "time": 0.81527} +{"mode": "train", "epoch": 136, "iter": 3300, "lr": 0.00217, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53891, "top5_acc": 0.78734, "loss_cls": 2.52625, "loss": 2.52625, "time": 0.82238} +{"mode": "train", "epoch": 136, "iter": 3400, "lr": 0.00216, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52828, "top5_acc": 0.77281, "loss_cls": 2.59561, "loss": 2.59561, "time": 0.81811} +{"mode": "train", "epoch": 136, "iter": 3500, "lr": 0.00215, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53812, "top5_acc": 0.775, "loss_cls": 2.58096, "loss": 2.58096, "time": 0.81592} +{"mode": "train", "epoch": 136, "iter": 3600, "lr": 0.00215, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53469, "top5_acc": 0.78266, "loss_cls": 2.5647, "loss": 2.5647, "time": 0.81303} +{"mode": "train", "epoch": 136, "iter": 3700, "lr": 0.00214, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53203, "top5_acc": 0.78125, "loss_cls": 2.58816, "loss": 2.58816, "time": 0.81644} +{"mode": "val", "epoch": 136, "iter": 309, "lr": 0.00213, "top1_acc": 0.43129, "top5_acc": 0.68029, "mean_class_accuracy": 0.43097} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00213, "memory": 15990, "data_time": 1.31008, "top1_acc": 0.56781, "top5_acc": 0.79406, "loss_cls": 2.47258, "loss": 2.47258, "time": 2.30031} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00212, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56875, "top5_acc": 0.79719, "loss_cls": 2.41601, "loss": 2.41601, "time": 0.82139} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00211, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53406, "top5_acc": 0.78516, "loss_cls": 2.53248, "loss": 2.53248, "time": 0.81775} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.0021, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55719, "top5_acc": 0.78844, "loss_cls": 2.49174, "loss": 2.49174, "time": 0.81258} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.00209, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56203, "top5_acc": 0.79875, "loss_cls": 2.45282, "loss": 2.45282, "time": 0.81435} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.00209, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56469, "top5_acc": 0.79672, "loss_cls": 2.45605, "loss": 2.45605, "time": 0.81974} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00208, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56188, "top5_acc": 0.80453, "loss_cls": 2.44297, "loss": 2.44297, "time": 0.8172} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00207, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56375, "top5_acc": 0.79734, "loss_cls": 2.44477, "loss": 2.44477, "time": 0.8158} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00206, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55156, "top5_acc": 0.80047, "loss_cls": 2.47893, "loss": 2.47893, "time": 0.81646} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00205, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54797, "top5_acc": 0.78781, "loss_cls": 2.52583, "loss": 2.52583, "time": 0.82103} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00205, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55688, "top5_acc": 0.79062, "loss_cls": 2.48114, "loss": 2.48114, "time": 0.81452} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00204, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55719, "top5_acc": 0.79453, "loss_cls": 2.46049, "loss": 2.46049, "time": 0.82315} +{"mode": "train", "epoch": 137, "iter": 1300, "lr": 0.00203, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55234, "top5_acc": 0.78797, "loss_cls": 2.49797, "loss": 2.49797, "time": 0.81745} +{"mode": "train", "epoch": 137, "iter": 1400, "lr": 0.00202, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54859, "top5_acc": 0.79141, "loss_cls": 2.51929, "loss": 2.51929, "time": 0.82786} +{"mode": "train", "epoch": 137, "iter": 1500, "lr": 0.00201, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55625, "top5_acc": 0.79547, "loss_cls": 2.45992, "loss": 2.45992, "time": 0.81888} +{"mode": "train", "epoch": 137, "iter": 1600, "lr": 0.00201, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.55266, "top5_acc": 0.80609, "loss_cls": 2.43263, "loss": 2.43263, "time": 0.81995} +{"mode": "train", "epoch": 137, "iter": 1700, "lr": 0.002, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54641, "top5_acc": 0.78828, "loss_cls": 2.52203, "loss": 2.52203, "time": 0.81759} +{"mode": "train", "epoch": 137, "iter": 1800, "lr": 0.00199, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55062, "top5_acc": 0.78781, "loss_cls": 2.47596, "loss": 2.47596, "time": 0.82031} +{"mode": "train", "epoch": 137, "iter": 1900, "lr": 0.00198, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55812, "top5_acc": 0.79391, "loss_cls": 2.45447, "loss": 2.45447, "time": 0.81518} +{"mode": "train", "epoch": 137, "iter": 2000, "lr": 0.00198, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5575, "top5_acc": 0.79094, "loss_cls": 2.47545, "loss": 2.47545, "time": 0.82043} +{"mode": "train", "epoch": 137, "iter": 2100, "lr": 0.00197, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54078, "top5_acc": 0.78047, "loss_cls": 2.55992, "loss": 2.55992, "time": 0.81965} +{"mode": "train", "epoch": 137, "iter": 2200, "lr": 0.00196, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54219, "top5_acc": 0.785, "loss_cls": 2.54258, "loss": 2.54258, "time": 0.81796} +{"mode": "train", "epoch": 137, "iter": 2300, "lr": 0.00195, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54406, "top5_acc": 0.78016, "loss_cls": 2.53892, "loss": 2.53892, "time": 0.81487} +{"mode": "train", "epoch": 137, "iter": 2400, "lr": 0.00194, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54984, "top5_acc": 0.7825, "loss_cls": 2.5142, "loss": 2.5142, "time": 0.81178} +{"mode": "train", "epoch": 137, "iter": 2500, "lr": 0.00194, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55, "top5_acc": 0.78953, "loss_cls": 2.52433, "loss": 2.52433, "time": 0.81457} +{"mode": "train", "epoch": 137, "iter": 2600, "lr": 0.00193, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55375, "top5_acc": 0.79125, "loss_cls": 2.49526, "loss": 2.49526, "time": 0.81231} +{"mode": "train", "epoch": 137, "iter": 2700, "lr": 0.00192, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56578, "top5_acc": 0.79922, "loss_cls": 2.42473, "loss": 2.42473, "time": 0.81031} +{"mode": "train", "epoch": 137, "iter": 2800, "lr": 0.00191, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55141, "top5_acc": 0.78906, "loss_cls": 2.51089, "loss": 2.51089, "time": 0.81522} +{"mode": "train", "epoch": 137, "iter": 2900, "lr": 0.00191, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55984, "top5_acc": 0.79656, "loss_cls": 2.46343, "loss": 2.46343, "time": 0.81089} +{"mode": "train", "epoch": 137, "iter": 3000, "lr": 0.0019, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54844, "top5_acc": 0.78141, "loss_cls": 2.53623, "loss": 2.53623, "time": 0.81539} +{"mode": "train", "epoch": 137, "iter": 3100, "lr": 0.00189, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54625, "top5_acc": 0.78172, "loss_cls": 2.53175, "loss": 2.53175, "time": 0.81499} +{"mode": "train", "epoch": 137, "iter": 3200, "lr": 0.00188, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54844, "top5_acc": 0.795, "loss_cls": 2.47882, "loss": 2.47882, "time": 0.8167} +{"mode": "train", "epoch": 137, "iter": 3300, "lr": 0.00188, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53688, "top5_acc": 0.78328, "loss_cls": 2.55527, "loss": 2.55527, "time": 0.81363} +{"mode": "train", "epoch": 137, "iter": 3400, "lr": 0.00187, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55453, "top5_acc": 0.78766, "loss_cls": 2.51491, "loss": 2.51491, "time": 0.81389} +{"mode": "train", "epoch": 137, "iter": 3500, "lr": 0.00186, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54094, "top5_acc": 0.78562, "loss_cls": 2.53008, "loss": 2.53008, "time": 0.8129} +{"mode": "train", "epoch": 137, "iter": 3600, "lr": 0.00185, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54438, "top5_acc": 0.78891, "loss_cls": 2.53834, "loss": 2.53834, "time": 0.81299} +{"mode": "train", "epoch": 137, "iter": 3700, "lr": 0.00185, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54391, "top5_acc": 0.78766, "loss_cls": 2.51598, "loss": 2.51598, "time": 0.81491} +{"mode": "val", "epoch": 137, "iter": 309, "lr": 0.00184, "top1_acc": 0.43631, "top5_acc": 0.6852, "mean_class_accuracy": 0.43609} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00183, "memory": 15990, "data_time": 1.29624, "top1_acc": 0.57688, "top5_acc": 0.80922, "loss_cls": 2.37787, "loss": 2.37787, "time": 2.28008} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00183, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57312, "top5_acc": 0.80656, "loss_cls": 2.3955, "loss": 2.3955, "time": 0.82696} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00182, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.57422, "top5_acc": 0.815, "loss_cls": 2.37546, "loss": 2.37546, "time": 0.82235} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00181, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57, "top5_acc": 0.80656, "loss_cls": 2.4003, "loss": 2.4003, "time": 0.82023} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.0018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56688, "top5_acc": 0.80094, "loss_cls": 2.40386, "loss": 2.40386, "time": 0.81298} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.0018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55875, "top5_acc": 0.79922, "loss_cls": 2.44494, "loss": 2.44494, "time": 0.81388} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00179, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56734, "top5_acc": 0.80203, "loss_cls": 2.4222, "loss": 2.4222, "time": 0.81559} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00178, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56641, "top5_acc": 0.80562, "loss_cls": 2.42202, "loss": 2.42202, "time": 0.81801} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00177, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56719, "top5_acc": 0.79812, "loss_cls": 2.42918, "loss": 2.42918, "time": 0.81175} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00177, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56969, "top5_acc": 0.80516, "loss_cls": 2.41735, "loss": 2.41735, "time": 0.81888} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.00176, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55766, "top5_acc": 0.8025, "loss_cls": 2.45674, "loss": 2.45674, "time": 0.81769} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.00175, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56375, "top5_acc": 0.79891, "loss_cls": 2.42793, "loss": 2.42793, "time": 0.81922} +{"mode": "train", "epoch": 138, "iter": 1300, "lr": 0.00175, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.56781, "top5_acc": 0.80328, "loss_cls": 2.40124, "loss": 2.40124, "time": 0.82428} +{"mode": "train", "epoch": 138, "iter": 1400, "lr": 0.00174, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56391, "top5_acc": 0.80094, "loss_cls": 2.42601, "loss": 2.42601, "time": 0.82117} +{"mode": "train", "epoch": 138, "iter": 1500, "lr": 0.00173, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56203, "top5_acc": 0.79109, "loss_cls": 2.45995, "loss": 2.45995, "time": 0.81426} +{"mode": "train", "epoch": 138, "iter": 1600, "lr": 0.00172, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54875, "top5_acc": 0.795, "loss_cls": 2.49141, "loss": 2.49141, "time": 0.818} +{"mode": "train", "epoch": 138, "iter": 1700, "lr": 0.00172, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56375, "top5_acc": 0.79078, "loss_cls": 2.44093, "loss": 2.44093, "time": 0.81284} +{"mode": "train", "epoch": 138, "iter": 1800, "lr": 0.00171, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56203, "top5_acc": 0.79609, "loss_cls": 2.45109, "loss": 2.45109, "time": 0.8149} +{"mode": "train", "epoch": 138, "iter": 1900, "lr": 0.0017, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57672, "top5_acc": 0.80047, "loss_cls": 2.41889, "loss": 2.41889, "time": 0.81664} +{"mode": "train", "epoch": 138, "iter": 2000, "lr": 0.00169, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.55359, "top5_acc": 0.79328, "loss_cls": 2.4547, "loss": 2.4547, "time": 0.81613} +{"mode": "train", "epoch": 138, "iter": 2100, "lr": 0.00169, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56078, "top5_acc": 0.79625, "loss_cls": 2.44688, "loss": 2.44688, "time": 0.81995} +{"mode": "train", "epoch": 138, "iter": 2200, "lr": 0.00168, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.56812, "top5_acc": 0.80672, "loss_cls": 2.4133, "loss": 2.4133, "time": 0.81863} +{"mode": "train", "epoch": 138, "iter": 2300, "lr": 0.00167, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56422, "top5_acc": 0.80547, "loss_cls": 2.41919, "loss": 2.41919, "time": 0.81662} +{"mode": "train", "epoch": 138, "iter": 2400, "lr": 0.00167, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56516, "top5_acc": 0.80281, "loss_cls": 2.41872, "loss": 2.41872, "time": 0.81929} +{"mode": "train", "epoch": 138, "iter": 2500, "lr": 0.00166, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55094, "top5_acc": 0.78562, "loss_cls": 2.50662, "loss": 2.50662, "time": 0.81675} +{"mode": "train", "epoch": 138, "iter": 2600, "lr": 0.00165, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56172, "top5_acc": 0.79453, "loss_cls": 2.4651, "loss": 2.4651, "time": 0.82364} +{"mode": "train", "epoch": 138, "iter": 2700, "lr": 0.00164, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56375, "top5_acc": 0.79188, "loss_cls": 2.43717, "loss": 2.43717, "time": 0.81538} +{"mode": "train", "epoch": 138, "iter": 2800, "lr": 0.00164, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54969, "top5_acc": 0.79672, "loss_cls": 2.4601, "loss": 2.4601, "time": 0.81923} +{"mode": "train", "epoch": 138, "iter": 2900, "lr": 0.00163, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55781, "top5_acc": 0.79609, "loss_cls": 2.45131, "loss": 2.45131, "time": 0.81411} +{"mode": "train", "epoch": 138, "iter": 3000, "lr": 0.00162, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55875, "top5_acc": 0.79641, "loss_cls": 2.44718, "loss": 2.44718, "time": 0.81365} +{"mode": "train", "epoch": 138, "iter": 3100, "lr": 0.00162, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55484, "top5_acc": 0.80516, "loss_cls": 2.43778, "loss": 2.43778, "time": 0.81217} +{"mode": "train", "epoch": 138, "iter": 3200, "lr": 0.00161, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56266, "top5_acc": 0.79469, "loss_cls": 2.47407, "loss": 2.47407, "time": 0.81816} +{"mode": "train", "epoch": 138, "iter": 3300, "lr": 0.0016, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56, "top5_acc": 0.79375, "loss_cls": 2.43034, "loss": 2.43034, "time": 0.81551} +{"mode": "train", "epoch": 138, "iter": 3400, "lr": 0.0016, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55188, "top5_acc": 0.78828, "loss_cls": 2.48993, "loss": 2.48993, "time": 0.81839} +{"mode": "train", "epoch": 138, "iter": 3500, "lr": 0.00159, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.56312, "top5_acc": 0.79312, "loss_cls": 2.46614, "loss": 2.46614, "time": 0.81176} +{"mode": "train", "epoch": 138, "iter": 3600, "lr": 0.00158, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55469, "top5_acc": 0.79188, "loss_cls": 2.48186, "loss": 2.48186, "time": 0.81781} +{"mode": "train", "epoch": 138, "iter": 3700, "lr": 0.00157, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.55875, "top5_acc": 0.79297, "loss_cls": 2.46034, "loss": 2.46034, "time": 0.81211} +{"mode": "val", "epoch": 138, "iter": 309, "lr": 0.00157, "top1_acc": 0.43803, "top5_acc": 0.6847, "mean_class_accuracy": 0.43776} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00156, "memory": 15990, "data_time": 1.26669, "top1_acc": 0.58203, "top5_acc": 0.80781, "loss_cls": 2.33181, "loss": 2.33181, "time": 2.24309} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00156, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58938, "top5_acc": 0.81719, "loss_cls": 2.32125, "loss": 2.32125, "time": 0.82109} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00155, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57766, "top5_acc": 0.81375, "loss_cls": 2.34919, "loss": 2.34919, "time": 0.81744} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00154, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57141, "top5_acc": 0.80406, "loss_cls": 2.39108, "loss": 2.39108, "time": 0.82094} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00154, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56312, "top5_acc": 0.80234, "loss_cls": 2.39524, "loss": 2.39524, "time": 0.81443} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00153, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57875, "top5_acc": 0.81297, "loss_cls": 2.3543, "loss": 2.3543, "time": 0.81898} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00152, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57656, "top5_acc": 0.80859, "loss_cls": 2.34856, "loss": 2.34856, "time": 0.81823} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00152, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57594, "top5_acc": 0.81422, "loss_cls": 2.35375, "loss": 2.35375, "time": 0.81459} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00151, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57219, "top5_acc": 0.80906, "loss_cls": 2.37816, "loss": 2.37816, "time": 0.82138} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.0015, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57719, "top5_acc": 0.81234, "loss_cls": 2.36322, "loss": 2.36322, "time": 0.80977} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.0015, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57328, "top5_acc": 0.80969, "loss_cls": 2.37965, "loss": 2.37965, "time": 0.81465} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00149, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57125, "top5_acc": 0.80469, "loss_cls": 2.39737, "loss": 2.39737, "time": 0.82173} +{"mode": "train", "epoch": 139, "iter": 1300, "lr": 0.00148, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56594, "top5_acc": 0.80375, "loss_cls": 2.39502, "loss": 2.39502, "time": 0.81783} +{"mode": "train", "epoch": 139, "iter": 1400, "lr": 0.00148, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.57797, "top5_acc": 0.80828, "loss_cls": 2.37071, "loss": 2.37071, "time": 0.82311} +{"mode": "train", "epoch": 139, "iter": 1500, "lr": 0.00147, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58656, "top5_acc": 0.8125, "loss_cls": 2.34011, "loss": 2.34011, "time": 0.82487} +{"mode": "train", "epoch": 139, "iter": 1600, "lr": 0.00146, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57047, "top5_acc": 0.80688, "loss_cls": 2.392, "loss": 2.392, "time": 0.82266} +{"mode": "train", "epoch": 139, "iter": 1700, "lr": 0.00145, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57531, "top5_acc": 0.80719, "loss_cls": 2.3551, "loss": 2.3551, "time": 0.81951} +{"mode": "train", "epoch": 139, "iter": 1800, "lr": 0.00145, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56953, "top5_acc": 0.80234, "loss_cls": 2.4017, "loss": 2.4017, "time": 0.81437} +{"mode": "train", "epoch": 139, "iter": 1900, "lr": 0.00144, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57078, "top5_acc": 0.8025, "loss_cls": 2.40684, "loss": 2.40684, "time": 0.82335} +{"mode": "train", "epoch": 139, "iter": 2000, "lr": 0.00143, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57406, "top5_acc": 0.8075, "loss_cls": 2.38504, "loss": 2.38504, "time": 0.81689} +{"mode": "train", "epoch": 139, "iter": 2100, "lr": 0.00143, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55547, "top5_acc": 0.79438, "loss_cls": 2.44494, "loss": 2.44494, "time": 0.82199} +{"mode": "train", "epoch": 139, "iter": 2200, "lr": 0.00142, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56844, "top5_acc": 0.80062, "loss_cls": 2.41988, "loss": 2.41988, "time": 0.81862} +{"mode": "train", "epoch": 139, "iter": 2300, "lr": 0.00142, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57078, "top5_acc": 0.80781, "loss_cls": 2.39624, "loss": 2.39624, "time": 0.81346} +{"mode": "train", "epoch": 139, "iter": 2400, "lr": 0.00141, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57578, "top5_acc": 0.81016, "loss_cls": 2.35868, "loss": 2.35868, "time": 0.81809} +{"mode": "train", "epoch": 139, "iter": 2500, "lr": 0.0014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56969, "top5_acc": 0.80656, "loss_cls": 2.39955, "loss": 2.39955, "time": 0.81454} +{"mode": "train", "epoch": 139, "iter": 2600, "lr": 0.0014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56719, "top5_acc": 0.80906, "loss_cls": 2.39849, "loss": 2.39849, "time": 0.81496} +{"mode": "train", "epoch": 139, "iter": 2700, "lr": 0.00139, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56828, "top5_acc": 0.80453, "loss_cls": 2.38533, "loss": 2.38533, "time": 0.81486} +{"mode": "train", "epoch": 139, "iter": 2800, "lr": 0.00138, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56578, "top5_acc": 0.80266, "loss_cls": 2.41052, "loss": 2.41052, "time": 0.81177} +{"mode": "train", "epoch": 139, "iter": 2900, "lr": 0.00138, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56437, "top5_acc": 0.80328, "loss_cls": 2.40239, "loss": 2.40239, "time": 0.81452} +{"mode": "train", "epoch": 139, "iter": 3000, "lr": 0.00137, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56047, "top5_acc": 0.80141, "loss_cls": 2.44078, "loss": 2.44078, "time": 0.81946} +{"mode": "train", "epoch": 139, "iter": 3100, "lr": 0.00136, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56172, "top5_acc": 0.80516, "loss_cls": 2.40813, "loss": 2.40813, "time": 0.81318} +{"mode": "train", "epoch": 139, "iter": 3200, "lr": 0.00136, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57047, "top5_acc": 0.8025, "loss_cls": 2.38895, "loss": 2.38895, "time": 0.81643} +{"mode": "train", "epoch": 139, "iter": 3300, "lr": 0.00135, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56781, "top5_acc": 0.80906, "loss_cls": 2.38706, "loss": 2.38706, "time": 0.81433} +{"mode": "train", "epoch": 139, "iter": 3400, "lr": 0.00134, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56219, "top5_acc": 0.79844, "loss_cls": 2.42986, "loss": 2.42986, "time": 0.81321} +{"mode": "train", "epoch": 139, "iter": 3500, "lr": 0.00134, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.56063, "top5_acc": 0.79656, "loss_cls": 2.44307, "loss": 2.44307, "time": 0.81461} +{"mode": "train", "epoch": 139, "iter": 3600, "lr": 0.00133, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57, "top5_acc": 0.80219, "loss_cls": 2.41942, "loss": 2.41942, "time": 0.81557} +{"mode": "train", "epoch": 139, "iter": 3700, "lr": 0.00132, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56672, "top5_acc": 0.79516, "loss_cls": 2.449, "loss": 2.449, "time": 0.8195} +{"mode": "val", "epoch": 139, "iter": 309, "lr": 0.00132, "top1_acc": 0.44254, "top5_acc": 0.68596, "mean_class_accuracy": 0.44234} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00131, "memory": 15990, "data_time": 1.30098, "top1_acc": 0.58844, "top5_acc": 0.81922, "loss_cls": 2.30228, "loss": 2.30228, "time": 2.28423} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00131, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59844, "top5_acc": 0.81938, "loss_cls": 2.26591, "loss": 2.26591, "time": 0.82329} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.0013, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59203, "top5_acc": 0.82984, "loss_cls": 2.26082, "loss": 2.26082, "time": 0.8241} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.0013, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.58953, "top5_acc": 0.81328, "loss_cls": 2.30599, "loss": 2.30599, "time": 0.81881} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00129, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58875, "top5_acc": 0.8175, "loss_cls": 2.30567, "loss": 2.30567, "time": 0.82106} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.00128, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58703, "top5_acc": 0.81703, "loss_cls": 2.28241, "loss": 2.28241, "time": 0.818} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.00128, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59094, "top5_acc": 0.82688, "loss_cls": 2.2634, "loss": 2.2634, "time": 0.82} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00127, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57359, "top5_acc": 0.80891, "loss_cls": 2.35371, "loss": 2.35371, "time": 0.81783} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00126, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58969, "top5_acc": 0.81625, "loss_cls": 2.28834, "loss": 2.28834, "time": 0.81527} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00126, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58344, "top5_acc": 0.81688, "loss_cls": 2.30854, "loss": 2.30854, "time": 0.81715} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00125, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59672, "top5_acc": 0.81984, "loss_cls": 2.28135, "loss": 2.28135, "time": 0.81586} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00125, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.57281, "top5_acc": 0.81453, "loss_cls": 2.34615, "loss": 2.34615, "time": 0.82513} +{"mode": "train", "epoch": 140, "iter": 1300, "lr": 0.00124, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.58234, "top5_acc": 0.81531, "loss_cls": 2.32532, "loss": 2.32532, "time": 0.81773} +{"mode": "train", "epoch": 140, "iter": 1400, "lr": 0.00123, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.57188, "top5_acc": 0.80375, "loss_cls": 2.39966, "loss": 2.39966, "time": 0.81805} +{"mode": "train", "epoch": 140, "iter": 1500, "lr": 0.00123, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58312, "top5_acc": 0.81031, "loss_cls": 2.32774, "loss": 2.32774, "time": 0.8267} +{"mode": "train", "epoch": 140, "iter": 1600, "lr": 0.00122, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58125, "top5_acc": 0.80859, "loss_cls": 2.35581, "loss": 2.35581, "time": 0.81633} +{"mode": "train", "epoch": 140, "iter": 1700, "lr": 0.00121, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59156, "top5_acc": 0.82422, "loss_cls": 2.30211, "loss": 2.30211, "time": 0.81917} +{"mode": "train", "epoch": 140, "iter": 1800, "lr": 0.00121, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57672, "top5_acc": 0.8125, "loss_cls": 2.3444, "loss": 2.3444, "time": 0.81363} +{"mode": "train", "epoch": 140, "iter": 1900, "lr": 0.0012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57031, "top5_acc": 0.80344, "loss_cls": 2.39505, "loss": 2.39505, "time": 0.81358} +{"mode": "train", "epoch": 140, "iter": 2000, "lr": 0.0012, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57656, "top5_acc": 0.79453, "loss_cls": 2.38601, "loss": 2.38601, "time": 0.82228} +{"mode": "train", "epoch": 140, "iter": 2100, "lr": 0.00119, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58016, "top5_acc": 0.81734, "loss_cls": 2.32787, "loss": 2.32787, "time": 0.81995} +{"mode": "train", "epoch": 140, "iter": 2200, "lr": 0.00118, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58078, "top5_acc": 0.81484, "loss_cls": 2.35038, "loss": 2.35038, "time": 0.82003} +{"mode": "train", "epoch": 140, "iter": 2300, "lr": 0.00118, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58188, "top5_acc": 0.80859, "loss_cls": 2.3546, "loss": 2.3546, "time": 0.82057} +{"mode": "train", "epoch": 140, "iter": 2400, "lr": 0.00117, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.575, "top5_acc": 0.80688, "loss_cls": 2.36403, "loss": 2.36403, "time": 0.81799} +{"mode": "train", "epoch": 140, "iter": 2500, "lr": 0.00117, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57906, "top5_acc": 0.81328, "loss_cls": 2.33768, "loss": 2.33768, "time": 0.81278} +{"mode": "train", "epoch": 140, "iter": 2600, "lr": 0.00116, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57422, "top5_acc": 0.80953, "loss_cls": 2.36384, "loss": 2.36384, "time": 0.81071} +{"mode": "train", "epoch": 140, "iter": 2700, "lr": 0.00115, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57734, "top5_acc": 0.80812, "loss_cls": 2.37651, "loss": 2.37651, "time": 0.81398} +{"mode": "train", "epoch": 140, "iter": 2800, "lr": 0.00115, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57406, "top5_acc": 0.80812, "loss_cls": 2.34696, "loss": 2.34696, "time": 0.81662} +{"mode": "train", "epoch": 140, "iter": 2900, "lr": 0.00114, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57922, "top5_acc": 0.80359, "loss_cls": 2.3702, "loss": 2.3702, "time": 0.81513} +{"mode": "train", "epoch": 140, "iter": 3000, "lr": 0.00114, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57594, "top5_acc": 0.80484, "loss_cls": 2.37449, "loss": 2.37449, "time": 0.81693} +{"mode": "train", "epoch": 140, "iter": 3100, "lr": 0.00113, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59562, "top5_acc": 0.82281, "loss_cls": 2.29945, "loss": 2.29945, "time": 0.82099} +{"mode": "train", "epoch": 140, "iter": 3200, "lr": 0.00112, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.56953, "top5_acc": 0.81141, "loss_cls": 2.34695, "loss": 2.34695, "time": 0.81865} +{"mode": "train", "epoch": 140, "iter": 3300, "lr": 0.00112, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58141, "top5_acc": 0.81688, "loss_cls": 2.32561, "loss": 2.32561, "time": 0.81164} +{"mode": "train", "epoch": 140, "iter": 3400, "lr": 0.00111, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57859, "top5_acc": 0.81297, "loss_cls": 2.33771, "loss": 2.33771, "time": 0.81076} +{"mode": "train", "epoch": 140, "iter": 3500, "lr": 0.00111, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57953, "top5_acc": 0.81141, "loss_cls": 2.34006, "loss": 2.34006, "time": 0.81525} +{"mode": "train", "epoch": 140, "iter": 3600, "lr": 0.0011, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58828, "top5_acc": 0.81766, "loss_cls": 2.32669, "loss": 2.32669, "time": 0.81558} +{"mode": "train", "epoch": 140, "iter": 3700, "lr": 0.0011, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.57953, "top5_acc": 0.81031, "loss_cls": 2.35648, "loss": 2.35648, "time": 0.81939} +{"mode": "val", "epoch": 140, "iter": 309, "lr": 0.00109, "top1_acc": 0.44309, "top5_acc": 0.69052, "mean_class_accuracy": 0.44287} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00109, "memory": 15990, "data_time": 1.29673, "top1_acc": 0.60969, "top5_acc": 0.83094, "loss_cls": 2.20633, "loss": 2.20633, "time": 2.27983} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00108, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60609, "top5_acc": 0.83188, "loss_cls": 2.20683, "loss": 2.20683, "time": 0.81577} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00108, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.61531, "top5_acc": 0.83984, "loss_cls": 2.17212, "loss": 2.17212, "time": 0.82588} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00107, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59359, "top5_acc": 0.82438, "loss_cls": 2.25806, "loss": 2.25806, "time": 0.80986} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00106, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59688, "top5_acc": 0.82906, "loss_cls": 2.26167, "loss": 2.26167, "time": 0.81521} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00106, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60344, "top5_acc": 0.83281, "loss_cls": 2.21074, "loss": 2.21074, "time": 0.81371} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00105, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59766, "top5_acc": 0.81812, "loss_cls": 2.28032, "loss": 2.28032, "time": 0.81692} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00105, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59609, "top5_acc": 0.82422, "loss_cls": 2.2575, "loss": 2.2575, "time": 0.81683} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00104, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59906, "top5_acc": 0.83094, "loss_cls": 2.24129, "loss": 2.24129, "time": 0.81102} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00104, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58469, "top5_acc": 0.81672, "loss_cls": 2.30312, "loss": 2.30312, "time": 0.81505} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00103, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58391, "top5_acc": 0.8175, "loss_cls": 2.31416, "loss": 2.31416, "time": 0.81472} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00102, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59859, "top5_acc": 0.82469, "loss_cls": 2.25582, "loss": 2.25582, "time": 0.82348} +{"mode": "train", "epoch": 141, "iter": 1300, "lr": 0.00102, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58812, "top5_acc": 0.81438, "loss_cls": 2.31072, "loss": 2.31072, "time": 0.82158} +{"mode": "train", "epoch": 141, "iter": 1400, "lr": 0.00101, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59391, "top5_acc": 0.81906, "loss_cls": 2.31761, "loss": 2.31761, "time": 0.82662} +{"mode": "train", "epoch": 141, "iter": 1500, "lr": 0.00101, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58516, "top5_acc": 0.81625, "loss_cls": 2.31697, "loss": 2.31697, "time": 0.81764} +{"mode": "train", "epoch": 141, "iter": 1600, "lr": 0.001, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.58203, "top5_acc": 0.81062, "loss_cls": 2.34502, "loss": 2.34502, "time": 0.82158} +{"mode": "train", "epoch": 141, "iter": 1700, "lr": 0.001, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59234, "top5_acc": 0.82359, "loss_cls": 2.29641, "loss": 2.29641, "time": 0.82066} +{"mode": "train", "epoch": 141, "iter": 1800, "lr": 0.00099, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58734, "top5_acc": 0.81922, "loss_cls": 2.29181, "loss": 2.29181, "time": 0.82281} +{"mode": "train", "epoch": 141, "iter": 1900, "lr": 0.00099, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59609, "top5_acc": 0.82641, "loss_cls": 2.2569, "loss": 2.2569, "time": 0.8149} +{"mode": "train", "epoch": 141, "iter": 2000, "lr": 0.00098, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59047, "top5_acc": 0.82094, "loss_cls": 2.2843, "loss": 2.2843, "time": 0.82485} +{"mode": "train", "epoch": 141, "iter": 2100, "lr": 0.00097, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59609, "top5_acc": 0.82516, "loss_cls": 2.26309, "loss": 2.26309, "time": 0.81564} +{"mode": "train", "epoch": 141, "iter": 2200, "lr": 0.00097, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58469, "top5_acc": 0.81484, "loss_cls": 2.30755, "loss": 2.30755, "time": 0.82111} +{"mode": "train", "epoch": 141, "iter": 2300, "lr": 0.00096, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58844, "top5_acc": 0.81547, "loss_cls": 2.27939, "loss": 2.27939, "time": 0.81577} +{"mode": "train", "epoch": 141, "iter": 2400, "lr": 0.00096, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59188, "top5_acc": 0.82469, "loss_cls": 2.27373, "loss": 2.27373, "time": 0.82167} +{"mode": "train", "epoch": 141, "iter": 2500, "lr": 0.00095, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59156, "top5_acc": 0.82031, "loss_cls": 2.28006, "loss": 2.28006, "time": 0.8162} +{"mode": "train", "epoch": 141, "iter": 2600, "lr": 0.00095, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60266, "top5_acc": 0.83016, "loss_cls": 2.24704, "loss": 2.24704, "time": 0.81761} +{"mode": "train", "epoch": 141, "iter": 2700, "lr": 0.00094, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60078, "top5_acc": 0.82797, "loss_cls": 2.23489, "loss": 2.23489, "time": 0.81272} +{"mode": "train", "epoch": 141, "iter": 2800, "lr": 0.00094, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.5975, "top5_acc": 0.82156, "loss_cls": 2.26259, "loss": 2.26259, "time": 0.81431} +{"mode": "train", "epoch": 141, "iter": 2900, "lr": 0.00093, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58484, "top5_acc": 0.81562, "loss_cls": 2.30081, "loss": 2.30081, "time": 0.81757} +{"mode": "train", "epoch": 141, "iter": 3000, "lr": 0.00093, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58188, "top5_acc": 0.81141, "loss_cls": 2.3321, "loss": 2.3321, "time": 0.81335} +{"mode": "train", "epoch": 141, "iter": 3100, "lr": 0.00092, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57906, "top5_acc": 0.80141, "loss_cls": 2.35626, "loss": 2.35626, "time": 0.81554} +{"mode": "train", "epoch": 141, "iter": 3200, "lr": 0.00091, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59453, "top5_acc": 0.82391, "loss_cls": 2.2626, "loss": 2.2626, "time": 0.81347} +{"mode": "train", "epoch": 141, "iter": 3300, "lr": 0.00091, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58875, "top5_acc": 0.82, "loss_cls": 2.26737, "loss": 2.26737, "time": 0.81744} +{"mode": "train", "epoch": 141, "iter": 3400, "lr": 0.0009, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58859, "top5_acc": 0.81406, "loss_cls": 2.29055, "loss": 2.29055, "time": 0.82264} +{"mode": "train", "epoch": 141, "iter": 3500, "lr": 0.0009, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59719, "top5_acc": 0.81578, "loss_cls": 2.29956, "loss": 2.29956, "time": 0.81867} +{"mode": "train", "epoch": 141, "iter": 3600, "lr": 0.00089, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59, "top5_acc": 0.81625, "loss_cls": 2.29682, "loss": 2.29682, "time": 0.81516} +{"mode": "train", "epoch": 141, "iter": 3700, "lr": 0.00089, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58812, "top5_acc": 0.8125, "loss_cls": 2.32272, "loss": 2.32272, "time": 0.81539} +{"mode": "val", "epoch": 141, "iter": 309, "lr": 0.00089, "top1_acc": 0.4475, "top5_acc": 0.69219, "mean_class_accuracy": 0.44732} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00088, "memory": 15990, "data_time": 1.33605, "top1_acc": 0.61422, "top5_acc": 0.83641, "loss_cls": 2.1565, "loss": 2.1565, "time": 2.33362} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00088, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.60781, "top5_acc": 0.83234, "loss_cls": 2.21415, "loss": 2.21415, "time": 0.81903} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00087, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.61406, "top5_acc": 0.83828, "loss_cls": 2.16576, "loss": 2.16576, "time": 0.8302} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00086, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60422, "top5_acc": 0.82719, "loss_cls": 2.2273, "loss": 2.2273, "time": 0.81541} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.00086, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.605, "top5_acc": 0.83562, "loss_cls": 2.21875, "loss": 2.21875, "time": 0.82061} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.00085, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60734, "top5_acc": 0.83453, "loss_cls": 2.1913, "loss": 2.1913, "time": 0.81705} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.00085, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.6125, "top5_acc": 0.82672, "loss_cls": 2.2032, "loss": 2.2032, "time": 0.81326} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00084, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.60859, "top5_acc": 0.83047, "loss_cls": 2.20585, "loss": 2.20585, "time": 0.81566} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00084, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60125, "top5_acc": 0.82734, "loss_cls": 2.23514, "loss": 2.23514, "time": 0.81017} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00083, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60625, "top5_acc": 0.83109, "loss_cls": 2.21104, "loss": 2.21104, "time": 0.81707} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00083, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60203, "top5_acc": 0.82922, "loss_cls": 2.21951, "loss": 2.21951, "time": 0.81852} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00082, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60031, "top5_acc": 0.83125, "loss_cls": 2.21867, "loss": 2.21867, "time": 0.81956} +{"mode": "train", "epoch": 142, "iter": 1300, "lr": 0.00082, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.60312, "top5_acc": 0.83297, "loss_cls": 2.20945, "loss": 2.20945, "time": 0.81932} +{"mode": "train", "epoch": 142, "iter": 1400, "lr": 0.00081, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.60938, "top5_acc": 0.8375, "loss_cls": 2.2096, "loss": 2.2096, "time": 0.82383} +{"mode": "train", "epoch": 142, "iter": 1500, "lr": 0.00081, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60141, "top5_acc": 0.82938, "loss_cls": 2.21545, "loss": 2.21545, "time": 0.81537} +{"mode": "train", "epoch": 142, "iter": 1600, "lr": 0.0008, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59766, "top5_acc": 0.82297, "loss_cls": 2.25055, "loss": 2.25055, "time": 0.82502} +{"mode": "train", "epoch": 142, "iter": 1700, "lr": 0.0008, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.60828, "top5_acc": 0.83469, "loss_cls": 2.18458, "loss": 2.18458, "time": 0.82256} +{"mode": "train", "epoch": 142, "iter": 1800, "lr": 0.00079, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60625, "top5_acc": 0.83234, "loss_cls": 2.18917, "loss": 2.18917, "time": 0.81834} +{"mode": "train", "epoch": 142, "iter": 1900, "lr": 0.00079, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61266, "top5_acc": 0.8325, "loss_cls": 2.1981, "loss": 2.1981, "time": 0.81119} +{"mode": "train", "epoch": 142, "iter": 2000, "lr": 0.00078, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59734, "top5_acc": 0.82438, "loss_cls": 2.25298, "loss": 2.25298, "time": 0.81615} +{"mode": "train", "epoch": 142, "iter": 2100, "lr": 0.00078, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59672, "top5_acc": 0.82328, "loss_cls": 2.24833, "loss": 2.24833, "time": 0.8131} +{"mode": "train", "epoch": 142, "iter": 2200, "lr": 0.00077, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.60453, "top5_acc": 0.82484, "loss_cls": 2.23263, "loss": 2.23263, "time": 0.81511} +{"mode": "train", "epoch": 142, "iter": 2300, "lr": 0.00077, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59766, "top5_acc": 0.82812, "loss_cls": 2.23013, "loss": 2.23013, "time": 0.82037} +{"mode": "train", "epoch": 142, "iter": 2400, "lr": 0.00076, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60188, "top5_acc": 0.83203, "loss_cls": 2.20348, "loss": 2.20348, "time": 0.82341} +{"mode": "train", "epoch": 142, "iter": 2500, "lr": 0.00076, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60781, "top5_acc": 0.83406, "loss_cls": 2.21012, "loss": 2.21012, "time": 0.82304} +{"mode": "train", "epoch": 142, "iter": 2600, "lr": 0.00075, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60344, "top5_acc": 0.8275, "loss_cls": 2.2214, "loss": 2.2214, "time": 0.81533} +{"mode": "train", "epoch": 142, "iter": 2700, "lr": 0.00075, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59781, "top5_acc": 0.81922, "loss_cls": 2.27327, "loss": 2.27327, "time": 0.81325} +{"mode": "train", "epoch": 142, "iter": 2800, "lr": 0.00075, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.605, "top5_acc": 0.83281, "loss_cls": 2.20173, "loss": 2.20173, "time": 0.81891} +{"mode": "train", "epoch": 142, "iter": 2900, "lr": 0.00074, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60547, "top5_acc": 0.83703, "loss_cls": 2.18951, "loss": 2.18951, "time": 0.8138} +{"mode": "train", "epoch": 142, "iter": 3000, "lr": 0.00074, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59516, "top5_acc": 0.82453, "loss_cls": 2.24356, "loss": 2.24356, "time": 0.81492} +{"mode": "train", "epoch": 142, "iter": 3100, "lr": 0.00073, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61078, "top5_acc": 0.83344, "loss_cls": 2.1791, "loss": 2.1791, "time": 0.81404} +{"mode": "train", "epoch": 142, "iter": 3200, "lr": 0.00073, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59688, "top5_acc": 0.8225, "loss_cls": 2.25922, "loss": 2.25922, "time": 0.81444} +{"mode": "train", "epoch": 142, "iter": 3300, "lr": 0.00072, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.60719, "top5_acc": 0.83188, "loss_cls": 2.20556, "loss": 2.20556, "time": 0.8168} +{"mode": "train", "epoch": 142, "iter": 3400, "lr": 0.00072, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59422, "top5_acc": 0.81922, "loss_cls": 2.24554, "loss": 2.24554, "time": 0.81339} +{"mode": "train", "epoch": 142, "iter": 3500, "lr": 0.00071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5925, "top5_acc": 0.82234, "loss_cls": 2.27626, "loss": 2.27626, "time": 0.81688} +{"mode": "train", "epoch": 142, "iter": 3600, "lr": 0.00071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59891, "top5_acc": 0.82203, "loss_cls": 2.25028, "loss": 2.25028, "time": 0.81075} +{"mode": "train", "epoch": 142, "iter": 3700, "lr": 0.0007, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59453, "top5_acc": 0.825, "loss_cls": 2.2606, "loss": 2.2606, "time": 0.81264} +{"mode": "val", "epoch": 142, "iter": 309, "lr": 0.0007, "top1_acc": 0.44588, "top5_acc": 0.68875, "mean_class_accuracy": 0.44562} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.0007, "memory": 15990, "data_time": 1.29397, "top1_acc": 0.62016, "top5_acc": 0.83859, "loss_cls": 2.11677, "loss": 2.11677, "time": 2.27188} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00069, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62094, "top5_acc": 0.83812, "loss_cls": 2.14265, "loss": 2.14265, "time": 0.82106} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00069, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62516, "top5_acc": 0.84109, "loss_cls": 2.11242, "loss": 2.11242, "time": 0.81949} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00068, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.62125, "top5_acc": 0.83844, "loss_cls": 2.1452, "loss": 2.1452, "time": 0.81876} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00068, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62641, "top5_acc": 0.84188, "loss_cls": 2.11431, "loss": 2.11431, "time": 0.81549} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00067, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62422, "top5_acc": 0.84766, "loss_cls": 2.11219, "loss": 2.11219, "time": 0.82531} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00067, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61344, "top5_acc": 0.83562, "loss_cls": 2.1601, "loss": 2.1601, "time": 0.81769} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00066, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60578, "top5_acc": 0.84016, "loss_cls": 2.16625, "loss": 2.16625, "time": 0.81371} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00066, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62109, "top5_acc": 0.83703, "loss_cls": 2.15879, "loss": 2.15879, "time": 0.81287} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00065, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.61203, "top5_acc": 0.84109, "loss_cls": 2.17588, "loss": 2.17588, "time": 0.81585} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63328, "top5_acc": 0.84594, "loss_cls": 2.09888, "loss": 2.09888, "time": 0.81641} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61516, "top5_acc": 0.83656, "loss_cls": 2.14774, "loss": 2.14774, "time": 0.8205} +{"mode": "train", "epoch": 143, "iter": 1300, "lr": 0.00064, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.615, "top5_acc": 0.8425, "loss_cls": 2.14638, "loss": 2.14638, "time": 0.82034} +{"mode": "train", "epoch": 143, "iter": 1400, "lr": 0.00064, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.61125, "top5_acc": 0.84062, "loss_cls": 2.16098, "loss": 2.16098, "time": 0.82715} +{"mode": "train", "epoch": 143, "iter": 1500, "lr": 0.00063, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61078, "top5_acc": 0.83344, "loss_cls": 2.18753, "loss": 2.18753, "time": 0.818} +{"mode": "train", "epoch": 143, "iter": 1600, "lr": 0.00063, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60734, "top5_acc": 0.83562, "loss_cls": 2.18291, "loss": 2.18291, "time": 0.81446} +{"mode": "train", "epoch": 143, "iter": 1700, "lr": 0.00062, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60422, "top5_acc": 0.83438, "loss_cls": 2.18935, "loss": 2.18935, "time": 0.81676} +{"mode": "train", "epoch": 143, "iter": 1800, "lr": 0.00062, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61969, "top5_acc": 0.83594, "loss_cls": 2.17409, "loss": 2.17409, "time": 0.82205} +{"mode": "train", "epoch": 143, "iter": 1900, "lr": 0.00061, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.61125, "top5_acc": 0.83938, "loss_cls": 2.17256, "loss": 2.17256, "time": 0.81889} +{"mode": "train", "epoch": 143, "iter": 2000, "lr": 0.00061, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.61234, "top5_acc": 0.82891, "loss_cls": 2.18387, "loss": 2.18387, "time": 0.82091} +{"mode": "train", "epoch": 143, "iter": 2100, "lr": 0.00061, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.60953, "top5_acc": 0.83703, "loss_cls": 2.14843, "loss": 2.14843, "time": 0.81365} +{"mode": "train", "epoch": 143, "iter": 2200, "lr": 0.0006, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.6225, "top5_acc": 0.83594, "loss_cls": 2.13761, "loss": 2.13761, "time": 0.81803} +{"mode": "train", "epoch": 143, "iter": 2300, "lr": 0.0006, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.59547, "top5_acc": 0.83172, "loss_cls": 2.20638, "loss": 2.20638, "time": 0.81536} +{"mode": "train", "epoch": 143, "iter": 2400, "lr": 0.00059, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60938, "top5_acc": 0.83719, "loss_cls": 2.16639, "loss": 2.16639, "time": 0.81126} +{"mode": "train", "epoch": 143, "iter": 2500, "lr": 0.00059, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61094, "top5_acc": 0.83359, "loss_cls": 2.19505, "loss": 2.19505, "time": 0.81485} +{"mode": "train", "epoch": 143, "iter": 2600, "lr": 0.00058, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61641, "top5_acc": 0.83719, "loss_cls": 2.15628, "loss": 2.15628, "time": 0.81437} +{"mode": "train", "epoch": 143, "iter": 2700, "lr": 0.00058, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61781, "top5_acc": 0.83641, "loss_cls": 2.14968, "loss": 2.14968, "time": 0.81284} +{"mode": "train", "epoch": 143, "iter": 2800, "lr": 0.00058, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60594, "top5_acc": 0.82938, "loss_cls": 2.20478, "loss": 2.20478, "time": 0.81814} +{"mode": "train", "epoch": 143, "iter": 2900, "lr": 0.00057, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.61375, "top5_acc": 0.8325, "loss_cls": 2.17837, "loss": 2.17837, "time": 0.8126} +{"mode": "train", "epoch": 143, "iter": 3000, "lr": 0.00057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61828, "top5_acc": 0.82844, "loss_cls": 2.18751, "loss": 2.18751, "time": 0.81635} +{"mode": "train", "epoch": 143, "iter": 3100, "lr": 0.00056, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61203, "top5_acc": 0.8325, "loss_cls": 2.20448, "loss": 2.20448, "time": 0.81724} +{"mode": "train", "epoch": 143, "iter": 3200, "lr": 0.00056, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.60688, "top5_acc": 0.83219, "loss_cls": 2.18929, "loss": 2.18929, "time": 0.81822} +{"mode": "train", "epoch": 143, "iter": 3300, "lr": 0.00055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59812, "top5_acc": 0.83, "loss_cls": 2.22608, "loss": 2.22608, "time": 0.81515} +{"mode": "train", "epoch": 143, "iter": 3400, "lr": 0.00055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.615, "top5_acc": 0.83016, "loss_cls": 2.16315, "loss": 2.16315, "time": 0.81404} +{"mode": "train", "epoch": 143, "iter": 3500, "lr": 0.00055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61344, "top5_acc": 0.83406, "loss_cls": 2.15802, "loss": 2.15802, "time": 0.81472} +{"mode": "train", "epoch": 143, "iter": 3600, "lr": 0.00054, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61484, "top5_acc": 0.83375, "loss_cls": 2.17535, "loss": 2.17535, "time": 0.81402} +{"mode": "train", "epoch": 143, "iter": 3700, "lr": 0.00054, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61687, "top5_acc": 0.83938, "loss_cls": 2.16683, "loss": 2.16683, "time": 0.81369} +{"mode": "val", "epoch": 143, "iter": 309, "lr": 0.00054, "top1_acc": 0.44765, "top5_acc": 0.69068, "mean_class_accuracy": 0.44746} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00053, "memory": 15990, "data_time": 1.28621, "top1_acc": 0.62672, "top5_acc": 0.84812, "loss_cls": 2.08795, "loss": 2.08795, "time": 2.25873} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00053, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64828, "top5_acc": 0.85219, "loss_cls": 2.0224, "loss": 2.0224, "time": 0.81297} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64172, "top5_acc": 0.855, "loss_cls": 2.04339, "loss": 2.04339, "time": 0.81577} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00052, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62391, "top5_acc": 0.85312, "loss_cls": 2.08813, "loss": 2.08813, "time": 0.83064} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00052, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.63344, "top5_acc": 0.85125, "loss_cls": 2.05184, "loss": 2.05184, "time": 0.81478} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00051, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62, "top5_acc": 0.84688, "loss_cls": 2.11987, "loss": 2.11987, "time": 0.81358} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00051, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63156, "top5_acc": 0.84984, "loss_cls": 2.07933, "loss": 2.07933, "time": 0.81794} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.0005, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63438, "top5_acc": 0.85, "loss_cls": 2.03908, "loss": 2.03908, "time": 0.81432} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.0005, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.63547, "top5_acc": 0.84828, "loss_cls": 2.05938, "loss": 2.05938, "time": 0.81244} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.0005, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62719, "top5_acc": 0.84562, "loss_cls": 2.10734, "loss": 2.10734, "time": 0.81341} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.00049, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62562, "top5_acc": 0.84719, "loss_cls": 2.10522, "loss": 2.10522, "time": 0.8193} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.00049, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.61922, "top5_acc": 0.84125, "loss_cls": 2.13017, "loss": 2.13017, "time": 0.82116} +{"mode": "train", "epoch": 144, "iter": 1300, "lr": 0.00048, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.61953, "top5_acc": 0.84188, "loss_cls": 2.14521, "loss": 2.14521, "time": 0.81949} +{"mode": "train", "epoch": 144, "iter": 1400, "lr": 0.00048, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.61844, "top5_acc": 0.84406, "loss_cls": 2.13614, "loss": 2.13614, "time": 0.82268} +{"mode": "train", "epoch": 144, "iter": 1500, "lr": 0.00048, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62344, "top5_acc": 0.83562, "loss_cls": 2.13077, "loss": 2.13077, "time": 0.81734} +{"mode": "train", "epoch": 144, "iter": 1600, "lr": 0.00047, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62313, "top5_acc": 0.84688, "loss_cls": 2.11832, "loss": 2.11832, "time": 0.82108} +{"mode": "train", "epoch": 144, "iter": 1700, "lr": 0.00047, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62078, "top5_acc": 0.84625, "loss_cls": 2.1219, "loss": 2.1219, "time": 0.81787} +{"mode": "train", "epoch": 144, "iter": 1800, "lr": 0.00047, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62375, "top5_acc": 0.84656, "loss_cls": 2.10063, "loss": 2.10063, "time": 0.81497} +{"mode": "train", "epoch": 144, "iter": 1900, "lr": 0.00046, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61766, "top5_acc": 0.83953, "loss_cls": 2.15478, "loss": 2.15478, "time": 0.8139} +{"mode": "train", "epoch": 144, "iter": 2000, "lr": 0.00046, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.60719, "top5_acc": 0.835, "loss_cls": 2.18395, "loss": 2.18395, "time": 0.82257} +{"mode": "train", "epoch": 144, "iter": 2100, "lr": 0.00045, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.62141, "top5_acc": 0.84094, "loss_cls": 2.11231, "loss": 2.11231, "time": 0.81736} +{"mode": "train", "epoch": 144, "iter": 2200, "lr": 0.00045, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.62828, "top5_acc": 0.83828, "loss_cls": 2.12382, "loss": 2.12382, "time": 0.81759} +{"mode": "train", "epoch": 144, "iter": 2300, "lr": 0.00045, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63047, "top5_acc": 0.84672, "loss_cls": 2.09632, "loss": 2.09632, "time": 0.81279} +{"mode": "train", "epoch": 144, "iter": 2400, "lr": 0.00044, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62281, "top5_acc": 0.83984, "loss_cls": 2.13589, "loss": 2.13589, "time": 0.81657} +{"mode": "train", "epoch": 144, "iter": 2500, "lr": 0.00044, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62547, "top5_acc": 0.83922, "loss_cls": 2.11641, "loss": 2.11641, "time": 0.81364} +{"mode": "train", "epoch": 144, "iter": 2600, "lr": 0.00044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63453, "top5_acc": 0.84312, "loss_cls": 2.07824, "loss": 2.07824, "time": 0.81739} +{"mode": "train", "epoch": 144, "iter": 2700, "lr": 0.00043, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62328, "top5_acc": 0.84594, "loss_cls": 2.12668, "loss": 2.12668, "time": 0.81355} +{"mode": "train", "epoch": 144, "iter": 2800, "lr": 0.00043, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63438, "top5_acc": 0.84922, "loss_cls": 2.08723, "loss": 2.08723, "time": 0.81206} +{"mode": "train", "epoch": 144, "iter": 2900, "lr": 0.00042, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61828, "top5_acc": 0.83984, "loss_cls": 2.14906, "loss": 2.14906, "time": 0.81721} +{"mode": "train", "epoch": 144, "iter": 3000, "lr": 0.00042, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.6225, "top5_acc": 0.83812, "loss_cls": 2.1396, "loss": 2.1396, "time": 0.81567} +{"mode": "train", "epoch": 144, "iter": 3100, "lr": 0.00042, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62297, "top5_acc": 0.83922, "loss_cls": 2.13117, "loss": 2.13117, "time": 0.81442} +{"mode": "train", "epoch": 144, "iter": 3200, "lr": 0.00041, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62156, "top5_acc": 0.84109, "loss_cls": 2.12593, "loss": 2.12593, "time": 0.81947} +{"mode": "train", "epoch": 144, "iter": 3300, "lr": 0.00041, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.62813, "top5_acc": 0.84578, "loss_cls": 2.10041, "loss": 2.10041, "time": 0.81661} +{"mode": "train", "epoch": 144, "iter": 3400, "lr": 0.00041, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62344, "top5_acc": 0.83844, "loss_cls": 2.13645, "loss": 2.13645, "time": 0.81411} +{"mode": "train", "epoch": 144, "iter": 3500, "lr": 0.0004, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.61953, "top5_acc": 0.84141, "loss_cls": 2.1083, "loss": 2.1083, "time": 0.80904} +{"mode": "train", "epoch": 144, "iter": 3600, "lr": 0.0004, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62094, "top5_acc": 0.83641, "loss_cls": 2.13321, "loss": 2.13321, "time": 0.81734} +{"mode": "train", "epoch": 144, "iter": 3700, "lr": 0.0004, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62438, "top5_acc": 0.84547, "loss_cls": 2.12986, "loss": 2.12986, "time": 0.82221} +{"mode": "val", "epoch": 144, "iter": 309, "lr": 0.00039, "top1_acc": 0.45024, "top5_acc": 0.69412, "mean_class_accuracy": 0.44999} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.00039, "memory": 15990, "data_time": 1.27217, "top1_acc": 0.65219, "top5_acc": 0.86391, "loss_cls": 1.97747, "loss": 1.97747, "time": 2.24908} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 0.00039, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64328, "top5_acc": 0.85969, "loss_cls": 2.00839, "loss": 2.00839, "time": 0.81789} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 0.00038, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64047, "top5_acc": 0.85188, "loss_cls": 2.04531, "loss": 2.04531, "time": 0.81556} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 0.00038, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63281, "top5_acc": 0.85422, "loss_cls": 2.05289, "loss": 2.05289, "time": 0.81692} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 0.00038, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63375, "top5_acc": 0.85188, "loss_cls": 2.0691, "loss": 2.0691, "time": 0.82016} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 0.00037, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.6375, "top5_acc": 0.85109, "loss_cls": 2.05856, "loss": 2.05856, "time": 0.8156} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 0.00037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63625, "top5_acc": 0.85297, "loss_cls": 2.05731, "loss": 2.05731, "time": 0.81312} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 0.00037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63594, "top5_acc": 0.84844, "loss_cls": 2.07062, "loss": 2.07062, "time": 0.81249} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 0.00036, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63141, "top5_acc": 0.85031, "loss_cls": 2.06508, "loss": 2.06508, "time": 0.81334} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 0.00036, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63844, "top5_acc": 0.84781, "loss_cls": 2.06316, "loss": 2.06316, "time": 0.81769} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 0.00036, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.6325, "top5_acc": 0.8475, "loss_cls": 2.06663, "loss": 2.06663, "time": 0.81829} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 0.00035, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.63266, "top5_acc": 0.84609, "loss_cls": 2.08504, "loss": 2.08504, "time": 0.82287} +{"mode": "train", "epoch": 145, "iter": 1300, "lr": 0.00035, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64078, "top5_acc": 0.8575, "loss_cls": 2.04394, "loss": 2.04394, "time": 0.81774} +{"mode": "train", "epoch": 145, "iter": 1400, "lr": 0.00035, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62391, "top5_acc": 0.84984, "loss_cls": 2.09005, "loss": 2.09005, "time": 0.82428} +{"mode": "train", "epoch": 145, "iter": 1500, "lr": 0.00034, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63906, "top5_acc": 0.85375, "loss_cls": 2.0575, "loss": 2.0575, "time": 0.82341} +{"mode": "train", "epoch": 145, "iter": 1600, "lr": 0.00034, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64219, "top5_acc": 0.84906, "loss_cls": 2.03558, "loss": 2.03558, "time": 0.81867} +{"mode": "train", "epoch": 145, "iter": 1700, "lr": 0.00034, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63391, "top5_acc": 0.85375, "loss_cls": 2.05096, "loss": 2.05096, "time": 0.81353} +{"mode": "train", "epoch": 145, "iter": 1800, "lr": 0.00033, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.62906, "top5_acc": 0.84109, "loss_cls": 2.10593, "loss": 2.10593, "time": 0.81623} +{"mode": "train", "epoch": 145, "iter": 1900, "lr": 0.00033, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.62859, "top5_acc": 0.84531, "loss_cls": 2.10025, "loss": 2.10025, "time": 0.81661} +{"mode": "train", "epoch": 145, "iter": 2000, "lr": 0.00033, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.63875, "top5_acc": 0.85391, "loss_cls": 2.04865, "loss": 2.04865, "time": 0.8185} +{"mode": "train", "epoch": 145, "iter": 2100, "lr": 0.00032, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.63922, "top5_acc": 0.85219, "loss_cls": 2.04627, "loss": 2.04627, "time": 0.81988} +{"mode": "train", "epoch": 145, "iter": 2200, "lr": 0.00032, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.64547, "top5_acc": 0.85391, "loss_cls": 2.01482, "loss": 2.01482, "time": 0.8216} +{"mode": "train", "epoch": 145, "iter": 2300, "lr": 0.00032, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63953, "top5_acc": 0.85484, "loss_cls": 2.04285, "loss": 2.04285, "time": 0.81523} +{"mode": "train", "epoch": 145, "iter": 2400, "lr": 0.00031, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63547, "top5_acc": 0.85172, "loss_cls": 2.03544, "loss": 2.03544, "time": 0.82302} +{"mode": "train", "epoch": 145, "iter": 2500, "lr": 0.00031, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64078, "top5_acc": 0.85094, "loss_cls": 2.04841, "loss": 2.04841, "time": 0.81553} +{"mode": "train", "epoch": 145, "iter": 2600, "lr": 0.00031, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.625, "top5_acc": 0.83938, "loss_cls": 2.13821, "loss": 2.13821, "time": 0.82085} +{"mode": "train", "epoch": 145, "iter": 2700, "lr": 0.00031, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63469, "top5_acc": 0.85094, "loss_cls": 2.05617, "loss": 2.05617, "time": 0.81485} +{"mode": "train", "epoch": 145, "iter": 2800, "lr": 0.0003, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63984, "top5_acc": 0.85625, "loss_cls": 2.03285, "loss": 2.03285, "time": 0.81747} +{"mode": "train", "epoch": 145, "iter": 2900, "lr": 0.0003, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63141, "top5_acc": 0.84984, "loss_cls": 2.07766, "loss": 2.07766, "time": 0.8158} +{"mode": "train", "epoch": 145, "iter": 3000, "lr": 0.0003, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63562, "top5_acc": 0.85547, "loss_cls": 2.03804, "loss": 2.03804, "time": 0.81283} +{"mode": "train", "epoch": 145, "iter": 3100, "lr": 0.00029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63625, "top5_acc": 0.84922, "loss_cls": 2.08861, "loss": 2.08861, "time": 0.81959} +{"mode": "train", "epoch": 145, "iter": 3200, "lr": 0.00029, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.63828, "top5_acc": 0.85078, "loss_cls": 2.05993, "loss": 2.05993, "time": 0.8164} +{"mode": "train", "epoch": 145, "iter": 3300, "lr": 0.00029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64562, "top5_acc": 0.86172, "loss_cls": 1.99995, "loss": 1.99995, "time": 0.81963} +{"mode": "train", "epoch": 145, "iter": 3400, "lr": 0.00028, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63234, "top5_acc": 0.84328, "loss_cls": 2.06447, "loss": 2.06447, "time": 0.81561} +{"mode": "train", "epoch": 145, "iter": 3500, "lr": 0.00028, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64031, "top5_acc": 0.84906, "loss_cls": 2.04034, "loss": 2.04034, "time": 0.81463} +{"mode": "train", "epoch": 145, "iter": 3600, "lr": 0.00028, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.62953, "top5_acc": 0.84047, "loss_cls": 2.09462, "loss": 2.09462, "time": 0.8166} +{"mode": "train", "epoch": 145, "iter": 3700, "lr": 0.00028, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.62781, "top5_acc": 0.84516, "loss_cls": 2.07348, "loss": 2.07348, "time": 0.82055} +{"mode": "val", "epoch": 145, "iter": 309, "lr": 0.00027, "top1_acc": 0.45236, "top5_acc": 0.69473, "mean_class_accuracy": 0.45214} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 0.00027, "memory": 15990, "data_time": 1.33625, "top1_acc": 0.64469, "top5_acc": 0.85797, "loss_cls": 1.99038, "loss": 1.99038, "time": 2.3266} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 0.00027, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.64156, "top5_acc": 0.85547, "loss_cls": 2.03101, "loss": 2.03101, "time": 0.82333} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 0.00027, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.65141, "top5_acc": 0.85953, "loss_cls": 2.00507, "loss": 2.00507, "time": 0.82098} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 0.00026, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65438, "top5_acc": 0.86438, "loss_cls": 1.95429, "loss": 1.95429, "time": 0.82047} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 0.00026, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.63094, "top5_acc": 0.85141, "loss_cls": 2.05943, "loss": 2.05943, "time": 0.82476} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 0.00026, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64547, "top5_acc": 0.85047, "loss_cls": 2.03379, "loss": 2.03379, "time": 0.82349} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 0.00025, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.63797, "top5_acc": 0.84891, "loss_cls": 2.06874, "loss": 2.06874, "time": 0.82047} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 0.00025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64875, "top5_acc": 0.86031, "loss_cls": 1.99807, "loss": 1.99807, "time": 0.81803} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 0.00025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65281, "top5_acc": 0.86078, "loss_cls": 1.9799, "loss": 1.9799, "time": 0.81402} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 0.00025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65, "top5_acc": 0.85547, "loss_cls": 1.9903, "loss": 1.9903, "time": 0.81209} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 0.00024, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64547, "top5_acc": 0.86312, "loss_cls": 1.97902, "loss": 1.97902, "time": 0.82051} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 0.00024, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63953, "top5_acc": 0.85172, "loss_cls": 2.04227, "loss": 2.04227, "time": 0.82069} +{"mode": "train", "epoch": 146, "iter": 1300, "lr": 0.00024, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.65672, "top5_acc": 0.86375, "loss_cls": 1.96419, "loss": 1.96419, "time": 0.82498} +{"mode": "train", "epoch": 146, "iter": 1400, "lr": 0.00023, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.6375, "top5_acc": 0.85031, "loss_cls": 2.05818, "loss": 2.05818, "time": 0.82176} +{"mode": "train", "epoch": 146, "iter": 1500, "lr": 0.00023, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65047, "top5_acc": 0.84859, "loss_cls": 2.01701, "loss": 2.01701, "time": 0.81764} +{"mode": "train", "epoch": 146, "iter": 1600, "lr": 0.00023, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64422, "top5_acc": 0.85672, "loss_cls": 2.03581, "loss": 2.03581, "time": 0.81447} +{"mode": "train", "epoch": 146, "iter": 1700, "lr": 0.00023, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.65094, "top5_acc": 0.85828, "loss_cls": 2.01074, "loss": 2.01074, "time": 0.81991} +{"mode": "train", "epoch": 146, "iter": 1800, "lr": 0.00022, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.63594, "top5_acc": 0.86, "loss_cls": 2.01687, "loss": 2.01687, "time": 0.81709} +{"mode": "train", "epoch": 146, "iter": 1900, "lr": 0.00022, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64406, "top5_acc": 0.85562, "loss_cls": 2.00193, "loss": 2.00193, "time": 0.81362} +{"mode": "train", "epoch": 146, "iter": 2000, "lr": 0.00022, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.64984, "top5_acc": 0.85484, "loss_cls": 2.02699, "loss": 2.02699, "time": 0.82656} +{"mode": "train", "epoch": 146, "iter": 2100, "lr": 0.00022, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64266, "top5_acc": 0.85391, "loss_cls": 2.04655, "loss": 2.04655, "time": 0.82033} +{"mode": "train", "epoch": 146, "iter": 2200, "lr": 0.00021, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65047, "top5_acc": 0.85312, "loss_cls": 2.01455, "loss": 2.01455, "time": 0.82432} +{"mode": "train", "epoch": 146, "iter": 2300, "lr": 0.00021, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64484, "top5_acc": 0.85906, "loss_cls": 2.01197, "loss": 2.01197, "time": 0.81513} +{"mode": "train", "epoch": 146, "iter": 2400, "lr": 0.00021, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65609, "top5_acc": 0.86141, "loss_cls": 1.9733, "loss": 1.9733, "time": 0.81527} +{"mode": "train", "epoch": 146, "iter": 2500, "lr": 0.00021, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.645, "top5_acc": 0.85922, "loss_cls": 2.01019, "loss": 2.01019, "time": 0.81696} +{"mode": "train", "epoch": 146, "iter": 2600, "lr": 0.0002, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63859, "top5_acc": 0.85219, "loss_cls": 2.00886, "loss": 2.00886, "time": 0.8161} +{"mode": "train", "epoch": 146, "iter": 2700, "lr": 0.0002, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64391, "top5_acc": 0.86422, "loss_cls": 1.9813, "loss": 1.9813, "time": 0.81753} +{"mode": "train", "epoch": 146, "iter": 2800, "lr": 0.0002, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64906, "top5_acc": 0.85938, "loss_cls": 1.98889, "loss": 1.98889, "time": 0.81643} +{"mode": "train", "epoch": 146, "iter": 2900, "lr": 0.0002, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.63859, "top5_acc": 0.84891, "loss_cls": 2.04259, "loss": 2.04259, "time": 0.81471} +{"mode": "train", "epoch": 146, "iter": 3000, "lr": 0.00019, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.63062, "top5_acc": 0.84891, "loss_cls": 2.05511, "loss": 2.05511, "time": 0.81778} +{"mode": "train", "epoch": 146, "iter": 3100, "lr": 0.00019, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64375, "top5_acc": 0.86078, "loss_cls": 2.00292, "loss": 2.00292, "time": 0.8124} +{"mode": "train", "epoch": 146, "iter": 3200, "lr": 0.00019, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64281, "top5_acc": 0.85391, "loss_cls": 2.03977, "loss": 2.03977, "time": 0.81796} +{"mode": "train", "epoch": 146, "iter": 3300, "lr": 0.00019, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64219, "top5_acc": 0.85281, "loss_cls": 2.01208, "loss": 2.01208, "time": 0.821} +{"mode": "train", "epoch": 146, "iter": 3400, "lr": 0.00018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64641, "top5_acc": 0.85203, "loss_cls": 2.03846, "loss": 2.03846, "time": 0.81431} +{"mode": "train", "epoch": 146, "iter": 3500, "lr": 0.00018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64844, "top5_acc": 0.85312, "loss_cls": 2.005, "loss": 2.005, "time": 0.82021} +{"mode": "train", "epoch": 146, "iter": 3600, "lr": 0.00018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64328, "top5_acc": 0.85328, "loss_cls": 2.01208, "loss": 2.01208, "time": 0.81589} +{"mode": "train", "epoch": 146, "iter": 3700, "lr": 0.00018, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65438, "top5_acc": 0.85406, "loss_cls": 2.00451, "loss": 2.00451, "time": 0.81884} +{"mode": "val", "epoch": 146, "iter": 309, "lr": 0.00018, "top1_acc": 0.44988, "top5_acc": 0.6928, "mean_class_accuracy": 0.4497} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 0.00017, "memory": 15990, "data_time": 1.31403, "top1_acc": 0.65844, "top5_acc": 0.86172, "loss_cls": 1.9536, "loss": 1.9536, "time": 2.29685} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 0.00017, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65438, "top5_acc": 0.86422, "loss_cls": 1.97395, "loss": 1.97395, "time": 0.81784} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 0.00017, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64953, "top5_acc": 0.85625, "loss_cls": 1.99179, "loss": 1.99179, "time": 0.81543} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 0.00017, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65828, "top5_acc": 0.86594, "loss_cls": 1.95631, "loss": 1.95631, "time": 0.81197} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 0.00016, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.64844, "top5_acc": 0.86469, "loss_cls": 1.989, "loss": 1.989, "time": 0.82769} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 0.00016, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65062, "top5_acc": 0.86047, "loss_cls": 1.96939, "loss": 1.96939, "time": 0.81806} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 0.00016, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.64703, "top5_acc": 0.85812, "loss_cls": 1.99909, "loss": 1.99909, "time": 0.82218} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 0.00016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66188, "top5_acc": 0.85938, "loss_cls": 1.96263, "loss": 1.96263, "time": 0.82005} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 0.00015, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64406, "top5_acc": 0.85703, "loss_cls": 2.00182, "loss": 2.00182, "time": 0.8157} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 0.00015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64703, "top5_acc": 0.86203, "loss_cls": 1.97754, "loss": 1.97754, "time": 0.81424} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 0.00015, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65766, "top5_acc": 0.86719, "loss_cls": 1.94747, "loss": 1.94747, "time": 0.81403} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 0.00015, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64438, "top5_acc": 0.85875, "loss_cls": 2.00224, "loss": 2.00224, "time": 0.81348} +{"mode": "train", "epoch": 147, "iter": 1300, "lr": 0.00015, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.65672, "top5_acc": 0.86266, "loss_cls": 1.95684, "loss": 1.95684, "time": 0.82237} +{"mode": "train", "epoch": 147, "iter": 1400, "lr": 0.00014, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.65672, "top5_acc": 0.87188, "loss_cls": 1.93047, "loss": 1.93047, "time": 0.82248} +{"mode": "train", "epoch": 147, "iter": 1500, "lr": 0.00014, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.64906, "top5_acc": 0.85703, "loss_cls": 2.01356, "loss": 2.01356, "time": 0.81752} +{"mode": "train", "epoch": 147, "iter": 1600, "lr": 0.00014, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65547, "top5_acc": 0.86562, "loss_cls": 1.94104, "loss": 1.94104, "time": 0.8189} +{"mode": "train", "epoch": 147, "iter": 1700, "lr": 0.00014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64406, "top5_acc": 0.85562, "loss_cls": 2.02568, "loss": 2.02568, "time": 0.82681} +{"mode": "train", "epoch": 147, "iter": 1800, "lr": 0.00014, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.64391, "top5_acc": 0.85609, "loss_cls": 2.00761, "loss": 2.00761, "time": 0.81327} +{"mode": "train", "epoch": 147, "iter": 1900, "lr": 0.00013, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65953, "top5_acc": 0.86531, "loss_cls": 1.94561, "loss": 1.94561, "time": 0.81234} +{"mode": "train", "epoch": 147, "iter": 2000, "lr": 0.00013, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.66547, "top5_acc": 0.86859, "loss_cls": 1.92639, "loss": 1.92639, "time": 0.81922} +{"mode": "train", "epoch": 147, "iter": 2100, "lr": 0.00013, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.64766, "top5_acc": 0.85984, "loss_cls": 1.99141, "loss": 1.99141, "time": 0.82034} +{"mode": "train", "epoch": 147, "iter": 2200, "lr": 0.00013, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65531, "top5_acc": 0.85484, "loss_cls": 1.99785, "loss": 1.99785, "time": 0.81202} +{"mode": "train", "epoch": 147, "iter": 2300, "lr": 0.00013, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64578, "top5_acc": 0.85312, "loss_cls": 2.02343, "loss": 2.02343, "time": 0.81976} +{"mode": "train", "epoch": 147, "iter": 2400, "lr": 0.00012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66328, "top5_acc": 0.85906, "loss_cls": 1.9729, "loss": 1.9729, "time": 0.81624} +{"mode": "train", "epoch": 147, "iter": 2500, "lr": 0.00012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.6475, "top5_acc": 0.86391, "loss_cls": 1.9849, "loss": 1.9849, "time": 0.81318} +{"mode": "train", "epoch": 147, "iter": 2600, "lr": 0.00012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65062, "top5_acc": 0.86391, "loss_cls": 1.97025, "loss": 1.97025, "time": 0.81723} +{"mode": "train", "epoch": 147, "iter": 2700, "lr": 0.00012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.6525, "top5_acc": 0.86531, "loss_cls": 1.97547, "loss": 1.97547, "time": 0.8174} +{"mode": "train", "epoch": 147, "iter": 2800, "lr": 0.00012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65609, "top5_acc": 0.86391, "loss_cls": 1.96968, "loss": 1.96968, "time": 0.81715} +{"mode": "train", "epoch": 147, "iter": 2900, "lr": 0.00011, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65312, "top5_acc": 0.86141, "loss_cls": 1.96551, "loss": 1.96551, "time": 0.81405} +{"mode": "train", "epoch": 147, "iter": 3000, "lr": 0.00011, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.6575, "top5_acc": 0.86656, "loss_cls": 1.94313, "loss": 1.94313, "time": 0.8094} +{"mode": "train", "epoch": 147, "iter": 3100, "lr": 0.00011, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65109, "top5_acc": 0.85891, "loss_cls": 1.97616, "loss": 1.97616, "time": 0.81388} +{"mode": "train", "epoch": 147, "iter": 3200, "lr": 0.00011, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65, "top5_acc": 0.85719, "loss_cls": 1.9818, "loss": 1.9818, "time": 0.81907} +{"mode": "train", "epoch": 147, "iter": 3300, "lr": 0.00011, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65453, "top5_acc": 0.86203, "loss_cls": 1.98996, "loss": 1.98996, "time": 0.81391} +{"mode": "train", "epoch": 147, "iter": 3400, "lr": 0.0001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65875, "top5_acc": 0.86406, "loss_cls": 1.96621, "loss": 1.96621, "time": 0.81748} +{"mode": "train", "epoch": 147, "iter": 3500, "lr": 0.0001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.64766, "top5_acc": 0.86266, "loss_cls": 2.00409, "loss": 2.00409, "time": 0.81787} +{"mode": "train", "epoch": 147, "iter": 3600, "lr": 0.0001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65469, "top5_acc": 0.86125, "loss_cls": 1.97414, "loss": 1.97414, "time": 0.81513} +{"mode": "train", "epoch": 147, "iter": 3700, "lr": 0.0001, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64547, "top5_acc": 0.85625, "loss_cls": 2.00541, "loss": 2.00541, "time": 0.81887} +{"mode": "val", "epoch": 147, "iter": 309, "lr": 0.0001, "top1_acc": 0.45084, "top5_acc": 0.69351, "mean_class_accuracy": 0.45063} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 0.0001, "memory": 15990, "data_time": 1.31398, "top1_acc": 0.65984, "top5_acc": 0.86578, "loss_cls": 1.95907, "loss": 1.95907, "time": 2.30178} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 0.0001, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66906, "top5_acc": 0.87234, "loss_cls": 1.89774, "loss": 1.89774, "time": 0.82166} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 9e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65875, "top5_acc": 0.86812, "loss_cls": 1.93594, "loss": 1.93594, "time": 0.8173} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 9e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.64781, "top5_acc": 0.86719, "loss_cls": 1.95634, "loss": 1.95634, "time": 0.81933} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 9e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66266, "top5_acc": 0.86578, "loss_cls": 1.95131, "loss": 1.95131, "time": 0.8252} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 9e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.65891, "top5_acc": 0.86531, "loss_cls": 1.94158, "loss": 1.94158, "time": 0.81806} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 9e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65891, "top5_acc": 0.86422, "loss_cls": 1.95298, "loss": 1.95298, "time": 0.82043} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 9e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66219, "top5_acc": 0.86172, "loss_cls": 1.94434, "loss": 1.94434, "time": 0.81764} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 8e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66859, "top5_acc": 0.87109, "loss_cls": 1.91829, "loss": 1.91829, "time": 0.81541} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 8e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66234, "top5_acc": 0.86406, "loss_cls": 1.93049, "loss": 1.93049, "time": 0.81618} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 8e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65656, "top5_acc": 0.86344, "loss_cls": 1.95447, "loss": 1.95447, "time": 0.81361} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 8e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.66406, "top5_acc": 0.86828, "loss_cls": 1.90803, "loss": 1.90803, "time": 0.81917} +{"mode": "train", "epoch": 148, "iter": 1300, "lr": 8e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65828, "top5_acc": 0.86375, "loss_cls": 1.94293, "loss": 1.94293, "time": 0.81647} +{"mode": "train", "epoch": 148, "iter": 1400, "lr": 8e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.66359, "top5_acc": 0.86625, "loss_cls": 1.92388, "loss": 1.92388, "time": 0.82065} +{"mode": "train", "epoch": 148, "iter": 1500, "lr": 7e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67609, "top5_acc": 0.87656, "loss_cls": 1.881, "loss": 1.881, "time": 0.81721} +{"mode": "train", "epoch": 148, "iter": 1600, "lr": 7e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66234, "top5_acc": 0.86469, "loss_cls": 1.9419, "loss": 1.9419, "time": 0.81851} +{"mode": "train", "epoch": 148, "iter": 1700, "lr": 7e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66328, "top5_acc": 0.86766, "loss_cls": 1.90915, "loss": 1.90915, "time": 0.82193} +{"mode": "train", "epoch": 148, "iter": 1800, "lr": 7e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66203, "top5_acc": 0.85969, "loss_cls": 1.96069, "loss": 1.96069, "time": 0.81336} +{"mode": "train", "epoch": 148, "iter": 1900, "lr": 7e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65547, "top5_acc": 0.85891, "loss_cls": 1.98159, "loss": 1.98159, "time": 0.81662} +{"mode": "train", "epoch": 148, "iter": 2000, "lr": 7e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65203, "top5_acc": 0.86141, "loss_cls": 1.9611, "loss": 1.9611, "time": 0.82157} +{"mode": "train", "epoch": 148, "iter": 2100, "lr": 7e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.64906, "top5_acc": 0.86172, "loss_cls": 1.96787, "loss": 1.96787, "time": 0.81653} +{"mode": "train", "epoch": 148, "iter": 2200, "lr": 6e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.65625, "top5_acc": 0.86641, "loss_cls": 1.96866, "loss": 1.96866, "time": 0.81741} +{"mode": "train", "epoch": 148, "iter": 2300, "lr": 6e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65188, "top5_acc": 0.85516, "loss_cls": 2.00539, "loss": 2.00539, "time": 0.81857} +{"mode": "train", "epoch": 148, "iter": 2400, "lr": 6e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.65797, "top5_acc": 0.86781, "loss_cls": 1.93996, "loss": 1.93996, "time": 0.81757} +{"mode": "train", "epoch": 148, "iter": 2500, "lr": 6e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65797, "top5_acc": 0.86938, "loss_cls": 1.92934, "loss": 1.92934, "time": 0.81913} +{"mode": "train", "epoch": 148, "iter": 2600, "lr": 6e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66719, "top5_acc": 0.87344, "loss_cls": 1.90038, "loss": 1.90038, "time": 0.81755} +{"mode": "train", "epoch": 148, "iter": 2700, "lr": 6e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65844, "top5_acc": 0.86703, "loss_cls": 1.94679, "loss": 1.94679, "time": 0.81064} +{"mode": "train", "epoch": 148, "iter": 2800, "lr": 6e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65812, "top5_acc": 0.86547, "loss_cls": 1.94939, "loss": 1.94939, "time": 0.81653} +{"mode": "train", "epoch": 148, "iter": 2900, "lr": 5e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.65438, "top5_acc": 0.86812, "loss_cls": 1.96693, "loss": 1.96693, "time": 0.81475} +{"mode": "train", "epoch": 148, "iter": 3000, "lr": 5e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65062, "top5_acc": 0.86734, "loss_cls": 1.9796, "loss": 1.9796, "time": 0.81323} +{"mode": "train", "epoch": 148, "iter": 3100, "lr": 5e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66031, "top5_acc": 0.86047, "loss_cls": 1.96197, "loss": 1.96197, "time": 0.81699} +{"mode": "train", "epoch": 148, "iter": 3200, "lr": 5e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65281, "top5_acc": 0.86047, "loss_cls": 1.98648, "loss": 1.98648, "time": 0.81472} +{"mode": "train", "epoch": 148, "iter": 3300, "lr": 5e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65172, "top5_acc": 0.86656, "loss_cls": 1.96744, "loss": 1.96744, "time": 0.81261} +{"mode": "train", "epoch": 148, "iter": 3400, "lr": 5e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.66781, "top5_acc": 0.86812, "loss_cls": 1.97473, "loss": 1.97473, "time": 0.81248} +{"mode": "train", "epoch": 148, "iter": 3500, "lr": 5e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66828, "top5_acc": 0.86938, "loss_cls": 1.92833, "loss": 1.92833, "time": 0.81489} +{"mode": "train", "epoch": 148, "iter": 3600, "lr": 5e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66453, "top5_acc": 0.86453, "loss_cls": 1.93991, "loss": 1.93991, "time": 0.81186} +{"mode": "train", "epoch": 148, "iter": 3700, "lr": 4e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65406, "top5_acc": 0.86203, "loss_cls": 1.96293, "loss": 1.96293, "time": 0.81316} +{"mode": "val", "epoch": 148, "iter": 309, "lr": 4e-05, "top1_acc": 0.45135, "top5_acc": 0.69275, "mean_class_accuracy": 0.45117} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 4e-05, "memory": 15990, "data_time": 1.27784, "top1_acc": 0.66484, "top5_acc": 0.86188, "loss_cls": 1.94191, "loss": 1.94191, "time": 2.25958} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 4e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66266, "top5_acc": 0.86969, "loss_cls": 1.90495, "loss": 1.90495, "time": 0.81577} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 4e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67156, "top5_acc": 0.87109, "loss_cls": 1.92379, "loss": 1.92379, "time": 0.80863} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 4e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65656, "top5_acc": 0.86953, "loss_cls": 1.93043, "loss": 1.93043, "time": 0.8111} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 4e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66, "top5_acc": 0.86188, "loss_cls": 1.95422, "loss": 1.95422, "time": 0.81659} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 4e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.66719, "top5_acc": 0.86609, "loss_cls": 1.93278, "loss": 1.93278, "time": 0.82912} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 4e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66922, "top5_acc": 0.86562, "loss_cls": 1.90754, "loss": 1.90754, "time": 0.81425} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 4e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.67422, "top5_acc": 0.87281, "loss_cls": 1.87932, "loss": 1.87932, "time": 0.81946} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66234, "top5_acc": 0.86141, "loss_cls": 1.95569, "loss": 1.95569, "time": 0.81337} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66984, "top5_acc": 0.87031, "loss_cls": 1.90497, "loss": 1.90497, "time": 0.81717} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 3e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66641, "top5_acc": 0.86781, "loss_cls": 1.91509, "loss": 1.91509, "time": 0.82183} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 3e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65531, "top5_acc": 0.85969, "loss_cls": 1.99523, "loss": 1.99523, "time": 0.8252} +{"mode": "train", "epoch": 149, "iter": 1300, "lr": 3e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.67, "top5_acc": 0.86625, "loss_cls": 1.9133, "loss": 1.9133, "time": 0.81785} +{"mode": "train", "epoch": 149, "iter": 1400, "lr": 3e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66312, "top5_acc": 0.86219, "loss_cls": 1.95133, "loss": 1.95133, "time": 0.82217} +{"mode": "train", "epoch": 149, "iter": 1500, "lr": 3e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67344, "top5_acc": 0.87094, "loss_cls": 1.90503, "loss": 1.90503, "time": 0.82783} +{"mode": "train", "epoch": 149, "iter": 1600, "lr": 3e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65625, "top5_acc": 0.8675, "loss_cls": 1.94875, "loss": 1.94875, "time": 0.81886} +{"mode": "train", "epoch": 149, "iter": 1700, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67, "top5_acc": 0.86766, "loss_cls": 1.93036, "loss": 1.93036, "time": 0.81796} +{"mode": "train", "epoch": 149, "iter": 1800, "lr": 3e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67047, "top5_acc": 0.87078, "loss_cls": 1.91392, "loss": 1.91392, "time": 0.81661} +{"mode": "train", "epoch": 149, "iter": 1900, "lr": 2e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65531, "top5_acc": 0.87375, "loss_cls": 1.93455, "loss": 1.93455, "time": 0.81729} +{"mode": "train", "epoch": 149, "iter": 2000, "lr": 2e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.65781, "top5_acc": 0.86656, "loss_cls": 1.95609, "loss": 1.95609, "time": 0.82143} +{"mode": "train", "epoch": 149, "iter": 2100, "lr": 2e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66594, "top5_acc": 0.87531, "loss_cls": 1.8885, "loss": 1.8885, "time": 0.82016} +{"mode": "train", "epoch": 149, "iter": 2200, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66797, "top5_acc": 0.87078, "loss_cls": 1.89485, "loss": 1.89485, "time": 0.82158} +{"mode": "train", "epoch": 149, "iter": 2300, "lr": 2e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65297, "top5_acc": 0.86281, "loss_cls": 1.95678, "loss": 1.95678, "time": 0.81146} +{"mode": "train", "epoch": 149, "iter": 2400, "lr": 2e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66516, "top5_acc": 0.87766, "loss_cls": 1.90294, "loss": 1.90294, "time": 0.81206} +{"mode": "train", "epoch": 149, "iter": 2500, "lr": 2e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67141, "top5_acc": 0.86922, "loss_cls": 1.90672, "loss": 1.90672, "time": 0.8198} +{"mode": "train", "epoch": 149, "iter": 2600, "lr": 2e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.67156, "top5_acc": 0.87188, "loss_cls": 1.88985, "loss": 1.88985, "time": 0.81637} +{"mode": "train", "epoch": 149, "iter": 2700, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66234, "top5_acc": 0.86516, "loss_cls": 1.92446, "loss": 1.92446, "time": 0.81516} +{"mode": "train", "epoch": 149, "iter": 2800, "lr": 2e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66312, "top5_acc": 0.86984, "loss_cls": 1.92477, "loss": 1.92477, "time": 0.82} +{"mode": "train", "epoch": 149, "iter": 2900, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66188, "top5_acc": 0.86594, "loss_cls": 1.91549, "loss": 1.91549, "time": 0.81335} +{"mode": "train", "epoch": 149, "iter": 3000, "lr": 2e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.65797, "top5_acc": 0.86188, "loss_cls": 1.95818, "loss": 1.95818, "time": 0.81552} +{"mode": "train", "epoch": 149, "iter": 3100, "lr": 2e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66109, "top5_acc": 0.87344, "loss_cls": 1.90734, "loss": 1.90734, "time": 0.81714} +{"mode": "train", "epoch": 149, "iter": 3200, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66531, "top5_acc": 0.86844, "loss_cls": 1.91603, "loss": 1.91603, "time": 0.81707} +{"mode": "train", "epoch": 149, "iter": 3300, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65594, "top5_acc": 0.86812, "loss_cls": 1.93509, "loss": 1.93509, "time": 0.81449} +{"mode": "train", "epoch": 149, "iter": 3400, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66078, "top5_acc": 0.86812, "loss_cls": 1.9329, "loss": 1.9329, "time": 0.81717} +{"mode": "train", "epoch": 149, "iter": 3500, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66172, "top5_acc": 0.86141, "loss_cls": 1.96213, "loss": 1.96213, "time": 0.81984} +{"mode": "train", "epoch": 149, "iter": 3600, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65453, "top5_acc": 0.86656, "loss_cls": 1.95342, "loss": 1.95342, "time": 0.815} +{"mode": "train", "epoch": 149, "iter": 3700, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66234, "top5_acc": 0.86656, "loss_cls": 1.94139, "loss": 1.94139, "time": 0.81212} +{"mode": "val", "epoch": 149, "iter": 309, "lr": 1e-05, "top1_acc": 0.45089, "top5_acc": 0.6923, "mean_class_accuracy": 0.45065} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 1e-05, "memory": 15990, "data_time": 1.28241, "top1_acc": 0.66391, "top5_acc": 0.87281, "loss_cls": 1.91654, "loss": 1.91654, "time": 2.25719} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.67047, "top5_acc": 0.87312, "loss_cls": 1.9009, "loss": 1.9009, "time": 0.81824} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 1e-05, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66594, "top5_acc": 0.875, "loss_cls": 1.91839, "loss": 1.91839, "time": 0.81378} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66594, "top5_acc": 0.87281, "loss_cls": 1.90325, "loss": 1.90325, "time": 0.81649} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66375, "top5_acc": 0.86844, "loss_cls": 1.91258, "loss": 1.91258, "time": 0.81474} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66484, "top5_acc": 0.86562, "loss_cls": 1.92863, "loss": 1.92863, "time": 0.82377} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66531, "top5_acc": 0.87062, "loss_cls": 1.91674, "loss": 1.91674, "time": 0.81624} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 1e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.67516, "top5_acc": 0.86578, "loss_cls": 1.90656, "loss": 1.90656, "time": 0.8174} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 1e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66594, "top5_acc": 0.86844, "loss_cls": 1.90474, "loss": 1.90474, "time": 0.81563} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66594, "top5_acc": 0.86578, "loss_cls": 1.92517, "loss": 1.92517, "time": 0.81413} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 1e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.66906, "top5_acc": 0.86891, "loss_cls": 1.92893, "loss": 1.92893, "time": 0.82096} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 1e-05, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66875, "top5_acc": 0.88094, "loss_cls": 1.87417, "loss": 1.87417, "time": 0.81794} +{"mode": "train", "epoch": 150, "iter": 1300, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.6725, "top5_acc": 0.86953, "loss_cls": 1.90207, "loss": 1.90207, "time": 0.82019} +{"mode": "train", "epoch": 150, "iter": 1400, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.67281, "top5_acc": 0.86812, "loss_cls": 1.89022, "loss": 1.89022, "time": 0.82168} +{"mode": "train", "epoch": 150, "iter": 1500, "lr": 0.0, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.66328, "top5_acc": 0.86531, "loss_cls": 1.94719, "loss": 1.94719, "time": 0.81563} +{"mode": "train", "epoch": 150, "iter": 1600, "lr": 0.0, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66703, "top5_acc": 0.86531, "loss_cls": 1.9449, "loss": 1.9449, "time": 0.82177} +{"mode": "train", "epoch": 150, "iter": 1700, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67078, "top5_acc": 0.86797, "loss_cls": 1.92722, "loss": 1.92722, "time": 0.81413} +{"mode": "train", "epoch": 150, "iter": 1800, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66781, "top5_acc": 0.87125, "loss_cls": 1.9001, "loss": 1.9001, "time": 0.81527} +{"mode": "train", "epoch": 150, "iter": 1900, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66766, "top5_acc": 0.87297, "loss_cls": 1.91463, "loss": 1.91463, "time": 0.82065} +{"mode": "train", "epoch": 150, "iter": 2000, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66344, "top5_acc": 0.86016, "loss_cls": 1.95209, "loss": 1.95209, "time": 0.82009} +{"mode": "train", "epoch": 150, "iter": 2100, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66719, "top5_acc": 0.87453, "loss_cls": 1.88576, "loss": 1.88576, "time": 0.82324} +{"mode": "train", "epoch": 150, "iter": 2200, "lr": 0.0, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.66547, "top5_acc": 0.87078, "loss_cls": 1.92949, "loss": 1.92949, "time": 0.81667} +{"mode": "train", "epoch": 150, "iter": 2300, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.65766, "top5_acc": 0.86875, "loss_cls": 1.94042, "loss": 1.94042, "time": 0.8168} +{"mode": "train", "epoch": 150, "iter": 2400, "lr": 0.0, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.66062, "top5_acc": 0.86141, "loss_cls": 1.94899, "loss": 1.94899, "time": 0.82995} +{"mode": "train", "epoch": 150, "iter": 2500, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66703, "top5_acc": 0.87156, "loss_cls": 1.90904, "loss": 1.90904, "time": 0.81946} +{"mode": "train", "epoch": 150, "iter": 2600, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66562, "top5_acc": 0.86938, "loss_cls": 1.93914, "loss": 1.93914, "time": 0.81216} +{"mode": "train", "epoch": 150, "iter": 2700, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66406, "top5_acc": 0.86438, "loss_cls": 1.91876, "loss": 1.91876, "time": 0.81512} +{"mode": "train", "epoch": 150, "iter": 2800, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.66906, "top5_acc": 0.86844, "loss_cls": 1.91365, "loss": 1.91365, "time": 0.81335} +{"mode": "train", "epoch": 150, "iter": 2900, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.67203, "top5_acc": 0.87016, "loss_cls": 1.89208, "loss": 1.89208, "time": 0.82517} +{"mode": "train", "epoch": 150, "iter": 3000, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.66234, "top5_acc": 0.86391, "loss_cls": 1.92872, "loss": 1.92872, "time": 0.81541} +{"mode": "train", "epoch": 150, "iter": 3100, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66953, "top5_acc": 0.87172, "loss_cls": 1.91793, "loss": 1.91793, "time": 0.81304} +{"mode": "train", "epoch": 150, "iter": 3200, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.65547, "top5_acc": 0.86859, "loss_cls": 1.96377, "loss": 1.96377, "time": 0.80851} +{"mode": "train", "epoch": 150, "iter": 3300, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.66906, "top5_acc": 0.87484, "loss_cls": 1.88885, "loss": 1.88885, "time": 0.81463} +{"mode": "train", "epoch": 150, "iter": 3400, "lr": 0.0, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.67234, "top5_acc": 0.87, "loss_cls": 1.90249, "loss": 1.90249, "time": 0.81534} +{"mode": "train", "epoch": 150, "iter": 3500, "lr": 0.0, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.65969, "top5_acc": 0.86859, "loss_cls": 1.94388, "loss": 1.94388, "time": 0.80577} +{"mode": "train", "epoch": 150, "iter": 3600, "lr": 0.0, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.66531, "top5_acc": 0.86562, "loss_cls": 1.92455, "loss": 1.92455, "time": 0.80901} +{"mode": "train", "epoch": 150, "iter": 3700, "lr": 0.0, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.66906, "top5_acc": 0.87312, "loss_cls": 1.89454, "loss": 1.89454, "time": 0.80661} +{"mode": "val", "epoch": 150, "iter": 309, "lr": 0.0, "top1_acc": 0.4512, "top5_acc": 0.6929, "mean_class_accuracy": 0.45099} diff --git a/k400/k_1/best_pred.pkl b/k400/k_1/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..a08daf209e46fd3d05690388c4392a8fba43e897 --- /dev/null +++ b/k400/k_1/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4b791d38b1ff32d689e94db08f7cf25c4e26ad0afa02ee7312a83dd778029ff2 +size 44884478 diff --git a/k400/k_1/best_top1_acc_epoch_145.pth b/k400/k_1/best_top1_acc_epoch_145.pth new file mode 100644 index 0000000000000000000000000000000000000000..efdf64236e9a0ce1a542a2a32e8f95140159f273 --- /dev/null +++ b/k400/k_1/best_top1_acc_epoch_145.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5623f62c6508c06ec52a7be4206dc119e8ff5e5fc4faa3a09d6009b228a4865c +size 32926705 diff --git a/k400/k_1/k_1.py b/k400/k_1/k_1.py new file mode 100644 index 0000000000000000000000000000000000000000..2735dc6f1f112aa12b87947c7348be3ae7a45958 --- /dev/null +++ b/k400/k_1/k_1.py @@ -0,0 +1,133 @@ +modality = 'k' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/k_1' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/k400/k_2/20241226_015115.log b/k400/k_2/20241226_015115.log new file mode 100644 index 0000000000000000000000000000000000000000..be09d199829c3ad3de40f57b9f44619d1ae07f3a --- /dev/null +++ b/k400/k_2/20241226_015115.log @@ -0,0 +1,7322 @@ +2024-12-26 01:51:15,530 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2024-12-26 01:51:15,901 - pyskl - INFO - Config: modality = 'k' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/k_2' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2024-12-26 01:51:15,901 - pyskl - INFO - Set random seed to 776052246, deterministic: False +2024-12-26 01:51:26,595 - pyskl - INFO - 239737 videos remain after valid thresholding +2024-12-26 01:51:41,733 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-12-26 01:51:41,735 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2 +2024-12-26 01:51:41,741 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2024-12-26 01:51:41,776 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2024-12-26 01:51:41,784 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2 by HardDiskBackend. +2024-12-26 01:55:21,356 - pyskl - INFO - Epoch [1][100/3746] lr: 1.000e-01, eta: 14 days, 6:38:35, time: 2.196, data_time: 1.479, memory: 15990, top1_acc: 0.0075, top5_acc: 0.0386, loss_cls: 6.3844, loss: 6.3844 +2024-12-26 01:56:32,010 - pyskl - INFO - Epoch [1][200/3746] lr: 1.000e-01, eta: 9 days, 10:24:36, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.0161, top5_acc: 0.0602, loss_cls: 6.2696, loss: 6.2696 +2024-12-26 01:57:42,907 - pyskl - INFO - Epoch [1][300/3746] lr: 1.000e-01, eta: 7 days, 19:46:43, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.0205, top5_acc: 0.0859, loss_cls: 6.0756, loss: 6.0756 +2024-12-26 01:58:53,326 - pyskl - INFO - Epoch [1][400/3746] lr: 1.000e-01, eta: 7 days, 0:16:02, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0278, top5_acc: 0.0984, loss_cls: 5.9621, loss: 5.9621 +2024-12-26 02:00:03,827 - pyskl - INFO - Epoch [1][500/3746] lr: 1.000e-01, eta: 6 days, 12:34:40, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0319, top5_acc: 0.1122, loss_cls: 5.8713, loss: 5.8713 +2024-12-26 02:01:14,658 - pyskl - INFO - Epoch [1][600/3746] lr: 1.000e-01, eta: 6 days, 4:51:51, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.0328, top5_acc: 0.1217, loss_cls: 5.8084, loss: 5.8084 +2024-12-26 02:02:25,296 - pyskl - INFO - Epoch [1][700/3746] lr: 1.000e-01, eta: 5 days, 23:18:21, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.0366, top5_acc: 0.1306, loss_cls: 5.7708, loss: 5.7708 +2024-12-26 02:03:36,210 - pyskl - INFO - Epoch [1][800/3746] lr: 1.000e-01, eta: 5 days, 19:11:09, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.0364, top5_acc: 0.1327, loss_cls: 5.7405, loss: 5.7405 +2024-12-26 02:04:47,151 - pyskl - INFO - Epoch [1][900/3746] lr: 1.000e-01, eta: 5 days, 15:58:55, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.0405, top5_acc: 0.1380, loss_cls: 5.6919, loss: 5.6919 +2024-12-26 02:05:58,416 - pyskl - INFO - Epoch [1][1000/3746] lr: 1.000e-01, eta: 5 days, 13:27:55, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.0419, top5_acc: 0.1544, loss_cls: 5.6630, loss: 5.6630 +2024-12-26 02:07:09,685 - pyskl - INFO - Epoch [1][1100/3746] lr: 1.000e-01, eta: 5 days, 11:24:11, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.0514, top5_acc: 0.1661, loss_cls: 5.6124, loss: 5.6124 +2024-12-26 02:08:21,058 - pyskl - INFO - Epoch [1][1200/3746] lr: 1.000e-01, eta: 5 days, 9:41:41, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0542, top5_acc: 0.1692, loss_cls: 5.6093, loss: 5.6093 +2024-12-26 02:09:32,489 - pyskl - INFO - Epoch [1][1300/3746] lr: 1.000e-01, eta: 5 days, 8:15:12, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0489, top5_acc: 0.1684, loss_cls: 5.6067, loss: 5.6067 +2024-12-26 02:10:44,521 - pyskl - INFO - Epoch [1][1400/3746] lr: 1.000e-01, eta: 5 days, 7:04:52, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.0508, top5_acc: 0.1773, loss_cls: 5.5457, loss: 5.5457 +2024-12-26 02:11:55,978 - pyskl - INFO - Epoch [1][1500/3746] lr: 1.000e-01, eta: 5 days, 6:00:14, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0550, top5_acc: 0.1800, loss_cls: 5.5539, loss: 5.5539 +2024-12-26 02:13:07,397 - pyskl - INFO - Epoch [1][1600/3746] lr: 1.000e-01, eta: 5 days, 5:03:16, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0620, top5_acc: 0.1894, loss_cls: 5.5140, loss: 5.5140 +2024-12-26 02:14:18,740 - pyskl - INFO - Epoch [1][1700/3746] lr: 1.000e-01, eta: 5 days, 4:12:27, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.0639, top5_acc: 0.2000, loss_cls: 5.4775, loss: 5.4775 +2024-12-26 02:15:30,173 - pyskl - INFO - Epoch [1][1800/3746] lr: 1.000e-01, eta: 5 days, 3:27:37, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0670, top5_acc: 0.2052, loss_cls: 5.4456, loss: 5.4456 +2024-12-26 02:16:41,738 - pyskl - INFO - Epoch [1][1900/3746] lr: 1.000e-01, eta: 5 days, 2:48:02, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.0664, top5_acc: 0.1989, loss_cls: 5.4679, loss: 5.4679 +2024-12-26 02:17:53,567 - pyskl - INFO - Epoch [1][2000/3746] lr: 1.000e-01, eta: 5 days, 2:13:31, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.0683, top5_acc: 0.2150, loss_cls: 5.4471, loss: 5.4471 +2024-12-26 02:19:04,931 - pyskl - INFO - Epoch [1][2100/3746] lr: 1.000e-01, eta: 5 days, 1:40:06, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0688, top5_acc: 0.2103, loss_cls: 5.4208, loss: 5.4208 +2024-12-26 02:20:16,540 - pyskl - INFO - Epoch [1][2200/3746] lr: 1.000e-01, eta: 5 days, 1:10:40, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.0794, top5_acc: 0.2281, loss_cls: 5.3477, loss: 5.3477 +2024-12-26 02:21:28,189 - pyskl - INFO - Epoch [1][2300/3746] lr: 1.000e-01, eta: 5 days, 0:43:51, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.0775, top5_acc: 0.2177, loss_cls: 5.3581, loss: 5.3581 +2024-12-26 02:22:39,665 - pyskl - INFO - Epoch [1][2400/3746] lr: 1.000e-01, eta: 5 days, 0:18:28, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0759, top5_acc: 0.2159, loss_cls: 5.3951, loss: 5.3951 +2024-12-26 02:23:51,059 - pyskl - INFO - Epoch [1][2500/3746] lr: 1.000e-01, eta: 4 days, 23:54:45, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0800, top5_acc: 0.2359, loss_cls: 5.3205, loss: 5.3205 +2024-12-26 02:25:02,458 - pyskl - INFO - Epoch [1][2600/3746] lr: 9.999e-02, eta: 4 days, 23:32:46, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0805, top5_acc: 0.2369, loss_cls: 5.3554, loss: 5.3554 +2024-12-26 02:26:14,025 - pyskl - INFO - Epoch [1][2700/3746] lr: 9.999e-02, eta: 4 days, 23:12:55, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.0800, top5_acc: 0.2398, loss_cls: 5.3037, loss: 5.3037 +2024-12-26 02:27:25,529 - pyskl - INFO - Epoch [1][2800/3746] lr: 9.999e-02, eta: 4 days, 22:54:10, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0862, top5_acc: 0.2459, loss_cls: 5.2303, loss: 5.2303 +2024-12-26 02:28:37,065 - pyskl - INFO - Epoch [1][2900/3746] lr: 9.999e-02, eta: 4 days, 22:36:45, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0805, top5_acc: 0.2456, loss_cls: 5.2553, loss: 5.2553 +2024-12-26 02:29:48,657 - pyskl - INFO - Epoch [1][3000/3746] lr: 9.999e-02, eta: 4 days, 22:20:35, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.0900, top5_acc: 0.2552, loss_cls: 5.2172, loss: 5.2172 +2024-12-26 02:31:00,281 - pyskl - INFO - Epoch [1][3100/3746] lr: 9.999e-02, eta: 4 days, 22:05:29, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.0972, top5_acc: 0.2577, loss_cls: 5.2406, loss: 5.2406 +2024-12-26 02:32:12,090 - pyskl - INFO - Epoch [1][3200/3746] lr: 9.999e-02, eta: 4 days, 21:51:47, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.0902, top5_acc: 0.2555, loss_cls: 5.2457, loss: 5.2457 +2024-12-26 02:33:23,834 - pyskl - INFO - Epoch [1][3300/3746] lr: 9.999e-02, eta: 4 days, 21:38:40, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.0883, top5_acc: 0.2556, loss_cls: 5.2295, loss: 5.2295 +2024-12-26 02:34:35,390 - pyskl - INFO - Epoch [1][3400/3746] lr: 9.999e-02, eta: 4 days, 21:25:44, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.0991, top5_acc: 0.2706, loss_cls: 5.1792, loss: 5.1792 +2024-12-26 02:35:46,849 - pyskl - INFO - Epoch [1][3500/3746] lr: 9.999e-02, eta: 4 days, 21:13:12, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0966, top5_acc: 0.2614, loss_cls: 5.2024, loss: 5.2024 +2024-12-26 02:36:58,083 - pyskl - INFO - Epoch [1][3600/3746] lr: 9.999e-02, eta: 4 days, 21:00:44, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1014, top5_acc: 0.2667, loss_cls: 5.1655, loss: 5.1655 +2024-12-26 02:38:09,503 - pyskl - INFO - Epoch [1][3700/3746] lr: 9.999e-02, eta: 4 days, 20:49:20, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0984, top5_acc: 0.2661, loss_cls: 5.1666, loss: 5.1666 +2024-12-26 02:38:43,841 - pyskl - INFO - Saving checkpoint at 1 epochs +2024-12-26 02:40:39,167 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 02:40:40,057 - pyskl - INFO - +top1_acc 0.0592 +top5_acc 0.1847 +2024-12-26 02:40:40,057 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 02:40:40,092 - pyskl - INFO - +mean_acc 0.0592 +2024-12-26 02:40:40,514 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2024-12-26 02:40:40,515 - pyskl - INFO - Best top1_acc is 0.0592 at 1 epoch. +2024-12-26 02:40:40,525 - pyskl - INFO - Epoch(val) [1][309] top1_acc: 0.0592, top5_acc: 0.1847, mean_class_accuracy: 0.0592 +2024-12-26 02:44:17,418 - pyskl - INFO - Epoch [2][100/3746] lr: 9.999e-02, eta: 5 days, 1:05:59, time: 2.169, data_time: 1.460, memory: 15990, top1_acc: 0.1037, top5_acc: 0.2748, loss_cls: 5.1446, loss: 5.1446 +2024-12-26 02:45:28,028 - pyskl - INFO - Epoch [2][200/3746] lr: 9.999e-02, eta: 5 days, 0:46:59, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1045, top5_acc: 0.2834, loss_cls: 5.1219, loss: 5.1219 +2024-12-26 02:46:38,760 - pyskl - INFO - Epoch [2][300/3746] lr: 9.999e-02, eta: 5 days, 0:29:08, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1009, top5_acc: 0.2766, loss_cls: 5.1541, loss: 5.1541 +2024-12-26 02:47:49,402 - pyskl - INFO - Epoch [2][400/3746] lr: 9.999e-02, eta: 5 days, 0:11:54, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1017, top5_acc: 0.2842, loss_cls: 5.1455, loss: 5.1455 +2024-12-26 02:48:59,950 - pyskl - INFO - Epoch [2][500/3746] lr: 9.999e-02, eta: 4 days, 23:55:12, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1094, top5_acc: 0.2856, loss_cls: 5.1159, loss: 5.1159 +2024-12-26 02:50:10,794 - pyskl - INFO - Epoch [2][600/3746] lr: 9.999e-02, eta: 4 days, 23:39:51, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1123, top5_acc: 0.2906, loss_cls: 5.0734, loss: 5.0734 +2024-12-26 02:51:22,159 - pyskl - INFO - Epoch [2][700/3746] lr: 9.998e-02, eta: 4 days, 23:26:14, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1062, top5_acc: 0.2992, loss_cls: 5.0527, loss: 5.0527 +2024-12-26 02:52:33,756 - pyskl - INFO - Epoch [2][800/3746] lr: 9.998e-02, eta: 4 days, 23:13:38, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1113, top5_acc: 0.2959, loss_cls: 5.0525, loss: 5.0525 +2024-12-26 02:53:45,134 - pyskl - INFO - Epoch [2][900/3746] lr: 9.998e-02, eta: 4 days, 23:01:06, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1191, top5_acc: 0.3070, loss_cls: 5.0124, loss: 5.0124 +2024-12-26 02:54:56,478 - pyskl - INFO - Epoch [2][1000/3746] lr: 9.998e-02, eta: 4 days, 22:48:58, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1192, top5_acc: 0.3077, loss_cls: 4.9974, loss: 4.9974 +2024-12-26 02:56:08,091 - pyskl - INFO - Epoch [2][1100/3746] lr: 9.998e-02, eta: 4 days, 22:37:48, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1202, top5_acc: 0.3109, loss_cls: 5.0002, loss: 5.0002 +2024-12-26 02:57:19,800 - pyskl - INFO - Epoch [2][1200/3746] lr: 9.998e-02, eta: 4 days, 22:27:13, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1219, top5_acc: 0.3153, loss_cls: 5.0079, loss: 5.0079 +2024-12-26 02:58:31,627 - pyskl - INFO - Epoch [2][1300/3746] lr: 9.998e-02, eta: 4 days, 22:17:13, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1208, top5_acc: 0.3172, loss_cls: 4.9719, loss: 4.9719 +2024-12-26 02:59:42,958 - pyskl - INFO - Epoch [2][1400/3746] lr: 9.998e-02, eta: 4 days, 22:06:40, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1277, top5_acc: 0.3200, loss_cls: 4.9567, loss: 4.9567 +2024-12-26 03:00:54,561 - pyskl - INFO - Epoch [2][1500/3746] lr: 9.998e-02, eta: 4 days, 21:56:58, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1255, top5_acc: 0.3195, loss_cls: 4.9394, loss: 4.9394 +2024-12-26 03:02:06,855 - pyskl - INFO - Epoch [2][1600/3746] lr: 9.998e-02, eta: 4 days, 21:48:47, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1259, top5_acc: 0.3278, loss_cls: 4.9079, loss: 4.9079 +2024-12-26 03:03:18,508 - pyskl - INFO - Epoch [2][1700/3746] lr: 9.998e-02, eta: 4 days, 21:39:45, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1292, top5_acc: 0.3187, loss_cls: 4.9491, loss: 4.9491 +2024-12-26 03:04:30,083 - pyskl - INFO - Epoch [2][1800/3746] lr: 9.998e-02, eta: 4 days, 21:30:53, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1269, top5_acc: 0.3284, loss_cls: 4.9355, loss: 4.9355 +2024-12-26 03:05:41,547 - pyskl - INFO - Epoch [2][1900/3746] lr: 9.998e-02, eta: 4 days, 21:22:05, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1328, top5_acc: 0.3320, loss_cls: 4.9067, loss: 4.9067 +2024-12-26 03:06:53,029 - pyskl - INFO - Epoch [2][2000/3746] lr: 9.997e-02, eta: 4 days, 21:13:36, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1355, top5_acc: 0.3441, loss_cls: 4.8725, loss: 4.8725 +2024-12-26 03:08:04,616 - pyskl - INFO - Epoch [2][2100/3746] lr: 9.997e-02, eta: 4 days, 21:05:32, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1397, top5_acc: 0.3395, loss_cls: 4.8525, loss: 4.8525 +2024-12-26 03:09:16,366 - pyskl - INFO - Epoch [2][2200/3746] lr: 9.997e-02, eta: 4 days, 20:57:56, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1314, top5_acc: 0.3373, loss_cls: 4.8830, loss: 4.8830 +2024-12-26 03:10:28,187 - pyskl - INFO - Epoch [2][2300/3746] lr: 9.997e-02, eta: 4 days, 20:50:40, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1417, top5_acc: 0.3506, loss_cls: 4.8555, loss: 4.8555 +2024-12-26 03:11:39,809 - pyskl - INFO - Epoch [2][2400/3746] lr: 9.997e-02, eta: 4 days, 20:43:18, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1350, top5_acc: 0.3281, loss_cls: 4.8768, loss: 4.8768 +2024-12-26 03:12:51,520 - pyskl - INFO - Epoch [2][2500/3746] lr: 9.997e-02, eta: 4 days, 20:36:15, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1392, top5_acc: 0.3497, loss_cls: 4.8335, loss: 4.8335 +2024-12-26 03:14:03,384 - pyskl - INFO - Epoch [2][2600/3746] lr: 9.997e-02, eta: 4 days, 20:29:37, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1398, top5_acc: 0.3353, loss_cls: 4.8368, loss: 4.8368 +2024-12-26 03:15:14,857 - pyskl - INFO - Epoch [2][2700/3746] lr: 9.997e-02, eta: 4 days, 20:22:36, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1406, top5_acc: 0.3514, loss_cls: 4.8247, loss: 4.8247 +2024-12-26 03:16:26,419 - pyskl - INFO - Epoch [2][2800/3746] lr: 9.997e-02, eta: 4 days, 20:15:52, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1388, top5_acc: 0.3447, loss_cls: 4.8203, loss: 4.8203 +2024-12-26 03:17:37,913 - pyskl - INFO - Epoch [2][2900/3746] lr: 9.997e-02, eta: 4 days, 20:09:13, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1414, top5_acc: 0.3519, loss_cls: 4.7985, loss: 4.7985 +2024-12-26 03:18:49,419 - pyskl - INFO - Epoch [2][3000/3746] lr: 9.996e-02, eta: 4 days, 20:02:45, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1475, top5_acc: 0.3561, loss_cls: 4.7858, loss: 4.7858 +2024-12-26 03:20:01,130 - pyskl - INFO - Epoch [2][3100/3746] lr: 9.996e-02, eta: 4 days, 19:56:43, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1425, top5_acc: 0.3541, loss_cls: 4.7961, loss: 4.7961 +2024-12-26 03:21:12,578 - pyskl - INFO - Epoch [2][3200/3746] lr: 9.996e-02, eta: 4 days, 19:50:27, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1445, top5_acc: 0.3552, loss_cls: 4.7769, loss: 4.7769 +2024-12-26 03:22:24,153 - pyskl - INFO - Epoch [2][3300/3746] lr: 9.996e-02, eta: 4 days, 19:44:31, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1508, top5_acc: 0.3622, loss_cls: 4.7463, loss: 4.7463 +2024-12-26 03:23:35,701 - pyskl - INFO - Epoch [2][3400/3746] lr: 9.996e-02, eta: 4 days, 19:38:40, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1520, top5_acc: 0.3681, loss_cls: 4.7267, loss: 4.7267 +2024-12-26 03:24:47,014 - pyskl - INFO - Epoch [2][3500/3746] lr: 9.996e-02, eta: 4 days, 19:32:39, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1489, top5_acc: 0.3681, loss_cls: 4.7527, loss: 4.7527 +2024-12-26 03:25:58,105 - pyskl - INFO - Epoch [2][3600/3746] lr: 9.996e-02, eta: 4 days, 19:26:30, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1541, top5_acc: 0.3658, loss_cls: 4.7623, loss: 4.7623 +2024-12-26 03:27:09,475 - pyskl - INFO - Epoch [2][3700/3746] lr: 9.996e-02, eta: 4 days, 19:20:49, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1541, top5_acc: 0.3720, loss_cls: 4.7340, loss: 4.7340 +2024-12-26 03:27:43,878 - pyskl - INFO - Saving checkpoint at 2 epochs +2024-12-26 03:29:40,050 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 03:29:40,703 - pyskl - INFO - +top1_acc 0.0962 +top5_acc 0.2713 +2024-12-26 03:29:40,703 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 03:29:40,738 - pyskl - INFO - +mean_acc 0.0963 +2024-12-26 03:29:40,742 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_1.pth was removed +2024-12-26 03:29:41,031 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2024-12-26 03:29:41,032 - pyskl - INFO - Best top1_acc is 0.0962 at 2 epoch. +2024-12-26 03:29:41,044 - pyskl - INFO - Epoch(val) [2][309] top1_acc: 0.0962, top5_acc: 0.2713, mean_class_accuracy: 0.0963 +2024-12-26 03:33:17,126 - pyskl - INFO - Epoch [3][100/3746] lr: 9.995e-02, eta: 4 days, 21:28:52, time: 2.161, data_time: 1.449, memory: 15990, top1_acc: 0.1577, top5_acc: 0.3780, loss_cls: 4.7212, loss: 4.7212 +2024-12-26 03:34:27,989 - pyskl - INFO - Epoch [3][200/3746] lr: 9.995e-02, eta: 4 days, 21:21:04, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1648, top5_acc: 0.3894, loss_cls: 4.6291, loss: 4.6291 +2024-12-26 03:35:38,759 - pyskl - INFO - Epoch [3][300/3746] lr: 9.995e-02, eta: 4 days, 21:13:19, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1553, top5_acc: 0.3800, loss_cls: 4.7001, loss: 4.7001 +2024-12-26 03:36:49,712 - pyskl - INFO - Epoch [3][400/3746] lr: 9.995e-02, eta: 4 days, 21:05:58, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1609, top5_acc: 0.3852, loss_cls: 4.6868, loss: 4.6868 +2024-12-26 03:38:00,588 - pyskl - INFO - Epoch [3][500/3746] lr: 9.995e-02, eta: 4 days, 20:58:40, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1606, top5_acc: 0.3762, loss_cls: 4.7043, loss: 4.7043 +2024-12-26 03:39:11,423 - pyskl - INFO - Epoch [3][600/3746] lr: 9.995e-02, eta: 4 days, 20:51:29, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1580, top5_acc: 0.3692, loss_cls: 4.7187, loss: 4.7187 +2024-12-26 03:40:22,386 - pyskl - INFO - Epoch [3][700/3746] lr: 9.995e-02, eta: 4 days, 20:44:35, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1753, top5_acc: 0.3897, loss_cls: 4.6329, loss: 4.6329 +2024-12-26 03:41:33,242 - pyskl - INFO - Epoch [3][800/3746] lr: 9.995e-02, eta: 4 days, 20:37:42, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1603, top5_acc: 0.3770, loss_cls: 4.6867, loss: 4.6867 +2024-12-26 03:42:44,250 - pyskl - INFO - Epoch [3][900/3746] lr: 9.994e-02, eta: 4 days, 20:31:07, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1658, top5_acc: 0.3858, loss_cls: 4.7061, loss: 4.7061 +2024-12-26 03:43:55,730 - pyskl - INFO - Epoch [3][1000/3746] lr: 9.994e-02, eta: 4 days, 20:25:11, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1698, top5_acc: 0.3964, loss_cls: 4.6056, loss: 4.6056 +2024-12-26 03:45:07,364 - pyskl - INFO - Epoch [3][1100/3746] lr: 9.994e-02, eta: 4 days, 20:19:31, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1628, top5_acc: 0.3925, loss_cls: 4.6305, loss: 4.6305 +2024-12-26 03:46:18,471 - pyskl - INFO - Epoch [3][1200/3746] lr: 9.994e-02, eta: 4 days, 20:13:24, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1630, top5_acc: 0.3856, loss_cls: 4.6762, loss: 4.6762 +2024-12-26 03:47:29,714 - pyskl - INFO - Epoch [3][1300/3746] lr: 9.994e-02, eta: 4 days, 20:07:32, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1630, top5_acc: 0.3912, loss_cls: 4.6601, loss: 4.6601 +2024-12-26 03:48:41,295 - pyskl - INFO - Epoch [3][1400/3746] lr: 9.994e-02, eta: 4 days, 20:02:07, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1773, top5_acc: 0.4088, loss_cls: 4.6039, loss: 4.6039 +2024-12-26 03:49:53,099 - pyskl - INFO - Epoch [3][1500/3746] lr: 9.994e-02, eta: 4 days, 19:57:02, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1709, top5_acc: 0.3889, loss_cls: 4.6612, loss: 4.6612 +2024-12-26 03:51:05,073 - pyskl - INFO - Epoch [3][1600/3746] lr: 9.994e-02, eta: 4 days, 19:52:13, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1756, top5_acc: 0.4033, loss_cls: 4.5998, loss: 4.5998 +2024-12-26 03:52:16,772 - pyskl - INFO - Epoch [3][1700/3746] lr: 9.993e-02, eta: 4 days, 19:47:11, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1711, top5_acc: 0.3925, loss_cls: 4.6306, loss: 4.6306 +2024-12-26 03:53:28,474 - pyskl - INFO - Epoch [3][1800/3746] lr: 9.993e-02, eta: 4 days, 19:42:15, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1691, top5_acc: 0.3920, loss_cls: 4.6399, loss: 4.6399 +2024-12-26 03:54:40,013 - pyskl - INFO - Epoch [3][1900/3746] lr: 9.993e-02, eta: 4 days, 19:37:14, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1645, top5_acc: 0.3942, loss_cls: 4.6440, loss: 4.6440 +2024-12-26 03:55:51,327 - pyskl - INFO - Epoch [3][2000/3746] lr: 9.993e-02, eta: 4 days, 19:32:04, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1616, top5_acc: 0.3833, loss_cls: 4.6494, loss: 4.6494 +2024-12-26 03:57:02,941 - pyskl - INFO - Epoch [3][2100/3746] lr: 9.993e-02, eta: 4 days, 19:27:17, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.1767, top5_acc: 0.4081, loss_cls: 4.5642, loss: 4.5642 +2024-12-26 03:58:14,354 - pyskl - INFO - Epoch [3][2200/3746] lr: 9.993e-02, eta: 4 days, 19:22:23, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1773, top5_acc: 0.3972, loss_cls: 4.6063, loss: 4.6063 +2024-12-26 03:59:25,758 - pyskl - INFO - Epoch [3][2300/3746] lr: 9.993e-02, eta: 4 days, 19:17:33, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1739, top5_acc: 0.4050, loss_cls: 4.5711, loss: 4.5711 +2024-12-26 04:00:37,466 - pyskl - INFO - Epoch [3][2400/3746] lr: 9.992e-02, eta: 4 days, 19:13:04, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1741, top5_acc: 0.3870, loss_cls: 4.6234, loss: 4.6234 +2024-12-26 04:01:49,321 - pyskl - INFO - Epoch [3][2500/3746] lr: 9.992e-02, eta: 4 days, 19:08:47, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1789, top5_acc: 0.3989, loss_cls: 4.5881, loss: 4.5881 +2024-12-26 04:03:00,744 - pyskl - INFO - Epoch [3][2600/3746] lr: 9.992e-02, eta: 4 days, 19:04:10, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1784, top5_acc: 0.3986, loss_cls: 4.6069, loss: 4.6069 +2024-12-26 04:04:12,493 - pyskl - INFO - Epoch [3][2700/3746] lr: 9.992e-02, eta: 4 days, 18:59:55, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1777, top5_acc: 0.3994, loss_cls: 4.5901, loss: 4.5901 +2024-12-26 04:05:24,151 - pyskl - INFO - Epoch [3][2800/3746] lr: 9.992e-02, eta: 4 days, 18:55:39, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1736, top5_acc: 0.3959, loss_cls: 4.5905, loss: 4.5905 +2024-12-26 04:06:35,792 - pyskl - INFO - Epoch [3][2900/3746] lr: 9.992e-02, eta: 4 days, 18:51:25, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1842, top5_acc: 0.4178, loss_cls: 4.4997, loss: 4.4997 +2024-12-26 04:07:47,554 - pyskl - INFO - Epoch [3][3000/3746] lr: 9.991e-02, eta: 4 days, 18:47:21, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1781, top5_acc: 0.3983, loss_cls: 4.5668, loss: 4.5668 +2024-12-26 04:08:59,191 - pyskl - INFO - Epoch [3][3100/3746] lr: 9.991e-02, eta: 4 days, 18:43:14, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1764, top5_acc: 0.4031, loss_cls: 4.5650, loss: 4.5650 +2024-12-26 04:10:10,761 - pyskl - INFO - Epoch [3][3200/3746] lr: 9.991e-02, eta: 4 days, 18:39:07, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1764, top5_acc: 0.4020, loss_cls: 4.5718, loss: 4.5718 +2024-12-26 04:11:22,514 - pyskl - INFO - Epoch [3][3300/3746] lr: 9.991e-02, eta: 4 days, 18:35:13, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1811, top5_acc: 0.4006, loss_cls: 4.5417, loss: 4.5417 +2024-12-26 04:12:33,526 - pyskl - INFO - Epoch [3][3400/3746] lr: 9.991e-02, eta: 4 days, 18:30:43, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1792, top5_acc: 0.4120, loss_cls: 4.5400, loss: 4.5400 +2024-12-26 04:13:45,038 - pyskl - INFO - Epoch [3][3500/3746] lr: 9.991e-02, eta: 4 days, 18:26:43, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1900, top5_acc: 0.4170, loss_cls: 4.5007, loss: 4.5007 +2024-12-26 04:14:56,348 - pyskl - INFO - Epoch [3][3600/3746] lr: 9.990e-02, eta: 4 days, 18:22:35, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1864, top5_acc: 0.4103, loss_cls: 4.5195, loss: 4.5195 +2024-12-26 04:16:07,923 - pyskl - INFO - Epoch [3][3700/3746] lr: 9.990e-02, eta: 4 days, 18:18:44, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1770, top5_acc: 0.4066, loss_cls: 4.5403, loss: 4.5403 +2024-12-26 04:16:42,719 - pyskl - INFO - Saving checkpoint at 3 epochs +2024-12-26 04:18:38,449 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 04:18:39,125 - pyskl - INFO - +top1_acc 0.1183 +top5_acc 0.3038 +2024-12-26 04:18:39,126 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 04:18:39,170 - pyskl - INFO - +mean_acc 0.1181 +2024-12-26 04:18:39,174 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_2.pth was removed +2024-12-26 04:18:39,424 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2024-12-26 04:18:39,425 - pyskl - INFO - Best top1_acc is 0.1183 at 3 epoch. +2024-12-26 04:18:39,439 - pyskl - INFO - Epoch(val) [3][309] top1_acc: 0.1183, top5_acc: 0.3038, mean_class_accuracy: 0.1181 +2024-12-26 04:22:14,390 - pyskl - INFO - Epoch [4][100/3746] lr: 9.990e-02, eta: 4 days, 19:42:35, time: 2.149, data_time: 1.436, memory: 15990, top1_acc: 0.1964, top5_acc: 0.4230, loss_cls: 4.4831, loss: 4.4831 +2024-12-26 04:23:25,597 - pyskl - INFO - Epoch [4][200/3746] lr: 9.990e-02, eta: 4 days, 19:37:45, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1837, top5_acc: 0.4214, loss_cls: 4.5179, loss: 4.5179 +2024-12-26 04:24:36,544 - pyskl - INFO - Epoch [4][300/3746] lr: 9.990e-02, eta: 4 days, 19:32:46, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1797, top5_acc: 0.4147, loss_cls: 4.5107, loss: 4.5107 +2024-12-26 04:25:47,526 - pyskl - INFO - Epoch [4][400/3746] lr: 9.989e-02, eta: 4 days, 19:27:53, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1947, top5_acc: 0.4291, loss_cls: 4.4573, loss: 4.4573 +2024-12-26 04:26:58,498 - pyskl - INFO - Epoch [4][500/3746] lr: 9.989e-02, eta: 4 days, 19:23:03, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1872, top5_acc: 0.4261, loss_cls: 4.4937, loss: 4.4937 +2024-12-26 04:28:09,525 - pyskl - INFO - Epoch [4][600/3746] lr: 9.989e-02, eta: 4 days, 19:18:20, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1841, top5_acc: 0.4147, loss_cls: 4.5354, loss: 4.5354 +2024-12-26 04:29:20,830 - pyskl - INFO - Epoch [4][700/3746] lr: 9.989e-02, eta: 4 days, 19:13:53, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1916, top5_acc: 0.4267, loss_cls: 4.4830, loss: 4.4830 +2024-12-26 04:30:31,771 - pyskl - INFO - Epoch [4][800/3746] lr: 9.989e-02, eta: 4 days, 19:09:12, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1836, top5_acc: 0.4166, loss_cls: 4.5187, loss: 4.5187 +2024-12-26 04:31:42,723 - pyskl - INFO - Epoch [4][900/3746] lr: 9.988e-02, eta: 4 days, 19:04:36, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1850, top5_acc: 0.4223, loss_cls: 4.4834, loss: 4.4834 +2024-12-26 04:32:54,027 - pyskl - INFO - Epoch [4][1000/3746] lr: 9.988e-02, eta: 4 days, 19:00:18, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1897, top5_acc: 0.4197, loss_cls: 4.4862, loss: 4.4862 +2024-12-26 04:34:04,862 - pyskl - INFO - Epoch [4][1100/3746] lr: 9.988e-02, eta: 4 days, 18:55:43, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1897, top5_acc: 0.4147, loss_cls: 4.5309, loss: 4.5309 +2024-12-26 04:35:15,856 - pyskl - INFO - Epoch [4][1200/3746] lr: 9.988e-02, eta: 4 days, 18:51:18, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1894, top5_acc: 0.4231, loss_cls: 4.4922, loss: 4.4922 +2024-12-26 04:36:26,866 - pyskl - INFO - Epoch [4][1300/3746] lr: 9.988e-02, eta: 4 days, 18:46:57, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1834, top5_acc: 0.4130, loss_cls: 4.5197, loss: 4.5197 +2024-12-26 04:37:37,936 - pyskl - INFO - Epoch [4][1400/3746] lr: 9.988e-02, eta: 4 days, 18:42:41, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1869, top5_acc: 0.4191, loss_cls: 4.4898, loss: 4.4898 +2024-12-26 04:38:49,280 - pyskl - INFO - Epoch [4][1500/3746] lr: 9.987e-02, eta: 4 days, 18:38:41, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4333, loss_cls: 4.4511, loss: 4.4511 +2024-12-26 04:40:00,477 - pyskl - INFO - Epoch [4][1600/3746] lr: 9.987e-02, eta: 4 days, 18:34:36, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1836, top5_acc: 0.4083, loss_cls: 4.5666, loss: 4.5666 +2024-12-26 04:41:11,705 - pyskl - INFO - Epoch [4][1700/3746] lr: 9.987e-02, eta: 4 days, 18:30:36, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1947, top5_acc: 0.4263, loss_cls: 4.5007, loss: 4.5007 +2024-12-26 04:42:23,349 - pyskl - INFO - Epoch [4][1800/3746] lr: 9.987e-02, eta: 4 days, 18:26:55, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1917, top5_acc: 0.4313, loss_cls: 4.4672, loss: 4.4672 +2024-12-26 04:43:34,809 - pyskl - INFO - Epoch [4][1900/3746] lr: 9.987e-02, eta: 4 days, 18:23:09, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4339, loss_cls: 4.4466, loss: 4.4466 +2024-12-26 04:44:46,558 - pyskl - INFO - Epoch [4][2000/3746] lr: 9.986e-02, eta: 4 days, 18:19:38, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1975, top5_acc: 0.4269, loss_cls: 4.4711, loss: 4.4711 +2024-12-26 04:45:57,904 - pyskl - INFO - Epoch [4][2100/3746] lr: 9.986e-02, eta: 4 days, 18:15:52, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4325, loss_cls: 4.4842, loss: 4.4842 +2024-12-26 04:47:09,637 - pyskl - INFO - Epoch [4][2200/3746] lr: 9.986e-02, eta: 4 days, 18:12:24, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1873, top5_acc: 0.4244, loss_cls: 4.4935, loss: 4.4935 +2024-12-26 04:48:21,073 - pyskl - INFO - Epoch [4][2300/3746] lr: 9.986e-02, eta: 4 days, 18:08:46, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1995, top5_acc: 0.4370, loss_cls: 4.4282, loss: 4.4282 +2024-12-26 04:49:32,849 - pyskl - INFO - Epoch [4][2400/3746] lr: 9.985e-02, eta: 4 days, 18:05:24, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4384, loss_cls: 4.4478, loss: 4.4478 +2024-12-26 04:50:44,507 - pyskl - INFO - Epoch [4][2500/3746] lr: 9.985e-02, eta: 4 days, 18:01:59, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4375, loss_cls: 4.4431, loss: 4.4431 +2024-12-26 04:51:56,190 - pyskl - INFO - Epoch [4][2600/3746] lr: 9.985e-02, eta: 4 days, 17:58:37, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1894, top5_acc: 0.4334, loss_cls: 4.4473, loss: 4.4473 +2024-12-26 04:53:07,979 - pyskl - INFO - Epoch [4][2700/3746] lr: 9.985e-02, eta: 4 days, 17:55:21, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4309, loss_cls: 4.4375, loss: 4.4375 +2024-12-26 04:54:19,566 - pyskl - INFO - Epoch [4][2800/3746] lr: 9.985e-02, eta: 4 days, 17:51:59, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2069, top5_acc: 0.4356, loss_cls: 4.4044, loss: 4.4044 +2024-12-26 04:55:31,778 - pyskl - INFO - Epoch [4][2900/3746] lr: 9.984e-02, eta: 4 days, 17:49:03, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1931, top5_acc: 0.4202, loss_cls: 4.4768, loss: 4.4768 +2024-12-26 04:56:43,606 - pyskl - INFO - Epoch [4][3000/3746] lr: 9.984e-02, eta: 4 days, 17:45:54, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1998, top5_acc: 0.4252, loss_cls: 4.4678, loss: 4.4678 +2024-12-26 04:57:55,309 - pyskl - INFO - Epoch [4][3100/3746] lr: 9.984e-02, eta: 4 days, 17:42:42, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1964, top5_acc: 0.4342, loss_cls: 4.4439, loss: 4.4439 +2024-12-26 04:59:07,111 - pyskl - INFO - Epoch [4][3200/3746] lr: 9.984e-02, eta: 4 days, 17:39:35, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4387, loss_cls: 4.4099, loss: 4.4099 +2024-12-26 05:00:18,737 - pyskl - INFO - Epoch [4][3300/3746] lr: 9.983e-02, eta: 4 days, 17:36:22, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4442, loss_cls: 4.4271, loss: 4.4271 +2024-12-26 05:01:30,108 - pyskl - INFO - Epoch [4][3400/3746] lr: 9.983e-02, eta: 4 days, 17:33:03, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4319, loss_cls: 4.4044, loss: 4.4044 +2024-12-26 05:02:41,516 - pyskl - INFO - Epoch [4][3500/3746] lr: 9.983e-02, eta: 4 days, 17:29:46, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4391, loss_cls: 4.4154, loss: 4.4154 +2024-12-26 05:03:52,804 - pyskl - INFO - Epoch [4][3600/3746] lr: 9.983e-02, eta: 4 days, 17:26:26, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4416, loss_cls: 4.4112, loss: 4.4112 +2024-12-26 05:05:04,536 - pyskl - INFO - Epoch [4][3700/3746] lr: 9.983e-02, eta: 4 days, 17:23:25, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1978, top5_acc: 0.4416, loss_cls: 4.4269, loss: 4.4269 +2024-12-26 05:05:39,896 - pyskl - INFO - Saving checkpoint at 4 epochs +2024-12-26 05:07:35,168 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 05:07:35,913 - pyskl - INFO - +top1_acc 0.1553 +top5_acc 0.3698 +2024-12-26 05:07:35,914 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 05:07:35,950 - pyskl - INFO - +mean_acc 0.1551 +2024-12-26 05:07:35,954 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_3.pth was removed +2024-12-26 05:07:36,197 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2024-12-26 05:07:36,198 - pyskl - INFO - Best top1_acc is 0.1553 at 4 epoch. +2024-12-26 05:07:36,208 - pyskl - INFO - Epoch(val) [4][309] top1_acc: 0.1553, top5_acc: 0.3698, mean_class_accuracy: 0.1551 +2024-12-26 05:11:08,729 - pyskl - INFO - Epoch [5][100/3746] lr: 9.982e-02, eta: 4 days, 18:24:10, time: 2.125, data_time: 1.410, memory: 15990, top1_acc: 0.2002, top5_acc: 0.4383, loss_cls: 4.4178, loss: 4.4178 +2024-12-26 05:12:20,106 - pyskl - INFO - Epoch [5][200/3746] lr: 9.982e-02, eta: 4 days, 18:20:32, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1934, top5_acc: 0.4387, loss_cls: 4.4262, loss: 4.4262 +2024-12-26 05:13:31,413 - pyskl - INFO - Epoch [5][300/3746] lr: 9.982e-02, eta: 4 days, 18:16:54, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4402, loss_cls: 4.3887, loss: 4.3887 +2024-12-26 05:14:42,298 - pyskl - INFO - Epoch [5][400/3746] lr: 9.982e-02, eta: 4 days, 18:13:04, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4450, loss_cls: 4.3729, loss: 4.3729 +2024-12-26 05:15:53,508 - pyskl - INFO - Epoch [5][500/3746] lr: 9.981e-02, eta: 4 days, 18:09:26, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4408, loss_cls: 4.3898, loss: 4.3898 +2024-12-26 05:17:04,662 - pyskl - INFO - Epoch [5][600/3746] lr: 9.981e-02, eta: 4 days, 18:05:49, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4347, loss_cls: 4.4035, loss: 4.4035 +2024-12-26 05:18:15,285 - pyskl - INFO - Epoch [5][700/3746] lr: 9.981e-02, eta: 4 days, 18:01:55, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2036, top5_acc: 0.4375, loss_cls: 4.4196, loss: 4.4196 +2024-12-26 05:19:26,198 - pyskl - INFO - Epoch [5][800/3746] lr: 9.981e-02, eta: 4 days, 17:58:13, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4345, loss_cls: 4.3615, loss: 4.3615 +2024-12-26 05:20:37,163 - pyskl - INFO - Epoch [5][900/3746] lr: 9.980e-02, eta: 4 days, 17:54:34, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1998, top5_acc: 0.4370, loss_cls: 4.4192, loss: 4.4192 +2024-12-26 05:21:48,315 - pyskl - INFO - Epoch [5][1000/3746] lr: 9.980e-02, eta: 4 days, 17:51:04, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4427, loss_cls: 4.3852, loss: 4.3852 +2024-12-26 05:22:59,662 - pyskl - INFO - Epoch [5][1100/3746] lr: 9.980e-02, eta: 4 days, 17:47:42, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4442, loss_cls: 4.3992, loss: 4.3992 +2024-12-26 05:24:10,770 - pyskl - INFO - Epoch [5][1200/3746] lr: 9.980e-02, eta: 4 days, 17:44:14, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4377, loss_cls: 4.3829, loss: 4.3829 +2024-12-26 05:25:22,523 - pyskl - INFO - Epoch [5][1300/3746] lr: 9.979e-02, eta: 4 days, 17:41:09, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1964, top5_acc: 0.4377, loss_cls: 4.4557, loss: 4.4557 +2024-12-26 05:26:33,815 - pyskl - INFO - Epoch [5][1400/3746] lr: 9.979e-02, eta: 4 days, 17:37:51, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4378, loss_cls: 4.3598, loss: 4.3598 +2024-12-26 05:27:44,992 - pyskl - INFO - Epoch [5][1500/3746] lr: 9.979e-02, eta: 4 days, 17:34:29, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4328, loss_cls: 4.4140, loss: 4.4140 +2024-12-26 05:28:56,525 - pyskl - INFO - Epoch [5][1600/3746] lr: 9.979e-02, eta: 4 days, 17:31:22, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4403, loss_cls: 4.4142, loss: 4.4142 +2024-12-26 05:30:08,263 - pyskl - INFO - Epoch [5][1700/3746] lr: 9.978e-02, eta: 4 days, 17:28:22, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2002, top5_acc: 0.4480, loss_cls: 4.3805, loss: 4.3805 +2024-12-26 05:31:19,730 - pyskl - INFO - Epoch [5][1800/3746] lr: 9.978e-02, eta: 4 days, 17:25:15, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4425, loss_cls: 4.3965, loss: 4.3965 +2024-12-26 05:32:30,944 - pyskl - INFO - Epoch [5][1900/3746] lr: 9.978e-02, eta: 4 days, 17:22:00, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4448, loss_cls: 4.3673, loss: 4.3673 +2024-12-26 05:33:42,431 - pyskl - INFO - Epoch [5][2000/3746] lr: 9.977e-02, eta: 4 days, 17:18:57, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4416, loss_cls: 4.3936, loss: 4.3936 +2024-12-26 05:34:54,367 - pyskl - INFO - Epoch [5][2100/3746] lr: 9.977e-02, eta: 4 days, 17:16:08, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2047, top5_acc: 0.4422, loss_cls: 4.3879, loss: 4.3879 +2024-12-26 05:36:05,764 - pyskl - INFO - Epoch [5][2200/3746] lr: 9.977e-02, eta: 4 days, 17:13:04, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4556, loss_cls: 4.3435, loss: 4.3435 +2024-12-26 05:37:17,445 - pyskl - INFO - Epoch [5][2300/3746] lr: 9.977e-02, eta: 4 days, 17:10:10, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4437, loss_cls: 4.3627, loss: 4.3627 +2024-12-26 05:38:28,761 - pyskl - INFO - Epoch [5][2400/3746] lr: 9.976e-02, eta: 4 days, 17:07:06, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4461, loss_cls: 4.3741, loss: 4.3741 +2024-12-26 05:39:40,268 - pyskl - INFO - Epoch [5][2500/3746] lr: 9.976e-02, eta: 4 days, 17:04:09, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4467, loss_cls: 4.3792, loss: 4.3792 +2024-12-26 05:40:52,198 - pyskl - INFO - Epoch [5][2600/3746] lr: 9.976e-02, eta: 4 days, 17:01:26, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4434, loss_cls: 4.3716, loss: 4.3716 +2024-12-26 05:42:04,478 - pyskl - INFO - Epoch [5][2700/3746] lr: 9.976e-02, eta: 4 days, 16:58:56, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4477, loss_cls: 4.3811, loss: 4.3811 +2024-12-26 05:43:16,387 - pyskl - INFO - Epoch [5][2800/3746] lr: 9.975e-02, eta: 4 days, 16:56:14, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4602, loss_cls: 4.3319, loss: 4.3319 +2024-12-26 05:44:28,321 - pyskl - INFO - Epoch [5][2900/3746] lr: 9.975e-02, eta: 4 days, 16:53:35, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4505, loss_cls: 4.3488, loss: 4.3488 +2024-12-26 05:45:40,285 - pyskl - INFO - Epoch [5][3000/3746] lr: 9.975e-02, eta: 4 days, 16:50:57, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4403, loss_cls: 4.4074, loss: 4.4074 +2024-12-26 05:46:52,073 - pyskl - INFO - Epoch [5][3100/3746] lr: 9.974e-02, eta: 4 days, 16:48:15, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4450, loss_cls: 4.3729, loss: 4.3729 +2024-12-26 05:48:03,795 - pyskl - INFO - Epoch [5][3200/3746] lr: 9.974e-02, eta: 4 days, 16:45:32, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4469, loss_cls: 4.3540, loss: 4.3540 +2024-12-26 05:49:15,857 - pyskl - INFO - Epoch [5][3300/3746] lr: 9.974e-02, eta: 4 days, 16:43:00, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4466, loss_cls: 4.3874, loss: 4.3874 +2024-12-26 05:50:27,571 - pyskl - INFO - Epoch [5][3400/3746] lr: 9.974e-02, eta: 4 days, 16:40:19, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2155, top5_acc: 0.4455, loss_cls: 4.3628, loss: 4.3628 +2024-12-26 05:51:38,751 - pyskl - INFO - Epoch [5][3500/3746] lr: 9.973e-02, eta: 4 days, 16:37:23, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4519, loss_cls: 4.3808, loss: 4.3808 +2024-12-26 05:52:49,819 - pyskl - INFO - Epoch [5][3600/3746] lr: 9.973e-02, eta: 4 days, 16:34:25, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4391, loss_cls: 4.4012, loss: 4.4012 +2024-12-26 05:54:01,032 - pyskl - INFO - Epoch [5][3700/3746] lr: 9.973e-02, eta: 4 days, 16:31:32, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4555, loss_cls: 4.3619, loss: 4.3619 +2024-12-26 05:54:36,439 - pyskl - INFO - Saving checkpoint at 5 epochs +2024-12-26 05:56:35,022 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 05:56:35,763 - pyskl - INFO - +top1_acc 0.1417 +top5_acc 0.3562 +2024-12-26 05:56:35,764 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 05:56:35,806 - pyskl - INFO - +mean_acc 0.1416 +2024-12-26 05:56:35,816 - pyskl - INFO - Epoch(val) [5][309] top1_acc: 0.1417, top5_acc: 0.3562, mean_class_accuracy: 0.1416 +2024-12-26 06:00:14,872 - pyskl - INFO - Epoch [6][100/3746] lr: 9.972e-02, eta: 4 days, 17:22:40, time: 2.190, data_time: 1.477, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4516, loss_cls: 4.3281, loss: 4.3281 +2024-12-26 06:01:26,170 - pyskl - INFO - Epoch [6][200/3746] lr: 9.972e-02, eta: 4 days, 17:19:34, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4614, loss_cls: 4.2736, loss: 4.2736 +2024-12-26 06:02:37,268 - pyskl - INFO - Epoch [6][300/3746] lr: 9.972e-02, eta: 4 days, 17:16:24, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4659, loss_cls: 4.3124, loss: 4.3124 +2024-12-26 06:03:49,045 - pyskl - INFO - Epoch [6][400/3746] lr: 9.971e-02, eta: 4 days, 17:13:34, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4528, loss_cls: 4.3231, loss: 4.3231 +2024-12-26 06:05:00,099 - pyskl - INFO - Epoch [6][500/3746] lr: 9.971e-02, eta: 4 days, 17:10:25, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4506, loss_cls: 4.3722, loss: 4.3722 +2024-12-26 06:06:11,402 - pyskl - INFO - Epoch [6][600/3746] lr: 9.971e-02, eta: 4 days, 17:07:24, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2155, top5_acc: 0.4487, loss_cls: 4.3624, loss: 4.3624 +2024-12-26 06:07:22,627 - pyskl - INFO - Epoch [6][700/3746] lr: 9.971e-02, eta: 4 days, 17:04:22, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4516, loss_cls: 4.3482, loss: 4.3482 +2024-12-26 06:08:33,926 - pyskl - INFO - Epoch [6][800/3746] lr: 9.970e-02, eta: 4 days, 17:01:23, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4439, loss_cls: 4.3746, loss: 4.3746 +2024-12-26 06:09:45,112 - pyskl - INFO - Epoch [6][900/3746] lr: 9.970e-02, eta: 4 days, 16:58:22, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4486, loss_cls: 4.3583, loss: 4.3583 +2024-12-26 06:10:56,758 - pyskl - INFO - Epoch [6][1000/3746] lr: 9.970e-02, eta: 4 days, 16:55:34, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4616, loss_cls: 4.3091, loss: 4.3091 +2024-12-26 06:12:07,671 - pyskl - INFO - Epoch [6][1100/3746] lr: 9.969e-02, eta: 4 days, 16:52:28, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4500, loss_cls: 4.3660, loss: 4.3660 +2024-12-26 06:13:19,170 - pyskl - INFO - Epoch [6][1200/3746] lr: 9.969e-02, eta: 4 days, 16:49:39, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4420, loss_cls: 4.3883, loss: 4.3883 +2024-12-26 06:14:30,481 - pyskl - INFO - Epoch [6][1300/3746] lr: 9.969e-02, eta: 4 days, 16:46:46, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4519, loss_cls: 4.3205, loss: 4.3205 +2024-12-26 06:15:42,261 - pyskl - INFO - Epoch [6][1400/3746] lr: 9.968e-02, eta: 4 days, 16:44:06, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2114, top5_acc: 0.4487, loss_cls: 4.3556, loss: 4.3556 +2024-12-26 06:16:54,268 - pyskl - INFO - Epoch [6][1500/3746] lr: 9.968e-02, eta: 4 days, 16:41:33, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2155, top5_acc: 0.4547, loss_cls: 4.3754, loss: 4.3754 +2024-12-26 06:18:05,553 - pyskl - INFO - Epoch [6][1600/3746] lr: 9.968e-02, eta: 4 days, 16:38:42, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4495, loss_cls: 4.3893, loss: 4.3893 +2024-12-26 06:19:16,928 - pyskl - INFO - Epoch [6][1700/3746] lr: 9.967e-02, eta: 4 days, 16:35:54, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4636, loss_cls: 4.2998, loss: 4.2998 +2024-12-26 06:20:29,088 - pyskl - INFO - Epoch [6][1800/3746] lr: 9.967e-02, eta: 4 days, 16:33:28, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4480, loss_cls: 4.3619, loss: 4.3619 +2024-12-26 06:21:40,985 - pyskl - INFO - Epoch [6][1900/3746] lr: 9.967e-02, eta: 4 days, 16:30:56, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4589, loss_cls: 4.3199, loss: 4.3199 +2024-12-26 06:22:52,927 - pyskl - INFO - Epoch [6][2000/3746] lr: 9.966e-02, eta: 4 days, 16:28:26, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4439, loss_cls: 4.3654, loss: 4.3654 +2024-12-26 06:24:04,663 - pyskl - INFO - Epoch [6][2100/3746] lr: 9.966e-02, eta: 4 days, 16:25:51, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4520, loss_cls: 4.3359, loss: 4.3359 +2024-12-26 06:25:16,281 - pyskl - INFO - Epoch [6][2200/3746] lr: 9.966e-02, eta: 4 days, 16:23:13, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4575, loss_cls: 4.3395, loss: 4.3395 +2024-12-26 06:26:28,379 - pyskl - INFO - Epoch [6][2300/3746] lr: 9.965e-02, eta: 4 days, 16:20:49, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4653, loss_cls: 4.3269, loss: 4.3269 +2024-12-26 06:27:40,121 - pyskl - INFO - Epoch [6][2400/3746] lr: 9.965e-02, eta: 4 days, 16:18:17, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4405, loss_cls: 4.3549, loss: 4.3549 +2024-12-26 06:28:52,375 - pyskl - INFO - Epoch [6][2500/3746] lr: 9.965e-02, eta: 4 days, 16:15:58, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4491, loss_cls: 4.3541, loss: 4.3541 +2024-12-26 06:30:03,953 - pyskl - INFO - Epoch [6][2600/3746] lr: 9.964e-02, eta: 4 days, 16:13:23, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4623, loss_cls: 4.3196, loss: 4.3196 +2024-12-26 06:31:16,049 - pyskl - INFO - Epoch [6][2700/3746] lr: 9.964e-02, eta: 4 days, 16:11:02, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4481, loss_cls: 4.3470, loss: 4.3470 +2024-12-26 06:32:28,332 - pyskl - INFO - Epoch [6][2800/3746] lr: 9.964e-02, eta: 4 days, 16:08:46, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4583, loss_cls: 4.3294, loss: 4.3294 +2024-12-26 06:33:39,986 - pyskl - INFO - Epoch [6][2900/3746] lr: 9.963e-02, eta: 4 days, 16:06:14, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4548, loss_cls: 4.3546, loss: 4.3546 +2024-12-26 06:34:52,196 - pyskl - INFO - Epoch [6][3000/3746] lr: 9.963e-02, eta: 4 days, 16:03:58, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4614, loss_cls: 4.3234, loss: 4.3234 +2024-12-26 06:36:04,076 - pyskl - INFO - Epoch [6][3100/3746] lr: 9.963e-02, eta: 4 days, 16:01:34, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4566, loss_cls: 4.3239, loss: 4.3239 +2024-12-26 06:37:15,877 - pyskl - INFO - Epoch [6][3200/3746] lr: 9.962e-02, eta: 4 days, 15:59:08, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4567, loss_cls: 4.3612, loss: 4.3612 +2024-12-26 06:38:27,593 - pyskl - INFO - Epoch [6][3300/3746] lr: 9.962e-02, eta: 4 days, 15:56:41, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4655, loss_cls: 4.2708, loss: 4.2708 +2024-12-26 06:39:39,231 - pyskl - INFO - Epoch [6][3400/3746] lr: 9.962e-02, eta: 4 days, 15:54:13, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4519, loss_cls: 4.3067, loss: 4.3067 +2024-12-26 06:40:50,677 - pyskl - INFO - Epoch [6][3500/3746] lr: 9.961e-02, eta: 4 days, 15:51:41, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4530, loss_cls: 4.3395, loss: 4.3395 +2024-12-26 06:42:01,757 - pyskl - INFO - Epoch [6][3600/3746] lr: 9.961e-02, eta: 4 days, 15:49:01, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4639, loss_cls: 4.2926, loss: 4.2926 +2024-12-26 06:43:13,431 - pyskl - INFO - Epoch [6][3700/3746] lr: 9.961e-02, eta: 4 days, 15:46:36, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4525, loss_cls: 4.3433, loss: 4.3433 +2024-12-26 06:43:48,694 - pyskl - INFO - Saving checkpoint at 6 epochs +2024-12-26 06:45:45,382 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 06:45:46,487 - pyskl - INFO - +top1_acc 0.1580 +top5_acc 0.3662 +2024-12-26 06:45:46,488 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 06:45:46,526 - pyskl - INFO - +mean_acc 0.1577 +2024-12-26 06:45:46,530 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_4.pth was removed +2024-12-26 06:45:46,792 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_6.pth. +2024-12-26 06:45:46,793 - pyskl - INFO - Best top1_acc is 0.1580 at 6 epoch. +2024-12-26 06:45:46,813 - pyskl - INFO - Epoch(val) [6][309] top1_acc: 0.1580, top5_acc: 0.3662, mean_class_accuracy: 0.1577 +2024-12-26 06:49:26,032 - pyskl - INFO - Epoch [7][100/3746] lr: 9.960e-02, eta: 4 days, 16:28:42, time: 2.192, data_time: 1.478, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4698, loss_cls: 4.2555, loss: 4.2555 +2024-12-26 06:50:37,426 - pyskl - INFO - Epoch [7][200/3746] lr: 9.960e-02, eta: 4 days, 16:25:59, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4583, loss_cls: 4.3106, loss: 4.3106 +2024-12-26 06:51:48,549 - pyskl - INFO - Epoch [7][300/3746] lr: 9.960e-02, eta: 4 days, 16:23:11, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4631, loss_cls: 4.2920, loss: 4.2920 +2024-12-26 06:52:59,679 - pyskl - INFO - Epoch [7][400/3746] lr: 9.959e-02, eta: 4 days, 16:20:24, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4637, loss_cls: 4.3263, loss: 4.3263 +2024-12-26 06:54:10,775 - pyskl - INFO - Epoch [7][500/3746] lr: 9.959e-02, eta: 4 days, 16:17:36, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4580, loss_cls: 4.3294, loss: 4.3294 +2024-12-26 06:55:21,799 - pyskl - INFO - Epoch [7][600/3746] lr: 9.958e-02, eta: 4 days, 16:14:48, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4639, loss_cls: 4.2931, loss: 4.2931 +2024-12-26 06:56:33,131 - pyskl - INFO - Epoch [7][700/3746] lr: 9.958e-02, eta: 4 days, 16:12:08, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4502, loss_cls: 4.3680, loss: 4.3680 +2024-12-26 06:57:44,209 - pyskl - INFO - Epoch [7][800/3746] lr: 9.958e-02, eta: 4 days, 16:09:23, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4547, loss_cls: 4.3029, loss: 4.3029 +2024-12-26 06:58:55,286 - pyskl - INFO - Epoch [7][900/3746] lr: 9.957e-02, eta: 4 days, 16:06:38, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4567, loss_cls: 4.3241, loss: 4.3241 +2024-12-26 07:00:06,798 - pyskl - INFO - Epoch [7][1000/3746] lr: 9.957e-02, eta: 4 days, 16:04:04, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4689, loss_cls: 4.2587, loss: 4.2587 +2024-12-26 07:01:18,048 - pyskl - INFO - Epoch [7][1100/3746] lr: 9.957e-02, eta: 4 days, 16:01:25, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4637, loss_cls: 4.3257, loss: 4.3257 +2024-12-26 07:02:29,393 - pyskl - INFO - Epoch [7][1200/3746] lr: 9.956e-02, eta: 4 days, 15:58:49, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4659, loss_cls: 4.2896, loss: 4.2896 +2024-12-26 07:03:41,034 - pyskl - INFO - Epoch [7][1300/3746] lr: 9.956e-02, eta: 4 days, 15:56:21, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4519, loss_cls: 4.3362, loss: 4.3362 +2024-12-26 07:04:52,389 - pyskl - INFO - Epoch [7][1400/3746] lr: 9.956e-02, eta: 4 days, 15:53:46, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4619, loss_cls: 4.2992, loss: 4.2992 +2024-12-26 07:06:03,931 - pyskl - INFO - Epoch [7][1500/3746] lr: 9.955e-02, eta: 4 days, 15:51:17, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4592, loss_cls: 4.3062, loss: 4.3062 +2024-12-26 07:07:15,635 - pyskl - INFO - Epoch [7][1600/3746] lr: 9.955e-02, eta: 4 days, 15:48:51, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4636, loss_cls: 4.3025, loss: 4.3025 +2024-12-26 07:08:27,392 - pyskl - INFO - Epoch [7][1700/3746] lr: 9.954e-02, eta: 4 days, 15:46:28, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4556, loss_cls: 4.2922, loss: 4.2922 +2024-12-26 07:09:39,024 - pyskl - INFO - Epoch [7][1800/3746] lr: 9.954e-02, eta: 4 days, 15:44:02, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4734, loss_cls: 4.2636, loss: 4.2636 +2024-12-26 07:10:50,901 - pyskl - INFO - Epoch [7][1900/3746] lr: 9.954e-02, eta: 4 days, 15:41:42, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4709, loss_cls: 4.2862, loss: 4.2862 +2024-12-26 07:12:02,632 - pyskl - INFO - Epoch [7][2000/3746] lr: 9.953e-02, eta: 4 days, 15:39:20, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4594, loss_cls: 4.3176, loss: 4.3176 +2024-12-26 07:13:14,077 - pyskl - INFO - Epoch [7][2100/3746] lr: 9.953e-02, eta: 4 days, 15:36:52, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4625, loss_cls: 4.3092, loss: 4.3092 +2024-12-26 07:14:25,652 - pyskl - INFO - Epoch [7][2200/3746] lr: 9.952e-02, eta: 4 days, 15:34:27, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4625, loss_cls: 4.2922, loss: 4.2922 +2024-12-26 07:15:37,346 - pyskl - INFO - Epoch [7][2300/3746] lr: 9.952e-02, eta: 4 days, 15:32:06, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4586, loss_cls: 4.3239, loss: 4.3239 +2024-12-26 07:16:49,244 - pyskl - INFO - Epoch [7][2400/3746] lr: 9.952e-02, eta: 4 days, 15:29:49, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4672, loss_cls: 4.2650, loss: 4.2650 +2024-12-26 07:18:01,064 - pyskl - INFO - Epoch [7][2500/3746] lr: 9.951e-02, eta: 4 days, 15:27:32, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4603, loss_cls: 4.3121, loss: 4.3121 +2024-12-26 07:19:12,934 - pyskl - INFO - Epoch [7][2600/3746] lr: 9.951e-02, eta: 4 days, 15:25:16, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4617, loss_cls: 4.2898, loss: 4.2898 +2024-12-26 07:20:25,002 - pyskl - INFO - Epoch [7][2700/3746] lr: 9.951e-02, eta: 4 days, 15:23:04, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4738, loss_cls: 4.2396, loss: 4.2396 +2024-12-26 07:21:36,631 - pyskl - INFO - Epoch [7][2800/3746] lr: 9.950e-02, eta: 4 days, 15:20:44, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4680, loss_cls: 4.2645, loss: 4.2645 +2024-12-26 07:22:48,456 - pyskl - INFO - Epoch [7][2900/3746] lr: 9.950e-02, eta: 4 days, 15:18:29, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4633, loss_cls: 4.2907, loss: 4.2907 +2024-12-26 07:24:00,408 - pyskl - INFO - Epoch [7][3000/3746] lr: 9.949e-02, eta: 4 days, 15:16:17, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4591, loss_cls: 4.2824, loss: 4.2824 +2024-12-26 07:25:11,945 - pyskl - INFO - Epoch [7][3100/3746] lr: 9.949e-02, eta: 4 days, 15:13:56, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4625, loss_cls: 4.2923, loss: 4.2923 +2024-12-26 07:26:23,582 - pyskl - INFO - Epoch [7][3200/3746] lr: 9.949e-02, eta: 4 days, 15:11:38, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4619, loss_cls: 4.2710, loss: 4.2710 +2024-12-26 07:27:35,251 - pyskl - INFO - Epoch [7][3300/3746] lr: 9.948e-02, eta: 4 days, 15:09:21, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4567, loss_cls: 4.3032, loss: 4.3032 +2024-12-26 07:28:46,740 - pyskl - INFO - Epoch [7][3400/3746] lr: 9.948e-02, eta: 4 days, 15:07:02, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4670, loss_cls: 4.2931, loss: 4.2931 +2024-12-26 07:29:58,033 - pyskl - INFO - Epoch [7][3500/3746] lr: 9.947e-02, eta: 4 days, 15:04:38, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4664, loss_cls: 4.2619, loss: 4.2619 +2024-12-26 07:31:09,263 - pyskl - INFO - Epoch [7][3600/3746] lr: 9.947e-02, eta: 4 days, 15:02:14, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4653, loss_cls: 4.2861, loss: 4.2861 +2024-12-26 07:32:20,625 - pyskl - INFO - Epoch [7][3700/3746] lr: 9.947e-02, eta: 4 days, 14:59:53, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4495, loss_cls: 4.3296, loss: 4.3296 +2024-12-26 07:32:55,457 - pyskl - INFO - Saving checkpoint at 7 epochs +2024-12-26 07:34:52,065 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 07:34:52,804 - pyskl - INFO - +top1_acc 0.1775 +top5_acc 0.4018 +2024-12-26 07:34:52,805 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 07:34:52,852 - pyskl - INFO - +mean_acc 0.1774 +2024-12-26 07:34:52,860 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_6.pth was removed +2024-12-26 07:34:53,139 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2024-12-26 07:34:53,140 - pyskl - INFO - Best top1_acc is 0.1775 at 7 epoch. +2024-12-26 07:34:53,152 - pyskl - INFO - Epoch(val) [7][309] top1_acc: 0.1775, top5_acc: 0.4018, mean_class_accuracy: 0.1774 +2024-12-26 07:38:22,058 - pyskl - INFO - Epoch [8][100/3746] lr: 9.946e-02, eta: 4 days, 15:31:59, time: 2.089, data_time: 1.366, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4713, loss_cls: 4.2270, loss: 4.2270 +2024-12-26 07:39:33,520 - pyskl - INFO - Epoch [8][200/3746] lr: 9.946e-02, eta: 4 days, 15:29:33, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4689, loss_cls: 4.2443, loss: 4.2443 +2024-12-26 07:40:44,924 - pyskl - INFO - Epoch [8][300/3746] lr: 9.945e-02, eta: 4 days, 15:27:06, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4734, loss_cls: 4.2469, loss: 4.2469 +2024-12-26 07:41:56,694 - pyskl - INFO - Epoch [8][400/3746] lr: 9.945e-02, eta: 4 days, 15:24:47, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4637, loss_cls: 4.2995, loss: 4.2995 +2024-12-26 07:43:07,657 - pyskl - INFO - Epoch [8][500/3746] lr: 9.944e-02, eta: 4 days, 15:22:13, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4697, loss_cls: 4.2858, loss: 4.2858 +2024-12-26 07:44:19,037 - pyskl - INFO - Epoch [8][600/3746] lr: 9.944e-02, eta: 4 days, 15:19:47, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4605, loss_cls: 4.3092, loss: 4.3092 +2024-12-26 07:45:30,425 - pyskl - INFO - Epoch [8][700/3746] lr: 9.943e-02, eta: 4 days, 15:17:23, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4562, loss_cls: 4.3085, loss: 4.3085 +2024-12-26 07:46:41,565 - pyskl - INFO - Epoch [8][800/3746] lr: 9.943e-02, eta: 4 days, 15:14:53, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4680, loss_cls: 4.2753, loss: 4.2753 +2024-12-26 07:47:52,678 - pyskl - INFO - Epoch [8][900/3746] lr: 9.943e-02, eta: 4 days, 15:12:24, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4658, loss_cls: 4.3022, loss: 4.3022 +2024-12-26 07:49:03,778 - pyskl - INFO - Epoch [8][1000/3746] lr: 9.942e-02, eta: 4 days, 15:09:56, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4653, loss_cls: 4.3068, loss: 4.3068 +2024-12-26 07:50:15,011 - pyskl - INFO - Epoch [8][1100/3746] lr: 9.942e-02, eta: 4 days, 15:07:30, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4616, loss_cls: 4.2631, loss: 4.2631 +2024-12-26 07:51:26,255 - pyskl - INFO - Epoch [8][1200/3746] lr: 9.941e-02, eta: 4 days, 15:05:05, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4738, loss_cls: 4.2554, loss: 4.2554 +2024-12-26 07:52:37,919 - pyskl - INFO - Epoch [8][1300/3746] lr: 9.941e-02, eta: 4 days, 15:02:49, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4823, loss_cls: 4.2240, loss: 4.2240 +2024-12-26 07:53:49,487 - pyskl - INFO - Epoch [8][1400/3746] lr: 9.940e-02, eta: 4 days, 15:00:31, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4688, loss_cls: 4.2470, loss: 4.2470 +2024-12-26 07:55:00,628 - pyskl - INFO - Epoch [8][1500/3746] lr: 9.940e-02, eta: 4 days, 14:58:06, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4656, loss_cls: 4.2722, loss: 4.2722 +2024-12-26 07:56:12,557 - pyskl - INFO - Epoch [8][1600/3746] lr: 9.940e-02, eta: 4 days, 14:55:56, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4728, loss_cls: 4.2800, loss: 4.2800 +2024-12-26 07:57:24,283 - pyskl - INFO - Epoch [8][1700/3746] lr: 9.939e-02, eta: 4 days, 14:53:43, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4731, loss_cls: 4.2645, loss: 4.2645 +2024-12-26 07:58:35,639 - pyskl - INFO - Epoch [8][1800/3746] lr: 9.939e-02, eta: 4 days, 14:51:24, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4778, loss_cls: 4.2095, loss: 4.2095 +2024-12-26 07:59:47,142 - pyskl - INFO - Epoch [8][1900/3746] lr: 9.938e-02, eta: 4 days, 14:49:07, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4630, loss_cls: 4.2812, loss: 4.2812 +2024-12-26 08:00:58,965 - pyskl - INFO - Epoch [8][2000/3746] lr: 9.938e-02, eta: 4 days, 14:46:57, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4677, loss_cls: 4.2889, loss: 4.2889 +2024-12-26 08:02:10,854 - pyskl - INFO - Epoch [8][2100/3746] lr: 9.937e-02, eta: 4 days, 14:44:49, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4708, loss_cls: 4.2567, loss: 4.2567 +2024-12-26 08:03:22,376 - pyskl - INFO - Epoch [8][2200/3746] lr: 9.937e-02, eta: 4 days, 14:42:34, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4744, loss_cls: 4.2466, loss: 4.2466 +2024-12-26 08:04:33,932 - pyskl - INFO - Epoch [8][2300/3746] lr: 9.937e-02, eta: 4 days, 14:40:21, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4750, loss_cls: 4.2531, loss: 4.2531 +2024-12-26 08:05:45,610 - pyskl - INFO - Epoch [8][2400/3746] lr: 9.936e-02, eta: 4 days, 14:38:10, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4558, loss_cls: 4.3012, loss: 4.3012 +2024-12-26 08:06:57,299 - pyskl - INFO - Epoch [8][2500/3746] lr: 9.936e-02, eta: 4 days, 14:35:59, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4731, loss_cls: 4.2445, loss: 4.2445 +2024-12-26 08:08:09,266 - pyskl - INFO - Epoch [8][2600/3746] lr: 9.935e-02, eta: 4 days, 14:33:54, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4592, loss_cls: 4.3176, loss: 4.3176 +2024-12-26 08:09:21,043 - pyskl - INFO - Epoch [8][2700/3746] lr: 9.935e-02, eta: 4 days, 14:31:46, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4622, loss_cls: 4.2837, loss: 4.2837 +2024-12-26 08:10:32,837 - pyskl - INFO - Epoch [8][2800/3746] lr: 9.934e-02, eta: 4 days, 14:29:39, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4713, loss_cls: 4.2359, loss: 4.2359 +2024-12-26 08:11:44,566 - pyskl - INFO - Epoch [8][2900/3746] lr: 9.934e-02, eta: 4 days, 14:27:31, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4708, loss_cls: 4.2497, loss: 4.2497 +2024-12-26 08:12:56,205 - pyskl - INFO - Epoch [8][3000/3746] lr: 9.933e-02, eta: 4 days, 14:25:22, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2155, top5_acc: 0.4498, loss_cls: 4.3108, loss: 4.3108 +2024-12-26 08:14:07,931 - pyskl - INFO - Epoch [8][3100/3746] lr: 9.933e-02, eta: 4 days, 14:23:14, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4709, loss_cls: 4.2730, loss: 4.2730 +2024-12-26 08:15:19,768 - pyskl - INFO - Epoch [8][3200/3746] lr: 9.933e-02, eta: 4 days, 14:21:09, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4736, loss_cls: 4.2266, loss: 4.2266 +2024-12-26 08:16:31,404 - pyskl - INFO - Epoch [8][3300/3746] lr: 9.932e-02, eta: 4 days, 14:19:01, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4647, loss_cls: 4.3152, loss: 4.3152 +2024-12-26 08:17:43,343 - pyskl - INFO - Epoch [8][3400/3746] lr: 9.932e-02, eta: 4 days, 14:16:59, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4761, loss_cls: 4.2576, loss: 4.2576 +2024-12-26 08:18:54,585 - pyskl - INFO - Epoch [8][3500/3746] lr: 9.931e-02, eta: 4 days, 14:14:44, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4662, loss_cls: 4.2675, loss: 4.2675 +2024-12-26 08:20:05,807 - pyskl - INFO - Epoch [8][3600/3746] lr: 9.931e-02, eta: 4 days, 14:12:30, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4709, loss_cls: 4.2489, loss: 4.2489 +2024-12-26 08:21:16,983 - pyskl - INFO - Epoch [8][3700/3746] lr: 9.930e-02, eta: 4 days, 14:10:15, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4734, loss_cls: 4.2511, loss: 4.2511 +2024-12-26 08:21:52,093 - pyskl - INFO - Saving checkpoint at 8 epochs +2024-12-26 08:23:50,204 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 08:23:50,910 - pyskl - INFO - +top1_acc 0.1619 +top5_acc 0.3696 +2024-12-26 08:23:50,910 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 08:23:50,949 - pyskl - INFO - +mean_acc 0.1617 +2024-12-26 08:23:50,960 - pyskl - INFO - Epoch(val) [8][309] top1_acc: 0.1619, top5_acc: 0.3696, mean_class_accuracy: 0.1617 +2024-12-26 08:27:20,659 - pyskl - INFO - Epoch [9][100/3746] lr: 9.930e-02, eta: 4 days, 14:38:10, time: 2.097, data_time: 1.381, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4619, loss_cls: 4.2824, loss: 4.2824 +2024-12-26 08:28:32,642 - pyskl - INFO - Epoch [9][200/3746] lr: 9.929e-02, eta: 4 days, 14:36:04, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4759, loss_cls: 4.2214, loss: 4.2214 +2024-12-26 08:29:43,644 - pyskl - INFO - Epoch [9][300/3746] lr: 9.929e-02, eta: 4 days, 14:33:41, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4772, loss_cls: 4.2601, loss: 4.2601 +2024-12-26 08:30:54,750 - pyskl - INFO - Epoch [9][400/3746] lr: 9.928e-02, eta: 4 days, 14:31:20, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4759, loss_cls: 4.2244, loss: 4.2244 +2024-12-26 08:32:06,090 - pyskl - INFO - Epoch [9][500/3746] lr: 9.928e-02, eta: 4 days, 14:29:04, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4697, loss_cls: 4.2438, loss: 4.2438 +2024-12-26 08:33:17,732 - pyskl - INFO - Epoch [9][600/3746] lr: 9.927e-02, eta: 4 days, 14:26:53, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4666, loss_cls: 4.2727, loss: 4.2727 +2024-12-26 08:34:28,442 - pyskl - INFO - Epoch [9][700/3746] lr: 9.927e-02, eta: 4 days, 14:24:27, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4673, loss_cls: 4.2418, loss: 4.2418 +2024-12-26 08:35:39,528 - pyskl - INFO - Epoch [9][800/3746] lr: 9.926e-02, eta: 4 days, 14:22:08, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4736, loss_cls: 4.2577, loss: 4.2577 +2024-12-26 08:36:50,933 - pyskl - INFO - Epoch [9][900/3746] lr: 9.926e-02, eta: 4 days, 14:19:54, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4669, loss_cls: 4.2447, loss: 4.2447 +2024-12-26 08:38:01,794 - pyskl - INFO - Epoch [9][1000/3746] lr: 9.925e-02, eta: 4 days, 14:17:32, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4694, loss_cls: 4.2711, loss: 4.2711 +2024-12-26 08:39:12,706 - pyskl - INFO - Epoch [9][1100/3746] lr: 9.925e-02, eta: 4 days, 14:15:11, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4769, loss_cls: 4.2187, loss: 4.2187 +2024-12-26 08:40:24,194 - pyskl - INFO - Epoch [9][1200/3746] lr: 9.924e-02, eta: 4 days, 14:13:00, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4733, loss_cls: 4.2443, loss: 4.2443 +2024-12-26 08:41:35,500 - pyskl - INFO - Epoch [9][1300/3746] lr: 9.924e-02, eta: 4 days, 14:10:47, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4697, loss_cls: 4.2598, loss: 4.2598 +2024-12-26 08:42:46,706 - pyskl - INFO - Epoch [9][1400/3746] lr: 9.923e-02, eta: 4 days, 14:08:32, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4781, loss_cls: 4.2375, loss: 4.2375 +2024-12-26 08:43:57,768 - pyskl - INFO - Epoch [9][1500/3746] lr: 9.923e-02, eta: 4 days, 14:06:15, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4809, loss_cls: 4.2005, loss: 4.2005 +2024-12-26 08:45:08,737 - pyskl - INFO - Epoch [9][1600/3746] lr: 9.922e-02, eta: 4 days, 14:03:58, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4642, loss_cls: 4.2717, loss: 4.2717 +2024-12-26 08:46:20,055 - pyskl - INFO - Epoch [9][1700/3746] lr: 9.922e-02, eta: 4 days, 14:01:46, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4773, loss_cls: 4.2300, loss: 4.2300 +2024-12-26 08:47:31,740 - pyskl - INFO - Epoch [9][1800/3746] lr: 9.921e-02, eta: 4 days, 13:59:41, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4770, loss_cls: 4.2448, loss: 4.2448 +2024-12-26 08:48:43,293 - pyskl - INFO - Epoch [9][1900/3746] lr: 9.921e-02, eta: 4 days, 13:57:34, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4722, loss_cls: 4.2283, loss: 4.2283 +2024-12-26 08:49:55,156 - pyskl - INFO - Epoch [9][2000/3746] lr: 9.920e-02, eta: 4 days, 13:55:32, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4717, loss_cls: 4.2465, loss: 4.2465 +2024-12-26 08:51:06,683 - pyskl - INFO - Epoch [9][2100/3746] lr: 9.920e-02, eta: 4 days, 13:53:26, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4725, loss_cls: 4.2521, loss: 4.2521 +2024-12-26 08:52:18,057 - pyskl - INFO - Epoch [9][2200/3746] lr: 9.919e-02, eta: 4 days, 13:51:17, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4598, loss_cls: 4.2989, loss: 4.2989 +2024-12-26 08:53:29,697 - pyskl - INFO - Epoch [9][2300/3746] lr: 9.919e-02, eta: 4 days, 13:49:13, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4759, loss_cls: 4.2815, loss: 4.2815 +2024-12-26 08:54:41,389 - pyskl - INFO - Epoch [9][2400/3746] lr: 9.918e-02, eta: 4 days, 13:47:10, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4755, loss_cls: 4.2522, loss: 4.2522 +2024-12-26 08:55:52,825 - pyskl - INFO - Epoch [9][2500/3746] lr: 9.918e-02, eta: 4 days, 13:45:03, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4694, loss_cls: 4.2726, loss: 4.2726 +2024-12-26 08:57:04,590 - pyskl - INFO - Epoch [9][2600/3746] lr: 9.917e-02, eta: 4 days, 13:43:01, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4681, loss_cls: 4.2563, loss: 4.2563 +2024-12-26 08:58:16,293 - pyskl - INFO - Epoch [9][2700/3746] lr: 9.917e-02, eta: 4 days, 13:41:00, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4797, loss_cls: 4.1994, loss: 4.1994 +2024-12-26 08:59:28,032 - pyskl - INFO - Epoch [9][2800/3746] lr: 9.916e-02, eta: 4 days, 13:38:59, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4736, loss_cls: 4.2410, loss: 4.2410 +2024-12-26 09:00:39,521 - pyskl - INFO - Epoch [9][2900/3746] lr: 9.916e-02, eta: 4 days, 13:36:54, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4878, loss_cls: 4.2387, loss: 4.2387 +2024-12-26 09:01:51,008 - pyskl - INFO - Epoch [9][3000/3746] lr: 9.915e-02, eta: 4 days, 13:34:49, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4706, loss_cls: 4.2669, loss: 4.2669 +2024-12-26 09:03:02,925 - pyskl - INFO - Epoch [9][3100/3746] lr: 9.915e-02, eta: 4 days, 13:32:52, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4741, loss_cls: 4.2648, loss: 4.2648 +2024-12-26 09:04:14,725 - pyskl - INFO - Epoch [9][3200/3746] lr: 9.914e-02, eta: 4 days, 13:30:53, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4764, loss_cls: 4.2135, loss: 4.2135 +2024-12-26 09:05:26,701 - pyskl - INFO - Epoch [9][3300/3746] lr: 9.914e-02, eta: 4 days, 13:28:58, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4781, loss_cls: 4.2143, loss: 4.2143 +2024-12-26 09:06:38,876 - pyskl - INFO - Epoch [9][3400/3746] lr: 9.913e-02, eta: 4 days, 13:27:05, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4659, loss_cls: 4.2795, loss: 4.2795 +2024-12-26 09:07:50,011 - pyskl - INFO - Epoch [9][3500/3746] lr: 9.913e-02, eta: 4 days, 13:24:57, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4656, loss_cls: 4.2691, loss: 4.2691 +2024-12-26 09:09:01,418 - pyskl - INFO - Epoch [9][3600/3746] lr: 9.912e-02, eta: 4 days, 13:22:53, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4809, loss_cls: 4.2169, loss: 4.2169 +2024-12-26 09:10:12,631 - pyskl - INFO - Epoch [9][3700/3746] lr: 9.912e-02, eta: 4 days, 13:20:46, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4892, loss_cls: 4.1922, loss: 4.1922 +2024-12-26 09:10:47,665 - pyskl - INFO - Saving checkpoint at 9 epochs +2024-12-26 09:12:44,918 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 09:12:45,582 - pyskl - INFO - +top1_acc 0.1567 +top5_acc 0.3692 +2024-12-26 09:12:45,583 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 09:12:45,622 - pyskl - INFO - +mean_acc 0.1566 +2024-12-26 09:12:45,631 - pyskl - INFO - Epoch(val) [9][309] top1_acc: 0.1567, top5_acc: 0.3692, mean_class_accuracy: 0.1566 +2024-12-26 09:16:12,995 - pyskl - INFO - Epoch [10][100/3746] lr: 9.911e-02, eta: 4 days, 13:44:37, time: 2.074, data_time: 1.356, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4869, loss_cls: 4.1465, loss: 4.1465 +2024-12-26 09:17:24,168 - pyskl - INFO - Epoch [10][200/3746] lr: 9.910e-02, eta: 4 days, 13:42:25, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4831, loss_cls: 4.1935, loss: 4.1935 +2024-12-26 09:18:35,278 - pyskl - INFO - Epoch [10][300/3746] lr: 9.910e-02, eta: 4 days, 13:40:13, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4891, loss_cls: 4.2103, loss: 4.2103 +2024-12-26 09:19:46,765 - pyskl - INFO - Epoch [10][400/3746] lr: 9.909e-02, eta: 4 days, 13:38:07, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4781, loss_cls: 4.2281, loss: 4.2281 +2024-12-26 09:20:57,926 - pyskl - INFO - Epoch [10][500/3746] lr: 9.909e-02, eta: 4 days, 13:35:57, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4745, loss_cls: 4.2366, loss: 4.2366 +2024-12-26 09:22:09,118 - pyskl - INFO - Epoch [10][600/3746] lr: 9.908e-02, eta: 4 days, 13:33:47, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4763, loss_cls: 4.2439, loss: 4.2439 +2024-12-26 09:23:20,202 - pyskl - INFO - Epoch [10][700/3746] lr: 9.908e-02, eta: 4 days, 13:31:36, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4869, loss_cls: 4.2350, loss: 4.2350 +2024-12-26 09:24:31,394 - pyskl - INFO - Epoch [10][800/3746] lr: 9.907e-02, eta: 4 days, 13:29:26, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4711, loss_cls: 4.2457, loss: 4.2457 +2024-12-26 09:25:42,518 - pyskl - INFO - Epoch [10][900/3746] lr: 9.907e-02, eta: 4 days, 13:27:17, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4764, loss_cls: 4.2129, loss: 4.2129 +2024-12-26 09:26:53,370 - pyskl - INFO - Epoch [10][1000/3746] lr: 9.906e-02, eta: 4 days, 13:25:03, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4747, loss_cls: 4.2172, loss: 4.2172 +2024-12-26 09:28:04,547 - pyskl - INFO - Epoch [10][1100/3746] lr: 9.906e-02, eta: 4 days, 13:22:55, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4891, loss_cls: 4.1736, loss: 4.1736 +2024-12-26 09:29:15,814 - pyskl - INFO - Epoch [10][1200/3746] lr: 9.905e-02, eta: 4 days, 13:20:48, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4678, loss_cls: 4.2299, loss: 4.2299 +2024-12-26 09:30:26,713 - pyskl - INFO - Epoch [10][1300/3746] lr: 9.905e-02, eta: 4 days, 13:18:36, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4781, loss_cls: 4.2321, loss: 4.2321 +2024-12-26 09:31:38,065 - pyskl - INFO - Epoch [10][1400/3746] lr: 9.904e-02, eta: 4 days, 13:16:31, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4763, loss_cls: 4.2337, loss: 4.2337 +2024-12-26 09:32:49,293 - pyskl - INFO - Epoch [10][1500/3746] lr: 9.903e-02, eta: 4 days, 13:14:25, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4691, loss_cls: 4.2446, loss: 4.2446 +2024-12-26 09:34:00,888 - pyskl - INFO - Epoch [10][1600/3746] lr: 9.903e-02, eta: 4 days, 13:12:25, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4680, loss_cls: 4.2759, loss: 4.2759 +2024-12-26 09:35:11,997 - pyskl - INFO - Epoch [10][1700/3746] lr: 9.902e-02, eta: 4 days, 13:10:17, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4722, loss_cls: 4.2519, loss: 4.2519 +2024-12-26 09:36:23,464 - pyskl - INFO - Epoch [10][1800/3746] lr: 9.902e-02, eta: 4 days, 13:08:15, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4788, loss_cls: 4.1910, loss: 4.1910 +2024-12-26 09:37:34,996 - pyskl - INFO - Epoch [10][1900/3746] lr: 9.901e-02, eta: 4 days, 13:06:15, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4745, loss_cls: 4.2509, loss: 4.2509 +2024-12-26 09:38:46,895 - pyskl - INFO - Epoch [10][2000/3746] lr: 9.901e-02, eta: 4 days, 13:04:20, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4752, loss_cls: 4.2392, loss: 4.2392 +2024-12-26 09:39:58,636 - pyskl - INFO - Epoch [10][2100/3746] lr: 9.900e-02, eta: 4 days, 13:02:23, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4689, loss_cls: 4.2595, loss: 4.2595 +2024-12-26 09:41:10,459 - pyskl - INFO - Epoch [10][2200/3746] lr: 9.900e-02, eta: 4 days, 13:00:27, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4822, loss_cls: 4.1973, loss: 4.1973 +2024-12-26 09:42:22,175 - pyskl - INFO - Epoch [10][2300/3746] lr: 9.899e-02, eta: 4 days, 12:58:30, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4714, loss_cls: 4.2419, loss: 4.2419 +2024-12-26 09:43:33,798 - pyskl - INFO - Epoch [10][2400/3746] lr: 9.898e-02, eta: 4 days, 12:56:32, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4808, loss_cls: 4.2077, loss: 4.2077 +2024-12-26 09:44:45,217 - pyskl - INFO - Epoch [10][2500/3746] lr: 9.898e-02, eta: 4 days, 12:54:32, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4636, loss_cls: 4.2828, loss: 4.2828 +2024-12-26 09:45:56,618 - pyskl - INFO - Epoch [10][2600/3746] lr: 9.897e-02, eta: 4 days, 12:52:31, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4758, loss_cls: 4.2167, loss: 4.2167 +2024-12-26 09:47:08,264 - pyskl - INFO - Epoch [10][2700/3746] lr: 9.897e-02, eta: 4 days, 12:50:34, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4584, loss_cls: 4.2948, loss: 4.2948 +2024-12-26 09:48:20,302 - pyskl - INFO - Epoch [10][2800/3746] lr: 9.896e-02, eta: 4 days, 12:48:43, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4705, loss_cls: 4.2356, loss: 4.2356 +2024-12-26 09:49:31,766 - pyskl - INFO - Epoch [10][2900/3746] lr: 9.896e-02, eta: 4 days, 12:46:44, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4838, loss_cls: 4.2014, loss: 4.2014 +2024-12-26 09:50:43,973 - pyskl - INFO - Epoch [10][3000/3746] lr: 9.895e-02, eta: 4 days, 12:44:56, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4730, loss_cls: 4.2485, loss: 4.2485 +2024-12-26 09:51:55,961 - pyskl - INFO - Epoch [10][3100/3746] lr: 9.894e-02, eta: 4 days, 12:43:05, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4797, loss_cls: 4.2043, loss: 4.2043 +2024-12-26 09:53:07,814 - pyskl - INFO - Epoch [10][3200/3746] lr: 9.894e-02, eta: 4 days, 12:41:12, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4842, loss_cls: 4.2072, loss: 4.2072 +2024-12-26 09:54:19,542 - pyskl - INFO - Epoch [10][3300/3746] lr: 9.893e-02, eta: 4 days, 12:39:18, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4713, loss_cls: 4.2564, loss: 4.2564 +2024-12-26 09:55:31,182 - pyskl - INFO - Epoch [10][3400/3746] lr: 9.893e-02, eta: 4 days, 12:37:23, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4783, loss_cls: 4.2444, loss: 4.2444 +2024-12-26 09:56:42,905 - pyskl - INFO - Epoch [10][3500/3746] lr: 9.892e-02, eta: 4 days, 12:35:29, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4752, loss_cls: 4.2416, loss: 4.2416 +2024-12-26 09:57:54,385 - pyskl - INFO - Epoch [10][3600/3746] lr: 9.892e-02, eta: 4 days, 12:33:32, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4798, loss_cls: 4.2234, loss: 4.2234 +2024-12-26 09:59:05,573 - pyskl - INFO - Epoch [10][3700/3746] lr: 9.891e-02, eta: 4 days, 12:31:31, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4791, loss_cls: 4.2080, loss: 4.2080 +2024-12-26 09:59:40,323 - pyskl - INFO - Saving checkpoint at 10 epochs +2024-12-26 10:01:37,625 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 10:01:38,357 - pyskl - INFO - +top1_acc 0.1796 +top5_acc 0.4159 +2024-12-26 10:01:38,357 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 10:01:38,399 - pyskl - INFO - +mean_acc 0.1792 +2024-12-26 10:01:38,404 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_7.pth was removed +2024-12-26 10:01:38,682 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_10.pth. +2024-12-26 10:01:38,683 - pyskl - INFO - Best top1_acc is 0.1796 at 10 epoch. +2024-12-26 10:01:38,696 - pyskl - INFO - Epoch(val) [10][309] top1_acc: 0.1796, top5_acc: 0.4159, mean_class_accuracy: 0.1792 +2024-12-26 10:05:12,506 - pyskl - INFO - Epoch [11][100/3746] lr: 9.890e-02, eta: 4 days, 12:54:09, time: 2.138, data_time: 1.417, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4813, loss_cls: 4.1867, loss: 4.1867 +2024-12-26 10:06:24,291 - pyskl - INFO - Epoch [11][200/3746] lr: 9.890e-02, eta: 4 days, 12:52:12, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4842, loss_cls: 4.1904, loss: 4.1904 +2024-12-26 10:07:36,128 - pyskl - INFO - Epoch [11][300/3746] lr: 9.889e-02, eta: 4 days, 12:50:17, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4688, loss_cls: 4.2295, loss: 4.2295 +2024-12-26 10:08:47,285 - pyskl - INFO - Epoch [11][400/3746] lr: 9.888e-02, eta: 4 days, 12:48:12, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4884, loss_cls: 4.1745, loss: 4.1745 +2024-12-26 10:09:59,202 - pyskl - INFO - Epoch [11][500/3746] lr: 9.888e-02, eta: 4 days, 12:46:19, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4672, loss_cls: 4.2280, loss: 4.2280 +2024-12-26 10:11:11,022 - pyskl - INFO - Epoch [11][600/3746] lr: 9.887e-02, eta: 4 days, 12:44:24, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4913, loss_cls: 4.1761, loss: 4.1761 +2024-12-26 10:12:22,899 - pyskl - INFO - Epoch [11][700/3746] lr: 9.887e-02, eta: 4 days, 12:42:30, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4703, loss_cls: 4.2160, loss: 4.2160 +2024-12-26 10:13:34,231 - pyskl - INFO - Epoch [11][800/3746] lr: 9.886e-02, eta: 4 days, 12:40:29, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4798, loss_cls: 4.2171, loss: 4.2171 +2024-12-26 10:14:46,092 - pyskl - INFO - Epoch [11][900/3746] lr: 9.885e-02, eta: 4 days, 12:38:35, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4786, loss_cls: 4.1894, loss: 4.1894 +2024-12-26 10:15:57,672 - pyskl - INFO - Epoch [11][1000/3746] lr: 9.885e-02, eta: 4 days, 12:36:38, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4841, loss_cls: 4.1863, loss: 4.1863 +2024-12-26 10:17:09,819 - pyskl - INFO - Epoch [11][1100/3746] lr: 9.884e-02, eta: 4 days, 12:34:48, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4633, loss_cls: 4.2697, loss: 4.2697 +2024-12-26 10:18:21,889 - pyskl - INFO - Epoch [11][1200/3746] lr: 9.884e-02, eta: 4 days, 12:32:58, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4895, loss_cls: 4.1710, loss: 4.1710 +2024-12-26 10:19:33,970 - pyskl - INFO - Epoch [11][1300/3746] lr: 9.883e-02, eta: 4 days, 12:31:08, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4783, loss_cls: 4.2510, loss: 4.2510 +2024-12-26 10:20:45,726 - pyskl - INFO - Epoch [11][1400/3746] lr: 9.882e-02, eta: 4 days, 12:29:14, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4844, loss_cls: 4.1975, loss: 4.1975 +2024-12-26 10:21:57,301 - pyskl - INFO - Epoch [11][1500/3746] lr: 9.882e-02, eta: 4 days, 12:27:18, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4802, loss_cls: 4.2217, loss: 4.2217 +2024-12-26 10:23:08,757 - pyskl - INFO - Epoch [11][1600/3746] lr: 9.881e-02, eta: 4 days, 12:25:20, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4767, loss_cls: 4.2156, loss: 4.2156 +2024-12-26 10:24:20,423 - pyskl - INFO - Epoch [11][1700/3746] lr: 9.881e-02, eta: 4 days, 12:23:26, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4745, loss_cls: 4.2094, loss: 4.2094 +2024-12-26 10:25:32,016 - pyskl - INFO - Epoch [11][1800/3746] lr: 9.880e-02, eta: 4 days, 12:21:31, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4906, loss_cls: 4.1680, loss: 4.1680 +2024-12-26 10:26:43,674 - pyskl - INFO - Epoch [11][1900/3746] lr: 9.879e-02, eta: 4 days, 12:19:36, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4689, loss_cls: 4.2613, loss: 4.2613 +2024-12-26 10:27:55,318 - pyskl - INFO - Epoch [11][2000/3746] lr: 9.879e-02, eta: 4 days, 12:17:42, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4881, loss_cls: 4.1992, loss: 4.1992 +2024-12-26 10:29:07,496 - pyskl - INFO - Epoch [11][2100/3746] lr: 9.878e-02, eta: 4 days, 12:15:55, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4798, loss_cls: 4.2083, loss: 4.2083 +2024-12-26 10:30:19,201 - pyskl - INFO - Epoch [11][2200/3746] lr: 9.878e-02, eta: 4 days, 12:14:02, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4781, loss_cls: 4.2255, loss: 4.2255 +2024-12-26 10:31:31,090 - pyskl - INFO - Epoch [11][2300/3746] lr: 9.877e-02, eta: 4 days, 12:12:12, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4797, loss_cls: 4.2240, loss: 4.2240 +2024-12-26 10:32:42,702 - pyskl - INFO - Epoch [11][2400/3746] lr: 9.876e-02, eta: 4 days, 12:10:18, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4831, loss_cls: 4.1888, loss: 4.1888 +2024-12-26 10:33:54,473 - pyskl - INFO - Epoch [11][2500/3746] lr: 9.876e-02, eta: 4 days, 12:08:27, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4733, loss_cls: 4.2219, loss: 4.2219 +2024-12-26 10:35:06,426 - pyskl - INFO - Epoch [11][2600/3746] lr: 9.875e-02, eta: 4 days, 12:06:38, time: 0.720, data_time: 0.001, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4747, loss_cls: 4.2138, loss: 4.2138 +2024-12-26 10:36:18,087 - pyskl - INFO - Epoch [11][2700/3746] lr: 9.874e-02, eta: 4 days, 12:04:45, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4756, loss_cls: 4.2543, loss: 4.2543 +2024-12-26 10:37:30,107 - pyskl - INFO - Epoch [11][2800/3746] lr: 9.874e-02, eta: 4 days, 12:02:57, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4792, loss_cls: 4.2062, loss: 4.2062 +2024-12-26 10:38:41,965 - pyskl - INFO - Epoch [11][2900/3746] lr: 9.873e-02, eta: 4 days, 12:01:08, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4769, loss_cls: 4.2147, loss: 4.2147 +2024-12-26 10:39:53,725 - pyskl - INFO - Epoch [11][3000/3746] lr: 9.873e-02, eta: 4 days, 11:59:17, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4741, loss_cls: 4.2154, loss: 4.2154 +2024-12-26 10:41:05,463 - pyskl - INFO - Epoch [11][3100/3746] lr: 9.872e-02, eta: 4 days, 11:57:26, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4736, loss_cls: 4.2401, loss: 4.2401 +2024-12-26 10:42:16,832 - pyskl - INFO - Epoch [11][3200/3746] lr: 9.871e-02, eta: 4 days, 11:55:31, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4739, loss_cls: 4.2128, loss: 4.2128 +2024-12-26 10:43:29,262 - pyskl - INFO - Epoch [11][3300/3746] lr: 9.871e-02, eta: 4 days, 11:53:50, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4825, loss_cls: 4.2049, loss: 4.2049 +2024-12-26 10:44:40,881 - pyskl - INFO - Epoch [11][3400/3746] lr: 9.870e-02, eta: 4 days, 11:51:58, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4878, loss_cls: 4.1960, loss: 4.1960 +2024-12-26 10:45:52,749 - pyskl - INFO - Epoch [11][3500/3746] lr: 9.869e-02, eta: 4 days, 11:50:09, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4697, loss_cls: 4.2490, loss: 4.2490 +2024-12-26 10:47:04,673 - pyskl - INFO - Epoch [11][3600/3746] lr: 9.869e-02, eta: 4 days, 11:48:22, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4747, loss_cls: 4.2457, loss: 4.2457 +2024-12-26 10:48:16,103 - pyskl - INFO - Epoch [11][3700/3746] lr: 9.868e-02, eta: 4 days, 11:46:28, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4800, loss_cls: 4.2087, loss: 4.2087 +2024-12-26 10:48:50,445 - pyskl - INFO - Saving checkpoint at 11 epochs +2024-12-26 10:50:46,171 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 10:50:46,846 - pyskl - INFO - +top1_acc 0.1356 +top5_acc 0.3438 +2024-12-26 10:50:46,847 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 10:50:46,892 - pyskl - INFO - +mean_acc 0.1355 +2024-12-26 10:50:46,904 - pyskl - INFO - Epoch(val) [11][309] top1_acc: 0.1356, top5_acc: 0.3438, mean_class_accuracy: 0.1355 +2024-12-26 10:54:20,040 - pyskl - INFO - Epoch [12][100/3746] lr: 9.867e-02, eta: 4 days, 12:06:35, time: 2.131, data_time: 1.417, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4841, loss_cls: 4.1832, loss: 4.1832 +2024-12-26 10:55:31,757 - pyskl - INFO - Epoch [12][200/3746] lr: 9.867e-02, eta: 4 days, 12:04:42, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4908, loss_cls: 4.1652, loss: 4.1652 +2024-12-26 10:56:43,169 - pyskl - INFO - Epoch [12][300/3746] lr: 9.866e-02, eta: 4 days, 12:02:45, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4733, loss_cls: 4.2495, loss: 4.2495 +2024-12-26 10:57:55,140 - pyskl - INFO - Epoch [12][400/3746] lr: 9.865e-02, eta: 4 days, 12:00:56, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4795, loss_cls: 4.2118, loss: 4.2118 +2024-12-26 10:59:06,778 - pyskl - INFO - Epoch [12][500/3746] lr: 9.865e-02, eta: 4 days, 11:59:02, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4836, loss_cls: 4.2036, loss: 4.2036 +2024-12-26 11:00:18,271 - pyskl - INFO - Epoch [12][600/3746] lr: 9.864e-02, eta: 4 days, 11:57:07, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4780, loss_cls: 4.2102, loss: 4.2102 +2024-12-26 11:01:29,888 - pyskl - INFO - Epoch [12][700/3746] lr: 9.863e-02, eta: 4 days, 11:55:14, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4863, loss_cls: 4.2063, loss: 4.2063 +2024-12-26 11:02:41,767 - pyskl - INFO - Epoch [12][800/3746] lr: 9.863e-02, eta: 4 days, 11:53:24, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4770, loss_cls: 4.2269, loss: 4.2269 +2024-12-26 11:03:53,520 - pyskl - INFO - Epoch [12][900/3746] lr: 9.862e-02, eta: 4 days, 11:51:33, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4920, loss_cls: 4.1407, loss: 4.1407 +2024-12-26 11:05:05,442 - pyskl - INFO - Epoch [12][1000/3746] lr: 9.861e-02, eta: 4 days, 11:49:44, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4858, loss_cls: 4.1948, loss: 4.1948 +2024-12-26 11:06:17,268 - pyskl - INFO - Epoch [12][1100/3746] lr: 9.861e-02, eta: 4 days, 11:47:54, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4811, loss_cls: 4.2037, loss: 4.2037 +2024-12-26 11:07:29,307 - pyskl - INFO - Epoch [12][1200/3746] lr: 9.860e-02, eta: 4 days, 11:46:07, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4838, loss_cls: 4.1954, loss: 4.1954 +2024-12-26 11:08:41,102 - pyskl - INFO - Epoch [12][1300/3746] lr: 9.859e-02, eta: 4 days, 11:44:17, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4769, loss_cls: 4.2249, loss: 4.2249 +2024-12-26 11:09:53,098 - pyskl - INFO - Epoch [12][1400/3746] lr: 9.859e-02, eta: 4 days, 11:42:29, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4838, loss_cls: 4.1475, loss: 4.1475 +2024-12-26 11:11:04,856 - pyskl - INFO - Epoch [12][1500/3746] lr: 9.858e-02, eta: 4 days, 11:40:39, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4786, loss_cls: 4.1869, loss: 4.1869 +2024-12-26 11:12:16,876 - pyskl - INFO - Epoch [12][1600/3746] lr: 9.857e-02, eta: 4 days, 11:38:53, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4842, loss_cls: 4.1927, loss: 4.1927 +2024-12-26 11:13:28,779 - pyskl - INFO - Epoch [12][1700/3746] lr: 9.857e-02, eta: 4 days, 11:37:05, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4758, loss_cls: 4.2181, loss: 4.2181 +2024-12-26 11:14:40,668 - pyskl - INFO - Epoch [12][1800/3746] lr: 9.856e-02, eta: 4 days, 11:35:17, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4794, loss_cls: 4.2039, loss: 4.2039 +2024-12-26 11:15:52,673 - pyskl - INFO - Epoch [12][1900/3746] lr: 9.855e-02, eta: 4 days, 11:33:30, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4784, loss_cls: 4.2558, loss: 4.2558 +2024-12-26 11:17:04,695 - pyskl - INFO - Epoch [12][2000/3746] lr: 9.855e-02, eta: 4 days, 11:31:44, time: 0.720, data_time: 0.001, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4805, loss_cls: 4.2184, loss: 4.2184 +2024-12-26 11:18:17,182 - pyskl - INFO - Epoch [12][2100/3746] lr: 9.854e-02, eta: 4 days, 11:30:04, time: 0.725, data_time: 0.001, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4869, loss_cls: 4.1955, loss: 4.1955 +2024-12-26 11:19:29,797 - pyskl - INFO - Epoch [12][2200/3746] lr: 9.853e-02, eta: 4 days, 11:28:25, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4798, loss_cls: 4.1613, loss: 4.1613 +2024-12-26 11:20:41,752 - pyskl - INFO - Epoch [12][2300/3746] lr: 9.853e-02, eta: 4 days, 11:26:39, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4719, loss_cls: 4.2227, loss: 4.2227 +2024-12-26 11:21:54,107 - pyskl - INFO - Epoch [12][2400/3746] lr: 9.852e-02, eta: 4 days, 11:24:57, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4934, loss_cls: 4.1584, loss: 4.1584 +2024-12-26 11:23:06,068 - pyskl - INFO - Epoch [12][2500/3746] lr: 9.851e-02, eta: 4 days, 11:23:11, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4839, loss_cls: 4.1630, loss: 4.1630 +2024-12-26 11:24:18,168 - pyskl - INFO - Epoch [12][2600/3746] lr: 9.851e-02, eta: 4 days, 11:21:27, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4767, loss_cls: 4.2257, loss: 4.2257 +2024-12-26 11:25:30,241 - pyskl - INFO - Epoch [12][2700/3746] lr: 9.850e-02, eta: 4 days, 11:19:43, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4811, loss_cls: 4.2120, loss: 4.2120 +2024-12-26 11:26:42,800 - pyskl - INFO - Epoch [12][2800/3746] lr: 9.849e-02, eta: 4 days, 11:18:04, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4916, loss_cls: 4.1493, loss: 4.1493 +2024-12-26 11:27:55,233 - pyskl - INFO - Epoch [12][2900/3746] lr: 9.849e-02, eta: 4 days, 11:16:24, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4827, loss_cls: 4.1983, loss: 4.1983 +2024-12-26 11:29:07,430 - pyskl - INFO - Epoch [12][3000/3746] lr: 9.848e-02, eta: 4 days, 11:14:42, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4742, loss_cls: 4.2076, loss: 4.2076 +2024-12-26 11:30:19,335 - pyskl - INFO - Epoch [12][3100/3746] lr: 9.847e-02, eta: 4 days, 11:12:56, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4847, loss_cls: 4.1808, loss: 4.1808 +2024-12-26 11:31:30,972 - pyskl - INFO - Epoch [12][3200/3746] lr: 9.847e-02, eta: 4 days, 11:11:07, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4808, loss_cls: 4.2128, loss: 4.2128 +2024-12-26 11:32:42,984 - pyskl - INFO - Epoch [12][3300/3746] lr: 9.846e-02, eta: 4 days, 11:09:23, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4895, loss_cls: 4.2244, loss: 4.2244 +2024-12-26 11:33:55,069 - pyskl - INFO - Epoch [12][3400/3746] lr: 9.845e-02, eta: 4 days, 11:07:40, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4809, loss_cls: 4.2103, loss: 4.2103 +2024-12-26 11:35:06,750 - pyskl - INFO - Epoch [12][3500/3746] lr: 9.845e-02, eta: 4 days, 11:05:52, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4736, loss_cls: 4.2687, loss: 4.2687 +2024-12-26 11:36:18,190 - pyskl - INFO - Epoch [12][3600/3746] lr: 9.844e-02, eta: 4 days, 11:04:01, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4736, loss_cls: 4.2336, loss: 4.2336 +2024-12-26 11:37:29,657 - pyskl - INFO - Epoch [12][3700/3746] lr: 9.843e-02, eta: 4 days, 11:02:12, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4875, loss_cls: 4.1557, loss: 4.1557 +2024-12-26 11:38:04,441 - pyskl - INFO - Saving checkpoint at 12 epochs +2024-12-26 11:40:02,141 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 11:40:02,838 - pyskl - INFO - +top1_acc 0.1534 +top5_acc 0.3513 +2024-12-26 11:40:02,839 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 11:40:02,882 - pyskl - INFO - +mean_acc 0.1532 +2024-12-26 11:40:02,894 - pyskl - INFO - Epoch(val) [12][309] top1_acc: 0.1534, top5_acc: 0.3513, mean_class_accuracy: 0.1532 +2024-12-26 11:43:34,125 - pyskl - INFO - Epoch [13][100/3746] lr: 9.842e-02, eta: 4 days, 11:19:57, time: 2.112, data_time: 1.391, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4856, loss_cls: 4.1751, loss: 4.1751 +2024-12-26 11:44:45,921 - pyskl - INFO - Epoch [13][200/3746] lr: 9.842e-02, eta: 4 days, 11:18:09, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4844, loss_cls: 4.1547, loss: 4.1547 +2024-12-26 11:45:57,768 - pyskl - INFO - Epoch [13][300/3746] lr: 9.841e-02, eta: 4 days, 11:16:21, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4875, loss_cls: 4.1898, loss: 4.1898 +2024-12-26 11:47:09,153 - pyskl - INFO - Epoch [13][400/3746] lr: 9.840e-02, eta: 4 days, 11:14:28, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4867, loss_cls: 4.1809, loss: 4.1809 +2024-12-26 11:48:20,629 - pyskl - INFO - Epoch [13][500/3746] lr: 9.839e-02, eta: 4 days, 11:12:36, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4859, loss_cls: 4.1729, loss: 4.1729 +2024-12-26 11:49:32,013 - pyskl - INFO - Epoch [13][600/3746] lr: 9.839e-02, eta: 4 days, 11:10:43, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4877, loss_cls: 4.2164, loss: 4.2164 +2024-12-26 11:50:43,296 - pyskl - INFO - Epoch [13][700/3746] lr: 9.838e-02, eta: 4 days, 11:08:50, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4786, loss_cls: 4.2098, loss: 4.2098 +2024-12-26 11:51:54,831 - pyskl - INFO - Epoch [13][800/3746] lr: 9.837e-02, eta: 4 days, 11:06:59, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4853, loss_cls: 4.1647, loss: 4.1647 +2024-12-26 11:53:06,473 - pyskl - INFO - Epoch [13][900/3746] lr: 9.837e-02, eta: 4 days, 11:05:10, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4791, loss_cls: 4.1860, loss: 4.1860 +2024-12-26 11:54:18,204 - pyskl - INFO - Epoch [13][1000/3746] lr: 9.836e-02, eta: 4 days, 11:03:22, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4861, loss_cls: 4.1730, loss: 4.1730 +2024-12-26 11:55:29,908 - pyskl - INFO - Epoch [13][1100/3746] lr: 9.835e-02, eta: 4 days, 11:01:33, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4783, loss_cls: 4.1967, loss: 4.1967 +2024-12-26 11:56:42,113 - pyskl - INFO - Epoch [13][1200/3746] lr: 9.834e-02, eta: 4 days, 10:59:51, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4825, loss_cls: 4.1843, loss: 4.1843 +2024-12-26 11:57:53,898 - pyskl - INFO - Epoch [13][1300/3746] lr: 9.834e-02, eta: 4 days, 10:58:04, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4928, loss_cls: 4.1667, loss: 4.1667 +2024-12-26 11:59:05,853 - pyskl - INFO - Epoch [13][1400/3746] lr: 9.833e-02, eta: 4 days, 10:56:19, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4783, loss_cls: 4.2136, loss: 4.2136 +2024-12-26 12:00:17,769 - pyskl - INFO - Epoch [13][1500/3746] lr: 9.832e-02, eta: 4 days, 10:54:33, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4936, loss_cls: 4.1405, loss: 4.1405 +2024-12-26 12:01:29,828 - pyskl - INFO - Epoch [13][1600/3746] lr: 9.832e-02, eta: 4 days, 10:52:50, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4939, loss_cls: 4.1577, loss: 4.1577 +2024-12-26 12:02:41,934 - pyskl - INFO - Epoch [13][1700/3746] lr: 9.831e-02, eta: 4 days, 10:51:07, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4892, loss_cls: 4.1837, loss: 4.1837 +2024-12-26 12:03:54,568 - pyskl - INFO - Epoch [13][1800/3746] lr: 9.830e-02, eta: 4 days, 10:49:30, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4891, loss_cls: 4.1797, loss: 4.1797 +2024-12-26 12:05:06,677 - pyskl - INFO - Epoch [13][1900/3746] lr: 9.829e-02, eta: 4 days, 10:47:47, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4805, loss_cls: 4.2071, loss: 4.2071 +2024-12-26 12:06:18,705 - pyskl - INFO - Epoch [13][2000/3746] lr: 9.829e-02, eta: 4 days, 10:46:04, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4902, loss_cls: 4.1431, loss: 4.1431 +2024-12-26 12:07:30,629 - pyskl - INFO - Epoch [13][2100/3746] lr: 9.828e-02, eta: 4 days, 10:44:20, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4797, loss_cls: 4.1878, loss: 4.1878 +2024-12-26 12:08:42,861 - pyskl - INFO - Epoch [13][2200/3746] lr: 9.827e-02, eta: 4 days, 10:42:39, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4792, loss_cls: 4.2137, loss: 4.2137 +2024-12-26 12:09:54,827 - pyskl - INFO - Epoch [13][2300/3746] lr: 9.827e-02, eta: 4 days, 10:40:55, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4789, loss_cls: 4.1996, loss: 4.1996 +2024-12-26 12:11:07,245 - pyskl - INFO - Epoch [13][2400/3746] lr: 9.826e-02, eta: 4 days, 10:39:16, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4873, loss_cls: 4.1971, loss: 4.1971 +2024-12-26 12:12:19,266 - pyskl - INFO - Epoch [13][2500/3746] lr: 9.825e-02, eta: 4 days, 10:37:34, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4761, loss_cls: 4.2128, loss: 4.2128 +2024-12-26 12:13:31,336 - pyskl - INFO - Epoch [13][2600/3746] lr: 9.824e-02, eta: 4 days, 10:35:51, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4798, loss_cls: 4.2204, loss: 4.2204 +2024-12-26 12:14:43,931 - pyskl - INFO - Epoch [13][2700/3746] lr: 9.824e-02, eta: 4 days, 10:34:15, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4766, loss_cls: 4.2157, loss: 4.2157 +2024-12-26 12:15:56,345 - pyskl - INFO - Epoch [13][2800/3746] lr: 9.823e-02, eta: 4 days, 10:32:37, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4886, loss_cls: 4.1605, loss: 4.1605 +2024-12-26 12:17:08,537 - pyskl - INFO - Epoch [13][2900/3746] lr: 9.822e-02, eta: 4 days, 10:30:56, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4930, loss_cls: 4.1510, loss: 4.1510 +2024-12-26 12:18:20,863 - pyskl - INFO - Epoch [13][3000/3746] lr: 9.821e-02, eta: 4 days, 10:29:17, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4775, loss_cls: 4.2405, loss: 4.2405 +2024-12-26 12:19:33,102 - pyskl - INFO - Epoch [13][3100/3746] lr: 9.821e-02, eta: 4 days, 10:27:38, time: 0.722, data_time: 0.001, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4788, loss_cls: 4.2123, loss: 4.2123 +2024-12-26 12:20:45,327 - pyskl - INFO - Epoch [13][3200/3746] lr: 9.820e-02, eta: 4 days, 10:25:58, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4766, loss_cls: 4.2320, loss: 4.2320 +2024-12-26 12:21:57,557 - pyskl - INFO - Epoch [13][3300/3746] lr: 9.819e-02, eta: 4 days, 10:24:18, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4822, loss_cls: 4.2034, loss: 4.2034 +2024-12-26 12:23:10,084 - pyskl - INFO - Epoch [13][3400/3746] lr: 9.818e-02, eta: 4 days, 10:22:42, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4784, loss_cls: 4.1857, loss: 4.1857 +2024-12-26 12:24:21,975 - pyskl - INFO - Epoch [13][3500/3746] lr: 9.818e-02, eta: 4 days, 10:20:59, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4892, loss_cls: 4.1808, loss: 4.1808 +2024-12-26 12:25:33,573 - pyskl - INFO - Epoch [13][3600/3746] lr: 9.817e-02, eta: 4 days, 10:19:13, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4788, loss_cls: 4.1875, loss: 4.1875 +2024-12-26 12:26:44,958 - pyskl - INFO - Epoch [13][3700/3746] lr: 9.816e-02, eta: 4 days, 10:17:25, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4827, loss_cls: 4.2080, loss: 4.2080 +2024-12-26 12:27:19,347 - pyskl - INFO - Saving checkpoint at 13 epochs +2024-12-26 12:29:17,244 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 12:29:18,028 - pyskl - INFO - +top1_acc 0.1774 +top5_acc 0.3975 +2024-12-26 12:29:18,028 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 12:29:18,073 - pyskl - INFO - +mean_acc 0.1773 +2024-12-26 12:29:18,086 - pyskl - INFO - Epoch(val) [13][309] top1_acc: 0.1774, top5_acc: 0.3975, mean_class_accuracy: 0.1773 +2024-12-26 12:32:50,846 - pyskl - INFO - Epoch [14][100/3746] lr: 9.815e-02, eta: 4 days, 10:33:49, time: 2.127, data_time: 1.412, memory: 15990, top1_acc: 0.2484, top5_acc: 0.5005, loss_cls: 4.1223, loss: 4.1223 +2024-12-26 12:34:02,588 - pyskl - INFO - Epoch [14][200/3746] lr: 9.814e-02, eta: 4 days, 10:32:02, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4802, loss_cls: 4.1843, loss: 4.1843 +2024-12-26 12:35:14,312 - pyskl - INFO - Epoch [14][300/3746] lr: 9.814e-02, eta: 4 days, 10:30:15, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4891, loss_cls: 4.1633, loss: 4.1633 +2024-12-26 12:36:26,019 - pyskl - INFO - Epoch [14][400/3746] lr: 9.813e-02, eta: 4 days, 10:28:29, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4948, loss_cls: 4.1283, loss: 4.1283 +2024-12-26 12:37:37,548 - pyskl - INFO - Epoch [14][500/3746] lr: 9.812e-02, eta: 4 days, 10:26:41, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4914, loss_cls: 4.1683, loss: 4.1683 +2024-12-26 12:38:48,750 - pyskl - INFO - Epoch [14][600/3746] lr: 9.811e-02, eta: 4 days, 10:24:49, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4858, loss_cls: 4.1973, loss: 4.1973 +2024-12-26 12:40:00,138 - pyskl - INFO - Epoch [14][700/3746] lr: 9.811e-02, eta: 4 days, 10:23:00, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4947, loss_cls: 4.1550, loss: 4.1550 +2024-12-26 12:41:11,538 - pyskl - INFO - Epoch [14][800/3746] lr: 9.810e-02, eta: 4 days, 10:21:10, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4863, loss_cls: 4.1537, loss: 4.1537 +2024-12-26 12:42:23,340 - pyskl - INFO - Epoch [14][900/3746] lr: 9.809e-02, eta: 4 days, 10:19:25, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4772, loss_cls: 4.2200, loss: 4.2200 +2024-12-26 12:43:35,126 - pyskl - INFO - Epoch [14][1000/3746] lr: 9.808e-02, eta: 4 days, 10:17:41, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4783, loss_cls: 4.1863, loss: 4.1863 +2024-12-26 12:44:46,916 - pyskl - INFO - Epoch [14][1100/3746] lr: 9.807e-02, eta: 4 days, 10:15:56, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4870, loss_cls: 4.1827, loss: 4.1827 +2024-12-26 12:45:58,697 - pyskl - INFO - Epoch [14][1200/3746] lr: 9.807e-02, eta: 4 days, 10:14:11, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4808, loss_cls: 4.2100, loss: 4.2100 +2024-12-26 12:47:10,500 - pyskl - INFO - Epoch [14][1300/3746] lr: 9.806e-02, eta: 4 days, 10:12:27, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4894, loss_cls: 4.1725, loss: 4.1725 +2024-12-26 12:48:22,327 - pyskl - INFO - Epoch [14][1400/3746] lr: 9.805e-02, eta: 4 days, 10:10:43, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4831, loss_cls: 4.1919, loss: 4.1919 +2024-12-26 12:49:34,145 - pyskl - INFO - Epoch [14][1500/3746] lr: 9.804e-02, eta: 4 days, 10:08:59, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4722, loss_cls: 4.2346, loss: 4.2346 +2024-12-26 12:50:46,160 - pyskl - INFO - Epoch [14][1600/3746] lr: 9.804e-02, eta: 4 days, 10:07:17, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4950, loss_cls: 4.1337, loss: 4.1337 +2024-12-26 12:51:58,153 - pyskl - INFO - Epoch [14][1700/3746] lr: 9.803e-02, eta: 4 days, 10:05:35, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4830, loss_cls: 4.1866, loss: 4.1866 +2024-12-26 12:53:09,894 - pyskl - INFO - Epoch [14][1800/3746] lr: 9.802e-02, eta: 4 days, 10:03:51, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4806, loss_cls: 4.2093, loss: 4.2093 +2024-12-26 12:54:22,018 - pyskl - INFO - Epoch [14][1900/3746] lr: 9.801e-02, eta: 4 days, 10:02:11, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4875, loss_cls: 4.1996, loss: 4.1996 +2024-12-26 12:55:34,164 - pyskl - INFO - Epoch [14][2000/3746] lr: 9.800e-02, eta: 4 days, 10:00:30, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4945, loss_cls: 4.1626, loss: 4.1626 +2024-12-26 12:56:46,278 - pyskl - INFO - Epoch [14][2100/3746] lr: 9.800e-02, eta: 4 days, 9:58:50, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4817, loss_cls: 4.1815, loss: 4.1815 +2024-12-26 12:57:58,373 - pyskl - INFO - Epoch [14][2200/3746] lr: 9.799e-02, eta: 4 days, 9:57:10, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4847, loss_cls: 4.1778, loss: 4.1778 +2024-12-26 12:59:10,537 - pyskl - INFO - Epoch [14][2300/3746] lr: 9.798e-02, eta: 4 days, 9:55:30, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4859, loss_cls: 4.1712, loss: 4.1712 +2024-12-26 13:00:22,513 - pyskl - INFO - Epoch [14][2400/3746] lr: 9.797e-02, eta: 4 days, 9:53:49, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4814, loss_cls: 4.2225, loss: 4.2225 +2024-12-26 13:01:34,555 - pyskl - INFO - Epoch [14][2500/3746] lr: 9.797e-02, eta: 4 days, 9:52:09, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4931, loss_cls: 4.1382, loss: 4.1382 +2024-12-26 13:02:46,505 - pyskl - INFO - Epoch [14][2600/3746] lr: 9.796e-02, eta: 4 days, 9:50:27, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5014, loss_cls: 4.1430, loss: 4.1430 +2024-12-26 13:03:58,540 - pyskl - INFO - Epoch [14][2700/3746] lr: 9.795e-02, eta: 4 days, 9:48:47, time: 0.720, data_time: 0.001, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4891, loss_cls: 4.1521, loss: 4.1521 +2024-12-26 13:05:10,748 - pyskl - INFO - Epoch [14][2800/3746] lr: 9.794e-02, eta: 4 days, 9:47:09, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4942, loss_cls: 4.1427, loss: 4.1427 +2024-12-26 13:06:23,207 - pyskl - INFO - Epoch [14][2900/3746] lr: 9.793e-02, eta: 4 days, 9:45:33, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4780, loss_cls: 4.2076, loss: 4.2076 +2024-12-26 13:07:35,250 - pyskl - INFO - Epoch [14][3000/3746] lr: 9.793e-02, eta: 4 days, 9:43:53, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4716, loss_cls: 4.2312, loss: 4.2312 +2024-12-26 13:08:47,367 - pyskl - INFO - Epoch [14][3100/3746] lr: 9.792e-02, eta: 4 days, 9:42:14, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4870, loss_cls: 4.1585, loss: 4.1585 +2024-12-26 13:09:59,576 - pyskl - INFO - Epoch [14][3200/3746] lr: 9.791e-02, eta: 4 days, 9:40:35, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4889, loss_cls: 4.1532, loss: 4.1532 +2024-12-26 13:11:12,296 - pyskl - INFO - Epoch [14][3300/3746] lr: 9.790e-02, eta: 4 days, 9:39:02, time: 0.727, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4736, loss_cls: 4.2080, loss: 4.2080 +2024-12-26 13:12:24,569 - pyskl - INFO - Epoch [14][3400/3746] lr: 9.789e-02, eta: 4 days, 9:37:25, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4842, loss_cls: 4.1842, loss: 4.1842 +2024-12-26 13:13:36,530 - pyskl - INFO - Epoch [14][3500/3746] lr: 9.789e-02, eta: 4 days, 9:35:45, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4841, loss_cls: 4.1929, loss: 4.1929 +2024-12-26 13:14:48,517 - pyskl - INFO - Epoch [14][3600/3746] lr: 9.788e-02, eta: 4 days, 9:34:05, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4892, loss_cls: 4.1614, loss: 4.1614 +2024-12-26 13:16:00,347 - pyskl - INFO - Epoch [14][3700/3746] lr: 9.787e-02, eta: 4 days, 9:32:24, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4830, loss_cls: 4.2049, loss: 4.2049 +2024-12-26 13:16:34,788 - pyskl - INFO - Saving checkpoint at 14 epochs +2024-12-26 13:18:33,076 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 13:18:33,785 - pyskl - INFO - +top1_acc 0.1603 +top5_acc 0.3652 +2024-12-26 13:18:33,785 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 13:18:33,833 - pyskl - INFO - +mean_acc 0.1600 +2024-12-26 13:18:33,847 - pyskl - INFO - Epoch(val) [14][309] top1_acc: 0.1603, top5_acc: 0.3652, mean_class_accuracy: 0.1600 +2024-12-26 13:22:04,214 - pyskl - INFO - Epoch [15][100/3746] lr: 9.786e-02, eta: 4 days, 9:46:59, time: 2.104, data_time: 1.387, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4923, loss_cls: 4.1242, loss: 4.1242 +2024-12-26 13:23:15,810 - pyskl - INFO - Epoch [15][200/3746] lr: 9.785e-02, eta: 4 days, 9:45:13, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4878, loss_cls: 4.1799, loss: 4.1799 +2024-12-26 13:24:27,525 - pyskl - INFO - Epoch [15][300/3746] lr: 9.784e-02, eta: 4 days, 9:43:29, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4764, loss_cls: 4.1677, loss: 4.1677 +2024-12-26 13:25:39,294 - pyskl - INFO - Epoch [15][400/3746] lr: 9.783e-02, eta: 4 days, 9:41:45, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4889, loss_cls: 4.1744, loss: 4.1744 +2024-12-26 13:26:51,129 - pyskl - INFO - Epoch [15][500/3746] lr: 9.783e-02, eta: 4 days, 9:40:03, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4795, loss_cls: 4.2048, loss: 4.2048 +2024-12-26 13:28:02,537 - pyskl - INFO - Epoch [15][600/3746] lr: 9.782e-02, eta: 4 days, 9:38:16, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4777, loss_cls: 4.2076, loss: 4.2076 +2024-12-26 13:29:13,959 - pyskl - INFO - Epoch [15][700/3746] lr: 9.781e-02, eta: 4 days, 9:36:30, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5064, loss_cls: 4.0990, loss: 4.0990 +2024-12-26 13:30:25,629 - pyskl - INFO - Epoch [15][800/3746] lr: 9.780e-02, eta: 4 days, 9:34:46, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4850, loss_cls: 4.1987, loss: 4.1987 +2024-12-26 13:31:37,483 - pyskl - INFO - Epoch [15][900/3746] lr: 9.779e-02, eta: 4 days, 9:33:03, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4923, loss_cls: 4.1435, loss: 4.1435 +2024-12-26 13:32:48,799 - pyskl - INFO - Epoch [15][1000/3746] lr: 9.778e-02, eta: 4 days, 9:31:16, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4772, loss_cls: 4.2031, loss: 4.2031 +2024-12-26 13:34:00,288 - pyskl - INFO - Epoch [15][1100/3746] lr: 9.778e-02, eta: 4 days, 9:29:31, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4884, loss_cls: 4.1472, loss: 4.1472 +2024-12-26 13:35:12,271 - pyskl - INFO - Epoch [15][1200/3746] lr: 9.777e-02, eta: 4 days, 9:27:51, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4952, loss_cls: 4.1502, loss: 4.1502 +2024-12-26 13:36:24,423 - pyskl - INFO - Epoch [15][1300/3746] lr: 9.776e-02, eta: 4 days, 9:26:12, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4798, loss_cls: 4.2083, loss: 4.2083 +2024-12-26 13:37:36,247 - pyskl - INFO - Epoch [15][1400/3746] lr: 9.775e-02, eta: 4 days, 9:24:30, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4859, loss_cls: 4.1693, loss: 4.1693 +2024-12-26 13:38:48,108 - pyskl - INFO - Epoch [15][1500/3746] lr: 9.774e-02, eta: 4 days, 9:22:49, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4745, loss_cls: 4.2130, loss: 4.2130 +2024-12-26 13:40:00,290 - pyskl - INFO - Epoch [15][1600/3746] lr: 9.773e-02, eta: 4 days, 9:21:10, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4897, loss_cls: 4.1516, loss: 4.1516 +2024-12-26 13:41:12,141 - pyskl - INFO - Epoch [15][1700/3746] lr: 9.773e-02, eta: 4 days, 9:19:29, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4833, loss_cls: 4.1789, loss: 4.1789 +2024-12-26 13:42:23,869 - pyskl - INFO - Epoch [15][1800/3746] lr: 9.772e-02, eta: 4 days, 9:17:47, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4838, loss_cls: 4.1865, loss: 4.1865 +2024-12-26 13:43:35,990 - pyskl - INFO - Epoch [15][1900/3746] lr: 9.771e-02, eta: 4 days, 9:16:08, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4847, loss_cls: 4.1877, loss: 4.1877 +2024-12-26 13:44:47,770 - pyskl - INFO - Epoch [15][2000/3746] lr: 9.770e-02, eta: 4 days, 9:14:27, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4955, loss_cls: 4.1514, loss: 4.1514 +2024-12-26 13:45:59,720 - pyskl - INFO - Epoch [15][2100/3746] lr: 9.769e-02, eta: 4 days, 9:12:47, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5023, loss_cls: 4.1358, loss: 4.1358 +2024-12-26 13:47:11,704 - pyskl - INFO - Epoch [15][2200/3746] lr: 9.768e-02, eta: 4 days, 9:11:07, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4863, loss_cls: 4.1604, loss: 4.1604 +2024-12-26 13:48:23,212 - pyskl - INFO - Epoch [15][2300/3746] lr: 9.768e-02, eta: 4 days, 9:09:24, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4973, loss_cls: 4.1619, loss: 4.1619 +2024-12-26 13:49:34,730 - pyskl - INFO - Epoch [15][2400/3746] lr: 9.767e-02, eta: 4 days, 9:07:40, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4886, loss_cls: 4.1827, loss: 4.1827 +2024-12-26 13:50:46,381 - pyskl - INFO - Epoch [15][2500/3746] lr: 9.766e-02, eta: 4 days, 9:05:58, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4836, loss_cls: 4.1944, loss: 4.1944 +2024-12-26 13:51:58,107 - pyskl - INFO - Epoch [15][2600/3746] lr: 9.765e-02, eta: 4 days, 9:04:16, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4864, loss_cls: 4.1681, loss: 4.1681 +2024-12-26 13:53:09,880 - pyskl - INFO - Epoch [15][2700/3746] lr: 9.764e-02, eta: 4 days, 9:02:36, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4894, loss_cls: 4.1635, loss: 4.1635 +2024-12-26 13:54:21,634 - pyskl - INFO - Epoch [15][2800/3746] lr: 9.763e-02, eta: 4 days, 9:00:55, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4834, loss_cls: 4.1917, loss: 4.1917 +2024-12-26 13:55:33,529 - pyskl - INFO - Epoch [15][2900/3746] lr: 9.763e-02, eta: 4 days, 8:59:15, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4816, loss_cls: 4.2291, loss: 4.2291 +2024-12-26 13:56:45,399 - pyskl - INFO - Epoch [15][3000/3746] lr: 9.762e-02, eta: 4 days, 8:57:35, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4898, loss_cls: 4.1487, loss: 4.1487 +2024-12-26 13:57:57,497 - pyskl - INFO - Epoch [15][3100/3746] lr: 9.761e-02, eta: 4 days, 8:55:58, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4970, loss_cls: 4.1322, loss: 4.1322 +2024-12-26 13:59:09,633 - pyskl - INFO - Epoch [15][3200/3746] lr: 9.760e-02, eta: 4 days, 8:54:21, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4769, loss_cls: 4.2357, loss: 4.2357 +2024-12-26 14:00:21,699 - pyskl - INFO - Epoch [15][3300/3746] lr: 9.759e-02, eta: 4 days, 8:52:43, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4800, loss_cls: 4.1793, loss: 4.1793 +2024-12-26 14:01:33,512 - pyskl - INFO - Epoch [15][3400/3746] lr: 9.758e-02, eta: 4 days, 8:51:03, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4919, loss_cls: 4.1822, loss: 4.1822 +2024-12-26 14:02:45,257 - pyskl - INFO - Epoch [15][3500/3746] lr: 9.757e-02, eta: 4 days, 8:49:23, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4970, loss_cls: 4.1594, loss: 4.1594 +2024-12-26 14:03:57,005 - pyskl - INFO - Epoch [15][3600/3746] lr: 9.757e-02, eta: 4 days, 8:47:43, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4948, loss_cls: 4.1420, loss: 4.1420 +2024-12-26 14:05:08,627 - pyskl - INFO - Epoch [15][3700/3746] lr: 9.756e-02, eta: 4 days, 8:46:02, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4816, loss_cls: 4.1831, loss: 4.1831 +2024-12-26 14:05:43,393 - pyskl - INFO - Saving checkpoint at 15 epochs +2024-12-26 14:07:39,563 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 14:07:40,298 - pyskl - INFO - +top1_acc 0.1493 +top5_acc 0.3544 +2024-12-26 14:07:40,298 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 14:07:40,352 - pyskl - INFO - +mean_acc 0.1493 +2024-12-26 14:07:40,372 - pyskl - INFO - Epoch(val) [15][309] top1_acc: 0.1493, top5_acc: 0.3544, mean_class_accuracy: 0.1493 +2024-12-26 14:11:12,150 - pyskl - INFO - Epoch [16][100/3746] lr: 9.754e-02, eta: 4 days, 8:59:37, time: 2.118, data_time: 1.398, memory: 15990, top1_acc: 0.2412, top5_acc: 0.5006, loss_cls: 4.1366, loss: 4.1366 +2024-12-26 14:12:23,726 - pyskl - INFO - Epoch [16][200/3746] lr: 9.754e-02, eta: 4 days, 8:57:54, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4952, loss_cls: 4.1301, loss: 4.1301 +2024-12-26 14:13:35,390 - pyskl - INFO - Epoch [16][300/3746] lr: 9.753e-02, eta: 4 days, 8:56:11, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5022, loss_cls: 4.0789, loss: 4.0789 +2024-12-26 14:14:47,064 - pyskl - INFO - Epoch [16][400/3746] lr: 9.752e-02, eta: 4 days, 8:54:29, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4884, loss_cls: 4.1638, loss: 4.1638 +2024-12-26 14:15:58,307 - pyskl - INFO - Epoch [16][500/3746] lr: 9.751e-02, eta: 4 days, 8:52:43, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4891, loss_cls: 4.1863, loss: 4.1863 +2024-12-26 14:17:10,022 - pyskl - INFO - Epoch [16][600/3746] lr: 9.750e-02, eta: 4 days, 8:51:02, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4863, loss_cls: 4.1970, loss: 4.1970 +2024-12-26 14:18:21,319 - pyskl - INFO - Epoch [16][700/3746] lr: 9.749e-02, eta: 4 days, 8:49:16, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4913, loss_cls: 4.1807, loss: 4.1807 +2024-12-26 14:19:32,675 - pyskl - INFO - Epoch [16][800/3746] lr: 9.748e-02, eta: 4 days, 8:47:32, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4925, loss_cls: 4.1432, loss: 4.1432 +2024-12-26 14:20:44,026 - pyskl - INFO - Epoch [16][900/3746] lr: 9.747e-02, eta: 4 days, 8:45:47, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4888, loss_cls: 4.1252, loss: 4.1252 +2024-12-26 14:21:55,570 - pyskl - INFO - Epoch [16][1000/3746] lr: 9.747e-02, eta: 4 days, 8:44:05, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4928, loss_cls: 4.1495, loss: 4.1495 +2024-12-26 14:23:07,339 - pyskl - INFO - Epoch [16][1100/3746] lr: 9.746e-02, eta: 4 days, 8:42:24, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4863, loss_cls: 4.2020, loss: 4.2020 +2024-12-26 14:24:18,984 - pyskl - INFO - Epoch [16][1200/3746] lr: 9.745e-02, eta: 4 days, 8:40:43, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4916, loss_cls: 4.1213, loss: 4.1213 +2024-12-26 14:25:30,962 - pyskl - INFO - Epoch [16][1300/3746] lr: 9.744e-02, eta: 4 days, 8:39:04, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4816, loss_cls: 4.2190, loss: 4.2190 +2024-12-26 14:26:42,909 - pyskl - INFO - Epoch [16][1400/3746] lr: 9.743e-02, eta: 4 days, 8:37:25, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4928, loss_cls: 4.1495, loss: 4.1495 +2024-12-26 14:27:55,019 - pyskl - INFO - Epoch [16][1500/3746] lr: 9.742e-02, eta: 4 days, 8:35:48, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4853, loss_cls: 4.1840, loss: 4.1840 +2024-12-26 14:29:06,755 - pyskl - INFO - Epoch [16][1600/3746] lr: 9.741e-02, eta: 4 days, 8:34:08, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4844, loss_cls: 4.1660, loss: 4.1660 +2024-12-26 14:30:18,598 - pyskl - INFO - Epoch [16][1700/3746] lr: 9.740e-02, eta: 4 days, 8:32:28, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4816, loss_cls: 4.1972, loss: 4.1972 +2024-12-26 14:31:30,090 - pyskl - INFO - Epoch [16][1800/3746] lr: 9.740e-02, eta: 4 days, 8:30:46, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4866, loss_cls: 4.1833, loss: 4.1833 +2024-12-26 14:32:41,767 - pyskl - INFO - Epoch [16][1900/3746] lr: 9.739e-02, eta: 4 days, 8:29:05, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4894, loss_cls: 4.1890, loss: 4.1890 +2024-12-26 14:33:53,835 - pyskl - INFO - Epoch [16][2000/3746] lr: 9.738e-02, eta: 4 days, 8:27:28, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5012, loss_cls: 4.1521, loss: 4.1521 +2024-12-26 14:35:05,447 - pyskl - INFO - Epoch [16][2100/3746] lr: 9.737e-02, eta: 4 days, 8:25:47, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4802, loss_cls: 4.1892, loss: 4.1892 +2024-12-26 14:36:17,140 - pyskl - INFO - Epoch [16][2200/3746] lr: 9.736e-02, eta: 4 days, 8:24:07, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4906, loss_cls: 4.1577, loss: 4.1577 +2024-12-26 14:37:29,234 - pyskl - INFO - Epoch [16][2300/3746] lr: 9.735e-02, eta: 4 days, 8:22:31, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4850, loss_cls: 4.1621, loss: 4.1621 +2024-12-26 14:38:40,912 - pyskl - INFO - Epoch [16][2400/3746] lr: 9.734e-02, eta: 4 days, 8:20:50, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4848, loss_cls: 4.1799, loss: 4.1799 +2024-12-26 14:39:52,817 - pyskl - INFO - Epoch [16][2500/3746] lr: 9.733e-02, eta: 4 days, 8:19:12, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.4941, loss_cls: 4.1168, loss: 4.1168 +2024-12-26 14:41:04,515 - pyskl - INFO - Epoch [16][2600/3746] lr: 9.732e-02, eta: 4 days, 8:17:33, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4903, loss_cls: 4.1730, loss: 4.1730 +2024-12-26 14:42:16,118 - pyskl - INFO - Epoch [16][2700/3746] lr: 9.731e-02, eta: 4 days, 8:15:52, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.5039, loss_cls: 4.1286, loss: 4.1286 +2024-12-26 14:43:27,785 - pyskl - INFO - Epoch [16][2800/3746] lr: 9.731e-02, eta: 4 days, 8:14:12, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4722, loss_cls: 4.2185, loss: 4.2185 +2024-12-26 14:44:39,454 - pyskl - INFO - Epoch [16][2900/3746] lr: 9.730e-02, eta: 4 days, 8:12:33, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4809, loss_cls: 4.2299, loss: 4.2299 +2024-12-26 14:45:51,621 - pyskl - INFO - Epoch [16][3000/3746] lr: 9.729e-02, eta: 4 days, 8:10:57, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4884, loss_cls: 4.1636, loss: 4.1636 +2024-12-26 14:47:03,337 - pyskl - INFO - Epoch [16][3100/3746] lr: 9.728e-02, eta: 4 days, 8:09:18, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4844, loss_cls: 4.2055, loss: 4.2055 +2024-12-26 14:48:15,013 - pyskl - INFO - Epoch [16][3200/3746] lr: 9.727e-02, eta: 4 days, 8:07:39, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4786, loss_cls: 4.2315, loss: 4.2315 +2024-12-26 14:49:26,915 - pyskl - INFO - Epoch [16][3300/3746] lr: 9.726e-02, eta: 4 days, 8:06:01, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4927, loss_cls: 4.1535, loss: 4.1535 +2024-12-26 14:50:38,863 - pyskl - INFO - Epoch [16][3400/3746] lr: 9.725e-02, eta: 4 days, 8:04:25, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4891, loss_cls: 4.1391, loss: 4.1391 +2024-12-26 14:51:50,841 - pyskl - INFO - Epoch [16][3500/3746] lr: 9.724e-02, eta: 4 days, 8:02:48, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4863, loss_cls: 4.1897, loss: 4.1897 +2024-12-26 14:53:02,336 - pyskl - INFO - Epoch [16][3600/3746] lr: 9.723e-02, eta: 4 days, 8:01:07, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4784, loss_cls: 4.2141, loss: 4.2141 +2024-12-26 14:54:14,317 - pyskl - INFO - Epoch [16][3700/3746] lr: 9.722e-02, eta: 4 days, 7:59:31, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4898, loss_cls: 4.1756, loss: 4.1756 +2024-12-26 14:54:48,924 - pyskl - INFO - Saving checkpoint at 16 epochs +2024-12-26 14:56:44,873 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 14:56:45,621 - pyskl - INFO - +top1_acc 0.1514 +top5_acc 0.3659 +2024-12-26 14:56:45,621 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 14:56:45,665 - pyskl - INFO - +mean_acc 0.1513 +2024-12-26 14:56:45,681 - pyskl - INFO - Epoch(val) [16][309] top1_acc: 0.1514, top5_acc: 0.3659, mean_class_accuracy: 0.1513 +2024-12-26 15:00:16,058 - pyskl - INFO - Epoch [17][100/3746] lr: 9.721e-02, eta: 4 days, 8:11:51, time: 2.104, data_time: 1.382, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4794, loss_cls: 4.1581, loss: 4.1581 +2024-12-26 15:01:27,674 - pyskl - INFO - Epoch [17][200/3746] lr: 9.720e-02, eta: 4 days, 8:10:10, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4952, loss_cls: 4.1198, loss: 4.1198 +2024-12-26 15:02:38,828 - pyskl - INFO - Epoch [17][300/3746] lr: 9.719e-02, eta: 4 days, 8:08:25, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4945, loss_cls: 4.1083, loss: 4.1083 +2024-12-26 15:03:50,980 - pyskl - INFO - Epoch [17][400/3746] lr: 9.718e-02, eta: 4 days, 8:06:49, time: 0.722, data_time: 0.001, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4798, loss_cls: 4.1706, loss: 4.1706 +2024-12-26 15:05:02,428 - pyskl - INFO - Epoch [17][500/3746] lr: 9.717e-02, eta: 4 days, 8:05:07, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4867, loss_cls: 4.1691, loss: 4.1691 +2024-12-26 15:06:13,997 - pyskl - INFO - Epoch [17][600/3746] lr: 9.716e-02, eta: 4 days, 8:03:26, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4869, loss_cls: 4.1872, loss: 4.1872 +2024-12-26 15:07:25,558 - pyskl - INFO - Epoch [17][700/3746] lr: 9.715e-02, eta: 4 days, 8:01:45, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4803, loss_cls: 4.1835, loss: 4.1835 +2024-12-26 15:08:37,234 - pyskl - INFO - Epoch [17][800/3746] lr: 9.714e-02, eta: 4 days, 8:00:06, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4928, loss_cls: 4.1786, loss: 4.1786 +2024-12-26 15:09:48,672 - pyskl - INFO - Epoch [17][900/3746] lr: 9.714e-02, eta: 4 days, 7:58:24, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4777, loss_cls: 4.2023, loss: 4.2023 +2024-12-26 15:10:59,704 - pyskl - INFO - Epoch [17][1000/3746] lr: 9.713e-02, eta: 4 days, 7:56:39, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5005, loss_cls: 4.1122, loss: 4.1122 +2024-12-26 15:12:11,551 - pyskl - INFO - Epoch [17][1100/3746] lr: 9.712e-02, eta: 4 days, 7:55:01, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4938, loss_cls: 4.1322, loss: 4.1322 +2024-12-26 15:13:23,504 - pyskl - INFO - Epoch [17][1200/3746] lr: 9.711e-02, eta: 4 days, 7:53:24, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4878, loss_cls: 4.1497, loss: 4.1497 +2024-12-26 15:14:35,406 - pyskl - INFO - Epoch [17][1300/3746] lr: 9.710e-02, eta: 4 days, 7:51:46, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4853, loss_cls: 4.1554, loss: 4.1554 +2024-12-26 15:15:47,543 - pyskl - INFO - Epoch [17][1400/3746] lr: 9.709e-02, eta: 4 days, 7:50:11, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4941, loss_cls: 4.1541, loss: 4.1541 +2024-12-26 15:16:59,936 - pyskl - INFO - Epoch [17][1500/3746] lr: 9.708e-02, eta: 4 days, 7:48:38, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4945, loss_cls: 4.1545, loss: 4.1545 +2024-12-26 15:18:12,225 - pyskl - INFO - Epoch [17][1600/3746] lr: 9.707e-02, eta: 4 days, 7:47:03, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4869, loss_cls: 4.1532, loss: 4.1532 +2024-12-26 15:19:24,197 - pyskl - INFO - Epoch [17][1700/3746] lr: 9.706e-02, eta: 4 days, 7:45:27, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4942, loss_cls: 4.1286, loss: 4.1286 +2024-12-26 15:20:36,214 - pyskl - INFO - Epoch [17][1800/3746] lr: 9.705e-02, eta: 4 days, 7:43:51, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4934, loss_cls: 4.1208, loss: 4.1208 +2024-12-26 15:21:48,157 - pyskl - INFO - Epoch [17][1900/3746] lr: 9.704e-02, eta: 4 days, 7:42:14, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4947, loss_cls: 4.1405, loss: 4.1405 +2024-12-26 15:22:59,915 - pyskl - INFO - Epoch [17][2000/3746] lr: 9.703e-02, eta: 4 days, 7:40:36, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4892, loss_cls: 4.1406, loss: 4.1406 +2024-12-26 15:24:12,019 - pyskl - INFO - Epoch [17][2100/3746] lr: 9.702e-02, eta: 4 days, 7:39:01, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4978, loss_cls: 4.1476, loss: 4.1476 +2024-12-26 15:25:24,174 - pyskl - INFO - Epoch [17][2200/3746] lr: 9.701e-02, eta: 4 days, 7:37:26, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4791, loss_cls: 4.2020, loss: 4.2020 +2024-12-26 15:26:36,202 - pyskl - INFO - Epoch [17][2300/3746] lr: 9.700e-02, eta: 4 days, 7:35:50, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4950, loss_cls: 4.1598, loss: 4.1598 +2024-12-26 15:27:48,212 - pyskl - INFO - Epoch [17][2400/3746] lr: 9.699e-02, eta: 4 days, 7:34:15, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4994, loss_cls: 4.0803, loss: 4.0803 +2024-12-26 15:29:00,414 - pyskl - INFO - Epoch [17][2500/3746] lr: 9.698e-02, eta: 4 days, 7:32:41, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4981, loss_cls: 4.1398, loss: 4.1398 +2024-12-26 15:30:12,540 - pyskl - INFO - Epoch [17][2600/3746] lr: 9.697e-02, eta: 4 days, 7:31:06, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4872, loss_cls: 4.1359, loss: 4.1359 +2024-12-26 15:31:24,364 - pyskl - INFO - Epoch [17][2700/3746] lr: 9.697e-02, eta: 4 days, 7:29:29, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4788, loss_cls: 4.2035, loss: 4.2035 +2024-12-26 15:32:36,689 - pyskl - INFO - Epoch [17][2800/3746] lr: 9.696e-02, eta: 4 days, 7:27:56, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4847, loss_cls: 4.1675, loss: 4.1675 +2024-12-26 15:33:48,980 - pyskl - INFO - Epoch [17][2900/3746] lr: 9.695e-02, eta: 4 days, 7:26:23, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4809, loss_cls: 4.2160, loss: 4.2160 +2024-12-26 15:35:01,025 - pyskl - INFO - Epoch [17][3000/3746] lr: 9.694e-02, eta: 4 days, 7:24:48, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4892, loss_cls: 4.1662, loss: 4.1662 +2024-12-26 15:36:13,213 - pyskl - INFO - Epoch [17][3100/3746] lr: 9.693e-02, eta: 4 days, 7:23:14, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.4889, loss_cls: 4.1513, loss: 4.1513 +2024-12-26 15:37:25,610 - pyskl - INFO - Epoch [17][3200/3746] lr: 9.692e-02, eta: 4 days, 7:21:42, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4797, loss_cls: 4.1726, loss: 4.1726 +2024-12-26 15:38:37,849 - pyskl - INFO - Epoch [17][3300/3746] lr: 9.691e-02, eta: 4 days, 7:20:09, time: 0.722, data_time: 0.001, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4783, loss_cls: 4.2216, loss: 4.2216 +2024-12-26 15:39:49,879 - pyskl - INFO - Epoch [17][3400/3746] lr: 9.690e-02, eta: 4 days, 7:18:34, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4977, loss_cls: 4.1687, loss: 4.1687 +2024-12-26 15:41:02,208 - pyskl - INFO - Epoch [17][3500/3746] lr: 9.689e-02, eta: 4 days, 7:17:01, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4845, loss_cls: 4.1608, loss: 4.1608 +2024-12-26 15:42:13,797 - pyskl - INFO - Epoch [17][3600/3746] lr: 9.688e-02, eta: 4 days, 7:15:23, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4883, loss_cls: 4.1715, loss: 4.1715 +2024-12-26 15:43:25,875 - pyskl - INFO - Epoch [17][3700/3746] lr: 9.687e-02, eta: 4 days, 7:13:49, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4834, loss_cls: 4.1418, loss: 4.1418 +2024-12-26 15:44:00,455 - pyskl - INFO - Saving checkpoint at 17 epochs +2024-12-26 15:45:56,663 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 15:45:57,419 - pyskl - INFO - +top1_acc 0.1678 +top5_acc 0.3887 +2024-12-26 15:45:57,419 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 15:45:57,467 - pyskl - INFO - +mean_acc 0.1677 +2024-12-26 15:45:57,482 - pyskl - INFO - Epoch(val) [17][309] top1_acc: 0.1678, top5_acc: 0.3887, mean_class_accuracy: 0.1677 +2024-12-26 15:49:29,158 - pyskl - INFO - Epoch [18][100/3746] lr: 9.685e-02, eta: 4 days, 7:25:23, time: 2.117, data_time: 1.394, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4959, loss_cls: 4.1166, loss: 4.1166 +2024-12-26 15:50:40,482 - pyskl - INFO - Epoch [18][200/3746] lr: 9.684e-02, eta: 4 days, 7:23:41, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.5011, loss_cls: 4.1038, loss: 4.1038 +2024-12-26 15:51:51,690 - pyskl - INFO - Epoch [18][300/3746] lr: 9.683e-02, eta: 4 days, 7:21:59, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4959, loss_cls: 4.1250, loss: 4.1250 +2024-12-26 15:53:03,137 - pyskl - INFO - Epoch [18][400/3746] lr: 9.683e-02, eta: 4 days, 7:20:19, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4917, loss_cls: 4.1630, loss: 4.1630 +2024-12-26 15:54:14,416 - pyskl - INFO - Epoch [18][500/3746] lr: 9.682e-02, eta: 4 days, 7:18:37, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4933, loss_cls: 4.1246, loss: 4.1246 +2024-12-26 15:55:25,838 - pyskl - INFO - Epoch [18][600/3746] lr: 9.681e-02, eta: 4 days, 7:16:57, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4942, loss_cls: 4.1380, loss: 4.1380 +2024-12-26 15:56:37,314 - pyskl - INFO - Epoch [18][700/3746] lr: 9.680e-02, eta: 4 days, 7:15:17, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4823, loss_cls: 4.1887, loss: 4.1887 +2024-12-26 15:57:49,134 - pyskl - INFO - Epoch [18][800/3746] lr: 9.679e-02, eta: 4 days, 7:13:40, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4881, loss_cls: 4.1614, loss: 4.1614 +2024-12-26 15:59:00,973 - pyskl - INFO - Epoch [18][900/3746] lr: 9.678e-02, eta: 4 days, 7:12:03, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4916, loss_cls: 4.1663, loss: 4.1663 +2024-12-26 16:00:12,368 - pyskl - INFO - Epoch [18][1000/3746] lr: 9.677e-02, eta: 4 days, 7:10:23, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4866, loss_cls: 4.1540, loss: 4.1540 +2024-12-26 16:01:23,876 - pyskl - INFO - Epoch [18][1100/3746] lr: 9.676e-02, eta: 4 days, 7:08:44, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4888, loss_cls: 4.1742, loss: 4.1742 +2024-12-26 16:02:35,685 - pyskl - INFO - Epoch [18][1200/3746] lr: 9.675e-02, eta: 4 days, 7:07:07, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4872, loss_cls: 4.1788, loss: 4.1788 +2024-12-26 16:03:47,476 - pyskl - INFO - Epoch [18][1300/3746] lr: 9.674e-02, eta: 4 days, 7:05:30, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4913, loss_cls: 4.1844, loss: 4.1844 +2024-12-26 16:04:59,083 - pyskl - INFO - Epoch [18][1400/3746] lr: 9.673e-02, eta: 4 days, 7:03:52, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4833, loss_cls: 4.1599, loss: 4.1599 +2024-12-26 16:06:10,671 - pyskl - INFO - Epoch [18][1500/3746] lr: 9.672e-02, eta: 4 days, 7:02:14, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4975, loss_cls: 4.1260, loss: 4.1260 +2024-12-26 16:07:22,354 - pyskl - INFO - Epoch [18][1600/3746] lr: 9.671e-02, eta: 4 days, 7:00:36, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4889, loss_cls: 4.1792, loss: 4.1792 +2024-12-26 16:08:34,022 - pyskl - INFO - Epoch [18][1700/3746] lr: 9.670e-02, eta: 4 days, 6:58:59, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.4988, loss_cls: 4.1267, loss: 4.1267 +2024-12-26 16:09:45,606 - pyskl - INFO - Epoch [18][1800/3746] lr: 9.669e-02, eta: 4 days, 6:57:21, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4847, loss_cls: 4.1576, loss: 4.1576 +2024-12-26 16:10:57,141 - pyskl - INFO - Epoch [18][1900/3746] lr: 9.668e-02, eta: 4 days, 6:55:42, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4941, loss_cls: 4.1231, loss: 4.1231 +2024-12-26 16:12:08,735 - pyskl - INFO - Epoch [18][2000/3746] lr: 9.667e-02, eta: 4 days, 6:54:05, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4952, loss_cls: 4.1706, loss: 4.1706 +2024-12-26 16:13:20,636 - pyskl - INFO - Epoch [18][2100/3746] lr: 9.666e-02, eta: 4 days, 6:52:29, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4880, loss_cls: 4.1719, loss: 4.1719 +2024-12-26 16:14:32,296 - pyskl - INFO - Epoch [18][2200/3746] lr: 9.665e-02, eta: 4 days, 6:50:52, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4856, loss_cls: 4.1701, loss: 4.1701 +2024-12-26 16:15:43,851 - pyskl - INFO - Epoch [18][2300/3746] lr: 9.664e-02, eta: 4 days, 6:49:14, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4917, loss_cls: 4.1558, loss: 4.1558 +2024-12-26 16:16:55,363 - pyskl - INFO - Epoch [18][2400/3746] lr: 9.663e-02, eta: 4 days, 6:47:36, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.4992, loss_cls: 4.1045, loss: 4.1045 +2024-12-26 16:18:06,839 - pyskl - INFO - Epoch [18][2500/3746] lr: 9.662e-02, eta: 4 days, 6:45:58, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4889, loss_cls: 4.1624, loss: 4.1624 +2024-12-26 16:19:18,303 - pyskl - INFO - Epoch [18][2600/3746] lr: 9.661e-02, eta: 4 days, 6:44:19, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4961, loss_cls: 4.1306, loss: 4.1306 +2024-12-26 16:20:29,911 - pyskl - INFO - Epoch [18][2700/3746] lr: 9.660e-02, eta: 4 days, 6:42:42, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4984, loss_cls: 4.1191, loss: 4.1191 +2024-12-26 16:21:41,636 - pyskl - INFO - Epoch [18][2800/3746] lr: 9.659e-02, eta: 4 days, 6:41:06, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4834, loss_cls: 4.1710, loss: 4.1710 +2024-12-26 16:22:53,285 - pyskl - INFO - Epoch [18][2900/3746] lr: 9.658e-02, eta: 4 days, 6:39:29, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4948, loss_cls: 4.1368, loss: 4.1368 +2024-12-26 16:24:04,982 - pyskl - INFO - Epoch [18][3000/3746] lr: 9.657e-02, eta: 4 days, 6:37:53, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4917, loss_cls: 4.1600, loss: 4.1600 +2024-12-26 16:25:16,712 - pyskl - INFO - Epoch [18][3100/3746] lr: 9.656e-02, eta: 4 days, 6:36:17, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4997, loss_cls: 4.0871, loss: 4.0871 +2024-12-26 16:26:28,394 - pyskl - INFO - Epoch [18][3200/3746] lr: 9.654e-02, eta: 4 days, 6:34:41, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4902, loss_cls: 4.1712, loss: 4.1712 +2024-12-26 16:27:40,313 - pyskl - INFO - Epoch [18][3300/3746] lr: 9.653e-02, eta: 4 days, 6:33:06, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4933, loss_cls: 4.1499, loss: 4.1499 +2024-12-26 16:28:52,189 - pyskl - INFO - Epoch [18][3400/3746] lr: 9.652e-02, eta: 4 days, 6:31:32, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4817, loss_cls: 4.2078, loss: 4.2078 +2024-12-26 16:30:04,074 - pyskl - INFO - Epoch [18][3500/3746] lr: 9.651e-02, eta: 4 days, 6:29:57, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.4930, loss_cls: 4.1274, loss: 4.1274 +2024-12-26 16:31:15,759 - pyskl - INFO - Epoch [18][3600/3746] lr: 9.650e-02, eta: 4 days, 6:28:21, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4986, loss_cls: 4.1420, loss: 4.1420 +2024-12-26 16:32:27,375 - pyskl - INFO - Epoch [18][3700/3746] lr: 9.649e-02, eta: 4 days, 6:26:45, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4936, loss_cls: 4.1337, loss: 4.1337 +2024-12-26 16:33:01,772 - pyskl - INFO - Saving checkpoint at 18 epochs +2024-12-26 16:34:57,825 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 16:34:58,557 - pyskl - INFO - +top1_acc 0.1774 +top5_acc 0.3946 +2024-12-26 16:34:58,557 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 16:34:58,598 - pyskl - INFO - +mean_acc 0.1770 +2024-12-26 16:34:58,610 - pyskl - INFO - Epoch(val) [18][309] top1_acc: 0.1774, top5_acc: 0.3946, mean_class_accuracy: 0.1770 +2024-12-26 16:38:29,164 - pyskl - INFO - Epoch [19][100/3746] lr: 9.648e-02, eta: 4 days, 6:37:20, time: 2.105, data_time: 1.387, memory: 15990, top1_acc: 0.2475, top5_acc: 0.5036, loss_cls: 4.1088, loss: 4.1088 +2024-12-26 16:39:40,850 - pyskl - INFO - Epoch [19][200/3746] lr: 9.647e-02, eta: 4 days, 6:35:43, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.4938, loss_cls: 4.1003, loss: 4.1003 +2024-12-26 16:40:52,186 - pyskl - INFO - Epoch [19][300/3746] lr: 9.646e-02, eta: 4 days, 6:34:04, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4927, loss_cls: 4.1419, loss: 4.1419 +2024-12-26 16:42:03,806 - pyskl - INFO - Epoch [19][400/3746] lr: 9.645e-02, eta: 4 days, 6:32:26, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4814, loss_cls: 4.1598, loss: 4.1598 +2024-12-26 16:43:15,387 - pyskl - INFO - Epoch [19][500/3746] lr: 9.644e-02, eta: 4 days, 6:30:49, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4964, loss_cls: 4.1243, loss: 4.1243 +2024-12-26 16:44:26,394 - pyskl - INFO - Epoch [19][600/3746] lr: 9.643e-02, eta: 4 days, 6:29:07, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4963, loss_cls: 4.1470, loss: 4.1470 +2024-12-26 16:45:38,175 - pyskl - INFO - Epoch [19][700/3746] lr: 9.642e-02, eta: 4 days, 6:27:31, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4963, loss_cls: 4.1174, loss: 4.1174 +2024-12-26 16:46:49,387 - pyskl - INFO - Epoch [19][800/3746] lr: 9.641e-02, eta: 4 days, 6:25:51, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4895, loss_cls: 4.1668, loss: 4.1668 +2024-12-26 16:48:00,986 - pyskl - INFO - Epoch [19][900/3746] lr: 9.640e-02, eta: 4 days, 6:24:14, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4928, loss_cls: 4.1662, loss: 4.1662 +2024-12-26 16:49:12,394 - pyskl - INFO - Epoch [19][1000/3746] lr: 9.639e-02, eta: 4 days, 6:22:36, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4883, loss_cls: 4.1715, loss: 4.1715 +2024-12-26 16:50:24,097 - pyskl - INFO - Epoch [19][1100/3746] lr: 9.637e-02, eta: 4 days, 6:21:00, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4956, loss_cls: 4.1479, loss: 4.1479 +2024-12-26 16:51:35,534 - pyskl - INFO - Epoch [19][1200/3746] lr: 9.636e-02, eta: 4 days, 6:19:22, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4972, loss_cls: 4.1404, loss: 4.1404 +2024-12-26 16:52:47,450 - pyskl - INFO - Epoch [19][1300/3746] lr: 9.635e-02, eta: 4 days, 6:17:47, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4909, loss_cls: 4.1359, loss: 4.1359 +2024-12-26 16:53:59,113 - pyskl - INFO - Epoch [19][1400/3746] lr: 9.634e-02, eta: 4 days, 6:16:11, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4867, loss_cls: 4.1289, loss: 4.1289 +2024-12-26 16:55:11,178 - pyskl - INFO - Epoch [19][1500/3746] lr: 9.633e-02, eta: 4 days, 6:14:38, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.5000, loss_cls: 4.1419, loss: 4.1419 +2024-12-26 16:56:22,999 - pyskl - INFO - Epoch [19][1600/3746] lr: 9.632e-02, eta: 4 days, 6:13:03, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4784, loss_cls: 4.2220, loss: 4.2220 +2024-12-26 16:57:35,323 - pyskl - INFO - Epoch [19][1700/3746] lr: 9.631e-02, eta: 4 days, 6:11:31, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4913, loss_cls: 4.1470, loss: 4.1470 +2024-12-26 16:58:46,953 - pyskl - INFO - Epoch [19][1800/3746] lr: 9.630e-02, eta: 4 days, 6:09:55, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4878, loss_cls: 4.1575, loss: 4.1575 +2024-12-26 16:59:59,043 - pyskl - INFO - Epoch [19][1900/3746] lr: 9.629e-02, eta: 4 days, 6:08:22, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4925, loss_cls: 4.1462, loss: 4.1462 +2024-12-26 17:01:11,474 - pyskl - INFO - Epoch [19][2000/3746] lr: 9.628e-02, eta: 4 days, 6:06:52, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4873, loss_cls: 4.1622, loss: 4.1622 +2024-12-26 17:02:23,879 - pyskl - INFO - Epoch [19][2100/3746] lr: 9.627e-02, eta: 4 days, 6:05:21, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4944, loss_cls: 4.1464, loss: 4.1464 +2024-12-26 17:03:35,747 - pyskl - INFO - Epoch [19][2200/3746] lr: 9.626e-02, eta: 4 days, 6:03:47, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4903, loss_cls: 4.1607, loss: 4.1607 +2024-12-26 17:04:47,535 - pyskl - INFO - Epoch [19][2300/3746] lr: 9.625e-02, eta: 4 days, 6:02:12, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5022, loss_cls: 4.0849, loss: 4.0849 +2024-12-26 17:05:59,641 - pyskl - INFO - Epoch [19][2400/3746] lr: 9.624e-02, eta: 4 days, 6:00:40, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4861, loss_cls: 4.1736, loss: 4.1736 +2024-12-26 17:07:11,752 - pyskl - INFO - Epoch [19][2500/3746] lr: 9.623e-02, eta: 4 days, 5:59:08, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4966, loss_cls: 4.1134, loss: 4.1134 +2024-12-26 17:08:23,829 - pyskl - INFO - Epoch [19][2600/3746] lr: 9.622e-02, eta: 4 days, 5:57:35, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4855, loss_cls: 4.2064, loss: 4.2064 +2024-12-26 17:09:35,494 - pyskl - INFO - Epoch [19][2700/3746] lr: 9.621e-02, eta: 4 days, 5:56:00, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4873, loss_cls: 4.1557, loss: 4.1557 +2024-12-26 17:10:47,541 - pyskl - INFO - Epoch [19][2800/3746] lr: 9.620e-02, eta: 4 days, 5:54:27, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4959, loss_cls: 4.1434, loss: 4.1434 +2024-12-26 17:11:59,708 - pyskl - INFO - Epoch [19][2900/3746] lr: 9.618e-02, eta: 4 days, 5:52:55, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4914, loss_cls: 4.1314, loss: 4.1314 +2024-12-26 17:13:11,919 - pyskl - INFO - Epoch [19][3000/3746] lr: 9.617e-02, eta: 4 days, 5:51:24, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4959, loss_cls: 4.0977, loss: 4.0977 +2024-12-26 17:14:24,202 - pyskl - INFO - Epoch [19][3100/3746] lr: 9.616e-02, eta: 4 days, 5:49:53, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4917, loss_cls: 4.1419, loss: 4.1419 +2024-12-26 17:15:36,451 - pyskl - INFO - Epoch [19][3200/3746] lr: 9.615e-02, eta: 4 days, 5:48:22, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4919, loss_cls: 4.1372, loss: 4.1372 +2024-12-26 17:16:48,497 - pyskl - INFO - Epoch [19][3300/3746] lr: 9.614e-02, eta: 4 days, 5:46:50, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4908, loss_cls: 4.1808, loss: 4.1808 +2024-12-26 17:18:00,500 - pyskl - INFO - Epoch [19][3400/3746] lr: 9.613e-02, eta: 4 days, 5:45:17, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4853, loss_cls: 4.1587, loss: 4.1587 +2024-12-26 17:19:12,525 - pyskl - INFO - Epoch [19][3500/3746] lr: 9.612e-02, eta: 4 days, 5:43:45, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4903, loss_cls: 4.1350, loss: 4.1350 +2024-12-26 17:20:24,040 - pyskl - INFO - Epoch [19][3600/3746] lr: 9.611e-02, eta: 4 days, 5:42:09, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2523, top5_acc: 0.4998, loss_cls: 4.1083, loss: 4.1083 +2024-12-26 17:21:36,376 - pyskl - INFO - Epoch [19][3700/3746] lr: 9.610e-02, eta: 4 days, 5:40:39, time: 0.723, data_time: 0.001, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4970, loss_cls: 4.1489, loss: 4.1489 +2024-12-26 17:22:11,152 - pyskl - INFO - Saving checkpoint at 19 epochs +2024-12-26 17:24:06,674 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 17:24:07,420 - pyskl - INFO - +top1_acc 0.1668 +top5_acc 0.3838 +2024-12-26 17:24:07,420 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 17:24:07,461 - pyskl - INFO - +mean_acc 0.1667 +2024-12-26 17:24:07,478 - pyskl - INFO - Epoch(val) [19][309] top1_acc: 0.1668, top5_acc: 0.3838, mean_class_accuracy: 0.1667 +2024-12-26 17:27:40,464 - pyskl - INFO - Epoch [20][100/3746] lr: 9.608e-02, eta: 4 days, 5:50:46, time: 2.130, data_time: 1.416, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5080, loss_cls: 4.1013, loss: 4.1013 +2024-12-26 17:28:52,197 - pyskl - INFO - Epoch [20][200/3746] lr: 9.607e-02, eta: 4 days, 5:49:11, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4958, loss_cls: 4.0933, loss: 4.0933 +2024-12-26 17:30:03,491 - pyskl - INFO - Epoch [20][300/3746] lr: 9.606e-02, eta: 4 days, 5:47:33, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4908, loss_cls: 4.1251, loss: 4.1251 +2024-12-26 17:31:15,098 - pyskl - INFO - Epoch [20][400/3746] lr: 9.605e-02, eta: 4 days, 5:45:57, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4863, loss_cls: 4.1794, loss: 4.1794 +2024-12-26 17:32:26,621 - pyskl - INFO - Epoch [20][500/3746] lr: 9.604e-02, eta: 4 days, 5:44:20, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4947, loss_cls: 4.1274, loss: 4.1274 +2024-12-26 17:33:37,759 - pyskl - INFO - Epoch [20][600/3746] lr: 9.603e-02, eta: 4 days, 5:42:41, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4913, loss_cls: 4.1627, loss: 4.1627 +2024-12-26 17:34:49,302 - pyskl - INFO - Epoch [20][700/3746] lr: 9.602e-02, eta: 4 days, 5:41:05, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4917, loss_cls: 4.1449, loss: 4.1449 +2024-12-26 17:36:00,452 - pyskl - INFO - Epoch [20][800/3746] lr: 9.601e-02, eta: 4 days, 5:39:26, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4975, loss_cls: 4.1222, loss: 4.1222 +2024-12-26 17:37:12,068 - pyskl - INFO - Epoch [20][900/3746] lr: 9.600e-02, eta: 4 days, 5:37:50, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4880, loss_cls: 4.1441, loss: 4.1441 +2024-12-26 17:38:23,145 - pyskl - INFO - Epoch [20][1000/3746] lr: 9.598e-02, eta: 4 days, 5:36:11, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.5084, loss_cls: 4.1033, loss: 4.1033 +2024-12-26 17:39:34,131 - pyskl - INFO - Epoch [20][1100/3746] lr: 9.597e-02, eta: 4 days, 5:34:31, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4917, loss_cls: 4.1482, loss: 4.1482 +2024-12-26 17:40:45,498 - pyskl - INFO - Epoch [20][1200/3746] lr: 9.596e-02, eta: 4 days, 5:32:54, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4941, loss_cls: 4.1574, loss: 4.1574 +2024-12-26 17:41:56,872 - pyskl - INFO - Epoch [20][1300/3746] lr: 9.595e-02, eta: 4 days, 5:31:17, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4919, loss_cls: 4.1524, loss: 4.1524 +2024-12-26 17:43:08,648 - pyskl - INFO - Epoch [20][1400/3746] lr: 9.594e-02, eta: 4 days, 5:29:43, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4972, loss_cls: 4.1272, loss: 4.1272 +2024-12-26 17:44:20,800 - pyskl - INFO - Epoch [20][1500/3746] lr: 9.593e-02, eta: 4 days, 5:28:11, time: 0.722, data_time: 0.001, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4945, loss_cls: 4.1523, loss: 4.1523 +2024-12-26 17:45:32,831 - pyskl - INFO - Epoch [20][1600/3746] lr: 9.592e-02, eta: 4 days, 5:26:39, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4984, loss_cls: 4.1342, loss: 4.1342 +2024-12-26 17:46:44,760 - pyskl - INFO - Epoch [20][1700/3746] lr: 9.591e-02, eta: 4 days, 5:25:06, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4867, loss_cls: 4.1865, loss: 4.1865 +2024-12-26 17:47:56,762 - pyskl - INFO - Epoch [20][1800/3746] lr: 9.590e-02, eta: 4 days, 5:23:33, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4914, loss_cls: 4.1140, loss: 4.1140 +2024-12-26 17:49:08,777 - pyskl - INFO - Epoch [20][1900/3746] lr: 9.588e-02, eta: 4 days, 5:22:01, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4755, loss_cls: 4.1846, loss: 4.1846 +2024-12-26 17:50:21,388 - pyskl - INFO - Epoch [20][2000/3746] lr: 9.587e-02, eta: 4 days, 5:20:32, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4955, loss_cls: 4.1209, loss: 4.1209 +2024-12-26 17:51:33,539 - pyskl - INFO - Epoch [20][2100/3746] lr: 9.586e-02, eta: 4 days, 5:19:01, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4958, loss_cls: 4.1257, loss: 4.1257 +2024-12-26 17:52:46,070 - pyskl - INFO - Epoch [20][2200/3746] lr: 9.585e-02, eta: 4 days, 5:17:32, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.4972, loss_cls: 4.1051, loss: 4.1051 +2024-12-26 17:53:58,348 - pyskl - INFO - Epoch [20][2300/3746] lr: 9.584e-02, eta: 4 days, 5:16:02, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4897, loss_cls: 4.1555, loss: 4.1555 +2024-12-26 17:55:10,474 - pyskl - INFO - Epoch [20][2400/3746] lr: 9.583e-02, eta: 4 days, 5:14:31, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.5017, loss_cls: 4.1207, loss: 4.1207 +2024-12-26 17:56:22,908 - pyskl - INFO - Epoch [20][2500/3746] lr: 9.582e-02, eta: 4 days, 5:13:01, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4959, loss_cls: 4.1537, loss: 4.1537 +2024-12-26 17:57:34,829 - pyskl - INFO - Epoch [20][2600/3746] lr: 9.581e-02, eta: 4 days, 5:11:29, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.4895, loss_cls: 4.1283, loss: 4.1283 +2024-12-26 17:58:46,748 - pyskl - INFO - Epoch [20][2700/3746] lr: 9.580e-02, eta: 4 days, 5:09:56, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4930, loss_cls: 4.1615, loss: 4.1615 +2024-12-26 17:59:59,558 - pyskl - INFO - Epoch [20][2800/3746] lr: 9.578e-02, eta: 4 days, 5:08:30, time: 0.728, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5017, loss_cls: 4.1078, loss: 4.1078 +2024-12-26 18:01:11,991 - pyskl - INFO - Epoch [20][2900/3746] lr: 9.577e-02, eta: 4 days, 5:07:01, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4939, loss_cls: 4.1199, loss: 4.1199 +2024-12-26 18:02:24,107 - pyskl - INFO - Epoch [20][3000/3746] lr: 9.576e-02, eta: 4 days, 5:05:30, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4958, loss_cls: 4.1343, loss: 4.1343 +2024-12-26 18:03:36,262 - pyskl - INFO - Epoch [20][3100/3746] lr: 9.575e-02, eta: 4 days, 5:03:59, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4920, loss_cls: 4.1320, loss: 4.1320 +2024-12-26 18:04:48,377 - pyskl - INFO - Epoch [20][3200/3746] lr: 9.574e-02, eta: 4 days, 5:02:28, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4964, loss_cls: 4.1168, loss: 4.1168 +2024-12-26 18:06:00,231 - pyskl - INFO - Epoch [20][3300/3746] lr: 9.573e-02, eta: 4 days, 5:00:55, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4914, loss_cls: 4.1847, loss: 4.1847 +2024-12-26 18:07:12,580 - pyskl - INFO - Epoch [20][3400/3746] lr: 9.572e-02, eta: 4 days, 4:59:26, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4878, loss_cls: 4.1655, loss: 4.1655 +2024-12-26 18:08:24,828 - pyskl - INFO - Epoch [20][3500/3746] lr: 9.571e-02, eta: 4 days, 4:57:56, time: 0.722, data_time: 0.001, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4953, loss_cls: 4.1263, loss: 4.1263 +2024-12-26 18:09:36,707 - pyskl - INFO - Epoch [20][3600/3746] lr: 9.569e-02, eta: 4 days, 4:56:23, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4992, loss_cls: 4.1108, loss: 4.1108 +2024-12-26 18:10:48,882 - pyskl - INFO - Epoch [20][3700/3746] lr: 9.568e-02, eta: 4 days, 4:54:53, time: 0.722, data_time: 0.001, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4927, loss_cls: 4.1328, loss: 4.1328 +2024-12-26 18:11:23,081 - pyskl - INFO - Saving checkpoint at 20 epochs +2024-12-26 18:13:19,997 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 18:13:20,710 - pyskl - INFO - +top1_acc 0.1845 +top5_acc 0.4104 +2024-12-26 18:13:20,711 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 18:13:20,755 - pyskl - INFO - +mean_acc 0.1844 +2024-12-26 18:13:20,762 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_10.pth was removed +2024-12-26 18:13:21,025 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_20.pth. +2024-12-26 18:13:21,029 - pyskl - INFO - Best top1_acc is 0.1845 at 20 epoch. +2024-12-26 18:13:21,043 - pyskl - INFO - Epoch(val) [20][309] top1_acc: 0.1845, top5_acc: 0.4104, mean_class_accuracy: 0.1844 +2024-12-26 18:16:51,636 - pyskl - INFO - Epoch [21][100/3746] lr: 9.567e-02, eta: 4 days, 5:04:04, time: 2.106, data_time: 1.388, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4877, loss_cls: 4.1553, loss: 4.1553 +2024-12-26 18:18:03,724 - pyskl - INFO - Epoch [21][200/3746] lr: 9.565e-02, eta: 4 days, 5:02:32, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5036, loss_cls: 4.0523, loss: 4.0523 +2024-12-26 18:19:15,922 - pyskl - INFO - Epoch [21][300/3746] lr: 9.564e-02, eta: 4 days, 5:01:01, time: 0.722, data_time: 0.001, memory: 15990, top1_acc: 0.2589, top5_acc: 0.4975, loss_cls: 4.0960, loss: 4.0960 +2024-12-26 18:20:27,914 - pyskl - INFO - Epoch [21][400/3746] lr: 9.563e-02, eta: 4 days, 4:59:29, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4947, loss_cls: 4.1278, loss: 4.1278 +2024-12-26 18:21:39,715 - pyskl - INFO - Epoch [21][500/3746] lr: 9.562e-02, eta: 4 days, 4:57:55, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4938, loss_cls: 4.1211, loss: 4.1211 +2024-12-26 18:22:51,061 - pyskl - INFO - Epoch [21][600/3746] lr: 9.561e-02, eta: 4 days, 4:56:19, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4948, loss_cls: 4.1266, loss: 4.1266 +2024-12-26 18:24:02,631 - pyskl - INFO - Epoch [21][700/3746] lr: 9.560e-02, eta: 4 days, 4:54:44, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.5038, loss_cls: 4.1037, loss: 4.1037 +2024-12-26 18:25:13,815 - pyskl - INFO - Epoch [21][800/3746] lr: 9.559e-02, eta: 4 days, 4:53:06, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.5039, loss_cls: 4.0952, loss: 4.0952 +2024-12-26 18:26:25,852 - pyskl - INFO - Epoch [21][900/3746] lr: 9.557e-02, eta: 4 days, 4:51:35, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4984, loss_cls: 4.1189, loss: 4.1189 +2024-12-26 18:27:37,618 - pyskl - INFO - Epoch [21][1000/3746] lr: 9.556e-02, eta: 4 days, 4:50:01, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5008, loss_cls: 4.0864, loss: 4.0864 +2024-12-26 18:28:48,904 - pyskl - INFO - Epoch [21][1100/3746] lr: 9.555e-02, eta: 4 days, 4:48:25, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.4948, loss_cls: 4.1089, loss: 4.1089 +2024-12-26 18:30:00,738 - pyskl - INFO - Epoch [21][1200/3746] lr: 9.554e-02, eta: 4 days, 4:46:52, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.5006, loss_cls: 4.1187, loss: 4.1187 +2024-12-26 18:31:12,726 - pyskl - INFO - Epoch [21][1300/3746] lr: 9.553e-02, eta: 4 days, 4:45:20, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4878, loss_cls: 4.1624, loss: 4.1624 +2024-12-26 18:32:24,630 - pyskl - INFO - Epoch [21][1400/3746] lr: 9.552e-02, eta: 4 days, 4:43:47, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5027, loss_cls: 4.1221, loss: 4.1221 +2024-12-26 18:33:36,358 - pyskl - INFO - Epoch [21][1500/3746] lr: 9.551e-02, eta: 4 days, 4:42:14, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4983, loss_cls: 4.1129, loss: 4.1129 +2024-12-26 18:34:48,571 - pyskl - INFO - Epoch [21][1600/3746] lr: 9.549e-02, eta: 4 days, 4:40:44, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4958, loss_cls: 4.1568, loss: 4.1568 +2024-12-26 18:36:00,258 - pyskl - INFO - Epoch [21][1700/3746] lr: 9.548e-02, eta: 4 days, 4:39:10, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5012, loss_cls: 4.1237, loss: 4.1237 +2024-12-26 18:37:12,376 - pyskl - INFO - Epoch [21][1800/3746] lr: 9.547e-02, eta: 4 days, 4:37:39, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4828, loss_cls: 4.1922, loss: 4.1922 +2024-12-26 18:38:23,975 - pyskl - INFO - Epoch [21][1900/3746] lr: 9.546e-02, eta: 4 days, 4:36:05, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4964, loss_cls: 4.1227, loss: 4.1227 +2024-12-26 18:39:36,125 - pyskl - INFO - Epoch [21][2000/3746] lr: 9.545e-02, eta: 4 days, 4:34:35, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4956, loss_cls: 4.1236, loss: 4.1236 +2024-12-26 18:40:47,794 - pyskl - INFO - Epoch [21][2100/3746] lr: 9.544e-02, eta: 4 days, 4:33:01, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4983, loss_cls: 4.1191, loss: 4.1191 +2024-12-26 18:41:59,700 - pyskl - INFO - Epoch [21][2200/3746] lr: 9.542e-02, eta: 4 days, 4:31:29, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4883, loss_cls: 4.1578, loss: 4.1578 +2024-12-26 18:43:11,512 - pyskl - INFO - Epoch [21][2300/3746] lr: 9.541e-02, eta: 4 days, 4:29:57, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4980, loss_cls: 4.1272, loss: 4.1272 +2024-12-26 18:44:23,108 - pyskl - INFO - Epoch [21][2400/3746] lr: 9.540e-02, eta: 4 days, 4:28:23, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5041, loss_cls: 4.0806, loss: 4.0806 +2024-12-26 18:45:35,017 - pyskl - INFO - Epoch [21][2500/3746] lr: 9.539e-02, eta: 4 days, 4:26:51, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4948, loss_cls: 4.1501, loss: 4.1501 +2024-12-26 18:46:47,309 - pyskl - INFO - Epoch [21][2600/3746] lr: 9.538e-02, eta: 4 days, 4:25:22, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4914, loss_cls: 4.1692, loss: 4.1692 +2024-12-26 18:47:59,591 - pyskl - INFO - Epoch [21][2700/3746] lr: 9.537e-02, eta: 4 days, 4:23:53, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4905, loss_cls: 4.1531, loss: 4.1531 +2024-12-26 18:49:11,474 - pyskl - INFO - Epoch [21][2800/3746] lr: 9.535e-02, eta: 4 days, 4:22:21, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4925, loss_cls: 4.1727, loss: 4.1727 +2024-12-26 18:50:23,603 - pyskl - INFO - Epoch [21][2900/3746] lr: 9.534e-02, eta: 4 days, 4:20:51, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4978, loss_cls: 4.1578, loss: 4.1578 +2024-12-26 18:51:35,381 - pyskl - INFO - Epoch [21][3000/3746] lr: 9.533e-02, eta: 4 days, 4:19:18, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4989, loss_cls: 4.1410, loss: 4.1410 +2024-12-26 18:52:47,386 - pyskl - INFO - Epoch [21][3100/3746] lr: 9.532e-02, eta: 4 days, 4:17:48, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4906, loss_cls: 4.1510, loss: 4.1510 +2024-12-26 18:53:58,704 - pyskl - INFO - Epoch [21][3200/3746] lr: 9.531e-02, eta: 4 days, 4:16:12, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.4992, loss_cls: 4.1323, loss: 4.1323 +2024-12-26 18:55:10,349 - pyskl - INFO - Epoch [21][3300/3746] lr: 9.529e-02, eta: 4 days, 4:14:39, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4950, loss_cls: 4.1395, loss: 4.1395 +2024-12-26 18:56:22,105 - pyskl - INFO - Epoch [21][3400/3746] lr: 9.528e-02, eta: 4 days, 4:13:07, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4938, loss_cls: 4.1500, loss: 4.1500 +2024-12-26 18:57:33,921 - pyskl - INFO - Epoch [21][3500/3746] lr: 9.527e-02, eta: 4 days, 4:11:35, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4841, loss_cls: 4.1717, loss: 4.1717 +2024-12-26 18:58:45,857 - pyskl - INFO - Epoch [21][3600/3746] lr: 9.526e-02, eta: 4 days, 4:10:04, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4902, loss_cls: 4.1402, loss: 4.1402 +2024-12-26 18:59:57,699 - pyskl - INFO - Epoch [21][3700/3746] lr: 9.525e-02, eta: 4 days, 4:08:33, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4970, loss_cls: 4.1224, loss: 4.1224 +2024-12-26 19:00:32,186 - pyskl - INFO - Saving checkpoint at 21 epochs +2024-12-26 19:02:28,549 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 19:02:29,221 - pyskl - INFO - +top1_acc 0.1938 +top5_acc 0.4258 +2024-12-26 19:02:29,221 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 19:02:29,266 - pyskl - INFO - +mean_acc 0.1936 +2024-12-26 19:02:29,271 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_20.pth was removed +2024-12-26 19:02:29,555 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_21.pth. +2024-12-26 19:02:29,556 - pyskl - INFO - Best top1_acc is 0.1938 at 21 epoch. +2024-12-26 19:02:29,570 - pyskl - INFO - Epoch(val) [21][309] top1_acc: 0.1938, top5_acc: 0.4258, mean_class_accuracy: 0.1936 +2024-12-26 19:06:01,689 - pyskl - INFO - Epoch [22][100/3746] lr: 9.523e-02, eta: 4 days, 4:17:17, time: 2.121, data_time: 1.406, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4980, loss_cls: 4.1292, loss: 4.1292 +2024-12-26 19:07:13,645 - pyskl - INFO - Epoch [22][200/3746] lr: 9.522e-02, eta: 4 days, 4:15:45, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4966, loss_cls: 4.1182, loss: 4.1182 +2024-12-26 19:08:25,090 - pyskl - INFO - Epoch [22][300/3746] lr: 9.521e-02, eta: 4 days, 4:14:10, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4991, loss_cls: 4.1086, loss: 4.1086 +2024-12-26 19:09:36,838 - pyskl - INFO - Epoch [22][400/3746] lr: 9.519e-02, eta: 4 days, 4:12:37, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4941, loss_cls: 4.1291, loss: 4.1291 +2024-12-26 19:10:48,841 - pyskl - INFO - Epoch [22][500/3746] lr: 9.518e-02, eta: 4 days, 4:11:06, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4958, loss_cls: 4.1068, loss: 4.1068 +2024-12-26 19:12:00,358 - pyskl - INFO - Epoch [22][600/3746] lr: 9.517e-02, eta: 4 days, 4:09:32, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5016, loss_cls: 4.1072, loss: 4.1072 +2024-12-26 19:13:11,490 - pyskl - INFO - Epoch [22][700/3746] lr: 9.516e-02, eta: 4 days, 4:07:56, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4866, loss_cls: 4.1563, loss: 4.1563 +2024-12-26 19:14:22,901 - pyskl - INFO - Epoch [22][800/3746] lr: 9.515e-02, eta: 4 days, 4:06:21, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4972, loss_cls: 4.1180, loss: 4.1180 +2024-12-26 19:15:34,743 - pyskl - INFO - Epoch [22][900/3746] lr: 9.513e-02, eta: 4 days, 4:04:49, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5084, loss_cls: 4.0581, loss: 4.0581 +2024-12-26 19:16:46,319 - pyskl - INFO - Epoch [22][1000/3746] lr: 9.512e-02, eta: 4 days, 4:03:15, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4959, loss_cls: 4.1435, loss: 4.1435 +2024-12-26 19:17:57,874 - pyskl - INFO - Epoch [22][1100/3746] lr: 9.511e-02, eta: 4 days, 4:01:42, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4977, loss_cls: 4.0997, loss: 4.0997 +2024-12-26 19:19:09,325 - pyskl - INFO - Epoch [22][1200/3746] lr: 9.510e-02, eta: 4 days, 4:00:07, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5156, loss_cls: 4.0573, loss: 4.0573 +2024-12-26 19:20:20,596 - pyskl - INFO - Epoch [22][1300/3746] lr: 9.509e-02, eta: 4 days, 3:58:32, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4963, loss_cls: 4.1236, loss: 4.1236 +2024-12-26 19:21:32,163 - pyskl - INFO - Epoch [22][1400/3746] lr: 9.507e-02, eta: 4 days, 3:56:59, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.4927, loss_cls: 4.1101, loss: 4.1101 +2024-12-26 19:22:44,222 - pyskl - INFO - Epoch [22][1500/3746] lr: 9.506e-02, eta: 4 days, 3:55:28, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4967, loss_cls: 4.1476, loss: 4.1476 +2024-12-26 19:23:56,364 - pyskl - INFO - Epoch [22][1600/3746] lr: 9.505e-02, eta: 4 days, 3:53:58, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4975, loss_cls: 4.0962, loss: 4.0962 +2024-12-26 19:25:08,507 - pyskl - INFO - Epoch [22][1700/3746] lr: 9.504e-02, eta: 4 days, 3:52:29, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4916, loss_cls: 4.1583, loss: 4.1583 +2024-12-26 19:26:20,112 - pyskl - INFO - Epoch [22][1800/3746] lr: 9.502e-02, eta: 4 days, 3:50:56, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5009, loss_cls: 4.0986, loss: 4.0986 +2024-12-26 19:27:31,950 - pyskl - INFO - Epoch [22][1900/3746] lr: 9.501e-02, eta: 4 days, 3:49:24, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5009, loss_cls: 4.1237, loss: 4.1237 +2024-12-26 19:28:43,623 - pyskl - INFO - Epoch [22][2000/3746] lr: 9.500e-02, eta: 4 days, 3:47:51, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4911, loss_cls: 4.1580, loss: 4.1580 +2024-12-26 19:29:55,513 - pyskl - INFO - Epoch [22][2100/3746] lr: 9.499e-02, eta: 4 days, 3:46:20, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5028, loss_cls: 4.1111, loss: 4.1111 +2024-12-26 19:31:07,312 - pyskl - INFO - Epoch [22][2200/3746] lr: 9.498e-02, eta: 4 days, 3:44:49, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4980, loss_cls: 4.1317, loss: 4.1317 +2024-12-26 19:32:19,043 - pyskl - INFO - Epoch [22][2300/3746] lr: 9.496e-02, eta: 4 days, 3:43:17, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5025, loss_cls: 4.0848, loss: 4.0848 +2024-12-26 19:33:30,893 - pyskl - INFO - Epoch [22][2400/3746] lr: 9.495e-02, eta: 4 days, 3:41:45, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4833, loss_cls: 4.1886, loss: 4.1886 +2024-12-26 19:34:42,381 - pyskl - INFO - Epoch [22][2500/3746] lr: 9.494e-02, eta: 4 days, 3:40:12, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4981, loss_cls: 4.1216, loss: 4.1216 +2024-12-26 19:35:54,134 - pyskl - INFO - Epoch [22][2600/3746] lr: 9.493e-02, eta: 4 days, 3:38:40, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4886, loss_cls: 4.1501, loss: 4.1501 +2024-12-26 19:37:05,805 - pyskl - INFO - Epoch [22][2700/3746] lr: 9.491e-02, eta: 4 days, 3:37:08, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4950, loss_cls: 4.1608, loss: 4.1608 +2024-12-26 19:38:17,878 - pyskl - INFO - Epoch [22][2800/3746] lr: 9.490e-02, eta: 4 days, 3:35:39, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4930, loss_cls: 4.1459, loss: 4.1459 +2024-12-26 19:39:29,392 - pyskl - INFO - Epoch [22][2900/3746] lr: 9.489e-02, eta: 4 days, 3:34:06, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4994, loss_cls: 4.1395, loss: 4.1395 +2024-12-26 19:40:41,411 - pyskl - INFO - Epoch [22][3000/3746] lr: 9.488e-02, eta: 4 days, 3:32:36, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4872, loss_cls: 4.1562, loss: 4.1562 +2024-12-26 19:41:53,561 - pyskl - INFO - Epoch [22][3100/3746] lr: 9.487e-02, eta: 4 days, 3:31:06, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4970, loss_cls: 4.1324, loss: 4.1324 +2024-12-26 19:43:06,039 - pyskl - INFO - Epoch [22][3200/3746] lr: 9.485e-02, eta: 4 days, 3:29:39, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4942, loss_cls: 4.1316, loss: 4.1316 +2024-12-26 19:44:17,716 - pyskl - INFO - Epoch [22][3300/3746] lr: 9.484e-02, eta: 4 days, 3:28:07, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4948, loss_cls: 4.1701, loss: 4.1701 +2024-12-26 19:45:29,385 - pyskl - INFO - Epoch [22][3400/3746] lr: 9.483e-02, eta: 4 days, 3:26:36, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5019, loss_cls: 4.1151, loss: 4.1151 +2024-12-26 19:46:41,258 - pyskl - INFO - Epoch [22][3500/3746] lr: 9.482e-02, eta: 4 days, 3:25:05, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4973, loss_cls: 4.1328, loss: 4.1328 +2024-12-26 19:47:53,029 - pyskl - INFO - Epoch [22][3600/3746] lr: 9.480e-02, eta: 4 days, 3:23:34, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4964, loss_cls: 4.1576, loss: 4.1576 +2024-12-26 19:49:04,912 - pyskl - INFO - Epoch [22][3700/3746] lr: 9.479e-02, eta: 4 days, 3:22:04, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4956, loss_cls: 4.1453, loss: 4.1453 +2024-12-26 19:49:39,139 - pyskl - INFO - Saving checkpoint at 22 epochs +2024-12-26 19:51:34,284 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 19:51:34,966 - pyskl - INFO - +top1_acc 0.1900 +top5_acc 0.4107 +2024-12-26 19:51:34,966 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 19:51:35,007 - pyskl - INFO - +mean_acc 0.1898 +2024-12-26 19:51:35,019 - pyskl - INFO - Epoch(val) [22][309] top1_acc: 0.1900, top5_acc: 0.4107, mean_class_accuracy: 0.1898 +2024-12-26 19:55:07,433 - pyskl - INFO - Epoch [23][100/3746] lr: 9.477e-02, eta: 4 days, 3:30:16, time: 2.124, data_time: 1.404, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5053, loss_cls: 4.0735, loss: 4.0735 +2024-12-26 19:56:19,195 - pyskl - INFO - Epoch [23][200/3746] lr: 9.476e-02, eta: 4 days, 3:28:44, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.5014, loss_cls: 4.1205, loss: 4.1205 +2024-12-26 19:57:30,912 - pyskl - INFO - Epoch [23][300/3746] lr: 9.475e-02, eta: 4 days, 3:27:12, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5128, loss_cls: 4.0391, loss: 4.0391 +2024-12-26 19:58:42,653 - pyskl - INFO - Epoch [23][400/3746] lr: 9.474e-02, eta: 4 days, 3:25:40, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4988, loss_cls: 4.1004, loss: 4.1004 +2024-12-26 19:59:54,191 - pyskl - INFO - Epoch [23][500/3746] lr: 9.472e-02, eta: 4 days, 3:24:07, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4931, loss_cls: 4.1440, loss: 4.1440 +2024-12-26 20:01:05,493 - pyskl - INFO - Epoch [23][600/3746] lr: 9.471e-02, eta: 4 days, 3:22:32, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5080, loss_cls: 4.0348, loss: 4.0348 +2024-12-26 20:02:16,941 - pyskl - INFO - Epoch [23][700/3746] lr: 9.470e-02, eta: 4 days, 3:20:59, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.4966, loss_cls: 4.0988, loss: 4.0988 +2024-12-26 20:03:28,677 - pyskl - INFO - Epoch [23][800/3746] lr: 9.469e-02, eta: 4 days, 3:19:27, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5000, loss_cls: 4.0837, loss: 4.0837 +2024-12-26 20:04:40,291 - pyskl - INFO - Epoch [23][900/3746] lr: 9.467e-02, eta: 4 days, 3:17:55, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5038, loss_cls: 4.0838, loss: 4.0838 +2024-12-26 20:05:51,897 - pyskl - INFO - Epoch [23][1000/3746] lr: 9.466e-02, eta: 4 days, 3:16:22, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4894, loss_cls: 4.1362, loss: 4.1362 +2024-12-26 20:07:03,291 - pyskl - INFO - Epoch [23][1100/3746] lr: 9.465e-02, eta: 4 days, 3:14:49, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.4991, loss_cls: 4.1069, loss: 4.1069 +2024-12-26 20:08:15,240 - pyskl - INFO - Epoch [23][1200/3746] lr: 9.464e-02, eta: 4 days, 3:13:18, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4922, loss_cls: 4.1619, loss: 4.1619 +2024-12-26 20:09:27,157 - pyskl - INFO - Epoch [23][1300/3746] lr: 9.462e-02, eta: 4 days, 3:11:48, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4903, loss_cls: 4.1564, loss: 4.1564 +2024-12-26 20:10:39,224 - pyskl - INFO - Epoch [23][1400/3746] lr: 9.461e-02, eta: 4 days, 3:10:18, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4942, loss_cls: 4.1399, loss: 4.1399 +2024-12-26 20:11:51,051 - pyskl - INFO - Epoch [23][1500/3746] lr: 9.460e-02, eta: 4 days, 3:08:47, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4898, loss_cls: 4.1404, loss: 4.1404 +2024-12-26 20:13:03,473 - pyskl - INFO - Epoch [23][1600/3746] lr: 9.459e-02, eta: 4 days, 3:07:20, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5042, loss_cls: 4.0772, loss: 4.0772 +2024-12-26 20:14:15,025 - pyskl - INFO - Epoch [23][1700/3746] lr: 9.457e-02, eta: 4 days, 3:05:48, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4886, loss_cls: 4.1647, loss: 4.1647 +2024-12-26 20:15:26,700 - pyskl - INFO - Epoch [23][1800/3746] lr: 9.456e-02, eta: 4 days, 3:04:16, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4984, loss_cls: 4.0865, loss: 4.0865 +2024-12-26 20:16:39,000 - pyskl - INFO - Epoch [23][1900/3746] lr: 9.455e-02, eta: 4 days, 3:02:48, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4953, loss_cls: 4.1151, loss: 4.1151 +2024-12-26 20:17:50,920 - pyskl - INFO - Epoch [23][2000/3746] lr: 9.453e-02, eta: 4 days, 3:01:18, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4863, loss_cls: 4.1673, loss: 4.1673 +2024-12-26 20:19:02,881 - pyskl - INFO - Epoch [23][2100/3746] lr: 9.452e-02, eta: 4 days, 2:59:48, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4997, loss_cls: 4.1288, loss: 4.1288 +2024-12-26 20:20:14,838 - pyskl - INFO - Epoch [23][2200/3746] lr: 9.451e-02, eta: 4 days, 2:58:18, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4806, loss_cls: 4.1856, loss: 4.1856 +2024-12-26 20:21:27,119 - pyskl - INFO - Epoch [23][2300/3746] lr: 9.450e-02, eta: 4 days, 2:56:50, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4928, loss_cls: 4.1454, loss: 4.1454 +2024-12-26 20:22:39,269 - pyskl - INFO - Epoch [23][2400/3746] lr: 9.448e-02, eta: 4 days, 2:55:21, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5039, loss_cls: 4.0864, loss: 4.0864 +2024-12-26 20:23:51,120 - pyskl - INFO - Epoch [23][2500/3746] lr: 9.447e-02, eta: 4 days, 2:53:51, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5066, loss_cls: 4.1029, loss: 4.1029 +2024-12-26 20:25:03,281 - pyskl - INFO - Epoch [23][2600/3746] lr: 9.446e-02, eta: 4 days, 2:52:22, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.5005, loss_cls: 4.1305, loss: 4.1305 +2024-12-26 20:26:15,348 - pyskl - INFO - Epoch [23][2700/3746] lr: 9.445e-02, eta: 4 days, 2:50:53, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4934, loss_cls: 4.1289, loss: 4.1289 +2024-12-26 20:27:27,472 - pyskl - INFO - Epoch [23][2800/3746] lr: 9.443e-02, eta: 4 days, 2:49:25, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4963, loss_cls: 4.1240, loss: 4.1240 +2024-12-26 20:28:39,498 - pyskl - INFO - Epoch [23][2900/3746] lr: 9.442e-02, eta: 4 days, 2:47:55, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5050, loss_cls: 4.0963, loss: 4.0963 +2024-12-26 20:29:51,922 - pyskl - INFO - Epoch [23][3000/3746] lr: 9.441e-02, eta: 4 days, 2:46:28, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5009, loss_cls: 4.1191, loss: 4.1191 +2024-12-26 20:31:04,027 - pyskl - INFO - Epoch [23][3100/3746] lr: 9.439e-02, eta: 4 days, 2:45:00, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4894, loss_cls: 4.1301, loss: 4.1301 +2024-12-26 20:32:16,014 - pyskl - INFO - Epoch [23][3200/3746] lr: 9.438e-02, eta: 4 days, 2:43:30, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.4973, loss_cls: 4.1302, loss: 4.1302 +2024-12-26 20:33:27,860 - pyskl - INFO - Epoch [23][3300/3746] lr: 9.437e-02, eta: 4 days, 2:42:00, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4938, loss_cls: 4.1172, loss: 4.1172 +2024-12-26 20:34:40,280 - pyskl - INFO - Epoch [23][3400/3746] lr: 9.436e-02, eta: 4 days, 2:40:34, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4953, loss_cls: 4.1250, loss: 4.1250 +2024-12-26 20:35:52,854 - pyskl - INFO - Epoch [23][3500/3746] lr: 9.434e-02, eta: 4 days, 2:39:08, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.4988, loss_cls: 4.0989, loss: 4.0989 +2024-12-26 20:37:05,005 - pyskl - INFO - Epoch [23][3600/3746] lr: 9.433e-02, eta: 4 days, 2:37:39, time: 0.722, data_time: 0.001, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5012, loss_cls: 4.0994, loss: 4.0994 +2024-12-26 20:38:16,929 - pyskl - INFO - Epoch [23][3700/3746] lr: 9.432e-02, eta: 4 days, 2:36:10, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5042, loss_cls: 4.0973, loss: 4.0973 +2024-12-26 20:38:51,348 - pyskl - INFO - Saving checkpoint at 23 epochs +2024-12-26 20:40:48,479 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 20:40:49,149 - pyskl - INFO - +top1_acc 0.1540 +top5_acc 0.3489 +2024-12-26 20:40:49,149 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 20:40:49,190 - pyskl - INFO - +mean_acc 0.1537 +2024-12-26 20:40:49,205 - pyskl - INFO - Epoch(val) [23][309] top1_acc: 0.1540, top5_acc: 0.3489, mean_class_accuracy: 0.1537 +2024-12-26 20:44:23,837 - pyskl - INFO - Epoch [24][100/3746] lr: 9.430e-02, eta: 4 days, 2:44:04, time: 2.146, data_time: 1.425, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4928, loss_cls: 4.1298, loss: 4.1298 +2024-12-26 20:45:35,597 - pyskl - INFO - Epoch [24][200/3746] lr: 9.428e-02, eta: 4 days, 2:42:33, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5119, loss_cls: 4.0566, loss: 4.0566 +2024-12-26 20:46:47,364 - pyskl - INFO - Epoch [24][300/3746] lr: 9.427e-02, eta: 4 days, 2:41:02, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5072, loss_cls: 4.0839, loss: 4.0839 +2024-12-26 20:47:58,925 - pyskl - INFO - Epoch [24][400/3746] lr: 9.426e-02, eta: 4 days, 2:39:30, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5025, loss_cls: 4.0677, loss: 4.0677 +2024-12-26 20:49:10,199 - pyskl - INFO - Epoch [24][500/3746] lr: 9.425e-02, eta: 4 days, 2:37:56, time: 0.713, data_time: 0.001, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4859, loss_cls: 4.1515, loss: 4.1515 +2024-12-26 20:50:21,669 - pyskl - INFO - Epoch [24][600/3746] lr: 9.423e-02, eta: 4 days, 2:36:24, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4927, loss_cls: 4.1462, loss: 4.1462 +2024-12-26 20:51:33,065 - pyskl - INFO - Epoch [24][700/3746] lr: 9.422e-02, eta: 4 days, 2:34:51, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5147, loss_cls: 4.0555, loss: 4.0555 +2024-12-26 20:52:44,521 - pyskl - INFO - Epoch [24][800/3746] lr: 9.421e-02, eta: 4 days, 2:33:18, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4978, loss_cls: 4.0995, loss: 4.0995 +2024-12-26 20:53:56,029 - pyskl - INFO - Epoch [24][900/3746] lr: 9.419e-02, eta: 4 days, 2:31:46, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5034, loss_cls: 4.0820, loss: 4.0820 +2024-12-26 20:55:07,374 - pyskl - INFO - Epoch [24][1000/3746] lr: 9.418e-02, eta: 4 days, 2:30:13, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5020, loss_cls: 4.0585, loss: 4.0585 +2024-12-26 20:56:18,525 - pyskl - INFO - Epoch [24][1100/3746] lr: 9.417e-02, eta: 4 days, 2:28:39, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.4975, loss_cls: 4.1292, loss: 4.1292 +2024-12-26 20:57:29,941 - pyskl - INFO - Epoch [24][1200/3746] lr: 9.415e-02, eta: 4 days, 2:27:07, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5038, loss_cls: 4.0901, loss: 4.0901 +2024-12-26 20:58:41,900 - pyskl - INFO - Epoch [24][1300/3746] lr: 9.414e-02, eta: 4 days, 2:25:37, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5088, loss_cls: 4.0860, loss: 4.0860 +2024-12-26 20:59:53,643 - pyskl - INFO - Epoch [24][1400/3746] lr: 9.413e-02, eta: 4 days, 2:24:07, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.4956, loss_cls: 4.0895, loss: 4.0895 +2024-12-26 21:01:04,945 - pyskl - INFO - Epoch [24][1500/3746] lr: 9.411e-02, eta: 4 days, 2:22:34, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4927, loss_cls: 4.1275, loss: 4.1275 +2024-12-26 21:02:16,500 - pyskl - INFO - Epoch [24][1600/3746] lr: 9.410e-02, eta: 4 days, 2:21:02, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4886, loss_cls: 4.1342, loss: 4.1342 +2024-12-26 21:03:28,214 - pyskl - INFO - Epoch [24][1700/3746] lr: 9.409e-02, eta: 4 days, 2:19:31, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5016, loss_cls: 4.1005, loss: 4.1005 +2024-12-26 21:04:39,898 - pyskl - INFO - Epoch [24][1800/3746] lr: 9.407e-02, eta: 4 days, 2:18:01, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4930, loss_cls: 4.1466, loss: 4.1466 +2024-12-26 21:05:51,566 - pyskl - INFO - Epoch [24][1900/3746] lr: 9.406e-02, eta: 4 days, 2:16:30, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4855, loss_cls: 4.1727, loss: 4.1727 +2024-12-26 21:07:03,547 - pyskl - INFO - Epoch [24][2000/3746] lr: 9.405e-02, eta: 4 days, 2:15:01, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4878, loss_cls: 4.1675, loss: 4.1675 +2024-12-26 21:08:15,115 - pyskl - INFO - Epoch [24][2100/3746] lr: 9.404e-02, eta: 4 days, 2:13:29, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4944, loss_cls: 4.1478, loss: 4.1478 +2024-12-26 21:09:26,524 - pyskl - INFO - Epoch [24][2200/3746] lr: 9.402e-02, eta: 4 days, 2:11:57, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5039, loss_cls: 4.1075, loss: 4.1075 +2024-12-26 21:10:38,009 - pyskl - INFO - Epoch [24][2300/3746] lr: 9.401e-02, eta: 4 days, 2:10:26, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.5017, loss_cls: 4.1129, loss: 4.1129 +2024-12-26 21:11:49,783 - pyskl - INFO - Epoch [24][2400/3746] lr: 9.400e-02, eta: 4 days, 2:08:56, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4942, loss_cls: 4.1313, loss: 4.1313 +2024-12-26 21:13:02,113 - pyskl - INFO - Epoch [24][2500/3746] lr: 9.398e-02, eta: 4 days, 2:07:29, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5050, loss_cls: 4.0706, loss: 4.0706 +2024-12-26 21:14:13,831 - pyskl - INFO - Epoch [24][2600/3746] lr: 9.397e-02, eta: 4 days, 2:05:58, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4916, loss_cls: 4.1272, loss: 4.1272 +2024-12-26 21:15:25,220 - pyskl - INFO - Epoch [24][2700/3746] lr: 9.396e-02, eta: 4 days, 2:04:26, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4956, loss_cls: 4.1220, loss: 4.1220 +2024-12-26 21:16:37,142 - pyskl - INFO - Epoch [24][2800/3746] lr: 9.394e-02, eta: 4 days, 2:02:57, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5012, loss_cls: 4.1105, loss: 4.1105 +2024-12-26 21:17:49,007 - pyskl - INFO - Epoch [24][2900/3746] lr: 9.393e-02, eta: 4 days, 2:01:28, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5073, loss_cls: 4.0988, loss: 4.0988 +2024-12-26 21:19:00,678 - pyskl - INFO - Epoch [24][3000/3746] lr: 9.392e-02, eta: 4 days, 1:59:58, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.4991, loss_cls: 4.1057, loss: 4.1057 +2024-12-26 21:20:12,301 - pyskl - INFO - Epoch [24][3100/3746] lr: 9.390e-02, eta: 4 days, 1:58:27, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4952, loss_cls: 4.1419, loss: 4.1419 +2024-12-26 21:21:24,176 - pyskl - INFO - Epoch [24][3200/3746] lr: 9.389e-02, eta: 4 days, 1:56:58, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4977, loss_cls: 4.1100, loss: 4.1100 +2024-12-26 21:22:36,144 - pyskl - INFO - Epoch [24][3300/3746] lr: 9.388e-02, eta: 4 days, 1:55:29, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4927, loss_cls: 4.1345, loss: 4.1345 +2024-12-26 21:23:47,779 - pyskl - INFO - Epoch [24][3400/3746] lr: 9.386e-02, eta: 4 days, 1:53:59, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4959, loss_cls: 4.1146, loss: 4.1146 +2024-12-26 21:24:59,558 - pyskl - INFO - Epoch [24][3500/3746] lr: 9.385e-02, eta: 4 days, 1:52:29, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4959, loss_cls: 4.1372, loss: 4.1372 +2024-12-26 21:26:11,176 - pyskl - INFO - Epoch [24][3600/3746] lr: 9.384e-02, eta: 4 days, 1:50:59, time: 0.716, data_time: 0.001, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4988, loss_cls: 4.1265, loss: 4.1265 +2024-12-26 21:27:22,905 - pyskl - INFO - Epoch [24][3700/3746] lr: 9.382e-02, eta: 4 days, 1:49:29, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.4986, loss_cls: 4.0870, loss: 4.0870 +2024-12-26 21:27:57,388 - pyskl - INFO - Saving checkpoint at 24 epochs +2024-12-26 21:29:53,396 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 21:29:54,077 - pyskl - INFO - +top1_acc 0.1729 +top5_acc 0.3898 +2024-12-26 21:29:54,077 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 21:29:54,122 - pyskl - INFO - +mean_acc 0.1728 +2024-12-26 21:29:54,139 - pyskl - INFO - Epoch(val) [24][309] top1_acc: 0.1729, top5_acc: 0.3898, mean_class_accuracy: 0.1728 +2024-12-26 21:33:24,764 - pyskl - INFO - Epoch [25][100/3746] lr: 9.380e-02, eta: 4 days, 1:56:34, time: 2.106, data_time: 1.386, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5011, loss_cls: 4.0859, loss: 4.0859 +2024-12-26 21:34:36,276 - pyskl - INFO - Epoch [25][200/3746] lr: 9.379e-02, eta: 4 days, 1:55:02, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4983, loss_cls: 4.1160, loss: 4.1160 +2024-12-26 21:35:47,791 - pyskl - INFO - Epoch [25][300/3746] lr: 9.378e-02, eta: 4 days, 1:53:31, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5078, loss_cls: 4.0701, loss: 4.0701 +2024-12-26 21:36:59,247 - pyskl - INFO - Epoch [25][400/3746] lr: 9.376e-02, eta: 4 days, 1:51:59, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5116, loss_cls: 4.0685, loss: 4.0685 +2024-12-26 21:38:10,239 - pyskl - INFO - Epoch [25][500/3746] lr: 9.375e-02, eta: 4 days, 1:50:25, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5030, loss_cls: 4.0777, loss: 4.0777 +2024-12-26 21:39:21,779 - pyskl - INFO - Epoch [25][600/3746] lr: 9.373e-02, eta: 4 days, 1:48:54, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4828, loss_cls: 4.1887, loss: 4.1887 +2024-12-26 21:40:33,500 - pyskl - INFO - Epoch [25][700/3746] lr: 9.372e-02, eta: 4 days, 1:47:23, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5050, loss_cls: 4.1008, loss: 4.1008 +2024-12-26 21:41:44,995 - pyskl - INFO - Epoch [25][800/3746] lr: 9.371e-02, eta: 4 days, 1:45:52, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5019, loss_cls: 4.0577, loss: 4.0577 +2024-12-26 21:42:56,245 - pyskl - INFO - Epoch [25][900/3746] lr: 9.369e-02, eta: 4 days, 1:44:19, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.4986, loss_cls: 4.0996, loss: 4.0996 +2024-12-26 21:44:07,963 - pyskl - INFO - Epoch [25][1000/3746] lr: 9.368e-02, eta: 4 days, 1:42:49, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5006, loss_cls: 4.1153, loss: 4.1153 +2024-12-26 21:45:19,450 - pyskl - INFO - Epoch [25][1100/3746] lr: 9.367e-02, eta: 4 days, 1:41:18, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4938, loss_cls: 4.1305, loss: 4.1305 +2024-12-26 21:46:31,228 - pyskl - INFO - Epoch [25][1200/3746] lr: 9.365e-02, eta: 4 days, 1:39:48, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4978, loss_cls: 4.1314, loss: 4.1314 +2024-12-26 21:47:42,455 - pyskl - INFO - Epoch [25][1300/3746] lr: 9.364e-02, eta: 4 days, 1:38:16, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4983, loss_cls: 4.1241, loss: 4.1241 +2024-12-26 21:48:53,703 - pyskl - INFO - Epoch [25][1400/3746] lr: 9.363e-02, eta: 4 days, 1:36:44, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5016, loss_cls: 4.0928, loss: 4.0928 +2024-12-26 21:50:05,439 - pyskl - INFO - Epoch [25][1500/3746] lr: 9.361e-02, eta: 4 days, 1:35:14, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.4948, loss_cls: 4.1230, loss: 4.1230 +2024-12-26 21:51:17,433 - pyskl - INFO - Epoch [25][1600/3746] lr: 9.360e-02, eta: 4 days, 1:33:45, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.4986, loss_cls: 4.1005, loss: 4.1005 +2024-12-26 21:52:29,039 - pyskl - INFO - Epoch [25][1700/3746] lr: 9.358e-02, eta: 4 days, 1:32:15, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5017, loss_cls: 4.1143, loss: 4.1143 +2024-12-26 21:53:40,556 - pyskl - INFO - Epoch [25][1800/3746] lr: 9.357e-02, eta: 4 days, 1:30:44, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4978, loss_cls: 4.1172, loss: 4.1172 +2024-12-26 21:54:52,782 - pyskl - INFO - Epoch [25][1900/3746] lr: 9.356e-02, eta: 4 days, 1:29:17, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5034, loss_cls: 4.1372, loss: 4.1372 +2024-12-26 21:56:04,616 - pyskl - INFO - Epoch [25][2000/3746] lr: 9.354e-02, eta: 4 days, 1:27:48, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4936, loss_cls: 4.1309, loss: 4.1309 +2024-12-26 21:57:16,678 - pyskl - INFO - Epoch [25][2100/3746] lr: 9.353e-02, eta: 4 days, 1:26:20, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.4995, loss_cls: 4.0777, loss: 4.0777 +2024-12-26 21:58:28,644 - pyskl - INFO - Epoch [25][2200/3746] lr: 9.352e-02, eta: 4 days, 1:24:52, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5009, loss_cls: 4.0988, loss: 4.0988 +2024-12-26 21:59:40,501 - pyskl - INFO - Epoch [25][2300/3746] lr: 9.350e-02, eta: 4 days, 1:23:23, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5047, loss_cls: 4.0804, loss: 4.0804 +2024-12-26 22:00:52,288 - pyskl - INFO - Epoch [25][2400/3746] lr: 9.349e-02, eta: 4 days, 1:21:54, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4923, loss_cls: 4.1502, loss: 4.1502 +2024-12-26 22:02:03,895 - pyskl - INFO - Epoch [25][2500/3746] lr: 9.347e-02, eta: 4 days, 1:20:24, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5045, loss_cls: 4.0808, loss: 4.0808 +2024-12-26 22:03:15,993 - pyskl - INFO - Epoch [25][2600/3746] lr: 9.346e-02, eta: 4 days, 1:18:56, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5028, loss_cls: 4.0897, loss: 4.0897 +2024-12-26 22:04:27,912 - pyskl - INFO - Epoch [25][2700/3746] lr: 9.345e-02, eta: 4 days, 1:17:28, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4978, loss_cls: 4.0938, loss: 4.0938 +2024-12-26 22:05:39,541 - pyskl - INFO - Epoch [25][2800/3746] lr: 9.343e-02, eta: 4 days, 1:15:58, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5009, loss_cls: 4.1242, loss: 4.1242 +2024-12-26 22:06:51,209 - pyskl - INFO - Epoch [25][2900/3746] lr: 9.342e-02, eta: 4 days, 1:14:28, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5056, loss_cls: 4.1129, loss: 4.1129 +2024-12-26 22:08:02,761 - pyskl - INFO - Epoch [25][3000/3746] lr: 9.341e-02, eta: 4 days, 1:12:58, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4994, loss_cls: 4.0883, loss: 4.0883 +2024-12-26 22:09:14,646 - pyskl - INFO - Epoch [25][3100/3746] lr: 9.339e-02, eta: 4 days, 1:11:30, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4933, loss_cls: 4.1355, loss: 4.1355 +2024-12-26 22:10:26,547 - pyskl - INFO - Epoch [25][3200/3746] lr: 9.338e-02, eta: 4 days, 1:10:01, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4975, loss_cls: 4.0957, loss: 4.0957 +2024-12-26 22:11:38,332 - pyskl - INFO - Epoch [25][3300/3746] lr: 9.336e-02, eta: 4 days, 1:08:32, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4997, loss_cls: 4.1226, loss: 4.1226 +2024-12-26 22:12:50,076 - pyskl - INFO - Epoch [25][3400/3746] lr: 9.335e-02, eta: 4 days, 1:07:03, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4969, loss_cls: 4.1354, loss: 4.1354 +2024-12-26 22:14:02,126 - pyskl - INFO - Epoch [25][3500/3746] lr: 9.334e-02, eta: 4 days, 1:05:36, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5008, loss_cls: 4.0902, loss: 4.0902 +2024-12-26 22:15:14,086 - pyskl - INFO - Epoch [25][3600/3746] lr: 9.332e-02, eta: 4 days, 1:04:08, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4991, loss_cls: 4.1056, loss: 4.1056 +2024-12-26 22:16:26,142 - pyskl - INFO - Epoch [25][3700/3746] lr: 9.331e-02, eta: 4 days, 1:02:41, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5105, loss_cls: 4.0625, loss: 4.0625 +2024-12-26 22:17:01,102 - pyskl - INFO - Saving checkpoint at 25 epochs +2024-12-26 22:18:56,523 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 22:18:57,223 - pyskl - INFO - +top1_acc 0.1796 +top5_acc 0.3976 +2024-12-26 22:18:57,224 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 22:18:57,265 - pyskl - INFO - +mean_acc 0.1795 +2024-12-26 22:18:57,280 - pyskl - INFO - Epoch(val) [25][309] top1_acc: 0.1796, top5_acc: 0.3976, mean_class_accuracy: 0.1795 +2024-12-26 22:22:28,241 - pyskl - INFO - Epoch [26][100/3746] lr: 9.329e-02, eta: 4 days, 1:09:21, time: 2.109, data_time: 1.394, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5066, loss_cls: 4.0866, loss: 4.0866 +2024-12-26 22:23:39,850 - pyskl - INFO - Epoch [26][200/3746] lr: 9.327e-02, eta: 4 days, 1:07:51, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5131, loss_cls: 4.0487, loss: 4.0487 +2024-12-26 22:24:51,427 - pyskl - INFO - Epoch [26][300/3746] lr: 9.326e-02, eta: 4 days, 1:06:21, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.4997, loss_cls: 4.0961, loss: 4.0961 +2024-12-26 22:26:03,043 - pyskl - INFO - Epoch [26][400/3746] lr: 9.325e-02, eta: 4 days, 1:04:51, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.4955, loss_cls: 4.0749, loss: 4.0749 +2024-12-26 22:27:14,637 - pyskl - INFO - Epoch [26][500/3746] lr: 9.323e-02, eta: 4 days, 1:03:21, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5055, loss_cls: 4.0773, loss: 4.0773 +2024-12-26 22:28:26,003 - pyskl - INFO - Epoch [26][600/3746] lr: 9.322e-02, eta: 4 days, 1:01:49, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4927, loss_cls: 4.1299, loss: 4.1299 +2024-12-26 22:29:37,578 - pyskl - INFO - Epoch [26][700/3746] lr: 9.320e-02, eta: 4 days, 1:00:19, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5106, loss_cls: 4.0678, loss: 4.0678 +2024-12-26 22:30:49,301 - pyskl - INFO - Epoch [26][800/3746] lr: 9.319e-02, eta: 4 days, 0:58:50, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5005, loss_cls: 4.0832, loss: 4.0832 +2024-12-26 22:32:00,669 - pyskl - INFO - Epoch [26][900/3746] lr: 9.318e-02, eta: 4 days, 0:57:19, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5033, loss_cls: 4.0838, loss: 4.0838 +2024-12-26 22:33:11,717 - pyskl - INFO - Epoch [26][1000/3746] lr: 9.316e-02, eta: 4 days, 0:55:46, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5033, loss_cls: 4.1209, loss: 4.1209 +2024-12-26 22:34:22,949 - pyskl - INFO - Epoch [26][1100/3746] lr: 9.315e-02, eta: 4 days, 0:54:14, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5003, loss_cls: 4.1195, loss: 4.1195 +2024-12-26 22:35:34,329 - pyskl - INFO - Epoch [26][1200/3746] lr: 9.313e-02, eta: 4 days, 0:52:43, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5027, loss_cls: 4.1087, loss: 4.1087 +2024-12-26 22:36:45,710 - pyskl - INFO - Epoch [26][1300/3746] lr: 9.312e-02, eta: 4 days, 0:51:12, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.4938, loss_cls: 4.1028, loss: 4.1028 +2024-12-26 22:37:57,116 - pyskl - INFO - Epoch [26][1400/3746] lr: 9.310e-02, eta: 4 days, 0:49:42, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5075, loss_cls: 4.0980, loss: 4.0980 +2024-12-26 22:39:08,562 - pyskl - INFO - Epoch [26][1500/3746] lr: 9.309e-02, eta: 4 days, 0:48:11, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5111, loss_cls: 4.0497, loss: 4.0497 +2024-12-26 22:40:20,366 - pyskl - INFO - Epoch [26][1600/3746] lr: 9.308e-02, eta: 4 days, 0:46:43, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4859, loss_cls: 4.1454, loss: 4.1454 +2024-12-26 22:41:31,931 - pyskl - INFO - Epoch [26][1700/3746] lr: 9.306e-02, eta: 4 days, 0:45:13, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5061, loss_cls: 4.1143, loss: 4.1143 +2024-12-26 22:42:43,541 - pyskl - INFO - Epoch [26][1800/3746] lr: 9.305e-02, eta: 4 days, 0:43:43, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4958, loss_cls: 4.1061, loss: 4.1061 +2024-12-26 22:43:54,892 - pyskl - INFO - Epoch [26][1900/3746] lr: 9.303e-02, eta: 4 days, 0:42:12, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5181, loss_cls: 4.0336, loss: 4.0336 +2024-12-26 22:45:06,318 - pyskl - INFO - Epoch [26][2000/3746] lr: 9.302e-02, eta: 4 days, 0:40:42, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4945, loss_cls: 4.1233, loss: 4.1233 +2024-12-26 22:46:17,824 - pyskl - INFO - Epoch [26][2100/3746] lr: 9.300e-02, eta: 4 days, 0:39:12, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4977, loss_cls: 4.1401, loss: 4.1401 +2024-12-26 22:47:29,598 - pyskl - INFO - Epoch [26][2200/3746] lr: 9.299e-02, eta: 4 days, 0:37:44, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4908, loss_cls: 4.1248, loss: 4.1248 +2024-12-26 22:48:41,360 - pyskl - INFO - Epoch [26][2300/3746] lr: 9.298e-02, eta: 4 days, 0:36:15, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4975, loss_cls: 4.1191, loss: 4.1191 +2024-12-26 22:49:52,921 - pyskl - INFO - Epoch [26][2400/3746] lr: 9.296e-02, eta: 4 days, 0:34:45, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.5025, loss_cls: 4.0896, loss: 4.0896 +2024-12-26 22:51:04,791 - pyskl - INFO - Epoch [26][2500/3746] lr: 9.295e-02, eta: 4 days, 0:33:17, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5061, loss_cls: 4.1129, loss: 4.1129 +2024-12-26 22:52:16,602 - pyskl - INFO - Epoch [26][2600/3746] lr: 9.293e-02, eta: 4 days, 0:31:49, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5048, loss_cls: 4.0839, loss: 4.0839 +2024-12-26 22:53:28,518 - pyskl - INFO - Epoch [26][2700/3746] lr: 9.292e-02, eta: 4 days, 0:30:21, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4969, loss_cls: 4.1022, loss: 4.1022 +2024-12-26 22:54:40,422 - pyskl - INFO - Epoch [26][2800/3746] lr: 9.290e-02, eta: 4 days, 0:28:54, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4942, loss_cls: 4.1678, loss: 4.1678 +2024-12-26 22:55:52,230 - pyskl - INFO - Epoch [26][2900/3746] lr: 9.289e-02, eta: 4 days, 0:27:25, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5019, loss_cls: 4.1157, loss: 4.1157 +2024-12-26 22:57:04,171 - pyskl - INFO - Epoch [26][3000/3746] lr: 9.288e-02, eta: 4 days, 0:25:58, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4998, loss_cls: 4.1159, loss: 4.1159 +2024-12-26 22:58:15,827 - pyskl - INFO - Epoch [26][3100/3746] lr: 9.286e-02, eta: 4 days, 0:24:29, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5077, loss_cls: 4.0672, loss: 4.0672 +2024-12-26 22:59:27,554 - pyskl - INFO - Epoch [26][3200/3746] lr: 9.285e-02, eta: 4 days, 0:23:00, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5062, loss_cls: 4.0907, loss: 4.0907 +2024-12-26 23:00:39,439 - pyskl - INFO - Epoch [26][3300/3746] lr: 9.283e-02, eta: 4 days, 0:21:33, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5081, loss_cls: 4.0890, loss: 4.0890 +2024-12-26 23:01:51,210 - pyskl - INFO - Epoch [26][3400/3746] lr: 9.282e-02, eta: 4 days, 0:20:05, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4972, loss_cls: 4.1447, loss: 4.1447 +2024-12-26 23:03:02,868 - pyskl - INFO - Epoch [26][3500/3746] lr: 9.280e-02, eta: 4 days, 0:18:36, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4972, loss_cls: 4.1243, loss: 4.1243 +2024-12-26 23:04:14,644 - pyskl - INFO - Epoch [26][3600/3746] lr: 9.279e-02, eta: 4 days, 0:17:08, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5017, loss_cls: 4.0916, loss: 4.0916 +2024-12-26 23:05:26,414 - pyskl - INFO - Epoch [26][3700/3746] lr: 9.278e-02, eta: 4 days, 0:15:40, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5008, loss_cls: 4.1198, loss: 4.1198 +2024-12-26 23:06:00,879 - pyskl - INFO - Saving checkpoint at 26 epochs +2024-12-26 23:07:57,055 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 23:07:57,740 - pyskl - INFO - +top1_acc 0.1912 +top5_acc 0.4199 +2024-12-26 23:07:57,740 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 23:07:57,788 - pyskl - INFO - +mean_acc 0.1913 +2024-12-26 23:07:57,805 - pyskl - INFO - Epoch(val) [26][309] top1_acc: 0.1912, top5_acc: 0.4199, mean_class_accuracy: 0.1913 +2024-12-26 23:11:32,769 - pyskl - INFO - Epoch [27][100/3746] lr: 9.275e-02, eta: 4 days, 0:22:16, time: 2.149, data_time: 1.429, memory: 15990, top1_acc: 0.2487, top5_acc: 0.5000, loss_cls: 4.1020, loss: 4.1020 +2024-12-26 23:12:44,813 - pyskl - INFO - Epoch [27][200/3746] lr: 9.274e-02, eta: 4 days, 0:20:48, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5098, loss_cls: 4.0777, loss: 4.0777 +2024-12-26 23:13:56,520 - pyskl - INFO - Epoch [27][300/3746] lr: 9.272e-02, eta: 4 days, 0:19:20, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5111, loss_cls: 4.0678, loss: 4.0678 +2024-12-26 23:15:08,008 - pyskl - INFO - Epoch [27][400/3746] lr: 9.271e-02, eta: 4 days, 0:17:50, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5050, loss_cls: 4.0695, loss: 4.0695 +2024-12-26 23:16:19,375 - pyskl - INFO - Epoch [27][500/3746] lr: 9.270e-02, eta: 4 days, 0:16:19, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5086, loss_cls: 4.0244, loss: 4.0244 +2024-12-26 23:17:30,914 - pyskl - INFO - Epoch [27][600/3746] lr: 9.268e-02, eta: 4 days, 0:14:49, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.4955, loss_cls: 4.1332, loss: 4.1332 +2024-12-26 23:18:42,573 - pyskl - INFO - Epoch [27][700/3746] lr: 9.267e-02, eta: 4 days, 0:13:20, time: 0.717, data_time: 0.001, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5012, loss_cls: 4.1432, loss: 4.1432 +2024-12-26 23:19:54,211 - pyskl - INFO - Epoch [27][800/3746] lr: 9.265e-02, eta: 4 days, 0:11:51, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5009, loss_cls: 4.0858, loss: 4.0858 +2024-12-26 23:21:05,528 - pyskl - INFO - Epoch [27][900/3746] lr: 9.264e-02, eta: 4 days, 0:10:21, time: 0.713, data_time: 0.001, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4994, loss_cls: 4.0957, loss: 4.0957 +2024-12-26 23:22:17,354 - pyskl - INFO - Epoch [27][1000/3746] lr: 9.262e-02, eta: 4 days, 0:08:53, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.5025, loss_cls: 4.0853, loss: 4.0853 +2024-12-26 23:23:29,150 - pyskl - INFO - Epoch [27][1100/3746] lr: 9.261e-02, eta: 4 days, 0:07:24, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4875, loss_cls: 4.1598, loss: 4.1598 +2024-12-26 23:24:40,597 - pyskl - INFO - Epoch [27][1200/3746] lr: 9.259e-02, eta: 4 days, 0:05:55, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5088, loss_cls: 4.0788, loss: 4.0788 +2024-12-26 23:25:51,987 - pyskl - INFO - Epoch [27][1300/3746] lr: 9.258e-02, eta: 4 days, 0:04:24, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4883, loss_cls: 4.1382, loss: 4.1382 +2024-12-26 23:27:04,220 - pyskl - INFO - Epoch [27][1400/3746] lr: 9.256e-02, eta: 4 days, 0:02:58, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5066, loss_cls: 4.0783, loss: 4.0783 +2024-12-26 23:28:16,325 - pyskl - INFO - Epoch [27][1500/3746] lr: 9.255e-02, eta: 4 days, 0:01:32, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5073, loss_cls: 4.0870, loss: 4.0870 +2024-12-26 23:29:28,088 - pyskl - INFO - Epoch [27][1600/3746] lr: 9.253e-02, eta: 4 days, 0:00:03, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4988, loss_cls: 4.1297, loss: 4.1297 +2024-12-26 23:30:39,853 - pyskl - INFO - Epoch [27][1700/3746] lr: 9.252e-02, eta: 3 days, 23:58:35, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4941, loss_cls: 4.1061, loss: 4.1061 +2024-12-26 23:31:51,390 - pyskl - INFO - Epoch [27][1800/3746] lr: 9.251e-02, eta: 3 days, 23:57:06, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4992, loss_cls: 4.1099, loss: 4.1099 +2024-12-26 23:33:03,608 - pyskl - INFO - Epoch [27][1900/3746] lr: 9.249e-02, eta: 3 days, 23:55:40, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4998, loss_cls: 4.1091, loss: 4.1091 +2024-12-26 23:34:14,983 - pyskl - INFO - Epoch [27][2000/3746] lr: 9.248e-02, eta: 3 days, 23:54:10, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5047, loss_cls: 4.0943, loss: 4.0943 +2024-12-26 23:35:26,507 - pyskl - INFO - Epoch [27][2100/3746] lr: 9.246e-02, eta: 3 days, 23:52:41, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.5050, loss_cls: 4.0968, loss: 4.0968 +2024-12-26 23:36:38,291 - pyskl - INFO - Epoch [27][2200/3746] lr: 9.245e-02, eta: 3 days, 23:51:13, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5058, loss_cls: 4.0752, loss: 4.0752 +2024-12-26 23:37:49,881 - pyskl - INFO - Epoch [27][2300/3746] lr: 9.243e-02, eta: 3 days, 23:49:44, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5075, loss_cls: 4.0937, loss: 4.0937 +2024-12-26 23:39:01,851 - pyskl - INFO - Epoch [27][2400/3746] lr: 9.242e-02, eta: 3 days, 23:48:17, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5055, loss_cls: 4.0986, loss: 4.0986 +2024-12-26 23:40:13,501 - pyskl - INFO - Epoch [27][2500/3746] lr: 9.240e-02, eta: 3 days, 23:46:49, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4964, loss_cls: 4.1363, loss: 4.1363 +2024-12-26 23:41:25,259 - pyskl - INFO - Epoch [27][2600/3746] lr: 9.239e-02, eta: 3 days, 23:45:21, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.5014, loss_cls: 4.1010, loss: 4.1010 +2024-12-26 23:42:37,443 - pyskl - INFO - Epoch [27][2700/3746] lr: 9.237e-02, eta: 3 days, 23:43:55, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5042, loss_cls: 4.1129, loss: 4.1129 +2024-12-26 23:43:49,270 - pyskl - INFO - Epoch [27][2800/3746] lr: 9.236e-02, eta: 3 days, 23:42:27, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5066, loss_cls: 4.0681, loss: 4.0681 +2024-12-26 23:45:01,011 - pyskl - INFO - Epoch [27][2900/3746] lr: 9.234e-02, eta: 3 days, 23:41:00, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5022, loss_cls: 4.0797, loss: 4.0797 +2024-12-26 23:46:12,735 - pyskl - INFO - Epoch [27][3000/3746] lr: 9.233e-02, eta: 3 days, 23:39:32, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.5019, loss_cls: 4.1165, loss: 4.1165 +2024-12-26 23:47:24,344 - pyskl - INFO - Epoch [27][3100/3746] lr: 9.231e-02, eta: 3 days, 23:38:03, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5033, loss_cls: 4.0714, loss: 4.0714 +2024-12-26 23:48:36,228 - pyskl - INFO - Epoch [27][3200/3746] lr: 9.230e-02, eta: 3 days, 23:36:36, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4955, loss_cls: 4.1171, loss: 4.1171 +2024-12-26 23:49:48,073 - pyskl - INFO - Epoch [27][3300/3746] lr: 9.228e-02, eta: 3 days, 23:35:09, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5034, loss_cls: 4.1058, loss: 4.1058 +2024-12-26 23:50:59,672 - pyskl - INFO - Epoch [27][3400/3746] lr: 9.227e-02, eta: 3 days, 23:33:40, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5094, loss_cls: 4.0724, loss: 4.0724 +2024-12-26 23:52:11,954 - pyskl - INFO - Epoch [27][3500/3746] lr: 9.225e-02, eta: 3 days, 23:32:15, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4922, loss_cls: 4.1468, loss: 4.1468 +2024-12-26 23:53:23,502 - pyskl - INFO - Epoch [27][3600/3746] lr: 9.224e-02, eta: 3 days, 23:30:46, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5153, loss_cls: 4.1019, loss: 4.1019 +2024-12-26 23:54:35,446 - pyskl - INFO - Epoch [27][3700/3746] lr: 9.222e-02, eta: 3 days, 23:29:20, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5000, loss_cls: 4.1085, loss: 4.1085 +2024-12-26 23:55:10,450 - pyskl - INFO - Saving checkpoint at 27 epochs +2024-12-26 23:57:07,728 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 23:57:08,432 - pyskl - INFO - +top1_acc 0.1716 +top5_acc 0.3927 +2024-12-26 23:57:08,432 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 23:57:08,476 - pyskl - INFO - +mean_acc 0.1713 +2024-12-26 23:57:08,489 - pyskl - INFO - Epoch(val) [27][309] top1_acc: 0.1716, top5_acc: 0.3927, mean_class_accuracy: 0.1713 +2024-12-27 00:00:43,767 - pyskl - INFO - Epoch [28][100/3746] lr: 9.220e-02, eta: 3 days, 23:35:35, time: 2.153, data_time: 1.436, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5169, loss_cls: 4.0448, loss: 4.0448 +2024-12-27 00:01:55,413 - pyskl - INFO - Epoch [28][200/3746] lr: 9.219e-02, eta: 3 days, 23:34:06, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5025, loss_cls: 4.0722, loss: 4.0722 +2024-12-27 00:03:06,928 - pyskl - INFO - Epoch [28][300/3746] lr: 9.217e-02, eta: 3 days, 23:32:37, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5020, loss_cls: 4.0865, loss: 4.0865 +2024-12-27 00:04:18,719 - pyskl - INFO - Epoch [28][400/3746] lr: 9.216e-02, eta: 3 days, 23:31:09, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5114, loss_cls: 4.0677, loss: 4.0677 +2024-12-27 00:05:30,465 - pyskl - INFO - Epoch [28][500/3746] lr: 9.214e-02, eta: 3 days, 23:29:41, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4959, loss_cls: 4.1165, loss: 4.1165 +2024-12-27 00:06:41,811 - pyskl - INFO - Epoch [28][600/3746] lr: 9.213e-02, eta: 3 days, 23:28:11, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4947, loss_cls: 4.1038, loss: 4.1038 +2024-12-27 00:07:53,451 - pyskl - INFO - Epoch [28][700/3746] lr: 9.211e-02, eta: 3 days, 23:26:43, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5088, loss_cls: 4.0177, loss: 4.0177 +2024-12-27 00:09:04,907 - pyskl - INFO - Epoch [28][800/3746] lr: 9.210e-02, eta: 3 days, 23:25:13, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5039, loss_cls: 4.0769, loss: 4.0769 +2024-12-27 00:10:16,770 - pyskl - INFO - Epoch [28][900/3746] lr: 9.208e-02, eta: 3 days, 23:23:46, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5086, loss_cls: 4.0701, loss: 4.0701 +2024-12-27 00:11:28,613 - pyskl - INFO - Epoch [28][1000/3746] lr: 9.207e-02, eta: 3 days, 23:22:19, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4944, loss_cls: 4.1515, loss: 4.1515 +2024-12-27 00:12:39,972 - pyskl - INFO - Epoch [28][1100/3746] lr: 9.205e-02, eta: 3 days, 23:20:49, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4991, loss_cls: 4.1312, loss: 4.1312 +2024-12-27 00:13:51,451 - pyskl - INFO - Epoch [28][1200/3746] lr: 9.204e-02, eta: 3 days, 23:19:20, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5066, loss_cls: 4.1057, loss: 4.1057 +2024-12-27 00:15:02,818 - pyskl - INFO - Epoch [28][1300/3746] lr: 9.202e-02, eta: 3 days, 23:17:50, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5033, loss_cls: 4.1016, loss: 4.1016 +2024-12-27 00:16:14,303 - pyskl - INFO - Epoch [28][1400/3746] lr: 9.201e-02, eta: 3 days, 23:16:21, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5044, loss_cls: 4.0741, loss: 4.0741 +2024-12-27 00:17:25,618 - pyskl - INFO - Epoch [28][1500/3746] lr: 9.199e-02, eta: 3 days, 23:14:52, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4989, loss_cls: 4.1127, loss: 4.1127 +2024-12-27 00:18:37,692 - pyskl - INFO - Epoch [28][1600/3746] lr: 9.198e-02, eta: 3 days, 23:13:26, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5012, loss_cls: 4.1014, loss: 4.1014 +2024-12-27 00:19:49,478 - pyskl - INFO - Epoch [28][1700/3746] lr: 9.196e-02, eta: 3 days, 23:11:58, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5020, loss_cls: 4.0856, loss: 4.0856 +2024-12-27 00:21:01,081 - pyskl - INFO - Epoch [28][1800/3746] lr: 9.194e-02, eta: 3 days, 23:10:30, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5005, loss_cls: 4.0861, loss: 4.0861 +2024-12-27 00:22:13,183 - pyskl - INFO - Epoch [28][1900/3746] lr: 9.193e-02, eta: 3 days, 23:09:04, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4989, loss_cls: 4.1092, loss: 4.1092 +2024-12-27 00:23:25,030 - pyskl - INFO - Epoch [28][2000/3746] lr: 9.191e-02, eta: 3 days, 23:07:37, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5000, loss_cls: 4.1159, loss: 4.1159 +2024-12-27 00:24:37,140 - pyskl - INFO - Epoch [28][2100/3746] lr: 9.190e-02, eta: 3 days, 23:06:11, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5138, loss_cls: 4.0799, loss: 4.0799 +2024-12-27 00:25:48,625 - pyskl - INFO - Epoch [28][2200/3746] lr: 9.188e-02, eta: 3 days, 23:04:42, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4933, loss_cls: 4.1416, loss: 4.1416 +2024-12-27 00:27:00,911 - pyskl - INFO - Epoch [28][2300/3746] lr: 9.187e-02, eta: 3 days, 23:03:17, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.4980, loss_cls: 4.0962, loss: 4.0962 +2024-12-27 00:28:12,643 - pyskl - INFO - Epoch [28][2400/3746] lr: 9.185e-02, eta: 3 days, 23:01:49, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5144, loss_cls: 4.0666, loss: 4.0666 +2024-12-27 00:29:24,768 - pyskl - INFO - Epoch [28][2500/3746] lr: 9.184e-02, eta: 3 days, 23:00:24, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5055, loss_cls: 4.0520, loss: 4.0520 +2024-12-27 00:30:36,635 - pyskl - INFO - Epoch [28][2600/3746] lr: 9.182e-02, eta: 3 days, 22:58:57, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5064, loss_cls: 4.0778, loss: 4.0778 +2024-12-27 00:31:48,700 - pyskl - INFO - Epoch [28][2700/3746] lr: 9.181e-02, eta: 3 days, 22:57:31, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5041, loss_cls: 4.0678, loss: 4.0678 +2024-12-27 00:33:00,552 - pyskl - INFO - Epoch [28][2800/3746] lr: 9.179e-02, eta: 3 days, 22:56:04, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.4958, loss_cls: 4.1443, loss: 4.1443 +2024-12-27 00:34:12,708 - pyskl - INFO - Epoch [28][2900/3746] lr: 9.178e-02, eta: 3 days, 22:54:38, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.5011, loss_cls: 4.1204, loss: 4.1204 +2024-12-27 00:35:24,897 - pyskl - INFO - Epoch [28][3000/3746] lr: 9.176e-02, eta: 3 days, 22:53:13, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5028, loss_cls: 4.0943, loss: 4.0943 +2024-12-27 00:36:37,102 - pyskl - INFO - Epoch [28][3100/3746] lr: 9.175e-02, eta: 3 days, 22:51:48, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5020, loss_cls: 4.1246, loss: 4.1246 +2024-12-27 00:37:49,382 - pyskl - INFO - Epoch [28][3200/3746] lr: 9.173e-02, eta: 3 days, 22:50:23, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.4992, loss_cls: 4.1095, loss: 4.1095 +2024-12-27 00:39:01,243 - pyskl - INFO - Epoch [28][3300/3746] lr: 9.172e-02, eta: 3 days, 22:48:56, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4930, loss_cls: 4.1451, loss: 4.1451 +2024-12-27 00:40:13,206 - pyskl - INFO - Epoch [28][3400/3746] lr: 9.170e-02, eta: 3 days, 22:47:30, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.5081, loss_cls: 4.1010, loss: 4.1010 +2024-12-27 00:41:25,079 - pyskl - INFO - Epoch [28][3500/3746] lr: 9.168e-02, eta: 3 days, 22:46:03, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4948, loss_cls: 4.1286, loss: 4.1286 +2024-12-27 00:42:37,536 - pyskl - INFO - Epoch [28][3600/3746] lr: 9.167e-02, eta: 3 days, 22:44:39, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5011, loss_cls: 4.1105, loss: 4.1105 +2024-12-27 00:43:49,337 - pyskl - INFO - Epoch [28][3700/3746] lr: 9.165e-02, eta: 3 days, 22:43:13, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5089, loss_cls: 4.0711, loss: 4.0711 +2024-12-27 00:44:24,217 - pyskl - INFO - Saving checkpoint at 28 epochs +2024-12-27 00:46:21,620 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 00:46:22,430 - pyskl - INFO - +top1_acc 0.1880 +top5_acc 0.4169 +2024-12-27 00:46:22,430 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 00:46:22,477 - pyskl - INFO - +mean_acc 0.1878 +2024-12-27 00:46:22,492 - pyskl - INFO - Epoch(val) [28][309] top1_acc: 0.1880, top5_acc: 0.4169, mean_class_accuracy: 0.1878 +2024-12-27 00:49:54,658 - pyskl - INFO - Epoch [29][100/3746] lr: 9.163e-02, eta: 3 days, 22:48:53, time: 2.122, data_time: 1.403, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5153, loss_cls: 4.0458, loss: 4.0458 +2024-12-27 00:51:06,287 - pyskl - INFO - Epoch [29][200/3746] lr: 9.162e-02, eta: 3 days, 22:47:25, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5095, loss_cls: 4.0503, loss: 4.0503 +2024-12-27 00:52:17,741 - pyskl - INFO - Epoch [29][300/3746] lr: 9.160e-02, eta: 3 days, 22:45:56, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5098, loss_cls: 4.0680, loss: 4.0680 +2024-12-27 00:53:29,591 - pyskl - INFO - Epoch [29][400/3746] lr: 9.158e-02, eta: 3 days, 22:44:29, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5050, loss_cls: 4.0815, loss: 4.0815 +2024-12-27 00:54:40,947 - pyskl - INFO - Epoch [29][500/3746] lr: 9.157e-02, eta: 3 days, 22:43:00, time: 0.714, data_time: 0.001, memory: 15990, top1_acc: 0.2473, top5_acc: 0.5005, loss_cls: 4.1358, loss: 4.1358 +2024-12-27 00:55:52,288 - pyskl - INFO - Epoch [29][600/3746] lr: 9.155e-02, eta: 3 days, 22:41:31, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5080, loss_cls: 4.0502, loss: 4.0502 +2024-12-27 00:57:04,155 - pyskl - INFO - Epoch [29][700/3746] lr: 9.154e-02, eta: 3 days, 22:40:04, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5081, loss_cls: 4.0545, loss: 4.0545 +2024-12-27 00:58:15,746 - pyskl - INFO - Epoch [29][800/3746] lr: 9.152e-02, eta: 3 days, 22:38:36, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4973, loss_cls: 4.1186, loss: 4.1186 +2024-12-27 00:59:26,961 - pyskl - INFO - Epoch [29][900/3746] lr: 9.151e-02, eta: 3 days, 22:37:06, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5006, loss_cls: 4.0913, loss: 4.0913 +2024-12-27 01:00:38,619 - pyskl - INFO - Epoch [29][1000/3746] lr: 9.149e-02, eta: 3 days, 22:35:38, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.4969, loss_cls: 4.1000, loss: 4.1000 +2024-12-27 01:01:50,453 - pyskl - INFO - Epoch [29][1100/3746] lr: 9.148e-02, eta: 3 days, 22:34:11, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4948, loss_cls: 4.0684, loss: 4.0684 +2024-12-27 01:03:01,747 - pyskl - INFO - Epoch [29][1200/3746] lr: 9.146e-02, eta: 3 days, 22:32:42, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5066, loss_cls: 4.0770, loss: 4.0770 +2024-12-27 01:04:13,153 - pyskl - INFO - Epoch [29][1300/3746] lr: 9.144e-02, eta: 3 days, 22:31:13, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5041, loss_cls: 4.0536, loss: 4.0536 +2024-12-27 01:05:25,137 - pyskl - INFO - Epoch [29][1400/3746] lr: 9.143e-02, eta: 3 days, 22:29:47, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4964, loss_cls: 4.1182, loss: 4.1182 +2024-12-27 01:06:36,304 - pyskl - INFO - Epoch [29][1500/3746] lr: 9.141e-02, eta: 3 days, 22:28:18, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5028, loss_cls: 4.0812, loss: 4.0812 +2024-12-27 01:07:48,012 - pyskl - INFO - Epoch [29][1600/3746] lr: 9.140e-02, eta: 3 days, 22:26:50, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5047, loss_cls: 4.0749, loss: 4.0749 +2024-12-27 01:09:00,316 - pyskl - INFO - Epoch [29][1700/3746] lr: 9.138e-02, eta: 3 days, 22:25:26, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5020, loss_cls: 4.0935, loss: 4.0935 +2024-12-27 01:10:12,343 - pyskl - INFO - Epoch [29][1800/3746] lr: 9.137e-02, eta: 3 days, 22:24:00, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5011, loss_cls: 4.0997, loss: 4.0997 +2024-12-27 01:11:24,269 - pyskl - INFO - Epoch [29][1900/3746] lr: 9.135e-02, eta: 3 days, 22:22:33, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5003, loss_cls: 4.0700, loss: 4.0700 +2024-12-27 01:12:36,004 - pyskl - INFO - Epoch [29][2000/3746] lr: 9.133e-02, eta: 3 days, 22:21:06, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5009, loss_cls: 4.0949, loss: 4.0949 +2024-12-27 01:13:48,118 - pyskl - INFO - Epoch [29][2100/3746] lr: 9.132e-02, eta: 3 days, 22:19:41, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5022, loss_cls: 4.0802, loss: 4.0802 +2024-12-27 01:15:00,013 - pyskl - INFO - Epoch [29][2200/3746] lr: 9.130e-02, eta: 3 days, 22:18:15, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4988, loss_cls: 4.1420, loss: 4.1420 +2024-12-27 01:16:12,051 - pyskl - INFO - Epoch [29][2300/3746] lr: 9.129e-02, eta: 3 days, 22:16:49, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5073, loss_cls: 4.0835, loss: 4.0835 +2024-12-27 01:17:23,692 - pyskl - INFO - Epoch [29][2400/3746] lr: 9.127e-02, eta: 3 days, 22:15:22, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5036, loss_cls: 4.0736, loss: 4.0736 +2024-12-27 01:18:35,651 - pyskl - INFO - Epoch [29][2500/3746] lr: 9.126e-02, eta: 3 days, 22:13:56, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5036, loss_cls: 4.1150, loss: 4.1150 +2024-12-27 01:19:47,187 - pyskl - INFO - Epoch [29][2600/3746] lr: 9.124e-02, eta: 3 days, 22:12:28, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4919, loss_cls: 4.1309, loss: 4.1309 +2024-12-27 01:20:59,281 - pyskl - INFO - Epoch [29][2700/3746] lr: 9.122e-02, eta: 3 days, 22:11:02, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4892, loss_cls: 4.1227, loss: 4.1227 +2024-12-27 01:22:11,328 - pyskl - INFO - Epoch [29][2800/3746] lr: 9.121e-02, eta: 3 days, 22:09:37, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5041, loss_cls: 4.1103, loss: 4.1103 +2024-12-27 01:23:23,362 - pyskl - INFO - Epoch [29][2900/3746] lr: 9.119e-02, eta: 3 days, 22:08:11, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5027, loss_cls: 4.1129, loss: 4.1129 +2024-12-27 01:24:35,456 - pyskl - INFO - Epoch [29][3000/3746] lr: 9.118e-02, eta: 3 days, 22:06:46, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5009, loss_cls: 4.1017, loss: 4.1017 +2024-12-27 01:25:47,349 - pyskl - INFO - Epoch [29][3100/3746] lr: 9.116e-02, eta: 3 days, 22:05:20, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5030, loss_cls: 4.1062, loss: 4.1062 +2024-12-27 01:26:58,849 - pyskl - INFO - Epoch [29][3200/3746] lr: 9.114e-02, eta: 3 days, 22:03:52, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4955, loss_cls: 4.1134, loss: 4.1134 +2024-12-27 01:28:10,964 - pyskl - INFO - Epoch [29][3300/3746] lr: 9.113e-02, eta: 3 days, 22:02:27, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4995, loss_cls: 4.1414, loss: 4.1414 +2024-12-27 01:29:22,931 - pyskl - INFO - Epoch [29][3400/3746] lr: 9.111e-02, eta: 3 days, 22:01:01, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5009, loss_cls: 4.1074, loss: 4.1074 +2024-12-27 01:30:35,248 - pyskl - INFO - Epoch [29][3500/3746] lr: 9.110e-02, eta: 3 days, 21:59:37, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5023, loss_cls: 4.0798, loss: 4.0798 +2024-12-27 01:31:47,325 - pyskl - INFO - Epoch [29][3600/3746] lr: 9.108e-02, eta: 3 days, 21:58:12, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5086, loss_cls: 4.0713, loss: 4.0713 +2024-12-27 01:32:58,861 - pyskl - INFO - Epoch [29][3700/3746] lr: 9.106e-02, eta: 3 days, 21:56:44, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4892, loss_cls: 4.1368, loss: 4.1368 +2024-12-27 01:33:33,166 - pyskl - INFO - Saving checkpoint at 29 epochs +2024-12-27 01:35:28,696 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 01:35:29,386 - pyskl - INFO - +top1_acc 0.1689 +top5_acc 0.3951 +2024-12-27 01:35:29,386 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 01:35:29,430 - pyskl - INFO - +mean_acc 0.1687 +2024-12-27 01:35:29,442 - pyskl - INFO - Epoch(val) [29][309] top1_acc: 0.1689, top5_acc: 0.3951, mean_class_accuracy: 0.1687 +2024-12-27 01:39:09,662 - pyskl - INFO - Epoch [30][100/3746] lr: 9.104e-02, eta: 3 days, 22:02:39, time: 2.202, data_time: 1.371, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5108, loss_cls: 4.0440, loss: 4.0440 +2024-12-27 01:40:32,627 - pyskl - INFO - Epoch [30][200/3746] lr: 9.103e-02, eta: 3 days, 22:01:59, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5089, loss_cls: 4.0482, loss: 4.0482 +2024-12-27 01:41:55,797 - pyskl - INFO - Epoch [30][300/3746] lr: 9.101e-02, eta: 3 days, 22:01:19, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5170, loss_cls: 4.0237, loss: 4.0237 +2024-12-27 01:43:18,300 - pyskl - INFO - Epoch [30][400/3746] lr: 9.099e-02, eta: 3 days, 22:00:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5100, loss_cls: 4.0466, loss: 4.0466 +2024-12-27 01:44:40,985 - pyskl - INFO - Epoch [30][500/3746] lr: 9.098e-02, eta: 3 days, 21:59:55, time: 0.827, data_time: 0.001, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5144, loss_cls: 4.0441, loss: 4.0441 +2024-12-27 01:46:03,671 - pyskl - INFO - Epoch [30][600/3746] lr: 9.096e-02, eta: 3 days, 21:59:13, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4927, loss_cls: 4.1214, loss: 4.1214 +2024-12-27 01:47:27,451 - pyskl - INFO - Epoch [30][700/3746] lr: 9.095e-02, eta: 3 days, 21:58:36, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5192, loss_cls: 4.0579, loss: 4.0579 +2024-12-27 01:48:50,957 - pyskl - INFO - Epoch [30][800/3746] lr: 9.093e-02, eta: 3 days, 21:57:57, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4983, loss_cls: 4.0945, loss: 4.0945 +2024-12-27 01:50:14,201 - pyskl - INFO - Epoch [30][900/3746] lr: 9.091e-02, eta: 3 days, 21:57:18, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.5075, loss_cls: 4.0775, loss: 4.0775 +2024-12-27 01:51:37,705 - pyskl - INFO - Epoch [30][1000/3746] lr: 9.090e-02, eta: 3 days, 21:56:39, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.4925, loss_cls: 4.1097, loss: 4.1097 +2024-12-27 01:53:01,572 - pyskl - INFO - Epoch [30][1100/3746] lr: 9.088e-02, eta: 3 days, 21:56:02, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5041, loss_cls: 4.0696, loss: 4.0696 +2024-12-27 01:54:25,077 - pyskl - INFO - Epoch [30][1200/3746] lr: 9.087e-02, eta: 3 days, 21:55:23, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4980, loss_cls: 4.1336, loss: 4.1336 +2024-12-27 01:55:48,252 - pyskl - INFO - Epoch [30][1300/3746] lr: 9.085e-02, eta: 3 days, 21:54:42, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.4939, loss_cls: 4.0951, loss: 4.0951 +2024-12-27 01:57:11,847 - pyskl - INFO - Epoch [30][1400/3746] lr: 9.083e-02, eta: 3 days, 21:54:04, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4961, loss_cls: 4.1020, loss: 4.1020 +2024-12-27 01:58:34,931 - pyskl - INFO - Epoch [30][1500/3746] lr: 9.082e-02, eta: 3 days, 21:53:23, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5050, loss_cls: 4.0766, loss: 4.0766 +2024-12-27 01:59:58,554 - pyskl - INFO - Epoch [30][1600/3746] lr: 9.080e-02, eta: 3 days, 21:52:44, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5072, loss_cls: 4.0800, loss: 4.0800 +2024-12-27 02:01:22,151 - pyskl - INFO - Epoch [30][1700/3746] lr: 9.078e-02, eta: 3 days, 21:52:05, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4963, loss_cls: 4.1227, loss: 4.1227 +2024-12-27 02:02:45,717 - pyskl - INFO - Epoch [30][1800/3746] lr: 9.077e-02, eta: 3 days, 21:51:26, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5028, loss_cls: 4.0812, loss: 4.0812 +2024-12-27 02:04:09,637 - pyskl - INFO - Epoch [30][1900/3746] lr: 9.075e-02, eta: 3 days, 21:50:48, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4923, loss_cls: 4.1223, loss: 4.1223 +2024-12-27 02:05:33,429 - pyskl - INFO - Epoch [30][2000/3746] lr: 9.074e-02, eta: 3 days, 21:50:10, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4953, loss_cls: 4.1174, loss: 4.1174 +2024-12-27 02:06:57,054 - pyskl - INFO - Epoch [30][2100/3746] lr: 9.072e-02, eta: 3 days, 21:49:31, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.4983, loss_cls: 4.1085, loss: 4.1085 +2024-12-27 02:08:20,463 - pyskl - INFO - Epoch [30][2200/3746] lr: 9.070e-02, eta: 3 days, 21:48:51, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5052, loss_cls: 4.0712, loss: 4.0712 +2024-12-27 02:09:44,342 - pyskl - INFO - Epoch [30][2300/3746] lr: 9.069e-02, eta: 3 days, 21:48:12, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.4980, loss_cls: 4.0979, loss: 4.0979 +2024-12-27 02:11:08,433 - pyskl - INFO - Epoch [30][2400/3746] lr: 9.067e-02, eta: 3 days, 21:47:35, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5098, loss_cls: 4.0954, loss: 4.0954 +2024-12-27 02:12:31,733 - pyskl - INFO - Epoch [30][2500/3746] lr: 9.065e-02, eta: 3 days, 21:46:54, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5045, loss_cls: 4.0933, loss: 4.0933 +2024-12-27 02:13:55,410 - pyskl - INFO - Epoch [30][2600/3746] lr: 9.064e-02, eta: 3 days, 21:46:15, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5111, loss_cls: 4.0569, loss: 4.0569 +2024-12-27 02:15:19,244 - pyskl - INFO - Epoch [30][2700/3746] lr: 9.062e-02, eta: 3 days, 21:45:36, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4936, loss_cls: 4.1012, loss: 4.1012 +2024-12-27 02:16:42,940 - pyskl - INFO - Epoch [30][2800/3746] lr: 9.061e-02, eta: 3 days, 21:44:56, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5116, loss_cls: 4.0589, loss: 4.0589 +2024-12-27 02:18:06,856 - pyskl - INFO - Epoch [30][2900/3746] lr: 9.059e-02, eta: 3 days, 21:44:18, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4967, loss_cls: 4.1138, loss: 4.1138 +2024-12-27 02:19:30,746 - pyskl - INFO - Epoch [30][3000/3746] lr: 9.057e-02, eta: 3 days, 21:43:39, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5175, loss_cls: 4.0608, loss: 4.0608 +2024-12-27 02:20:54,236 - pyskl - INFO - Epoch [30][3100/3746] lr: 9.056e-02, eta: 3 days, 21:42:59, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4984, loss_cls: 4.0978, loss: 4.0978 +2024-12-27 02:22:18,151 - pyskl - INFO - Epoch [30][3200/3746] lr: 9.054e-02, eta: 3 days, 21:42:20, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5084, loss_cls: 4.0706, loss: 4.0706 +2024-12-27 02:23:42,052 - pyskl - INFO - Epoch [30][3300/3746] lr: 9.052e-02, eta: 3 days, 21:41:41, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5028, loss_cls: 4.0610, loss: 4.0610 +2024-12-27 02:25:06,050 - pyskl - INFO - Epoch [30][3400/3746] lr: 9.051e-02, eta: 3 days, 21:41:02, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5052, loss_cls: 4.0939, loss: 4.0939 +2024-12-27 02:26:29,787 - pyskl - INFO - Epoch [30][3500/3746] lr: 9.049e-02, eta: 3 days, 21:40:22, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5059, loss_cls: 4.1011, loss: 4.1011 +2024-12-27 02:27:52,935 - pyskl - INFO - Epoch [30][3600/3746] lr: 9.047e-02, eta: 3 days, 21:39:40, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5066, loss_cls: 4.0886, loss: 4.0886 +2024-12-27 02:29:15,563 - pyskl - INFO - Epoch [30][3700/3746] lr: 9.046e-02, eta: 3 days, 21:38:56, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5148, loss_cls: 4.0349, loss: 4.0349 +2024-12-27 02:29:55,445 - pyskl - INFO - Saving checkpoint at 30 epochs +2024-12-27 02:31:51,443 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 02:31:52,137 - pyskl - INFO - +top1_acc 0.1883 +top5_acc 0.4156 +2024-12-27 02:31:52,137 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 02:31:52,179 - pyskl - INFO - +mean_acc 0.1881 +2024-12-27 02:31:52,191 - pyskl - INFO - Epoch(val) [30][309] top1_acc: 0.1883, top5_acc: 0.4156, mean_class_accuracy: 0.1881 +2024-12-27 02:35:54,538 - pyskl - INFO - Epoch [31][100/3746] lr: 9.043e-02, eta: 3 days, 21:45:57, time: 2.423, data_time: 1.385, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5202, loss_cls: 4.2359, loss: 4.2359 +2024-12-27 02:37:20,354 - pyskl - INFO - Epoch [31][200/3746] lr: 9.042e-02, eta: 3 days, 21:45:24, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5130, loss_cls: 4.2621, loss: 4.2621 +2024-12-27 02:38:46,339 - pyskl - INFO - Epoch [31][300/3746] lr: 9.040e-02, eta: 3 days, 21:44:53, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5058, loss_cls: 4.2803, loss: 4.2803 +2024-12-27 02:40:11,532 - pyskl - INFO - Epoch [31][400/3746] lr: 9.039e-02, eta: 3 days, 21:44:18, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5100, loss_cls: 4.2815, loss: 4.2815 +2024-12-27 02:41:36,495 - pyskl - INFO - Epoch [31][500/3746] lr: 9.037e-02, eta: 3 days, 21:43:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5253, loss_cls: 4.1889, loss: 4.1889 +2024-12-27 02:43:01,387 - pyskl - INFO - Epoch [31][600/3746] lr: 9.035e-02, eta: 3 days, 21:43:05, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5011, loss_cls: 4.3162, loss: 4.3162 +2024-12-27 02:44:26,671 - pyskl - INFO - Epoch [31][700/3746] lr: 9.034e-02, eta: 3 days, 21:42:30, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5055, loss_cls: 4.2779, loss: 4.2779 +2024-12-27 02:45:51,431 - pyskl - INFO - Epoch [31][800/3746] lr: 9.032e-02, eta: 3 days, 21:41:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5067, loss_cls: 4.2938, loss: 4.2938 +2024-12-27 02:47:16,000 - pyskl - INFO - Epoch [31][900/3746] lr: 9.030e-02, eta: 3 days, 21:41:15, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5050, loss_cls: 4.2865, loss: 4.2865 +2024-12-27 02:48:40,560 - pyskl - INFO - Epoch [31][1000/3746] lr: 9.029e-02, eta: 3 days, 21:40:37, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5119, loss_cls: 4.2881, loss: 4.2881 +2024-12-27 02:50:05,227 - pyskl - INFO - Epoch [31][1100/3746] lr: 9.027e-02, eta: 3 days, 21:39:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4964, loss_cls: 4.3805, loss: 4.3805 +2024-12-27 02:51:30,173 - pyskl - INFO - Epoch [31][1200/3746] lr: 9.025e-02, eta: 3 days, 21:39:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4984, loss_cls: 4.3437, loss: 4.3437 +2024-12-27 02:52:55,601 - pyskl - INFO - Epoch [31][1300/3746] lr: 9.024e-02, eta: 3 days, 21:38:48, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4970, loss_cls: 4.3259, loss: 4.3259 +2024-12-27 02:54:20,690 - pyskl - INFO - Epoch [31][1400/3746] lr: 9.022e-02, eta: 3 days, 21:38:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5148, loss_cls: 4.2834, loss: 4.2834 +2024-12-27 02:55:46,152 - pyskl - INFO - Epoch [31][1500/3746] lr: 9.020e-02, eta: 3 days, 21:37:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5125, loss_cls: 4.2853, loss: 4.2853 +2024-12-27 02:57:11,436 - pyskl - INFO - Epoch [31][1600/3746] lr: 9.019e-02, eta: 3 days, 21:37:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5111, loss_cls: 4.2586, loss: 4.2586 +2024-12-27 02:58:36,386 - pyskl - INFO - Epoch [31][1700/3746] lr: 9.017e-02, eta: 3 days, 21:36:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4955, loss_cls: 4.3456, loss: 4.3456 +2024-12-27 03:00:01,709 - pyskl - INFO - Epoch [31][1800/3746] lr: 9.015e-02, eta: 3 days, 21:35:48, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5086, loss_cls: 4.2658, loss: 4.2658 +2024-12-27 03:01:26,546 - pyskl - INFO - Epoch [31][1900/3746] lr: 9.014e-02, eta: 3 days, 21:35:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5003, loss_cls: 4.3103, loss: 4.3103 +2024-12-27 03:02:51,917 - pyskl - INFO - Epoch [31][2000/3746] lr: 9.012e-02, eta: 3 days, 21:34:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5070, loss_cls: 4.2785, loss: 4.2785 +2024-12-27 03:04:17,188 - pyskl - INFO - Epoch [31][2100/3746] lr: 9.010e-02, eta: 3 days, 21:33:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5038, loss_cls: 4.3067, loss: 4.3067 +2024-12-27 03:05:42,592 - pyskl - INFO - Epoch [31][2200/3746] lr: 9.009e-02, eta: 3 days, 21:33:22, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5064, loss_cls: 4.2607, loss: 4.2607 +2024-12-27 03:07:07,820 - pyskl - INFO - Epoch [31][2300/3746] lr: 9.007e-02, eta: 3 days, 21:32:46, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5059, loss_cls: 4.2795, loss: 4.2795 +2024-12-27 03:08:32,809 - pyskl - INFO - Epoch [31][2400/3746] lr: 9.005e-02, eta: 3 days, 21:32:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4991, loss_cls: 4.3185, loss: 4.3185 +2024-12-27 03:09:58,211 - pyskl - INFO - Epoch [31][2500/3746] lr: 9.004e-02, eta: 3 days, 21:31:32, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5191, loss_cls: 4.2591, loss: 4.2591 +2024-12-27 03:11:23,463 - pyskl - INFO - Epoch [31][2600/3746] lr: 9.002e-02, eta: 3 days, 21:30:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4986, loss_cls: 4.3370, loss: 4.3370 +2024-12-27 03:12:48,397 - pyskl - INFO - Epoch [31][2700/3746] lr: 9.000e-02, eta: 3 days, 21:30:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5042, loss_cls: 4.3176, loss: 4.3176 +2024-12-27 03:14:13,665 - pyskl - INFO - Epoch [31][2800/3746] lr: 8.999e-02, eta: 3 days, 21:29:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5055, loss_cls: 4.2760, loss: 4.2760 +2024-12-27 03:15:39,092 - pyskl - INFO - Epoch [31][2900/3746] lr: 8.997e-02, eta: 3 days, 21:29:04, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5017, loss_cls: 4.2970, loss: 4.2970 +2024-12-27 03:17:04,124 - pyskl - INFO - Epoch [31][3000/3746] lr: 8.995e-02, eta: 3 days, 21:28:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5069, loss_cls: 4.2902, loss: 4.2902 +2024-12-27 03:18:29,642 - pyskl - INFO - Epoch [31][3100/3746] lr: 8.994e-02, eta: 3 days, 21:27:50, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5117, loss_cls: 4.2841, loss: 4.2841 +2024-12-27 03:19:54,579 - pyskl - INFO - Epoch [31][3200/3746] lr: 8.992e-02, eta: 3 days, 21:27:12, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5028, loss_cls: 4.3075, loss: 4.3075 +2024-12-27 03:21:20,349 - pyskl - INFO - Epoch [31][3300/3746] lr: 8.990e-02, eta: 3 days, 21:26:36, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5098, loss_cls: 4.3061, loss: 4.3061 +2024-12-27 03:22:45,849 - pyskl - INFO - Epoch [31][3400/3746] lr: 8.989e-02, eta: 3 days, 21:26:00, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4992, loss_cls: 4.3094, loss: 4.3094 +2024-12-27 03:24:11,313 - pyskl - INFO - Epoch [31][3500/3746] lr: 8.987e-02, eta: 3 days, 21:25:23, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5088, loss_cls: 4.2989, loss: 4.2989 +2024-12-27 03:25:36,848 - pyskl - INFO - Epoch [31][3600/3746] lr: 8.985e-02, eta: 3 days, 21:24:47, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4936, loss_cls: 4.3429, loss: 4.3429 +2024-12-27 03:27:01,552 - pyskl - INFO - Epoch [31][3700/3746] lr: 8.983e-02, eta: 3 days, 21:24:07, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5056, loss_cls: 4.2931, loss: 4.2931 +2024-12-27 03:27:42,051 - pyskl - INFO - Saving checkpoint at 31 epochs +2024-12-27 03:29:38,868 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 03:29:39,537 - pyskl - INFO - +top1_acc 0.1937 +top5_acc 0.4120 +2024-12-27 03:29:39,537 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 03:29:39,578 - pyskl - INFO - +mean_acc 0.1937 +2024-12-27 03:29:39,591 - pyskl - INFO - Epoch(val) [31][309] top1_acc: 0.1937, top5_acc: 0.4120, mean_class_accuracy: 0.1937 +2024-12-27 03:33:50,048 - pyskl - INFO - Epoch [32][100/3746] lr: 8.981e-02, eta: 3 days, 21:31:15, time: 2.504, data_time: 1.476, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5103, loss_cls: 4.2851, loss: 4.2851 +2024-12-27 03:35:15,408 - pyskl - INFO - Epoch [32][200/3746] lr: 8.979e-02, eta: 3 days, 21:30:37, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5075, loss_cls: 4.2795, loss: 4.2795 +2024-12-27 03:36:41,481 - pyskl - INFO - Epoch [32][300/3746] lr: 8.978e-02, eta: 3 days, 21:30:02, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5216, loss_cls: 4.2319, loss: 4.2319 +2024-12-27 03:38:07,136 - pyskl - INFO - Epoch [32][400/3746] lr: 8.976e-02, eta: 3 days, 21:29:25, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5098, loss_cls: 4.2784, loss: 4.2784 +2024-12-27 03:39:32,066 - pyskl - INFO - Epoch [32][500/3746] lr: 8.974e-02, eta: 3 days, 21:28:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4952, loss_cls: 4.3176, loss: 4.3176 +2024-12-27 03:40:56,977 - pyskl - INFO - Epoch [32][600/3746] lr: 8.973e-02, eta: 3 days, 21:28:05, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5095, loss_cls: 4.2910, loss: 4.2910 +2024-12-27 03:42:22,253 - pyskl - INFO - Epoch [32][700/3746] lr: 8.971e-02, eta: 3 days, 21:27:26, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5198, loss_cls: 4.2210, loss: 4.2210 +2024-12-27 03:43:47,124 - pyskl - INFO - Epoch [32][800/3746] lr: 8.969e-02, eta: 3 days, 21:26:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5056, loss_cls: 4.2841, loss: 4.2841 +2024-12-27 03:45:12,933 - pyskl - INFO - Epoch [32][900/3746] lr: 8.967e-02, eta: 3 days, 21:26:09, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5200, loss_cls: 4.2289, loss: 4.2289 +2024-12-27 03:46:38,204 - pyskl - INFO - Epoch [32][1000/3746] lr: 8.966e-02, eta: 3 days, 21:25:30, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5016, loss_cls: 4.3169, loss: 4.3169 +2024-12-27 03:48:03,620 - pyskl - INFO - Epoch [32][1100/3746] lr: 8.964e-02, eta: 3 days, 21:24:52, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5344, loss_cls: 4.1784, loss: 4.1784 +2024-12-27 03:49:29,303 - pyskl - INFO - Epoch [32][1200/3746] lr: 8.962e-02, eta: 3 days, 21:24:14, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5009, loss_cls: 4.3205, loss: 4.3205 +2024-12-27 03:50:55,034 - pyskl - INFO - Epoch [32][1300/3746] lr: 8.961e-02, eta: 3 days, 21:23:37, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5147, loss_cls: 4.2596, loss: 4.2596 +2024-12-27 03:52:20,678 - pyskl - INFO - Epoch [32][1400/3746] lr: 8.959e-02, eta: 3 days, 21:22:59, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5064, loss_cls: 4.3128, loss: 4.3128 +2024-12-27 03:53:46,316 - pyskl - INFO - Epoch [32][1500/3746] lr: 8.957e-02, eta: 3 days, 21:22:21, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5102, loss_cls: 4.2553, loss: 4.2553 +2024-12-27 03:55:12,013 - pyskl - INFO - Epoch [32][1600/3746] lr: 8.955e-02, eta: 3 days, 21:21:43, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4984, loss_cls: 4.3437, loss: 4.3437 +2024-12-27 03:56:37,808 - pyskl - INFO - Epoch [32][1700/3746] lr: 8.954e-02, eta: 3 days, 21:21:06, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5175, loss_cls: 4.2655, loss: 4.2655 +2024-12-27 03:58:03,488 - pyskl - INFO - Epoch [32][1800/3746] lr: 8.952e-02, eta: 3 days, 21:20:28, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5056, loss_cls: 4.3013, loss: 4.3013 +2024-12-27 03:59:29,363 - pyskl - INFO - Epoch [32][1900/3746] lr: 8.950e-02, eta: 3 days, 21:19:51, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4966, loss_cls: 4.3226, loss: 4.3226 +2024-12-27 04:00:54,821 - pyskl - INFO - Epoch [32][2000/3746] lr: 8.949e-02, eta: 3 days, 21:19:11, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5161, loss_cls: 4.2851, loss: 4.2851 +2024-12-27 04:02:21,030 - pyskl - INFO - Epoch [32][2100/3746] lr: 8.947e-02, eta: 3 days, 21:18:35, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5028, loss_cls: 4.3007, loss: 4.3007 +2024-12-27 04:03:46,890 - pyskl - INFO - Epoch [32][2200/3746] lr: 8.945e-02, eta: 3 days, 21:17:58, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5075, loss_cls: 4.2970, loss: 4.2970 +2024-12-27 04:05:12,665 - pyskl - INFO - Epoch [32][2300/3746] lr: 8.943e-02, eta: 3 days, 21:17:19, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5011, loss_cls: 4.3192, loss: 4.3192 +2024-12-27 04:06:38,723 - pyskl - INFO - Epoch [32][2400/3746] lr: 8.942e-02, eta: 3 days, 21:16:42, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5036, loss_cls: 4.3176, loss: 4.3176 +2024-12-27 04:08:04,605 - pyskl - INFO - Epoch [32][2500/3746] lr: 8.940e-02, eta: 3 days, 21:16:05, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5053, loss_cls: 4.2891, loss: 4.2891 +2024-12-27 04:09:30,366 - pyskl - INFO - Epoch [32][2600/3746] lr: 8.938e-02, eta: 3 days, 21:15:26, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5078, loss_cls: 4.3078, loss: 4.3078 +2024-12-27 04:10:56,316 - pyskl - INFO - Epoch [32][2700/3746] lr: 8.937e-02, eta: 3 days, 21:14:48, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4988, loss_cls: 4.3157, loss: 4.3157 +2024-12-27 04:12:21,971 - pyskl - INFO - Epoch [32][2800/3746] lr: 8.935e-02, eta: 3 days, 21:14:09, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5067, loss_cls: 4.3018, loss: 4.3018 +2024-12-27 04:13:47,657 - pyskl - INFO - Epoch [32][2900/3746] lr: 8.933e-02, eta: 3 days, 21:13:31, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5022, loss_cls: 4.2755, loss: 4.2755 +2024-12-27 04:15:13,788 - pyskl - INFO - Epoch [32][3000/3746] lr: 8.931e-02, eta: 3 days, 21:12:53, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5069, loss_cls: 4.2734, loss: 4.2734 +2024-12-27 04:16:39,623 - pyskl - INFO - Epoch [32][3100/3746] lr: 8.930e-02, eta: 3 days, 21:12:15, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5131, loss_cls: 4.2792, loss: 4.2792 +2024-12-27 04:18:05,372 - pyskl - INFO - Epoch [32][3200/3746] lr: 8.928e-02, eta: 3 days, 21:11:36, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.5022, loss_cls: 4.3094, loss: 4.3094 +2024-12-27 04:19:30,835 - pyskl - INFO - Epoch [32][3300/3746] lr: 8.926e-02, eta: 3 days, 21:10:56, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5086, loss_cls: 4.2625, loss: 4.2625 +2024-12-27 04:20:56,556 - pyskl - INFO - Epoch [32][3400/3746] lr: 8.924e-02, eta: 3 days, 21:10:17, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5178, loss_cls: 4.2308, loss: 4.2308 +2024-12-27 04:22:22,584 - pyskl - INFO - Epoch [32][3500/3746] lr: 8.923e-02, eta: 3 days, 21:09:39, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4948, loss_cls: 4.3431, loss: 4.3431 +2024-12-27 04:23:47,771 - pyskl - INFO - Epoch [32][3600/3746] lr: 8.921e-02, eta: 3 days, 21:08:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5084, loss_cls: 4.2801, loss: 4.2801 +2024-12-27 04:25:12,861 - pyskl - INFO - Epoch [32][3700/3746] lr: 8.919e-02, eta: 3 days, 21:08:16, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5098, loss_cls: 4.2653, loss: 4.2653 +2024-12-27 04:25:53,793 - pyskl - INFO - Saving checkpoint at 32 epochs +2024-12-27 04:27:50,309 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 04:27:51,036 - pyskl - INFO - +top1_acc 0.1755 +top5_acc 0.3903 +2024-12-27 04:27:51,036 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 04:27:51,077 - pyskl - INFO - +mean_acc 0.1751 +2024-12-27 04:27:51,090 - pyskl - INFO - Epoch(val) [32][309] top1_acc: 0.1755, top5_acc: 0.3903, mean_class_accuracy: 0.1751 +2024-12-27 04:31:56,338 - pyskl - INFO - Epoch [33][100/3746] lr: 8.917e-02, eta: 3 days, 21:14:40, time: 2.452, data_time: 1.425, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5264, loss_cls: 4.2047, loss: 4.2047 +2024-12-27 04:33:21,659 - pyskl - INFO - Epoch [33][200/3746] lr: 8.915e-02, eta: 3 days, 21:13:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5081, loss_cls: 4.2573, loss: 4.2573 +2024-12-27 04:34:47,185 - pyskl - INFO - Epoch [33][300/3746] lr: 8.913e-02, eta: 3 days, 21:13:18, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5223, loss_cls: 4.2242, loss: 4.2242 +2024-12-27 04:36:12,688 - pyskl - INFO - Epoch [33][400/3746] lr: 8.912e-02, eta: 3 days, 21:12:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5131, loss_cls: 4.2568, loss: 4.2568 +2024-12-27 04:37:37,452 - pyskl - INFO - Epoch [33][500/3746] lr: 8.910e-02, eta: 3 days, 21:11:53, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5077, loss_cls: 4.2697, loss: 4.2697 +2024-12-27 04:39:02,160 - pyskl - INFO - Epoch [33][600/3746] lr: 8.908e-02, eta: 3 days, 21:11:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5041, loss_cls: 4.2773, loss: 4.2773 +2024-12-27 04:40:27,523 - pyskl - INFO - Epoch [33][700/3746] lr: 8.906e-02, eta: 3 days, 21:10:27, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5070, loss_cls: 4.2905, loss: 4.2905 +2024-12-27 04:41:53,230 - pyskl - INFO - Epoch [33][800/3746] lr: 8.905e-02, eta: 3 days, 21:09:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5036, loss_cls: 4.3053, loss: 4.3053 +2024-12-27 04:43:18,929 - pyskl - INFO - Epoch [33][900/3746] lr: 8.903e-02, eta: 3 days, 21:09:06, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.4970, loss_cls: 4.3140, loss: 4.3140 +2024-12-27 04:44:44,308 - pyskl - INFO - Epoch [33][1000/3746] lr: 8.901e-02, eta: 3 days, 21:08:24, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5117, loss_cls: 4.3052, loss: 4.3052 +2024-12-27 04:46:09,699 - pyskl - INFO - Epoch [33][1100/3746] lr: 8.899e-02, eta: 3 days, 21:07:43, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5114, loss_cls: 4.2760, loss: 4.2760 +2024-12-27 04:47:35,343 - pyskl - INFO - Epoch [33][1200/3746] lr: 8.898e-02, eta: 3 days, 21:07:01, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5094, loss_cls: 4.2957, loss: 4.2957 +2024-12-27 04:49:00,981 - pyskl - INFO - Epoch [33][1300/3746] lr: 8.896e-02, eta: 3 days, 21:06:20, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5136, loss_cls: 4.2631, loss: 4.2631 +2024-12-27 04:50:26,976 - pyskl - INFO - Epoch [33][1400/3746] lr: 8.894e-02, eta: 3 days, 21:05:40, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4972, loss_cls: 4.3243, loss: 4.3243 +2024-12-27 04:51:52,916 - pyskl - INFO - Epoch [33][1500/3746] lr: 8.892e-02, eta: 3 days, 21:05:00, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5123, loss_cls: 4.2901, loss: 4.2901 +2024-12-27 04:53:18,839 - pyskl - INFO - Epoch [33][1600/3746] lr: 8.891e-02, eta: 3 days, 21:04:20, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5117, loss_cls: 4.2560, loss: 4.2560 +2024-12-27 04:54:44,904 - pyskl - INFO - Epoch [33][1700/3746] lr: 8.889e-02, eta: 3 days, 21:03:40, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5080, loss_cls: 4.2674, loss: 4.2674 +2024-12-27 04:56:10,830 - pyskl - INFO - Epoch [33][1800/3746] lr: 8.887e-02, eta: 3 days, 21:02:59, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5088, loss_cls: 4.3008, loss: 4.3008 +2024-12-27 04:57:36,521 - pyskl - INFO - Epoch [33][1900/3746] lr: 8.885e-02, eta: 3 days, 21:02:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5078, loss_cls: 4.2745, loss: 4.2745 +2024-12-27 04:59:02,437 - pyskl - INFO - Epoch [33][2000/3746] lr: 8.884e-02, eta: 3 days, 21:01:37, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5039, loss_cls: 4.2902, loss: 4.2902 +2024-12-27 05:00:28,162 - pyskl - INFO - Epoch [33][2100/3746] lr: 8.882e-02, eta: 3 days, 21:00:56, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5145, loss_cls: 4.2554, loss: 4.2554 +2024-12-27 05:01:54,333 - pyskl - INFO - Epoch [33][2200/3746] lr: 8.880e-02, eta: 3 days, 21:00:16, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5055, loss_cls: 4.2671, loss: 4.2671 +2024-12-27 05:03:19,921 - pyskl - INFO - Epoch [33][2300/3746] lr: 8.878e-02, eta: 3 days, 20:59:34, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5102, loss_cls: 4.2661, loss: 4.2661 +2024-12-27 05:04:45,849 - pyskl - INFO - Epoch [33][2400/3746] lr: 8.876e-02, eta: 3 days, 20:58:53, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5056, loss_cls: 4.2794, loss: 4.2794 +2024-12-27 05:06:11,779 - pyskl - INFO - Epoch [33][2500/3746] lr: 8.875e-02, eta: 3 days, 20:58:12, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5067, loss_cls: 4.2916, loss: 4.2916 +2024-12-27 05:07:37,319 - pyskl - INFO - Epoch [33][2600/3746] lr: 8.873e-02, eta: 3 days, 20:57:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5156, loss_cls: 4.2492, loss: 4.2492 +2024-12-27 05:09:03,157 - pyskl - INFO - Epoch [33][2700/3746] lr: 8.871e-02, eta: 3 days, 20:56:48, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5014, loss_cls: 4.2854, loss: 4.2854 +2024-12-27 05:10:29,109 - pyskl - INFO - Epoch [33][2800/3746] lr: 8.869e-02, eta: 3 days, 20:56:07, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5031, loss_cls: 4.3040, loss: 4.3040 +2024-12-27 05:11:54,898 - pyskl - INFO - Epoch [33][2900/3746] lr: 8.868e-02, eta: 3 days, 20:55:25, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5078, loss_cls: 4.3099, loss: 4.3099 +2024-12-27 05:13:20,976 - pyskl - INFO - Epoch [33][3000/3746] lr: 8.866e-02, eta: 3 days, 20:54:44, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5027, loss_cls: 4.3032, loss: 4.3032 +2024-12-27 05:14:46,683 - pyskl - INFO - Epoch [33][3100/3746] lr: 8.864e-02, eta: 3 days, 20:54:02, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5070, loss_cls: 4.2867, loss: 4.2867 +2024-12-27 05:16:12,370 - pyskl - INFO - Epoch [33][3200/3746] lr: 8.862e-02, eta: 3 days, 20:53:20, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5017, loss_cls: 4.3017, loss: 4.3017 +2024-12-27 05:17:38,236 - pyskl - INFO - Epoch [33][3300/3746] lr: 8.861e-02, eta: 3 days, 20:52:38, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4995, loss_cls: 4.3298, loss: 4.3298 +2024-12-27 05:19:04,083 - pyskl - INFO - Epoch [33][3400/3746] lr: 8.859e-02, eta: 3 days, 20:51:56, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5078, loss_cls: 4.2824, loss: 4.2824 +2024-12-27 05:20:29,152 - pyskl - INFO - Epoch [33][3500/3746] lr: 8.857e-02, eta: 3 days, 20:51:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4953, loss_cls: 4.3176, loss: 4.3176 +2024-12-27 05:21:53,796 - pyskl - INFO - Epoch [33][3600/3746] lr: 8.855e-02, eta: 3 days, 20:50:25, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4952, loss_cls: 4.3433, loss: 4.3433 +2024-12-27 05:23:18,038 - pyskl - INFO - Epoch [33][3700/3746] lr: 8.853e-02, eta: 3 days, 20:49:37, time: 0.842, data_time: 0.001, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5072, loss_cls: 4.2790, loss: 4.2790 +2024-12-27 05:23:59,214 - pyskl - INFO - Saving checkpoint at 33 epochs +2024-12-27 05:25:57,328 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 05:25:58,206 - pyskl - INFO - +top1_acc 0.2054 +top5_acc 0.4370 +2024-12-27 05:25:58,207 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 05:25:58,249 - pyskl - INFO - +mean_acc 0.2052 +2024-12-27 05:25:58,254 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_21.pth was removed +2024-12-27 05:25:58,513 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_33.pth. +2024-12-27 05:25:58,514 - pyskl - INFO - Best top1_acc is 0.2054 at 33 epoch. +2024-12-27 05:25:58,528 - pyskl - INFO - Epoch(val) [33][309] top1_acc: 0.2054, top5_acc: 0.4370, mean_class_accuracy: 0.2052 +2024-12-27 05:30:10,177 - pyskl - INFO - Epoch [34][100/3746] lr: 8.851e-02, eta: 3 days, 20:56:03, time: 2.516, data_time: 1.485, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5097, loss_cls: 4.2535, loss: 4.2535 +2024-12-27 05:31:36,149 - pyskl - INFO - Epoch [34][200/3746] lr: 8.849e-02, eta: 3 days, 20:55:21, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5155, loss_cls: 4.2628, loss: 4.2628 +2024-12-27 05:33:02,068 - pyskl - INFO - Epoch [34][300/3746] lr: 8.847e-02, eta: 3 days, 20:54:38, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5159, loss_cls: 4.2508, loss: 4.2508 +2024-12-27 05:34:28,173 - pyskl - INFO - Epoch [34][400/3746] lr: 8.845e-02, eta: 3 days, 20:53:56, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5166, loss_cls: 4.2136, loss: 4.2136 +2024-12-27 05:35:54,075 - pyskl - INFO - Epoch [34][500/3746] lr: 8.844e-02, eta: 3 days, 20:53:13, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5083, loss_cls: 4.2934, loss: 4.2934 +2024-12-27 05:37:19,563 - pyskl - INFO - Epoch [34][600/3746] lr: 8.842e-02, eta: 3 days, 20:52:29, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5136, loss_cls: 4.2792, loss: 4.2792 +2024-12-27 05:38:44,560 - pyskl - INFO - Epoch [34][700/3746] lr: 8.840e-02, eta: 3 days, 20:51:43, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5208, loss_cls: 4.2247, loss: 4.2247 +2024-12-27 05:40:09,461 - pyskl - INFO - Epoch [34][800/3746] lr: 8.838e-02, eta: 3 days, 20:50:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5012, loss_cls: 4.3125, loss: 4.3125 +2024-12-27 05:41:35,225 - pyskl - INFO - Epoch [34][900/3746] lr: 8.836e-02, eta: 3 days, 20:50:13, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5158, loss_cls: 4.2309, loss: 4.2309 +2024-12-27 05:43:00,749 - pyskl - INFO - Epoch [34][1000/3746] lr: 8.835e-02, eta: 3 days, 20:49:28, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5061, loss_cls: 4.2977, loss: 4.2977 +2024-12-27 05:44:26,672 - pyskl - INFO - Epoch [34][1100/3746] lr: 8.833e-02, eta: 3 days, 20:48:45, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5188, loss_cls: 4.2670, loss: 4.2670 +2024-12-27 05:45:52,481 - pyskl - INFO - Epoch [34][1200/3746] lr: 8.831e-02, eta: 3 days, 20:48:02, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5061, loss_cls: 4.3047, loss: 4.3047 +2024-12-27 05:47:18,210 - pyskl - INFO - Epoch [34][1300/3746] lr: 8.829e-02, eta: 3 days, 20:47:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5038, loss_cls: 4.2937, loss: 4.2937 +2024-12-27 05:48:43,634 - pyskl - INFO - Epoch [34][1400/3746] lr: 8.828e-02, eta: 3 days, 20:46:33, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.4980, loss_cls: 4.2992, loss: 4.2992 +2024-12-27 05:50:09,331 - pyskl - INFO - Epoch [34][1500/3746] lr: 8.826e-02, eta: 3 days, 20:45:49, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5109, loss_cls: 4.2947, loss: 4.2947 +2024-12-27 05:51:35,080 - pyskl - INFO - Epoch [34][1600/3746] lr: 8.824e-02, eta: 3 days, 20:45:04, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5127, loss_cls: 4.2619, loss: 4.2619 +2024-12-27 05:53:00,851 - pyskl - INFO - Epoch [34][1700/3746] lr: 8.822e-02, eta: 3 days, 20:44:20, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5091, loss_cls: 4.2746, loss: 4.2746 +2024-12-27 05:54:26,615 - pyskl - INFO - Epoch [34][1800/3746] lr: 8.820e-02, eta: 3 days, 20:43:36, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5161, loss_cls: 4.2636, loss: 4.2636 +2024-12-27 05:55:52,179 - pyskl - INFO - Epoch [34][1900/3746] lr: 8.819e-02, eta: 3 days, 20:42:51, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5097, loss_cls: 4.2782, loss: 4.2782 +2024-12-27 05:57:17,795 - pyskl - INFO - Epoch [34][2000/3746] lr: 8.817e-02, eta: 3 days, 20:42:07, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5112, loss_cls: 4.2827, loss: 4.2827 +2024-12-27 05:58:43,645 - pyskl - INFO - Epoch [34][2100/3746] lr: 8.815e-02, eta: 3 days, 20:41:23, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5081, loss_cls: 4.2645, loss: 4.2645 +2024-12-27 06:00:09,422 - pyskl - INFO - Epoch [34][2200/3746] lr: 8.813e-02, eta: 3 days, 20:40:38, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5058, loss_cls: 4.2646, loss: 4.2646 +2024-12-27 06:01:35,319 - pyskl - INFO - Epoch [34][2300/3746] lr: 8.811e-02, eta: 3 days, 20:39:54, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5184, loss_cls: 4.2402, loss: 4.2402 +2024-12-27 06:03:00,920 - pyskl - INFO - Epoch [34][2400/3746] lr: 8.809e-02, eta: 3 days, 20:39:09, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.4981, loss_cls: 4.3056, loss: 4.3056 +2024-12-27 06:04:26,410 - pyskl - INFO - Epoch [34][2500/3746] lr: 8.808e-02, eta: 3 days, 20:38:24, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5034, loss_cls: 4.2618, loss: 4.2618 +2024-12-27 06:05:52,078 - pyskl - INFO - Epoch [34][2600/3746] lr: 8.806e-02, eta: 3 days, 20:37:39, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5080, loss_cls: 4.3216, loss: 4.3216 +2024-12-27 06:07:17,805 - pyskl - INFO - Epoch [34][2700/3746] lr: 8.804e-02, eta: 3 days, 20:36:54, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5047, loss_cls: 4.3011, loss: 4.3011 +2024-12-27 06:08:43,333 - pyskl - INFO - Epoch [34][2800/3746] lr: 8.802e-02, eta: 3 days, 20:36:08, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.5017, loss_cls: 4.3226, loss: 4.3226 +2024-12-27 06:10:09,393 - pyskl - INFO - Epoch [34][2900/3746] lr: 8.800e-02, eta: 3 days, 20:35:24, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5136, loss_cls: 4.2444, loss: 4.2444 +2024-12-27 06:11:35,543 - pyskl - INFO - Epoch [34][3000/3746] lr: 8.799e-02, eta: 3 days, 20:34:41, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5039, loss_cls: 4.3173, loss: 4.3173 +2024-12-27 06:13:01,349 - pyskl - INFO - Epoch [34][3100/3746] lr: 8.797e-02, eta: 3 days, 20:33:56, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5036, loss_cls: 4.3005, loss: 4.3005 +2024-12-27 06:14:26,897 - pyskl - INFO - Epoch [34][3200/3746] lr: 8.795e-02, eta: 3 days, 20:33:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5053, loss_cls: 4.2893, loss: 4.2893 +2024-12-27 06:15:52,928 - pyskl - INFO - Epoch [34][3300/3746] lr: 8.793e-02, eta: 3 days, 20:32:26, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5033, loss_cls: 4.2940, loss: 4.2940 +2024-12-27 06:17:18,469 - pyskl - INFO - Epoch [34][3400/3746] lr: 8.791e-02, eta: 3 days, 20:31:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5111, loss_cls: 4.2536, loss: 4.2536 +2024-12-27 06:18:43,413 - pyskl - INFO - Epoch [34][3500/3746] lr: 8.789e-02, eta: 3 days, 20:30:52, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5052, loss_cls: 4.2608, loss: 4.2608 +2024-12-27 06:20:07,519 - pyskl - INFO - Epoch [34][3600/3746] lr: 8.788e-02, eta: 3 days, 20:30:01, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5011, loss_cls: 4.2466, loss: 4.2466 +2024-12-27 06:21:32,413 - pyskl - INFO - Epoch [34][3700/3746] lr: 8.786e-02, eta: 3 days, 20:29:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5030, loss_cls: 4.3115, loss: 4.3115 +2024-12-27 06:22:13,740 - pyskl - INFO - Saving checkpoint at 34 epochs +2024-12-27 06:24:10,615 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 06:24:11,343 - pyskl - INFO - +top1_acc 0.1754 +top5_acc 0.3904 +2024-12-27 06:24:11,343 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 06:24:11,383 - pyskl - INFO - +mean_acc 0.1755 +2024-12-27 06:24:11,394 - pyskl - INFO - Epoch(val) [34][309] top1_acc: 0.1754, top5_acc: 0.3904, mean_class_accuracy: 0.1755 +2024-12-27 06:28:18,050 - pyskl - INFO - Epoch [35][100/3746] lr: 8.783e-02, eta: 3 days, 20:35:00, time: 2.466, data_time: 1.432, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5081, loss_cls: 4.2458, loss: 4.2458 +2024-12-27 06:29:43,992 - pyskl - INFO - Epoch [35][200/3746] lr: 8.781e-02, eta: 3 days, 20:34:15, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5178, loss_cls: 4.2350, loss: 4.2350 +2024-12-27 06:31:10,168 - pyskl - INFO - Epoch [35][300/3746] lr: 8.780e-02, eta: 3 days, 20:33:30, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5130, loss_cls: 4.2435, loss: 4.2435 +2024-12-27 06:32:35,948 - pyskl - INFO - Epoch [35][400/3746] lr: 8.778e-02, eta: 3 days, 20:32:44, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5038, loss_cls: 4.2688, loss: 4.2688 +2024-12-27 06:34:01,654 - pyskl - INFO - Epoch [35][500/3746] lr: 8.776e-02, eta: 3 days, 20:31:58, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5134, loss_cls: 4.2128, loss: 4.2128 +2024-12-27 06:35:26,930 - pyskl - INFO - Epoch [35][600/3746] lr: 8.774e-02, eta: 3 days, 20:31:10, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5177, loss_cls: 4.2482, loss: 4.2482 +2024-12-27 06:36:51,676 - pyskl - INFO - Epoch [35][700/3746] lr: 8.772e-02, eta: 3 days, 20:30:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5073, loss_cls: 4.2745, loss: 4.2745 +2024-12-27 06:38:17,156 - pyskl - INFO - Epoch [35][800/3746] lr: 8.770e-02, eta: 3 days, 20:29:33, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5109, loss_cls: 4.2584, loss: 4.2584 +2024-12-27 06:39:42,051 - pyskl - INFO - Epoch [35][900/3746] lr: 8.769e-02, eta: 3 days, 20:28:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5053, loss_cls: 4.2768, loss: 4.2768 +2024-12-27 06:41:06,801 - pyskl - INFO - Epoch [35][1000/3746] lr: 8.767e-02, eta: 3 days, 20:27:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5077, loss_cls: 4.2508, loss: 4.2508 +2024-12-27 06:42:31,618 - pyskl - INFO - Epoch [35][1100/3746] lr: 8.765e-02, eta: 3 days, 20:27:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5073, loss_cls: 4.2838, loss: 4.2838 +2024-12-27 06:43:56,181 - pyskl - INFO - Epoch [35][1200/3746] lr: 8.763e-02, eta: 3 days, 20:26:14, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5141, loss_cls: 4.2335, loss: 4.2335 +2024-12-27 06:45:20,711 - pyskl - INFO - Epoch [35][1300/3746] lr: 8.761e-02, eta: 3 days, 20:25:23, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5047, loss_cls: 4.2822, loss: 4.2822 +2024-12-27 06:46:45,679 - pyskl - INFO - Epoch [35][1400/3746] lr: 8.759e-02, eta: 3 days, 20:24:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5164, loss_cls: 4.2807, loss: 4.2807 +2024-12-27 06:48:10,373 - pyskl - INFO - Epoch [35][1500/3746] lr: 8.757e-02, eta: 3 days, 20:23:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5003, loss_cls: 4.3021, loss: 4.3021 +2024-12-27 06:49:35,421 - pyskl - INFO - Epoch [35][1600/3746] lr: 8.756e-02, eta: 3 days, 20:22:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5023, loss_cls: 4.2518, loss: 4.2518 +2024-12-27 06:50:59,822 - pyskl - INFO - Epoch [35][1700/3746] lr: 8.754e-02, eta: 3 days, 20:22:03, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5095, loss_cls: 4.2749, loss: 4.2749 +2024-12-27 06:52:24,427 - pyskl - INFO - Epoch [35][1800/3746] lr: 8.752e-02, eta: 3 days, 20:21:12, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5116, loss_cls: 4.2477, loss: 4.2477 +2024-12-27 06:53:49,340 - pyskl - INFO - Epoch [35][1900/3746] lr: 8.750e-02, eta: 3 days, 20:20:22, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5055, loss_cls: 4.2926, loss: 4.2926 +2024-12-27 06:55:14,721 - pyskl - INFO - Epoch [35][2000/3746] lr: 8.748e-02, eta: 3 days, 20:19:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5241, loss_cls: 4.1995, loss: 4.1995 +2024-12-27 06:56:39,904 - pyskl - INFO - Epoch [35][2100/3746] lr: 8.746e-02, eta: 3 days, 20:18:45, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.5020, loss_cls: 4.3085, loss: 4.3085 +2024-12-27 06:58:05,524 - pyskl - INFO - Epoch [35][2200/3746] lr: 8.745e-02, eta: 3 days, 20:17:57, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5011, loss_cls: 4.3015, loss: 4.3015 +2024-12-27 06:59:30,961 - pyskl - INFO - Epoch [35][2300/3746] lr: 8.743e-02, eta: 3 days, 20:17:09, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5098, loss_cls: 4.2750, loss: 4.2750 +2024-12-27 07:00:56,482 - pyskl - INFO - Epoch [35][2400/3746] lr: 8.741e-02, eta: 3 days, 20:16:21, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5134, loss_cls: 4.2611, loss: 4.2611 +2024-12-27 07:02:21,970 - pyskl - INFO - Epoch [35][2500/3746] lr: 8.739e-02, eta: 3 days, 20:15:33, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5102, loss_cls: 4.2771, loss: 4.2771 +2024-12-27 07:03:47,137 - pyskl - INFO - Epoch [35][2600/3746] lr: 8.737e-02, eta: 3 days, 20:14:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5072, loss_cls: 4.3064, loss: 4.3064 +2024-12-27 07:05:12,539 - pyskl - INFO - Epoch [35][2700/3746] lr: 8.735e-02, eta: 3 days, 20:13:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5073, loss_cls: 4.3030, loss: 4.3030 +2024-12-27 07:06:38,140 - pyskl - INFO - Epoch [35][2800/3746] lr: 8.733e-02, eta: 3 days, 20:13:07, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5116, loss_cls: 4.2462, loss: 4.2462 +2024-12-27 07:08:03,657 - pyskl - INFO - Epoch [35][2900/3746] lr: 8.732e-02, eta: 3 days, 20:12:19, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5108, loss_cls: 4.2873, loss: 4.2873 +2024-12-27 07:09:28,952 - pyskl - INFO - Epoch [35][3000/3746] lr: 8.730e-02, eta: 3 days, 20:11:29, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5125, loss_cls: 4.2874, loss: 4.2874 +2024-12-27 07:10:54,813 - pyskl - INFO - Epoch [35][3100/3746] lr: 8.728e-02, eta: 3 days, 20:10:42, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5055, loss_cls: 4.2803, loss: 4.2803 +2024-12-27 07:12:20,536 - pyskl - INFO - Epoch [35][3200/3746] lr: 8.726e-02, eta: 3 days, 20:09:54, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5000, loss_cls: 4.3144, loss: 4.3144 +2024-12-27 07:13:46,296 - pyskl - INFO - Epoch [35][3300/3746] lr: 8.724e-02, eta: 3 days, 20:09:07, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5022, loss_cls: 4.3057, loss: 4.3057 +2024-12-27 07:15:11,515 - pyskl - INFO - Epoch [35][3400/3746] lr: 8.722e-02, eta: 3 days, 20:08:17, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5080, loss_cls: 4.2748, loss: 4.2748 +2024-12-27 07:16:36,942 - pyskl - INFO - Epoch [35][3500/3746] lr: 8.720e-02, eta: 3 days, 20:07:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5122, loss_cls: 4.2582, loss: 4.2582 +2024-12-27 07:18:02,061 - pyskl - INFO - Epoch [35][3600/3746] lr: 8.718e-02, eta: 3 days, 20:06:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5062, loss_cls: 4.2837, loss: 4.2837 +2024-12-27 07:19:27,353 - pyskl - INFO - Epoch [35][3700/3746] lr: 8.717e-02, eta: 3 days, 20:05:48, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5105, loss_cls: 4.2497, loss: 4.2497 +2024-12-27 07:20:08,330 - pyskl - INFO - Saving checkpoint at 35 epochs +2024-12-27 07:22:06,403 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 07:22:07,270 - pyskl - INFO - +top1_acc 0.2095 +top5_acc 0.4469 +2024-12-27 07:22:07,271 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 07:22:07,316 - pyskl - INFO - +mean_acc 0.2093 +2024-12-27 07:22:07,321 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_33.pth was removed +2024-12-27 07:22:07,605 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_35.pth. +2024-12-27 07:22:07,606 - pyskl - INFO - Best top1_acc is 0.2095 at 35 epoch. +2024-12-27 07:22:07,624 - pyskl - INFO - Epoch(val) [35][309] top1_acc: 0.2095, top5_acc: 0.4469, mean_class_accuracy: 0.2093 +2024-12-27 07:26:11,441 - pyskl - INFO - Epoch [36][100/3746] lr: 8.714e-02, eta: 3 days, 20:11:08, time: 2.438, data_time: 1.420, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5130, loss_cls: 4.2475, loss: 4.2475 +2024-12-27 07:27:36,506 - pyskl - INFO - Epoch [36][200/3746] lr: 8.712e-02, eta: 3 days, 20:10:17, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5209, loss_cls: 4.2202, loss: 4.2202 +2024-12-27 07:29:01,572 - pyskl - INFO - Epoch [36][300/3746] lr: 8.710e-02, eta: 3 days, 20:09:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5142, loss_cls: 4.2564, loss: 4.2564 +2024-12-27 07:30:26,559 - pyskl - INFO - Epoch [36][400/3746] lr: 8.708e-02, eta: 3 days, 20:08:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5184, loss_cls: 4.2327, loss: 4.2327 +2024-12-27 07:31:51,611 - pyskl - INFO - Epoch [36][500/3746] lr: 8.706e-02, eta: 3 days, 20:07:44, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5191, loss_cls: 4.2373, loss: 4.2373 +2024-12-27 07:33:17,157 - pyskl - INFO - Epoch [36][600/3746] lr: 8.704e-02, eta: 3 days, 20:06:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5136, loss_cls: 4.2527, loss: 4.2527 +2024-12-27 07:34:42,248 - pyskl - INFO - Epoch [36][700/3746] lr: 8.703e-02, eta: 3 days, 20:06:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5144, loss_cls: 4.2467, loss: 4.2467 +2024-12-27 07:36:06,979 - pyskl - INFO - Epoch [36][800/3746] lr: 8.701e-02, eta: 3 days, 20:05:11, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5114, loss_cls: 4.2460, loss: 4.2460 +2024-12-27 07:37:32,004 - pyskl - INFO - Epoch [36][900/3746] lr: 8.699e-02, eta: 3 days, 20:04:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5108, loss_cls: 4.2528, loss: 4.2528 +2024-12-27 07:38:56,937 - pyskl - INFO - Epoch [36][1000/3746] lr: 8.697e-02, eta: 3 days, 20:03:28, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5172, loss_cls: 4.2460, loss: 4.2460 +2024-12-27 07:40:21,790 - pyskl - INFO - Epoch [36][1100/3746] lr: 8.695e-02, eta: 3 days, 20:02:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5111, loss_cls: 4.2892, loss: 4.2892 +2024-12-27 07:41:46,422 - pyskl - INFO - Epoch [36][1200/3746] lr: 8.693e-02, eta: 3 days, 20:01:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5112, loss_cls: 4.2500, loss: 4.2500 +2024-12-27 07:43:11,616 - pyskl - INFO - Epoch [36][1300/3746] lr: 8.691e-02, eta: 3 days, 20:00:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5169, loss_cls: 4.2520, loss: 4.2520 +2024-12-27 07:44:36,174 - pyskl - INFO - Epoch [36][1400/3746] lr: 8.689e-02, eta: 3 days, 20:00:00, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4986, loss_cls: 4.3088, loss: 4.3088 +2024-12-27 07:46:01,079 - pyskl - INFO - Epoch [36][1500/3746] lr: 8.688e-02, eta: 3 days, 19:59:08, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5105, loss_cls: 4.2594, loss: 4.2594 +2024-12-27 07:47:26,201 - pyskl - INFO - Epoch [36][1600/3746] lr: 8.686e-02, eta: 3 days, 19:58:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5105, loss_cls: 4.2082, loss: 4.2082 +2024-12-27 07:48:51,365 - pyskl - INFO - Epoch [36][1700/3746] lr: 8.684e-02, eta: 3 days, 19:57:25, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5045, loss_cls: 4.3013, loss: 4.3013 +2024-12-27 07:50:16,105 - pyskl - INFO - Epoch [36][1800/3746] lr: 8.682e-02, eta: 3 days, 19:56:32, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5069, loss_cls: 4.3020, loss: 4.3020 +2024-12-27 07:51:40,945 - pyskl - INFO - Epoch [36][1900/3746] lr: 8.680e-02, eta: 3 days, 19:55:40, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5059, loss_cls: 4.2935, loss: 4.2935 +2024-12-27 07:53:05,688 - pyskl - INFO - Epoch [36][2000/3746] lr: 8.678e-02, eta: 3 days, 19:54:47, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5105, loss_cls: 4.2619, loss: 4.2619 +2024-12-27 07:54:31,311 - pyskl - INFO - Epoch [36][2100/3746] lr: 8.676e-02, eta: 3 days, 19:53:57, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5169, loss_cls: 4.2632, loss: 4.2632 +2024-12-27 07:55:56,798 - pyskl - INFO - Epoch [36][2200/3746] lr: 8.674e-02, eta: 3 days, 19:53:07, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5044, loss_cls: 4.2945, loss: 4.2945 +2024-12-27 07:57:22,195 - pyskl - INFO - Epoch [36][2300/3746] lr: 8.672e-02, eta: 3 days, 19:52:16, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5230, loss_cls: 4.2494, loss: 4.2494 +2024-12-27 07:58:47,828 - pyskl - INFO - Epoch [36][2400/3746] lr: 8.671e-02, eta: 3 days, 19:51:26, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5148, loss_cls: 4.2379, loss: 4.2379 +2024-12-27 08:00:13,335 - pyskl - INFO - Epoch [36][2500/3746] lr: 8.669e-02, eta: 3 days, 19:50:35, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5094, loss_cls: 4.2901, loss: 4.2901 +2024-12-27 08:01:38,988 - pyskl - INFO - Epoch [36][2600/3746] lr: 8.667e-02, eta: 3 days, 19:49:45, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.4981, loss_cls: 4.3179, loss: 4.3179 +2024-12-27 08:03:04,381 - pyskl - INFO - Epoch [36][2700/3746] lr: 8.665e-02, eta: 3 days, 19:48:54, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5117, loss_cls: 4.2840, loss: 4.2840 +2024-12-27 08:04:29,714 - pyskl - INFO - Epoch [36][2800/3746] lr: 8.663e-02, eta: 3 days, 19:48:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5100, loss_cls: 4.2839, loss: 4.2839 +2024-12-27 08:05:55,356 - pyskl - INFO - Epoch [36][2900/3746] lr: 8.661e-02, eta: 3 days, 19:47:13, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5194, loss_cls: 4.2430, loss: 4.2430 +2024-12-27 08:07:20,563 - pyskl - INFO - Epoch [36][3000/3746] lr: 8.659e-02, eta: 3 days, 19:46:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5070, loss_cls: 4.2552, loss: 4.2552 +2024-12-27 08:08:46,209 - pyskl - INFO - Epoch [36][3100/3746] lr: 8.657e-02, eta: 3 days, 19:45:31, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5053, loss_cls: 4.2683, loss: 4.2683 +2024-12-27 08:10:11,745 - pyskl - INFO - Epoch [36][3200/3746] lr: 8.655e-02, eta: 3 days, 19:44:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5077, loss_cls: 4.2483, loss: 4.2483 +2024-12-27 08:11:37,663 - pyskl - INFO - Epoch [36][3300/3746] lr: 8.653e-02, eta: 3 days, 19:43:50, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5111, loss_cls: 4.2789, loss: 4.2789 +2024-12-27 08:13:02,870 - pyskl - INFO - Epoch [36][3400/3746] lr: 8.651e-02, eta: 3 days, 19:42:58, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5105, loss_cls: 4.2656, loss: 4.2656 +2024-12-27 08:14:27,342 - pyskl - INFO - Epoch [36][3500/3746] lr: 8.650e-02, eta: 3 days, 19:42:04, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5119, loss_cls: 4.2565, loss: 4.2565 +2024-12-27 08:15:52,115 - pyskl - INFO - Epoch [36][3600/3746] lr: 8.648e-02, eta: 3 days, 19:41:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5200, loss_cls: 4.2309, loss: 4.2309 +2024-12-27 08:17:17,511 - pyskl - INFO - Epoch [36][3700/3746] lr: 8.646e-02, eta: 3 days, 19:40:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.4998, loss_cls: 4.3104, loss: 4.3104 +2024-12-27 08:17:58,493 - pyskl - INFO - Saving checkpoint at 36 epochs +2024-12-27 08:19:55,713 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 08:19:56,632 - pyskl - INFO - +top1_acc 0.2118 +top5_acc 0.4495 +2024-12-27 08:19:56,632 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 08:19:56,677 - pyskl - INFO - +mean_acc 0.2116 +2024-12-27 08:19:56,681 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_35.pth was removed +2024-12-27 08:19:56,945 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_36.pth. +2024-12-27 08:19:56,946 - pyskl - INFO - Best top1_acc is 0.2118 at 36 epoch. +2024-12-27 08:19:56,962 - pyskl - INFO - Epoch(val) [36][309] top1_acc: 0.2118, top5_acc: 0.4495, mean_class_accuracy: 0.2116 +2024-12-27 08:23:59,284 - pyskl - INFO - Epoch [37][100/3746] lr: 8.643e-02, eta: 3 days, 19:45:16, time: 2.423, data_time: 1.404, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5058, loss_cls: 4.2949, loss: 4.2949 +2024-12-27 08:25:23,620 - pyskl - INFO - Epoch [37][200/3746] lr: 8.641e-02, eta: 3 days, 19:44:20, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5142, loss_cls: 4.2454, loss: 4.2454 +2024-12-27 08:26:48,212 - pyskl - INFO - Epoch [37][300/3746] lr: 8.639e-02, eta: 3 days, 19:43:26, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5167, loss_cls: 4.2304, loss: 4.2304 +2024-12-27 08:28:13,018 - pyskl - INFO - Epoch [37][400/3746] lr: 8.637e-02, eta: 3 days, 19:42:32, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5175, loss_cls: 4.2377, loss: 4.2377 +2024-12-27 08:29:37,596 - pyskl - INFO - Epoch [37][500/3746] lr: 8.635e-02, eta: 3 days, 19:41:37, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5183, loss_cls: 4.2370, loss: 4.2370 +2024-12-27 08:31:02,247 - pyskl - INFO - Epoch [37][600/3746] lr: 8.633e-02, eta: 3 days, 19:40:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5105, loss_cls: 4.2603, loss: 4.2603 +2024-12-27 08:32:27,073 - pyskl - INFO - Epoch [37][700/3746] lr: 8.631e-02, eta: 3 days, 19:39:49, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5161, loss_cls: 4.2455, loss: 4.2455 +2024-12-27 08:33:50,944 - pyskl - INFO - Epoch [37][800/3746] lr: 8.630e-02, eta: 3 days, 19:38:51, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5136, loss_cls: 4.2611, loss: 4.2611 +2024-12-27 08:35:15,837 - pyskl - INFO - Epoch [37][900/3746] lr: 8.628e-02, eta: 3 days, 19:37:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5134, loss_cls: 4.2549, loss: 4.2549 +2024-12-27 08:36:40,535 - pyskl - INFO - Epoch [37][1000/3746] lr: 8.626e-02, eta: 3 days, 19:37:03, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5108, loss_cls: 4.2646, loss: 4.2646 +2024-12-27 08:38:05,259 - pyskl - INFO - Epoch [37][1100/3746] lr: 8.624e-02, eta: 3 days, 19:36:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5228, loss_cls: 4.2170, loss: 4.2170 +2024-12-27 08:39:30,302 - pyskl - INFO - Epoch [37][1200/3746] lr: 8.622e-02, eta: 3 days, 19:35:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5094, loss_cls: 4.2848, loss: 4.2848 +2024-12-27 08:40:54,508 - pyskl - INFO - Epoch [37][1300/3746] lr: 8.620e-02, eta: 3 days, 19:34:19, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5069, loss_cls: 4.2435, loss: 4.2435 +2024-12-27 08:42:18,942 - pyskl - INFO - Epoch [37][1400/3746] lr: 8.618e-02, eta: 3 days, 19:33:23, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5150, loss_cls: 4.2291, loss: 4.2291 +2024-12-27 08:43:43,431 - pyskl - INFO - Epoch [37][1500/3746] lr: 8.616e-02, eta: 3 days, 19:32:28, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5167, loss_cls: 4.2368, loss: 4.2368 +2024-12-27 08:45:07,730 - pyskl - INFO - Epoch [37][1600/3746] lr: 8.614e-02, eta: 3 days, 19:31:32, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5177, loss_cls: 4.2314, loss: 4.2314 +2024-12-27 08:46:32,213 - pyskl - INFO - Epoch [37][1700/3746] lr: 8.612e-02, eta: 3 days, 19:30:36, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5053, loss_cls: 4.2732, loss: 4.2732 +2024-12-27 08:47:56,667 - pyskl - INFO - Epoch [37][1800/3746] lr: 8.610e-02, eta: 3 days, 19:29:40, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5084, loss_cls: 4.2628, loss: 4.2628 +2024-12-27 08:49:21,186 - pyskl - INFO - Epoch [37][1900/3746] lr: 8.608e-02, eta: 3 days, 19:28:45, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5064, loss_cls: 4.2895, loss: 4.2895 +2024-12-27 08:50:45,494 - pyskl - INFO - Epoch [37][2000/3746] lr: 8.606e-02, eta: 3 days, 19:27:49, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5050, loss_cls: 4.3224, loss: 4.3224 +2024-12-27 08:52:10,098 - pyskl - INFO - Epoch [37][2100/3746] lr: 8.604e-02, eta: 3 days, 19:26:53, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5164, loss_cls: 4.2382, loss: 4.2382 +2024-12-27 08:53:35,193 - pyskl - INFO - Epoch [37][2200/3746] lr: 8.602e-02, eta: 3 days, 19:26:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5203, loss_cls: 4.2133, loss: 4.2133 +2024-12-27 08:55:00,272 - pyskl - INFO - Epoch [37][2300/3746] lr: 8.601e-02, eta: 3 days, 19:25:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5047, loss_cls: 4.2762, loss: 4.2762 +2024-12-27 08:56:25,298 - pyskl - INFO - Epoch [37][2400/3746] lr: 8.599e-02, eta: 3 days, 19:24:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5109, loss_cls: 4.2679, loss: 4.2679 +2024-12-27 08:57:50,203 - pyskl - INFO - Epoch [37][2500/3746] lr: 8.597e-02, eta: 3 days, 19:23:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5030, loss_cls: 4.2710, loss: 4.2710 +2024-12-27 08:59:15,514 - pyskl - INFO - Epoch [37][2600/3746] lr: 8.595e-02, eta: 3 days, 19:22:24, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5089, loss_cls: 4.2832, loss: 4.2832 +2024-12-27 09:00:40,532 - pyskl - INFO - Epoch [37][2700/3746] lr: 8.593e-02, eta: 3 days, 19:21:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5133, loss_cls: 4.2708, loss: 4.2708 +2024-12-27 09:02:05,839 - pyskl - INFO - Epoch [37][2800/3746] lr: 8.591e-02, eta: 3 days, 19:20:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.4992, loss_cls: 4.3163, loss: 4.3163 +2024-12-27 09:03:31,070 - pyskl - INFO - Epoch [37][2900/3746] lr: 8.589e-02, eta: 3 days, 19:19:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5175, loss_cls: 4.2127, loss: 4.2127 +2024-12-27 09:04:56,244 - pyskl - INFO - Epoch [37][3000/3746] lr: 8.587e-02, eta: 3 days, 19:18:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5064, loss_cls: 4.2737, loss: 4.2737 +2024-12-27 09:06:21,467 - pyskl - INFO - Epoch [37][3100/3746] lr: 8.585e-02, eta: 3 days, 19:17:54, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5044, loss_cls: 4.2676, loss: 4.2676 +2024-12-27 09:07:46,201 - pyskl - INFO - Epoch [37][3200/3746] lr: 8.583e-02, eta: 3 days, 19:16:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5097, loss_cls: 4.2736, loss: 4.2736 +2024-12-27 09:09:11,209 - pyskl - INFO - Epoch [37][3300/3746] lr: 8.581e-02, eta: 3 days, 19:16:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5108, loss_cls: 4.2705, loss: 4.2705 +2024-12-27 09:10:36,059 - pyskl - INFO - Epoch [37][3400/3746] lr: 8.579e-02, eta: 3 days, 19:15:09, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5070, loss_cls: 4.2752, loss: 4.2752 +2024-12-27 09:12:00,626 - pyskl - INFO - Epoch [37][3500/3746] lr: 8.577e-02, eta: 3 days, 19:14:13, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5075, loss_cls: 4.2513, loss: 4.2513 +2024-12-27 09:13:24,975 - pyskl - INFO - Epoch [37][3600/3746] lr: 8.575e-02, eta: 3 days, 19:13:16, time: 0.843, data_time: 0.001, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5122, loss_cls: 4.2434, loss: 4.2434 +2024-12-27 09:14:50,189 - pyskl - INFO - Epoch [37][3700/3746] lr: 8.573e-02, eta: 3 days, 19:12:22, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5077, loss_cls: 4.2999, loss: 4.2999 +2024-12-27 09:15:30,761 - pyskl - INFO - Saving checkpoint at 37 epochs +2024-12-27 09:17:27,892 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 09:17:28,902 - pyskl - INFO - +top1_acc 0.1819 +top5_acc 0.4074 +2024-12-27 09:17:28,902 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 09:17:28,943 - pyskl - INFO - +mean_acc 0.1817 +2024-12-27 09:17:28,960 - pyskl - INFO - Epoch(val) [37][309] top1_acc: 0.1819, top5_acc: 0.4074, mean_class_accuracy: 0.1817 +2024-12-27 09:21:31,065 - pyskl - INFO - Epoch [38][100/3746] lr: 8.570e-02, eta: 3 days, 19:17:02, time: 2.421, data_time: 1.391, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5214, loss_cls: 4.2190, loss: 4.2190 +2024-12-27 09:22:56,171 - pyskl - INFO - Epoch [38][200/3746] lr: 8.568e-02, eta: 3 days, 19:16:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5139, loss_cls: 4.2542, loss: 4.2542 +2024-12-27 09:24:21,160 - pyskl - INFO - Epoch [38][300/3746] lr: 8.567e-02, eta: 3 days, 19:15:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5184, loss_cls: 4.2510, loss: 4.2510 +2024-12-27 09:25:45,610 - pyskl - INFO - Epoch [38][400/3746] lr: 8.565e-02, eta: 3 days, 19:14:15, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5234, loss_cls: 4.1971, loss: 4.1971 +2024-12-27 09:27:09,860 - pyskl - INFO - Epoch [38][500/3746] lr: 8.563e-02, eta: 3 days, 19:13:17, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5211, loss_cls: 4.1937, loss: 4.1937 +2024-12-27 09:28:34,765 - pyskl - INFO - Epoch [38][600/3746] lr: 8.561e-02, eta: 3 days, 19:12:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5089, loss_cls: 4.2608, loss: 4.2608 +2024-12-27 09:29:59,583 - pyskl - INFO - Epoch [38][700/3746] lr: 8.559e-02, eta: 3 days, 19:11:25, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5050, loss_cls: 4.2708, loss: 4.2708 +2024-12-27 09:31:24,306 - pyskl - INFO - Epoch [38][800/3746] lr: 8.557e-02, eta: 3 days, 19:10:29, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5100, loss_cls: 4.2487, loss: 4.2487 +2024-12-27 09:32:49,000 - pyskl - INFO - Epoch [38][900/3746] lr: 8.555e-02, eta: 3 days, 19:09:32, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5167, loss_cls: 4.2381, loss: 4.2381 +2024-12-27 09:34:14,531 - pyskl - INFO - Epoch [38][1000/3746] lr: 8.553e-02, eta: 3 days, 19:08:39, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5081, loss_cls: 4.2833, loss: 4.2833 +2024-12-27 09:35:39,673 - pyskl - INFO - Epoch [38][1100/3746] lr: 8.551e-02, eta: 3 days, 19:07:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5247, loss_cls: 4.1949, loss: 4.1949 +2024-12-27 09:37:04,534 - pyskl - INFO - Epoch [38][1200/3746] lr: 8.549e-02, eta: 3 days, 19:06:47, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5073, loss_cls: 4.2958, loss: 4.2958 +2024-12-27 09:38:30,169 - pyskl - INFO - Epoch [38][1300/3746] lr: 8.547e-02, eta: 3 days, 19:05:53, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5259, loss_cls: 4.1890, loss: 4.1890 +2024-12-27 09:39:55,428 - pyskl - INFO - Epoch [38][1400/3746] lr: 8.545e-02, eta: 3 days, 19:04:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5116, loss_cls: 4.2649, loss: 4.2649 +2024-12-27 09:41:20,826 - pyskl - INFO - Epoch [38][1500/3746] lr: 8.543e-02, eta: 3 days, 19:04:04, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5147, loss_cls: 4.2633, loss: 4.2633 +2024-12-27 09:42:46,713 - pyskl - INFO - Epoch [38][1600/3746] lr: 8.541e-02, eta: 3 days, 19:03:11, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5142, loss_cls: 4.2674, loss: 4.2674 +2024-12-27 09:44:11,783 - pyskl - INFO - Epoch [38][1700/3746] lr: 8.539e-02, eta: 3 days, 19:02:15, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5161, loss_cls: 4.2369, loss: 4.2369 +2024-12-27 09:45:37,291 - pyskl - INFO - Epoch [38][1800/3746] lr: 8.537e-02, eta: 3 days, 19:01:21, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5108, loss_cls: 4.2601, loss: 4.2601 +2024-12-27 09:47:02,505 - pyskl - INFO - Epoch [38][1900/3746] lr: 8.535e-02, eta: 3 days, 19:00:25, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5142, loss_cls: 4.2553, loss: 4.2553 +2024-12-27 09:48:28,252 - pyskl - INFO - Epoch [38][2000/3746] lr: 8.533e-02, eta: 3 days, 18:59:31, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5070, loss_cls: 4.3097, loss: 4.3097 +2024-12-27 09:49:53,669 - pyskl - INFO - Epoch [38][2100/3746] lr: 8.531e-02, eta: 3 days, 18:58:37, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5058, loss_cls: 4.2905, loss: 4.2905 +2024-12-27 09:51:19,317 - pyskl - INFO - Epoch [38][2200/3746] lr: 8.529e-02, eta: 3 days, 18:57:42, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5147, loss_cls: 4.2423, loss: 4.2423 +2024-12-27 09:52:44,568 - pyskl - INFO - Epoch [38][2300/3746] lr: 8.527e-02, eta: 3 days, 18:56:47, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5127, loss_cls: 4.2513, loss: 4.2513 +2024-12-27 09:54:09,685 - pyskl - INFO - Epoch [38][2400/3746] lr: 8.525e-02, eta: 3 days, 18:55:51, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5106, loss_cls: 4.2353, loss: 4.2353 +2024-12-27 09:55:35,001 - pyskl - INFO - Epoch [38][2500/3746] lr: 8.523e-02, eta: 3 days, 18:54:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5000, loss_cls: 4.3054, loss: 4.3054 +2024-12-27 09:57:00,152 - pyskl - INFO - Epoch [38][2600/3746] lr: 8.521e-02, eta: 3 days, 18:54:00, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5275, loss_cls: 4.2108, loss: 4.2108 +2024-12-27 09:58:25,424 - pyskl - INFO - Epoch [38][2700/3746] lr: 8.519e-02, eta: 3 days, 18:53:05, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5175, loss_cls: 4.2240, loss: 4.2240 +2024-12-27 09:59:50,808 - pyskl - INFO - Epoch [38][2800/3746] lr: 8.517e-02, eta: 3 days, 18:52:09, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5233, loss_cls: 4.2633, loss: 4.2633 +2024-12-27 10:01:16,227 - pyskl - INFO - Epoch [38][2900/3746] lr: 8.515e-02, eta: 3 days, 18:51:14, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5156, loss_cls: 4.2531, loss: 4.2531 +2024-12-27 10:02:41,605 - pyskl - INFO - Epoch [38][3000/3746] lr: 8.513e-02, eta: 3 days, 18:50:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5102, loss_cls: 4.2541, loss: 4.2541 +2024-12-27 10:04:07,538 - pyskl - INFO - Epoch [38][3100/3746] lr: 8.511e-02, eta: 3 days, 18:49:25, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5208, loss_cls: 4.2456, loss: 4.2456 +2024-12-27 10:05:32,927 - pyskl - INFO - Epoch [38][3200/3746] lr: 8.509e-02, eta: 3 days, 18:48:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5231, loss_cls: 4.2007, loss: 4.2007 +2024-12-27 10:06:58,293 - pyskl - INFO - Epoch [38][3300/3746] lr: 8.507e-02, eta: 3 days, 18:47:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5102, loss_cls: 4.2491, loss: 4.2491 +2024-12-27 10:08:23,337 - pyskl - INFO - Epoch [38][3400/3746] lr: 8.505e-02, eta: 3 days, 18:46:38, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5017, loss_cls: 4.2943, loss: 4.2943 +2024-12-27 10:09:48,177 - pyskl - INFO - Epoch [38][3500/3746] lr: 8.503e-02, eta: 3 days, 18:45:41, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5028, loss_cls: 4.3021, loss: 4.3021 +2024-12-27 10:11:12,693 - pyskl - INFO - Epoch [38][3600/3746] lr: 8.501e-02, eta: 3 days, 18:44:43, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5089, loss_cls: 4.2652, loss: 4.2652 +2024-12-27 10:12:37,872 - pyskl - INFO - Epoch [38][3700/3746] lr: 8.499e-02, eta: 3 days, 18:43:46, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5130, loss_cls: 4.2404, loss: 4.2404 +2024-12-27 10:13:18,958 - pyskl - INFO - Saving checkpoint at 38 epochs +2024-12-27 10:15:14,795 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 10:15:15,730 - pyskl - INFO - +top1_acc 0.1756 +top5_acc 0.3868 +2024-12-27 10:15:15,730 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 10:15:15,779 - pyskl - INFO - +mean_acc 0.1754 +2024-12-27 10:15:15,792 - pyskl - INFO - Epoch(val) [38][309] top1_acc: 0.1756, top5_acc: 0.3868, mean_class_accuracy: 0.1754 +2024-12-27 10:19:25,591 - pyskl - INFO - Epoch [39][100/3746] lr: 8.496e-02, eta: 3 days, 18:48:34, time: 2.498, data_time: 1.477, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5194, loss_cls: 4.2154, loss: 4.2154 +2024-12-27 10:20:50,502 - pyskl - INFO - Epoch [39][200/3746] lr: 8.494e-02, eta: 3 days, 18:47:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5173, loss_cls: 4.2262, loss: 4.2262 +2024-12-27 10:22:15,799 - pyskl - INFO - Epoch [39][300/3746] lr: 8.492e-02, eta: 3 days, 18:46:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5142, loss_cls: 4.2319, loss: 4.2319 +2024-12-27 10:23:41,120 - pyskl - INFO - Epoch [39][400/3746] lr: 8.490e-02, eta: 3 days, 18:45:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5184, loss_cls: 4.2024, loss: 4.2024 +2024-12-27 10:25:06,454 - pyskl - INFO - Epoch [39][500/3746] lr: 8.488e-02, eta: 3 days, 18:44:47, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5145, loss_cls: 4.2423, loss: 4.2423 +2024-12-27 10:26:31,474 - pyskl - INFO - Epoch [39][600/3746] lr: 8.486e-02, eta: 3 days, 18:43:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5105, loss_cls: 4.2534, loss: 4.2534 +2024-12-27 10:27:56,263 - pyskl - INFO - Epoch [39][700/3746] lr: 8.484e-02, eta: 3 days, 18:42:52, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5211, loss_cls: 4.2296, loss: 4.2296 +2024-12-27 10:29:21,133 - pyskl - INFO - Epoch [39][800/3746] lr: 8.482e-02, eta: 3 days, 18:41:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5334, loss_cls: 4.1564, loss: 4.1564 +2024-12-27 10:30:45,460 - pyskl - INFO - Epoch [39][900/3746] lr: 8.480e-02, eta: 3 days, 18:40:55, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5108, loss_cls: 4.2924, loss: 4.2924 +2024-12-27 10:32:09,990 - pyskl - INFO - Epoch [39][1000/3746] lr: 8.478e-02, eta: 3 days, 18:39:56, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5272, loss_cls: 4.2003, loss: 4.2003 +2024-12-27 10:33:34,562 - pyskl - INFO - Epoch [39][1100/3746] lr: 8.476e-02, eta: 3 days, 18:38:57, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5112, loss_cls: 4.2254, loss: 4.2254 +2024-12-27 10:34:59,061 - pyskl - INFO - Epoch [39][1200/3746] lr: 8.474e-02, eta: 3 days, 18:37:58, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5111, loss_cls: 4.2512, loss: 4.2512 +2024-12-27 10:36:23,751 - pyskl - INFO - Epoch [39][1300/3746] lr: 8.472e-02, eta: 3 days, 18:37:00, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5044, loss_cls: 4.3357, loss: 4.3357 +2024-12-27 10:37:48,497 - pyskl - INFO - Epoch [39][1400/3746] lr: 8.470e-02, eta: 3 days, 18:36:01, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5158, loss_cls: 4.2411, loss: 4.2411 +2024-12-27 10:39:13,086 - pyskl - INFO - Epoch [39][1500/3746] lr: 8.468e-02, eta: 3 days, 18:35:03, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5000, loss_cls: 4.2877, loss: 4.2877 +2024-12-27 10:40:37,870 - pyskl - INFO - Epoch [39][1600/3746] lr: 8.466e-02, eta: 3 days, 18:34:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5077, loss_cls: 4.2702, loss: 4.2702 +2024-12-27 10:42:02,360 - pyskl - INFO - Epoch [39][1700/3746] lr: 8.464e-02, eta: 3 days, 18:33:05, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5158, loss_cls: 4.2507, loss: 4.2507 +2024-12-27 10:43:26,949 - pyskl - INFO - Epoch [39][1800/3746] lr: 8.462e-02, eta: 3 days, 18:32:06, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5045, loss_cls: 4.2724, loss: 4.2724 +2024-12-27 10:44:51,546 - pyskl - INFO - Epoch [39][1900/3746] lr: 8.460e-02, eta: 3 days, 18:31:07, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5003, loss_cls: 4.2882, loss: 4.2882 +2024-12-27 10:46:16,288 - pyskl - INFO - Epoch [39][2000/3746] lr: 8.458e-02, eta: 3 days, 18:30:08, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5148, loss_cls: 4.2617, loss: 4.2617 +2024-12-27 10:47:40,638 - pyskl - INFO - Epoch [39][2100/3746] lr: 8.456e-02, eta: 3 days, 18:29:09, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5058, loss_cls: 4.2762, loss: 4.2762 +2024-12-27 10:49:04,912 - pyskl - INFO - Epoch [39][2200/3746] lr: 8.454e-02, eta: 3 days, 18:28:09, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5228, loss_cls: 4.2547, loss: 4.2547 +2024-12-27 10:50:30,320 - pyskl - INFO - Epoch [39][2300/3746] lr: 8.452e-02, eta: 3 days, 18:27:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5086, loss_cls: 4.2812, loss: 4.2812 +2024-12-27 10:51:55,344 - pyskl - INFO - Epoch [39][2400/3746] lr: 8.450e-02, eta: 3 days, 18:26:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5180, loss_cls: 4.2294, loss: 4.2294 +2024-12-27 10:53:20,759 - pyskl - INFO - Epoch [39][2500/3746] lr: 8.448e-02, eta: 3 days, 18:25:17, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5223, loss_cls: 4.2143, loss: 4.2143 +2024-12-27 10:54:46,601 - pyskl - INFO - Epoch [39][2600/3746] lr: 8.446e-02, eta: 3 days, 18:24:21, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5123, loss_cls: 4.2575, loss: 4.2575 +2024-12-27 10:56:12,215 - pyskl - INFO - Epoch [39][2700/3746] lr: 8.444e-02, eta: 3 days, 18:23:25, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5159, loss_cls: 4.2141, loss: 4.2141 +2024-12-27 10:57:37,547 - pyskl - INFO - Epoch [39][2800/3746] lr: 8.442e-02, eta: 3 days, 18:22:28, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5134, loss_cls: 4.2150, loss: 4.2150 +2024-12-27 10:59:02,910 - pyskl - INFO - Epoch [39][2900/3746] lr: 8.440e-02, eta: 3 days, 18:21:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5131, loss_cls: 4.2539, loss: 4.2539 +2024-12-27 11:00:28,562 - pyskl - INFO - Epoch [39][3000/3746] lr: 8.438e-02, eta: 3 days, 18:20:34, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5172, loss_cls: 4.2285, loss: 4.2285 +2024-12-27 11:01:54,143 - pyskl - INFO - Epoch [39][3100/3746] lr: 8.436e-02, eta: 3 days, 18:19:38, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5197, loss_cls: 4.2185, loss: 4.2185 +2024-12-27 11:03:20,004 - pyskl - INFO - Epoch [39][3200/3746] lr: 8.434e-02, eta: 3 days, 18:18:42, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5094, loss_cls: 4.2557, loss: 4.2557 +2024-12-27 11:04:45,399 - pyskl - INFO - Epoch [39][3300/3746] lr: 8.432e-02, eta: 3 days, 18:17:45, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5134, loss_cls: 4.2445, loss: 4.2445 +2024-12-27 11:06:10,684 - pyskl - INFO - Epoch [39][3400/3746] lr: 8.430e-02, eta: 3 days, 18:16:47, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5128, loss_cls: 4.2438, loss: 4.2438 +2024-12-27 11:07:35,946 - pyskl - INFO - Epoch [39][3500/3746] lr: 8.428e-02, eta: 3 days, 18:15:50, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5166, loss_cls: 4.2272, loss: 4.2272 +2024-12-27 11:09:01,060 - pyskl - INFO - Epoch [39][3600/3746] lr: 8.426e-02, eta: 3 days, 18:14:51, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5139, loss_cls: 4.2497, loss: 4.2497 +2024-12-27 11:10:26,499 - pyskl - INFO - Epoch [39][3700/3746] lr: 8.424e-02, eta: 3 days, 18:13:54, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5192, loss_cls: 4.2273, loss: 4.2273 +2024-12-27 11:11:07,418 - pyskl - INFO - Saving checkpoint at 39 epochs +2024-12-27 11:13:05,085 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 11:13:06,113 - pyskl - INFO - +top1_acc 0.1957 +top5_acc 0.4217 +2024-12-27 11:13:06,113 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 11:13:06,174 - pyskl - INFO - +mean_acc 0.1956 +2024-12-27 11:13:06,194 - pyskl - INFO - Epoch(val) [39][309] top1_acc: 0.1957, top5_acc: 0.4217, mean_class_accuracy: 0.1956 +2024-12-27 11:17:09,978 - pyskl - INFO - Epoch [40][100/3746] lr: 8.421e-02, eta: 3 days, 18:18:09, time: 2.438, data_time: 1.416, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5109, loss_cls: 4.2303, loss: 4.2303 +2024-12-27 11:18:34,592 - pyskl - INFO - Epoch [40][200/3746] lr: 8.419e-02, eta: 3 days, 18:17:09, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5216, loss_cls: 4.2077, loss: 4.2077 +2024-12-27 11:19:59,916 - pyskl - INFO - Epoch [40][300/3746] lr: 8.417e-02, eta: 3 days, 18:16:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5181, loss_cls: 4.2537, loss: 4.2537 +2024-12-27 11:21:24,995 - pyskl - INFO - Epoch [40][400/3746] lr: 8.415e-02, eta: 3 days, 18:15:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5250, loss_cls: 4.2109, loss: 4.2109 +2024-12-27 11:22:49,710 - pyskl - INFO - Epoch [40][500/3746] lr: 8.413e-02, eta: 3 days, 18:14:12, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5195, loss_cls: 4.2079, loss: 4.2079 +2024-12-27 11:24:14,420 - pyskl - INFO - Epoch [40][600/3746] lr: 8.411e-02, eta: 3 days, 18:13:13, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5088, loss_cls: 4.2861, loss: 4.2861 +2024-12-27 11:25:39,150 - pyskl - INFO - Epoch [40][700/3746] lr: 8.408e-02, eta: 3 days, 18:12:13, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5209, loss_cls: 4.2270, loss: 4.2270 +2024-12-27 11:27:03,961 - pyskl - INFO - Epoch [40][800/3746] lr: 8.406e-02, eta: 3 days, 18:11:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5103, loss_cls: 4.2846, loss: 4.2846 +2024-12-27 11:28:28,109 - pyskl - INFO - Epoch [40][900/3746] lr: 8.404e-02, eta: 3 days, 18:10:12, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5214, loss_cls: 4.2271, loss: 4.2271 +2024-12-27 11:29:52,869 - pyskl - INFO - Epoch [40][1000/3746] lr: 8.402e-02, eta: 3 days, 18:09:12, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5217, loss_cls: 4.2134, loss: 4.2134 +2024-12-27 11:31:18,095 - pyskl - INFO - Epoch [40][1100/3746] lr: 8.400e-02, eta: 3 days, 18:08:13, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5125, loss_cls: 4.2650, loss: 4.2650 +2024-12-27 11:32:42,665 - pyskl - INFO - Epoch [40][1200/3746] lr: 8.398e-02, eta: 3 days, 18:07:13, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5214, loss_cls: 4.2091, loss: 4.2091 +2024-12-27 11:34:07,347 - pyskl - INFO - Epoch [40][1300/3746] lr: 8.396e-02, eta: 3 days, 18:06:13, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5145, loss_cls: 4.2318, loss: 4.2318 +2024-12-27 11:35:31,966 - pyskl - INFO - Epoch [40][1400/3746] lr: 8.394e-02, eta: 3 days, 18:05:12, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5159, loss_cls: 4.2723, loss: 4.2723 +2024-12-27 11:36:56,966 - pyskl - INFO - Epoch [40][1500/3746] lr: 8.392e-02, eta: 3 days, 18:04:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5161, loss_cls: 4.2681, loss: 4.2681 +2024-12-27 11:38:22,028 - pyskl - INFO - Epoch [40][1600/3746] lr: 8.390e-02, eta: 3 days, 18:03:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5047, loss_cls: 4.2782, loss: 4.2782 +2024-12-27 11:39:46,781 - pyskl - INFO - Epoch [40][1700/3746] lr: 8.388e-02, eta: 3 days, 18:02:14, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5089, loss_cls: 4.2419, loss: 4.2419 +2024-12-27 11:41:11,278 - pyskl - INFO - Epoch [40][1800/3746] lr: 8.386e-02, eta: 3 days, 18:01:13, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5212, loss_cls: 4.2243, loss: 4.2243 +2024-12-27 11:42:35,958 - pyskl - INFO - Epoch [40][1900/3746] lr: 8.384e-02, eta: 3 days, 18:00:12, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5083, loss_cls: 4.2535, loss: 4.2535 +2024-12-27 11:44:00,916 - pyskl - INFO - Epoch [40][2000/3746] lr: 8.382e-02, eta: 3 days, 17:59:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5127, loss_cls: 4.2493, loss: 4.2493 +2024-12-27 11:45:25,361 - pyskl - INFO - Epoch [40][2100/3746] lr: 8.380e-02, eta: 3 days, 17:58:12, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5112, loss_cls: 4.2654, loss: 4.2654 +2024-12-27 11:46:49,999 - pyskl - INFO - Epoch [40][2200/3746] lr: 8.378e-02, eta: 3 days, 17:57:11, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5038, loss_cls: 4.2940, loss: 4.2940 +2024-12-27 11:48:14,992 - pyskl - INFO - Epoch [40][2300/3746] lr: 8.376e-02, eta: 3 days, 17:56:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5162, loss_cls: 4.2342, loss: 4.2342 +2024-12-27 11:49:39,817 - pyskl - INFO - Epoch [40][2400/3746] lr: 8.374e-02, eta: 3 days, 17:55:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.4997, loss_cls: 4.3030, loss: 4.3030 +2024-12-27 11:51:04,797 - pyskl - INFO - Epoch [40][2500/3746] lr: 8.371e-02, eta: 3 days, 17:54:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5098, loss_cls: 4.2369, loss: 4.2369 +2024-12-27 11:52:30,033 - pyskl - INFO - Epoch [40][2600/3746] lr: 8.369e-02, eta: 3 days, 17:53:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5127, loss_cls: 4.2450, loss: 4.2450 +2024-12-27 11:53:55,584 - pyskl - INFO - Epoch [40][2700/3746] lr: 8.367e-02, eta: 3 days, 17:52:14, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5295, loss_cls: 4.1854, loss: 4.1854 +2024-12-27 11:55:21,201 - pyskl - INFO - Epoch [40][2800/3746] lr: 8.365e-02, eta: 3 days, 17:51:16, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5066, loss_cls: 4.2806, loss: 4.2806 +2024-12-27 11:56:46,707 - pyskl - INFO - Epoch [40][2900/3746] lr: 8.363e-02, eta: 3 days, 17:50:18, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5083, loss_cls: 4.2702, loss: 4.2702 +2024-12-27 11:58:12,110 - pyskl - INFO - Epoch [40][3000/3746] lr: 8.361e-02, eta: 3 days, 17:49:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5195, loss_cls: 4.2022, loss: 4.2022 +2024-12-27 11:59:37,366 - pyskl - INFO - Epoch [40][3100/3746] lr: 8.359e-02, eta: 3 days, 17:48:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5133, loss_cls: 4.2284, loss: 4.2284 +2024-12-27 12:01:02,435 - pyskl - INFO - Epoch [40][3200/3746] lr: 8.357e-02, eta: 3 days, 17:47:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5139, loss_cls: 4.2533, loss: 4.2533 +2024-12-27 12:02:27,788 - pyskl - INFO - Epoch [40][3300/3746] lr: 8.355e-02, eta: 3 days, 17:46:21, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5184, loss_cls: 4.2013, loss: 4.2013 +2024-12-27 12:03:52,776 - pyskl - INFO - Epoch [40][3400/3746] lr: 8.353e-02, eta: 3 days, 17:45:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5059, loss_cls: 4.2791, loss: 4.2791 +2024-12-27 12:05:17,910 - pyskl - INFO - Epoch [40][3500/3746] lr: 8.351e-02, eta: 3 days, 17:44:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5222, loss_cls: 4.2257, loss: 4.2257 +2024-12-27 12:06:42,397 - pyskl - INFO - Epoch [40][3600/3746] lr: 8.349e-02, eta: 3 days, 17:43:20, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5169, loss_cls: 4.2232, loss: 4.2232 +2024-12-27 12:08:07,697 - pyskl - INFO - Epoch [40][3700/3746] lr: 8.347e-02, eta: 3 days, 17:42:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5159, loss_cls: 4.2451, loss: 4.2451 +2024-12-27 12:08:48,713 - pyskl - INFO - Saving checkpoint at 40 epochs +2024-12-27 12:10:45,508 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 12:10:46,481 - pyskl - INFO - +top1_acc 0.1968 +top5_acc 0.4259 +2024-12-27 12:10:46,481 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 12:10:46,521 - pyskl - INFO - +mean_acc 0.1965 +2024-12-27 12:10:46,534 - pyskl - INFO - Epoch(val) [40][309] top1_acc: 0.1968, top5_acc: 0.4259, mean_class_accuracy: 0.1965 +2024-12-27 12:14:49,922 - pyskl - INFO - Epoch [41][100/3746] lr: 8.344e-02, eta: 3 days, 17:46:21, time: 2.434, data_time: 1.411, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5294, loss_cls: 4.1904, loss: 4.1904 +2024-12-27 12:16:14,396 - pyskl - INFO - Epoch [41][200/3746] lr: 8.342e-02, eta: 3 days, 17:45:19, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5292, loss_cls: 4.2089, loss: 4.2089 +2024-12-27 12:17:39,132 - pyskl - INFO - Epoch [41][300/3746] lr: 8.339e-02, eta: 3 days, 17:44:17, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5203, loss_cls: 4.2215, loss: 4.2215 +2024-12-27 12:19:03,845 - pyskl - INFO - Epoch [41][400/3746] lr: 8.337e-02, eta: 3 days, 17:43:16, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5189, loss_cls: 4.1987, loss: 4.1987 +2024-12-27 12:20:28,929 - pyskl - INFO - Epoch [41][500/3746] lr: 8.335e-02, eta: 3 days, 17:42:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5191, loss_cls: 4.2143, loss: 4.2143 +2024-12-27 12:21:53,892 - pyskl - INFO - Epoch [41][600/3746] lr: 8.333e-02, eta: 3 days, 17:41:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5194, loss_cls: 4.2479, loss: 4.2479 +2024-12-27 12:23:19,218 - pyskl - INFO - Epoch [41][700/3746] lr: 8.331e-02, eta: 3 days, 17:40:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5212, loss_cls: 4.2232, loss: 4.2232 +2024-12-27 12:24:44,601 - pyskl - INFO - Epoch [41][800/3746] lr: 8.329e-02, eta: 3 days, 17:39:16, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5128, loss_cls: 4.2439, loss: 4.2439 +2024-12-27 12:26:09,163 - pyskl - INFO - Epoch [41][900/3746] lr: 8.327e-02, eta: 3 days, 17:38:14, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5064, loss_cls: 4.2295, loss: 4.2295 +2024-12-27 12:27:33,277 - pyskl - INFO - Epoch [41][1000/3746] lr: 8.325e-02, eta: 3 days, 17:37:11, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5141, loss_cls: 4.2388, loss: 4.2388 +2024-12-27 12:28:58,070 - pyskl - INFO - Epoch [41][1100/3746] lr: 8.323e-02, eta: 3 days, 17:36:09, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5172, loss_cls: 4.2442, loss: 4.2442 +2024-12-27 12:30:22,734 - pyskl - INFO - Epoch [41][1200/3746] lr: 8.321e-02, eta: 3 days, 17:35:08, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5159, loss_cls: 4.2271, loss: 4.2271 +2024-12-27 12:31:47,480 - pyskl - INFO - Epoch [41][1300/3746] lr: 8.319e-02, eta: 3 days, 17:34:06, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5252, loss_cls: 4.2092, loss: 4.2092 +2024-12-27 12:33:12,116 - pyskl - INFO - Epoch [41][1400/3746] lr: 8.316e-02, eta: 3 days, 17:33:04, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5164, loss_cls: 4.2657, loss: 4.2657 +2024-12-27 12:34:36,650 - pyskl - INFO - Epoch [41][1500/3746] lr: 8.314e-02, eta: 3 days, 17:32:02, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5147, loss_cls: 4.2508, loss: 4.2508 +2024-12-27 12:36:00,947 - pyskl - INFO - Epoch [41][1600/3746] lr: 8.312e-02, eta: 3 days, 17:30:59, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5152, loss_cls: 4.2433, loss: 4.2433 +2024-12-27 12:37:25,565 - pyskl - INFO - Epoch [41][1700/3746] lr: 8.310e-02, eta: 3 days, 17:29:57, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5273, loss_cls: 4.1981, loss: 4.1981 +2024-12-27 12:38:50,252 - pyskl - INFO - Epoch [41][1800/3746] lr: 8.308e-02, eta: 3 days, 17:28:56, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5159, loss_cls: 4.1956, loss: 4.1956 +2024-12-27 12:40:14,409 - pyskl - INFO - Epoch [41][1900/3746] lr: 8.306e-02, eta: 3 days, 17:27:52, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5247, loss_cls: 4.1851, loss: 4.1851 +2024-12-27 12:41:39,346 - pyskl - INFO - Epoch [41][2000/3746] lr: 8.304e-02, eta: 3 days, 17:26:51, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5078, loss_cls: 4.2925, loss: 4.2925 +2024-12-27 12:43:03,842 - pyskl - INFO - Epoch [41][2100/3746] lr: 8.302e-02, eta: 3 days, 17:25:49, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5222, loss_cls: 4.1987, loss: 4.1987 +2024-12-27 12:44:28,288 - pyskl - INFO - Epoch [41][2200/3746] lr: 8.300e-02, eta: 3 days, 17:24:46, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5169, loss_cls: 4.2237, loss: 4.2237 +2024-12-27 12:45:53,144 - pyskl - INFO - Epoch [41][2300/3746] lr: 8.298e-02, eta: 3 days, 17:23:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5203, loss_cls: 4.2147, loss: 4.2147 +2024-12-27 12:47:17,830 - pyskl - INFO - Epoch [41][2400/3746] lr: 8.296e-02, eta: 3 days, 17:22:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5150, loss_cls: 4.2671, loss: 4.2671 +2024-12-27 12:48:42,708 - pyskl - INFO - Epoch [41][2500/3746] lr: 8.293e-02, eta: 3 days, 17:21:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5098, loss_cls: 4.2206, loss: 4.2206 +2024-12-27 12:50:07,440 - pyskl - INFO - Epoch [41][2600/3746] lr: 8.291e-02, eta: 3 days, 17:20:39, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5100, loss_cls: 4.2508, loss: 4.2508 +2024-12-27 12:51:32,245 - pyskl - INFO - Epoch [41][2700/3746] lr: 8.289e-02, eta: 3 days, 17:19:37, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5125, loss_cls: 4.2472, loss: 4.2472 +2024-12-27 12:52:57,355 - pyskl - INFO - Epoch [41][2800/3746] lr: 8.287e-02, eta: 3 days, 17:18:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5156, loss_cls: 4.2493, loss: 4.2493 +2024-12-27 12:54:22,596 - pyskl - INFO - Epoch [41][2900/3746] lr: 8.285e-02, eta: 3 days, 17:17:36, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5216, loss_cls: 4.2515, loss: 4.2515 +2024-12-27 12:55:47,930 - pyskl - INFO - Epoch [41][3000/3746] lr: 8.283e-02, eta: 3 days, 17:16:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5245, loss_cls: 4.2302, loss: 4.2302 +2024-12-27 12:57:13,284 - pyskl - INFO - Epoch [41][3100/3746] lr: 8.281e-02, eta: 3 days, 17:15:35, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5184, loss_cls: 4.2573, loss: 4.2573 +2024-12-27 12:58:38,767 - pyskl - INFO - Epoch [41][3200/3746] lr: 8.279e-02, eta: 3 days, 17:14:35, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5180, loss_cls: 4.2568, loss: 4.2568 +2024-12-27 13:00:04,100 - pyskl - INFO - Epoch [41][3300/3746] lr: 8.277e-02, eta: 3 days, 17:13:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5317, loss_cls: 4.1614, loss: 4.1614 +2024-12-27 13:01:29,346 - pyskl - INFO - Epoch [41][3400/3746] lr: 8.274e-02, eta: 3 days, 17:12:33, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5219, loss_cls: 4.2256, loss: 4.2256 +2024-12-27 13:02:54,101 - pyskl - INFO - Epoch [41][3500/3746] lr: 8.272e-02, eta: 3 days, 17:11:31, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5122, loss_cls: 4.2552, loss: 4.2552 +2024-12-27 13:04:18,716 - pyskl - INFO - Epoch [41][3600/3746] lr: 8.270e-02, eta: 3 days, 17:10:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5173, loss_cls: 4.2292, loss: 4.2292 +2024-12-27 13:05:44,083 - pyskl - INFO - Epoch [41][3700/3746] lr: 8.268e-02, eta: 3 days, 17:09:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5089, loss_cls: 4.2574, loss: 4.2574 +2024-12-27 13:06:24,915 - pyskl - INFO - Saving checkpoint at 41 epochs +2024-12-27 13:08:22,533 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 13:08:23,507 - pyskl - INFO - +top1_acc 0.1922 +top5_acc 0.4123 +2024-12-27 13:08:23,507 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 13:08:23,550 - pyskl - INFO - +mean_acc 0.1921 +2024-12-27 13:08:23,562 - pyskl - INFO - Epoch(val) [41][309] top1_acc: 0.1922, top5_acc: 0.4123, mean_class_accuracy: 0.1921 +2024-12-27 13:12:29,250 - pyskl - INFO - Epoch [42][100/3746] lr: 8.265e-02, eta: 3 days, 17:13:21, time: 2.457, data_time: 1.425, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5297, loss_cls: 4.1739, loss: 4.1739 +2024-12-27 13:13:54,084 - pyskl - INFO - Epoch [42][200/3746] lr: 8.263e-02, eta: 3 days, 17:12:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5166, loss_cls: 4.2264, loss: 4.2264 +2024-12-27 13:15:19,129 - pyskl - INFO - Epoch [42][300/3746] lr: 8.261e-02, eta: 3 days, 17:11:17, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5192, loss_cls: 4.2035, loss: 4.2035 +2024-12-27 13:16:43,896 - pyskl - INFO - Epoch [42][400/3746] lr: 8.259e-02, eta: 3 days, 17:10:14, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5305, loss_cls: 4.1774, loss: 4.1774 +2024-12-27 13:18:08,920 - pyskl - INFO - Epoch [42][500/3746] lr: 8.257e-02, eta: 3 days, 17:09:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5139, loss_cls: 4.2730, loss: 4.2730 +2024-12-27 13:19:33,740 - pyskl - INFO - Epoch [42][600/3746] lr: 8.254e-02, eta: 3 days, 17:08:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5262, loss_cls: 4.1878, loss: 4.1878 +2024-12-27 13:20:58,605 - pyskl - INFO - Epoch [42][700/3746] lr: 8.252e-02, eta: 3 days, 17:07:07, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5211, loss_cls: 4.1844, loss: 4.1844 +2024-12-27 13:22:23,965 - pyskl - INFO - Epoch [42][800/3746] lr: 8.250e-02, eta: 3 days, 17:06:06, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5306, loss_cls: 4.2054, loss: 4.2054 +2024-12-27 13:23:49,008 - pyskl - INFO - Epoch [42][900/3746] lr: 8.248e-02, eta: 3 days, 17:05:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5211, loss_cls: 4.2001, loss: 4.2001 +2024-12-27 13:25:14,118 - pyskl - INFO - Epoch [42][1000/3746] lr: 8.246e-02, eta: 3 days, 17:04:02, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5123, loss_cls: 4.2733, loss: 4.2733 +2024-12-27 13:26:39,147 - pyskl - INFO - Epoch [42][1100/3746] lr: 8.244e-02, eta: 3 days, 17:03:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5091, loss_cls: 4.2944, loss: 4.2944 +2024-12-27 13:28:03,987 - pyskl - INFO - Epoch [42][1200/3746] lr: 8.242e-02, eta: 3 days, 17:01:57, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5081, loss_cls: 4.2945, loss: 4.2945 +2024-12-27 13:29:28,588 - pyskl - INFO - Epoch [42][1300/3746] lr: 8.240e-02, eta: 3 days, 17:00:54, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5198, loss_cls: 4.2202, loss: 4.2202 +2024-12-27 13:30:53,451 - pyskl - INFO - Epoch [42][1400/3746] lr: 8.237e-02, eta: 3 days, 16:59:51, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5120, loss_cls: 4.2539, loss: 4.2539 +2024-12-27 13:32:18,122 - pyskl - INFO - Epoch [42][1500/3746] lr: 8.235e-02, eta: 3 days, 16:58:48, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5228, loss_cls: 4.2047, loss: 4.2047 +2024-12-27 13:33:43,381 - pyskl - INFO - Epoch [42][1600/3746] lr: 8.233e-02, eta: 3 days, 16:57:47, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5202, loss_cls: 4.2235, loss: 4.2235 +2024-12-27 13:35:08,494 - pyskl - INFO - Epoch [42][1700/3746] lr: 8.231e-02, eta: 3 days, 16:56:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5153, loss_cls: 4.2404, loss: 4.2404 +2024-12-27 13:36:33,986 - pyskl - INFO - Epoch [42][1800/3746] lr: 8.229e-02, eta: 3 days, 16:55:43, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5123, loss_cls: 4.2638, loss: 4.2638 +2024-12-27 13:37:58,890 - pyskl - INFO - Epoch [42][1900/3746] lr: 8.227e-02, eta: 3 days, 16:54:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5111, loss_cls: 4.2539, loss: 4.2539 +2024-12-27 13:39:23,967 - pyskl - INFO - Epoch [42][2000/3746] lr: 8.225e-02, eta: 3 days, 16:53:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5253, loss_cls: 4.2107, loss: 4.2107 +2024-12-27 13:40:49,141 - pyskl - INFO - Epoch [42][2100/3746] lr: 8.222e-02, eta: 3 days, 16:52:36, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5117, loss_cls: 4.2588, loss: 4.2588 +2024-12-27 13:42:14,493 - pyskl - INFO - Epoch [42][2200/3746] lr: 8.220e-02, eta: 3 days, 16:51:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5197, loss_cls: 4.2272, loss: 4.2272 +2024-12-27 13:43:39,063 - pyskl - INFO - Epoch [42][2300/3746] lr: 8.218e-02, eta: 3 days, 16:50:31, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5158, loss_cls: 4.2396, loss: 4.2396 +2024-12-27 13:45:04,131 - pyskl - INFO - Epoch [42][2400/3746] lr: 8.216e-02, eta: 3 days, 16:49:29, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5159, loss_cls: 4.2386, loss: 4.2386 +2024-12-27 13:46:28,789 - pyskl - INFO - Epoch [42][2500/3746] lr: 8.214e-02, eta: 3 days, 16:48:25, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5258, loss_cls: 4.1912, loss: 4.1912 +2024-12-27 13:47:53,514 - pyskl - INFO - Epoch [42][2600/3746] lr: 8.212e-02, eta: 3 days, 16:47:22, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5016, loss_cls: 4.2715, loss: 4.2715 +2024-12-27 13:49:17,725 - pyskl - INFO - Epoch [42][2700/3746] lr: 8.210e-02, eta: 3 days, 16:46:17, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5159, loss_cls: 4.1991, loss: 4.1991 +2024-12-27 13:50:42,375 - pyskl - INFO - Epoch [42][2800/3746] lr: 8.207e-02, eta: 3 days, 16:45:14, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5295, loss_cls: 4.2040, loss: 4.2040 +2024-12-27 13:52:06,631 - pyskl - INFO - Epoch [42][2900/3746] lr: 8.205e-02, eta: 3 days, 16:44:09, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5111, loss_cls: 4.2498, loss: 4.2498 +2024-12-27 13:53:31,969 - pyskl - INFO - Epoch [42][3000/3746] lr: 8.203e-02, eta: 3 days, 16:43:07, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5203, loss_cls: 4.2300, loss: 4.2300 +2024-12-27 13:54:57,127 - pyskl - INFO - Epoch [42][3100/3746] lr: 8.201e-02, eta: 3 days, 16:42:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5197, loss_cls: 4.2119, loss: 4.2119 +2024-12-27 13:56:22,329 - pyskl - INFO - Epoch [42][3200/3746] lr: 8.199e-02, eta: 3 days, 16:41:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5211, loss_cls: 4.2105, loss: 4.2105 +2024-12-27 13:57:47,674 - pyskl - INFO - Epoch [42][3300/3746] lr: 8.197e-02, eta: 3 days, 16:40:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5231, loss_cls: 4.2009, loss: 4.2009 +2024-12-27 13:59:12,405 - pyskl - INFO - Epoch [42][3400/3746] lr: 8.195e-02, eta: 3 days, 16:38:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5138, loss_cls: 4.2362, loss: 4.2362 +2024-12-27 14:00:37,089 - pyskl - INFO - Epoch [42][3500/3746] lr: 8.192e-02, eta: 3 days, 16:37:53, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5161, loss_cls: 4.2630, loss: 4.2630 +2024-12-27 14:02:01,969 - pyskl - INFO - Epoch [42][3600/3746] lr: 8.190e-02, eta: 3 days, 16:36:50, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5255, loss_cls: 4.1693, loss: 4.1693 +2024-12-27 14:03:26,491 - pyskl - INFO - Epoch [42][3700/3746] lr: 8.188e-02, eta: 3 days, 16:35:46, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5162, loss_cls: 4.2378, loss: 4.2378 +2024-12-27 14:04:07,167 - pyskl - INFO - Saving checkpoint at 42 epochs +2024-12-27 14:06:05,163 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 14:06:05,938 - pyskl - INFO - +top1_acc 0.1943 +top5_acc 0.4199 +2024-12-27 14:06:05,938 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 14:06:05,985 - pyskl - INFO - +mean_acc 0.1941 +2024-12-27 14:06:06,000 - pyskl - INFO - Epoch(val) [42][309] top1_acc: 0.1943, top5_acc: 0.4199, mean_class_accuracy: 0.1941 +2024-12-27 14:10:22,868 - pyskl - INFO - Epoch [43][100/3746] lr: 8.185e-02, eta: 3 days, 16:39:55, time: 2.569, data_time: 1.539, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5158, loss_cls: 4.2153, loss: 4.2153 +2024-12-27 14:11:48,151 - pyskl - INFO - Epoch [43][200/3746] lr: 8.183e-02, eta: 3 days, 16:38:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5208, loss_cls: 4.2034, loss: 4.2034 +2024-12-27 14:13:13,493 - pyskl - INFO - Epoch [43][300/3746] lr: 8.181e-02, eta: 3 days, 16:37:50, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5253, loss_cls: 4.2181, loss: 4.2181 +2024-12-27 14:14:38,098 - pyskl - INFO - Epoch [43][400/3746] lr: 8.179e-02, eta: 3 days, 16:36:46, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5311, loss_cls: 4.1582, loss: 4.1582 +2024-12-27 14:16:02,859 - pyskl - INFO - Epoch [43][500/3746] lr: 8.176e-02, eta: 3 days, 16:35:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5295, loss_cls: 4.1827, loss: 4.1827 +2024-12-27 14:17:27,655 - pyskl - INFO - Epoch [43][600/3746] lr: 8.174e-02, eta: 3 days, 16:34:38, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5119, loss_cls: 4.2712, loss: 4.2712 +2024-12-27 14:18:52,262 - pyskl - INFO - Epoch [43][700/3746] lr: 8.172e-02, eta: 3 days, 16:33:33, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5250, loss_cls: 4.1920, loss: 4.1920 +2024-12-27 14:20:16,935 - pyskl - INFO - Epoch [43][800/3746] lr: 8.170e-02, eta: 3 days, 16:32:29, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5209, loss_cls: 4.2351, loss: 4.2351 +2024-12-27 14:21:41,806 - pyskl - INFO - Epoch [43][900/3746] lr: 8.168e-02, eta: 3 days, 16:31:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5145, loss_cls: 4.2162, loss: 4.2162 +2024-12-27 14:23:06,208 - pyskl - INFO - Epoch [43][1000/3746] lr: 8.166e-02, eta: 3 days, 16:30:20, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5248, loss_cls: 4.2129, loss: 4.2129 +2024-12-27 14:24:30,975 - pyskl - INFO - Epoch [43][1100/3746] lr: 8.163e-02, eta: 3 days, 16:29:16, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5227, loss_cls: 4.2173, loss: 4.2173 +2024-12-27 14:25:55,477 - pyskl - INFO - Epoch [43][1200/3746] lr: 8.161e-02, eta: 3 days, 16:28:11, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5203, loss_cls: 4.2439, loss: 4.2439 +2024-12-27 14:27:20,484 - pyskl - INFO - Epoch [43][1300/3746] lr: 8.159e-02, eta: 3 days, 16:27:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5106, loss_cls: 4.2824, loss: 4.2824 +2024-12-27 14:28:44,879 - pyskl - INFO - Epoch [43][1400/3746] lr: 8.157e-02, eta: 3 days, 16:26:02, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5248, loss_cls: 4.2135, loss: 4.2135 +2024-12-27 14:30:08,928 - pyskl - INFO - Epoch [43][1500/3746] lr: 8.155e-02, eta: 3 days, 16:24:56, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5184, loss_cls: 4.2123, loss: 4.2123 +2024-12-27 14:31:33,417 - pyskl - INFO - Epoch [43][1600/3746] lr: 8.153e-02, eta: 3 days, 16:23:51, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5148, loss_cls: 4.2445, loss: 4.2445 +2024-12-27 14:32:58,086 - pyskl - INFO - Epoch [43][1700/3746] lr: 8.150e-02, eta: 3 days, 16:22:47, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5262, loss_cls: 4.1837, loss: 4.1837 +2024-12-27 14:34:22,604 - pyskl - INFO - Epoch [43][1800/3746] lr: 8.148e-02, eta: 3 days, 16:21:42, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5272, loss_cls: 4.2237, loss: 4.2237 +2024-12-27 14:35:47,040 - pyskl - INFO - Epoch [43][1900/3746] lr: 8.146e-02, eta: 3 days, 16:20:37, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5103, loss_cls: 4.2228, loss: 4.2228 +2024-12-27 14:37:11,505 - pyskl - INFO - Epoch [43][2000/3746] lr: 8.144e-02, eta: 3 days, 16:19:32, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5166, loss_cls: 4.2341, loss: 4.2341 +2024-12-27 14:38:36,061 - pyskl - INFO - Epoch [43][2100/3746] lr: 8.142e-02, eta: 3 days, 16:18:27, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5217, loss_cls: 4.2023, loss: 4.2023 +2024-12-27 14:40:01,496 - pyskl - INFO - Epoch [43][2200/3746] lr: 8.140e-02, eta: 3 days, 16:17:24, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5105, loss_cls: 4.2597, loss: 4.2597 +2024-12-27 14:41:26,188 - pyskl - INFO - Epoch [43][2300/3746] lr: 8.137e-02, eta: 3 days, 16:16:19, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5183, loss_cls: 4.2016, loss: 4.2016 +2024-12-27 14:42:51,235 - pyskl - INFO - Epoch [43][2400/3746] lr: 8.135e-02, eta: 3 days, 16:15:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5208, loss_cls: 4.2040, loss: 4.2040 +2024-12-27 14:44:15,610 - pyskl - INFO - Epoch [43][2500/3746] lr: 8.133e-02, eta: 3 days, 16:14:10, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5123, loss_cls: 4.2759, loss: 4.2759 +2024-12-27 14:45:40,390 - pyskl - INFO - Epoch [43][2600/3746] lr: 8.131e-02, eta: 3 days, 16:13:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5303, loss_cls: 4.1791, loss: 4.1791 +2024-12-27 14:47:06,044 - pyskl - INFO - Epoch [43][2700/3746] lr: 8.129e-02, eta: 3 days, 16:12:03, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5236, loss_cls: 4.2052, loss: 4.2052 +2024-12-27 14:48:31,296 - pyskl - INFO - Epoch [43][2800/3746] lr: 8.126e-02, eta: 3 days, 16:11:00, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5188, loss_cls: 4.2399, loss: 4.2399 +2024-12-27 14:49:56,602 - pyskl - INFO - Epoch [43][2900/3746] lr: 8.124e-02, eta: 3 days, 16:09:57, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5083, loss_cls: 4.2578, loss: 4.2578 +2024-12-27 14:51:21,595 - pyskl - INFO - Epoch [43][3000/3746] lr: 8.122e-02, eta: 3 days, 16:08:52, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5053, loss_cls: 4.2824, loss: 4.2824 +2024-12-27 14:52:47,359 - pyskl - INFO - Epoch [43][3100/3746] lr: 8.120e-02, eta: 3 days, 16:07:50, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5289, loss_cls: 4.2062, loss: 4.2062 +2024-12-27 14:54:12,925 - pyskl - INFO - Epoch [43][3200/3746] lr: 8.118e-02, eta: 3 days, 16:06:48, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5223, loss_cls: 4.1899, loss: 4.1899 +2024-12-27 14:55:38,403 - pyskl - INFO - Epoch [43][3300/3746] lr: 8.116e-02, eta: 3 days, 16:05:45, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5141, loss_cls: 4.2403, loss: 4.2403 +2024-12-27 14:57:03,846 - pyskl - INFO - Epoch [43][3400/3746] lr: 8.113e-02, eta: 3 days, 16:04:42, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5133, loss_cls: 4.2436, loss: 4.2436 +2024-12-27 14:58:29,276 - pyskl - INFO - Epoch [43][3500/3746] lr: 8.111e-02, eta: 3 days, 16:03:39, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5186, loss_cls: 4.2033, loss: 4.2033 +2024-12-27 14:59:55,020 - pyskl - INFO - Epoch [43][3600/3746] lr: 8.109e-02, eta: 3 days, 16:02:36, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5152, loss_cls: 4.2274, loss: 4.2274 +2024-12-27 15:01:20,391 - pyskl - INFO - Epoch [43][3700/3746] lr: 8.107e-02, eta: 3 days, 16:01:33, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5244, loss_cls: 4.2243, loss: 4.2243 +2024-12-27 15:02:01,235 - pyskl - INFO - Saving checkpoint at 43 epochs +2024-12-27 15:03:57,919 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 15:03:58,760 - pyskl - INFO - +top1_acc 0.1820 +top5_acc 0.4118 +2024-12-27 15:03:58,760 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 15:03:58,812 - pyskl - INFO - +mean_acc 0.1819 +2024-12-27 15:03:58,826 - pyskl - INFO - Epoch(val) [43][309] top1_acc: 0.1820, top5_acc: 0.4118, mean_class_accuracy: 0.1819 +2024-12-27 15:08:14,922 - pyskl - INFO - Epoch [44][100/3746] lr: 8.104e-02, eta: 3 days, 16:05:27, time: 2.561, data_time: 1.519, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5258, loss_cls: 4.1582, loss: 4.1582 +2024-12-27 15:09:40,000 - pyskl - INFO - Epoch [44][200/3746] lr: 8.101e-02, eta: 3 days, 16:04:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5252, loss_cls: 4.1770, loss: 4.1770 +2024-12-27 15:11:05,029 - pyskl - INFO - Epoch [44][300/3746] lr: 8.099e-02, eta: 3 days, 16:03:18, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5342, loss_cls: 4.1663, loss: 4.1663 +2024-12-27 15:12:30,277 - pyskl - INFO - Epoch [44][400/3746] lr: 8.097e-02, eta: 3 days, 16:02:14, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5211, loss_cls: 4.2270, loss: 4.2270 +2024-12-27 15:13:55,270 - pyskl - INFO - Epoch [44][500/3746] lr: 8.095e-02, eta: 3 days, 16:01:10, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5189, loss_cls: 4.2160, loss: 4.2160 +2024-12-27 15:15:20,198 - pyskl - INFO - Epoch [44][600/3746] lr: 8.093e-02, eta: 3 days, 16:00:05, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5173, loss_cls: 4.2360, loss: 4.2360 +2024-12-27 15:16:45,256 - pyskl - INFO - Epoch [44][700/3746] lr: 8.090e-02, eta: 3 days, 15:59:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5284, loss_cls: 4.1956, loss: 4.1956 +2024-12-27 15:18:10,855 - pyskl - INFO - Epoch [44][800/3746] lr: 8.088e-02, eta: 3 days, 15:57:57, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5144, loss_cls: 4.2520, loss: 4.2520 +2024-12-27 15:19:35,946 - pyskl - INFO - Epoch [44][900/3746] lr: 8.086e-02, eta: 3 days, 15:56:52, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5184, loss_cls: 4.2290, loss: 4.2290 +2024-12-27 15:21:00,607 - pyskl - INFO - Epoch [44][1000/3746] lr: 8.084e-02, eta: 3 days, 15:55:47, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5162, loss_cls: 4.2458, loss: 4.2458 +2024-12-27 15:22:26,452 - pyskl - INFO - Epoch [44][1100/3746] lr: 8.082e-02, eta: 3 days, 15:54:44, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5225, loss_cls: 4.2070, loss: 4.2070 +2024-12-27 15:23:51,901 - pyskl - INFO - Epoch [44][1200/3746] lr: 8.079e-02, eta: 3 days, 15:53:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5328, loss_cls: 4.1541, loss: 4.1541 +2024-12-27 15:25:17,401 - pyskl - INFO - Epoch [44][1300/3746] lr: 8.077e-02, eta: 3 days, 15:52:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5173, loss_cls: 4.2443, loss: 4.2443 +2024-12-27 15:26:43,082 - pyskl - INFO - Epoch [44][1400/3746] lr: 8.075e-02, eta: 3 days, 15:51:33, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5270, loss_cls: 4.2212, loss: 4.2212 +2024-12-27 15:28:08,029 - pyskl - INFO - Epoch [44][1500/3746] lr: 8.073e-02, eta: 3 days, 15:50:28, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5081, loss_cls: 4.2626, loss: 4.2626 +2024-12-27 15:29:33,652 - pyskl - INFO - Epoch [44][1600/3746] lr: 8.071e-02, eta: 3 days, 15:49:25, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5139, loss_cls: 4.2137, loss: 4.2137 +2024-12-27 15:30:58,902 - pyskl - INFO - Epoch [44][1700/3746] lr: 8.068e-02, eta: 3 days, 15:48:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5178, loss_cls: 4.2167, loss: 4.2167 +2024-12-27 15:32:24,142 - pyskl - INFO - Epoch [44][1800/3746] lr: 8.066e-02, eta: 3 days, 15:47:16, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5233, loss_cls: 4.2011, loss: 4.2011 +2024-12-27 15:33:49,780 - pyskl - INFO - Epoch [44][1900/3746] lr: 8.064e-02, eta: 3 days, 15:46:13, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5342, loss_cls: 4.1398, loss: 4.1398 +2024-12-27 15:35:14,932 - pyskl - INFO - Epoch [44][2000/3746] lr: 8.062e-02, eta: 3 days, 15:45:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5131, loss_cls: 4.2480, loss: 4.2480 +2024-12-27 15:36:40,345 - pyskl - INFO - Epoch [44][2100/3746] lr: 8.060e-02, eta: 3 days, 15:44:04, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5292, loss_cls: 4.1875, loss: 4.1875 +2024-12-27 15:38:06,193 - pyskl - INFO - Epoch [44][2200/3746] lr: 8.057e-02, eta: 3 days, 15:43:01, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5095, loss_cls: 4.2768, loss: 4.2768 +2024-12-27 15:39:31,744 - pyskl - INFO - Epoch [44][2300/3746] lr: 8.055e-02, eta: 3 days, 15:41:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5172, loss_cls: 4.2612, loss: 4.2612 +2024-12-27 15:40:57,739 - pyskl - INFO - Epoch [44][2400/3746] lr: 8.053e-02, eta: 3 days, 15:40:55, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5223, loss_cls: 4.1946, loss: 4.1946 +2024-12-27 15:42:23,037 - pyskl - INFO - Epoch [44][2500/3746] lr: 8.051e-02, eta: 3 days, 15:39:50, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5238, loss_cls: 4.1898, loss: 4.1898 +2024-12-27 15:43:48,034 - pyskl - INFO - Epoch [44][2600/3746] lr: 8.048e-02, eta: 3 days, 15:38:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5172, loss_cls: 4.2484, loss: 4.2484 +2024-12-27 15:45:13,776 - pyskl - INFO - Epoch [44][2700/3746] lr: 8.046e-02, eta: 3 days, 15:37:41, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5131, loss_cls: 4.2221, loss: 4.2221 +2024-12-27 15:46:39,305 - pyskl - INFO - Epoch [44][2800/3746] lr: 8.044e-02, eta: 3 days, 15:36:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5127, loss_cls: 4.2412, loss: 4.2412 +2024-12-27 15:48:05,262 - pyskl - INFO - Epoch [44][2900/3746] lr: 8.042e-02, eta: 3 days, 15:35:35, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5202, loss_cls: 4.2233, loss: 4.2233 +2024-12-27 15:49:31,081 - pyskl - INFO - Epoch [44][3000/3746] lr: 8.040e-02, eta: 3 days, 15:34:31, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5269, loss_cls: 4.1849, loss: 4.1849 +2024-12-27 15:50:56,666 - pyskl - INFO - Epoch [44][3100/3746] lr: 8.037e-02, eta: 3 days, 15:33:27, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5120, loss_cls: 4.2467, loss: 4.2467 +2024-12-27 15:52:22,278 - pyskl - INFO - Epoch [44][3200/3746] lr: 8.035e-02, eta: 3 days, 15:32:23, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5286, loss_cls: 4.1517, loss: 4.1517 +2024-12-27 15:53:47,889 - pyskl - INFO - Epoch [44][3300/3746] lr: 8.033e-02, eta: 3 days, 15:31:20, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5123, loss_cls: 4.2408, loss: 4.2408 +2024-12-27 15:55:13,131 - pyskl - INFO - Epoch [44][3400/3746] lr: 8.031e-02, eta: 3 days, 15:30:15, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5177, loss_cls: 4.2416, loss: 4.2416 +2024-12-27 15:56:38,516 - pyskl - INFO - Epoch [44][3500/3746] lr: 8.028e-02, eta: 3 days, 15:29:10, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5275, loss_cls: 4.1942, loss: 4.1942 +2024-12-27 15:58:04,135 - pyskl - INFO - Epoch [44][3600/3746] lr: 8.026e-02, eta: 3 days, 15:28:06, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5209, loss_cls: 4.2040, loss: 4.2040 +2024-12-27 15:59:30,236 - pyskl - INFO - Epoch [44][3700/3746] lr: 8.024e-02, eta: 3 days, 15:27:04, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5212, loss_cls: 4.2033, loss: 4.2033 +2024-12-27 16:00:11,177 - pyskl - INFO - Saving checkpoint at 44 epochs +2024-12-27 16:02:09,656 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 16:02:10,616 - pyskl - INFO - +top1_acc 0.2002 +top5_acc 0.4274 +2024-12-27 16:02:10,617 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 16:02:10,657 - pyskl - INFO - +mean_acc 0.1999 +2024-12-27 16:02:10,670 - pyskl - INFO - Epoch(val) [44][309] top1_acc: 0.2002, top5_acc: 0.4274, mean_class_accuracy: 0.1999 +2024-12-27 16:06:26,321 - pyskl - INFO - Epoch [45][100/3746] lr: 8.021e-02, eta: 3 days, 15:30:44, time: 2.556, data_time: 1.519, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5406, loss_cls: 4.1477, loss: 4.1477 +2024-12-27 16:07:51,536 - pyskl - INFO - Epoch [45][200/3746] lr: 8.019e-02, eta: 3 days, 15:29:39, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5148, loss_cls: 4.2307, loss: 4.2307 +2024-12-27 16:09:16,095 - pyskl - INFO - Epoch [45][300/3746] lr: 8.016e-02, eta: 3 days, 15:28:32, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5247, loss_cls: 4.1861, loss: 4.1861 +2024-12-27 16:10:40,676 - pyskl - INFO - Epoch [45][400/3746] lr: 8.014e-02, eta: 3 days, 15:27:25, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5186, loss_cls: 4.2155, loss: 4.2155 +2024-12-27 16:12:05,256 - pyskl - INFO - Epoch [45][500/3746] lr: 8.012e-02, eta: 3 days, 15:26:19, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5209, loss_cls: 4.1989, loss: 4.1989 +2024-12-27 16:13:30,351 - pyskl - INFO - Epoch [45][600/3746] lr: 8.010e-02, eta: 3 days, 15:25:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5205, loss_cls: 4.1902, loss: 4.1902 +2024-12-27 16:14:55,882 - pyskl - INFO - Epoch [45][700/3746] lr: 8.007e-02, eta: 3 days, 15:24:08, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5286, loss_cls: 4.2169, loss: 4.2169 +2024-12-27 16:16:20,912 - pyskl - INFO - Epoch [45][800/3746] lr: 8.005e-02, eta: 3 days, 15:23:02, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5181, loss_cls: 4.2071, loss: 4.2071 +2024-12-27 16:17:46,271 - pyskl - INFO - Epoch [45][900/3746] lr: 8.003e-02, eta: 3 days, 15:21:57, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5283, loss_cls: 4.1569, loss: 4.1569 +2024-12-27 16:19:11,078 - pyskl - INFO - Epoch [45][1000/3746] lr: 8.001e-02, eta: 3 days, 15:20:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5188, loss_cls: 4.2053, loss: 4.2053 +2024-12-27 16:20:35,479 - pyskl - INFO - Epoch [45][1100/3746] lr: 7.998e-02, eta: 3 days, 15:19:43, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5219, loss_cls: 4.2266, loss: 4.2266 +2024-12-27 16:22:00,157 - pyskl - INFO - Epoch [45][1200/3746] lr: 7.996e-02, eta: 3 days, 15:18:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5205, loss_cls: 4.1902, loss: 4.1902 +2024-12-27 16:23:25,621 - pyskl - INFO - Epoch [45][1300/3746] lr: 7.994e-02, eta: 3 days, 15:17:32, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5292, loss_cls: 4.1786, loss: 4.1786 +2024-12-27 16:24:50,579 - pyskl - INFO - Epoch [45][1400/3746] lr: 7.992e-02, eta: 3 days, 15:16:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5261, loss_cls: 4.1934, loss: 4.1934 +2024-12-27 16:26:15,242 - pyskl - INFO - Epoch [45][1500/3746] lr: 7.990e-02, eta: 3 days, 15:15:19, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5178, loss_cls: 4.2033, loss: 4.2033 +2024-12-27 16:27:40,278 - pyskl - INFO - Epoch [45][1600/3746] lr: 7.987e-02, eta: 3 days, 15:14:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5209, loss_cls: 4.2054, loss: 4.2054 +2024-12-27 16:29:05,007 - pyskl - INFO - Epoch [45][1700/3746] lr: 7.985e-02, eta: 3 days, 15:13:06, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5309, loss_cls: 4.1772, loss: 4.1772 +2024-12-27 16:30:30,206 - pyskl - INFO - Epoch [45][1800/3746] lr: 7.983e-02, eta: 3 days, 15:12:00, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5172, loss_cls: 4.2302, loss: 4.2302 +2024-12-27 16:31:55,426 - pyskl - INFO - Epoch [45][1900/3746] lr: 7.981e-02, eta: 3 days, 15:10:54, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5159, loss_cls: 4.2270, loss: 4.2270 +2024-12-27 16:33:20,693 - pyskl - INFO - Epoch [45][2000/3746] lr: 7.978e-02, eta: 3 days, 15:09:49, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5123, loss_cls: 4.2482, loss: 4.2482 +2024-12-27 16:34:46,436 - pyskl - INFO - Epoch [45][2100/3746] lr: 7.976e-02, eta: 3 days, 15:08:44, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5216, loss_cls: 4.2191, loss: 4.2191 +2024-12-27 16:36:11,509 - pyskl - INFO - Epoch [45][2200/3746] lr: 7.974e-02, eta: 3 days, 15:07:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5311, loss_cls: 4.1996, loss: 4.1996 +2024-12-27 16:37:37,030 - pyskl - INFO - Epoch [45][2300/3746] lr: 7.972e-02, eta: 3 days, 15:06:33, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5181, loss_cls: 4.2222, loss: 4.2222 +2024-12-27 16:39:02,643 - pyskl - INFO - Epoch [45][2400/3746] lr: 7.969e-02, eta: 3 days, 15:05:28, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5241, loss_cls: 4.2028, loss: 4.2028 +2024-12-27 16:40:28,125 - pyskl - INFO - Epoch [45][2500/3746] lr: 7.967e-02, eta: 3 days, 15:04:23, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5272, loss_cls: 4.1866, loss: 4.1866 +2024-12-27 16:41:53,980 - pyskl - INFO - Epoch [45][2600/3746] lr: 7.965e-02, eta: 3 days, 15:03:19, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5136, loss_cls: 4.2573, loss: 4.2573 +2024-12-27 16:43:19,183 - pyskl - INFO - Epoch [45][2700/3746] lr: 7.963e-02, eta: 3 days, 15:02:13, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5277, loss_cls: 4.1984, loss: 4.1984 +2024-12-27 16:44:44,840 - pyskl - INFO - Epoch [45][2800/3746] lr: 7.960e-02, eta: 3 days, 15:01:08, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5170, loss_cls: 4.2430, loss: 4.2430 +2024-12-27 16:46:10,280 - pyskl - INFO - Epoch [45][2900/3746] lr: 7.958e-02, eta: 3 days, 15:00:03, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5209, loss_cls: 4.2003, loss: 4.2003 +2024-12-27 16:47:35,760 - pyskl - INFO - Epoch [45][3000/3746] lr: 7.956e-02, eta: 3 days, 14:58:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5295, loss_cls: 4.1605, loss: 4.1605 +2024-12-27 16:49:01,024 - pyskl - INFO - Epoch [45][3100/3746] lr: 7.954e-02, eta: 3 days, 14:57:51, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5305, loss_cls: 4.1714, loss: 4.1714 +2024-12-27 16:50:26,405 - pyskl - INFO - Epoch [45][3200/3746] lr: 7.951e-02, eta: 3 days, 14:56:46, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5261, loss_cls: 4.1892, loss: 4.1892 +2024-12-27 16:51:51,746 - pyskl - INFO - Epoch [45][3300/3746] lr: 7.949e-02, eta: 3 days, 14:55:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5112, loss_cls: 4.2385, loss: 4.2385 +2024-12-27 16:53:16,517 - pyskl - INFO - Epoch [45][3400/3746] lr: 7.947e-02, eta: 3 days, 14:54:33, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5248, loss_cls: 4.1733, loss: 4.1733 +2024-12-27 16:54:41,127 - pyskl - INFO - Epoch [45][3500/3746] lr: 7.945e-02, eta: 3 days, 14:53:26, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5148, loss_cls: 4.1954, loss: 4.1954 +2024-12-27 16:56:05,525 - pyskl - INFO - Epoch [45][3600/3746] lr: 7.942e-02, eta: 3 days, 14:52:18, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5202, loss_cls: 4.2351, loss: 4.2351 +2024-12-27 16:57:30,823 - pyskl - INFO - Epoch [45][3700/3746] lr: 7.940e-02, eta: 3 days, 14:51:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5144, loss_cls: 4.2691, loss: 4.2691 +2024-12-27 16:58:11,645 - pyskl - INFO - Saving checkpoint at 45 epochs +2024-12-27 17:00:08,857 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 17:00:09,670 - pyskl - INFO - +top1_acc 0.2098 +top5_acc 0.4419 +2024-12-27 17:00:09,670 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 17:00:09,721 - pyskl - INFO - +mean_acc 0.2095 +2024-12-27 17:00:09,734 - pyskl - INFO - Epoch(val) [45][309] top1_acc: 0.2098, top5_acc: 0.4419, mean_class_accuracy: 0.2095 +2024-12-27 17:04:25,725 - pyskl - INFO - Epoch [46][100/3746] lr: 7.937e-02, eta: 3 days, 14:54:42, time: 2.560, data_time: 1.518, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5238, loss_cls: 4.1657, loss: 4.1657 +2024-12-27 17:05:51,059 - pyskl - INFO - Epoch [46][200/3746] lr: 7.934e-02, eta: 3 days, 14:53:36, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5228, loss_cls: 4.1901, loss: 4.1901 +2024-12-27 17:07:16,305 - pyskl - INFO - Epoch [46][300/3746] lr: 7.932e-02, eta: 3 days, 14:52:29, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5300, loss_cls: 4.1866, loss: 4.1866 +2024-12-27 17:08:41,524 - pyskl - INFO - Epoch [46][400/3746] lr: 7.930e-02, eta: 3 days, 14:51:23, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5252, loss_cls: 4.1879, loss: 4.1879 +2024-12-27 17:10:07,123 - pyskl - INFO - Epoch [46][500/3746] lr: 7.928e-02, eta: 3 days, 14:50:17, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5205, loss_cls: 4.2101, loss: 4.2101 +2024-12-27 17:11:32,630 - pyskl - INFO - Epoch [46][600/3746] lr: 7.925e-02, eta: 3 days, 14:49:12, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5178, loss_cls: 4.1951, loss: 4.1951 +2024-12-27 17:12:58,116 - pyskl - INFO - Epoch [46][700/3746] lr: 7.923e-02, eta: 3 days, 14:48:06, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5255, loss_cls: 4.1647, loss: 4.1647 +2024-12-27 17:14:24,031 - pyskl - INFO - Epoch [46][800/3746] lr: 7.921e-02, eta: 3 days, 14:47:01, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5091, loss_cls: 4.2639, loss: 4.2639 +2024-12-27 17:15:49,515 - pyskl - INFO - Epoch [46][900/3746] lr: 7.919e-02, eta: 3 days, 14:45:55, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5202, loss_cls: 4.2089, loss: 4.2089 +2024-12-27 17:17:14,386 - pyskl - INFO - Epoch [46][1000/3746] lr: 7.916e-02, eta: 3 days, 14:44:47, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5167, loss_cls: 4.2590, loss: 4.2590 +2024-12-27 17:18:39,076 - pyskl - INFO - Epoch [46][1100/3746] lr: 7.914e-02, eta: 3 days, 14:43:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5262, loss_cls: 4.1800, loss: 4.1800 +2024-12-27 17:20:04,457 - pyskl - INFO - Epoch [46][1200/3746] lr: 7.912e-02, eta: 3 days, 14:42:33, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5258, loss_cls: 4.2041, loss: 4.2041 +2024-12-27 17:21:29,745 - pyskl - INFO - Epoch [46][1300/3746] lr: 7.909e-02, eta: 3 days, 14:41:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5144, loss_cls: 4.2312, loss: 4.2312 +2024-12-27 17:22:55,178 - pyskl - INFO - Epoch [46][1400/3746] lr: 7.907e-02, eta: 3 days, 14:40:21, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5323, loss_cls: 4.1760, loss: 4.1760 +2024-12-27 17:24:20,731 - pyskl - INFO - Epoch [46][1500/3746] lr: 7.905e-02, eta: 3 days, 14:39:15, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5188, loss_cls: 4.2181, loss: 4.2181 +2024-12-27 17:25:46,210 - pyskl - INFO - Epoch [46][1600/3746] lr: 7.903e-02, eta: 3 days, 14:38:09, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5256, loss_cls: 4.1562, loss: 4.1562 +2024-12-27 17:27:11,684 - pyskl - INFO - Epoch [46][1700/3746] lr: 7.900e-02, eta: 3 days, 14:37:02, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5305, loss_cls: 4.2087, loss: 4.2087 +2024-12-27 17:28:36,859 - pyskl - INFO - Epoch [46][1800/3746] lr: 7.898e-02, eta: 3 days, 14:35:56, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5245, loss_cls: 4.1976, loss: 4.1976 +2024-12-27 17:30:01,553 - pyskl - INFO - Epoch [46][1900/3746] lr: 7.896e-02, eta: 3 days, 14:34:48, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5242, loss_cls: 4.2091, loss: 4.2091 +2024-12-27 17:31:26,682 - pyskl - INFO - Epoch [46][2000/3746] lr: 7.894e-02, eta: 3 days, 14:33:41, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5227, loss_cls: 4.1930, loss: 4.1930 +2024-12-27 17:32:52,041 - pyskl - INFO - Epoch [46][2100/3746] lr: 7.891e-02, eta: 3 days, 14:32:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5209, loss_cls: 4.2033, loss: 4.2033 +2024-12-27 17:34:17,148 - pyskl - INFO - Epoch [46][2200/3746] lr: 7.889e-02, eta: 3 days, 14:31:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5242, loss_cls: 4.1932, loss: 4.1932 +2024-12-27 17:35:42,192 - pyskl - INFO - Epoch [46][2300/3746] lr: 7.887e-02, eta: 3 days, 14:30:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5353, loss_cls: 4.1547, loss: 4.1547 +2024-12-27 17:37:07,671 - pyskl - INFO - Epoch [46][2400/3746] lr: 7.884e-02, eta: 3 days, 14:29:13, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5244, loss_cls: 4.2027, loss: 4.2027 +2024-12-27 17:38:33,077 - pyskl - INFO - Epoch [46][2500/3746] lr: 7.882e-02, eta: 3 days, 14:28:07, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5228, loss_cls: 4.2131, loss: 4.2131 +2024-12-27 17:39:58,609 - pyskl - INFO - Epoch [46][2600/3746] lr: 7.880e-02, eta: 3 days, 14:27:01, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5255, loss_cls: 4.1775, loss: 4.1775 +2024-12-27 17:41:24,122 - pyskl - INFO - Epoch [46][2700/3746] lr: 7.878e-02, eta: 3 days, 14:25:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5203, loss_cls: 4.2417, loss: 4.2417 +2024-12-27 17:42:49,718 - pyskl - INFO - Epoch [46][2800/3746] lr: 7.875e-02, eta: 3 days, 14:24:48, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5270, loss_cls: 4.1685, loss: 4.1685 +2024-12-27 17:44:15,443 - pyskl - INFO - Epoch [46][2900/3746] lr: 7.873e-02, eta: 3 days, 14:23:42, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5306, loss_cls: 4.1775, loss: 4.1775 +2024-12-27 17:45:41,360 - pyskl - INFO - Epoch [46][3000/3746] lr: 7.871e-02, eta: 3 days, 14:22:37, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5142, loss_cls: 4.2147, loss: 4.2147 +2024-12-27 17:47:06,550 - pyskl - INFO - Epoch [46][3100/3746] lr: 7.868e-02, eta: 3 days, 14:21:30, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5269, loss_cls: 4.1712, loss: 4.1712 +2024-12-27 17:48:31,890 - pyskl - INFO - Epoch [46][3200/3746] lr: 7.866e-02, eta: 3 days, 14:20:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5267, loss_cls: 4.1836, loss: 4.1836 +2024-12-27 17:49:57,491 - pyskl - INFO - Epoch [46][3300/3746] lr: 7.864e-02, eta: 3 days, 14:19:17, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5256, loss_cls: 4.1953, loss: 4.1953 +2024-12-27 17:51:22,156 - pyskl - INFO - Epoch [46][3400/3746] lr: 7.862e-02, eta: 3 days, 14:18:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5284, loss_cls: 4.1943, loss: 4.1943 +2024-12-27 17:52:46,903 - pyskl - INFO - Epoch [46][3500/3746] lr: 7.859e-02, eta: 3 days, 14:17:00, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5233, loss_cls: 4.1970, loss: 4.1970 +2024-12-27 17:54:12,264 - pyskl - INFO - Epoch [46][3600/3746] lr: 7.857e-02, eta: 3 days, 14:15:54, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5241, loss_cls: 4.2025, loss: 4.2025 +2024-12-27 17:55:37,871 - pyskl - INFO - Epoch [46][3700/3746] lr: 7.855e-02, eta: 3 days, 14:14:47, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5228, loss_cls: 4.1992, loss: 4.1992 +2024-12-27 17:56:19,262 - pyskl - INFO - Saving checkpoint at 46 epochs +2024-12-27 17:58:17,127 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 17:58:17,846 - pyskl - INFO - +top1_acc 0.2088 +top5_acc 0.4404 +2024-12-27 17:58:17,846 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 17:58:17,894 - pyskl - INFO - +mean_acc 0.2086 +2024-12-27 17:58:17,907 - pyskl - INFO - Epoch(val) [46][309] top1_acc: 0.2088, top5_acc: 0.4404, mean_class_accuracy: 0.2086 +2024-12-27 18:02:33,153 - pyskl - INFO - Epoch [47][100/3746] lr: 7.851e-02, eta: 3 days, 14:18:05, time: 2.552, data_time: 1.511, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5228, loss_cls: 4.1888, loss: 4.1888 +2024-12-27 18:03:57,916 - pyskl - INFO - Epoch [47][200/3746] lr: 7.849e-02, eta: 3 days, 14:16:56, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5261, loss_cls: 4.1657, loss: 4.1657 +2024-12-27 18:05:22,863 - pyskl - INFO - Epoch [47][300/3746] lr: 7.847e-02, eta: 3 days, 14:15:48, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5314, loss_cls: 4.1529, loss: 4.1529 +2024-12-27 18:06:47,789 - pyskl - INFO - Epoch [47][400/3746] lr: 7.844e-02, eta: 3 days, 14:14:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5288, loss_cls: 4.1688, loss: 4.1688 +2024-12-27 18:08:12,591 - pyskl - INFO - Epoch [47][500/3746] lr: 7.842e-02, eta: 3 days, 14:13:31, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5269, loss_cls: 4.1867, loss: 4.1867 +2024-12-27 18:09:37,604 - pyskl - INFO - Epoch [47][600/3746] lr: 7.840e-02, eta: 3 days, 14:12:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5141, loss_cls: 4.2485, loss: 4.2485 +2024-12-27 18:11:02,637 - pyskl - INFO - Epoch [47][700/3746] lr: 7.838e-02, eta: 3 days, 14:11:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5281, loss_cls: 4.1803, loss: 4.1803 +2024-12-27 18:12:27,405 - pyskl - INFO - Epoch [47][800/3746] lr: 7.835e-02, eta: 3 days, 14:10:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5283, loss_cls: 4.1922, loss: 4.1922 +2024-12-27 18:13:52,366 - pyskl - INFO - Epoch [47][900/3746] lr: 7.833e-02, eta: 3 days, 14:08:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5275, loss_cls: 4.1656, loss: 4.1656 +2024-12-27 18:15:17,398 - pyskl - INFO - Epoch [47][1000/3746] lr: 7.831e-02, eta: 3 days, 14:07:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5377, loss_cls: 4.1570, loss: 4.1570 +2024-12-27 18:16:41,812 - pyskl - INFO - Epoch [47][1100/3746] lr: 7.828e-02, eta: 3 days, 14:06:41, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5248, loss_cls: 4.1941, loss: 4.1941 +2024-12-27 18:18:06,391 - pyskl - INFO - Epoch [47][1200/3746] lr: 7.826e-02, eta: 3 days, 14:05:32, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5234, loss_cls: 4.1459, loss: 4.1459 +2024-12-27 18:19:31,201 - pyskl - INFO - Epoch [47][1300/3746] lr: 7.824e-02, eta: 3 days, 14:04:23, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5225, loss_cls: 4.2021, loss: 4.2021 +2024-12-27 18:20:55,794 - pyskl - INFO - Epoch [47][1400/3746] lr: 7.821e-02, eta: 3 days, 14:03:14, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5139, loss_cls: 4.2120, loss: 4.2120 +2024-12-27 18:22:20,446 - pyskl - INFO - Epoch [47][1500/3746] lr: 7.819e-02, eta: 3 days, 14:02:05, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5352, loss_cls: 4.1715, loss: 4.1715 +2024-12-27 18:23:44,862 - pyskl - INFO - Epoch [47][1600/3746] lr: 7.817e-02, eta: 3 days, 14:00:56, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5181, loss_cls: 4.2203, loss: 4.2203 +2024-12-27 18:25:09,589 - pyskl - INFO - Epoch [47][1700/3746] lr: 7.814e-02, eta: 3 days, 13:59:47, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5209, loss_cls: 4.1884, loss: 4.1884 +2024-12-27 18:26:34,155 - pyskl - INFO - Epoch [47][1800/3746] lr: 7.812e-02, eta: 3 days, 13:58:38, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5211, loss_cls: 4.2218, loss: 4.2218 +2024-12-27 18:27:58,945 - pyskl - INFO - Epoch [47][1900/3746] lr: 7.810e-02, eta: 3 days, 13:57:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5156, loss_cls: 4.2435, loss: 4.2435 +2024-12-27 18:29:23,746 - pyskl - INFO - Epoch [47][2000/3746] lr: 7.808e-02, eta: 3 days, 13:56:20, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5198, loss_cls: 4.2281, loss: 4.2281 +2024-12-27 18:30:49,092 - pyskl - INFO - Epoch [47][2100/3746] lr: 7.805e-02, eta: 3 days, 13:55:13, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5242, loss_cls: 4.1823, loss: 4.1823 +2024-12-27 18:32:14,164 - pyskl - INFO - Epoch [47][2200/3746] lr: 7.803e-02, eta: 3 days, 13:54:04, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5298, loss_cls: 4.1850, loss: 4.1850 +2024-12-27 18:33:39,458 - pyskl - INFO - Epoch [47][2300/3746] lr: 7.801e-02, eta: 3 days, 13:52:57, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5345, loss_cls: 4.1392, loss: 4.1392 +2024-12-27 18:35:04,829 - pyskl - INFO - Epoch [47][2400/3746] lr: 7.798e-02, eta: 3 days, 13:51:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5241, loss_cls: 4.2151, loss: 4.2151 +2024-12-27 18:36:30,125 - pyskl - INFO - Epoch [47][2500/3746] lr: 7.796e-02, eta: 3 days, 13:50:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5231, loss_cls: 4.1803, loss: 4.1803 +2024-12-27 18:37:55,178 - pyskl - INFO - Epoch [47][2600/3746] lr: 7.794e-02, eta: 3 days, 13:49:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5230, loss_cls: 4.2355, loss: 4.2355 +2024-12-27 18:39:20,261 - pyskl - INFO - Epoch [47][2700/3746] lr: 7.791e-02, eta: 3 days, 13:48:25, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5175, loss_cls: 4.2288, loss: 4.2288 +2024-12-27 18:40:45,541 - pyskl - INFO - Epoch [47][2800/3746] lr: 7.789e-02, eta: 3 days, 13:47:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5220, loss_cls: 4.2071, loss: 4.2071 +2024-12-27 18:42:10,378 - pyskl - INFO - Epoch [47][2900/3746] lr: 7.787e-02, eta: 3 days, 13:46:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5300, loss_cls: 4.1553, loss: 4.1553 +2024-12-27 18:43:35,496 - pyskl - INFO - Epoch [47][3000/3746] lr: 7.784e-02, eta: 3 days, 13:45:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5223, loss_cls: 4.2279, loss: 4.2279 +2024-12-27 18:45:00,306 - pyskl - INFO - Epoch [47][3100/3746] lr: 7.782e-02, eta: 3 days, 13:43:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5333, loss_cls: 4.1465, loss: 4.1465 +2024-12-27 18:46:25,725 - pyskl - INFO - Epoch [47][3200/3746] lr: 7.780e-02, eta: 3 days, 13:42:43, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5234, loss_cls: 4.2125, loss: 4.2125 +2024-12-27 18:47:50,897 - pyskl - INFO - Epoch [47][3300/3746] lr: 7.777e-02, eta: 3 days, 13:41:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5280, loss_cls: 4.1780, loss: 4.1780 +2024-12-27 18:49:15,800 - pyskl - INFO - Epoch [47][3400/3746] lr: 7.775e-02, eta: 3 days, 13:40:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5245, loss_cls: 4.2408, loss: 4.2408 +2024-12-27 18:50:40,652 - pyskl - INFO - Epoch [47][3500/3746] lr: 7.773e-02, eta: 3 days, 13:39:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5233, loss_cls: 4.1697, loss: 4.1697 +2024-12-27 18:52:05,613 - pyskl - INFO - Epoch [47][3600/3746] lr: 7.770e-02, eta: 3 days, 13:38:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5266, loss_cls: 4.1663, loss: 4.1663 +2024-12-27 18:53:30,618 - pyskl - INFO - Epoch [47][3700/3746] lr: 7.768e-02, eta: 3 days, 13:37:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5155, loss_cls: 4.2229, loss: 4.2229 +2024-12-27 18:54:11,997 - pyskl - INFO - Saving checkpoint at 47 epochs +2024-12-27 18:56:09,972 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 18:56:10,719 - pyskl - INFO - +top1_acc 0.2014 +top5_acc 0.4341 +2024-12-27 18:56:10,719 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 18:56:10,764 - pyskl - INFO - +mean_acc 0.2011 +2024-12-27 18:56:10,779 - pyskl - INFO - Epoch(val) [47][309] top1_acc: 0.2014, top5_acc: 0.4341, mean_class_accuracy: 0.2011 +2024-12-27 19:00:29,255 - pyskl - INFO - Epoch [48][100/3746] lr: 7.765e-02, eta: 3 days, 13:40:14, time: 2.585, data_time: 1.547, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5348, loss_cls: 4.1477, loss: 4.1477 +2024-12-27 19:01:54,945 - pyskl - INFO - Epoch [48][200/3746] lr: 7.762e-02, eta: 3 days, 13:39:07, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5286, loss_cls: 4.1166, loss: 4.1166 +2024-12-27 19:03:19,755 - pyskl - INFO - Epoch [48][300/3746] lr: 7.760e-02, eta: 3 days, 13:37:57, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5303, loss_cls: 4.1435, loss: 4.1435 +2024-12-27 19:04:45,011 - pyskl - INFO - Epoch [48][400/3746] lr: 7.758e-02, eta: 3 days, 13:36:49, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5394, loss_cls: 4.1169, loss: 4.1169 +2024-12-27 19:06:10,963 - pyskl - INFO - Epoch [48][500/3746] lr: 7.755e-02, eta: 3 days, 13:35:42, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5352, loss_cls: 4.1355, loss: 4.1355 +2024-12-27 19:07:36,494 - pyskl - INFO - Epoch [48][600/3746] lr: 7.753e-02, eta: 3 days, 13:34:34, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5200, loss_cls: 4.1819, loss: 4.1819 +2024-12-27 19:09:01,984 - pyskl - INFO - Epoch [48][700/3746] lr: 7.751e-02, eta: 3 days, 13:33:26, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5166, loss_cls: 4.2374, loss: 4.2374 +2024-12-27 19:10:27,693 - pyskl - INFO - Epoch [48][800/3746] lr: 7.748e-02, eta: 3 days, 13:32:19, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5311, loss_cls: 4.1409, loss: 4.1409 +2024-12-27 19:11:53,483 - pyskl - INFO - Epoch [48][900/3746] lr: 7.746e-02, eta: 3 days, 13:31:12, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5264, loss_cls: 4.1609, loss: 4.1609 +2024-12-27 19:13:18,761 - pyskl - INFO - Epoch [48][1000/3746] lr: 7.744e-02, eta: 3 days, 13:30:03, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5202, loss_cls: 4.1913, loss: 4.1913 +2024-12-27 19:14:43,219 - pyskl - INFO - Epoch [48][1100/3746] lr: 7.741e-02, eta: 3 days, 13:28:53, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5275, loss_cls: 4.1824, loss: 4.1824 +2024-12-27 19:16:07,970 - pyskl - INFO - Epoch [48][1200/3746] lr: 7.739e-02, eta: 3 days, 13:27:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5198, loss_cls: 4.1895, loss: 4.1895 +2024-12-27 19:17:32,508 - pyskl - INFO - Epoch [48][1300/3746] lr: 7.737e-02, eta: 3 days, 13:26:33, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5270, loss_cls: 4.1771, loss: 4.1771 +2024-12-27 19:18:57,180 - pyskl - INFO - Epoch [48][1400/3746] lr: 7.734e-02, eta: 3 days, 13:25:23, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5288, loss_cls: 4.1860, loss: 4.1860 +2024-12-27 19:20:21,749 - pyskl - INFO - Epoch [48][1500/3746] lr: 7.732e-02, eta: 3 days, 13:24:13, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5245, loss_cls: 4.1963, loss: 4.1963 +2024-12-27 19:21:45,807 - pyskl - INFO - Epoch [48][1600/3746] lr: 7.730e-02, eta: 3 days, 13:23:02, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5244, loss_cls: 4.2095, loss: 4.2095 +2024-12-27 19:23:10,808 - pyskl - INFO - Epoch [48][1700/3746] lr: 7.727e-02, eta: 3 days, 13:21:53, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5280, loss_cls: 4.1865, loss: 4.1865 +2024-12-27 19:24:35,656 - pyskl - INFO - Epoch [48][1800/3746] lr: 7.725e-02, eta: 3 days, 13:20:43, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5236, loss_cls: 4.1972, loss: 4.1972 +2024-12-27 19:26:00,710 - pyskl - INFO - Epoch [48][1900/3746] lr: 7.723e-02, eta: 3 days, 13:19:34, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5127, loss_cls: 4.2541, loss: 4.2541 +2024-12-27 19:27:25,376 - pyskl - INFO - Epoch [48][2000/3746] lr: 7.720e-02, eta: 3 days, 13:18:24, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5191, loss_cls: 4.1853, loss: 4.1853 +2024-12-27 19:28:50,338 - pyskl - INFO - Epoch [48][2100/3746] lr: 7.718e-02, eta: 3 days, 13:17:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5203, loss_cls: 4.2006, loss: 4.2006 +2024-12-27 19:30:14,998 - pyskl - INFO - Epoch [48][2200/3746] lr: 7.716e-02, eta: 3 days, 13:16:05, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5197, loss_cls: 4.2569, loss: 4.2569 +2024-12-27 19:31:39,833 - pyskl - INFO - Epoch [48][2300/3746] lr: 7.713e-02, eta: 3 days, 13:14:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5266, loss_cls: 4.2093, loss: 4.2093 +2024-12-27 19:33:05,061 - pyskl - INFO - Epoch [48][2400/3746] lr: 7.711e-02, eta: 3 days, 13:13:46, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5233, loss_cls: 4.1873, loss: 4.1873 +2024-12-27 19:34:30,516 - pyskl - INFO - Epoch [48][2500/3746] lr: 7.709e-02, eta: 3 days, 13:12:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5220, loss_cls: 4.1932, loss: 4.1932 +2024-12-27 19:35:56,127 - pyskl - INFO - Epoch [48][2600/3746] lr: 7.706e-02, eta: 3 days, 13:11:30, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5356, loss_cls: 4.1431, loss: 4.1431 +2024-12-27 19:37:21,424 - pyskl - INFO - Epoch [48][2700/3746] lr: 7.704e-02, eta: 3 days, 13:10:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5258, loss_cls: 4.1897, loss: 4.1897 +2024-12-27 19:38:46,821 - pyskl - INFO - Epoch [48][2800/3746] lr: 7.701e-02, eta: 3 days, 13:09:13, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5325, loss_cls: 4.1853, loss: 4.1853 +2024-12-27 19:40:12,754 - pyskl - INFO - Epoch [48][2900/3746] lr: 7.699e-02, eta: 3 days, 13:08:05, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5242, loss_cls: 4.1961, loss: 4.1961 +2024-12-27 19:41:38,486 - pyskl - INFO - Epoch [48][3000/3746] lr: 7.697e-02, eta: 3 days, 13:06:57, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5350, loss_cls: 4.1404, loss: 4.1404 +2024-12-27 19:43:04,125 - pyskl - INFO - Epoch [48][3100/3746] lr: 7.694e-02, eta: 3 days, 13:05:49, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5188, loss_cls: 4.2085, loss: 4.2085 +2024-12-27 19:44:29,704 - pyskl - INFO - Epoch [48][3200/3746] lr: 7.692e-02, eta: 3 days, 13:04:41, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5305, loss_cls: 4.2007, loss: 4.2007 +2024-12-27 19:45:54,965 - pyskl - INFO - Epoch [48][3300/3746] lr: 7.690e-02, eta: 3 days, 13:03:32, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5386, loss_cls: 4.1155, loss: 4.1155 +2024-12-27 19:47:20,533 - pyskl - INFO - Epoch [48][3400/3746] lr: 7.687e-02, eta: 3 days, 13:02:24, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5284, loss_cls: 4.1619, loss: 4.1619 +2024-12-27 19:48:45,802 - pyskl - INFO - Epoch [48][3500/3746] lr: 7.685e-02, eta: 3 days, 13:01:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5233, loss_cls: 4.1954, loss: 4.1954 +2024-12-27 19:50:11,560 - pyskl - INFO - Epoch [48][3600/3746] lr: 7.683e-02, eta: 3 days, 13:00:07, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5155, loss_cls: 4.2267, loss: 4.2267 +2024-12-27 19:51:36,970 - pyskl - INFO - Epoch [48][3700/3746] lr: 7.680e-02, eta: 3 days, 12:58:58, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5208, loss_cls: 4.2349, loss: 4.2349 +2024-12-27 19:52:18,275 - pyskl - INFO - Saving checkpoint at 48 epochs +2024-12-27 19:54:15,897 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 19:54:16,593 - pyskl - INFO - +top1_acc 0.2081 +top5_acc 0.4384 +2024-12-27 19:54:16,593 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 19:54:16,643 - pyskl - INFO - +mean_acc 0.2080 +2024-12-27 19:54:16,659 - pyskl - INFO - Epoch(val) [48][309] top1_acc: 0.2081, top5_acc: 0.4384, mean_class_accuracy: 0.2080 +2024-12-27 19:58:37,062 - pyskl - INFO - Epoch [49][100/3746] lr: 7.677e-02, eta: 3 days, 13:02:06, time: 2.604, data_time: 1.552, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5259, loss_cls: 4.1957, loss: 4.1957 +2024-12-27 20:00:02,639 - pyskl - INFO - Epoch [49][200/3746] lr: 7.674e-02, eta: 3 days, 13:00:57, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5381, loss_cls: 4.1374, loss: 4.1374 +2024-12-27 20:01:27,364 - pyskl - INFO - Epoch [49][300/3746] lr: 7.672e-02, eta: 3 days, 12:59:47, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5236, loss_cls: 4.1765, loss: 4.1765 +2024-12-27 20:02:52,670 - pyskl - INFO - Epoch [49][400/3746] lr: 7.670e-02, eta: 3 days, 12:58:38, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5297, loss_cls: 4.1508, loss: 4.1508 +2024-12-27 20:04:18,288 - pyskl - INFO - Epoch [49][500/3746] lr: 7.667e-02, eta: 3 days, 12:57:29, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5264, loss_cls: 4.1667, loss: 4.1667 +2024-12-27 20:05:43,962 - pyskl - INFO - Epoch [49][600/3746] lr: 7.665e-02, eta: 3 days, 12:56:21, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5403, loss_cls: 4.1372, loss: 4.1372 +2024-12-27 20:07:09,611 - pyskl - INFO - Epoch [49][700/3746] lr: 7.663e-02, eta: 3 days, 12:55:12, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5267, loss_cls: 4.1740, loss: 4.1740 +2024-12-27 20:08:35,168 - pyskl - INFO - Epoch [49][800/3746] lr: 7.660e-02, eta: 3 days, 12:54:03, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5344, loss_cls: 4.1523, loss: 4.1523 +2024-12-27 20:10:00,435 - pyskl - INFO - Epoch [49][900/3746] lr: 7.658e-02, eta: 3 days, 12:52:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5283, loss_cls: 4.1709, loss: 4.1709 +2024-12-27 20:11:25,506 - pyskl - INFO - Epoch [49][1000/3746] lr: 7.656e-02, eta: 3 days, 12:51:44, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5289, loss_cls: 4.1880, loss: 4.1880 +2024-12-27 20:12:50,250 - pyskl - INFO - Epoch [49][1100/3746] lr: 7.653e-02, eta: 3 days, 12:50:34, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5292, loss_cls: 4.1813, loss: 4.1813 +2024-12-27 20:14:14,795 - pyskl - INFO - Epoch [49][1200/3746] lr: 7.651e-02, eta: 3 days, 12:49:23, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5234, loss_cls: 4.1832, loss: 4.1832 +2024-12-27 20:15:39,591 - pyskl - INFO - Epoch [49][1300/3746] lr: 7.648e-02, eta: 3 days, 12:48:12, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5267, loss_cls: 4.1669, loss: 4.1669 +2024-12-27 20:17:04,352 - pyskl - INFO - Epoch [49][1400/3746] lr: 7.646e-02, eta: 3 days, 12:47:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5272, loss_cls: 4.1722, loss: 4.1722 +2024-12-27 20:18:29,171 - pyskl - INFO - Epoch [49][1500/3746] lr: 7.644e-02, eta: 3 days, 12:45:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5175, loss_cls: 4.2590, loss: 4.2590 +2024-12-27 20:19:53,787 - pyskl - INFO - Epoch [49][1600/3746] lr: 7.641e-02, eta: 3 days, 12:44:40, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5342, loss_cls: 4.1240, loss: 4.1240 +2024-12-27 20:21:18,482 - pyskl - INFO - Epoch [49][1700/3746] lr: 7.639e-02, eta: 3 days, 12:43:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5336, loss_cls: 4.1435, loss: 4.1435 +2024-12-27 20:22:42,517 - pyskl - INFO - Epoch [49][1800/3746] lr: 7.637e-02, eta: 3 days, 12:42:18, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5241, loss_cls: 4.1912, loss: 4.1912 +2024-12-27 20:24:07,401 - pyskl - INFO - Epoch [49][1900/3746] lr: 7.634e-02, eta: 3 days, 12:41:07, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5191, loss_cls: 4.1980, loss: 4.1980 +2024-12-27 20:25:32,559 - pyskl - INFO - Epoch [49][2000/3746] lr: 7.632e-02, eta: 3 days, 12:39:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5189, loss_cls: 4.2410, loss: 4.2410 +2024-12-27 20:26:58,181 - pyskl - INFO - Epoch [49][2100/3746] lr: 7.629e-02, eta: 3 days, 12:38:49, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5363, loss_cls: 4.1171, loss: 4.1171 +2024-12-27 20:28:23,896 - pyskl - INFO - Epoch [49][2200/3746] lr: 7.627e-02, eta: 3 days, 12:37:40, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5297, loss_cls: 4.1536, loss: 4.1536 +2024-12-27 20:29:49,620 - pyskl - INFO - Epoch [49][2300/3746] lr: 7.625e-02, eta: 3 days, 12:36:31, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5241, loss_cls: 4.2198, loss: 4.2198 +2024-12-27 20:31:14,857 - pyskl - INFO - Epoch [49][2400/3746] lr: 7.622e-02, eta: 3 days, 12:35:22, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5177, loss_cls: 4.2439, loss: 4.2439 +2024-12-27 20:32:40,073 - pyskl - INFO - Epoch [49][2500/3746] lr: 7.620e-02, eta: 3 days, 12:34:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5216, loss_cls: 4.2011, loss: 4.2011 +2024-12-27 20:34:05,252 - pyskl - INFO - Epoch [49][2600/3746] lr: 7.618e-02, eta: 3 days, 12:33:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5192, loss_cls: 4.2291, loss: 4.2291 +2024-12-27 20:35:30,403 - pyskl - INFO - Epoch [49][2700/3746] lr: 7.615e-02, eta: 3 days, 12:31:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5255, loss_cls: 4.1407, loss: 4.1407 +2024-12-27 20:36:56,038 - pyskl - INFO - Epoch [49][2800/3746] lr: 7.613e-02, eta: 3 days, 12:30:43, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5273, loss_cls: 4.1433, loss: 4.1433 +2024-12-27 20:38:21,004 - pyskl - INFO - Epoch [49][2900/3746] lr: 7.610e-02, eta: 3 days, 12:29:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5280, loss_cls: 4.1556, loss: 4.1556 +2024-12-27 20:39:46,637 - pyskl - INFO - Epoch [49][3000/3746] lr: 7.608e-02, eta: 3 days, 12:28:24, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5152, loss_cls: 4.1917, loss: 4.1917 +2024-12-27 20:41:12,176 - pyskl - INFO - Epoch [49][3100/3746] lr: 7.606e-02, eta: 3 days, 12:27:15, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5234, loss_cls: 4.1603, loss: 4.1603 +2024-12-27 20:42:37,739 - pyskl - INFO - Epoch [49][3200/3746] lr: 7.603e-02, eta: 3 days, 12:26:05, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5167, loss_cls: 4.2316, loss: 4.2316 +2024-12-27 20:44:02,737 - pyskl - INFO - Epoch [49][3300/3746] lr: 7.601e-02, eta: 3 days, 12:24:55, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5164, loss_cls: 4.1877, loss: 4.1877 +2024-12-27 20:45:27,197 - pyskl - INFO - Epoch [49][3400/3746] lr: 7.598e-02, eta: 3 days, 12:23:44, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5148, loss_cls: 4.2176, loss: 4.2176 +2024-12-27 20:46:52,064 - pyskl - INFO - Epoch [49][3500/3746] lr: 7.596e-02, eta: 3 days, 12:22:33, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5289, loss_cls: 4.1672, loss: 4.1672 +2024-12-27 20:48:17,700 - pyskl - INFO - Epoch [49][3600/3746] lr: 7.594e-02, eta: 3 days, 12:21:24, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5277, loss_cls: 4.1840, loss: 4.1840 +2024-12-27 20:49:43,034 - pyskl - INFO - Epoch [49][3700/3746] lr: 7.591e-02, eta: 3 days, 12:20:14, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5259, loss_cls: 4.1627, loss: 4.1627 +2024-12-27 20:50:24,219 - pyskl - INFO - Saving checkpoint at 49 epochs +2024-12-27 20:52:22,735 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 20:52:23,570 - pyskl - INFO - +top1_acc 0.1932 +top5_acc 0.4294 +2024-12-27 20:52:23,571 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 20:52:23,618 - pyskl - INFO - +mean_acc 0.1932 +2024-12-27 20:52:23,632 - pyskl - INFO - Epoch(val) [49][309] top1_acc: 0.1932, top5_acc: 0.4294, mean_class_accuracy: 0.1932 +2024-12-27 20:56:36,930 - pyskl - INFO - Epoch [50][100/3746] lr: 7.588e-02, eta: 3 days, 12:22:57, time: 2.533, data_time: 1.489, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5453, loss_cls: 4.1091, loss: 4.1091 +2024-12-27 20:58:02,834 - pyskl - INFO - Epoch [50][200/3746] lr: 7.585e-02, eta: 3 days, 12:21:49, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5233, loss_cls: 4.1616, loss: 4.1616 +2024-12-27 20:59:28,178 - pyskl - INFO - Epoch [50][300/3746] lr: 7.583e-02, eta: 3 days, 12:20:39, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5356, loss_cls: 4.1219, loss: 4.1219 +2024-12-27 21:00:52,682 - pyskl - INFO - Epoch [50][400/3746] lr: 7.581e-02, eta: 3 days, 12:19:27, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5303, loss_cls: 4.1484, loss: 4.1484 +2024-12-27 21:02:17,420 - pyskl - INFO - Epoch [50][500/3746] lr: 7.578e-02, eta: 3 days, 12:18:16, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5348, loss_cls: 4.1270, loss: 4.1270 +2024-12-27 21:03:42,305 - pyskl - INFO - Epoch [50][600/3746] lr: 7.576e-02, eta: 3 days, 12:17:05, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5220, loss_cls: 4.2308, loss: 4.2308 +2024-12-27 21:05:06,983 - pyskl - INFO - Epoch [50][700/3746] lr: 7.573e-02, eta: 3 days, 12:15:53, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5317, loss_cls: 4.1413, loss: 4.1413 +2024-12-27 21:06:31,778 - pyskl - INFO - Epoch [50][800/3746] lr: 7.571e-02, eta: 3 days, 12:14:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5331, loss_cls: 4.1646, loss: 4.1646 +2024-12-27 21:07:56,725 - pyskl - INFO - Epoch [50][900/3746] lr: 7.569e-02, eta: 3 days, 12:13:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5267, loss_cls: 4.1919, loss: 4.1919 +2024-12-27 21:09:21,814 - pyskl - INFO - Epoch [50][1000/3746] lr: 7.566e-02, eta: 3 days, 12:12:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5306, loss_cls: 4.1654, loss: 4.1654 +2024-12-27 21:10:46,511 - pyskl - INFO - Epoch [50][1100/3746] lr: 7.564e-02, eta: 3 days, 12:11:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5289, loss_cls: 4.2025, loss: 4.2025 +2024-12-27 21:12:11,249 - pyskl - INFO - Epoch [50][1200/3746] lr: 7.561e-02, eta: 3 days, 12:09:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5280, loss_cls: 4.1622, loss: 4.1622 +2024-12-27 21:13:36,086 - pyskl - INFO - Epoch [50][1300/3746] lr: 7.559e-02, eta: 3 days, 12:08:47, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5211, loss_cls: 4.1825, loss: 4.1825 +2024-12-27 21:15:00,745 - pyskl - INFO - Epoch [50][1400/3746] lr: 7.557e-02, eta: 3 days, 12:07:35, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5288, loss_cls: 4.1509, loss: 4.1509 +2024-12-27 21:16:25,759 - pyskl - INFO - Epoch [50][1500/3746] lr: 7.554e-02, eta: 3 days, 12:06:24, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5255, loss_cls: 4.1721, loss: 4.1721 +2024-12-27 21:17:50,758 - pyskl - INFO - Epoch [50][1600/3746] lr: 7.552e-02, eta: 3 days, 12:05:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5245, loss_cls: 4.2256, loss: 4.2256 +2024-12-27 21:19:15,935 - pyskl - INFO - Epoch [50][1700/3746] lr: 7.549e-02, eta: 3 days, 12:04:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5208, loss_cls: 4.1569, loss: 4.1569 +2024-12-27 21:20:40,721 - pyskl - INFO - Epoch [50][1800/3746] lr: 7.547e-02, eta: 3 days, 12:02:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5259, loss_cls: 4.1730, loss: 4.1730 +2024-12-27 21:22:04,960 - pyskl - INFO - Epoch [50][1900/3746] lr: 7.545e-02, eta: 3 days, 12:01:39, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5288, loss_cls: 4.1733, loss: 4.1733 +2024-12-27 21:23:29,611 - pyskl - INFO - Epoch [50][2000/3746] lr: 7.542e-02, eta: 3 days, 12:00:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5197, loss_cls: 4.2022, loss: 4.2022 +2024-12-27 21:24:54,128 - pyskl - INFO - Epoch [50][2100/3746] lr: 7.540e-02, eta: 3 days, 11:59:15, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5234, loss_cls: 4.1486, loss: 4.1486 +2024-12-27 21:26:18,897 - pyskl - INFO - Epoch [50][2200/3746] lr: 7.537e-02, eta: 3 days, 11:58:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5264, loss_cls: 4.1907, loss: 4.1907 +2024-12-27 21:27:43,678 - pyskl - INFO - Epoch [50][2300/3746] lr: 7.535e-02, eta: 3 days, 11:56:52, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5250, loss_cls: 4.1786, loss: 4.1786 +2024-12-27 21:29:08,347 - pyskl - INFO - Epoch [50][2400/3746] lr: 7.533e-02, eta: 3 days, 11:55:41, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5245, loss_cls: 4.2001, loss: 4.2001 +2024-12-27 21:30:32,917 - pyskl - INFO - Epoch [50][2500/3746] lr: 7.530e-02, eta: 3 days, 11:54:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5188, loss_cls: 4.2349, loss: 4.2349 +2024-12-27 21:31:57,682 - pyskl - INFO - Epoch [50][2600/3746] lr: 7.528e-02, eta: 3 days, 11:53:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5309, loss_cls: 4.1418, loss: 4.1418 +2024-12-27 21:33:22,666 - pyskl - INFO - Epoch [50][2700/3746] lr: 7.525e-02, eta: 3 days, 11:52:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5292, loss_cls: 4.1857, loss: 4.1857 +2024-12-27 21:34:47,605 - pyskl - INFO - Epoch [50][2800/3746] lr: 7.523e-02, eta: 3 days, 11:50:55, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5386, loss_cls: 4.1312, loss: 4.1312 +2024-12-27 21:36:12,499 - pyskl - INFO - Epoch [50][2900/3746] lr: 7.520e-02, eta: 3 days, 11:49:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5191, loss_cls: 4.2224, loss: 4.2224 +2024-12-27 21:37:37,477 - pyskl - INFO - Epoch [50][3000/3746] lr: 7.518e-02, eta: 3 days, 11:48:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5270, loss_cls: 4.1615, loss: 4.1615 +2024-12-27 21:39:02,205 - pyskl - INFO - Epoch [50][3100/3746] lr: 7.516e-02, eta: 3 days, 11:47:21, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5375, loss_cls: 4.1276, loss: 4.1276 +2024-12-27 21:40:27,279 - pyskl - INFO - Epoch [50][3200/3746] lr: 7.513e-02, eta: 3 days, 11:46:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5344, loss_cls: 4.1292, loss: 4.1292 +2024-12-27 21:41:52,540 - pyskl - INFO - Epoch [50][3300/3746] lr: 7.511e-02, eta: 3 days, 11:45:00, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5244, loss_cls: 4.2031, loss: 4.2031 +2024-12-27 21:43:17,755 - pyskl - INFO - Epoch [50][3400/3746] lr: 7.508e-02, eta: 3 days, 11:43:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5322, loss_cls: 4.1328, loss: 4.1328 +2024-12-27 21:44:42,026 - pyskl - INFO - Epoch [50][3500/3746] lr: 7.506e-02, eta: 3 days, 11:42:36, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5214, loss_cls: 4.1905, loss: 4.1905 +2024-12-27 21:46:06,828 - pyskl - INFO - Epoch [50][3600/3746] lr: 7.504e-02, eta: 3 days, 11:41:25, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5350, loss_cls: 4.1203, loss: 4.1203 +2024-12-27 21:47:31,521 - pyskl - INFO - Epoch [50][3700/3746] lr: 7.501e-02, eta: 3 days, 11:40:13, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5183, loss_cls: 4.2287, loss: 4.2287 +2024-12-27 21:48:12,333 - pyskl - INFO - Saving checkpoint at 50 epochs +2024-12-27 21:50:10,932 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 21:50:11,606 - pyskl - INFO - +top1_acc 0.2216 +top5_acc 0.4549 +2024-12-27 21:50:11,606 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 21:50:11,652 - pyskl - INFO - +mean_acc 0.2215 +2024-12-27 21:50:11,657 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_36.pth was removed +2024-12-27 21:50:11,931 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_50.pth. +2024-12-27 21:50:11,932 - pyskl - INFO - Best top1_acc is 0.2216 at 50 epoch. +2024-12-27 21:50:11,944 - pyskl - INFO - Epoch(val) [50][309] top1_acc: 0.2216, top5_acc: 0.4549, mean_class_accuracy: 0.2215 +2024-12-27 21:54:25,607 - pyskl - INFO - Epoch [51][100/3746] lr: 7.498e-02, eta: 3 days, 11:42:48, time: 2.537, data_time: 1.509, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5392, loss_cls: 4.1162, loss: 4.1162 +2024-12-27 21:55:51,140 - pyskl - INFO - Epoch [51][200/3746] lr: 7.495e-02, eta: 3 days, 11:41:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5308, loss_cls: 4.1556, loss: 4.1556 +2024-12-27 21:57:16,654 - pyskl - INFO - Epoch [51][300/3746] lr: 7.493e-02, eta: 3 days, 11:40:27, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5286, loss_cls: 4.1346, loss: 4.1346 +2024-12-27 21:58:41,338 - pyskl - INFO - Epoch [51][400/3746] lr: 7.490e-02, eta: 3 days, 11:39:15, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5323, loss_cls: 4.1387, loss: 4.1387 +2024-12-27 22:00:06,367 - pyskl - INFO - Epoch [51][500/3746] lr: 7.488e-02, eta: 3 days, 11:38:03, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5278, loss_cls: 4.1682, loss: 4.1682 +2024-12-27 22:01:30,723 - pyskl - INFO - Epoch [51][600/3746] lr: 7.485e-02, eta: 3 days, 11:36:51, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5336, loss_cls: 4.1168, loss: 4.1168 +2024-12-27 22:02:55,572 - pyskl - INFO - Epoch [51][700/3746] lr: 7.483e-02, eta: 3 days, 11:35:39, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5208, loss_cls: 4.2045, loss: 4.2045 +2024-12-27 22:04:20,533 - pyskl - INFO - Epoch [51][800/3746] lr: 7.481e-02, eta: 3 days, 11:34:27, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5405, loss_cls: 4.1288, loss: 4.1288 +2024-12-27 22:05:44,927 - pyskl - INFO - Epoch [51][900/3746] lr: 7.478e-02, eta: 3 days, 11:33:15, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5264, loss_cls: 4.1724, loss: 4.1724 +2024-12-27 22:07:09,651 - pyskl - INFO - Epoch [51][1000/3746] lr: 7.476e-02, eta: 3 days, 11:32:02, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5272, loss_cls: 4.1635, loss: 4.1635 +2024-12-27 22:08:34,514 - pyskl - INFO - Epoch [51][1100/3746] lr: 7.473e-02, eta: 3 days, 11:30:51, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5264, loss_cls: 4.1690, loss: 4.1690 +2024-12-27 22:09:59,079 - pyskl - INFO - Epoch [51][1200/3746] lr: 7.471e-02, eta: 3 days, 11:29:38, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5275, loss_cls: 4.1575, loss: 4.1575 +2024-12-27 22:11:23,785 - pyskl - INFO - Epoch [51][1300/3746] lr: 7.468e-02, eta: 3 days, 11:28:26, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5333, loss_cls: 4.1414, loss: 4.1414 +2024-12-27 22:12:48,761 - pyskl - INFO - Epoch [51][1400/3746] lr: 7.466e-02, eta: 3 days, 11:27:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5331, loss_cls: 4.1599, loss: 4.1599 +2024-12-27 22:14:13,711 - pyskl - INFO - Epoch [51][1500/3746] lr: 7.464e-02, eta: 3 days, 11:26:03, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5327, loss_cls: 4.1545, loss: 4.1545 +2024-12-27 22:15:39,372 - pyskl - INFO - Epoch [51][1600/3746] lr: 7.461e-02, eta: 3 days, 11:24:52, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5286, loss_cls: 4.1829, loss: 4.1829 +2024-12-27 22:17:04,561 - pyskl - INFO - Epoch [51][1700/3746] lr: 7.459e-02, eta: 3 days, 11:23:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5234, loss_cls: 4.1852, loss: 4.1852 +2024-12-27 22:18:29,395 - pyskl - INFO - Epoch [51][1800/3746] lr: 7.456e-02, eta: 3 days, 11:22:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5350, loss_cls: 4.1501, loss: 4.1501 +2024-12-27 22:19:54,150 - pyskl - INFO - Epoch [51][1900/3746] lr: 7.454e-02, eta: 3 days, 11:21:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5216, loss_cls: 4.1768, loss: 4.1768 +2024-12-27 22:21:18,858 - pyskl - INFO - Epoch [51][2000/3746] lr: 7.451e-02, eta: 3 days, 11:20:05, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5305, loss_cls: 4.2056, loss: 4.2056 +2024-12-27 22:22:43,169 - pyskl - INFO - Epoch [51][2100/3746] lr: 7.449e-02, eta: 3 days, 11:18:52, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5319, loss_cls: 4.1610, loss: 4.1610 +2024-12-27 22:24:07,789 - pyskl - INFO - Epoch [51][2200/3746] lr: 7.447e-02, eta: 3 days, 11:17:39, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5312, loss_cls: 4.1645, loss: 4.1645 +2024-12-27 22:25:32,482 - pyskl - INFO - Epoch [51][2300/3746] lr: 7.444e-02, eta: 3 days, 11:16:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5255, loss_cls: 4.1801, loss: 4.1801 +2024-12-27 22:26:57,452 - pyskl - INFO - Epoch [51][2400/3746] lr: 7.442e-02, eta: 3 days, 11:15:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5297, loss_cls: 4.2018, loss: 4.2018 +2024-12-27 22:28:22,083 - pyskl - INFO - Epoch [51][2500/3746] lr: 7.439e-02, eta: 3 days, 11:14:02, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5342, loss_cls: 4.1213, loss: 4.1213 +2024-12-27 22:29:47,132 - pyskl - INFO - Epoch [51][2600/3746] lr: 7.437e-02, eta: 3 days, 11:12:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5292, loss_cls: 4.1600, loss: 4.1600 +2024-12-27 22:31:12,290 - pyskl - INFO - Epoch [51][2700/3746] lr: 7.434e-02, eta: 3 days, 11:11:39, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5370, loss_cls: 4.1168, loss: 4.1168 +2024-12-27 22:32:37,314 - pyskl - INFO - Epoch [51][2800/3746] lr: 7.432e-02, eta: 3 days, 11:10:28, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5289, loss_cls: 4.1740, loss: 4.1740 +2024-12-27 22:34:02,455 - pyskl - INFO - Epoch [51][2900/3746] lr: 7.429e-02, eta: 3 days, 11:09:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5259, loss_cls: 4.1676, loss: 4.1676 +2024-12-27 22:35:27,144 - pyskl - INFO - Epoch [51][3000/3746] lr: 7.427e-02, eta: 3 days, 11:08:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5252, loss_cls: 4.1984, loss: 4.1984 +2024-12-27 22:36:52,109 - pyskl - INFO - Epoch [51][3100/3746] lr: 7.425e-02, eta: 3 days, 11:06:52, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5223, loss_cls: 4.2083, loss: 4.2083 +2024-12-27 22:38:17,655 - pyskl - INFO - Epoch [51][3200/3746] lr: 7.422e-02, eta: 3 days, 11:05:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5186, loss_cls: 4.2054, loss: 4.2054 +2024-12-27 22:39:43,083 - pyskl - INFO - Epoch [51][3300/3746] lr: 7.420e-02, eta: 3 days, 11:04:30, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5164, loss_cls: 4.1865, loss: 4.1865 +2024-12-27 22:41:08,323 - pyskl - INFO - Epoch [51][3400/3746] lr: 7.417e-02, eta: 3 days, 11:03:18, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5323, loss_cls: 4.1539, loss: 4.1539 +2024-12-27 22:42:33,490 - pyskl - INFO - Epoch [51][3500/3746] lr: 7.415e-02, eta: 3 days, 11:02:07, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5155, loss_cls: 4.1971, loss: 4.1971 +2024-12-27 22:43:58,580 - pyskl - INFO - Epoch [51][3600/3746] lr: 7.412e-02, eta: 3 days, 11:00:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5300, loss_cls: 4.1465, loss: 4.1465 +2024-12-27 22:45:23,525 - pyskl - INFO - Epoch [51][3700/3746] lr: 7.410e-02, eta: 3 days, 10:59:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5345, loss_cls: 4.1046, loss: 4.1046 +2024-12-27 22:46:04,597 - pyskl - INFO - Saving checkpoint at 51 epochs +2024-12-27 22:48:02,433 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 22:48:03,149 - pyskl - INFO - +top1_acc 0.2290 +top5_acc 0.4605 +2024-12-27 22:48:03,149 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 22:48:03,196 - pyskl - INFO - +mean_acc 0.2287 +2024-12-27 22:48:03,200 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_50.pth was removed +2024-12-27 22:48:03,524 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_51.pth. +2024-12-27 22:48:03,524 - pyskl - INFO - Best top1_acc is 0.2290 at 51 epoch. +2024-12-27 22:48:03,543 - pyskl - INFO - Epoch(val) [51][309] top1_acc: 0.2290, top5_acc: 0.4605, mean_class_accuracy: 0.2287 +2024-12-27 22:52:14,544 - pyskl - INFO - Epoch [52][100/3746] lr: 7.406e-02, eta: 3 days, 11:02:04, time: 2.510, data_time: 1.469, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5416, loss_cls: 4.1335, loss: 4.1335 +2024-12-27 22:53:39,564 - pyskl - INFO - Epoch [52][200/3746] lr: 7.404e-02, eta: 3 days, 11:00:52, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5441, loss_cls: 4.0876, loss: 4.0876 +2024-12-27 22:55:05,032 - pyskl - INFO - Epoch [52][300/3746] lr: 7.401e-02, eta: 3 days, 10:59:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5389, loss_cls: 4.1071, loss: 4.1071 +2024-12-27 22:56:30,290 - pyskl - INFO - Epoch [52][400/3746] lr: 7.399e-02, eta: 3 days, 10:58:29, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5272, loss_cls: 4.1745, loss: 4.1745 +2024-12-27 22:57:55,948 - pyskl - INFO - Epoch [52][500/3746] lr: 7.397e-02, eta: 3 days, 10:57:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5317, loss_cls: 4.1455, loss: 4.1455 +2024-12-27 22:59:21,616 - pyskl - INFO - Epoch [52][600/3746] lr: 7.394e-02, eta: 3 days, 10:56:07, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5317, loss_cls: 4.1827, loss: 4.1827 +2024-12-27 23:00:47,067 - pyskl - INFO - Epoch [52][700/3746] lr: 7.392e-02, eta: 3 days, 10:54:56, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5327, loss_cls: 4.1310, loss: 4.1310 +2024-12-27 23:02:12,866 - pyskl - INFO - Epoch [52][800/3746] lr: 7.389e-02, eta: 3 days, 10:53:45, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5369, loss_cls: 4.1038, loss: 4.1038 +2024-12-27 23:03:38,008 - pyskl - INFO - Epoch [52][900/3746] lr: 7.387e-02, eta: 3 days, 10:52:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5273, loss_cls: 4.1501, loss: 4.1501 +2024-12-27 23:05:03,517 - pyskl - INFO - Epoch [52][1000/3746] lr: 7.384e-02, eta: 3 days, 10:51:22, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5389, loss_cls: 4.1540, loss: 4.1540 +2024-12-27 23:06:28,709 - pyskl - INFO - Epoch [52][1100/3746] lr: 7.382e-02, eta: 3 days, 10:50:10, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5255, loss_cls: 4.1864, loss: 4.1864 +2024-12-27 23:07:54,128 - pyskl - INFO - Epoch [52][1200/3746] lr: 7.379e-02, eta: 3 days, 10:48:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5248, loss_cls: 4.1594, loss: 4.1594 +2024-12-27 23:09:18,786 - pyskl - INFO - Epoch [52][1300/3746] lr: 7.377e-02, eta: 3 days, 10:47:46, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5348, loss_cls: 4.1700, loss: 4.1700 +2024-12-27 23:10:43,635 - pyskl - INFO - Epoch [52][1400/3746] lr: 7.374e-02, eta: 3 days, 10:46:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5311, loss_cls: 4.1524, loss: 4.1524 +2024-12-27 23:12:08,340 - pyskl - INFO - Epoch [52][1500/3746] lr: 7.372e-02, eta: 3 days, 10:45:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5383, loss_cls: 4.1370, loss: 4.1370 +2024-12-27 23:13:33,421 - pyskl - INFO - Epoch [52][1600/3746] lr: 7.370e-02, eta: 3 days, 10:44:08, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5444, loss_cls: 4.1088, loss: 4.1088 +2024-12-27 23:14:58,788 - pyskl - INFO - Epoch [52][1700/3746] lr: 7.367e-02, eta: 3 days, 10:42:56, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5275, loss_cls: 4.1790, loss: 4.1790 +2024-12-27 23:16:23,380 - pyskl - INFO - Epoch [52][1800/3746] lr: 7.365e-02, eta: 3 days, 10:41:43, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5337, loss_cls: 4.1298, loss: 4.1298 +2024-12-27 23:17:48,302 - pyskl - INFO - Epoch [52][1900/3746] lr: 7.362e-02, eta: 3 days, 10:40:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5239, loss_cls: 4.1688, loss: 4.1688 +2024-12-27 23:19:13,541 - pyskl - INFO - Epoch [52][2000/3746] lr: 7.360e-02, eta: 3 days, 10:39:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5227, loss_cls: 4.2092, loss: 4.2092 +2024-12-27 23:20:38,224 - pyskl - INFO - Epoch [52][2100/3746] lr: 7.357e-02, eta: 3 days, 10:38:06, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5291, loss_cls: 4.1619, loss: 4.1619 +2024-12-27 23:22:03,297 - pyskl - INFO - Epoch [52][2200/3746] lr: 7.355e-02, eta: 3 days, 10:36:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5241, loss_cls: 4.2000, loss: 4.2000 +2024-12-27 23:23:28,646 - pyskl - INFO - Epoch [52][2300/3746] lr: 7.352e-02, eta: 3 days, 10:35:42, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5220, loss_cls: 4.2113, loss: 4.2113 +2024-12-27 23:24:53,603 - pyskl - INFO - Epoch [52][2400/3746] lr: 7.350e-02, eta: 3 days, 10:34:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5225, loss_cls: 4.1922, loss: 4.1922 +2024-12-27 23:26:18,942 - pyskl - INFO - Epoch [52][2500/3746] lr: 7.347e-02, eta: 3 days, 10:33:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5322, loss_cls: 4.1502, loss: 4.1502 +2024-12-27 23:27:44,055 - pyskl - INFO - Epoch [52][2600/3746] lr: 7.345e-02, eta: 3 days, 10:32:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5389, loss_cls: 4.1369, loss: 4.1369 +2024-12-27 23:29:08,982 - pyskl - INFO - Epoch [52][2700/3746] lr: 7.342e-02, eta: 3 days, 10:30:52, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5337, loss_cls: 4.1361, loss: 4.1361 +2024-12-27 23:30:33,905 - pyskl - INFO - Epoch [52][2800/3746] lr: 7.340e-02, eta: 3 days, 10:29:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5328, loss_cls: 4.1244, loss: 4.1244 +2024-12-27 23:31:59,181 - pyskl - INFO - Epoch [52][2900/3746] lr: 7.337e-02, eta: 3 days, 10:28:28, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5325, loss_cls: 4.1570, loss: 4.1570 +2024-12-27 23:33:24,387 - pyskl - INFO - Epoch [52][3000/3746] lr: 7.335e-02, eta: 3 days, 10:27:16, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5259, loss_cls: 4.1541, loss: 4.1541 +2024-12-27 23:34:49,270 - pyskl - INFO - Epoch [52][3100/3746] lr: 7.332e-02, eta: 3 days, 10:26:03, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5308, loss_cls: 4.1611, loss: 4.1611 +2024-12-27 23:36:14,605 - pyskl - INFO - Epoch [52][3200/3746] lr: 7.330e-02, eta: 3 days, 10:24:51, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5348, loss_cls: 4.1411, loss: 4.1411 +2024-12-27 23:37:39,077 - pyskl - INFO - Epoch [52][3300/3746] lr: 7.328e-02, eta: 3 days, 10:23:37, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5392, loss_cls: 4.1167, loss: 4.1167 +2024-12-27 23:39:03,768 - pyskl - INFO - Epoch [52][3400/3746] lr: 7.325e-02, eta: 3 days, 10:22:24, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5264, loss_cls: 4.1751, loss: 4.1751 +2024-12-27 23:40:28,343 - pyskl - INFO - Epoch [52][3500/3746] lr: 7.323e-02, eta: 3 days, 10:21:11, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5384, loss_cls: 4.1397, loss: 4.1397 +2024-12-27 23:41:53,073 - pyskl - INFO - Epoch [52][3600/3746] lr: 7.320e-02, eta: 3 days, 10:19:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5255, loss_cls: 4.1549, loss: 4.1549 +2024-12-27 23:43:18,055 - pyskl - INFO - Epoch [52][3700/3746] lr: 7.318e-02, eta: 3 days, 10:18:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5244, loss_cls: 4.2116, loss: 4.2116 +2024-12-27 23:43:58,913 - pyskl - INFO - Saving checkpoint at 52 epochs +2024-12-27 23:45:57,068 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 23:45:57,847 - pyskl - INFO - +top1_acc 0.2229 +top5_acc 0.4615 +2024-12-27 23:45:57,847 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 23:45:57,890 - pyskl - INFO - +mean_acc 0.2226 +2024-12-27 23:45:57,901 - pyskl - INFO - Epoch(val) [52][309] top1_acc: 0.2229, top5_acc: 0.4615, mean_class_accuracy: 0.2226 +2024-12-27 23:50:18,985 - pyskl - INFO - Epoch [53][100/3746] lr: 7.314e-02, eta: 3 days, 10:21:17, time: 2.611, data_time: 1.586, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5455, loss_cls: 4.0967, loss: 4.0967 +2024-12-27 23:51:44,468 - pyskl - INFO - Epoch [53][200/3746] lr: 7.312e-02, eta: 3 days, 10:20:05, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5245, loss_cls: 4.1427, loss: 4.1427 +2024-12-27 23:53:10,190 - pyskl - INFO - Epoch [53][300/3746] lr: 7.309e-02, eta: 3 days, 10:18:54, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5372, loss_cls: 4.1289, loss: 4.1289 +2024-12-27 23:54:35,494 - pyskl - INFO - Epoch [53][400/3746] lr: 7.307e-02, eta: 3 days, 10:17:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5273, loss_cls: 4.1681, loss: 4.1681 +2024-12-27 23:56:00,545 - pyskl - INFO - Epoch [53][500/3746] lr: 7.304e-02, eta: 3 days, 10:16:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5369, loss_cls: 4.1510, loss: 4.1510 +2024-12-27 23:57:25,555 - pyskl - INFO - Epoch [53][600/3746] lr: 7.302e-02, eta: 3 days, 10:15:16, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5373, loss_cls: 4.1318, loss: 4.1318 +2024-12-27 23:58:50,568 - pyskl - INFO - Epoch [53][700/3746] lr: 7.299e-02, eta: 3 days, 10:14:03, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5328, loss_cls: 4.1860, loss: 4.1860 +2024-12-28 00:00:15,424 - pyskl - INFO - Epoch [53][800/3746] lr: 7.297e-02, eta: 3 days, 10:12:49, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5278, loss_cls: 4.1806, loss: 4.1806 +2024-12-28 00:01:40,340 - pyskl - INFO - Epoch [53][900/3746] lr: 7.294e-02, eta: 3 days, 10:11:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5398, loss_cls: 4.1202, loss: 4.1202 +2024-12-28 00:03:05,658 - pyskl - INFO - Epoch [53][1000/3746] lr: 7.292e-02, eta: 3 days, 10:10:24, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5348, loss_cls: 4.1511, loss: 4.1511 +2024-12-28 00:04:30,721 - pyskl - INFO - Epoch [53][1100/3746] lr: 7.289e-02, eta: 3 days, 10:09:11, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5355, loss_cls: 4.1414, loss: 4.1414 +2024-12-28 00:05:54,903 - pyskl - INFO - Epoch [53][1200/3746] lr: 7.287e-02, eta: 3 days, 10:07:57, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5363, loss_cls: 4.1470, loss: 4.1470 +2024-12-28 00:07:19,390 - pyskl - INFO - Epoch [53][1300/3746] lr: 7.284e-02, eta: 3 days, 10:06:43, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5359, loss_cls: 4.1575, loss: 4.1575 +2024-12-28 00:08:44,052 - pyskl - INFO - Epoch [53][1400/3746] lr: 7.282e-02, eta: 3 days, 10:05:29, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5291, loss_cls: 4.1449, loss: 4.1449 +2024-12-28 00:10:08,710 - pyskl - INFO - Epoch [53][1500/3746] lr: 7.279e-02, eta: 3 days, 10:04:15, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5241, loss_cls: 4.1641, loss: 4.1641 +2024-12-28 00:11:33,348 - pyskl - INFO - Epoch [53][1600/3746] lr: 7.277e-02, eta: 3 days, 10:03:02, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5262, loss_cls: 4.1610, loss: 4.1610 +2024-12-28 00:12:57,661 - pyskl - INFO - Epoch [53][1700/3746] lr: 7.274e-02, eta: 3 days, 10:01:47, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5284, loss_cls: 4.1875, loss: 4.1875 +2024-12-28 00:14:22,232 - pyskl - INFO - Epoch [53][1800/3746] lr: 7.272e-02, eta: 3 days, 10:00:34, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5222, loss_cls: 4.1592, loss: 4.1592 +2024-12-28 00:15:46,990 - pyskl - INFO - Epoch [53][1900/3746] lr: 7.269e-02, eta: 3 days, 9:59:20, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5341, loss_cls: 4.1655, loss: 4.1655 +2024-12-28 00:17:11,550 - pyskl - INFO - Epoch [53][2000/3746] lr: 7.267e-02, eta: 3 days, 9:58:06, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5353, loss_cls: 4.1348, loss: 4.1348 +2024-12-28 00:18:36,011 - pyskl - INFO - Epoch [53][2100/3746] lr: 7.264e-02, eta: 3 days, 9:56:52, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5431, loss_cls: 4.0963, loss: 4.0963 +2024-12-28 00:20:00,455 - pyskl - INFO - Epoch [53][2200/3746] lr: 7.262e-02, eta: 3 days, 9:55:38, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5337, loss_cls: 4.1144, loss: 4.1144 +2024-12-28 00:21:24,979 - pyskl - INFO - Epoch [53][2300/3746] lr: 7.259e-02, eta: 3 days, 9:54:24, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5328, loss_cls: 4.1472, loss: 4.1472 +2024-12-28 00:22:49,626 - pyskl - INFO - Epoch [53][2400/3746] lr: 7.257e-02, eta: 3 days, 9:53:10, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5316, loss_cls: 4.1651, loss: 4.1651 +2024-12-28 00:24:14,409 - pyskl - INFO - Epoch [53][2500/3746] lr: 7.254e-02, eta: 3 days, 9:51:57, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5395, loss_cls: 4.1382, loss: 4.1382 +2024-12-28 00:25:38,890 - pyskl - INFO - Epoch [53][2600/3746] lr: 7.252e-02, eta: 3 days, 9:50:43, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5314, loss_cls: 4.1674, loss: 4.1674 +2024-12-28 00:27:03,944 - pyskl - INFO - Epoch [53][2700/3746] lr: 7.249e-02, eta: 3 days, 9:49:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5347, loss_cls: 4.1325, loss: 4.1325 +2024-12-28 00:28:28,489 - pyskl - INFO - Epoch [53][2800/3746] lr: 7.247e-02, eta: 3 days, 9:48:16, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5297, loss_cls: 4.1458, loss: 4.1458 +2024-12-28 00:29:52,995 - pyskl - INFO - Epoch [53][2900/3746] lr: 7.244e-02, eta: 3 days, 9:47:02, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5361, loss_cls: 4.1196, loss: 4.1196 +2024-12-28 00:31:18,019 - pyskl - INFO - Epoch [53][3000/3746] lr: 7.242e-02, eta: 3 days, 9:45:48, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5281, loss_cls: 4.1716, loss: 4.1716 +2024-12-28 00:32:43,019 - pyskl - INFO - Epoch [53][3100/3746] lr: 7.239e-02, eta: 3 days, 9:44:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5325, loss_cls: 4.1172, loss: 4.1172 +2024-12-28 00:34:07,746 - pyskl - INFO - Epoch [53][3200/3746] lr: 7.237e-02, eta: 3 days, 9:43:22, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5262, loss_cls: 4.1637, loss: 4.1637 +2024-12-28 00:35:33,090 - pyskl - INFO - Epoch [53][3300/3746] lr: 7.234e-02, eta: 3 days, 9:42:09, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5363, loss_cls: 4.1417, loss: 4.1417 +2024-12-28 00:36:57,858 - pyskl - INFO - Epoch [53][3400/3746] lr: 7.232e-02, eta: 3 days, 9:40:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5363, loss_cls: 4.1389, loss: 4.1389 +2024-12-28 00:38:22,159 - pyskl - INFO - Epoch [53][3500/3746] lr: 7.229e-02, eta: 3 days, 9:39:41, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5364, loss_cls: 4.1166, loss: 4.1166 +2024-12-28 00:39:47,267 - pyskl - INFO - Epoch [53][3600/3746] lr: 7.227e-02, eta: 3 days, 9:38:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5302, loss_cls: 4.1700, loss: 4.1700 +2024-12-28 00:41:12,986 - pyskl - INFO - Epoch [53][3700/3746] lr: 7.224e-02, eta: 3 days, 9:37:16, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5334, loss_cls: 4.1279, loss: 4.1279 +2024-12-28 00:41:54,153 - pyskl - INFO - Saving checkpoint at 53 epochs +2024-12-28 00:43:54,200 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 00:43:55,298 - pyskl - INFO - +top1_acc 0.2042 +top5_acc 0.4421 +2024-12-28 00:43:55,298 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 00:43:55,351 - pyskl - INFO - +mean_acc 0.2038 +2024-12-28 00:43:55,365 - pyskl - INFO - Epoch(val) [53][309] top1_acc: 0.2042, top5_acc: 0.4421, mean_class_accuracy: 0.2038 +2024-12-28 00:48:15,994 - pyskl - INFO - Epoch [54][100/3746] lr: 7.221e-02, eta: 3 days, 9:39:38, time: 2.606, data_time: 1.578, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5430, loss_cls: 4.0710, loss: 4.0710 +2024-12-28 00:49:41,104 - pyskl - INFO - Epoch [54][200/3746] lr: 7.218e-02, eta: 3 days, 9:38:25, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5391, loss_cls: 4.1253, loss: 4.1253 +2024-12-28 00:51:06,153 - pyskl - INFO - Epoch [54][300/3746] lr: 7.216e-02, eta: 3 days, 9:37:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5484, loss_cls: 4.0857, loss: 4.0857 +2024-12-28 00:52:30,785 - pyskl - INFO - Epoch [54][400/3746] lr: 7.213e-02, eta: 3 days, 9:35:58, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5427, loss_cls: 4.1100, loss: 4.1100 +2024-12-28 00:53:55,654 - pyskl - INFO - Epoch [54][500/3746] lr: 7.211e-02, eta: 3 days, 9:34:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5370, loss_cls: 4.1215, loss: 4.1215 +2024-12-28 00:55:20,479 - pyskl - INFO - Epoch [54][600/3746] lr: 7.208e-02, eta: 3 days, 9:33:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5316, loss_cls: 4.1389, loss: 4.1389 +2024-12-28 00:56:45,107 - pyskl - INFO - Epoch [54][700/3746] lr: 7.206e-02, eta: 3 days, 9:32:16, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5395, loss_cls: 4.1164, loss: 4.1164 +2024-12-28 00:58:09,595 - pyskl - INFO - Epoch [54][800/3746] lr: 7.203e-02, eta: 3 days, 9:31:01, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5278, loss_cls: 4.1389, loss: 4.1389 +2024-12-28 00:59:34,300 - pyskl - INFO - Epoch [54][900/3746] lr: 7.201e-02, eta: 3 days, 9:29:47, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5336, loss_cls: 4.1230, loss: 4.1230 +2024-12-28 01:00:59,166 - pyskl - INFO - Epoch [54][1000/3746] lr: 7.198e-02, eta: 3 days, 9:28:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5323, loss_cls: 4.1564, loss: 4.1564 +2024-12-28 01:02:23,920 - pyskl - INFO - Epoch [54][1100/3746] lr: 7.196e-02, eta: 3 days, 9:27:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5378, loss_cls: 4.1405, loss: 4.1405 +2024-12-28 01:03:48,825 - pyskl - INFO - Epoch [54][1200/3746] lr: 7.193e-02, eta: 3 days, 9:26:05, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5345, loss_cls: 4.1334, loss: 4.1334 +2024-12-28 01:05:13,546 - pyskl - INFO - Epoch [54][1300/3746] lr: 7.191e-02, eta: 3 days, 9:24:51, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5330, loss_cls: 4.1587, loss: 4.1587 +2024-12-28 01:06:38,336 - pyskl - INFO - Epoch [54][1400/3746] lr: 7.188e-02, eta: 3 days, 9:23:37, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5431, loss_cls: 4.0809, loss: 4.0809 +2024-12-28 01:08:03,209 - pyskl - INFO - Epoch [54][1500/3746] lr: 7.186e-02, eta: 3 days, 9:22:24, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5300, loss_cls: 4.1628, loss: 4.1628 +2024-12-28 01:09:28,147 - pyskl - INFO - Epoch [54][1600/3746] lr: 7.183e-02, eta: 3 days, 9:21:10, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5314, loss_cls: 4.1476, loss: 4.1476 +2024-12-28 01:10:52,993 - pyskl - INFO - Epoch [54][1700/3746] lr: 7.181e-02, eta: 3 days, 9:19:56, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5320, loss_cls: 4.1679, loss: 4.1679 +2024-12-28 01:12:17,696 - pyskl - INFO - Epoch [54][1800/3746] lr: 7.178e-02, eta: 3 days, 9:18:42, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5195, loss_cls: 4.1962, loss: 4.1962 +2024-12-28 01:13:42,194 - pyskl - INFO - Epoch [54][1900/3746] lr: 7.176e-02, eta: 3 days, 9:17:27, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5292, loss_cls: 4.1581, loss: 4.1581 +2024-12-28 01:15:06,915 - pyskl - INFO - Epoch [54][2000/3746] lr: 7.173e-02, eta: 3 days, 9:16:13, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5430, loss_cls: 4.1383, loss: 4.1383 +2024-12-28 01:16:32,072 - pyskl - INFO - Epoch [54][2100/3746] lr: 7.170e-02, eta: 3 days, 9:14:59, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5288, loss_cls: 4.1779, loss: 4.1779 +2024-12-28 01:17:57,009 - pyskl - INFO - Epoch [54][2200/3746] lr: 7.168e-02, eta: 3 days, 9:13:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5175, loss_cls: 4.2042, loss: 4.2042 +2024-12-28 01:19:22,234 - pyskl - INFO - Epoch [54][2300/3746] lr: 7.165e-02, eta: 3 days, 9:12:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5236, loss_cls: 4.1889, loss: 4.1889 +2024-12-28 01:20:47,147 - pyskl - INFO - Epoch [54][2400/3746] lr: 7.163e-02, eta: 3 days, 9:11:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5492, loss_cls: 4.0712, loss: 4.0712 +2024-12-28 01:22:12,121 - pyskl - INFO - Epoch [54][2500/3746] lr: 7.160e-02, eta: 3 days, 9:10:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5291, loss_cls: 4.1403, loss: 4.1403 +2024-12-28 01:23:37,571 - pyskl - INFO - Epoch [54][2600/3746] lr: 7.158e-02, eta: 3 days, 9:08:52, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5325, loss_cls: 4.1741, loss: 4.1741 +2024-12-28 01:25:03,295 - pyskl - INFO - Epoch [54][2700/3746] lr: 7.155e-02, eta: 3 days, 9:07:39, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5278, loss_cls: 4.1543, loss: 4.1543 +2024-12-28 01:26:28,879 - pyskl - INFO - Epoch [54][2800/3746] lr: 7.153e-02, eta: 3 days, 9:06:26, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5320, loss_cls: 4.1551, loss: 4.1551 +2024-12-28 01:27:54,644 - pyskl - INFO - Epoch [54][2900/3746] lr: 7.150e-02, eta: 3 days, 9:05:14, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5311, loss_cls: 4.1446, loss: 4.1446 +2024-12-28 01:29:20,048 - pyskl - INFO - Epoch [54][3000/3746] lr: 7.148e-02, eta: 3 days, 9:04:01, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5364, loss_cls: 4.1140, loss: 4.1140 +2024-12-28 01:30:45,325 - pyskl - INFO - Epoch [54][3100/3746] lr: 7.145e-02, eta: 3 days, 9:02:48, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5294, loss_cls: 4.1396, loss: 4.1396 +2024-12-28 01:32:11,034 - pyskl - INFO - Epoch [54][3200/3746] lr: 7.143e-02, eta: 3 days, 9:01:35, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5434, loss_cls: 4.1284, loss: 4.1284 +2024-12-28 01:33:37,017 - pyskl - INFO - Epoch [54][3300/3746] lr: 7.140e-02, eta: 3 days, 9:00:23, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5417, loss_cls: 4.1232, loss: 4.1232 +2024-12-28 01:35:02,673 - pyskl - INFO - Epoch [54][3400/3746] lr: 7.138e-02, eta: 3 days, 8:59:10, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5348, loss_cls: 4.1192, loss: 4.1192 +2024-12-28 01:36:27,731 - pyskl - INFO - Epoch [54][3500/3746] lr: 7.135e-02, eta: 3 days, 8:57:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5317, loss_cls: 4.1719, loss: 4.1719 +2024-12-28 01:37:52,203 - pyskl - INFO - Epoch [54][3600/3746] lr: 7.133e-02, eta: 3 days, 8:56:42, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5319, loss_cls: 4.1786, loss: 4.1786 +2024-12-28 01:39:16,920 - pyskl - INFO - Epoch [54][3700/3746] lr: 7.130e-02, eta: 3 days, 8:55:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5267, loss_cls: 4.1674, loss: 4.1674 +2024-12-28 01:39:57,745 - pyskl - INFO - Saving checkpoint at 54 epochs +2024-12-28 01:41:56,718 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 01:41:57,551 - pyskl - INFO - +top1_acc 0.2216 +top5_acc 0.4622 +2024-12-28 01:41:57,551 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 01:41:57,604 - pyskl - INFO - +mean_acc 0.2212 +2024-12-28 01:41:57,618 - pyskl - INFO - Epoch(val) [54][309] top1_acc: 0.2216, top5_acc: 0.4622, mean_class_accuracy: 0.2212 +2024-12-28 01:46:15,534 - pyskl - INFO - Epoch [55][100/3746] lr: 7.126e-02, eta: 3 days, 8:57:37, time: 2.579, data_time: 1.543, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5405, loss_cls: 4.0873, loss: 4.0873 +2024-12-28 01:47:40,677 - pyskl - INFO - Epoch [55][200/3746] lr: 7.124e-02, eta: 3 days, 8:56:23, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5491, loss_cls: 4.0490, loss: 4.0490 +2024-12-28 01:49:05,841 - pyskl - INFO - Epoch [55][300/3746] lr: 7.121e-02, eta: 3 days, 8:55:09, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5419, loss_cls: 4.0920, loss: 4.0920 +2024-12-28 01:50:30,651 - pyskl - INFO - Epoch [55][400/3746] lr: 7.119e-02, eta: 3 days, 8:53:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5428, loss_cls: 4.0959, loss: 4.0959 +2024-12-28 01:51:55,049 - pyskl - INFO - Epoch [55][500/3746] lr: 7.116e-02, eta: 3 days, 8:52:40, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5342, loss_cls: 4.1383, loss: 4.1383 +2024-12-28 01:53:19,654 - pyskl - INFO - Epoch [55][600/3746] lr: 7.114e-02, eta: 3 days, 8:51:25, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5309, loss_cls: 4.1430, loss: 4.1430 +2024-12-28 01:54:44,330 - pyskl - INFO - Epoch [55][700/3746] lr: 7.111e-02, eta: 3 days, 8:50:10, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5363, loss_cls: 4.1444, loss: 4.1444 +2024-12-28 01:56:08,577 - pyskl - INFO - Epoch [55][800/3746] lr: 7.109e-02, eta: 3 days, 8:48:55, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5447, loss_cls: 4.0750, loss: 4.0750 +2024-12-28 01:57:33,109 - pyskl - INFO - Epoch [55][900/3746] lr: 7.106e-02, eta: 3 days, 8:47:40, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5453, loss_cls: 4.0703, loss: 4.0703 +2024-12-28 01:58:57,386 - pyskl - INFO - Epoch [55][1000/3746] lr: 7.104e-02, eta: 3 days, 8:46:24, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5419, loss_cls: 4.1272, loss: 4.1272 +2024-12-28 02:00:22,199 - pyskl - INFO - Epoch [55][1100/3746] lr: 7.101e-02, eta: 3 days, 8:45:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5312, loss_cls: 4.1406, loss: 4.1406 +2024-12-28 02:01:47,102 - pyskl - INFO - Epoch [55][1200/3746] lr: 7.099e-02, eta: 3 days, 8:43:55, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5262, loss_cls: 4.2036, loss: 4.2036 +2024-12-28 02:03:11,595 - pyskl - INFO - Epoch [55][1300/3746] lr: 7.096e-02, eta: 3 days, 8:42:40, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5220, loss_cls: 4.1833, loss: 4.1833 +2024-12-28 02:04:36,467 - pyskl - INFO - Epoch [55][1400/3746] lr: 7.093e-02, eta: 3 days, 8:41:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5209, loss_cls: 4.1584, loss: 4.1584 +2024-12-28 02:06:01,499 - pyskl - INFO - Epoch [55][1500/3746] lr: 7.091e-02, eta: 3 days, 8:40:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5423, loss_cls: 4.1090, loss: 4.1090 +2024-12-28 02:07:26,446 - pyskl - INFO - Epoch [55][1600/3746] lr: 7.088e-02, eta: 3 days, 8:38:57, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5331, loss_cls: 4.1678, loss: 4.1678 +2024-12-28 02:08:50,661 - pyskl - INFO - Epoch [55][1700/3746] lr: 7.086e-02, eta: 3 days, 8:37:42, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5345, loss_cls: 4.1395, loss: 4.1395 +2024-12-28 02:10:14,946 - pyskl - INFO - Epoch [55][1800/3746] lr: 7.083e-02, eta: 3 days, 8:36:26, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5283, loss_cls: 4.1310, loss: 4.1310 +2024-12-28 02:11:40,114 - pyskl - INFO - Epoch [55][1900/3746] lr: 7.081e-02, eta: 3 days, 8:35:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5395, loss_cls: 4.1191, loss: 4.1191 +2024-12-28 02:13:05,000 - pyskl - INFO - Epoch [55][2000/3746] lr: 7.078e-02, eta: 3 days, 8:33:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5319, loss_cls: 4.1282, loss: 4.1282 +2024-12-28 02:14:30,425 - pyskl - INFO - Epoch [55][2100/3746] lr: 7.076e-02, eta: 3 days, 8:32:44, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5358, loss_cls: 4.1518, loss: 4.1518 +2024-12-28 02:15:55,317 - pyskl - INFO - Epoch [55][2200/3746] lr: 7.073e-02, eta: 3 days, 8:31:30, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5347, loss_cls: 4.1591, loss: 4.1591 +2024-12-28 02:17:20,229 - pyskl - INFO - Epoch [55][2300/3746] lr: 7.071e-02, eta: 3 days, 8:30:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5355, loss_cls: 4.1545, loss: 4.1545 +2024-12-28 02:18:45,859 - pyskl - INFO - Epoch [55][2400/3746] lr: 7.068e-02, eta: 3 days, 8:29:02, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5352, loss_cls: 4.1753, loss: 4.1753 +2024-12-28 02:20:10,684 - pyskl - INFO - Epoch [55][2500/3746] lr: 7.065e-02, eta: 3 days, 8:27:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5359, loss_cls: 4.1323, loss: 4.1323 +2024-12-28 02:21:36,627 - pyskl - INFO - Epoch [55][2600/3746] lr: 7.063e-02, eta: 3 days, 8:26:35, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5350, loss_cls: 4.1451, loss: 4.1451 +2024-12-28 02:23:01,883 - pyskl - INFO - Epoch [55][2700/3746] lr: 7.060e-02, eta: 3 days, 8:25:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5309, loss_cls: 4.1655, loss: 4.1655 +2024-12-28 02:24:27,667 - pyskl - INFO - Epoch [55][2800/3746] lr: 7.058e-02, eta: 3 days, 8:24:08, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5358, loss_cls: 4.1468, loss: 4.1468 +2024-12-28 02:25:53,289 - pyskl - INFO - Epoch [55][2900/3746] lr: 7.055e-02, eta: 3 days, 8:22:55, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5414, loss_cls: 4.1357, loss: 4.1357 +2024-12-28 02:27:18,972 - pyskl - INFO - Epoch [55][3000/3746] lr: 7.053e-02, eta: 3 days, 8:21:41, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5330, loss_cls: 4.1604, loss: 4.1604 +2024-12-28 02:28:45,145 - pyskl - INFO - Epoch [55][3100/3746] lr: 7.050e-02, eta: 3 days, 8:20:29, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5336, loss_cls: 4.1548, loss: 4.1548 +2024-12-28 02:30:10,797 - pyskl - INFO - Epoch [55][3200/3746] lr: 7.048e-02, eta: 3 days, 8:19:16, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5322, loss_cls: 4.1346, loss: 4.1346 +2024-12-28 02:31:36,715 - pyskl - INFO - Epoch [55][3300/3746] lr: 7.045e-02, eta: 3 days, 8:18:03, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5305, loss_cls: 4.1273, loss: 4.1273 +2024-12-28 02:33:01,926 - pyskl - INFO - Epoch [55][3400/3746] lr: 7.043e-02, eta: 3 days, 8:16:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5352, loss_cls: 4.1349, loss: 4.1349 +2024-12-28 02:34:26,823 - pyskl - INFO - Epoch [55][3500/3746] lr: 7.040e-02, eta: 3 days, 8:15:34, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5186, loss_cls: 4.1875, loss: 4.1875 +2024-12-28 02:35:52,444 - pyskl - INFO - Epoch [55][3600/3746] lr: 7.037e-02, eta: 3 days, 8:14:21, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5387, loss_cls: 4.1103, loss: 4.1103 +2024-12-28 02:37:17,447 - pyskl - INFO - Epoch [55][3700/3746] lr: 7.035e-02, eta: 3 days, 8:13:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5359, loss_cls: 4.0984, loss: 4.0984 +2024-12-28 02:37:58,775 - pyskl - INFO - Saving checkpoint at 55 epochs +2024-12-28 02:39:57,623 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 02:39:58,368 - pyskl - INFO - +top1_acc 0.2227 +top5_acc 0.4721 +2024-12-28 02:39:58,368 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 02:39:58,425 - pyskl - INFO - +mean_acc 0.2226 +2024-12-28 02:39:58,445 - pyskl - INFO - Epoch(val) [55][309] top1_acc: 0.2227, top5_acc: 0.4721, mean_class_accuracy: 0.2226 +2024-12-28 02:44:16,954 - pyskl - INFO - Epoch [56][100/3746] lr: 7.031e-02, eta: 3 days, 8:15:10, time: 2.585, data_time: 1.537, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5433, loss_cls: 4.1071, loss: 4.1071 +2024-12-28 02:45:42,660 - pyskl - INFO - Epoch [56][200/3746] lr: 7.029e-02, eta: 3 days, 8:13:56, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5323, loss_cls: 4.1467, loss: 4.1467 +2024-12-28 02:47:08,153 - pyskl - INFO - Epoch [56][300/3746] lr: 7.026e-02, eta: 3 days, 8:12:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5312, loss_cls: 4.1397, loss: 4.1397 +2024-12-28 02:48:33,328 - pyskl - INFO - Epoch [56][400/3746] lr: 7.023e-02, eta: 3 days, 8:11:28, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5419, loss_cls: 4.0922, loss: 4.0922 +2024-12-28 02:49:58,896 - pyskl - INFO - Epoch [56][500/3746] lr: 7.021e-02, eta: 3 days, 8:10:14, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5350, loss_cls: 4.1160, loss: 4.1160 +2024-12-28 02:51:24,052 - pyskl - INFO - Epoch [56][600/3746] lr: 7.018e-02, eta: 3 days, 8:09:00, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5458, loss_cls: 4.0969, loss: 4.0969 +2024-12-28 02:52:49,515 - pyskl - INFO - Epoch [56][700/3746] lr: 7.016e-02, eta: 3 days, 8:07:46, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5456, loss_cls: 4.0896, loss: 4.0896 +2024-12-28 02:54:15,072 - pyskl - INFO - Epoch [56][800/3746] lr: 7.013e-02, eta: 3 days, 8:06:32, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5341, loss_cls: 4.1254, loss: 4.1254 +2024-12-28 02:55:40,455 - pyskl - INFO - Epoch [56][900/3746] lr: 7.011e-02, eta: 3 days, 8:05:18, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5409, loss_cls: 4.0843, loss: 4.0843 +2024-12-28 02:57:05,980 - pyskl - INFO - Epoch [56][1000/3746] lr: 7.008e-02, eta: 3 days, 8:04:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5416, loss_cls: 4.1231, loss: 4.1231 +2024-12-28 02:58:31,557 - pyskl - INFO - Epoch [56][1100/3746] lr: 7.006e-02, eta: 3 days, 8:02:50, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5345, loss_cls: 4.1368, loss: 4.1368 +2024-12-28 02:59:57,094 - pyskl - INFO - Epoch [56][1200/3746] lr: 7.003e-02, eta: 3 days, 8:01:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5336, loss_cls: 4.1299, loss: 4.1299 +2024-12-28 03:01:22,138 - pyskl - INFO - Epoch [56][1300/3746] lr: 7.000e-02, eta: 3 days, 8:00:22, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5355, loss_cls: 4.1124, loss: 4.1124 +2024-12-28 03:02:46,731 - pyskl - INFO - Epoch [56][1400/3746] lr: 6.998e-02, eta: 3 days, 7:59:06, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5445, loss_cls: 4.1003, loss: 4.1003 +2024-12-28 03:04:11,494 - pyskl - INFO - Epoch [56][1500/3746] lr: 6.995e-02, eta: 3 days, 7:57:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5281, loss_cls: 4.1303, loss: 4.1303 +2024-12-28 03:05:35,981 - pyskl - INFO - Epoch [56][1600/3746] lr: 6.993e-02, eta: 3 days, 7:56:35, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5305, loss_cls: 4.1702, loss: 4.1702 +2024-12-28 03:07:01,025 - pyskl - INFO - Epoch [56][1700/3746] lr: 6.990e-02, eta: 3 days, 7:55:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5241, loss_cls: 4.1740, loss: 4.1740 +2024-12-28 03:08:26,053 - pyskl - INFO - Epoch [56][1800/3746] lr: 6.988e-02, eta: 3 days, 7:54:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5411, loss_cls: 4.1667, loss: 4.1667 +2024-12-28 03:09:51,680 - pyskl - INFO - Epoch [56][1900/3746] lr: 6.985e-02, eta: 3 days, 7:52:52, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5381, loss_cls: 4.1423, loss: 4.1423 +2024-12-28 03:11:17,273 - pyskl - INFO - Epoch [56][2000/3746] lr: 6.983e-02, eta: 3 days, 7:51:38, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5363, loss_cls: 4.1431, loss: 4.1431 +2024-12-28 03:12:42,843 - pyskl - INFO - Epoch [56][2100/3746] lr: 6.980e-02, eta: 3 days, 7:50:24, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5230, loss_cls: 4.1621, loss: 4.1621 +2024-12-28 03:14:07,852 - pyskl - INFO - Epoch [56][2200/3746] lr: 6.977e-02, eta: 3 days, 7:49:10, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5364, loss_cls: 4.1348, loss: 4.1348 +2024-12-28 03:15:33,246 - pyskl - INFO - Epoch [56][2300/3746] lr: 6.975e-02, eta: 3 days, 7:47:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5311, loss_cls: 4.1517, loss: 4.1517 +2024-12-28 03:16:58,944 - pyskl - INFO - Epoch [56][2400/3746] lr: 6.972e-02, eta: 3 days, 7:46:42, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5380, loss_cls: 4.1486, loss: 4.1486 +2024-12-28 03:18:24,405 - pyskl - INFO - Epoch [56][2500/3746] lr: 6.970e-02, eta: 3 days, 7:45:27, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5294, loss_cls: 4.1312, loss: 4.1312 +2024-12-28 03:19:50,021 - pyskl - INFO - Epoch [56][2600/3746] lr: 6.967e-02, eta: 3 days, 7:44:14, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5355, loss_cls: 4.1182, loss: 4.1182 +2024-12-28 03:21:15,243 - pyskl - INFO - Epoch [56][2700/3746] lr: 6.965e-02, eta: 3 days, 7:42:59, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5252, loss_cls: 4.1538, loss: 4.1538 +2024-12-28 03:22:40,792 - pyskl - INFO - Epoch [56][2800/3746] lr: 6.962e-02, eta: 3 days, 7:41:45, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5322, loss_cls: 4.1471, loss: 4.1471 +2024-12-28 03:24:06,521 - pyskl - INFO - Epoch [56][2900/3746] lr: 6.959e-02, eta: 3 days, 7:40:31, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5359, loss_cls: 4.1424, loss: 4.1424 +2024-12-28 03:25:32,550 - pyskl - INFO - Epoch [56][3000/3746] lr: 6.957e-02, eta: 3 days, 7:39:18, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5392, loss_cls: 4.1103, loss: 4.1103 +2024-12-28 03:26:57,966 - pyskl - INFO - Epoch [56][3100/3746] lr: 6.954e-02, eta: 3 days, 7:38:04, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5402, loss_cls: 4.1153, loss: 4.1153 +2024-12-28 03:28:23,185 - pyskl - INFO - Epoch [56][3200/3746] lr: 6.952e-02, eta: 3 days, 7:36:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5339, loss_cls: 4.1375, loss: 4.1375 +2024-12-28 03:29:48,605 - pyskl - INFO - Epoch [56][3300/3746] lr: 6.949e-02, eta: 3 days, 7:35:35, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5319, loss_cls: 4.1241, loss: 4.1241 +2024-12-28 03:31:13,991 - pyskl - INFO - Epoch [56][3400/3746] lr: 6.947e-02, eta: 3 days, 7:34:21, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5366, loss_cls: 4.1304, loss: 4.1304 +2024-12-28 03:32:38,939 - pyskl - INFO - Epoch [56][3500/3746] lr: 6.944e-02, eta: 3 days, 7:33:06, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5430, loss_cls: 4.1014, loss: 4.1014 +2024-12-28 03:34:04,423 - pyskl - INFO - Epoch [56][3600/3746] lr: 6.941e-02, eta: 3 days, 7:31:51, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5370, loss_cls: 4.1323, loss: 4.1323 +2024-12-28 03:35:30,105 - pyskl - INFO - Epoch [56][3700/3746] lr: 6.939e-02, eta: 3 days, 7:30:37, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5398, loss_cls: 4.0973, loss: 4.0973 +2024-12-28 03:36:11,428 - pyskl - INFO - Saving checkpoint at 56 epochs +2024-12-28 03:38:09,524 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 03:38:10,616 - pyskl - INFO - +top1_acc 0.2134 +top5_acc 0.4484 +2024-12-28 03:38:10,616 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 03:38:10,660 - pyskl - INFO - +mean_acc 0.2133 +2024-12-28 03:38:10,675 - pyskl - INFO - Epoch(val) [56][309] top1_acc: 0.2134, top5_acc: 0.4484, mean_class_accuracy: 0.2133 +2024-12-28 03:42:30,352 - pyskl - INFO - Epoch [57][100/3746] lr: 6.935e-02, eta: 3 days, 7:32:35, time: 2.597, data_time: 1.554, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5497, loss_cls: 4.0209, loss: 4.0209 +2024-12-28 03:43:55,418 - pyskl - INFO - Epoch [57][200/3746] lr: 6.932e-02, eta: 3 days, 7:31:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5419, loss_cls: 4.0863, loss: 4.0863 +2024-12-28 03:45:20,499 - pyskl - INFO - Epoch [57][300/3746] lr: 6.930e-02, eta: 3 days, 7:30:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5464, loss_cls: 4.0455, loss: 4.0455 +2024-12-28 03:46:45,338 - pyskl - INFO - Epoch [57][400/3746] lr: 6.927e-02, eta: 3 days, 7:28:50, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5439, loss_cls: 4.1332, loss: 4.1332 +2024-12-28 03:48:10,960 - pyskl - INFO - Epoch [57][500/3746] lr: 6.925e-02, eta: 3 days, 7:27:35, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5259, loss_cls: 4.1649, loss: 4.1649 +2024-12-28 03:49:37,097 - pyskl - INFO - Epoch [57][600/3746] lr: 6.922e-02, eta: 3 days, 7:26:22, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5459, loss_cls: 4.0947, loss: 4.0947 +2024-12-28 03:51:02,819 - pyskl - INFO - Epoch [57][700/3746] lr: 6.920e-02, eta: 3 days, 7:25:08, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5495, loss_cls: 4.0734, loss: 4.0734 +2024-12-28 03:52:28,517 - pyskl - INFO - Epoch [57][800/3746] lr: 6.917e-02, eta: 3 days, 7:23:54, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5387, loss_cls: 4.1059, loss: 4.1059 +2024-12-28 03:53:54,684 - pyskl - INFO - Epoch [57][900/3746] lr: 6.914e-02, eta: 3 days, 7:22:40, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5322, loss_cls: 4.1170, loss: 4.1170 +2024-12-28 03:55:20,526 - pyskl - INFO - Epoch [57][1000/3746] lr: 6.912e-02, eta: 3 days, 7:21:27, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5453, loss_cls: 4.0684, loss: 4.0684 +2024-12-28 03:56:46,135 - pyskl - INFO - Epoch [57][1100/3746] lr: 6.909e-02, eta: 3 days, 7:20:12, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5292, loss_cls: 4.1434, loss: 4.1434 +2024-12-28 03:58:11,438 - pyskl - INFO - Epoch [57][1200/3746] lr: 6.907e-02, eta: 3 days, 7:18:57, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5452, loss_cls: 4.0823, loss: 4.0823 +2024-12-28 03:59:36,007 - pyskl - INFO - Epoch [57][1300/3746] lr: 6.904e-02, eta: 3 days, 7:17:41, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5258, loss_cls: 4.1787, loss: 4.1787 +2024-12-28 04:01:00,672 - pyskl - INFO - Epoch [57][1400/3746] lr: 6.901e-02, eta: 3 days, 7:16:25, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5355, loss_cls: 4.1319, loss: 4.1319 +2024-12-28 04:02:25,445 - pyskl - INFO - Epoch [57][1500/3746] lr: 6.899e-02, eta: 3 days, 7:15:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5398, loss_cls: 4.1163, loss: 4.1163 +2024-12-28 04:03:50,567 - pyskl - INFO - Epoch [57][1600/3746] lr: 6.896e-02, eta: 3 days, 7:13:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5448, loss_cls: 4.0923, loss: 4.0923 +2024-12-28 04:05:15,512 - pyskl - INFO - Epoch [57][1700/3746] lr: 6.894e-02, eta: 3 days, 7:12:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5387, loss_cls: 4.1209, loss: 4.1209 +2024-12-28 04:06:40,457 - pyskl - INFO - Epoch [57][1800/3746] lr: 6.891e-02, eta: 3 days, 7:11:24, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5367, loss_cls: 4.0976, loss: 4.0976 +2024-12-28 04:08:05,494 - pyskl - INFO - Epoch [57][1900/3746] lr: 6.889e-02, eta: 3 days, 7:10:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5400, loss_cls: 4.1146, loss: 4.1146 +2024-12-28 04:09:30,169 - pyskl - INFO - Epoch [57][2000/3746] lr: 6.886e-02, eta: 3 days, 7:08:52, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5373, loss_cls: 4.1040, loss: 4.1040 +2024-12-28 04:10:54,966 - pyskl - INFO - Epoch [57][2100/3746] lr: 6.883e-02, eta: 3 days, 7:07:37, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5414, loss_cls: 4.1129, loss: 4.1129 +2024-12-28 04:12:19,587 - pyskl - INFO - Epoch [57][2200/3746] lr: 6.881e-02, eta: 3 days, 7:06:21, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5323, loss_cls: 4.1304, loss: 4.1304 +2024-12-28 04:13:44,772 - pyskl - INFO - Epoch [57][2300/3746] lr: 6.878e-02, eta: 3 days, 7:05:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5445, loss_cls: 4.0742, loss: 4.0742 +2024-12-28 04:15:09,597 - pyskl - INFO - Epoch [57][2400/3746] lr: 6.876e-02, eta: 3 days, 7:03:50, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5428, loss_cls: 4.1262, loss: 4.1262 +2024-12-28 04:16:34,825 - pyskl - INFO - Epoch [57][2500/3746] lr: 6.873e-02, eta: 3 days, 7:02:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5291, loss_cls: 4.1470, loss: 4.1470 +2024-12-28 04:17:59,051 - pyskl - INFO - Epoch [57][2600/3746] lr: 6.870e-02, eta: 3 days, 7:01:18, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5283, loss_cls: 4.1568, loss: 4.1568 +2024-12-28 04:19:23,524 - pyskl - INFO - Epoch [57][2700/3746] lr: 6.868e-02, eta: 3 days, 7:00:02, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5387, loss_cls: 4.1082, loss: 4.1082 +2024-12-28 04:20:47,979 - pyskl - INFO - Epoch [57][2800/3746] lr: 6.865e-02, eta: 3 days, 6:58:45, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5364, loss_cls: 4.1529, loss: 4.1529 +2024-12-28 04:22:13,160 - pyskl - INFO - Epoch [57][2900/3746] lr: 6.863e-02, eta: 3 days, 6:57:30, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5280, loss_cls: 4.1940, loss: 4.1940 +2024-12-28 04:23:38,192 - pyskl - INFO - Epoch [57][3000/3746] lr: 6.860e-02, eta: 3 days, 6:56:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5372, loss_cls: 4.1143, loss: 4.1143 +2024-12-28 04:25:03,058 - pyskl - INFO - Epoch [57][3100/3746] lr: 6.857e-02, eta: 3 days, 6:54:59, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5434, loss_cls: 4.1081, loss: 4.1081 +2024-12-28 04:26:27,933 - pyskl - INFO - Epoch [57][3200/3746] lr: 6.855e-02, eta: 3 days, 6:53:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5391, loss_cls: 4.1173, loss: 4.1173 +2024-12-28 04:27:52,701 - pyskl - INFO - Epoch [57][3300/3746] lr: 6.852e-02, eta: 3 days, 6:52:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5325, loss_cls: 4.1533, loss: 4.1533 +2024-12-28 04:29:17,241 - pyskl - INFO - Epoch [57][3400/3746] lr: 6.850e-02, eta: 3 days, 6:51:11, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5433, loss_cls: 4.1258, loss: 4.1258 +2024-12-28 04:30:42,252 - pyskl - INFO - Epoch [57][3500/3746] lr: 6.847e-02, eta: 3 days, 6:49:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5448, loss_cls: 4.0563, loss: 4.0563 +2024-12-28 04:32:07,752 - pyskl - INFO - Epoch [57][3600/3746] lr: 6.844e-02, eta: 3 days, 6:48:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5384, loss_cls: 4.1369, loss: 4.1369 +2024-12-28 04:33:32,948 - pyskl - INFO - Epoch [57][3700/3746] lr: 6.842e-02, eta: 3 days, 6:47:26, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5487, loss_cls: 4.0282, loss: 4.0282 +2024-12-28 04:34:14,100 - pyskl - INFO - Saving checkpoint at 57 epochs +2024-12-28 04:36:12,519 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 04:36:13,610 - pyskl - INFO - +top1_acc 0.2220 +top5_acc 0.4650 +2024-12-28 04:36:13,610 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 04:36:13,650 - pyskl - INFO - +mean_acc 0.2217 +2024-12-28 04:36:13,663 - pyskl - INFO - Epoch(val) [57][309] top1_acc: 0.2220, top5_acc: 0.4650, mean_class_accuracy: 0.2217 +2024-12-28 04:40:30,416 - pyskl - INFO - Epoch [58][100/3746] lr: 6.838e-02, eta: 3 days, 6:49:12, time: 2.567, data_time: 1.532, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5528, loss_cls: 4.0488, loss: 4.0488 +2024-12-28 04:41:55,546 - pyskl - INFO - Epoch [58][200/3746] lr: 6.835e-02, eta: 3 days, 6:47:56, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5472, loss_cls: 4.0836, loss: 4.0836 +2024-12-28 04:43:21,090 - pyskl - INFO - Epoch [58][300/3746] lr: 6.833e-02, eta: 3 days, 6:46:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5417, loss_cls: 4.0990, loss: 4.0990 +2024-12-28 04:44:45,568 - pyskl - INFO - Epoch [58][400/3746] lr: 6.830e-02, eta: 3 days, 6:45:25, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5534, loss_cls: 4.0577, loss: 4.0577 +2024-12-28 04:46:10,387 - pyskl - INFO - Epoch [58][500/3746] lr: 6.828e-02, eta: 3 days, 6:44:09, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5328, loss_cls: 4.1338, loss: 4.1338 +2024-12-28 04:47:35,919 - pyskl - INFO - Epoch [58][600/3746] lr: 6.825e-02, eta: 3 days, 6:42:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5341, loss_cls: 4.1153, loss: 4.1153 +2024-12-28 04:49:00,954 - pyskl - INFO - Epoch [58][700/3746] lr: 6.822e-02, eta: 3 days, 6:41:38, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5472, loss_cls: 4.0567, loss: 4.0567 +2024-12-28 04:50:25,851 - pyskl - INFO - Epoch [58][800/3746] lr: 6.820e-02, eta: 3 days, 6:40:22, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5402, loss_cls: 4.1126, loss: 4.1126 +2024-12-28 04:51:50,289 - pyskl - INFO - Epoch [58][900/3746] lr: 6.817e-02, eta: 3 days, 6:39:05, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5450, loss_cls: 4.0400, loss: 4.0400 +2024-12-28 04:53:15,346 - pyskl - INFO - Epoch [58][1000/3746] lr: 6.815e-02, eta: 3 days, 6:37:50, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5273, loss_cls: 4.1548, loss: 4.1548 +2024-12-28 04:54:41,155 - pyskl - INFO - Epoch [58][1100/3746] lr: 6.812e-02, eta: 3 days, 6:36:35, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5302, loss_cls: 4.1494, loss: 4.1494 +2024-12-28 04:56:06,550 - pyskl - INFO - Epoch [58][1200/3746] lr: 6.809e-02, eta: 3 days, 6:35:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5494, loss_cls: 4.0448, loss: 4.0448 +2024-12-28 04:57:32,019 - pyskl - INFO - Epoch [58][1300/3746] lr: 6.807e-02, eta: 3 days, 6:34:05, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5498, loss_cls: 4.0862, loss: 4.0862 +2024-12-28 04:58:57,460 - pyskl - INFO - Epoch [58][1400/3746] lr: 6.804e-02, eta: 3 days, 6:32:50, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5420, loss_cls: 4.0986, loss: 4.0986 +2024-12-28 05:00:22,480 - pyskl - INFO - Epoch [58][1500/3746] lr: 6.802e-02, eta: 3 days, 6:31:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5370, loss_cls: 4.1335, loss: 4.1335 +2024-12-28 05:01:47,598 - pyskl - INFO - Epoch [58][1600/3746] lr: 6.799e-02, eta: 3 days, 6:30:18, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5392, loss_cls: 4.0819, loss: 4.0819 +2024-12-28 05:03:12,361 - pyskl - INFO - Epoch [58][1700/3746] lr: 6.796e-02, eta: 3 days, 6:29:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5500, loss_cls: 4.0736, loss: 4.0736 +2024-12-28 05:04:37,492 - pyskl - INFO - Epoch [58][1800/3746] lr: 6.794e-02, eta: 3 days, 6:27:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5430, loss_cls: 4.0778, loss: 4.0778 +2024-12-28 05:06:02,537 - pyskl - INFO - Epoch [58][1900/3746] lr: 6.791e-02, eta: 3 days, 6:26:31, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5211, loss_cls: 4.2115, loss: 4.2115 +2024-12-28 05:07:27,831 - pyskl - INFO - Epoch [58][2000/3746] lr: 6.789e-02, eta: 3 days, 6:25:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5413, loss_cls: 4.1098, loss: 4.1098 +2024-12-28 05:08:52,277 - pyskl - INFO - Epoch [58][2100/3746] lr: 6.786e-02, eta: 3 days, 6:23:58, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5425, loss_cls: 4.1035, loss: 4.1035 +2024-12-28 05:10:17,195 - pyskl - INFO - Epoch [58][2200/3746] lr: 6.783e-02, eta: 3 days, 6:22:42, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5367, loss_cls: 4.1437, loss: 4.1437 +2024-12-28 05:11:42,523 - pyskl - INFO - Epoch [58][2300/3746] lr: 6.781e-02, eta: 3 days, 6:21:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5403, loss_cls: 4.0977, loss: 4.0977 +2024-12-28 05:13:07,290 - pyskl - INFO - Epoch [58][2400/3746] lr: 6.778e-02, eta: 3 days, 6:20:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5414, loss_cls: 4.1177, loss: 4.1177 +2024-12-28 05:14:32,510 - pyskl - INFO - Epoch [58][2500/3746] lr: 6.775e-02, eta: 3 days, 6:18:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5377, loss_cls: 4.1070, loss: 4.1070 +2024-12-28 05:15:57,392 - pyskl - INFO - Epoch [58][2600/3746] lr: 6.773e-02, eta: 3 days, 6:17:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5522, loss_cls: 4.0389, loss: 4.0389 +2024-12-28 05:17:22,020 - pyskl - INFO - Epoch [58][2700/3746] lr: 6.770e-02, eta: 3 days, 6:16:22, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5341, loss_cls: 4.1277, loss: 4.1277 +2024-12-28 05:18:46,919 - pyskl - INFO - Epoch [58][2800/3746] lr: 6.768e-02, eta: 3 days, 6:15:06, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5281, loss_cls: 4.1304, loss: 4.1304 +2024-12-28 05:20:12,631 - pyskl - INFO - Epoch [58][2900/3746] lr: 6.765e-02, eta: 3 days, 6:13:52, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5439, loss_cls: 4.1039, loss: 4.1039 +2024-12-28 05:21:37,966 - pyskl - INFO - Epoch [58][3000/3746] lr: 6.762e-02, eta: 3 days, 6:12:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5330, loss_cls: 4.1487, loss: 4.1487 +2024-12-28 05:23:04,045 - pyskl - INFO - Epoch [58][3100/3746] lr: 6.760e-02, eta: 3 days, 6:11:22, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5470, loss_cls: 4.0876, loss: 4.0876 +2024-12-28 05:24:29,343 - pyskl - INFO - Epoch [58][3200/3746] lr: 6.757e-02, eta: 3 days, 6:10:06, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5467, loss_cls: 4.0792, loss: 4.0792 +2024-12-28 05:25:54,931 - pyskl - INFO - Epoch [58][3300/3746] lr: 6.755e-02, eta: 3 days, 6:08:51, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5309, loss_cls: 4.1886, loss: 4.1886 +2024-12-28 05:27:19,630 - pyskl - INFO - Epoch [58][3400/3746] lr: 6.752e-02, eta: 3 days, 6:07:35, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5411, loss_cls: 4.1214, loss: 4.1214 +2024-12-28 05:28:44,560 - pyskl - INFO - Epoch [58][3500/3746] lr: 6.749e-02, eta: 3 days, 6:06:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5384, loss_cls: 4.0949, loss: 4.0949 +2024-12-28 05:30:09,105 - pyskl - INFO - Epoch [58][3600/3746] lr: 6.747e-02, eta: 3 days, 6:05:02, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5416, loss_cls: 4.1270, loss: 4.1270 +2024-12-28 05:31:34,292 - pyskl - INFO - Epoch [58][3700/3746] lr: 6.744e-02, eta: 3 days, 6:03:46, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5427, loss_cls: 4.1106, loss: 4.1106 +2024-12-28 05:32:15,575 - pyskl - INFO - Saving checkpoint at 58 epochs +2024-12-28 05:34:12,280 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 05:34:13,042 - pyskl - INFO - +top1_acc 0.2283 +top5_acc 0.4721 +2024-12-28 05:34:13,042 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 05:34:13,094 - pyskl - INFO - +mean_acc 0.2284 +2024-12-28 05:34:13,112 - pyskl - INFO - Epoch(val) [58][309] top1_acc: 0.2283, top5_acc: 0.4721, mean_class_accuracy: 0.2284 +2024-12-28 05:38:32,733 - pyskl - INFO - Epoch [59][100/3746] lr: 6.740e-02, eta: 3 days, 6:05:30, time: 2.596, data_time: 1.554, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5483, loss_cls: 4.0457, loss: 4.0457 +2024-12-28 05:39:58,449 - pyskl - INFO - Epoch [59][200/3746] lr: 6.738e-02, eta: 3 days, 6:04:15, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5377, loss_cls: 4.0837, loss: 4.0837 +2024-12-28 05:41:23,760 - pyskl - INFO - Epoch [59][300/3746] lr: 6.735e-02, eta: 3 days, 6:02:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5336, loss_cls: 4.1096, loss: 4.1096 +2024-12-28 05:42:48,629 - pyskl - INFO - Epoch [59][400/3746] lr: 6.732e-02, eta: 3 days, 6:01:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5452, loss_cls: 4.0338, loss: 4.0338 +2024-12-28 05:44:13,372 - pyskl - INFO - Epoch [59][500/3746] lr: 6.730e-02, eta: 3 days, 6:00:26, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5427, loss_cls: 4.1043, loss: 4.1043 +2024-12-28 05:45:38,675 - pyskl - INFO - Epoch [59][600/3746] lr: 6.727e-02, eta: 3 days, 5:59:10, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5372, loss_cls: 4.1028, loss: 4.1028 +2024-12-28 05:47:03,797 - pyskl - INFO - Epoch [59][700/3746] lr: 6.725e-02, eta: 3 days, 5:57:54, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5406, loss_cls: 4.1033, loss: 4.1033 +2024-12-28 05:48:28,733 - pyskl - INFO - Epoch [59][800/3746] lr: 6.722e-02, eta: 3 days, 5:56:38, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5372, loss_cls: 4.1126, loss: 4.1126 +2024-12-28 05:49:53,110 - pyskl - INFO - Epoch [59][900/3746] lr: 6.719e-02, eta: 3 days, 5:55:21, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5441, loss_cls: 4.1065, loss: 4.1065 +2024-12-28 05:51:18,206 - pyskl - INFO - Epoch [59][1000/3746] lr: 6.717e-02, eta: 3 days, 5:54:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5487, loss_cls: 4.0749, loss: 4.0749 +2024-12-28 05:52:42,651 - pyskl - INFO - Epoch [59][1100/3746] lr: 6.714e-02, eta: 3 days, 5:52:47, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5414, loss_cls: 4.0874, loss: 4.0874 +2024-12-28 05:54:07,640 - pyskl - INFO - Epoch [59][1200/3746] lr: 6.711e-02, eta: 3 days, 5:51:31, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5461, loss_cls: 4.0998, loss: 4.0998 +2024-12-28 05:55:32,549 - pyskl - INFO - Epoch [59][1300/3746] lr: 6.709e-02, eta: 3 days, 5:50:15, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5389, loss_cls: 4.1231, loss: 4.1231 +2024-12-28 05:56:57,017 - pyskl - INFO - Epoch [59][1400/3746] lr: 6.706e-02, eta: 3 days, 5:48:57, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5492, loss_cls: 4.0517, loss: 4.0517 +2024-12-28 05:58:21,386 - pyskl - INFO - Epoch [59][1500/3746] lr: 6.704e-02, eta: 3 days, 5:47:40, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5530, loss_cls: 4.0471, loss: 4.0471 +2024-12-28 05:59:46,255 - pyskl - INFO - Epoch [59][1600/3746] lr: 6.701e-02, eta: 3 days, 5:46:24, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5427, loss_cls: 4.0907, loss: 4.0907 +2024-12-28 06:01:11,101 - pyskl - INFO - Epoch [59][1700/3746] lr: 6.698e-02, eta: 3 days, 5:45:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5352, loss_cls: 4.1061, loss: 4.1061 +2024-12-28 06:02:35,848 - pyskl - INFO - Epoch [59][1800/3746] lr: 6.696e-02, eta: 3 days, 5:43:50, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5392, loss_cls: 4.1174, loss: 4.1174 +2024-12-28 06:04:00,546 - pyskl - INFO - Epoch [59][1900/3746] lr: 6.693e-02, eta: 3 days, 5:42:34, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5322, loss_cls: 4.1237, loss: 4.1237 +2024-12-28 06:05:25,145 - pyskl - INFO - Epoch [59][2000/3746] lr: 6.690e-02, eta: 3 days, 5:41:17, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5352, loss_cls: 4.1296, loss: 4.1296 +2024-12-28 06:06:50,402 - pyskl - INFO - Epoch [59][2100/3746] lr: 6.688e-02, eta: 3 days, 5:40:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5414, loss_cls: 4.0793, loss: 4.0793 +2024-12-28 06:08:15,305 - pyskl - INFO - Epoch [59][2200/3746] lr: 6.685e-02, eta: 3 days, 5:38:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5380, loss_cls: 4.1123, loss: 4.1123 +2024-12-28 06:09:40,267 - pyskl - INFO - Epoch [59][2300/3746] lr: 6.682e-02, eta: 3 days, 5:37:28, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5403, loss_cls: 4.0967, loss: 4.0967 +2024-12-28 06:11:05,148 - pyskl - INFO - Epoch [59][2400/3746] lr: 6.680e-02, eta: 3 days, 5:36:11, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5452, loss_cls: 4.1223, loss: 4.1223 +2024-12-28 06:12:30,340 - pyskl - INFO - Epoch [59][2500/3746] lr: 6.677e-02, eta: 3 days, 5:34:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5358, loss_cls: 4.1165, loss: 4.1165 +2024-12-28 06:13:54,703 - pyskl - INFO - Epoch [59][2600/3746] lr: 6.675e-02, eta: 3 days, 5:33:38, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5430, loss_cls: 4.0617, loss: 4.0617 +2024-12-28 06:15:19,876 - pyskl - INFO - Epoch [59][2700/3746] lr: 6.672e-02, eta: 3 days, 5:32:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5298, loss_cls: 4.1144, loss: 4.1144 +2024-12-28 06:16:44,359 - pyskl - INFO - Epoch [59][2800/3746] lr: 6.669e-02, eta: 3 days, 5:31:04, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5467, loss_cls: 4.0663, loss: 4.0663 +2024-12-28 06:18:09,430 - pyskl - INFO - Epoch [59][2900/3746] lr: 6.667e-02, eta: 3 days, 5:29:48, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5297, loss_cls: 4.1336, loss: 4.1336 +2024-12-28 06:19:34,707 - pyskl - INFO - Epoch [59][3000/3746] lr: 6.664e-02, eta: 3 days, 5:28:32, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5348, loss_cls: 4.1112, loss: 4.1112 +2024-12-28 06:20:59,840 - pyskl - INFO - Epoch [59][3100/3746] lr: 6.661e-02, eta: 3 days, 5:27:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5419, loss_cls: 4.0884, loss: 4.0884 +2024-12-28 06:22:25,006 - pyskl - INFO - Epoch [59][3200/3746] lr: 6.659e-02, eta: 3 days, 5:25:59, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5453, loss_cls: 4.0852, loss: 4.0852 +2024-12-28 06:23:50,551 - pyskl - INFO - Epoch [59][3300/3746] lr: 6.656e-02, eta: 3 days, 5:24:44, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5391, loss_cls: 4.0683, loss: 4.0683 +2024-12-28 06:25:15,661 - pyskl - INFO - Epoch [59][3400/3746] lr: 6.653e-02, eta: 3 days, 5:23:28, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5336, loss_cls: 4.1406, loss: 4.1406 +2024-12-28 06:26:40,452 - pyskl - INFO - Epoch [59][3500/3746] lr: 6.651e-02, eta: 3 days, 5:22:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5472, loss_cls: 4.0503, loss: 4.0503 +2024-12-28 06:28:05,390 - pyskl - INFO - Epoch [59][3600/3746] lr: 6.648e-02, eta: 3 days, 5:20:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5311, loss_cls: 4.1346, loss: 4.1346 +2024-12-28 06:29:29,966 - pyskl - INFO - Epoch [59][3700/3746] lr: 6.646e-02, eta: 3 days, 5:19:37, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5337, loss_cls: 4.1182, loss: 4.1182 +2024-12-28 06:30:11,099 - pyskl - INFO - Saving checkpoint at 59 epochs +2024-12-28 06:32:11,032 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 06:32:11,709 - pyskl - INFO - +top1_acc 0.2307 +top5_acc 0.4696 +2024-12-28 06:32:11,709 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 06:32:11,754 - pyskl - INFO - +mean_acc 0.2305 +2024-12-28 06:32:11,759 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_51.pth was removed +2024-12-28 06:32:12,117 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_59.pth. +2024-12-28 06:32:12,118 - pyskl - INFO - Best top1_acc is 0.2307 at 59 epoch. +2024-12-28 06:32:12,135 - pyskl - INFO - Epoch(val) [59][309] top1_acc: 0.2307, top5_acc: 0.4696, mean_class_accuracy: 0.2305 +2024-12-28 06:36:27,760 - pyskl - INFO - Epoch [60][100/3746] lr: 6.642e-02, eta: 3 days, 5:21:08, time: 2.556, data_time: 1.521, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5663, loss_cls: 3.9986, loss: 3.9986 +2024-12-28 06:37:53,006 - pyskl - INFO - Epoch [60][200/3746] lr: 6.639e-02, eta: 3 days, 5:19:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5497, loss_cls: 4.0676, loss: 4.0676 +2024-12-28 06:39:18,171 - pyskl - INFO - Epoch [60][300/3746] lr: 6.636e-02, eta: 3 days, 5:18:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5495, loss_cls: 4.0567, loss: 4.0567 +2024-12-28 06:40:42,829 - pyskl - INFO - Epoch [60][400/3746] lr: 6.634e-02, eta: 3 days, 5:17:18, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5492, loss_cls: 4.0657, loss: 4.0657 +2024-12-28 06:42:08,087 - pyskl - INFO - Epoch [60][500/3746] lr: 6.631e-02, eta: 3 days, 5:16:02, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5375, loss_cls: 4.1080, loss: 4.1080 +2024-12-28 06:43:33,406 - pyskl - INFO - Epoch [60][600/3746] lr: 6.629e-02, eta: 3 days, 5:14:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5430, loss_cls: 4.1049, loss: 4.1049 +2024-12-28 06:44:58,784 - pyskl - INFO - Epoch [60][700/3746] lr: 6.626e-02, eta: 3 days, 5:13:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5352, loss_cls: 4.1072, loss: 4.1072 +2024-12-28 06:46:24,240 - pyskl - INFO - Epoch [60][800/3746] lr: 6.623e-02, eta: 3 days, 5:12:14, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5464, loss_cls: 4.0845, loss: 4.0845 +2024-12-28 06:47:49,698 - pyskl - INFO - Epoch [60][900/3746] lr: 6.621e-02, eta: 3 days, 5:10:58, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5386, loss_cls: 4.1132, loss: 4.1132 +2024-12-28 06:49:14,629 - pyskl - INFO - Epoch [60][1000/3746] lr: 6.618e-02, eta: 3 days, 5:09:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5550, loss_cls: 4.0368, loss: 4.0368 +2024-12-28 06:50:39,673 - pyskl - INFO - Epoch [60][1100/3746] lr: 6.615e-02, eta: 3 days, 5:08:24, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5325, loss_cls: 4.1163, loss: 4.1163 +2024-12-28 06:52:05,047 - pyskl - INFO - Epoch [60][1200/3746] lr: 6.613e-02, eta: 3 days, 5:07:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5492, loss_cls: 4.0501, loss: 4.0501 +2024-12-28 06:53:30,089 - pyskl - INFO - Epoch [60][1300/3746] lr: 6.610e-02, eta: 3 days, 5:05:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5456, loss_cls: 4.0801, loss: 4.0801 +2024-12-28 06:54:54,613 - pyskl - INFO - Epoch [60][1400/3746] lr: 6.607e-02, eta: 3 days, 5:04:34, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5400, loss_cls: 4.1016, loss: 4.1016 +2024-12-28 06:56:18,939 - pyskl - INFO - Epoch [60][1500/3746] lr: 6.605e-02, eta: 3 days, 5:03:16, time: 0.843, data_time: 0.001, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5345, loss_cls: 4.1443, loss: 4.1443 +2024-12-28 06:57:43,891 - pyskl - INFO - Epoch [60][1600/3746] lr: 6.602e-02, eta: 3 days, 5:01:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5444, loss_cls: 4.0865, loss: 4.0865 +2024-12-28 06:59:09,398 - pyskl - INFO - Epoch [60][1700/3746] lr: 6.599e-02, eta: 3 days, 5:00:43, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5472, loss_cls: 4.0840, loss: 4.0840 +2024-12-28 07:00:33,821 - pyskl - INFO - Epoch [60][1800/3746] lr: 6.597e-02, eta: 3 days, 4:59:26, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5509, loss_cls: 4.0727, loss: 4.0727 +2024-12-28 07:01:58,824 - pyskl - INFO - Epoch [60][1900/3746] lr: 6.594e-02, eta: 3 days, 4:58:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5409, loss_cls: 4.0881, loss: 4.0881 +2024-12-28 07:03:23,956 - pyskl - INFO - Epoch [60][2000/3746] lr: 6.591e-02, eta: 3 days, 4:56:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5384, loss_cls: 4.0907, loss: 4.0907 +2024-12-28 07:04:49,297 - pyskl - INFO - Epoch [60][2100/3746] lr: 6.589e-02, eta: 3 days, 4:55:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5470, loss_cls: 4.0723, loss: 4.0723 +2024-12-28 07:06:15,008 - pyskl - INFO - Epoch [60][2200/3746] lr: 6.586e-02, eta: 3 days, 4:54:20, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5356, loss_cls: 4.1388, loss: 4.1388 +2024-12-28 07:07:40,481 - pyskl - INFO - Epoch [60][2300/3746] lr: 6.584e-02, eta: 3 days, 4:53:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5477, loss_cls: 4.0900, loss: 4.0900 +2024-12-28 07:09:05,982 - pyskl - INFO - Epoch [60][2400/3746] lr: 6.581e-02, eta: 3 days, 4:51:48, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5398, loss_cls: 4.0887, loss: 4.0887 +2024-12-28 07:10:31,159 - pyskl - INFO - Epoch [60][2500/3746] lr: 6.578e-02, eta: 3 days, 4:50:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5294, loss_cls: 4.1353, loss: 4.1353 +2024-12-28 07:11:56,527 - pyskl - INFO - Epoch [60][2600/3746] lr: 6.576e-02, eta: 3 days, 4:49:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5417, loss_cls: 4.1001, loss: 4.1001 +2024-12-28 07:13:21,730 - pyskl - INFO - Epoch [60][2700/3746] lr: 6.573e-02, eta: 3 days, 4:47:59, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5437, loss_cls: 4.1100, loss: 4.1100 +2024-12-28 07:14:47,163 - pyskl - INFO - Epoch [60][2800/3746] lr: 6.570e-02, eta: 3 days, 4:46:42, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5363, loss_cls: 4.1175, loss: 4.1175 +2024-12-28 07:16:12,488 - pyskl - INFO - Epoch [60][2900/3746] lr: 6.568e-02, eta: 3 days, 4:45:26, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5519, loss_cls: 4.0628, loss: 4.0628 +2024-12-28 07:17:37,825 - pyskl - INFO - Epoch [60][3000/3746] lr: 6.565e-02, eta: 3 days, 4:44:10, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5459, loss_cls: 4.0886, loss: 4.0886 +2024-12-28 07:19:03,043 - pyskl - INFO - Epoch [60][3100/3746] lr: 6.562e-02, eta: 3 days, 4:42:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5405, loss_cls: 4.0684, loss: 4.0684 +2024-12-28 07:20:28,433 - pyskl - INFO - Epoch [60][3200/3746] lr: 6.560e-02, eta: 3 days, 4:41:37, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5392, loss_cls: 4.1068, loss: 4.1068 +2024-12-28 07:21:53,481 - pyskl - INFO - Epoch [60][3300/3746] lr: 6.557e-02, eta: 3 days, 4:40:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5455, loss_cls: 4.0904, loss: 4.0904 +2024-12-28 07:23:18,587 - pyskl - INFO - Epoch [60][3400/3746] lr: 6.554e-02, eta: 3 days, 4:39:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5392, loss_cls: 4.0955, loss: 4.0955 +2024-12-28 07:24:44,275 - pyskl - INFO - Epoch [60][3500/3746] lr: 6.552e-02, eta: 3 days, 4:37:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5541, loss_cls: 4.0467, loss: 4.0467 +2024-12-28 07:26:09,610 - pyskl - INFO - Epoch [60][3600/3746] lr: 6.549e-02, eta: 3 days, 4:36:31, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5288, loss_cls: 4.1376, loss: 4.1376 +2024-12-28 07:27:34,930 - pyskl - INFO - Epoch [60][3700/3746] lr: 6.546e-02, eta: 3 days, 4:35:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5463, loss_cls: 4.0738, loss: 4.0738 +2024-12-28 07:28:16,282 - pyskl - INFO - Saving checkpoint at 60 epochs +2024-12-28 07:30:14,719 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 07:30:15,903 - pyskl - INFO - +top1_acc 0.2349 +top5_acc 0.4786 +2024-12-28 07:30:15,903 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 07:30:15,958 - pyskl - INFO - +mean_acc 0.2348 +2024-12-28 07:30:15,962 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_59.pth was removed +2024-12-28 07:30:16,213 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_60.pth. +2024-12-28 07:30:16,214 - pyskl - INFO - Best top1_acc is 0.2349 at 60 epoch. +2024-12-28 07:30:16,227 - pyskl - INFO - Epoch(val) [60][309] top1_acc: 0.2349, top5_acc: 0.4786, mean_class_accuracy: 0.2348 +2024-12-28 07:34:34,628 - pyskl - INFO - Epoch [61][100/3746] lr: 6.542e-02, eta: 3 days, 4:36:44, time: 2.584, data_time: 1.541, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5573, loss_cls: 4.0233, loss: 4.0233 +2024-12-28 07:36:00,386 - pyskl - INFO - Epoch [61][200/3746] lr: 6.540e-02, eta: 3 days, 4:35:28, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5517, loss_cls: 4.0666, loss: 4.0666 +2024-12-28 07:37:25,977 - pyskl - INFO - Epoch [61][300/3746] lr: 6.537e-02, eta: 3 days, 4:34:11, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5503, loss_cls: 4.0435, loss: 4.0435 +2024-12-28 07:38:50,555 - pyskl - INFO - Epoch [61][400/3746] lr: 6.534e-02, eta: 3 days, 4:32:54, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5367, loss_cls: 4.1324, loss: 4.1324 +2024-12-28 07:40:15,439 - pyskl - INFO - Epoch [61][500/3746] lr: 6.532e-02, eta: 3 days, 4:31:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5503, loss_cls: 4.0705, loss: 4.0705 +2024-12-28 07:41:40,613 - pyskl - INFO - Epoch [61][600/3746] lr: 6.529e-02, eta: 3 days, 4:30:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5608, loss_cls: 3.9953, loss: 3.9953 +2024-12-28 07:43:05,594 - pyskl - INFO - Epoch [61][700/3746] lr: 6.526e-02, eta: 3 days, 4:29:02, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5425, loss_cls: 4.0793, loss: 4.0793 +2024-12-28 07:44:30,283 - pyskl - INFO - Epoch [61][800/3746] lr: 6.524e-02, eta: 3 days, 4:27:45, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5427, loss_cls: 4.0688, loss: 4.0688 +2024-12-28 07:45:55,272 - pyskl - INFO - Epoch [61][900/3746] lr: 6.521e-02, eta: 3 days, 4:26:28, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5450, loss_cls: 4.1016, loss: 4.1016 +2024-12-28 07:47:20,257 - pyskl - INFO - Epoch [61][1000/3746] lr: 6.519e-02, eta: 3 days, 4:25:10, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5420, loss_cls: 4.0907, loss: 4.0907 +2024-12-28 07:48:44,983 - pyskl - INFO - Epoch [61][1100/3746] lr: 6.516e-02, eta: 3 days, 4:23:53, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5380, loss_cls: 4.1437, loss: 4.1437 +2024-12-28 07:50:09,714 - pyskl - INFO - Epoch [61][1200/3746] lr: 6.513e-02, eta: 3 days, 4:22:35, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5558, loss_cls: 3.9989, loss: 3.9989 +2024-12-28 07:51:34,318 - pyskl - INFO - Epoch [61][1300/3746] lr: 6.511e-02, eta: 3 days, 4:21:18, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5394, loss_cls: 4.1043, loss: 4.1043 +2024-12-28 07:52:59,112 - pyskl - INFO - Epoch [61][1400/3746] lr: 6.508e-02, eta: 3 days, 4:20:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5492, loss_cls: 4.0658, loss: 4.0658 +2024-12-28 07:54:23,548 - pyskl - INFO - Epoch [61][1500/3746] lr: 6.505e-02, eta: 3 days, 4:18:42, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5413, loss_cls: 4.1238, loss: 4.1238 +2024-12-28 07:55:48,201 - pyskl - INFO - Epoch [61][1600/3746] lr: 6.503e-02, eta: 3 days, 4:17:24, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5486, loss_cls: 4.0666, loss: 4.0666 +2024-12-28 07:57:12,743 - pyskl - INFO - Epoch [61][1700/3746] lr: 6.500e-02, eta: 3 days, 4:16:07, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5522, loss_cls: 4.0468, loss: 4.0468 +2024-12-28 07:58:37,241 - pyskl - INFO - Epoch [61][1800/3746] lr: 6.497e-02, eta: 3 days, 4:14:49, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5528, loss_cls: 4.0708, loss: 4.0708 +2024-12-28 08:00:01,657 - pyskl - INFO - Epoch [61][1900/3746] lr: 6.495e-02, eta: 3 days, 4:13:31, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5459, loss_cls: 4.1029, loss: 4.1029 +2024-12-28 08:01:26,254 - pyskl - INFO - Epoch [61][2000/3746] lr: 6.492e-02, eta: 3 days, 4:12:13, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5495, loss_cls: 4.0120, loss: 4.0120 +2024-12-28 08:02:51,078 - pyskl - INFO - Epoch [61][2100/3746] lr: 6.489e-02, eta: 3 days, 4:10:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5391, loss_cls: 4.1048, loss: 4.1048 +2024-12-28 08:04:15,774 - pyskl - INFO - Epoch [61][2200/3746] lr: 6.487e-02, eta: 3 days, 4:09:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5459, loss_cls: 4.0539, loss: 4.0539 +2024-12-28 08:05:40,752 - pyskl - INFO - Epoch [61][2300/3746] lr: 6.484e-02, eta: 3 days, 4:08:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5503, loss_cls: 4.0456, loss: 4.0456 +2024-12-28 08:07:05,217 - pyskl - INFO - Epoch [61][2400/3746] lr: 6.481e-02, eta: 3 days, 4:07:02, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5472, loss_cls: 4.0340, loss: 4.0340 +2024-12-28 08:08:29,773 - pyskl - INFO - Epoch [61][2500/3746] lr: 6.478e-02, eta: 3 days, 4:05:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5411, loss_cls: 4.0839, loss: 4.0839 +2024-12-28 08:09:54,206 - pyskl - INFO - Epoch [61][2600/3746] lr: 6.476e-02, eta: 3 days, 4:04:26, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5436, loss_cls: 4.0883, loss: 4.0883 +2024-12-28 08:11:19,036 - pyskl - INFO - Epoch [61][2700/3746] lr: 6.473e-02, eta: 3 days, 4:03:09, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5467, loss_cls: 4.0701, loss: 4.0701 +2024-12-28 08:12:43,782 - pyskl - INFO - Epoch [61][2800/3746] lr: 6.470e-02, eta: 3 days, 4:01:51, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5428, loss_cls: 4.0857, loss: 4.0857 +2024-12-28 08:14:08,384 - pyskl - INFO - Epoch [61][2900/3746] lr: 6.468e-02, eta: 3 days, 4:00:33, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5395, loss_cls: 4.0796, loss: 4.0796 +2024-12-28 08:15:33,058 - pyskl - INFO - Epoch [61][3000/3746] lr: 6.465e-02, eta: 3 days, 3:59:16, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5425, loss_cls: 4.0740, loss: 4.0740 +2024-12-28 08:16:57,749 - pyskl - INFO - Epoch [61][3100/3746] lr: 6.462e-02, eta: 3 days, 3:57:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5523, loss_cls: 4.0275, loss: 4.0275 +2024-12-28 08:18:23,000 - pyskl - INFO - Epoch [61][3200/3746] lr: 6.460e-02, eta: 3 days, 3:56:41, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5352, loss_cls: 4.1417, loss: 4.1417 +2024-12-28 08:19:47,542 - pyskl - INFO - Epoch [61][3300/3746] lr: 6.457e-02, eta: 3 days, 3:55:23, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5394, loss_cls: 4.0809, loss: 4.0809 +2024-12-28 08:21:12,234 - pyskl - INFO - Epoch [61][3400/3746] lr: 6.454e-02, eta: 3 days, 3:54:05, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5422, loss_cls: 4.0916, loss: 4.0916 +2024-12-28 08:22:37,738 - pyskl - INFO - Epoch [61][3500/3746] lr: 6.452e-02, eta: 3 days, 3:52:49, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5434, loss_cls: 4.0829, loss: 4.0829 +2024-12-28 08:24:02,835 - pyskl - INFO - Epoch [61][3600/3746] lr: 6.449e-02, eta: 3 days, 3:51:32, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5416, loss_cls: 4.0855, loss: 4.0855 +2024-12-28 08:25:28,483 - pyskl - INFO - Epoch [61][3700/3746] lr: 6.446e-02, eta: 3 days, 3:50:15, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5373, loss_cls: 4.1132, loss: 4.1132 +2024-12-28 08:26:09,618 - pyskl - INFO - Saving checkpoint at 61 epochs +2024-12-28 08:28:08,256 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 08:28:09,296 - pyskl - INFO - +top1_acc 0.2226 +top5_acc 0.4568 +2024-12-28 08:28:09,297 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 08:28:09,346 - pyskl - INFO - +mean_acc 0.2224 +2024-12-28 08:28:09,361 - pyskl - INFO - Epoch(val) [61][309] top1_acc: 0.2226, top5_acc: 0.4568, mean_class_accuracy: 0.2224 +2024-12-28 08:32:21,460 - pyskl - INFO - Epoch [62][100/3746] lr: 6.443e-02, eta: 3 days, 3:51:29, time: 2.521, data_time: 1.484, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5455, loss_cls: 4.0358, loss: 4.0358 +2024-12-28 08:33:46,743 - pyskl - INFO - Epoch [62][200/3746] lr: 6.440e-02, eta: 3 days, 3:50:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5566, loss_cls: 4.0170, loss: 4.0170 +2024-12-28 08:35:11,717 - pyskl - INFO - Epoch [62][300/3746] lr: 6.437e-02, eta: 3 days, 3:48:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5498, loss_cls: 4.0575, loss: 4.0575 +2024-12-28 08:36:36,336 - pyskl - INFO - Epoch [62][400/3746] lr: 6.434e-02, eta: 3 days, 3:47:36, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5534, loss_cls: 4.0597, loss: 4.0597 +2024-12-28 08:38:01,391 - pyskl - INFO - Epoch [62][500/3746] lr: 6.432e-02, eta: 3 days, 3:46:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5464, loss_cls: 4.0570, loss: 4.0570 +2024-12-28 08:39:26,316 - pyskl - INFO - Epoch [62][600/3746] lr: 6.429e-02, eta: 3 days, 3:45:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5428, loss_cls: 4.0689, loss: 4.0689 +2024-12-28 08:40:51,071 - pyskl - INFO - Epoch [62][700/3746] lr: 6.426e-02, eta: 3 days, 3:43:43, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5416, loss_cls: 4.0870, loss: 4.0870 +2024-12-28 08:42:15,839 - pyskl - INFO - Epoch [62][800/3746] lr: 6.424e-02, eta: 3 days, 3:42:25, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5383, loss_cls: 4.0802, loss: 4.0802 +2024-12-28 08:43:40,437 - pyskl - INFO - Epoch [62][900/3746] lr: 6.421e-02, eta: 3 days, 3:41:07, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5552, loss_cls: 4.0493, loss: 4.0493 +2024-12-28 08:45:05,513 - pyskl - INFO - Epoch [62][1000/3746] lr: 6.418e-02, eta: 3 days, 3:39:50, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5473, loss_cls: 4.1039, loss: 4.1039 +2024-12-28 08:46:30,031 - pyskl - INFO - Epoch [62][1100/3746] lr: 6.416e-02, eta: 3 days, 3:38:32, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5430, loss_cls: 4.0827, loss: 4.0827 +2024-12-28 08:47:54,839 - pyskl - INFO - Epoch [62][1200/3746] lr: 6.413e-02, eta: 3 days, 3:37:14, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5375, loss_cls: 4.0822, loss: 4.0822 +2024-12-28 08:49:19,251 - pyskl - INFO - Epoch [62][1300/3746] lr: 6.410e-02, eta: 3 days, 3:35:56, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5398, loss_cls: 4.0894, loss: 4.0894 +2024-12-28 08:50:44,099 - pyskl - INFO - Epoch [62][1400/3746] lr: 6.408e-02, eta: 3 days, 3:34:38, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5433, loss_cls: 4.0620, loss: 4.0620 +2024-12-28 08:52:08,811 - pyskl - INFO - Epoch [62][1500/3746] lr: 6.405e-02, eta: 3 days, 3:33:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5478, loss_cls: 4.0239, loss: 4.0239 +2024-12-28 08:53:33,645 - pyskl - INFO - Epoch [62][1600/3746] lr: 6.402e-02, eta: 3 days, 3:32:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5400, loss_cls: 4.0761, loss: 4.0761 +2024-12-28 08:54:58,438 - pyskl - INFO - Epoch [62][1700/3746] lr: 6.400e-02, eta: 3 days, 3:30:44, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5481, loss_cls: 4.0628, loss: 4.0628 +2024-12-28 08:56:23,010 - pyskl - INFO - Epoch [62][1800/3746] lr: 6.397e-02, eta: 3 days, 3:29:26, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5505, loss_cls: 4.0556, loss: 4.0556 +2024-12-28 08:57:47,662 - pyskl - INFO - Epoch [62][1900/3746] lr: 6.394e-02, eta: 3 days, 3:28:08, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5481, loss_cls: 4.0418, loss: 4.0418 +2024-12-28 08:59:12,456 - pyskl - INFO - Epoch [62][2000/3746] lr: 6.392e-02, eta: 3 days, 3:26:50, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5597, loss_cls: 4.0234, loss: 4.0234 +2024-12-28 09:00:37,393 - pyskl - INFO - Epoch [62][2100/3746] lr: 6.389e-02, eta: 3 days, 3:25:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5437, loss_cls: 4.0500, loss: 4.0500 +2024-12-28 09:02:01,774 - pyskl - INFO - Epoch [62][2200/3746] lr: 6.386e-02, eta: 3 days, 3:24:14, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5427, loss_cls: 4.1037, loss: 4.1037 +2024-12-28 09:03:26,697 - pyskl - INFO - Epoch [62][2300/3746] lr: 6.384e-02, eta: 3 days, 3:22:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5491, loss_cls: 4.0757, loss: 4.0757 +2024-12-28 09:04:51,508 - pyskl - INFO - Epoch [62][2400/3746] lr: 6.381e-02, eta: 3 days, 3:21:38, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5477, loss_cls: 4.0993, loss: 4.0993 +2024-12-28 09:06:16,436 - pyskl - INFO - Epoch [62][2500/3746] lr: 6.378e-02, eta: 3 days, 3:20:20, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5427, loss_cls: 4.0677, loss: 4.0677 +2024-12-28 09:07:41,413 - pyskl - INFO - Epoch [62][2600/3746] lr: 6.375e-02, eta: 3 days, 3:19:03, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5420, loss_cls: 4.1005, loss: 4.1005 +2024-12-28 09:09:06,278 - pyskl - INFO - Epoch [62][2700/3746] lr: 6.373e-02, eta: 3 days, 3:17:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5467, loss_cls: 4.0899, loss: 4.0899 +2024-12-28 09:10:31,211 - pyskl - INFO - Epoch [62][2800/3746] lr: 6.370e-02, eta: 3 days, 3:16:27, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5372, loss_cls: 4.1133, loss: 4.1133 +2024-12-28 09:11:56,271 - pyskl - INFO - Epoch [62][2900/3746] lr: 6.367e-02, eta: 3 days, 3:15:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5431, loss_cls: 4.0983, loss: 4.0983 +2024-12-28 09:13:21,133 - pyskl - INFO - Epoch [62][3000/3746] lr: 6.365e-02, eta: 3 days, 3:13:52, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5461, loss_cls: 4.0930, loss: 4.0930 +2024-12-28 09:14:45,988 - pyskl - INFO - Epoch [62][3100/3746] lr: 6.362e-02, eta: 3 days, 3:12:34, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5372, loss_cls: 4.1014, loss: 4.1014 +2024-12-28 09:16:11,801 - pyskl - INFO - Epoch [62][3200/3746] lr: 6.359e-02, eta: 3 days, 3:11:17, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5475, loss_cls: 4.0838, loss: 4.0838 +2024-12-28 09:17:35,864 - pyskl - INFO - Epoch [62][3300/3746] lr: 6.357e-02, eta: 3 days, 3:09:58, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5461, loss_cls: 4.0527, loss: 4.0527 +2024-12-28 09:19:00,796 - pyskl - INFO - Epoch [62][3400/3746] lr: 6.354e-02, eta: 3 days, 3:08:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5466, loss_cls: 4.0555, loss: 4.0555 +2024-12-28 09:20:25,683 - pyskl - INFO - Epoch [62][3500/3746] lr: 6.351e-02, eta: 3 days, 3:07:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5455, loss_cls: 4.0558, loss: 4.0558 +2024-12-28 09:21:50,667 - pyskl - INFO - Epoch [62][3600/3746] lr: 6.349e-02, eta: 3 days, 3:06:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5409, loss_cls: 4.0809, loss: 4.0809 +2024-12-28 09:23:15,413 - pyskl - INFO - Epoch [62][3700/3746] lr: 6.346e-02, eta: 3 days, 3:04:47, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5453, loss_cls: 4.0797, loss: 4.0797 +2024-12-28 09:23:56,140 - pyskl - INFO - Saving checkpoint at 62 epochs +2024-12-28 09:25:54,049 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 09:25:54,838 - pyskl - INFO - +top1_acc 0.2216 +top5_acc 0.4661 +2024-12-28 09:25:54,838 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 09:25:54,886 - pyskl - INFO - +mean_acc 0.2214 +2024-12-28 09:25:54,899 - pyskl - INFO - Epoch(val) [62][309] top1_acc: 0.2216, top5_acc: 0.4661, mean_class_accuracy: 0.2214 +2024-12-28 09:30:07,521 - pyskl - INFO - Epoch [63][100/3746] lr: 6.342e-02, eta: 3 days, 3:05:56, time: 2.526, data_time: 1.487, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5561, loss_cls: 4.0228, loss: 4.0228 +2024-12-28 09:31:32,175 - pyskl - INFO - Epoch [63][200/3746] lr: 6.339e-02, eta: 3 days, 3:04:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5464, loss_cls: 4.0760, loss: 4.0760 +2024-12-28 09:32:57,301 - pyskl - INFO - Epoch [63][300/3746] lr: 6.337e-02, eta: 3 days, 3:03:20, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5487, loss_cls: 4.0463, loss: 4.0463 +2024-12-28 09:34:21,817 - pyskl - INFO - Epoch [63][400/3746] lr: 6.334e-02, eta: 3 days, 3:02:01, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5537, loss_cls: 4.0068, loss: 4.0068 +2024-12-28 09:35:46,807 - pyskl - INFO - Epoch [63][500/3746] lr: 6.331e-02, eta: 3 days, 3:00:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5444, loss_cls: 4.0719, loss: 4.0719 +2024-12-28 09:37:11,829 - pyskl - INFO - Epoch [63][600/3746] lr: 6.328e-02, eta: 3 days, 2:59:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5569, loss_cls: 4.0123, loss: 4.0123 +2024-12-28 09:38:36,890 - pyskl - INFO - Epoch [63][700/3746] lr: 6.326e-02, eta: 3 days, 2:58:08, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5423, loss_cls: 4.0590, loss: 4.0590 +2024-12-28 09:40:02,220 - pyskl - INFO - Epoch [63][800/3746] lr: 6.323e-02, eta: 3 days, 2:56:50, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5455, loss_cls: 4.0729, loss: 4.0729 +2024-12-28 09:41:26,994 - pyskl - INFO - Epoch [63][900/3746] lr: 6.320e-02, eta: 3 days, 2:55:32, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5370, loss_cls: 4.1350, loss: 4.1350 +2024-12-28 09:42:51,937 - pyskl - INFO - Epoch [63][1000/3746] lr: 6.318e-02, eta: 3 days, 2:54:14, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5447, loss_cls: 4.0744, loss: 4.0744 +2024-12-28 09:44:16,961 - pyskl - INFO - Epoch [63][1100/3746] lr: 6.315e-02, eta: 3 days, 2:52:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5372, loss_cls: 4.0801, loss: 4.0801 +2024-12-28 09:45:41,993 - pyskl - INFO - Epoch [63][1200/3746] lr: 6.312e-02, eta: 3 days, 2:51:39, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5584, loss_cls: 4.0091, loss: 4.0091 +2024-12-28 09:47:07,197 - pyskl - INFO - Epoch [63][1300/3746] lr: 6.310e-02, eta: 3 days, 2:50:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5477, loss_cls: 4.0538, loss: 4.0538 +2024-12-28 09:48:32,437 - pyskl - INFO - Epoch [63][1400/3746] lr: 6.307e-02, eta: 3 days, 2:49:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5484, loss_cls: 4.0359, loss: 4.0359 +2024-12-28 09:49:56,890 - pyskl - INFO - Epoch [63][1500/3746] lr: 6.304e-02, eta: 3 days, 2:47:45, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5514, loss_cls: 4.0178, loss: 4.0178 +2024-12-28 09:51:21,685 - pyskl - INFO - Epoch [63][1600/3746] lr: 6.301e-02, eta: 3 days, 2:46:26, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5381, loss_cls: 4.1249, loss: 4.1249 +2024-12-28 09:52:47,172 - pyskl - INFO - Epoch [63][1700/3746] lr: 6.299e-02, eta: 3 days, 2:45:09, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5519, loss_cls: 4.0512, loss: 4.0512 +2024-12-28 09:54:12,672 - pyskl - INFO - Epoch [63][1800/3746] lr: 6.296e-02, eta: 3 days, 2:43:52, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5527, loss_cls: 4.0374, loss: 4.0374 +2024-12-28 09:55:38,535 - pyskl - INFO - Epoch [63][1900/3746] lr: 6.293e-02, eta: 3 days, 2:42:35, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5364, loss_cls: 4.1185, loss: 4.1185 +2024-12-28 09:57:04,257 - pyskl - INFO - Epoch [63][2000/3746] lr: 6.291e-02, eta: 3 days, 2:41:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5463, loss_cls: 4.0780, loss: 4.0780 +2024-12-28 09:58:29,565 - pyskl - INFO - Epoch [63][2100/3746] lr: 6.288e-02, eta: 3 days, 2:40:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5450, loss_cls: 4.0669, loss: 4.0669 +2024-12-28 09:59:55,489 - pyskl - INFO - Epoch [63][2200/3746] lr: 6.285e-02, eta: 3 days, 2:38:44, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5606, loss_cls: 4.0090, loss: 4.0090 +2024-12-28 10:01:20,903 - pyskl - INFO - Epoch [63][2300/3746] lr: 6.283e-02, eta: 3 days, 2:37:27, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5575, loss_cls: 4.0042, loss: 4.0042 +2024-12-28 10:02:46,667 - pyskl - INFO - Epoch [63][2400/3746] lr: 6.280e-02, eta: 3 days, 2:36:10, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5305, loss_cls: 4.1285, loss: 4.1285 +2024-12-28 10:04:12,318 - pyskl - INFO - Epoch [63][2500/3746] lr: 6.277e-02, eta: 3 days, 2:34:53, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5508, loss_cls: 4.0596, loss: 4.0596 +2024-12-28 10:05:38,408 - pyskl - INFO - Epoch [63][2600/3746] lr: 6.274e-02, eta: 3 days, 2:33:36, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5475, loss_cls: 4.0719, loss: 4.0719 +2024-12-28 10:07:03,661 - pyskl - INFO - Epoch [63][2700/3746] lr: 6.272e-02, eta: 3 days, 2:32:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5527, loss_cls: 4.0488, loss: 4.0488 +2024-12-28 10:08:29,102 - pyskl - INFO - Epoch [63][2800/3746] lr: 6.269e-02, eta: 3 days, 2:31:01, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5416, loss_cls: 4.0913, loss: 4.0913 +2024-12-28 10:09:54,409 - pyskl - INFO - Epoch [63][2900/3746] lr: 6.266e-02, eta: 3 days, 2:29:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5367, loss_cls: 4.1160, loss: 4.1160 +2024-12-28 10:11:19,827 - pyskl - INFO - Epoch [63][3000/3746] lr: 6.264e-02, eta: 3 days, 2:28:26, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5439, loss_cls: 4.0876, loss: 4.0876 +2024-12-28 10:12:45,533 - pyskl - INFO - Epoch [63][3100/3746] lr: 6.261e-02, eta: 3 days, 2:27:09, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5467, loss_cls: 4.0539, loss: 4.0539 +2024-12-28 10:14:10,978 - pyskl - INFO - Epoch [63][3200/3746] lr: 6.258e-02, eta: 3 days, 2:25:52, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5459, loss_cls: 4.0720, loss: 4.0720 +2024-12-28 10:15:36,124 - pyskl - INFO - Epoch [63][3300/3746] lr: 6.256e-02, eta: 3 days, 2:24:34, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5584, loss_cls: 3.9842, loss: 3.9842 +2024-12-28 10:17:00,837 - pyskl - INFO - Epoch [63][3400/3746] lr: 6.253e-02, eta: 3 days, 2:23:15, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5400, loss_cls: 4.0717, loss: 4.0717 +2024-12-28 10:18:26,138 - pyskl - INFO - Epoch [63][3500/3746] lr: 6.250e-02, eta: 3 days, 2:21:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5505, loss_cls: 4.0352, loss: 4.0352 +2024-12-28 10:19:51,585 - pyskl - INFO - Epoch [63][3600/3746] lr: 6.247e-02, eta: 3 days, 2:20:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5513, loss_cls: 4.0709, loss: 4.0709 +2024-12-28 10:21:16,927 - pyskl - INFO - Epoch [63][3700/3746] lr: 6.245e-02, eta: 3 days, 2:19:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5425, loss_cls: 4.0874, loss: 4.0874 +2024-12-28 10:21:57,561 - pyskl - INFO - Saving checkpoint at 63 epochs +2024-12-28 10:23:56,531 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 10:23:57,356 - pyskl - INFO - +top1_acc 0.2275 +top5_acc 0.4659 +2024-12-28 10:23:57,357 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 10:23:57,405 - pyskl - INFO - +mean_acc 0.2271 +2024-12-28 10:23:57,419 - pyskl - INFO - Epoch(val) [63][309] top1_acc: 0.2275, top5_acc: 0.4659, mean_class_accuracy: 0.2271 +2024-12-28 10:28:08,681 - pyskl - INFO - Epoch [64][100/3746] lr: 6.241e-02, eta: 3 days, 2:20:24, time: 2.513, data_time: 1.475, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5528, loss_cls: 4.0119, loss: 4.0119 +2024-12-28 10:29:34,480 - pyskl - INFO - Epoch [64][200/3746] lr: 6.238e-02, eta: 3 days, 2:19:07, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5530, loss_cls: 4.0248, loss: 4.0248 +2024-12-28 10:31:00,637 - pyskl - INFO - Epoch [64][300/3746] lr: 6.235e-02, eta: 3 days, 2:17:50, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5458, loss_cls: 4.0422, loss: 4.0422 +2024-12-28 10:32:25,710 - pyskl - INFO - Epoch [64][400/3746] lr: 6.233e-02, eta: 3 days, 2:16:32, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5506, loss_cls: 4.0288, loss: 4.0288 +2024-12-28 10:33:50,767 - pyskl - INFO - Epoch [64][500/3746] lr: 6.230e-02, eta: 3 days, 2:15:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5427, loss_cls: 4.0821, loss: 4.0821 +2024-12-28 10:35:16,338 - pyskl - INFO - Epoch [64][600/3746] lr: 6.227e-02, eta: 3 days, 2:13:57, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5523, loss_cls: 4.0171, loss: 4.0171 +2024-12-28 10:36:42,098 - pyskl - INFO - Epoch [64][700/3746] lr: 6.225e-02, eta: 3 days, 2:12:40, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5469, loss_cls: 4.0603, loss: 4.0603 +2024-12-28 10:38:07,908 - pyskl - INFO - Epoch [64][800/3746] lr: 6.222e-02, eta: 3 days, 2:11:22, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5548, loss_cls: 4.0335, loss: 4.0335 +2024-12-28 10:39:33,822 - pyskl - INFO - Epoch [64][900/3746] lr: 6.219e-02, eta: 3 days, 2:10:05, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5500, loss_cls: 4.0514, loss: 4.0514 +2024-12-28 10:40:59,833 - pyskl - INFO - Epoch [64][1000/3746] lr: 6.216e-02, eta: 3 days, 2:08:49, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5384, loss_cls: 4.0945, loss: 4.0945 +2024-12-28 10:42:25,625 - pyskl - INFO - Epoch [64][1100/3746] lr: 6.214e-02, eta: 3 days, 2:07:31, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5523, loss_cls: 4.0600, loss: 4.0600 +2024-12-28 10:43:51,706 - pyskl - INFO - Epoch [64][1200/3746] lr: 6.211e-02, eta: 3 days, 2:06:15, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5384, loss_cls: 4.0971, loss: 4.0971 +2024-12-28 10:45:17,690 - pyskl - INFO - Epoch [64][1300/3746] lr: 6.208e-02, eta: 3 days, 2:04:58, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5469, loss_cls: 4.0643, loss: 4.0643 +2024-12-28 10:46:43,151 - pyskl - INFO - Epoch [64][1400/3746] lr: 6.206e-02, eta: 3 days, 2:03:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5364, loss_cls: 4.0772, loss: 4.0772 +2024-12-28 10:48:07,824 - pyskl - INFO - Epoch [64][1500/3746] lr: 6.203e-02, eta: 3 days, 2:02:21, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5433, loss_cls: 4.0728, loss: 4.0728 +2024-12-28 10:49:32,468 - pyskl - INFO - Epoch [64][1600/3746] lr: 6.200e-02, eta: 3 days, 2:01:02, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5473, loss_cls: 4.0806, loss: 4.0806 +2024-12-28 10:50:57,034 - pyskl - INFO - Epoch [64][1700/3746] lr: 6.197e-02, eta: 3 days, 1:59:44, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5517, loss_cls: 4.0342, loss: 4.0342 +2024-12-28 10:52:22,363 - pyskl - INFO - Epoch [64][1800/3746] lr: 6.195e-02, eta: 3 days, 1:58:26, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5567, loss_cls: 4.0285, loss: 4.0285 +2024-12-28 10:53:47,311 - pyskl - INFO - Epoch [64][1900/3746] lr: 6.192e-02, eta: 3 days, 1:57:07, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5406, loss_cls: 4.0643, loss: 4.0643 +2024-12-28 10:55:12,094 - pyskl - INFO - Epoch [64][2000/3746] lr: 6.189e-02, eta: 3 days, 1:55:49, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5523, loss_cls: 4.0382, loss: 4.0382 +2024-12-28 10:56:36,773 - pyskl - INFO - Epoch [64][2100/3746] lr: 6.187e-02, eta: 3 days, 1:54:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5558, loss_cls: 4.0132, loss: 4.0132 +2024-12-28 10:58:01,420 - pyskl - INFO - Epoch [64][2200/3746] lr: 6.184e-02, eta: 3 days, 1:53:11, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5622, loss_cls: 4.0135, loss: 4.0135 +2024-12-28 10:59:26,136 - pyskl - INFO - Epoch [64][2300/3746] lr: 6.181e-02, eta: 3 days, 1:51:52, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5453, loss_cls: 4.0858, loss: 4.0858 +2024-12-28 11:00:50,970 - pyskl - INFO - Epoch [64][2400/3746] lr: 6.178e-02, eta: 3 days, 1:50:34, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5481, loss_cls: 4.0463, loss: 4.0463 +2024-12-28 11:02:15,848 - pyskl - INFO - Epoch [64][2500/3746] lr: 6.176e-02, eta: 3 days, 1:49:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5289, loss_cls: 4.1353, loss: 4.1353 +2024-12-28 11:03:40,950 - pyskl - INFO - Epoch [64][2600/3746] lr: 6.173e-02, eta: 3 days, 1:47:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5441, loss_cls: 4.0686, loss: 4.0686 +2024-12-28 11:05:05,777 - pyskl - INFO - Epoch [64][2700/3746] lr: 6.170e-02, eta: 3 days, 1:46:39, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5516, loss_cls: 4.0444, loss: 4.0444 +2024-12-28 11:06:30,781 - pyskl - INFO - Epoch [64][2800/3746] lr: 6.168e-02, eta: 3 days, 1:45:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5461, loss_cls: 4.0632, loss: 4.0632 +2024-12-28 11:07:55,690 - pyskl - INFO - Epoch [64][2900/3746] lr: 6.165e-02, eta: 3 days, 1:44:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5397, loss_cls: 4.0955, loss: 4.0955 +2024-12-28 11:09:20,624 - pyskl - INFO - Epoch [64][3000/3746] lr: 6.162e-02, eta: 3 days, 1:42:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5548, loss_cls: 4.0195, loss: 4.0195 +2024-12-28 11:10:45,539 - pyskl - INFO - Epoch [64][3100/3746] lr: 6.159e-02, eta: 3 days, 1:41:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5411, loss_cls: 4.0746, loss: 4.0746 +2024-12-28 11:12:10,279 - pyskl - INFO - Epoch [64][3200/3746] lr: 6.157e-02, eta: 3 days, 1:40:06, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5519, loss_cls: 4.0454, loss: 4.0454 +2024-12-28 11:13:35,199 - pyskl - INFO - Epoch [64][3300/3746] lr: 6.154e-02, eta: 3 days, 1:38:48, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5525, loss_cls: 4.0353, loss: 4.0353 +2024-12-28 11:14:59,554 - pyskl - INFO - Epoch [64][3400/3746] lr: 6.151e-02, eta: 3 days, 1:37:28, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5534, loss_cls: 4.0391, loss: 4.0391 +2024-12-28 11:16:23,944 - pyskl - INFO - Epoch [64][3500/3746] lr: 6.148e-02, eta: 3 days, 1:36:09, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5466, loss_cls: 4.0935, loss: 4.0935 +2024-12-28 11:17:48,385 - pyskl - INFO - Epoch [64][3600/3746] lr: 6.146e-02, eta: 3 days, 1:34:50, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5486, loss_cls: 4.0537, loss: 4.0537 +2024-12-28 11:19:13,019 - pyskl - INFO - Epoch [64][3700/3746] lr: 6.143e-02, eta: 3 days, 1:33:31, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5464, loss_cls: 4.0473, loss: 4.0473 +2024-12-28 11:19:53,987 - pyskl - INFO - Saving checkpoint at 64 epochs +2024-12-28 11:21:53,327 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 11:21:54,036 - pyskl - INFO - +top1_acc 0.2083 +top5_acc 0.4478 +2024-12-28 11:21:54,037 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 11:21:54,081 - pyskl - INFO - +mean_acc 0.2083 +2024-12-28 11:21:54,094 - pyskl - INFO - Epoch(val) [64][309] top1_acc: 0.2083, top5_acc: 0.4478, mean_class_accuracy: 0.2083 +2024-12-28 11:26:10,512 - pyskl - INFO - Epoch [65][100/3746] lr: 6.139e-02, eta: 3 days, 1:34:34, time: 2.564, data_time: 1.534, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5531, loss_cls: 4.0517, loss: 4.0517 +2024-12-28 11:27:35,993 - pyskl - INFO - Epoch [65][200/3746] lr: 6.136e-02, eta: 3 days, 1:33:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5600, loss_cls: 4.0081, loss: 4.0081 +2024-12-28 11:29:01,368 - pyskl - INFO - Epoch [65][300/3746] lr: 6.134e-02, eta: 3 days, 1:31:58, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5559, loss_cls: 4.0022, loss: 4.0022 +2024-12-28 11:30:26,701 - pyskl - INFO - Epoch [65][400/3746] lr: 6.131e-02, eta: 3 days, 1:30:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5527, loss_cls: 4.0491, loss: 4.0491 +2024-12-28 11:31:51,636 - pyskl - INFO - Epoch [65][500/3746] lr: 6.128e-02, eta: 3 days, 1:29:21, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5519, loss_cls: 4.0039, loss: 4.0039 +2024-12-28 11:33:17,206 - pyskl - INFO - Epoch [65][600/3746] lr: 6.125e-02, eta: 3 days, 1:28:04, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5648, loss_cls: 3.9632, loss: 3.9632 +2024-12-28 11:34:42,619 - pyskl - INFO - Epoch [65][700/3746] lr: 6.123e-02, eta: 3 days, 1:26:46, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5492, loss_cls: 4.0297, loss: 4.0297 +2024-12-28 11:36:07,526 - pyskl - INFO - Epoch [65][800/3746] lr: 6.120e-02, eta: 3 days, 1:25:27, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5463, loss_cls: 4.0416, loss: 4.0416 +2024-12-28 11:37:32,949 - pyskl - INFO - Epoch [65][900/3746] lr: 6.117e-02, eta: 3 days, 1:24:09, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5527, loss_cls: 4.0302, loss: 4.0302 +2024-12-28 11:38:58,490 - pyskl - INFO - Epoch [65][1000/3746] lr: 6.115e-02, eta: 3 days, 1:22:51, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5459, loss_cls: 4.0450, loss: 4.0450 +2024-12-28 11:40:24,049 - pyskl - INFO - Epoch [65][1100/3746] lr: 6.112e-02, eta: 3 days, 1:21:33, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5552, loss_cls: 4.0087, loss: 4.0087 +2024-12-28 11:41:49,244 - pyskl - INFO - Epoch [65][1200/3746] lr: 6.109e-02, eta: 3 days, 1:20:15, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5558, loss_cls: 4.0072, loss: 4.0072 +2024-12-28 11:43:13,977 - pyskl - INFO - Epoch [65][1300/3746] lr: 6.106e-02, eta: 3 days, 1:18:56, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5550, loss_cls: 4.0101, loss: 4.0101 +2024-12-28 11:44:38,878 - pyskl - INFO - Epoch [65][1400/3746] lr: 6.104e-02, eta: 3 days, 1:17:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5431, loss_cls: 4.1055, loss: 4.1055 +2024-12-28 11:46:03,894 - pyskl - INFO - Epoch [65][1500/3746] lr: 6.101e-02, eta: 3 days, 1:16:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5434, loss_cls: 4.0503, loss: 4.0503 +2024-12-28 11:47:28,253 - pyskl - INFO - Epoch [65][1600/3746] lr: 6.098e-02, eta: 3 days, 1:14:59, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5394, loss_cls: 4.1063, loss: 4.1063 +2024-12-28 11:48:53,298 - pyskl - INFO - Epoch [65][1700/3746] lr: 6.095e-02, eta: 3 days, 1:13:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5502, loss_cls: 4.0625, loss: 4.0625 +2024-12-28 11:50:19,063 - pyskl - INFO - Epoch [65][1800/3746] lr: 6.093e-02, eta: 3 days, 1:12:23, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5537, loss_cls: 4.0380, loss: 4.0380 +2024-12-28 11:51:44,748 - pyskl - INFO - Epoch [65][1900/3746] lr: 6.090e-02, eta: 3 days, 1:11:05, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5498, loss_cls: 4.0745, loss: 4.0745 +2024-12-28 11:53:10,880 - pyskl - INFO - Epoch [65][2000/3746] lr: 6.087e-02, eta: 3 days, 1:09:48, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5397, loss_cls: 4.0675, loss: 4.0675 +2024-12-28 11:54:36,458 - pyskl - INFO - Epoch [65][2100/3746] lr: 6.085e-02, eta: 3 days, 1:08:30, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5605, loss_cls: 4.0179, loss: 4.0179 +2024-12-28 11:56:02,038 - pyskl - INFO - Epoch [65][2200/3746] lr: 6.082e-02, eta: 3 days, 1:07:12, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5542, loss_cls: 4.0341, loss: 4.0341 +2024-12-28 11:57:27,288 - pyskl - INFO - Epoch [65][2300/3746] lr: 6.079e-02, eta: 3 days, 1:05:54, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5527, loss_cls: 4.0550, loss: 4.0550 +2024-12-28 11:58:52,969 - pyskl - INFO - Epoch [65][2400/3746] lr: 6.076e-02, eta: 3 days, 1:04:36, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5455, loss_cls: 4.0864, loss: 4.0864 +2024-12-28 12:00:18,044 - pyskl - INFO - Epoch [65][2500/3746] lr: 6.074e-02, eta: 3 days, 1:03:17, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5592, loss_cls: 4.0188, loss: 4.0188 +2024-12-28 12:01:44,133 - pyskl - INFO - Epoch [65][2600/3746] lr: 6.071e-02, eta: 3 days, 1:02:00, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5572, loss_cls: 4.0101, loss: 4.0101 +2024-12-28 12:03:09,379 - pyskl - INFO - Epoch [65][2700/3746] lr: 6.068e-02, eta: 3 days, 1:00:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5389, loss_cls: 4.0872, loss: 4.0872 +2024-12-28 12:04:35,219 - pyskl - INFO - Epoch [65][2800/3746] lr: 6.065e-02, eta: 3 days, 0:59:24, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5447, loss_cls: 4.0786, loss: 4.0786 +2024-12-28 12:06:00,835 - pyskl - INFO - Epoch [65][2900/3746] lr: 6.063e-02, eta: 3 days, 0:58:06, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5672, loss_cls: 3.9941, loss: 3.9941 +2024-12-28 12:07:26,417 - pyskl - INFO - Epoch [65][3000/3746] lr: 6.060e-02, eta: 3 days, 0:56:48, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5452, loss_cls: 4.0426, loss: 4.0426 +2024-12-28 12:08:51,550 - pyskl - INFO - Epoch [65][3100/3746] lr: 6.057e-02, eta: 3 days, 0:55:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5480, loss_cls: 4.0609, loss: 4.0609 +2024-12-28 12:10:16,805 - pyskl - INFO - Epoch [65][3200/3746] lr: 6.055e-02, eta: 3 days, 0:54:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5489, loss_cls: 4.0331, loss: 4.0331 +2024-12-28 12:11:42,066 - pyskl - INFO - Epoch [65][3300/3746] lr: 6.052e-02, eta: 3 days, 0:52:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5463, loss_cls: 4.0630, loss: 4.0630 +2024-12-28 12:13:06,964 - pyskl - INFO - Epoch [65][3400/3746] lr: 6.049e-02, eta: 3 days, 0:51:34, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5477, loss_cls: 4.0430, loss: 4.0430 +2024-12-28 12:14:31,849 - pyskl - INFO - Epoch [65][3500/3746] lr: 6.046e-02, eta: 3 days, 0:50:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5533, loss_cls: 4.0098, loss: 4.0098 +2024-12-28 12:15:56,373 - pyskl - INFO - Epoch [65][3600/3746] lr: 6.044e-02, eta: 3 days, 0:48:56, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5394, loss_cls: 4.0792, loss: 4.0792 +2024-12-28 12:17:21,167 - pyskl - INFO - Epoch [65][3700/3746] lr: 6.041e-02, eta: 3 days, 0:47:37, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5494, loss_cls: 4.0538, loss: 4.0538 +2024-12-28 12:18:02,198 - pyskl - INFO - Saving checkpoint at 65 epochs +2024-12-28 12:20:02,057 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 12:20:02,805 - pyskl - INFO - +top1_acc 0.2443 +top5_acc 0.4882 +2024-12-28 12:20:02,805 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 12:20:02,858 - pyskl - INFO - +mean_acc 0.2442 +2024-12-28 12:20:02,870 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_60.pth was removed +2024-12-28 12:20:03,180 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_65.pth. +2024-12-28 12:20:03,181 - pyskl - INFO - Best top1_acc is 0.2443 at 65 epoch. +2024-12-28 12:20:03,191 - pyskl - INFO - Epoch(val) [65][309] top1_acc: 0.2443, top5_acc: 0.4882, mean_class_accuracy: 0.2442 +2024-12-28 12:24:28,027 - pyskl - INFO - Epoch [66][100/3746] lr: 6.037e-02, eta: 3 days, 0:48:46, time: 2.648, data_time: 1.623, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5522, loss_cls: 4.0013, loss: 4.0013 +2024-12-28 12:25:52,748 - pyskl - INFO - Epoch [66][200/3746] lr: 6.034e-02, eta: 3 days, 0:47:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5675, loss_cls: 3.9576, loss: 3.9576 +2024-12-28 12:27:17,833 - pyskl - INFO - Epoch [66][300/3746] lr: 6.031e-02, eta: 3 days, 0:46:08, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5573, loss_cls: 4.0068, loss: 4.0068 +2024-12-28 12:28:42,286 - pyskl - INFO - Epoch [66][400/3746] lr: 6.029e-02, eta: 3 days, 0:44:48, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5613, loss_cls: 4.0123, loss: 4.0123 +2024-12-28 12:30:07,038 - pyskl - INFO - Epoch [66][500/3746] lr: 6.026e-02, eta: 3 days, 0:43:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5494, loss_cls: 4.0435, loss: 4.0435 +2024-12-28 12:31:32,037 - pyskl - INFO - Epoch [66][600/3746] lr: 6.023e-02, eta: 3 days, 0:42:10, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5595, loss_cls: 4.0130, loss: 4.0130 +2024-12-28 12:32:56,775 - pyskl - INFO - Epoch [66][700/3746] lr: 6.020e-02, eta: 3 days, 0:40:51, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5497, loss_cls: 4.0412, loss: 4.0412 +2024-12-28 12:34:21,274 - pyskl - INFO - Epoch [66][800/3746] lr: 6.018e-02, eta: 3 days, 0:39:31, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5536, loss_cls: 3.9996, loss: 3.9996 +2024-12-28 12:35:45,808 - pyskl - INFO - Epoch [66][900/3746] lr: 6.015e-02, eta: 3 days, 0:38:12, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5533, loss_cls: 4.0443, loss: 4.0443 +2024-12-28 12:37:10,510 - pyskl - INFO - Epoch [66][1000/3746] lr: 6.012e-02, eta: 3 days, 0:36:52, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5531, loss_cls: 4.0327, loss: 4.0327 +2024-12-28 12:38:35,447 - pyskl - INFO - Epoch [66][1100/3746] lr: 6.009e-02, eta: 3 days, 0:35:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5584, loss_cls: 4.0042, loss: 4.0042 +2024-12-28 12:40:00,120 - pyskl - INFO - Epoch [66][1200/3746] lr: 6.007e-02, eta: 3 days, 0:34:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5539, loss_cls: 4.0421, loss: 4.0421 +2024-12-28 12:41:24,926 - pyskl - INFO - Epoch [66][1300/3746] lr: 6.004e-02, eta: 3 days, 0:32:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5459, loss_cls: 4.0311, loss: 4.0311 +2024-12-28 12:42:49,520 - pyskl - INFO - Epoch [66][1400/3746] lr: 6.001e-02, eta: 3 days, 0:31:35, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5528, loss_cls: 4.0364, loss: 4.0364 +2024-12-28 12:44:14,168 - pyskl - INFO - Epoch [66][1500/3746] lr: 5.999e-02, eta: 3 days, 0:30:16, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5444, loss_cls: 4.0730, loss: 4.0730 +2024-12-28 12:45:38,927 - pyskl - INFO - Epoch [66][1600/3746] lr: 5.996e-02, eta: 3 days, 0:28:57, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5525, loss_cls: 4.0217, loss: 4.0217 +2024-12-28 12:47:03,636 - pyskl - INFO - Epoch [66][1700/3746] lr: 5.993e-02, eta: 3 days, 0:27:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5480, loss_cls: 4.0513, loss: 4.0513 +2024-12-28 12:48:28,195 - pyskl - INFO - Epoch [66][1800/3746] lr: 5.990e-02, eta: 3 days, 0:26:18, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5534, loss_cls: 4.0110, loss: 4.0110 +2024-12-28 12:49:52,861 - pyskl - INFO - Epoch [66][1900/3746] lr: 5.988e-02, eta: 3 days, 0:24:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5520, loss_cls: 4.0318, loss: 4.0318 +2024-12-28 12:51:17,740 - pyskl - INFO - Epoch [66][2000/3746] lr: 5.985e-02, eta: 3 days, 0:23:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5475, loss_cls: 4.0640, loss: 4.0640 +2024-12-28 12:52:43,176 - pyskl - INFO - Epoch [66][2100/3746] lr: 5.982e-02, eta: 3 days, 0:22:21, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5409, loss_cls: 4.0804, loss: 4.0804 +2024-12-28 12:54:08,344 - pyskl - INFO - Epoch [66][2200/3746] lr: 5.979e-02, eta: 3 days, 0:21:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5491, loss_cls: 4.0143, loss: 4.0143 +2024-12-28 12:55:32,922 - pyskl - INFO - Epoch [66][2300/3746] lr: 5.977e-02, eta: 3 days, 0:19:43, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5513, loss_cls: 4.0480, loss: 4.0480 +2024-12-28 12:56:57,239 - pyskl - INFO - Epoch [66][2400/3746] lr: 5.974e-02, eta: 3 days, 0:18:23, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5633, loss_cls: 3.9941, loss: 3.9941 +2024-12-28 12:58:22,326 - pyskl - INFO - Epoch [66][2500/3746] lr: 5.971e-02, eta: 3 days, 0:17:04, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5566, loss_cls: 4.0077, loss: 4.0077 +2024-12-28 12:59:47,415 - pyskl - INFO - Epoch [66][2600/3746] lr: 5.968e-02, eta: 3 days, 0:15:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5542, loss_cls: 3.9991, loss: 3.9991 +2024-12-28 13:01:12,978 - pyskl - INFO - Epoch [66][2700/3746] lr: 5.966e-02, eta: 3 days, 0:14:27, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5583, loss_cls: 4.0515, loss: 4.0515 +2024-12-28 13:02:37,789 - pyskl - INFO - Epoch [66][2800/3746] lr: 5.963e-02, eta: 3 days, 0:13:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5578, loss_cls: 3.9987, loss: 3.9987 +2024-12-28 13:04:02,791 - pyskl - INFO - Epoch [66][2900/3746] lr: 5.960e-02, eta: 3 days, 0:11:48, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5413, loss_cls: 4.0602, loss: 4.0602 +2024-12-28 13:05:27,512 - pyskl - INFO - Epoch [66][3000/3746] lr: 5.957e-02, eta: 3 days, 0:10:29, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5561, loss_cls: 4.0137, loss: 4.0137 +2024-12-28 13:06:52,626 - pyskl - INFO - Epoch [66][3100/3746] lr: 5.955e-02, eta: 3 days, 0:09:10, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5533, loss_cls: 4.0327, loss: 4.0327 +2024-12-28 13:08:18,118 - pyskl - INFO - Epoch [66][3200/3746] lr: 5.952e-02, eta: 3 days, 0:07:52, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5386, loss_cls: 4.0880, loss: 4.0880 +2024-12-28 13:09:42,824 - pyskl - INFO - Epoch [66][3300/3746] lr: 5.949e-02, eta: 3 days, 0:06:32, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5392, loss_cls: 4.0825, loss: 4.0825 +2024-12-28 13:11:07,577 - pyskl - INFO - Epoch [66][3400/3746] lr: 5.946e-02, eta: 3 days, 0:05:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5450, loss_cls: 4.0670, loss: 4.0670 +2024-12-28 13:12:31,803 - pyskl - INFO - Epoch [66][3500/3746] lr: 5.944e-02, eta: 3 days, 0:03:53, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5492, loss_cls: 4.0413, loss: 4.0413 +2024-12-28 13:13:56,330 - pyskl - INFO - Epoch [66][3600/3746] lr: 5.941e-02, eta: 3 days, 0:02:33, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5614, loss_cls: 3.9749, loss: 3.9749 +2024-12-28 13:15:20,974 - pyskl - INFO - Epoch [66][3700/3746] lr: 5.938e-02, eta: 3 days, 0:01:14, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5547, loss_cls: 4.0174, loss: 4.0174 +2024-12-28 13:16:02,247 - pyskl - INFO - Saving checkpoint at 66 epochs +2024-12-28 13:18:01,843 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 13:18:02,561 - pyskl - INFO - +top1_acc 0.2438 +top5_acc 0.4901 +2024-12-28 13:18:02,561 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 13:18:02,603 - pyskl - INFO - +mean_acc 0.2439 +2024-12-28 13:18:02,617 - pyskl - INFO - Epoch(val) [66][309] top1_acc: 0.2438, top5_acc: 0.4901, mean_class_accuracy: 0.2439 +2024-12-28 13:22:24,997 - pyskl - INFO - Epoch [67][100/3746] lr: 5.934e-02, eta: 3 days, 0:02:14, time: 2.624, data_time: 1.582, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5642, loss_cls: 3.9982, loss: 3.9982 +2024-12-28 13:23:50,181 - pyskl - INFO - Epoch [67][200/3746] lr: 5.931e-02, eta: 3 days, 0:00:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5563, loss_cls: 4.0047, loss: 4.0047 +2024-12-28 13:25:15,688 - pyskl - INFO - Epoch [67][300/3746] lr: 5.929e-02, eta: 2 days, 23:59:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5653, loss_cls: 3.9492, loss: 3.9492 +2024-12-28 13:26:41,197 - pyskl - INFO - Epoch [67][400/3746] lr: 5.926e-02, eta: 2 days, 23:58:18, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5503, loss_cls: 4.0239, loss: 4.0239 +2024-12-28 13:28:06,147 - pyskl - INFO - Epoch [67][500/3746] lr: 5.923e-02, eta: 2 days, 23:56:59, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5584, loss_cls: 4.0005, loss: 4.0005 +2024-12-28 13:29:31,936 - pyskl - INFO - Epoch [67][600/3746] lr: 5.920e-02, eta: 2 days, 23:55:41, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5552, loss_cls: 4.0231, loss: 4.0231 +2024-12-28 13:30:58,136 - pyskl - INFO - Epoch [67][700/3746] lr: 5.918e-02, eta: 2 days, 23:54:23, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5587, loss_cls: 4.0002, loss: 4.0002 +2024-12-28 13:32:24,207 - pyskl - INFO - Epoch [67][800/3746] lr: 5.915e-02, eta: 2 days, 23:53:05, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5594, loss_cls: 4.0073, loss: 4.0073 +2024-12-28 13:33:49,819 - pyskl - INFO - Epoch [67][900/3746] lr: 5.912e-02, eta: 2 days, 23:51:47, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5600, loss_cls: 4.0185, loss: 4.0185 +2024-12-28 13:35:15,642 - pyskl - INFO - Epoch [67][1000/3746] lr: 5.909e-02, eta: 2 days, 23:50:28, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5592, loss_cls: 3.9768, loss: 3.9768 +2024-12-28 13:36:40,905 - pyskl - INFO - Epoch [67][1100/3746] lr: 5.907e-02, eta: 2 days, 23:49:10, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5517, loss_cls: 4.0433, loss: 4.0433 +2024-12-28 13:38:06,240 - pyskl - INFO - Epoch [67][1200/3746] lr: 5.904e-02, eta: 2 days, 23:47:51, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5487, loss_cls: 4.0517, loss: 4.0517 +2024-12-28 13:39:32,009 - pyskl - INFO - Epoch [67][1300/3746] lr: 5.901e-02, eta: 2 days, 23:46:32, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5480, loss_cls: 4.0214, loss: 4.0214 +2024-12-28 13:40:57,366 - pyskl - INFO - Epoch [67][1400/3746] lr: 5.898e-02, eta: 2 days, 23:45:14, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5506, loss_cls: 4.0131, loss: 4.0131 +2024-12-28 13:42:22,189 - pyskl - INFO - Epoch [67][1500/3746] lr: 5.896e-02, eta: 2 days, 23:43:54, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5533, loss_cls: 4.0208, loss: 4.0208 +2024-12-28 13:43:46,846 - pyskl - INFO - Epoch [67][1600/3746] lr: 5.893e-02, eta: 2 days, 23:42:34, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5486, loss_cls: 4.0400, loss: 4.0400 +2024-12-28 13:45:11,387 - pyskl - INFO - Epoch [67][1700/3746] lr: 5.890e-02, eta: 2 days, 23:41:15, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5552, loss_cls: 4.0268, loss: 4.0268 +2024-12-28 13:46:36,419 - pyskl - INFO - Epoch [67][1800/3746] lr: 5.887e-02, eta: 2 days, 23:39:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5539, loss_cls: 4.0251, loss: 4.0251 +2024-12-28 13:48:01,281 - pyskl - INFO - Epoch [67][1900/3746] lr: 5.885e-02, eta: 2 days, 23:38:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5520, loss_cls: 4.0266, loss: 4.0266 +2024-12-28 13:49:26,415 - pyskl - INFO - Epoch [67][2000/3746] lr: 5.882e-02, eta: 2 days, 23:37:17, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5614, loss_cls: 3.9815, loss: 3.9815 +2024-12-28 13:50:51,675 - pyskl - INFO - Epoch [67][2100/3746] lr: 5.879e-02, eta: 2 days, 23:35:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5573, loss_cls: 3.9957, loss: 3.9957 +2024-12-28 13:52:17,039 - pyskl - INFO - Epoch [67][2200/3746] lr: 5.876e-02, eta: 2 days, 23:34:39, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5494, loss_cls: 4.0472, loss: 4.0472 +2024-12-28 13:53:42,417 - pyskl - INFO - Epoch [67][2300/3746] lr: 5.874e-02, eta: 2 days, 23:33:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5498, loss_cls: 4.0213, loss: 4.0213 +2024-12-28 13:55:07,969 - pyskl - INFO - Epoch [67][2400/3746] lr: 5.871e-02, eta: 2 days, 23:32:02, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5534, loss_cls: 4.0121, loss: 4.0121 +2024-12-28 13:56:32,999 - pyskl - INFO - Epoch [67][2500/3746] lr: 5.868e-02, eta: 2 days, 23:30:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5498, loss_cls: 4.0409, loss: 4.0409 +2024-12-28 13:57:57,995 - pyskl - INFO - Epoch [67][2600/3746] lr: 5.865e-02, eta: 2 days, 23:29:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5514, loss_cls: 4.0537, loss: 4.0537 +2024-12-28 13:59:23,776 - pyskl - INFO - Epoch [67][2700/3746] lr: 5.863e-02, eta: 2 days, 23:28:05, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5533, loss_cls: 4.0645, loss: 4.0645 +2024-12-28 14:00:49,309 - pyskl - INFO - Epoch [67][2800/3746] lr: 5.860e-02, eta: 2 days, 23:26:46, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5611, loss_cls: 3.9975, loss: 3.9975 +2024-12-28 14:02:15,143 - pyskl - INFO - Epoch [67][2900/3746] lr: 5.857e-02, eta: 2 days, 23:25:28, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5580, loss_cls: 4.0040, loss: 4.0040 +2024-12-28 14:03:40,487 - pyskl - INFO - Epoch [67][3000/3746] lr: 5.854e-02, eta: 2 days, 23:24:09, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5492, loss_cls: 4.0487, loss: 4.0487 +2024-12-28 14:05:06,406 - pyskl - INFO - Epoch [67][3100/3746] lr: 5.852e-02, eta: 2 days, 23:22:51, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5556, loss_cls: 4.0080, loss: 4.0080 +2024-12-28 14:06:31,522 - pyskl - INFO - Epoch [67][3200/3746] lr: 5.849e-02, eta: 2 days, 23:21:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5437, loss_cls: 4.0704, loss: 4.0704 +2024-12-28 14:07:56,347 - pyskl - INFO - Epoch [67][3300/3746] lr: 5.846e-02, eta: 2 days, 23:20:12, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5575, loss_cls: 4.0108, loss: 4.0108 +2024-12-28 14:09:21,469 - pyskl - INFO - Epoch [67][3400/3746] lr: 5.843e-02, eta: 2 days, 23:18:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5577, loss_cls: 4.0383, loss: 4.0383 +2024-12-28 14:10:47,120 - pyskl - INFO - Epoch [67][3500/3746] lr: 5.841e-02, eta: 2 days, 23:17:34, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5533, loss_cls: 4.0292, loss: 4.0292 +2024-12-28 14:12:12,644 - pyskl - INFO - Epoch [67][3600/3746] lr: 5.838e-02, eta: 2 days, 23:16:15, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5466, loss_cls: 4.0308, loss: 4.0308 +2024-12-28 14:13:38,764 - pyskl - INFO - Epoch [67][3700/3746] lr: 5.835e-02, eta: 2 days, 23:14:57, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5537, loss_cls: 4.0211, loss: 4.0211 +2024-12-28 14:14:20,378 - pyskl - INFO - Saving checkpoint at 67 epochs +2024-12-28 14:16:21,309 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 14:16:22,067 - pyskl - INFO - +top1_acc 0.2342 +top5_acc 0.4677 +2024-12-28 14:16:22,067 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 14:16:22,114 - pyskl - INFO - +mean_acc 0.2339 +2024-12-28 14:16:22,126 - pyskl - INFO - Epoch(val) [67][309] top1_acc: 0.2342, top5_acc: 0.4677, mean_class_accuracy: 0.2339 +2024-12-28 14:20:48,148 - pyskl - INFO - Epoch [68][100/3746] lr: 5.831e-02, eta: 2 days, 23:15:57, time: 2.660, data_time: 1.619, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5522, loss_cls: 3.9945, loss: 3.9945 +2024-12-28 14:22:13,659 - pyskl - INFO - Epoch [68][200/3746] lr: 5.828e-02, eta: 2 days, 23:14:38, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5555, loss_cls: 4.0087, loss: 4.0087 +2024-12-28 14:23:38,694 - pyskl - INFO - Epoch [68][300/3746] lr: 5.826e-02, eta: 2 days, 23:13:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5647, loss_cls: 3.9671, loss: 3.9671 +2024-12-28 14:25:03,781 - pyskl - INFO - Epoch [68][400/3746] lr: 5.823e-02, eta: 2 days, 23:11:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5573, loss_cls: 3.9978, loss: 3.9978 +2024-12-28 14:26:29,747 - pyskl - INFO - Epoch [68][500/3746] lr: 5.820e-02, eta: 2 days, 23:10:41, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5572, loss_cls: 4.0046, loss: 4.0046 +2024-12-28 14:27:54,943 - pyskl - INFO - Epoch [68][600/3746] lr: 5.817e-02, eta: 2 days, 23:09:22, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5637, loss_cls: 3.9626, loss: 3.9626 +2024-12-28 14:29:19,844 - pyskl - INFO - Epoch [68][700/3746] lr: 5.815e-02, eta: 2 days, 23:08:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5541, loss_cls: 4.0411, loss: 4.0411 +2024-12-28 14:30:44,620 - pyskl - INFO - Epoch [68][800/3746] lr: 5.812e-02, eta: 2 days, 23:06:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5602, loss_cls: 3.9861, loss: 3.9861 +2024-12-28 14:32:09,629 - pyskl - INFO - Epoch [68][900/3746] lr: 5.809e-02, eta: 2 days, 23:05:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5583, loss_cls: 4.0183, loss: 4.0183 +2024-12-28 14:33:34,703 - pyskl - INFO - Epoch [68][1000/3746] lr: 5.806e-02, eta: 2 days, 23:04:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5567, loss_cls: 3.9948, loss: 3.9948 +2024-12-28 14:34:59,669 - pyskl - INFO - Epoch [68][1100/3746] lr: 5.804e-02, eta: 2 days, 23:02:44, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5533, loss_cls: 4.0377, loss: 4.0377 +2024-12-28 14:36:24,720 - pyskl - INFO - Epoch [68][1200/3746] lr: 5.801e-02, eta: 2 days, 23:01:24, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5500, loss_cls: 4.0468, loss: 4.0468 +2024-12-28 14:37:49,570 - pyskl - INFO - Epoch [68][1300/3746] lr: 5.798e-02, eta: 2 days, 23:00:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5548, loss_cls: 4.0132, loss: 4.0132 +2024-12-28 14:39:14,308 - pyskl - INFO - Epoch [68][1400/3746] lr: 5.795e-02, eta: 2 days, 22:58:45, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5506, loss_cls: 4.0283, loss: 4.0283 +2024-12-28 14:40:39,244 - pyskl - INFO - Epoch [68][1500/3746] lr: 5.792e-02, eta: 2 days, 22:57:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5508, loss_cls: 4.0061, loss: 4.0061 +2024-12-28 14:42:03,972 - pyskl - INFO - Epoch [68][1600/3746] lr: 5.790e-02, eta: 2 days, 22:56:05, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5580, loss_cls: 4.0125, loss: 4.0125 +2024-12-28 14:43:29,347 - pyskl - INFO - Epoch [68][1700/3746] lr: 5.787e-02, eta: 2 days, 22:54:46, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5516, loss_cls: 4.0203, loss: 4.0203 +2024-12-28 14:44:55,179 - pyskl - INFO - Epoch [68][1800/3746] lr: 5.784e-02, eta: 2 days, 22:53:27, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5550, loss_cls: 4.0266, loss: 4.0266 +2024-12-28 14:46:20,667 - pyskl - INFO - Epoch [68][1900/3746] lr: 5.781e-02, eta: 2 days, 22:52:09, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5530, loss_cls: 4.0582, loss: 4.0582 +2024-12-28 14:47:46,496 - pyskl - INFO - Epoch [68][2000/3746] lr: 5.779e-02, eta: 2 days, 22:50:50, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5644, loss_cls: 3.9962, loss: 3.9962 +2024-12-28 14:49:11,970 - pyskl - INFO - Epoch [68][2100/3746] lr: 5.776e-02, eta: 2 days, 22:49:31, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5539, loss_cls: 4.0371, loss: 4.0371 +2024-12-28 14:50:37,347 - pyskl - INFO - Epoch [68][2200/3746] lr: 5.773e-02, eta: 2 days, 22:48:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5467, loss_cls: 4.0673, loss: 4.0673 +2024-12-28 14:52:02,666 - pyskl - INFO - Epoch [68][2300/3746] lr: 5.770e-02, eta: 2 days, 22:46:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5522, loss_cls: 3.9972, loss: 3.9972 +2024-12-28 14:53:28,080 - pyskl - INFO - Epoch [68][2400/3746] lr: 5.768e-02, eta: 2 days, 22:45:33, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5517, loss_cls: 4.0282, loss: 4.0282 +2024-12-28 14:54:53,204 - pyskl - INFO - Epoch [68][2500/3746] lr: 5.765e-02, eta: 2 days, 22:44:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5537, loss_cls: 4.0023, loss: 4.0023 +2024-12-28 14:56:18,449 - pyskl - INFO - Epoch [68][2600/3746] lr: 5.762e-02, eta: 2 days, 22:42:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5516, loss_cls: 3.9986, loss: 3.9986 +2024-12-28 14:57:43,866 - pyskl - INFO - Epoch [68][2700/3746] lr: 5.759e-02, eta: 2 days, 22:41:36, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5450, loss_cls: 4.0625, loss: 4.0625 +2024-12-28 14:59:09,356 - pyskl - INFO - Epoch [68][2800/3746] lr: 5.757e-02, eta: 2 days, 22:40:17, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5284, loss_cls: 4.0947, loss: 4.0947 +2024-12-28 15:00:34,435 - pyskl - INFO - Epoch [68][2900/3746] lr: 5.754e-02, eta: 2 days, 22:38:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5555, loss_cls: 4.0295, loss: 4.0295 +2024-12-28 15:02:00,088 - pyskl - INFO - Epoch [68][3000/3746] lr: 5.751e-02, eta: 2 days, 22:37:38, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5609, loss_cls: 4.0136, loss: 4.0136 +2024-12-28 15:03:25,154 - pyskl - INFO - Epoch [68][3100/3746] lr: 5.748e-02, eta: 2 days, 22:36:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5625, loss_cls: 3.9839, loss: 3.9839 +2024-12-28 15:04:49,719 - pyskl - INFO - Epoch [68][3200/3746] lr: 5.746e-02, eta: 2 days, 22:34:58, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5502, loss_cls: 4.0102, loss: 4.0102 +2024-12-28 15:06:14,205 - pyskl - INFO - Epoch [68][3300/3746] lr: 5.743e-02, eta: 2 days, 22:33:38, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5577, loss_cls: 3.9887, loss: 3.9887 +2024-12-28 15:07:39,370 - pyskl - INFO - Epoch [68][3400/3746] lr: 5.740e-02, eta: 2 days, 22:32:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5656, loss_cls: 3.9787, loss: 3.9787 +2024-12-28 15:09:04,646 - pyskl - INFO - Epoch [68][3500/3746] lr: 5.737e-02, eta: 2 days, 22:30:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5652, loss_cls: 3.9464, loss: 3.9464 +2024-12-28 15:10:30,465 - pyskl - INFO - Epoch [68][3600/3746] lr: 5.734e-02, eta: 2 days, 22:29:41, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5534, loss_cls: 4.0257, loss: 4.0257 +2024-12-28 15:11:56,488 - pyskl - INFO - Epoch [68][3700/3746] lr: 5.732e-02, eta: 2 days, 22:28:22, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5509, loss_cls: 4.0419, loss: 4.0419 +2024-12-28 15:12:37,923 - pyskl - INFO - Saving checkpoint at 68 epochs +2024-12-28 15:14:38,818 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 15:14:39,609 - pyskl - INFO - +top1_acc 0.2489 +top5_acc 0.4884 +2024-12-28 15:14:39,609 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 15:14:39,687 - pyskl - INFO - +mean_acc 0.2486 +2024-12-28 15:14:39,693 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_65.pth was removed +2024-12-28 15:14:40,071 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_68.pth. +2024-12-28 15:14:40,072 - pyskl - INFO - Best top1_acc is 0.2489 at 68 epoch. +2024-12-28 15:14:40,093 - pyskl - INFO - Epoch(val) [68][309] top1_acc: 0.2489, top5_acc: 0.4884, mean_class_accuracy: 0.2486 +2024-12-28 15:19:03,364 - pyskl - INFO - Epoch [69][100/3746] lr: 5.728e-02, eta: 2 days, 22:29:14, time: 2.633, data_time: 1.604, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5778, loss_cls: 3.8928, loss: 3.8928 +2024-12-28 15:20:28,324 - pyskl - INFO - Epoch [69][200/3746] lr: 5.725e-02, eta: 2 days, 22:27:54, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5600, loss_cls: 3.9997, loss: 3.9997 +2024-12-28 15:21:53,140 - pyskl - INFO - Epoch [69][300/3746] lr: 5.722e-02, eta: 2 days, 22:26:34, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5587, loss_cls: 3.9579, loss: 3.9579 +2024-12-28 15:23:17,990 - pyskl - INFO - Epoch [69][400/3746] lr: 5.719e-02, eta: 2 days, 22:25:14, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5495, loss_cls: 3.9843, loss: 3.9843 +2024-12-28 15:24:43,372 - pyskl - INFO - Epoch [69][500/3746] lr: 5.717e-02, eta: 2 days, 22:23:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5622, loss_cls: 3.9854, loss: 3.9854 +2024-12-28 15:26:08,320 - pyskl - INFO - Epoch [69][600/3746] lr: 5.714e-02, eta: 2 days, 22:22:35, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5681, loss_cls: 3.9720, loss: 3.9720 +2024-12-28 15:27:33,903 - pyskl - INFO - Epoch [69][700/3746] lr: 5.711e-02, eta: 2 days, 22:21:16, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5505, loss_cls: 4.0342, loss: 4.0342 +2024-12-28 15:28:59,041 - pyskl - INFO - Epoch [69][800/3746] lr: 5.708e-02, eta: 2 days, 22:19:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5567, loss_cls: 4.0165, loss: 4.0165 +2024-12-28 15:30:24,568 - pyskl - INFO - Epoch [69][900/3746] lr: 5.706e-02, eta: 2 days, 22:18:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5558, loss_cls: 4.0182, loss: 4.0182 +2024-12-28 15:31:50,502 - pyskl - INFO - Epoch [69][1000/3746] lr: 5.703e-02, eta: 2 days, 22:17:18, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5481, loss_cls: 4.0447, loss: 4.0447 +2024-12-28 15:33:16,405 - pyskl - INFO - Epoch [69][1100/3746] lr: 5.700e-02, eta: 2 days, 22:15:59, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5552, loss_cls: 3.9773, loss: 3.9773 +2024-12-28 15:34:42,002 - pyskl - INFO - Epoch [69][1200/3746] lr: 5.697e-02, eta: 2 days, 22:14:40, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5589, loss_cls: 4.0092, loss: 4.0092 +2024-12-28 15:36:07,451 - pyskl - INFO - Epoch [69][1300/3746] lr: 5.694e-02, eta: 2 days, 22:13:21, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5564, loss_cls: 4.0121, loss: 4.0121 +2024-12-28 15:37:32,151 - pyskl - INFO - Epoch [69][1400/3746] lr: 5.692e-02, eta: 2 days, 22:12:01, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5578, loss_cls: 4.0187, loss: 4.0187 +2024-12-28 15:38:57,410 - pyskl - INFO - Epoch [69][1500/3746] lr: 5.689e-02, eta: 2 days, 22:10:41, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5516, loss_cls: 4.0657, loss: 4.0657 +2024-12-28 15:40:22,263 - pyskl - INFO - Epoch [69][1600/3746] lr: 5.686e-02, eta: 2 days, 22:09:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5661, loss_cls: 3.9273, loss: 3.9273 +2024-12-28 15:41:46,638 - pyskl - INFO - Epoch [69][1700/3746] lr: 5.683e-02, eta: 2 days, 22:08:00, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5572, loss_cls: 3.9679, loss: 3.9679 +2024-12-28 15:43:11,448 - pyskl - INFO - Epoch [69][1800/3746] lr: 5.681e-02, eta: 2 days, 22:06:40, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5600, loss_cls: 3.9737, loss: 3.9737 +2024-12-28 15:44:35,975 - pyskl - INFO - Epoch [69][1900/3746] lr: 5.678e-02, eta: 2 days, 22:05:20, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5547, loss_cls: 4.0147, loss: 4.0147 +2024-12-28 15:46:00,526 - pyskl - INFO - Epoch [69][2000/3746] lr: 5.675e-02, eta: 2 days, 22:04:00, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5473, loss_cls: 4.0338, loss: 4.0338 +2024-12-28 15:47:25,061 - pyskl - INFO - Epoch [69][2100/3746] lr: 5.672e-02, eta: 2 days, 22:02:39, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5498, loss_cls: 4.0234, loss: 4.0234 +2024-12-28 15:48:49,493 - pyskl - INFO - Epoch [69][2200/3746] lr: 5.670e-02, eta: 2 days, 22:01:19, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5509, loss_cls: 4.0389, loss: 4.0389 +2024-12-28 15:50:14,458 - pyskl - INFO - Epoch [69][2300/3746] lr: 5.667e-02, eta: 2 days, 21:59:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5487, loss_cls: 4.0260, loss: 4.0260 +2024-12-28 15:51:39,526 - pyskl - INFO - Epoch [69][2400/3746] lr: 5.664e-02, eta: 2 days, 21:58:39, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5539, loss_cls: 4.0284, loss: 4.0284 +2024-12-28 15:53:04,307 - pyskl - INFO - Epoch [69][2500/3746] lr: 5.661e-02, eta: 2 days, 21:57:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5528, loss_cls: 4.0015, loss: 4.0015 +2024-12-28 15:54:29,600 - pyskl - INFO - Epoch [69][2600/3746] lr: 5.658e-02, eta: 2 days, 21:55:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5619, loss_cls: 3.9830, loss: 3.9830 +2024-12-28 15:55:55,687 - pyskl - INFO - Epoch [69][2700/3746] lr: 5.656e-02, eta: 2 days, 21:54:41, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5736, loss_cls: 3.9501, loss: 3.9501 +2024-12-28 15:57:20,908 - pyskl - INFO - Epoch [69][2800/3746] lr: 5.653e-02, eta: 2 days, 21:53:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5481, loss_cls: 4.0425, loss: 4.0425 +2024-12-28 15:58:46,351 - pyskl - INFO - Epoch [69][2900/3746] lr: 5.650e-02, eta: 2 days, 21:52:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5625, loss_cls: 3.9958, loss: 3.9958 +2024-12-28 16:00:11,660 - pyskl - INFO - Epoch [69][3000/3746] lr: 5.647e-02, eta: 2 days, 21:50:42, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5489, loss_cls: 4.0084, loss: 4.0084 +2024-12-28 16:01:37,387 - pyskl - INFO - Epoch [69][3100/3746] lr: 5.645e-02, eta: 2 days, 21:49:23, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5616, loss_cls: 4.0212, loss: 4.0212 +2024-12-28 16:03:02,209 - pyskl - INFO - Epoch [69][3200/3746] lr: 5.642e-02, eta: 2 days, 21:48:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5656, loss_cls: 3.9804, loss: 3.9804 +2024-12-28 16:04:27,261 - pyskl - INFO - Epoch [69][3300/3746] lr: 5.639e-02, eta: 2 days, 21:46:43, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5530, loss_cls: 3.9968, loss: 3.9968 +2024-12-28 16:05:52,281 - pyskl - INFO - Epoch [69][3400/3746] lr: 5.636e-02, eta: 2 days, 21:45:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5584, loss_cls: 3.9924, loss: 3.9924 +2024-12-28 16:07:17,773 - pyskl - INFO - Epoch [69][3500/3746] lr: 5.634e-02, eta: 2 days, 21:44:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5625, loss_cls: 3.9778, loss: 3.9778 +2024-12-28 16:08:42,635 - pyskl - INFO - Epoch [69][3600/3746] lr: 5.631e-02, eta: 2 days, 21:42:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5536, loss_cls: 4.0480, loss: 4.0480 +2024-12-28 16:10:07,592 - pyskl - INFO - Epoch [69][3700/3746] lr: 5.628e-02, eta: 2 days, 21:41:24, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5533, loss_cls: 4.0067, loss: 4.0067 +2024-12-28 16:10:49,072 - pyskl - INFO - Saving checkpoint at 69 epochs +2024-12-28 16:12:48,482 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 16:12:49,237 - pyskl - INFO - +top1_acc 0.2488 +top5_acc 0.4887 +2024-12-28 16:12:49,238 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 16:12:49,282 - pyskl - INFO - +mean_acc 0.2487 +2024-12-28 16:12:49,295 - pyskl - INFO - Epoch(val) [69][309] top1_acc: 0.2488, top5_acc: 0.4887, mean_class_accuracy: 0.2487 +2024-12-28 16:17:13,584 - pyskl - INFO - Epoch [70][100/3746] lr: 5.624e-02, eta: 2 days, 21:42:11, time: 2.643, data_time: 1.596, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5661, loss_cls: 3.9252, loss: 3.9252 +2024-12-28 16:18:38,812 - pyskl - INFO - Epoch [70][200/3746] lr: 5.621e-02, eta: 2 days, 21:40:52, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5667, loss_cls: 3.9289, loss: 3.9289 +2024-12-28 16:20:03,708 - pyskl - INFO - Epoch [70][300/3746] lr: 5.618e-02, eta: 2 days, 21:39:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5613, loss_cls: 3.9922, loss: 3.9922 +2024-12-28 16:21:28,098 - pyskl - INFO - Epoch [70][400/3746] lr: 5.616e-02, eta: 2 days, 21:38:11, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5686, loss_cls: 3.9521, loss: 3.9521 +2024-12-28 16:22:52,975 - pyskl - INFO - Epoch [70][500/3746] lr: 5.613e-02, eta: 2 days, 21:36:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5636, loss_cls: 3.9578, loss: 3.9578 +2024-12-28 16:24:17,591 - pyskl - INFO - Epoch [70][600/3746] lr: 5.610e-02, eta: 2 days, 21:35:30, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5592, loss_cls: 3.9811, loss: 3.9811 +2024-12-28 16:25:41,976 - pyskl - INFO - Epoch [70][700/3746] lr: 5.607e-02, eta: 2 days, 21:34:09, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5592, loss_cls: 3.9664, loss: 3.9664 +2024-12-28 16:27:06,623 - pyskl - INFO - Epoch [70][800/3746] lr: 5.605e-02, eta: 2 days, 21:32:49, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5697, loss_cls: 3.9434, loss: 3.9434 +2024-12-28 16:28:31,460 - pyskl - INFO - Epoch [70][900/3746] lr: 5.602e-02, eta: 2 days, 21:31:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5509, loss_cls: 4.0381, loss: 4.0381 +2024-12-28 16:29:56,070 - pyskl - INFO - Epoch [70][1000/3746] lr: 5.599e-02, eta: 2 days, 21:30:08, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5527, loss_cls: 4.0325, loss: 4.0325 +2024-12-28 16:31:20,115 - pyskl - INFO - Epoch [70][1100/3746] lr: 5.596e-02, eta: 2 days, 21:28:47, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5511, loss_cls: 3.9989, loss: 3.9989 +2024-12-28 16:32:44,865 - pyskl - INFO - Epoch [70][1200/3746] lr: 5.593e-02, eta: 2 days, 21:27:26, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5583, loss_cls: 3.9731, loss: 3.9731 +2024-12-28 16:34:09,806 - pyskl - INFO - Epoch [70][1300/3746] lr: 5.591e-02, eta: 2 days, 21:26:06, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5614, loss_cls: 3.9638, loss: 3.9638 +2024-12-28 16:35:34,445 - pyskl - INFO - Epoch [70][1400/3746] lr: 5.588e-02, eta: 2 days, 21:24:45, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5622, loss_cls: 3.9722, loss: 3.9722 +2024-12-28 16:36:59,030 - pyskl - INFO - Epoch [70][1500/3746] lr: 5.585e-02, eta: 2 days, 21:23:25, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5616, loss_cls: 3.9845, loss: 3.9845 +2024-12-28 16:38:24,293 - pyskl - INFO - Epoch [70][1600/3746] lr: 5.582e-02, eta: 2 days, 21:22:05, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5472, loss_cls: 4.0223, loss: 4.0223 +2024-12-28 16:39:48,811 - pyskl - INFO - Epoch [70][1700/3746] lr: 5.580e-02, eta: 2 days, 21:20:44, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5587, loss_cls: 4.0107, loss: 4.0107 +2024-12-28 16:41:13,597 - pyskl - INFO - Epoch [70][1800/3746] lr: 5.577e-02, eta: 2 days, 21:19:24, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5511, loss_cls: 4.0195, loss: 4.0195 +2024-12-28 16:42:38,670 - pyskl - INFO - Epoch [70][1900/3746] lr: 5.574e-02, eta: 2 days, 21:18:04, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5578, loss_cls: 3.9993, loss: 3.9993 +2024-12-28 16:44:03,971 - pyskl - INFO - Epoch [70][2000/3746] lr: 5.571e-02, eta: 2 days, 21:16:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5558, loss_cls: 3.9812, loss: 3.9812 +2024-12-28 16:45:29,698 - pyskl - INFO - Epoch [70][2100/3746] lr: 5.568e-02, eta: 2 days, 21:15:25, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5619, loss_cls: 3.9768, loss: 3.9768 +2024-12-28 16:46:55,251 - pyskl - INFO - Epoch [70][2200/3746] lr: 5.566e-02, eta: 2 days, 21:14:05, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5716, loss_cls: 3.9496, loss: 3.9496 +2024-12-28 16:48:20,825 - pyskl - INFO - Epoch [70][2300/3746] lr: 5.563e-02, eta: 2 days, 21:12:46, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5513, loss_cls: 4.0450, loss: 4.0450 +2024-12-28 16:49:46,734 - pyskl - INFO - Epoch [70][2400/3746] lr: 5.560e-02, eta: 2 days, 21:11:27, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5689, loss_cls: 3.9464, loss: 3.9464 +2024-12-28 16:51:12,203 - pyskl - INFO - Epoch [70][2500/3746] lr: 5.557e-02, eta: 2 days, 21:10:07, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5623, loss_cls: 3.9678, loss: 3.9678 +2024-12-28 16:52:37,317 - pyskl - INFO - Epoch [70][2600/3746] lr: 5.555e-02, eta: 2 days, 21:08:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5625, loss_cls: 3.9899, loss: 3.9899 +2024-12-28 16:54:02,609 - pyskl - INFO - Epoch [70][2700/3746] lr: 5.552e-02, eta: 2 days, 21:07:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5536, loss_cls: 4.0131, loss: 4.0131 +2024-12-28 16:55:28,374 - pyskl - INFO - Epoch [70][2800/3746] lr: 5.549e-02, eta: 2 days, 21:06:08, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5537, loss_cls: 4.0404, loss: 4.0404 +2024-12-28 16:56:53,602 - pyskl - INFO - Epoch [70][2900/3746] lr: 5.546e-02, eta: 2 days, 21:04:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5473, loss_cls: 4.0502, loss: 4.0502 +2024-12-28 16:58:19,351 - pyskl - INFO - Epoch [70][3000/3746] lr: 5.543e-02, eta: 2 days, 21:03:29, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5608, loss_cls: 4.0064, loss: 4.0064 +2024-12-28 16:59:44,905 - pyskl - INFO - Epoch [70][3100/3746] lr: 5.541e-02, eta: 2 days, 21:02:09, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5572, loss_cls: 4.0179, loss: 4.0179 +2024-12-28 17:01:10,062 - pyskl - INFO - Epoch [70][3200/3746] lr: 5.538e-02, eta: 2 days, 21:00:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5623, loss_cls: 3.9974, loss: 3.9974 +2024-12-28 17:02:35,569 - pyskl - INFO - Epoch [70][3300/3746] lr: 5.535e-02, eta: 2 days, 20:59:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5573, loss_cls: 4.0263, loss: 4.0263 +2024-12-28 17:04:00,528 - pyskl - INFO - Epoch [70][3400/3746] lr: 5.532e-02, eta: 2 days, 20:58:10, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5619, loss_cls: 4.0083, loss: 4.0083 +2024-12-28 17:05:25,996 - pyskl - INFO - Epoch [70][3500/3746] lr: 5.530e-02, eta: 2 days, 20:56:50, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5592, loss_cls: 3.9965, loss: 3.9965 +2024-12-28 17:06:51,083 - pyskl - INFO - Epoch [70][3600/3746] lr: 5.527e-02, eta: 2 days, 20:55:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5644, loss_cls: 3.9478, loss: 3.9478 +2024-12-28 17:08:16,265 - pyskl - INFO - Epoch [70][3700/3746] lr: 5.524e-02, eta: 2 days, 20:54:10, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5517, loss_cls: 4.0218, loss: 4.0218 +2024-12-28 17:08:57,437 - pyskl - INFO - Saving checkpoint at 70 epochs +2024-12-28 17:10:55,525 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 17:10:56,272 - pyskl - INFO - +top1_acc 0.2329 +top5_acc 0.4732 +2024-12-28 17:10:56,272 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 17:10:56,331 - pyskl - INFO - +mean_acc 0.2330 +2024-12-28 17:10:56,353 - pyskl - INFO - Epoch(val) [70][309] top1_acc: 0.2329, top5_acc: 0.4732, mean_class_accuracy: 0.2330 +2024-12-28 17:15:16,997 - pyskl - INFO - Epoch [71][100/3746] lr: 5.520e-02, eta: 2 days, 20:54:49, time: 2.606, data_time: 1.574, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5769, loss_cls: 3.8948, loss: 3.8948 +2024-12-28 17:16:42,165 - pyskl - INFO - Epoch [71][200/3746] lr: 5.517e-02, eta: 2 days, 20:53:28, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5658, loss_cls: 3.9539, loss: 3.9539 +2024-12-28 17:18:07,609 - pyskl - INFO - Epoch [71][300/3746] lr: 5.514e-02, eta: 2 days, 20:52:09, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5628, loss_cls: 3.9257, loss: 3.9257 +2024-12-28 17:19:32,683 - pyskl - INFO - Epoch [71][400/3746] lr: 5.512e-02, eta: 2 days, 20:50:48, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5594, loss_cls: 3.9969, loss: 3.9969 +2024-12-28 17:20:58,491 - pyskl - INFO - Epoch [71][500/3746] lr: 5.509e-02, eta: 2 days, 20:49:29, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5558, loss_cls: 4.0039, loss: 4.0039 +2024-12-28 17:22:23,200 - pyskl - INFO - Epoch [71][600/3746] lr: 5.506e-02, eta: 2 days, 20:48:08, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5616, loss_cls: 4.0123, loss: 4.0123 +2024-12-28 17:23:48,535 - pyskl - INFO - Epoch [71][700/3746] lr: 5.503e-02, eta: 2 days, 20:46:48, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5469, loss_cls: 4.0226, loss: 4.0226 +2024-12-28 17:25:14,034 - pyskl - INFO - Epoch [71][800/3746] lr: 5.500e-02, eta: 2 days, 20:45:29, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5659, loss_cls: 3.9578, loss: 3.9578 +2024-12-28 17:26:39,160 - pyskl - INFO - Epoch [71][900/3746] lr: 5.498e-02, eta: 2 days, 20:44:08, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5794, loss_cls: 3.9262, loss: 3.9262 +2024-12-28 17:28:04,346 - pyskl - INFO - Epoch [71][1000/3746] lr: 5.495e-02, eta: 2 days, 20:42:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5667, loss_cls: 3.9665, loss: 3.9665 +2024-12-28 17:29:29,927 - pyskl - INFO - Epoch [71][1100/3746] lr: 5.492e-02, eta: 2 days, 20:41:29, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5509, loss_cls: 4.0086, loss: 4.0086 +2024-12-28 17:30:55,469 - pyskl - INFO - Epoch [71][1200/3746] lr: 5.489e-02, eta: 2 days, 20:40:09, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5598, loss_cls: 3.9515, loss: 3.9515 +2024-12-28 17:32:20,834 - pyskl - INFO - Epoch [71][1300/3746] lr: 5.487e-02, eta: 2 days, 20:38:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5645, loss_cls: 3.9445, loss: 3.9445 +2024-12-28 17:33:45,958 - pyskl - INFO - Epoch [71][1400/3746] lr: 5.484e-02, eta: 2 days, 20:37:29, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5661, loss_cls: 3.9800, loss: 3.9800 +2024-12-28 17:35:11,270 - pyskl - INFO - Epoch [71][1500/3746] lr: 5.481e-02, eta: 2 days, 20:36:09, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5569, loss_cls: 4.0056, loss: 4.0056 +2024-12-28 17:36:36,421 - pyskl - INFO - Epoch [71][1600/3746] lr: 5.478e-02, eta: 2 days, 20:34:48, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5553, loss_cls: 4.0212, loss: 4.0212 +2024-12-28 17:38:01,507 - pyskl - INFO - Epoch [71][1700/3746] lr: 5.475e-02, eta: 2 days, 20:33:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5597, loss_cls: 4.0125, loss: 4.0125 +2024-12-28 17:39:25,877 - pyskl - INFO - Epoch [71][1800/3746] lr: 5.473e-02, eta: 2 days, 20:32:07, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5594, loss_cls: 3.9825, loss: 3.9825 +2024-12-28 17:40:50,426 - pyskl - INFO - Epoch [71][1900/3746] lr: 5.470e-02, eta: 2 days, 20:30:46, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5667, loss_cls: 3.9544, loss: 3.9544 +2024-12-28 17:42:14,777 - pyskl - INFO - Epoch [71][2000/3746] lr: 5.467e-02, eta: 2 days, 20:29:25, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5619, loss_cls: 3.9627, loss: 3.9627 +2024-12-28 17:43:39,874 - pyskl - INFO - Epoch [71][2100/3746] lr: 5.464e-02, eta: 2 days, 20:28:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5645, loss_cls: 3.9596, loss: 3.9596 +2024-12-28 17:45:04,443 - pyskl - INFO - Epoch [71][2200/3746] lr: 5.461e-02, eta: 2 days, 20:26:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5686, loss_cls: 3.9371, loss: 3.9371 +2024-12-28 17:46:29,437 - pyskl - INFO - Epoch [71][2300/3746] lr: 5.459e-02, eta: 2 days, 20:25:24, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5697, loss_cls: 3.9441, loss: 3.9441 +2024-12-28 17:47:54,046 - pyskl - INFO - Epoch [71][2400/3746] lr: 5.456e-02, eta: 2 days, 20:24:03, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5678, loss_cls: 3.9538, loss: 3.9538 +2024-12-28 17:49:19,412 - pyskl - INFO - Epoch [71][2500/3746] lr: 5.453e-02, eta: 2 days, 20:22:43, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5592, loss_cls: 3.9928, loss: 3.9928 +2024-12-28 17:50:44,168 - pyskl - INFO - Epoch [71][2600/3746] lr: 5.450e-02, eta: 2 days, 20:21:22, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5622, loss_cls: 3.9819, loss: 3.9819 +2024-12-28 17:52:09,602 - pyskl - INFO - Epoch [71][2700/3746] lr: 5.448e-02, eta: 2 days, 20:20:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5683, loss_cls: 3.9511, loss: 3.9511 +2024-12-28 17:53:34,520 - pyskl - INFO - Epoch [71][2800/3746] lr: 5.445e-02, eta: 2 days, 20:18:42, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5647, loss_cls: 3.9503, loss: 3.9503 +2024-12-28 17:54:59,467 - pyskl - INFO - Epoch [71][2900/3746] lr: 5.442e-02, eta: 2 days, 20:17:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5555, loss_cls: 4.0093, loss: 4.0093 +2024-12-28 17:56:24,098 - pyskl - INFO - Epoch [71][3000/3746] lr: 5.439e-02, eta: 2 days, 20:16:00, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5513, loss_cls: 3.9884, loss: 3.9884 +2024-12-28 17:57:49,011 - pyskl - INFO - Epoch [71][3100/3746] lr: 5.436e-02, eta: 2 days, 20:14:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5589, loss_cls: 4.0172, loss: 4.0172 +2024-12-28 17:59:13,950 - pyskl - INFO - Epoch [71][3200/3746] lr: 5.434e-02, eta: 2 days, 20:13:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5580, loss_cls: 4.0105, loss: 4.0105 +2024-12-28 18:00:38,681 - pyskl - INFO - Epoch [71][3300/3746] lr: 5.431e-02, eta: 2 days, 20:11:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5597, loss_cls: 4.0103, loss: 4.0103 +2024-12-28 18:02:03,169 - pyskl - INFO - Epoch [71][3400/3746] lr: 5.428e-02, eta: 2 days, 20:10:38, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5564, loss_cls: 4.0010, loss: 4.0010 +2024-12-28 18:03:27,755 - pyskl - INFO - Epoch [71][3500/3746] lr: 5.425e-02, eta: 2 days, 20:09:17, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5578, loss_cls: 3.9985, loss: 3.9985 +2024-12-28 18:04:52,242 - pyskl - INFO - Epoch [71][3600/3746] lr: 5.422e-02, eta: 2 days, 20:07:56, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5548, loss_cls: 3.9981, loss: 3.9981 +2024-12-28 18:06:16,801 - pyskl - INFO - Epoch [71][3700/3746] lr: 5.420e-02, eta: 2 days, 20:06:35, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5616, loss_cls: 3.9918, loss: 3.9918 +2024-12-28 18:06:57,642 - pyskl - INFO - Saving checkpoint at 71 epochs +2024-12-28 18:08:56,435 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 18:08:57,156 - pyskl - INFO - +top1_acc 0.2461 +top5_acc 0.4909 +2024-12-28 18:08:57,156 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 18:08:57,202 - pyskl - INFO - +mean_acc 0.2457 +2024-12-28 18:08:57,216 - pyskl - INFO - Epoch(val) [71][309] top1_acc: 0.2461, top5_acc: 0.4909, mean_class_accuracy: 0.2457 +2024-12-28 18:13:13,026 - pyskl - INFO - Epoch [72][100/3746] lr: 5.416e-02, eta: 2 days, 20:07:04, time: 2.558, data_time: 1.517, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5734, loss_cls: 3.9382, loss: 3.9382 +2024-12-28 18:14:38,409 - pyskl - INFO - Epoch [72][200/3746] lr: 5.413e-02, eta: 2 days, 20:05:44, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5692, loss_cls: 3.9270, loss: 3.9270 +2024-12-28 18:16:03,474 - pyskl - INFO - Epoch [72][300/3746] lr: 5.410e-02, eta: 2 days, 20:04:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5781, loss_cls: 3.9286, loss: 3.9286 +2024-12-28 18:17:28,744 - pyskl - INFO - Epoch [72][400/3746] lr: 5.407e-02, eta: 2 days, 20:03:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5680, loss_cls: 3.9649, loss: 3.9649 +2024-12-28 18:18:53,933 - pyskl - INFO - Epoch [72][500/3746] lr: 5.404e-02, eta: 2 days, 20:01:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5759, loss_cls: 3.9235, loss: 3.9235 +2024-12-28 18:20:18,548 - pyskl - INFO - Epoch [72][600/3746] lr: 5.402e-02, eta: 2 days, 20:00:22, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5592, loss_cls: 3.9836, loss: 3.9836 +2024-12-28 18:21:43,425 - pyskl - INFO - Epoch [72][700/3746] lr: 5.399e-02, eta: 2 days, 19:59:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5633, loss_cls: 3.9629, loss: 3.9629 +2024-12-28 18:23:08,414 - pyskl - INFO - Epoch [72][800/3746] lr: 5.396e-02, eta: 2 days, 19:57:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5609, loss_cls: 3.9877, loss: 3.9877 +2024-12-28 18:24:33,319 - pyskl - INFO - Epoch [72][900/3746] lr: 5.393e-02, eta: 2 days, 19:56:20, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5591, loss_cls: 3.9781, loss: 3.9781 +2024-12-28 18:25:58,798 - pyskl - INFO - Epoch [72][1000/3746] lr: 5.391e-02, eta: 2 days, 19:54:59, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5698, loss_cls: 3.9303, loss: 3.9303 +2024-12-28 18:27:23,981 - pyskl - INFO - Epoch [72][1100/3746] lr: 5.388e-02, eta: 2 days, 19:53:39, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5589, loss_cls: 3.9877, loss: 3.9877 +2024-12-28 18:28:49,091 - pyskl - INFO - Epoch [72][1200/3746] lr: 5.385e-02, eta: 2 days, 19:52:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5542, loss_cls: 4.0300, loss: 4.0300 +2024-12-28 18:30:14,403 - pyskl - INFO - Epoch [72][1300/3746] lr: 5.382e-02, eta: 2 days, 19:50:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5577, loss_cls: 3.9659, loss: 3.9659 +2024-12-28 18:31:39,572 - pyskl - INFO - Epoch [72][1400/3746] lr: 5.379e-02, eta: 2 days, 19:49:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5627, loss_cls: 3.9482, loss: 3.9482 +2024-12-28 18:33:04,357 - pyskl - INFO - Epoch [72][1500/3746] lr: 5.377e-02, eta: 2 days, 19:48:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5705, loss_cls: 3.9457, loss: 3.9457 +2024-12-28 18:34:29,651 - pyskl - INFO - Epoch [72][1600/3746] lr: 5.374e-02, eta: 2 days, 19:46:57, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5520, loss_cls: 4.0554, loss: 4.0554 +2024-12-28 18:35:54,686 - pyskl - INFO - Epoch [72][1700/3746] lr: 5.371e-02, eta: 2 days, 19:45:36, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5595, loss_cls: 3.9815, loss: 3.9815 +2024-12-28 18:37:20,116 - pyskl - INFO - Epoch [72][1800/3746] lr: 5.368e-02, eta: 2 days, 19:44:16, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5589, loss_cls: 3.9815, loss: 3.9815 +2024-12-28 18:38:45,603 - pyskl - INFO - Epoch [72][1900/3746] lr: 5.365e-02, eta: 2 days, 19:42:56, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5587, loss_cls: 3.9930, loss: 3.9930 +2024-12-28 18:40:11,636 - pyskl - INFO - Epoch [72][2000/3746] lr: 5.363e-02, eta: 2 days, 19:41:37, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5581, loss_cls: 3.9879, loss: 3.9879 +2024-12-28 18:41:36,893 - pyskl - INFO - Epoch [72][2100/3746] lr: 5.360e-02, eta: 2 days, 19:40:16, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5652, loss_cls: 3.9534, loss: 3.9534 +2024-12-28 18:43:02,380 - pyskl - INFO - Epoch [72][2200/3746] lr: 5.357e-02, eta: 2 days, 19:38:56, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5583, loss_cls: 3.9785, loss: 3.9785 +2024-12-28 18:44:28,212 - pyskl - INFO - Epoch [72][2300/3746] lr: 5.354e-02, eta: 2 days, 19:37:36, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5617, loss_cls: 4.0061, loss: 4.0061 +2024-12-28 18:45:53,578 - pyskl - INFO - Epoch [72][2400/3746] lr: 5.352e-02, eta: 2 days, 19:36:16, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5564, loss_cls: 3.9848, loss: 3.9848 +2024-12-28 18:47:19,310 - pyskl - INFO - Epoch [72][2500/3746] lr: 5.349e-02, eta: 2 days, 19:34:56, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5684, loss_cls: 3.9427, loss: 3.9427 +2024-12-28 18:48:45,065 - pyskl - INFO - Epoch [72][2600/3746] lr: 5.346e-02, eta: 2 days, 19:33:37, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5617, loss_cls: 4.0026, loss: 4.0026 +2024-12-28 18:50:10,406 - pyskl - INFO - Epoch [72][2700/3746] lr: 5.343e-02, eta: 2 days, 19:32:16, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5622, loss_cls: 3.9694, loss: 3.9694 +2024-12-28 18:51:36,099 - pyskl - INFO - Epoch [72][2800/3746] lr: 5.340e-02, eta: 2 days, 19:30:56, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5575, loss_cls: 3.9882, loss: 3.9882 +2024-12-28 18:53:01,818 - pyskl - INFO - Epoch [72][2900/3746] lr: 5.338e-02, eta: 2 days, 19:29:37, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5695, loss_cls: 3.9443, loss: 3.9443 +2024-12-28 18:54:26,972 - pyskl - INFO - Epoch [72][3000/3746] lr: 5.335e-02, eta: 2 days, 19:28:16, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5564, loss_cls: 3.9890, loss: 3.9890 +2024-12-28 18:55:52,446 - pyskl - INFO - Epoch [72][3100/3746] lr: 5.332e-02, eta: 2 days, 19:26:56, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5764, loss_cls: 3.9249, loss: 3.9249 +2024-12-28 18:57:16,842 - pyskl - INFO - Epoch [72][3200/3746] lr: 5.329e-02, eta: 2 days, 19:25:35, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5641, loss_cls: 3.9477, loss: 3.9477 +2024-12-28 18:58:41,615 - pyskl - INFO - Epoch [72][3300/3746] lr: 5.326e-02, eta: 2 days, 19:24:14, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5775, loss_cls: 3.9176, loss: 3.9176 +2024-12-28 19:00:07,056 - pyskl - INFO - Epoch [72][3400/3746] lr: 5.324e-02, eta: 2 days, 19:22:54, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5553, loss_cls: 4.0174, loss: 4.0174 +2024-12-28 19:01:32,183 - pyskl - INFO - Epoch [72][3500/3746] lr: 5.321e-02, eta: 2 days, 19:21:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5527, loss_cls: 4.0415, loss: 4.0415 +2024-12-28 19:02:57,340 - pyskl - INFO - Epoch [72][3600/3746] lr: 5.318e-02, eta: 2 days, 19:20:13, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5695, loss_cls: 3.9815, loss: 3.9815 +2024-12-28 19:04:22,452 - pyskl - INFO - Epoch [72][3700/3746] lr: 5.315e-02, eta: 2 days, 19:18:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5558, loss_cls: 3.9905, loss: 3.9905 +2024-12-28 19:05:03,387 - pyskl - INFO - Saving checkpoint at 72 epochs +2024-12-28 19:07:02,275 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 19:07:03,091 - pyskl - INFO - +top1_acc 0.2351 +top5_acc 0.4763 +2024-12-28 19:07:03,092 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 19:07:03,149 - pyskl - INFO - +mean_acc 0.2349 +2024-12-28 19:07:03,168 - pyskl - INFO - Epoch(val) [72][309] top1_acc: 0.2351, top5_acc: 0.4763, mean_class_accuracy: 0.2349 +2024-12-28 19:11:19,657 - pyskl - INFO - Epoch [73][100/3746] lr: 5.311e-02, eta: 2 days, 19:19:18, time: 2.565, data_time: 1.526, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5725, loss_cls: 3.9223, loss: 3.9223 +2024-12-28 19:12:44,879 - pyskl - INFO - Epoch [73][200/3746] lr: 5.308e-02, eta: 2 days, 19:17:57, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5675, loss_cls: 3.9211, loss: 3.9211 +2024-12-28 19:14:10,443 - pyskl - INFO - Epoch [73][300/3746] lr: 5.306e-02, eta: 2 days, 19:16:37, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5756, loss_cls: 3.9172, loss: 3.9172 +2024-12-28 19:15:34,991 - pyskl - INFO - Epoch [73][400/3746] lr: 5.303e-02, eta: 2 days, 19:15:16, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5673, loss_cls: 3.9365, loss: 3.9365 +2024-12-28 19:17:00,154 - pyskl - INFO - Epoch [73][500/3746] lr: 5.300e-02, eta: 2 days, 19:13:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5602, loss_cls: 3.9936, loss: 3.9936 +2024-12-28 19:18:25,192 - pyskl - INFO - Epoch [73][600/3746] lr: 5.297e-02, eta: 2 days, 19:12:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5691, loss_cls: 3.9519, loss: 3.9519 +2024-12-28 19:19:50,020 - pyskl - INFO - Epoch [73][700/3746] lr: 5.294e-02, eta: 2 days, 19:11:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5725, loss_cls: 3.9284, loss: 3.9284 +2024-12-28 19:21:14,801 - pyskl - INFO - Epoch [73][800/3746] lr: 5.292e-02, eta: 2 days, 19:09:52, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5713, loss_cls: 3.9203, loss: 3.9203 +2024-12-28 19:22:39,585 - pyskl - INFO - Epoch [73][900/3746] lr: 5.289e-02, eta: 2 days, 19:08:31, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5550, loss_cls: 4.0006, loss: 4.0006 +2024-12-28 19:24:04,844 - pyskl - INFO - Epoch [73][1000/3746] lr: 5.286e-02, eta: 2 days, 19:07:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5616, loss_cls: 3.9717, loss: 3.9717 +2024-12-28 19:25:29,445 - pyskl - INFO - Epoch [73][1100/3746] lr: 5.283e-02, eta: 2 days, 19:05:49, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5637, loss_cls: 3.9665, loss: 3.9665 +2024-12-28 19:26:54,005 - pyskl - INFO - Epoch [73][1200/3746] lr: 5.280e-02, eta: 2 days, 19:04:28, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5709, loss_cls: 3.9542, loss: 3.9542 +2024-12-28 19:28:19,669 - pyskl - INFO - Epoch [73][1300/3746] lr: 5.278e-02, eta: 2 days, 19:03:08, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5669, loss_cls: 3.9663, loss: 3.9663 +2024-12-28 19:29:44,453 - pyskl - INFO - Epoch [73][1400/3746] lr: 5.275e-02, eta: 2 days, 19:01:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5637, loss_cls: 3.9730, loss: 3.9730 +2024-12-28 19:31:08,907 - pyskl - INFO - Epoch [73][1500/3746] lr: 5.272e-02, eta: 2 days, 19:00:25, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5637, loss_cls: 3.9557, loss: 3.9557 +2024-12-28 19:32:34,259 - pyskl - INFO - Epoch [73][1600/3746] lr: 5.269e-02, eta: 2 days, 18:59:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5667, loss_cls: 3.9259, loss: 3.9259 +2024-12-28 19:33:58,956 - pyskl - INFO - Epoch [73][1700/3746] lr: 5.267e-02, eta: 2 days, 18:57:44, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5602, loss_cls: 3.9617, loss: 3.9617 +2024-12-28 19:35:23,957 - pyskl - INFO - Epoch [73][1800/3746] lr: 5.264e-02, eta: 2 days, 18:56:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5661, loss_cls: 3.9541, loss: 3.9541 +2024-12-28 19:36:48,653 - pyskl - INFO - Epoch [73][1900/3746] lr: 5.261e-02, eta: 2 days, 18:55:02, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5673, loss_cls: 3.9546, loss: 3.9546 +2024-12-28 19:38:13,038 - pyskl - INFO - Epoch [73][2000/3746] lr: 5.258e-02, eta: 2 days, 18:53:40, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5681, loss_cls: 3.9256, loss: 3.9256 +2024-12-28 19:39:37,693 - pyskl - INFO - Epoch [73][2100/3746] lr: 5.255e-02, eta: 2 days, 18:52:19, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5605, loss_cls: 3.9840, loss: 3.9840 +2024-12-28 19:41:02,271 - pyskl - INFO - Epoch [73][2200/3746] lr: 5.253e-02, eta: 2 days, 18:50:58, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5631, loss_cls: 3.9866, loss: 3.9866 +2024-12-28 19:42:26,708 - pyskl - INFO - Epoch [73][2300/3746] lr: 5.250e-02, eta: 2 days, 18:49:36, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5664, loss_cls: 3.9835, loss: 3.9835 +2024-12-28 19:43:51,139 - pyskl - INFO - Epoch [73][2400/3746] lr: 5.247e-02, eta: 2 days, 18:48:15, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5730, loss_cls: 3.9435, loss: 3.9435 +2024-12-28 19:45:15,853 - pyskl - INFO - Epoch [73][2500/3746] lr: 5.244e-02, eta: 2 days, 18:46:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5644, loss_cls: 3.9582, loss: 3.9582 +2024-12-28 19:46:40,030 - pyskl - INFO - Epoch [73][2600/3746] lr: 5.241e-02, eta: 2 days, 18:45:32, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5605, loss_cls: 3.9511, loss: 3.9511 +2024-12-28 19:48:04,283 - pyskl - INFO - Epoch [73][2700/3746] lr: 5.239e-02, eta: 2 days, 18:44:10, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5584, loss_cls: 4.0170, loss: 4.0170 +2024-12-28 19:49:28,515 - pyskl - INFO - Epoch [73][2800/3746] lr: 5.236e-02, eta: 2 days, 18:42:49, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5681, loss_cls: 3.9205, loss: 3.9205 +2024-12-28 19:50:53,382 - pyskl - INFO - Epoch [73][2900/3746] lr: 5.233e-02, eta: 2 days, 18:41:28, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5502, loss_cls: 4.0135, loss: 4.0135 +2024-12-28 19:52:17,821 - pyskl - INFO - Epoch [73][3000/3746] lr: 5.230e-02, eta: 2 days, 18:40:06, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5603, loss_cls: 3.9753, loss: 3.9753 +2024-12-28 19:53:42,428 - pyskl - INFO - Epoch [73][3100/3746] lr: 5.227e-02, eta: 2 days, 18:38:45, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5652, loss_cls: 3.9862, loss: 3.9862 +2024-12-28 19:55:06,902 - pyskl - INFO - Epoch [73][3200/3746] lr: 5.225e-02, eta: 2 days, 18:37:24, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5622, loss_cls: 3.9620, loss: 3.9620 +2024-12-28 19:56:31,231 - pyskl - INFO - Epoch [73][3300/3746] lr: 5.222e-02, eta: 2 days, 18:36:02, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5573, loss_cls: 3.9902, loss: 3.9902 +2024-12-28 19:57:56,196 - pyskl - INFO - Epoch [73][3400/3746] lr: 5.219e-02, eta: 2 days, 18:34:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5689, loss_cls: 3.9434, loss: 3.9434 +2024-12-28 19:59:21,657 - pyskl - INFO - Epoch [73][3500/3746] lr: 5.216e-02, eta: 2 days, 18:33:21, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5584, loss_cls: 3.9755, loss: 3.9755 +2024-12-28 20:00:46,646 - pyskl - INFO - Epoch [73][3600/3746] lr: 5.213e-02, eta: 2 days, 18:32:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5692, loss_cls: 3.9633, loss: 3.9633 +2024-12-28 20:02:11,644 - pyskl - INFO - Epoch [73][3700/3746] lr: 5.211e-02, eta: 2 days, 18:30:39, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5534, loss_cls: 3.9872, loss: 3.9872 +2024-12-28 20:02:52,665 - pyskl - INFO - Saving checkpoint at 73 epochs +2024-12-28 20:04:51,241 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 20:04:52,118 - pyskl - INFO - +top1_acc 0.2486 +top5_acc 0.4935 +2024-12-28 20:04:52,118 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 20:04:52,161 - pyskl - INFO - +mean_acc 0.2484 +2024-12-28 20:04:52,173 - pyskl - INFO - Epoch(val) [73][309] top1_acc: 0.2486, top5_acc: 0.4935, mean_class_accuracy: 0.2484 +2024-12-28 20:09:09,496 - pyskl - INFO - Epoch [74][100/3746] lr: 5.207e-02, eta: 2 days, 18:31:01, time: 2.573, data_time: 1.530, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5641, loss_cls: 3.9536, loss: 3.9536 +2024-12-28 20:10:34,959 - pyskl - INFO - Epoch [74][200/3746] lr: 5.204e-02, eta: 2 days, 18:29:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5687, loss_cls: 3.9137, loss: 3.9137 +2024-12-28 20:12:00,517 - pyskl - INFO - Epoch [74][300/3746] lr: 5.201e-02, eta: 2 days, 18:28:20, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5705, loss_cls: 3.9140, loss: 3.9140 +2024-12-28 20:13:25,253 - pyskl - INFO - Epoch [74][400/3746] lr: 5.198e-02, eta: 2 days, 18:26:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5716, loss_cls: 3.9507, loss: 3.9507 +2024-12-28 20:14:50,295 - pyskl - INFO - Epoch [74][500/3746] lr: 5.195e-02, eta: 2 days, 18:25:38, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5733, loss_cls: 3.9180, loss: 3.9180 +2024-12-28 20:16:15,218 - pyskl - INFO - Epoch [74][600/3746] lr: 5.193e-02, eta: 2 days, 18:24:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5644, loss_cls: 3.9180, loss: 3.9180 +2024-12-28 20:17:40,110 - pyskl - INFO - Epoch [74][700/3746] lr: 5.190e-02, eta: 2 days, 18:22:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5611, loss_cls: 3.9537, loss: 3.9537 +2024-12-28 20:19:04,826 - pyskl - INFO - Epoch [74][800/3746] lr: 5.187e-02, eta: 2 days, 18:21:35, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5537, loss_cls: 4.0018, loss: 4.0018 +2024-12-28 20:20:29,260 - pyskl - INFO - Epoch [74][900/3746] lr: 5.184e-02, eta: 2 days, 18:20:13, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5748, loss_cls: 3.9295, loss: 3.9295 +2024-12-28 20:21:54,144 - pyskl - INFO - Epoch [74][1000/3746] lr: 5.181e-02, eta: 2 days, 18:18:52, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5630, loss_cls: 3.9474, loss: 3.9474 +2024-12-28 20:23:18,378 - pyskl - INFO - Epoch [74][1100/3746] lr: 5.179e-02, eta: 2 days, 18:17:30, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5687, loss_cls: 3.9306, loss: 3.9306 +2024-12-28 20:24:42,911 - pyskl - INFO - Epoch [74][1200/3746] lr: 5.176e-02, eta: 2 days, 18:16:08, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5584, loss_cls: 3.9653, loss: 3.9653 +2024-12-28 20:26:07,872 - pyskl - INFO - Epoch [74][1300/3746] lr: 5.173e-02, eta: 2 days, 18:14:47, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5666, loss_cls: 3.9447, loss: 3.9447 +2024-12-28 20:27:32,465 - pyskl - INFO - Epoch [74][1400/3746] lr: 5.170e-02, eta: 2 days, 18:13:26, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5794, loss_cls: 3.8882, loss: 3.8882 +2024-12-28 20:28:57,243 - pyskl - INFO - Epoch [74][1500/3746] lr: 5.168e-02, eta: 2 days, 18:12:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5703, loss_cls: 3.9240, loss: 3.9240 +2024-12-28 20:30:22,427 - pyskl - INFO - Epoch [74][1600/3746] lr: 5.165e-02, eta: 2 days, 18:10:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5666, loss_cls: 3.9445, loss: 3.9445 +2024-12-28 20:31:46,950 - pyskl - INFO - Epoch [74][1700/3746] lr: 5.162e-02, eta: 2 days, 18:09:22, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5653, loss_cls: 3.9768, loss: 3.9768 +2024-12-28 20:33:11,442 - pyskl - INFO - Epoch [74][1800/3746] lr: 5.159e-02, eta: 2 days, 18:08:01, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5605, loss_cls: 3.9699, loss: 3.9699 +2024-12-28 20:34:36,201 - pyskl - INFO - Epoch [74][1900/3746] lr: 5.156e-02, eta: 2 days, 18:06:39, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5698, loss_cls: 3.9277, loss: 3.9277 +2024-12-28 20:36:00,945 - pyskl - INFO - Epoch [74][2000/3746] lr: 5.154e-02, eta: 2 days, 18:05:18, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5727, loss_cls: 3.9159, loss: 3.9159 +2024-12-28 20:37:25,793 - pyskl - INFO - Epoch [74][2100/3746] lr: 5.151e-02, eta: 2 days, 18:03:57, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5672, loss_cls: 3.9324, loss: 3.9324 +2024-12-28 20:38:50,476 - pyskl - INFO - Epoch [74][2200/3746] lr: 5.148e-02, eta: 2 days, 18:02:36, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5747, loss_cls: 3.9081, loss: 3.9081 +2024-12-28 20:40:14,956 - pyskl - INFO - Epoch [74][2300/3746] lr: 5.145e-02, eta: 2 days, 18:01:14, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5631, loss_cls: 3.9370, loss: 3.9370 +2024-12-28 20:41:39,362 - pyskl - INFO - Epoch [74][2400/3746] lr: 5.142e-02, eta: 2 days, 17:59:52, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5659, loss_cls: 3.9632, loss: 3.9632 +2024-12-28 20:43:03,644 - pyskl - INFO - Epoch [74][2500/3746] lr: 5.140e-02, eta: 2 days, 17:58:31, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5627, loss_cls: 3.9896, loss: 3.9896 +2024-12-28 20:44:28,112 - pyskl - INFO - Epoch [74][2600/3746] lr: 5.137e-02, eta: 2 days, 17:57:09, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5639, loss_cls: 3.9736, loss: 3.9736 +2024-12-28 20:45:52,621 - pyskl - INFO - Epoch [74][2700/3746] lr: 5.134e-02, eta: 2 days, 17:55:47, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5655, loss_cls: 3.9558, loss: 3.9558 +2024-12-28 20:47:17,337 - pyskl - INFO - Epoch [74][2800/3746] lr: 5.131e-02, eta: 2 days, 17:54:26, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5698, loss_cls: 3.9719, loss: 3.9719 +2024-12-28 20:48:42,391 - pyskl - INFO - Epoch [74][2900/3746] lr: 5.128e-02, eta: 2 days, 17:53:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5714, loss_cls: 3.9457, loss: 3.9457 +2024-12-28 20:50:07,269 - pyskl - INFO - Epoch [74][3000/3746] lr: 5.126e-02, eta: 2 days, 17:51:44, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5677, loss_cls: 3.9612, loss: 3.9612 +2024-12-28 20:51:32,075 - pyskl - INFO - Epoch [74][3100/3746] lr: 5.123e-02, eta: 2 days, 17:50:22, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5622, loss_cls: 3.9778, loss: 3.9778 +2024-12-28 20:52:56,570 - pyskl - INFO - Epoch [74][3200/3746] lr: 5.120e-02, eta: 2 days, 17:49:01, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5652, loss_cls: 3.9679, loss: 3.9679 +2024-12-28 20:54:21,472 - pyskl - INFO - Epoch [74][3300/3746] lr: 5.117e-02, eta: 2 days, 17:47:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5667, loss_cls: 3.9426, loss: 3.9426 +2024-12-28 20:55:45,822 - pyskl - INFO - Epoch [74][3400/3746] lr: 5.114e-02, eta: 2 days, 17:46:18, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5664, loss_cls: 3.9505, loss: 3.9505 +2024-12-28 20:57:10,297 - pyskl - INFO - Epoch [74][3500/3746] lr: 5.112e-02, eta: 2 days, 17:44:56, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5595, loss_cls: 3.9812, loss: 3.9812 +2024-12-28 20:58:34,526 - pyskl - INFO - Epoch [74][3600/3746] lr: 5.109e-02, eta: 2 days, 17:43:34, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5505, loss_cls: 3.9893, loss: 3.9893 +2024-12-28 20:59:58,473 - pyskl - INFO - Epoch [74][3700/3746] lr: 5.106e-02, eta: 2 days, 17:42:12, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5656, loss_cls: 3.9592, loss: 3.9592 +2024-12-28 21:00:39,390 - pyskl - INFO - Saving checkpoint at 74 epochs +2024-12-28 21:02:39,788 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 21:02:40,487 - pyskl - INFO - +top1_acc 0.2605 +top5_acc 0.5041 +2024-12-28 21:02:40,487 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 21:02:40,543 - pyskl - INFO - +mean_acc 0.2601 +2024-12-28 21:02:40,548 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_68.pth was removed +2024-12-28 21:02:40,876 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_74.pth. +2024-12-28 21:02:40,877 - pyskl - INFO - Best top1_acc is 0.2605 at 74 epoch. +2024-12-28 21:02:40,898 - pyskl - INFO - Epoch(val) [74][309] top1_acc: 0.2605, top5_acc: 0.5041, mean_class_accuracy: 0.2601 +2024-12-28 21:06:51,621 - pyskl - INFO - Epoch [75][100/3746] lr: 5.102e-02, eta: 2 days, 17:42:24, time: 2.507, data_time: 1.479, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5853, loss_cls: 3.8696, loss: 3.8696 +2024-12-28 21:08:16,367 - pyskl - INFO - Epoch [75][200/3746] lr: 5.099e-02, eta: 2 days, 17:41:02, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5791, loss_cls: 3.8904, loss: 3.8904 +2024-12-28 21:09:41,822 - pyskl - INFO - Epoch [75][300/3746] lr: 5.096e-02, eta: 2 days, 17:39:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5744, loss_cls: 3.8930, loss: 3.8930 +2024-12-28 21:11:06,419 - pyskl - INFO - Epoch [75][400/3746] lr: 5.094e-02, eta: 2 days, 17:38:20, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5705, loss_cls: 3.9357, loss: 3.9357 +2024-12-28 21:12:30,751 - pyskl - INFO - Epoch [75][500/3746] lr: 5.091e-02, eta: 2 days, 17:36:58, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5672, loss_cls: 3.9220, loss: 3.9220 +2024-12-28 21:13:55,223 - pyskl - INFO - Epoch [75][600/3746] lr: 5.088e-02, eta: 2 days, 17:35:36, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5697, loss_cls: 3.9098, loss: 3.9098 +2024-12-28 21:15:19,544 - pyskl - INFO - Epoch [75][700/3746] lr: 5.085e-02, eta: 2 days, 17:34:15, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5664, loss_cls: 3.9702, loss: 3.9702 +2024-12-28 21:16:44,065 - pyskl - INFO - Epoch [75][800/3746] lr: 5.082e-02, eta: 2 days, 17:32:53, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5669, loss_cls: 3.9401, loss: 3.9401 +2024-12-28 21:18:08,450 - pyskl - INFO - Epoch [75][900/3746] lr: 5.080e-02, eta: 2 days, 17:31:31, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5772, loss_cls: 3.9060, loss: 3.9060 +2024-12-28 21:19:33,072 - pyskl - INFO - Epoch [75][1000/3746] lr: 5.077e-02, eta: 2 days, 17:30:09, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5631, loss_cls: 3.9474, loss: 3.9474 +2024-12-28 21:20:57,312 - pyskl - INFO - Epoch [75][1100/3746] lr: 5.074e-02, eta: 2 days, 17:28:48, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5687, loss_cls: 3.9348, loss: 3.9348 +2024-12-28 21:22:22,186 - pyskl - INFO - Epoch [75][1200/3746] lr: 5.071e-02, eta: 2 days, 17:27:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5794, loss_cls: 3.9013, loss: 3.9013 +2024-12-28 21:23:46,436 - pyskl - INFO - Epoch [75][1300/3746] lr: 5.068e-02, eta: 2 days, 17:26:04, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5711, loss_cls: 3.9267, loss: 3.9267 +2024-12-28 21:25:10,617 - pyskl - INFO - Epoch [75][1400/3746] lr: 5.066e-02, eta: 2 days, 17:24:42, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5650, loss_cls: 3.9288, loss: 3.9288 +2024-12-28 21:26:35,265 - pyskl - INFO - Epoch [75][1500/3746] lr: 5.063e-02, eta: 2 days, 17:23:21, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5670, loss_cls: 3.9372, loss: 3.9372 +2024-12-28 21:28:00,195 - pyskl - INFO - Epoch [75][1600/3746] lr: 5.060e-02, eta: 2 days, 17:21:59, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5652, loss_cls: 3.9315, loss: 3.9315 +2024-12-28 21:29:24,824 - pyskl - INFO - Epoch [75][1700/3746] lr: 5.057e-02, eta: 2 days, 17:20:38, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5769, loss_cls: 3.9117, loss: 3.9117 +2024-12-28 21:30:49,329 - pyskl - INFO - Epoch [75][1800/3746] lr: 5.054e-02, eta: 2 days, 17:19:16, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5648, loss_cls: 3.9701, loss: 3.9701 +2024-12-28 21:32:13,706 - pyskl - INFO - Epoch [75][1900/3746] lr: 5.052e-02, eta: 2 days, 17:17:54, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5656, loss_cls: 3.9722, loss: 3.9722 +2024-12-28 21:33:38,393 - pyskl - INFO - Epoch [75][2000/3746] lr: 5.049e-02, eta: 2 days, 17:16:33, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5563, loss_cls: 3.9631, loss: 3.9631 +2024-12-28 21:35:02,731 - pyskl - INFO - Epoch [75][2100/3746] lr: 5.046e-02, eta: 2 days, 17:15:11, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5647, loss_cls: 3.9787, loss: 3.9787 +2024-12-28 21:36:27,363 - pyskl - INFO - Epoch [75][2200/3746] lr: 5.043e-02, eta: 2 days, 17:13:49, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5628, loss_cls: 3.9469, loss: 3.9469 +2024-12-28 21:37:52,307 - pyskl - INFO - Epoch [75][2300/3746] lr: 5.040e-02, eta: 2 days, 17:12:28, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5659, loss_cls: 3.9623, loss: 3.9623 +2024-12-28 21:39:16,706 - pyskl - INFO - Epoch [75][2400/3746] lr: 5.038e-02, eta: 2 days, 17:11:06, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5728, loss_cls: 3.9054, loss: 3.9054 +2024-12-28 21:40:41,077 - pyskl - INFO - Epoch [75][2500/3746] lr: 5.035e-02, eta: 2 days, 17:09:44, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5617, loss_cls: 3.9850, loss: 3.9850 +2024-12-28 21:42:05,963 - pyskl - INFO - Epoch [75][2600/3746] lr: 5.032e-02, eta: 2 days, 17:08:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5720, loss_cls: 3.9352, loss: 3.9352 +2024-12-28 21:43:31,219 - pyskl - INFO - Epoch [75][2700/3746] lr: 5.029e-02, eta: 2 days, 17:07:02, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5755, loss_cls: 3.9099, loss: 3.9099 +2024-12-28 21:44:56,377 - pyskl - INFO - Epoch [75][2800/3746] lr: 5.026e-02, eta: 2 days, 17:05:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5811, loss_cls: 3.9090, loss: 3.9090 +2024-12-28 21:46:21,372 - pyskl - INFO - Epoch [75][2900/3746] lr: 5.024e-02, eta: 2 days, 17:04:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5667, loss_cls: 3.9462, loss: 3.9462 +2024-12-28 21:47:46,122 - pyskl - INFO - Epoch [75][3000/3746] lr: 5.021e-02, eta: 2 days, 17:02:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5544, loss_cls: 3.9850, loss: 3.9850 +2024-12-28 21:49:11,406 - pyskl - INFO - Epoch [75][3100/3746] lr: 5.018e-02, eta: 2 days, 17:01:37, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5703, loss_cls: 3.9079, loss: 3.9079 +2024-12-28 21:50:35,983 - pyskl - INFO - Epoch [75][3200/3746] lr: 5.015e-02, eta: 2 days, 17:00:15, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5633, loss_cls: 3.9618, loss: 3.9618 +2024-12-28 21:52:01,005 - pyskl - INFO - Epoch [75][3300/3746] lr: 5.012e-02, eta: 2 days, 16:58:54, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5673, loss_cls: 3.9390, loss: 3.9390 +2024-12-28 21:53:25,448 - pyskl - INFO - Epoch [75][3400/3746] lr: 5.010e-02, eta: 2 days, 16:57:32, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5614, loss_cls: 3.9847, loss: 3.9847 +2024-12-28 21:54:50,206 - pyskl - INFO - Epoch [75][3500/3746] lr: 5.007e-02, eta: 2 days, 16:56:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5631, loss_cls: 3.9542, loss: 3.9542 +2024-12-28 21:56:15,296 - pyskl - INFO - Epoch [75][3600/3746] lr: 5.004e-02, eta: 2 days, 16:54:49, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5748, loss_cls: 3.9153, loss: 3.9153 +2024-12-28 21:57:39,991 - pyskl - INFO - Epoch [75][3700/3746] lr: 5.001e-02, eta: 2 days, 16:53:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5669, loss_cls: 3.9603, loss: 3.9603 +2024-12-28 21:58:20,811 - pyskl - INFO - Saving checkpoint at 75 epochs +2024-12-28 22:00:19,679 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 22:00:20,376 - pyskl - INFO - +top1_acc 0.2663 +top5_acc 0.5089 +2024-12-28 22:00:20,376 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 22:00:20,419 - pyskl - INFO - +mean_acc 0.2660 +2024-12-28 22:00:20,423 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_74.pth was removed +2024-12-28 22:00:20,717 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_75.pth. +2024-12-28 22:00:20,717 - pyskl - INFO - Best top1_acc is 0.2663 at 75 epoch. +2024-12-28 22:00:20,732 - pyskl - INFO - Epoch(val) [75][309] top1_acc: 0.2663, top5_acc: 0.5089, mean_class_accuracy: 0.2660 +2024-12-28 22:04:34,331 - pyskl - INFO - Epoch [76][100/3746] lr: 4.997e-02, eta: 2 days, 16:53:39, time: 2.536, data_time: 1.512, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5820, loss_cls: 3.8564, loss: 3.8564 +2024-12-28 22:05:59,452 - pyskl - INFO - Epoch [76][200/3746] lr: 4.994e-02, eta: 2 days, 16:52:17, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5837, loss_cls: 3.8763, loss: 3.8763 +2024-12-28 22:07:24,185 - pyskl - INFO - Epoch [76][300/3746] lr: 4.992e-02, eta: 2 days, 16:50:56, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5753, loss_cls: 3.9057, loss: 3.9057 +2024-12-28 22:08:49,249 - pyskl - INFO - Epoch [76][400/3746] lr: 4.989e-02, eta: 2 days, 16:49:34, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5747, loss_cls: 3.9046, loss: 3.9046 +2024-12-28 22:10:13,932 - pyskl - INFO - Epoch [76][500/3746] lr: 4.986e-02, eta: 2 days, 16:48:13, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5706, loss_cls: 3.9205, loss: 3.9205 +2024-12-28 22:11:38,930 - pyskl - INFO - Epoch [76][600/3746] lr: 4.983e-02, eta: 2 days, 16:46:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5669, loss_cls: 3.9463, loss: 3.9463 +2024-12-28 22:13:03,648 - pyskl - INFO - Epoch [76][700/3746] lr: 4.980e-02, eta: 2 days, 16:45:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5741, loss_cls: 3.9191, loss: 3.9191 +2024-12-28 22:14:28,600 - pyskl - INFO - Epoch [76][800/3746] lr: 4.978e-02, eta: 2 days, 16:44:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5708, loss_cls: 3.9428, loss: 3.9428 +2024-12-28 22:15:53,115 - pyskl - INFO - Epoch [76][900/3746] lr: 4.975e-02, eta: 2 days, 16:42:46, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5713, loss_cls: 3.9410, loss: 3.9410 +2024-12-28 22:17:18,038 - pyskl - INFO - Epoch [76][1000/3746] lr: 4.972e-02, eta: 2 days, 16:41:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5817, loss_cls: 3.8963, loss: 3.8963 +2024-12-28 22:18:42,616 - pyskl - INFO - Epoch [76][1100/3746] lr: 4.969e-02, eta: 2 days, 16:40:03, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5775, loss_cls: 3.9098, loss: 3.9098 +2024-12-28 22:20:07,084 - pyskl - INFO - Epoch [76][1200/3746] lr: 4.966e-02, eta: 2 days, 16:38:41, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5702, loss_cls: 3.9120, loss: 3.9120 +2024-12-28 22:21:31,813 - pyskl - INFO - Epoch [76][1300/3746] lr: 4.964e-02, eta: 2 days, 16:37:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5666, loss_cls: 3.9319, loss: 3.9319 +2024-12-28 22:22:56,215 - pyskl - INFO - Epoch [76][1400/3746] lr: 4.961e-02, eta: 2 days, 16:35:58, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5748, loss_cls: 3.9360, loss: 3.9360 +2024-12-28 22:24:21,337 - pyskl - INFO - Epoch [76][1500/3746] lr: 4.958e-02, eta: 2 days, 16:34:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5767, loss_cls: 3.8990, loss: 3.8990 +2024-12-28 22:25:45,627 - pyskl - INFO - Epoch [76][1600/3746] lr: 4.955e-02, eta: 2 days, 16:33:14, time: 0.843, data_time: 0.001, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5670, loss_cls: 3.9458, loss: 3.9458 +2024-12-28 22:27:10,724 - pyskl - INFO - Epoch [76][1700/3746] lr: 4.953e-02, eta: 2 days, 16:31:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5741, loss_cls: 3.9569, loss: 3.9569 +2024-12-28 22:28:35,758 - pyskl - INFO - Epoch [76][1800/3746] lr: 4.950e-02, eta: 2 days, 16:30:31, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5736, loss_cls: 3.9005, loss: 3.9005 +2024-12-28 22:30:00,237 - pyskl - INFO - Epoch [76][1900/3746] lr: 4.947e-02, eta: 2 days, 16:29:10, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5780, loss_cls: 3.9082, loss: 3.9082 +2024-12-28 22:31:24,826 - pyskl - INFO - Epoch [76][2000/3746] lr: 4.944e-02, eta: 2 days, 16:27:48, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5663, loss_cls: 3.9544, loss: 3.9544 +2024-12-28 22:32:49,315 - pyskl - INFO - Epoch [76][2100/3746] lr: 4.941e-02, eta: 2 days, 16:26:26, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5827, loss_cls: 3.8695, loss: 3.8695 +2024-12-28 22:34:13,951 - pyskl - INFO - Epoch [76][2200/3746] lr: 4.939e-02, eta: 2 days, 16:25:04, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5720, loss_cls: 3.9271, loss: 3.9271 +2024-12-28 22:35:38,303 - pyskl - INFO - Epoch [76][2300/3746] lr: 4.936e-02, eta: 2 days, 16:23:42, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5663, loss_cls: 3.9486, loss: 3.9486 +2024-12-28 22:37:02,825 - pyskl - INFO - Epoch [76][2400/3746] lr: 4.933e-02, eta: 2 days, 16:22:20, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5705, loss_cls: 3.9337, loss: 3.9337 +2024-12-28 22:38:27,546 - pyskl - INFO - Epoch [76][2500/3746] lr: 4.930e-02, eta: 2 days, 16:20:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5592, loss_cls: 3.9624, loss: 3.9624 +2024-12-28 22:39:52,012 - pyskl - INFO - Epoch [76][2600/3746] lr: 4.927e-02, eta: 2 days, 16:19:36, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5613, loss_cls: 4.0127, loss: 4.0127 +2024-12-28 22:41:16,285 - pyskl - INFO - Epoch [76][2700/3746] lr: 4.925e-02, eta: 2 days, 16:18:14, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5669, loss_cls: 3.9341, loss: 3.9341 +2024-12-28 22:42:41,116 - pyskl - INFO - Epoch [76][2800/3746] lr: 4.922e-02, eta: 2 days, 16:16:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5622, loss_cls: 3.9502, loss: 3.9502 +2024-12-28 22:44:06,274 - pyskl - INFO - Epoch [76][2900/3746] lr: 4.919e-02, eta: 2 days, 16:15:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5739, loss_cls: 3.9272, loss: 3.9272 +2024-12-28 22:45:30,808 - pyskl - INFO - Epoch [76][3000/3746] lr: 4.916e-02, eta: 2 days, 16:14:10, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5703, loss_cls: 3.9254, loss: 3.9254 +2024-12-28 22:46:55,753 - pyskl - INFO - Epoch [76][3100/3746] lr: 4.913e-02, eta: 2 days, 16:12:48, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5797, loss_cls: 3.8831, loss: 3.8831 +2024-12-28 22:48:20,880 - pyskl - INFO - Epoch [76][3200/3746] lr: 4.911e-02, eta: 2 days, 16:11:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5714, loss_cls: 3.9039, loss: 3.9039 +2024-12-28 22:49:45,670 - pyskl - INFO - Epoch [76][3300/3746] lr: 4.908e-02, eta: 2 days, 16:10:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5728, loss_cls: 3.9279, loss: 3.9279 +2024-12-28 22:51:10,267 - pyskl - INFO - Epoch [76][3400/3746] lr: 4.905e-02, eta: 2 days, 16:08:43, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5695, loss_cls: 3.9337, loss: 3.9337 +2024-12-28 22:52:34,653 - pyskl - INFO - Epoch [76][3500/3746] lr: 4.902e-02, eta: 2 days, 16:07:21, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5630, loss_cls: 3.9585, loss: 3.9585 +2024-12-28 22:53:59,334 - pyskl - INFO - Epoch [76][3600/3746] lr: 4.899e-02, eta: 2 days, 16:05:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5602, loss_cls: 4.0088, loss: 4.0088 +2024-12-28 22:55:23,667 - pyskl - INFO - Epoch [76][3700/3746] lr: 4.897e-02, eta: 2 days, 16:04:37, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5681, loss_cls: 3.9161, loss: 3.9161 +2024-12-28 22:56:04,465 - pyskl - INFO - Saving checkpoint at 76 epochs +2024-12-28 22:58:04,016 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 22:58:05,028 - pyskl - INFO - +top1_acc 0.2593 +top5_acc 0.5025 +2024-12-28 22:58:05,028 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 22:58:05,072 - pyskl - INFO - +mean_acc 0.2590 +2024-12-28 22:58:05,087 - pyskl - INFO - Epoch(val) [76][309] top1_acc: 0.2593, top5_acc: 0.5025, mean_class_accuracy: 0.2590 +2024-12-28 23:02:18,869 - pyskl - INFO - Epoch [77][100/3746] lr: 4.893e-02, eta: 2 days, 16:04:45, time: 2.538, data_time: 1.504, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5836, loss_cls: 3.8628, loss: 3.8628 +2024-12-28 23:03:44,177 - pyskl - INFO - Epoch [77][200/3746] lr: 4.890e-02, eta: 2 days, 16:03:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5808, loss_cls: 3.8671, loss: 3.8671 +2024-12-28 23:05:09,158 - pyskl - INFO - Epoch [77][300/3746] lr: 4.887e-02, eta: 2 days, 16:02:02, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5741, loss_cls: 3.9206, loss: 3.9206 +2024-12-28 23:06:34,298 - pyskl - INFO - Epoch [77][400/3746] lr: 4.884e-02, eta: 2 days, 16:00:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5789, loss_cls: 3.8611, loss: 3.8611 +2024-12-28 23:07:58,754 - pyskl - INFO - Epoch [77][500/3746] lr: 4.881e-02, eta: 2 days, 15:59:18, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5770, loss_cls: 3.9181, loss: 3.9181 +2024-12-28 23:09:24,021 - pyskl - INFO - Epoch [77][600/3746] lr: 4.879e-02, eta: 2 days, 15:57:57, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5733, loss_cls: 3.8945, loss: 3.8945 +2024-12-28 23:10:48,807 - pyskl - INFO - Epoch [77][700/3746] lr: 4.876e-02, eta: 2 days, 15:56:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5772, loss_cls: 3.8889, loss: 3.8889 +2024-12-28 23:12:13,431 - pyskl - INFO - Epoch [77][800/3746] lr: 4.873e-02, eta: 2 days, 15:55:13, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5775, loss_cls: 3.9074, loss: 3.9074 +2024-12-28 23:13:38,186 - pyskl - INFO - Epoch [77][900/3746] lr: 4.870e-02, eta: 2 days, 15:53:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5745, loss_cls: 3.8938, loss: 3.8938 +2024-12-28 23:15:02,568 - pyskl - INFO - Epoch [77][1000/3746] lr: 4.867e-02, eta: 2 days, 15:52:29, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5719, loss_cls: 3.9359, loss: 3.9359 +2024-12-28 23:16:27,162 - pyskl - INFO - Epoch [77][1100/3746] lr: 4.865e-02, eta: 2 days, 15:51:07, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5764, loss_cls: 3.8995, loss: 3.8995 +2024-12-28 23:17:51,631 - pyskl - INFO - Epoch [77][1200/3746] lr: 4.862e-02, eta: 2 days, 15:49:45, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5758, loss_cls: 3.9041, loss: 3.9041 +2024-12-28 23:19:16,281 - pyskl - INFO - Epoch [77][1300/3746] lr: 4.859e-02, eta: 2 days, 15:48:23, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5766, loss_cls: 3.9109, loss: 3.9109 +2024-12-28 23:20:41,443 - pyskl - INFO - Epoch [77][1400/3746] lr: 4.856e-02, eta: 2 days, 15:47:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5750, loss_cls: 3.9156, loss: 3.9156 +2024-12-28 23:22:06,618 - pyskl - INFO - Epoch [77][1500/3746] lr: 4.853e-02, eta: 2 days, 15:45:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5706, loss_cls: 3.9621, loss: 3.9621 +2024-12-28 23:23:31,173 - pyskl - INFO - Epoch [77][1600/3746] lr: 4.851e-02, eta: 2 days, 15:44:19, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5736, loss_cls: 3.9104, loss: 3.9104 +2024-12-28 23:24:55,598 - pyskl - INFO - Epoch [77][1700/3746] lr: 4.848e-02, eta: 2 days, 15:42:56, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5748, loss_cls: 3.9179, loss: 3.9179 +2024-12-28 23:26:20,236 - pyskl - INFO - Epoch [77][1800/3746] lr: 4.845e-02, eta: 2 days, 15:41:34, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5709, loss_cls: 3.9327, loss: 3.9327 +2024-12-28 23:27:45,239 - pyskl - INFO - Epoch [77][1900/3746] lr: 4.842e-02, eta: 2 days, 15:40:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5656, loss_cls: 3.9198, loss: 3.9198 +2024-12-28 23:29:10,272 - pyskl - INFO - Epoch [77][2000/3746] lr: 4.839e-02, eta: 2 days, 15:38:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5756, loss_cls: 3.9381, loss: 3.9381 +2024-12-28 23:30:35,514 - pyskl - INFO - Epoch [77][2100/3746] lr: 4.837e-02, eta: 2 days, 15:37:30, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5603, loss_cls: 3.9417, loss: 3.9417 +2024-12-28 23:32:00,720 - pyskl - INFO - Epoch [77][2200/3746] lr: 4.834e-02, eta: 2 days, 15:36:09, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5708, loss_cls: 3.9415, loss: 3.9415 +2024-12-28 23:33:26,154 - pyskl - INFO - Epoch [77][2300/3746] lr: 4.831e-02, eta: 2 days, 15:34:47, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5772, loss_cls: 3.8884, loss: 3.8884 +2024-12-28 23:34:51,403 - pyskl - INFO - Epoch [77][2400/3746] lr: 4.828e-02, eta: 2 days, 15:33:26, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5791, loss_cls: 3.8879, loss: 3.8879 +2024-12-28 23:36:16,514 - pyskl - INFO - Epoch [77][2500/3746] lr: 4.825e-02, eta: 2 days, 15:32:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5717, loss_cls: 3.8985, loss: 3.8985 +2024-12-28 23:37:41,236 - pyskl - INFO - Epoch [77][2600/3746] lr: 4.823e-02, eta: 2 days, 15:30:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5734, loss_cls: 3.9094, loss: 3.9094 +2024-12-28 23:39:06,197 - pyskl - INFO - Epoch [77][2700/3746] lr: 4.820e-02, eta: 2 days, 15:29:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5652, loss_cls: 3.9502, loss: 3.9502 +2024-12-28 23:40:31,681 - pyskl - INFO - Epoch [77][2800/3746] lr: 4.817e-02, eta: 2 days, 15:28:00, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5666, loss_cls: 3.9811, loss: 3.9811 +2024-12-28 23:41:56,868 - pyskl - INFO - Epoch [77][2900/3746] lr: 4.814e-02, eta: 2 days, 15:26:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5816, loss_cls: 3.8574, loss: 3.8574 +2024-12-28 23:43:21,861 - pyskl - INFO - Epoch [77][3000/3746] lr: 4.811e-02, eta: 2 days, 15:25:17, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5787, loss_cls: 3.9114, loss: 3.9114 +2024-12-28 23:44:46,870 - pyskl - INFO - Epoch [77][3100/3746] lr: 4.809e-02, eta: 2 days, 15:23:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5823, loss_cls: 3.8947, loss: 3.8947 +2024-12-28 23:46:11,594 - pyskl - INFO - Epoch [77][3200/3746] lr: 4.806e-02, eta: 2 days, 15:22:33, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5752, loss_cls: 3.9335, loss: 3.9335 +2024-12-28 23:47:36,646 - pyskl - INFO - Epoch [77][3300/3746] lr: 4.803e-02, eta: 2 days, 15:21:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5714, loss_cls: 3.9279, loss: 3.9279 +2024-12-28 23:49:01,257 - pyskl - INFO - Epoch [77][3400/3746] lr: 4.800e-02, eta: 2 days, 15:19:50, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5709, loss_cls: 3.9557, loss: 3.9557 +2024-12-28 23:50:25,805 - pyskl - INFO - Epoch [77][3500/3746] lr: 4.798e-02, eta: 2 days, 15:18:28, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5656, loss_cls: 3.9337, loss: 3.9337 +2024-12-28 23:51:50,627 - pyskl - INFO - Epoch [77][3600/3746] lr: 4.795e-02, eta: 2 days, 15:17:06, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5722, loss_cls: 3.9281, loss: 3.9281 +2024-12-28 23:53:15,101 - pyskl - INFO - Epoch [77][3700/3746] lr: 4.792e-02, eta: 2 days, 15:15:44, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5786, loss_cls: 3.9146, loss: 3.9146 +2024-12-28 23:53:55,700 - pyskl - INFO - Saving checkpoint at 77 epochs +2024-12-28 23:55:54,711 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 23:55:55,403 - pyskl - INFO - +top1_acc 0.2559 +top5_acc 0.4980 +2024-12-28 23:55:55,403 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 23:55:55,448 - pyskl - INFO - +mean_acc 0.2558 +2024-12-28 23:55:55,464 - pyskl - INFO - Epoch(val) [77][309] top1_acc: 0.2559, top5_acc: 0.4980, mean_class_accuracy: 0.2558 +2024-12-29 00:00:08,409 - pyskl - INFO - Epoch [78][100/3746] lr: 4.788e-02, eta: 2 days, 15:15:47, time: 2.529, data_time: 1.502, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5887, loss_cls: 3.8183, loss: 3.8183 +2024-12-29 00:01:33,486 - pyskl - INFO - Epoch [78][200/3746] lr: 4.785e-02, eta: 2 days, 15:14:25, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5852, loss_cls: 3.8311, loss: 3.8311 +2024-12-29 00:02:58,355 - pyskl - INFO - Epoch [78][300/3746] lr: 4.782e-02, eta: 2 days, 15:13:03, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5831, loss_cls: 3.8664, loss: 3.8664 +2024-12-29 00:04:23,613 - pyskl - INFO - Epoch [78][400/3746] lr: 4.779e-02, eta: 2 days, 15:11:42, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5786, loss_cls: 3.9117, loss: 3.9117 +2024-12-29 00:05:48,756 - pyskl - INFO - Epoch [78][500/3746] lr: 4.777e-02, eta: 2 days, 15:10:20, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5673, loss_cls: 3.8979, loss: 3.8979 +2024-12-29 00:07:13,443 - pyskl - INFO - Epoch [78][600/3746] lr: 4.774e-02, eta: 2 days, 15:08:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5694, loss_cls: 3.9047, loss: 3.9047 +2024-12-29 00:08:38,562 - pyskl - INFO - Epoch [78][700/3746] lr: 4.771e-02, eta: 2 days, 15:07:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5758, loss_cls: 3.9420, loss: 3.9420 +2024-12-29 00:10:03,606 - pyskl - INFO - Epoch [78][800/3746] lr: 4.768e-02, eta: 2 days, 15:06:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5692, loss_cls: 3.9082, loss: 3.9082 +2024-12-29 00:11:28,075 - pyskl - INFO - Epoch [78][900/3746] lr: 4.766e-02, eta: 2 days, 15:04:53, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5734, loss_cls: 3.9032, loss: 3.9032 +2024-12-29 00:12:53,026 - pyskl - INFO - Epoch [78][1000/3746] lr: 4.763e-02, eta: 2 days, 15:03:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5686, loss_cls: 3.9136, loss: 3.9136 +2024-12-29 00:14:17,695 - pyskl - INFO - Epoch [78][1100/3746] lr: 4.760e-02, eta: 2 days, 15:02:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5723, loss_cls: 3.9234, loss: 3.9234 +2024-12-29 00:15:42,523 - pyskl - INFO - Epoch [78][1200/3746] lr: 4.757e-02, eta: 2 days, 15:00:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5777, loss_cls: 3.8749, loss: 3.8749 +2024-12-29 00:17:07,377 - pyskl - INFO - Epoch [78][1300/3746] lr: 4.754e-02, eta: 2 days, 14:59:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5745, loss_cls: 3.8847, loss: 3.8847 +2024-12-29 00:18:32,090 - pyskl - INFO - Epoch [78][1400/3746] lr: 4.752e-02, eta: 2 days, 14:58:03, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5877, loss_cls: 3.8490, loss: 3.8490 +2024-12-29 00:19:56,694 - pyskl - INFO - Epoch [78][1500/3746] lr: 4.749e-02, eta: 2 days, 14:56:41, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5778, loss_cls: 3.9072, loss: 3.9072 +2024-12-29 00:21:21,527 - pyskl - INFO - Epoch [78][1600/3746] lr: 4.746e-02, eta: 2 days, 14:55:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5856, loss_cls: 3.8617, loss: 3.8617 +2024-12-29 00:22:46,862 - pyskl - INFO - Epoch [78][1700/3746] lr: 4.743e-02, eta: 2 days, 14:53:57, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5830, loss_cls: 3.8935, loss: 3.8935 +2024-12-29 00:24:11,260 - pyskl - INFO - Epoch [78][1800/3746] lr: 4.740e-02, eta: 2 days, 14:52:35, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5750, loss_cls: 3.9059, loss: 3.9059 +2024-12-29 00:25:35,724 - pyskl - INFO - Epoch [78][1900/3746] lr: 4.738e-02, eta: 2 days, 14:51:13, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5700, loss_cls: 3.9053, loss: 3.9053 +2024-12-29 00:27:00,376 - pyskl - INFO - Epoch [78][2000/3746] lr: 4.735e-02, eta: 2 days, 14:49:51, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5758, loss_cls: 3.9126, loss: 3.9126 +2024-12-29 00:28:25,054 - pyskl - INFO - Epoch [78][2100/3746] lr: 4.732e-02, eta: 2 days, 14:48:29, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5702, loss_cls: 3.9167, loss: 3.9167 +2024-12-29 00:29:49,462 - pyskl - INFO - Epoch [78][2200/3746] lr: 4.729e-02, eta: 2 days, 14:47:06, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5773, loss_cls: 3.9222, loss: 3.9222 +2024-12-29 00:31:13,908 - pyskl - INFO - Epoch [78][2300/3746] lr: 4.726e-02, eta: 2 days, 14:45:44, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5755, loss_cls: 3.8927, loss: 3.8927 +2024-12-29 00:32:38,500 - pyskl - INFO - Epoch [78][2400/3746] lr: 4.724e-02, eta: 2 days, 14:44:22, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5814, loss_cls: 3.8788, loss: 3.8788 +2024-12-29 00:34:02,993 - pyskl - INFO - Epoch [78][2500/3746] lr: 4.721e-02, eta: 2 days, 14:43:00, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5742, loss_cls: 3.9274, loss: 3.9274 +2024-12-29 00:35:27,915 - pyskl - INFO - Epoch [78][2600/3746] lr: 4.718e-02, eta: 2 days, 14:41:38, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5708, loss_cls: 3.9262, loss: 3.9262 +2024-12-29 00:36:52,538 - pyskl - INFO - Epoch [78][2700/3746] lr: 4.715e-02, eta: 2 days, 14:40:16, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5650, loss_cls: 3.9311, loss: 3.9311 +2024-12-29 00:38:17,184 - pyskl - INFO - Epoch [78][2800/3746] lr: 4.712e-02, eta: 2 days, 14:38:54, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5756, loss_cls: 3.8989, loss: 3.8989 +2024-12-29 00:39:41,675 - pyskl - INFO - Epoch [78][2900/3746] lr: 4.710e-02, eta: 2 days, 14:37:31, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5716, loss_cls: 3.8989, loss: 3.8989 +2024-12-29 00:41:06,437 - pyskl - INFO - Epoch [78][3000/3746] lr: 4.707e-02, eta: 2 days, 14:36:09, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5805, loss_cls: 3.8578, loss: 3.8578 +2024-12-29 00:42:31,282 - pyskl - INFO - Epoch [78][3100/3746] lr: 4.704e-02, eta: 2 days, 14:34:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5714, loss_cls: 3.9231, loss: 3.9231 +2024-12-29 00:43:55,770 - pyskl - INFO - Epoch [78][3200/3746] lr: 4.701e-02, eta: 2 days, 14:33:25, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5691, loss_cls: 3.9252, loss: 3.9252 +2024-12-29 00:45:20,403 - pyskl - INFO - Epoch [78][3300/3746] lr: 4.699e-02, eta: 2 days, 14:32:03, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5630, loss_cls: 3.9581, loss: 3.9581 +2024-12-29 00:46:45,275 - pyskl - INFO - Epoch [78][3400/3746] lr: 4.696e-02, eta: 2 days, 14:30:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5778, loss_cls: 3.8806, loss: 3.8806 +2024-12-29 00:48:10,347 - pyskl - INFO - Epoch [78][3500/3746] lr: 4.693e-02, eta: 2 days, 14:29:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5619, loss_cls: 3.9587, loss: 3.9587 +2024-12-29 00:49:35,876 - pyskl - INFO - Epoch [78][3600/3746] lr: 4.690e-02, eta: 2 days, 14:27:58, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5784, loss_cls: 3.8821, loss: 3.8821 +2024-12-29 00:51:01,311 - pyskl - INFO - Epoch [78][3700/3746] lr: 4.687e-02, eta: 2 days, 14:26:37, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5719, loss_cls: 3.8993, loss: 3.8993 +2024-12-29 00:51:42,283 - pyskl - INFO - Saving checkpoint at 78 epochs +2024-12-29 00:53:40,811 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 00:53:41,745 - pyskl - INFO - +top1_acc 0.2676 +top5_acc 0.5116 +2024-12-29 00:53:41,745 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 00:53:41,797 - pyskl - INFO - +mean_acc 0.2674 +2024-12-29 00:53:41,802 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_75.pth was removed +2024-12-29 00:53:42,102 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_78.pth. +2024-12-29 00:53:42,103 - pyskl - INFO - Best top1_acc is 0.2676 at 78 epoch. +2024-12-29 00:53:42,117 - pyskl - INFO - Epoch(val) [78][309] top1_acc: 0.2676, top5_acc: 0.5116, mean_class_accuracy: 0.2674 +2024-12-29 00:57:58,320 - pyskl - INFO - Epoch [79][100/3746] lr: 4.683e-02, eta: 2 days, 14:26:39, time: 2.562, data_time: 1.522, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5922, loss_cls: 3.8068, loss: 3.8068 +2024-12-29 00:59:24,110 - pyskl - INFO - Epoch [79][200/3746] lr: 4.680e-02, eta: 2 days, 14:25:18, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5845, loss_cls: 3.8493, loss: 3.8493 +2024-12-29 01:00:49,494 - pyskl - INFO - Epoch [79][300/3746] lr: 4.678e-02, eta: 2 days, 14:23:57, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5781, loss_cls: 3.8842, loss: 3.8842 +2024-12-29 01:02:14,494 - pyskl - INFO - Epoch [79][400/3746] lr: 4.675e-02, eta: 2 days, 14:22:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5795, loss_cls: 3.8611, loss: 3.8611 +2024-12-29 01:03:39,542 - pyskl - INFO - Epoch [79][500/3746] lr: 4.672e-02, eta: 2 days, 14:21:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5827, loss_cls: 3.8526, loss: 3.8526 +2024-12-29 01:05:04,225 - pyskl - INFO - Epoch [79][600/3746] lr: 4.669e-02, eta: 2 days, 14:19:51, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5802, loss_cls: 3.8651, loss: 3.8651 +2024-12-29 01:06:28,896 - pyskl - INFO - Epoch [79][700/3746] lr: 4.667e-02, eta: 2 days, 14:18:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5833, loss_cls: 3.8466, loss: 3.8466 +2024-12-29 01:07:53,423 - pyskl - INFO - Epoch [79][800/3746] lr: 4.664e-02, eta: 2 days, 14:17:06, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5777, loss_cls: 3.8865, loss: 3.8865 +2024-12-29 01:09:18,093 - pyskl - INFO - Epoch [79][900/3746] lr: 4.661e-02, eta: 2 days, 14:15:44, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5847, loss_cls: 3.8283, loss: 3.8283 +2024-12-29 01:10:42,953 - pyskl - INFO - Epoch [79][1000/3746] lr: 4.658e-02, eta: 2 days, 14:14:22, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5837, loss_cls: 3.8517, loss: 3.8517 +2024-12-29 01:12:07,375 - pyskl - INFO - Epoch [79][1100/3746] lr: 4.655e-02, eta: 2 days, 14:12:59, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5778, loss_cls: 3.8583, loss: 3.8583 +2024-12-29 01:13:32,222 - pyskl - INFO - Epoch [79][1200/3746] lr: 4.653e-02, eta: 2 days, 14:11:37, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5745, loss_cls: 3.9352, loss: 3.9352 +2024-12-29 01:14:57,104 - pyskl - INFO - Epoch [79][1300/3746] lr: 4.650e-02, eta: 2 days, 14:10:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5716, loss_cls: 3.8967, loss: 3.8967 +2024-12-29 01:16:21,830 - pyskl - INFO - Epoch [79][1400/3746] lr: 4.647e-02, eta: 2 days, 14:08:53, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5717, loss_cls: 3.8660, loss: 3.8660 +2024-12-29 01:17:46,144 - pyskl - INFO - Epoch [79][1500/3746] lr: 4.644e-02, eta: 2 days, 14:07:31, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5748, loss_cls: 3.9009, loss: 3.9009 +2024-12-29 01:19:11,620 - pyskl - INFO - Epoch [79][1600/3746] lr: 4.641e-02, eta: 2 days, 14:06:09, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5806, loss_cls: 3.8540, loss: 3.8540 +2024-12-29 01:20:36,865 - pyskl - INFO - Epoch [79][1700/3746] lr: 4.639e-02, eta: 2 days, 14:04:47, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5661, loss_cls: 3.9218, loss: 3.9218 +2024-12-29 01:22:01,391 - pyskl - INFO - Epoch [79][1800/3746] lr: 4.636e-02, eta: 2 days, 14:03:25, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5847, loss_cls: 3.8567, loss: 3.8567 +2024-12-29 01:23:26,009 - pyskl - INFO - Epoch [79][1900/3746] lr: 4.633e-02, eta: 2 days, 14:02:03, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5716, loss_cls: 3.8941, loss: 3.8941 +2024-12-29 01:24:50,439 - pyskl - INFO - Epoch [79][2000/3746] lr: 4.630e-02, eta: 2 days, 14:00:40, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5775, loss_cls: 3.8765, loss: 3.8765 +2024-12-29 01:26:15,191 - pyskl - INFO - Epoch [79][2100/3746] lr: 4.628e-02, eta: 2 days, 13:59:18, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5777, loss_cls: 3.8903, loss: 3.8903 +2024-12-29 01:27:39,992 - pyskl - INFO - Epoch [79][2200/3746] lr: 4.625e-02, eta: 2 days, 13:57:56, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5816, loss_cls: 3.8464, loss: 3.8464 +2024-12-29 01:29:04,832 - pyskl - INFO - Epoch [79][2300/3746] lr: 4.622e-02, eta: 2 days, 13:56:34, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5761, loss_cls: 3.9149, loss: 3.9149 +2024-12-29 01:30:29,273 - pyskl - INFO - Epoch [79][2400/3746] lr: 4.619e-02, eta: 2 days, 13:55:12, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5750, loss_cls: 3.8759, loss: 3.8759 +2024-12-29 01:31:54,012 - pyskl - INFO - Epoch [79][2500/3746] lr: 4.616e-02, eta: 2 days, 13:53:49, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5630, loss_cls: 3.9529, loss: 3.9529 +2024-12-29 01:33:18,297 - pyskl - INFO - Epoch [79][2600/3746] lr: 4.614e-02, eta: 2 days, 13:52:27, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5728, loss_cls: 3.9329, loss: 3.9329 +2024-12-29 01:34:43,295 - pyskl - INFO - Epoch [79][2700/3746] lr: 4.611e-02, eta: 2 days, 13:51:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5714, loss_cls: 3.9266, loss: 3.9266 +2024-12-29 01:36:08,126 - pyskl - INFO - Epoch [79][2800/3746] lr: 4.608e-02, eta: 2 days, 13:49:43, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5723, loss_cls: 3.9181, loss: 3.9181 +2024-12-29 01:37:32,938 - pyskl - INFO - Epoch [79][2900/3746] lr: 4.605e-02, eta: 2 days, 13:48:21, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5861, loss_cls: 3.8907, loss: 3.8907 +2024-12-29 01:38:58,005 - pyskl - INFO - Epoch [79][3000/3746] lr: 4.602e-02, eta: 2 days, 13:46:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5792, loss_cls: 3.8871, loss: 3.8871 +2024-12-29 01:40:23,027 - pyskl - INFO - Epoch [79][3100/3746] lr: 4.600e-02, eta: 2 days, 13:45:37, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5722, loss_cls: 3.8874, loss: 3.8874 +2024-12-29 01:41:47,246 - pyskl - INFO - Epoch [79][3200/3746] lr: 4.597e-02, eta: 2 days, 13:44:14, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5716, loss_cls: 3.8986, loss: 3.8986 +2024-12-29 01:43:12,020 - pyskl - INFO - Epoch [79][3300/3746] lr: 4.594e-02, eta: 2 days, 13:42:52, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5731, loss_cls: 3.9228, loss: 3.9228 +2024-12-29 01:44:36,802 - pyskl - INFO - Epoch [79][3400/3746] lr: 4.591e-02, eta: 2 days, 13:41:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5731, loss_cls: 3.8995, loss: 3.8995 +2024-12-29 01:46:01,871 - pyskl - INFO - Epoch [79][3500/3746] lr: 4.588e-02, eta: 2 days, 13:40:08, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5625, loss_cls: 3.9517, loss: 3.9517 +2024-12-29 01:47:26,976 - pyskl - INFO - Epoch [79][3600/3746] lr: 4.586e-02, eta: 2 days, 13:38:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5759, loss_cls: 3.9322, loss: 3.9322 +2024-12-29 01:48:52,148 - pyskl - INFO - Epoch [79][3700/3746] lr: 4.583e-02, eta: 2 days, 13:37:24, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5713, loss_cls: 3.9102, loss: 3.9102 +2024-12-29 01:49:32,949 - pyskl - INFO - Saving checkpoint at 79 epochs +2024-12-29 01:51:31,602 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 01:51:32,388 - pyskl - INFO - +top1_acc 0.2627 +top5_acc 0.5096 +2024-12-29 01:51:32,389 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 01:51:32,445 - pyskl - INFO - +mean_acc 0.2625 +2024-12-29 01:51:32,464 - pyskl - INFO - Epoch(val) [79][309] top1_acc: 0.2627, top5_acc: 0.5096, mean_class_accuracy: 0.2625 +2024-12-29 01:55:49,653 - pyskl - INFO - Epoch [80][100/3746] lr: 4.579e-02, eta: 2 days, 13:37:24, time: 2.572, data_time: 1.545, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5969, loss_cls: 3.7898, loss: 3.7898 +2024-12-29 01:57:14,897 - pyskl - INFO - Epoch [80][200/3746] lr: 4.576e-02, eta: 2 days, 13:36:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5817, loss_cls: 3.8527, loss: 3.8527 +2024-12-29 01:58:39,897 - pyskl - INFO - Epoch [80][300/3746] lr: 4.573e-02, eta: 2 days, 13:34:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5837, loss_cls: 3.8671, loss: 3.8671 +2024-12-29 02:00:04,747 - pyskl - INFO - Epoch [80][400/3746] lr: 4.570e-02, eta: 2 days, 13:33:18, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5795, loss_cls: 3.8910, loss: 3.8910 +2024-12-29 02:01:29,899 - pyskl - INFO - Epoch [80][500/3746] lr: 4.568e-02, eta: 2 days, 13:31:56, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5780, loss_cls: 3.8709, loss: 3.8709 +2024-12-29 02:02:54,676 - pyskl - INFO - Epoch [80][600/3746] lr: 4.565e-02, eta: 2 days, 13:30:34, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5906, loss_cls: 3.8568, loss: 3.8568 +2024-12-29 02:04:19,728 - pyskl - INFO - Epoch [80][700/3746] lr: 4.562e-02, eta: 2 days, 13:29:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5806, loss_cls: 3.8687, loss: 3.8687 +2024-12-29 02:05:44,716 - pyskl - INFO - Epoch [80][800/3746] lr: 4.559e-02, eta: 2 days, 13:27:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5733, loss_cls: 3.8993, loss: 3.8993 +2024-12-29 02:07:09,680 - pyskl - INFO - Epoch [80][900/3746] lr: 4.557e-02, eta: 2 days, 13:26:28, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5792, loss_cls: 3.8590, loss: 3.8590 +2024-12-29 02:08:34,590 - pyskl - INFO - Epoch [80][1000/3746] lr: 4.554e-02, eta: 2 days, 13:25:06, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5797, loss_cls: 3.8425, loss: 3.8425 +2024-12-29 02:09:59,247 - pyskl - INFO - Epoch [80][1100/3746] lr: 4.551e-02, eta: 2 days, 13:23:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5716, loss_cls: 3.8920, loss: 3.8920 +2024-12-29 02:11:24,112 - pyskl - INFO - Epoch [80][1200/3746] lr: 4.548e-02, eta: 2 days, 13:22:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5775, loss_cls: 3.8857, loss: 3.8857 +2024-12-29 02:12:48,901 - pyskl - INFO - Epoch [80][1300/3746] lr: 4.545e-02, eta: 2 days, 13:20:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5675, loss_cls: 3.9096, loss: 3.9096 +2024-12-29 02:14:13,653 - pyskl - INFO - Epoch [80][1400/3746] lr: 4.543e-02, eta: 2 days, 13:19:37, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5725, loss_cls: 3.9001, loss: 3.9001 +2024-12-29 02:15:38,008 - pyskl - INFO - Epoch [80][1500/3746] lr: 4.540e-02, eta: 2 days, 13:18:14, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5756, loss_cls: 3.8891, loss: 3.8891 +2024-12-29 02:17:02,945 - pyskl - INFO - Epoch [80][1600/3746] lr: 4.537e-02, eta: 2 days, 13:16:52, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5766, loss_cls: 3.8605, loss: 3.8605 +2024-12-29 02:18:27,631 - pyskl - INFO - Epoch [80][1700/3746] lr: 4.534e-02, eta: 2 days, 13:15:30, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5752, loss_cls: 3.8963, loss: 3.8963 +2024-12-29 02:19:52,298 - pyskl - INFO - Epoch [80][1800/3746] lr: 4.532e-02, eta: 2 days, 13:14:07, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5798, loss_cls: 3.8439, loss: 3.8439 +2024-12-29 02:21:16,925 - pyskl - INFO - Epoch [80][1900/3746] lr: 4.529e-02, eta: 2 days, 13:12:45, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5827, loss_cls: 3.8440, loss: 3.8440 +2024-12-29 02:22:41,645 - pyskl - INFO - Epoch [80][2000/3746] lr: 4.526e-02, eta: 2 days, 13:11:22, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5841, loss_cls: 3.8781, loss: 3.8781 +2024-12-29 02:24:06,176 - pyskl - INFO - Epoch [80][2100/3746] lr: 4.523e-02, eta: 2 days, 13:10:00, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5834, loss_cls: 3.8335, loss: 3.8335 +2024-12-29 02:25:31,153 - pyskl - INFO - Epoch [80][2200/3746] lr: 4.520e-02, eta: 2 days, 13:08:38, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5866, loss_cls: 3.8721, loss: 3.8721 +2024-12-29 02:26:55,937 - pyskl - INFO - Epoch [80][2300/3746] lr: 4.518e-02, eta: 2 days, 13:07:16, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5709, loss_cls: 3.9226, loss: 3.9226 +2024-12-29 02:28:20,928 - pyskl - INFO - Epoch [80][2400/3746] lr: 4.515e-02, eta: 2 days, 13:05:53, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5800, loss_cls: 3.8876, loss: 3.8876 +2024-12-29 02:29:45,465 - pyskl - INFO - Epoch [80][2500/3746] lr: 4.512e-02, eta: 2 days, 13:04:31, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5906, loss_cls: 3.8577, loss: 3.8577 +2024-12-29 02:31:10,498 - pyskl - INFO - Epoch [80][2600/3746] lr: 4.509e-02, eta: 2 days, 13:03:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5808, loss_cls: 3.8654, loss: 3.8654 +2024-12-29 02:32:35,227 - pyskl - INFO - Epoch [80][2700/3746] lr: 4.506e-02, eta: 2 days, 13:01:47, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5827, loss_cls: 3.8468, loss: 3.8468 +2024-12-29 02:34:00,413 - pyskl - INFO - Epoch [80][2800/3746] lr: 4.504e-02, eta: 2 days, 13:00:25, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5753, loss_cls: 3.8925, loss: 3.8925 +2024-12-29 02:35:25,967 - pyskl - INFO - Epoch [80][2900/3746] lr: 4.501e-02, eta: 2 days, 12:59:03, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5683, loss_cls: 3.9367, loss: 3.9367 +2024-12-29 02:36:51,269 - pyskl - INFO - Epoch [80][3000/3746] lr: 4.498e-02, eta: 2 days, 12:57:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5814, loss_cls: 3.8800, loss: 3.8800 +2024-12-29 02:38:16,233 - pyskl - INFO - Epoch [80][3100/3746] lr: 4.495e-02, eta: 2 days, 12:56:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5758, loss_cls: 3.8940, loss: 3.8940 +2024-12-29 02:39:41,003 - pyskl - INFO - Epoch [80][3200/3746] lr: 4.493e-02, eta: 2 days, 12:54:57, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5772, loss_cls: 3.9059, loss: 3.9059 +2024-12-29 02:41:05,615 - pyskl - INFO - Epoch [80][3300/3746] lr: 4.490e-02, eta: 2 days, 12:53:34, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5828, loss_cls: 3.8934, loss: 3.8934 +2024-12-29 02:42:30,305 - pyskl - INFO - Epoch [80][3400/3746] lr: 4.487e-02, eta: 2 days, 12:52:12, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5706, loss_cls: 3.9162, loss: 3.9162 +2024-12-29 02:43:55,032 - pyskl - INFO - Epoch [80][3500/3746] lr: 4.484e-02, eta: 2 days, 12:50:50, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5734, loss_cls: 3.9067, loss: 3.9067 +2024-12-29 02:45:20,102 - pyskl - INFO - Epoch [80][3600/3746] lr: 4.481e-02, eta: 2 days, 12:49:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5758, loss_cls: 3.8673, loss: 3.8673 +2024-12-29 02:46:45,147 - pyskl - INFO - Epoch [80][3700/3746] lr: 4.479e-02, eta: 2 days, 12:48:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5805, loss_cls: 3.8729, loss: 3.8729 +2024-12-29 02:47:26,030 - pyskl - INFO - Saving checkpoint at 80 epochs +2024-12-29 02:49:24,428 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 02:49:25,141 - pyskl - INFO - +top1_acc 0.2541 +top5_acc 0.5012 +2024-12-29 02:49:25,141 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 02:49:25,182 - pyskl - INFO - +mean_acc 0.2541 +2024-12-29 02:49:25,198 - pyskl - INFO - Epoch(val) [80][309] top1_acc: 0.2541, top5_acc: 0.5012, mean_class_accuracy: 0.2541 +2024-12-29 02:53:49,655 - pyskl - INFO - Epoch [81][100/3746] lr: 4.475e-02, eta: 2 days, 12:48:08, time: 2.644, data_time: 1.610, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5861, loss_cls: 3.8152, loss: 3.8152 +2024-12-29 02:55:14,961 - pyskl - INFO - Epoch [81][200/3746] lr: 4.472e-02, eta: 2 days, 12:46:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5825, loss_cls: 3.8360, loss: 3.8360 +2024-12-29 02:56:40,209 - pyskl - INFO - Epoch [81][300/3746] lr: 4.469e-02, eta: 2 days, 12:45:24, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5798, loss_cls: 3.8595, loss: 3.8595 +2024-12-29 02:58:05,359 - pyskl - INFO - Epoch [81][400/3746] lr: 4.466e-02, eta: 2 days, 12:44:02, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5855, loss_cls: 3.8281, loss: 3.8281 +2024-12-29 02:59:30,808 - pyskl - INFO - Epoch [81][500/3746] lr: 4.463e-02, eta: 2 days, 12:42:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5923, loss_cls: 3.7844, loss: 3.7844 +2024-12-29 03:00:55,420 - pyskl - INFO - Epoch [81][600/3746] lr: 4.461e-02, eta: 2 days, 12:41:18, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5823, loss_cls: 3.8845, loss: 3.8845 +2024-12-29 03:02:20,685 - pyskl - INFO - Epoch [81][700/3746] lr: 4.458e-02, eta: 2 days, 12:39:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5839, loss_cls: 3.8332, loss: 3.8332 +2024-12-29 03:03:45,557 - pyskl - INFO - Epoch [81][800/3746] lr: 4.455e-02, eta: 2 days, 12:38:34, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5875, loss_cls: 3.8039, loss: 3.8039 +2024-12-29 03:05:10,825 - pyskl - INFO - Epoch [81][900/3746] lr: 4.452e-02, eta: 2 days, 12:37:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5786, loss_cls: 3.8932, loss: 3.8932 +2024-12-29 03:06:35,717 - pyskl - INFO - Epoch [81][1000/3746] lr: 4.450e-02, eta: 2 days, 12:35:49, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5786, loss_cls: 3.8723, loss: 3.8723 +2024-12-29 03:08:01,438 - pyskl - INFO - Epoch [81][1100/3746] lr: 4.447e-02, eta: 2 days, 12:34:28, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5823, loss_cls: 3.8888, loss: 3.8888 +2024-12-29 03:09:26,861 - pyskl - INFO - Epoch [81][1200/3746] lr: 4.444e-02, eta: 2 days, 12:33:06, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5966, loss_cls: 3.8059, loss: 3.8059 +2024-12-29 03:10:51,772 - pyskl - INFO - Epoch [81][1300/3746] lr: 4.441e-02, eta: 2 days, 12:31:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5795, loss_cls: 3.8646, loss: 3.8646 +2024-12-29 03:12:17,175 - pyskl - INFO - Epoch [81][1400/3746] lr: 4.438e-02, eta: 2 days, 12:30:22, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5795, loss_cls: 3.8904, loss: 3.8904 +2024-12-29 03:13:42,412 - pyskl - INFO - Epoch [81][1500/3746] lr: 4.436e-02, eta: 2 days, 12:29:00, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5819, loss_cls: 3.8632, loss: 3.8632 +2024-12-29 03:15:07,760 - pyskl - INFO - Epoch [81][1600/3746] lr: 4.433e-02, eta: 2 days, 12:27:38, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5795, loss_cls: 3.9059, loss: 3.9059 +2024-12-29 03:16:32,671 - pyskl - INFO - Epoch [81][1700/3746] lr: 4.430e-02, eta: 2 days, 12:26:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5841, loss_cls: 3.8680, loss: 3.8680 +2024-12-29 03:17:57,097 - pyskl - INFO - Epoch [81][1800/3746] lr: 4.427e-02, eta: 2 days, 12:24:53, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5837, loss_cls: 3.8623, loss: 3.8623 +2024-12-29 03:19:21,935 - pyskl - INFO - Epoch [81][1900/3746] lr: 4.425e-02, eta: 2 days, 12:23:30, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5686, loss_cls: 3.9069, loss: 3.9069 +2024-12-29 03:20:46,657 - pyskl - INFO - Epoch [81][2000/3746] lr: 4.422e-02, eta: 2 days, 12:22:08, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5700, loss_cls: 3.8963, loss: 3.8963 +2024-12-29 03:22:11,698 - pyskl - INFO - Epoch [81][2100/3746] lr: 4.419e-02, eta: 2 days, 12:20:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5759, loss_cls: 3.9058, loss: 3.9058 +2024-12-29 03:23:37,130 - pyskl - INFO - Epoch [81][2200/3746] lr: 4.416e-02, eta: 2 days, 12:19:24, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5845, loss_cls: 3.8511, loss: 3.8511 +2024-12-29 03:25:02,311 - pyskl - INFO - Epoch [81][2300/3746] lr: 4.413e-02, eta: 2 days, 12:18:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5842, loss_cls: 3.8403, loss: 3.8403 +2024-12-29 03:26:27,388 - pyskl - INFO - Epoch [81][2400/3746] lr: 4.411e-02, eta: 2 days, 12:16:39, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5780, loss_cls: 3.8737, loss: 3.8737 +2024-12-29 03:27:52,535 - pyskl - INFO - Epoch [81][2500/3746] lr: 4.408e-02, eta: 2 days, 12:15:17, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5813, loss_cls: 3.8471, loss: 3.8471 +2024-12-29 03:29:17,814 - pyskl - INFO - Epoch [81][2600/3746] lr: 4.405e-02, eta: 2 days, 12:13:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5741, loss_cls: 3.9074, loss: 3.9074 +2024-12-29 03:30:42,846 - pyskl - INFO - Epoch [81][2700/3746] lr: 4.402e-02, eta: 2 days, 12:12:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5766, loss_cls: 3.9115, loss: 3.9115 +2024-12-29 03:32:07,390 - pyskl - INFO - Epoch [81][2800/3746] lr: 4.400e-02, eta: 2 days, 12:11:10, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5869, loss_cls: 3.8337, loss: 3.8337 +2024-12-29 03:33:31,900 - pyskl - INFO - Epoch [81][2900/3746] lr: 4.397e-02, eta: 2 days, 12:09:48, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5786, loss_cls: 3.8840, loss: 3.8840 +2024-12-29 03:34:56,491 - pyskl - INFO - Epoch [81][3000/3746] lr: 4.394e-02, eta: 2 days, 12:08:25, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5898, loss_cls: 3.8206, loss: 3.8206 +2024-12-29 03:36:21,459 - pyskl - INFO - Epoch [81][3100/3746] lr: 4.391e-02, eta: 2 days, 12:07:03, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5809, loss_cls: 3.8776, loss: 3.8776 +2024-12-29 03:37:46,324 - pyskl - INFO - Epoch [81][3200/3746] lr: 4.389e-02, eta: 2 days, 12:05:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5839, loss_cls: 3.8438, loss: 3.8438 +2024-12-29 03:39:10,826 - pyskl - INFO - Epoch [81][3300/3746] lr: 4.386e-02, eta: 2 days, 12:04:18, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5773, loss_cls: 3.8754, loss: 3.8754 +2024-12-29 03:40:35,751 - pyskl - INFO - Epoch [81][3400/3746] lr: 4.383e-02, eta: 2 days, 12:02:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5848, loss_cls: 3.8683, loss: 3.8683 +2024-12-29 03:42:01,088 - pyskl - INFO - Epoch [81][3500/3746] lr: 4.380e-02, eta: 2 days, 12:01:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5872, loss_cls: 3.8138, loss: 3.8138 +2024-12-29 03:43:26,087 - pyskl - INFO - Epoch [81][3600/3746] lr: 4.377e-02, eta: 2 days, 12:00:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5763, loss_cls: 3.9253, loss: 3.9253 +2024-12-29 03:44:50,774 - pyskl - INFO - Epoch [81][3700/3746] lr: 4.375e-02, eta: 2 days, 11:58:49, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5769, loss_cls: 3.8876, loss: 3.8876 +2024-12-29 03:45:31,539 - pyskl - INFO - Saving checkpoint at 81 epochs +2024-12-29 03:47:29,811 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 03:47:30,595 - pyskl - INFO - +top1_acc 0.2660 +top5_acc 0.5181 +2024-12-29 03:47:30,596 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 03:47:30,640 - pyskl - INFO - +mean_acc 0.2658 +2024-12-29 03:47:30,653 - pyskl - INFO - Epoch(val) [81][309] top1_acc: 0.2660, top5_acc: 0.5181, mean_class_accuracy: 0.2658 +2024-12-29 03:51:47,129 - pyskl - INFO - Epoch [82][100/3746] lr: 4.371e-02, eta: 2 days, 11:58:41, time: 2.565, data_time: 1.531, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5961, loss_cls: 3.7989, loss: 3.7989 +2024-12-29 03:53:12,360 - pyskl - INFO - Epoch [82][200/3746] lr: 4.368e-02, eta: 2 days, 11:57:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.5977, loss_cls: 3.7735, loss: 3.7735 +2024-12-29 03:54:37,728 - pyskl - INFO - Epoch [82][300/3746] lr: 4.365e-02, eta: 2 days, 11:55:57, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5803, loss_cls: 3.8559, loss: 3.8559 +2024-12-29 03:56:02,801 - pyskl - INFO - Epoch [82][400/3746] lr: 4.362e-02, eta: 2 days, 11:54:35, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5939, loss_cls: 3.8155, loss: 3.8155 +2024-12-29 03:57:28,196 - pyskl - INFO - Epoch [82][500/3746] lr: 4.359e-02, eta: 2 days, 11:53:13, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5861, loss_cls: 3.8177, loss: 3.8177 +2024-12-29 03:58:53,008 - pyskl - INFO - Epoch [82][600/3746] lr: 4.357e-02, eta: 2 days, 11:51:50, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5800, loss_cls: 3.8449, loss: 3.8449 +2024-12-29 04:00:18,143 - pyskl - INFO - Epoch [82][700/3746] lr: 4.354e-02, eta: 2 days, 11:50:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5797, loss_cls: 3.8623, loss: 3.8623 +2024-12-29 04:01:43,123 - pyskl - INFO - Epoch [82][800/3746] lr: 4.351e-02, eta: 2 days, 11:49:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5889, loss_cls: 3.8512, loss: 3.8512 +2024-12-29 04:03:07,860 - pyskl - INFO - Epoch [82][900/3746] lr: 4.348e-02, eta: 2 days, 11:47:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5775, loss_cls: 3.8765, loss: 3.8765 +2024-12-29 04:04:32,724 - pyskl - INFO - Epoch [82][1000/3746] lr: 4.346e-02, eta: 2 days, 11:46:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.5997, loss_cls: 3.7848, loss: 3.7848 +2024-12-29 04:05:57,811 - pyskl - INFO - Epoch [82][1100/3746] lr: 4.343e-02, eta: 2 days, 11:44:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5797, loss_cls: 3.8561, loss: 3.8561 +2024-12-29 04:07:22,867 - pyskl - INFO - Epoch [82][1200/3746] lr: 4.340e-02, eta: 2 days, 11:43:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5825, loss_cls: 3.8786, loss: 3.8786 +2024-12-29 04:08:47,448 - pyskl - INFO - Epoch [82][1300/3746] lr: 4.337e-02, eta: 2 days, 11:42:13, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5864, loss_cls: 3.8304, loss: 3.8304 +2024-12-29 04:10:11,976 - pyskl - INFO - Epoch [82][1400/3746] lr: 4.335e-02, eta: 2 days, 11:40:50, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5877, loss_cls: 3.8207, loss: 3.8207 +2024-12-29 04:11:36,697 - pyskl - INFO - Epoch [82][1500/3746] lr: 4.332e-02, eta: 2 days, 11:39:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5850, loss_cls: 3.8441, loss: 3.8441 +2024-12-29 04:13:00,813 - pyskl - INFO - Epoch [82][1600/3746] lr: 4.329e-02, eta: 2 days, 11:38:05, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5795, loss_cls: 3.8214, loss: 3.8214 +2024-12-29 04:14:25,516 - pyskl - INFO - Epoch [82][1700/3746] lr: 4.326e-02, eta: 2 days, 11:36:42, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5716, loss_cls: 3.8826, loss: 3.8826 +2024-12-29 04:15:50,368 - pyskl - INFO - Epoch [82][1800/3746] lr: 4.323e-02, eta: 2 days, 11:35:20, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5822, loss_cls: 3.8281, loss: 3.8281 +2024-12-29 04:17:14,695 - pyskl - INFO - Epoch [82][1900/3746] lr: 4.321e-02, eta: 2 days, 11:33:57, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5823, loss_cls: 3.8843, loss: 3.8843 +2024-12-29 04:18:39,361 - pyskl - INFO - Epoch [82][2000/3746] lr: 4.318e-02, eta: 2 days, 11:32:34, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5881, loss_cls: 3.8162, loss: 3.8162 +2024-12-29 04:20:03,970 - pyskl - INFO - Epoch [82][2100/3746] lr: 4.315e-02, eta: 2 days, 11:31:11, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.5878, loss_cls: 3.7812, loss: 3.7812 +2024-12-29 04:21:28,435 - pyskl - INFO - Epoch [82][2200/3746] lr: 4.312e-02, eta: 2 days, 11:29:48, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5781, loss_cls: 3.8877, loss: 3.8877 +2024-12-29 04:22:52,822 - pyskl - INFO - Epoch [82][2300/3746] lr: 4.310e-02, eta: 2 days, 11:28:26, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5825, loss_cls: 3.8479, loss: 3.8479 +2024-12-29 04:24:17,339 - pyskl - INFO - Epoch [82][2400/3746] lr: 4.307e-02, eta: 2 days, 11:27:03, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5850, loss_cls: 3.8354, loss: 3.8354 +2024-12-29 04:25:42,058 - pyskl - INFO - Epoch [82][2500/3746] lr: 4.304e-02, eta: 2 days, 11:25:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5981, loss_cls: 3.8117, loss: 3.8117 +2024-12-29 04:27:06,769 - pyskl - INFO - Epoch [82][2600/3746] lr: 4.301e-02, eta: 2 days, 11:24:17, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5811, loss_cls: 3.8623, loss: 3.8623 +2024-12-29 04:28:31,569 - pyskl - INFO - Epoch [82][2700/3746] lr: 4.299e-02, eta: 2 days, 11:22:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5820, loss_cls: 3.8702, loss: 3.8702 +2024-12-29 04:29:57,272 - pyskl - INFO - Epoch [82][2800/3746] lr: 4.296e-02, eta: 2 days, 11:21:33, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5781, loss_cls: 3.8954, loss: 3.8954 +2024-12-29 04:31:22,672 - pyskl - INFO - Epoch [82][2900/3746] lr: 4.293e-02, eta: 2 days, 11:20:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5766, loss_cls: 3.8457, loss: 3.8457 +2024-12-29 04:32:47,884 - pyskl - INFO - Epoch [82][3000/3746] lr: 4.290e-02, eta: 2 days, 11:18:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5811, loss_cls: 3.8665, loss: 3.8665 +2024-12-29 04:34:12,845 - pyskl - INFO - Epoch [82][3100/3746] lr: 4.287e-02, eta: 2 days, 11:17:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5853, loss_cls: 3.8666, loss: 3.8666 +2024-12-29 04:35:37,862 - pyskl - INFO - Epoch [82][3200/3746] lr: 4.285e-02, eta: 2 days, 11:16:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5814, loss_cls: 3.8782, loss: 3.8782 +2024-12-29 04:37:03,108 - pyskl - INFO - Epoch [82][3300/3746] lr: 4.282e-02, eta: 2 days, 11:14:42, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5936, loss_cls: 3.8422, loss: 3.8422 +2024-12-29 04:38:29,167 - pyskl - INFO - Epoch [82][3400/3746] lr: 4.279e-02, eta: 2 days, 11:13:20, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5763, loss_cls: 3.9125, loss: 3.9125 +2024-12-29 04:39:54,578 - pyskl - INFO - Epoch [82][3500/3746] lr: 4.276e-02, eta: 2 days, 11:11:58, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5787, loss_cls: 3.8838, loss: 3.8838 +2024-12-29 04:41:20,050 - pyskl - INFO - Epoch [82][3600/3746] lr: 4.274e-02, eta: 2 days, 11:10:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5733, loss_cls: 3.8483, loss: 3.8483 +2024-12-29 04:42:45,640 - pyskl - INFO - Epoch [82][3700/3746] lr: 4.271e-02, eta: 2 days, 11:09:14, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5747, loss_cls: 3.9112, loss: 3.9112 +2024-12-29 04:43:26,530 - pyskl - INFO - Saving checkpoint at 82 epochs +2024-12-29 04:45:25,260 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 04:45:26,171 - pyskl - INFO - +top1_acc 0.2733 +top5_acc 0.5231 +2024-12-29 04:45:26,171 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 04:45:26,213 - pyskl - INFO - +mean_acc 0.2731 +2024-12-29 04:45:26,217 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_78.pth was removed +2024-12-29 04:45:26,494 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_82.pth. +2024-12-29 04:45:26,495 - pyskl - INFO - Best top1_acc is 0.2733 at 82 epoch. +2024-12-29 04:45:26,515 - pyskl - INFO - Epoch(val) [82][309] top1_acc: 0.2733, top5_acc: 0.5231, mean_class_accuracy: 0.2731 +2024-12-29 04:49:43,260 - pyskl - INFO - Epoch [83][100/3746] lr: 4.267e-02, eta: 2 days, 11:09:04, time: 2.567, data_time: 1.525, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5961, loss_cls: 3.7701, loss: 3.7701 +2024-12-29 04:51:09,314 - pyskl - INFO - Epoch [83][200/3746] lr: 4.264e-02, eta: 2 days, 11:07:42, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5848, loss_cls: 3.8117, loss: 3.8117 +2024-12-29 04:52:35,430 - pyskl - INFO - Epoch [83][300/3746] lr: 4.261e-02, eta: 2 days, 11:06:21, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.5877, loss_cls: 3.8050, loss: 3.8050 +2024-12-29 04:54:01,184 - pyskl - INFO - Epoch [83][400/3746] lr: 4.259e-02, eta: 2 days, 11:04:59, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5844, loss_cls: 3.8274, loss: 3.8274 +2024-12-29 04:55:26,431 - pyskl - INFO - Epoch [83][500/3746] lr: 4.256e-02, eta: 2 days, 11:03:36, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5956, loss_cls: 3.7920, loss: 3.7920 +2024-12-29 04:56:51,674 - pyskl - INFO - Epoch [83][600/3746] lr: 4.253e-02, eta: 2 days, 11:02:14, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5939, loss_cls: 3.8123, loss: 3.8123 +2024-12-29 04:58:16,879 - pyskl - INFO - Epoch [83][700/3746] lr: 4.250e-02, eta: 2 days, 11:00:52, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5828, loss_cls: 3.8427, loss: 3.8427 +2024-12-29 04:59:42,768 - pyskl - INFO - Epoch [83][800/3746] lr: 4.247e-02, eta: 2 days, 10:59:30, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5825, loss_cls: 3.8631, loss: 3.8631 +2024-12-29 05:01:08,136 - pyskl - INFO - Epoch [83][900/3746] lr: 4.245e-02, eta: 2 days, 10:58:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5955, loss_cls: 3.8270, loss: 3.8270 +2024-12-29 05:02:34,091 - pyskl - INFO - Epoch [83][1000/3746] lr: 4.242e-02, eta: 2 days, 10:56:46, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5855, loss_cls: 3.8387, loss: 3.8387 +2024-12-29 05:03:59,924 - pyskl - INFO - Epoch [83][1100/3746] lr: 4.239e-02, eta: 2 days, 10:55:24, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5853, loss_cls: 3.7992, loss: 3.7992 +2024-12-29 05:05:25,811 - pyskl - INFO - Epoch [83][1200/3746] lr: 4.236e-02, eta: 2 days, 10:54:03, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5856, loss_cls: 3.8453, loss: 3.8453 +2024-12-29 05:06:51,291 - pyskl - INFO - Epoch [83][1300/3746] lr: 4.234e-02, eta: 2 days, 10:52:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5905, loss_cls: 3.8271, loss: 3.8271 +2024-12-29 05:08:17,044 - pyskl - INFO - Epoch [83][1400/3746] lr: 4.231e-02, eta: 2 days, 10:51:18, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5883, loss_cls: 3.8438, loss: 3.8438 +2024-12-29 05:09:42,956 - pyskl - INFO - Epoch [83][1500/3746] lr: 4.228e-02, eta: 2 days, 10:49:57, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5847, loss_cls: 3.8555, loss: 3.8555 +2024-12-29 05:11:09,094 - pyskl - INFO - Epoch [83][1600/3746] lr: 4.225e-02, eta: 2 days, 10:48:35, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5845, loss_cls: 3.8275, loss: 3.8275 +2024-12-29 05:12:34,203 - pyskl - INFO - Epoch [83][1700/3746] lr: 4.223e-02, eta: 2 days, 10:47:13, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5737, loss_cls: 3.8704, loss: 3.8704 +2024-12-29 05:13:59,216 - pyskl - INFO - Epoch [83][1800/3746] lr: 4.220e-02, eta: 2 days, 10:45:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5853, loss_cls: 3.8400, loss: 3.8400 +2024-12-29 05:15:23,602 - pyskl - INFO - Epoch [83][1900/3746] lr: 4.217e-02, eta: 2 days, 10:44:27, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5708, loss_cls: 3.8941, loss: 3.8941 +2024-12-29 05:16:48,631 - pyskl - INFO - Epoch [83][2000/3746] lr: 4.214e-02, eta: 2 days, 10:43:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5931, loss_cls: 3.8041, loss: 3.8041 +2024-12-29 05:18:14,055 - pyskl - INFO - Epoch [83][2100/3746] lr: 4.212e-02, eta: 2 days, 10:41:42, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5859, loss_cls: 3.8295, loss: 3.8295 +2024-12-29 05:19:39,274 - pyskl - INFO - Epoch [83][2200/3746] lr: 4.209e-02, eta: 2 days, 10:40:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5989, loss_cls: 3.8013, loss: 3.8013 +2024-12-29 05:21:04,973 - pyskl - INFO - Epoch [83][2300/3746] lr: 4.206e-02, eta: 2 days, 10:38:58, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5823, loss_cls: 3.8510, loss: 3.8510 +2024-12-29 05:22:30,534 - pyskl - INFO - Epoch [83][2400/3746] lr: 4.203e-02, eta: 2 days, 10:37:36, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5845, loss_cls: 3.8559, loss: 3.8559 +2024-12-29 05:23:56,361 - pyskl - INFO - Epoch [83][2500/3746] lr: 4.201e-02, eta: 2 days, 10:36:14, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5798, loss_cls: 3.8443, loss: 3.8443 +2024-12-29 05:25:22,209 - pyskl - INFO - Epoch [83][2600/3746] lr: 4.198e-02, eta: 2 days, 10:34:52, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5845, loss_cls: 3.8274, loss: 3.8274 +2024-12-29 05:26:47,789 - pyskl - INFO - Epoch [83][2700/3746] lr: 4.195e-02, eta: 2 days, 10:33:30, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5970, loss_cls: 3.8178, loss: 3.8178 +2024-12-29 05:28:13,985 - pyskl - INFO - Epoch [83][2800/3746] lr: 4.192e-02, eta: 2 days, 10:32:09, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5928, loss_cls: 3.8096, loss: 3.8096 +2024-12-29 05:29:40,043 - pyskl - INFO - Epoch [83][2900/3746] lr: 4.190e-02, eta: 2 days, 10:30:47, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5763, loss_cls: 3.8680, loss: 3.8680 +2024-12-29 05:31:05,690 - pyskl - INFO - Epoch [83][3000/3746] lr: 4.187e-02, eta: 2 days, 10:29:25, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5891, loss_cls: 3.8149, loss: 3.8149 +2024-12-29 05:32:30,435 - pyskl - INFO - Epoch [83][3100/3746] lr: 4.184e-02, eta: 2 days, 10:28:02, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5720, loss_cls: 3.9100, loss: 3.9100 +2024-12-29 05:33:55,520 - pyskl - INFO - Epoch [83][3200/3746] lr: 4.181e-02, eta: 2 days, 10:26:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5800, loss_cls: 3.8309, loss: 3.8309 +2024-12-29 05:35:20,684 - pyskl - INFO - Epoch [83][3300/3746] lr: 4.178e-02, eta: 2 days, 10:25:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5916, loss_cls: 3.8277, loss: 3.8277 +2024-12-29 05:36:46,071 - pyskl - INFO - Epoch [83][3400/3746] lr: 4.176e-02, eta: 2 days, 10:23:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5939, loss_cls: 3.8242, loss: 3.8242 +2024-12-29 05:38:11,739 - pyskl - INFO - Epoch [83][3500/3746] lr: 4.173e-02, eta: 2 days, 10:22:33, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5786, loss_cls: 3.8692, loss: 3.8692 +2024-12-29 05:39:37,271 - pyskl - INFO - Epoch [83][3600/3746] lr: 4.170e-02, eta: 2 days, 10:21:11, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5769, loss_cls: 3.8429, loss: 3.8429 +2024-12-29 05:41:02,713 - pyskl - INFO - Epoch [83][3700/3746] lr: 4.167e-02, eta: 2 days, 10:19:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5820, loss_cls: 3.8820, loss: 3.8820 +2024-12-29 05:41:44,138 - pyskl - INFO - Saving checkpoint at 83 epochs +2024-12-29 05:43:41,860 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 05:43:42,646 - pyskl - INFO - +top1_acc 0.2686 +top5_acc 0.5177 +2024-12-29 05:43:42,646 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 05:43:42,691 - pyskl - INFO - +mean_acc 0.2682 +2024-12-29 05:43:42,704 - pyskl - INFO - Epoch(val) [83][309] top1_acc: 0.2686, top5_acc: 0.5177, mean_class_accuracy: 0.2682 +2024-12-29 05:47:57,614 - pyskl - INFO - Epoch [84][100/3746] lr: 4.163e-02, eta: 2 days, 10:19:34, time: 2.549, data_time: 1.502, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5913, loss_cls: 3.7754, loss: 3.7754 +2024-12-29 05:49:23,044 - pyskl - INFO - Epoch [84][200/3746] lr: 4.161e-02, eta: 2 days, 10:18:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5977, loss_cls: 3.7887, loss: 3.7887 +2024-12-29 05:50:48,495 - pyskl - INFO - Epoch [84][300/3746] lr: 4.158e-02, eta: 2 days, 10:16:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5933, loss_cls: 3.8097, loss: 3.8097 +2024-12-29 05:52:13,857 - pyskl - INFO - Epoch [84][400/3746] lr: 4.155e-02, eta: 2 days, 10:15:27, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5863, loss_cls: 3.7771, loss: 3.7771 +2024-12-29 05:53:38,477 - pyskl - INFO - Epoch [84][500/3746] lr: 4.152e-02, eta: 2 days, 10:14:04, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5906, loss_cls: 3.8089, loss: 3.8089 +2024-12-29 05:55:02,745 - pyskl - INFO - Epoch [84][600/3746] lr: 4.150e-02, eta: 2 days, 10:12:41, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5859, loss_cls: 3.8106, loss: 3.8106 +2024-12-29 05:56:28,196 - pyskl - INFO - Epoch [84][700/3746] lr: 4.147e-02, eta: 2 days, 10:11:18, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5900, loss_cls: 3.8528, loss: 3.8528 +2024-12-29 05:57:53,526 - pyskl - INFO - Epoch [84][800/3746] lr: 4.144e-02, eta: 2 days, 10:09:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5920, loss_cls: 3.7971, loss: 3.7971 +2024-12-29 05:59:19,476 - pyskl - INFO - Epoch [84][900/3746] lr: 4.141e-02, eta: 2 days, 10:08:34, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5883, loss_cls: 3.8351, loss: 3.8351 +2024-12-29 06:00:44,759 - pyskl - INFO - Epoch [84][1000/3746] lr: 4.139e-02, eta: 2 days, 10:07:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5917, loss_cls: 3.7682, loss: 3.7682 +2024-12-29 06:02:09,864 - pyskl - INFO - Epoch [84][1100/3746] lr: 4.136e-02, eta: 2 days, 10:05:49, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5925, loss_cls: 3.8012, loss: 3.8012 +2024-12-29 06:03:35,262 - pyskl - INFO - Epoch [84][1200/3746] lr: 4.133e-02, eta: 2 days, 10:04:27, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5927, loss_cls: 3.8115, loss: 3.8115 +2024-12-29 06:05:01,364 - pyskl - INFO - Epoch [84][1300/3746] lr: 4.130e-02, eta: 2 days, 10:03:05, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5870, loss_cls: 3.8214, loss: 3.8214 +2024-12-29 06:06:26,975 - pyskl - INFO - Epoch [84][1400/3746] lr: 4.128e-02, eta: 2 days, 10:01:43, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5880, loss_cls: 3.8212, loss: 3.8212 +2024-12-29 06:07:52,659 - pyskl - INFO - Epoch [84][1500/3746] lr: 4.125e-02, eta: 2 days, 10:00:21, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5922, loss_cls: 3.8156, loss: 3.8156 +2024-12-29 06:09:18,495 - pyskl - INFO - Epoch [84][1600/3746] lr: 4.122e-02, eta: 2 days, 9:58:59, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5906, loss_cls: 3.8054, loss: 3.8054 +2024-12-29 06:10:43,811 - pyskl - INFO - Epoch [84][1700/3746] lr: 4.119e-02, eta: 2 days, 9:57:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5900, loss_cls: 3.8055, loss: 3.8055 +2024-12-29 06:12:08,524 - pyskl - INFO - Epoch [84][1800/3746] lr: 4.117e-02, eta: 2 days, 9:56:13, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5845, loss_cls: 3.8585, loss: 3.8585 +2024-12-29 06:13:32,790 - pyskl - INFO - Epoch [84][1900/3746] lr: 4.114e-02, eta: 2 days, 9:54:50, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5887, loss_cls: 3.7960, loss: 3.7960 +2024-12-29 06:14:57,057 - pyskl - INFO - Epoch [84][2000/3746] lr: 4.111e-02, eta: 2 days, 9:53:27, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5719, loss_cls: 3.9319, loss: 3.9319 +2024-12-29 06:16:21,754 - pyskl - INFO - Epoch [84][2100/3746] lr: 4.108e-02, eta: 2 days, 9:52:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5905, loss_cls: 3.8226, loss: 3.8226 +2024-12-29 06:17:46,748 - pyskl - INFO - Epoch [84][2200/3746] lr: 4.106e-02, eta: 2 days, 9:50:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5942, loss_cls: 3.7822, loss: 3.7822 +2024-12-29 06:19:11,834 - pyskl - INFO - Epoch [84][2300/3746] lr: 4.103e-02, eta: 2 days, 9:49:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5753, loss_cls: 3.8782, loss: 3.8782 +2024-12-29 06:20:36,459 - pyskl - INFO - Epoch [84][2400/3746] lr: 4.100e-02, eta: 2 days, 9:47:56, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5848, loss_cls: 3.8440, loss: 3.8440 +2024-12-29 06:22:01,333 - pyskl - INFO - Epoch [84][2500/3746] lr: 4.097e-02, eta: 2 days, 9:46:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5820, loss_cls: 3.8518, loss: 3.8518 +2024-12-29 06:23:26,536 - pyskl - INFO - Epoch [84][2600/3746] lr: 4.095e-02, eta: 2 days, 9:45:10, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5884, loss_cls: 3.8300, loss: 3.8300 +2024-12-29 06:24:51,028 - pyskl - INFO - Epoch [84][2700/3746] lr: 4.092e-02, eta: 2 days, 9:43:47, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5922, loss_cls: 3.7923, loss: 3.7923 +2024-12-29 06:26:15,615 - pyskl - INFO - Epoch [84][2800/3746] lr: 4.089e-02, eta: 2 days, 9:42:24, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5900, loss_cls: 3.8290, loss: 3.8290 +2024-12-29 06:27:40,058 - pyskl - INFO - Epoch [84][2900/3746] lr: 4.086e-02, eta: 2 days, 9:41:01, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5900, loss_cls: 3.7914, loss: 3.7914 +2024-12-29 06:29:04,615 - pyskl - INFO - Epoch [84][3000/3746] lr: 4.084e-02, eta: 2 days, 9:39:38, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5852, loss_cls: 3.8283, loss: 3.8283 +2024-12-29 06:30:29,745 - pyskl - INFO - Epoch [84][3100/3746] lr: 4.081e-02, eta: 2 days, 9:38:15, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5772, loss_cls: 3.8794, loss: 3.8794 +2024-12-29 06:31:54,406 - pyskl - INFO - Epoch [84][3200/3746] lr: 4.078e-02, eta: 2 days, 9:36:52, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5816, loss_cls: 3.8787, loss: 3.8787 +2024-12-29 06:33:18,901 - pyskl - INFO - Epoch [84][3300/3746] lr: 4.075e-02, eta: 2 days, 9:35:29, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5884, loss_cls: 3.8337, loss: 3.8337 +2024-12-29 06:34:43,617 - pyskl - INFO - Epoch [84][3400/3746] lr: 4.073e-02, eta: 2 days, 9:34:06, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5809, loss_cls: 3.8548, loss: 3.8548 +2024-12-29 06:36:07,903 - pyskl - INFO - Epoch [84][3500/3746] lr: 4.070e-02, eta: 2 days, 9:32:43, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5884, loss_cls: 3.8246, loss: 3.8246 +2024-12-29 06:37:32,460 - pyskl - INFO - Epoch [84][3600/3746] lr: 4.067e-02, eta: 2 days, 9:31:20, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5811, loss_cls: 3.8489, loss: 3.8489 +2024-12-29 06:38:57,066 - pyskl - INFO - Epoch [84][3700/3746] lr: 4.064e-02, eta: 2 days, 9:29:57, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5870, loss_cls: 3.8312, loss: 3.8312 +2024-12-29 06:39:37,718 - pyskl - INFO - Saving checkpoint at 84 epochs +2024-12-29 06:41:36,342 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 06:41:37,024 - pyskl - INFO - +top1_acc 0.2730 +top5_acc 0.5212 +2024-12-29 06:41:37,024 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 06:41:37,072 - pyskl - INFO - +mean_acc 0.2728 +2024-12-29 06:41:37,086 - pyskl - INFO - Epoch(val) [84][309] top1_acc: 0.2730, top5_acc: 0.5212, mean_class_accuracy: 0.2728 +2024-12-29 06:45:49,032 - pyskl - INFO - Epoch [85][100/3746] lr: 4.060e-02, eta: 2 days, 9:29:37, time: 2.519, data_time: 1.484, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5922, loss_cls: 3.7845, loss: 3.7845 +2024-12-29 06:47:14,261 - pyskl - INFO - Epoch [85][200/3746] lr: 4.058e-02, eta: 2 days, 9:28:14, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.6017, loss_cls: 3.7543, loss: 3.7543 +2024-12-29 06:48:39,641 - pyskl - INFO - Epoch [85][300/3746] lr: 4.055e-02, eta: 2 days, 9:26:52, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6031, loss_cls: 3.7472, loss: 3.7472 +2024-12-29 06:50:04,684 - pyskl - INFO - Epoch [85][400/3746] lr: 4.052e-02, eta: 2 days, 9:25:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5894, loss_cls: 3.8249, loss: 3.8249 +2024-12-29 06:51:29,742 - pyskl - INFO - Epoch [85][500/3746] lr: 4.049e-02, eta: 2 days, 9:24:06, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5919, loss_cls: 3.7974, loss: 3.7974 +2024-12-29 06:52:54,383 - pyskl - INFO - Epoch [85][600/3746] lr: 4.047e-02, eta: 2 days, 9:22:43, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5938, loss_cls: 3.8176, loss: 3.8176 +2024-12-29 06:54:19,600 - pyskl - INFO - Epoch [85][700/3746] lr: 4.044e-02, eta: 2 days, 9:21:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5931, loss_cls: 3.7699, loss: 3.7699 +2024-12-29 06:55:43,955 - pyskl - INFO - Epoch [85][800/3746] lr: 4.041e-02, eta: 2 days, 9:19:57, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.6020, loss_cls: 3.7455, loss: 3.7455 +2024-12-29 06:57:08,901 - pyskl - INFO - Epoch [85][900/3746] lr: 4.038e-02, eta: 2 days, 9:18:34, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5895, loss_cls: 3.8271, loss: 3.8271 +2024-12-29 06:58:33,818 - pyskl - INFO - Epoch [85][1000/3746] lr: 4.036e-02, eta: 2 days, 9:17:12, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.5998, loss_cls: 3.7885, loss: 3.7885 +2024-12-29 06:59:59,579 - pyskl - INFO - Epoch [85][1100/3746] lr: 4.033e-02, eta: 2 days, 9:15:49, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5758, loss_cls: 3.8574, loss: 3.8574 +2024-12-29 07:01:25,500 - pyskl - INFO - Epoch [85][1200/3746] lr: 4.030e-02, eta: 2 days, 9:14:27, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5783, loss_cls: 3.8537, loss: 3.8537 +2024-12-29 07:02:50,848 - pyskl - INFO - Epoch [85][1300/3746] lr: 4.027e-02, eta: 2 days, 9:13:05, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5827, loss_cls: 3.8366, loss: 3.8366 +2024-12-29 07:04:16,390 - pyskl - INFO - Epoch [85][1400/3746] lr: 4.025e-02, eta: 2 days, 9:11:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5973, loss_cls: 3.7805, loss: 3.7805 +2024-12-29 07:05:41,917 - pyskl - INFO - Epoch [85][1500/3746] lr: 4.022e-02, eta: 2 days, 9:10:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5947, loss_cls: 3.8158, loss: 3.8158 +2024-12-29 07:07:07,227 - pyskl - INFO - Epoch [85][1600/3746] lr: 4.019e-02, eta: 2 days, 9:08:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5964, loss_cls: 3.7670, loss: 3.7670 +2024-12-29 07:08:32,477 - pyskl - INFO - Epoch [85][1700/3746] lr: 4.016e-02, eta: 2 days, 9:07:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5977, loss_cls: 3.7804, loss: 3.7804 +2024-12-29 07:09:57,776 - pyskl - INFO - Epoch [85][1800/3746] lr: 4.014e-02, eta: 2 days, 9:06:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5878, loss_cls: 3.8279, loss: 3.8279 +2024-12-29 07:11:22,435 - pyskl - INFO - Epoch [85][1900/3746] lr: 4.011e-02, eta: 2 days, 9:04:49, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5994, loss_cls: 3.7792, loss: 3.7792 +2024-12-29 07:12:47,360 - pyskl - INFO - Epoch [85][2000/3746] lr: 4.008e-02, eta: 2 days, 9:03:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5908, loss_cls: 3.7906, loss: 3.7906 +2024-12-29 07:14:12,009 - pyskl - INFO - Epoch [85][2100/3746] lr: 4.006e-02, eta: 2 days, 9:02:03, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5878, loss_cls: 3.7977, loss: 3.7977 +2024-12-29 07:15:36,845 - pyskl - INFO - Epoch [85][2200/3746] lr: 4.003e-02, eta: 2 days, 9:00:40, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5822, loss_cls: 3.8293, loss: 3.8293 +2024-12-29 07:17:01,121 - pyskl - INFO - Epoch [85][2300/3746] lr: 4.000e-02, eta: 2 days, 8:59:17, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5925, loss_cls: 3.8182, loss: 3.8182 +2024-12-29 07:18:25,727 - pyskl - INFO - Epoch [85][2400/3746] lr: 3.997e-02, eta: 2 days, 8:57:54, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5880, loss_cls: 3.8254, loss: 3.8254 +2024-12-29 07:19:50,417 - pyskl - INFO - Epoch [85][2500/3746] lr: 3.995e-02, eta: 2 days, 8:56:31, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5845, loss_cls: 3.8336, loss: 3.8336 +2024-12-29 07:21:15,131 - pyskl - INFO - Epoch [85][2600/3746] lr: 3.992e-02, eta: 2 days, 8:55:08, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5873, loss_cls: 3.8689, loss: 3.8689 +2024-12-29 07:22:39,806 - pyskl - INFO - Epoch [85][2700/3746] lr: 3.989e-02, eta: 2 days, 8:53:45, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5914, loss_cls: 3.8123, loss: 3.8123 +2024-12-29 07:24:04,446 - pyskl - INFO - Epoch [85][2800/3746] lr: 3.986e-02, eta: 2 days, 8:52:22, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5955, loss_cls: 3.7895, loss: 3.7895 +2024-12-29 07:25:29,332 - pyskl - INFO - Epoch [85][2900/3746] lr: 3.984e-02, eta: 2 days, 8:50:59, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5903, loss_cls: 3.8429, loss: 3.8429 +2024-12-29 07:26:54,046 - pyskl - INFO - Epoch [85][3000/3746] lr: 3.981e-02, eta: 2 days, 8:49:36, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5877, loss_cls: 3.8213, loss: 3.8213 +2024-12-29 07:28:18,729 - pyskl - INFO - Epoch [85][3100/3746] lr: 3.978e-02, eta: 2 days, 8:48:13, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5864, loss_cls: 3.8170, loss: 3.8170 +2024-12-29 07:29:43,724 - pyskl - INFO - Epoch [85][3200/3746] lr: 3.975e-02, eta: 2 days, 8:46:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5919, loss_cls: 3.7724, loss: 3.7724 +2024-12-29 07:31:08,450 - pyskl - INFO - Epoch [85][3300/3746] lr: 3.973e-02, eta: 2 days, 8:45:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5811, loss_cls: 3.8676, loss: 3.8676 +2024-12-29 07:32:33,396 - pyskl - INFO - Epoch [85][3400/3746] lr: 3.970e-02, eta: 2 days, 8:44:04, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5906, loss_cls: 3.8099, loss: 3.8099 +2024-12-29 07:33:57,956 - pyskl - INFO - Epoch [85][3500/3746] lr: 3.967e-02, eta: 2 days, 8:42:41, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5873, loss_cls: 3.8318, loss: 3.8318 +2024-12-29 07:35:22,448 - pyskl - INFO - Epoch [85][3600/3746] lr: 3.964e-02, eta: 2 days, 8:41:18, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6023, loss_cls: 3.7692, loss: 3.7692 +2024-12-29 07:36:47,414 - pyskl - INFO - Epoch [85][3700/3746] lr: 3.962e-02, eta: 2 days, 8:39:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5847, loss_cls: 3.8527, loss: 3.8527 +2024-12-29 07:37:28,176 - pyskl - INFO - Saving checkpoint at 85 epochs +2024-12-29 07:39:27,799 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 07:39:28,489 - pyskl - INFO - +top1_acc 0.2709 +top5_acc 0.5228 +2024-12-29 07:39:28,489 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 07:39:28,540 - pyskl - INFO - +mean_acc 0.2708 +2024-12-29 07:39:28,558 - pyskl - INFO - Epoch(val) [85][309] top1_acc: 0.2709, top5_acc: 0.5228, mean_class_accuracy: 0.2708 +2024-12-29 07:43:46,729 - pyskl - INFO - Epoch [86][100/3746] lr: 3.958e-02, eta: 2 days, 8:39:36, time: 2.582, data_time: 1.541, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6005, loss_cls: 3.7183, loss: 3.7183 +2024-12-29 07:45:12,057 - pyskl - INFO - Epoch [86][200/3746] lr: 3.955e-02, eta: 2 days, 8:38:14, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5986, loss_cls: 3.7965, loss: 3.7965 +2024-12-29 07:46:37,711 - pyskl - INFO - Epoch [86][300/3746] lr: 3.952e-02, eta: 2 days, 8:36:51, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5920, loss_cls: 3.8132, loss: 3.8132 +2024-12-29 07:48:02,703 - pyskl - INFO - Epoch [86][400/3746] lr: 3.950e-02, eta: 2 days, 8:35:28, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.5986, loss_cls: 3.7465, loss: 3.7465 +2024-12-29 07:49:28,089 - pyskl - INFO - Epoch [86][500/3746] lr: 3.947e-02, eta: 2 days, 8:34:06, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6002, loss_cls: 3.7515, loss: 3.7515 +2024-12-29 07:50:53,122 - pyskl - INFO - Epoch [86][600/3746] lr: 3.944e-02, eta: 2 days, 8:32:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5861, loss_cls: 3.8388, loss: 3.8388 +2024-12-29 07:52:17,992 - pyskl - INFO - Epoch [86][700/3746] lr: 3.941e-02, eta: 2 days, 8:31:20, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5964, loss_cls: 3.7764, loss: 3.7764 +2024-12-29 07:53:43,060 - pyskl - INFO - Epoch [86][800/3746] lr: 3.939e-02, eta: 2 days, 8:29:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5995, loss_cls: 3.7984, loss: 3.7984 +2024-12-29 07:55:07,937 - pyskl - INFO - Epoch [86][900/3746] lr: 3.936e-02, eta: 2 days, 8:28:34, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5895, loss_cls: 3.8283, loss: 3.8283 +2024-12-29 07:56:32,437 - pyskl - INFO - Epoch [86][1000/3746] lr: 3.933e-02, eta: 2 days, 8:27:11, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5961, loss_cls: 3.7829, loss: 3.7829 +2024-12-29 07:57:57,356 - pyskl - INFO - Epoch [86][1100/3746] lr: 3.930e-02, eta: 2 days, 8:25:48, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5998, loss_cls: 3.7817, loss: 3.7817 +2024-12-29 07:59:22,436 - pyskl - INFO - Epoch [86][1200/3746] lr: 3.928e-02, eta: 2 days, 8:24:25, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5914, loss_cls: 3.7800, loss: 3.7800 +2024-12-29 08:00:47,427 - pyskl - INFO - Epoch [86][1300/3746] lr: 3.925e-02, eta: 2 days, 8:23:02, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.5914, loss_cls: 3.8219, loss: 3.8219 +2024-12-29 08:02:12,177 - pyskl - INFO - Epoch [86][1400/3746] lr: 3.922e-02, eta: 2 days, 8:21:39, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5848, loss_cls: 3.8284, loss: 3.8284 +2024-12-29 08:03:36,969 - pyskl - INFO - Epoch [86][1500/3746] lr: 3.919e-02, eta: 2 days, 8:20:16, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5814, loss_cls: 3.8411, loss: 3.8411 +2024-12-29 08:05:01,337 - pyskl - INFO - Epoch [86][1600/3746] lr: 3.917e-02, eta: 2 days, 8:18:53, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.5886, loss_cls: 3.8112, loss: 3.8112 +2024-12-29 08:06:26,471 - pyskl - INFO - Epoch [86][1700/3746] lr: 3.914e-02, eta: 2 days, 8:17:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5948, loss_cls: 3.8223, loss: 3.8223 +2024-12-29 08:07:51,716 - pyskl - INFO - Epoch [86][1800/3746] lr: 3.911e-02, eta: 2 days, 8:16:07, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5839, loss_cls: 3.8506, loss: 3.8506 +2024-12-29 08:09:16,393 - pyskl - INFO - Epoch [86][1900/3746] lr: 3.909e-02, eta: 2 days, 8:14:44, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5795, loss_cls: 3.8294, loss: 3.8294 +2024-12-29 08:10:41,277 - pyskl - INFO - Epoch [86][2000/3746] lr: 3.906e-02, eta: 2 days, 8:13:21, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.3477, top5_acc: 0.5938, loss_cls: 3.7655, loss: 3.7655 +2024-12-29 08:12:06,382 - pyskl - INFO - Epoch [86][2100/3746] lr: 3.903e-02, eta: 2 days, 8:11:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5825, loss_cls: 3.8251, loss: 3.8251 +2024-12-29 08:13:31,431 - pyskl - INFO - Epoch [86][2200/3746] lr: 3.900e-02, eta: 2 days, 8:10:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3425, top5_acc: 0.6006, loss_cls: 3.7504, loss: 3.7504 +2024-12-29 08:14:57,287 - pyskl - INFO - Epoch [86][2300/3746] lr: 3.898e-02, eta: 2 days, 8:09:13, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5967, loss_cls: 3.7618, loss: 3.7618 +2024-12-29 08:16:22,808 - pyskl - INFO - Epoch [86][2400/3746] lr: 3.895e-02, eta: 2 days, 8:07:50, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6019, loss_cls: 3.7733, loss: 3.7733 +2024-12-29 08:17:47,890 - pyskl - INFO - Epoch [86][2500/3746] lr: 3.892e-02, eta: 2 days, 8:06:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5991, loss_cls: 3.7554, loss: 3.7554 +2024-12-29 08:19:13,462 - pyskl - INFO - Epoch [86][2600/3746] lr: 3.889e-02, eta: 2 days, 8:05:05, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5972, loss_cls: 3.7845, loss: 3.7845 +2024-12-29 08:20:38,412 - pyskl - INFO - Epoch [86][2700/3746] lr: 3.887e-02, eta: 2 days, 8:03:42, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5995, loss_cls: 3.7631, loss: 3.7631 +2024-12-29 08:22:03,810 - pyskl - INFO - Epoch [86][2800/3746] lr: 3.884e-02, eta: 2 days, 8:02:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3452, top5_acc: 0.5984, loss_cls: 3.7346, loss: 3.7346 +2024-12-29 08:23:28,944 - pyskl - INFO - Epoch [86][2900/3746] lr: 3.881e-02, eta: 2 days, 8:00:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5836, loss_cls: 3.8475, loss: 3.8475 +2024-12-29 08:24:54,197 - pyskl - INFO - Epoch [86][3000/3746] lr: 3.879e-02, eta: 2 days, 7:59:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5972, loss_cls: 3.8093, loss: 3.8093 +2024-12-29 08:26:19,815 - pyskl - INFO - Epoch [86][3100/3746] lr: 3.876e-02, eta: 2 days, 7:58:11, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5863, loss_cls: 3.8143, loss: 3.8143 +2024-12-29 08:27:44,709 - pyskl - INFO - Epoch [86][3200/3746] lr: 3.873e-02, eta: 2 days, 7:56:48, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5933, loss_cls: 3.7829, loss: 3.7829 +2024-12-29 08:29:09,144 - pyskl - INFO - Epoch [86][3300/3746] lr: 3.870e-02, eta: 2 days, 7:55:25, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.6003, loss_cls: 3.8176, loss: 3.8176 +2024-12-29 08:30:33,630 - pyskl - INFO - Epoch [86][3400/3746] lr: 3.868e-02, eta: 2 days, 7:54:02, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5866, loss_cls: 3.8347, loss: 3.8347 +2024-12-29 08:31:59,289 - pyskl - INFO - Epoch [86][3500/3746] lr: 3.865e-02, eta: 2 days, 7:52:39, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5820, loss_cls: 3.8480, loss: 3.8480 +2024-12-29 08:33:24,119 - pyskl - INFO - Epoch [86][3600/3746] lr: 3.862e-02, eta: 2 days, 7:51:16, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5898, loss_cls: 3.8185, loss: 3.8185 +2024-12-29 08:34:49,254 - pyskl - INFO - Epoch [86][3700/3746] lr: 3.860e-02, eta: 2 days, 7:49:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5842, loss_cls: 3.8711, loss: 3.8711 +2024-12-29 08:35:30,630 - pyskl - INFO - Saving checkpoint at 86 epochs +2024-12-29 08:37:29,685 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 08:37:30,578 - pyskl - INFO - +top1_acc 0.2781 +top5_acc 0.5249 +2024-12-29 08:37:30,578 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 08:37:30,631 - pyskl - INFO - +mean_acc 0.2779 +2024-12-29 08:37:30,636 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_82.pth was removed +2024-12-29 08:37:30,933 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_86.pth. +2024-12-29 08:37:30,934 - pyskl - INFO - Best top1_acc is 0.2781 at 86 epoch. +2024-12-29 08:37:30,952 - pyskl - INFO - Epoch(val) [86][309] top1_acc: 0.2781, top5_acc: 0.5249, mean_class_accuracy: 0.2779 +2024-12-29 08:41:46,439 - pyskl - INFO - Epoch [87][100/3746] lr: 3.856e-02, eta: 2 days, 7:49:30, time: 2.555, data_time: 1.512, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6055, loss_cls: 3.7072, loss: 3.7072 +2024-12-29 08:43:12,310 - pyskl - INFO - Epoch [87][200/3746] lr: 3.853e-02, eta: 2 days, 7:48:07, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6052, loss_cls: 3.7344, loss: 3.7344 +2024-12-29 08:44:38,392 - pyskl - INFO - Epoch [87][300/3746] lr: 3.850e-02, eta: 2 days, 7:46:45, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.5988, loss_cls: 3.7579, loss: 3.7579 +2024-12-29 08:46:03,516 - pyskl - INFO - Epoch [87][400/3746] lr: 3.847e-02, eta: 2 days, 7:45:22, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6023, loss_cls: 3.7496, loss: 3.7496 +2024-12-29 08:47:28,634 - pyskl - INFO - Epoch [87][500/3746] lr: 3.845e-02, eta: 2 days, 7:43:59, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3450, top5_acc: 0.5933, loss_cls: 3.7601, loss: 3.7601 +2024-12-29 08:48:53,778 - pyskl - INFO - Epoch [87][600/3746] lr: 3.842e-02, eta: 2 days, 7:42:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.6003, loss_cls: 3.7894, loss: 3.7894 +2024-12-29 08:50:18,478 - pyskl - INFO - Epoch [87][700/3746] lr: 3.839e-02, eta: 2 days, 7:41:13, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5936, loss_cls: 3.7868, loss: 3.7868 +2024-12-29 08:51:43,345 - pyskl - INFO - Epoch [87][800/3746] lr: 3.837e-02, eta: 2 days, 7:39:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3452, top5_acc: 0.6038, loss_cls: 3.7303, loss: 3.7303 +2024-12-29 08:53:07,959 - pyskl - INFO - Epoch [87][900/3746] lr: 3.834e-02, eta: 2 days, 7:38:27, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.5950, loss_cls: 3.7518, loss: 3.7518 +2024-12-29 08:54:32,621 - pyskl - INFO - Epoch [87][1000/3746] lr: 3.831e-02, eta: 2 days, 7:37:03, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.5942, loss_cls: 3.7676, loss: 3.7676 +2024-12-29 08:55:57,333 - pyskl - INFO - Epoch [87][1100/3746] lr: 3.828e-02, eta: 2 days, 7:35:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.5992, loss_cls: 3.7676, loss: 3.7676 +2024-12-29 08:57:22,533 - pyskl - INFO - Epoch [87][1200/3746] lr: 3.826e-02, eta: 2 days, 7:34:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5948, loss_cls: 3.7792, loss: 3.7792 +2024-12-29 08:58:47,624 - pyskl - INFO - Epoch [87][1300/3746] lr: 3.823e-02, eta: 2 days, 7:32:54, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5950, loss_cls: 3.7728, loss: 3.7728 +2024-12-29 09:00:13,485 - pyskl - INFO - Epoch [87][1400/3746] lr: 3.820e-02, eta: 2 days, 7:31:32, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.5906, loss_cls: 3.7810, loss: 3.7810 +2024-12-29 09:01:38,181 - pyskl - INFO - Epoch [87][1500/3746] lr: 3.817e-02, eta: 2 days, 7:30:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5948, loss_cls: 3.7538, loss: 3.7538 +2024-12-29 09:03:03,073 - pyskl - INFO - Epoch [87][1600/3746] lr: 3.815e-02, eta: 2 days, 7:28:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.5964, loss_cls: 3.7720, loss: 3.7720 +2024-12-29 09:04:28,279 - pyskl - INFO - Epoch [87][1700/3746] lr: 3.812e-02, eta: 2 days, 7:27:23, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.5944, loss_cls: 3.7820, loss: 3.7820 +2024-12-29 09:05:53,438 - pyskl - INFO - Epoch [87][1800/3746] lr: 3.809e-02, eta: 2 days, 7:26:00, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5848, loss_cls: 3.8074, loss: 3.8074 +2024-12-29 09:07:18,648 - pyskl - INFO - Epoch [87][1900/3746] lr: 3.807e-02, eta: 2 days, 7:24:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.5948, loss_cls: 3.7829, loss: 3.7829 +2024-12-29 09:08:43,330 - pyskl - INFO - Epoch [87][2000/3746] lr: 3.804e-02, eta: 2 days, 7:23:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.5997, loss_cls: 3.7722, loss: 3.7722 +2024-12-29 09:10:08,017 - pyskl - INFO - Epoch [87][2100/3746] lr: 3.801e-02, eta: 2 days, 7:21:50, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6081, loss_cls: 3.7297, loss: 3.7297 +2024-12-29 09:11:32,814 - pyskl - INFO - Epoch [87][2200/3746] lr: 3.798e-02, eta: 2 days, 7:20:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5972, loss_cls: 3.7924, loss: 3.7924 +2024-12-29 09:12:57,544 - pyskl - INFO - Epoch [87][2300/3746] lr: 3.796e-02, eta: 2 days, 7:19:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5998, loss_cls: 3.7808, loss: 3.7808 +2024-12-29 09:14:21,760 - pyskl - INFO - Epoch [87][2400/3746] lr: 3.793e-02, eta: 2 days, 7:17:40, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5864, loss_cls: 3.8287, loss: 3.8287 +2024-12-29 09:15:46,413 - pyskl - INFO - Epoch [87][2500/3746] lr: 3.790e-02, eta: 2 days, 7:16:17, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5870, loss_cls: 3.8224, loss: 3.8224 +2024-12-29 09:17:11,149 - pyskl - INFO - Epoch [87][2600/3746] lr: 3.788e-02, eta: 2 days, 7:14:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.5944, loss_cls: 3.7627, loss: 3.7627 +2024-12-29 09:18:36,129 - pyskl - INFO - Epoch [87][2700/3746] lr: 3.785e-02, eta: 2 days, 7:13:31, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.5953, loss_cls: 3.7596, loss: 3.7596 +2024-12-29 09:20:01,088 - pyskl - INFO - Epoch [87][2800/3746] lr: 3.782e-02, eta: 2 days, 7:12:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5920, loss_cls: 3.8350, loss: 3.8350 +2024-12-29 09:21:26,050 - pyskl - INFO - Epoch [87][2900/3746] lr: 3.779e-02, eta: 2 days, 7:10:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5845, loss_cls: 3.8560, loss: 3.8560 +2024-12-29 09:22:50,662 - pyskl - INFO - Epoch [87][3000/3746] lr: 3.777e-02, eta: 2 days, 7:09:21, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5986, loss_cls: 3.7754, loss: 3.7754 +2024-12-29 09:24:15,743 - pyskl - INFO - Epoch [87][3100/3746] lr: 3.774e-02, eta: 2 days, 7:07:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5866, loss_cls: 3.8302, loss: 3.8302 +2024-12-29 09:25:40,937 - pyskl - INFO - Epoch [87][3200/3746] lr: 3.771e-02, eta: 2 days, 7:06:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5913, loss_cls: 3.7714, loss: 3.7714 +2024-12-29 09:27:05,864 - pyskl - INFO - Epoch [87][3300/3746] lr: 3.769e-02, eta: 2 days, 7:05:12, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6002, loss_cls: 3.7629, loss: 3.7629 +2024-12-29 09:28:30,539 - pyskl - INFO - Epoch [87][3400/3746] lr: 3.766e-02, eta: 2 days, 7:03:49, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5778, loss_cls: 3.8428, loss: 3.8428 +2024-12-29 09:29:55,270 - pyskl - INFO - Epoch [87][3500/3746] lr: 3.763e-02, eta: 2 days, 7:02:26, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5858, loss_cls: 3.8272, loss: 3.8272 +2024-12-29 09:31:19,704 - pyskl - INFO - Epoch [87][3600/3746] lr: 3.761e-02, eta: 2 days, 7:01:02, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5936, loss_cls: 3.7772, loss: 3.7772 +2024-12-29 09:32:44,317 - pyskl - INFO - Epoch [87][3700/3746] lr: 3.758e-02, eta: 2 days, 6:59:39, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5884, loss_cls: 3.8339, loss: 3.8339 +2024-12-29 09:33:25,299 - pyskl - INFO - Saving checkpoint at 87 epochs +2024-12-29 09:35:24,047 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 09:35:24,734 - pyskl - INFO - +top1_acc 0.2777 +top5_acc 0.5319 +2024-12-29 09:35:24,735 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 09:35:24,785 - pyskl - INFO - +mean_acc 0.2775 +2024-12-29 09:35:24,803 - pyskl - INFO - Epoch(val) [87][309] top1_acc: 0.2777, top5_acc: 0.5319, mean_class_accuracy: 0.2775 +2024-12-29 09:39:41,920 - pyskl - INFO - Epoch [88][100/3746] lr: 3.754e-02, eta: 2 days, 6:59:14, time: 2.571, data_time: 1.533, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6070, loss_cls: 3.7040, loss: 3.7040 +2024-12-29 09:41:07,462 - pyskl - INFO - Epoch [88][200/3746] lr: 3.751e-02, eta: 2 days, 6:57:51, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5962, loss_cls: 3.7872, loss: 3.7872 +2024-12-29 09:42:32,953 - pyskl - INFO - Epoch [88][300/3746] lr: 3.748e-02, eta: 2 days, 6:56:28, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.6017, loss_cls: 3.7600, loss: 3.7600 +2024-12-29 09:43:58,138 - pyskl - INFO - Epoch [88][400/3746] lr: 3.746e-02, eta: 2 days, 6:55:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.6077, loss_cls: 3.7298, loss: 3.7298 +2024-12-29 09:45:23,038 - pyskl - INFO - Epoch [88][500/3746] lr: 3.743e-02, eta: 2 days, 6:53:42, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5903, loss_cls: 3.7848, loss: 3.7848 +2024-12-29 09:46:47,899 - pyskl - INFO - Epoch [88][600/3746] lr: 3.740e-02, eta: 2 days, 6:52:19, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6041, loss_cls: 3.7206, loss: 3.7206 +2024-12-29 09:48:12,895 - pyskl - INFO - Epoch [88][700/3746] lr: 3.738e-02, eta: 2 days, 6:50:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6014, loss_cls: 3.7247, loss: 3.7247 +2024-12-29 09:49:37,778 - pyskl - INFO - Epoch [88][800/3746] lr: 3.735e-02, eta: 2 days, 6:49:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.5955, loss_cls: 3.7657, loss: 3.7657 +2024-12-29 09:51:02,815 - pyskl - INFO - Epoch [88][900/3746] lr: 3.732e-02, eta: 2 days, 6:48:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6014, loss_cls: 3.7529, loss: 3.7529 +2024-12-29 09:52:27,687 - pyskl - INFO - Epoch [88][1000/3746] lr: 3.730e-02, eta: 2 days, 6:46:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5972, loss_cls: 3.7760, loss: 3.7760 +2024-12-29 09:53:52,190 - pyskl - INFO - Epoch [88][1100/3746] lr: 3.727e-02, eta: 2 days, 6:45:23, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5991, loss_cls: 3.7481, loss: 3.7481 +2024-12-29 09:55:17,234 - pyskl - INFO - Epoch [88][1200/3746] lr: 3.724e-02, eta: 2 days, 6:44:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.5878, loss_cls: 3.7678, loss: 3.7678 +2024-12-29 09:56:42,101 - pyskl - INFO - Epoch [88][1300/3746] lr: 3.721e-02, eta: 2 days, 6:42:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.5919, loss_cls: 3.8267, loss: 3.8267 +2024-12-29 09:58:07,093 - pyskl - INFO - Epoch [88][1400/3746] lr: 3.719e-02, eta: 2 days, 6:41:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.5998, loss_cls: 3.7445, loss: 3.7445 +2024-12-29 09:59:32,114 - pyskl - INFO - Epoch [88][1500/3746] lr: 3.716e-02, eta: 2 days, 6:39:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5997, loss_cls: 3.7574, loss: 3.7574 +2024-12-29 10:00:57,222 - pyskl - INFO - Epoch [88][1600/3746] lr: 3.713e-02, eta: 2 days, 6:38:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5977, loss_cls: 3.7811, loss: 3.7811 +2024-12-29 10:02:22,311 - pyskl - INFO - Epoch [88][1700/3746] lr: 3.711e-02, eta: 2 days, 6:37:04, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5986, loss_cls: 3.7416, loss: 3.7416 +2024-12-29 10:03:47,416 - pyskl - INFO - Epoch [88][1800/3746] lr: 3.708e-02, eta: 2 days, 6:35:41, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5944, loss_cls: 3.7797, loss: 3.7797 +2024-12-29 10:05:13,042 - pyskl - INFO - Epoch [88][1900/3746] lr: 3.705e-02, eta: 2 days, 6:34:18, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5961, loss_cls: 3.7561, loss: 3.7561 +2024-12-29 10:06:38,245 - pyskl - INFO - Epoch [88][2000/3746] lr: 3.703e-02, eta: 2 days, 6:32:55, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5989, loss_cls: 3.7812, loss: 3.7812 +2024-12-29 10:08:04,203 - pyskl - INFO - Epoch [88][2100/3746] lr: 3.700e-02, eta: 2 days, 6:31:33, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.5936, loss_cls: 3.7800, loss: 3.7800 +2024-12-29 10:09:29,153 - pyskl - INFO - Epoch [88][2200/3746] lr: 3.697e-02, eta: 2 days, 6:30:09, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.6005, loss_cls: 3.7422, loss: 3.7422 +2024-12-29 10:10:54,038 - pyskl - INFO - Epoch [88][2300/3746] lr: 3.694e-02, eta: 2 days, 6:28:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.5967, loss_cls: 3.7539, loss: 3.7539 +2024-12-29 10:12:19,189 - pyskl - INFO - Epoch [88][2400/3746] lr: 3.692e-02, eta: 2 days, 6:27:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6033, loss_cls: 3.7423, loss: 3.7423 +2024-12-29 10:13:44,684 - pyskl - INFO - Epoch [88][2500/3746] lr: 3.689e-02, eta: 2 days, 6:26:00, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.5964, loss_cls: 3.7686, loss: 3.7686 +2024-12-29 10:15:09,576 - pyskl - INFO - Epoch [88][2600/3746] lr: 3.686e-02, eta: 2 days, 6:24:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.5955, loss_cls: 3.8010, loss: 3.8010 +2024-12-29 10:16:34,537 - pyskl - INFO - Epoch [88][2700/3746] lr: 3.684e-02, eta: 2 days, 6:23:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5877, loss_cls: 3.8078, loss: 3.8078 +2024-12-29 10:17:59,534 - pyskl - INFO - Epoch [88][2800/3746] lr: 3.681e-02, eta: 2 days, 6:21:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5880, loss_cls: 3.8296, loss: 3.8296 +2024-12-29 10:19:24,223 - pyskl - INFO - Epoch [88][2900/3746] lr: 3.678e-02, eta: 2 days, 6:20:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5939, loss_cls: 3.8136, loss: 3.8136 +2024-12-29 10:20:48,992 - pyskl - INFO - Epoch [88][3000/3746] lr: 3.676e-02, eta: 2 days, 6:19:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6036, loss_cls: 3.7505, loss: 3.7505 +2024-12-29 10:22:13,695 - pyskl - INFO - Epoch [88][3100/3746] lr: 3.673e-02, eta: 2 days, 6:17:41, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5942, loss_cls: 3.7923, loss: 3.7923 +2024-12-29 10:23:38,572 - pyskl - INFO - Epoch [88][3200/3746] lr: 3.670e-02, eta: 2 days, 6:16:17, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5878, loss_cls: 3.7868, loss: 3.7868 +2024-12-29 10:25:03,420 - pyskl - INFO - Epoch [88][3300/3746] lr: 3.667e-02, eta: 2 days, 6:14:54, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5873, loss_cls: 3.8306, loss: 3.8306 +2024-12-29 10:26:27,987 - pyskl - INFO - Epoch [88][3400/3746] lr: 3.665e-02, eta: 2 days, 6:13:31, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.5992, loss_cls: 3.7557, loss: 3.7557 +2024-12-29 10:27:52,523 - pyskl - INFO - Epoch [88][3500/3746] lr: 3.662e-02, eta: 2 days, 6:12:07, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.5966, loss_cls: 3.7220, loss: 3.7220 +2024-12-29 10:29:17,202 - pyskl - INFO - Epoch [88][3600/3746] lr: 3.659e-02, eta: 2 days, 6:10:44, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5988, loss_cls: 3.7994, loss: 3.7994 +2024-12-29 10:30:41,806 - pyskl - INFO - Epoch [88][3700/3746] lr: 3.657e-02, eta: 2 days, 6:09:20, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5913, loss_cls: 3.8084, loss: 3.8084 +2024-12-29 10:31:22,677 - pyskl - INFO - Saving checkpoint at 88 epochs +2024-12-29 10:33:22,473 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 10:33:23,260 - pyskl - INFO - +top1_acc 0.2770 +top5_acc 0.5240 +2024-12-29 10:33:23,260 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 10:33:23,317 - pyskl - INFO - +mean_acc 0.2768 +2024-12-29 10:33:23,332 - pyskl - INFO - Epoch(val) [88][309] top1_acc: 0.2770, top5_acc: 0.5240, mean_class_accuracy: 0.2768 +2024-12-29 10:37:40,985 - pyskl - INFO - Epoch [89][100/3746] lr: 3.653e-02, eta: 2 days, 6:08:53, time: 2.576, data_time: 1.535, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6127, loss_cls: 3.7083, loss: 3.7083 +2024-12-29 10:39:05,820 - pyskl - INFO - Epoch [89][200/3746] lr: 3.650e-02, eta: 2 days, 6:07:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.6039, loss_cls: 3.7243, loss: 3.7243 +2024-12-29 10:40:31,231 - pyskl - INFO - Epoch [89][300/3746] lr: 3.647e-02, eta: 2 days, 6:06:07, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.6003, loss_cls: 3.7461, loss: 3.7461 +2024-12-29 10:41:56,761 - pyskl - INFO - Epoch [89][400/3746] lr: 3.645e-02, eta: 2 days, 6:04:44, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6102, loss_cls: 3.7310, loss: 3.7310 +2024-12-29 10:43:21,979 - pyskl - INFO - Epoch [89][500/3746] lr: 3.642e-02, eta: 2 days, 6:03:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6055, loss_cls: 3.7038, loss: 3.7038 +2024-12-29 10:44:46,742 - pyskl - INFO - Epoch [89][600/3746] lr: 3.639e-02, eta: 2 days, 6:01:57, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.5958, loss_cls: 3.7459, loss: 3.7459 +2024-12-29 10:46:11,652 - pyskl - INFO - Epoch [89][700/3746] lr: 3.637e-02, eta: 2 days, 6:00:34, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.5964, loss_cls: 3.7628, loss: 3.7628 +2024-12-29 10:47:36,735 - pyskl - INFO - Epoch [89][800/3746] lr: 3.634e-02, eta: 2 days, 5:59:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6052, loss_cls: 3.7447, loss: 3.7447 +2024-12-29 10:49:01,699 - pyskl - INFO - Epoch [89][900/3746] lr: 3.631e-02, eta: 2 days, 5:57:47, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.6077, loss_cls: 3.7336, loss: 3.7336 +2024-12-29 10:50:26,945 - pyskl - INFO - Epoch [89][1000/3746] lr: 3.629e-02, eta: 2 days, 5:56:24, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6111, loss_cls: 3.7076, loss: 3.7076 +2024-12-29 10:51:52,007 - pyskl - INFO - Epoch [89][1100/3746] lr: 3.626e-02, eta: 2 days, 5:55:01, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.5873, loss_cls: 3.7963, loss: 3.7963 +2024-12-29 10:53:17,157 - pyskl - INFO - Epoch [89][1200/3746] lr: 3.623e-02, eta: 2 days, 5:53:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.5902, loss_cls: 3.7640, loss: 3.7640 +2024-12-29 10:54:42,194 - pyskl - INFO - Epoch [89][1300/3746] lr: 3.620e-02, eta: 2 days, 5:52:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5930, loss_cls: 3.8012, loss: 3.8012 +2024-12-29 10:56:07,456 - pyskl - INFO - Epoch [89][1400/3746] lr: 3.618e-02, eta: 2 days, 5:50:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6058, loss_cls: 3.6940, loss: 3.6940 +2024-12-29 10:57:32,439 - pyskl - INFO - Epoch [89][1500/3746] lr: 3.615e-02, eta: 2 days, 5:49:28, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6062, loss_cls: 3.7415, loss: 3.7415 +2024-12-29 10:58:57,218 - pyskl - INFO - Epoch [89][1600/3746] lr: 3.612e-02, eta: 2 days, 5:48:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.5913, loss_cls: 3.8123, loss: 3.8123 +2024-12-29 11:00:22,095 - pyskl - INFO - Epoch [89][1700/3746] lr: 3.610e-02, eta: 2 days, 5:46:42, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6044, loss_cls: 3.7255, loss: 3.7255 +2024-12-29 11:01:47,332 - pyskl - INFO - Epoch [89][1800/3746] lr: 3.607e-02, eta: 2 days, 5:45:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.6002, loss_cls: 3.7586, loss: 3.7586 +2024-12-29 11:03:12,889 - pyskl - INFO - Epoch [89][1900/3746] lr: 3.604e-02, eta: 2 days, 5:43:56, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6012, loss_cls: 3.7416, loss: 3.7416 +2024-12-29 11:04:37,970 - pyskl - INFO - Epoch [89][2000/3746] lr: 3.602e-02, eta: 2 days, 5:42:32, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.6034, loss_cls: 3.7657, loss: 3.7657 +2024-12-29 11:06:03,048 - pyskl - INFO - Epoch [89][2100/3746] lr: 3.599e-02, eta: 2 days, 5:41:09, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6022, loss_cls: 3.7173, loss: 3.7173 +2024-12-29 11:07:27,967 - pyskl - INFO - Epoch [89][2200/3746] lr: 3.596e-02, eta: 2 days, 5:39:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5870, loss_cls: 3.8120, loss: 3.8120 +2024-12-29 11:08:53,099 - pyskl - INFO - Epoch [89][2300/3746] lr: 3.594e-02, eta: 2 days, 5:38:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.5953, loss_cls: 3.7808, loss: 3.7808 +2024-12-29 11:10:18,218 - pyskl - INFO - Epoch [89][2400/3746] lr: 3.591e-02, eta: 2 days, 5:37:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.6047, loss_cls: 3.7380, loss: 3.7380 +2024-12-29 11:11:43,072 - pyskl - INFO - Epoch [89][2500/3746] lr: 3.588e-02, eta: 2 days, 5:35:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6036, loss_cls: 3.7581, loss: 3.7581 +2024-12-29 11:13:07,985 - pyskl - INFO - Epoch [89][2600/3746] lr: 3.586e-02, eta: 2 days, 5:34:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.5948, loss_cls: 3.7533, loss: 3.7533 +2024-12-29 11:14:32,931 - pyskl - INFO - Epoch [89][2700/3746] lr: 3.583e-02, eta: 2 days, 5:32:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5942, loss_cls: 3.7970, loss: 3.7970 +2024-12-29 11:15:57,881 - pyskl - INFO - Epoch [89][2800/3746] lr: 3.580e-02, eta: 2 days, 5:31:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.5998, loss_cls: 3.7844, loss: 3.7844 +2024-12-29 11:17:23,105 - pyskl - INFO - Epoch [89][2900/3746] lr: 3.578e-02, eta: 2 days, 5:30:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.5948, loss_cls: 3.7642, loss: 3.7642 +2024-12-29 11:18:47,689 - pyskl - INFO - Epoch [89][3000/3746] lr: 3.575e-02, eta: 2 days, 5:28:40, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5997, loss_cls: 3.7836, loss: 3.7836 +2024-12-29 11:20:12,704 - pyskl - INFO - Epoch [89][3100/3746] lr: 3.572e-02, eta: 2 days, 5:27:16, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6000, loss_cls: 3.7574, loss: 3.7574 +2024-12-29 11:21:37,888 - pyskl - INFO - Epoch [89][3200/3746] lr: 3.569e-02, eta: 2 days, 5:25:53, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6023, loss_cls: 3.7548, loss: 3.7548 +2024-12-29 11:23:02,824 - pyskl - INFO - Epoch [89][3300/3746] lr: 3.567e-02, eta: 2 days, 5:24:30, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5948, loss_cls: 3.7676, loss: 3.7676 +2024-12-29 11:24:27,595 - pyskl - INFO - Epoch [89][3400/3746] lr: 3.564e-02, eta: 2 days, 5:23:06, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.6022, loss_cls: 3.7947, loss: 3.7947 +2024-12-29 11:25:52,474 - pyskl - INFO - Epoch [89][3500/3746] lr: 3.561e-02, eta: 2 days, 5:21:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.5953, loss_cls: 3.7713, loss: 3.7713 +2024-12-29 11:27:17,089 - pyskl - INFO - Epoch [89][3600/3746] lr: 3.559e-02, eta: 2 days, 5:20:20, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.5958, loss_cls: 3.7564, loss: 3.7564 +2024-12-29 11:28:42,316 - pyskl - INFO - Epoch [89][3700/3746] lr: 3.556e-02, eta: 2 days, 5:18:56, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6061, loss_cls: 3.7405, loss: 3.7405 +2024-12-29 11:29:23,181 - pyskl - INFO - Saving checkpoint at 89 epochs +2024-12-29 11:31:22,726 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 11:31:23,519 - pyskl - INFO - +top1_acc 0.2818 +top5_acc 0.5374 +2024-12-29 11:31:23,519 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 11:31:23,576 - pyskl - INFO - +mean_acc 0.2815 +2024-12-29 11:31:23,580 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_86.pth was removed +2024-12-29 11:31:23,873 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_89.pth. +2024-12-29 11:31:23,874 - pyskl - INFO - Best top1_acc is 0.2818 at 89 epoch. +2024-12-29 11:31:23,888 - pyskl - INFO - Epoch(val) [89][309] top1_acc: 0.2818, top5_acc: 0.5374, mean_class_accuracy: 0.2815 +2024-12-29 11:35:38,615 - pyskl - INFO - Epoch [90][100/3746] lr: 3.552e-02, eta: 2 days, 5:18:24, time: 2.547, data_time: 1.513, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6147, loss_cls: 3.6636, loss: 3.6636 +2024-12-29 11:37:04,056 - pyskl - INFO - Epoch [90][200/3746] lr: 3.550e-02, eta: 2 days, 5:17:01, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.6116, loss_cls: 3.7045, loss: 3.7045 +2024-12-29 11:38:29,047 - pyskl - INFO - Epoch [90][300/3746] lr: 3.547e-02, eta: 2 days, 5:15:38, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.6055, loss_cls: 3.7342, loss: 3.7342 +2024-12-29 11:39:54,458 - pyskl - INFO - Epoch [90][400/3746] lr: 3.544e-02, eta: 2 days, 5:14:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6133, loss_cls: 3.7028, loss: 3.7028 +2024-12-29 11:41:19,476 - pyskl - INFO - Epoch [90][500/3746] lr: 3.541e-02, eta: 2 days, 5:12:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.5970, loss_cls: 3.7415, loss: 3.7415 +2024-12-29 11:42:44,692 - pyskl - INFO - Epoch [90][600/3746] lr: 3.539e-02, eta: 2 days, 5:11:28, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6067, loss_cls: 3.7248, loss: 3.7248 +2024-12-29 11:44:09,469 - pyskl - INFO - Epoch [90][700/3746] lr: 3.536e-02, eta: 2 days, 5:10:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6031, loss_cls: 3.7007, loss: 3.7007 +2024-12-29 11:45:34,932 - pyskl - INFO - Epoch [90][800/3746] lr: 3.533e-02, eta: 2 days, 5:08:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.6025, loss_cls: 3.7462, loss: 3.7462 +2024-12-29 11:47:00,370 - pyskl - INFO - Epoch [90][900/3746] lr: 3.531e-02, eta: 2 days, 5:07:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6003, loss_cls: 3.7321, loss: 3.7321 +2024-12-29 11:48:25,960 - pyskl - INFO - Epoch [90][1000/3746] lr: 3.528e-02, eta: 2 days, 5:05:56, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6038, loss_cls: 3.7422, loss: 3.7422 +2024-12-29 11:49:51,611 - pyskl - INFO - Epoch [90][1100/3746] lr: 3.525e-02, eta: 2 days, 5:04:33, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.5948, loss_cls: 3.7306, loss: 3.7306 +2024-12-29 11:51:17,207 - pyskl - INFO - Epoch [90][1200/3746] lr: 3.523e-02, eta: 2 days, 5:03:10, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.6039, loss_cls: 3.7465, loss: 3.7465 +2024-12-29 11:52:42,957 - pyskl - INFO - Epoch [90][1300/3746] lr: 3.520e-02, eta: 2 days, 5:01:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6083, loss_cls: 3.7521, loss: 3.7521 +2024-12-29 11:54:08,576 - pyskl - INFO - Epoch [90][1400/3746] lr: 3.517e-02, eta: 2 days, 5:00:24, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6105, loss_cls: 3.6956, loss: 3.6956 +2024-12-29 11:55:34,305 - pyskl - INFO - Epoch [90][1500/3746] lr: 3.515e-02, eta: 2 days, 4:59:01, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6047, loss_cls: 3.7146, loss: 3.7146 +2024-12-29 11:57:00,311 - pyskl - INFO - Epoch [90][1600/3746] lr: 3.512e-02, eta: 2 days, 4:57:38, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6017, loss_cls: 3.7296, loss: 3.7296 +2024-12-29 11:58:26,316 - pyskl - INFO - Epoch [90][1700/3746] lr: 3.509e-02, eta: 2 days, 4:56:16, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6067, loss_cls: 3.7292, loss: 3.7292 +2024-12-29 11:59:52,461 - pyskl - INFO - Epoch [90][1800/3746] lr: 3.507e-02, eta: 2 days, 4:54:53, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6045, loss_cls: 3.7363, loss: 3.7363 +2024-12-29 12:01:17,770 - pyskl - INFO - Epoch [90][1900/3746] lr: 3.504e-02, eta: 2 days, 4:53:30, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6020, loss_cls: 3.7500, loss: 3.7500 +2024-12-29 12:02:43,096 - pyskl - INFO - Epoch [90][2000/3746] lr: 3.501e-02, eta: 2 days, 4:52:07, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3439, top5_acc: 0.5947, loss_cls: 3.7399, loss: 3.7399 +2024-12-29 12:04:08,598 - pyskl - INFO - Epoch [90][2100/3746] lr: 3.499e-02, eta: 2 days, 4:50:44, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6052, loss_cls: 3.6987, loss: 3.6987 +2024-12-29 12:05:34,156 - pyskl - INFO - Epoch [90][2200/3746] lr: 3.496e-02, eta: 2 days, 4:49:21, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3423, top5_acc: 0.5986, loss_cls: 3.7707, loss: 3.7707 +2024-12-29 12:06:59,527 - pyskl - INFO - Epoch [90][2300/3746] lr: 3.493e-02, eta: 2 days, 4:47:58, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6105, loss_cls: 3.7082, loss: 3.7082 +2024-12-29 12:08:24,914 - pyskl - INFO - Epoch [90][2400/3746] lr: 3.491e-02, eta: 2 days, 4:46:35, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6025, loss_cls: 3.7510, loss: 3.7510 +2024-12-29 12:09:50,505 - pyskl - INFO - Epoch [90][2500/3746] lr: 3.488e-02, eta: 2 days, 4:45:12, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.6055, loss_cls: 3.7614, loss: 3.7614 +2024-12-29 12:11:15,990 - pyskl - INFO - Epoch [90][2600/3746] lr: 3.485e-02, eta: 2 days, 4:43:49, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6064, loss_cls: 3.7529, loss: 3.7529 +2024-12-29 12:12:41,539 - pyskl - INFO - Epoch [90][2700/3746] lr: 3.483e-02, eta: 2 days, 4:42:26, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6020, loss_cls: 3.7381, loss: 3.7381 +2024-12-29 12:14:06,647 - pyskl - INFO - Epoch [90][2800/3746] lr: 3.480e-02, eta: 2 days, 4:41:02, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.6025, loss_cls: 3.7577, loss: 3.7577 +2024-12-29 12:15:32,138 - pyskl - INFO - Epoch [90][2900/3746] lr: 3.477e-02, eta: 2 days, 4:39:39, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.6000, loss_cls: 3.7508, loss: 3.7508 +2024-12-29 12:16:57,479 - pyskl - INFO - Epoch [90][3000/3746] lr: 3.475e-02, eta: 2 days, 4:38:16, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6125, loss_cls: 3.6942, loss: 3.6942 +2024-12-29 12:18:22,646 - pyskl - INFO - Epoch [90][3100/3746] lr: 3.472e-02, eta: 2 days, 4:36:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6041, loss_cls: 3.7712, loss: 3.7712 +2024-12-29 12:19:47,689 - pyskl - INFO - Epoch [90][3200/3746] lr: 3.469e-02, eta: 2 days, 4:35:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.5944, loss_cls: 3.7762, loss: 3.7762 +2024-12-29 12:21:12,718 - pyskl - INFO - Epoch [90][3300/3746] lr: 3.467e-02, eta: 2 days, 4:34:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.5936, loss_cls: 3.7903, loss: 3.7903 +2024-12-29 12:22:37,277 - pyskl - INFO - Epoch [90][3400/3746] lr: 3.464e-02, eta: 2 days, 4:32:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5948, loss_cls: 3.8074, loss: 3.8074 +2024-12-29 12:24:01,907 - pyskl - INFO - Epoch [90][3500/3746] lr: 3.461e-02, eta: 2 days, 4:31:19, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5856, loss_cls: 3.8078, loss: 3.8078 +2024-12-29 12:25:26,860 - pyskl - INFO - Epoch [90][3600/3746] lr: 3.459e-02, eta: 2 days, 4:29:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.5978, loss_cls: 3.7360, loss: 3.7360 +2024-12-29 12:26:51,741 - pyskl - INFO - Epoch [90][3700/3746] lr: 3.456e-02, eta: 2 days, 4:28:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.5953, loss_cls: 3.7608, loss: 3.7608 +2024-12-29 12:27:32,847 - pyskl - INFO - Saving checkpoint at 90 epochs +2024-12-29 12:29:32,345 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 12:29:33,043 - pyskl - INFO - +top1_acc 0.2894 +top5_acc 0.5467 +2024-12-29 12:29:33,043 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 12:29:33,086 - pyskl - INFO - +mean_acc 0.2893 +2024-12-29 12:29:33,090 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_89.pth was removed +2024-12-29 12:29:33,414 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_90.pth. +2024-12-29 12:29:33,415 - pyskl - INFO - Best top1_acc is 0.2894 at 90 epoch. +2024-12-29 12:29:33,431 - pyskl - INFO - Epoch(val) [90][309] top1_acc: 0.2894, top5_acc: 0.5467, mean_class_accuracy: 0.2893 +2024-12-29 12:33:52,592 - pyskl - INFO - Epoch [91][100/3746] lr: 3.452e-02, eta: 2 days, 4:28:00, time: 2.591, data_time: 1.548, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6081, loss_cls: 3.7068, loss: 3.7068 +2024-12-29 12:35:17,996 - pyskl - INFO - Epoch [91][200/3746] lr: 3.450e-02, eta: 2 days, 4:26:37, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6091, loss_cls: 3.6823, loss: 3.6823 +2024-12-29 12:36:43,284 - pyskl - INFO - Epoch [91][300/3746] lr: 3.447e-02, eta: 2 days, 4:25:14, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.6062, loss_cls: 3.7368, loss: 3.7368 +2024-12-29 12:38:08,462 - pyskl - INFO - Epoch [91][400/3746] lr: 3.444e-02, eta: 2 days, 4:23:50, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6131, loss_cls: 3.6758, loss: 3.6758 +2024-12-29 12:39:34,150 - pyskl - INFO - Epoch [91][500/3746] lr: 3.442e-02, eta: 2 days, 4:22:27, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6208, loss_cls: 3.6604, loss: 3.6604 +2024-12-29 12:40:59,120 - pyskl - INFO - Epoch [91][600/3746] lr: 3.439e-02, eta: 2 days, 4:21:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6136, loss_cls: 3.6980, loss: 3.6980 +2024-12-29 12:42:24,562 - pyskl - INFO - Epoch [91][700/3746] lr: 3.436e-02, eta: 2 days, 4:19:41, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.6041, loss_cls: 3.7385, loss: 3.7385 +2024-12-29 12:43:49,478 - pyskl - INFO - Epoch [91][800/3746] lr: 3.434e-02, eta: 2 days, 4:18:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.6019, loss_cls: 3.7513, loss: 3.7513 +2024-12-29 12:45:14,259 - pyskl - INFO - Epoch [91][900/3746] lr: 3.431e-02, eta: 2 days, 4:16:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6178, loss_cls: 3.7146, loss: 3.7146 +2024-12-29 12:46:38,989 - pyskl - INFO - Epoch [91][1000/3746] lr: 3.428e-02, eta: 2 days, 4:15:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6128, loss_cls: 3.6789, loss: 3.6789 +2024-12-29 12:48:03,333 - pyskl - INFO - Epoch [91][1100/3746] lr: 3.426e-02, eta: 2 days, 4:14:06, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6147, loss_cls: 3.6785, loss: 3.6785 +2024-12-29 12:49:28,028 - pyskl - INFO - Epoch [91][1200/3746] lr: 3.423e-02, eta: 2 days, 4:12:42, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.6016, loss_cls: 3.7262, loss: 3.7262 +2024-12-29 12:50:52,749 - pyskl - INFO - Epoch [91][1300/3746] lr: 3.420e-02, eta: 2 days, 4:11:19, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6062, loss_cls: 3.7176, loss: 3.7176 +2024-12-29 12:52:18,193 - pyskl - INFO - Epoch [91][1400/3746] lr: 3.418e-02, eta: 2 days, 4:09:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6067, loss_cls: 3.7190, loss: 3.7190 +2024-12-29 12:53:42,976 - pyskl - INFO - Epoch [91][1500/3746] lr: 3.415e-02, eta: 2 days, 4:08:32, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6106, loss_cls: 3.7043, loss: 3.7043 +2024-12-29 12:55:08,050 - pyskl - INFO - Epoch [91][1600/3746] lr: 3.412e-02, eta: 2 days, 4:07:08, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.6003, loss_cls: 3.7334, loss: 3.7334 +2024-12-29 12:56:32,773 - pyskl - INFO - Epoch [91][1700/3746] lr: 3.410e-02, eta: 2 days, 4:05:45, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.6008, loss_cls: 3.7615, loss: 3.7615 +2024-12-29 12:57:57,511 - pyskl - INFO - Epoch [91][1800/3746] lr: 3.407e-02, eta: 2 days, 4:04:21, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6000, loss_cls: 3.7108, loss: 3.7108 +2024-12-29 12:59:22,412 - pyskl - INFO - Epoch [91][1900/3746] lr: 3.405e-02, eta: 2 days, 4:02:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.6086, loss_cls: 3.7316, loss: 3.7316 +2024-12-29 13:00:46,652 - pyskl - INFO - Epoch [91][2000/3746] lr: 3.402e-02, eta: 2 days, 4:01:34, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6191, loss_cls: 3.6695, loss: 3.6695 +2024-12-29 13:02:11,896 - pyskl - INFO - Epoch [91][2100/3746] lr: 3.399e-02, eta: 2 days, 4:00:10, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5995, loss_cls: 3.7254, loss: 3.7254 +2024-12-29 13:03:36,170 - pyskl - INFO - Epoch [91][2200/3746] lr: 3.397e-02, eta: 2 days, 3:58:46, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6038, loss_cls: 3.7177, loss: 3.7177 +2024-12-29 13:05:00,892 - pyskl - INFO - Epoch [91][2300/3746] lr: 3.394e-02, eta: 2 days, 3:57:23, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6075, loss_cls: 3.6947, loss: 3.6947 +2024-12-29 13:06:26,167 - pyskl - INFO - Epoch [91][2400/3746] lr: 3.391e-02, eta: 2 days, 3:55:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.5928, loss_cls: 3.7535, loss: 3.7535 +2024-12-29 13:07:51,503 - pyskl - INFO - Epoch [91][2500/3746] lr: 3.389e-02, eta: 2 days, 3:54:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6039, loss_cls: 3.7409, loss: 3.7409 +2024-12-29 13:09:16,253 - pyskl - INFO - Epoch [91][2600/3746] lr: 3.386e-02, eta: 2 days, 3:53:12, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.5988, loss_cls: 3.7187, loss: 3.7187 +2024-12-29 13:10:40,336 - pyskl - INFO - Epoch [91][2700/3746] lr: 3.383e-02, eta: 2 days, 3:51:48, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.5962, loss_cls: 3.7601, loss: 3.7601 +2024-12-29 13:12:05,472 - pyskl - INFO - Epoch [91][2800/3746] lr: 3.381e-02, eta: 2 days, 3:50:25, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6034, loss_cls: 3.7211, loss: 3.7211 +2024-12-29 13:13:31,701 - pyskl - INFO - Epoch [91][2900/3746] lr: 3.378e-02, eta: 2 days, 3:49:02, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.5977, loss_cls: 3.7803, loss: 3.7803 +2024-12-29 13:14:58,089 - pyskl - INFO - Epoch [91][3000/3746] lr: 3.375e-02, eta: 2 days, 3:47:40, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6194, loss_cls: 3.6495, loss: 3.6495 +2024-12-29 13:16:23,764 - pyskl - INFO - Epoch [91][3100/3746] lr: 3.373e-02, eta: 2 days, 3:46:17, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5956, loss_cls: 3.7600, loss: 3.7600 +2024-12-29 13:17:49,702 - pyskl - INFO - Epoch [91][3200/3746] lr: 3.370e-02, eta: 2 days, 3:44:54, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6044, loss_cls: 3.7323, loss: 3.7323 +2024-12-29 13:19:15,670 - pyskl - INFO - Epoch [91][3300/3746] lr: 3.367e-02, eta: 2 days, 3:43:31, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.6025, loss_cls: 3.7584, loss: 3.7584 +2024-12-29 13:20:41,315 - pyskl - INFO - Epoch [91][3400/3746] lr: 3.365e-02, eta: 2 days, 3:42:08, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.5989, loss_cls: 3.7255, loss: 3.7255 +2024-12-29 13:22:07,355 - pyskl - INFO - Epoch [91][3500/3746] lr: 3.362e-02, eta: 2 days, 3:40:45, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5944, loss_cls: 3.7753, loss: 3.7753 +2024-12-29 13:23:33,867 - pyskl - INFO - Epoch [91][3600/3746] lr: 3.360e-02, eta: 2 days, 3:39:23, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3425, top5_acc: 0.5962, loss_cls: 3.7765, loss: 3.7765 +2024-12-29 13:25:00,396 - pyskl - INFO - Epoch [91][3700/3746] lr: 3.357e-02, eta: 2 days, 3:38:00, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5959, loss_cls: 3.7862, loss: 3.7862 +2024-12-29 13:25:42,215 - pyskl - INFO - Saving checkpoint at 91 epochs +2024-12-29 13:27:47,142 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 13:27:47,956 - pyskl - INFO - +top1_acc 0.2903 +top5_acc 0.5415 +2024-12-29 13:27:47,957 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 13:27:48,027 - pyskl - INFO - +mean_acc 0.2901 +2024-12-29 13:27:48,032 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_90.pth was removed +2024-12-29 13:27:48,381 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_91.pth. +2024-12-29 13:27:48,382 - pyskl - INFO - Best top1_acc is 0.2903 at 91 epoch. +2024-12-29 13:27:48,406 - pyskl - INFO - Epoch(val) [91][309] top1_acc: 0.2903, top5_acc: 0.5415, mean_class_accuracy: 0.2901 +2024-12-29 13:32:24,672 - pyskl - INFO - Epoch [92][100/3746] lr: 3.353e-02, eta: 2 days, 3:37:37, time: 2.763, data_time: 1.718, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6231, loss_cls: 3.6305, loss: 3.6305 +2024-12-29 13:33:50,831 - pyskl - INFO - Epoch [92][200/3746] lr: 3.350e-02, eta: 2 days, 3:36:14, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6198, loss_cls: 3.6315, loss: 3.6315 +2024-12-29 13:35:16,653 - pyskl - INFO - Epoch [92][300/3746] lr: 3.348e-02, eta: 2 days, 3:34:51, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6197, loss_cls: 3.6777, loss: 3.6777 +2024-12-29 13:36:42,267 - pyskl - INFO - Epoch [92][400/3746] lr: 3.345e-02, eta: 2 days, 3:33:28, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6123, loss_cls: 3.6787, loss: 3.6787 +2024-12-29 13:38:08,118 - pyskl - INFO - Epoch [92][500/3746] lr: 3.342e-02, eta: 2 days, 3:32:05, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6158, loss_cls: 3.6746, loss: 3.6746 +2024-12-29 13:39:33,618 - pyskl - INFO - Epoch [92][600/3746] lr: 3.340e-02, eta: 2 days, 3:30:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6097, loss_cls: 3.6993, loss: 3.6993 +2024-12-29 13:40:59,359 - pyskl - INFO - Epoch [92][700/3746] lr: 3.337e-02, eta: 2 days, 3:29:18, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6136, loss_cls: 3.6769, loss: 3.6769 +2024-12-29 13:42:25,449 - pyskl - INFO - Epoch [92][800/3746] lr: 3.335e-02, eta: 2 days, 3:27:55, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6178, loss_cls: 3.7073, loss: 3.7073 +2024-12-29 13:43:51,604 - pyskl - INFO - Epoch [92][900/3746] lr: 3.332e-02, eta: 2 days, 3:26:33, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6105, loss_cls: 3.6991, loss: 3.6991 +2024-12-29 13:45:17,584 - pyskl - INFO - Epoch [92][1000/3746] lr: 3.329e-02, eta: 2 days, 3:25:10, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6041, loss_cls: 3.7111, loss: 3.7111 +2024-12-29 13:46:43,276 - pyskl - INFO - Epoch [92][1100/3746] lr: 3.327e-02, eta: 2 days, 3:23:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6152, loss_cls: 3.6651, loss: 3.6651 +2024-12-29 13:48:09,453 - pyskl - INFO - Epoch [92][1200/3746] lr: 3.324e-02, eta: 2 days, 3:22:24, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6056, loss_cls: 3.7240, loss: 3.7240 +2024-12-29 13:49:35,730 - pyskl - INFO - Epoch [92][1300/3746] lr: 3.321e-02, eta: 2 days, 3:21:01, time: 0.863, data_time: 0.001, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6136, loss_cls: 3.6779, loss: 3.6779 +2024-12-29 13:51:01,709 - pyskl - INFO - Epoch [92][1400/3746] lr: 3.319e-02, eta: 2 days, 3:19:38, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6058, loss_cls: 3.7298, loss: 3.7298 +2024-12-29 13:52:27,467 - pyskl - INFO - Epoch [92][1500/3746] lr: 3.316e-02, eta: 2 days, 3:18:15, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6048, loss_cls: 3.7394, loss: 3.7394 +2024-12-29 13:53:53,445 - pyskl - INFO - Epoch [92][1600/3746] lr: 3.314e-02, eta: 2 days, 3:16:52, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6131, loss_cls: 3.6905, loss: 3.6905 +2024-12-29 13:55:19,532 - pyskl - INFO - Epoch [92][1700/3746] lr: 3.311e-02, eta: 2 days, 3:15:29, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.3439, top5_acc: 0.5988, loss_cls: 3.7438, loss: 3.7438 +2024-12-29 13:56:45,896 - pyskl - INFO - Epoch [92][1800/3746] lr: 3.308e-02, eta: 2 days, 3:14:06, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6066, loss_cls: 3.6840, loss: 3.6840 +2024-12-29 13:58:12,293 - pyskl - INFO - Epoch [92][1900/3746] lr: 3.306e-02, eta: 2 days, 3:12:44, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6123, loss_cls: 3.7020, loss: 3.7020 +2024-12-29 13:59:37,576 - pyskl - INFO - Epoch [92][2000/3746] lr: 3.303e-02, eta: 2 days, 3:11:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3425, top5_acc: 0.5941, loss_cls: 3.7462, loss: 3.7462 +2024-12-29 14:01:03,152 - pyskl - INFO - Epoch [92][2100/3746] lr: 3.300e-02, eta: 2 days, 3:09:57, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6266, loss_cls: 3.6314, loss: 3.6314 +2024-12-29 14:02:29,215 - pyskl - INFO - Epoch [92][2200/3746] lr: 3.298e-02, eta: 2 days, 3:08:34, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6105, loss_cls: 3.7127, loss: 3.7127 +2024-12-29 14:03:54,955 - pyskl - INFO - Epoch [92][2300/3746] lr: 3.295e-02, eta: 2 days, 3:07:11, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.5975, loss_cls: 3.7291, loss: 3.7291 +2024-12-29 14:05:20,725 - pyskl - INFO - Epoch [92][2400/3746] lr: 3.292e-02, eta: 2 days, 3:05:48, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5972, loss_cls: 3.7531, loss: 3.7531 +2024-12-29 14:06:46,008 - pyskl - INFO - Epoch [92][2500/3746] lr: 3.290e-02, eta: 2 days, 3:04:24, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6047, loss_cls: 3.7049, loss: 3.7049 +2024-12-29 14:08:12,055 - pyskl - INFO - Epoch [92][2600/3746] lr: 3.287e-02, eta: 2 days, 3:03:01, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.5961, loss_cls: 3.7470, loss: 3.7470 +2024-12-29 14:09:37,768 - pyskl - INFO - Epoch [92][2700/3746] lr: 3.285e-02, eta: 2 days, 3:01:38, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6047, loss_cls: 3.7140, loss: 3.7140 +2024-12-29 14:11:03,137 - pyskl - INFO - Epoch [92][2800/3746] lr: 3.282e-02, eta: 2 days, 3:00:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6119, loss_cls: 3.7035, loss: 3.7035 +2024-12-29 14:12:28,771 - pyskl - INFO - Epoch [92][2900/3746] lr: 3.279e-02, eta: 2 days, 2:58:52, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.5967, loss_cls: 3.7441, loss: 3.7441 +2024-12-29 14:13:54,686 - pyskl - INFO - Epoch [92][3000/3746] lr: 3.277e-02, eta: 2 days, 2:57:29, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5961, loss_cls: 3.7693, loss: 3.7693 +2024-12-29 14:15:20,934 - pyskl - INFO - Epoch [92][3100/3746] lr: 3.274e-02, eta: 2 days, 2:56:06, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.3417, top5_acc: 0.6058, loss_cls: 3.7083, loss: 3.7083 +2024-12-29 14:16:47,252 - pyskl - INFO - Epoch [92][3200/3746] lr: 3.271e-02, eta: 2 days, 2:54:43, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6081, loss_cls: 3.7286, loss: 3.7286 +2024-12-29 14:18:13,336 - pyskl - INFO - Epoch [92][3300/3746] lr: 3.269e-02, eta: 2 days, 2:53:20, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6147, loss_cls: 3.6850, loss: 3.6850 +2024-12-29 14:19:39,475 - pyskl - INFO - Epoch [92][3400/3746] lr: 3.266e-02, eta: 2 days, 2:51:57, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.5969, loss_cls: 3.7219, loss: 3.7219 +2024-12-29 14:21:06,094 - pyskl - INFO - Epoch [92][3500/3746] lr: 3.264e-02, eta: 2 days, 2:50:35, time: 0.866, data_time: 0.001, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6077, loss_cls: 3.7253, loss: 3.7253 +2024-12-29 14:22:32,608 - pyskl - INFO - Epoch [92][3600/3746] lr: 3.261e-02, eta: 2 days, 2:49:12, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5998, loss_cls: 3.7365, loss: 3.7365 +2024-12-29 14:23:59,377 - pyskl - INFO - Epoch [92][3700/3746] lr: 3.258e-02, eta: 2 days, 2:47:50, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5938, loss_cls: 3.7912, loss: 3.7912 +2024-12-29 14:24:41,560 - pyskl - INFO - Saving checkpoint at 92 epochs +2024-12-29 14:26:41,489 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 14:26:42,216 - pyskl - INFO - +top1_acc 0.2849 +top5_acc 0.5357 +2024-12-29 14:26:42,216 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 14:26:42,267 - pyskl - INFO - +mean_acc 0.2847 +2024-12-29 14:26:42,281 - pyskl - INFO - Epoch(val) [92][309] top1_acc: 0.2849, top5_acc: 0.5357, mean_class_accuracy: 0.2847 +2024-12-29 14:31:12,207 - pyskl - INFO - Epoch [93][100/3746] lr: 3.255e-02, eta: 2 days, 2:47:19, time: 2.699, data_time: 1.635, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6222, loss_cls: 3.6763, loss: 3.6763 +2024-12-29 14:32:39,421 - pyskl - INFO - Epoch [93][200/3746] lr: 3.252e-02, eta: 2 days, 2:45:57, time: 0.872, data_time: 0.001, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6227, loss_cls: 3.6213, loss: 3.6213 +2024-12-29 14:34:05,431 - pyskl - INFO - Epoch [93][300/3746] lr: 3.249e-02, eta: 2 days, 2:44:34, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6170, loss_cls: 3.6578, loss: 3.6578 +2024-12-29 14:35:31,791 - pyskl - INFO - Epoch [93][400/3746] lr: 3.247e-02, eta: 2 days, 2:43:11, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6127, loss_cls: 3.6925, loss: 3.6925 +2024-12-29 14:36:57,851 - pyskl - INFO - Epoch [93][500/3746] lr: 3.244e-02, eta: 2 days, 2:41:48, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6188, loss_cls: 3.6418, loss: 3.6418 +2024-12-29 14:38:24,472 - pyskl - INFO - Epoch [93][600/3746] lr: 3.241e-02, eta: 2 days, 2:40:26, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6041, loss_cls: 3.7160, loss: 3.7160 +2024-12-29 14:39:51,056 - pyskl - INFO - Epoch [93][700/3746] lr: 3.239e-02, eta: 2 days, 2:39:03, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6220, loss_cls: 3.6456, loss: 3.6456 +2024-12-29 14:41:18,116 - pyskl - INFO - Epoch [93][800/3746] lr: 3.236e-02, eta: 2 days, 2:37:40, time: 0.871, data_time: 0.001, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6120, loss_cls: 3.6853, loss: 3.6853 +2024-12-29 14:42:44,941 - pyskl - INFO - Epoch [93][900/3746] lr: 3.234e-02, eta: 2 days, 2:36:18, time: 0.868, data_time: 0.001, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6039, loss_cls: 3.7302, loss: 3.7302 +2024-12-29 14:44:11,527 - pyskl - INFO - Epoch [93][1000/3746] lr: 3.231e-02, eta: 2 days, 2:34:55, time: 0.866, data_time: 0.001, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6031, loss_cls: 3.7364, loss: 3.7364 +2024-12-29 14:45:38,271 - pyskl - INFO - Epoch [93][1100/3746] lr: 3.228e-02, eta: 2 days, 2:33:33, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6025, loss_cls: 3.6974, loss: 3.6974 +2024-12-29 14:47:04,961 - pyskl - INFO - Epoch [93][1200/3746] lr: 3.226e-02, eta: 2 days, 2:32:10, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6138, loss_cls: 3.6395, loss: 3.6395 +2024-12-29 14:48:31,276 - pyskl - INFO - Epoch [93][1300/3746] lr: 3.223e-02, eta: 2 days, 2:30:47, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6122, loss_cls: 3.6842, loss: 3.6842 +2024-12-29 14:49:57,461 - pyskl - INFO - Epoch [93][1400/3746] lr: 3.221e-02, eta: 2 days, 2:29:24, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6197, loss_cls: 3.6676, loss: 3.6676 +2024-12-29 14:51:24,737 - pyskl - INFO - Epoch [93][1500/3746] lr: 3.218e-02, eta: 2 days, 2:28:02, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6014, loss_cls: 3.7136, loss: 3.7136 +2024-12-29 14:52:51,475 - pyskl - INFO - Epoch [93][1600/3746] lr: 3.215e-02, eta: 2 days, 2:26:39, time: 0.867, data_time: 0.001, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6095, loss_cls: 3.6656, loss: 3.6656 +2024-12-29 14:54:18,316 - pyskl - INFO - Epoch [93][1700/3746] lr: 3.213e-02, eta: 2 days, 2:25:17, time: 0.868, data_time: 0.001, memory: 15990, top1_acc: 0.3452, top5_acc: 0.5941, loss_cls: 3.7216, loss: 3.7216 +2024-12-29 14:55:45,064 - pyskl - INFO - Epoch [93][1800/3746] lr: 3.210e-02, eta: 2 days, 2:23:54, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6084, loss_cls: 3.7350, loss: 3.7350 +2024-12-29 14:57:11,695 - pyskl - INFO - Epoch [93][1900/3746] lr: 3.207e-02, eta: 2 days, 2:22:31, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6078, loss_cls: 3.7132, loss: 3.7132 +2024-12-29 14:58:38,632 - pyskl - INFO - Epoch [93][2000/3746] lr: 3.205e-02, eta: 2 days, 2:21:09, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6142, loss_cls: 3.6772, loss: 3.6772 +2024-12-29 15:00:04,497 - pyskl - INFO - Epoch [93][2100/3746] lr: 3.202e-02, eta: 2 days, 2:19:46, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5973, loss_cls: 3.7569, loss: 3.7569 +2024-12-29 15:01:31,284 - pyskl - INFO - Epoch [93][2200/3746] lr: 3.200e-02, eta: 2 days, 2:18:23, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6072, loss_cls: 3.7251, loss: 3.7251 +2024-12-29 15:02:57,349 - pyskl - INFO - Epoch [93][2300/3746] lr: 3.197e-02, eta: 2 days, 2:17:00, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6084, loss_cls: 3.6900, loss: 3.6900 +2024-12-29 15:04:23,647 - pyskl - INFO - Epoch [93][2400/3746] lr: 3.194e-02, eta: 2 days, 2:15:37, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6144, loss_cls: 3.6738, loss: 3.6738 +2024-12-29 15:05:49,934 - pyskl - INFO - Epoch [93][2500/3746] lr: 3.192e-02, eta: 2 days, 2:14:14, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6081, loss_cls: 3.6950, loss: 3.6950 +2024-12-29 15:07:16,723 - pyskl - INFO - Epoch [93][2600/3746] lr: 3.189e-02, eta: 2 days, 2:12:51, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6089, loss_cls: 3.7065, loss: 3.7065 +2024-12-29 15:08:42,928 - pyskl - INFO - Epoch [93][2700/3746] lr: 3.187e-02, eta: 2 days, 2:11:28, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6103, loss_cls: 3.6735, loss: 3.6735 +2024-12-29 15:10:09,796 - pyskl - INFO - Epoch [93][2800/3746] lr: 3.184e-02, eta: 2 days, 2:10:06, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6172, loss_cls: 3.6763, loss: 3.6763 +2024-12-29 15:11:36,428 - pyskl - INFO - Epoch [93][2900/3746] lr: 3.181e-02, eta: 2 days, 2:08:43, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6072, loss_cls: 3.6937, loss: 3.6937 +2024-12-29 15:13:03,459 - pyskl - INFO - Epoch [93][3000/3746] lr: 3.179e-02, eta: 2 days, 2:07:21, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6086, loss_cls: 3.7145, loss: 3.7145 +2024-12-29 15:14:30,016 - pyskl - INFO - Epoch [93][3100/3746] lr: 3.176e-02, eta: 2 days, 2:05:58, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6144, loss_cls: 3.6453, loss: 3.6453 +2024-12-29 15:15:56,445 - pyskl - INFO - Epoch [93][3200/3746] lr: 3.174e-02, eta: 2 days, 2:04:35, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6061, loss_cls: 3.7139, loss: 3.7139 +2024-12-29 15:17:22,204 - pyskl - INFO - Epoch [93][3300/3746] lr: 3.171e-02, eta: 2 days, 2:03:12, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6059, loss_cls: 3.7057, loss: 3.7057 +2024-12-29 15:18:47,996 - pyskl - INFO - Epoch [93][3400/3746] lr: 3.168e-02, eta: 2 days, 2:01:49, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6123, loss_cls: 3.6457, loss: 3.6457 +2024-12-29 15:20:14,280 - pyskl - INFO - Epoch [93][3500/3746] lr: 3.166e-02, eta: 2 days, 2:00:26, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6108, loss_cls: 3.6976, loss: 3.6976 +2024-12-29 15:21:40,361 - pyskl - INFO - Epoch [93][3600/3746] lr: 3.163e-02, eta: 2 days, 1:59:03, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6039, loss_cls: 3.7263, loss: 3.7263 +2024-12-29 15:23:06,434 - pyskl - INFO - Epoch [93][3700/3746] lr: 3.161e-02, eta: 2 days, 1:57:39, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6200, loss_cls: 3.6333, loss: 3.6333 +2024-12-29 15:23:48,365 - pyskl - INFO - Saving checkpoint at 93 epochs +2024-12-29 15:25:48,268 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 15:25:48,949 - pyskl - INFO - +top1_acc 0.2912 +top5_acc 0.5421 +2024-12-29 15:25:48,949 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 15:25:48,991 - pyskl - INFO - +mean_acc 0.2909 +2024-12-29 15:25:48,996 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_91.pth was removed +2024-12-29 15:25:49,253 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_93.pth. +2024-12-29 15:25:49,254 - pyskl - INFO - Best top1_acc is 0.2912 at 93 epoch. +2024-12-29 15:25:49,268 - pyskl - INFO - Epoch(val) [93][309] top1_acc: 0.2912, top5_acc: 0.5421, mean_class_accuracy: 0.2909 +2024-12-29 15:30:23,391 - pyskl - INFO - Epoch [94][100/3746] lr: 3.157e-02, eta: 2 days, 1:57:09, time: 2.741, data_time: 1.696, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6194, loss_cls: 3.6337, loss: 3.6337 +2024-12-29 15:31:49,233 - pyskl - INFO - Epoch [94][200/3746] lr: 3.154e-02, eta: 2 days, 1:55:46, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6255, loss_cls: 3.6036, loss: 3.6036 +2024-12-29 15:33:15,816 - pyskl - INFO - Epoch [94][300/3746] lr: 3.152e-02, eta: 2 days, 1:54:23, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6233, loss_cls: 3.5996, loss: 3.5996 +2024-12-29 15:34:41,844 - pyskl - INFO - Epoch [94][400/3746] lr: 3.149e-02, eta: 2 days, 1:53:00, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6122, loss_cls: 3.6770, loss: 3.6770 +2024-12-29 15:36:07,841 - pyskl - INFO - Epoch [94][500/3746] lr: 3.146e-02, eta: 2 days, 1:51:37, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6039, loss_cls: 3.6674, loss: 3.6674 +2024-12-29 15:37:33,729 - pyskl - INFO - Epoch [94][600/3746] lr: 3.144e-02, eta: 2 days, 1:50:13, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6183, loss_cls: 3.6616, loss: 3.6616 +2024-12-29 15:38:59,901 - pyskl - INFO - Epoch [94][700/3746] lr: 3.141e-02, eta: 2 days, 1:48:50, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6272, loss_cls: 3.6012, loss: 3.6012 +2024-12-29 15:40:25,787 - pyskl - INFO - Epoch [94][800/3746] lr: 3.139e-02, eta: 2 days, 1:47:27, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6247, loss_cls: 3.6225, loss: 3.6225 +2024-12-29 15:41:51,922 - pyskl - INFO - Epoch [94][900/3746] lr: 3.136e-02, eta: 2 days, 1:46:04, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6017, loss_cls: 3.7051, loss: 3.7051 +2024-12-29 15:43:18,190 - pyskl - INFO - Epoch [94][1000/3746] lr: 3.133e-02, eta: 2 days, 1:44:41, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6123, loss_cls: 3.6854, loss: 3.6854 +2024-12-29 15:44:44,373 - pyskl - INFO - Epoch [94][1100/3746] lr: 3.131e-02, eta: 2 days, 1:43:18, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6114, loss_cls: 3.6911, loss: 3.6911 +2024-12-29 15:46:11,160 - pyskl - INFO - Epoch [94][1200/3746] lr: 3.128e-02, eta: 2 days, 1:41:55, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6088, loss_cls: 3.6954, loss: 3.6954 +2024-12-29 15:47:37,129 - pyskl - INFO - Epoch [94][1300/3746] lr: 3.126e-02, eta: 2 days, 1:40:32, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6103, loss_cls: 3.6824, loss: 3.6824 +2024-12-29 15:49:02,168 - pyskl - INFO - Epoch [94][1400/3746] lr: 3.123e-02, eta: 2 days, 1:39:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6102, loss_cls: 3.6742, loss: 3.6742 +2024-12-29 15:50:27,529 - pyskl - INFO - Epoch [94][1500/3746] lr: 3.120e-02, eta: 2 days, 1:37:44, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6039, loss_cls: 3.7125, loss: 3.7125 +2024-12-29 15:51:52,662 - pyskl - INFO - Epoch [94][1600/3746] lr: 3.118e-02, eta: 2 days, 1:36:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6241, loss_cls: 3.6361, loss: 3.6361 +2024-12-29 15:53:18,048 - pyskl - INFO - Epoch [94][1700/3746] lr: 3.115e-02, eta: 2 days, 1:34:57, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6142, loss_cls: 3.6736, loss: 3.6736 +2024-12-29 15:54:43,610 - pyskl - INFO - Epoch [94][1800/3746] lr: 3.113e-02, eta: 2 days, 1:33:33, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6125, loss_cls: 3.7102, loss: 3.7102 +2024-12-29 15:56:09,181 - pyskl - INFO - Epoch [94][1900/3746] lr: 3.110e-02, eta: 2 days, 1:32:10, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6223, loss_cls: 3.6421, loss: 3.6421 +2024-12-29 15:57:36,029 - pyskl - INFO - Epoch [94][2000/3746] lr: 3.108e-02, eta: 2 days, 1:30:47, time: 0.868, data_time: 0.001, memory: 15990, top1_acc: 0.3434, top5_acc: 0.6028, loss_cls: 3.7021, loss: 3.7021 +2024-12-29 15:59:01,619 - pyskl - INFO - Epoch [94][2100/3746] lr: 3.105e-02, eta: 2 days, 1:29:24, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6047, loss_cls: 3.7223, loss: 3.7223 +2024-12-29 16:00:27,607 - pyskl - INFO - Epoch [94][2200/3746] lr: 3.102e-02, eta: 2 days, 1:28:01, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6217, loss_cls: 3.6645, loss: 3.6645 +2024-12-29 16:01:53,084 - pyskl - INFO - Epoch [94][2300/3746] lr: 3.100e-02, eta: 2 days, 1:26:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6078, loss_cls: 3.6907, loss: 3.6907 +2024-12-29 16:03:18,925 - pyskl - INFO - Epoch [94][2400/3746] lr: 3.097e-02, eta: 2 days, 1:25:14, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6133, loss_cls: 3.6788, loss: 3.6788 +2024-12-29 16:04:44,436 - pyskl - INFO - Epoch [94][2500/3746] lr: 3.095e-02, eta: 2 days, 1:23:50, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6083, loss_cls: 3.6679, loss: 3.6679 +2024-12-29 16:06:10,495 - pyskl - INFO - Epoch [94][2600/3746] lr: 3.092e-02, eta: 2 days, 1:22:27, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6056, loss_cls: 3.7390, loss: 3.7390 +2024-12-29 16:07:36,533 - pyskl - INFO - Epoch [94][2700/3746] lr: 3.089e-02, eta: 2 days, 1:21:04, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6133, loss_cls: 3.6686, loss: 3.6686 +2024-12-29 16:09:02,291 - pyskl - INFO - Epoch [94][2800/3746] lr: 3.087e-02, eta: 2 days, 1:19:40, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6134, loss_cls: 3.6974, loss: 3.6974 +2024-12-29 16:10:27,585 - pyskl - INFO - Epoch [94][2900/3746] lr: 3.084e-02, eta: 2 days, 1:18:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6030, loss_cls: 3.7343, loss: 3.7343 +2024-12-29 16:11:53,790 - pyskl - INFO - Epoch [94][3000/3746] lr: 3.082e-02, eta: 2 days, 1:16:54, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3622, top5_acc: 0.6153, loss_cls: 3.6492, loss: 3.6492 +2024-12-29 16:13:19,831 - pyskl - INFO - Epoch [94][3100/3746] lr: 3.079e-02, eta: 2 days, 1:15:30, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.5994, loss_cls: 3.7528, loss: 3.7528 +2024-12-29 16:14:45,419 - pyskl - INFO - Epoch [94][3200/3746] lr: 3.077e-02, eta: 2 days, 1:14:07, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6195, loss_cls: 3.6550, loss: 3.6550 +2024-12-29 16:16:10,994 - pyskl - INFO - Epoch [94][3300/3746] lr: 3.074e-02, eta: 2 days, 1:12:43, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6075, loss_cls: 3.6930, loss: 3.6930 +2024-12-29 16:17:36,849 - pyskl - INFO - Epoch [94][3400/3746] lr: 3.071e-02, eta: 2 days, 1:11:20, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6116, loss_cls: 3.6738, loss: 3.6738 +2024-12-29 16:19:03,063 - pyskl - INFO - Epoch [94][3500/3746] lr: 3.069e-02, eta: 2 days, 1:09:57, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6081, loss_cls: 3.7132, loss: 3.7132 +2024-12-29 16:20:29,062 - pyskl - INFO - Epoch [94][3600/3746] lr: 3.066e-02, eta: 2 days, 1:08:34, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6092, loss_cls: 3.6766, loss: 3.6766 +2024-12-29 16:21:54,460 - pyskl - INFO - Epoch [94][3700/3746] lr: 3.064e-02, eta: 2 days, 1:07:10, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6208, loss_cls: 3.6508, loss: 3.6508 +2024-12-29 16:22:36,092 - pyskl - INFO - Saving checkpoint at 94 epochs +2024-12-29 16:24:35,135 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 16:24:35,810 - pyskl - INFO - +top1_acc 0.3001 +top5_acc 0.5480 +2024-12-29 16:24:35,811 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 16:24:35,858 - pyskl - INFO - +mean_acc 0.2998 +2024-12-29 16:24:35,865 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_93.pth was removed +2024-12-29 16:24:36,179 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_94.pth. +2024-12-29 16:24:36,180 - pyskl - INFO - Best top1_acc is 0.3001 at 94 epoch. +2024-12-29 16:24:36,199 - pyskl - INFO - Epoch(val) [94][309] top1_acc: 0.3001, top5_acc: 0.5480, mean_class_accuracy: 0.2998 +2024-12-29 16:29:02,296 - pyskl - INFO - Epoch [95][100/3746] lr: 3.060e-02, eta: 2 days, 1:06:32, time: 2.661, data_time: 1.610, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6152, loss_cls: 3.6498, loss: 3.6498 +2024-12-29 16:30:28,495 - pyskl - INFO - Epoch [95][200/3746] lr: 3.057e-02, eta: 2 days, 1:05:09, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6214, loss_cls: 3.6253, loss: 3.6253 +2024-12-29 16:31:54,732 - pyskl - INFO - Epoch [95][300/3746] lr: 3.055e-02, eta: 2 days, 1:03:46, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6281, loss_cls: 3.6345, loss: 3.6345 +2024-12-29 16:33:19,795 - pyskl - INFO - Epoch [95][400/3746] lr: 3.052e-02, eta: 2 days, 1:02:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6181, loss_cls: 3.6216, loss: 3.6216 +2024-12-29 16:34:45,556 - pyskl - INFO - Epoch [95][500/3746] lr: 3.050e-02, eta: 2 days, 1:00:58, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6081, loss_cls: 3.6868, loss: 3.6868 +2024-12-29 16:36:11,156 - pyskl - INFO - Epoch [95][600/3746] lr: 3.047e-02, eta: 2 days, 0:59:35, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6111, loss_cls: 3.6774, loss: 3.6774 +2024-12-29 16:37:36,523 - pyskl - INFO - Epoch [95][700/3746] lr: 3.044e-02, eta: 2 days, 0:58:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6203, loss_cls: 3.6370, loss: 3.6370 +2024-12-29 16:39:01,692 - pyskl - INFO - Epoch [95][800/3746] lr: 3.042e-02, eta: 2 days, 0:56:47, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6248, loss_cls: 3.6265, loss: 3.6265 +2024-12-29 16:40:27,491 - pyskl - INFO - Epoch [95][900/3746] lr: 3.039e-02, eta: 2 days, 0:55:24, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6152, loss_cls: 3.6587, loss: 3.6587 +2024-12-29 16:41:53,939 - pyskl - INFO - Epoch [95][1000/3746] lr: 3.037e-02, eta: 2 days, 0:54:01, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6227, loss_cls: 3.6294, loss: 3.6294 +2024-12-29 16:43:19,814 - pyskl - INFO - Epoch [95][1100/3746] lr: 3.034e-02, eta: 2 days, 0:52:37, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6155, loss_cls: 3.6579, loss: 3.6579 +2024-12-29 16:44:45,189 - pyskl - INFO - Epoch [95][1200/3746] lr: 3.032e-02, eta: 2 days, 0:51:14, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6145, loss_cls: 3.6502, loss: 3.6502 +2024-12-29 16:46:11,195 - pyskl - INFO - Epoch [95][1300/3746] lr: 3.029e-02, eta: 2 days, 0:49:50, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6144, loss_cls: 3.6637, loss: 3.6637 +2024-12-29 16:47:37,901 - pyskl - INFO - Epoch [95][1400/3746] lr: 3.026e-02, eta: 2 days, 0:48:28, time: 0.867, data_time: 0.001, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6167, loss_cls: 3.6692, loss: 3.6692 +2024-12-29 16:49:04,171 - pyskl - INFO - Epoch [95][1500/3746] lr: 3.024e-02, eta: 2 days, 0:47:04, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6216, loss_cls: 3.6581, loss: 3.6581 +2024-12-29 16:50:30,552 - pyskl - INFO - Epoch [95][1600/3746] lr: 3.021e-02, eta: 2 days, 0:45:41, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6250, loss_cls: 3.6140, loss: 3.6140 +2024-12-29 16:51:57,605 - pyskl - INFO - Epoch [95][1700/3746] lr: 3.019e-02, eta: 2 days, 0:44:19, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6172, loss_cls: 3.6468, loss: 3.6468 +2024-12-29 16:53:24,636 - pyskl - INFO - Epoch [95][1800/3746] lr: 3.016e-02, eta: 2 days, 0:42:56, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6156, loss_cls: 3.6536, loss: 3.6536 +2024-12-29 16:54:50,837 - pyskl - INFO - Epoch [95][1900/3746] lr: 3.014e-02, eta: 2 days, 0:41:33, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6192, loss_cls: 3.6662, loss: 3.6662 +2024-12-29 16:56:17,292 - pyskl - INFO - Epoch [95][2000/3746] lr: 3.011e-02, eta: 2 days, 0:40:10, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6238, loss_cls: 3.6361, loss: 3.6361 +2024-12-29 16:57:42,666 - pyskl - INFO - Epoch [95][2100/3746] lr: 3.008e-02, eta: 2 days, 0:38:46, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6161, loss_cls: 3.6647, loss: 3.6647 +2024-12-29 16:59:09,134 - pyskl - INFO - Epoch [95][2200/3746] lr: 3.006e-02, eta: 2 days, 0:37:23, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6148, loss_cls: 3.6630, loss: 3.6630 +2024-12-29 17:00:34,588 - pyskl - INFO - Epoch [95][2300/3746] lr: 3.003e-02, eta: 2 days, 0:35:59, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6077, loss_cls: 3.7017, loss: 3.7017 +2024-12-29 17:02:00,471 - pyskl - INFO - Epoch [95][2400/3746] lr: 3.001e-02, eta: 2 days, 0:34:36, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6112, loss_cls: 3.6887, loss: 3.6887 +2024-12-29 17:03:26,830 - pyskl - INFO - Epoch [95][2500/3746] lr: 2.998e-02, eta: 2 days, 0:33:12, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6167, loss_cls: 3.6427, loss: 3.6427 +2024-12-29 17:04:53,352 - pyskl - INFO - Epoch [95][2600/3746] lr: 2.996e-02, eta: 2 days, 0:31:49, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6162, loss_cls: 3.6786, loss: 3.6786 +2024-12-29 17:06:19,943 - pyskl - INFO - Epoch [95][2700/3746] lr: 2.993e-02, eta: 2 days, 0:30:26, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6130, loss_cls: 3.7109, loss: 3.7109 +2024-12-29 17:07:46,617 - pyskl - INFO - Epoch [95][2800/3746] lr: 2.991e-02, eta: 2 days, 0:29:03, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6161, loss_cls: 3.6606, loss: 3.6606 +2024-12-29 17:09:13,214 - pyskl - INFO - Epoch [95][2900/3746] lr: 2.988e-02, eta: 2 days, 0:27:40, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6134, loss_cls: 3.7064, loss: 3.7064 +2024-12-29 17:10:39,107 - pyskl - INFO - Epoch [95][3000/3746] lr: 2.985e-02, eta: 2 days, 0:26:17, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6077, loss_cls: 3.6723, loss: 3.6723 +2024-12-29 17:12:04,615 - pyskl - INFO - Epoch [95][3100/3746] lr: 2.983e-02, eta: 2 days, 0:24:53, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6136, loss_cls: 3.6865, loss: 3.6865 +2024-12-29 17:13:30,065 - pyskl - INFO - Epoch [95][3200/3746] lr: 2.980e-02, eta: 2 days, 0:23:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3494, top5_acc: 0.6141, loss_cls: 3.6841, loss: 3.6841 +2024-12-29 17:14:55,911 - pyskl - INFO - Epoch [95][3300/3746] lr: 2.978e-02, eta: 2 days, 0:22:06, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6106, loss_cls: 3.6788, loss: 3.6788 +2024-12-29 17:16:21,361 - pyskl - INFO - Epoch [95][3400/3746] lr: 2.975e-02, eta: 2 days, 0:20:43, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3622, top5_acc: 0.6153, loss_cls: 3.6259, loss: 3.6259 +2024-12-29 17:17:47,298 - pyskl - INFO - Epoch [95][3500/3746] lr: 2.973e-02, eta: 2 days, 0:19:19, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6181, loss_cls: 3.6392, loss: 3.6392 +2024-12-29 17:19:12,738 - pyskl - INFO - Epoch [95][3600/3746] lr: 2.970e-02, eta: 2 days, 0:17:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6105, loss_cls: 3.7013, loss: 3.7013 +2024-12-29 17:20:37,810 - pyskl - INFO - Epoch [95][3700/3746] lr: 2.968e-02, eta: 2 days, 0:16:32, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6041, loss_cls: 3.7105, loss: 3.7105 +2024-12-29 17:21:19,075 - pyskl - INFO - Saving checkpoint at 95 epochs +2024-12-29 17:23:19,453 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 17:23:20,310 - pyskl - INFO - +top1_acc 0.3050 +top5_acc 0.5600 +2024-12-29 17:23:20,310 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 17:23:20,357 - pyskl - INFO - +mean_acc 0.3048 +2024-12-29 17:23:20,361 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_94.pth was removed +2024-12-29 17:23:20,628 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_95.pth. +2024-12-29 17:23:20,628 - pyskl - INFO - Best top1_acc is 0.3050 at 95 epoch. +2024-12-29 17:23:20,642 - pyskl - INFO - Epoch(val) [95][309] top1_acc: 0.3050, top5_acc: 0.5600, mean_class_accuracy: 0.3048 +2024-12-29 17:27:43,622 - pyskl - INFO - Epoch [96][100/3746] lr: 2.964e-02, eta: 2 days, 0:15:49, time: 2.630, data_time: 1.573, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6289, loss_cls: 3.5715, loss: 3.5715 +2024-12-29 17:29:09,859 - pyskl - INFO - Epoch [96][200/3746] lr: 2.961e-02, eta: 2 days, 0:14:26, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6262, loss_cls: 3.6183, loss: 3.6183 +2024-12-29 17:30:35,278 - pyskl - INFO - Epoch [96][300/3746] lr: 2.959e-02, eta: 2 days, 0:13:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6184, loss_cls: 3.6298, loss: 3.6298 +2024-12-29 17:32:00,826 - pyskl - INFO - Epoch [96][400/3746] lr: 2.956e-02, eta: 2 days, 0:11:39, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6277, loss_cls: 3.6133, loss: 3.6133 +2024-12-29 17:33:27,386 - pyskl - INFO - Epoch [96][500/3746] lr: 2.954e-02, eta: 2 days, 0:10:15, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6272, loss_cls: 3.5859, loss: 3.5859 +2024-12-29 17:34:53,875 - pyskl - INFO - Epoch [96][600/3746] lr: 2.951e-02, eta: 2 days, 0:08:52, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6289, loss_cls: 3.5939, loss: 3.5939 +2024-12-29 17:36:20,410 - pyskl - INFO - Epoch [96][700/3746] lr: 2.948e-02, eta: 2 days, 0:07:29, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6167, loss_cls: 3.6759, loss: 3.6759 +2024-12-29 17:37:46,957 - pyskl - INFO - Epoch [96][800/3746] lr: 2.946e-02, eta: 2 days, 0:06:06, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6186, loss_cls: 3.6387, loss: 3.6387 +2024-12-29 17:39:13,328 - pyskl - INFO - Epoch [96][900/3746] lr: 2.943e-02, eta: 2 days, 0:04:43, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6134, loss_cls: 3.6473, loss: 3.6473 +2024-12-29 17:40:39,642 - pyskl - INFO - Epoch [96][1000/3746] lr: 2.941e-02, eta: 2 days, 0:03:20, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6208, loss_cls: 3.6320, loss: 3.6320 +2024-12-29 17:42:06,831 - pyskl - INFO - Epoch [96][1100/3746] lr: 2.938e-02, eta: 2 days, 0:01:57, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6145, loss_cls: 3.6759, loss: 3.6759 +2024-12-29 17:43:33,360 - pyskl - INFO - Epoch [96][1200/3746] lr: 2.936e-02, eta: 2 days, 0:00:34, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6184, loss_cls: 3.6655, loss: 3.6655 +2024-12-29 17:45:00,741 - pyskl - INFO - Epoch [96][1300/3746] lr: 2.933e-02, eta: 1 day, 23:59:11, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6277, loss_cls: 3.6227, loss: 3.6227 +2024-12-29 17:46:27,507 - pyskl - INFO - Epoch [96][1400/3746] lr: 2.931e-02, eta: 1 day, 23:57:48, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6322, loss_cls: 3.5688, loss: 3.5688 +2024-12-29 17:47:54,279 - pyskl - INFO - Epoch [96][1500/3746] lr: 2.928e-02, eta: 1 day, 23:56:25, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6177, loss_cls: 3.6444, loss: 3.6444 +2024-12-29 17:49:20,932 - pyskl - INFO - Epoch [96][1600/3746] lr: 2.926e-02, eta: 1 day, 23:55:02, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6158, loss_cls: 3.6454, loss: 3.6454 +2024-12-29 17:50:47,272 - pyskl - INFO - Epoch [96][1700/3746] lr: 2.923e-02, eta: 1 day, 23:53:39, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6225, loss_cls: 3.6448, loss: 3.6448 +2024-12-29 17:52:14,304 - pyskl - INFO - Epoch [96][1800/3746] lr: 2.920e-02, eta: 1 day, 23:52:16, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6203, loss_cls: 3.6279, loss: 3.6279 +2024-12-29 17:53:40,466 - pyskl - INFO - Epoch [96][1900/3746] lr: 2.918e-02, eta: 1 day, 23:50:52, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6123, loss_cls: 3.6945, loss: 3.6945 +2024-12-29 17:55:06,155 - pyskl - INFO - Epoch [96][2000/3746] lr: 2.915e-02, eta: 1 day, 23:49:29, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6244, loss_cls: 3.5880, loss: 3.5880 +2024-12-29 17:56:31,786 - pyskl - INFO - Epoch [96][2100/3746] lr: 2.913e-02, eta: 1 day, 23:48:05, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6214, loss_cls: 3.6288, loss: 3.6288 +2024-12-29 17:57:57,359 - pyskl - INFO - Epoch [96][2200/3746] lr: 2.910e-02, eta: 1 day, 23:46:41, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6177, loss_cls: 3.6400, loss: 3.6400 +2024-12-29 17:59:23,520 - pyskl - INFO - Epoch [96][2300/3746] lr: 2.908e-02, eta: 1 day, 23:45:18, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6070, loss_cls: 3.7181, loss: 3.7181 +2024-12-29 18:00:48,980 - pyskl - INFO - Epoch [96][2400/3746] lr: 2.905e-02, eta: 1 day, 23:43:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6156, loss_cls: 3.6600, loss: 3.6600 +2024-12-29 18:02:14,735 - pyskl - INFO - Epoch [96][2500/3746] lr: 2.903e-02, eta: 1 day, 23:42:31, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6214, loss_cls: 3.6453, loss: 3.6453 +2024-12-29 18:03:39,884 - pyskl - INFO - Epoch [96][2600/3746] lr: 2.900e-02, eta: 1 day, 23:41:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6122, loss_cls: 3.6718, loss: 3.6718 +2024-12-29 18:05:05,325 - pyskl - INFO - Epoch [96][2700/3746] lr: 2.898e-02, eta: 1 day, 23:39:43, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6225, loss_cls: 3.6328, loss: 3.6328 +2024-12-29 18:06:30,544 - pyskl - INFO - Epoch [96][2800/3746] lr: 2.895e-02, eta: 1 day, 23:38:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6170, loss_cls: 3.6657, loss: 3.6657 +2024-12-29 18:07:56,064 - pyskl - INFO - Epoch [96][2900/3746] lr: 2.893e-02, eta: 1 day, 23:36:55, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6136, loss_cls: 3.6861, loss: 3.6861 +2024-12-29 18:09:21,729 - pyskl - INFO - Epoch [96][3000/3746] lr: 2.890e-02, eta: 1 day, 23:35:32, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6136, loss_cls: 3.6360, loss: 3.6360 +2024-12-29 18:10:47,126 - pyskl - INFO - Epoch [96][3100/3746] lr: 2.887e-02, eta: 1 day, 23:34:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6177, loss_cls: 3.6534, loss: 3.6534 +2024-12-29 18:12:12,460 - pyskl - INFO - Epoch [96][3200/3746] lr: 2.885e-02, eta: 1 day, 23:32:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6103, loss_cls: 3.7050, loss: 3.7050 +2024-12-29 18:13:37,802 - pyskl - INFO - Epoch [96][3300/3746] lr: 2.882e-02, eta: 1 day, 23:31:20, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6217, loss_cls: 3.6383, loss: 3.6383 +2024-12-29 18:15:03,083 - pyskl - INFO - Epoch [96][3400/3746] lr: 2.880e-02, eta: 1 day, 23:29:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3517, top5_acc: 0.6075, loss_cls: 3.6797, loss: 3.6797 +2024-12-29 18:16:28,696 - pyskl - INFO - Epoch [96][3500/3746] lr: 2.877e-02, eta: 1 day, 23:28:32, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6073, loss_cls: 3.7069, loss: 3.7069 +2024-12-29 18:17:54,038 - pyskl - INFO - Epoch [96][3600/3746] lr: 2.875e-02, eta: 1 day, 23:27:09, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6166, loss_cls: 3.6434, loss: 3.6434 +2024-12-29 18:19:19,097 - pyskl - INFO - Epoch [96][3700/3746] lr: 2.872e-02, eta: 1 day, 23:25:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6256, loss_cls: 3.6116, loss: 3.6116 +2024-12-29 18:20:00,412 - pyskl - INFO - Saving checkpoint at 96 epochs +2024-12-29 18:22:01,698 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 18:22:02,468 - pyskl - INFO - +top1_acc 0.3066 +top5_acc 0.5604 +2024-12-29 18:22:02,468 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 18:22:02,514 - pyskl - INFO - +mean_acc 0.3064 +2024-12-29 18:22:02,519 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_95.pth was removed +2024-12-29 18:22:02,793 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_96.pth. +2024-12-29 18:22:02,794 - pyskl - INFO - Best top1_acc is 0.3066 at 96 epoch. +2024-12-29 18:22:02,811 - pyskl - INFO - Epoch(val) [96][309] top1_acc: 0.3066, top5_acc: 0.5604, mean_class_accuracy: 0.3064 +2024-12-29 18:26:18,136 - pyskl - INFO - Epoch [97][100/3746] lr: 2.869e-02, eta: 1 day, 23:24:56, time: 2.553, data_time: 1.516, memory: 15990, top1_acc: 0.3741, top5_acc: 0.6277, loss_cls: 3.5525, loss: 3.5525 +2024-12-29 18:27:43,941 - pyskl - INFO - Epoch [97][200/3746] lr: 2.866e-02, eta: 1 day, 23:23:32, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6205, loss_cls: 3.6148, loss: 3.6148 +2024-12-29 18:29:09,347 - pyskl - INFO - Epoch [97][300/3746] lr: 2.864e-02, eta: 1 day, 23:22:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6284, loss_cls: 3.5968, loss: 3.5968 +2024-12-29 18:30:34,858 - pyskl - INFO - Epoch [97][400/3746] lr: 2.861e-02, eta: 1 day, 23:20:44, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6350, loss_cls: 3.5466, loss: 3.5466 +2024-12-29 18:32:00,379 - pyskl - INFO - Epoch [97][500/3746] lr: 2.858e-02, eta: 1 day, 23:19:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6345, loss_cls: 3.5496, loss: 3.5496 +2024-12-29 18:33:25,882 - pyskl - INFO - Epoch [97][600/3746] lr: 2.856e-02, eta: 1 day, 23:17:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6134, loss_cls: 3.6538, loss: 3.6538 +2024-12-29 18:34:51,297 - pyskl - INFO - Epoch [97][700/3746] lr: 2.853e-02, eta: 1 day, 23:16:33, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6212, loss_cls: 3.6575, loss: 3.6575 +2024-12-29 18:36:16,093 - pyskl - INFO - Epoch [97][800/3746] lr: 2.851e-02, eta: 1 day, 23:15:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6136, loss_cls: 3.6774, loss: 3.6774 +2024-12-29 18:37:41,665 - pyskl - INFO - Epoch [97][900/3746] lr: 2.848e-02, eta: 1 day, 23:13:45, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6278, loss_cls: 3.6288, loss: 3.6288 +2024-12-29 18:39:06,765 - pyskl - INFO - Epoch [97][1000/3746] lr: 2.846e-02, eta: 1 day, 23:12:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6223, loss_cls: 3.6251, loss: 3.6251 +2024-12-29 18:40:32,162 - pyskl - INFO - Epoch [97][1100/3746] lr: 2.843e-02, eta: 1 day, 23:10:57, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6259, loss_cls: 3.6117, loss: 3.6117 +2024-12-29 18:41:57,139 - pyskl - INFO - Epoch [97][1200/3746] lr: 2.841e-02, eta: 1 day, 23:09:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6180, loss_cls: 3.6397, loss: 3.6397 +2024-12-29 18:43:22,352 - pyskl - INFO - Epoch [97][1300/3746] lr: 2.838e-02, eta: 1 day, 23:08:09, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6148, loss_cls: 3.6593, loss: 3.6593 +2024-12-29 18:44:47,680 - pyskl - INFO - Epoch [97][1400/3746] lr: 2.836e-02, eta: 1 day, 23:06:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6208, loss_cls: 3.6157, loss: 3.6157 +2024-12-29 18:46:13,520 - pyskl - INFO - Epoch [97][1500/3746] lr: 2.833e-02, eta: 1 day, 23:05:21, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6222, loss_cls: 3.6029, loss: 3.6029 +2024-12-29 18:47:38,899 - pyskl - INFO - Epoch [97][1600/3746] lr: 2.831e-02, eta: 1 day, 23:03:57, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6164, loss_cls: 3.6257, loss: 3.6257 +2024-12-29 18:49:04,289 - pyskl - INFO - Epoch [97][1700/3746] lr: 2.828e-02, eta: 1 day, 23:02:33, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6248, loss_cls: 3.5977, loss: 3.5977 +2024-12-29 18:50:29,715 - pyskl - INFO - Epoch [97][1800/3746] lr: 2.826e-02, eta: 1 day, 23:01:09, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6150, loss_cls: 3.6392, loss: 3.6392 +2024-12-29 18:51:55,125 - pyskl - INFO - Epoch [97][1900/3746] lr: 2.823e-02, eta: 1 day, 22:59:45, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6155, loss_cls: 3.6692, loss: 3.6692 +2024-12-29 18:53:20,212 - pyskl - INFO - Epoch [97][2000/3746] lr: 2.821e-02, eta: 1 day, 22:58:21, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6200, loss_cls: 3.6319, loss: 3.6319 +2024-12-29 18:54:45,600 - pyskl - INFO - Epoch [97][2100/3746] lr: 2.818e-02, eta: 1 day, 22:56:58, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6283, loss_cls: 3.5609, loss: 3.5609 +2024-12-29 18:56:11,697 - pyskl - INFO - Epoch [97][2200/3746] lr: 2.816e-02, eta: 1 day, 22:55:34, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6164, loss_cls: 3.6394, loss: 3.6394 +2024-12-29 18:57:38,360 - pyskl - INFO - Epoch [97][2300/3746] lr: 2.813e-02, eta: 1 day, 22:54:11, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6098, loss_cls: 3.6752, loss: 3.6752 +2024-12-29 18:59:04,968 - pyskl - INFO - Epoch [97][2400/3746] lr: 2.811e-02, eta: 1 day, 22:52:48, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6186, loss_cls: 3.6257, loss: 3.6257 +2024-12-29 19:00:31,212 - pyskl - INFO - Epoch [97][2500/3746] lr: 2.808e-02, eta: 1 day, 22:51:24, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6148, loss_cls: 3.6657, loss: 3.6657 +2024-12-29 19:01:57,653 - pyskl - INFO - Epoch [97][2600/3746] lr: 2.806e-02, eta: 1 day, 22:50:01, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6181, loss_cls: 3.6781, loss: 3.6781 +2024-12-29 19:03:23,808 - pyskl - INFO - Epoch [97][2700/3746] lr: 2.803e-02, eta: 1 day, 22:48:37, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6114, loss_cls: 3.6507, loss: 3.6507 +2024-12-29 19:04:49,957 - pyskl - INFO - Epoch [97][2800/3746] lr: 2.801e-02, eta: 1 day, 22:47:14, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6131, loss_cls: 3.6516, loss: 3.6516 +2024-12-29 19:06:16,453 - pyskl - INFO - Epoch [97][2900/3746] lr: 2.798e-02, eta: 1 day, 22:45:51, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6233, loss_cls: 3.6019, loss: 3.6019 +2024-12-29 19:07:41,555 - pyskl - INFO - Epoch [97][3000/3746] lr: 2.796e-02, eta: 1 day, 22:44:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6322, loss_cls: 3.5785, loss: 3.5785 +2024-12-29 19:09:07,800 - pyskl - INFO - Epoch [97][3100/3746] lr: 2.793e-02, eta: 1 day, 22:43:03, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6331, loss_cls: 3.5768, loss: 3.5768 +2024-12-29 19:10:33,830 - pyskl - INFO - Epoch [97][3200/3746] lr: 2.791e-02, eta: 1 day, 22:41:39, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6170, loss_cls: 3.6437, loss: 3.6437 +2024-12-29 19:12:00,115 - pyskl - INFO - Epoch [97][3300/3746] lr: 2.788e-02, eta: 1 day, 22:40:16, time: 0.863, data_time: 0.001, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6117, loss_cls: 3.6618, loss: 3.6618 +2024-12-29 19:13:25,545 - pyskl - INFO - Epoch [97][3400/3746] lr: 2.786e-02, eta: 1 day, 22:38:52, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6172, loss_cls: 3.6464, loss: 3.6464 +2024-12-29 19:14:51,173 - pyskl - INFO - Epoch [97][3500/3746] lr: 2.783e-02, eta: 1 day, 22:37:28, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6145, loss_cls: 3.6683, loss: 3.6683 +2024-12-29 19:16:17,044 - pyskl - INFO - Epoch [97][3600/3746] lr: 2.781e-02, eta: 1 day, 22:36:05, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6177, loss_cls: 3.6407, loss: 3.6407 +2024-12-29 19:17:42,333 - pyskl - INFO - Epoch [97][3700/3746] lr: 2.778e-02, eta: 1 day, 22:34:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6153, loss_cls: 3.6457, loss: 3.6457 +2024-12-29 19:18:23,298 - pyskl - INFO - Saving checkpoint at 97 epochs +2024-12-29 19:20:24,014 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 19:20:24,758 - pyskl - INFO - +top1_acc 0.3039 +top5_acc 0.5578 +2024-12-29 19:20:24,758 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 19:20:24,805 - pyskl - INFO - +mean_acc 0.3035 +2024-12-29 19:20:24,820 - pyskl - INFO - Epoch(val) [97][309] top1_acc: 0.3039, top5_acc: 0.5578, mean_class_accuracy: 0.3035 +2024-12-29 19:24:43,116 - pyskl - INFO - Epoch [98][100/3746] lr: 2.774e-02, eta: 1 day, 22:33:51, time: 2.583, data_time: 1.553, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6328, loss_cls: 3.5517, loss: 3.5517 +2024-12-29 19:26:09,325 - pyskl - INFO - Epoch [98][200/3746] lr: 2.772e-02, eta: 1 day, 22:32:28, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6362, loss_cls: 3.5083, loss: 3.5083 +2024-12-29 19:27:34,852 - pyskl - INFO - Epoch [98][300/3746] lr: 2.769e-02, eta: 1 day, 22:31:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6317, loss_cls: 3.5523, loss: 3.5523 +2024-12-29 19:29:00,140 - pyskl - INFO - Epoch [98][400/3746] lr: 2.767e-02, eta: 1 day, 22:29:40, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6191, loss_cls: 3.6183, loss: 3.6183 +2024-12-29 19:30:25,798 - pyskl - INFO - Epoch [98][500/3746] lr: 2.764e-02, eta: 1 day, 22:28:16, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6219, loss_cls: 3.6068, loss: 3.6068 +2024-12-29 19:31:51,195 - pyskl - INFO - Epoch [98][600/3746] lr: 2.762e-02, eta: 1 day, 22:26:52, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6230, loss_cls: 3.6179, loss: 3.6179 +2024-12-29 19:33:16,260 - pyskl - INFO - Epoch [98][700/3746] lr: 2.759e-02, eta: 1 day, 22:25:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6167, loss_cls: 3.6391, loss: 3.6391 +2024-12-29 19:34:41,492 - pyskl - INFO - Epoch [98][800/3746] lr: 2.757e-02, eta: 1 day, 22:24:04, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6159, loss_cls: 3.6512, loss: 3.6512 +2024-12-29 19:36:07,236 - pyskl - INFO - Epoch [98][900/3746] lr: 2.754e-02, eta: 1 day, 22:22:40, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6327, loss_cls: 3.5958, loss: 3.5958 +2024-12-29 19:37:33,041 - pyskl - INFO - Epoch [98][1000/3746] lr: 2.752e-02, eta: 1 day, 22:21:16, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6245, loss_cls: 3.6050, loss: 3.6050 +2024-12-29 19:38:58,854 - pyskl - INFO - Epoch [98][1100/3746] lr: 2.749e-02, eta: 1 day, 22:19:52, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6258, loss_cls: 3.5856, loss: 3.5856 +2024-12-29 19:40:24,591 - pyskl - INFO - Epoch [98][1200/3746] lr: 2.747e-02, eta: 1 day, 22:18:29, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6269, loss_cls: 3.6141, loss: 3.6141 +2024-12-29 19:41:50,193 - pyskl - INFO - Epoch [98][1300/3746] lr: 2.744e-02, eta: 1 day, 22:17:05, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6312, loss_cls: 3.5623, loss: 3.5623 +2024-12-29 19:43:15,877 - pyskl - INFO - Epoch [98][1400/3746] lr: 2.742e-02, eta: 1 day, 22:15:41, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6209, loss_cls: 3.6261, loss: 3.6261 +2024-12-29 19:44:41,674 - pyskl - INFO - Epoch [98][1500/3746] lr: 2.739e-02, eta: 1 day, 22:14:17, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6170, loss_cls: 3.6086, loss: 3.6086 +2024-12-29 19:46:07,668 - pyskl - INFO - Epoch [98][1600/3746] lr: 2.737e-02, eta: 1 day, 22:12:53, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6312, loss_cls: 3.5746, loss: 3.5746 +2024-12-29 19:47:33,254 - pyskl - INFO - Epoch [98][1700/3746] lr: 2.734e-02, eta: 1 day, 22:11:30, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6155, loss_cls: 3.6333, loss: 3.6333 +2024-12-29 19:48:58,315 - pyskl - INFO - Epoch [98][1800/3746] lr: 2.732e-02, eta: 1 day, 22:10:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6203, loss_cls: 3.6486, loss: 3.6486 +2024-12-29 19:50:23,508 - pyskl - INFO - Epoch [98][1900/3746] lr: 2.729e-02, eta: 1 day, 22:08:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6406, loss_cls: 3.5407, loss: 3.5407 +2024-12-29 19:51:48,968 - pyskl - INFO - Epoch [98][2000/3746] lr: 2.727e-02, eta: 1 day, 22:07:17, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6258, loss_cls: 3.5961, loss: 3.5961 +2024-12-29 19:53:14,509 - pyskl - INFO - Epoch [98][2100/3746] lr: 2.724e-02, eta: 1 day, 22:05:53, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6283, loss_cls: 3.5699, loss: 3.5699 +2024-12-29 19:54:39,430 - pyskl - INFO - Epoch [98][2200/3746] lr: 2.722e-02, eta: 1 day, 22:04:29, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6300, loss_cls: 3.5999, loss: 3.5999 +2024-12-29 19:56:04,318 - pyskl - INFO - Epoch [98][2300/3746] lr: 2.719e-02, eta: 1 day, 22:03:05, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6206, loss_cls: 3.6089, loss: 3.6089 +2024-12-29 19:57:30,538 - pyskl - INFO - Epoch [98][2400/3746] lr: 2.717e-02, eta: 1 day, 22:01:41, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6208, loss_cls: 3.6063, loss: 3.6063 +2024-12-29 19:58:56,314 - pyskl - INFO - Epoch [98][2500/3746] lr: 2.714e-02, eta: 1 day, 22:00:18, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6231, loss_cls: 3.6224, loss: 3.6224 +2024-12-29 20:00:23,248 - pyskl - INFO - Epoch [98][2600/3746] lr: 2.712e-02, eta: 1 day, 21:58:54, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6106, loss_cls: 3.6672, loss: 3.6672 +2024-12-29 20:01:48,977 - pyskl - INFO - Epoch [98][2700/3746] lr: 2.709e-02, eta: 1 day, 21:57:31, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6059, loss_cls: 3.6767, loss: 3.6767 +2024-12-29 20:03:14,832 - pyskl - INFO - Epoch [98][2800/3746] lr: 2.707e-02, eta: 1 day, 21:56:07, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6308, loss_cls: 3.5807, loss: 3.5807 +2024-12-29 20:04:40,751 - pyskl - INFO - Epoch [98][2900/3746] lr: 2.705e-02, eta: 1 day, 21:54:43, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6234, loss_cls: 3.5990, loss: 3.5990 +2024-12-29 20:06:06,906 - pyskl - INFO - Epoch [98][3000/3746] lr: 2.702e-02, eta: 1 day, 21:53:19, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6084, loss_cls: 3.6936, loss: 3.6936 +2024-12-29 20:07:32,981 - pyskl - INFO - Epoch [98][3100/3746] lr: 2.700e-02, eta: 1 day, 21:51:56, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6286, loss_cls: 3.5825, loss: 3.5825 +2024-12-29 20:08:58,286 - pyskl - INFO - Epoch [98][3200/3746] lr: 2.697e-02, eta: 1 day, 21:50:32, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6206, loss_cls: 3.6058, loss: 3.6058 +2024-12-29 20:10:23,688 - pyskl - INFO - Epoch [98][3300/3746] lr: 2.695e-02, eta: 1 day, 21:49:08, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6194, loss_cls: 3.6402, loss: 3.6402 +2024-12-29 20:11:50,270 - pyskl - INFO - Epoch [98][3400/3746] lr: 2.692e-02, eta: 1 day, 21:47:44, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3594, top5_acc: 0.6205, loss_cls: 3.6172, loss: 3.6172 +2024-12-29 20:13:16,999 - pyskl - INFO - Epoch [98][3500/3746] lr: 2.690e-02, eta: 1 day, 21:46:21, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6131, loss_cls: 3.6270, loss: 3.6270 +2024-12-29 20:14:43,673 - pyskl - INFO - Epoch [98][3600/3746] lr: 2.687e-02, eta: 1 day, 21:44:58, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6217, loss_cls: 3.5983, loss: 3.5983 +2024-12-29 20:16:10,573 - pyskl - INFO - Epoch [98][3700/3746] lr: 2.685e-02, eta: 1 day, 21:43:35, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6105, loss_cls: 3.6697, loss: 3.6697 +2024-12-29 20:16:51,962 - pyskl - INFO - Saving checkpoint at 98 epochs +2024-12-29 20:18:51,845 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 20:18:53,010 - pyskl - INFO - +top1_acc 0.3092 +top5_acc 0.5648 +2024-12-29 20:18:53,011 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 20:18:53,058 - pyskl - INFO - +mean_acc 0.3090 +2024-12-29 20:18:53,063 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_96.pth was removed +2024-12-29 20:18:53,323 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_98.pth. +2024-12-29 20:18:53,324 - pyskl - INFO - Best top1_acc is 0.3092 at 98 epoch. +2024-12-29 20:18:53,337 - pyskl - INFO - Epoch(val) [98][309] top1_acc: 0.3092, top5_acc: 0.5648, mean_class_accuracy: 0.3090 +2024-12-29 20:23:12,277 - pyskl - INFO - Epoch [99][100/3746] lr: 2.681e-02, eta: 1 day, 21:42:43, time: 2.589, data_time: 1.546, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6300, loss_cls: 3.5548, loss: 3.5548 +2024-12-29 20:24:38,216 - pyskl - INFO - Epoch [99][200/3746] lr: 2.679e-02, eta: 1 day, 21:41:19, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6305, loss_cls: 3.6018, loss: 3.6018 +2024-12-29 20:26:04,126 - pyskl - INFO - Epoch [99][300/3746] lr: 2.676e-02, eta: 1 day, 21:39:56, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6306, loss_cls: 3.5493, loss: 3.5493 +2024-12-29 20:27:29,459 - pyskl - INFO - Epoch [99][400/3746] lr: 2.674e-02, eta: 1 day, 21:38:31, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6334, loss_cls: 3.5445, loss: 3.5445 +2024-12-29 20:28:55,049 - pyskl - INFO - Epoch [99][500/3746] lr: 2.671e-02, eta: 1 day, 21:37:07, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6294, loss_cls: 3.5784, loss: 3.5784 +2024-12-29 20:30:21,365 - pyskl - INFO - Epoch [99][600/3746] lr: 2.669e-02, eta: 1 day, 21:35:44, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6305, loss_cls: 3.5857, loss: 3.5857 +2024-12-29 20:31:47,127 - pyskl - INFO - Epoch [99][700/3746] lr: 2.666e-02, eta: 1 day, 21:34:20, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6230, loss_cls: 3.5981, loss: 3.5981 +2024-12-29 20:33:13,330 - pyskl - INFO - Epoch [99][800/3746] lr: 2.664e-02, eta: 1 day, 21:32:56, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6225, loss_cls: 3.5954, loss: 3.5954 +2024-12-29 20:34:39,366 - pyskl - INFO - Epoch [99][900/3746] lr: 2.661e-02, eta: 1 day, 21:31:33, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6389, loss_cls: 3.5587, loss: 3.5587 +2024-12-29 20:36:05,609 - pyskl - INFO - Epoch [99][1000/3746] lr: 2.659e-02, eta: 1 day, 21:30:09, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6306, loss_cls: 3.5551, loss: 3.5551 +2024-12-29 20:37:31,706 - pyskl - INFO - Epoch [99][1100/3746] lr: 2.656e-02, eta: 1 day, 21:28:45, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6309, loss_cls: 3.5912, loss: 3.5912 +2024-12-29 20:38:57,307 - pyskl - INFO - Epoch [99][1200/3746] lr: 2.654e-02, eta: 1 day, 21:27:21, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6258, loss_cls: 3.5891, loss: 3.5891 +2024-12-29 20:40:23,750 - pyskl - INFO - Epoch [99][1300/3746] lr: 2.651e-02, eta: 1 day, 21:25:58, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3588, top5_acc: 0.6191, loss_cls: 3.6448, loss: 3.6448 +2024-12-29 20:41:50,219 - pyskl - INFO - Epoch [99][1400/3746] lr: 2.649e-02, eta: 1 day, 21:24:34, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6403, loss_cls: 3.5595, loss: 3.5595 +2024-12-29 20:43:16,665 - pyskl - INFO - Epoch [99][1500/3746] lr: 2.646e-02, eta: 1 day, 21:23:11, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6280, loss_cls: 3.5783, loss: 3.5783 +2024-12-29 20:44:42,875 - pyskl - INFO - Epoch [99][1600/3746] lr: 2.644e-02, eta: 1 day, 21:21:47, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6208, loss_cls: 3.6137, loss: 3.6137 +2024-12-29 20:46:09,238 - pyskl - INFO - Epoch [99][1700/3746] lr: 2.642e-02, eta: 1 day, 21:20:24, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6220, loss_cls: 3.5973, loss: 3.5973 +2024-12-29 20:47:35,920 - pyskl - INFO - Epoch [99][1800/3746] lr: 2.639e-02, eta: 1 day, 21:19:00, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6225, loss_cls: 3.6299, loss: 3.6299 +2024-12-29 20:49:01,826 - pyskl - INFO - Epoch [99][1900/3746] lr: 2.637e-02, eta: 1 day, 21:17:36, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6277, loss_cls: 3.5765, loss: 3.5765 +2024-12-29 20:50:28,399 - pyskl - INFO - Epoch [99][2000/3746] lr: 2.634e-02, eta: 1 day, 21:16:13, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3691, top5_acc: 0.6325, loss_cls: 3.5504, loss: 3.5504 +2024-12-29 20:51:55,250 - pyskl - INFO - Epoch [99][2100/3746] lr: 2.632e-02, eta: 1 day, 21:14:50, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6302, loss_cls: 3.5677, loss: 3.5677 +2024-12-29 20:53:21,290 - pyskl - INFO - Epoch [99][2200/3746] lr: 2.629e-02, eta: 1 day, 21:13:26, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6333, loss_cls: 3.5795, loss: 3.5795 +2024-12-29 20:54:46,426 - pyskl - INFO - Epoch [99][2300/3746] lr: 2.627e-02, eta: 1 day, 21:12:02, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3680, top5_acc: 0.6273, loss_cls: 3.5737, loss: 3.5737 +2024-12-29 20:56:12,490 - pyskl - INFO - Epoch [99][2400/3746] lr: 2.624e-02, eta: 1 day, 21:10:38, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6162, loss_cls: 3.6664, loss: 3.6664 +2024-12-29 20:57:38,550 - pyskl - INFO - Epoch [99][2500/3746] lr: 2.622e-02, eta: 1 day, 21:09:14, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3656, top5_acc: 0.6333, loss_cls: 3.5808, loss: 3.5808 +2024-12-29 20:59:04,314 - pyskl - INFO - Epoch [99][2600/3746] lr: 2.619e-02, eta: 1 day, 21:07:50, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6308, loss_cls: 3.6052, loss: 3.6052 +2024-12-29 21:00:30,368 - pyskl - INFO - Epoch [99][2700/3746] lr: 2.617e-02, eta: 1 day, 21:06:26, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6156, loss_cls: 3.6477, loss: 3.6477 +2024-12-29 21:01:56,574 - pyskl - INFO - Epoch [99][2800/3746] lr: 2.614e-02, eta: 1 day, 21:05:03, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6202, loss_cls: 3.6125, loss: 3.6125 +2024-12-29 21:03:22,920 - pyskl - INFO - Epoch [99][2900/3746] lr: 2.612e-02, eta: 1 day, 21:03:39, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6311, loss_cls: 3.5718, loss: 3.5718 +2024-12-29 21:04:49,096 - pyskl - INFO - Epoch [99][3000/3746] lr: 2.610e-02, eta: 1 day, 21:02:16, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6359, loss_cls: 3.5433, loss: 3.5433 +2024-12-29 21:06:14,724 - pyskl - INFO - Epoch [99][3100/3746] lr: 2.607e-02, eta: 1 day, 21:00:52, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6209, loss_cls: 3.5777, loss: 3.5777 +2024-12-29 21:07:39,790 - pyskl - INFO - Epoch [99][3200/3746] lr: 2.605e-02, eta: 1 day, 20:59:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6286, loss_cls: 3.5693, loss: 3.5693 +2024-12-29 21:09:05,511 - pyskl - INFO - Epoch [99][3300/3746] lr: 2.602e-02, eta: 1 day, 20:58:03, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3648, top5_acc: 0.6284, loss_cls: 3.5965, loss: 3.5965 +2024-12-29 21:10:32,128 - pyskl - INFO - Epoch [99][3400/3746] lr: 2.600e-02, eta: 1 day, 20:56:40, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6231, loss_cls: 3.5981, loss: 3.5981 +2024-12-29 21:11:58,017 - pyskl - INFO - Epoch [99][3500/3746] lr: 2.597e-02, eta: 1 day, 20:55:16, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6273, loss_cls: 3.6021, loss: 3.6021 +2024-12-29 21:13:24,362 - pyskl - INFO - Epoch [99][3600/3746] lr: 2.595e-02, eta: 1 day, 20:53:52, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6184, loss_cls: 3.6558, loss: 3.6558 +2024-12-29 21:14:50,613 - pyskl - INFO - Epoch [99][3700/3746] lr: 2.592e-02, eta: 1 day, 20:52:29, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3622, top5_acc: 0.6112, loss_cls: 3.6573, loss: 3.6573 +2024-12-29 21:15:32,287 - pyskl - INFO - Saving checkpoint at 99 epochs +2024-12-29 21:17:32,694 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 21:17:33,677 - pyskl - INFO - +top1_acc 0.3114 +top5_acc 0.5673 +2024-12-29 21:17:33,678 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 21:17:33,724 - pyskl - INFO - +mean_acc 0.3111 +2024-12-29 21:17:33,729 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_98.pth was removed +2024-12-29 21:17:34,032 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_99.pth. +2024-12-29 21:17:34,037 - pyskl - INFO - Best top1_acc is 0.3114 at 99 epoch. +2024-12-29 21:17:34,059 - pyskl - INFO - Epoch(val) [99][309] top1_acc: 0.3114, top5_acc: 0.5673, mean_class_accuracy: 0.3111 +2024-12-29 21:21:54,184 - pyskl - INFO - Epoch [100][100/3746] lr: 2.589e-02, eta: 1 day, 20:51:36, time: 2.601, data_time: 1.567, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6367, loss_cls: 3.5035, loss: 3.5035 +2024-12-29 21:23:19,362 - pyskl - INFO - Epoch [100][200/3746] lr: 2.586e-02, eta: 1 day, 20:50:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6362, loss_cls: 3.5377, loss: 3.5377 +2024-12-29 21:24:44,546 - pyskl - INFO - Epoch [100][300/3746] lr: 2.584e-02, eta: 1 day, 20:48:47, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6284, loss_cls: 3.5495, loss: 3.5495 +2024-12-29 21:26:10,366 - pyskl - INFO - Epoch [100][400/3746] lr: 2.581e-02, eta: 1 day, 20:47:23, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6430, loss_cls: 3.5411, loss: 3.5411 +2024-12-29 21:27:35,188 - pyskl - INFO - Epoch [100][500/3746] lr: 2.579e-02, eta: 1 day, 20:45:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6258, loss_cls: 3.5664, loss: 3.5664 +2024-12-29 21:29:00,262 - pyskl - INFO - Epoch [100][600/3746] lr: 2.577e-02, eta: 1 day, 20:44:34, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6230, loss_cls: 3.5679, loss: 3.5679 +2024-12-29 21:30:24,534 - pyskl - INFO - Epoch [100][700/3746] lr: 2.574e-02, eta: 1 day, 20:43:10, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6364, loss_cls: 3.5642, loss: 3.5642 +2024-12-29 21:31:49,446 - pyskl - INFO - Epoch [100][800/3746] lr: 2.572e-02, eta: 1 day, 20:41:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6281, loss_cls: 3.5765, loss: 3.5765 +2024-12-29 21:33:13,847 - pyskl - INFO - Epoch [100][900/3746] lr: 2.569e-02, eta: 1 day, 20:40:21, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6325, loss_cls: 3.5414, loss: 3.5414 +2024-12-29 21:34:39,184 - pyskl - INFO - Epoch [100][1000/3746] lr: 2.567e-02, eta: 1 day, 20:38:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6320, loss_cls: 3.5786, loss: 3.5786 +2024-12-29 21:36:04,353 - pyskl - INFO - Epoch [100][1100/3746] lr: 2.564e-02, eta: 1 day, 20:37:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6319, loss_cls: 3.5510, loss: 3.5510 +2024-12-29 21:37:29,248 - pyskl - INFO - Epoch [100][1200/3746] lr: 2.562e-02, eta: 1 day, 20:36:08, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6295, loss_cls: 3.5764, loss: 3.5764 +2024-12-29 21:38:54,203 - pyskl - INFO - Epoch [100][1300/3746] lr: 2.559e-02, eta: 1 day, 20:34:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6331, loss_cls: 3.5173, loss: 3.5173 +2024-12-29 21:40:18,758 - pyskl - INFO - Epoch [100][1400/3746] lr: 2.557e-02, eta: 1 day, 20:33:19, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6336, loss_cls: 3.5529, loss: 3.5529 +2024-12-29 21:41:43,663 - pyskl - INFO - Epoch [100][1500/3746] lr: 2.555e-02, eta: 1 day, 20:31:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6336, loss_cls: 3.5681, loss: 3.5681 +2024-12-29 21:43:08,778 - pyskl - INFO - Epoch [100][1600/3746] lr: 2.552e-02, eta: 1 day, 20:30:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6256, loss_cls: 3.5921, loss: 3.5921 +2024-12-29 21:44:33,402 - pyskl - INFO - Epoch [100][1700/3746] lr: 2.550e-02, eta: 1 day, 20:29:05, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6228, loss_cls: 3.6082, loss: 3.6082 +2024-12-29 21:45:57,969 - pyskl - INFO - Epoch [100][1800/3746] lr: 2.547e-02, eta: 1 day, 20:27:41, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6336, loss_cls: 3.5518, loss: 3.5518 +2024-12-29 21:47:22,984 - pyskl - INFO - Epoch [100][1900/3746] lr: 2.545e-02, eta: 1 day, 20:26:17, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6341, loss_cls: 3.5682, loss: 3.5682 +2024-12-29 21:48:48,335 - pyskl - INFO - Epoch [100][2000/3746] lr: 2.542e-02, eta: 1 day, 20:24:52, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6247, loss_cls: 3.5947, loss: 3.5947 +2024-12-29 21:50:13,041 - pyskl - INFO - Epoch [100][2100/3746] lr: 2.540e-02, eta: 1 day, 20:23:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6266, loss_cls: 3.6124, loss: 3.6124 +2024-12-29 21:51:38,074 - pyskl - INFO - Epoch [100][2200/3746] lr: 2.538e-02, eta: 1 day, 20:22:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6291, loss_cls: 3.5659, loss: 3.5659 +2024-12-29 21:53:02,501 - pyskl - INFO - Epoch [100][2300/3746] lr: 2.535e-02, eta: 1 day, 20:20:39, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6303, loss_cls: 3.5563, loss: 3.5563 +2024-12-29 21:54:27,785 - pyskl - INFO - Epoch [100][2400/3746] lr: 2.533e-02, eta: 1 day, 20:19:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6222, loss_cls: 3.5954, loss: 3.5954 +2024-12-29 21:55:52,545 - pyskl - INFO - Epoch [100][2500/3746] lr: 2.530e-02, eta: 1 day, 20:17:50, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6223, loss_cls: 3.5930, loss: 3.5930 +2024-12-29 21:57:17,325 - pyskl - INFO - Epoch [100][2600/3746] lr: 2.528e-02, eta: 1 day, 20:16:26, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6202, loss_cls: 3.6151, loss: 3.6151 +2024-12-29 21:58:42,424 - pyskl - INFO - Epoch [100][2700/3746] lr: 2.525e-02, eta: 1 day, 20:15:01, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6281, loss_cls: 3.5601, loss: 3.5601 +2024-12-29 22:00:08,004 - pyskl - INFO - Epoch [100][2800/3746] lr: 2.523e-02, eta: 1 day, 20:13:37, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3720, top5_acc: 0.6336, loss_cls: 3.5437, loss: 3.5437 +2024-12-29 22:01:33,355 - pyskl - INFO - Epoch [100][2900/3746] lr: 2.521e-02, eta: 1 day, 20:12:13, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6314, loss_cls: 3.5476, loss: 3.5476 +2024-12-29 22:02:57,971 - pyskl - INFO - Epoch [100][3000/3746] lr: 2.518e-02, eta: 1 day, 20:10:49, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.3686, top5_acc: 0.6373, loss_cls: 3.5575, loss: 3.5575 +2024-12-29 22:04:22,866 - pyskl - INFO - Epoch [100][3100/3746] lr: 2.516e-02, eta: 1 day, 20:09:24, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6261, loss_cls: 3.6059, loss: 3.6059 +2024-12-29 22:05:47,255 - pyskl - INFO - Epoch [100][3200/3746] lr: 2.513e-02, eta: 1 day, 20:07:59, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6239, loss_cls: 3.6409, loss: 3.6409 +2024-12-29 22:07:12,426 - pyskl - INFO - Epoch [100][3300/3746] lr: 2.511e-02, eta: 1 day, 20:06:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6234, loss_cls: 3.6033, loss: 3.6033 +2024-12-29 22:08:37,763 - pyskl - INFO - Epoch [100][3400/3746] lr: 2.508e-02, eta: 1 day, 20:05:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6192, loss_cls: 3.6377, loss: 3.6377 +2024-12-29 22:10:03,416 - pyskl - INFO - Epoch [100][3500/3746] lr: 2.506e-02, eta: 1 day, 20:03:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3720, top5_acc: 0.6242, loss_cls: 3.5984, loss: 3.5984 +2024-12-29 22:11:28,736 - pyskl - INFO - Epoch [100][3600/3746] lr: 2.504e-02, eta: 1 day, 20:02:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6170, loss_cls: 3.6473, loss: 3.6473 +2024-12-29 22:12:54,076 - pyskl - INFO - Epoch [100][3700/3746] lr: 2.501e-02, eta: 1 day, 20:00:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6233, loss_cls: 3.6255, loss: 3.6255 +2024-12-29 22:13:35,195 - pyskl - INFO - Saving checkpoint at 100 epochs +2024-12-29 22:15:32,789 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 22:15:33,600 - pyskl - INFO - +top1_acc 0.3131 +top5_acc 0.5715 +2024-12-29 22:15:33,600 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 22:15:33,644 - pyskl - INFO - +mean_acc 0.3129 +2024-12-29 22:15:33,650 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_99.pth was removed +2024-12-29 22:15:33,930 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_100.pth. +2024-12-29 22:15:33,930 - pyskl - INFO - Best top1_acc is 0.3131 at 100 epoch. +2024-12-29 22:15:33,947 - pyskl - INFO - Epoch(val) [100][309] top1_acc: 0.3131, top5_acc: 0.5715, mean_class_accuracy: 0.3129 +2024-12-29 22:19:53,069 - pyskl - INFO - Epoch [101][100/3746] lr: 2.498e-02, eta: 1 day, 20:00:03, time: 2.591, data_time: 1.540, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6406, loss_cls: 3.5151, loss: 3.5151 +2024-12-29 22:21:18,861 - pyskl - INFO - Epoch [101][200/3746] lr: 2.495e-02, eta: 1 day, 19:58:39, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6322, loss_cls: 3.5839, loss: 3.5839 +2024-12-29 22:22:44,123 - pyskl - INFO - Epoch [101][300/3746] lr: 2.493e-02, eta: 1 day, 19:57:14, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3844, top5_acc: 0.6480, loss_cls: 3.4887, loss: 3.4887 +2024-12-29 22:24:08,979 - pyskl - INFO - Epoch [101][400/3746] lr: 2.490e-02, eta: 1 day, 19:55:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6394, loss_cls: 3.5182, loss: 3.5182 +2024-12-29 22:25:33,939 - pyskl - INFO - Epoch [101][500/3746] lr: 2.488e-02, eta: 1 day, 19:54:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6470, loss_cls: 3.4852, loss: 3.4852 +2024-12-29 22:26:58,978 - pyskl - INFO - Epoch [101][600/3746] lr: 2.486e-02, eta: 1 day, 19:53:01, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6444, loss_cls: 3.4745, loss: 3.4745 +2024-12-29 22:28:24,556 - pyskl - INFO - Epoch [101][700/3746] lr: 2.483e-02, eta: 1 day, 19:51:37, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6358, loss_cls: 3.5321, loss: 3.5321 +2024-12-29 22:29:50,202 - pyskl - INFO - Epoch [101][800/3746] lr: 2.481e-02, eta: 1 day, 19:50:13, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3708, top5_acc: 0.6358, loss_cls: 3.5242, loss: 3.5242 +2024-12-29 22:31:15,861 - pyskl - INFO - Epoch [101][900/3746] lr: 2.478e-02, eta: 1 day, 19:48:49, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6323, loss_cls: 3.5380, loss: 3.5380 +2024-12-29 22:32:42,273 - pyskl - INFO - Epoch [101][1000/3746] lr: 2.476e-02, eta: 1 day, 19:47:25, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6408, loss_cls: 3.5269, loss: 3.5269 +2024-12-29 22:34:07,938 - pyskl - INFO - Epoch [101][1100/3746] lr: 2.473e-02, eta: 1 day, 19:46:01, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6436, loss_cls: 3.5024, loss: 3.5024 +2024-12-29 22:35:33,512 - pyskl - INFO - Epoch [101][1200/3746] lr: 2.471e-02, eta: 1 day, 19:44:37, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6280, loss_cls: 3.5774, loss: 3.5774 +2024-12-29 22:36:59,391 - pyskl - INFO - Epoch [101][1300/3746] lr: 2.469e-02, eta: 1 day, 19:43:13, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6197, loss_cls: 3.6112, loss: 3.6112 +2024-12-29 22:38:24,866 - pyskl - INFO - Epoch [101][1400/3746] lr: 2.466e-02, eta: 1 day, 19:41:49, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6350, loss_cls: 3.5344, loss: 3.5344 +2024-12-29 22:39:50,389 - pyskl - INFO - Epoch [101][1500/3746] lr: 2.464e-02, eta: 1 day, 19:40:24, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6327, loss_cls: 3.5821, loss: 3.5821 +2024-12-29 22:41:16,304 - pyskl - INFO - Epoch [101][1600/3746] lr: 2.461e-02, eta: 1 day, 19:39:00, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6323, loss_cls: 3.5916, loss: 3.5916 +2024-12-29 22:42:42,259 - pyskl - INFO - Epoch [101][1700/3746] lr: 2.459e-02, eta: 1 day, 19:37:36, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6327, loss_cls: 3.5314, loss: 3.5314 +2024-12-29 22:44:07,657 - pyskl - INFO - Epoch [101][1800/3746] lr: 2.457e-02, eta: 1 day, 19:36:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6228, loss_cls: 3.6269, loss: 3.6269 +2024-12-29 22:45:33,059 - pyskl - INFO - Epoch [101][1900/3746] lr: 2.454e-02, eta: 1 day, 19:34:48, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6331, loss_cls: 3.5738, loss: 3.5738 +2024-12-29 22:46:58,977 - pyskl - INFO - Epoch [101][2000/3746] lr: 2.452e-02, eta: 1 day, 19:33:24, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6459, loss_cls: 3.4993, loss: 3.4993 +2024-12-29 22:48:24,066 - pyskl - INFO - Epoch [101][2100/3746] lr: 2.449e-02, eta: 1 day, 19:32:00, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3731, top5_acc: 0.6312, loss_cls: 3.5458, loss: 3.5458 +2024-12-29 22:49:49,498 - pyskl - INFO - Epoch [101][2200/3746] lr: 2.447e-02, eta: 1 day, 19:30:35, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6344, loss_cls: 3.5822, loss: 3.5822 +2024-12-29 22:51:14,543 - pyskl - INFO - Epoch [101][2300/3746] lr: 2.445e-02, eta: 1 day, 19:29:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6281, loss_cls: 3.5791, loss: 3.5791 +2024-12-29 22:52:39,861 - pyskl - INFO - Epoch [101][2400/3746] lr: 2.442e-02, eta: 1 day, 19:27:47, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6228, loss_cls: 3.5980, loss: 3.5980 +2024-12-29 22:54:05,261 - pyskl - INFO - Epoch [101][2500/3746] lr: 2.440e-02, eta: 1 day, 19:26:22, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6311, loss_cls: 3.5913, loss: 3.5913 +2024-12-29 22:55:30,947 - pyskl - INFO - Epoch [101][2600/3746] lr: 2.437e-02, eta: 1 day, 19:24:58, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6372, loss_cls: 3.5167, loss: 3.5167 +2024-12-29 22:56:56,594 - pyskl - INFO - Epoch [101][2700/3746] lr: 2.435e-02, eta: 1 day, 19:23:34, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6297, loss_cls: 3.5938, loss: 3.5938 +2024-12-29 22:58:22,138 - pyskl - INFO - Epoch [101][2800/3746] lr: 2.433e-02, eta: 1 day, 19:22:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6305, loss_cls: 3.5653, loss: 3.5653 +2024-12-29 22:59:47,923 - pyskl - INFO - Epoch [101][2900/3746] lr: 2.430e-02, eta: 1 day, 19:20:46, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6272, loss_cls: 3.5541, loss: 3.5541 +2024-12-29 23:01:13,408 - pyskl - INFO - Epoch [101][3000/3746] lr: 2.428e-02, eta: 1 day, 19:19:22, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6269, loss_cls: 3.5814, loss: 3.5814 +2024-12-29 23:02:38,168 - pyskl - INFO - Epoch [101][3100/3746] lr: 2.425e-02, eta: 1 day, 19:17:57, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6309, loss_cls: 3.5567, loss: 3.5567 +2024-12-29 23:04:02,667 - pyskl - INFO - Epoch [101][3200/3746] lr: 2.423e-02, eta: 1 day, 19:16:32, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6347, loss_cls: 3.5451, loss: 3.5451 +2024-12-29 23:05:27,676 - pyskl - INFO - Epoch [101][3300/3746] lr: 2.421e-02, eta: 1 day, 19:15:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6322, loss_cls: 3.5398, loss: 3.5398 +2024-12-29 23:06:53,052 - pyskl - INFO - Epoch [101][3400/3746] lr: 2.418e-02, eta: 1 day, 19:13:44, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6216, loss_cls: 3.5982, loss: 3.5982 +2024-12-29 23:08:18,451 - pyskl - INFO - Epoch [101][3500/3746] lr: 2.416e-02, eta: 1 day, 19:12:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6273, loss_cls: 3.6006, loss: 3.6006 +2024-12-29 23:09:44,488 - pyskl - INFO - Epoch [101][3600/3746] lr: 2.413e-02, eta: 1 day, 19:10:56, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6309, loss_cls: 3.5678, loss: 3.5678 +2024-12-29 23:11:09,554 - pyskl - INFO - Epoch [101][3700/3746] lr: 2.411e-02, eta: 1 day, 19:09:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6219, loss_cls: 3.5862, loss: 3.5862 +2024-12-29 23:11:50,503 - pyskl - INFO - Saving checkpoint at 101 epochs +2024-12-29 23:13:50,543 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 23:13:51,397 - pyskl - INFO - +top1_acc 0.3134 +top5_acc 0.5639 +2024-12-29 23:13:51,398 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 23:13:51,442 - pyskl - INFO - +mean_acc 0.3130 +2024-12-29 23:13:51,448 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_100.pth was removed +2024-12-29 23:13:51,804 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_101.pth. +2024-12-29 23:13:51,805 - pyskl - INFO - Best top1_acc is 0.3134 at 101 epoch. +2024-12-29 23:13:51,818 - pyskl - INFO - Epoch(val) [101][309] top1_acc: 0.3134, top5_acc: 0.5639, mean_class_accuracy: 0.3130 +2024-12-29 23:18:11,572 - pyskl - INFO - Epoch [102][100/3746] lr: 2.407e-02, eta: 1 day, 19:08:34, time: 2.597, data_time: 1.565, memory: 15990, top1_acc: 0.3762, top5_acc: 0.6394, loss_cls: 3.5072, loss: 3.5072 +2024-12-29 23:19:37,262 - pyskl - INFO - Epoch [102][200/3746] lr: 2.405e-02, eta: 1 day, 19:07:09, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6384, loss_cls: 3.5328, loss: 3.5328 +2024-12-29 23:21:02,455 - pyskl - INFO - Epoch [102][300/3746] lr: 2.403e-02, eta: 1 day, 19:05:45, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6488, loss_cls: 3.4636, loss: 3.4636 +2024-12-29 23:22:27,809 - pyskl - INFO - Epoch [102][400/3746] lr: 2.400e-02, eta: 1 day, 19:04:21, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3837, top5_acc: 0.6531, loss_cls: 3.4659, loss: 3.4659 +2024-12-29 23:23:52,753 - pyskl - INFO - Epoch [102][500/3746] lr: 2.398e-02, eta: 1 day, 19:02:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6316, loss_cls: 3.5469, loss: 3.5469 +2024-12-29 23:25:17,510 - pyskl - INFO - Epoch [102][600/3746] lr: 2.396e-02, eta: 1 day, 19:01:32, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6456, loss_cls: 3.4485, loss: 3.4485 +2024-12-29 23:26:42,798 - pyskl - INFO - Epoch [102][700/3746] lr: 2.393e-02, eta: 1 day, 19:00:07, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6305, loss_cls: 3.5456, loss: 3.5456 +2024-12-29 23:28:07,952 - pyskl - INFO - Epoch [102][800/3746] lr: 2.391e-02, eta: 1 day, 18:58:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6239, loss_cls: 3.5868, loss: 3.5868 +2024-12-29 23:29:33,324 - pyskl - INFO - Epoch [102][900/3746] lr: 2.388e-02, eta: 1 day, 18:57:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6348, loss_cls: 3.5257, loss: 3.5257 +2024-12-29 23:30:58,974 - pyskl - INFO - Epoch [102][1000/3746] lr: 2.386e-02, eta: 1 day, 18:55:54, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6355, loss_cls: 3.5502, loss: 3.5502 +2024-12-29 23:32:24,187 - pyskl - INFO - Epoch [102][1100/3746] lr: 2.384e-02, eta: 1 day, 18:54:30, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6327, loss_cls: 3.5296, loss: 3.5296 +2024-12-29 23:33:49,471 - pyskl - INFO - Epoch [102][1200/3746] lr: 2.381e-02, eta: 1 day, 18:53:06, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6319, loss_cls: 3.5465, loss: 3.5465 +2024-12-29 23:35:14,621 - pyskl - INFO - Epoch [102][1300/3746] lr: 2.379e-02, eta: 1 day, 18:51:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6331, loss_cls: 3.5336, loss: 3.5336 +2024-12-29 23:36:40,031 - pyskl - INFO - Epoch [102][1400/3746] lr: 2.376e-02, eta: 1 day, 18:50:17, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3819, top5_acc: 0.6408, loss_cls: 3.5111, loss: 3.5111 +2024-12-29 23:38:04,859 - pyskl - INFO - Epoch [102][1500/3746] lr: 2.374e-02, eta: 1 day, 18:48:52, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6309, loss_cls: 3.5631, loss: 3.5631 +2024-12-29 23:39:30,388 - pyskl - INFO - Epoch [102][1600/3746] lr: 2.372e-02, eta: 1 day, 18:47:28, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3706, top5_acc: 0.6378, loss_cls: 3.5378, loss: 3.5378 +2024-12-29 23:40:55,687 - pyskl - INFO - Epoch [102][1700/3746] lr: 2.369e-02, eta: 1 day, 18:46:04, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3812, top5_acc: 0.6286, loss_cls: 3.5748, loss: 3.5748 +2024-12-29 23:42:21,021 - pyskl - INFO - Epoch [102][1800/3746] lr: 2.367e-02, eta: 1 day, 18:44:39, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3656, top5_acc: 0.6281, loss_cls: 3.5867, loss: 3.5867 +2024-12-29 23:43:46,102 - pyskl - INFO - Epoch [102][1900/3746] lr: 2.365e-02, eta: 1 day, 18:43:15, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6386, loss_cls: 3.5215, loss: 3.5215 +2024-12-29 23:45:11,579 - pyskl - INFO - Epoch [102][2000/3746] lr: 2.362e-02, eta: 1 day, 18:41:51, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3723, top5_acc: 0.6369, loss_cls: 3.5317, loss: 3.5317 +2024-12-29 23:46:36,916 - pyskl - INFO - Epoch [102][2100/3746] lr: 2.360e-02, eta: 1 day, 18:40:26, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6300, loss_cls: 3.5396, loss: 3.5396 +2024-12-29 23:48:02,089 - pyskl - INFO - Epoch [102][2200/3746] lr: 2.357e-02, eta: 1 day, 18:39:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6319, loss_cls: 3.5522, loss: 3.5522 +2024-12-29 23:49:27,088 - pyskl - INFO - Epoch [102][2300/3746] lr: 2.355e-02, eta: 1 day, 18:37:37, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6405, loss_cls: 3.5102, loss: 3.5102 +2024-12-29 23:50:52,402 - pyskl - INFO - Epoch [102][2400/3746] lr: 2.353e-02, eta: 1 day, 18:36:13, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3762, top5_acc: 0.6419, loss_cls: 3.5075, loss: 3.5075 +2024-12-29 23:52:18,430 - pyskl - INFO - Epoch [102][2500/3746] lr: 2.350e-02, eta: 1 day, 18:34:49, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6378, loss_cls: 3.5321, loss: 3.5321 +2024-12-29 23:53:43,891 - pyskl - INFO - Epoch [102][2600/3746] lr: 2.348e-02, eta: 1 day, 18:33:25, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3691, top5_acc: 0.6292, loss_cls: 3.5861, loss: 3.5861 +2024-12-29 23:55:09,826 - pyskl - INFO - Epoch [102][2700/3746] lr: 2.346e-02, eta: 1 day, 18:32:01, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6388, loss_cls: 3.5203, loss: 3.5203 +2024-12-29 23:56:35,636 - pyskl - INFO - Epoch [102][2800/3746] lr: 2.343e-02, eta: 1 day, 18:30:36, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6448, loss_cls: 3.5171, loss: 3.5171 +2024-12-29 23:58:01,859 - pyskl - INFO - Epoch [102][2900/3746] lr: 2.341e-02, eta: 1 day, 18:29:12, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6267, loss_cls: 3.5732, loss: 3.5732 +2024-12-29 23:59:27,410 - pyskl - INFO - Epoch [102][3000/3746] lr: 2.339e-02, eta: 1 day, 18:27:48, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6286, loss_cls: 3.5659, loss: 3.5659 +2024-12-30 00:00:52,920 - pyskl - INFO - Epoch [102][3100/3746] lr: 2.336e-02, eta: 1 day, 18:26:24, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6392, loss_cls: 3.5603, loss: 3.5603 +2024-12-30 00:02:17,728 - pyskl - INFO - Epoch [102][3200/3746] lr: 2.334e-02, eta: 1 day, 18:24:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6400, loss_cls: 3.5478, loss: 3.5478 +2024-12-30 00:03:42,941 - pyskl - INFO - Epoch [102][3300/3746] lr: 2.331e-02, eta: 1 day, 18:23:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6273, loss_cls: 3.5845, loss: 3.5845 +2024-12-30 00:05:08,083 - pyskl - INFO - Epoch [102][3400/3746] lr: 2.329e-02, eta: 1 day, 18:22:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6314, loss_cls: 3.5478, loss: 3.5478 +2024-12-30 00:06:33,398 - pyskl - INFO - Epoch [102][3500/3746] lr: 2.327e-02, eta: 1 day, 18:20:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3848, top5_acc: 0.6442, loss_cls: 3.5024, loss: 3.5024 +2024-12-30 00:07:58,955 - pyskl - INFO - Epoch [102][3600/3746] lr: 2.324e-02, eta: 1 day, 18:19:22, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6372, loss_cls: 3.5449, loss: 3.5449 +2024-12-30 00:09:24,514 - pyskl - INFO - Epoch [102][3700/3746] lr: 2.322e-02, eta: 1 day, 18:17:58, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6286, loss_cls: 3.5648, loss: 3.5648 +2024-12-30 00:10:06,047 - pyskl - INFO - Saving checkpoint at 102 epochs +2024-12-30 00:12:07,504 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 00:12:08,209 - pyskl - INFO - +top1_acc 0.3121 +top5_acc 0.5697 +2024-12-30 00:12:08,209 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 00:12:08,250 - pyskl - INFO - +mean_acc 0.3117 +2024-12-30 00:12:08,263 - pyskl - INFO - Epoch(val) [102][309] top1_acc: 0.3121, top5_acc: 0.5697, mean_class_accuracy: 0.3117 +2024-12-30 00:16:31,984 - pyskl - INFO - Epoch [103][100/3746] lr: 2.319e-02, eta: 1 day, 18:17:00, time: 2.637, data_time: 1.591, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6503, loss_cls: 3.4834, loss: 3.4834 +2024-12-30 00:17:57,022 - pyskl - INFO - Epoch [103][200/3746] lr: 2.316e-02, eta: 1 day, 18:15:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6489, loss_cls: 3.4671, loss: 3.4671 +2024-12-30 00:19:22,488 - pyskl - INFO - Epoch [103][300/3746] lr: 2.314e-02, eta: 1 day, 18:14:11, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6462, loss_cls: 3.4826, loss: 3.4826 +2024-12-30 00:20:47,703 - pyskl - INFO - Epoch [103][400/3746] lr: 2.311e-02, eta: 1 day, 18:12:47, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6431, loss_cls: 3.5342, loss: 3.5342 +2024-12-30 00:22:12,776 - pyskl - INFO - Epoch [103][500/3746] lr: 2.309e-02, eta: 1 day, 18:11:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6495, loss_cls: 3.4864, loss: 3.4864 +2024-12-30 00:23:37,751 - pyskl - INFO - Epoch [103][600/3746] lr: 2.307e-02, eta: 1 day, 18:09:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6428, loss_cls: 3.4998, loss: 3.4998 +2024-12-30 00:25:02,896 - pyskl - INFO - Epoch [103][700/3746] lr: 2.304e-02, eta: 1 day, 18:08:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6370, loss_cls: 3.4841, loss: 3.4841 +2024-12-30 00:26:27,386 - pyskl - INFO - Epoch [103][800/3746] lr: 2.302e-02, eta: 1 day, 18:07:08, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6384, loss_cls: 3.5460, loss: 3.5460 +2024-12-30 00:27:52,037 - pyskl - INFO - Epoch [103][900/3746] lr: 2.300e-02, eta: 1 day, 18:05:43, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6362, loss_cls: 3.5108, loss: 3.5108 +2024-12-30 00:29:16,831 - pyskl - INFO - Epoch [103][1000/3746] lr: 2.297e-02, eta: 1 day, 18:04:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6331, loss_cls: 3.5045, loss: 3.5045 +2024-12-30 00:30:41,834 - pyskl - INFO - Epoch [103][1100/3746] lr: 2.295e-02, eta: 1 day, 18:02:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3691, top5_acc: 0.6305, loss_cls: 3.5585, loss: 3.5585 +2024-12-30 00:32:07,124 - pyskl - INFO - Epoch [103][1200/3746] lr: 2.293e-02, eta: 1 day, 18:01:30, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6389, loss_cls: 3.5214, loss: 3.5214 +2024-12-30 00:33:32,188 - pyskl - INFO - Epoch [103][1300/3746] lr: 2.290e-02, eta: 1 day, 18:00:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6383, loss_cls: 3.5531, loss: 3.5531 +2024-12-30 00:34:57,118 - pyskl - INFO - Epoch [103][1400/3746] lr: 2.288e-02, eta: 1 day, 17:58:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6378, loss_cls: 3.5030, loss: 3.5030 +2024-12-30 00:36:22,449 - pyskl - INFO - Epoch [103][1500/3746] lr: 2.286e-02, eta: 1 day, 17:57:16, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6438, loss_cls: 3.4673, loss: 3.4673 +2024-12-30 00:37:47,967 - pyskl - INFO - Epoch [103][1600/3746] lr: 2.283e-02, eta: 1 day, 17:55:52, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3872, top5_acc: 0.6555, loss_cls: 3.4687, loss: 3.4687 +2024-12-30 00:39:13,226 - pyskl - INFO - Epoch [103][1700/3746] lr: 2.281e-02, eta: 1 day, 17:54:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6372, loss_cls: 3.5316, loss: 3.5316 +2024-12-30 00:40:38,526 - pyskl - INFO - Epoch [103][1800/3746] lr: 2.279e-02, eta: 1 day, 17:53:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6302, loss_cls: 3.5683, loss: 3.5683 +2024-12-30 00:42:04,069 - pyskl - INFO - Epoch [103][1900/3746] lr: 2.276e-02, eta: 1 day, 17:51:39, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6380, loss_cls: 3.5168, loss: 3.5168 +2024-12-30 00:43:29,656 - pyskl - INFO - Epoch [103][2000/3746] lr: 2.274e-02, eta: 1 day, 17:50:14, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6319, loss_cls: 3.5300, loss: 3.5300 +2024-12-30 00:44:55,092 - pyskl - INFO - Epoch [103][2100/3746] lr: 2.272e-02, eta: 1 day, 17:48:50, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6377, loss_cls: 3.5548, loss: 3.5548 +2024-12-30 00:46:20,095 - pyskl - INFO - Epoch [103][2200/3746] lr: 2.269e-02, eta: 1 day, 17:47:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6328, loss_cls: 3.5426, loss: 3.5426 +2024-12-30 00:47:45,147 - pyskl - INFO - Epoch [103][2300/3746] lr: 2.267e-02, eta: 1 day, 17:46:01, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6400, loss_cls: 3.5241, loss: 3.5241 +2024-12-30 00:49:10,442 - pyskl - INFO - Epoch [103][2400/3746] lr: 2.264e-02, eta: 1 day, 17:44:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6327, loss_cls: 3.5263, loss: 3.5263 +2024-12-30 00:50:35,153 - pyskl - INFO - Epoch [103][2500/3746] lr: 2.262e-02, eta: 1 day, 17:43:12, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6328, loss_cls: 3.5293, loss: 3.5293 +2024-12-30 00:51:59,908 - pyskl - INFO - Epoch [103][2600/3746] lr: 2.260e-02, eta: 1 day, 17:41:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6295, loss_cls: 3.5731, loss: 3.5731 +2024-12-30 00:53:24,702 - pyskl - INFO - Epoch [103][2700/3746] lr: 2.257e-02, eta: 1 day, 17:40:22, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6348, loss_cls: 3.5623, loss: 3.5623 +2024-12-30 00:54:49,000 - pyskl - INFO - Epoch [103][2800/3746] lr: 2.255e-02, eta: 1 day, 17:38:57, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6305, loss_cls: 3.5260, loss: 3.5260 +2024-12-30 00:56:14,222 - pyskl - INFO - Epoch [103][2900/3746] lr: 2.253e-02, eta: 1 day, 17:37:33, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6391, loss_cls: 3.5141, loss: 3.5141 +2024-12-30 00:57:39,258 - pyskl - INFO - Epoch [103][3000/3746] lr: 2.250e-02, eta: 1 day, 17:36:08, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6395, loss_cls: 3.5337, loss: 3.5337 +2024-12-30 00:59:04,716 - pyskl - INFO - Epoch [103][3100/3746] lr: 2.248e-02, eta: 1 day, 17:34:44, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6473, loss_cls: 3.4850, loss: 3.4850 +2024-12-30 01:00:29,859 - pyskl - INFO - Epoch [103][3200/3746] lr: 2.246e-02, eta: 1 day, 17:33:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6448, loss_cls: 3.4802, loss: 3.4802 +2024-12-30 01:01:54,686 - pyskl - INFO - Epoch [103][3300/3746] lr: 2.243e-02, eta: 1 day, 17:31:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6333, loss_cls: 3.5417, loss: 3.5417 +2024-12-30 01:03:20,139 - pyskl - INFO - Epoch [103][3400/3746] lr: 2.241e-02, eta: 1 day, 17:30:31, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6295, loss_cls: 3.5420, loss: 3.5420 +2024-12-30 01:04:45,023 - pyskl - INFO - Epoch [103][3500/3746] lr: 2.239e-02, eta: 1 day, 17:29:06, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6300, loss_cls: 3.5502, loss: 3.5502 +2024-12-30 01:06:09,770 - pyskl - INFO - Epoch [103][3600/3746] lr: 2.236e-02, eta: 1 day, 17:27:41, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6331, loss_cls: 3.5585, loss: 3.5585 +2024-12-30 01:07:34,536 - pyskl - INFO - Epoch [103][3700/3746] lr: 2.234e-02, eta: 1 day, 17:26:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6309, loss_cls: 3.5267, loss: 3.5267 +2024-12-30 01:08:15,397 - pyskl - INFO - Saving checkpoint at 103 epochs +2024-12-30 01:10:14,335 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 01:10:15,073 - pyskl - INFO - +top1_acc 0.3320 +top5_acc 0.5884 +2024-12-30 01:10:15,073 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 01:10:15,123 - pyskl - INFO - +mean_acc 0.3317 +2024-12-30 01:10:15,128 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_101.pth was removed +2024-12-30 01:10:15,400 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_103.pth. +2024-12-30 01:10:15,401 - pyskl - INFO - Best top1_acc is 0.3320 at 103 epoch. +2024-12-30 01:10:15,413 - pyskl - INFO - Epoch(val) [103][309] top1_acc: 0.3320, top5_acc: 0.5884, mean_class_accuracy: 0.3317 +2024-12-30 01:14:26,577 - pyskl - INFO - Epoch [104][100/3746] lr: 2.231e-02, eta: 1 day, 17:25:11, time: 2.512, data_time: 1.484, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6564, loss_cls: 3.4132, loss: 3.4132 +2024-12-30 01:15:51,746 - pyskl - INFO - Epoch [104][200/3746] lr: 2.228e-02, eta: 1 day, 17:23:46, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6552, loss_cls: 3.4330, loss: 3.4330 +2024-12-30 01:17:16,667 - pyskl - INFO - Epoch [104][300/3746] lr: 2.226e-02, eta: 1 day, 17:22:22, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6545, loss_cls: 3.4404, loss: 3.4404 +2024-12-30 01:18:41,544 - pyskl - INFO - Epoch [104][400/3746] lr: 2.224e-02, eta: 1 day, 17:20:57, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6427, loss_cls: 3.4906, loss: 3.4906 +2024-12-30 01:20:06,935 - pyskl - INFO - Epoch [104][500/3746] lr: 2.221e-02, eta: 1 day, 17:19:33, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6409, loss_cls: 3.5138, loss: 3.5138 +2024-12-30 01:21:32,273 - pyskl - INFO - Epoch [104][600/3746] lr: 2.219e-02, eta: 1 day, 17:18:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6522, loss_cls: 3.4221, loss: 3.4221 +2024-12-30 01:22:57,801 - pyskl - INFO - Epoch [104][700/3746] lr: 2.217e-02, eta: 1 day, 17:16:44, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6455, loss_cls: 3.4770, loss: 3.4770 +2024-12-30 01:24:23,314 - pyskl - INFO - Epoch [104][800/3746] lr: 2.214e-02, eta: 1 day, 17:15:19, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6420, loss_cls: 3.5053, loss: 3.5053 +2024-12-30 01:25:48,393 - pyskl - INFO - Epoch [104][900/3746] lr: 2.212e-02, eta: 1 day, 17:13:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6470, loss_cls: 3.4740, loss: 3.4740 +2024-12-30 01:27:13,538 - pyskl - INFO - Epoch [104][1000/3746] lr: 2.210e-02, eta: 1 day, 17:12:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6466, loss_cls: 3.4589, loss: 3.4589 +2024-12-30 01:28:38,313 - pyskl - INFO - Epoch [104][1100/3746] lr: 2.208e-02, eta: 1 day, 17:11:06, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6466, loss_cls: 3.5050, loss: 3.5050 +2024-12-30 01:30:03,504 - pyskl - INFO - Epoch [104][1200/3746] lr: 2.205e-02, eta: 1 day, 17:09:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6436, loss_cls: 3.4740, loss: 3.4740 +2024-12-30 01:31:28,299 - pyskl - INFO - Epoch [104][1300/3746] lr: 2.203e-02, eta: 1 day, 17:08:16, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6422, loss_cls: 3.5011, loss: 3.5011 +2024-12-30 01:32:53,017 - pyskl - INFO - Epoch [104][1400/3746] lr: 2.201e-02, eta: 1 day, 17:06:52, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6342, loss_cls: 3.5638, loss: 3.5638 +2024-12-30 01:34:18,088 - pyskl - INFO - Epoch [104][1500/3746] lr: 2.198e-02, eta: 1 day, 17:05:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3822, top5_acc: 0.6423, loss_cls: 3.5012, loss: 3.5012 +2024-12-30 01:35:43,447 - pyskl - INFO - Epoch [104][1600/3746] lr: 2.196e-02, eta: 1 day, 17:04:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6436, loss_cls: 3.4764, loss: 3.4764 +2024-12-30 01:37:08,612 - pyskl - INFO - Epoch [104][1700/3746] lr: 2.194e-02, eta: 1 day, 17:02:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3848, top5_acc: 0.6344, loss_cls: 3.5080, loss: 3.5080 +2024-12-30 01:38:34,239 - pyskl - INFO - Epoch [104][1800/3746] lr: 2.191e-02, eta: 1 day, 17:01:14, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6308, loss_cls: 3.5491, loss: 3.5491 +2024-12-30 01:39:59,681 - pyskl - INFO - Epoch [104][1900/3746] lr: 2.189e-02, eta: 1 day, 16:59:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6358, loss_cls: 3.5168, loss: 3.5168 +2024-12-30 01:41:25,242 - pyskl - INFO - Epoch [104][2000/3746] lr: 2.187e-02, eta: 1 day, 16:58:25, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6434, loss_cls: 3.5000, loss: 3.5000 +2024-12-30 01:42:50,779 - pyskl - INFO - Epoch [104][2100/3746] lr: 2.184e-02, eta: 1 day, 16:57:00, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6359, loss_cls: 3.5470, loss: 3.5470 +2024-12-30 01:44:16,023 - pyskl - INFO - Epoch [104][2200/3746] lr: 2.182e-02, eta: 1 day, 16:55:36, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6464, loss_cls: 3.4956, loss: 3.4956 +2024-12-30 01:45:40,609 - pyskl - INFO - Epoch [104][2300/3746] lr: 2.180e-02, eta: 1 day, 16:54:11, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.3933, top5_acc: 0.6453, loss_cls: 3.4625, loss: 3.4625 +2024-12-30 01:47:05,027 - pyskl - INFO - Epoch [104][2400/3746] lr: 2.177e-02, eta: 1 day, 16:52:46, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6528, loss_cls: 3.4372, loss: 3.4372 +2024-12-30 01:48:29,479 - pyskl - INFO - Epoch [104][2500/3746] lr: 2.175e-02, eta: 1 day, 16:51:21, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6369, loss_cls: 3.5755, loss: 3.5755 +2024-12-30 01:49:54,001 - pyskl - INFO - Epoch [104][2600/3746] lr: 2.173e-02, eta: 1 day, 16:49:56, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3844, top5_acc: 0.6359, loss_cls: 3.5095, loss: 3.5095 +2024-12-30 01:51:18,926 - pyskl - INFO - Epoch [104][2700/3746] lr: 2.171e-02, eta: 1 day, 16:48:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6431, loss_cls: 3.4863, loss: 3.4863 +2024-12-30 01:52:43,782 - pyskl - INFO - Epoch [104][2800/3746] lr: 2.168e-02, eta: 1 day, 16:47:07, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6266, loss_cls: 3.5575, loss: 3.5575 +2024-12-30 01:54:08,787 - pyskl - INFO - Epoch [104][2900/3746] lr: 2.166e-02, eta: 1 day, 16:45:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6364, loss_cls: 3.5547, loss: 3.5547 +2024-12-30 01:55:33,733 - pyskl - INFO - Epoch [104][3000/3746] lr: 2.164e-02, eta: 1 day, 16:44:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6339, loss_cls: 3.5399, loss: 3.5399 +2024-12-30 01:56:58,874 - pyskl - INFO - Epoch [104][3100/3746] lr: 2.161e-02, eta: 1 day, 16:42:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6402, loss_cls: 3.5137, loss: 3.5137 +2024-12-30 01:58:23,857 - pyskl - INFO - Epoch [104][3200/3746] lr: 2.159e-02, eta: 1 day, 16:41:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6389, loss_cls: 3.5090, loss: 3.5090 +2024-12-30 01:59:49,185 - pyskl - INFO - Epoch [104][3300/3746] lr: 2.157e-02, eta: 1 day, 16:40:04, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6409, loss_cls: 3.5472, loss: 3.5472 +2024-12-30 02:01:14,419 - pyskl - INFO - Epoch [104][3400/3746] lr: 2.154e-02, eta: 1 day, 16:38:40, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3809, top5_acc: 0.6433, loss_cls: 3.4864, loss: 3.4864 +2024-12-30 02:02:39,840 - pyskl - INFO - Epoch [104][3500/3746] lr: 2.152e-02, eta: 1 day, 16:37:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6509, loss_cls: 3.4561, loss: 3.4561 +2024-12-30 02:04:04,924 - pyskl - INFO - Epoch [104][3600/3746] lr: 2.150e-02, eta: 1 day, 16:35:51, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6425, loss_cls: 3.5160, loss: 3.5160 +2024-12-30 02:05:29,732 - pyskl - INFO - Epoch [104][3700/3746] lr: 2.148e-02, eta: 1 day, 16:34:26, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6366, loss_cls: 3.5310, loss: 3.5310 +2024-12-30 02:06:11,066 - pyskl - INFO - Saving checkpoint at 104 epochs +2024-12-30 02:08:10,339 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 02:08:11,115 - pyskl - INFO - +top1_acc 0.3252 +top5_acc 0.5845 +2024-12-30 02:08:11,115 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 02:08:11,162 - pyskl - INFO - +mean_acc 0.3249 +2024-12-30 02:08:11,175 - pyskl - INFO - Epoch(val) [104][309] top1_acc: 0.3252, top5_acc: 0.5845, mean_class_accuracy: 0.3249 +2024-12-30 02:12:28,561 - pyskl - INFO - Epoch [105][100/3746] lr: 2.144e-02, eta: 1 day, 16:33:21, time: 2.574, data_time: 1.540, memory: 15990, top1_acc: 0.3848, top5_acc: 0.6548, loss_cls: 3.4231, loss: 3.4231 +2024-12-30 02:13:53,481 - pyskl - INFO - Epoch [105][200/3746] lr: 2.142e-02, eta: 1 day, 16:31:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6472, loss_cls: 3.4341, loss: 3.4341 +2024-12-30 02:15:18,703 - pyskl - INFO - Epoch [105][300/3746] lr: 2.140e-02, eta: 1 day, 16:30:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6412, loss_cls: 3.4751, loss: 3.4751 +2024-12-30 02:16:44,020 - pyskl - INFO - Epoch [105][400/3746] lr: 2.137e-02, eta: 1 day, 16:29:07, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6456, loss_cls: 3.4473, loss: 3.4473 +2024-12-30 02:18:09,340 - pyskl - INFO - Epoch [105][500/3746] lr: 2.135e-02, eta: 1 day, 16:27:43, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6434, loss_cls: 3.4648, loss: 3.4648 +2024-12-30 02:19:34,374 - pyskl - INFO - Epoch [105][600/3746] lr: 2.133e-02, eta: 1 day, 16:26:18, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6434, loss_cls: 3.4844, loss: 3.4844 +2024-12-30 02:20:59,667 - pyskl - INFO - Epoch [105][700/3746] lr: 2.130e-02, eta: 1 day, 16:24:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3858, top5_acc: 0.6450, loss_cls: 3.4715, loss: 3.4715 +2024-12-30 02:22:24,658 - pyskl - INFO - Epoch [105][800/3746] lr: 2.128e-02, eta: 1 day, 16:23:29, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3927, top5_acc: 0.6475, loss_cls: 3.4582, loss: 3.4582 +2024-12-30 02:23:49,771 - pyskl - INFO - Epoch [105][900/3746] lr: 2.126e-02, eta: 1 day, 16:22:04, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6417, loss_cls: 3.4838, loss: 3.4838 +2024-12-30 02:25:15,107 - pyskl - INFO - Epoch [105][1000/3746] lr: 2.124e-02, eta: 1 day, 16:20:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6542, loss_cls: 3.4337, loss: 3.4337 +2024-12-30 02:26:39,599 - pyskl - INFO - Epoch [105][1100/3746] lr: 2.121e-02, eta: 1 day, 16:19:15, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6509, loss_cls: 3.4126, loss: 3.4126 +2024-12-30 02:28:04,664 - pyskl - INFO - Epoch [105][1200/3746] lr: 2.119e-02, eta: 1 day, 16:17:50, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6428, loss_cls: 3.5101, loss: 3.5101 +2024-12-30 02:29:30,150 - pyskl - INFO - Epoch [105][1300/3746] lr: 2.117e-02, eta: 1 day, 16:16:26, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6395, loss_cls: 3.5208, loss: 3.5208 +2024-12-30 02:30:55,469 - pyskl - INFO - Epoch [105][1400/3746] lr: 2.114e-02, eta: 1 day, 16:15:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6520, loss_cls: 3.4377, loss: 3.4377 +2024-12-30 02:32:20,382 - pyskl - INFO - Epoch [105][1500/3746] lr: 2.112e-02, eta: 1 day, 16:13:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6422, loss_cls: 3.4892, loss: 3.4892 +2024-12-30 02:33:45,588 - pyskl - INFO - Epoch [105][1600/3746] lr: 2.110e-02, eta: 1 day, 16:12:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3844, top5_acc: 0.6478, loss_cls: 3.4770, loss: 3.4770 +2024-12-30 02:35:10,510 - pyskl - INFO - Epoch [105][1700/3746] lr: 2.108e-02, eta: 1 day, 16:10:47, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6430, loss_cls: 3.5069, loss: 3.5069 +2024-12-30 02:36:35,870 - pyskl - INFO - Epoch [105][1800/3746] lr: 2.105e-02, eta: 1 day, 16:09:23, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6516, loss_cls: 3.4110, loss: 3.4110 +2024-12-30 02:38:00,745 - pyskl - INFO - Epoch [105][1900/3746] lr: 2.103e-02, eta: 1 day, 16:07:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6430, loss_cls: 3.5125, loss: 3.5125 +2024-12-30 02:39:26,166 - pyskl - INFO - Epoch [105][2000/3746] lr: 2.101e-02, eta: 1 day, 16:06:33, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6392, loss_cls: 3.5144, loss: 3.5144 +2024-12-30 02:40:51,180 - pyskl - INFO - Epoch [105][2100/3746] lr: 2.098e-02, eta: 1 day, 16:05:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6436, loss_cls: 3.5041, loss: 3.5041 +2024-12-30 02:42:16,164 - pyskl - INFO - Epoch [105][2200/3746] lr: 2.096e-02, eta: 1 day, 16:03:44, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6366, loss_cls: 3.5337, loss: 3.5337 +2024-12-30 02:43:41,184 - pyskl - INFO - Epoch [105][2300/3746] lr: 2.094e-02, eta: 1 day, 16:02:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6344, loss_cls: 3.5201, loss: 3.5201 +2024-12-30 02:45:06,386 - pyskl - INFO - Epoch [105][2400/3746] lr: 2.092e-02, eta: 1 day, 16:00:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3822, top5_acc: 0.6442, loss_cls: 3.4942, loss: 3.4942 +2024-12-30 02:46:31,908 - pyskl - INFO - Epoch [105][2500/3746] lr: 2.089e-02, eta: 1 day, 15:59:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6483, loss_cls: 3.4865, loss: 3.4865 +2024-12-30 02:47:57,341 - pyskl - INFO - Epoch [105][2600/3746] lr: 2.087e-02, eta: 1 day, 15:58:06, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3762, top5_acc: 0.6436, loss_cls: 3.5067, loss: 3.5067 +2024-12-30 02:49:22,914 - pyskl - INFO - Epoch [105][2700/3746] lr: 2.085e-02, eta: 1 day, 15:56:41, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6475, loss_cls: 3.4797, loss: 3.4797 +2024-12-30 02:50:48,324 - pyskl - INFO - Epoch [105][2800/3746] lr: 2.083e-02, eta: 1 day, 15:55:17, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6425, loss_cls: 3.4851, loss: 3.4851 +2024-12-30 02:52:13,641 - pyskl - INFO - Epoch [105][2900/3746] lr: 2.080e-02, eta: 1 day, 15:53:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6398, loss_cls: 3.5085, loss: 3.5085 +2024-12-30 02:53:38,715 - pyskl - INFO - Epoch [105][3000/3746] lr: 2.078e-02, eta: 1 day, 15:52:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6423, loss_cls: 3.4651, loss: 3.4651 +2024-12-30 02:55:03,808 - pyskl - INFO - Epoch [105][3100/3746] lr: 2.076e-02, eta: 1 day, 15:51:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6425, loss_cls: 3.5077, loss: 3.5077 +2024-12-30 02:56:28,984 - pyskl - INFO - Epoch [105][3200/3746] lr: 2.073e-02, eta: 1 day, 15:49:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6409, loss_cls: 3.5261, loss: 3.5261 +2024-12-30 02:57:54,759 - pyskl - INFO - Epoch [105][3300/3746] lr: 2.071e-02, eta: 1 day, 15:48:14, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6422, loss_cls: 3.4852, loss: 3.4852 +2024-12-30 02:59:20,738 - pyskl - INFO - Epoch [105][3400/3746] lr: 2.069e-02, eta: 1 day, 15:46:50, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6336, loss_cls: 3.5096, loss: 3.5096 +2024-12-30 03:00:46,067 - pyskl - INFO - Epoch [105][3500/3746] lr: 2.067e-02, eta: 1 day, 15:45:25, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6436, loss_cls: 3.4571, loss: 3.4571 +2024-12-30 03:02:11,959 - pyskl - INFO - Epoch [105][3600/3746] lr: 2.064e-02, eta: 1 day, 15:44:01, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6444, loss_cls: 3.4590, loss: 3.4590 +2024-12-30 03:03:37,634 - pyskl - INFO - Epoch [105][3700/3746] lr: 2.062e-02, eta: 1 day, 15:42:36, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6347, loss_cls: 3.5271, loss: 3.5271 +2024-12-30 03:04:19,197 - pyskl - INFO - Saving checkpoint at 105 epochs +2024-12-30 03:06:20,950 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 03:06:21,648 - pyskl - INFO - +top1_acc 0.3313 +top5_acc 0.5883 +2024-12-30 03:06:21,648 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 03:06:21,701 - pyskl - INFO - +mean_acc 0.3310 +2024-12-30 03:06:21,719 - pyskl - INFO - Epoch(val) [105][309] top1_acc: 0.3313, top5_acc: 0.5883, mean_class_accuracy: 0.3310 +2024-12-30 03:10:44,707 - pyskl - INFO - Epoch [106][100/3746] lr: 2.059e-02, eta: 1 day, 15:41:32, time: 2.630, data_time: 1.598, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6573, loss_cls: 3.4004, loss: 3.4004 +2024-12-30 03:12:10,175 - pyskl - INFO - Epoch [106][200/3746] lr: 2.057e-02, eta: 1 day, 15:40:08, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6606, loss_cls: 3.4183, loss: 3.4183 +2024-12-30 03:13:35,176 - pyskl - INFO - Epoch [106][300/3746] lr: 2.054e-02, eta: 1 day, 15:38:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6648, loss_cls: 3.3977, loss: 3.3977 +2024-12-30 03:15:00,874 - pyskl - INFO - Epoch [106][400/3746] lr: 2.052e-02, eta: 1 day, 15:37:19, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6491, loss_cls: 3.4368, loss: 3.4368 +2024-12-30 03:16:26,217 - pyskl - INFO - Epoch [106][500/3746] lr: 2.050e-02, eta: 1 day, 15:35:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6420, loss_cls: 3.4709, loss: 3.4709 +2024-12-30 03:17:51,280 - pyskl - INFO - Epoch [106][600/3746] lr: 2.048e-02, eta: 1 day, 15:34:29, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3909, top5_acc: 0.6534, loss_cls: 3.4220, loss: 3.4220 +2024-12-30 03:19:16,335 - pyskl - INFO - Epoch [106][700/3746] lr: 2.045e-02, eta: 1 day, 15:33:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6505, loss_cls: 3.4386, loss: 3.4386 +2024-12-30 03:20:41,672 - pyskl - INFO - Epoch [106][800/3746] lr: 2.043e-02, eta: 1 day, 15:31:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6453, loss_cls: 3.4623, loss: 3.4623 +2024-12-30 03:22:06,424 - pyskl - INFO - Epoch [106][900/3746] lr: 2.041e-02, eta: 1 day, 15:30:15, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3831, top5_acc: 0.6452, loss_cls: 3.4932, loss: 3.4932 +2024-12-30 03:23:30,874 - pyskl - INFO - Epoch [106][1000/3746] lr: 2.039e-02, eta: 1 day, 15:28:50, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6595, loss_cls: 3.3668, loss: 3.3668 +2024-12-30 03:24:56,369 - pyskl - INFO - Epoch [106][1100/3746] lr: 2.036e-02, eta: 1 day, 15:27:26, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6488, loss_cls: 3.4404, loss: 3.4404 +2024-12-30 03:26:21,000 - pyskl - INFO - Epoch [106][1200/3746] lr: 2.034e-02, eta: 1 day, 15:26:01, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3858, top5_acc: 0.6427, loss_cls: 3.4745, loss: 3.4745 +2024-12-30 03:27:46,025 - pyskl - INFO - Epoch [106][1300/3746] lr: 2.032e-02, eta: 1 day, 15:24:36, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6498, loss_cls: 3.4212, loss: 3.4212 +2024-12-30 03:29:10,770 - pyskl - INFO - Epoch [106][1400/3746] lr: 2.030e-02, eta: 1 day, 15:23:11, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6422, loss_cls: 3.4729, loss: 3.4729 +2024-12-30 03:30:35,175 - pyskl - INFO - Epoch [106][1500/3746] lr: 2.027e-02, eta: 1 day, 15:21:46, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6456, loss_cls: 3.4720, loss: 3.4720 +2024-12-30 03:31:59,783 - pyskl - INFO - Epoch [106][1600/3746] lr: 2.025e-02, eta: 1 day, 15:20:21, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4017, top5_acc: 0.6591, loss_cls: 3.4037, loss: 3.4037 +2024-12-30 03:33:24,220 - pyskl - INFO - Epoch [106][1700/3746] lr: 2.023e-02, eta: 1 day, 15:18:56, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6514, loss_cls: 3.4846, loss: 3.4846 +2024-12-30 03:34:48,961 - pyskl - INFO - Epoch [106][1800/3746] lr: 2.021e-02, eta: 1 day, 15:17:31, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6370, loss_cls: 3.5058, loss: 3.5058 +2024-12-30 03:36:13,948 - pyskl - INFO - Epoch [106][1900/3746] lr: 2.018e-02, eta: 1 day, 15:16:07, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6447, loss_cls: 3.4731, loss: 3.4731 +2024-12-30 03:37:38,864 - pyskl - INFO - Epoch [106][2000/3746] lr: 2.016e-02, eta: 1 day, 15:14:42, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6481, loss_cls: 3.4790, loss: 3.4790 +2024-12-30 03:39:03,486 - pyskl - INFO - Epoch [106][2100/3746] lr: 2.014e-02, eta: 1 day, 15:13:17, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6483, loss_cls: 3.4591, loss: 3.4591 +2024-12-30 03:40:27,647 - pyskl - INFO - Epoch [106][2200/3746] lr: 2.012e-02, eta: 1 day, 15:11:52, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6514, loss_cls: 3.4202, loss: 3.4202 +2024-12-30 03:41:51,896 - pyskl - INFO - Epoch [106][2300/3746] lr: 2.009e-02, eta: 1 day, 15:10:27, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6419, loss_cls: 3.5034, loss: 3.5034 +2024-12-30 03:43:16,686 - pyskl - INFO - Epoch [106][2400/3746] lr: 2.007e-02, eta: 1 day, 15:09:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3966, top5_acc: 0.6475, loss_cls: 3.4340, loss: 3.4340 +2024-12-30 03:44:40,819 - pyskl - INFO - Epoch [106][2500/3746] lr: 2.005e-02, eta: 1 day, 15:07:37, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6436, loss_cls: 3.4707, loss: 3.4707 +2024-12-30 03:46:05,179 - pyskl - INFO - Epoch [106][2600/3746] lr: 2.003e-02, eta: 1 day, 15:06:12, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6564, loss_cls: 3.4085, loss: 3.4085 +2024-12-30 03:47:29,792 - pyskl - INFO - Epoch [106][2700/3746] lr: 2.000e-02, eta: 1 day, 15:04:47, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3806, top5_acc: 0.6386, loss_cls: 3.4945, loss: 3.4945 +2024-12-30 03:48:54,244 - pyskl - INFO - Epoch [106][2800/3746] lr: 1.998e-02, eta: 1 day, 15:03:22, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6406, loss_cls: 3.4733, loss: 3.4733 +2024-12-30 03:50:18,471 - pyskl - INFO - Epoch [106][2900/3746] lr: 1.996e-02, eta: 1 day, 15:01:57, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6425, loss_cls: 3.5150, loss: 3.5150 +2024-12-30 03:51:42,774 - pyskl - INFO - Epoch [106][3000/3746] lr: 1.994e-02, eta: 1 day, 15:00:32, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3933, top5_acc: 0.6552, loss_cls: 3.4475, loss: 3.4475 +2024-12-30 03:53:07,912 - pyskl - INFO - Epoch [106][3100/3746] lr: 1.991e-02, eta: 1 day, 14:59:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6492, loss_cls: 3.4671, loss: 3.4671 +2024-12-30 03:54:33,174 - pyskl - INFO - Epoch [106][3200/3746] lr: 1.989e-02, eta: 1 day, 14:57:43, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6456, loss_cls: 3.4766, loss: 3.4766 +2024-12-30 03:55:58,724 - pyskl - INFO - Epoch [106][3300/3746] lr: 1.987e-02, eta: 1 day, 14:56:18, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6350, loss_cls: 3.5358, loss: 3.5358 +2024-12-30 03:57:23,824 - pyskl - INFO - Epoch [106][3400/3746] lr: 1.985e-02, eta: 1 day, 14:54:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6433, loss_cls: 3.4781, loss: 3.4781 +2024-12-30 03:58:48,967 - pyskl - INFO - Epoch [106][3500/3746] lr: 1.983e-02, eta: 1 day, 14:53:29, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6377, loss_cls: 3.4995, loss: 3.4995 +2024-12-30 04:00:14,013 - pyskl - INFO - Epoch [106][3600/3746] lr: 1.980e-02, eta: 1 day, 14:52:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6375, loss_cls: 3.4937, loss: 3.4937 +2024-12-30 04:01:39,141 - pyskl - INFO - Epoch [106][3700/3746] lr: 1.978e-02, eta: 1 day, 14:50:39, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3822, top5_acc: 0.6286, loss_cls: 3.5420, loss: 3.5420 +2024-12-30 04:02:20,569 - pyskl - INFO - Saving checkpoint at 106 epochs +2024-12-30 04:04:20,530 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 04:04:21,209 - pyskl - INFO - +top1_acc 0.3352 +top5_acc 0.5962 +2024-12-30 04:04:21,209 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 04:04:21,252 - pyskl - INFO - +mean_acc 0.3350 +2024-12-30 04:04:21,257 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_103.pth was removed +2024-12-30 04:04:21,624 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_106.pth. +2024-12-30 04:04:21,625 - pyskl - INFO - Best top1_acc is 0.3352 at 106 epoch. +2024-12-30 04:04:21,647 - pyskl - INFO - Epoch(val) [106][309] top1_acc: 0.3352, top5_acc: 0.5962, mean_class_accuracy: 0.3350 +2024-12-30 04:08:49,012 - pyskl - INFO - Epoch [107][100/3746] lr: 1.975e-02, eta: 1 day, 14:49:35, time: 2.674, data_time: 1.608, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6670, loss_cls: 3.3989, loss: 3.3989 +2024-12-30 04:10:14,672 - pyskl - INFO - Epoch [107][200/3746] lr: 1.973e-02, eta: 1 day, 14:48:10, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.3945, top5_acc: 0.6606, loss_cls: 3.4155, loss: 3.4155 +2024-12-30 04:11:39,790 - pyskl - INFO - Epoch [107][300/3746] lr: 1.970e-02, eta: 1 day, 14:46:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6617, loss_cls: 3.3527, loss: 3.3527 +2024-12-30 04:13:04,718 - pyskl - INFO - Epoch [107][400/3746] lr: 1.968e-02, eta: 1 day, 14:45:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6538, loss_cls: 3.4193, loss: 3.4193 +2024-12-30 04:14:30,137 - pyskl - INFO - Epoch [107][500/3746] lr: 1.966e-02, eta: 1 day, 14:43:56, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6545, loss_cls: 3.4092, loss: 3.4092 +2024-12-30 04:15:55,681 - pyskl - INFO - Epoch [107][600/3746] lr: 1.964e-02, eta: 1 day, 14:42:32, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6533, loss_cls: 3.4118, loss: 3.4118 +2024-12-30 04:17:21,009 - pyskl - INFO - Epoch [107][700/3746] lr: 1.961e-02, eta: 1 day, 14:41:07, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6544, loss_cls: 3.4127, loss: 3.4127 +2024-12-30 04:18:46,558 - pyskl - INFO - Epoch [107][800/3746] lr: 1.959e-02, eta: 1 day, 14:39:42, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3945, top5_acc: 0.6545, loss_cls: 3.4256, loss: 3.4256 +2024-12-30 04:20:11,948 - pyskl - INFO - Epoch [107][900/3746] lr: 1.957e-02, eta: 1 day, 14:38:18, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6600, loss_cls: 3.4119, loss: 3.4119 +2024-12-30 04:21:37,063 - pyskl - INFO - Epoch [107][1000/3746] lr: 1.955e-02, eta: 1 day, 14:36:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6539, loss_cls: 3.4199, loss: 3.4199 +2024-12-30 04:23:02,374 - pyskl - INFO - Epoch [107][1100/3746] lr: 1.953e-02, eta: 1 day, 14:35:28, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6589, loss_cls: 3.4036, loss: 3.4036 +2024-12-30 04:24:27,832 - pyskl - INFO - Epoch [107][1200/3746] lr: 1.950e-02, eta: 1 day, 14:34:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3831, top5_acc: 0.6505, loss_cls: 3.4628, loss: 3.4628 +2024-12-30 04:25:52,899 - pyskl - INFO - Epoch [107][1300/3746] lr: 1.948e-02, eta: 1 day, 14:32:39, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6547, loss_cls: 3.4586, loss: 3.4586 +2024-12-30 04:27:17,847 - pyskl - INFO - Epoch [107][1400/3746] lr: 1.946e-02, eta: 1 day, 14:31:14, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3972, top5_acc: 0.6453, loss_cls: 3.4437, loss: 3.4437 +2024-12-30 04:28:43,114 - pyskl - INFO - Epoch [107][1500/3746] lr: 1.944e-02, eta: 1 day, 14:29:49, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6430, loss_cls: 3.4844, loss: 3.4844 +2024-12-30 04:30:07,980 - pyskl - INFO - Epoch [107][1600/3746] lr: 1.942e-02, eta: 1 day, 14:28:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6480, loss_cls: 3.4686, loss: 3.4686 +2024-12-30 04:31:33,522 - pyskl - INFO - Epoch [107][1700/3746] lr: 1.939e-02, eta: 1 day, 14:27:00, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6592, loss_cls: 3.4500, loss: 3.4500 +2024-12-30 04:32:58,705 - pyskl - INFO - Epoch [107][1800/3746] lr: 1.937e-02, eta: 1 day, 14:25:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3898, top5_acc: 0.6366, loss_cls: 3.4904, loss: 3.4904 +2024-12-30 04:34:23,867 - pyskl - INFO - Epoch [107][1900/3746] lr: 1.935e-02, eta: 1 day, 14:24:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6419, loss_cls: 3.4733, loss: 3.4733 +2024-12-30 04:35:48,976 - pyskl - INFO - Epoch [107][2000/3746] lr: 1.933e-02, eta: 1 day, 14:22:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6552, loss_cls: 3.4736, loss: 3.4736 +2024-12-30 04:37:13,714 - pyskl - INFO - Epoch [107][2100/3746] lr: 1.930e-02, eta: 1 day, 14:21:21, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6467, loss_cls: 3.4342, loss: 3.4342 +2024-12-30 04:38:38,867 - pyskl - INFO - Epoch [107][2200/3746] lr: 1.928e-02, eta: 1 day, 14:19:56, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6497, loss_cls: 3.4488, loss: 3.4488 +2024-12-30 04:40:03,964 - pyskl - INFO - Epoch [107][2300/3746] lr: 1.926e-02, eta: 1 day, 14:18:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6397, loss_cls: 3.5079, loss: 3.5079 +2024-12-30 04:41:29,373 - pyskl - INFO - Epoch [107][2400/3746] lr: 1.924e-02, eta: 1 day, 14:17:07, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6373, loss_cls: 3.5106, loss: 3.5106 +2024-12-30 04:42:54,935 - pyskl - INFO - Epoch [107][2500/3746] lr: 1.922e-02, eta: 1 day, 14:15:42, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6505, loss_cls: 3.4401, loss: 3.4401 +2024-12-30 04:44:20,146 - pyskl - INFO - Epoch [107][2600/3746] lr: 1.919e-02, eta: 1 day, 14:14:18, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6523, loss_cls: 3.4415, loss: 3.4415 +2024-12-30 04:45:44,771 - pyskl - INFO - Epoch [107][2700/3746] lr: 1.917e-02, eta: 1 day, 14:12:53, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4005, top5_acc: 0.6489, loss_cls: 3.4176, loss: 3.4176 +2024-12-30 04:47:09,856 - pyskl - INFO - Epoch [107][2800/3746] lr: 1.915e-02, eta: 1 day, 14:11:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6583, loss_cls: 3.4219, loss: 3.4219 +2024-12-30 04:48:34,487 - pyskl - INFO - Epoch [107][2900/3746] lr: 1.913e-02, eta: 1 day, 14:10:03, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3914, top5_acc: 0.6478, loss_cls: 3.4595, loss: 3.4595 +2024-12-30 04:49:59,662 - pyskl - INFO - Epoch [107][3000/3746] lr: 1.911e-02, eta: 1 day, 14:08:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6564, loss_cls: 3.4013, loss: 3.4013 +2024-12-30 04:51:24,739 - pyskl - INFO - Epoch [107][3100/3746] lr: 1.908e-02, eta: 1 day, 14:07:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6508, loss_cls: 3.4163, loss: 3.4163 +2024-12-30 04:52:49,704 - pyskl - INFO - Epoch [107][3200/3746] lr: 1.906e-02, eta: 1 day, 14:05:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6452, loss_cls: 3.4405, loss: 3.4405 +2024-12-30 04:54:14,967 - pyskl - INFO - Epoch [107][3300/3746] lr: 1.904e-02, eta: 1 day, 14:04:24, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6550, loss_cls: 3.4094, loss: 3.4094 +2024-12-30 04:55:39,697 - pyskl - INFO - Epoch [107][3400/3746] lr: 1.902e-02, eta: 1 day, 14:02:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6398, loss_cls: 3.4916, loss: 3.4916 +2024-12-30 04:57:04,922 - pyskl - INFO - Epoch [107][3500/3746] lr: 1.900e-02, eta: 1 day, 14:01:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3844, top5_acc: 0.6464, loss_cls: 3.4830, loss: 3.4830 +2024-12-30 04:58:29,812 - pyskl - INFO - Epoch [107][3600/3746] lr: 1.897e-02, eta: 1 day, 14:00:09, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3914, top5_acc: 0.6433, loss_cls: 3.4770, loss: 3.4770 +2024-12-30 04:59:55,103 - pyskl - INFO - Epoch [107][3700/3746] lr: 1.895e-02, eta: 1 day, 13:58:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6412, loss_cls: 3.5099, loss: 3.5099 +2024-12-30 05:00:36,273 - pyskl - INFO - Saving checkpoint at 107 epochs +2024-12-30 05:02:36,445 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 05:02:37,129 - pyskl - INFO - +top1_acc 0.3319 +top5_acc 0.5898 +2024-12-30 05:02:37,130 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 05:02:37,170 - pyskl - INFO - +mean_acc 0.3317 +2024-12-30 05:02:37,183 - pyskl - INFO - Epoch(val) [107][309] top1_acc: 0.3319, top5_acc: 0.5898, mean_class_accuracy: 0.3317 +2024-12-30 05:06:57,370 - pyskl - INFO - Epoch [108][100/3746] lr: 1.892e-02, eta: 1 day, 13:57:36, time: 2.602, data_time: 1.576, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6723, loss_cls: 3.3550, loss: 3.3550 +2024-12-30 05:08:22,234 - pyskl - INFO - Epoch [108][200/3746] lr: 1.890e-02, eta: 1 day, 13:56:11, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6706, loss_cls: 3.3201, loss: 3.3201 +2024-12-30 05:09:47,165 - pyskl - INFO - Epoch [108][300/3746] lr: 1.888e-02, eta: 1 day, 13:54:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4044, top5_acc: 0.6603, loss_cls: 3.3625, loss: 3.3625 +2024-12-30 05:11:11,951 - pyskl - INFO - Epoch [108][400/3746] lr: 1.886e-02, eta: 1 day, 13:53:21, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6591, loss_cls: 3.3917, loss: 3.3917 +2024-12-30 05:12:36,943 - pyskl - INFO - Epoch [108][500/3746] lr: 1.883e-02, eta: 1 day, 13:51:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6545, loss_cls: 3.4281, loss: 3.4281 +2024-12-30 05:14:02,213 - pyskl - INFO - Epoch [108][600/3746] lr: 1.881e-02, eta: 1 day, 13:50:31, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6511, loss_cls: 3.4095, loss: 3.4095 +2024-12-30 05:15:27,461 - pyskl - INFO - Epoch [108][700/3746] lr: 1.879e-02, eta: 1 day, 13:49:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3941, top5_acc: 0.6508, loss_cls: 3.4303, loss: 3.4303 +2024-12-30 05:16:53,186 - pyskl - INFO - Epoch [108][800/3746] lr: 1.877e-02, eta: 1 day, 13:47:42, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6550, loss_cls: 3.4053, loss: 3.4053 +2024-12-30 05:18:18,360 - pyskl - INFO - Epoch [108][900/3746] lr: 1.875e-02, eta: 1 day, 13:46:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6559, loss_cls: 3.4122, loss: 3.4122 +2024-12-30 05:19:43,772 - pyskl - INFO - Epoch [108][1000/3746] lr: 1.872e-02, eta: 1 day, 13:44:52, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6553, loss_cls: 3.4063, loss: 3.4063 +2024-12-30 05:21:08,553 - pyskl - INFO - Epoch [108][1100/3746] lr: 1.870e-02, eta: 1 day, 13:43:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6538, loss_cls: 3.4100, loss: 3.4100 +2024-12-30 05:22:34,252 - pyskl - INFO - Epoch [108][1200/3746] lr: 1.868e-02, eta: 1 day, 13:42:03, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6472, loss_cls: 3.4836, loss: 3.4836 +2024-12-30 05:23:59,106 - pyskl - INFO - Epoch [108][1300/3746] lr: 1.866e-02, eta: 1 day, 13:40:38, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6523, loss_cls: 3.4288, loss: 3.4288 +2024-12-30 05:25:24,453 - pyskl - INFO - Epoch [108][1400/3746] lr: 1.864e-02, eta: 1 day, 13:39:13, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6620, loss_cls: 3.4005, loss: 3.4005 +2024-12-30 05:26:49,444 - pyskl - INFO - Epoch [108][1500/3746] lr: 1.862e-02, eta: 1 day, 13:37:48, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3997, top5_acc: 0.6584, loss_cls: 3.3932, loss: 3.3932 +2024-12-30 05:28:14,985 - pyskl - INFO - Epoch [108][1600/3746] lr: 1.859e-02, eta: 1 day, 13:36:24, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6539, loss_cls: 3.4593, loss: 3.4593 +2024-12-30 05:29:39,964 - pyskl - INFO - Epoch [108][1700/3746] lr: 1.857e-02, eta: 1 day, 13:34:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6541, loss_cls: 3.4277, loss: 3.4277 +2024-12-30 05:31:05,786 - pyskl - INFO - Epoch [108][1800/3746] lr: 1.855e-02, eta: 1 day, 13:33:34, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6533, loss_cls: 3.4138, loss: 3.4138 +2024-12-30 05:32:31,246 - pyskl - INFO - Epoch [108][1900/3746] lr: 1.853e-02, eta: 1 day, 13:32:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3966, top5_acc: 0.6517, loss_cls: 3.4238, loss: 3.4238 +2024-12-30 05:33:56,517 - pyskl - INFO - Epoch [108][2000/3746] lr: 1.851e-02, eta: 1 day, 13:30:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6508, loss_cls: 3.4361, loss: 3.4361 +2024-12-30 05:35:21,914 - pyskl - INFO - Epoch [108][2100/3746] lr: 1.848e-02, eta: 1 day, 13:29:20, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6548, loss_cls: 3.4308, loss: 3.4308 +2024-12-30 05:36:47,381 - pyskl - INFO - Epoch [108][2200/3746] lr: 1.846e-02, eta: 1 day, 13:27:56, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6466, loss_cls: 3.4288, loss: 3.4288 +2024-12-30 05:38:12,655 - pyskl - INFO - Epoch [108][2300/3746] lr: 1.844e-02, eta: 1 day, 13:26:31, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3848, top5_acc: 0.6530, loss_cls: 3.4372, loss: 3.4372 +2024-12-30 05:39:37,498 - pyskl - INFO - Epoch [108][2400/3746] lr: 1.842e-02, eta: 1 day, 13:25:06, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6441, loss_cls: 3.4588, loss: 3.4588 +2024-12-30 05:41:03,077 - pyskl - INFO - Epoch [108][2500/3746] lr: 1.840e-02, eta: 1 day, 13:23:41, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6492, loss_cls: 3.4528, loss: 3.4528 +2024-12-30 05:42:28,245 - pyskl - INFO - Epoch [108][2600/3746] lr: 1.838e-02, eta: 1 day, 13:22:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6530, loss_cls: 3.4490, loss: 3.4490 +2024-12-30 05:43:53,287 - pyskl - INFO - Epoch [108][2700/3746] lr: 1.835e-02, eta: 1 day, 13:20:52, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6639, loss_cls: 3.3609, loss: 3.3609 +2024-12-30 05:45:18,514 - pyskl - INFO - Epoch [108][2800/3746] lr: 1.833e-02, eta: 1 day, 13:19:27, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3812, top5_acc: 0.6438, loss_cls: 3.4700, loss: 3.4700 +2024-12-30 05:46:43,653 - pyskl - INFO - Epoch [108][2900/3746] lr: 1.831e-02, eta: 1 day, 13:18:02, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3986, top5_acc: 0.6583, loss_cls: 3.4013, loss: 3.4013 +2024-12-30 05:48:08,592 - pyskl - INFO - Epoch [108][3000/3746] lr: 1.829e-02, eta: 1 day, 13:16:37, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6517, loss_cls: 3.4478, loss: 3.4478 +2024-12-30 05:49:33,560 - pyskl - INFO - Epoch [108][3100/3746] lr: 1.827e-02, eta: 1 day, 13:15:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6578, loss_cls: 3.4004, loss: 3.4004 +2024-12-30 05:50:57,700 - pyskl - INFO - Epoch [108][3200/3746] lr: 1.825e-02, eta: 1 day, 13:13:47, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6556, loss_cls: 3.4142, loss: 3.4142 +2024-12-30 05:52:22,169 - pyskl - INFO - Epoch [108][3300/3746] lr: 1.823e-02, eta: 1 day, 13:12:22, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6436, loss_cls: 3.4420, loss: 3.4420 +2024-12-30 05:53:46,854 - pyskl - INFO - Epoch [108][3400/3746] lr: 1.820e-02, eta: 1 day, 13:10:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6550, loss_cls: 3.4029, loss: 3.4029 +2024-12-30 05:55:11,532 - pyskl - INFO - Epoch [108][3500/3746] lr: 1.818e-02, eta: 1 day, 13:09:32, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6420, loss_cls: 3.4598, loss: 3.4598 +2024-12-30 05:56:36,991 - pyskl - INFO - Epoch [108][3600/3746] lr: 1.816e-02, eta: 1 day, 13:08:08, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6483, loss_cls: 3.4452, loss: 3.4452 +2024-12-30 05:58:02,319 - pyskl - INFO - Epoch [108][3700/3746] lr: 1.814e-02, eta: 1 day, 13:06:43, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3903, top5_acc: 0.6545, loss_cls: 3.4287, loss: 3.4287 +2024-12-30 05:58:43,783 - pyskl - INFO - Saving checkpoint at 108 epochs +2024-12-30 06:00:44,005 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 06:00:44,838 - pyskl - INFO - +top1_acc 0.3389 +top5_acc 0.5947 +2024-12-30 06:00:44,839 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 06:00:44,890 - pyskl - INFO - +mean_acc 0.3386 +2024-12-30 06:00:44,895 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_106.pth was removed +2024-12-30 06:00:45,176 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_108.pth. +2024-12-30 06:00:45,177 - pyskl - INFO - Best top1_acc is 0.3389 at 108 epoch. +2024-12-30 06:00:45,189 - pyskl - INFO - Epoch(val) [108][309] top1_acc: 0.3389, top5_acc: 0.5947, mean_class_accuracy: 0.3386 +2024-12-30 06:04:55,043 - pyskl - INFO - Epoch [109][100/3746] lr: 1.811e-02, eta: 1 day, 13:05:28, time: 2.498, data_time: 1.463, memory: 15990, top1_acc: 0.4163, top5_acc: 0.6675, loss_cls: 3.3222, loss: 3.3222 +2024-12-30 06:06:19,770 - pyskl - INFO - Epoch [109][200/3746] lr: 1.809e-02, eta: 1 day, 13:04:03, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6553, loss_cls: 3.4243, loss: 3.4243 +2024-12-30 06:07:45,588 - pyskl - INFO - Epoch [109][300/3746] lr: 1.806e-02, eta: 1 day, 13:02:38, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.4091, top5_acc: 0.6591, loss_cls: 3.3638, loss: 3.3638 +2024-12-30 06:09:10,074 - pyskl - INFO - Epoch [109][400/3746] lr: 1.804e-02, eta: 1 day, 13:01:13, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6605, loss_cls: 3.3652, loss: 3.3652 +2024-12-30 06:10:34,963 - pyskl - INFO - Epoch [109][500/3746] lr: 1.802e-02, eta: 1 day, 12:59:48, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6591, loss_cls: 3.3893, loss: 3.3893 +2024-12-30 06:12:00,070 - pyskl - INFO - Epoch [109][600/3746] lr: 1.800e-02, eta: 1 day, 12:58:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4016, top5_acc: 0.6673, loss_cls: 3.3802, loss: 3.3802 +2024-12-30 06:13:24,772 - pyskl - INFO - Epoch [109][700/3746] lr: 1.798e-02, eta: 1 day, 12:56:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3986, top5_acc: 0.6512, loss_cls: 3.4117, loss: 3.4117 +2024-12-30 06:14:49,906 - pyskl - INFO - Epoch [109][800/3746] lr: 1.796e-02, eta: 1 day, 12:55:34, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6670, loss_cls: 3.3592, loss: 3.3592 +2024-12-30 06:16:15,097 - pyskl - INFO - Epoch [109][900/3746] lr: 1.794e-02, eta: 1 day, 12:54:09, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6603, loss_cls: 3.3664, loss: 3.3664 +2024-12-30 06:17:40,518 - pyskl - INFO - Epoch [109][1000/3746] lr: 1.791e-02, eta: 1 day, 12:52:44, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6616, loss_cls: 3.3957, loss: 3.3957 +2024-12-30 06:19:05,628 - pyskl - INFO - Epoch [109][1100/3746] lr: 1.789e-02, eta: 1 day, 12:51:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6569, loss_cls: 3.3965, loss: 3.3965 +2024-12-30 06:20:30,845 - pyskl - INFO - Epoch [109][1200/3746] lr: 1.787e-02, eta: 1 day, 12:49:54, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4033, top5_acc: 0.6587, loss_cls: 3.3826, loss: 3.3826 +2024-12-30 06:21:55,824 - pyskl - INFO - Epoch [109][1300/3746] lr: 1.785e-02, eta: 1 day, 12:48:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4008, top5_acc: 0.6605, loss_cls: 3.3657, loss: 3.3657 +2024-12-30 06:23:21,262 - pyskl - INFO - Epoch [109][1400/3746] lr: 1.783e-02, eta: 1 day, 12:47:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3997, top5_acc: 0.6613, loss_cls: 3.3950, loss: 3.3950 +2024-12-30 06:24:46,670 - pyskl - INFO - Epoch [109][1500/3746] lr: 1.781e-02, eta: 1 day, 12:45:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6406, loss_cls: 3.4633, loss: 3.4633 +2024-12-30 06:26:12,077 - pyskl - INFO - Epoch [109][1600/3746] lr: 1.779e-02, eta: 1 day, 12:44:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6727, loss_cls: 3.3393, loss: 3.3393 +2024-12-30 06:27:36,581 - pyskl - INFO - Epoch [109][1700/3746] lr: 1.776e-02, eta: 1 day, 12:42:50, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6581, loss_cls: 3.3645, loss: 3.3645 +2024-12-30 06:29:01,748 - pyskl - INFO - Epoch [109][1800/3746] lr: 1.774e-02, eta: 1 day, 12:41:25, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6584, loss_cls: 3.3929, loss: 3.3929 +2024-12-30 06:30:27,558 - pyskl - INFO - Epoch [109][1900/3746] lr: 1.772e-02, eta: 1 day, 12:40:01, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6527, loss_cls: 3.4335, loss: 3.4335 +2024-12-30 06:31:52,694 - pyskl - INFO - Epoch [109][2000/3746] lr: 1.770e-02, eta: 1 day, 12:38:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6594, loss_cls: 3.4123, loss: 3.4123 +2024-12-30 06:33:17,955 - pyskl - INFO - Epoch [109][2100/3746] lr: 1.768e-02, eta: 1 day, 12:37:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6561, loss_cls: 3.3861, loss: 3.3861 +2024-12-30 06:34:43,460 - pyskl - INFO - Epoch [109][2200/3746] lr: 1.766e-02, eta: 1 day, 12:35:46, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6520, loss_cls: 3.4199, loss: 3.4199 +2024-12-30 06:36:08,598 - pyskl - INFO - Epoch [109][2300/3746] lr: 1.764e-02, eta: 1 day, 12:34:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6486, loss_cls: 3.4377, loss: 3.4377 +2024-12-30 06:37:33,656 - pyskl - INFO - Epoch [109][2400/3746] lr: 1.761e-02, eta: 1 day, 12:32:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6469, loss_cls: 3.4457, loss: 3.4457 +2024-12-30 06:38:58,687 - pyskl - INFO - Epoch [109][2500/3746] lr: 1.759e-02, eta: 1 day, 12:31:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6466, loss_cls: 3.4354, loss: 3.4354 +2024-12-30 06:40:23,404 - pyskl - INFO - Epoch [109][2600/3746] lr: 1.757e-02, eta: 1 day, 12:30:07, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6556, loss_cls: 3.4044, loss: 3.4044 +2024-12-30 06:41:48,072 - pyskl - INFO - Epoch [109][2700/3746] lr: 1.755e-02, eta: 1 day, 12:28:42, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6502, loss_cls: 3.4722, loss: 3.4722 +2024-12-30 06:43:12,958 - pyskl - INFO - Epoch [109][2800/3746] lr: 1.753e-02, eta: 1 day, 12:27:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6492, loss_cls: 3.4789, loss: 3.4789 +2024-12-30 06:44:38,182 - pyskl - INFO - Epoch [109][2900/3746] lr: 1.751e-02, eta: 1 day, 12:25:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6489, loss_cls: 3.4584, loss: 3.4584 +2024-12-30 06:46:03,015 - pyskl - INFO - Epoch [109][3000/3746] lr: 1.749e-02, eta: 1 day, 12:24:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6539, loss_cls: 3.4282, loss: 3.4282 +2024-12-30 06:47:28,078 - pyskl - INFO - Epoch [109][3100/3746] lr: 1.747e-02, eta: 1 day, 12:23:02, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6545, loss_cls: 3.3930, loss: 3.3930 +2024-12-30 06:48:52,581 - pyskl - INFO - Epoch [109][3200/3746] lr: 1.744e-02, eta: 1 day, 12:21:37, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6533, loss_cls: 3.3848, loss: 3.3848 +2024-12-30 06:50:17,469 - pyskl - INFO - Epoch [109][3300/3746] lr: 1.742e-02, eta: 1 day, 12:20:12, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6523, loss_cls: 3.4119, loss: 3.4119 +2024-12-30 06:51:42,063 - pyskl - INFO - Epoch [109][3400/3746] lr: 1.740e-02, eta: 1 day, 12:18:47, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6536, loss_cls: 3.4408, loss: 3.4408 +2024-12-30 06:53:06,745 - pyskl - INFO - Epoch [109][3500/3746] lr: 1.738e-02, eta: 1 day, 12:17:22, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6533, loss_cls: 3.4087, loss: 3.4087 +2024-12-30 06:54:31,230 - pyskl - INFO - Epoch [109][3600/3746] lr: 1.736e-02, eta: 1 day, 12:15:57, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6453, loss_cls: 3.4368, loss: 3.4368 +2024-12-30 06:55:55,687 - pyskl - INFO - Epoch [109][3700/3746] lr: 1.734e-02, eta: 1 day, 12:14:32, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6591, loss_cls: 3.4017, loss: 3.4017 +2024-12-30 06:56:36,437 - pyskl - INFO - Saving checkpoint at 109 epochs +2024-12-30 06:58:34,301 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 06:58:34,977 - pyskl - INFO - +top1_acc 0.3474 +top5_acc 0.6010 +2024-12-30 06:58:34,977 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 06:58:35,018 - pyskl - INFO - +mean_acc 0.3471 +2024-12-30 06:58:35,022 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_108.pth was removed +2024-12-30 06:58:35,503 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_109.pth. +2024-12-30 06:58:35,504 - pyskl - INFO - Best top1_acc is 0.3474 at 109 epoch. +2024-12-30 06:58:35,520 - pyskl - INFO - Epoch(val) [109][309] top1_acc: 0.3474, top5_acc: 0.6010, mean_class_accuracy: 0.3471 +2024-12-30 07:02:52,979 - pyskl - INFO - Epoch [110][100/3746] lr: 1.731e-02, eta: 1 day, 12:13:18, time: 2.574, data_time: 1.534, memory: 15990, top1_acc: 0.4188, top5_acc: 0.6722, loss_cls: 3.2905, loss: 3.2905 +2024-12-30 07:04:18,488 - pyskl - INFO - Epoch [110][200/3746] lr: 1.729e-02, eta: 1 day, 12:11:53, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.4145, top5_acc: 0.6642, loss_cls: 3.3291, loss: 3.3291 +2024-12-30 07:05:43,873 - pyskl - INFO - Epoch [110][300/3746] lr: 1.727e-02, eta: 1 day, 12:10:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6689, loss_cls: 3.3332, loss: 3.3332 +2024-12-30 07:07:08,542 - pyskl - INFO - Epoch [110][400/3746] lr: 1.724e-02, eta: 1 day, 12:09:03, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4177, top5_acc: 0.6655, loss_cls: 3.2924, loss: 3.2924 +2024-12-30 07:08:33,347 - pyskl - INFO - Epoch [110][500/3746] lr: 1.722e-02, eta: 1 day, 12:07:38, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6650, loss_cls: 3.3385, loss: 3.3385 +2024-12-30 07:09:58,517 - pyskl - INFO - Epoch [110][600/3746] lr: 1.720e-02, eta: 1 day, 12:06:13, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4009, top5_acc: 0.6658, loss_cls: 3.3600, loss: 3.3600 +2024-12-30 07:11:23,704 - pyskl - INFO - Epoch [110][700/3746] lr: 1.718e-02, eta: 1 day, 12:04:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6587, loss_cls: 3.3938, loss: 3.3938 +2024-12-30 07:12:48,668 - pyskl - INFO - Epoch [110][800/3746] lr: 1.716e-02, eta: 1 day, 12:03:24, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6628, loss_cls: 3.3418, loss: 3.3418 +2024-12-30 07:14:13,730 - pyskl - INFO - Epoch [110][900/3746] lr: 1.714e-02, eta: 1 day, 12:01:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4070, top5_acc: 0.6713, loss_cls: 3.3331, loss: 3.3331 +2024-12-30 07:15:39,128 - pyskl - INFO - Epoch [110][1000/3746] lr: 1.712e-02, eta: 1 day, 12:00:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6609, loss_cls: 3.3663, loss: 3.3663 +2024-12-30 07:17:04,074 - pyskl - INFO - Epoch [110][1100/3746] lr: 1.710e-02, eta: 1 day, 11:59:09, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4025, top5_acc: 0.6594, loss_cls: 3.3871, loss: 3.3871 +2024-12-30 07:18:29,260 - pyskl - INFO - Epoch [110][1200/3746] lr: 1.708e-02, eta: 1 day, 11:57:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6648, loss_cls: 3.3931, loss: 3.3931 +2024-12-30 07:19:54,166 - pyskl - INFO - Epoch [110][1300/3746] lr: 1.705e-02, eta: 1 day, 11:56:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4138, top5_acc: 0.6639, loss_cls: 3.3251, loss: 3.3251 +2024-12-30 07:21:19,178 - pyskl - INFO - Epoch [110][1400/3746] lr: 1.703e-02, eta: 1 day, 11:54:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4102, top5_acc: 0.6717, loss_cls: 3.3353, loss: 3.3353 +2024-12-30 07:22:43,878 - pyskl - INFO - Epoch [110][1500/3746] lr: 1.701e-02, eta: 1 day, 11:53:29, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6417, loss_cls: 3.4616, loss: 3.4616 +2024-12-30 07:24:08,945 - pyskl - INFO - Epoch [110][1600/3746] lr: 1.699e-02, eta: 1 day, 11:52:04, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6520, loss_cls: 3.4251, loss: 3.4251 +2024-12-30 07:25:34,173 - pyskl - INFO - Epoch [110][1700/3746] lr: 1.697e-02, eta: 1 day, 11:50:39, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6594, loss_cls: 3.3787, loss: 3.3787 +2024-12-30 07:26:59,202 - pyskl - INFO - Epoch [110][1800/3746] lr: 1.695e-02, eta: 1 day, 11:49:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6497, loss_cls: 3.3979, loss: 3.3979 +2024-12-30 07:28:24,270 - pyskl - INFO - Epoch [110][1900/3746] lr: 1.693e-02, eta: 1 day, 11:47:50, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6578, loss_cls: 3.3997, loss: 3.3997 +2024-12-30 07:29:49,532 - pyskl - INFO - Epoch [110][2000/3746] lr: 1.691e-02, eta: 1 day, 11:46:25, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6647, loss_cls: 3.3650, loss: 3.3650 +2024-12-30 07:31:15,055 - pyskl - INFO - Epoch [110][2100/3746] lr: 1.689e-02, eta: 1 day, 11:45:00, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6602, loss_cls: 3.4093, loss: 3.4093 +2024-12-30 07:32:40,371 - pyskl - INFO - Epoch [110][2200/3746] lr: 1.687e-02, eta: 1 day, 11:43:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6591, loss_cls: 3.4308, loss: 3.4308 +2024-12-30 07:34:05,398 - pyskl - INFO - Epoch [110][2300/3746] lr: 1.685e-02, eta: 1 day, 11:42:10, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6502, loss_cls: 3.3782, loss: 3.3782 +2024-12-30 07:35:30,171 - pyskl - INFO - Epoch [110][2400/3746] lr: 1.682e-02, eta: 1 day, 11:40:45, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6577, loss_cls: 3.4457, loss: 3.4457 +2024-12-30 07:36:55,022 - pyskl - INFO - Epoch [110][2500/3746] lr: 1.680e-02, eta: 1 day, 11:39:20, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6663, loss_cls: 3.3750, loss: 3.3750 +2024-12-30 07:38:19,392 - pyskl - INFO - Epoch [110][2600/3746] lr: 1.678e-02, eta: 1 day, 11:37:55, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6561, loss_cls: 3.4090, loss: 3.4090 +2024-12-30 07:39:44,495 - pyskl - INFO - Epoch [110][2700/3746] lr: 1.676e-02, eta: 1 day, 11:36:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6658, loss_cls: 3.3895, loss: 3.3895 +2024-12-30 07:41:09,523 - pyskl - INFO - Epoch [110][2800/3746] lr: 1.674e-02, eta: 1 day, 11:35:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6486, loss_cls: 3.4466, loss: 3.4466 +2024-12-30 07:42:34,485 - pyskl - INFO - Epoch [110][2900/3746] lr: 1.672e-02, eta: 1 day, 11:33:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6475, loss_cls: 3.4413, loss: 3.4413 +2024-12-30 07:43:58,840 - pyskl - INFO - Epoch [110][3000/3746] lr: 1.670e-02, eta: 1 day, 11:32:15, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4016, top5_acc: 0.6573, loss_cls: 3.3781, loss: 3.3781 +2024-12-30 07:45:24,074 - pyskl - INFO - Epoch [110][3100/3746] lr: 1.668e-02, eta: 1 day, 11:30:50, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4042, top5_acc: 0.6562, loss_cls: 3.3919, loss: 3.3919 +2024-12-30 07:46:48,524 - pyskl - INFO - Epoch [110][3200/3746] lr: 1.666e-02, eta: 1 day, 11:29:25, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6586, loss_cls: 3.4039, loss: 3.4039 +2024-12-30 07:48:13,081 - pyskl - INFO - Epoch [110][3300/3746] lr: 1.664e-02, eta: 1 day, 11:28:00, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3970, top5_acc: 0.6567, loss_cls: 3.4059, loss: 3.4059 +2024-12-30 07:49:37,277 - pyskl - INFO - Epoch [110][3400/3746] lr: 1.662e-02, eta: 1 day, 11:26:35, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6575, loss_cls: 3.3471, loss: 3.3471 +2024-12-30 07:51:01,616 - pyskl - INFO - Epoch [110][3500/3746] lr: 1.659e-02, eta: 1 day, 11:25:09, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6655, loss_cls: 3.3679, loss: 3.3679 +2024-12-30 07:52:26,248 - pyskl - INFO - Epoch [110][3600/3746] lr: 1.657e-02, eta: 1 day, 11:23:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6509, loss_cls: 3.4465, loss: 3.4465 +2024-12-30 07:53:50,976 - pyskl - INFO - Epoch [110][3700/3746] lr: 1.655e-02, eta: 1 day, 11:22:19, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6611, loss_cls: 3.3754, loss: 3.3754 +2024-12-30 07:54:31,525 - pyskl - INFO - Saving checkpoint at 110 epochs +2024-12-30 07:56:29,662 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 07:56:30,341 - pyskl - INFO - +top1_acc 0.3387 +top5_acc 0.5956 +2024-12-30 07:56:30,341 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 07:56:30,384 - pyskl - INFO - +mean_acc 0.3384 +2024-12-30 07:56:30,397 - pyskl - INFO - Epoch(val) [110][309] top1_acc: 0.3387, top5_acc: 0.5956, mean_class_accuracy: 0.3384 +2024-12-30 08:00:51,641 - pyskl - INFO - Epoch [111][100/3746] lr: 1.652e-02, eta: 1 day, 11:21:05, time: 2.612, data_time: 1.585, memory: 15990, top1_acc: 0.4113, top5_acc: 0.6734, loss_cls: 3.2874, loss: 3.2874 +2024-12-30 08:02:16,828 - pyskl - INFO - Epoch [111][200/3746] lr: 1.650e-02, eta: 1 day, 11:19:40, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4189, top5_acc: 0.6789, loss_cls: 3.2886, loss: 3.2886 +2024-12-30 08:03:41,931 - pyskl - INFO - Epoch [111][300/3746] lr: 1.648e-02, eta: 1 day, 11:18:15, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6742, loss_cls: 3.3252, loss: 3.3252 +2024-12-30 08:05:07,366 - pyskl - INFO - Epoch [111][400/3746] lr: 1.646e-02, eta: 1 day, 11:16:50, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6581, loss_cls: 3.3584, loss: 3.3584 +2024-12-30 08:06:32,614 - pyskl - INFO - Epoch [111][500/3746] lr: 1.644e-02, eta: 1 day, 11:15:26, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6659, loss_cls: 3.3300, loss: 3.3300 +2024-12-30 08:07:57,162 - pyskl - INFO - Epoch [111][600/3746] lr: 1.642e-02, eta: 1 day, 11:14:00, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4155, top5_acc: 0.6677, loss_cls: 3.3212, loss: 3.3212 +2024-12-30 08:09:22,348 - pyskl - INFO - Epoch [111][700/3746] lr: 1.640e-02, eta: 1 day, 11:12:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6708, loss_cls: 3.3357, loss: 3.3357 +2024-12-30 08:10:47,276 - pyskl - INFO - Epoch [111][800/3746] lr: 1.638e-02, eta: 1 day, 11:11:10, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4155, top5_acc: 0.6797, loss_cls: 3.2782, loss: 3.2782 +2024-12-30 08:12:12,348 - pyskl - INFO - Epoch [111][900/3746] lr: 1.636e-02, eta: 1 day, 11:09:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6648, loss_cls: 3.3601, loss: 3.3601 +2024-12-30 08:13:37,204 - pyskl - INFO - Epoch [111][1000/3746] lr: 1.634e-02, eta: 1 day, 11:08:20, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6536, loss_cls: 3.4074, loss: 3.4074 +2024-12-30 08:15:01,945 - pyskl - INFO - Epoch [111][1100/3746] lr: 1.632e-02, eta: 1 day, 11:06:55, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4050, top5_acc: 0.6706, loss_cls: 3.3608, loss: 3.3608 +2024-12-30 08:16:26,593 - pyskl - INFO - Epoch [111][1200/3746] lr: 1.630e-02, eta: 1 day, 11:05:30, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6722, loss_cls: 3.3266, loss: 3.3266 +2024-12-30 08:17:51,505 - pyskl - INFO - Epoch [111][1300/3746] lr: 1.627e-02, eta: 1 day, 11:04:05, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6723, loss_cls: 3.3252, loss: 3.3252 +2024-12-30 08:19:16,240 - pyskl - INFO - Epoch [111][1400/3746] lr: 1.625e-02, eta: 1 day, 11:02:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6663, loss_cls: 3.3829, loss: 3.3829 +2024-12-30 08:20:41,259 - pyskl - INFO - Epoch [111][1500/3746] lr: 1.623e-02, eta: 1 day, 11:01:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3986, top5_acc: 0.6547, loss_cls: 3.3627, loss: 3.3627 +2024-12-30 08:22:06,091 - pyskl - INFO - Epoch [111][1600/3746] lr: 1.621e-02, eta: 1 day, 10:59:50, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6636, loss_cls: 3.3375, loss: 3.3375 +2024-12-30 08:23:30,662 - pyskl - INFO - Epoch [111][1700/3746] lr: 1.619e-02, eta: 1 day, 10:58:25, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4116, top5_acc: 0.6627, loss_cls: 3.3479, loss: 3.3479 +2024-12-30 08:24:55,184 - pyskl - INFO - Epoch [111][1800/3746] lr: 1.617e-02, eta: 1 day, 10:57:00, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4044, top5_acc: 0.6622, loss_cls: 3.3763, loss: 3.3763 +2024-12-30 08:26:20,217 - pyskl - INFO - Epoch [111][1900/3746] lr: 1.615e-02, eta: 1 day, 10:55:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6622, loss_cls: 3.3649, loss: 3.3649 +2024-12-30 08:27:45,221 - pyskl - INFO - Epoch [111][2000/3746] lr: 1.613e-02, eta: 1 day, 10:54:10, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6611, loss_cls: 3.3893, loss: 3.3893 +2024-12-30 08:29:10,654 - pyskl - INFO - Epoch [111][2100/3746] lr: 1.611e-02, eta: 1 day, 10:52:45, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6625, loss_cls: 3.3955, loss: 3.3955 +2024-12-30 08:30:35,803 - pyskl - INFO - Epoch [111][2200/3746] lr: 1.609e-02, eta: 1 day, 10:51:20, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6691, loss_cls: 3.3226, loss: 3.3226 +2024-12-30 08:32:01,262 - pyskl - INFO - Epoch [111][2300/3746] lr: 1.607e-02, eta: 1 day, 10:49:55, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6647, loss_cls: 3.3575, loss: 3.3575 +2024-12-30 08:33:26,844 - pyskl - INFO - Epoch [111][2400/3746] lr: 1.605e-02, eta: 1 day, 10:48:30, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6669, loss_cls: 3.3543, loss: 3.3543 +2024-12-30 08:34:51,973 - pyskl - INFO - Epoch [111][2500/3746] lr: 1.603e-02, eta: 1 day, 10:47:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6619, loss_cls: 3.3523, loss: 3.3523 +2024-12-30 08:36:16,837 - pyskl - INFO - Epoch [111][2600/3746] lr: 1.601e-02, eta: 1 day, 10:45:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6637, loss_cls: 3.3577, loss: 3.3577 +2024-12-30 08:37:41,730 - pyskl - INFO - Epoch [111][2700/3746] lr: 1.599e-02, eta: 1 day, 10:44:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6523, loss_cls: 3.3944, loss: 3.3944 +2024-12-30 08:39:06,795 - pyskl - INFO - Epoch [111][2800/3746] lr: 1.597e-02, eta: 1 day, 10:42:50, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6634, loss_cls: 3.4129, loss: 3.4129 +2024-12-30 08:40:31,662 - pyskl - INFO - Epoch [111][2900/3746] lr: 1.595e-02, eta: 1 day, 10:41:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4073, top5_acc: 0.6648, loss_cls: 3.3797, loss: 3.3797 +2024-12-30 08:41:56,395 - pyskl - INFO - Epoch [111][3000/3746] lr: 1.593e-02, eta: 1 day, 10:40:00, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4173, top5_acc: 0.6584, loss_cls: 3.3575, loss: 3.3575 +2024-12-30 08:43:21,505 - pyskl - INFO - Epoch [111][3100/3746] lr: 1.590e-02, eta: 1 day, 10:38:35, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6544, loss_cls: 3.3895, loss: 3.3895 +2024-12-30 08:44:46,749 - pyskl - INFO - Epoch [111][3200/3746] lr: 1.588e-02, eta: 1 day, 10:37:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6591, loss_cls: 3.3850, loss: 3.3850 +2024-12-30 08:46:11,578 - pyskl - INFO - Epoch [111][3300/3746] lr: 1.586e-02, eta: 1 day, 10:35:45, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6627, loss_cls: 3.3543, loss: 3.3543 +2024-12-30 08:47:36,394 - pyskl - INFO - Epoch [111][3400/3746] lr: 1.584e-02, eta: 1 day, 10:34:20, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4016, top5_acc: 0.6577, loss_cls: 3.3782, loss: 3.3782 +2024-12-30 08:49:01,462 - pyskl - INFO - Epoch [111][3500/3746] lr: 1.582e-02, eta: 1 day, 10:32:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4095, top5_acc: 0.6650, loss_cls: 3.3583, loss: 3.3583 +2024-12-30 08:50:26,244 - pyskl - INFO - Epoch [111][3600/3746] lr: 1.580e-02, eta: 1 day, 10:31:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6652, loss_cls: 3.3359, loss: 3.3359 +2024-12-30 08:51:51,214 - pyskl - INFO - Epoch [111][3700/3746] lr: 1.578e-02, eta: 1 day, 10:30:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6677, loss_cls: 3.3622, loss: 3.3622 +2024-12-30 08:52:32,136 - pyskl - INFO - Saving checkpoint at 111 epochs +2024-12-30 08:54:31,549 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 08:54:32,256 - pyskl - INFO - +top1_acc 0.3477 +top5_acc 0.6023 +2024-12-30 08:54:32,257 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 08:54:32,304 - pyskl - INFO - +mean_acc 0.3474 +2024-12-30 08:54:32,309 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_109.pth was removed +2024-12-30 08:54:32,590 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_111.pth. +2024-12-30 08:54:32,591 - pyskl - INFO - Best top1_acc is 0.3477 at 111 epoch. +2024-12-30 08:54:32,608 - pyskl - INFO - Epoch(val) [111][309] top1_acc: 0.3477, top5_acc: 0.6023, mean_class_accuracy: 0.3474 +2024-12-30 08:58:54,864 - pyskl - INFO - Epoch [112][100/3746] lr: 1.575e-02, eta: 1 day, 10:28:50, time: 2.622, data_time: 1.595, memory: 15990, top1_acc: 0.4169, top5_acc: 0.6705, loss_cls: 3.2968, loss: 3.2968 +2024-12-30 09:00:20,176 - pyskl - INFO - Epoch [112][200/3746] lr: 1.573e-02, eta: 1 day, 10:27:25, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6773, loss_cls: 3.2722, loss: 3.2722 +2024-12-30 09:01:45,294 - pyskl - INFO - Epoch [112][300/3746] lr: 1.571e-02, eta: 1 day, 10:26:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4167, top5_acc: 0.6775, loss_cls: 3.2870, loss: 3.2870 +2024-12-30 09:03:10,434 - pyskl - INFO - Epoch [112][400/3746] lr: 1.569e-02, eta: 1 day, 10:24:35, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6684, loss_cls: 3.3131, loss: 3.3131 +2024-12-30 09:04:35,277 - pyskl - INFO - Epoch [112][500/3746] lr: 1.567e-02, eta: 1 day, 10:23:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4327, top5_acc: 0.6897, loss_cls: 3.2059, loss: 3.2059 +2024-12-30 09:06:00,106 - pyskl - INFO - Epoch [112][600/3746] lr: 1.565e-02, eta: 1 day, 10:21:45, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6644, loss_cls: 3.3540, loss: 3.3540 +2024-12-30 09:07:24,931 - pyskl - INFO - Epoch [112][700/3746] lr: 1.563e-02, eta: 1 day, 10:20:20, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6769, loss_cls: 3.2699, loss: 3.2699 +2024-12-30 09:08:49,896 - pyskl - INFO - Epoch [112][800/3746] lr: 1.561e-02, eta: 1 day, 10:18:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6636, loss_cls: 3.3452, loss: 3.3452 +2024-12-30 09:10:14,554 - pyskl - INFO - Epoch [112][900/3746] lr: 1.559e-02, eta: 1 day, 10:17:29, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4095, top5_acc: 0.6664, loss_cls: 3.3275, loss: 3.3275 +2024-12-30 09:11:38,953 - pyskl - INFO - Epoch [112][1000/3746] lr: 1.557e-02, eta: 1 day, 10:16:04, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6758, loss_cls: 3.2894, loss: 3.2894 +2024-12-30 09:13:03,542 - pyskl - INFO - Epoch [112][1100/3746] lr: 1.555e-02, eta: 1 day, 10:14:39, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4141, top5_acc: 0.6748, loss_cls: 3.3117, loss: 3.3117 +2024-12-30 09:14:28,277 - pyskl - INFO - Epoch [112][1200/3746] lr: 1.553e-02, eta: 1 day, 10:13:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6598, loss_cls: 3.3542, loss: 3.3542 +2024-12-30 09:15:53,342 - pyskl - INFO - Epoch [112][1300/3746] lr: 1.551e-02, eta: 1 day, 10:11:49, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4117, top5_acc: 0.6689, loss_cls: 3.3284, loss: 3.3284 +2024-12-30 09:17:17,920 - pyskl - INFO - Epoch [112][1400/3746] lr: 1.549e-02, eta: 1 day, 10:10:24, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4173, top5_acc: 0.6617, loss_cls: 3.3271, loss: 3.3271 +2024-12-30 09:18:43,145 - pyskl - INFO - Epoch [112][1500/3746] lr: 1.547e-02, eta: 1 day, 10:08:59, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4078, top5_acc: 0.6686, loss_cls: 3.3191, loss: 3.3191 +2024-12-30 09:20:07,941 - pyskl - INFO - Epoch [112][1600/3746] lr: 1.545e-02, eta: 1 day, 10:07:34, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6645, loss_cls: 3.3243, loss: 3.3243 +2024-12-30 09:21:32,901 - pyskl - INFO - Epoch [112][1700/3746] lr: 1.543e-02, eta: 1 day, 10:06:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6747, loss_cls: 3.2984, loss: 3.2984 +2024-12-30 09:22:57,632 - pyskl - INFO - Epoch [112][1800/3746] lr: 1.541e-02, eta: 1 day, 10:04:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4117, top5_acc: 0.6598, loss_cls: 3.3503, loss: 3.3503 +2024-12-30 09:24:22,566 - pyskl - INFO - Epoch [112][1900/3746] lr: 1.539e-02, eta: 1 day, 10:03:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6619, loss_cls: 3.3716, loss: 3.3716 +2024-12-30 09:25:47,213 - pyskl - INFO - Epoch [112][2000/3746] lr: 1.537e-02, eta: 1 day, 10:01:53, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6634, loss_cls: 3.3500, loss: 3.3500 +2024-12-30 09:27:12,112 - pyskl - INFO - Epoch [112][2100/3746] lr: 1.535e-02, eta: 1 day, 10:00:28, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6677, loss_cls: 3.3259, loss: 3.3259 +2024-12-30 09:28:36,529 - pyskl - INFO - Epoch [112][2200/3746] lr: 1.533e-02, eta: 1 day, 9:59:03, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6600, loss_cls: 3.3931, loss: 3.3931 +2024-12-30 09:30:01,530 - pyskl - INFO - Epoch [112][2300/3746] lr: 1.531e-02, eta: 1 day, 9:57:38, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6702, loss_cls: 3.3484, loss: 3.3484 +2024-12-30 09:31:27,188 - pyskl - INFO - Epoch [112][2400/3746] lr: 1.529e-02, eta: 1 day, 9:56:13, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6664, loss_cls: 3.3197, loss: 3.3197 +2024-12-30 09:32:52,472 - pyskl - INFO - Epoch [112][2500/3746] lr: 1.527e-02, eta: 1 day, 9:54:48, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6642, loss_cls: 3.3551, loss: 3.3551 +2024-12-30 09:34:17,610 - pyskl - INFO - Epoch [112][2600/3746] lr: 1.525e-02, eta: 1 day, 9:53:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4114, top5_acc: 0.6709, loss_cls: 3.3166, loss: 3.3166 +2024-12-30 09:35:42,660 - pyskl - INFO - Epoch [112][2700/3746] lr: 1.523e-02, eta: 1 day, 9:51:58, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4073, top5_acc: 0.6692, loss_cls: 3.3334, loss: 3.3334 +2024-12-30 09:37:07,705 - pyskl - INFO - Epoch [112][2800/3746] lr: 1.521e-02, eta: 1 day, 9:50:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4031, top5_acc: 0.6620, loss_cls: 3.3581, loss: 3.3581 +2024-12-30 09:38:33,186 - pyskl - INFO - Epoch [112][2900/3746] lr: 1.519e-02, eta: 1 day, 9:49:08, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6562, loss_cls: 3.3967, loss: 3.3967 +2024-12-30 09:39:58,876 - pyskl - INFO - Epoch [112][3000/3746] lr: 1.517e-02, eta: 1 day, 9:47:44, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6545, loss_cls: 3.3848, loss: 3.3848 +2024-12-30 09:41:24,034 - pyskl - INFO - Epoch [112][3100/3746] lr: 1.515e-02, eta: 1 day, 9:46:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6630, loss_cls: 3.3481, loss: 3.3481 +2024-12-30 09:42:48,493 - pyskl - INFO - Epoch [112][3200/3746] lr: 1.513e-02, eta: 1 day, 9:44:53, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4164, top5_acc: 0.6728, loss_cls: 3.3281, loss: 3.3281 +2024-12-30 09:44:13,185 - pyskl - INFO - Epoch [112][3300/3746] lr: 1.511e-02, eta: 1 day, 9:43:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6667, loss_cls: 3.3669, loss: 3.3669 +2024-12-30 09:45:37,859 - pyskl - INFO - Epoch [112][3400/3746] lr: 1.509e-02, eta: 1 day, 9:42:03, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6647, loss_cls: 3.3621, loss: 3.3621 +2024-12-30 09:47:02,444 - pyskl - INFO - Epoch [112][3500/3746] lr: 1.507e-02, eta: 1 day, 9:40:38, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6564, loss_cls: 3.3627, loss: 3.3627 +2024-12-30 09:48:27,196 - pyskl - INFO - Epoch [112][3600/3746] lr: 1.505e-02, eta: 1 day, 9:39:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4025, top5_acc: 0.6622, loss_cls: 3.3792, loss: 3.3792 +2024-12-30 09:49:51,838 - pyskl - INFO - Epoch [112][3700/3746] lr: 1.503e-02, eta: 1 day, 9:37:48, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6556, loss_cls: 3.3912, loss: 3.3912 +2024-12-30 09:50:32,616 - pyskl - INFO - Saving checkpoint at 112 epochs +2024-12-30 09:52:31,449 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 09:52:32,123 - pyskl - INFO - +top1_acc 0.3506 +top5_acc 0.6074 +2024-12-30 09:52:32,124 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 09:52:32,167 - pyskl - INFO - +mean_acc 0.3504 +2024-12-30 09:52:32,172 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_111.pth was removed +2024-12-30 09:52:32,458 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_112.pth. +2024-12-30 09:52:32,459 - pyskl - INFO - Best top1_acc is 0.3506 at 112 epoch. +2024-12-30 09:52:32,472 - pyskl - INFO - Epoch(val) [112][309] top1_acc: 0.3506, top5_acc: 0.6074, mean_class_accuracy: 0.3504 +2024-12-30 09:56:56,174 - pyskl - INFO - Epoch [113][100/3746] lr: 1.500e-02, eta: 1 day, 9:36:31, time: 2.637, data_time: 1.607, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6806, loss_cls: 3.2502, loss: 3.2502 +2024-12-30 09:58:21,905 - pyskl - INFO - Epoch [113][200/3746] lr: 1.498e-02, eta: 1 day, 9:35:06, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6828, loss_cls: 3.2348, loss: 3.2348 +2024-12-30 09:59:46,925 - pyskl - INFO - Epoch [113][300/3746] lr: 1.496e-02, eta: 1 day, 9:33:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4158, top5_acc: 0.6764, loss_cls: 3.2877, loss: 3.2877 +2024-12-30 10:01:12,117 - pyskl - INFO - Epoch [113][400/3746] lr: 1.494e-02, eta: 1 day, 9:32:16, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6783, loss_cls: 3.3102, loss: 3.3102 +2024-12-30 10:02:37,669 - pyskl - INFO - Epoch [113][500/3746] lr: 1.492e-02, eta: 1 day, 9:30:51, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4164, top5_acc: 0.6773, loss_cls: 3.2679, loss: 3.2679 +2024-12-30 10:04:02,614 - pyskl - INFO - Epoch [113][600/3746] lr: 1.490e-02, eta: 1 day, 9:29:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6786, loss_cls: 3.2599, loss: 3.2599 +2024-12-30 10:05:27,346 - pyskl - INFO - Epoch [113][700/3746] lr: 1.488e-02, eta: 1 day, 9:28:01, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4136, top5_acc: 0.6739, loss_cls: 3.3187, loss: 3.3187 +2024-12-30 10:06:52,373 - pyskl - INFO - Epoch [113][800/3746] lr: 1.486e-02, eta: 1 day, 9:26:36, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6769, loss_cls: 3.2523, loss: 3.2523 +2024-12-30 10:08:17,102 - pyskl - INFO - Epoch [113][900/3746] lr: 1.484e-02, eta: 1 day, 9:25:10, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6703, loss_cls: 3.3070, loss: 3.3070 +2024-12-30 10:09:41,778 - pyskl - INFO - Epoch [113][1000/3746] lr: 1.482e-02, eta: 1 day, 9:23:45, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6731, loss_cls: 3.3402, loss: 3.3402 +2024-12-30 10:11:06,884 - pyskl - INFO - Epoch [113][1100/3746] lr: 1.480e-02, eta: 1 day, 9:22:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4158, top5_acc: 0.6770, loss_cls: 3.2748, loss: 3.2748 +2024-12-30 10:12:31,808 - pyskl - INFO - Epoch [113][1200/3746] lr: 1.478e-02, eta: 1 day, 9:20:55, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6839, loss_cls: 3.2366, loss: 3.2366 +2024-12-30 10:13:57,046 - pyskl - INFO - Epoch [113][1300/3746] lr: 1.476e-02, eta: 1 day, 9:19:30, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4175, top5_acc: 0.6753, loss_cls: 3.3124, loss: 3.3124 +2024-12-30 10:15:22,455 - pyskl - INFO - Epoch [113][1400/3746] lr: 1.474e-02, eta: 1 day, 9:18:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6705, loss_cls: 3.2928, loss: 3.2928 +2024-12-30 10:16:47,562 - pyskl - INFO - Epoch [113][1500/3746] lr: 1.472e-02, eta: 1 day, 9:16:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4158, top5_acc: 0.6736, loss_cls: 3.3148, loss: 3.3148 +2024-12-30 10:18:12,317 - pyskl - INFO - Epoch [113][1600/3746] lr: 1.470e-02, eta: 1 day, 9:15:15, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4141, top5_acc: 0.6758, loss_cls: 3.2873, loss: 3.2873 +2024-12-30 10:19:37,605 - pyskl - INFO - Epoch [113][1700/3746] lr: 1.468e-02, eta: 1 day, 9:13:50, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6637, loss_cls: 3.3714, loss: 3.3714 +2024-12-30 10:21:03,049 - pyskl - INFO - Epoch [113][1800/3746] lr: 1.466e-02, eta: 1 day, 9:12:25, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6639, loss_cls: 3.3815, loss: 3.3815 +2024-12-30 10:22:27,850 - pyskl - INFO - Epoch [113][1900/3746] lr: 1.464e-02, eta: 1 day, 9:11:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6703, loss_cls: 3.2997, loss: 3.2997 +2024-12-30 10:23:52,192 - pyskl - INFO - Epoch [113][2000/3746] lr: 1.462e-02, eta: 1 day, 9:09:35, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4158, top5_acc: 0.6811, loss_cls: 3.2651, loss: 3.2651 +2024-12-30 10:25:17,823 - pyskl - INFO - Epoch [113][2100/3746] lr: 1.460e-02, eta: 1 day, 9:08:10, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6705, loss_cls: 3.3355, loss: 3.3355 +2024-12-30 10:26:42,884 - pyskl - INFO - Epoch [113][2200/3746] lr: 1.458e-02, eta: 1 day, 9:06:45, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6706, loss_cls: 3.3039, loss: 3.3039 +2024-12-30 10:28:08,356 - pyskl - INFO - Epoch [113][2300/3746] lr: 1.456e-02, eta: 1 day, 9:05:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4153, top5_acc: 0.6580, loss_cls: 3.3539, loss: 3.3539 +2024-12-30 10:29:33,417 - pyskl - INFO - Epoch [113][2400/3746] lr: 1.454e-02, eta: 1 day, 9:03:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4003, top5_acc: 0.6634, loss_cls: 3.3626, loss: 3.3626 +2024-12-30 10:30:58,690 - pyskl - INFO - Epoch [113][2500/3746] lr: 1.452e-02, eta: 1 day, 9:02:30, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6687, loss_cls: 3.3319, loss: 3.3319 +2024-12-30 10:32:23,674 - pyskl - INFO - Epoch [113][2600/3746] lr: 1.450e-02, eta: 1 day, 9:01:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6580, loss_cls: 3.3793, loss: 3.3793 +2024-12-30 10:33:48,788 - pyskl - INFO - Epoch [113][2700/3746] lr: 1.448e-02, eta: 1 day, 8:59:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6800, loss_cls: 3.2715, loss: 3.2715 +2024-12-30 10:35:13,893 - pyskl - INFO - Epoch [113][2800/3746] lr: 1.446e-02, eta: 1 day, 8:58:15, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.4125, top5_acc: 0.6698, loss_cls: 3.3530, loss: 3.3530 +2024-12-30 10:36:38,834 - pyskl - INFO - Epoch [113][2900/3746] lr: 1.444e-02, eta: 1 day, 8:56:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4095, top5_acc: 0.6625, loss_cls: 3.3428, loss: 3.3428 +2024-12-30 10:38:03,863 - pyskl - INFO - Epoch [113][3000/3746] lr: 1.442e-02, eta: 1 day, 8:55:24, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6544, loss_cls: 3.3707, loss: 3.3707 +2024-12-30 10:39:28,940 - pyskl - INFO - Epoch [113][3100/3746] lr: 1.440e-02, eta: 1 day, 8:53:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6573, loss_cls: 3.4253, loss: 3.4253 +2024-12-30 10:40:53,902 - pyskl - INFO - Epoch [113][3200/3746] lr: 1.438e-02, eta: 1 day, 8:52:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6702, loss_cls: 3.3083, loss: 3.3083 +2024-12-30 10:42:18,651 - pyskl - INFO - Epoch [113][3300/3746] lr: 1.436e-02, eta: 1 day, 8:51:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6675, loss_cls: 3.3349, loss: 3.3349 +2024-12-30 10:43:43,831 - pyskl - INFO - Epoch [113][3400/3746] lr: 1.434e-02, eta: 1 day, 8:49:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6639, loss_cls: 3.3749, loss: 3.3749 +2024-12-30 10:45:09,489 - pyskl - INFO - Epoch [113][3500/3746] lr: 1.432e-02, eta: 1 day, 8:48:19, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6647, loss_cls: 3.3327, loss: 3.3327 +2024-12-30 10:46:34,218 - pyskl - INFO - Epoch [113][3600/3746] lr: 1.431e-02, eta: 1 day, 8:46:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6692, loss_cls: 3.3410, loss: 3.3410 +2024-12-30 10:47:59,586 - pyskl - INFO - Epoch [113][3700/3746] lr: 1.429e-02, eta: 1 day, 8:45:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6611, loss_cls: 3.3658, loss: 3.3658 +2024-12-30 10:48:40,468 - pyskl - INFO - Saving checkpoint at 113 epochs +2024-12-30 10:50:38,099 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 10:50:38,778 - pyskl - INFO - +top1_acc 0.3515 +top5_acc 0.6049 +2024-12-30 10:50:38,778 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 10:50:38,823 - pyskl - INFO - +mean_acc 0.3511 +2024-12-30 10:50:38,828 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_112.pth was removed +2024-12-30 10:50:39,137 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_113.pth. +2024-12-30 10:50:39,138 - pyskl - INFO - Best top1_acc is 0.3515 at 113 epoch. +2024-12-30 10:50:39,156 - pyskl - INFO - Epoch(val) [113][309] top1_acc: 0.3515, top5_acc: 0.6049, mean_class_accuracy: 0.3511 +2024-12-30 10:55:00,165 - pyskl - INFO - Epoch [114][100/3746] lr: 1.426e-02, eta: 1 day, 8:44:10, time: 2.610, data_time: 1.556, memory: 15990, top1_acc: 0.4317, top5_acc: 0.6811, loss_cls: 3.2134, loss: 3.2134 +2024-12-30 10:56:25,841 - pyskl - INFO - Epoch [114][200/3746] lr: 1.424e-02, eta: 1 day, 8:42:45, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.4295, top5_acc: 0.6789, loss_cls: 3.2482, loss: 3.2482 +2024-12-30 10:57:50,942 - pyskl - INFO - Epoch [114][300/3746] lr: 1.422e-02, eta: 1 day, 8:41:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4169, top5_acc: 0.6789, loss_cls: 3.2790, loss: 3.2790 +2024-12-30 10:59:16,849 - pyskl - INFO - Epoch [114][400/3746] lr: 1.420e-02, eta: 1 day, 8:39:55, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4302, top5_acc: 0.6920, loss_cls: 3.2131, loss: 3.2131 +2024-12-30 11:00:42,900 - pyskl - INFO - Epoch [114][500/3746] lr: 1.418e-02, eta: 1 day, 8:38:30, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4263, top5_acc: 0.6791, loss_cls: 3.2377, loss: 3.2377 +2024-12-30 11:02:09,082 - pyskl - INFO - Epoch [114][600/3746] lr: 1.416e-02, eta: 1 day, 8:37:05, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6844, loss_cls: 3.2497, loss: 3.2497 +2024-12-30 11:03:35,248 - pyskl - INFO - Epoch [114][700/3746] lr: 1.414e-02, eta: 1 day, 8:35:41, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4273, top5_acc: 0.6778, loss_cls: 3.2296, loss: 3.2296 +2024-12-30 11:05:01,096 - pyskl - INFO - Epoch [114][800/3746] lr: 1.412e-02, eta: 1 day, 8:34:16, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6808, loss_cls: 3.2614, loss: 3.2614 +2024-12-30 11:06:26,785 - pyskl - INFO - Epoch [114][900/3746] lr: 1.410e-02, eta: 1 day, 8:32:51, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6797, loss_cls: 3.2517, loss: 3.2517 +2024-12-30 11:07:52,751 - pyskl - INFO - Epoch [114][1000/3746] lr: 1.408e-02, eta: 1 day, 8:31:26, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4125, top5_acc: 0.6720, loss_cls: 3.3201, loss: 3.3201 +2024-12-30 11:09:18,293 - pyskl - INFO - Epoch [114][1100/3746] lr: 1.406e-02, eta: 1 day, 8:30:01, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4211, top5_acc: 0.6706, loss_cls: 3.3090, loss: 3.3090 +2024-12-30 11:10:44,067 - pyskl - INFO - Epoch [114][1200/3746] lr: 1.404e-02, eta: 1 day, 8:28:36, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4167, top5_acc: 0.6783, loss_cls: 3.2684, loss: 3.2684 +2024-12-30 11:12:09,721 - pyskl - INFO - Epoch [114][1300/3746] lr: 1.402e-02, eta: 1 day, 8:27:11, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4181, top5_acc: 0.6780, loss_cls: 3.2879, loss: 3.2879 +2024-12-30 11:13:35,167 - pyskl - INFO - Epoch [114][1400/3746] lr: 1.400e-02, eta: 1 day, 8:25:46, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4167, top5_acc: 0.6725, loss_cls: 3.2795, loss: 3.2795 +2024-12-30 11:15:00,759 - pyskl - INFO - Epoch [114][1500/3746] lr: 1.398e-02, eta: 1 day, 8:24:21, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6797, loss_cls: 3.2524, loss: 3.2524 +2024-12-30 11:16:26,480 - pyskl - INFO - Epoch [114][1600/3746] lr: 1.397e-02, eta: 1 day, 8:22:57, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6661, loss_cls: 3.3336, loss: 3.3336 +2024-12-30 11:17:52,128 - pyskl - INFO - Epoch [114][1700/3746] lr: 1.395e-02, eta: 1 day, 8:21:32, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4113, top5_acc: 0.6637, loss_cls: 3.3530, loss: 3.3530 +2024-12-30 11:19:17,854 - pyskl - INFO - Epoch [114][1800/3746] lr: 1.393e-02, eta: 1 day, 8:20:07, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6689, loss_cls: 3.3446, loss: 3.3446 +2024-12-30 11:20:43,164 - pyskl - INFO - Epoch [114][1900/3746] lr: 1.391e-02, eta: 1 day, 8:18:42, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6858, loss_cls: 3.2580, loss: 3.2580 +2024-12-30 11:22:08,703 - pyskl - INFO - Epoch [114][2000/3746] lr: 1.389e-02, eta: 1 day, 8:17:17, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4106, top5_acc: 0.6750, loss_cls: 3.3265, loss: 3.3265 +2024-12-30 11:23:34,240 - pyskl - INFO - Epoch [114][2100/3746] lr: 1.387e-02, eta: 1 day, 8:15:52, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6770, loss_cls: 3.2289, loss: 3.2289 +2024-12-30 11:24:59,972 - pyskl - INFO - Epoch [114][2200/3746] lr: 1.385e-02, eta: 1 day, 8:14:27, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4169, top5_acc: 0.6698, loss_cls: 3.3018, loss: 3.3018 +2024-12-30 11:26:25,120 - pyskl - INFO - Epoch [114][2300/3746] lr: 1.383e-02, eta: 1 day, 8:13:02, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6734, loss_cls: 3.3132, loss: 3.3132 +2024-12-30 11:27:50,017 - pyskl - INFO - Epoch [114][2400/3746] lr: 1.381e-02, eta: 1 day, 8:11:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4070, top5_acc: 0.6684, loss_cls: 3.3254, loss: 3.3254 +2024-12-30 11:29:14,626 - pyskl - INFO - Epoch [114][2500/3746] lr: 1.379e-02, eta: 1 day, 8:10:12, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6814, loss_cls: 3.2706, loss: 3.2706 +2024-12-30 11:30:39,358 - pyskl - INFO - Epoch [114][2600/3746] lr: 1.377e-02, eta: 1 day, 8:08:46, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6692, loss_cls: 3.3472, loss: 3.3472 +2024-12-30 11:32:04,343 - pyskl - INFO - Epoch [114][2700/3746] lr: 1.375e-02, eta: 1 day, 8:07:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6716, loss_cls: 3.3129, loss: 3.3129 +2024-12-30 11:33:29,439 - pyskl - INFO - Epoch [114][2800/3746] lr: 1.373e-02, eta: 1 day, 8:05:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6648, loss_cls: 3.3265, loss: 3.3265 +2024-12-30 11:34:54,402 - pyskl - INFO - Epoch [114][2900/3746] lr: 1.371e-02, eta: 1 day, 8:04:31, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6703, loss_cls: 3.2899, loss: 3.2899 +2024-12-30 11:36:19,516 - pyskl - INFO - Epoch [114][3000/3746] lr: 1.369e-02, eta: 1 day, 8:03:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6759, loss_cls: 3.3169, loss: 3.3169 +2024-12-30 11:37:44,179 - pyskl - INFO - Epoch [114][3100/3746] lr: 1.368e-02, eta: 1 day, 8:01:41, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4167, top5_acc: 0.6728, loss_cls: 3.3031, loss: 3.3031 +2024-12-30 11:39:08,880 - pyskl - INFO - Epoch [114][3200/3746] lr: 1.366e-02, eta: 1 day, 8:00:15, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4164, top5_acc: 0.6781, loss_cls: 3.3039, loss: 3.3039 +2024-12-30 11:40:33,513 - pyskl - INFO - Epoch [114][3300/3746] lr: 1.364e-02, eta: 1 day, 7:58:50, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6783, loss_cls: 3.2915, loss: 3.2915 +2024-12-30 11:41:58,059 - pyskl - INFO - Epoch [114][3400/3746] lr: 1.362e-02, eta: 1 day, 7:57:25, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4228, top5_acc: 0.6761, loss_cls: 3.2706, loss: 3.2706 +2024-12-30 11:43:22,746 - pyskl - INFO - Epoch [114][3500/3746] lr: 1.360e-02, eta: 1 day, 7:56:00, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4095, top5_acc: 0.6630, loss_cls: 3.3346, loss: 3.3346 +2024-12-30 11:44:47,543 - pyskl - INFO - Epoch [114][3600/3746] lr: 1.358e-02, eta: 1 day, 7:54:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6739, loss_cls: 3.3019, loss: 3.3019 +2024-12-30 11:46:12,673 - pyskl - INFO - Epoch [114][3700/3746] lr: 1.356e-02, eta: 1 day, 7:53:09, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6672, loss_cls: 3.3120, loss: 3.3120 +2024-12-30 11:46:53,795 - pyskl - INFO - Saving checkpoint at 114 epochs +2024-12-30 11:48:54,727 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 11:48:55,447 - pyskl - INFO - +top1_acc 0.3570 +top5_acc 0.6107 +2024-12-30 11:48:55,448 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 11:48:55,504 - pyskl - INFO - +mean_acc 0.3568 +2024-12-30 11:48:55,516 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_113.pth was removed +2024-12-30 11:48:55,968 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_114.pth. +2024-12-30 11:48:55,969 - pyskl - INFO - Best top1_acc is 0.3570 at 114 epoch. +2024-12-30 11:48:55,986 - pyskl - INFO - Epoch(val) [114][309] top1_acc: 0.3570, top5_acc: 0.6107, mean_class_accuracy: 0.3568 +2024-12-30 11:53:23,403 - pyskl - INFO - Epoch [115][100/3746] lr: 1.353e-02, eta: 1 day, 7:51:50, time: 2.674, data_time: 1.640, memory: 15990, top1_acc: 0.4381, top5_acc: 0.6989, loss_cls: 3.1546, loss: 3.1546 +2024-12-30 11:54:48,939 - pyskl - INFO - Epoch [115][200/3746] lr: 1.351e-02, eta: 1 day, 7:50:25, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6878, loss_cls: 3.2312, loss: 3.2312 +2024-12-30 11:56:14,107 - pyskl - INFO - Epoch [115][300/3746] lr: 1.349e-02, eta: 1 day, 7:49:00, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4300, top5_acc: 0.6847, loss_cls: 3.2081, loss: 3.2081 +2024-12-30 11:57:39,140 - pyskl - INFO - Epoch [115][400/3746] lr: 1.348e-02, eta: 1 day, 7:47:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6866, loss_cls: 3.2148, loss: 3.2148 +2024-12-30 11:59:04,677 - pyskl - INFO - Epoch [115][500/3746] lr: 1.346e-02, eta: 1 day, 7:46:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4183, top5_acc: 0.6700, loss_cls: 3.3006, loss: 3.3006 +2024-12-30 12:00:30,402 - pyskl - INFO - Epoch [115][600/3746] lr: 1.344e-02, eta: 1 day, 7:44:45, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4202, top5_acc: 0.6861, loss_cls: 3.2652, loss: 3.2652 +2024-12-30 12:01:55,871 - pyskl - INFO - Epoch [115][700/3746] lr: 1.342e-02, eta: 1 day, 7:43:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4247, top5_acc: 0.6800, loss_cls: 3.2372, loss: 3.2372 +2024-12-30 12:03:21,202 - pyskl - INFO - Epoch [115][800/3746] lr: 1.340e-02, eta: 1 day, 7:41:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6798, loss_cls: 3.2372, loss: 3.2372 +2024-12-30 12:04:46,040 - pyskl - INFO - Epoch [115][900/3746] lr: 1.338e-02, eta: 1 day, 7:40:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4152, top5_acc: 0.6633, loss_cls: 3.3233, loss: 3.3233 +2024-12-30 12:06:11,480 - pyskl - INFO - Epoch [115][1000/3746] lr: 1.336e-02, eta: 1 day, 7:39:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4220, top5_acc: 0.6769, loss_cls: 3.2937, loss: 3.2937 +2024-12-30 12:07:36,433 - pyskl - INFO - Epoch [115][1100/3746] lr: 1.334e-02, eta: 1 day, 7:37:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4238, top5_acc: 0.6906, loss_cls: 3.2407, loss: 3.2407 +2024-12-30 12:09:01,893 - pyskl - INFO - Epoch [115][1200/3746] lr: 1.332e-02, eta: 1 day, 7:36:15, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6723, loss_cls: 3.3176, loss: 3.3176 +2024-12-30 12:10:27,450 - pyskl - INFO - Epoch [115][1300/3746] lr: 1.330e-02, eta: 1 day, 7:34:50, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4167, top5_acc: 0.6787, loss_cls: 3.2913, loss: 3.2913 +2024-12-30 12:11:52,770 - pyskl - INFO - Epoch [115][1400/3746] lr: 1.328e-02, eta: 1 day, 7:33:25, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4281, top5_acc: 0.6822, loss_cls: 3.2229, loss: 3.2229 +2024-12-30 12:13:18,175 - pyskl - INFO - Epoch [115][1500/3746] lr: 1.327e-02, eta: 1 day, 7:32:00, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4158, top5_acc: 0.6766, loss_cls: 3.3084, loss: 3.3084 +2024-12-30 12:14:43,420 - pyskl - INFO - Epoch [115][1600/3746] lr: 1.325e-02, eta: 1 day, 7:30:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4238, top5_acc: 0.6770, loss_cls: 3.2382, loss: 3.2382 +2024-12-30 12:16:08,880 - pyskl - INFO - Epoch [115][1700/3746] lr: 1.323e-02, eta: 1 day, 7:29:09, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6866, loss_cls: 3.2738, loss: 3.2738 +2024-12-30 12:17:34,238 - pyskl - INFO - Epoch [115][1800/3746] lr: 1.321e-02, eta: 1 day, 7:27:44, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6719, loss_cls: 3.2755, loss: 3.2755 +2024-12-30 12:18:59,639 - pyskl - INFO - Epoch [115][1900/3746] lr: 1.319e-02, eta: 1 day, 7:26:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6741, loss_cls: 3.2490, loss: 3.2490 +2024-12-30 12:20:24,560 - pyskl - INFO - Epoch [115][2000/3746] lr: 1.317e-02, eta: 1 day, 7:24:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6814, loss_cls: 3.2743, loss: 3.2743 +2024-12-30 12:21:49,475 - pyskl - INFO - Epoch [115][2100/3746] lr: 1.315e-02, eta: 1 day, 7:23:29, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6767, loss_cls: 3.2697, loss: 3.2697 +2024-12-30 12:23:14,521 - pyskl - INFO - Epoch [115][2200/3746] lr: 1.313e-02, eta: 1 day, 7:22:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4119, top5_acc: 0.6672, loss_cls: 3.3303, loss: 3.3303 +2024-12-30 12:24:39,383 - pyskl - INFO - Epoch [115][2300/3746] lr: 1.311e-02, eta: 1 day, 7:20:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4173, top5_acc: 0.6766, loss_cls: 3.2893, loss: 3.2893 +2024-12-30 12:26:04,150 - pyskl - INFO - Epoch [115][2400/3746] lr: 1.310e-02, eta: 1 day, 7:19:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4270, top5_acc: 0.6758, loss_cls: 3.2413, loss: 3.2413 +2024-12-30 12:27:29,334 - pyskl - INFO - Epoch [115][2500/3746] lr: 1.308e-02, eta: 1 day, 7:17:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4161, top5_acc: 0.6683, loss_cls: 3.2711, loss: 3.2711 +2024-12-30 12:28:55,031 - pyskl - INFO - Epoch [115][2600/3746] lr: 1.306e-02, eta: 1 day, 7:16:23, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4189, top5_acc: 0.6817, loss_cls: 3.2531, loss: 3.2531 +2024-12-30 12:30:20,268 - pyskl - INFO - Epoch [115][2700/3746] lr: 1.304e-02, eta: 1 day, 7:14:58, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6922, loss_cls: 3.2250, loss: 3.2250 +2024-12-30 12:31:45,103 - pyskl - INFO - Epoch [115][2800/3746] lr: 1.302e-02, eta: 1 day, 7:13:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4228, top5_acc: 0.6764, loss_cls: 3.2825, loss: 3.2825 +2024-12-30 12:33:10,492 - pyskl - INFO - Epoch [115][2900/3746] lr: 1.300e-02, eta: 1 day, 7:12:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4070, top5_acc: 0.6778, loss_cls: 3.2892, loss: 3.2892 +2024-12-30 12:34:35,655 - pyskl - INFO - Epoch [115][3000/3746] lr: 1.298e-02, eta: 1 day, 7:10:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6802, loss_cls: 3.2660, loss: 3.2660 +2024-12-30 12:36:00,521 - pyskl - INFO - Epoch [115][3100/3746] lr: 1.296e-02, eta: 1 day, 7:09:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4156, top5_acc: 0.6787, loss_cls: 3.2589, loss: 3.2589 +2024-12-30 12:37:25,414 - pyskl - INFO - Epoch [115][3200/3746] lr: 1.295e-02, eta: 1 day, 7:07:52, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6773, loss_cls: 3.3025, loss: 3.3025 +2024-12-30 12:38:50,559 - pyskl - INFO - Epoch [115][3300/3746] lr: 1.293e-02, eta: 1 day, 7:06:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6714, loss_cls: 3.2933, loss: 3.2933 +2024-12-30 12:40:15,487 - pyskl - INFO - Epoch [115][3400/3746] lr: 1.291e-02, eta: 1 day, 7:05:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6686, loss_cls: 3.3208, loss: 3.3208 +2024-12-30 12:41:40,586 - pyskl - INFO - Epoch [115][3500/3746] lr: 1.289e-02, eta: 1 day, 7:03:37, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6820, loss_cls: 3.2528, loss: 3.2528 +2024-12-30 12:43:05,264 - pyskl - INFO - Epoch [115][3600/3746] lr: 1.287e-02, eta: 1 day, 7:02:12, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6777, loss_cls: 3.2647, loss: 3.2647 +2024-12-30 12:44:30,135 - pyskl - INFO - Epoch [115][3700/3746] lr: 1.285e-02, eta: 1 day, 7:00:47, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6705, loss_cls: 3.3363, loss: 3.3363 +2024-12-30 12:45:11,408 - pyskl - INFO - Saving checkpoint at 115 epochs +2024-12-30 12:47:11,264 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 12:47:12,051 - pyskl - INFO - +top1_acc 0.3584 +top5_acc 0.6188 +2024-12-30 12:47:12,051 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 12:47:12,101 - pyskl - INFO - +mean_acc 0.3580 +2024-12-30 12:47:12,106 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_114.pth was removed +2024-12-30 12:47:12,384 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_115.pth. +2024-12-30 12:47:12,384 - pyskl - INFO - Best top1_acc is 0.3584 at 115 epoch. +2024-12-30 12:47:12,398 - pyskl - INFO - Epoch(val) [115][309] top1_acc: 0.3584, top5_acc: 0.6188, mean_class_accuracy: 0.3580 +2024-12-30 12:51:34,309 - pyskl - INFO - Epoch [116][100/3746] lr: 1.282e-02, eta: 1 day, 6:59:24, time: 2.619, data_time: 1.588, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6936, loss_cls: 3.1755, loss: 3.1755 +2024-12-30 12:52:59,271 - pyskl - INFO - Epoch [116][200/3746] lr: 1.281e-02, eta: 1 day, 6:57:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6931, loss_cls: 3.1986, loss: 3.1986 +2024-12-30 12:54:24,201 - pyskl - INFO - Epoch [116][300/3746] lr: 1.279e-02, eta: 1 day, 6:56:34, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.6853, loss_cls: 3.2198, loss: 3.2198 +2024-12-30 12:55:49,210 - pyskl - INFO - Epoch [116][400/3746] lr: 1.277e-02, eta: 1 day, 6:55:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4478, top5_acc: 0.6998, loss_cls: 3.1344, loss: 3.1344 +2024-12-30 12:57:14,359 - pyskl - INFO - Epoch [116][500/3746] lr: 1.275e-02, eta: 1 day, 6:53:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4389, top5_acc: 0.6922, loss_cls: 3.1692, loss: 3.1692 +2024-12-30 12:58:40,050 - pyskl - INFO - Epoch [116][600/3746] lr: 1.273e-02, eta: 1 day, 6:52:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4334, top5_acc: 0.6863, loss_cls: 3.2096, loss: 3.2096 +2024-12-30 13:00:05,231 - pyskl - INFO - Epoch [116][700/3746] lr: 1.271e-02, eta: 1 day, 6:50:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4309, top5_acc: 0.6892, loss_cls: 3.2064, loss: 3.2064 +2024-12-30 13:01:30,345 - pyskl - INFO - Epoch [116][800/3746] lr: 1.269e-02, eta: 1 day, 6:49:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4470, top5_acc: 0.6963, loss_cls: 3.1576, loss: 3.1576 +2024-12-30 13:02:55,225 - pyskl - INFO - Epoch [116][900/3746] lr: 1.268e-02, eta: 1 day, 6:48:03, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4297, top5_acc: 0.6892, loss_cls: 3.2224, loss: 3.2224 +2024-12-30 13:04:20,437 - pyskl - INFO - Epoch [116][1000/3746] lr: 1.266e-02, eta: 1 day, 6:46:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6777, loss_cls: 3.2425, loss: 3.2425 +2024-12-30 13:05:46,045 - pyskl - INFO - Epoch [116][1100/3746] lr: 1.264e-02, eta: 1 day, 6:45:13, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6800, loss_cls: 3.2400, loss: 3.2400 +2024-12-30 13:07:11,177 - pyskl - INFO - Epoch [116][1200/3746] lr: 1.262e-02, eta: 1 day, 6:43:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6705, loss_cls: 3.2712, loss: 3.2712 +2024-12-30 13:08:36,881 - pyskl - INFO - Epoch [116][1300/3746] lr: 1.260e-02, eta: 1 day, 6:42:22, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6764, loss_cls: 3.2457, loss: 3.2457 +2024-12-30 13:10:01,862 - pyskl - INFO - Epoch [116][1400/3746] lr: 1.258e-02, eta: 1 day, 6:40:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4195, top5_acc: 0.6778, loss_cls: 3.2656, loss: 3.2656 +2024-12-30 13:11:26,832 - pyskl - INFO - Epoch [116][1500/3746] lr: 1.256e-02, eta: 1 day, 6:39:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6773, loss_cls: 3.2610, loss: 3.2610 +2024-12-30 13:12:51,707 - pyskl - INFO - Epoch [116][1600/3746] lr: 1.255e-02, eta: 1 day, 6:38:07, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6761, loss_cls: 3.2902, loss: 3.2902 +2024-12-30 13:14:16,741 - pyskl - INFO - Epoch [116][1700/3746] lr: 1.253e-02, eta: 1 day, 6:36:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6827, loss_cls: 3.2326, loss: 3.2326 +2024-12-30 13:15:41,970 - pyskl - INFO - Epoch [116][1800/3746] lr: 1.251e-02, eta: 1 day, 6:35:16, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4167, top5_acc: 0.6687, loss_cls: 3.3047, loss: 3.3047 +2024-12-30 13:17:07,092 - pyskl - INFO - Epoch [116][1900/3746] lr: 1.249e-02, eta: 1 day, 6:33:51, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.4247, top5_acc: 0.6858, loss_cls: 3.2261, loss: 3.2261 +2024-12-30 13:18:32,475 - pyskl - INFO - Epoch [116][2000/3746] lr: 1.247e-02, eta: 1 day, 6:32:26, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4298, top5_acc: 0.6858, loss_cls: 3.2064, loss: 3.2064 +2024-12-30 13:19:57,529 - pyskl - INFO - Epoch [116][2100/3746] lr: 1.245e-02, eta: 1 day, 6:31:01, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4130, top5_acc: 0.6752, loss_cls: 3.2957, loss: 3.2957 +2024-12-30 13:21:23,040 - pyskl - INFO - Epoch [116][2200/3746] lr: 1.243e-02, eta: 1 day, 6:29:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6880, loss_cls: 3.2195, loss: 3.2195 +2024-12-30 13:22:47,856 - pyskl - INFO - Epoch [116][2300/3746] lr: 1.242e-02, eta: 1 day, 6:28:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4314, top5_acc: 0.6850, loss_cls: 3.1915, loss: 3.1915 +2024-12-30 13:24:12,812 - pyskl - INFO - Epoch [116][2400/3746] lr: 1.240e-02, eta: 1 day, 6:26:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6769, loss_cls: 3.2521, loss: 3.2521 +2024-12-30 13:25:37,851 - pyskl - INFO - Epoch [116][2500/3746] lr: 1.238e-02, eta: 1 day, 6:25:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4158, top5_acc: 0.6761, loss_cls: 3.3036, loss: 3.3036 +2024-12-30 13:27:02,415 - pyskl - INFO - Epoch [116][2600/3746] lr: 1.236e-02, eta: 1 day, 6:23:55, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6756, loss_cls: 3.2890, loss: 3.2890 +2024-12-30 13:28:27,612 - pyskl - INFO - Epoch [116][2700/3746] lr: 1.234e-02, eta: 1 day, 6:22:30, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4166, top5_acc: 0.6761, loss_cls: 3.2713, loss: 3.2713 +2024-12-30 13:29:52,348 - pyskl - INFO - Epoch [116][2800/3746] lr: 1.232e-02, eta: 1 day, 6:21:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4166, top5_acc: 0.6592, loss_cls: 3.3502, loss: 3.3502 +2024-12-30 13:31:16,991 - pyskl - INFO - Epoch [116][2900/3746] lr: 1.231e-02, eta: 1 day, 6:19:39, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4197, top5_acc: 0.6809, loss_cls: 3.2523, loss: 3.2523 +2024-12-30 13:32:41,529 - pyskl - INFO - Epoch [116][3000/3746] lr: 1.229e-02, eta: 1 day, 6:18:14, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4177, top5_acc: 0.6834, loss_cls: 3.2468, loss: 3.2468 +2024-12-30 13:34:06,597 - pyskl - INFO - Epoch [116][3100/3746] lr: 1.227e-02, eta: 1 day, 6:16:49, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6828, loss_cls: 3.2325, loss: 3.2325 +2024-12-30 13:35:31,954 - pyskl - INFO - Epoch [116][3200/3746] lr: 1.225e-02, eta: 1 day, 6:15:24, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4188, top5_acc: 0.6797, loss_cls: 3.2703, loss: 3.2703 +2024-12-30 13:36:56,790 - pyskl - INFO - Epoch [116][3300/3746] lr: 1.223e-02, eta: 1 day, 6:13:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6748, loss_cls: 3.3140, loss: 3.3140 +2024-12-30 13:38:21,802 - pyskl - INFO - Epoch [116][3400/3746] lr: 1.221e-02, eta: 1 day, 6:12:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6737, loss_cls: 3.2637, loss: 3.2637 +2024-12-30 13:39:47,189 - pyskl - INFO - Epoch [116][3500/3746] lr: 1.220e-02, eta: 1 day, 6:11:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6839, loss_cls: 3.2403, loss: 3.2403 +2024-12-30 13:41:12,400 - pyskl - INFO - Epoch [116][3600/3746] lr: 1.218e-02, eta: 1 day, 6:09:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4277, top5_acc: 0.6786, loss_cls: 3.2543, loss: 3.2543 +2024-12-30 13:42:38,084 - pyskl - INFO - Epoch [116][3700/3746] lr: 1.216e-02, eta: 1 day, 6:08:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4300, top5_acc: 0.6783, loss_cls: 3.2443, loss: 3.2443 +2024-12-30 13:43:19,770 - pyskl - INFO - Saving checkpoint at 116 epochs +2024-12-30 13:45:20,295 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 13:45:21,209 - pyskl - INFO - +top1_acc 0.3428 +top5_acc 0.5952 +2024-12-30 13:45:21,209 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 13:45:21,267 - pyskl - INFO - +mean_acc 0.3426 +2024-12-30 13:45:21,281 - pyskl - INFO - Epoch(val) [116][309] top1_acc: 0.3428, top5_acc: 0.5952, mean_class_accuracy: 0.3426 +2024-12-30 13:49:38,966 - pyskl - INFO - Epoch [117][100/3746] lr: 1.213e-02, eta: 1 day, 6:06:53, time: 2.577, data_time: 1.545, memory: 15990, top1_acc: 0.4331, top5_acc: 0.6964, loss_cls: 3.1731, loss: 3.1731 +2024-12-30 13:51:03,468 - pyskl - INFO - Epoch [117][200/3746] lr: 1.211e-02, eta: 1 day, 6:05:27, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4248, top5_acc: 0.6870, loss_cls: 3.2172, loss: 3.2172 +2024-12-30 13:52:28,259 - pyskl - INFO - Epoch [117][300/3746] lr: 1.210e-02, eta: 1 day, 6:04:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4331, top5_acc: 0.6920, loss_cls: 3.2052, loss: 3.2052 +2024-12-30 13:53:52,979 - pyskl - INFO - Epoch [117][400/3746] lr: 1.208e-02, eta: 1 day, 6:02:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6867, loss_cls: 3.2206, loss: 3.2206 +2024-12-30 13:55:17,810 - pyskl - INFO - Epoch [117][500/3746] lr: 1.206e-02, eta: 1 day, 6:01:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4281, top5_acc: 0.6878, loss_cls: 3.2213, loss: 3.2213 +2024-12-30 13:56:43,264 - pyskl - INFO - Epoch [117][600/3746] lr: 1.204e-02, eta: 1 day, 5:59:46, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4450, top5_acc: 0.6925, loss_cls: 3.1559, loss: 3.1559 +2024-12-30 13:58:08,532 - pyskl - INFO - Epoch [117][700/3746] lr: 1.202e-02, eta: 1 day, 5:58:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4431, top5_acc: 0.6913, loss_cls: 3.1423, loss: 3.1423 +2024-12-30 13:59:33,535 - pyskl - INFO - Epoch [117][800/3746] lr: 1.200e-02, eta: 1 day, 5:56:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4259, top5_acc: 0.6855, loss_cls: 3.2240, loss: 3.2240 +2024-12-30 14:00:59,170 - pyskl - INFO - Epoch [117][900/3746] lr: 1.199e-02, eta: 1 day, 5:55:31, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4330, top5_acc: 0.6863, loss_cls: 3.1790, loss: 3.1790 +2024-12-30 14:02:24,434 - pyskl - INFO - Epoch [117][1000/3746] lr: 1.197e-02, eta: 1 day, 5:54:06, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4327, top5_acc: 0.6909, loss_cls: 3.2126, loss: 3.2126 +2024-12-30 14:03:49,900 - pyskl - INFO - Epoch [117][1100/3746] lr: 1.195e-02, eta: 1 day, 5:52:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4309, top5_acc: 0.6861, loss_cls: 3.2017, loss: 3.2017 +2024-12-30 14:05:15,364 - pyskl - INFO - Epoch [117][1200/3746] lr: 1.193e-02, eta: 1 day, 5:51:15, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4405, top5_acc: 0.6986, loss_cls: 3.1636, loss: 3.1636 +2024-12-30 14:06:41,545 - pyskl - INFO - Epoch [117][1300/3746] lr: 1.191e-02, eta: 1 day, 5:49:50, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4211, top5_acc: 0.6766, loss_cls: 3.2851, loss: 3.2851 +2024-12-30 14:08:07,093 - pyskl - INFO - Epoch [117][1400/3746] lr: 1.190e-02, eta: 1 day, 5:48:25, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4264, top5_acc: 0.6931, loss_cls: 3.1965, loss: 3.1965 +2024-12-30 14:09:33,361 - pyskl - INFO - Epoch [117][1500/3746] lr: 1.188e-02, eta: 1 day, 5:47:00, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4322, top5_acc: 0.6872, loss_cls: 3.1989, loss: 3.1989 +2024-12-30 14:10:59,145 - pyskl - INFO - Epoch [117][1600/3746] lr: 1.186e-02, eta: 1 day, 5:45:35, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4138, top5_acc: 0.6748, loss_cls: 3.2627, loss: 3.2627 +2024-12-30 14:12:25,216 - pyskl - INFO - Epoch [117][1700/3746] lr: 1.184e-02, eta: 1 day, 5:44:11, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6884, loss_cls: 3.2146, loss: 3.2146 +2024-12-30 14:13:51,765 - pyskl - INFO - Epoch [117][1800/3746] lr: 1.182e-02, eta: 1 day, 5:42:46, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6873, loss_cls: 3.1715, loss: 3.1715 +2024-12-30 14:15:17,783 - pyskl - INFO - Epoch [117][1900/3746] lr: 1.181e-02, eta: 1 day, 5:41:21, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4331, top5_acc: 0.6844, loss_cls: 3.2270, loss: 3.2270 +2024-12-30 14:16:43,853 - pyskl - INFO - Epoch [117][2000/3746] lr: 1.179e-02, eta: 1 day, 5:39:56, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6847, loss_cls: 3.2084, loss: 3.2084 +2024-12-30 14:18:10,308 - pyskl - INFO - Epoch [117][2100/3746] lr: 1.177e-02, eta: 1 day, 5:38:31, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.4275, top5_acc: 0.6859, loss_cls: 3.2063, loss: 3.2063 +2024-12-30 14:19:36,140 - pyskl - INFO - Epoch [117][2200/3746] lr: 1.175e-02, eta: 1 day, 5:37:06, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.4359, top5_acc: 0.6944, loss_cls: 3.1812, loss: 3.1812 +2024-12-30 14:21:01,928 - pyskl - INFO - Epoch [117][2300/3746] lr: 1.173e-02, eta: 1 day, 5:35:41, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4252, top5_acc: 0.6836, loss_cls: 3.2357, loss: 3.2357 +2024-12-30 14:22:27,386 - pyskl - INFO - Epoch [117][2400/3746] lr: 1.172e-02, eta: 1 day, 5:34:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6886, loss_cls: 3.2162, loss: 3.2162 +2024-12-30 14:23:53,115 - pyskl - INFO - Epoch [117][2500/3746] lr: 1.170e-02, eta: 1 day, 5:32:51, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6809, loss_cls: 3.2283, loss: 3.2283 +2024-12-30 14:25:18,292 - pyskl - INFO - Epoch [117][2600/3746] lr: 1.168e-02, eta: 1 day, 5:31:26, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6808, loss_cls: 3.2461, loss: 3.2461 +2024-12-30 14:26:43,985 - pyskl - INFO - Epoch [117][2700/3746] lr: 1.166e-02, eta: 1 day, 5:30:01, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.4263, top5_acc: 0.6820, loss_cls: 3.2563, loss: 3.2563 +2024-12-30 14:28:09,150 - pyskl - INFO - Epoch [117][2800/3746] lr: 1.164e-02, eta: 1 day, 5:28:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4303, top5_acc: 0.6825, loss_cls: 3.2109, loss: 3.2109 +2024-12-30 14:29:34,233 - pyskl - INFO - Epoch [117][2900/3746] lr: 1.163e-02, eta: 1 day, 5:27:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4202, top5_acc: 0.6833, loss_cls: 3.2462, loss: 3.2462 +2024-12-30 14:30:59,306 - pyskl - INFO - Epoch [117][3000/3746] lr: 1.161e-02, eta: 1 day, 5:25:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.6931, loss_cls: 3.1939, loss: 3.1939 +2024-12-30 14:32:24,296 - pyskl - INFO - Epoch [117][3100/3746] lr: 1.159e-02, eta: 1 day, 5:24:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6687, loss_cls: 3.3160, loss: 3.3160 +2024-12-30 14:33:49,386 - pyskl - INFO - Epoch [117][3200/3746] lr: 1.157e-02, eta: 1 day, 5:22:54, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4283, top5_acc: 0.6894, loss_cls: 3.1862, loss: 3.1862 +2024-12-30 14:35:14,272 - pyskl - INFO - Epoch [117][3300/3746] lr: 1.155e-02, eta: 1 day, 5:21:29, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4300, top5_acc: 0.6855, loss_cls: 3.2104, loss: 3.2104 +2024-12-30 14:36:39,630 - pyskl - INFO - Epoch [117][3400/3746] lr: 1.154e-02, eta: 1 day, 5:20:04, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6789, loss_cls: 3.2694, loss: 3.2694 +2024-12-30 14:38:04,583 - pyskl - INFO - Epoch [117][3500/3746] lr: 1.152e-02, eta: 1 day, 5:18:39, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4317, top5_acc: 0.6864, loss_cls: 3.2345, loss: 3.2345 +2024-12-30 14:39:29,372 - pyskl - INFO - Epoch [117][3600/3746] lr: 1.150e-02, eta: 1 day, 5:17:14, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4258, top5_acc: 0.6742, loss_cls: 3.2566, loss: 3.2566 +2024-12-30 14:40:54,214 - pyskl - INFO - Epoch [117][3700/3746] lr: 1.148e-02, eta: 1 day, 5:15:48, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4181, top5_acc: 0.6756, loss_cls: 3.2698, loss: 3.2698 +2024-12-30 14:41:35,306 - pyskl - INFO - Saving checkpoint at 117 epochs +2024-12-30 14:43:33,155 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 14:43:33,821 - pyskl - INFO - +top1_acc 0.3603 +top5_acc 0.6091 +2024-12-30 14:43:33,821 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 14:43:33,862 - pyskl - INFO - +mean_acc 0.3600 +2024-12-30 14:43:33,868 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_115.pth was removed +2024-12-30 14:43:34,142 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_117.pth. +2024-12-30 14:43:34,143 - pyskl - INFO - Best top1_acc is 0.3603 at 117 epoch. +2024-12-30 14:43:34,155 - pyskl - INFO - Epoch(val) [117][309] top1_acc: 0.3603, top5_acc: 0.6091, mean_class_accuracy: 0.3600 +2024-12-30 14:47:45,852 - pyskl - INFO - Epoch [118][100/3746] lr: 1.146e-02, eta: 1 day, 5:14:20, time: 2.517, data_time: 1.483, memory: 15990, top1_acc: 0.4445, top5_acc: 0.7005, loss_cls: 3.1238, loss: 3.1238 +2024-12-30 14:49:10,874 - pyskl - INFO - Epoch [118][200/3746] lr: 1.144e-02, eta: 1 day, 5:12:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4536, top5_acc: 0.7100, loss_cls: 3.0837, loss: 3.0837 +2024-12-30 14:50:36,159 - pyskl - INFO - Epoch [118][300/3746] lr: 1.142e-02, eta: 1 day, 5:11:29, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4517, top5_acc: 0.7069, loss_cls: 3.0866, loss: 3.0866 +2024-12-30 14:52:01,586 - pyskl - INFO - Epoch [118][400/3746] lr: 1.140e-02, eta: 1 day, 5:10:04, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4392, top5_acc: 0.6895, loss_cls: 3.1674, loss: 3.1674 +2024-12-30 14:53:27,073 - pyskl - INFO - Epoch [118][500/3746] lr: 1.139e-02, eta: 1 day, 5:08:39, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4331, top5_acc: 0.7003, loss_cls: 3.1382, loss: 3.1382 +2024-12-30 14:54:52,322 - pyskl - INFO - Epoch [118][600/3746] lr: 1.137e-02, eta: 1 day, 5:07:14, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7097, loss_cls: 3.0863, loss: 3.0863 +2024-12-30 14:56:17,487 - pyskl - INFO - Epoch [118][700/3746] lr: 1.135e-02, eta: 1 day, 5:05:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4372, top5_acc: 0.6877, loss_cls: 3.1949, loss: 3.1949 +2024-12-30 14:57:42,519 - pyskl - INFO - Epoch [118][800/3746] lr: 1.133e-02, eta: 1 day, 5:04:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6905, loss_cls: 3.1969, loss: 3.1969 +2024-12-30 14:59:07,479 - pyskl - INFO - Epoch [118][900/3746] lr: 1.131e-02, eta: 1 day, 5:02:58, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4338, top5_acc: 0.6895, loss_cls: 3.1673, loss: 3.1673 +2024-12-30 15:00:32,998 - pyskl - INFO - Epoch [118][1000/3746] lr: 1.130e-02, eta: 1 day, 5:01:33, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4327, top5_acc: 0.6847, loss_cls: 3.1989, loss: 3.1989 +2024-12-30 15:01:58,285 - pyskl - INFO - Epoch [118][1100/3746] lr: 1.128e-02, eta: 1 day, 5:00:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6952, loss_cls: 3.1470, loss: 3.1470 +2024-12-30 15:03:23,745 - pyskl - INFO - Epoch [118][1200/3746] lr: 1.126e-02, eta: 1 day, 4:58:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4272, top5_acc: 0.6828, loss_cls: 3.2116, loss: 3.2116 +2024-12-30 15:04:49,071 - pyskl - INFO - Epoch [118][1300/3746] lr: 1.124e-02, eta: 1 day, 4:57:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.6936, loss_cls: 3.1651, loss: 3.1651 +2024-12-30 15:06:14,722 - pyskl - INFO - Epoch [118][1400/3746] lr: 1.123e-02, eta: 1 day, 4:55:52, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4314, top5_acc: 0.6881, loss_cls: 3.2039, loss: 3.2039 +2024-12-30 15:07:40,435 - pyskl - INFO - Epoch [118][1500/3746] lr: 1.121e-02, eta: 1 day, 4:54:27, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4323, top5_acc: 0.6916, loss_cls: 3.2163, loss: 3.2163 +2024-12-30 15:09:05,731 - pyskl - INFO - Epoch [118][1600/3746] lr: 1.119e-02, eta: 1 day, 4:53:02, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.6916, loss_cls: 3.1809, loss: 3.1809 +2024-12-30 15:10:31,632 - pyskl - INFO - Epoch [118][1700/3746] lr: 1.117e-02, eta: 1 day, 4:51:37, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.6934, loss_cls: 3.1852, loss: 3.1852 +2024-12-30 15:11:57,215 - pyskl - INFO - Epoch [118][1800/3746] lr: 1.116e-02, eta: 1 day, 4:50:12, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4313, top5_acc: 0.6909, loss_cls: 3.2074, loss: 3.2074 +2024-12-30 15:13:22,942 - pyskl - INFO - Epoch [118][1900/3746] lr: 1.114e-02, eta: 1 day, 4:48:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4269, top5_acc: 0.6878, loss_cls: 3.1964, loss: 3.1964 +2024-12-30 15:14:48,429 - pyskl - INFO - Epoch [118][2000/3746] lr: 1.112e-02, eta: 1 day, 4:47:22, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4269, top5_acc: 0.6917, loss_cls: 3.1982, loss: 3.1982 +2024-12-30 15:16:14,037 - pyskl - INFO - Epoch [118][2100/3746] lr: 1.110e-02, eta: 1 day, 4:45:56, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6848, loss_cls: 3.2339, loss: 3.2339 +2024-12-30 15:17:39,870 - pyskl - INFO - Epoch [118][2200/3746] lr: 1.109e-02, eta: 1 day, 4:44:31, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4477, top5_acc: 0.6892, loss_cls: 3.1784, loss: 3.1784 +2024-12-30 15:19:05,546 - pyskl - INFO - Epoch [118][2300/3746] lr: 1.107e-02, eta: 1 day, 4:43:06, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6869, loss_cls: 3.2413, loss: 3.2413 +2024-12-30 15:20:30,589 - pyskl - INFO - Epoch [118][2400/3746] lr: 1.105e-02, eta: 1 day, 4:41:41, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.4308, top5_acc: 0.6836, loss_cls: 3.2067, loss: 3.2067 +2024-12-30 15:21:55,345 - pyskl - INFO - Epoch [118][2500/3746] lr: 1.103e-02, eta: 1 day, 4:40:16, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4295, top5_acc: 0.6884, loss_cls: 3.1801, loss: 3.1801 +2024-12-30 15:23:20,408 - pyskl - INFO - Epoch [118][2600/3746] lr: 1.102e-02, eta: 1 day, 4:38:50, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6811, loss_cls: 3.2409, loss: 3.2409 +2024-12-30 15:24:45,947 - pyskl - INFO - Epoch [118][2700/3746] lr: 1.100e-02, eta: 1 day, 4:37:25, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4269, top5_acc: 0.6848, loss_cls: 3.2149, loss: 3.2149 +2024-12-30 15:26:10,707 - pyskl - INFO - Epoch [118][2800/3746] lr: 1.098e-02, eta: 1 day, 4:36:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4273, top5_acc: 0.6720, loss_cls: 3.2837, loss: 3.2837 +2024-12-30 15:27:35,554 - pyskl - INFO - Epoch [118][2900/3746] lr: 1.096e-02, eta: 1 day, 4:34:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6786, loss_cls: 3.2308, loss: 3.2308 +2024-12-30 15:29:00,096 - pyskl - INFO - Epoch [118][3000/3746] lr: 1.095e-02, eta: 1 day, 4:33:09, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4381, top5_acc: 0.6817, loss_cls: 3.2087, loss: 3.2087 +2024-12-30 15:30:24,947 - pyskl - INFO - Epoch [118][3100/3746] lr: 1.093e-02, eta: 1 day, 4:31:44, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4286, top5_acc: 0.6864, loss_cls: 3.1951, loss: 3.1951 +2024-12-30 15:31:50,396 - pyskl - INFO - Epoch [118][3200/3746] lr: 1.091e-02, eta: 1 day, 4:30:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6908, loss_cls: 3.2192, loss: 3.2192 +2024-12-30 15:33:15,175 - pyskl - INFO - Epoch [118][3300/3746] lr: 1.089e-02, eta: 1 day, 4:28:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4277, top5_acc: 0.6916, loss_cls: 3.1789, loss: 3.1789 +2024-12-30 15:34:39,839 - pyskl - INFO - Epoch [118][3400/3746] lr: 1.088e-02, eta: 1 day, 4:27:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4291, top5_acc: 0.6819, loss_cls: 3.2475, loss: 3.2475 +2024-12-30 15:36:04,698 - pyskl - INFO - Epoch [118][3500/3746] lr: 1.086e-02, eta: 1 day, 4:26:03, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6802, loss_cls: 3.2206, loss: 3.2206 +2024-12-30 15:37:30,006 - pyskl - INFO - Epoch [118][3600/3746] lr: 1.084e-02, eta: 1 day, 4:24:37, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4375, top5_acc: 0.6856, loss_cls: 3.2003, loss: 3.2003 +2024-12-30 15:38:55,106 - pyskl - INFO - Epoch [118][3700/3746] lr: 1.082e-02, eta: 1 day, 4:23:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6825, loss_cls: 3.2339, loss: 3.2339 +2024-12-30 15:39:36,267 - pyskl - INFO - Saving checkpoint at 118 epochs +2024-12-30 15:41:35,651 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 15:41:36,523 - pyskl - INFO - +top1_acc 0.3648 +top5_acc 0.6201 +2024-12-30 15:41:36,523 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 15:41:36,568 - pyskl - INFO - +mean_acc 0.3646 +2024-12-30 15:41:36,573 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_117.pth was removed +2024-12-30 15:41:36,858 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2024-12-30 15:41:36,858 - pyskl - INFO - Best top1_acc is 0.3648 at 118 epoch. +2024-12-30 15:41:36,871 - pyskl - INFO - Epoch(val) [118][309] top1_acc: 0.3648, top5_acc: 0.6201, mean_class_accuracy: 0.3646 +2024-12-30 15:45:54,337 - pyskl - INFO - Epoch [119][100/3746] lr: 1.080e-02, eta: 1 day, 4:21:44, time: 2.575, data_time: 1.545, memory: 15990, top1_acc: 0.4514, top5_acc: 0.7011, loss_cls: 3.1093, loss: 3.1093 +2024-12-30 15:47:19,887 - pyskl - INFO - Epoch [119][200/3746] lr: 1.078e-02, eta: 1 day, 4:20:19, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4458, top5_acc: 0.7116, loss_cls: 3.0871, loss: 3.0871 +2024-12-30 15:48:45,715 - pyskl - INFO - Epoch [119][300/3746] lr: 1.076e-02, eta: 1 day, 4:18:54, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.4314, top5_acc: 0.6992, loss_cls: 3.1564, loss: 3.1564 +2024-12-30 15:50:10,605 - pyskl - INFO - Epoch [119][400/3746] lr: 1.075e-02, eta: 1 day, 4:17:28, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4381, top5_acc: 0.6966, loss_cls: 3.1466, loss: 3.1466 +2024-12-30 15:51:35,550 - pyskl - INFO - Epoch [119][500/3746] lr: 1.073e-02, eta: 1 day, 4:16:03, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4378, top5_acc: 0.6916, loss_cls: 3.1718, loss: 3.1718 +2024-12-30 15:53:00,452 - pyskl - INFO - Epoch [119][600/3746] lr: 1.071e-02, eta: 1 day, 4:14:38, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4444, top5_acc: 0.6991, loss_cls: 3.1202, loss: 3.1202 +2024-12-30 15:54:25,615 - pyskl - INFO - Epoch [119][700/3746] lr: 1.069e-02, eta: 1 day, 4:13:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4433, top5_acc: 0.7072, loss_cls: 3.1309, loss: 3.1309 +2024-12-30 15:55:50,442 - pyskl - INFO - Epoch [119][800/3746] lr: 1.068e-02, eta: 1 day, 4:11:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4462, top5_acc: 0.7028, loss_cls: 3.1125, loss: 3.1125 +2024-12-30 15:57:15,240 - pyskl - INFO - Epoch [119][900/3746] lr: 1.066e-02, eta: 1 day, 4:10:22, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4473, top5_acc: 0.7006, loss_cls: 3.1116, loss: 3.1116 +2024-12-30 15:58:40,735 - pyskl - INFO - Epoch [119][1000/3746] lr: 1.064e-02, eta: 1 day, 4:08:56, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4434, top5_acc: 0.6972, loss_cls: 3.1182, loss: 3.1182 +2024-12-30 16:00:06,432 - pyskl - INFO - Epoch [119][1100/3746] lr: 1.063e-02, eta: 1 day, 4:07:31, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4416, top5_acc: 0.6964, loss_cls: 3.1483, loss: 3.1483 +2024-12-30 16:01:31,762 - pyskl - INFO - Epoch [119][1200/3746] lr: 1.061e-02, eta: 1 day, 4:06:06, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4387, top5_acc: 0.6986, loss_cls: 3.1599, loss: 3.1599 +2024-12-30 16:02:57,122 - pyskl - INFO - Epoch [119][1300/3746] lr: 1.059e-02, eta: 1 day, 4:04:41, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4381, top5_acc: 0.6972, loss_cls: 3.1566, loss: 3.1566 +2024-12-30 16:04:22,031 - pyskl - INFO - Epoch [119][1400/3746] lr: 1.057e-02, eta: 1 day, 4:03:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4252, top5_acc: 0.6939, loss_cls: 3.1826, loss: 3.1826 +2024-12-30 16:05:46,959 - pyskl - INFO - Epoch [119][1500/3746] lr: 1.056e-02, eta: 1 day, 4:01:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4320, top5_acc: 0.6870, loss_cls: 3.2045, loss: 3.2045 +2024-12-30 16:07:11,636 - pyskl - INFO - Epoch [119][1600/3746] lr: 1.054e-02, eta: 1 day, 4:00:25, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4478, top5_acc: 0.6914, loss_cls: 3.1411, loss: 3.1411 +2024-12-30 16:08:36,423 - pyskl - INFO - Epoch [119][1700/3746] lr: 1.052e-02, eta: 1 day, 3:58:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4378, top5_acc: 0.6825, loss_cls: 3.1858, loss: 3.1858 +2024-12-30 16:10:01,302 - pyskl - INFO - Epoch [119][1800/3746] lr: 1.050e-02, eta: 1 day, 3:57:34, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.6948, loss_cls: 3.1451, loss: 3.1451 +2024-12-30 16:11:26,362 - pyskl - INFO - Epoch [119][1900/3746] lr: 1.049e-02, eta: 1 day, 3:56:09, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.6987, loss_cls: 3.1244, loss: 3.1244 +2024-12-30 16:12:51,131 - pyskl - INFO - Epoch [119][2000/3746] lr: 1.047e-02, eta: 1 day, 3:54:43, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4345, top5_acc: 0.6961, loss_cls: 3.1771, loss: 3.1771 +2024-12-30 16:14:15,963 - pyskl - INFO - Epoch [119][2100/3746] lr: 1.045e-02, eta: 1 day, 3:53:18, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4302, top5_acc: 0.6891, loss_cls: 3.2075, loss: 3.2075 +2024-12-30 16:15:40,982 - pyskl - INFO - Epoch [119][2200/3746] lr: 1.044e-02, eta: 1 day, 3:51:53, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4298, top5_acc: 0.6837, loss_cls: 3.2052, loss: 3.2052 +2024-12-30 16:17:06,527 - pyskl - INFO - Epoch [119][2300/3746] lr: 1.042e-02, eta: 1 day, 3:50:27, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.4320, top5_acc: 0.6867, loss_cls: 3.2184, loss: 3.2184 +2024-12-30 16:18:31,692 - pyskl - INFO - Epoch [119][2400/3746] lr: 1.040e-02, eta: 1 day, 3:49:02, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.4300, top5_acc: 0.6841, loss_cls: 3.2144, loss: 3.2144 +2024-12-30 16:19:56,627 - pyskl - INFO - Epoch [119][2500/3746] lr: 1.039e-02, eta: 1 day, 3:47:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6830, loss_cls: 3.2013, loss: 3.2013 +2024-12-30 16:21:21,851 - pyskl - INFO - Epoch [119][2600/3746] lr: 1.037e-02, eta: 1 day, 3:46:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4387, top5_acc: 0.6839, loss_cls: 3.1949, loss: 3.1949 +2024-12-30 16:22:47,194 - pyskl - INFO - Epoch [119][2700/3746] lr: 1.035e-02, eta: 1 day, 3:44:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4453, top5_acc: 0.6927, loss_cls: 3.1457, loss: 3.1457 +2024-12-30 16:24:12,030 - pyskl - INFO - Epoch [119][2800/3746] lr: 1.033e-02, eta: 1 day, 3:43:21, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4403, top5_acc: 0.6984, loss_cls: 3.1584, loss: 3.1584 +2024-12-30 16:25:36,907 - pyskl - INFO - Epoch [119][2900/3746] lr: 1.032e-02, eta: 1 day, 3:41:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4286, top5_acc: 0.6891, loss_cls: 3.1951, loss: 3.1951 +2024-12-30 16:27:02,065 - pyskl - INFO - Epoch [119][3000/3746] lr: 1.030e-02, eta: 1 day, 3:40:30, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4402, top5_acc: 0.6948, loss_cls: 3.1543, loss: 3.1543 +2024-12-30 16:28:27,921 - pyskl - INFO - Epoch [119][3100/3746] lr: 1.028e-02, eta: 1 day, 3:39:05, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4414, top5_acc: 0.6964, loss_cls: 3.1510, loss: 3.1510 +2024-12-30 16:29:53,416 - pyskl - INFO - Epoch [119][3200/3746] lr: 1.027e-02, eta: 1 day, 3:37:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4356, top5_acc: 0.6941, loss_cls: 3.1587, loss: 3.1587 +2024-12-30 16:31:18,957 - pyskl - INFO - Epoch [119][3300/3746] lr: 1.025e-02, eta: 1 day, 3:36:15, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6969, loss_cls: 3.2081, loss: 3.2081 +2024-12-30 16:32:44,628 - pyskl - INFO - Epoch [119][3400/3746] lr: 1.023e-02, eta: 1 day, 3:34:50, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4445, top5_acc: 0.6894, loss_cls: 3.1602, loss: 3.1602 +2024-12-30 16:34:10,520 - pyskl - INFO - Epoch [119][3500/3746] lr: 1.022e-02, eta: 1 day, 3:33:25, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4394, top5_acc: 0.6958, loss_cls: 3.1830, loss: 3.1830 +2024-12-30 16:35:36,466 - pyskl - INFO - Epoch [119][3600/3746] lr: 1.020e-02, eta: 1 day, 3:32:00, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4338, top5_acc: 0.6906, loss_cls: 3.2060, loss: 3.2060 +2024-12-30 16:37:02,261 - pyskl - INFO - Epoch [119][3700/3746] lr: 1.018e-02, eta: 1 day, 3:30:35, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4203, top5_acc: 0.6777, loss_cls: 3.2446, loss: 3.2446 +2024-12-30 16:37:43,643 - pyskl - INFO - Saving checkpoint at 119 epochs +2024-12-30 16:39:43,293 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 16:39:44,149 - pyskl - INFO - +top1_acc 0.3746 +top5_acc 0.6302 +2024-12-30 16:39:44,149 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 16:39:44,202 - pyskl - INFO - +mean_acc 0.3743 +2024-12-30 16:39:44,208 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_118.pth was removed +2024-12-30 16:39:44,548 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_119.pth. +2024-12-30 16:39:44,550 - pyskl - INFO - Best top1_acc is 0.3746 at 119 epoch. +2024-12-30 16:39:44,567 - pyskl - INFO - Epoch(val) [119][309] top1_acc: 0.3746, top5_acc: 0.6302, mean_class_accuracy: 0.3743 +2024-12-30 16:44:03,384 - pyskl - INFO - Epoch [120][100/3746] lr: 1.016e-02, eta: 1 day, 3:29:05, time: 2.588, data_time: 1.552, memory: 15990, top1_acc: 0.4575, top5_acc: 0.7152, loss_cls: 3.0482, loss: 3.0482 +2024-12-30 16:45:28,770 - pyskl - INFO - Epoch [120][200/3746] lr: 1.014e-02, eta: 1 day, 3:27:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7055, loss_cls: 3.0899, loss: 3.0899 +2024-12-30 16:46:53,644 - pyskl - INFO - Epoch [120][300/3746] lr: 1.012e-02, eta: 1 day, 3:26:14, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.4541, top5_acc: 0.7095, loss_cls: 3.0739, loss: 3.0739 +2024-12-30 16:48:18,800 - pyskl - INFO - Epoch [120][400/3746] lr: 1.011e-02, eta: 1 day, 3:24:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4559, top5_acc: 0.7056, loss_cls: 3.0621, loss: 3.0621 +2024-12-30 16:49:43,873 - pyskl - INFO - Epoch [120][500/3746] lr: 1.009e-02, eta: 1 day, 3:23:24, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4492, top5_acc: 0.6978, loss_cls: 3.1236, loss: 3.1236 +2024-12-30 16:51:09,079 - pyskl - INFO - Epoch [120][600/3746] lr: 1.007e-02, eta: 1 day, 3:21:59, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4542, top5_acc: 0.7017, loss_cls: 3.0978, loss: 3.0978 +2024-12-30 16:52:34,375 - pyskl - INFO - Epoch [120][700/3746] lr: 1.006e-02, eta: 1 day, 3:20:33, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4405, top5_acc: 0.6966, loss_cls: 3.1333, loss: 3.1333 +2024-12-30 16:53:58,966 - pyskl - INFO - Epoch [120][800/3746] lr: 1.004e-02, eta: 1 day, 3:19:08, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.6958, loss_cls: 3.1383, loss: 3.1383 +2024-12-30 16:55:24,615 - pyskl - INFO - Epoch [120][900/3746] lr: 1.002e-02, eta: 1 day, 3:17:43, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4570, top5_acc: 0.6991, loss_cls: 3.1077, loss: 3.1077 +2024-12-30 16:56:49,689 - pyskl - INFO - Epoch [120][1000/3746] lr: 1.001e-02, eta: 1 day, 3:16:17, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4523, top5_acc: 0.7059, loss_cls: 3.1143, loss: 3.1143 +2024-12-30 16:58:14,999 - pyskl - INFO - Epoch [120][1100/3746] lr: 9.989e-03, eta: 1 day, 3:14:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4378, top5_acc: 0.6981, loss_cls: 3.1487, loss: 3.1487 +2024-12-30 16:59:40,420 - pyskl - INFO - Epoch [120][1200/3746] lr: 9.972e-03, eta: 1 day, 3:13:27, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4402, top5_acc: 0.6922, loss_cls: 3.1565, loss: 3.1565 +2024-12-30 17:01:05,998 - pyskl - INFO - Epoch [120][1300/3746] lr: 9.955e-03, eta: 1 day, 3:12:02, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4483, top5_acc: 0.6966, loss_cls: 3.1321, loss: 3.1321 +2024-12-30 17:02:31,110 - pyskl - INFO - Epoch [120][1400/3746] lr: 9.938e-03, eta: 1 day, 3:10:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4442, top5_acc: 0.6980, loss_cls: 3.1459, loss: 3.1459 +2024-12-30 17:03:56,086 - pyskl - INFO - Epoch [120][1500/3746] lr: 9.922e-03, eta: 1 day, 3:09:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4484, top5_acc: 0.7000, loss_cls: 3.1005, loss: 3.1005 +2024-12-30 17:05:20,919 - pyskl - INFO - Epoch [120][1600/3746] lr: 9.905e-03, eta: 1 day, 3:07:45, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4459, top5_acc: 0.6991, loss_cls: 3.1405, loss: 3.1405 +2024-12-30 17:06:45,638 - pyskl - INFO - Epoch [120][1700/3746] lr: 9.888e-03, eta: 1 day, 3:06:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.7075, loss_cls: 3.1342, loss: 3.1342 +2024-12-30 17:08:11,122 - pyskl - INFO - Epoch [120][1800/3746] lr: 9.871e-03, eta: 1 day, 3:04:55, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6928, loss_cls: 3.1771, loss: 3.1771 +2024-12-30 17:09:36,244 - pyskl - INFO - Epoch [120][1900/3746] lr: 9.855e-03, eta: 1 day, 3:03:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4386, top5_acc: 0.6913, loss_cls: 3.1590, loss: 3.1590 +2024-12-30 17:11:01,302 - pyskl - INFO - Epoch [120][2000/3746] lr: 9.838e-03, eta: 1 day, 3:02:04, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.7028, loss_cls: 3.1020, loss: 3.1020 +2024-12-30 17:12:26,876 - pyskl - INFO - Epoch [120][2100/3746] lr: 9.821e-03, eta: 1 day, 3:00:39, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4342, top5_acc: 0.6950, loss_cls: 3.1663, loss: 3.1663 +2024-12-30 17:13:52,528 - pyskl - INFO - Epoch [120][2200/3746] lr: 9.805e-03, eta: 1 day, 2:59:14, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4387, top5_acc: 0.6937, loss_cls: 3.1631, loss: 3.1631 +2024-12-30 17:15:17,618 - pyskl - INFO - Epoch [120][2300/3746] lr: 9.788e-03, eta: 1 day, 2:57:48, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4238, top5_acc: 0.6866, loss_cls: 3.1952, loss: 3.1952 +2024-12-30 17:16:42,748 - pyskl - INFO - Epoch [120][2400/3746] lr: 9.772e-03, eta: 1 day, 2:56:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4459, top5_acc: 0.6894, loss_cls: 3.1593, loss: 3.1593 +2024-12-30 17:18:07,443 - pyskl - INFO - Epoch [120][2500/3746] lr: 9.755e-03, eta: 1 day, 2:54:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4534, top5_acc: 0.6998, loss_cls: 3.1316, loss: 3.1316 +2024-12-30 17:19:33,166 - pyskl - INFO - Epoch [120][2600/3746] lr: 9.738e-03, eta: 1 day, 2:53:33, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.4322, top5_acc: 0.6980, loss_cls: 3.1603, loss: 3.1603 +2024-12-30 17:20:58,479 - pyskl - INFO - Epoch [120][2700/3746] lr: 9.722e-03, eta: 1 day, 2:52:07, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.7023, loss_cls: 3.1587, loss: 3.1587 +2024-12-30 17:22:23,207 - pyskl - INFO - Epoch [120][2800/3746] lr: 9.705e-03, eta: 1 day, 2:50:42, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4381, top5_acc: 0.6978, loss_cls: 3.1523, loss: 3.1523 +2024-12-30 17:23:47,963 - pyskl - INFO - Epoch [120][2900/3746] lr: 9.689e-03, eta: 1 day, 2:49:16, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.6995, loss_cls: 3.1289, loss: 3.1289 +2024-12-30 17:25:12,824 - pyskl - INFO - Epoch [120][3000/3746] lr: 9.672e-03, eta: 1 day, 2:47:51, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4347, top5_acc: 0.6883, loss_cls: 3.1977, loss: 3.1977 +2024-12-30 17:26:37,352 - pyskl - INFO - Epoch [120][3100/3746] lr: 9.656e-03, eta: 1 day, 2:46:26, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4403, top5_acc: 0.6919, loss_cls: 3.1611, loss: 3.1611 +2024-12-30 17:28:02,198 - pyskl - INFO - Epoch [120][3200/3746] lr: 9.639e-03, eta: 1 day, 2:45:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4434, top5_acc: 0.6944, loss_cls: 3.1471, loss: 3.1471 +2024-12-30 17:29:27,360 - pyskl - INFO - Epoch [120][3300/3746] lr: 9.623e-03, eta: 1 day, 2:43:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4417, top5_acc: 0.6944, loss_cls: 3.1430, loss: 3.1430 +2024-12-30 17:30:51,859 - pyskl - INFO - Epoch [120][3400/3746] lr: 9.606e-03, eta: 1 day, 2:42:09, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4431, top5_acc: 0.7002, loss_cls: 3.1295, loss: 3.1295 +2024-12-30 17:32:16,819 - pyskl - INFO - Epoch [120][3500/3746] lr: 9.590e-03, eta: 1 day, 2:40:44, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4447, top5_acc: 0.6959, loss_cls: 3.1531, loss: 3.1531 +2024-12-30 17:33:42,150 - pyskl - INFO - Epoch [120][3600/3746] lr: 9.573e-03, eta: 1 day, 2:39:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.6913, loss_cls: 3.1820, loss: 3.1820 +2024-12-30 17:35:07,611 - pyskl - INFO - Epoch [120][3700/3746] lr: 9.557e-03, eta: 1 day, 2:37:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6908, loss_cls: 3.2086, loss: 3.2086 +2024-12-30 17:35:48,849 - pyskl - INFO - Saving checkpoint at 120 epochs +2024-12-30 17:37:47,791 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 17:37:48,471 - pyskl - INFO - +top1_acc 0.3701 +top5_acc 0.6297 +2024-12-30 17:37:48,471 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 17:37:48,517 - pyskl - INFO - +mean_acc 0.3699 +2024-12-30 17:37:48,531 - pyskl - INFO - Epoch(val) [120][309] top1_acc: 0.3701, top5_acc: 0.6297, mean_class_accuracy: 0.3699 +2024-12-30 17:42:02,664 - pyskl - INFO - Epoch [121][100/3746] lr: 9.533e-03, eta: 1 day, 2:36:21, time: 2.541, data_time: 1.505, memory: 15990, top1_acc: 0.4505, top5_acc: 0.7039, loss_cls: 3.0955, loss: 3.0955 +2024-12-30 17:43:27,786 - pyskl - INFO - Epoch [121][200/3746] lr: 9.516e-03, eta: 1 day, 2:34:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4584, top5_acc: 0.7131, loss_cls: 3.0436, loss: 3.0436 +2024-12-30 17:44:52,787 - pyskl - INFO - Epoch [121][300/3746] lr: 9.500e-03, eta: 1 day, 2:33:31, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4598, top5_acc: 0.7139, loss_cls: 3.0245, loss: 3.0245 +2024-12-30 17:46:17,290 - pyskl - INFO - Epoch [121][400/3746] lr: 9.484e-03, eta: 1 day, 2:32:05, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4580, top5_acc: 0.7045, loss_cls: 3.0734, loss: 3.0734 +2024-12-30 17:47:42,247 - pyskl - INFO - Epoch [121][500/3746] lr: 9.467e-03, eta: 1 day, 2:30:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4480, top5_acc: 0.7037, loss_cls: 3.0901, loss: 3.0901 +2024-12-30 17:49:06,930 - pyskl - INFO - Epoch [121][600/3746] lr: 9.451e-03, eta: 1 day, 2:29:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4587, top5_acc: 0.7075, loss_cls: 3.0540, loss: 3.0540 +2024-12-30 17:50:31,694 - pyskl - INFO - Epoch [121][700/3746] lr: 9.435e-03, eta: 1 day, 2:27:49, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4556, top5_acc: 0.7059, loss_cls: 3.0727, loss: 3.0727 +2024-12-30 17:51:56,649 - pyskl - INFO - Epoch [121][800/3746] lr: 9.418e-03, eta: 1 day, 2:26:24, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.7066, loss_cls: 3.0945, loss: 3.0945 +2024-12-30 17:53:22,075 - pyskl - INFO - Epoch [121][900/3746] lr: 9.402e-03, eta: 1 day, 2:24:58, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.7081, loss_cls: 3.0820, loss: 3.0820 +2024-12-30 17:54:46,958 - pyskl - INFO - Epoch [121][1000/3746] lr: 9.386e-03, eta: 1 day, 2:23:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4606, top5_acc: 0.7072, loss_cls: 3.0469, loss: 3.0469 +2024-12-30 17:56:12,225 - pyskl - INFO - Epoch [121][1100/3746] lr: 9.369e-03, eta: 1 day, 2:22:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.7011, loss_cls: 3.1252, loss: 3.1252 +2024-12-30 17:57:37,280 - pyskl - INFO - Epoch [121][1200/3746] lr: 9.353e-03, eta: 1 day, 2:20:42, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4442, top5_acc: 0.6930, loss_cls: 3.1675, loss: 3.1675 +2024-12-30 17:59:02,586 - pyskl - INFO - Epoch [121][1300/3746] lr: 9.337e-03, eta: 1 day, 2:19:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4378, top5_acc: 0.7009, loss_cls: 3.1444, loss: 3.1444 +2024-12-30 18:00:27,770 - pyskl - INFO - Epoch [121][1400/3746] lr: 9.321e-03, eta: 1 day, 2:17:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4486, top5_acc: 0.7041, loss_cls: 3.1211, loss: 3.1211 +2024-12-30 18:01:52,929 - pyskl - INFO - Epoch [121][1500/3746] lr: 9.304e-03, eta: 1 day, 2:16:26, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.7064, loss_cls: 3.1007, loss: 3.1007 +2024-12-30 18:03:17,862 - pyskl - INFO - Epoch [121][1600/3746] lr: 9.288e-03, eta: 1 day, 2:15:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4480, top5_acc: 0.6987, loss_cls: 3.1368, loss: 3.1368 +2024-12-30 18:04:43,151 - pyskl - INFO - Epoch [121][1700/3746] lr: 9.272e-03, eta: 1 day, 2:13:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4409, top5_acc: 0.6977, loss_cls: 3.1548, loss: 3.1548 +2024-12-30 18:06:08,846 - pyskl - INFO - Epoch [121][1800/3746] lr: 9.256e-03, eta: 1 day, 2:12:10, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4439, top5_acc: 0.7000, loss_cls: 3.1080, loss: 3.1080 +2024-12-30 18:07:34,361 - pyskl - INFO - Epoch [121][1900/3746] lr: 9.239e-03, eta: 1 day, 2:10:45, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4320, top5_acc: 0.6869, loss_cls: 3.1920, loss: 3.1920 +2024-12-30 18:08:59,447 - pyskl - INFO - Epoch [121][2000/3746] lr: 9.223e-03, eta: 1 day, 2:09:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4381, top5_acc: 0.6952, loss_cls: 3.1499, loss: 3.1499 +2024-12-30 18:10:24,451 - pyskl - INFO - Epoch [121][2100/3746] lr: 9.207e-03, eta: 1 day, 2:07:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4437, top5_acc: 0.6972, loss_cls: 3.1148, loss: 3.1148 +2024-12-30 18:11:50,121 - pyskl - INFO - Epoch [121][2200/3746] lr: 9.191e-03, eta: 1 day, 2:06:29, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4550, top5_acc: 0.7136, loss_cls: 3.0725, loss: 3.0725 +2024-12-30 18:13:14,865 - pyskl - INFO - Epoch [121][2300/3746] lr: 9.175e-03, eta: 1 day, 2:05:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4473, top5_acc: 0.7025, loss_cls: 3.1052, loss: 3.1052 +2024-12-30 18:14:39,822 - pyskl - INFO - Epoch [121][2400/3746] lr: 9.159e-03, eta: 1 day, 2:03:38, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.6964, loss_cls: 3.1392, loss: 3.1392 +2024-12-30 18:16:04,404 - pyskl - INFO - Epoch [121][2500/3746] lr: 9.142e-03, eta: 1 day, 2:02:13, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4475, top5_acc: 0.7037, loss_cls: 3.1141, loss: 3.1141 +2024-12-30 18:17:29,441 - pyskl - INFO - Epoch [121][2600/3746] lr: 9.126e-03, eta: 1 day, 2:00:47, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.4405, top5_acc: 0.6934, loss_cls: 3.1329, loss: 3.1329 +2024-12-30 18:18:54,248 - pyskl - INFO - Epoch [121][2700/3746] lr: 9.110e-03, eta: 1 day, 1:59:22, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4311, top5_acc: 0.6967, loss_cls: 3.1558, loss: 3.1558 +2024-12-30 18:20:18,480 - pyskl - INFO - Epoch [121][2800/3746] lr: 9.094e-03, eta: 1 day, 1:57:56, time: 0.842, data_time: 0.001, memory: 15990, top1_acc: 0.4384, top5_acc: 0.6825, loss_cls: 3.1973, loss: 3.1973 +2024-12-30 18:21:43,070 - pyskl - INFO - Epoch [121][2900/3746] lr: 9.078e-03, eta: 1 day, 1:56:31, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4386, top5_acc: 0.7066, loss_cls: 3.1173, loss: 3.1173 +2024-12-30 18:23:07,753 - pyskl - INFO - Epoch [121][3000/3746] lr: 9.062e-03, eta: 1 day, 1:55:05, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4309, top5_acc: 0.6961, loss_cls: 3.1483, loss: 3.1483 +2024-12-30 18:24:32,983 - pyskl - INFO - Epoch [121][3100/3746] lr: 9.046e-03, eta: 1 day, 1:53:40, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.7013, loss_cls: 3.1065, loss: 3.1065 +2024-12-30 18:25:57,725 - pyskl - INFO - Epoch [121][3200/3746] lr: 9.030e-03, eta: 1 day, 1:52:15, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4544, top5_acc: 0.7044, loss_cls: 3.0889, loss: 3.0889 +2024-12-30 18:27:22,826 - pyskl - INFO - Epoch [121][3300/3746] lr: 9.014e-03, eta: 1 day, 1:50:49, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4570, top5_acc: 0.7189, loss_cls: 3.0247, loss: 3.0247 +2024-12-30 18:28:47,563 - pyskl - INFO - Epoch [121][3400/3746] lr: 8.998e-03, eta: 1 day, 1:49:24, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4403, top5_acc: 0.6966, loss_cls: 3.1524, loss: 3.1524 +2024-12-30 18:30:12,696 - pyskl - INFO - Epoch [121][3500/3746] lr: 8.982e-03, eta: 1 day, 1:47:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.7064, loss_cls: 3.1009, loss: 3.1009 +2024-12-30 18:31:38,117 - pyskl - INFO - Epoch [121][3600/3746] lr: 8.966e-03, eta: 1 day, 1:46:33, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4461, top5_acc: 0.7125, loss_cls: 3.0809, loss: 3.0809 +2024-12-30 18:33:02,924 - pyskl - INFO - Epoch [121][3700/3746] lr: 8.950e-03, eta: 1 day, 1:45:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.7027, loss_cls: 3.1318, loss: 3.1318 +2024-12-30 18:33:43,425 - pyskl - INFO - Saving checkpoint at 121 epochs +2024-12-30 18:35:39,887 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 18:35:40,559 - pyskl - INFO - +top1_acc 0.3840 +top5_acc 0.6362 +2024-12-30 18:35:40,559 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 18:35:40,612 - pyskl - INFO - +mean_acc 0.3837 +2024-12-30 18:35:40,617 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_119.pth was removed +2024-12-30 18:35:41,070 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_121.pth. +2024-12-30 18:35:41,071 - pyskl - INFO - Best top1_acc is 0.3840 at 121 epoch. +2024-12-30 18:35:41,088 - pyskl - INFO - Epoch(val) [121][309] top1_acc: 0.3840, top5_acc: 0.6362, mean_class_accuracy: 0.3837 +2024-12-30 18:39:59,454 - pyskl - INFO - Epoch [122][100/3746] lr: 8.927e-03, eta: 1 day, 1:43:35, time: 2.584, data_time: 1.524, memory: 15990, top1_acc: 0.4681, top5_acc: 0.7225, loss_cls: 3.0014, loss: 3.0014 +2024-12-30 18:41:24,700 - pyskl - INFO - Epoch [122][200/3746] lr: 8.911e-03, eta: 1 day, 1:42:10, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4597, top5_acc: 0.7197, loss_cls: 3.0180, loss: 3.0180 +2024-12-30 18:42:50,298 - pyskl - INFO - Epoch [122][300/3746] lr: 8.895e-03, eta: 1 day, 1:40:45, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4669, top5_acc: 0.7156, loss_cls: 3.0213, loss: 3.0213 +2024-12-30 18:44:15,248 - pyskl - INFO - Epoch [122][400/3746] lr: 8.879e-03, eta: 1 day, 1:39:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4447, top5_acc: 0.7117, loss_cls: 3.0465, loss: 3.0465 +2024-12-30 18:45:40,790 - pyskl - INFO - Epoch [122][500/3746] lr: 8.863e-03, eta: 1 day, 1:37:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4592, top5_acc: 0.7163, loss_cls: 3.0381, loss: 3.0381 +2024-12-30 18:47:06,125 - pyskl - INFO - Epoch [122][600/3746] lr: 8.847e-03, eta: 1 day, 1:36:29, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4561, top5_acc: 0.7120, loss_cls: 3.0799, loss: 3.0799 +2024-12-30 18:48:31,530 - pyskl - INFO - Epoch [122][700/3746] lr: 8.831e-03, eta: 1 day, 1:35:03, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4625, top5_acc: 0.7164, loss_cls: 3.0504, loss: 3.0504 +2024-12-30 18:49:57,058 - pyskl - INFO - Epoch [122][800/3746] lr: 8.815e-03, eta: 1 day, 1:33:38, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7045, loss_cls: 3.0765, loss: 3.0765 +2024-12-30 18:51:22,750 - pyskl - INFO - Epoch [122][900/3746] lr: 8.800e-03, eta: 1 day, 1:32:13, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4561, top5_acc: 0.7153, loss_cls: 3.0514, loss: 3.0514 +2024-12-30 18:52:48,082 - pyskl - INFO - Epoch [122][1000/3746] lr: 8.784e-03, eta: 1 day, 1:30:47, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4658, top5_acc: 0.7170, loss_cls: 3.0324, loss: 3.0324 +2024-12-30 18:54:13,628 - pyskl - INFO - Epoch [122][1100/3746] lr: 8.768e-03, eta: 1 day, 1:29:22, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.7070, loss_cls: 3.0644, loss: 3.0644 +2024-12-30 18:55:39,311 - pyskl - INFO - Epoch [122][1200/3746] lr: 8.752e-03, eta: 1 day, 1:27:57, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.6958, loss_cls: 3.0661, loss: 3.0661 +2024-12-30 18:57:04,653 - pyskl - INFO - Epoch [122][1300/3746] lr: 8.736e-03, eta: 1 day, 1:26:32, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4494, top5_acc: 0.6966, loss_cls: 3.0921, loss: 3.0921 +2024-12-30 18:58:30,532 - pyskl - INFO - Epoch [122][1400/3746] lr: 8.721e-03, eta: 1 day, 1:25:06, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4470, top5_acc: 0.7083, loss_cls: 3.0779, loss: 3.0779 +2024-12-30 18:59:55,825 - pyskl - INFO - Epoch [122][1500/3746] lr: 8.705e-03, eta: 1 day, 1:23:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4484, top5_acc: 0.7069, loss_cls: 3.0633, loss: 3.0633 +2024-12-30 19:01:20,907 - pyskl - INFO - Epoch [122][1600/3746] lr: 8.689e-03, eta: 1 day, 1:22:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7120, loss_cls: 3.0577, loss: 3.0577 +2024-12-30 19:02:45,932 - pyskl - INFO - Epoch [122][1700/3746] lr: 8.673e-03, eta: 1 day, 1:20:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4441, top5_acc: 0.7028, loss_cls: 3.1259, loss: 3.1259 +2024-12-30 19:04:11,464 - pyskl - INFO - Epoch [122][1800/3746] lr: 8.658e-03, eta: 1 day, 1:19:25, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4416, top5_acc: 0.6886, loss_cls: 3.1586, loss: 3.1586 +2024-12-30 19:05:36,598 - pyskl - INFO - Epoch [122][1900/3746] lr: 8.642e-03, eta: 1 day, 1:18:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4520, top5_acc: 0.7055, loss_cls: 3.0899, loss: 3.0899 +2024-12-30 19:07:01,669 - pyskl - INFO - Epoch [122][2000/3746] lr: 8.626e-03, eta: 1 day, 1:16:34, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4550, top5_acc: 0.7095, loss_cls: 3.0545, loss: 3.0545 +2024-12-30 19:08:27,086 - pyskl - INFO - Epoch [122][2100/3746] lr: 8.610e-03, eta: 1 day, 1:15:09, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7075, loss_cls: 3.0831, loss: 3.0831 +2024-12-30 19:09:52,148 - pyskl - INFO - Epoch [122][2200/3746] lr: 8.595e-03, eta: 1 day, 1:13:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4392, top5_acc: 0.6992, loss_cls: 3.1532, loss: 3.1532 +2024-12-30 19:11:17,449 - pyskl - INFO - Epoch [122][2300/3746] lr: 8.579e-03, eta: 1 day, 1:12:18, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.4520, top5_acc: 0.7078, loss_cls: 3.0803, loss: 3.0803 +2024-12-30 19:12:42,700 - pyskl - INFO - Epoch [122][2400/3746] lr: 8.563e-03, eta: 1 day, 1:10:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4458, top5_acc: 0.7037, loss_cls: 3.0890, loss: 3.0890 +2024-12-30 19:14:07,487 - pyskl - INFO - Epoch [122][2500/3746] lr: 8.548e-03, eta: 1 day, 1:09:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4509, top5_acc: 0.7064, loss_cls: 3.0981, loss: 3.0981 +2024-12-30 19:15:32,806 - pyskl - INFO - Epoch [122][2600/3746] lr: 8.532e-03, eta: 1 day, 1:08:02, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4442, top5_acc: 0.7034, loss_cls: 3.1078, loss: 3.1078 +2024-12-30 19:16:57,364 - pyskl - INFO - Epoch [122][2700/3746] lr: 8.517e-03, eta: 1 day, 1:06:36, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4489, top5_acc: 0.6994, loss_cls: 3.1207, loss: 3.1207 +2024-12-30 19:18:22,040 - pyskl - INFO - Epoch [122][2800/3746] lr: 8.501e-03, eta: 1 day, 1:05:11, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4483, top5_acc: 0.7045, loss_cls: 3.1085, loss: 3.1085 +2024-12-30 19:19:46,649 - pyskl - INFO - Epoch [122][2900/3746] lr: 8.485e-03, eta: 1 day, 1:03:45, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4475, top5_acc: 0.6992, loss_cls: 3.1291, loss: 3.1291 +2024-12-30 19:21:11,815 - pyskl - INFO - Epoch [122][3000/3746] lr: 8.470e-03, eta: 1 day, 1:02:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4477, top5_acc: 0.7077, loss_cls: 3.0559, loss: 3.0559 +2024-12-30 19:22:36,751 - pyskl - INFO - Epoch [122][3100/3746] lr: 8.454e-03, eta: 1 day, 1:00:55, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4486, top5_acc: 0.7150, loss_cls: 3.0757, loss: 3.0757 +2024-12-30 19:24:01,721 - pyskl - INFO - Epoch [122][3200/3746] lr: 8.439e-03, eta: 1 day, 0:59:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4537, top5_acc: 0.7061, loss_cls: 3.0732, loss: 3.0732 +2024-12-30 19:25:26,040 - pyskl - INFO - Epoch [122][3300/3746] lr: 8.423e-03, eta: 1 day, 0:58:04, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4492, top5_acc: 0.7041, loss_cls: 3.1039, loss: 3.1039 +2024-12-30 19:26:50,248 - pyskl - INFO - Epoch [122][3400/3746] lr: 8.408e-03, eta: 1 day, 0:56:38, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4572, top5_acc: 0.7016, loss_cls: 3.1117, loss: 3.1117 +2024-12-30 19:28:14,908 - pyskl - INFO - Epoch [122][3500/3746] lr: 8.392e-03, eta: 1 day, 0:55:13, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4475, top5_acc: 0.7067, loss_cls: 3.1041, loss: 3.1041 +2024-12-30 19:29:39,111 - pyskl - INFO - Epoch [122][3600/3746] lr: 8.377e-03, eta: 1 day, 0:53:47, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4505, top5_acc: 0.7039, loss_cls: 3.0931, loss: 3.0931 +2024-12-30 19:31:04,146 - pyskl - INFO - Epoch [122][3700/3746] lr: 8.361e-03, eta: 1 day, 0:52:22, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4517, top5_acc: 0.7019, loss_cls: 3.1219, loss: 3.1219 +2024-12-30 19:31:45,021 - pyskl - INFO - Saving checkpoint at 122 epochs +2024-12-30 19:33:44,930 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 19:33:45,623 - pyskl - INFO - +top1_acc 0.3760 +top5_acc 0.6337 +2024-12-30 19:33:45,623 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 19:33:45,682 - pyskl - INFO - +mean_acc 0.3758 +2024-12-30 19:33:45,696 - pyskl - INFO - Epoch(val) [122][309] top1_acc: 0.3760, top5_acc: 0.6337, mean_class_accuracy: 0.3758 +2024-12-30 19:38:03,542 - pyskl - INFO - Epoch [123][100/3746] lr: 8.339e-03, eta: 1 day, 0:50:48, time: 2.578, data_time: 1.540, memory: 15990, top1_acc: 0.4648, top5_acc: 0.7245, loss_cls: 2.9800, loss: 2.9800 +2024-12-30 19:39:28,643 - pyskl - INFO - Epoch [123][200/3746] lr: 8.323e-03, eta: 1 day, 0:49:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4562, top5_acc: 0.7113, loss_cls: 3.0511, loss: 3.0511 +2024-12-30 19:40:53,329 - pyskl - INFO - Epoch [123][300/3746] lr: 8.308e-03, eta: 1 day, 0:47:57, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4597, top5_acc: 0.7156, loss_cls: 3.0248, loss: 3.0248 +2024-12-30 19:42:18,582 - pyskl - INFO - Epoch [123][400/3746] lr: 8.292e-03, eta: 1 day, 0:46:31, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4634, top5_acc: 0.7142, loss_cls: 3.0442, loss: 3.0442 +2024-12-30 19:43:43,123 - pyskl - INFO - Epoch [123][500/3746] lr: 8.277e-03, eta: 1 day, 0:45:06, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7245, loss_cls: 2.9687, loss: 2.9687 +2024-12-30 19:45:08,345 - pyskl - INFO - Epoch [123][600/3746] lr: 8.262e-03, eta: 1 day, 0:43:40, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4672, top5_acc: 0.7194, loss_cls: 3.0213, loss: 3.0213 +2024-12-30 19:46:33,060 - pyskl - INFO - Epoch [123][700/3746] lr: 8.246e-03, eta: 1 day, 0:42:15, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4575, top5_acc: 0.7147, loss_cls: 3.0386, loss: 3.0386 +2024-12-30 19:47:58,604 - pyskl - INFO - Epoch [123][800/3746] lr: 8.231e-03, eta: 1 day, 0:40:49, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4519, top5_acc: 0.7194, loss_cls: 3.0366, loss: 3.0366 +2024-12-30 19:49:23,788 - pyskl - INFO - Epoch [123][900/3746] lr: 8.215e-03, eta: 1 day, 0:39:24, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4589, top5_acc: 0.7245, loss_cls: 3.0039, loss: 3.0039 +2024-12-30 19:50:49,088 - pyskl - INFO - Epoch [123][1000/3746] lr: 8.200e-03, eta: 1 day, 0:37:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7153, loss_cls: 3.0621, loss: 3.0621 +2024-12-30 19:52:14,316 - pyskl - INFO - Epoch [123][1100/3746] lr: 8.185e-03, eta: 1 day, 0:36:33, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4491, top5_acc: 0.7083, loss_cls: 3.0743, loss: 3.0743 +2024-12-30 19:53:39,618 - pyskl - INFO - Epoch [123][1200/3746] lr: 8.169e-03, eta: 1 day, 0:35:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4536, top5_acc: 0.7127, loss_cls: 3.0664, loss: 3.0664 +2024-12-30 19:55:04,506 - pyskl - INFO - Epoch [123][1300/3746] lr: 8.154e-03, eta: 1 day, 0:33:42, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4427, top5_acc: 0.7084, loss_cls: 3.0673, loss: 3.0673 +2024-12-30 19:56:29,744 - pyskl - INFO - Epoch [123][1400/3746] lr: 8.139e-03, eta: 1 day, 0:32:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4595, top5_acc: 0.7077, loss_cls: 3.0607, loss: 3.0607 +2024-12-30 19:57:54,883 - pyskl - INFO - Epoch [123][1500/3746] lr: 8.124e-03, eta: 1 day, 0:30:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4597, top5_acc: 0.7081, loss_cls: 3.0552, loss: 3.0552 +2024-12-30 19:59:20,260 - pyskl - INFO - Epoch [123][1600/3746] lr: 8.108e-03, eta: 1 day, 0:29:26, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4628, top5_acc: 0.7117, loss_cls: 3.0364, loss: 3.0364 +2024-12-30 20:00:45,292 - pyskl - INFO - Epoch [123][1700/3746] lr: 8.093e-03, eta: 1 day, 0:28:01, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4527, top5_acc: 0.7077, loss_cls: 3.0843, loss: 3.0843 +2024-12-30 20:02:09,996 - pyskl - INFO - Epoch [123][1800/3746] lr: 8.078e-03, eta: 1 day, 0:26:35, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4636, top5_acc: 0.7169, loss_cls: 3.0240, loss: 3.0240 +2024-12-30 20:03:35,513 - pyskl - INFO - Epoch [123][1900/3746] lr: 8.063e-03, eta: 1 day, 0:25:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4556, top5_acc: 0.7144, loss_cls: 3.0502, loss: 3.0502 +2024-12-30 20:05:00,531 - pyskl - INFO - Epoch [123][2000/3746] lr: 8.047e-03, eta: 1 day, 0:23:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4525, top5_acc: 0.7092, loss_cls: 3.0710, loss: 3.0710 +2024-12-30 20:06:25,273 - pyskl - INFO - Epoch [123][2100/3746] lr: 8.032e-03, eta: 1 day, 0:22:19, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4556, top5_acc: 0.7105, loss_cls: 3.0383, loss: 3.0383 +2024-12-30 20:07:50,648 - pyskl - INFO - Epoch [123][2200/3746] lr: 8.017e-03, eta: 1 day, 0:20:54, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7058, loss_cls: 3.0594, loss: 3.0594 +2024-12-30 20:09:15,698 - pyskl - INFO - Epoch [123][2300/3746] lr: 8.002e-03, eta: 1 day, 0:19:28, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7141, loss_cls: 3.0521, loss: 3.0521 +2024-12-30 20:10:40,533 - pyskl - INFO - Epoch [123][2400/3746] lr: 7.987e-03, eta: 1 day, 0:18:03, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.4552, top5_acc: 0.7061, loss_cls: 3.0744, loss: 3.0744 +2024-12-30 20:12:05,083 - pyskl - INFO - Epoch [123][2500/3746] lr: 7.971e-03, eta: 1 day, 0:16:37, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4556, top5_acc: 0.7208, loss_cls: 3.0277, loss: 3.0277 +2024-12-30 20:13:30,085 - pyskl - INFO - Epoch [123][2600/3746] lr: 7.956e-03, eta: 1 day, 0:15:12, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.4511, top5_acc: 0.6950, loss_cls: 3.0996, loss: 3.0996 +2024-12-30 20:14:54,973 - pyskl - INFO - Epoch [123][2700/3746] lr: 7.941e-03, eta: 1 day, 0:13:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4483, top5_acc: 0.7006, loss_cls: 3.1209, loss: 3.1209 +2024-12-30 20:16:19,757 - pyskl - INFO - Epoch [123][2800/3746] lr: 7.926e-03, eta: 1 day, 0:12:21, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4550, top5_acc: 0.7109, loss_cls: 3.0558, loss: 3.0558 +2024-12-30 20:17:44,741 - pyskl - INFO - Epoch [123][2900/3746] lr: 7.911e-03, eta: 1 day, 0:10:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4489, top5_acc: 0.7014, loss_cls: 3.0900, loss: 3.0900 +2024-12-30 20:19:10,418 - pyskl - INFO - Epoch [123][3000/3746] lr: 7.896e-03, eta: 1 day, 0:09:30, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4520, top5_acc: 0.7042, loss_cls: 3.0981, loss: 3.0981 +2024-12-30 20:20:36,036 - pyskl - INFO - Epoch [123][3100/3746] lr: 7.881e-03, eta: 1 day, 0:08:05, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4558, top5_acc: 0.7092, loss_cls: 3.0654, loss: 3.0654 +2024-12-30 20:22:01,041 - pyskl - INFO - Epoch [123][3200/3746] lr: 7.866e-03, eta: 1 day, 0:06:39, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4695, top5_acc: 0.7255, loss_cls: 2.9963, loss: 2.9963 +2024-12-30 20:23:26,462 - pyskl - INFO - Epoch [123][3300/3746] lr: 7.851e-03, eta: 1 day, 0:05:14, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4442, top5_acc: 0.6994, loss_cls: 3.1323, loss: 3.1323 +2024-12-30 20:24:51,900 - pyskl - INFO - Epoch [123][3400/3746] lr: 7.836e-03, eta: 1 day, 0:03:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4637, top5_acc: 0.7195, loss_cls: 3.0340, loss: 3.0340 +2024-12-30 20:26:17,610 - pyskl - INFO - Epoch [123][3500/3746] lr: 7.821e-03, eta: 1 day, 0:02:23, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4508, top5_acc: 0.7047, loss_cls: 3.0662, loss: 3.0662 +2024-12-30 20:27:42,988 - pyskl - INFO - Epoch [123][3600/3746] lr: 7.806e-03, eta: 1 day, 0:00:58, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4564, top5_acc: 0.7066, loss_cls: 3.0816, loss: 3.0816 +2024-12-30 20:29:08,481 - pyskl - INFO - Epoch [123][3700/3746] lr: 7.791e-03, eta: 23:59:33, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4494, top5_acc: 0.7117, loss_cls: 3.0937, loss: 3.0937 +2024-12-30 20:29:49,831 - pyskl - INFO - Saving checkpoint at 123 epochs +2024-12-30 20:31:48,747 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 20:31:49,652 - pyskl - INFO - +top1_acc 0.3877 +top5_acc 0.6391 +2024-12-30 20:31:49,652 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 20:31:49,694 - pyskl - INFO - +mean_acc 0.3875 +2024-12-30 20:31:49,701 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_121.pth was removed +2024-12-30 20:31:49,968 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_123.pth. +2024-12-30 20:31:49,969 - pyskl - INFO - Best top1_acc is 0.3877 at 123 epoch. +2024-12-30 20:31:49,982 - pyskl - INFO - Epoch(val) [123][309] top1_acc: 0.3877, top5_acc: 0.6391, mean_class_accuracy: 0.3875 +2024-12-30 20:36:02,836 - pyskl - INFO - Epoch [124][100/3746] lr: 7.769e-03, eta: 23:57:56, time: 2.528, data_time: 1.503, memory: 15990, top1_acc: 0.4736, top5_acc: 0.7275, loss_cls: 2.9402, loss: 2.9402 +2024-12-30 20:37:27,731 - pyskl - INFO - Epoch [124][200/3746] lr: 7.754e-03, eta: 23:56:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4702, top5_acc: 0.7217, loss_cls: 2.9854, loss: 2.9854 +2024-12-30 20:38:52,939 - pyskl - INFO - Epoch [124][300/3746] lr: 7.739e-03, eta: 23:55:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4617, top5_acc: 0.7113, loss_cls: 3.0104, loss: 3.0104 +2024-12-30 20:40:17,439 - pyskl - INFO - Epoch [124][400/3746] lr: 7.724e-03, eta: 23:53:40, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4752, top5_acc: 0.7320, loss_cls: 2.9189, loss: 2.9189 +2024-12-30 20:41:42,166 - pyskl - INFO - Epoch [124][500/3746] lr: 7.709e-03, eta: 23:52:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4647, top5_acc: 0.7228, loss_cls: 3.0078, loss: 3.0078 +2024-12-30 20:43:06,912 - pyskl - INFO - Epoch [124][600/3746] lr: 7.694e-03, eta: 23:50:49, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4736, top5_acc: 0.7298, loss_cls: 2.9543, loss: 2.9543 +2024-12-30 20:44:31,262 - pyskl - INFO - Epoch [124][700/3746] lr: 7.679e-03, eta: 23:49:23, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4758, top5_acc: 0.7273, loss_cls: 2.9654, loss: 2.9654 +2024-12-30 20:45:55,788 - pyskl - INFO - Epoch [124][800/3746] lr: 7.664e-03, eta: 23:47:58, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4692, top5_acc: 0.7205, loss_cls: 2.9771, loss: 2.9771 +2024-12-30 20:47:20,598 - pyskl - INFO - Epoch [124][900/3746] lr: 7.649e-03, eta: 23:46:32, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4689, top5_acc: 0.7225, loss_cls: 2.9961, loss: 2.9961 +2024-12-30 20:48:45,774 - pyskl - INFO - Epoch [124][1000/3746] lr: 7.635e-03, eta: 23:45:07, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4664, top5_acc: 0.7266, loss_cls: 2.9872, loss: 2.9872 +2024-12-30 20:50:11,228 - pyskl - INFO - Epoch [124][1100/3746] lr: 7.620e-03, eta: 23:43:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4625, top5_acc: 0.7100, loss_cls: 3.0519, loss: 3.0519 +2024-12-30 20:51:36,053 - pyskl - INFO - Epoch [124][1200/3746] lr: 7.605e-03, eta: 23:42:16, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4545, top5_acc: 0.7111, loss_cls: 3.0547, loss: 3.0547 +2024-12-30 20:53:01,118 - pyskl - INFO - Epoch [124][1300/3746] lr: 7.590e-03, eta: 23:40:50, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4555, top5_acc: 0.7055, loss_cls: 3.0681, loss: 3.0681 +2024-12-30 20:54:25,402 - pyskl - INFO - Epoch [124][1400/3746] lr: 7.575e-03, eta: 23:39:25, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4609, top5_acc: 0.7080, loss_cls: 3.0336, loss: 3.0336 +2024-12-30 20:55:50,453 - pyskl - INFO - Epoch [124][1500/3746] lr: 7.561e-03, eta: 23:37:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7125, loss_cls: 3.0342, loss: 3.0342 +2024-12-30 20:57:15,309 - pyskl - INFO - Epoch [124][1600/3746] lr: 7.546e-03, eta: 23:36:34, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4605, top5_acc: 0.7200, loss_cls: 3.0405, loss: 3.0405 +2024-12-30 20:58:39,591 - pyskl - INFO - Epoch [124][1700/3746] lr: 7.531e-03, eta: 23:35:08, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4620, top5_acc: 0.7164, loss_cls: 3.0171, loss: 3.0171 +2024-12-30 21:00:04,592 - pyskl - INFO - Epoch [124][1800/3746] lr: 7.516e-03, eta: 23:33:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4625, top5_acc: 0.7217, loss_cls: 3.0154, loss: 3.0154 +2024-12-30 21:01:29,423 - pyskl - INFO - Epoch [124][1900/3746] lr: 7.502e-03, eta: 23:32:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4652, top5_acc: 0.7139, loss_cls: 2.9989, loss: 2.9989 +2024-12-30 21:02:54,369 - pyskl - INFO - Epoch [124][2000/3746] lr: 7.487e-03, eta: 23:30:52, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4528, top5_acc: 0.7211, loss_cls: 3.0388, loss: 3.0388 +2024-12-30 21:04:19,088 - pyskl - INFO - Epoch [124][2100/3746] lr: 7.472e-03, eta: 23:29:26, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4555, top5_acc: 0.7183, loss_cls: 3.0256, loss: 3.0256 +2024-12-30 21:05:43,910 - pyskl - INFO - Epoch [124][2200/3746] lr: 7.457e-03, eta: 23:28:01, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.4612, top5_acc: 0.7186, loss_cls: 3.0128, loss: 3.0128 +2024-12-30 21:07:08,918 - pyskl - INFO - Epoch [124][2300/3746] lr: 7.443e-03, eta: 23:26:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7166, loss_cls: 3.0170, loss: 3.0170 +2024-12-30 21:08:34,024 - pyskl - INFO - Epoch [124][2400/3746] lr: 7.428e-03, eta: 23:25:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4734, top5_acc: 0.7208, loss_cls: 2.9699, loss: 2.9699 +2024-12-30 21:09:58,575 - pyskl - INFO - Epoch [124][2500/3746] lr: 7.413e-03, eta: 23:23:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4620, top5_acc: 0.7177, loss_cls: 3.0110, loss: 3.0110 +2024-12-30 21:11:23,486 - pyskl - INFO - Epoch [124][2600/3746] lr: 7.399e-03, eta: 23:22:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4477, top5_acc: 0.7123, loss_cls: 3.0603, loss: 3.0603 +2024-12-30 21:12:47,744 - pyskl - INFO - Epoch [124][2700/3746] lr: 7.384e-03, eta: 23:20:53, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4541, top5_acc: 0.7095, loss_cls: 3.0821, loss: 3.0821 +2024-12-30 21:14:12,341 - pyskl - INFO - Epoch [124][2800/3746] lr: 7.370e-03, eta: 23:19:27, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.4539, top5_acc: 0.7080, loss_cls: 3.0531, loss: 3.0531 +2024-12-30 21:15:36,650 - pyskl - INFO - Epoch [124][2900/3746] lr: 7.355e-03, eta: 23:18:02, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4616, top5_acc: 0.7098, loss_cls: 3.0529, loss: 3.0529 +2024-12-30 21:17:01,282 - pyskl - INFO - Epoch [124][3000/3746] lr: 7.340e-03, eta: 23:16:36, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.7077, loss_cls: 3.0819, loss: 3.0819 +2024-12-30 21:18:25,843 - pyskl - INFO - Epoch [124][3100/3746] lr: 7.326e-03, eta: 23:15:11, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4578, top5_acc: 0.6978, loss_cls: 3.0990, loss: 3.0990 +2024-12-30 21:19:50,741 - pyskl - INFO - Epoch [124][3200/3746] lr: 7.311e-03, eta: 23:13:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4519, top5_acc: 0.7077, loss_cls: 3.0520, loss: 3.0520 +2024-12-30 21:21:15,324 - pyskl - INFO - Epoch [124][3300/3746] lr: 7.297e-03, eta: 23:12:20, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4528, top5_acc: 0.7145, loss_cls: 3.0470, loss: 3.0470 +2024-12-30 21:22:40,130 - pyskl - INFO - Epoch [124][3400/3746] lr: 7.282e-03, eta: 23:10:54, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4644, top5_acc: 0.7106, loss_cls: 3.0383, loss: 3.0383 +2024-12-30 21:24:04,671 - pyskl - INFO - Epoch [124][3500/3746] lr: 7.268e-03, eta: 23:09:29, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4605, top5_acc: 0.7133, loss_cls: 3.0024, loss: 3.0024 +2024-12-30 21:25:29,455 - pyskl - INFO - Epoch [124][3600/3746] lr: 7.253e-03, eta: 23:08:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4644, top5_acc: 0.7178, loss_cls: 3.0189, loss: 3.0189 +2024-12-30 21:26:53,768 - pyskl - INFO - Epoch [124][3700/3746] lr: 7.239e-03, eta: 23:06:38, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4520, top5_acc: 0.7063, loss_cls: 3.0820, loss: 3.0820 +2024-12-30 21:27:34,359 - pyskl - INFO - Saving checkpoint at 124 epochs +2024-12-30 21:29:32,828 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 21:29:33,561 - pyskl - INFO - +top1_acc 0.3843 +top5_acc 0.6414 +2024-12-30 21:29:33,561 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 21:29:33,605 - pyskl - INFO - +mean_acc 0.3842 +2024-12-30 21:29:33,620 - pyskl - INFO - Epoch(val) [124][309] top1_acc: 0.3843, top5_acc: 0.6414, mean_class_accuracy: 0.3842 +2024-12-30 21:33:45,206 - pyskl - INFO - Epoch [125][100/3746] lr: 7.217e-03, eta: 23:04:59, time: 2.516, data_time: 1.489, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7395, loss_cls: 2.8927, loss: 2.8927 +2024-12-30 21:35:10,253 - pyskl - INFO - Epoch [125][200/3746] lr: 7.203e-03, eta: 23:03:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4686, top5_acc: 0.7281, loss_cls: 2.9377, loss: 2.9377 +2024-12-30 21:36:35,258 - pyskl - INFO - Epoch [125][300/3746] lr: 7.189e-03, eta: 23:02:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4742, top5_acc: 0.7241, loss_cls: 2.9593, loss: 2.9593 +2024-12-30 21:37:59,672 - pyskl - INFO - Epoch [125][400/3746] lr: 7.174e-03, eta: 23:00:43, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4745, top5_acc: 0.7266, loss_cls: 2.9401, loss: 2.9401 +2024-12-30 21:39:24,648 - pyskl - INFO - Epoch [125][500/3746] lr: 7.160e-03, eta: 22:59:17, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4819, top5_acc: 0.7258, loss_cls: 2.9466, loss: 2.9466 +2024-12-30 21:40:49,263 - pyskl - INFO - Epoch [125][600/3746] lr: 7.145e-03, eta: 22:57:52, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4637, top5_acc: 0.7169, loss_cls: 2.9936, loss: 2.9936 +2024-12-30 21:42:14,015 - pyskl - INFO - Epoch [125][700/3746] lr: 7.131e-03, eta: 22:56:26, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4761, top5_acc: 0.7233, loss_cls: 2.9427, loss: 2.9427 +2024-12-30 21:43:38,957 - pyskl - INFO - Epoch [125][800/3746] lr: 7.117e-03, eta: 22:55:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4728, top5_acc: 0.7194, loss_cls: 2.9674, loss: 2.9674 +2024-12-30 21:45:03,372 - pyskl - INFO - Epoch [125][900/3746] lr: 7.102e-03, eta: 22:53:35, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7103, loss_cls: 3.0210, loss: 3.0210 +2024-12-30 21:46:28,080 - pyskl - INFO - Epoch [125][1000/3746] lr: 7.088e-03, eta: 22:52:10, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4683, top5_acc: 0.7233, loss_cls: 2.9886, loss: 2.9886 +2024-12-30 21:47:52,501 - pyskl - INFO - Epoch [125][1100/3746] lr: 7.073e-03, eta: 22:50:44, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4723, top5_acc: 0.7295, loss_cls: 2.9506, loss: 2.9506 +2024-12-30 21:49:17,390 - pyskl - INFO - Epoch [125][1200/3746] lr: 7.059e-03, eta: 22:49:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4677, top5_acc: 0.7244, loss_cls: 3.0001, loss: 3.0001 +2024-12-30 21:50:42,080 - pyskl - INFO - Epoch [125][1300/3746] lr: 7.045e-03, eta: 22:47:53, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4684, top5_acc: 0.7280, loss_cls: 2.9560, loss: 2.9560 +2024-12-30 21:52:07,025 - pyskl - INFO - Epoch [125][1400/3746] lr: 7.031e-03, eta: 22:46:27, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4627, top5_acc: 0.7234, loss_cls: 2.9749, loss: 2.9749 +2024-12-30 21:53:31,671 - pyskl - INFO - Epoch [125][1500/3746] lr: 7.016e-03, eta: 22:45:02, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4688, top5_acc: 0.7173, loss_cls: 2.9877, loss: 2.9877 +2024-12-30 21:54:56,547 - pyskl - INFO - Epoch [125][1600/3746] lr: 7.002e-03, eta: 22:43:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4773, top5_acc: 0.7298, loss_cls: 2.9447, loss: 2.9447 +2024-12-30 21:56:20,866 - pyskl - INFO - Epoch [125][1700/3746] lr: 6.988e-03, eta: 22:42:11, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4661, top5_acc: 0.7142, loss_cls: 3.0056, loss: 3.0056 +2024-12-30 21:57:45,606 - pyskl - INFO - Epoch [125][1800/3746] lr: 6.973e-03, eta: 22:40:45, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4623, top5_acc: 0.7134, loss_cls: 3.0185, loss: 3.0185 +2024-12-30 21:59:10,155 - pyskl - INFO - Epoch [125][1900/3746] lr: 6.959e-03, eta: 22:39:20, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4692, top5_acc: 0.7172, loss_cls: 3.0044, loss: 3.0044 +2024-12-30 22:00:34,824 - pyskl - INFO - Epoch [125][2000/3746] lr: 6.945e-03, eta: 22:37:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4677, top5_acc: 0.7223, loss_cls: 3.0198, loss: 3.0198 +2024-12-30 22:01:59,356 - pyskl - INFO - Epoch [125][2100/3746] lr: 6.931e-03, eta: 22:36:28, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4723, top5_acc: 0.7238, loss_cls: 2.9567, loss: 2.9567 +2024-12-30 22:03:23,741 - pyskl - INFO - Epoch [125][2200/3746] lr: 6.917e-03, eta: 22:35:03, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4584, top5_acc: 0.7133, loss_cls: 3.0429, loss: 3.0429 +2024-12-30 22:04:48,394 - pyskl - INFO - Epoch [125][2300/3746] lr: 6.902e-03, eta: 22:33:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4633, top5_acc: 0.7147, loss_cls: 3.0233, loss: 3.0233 +2024-12-30 22:06:12,798 - pyskl - INFO - Epoch [125][2400/3746] lr: 6.888e-03, eta: 22:32:12, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4662, top5_acc: 0.7220, loss_cls: 2.9998, loss: 2.9998 +2024-12-30 22:07:37,600 - pyskl - INFO - Epoch [125][2500/3746] lr: 6.874e-03, eta: 22:30:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4698, top5_acc: 0.7230, loss_cls: 2.9809, loss: 2.9809 +2024-12-30 22:09:02,678 - pyskl - INFO - Epoch [125][2600/3746] lr: 6.860e-03, eta: 22:29:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4672, top5_acc: 0.7245, loss_cls: 2.9998, loss: 2.9998 +2024-12-30 22:10:27,388 - pyskl - INFO - Epoch [125][2700/3746] lr: 6.846e-03, eta: 22:27:55, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4603, top5_acc: 0.7113, loss_cls: 3.0363, loss: 3.0363 +2024-12-30 22:11:52,138 - pyskl - INFO - Epoch [125][2800/3746] lr: 6.832e-03, eta: 22:26:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4694, top5_acc: 0.7227, loss_cls: 2.9718, loss: 2.9718 +2024-12-30 22:13:17,073 - pyskl - INFO - Epoch [125][2900/3746] lr: 6.818e-03, eta: 22:25:04, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4706, top5_acc: 0.7192, loss_cls: 3.0085, loss: 3.0085 +2024-12-30 22:14:41,807 - pyskl - INFO - Epoch [125][3000/3746] lr: 6.804e-03, eta: 22:23:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7116, loss_cls: 3.0520, loss: 3.0520 +2024-12-30 22:16:06,872 - pyskl - INFO - Epoch [125][3100/3746] lr: 6.789e-03, eta: 22:22:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4694, top5_acc: 0.7208, loss_cls: 2.9925, loss: 2.9925 +2024-12-30 22:17:31,724 - pyskl - INFO - Epoch [125][3200/3746] lr: 6.775e-03, eta: 22:20:47, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7188, loss_cls: 2.9984, loss: 2.9984 +2024-12-30 22:18:56,673 - pyskl - INFO - Epoch [125][3300/3746] lr: 6.761e-03, eta: 22:19:22, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4561, top5_acc: 0.7091, loss_cls: 3.0828, loss: 3.0828 +2024-12-30 22:20:21,240 - pyskl - INFO - Epoch [125][3400/3746] lr: 6.747e-03, eta: 22:17:56, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4569, top5_acc: 0.7119, loss_cls: 3.0480, loss: 3.0480 +2024-12-30 22:21:45,753 - pyskl - INFO - Epoch [125][3500/3746] lr: 6.733e-03, eta: 22:16:31, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7147, loss_cls: 3.0212, loss: 3.0212 +2024-12-30 22:23:10,560 - pyskl - INFO - Epoch [125][3600/3746] lr: 6.719e-03, eta: 22:15:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4619, top5_acc: 0.7116, loss_cls: 3.0144, loss: 3.0144 +2024-12-30 22:24:35,106 - pyskl - INFO - Epoch [125][3700/3746] lr: 6.705e-03, eta: 22:13:40, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4714, top5_acc: 0.7228, loss_cls: 2.9850, loss: 2.9850 +2024-12-30 22:25:15,863 - pyskl - INFO - Saving checkpoint at 125 epochs +2024-12-30 22:27:14,081 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 22:27:14,763 - pyskl - INFO - +top1_acc 0.3964 +top5_acc 0.6551 +2024-12-30 22:27:14,764 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 22:27:14,809 - pyskl - INFO - +mean_acc 0.3962 +2024-12-30 22:27:14,814 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_123.pth was removed +2024-12-30 22:27:15,149 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_125.pth. +2024-12-30 22:27:15,150 - pyskl - INFO - Best top1_acc is 0.3964 at 125 epoch. +2024-12-30 22:27:15,162 - pyskl - INFO - Epoch(val) [125][309] top1_acc: 0.3964, top5_acc: 0.6551, mean_class_accuracy: 0.3962 +2024-12-30 22:31:26,239 - pyskl - INFO - Epoch [126][100/3746] lr: 6.685e-03, eta: 22:12:00, time: 2.511, data_time: 1.479, memory: 15990, top1_acc: 0.4755, top5_acc: 0.7184, loss_cls: 2.9597, loss: 2.9597 +2024-12-30 22:32:51,151 - pyskl - INFO - Epoch [126][200/3746] lr: 6.671e-03, eta: 22:10:35, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4844, top5_acc: 0.7345, loss_cls: 2.8791, loss: 2.8791 +2024-12-30 22:34:16,113 - pyskl - INFO - Epoch [126][300/3746] lr: 6.657e-03, eta: 22:09:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4859, top5_acc: 0.7292, loss_cls: 2.8961, loss: 2.8961 +2024-12-30 22:35:40,796 - pyskl - INFO - Epoch [126][400/3746] lr: 6.643e-03, eta: 22:07:44, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.4719, top5_acc: 0.7233, loss_cls: 2.9528, loss: 2.9528 +2024-12-30 22:37:05,959 - pyskl - INFO - Epoch [126][500/3746] lr: 6.629e-03, eta: 22:06:18, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.4764, top5_acc: 0.7262, loss_cls: 2.9416, loss: 2.9416 +2024-12-30 22:38:30,841 - pyskl - INFO - Epoch [126][600/3746] lr: 6.615e-03, eta: 22:04:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4742, top5_acc: 0.7298, loss_cls: 2.9523, loss: 2.9523 +2024-12-30 22:39:55,731 - pyskl - INFO - Epoch [126][700/3746] lr: 6.601e-03, eta: 22:03:27, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4683, top5_acc: 0.7217, loss_cls: 2.9750, loss: 2.9750 +2024-12-30 22:41:20,237 - pyskl - INFO - Epoch [126][800/3746] lr: 6.587e-03, eta: 22:02:01, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4778, top5_acc: 0.7348, loss_cls: 2.9267, loss: 2.9267 +2024-12-30 22:42:45,291 - pyskl - INFO - Epoch [126][900/3746] lr: 6.574e-03, eta: 22:00:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7323, loss_cls: 2.9110, loss: 2.9110 +2024-12-30 22:44:10,358 - pyskl - INFO - Epoch [126][1000/3746] lr: 6.560e-03, eta: 21:59:10, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.4786, top5_acc: 0.7300, loss_cls: 2.9389, loss: 2.9389 +2024-12-30 22:45:35,231 - pyskl - INFO - Epoch [126][1100/3746] lr: 6.546e-03, eta: 21:57:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4745, top5_acc: 0.7317, loss_cls: 2.9395, loss: 2.9395 +2024-12-30 22:47:00,215 - pyskl - INFO - Epoch [126][1200/3746] lr: 6.532e-03, eta: 21:56:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4783, top5_acc: 0.7297, loss_cls: 2.9272, loss: 2.9272 +2024-12-30 22:48:24,847 - pyskl - INFO - Epoch [126][1300/3746] lr: 6.518e-03, eta: 21:54:54, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4728, top5_acc: 0.7302, loss_cls: 2.9361, loss: 2.9361 +2024-12-30 22:49:49,590 - pyskl - INFO - Epoch [126][1400/3746] lr: 6.505e-03, eta: 21:53:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4670, top5_acc: 0.7234, loss_cls: 2.9888, loss: 2.9888 +2024-12-30 22:51:14,580 - pyskl - INFO - Epoch [126][1500/3746] lr: 6.491e-03, eta: 21:52:03, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4777, top5_acc: 0.7359, loss_cls: 2.9288, loss: 2.9288 +2024-12-30 22:52:39,203 - pyskl - INFO - Epoch [126][1600/3746] lr: 6.477e-03, eta: 21:50:37, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4797, top5_acc: 0.7338, loss_cls: 2.9117, loss: 2.9117 +2024-12-30 22:54:03,948 - pyskl - INFO - Epoch [126][1700/3746] lr: 6.463e-03, eta: 21:49:12, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4747, top5_acc: 0.7236, loss_cls: 2.9555, loss: 2.9555 +2024-12-30 22:55:28,910 - pyskl - INFO - Epoch [126][1800/3746] lr: 6.449e-03, eta: 21:47:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4834, top5_acc: 0.7267, loss_cls: 2.9383, loss: 2.9383 +2024-12-30 22:56:53,598 - pyskl - INFO - Epoch [126][1900/3746] lr: 6.436e-03, eta: 21:46:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4702, top5_acc: 0.7270, loss_cls: 2.9678, loss: 2.9678 +2024-12-30 22:58:18,577 - pyskl - INFO - Epoch [126][2000/3746] lr: 6.422e-03, eta: 21:44:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4747, top5_acc: 0.7292, loss_cls: 2.9341, loss: 2.9341 +2024-12-30 22:59:43,201 - pyskl - INFO - Epoch [126][2100/3746] lr: 6.408e-03, eta: 21:43:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7262, loss_cls: 2.9698, loss: 2.9698 +2024-12-30 23:01:08,037 - pyskl - INFO - Epoch [126][2200/3746] lr: 6.395e-03, eta: 21:42:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4664, top5_acc: 0.7191, loss_cls: 2.9934, loss: 2.9934 +2024-12-30 23:02:32,798 - pyskl - INFO - Epoch [126][2300/3746] lr: 6.381e-03, eta: 21:40:38, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4788, top5_acc: 0.7247, loss_cls: 2.9673, loss: 2.9673 +2024-12-30 23:03:57,703 - pyskl - INFO - Epoch [126][2400/3746] lr: 6.367e-03, eta: 21:39:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4778, top5_acc: 0.7264, loss_cls: 2.9402, loss: 2.9402 +2024-12-30 23:05:22,415 - pyskl - INFO - Epoch [126][2500/3746] lr: 6.354e-03, eta: 21:37:47, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.4731, top5_acc: 0.7278, loss_cls: 2.9517, loss: 2.9517 +2024-12-30 23:06:47,063 - pyskl - INFO - Epoch [126][2600/3746] lr: 6.340e-03, eta: 21:36:22, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.4587, top5_acc: 0.7119, loss_cls: 3.0331, loss: 3.0331 +2024-12-30 23:08:11,908 - pyskl - INFO - Epoch [126][2700/3746] lr: 6.326e-03, eta: 21:34:56, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4792, top5_acc: 0.7288, loss_cls: 2.9373, loss: 2.9373 +2024-12-30 23:09:36,216 - pyskl - INFO - Epoch [126][2800/3746] lr: 6.313e-03, eta: 21:33:30, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4775, top5_acc: 0.7234, loss_cls: 2.9688, loss: 2.9688 +2024-12-30 23:11:01,650 - pyskl - INFO - Epoch [126][2900/3746] lr: 6.299e-03, eta: 21:32:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4637, top5_acc: 0.7239, loss_cls: 2.9885, loss: 2.9885 +2024-12-30 23:12:26,175 - pyskl - INFO - Epoch [126][3000/3746] lr: 6.286e-03, eta: 21:30:39, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7198, loss_cls: 3.0073, loss: 3.0073 +2024-12-30 23:13:51,029 - pyskl - INFO - Epoch [126][3100/3746] lr: 6.272e-03, eta: 21:29:14, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7230, loss_cls: 2.9876, loss: 2.9876 +2024-12-30 23:15:15,842 - pyskl - INFO - Epoch [126][3200/3746] lr: 6.259e-03, eta: 21:27:48, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4631, top5_acc: 0.7152, loss_cls: 3.0310, loss: 3.0310 +2024-12-30 23:16:40,841 - pyskl - INFO - Epoch [126][3300/3746] lr: 6.245e-03, eta: 21:26:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4761, top5_acc: 0.7181, loss_cls: 2.9716, loss: 2.9716 +2024-12-30 23:18:05,871 - pyskl - INFO - Epoch [126][3400/3746] lr: 6.231e-03, eta: 21:24:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4719, top5_acc: 0.7142, loss_cls: 2.9799, loss: 2.9799 +2024-12-30 23:19:31,041 - pyskl - INFO - Epoch [126][3500/3746] lr: 6.218e-03, eta: 21:23:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4658, top5_acc: 0.7197, loss_cls: 2.9932, loss: 2.9932 +2024-12-30 23:20:56,090 - pyskl - INFO - Epoch [126][3600/3746] lr: 6.204e-03, eta: 21:22:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4717, top5_acc: 0.7158, loss_cls: 2.9904, loss: 2.9904 +2024-12-30 23:22:21,525 - pyskl - INFO - Epoch [126][3700/3746] lr: 6.191e-03, eta: 21:20:41, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4584, top5_acc: 0.7177, loss_cls: 3.0138, loss: 3.0138 +2024-12-30 23:23:02,495 - pyskl - INFO - Saving checkpoint at 126 epochs +2024-12-30 23:25:01,668 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 23:25:02,605 - pyskl - INFO - +top1_acc 0.3935 +top5_acc 0.6494 +2024-12-30 23:25:02,606 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 23:25:02,658 - pyskl - INFO - +mean_acc 0.3932 +2024-12-30 23:25:02,673 - pyskl - INFO - Epoch(val) [126][309] top1_acc: 0.3935, top5_acc: 0.6494, mean_class_accuracy: 0.3932 +2024-12-30 23:29:18,241 - pyskl - INFO - Epoch [127][100/3746] lr: 6.171e-03, eta: 21:19:01, time: 2.556, data_time: 1.530, memory: 15990, top1_acc: 0.4795, top5_acc: 0.7312, loss_cls: 2.8966, loss: 2.8966 +2024-12-30 23:30:43,489 - pyskl - INFO - Epoch [127][200/3746] lr: 6.158e-03, eta: 21:17:36, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4788, top5_acc: 0.7353, loss_cls: 2.9016, loss: 2.9016 +2024-12-30 23:32:08,435 - pyskl - INFO - Epoch [127][300/3746] lr: 6.144e-03, eta: 21:16:10, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4877, top5_acc: 0.7403, loss_cls: 2.8714, loss: 2.8714 +2024-12-30 23:33:32,450 - pyskl - INFO - Epoch [127][400/3746] lr: 6.131e-03, eta: 21:14:44, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7361, loss_cls: 2.9054, loss: 2.9054 +2024-12-30 23:34:56,659 - pyskl - INFO - Epoch [127][500/3746] lr: 6.118e-03, eta: 21:13:19, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.4945, top5_acc: 0.7386, loss_cls: 2.8800, loss: 2.8800 +2024-12-30 23:36:21,314 - pyskl - INFO - Epoch [127][600/3746] lr: 6.104e-03, eta: 21:11:53, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4816, top5_acc: 0.7344, loss_cls: 2.9142, loss: 2.9142 +2024-12-30 23:37:45,937 - pyskl - INFO - Epoch [127][700/3746] lr: 6.091e-03, eta: 21:10:27, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4889, top5_acc: 0.7384, loss_cls: 2.8612, loss: 2.8612 +2024-12-30 23:39:10,572 - pyskl - INFO - Epoch [127][800/3746] lr: 6.077e-03, eta: 21:09:02, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4881, top5_acc: 0.7344, loss_cls: 2.8974, loss: 2.8974 +2024-12-30 23:40:35,460 - pyskl - INFO - Epoch [127][900/3746] lr: 6.064e-03, eta: 21:07:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4850, top5_acc: 0.7327, loss_cls: 2.8946, loss: 2.8946 +2024-12-30 23:42:00,576 - pyskl - INFO - Epoch [127][1000/3746] lr: 6.051e-03, eta: 21:06:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4906, top5_acc: 0.7355, loss_cls: 2.8783, loss: 2.8783 +2024-12-30 23:43:25,527 - pyskl - INFO - Epoch [127][1100/3746] lr: 6.037e-03, eta: 21:04:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4825, top5_acc: 0.7330, loss_cls: 2.9064, loss: 2.9064 +2024-12-30 23:44:49,974 - pyskl - INFO - Epoch [127][1200/3746] lr: 6.024e-03, eta: 21:03:20, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4725, top5_acc: 0.7314, loss_cls: 2.9718, loss: 2.9718 +2024-12-30 23:46:15,034 - pyskl - INFO - Epoch [127][1300/3746] lr: 6.011e-03, eta: 21:01:54, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7353, loss_cls: 2.9237, loss: 2.9237 +2024-12-30 23:47:40,151 - pyskl - INFO - Epoch [127][1400/3746] lr: 5.998e-03, eta: 21:00:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4831, top5_acc: 0.7311, loss_cls: 2.9213, loss: 2.9213 +2024-12-30 23:49:04,734 - pyskl - INFO - Epoch [127][1500/3746] lr: 5.984e-03, eta: 20:59:03, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7375, loss_cls: 2.8932, loss: 2.8932 +2024-12-30 23:50:29,257 - pyskl - INFO - Epoch [127][1600/3746] lr: 5.971e-03, eta: 20:57:37, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4717, top5_acc: 0.7261, loss_cls: 2.9308, loss: 2.9308 +2024-12-30 23:51:54,185 - pyskl - INFO - Epoch [127][1700/3746] lr: 5.958e-03, eta: 20:56:12, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4823, top5_acc: 0.7322, loss_cls: 2.8999, loss: 2.8999 +2024-12-30 23:53:18,865 - pyskl - INFO - Epoch [127][1800/3746] lr: 5.945e-03, eta: 20:54:46, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4775, top5_acc: 0.7283, loss_cls: 2.9393, loss: 2.9393 +2024-12-30 23:54:43,629 - pyskl - INFO - Epoch [127][1900/3746] lr: 5.931e-03, eta: 20:53:21, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4802, top5_acc: 0.7323, loss_cls: 2.9265, loss: 2.9265 +2024-12-30 23:56:08,112 - pyskl - INFO - Epoch [127][2000/3746] lr: 5.918e-03, eta: 20:51:55, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4741, top5_acc: 0.7331, loss_cls: 2.9452, loss: 2.9452 +2024-12-30 23:57:32,950 - pyskl - INFO - Epoch [127][2100/3746] lr: 5.905e-03, eta: 20:50:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4767, top5_acc: 0.7273, loss_cls: 2.9738, loss: 2.9738 +2024-12-30 23:58:57,587 - pyskl - INFO - Epoch [127][2200/3746] lr: 5.892e-03, eta: 20:49:04, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4795, top5_acc: 0.7366, loss_cls: 2.8919, loss: 2.8919 +2024-12-31 00:00:22,382 - pyskl - INFO - Epoch [127][2300/3746] lr: 5.879e-03, eta: 20:47:38, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4773, top5_acc: 0.7269, loss_cls: 2.9602, loss: 2.9602 +2024-12-31 00:01:47,298 - pyskl - INFO - Epoch [127][2400/3746] lr: 5.866e-03, eta: 20:46:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4758, top5_acc: 0.7217, loss_cls: 2.9646, loss: 2.9646 +2024-12-31 00:03:12,739 - pyskl - INFO - Epoch [127][2500/3746] lr: 5.852e-03, eta: 20:44:47, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.4828, top5_acc: 0.7336, loss_cls: 2.9058, loss: 2.9058 +2024-12-31 00:04:38,188 - pyskl - INFO - Epoch [127][2600/3746] lr: 5.839e-03, eta: 20:43:22, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.4750, top5_acc: 0.7316, loss_cls: 2.9268, loss: 2.9268 +2024-12-31 00:06:02,984 - pyskl - INFO - Epoch [127][2700/3746] lr: 5.826e-03, eta: 20:41:56, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.4761, top5_acc: 0.7292, loss_cls: 2.9435, loss: 2.9435 +2024-12-31 00:07:27,556 - pyskl - INFO - Epoch [127][2800/3746] lr: 5.813e-03, eta: 20:40:30, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4786, top5_acc: 0.7275, loss_cls: 2.9390, loss: 2.9390 +2024-12-31 00:08:51,848 - pyskl - INFO - Epoch [127][2900/3746] lr: 5.800e-03, eta: 20:39:05, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4741, top5_acc: 0.7306, loss_cls: 2.9361, loss: 2.9361 +2024-12-31 00:10:16,262 - pyskl - INFO - Epoch [127][3000/3746] lr: 5.787e-03, eta: 20:37:39, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4741, top5_acc: 0.7258, loss_cls: 2.9554, loss: 2.9554 +2024-12-31 00:11:40,891 - pyskl - INFO - Epoch [127][3100/3746] lr: 5.774e-03, eta: 20:36:14, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4708, top5_acc: 0.7269, loss_cls: 2.9488, loss: 2.9488 +2024-12-31 00:13:05,452 - pyskl - INFO - Epoch [127][3200/3746] lr: 5.761e-03, eta: 20:34:48, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4756, top5_acc: 0.7291, loss_cls: 2.9166, loss: 2.9166 +2024-12-31 00:14:30,182 - pyskl - INFO - Epoch [127][3300/3746] lr: 5.748e-03, eta: 20:33:22, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4745, top5_acc: 0.7275, loss_cls: 2.9216, loss: 2.9216 +2024-12-31 00:15:55,055 - pyskl - INFO - Epoch [127][3400/3746] lr: 5.735e-03, eta: 20:31:57, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4689, top5_acc: 0.7197, loss_cls: 2.9608, loss: 2.9608 +2024-12-31 00:17:20,439 - pyskl - INFO - Epoch [127][3500/3746] lr: 5.722e-03, eta: 20:30:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4683, top5_acc: 0.7181, loss_cls: 3.0172, loss: 3.0172 +2024-12-31 00:18:44,933 - pyskl - INFO - Epoch [127][3600/3746] lr: 5.709e-03, eta: 20:29:06, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7227, loss_cls: 2.9842, loss: 2.9842 +2024-12-31 00:20:09,923 - pyskl - INFO - Epoch [127][3700/3746] lr: 5.696e-03, eta: 20:27:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4717, top5_acc: 0.7288, loss_cls: 2.9419, loss: 2.9419 +2024-12-31 00:20:50,857 - pyskl - INFO - Saving checkpoint at 127 epochs +2024-12-31 00:22:49,335 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 00:22:50,073 - pyskl - INFO - +top1_acc 0.4048 +top5_acc 0.6551 +2024-12-31 00:22:50,073 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 00:22:50,125 - pyskl - INFO - +mean_acc 0.4044 +2024-12-31 00:22:50,130 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_125.pth was removed +2024-12-31 00:22:50,410 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_127.pth. +2024-12-31 00:22:50,411 - pyskl - INFO - Best top1_acc is 0.4048 at 127 epoch. +2024-12-31 00:22:50,423 - pyskl - INFO - Epoch(val) [127][309] top1_acc: 0.4048, top5_acc: 0.6551, mean_class_accuracy: 0.4044 +2024-12-31 00:27:07,891 - pyskl - INFO - Epoch [128][100/3746] lr: 5.677e-03, eta: 20:25:59, time: 2.575, data_time: 1.544, memory: 15990, top1_acc: 0.4994, top5_acc: 0.7442, loss_cls: 2.8204, loss: 2.8204 +2024-12-31 00:28:33,098 - pyskl - INFO - Epoch [128][200/3746] lr: 5.664e-03, eta: 20:24:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5131, top5_acc: 0.7514, loss_cls: 2.7824, loss: 2.7824 +2024-12-31 00:29:58,283 - pyskl - INFO - Epoch [128][300/3746] lr: 5.651e-03, eta: 20:23:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4986, top5_acc: 0.7514, loss_cls: 2.7888, loss: 2.7888 +2024-12-31 00:31:23,253 - pyskl - INFO - Epoch [128][400/3746] lr: 5.638e-03, eta: 20:21:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4863, top5_acc: 0.7317, loss_cls: 2.8952, loss: 2.8952 +2024-12-31 00:32:48,131 - pyskl - INFO - Epoch [128][500/3746] lr: 5.625e-03, eta: 20:20:17, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.4963, top5_acc: 0.7408, loss_cls: 2.8527, loss: 2.8527 +2024-12-31 00:34:13,000 - pyskl - INFO - Epoch [128][600/3746] lr: 5.612e-03, eta: 20:18:52, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4956, top5_acc: 0.7494, loss_cls: 2.8436, loss: 2.8436 +2024-12-31 00:35:37,742 - pyskl - INFO - Epoch [128][700/3746] lr: 5.600e-03, eta: 20:17:26, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4850, top5_acc: 0.7431, loss_cls: 2.8692, loss: 2.8692 +2024-12-31 00:37:02,354 - pyskl - INFO - Epoch [128][800/3746] lr: 5.587e-03, eta: 20:16:00, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4964, top5_acc: 0.7386, loss_cls: 2.8717, loss: 2.8717 +2024-12-31 00:38:27,168 - pyskl - INFO - Epoch [128][900/3746] lr: 5.574e-03, eta: 20:14:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4836, top5_acc: 0.7395, loss_cls: 2.8925, loss: 2.8925 +2024-12-31 00:39:52,170 - pyskl - INFO - Epoch [128][1000/3746] lr: 5.561e-03, eta: 20:13:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4906, top5_acc: 0.7362, loss_cls: 2.9043, loss: 2.9043 +2024-12-31 00:41:16,601 - pyskl - INFO - Epoch [128][1100/3746] lr: 5.548e-03, eta: 20:11:44, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4894, top5_acc: 0.7359, loss_cls: 2.8701, loss: 2.8701 +2024-12-31 00:42:41,236 - pyskl - INFO - Epoch [128][1200/3746] lr: 5.536e-03, eta: 20:10:18, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4791, top5_acc: 0.7328, loss_cls: 2.9186, loss: 2.9186 +2024-12-31 00:44:06,336 - pyskl - INFO - Epoch [128][1300/3746] lr: 5.523e-03, eta: 20:08:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4784, top5_acc: 0.7402, loss_cls: 2.8818, loss: 2.8818 +2024-12-31 00:45:31,140 - pyskl - INFO - Epoch [128][1400/3746] lr: 5.510e-03, eta: 20:07:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4830, top5_acc: 0.7408, loss_cls: 2.8841, loss: 2.8841 +2024-12-31 00:46:56,523 - pyskl - INFO - Epoch [128][1500/3746] lr: 5.497e-03, eta: 20:06:01, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4878, top5_acc: 0.7400, loss_cls: 2.8840, loss: 2.8840 +2024-12-31 00:48:21,850 - pyskl - INFO - Epoch [128][1600/3746] lr: 5.485e-03, eta: 20:04:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4744, top5_acc: 0.7331, loss_cls: 2.9288, loss: 2.9288 +2024-12-31 00:49:46,755 - pyskl - INFO - Epoch [128][1700/3746] lr: 5.472e-03, eta: 20:03:10, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4908, top5_acc: 0.7319, loss_cls: 2.9082, loss: 2.9082 +2024-12-31 00:51:11,296 - pyskl - INFO - Epoch [128][1800/3746] lr: 5.459e-03, eta: 20:01:45, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4923, top5_acc: 0.7389, loss_cls: 2.8737, loss: 2.8737 +2024-12-31 00:52:36,132 - pyskl - INFO - Epoch [128][1900/3746] lr: 5.446e-03, eta: 20:00:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4798, top5_acc: 0.7327, loss_cls: 2.8904, loss: 2.8904 +2024-12-31 00:54:01,031 - pyskl - INFO - Epoch [128][2000/3746] lr: 5.434e-03, eta: 19:58:53, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4817, top5_acc: 0.7345, loss_cls: 2.8786, loss: 2.8786 +2024-12-31 00:55:25,791 - pyskl - INFO - Epoch [128][2100/3746] lr: 5.421e-03, eta: 19:57:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4873, top5_acc: 0.7384, loss_cls: 2.8541, loss: 2.8541 +2024-12-31 00:56:50,678 - pyskl - INFO - Epoch [128][2200/3746] lr: 5.408e-03, eta: 19:56:02, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.4766, top5_acc: 0.7294, loss_cls: 2.9204, loss: 2.9204 +2024-12-31 00:58:15,980 - pyskl - INFO - Epoch [128][2300/3746] lr: 5.396e-03, eta: 19:54:37, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4855, top5_acc: 0.7345, loss_cls: 2.9032, loss: 2.9032 +2024-12-31 00:59:40,961 - pyskl - INFO - Epoch [128][2400/3746] lr: 5.383e-03, eta: 19:53:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4808, top5_acc: 0.7342, loss_cls: 2.9165, loss: 2.9165 +2024-12-31 01:01:06,203 - pyskl - INFO - Epoch [128][2500/3746] lr: 5.370e-03, eta: 19:51:46, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4847, top5_acc: 0.7291, loss_cls: 2.9086, loss: 2.9086 +2024-12-31 01:02:30,915 - pyskl - INFO - Epoch [128][2600/3746] lr: 5.358e-03, eta: 19:50:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7241, loss_cls: 2.9085, loss: 2.9085 +2024-12-31 01:03:55,744 - pyskl - INFO - Epoch [128][2700/3746] lr: 5.345e-03, eta: 19:48:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4714, top5_acc: 0.7222, loss_cls: 2.9793, loss: 2.9793 +2024-12-31 01:05:20,475 - pyskl - INFO - Epoch [128][2800/3746] lr: 5.333e-03, eta: 19:47:29, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4834, top5_acc: 0.7259, loss_cls: 2.9194, loss: 2.9194 +2024-12-31 01:06:45,009 - pyskl - INFO - Epoch [128][2900/3746] lr: 5.320e-03, eta: 19:46:03, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4813, top5_acc: 0.7391, loss_cls: 2.9043, loss: 2.9043 +2024-12-31 01:08:10,066 - pyskl - INFO - Epoch [128][3000/3746] lr: 5.308e-03, eta: 19:44:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4775, top5_acc: 0.7373, loss_cls: 2.9148, loss: 2.9148 +2024-12-31 01:09:34,436 - pyskl - INFO - Epoch [128][3100/3746] lr: 5.295e-03, eta: 19:43:12, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4739, top5_acc: 0.7283, loss_cls: 2.9330, loss: 2.9330 +2024-12-31 01:10:59,213 - pyskl - INFO - Epoch [128][3200/3746] lr: 5.283e-03, eta: 19:41:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4766, top5_acc: 0.7216, loss_cls: 2.9417, loss: 2.9417 +2024-12-31 01:12:23,701 - pyskl - INFO - Epoch [128][3300/3746] lr: 5.270e-03, eta: 19:40:21, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4839, top5_acc: 0.7347, loss_cls: 2.8955, loss: 2.8955 +2024-12-31 01:13:48,172 - pyskl - INFO - Epoch [128][3400/3746] lr: 5.258e-03, eta: 19:38:55, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4830, top5_acc: 0.7350, loss_cls: 2.8825, loss: 2.8825 +2024-12-31 01:15:12,487 - pyskl - INFO - Epoch [128][3500/3746] lr: 5.245e-03, eta: 19:37:30, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.4870, top5_acc: 0.7416, loss_cls: 2.8729, loss: 2.8729 +2024-12-31 01:16:37,813 - pyskl - INFO - Epoch [128][3600/3746] lr: 5.233e-03, eta: 19:36:04, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4783, top5_acc: 0.7305, loss_cls: 2.9212, loss: 2.9212 +2024-12-31 01:18:02,474 - pyskl - INFO - Epoch [128][3700/3746] lr: 5.220e-03, eta: 19:34:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4872, top5_acc: 0.7358, loss_cls: 2.8989, loss: 2.8989 +2024-12-31 01:18:43,550 - pyskl - INFO - Saving checkpoint at 128 epochs +2024-12-31 01:20:42,483 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 01:20:43,197 - pyskl - INFO - +top1_acc 0.4057 +top5_acc 0.6570 +2024-12-31 01:20:43,198 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 01:20:43,239 - pyskl - INFO - +mean_acc 0.4055 +2024-12-31 01:20:43,243 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_127.pth was removed +2024-12-31 01:20:43,550 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_128.pth. +2024-12-31 01:20:43,551 - pyskl - INFO - Best top1_acc is 0.4057 at 128 epoch. +2024-12-31 01:20:43,564 - pyskl - INFO - Epoch(val) [128][309] top1_acc: 0.4057, top5_acc: 0.6570, mean_class_accuracy: 0.4055 +2024-12-31 01:24:56,842 - pyskl - INFO - Epoch [129][100/3746] lr: 5.202e-03, eta: 19:32:56, time: 2.533, data_time: 1.512, memory: 15990, top1_acc: 0.4948, top5_acc: 0.7411, loss_cls: 2.8505, loss: 2.8505 +2024-12-31 01:26:21,823 - pyskl - INFO - Epoch [129][200/3746] lr: 5.190e-03, eta: 19:31:30, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4973, top5_acc: 0.7459, loss_cls: 2.8212, loss: 2.8212 +2024-12-31 01:27:46,937 - pyskl - INFO - Epoch [129][300/3746] lr: 5.177e-03, eta: 19:30:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4964, top5_acc: 0.7473, loss_cls: 2.8121, loss: 2.8121 +2024-12-31 01:29:11,838 - pyskl - INFO - Epoch [129][400/3746] lr: 5.165e-03, eta: 19:28:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5002, top5_acc: 0.7564, loss_cls: 2.7794, loss: 2.7794 +2024-12-31 01:30:36,561 - pyskl - INFO - Epoch [129][500/3746] lr: 5.153e-03, eta: 19:27:13, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4933, top5_acc: 0.7436, loss_cls: 2.8330, loss: 2.8330 +2024-12-31 01:32:01,403 - pyskl - INFO - Epoch [129][600/3746] lr: 5.140e-03, eta: 19:25:48, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4975, top5_acc: 0.7488, loss_cls: 2.8129, loss: 2.8129 +2024-12-31 01:33:25,854 - pyskl - INFO - Epoch [129][700/3746] lr: 5.128e-03, eta: 19:24:22, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5120, top5_acc: 0.7525, loss_cls: 2.7797, loss: 2.7797 +2024-12-31 01:34:50,566 - pyskl - INFO - Epoch [129][800/3746] lr: 5.116e-03, eta: 19:22:56, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5003, top5_acc: 0.7459, loss_cls: 2.8099, loss: 2.8099 +2024-12-31 01:36:15,061 - pyskl - INFO - Epoch [129][900/3746] lr: 5.103e-03, eta: 19:21:31, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4895, top5_acc: 0.7445, loss_cls: 2.8477, loss: 2.8477 +2024-12-31 01:37:39,622 - pyskl - INFO - Epoch [129][1000/3746] lr: 5.091e-03, eta: 19:20:05, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4998, top5_acc: 0.7458, loss_cls: 2.8328, loss: 2.8328 +2024-12-31 01:39:04,244 - pyskl - INFO - Epoch [129][1100/3746] lr: 5.079e-03, eta: 19:18:39, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4970, top5_acc: 0.7516, loss_cls: 2.7946, loss: 2.7946 +2024-12-31 01:40:29,333 - pyskl - INFO - Epoch [129][1200/3746] lr: 5.066e-03, eta: 19:17:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4852, top5_acc: 0.7384, loss_cls: 2.8948, loss: 2.8948 +2024-12-31 01:41:54,246 - pyskl - INFO - Epoch [129][1300/3746] lr: 5.054e-03, eta: 19:15:48, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4980, top5_acc: 0.7539, loss_cls: 2.7926, loss: 2.7926 +2024-12-31 01:43:18,886 - pyskl - INFO - Epoch [129][1400/3746] lr: 5.042e-03, eta: 19:14:23, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4930, top5_acc: 0.7386, loss_cls: 2.8723, loss: 2.8723 +2024-12-31 01:44:44,614 - pyskl - INFO - Epoch [129][1500/3746] lr: 5.030e-03, eta: 19:12:57, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4838, top5_acc: 0.7361, loss_cls: 2.8702, loss: 2.8702 +2024-12-31 01:46:10,995 - pyskl - INFO - Epoch [129][1600/3746] lr: 5.017e-03, eta: 19:11:32, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.4873, top5_acc: 0.7355, loss_cls: 2.9059, loss: 2.9059 +2024-12-31 01:47:36,632 - pyskl - INFO - Epoch [129][1700/3746] lr: 5.005e-03, eta: 19:10:06, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5005, top5_acc: 0.7448, loss_cls: 2.8139, loss: 2.8139 +2024-12-31 01:49:02,387 - pyskl - INFO - Epoch [129][1800/3746] lr: 4.993e-03, eta: 19:08:41, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7467, loss_cls: 2.8415, loss: 2.8415 +2024-12-31 01:50:28,346 - pyskl - INFO - Epoch [129][1900/3746] lr: 4.981e-03, eta: 19:07:15, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4861, top5_acc: 0.7312, loss_cls: 2.8952, loss: 2.8952 +2024-12-31 01:51:54,375 - pyskl - INFO - Epoch [129][2000/3746] lr: 4.969e-03, eta: 19:05:50, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4736, top5_acc: 0.7336, loss_cls: 2.9059, loss: 2.9059 +2024-12-31 01:53:20,878 - pyskl - INFO - Epoch [129][2100/3746] lr: 4.957e-03, eta: 19:04:25, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5008, top5_acc: 0.7381, loss_cls: 2.8510, loss: 2.8510 +2024-12-31 01:54:47,133 - pyskl - INFO - Epoch [129][2200/3746] lr: 4.944e-03, eta: 19:02:59, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4805, top5_acc: 0.7373, loss_cls: 2.8936, loss: 2.8936 +2024-12-31 01:56:13,630 - pyskl - INFO - Epoch [129][2300/3746] lr: 4.932e-03, eta: 19:01:34, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.4855, top5_acc: 0.7327, loss_cls: 2.8929, loss: 2.8929 +2024-12-31 01:57:39,723 - pyskl - INFO - Epoch [129][2400/3746] lr: 4.920e-03, eta: 19:00:09, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.4981, top5_acc: 0.7419, loss_cls: 2.8383, loss: 2.8383 +2024-12-31 01:59:05,717 - pyskl - INFO - Epoch [129][2500/3746] lr: 4.908e-03, eta: 18:58:43, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4770, top5_acc: 0.7389, loss_cls: 2.8805, loss: 2.8805 +2024-12-31 02:00:30,480 - pyskl - INFO - Epoch [129][2600/3746] lr: 4.896e-03, eta: 18:57:18, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4895, top5_acc: 0.7388, loss_cls: 2.8635, loss: 2.8635 +2024-12-31 02:01:56,087 - pyskl - INFO - Epoch [129][2700/3746] lr: 4.884e-03, eta: 18:55:52, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.4869, top5_acc: 0.7355, loss_cls: 2.8655, loss: 2.8655 +2024-12-31 02:03:21,907 - pyskl - INFO - Epoch [129][2800/3746] lr: 4.872e-03, eta: 18:54:27, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4814, top5_acc: 0.7414, loss_cls: 2.8820, loss: 2.8820 +2024-12-31 02:04:47,568 - pyskl - INFO - Epoch [129][2900/3746] lr: 4.860e-03, eta: 18:53:01, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4850, top5_acc: 0.7414, loss_cls: 2.8796, loss: 2.8796 +2024-12-31 02:06:13,133 - pyskl - INFO - Epoch [129][3000/3746] lr: 4.848e-03, eta: 18:51:36, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4941, top5_acc: 0.7439, loss_cls: 2.8590, loss: 2.8590 +2024-12-31 02:07:38,949 - pyskl - INFO - Epoch [129][3100/3746] lr: 4.836e-03, eta: 18:50:10, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4886, top5_acc: 0.7477, loss_cls: 2.8555, loss: 2.8555 +2024-12-31 02:09:04,717 - pyskl - INFO - Epoch [129][3200/3746] lr: 4.824e-03, eta: 18:48:45, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4977, top5_acc: 0.7381, loss_cls: 2.8352, loss: 2.8352 +2024-12-31 02:10:30,795 - pyskl - INFO - Epoch [129][3300/3746] lr: 4.812e-03, eta: 18:47:19, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4858, top5_acc: 0.7355, loss_cls: 2.8950, loss: 2.8950 +2024-12-31 02:11:56,327 - pyskl - INFO - Epoch [129][3400/3746] lr: 4.800e-03, eta: 18:45:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4816, top5_acc: 0.7316, loss_cls: 2.9057, loss: 2.9057 +2024-12-31 02:13:22,130 - pyskl - INFO - Epoch [129][3500/3746] lr: 4.788e-03, eta: 18:44:28, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4806, top5_acc: 0.7273, loss_cls: 2.9095, loss: 2.9095 +2024-12-31 02:14:47,771 - pyskl - INFO - Epoch [129][3600/3746] lr: 4.776e-03, eta: 18:43:03, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4905, top5_acc: 0.7416, loss_cls: 2.8529, loss: 2.8529 +2024-12-31 02:16:13,353 - pyskl - INFO - Epoch [129][3700/3746] lr: 4.764e-03, eta: 18:41:37, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4850, top5_acc: 0.7366, loss_cls: 2.8834, loss: 2.8834 +2024-12-31 02:16:54,648 - pyskl - INFO - Saving checkpoint at 129 epochs +2024-12-31 02:18:54,194 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 02:18:54,867 - pyskl - INFO - +top1_acc 0.4074 +top5_acc 0.6625 +2024-12-31 02:18:54,867 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 02:18:54,913 - pyskl - INFO - +mean_acc 0.4072 +2024-12-31 02:18:54,922 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_128.pth was removed +2024-12-31 02:18:55,508 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_129.pth. +2024-12-31 02:18:55,509 - pyskl - INFO - Best top1_acc is 0.4074 at 129 epoch. +2024-12-31 02:18:55,533 - pyskl - INFO - Epoch(val) [129][309] top1_acc: 0.4074, top5_acc: 0.6625, mean_class_accuracy: 0.4072 +2024-12-31 02:23:16,038 - pyskl - INFO - Epoch [130][100/3746] lr: 4.747e-03, eta: 18:39:55, time: 2.605, data_time: 1.541, memory: 15990, top1_acc: 0.5095, top5_acc: 0.7516, loss_cls: 2.7549, loss: 2.7549 +2024-12-31 02:24:41,989 - pyskl - INFO - Epoch [130][200/3746] lr: 4.735e-03, eta: 18:38:29, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5059, top5_acc: 0.7566, loss_cls: 2.7611, loss: 2.7611 +2024-12-31 02:26:07,793 - pyskl - INFO - Epoch [130][300/3746] lr: 4.723e-03, eta: 18:37:04, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5058, top5_acc: 0.7473, loss_cls: 2.7795, loss: 2.7795 +2024-12-31 02:27:33,973 - pyskl - INFO - Epoch [130][400/3746] lr: 4.711e-03, eta: 18:35:38, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.5100, top5_acc: 0.7589, loss_cls: 2.7539, loss: 2.7539 +2024-12-31 02:28:59,402 - pyskl - INFO - Epoch [130][500/3746] lr: 4.699e-03, eta: 18:34:13, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.5005, top5_acc: 0.7516, loss_cls: 2.7901, loss: 2.7901 +2024-12-31 02:30:25,315 - pyskl - INFO - Epoch [130][600/3746] lr: 4.688e-03, eta: 18:32:47, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.4928, top5_acc: 0.7462, loss_cls: 2.8206, loss: 2.8206 +2024-12-31 02:31:51,673 - pyskl - INFO - Epoch [130][700/3746] lr: 4.676e-03, eta: 18:31:22, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5025, top5_acc: 0.7555, loss_cls: 2.7689, loss: 2.7689 +2024-12-31 02:33:17,819 - pyskl - INFO - Epoch [130][800/3746] lr: 4.664e-03, eta: 18:29:56, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5034, top5_acc: 0.7523, loss_cls: 2.7702, loss: 2.7702 +2024-12-31 02:34:44,582 - pyskl - INFO - Epoch [130][900/3746] lr: 4.652e-03, eta: 18:28:31, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5095, top5_acc: 0.7467, loss_cls: 2.7649, loss: 2.7649 +2024-12-31 02:36:10,704 - pyskl - INFO - Epoch [130][1000/3746] lr: 4.640e-03, eta: 18:27:06, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5061, top5_acc: 0.7522, loss_cls: 2.7836, loss: 2.7836 +2024-12-31 02:37:37,485 - pyskl - INFO - Epoch [130][1100/3746] lr: 4.629e-03, eta: 18:25:40, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.4930, top5_acc: 0.7477, loss_cls: 2.8408, loss: 2.8408 +2024-12-31 02:39:04,237 - pyskl - INFO - Epoch [130][1200/3746] lr: 4.617e-03, eta: 18:24:15, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.4886, top5_acc: 0.7395, loss_cls: 2.8574, loss: 2.8574 +2024-12-31 02:40:30,759 - pyskl - INFO - Epoch [130][1300/3746] lr: 4.605e-03, eta: 18:22:50, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.4950, top5_acc: 0.7517, loss_cls: 2.8069, loss: 2.8069 +2024-12-31 02:41:56,894 - pyskl - INFO - Epoch [130][1400/3746] lr: 4.594e-03, eta: 18:21:24, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5033, top5_acc: 0.7536, loss_cls: 2.8008, loss: 2.8008 +2024-12-31 02:43:23,927 - pyskl - INFO - Epoch [130][1500/3746] lr: 4.582e-03, eta: 18:19:59, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5072, top5_acc: 0.7530, loss_cls: 2.7767, loss: 2.7767 +2024-12-31 02:44:50,424 - pyskl - INFO - Epoch [130][1600/3746] lr: 4.570e-03, eta: 18:18:34, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.5012, top5_acc: 0.7506, loss_cls: 2.8104, loss: 2.8104 +2024-12-31 02:46:17,151 - pyskl - INFO - Epoch [130][1700/3746] lr: 4.558e-03, eta: 18:17:08, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.4948, top5_acc: 0.7364, loss_cls: 2.8429, loss: 2.8429 +2024-12-31 02:47:44,384 - pyskl - INFO - Epoch [130][1800/3746] lr: 4.547e-03, eta: 18:15:43, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5019, top5_acc: 0.7478, loss_cls: 2.8205, loss: 2.8205 +2024-12-31 02:49:11,948 - pyskl - INFO - Epoch [130][1900/3746] lr: 4.535e-03, eta: 18:14:18, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7489, loss_cls: 2.8062, loss: 2.8062 +2024-12-31 02:50:39,125 - pyskl - INFO - Epoch [130][2000/3746] lr: 4.524e-03, eta: 18:12:52, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5069, top5_acc: 0.7520, loss_cls: 2.8045, loss: 2.8045 +2024-12-31 02:52:06,356 - pyskl - INFO - Epoch [130][2100/3746] lr: 4.512e-03, eta: 18:11:27, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.4883, top5_acc: 0.7441, loss_cls: 2.8497, loss: 2.8497 +2024-12-31 02:53:34,000 - pyskl - INFO - Epoch [130][2200/3746] lr: 4.500e-03, eta: 18:10:02, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.4959, top5_acc: 0.7422, loss_cls: 2.8635, loss: 2.8635 +2024-12-31 02:55:01,509 - pyskl - INFO - Epoch [130][2300/3746] lr: 4.489e-03, eta: 18:08:37, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.4988, top5_acc: 0.7467, loss_cls: 2.7921, loss: 2.7921 +2024-12-31 02:56:27,722 - pyskl - INFO - Epoch [130][2400/3746] lr: 4.477e-03, eta: 18:07:11, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.4964, top5_acc: 0.7433, loss_cls: 2.8405, loss: 2.8405 +2024-12-31 02:57:54,372 - pyskl - INFO - Epoch [130][2500/3746] lr: 4.466e-03, eta: 18:05:46, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.4955, top5_acc: 0.7462, loss_cls: 2.8101, loss: 2.8101 +2024-12-31 02:59:19,879 - pyskl - INFO - Epoch [130][2600/3746] lr: 4.454e-03, eta: 18:04:20, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.4944, top5_acc: 0.7367, loss_cls: 2.8545, loss: 2.8545 +2024-12-31 03:00:46,310 - pyskl - INFO - Epoch [130][2700/3746] lr: 4.443e-03, eta: 18:02:55, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.4864, top5_acc: 0.7361, loss_cls: 2.8896, loss: 2.8896 +2024-12-31 03:02:12,599 - pyskl - INFO - Epoch [130][2800/3746] lr: 4.431e-03, eta: 18:01:30, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4852, top5_acc: 0.7359, loss_cls: 2.8612, loss: 2.8612 +2024-12-31 03:03:39,102 - pyskl - INFO - Epoch [130][2900/3746] lr: 4.420e-03, eta: 18:00:04, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.4970, top5_acc: 0.7455, loss_cls: 2.8229, loss: 2.8229 +2024-12-31 03:05:05,427 - pyskl - INFO - Epoch [130][3000/3746] lr: 4.408e-03, eta: 17:58:39, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4889, top5_acc: 0.7403, loss_cls: 2.8473, loss: 2.8473 +2024-12-31 03:06:31,712 - pyskl - INFO - Epoch [130][3100/3746] lr: 4.397e-03, eta: 17:57:13, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4959, top5_acc: 0.7403, loss_cls: 2.8399, loss: 2.8399 +2024-12-31 03:07:58,103 - pyskl - INFO - Epoch [130][3200/3746] lr: 4.385e-03, eta: 17:55:48, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.4988, top5_acc: 0.7530, loss_cls: 2.7956, loss: 2.7956 +2024-12-31 03:09:25,239 - pyskl - INFO - Epoch [130][3300/3746] lr: 4.374e-03, eta: 17:54:23, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5016, top5_acc: 0.7472, loss_cls: 2.8129, loss: 2.8129 +2024-12-31 03:10:51,394 - pyskl - INFO - Epoch [130][3400/3746] lr: 4.362e-03, eta: 17:52:57, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4894, top5_acc: 0.7367, loss_cls: 2.8616, loss: 2.8616 +2024-12-31 03:12:17,727 - pyskl - INFO - Epoch [130][3500/3746] lr: 4.351e-03, eta: 17:51:32, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5039, top5_acc: 0.7425, loss_cls: 2.8304, loss: 2.8304 +2024-12-31 03:13:43,811 - pyskl - INFO - Epoch [130][3600/3746] lr: 4.339e-03, eta: 17:50:06, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4913, top5_acc: 0.7495, loss_cls: 2.8347, loss: 2.8347 +2024-12-31 03:15:09,770 - pyskl - INFO - Epoch [130][3700/3746] lr: 4.328e-03, eta: 17:48:41, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4967, top5_acc: 0.7469, loss_cls: 2.8208, loss: 2.8208 +2024-12-31 03:15:51,005 - pyskl - INFO - Saving checkpoint at 130 epochs +2024-12-31 03:17:52,456 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 03:17:53,378 - pyskl - INFO - +top1_acc 0.4131 +top5_acc 0.6646 +2024-12-31 03:17:53,378 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 03:17:53,440 - pyskl - INFO - +mean_acc 0.4127 +2024-12-31 03:17:53,445 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_129.pth was removed +2024-12-31 03:17:53,781 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_130.pth. +2024-12-31 03:17:53,782 - pyskl - INFO - Best top1_acc is 0.4131 at 130 epoch. +2024-12-31 03:17:53,805 - pyskl - INFO - Epoch(val) [130][309] top1_acc: 0.4131, top5_acc: 0.6646, mean_class_accuracy: 0.4127 +2024-12-31 03:22:09,612 - pyskl - INFO - Epoch [131][100/3746] lr: 4.311e-03, eta: 17:46:56, time: 2.558, data_time: 1.514, memory: 15990, top1_acc: 0.5091, top5_acc: 0.7645, loss_cls: 2.7102, loss: 2.7102 +2024-12-31 03:23:35,876 - pyskl - INFO - Epoch [131][200/3746] lr: 4.300e-03, eta: 17:45:31, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5181, top5_acc: 0.7631, loss_cls: 2.6994, loss: 2.6994 +2024-12-31 03:25:02,437 - pyskl - INFO - Epoch [131][300/3746] lr: 4.289e-03, eta: 17:44:05, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5162, top5_acc: 0.7603, loss_cls: 2.7200, loss: 2.7200 +2024-12-31 03:26:28,251 - pyskl - INFO - Epoch [131][400/3746] lr: 4.277e-03, eta: 17:42:40, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5108, top5_acc: 0.7564, loss_cls: 2.7393, loss: 2.7393 +2024-12-31 03:27:54,630 - pyskl - INFO - Epoch [131][500/3746] lr: 4.266e-03, eta: 17:41:14, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7438, loss_cls: 2.8297, loss: 2.8297 +2024-12-31 03:29:19,882 - pyskl - INFO - Epoch [131][600/3746] lr: 4.255e-03, eta: 17:39:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5192, top5_acc: 0.7677, loss_cls: 2.6976, loss: 2.6976 +2024-12-31 03:30:45,618 - pyskl - INFO - Epoch [131][700/3746] lr: 4.244e-03, eta: 17:38:23, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5080, top5_acc: 0.7555, loss_cls: 2.7624, loss: 2.7624 +2024-12-31 03:32:12,238 - pyskl - INFO - Epoch [131][800/3746] lr: 4.232e-03, eta: 17:36:58, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5138, top5_acc: 0.7580, loss_cls: 2.7329, loss: 2.7329 +2024-12-31 03:33:38,680 - pyskl - INFO - Epoch [131][900/3746] lr: 4.221e-03, eta: 17:35:32, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5106, top5_acc: 0.7545, loss_cls: 2.7488, loss: 2.7488 +2024-12-31 03:35:05,225 - pyskl - INFO - Epoch [131][1000/3746] lr: 4.210e-03, eta: 17:34:07, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5089, top5_acc: 0.7541, loss_cls: 2.7423, loss: 2.7423 +2024-12-31 03:36:32,116 - pyskl - INFO - Epoch [131][1100/3746] lr: 4.199e-03, eta: 17:32:42, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5197, top5_acc: 0.7580, loss_cls: 2.7390, loss: 2.7390 +2024-12-31 03:37:59,226 - pyskl - INFO - Epoch [131][1200/3746] lr: 4.187e-03, eta: 17:31:16, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5144, top5_acc: 0.7548, loss_cls: 2.7644, loss: 2.7644 +2024-12-31 03:39:25,631 - pyskl - INFO - Epoch [131][1300/3746] lr: 4.176e-03, eta: 17:29:51, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5041, top5_acc: 0.7625, loss_cls: 2.7534, loss: 2.7534 +2024-12-31 03:40:52,411 - pyskl - INFO - Epoch [131][1400/3746] lr: 4.165e-03, eta: 17:28:26, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5092, top5_acc: 0.7575, loss_cls: 2.7757, loss: 2.7757 +2024-12-31 03:42:19,159 - pyskl - INFO - Epoch [131][1500/3746] lr: 4.154e-03, eta: 17:27:00, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5117, top5_acc: 0.7597, loss_cls: 2.7390, loss: 2.7390 +2024-12-31 03:43:46,320 - pyskl - INFO - Epoch [131][1600/3746] lr: 4.143e-03, eta: 17:25:35, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5053, top5_acc: 0.7567, loss_cls: 2.7671, loss: 2.7671 +2024-12-31 03:45:13,489 - pyskl - INFO - Epoch [131][1700/3746] lr: 4.132e-03, eta: 17:24:10, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5148, top5_acc: 0.7539, loss_cls: 2.7797, loss: 2.7797 +2024-12-31 03:46:40,408 - pyskl - INFO - Epoch [131][1800/3746] lr: 4.120e-03, eta: 17:22:44, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.4998, top5_acc: 0.7377, loss_cls: 2.8461, loss: 2.8461 +2024-12-31 03:48:07,618 - pyskl - INFO - Epoch [131][1900/3746] lr: 4.109e-03, eta: 17:21:19, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.4986, top5_acc: 0.7503, loss_cls: 2.8214, loss: 2.8214 +2024-12-31 03:49:34,639 - pyskl - INFO - Epoch [131][2000/3746] lr: 4.098e-03, eta: 17:19:53, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5009, top5_acc: 0.7527, loss_cls: 2.7873, loss: 2.7873 +2024-12-31 03:51:01,551 - pyskl - INFO - Epoch [131][2100/3746] lr: 4.087e-03, eta: 17:18:28, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.4972, top5_acc: 0.7445, loss_cls: 2.8312, loss: 2.8312 +2024-12-31 03:52:28,439 - pyskl - INFO - Epoch [131][2200/3746] lr: 4.076e-03, eta: 17:17:03, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5066, top5_acc: 0.7458, loss_cls: 2.7897, loss: 2.7897 +2024-12-31 03:53:55,023 - pyskl - INFO - Epoch [131][2300/3746] lr: 4.065e-03, eta: 17:15:37, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7470, loss_cls: 2.7935, loss: 2.7935 +2024-12-31 03:55:21,081 - pyskl - INFO - Epoch [131][2400/3746] lr: 4.054e-03, eta: 17:14:12, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.5030, top5_acc: 0.7472, loss_cls: 2.8222, loss: 2.8222 +2024-12-31 03:56:46,949 - pyskl - INFO - Epoch [131][2500/3746] lr: 4.043e-03, eta: 17:12:46, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4991, top5_acc: 0.7459, loss_cls: 2.8108, loss: 2.8108 +2024-12-31 03:58:11,665 - pyskl - INFO - Epoch [131][2600/3746] lr: 4.032e-03, eta: 17:11:21, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.5022, top5_acc: 0.7481, loss_cls: 2.7984, loss: 2.7984 +2024-12-31 03:59:38,335 - pyskl - INFO - Epoch [131][2700/3746] lr: 4.021e-03, eta: 17:09:55, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5028, top5_acc: 0.7459, loss_cls: 2.8196, loss: 2.8196 +2024-12-31 04:01:04,615 - pyskl - INFO - Epoch [131][2800/3746] lr: 4.010e-03, eta: 17:08:30, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4961, top5_acc: 0.7486, loss_cls: 2.8279, loss: 2.8279 +2024-12-31 04:02:31,367 - pyskl - INFO - Epoch [131][2900/3746] lr: 3.999e-03, eta: 17:07:04, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5080, top5_acc: 0.7508, loss_cls: 2.7955, loss: 2.7955 +2024-12-31 04:03:57,328 - pyskl - INFO - Epoch [131][3000/3746] lr: 3.988e-03, eta: 17:05:39, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4975, top5_acc: 0.7450, loss_cls: 2.8001, loss: 2.8001 +2024-12-31 04:05:23,769 - pyskl - INFO - Epoch [131][3100/3746] lr: 3.977e-03, eta: 17:04:13, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.4997, top5_acc: 0.7466, loss_cls: 2.8167, loss: 2.8167 +2024-12-31 04:06:50,272 - pyskl - INFO - Epoch [131][3200/3746] lr: 3.966e-03, eta: 17:02:48, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5039, top5_acc: 0.7578, loss_cls: 2.7696, loss: 2.7696 +2024-12-31 04:08:16,267 - pyskl - INFO - Epoch [131][3300/3746] lr: 3.955e-03, eta: 17:01:22, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4978, top5_acc: 0.7473, loss_cls: 2.8198, loss: 2.8198 +2024-12-31 04:09:42,388 - pyskl - INFO - Epoch [131][3400/3746] lr: 3.945e-03, eta: 16:59:57, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4975, top5_acc: 0.7494, loss_cls: 2.8098, loss: 2.8098 +2024-12-31 04:11:09,366 - pyskl - INFO - Epoch [131][3500/3746] lr: 3.934e-03, eta: 16:58:32, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.4995, top5_acc: 0.7483, loss_cls: 2.8138, loss: 2.8138 +2024-12-31 04:12:36,616 - pyskl - INFO - Epoch [131][3600/3746] lr: 3.923e-03, eta: 16:57:06, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5005, top5_acc: 0.7503, loss_cls: 2.7934, loss: 2.7934 +2024-12-31 04:14:03,471 - pyskl - INFO - Epoch [131][3700/3746] lr: 3.912e-03, eta: 16:55:41, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5075, top5_acc: 0.7520, loss_cls: 2.7890, loss: 2.7890 +2024-12-31 04:14:45,460 - pyskl - INFO - Saving checkpoint at 131 epochs +2024-12-31 04:16:46,165 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 04:16:47,312 - pyskl - INFO - +top1_acc 0.4204 +top5_acc 0.6703 +2024-12-31 04:16:47,312 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 04:16:47,355 - pyskl - INFO - +mean_acc 0.4200 +2024-12-31 04:16:47,360 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_130.pth was removed +2024-12-31 04:16:47,652 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_131.pth. +2024-12-31 04:16:47,653 - pyskl - INFO - Best top1_acc is 0.4204 at 131 epoch. +2024-12-31 04:16:47,672 - pyskl - INFO - Epoch(val) [131][309] top1_acc: 0.4204, top5_acc: 0.6703, mean_class_accuracy: 0.4200 +2024-12-31 04:21:08,601 - pyskl - INFO - Epoch [132][100/3746] lr: 3.896e-03, eta: 16:53:56, time: 2.609, data_time: 1.537, memory: 15990, top1_acc: 0.5306, top5_acc: 0.7816, loss_cls: 2.6112, loss: 2.6112 +2024-12-31 04:22:35,739 - pyskl - INFO - Epoch [132][200/3746] lr: 3.885e-03, eta: 16:52:30, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5239, top5_acc: 0.7728, loss_cls: 2.6481, loss: 2.6481 +2024-12-31 04:24:02,830 - pyskl - INFO - Epoch [132][300/3746] lr: 3.875e-03, eta: 16:51:05, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5164, top5_acc: 0.7592, loss_cls: 2.7314, loss: 2.7314 +2024-12-31 04:25:30,445 - pyskl - INFO - Epoch [132][400/3746] lr: 3.864e-03, eta: 16:49:40, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.5192, top5_acc: 0.7664, loss_cls: 2.6753, loss: 2.6753 +2024-12-31 04:26:56,183 - pyskl - INFO - Epoch [132][500/3746] lr: 3.853e-03, eta: 16:48:14, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.5184, top5_acc: 0.7602, loss_cls: 2.7045, loss: 2.7045 +2024-12-31 04:28:21,713 - pyskl - INFO - Epoch [132][600/3746] lr: 3.842e-03, eta: 16:46:48, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.5205, top5_acc: 0.7636, loss_cls: 2.6935, loss: 2.6935 +2024-12-31 04:29:47,513 - pyskl - INFO - Epoch [132][700/3746] lr: 3.831e-03, eta: 16:45:23, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5159, top5_acc: 0.7677, loss_cls: 2.6970, loss: 2.6970 +2024-12-31 04:31:13,817 - pyskl - INFO - Epoch [132][800/3746] lr: 3.821e-03, eta: 16:43:57, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5167, top5_acc: 0.7645, loss_cls: 2.7076, loss: 2.7076 +2024-12-31 04:32:40,085 - pyskl - INFO - Epoch [132][900/3746] lr: 3.810e-03, eta: 16:42:32, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5155, top5_acc: 0.7612, loss_cls: 2.7164, loss: 2.7164 +2024-12-31 04:34:06,893 - pyskl - INFO - Epoch [132][1000/3746] lr: 3.799e-03, eta: 16:41:06, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5166, top5_acc: 0.7650, loss_cls: 2.6965, loss: 2.6965 +2024-12-31 04:35:33,923 - pyskl - INFO - Epoch [132][1100/3746] lr: 3.789e-03, eta: 16:39:41, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5103, top5_acc: 0.7453, loss_cls: 2.7876, loss: 2.7876 +2024-12-31 04:37:00,643 - pyskl - INFO - Epoch [132][1200/3746] lr: 3.778e-03, eta: 16:38:16, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5055, top5_acc: 0.7573, loss_cls: 2.7432, loss: 2.7432 +2024-12-31 04:38:27,704 - pyskl - INFO - Epoch [132][1300/3746] lr: 3.767e-03, eta: 16:36:50, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5059, top5_acc: 0.7516, loss_cls: 2.7712, loss: 2.7712 +2024-12-31 04:39:54,245 - pyskl - INFO - Epoch [132][1400/3746] lr: 3.757e-03, eta: 16:35:25, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.5078, top5_acc: 0.7514, loss_cls: 2.7346, loss: 2.7346 +2024-12-31 04:41:20,859 - pyskl - INFO - Epoch [132][1500/3746] lr: 3.746e-03, eta: 16:33:59, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5189, top5_acc: 0.7628, loss_cls: 2.7185, loss: 2.7185 +2024-12-31 04:42:48,155 - pyskl - INFO - Epoch [132][1600/3746] lr: 3.735e-03, eta: 16:32:34, time: 0.873, data_time: 0.001, memory: 15990, top1_acc: 0.5147, top5_acc: 0.7512, loss_cls: 2.7473, loss: 2.7473 +2024-12-31 04:44:14,742 - pyskl - INFO - Epoch [132][1700/3746] lr: 3.725e-03, eta: 16:31:09, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5112, top5_acc: 0.7586, loss_cls: 2.7522, loss: 2.7522 +2024-12-31 04:45:41,261 - pyskl - INFO - Epoch [132][1800/3746] lr: 3.714e-03, eta: 16:29:43, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5050, top5_acc: 0.7572, loss_cls: 2.7401, loss: 2.7401 +2024-12-31 04:47:07,782 - pyskl - INFO - Epoch [132][1900/3746] lr: 3.704e-03, eta: 16:28:18, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5123, top5_acc: 0.7583, loss_cls: 2.7547, loss: 2.7547 +2024-12-31 04:48:33,952 - pyskl - INFO - Epoch [132][2000/3746] lr: 3.693e-03, eta: 16:26:52, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5073, top5_acc: 0.7550, loss_cls: 2.7485, loss: 2.7485 +2024-12-31 04:50:00,571 - pyskl - INFO - Epoch [132][2100/3746] lr: 3.683e-03, eta: 16:25:27, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.4997, top5_acc: 0.7486, loss_cls: 2.8037, loss: 2.8037 +2024-12-31 04:51:27,885 - pyskl - INFO - Epoch [132][2200/3746] lr: 3.672e-03, eta: 16:24:01, time: 0.873, data_time: 0.001, memory: 15990, top1_acc: 0.5059, top5_acc: 0.7558, loss_cls: 2.7618, loss: 2.7618 +2024-12-31 04:52:54,811 - pyskl - INFO - Epoch [132][2300/3746] lr: 3.662e-03, eta: 16:22:36, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5019, top5_acc: 0.7538, loss_cls: 2.7892, loss: 2.7892 +2024-12-31 04:54:21,006 - pyskl - INFO - Epoch [132][2400/3746] lr: 3.651e-03, eta: 16:21:10, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5125, top5_acc: 0.7525, loss_cls: 2.7635, loss: 2.7635 +2024-12-31 04:55:47,022 - pyskl - INFO - Epoch [132][2500/3746] lr: 3.641e-03, eta: 16:19:45, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5211, top5_acc: 0.7641, loss_cls: 2.6862, loss: 2.6862 +2024-12-31 04:57:12,852 - pyskl - INFO - Epoch [132][2600/3746] lr: 3.630e-03, eta: 16:18:19, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.5084, top5_acc: 0.7525, loss_cls: 2.7366, loss: 2.7366 +2024-12-31 04:58:39,181 - pyskl - INFO - Epoch [132][2700/3746] lr: 3.620e-03, eta: 16:16:54, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5152, top5_acc: 0.7606, loss_cls: 2.7239, loss: 2.7239 +2024-12-31 05:00:06,064 - pyskl - INFO - Epoch [132][2800/3746] lr: 3.609e-03, eta: 16:15:28, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5009, top5_acc: 0.7450, loss_cls: 2.7956, loss: 2.7956 +2024-12-31 05:01:32,723 - pyskl - INFO - Epoch [132][2900/3746] lr: 3.599e-03, eta: 16:14:03, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5073, top5_acc: 0.7536, loss_cls: 2.7607, loss: 2.7607 +2024-12-31 05:02:59,229 - pyskl - INFO - Epoch [132][3000/3746] lr: 3.588e-03, eta: 16:12:37, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5122, top5_acc: 0.7652, loss_cls: 2.7319, loss: 2.7319 +2024-12-31 05:04:26,520 - pyskl - INFO - Epoch [132][3100/3746] lr: 3.578e-03, eta: 16:11:12, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.5061, top5_acc: 0.7473, loss_cls: 2.8035, loss: 2.8035 +2024-12-31 05:05:52,817 - pyskl - INFO - Epoch [132][3200/3746] lr: 3.568e-03, eta: 16:09:46, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5066, top5_acc: 0.7519, loss_cls: 2.7689, loss: 2.7689 +2024-12-31 05:07:19,606 - pyskl - INFO - Epoch [132][3300/3746] lr: 3.557e-03, eta: 16:08:21, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5112, top5_acc: 0.7669, loss_cls: 2.6990, loss: 2.6990 +2024-12-31 05:08:45,846 - pyskl - INFO - Epoch [132][3400/3746] lr: 3.547e-03, eta: 16:06:56, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5200, top5_acc: 0.7591, loss_cls: 2.7168, loss: 2.7168 +2024-12-31 05:10:12,547 - pyskl - INFO - Epoch [132][3500/3746] lr: 3.537e-03, eta: 16:05:30, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5084, top5_acc: 0.7552, loss_cls: 2.7516, loss: 2.7516 +2024-12-31 05:11:39,482 - pyskl - INFO - Epoch [132][3600/3746] lr: 3.526e-03, eta: 16:04:05, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.4955, top5_acc: 0.7488, loss_cls: 2.8004, loss: 2.8004 +2024-12-31 05:13:05,894 - pyskl - INFO - Epoch [132][3700/3746] lr: 3.516e-03, eta: 16:02:39, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5130, top5_acc: 0.7586, loss_cls: 2.7537, loss: 2.7537 +2024-12-31 05:13:48,008 - pyskl - INFO - Saving checkpoint at 132 epochs +2024-12-31 05:15:47,448 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 05:15:48,177 - pyskl - INFO - +top1_acc 0.4189 +top5_acc 0.6741 +2024-12-31 05:15:48,178 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 05:15:48,225 - pyskl - INFO - +mean_acc 0.4186 +2024-12-31 05:15:48,238 - pyskl - INFO - Epoch(val) [132][309] top1_acc: 0.4189, top5_acc: 0.6741, mean_class_accuracy: 0.4186 +2024-12-31 05:20:11,796 - pyskl - INFO - Epoch [133][100/3746] lr: 3.501e-03, eta: 16:00:53, time: 2.635, data_time: 1.572, memory: 15990, top1_acc: 0.5278, top5_acc: 0.7705, loss_cls: 2.6711, loss: 2.6711 +2024-12-31 05:21:38,723 - pyskl - INFO - Epoch [133][200/3746] lr: 3.491e-03, eta: 15:59:28, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5291, top5_acc: 0.7633, loss_cls: 2.6584, loss: 2.6584 +2024-12-31 05:23:05,762 - pyskl - INFO - Epoch [133][300/3746] lr: 3.480e-03, eta: 15:58:02, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5417, top5_acc: 0.7842, loss_cls: 2.5752, loss: 2.5752 +2024-12-31 05:24:32,952 - pyskl - INFO - Epoch [133][400/3746] lr: 3.470e-03, eta: 15:56:37, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7731, loss_cls: 2.6451, loss: 2.6451 +2024-12-31 05:25:58,685 - pyskl - INFO - Epoch [133][500/3746] lr: 3.460e-03, eta: 15:55:11, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5359, top5_acc: 0.7673, loss_cls: 2.6674, loss: 2.6674 +2024-12-31 05:27:24,259 - pyskl - INFO - Epoch [133][600/3746] lr: 3.450e-03, eta: 15:53:45, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.5337, top5_acc: 0.7750, loss_cls: 2.6244, loss: 2.6244 +2024-12-31 05:28:49,372 - pyskl - INFO - Epoch [133][700/3746] lr: 3.440e-03, eta: 15:52:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5227, top5_acc: 0.7658, loss_cls: 2.6852, loss: 2.6852 +2024-12-31 05:30:15,255 - pyskl - INFO - Epoch [133][800/3746] lr: 3.429e-03, eta: 15:50:54, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5216, top5_acc: 0.7733, loss_cls: 2.6539, loss: 2.6539 +2024-12-31 05:31:41,924 - pyskl - INFO - Epoch [133][900/3746] lr: 3.419e-03, eta: 15:49:29, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5277, top5_acc: 0.7597, loss_cls: 2.6815, loss: 2.6815 +2024-12-31 05:33:08,818 - pyskl - INFO - Epoch [133][1000/3746] lr: 3.409e-03, eta: 15:48:03, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5216, top5_acc: 0.7680, loss_cls: 2.6751, loss: 2.6751 +2024-12-31 05:34:35,272 - pyskl - INFO - Epoch [133][1100/3746] lr: 3.399e-03, eta: 15:46:38, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5214, top5_acc: 0.7620, loss_cls: 2.7062, loss: 2.7062 +2024-12-31 05:36:01,833 - pyskl - INFO - Epoch [133][1200/3746] lr: 3.389e-03, eta: 15:45:12, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5258, top5_acc: 0.7631, loss_cls: 2.6623, loss: 2.6623 +2024-12-31 05:37:29,075 - pyskl - INFO - Epoch [133][1300/3746] lr: 3.379e-03, eta: 15:43:47, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5216, top5_acc: 0.7595, loss_cls: 2.6899, loss: 2.6899 +2024-12-31 05:38:56,044 - pyskl - INFO - Epoch [133][1400/3746] lr: 3.369e-03, eta: 15:42:21, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5069, top5_acc: 0.7644, loss_cls: 2.7166, loss: 2.7166 +2024-12-31 05:40:22,930 - pyskl - INFO - Epoch [133][1500/3746] lr: 3.359e-03, eta: 15:40:56, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5091, top5_acc: 0.7650, loss_cls: 2.6953, loss: 2.6953 +2024-12-31 05:41:49,635 - pyskl - INFO - Epoch [133][1600/3746] lr: 3.348e-03, eta: 15:39:30, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5189, top5_acc: 0.7708, loss_cls: 2.7079, loss: 2.7079 +2024-12-31 05:43:16,775 - pyskl - INFO - Epoch [133][1700/3746] lr: 3.338e-03, eta: 15:38:05, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5156, top5_acc: 0.7633, loss_cls: 2.7282, loss: 2.7282 +2024-12-31 05:44:43,448 - pyskl - INFO - Epoch [133][1800/3746] lr: 3.328e-03, eta: 15:36:39, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5233, top5_acc: 0.7628, loss_cls: 2.6958, loss: 2.6958 +2024-12-31 05:46:10,502 - pyskl - INFO - Epoch [133][1900/3746] lr: 3.318e-03, eta: 15:35:14, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5092, top5_acc: 0.7605, loss_cls: 2.7120, loss: 2.7120 +2024-12-31 05:47:37,883 - pyskl - INFO - Epoch [133][2000/3746] lr: 3.308e-03, eta: 15:33:49, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.5186, top5_acc: 0.7631, loss_cls: 2.6902, loss: 2.6902 +2024-12-31 05:49:04,633 - pyskl - INFO - Epoch [133][2100/3746] lr: 3.298e-03, eta: 15:32:23, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5214, top5_acc: 0.7622, loss_cls: 2.6962, loss: 2.6962 +2024-12-31 05:50:31,371 - pyskl - INFO - Epoch [133][2200/3746] lr: 3.288e-03, eta: 15:30:58, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5134, top5_acc: 0.7608, loss_cls: 2.7384, loss: 2.7384 +2024-12-31 05:51:58,042 - pyskl - INFO - Epoch [133][2300/3746] lr: 3.278e-03, eta: 15:29:32, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5216, top5_acc: 0.7681, loss_cls: 2.6849, loss: 2.6849 +2024-12-31 05:53:24,701 - pyskl - INFO - Epoch [133][2400/3746] lr: 3.268e-03, eta: 15:28:07, time: 0.867, data_time: 0.001, memory: 15990, top1_acc: 0.5209, top5_acc: 0.7630, loss_cls: 2.6905, loss: 2.6905 +2024-12-31 05:54:50,640 - pyskl - INFO - Epoch [133][2500/3746] lr: 3.259e-03, eta: 15:26:41, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.5019, top5_acc: 0.7564, loss_cls: 2.7674, loss: 2.7674 +2024-12-31 05:56:16,355 - pyskl - INFO - Epoch [133][2600/3746] lr: 3.249e-03, eta: 15:25:15, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5094, top5_acc: 0.7605, loss_cls: 2.7314, loss: 2.7314 +2024-12-31 05:57:41,716 - pyskl - INFO - Epoch [133][2700/3746] lr: 3.239e-03, eta: 15:23:50, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.5192, top5_acc: 0.7666, loss_cls: 2.6860, loss: 2.6860 +2024-12-31 05:59:07,157 - pyskl - INFO - Epoch [133][2800/3746] lr: 3.229e-03, eta: 15:22:24, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5186, top5_acc: 0.7619, loss_cls: 2.7075, loss: 2.7075 +2024-12-31 06:00:32,782 - pyskl - INFO - Epoch [133][2900/3746] lr: 3.219e-03, eta: 15:20:58, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5006, top5_acc: 0.7527, loss_cls: 2.7806, loss: 2.7806 +2024-12-31 06:01:58,929 - pyskl - INFO - Epoch [133][3000/3746] lr: 3.209e-03, eta: 15:19:33, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5091, top5_acc: 0.7639, loss_cls: 2.7217, loss: 2.7217 +2024-12-31 06:03:25,080 - pyskl - INFO - Epoch [133][3100/3746] lr: 3.199e-03, eta: 15:18:07, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5238, top5_acc: 0.7694, loss_cls: 2.6521, loss: 2.6521 +2024-12-31 06:04:51,512 - pyskl - INFO - Epoch [133][3200/3746] lr: 3.189e-03, eta: 15:16:42, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5261, top5_acc: 0.7666, loss_cls: 2.6833, loss: 2.6833 +2024-12-31 06:06:17,900 - pyskl - INFO - Epoch [133][3300/3746] lr: 3.180e-03, eta: 15:15:16, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5044, top5_acc: 0.7552, loss_cls: 2.7494, loss: 2.7494 +2024-12-31 06:07:44,736 - pyskl - INFO - Epoch [133][3400/3746] lr: 3.170e-03, eta: 15:13:51, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5194, top5_acc: 0.7631, loss_cls: 2.6945, loss: 2.6945 +2024-12-31 06:09:10,894 - pyskl - INFO - Epoch [133][3500/3746] lr: 3.160e-03, eta: 15:12:25, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5023, top5_acc: 0.7506, loss_cls: 2.7467, loss: 2.7467 +2024-12-31 06:10:37,747 - pyskl - INFO - Epoch [133][3600/3746] lr: 3.150e-03, eta: 15:11:00, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5075, top5_acc: 0.7591, loss_cls: 2.7507, loss: 2.7507 +2024-12-31 06:12:04,093 - pyskl - INFO - Epoch [133][3700/3746] lr: 3.140e-03, eta: 15:09:34, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5180, top5_acc: 0.7691, loss_cls: 2.6946, loss: 2.6946 +2024-12-31 06:12:45,568 - pyskl - INFO - Saving checkpoint at 133 epochs +2024-12-31 06:14:45,147 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 06:14:45,943 - pyskl - INFO - +top1_acc 0.4232 +top5_acc 0.6756 +2024-12-31 06:14:45,943 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 06:14:45,990 - pyskl - INFO - +mean_acc 0.4230 +2024-12-31 06:14:45,995 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_131.pth was removed +2024-12-31 06:14:46,288 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_133.pth. +2024-12-31 06:14:46,289 - pyskl - INFO - Best top1_acc is 0.4232 at 133 epoch. +2024-12-31 06:14:46,304 - pyskl - INFO - Epoch(val) [133][309] top1_acc: 0.4232, top5_acc: 0.6756, mean_class_accuracy: 0.4230 +2024-12-31 06:19:09,616 - pyskl - INFO - Epoch [134][100/3746] lr: 3.126e-03, eta: 15:07:47, time: 2.633, data_time: 1.563, memory: 15990, top1_acc: 0.5491, top5_acc: 0.7870, loss_cls: 2.5371, loss: 2.5371 +2024-12-31 06:20:36,838 - pyskl - INFO - Epoch [134][200/3746] lr: 3.117e-03, eta: 15:06:21, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5234, top5_acc: 0.7744, loss_cls: 2.6285, loss: 2.6285 +2024-12-31 06:22:03,856 - pyskl - INFO - Epoch [134][300/3746] lr: 3.107e-03, eta: 15:04:56, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5270, top5_acc: 0.7734, loss_cls: 2.6608, loss: 2.6608 +2024-12-31 06:23:31,488 - pyskl - INFO - Epoch [134][400/3746] lr: 3.097e-03, eta: 15:03:30, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.5348, top5_acc: 0.7777, loss_cls: 2.6294, loss: 2.6294 +2024-12-31 06:24:59,026 - pyskl - INFO - Epoch [134][500/3746] lr: 3.087e-03, eta: 15:02:05, time: 0.875, data_time: 0.000, memory: 15990, top1_acc: 0.5344, top5_acc: 0.7830, loss_cls: 2.6091, loss: 2.6091 +2024-12-31 06:26:25,101 - pyskl - INFO - Epoch [134][600/3746] lr: 3.078e-03, eta: 15:00:39, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.5339, top5_acc: 0.7711, loss_cls: 2.6427, loss: 2.6427 +2024-12-31 06:27:50,426 - pyskl - INFO - Epoch [134][700/3746] lr: 3.068e-03, eta: 14:59:14, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5342, top5_acc: 0.7892, loss_cls: 2.5938, loss: 2.5938 +2024-12-31 06:29:15,710 - pyskl - INFO - Epoch [134][800/3746] lr: 3.059e-03, eta: 14:57:48, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.5330, top5_acc: 0.7720, loss_cls: 2.6177, loss: 2.6177 +2024-12-31 06:30:41,774 - pyskl - INFO - Epoch [134][900/3746] lr: 3.049e-03, eta: 14:56:22, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5366, top5_acc: 0.7738, loss_cls: 2.6455, loss: 2.6455 +2024-12-31 06:32:08,443 - pyskl - INFO - Epoch [134][1000/3746] lr: 3.039e-03, eta: 14:54:57, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5366, top5_acc: 0.7767, loss_cls: 2.6137, loss: 2.6137 +2024-12-31 06:33:34,577 - pyskl - INFO - Epoch [134][1100/3746] lr: 3.030e-03, eta: 14:53:31, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5320, top5_acc: 0.7772, loss_cls: 2.6143, loss: 2.6143 +2024-12-31 06:35:01,049 - pyskl - INFO - Epoch [134][1200/3746] lr: 3.020e-03, eta: 14:52:06, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5175, top5_acc: 0.7642, loss_cls: 2.6858, loss: 2.6858 +2024-12-31 06:36:27,745 - pyskl - INFO - Epoch [134][1300/3746] lr: 3.011e-03, eta: 14:50:40, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5441, top5_acc: 0.7802, loss_cls: 2.5965, loss: 2.5965 +2024-12-31 06:37:54,368 - pyskl - INFO - Epoch [134][1400/3746] lr: 3.001e-03, eta: 14:49:14, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5247, top5_acc: 0.7655, loss_cls: 2.6724, loss: 2.6724 +2024-12-31 06:39:20,646 - pyskl - INFO - Epoch [134][1500/3746] lr: 2.991e-03, eta: 14:47:49, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5306, top5_acc: 0.7722, loss_cls: 2.6444, loss: 2.6444 +2024-12-31 06:40:47,591 - pyskl - INFO - Epoch [134][1600/3746] lr: 2.982e-03, eta: 14:46:23, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5327, top5_acc: 0.7738, loss_cls: 2.6527, loss: 2.6527 +2024-12-31 06:42:13,904 - pyskl - INFO - Epoch [134][1700/3746] lr: 2.972e-03, eta: 14:44:58, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5322, top5_acc: 0.7647, loss_cls: 2.6604, loss: 2.6604 +2024-12-31 06:43:40,365 - pyskl - INFO - Epoch [134][1800/3746] lr: 2.963e-03, eta: 14:43:32, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5198, top5_acc: 0.7616, loss_cls: 2.7316, loss: 2.7316 +2024-12-31 06:45:06,559 - pyskl - INFO - Epoch [134][1900/3746] lr: 2.953e-03, eta: 14:42:07, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5345, top5_acc: 0.7733, loss_cls: 2.6266, loss: 2.6266 +2024-12-31 06:46:32,886 - pyskl - INFO - Epoch [134][2000/3746] lr: 2.944e-03, eta: 14:40:41, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5298, top5_acc: 0.7688, loss_cls: 2.6679, loss: 2.6679 +2024-12-31 06:47:58,773 - pyskl - INFO - Epoch [134][2100/3746] lr: 2.935e-03, eta: 14:39:15, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5308, top5_acc: 0.7753, loss_cls: 2.6224, loss: 2.6224 +2024-12-31 06:49:25,037 - pyskl - INFO - Epoch [134][2200/3746] lr: 2.925e-03, eta: 14:37:50, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5256, top5_acc: 0.7656, loss_cls: 2.6527, loss: 2.6527 +2024-12-31 06:50:51,645 - pyskl - INFO - Epoch [134][2300/3746] lr: 2.916e-03, eta: 14:36:24, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5305, top5_acc: 0.7752, loss_cls: 2.6212, loss: 2.6212 +2024-12-31 06:52:17,653 - pyskl - INFO - Epoch [134][2400/3746] lr: 2.906e-03, eta: 14:34:59, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.5214, top5_acc: 0.7558, loss_cls: 2.6942, loss: 2.6942 +2024-12-31 06:53:43,427 - pyskl - INFO - Epoch [134][2500/3746] lr: 2.897e-03, eta: 14:33:33, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5328, top5_acc: 0.7709, loss_cls: 2.6590, loss: 2.6590 +2024-12-31 06:55:09,237 - pyskl - INFO - Epoch [134][2600/3746] lr: 2.888e-03, eta: 14:32:07, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5272, top5_acc: 0.7698, loss_cls: 2.6581, loss: 2.6581 +2024-12-31 06:56:35,020 - pyskl - INFO - Epoch [134][2700/3746] lr: 2.878e-03, eta: 14:30:42, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5319, top5_acc: 0.7772, loss_cls: 2.6347, loss: 2.6347 +2024-12-31 06:58:01,351 - pyskl - INFO - Epoch [134][2800/3746] lr: 2.869e-03, eta: 14:29:16, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5261, top5_acc: 0.7747, loss_cls: 2.6698, loss: 2.6698 +2024-12-31 06:59:28,041 - pyskl - INFO - Epoch [134][2900/3746] lr: 2.860e-03, eta: 14:27:51, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5162, top5_acc: 0.7647, loss_cls: 2.7061, loss: 2.7061 +2024-12-31 07:00:53,951 - pyskl - INFO - Epoch [134][3000/3746] lr: 2.850e-03, eta: 14:26:25, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5242, top5_acc: 0.7636, loss_cls: 2.6978, loss: 2.6978 +2024-12-31 07:02:20,464 - pyskl - INFO - Epoch [134][3100/3746] lr: 2.841e-03, eta: 14:24:59, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5220, top5_acc: 0.7639, loss_cls: 2.7072, loss: 2.7072 +2024-12-31 07:03:47,181 - pyskl - INFO - Epoch [134][3200/3746] lr: 2.832e-03, eta: 14:23:34, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5195, top5_acc: 0.7675, loss_cls: 2.6733, loss: 2.6733 +2024-12-31 07:05:13,496 - pyskl - INFO - Epoch [134][3300/3746] lr: 2.822e-03, eta: 14:22:08, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5289, top5_acc: 0.7788, loss_cls: 2.6102, loss: 2.6102 +2024-12-31 07:06:39,864 - pyskl - INFO - Epoch [134][3400/3746] lr: 2.813e-03, eta: 14:20:43, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5230, top5_acc: 0.7748, loss_cls: 2.6723, loss: 2.6723 +2024-12-31 07:08:06,647 - pyskl - INFO - Epoch [134][3500/3746] lr: 2.804e-03, eta: 14:19:17, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5277, top5_acc: 0.7725, loss_cls: 2.6224, loss: 2.6224 +2024-12-31 07:09:32,963 - pyskl - INFO - Epoch [134][3600/3746] lr: 2.795e-03, eta: 14:17:51, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5275, top5_acc: 0.7652, loss_cls: 2.6622, loss: 2.6622 +2024-12-31 07:10:59,619 - pyskl - INFO - Epoch [134][3700/3746] lr: 2.786e-03, eta: 14:16:26, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5219, top5_acc: 0.7619, loss_cls: 2.7089, loss: 2.7089 +2024-12-31 07:11:41,256 - pyskl - INFO - Saving checkpoint at 134 epochs +2024-12-31 07:13:41,783 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 07:13:42,529 - pyskl - INFO - +top1_acc 0.4226 +top5_acc 0.6753 +2024-12-31 07:13:42,529 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 07:13:42,632 - pyskl - INFO - +mean_acc 0.4224 +2024-12-31 07:13:42,667 - pyskl - INFO - Epoch(val) [134][309] top1_acc: 0.4226, top5_acc: 0.6753, mean_class_accuracy: 0.4224 +2024-12-31 07:18:06,447 - pyskl - INFO - Epoch [135][100/3746] lr: 2.772e-03, eta: 14:14:37, time: 2.638, data_time: 1.585, memory: 15990, top1_acc: 0.5517, top5_acc: 0.7881, loss_cls: 2.5282, loss: 2.5282 +2024-12-31 07:19:33,851 - pyskl - INFO - Epoch [135][200/3746] lr: 2.763e-03, eta: 14:13:12, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.5481, top5_acc: 0.7891, loss_cls: 2.5191, loss: 2.5191 +2024-12-31 07:21:00,785 - pyskl - INFO - Epoch [135][300/3746] lr: 2.754e-03, eta: 14:11:46, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5380, top5_acc: 0.7816, loss_cls: 2.5776, loss: 2.5776 +2024-12-31 07:22:27,039 - pyskl - INFO - Epoch [135][400/3746] lr: 2.745e-03, eta: 14:10:21, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5444, top5_acc: 0.7909, loss_cls: 2.5385, loss: 2.5385 +2024-12-31 07:23:53,968 - pyskl - INFO - Epoch [135][500/3746] lr: 2.735e-03, eta: 14:08:55, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5448, top5_acc: 0.7827, loss_cls: 2.5754, loss: 2.5754 +2024-12-31 07:25:20,521 - pyskl - INFO - Epoch [135][600/3746] lr: 2.726e-03, eta: 14:07:30, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5452, top5_acc: 0.7780, loss_cls: 2.5758, loss: 2.5758 +2024-12-31 07:26:46,194 - pyskl - INFO - Epoch [135][700/3746] lr: 2.717e-03, eta: 14:06:04, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5447, top5_acc: 0.7750, loss_cls: 2.6052, loss: 2.6052 +2024-12-31 07:28:12,300 - pyskl - INFO - Epoch [135][800/3746] lr: 2.708e-03, eta: 14:04:38, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.5378, top5_acc: 0.7802, loss_cls: 2.6010, loss: 2.6010 +2024-12-31 07:29:37,829 - pyskl - INFO - Epoch [135][900/3746] lr: 2.699e-03, eta: 14:03:12, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5353, top5_acc: 0.7780, loss_cls: 2.6060, loss: 2.6060 +2024-12-31 07:31:04,193 - pyskl - INFO - Epoch [135][1000/3746] lr: 2.690e-03, eta: 14:01:47, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.5489, top5_acc: 0.7859, loss_cls: 2.5455, loss: 2.5455 +2024-12-31 07:32:30,593 - pyskl - INFO - Epoch [135][1100/3746] lr: 2.681e-03, eta: 14:00:21, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5463, top5_acc: 0.7883, loss_cls: 2.5540, loss: 2.5540 +2024-12-31 07:33:57,181 - pyskl - INFO - Epoch [135][1200/3746] lr: 2.672e-03, eta: 13:58:56, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5353, top5_acc: 0.7770, loss_cls: 2.6028, loss: 2.6028 +2024-12-31 07:35:23,714 - pyskl - INFO - Epoch [135][1300/3746] lr: 2.663e-03, eta: 13:57:30, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5353, top5_acc: 0.7758, loss_cls: 2.6177, loss: 2.6177 +2024-12-31 07:36:50,381 - pyskl - INFO - Epoch [135][1400/3746] lr: 2.654e-03, eta: 13:56:04, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5400, top5_acc: 0.7719, loss_cls: 2.6024, loss: 2.6024 +2024-12-31 07:38:16,959 - pyskl - INFO - Epoch [135][1500/3746] lr: 2.645e-03, eta: 13:54:39, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5377, top5_acc: 0.7739, loss_cls: 2.6056, loss: 2.6056 +2024-12-31 07:39:43,426 - pyskl - INFO - Epoch [135][1600/3746] lr: 2.636e-03, eta: 13:53:13, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5339, top5_acc: 0.7730, loss_cls: 2.6393, loss: 2.6393 +2024-12-31 07:41:10,143 - pyskl - INFO - Epoch [135][1700/3746] lr: 2.627e-03, eta: 13:51:48, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5205, top5_acc: 0.7700, loss_cls: 2.6675, loss: 2.6675 +2024-12-31 07:42:36,987 - pyskl - INFO - Epoch [135][1800/3746] lr: 2.618e-03, eta: 13:50:22, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5253, top5_acc: 0.7775, loss_cls: 2.6133, loss: 2.6133 +2024-12-31 07:44:03,880 - pyskl - INFO - Epoch [135][1900/3746] lr: 2.609e-03, eta: 13:48:57, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5339, top5_acc: 0.7734, loss_cls: 2.6115, loss: 2.6115 +2024-12-31 07:45:30,830 - pyskl - INFO - Epoch [135][2000/3746] lr: 2.600e-03, eta: 13:47:31, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5384, top5_acc: 0.7794, loss_cls: 2.6132, loss: 2.6132 +2024-12-31 07:46:58,025 - pyskl - INFO - Epoch [135][2100/3746] lr: 2.591e-03, eta: 13:46:05, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5423, top5_acc: 0.7767, loss_cls: 2.6020, loss: 2.6020 +2024-12-31 07:48:24,656 - pyskl - INFO - Epoch [135][2200/3746] lr: 2.583e-03, eta: 13:44:40, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5381, top5_acc: 0.7770, loss_cls: 2.6038, loss: 2.6038 +2024-12-31 07:49:51,122 - pyskl - INFO - Epoch [135][2300/3746] lr: 2.574e-03, eta: 13:43:14, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5337, top5_acc: 0.7748, loss_cls: 2.6136, loss: 2.6136 +2024-12-31 07:51:17,050 - pyskl - INFO - Epoch [135][2400/3746] lr: 2.565e-03, eta: 13:41:49, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5384, top5_acc: 0.7837, loss_cls: 2.6121, loss: 2.6121 +2024-12-31 07:52:42,773 - pyskl - INFO - Epoch [135][2500/3746] lr: 2.556e-03, eta: 13:40:23, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.5359, top5_acc: 0.7700, loss_cls: 2.6234, loss: 2.6234 +2024-12-31 07:54:08,215 - pyskl - INFO - Epoch [135][2600/3746] lr: 2.547e-03, eta: 13:38:57, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5311, top5_acc: 0.7683, loss_cls: 2.6365, loss: 2.6365 +2024-12-31 07:55:33,529 - pyskl - INFO - Epoch [135][2700/3746] lr: 2.538e-03, eta: 13:37:31, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.5217, top5_acc: 0.7703, loss_cls: 2.6357, loss: 2.6357 +2024-12-31 07:57:00,279 - pyskl - INFO - Epoch [135][2800/3746] lr: 2.530e-03, eta: 13:36:06, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5380, top5_acc: 0.7784, loss_cls: 2.5993, loss: 2.5993 +2024-12-31 07:58:26,860 - pyskl - INFO - Epoch [135][2900/3746] lr: 2.521e-03, eta: 13:34:40, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5194, top5_acc: 0.7727, loss_cls: 2.6456, loss: 2.6456 +2024-12-31 07:59:53,412 - pyskl - INFO - Epoch [135][3000/3746] lr: 2.512e-03, eta: 13:33:15, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5333, top5_acc: 0.7778, loss_cls: 2.6011, loss: 2.6011 +2024-12-31 08:01:20,229 - pyskl - INFO - Epoch [135][3100/3746] lr: 2.503e-03, eta: 13:31:49, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5258, top5_acc: 0.7698, loss_cls: 2.6654, loss: 2.6654 +2024-12-31 08:02:46,929 - pyskl - INFO - Epoch [135][3200/3746] lr: 2.495e-03, eta: 13:30:23, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5477, top5_acc: 0.7766, loss_cls: 2.5708, loss: 2.5708 +2024-12-31 08:04:13,878 - pyskl - INFO - Epoch [135][3300/3746] lr: 2.486e-03, eta: 13:28:58, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5273, top5_acc: 0.7722, loss_cls: 2.6560, loss: 2.6560 +2024-12-31 08:05:41,109 - pyskl - INFO - Epoch [135][3400/3746] lr: 2.477e-03, eta: 13:27:32, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5341, top5_acc: 0.7816, loss_cls: 2.5938, loss: 2.5938 +2024-12-31 08:07:07,913 - pyskl - INFO - Epoch [135][3500/3746] lr: 2.469e-03, eta: 13:26:07, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5262, top5_acc: 0.7642, loss_cls: 2.6563, loss: 2.6563 +2024-12-31 08:08:35,212 - pyskl - INFO - Epoch [135][3600/3746] lr: 2.460e-03, eta: 13:24:41, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.5256, top5_acc: 0.7670, loss_cls: 2.6533, loss: 2.6533 +2024-12-31 08:10:02,399 - pyskl - INFO - Epoch [135][3700/3746] lr: 2.451e-03, eta: 13:23:16, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5333, top5_acc: 0.7767, loss_cls: 2.6080, loss: 2.6080 +2024-12-31 08:10:44,412 - pyskl - INFO - Saving checkpoint at 135 epochs +2024-12-31 08:12:44,431 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 08:12:45,383 - pyskl - INFO - +top1_acc 0.4293 +top5_acc 0.6823 +2024-12-31 08:12:45,383 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 08:12:45,426 - pyskl - INFO - +mean_acc 0.4290 +2024-12-31 08:12:45,431 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_133.pth was removed +2024-12-31 08:12:45,736 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_135.pth. +2024-12-31 08:12:45,737 - pyskl - INFO - Best top1_acc is 0.4293 at 135 epoch. +2024-12-31 08:12:45,753 - pyskl - INFO - Epoch(val) [135][309] top1_acc: 0.4293, top5_acc: 0.6823, mean_class_accuracy: 0.4290 +2024-12-31 08:17:13,768 - pyskl - INFO - Epoch [136][100/3746] lr: 2.439e-03, eta: 13:21:26, time: 2.680, data_time: 1.603, memory: 15990, top1_acc: 0.5514, top5_acc: 0.7880, loss_cls: 2.5245, loss: 2.5245 +2024-12-31 08:18:41,426 - pyskl - INFO - Epoch [136][200/3746] lr: 2.430e-03, eta: 13:20:01, time: 0.877, data_time: 0.000, memory: 15990, top1_acc: 0.5544, top5_acc: 0.7947, loss_cls: 2.5009, loss: 2.5009 +2024-12-31 08:20:08,747 - pyskl - INFO - Epoch [136][300/3746] lr: 2.421e-03, eta: 13:18:35, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.5553, top5_acc: 0.7906, loss_cls: 2.5073, loss: 2.5073 +2024-12-31 08:21:36,369 - pyskl - INFO - Epoch [136][400/3746] lr: 2.413e-03, eta: 13:17:10, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.5550, top5_acc: 0.8013, loss_cls: 2.4918, loss: 2.4918 +2024-12-31 08:23:03,754 - pyskl - INFO - Epoch [136][500/3746] lr: 2.404e-03, eta: 13:15:44, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.5483, top5_acc: 0.7894, loss_cls: 2.5630, loss: 2.5630 +2024-12-31 08:24:31,015 - pyskl - INFO - Epoch [136][600/3746] lr: 2.396e-03, eta: 13:14:19, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.5539, top5_acc: 0.7889, loss_cls: 2.5292, loss: 2.5292 +2024-12-31 08:25:56,654 - pyskl - INFO - Epoch [136][700/3746] lr: 2.387e-03, eta: 13:12:53, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5516, top5_acc: 0.7930, loss_cls: 2.5130, loss: 2.5130 +2024-12-31 08:27:22,431 - pyskl - INFO - Epoch [136][800/3746] lr: 2.379e-03, eta: 13:11:27, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5584, top5_acc: 0.7975, loss_cls: 2.4896, loss: 2.4896 +2024-12-31 08:28:47,896 - pyskl - INFO - Epoch [136][900/3746] lr: 2.370e-03, eta: 13:10:01, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.5492, top5_acc: 0.7845, loss_cls: 2.5496, loss: 2.5496 +2024-12-31 08:30:14,269 - pyskl - INFO - Epoch [136][1000/3746] lr: 2.362e-03, eta: 13:08:36, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.5548, top5_acc: 0.7897, loss_cls: 2.5258, loss: 2.5258 +2024-12-31 08:31:40,885 - pyskl - INFO - Epoch [136][1100/3746] lr: 2.353e-03, eta: 13:07:10, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5337, top5_acc: 0.7738, loss_cls: 2.6145, loss: 2.6145 +2024-12-31 08:33:07,642 - pyskl - INFO - Epoch [136][1200/3746] lr: 2.345e-03, eta: 13:05:45, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5436, top5_acc: 0.7917, loss_cls: 2.5411, loss: 2.5411 +2024-12-31 08:34:35,009 - pyskl - INFO - Epoch [136][1300/3746] lr: 2.336e-03, eta: 13:04:19, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.5423, top5_acc: 0.7877, loss_cls: 2.5727, loss: 2.5727 +2024-12-31 08:36:02,384 - pyskl - INFO - Epoch [136][1400/3746] lr: 2.328e-03, eta: 13:02:53, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.5322, top5_acc: 0.7756, loss_cls: 2.6119, loss: 2.6119 +2024-12-31 08:37:29,135 - pyskl - INFO - Epoch [136][1500/3746] lr: 2.319e-03, eta: 13:01:28, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5606, top5_acc: 0.8017, loss_cls: 2.4891, loss: 2.4891 +2024-12-31 08:38:55,841 - pyskl - INFO - Epoch [136][1600/3746] lr: 2.311e-03, eta: 13:00:02, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5372, top5_acc: 0.7798, loss_cls: 2.5669, loss: 2.5669 +2024-12-31 08:40:22,578 - pyskl - INFO - Epoch [136][1700/3746] lr: 2.303e-03, eta: 12:58:37, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5441, top5_acc: 0.7852, loss_cls: 2.5443, loss: 2.5443 +2024-12-31 08:41:49,617 - pyskl - INFO - Epoch [136][1800/3746] lr: 2.294e-03, eta: 12:57:11, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5361, top5_acc: 0.7802, loss_cls: 2.5853, loss: 2.5853 +2024-12-31 08:43:16,774 - pyskl - INFO - Epoch [136][1900/3746] lr: 2.286e-03, eta: 12:55:45, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5423, top5_acc: 0.7877, loss_cls: 2.5546, loss: 2.5546 +2024-12-31 08:44:43,878 - pyskl - INFO - Epoch [136][2000/3746] lr: 2.277e-03, eta: 12:54:20, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5352, top5_acc: 0.7887, loss_cls: 2.5599, loss: 2.5599 +2024-12-31 08:46:10,963 - pyskl - INFO - Epoch [136][2100/3746] lr: 2.269e-03, eta: 12:52:54, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5408, top5_acc: 0.7852, loss_cls: 2.5657, loss: 2.5657 +2024-12-31 08:47:37,770 - pyskl - INFO - Epoch [136][2200/3746] lr: 2.261e-03, eta: 12:51:29, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5328, top5_acc: 0.7742, loss_cls: 2.5988, loss: 2.5988 +2024-12-31 08:49:03,597 - pyskl - INFO - Epoch [136][2300/3746] lr: 2.253e-03, eta: 12:50:03, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5463, top5_acc: 0.7823, loss_cls: 2.5592, loss: 2.5592 +2024-12-31 08:50:29,455 - pyskl - INFO - Epoch [136][2400/3746] lr: 2.244e-03, eta: 12:48:37, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.5445, top5_acc: 0.7886, loss_cls: 2.5267, loss: 2.5267 +2024-12-31 08:51:54,491 - pyskl - INFO - Epoch [136][2500/3746] lr: 2.236e-03, eta: 12:47:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5523, top5_acc: 0.7845, loss_cls: 2.5413, loss: 2.5413 +2024-12-31 08:53:20,053 - pyskl - INFO - Epoch [136][2600/3746] lr: 2.228e-03, eta: 12:45:46, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5402, top5_acc: 0.7850, loss_cls: 2.5844, loss: 2.5844 +2024-12-31 08:54:44,937 - pyskl - INFO - Epoch [136][2700/3746] lr: 2.219e-03, eta: 12:44:20, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5364, top5_acc: 0.7756, loss_cls: 2.6051, loss: 2.6051 +2024-12-31 08:56:10,654 - pyskl - INFO - Epoch [136][2800/3746] lr: 2.211e-03, eta: 12:42:54, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5397, top5_acc: 0.7762, loss_cls: 2.5772, loss: 2.5772 +2024-12-31 08:57:36,636 - pyskl - INFO - Epoch [136][2900/3746] lr: 2.203e-03, eta: 12:41:28, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5392, top5_acc: 0.7881, loss_cls: 2.5653, loss: 2.5653 +2024-12-31 08:59:02,391 - pyskl - INFO - Epoch [136][3000/3746] lr: 2.195e-03, eta: 12:40:03, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5300, top5_acc: 0.7625, loss_cls: 2.6483, loss: 2.6483 +2024-12-31 09:00:27,626 - pyskl - INFO - Epoch [136][3100/3746] lr: 2.187e-03, eta: 12:38:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5430, top5_acc: 0.7883, loss_cls: 2.5779, loss: 2.5779 +2024-12-31 09:01:53,190 - pyskl - INFO - Epoch [136][3200/3746] lr: 2.178e-03, eta: 12:37:11, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5455, top5_acc: 0.7789, loss_cls: 2.5715, loss: 2.5715 +2024-12-31 09:03:18,648 - pyskl - INFO - Epoch [136][3300/3746] lr: 2.170e-03, eta: 12:35:45, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5416, top5_acc: 0.7833, loss_cls: 2.5745, loss: 2.5745 +2024-12-31 09:04:44,483 - pyskl - INFO - Epoch [136][3400/3746] lr: 2.162e-03, eta: 12:34:19, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5377, top5_acc: 0.7836, loss_cls: 2.5871, loss: 2.5871 +2024-12-31 09:06:09,909 - pyskl - INFO - Epoch [136][3500/3746] lr: 2.154e-03, eta: 12:32:54, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5464, top5_acc: 0.7861, loss_cls: 2.5359, loss: 2.5359 +2024-12-31 09:07:35,136 - pyskl - INFO - Epoch [136][3600/3746] lr: 2.146e-03, eta: 12:31:28, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5344, top5_acc: 0.7720, loss_cls: 2.6056, loss: 2.6056 +2024-12-31 09:09:00,387 - pyskl - INFO - Epoch [136][3700/3746] lr: 2.138e-03, eta: 12:30:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5366, top5_acc: 0.7756, loss_cls: 2.6134, loss: 2.6134 +2024-12-31 09:09:42,005 - pyskl - INFO - Saving checkpoint at 136 epochs +2024-12-31 09:11:41,106 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 09:11:41,951 - pyskl - INFO - +top1_acc 0.4296 +top5_acc 0.6831 +2024-12-31 09:11:41,951 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 09:11:42,009 - pyskl - INFO - +mean_acc 0.4293 +2024-12-31 09:11:42,014 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_135.pth was removed +2024-12-31 09:11:42,349 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_136.pth. +2024-12-31 09:11:42,350 - pyskl - INFO - Best top1_acc is 0.4296 at 136 epoch. +2024-12-31 09:11:42,367 - pyskl - INFO - Epoch(val) [136][309] top1_acc: 0.4296, top5_acc: 0.6831, mean_class_accuracy: 0.4293 +2024-12-31 09:16:07,011 - pyskl - INFO - Epoch [137][100/3746] lr: 2.126e-03, eta: 12:28:11, time: 2.646, data_time: 1.564, memory: 15990, top1_acc: 0.5639, top5_acc: 0.8031, loss_cls: 2.4426, loss: 2.4426 +2024-12-31 09:17:32,898 - pyskl - INFO - Epoch [137][200/3746] lr: 2.118e-03, eta: 12:26:46, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5617, top5_acc: 0.8067, loss_cls: 2.4580, loss: 2.4580 +2024-12-31 09:18:59,547 - pyskl - INFO - Epoch [137][300/3746] lr: 2.110e-03, eta: 12:25:20, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5667, top5_acc: 0.8002, loss_cls: 2.4473, loss: 2.4473 +2024-12-31 09:20:25,739 - pyskl - INFO - Epoch [137][400/3746] lr: 2.102e-03, eta: 12:23:54, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5575, top5_acc: 0.7973, loss_cls: 2.4817, loss: 2.4817 +2024-12-31 09:21:52,155 - pyskl - INFO - Epoch [137][500/3746] lr: 2.094e-03, eta: 12:22:28, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5573, top5_acc: 0.8009, loss_cls: 2.4728, loss: 2.4728 +2024-12-31 09:23:18,550 - pyskl - INFO - Epoch [137][600/3746] lr: 2.086e-03, eta: 12:21:03, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5592, top5_acc: 0.7981, loss_cls: 2.4942, loss: 2.4942 +2024-12-31 09:24:44,109 - pyskl - INFO - Epoch [137][700/3746] lr: 2.078e-03, eta: 12:19:37, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5533, top5_acc: 0.7895, loss_cls: 2.5122, loss: 2.5122 +2024-12-31 09:26:10,805 - pyskl - INFO - Epoch [137][800/3746] lr: 2.070e-03, eta: 12:18:11, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5533, top5_acc: 0.7994, loss_cls: 2.4978, loss: 2.4978 +2024-12-31 09:27:36,729 - pyskl - INFO - Epoch [137][900/3746] lr: 2.062e-03, eta: 12:16:46, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5616, top5_acc: 0.7970, loss_cls: 2.4793, loss: 2.4793 +2024-12-31 09:29:03,271 - pyskl - INFO - Epoch [137][1000/3746] lr: 2.054e-03, eta: 12:15:20, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.5527, top5_acc: 0.7883, loss_cls: 2.5171, loss: 2.5171 +2024-12-31 09:30:30,726 - pyskl - INFO - Epoch [137][1100/3746] lr: 2.046e-03, eta: 12:13:54, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.5541, top5_acc: 0.7959, loss_cls: 2.5021, loss: 2.5021 +2024-12-31 09:31:58,652 - pyskl - INFO - Epoch [137][1200/3746] lr: 2.038e-03, eta: 12:12:29, time: 0.879, data_time: 0.001, memory: 15990, top1_acc: 0.5658, top5_acc: 0.7948, loss_cls: 2.4628, loss: 2.4628 +2024-12-31 09:33:25,516 - pyskl - INFO - Epoch [137][1300/3746] lr: 2.030e-03, eta: 12:11:03, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5516, top5_acc: 0.7836, loss_cls: 2.5426, loss: 2.5426 +2024-12-31 09:34:52,270 - pyskl - INFO - Epoch [137][1400/3746] lr: 2.022e-03, eta: 12:09:37, time: 0.868, data_time: 0.001, memory: 15990, top1_acc: 0.5541, top5_acc: 0.7916, loss_cls: 2.4891, loss: 2.4891 +2024-12-31 09:36:19,479 - pyskl - INFO - Epoch [137][1500/3746] lr: 2.015e-03, eta: 12:08:12, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5513, top5_acc: 0.7842, loss_cls: 2.5308, loss: 2.5308 +2024-12-31 09:37:46,795 - pyskl - INFO - Epoch [137][1600/3746] lr: 2.007e-03, eta: 12:06:46, time: 0.873, data_time: 0.001, memory: 15990, top1_acc: 0.5448, top5_acc: 0.7956, loss_cls: 2.5013, loss: 2.5013 +2024-12-31 09:39:14,222 - pyskl - INFO - Epoch [137][1700/3746] lr: 1.999e-03, eta: 12:05:21, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.5464, top5_acc: 0.7863, loss_cls: 2.5337, loss: 2.5337 +2024-12-31 09:40:41,769 - pyskl - INFO - Epoch [137][1800/3746] lr: 1.991e-03, eta: 12:03:55, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.5514, top5_acc: 0.7864, loss_cls: 2.5224, loss: 2.5224 +2024-12-31 09:42:09,342 - pyskl - INFO - Epoch [137][1900/3746] lr: 1.983e-03, eta: 12:02:29, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.5561, top5_acc: 0.7923, loss_cls: 2.5158, loss: 2.5158 +2024-12-31 09:43:37,030 - pyskl - INFO - Epoch [137][2000/3746] lr: 1.976e-03, eta: 12:01:04, time: 0.877, data_time: 0.000, memory: 15990, top1_acc: 0.5531, top5_acc: 0.7881, loss_cls: 2.5366, loss: 2.5366 +2024-12-31 09:45:04,606 - pyskl - INFO - Epoch [137][2100/3746] lr: 1.968e-03, eta: 11:59:38, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.5525, top5_acc: 0.7955, loss_cls: 2.4953, loss: 2.4953 +2024-12-31 09:46:31,074 - pyskl - INFO - Epoch [137][2200/3746] lr: 1.960e-03, eta: 11:58:13, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5614, top5_acc: 0.7970, loss_cls: 2.4813, loss: 2.4813 +2024-12-31 09:47:57,573 - pyskl - INFO - Epoch [137][2300/3746] lr: 1.952e-03, eta: 11:56:47, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5475, top5_acc: 0.7878, loss_cls: 2.5271, loss: 2.5271 +2024-12-31 09:49:23,716 - pyskl - INFO - Epoch [137][2400/3746] lr: 1.944e-03, eta: 11:55:21, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5459, top5_acc: 0.7845, loss_cls: 2.5758, loss: 2.5758 +2024-12-31 09:50:49,942 - pyskl - INFO - Epoch [137][2500/3746] lr: 1.937e-03, eta: 11:53:55, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.5519, top5_acc: 0.7970, loss_cls: 2.4994, loss: 2.4994 +2024-12-31 09:52:15,698 - pyskl - INFO - Epoch [137][2600/3746] lr: 1.929e-03, eta: 11:52:30, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5437, top5_acc: 0.7884, loss_cls: 2.5475, loss: 2.5475 +2024-12-31 09:53:41,543 - pyskl - INFO - Epoch [137][2700/3746] lr: 1.921e-03, eta: 11:51:04, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5416, top5_acc: 0.7864, loss_cls: 2.5555, loss: 2.5555 +2024-12-31 09:55:08,192 - pyskl - INFO - Epoch [137][2800/3746] lr: 1.914e-03, eta: 11:49:38, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5439, top5_acc: 0.7853, loss_cls: 2.5637, loss: 2.5637 +2024-12-31 09:56:35,915 - pyskl - INFO - Epoch [137][2900/3746] lr: 1.906e-03, eta: 11:48:13, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.5575, top5_acc: 0.7911, loss_cls: 2.4819, loss: 2.4819 +2024-12-31 09:58:04,011 - pyskl - INFO - Epoch [137][3000/3746] lr: 1.898e-03, eta: 11:46:47, time: 0.881, data_time: 0.001, memory: 15990, top1_acc: 0.5563, top5_acc: 0.7887, loss_cls: 2.5225, loss: 2.5225 +2024-12-31 09:59:32,117 - pyskl - INFO - Epoch [137][3100/3746] lr: 1.891e-03, eta: 11:45:21, time: 0.881, data_time: 0.000, memory: 15990, top1_acc: 0.5600, top5_acc: 0.7922, loss_cls: 2.4979, loss: 2.4979 +2024-12-31 10:01:00,365 - pyskl - INFO - Epoch [137][3200/3746] lr: 1.883e-03, eta: 11:43:56, time: 0.882, data_time: 0.001, memory: 15990, top1_acc: 0.5511, top5_acc: 0.7881, loss_cls: 2.5280, loss: 2.5280 +2024-12-31 10:02:28,395 - pyskl - INFO - Epoch [137][3300/3746] lr: 1.876e-03, eta: 11:42:30, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.5513, top5_acc: 0.7872, loss_cls: 2.5325, loss: 2.5325 +2024-12-31 10:03:56,159 - pyskl - INFO - Epoch [137][3400/3746] lr: 1.868e-03, eta: 11:41:05, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.5645, top5_acc: 0.7969, loss_cls: 2.4796, loss: 2.4796 +2024-12-31 10:05:24,223 - pyskl - INFO - Epoch [137][3500/3746] lr: 1.860e-03, eta: 11:39:39, time: 0.881, data_time: 0.001, memory: 15990, top1_acc: 0.5534, top5_acc: 0.7867, loss_cls: 2.5114, loss: 2.5114 +2024-12-31 10:06:52,417 - pyskl - INFO - Epoch [137][3600/3746] lr: 1.853e-03, eta: 11:38:14, time: 0.882, data_time: 0.001, memory: 15990, top1_acc: 0.5470, top5_acc: 0.7858, loss_cls: 2.5265, loss: 2.5265 +2024-12-31 10:08:20,424 - pyskl - INFO - Epoch [137][3700/3746] lr: 1.845e-03, eta: 11:36:48, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.5453, top5_acc: 0.7923, loss_cls: 2.5247, loss: 2.5247 +2024-12-31 10:09:02,741 - pyskl - INFO - Saving checkpoint at 137 epochs +2024-12-31 10:11:07,135 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 10:11:07,821 - pyskl - INFO - +top1_acc 0.4331 +top5_acc 0.6859 +2024-12-31 10:11:07,822 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 10:11:07,859 - pyskl - INFO - +mean_acc 0.4329 +2024-12-31 10:11:07,864 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_136.pth was removed +2024-12-31 10:11:08,113 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_137.pth. +2024-12-31 10:11:08,114 - pyskl - INFO - Best top1_acc is 0.4331 at 137 epoch. +2024-12-31 10:11:08,125 - pyskl - INFO - Epoch(val) [137][309] top1_acc: 0.4331, top5_acc: 0.6859, mean_class_accuracy: 0.4329 +2024-12-31 10:15:31,018 - pyskl - INFO - Epoch [138][100/3746] lr: 1.834e-03, eta: 11:34:56, time: 2.629, data_time: 1.585, memory: 15990, top1_acc: 0.5630, top5_acc: 0.8028, loss_cls: 2.4482, loss: 2.4482 +2024-12-31 10:16:56,107 - pyskl - INFO - Epoch [138][200/3746] lr: 1.827e-03, eta: 11:33:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5756, top5_acc: 0.8061, loss_cls: 2.4007, loss: 2.4007 +2024-12-31 10:18:21,398 - pyskl - INFO - Epoch [138][300/3746] lr: 1.819e-03, eta: 11:32:04, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5628, top5_acc: 0.8073, loss_cls: 2.4284, loss: 2.4284 +2024-12-31 10:19:46,204 - pyskl - INFO - Epoch [138][400/3746] lr: 1.812e-03, eta: 11:30:38, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5702, top5_acc: 0.8086, loss_cls: 2.4257, loss: 2.4257 +2024-12-31 10:21:11,207 - pyskl - INFO - Epoch [138][500/3746] lr: 1.805e-03, eta: 11:29:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5733, top5_acc: 0.8023, loss_cls: 2.4412, loss: 2.4412 +2024-12-31 10:22:36,316 - pyskl - INFO - Epoch [138][600/3746] lr: 1.797e-03, eta: 11:27:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5594, top5_acc: 0.8027, loss_cls: 2.4446, loss: 2.4446 +2024-12-31 10:24:01,673 - pyskl - INFO - Epoch [138][700/3746] lr: 1.790e-03, eta: 11:26:21, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5684, top5_acc: 0.7987, loss_cls: 2.4328, loss: 2.4328 +2024-12-31 10:25:26,749 - pyskl - INFO - Epoch [138][800/3746] lr: 1.782e-03, eta: 11:24:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5642, top5_acc: 0.7952, loss_cls: 2.4469, loss: 2.4469 +2024-12-31 10:26:51,702 - pyskl - INFO - Epoch [138][900/3746] lr: 1.775e-03, eta: 11:23:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5792, top5_acc: 0.7983, loss_cls: 2.4129, loss: 2.4129 +2024-12-31 10:28:16,349 - pyskl - INFO - Epoch [138][1000/3746] lr: 1.768e-03, eta: 11:22:03, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5673, top5_acc: 0.7980, loss_cls: 2.4715, loss: 2.4715 +2024-12-31 10:29:41,072 - pyskl - INFO - Epoch [138][1100/3746] lr: 1.760e-03, eta: 11:20:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5706, top5_acc: 0.8023, loss_cls: 2.4307, loss: 2.4307 +2024-12-31 10:31:06,476 - pyskl - INFO - Epoch [138][1200/3746] lr: 1.753e-03, eta: 11:19:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5684, top5_acc: 0.8064, loss_cls: 2.4178, loss: 2.4178 +2024-12-31 10:32:31,551 - pyskl - INFO - Epoch [138][1300/3746] lr: 1.745e-03, eta: 11:17:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5708, top5_acc: 0.8063, loss_cls: 2.4248, loss: 2.4248 +2024-12-31 10:33:56,850 - pyskl - INFO - Epoch [138][1400/3746] lr: 1.738e-03, eta: 11:16:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5713, top5_acc: 0.7997, loss_cls: 2.4255, loss: 2.4255 +2024-12-31 10:35:22,116 - pyskl - INFO - Epoch [138][1500/3746] lr: 1.731e-03, eta: 11:14:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5777, top5_acc: 0.8005, loss_cls: 2.4291, loss: 2.4291 +2024-12-31 10:36:47,087 - pyskl - INFO - Epoch [138][1600/3746] lr: 1.724e-03, eta: 11:13:28, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5709, top5_acc: 0.7944, loss_cls: 2.4690, loss: 2.4690 +2024-12-31 10:38:12,027 - pyskl - INFO - Epoch [138][1700/3746] lr: 1.716e-03, eta: 11:12:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5603, top5_acc: 0.7992, loss_cls: 2.4768, loss: 2.4768 +2024-12-31 10:39:37,285 - pyskl - INFO - Epoch [138][1800/3746] lr: 1.709e-03, eta: 11:10:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5622, top5_acc: 0.7937, loss_cls: 2.4941, loss: 2.4941 +2024-12-31 10:41:02,201 - pyskl - INFO - Epoch [138][1900/3746] lr: 1.702e-03, eta: 11:09:10, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5598, top5_acc: 0.7941, loss_cls: 2.4918, loss: 2.4918 +2024-12-31 10:42:27,360 - pyskl - INFO - Epoch [138][2000/3746] lr: 1.695e-03, eta: 11:07:45, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5527, top5_acc: 0.7922, loss_cls: 2.5102, loss: 2.5102 +2024-12-31 10:43:52,416 - pyskl - INFO - Epoch [138][2100/3746] lr: 1.687e-03, eta: 11:06:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5605, top5_acc: 0.7983, loss_cls: 2.4775, loss: 2.4775 +2024-12-31 10:45:17,669 - pyskl - INFO - Epoch [138][2200/3746] lr: 1.680e-03, eta: 11:04:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5652, top5_acc: 0.7970, loss_cls: 2.4429, loss: 2.4429 +2024-12-31 10:46:42,420 - pyskl - INFO - Epoch [138][2300/3746] lr: 1.673e-03, eta: 11:03:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5597, top5_acc: 0.7986, loss_cls: 2.4711, loss: 2.4711 +2024-12-31 10:48:07,254 - pyskl - INFO - Epoch [138][2400/3746] lr: 1.666e-03, eta: 11:02:01, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5709, top5_acc: 0.8019, loss_cls: 2.4181, loss: 2.4181 +2024-12-31 10:49:32,410 - pyskl - INFO - Epoch [138][2500/3746] lr: 1.659e-03, eta: 11:00:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5550, top5_acc: 0.7973, loss_cls: 2.5014, loss: 2.5014 +2024-12-31 10:50:57,674 - pyskl - INFO - Epoch [138][2600/3746] lr: 1.652e-03, eta: 10:59:09, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.5645, top5_acc: 0.8034, loss_cls: 2.4388, loss: 2.4388 +2024-12-31 10:52:22,143 - pyskl - INFO - Epoch [138][2700/3746] lr: 1.644e-03, eta: 10:57:43, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5589, top5_acc: 0.7947, loss_cls: 2.4620, loss: 2.4620 +2024-12-31 10:53:47,620 - pyskl - INFO - Epoch [138][2800/3746] lr: 1.637e-03, eta: 10:56:18, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5484, top5_acc: 0.7930, loss_cls: 2.5217, loss: 2.5217 +2024-12-31 10:55:12,520 - pyskl - INFO - Epoch [138][2900/3746] lr: 1.630e-03, eta: 10:54:52, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5652, top5_acc: 0.8028, loss_cls: 2.4559, loss: 2.4559 +2024-12-31 10:56:37,594 - pyskl - INFO - Epoch [138][3000/3746] lr: 1.623e-03, eta: 10:53:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5542, top5_acc: 0.7942, loss_cls: 2.4966, loss: 2.4966 +2024-12-31 10:58:02,074 - pyskl - INFO - Epoch [138][3100/3746] lr: 1.616e-03, eta: 10:52:00, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5619, top5_acc: 0.7977, loss_cls: 2.4921, loss: 2.4921 +2024-12-31 10:59:26,887 - pyskl - INFO - Epoch [138][3200/3746] lr: 1.609e-03, eta: 10:50:34, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5556, top5_acc: 0.7900, loss_cls: 2.5069, loss: 2.5069 +2024-12-31 11:00:51,750 - pyskl - INFO - Epoch [138][3300/3746] lr: 1.602e-03, eta: 10:49:08, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5714, top5_acc: 0.7981, loss_cls: 2.4418, loss: 2.4418 +2024-12-31 11:02:16,376 - pyskl - INFO - Epoch [138][3400/3746] lr: 1.595e-03, eta: 10:47:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5611, top5_acc: 0.8014, loss_cls: 2.4741, loss: 2.4741 +2024-12-31 11:03:41,488 - pyskl - INFO - Epoch [138][3500/3746] lr: 1.588e-03, eta: 10:46:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5777, top5_acc: 0.8041, loss_cls: 2.4214, loss: 2.4214 +2024-12-31 11:05:06,084 - pyskl - INFO - Epoch [138][3600/3746] lr: 1.581e-03, eta: 10:44:51, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5497, top5_acc: 0.7900, loss_cls: 2.5028, loss: 2.5028 +2024-12-31 11:06:30,674 - pyskl - INFO - Epoch [138][3700/3746] lr: 1.574e-03, eta: 10:43:25, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5587, top5_acc: 0.8020, loss_cls: 2.4551, loss: 2.4551 +2024-12-31 11:07:11,680 - pyskl - INFO - Saving checkpoint at 138 epochs +2024-12-31 11:09:09,153 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 11:09:09,825 - pyskl - INFO - +top1_acc 0.4404 +top5_acc 0.6862 +2024-12-31 11:09:09,825 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 11:09:09,873 - pyskl - INFO - +mean_acc 0.4402 +2024-12-31 11:09:09,879 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_137.pth was removed +2024-12-31 11:09:10,186 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_138.pth. +2024-12-31 11:09:10,187 - pyskl - INFO - Best top1_acc is 0.4404 at 138 epoch. +2024-12-31 11:09:10,201 - pyskl - INFO - Epoch(val) [138][309] top1_acc: 0.4404, top5_acc: 0.6862, mean_class_accuracy: 0.4402 +2024-12-31 11:13:27,393 - pyskl - INFO - Epoch [139][100/3746] lr: 1.564e-03, eta: 10:41:31, time: 2.572, data_time: 1.534, memory: 15990, top1_acc: 0.5884, top5_acc: 0.8034, loss_cls: 2.3604, loss: 2.3604 +2024-12-31 11:14:52,175 - pyskl - INFO - Epoch [139][200/3746] lr: 1.557e-03, eta: 10:40:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5802, top5_acc: 0.8169, loss_cls: 2.3617, loss: 2.3617 +2024-12-31 11:16:17,172 - pyskl - INFO - Epoch [139][300/3746] lr: 1.550e-03, eta: 10:38:39, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5923, top5_acc: 0.8163, loss_cls: 2.3202, loss: 2.3202 +2024-12-31 11:17:42,291 - pyskl - INFO - Epoch [139][400/3746] lr: 1.543e-03, eta: 10:37:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5736, top5_acc: 0.8042, loss_cls: 2.4245, loss: 2.4245 +2024-12-31 11:19:07,788 - pyskl - INFO - Epoch [139][500/3746] lr: 1.536e-03, eta: 10:35:47, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5759, top5_acc: 0.8092, loss_cls: 2.3958, loss: 2.3958 +2024-12-31 11:20:33,020 - pyskl - INFO - Epoch [139][600/3746] lr: 1.529e-03, eta: 10:34:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5822, top5_acc: 0.8159, loss_cls: 2.3708, loss: 2.3708 +2024-12-31 11:21:58,152 - pyskl - INFO - Epoch [139][700/3746] lr: 1.523e-03, eta: 10:32:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5864, top5_acc: 0.8192, loss_cls: 2.3225, loss: 2.3225 +2024-12-31 11:23:23,119 - pyskl - INFO - Epoch [139][800/3746] lr: 1.516e-03, eta: 10:31:30, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5730, top5_acc: 0.8111, loss_cls: 2.3764, loss: 2.3764 +2024-12-31 11:24:48,658 - pyskl - INFO - Epoch [139][900/3746] lr: 1.509e-03, eta: 10:30:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5872, top5_acc: 0.8181, loss_cls: 2.3423, loss: 2.3423 +2024-12-31 11:26:13,909 - pyskl - INFO - Epoch [139][1000/3746] lr: 1.502e-03, eta: 10:28:38, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5767, top5_acc: 0.8141, loss_cls: 2.3730, loss: 2.3730 +2024-12-31 11:27:38,778 - pyskl - INFO - Epoch [139][1100/3746] lr: 1.495e-03, eta: 10:27:12, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5859, top5_acc: 0.8083, loss_cls: 2.3711, loss: 2.3711 +2024-12-31 11:29:03,435 - pyskl - INFO - Epoch [139][1200/3746] lr: 1.489e-03, eta: 10:25:46, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5645, top5_acc: 0.8056, loss_cls: 2.4279, loss: 2.4279 +2024-12-31 11:30:28,855 - pyskl - INFO - Epoch [139][1300/3746] lr: 1.482e-03, eta: 10:24:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5719, top5_acc: 0.8075, loss_cls: 2.3907, loss: 2.3907 +2024-12-31 11:31:53,487 - pyskl - INFO - Epoch [139][1400/3746] lr: 1.475e-03, eta: 10:22:54, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5825, top5_acc: 0.8130, loss_cls: 2.3684, loss: 2.3684 +2024-12-31 11:33:18,107 - pyskl - INFO - Epoch [139][1500/3746] lr: 1.468e-03, eta: 10:21:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5684, top5_acc: 0.8070, loss_cls: 2.4055, loss: 2.4055 +2024-12-31 11:34:42,854 - pyskl - INFO - Epoch [139][1600/3746] lr: 1.462e-03, eta: 10:20:03, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5723, top5_acc: 0.8022, loss_cls: 2.4031, loss: 2.4031 +2024-12-31 11:36:07,405 - pyskl - INFO - Epoch [139][1700/3746] lr: 1.455e-03, eta: 10:18:37, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5695, top5_acc: 0.8052, loss_cls: 2.4105, loss: 2.4105 +2024-12-31 11:37:32,151 - pyskl - INFO - Epoch [139][1800/3746] lr: 1.448e-03, eta: 10:17:11, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5591, top5_acc: 0.7992, loss_cls: 2.4525, loss: 2.4525 +2024-12-31 11:38:56,741 - pyskl - INFO - Epoch [139][1900/3746] lr: 1.442e-03, eta: 10:15:45, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5756, top5_acc: 0.8163, loss_cls: 2.3715, loss: 2.3715 +2024-12-31 11:40:21,985 - pyskl - INFO - Epoch [139][2000/3746] lr: 1.435e-03, eta: 10:14:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5723, top5_acc: 0.7998, loss_cls: 2.4272, loss: 2.4272 +2024-12-31 11:41:46,795 - pyskl - INFO - Epoch [139][2100/3746] lr: 1.428e-03, eta: 10:12:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5717, top5_acc: 0.7977, loss_cls: 2.4223, loss: 2.4223 +2024-12-31 11:43:11,870 - pyskl - INFO - Epoch [139][2200/3746] lr: 1.422e-03, eta: 10:11:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5686, top5_acc: 0.8048, loss_cls: 2.4031, loss: 2.4031 +2024-12-31 11:44:36,047 - pyskl - INFO - Epoch [139][2300/3746] lr: 1.415e-03, eta: 10:10:01, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5795, top5_acc: 0.8095, loss_cls: 2.3892, loss: 2.3892 +2024-12-31 11:46:00,850 - pyskl - INFO - Epoch [139][2400/3746] lr: 1.408e-03, eta: 10:08:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5717, top5_acc: 0.8047, loss_cls: 2.4118, loss: 2.4118 +2024-12-31 11:47:26,363 - pyskl - INFO - Epoch [139][2500/3746] lr: 1.402e-03, eta: 10:07:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5722, top5_acc: 0.8039, loss_cls: 2.4223, loss: 2.4223 +2024-12-31 11:48:50,980 - pyskl - INFO - Epoch [139][2600/3746] lr: 1.395e-03, eta: 10:05:44, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5602, top5_acc: 0.7959, loss_cls: 2.4521, loss: 2.4521 +2024-12-31 11:50:14,910 - pyskl - INFO - Epoch [139][2700/3746] lr: 1.389e-03, eta: 10:04:18, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5669, top5_acc: 0.7987, loss_cls: 2.4344, loss: 2.4344 +2024-12-31 11:51:39,166 - pyskl - INFO - Epoch [139][2800/3746] lr: 1.382e-03, eta: 10:02:52, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5745, top5_acc: 0.8137, loss_cls: 2.3878, loss: 2.3878 +2024-12-31 11:53:04,493 - pyskl - INFO - Epoch [139][2900/3746] lr: 1.376e-03, eta: 10:01:26, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5745, top5_acc: 0.8077, loss_cls: 2.3922, loss: 2.3922 +2024-12-31 11:54:30,171 - pyskl - INFO - Epoch [139][3000/3746] lr: 1.369e-03, eta: 10:00:00, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5752, top5_acc: 0.8094, loss_cls: 2.4271, loss: 2.4271 +2024-12-31 11:55:56,380 - pyskl - INFO - Epoch [139][3100/3746] lr: 1.363e-03, eta: 9:58:34, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5597, top5_acc: 0.8073, loss_cls: 2.4564, loss: 2.4564 +2024-12-31 11:57:22,192 - pyskl - INFO - Epoch [139][3200/3746] lr: 1.356e-03, eta: 9:57:08, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5656, top5_acc: 0.8037, loss_cls: 2.4395, loss: 2.4395 +2024-12-31 11:58:47,907 - pyskl - INFO - Epoch [139][3300/3746] lr: 1.350e-03, eta: 9:55:43, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5720, top5_acc: 0.8137, loss_cls: 2.4181, loss: 2.4181 +2024-12-31 12:00:13,059 - pyskl - INFO - Epoch [139][3400/3746] lr: 1.343e-03, eta: 9:54:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5639, top5_acc: 0.8025, loss_cls: 2.4432, loss: 2.4432 +2024-12-31 12:01:38,013 - pyskl - INFO - Epoch [139][3500/3746] lr: 1.337e-03, eta: 9:52:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5736, top5_acc: 0.8017, loss_cls: 2.4156, loss: 2.4156 +2024-12-31 12:03:03,829 - pyskl - INFO - Epoch [139][3600/3746] lr: 1.330e-03, eta: 9:51:25, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5611, top5_acc: 0.7902, loss_cls: 2.4812, loss: 2.4812 +2024-12-31 12:04:29,199 - pyskl - INFO - Epoch [139][3700/3746] lr: 1.324e-03, eta: 9:49:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5559, top5_acc: 0.8069, loss_cls: 2.4355, loss: 2.4355 +2024-12-31 12:05:10,609 - pyskl - INFO - Saving checkpoint at 139 epochs +2024-12-31 12:07:10,881 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 12:07:11,678 - pyskl - INFO - +top1_acc 0.4366 +top5_acc 0.6861 +2024-12-31 12:07:11,678 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 12:07:11,731 - pyskl - INFO - +mean_acc 0.4364 +2024-12-31 12:07:11,746 - pyskl - INFO - Epoch(val) [139][309] top1_acc: 0.4366, top5_acc: 0.6861, mean_class_accuracy: 0.4364 +2024-12-31 12:11:31,550 - pyskl - INFO - Epoch [140][100/3746] lr: 1.315e-03, eta: 9:48:05, time: 2.598, data_time: 1.568, memory: 15990, top1_acc: 0.5966, top5_acc: 0.8242, loss_cls: 2.3040, loss: 2.3040 +2024-12-31 12:12:56,063 - pyskl - INFO - Epoch [140][200/3746] lr: 1.308e-03, eta: 9:46:39, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5944, top5_acc: 0.8283, loss_cls: 2.3027, loss: 2.3027 +2024-12-31 12:14:21,470 - pyskl - INFO - Epoch [140][300/3746] lr: 1.302e-03, eta: 9:45:13, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5892, top5_acc: 0.8142, loss_cls: 2.3347, loss: 2.3347 +2024-12-31 12:15:46,576 - pyskl - INFO - Epoch [140][400/3746] lr: 1.296e-03, eta: 9:43:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5930, top5_acc: 0.8200, loss_cls: 2.3017, loss: 2.3017 +2024-12-31 12:17:11,897 - pyskl - INFO - Epoch [140][500/3746] lr: 1.289e-03, eta: 9:42:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5984, top5_acc: 0.8283, loss_cls: 2.2680, loss: 2.2680 +2024-12-31 12:18:37,689 - pyskl - INFO - Epoch [140][600/3746] lr: 1.283e-03, eta: 9:40:55, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.5942, top5_acc: 0.8169, loss_cls: 2.3135, loss: 2.3135 +2024-12-31 12:20:03,093 - pyskl - INFO - Epoch [140][700/3746] lr: 1.277e-03, eta: 9:39:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5914, top5_acc: 0.8261, loss_cls: 2.3079, loss: 2.3079 +2024-12-31 12:21:28,361 - pyskl - INFO - Epoch [140][800/3746] lr: 1.271e-03, eta: 9:38:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5956, top5_acc: 0.8216, loss_cls: 2.2959, loss: 2.2959 +2024-12-31 12:22:53,669 - pyskl - INFO - Epoch [140][900/3746] lr: 1.264e-03, eta: 9:36:37, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5855, top5_acc: 0.8122, loss_cls: 2.3550, loss: 2.3550 +2024-12-31 12:24:19,515 - pyskl - INFO - Epoch [140][1000/3746] lr: 1.258e-03, eta: 9:35:12, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5741, top5_acc: 0.8087, loss_cls: 2.3993, loss: 2.3993 +2024-12-31 12:25:45,353 - pyskl - INFO - Epoch [140][1100/3746] lr: 1.252e-03, eta: 9:33:46, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5933, top5_acc: 0.8161, loss_cls: 2.3201, loss: 2.3201 +2024-12-31 12:27:11,038 - pyskl - INFO - Epoch [140][1200/3746] lr: 1.246e-03, eta: 9:32:20, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5886, top5_acc: 0.8136, loss_cls: 2.3263, loss: 2.3263 +2024-12-31 12:28:36,813 - pyskl - INFO - Epoch [140][1300/3746] lr: 1.239e-03, eta: 9:30:54, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5842, top5_acc: 0.8191, loss_cls: 2.3414, loss: 2.3414 +2024-12-31 12:30:02,378 - pyskl - INFO - Epoch [140][1400/3746] lr: 1.233e-03, eta: 9:29:28, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5927, top5_acc: 0.8208, loss_cls: 2.3174, loss: 2.3174 +2024-12-31 12:31:28,071 - pyskl - INFO - Epoch [140][1500/3746] lr: 1.227e-03, eta: 9:28:02, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5972, top5_acc: 0.8241, loss_cls: 2.2931, loss: 2.2931 +2024-12-31 12:32:53,569 - pyskl - INFO - Epoch [140][1600/3746] lr: 1.221e-03, eta: 9:26:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5791, top5_acc: 0.8144, loss_cls: 2.3726, loss: 2.3726 +2024-12-31 12:34:18,819 - pyskl - INFO - Epoch [140][1700/3746] lr: 1.215e-03, eta: 9:25:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5911, top5_acc: 0.8167, loss_cls: 2.3234, loss: 2.3234 +2024-12-31 12:35:43,900 - pyskl - INFO - Epoch [140][1800/3746] lr: 1.209e-03, eta: 9:23:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5816, top5_acc: 0.8167, loss_cls: 2.3365, loss: 2.3365 +2024-12-31 12:37:09,591 - pyskl - INFO - Epoch [140][1900/3746] lr: 1.203e-03, eta: 9:22:19, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5808, top5_acc: 0.8144, loss_cls: 2.3402, loss: 2.3402 +2024-12-31 12:38:34,694 - pyskl - INFO - Epoch [140][2000/3746] lr: 1.196e-03, eta: 9:20:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5828, top5_acc: 0.8100, loss_cls: 2.3713, loss: 2.3713 +2024-12-31 12:40:00,111 - pyskl - INFO - Epoch [140][2100/3746] lr: 1.190e-03, eta: 9:19:27, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.5786, top5_acc: 0.8103, loss_cls: 2.3811, loss: 2.3811 +2024-12-31 12:41:25,183 - pyskl - INFO - Epoch [140][2200/3746] lr: 1.184e-03, eta: 9:18:01, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5836, top5_acc: 0.8152, loss_cls: 2.3546, loss: 2.3546 +2024-12-31 12:42:49,775 - pyskl - INFO - Epoch [140][2300/3746] lr: 1.178e-03, eta: 9:16:35, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5841, top5_acc: 0.8172, loss_cls: 2.3400, loss: 2.3400 +2024-12-31 12:44:14,551 - pyskl - INFO - Epoch [140][2400/3746] lr: 1.172e-03, eta: 9:15:09, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.5764, top5_acc: 0.8097, loss_cls: 2.3734, loss: 2.3734 +2024-12-31 12:45:39,535 - pyskl - INFO - Epoch [140][2500/3746] lr: 1.166e-03, eta: 9:13:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5897, top5_acc: 0.8148, loss_cls: 2.3552, loss: 2.3552 +2024-12-31 12:47:04,235 - pyskl - INFO - Epoch [140][2600/3746] lr: 1.160e-03, eta: 9:12:18, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5836, top5_acc: 0.8145, loss_cls: 2.3514, loss: 2.3514 +2024-12-31 12:48:29,597 - pyskl - INFO - Epoch [140][2700/3746] lr: 1.154e-03, eta: 9:10:52, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5819, top5_acc: 0.8072, loss_cls: 2.3830, loss: 2.3830 +2024-12-31 12:49:54,525 - pyskl - INFO - Epoch [140][2800/3746] lr: 1.148e-03, eta: 9:09:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5728, top5_acc: 0.8073, loss_cls: 2.3859, loss: 2.3859 +2024-12-31 12:51:19,024 - pyskl - INFO - Epoch [140][2900/3746] lr: 1.142e-03, eta: 9:08:00, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.5747, top5_acc: 0.8122, loss_cls: 2.3598, loss: 2.3598 +2024-12-31 12:52:43,990 - pyskl - INFO - Epoch [140][3000/3746] lr: 1.136e-03, eta: 9:06:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5783, top5_acc: 0.8105, loss_cls: 2.3807, loss: 2.3807 +2024-12-31 12:54:09,167 - pyskl - INFO - Epoch [140][3100/3746] lr: 1.131e-03, eta: 9:05:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5739, top5_acc: 0.8106, loss_cls: 2.4016, loss: 2.4016 +2024-12-31 12:55:33,860 - pyskl - INFO - Epoch [140][3200/3746] lr: 1.125e-03, eta: 9:03:42, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5819, top5_acc: 0.8142, loss_cls: 2.3768, loss: 2.3768 +2024-12-31 12:56:58,832 - pyskl - INFO - Epoch [140][3300/3746] lr: 1.119e-03, eta: 9:02:16, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5894, top5_acc: 0.8137, loss_cls: 2.3463, loss: 2.3463 +2024-12-31 12:58:23,444 - pyskl - INFO - Epoch [140][3400/3746] lr: 1.113e-03, eta: 9:00:50, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5877, top5_acc: 0.8139, loss_cls: 2.3608, loss: 2.3608 +2024-12-31 12:59:47,911 - pyskl - INFO - Epoch [140][3500/3746] lr: 1.107e-03, eta: 8:59:24, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5750, top5_acc: 0.8155, loss_cls: 2.3643, loss: 2.3643 +2024-12-31 13:01:12,757 - pyskl - INFO - Epoch [140][3600/3746] lr: 1.101e-03, eta: 8:57:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5737, top5_acc: 0.8050, loss_cls: 2.4062, loss: 2.4062 +2024-12-31 13:02:37,719 - pyskl - INFO - Epoch [140][3700/3746] lr: 1.095e-03, eta: 8:56:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5809, top5_acc: 0.8084, loss_cls: 2.3609, loss: 2.3609 +2024-12-31 13:03:19,126 - pyskl - INFO - Saving checkpoint at 140 epochs +2024-12-31 13:05:20,041 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 13:05:20,946 - pyskl - INFO - +top1_acc 0.4411 +top5_acc 0.6895 +2024-12-31 13:05:20,946 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 13:05:20,994 - pyskl - INFO - +mean_acc 0.4409 +2024-12-31 13:05:20,999 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_138.pth was removed +2024-12-31 13:05:21,273 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_140.pth. +2024-12-31 13:05:21,274 - pyskl - INFO - Best top1_acc is 0.4411 at 140 epoch. +2024-12-31 13:05:21,287 - pyskl - INFO - Epoch(val) [140][309] top1_acc: 0.4411, top5_acc: 0.6895, mean_class_accuracy: 0.4409 +2024-12-31 13:09:48,244 - pyskl - INFO - Epoch [141][100/3746] lr: 1.087e-03, eta: 8:54:37, time: 2.669, data_time: 1.624, memory: 15990, top1_acc: 0.6091, top5_acc: 0.8355, loss_cls: 2.2193, loss: 2.2193 +2024-12-31 13:11:14,841 - pyskl - INFO - Epoch [141][200/3746] lr: 1.081e-03, eta: 8:53:12, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.6117, top5_acc: 0.8286, loss_cls: 2.2148, loss: 2.2148 +2024-12-31 13:12:41,066 - pyskl - INFO - Epoch [141][300/3746] lr: 1.075e-03, eta: 8:51:46, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.6012, top5_acc: 0.8300, loss_cls: 2.2751, loss: 2.2751 +2024-12-31 13:14:07,225 - pyskl - INFO - Epoch [141][400/3746] lr: 1.070e-03, eta: 8:50:20, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5989, top5_acc: 0.8281, loss_cls: 2.2578, loss: 2.2578 +2024-12-31 13:15:33,043 - pyskl - INFO - Epoch [141][500/3746] lr: 1.064e-03, eta: 8:48:54, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5934, top5_acc: 0.8216, loss_cls: 2.3023, loss: 2.3023 +2024-12-31 13:16:58,619 - pyskl - INFO - Epoch [141][600/3746] lr: 1.058e-03, eta: 8:47:28, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6034, top5_acc: 0.8242, loss_cls: 2.2704, loss: 2.2704 +2024-12-31 13:18:23,900 - pyskl - INFO - Epoch [141][700/3746] lr: 1.052e-03, eta: 8:46:02, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5953, top5_acc: 0.8244, loss_cls: 2.3194, loss: 2.3194 +2024-12-31 13:19:48,834 - pyskl - INFO - Epoch [141][800/3746] lr: 1.047e-03, eta: 8:44:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6102, top5_acc: 0.8317, loss_cls: 2.2239, loss: 2.2239 +2024-12-31 13:21:14,090 - pyskl - INFO - Epoch [141][900/3746] lr: 1.041e-03, eta: 8:43:10, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6025, top5_acc: 0.8242, loss_cls: 2.2930, loss: 2.2930 +2024-12-31 13:22:39,308 - pyskl - INFO - Epoch [141][1000/3746] lr: 1.035e-03, eta: 8:41:45, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5922, top5_acc: 0.8223, loss_cls: 2.2912, loss: 2.2912 +2024-12-31 13:24:04,788 - pyskl - INFO - Epoch [141][1100/3746] lr: 1.030e-03, eta: 8:40:19, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6014, top5_acc: 0.8339, loss_cls: 2.2495, loss: 2.2495 +2024-12-31 13:25:30,127 - pyskl - INFO - Epoch [141][1200/3746] lr: 1.024e-03, eta: 8:38:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5945, top5_acc: 0.8291, loss_cls: 2.2755, loss: 2.2755 +2024-12-31 13:26:55,212 - pyskl - INFO - Epoch [141][1300/3746] lr: 1.018e-03, eta: 8:37:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6008, top5_acc: 0.8266, loss_cls: 2.2652, loss: 2.2652 +2024-12-31 13:28:20,467 - pyskl - INFO - Epoch [141][1400/3746] lr: 1.013e-03, eta: 8:36:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6003, top5_acc: 0.8194, loss_cls: 2.2756, loss: 2.2756 +2024-12-31 13:29:45,337 - pyskl - INFO - Epoch [141][1500/3746] lr: 1.007e-03, eta: 8:34:35, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5870, top5_acc: 0.8192, loss_cls: 2.3031, loss: 2.3031 +2024-12-31 13:31:10,040 - pyskl - INFO - Epoch [141][1600/3746] lr: 1.002e-03, eta: 8:33:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5905, top5_acc: 0.8222, loss_cls: 2.3099, loss: 2.3099 +2024-12-31 13:32:34,647 - pyskl - INFO - Epoch [141][1700/3746] lr: 9.961e-04, eta: 8:31:43, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5981, top5_acc: 0.8225, loss_cls: 2.2871, loss: 2.2871 +2024-12-31 13:33:59,913 - pyskl - INFO - Epoch [141][1800/3746] lr: 9.905e-04, eta: 8:30:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5988, top5_acc: 0.8230, loss_cls: 2.2919, loss: 2.2919 +2024-12-31 13:35:25,093 - pyskl - INFO - Epoch [141][1900/3746] lr: 9.850e-04, eta: 8:28:51, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5992, top5_acc: 0.8298, loss_cls: 2.2610, loss: 2.2610 +2024-12-31 13:36:49,909 - pyskl - INFO - Epoch [141][2000/3746] lr: 9.795e-04, eta: 8:27:25, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5944, top5_acc: 0.8244, loss_cls: 2.2727, loss: 2.2727 +2024-12-31 13:38:14,963 - pyskl - INFO - Epoch [141][2100/3746] lr: 9.740e-04, eta: 8:25:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5966, top5_acc: 0.8281, loss_cls: 2.2838, loss: 2.2838 +2024-12-31 13:39:40,117 - pyskl - INFO - Epoch [141][2200/3746] lr: 9.685e-04, eta: 8:24:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5870, top5_acc: 0.8208, loss_cls: 2.3064, loss: 2.3064 +2024-12-31 13:41:05,110 - pyskl - INFO - Epoch [141][2300/3746] lr: 9.630e-04, eta: 8:23:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5961, top5_acc: 0.8219, loss_cls: 2.2614, loss: 2.2614 +2024-12-31 13:42:29,662 - pyskl - INFO - Epoch [141][2400/3746] lr: 9.576e-04, eta: 8:21:42, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5928, top5_acc: 0.8227, loss_cls: 2.3079, loss: 2.3079 +2024-12-31 13:43:54,796 - pyskl - INFO - Epoch [141][2500/3746] lr: 9.522e-04, eta: 8:20:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5848, top5_acc: 0.8202, loss_cls: 2.3241, loss: 2.3241 +2024-12-31 13:45:19,827 - pyskl - INFO - Epoch [141][2600/3746] lr: 9.467e-04, eta: 8:18:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5878, top5_acc: 0.8213, loss_cls: 2.3348, loss: 2.3348 +2024-12-31 13:46:44,538 - pyskl - INFO - Epoch [141][2700/3746] lr: 9.413e-04, eta: 8:17:24, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6039, top5_acc: 0.8289, loss_cls: 2.2648, loss: 2.2648 +2024-12-31 13:48:08,734 - pyskl - INFO - Epoch [141][2800/3746] lr: 9.359e-04, eta: 8:15:58, time: 0.842, data_time: 0.001, memory: 15990, top1_acc: 0.6047, top5_acc: 0.8298, loss_cls: 2.2508, loss: 2.2508 +2024-12-31 13:49:33,854 - pyskl - INFO - Epoch [141][2900/3746] lr: 9.306e-04, eta: 8:14:32, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5970, top5_acc: 0.8272, loss_cls: 2.2816, loss: 2.2816 +2024-12-31 13:50:58,566 - pyskl - INFO - Epoch [141][3000/3746] lr: 9.252e-04, eta: 8:13:06, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5869, top5_acc: 0.8173, loss_cls: 2.3119, loss: 2.3119 +2024-12-31 13:52:23,741 - pyskl - INFO - Epoch [141][3100/3746] lr: 9.199e-04, eta: 8:11:40, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5820, top5_acc: 0.8084, loss_cls: 2.3714, loss: 2.3714 +2024-12-31 13:53:48,681 - pyskl - INFO - Epoch [141][3200/3746] lr: 9.145e-04, eta: 8:10:14, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5984, top5_acc: 0.8187, loss_cls: 2.2962, loss: 2.2962 +2024-12-31 13:55:13,937 - pyskl - INFO - Epoch [141][3300/3746] lr: 9.092e-04, eta: 8:08:48, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5981, top5_acc: 0.8237, loss_cls: 2.2987, loss: 2.2987 +2024-12-31 13:56:38,593 - pyskl - INFO - Epoch [141][3400/3746] lr: 9.039e-04, eta: 8:07:22, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5964, top5_acc: 0.8153, loss_cls: 2.3032, loss: 2.3032 +2024-12-31 13:58:03,038 - pyskl - INFO - Epoch [141][3500/3746] lr: 8.986e-04, eta: 8:05:57, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5881, top5_acc: 0.8192, loss_cls: 2.3154, loss: 2.3154 +2024-12-31 13:59:27,935 - pyskl - INFO - Epoch [141][3600/3746] lr: 8.934e-04, eta: 8:04:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5967, top5_acc: 0.8191, loss_cls: 2.2958, loss: 2.2958 +2024-12-31 14:00:53,336 - pyskl - INFO - Epoch [141][3700/3746] lr: 8.881e-04, eta: 8:03:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5972, top5_acc: 0.8220, loss_cls: 2.3088, loss: 2.3088 +2024-12-31 14:01:34,561 - pyskl - INFO - Saving checkpoint at 141 epochs +2024-12-31 14:03:33,222 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 14:03:33,927 - pyskl - INFO - +top1_acc 0.4451 +top5_acc 0.6913 +2024-12-31 14:03:33,927 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 14:03:33,992 - pyskl - INFO - +mean_acc 0.4448 +2024-12-31 14:03:33,997 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_140.pth was removed +2024-12-31 14:03:34,310 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_141.pth. +2024-12-31 14:03:34,311 - pyskl - INFO - Best top1_acc is 0.4451 at 141 epoch. +2024-12-31 14:03:34,337 - pyskl - INFO - Epoch(val) [141][309] top1_acc: 0.4451, top5_acc: 0.6913, mean_class_accuracy: 0.4448 +2024-12-31 14:07:58,226 - pyskl - INFO - Epoch [142][100/3746] lr: 8.805e-04, eta: 8:01:08, time: 2.639, data_time: 1.601, memory: 15990, top1_acc: 0.6123, top5_acc: 0.8442, loss_cls: 2.1598, loss: 2.1598 +2024-12-31 14:09:23,803 - pyskl - INFO - Epoch [142][200/3746] lr: 8.752e-04, eta: 7:59:42, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6203, top5_acc: 0.8450, loss_cls: 2.1673, loss: 2.1673 +2024-12-31 14:10:49,153 - pyskl - INFO - Epoch [142][300/3746] lr: 8.700e-04, eta: 7:58:16, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.6186, top5_acc: 0.8395, loss_cls: 2.1926, loss: 2.1926 +2024-12-31 14:12:14,056 - pyskl - INFO - Epoch [142][400/3746] lr: 8.649e-04, eta: 7:56:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6164, top5_acc: 0.8394, loss_cls: 2.1921, loss: 2.1921 +2024-12-31 14:13:38,879 - pyskl - INFO - Epoch [142][500/3746] lr: 8.597e-04, eta: 7:55:24, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6183, top5_acc: 0.8414, loss_cls: 2.1823, loss: 2.1823 +2024-12-31 14:15:03,908 - pyskl - INFO - Epoch [142][600/3746] lr: 8.545e-04, eta: 7:53:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6005, top5_acc: 0.8350, loss_cls: 2.2473, loss: 2.2473 +2024-12-31 14:16:29,787 - pyskl - INFO - Epoch [142][700/3746] lr: 8.494e-04, eta: 7:52:33, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.6053, top5_acc: 0.8242, loss_cls: 2.2412, loss: 2.2412 +2024-12-31 14:17:54,562 - pyskl - INFO - Epoch [142][800/3746] lr: 8.443e-04, eta: 7:51:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6100, top5_acc: 0.8294, loss_cls: 2.2332, loss: 2.2332 +2024-12-31 14:19:19,536 - pyskl - INFO - Epoch [142][900/3746] lr: 8.392e-04, eta: 7:49:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6159, top5_acc: 0.8397, loss_cls: 2.1846, loss: 2.1846 +2024-12-31 14:20:44,042 - pyskl - INFO - Epoch [142][1000/3746] lr: 8.341e-04, eta: 7:48:15, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6106, top5_acc: 0.8398, loss_cls: 2.1857, loss: 2.1857 +2024-12-31 14:22:09,136 - pyskl - INFO - Epoch [142][1100/3746] lr: 8.290e-04, eta: 7:46:49, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6103, top5_acc: 0.8373, loss_cls: 2.2115, loss: 2.2115 +2024-12-31 14:23:34,163 - pyskl - INFO - Epoch [142][1200/3746] lr: 8.239e-04, eta: 7:45:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6106, top5_acc: 0.8297, loss_cls: 2.2399, loss: 2.2399 +2024-12-31 14:24:59,169 - pyskl - INFO - Epoch [142][1300/3746] lr: 8.189e-04, eta: 7:43:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6119, top5_acc: 0.8423, loss_cls: 2.1934, loss: 2.1934 +2024-12-31 14:26:24,474 - pyskl - INFO - Epoch [142][1400/3746] lr: 8.139e-04, eta: 7:42:31, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6119, top5_acc: 0.8380, loss_cls: 2.1888, loss: 2.1888 +2024-12-31 14:27:50,761 - pyskl - INFO - Epoch [142][1500/3746] lr: 8.088e-04, eta: 7:41:05, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.6077, top5_acc: 0.8389, loss_cls: 2.2152, loss: 2.2152 +2024-12-31 14:29:16,495 - pyskl - INFO - Epoch [142][1600/3746] lr: 8.038e-04, eta: 7:39:39, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6089, top5_acc: 0.8287, loss_cls: 2.2232, loss: 2.2232 +2024-12-31 14:30:42,797 - pyskl - INFO - Epoch [142][1700/3746] lr: 7.989e-04, eta: 7:38:13, time: 0.863, data_time: 0.001, memory: 15990, top1_acc: 0.6084, top5_acc: 0.8367, loss_cls: 2.2174, loss: 2.2174 +2024-12-31 14:32:08,135 - pyskl - INFO - Epoch [142][1800/3746] lr: 7.939e-04, eta: 7:36:48, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6138, top5_acc: 0.8259, loss_cls: 2.2424, loss: 2.2424 +2024-12-31 14:33:34,337 - pyskl - INFO - Epoch [142][1900/3746] lr: 7.889e-04, eta: 7:35:22, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.6002, top5_acc: 0.8225, loss_cls: 2.2703, loss: 2.2703 +2024-12-31 14:34:59,832 - pyskl - INFO - Epoch [142][2000/3746] lr: 7.840e-04, eta: 7:33:56, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6027, top5_acc: 0.8245, loss_cls: 2.2791, loss: 2.2791 +2024-12-31 14:36:26,137 - pyskl - INFO - Epoch [142][2100/3746] lr: 7.791e-04, eta: 7:32:30, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.6014, top5_acc: 0.8345, loss_cls: 2.2510, loss: 2.2510 +2024-12-31 14:37:51,787 - pyskl - INFO - Epoch [142][2200/3746] lr: 7.742e-04, eta: 7:31:04, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6156, top5_acc: 0.8331, loss_cls: 2.2172, loss: 2.2172 +2024-12-31 14:39:17,661 - pyskl - INFO - Epoch [142][2300/3746] lr: 7.693e-04, eta: 7:29:38, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.6066, top5_acc: 0.8334, loss_cls: 2.2312, loss: 2.2312 +2024-12-31 14:40:43,010 - pyskl - INFO - Epoch [142][2400/3746] lr: 7.644e-04, eta: 7:28:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6208, top5_acc: 0.8347, loss_cls: 2.1976, loss: 2.1976 +2024-12-31 14:42:08,292 - pyskl - INFO - Epoch [142][2500/3746] lr: 7.595e-04, eta: 7:26:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5959, top5_acc: 0.8250, loss_cls: 2.2595, loss: 2.2595 +2024-12-31 14:43:33,571 - pyskl - INFO - Epoch [142][2600/3746] lr: 7.547e-04, eta: 7:25:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6062, top5_acc: 0.8287, loss_cls: 2.2526, loss: 2.2526 +2024-12-31 14:44:58,506 - pyskl - INFO - Epoch [142][2700/3746] lr: 7.499e-04, eta: 7:23:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6128, top5_acc: 0.8369, loss_cls: 2.2045, loss: 2.2045 +2024-12-31 14:46:22,986 - pyskl - INFO - Epoch [142][2800/3746] lr: 7.450e-04, eta: 7:22:29, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5902, top5_acc: 0.8225, loss_cls: 2.2863, loss: 2.2863 +2024-12-31 14:47:47,663 - pyskl - INFO - Epoch [142][2900/3746] lr: 7.402e-04, eta: 7:21:03, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6048, top5_acc: 0.8308, loss_cls: 2.2216, loss: 2.2216 +2024-12-31 14:49:12,401 - pyskl - INFO - Epoch [142][3000/3746] lr: 7.355e-04, eta: 7:19:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6142, top5_acc: 0.8434, loss_cls: 2.1739, loss: 2.1739 +2024-12-31 14:50:37,838 - pyskl - INFO - Epoch [142][3100/3746] lr: 7.307e-04, eta: 7:18:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5925, top5_acc: 0.8308, loss_cls: 2.2666, loss: 2.2666 +2024-12-31 14:52:03,640 - pyskl - INFO - Epoch [142][3200/3746] lr: 7.259e-04, eta: 7:16:45, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6052, top5_acc: 0.8372, loss_cls: 2.2265, loss: 2.2265 +2024-12-31 14:53:28,880 - pyskl - INFO - Epoch [142][3300/3746] lr: 7.212e-04, eta: 7:15:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5967, top5_acc: 0.8198, loss_cls: 2.2755, loss: 2.2755 +2024-12-31 14:54:54,576 - pyskl - INFO - Epoch [142][3400/3746] lr: 7.165e-04, eta: 7:13:53, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5925, top5_acc: 0.8277, loss_cls: 2.2803, loss: 2.2803 +2024-12-31 14:56:19,912 - pyskl - INFO - Epoch [142][3500/3746] lr: 7.118e-04, eta: 7:12:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6036, top5_acc: 0.8291, loss_cls: 2.2611, loss: 2.2611 +2024-12-31 14:57:45,722 - pyskl - INFO - Epoch [142][3600/3746] lr: 7.071e-04, eta: 7:11:01, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6139, top5_acc: 0.8316, loss_cls: 2.2235, loss: 2.2235 +2024-12-31 14:59:10,575 - pyskl - INFO - Epoch [142][3700/3746] lr: 7.024e-04, eta: 7:09:35, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6055, top5_acc: 0.8305, loss_cls: 2.2422, loss: 2.2422 +2024-12-31 14:59:51,597 - pyskl - INFO - Saving checkpoint at 142 epochs +2024-12-31 15:01:50,070 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 15:01:50,843 - pyskl - INFO - +top1_acc 0.4464 +top5_acc 0.6929 +2024-12-31 15:01:50,844 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 15:01:50,888 - pyskl - INFO - +mean_acc 0.4462 +2024-12-31 15:01:50,892 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_141.pth was removed +2024-12-31 15:01:51,168 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_142.pth. +2024-12-31 15:01:51,168 - pyskl - INFO - Best top1_acc is 0.4464 at 142 epoch. +2024-12-31 15:01:51,181 - pyskl - INFO - Epoch(val) [142][309] top1_acc: 0.4464, top5_acc: 0.6929, mean_class_accuracy: 0.4462 +2024-12-31 15:06:08,687 - pyskl - INFO - Epoch [143][100/3746] lr: 6.956e-04, eta: 7:07:37, time: 2.575, data_time: 1.546, memory: 15990, top1_acc: 0.6334, top5_acc: 0.8491, loss_cls: 2.0995, loss: 2.0995 +2024-12-31 15:07:34,240 - pyskl - INFO - Epoch [143][200/3746] lr: 6.910e-04, eta: 7:06:11, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6262, top5_acc: 0.8433, loss_cls: 2.1502, loss: 2.1502 +2024-12-31 15:08:59,997 - pyskl - INFO - Epoch [143][300/3746] lr: 6.863e-04, eta: 7:04:45, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6159, top5_acc: 0.8337, loss_cls: 2.1801, loss: 2.1801 +2024-12-31 15:10:25,702 - pyskl - INFO - Epoch [143][400/3746] lr: 6.817e-04, eta: 7:03:20, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6331, top5_acc: 0.8470, loss_cls: 2.1181, loss: 2.1181 +2024-12-31 15:11:51,739 - pyskl - INFO - Epoch [143][500/3746] lr: 6.771e-04, eta: 7:01:54, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6203, top5_acc: 0.8392, loss_cls: 2.1579, loss: 2.1579 +2024-12-31 15:13:17,517 - pyskl - INFO - Epoch [143][600/3746] lr: 6.725e-04, eta: 7:00:28, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6258, top5_acc: 0.8406, loss_cls: 2.1550, loss: 2.1550 +2024-12-31 15:14:43,341 - pyskl - INFO - Epoch [143][700/3746] lr: 6.680e-04, eta: 6:59:02, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.6264, top5_acc: 0.8448, loss_cls: 2.1322, loss: 2.1322 +2024-12-31 15:16:08,443 - pyskl - INFO - Epoch [143][800/3746] lr: 6.634e-04, eta: 6:57:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6225, top5_acc: 0.8406, loss_cls: 2.1587, loss: 2.1587 +2024-12-31 15:17:33,888 - pyskl - INFO - Epoch [143][900/3746] lr: 6.589e-04, eta: 6:56:10, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.6248, top5_acc: 0.8400, loss_cls: 2.1650, loss: 2.1650 +2024-12-31 15:18:58,841 - pyskl - INFO - Epoch [143][1000/3746] lr: 6.544e-04, eta: 6:54:44, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6139, top5_acc: 0.8398, loss_cls: 2.1814, loss: 2.1814 +2024-12-31 15:20:23,555 - pyskl - INFO - Epoch [143][1100/3746] lr: 6.499e-04, eta: 6:53:18, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6269, top5_acc: 0.8433, loss_cls: 2.1473, loss: 2.1473 +2024-12-31 15:21:49,068 - pyskl - INFO - Epoch [143][1200/3746] lr: 6.454e-04, eta: 6:51:52, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6234, top5_acc: 0.8422, loss_cls: 2.1608, loss: 2.1608 +2024-12-31 15:23:14,040 - pyskl - INFO - Epoch [143][1300/3746] lr: 6.409e-04, eta: 6:50:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6177, top5_acc: 0.8392, loss_cls: 2.1773, loss: 2.1773 +2024-12-31 15:24:38,987 - pyskl - INFO - Epoch [143][1400/3746] lr: 6.365e-04, eta: 6:49:00, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6172, top5_acc: 0.8366, loss_cls: 2.1822, loss: 2.1822 +2024-12-31 15:26:03,711 - pyskl - INFO - Epoch [143][1500/3746] lr: 6.320e-04, eta: 6:47:34, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6225, top5_acc: 0.8344, loss_cls: 2.1784, loss: 2.1784 +2024-12-31 15:27:28,472 - pyskl - INFO - Epoch [143][1600/3746] lr: 6.276e-04, eta: 6:46:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6253, top5_acc: 0.8383, loss_cls: 2.1509, loss: 2.1509 +2024-12-31 15:28:53,413 - pyskl - INFO - Epoch [143][1700/3746] lr: 6.232e-04, eta: 6:44:42, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6208, top5_acc: 0.8452, loss_cls: 2.1439, loss: 2.1439 +2024-12-31 15:30:18,412 - pyskl - INFO - Epoch [143][1800/3746] lr: 6.188e-04, eta: 6:43:16, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6095, top5_acc: 0.8416, loss_cls: 2.1652, loss: 2.1652 +2024-12-31 15:31:43,271 - pyskl - INFO - Epoch [143][1900/3746] lr: 6.144e-04, eta: 6:41:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6114, top5_acc: 0.8397, loss_cls: 2.1709, loss: 2.1709 +2024-12-31 15:33:08,163 - pyskl - INFO - Epoch [143][2000/3746] lr: 6.101e-04, eta: 6:40:24, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6236, top5_acc: 0.8445, loss_cls: 2.1457, loss: 2.1457 +2024-12-31 15:34:33,376 - pyskl - INFO - Epoch [143][2100/3746] lr: 6.057e-04, eta: 6:38:59, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.6212, top5_acc: 0.8431, loss_cls: 2.1610, loss: 2.1610 +2024-12-31 15:35:58,955 - pyskl - INFO - Epoch [143][2200/3746] lr: 6.014e-04, eta: 6:37:33, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.6134, top5_acc: 0.8356, loss_cls: 2.2009, loss: 2.2009 +2024-12-31 15:37:23,914 - pyskl - INFO - Epoch [143][2300/3746] lr: 5.971e-04, eta: 6:36:07, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.6264, top5_acc: 0.8373, loss_cls: 2.1699, loss: 2.1699 +2024-12-31 15:38:48,829 - pyskl - INFO - Epoch [143][2400/3746] lr: 5.928e-04, eta: 6:34:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6316, top5_acc: 0.8444, loss_cls: 2.1330, loss: 2.1330 +2024-12-31 15:40:13,511 - pyskl - INFO - Epoch [143][2500/3746] lr: 5.885e-04, eta: 6:33:15, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6133, top5_acc: 0.8447, loss_cls: 2.1893, loss: 2.1893 +2024-12-31 15:41:38,448 - pyskl - INFO - Epoch [143][2600/3746] lr: 5.842e-04, eta: 6:31:49, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6173, top5_acc: 0.8400, loss_cls: 2.1606, loss: 2.1606 +2024-12-31 15:43:03,550 - pyskl - INFO - Epoch [143][2700/3746] lr: 5.800e-04, eta: 6:30:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6098, top5_acc: 0.8372, loss_cls: 2.1904, loss: 2.1904 +2024-12-31 15:44:28,359 - pyskl - INFO - Epoch [143][2800/3746] lr: 5.757e-04, eta: 6:28:57, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6116, top5_acc: 0.8336, loss_cls: 2.2027, loss: 2.2027 +2024-12-31 15:45:53,478 - pyskl - INFO - Epoch [143][2900/3746] lr: 5.715e-04, eta: 6:27:31, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.6277, top5_acc: 0.8406, loss_cls: 2.1621, loss: 2.1621 +2024-12-31 15:47:18,559 - pyskl - INFO - Epoch [143][3000/3746] lr: 5.673e-04, eta: 6:26:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6200, top5_acc: 0.8430, loss_cls: 2.1688, loss: 2.1688 +2024-12-31 15:48:43,196 - pyskl - INFO - Epoch [143][3100/3746] lr: 5.631e-04, eta: 6:24:39, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6077, top5_acc: 0.8367, loss_cls: 2.2133, loss: 2.2133 +2024-12-31 15:50:08,580 - pyskl - INFO - Epoch [143][3200/3746] lr: 5.590e-04, eta: 6:23:13, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6144, top5_acc: 0.8323, loss_cls: 2.2045, loss: 2.2045 +2024-12-31 15:51:34,021 - pyskl - INFO - Epoch [143][3300/3746] lr: 5.548e-04, eta: 6:21:47, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6170, top5_acc: 0.8353, loss_cls: 2.1858, loss: 2.1858 +2024-12-31 15:52:59,506 - pyskl - INFO - Epoch [143][3400/3746] lr: 5.506e-04, eta: 6:20:21, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6258, top5_acc: 0.8452, loss_cls: 2.1438, loss: 2.1438 +2024-12-31 15:54:24,790 - pyskl - INFO - Epoch [143][3500/3746] lr: 5.465e-04, eta: 6:18:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6078, top5_acc: 0.8348, loss_cls: 2.2017, loss: 2.2017 +2024-12-31 15:55:49,625 - pyskl - INFO - Epoch [143][3600/3746] lr: 5.424e-04, eta: 6:17:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6258, top5_acc: 0.8447, loss_cls: 2.1483, loss: 2.1483 +2024-12-31 15:57:15,383 - pyskl - INFO - Epoch [143][3700/3746] lr: 5.383e-04, eta: 6:16:03, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6134, top5_acc: 0.8325, loss_cls: 2.1890, loss: 2.1890 +2024-12-31 15:57:56,769 - pyskl - INFO - Saving checkpoint at 143 epochs +2024-12-31 15:59:56,667 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 15:59:57,571 - pyskl - INFO - +top1_acc 0.4441 +top5_acc 0.6933 +2024-12-31 15:59:57,571 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 15:59:57,619 - pyskl - INFO - +mean_acc 0.4439 +2024-12-31 15:59:57,636 - pyskl - INFO - Epoch(val) [143][309] top1_acc: 0.4441, top5_acc: 0.6933, mean_class_accuracy: 0.4439 +2024-12-31 16:04:19,684 - pyskl - INFO - Epoch [144][100/3746] lr: 5.323e-04, eta: 6:14:05, time: 2.620, data_time: 1.579, memory: 15990, top1_acc: 0.6331, top5_acc: 0.8545, loss_cls: 2.0931, loss: 2.0931 +2024-12-31 16:05:45,353 - pyskl - INFO - Epoch [144][200/3746] lr: 5.283e-04, eta: 6:12:39, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6391, top5_acc: 0.8558, loss_cls: 2.0658, loss: 2.0658 +2024-12-31 16:07:11,516 - pyskl - INFO - Epoch [144][300/3746] lr: 5.242e-04, eta: 6:11:13, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.6423, top5_acc: 0.8603, loss_cls: 2.0411, loss: 2.0411 +2024-12-31 16:08:37,217 - pyskl - INFO - Epoch [144][400/3746] lr: 5.202e-04, eta: 6:09:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6453, top5_acc: 0.8517, loss_cls: 2.0680, loss: 2.0680 +2024-12-31 16:10:03,285 - pyskl - INFO - Epoch [144][500/3746] lr: 5.162e-04, eta: 6:08:21, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.6314, top5_acc: 0.8455, loss_cls: 2.1013, loss: 2.1013 +2024-12-31 16:11:28,848 - pyskl - INFO - Epoch [144][600/3746] lr: 5.122e-04, eta: 6:06:55, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6309, top5_acc: 0.8464, loss_cls: 2.1090, loss: 2.1090 +2024-12-31 16:12:54,286 - pyskl - INFO - Epoch [144][700/3746] lr: 5.082e-04, eta: 6:05:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6353, top5_acc: 0.8464, loss_cls: 2.0927, loss: 2.0927 +2024-12-31 16:14:19,042 - pyskl - INFO - Epoch [144][800/3746] lr: 5.042e-04, eta: 6:04:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6366, top5_acc: 0.8500, loss_cls: 2.0976, loss: 2.0976 +2024-12-31 16:15:44,981 - pyskl - INFO - Epoch [144][900/3746] lr: 5.003e-04, eta: 6:02:37, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6320, top5_acc: 0.8511, loss_cls: 2.1010, loss: 2.1010 +2024-12-31 16:17:10,642 - pyskl - INFO - Epoch [144][1000/3746] lr: 4.964e-04, eta: 6:01:11, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6325, top5_acc: 0.8478, loss_cls: 2.1202, loss: 2.1202 +2024-12-31 16:18:36,247 - pyskl - INFO - Epoch [144][1100/3746] lr: 4.924e-04, eta: 5:59:45, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6291, top5_acc: 0.8505, loss_cls: 2.1180, loss: 2.1180 +2024-12-31 16:20:02,296 - pyskl - INFO - Epoch [144][1200/3746] lr: 4.885e-04, eta: 5:58:19, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.6339, top5_acc: 0.8461, loss_cls: 2.1108, loss: 2.1108 +2024-12-31 16:21:28,196 - pyskl - INFO - Epoch [144][1300/3746] lr: 4.846e-04, eta: 5:56:54, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6350, top5_acc: 0.8500, loss_cls: 2.1159, loss: 2.1159 +2024-12-31 16:22:53,593 - pyskl - INFO - Epoch [144][1400/3746] lr: 4.808e-04, eta: 5:55:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6342, top5_acc: 0.8508, loss_cls: 2.1208, loss: 2.1208 +2024-12-31 16:24:18,681 - pyskl - INFO - Epoch [144][1500/3746] lr: 4.769e-04, eta: 5:54:02, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6334, top5_acc: 0.8512, loss_cls: 2.0884, loss: 2.0884 +2024-12-31 16:25:44,444 - pyskl - INFO - Epoch [144][1600/3746] lr: 4.731e-04, eta: 5:52:36, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6227, top5_acc: 0.8475, loss_cls: 2.1435, loss: 2.1435 +2024-12-31 16:27:09,718 - pyskl - INFO - Epoch [144][1700/3746] lr: 4.692e-04, eta: 5:51:10, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6302, top5_acc: 0.8516, loss_cls: 2.1152, loss: 2.1152 +2024-12-31 16:28:35,503 - pyskl - INFO - Epoch [144][1800/3746] lr: 4.654e-04, eta: 5:49:44, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6431, top5_acc: 0.8467, loss_cls: 2.0915, loss: 2.0915 +2024-12-31 16:30:01,218 - pyskl - INFO - Epoch [144][1900/3746] lr: 4.616e-04, eta: 5:48:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6316, top5_acc: 0.8455, loss_cls: 2.1127, loss: 2.1127 +2024-12-31 16:31:27,550 - pyskl - INFO - Epoch [144][2000/3746] lr: 4.578e-04, eta: 5:46:52, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.6278, top5_acc: 0.8480, loss_cls: 2.1173, loss: 2.1173 +2024-12-31 16:32:53,090 - pyskl - INFO - Epoch [144][2100/3746] lr: 4.541e-04, eta: 5:45:26, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.6219, top5_acc: 0.8486, loss_cls: 2.1440, loss: 2.1440 +2024-12-31 16:34:18,472 - pyskl - INFO - Epoch [144][2200/3746] lr: 4.503e-04, eta: 5:44:00, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6283, top5_acc: 0.8466, loss_cls: 2.1225, loss: 2.1225 +2024-12-31 16:35:43,279 - pyskl - INFO - Epoch [144][2300/3746] lr: 4.466e-04, eta: 5:42:34, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6316, top5_acc: 0.8473, loss_cls: 2.1195, loss: 2.1195 +2024-12-31 16:37:08,108 - pyskl - INFO - Epoch [144][2400/3746] lr: 4.429e-04, eta: 5:41:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6364, top5_acc: 0.8456, loss_cls: 2.1135, loss: 2.1135 +2024-12-31 16:38:33,311 - pyskl - INFO - Epoch [144][2500/3746] lr: 4.392e-04, eta: 5:39:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6309, top5_acc: 0.8481, loss_cls: 2.1086, loss: 2.1086 +2024-12-31 16:39:58,742 - pyskl - INFO - Epoch [144][2600/3746] lr: 4.355e-04, eta: 5:38:16, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6252, top5_acc: 0.8462, loss_cls: 2.0980, loss: 2.0980 +2024-12-31 16:41:23,675 - pyskl - INFO - Epoch [144][2700/3746] lr: 4.318e-04, eta: 5:36:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6353, top5_acc: 0.8511, loss_cls: 2.0994, loss: 2.0994 +2024-12-31 16:42:48,524 - pyskl - INFO - Epoch [144][2800/3746] lr: 4.281e-04, eta: 5:35:24, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6181, top5_acc: 0.8448, loss_cls: 2.1695, loss: 2.1695 +2024-12-31 16:44:13,417 - pyskl - INFO - Epoch [144][2900/3746] lr: 4.245e-04, eta: 5:33:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6205, top5_acc: 0.8409, loss_cls: 2.1423, loss: 2.1423 +2024-12-31 16:45:38,069 - pyskl - INFO - Epoch [144][3000/3746] lr: 4.209e-04, eta: 5:32:32, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6338, top5_acc: 0.8436, loss_cls: 2.1085, loss: 2.1085 +2024-12-31 16:47:03,222 - pyskl - INFO - Epoch [144][3100/3746] lr: 4.173e-04, eta: 5:31:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6342, top5_acc: 0.8470, loss_cls: 2.1022, loss: 2.1022 +2024-12-31 16:48:28,624 - pyskl - INFO - Epoch [144][3200/3746] lr: 4.137e-04, eta: 5:29:40, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.6366, top5_acc: 0.8484, loss_cls: 2.1019, loss: 2.1019 +2024-12-31 16:49:52,847 - pyskl - INFO - Epoch [144][3300/3746] lr: 4.101e-04, eta: 5:28:14, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6291, top5_acc: 0.8498, loss_cls: 2.1072, loss: 2.1072 +2024-12-31 16:51:19,201 - pyskl - INFO - Epoch [144][3400/3746] lr: 4.065e-04, eta: 5:26:49, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.6239, top5_acc: 0.8358, loss_cls: 2.1622, loss: 2.1622 +2024-12-31 16:52:44,972 - pyskl - INFO - Epoch [144][3500/3746] lr: 4.030e-04, eta: 5:25:23, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.6330, top5_acc: 0.8430, loss_cls: 2.1202, loss: 2.1202 +2024-12-31 16:54:10,748 - pyskl - INFO - Epoch [144][3600/3746] lr: 3.994e-04, eta: 5:23:57, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6264, top5_acc: 0.8408, loss_cls: 2.1837, loss: 2.1837 +2024-12-31 16:55:36,523 - pyskl - INFO - Epoch [144][3700/3746] lr: 3.959e-04, eta: 5:22:31, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6248, top5_acc: 0.8417, loss_cls: 2.1350, loss: 2.1350 +2024-12-31 16:56:17,733 - pyskl - INFO - Saving checkpoint at 144 epochs +2024-12-31 16:58:16,635 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 16:58:17,339 - pyskl - INFO - +top1_acc 0.4499 +top5_acc 0.6970 +2024-12-31 16:58:17,340 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 16:58:17,389 - pyskl - INFO - +mean_acc 0.4497 +2024-12-31 16:58:17,394 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_142.pth was removed +2024-12-31 16:58:17,758 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_144.pth. +2024-12-31 16:58:17,758 - pyskl - INFO - Best top1_acc is 0.4499 at 144 epoch. +2024-12-31 16:58:17,784 - pyskl - INFO - Epoch(val) [144][309] top1_acc: 0.4499, top5_acc: 0.6970, mean_class_accuracy: 0.4497 +2024-12-31 17:02:38,929 - pyskl - INFO - Epoch [145][100/3746] lr: 3.908e-04, eta: 5:20:31, time: 2.611, data_time: 1.577, memory: 15990, top1_acc: 0.6547, top5_acc: 0.8642, loss_cls: 2.0095, loss: 2.0095 +2024-12-31 17:04:04,971 - pyskl - INFO - Epoch [145][200/3746] lr: 3.873e-04, eta: 5:19:05, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6541, top5_acc: 0.8653, loss_cls: 2.0036, loss: 2.0036 +2024-12-31 17:05:30,362 - pyskl - INFO - Epoch [145][300/3746] lr: 3.839e-04, eta: 5:17:39, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6434, top5_acc: 0.8578, loss_cls: 2.0695, loss: 2.0695 +2024-12-31 17:06:56,036 - pyskl - INFO - Epoch [145][400/3746] lr: 3.804e-04, eta: 5:16:13, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6362, top5_acc: 0.8539, loss_cls: 2.0757, loss: 2.0757 +2024-12-31 17:08:21,566 - pyskl - INFO - Epoch [145][500/3746] lr: 3.770e-04, eta: 5:14:47, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6411, top5_acc: 0.8512, loss_cls: 2.0727, loss: 2.0727 +2024-12-31 17:09:47,257 - pyskl - INFO - Epoch [145][600/3746] lr: 3.736e-04, eta: 5:13:21, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.6405, top5_acc: 0.8578, loss_cls: 2.0685, loss: 2.0685 +2024-12-31 17:11:12,806 - pyskl - INFO - Epoch [145][700/3746] lr: 3.702e-04, eta: 5:11:55, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6545, top5_acc: 0.8578, loss_cls: 2.0185, loss: 2.0185 +2024-12-31 17:12:37,928 - pyskl - INFO - Epoch [145][800/3746] lr: 3.668e-04, eta: 5:10:29, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6395, top5_acc: 0.8519, loss_cls: 2.0819, loss: 2.0819 +2024-12-31 17:14:03,223 - pyskl - INFO - Epoch [145][900/3746] lr: 3.634e-04, eta: 5:09:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6492, top5_acc: 0.8609, loss_cls: 2.0387, loss: 2.0387 +2024-12-31 17:15:27,965 - pyskl - INFO - Epoch [145][1000/3746] lr: 3.600e-04, eta: 5:07:37, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6536, top5_acc: 0.8631, loss_cls: 2.0218, loss: 2.0218 +2024-12-31 17:16:53,270 - pyskl - INFO - Epoch [145][1100/3746] lr: 3.567e-04, eta: 5:06:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6283, top5_acc: 0.8505, loss_cls: 2.1174, loss: 2.1174 +2024-12-31 17:18:18,196 - pyskl - INFO - Epoch [145][1200/3746] lr: 3.534e-04, eta: 5:04:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6439, top5_acc: 0.8580, loss_cls: 2.0418, loss: 2.0418 +2024-12-31 17:19:42,974 - pyskl - INFO - Epoch [145][1300/3746] lr: 3.501e-04, eta: 5:03:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6538, top5_acc: 0.8616, loss_cls: 2.0113, loss: 2.0113 +2024-12-31 17:21:07,693 - pyskl - INFO - Epoch [145][1400/3746] lr: 3.468e-04, eta: 5:01:53, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6452, top5_acc: 0.8548, loss_cls: 2.0523, loss: 2.0523 +2024-12-31 17:22:32,884 - pyskl - INFO - Epoch [145][1500/3746] lr: 3.435e-04, eta: 5:00:27, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6320, top5_acc: 0.8539, loss_cls: 2.0760, loss: 2.0760 +2024-12-31 17:23:57,932 - pyskl - INFO - Epoch [145][1600/3746] lr: 3.402e-04, eta: 4:59:01, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6472, top5_acc: 0.8553, loss_cls: 2.0526, loss: 2.0526 +2024-12-31 17:25:22,751 - pyskl - INFO - Epoch [145][1700/3746] lr: 3.370e-04, eta: 4:57:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6400, top5_acc: 0.8506, loss_cls: 2.0786, loss: 2.0786 +2024-12-31 17:26:47,478 - pyskl - INFO - Epoch [145][1800/3746] lr: 3.337e-04, eta: 4:56:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6400, top5_acc: 0.8481, loss_cls: 2.0559, loss: 2.0559 +2024-12-31 17:28:12,395 - pyskl - INFO - Epoch [145][1900/3746] lr: 3.305e-04, eta: 4:54:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6412, top5_acc: 0.8516, loss_cls: 2.0660, loss: 2.0660 +2024-12-31 17:29:37,005 - pyskl - INFO - Epoch [145][2000/3746] lr: 3.273e-04, eta: 4:53:17, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6506, top5_acc: 0.8595, loss_cls: 2.0245, loss: 2.0245 +2024-12-31 17:31:02,631 - pyskl - INFO - Epoch [145][2100/3746] lr: 3.241e-04, eta: 4:51:51, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.6464, top5_acc: 0.8577, loss_cls: 2.0399, loss: 2.0399 +2024-12-31 17:32:28,151 - pyskl - INFO - Epoch [145][2200/3746] lr: 3.210e-04, eta: 4:50:25, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6411, top5_acc: 0.8542, loss_cls: 2.0722, loss: 2.0722 +2024-12-31 17:33:53,106 - pyskl - INFO - Epoch [145][2300/3746] lr: 3.178e-04, eta: 4:49:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6406, top5_acc: 0.8531, loss_cls: 2.0739, loss: 2.0739 +2024-12-31 17:35:18,336 - pyskl - INFO - Epoch [145][2400/3746] lr: 3.147e-04, eta: 4:47:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6292, top5_acc: 0.8489, loss_cls: 2.1210, loss: 2.1210 +2024-12-31 17:36:43,773 - pyskl - INFO - Epoch [145][2500/3746] lr: 3.116e-04, eta: 4:46:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6438, top5_acc: 0.8523, loss_cls: 2.0820, loss: 2.0820 +2024-12-31 17:38:09,086 - pyskl - INFO - Epoch [145][2600/3746] lr: 3.084e-04, eta: 4:44:42, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6298, top5_acc: 0.8500, loss_cls: 2.1027, loss: 2.1027 +2024-12-31 17:39:34,549 - pyskl - INFO - Epoch [145][2700/3746] lr: 3.054e-04, eta: 4:43:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6475, top5_acc: 0.8555, loss_cls: 2.0458, loss: 2.0458 +2024-12-31 17:40:59,636 - pyskl - INFO - Epoch [145][2800/3746] lr: 3.023e-04, eta: 4:41:50, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6455, top5_acc: 0.8592, loss_cls: 2.0278, loss: 2.0278 +2024-12-31 17:42:24,483 - pyskl - INFO - Epoch [145][2900/3746] lr: 2.992e-04, eta: 4:40:24, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6391, top5_acc: 0.8572, loss_cls: 2.0742, loss: 2.0742 +2024-12-31 17:43:49,808 - pyskl - INFO - Epoch [145][3000/3746] lr: 2.962e-04, eta: 4:38:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6462, top5_acc: 0.8502, loss_cls: 2.0726, loss: 2.0726 +2024-12-31 17:45:14,800 - pyskl - INFO - Epoch [145][3100/3746] lr: 2.931e-04, eta: 4:37:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6459, top5_acc: 0.8530, loss_cls: 2.0543, loss: 2.0543 +2024-12-31 17:46:40,013 - pyskl - INFO - Epoch [145][3200/3746] lr: 2.901e-04, eta: 4:36:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6520, top5_acc: 0.8588, loss_cls: 2.0184, loss: 2.0184 +2024-12-31 17:48:05,122 - pyskl - INFO - Epoch [145][3300/3746] lr: 2.871e-04, eta: 4:34:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6336, top5_acc: 0.8523, loss_cls: 2.0853, loss: 2.0853 +2024-12-31 17:49:30,078 - pyskl - INFO - Epoch [145][3400/3746] lr: 2.841e-04, eta: 4:33:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6455, top5_acc: 0.8572, loss_cls: 2.0448, loss: 2.0448 +2024-12-31 17:50:54,480 - pyskl - INFO - Epoch [145][3500/3746] lr: 2.812e-04, eta: 4:31:48, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6441, top5_acc: 0.8502, loss_cls: 2.0695, loss: 2.0695 +2024-12-31 17:52:19,210 - pyskl - INFO - Epoch [145][3600/3746] lr: 2.782e-04, eta: 4:30:22, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6359, top5_acc: 0.8512, loss_cls: 2.1011, loss: 2.1011 +2024-12-31 17:53:43,842 - pyskl - INFO - Epoch [145][3700/3746] lr: 2.753e-04, eta: 4:28:56, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6378, top5_acc: 0.8542, loss_cls: 2.0659, loss: 2.0659 +2024-12-31 17:54:24,820 - pyskl - INFO - Saving checkpoint at 145 epochs +2024-12-31 17:56:23,422 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 17:56:24,121 - pyskl - INFO - +top1_acc 0.4504 +top5_acc 0.6957 +2024-12-31 17:56:24,121 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 17:56:24,165 - pyskl - INFO - +mean_acc 0.4502 +2024-12-31 17:56:24,170 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_144.pth was removed +2024-12-31 17:56:24,481 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_145.pth. +2024-12-31 17:56:24,482 - pyskl - INFO - Best top1_acc is 0.4504 at 145 epoch. +2024-12-31 17:56:24,497 - pyskl - INFO - Epoch(val) [145][309] top1_acc: 0.4504, top5_acc: 0.6957, mean_class_accuracy: 0.4502 +2024-12-31 18:00:32,587 - pyskl - INFO - Epoch [146][100/3746] lr: 2.710e-04, eta: 4:26:55, time: 2.481, data_time: 1.463, memory: 15990, top1_acc: 0.6619, top5_acc: 0.8695, loss_cls: 1.9842, loss: 1.9842 +2024-12-31 18:01:57,667 - pyskl - INFO - Epoch [146][200/3746] lr: 2.681e-04, eta: 4:25:29, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6552, top5_acc: 0.8620, loss_cls: 1.9982, loss: 1.9982 +2024-12-31 18:03:22,551 - pyskl - INFO - Epoch [146][300/3746] lr: 2.652e-04, eta: 4:24:03, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6598, top5_acc: 0.8677, loss_cls: 1.9614, loss: 1.9614 +2024-12-31 18:04:47,421 - pyskl - INFO - Epoch [146][400/3746] lr: 2.624e-04, eta: 4:22:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6686, top5_acc: 0.8700, loss_cls: 1.9568, loss: 1.9568 +2024-12-31 18:06:12,922 - pyskl - INFO - Epoch [146][500/3746] lr: 2.595e-04, eta: 4:21:11, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6514, top5_acc: 0.8612, loss_cls: 2.0318, loss: 2.0318 +2024-12-31 18:07:37,933 - pyskl - INFO - Epoch [146][600/3746] lr: 2.567e-04, eta: 4:19:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6570, top5_acc: 0.8602, loss_cls: 2.0237, loss: 2.0237 +2024-12-31 18:09:03,084 - pyskl - INFO - Epoch [146][700/3746] lr: 2.539e-04, eta: 4:18:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6503, top5_acc: 0.8634, loss_cls: 2.0082, loss: 2.0082 +2024-12-31 18:10:27,864 - pyskl - INFO - Epoch [146][800/3746] lr: 2.511e-04, eta: 4:16:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6517, top5_acc: 0.8600, loss_cls: 2.0038, loss: 2.0038 +2024-12-31 18:11:52,151 - pyskl - INFO - Epoch [146][900/3746] lr: 2.483e-04, eta: 4:15:27, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6461, top5_acc: 0.8625, loss_cls: 2.0349, loss: 2.0349 +2024-12-31 18:13:16,797 - pyskl - INFO - Epoch [146][1000/3746] lr: 2.455e-04, eta: 4:14:01, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6530, top5_acc: 0.8653, loss_cls: 1.9975, loss: 1.9975 +2024-12-31 18:14:41,349 - pyskl - INFO - Epoch [146][1100/3746] lr: 2.427e-04, eta: 4:12:35, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6567, top5_acc: 0.8639, loss_cls: 1.9875, loss: 1.9875 +2024-12-31 18:16:06,493 - pyskl - INFO - Epoch [146][1200/3746] lr: 2.400e-04, eta: 4:11:09, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6530, top5_acc: 0.8556, loss_cls: 2.0101, loss: 2.0101 +2024-12-31 18:17:31,075 - pyskl - INFO - Epoch [146][1300/3746] lr: 2.373e-04, eta: 4:09:43, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6508, top5_acc: 0.8623, loss_cls: 2.0010, loss: 2.0010 +2024-12-31 18:18:55,727 - pyskl - INFO - Epoch [146][1400/3746] lr: 2.345e-04, eta: 4:08:17, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6519, top5_acc: 0.8573, loss_cls: 2.0259, loss: 2.0259 +2024-12-31 18:20:20,781 - pyskl - INFO - Epoch [146][1500/3746] lr: 2.318e-04, eta: 4:06:51, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6558, top5_acc: 0.8556, loss_cls: 2.0350, loss: 2.0350 +2024-12-31 18:21:45,407 - pyskl - INFO - Epoch [146][1600/3746] lr: 2.292e-04, eta: 4:05:25, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6500, top5_acc: 0.8633, loss_cls: 2.0058, loss: 2.0058 +2024-12-31 18:23:10,176 - pyskl - INFO - Epoch [146][1700/3746] lr: 2.265e-04, eta: 4:03:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6569, top5_acc: 0.8609, loss_cls: 1.9982, loss: 1.9982 +2024-12-31 18:24:34,994 - pyskl - INFO - Epoch [146][1800/3746] lr: 2.239e-04, eta: 4:02:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6558, top5_acc: 0.8636, loss_cls: 2.0054, loss: 2.0054 +2024-12-31 18:26:00,100 - pyskl - INFO - Epoch [146][1900/3746] lr: 2.212e-04, eta: 4:01:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6650, top5_acc: 0.8642, loss_cls: 1.9674, loss: 1.9674 +2024-12-31 18:27:25,037 - pyskl - INFO - Epoch [146][2000/3746] lr: 2.186e-04, eta: 3:59:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6539, top5_acc: 0.8627, loss_cls: 2.0051, loss: 2.0051 +2024-12-31 18:28:50,297 - pyskl - INFO - Epoch [146][2100/3746] lr: 2.160e-04, eta: 3:58:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6458, top5_acc: 0.8603, loss_cls: 2.0139, loss: 2.0139 +2024-12-31 18:30:15,644 - pyskl - INFO - Epoch [146][2200/3746] lr: 2.134e-04, eta: 3:56:49, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6425, top5_acc: 0.8603, loss_cls: 2.0276, loss: 2.0276 +2024-12-31 18:31:40,401 - pyskl - INFO - Epoch [146][2300/3746] lr: 2.108e-04, eta: 3:55:23, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6544, top5_acc: 0.8645, loss_cls: 1.9783, loss: 1.9783 +2024-12-31 18:33:05,492 - pyskl - INFO - Epoch [146][2400/3746] lr: 2.083e-04, eta: 3:53:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6492, top5_acc: 0.8575, loss_cls: 2.0330, loss: 2.0330 +2024-12-31 18:34:30,251 - pyskl - INFO - Epoch [146][2500/3746] lr: 2.057e-04, eta: 3:52:31, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6514, top5_acc: 0.8566, loss_cls: 2.0306, loss: 2.0306 +2024-12-31 18:35:55,048 - pyskl - INFO - Epoch [146][2600/3746] lr: 2.032e-04, eta: 3:51:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6533, top5_acc: 0.8616, loss_cls: 2.0076, loss: 2.0076 +2024-12-31 18:37:19,846 - pyskl - INFO - Epoch [146][2700/3746] lr: 2.007e-04, eta: 3:49:39, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6480, top5_acc: 0.8564, loss_cls: 2.0384, loss: 2.0384 +2024-12-31 18:38:44,702 - pyskl - INFO - Epoch [146][2800/3746] lr: 1.982e-04, eta: 3:48:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6472, top5_acc: 0.8597, loss_cls: 2.0463, loss: 2.0463 +2024-12-31 18:40:09,728 - pyskl - INFO - Epoch [146][2900/3746] lr: 1.957e-04, eta: 3:46:47, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6616, top5_acc: 0.8661, loss_cls: 1.9670, loss: 1.9670 +2024-12-31 18:41:34,102 - pyskl - INFO - Epoch [146][3000/3746] lr: 1.933e-04, eta: 3:45:21, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6522, top5_acc: 0.8591, loss_cls: 2.0131, loss: 2.0131 +2024-12-31 18:42:59,237 - pyskl - INFO - Epoch [146][3100/3746] lr: 1.908e-04, eta: 3:43:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6547, top5_acc: 0.8531, loss_cls: 2.0179, loss: 2.0179 +2024-12-31 18:44:23,956 - pyskl - INFO - Epoch [146][3200/3746] lr: 1.884e-04, eta: 3:42:29, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6431, top5_acc: 0.8578, loss_cls: 2.0547, loss: 2.0547 +2024-12-31 18:45:48,751 - pyskl - INFO - Epoch [146][3300/3746] lr: 1.860e-04, eta: 3:41:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6488, top5_acc: 0.8620, loss_cls: 2.0164, loss: 2.0164 +2024-12-31 18:47:13,290 - pyskl - INFO - Epoch [146][3400/3746] lr: 1.836e-04, eta: 3:39:37, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6489, top5_acc: 0.8586, loss_cls: 2.0307, loss: 2.0307 +2024-12-31 18:48:38,390 - pyskl - INFO - Epoch [146][3500/3746] lr: 1.812e-04, eta: 3:38:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6488, top5_acc: 0.8653, loss_cls: 2.0048, loss: 2.0048 +2024-12-31 18:50:03,092 - pyskl - INFO - Epoch [146][3600/3746] lr: 1.788e-04, eta: 3:36:45, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6486, top5_acc: 0.8622, loss_cls: 2.0241, loss: 2.0241 +2024-12-31 18:51:28,078 - pyskl - INFO - Epoch [146][3700/3746] lr: 1.765e-04, eta: 3:35:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6553, top5_acc: 0.8627, loss_cls: 2.0156, loss: 2.0156 +2024-12-31 18:52:08,520 - pyskl - INFO - Saving checkpoint at 146 epochs +2024-12-31 18:54:06,505 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 18:54:07,201 - pyskl - INFO - +top1_acc 0.4471 +top5_acc 0.6962 +2024-12-31 18:54:07,201 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 18:54:07,242 - pyskl - INFO - +mean_acc 0.4469 +2024-12-31 18:54:07,254 - pyskl - INFO - Epoch(val) [146][309] top1_acc: 0.4471, top5_acc: 0.6962, mean_class_accuracy: 0.4469 +2024-12-31 18:58:18,149 - pyskl - INFO - Epoch [147][100/3746] lr: 1.730e-04, eta: 3:33:17, time: 2.509, data_time: 1.479, memory: 15990, top1_acc: 0.6634, top5_acc: 0.8725, loss_cls: 1.9655, loss: 1.9655 +2024-12-31 18:59:43,602 - pyskl - INFO - Epoch [147][200/3746] lr: 1.707e-04, eta: 3:31:51, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6664, top5_acc: 0.8722, loss_cls: 1.9553, loss: 1.9553 +2024-12-31 19:01:08,577 - pyskl - INFO - Epoch [147][300/3746] lr: 1.684e-04, eta: 3:30:25, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.6609, top5_acc: 0.8673, loss_cls: 1.9805, loss: 1.9805 +2024-12-31 19:02:33,607 - pyskl - INFO - Epoch [147][400/3746] lr: 1.661e-04, eta: 3:28:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6647, top5_acc: 0.8720, loss_cls: 1.9320, loss: 1.9320 +2024-12-31 19:03:58,332 - pyskl - INFO - Epoch [147][500/3746] lr: 1.639e-04, eta: 3:27:33, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6602, top5_acc: 0.8703, loss_cls: 1.9668, loss: 1.9668 +2024-12-31 19:05:23,328 - pyskl - INFO - Epoch [147][600/3746] lr: 1.616e-04, eta: 3:26:07, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6478, top5_acc: 0.8616, loss_cls: 2.0202, loss: 2.0202 +2024-12-31 19:06:48,878 - pyskl - INFO - Epoch [147][700/3746] lr: 1.594e-04, eta: 3:24:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6637, top5_acc: 0.8712, loss_cls: 1.9538, loss: 1.9538 +2024-12-31 19:08:13,672 - pyskl - INFO - Epoch [147][800/3746] lr: 1.572e-04, eta: 3:23:15, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6644, top5_acc: 0.8680, loss_cls: 1.9688, loss: 1.9688 +2024-12-31 19:09:38,926 - pyskl - INFO - Epoch [147][900/3746] lr: 1.550e-04, eta: 3:21:49, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6558, top5_acc: 0.8612, loss_cls: 1.9882, loss: 1.9882 +2024-12-31 19:11:04,284 - pyskl - INFO - Epoch [147][1000/3746] lr: 1.528e-04, eta: 3:20:23, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6645, top5_acc: 0.8705, loss_cls: 1.9551, loss: 1.9551 +2024-12-31 19:12:29,488 - pyskl - INFO - Epoch [147][1100/3746] lr: 1.506e-04, eta: 3:18:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6730, top5_acc: 0.8688, loss_cls: 1.9350, loss: 1.9350 +2024-12-31 19:13:55,049 - pyskl - INFO - Epoch [147][1200/3746] lr: 1.484e-04, eta: 3:17:31, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6614, top5_acc: 0.8666, loss_cls: 1.9699, loss: 1.9699 +2024-12-31 19:15:20,200 - pyskl - INFO - Epoch [147][1300/3746] lr: 1.463e-04, eta: 3:16:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6525, top5_acc: 0.8609, loss_cls: 2.0025, loss: 2.0025 +2024-12-31 19:16:45,409 - pyskl - INFO - Epoch [147][1400/3746] lr: 1.442e-04, eta: 3:14:39, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6600, top5_acc: 0.8678, loss_cls: 1.9764, loss: 1.9764 +2024-12-31 19:18:10,776 - pyskl - INFO - Epoch [147][1500/3746] lr: 1.420e-04, eta: 3:13:13, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6702, top5_acc: 0.8675, loss_cls: 1.9405, loss: 1.9405 +2024-12-31 19:19:36,361 - pyskl - INFO - Epoch [147][1600/3746] lr: 1.399e-04, eta: 3:11:47, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6602, top5_acc: 0.8691, loss_cls: 1.9886, loss: 1.9886 +2024-12-31 19:21:01,902 - pyskl - INFO - Epoch [147][1700/3746] lr: 1.379e-04, eta: 3:10:21, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6572, top5_acc: 0.8644, loss_cls: 1.9871, loss: 1.9871 +2024-12-31 19:22:27,763 - pyskl - INFO - Epoch [147][1800/3746] lr: 1.358e-04, eta: 3:08:55, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6656, top5_acc: 0.8698, loss_cls: 1.9625, loss: 1.9625 +2024-12-31 19:23:53,670 - pyskl - INFO - Epoch [147][1900/3746] lr: 1.337e-04, eta: 3:07:29, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6578, top5_acc: 0.8692, loss_cls: 1.9481, loss: 1.9481 +2024-12-31 19:25:19,077 - pyskl - INFO - Epoch [147][2000/3746] lr: 1.317e-04, eta: 3:06:03, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6594, top5_acc: 0.8723, loss_cls: 1.9632, loss: 1.9632 +2024-12-31 19:26:44,264 - pyskl - INFO - Epoch [147][2100/3746] lr: 1.297e-04, eta: 3:04:37, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.6625, top5_acc: 0.8709, loss_cls: 1.9780, loss: 1.9780 +2024-12-31 19:28:09,006 - pyskl - INFO - Epoch [147][2200/3746] lr: 1.277e-04, eta: 3:03:11, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6663, top5_acc: 0.8733, loss_cls: 1.9510, loss: 1.9510 +2024-12-31 19:29:33,910 - pyskl - INFO - Epoch [147][2300/3746] lr: 1.257e-04, eta: 3:01:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6623, top5_acc: 0.8616, loss_cls: 1.9797, loss: 1.9797 +2024-12-31 19:30:58,237 - pyskl - INFO - Epoch [147][2400/3746] lr: 1.237e-04, eta: 3:00:19, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6637, top5_acc: 0.8752, loss_cls: 1.9281, loss: 1.9281 +2024-12-31 19:32:23,356 - pyskl - INFO - Epoch [147][2500/3746] lr: 1.218e-04, eta: 2:58:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6536, top5_acc: 0.8611, loss_cls: 1.9876, loss: 1.9876 +2024-12-31 19:33:48,180 - pyskl - INFO - Epoch [147][2600/3746] lr: 1.198e-04, eta: 2:57:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6509, top5_acc: 0.8648, loss_cls: 1.9890, loss: 1.9890 +2024-12-31 19:35:13,169 - pyskl - INFO - Epoch [147][2700/3746] lr: 1.179e-04, eta: 2:56:01, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6619, top5_acc: 0.8636, loss_cls: 1.9602, loss: 1.9602 +2024-12-31 19:36:38,176 - pyskl - INFO - Epoch [147][2800/3746] lr: 1.160e-04, eta: 2:54:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6544, top5_acc: 0.8630, loss_cls: 2.0104, loss: 2.0104 +2024-12-31 19:38:02,569 - pyskl - INFO - Epoch [147][2900/3746] lr: 1.141e-04, eta: 2:53:09, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.6613, top5_acc: 0.8592, loss_cls: 1.9827, loss: 1.9827 +2024-12-31 19:39:27,100 - pyskl - INFO - Epoch [147][3000/3746] lr: 1.122e-04, eta: 2:51:43, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6483, top5_acc: 0.8566, loss_cls: 2.0240, loss: 2.0240 +2024-12-31 19:40:52,125 - pyskl - INFO - Epoch [147][3100/3746] lr: 1.103e-04, eta: 2:50:17, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6647, top5_acc: 0.8683, loss_cls: 1.9572, loss: 1.9572 +2024-12-31 19:42:16,633 - pyskl - INFO - Epoch [147][3200/3746] lr: 1.085e-04, eta: 2:48:51, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6639, top5_acc: 0.8698, loss_cls: 1.9640, loss: 1.9640 +2024-12-31 19:43:41,447 - pyskl - INFO - Epoch [147][3300/3746] lr: 1.067e-04, eta: 2:47:25, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6597, top5_acc: 0.8650, loss_cls: 1.9734, loss: 1.9734 +2024-12-31 19:45:06,232 - pyskl - INFO - Epoch [147][3400/3746] lr: 1.048e-04, eta: 2:45:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6656, top5_acc: 0.8691, loss_cls: 1.9507, loss: 1.9507 +2024-12-31 19:46:31,107 - pyskl - INFO - Epoch [147][3500/3746] lr: 1.030e-04, eta: 2:44:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6616, top5_acc: 0.8602, loss_cls: 1.9939, loss: 1.9939 +2024-12-31 19:47:55,761 - pyskl - INFO - Epoch [147][3600/3746] lr: 1.013e-04, eta: 2:43:07, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6547, top5_acc: 0.8684, loss_cls: 1.9730, loss: 1.9730 +2024-12-31 19:49:21,128 - pyskl - INFO - Epoch [147][3700/3746] lr: 9.949e-05, eta: 2:41:41, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6617, top5_acc: 0.8648, loss_cls: 1.9787, loss: 1.9787 +2024-12-31 19:50:01,851 - pyskl - INFO - Saving checkpoint at 147 epochs +2024-12-31 19:52:00,387 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 19:52:01,252 - pyskl - INFO - +top1_acc 0.4497 +top5_acc 0.6961 +2024-12-31 19:52:01,252 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 19:52:01,294 - pyskl - INFO - +mean_acc 0.4495 +2024-12-31 19:52:01,307 - pyskl - INFO - Epoch(val) [147][309] top1_acc: 0.4497, top5_acc: 0.6961, mean_class_accuracy: 0.4495 +2024-12-31 19:56:15,929 - pyskl - INFO - Epoch [148][100/3746] lr: 9.693e-05, eta: 2:39:38, time: 2.546, data_time: 1.506, memory: 15990, top1_acc: 0.6713, top5_acc: 0.8747, loss_cls: 1.9148, loss: 1.9148 +2024-12-31 19:57:41,319 - pyskl - INFO - Epoch [148][200/3746] lr: 9.520e-05, eta: 2:38:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6775, top5_acc: 0.8758, loss_cls: 1.9183, loss: 1.9183 +2024-12-31 19:59:06,429 - pyskl - INFO - Epoch [148][300/3746] lr: 9.348e-05, eta: 2:36:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6711, top5_acc: 0.8767, loss_cls: 1.9136, loss: 1.9136 +2024-12-31 20:00:31,327 - pyskl - INFO - Epoch [148][400/3746] lr: 9.178e-05, eta: 2:35:20, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6616, top5_acc: 0.8702, loss_cls: 1.9549, loss: 1.9549 +2024-12-31 20:01:56,581 - pyskl - INFO - Epoch [148][500/3746] lr: 9.010e-05, eta: 2:33:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6692, top5_acc: 0.8714, loss_cls: 1.9348, loss: 1.9348 +2024-12-31 20:03:21,782 - pyskl - INFO - Epoch [148][600/3746] lr: 8.843e-05, eta: 2:32:28, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6742, top5_acc: 0.8688, loss_cls: 1.9222, loss: 1.9222 +2024-12-31 20:04:47,255 - pyskl - INFO - Epoch [148][700/3746] lr: 8.678e-05, eta: 2:31:02, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6647, top5_acc: 0.8644, loss_cls: 1.9703, loss: 1.9703 +2024-12-31 20:06:12,179 - pyskl - INFO - Epoch [148][800/3746] lr: 8.514e-05, eta: 2:29:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6678, top5_acc: 0.8708, loss_cls: 1.9279, loss: 1.9279 +2024-12-31 20:07:36,998 - pyskl - INFO - Epoch [148][900/3746] lr: 8.351e-05, eta: 2:28:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6656, top5_acc: 0.8723, loss_cls: 1.9316, loss: 1.9316 +2024-12-31 20:09:02,068 - pyskl - INFO - Epoch [148][1000/3746] lr: 8.191e-05, eta: 2:26:44, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6697, top5_acc: 0.8769, loss_cls: 1.9267, loss: 1.9267 +2024-12-31 20:10:27,557 - pyskl - INFO - Epoch [148][1100/3746] lr: 8.031e-05, eta: 2:25:18, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6622, top5_acc: 0.8717, loss_cls: 1.9453, loss: 1.9453 +2024-12-31 20:11:52,906 - pyskl - INFO - Epoch [148][1200/3746] lr: 7.874e-05, eta: 2:23:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6709, top5_acc: 0.8709, loss_cls: 1.9441, loss: 1.9441 +2024-12-31 20:13:18,287 - pyskl - INFO - Epoch [148][1300/3746] lr: 7.718e-05, eta: 2:22:26, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6672, top5_acc: 0.8741, loss_cls: 1.9146, loss: 1.9146 +2024-12-31 20:14:43,917 - pyskl - INFO - Epoch [148][1400/3746] lr: 7.563e-05, eta: 2:21:00, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6709, top5_acc: 0.8736, loss_cls: 1.9143, loss: 1.9143 +2024-12-31 20:16:09,508 - pyskl - INFO - Epoch [148][1500/3746] lr: 7.410e-05, eta: 2:19:34, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6727, top5_acc: 0.8667, loss_cls: 1.9459, loss: 1.9459 +2024-12-31 20:17:35,563 - pyskl - INFO - Epoch [148][1600/3746] lr: 7.259e-05, eta: 2:18:08, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.6773, top5_acc: 0.8712, loss_cls: 1.9228, loss: 1.9228 +2024-12-31 20:19:01,064 - pyskl - INFO - Epoch [148][1700/3746] lr: 7.109e-05, eta: 2:16:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6634, top5_acc: 0.8717, loss_cls: 1.9384, loss: 1.9384 +2024-12-31 20:20:26,436 - pyskl - INFO - Epoch [148][1800/3746] lr: 6.961e-05, eta: 2:15:16, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6700, top5_acc: 0.8716, loss_cls: 1.9419, loss: 1.9419 +2024-12-31 20:21:51,764 - pyskl - INFO - Epoch [148][1900/3746] lr: 6.814e-05, eta: 2:13:50, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6717, top5_acc: 0.8683, loss_cls: 1.9342, loss: 1.9342 +2024-12-31 20:23:17,260 - pyskl - INFO - Epoch [148][2000/3746] lr: 6.669e-05, eta: 2:12:24, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6586, top5_acc: 0.8670, loss_cls: 1.9677, loss: 1.9677 +2024-12-31 20:24:42,548 - pyskl - INFO - Epoch [148][2100/3746] lr: 6.526e-05, eta: 2:10:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6681, top5_acc: 0.8772, loss_cls: 1.9360, loss: 1.9360 +2024-12-31 20:26:06,919 - pyskl - INFO - Epoch [148][2200/3746] lr: 6.384e-05, eta: 2:09:32, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.6591, top5_acc: 0.8775, loss_cls: 1.9368, loss: 1.9368 +2024-12-31 20:27:31,945 - pyskl - INFO - Epoch [148][2300/3746] lr: 6.243e-05, eta: 2:08:06, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.6769, top5_acc: 0.8741, loss_cls: 1.9217, loss: 1.9217 +2024-12-31 20:28:57,498 - pyskl - INFO - Epoch [148][2400/3746] lr: 6.104e-05, eta: 2:06:40, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6584, top5_acc: 0.8594, loss_cls: 1.9854, loss: 1.9854 +2024-12-31 20:30:23,369 - pyskl - INFO - Epoch [148][2500/3746] lr: 5.967e-05, eta: 2:05:14, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6794, top5_acc: 0.8733, loss_cls: 1.9101, loss: 1.9101 +2024-12-31 20:31:48,717 - pyskl - INFO - Epoch [148][2600/3746] lr: 5.831e-05, eta: 2:03:48, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6711, top5_acc: 0.8756, loss_cls: 1.9205, loss: 1.9205 +2024-12-31 20:33:14,020 - pyskl - INFO - Epoch [148][2700/3746] lr: 5.697e-05, eta: 2:02:22, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6714, top5_acc: 0.8714, loss_cls: 1.9477, loss: 1.9477 +2024-12-31 20:34:38,940 - pyskl - INFO - Epoch [148][2800/3746] lr: 5.564e-05, eta: 2:00:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6741, top5_acc: 0.8741, loss_cls: 1.9167, loss: 1.9167 +2024-12-31 20:36:03,361 - pyskl - INFO - Epoch [148][2900/3746] lr: 5.433e-05, eta: 1:59:30, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6663, top5_acc: 0.8692, loss_cls: 1.9461, loss: 1.9461 +2024-12-31 20:37:28,061 - pyskl - INFO - Epoch [148][3000/3746] lr: 5.304e-05, eta: 1:58:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6652, top5_acc: 0.8723, loss_cls: 1.9375, loss: 1.9375 +2024-12-31 20:38:53,126 - pyskl - INFO - Epoch [148][3100/3746] lr: 5.176e-05, eta: 1:56:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6617, top5_acc: 0.8688, loss_cls: 1.9802, loss: 1.9802 +2024-12-31 20:40:17,792 - pyskl - INFO - Epoch [148][3200/3746] lr: 5.050e-05, eta: 1:55:12, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6780, top5_acc: 0.8802, loss_cls: 1.8984, loss: 1.8984 +2024-12-31 20:41:42,980 - pyskl - INFO - Epoch [148][3300/3746] lr: 4.925e-05, eta: 1:53:46, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6811, top5_acc: 0.8753, loss_cls: 1.9244, loss: 1.9244 +2024-12-31 20:43:08,200 - pyskl - INFO - Epoch [148][3400/3746] lr: 4.801e-05, eta: 1:52:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6842, top5_acc: 0.8797, loss_cls: 1.8773, loss: 1.8773 +2024-12-31 20:44:33,191 - pyskl - INFO - Epoch [148][3500/3746] lr: 4.680e-05, eta: 1:50:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6575, top5_acc: 0.8717, loss_cls: 1.9682, loss: 1.9682 +2024-12-31 20:45:57,934 - pyskl - INFO - Epoch [148][3600/3746] lr: 4.560e-05, eta: 1:49:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6600, top5_acc: 0.8630, loss_cls: 1.9630, loss: 1.9630 +2024-12-31 20:47:22,722 - pyskl - INFO - Epoch [148][3700/3746] lr: 4.441e-05, eta: 1:48:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6519, top5_acc: 0.8672, loss_cls: 1.9897, loss: 1.9897 +2024-12-31 20:48:03,386 - pyskl - INFO - Saving checkpoint at 148 epochs +2024-12-31 20:50:02,094 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 20:50:03,053 - pyskl - INFO - +top1_acc 0.4478 +top5_acc 0.6967 +2024-12-31 20:50:03,053 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 20:50:03,096 - pyskl - INFO - +mean_acc 0.4476 +2024-12-31 20:50:03,110 - pyskl - INFO - Epoch(val) [148][309] top1_acc: 0.4478, top5_acc: 0.6967, mean_class_accuracy: 0.4476 +2024-12-31 20:54:11,603 - pyskl - INFO - Epoch [149][100/3746] lr: 4.271e-05, eta: 1:45:58, time: 2.485, data_time: 1.463, memory: 15990, top1_acc: 0.6734, top5_acc: 0.8791, loss_cls: 1.9022, loss: 1.9022 +2024-12-31 20:55:36,898 - pyskl - INFO - Epoch [149][200/3746] lr: 4.156e-05, eta: 1:44:32, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6672, top5_acc: 0.8689, loss_cls: 1.9611, loss: 1.9611 +2024-12-31 20:57:01,641 - pyskl - INFO - Epoch [149][300/3746] lr: 4.043e-05, eta: 1:43:06, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6713, top5_acc: 0.8725, loss_cls: 1.9401, loss: 1.9401 +2024-12-31 20:58:26,827 - pyskl - INFO - Epoch [149][400/3746] lr: 3.931e-05, eta: 1:41:40, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6659, top5_acc: 0.8680, loss_cls: 1.9382, loss: 1.9382 +2024-12-31 20:59:51,479 - pyskl - INFO - Epoch [149][500/3746] lr: 3.821e-05, eta: 1:40:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6755, top5_acc: 0.8659, loss_cls: 1.9402, loss: 1.9402 +2024-12-31 21:01:16,272 - pyskl - INFO - Epoch [149][600/3746] lr: 3.713e-05, eta: 1:38:48, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6741, top5_acc: 0.8723, loss_cls: 1.9185, loss: 1.9185 +2024-12-31 21:02:40,647 - pyskl - INFO - Epoch [149][700/3746] lr: 3.606e-05, eta: 1:37:21, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6758, top5_acc: 0.8758, loss_cls: 1.9210, loss: 1.9210 +2024-12-31 21:04:05,677 - pyskl - INFO - Epoch [149][800/3746] lr: 3.500e-05, eta: 1:35:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6698, top5_acc: 0.8733, loss_cls: 1.9342, loss: 1.9342 +2024-12-31 21:05:30,075 - pyskl - INFO - Epoch [149][900/3746] lr: 3.397e-05, eta: 1:34:29, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6741, top5_acc: 0.8769, loss_cls: 1.9058, loss: 1.9058 +2024-12-31 21:06:54,791 - pyskl - INFO - Epoch [149][1000/3746] lr: 3.294e-05, eta: 1:33:03, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6694, top5_acc: 0.8719, loss_cls: 1.9482, loss: 1.9482 +2024-12-31 21:08:18,942 - pyskl - INFO - Epoch [149][1100/3746] lr: 3.194e-05, eta: 1:31:37, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6741, top5_acc: 0.8762, loss_cls: 1.8969, loss: 1.8969 +2024-12-31 21:09:43,555 - pyskl - INFO - Epoch [149][1200/3746] lr: 3.095e-05, eta: 1:30:11, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6759, top5_acc: 0.8773, loss_cls: 1.9111, loss: 1.9111 +2024-12-31 21:11:08,380 - pyskl - INFO - Epoch [149][1300/3746] lr: 2.997e-05, eta: 1:28:45, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6709, top5_acc: 0.8745, loss_cls: 1.9416, loss: 1.9416 +2024-12-31 21:12:33,198 - pyskl - INFO - Epoch [149][1400/3746] lr: 2.901e-05, eta: 1:27:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6752, top5_acc: 0.8628, loss_cls: 1.9437, loss: 1.9437 +2024-12-31 21:13:57,698 - pyskl - INFO - Epoch [149][1500/3746] lr: 2.807e-05, eta: 1:25:53, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6733, top5_acc: 0.8692, loss_cls: 1.9196, loss: 1.9196 +2024-12-31 21:15:22,147 - pyskl - INFO - Epoch [149][1600/3746] lr: 2.714e-05, eta: 1:24:27, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6830, top5_acc: 0.8834, loss_cls: 1.8627, loss: 1.8627 +2024-12-31 21:16:46,513 - pyskl - INFO - Epoch [149][1700/3746] lr: 2.622e-05, eta: 1:23:01, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6753, top5_acc: 0.8745, loss_cls: 1.8898, loss: 1.8898 +2024-12-31 21:18:11,325 - pyskl - INFO - Epoch [149][1800/3746] lr: 2.533e-05, eta: 1:21:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6808, top5_acc: 0.8730, loss_cls: 1.9368, loss: 1.9368 +2024-12-31 21:19:35,724 - pyskl - INFO - Epoch [149][1900/3746] lr: 2.444e-05, eta: 1:20:09, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6823, top5_acc: 0.8727, loss_cls: 1.9069, loss: 1.9069 +2024-12-31 21:21:00,622 - pyskl - INFO - Epoch [149][2000/3746] lr: 2.358e-05, eta: 1:18:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6722, top5_acc: 0.8784, loss_cls: 1.9217, loss: 1.9217 +2024-12-31 21:22:25,520 - pyskl - INFO - Epoch [149][2100/3746] lr: 2.273e-05, eta: 1:17:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6673, top5_acc: 0.8719, loss_cls: 1.9473, loss: 1.9473 +2024-12-31 21:23:50,952 - pyskl - INFO - Epoch [149][2200/3746] lr: 2.189e-05, eta: 1:15:51, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6677, top5_acc: 0.8709, loss_cls: 1.9296, loss: 1.9296 +2024-12-31 21:25:15,909 - pyskl - INFO - Epoch [149][2300/3746] lr: 2.107e-05, eta: 1:14:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6717, top5_acc: 0.8727, loss_cls: 1.9219, loss: 1.9219 +2024-12-31 21:26:40,782 - pyskl - INFO - Epoch [149][2400/3746] lr: 2.027e-05, eta: 1:12:59, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6691, top5_acc: 0.8731, loss_cls: 1.9215, loss: 1.9215 +2024-12-31 21:28:05,483 - pyskl - INFO - Epoch [149][2500/3746] lr: 1.948e-05, eta: 1:11:33, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6608, top5_acc: 0.8725, loss_cls: 1.9639, loss: 1.9639 +2024-12-31 21:29:30,270 - pyskl - INFO - Epoch [149][2600/3746] lr: 1.871e-05, eta: 1:10:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6698, top5_acc: 0.8800, loss_cls: 1.9048, loss: 1.9048 +2024-12-31 21:30:54,948 - pyskl - INFO - Epoch [149][2700/3746] lr: 1.795e-05, eta: 1:08:41, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6697, top5_acc: 0.8731, loss_cls: 1.9317, loss: 1.9317 +2024-12-31 21:32:20,392 - pyskl - INFO - Epoch [149][2800/3746] lr: 1.721e-05, eta: 1:07:15, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.6756, top5_acc: 0.8744, loss_cls: 1.8907, loss: 1.8907 +2024-12-31 21:33:45,958 - pyskl - INFO - Epoch [149][2900/3746] lr: 1.649e-05, eta: 1:05:49, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6758, top5_acc: 0.8784, loss_cls: 1.9006, loss: 1.9006 +2024-12-31 21:35:11,021 - pyskl - INFO - Epoch [149][3000/3746] lr: 1.578e-05, eta: 1:04:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6814, top5_acc: 0.8783, loss_cls: 1.8851, loss: 1.8851 +2024-12-31 21:36:35,986 - pyskl - INFO - Epoch [149][3100/3746] lr: 1.508e-05, eta: 1:02:57, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.6791, top5_acc: 0.8789, loss_cls: 1.8824, loss: 1.8824 +2024-12-31 21:38:00,974 - pyskl - INFO - Epoch [149][3200/3746] lr: 1.440e-05, eta: 1:01:31, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6672, top5_acc: 0.8758, loss_cls: 1.9155, loss: 1.9155 +2024-12-31 21:39:26,142 - pyskl - INFO - Epoch [149][3300/3746] lr: 1.374e-05, eta: 1:00:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6825, top5_acc: 0.8842, loss_cls: 1.8769, loss: 1.8769 +2024-12-31 21:40:51,157 - pyskl - INFO - Epoch [149][3400/3746] lr: 1.309e-05, eta: 0:58:39, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6652, top5_acc: 0.8662, loss_cls: 1.9575, loss: 1.9575 +2024-12-31 21:42:15,584 - pyskl - INFO - Epoch [149][3500/3746] lr: 1.246e-05, eta: 0:57:13, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6752, top5_acc: 0.8780, loss_cls: 1.9133, loss: 1.9133 +2024-12-31 21:43:40,718 - pyskl - INFO - Epoch [149][3600/3746] lr: 1.184e-05, eta: 0:55:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6639, top5_acc: 0.8750, loss_cls: 1.9586, loss: 1.9586 +2024-12-31 21:45:05,710 - pyskl - INFO - Epoch [149][3700/3746] lr: 1.124e-05, eta: 0:54:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6750, top5_acc: 0.8772, loss_cls: 1.9290, loss: 1.9290 +2024-12-31 21:45:46,541 - pyskl - INFO - Saving checkpoint at 149 epochs +2024-12-31 21:47:44,610 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 21:47:45,385 - pyskl - INFO - +top1_acc 0.4479 +top5_acc 0.6957 +2024-12-31 21:47:45,385 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 21:47:45,439 - pyskl - INFO - +mean_acc 0.4477 +2024-12-31 21:47:45,455 - pyskl - INFO - Epoch(val) [149][309] top1_acc: 0.4479, top5_acc: 0.6957, mean_class_accuracy: 0.4477 +2024-12-31 21:52:01,535 - pyskl - INFO - Epoch [150][100/3746] lr: 1.039e-05, eta: 0:52:16, time: 2.561, data_time: 1.525, memory: 15990, top1_acc: 0.6734, top5_acc: 0.8773, loss_cls: 1.9240, loss: 1.9240 +2024-12-31 21:53:26,715 - pyskl - INFO - Epoch [150][200/3746] lr: 9.832e-06, eta: 0:50:50, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6733, top5_acc: 0.8775, loss_cls: 1.9033, loss: 1.9033 +2024-12-31 21:54:51,950 - pyskl - INFO - Epoch [150][300/3746] lr: 9.285e-06, eta: 0:49:24, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6880, top5_acc: 0.8842, loss_cls: 1.8542, loss: 1.8542 +2024-12-31 21:56:17,139 - pyskl - INFO - Epoch [150][400/3746] lr: 8.754e-06, eta: 0:47:58, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6781, top5_acc: 0.8764, loss_cls: 1.9021, loss: 1.9021 +2024-12-31 21:57:42,524 - pyskl - INFO - Epoch [150][500/3746] lr: 8.239e-06, eta: 0:46:32, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6817, top5_acc: 0.8802, loss_cls: 1.8837, loss: 1.8837 +2024-12-31 21:59:07,890 - pyskl - INFO - Epoch [150][600/3746] lr: 7.739e-06, eta: 0:45:06, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6709, top5_acc: 0.8756, loss_cls: 1.9142, loss: 1.9142 +2024-12-31 22:00:33,241 - pyskl - INFO - Epoch [150][700/3746] lr: 7.255e-06, eta: 0:43:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6733, top5_acc: 0.8778, loss_cls: 1.9053, loss: 1.9053 +2024-12-31 22:01:58,217 - pyskl - INFO - Epoch [150][800/3746] lr: 6.787e-06, eta: 0:42:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6667, top5_acc: 0.8723, loss_cls: 1.9208, loss: 1.9208 +2024-12-31 22:03:22,977 - pyskl - INFO - Epoch [150][900/3746] lr: 6.334e-06, eta: 0:40:48, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6827, top5_acc: 0.8800, loss_cls: 1.8831, loss: 1.8831 +2024-12-31 22:04:48,058 - pyskl - INFO - Epoch [150][1000/3746] lr: 5.897e-06, eta: 0:39:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6817, top5_acc: 0.8850, loss_cls: 1.8540, loss: 1.8540 +2024-12-31 22:06:12,644 - pyskl - INFO - Epoch [150][1100/3746] lr: 5.475e-06, eta: 0:37:56, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6748, top5_acc: 0.8759, loss_cls: 1.9259, loss: 1.9259 +2024-12-31 22:07:37,688 - pyskl - INFO - Epoch [150][1200/3746] lr: 5.070e-06, eta: 0:36:30, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6816, top5_acc: 0.8762, loss_cls: 1.9064, loss: 1.9064 +2024-12-31 22:09:02,518 - pyskl - INFO - Epoch [150][1300/3746] lr: 4.679e-06, eta: 0:35:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6734, top5_acc: 0.8723, loss_cls: 1.9279, loss: 1.9279 +2024-12-31 22:10:27,752 - pyskl - INFO - Epoch [150][1400/3746] lr: 4.305e-06, eta: 0:33:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6789, top5_acc: 0.8780, loss_cls: 1.8912, loss: 1.8912 +2024-12-31 22:11:52,712 - pyskl - INFO - Epoch [150][1500/3746] lr: 3.946e-06, eta: 0:32:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6639, top5_acc: 0.8677, loss_cls: 1.9417, loss: 1.9417 +2024-12-31 22:13:17,631 - pyskl - INFO - Epoch [150][1600/3746] lr: 3.602e-06, eta: 0:30:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6741, top5_acc: 0.8698, loss_cls: 1.9338, loss: 1.9338 +2024-12-31 22:14:42,522 - pyskl - INFO - Epoch [150][1700/3746] lr: 3.275e-06, eta: 0:29:20, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6792, top5_acc: 0.8755, loss_cls: 1.8938, loss: 1.8938 +2024-12-31 22:16:07,524 - pyskl - INFO - Epoch [150][1800/3746] lr: 2.962e-06, eta: 0:27:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6806, top5_acc: 0.8750, loss_cls: 1.9100, loss: 1.9100 +2024-12-31 22:17:32,719 - pyskl - INFO - Epoch [150][1900/3746] lr: 2.666e-06, eta: 0:26:28, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6675, top5_acc: 0.8752, loss_cls: 1.9417, loss: 1.9417 +2024-12-31 22:18:57,312 - pyskl - INFO - Epoch [150][2000/3746] lr: 2.385e-06, eta: 0:25:02, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6737, top5_acc: 0.8753, loss_cls: 1.9047, loss: 1.9047 +2024-12-31 22:20:22,643 - pyskl - INFO - Epoch [150][2100/3746] lr: 2.120e-06, eta: 0:23:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6808, top5_acc: 0.8834, loss_cls: 1.8940, loss: 1.8940 +2024-12-31 22:21:47,341 - pyskl - INFO - Epoch [150][2200/3746] lr: 1.870e-06, eta: 0:22:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6730, top5_acc: 0.8731, loss_cls: 1.9225, loss: 1.9225 +2024-12-31 22:23:12,561 - pyskl - INFO - Epoch [150][2300/3746] lr: 1.636e-06, eta: 0:20:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6727, top5_acc: 0.8784, loss_cls: 1.8842, loss: 1.8842 +2024-12-31 22:24:37,580 - pyskl - INFO - Epoch [150][2400/3746] lr: 1.418e-06, eta: 0:19:17, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6750, top5_acc: 0.8772, loss_cls: 1.9073, loss: 1.9073 +2024-12-31 22:26:02,870 - pyskl - INFO - Epoch [150][2500/3746] lr: 1.215e-06, eta: 0:17:51, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6613, top5_acc: 0.8683, loss_cls: 1.9451, loss: 1.9451 +2024-12-31 22:27:27,763 - pyskl - INFO - Epoch [150][2600/3746] lr: 1.028e-06, eta: 0:16:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6811, top5_acc: 0.8764, loss_cls: 1.8913, loss: 1.8913 +2024-12-31 22:28:53,290 - pyskl - INFO - Epoch [150][2700/3746] lr: 8.567e-07, eta: 0:14:59, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6763, top5_acc: 0.8739, loss_cls: 1.9053, loss: 1.9053 +2024-12-31 22:30:18,582 - pyskl - INFO - Epoch [150][2800/3746] lr: 7.008e-07, eta: 0:13:33, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6787, top5_acc: 0.8770, loss_cls: 1.8991, loss: 1.8991 +2024-12-31 22:31:44,409 - pyskl - INFO - Epoch [150][2900/3746] lr: 5.606e-07, eta: 0:12:07, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6620, top5_acc: 0.8658, loss_cls: 1.9619, loss: 1.9619 +2024-12-31 22:33:08,786 - pyskl - INFO - Epoch [150][3000/3746] lr: 4.361e-07, eta: 0:10:41, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6736, top5_acc: 0.8773, loss_cls: 1.9195, loss: 1.9195 +2024-12-31 22:34:33,412 - pyskl - INFO - Epoch [150][3100/3746] lr: 3.271e-07, eta: 0:09:15, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6770, top5_acc: 0.8747, loss_cls: 1.8881, loss: 1.8881 +2024-12-31 22:35:58,422 - pyskl - INFO - Epoch [150][3200/3746] lr: 2.338e-07, eta: 0:07:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6744, top5_acc: 0.8722, loss_cls: 1.9336, loss: 1.9336 +2024-12-31 22:37:22,919 - pyskl - INFO - Epoch [150][3300/3746] lr: 1.561e-07, eta: 0:06:23, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6747, top5_acc: 0.8719, loss_cls: 1.9379, loss: 1.9379 +2024-12-31 22:38:47,520 - pyskl - INFO - Epoch [150][3400/3746] lr: 9.410e-08, eta: 0:04:57, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.6787, top5_acc: 0.8827, loss_cls: 1.8901, loss: 1.8901 +2024-12-31 22:40:12,268 - pyskl - INFO - Epoch [150][3500/3746] lr: 4.768e-08, eta: 0:03:31, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6725, top5_acc: 0.8753, loss_cls: 1.9129, loss: 1.9129 +2024-12-31 22:41:37,103 - pyskl - INFO - Epoch [150][3600/3746] lr: 1.689e-08, eta: 0:02:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6758, top5_acc: 0.8795, loss_cls: 1.9018, loss: 1.9018 +2024-12-31 22:43:01,954 - pyskl - INFO - Epoch [150][3700/3746] lr: 1.726e-09, eta: 0:00:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6664, top5_acc: 0.8747, loss_cls: 1.9258, loss: 1.9258 +2024-12-31 22:43:42,702 - pyskl - INFO - Saving checkpoint at 150 epochs +2024-12-31 22:45:41,670 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 22:45:42,336 - pyskl - INFO - +top1_acc 0.4483 +top5_acc 0.6938 +2024-12-31 22:45:42,336 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 22:45:42,382 - pyskl - INFO - +mean_acc 0.4481 +2024-12-31 22:45:42,397 - pyskl - INFO - Epoch(val) [150][309] top1_acc: 0.4483, top5_acc: 0.6938, mean_class_accuracy: 0.4481 +2024-12-31 22:45:58,805 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-12-31 22:58:55,450 - pyskl - INFO - Testing results of the last checkpoint +2024-12-31 22:58:55,450 - pyskl - INFO - top1_acc: 0.4622 +2024-12-31 22:58:55,450 - pyskl - INFO - top5_acc: 0.7085 +2024-12-31 22:58:55,450 - pyskl - INFO - mean_class_accuracy: 0.4620 +2024-12-31 22:58:55,451 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/k400/k_2/best_top1_acc_epoch_145.pth +2024-12-31 23:11:53,589 - pyskl - INFO - Testing results of the best checkpoint +2024-12-31 23:11:53,589 - pyskl - INFO - top1_acc: 0.4599 +2024-12-31 23:11:53,589 - pyskl - INFO - top5_acc: 0.7061 +2024-12-31 23:11:53,589 - pyskl - INFO - mean_class_accuracy: 0.4596 diff --git a/k400/k_2/20241226_015115.log.json b/k400/k_2/20241226_015115.log.json new file mode 100644 index 0000000000000000000000000000000000000000..b206461b4a68e48d5120c6c84138b7619e3dd1eb --- /dev/null +++ b/k400/k_2/20241226_015115.log.json @@ -0,0 +1,5701 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 776052246, "config_name": "k_2.py", "work_dir": "k_2", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.1, "memory": 15990, "data_time": 1.4794, "top1_acc": 0.0075, "top5_acc": 0.03859, "loss_cls": 6.38441, "loss": 6.38441, "time": 2.19565} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.1, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.01609, "top5_acc": 0.06016, "loss_cls": 6.26958, "loss": 6.26958, "time": 0.70653} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.1, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.02047, "top5_acc": 0.08594, "loss_cls": 6.07558, "loss": 6.07558, "time": 0.70896} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.1, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.02781, "top5_acc": 0.09844, "loss_cls": 5.96214, "loss": 5.96214, "time": 0.70419} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.1, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.03188, "top5_acc": 0.11219, "loss_cls": 5.87126, "loss": 5.87126, "time": 0.70499} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.1, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.03281, "top5_acc": 0.12172, "loss_cls": 5.8084, "loss": 5.8084, "time": 0.7083} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.1, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.03656, "top5_acc": 0.13062, "loss_cls": 5.77084, "loss": 5.77084, "time": 0.70637} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.1, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.03641, "top5_acc": 0.13266, "loss_cls": 5.74048, "loss": 5.74048, "time": 0.70913} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.1, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.04047, "top5_acc": 0.13797, "loss_cls": 5.69188, "loss": 5.69188, "time": 0.7094} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.1, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.04188, "top5_acc": 0.15438, "loss_cls": 5.66302, "loss": 5.66302, "time": 0.71264} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.1, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.05141, "top5_acc": 0.16609, "loss_cls": 5.6124, "loss": 5.6124, "time": 0.71268} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.1, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.05422, "top5_acc": 0.16922, "loss_cls": 5.60929, "loss": 5.60929, "time": 0.71371} +{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.1, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.04891, "top5_acc": 0.16844, "loss_cls": 5.60671, "loss": 5.60671, "time": 0.7143} +{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.1, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.05078, "top5_acc": 0.17734, "loss_cls": 5.54569, "loss": 5.54569, "time": 0.72026} +{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.1, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.055, "top5_acc": 0.18, "loss_cls": 5.55394, "loss": 5.55394, "time": 0.7146} +{"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.1, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.06203, "top5_acc": 0.18938, "loss_cls": 5.51402, "loss": 5.51402, "time": 0.71415} +{"mode": "train", "epoch": 1, "iter": 1700, "lr": 0.1, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.06391, "top5_acc": 0.2, "loss_cls": 5.47746, "loss": 5.47746, "time": 0.71341} +{"mode": "train", "epoch": 1, "iter": 1800, "lr": 0.1, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.06703, "top5_acc": 0.20516, "loss_cls": 5.44558, "loss": 5.44558, "time": 0.71433} +{"mode": "train", "epoch": 1, "iter": 1900, "lr": 0.1, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.06641, "top5_acc": 0.19891, "loss_cls": 5.46793, "loss": 5.46793, "time": 0.71564} +{"mode": "train", "epoch": 1, "iter": 2000, "lr": 0.1, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.06828, "top5_acc": 0.215, "loss_cls": 5.44709, "loss": 5.44709, "time": 0.71827} +{"mode": "train", "epoch": 1, "iter": 2100, "lr": 0.1, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.06875, "top5_acc": 0.21031, "loss_cls": 5.42076, "loss": 5.42076, "time": 0.71359} +{"mode": "train", "epoch": 1, "iter": 2200, "lr": 0.1, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.07938, "top5_acc": 0.22812, "loss_cls": 5.34765, "loss": 5.34765, "time": 0.7161} +{"mode": "train", "epoch": 1, "iter": 2300, "lr": 0.1, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.0775, "top5_acc": 0.21766, "loss_cls": 5.35809, "loss": 5.35809, "time": 0.71648} +{"mode": "train", "epoch": 1, "iter": 2400, "lr": 0.1, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.07594, "top5_acc": 0.21594, "loss_cls": 5.39511, "loss": 5.39511, "time": 0.71472} +{"mode": "train", "epoch": 1, "iter": 2500, "lr": 0.1, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.08, "top5_acc": 0.23594, "loss_cls": 5.3205, "loss": 5.3205, "time": 0.71394} +{"mode": "train", "epoch": 1, "iter": 2600, "lr": 0.09999, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.08047, "top5_acc": 0.23688, "loss_cls": 5.3554, "loss": 5.3554, "time": 0.71399} +{"mode": "train", "epoch": 1, "iter": 2700, "lr": 0.09999, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.08, "top5_acc": 0.23984, "loss_cls": 5.30375, "loss": 5.30375, "time": 0.71566} +{"mode": "train", "epoch": 1, "iter": 2800, "lr": 0.09999, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.08625, "top5_acc": 0.24594, "loss_cls": 5.23025, "loss": 5.23025, "time": 0.71501} +{"mode": "train", "epoch": 1, "iter": 2900, "lr": 0.09999, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.08047, "top5_acc": 0.24562, "loss_cls": 5.2553, "loss": 5.2553, "time": 0.71535} +{"mode": "train", "epoch": 1, "iter": 3000, "lr": 0.09999, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.09, "top5_acc": 0.25516, "loss_cls": 5.21719, "loss": 5.21719, "time": 0.71591} +{"mode": "train", "epoch": 1, "iter": 3100, "lr": 0.09999, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.09719, "top5_acc": 0.25766, "loss_cls": 5.24062, "loss": 5.24062, "time": 0.71623} +{"mode": "train", "epoch": 1, "iter": 3200, "lr": 0.09999, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.09016, "top5_acc": 0.25547, "loss_cls": 5.24574, "loss": 5.24574, "time": 0.71808} +{"mode": "train", "epoch": 1, "iter": 3300, "lr": 0.09999, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.08828, "top5_acc": 0.25562, "loss_cls": 5.22946, "loss": 5.22946, "time": 0.71742} +{"mode": "train", "epoch": 1, "iter": 3400, "lr": 0.09999, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.09906, "top5_acc": 0.27062, "loss_cls": 5.1792, "loss": 5.1792, "time": 0.71555} +{"mode": "train", "epoch": 1, "iter": 3500, "lr": 0.09999, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.09656, "top5_acc": 0.26141, "loss_cls": 5.20241, "loss": 5.20241, "time": 0.71458} +{"mode": "train", "epoch": 1, "iter": 3600, "lr": 0.09999, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.10141, "top5_acc": 0.26672, "loss_cls": 5.16554, "loss": 5.16554, "time": 0.71233} +{"mode": "train", "epoch": 1, "iter": 3700, "lr": 0.09999, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.09844, "top5_acc": 0.26609, "loss_cls": 5.16665, "loss": 5.16665, "time": 0.71419} +{"mode": "val", "epoch": 1, "iter": 309, "lr": 0.09999, "top1_acc": 0.05921, "top5_acc": 0.18467, "mean_class_accuracy": 0.05923} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.09999, "memory": 15990, "data_time": 1.45983, "top1_acc": 0.10375, "top5_acc": 0.27484, "loss_cls": 5.1446, "loss": 5.1446, "time": 2.16884} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.09999, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.10453, "top5_acc": 0.28344, "loss_cls": 5.12193, "loss": 5.12193, "time": 0.70609} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.09999, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.10094, "top5_acc": 0.27656, "loss_cls": 5.15406, "loss": 5.15406, "time": 0.70731} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.09999, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.10172, "top5_acc": 0.28422, "loss_cls": 5.14547, "loss": 5.14547, "time": 0.7064} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.09999, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.10938, "top5_acc": 0.28562, "loss_cls": 5.11588, "loss": 5.11588, "time": 0.70547} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.09999, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.11234, "top5_acc": 0.29063, "loss_cls": 5.07342, "loss": 5.07342, "time": 0.70843} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.09998, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.10625, "top5_acc": 0.29922, "loss_cls": 5.05265, "loss": 5.05265, "time": 0.71364} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.09998, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.11125, "top5_acc": 0.29594, "loss_cls": 5.05252, "loss": 5.05252, "time": 0.71595} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.09998, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.11906, "top5_acc": 0.30703, "loss_cls": 5.01241, "loss": 5.01241, "time": 0.71378} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.09998, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.11922, "top5_acc": 0.30766, "loss_cls": 4.99736, "loss": 4.99736, "time": 0.71342} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.09998, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.12016, "top5_acc": 0.31094, "loss_cls": 5.00018, "loss": 5.00018, "time": 0.71611} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.09998, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.12188, "top5_acc": 0.31531, "loss_cls": 5.00786, "loss": 5.00786, "time": 0.71706} +{"mode": "train", "epoch": 2, "iter": 1300, "lr": 0.09998, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.12078, "top5_acc": 0.31719, "loss_cls": 4.97192, "loss": 4.97192, "time": 0.71828} +{"mode": "train", "epoch": 2, "iter": 1400, "lr": 0.09998, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.12766, "top5_acc": 0.32, "loss_cls": 4.95668, "loss": 4.95668, "time": 0.7133} +{"mode": "train", "epoch": 2, "iter": 1500, "lr": 0.09998, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.12547, "top5_acc": 0.31953, "loss_cls": 4.93942, "loss": 4.93942, "time": 0.71601} +{"mode": "train", "epoch": 2, "iter": 1600, "lr": 0.09998, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.12594, "top5_acc": 0.32781, "loss_cls": 4.90792, "loss": 4.90792, "time": 0.72293} +{"mode": "train", "epoch": 2, "iter": 1700, "lr": 0.09998, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.12922, "top5_acc": 0.31875, "loss_cls": 4.9491, "loss": 4.9491, "time": 0.71652} +{"mode": "train", "epoch": 2, "iter": 1800, "lr": 0.09998, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.12687, "top5_acc": 0.32844, "loss_cls": 4.93554, "loss": 4.93554, "time": 0.71573} +{"mode": "train", "epoch": 2, "iter": 1900, "lr": 0.09998, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.13281, "top5_acc": 0.33203, "loss_cls": 4.90666, "loss": 4.90666, "time": 0.71462} +{"mode": "train", "epoch": 2, "iter": 2000, "lr": 0.09997, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.13547, "top5_acc": 0.34406, "loss_cls": 4.87246, "loss": 4.87246, "time": 0.71481} +{"mode": "train", "epoch": 2, "iter": 2100, "lr": 0.09997, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.13969, "top5_acc": 0.33953, "loss_cls": 4.85252, "loss": 4.85252, "time": 0.71586} +{"mode": "train", "epoch": 2, "iter": 2200, "lr": 0.09997, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.13141, "top5_acc": 0.33734, "loss_cls": 4.88304, "loss": 4.88304, "time": 0.71749} +{"mode": "train", "epoch": 2, "iter": 2300, "lr": 0.09997, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.14172, "top5_acc": 0.35062, "loss_cls": 4.85546, "loss": 4.85546, "time": 0.71819} +{"mode": "train", "epoch": 2, "iter": 2400, "lr": 0.09997, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.135, "top5_acc": 0.32812, "loss_cls": 4.87684, "loss": 4.87684, "time": 0.71622} +{"mode": "train", "epoch": 2, "iter": 2500, "lr": 0.09997, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.13922, "top5_acc": 0.34969, "loss_cls": 4.83351, "loss": 4.83351, "time": 0.71709} +{"mode": "train", "epoch": 2, "iter": 2600, "lr": 0.09997, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.13984, "top5_acc": 0.33531, "loss_cls": 4.83681, "loss": 4.83681, "time": 0.71863} +{"mode": "train", "epoch": 2, "iter": 2700, "lr": 0.09997, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.14062, "top5_acc": 0.35141, "loss_cls": 4.82472, "loss": 4.82472, "time": 0.71471} +{"mode": "train", "epoch": 2, "iter": 2800, "lr": 0.09997, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.13875, "top5_acc": 0.34469, "loss_cls": 4.82029, "loss": 4.82029, "time": 0.71561} +{"mode": "train", "epoch": 2, "iter": 2900, "lr": 0.09997, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.14141, "top5_acc": 0.35188, "loss_cls": 4.79852, "loss": 4.79852, "time": 0.71493} +{"mode": "train", "epoch": 2, "iter": 3000, "lr": 0.09996, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.1475, "top5_acc": 0.35609, "loss_cls": 4.7858, "loss": 4.7858, "time": 0.71504} +{"mode": "train", "epoch": 2, "iter": 3100, "lr": 0.09996, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.1425, "top5_acc": 0.35406, "loss_cls": 4.79608, "loss": 4.79608, "time": 0.7171} +{"mode": "train", "epoch": 2, "iter": 3200, "lr": 0.09996, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.14453, "top5_acc": 0.35516, "loss_cls": 4.77689, "loss": 4.77689, "time": 0.71445} +{"mode": "train", "epoch": 2, "iter": 3300, "lr": 0.09996, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.15078, "top5_acc": 0.36219, "loss_cls": 4.74634, "loss": 4.74634, "time": 0.71574} +{"mode": "train", "epoch": 2, "iter": 3400, "lr": 0.09996, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.15203, "top5_acc": 0.36812, "loss_cls": 4.72671, "loss": 4.72671, "time": 0.71547} +{"mode": "train", "epoch": 2, "iter": 3500, "lr": 0.09996, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.14891, "top5_acc": 0.36812, "loss_cls": 4.75269, "loss": 4.75269, "time": 0.71313} +{"mode": "train", "epoch": 2, "iter": 3600, "lr": 0.09996, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.15406, "top5_acc": 0.36578, "loss_cls": 4.76233, "loss": 4.76233, "time": 0.7109} +{"mode": "train", "epoch": 2, "iter": 3700, "lr": 0.09996, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.15406, "top5_acc": 0.37203, "loss_cls": 4.73399, "loss": 4.73399, "time": 0.71369} +{"mode": "val", "epoch": 2, "iter": 309, "lr": 0.09996, "top1_acc": 0.09619, "top5_acc": 0.27129, "mean_class_accuracy": 0.09635} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.09995, "memory": 15990, "data_time": 1.4487, "top1_acc": 0.15766, "top5_acc": 0.37797, "loss_cls": 4.72125, "loss": 4.72125, "time": 2.16074} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.09995, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.16484, "top5_acc": 0.38938, "loss_cls": 4.62915, "loss": 4.62915, "time": 0.70862} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.09995, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.15531, "top5_acc": 0.38, "loss_cls": 4.70014, "loss": 4.70014, "time": 0.70769} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.09995, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.16094, "top5_acc": 0.38516, "loss_cls": 4.68684, "loss": 4.68684, "time": 0.70951} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.09995, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.16062, "top5_acc": 0.37625, "loss_cls": 4.70426, "loss": 4.70426, "time": 0.70875} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.09995, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.15797, "top5_acc": 0.36922, "loss_cls": 4.71872, "loss": 4.71872, "time": 0.70834} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.09995, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.17531, "top5_acc": 0.38969, "loss_cls": 4.63292, "loss": 4.63292, "time": 0.70962} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.09995, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.16031, "top5_acc": 0.37703, "loss_cls": 4.68672, "loss": 4.68672, "time": 0.70856} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.09994, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.16578, "top5_acc": 0.38578, "loss_cls": 4.70611, "loss": 4.70611, "time": 0.71007} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.09994, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.16984, "top5_acc": 0.39641, "loss_cls": 4.60557, "loss": 4.60557, "time": 0.71479} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.09994, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.16281, "top5_acc": 0.3925, "loss_cls": 4.63049, "loss": 4.63049, "time": 0.71633} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.09994, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.16297, "top5_acc": 0.38562, "loss_cls": 4.6762, "loss": 4.6762, "time": 0.71105} +{"mode": "train", "epoch": 3, "iter": 1300, "lr": 0.09994, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.16297, "top5_acc": 0.39125, "loss_cls": 4.66008, "loss": 4.66008, "time": 0.71241} +{"mode": "train", "epoch": 3, "iter": 1400, "lr": 0.09994, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.17734, "top5_acc": 0.40875, "loss_cls": 4.60387, "loss": 4.60387, "time": 0.71581} +{"mode": "train", "epoch": 3, "iter": 1500, "lr": 0.09994, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.17094, "top5_acc": 0.38891, "loss_cls": 4.66125, "loss": 4.66125, "time": 0.71803} +{"mode": "train", "epoch": 3, "iter": 1600, "lr": 0.09994, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.17562, "top5_acc": 0.40328, "loss_cls": 4.59981, "loss": 4.59981, "time": 0.71972} +{"mode": "train", "epoch": 3, "iter": 1700, "lr": 0.09993, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.17109, "top5_acc": 0.3925, "loss_cls": 4.63056, "loss": 4.63056, "time": 0.71698} +{"mode": "train", "epoch": 3, "iter": 1800, "lr": 0.09993, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.16906, "top5_acc": 0.39203, "loss_cls": 4.63989, "loss": 4.63989, "time": 0.717} +{"mode": "train", "epoch": 3, "iter": 1900, "lr": 0.09993, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.16453, "top5_acc": 0.39422, "loss_cls": 4.64402, "loss": 4.64402, "time": 0.71539} +{"mode": "train", "epoch": 3, "iter": 2000, "lr": 0.09993, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.16156, "top5_acc": 0.38328, "loss_cls": 4.64938, "loss": 4.64938, "time": 0.71309} +{"mode": "train", "epoch": 3, "iter": 2100, "lr": 0.09993, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.17672, "top5_acc": 0.40812, "loss_cls": 4.56424, "loss": 4.56424, "time": 0.71615} +{"mode": "train", "epoch": 3, "iter": 2200, "lr": 0.09993, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.17734, "top5_acc": 0.39719, "loss_cls": 4.60634, "loss": 4.60634, "time": 0.71412} +{"mode": "train", "epoch": 3, "iter": 2300, "lr": 0.09993, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.17391, "top5_acc": 0.405, "loss_cls": 4.57113, "loss": 4.57113, "time": 0.71403} +{"mode": "train", "epoch": 3, "iter": 2400, "lr": 0.09992, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.17406, "top5_acc": 0.38703, "loss_cls": 4.62338, "loss": 4.62338, "time": 0.71707} +{"mode": "train", "epoch": 3, "iter": 2500, "lr": 0.09992, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.17891, "top5_acc": 0.39891, "loss_cls": 4.58806, "loss": 4.58806, "time": 0.71853} +{"mode": "train", "epoch": 3, "iter": 2600, "lr": 0.09992, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.17844, "top5_acc": 0.39859, "loss_cls": 4.60688, "loss": 4.60688, "time": 0.71422} +{"mode": "train", "epoch": 3, "iter": 2700, "lr": 0.09992, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.17766, "top5_acc": 0.39938, "loss_cls": 4.59008, "loss": 4.59008, "time": 0.71747} +{"mode": "train", "epoch": 3, "iter": 2800, "lr": 0.09992, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.17359, "top5_acc": 0.39594, "loss_cls": 4.59052, "loss": 4.59052, "time": 0.71657} +{"mode": "train", "epoch": 3, "iter": 2900, "lr": 0.09992, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.18422, "top5_acc": 0.41781, "loss_cls": 4.49966, "loss": 4.49966, "time": 0.71639} +{"mode": "train", "epoch": 3, "iter": 3000, "lr": 0.09991, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.17812, "top5_acc": 0.39828, "loss_cls": 4.56676, "loss": 4.56676, "time": 0.71761} +{"mode": "train", "epoch": 3, "iter": 3100, "lr": 0.09991, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.17641, "top5_acc": 0.40312, "loss_cls": 4.56504, "loss": 4.56504, "time": 0.71636} +{"mode": "train", "epoch": 3, "iter": 3200, "lr": 0.09991, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.17641, "top5_acc": 0.40203, "loss_cls": 4.57183, "loss": 4.57183, "time": 0.71568} +{"mode": "train", "epoch": 3, "iter": 3300, "lr": 0.09991, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.18109, "top5_acc": 0.40062, "loss_cls": 4.54169, "loss": 4.54169, "time": 0.71753} +{"mode": "train", "epoch": 3, "iter": 3400, "lr": 0.09991, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.17922, "top5_acc": 0.41203, "loss_cls": 4.54001, "loss": 4.54001, "time": 0.71011} +{"mode": "train", "epoch": 3, "iter": 3500, "lr": 0.09991, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.19, "top5_acc": 0.41703, "loss_cls": 4.50071, "loss": 4.50071, "time": 0.71511} +{"mode": "train", "epoch": 3, "iter": 3600, "lr": 0.0999, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.18641, "top5_acc": 0.41031, "loss_cls": 4.51951, "loss": 4.51951, "time": 0.71309} +{"mode": "train", "epoch": 3, "iter": 3700, "lr": 0.0999, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.17703, "top5_acc": 0.40656, "loss_cls": 4.54029, "loss": 4.54029, "time": 0.71574} +{"mode": "val", "epoch": 3, "iter": 309, "lr": 0.0999, "top1_acc": 0.11827, "top5_acc": 0.3038, "mean_class_accuracy": 0.11813} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.0999, "memory": 15990, "data_time": 1.43646, "top1_acc": 0.19641, "top5_acc": 0.42297, "loss_cls": 4.48309, "loss": 4.48309, "time": 2.14941} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.0999, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.18375, "top5_acc": 0.42141, "loss_cls": 4.51786, "loss": 4.51786, "time": 0.71206} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.0999, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.17969, "top5_acc": 0.41469, "loss_cls": 4.51067, "loss": 4.51067, "time": 0.70946} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.09989, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19469, "top5_acc": 0.42906, "loss_cls": 4.45726, "loss": 4.45726, "time": 0.70981} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.09989, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18719, "top5_acc": 0.42609, "loss_cls": 4.49373, "loss": 4.49373, "time": 0.70971} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.09989, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18406, "top5_acc": 0.41469, "loss_cls": 4.53542, "loss": 4.53542, "time": 0.71026} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.09989, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19156, "top5_acc": 0.42672, "loss_cls": 4.48303, "loss": 4.48303, "time": 0.71304} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.09989, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.18359, "top5_acc": 0.41656, "loss_cls": 4.51873, "loss": 4.51873, "time": 0.7094} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.09988, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.185, "top5_acc": 0.42234, "loss_cls": 4.48336, "loss": 4.48336, "time": 0.70951} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.09988, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.18969, "top5_acc": 0.41969, "loss_cls": 4.48617, "loss": 4.48617, "time": 0.71304} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.09988, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.18969, "top5_acc": 0.41469, "loss_cls": 4.53092, "loss": 4.53092, "time": 0.70833} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.09988, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.18938, "top5_acc": 0.42312, "loss_cls": 4.49221, "loss": 4.49221, "time": 0.70994} +{"mode": "train", "epoch": 4, "iter": 1300, "lr": 0.09988, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.18344, "top5_acc": 0.41297, "loss_cls": 4.51971, "loss": 4.51971, "time": 0.71008} +{"mode": "train", "epoch": 4, "iter": 1400, "lr": 0.09988, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.18688, "top5_acc": 0.41906, "loss_cls": 4.48985, "loss": 4.48985, "time": 0.71069} +{"mode": "train", "epoch": 4, "iter": 1500, "lr": 0.09987, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20344, "top5_acc": 0.43328, "loss_cls": 4.45107, "loss": 4.45107, "time": 0.71344} +{"mode": "train", "epoch": 4, "iter": 1600, "lr": 0.09987, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.18359, "top5_acc": 0.40828, "loss_cls": 4.56661, "loss": 4.56661, "time": 0.71196} +{"mode": "train", "epoch": 4, "iter": 1700, "lr": 0.09987, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19469, "top5_acc": 0.42625, "loss_cls": 4.50072, "loss": 4.50072, "time": 0.71226} +{"mode": "train", "epoch": 4, "iter": 1800, "lr": 0.09987, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.19172, "top5_acc": 0.43125, "loss_cls": 4.46721, "loss": 4.46721, "time": 0.71643} +{"mode": "train", "epoch": 4, "iter": 1900, "lr": 0.09987, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.19797, "top5_acc": 0.43391, "loss_cls": 4.44658, "loss": 4.44658, "time": 0.71459} +{"mode": "train", "epoch": 4, "iter": 2000, "lr": 0.09986, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.1975, "top5_acc": 0.42688, "loss_cls": 4.47106, "loss": 4.47106, "time": 0.71748} +{"mode": "train", "epoch": 4, "iter": 2100, "lr": 0.09986, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20156, "top5_acc": 0.4325, "loss_cls": 4.48423, "loss": 4.48423, "time": 0.71345} +{"mode": "train", "epoch": 4, "iter": 2200, "lr": 0.09986, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.18734, "top5_acc": 0.42438, "loss_cls": 4.49352, "loss": 4.49352, "time": 0.71731} +{"mode": "train", "epoch": 4, "iter": 2300, "lr": 0.09986, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.19953, "top5_acc": 0.43703, "loss_cls": 4.42817, "loss": 4.42817, "time": 0.71435} +{"mode": "train", "epoch": 4, "iter": 2400, "lr": 0.09985, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.19938, "top5_acc": 0.43844, "loss_cls": 4.44783, "loss": 4.44783, "time": 0.71775} +{"mode": "train", "epoch": 4, "iter": 2500, "lr": 0.09985, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20031, "top5_acc": 0.4375, "loss_cls": 4.44311, "loss": 4.44311, "time": 0.71656} +{"mode": "train", "epoch": 4, "iter": 2600, "lr": 0.09985, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.18938, "top5_acc": 0.43344, "loss_cls": 4.44733, "loss": 4.44733, "time": 0.71682} +{"mode": "train", "epoch": 4, "iter": 2700, "lr": 0.09985, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.19938, "top5_acc": 0.43094, "loss_cls": 4.4375, "loss": 4.4375, "time": 0.71787} +{"mode": "train", "epoch": 4, "iter": 2800, "lr": 0.09985, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20688, "top5_acc": 0.43562, "loss_cls": 4.40445, "loss": 4.40445, "time": 0.71586} +{"mode": "train", "epoch": 4, "iter": 2900, "lr": 0.09984, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.19312, "top5_acc": 0.42016, "loss_cls": 4.47677, "loss": 4.47677, "time": 0.7221} +{"mode": "train", "epoch": 4, "iter": 3000, "lr": 0.09984, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.19984, "top5_acc": 0.42516, "loss_cls": 4.46782, "loss": 4.46782, "time": 0.71826} +{"mode": "train", "epoch": 4, "iter": 3100, "lr": 0.09984, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.19641, "top5_acc": 0.43422, "loss_cls": 4.44393, "loss": 4.44393, "time": 0.71702} +{"mode": "train", "epoch": 4, "iter": 3200, "lr": 0.09984, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20609, "top5_acc": 0.43875, "loss_cls": 4.40994, "loss": 4.40994, "time": 0.718} +{"mode": "train", "epoch": 4, "iter": 3300, "lr": 0.09983, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20641, "top5_acc": 0.44422, "loss_cls": 4.42712, "loss": 4.42712, "time": 0.71625} +{"mode": "train", "epoch": 4, "iter": 3400, "lr": 0.09983, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20484, "top5_acc": 0.43188, "loss_cls": 4.4044, "loss": 4.4044, "time": 0.7137} +{"mode": "train", "epoch": 4, "iter": 3500, "lr": 0.09983, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20172, "top5_acc": 0.43906, "loss_cls": 4.41538, "loss": 4.41538, "time": 0.71407} +{"mode": "train", "epoch": 4, "iter": 3600, "lr": 0.09983, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20391, "top5_acc": 0.44156, "loss_cls": 4.41125, "loss": 4.41125, "time": 0.71287} +{"mode": "train", "epoch": 4, "iter": 3700, "lr": 0.09983, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19781, "top5_acc": 0.44156, "loss_cls": 4.42692, "loss": 4.42692, "time": 0.71731} +{"mode": "val", "epoch": 4, "iter": 309, "lr": 0.09982, "top1_acc": 0.1553, "top5_acc": 0.3698, "mean_class_accuracy": 0.15514} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.09982, "memory": 15990, "data_time": 1.40985, "top1_acc": 0.20016, "top5_acc": 0.43828, "loss_cls": 4.41783, "loss": 4.41783, "time": 2.12514} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.09982, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.19344, "top5_acc": 0.43875, "loss_cls": 4.4262, "loss": 4.4262, "time": 0.71376} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.09982, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20703, "top5_acc": 0.44016, "loss_cls": 4.38872, "loss": 4.38872, "time": 0.71305} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.09982, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.20906, "top5_acc": 0.445, "loss_cls": 4.37285, "loss": 4.37285, "time": 0.70884} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.09981, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20438, "top5_acc": 0.44078, "loss_cls": 4.38983, "loss": 4.38983, "time": 0.71209} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.09981, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20219, "top5_acc": 0.43469, "loss_cls": 4.40346, "loss": 4.40346, "time": 0.71153} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.09981, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20359, "top5_acc": 0.4375, "loss_cls": 4.41956, "loss": 4.41956, "time": 0.70622} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.09981, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20656, "top5_acc": 0.43453, "loss_cls": 4.36152, "loss": 4.36152, "time": 0.70912} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.0998, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19984, "top5_acc": 0.43703, "loss_cls": 4.41916, "loss": 4.41916, "time": 0.70964} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.0998, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20859, "top5_acc": 0.44266, "loss_cls": 4.38524, "loss": 4.38524, "time": 0.71151} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.0998, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20422, "top5_acc": 0.44422, "loss_cls": 4.3992, "loss": 4.3992, "time": 0.71345} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.0998, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21203, "top5_acc": 0.43766, "loss_cls": 4.38289, "loss": 4.38289, "time": 0.71108} +{"mode": "train", "epoch": 5, "iter": 1300, "lr": 0.09979, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.19641, "top5_acc": 0.43766, "loss_cls": 4.45574, "loss": 4.45574, "time": 0.71751} +{"mode": "train", "epoch": 5, "iter": 1400, "lr": 0.09979, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21062, "top5_acc": 0.43781, "loss_cls": 4.35976, "loss": 4.35976, "time": 0.71292} +{"mode": "train", "epoch": 5, "iter": 1500, "lr": 0.09979, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20312, "top5_acc": 0.43281, "loss_cls": 4.41398, "loss": 4.41398, "time": 0.71175} +{"mode": "train", "epoch": 5, "iter": 1600, "lr": 0.09979, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.19875, "top5_acc": 0.44031, "loss_cls": 4.41418, "loss": 4.41418, "time": 0.71533} +{"mode": "train", "epoch": 5, "iter": 1700, "lr": 0.09978, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20016, "top5_acc": 0.44797, "loss_cls": 4.38051, "loss": 4.38051, "time": 0.71738} +{"mode": "train", "epoch": 5, "iter": 1800, "lr": 0.09978, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20625, "top5_acc": 0.4425, "loss_cls": 4.39648, "loss": 4.39648, "time": 0.71466} +{"mode": "train", "epoch": 5, "iter": 1900, "lr": 0.09978, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20984, "top5_acc": 0.44484, "loss_cls": 4.3673, "loss": 4.3673, "time": 0.71213} +{"mode": "train", "epoch": 5, "iter": 2000, "lr": 0.09977, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21094, "top5_acc": 0.44156, "loss_cls": 4.39359, "loss": 4.39359, "time": 0.71486} +{"mode": "train", "epoch": 5, "iter": 2100, "lr": 0.09977, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20469, "top5_acc": 0.44219, "loss_cls": 4.38789, "loss": 4.38789, "time": 0.71935} +{"mode": "train", "epoch": 5, "iter": 2200, "lr": 0.09977, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21047, "top5_acc": 0.45562, "loss_cls": 4.34346, "loss": 4.34346, "time": 0.71396} +{"mode": "train", "epoch": 5, "iter": 2300, "lr": 0.09977, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.20672, "top5_acc": 0.44375, "loss_cls": 4.36267, "loss": 4.36267, "time": 0.7168} +{"mode": "train", "epoch": 5, "iter": 2400, "lr": 0.09976, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20891, "top5_acc": 0.44609, "loss_cls": 4.37411, "loss": 4.37411, "time": 0.71314} +{"mode": "train", "epoch": 5, "iter": 2500, "lr": 0.09976, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21156, "top5_acc": 0.44672, "loss_cls": 4.37916, "loss": 4.37916, "time": 0.71506} +{"mode": "train", "epoch": 5, "iter": 2600, "lr": 0.09976, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20656, "top5_acc": 0.44344, "loss_cls": 4.37164, "loss": 4.37164, "time": 0.71928} +{"mode": "train", "epoch": 5, "iter": 2700, "lr": 0.09976, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21219, "top5_acc": 0.44766, "loss_cls": 4.38111, "loss": 4.38111, "time": 0.72279} +{"mode": "train", "epoch": 5, "iter": 2800, "lr": 0.09975, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21797, "top5_acc": 0.46016, "loss_cls": 4.33189, "loss": 4.33189, "time": 0.71908} +{"mode": "train", "epoch": 5, "iter": 2900, "lr": 0.09975, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21219, "top5_acc": 0.45047, "loss_cls": 4.34884, "loss": 4.34884, "time": 0.71932} +{"mode": "train", "epoch": 5, "iter": 3000, "lr": 0.09975, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.20766, "top5_acc": 0.44031, "loss_cls": 4.40743, "loss": 4.40743, "time": 0.71963} +{"mode": "train", "epoch": 5, "iter": 3100, "lr": 0.09974, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21047, "top5_acc": 0.445, "loss_cls": 4.37293, "loss": 4.37293, "time": 0.71787} +{"mode": "train", "epoch": 5, "iter": 3200, "lr": 0.09974, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21344, "top5_acc": 0.44688, "loss_cls": 4.354, "loss": 4.354, "time": 0.71719} +{"mode": "train", "epoch": 5, "iter": 3300, "lr": 0.09974, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20891, "top5_acc": 0.44656, "loss_cls": 4.38736, "loss": 4.38736, "time": 0.72063} +{"mode": "train", "epoch": 5, "iter": 3400, "lr": 0.09974, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21547, "top5_acc": 0.44547, "loss_cls": 4.36281, "loss": 4.36281, "time": 0.71713} +{"mode": "train", "epoch": 5, "iter": 3500, "lr": 0.09973, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21031, "top5_acc": 0.45188, "loss_cls": 4.38082, "loss": 4.38082, "time": 0.71179} +{"mode": "train", "epoch": 5, "iter": 3600, "lr": 0.09973, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20578, "top5_acc": 0.43906, "loss_cls": 4.40116, "loss": 4.40116, "time": 0.71067} +{"mode": "train", "epoch": 5, "iter": 3700, "lr": 0.09973, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21469, "top5_acc": 0.45547, "loss_cls": 4.36188, "loss": 4.36188, "time": 0.71212} +{"mode": "val", "epoch": 5, "iter": 309, "lr": 0.09973, "top1_acc": 0.14172, "top5_acc": 0.35618, "mean_class_accuracy": 0.14163} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.09972, "memory": 15990, "data_time": 1.47721, "top1_acc": 0.21453, "top5_acc": 0.45156, "loss_cls": 4.32814, "loss": 4.32814, "time": 2.19048} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.09972, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22203, "top5_acc": 0.46141, "loss_cls": 4.27359, "loss": 4.27359, "time": 0.71296} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.09972, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22266, "top5_acc": 0.46594, "loss_cls": 4.31245, "loss": 4.31245, "time": 0.71097} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.09971, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20953, "top5_acc": 0.45281, "loss_cls": 4.32315, "loss": 4.32315, "time": 0.71776} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.09971, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21484, "top5_acc": 0.45062, "loss_cls": 4.37223, "loss": 4.37223, "time": 0.71052} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.09971, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.21547, "top5_acc": 0.44875, "loss_cls": 4.36242, "loss": 4.36242, "time": 0.71302} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.09971, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2125, "top5_acc": 0.45156, "loss_cls": 4.34823, "loss": 4.34823, "time": 0.71224} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.0997, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20797, "top5_acc": 0.44391, "loss_cls": 4.37462, "loss": 4.37462, "time": 0.71297} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.0997, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21594, "top5_acc": 0.44859, "loss_cls": 4.35829, "loss": 4.35829, "time": 0.71186} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.0997, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22031, "top5_acc": 0.46156, "loss_cls": 4.30909, "loss": 4.30909, "time": 0.71645} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.09969, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21516, "top5_acc": 0.45, "loss_cls": 4.36602, "loss": 4.36602, "time": 0.70912} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.09969, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.205, "top5_acc": 0.44203, "loss_cls": 4.38826, "loss": 4.38826, "time": 0.71498} +{"mode": "train", "epoch": 6, "iter": 1300, "lr": 0.09969, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21203, "top5_acc": 0.45188, "loss_cls": 4.32046, "loss": 4.32046, "time": 0.7131} +{"mode": "train", "epoch": 6, "iter": 1400, "lr": 0.09968, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21141, "top5_acc": 0.44875, "loss_cls": 4.35563, "loss": 4.35563, "time": 0.71779} +{"mode": "train", "epoch": 6, "iter": 1500, "lr": 0.09968, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21547, "top5_acc": 0.45469, "loss_cls": 4.37536, "loss": 4.37536, "time": 0.72005} +{"mode": "train", "epoch": 6, "iter": 1600, "lr": 0.09968, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20703, "top5_acc": 0.44953, "loss_cls": 4.38929, "loss": 4.38929, "time": 0.71284} +{"mode": "train", "epoch": 6, "iter": 1700, "lr": 0.09967, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21906, "top5_acc": 0.46359, "loss_cls": 4.2998, "loss": 4.2998, "time": 0.71374} +{"mode": "train", "epoch": 6, "iter": 1800, "lr": 0.09967, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21922, "top5_acc": 0.44797, "loss_cls": 4.36189, "loss": 4.36189, "time": 0.72157} +{"mode": "train", "epoch": 6, "iter": 1900, "lr": 0.09967, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21875, "top5_acc": 0.45891, "loss_cls": 4.31988, "loss": 4.31988, "time": 0.71898} +{"mode": "train", "epoch": 6, "iter": 2000, "lr": 0.09966, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20797, "top5_acc": 0.44391, "loss_cls": 4.36542, "loss": 4.36542, "time": 0.71938} +{"mode": "train", "epoch": 6, "iter": 2100, "lr": 0.09966, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.20969, "top5_acc": 0.45203, "loss_cls": 4.3359, "loss": 4.3359, "time": 0.71736} +{"mode": "train", "epoch": 6, "iter": 2200, "lr": 0.09966, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21281, "top5_acc": 0.4575, "loss_cls": 4.3395, "loss": 4.3395, "time": 0.71618} +{"mode": "train", "epoch": 6, "iter": 2300, "lr": 0.09965, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21531, "top5_acc": 0.46531, "loss_cls": 4.3269, "loss": 4.3269, "time": 0.72097} +{"mode": "train", "epoch": 6, "iter": 2400, "lr": 0.09965, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21125, "top5_acc": 0.44047, "loss_cls": 4.35491, "loss": 4.35491, "time": 0.7174} +{"mode": "train", "epoch": 6, "iter": 2500, "lr": 0.09965, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21125, "top5_acc": 0.44906, "loss_cls": 4.3541, "loss": 4.3541, "time": 0.72253} +{"mode": "train", "epoch": 6, "iter": 2600, "lr": 0.09964, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22047, "top5_acc": 0.46234, "loss_cls": 4.3196, "loss": 4.3196, "time": 0.71577} +{"mode": "train", "epoch": 6, "iter": 2700, "lr": 0.09964, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.21031, "top5_acc": 0.44812, "loss_cls": 4.34697, "loss": 4.34697, "time": 0.72094} +{"mode": "train", "epoch": 6, "iter": 2800, "lr": 0.09964, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21953, "top5_acc": 0.45828, "loss_cls": 4.32939, "loss": 4.32939, "time": 0.72282} +{"mode": "train", "epoch": 6, "iter": 2900, "lr": 0.09963, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.205, "top5_acc": 0.45484, "loss_cls": 4.35463, "loss": 4.35463, "time": 0.71651} +{"mode": "train", "epoch": 6, "iter": 3000, "lr": 0.09963, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.21422, "top5_acc": 0.46141, "loss_cls": 4.32336, "loss": 4.32336, "time": 0.72209} +{"mode": "train", "epoch": 6, "iter": 3100, "lr": 0.09963, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21172, "top5_acc": 0.45656, "loss_cls": 4.32388, "loss": 4.32388, "time": 0.71878} +{"mode": "train", "epoch": 6, "iter": 3200, "lr": 0.09962, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21391, "top5_acc": 0.45672, "loss_cls": 4.3612, "loss": 4.3612, "time": 0.71799} +{"mode": "train", "epoch": 6, "iter": 3300, "lr": 0.09962, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.22672, "top5_acc": 0.46547, "loss_cls": 4.27084, "loss": 4.27084, "time": 0.71717} +{"mode": "train", "epoch": 6, "iter": 3400, "lr": 0.09962, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21906, "top5_acc": 0.45188, "loss_cls": 4.30673, "loss": 4.30673, "time": 0.71637} +{"mode": "train", "epoch": 6, "iter": 3500, "lr": 0.09961, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2125, "top5_acc": 0.45297, "loss_cls": 4.33954, "loss": 4.33954, "time": 0.71446} +{"mode": "train", "epoch": 6, "iter": 3600, "lr": 0.09961, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22625, "top5_acc": 0.46391, "loss_cls": 4.29263, "loss": 4.29263, "time": 0.71077} +{"mode": "train", "epoch": 6, "iter": 3700, "lr": 0.09961, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.21109, "top5_acc": 0.4525, "loss_cls": 4.34325, "loss": 4.34325, "time": 0.71673} +{"mode": "val", "epoch": 6, "iter": 309, "lr": 0.09961, "top1_acc": 0.15803, "top5_acc": 0.36621, "mean_class_accuracy": 0.15771} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0996, "memory": 15990, "data_time": 1.47759, "top1_acc": 0.21641, "top5_acc": 0.46984, "loss_cls": 4.25552, "loss": 4.25552, "time": 2.19208} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0996, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21688, "top5_acc": 0.45828, "loss_cls": 4.31056, "loss": 4.31056, "time": 0.71392} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.0996, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21484, "top5_acc": 0.46312, "loss_cls": 4.29205, "loss": 4.29205, "time": 0.71121} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.09959, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21906, "top5_acc": 0.46375, "loss_cls": 4.32627, "loss": 4.32627, "time": 0.7113} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.09959, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21922, "top5_acc": 0.45797, "loss_cls": 4.32938, "loss": 4.32938, "time": 0.71094} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.09958, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22312, "top5_acc": 0.46391, "loss_cls": 4.29305, "loss": 4.29305, "time": 0.71024} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.09958, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20953, "top5_acc": 0.45016, "loss_cls": 4.36803, "loss": 4.36803, "time": 0.71331} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.09958, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21781, "top5_acc": 0.45469, "loss_cls": 4.30287, "loss": 4.30287, "time": 0.71076} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.09957, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20984, "top5_acc": 0.45672, "loss_cls": 4.32413, "loss": 4.32413, "time": 0.71077} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.09957, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22344, "top5_acc": 0.46891, "loss_cls": 4.2587, "loss": 4.2587, "time": 0.71511} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.09957, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21594, "top5_acc": 0.46375, "loss_cls": 4.32575, "loss": 4.32575, "time": 0.71249} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.09956, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22016, "top5_acc": 0.46594, "loss_cls": 4.28957, "loss": 4.28957, "time": 0.71344} +{"mode": "train", "epoch": 7, "iter": 1300, "lr": 0.09956, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.2225, "top5_acc": 0.45188, "loss_cls": 4.33623, "loss": 4.33623, "time": 0.71639} +{"mode": "train", "epoch": 7, "iter": 1400, "lr": 0.09956, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21828, "top5_acc": 0.46188, "loss_cls": 4.29924, "loss": 4.29924, "time": 0.71354} +{"mode": "train", "epoch": 7, "iter": 1500, "lr": 0.09955, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22234, "top5_acc": 0.45922, "loss_cls": 4.30621, "loss": 4.30621, "time": 0.7154} +{"mode": "train", "epoch": 7, "iter": 1600, "lr": 0.09955, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21734, "top5_acc": 0.46359, "loss_cls": 4.30252, "loss": 4.30252, "time": 0.71703} +{"mode": "train", "epoch": 7, "iter": 1700, "lr": 0.09954, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22141, "top5_acc": 0.45562, "loss_cls": 4.29224, "loss": 4.29224, "time": 0.71756} +{"mode": "train", "epoch": 7, "iter": 1800, "lr": 0.09954, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22219, "top5_acc": 0.47344, "loss_cls": 4.2636, "loss": 4.2636, "time": 0.7163} +{"mode": "train", "epoch": 7, "iter": 1900, "lr": 0.09954, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21641, "top5_acc": 0.47094, "loss_cls": 4.28618, "loss": 4.28618, "time": 0.71875} +{"mode": "train", "epoch": 7, "iter": 2000, "lr": 0.09953, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22109, "top5_acc": 0.45938, "loss_cls": 4.31759, "loss": 4.31759, "time": 0.71732} +{"mode": "train", "epoch": 7, "iter": 2100, "lr": 0.09953, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21531, "top5_acc": 0.4625, "loss_cls": 4.3092, "loss": 4.3092, "time": 0.71443} +{"mode": "train", "epoch": 7, "iter": 2200, "lr": 0.09952, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21562, "top5_acc": 0.4625, "loss_cls": 4.29219, "loss": 4.29219, "time": 0.71574} +{"mode": "train", "epoch": 7, "iter": 2300, "lr": 0.09952, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21172, "top5_acc": 0.45859, "loss_cls": 4.32386, "loss": 4.32386, "time": 0.71693} +{"mode": "train", "epoch": 7, "iter": 2400, "lr": 0.09952, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23141, "top5_acc": 0.46719, "loss_cls": 4.26501, "loss": 4.26501, "time": 0.71897} +{"mode": "train", "epoch": 7, "iter": 2500, "lr": 0.09951, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21844, "top5_acc": 0.46031, "loss_cls": 4.31213, "loss": 4.31213, "time": 0.71819} +{"mode": "train", "epoch": 7, "iter": 2600, "lr": 0.09951, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21734, "top5_acc": 0.46172, "loss_cls": 4.28983, "loss": 4.28983, "time": 0.71868} +{"mode": "train", "epoch": 7, "iter": 2700, "lr": 0.09951, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22516, "top5_acc": 0.47375, "loss_cls": 4.2396, "loss": 4.2396, "time": 0.72065} +{"mode": "train", "epoch": 7, "iter": 2800, "lr": 0.0995, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22219, "top5_acc": 0.46797, "loss_cls": 4.26449, "loss": 4.26449, "time": 0.71629} +{"mode": "train", "epoch": 7, "iter": 2900, "lr": 0.0995, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22109, "top5_acc": 0.46328, "loss_cls": 4.29068, "loss": 4.29068, "time": 0.71824} +{"mode": "train", "epoch": 7, "iter": 3000, "lr": 0.09949, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21812, "top5_acc": 0.45906, "loss_cls": 4.28239, "loss": 4.28239, "time": 0.71951} +{"mode": "train", "epoch": 7, "iter": 3100, "lr": 0.09949, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21609, "top5_acc": 0.4625, "loss_cls": 4.29226, "loss": 4.29226, "time": 0.71535} +{"mode": "train", "epoch": 7, "iter": 3200, "lr": 0.09949, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22406, "top5_acc": 0.46188, "loss_cls": 4.27098, "loss": 4.27098, "time": 0.71636} +{"mode": "train", "epoch": 7, "iter": 3300, "lr": 0.09948, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.21969, "top5_acc": 0.45672, "loss_cls": 4.30316, "loss": 4.30316, "time": 0.71667} +{"mode": "train", "epoch": 7, "iter": 3400, "lr": 0.09948, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21875, "top5_acc": 0.46703, "loss_cls": 4.29307, "loss": 4.29307, "time": 0.71488} +{"mode": "train", "epoch": 7, "iter": 3500, "lr": 0.09947, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22984, "top5_acc": 0.46641, "loss_cls": 4.2619, "loss": 4.2619, "time": 0.71291} +{"mode": "train", "epoch": 7, "iter": 3600, "lr": 0.09947, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22344, "top5_acc": 0.46531, "loss_cls": 4.28606, "loss": 4.28606, "time": 0.7123} +{"mode": "train", "epoch": 7, "iter": 3700, "lr": 0.09947, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.22125, "top5_acc": 0.44953, "loss_cls": 4.32955, "loss": 4.32955, "time": 0.7136} +{"mode": "val", "epoch": 7, "iter": 309, "lr": 0.09946, "top1_acc": 0.17753, "top5_acc": 0.40181, "mean_class_accuracy": 0.1774} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.09946, "memory": 15990, "data_time": 1.3663, "top1_acc": 0.23266, "top5_acc": 0.47125, "loss_cls": 4.22702, "loss": 4.22702, "time": 2.08897} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.09946, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22828, "top5_acc": 0.46891, "loss_cls": 4.24433, "loss": 4.24433, "time": 0.71461} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.09945, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23562, "top5_acc": 0.47344, "loss_cls": 4.24695, "loss": 4.24695, "time": 0.71402} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.09945, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21891, "top5_acc": 0.46375, "loss_cls": 4.29954, "loss": 4.29954, "time": 0.71769} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.09944, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22406, "top5_acc": 0.46969, "loss_cls": 4.28581, "loss": 4.28581, "time": 0.70961} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.09944, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22141, "top5_acc": 0.46047, "loss_cls": 4.30925, "loss": 4.30925, "time": 0.7138} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.09943, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22141, "top5_acc": 0.45625, "loss_cls": 4.30848, "loss": 4.30848, "time": 0.71387} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.09943, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22312, "top5_acc": 0.46797, "loss_cls": 4.27525, "loss": 4.27525, "time": 0.71138} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.09943, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21562, "top5_acc": 0.46578, "loss_cls": 4.30224, "loss": 4.30224, "time": 0.71112} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.09942, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22, "top5_acc": 0.46531, "loss_cls": 4.30676, "loss": 4.30676, "time": 0.71098} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.09942, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22438, "top5_acc": 0.46156, "loss_cls": 4.26308, "loss": 4.26308, "time": 0.71232} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.09941, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22781, "top5_acc": 0.47375, "loss_cls": 4.25538, "loss": 4.25538, "time": 0.71244} +{"mode": "train", "epoch": 8, "iter": 1300, "lr": 0.09941, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23234, "top5_acc": 0.48234, "loss_cls": 4.22398, "loss": 4.22398, "time": 0.71662} +{"mode": "train", "epoch": 8, "iter": 1400, "lr": 0.0994, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22969, "top5_acc": 0.46875, "loss_cls": 4.24702, "loss": 4.24702, "time": 0.71567} +{"mode": "train", "epoch": 8, "iter": 1500, "lr": 0.0994, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22469, "top5_acc": 0.46562, "loss_cls": 4.27221, "loss": 4.27221, "time": 0.7114} +{"mode": "train", "epoch": 8, "iter": 1600, "lr": 0.0994, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22266, "top5_acc": 0.47281, "loss_cls": 4.27999, "loss": 4.27999, "time": 0.71928} +{"mode": "train", "epoch": 8, "iter": 1700, "lr": 0.09939, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.235, "top5_acc": 0.47312, "loss_cls": 4.2645, "loss": 4.2645, "time": 0.71725} +{"mode": "train", "epoch": 8, "iter": 1800, "lr": 0.09939, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23281, "top5_acc": 0.47781, "loss_cls": 4.20954, "loss": 4.20954, "time": 0.71354} +{"mode": "train", "epoch": 8, "iter": 1900, "lr": 0.09938, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22078, "top5_acc": 0.46297, "loss_cls": 4.28118, "loss": 4.28118, "time": 0.71502} +{"mode": "train", "epoch": 8, "iter": 2000, "lr": 0.09938, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21828, "top5_acc": 0.46766, "loss_cls": 4.2889, "loss": 4.2889, "time": 0.71821} +{"mode": "train", "epoch": 8, "iter": 2100, "lr": 0.09937, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23469, "top5_acc": 0.47078, "loss_cls": 4.25674, "loss": 4.25674, "time": 0.71888} +{"mode": "train", "epoch": 8, "iter": 2200, "lr": 0.09937, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23234, "top5_acc": 0.47438, "loss_cls": 4.24657, "loss": 4.24657, "time": 0.71521} +{"mode": "train", "epoch": 8, "iter": 2300, "lr": 0.09937, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22203, "top5_acc": 0.475, "loss_cls": 4.25311, "loss": 4.25311, "time": 0.71555} +{"mode": "train", "epoch": 8, "iter": 2400, "lr": 0.09936, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.21594, "top5_acc": 0.45578, "loss_cls": 4.30119, "loss": 4.30119, "time": 0.71676} +{"mode": "train", "epoch": 8, "iter": 2500, "lr": 0.09936, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23219, "top5_acc": 0.47312, "loss_cls": 4.24446, "loss": 4.24446, "time": 0.71689} +{"mode": "train", "epoch": 8, "iter": 2600, "lr": 0.09935, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22359, "top5_acc": 0.45922, "loss_cls": 4.31765, "loss": 4.31765, "time": 0.71965} +{"mode": "train", "epoch": 8, "iter": 2700, "lr": 0.09935, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22812, "top5_acc": 0.46219, "loss_cls": 4.2837, "loss": 4.2837, "time": 0.71776} +{"mode": "train", "epoch": 8, "iter": 2800, "lr": 0.09934, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22938, "top5_acc": 0.47125, "loss_cls": 4.23592, "loss": 4.23592, "time": 0.71792} +{"mode": "train", "epoch": 8, "iter": 2900, "lr": 0.09934, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22922, "top5_acc": 0.47078, "loss_cls": 4.24969, "loss": 4.24969, "time": 0.71727} +{"mode": "train", "epoch": 8, "iter": 3000, "lr": 0.09933, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21547, "top5_acc": 0.44984, "loss_cls": 4.31081, "loss": 4.31081, "time": 0.71637} +{"mode": "train", "epoch": 8, "iter": 3100, "lr": 0.09933, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22203, "top5_acc": 0.47094, "loss_cls": 4.27303, "loss": 4.27303, "time": 0.71726} +{"mode": "train", "epoch": 8, "iter": 3200, "lr": 0.09933, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23047, "top5_acc": 0.47359, "loss_cls": 4.22659, "loss": 4.22659, "time": 0.71836} +{"mode": "train", "epoch": 8, "iter": 3300, "lr": 0.09932, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22281, "top5_acc": 0.46469, "loss_cls": 4.31516, "loss": 4.31516, "time": 0.71635} +{"mode": "train", "epoch": 8, "iter": 3400, "lr": 0.09932, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23062, "top5_acc": 0.47609, "loss_cls": 4.25761, "loss": 4.25761, "time": 0.71937} +{"mode": "train", "epoch": 8, "iter": 3500, "lr": 0.09931, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22328, "top5_acc": 0.46625, "loss_cls": 4.2675, "loss": 4.2675, "time": 0.71241} +{"mode": "train", "epoch": 8, "iter": 3600, "lr": 0.09931, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23453, "top5_acc": 0.47094, "loss_cls": 4.24888, "loss": 4.24888, "time": 0.71221} +{"mode": "train", "epoch": 8, "iter": 3700, "lr": 0.0993, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23219, "top5_acc": 0.47344, "loss_cls": 4.25111, "loss": 4.25111, "time": 0.71175} +{"mode": "val", "epoch": 8, "iter": 309, "lr": 0.0993, "top1_acc": 0.16193, "top5_acc": 0.3696, "mean_class_accuracy": 0.16167} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.0993, "memory": 15990, "data_time": 1.38117, "top1_acc": 0.22594, "top5_acc": 0.46188, "loss_cls": 4.28239, "loss": 4.28239, "time": 2.0969} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.09929, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23219, "top5_acc": 0.47594, "loss_cls": 4.22137, "loss": 4.22137, "time": 0.71982} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.09929, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22891, "top5_acc": 0.47719, "loss_cls": 4.26012, "loss": 4.26012, "time": 0.71001} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.09928, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23688, "top5_acc": 0.47594, "loss_cls": 4.2244, "loss": 4.2244, "time": 0.71105} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.09928, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22391, "top5_acc": 0.46969, "loss_cls": 4.24378, "loss": 4.24378, "time": 0.71338} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.09927, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23203, "top5_acc": 0.46656, "loss_cls": 4.2727, "loss": 4.2727, "time": 0.7164} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.09927, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23203, "top5_acc": 0.46734, "loss_cls": 4.24185, "loss": 4.24185, "time": 0.7071} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.09926, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22984, "top5_acc": 0.47359, "loss_cls": 4.2577, "loss": 4.2577, "time": 0.71086} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.09926, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22516, "top5_acc": 0.46688, "loss_cls": 4.24465, "loss": 4.24465, "time": 0.71404} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.09925, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22344, "top5_acc": 0.46938, "loss_cls": 4.2711, "loss": 4.2711, "time": 0.70859} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.09925, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23328, "top5_acc": 0.47688, "loss_cls": 4.21872, "loss": 4.21872, "time": 0.70912} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.09924, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22984, "top5_acc": 0.47328, "loss_cls": 4.24429, "loss": 4.24429, "time": 0.71487} +{"mode": "train", "epoch": 9, "iter": 1300, "lr": 0.09924, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22656, "top5_acc": 0.46969, "loss_cls": 4.25984, "loss": 4.25984, "time": 0.71304} +{"mode": "train", "epoch": 9, "iter": 1400, "lr": 0.09923, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23531, "top5_acc": 0.47812, "loss_cls": 4.23755, "loss": 4.23755, "time": 0.71206} +{"mode": "train", "epoch": 9, "iter": 1500, "lr": 0.09923, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23844, "top5_acc": 0.48094, "loss_cls": 4.20045, "loss": 4.20045, "time": 0.71061} +{"mode": "train", "epoch": 9, "iter": 1600, "lr": 0.09922, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22125, "top5_acc": 0.46422, "loss_cls": 4.27169, "loss": 4.27169, "time": 0.70966} +{"mode": "train", "epoch": 9, "iter": 1700, "lr": 0.09922, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23281, "top5_acc": 0.47734, "loss_cls": 4.23, "loss": 4.23, "time": 0.71317} +{"mode": "train", "epoch": 9, "iter": 1800, "lr": 0.09921, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22812, "top5_acc": 0.47703, "loss_cls": 4.24484, "loss": 4.24484, "time": 0.71685} +{"mode": "train", "epoch": 9, "iter": 1900, "lr": 0.09921, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.23656, "top5_acc": 0.47219, "loss_cls": 4.22834, "loss": 4.22834, "time": 0.71552} +{"mode": "train", "epoch": 9, "iter": 2000, "lr": 0.0992, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23703, "top5_acc": 0.47172, "loss_cls": 4.24645, "loss": 4.24645, "time": 0.71862} +{"mode": "train", "epoch": 9, "iter": 2100, "lr": 0.0992, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22266, "top5_acc": 0.4725, "loss_cls": 4.25205, "loss": 4.25205, "time": 0.71525} +{"mode": "train", "epoch": 9, "iter": 2200, "lr": 0.09919, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.22734, "top5_acc": 0.45984, "loss_cls": 4.29891, "loss": 4.29891, "time": 0.71373} +{"mode": "train", "epoch": 9, "iter": 2300, "lr": 0.09919, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2325, "top5_acc": 0.47594, "loss_cls": 4.28145, "loss": 4.28145, "time": 0.71639} +{"mode": "train", "epoch": 9, "iter": 2400, "lr": 0.09918, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22219, "top5_acc": 0.47547, "loss_cls": 4.2522, "loss": 4.2522, "time": 0.71691} +{"mode": "train", "epoch": 9, "iter": 2500, "lr": 0.09918, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.22375, "top5_acc": 0.46938, "loss_cls": 4.27257, "loss": 4.27257, "time": 0.71432} +{"mode": "train", "epoch": 9, "iter": 2600, "lr": 0.09917, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22969, "top5_acc": 0.46812, "loss_cls": 4.25632, "loss": 4.25632, "time": 0.71764} +{"mode": "train", "epoch": 9, "iter": 2700, "lr": 0.09917, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24172, "top5_acc": 0.47969, "loss_cls": 4.19936, "loss": 4.19936, "time": 0.71703} +{"mode": "train", "epoch": 9, "iter": 2800, "lr": 0.09916, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22641, "top5_acc": 0.47359, "loss_cls": 4.24104, "loss": 4.24104, "time": 0.71736} +{"mode": "train", "epoch": 9, "iter": 2900, "lr": 0.09916, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.23219, "top5_acc": 0.48781, "loss_cls": 4.2387, "loss": 4.2387, "time": 0.71489} +{"mode": "train", "epoch": 9, "iter": 3000, "lr": 0.09915, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23203, "top5_acc": 0.47062, "loss_cls": 4.26692, "loss": 4.26692, "time": 0.71485} +{"mode": "train", "epoch": 9, "iter": 3100, "lr": 0.09915, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23203, "top5_acc": 0.47406, "loss_cls": 4.26483, "loss": 4.26483, "time": 0.71917} +{"mode": "train", "epoch": 9, "iter": 3200, "lr": 0.09914, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24016, "top5_acc": 0.47641, "loss_cls": 4.21348, "loss": 4.21348, "time": 0.71799} +{"mode": "train", "epoch": 9, "iter": 3300, "lr": 0.09914, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23812, "top5_acc": 0.47812, "loss_cls": 4.21433, "loss": 4.21433, "time": 0.71974} +{"mode": "train", "epoch": 9, "iter": 3400, "lr": 0.09913, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.22109, "top5_acc": 0.46594, "loss_cls": 4.27954, "loss": 4.27954, "time": 0.72174} +{"mode": "train", "epoch": 9, "iter": 3500, "lr": 0.09913, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22266, "top5_acc": 0.46562, "loss_cls": 4.26907, "loss": 4.26907, "time": 0.71133} +{"mode": "train", "epoch": 9, "iter": 3600, "lr": 0.09912, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23078, "top5_acc": 0.48094, "loss_cls": 4.21693, "loss": 4.21693, "time": 0.71406} +{"mode": "train", "epoch": 9, "iter": 3700, "lr": 0.09912, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23672, "top5_acc": 0.48922, "loss_cls": 4.1922, "loss": 4.1922, "time": 0.71212} +{"mode": "val", "epoch": 9, "iter": 309, "lr": 0.09911, "top1_acc": 0.15666, "top5_acc": 0.36924, "mean_class_accuracy": 0.15662} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.09911, "memory": 15990, "data_time": 1.35564, "top1_acc": 0.24031, "top5_acc": 0.48688, "loss_cls": 4.14652, "loss": 4.14652, "time": 2.07355} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.0991, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23516, "top5_acc": 0.48312, "loss_cls": 4.19349, "loss": 4.19349, "time": 0.71172} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.0991, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22922, "top5_acc": 0.48906, "loss_cls": 4.21027, "loss": 4.21027, "time": 0.71109} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.09909, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23141, "top5_acc": 0.47812, "loss_cls": 4.2281, "loss": 4.2281, "time": 0.71486} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.09909, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23578, "top5_acc": 0.47453, "loss_cls": 4.23661, "loss": 4.23661, "time": 0.7116} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.09908, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23188, "top5_acc": 0.47625, "loss_cls": 4.24386, "loss": 4.24386, "time": 0.71191} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.09908, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22891, "top5_acc": 0.48688, "loss_cls": 4.23499, "loss": 4.23499, "time": 0.71083} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.09907, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23109, "top5_acc": 0.47109, "loss_cls": 4.24574, "loss": 4.24574, "time": 0.71191} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.09907, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23141, "top5_acc": 0.47641, "loss_cls": 4.2129, "loss": 4.2129, "time": 0.71123} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.09906, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23703, "top5_acc": 0.47469, "loss_cls": 4.21725, "loss": 4.21725, "time": 0.7085} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.09906, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24172, "top5_acc": 0.48906, "loss_cls": 4.17362, "loss": 4.17362, "time": 0.71176} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.09905, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22859, "top5_acc": 0.46781, "loss_cls": 4.22995, "loss": 4.22995, "time": 0.71267} +{"mode": "train", "epoch": 10, "iter": 1300, "lr": 0.09905, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23203, "top5_acc": 0.47812, "loss_cls": 4.23207, "loss": 4.23207, "time": 0.70898} +{"mode": "train", "epoch": 10, "iter": 1400, "lr": 0.09904, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22828, "top5_acc": 0.47625, "loss_cls": 4.23371, "loss": 4.23371, "time": 0.71351} +{"mode": "train", "epoch": 10, "iter": 1500, "lr": 0.09903, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23047, "top5_acc": 0.46906, "loss_cls": 4.24457, "loss": 4.24457, "time": 0.71226} +{"mode": "train", "epoch": 10, "iter": 1600, "lr": 0.09903, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23188, "top5_acc": 0.46797, "loss_cls": 4.27592, "loss": 4.27592, "time": 0.71594} +{"mode": "train", "epoch": 10, "iter": 1700, "lr": 0.09902, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23031, "top5_acc": 0.47219, "loss_cls": 4.25186, "loss": 4.25186, "time": 0.71108} +{"mode": "train", "epoch": 10, "iter": 1800, "lr": 0.09902, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23594, "top5_acc": 0.47875, "loss_cls": 4.19102, "loss": 4.19102, "time": 0.71464} +{"mode": "train", "epoch": 10, "iter": 1900, "lr": 0.09901, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22469, "top5_acc": 0.47453, "loss_cls": 4.25092, "loss": 4.25092, "time": 0.71532} +{"mode": "train", "epoch": 10, "iter": 2000, "lr": 0.09901, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.22344, "top5_acc": 0.47516, "loss_cls": 4.23924, "loss": 4.23924, "time": 0.71896} +{"mode": "train", "epoch": 10, "iter": 2100, "lr": 0.099, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23281, "top5_acc": 0.46891, "loss_cls": 4.2595, "loss": 4.2595, "time": 0.71742} +{"mode": "train", "epoch": 10, "iter": 2200, "lr": 0.099, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23891, "top5_acc": 0.48219, "loss_cls": 4.19728, "loss": 4.19728, "time": 0.71821} +{"mode": "train", "epoch": 10, "iter": 2300, "lr": 0.09899, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.23016, "top5_acc": 0.47141, "loss_cls": 4.24187, "loss": 4.24187, "time": 0.71715} +{"mode": "train", "epoch": 10, "iter": 2400, "lr": 0.09898, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23625, "top5_acc": 0.48078, "loss_cls": 4.20771, "loss": 4.20771, "time": 0.71623} +{"mode": "train", "epoch": 10, "iter": 2500, "lr": 0.09898, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22781, "top5_acc": 0.46359, "loss_cls": 4.28278, "loss": 4.28278, "time": 0.71417} +{"mode": "train", "epoch": 10, "iter": 2600, "lr": 0.09897, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23609, "top5_acc": 0.47578, "loss_cls": 4.21674, "loss": 4.21674, "time": 0.714} +{"mode": "train", "epoch": 10, "iter": 2700, "lr": 0.09897, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21984, "top5_acc": 0.45844, "loss_cls": 4.29484, "loss": 4.29484, "time": 0.71644} +{"mode": "train", "epoch": 10, "iter": 2800, "lr": 0.09896, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23609, "top5_acc": 0.47047, "loss_cls": 4.23563, "loss": 4.23563, "time": 0.72037} +{"mode": "train", "epoch": 10, "iter": 2900, "lr": 0.09896, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22969, "top5_acc": 0.48375, "loss_cls": 4.20145, "loss": 4.20145, "time": 0.71463} +{"mode": "train", "epoch": 10, "iter": 3000, "lr": 0.09895, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.23203, "top5_acc": 0.47297, "loss_cls": 4.24845, "loss": 4.24845, "time": 0.72205} +{"mode": "train", "epoch": 10, "iter": 3100, "lr": 0.09894, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23281, "top5_acc": 0.47969, "loss_cls": 4.20426, "loss": 4.20426, "time": 0.71986} +{"mode": "train", "epoch": 10, "iter": 3200, "lr": 0.09894, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23969, "top5_acc": 0.48422, "loss_cls": 4.2072, "loss": 4.2072, "time": 0.71852} +{"mode": "train", "epoch": 10, "iter": 3300, "lr": 0.09893, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.23328, "top5_acc": 0.47125, "loss_cls": 4.2564, "loss": 4.2564, "time": 0.71727} +{"mode": "train", "epoch": 10, "iter": 3400, "lr": 0.09893, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22609, "top5_acc": 0.47828, "loss_cls": 4.24439, "loss": 4.24439, "time": 0.71639} +{"mode": "train", "epoch": 10, "iter": 3500, "lr": 0.09892, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23188, "top5_acc": 0.47516, "loss_cls": 4.24158, "loss": 4.24158, "time": 0.71722} +{"mode": "train", "epoch": 10, "iter": 3600, "lr": 0.09892, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22562, "top5_acc": 0.47984, "loss_cls": 4.22337, "loss": 4.22337, "time": 0.71478} +{"mode": "train", "epoch": 10, "iter": 3700, "lr": 0.09891, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23531, "top5_acc": 0.47906, "loss_cls": 4.20797, "loss": 4.20797, "time": 0.71187} +{"mode": "val", "epoch": 10, "iter": 309, "lr": 0.09891, "top1_acc": 0.17961, "top5_acc": 0.41594, "mean_class_accuracy": 0.17919} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.0989, "memory": 15990, "data_time": 1.41718, "top1_acc": 0.23703, "top5_acc": 0.48125, "loss_cls": 4.18667, "loss": 4.18667, "time": 2.138} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.0989, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.23797, "top5_acc": 0.48422, "loss_cls": 4.19038, "loss": 4.19038, "time": 0.71784} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.09889, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23766, "top5_acc": 0.46875, "loss_cls": 4.22954, "loss": 4.22954, "time": 0.71836} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.09888, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23766, "top5_acc": 0.48844, "loss_cls": 4.17446, "loss": 4.17446, "time": 0.71155} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.09888, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23031, "top5_acc": 0.46719, "loss_cls": 4.22796, "loss": 4.22796, "time": 0.71916} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.09887, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23609, "top5_acc": 0.49125, "loss_cls": 4.17608, "loss": 4.17608, "time": 0.71819} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.09887, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23484, "top5_acc": 0.47031, "loss_cls": 4.21599, "loss": 4.21599, "time": 0.71876} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.09886, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23281, "top5_acc": 0.47984, "loss_cls": 4.21707, "loss": 4.21707, "time": 0.7133} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.09885, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23859, "top5_acc": 0.47859, "loss_cls": 4.18941, "loss": 4.18941, "time": 0.7186} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.09885, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23281, "top5_acc": 0.48406, "loss_cls": 4.18626, "loss": 4.18626, "time": 0.71578} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.09884, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22109, "top5_acc": 0.46328, "loss_cls": 4.26969, "loss": 4.26969, "time": 0.72146} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.09884, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24219, "top5_acc": 0.48953, "loss_cls": 4.17097, "loss": 4.17097, "time": 0.72067} +{"mode": "train", "epoch": 11, "iter": 1300, "lr": 0.09883, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22781, "top5_acc": 0.47828, "loss_cls": 4.25103, "loss": 4.25103, "time": 0.72082} +{"mode": "train", "epoch": 11, "iter": 1400, "lr": 0.09882, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23578, "top5_acc": 0.48438, "loss_cls": 4.19748, "loss": 4.19748, "time": 0.71755} +{"mode": "train", "epoch": 11, "iter": 1500, "lr": 0.09882, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23812, "top5_acc": 0.48016, "loss_cls": 4.22169, "loss": 4.22169, "time": 0.71574} +{"mode": "train", "epoch": 11, "iter": 1600, "lr": 0.09881, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23297, "top5_acc": 0.47672, "loss_cls": 4.21555, "loss": 4.21555, "time": 0.71455} +{"mode": "train", "epoch": 11, "iter": 1700, "lr": 0.09881, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23531, "top5_acc": 0.47453, "loss_cls": 4.2094, "loss": 4.2094, "time": 0.71664} +{"mode": "train", "epoch": 11, "iter": 1800, "lr": 0.0988, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23938, "top5_acc": 0.49062, "loss_cls": 4.16796, "loss": 4.16796, "time": 0.71592} +{"mode": "train", "epoch": 11, "iter": 1900, "lr": 0.09879, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22656, "top5_acc": 0.46891, "loss_cls": 4.26129, "loss": 4.26129, "time": 0.71657} +{"mode": "train", "epoch": 11, "iter": 2000, "lr": 0.09879, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.235, "top5_acc": 0.48812, "loss_cls": 4.19915, "loss": 4.19915, "time": 0.71643} +{"mode": "train", "epoch": 11, "iter": 2100, "lr": 0.09878, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23719, "top5_acc": 0.47984, "loss_cls": 4.2083, "loss": 4.2083, "time": 0.72177} +{"mode": "train", "epoch": 11, "iter": 2200, "lr": 0.09878, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23625, "top5_acc": 0.47812, "loss_cls": 4.22549, "loss": 4.22549, "time": 0.71704} +{"mode": "train", "epoch": 11, "iter": 2300, "lr": 0.09877, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22625, "top5_acc": 0.47969, "loss_cls": 4.22397, "loss": 4.22397, "time": 0.71888} +{"mode": "train", "epoch": 11, "iter": 2400, "lr": 0.09876, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23938, "top5_acc": 0.48312, "loss_cls": 4.18877, "loss": 4.18877, "time": 0.7161} +{"mode": "train", "epoch": 11, "iter": 2500, "lr": 0.09876, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.23219, "top5_acc": 0.47328, "loss_cls": 4.22187, "loss": 4.22187, "time": 0.7177} +{"mode": "train", "epoch": 11, "iter": 2600, "lr": 0.09875, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.22953, "top5_acc": 0.47469, "loss_cls": 4.21383, "loss": 4.21383, "time": 0.71952} +{"mode": "train", "epoch": 11, "iter": 2700, "lr": 0.09874, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23016, "top5_acc": 0.47562, "loss_cls": 4.25431, "loss": 4.25431, "time": 0.71659} +{"mode": "train", "epoch": 11, "iter": 2800, "lr": 0.09874, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24125, "top5_acc": 0.47922, "loss_cls": 4.20622, "loss": 4.20622, "time": 0.72019} +{"mode": "train", "epoch": 11, "iter": 2900, "lr": 0.09873, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23375, "top5_acc": 0.47688, "loss_cls": 4.2147, "loss": 4.2147, "time": 0.71857} +{"mode": "train", "epoch": 11, "iter": 3000, "lr": 0.09873, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.23453, "top5_acc": 0.47406, "loss_cls": 4.21536, "loss": 4.21536, "time": 0.71759} +{"mode": "train", "epoch": 11, "iter": 3100, "lr": 0.09872, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23328, "top5_acc": 0.47359, "loss_cls": 4.24009, "loss": 4.24009, "time": 0.71737} +{"mode": "train", "epoch": 11, "iter": 3200, "lr": 0.09871, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23562, "top5_acc": 0.47391, "loss_cls": 4.21277, "loss": 4.21277, "time": 0.71369} +{"mode": "train", "epoch": 11, "iter": 3300, "lr": 0.09871, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24234, "top5_acc": 0.4825, "loss_cls": 4.20488, "loss": 4.20488, "time": 0.72428} +{"mode": "train", "epoch": 11, "iter": 3400, "lr": 0.0987, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24094, "top5_acc": 0.48781, "loss_cls": 4.196, "loss": 4.196, "time": 0.71618} +{"mode": "train", "epoch": 11, "iter": 3500, "lr": 0.09869, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23766, "top5_acc": 0.46969, "loss_cls": 4.24905, "loss": 4.24905, "time": 0.71868} +{"mode": "train", "epoch": 11, "iter": 3600, "lr": 0.09869, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23109, "top5_acc": 0.47469, "loss_cls": 4.24568, "loss": 4.24568, "time": 0.71923} +{"mode": "train", "epoch": 11, "iter": 3700, "lr": 0.09868, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23797, "top5_acc": 0.48, "loss_cls": 4.20866, "loss": 4.20866, "time": 0.71429} +{"mode": "val", "epoch": 11, "iter": 309, "lr": 0.09868, "top1_acc": 0.13564, "top5_acc": 0.34382, "mean_class_accuracy": 0.13545} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.09867, "memory": 15990, "data_time": 1.41744, "top1_acc": 0.24094, "top5_acc": 0.48406, "loss_cls": 4.18317, "loss": 4.18317, "time": 2.13126} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.09867, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24125, "top5_acc": 0.49078, "loss_cls": 4.16516, "loss": 4.16516, "time": 0.71716} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.09866, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23156, "top5_acc": 0.47328, "loss_cls": 4.24949, "loss": 4.24949, "time": 0.71411} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.09865, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23203, "top5_acc": 0.47953, "loss_cls": 4.21175, "loss": 4.21175, "time": 0.71969} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.09865, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24156, "top5_acc": 0.48359, "loss_cls": 4.20361, "loss": 4.20361, "time": 0.71636} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.09864, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23703, "top5_acc": 0.47797, "loss_cls": 4.2102, "loss": 4.2102, "time": 0.71492} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.09863, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23453, "top5_acc": 0.48625, "loss_cls": 4.20635, "loss": 4.20635, "time": 0.71616} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.09863, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23078, "top5_acc": 0.47703, "loss_cls": 4.22695, "loss": 4.22695, "time": 0.71877} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.09862, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25203, "top5_acc": 0.49203, "loss_cls": 4.14067, "loss": 4.14067, "time": 0.71752} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.09861, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24156, "top5_acc": 0.48578, "loss_cls": 4.19483, "loss": 4.19483, "time": 0.7192} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.09861, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23562, "top5_acc": 0.48109, "loss_cls": 4.20369, "loss": 4.20369, "time": 0.71826} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.0986, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23719, "top5_acc": 0.48375, "loss_cls": 4.19542, "loss": 4.19542, "time": 0.72037} +{"mode": "train", "epoch": 12, "iter": 1300, "lr": 0.09859, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23312, "top5_acc": 0.47688, "loss_cls": 4.22488, "loss": 4.22488, "time": 0.71794} +{"mode": "train", "epoch": 12, "iter": 1400, "lr": 0.09859, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25266, "top5_acc": 0.48375, "loss_cls": 4.14749, "loss": 4.14749, "time": 0.71995} +{"mode": "train", "epoch": 12, "iter": 1500, "lr": 0.09858, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24078, "top5_acc": 0.47859, "loss_cls": 4.18688, "loss": 4.18688, "time": 0.71756} +{"mode": "train", "epoch": 12, "iter": 1600, "lr": 0.09857, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23906, "top5_acc": 0.48422, "loss_cls": 4.19271, "loss": 4.19271, "time": 0.7202} +{"mode": "train", "epoch": 12, "iter": 1700, "lr": 0.09857, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23766, "top5_acc": 0.47578, "loss_cls": 4.2181, "loss": 4.2181, "time": 0.71901} +{"mode": "train", "epoch": 12, "iter": 1800, "lr": 0.09856, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23328, "top5_acc": 0.47938, "loss_cls": 4.20395, "loss": 4.20395, "time": 0.71888} +{"mode": "train", "epoch": 12, "iter": 1900, "lr": 0.09855, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22672, "top5_acc": 0.47844, "loss_cls": 4.25579, "loss": 4.25579, "time": 0.72004} +{"mode": "train", "epoch": 12, "iter": 2000, "lr": 0.09855, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.23609, "top5_acc": 0.48047, "loss_cls": 4.21836, "loss": 4.21836, "time": 0.7202} +{"mode": "train", "epoch": 12, "iter": 2100, "lr": 0.09854, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.23469, "top5_acc": 0.48688, "loss_cls": 4.19553, "loss": 4.19553, "time": 0.72485} +{"mode": "train", "epoch": 12, "iter": 2200, "lr": 0.09853, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23984, "top5_acc": 0.47984, "loss_cls": 4.16133, "loss": 4.16133, "time": 0.72614} +{"mode": "train", "epoch": 12, "iter": 2300, "lr": 0.09853, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.23297, "top5_acc": 0.47188, "loss_cls": 4.22272, "loss": 4.22272, "time": 0.71953} +{"mode": "train", "epoch": 12, "iter": 2400, "lr": 0.09852, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24, "top5_acc": 0.49344, "loss_cls": 4.15839, "loss": 4.15839, "time": 0.72354} +{"mode": "train", "epoch": 12, "iter": 2500, "lr": 0.09851, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24016, "top5_acc": 0.48391, "loss_cls": 4.163, "loss": 4.163, "time": 0.7196} +{"mode": "train", "epoch": 12, "iter": 2600, "lr": 0.09851, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.23016, "top5_acc": 0.47672, "loss_cls": 4.2257, "loss": 4.2257, "time": 0.72097} +{"mode": "train", "epoch": 12, "iter": 2700, "lr": 0.0985, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.23938, "top5_acc": 0.48109, "loss_cls": 4.21196, "loss": 4.21196, "time": 0.72071} +{"mode": "train", "epoch": 12, "iter": 2800, "lr": 0.09849, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23938, "top5_acc": 0.49156, "loss_cls": 4.1493, "loss": 4.1493, "time": 0.72559} +{"mode": "train", "epoch": 12, "iter": 2900, "lr": 0.09849, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.23781, "top5_acc": 0.48266, "loss_cls": 4.19832, "loss": 4.19832, "time": 0.72429} +{"mode": "train", "epoch": 12, "iter": 3000, "lr": 0.09848, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23297, "top5_acc": 0.47422, "loss_cls": 4.20764, "loss": 4.20764, "time": 0.72196} +{"mode": "train", "epoch": 12, "iter": 3100, "lr": 0.09847, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.23734, "top5_acc": 0.48469, "loss_cls": 4.18083, "loss": 4.18083, "time": 0.71903} +{"mode": "train", "epoch": 12, "iter": 3200, "lr": 0.09847, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23062, "top5_acc": 0.48078, "loss_cls": 4.2128, "loss": 4.2128, "time": 0.71635} +{"mode": "train", "epoch": 12, "iter": 3300, "lr": 0.09846, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.2325, "top5_acc": 0.48953, "loss_cls": 4.22436, "loss": 4.22436, "time": 0.72012} +{"mode": "train", "epoch": 12, "iter": 3400, "lr": 0.09845, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22734, "top5_acc": 0.48094, "loss_cls": 4.21032, "loss": 4.21032, "time": 0.72083} +{"mode": "train", "epoch": 12, "iter": 3500, "lr": 0.09845, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22922, "top5_acc": 0.47359, "loss_cls": 4.26872, "loss": 4.26872, "time": 0.71681} +{"mode": "train", "epoch": 12, "iter": 3600, "lr": 0.09844, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23406, "top5_acc": 0.47359, "loss_cls": 4.23364, "loss": 4.23364, "time": 0.71439} +{"mode": "train", "epoch": 12, "iter": 3700, "lr": 0.09843, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24266, "top5_acc": 0.4875, "loss_cls": 4.15565, "loss": 4.15565, "time": 0.71466} +{"mode": "val", "epoch": 12, "iter": 309, "lr": 0.09843, "top1_acc": 0.15337, "top5_acc": 0.35131, "mean_class_accuracy": 0.15321} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.09842, "memory": 15990, "data_time": 1.39128, "top1_acc": 0.24188, "top5_acc": 0.48562, "loss_cls": 4.17514, "loss": 4.17514, "time": 2.11219} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.09842, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.24188, "top5_acc": 0.48438, "loss_cls": 4.15468, "loss": 4.15468, "time": 0.71794} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.09841, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24422, "top5_acc": 0.4875, "loss_cls": 4.18979, "loss": 4.18979, "time": 0.71846} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.0984, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24422, "top5_acc": 0.48672, "loss_cls": 4.18091, "loss": 4.18091, "time": 0.71383} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.09839, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24688, "top5_acc": 0.48594, "loss_cls": 4.17292, "loss": 4.17292, "time": 0.71474} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.09839, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.23938, "top5_acc": 0.48766, "loss_cls": 4.21637, "loss": 4.21637, "time": 0.71383} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.09838, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23141, "top5_acc": 0.47859, "loss_cls": 4.2098, "loss": 4.2098, "time": 0.71283} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.09837, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24219, "top5_acc": 0.48531, "loss_cls": 4.16473, "loss": 4.16473, "time": 0.71533} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.09837, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23891, "top5_acc": 0.47906, "loss_cls": 4.18598, "loss": 4.18598, "time": 0.7164} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.09836, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24016, "top5_acc": 0.48609, "loss_cls": 4.17302, "loss": 4.17302, "time": 0.71729} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.09835, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23844, "top5_acc": 0.47828, "loss_cls": 4.19668, "loss": 4.19668, "time": 0.71704} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.09834, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24562, "top5_acc": 0.4825, "loss_cls": 4.18432, "loss": 4.18432, "time": 0.72204} +{"mode": "train", "epoch": 13, "iter": 1300, "lr": 0.09834, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23953, "top5_acc": 0.49281, "loss_cls": 4.16667, "loss": 4.16667, "time": 0.71783} +{"mode": "train", "epoch": 13, "iter": 1400, "lr": 0.09833, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22781, "top5_acc": 0.47828, "loss_cls": 4.21362, "loss": 4.21362, "time": 0.71954} +{"mode": "train", "epoch": 13, "iter": 1500, "lr": 0.09832, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24547, "top5_acc": 0.49359, "loss_cls": 4.14045, "loss": 4.14045, "time": 0.71914} +{"mode": "train", "epoch": 13, "iter": 1600, "lr": 0.09832, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.24047, "top5_acc": 0.49391, "loss_cls": 4.15767, "loss": 4.15767, "time": 0.72057} +{"mode": "train", "epoch": 13, "iter": 1700, "lr": 0.09831, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.24281, "top5_acc": 0.48922, "loss_cls": 4.18373, "loss": 4.18373, "time": 0.72105} +{"mode": "train", "epoch": 13, "iter": 1800, "lr": 0.0983, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24203, "top5_acc": 0.48906, "loss_cls": 4.1797, "loss": 4.1797, "time": 0.72634} +{"mode": "train", "epoch": 13, "iter": 1900, "lr": 0.09829, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24, "top5_acc": 0.48047, "loss_cls": 4.20712, "loss": 4.20712, "time": 0.72108} +{"mode": "train", "epoch": 13, "iter": 2000, "lr": 0.09829, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24422, "top5_acc": 0.49016, "loss_cls": 4.14313, "loss": 4.14313, "time": 0.72026} +{"mode": "train", "epoch": 13, "iter": 2100, "lr": 0.09828, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.23328, "top5_acc": 0.47969, "loss_cls": 4.18778, "loss": 4.18778, "time": 0.71922} +{"mode": "train", "epoch": 13, "iter": 2200, "lr": 0.09827, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.23906, "top5_acc": 0.47922, "loss_cls": 4.21365, "loss": 4.21365, "time": 0.72231} +{"mode": "train", "epoch": 13, "iter": 2300, "lr": 0.09827, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23734, "top5_acc": 0.47891, "loss_cls": 4.19959, "loss": 4.19959, "time": 0.71966} +{"mode": "train", "epoch": 13, "iter": 2400, "lr": 0.09826, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23422, "top5_acc": 0.48734, "loss_cls": 4.19708, "loss": 4.19708, "time": 0.72416} +{"mode": "train", "epoch": 13, "iter": 2500, "lr": 0.09825, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23453, "top5_acc": 0.47609, "loss_cls": 4.21282, "loss": 4.21282, "time": 0.7202} +{"mode": "train", "epoch": 13, "iter": 2600, "lr": 0.09824, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23219, "top5_acc": 0.47984, "loss_cls": 4.22038, "loss": 4.22038, "time": 0.72069} +{"mode": "train", "epoch": 13, "iter": 2700, "lr": 0.09824, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24125, "top5_acc": 0.47656, "loss_cls": 4.21566, "loss": 4.21566, "time": 0.72592} +{"mode": "train", "epoch": 13, "iter": 2800, "lr": 0.09823, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24734, "top5_acc": 0.48859, "loss_cls": 4.1605, "loss": 4.1605, "time": 0.72414} +{"mode": "train", "epoch": 13, "iter": 2900, "lr": 0.09822, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24422, "top5_acc": 0.49297, "loss_cls": 4.15099, "loss": 4.15099, "time": 0.72191} +{"mode": "train", "epoch": 13, "iter": 3000, "lr": 0.09821, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23172, "top5_acc": 0.4775, "loss_cls": 4.24046, "loss": 4.24046, "time": 0.72324} +{"mode": "train", "epoch": 13, "iter": 3100, "lr": 0.09821, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.23672, "top5_acc": 0.47875, "loss_cls": 4.21232, "loss": 4.21232, "time": 0.72237} +{"mode": "train", "epoch": 13, "iter": 3200, "lr": 0.0982, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23469, "top5_acc": 0.47656, "loss_cls": 4.232, "loss": 4.232, "time": 0.72224} +{"mode": "train", "epoch": 13, "iter": 3300, "lr": 0.09819, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23531, "top5_acc": 0.48219, "loss_cls": 4.20342, "loss": 4.20342, "time": 0.72228} +{"mode": "train", "epoch": 13, "iter": 3400, "lr": 0.09818, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.24203, "top5_acc": 0.47844, "loss_cls": 4.18573, "loss": 4.18573, "time": 0.72524} +{"mode": "train", "epoch": 13, "iter": 3500, "lr": 0.09818, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.23547, "top5_acc": 0.48922, "loss_cls": 4.18082, "loss": 4.18082, "time": 0.71891} +{"mode": "train", "epoch": 13, "iter": 3600, "lr": 0.09817, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.24094, "top5_acc": 0.47875, "loss_cls": 4.18755, "loss": 4.18755, "time": 0.71595} +{"mode": "train", "epoch": 13, "iter": 3700, "lr": 0.09816, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22938, "top5_acc": 0.48266, "loss_cls": 4.20795, "loss": 4.20795, "time": 0.71386} +{"mode": "val", "epoch": 13, "iter": 309, "lr": 0.09816, "top1_acc": 0.17738, "top5_acc": 0.39746, "mean_class_accuracy": 0.17725} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.09815, "memory": 15990, "data_time": 1.41243, "top1_acc": 0.24844, "top5_acc": 0.50047, "loss_cls": 4.12228, "loss": 4.12228, "time": 2.1275} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.09814, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23156, "top5_acc": 0.48016, "loss_cls": 4.18432, "loss": 4.18432, "time": 0.71739} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.09814, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.24109, "top5_acc": 0.48906, "loss_cls": 4.16328, "loss": 4.16328, "time": 0.71723} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.09813, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24516, "top5_acc": 0.49484, "loss_cls": 4.12833, "loss": 4.12833, "time": 0.71705} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.09812, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23531, "top5_acc": 0.49141, "loss_cls": 4.1683, "loss": 4.1683, "time": 0.71528} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.09811, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23922, "top5_acc": 0.48578, "loss_cls": 4.19734, "loss": 4.19734, "time": 0.71201} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.09811, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23938, "top5_acc": 0.49469, "loss_cls": 4.15496, "loss": 4.15496, "time": 0.71387} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.0981, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24875, "top5_acc": 0.48625, "loss_cls": 4.15374, "loss": 4.15374, "time": 0.71399} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.09809, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22922, "top5_acc": 0.47719, "loss_cls": 4.22003, "loss": 4.22003, "time": 0.718} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.09808, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2375, "top5_acc": 0.47828, "loss_cls": 4.1863, "loss": 4.1863, "time": 0.71785} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.09807, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24406, "top5_acc": 0.48703, "loss_cls": 4.18272, "loss": 4.18272, "time": 0.71788} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.09807, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.24141, "top5_acc": 0.48078, "loss_cls": 4.21002, "loss": 4.21002, "time": 0.71781} +{"mode": "train", "epoch": 14, "iter": 1300, "lr": 0.09806, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24578, "top5_acc": 0.48938, "loss_cls": 4.17254, "loss": 4.17254, "time": 0.71802} +{"mode": "train", "epoch": 14, "iter": 1400, "lr": 0.09805, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23188, "top5_acc": 0.48312, "loss_cls": 4.1919, "loss": 4.1919, "time": 0.71825} +{"mode": "train", "epoch": 14, "iter": 1500, "lr": 0.09804, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22703, "top5_acc": 0.47219, "loss_cls": 4.23461, "loss": 4.23461, "time": 0.71818} +{"mode": "train", "epoch": 14, "iter": 1600, "lr": 0.09804, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24344, "top5_acc": 0.495, "loss_cls": 4.13374, "loss": 4.13374, "time": 0.72014} +{"mode": "train", "epoch": 14, "iter": 1700, "lr": 0.09803, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23844, "top5_acc": 0.48297, "loss_cls": 4.18665, "loss": 4.18665, "time": 0.71991} +{"mode": "train", "epoch": 14, "iter": 1800, "lr": 0.09802, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24016, "top5_acc": 0.48062, "loss_cls": 4.20929, "loss": 4.20929, "time": 0.7174} +{"mode": "train", "epoch": 14, "iter": 1900, "lr": 0.09801, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.23594, "top5_acc": 0.4875, "loss_cls": 4.19962, "loss": 4.19962, "time": 0.72122} +{"mode": "train", "epoch": 14, "iter": 2000, "lr": 0.098, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24969, "top5_acc": 0.49453, "loss_cls": 4.16264, "loss": 4.16264, "time": 0.72144} +{"mode": "train", "epoch": 14, "iter": 2100, "lr": 0.098, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.2425, "top5_acc": 0.48172, "loss_cls": 4.18153, "loss": 4.18153, "time": 0.72114} +{"mode": "train", "epoch": 14, "iter": 2200, "lr": 0.09799, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24672, "top5_acc": 0.48469, "loss_cls": 4.17781, "loss": 4.17781, "time": 0.72092} +{"mode": "train", "epoch": 14, "iter": 2300, "lr": 0.09798, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24578, "top5_acc": 0.48594, "loss_cls": 4.1712, "loss": 4.1712, "time": 0.72165} +{"mode": "train", "epoch": 14, "iter": 2400, "lr": 0.09797, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23328, "top5_acc": 0.48141, "loss_cls": 4.22252, "loss": 4.22252, "time": 0.71974} +{"mode": "train", "epoch": 14, "iter": 2500, "lr": 0.09797, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24438, "top5_acc": 0.49312, "loss_cls": 4.13822, "loss": 4.13822, "time": 0.72038} +{"mode": "train", "epoch": 14, "iter": 2600, "lr": 0.09796, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.25281, "top5_acc": 0.50141, "loss_cls": 4.14301, "loss": 4.14301, "time": 0.71951} +{"mode": "train", "epoch": 14, "iter": 2700, "lr": 0.09795, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.23938, "top5_acc": 0.48906, "loss_cls": 4.15215, "loss": 4.15215, "time": 0.72034} +{"mode": "train", "epoch": 14, "iter": 2800, "lr": 0.09794, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24859, "top5_acc": 0.49422, "loss_cls": 4.14271, "loss": 4.14271, "time": 0.72206} +{"mode": "train", "epoch": 14, "iter": 2900, "lr": 0.09793, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2425, "top5_acc": 0.47797, "loss_cls": 4.20757, "loss": 4.20757, "time": 0.72457} +{"mode": "train", "epoch": 14, "iter": 3000, "lr": 0.09793, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23359, "top5_acc": 0.47156, "loss_cls": 4.23121, "loss": 4.23121, "time": 0.72038} +{"mode": "train", "epoch": 14, "iter": 3100, "lr": 0.09792, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.2375, "top5_acc": 0.48703, "loss_cls": 4.15851, "loss": 4.15851, "time": 0.72119} +{"mode": "train", "epoch": 14, "iter": 3200, "lr": 0.09791, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.24688, "top5_acc": 0.48891, "loss_cls": 4.15318, "loss": 4.15318, "time": 0.72208} +{"mode": "train", "epoch": 14, "iter": 3300, "lr": 0.0979, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23203, "top5_acc": 0.47359, "loss_cls": 4.20796, "loss": 4.20796, "time": 0.72719} +{"mode": "train", "epoch": 14, "iter": 3400, "lr": 0.09789, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24281, "top5_acc": 0.48422, "loss_cls": 4.18416, "loss": 4.18416, "time": 0.72267} +{"mode": "train", "epoch": 14, "iter": 3500, "lr": 0.09789, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.24328, "top5_acc": 0.48406, "loss_cls": 4.1929, "loss": 4.1929, "time": 0.71963} +{"mode": "train", "epoch": 14, "iter": 3600, "lr": 0.09788, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24562, "top5_acc": 0.48922, "loss_cls": 4.16143, "loss": 4.16143, "time": 0.71986} +{"mode": "train", "epoch": 14, "iter": 3700, "lr": 0.09787, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.24203, "top5_acc": 0.48297, "loss_cls": 4.20486, "loss": 4.20486, "time": 0.71828} +{"mode": "val", "epoch": 14, "iter": 309, "lr": 0.09787, "top1_acc": 0.16026, "top5_acc": 0.36519, "mean_class_accuracy": 0.16003} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.09786, "memory": 15990, "data_time": 1.38658, "top1_acc": 0.24797, "top5_acc": 0.49234, "loss_cls": 4.12418, "loss": 4.12418, "time": 2.10355} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.09785, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23953, "top5_acc": 0.48781, "loss_cls": 4.17986, "loss": 4.17986, "time": 0.71594} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.09784, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24094, "top5_acc": 0.47641, "loss_cls": 4.16774, "loss": 4.16774, "time": 0.71713} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.09783, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24375, "top5_acc": 0.48891, "loss_cls": 4.17445, "loss": 4.17445, "time": 0.71768} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.09783, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24359, "top5_acc": 0.47953, "loss_cls": 4.20478, "loss": 4.20478, "time": 0.71833} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.09782, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23703, "top5_acc": 0.47766, "loss_cls": 4.20756, "loss": 4.20756, "time": 0.71407} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.09781, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25203, "top5_acc": 0.50641, "loss_cls": 4.09896, "loss": 4.09896, "time": 0.71422} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.0978, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.23547, "top5_acc": 0.485, "loss_cls": 4.19873, "loss": 4.19873, "time": 0.71668} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.09779, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25031, "top5_acc": 0.49234, "loss_cls": 4.1435, "loss": 4.1435, "time": 0.71854} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.09778, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23391, "top5_acc": 0.47719, "loss_cls": 4.20307, "loss": 4.20307, "time": 0.71314} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.09778, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24328, "top5_acc": 0.48844, "loss_cls": 4.14723, "loss": 4.14723, "time": 0.71488} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.09777, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24844, "top5_acc": 0.49516, "loss_cls": 4.15015, "loss": 4.15015, "time": 0.71982} +{"mode": "train", "epoch": 15, "iter": 1300, "lr": 0.09776, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23688, "top5_acc": 0.47984, "loss_cls": 4.20828, "loss": 4.20828, "time": 0.72149} +{"mode": "train", "epoch": 15, "iter": 1400, "lr": 0.09775, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24016, "top5_acc": 0.48594, "loss_cls": 4.16931, "loss": 4.16931, "time": 0.71823} +{"mode": "train", "epoch": 15, "iter": 1500, "lr": 0.09774, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23875, "top5_acc": 0.47453, "loss_cls": 4.21305, "loss": 4.21305, "time": 0.7186} +{"mode": "train", "epoch": 15, "iter": 1600, "lr": 0.09773, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25328, "top5_acc": 0.48969, "loss_cls": 4.15156, "loss": 4.15156, "time": 0.72181} +{"mode": "train", "epoch": 15, "iter": 1700, "lr": 0.09773, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.24344, "top5_acc": 0.48328, "loss_cls": 4.17889, "loss": 4.17889, "time": 0.71849} +{"mode": "train", "epoch": 15, "iter": 1800, "lr": 0.09772, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24188, "top5_acc": 0.48375, "loss_cls": 4.18652, "loss": 4.18652, "time": 0.71728} +{"mode": "train", "epoch": 15, "iter": 1900, "lr": 0.09771, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23797, "top5_acc": 0.48469, "loss_cls": 4.18768, "loss": 4.18768, "time": 0.72118} +{"mode": "train", "epoch": 15, "iter": 2000, "lr": 0.0977, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25031, "top5_acc": 0.49547, "loss_cls": 4.1514, "loss": 4.1514, "time": 0.7178} +{"mode": "train", "epoch": 15, "iter": 2100, "lr": 0.09769, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25125, "top5_acc": 0.50234, "loss_cls": 4.13578, "loss": 4.13578, "time": 0.71948} +{"mode": "train", "epoch": 15, "iter": 2200, "lr": 0.09768, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24156, "top5_acc": 0.48625, "loss_cls": 4.16039, "loss": 4.16039, "time": 0.71982} +{"mode": "train", "epoch": 15, "iter": 2300, "lr": 0.09768, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24406, "top5_acc": 0.49734, "loss_cls": 4.16187, "loss": 4.16187, "time": 0.71508} +{"mode": "train", "epoch": 15, "iter": 2400, "lr": 0.09767, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23875, "top5_acc": 0.48859, "loss_cls": 4.18267, "loss": 4.18267, "time": 0.71517} +{"mode": "train", "epoch": 15, "iter": 2500, "lr": 0.09766, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24422, "top5_acc": 0.48359, "loss_cls": 4.19436, "loss": 4.19436, "time": 0.7165} +{"mode": "train", "epoch": 15, "iter": 2600, "lr": 0.09765, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24062, "top5_acc": 0.48641, "loss_cls": 4.16807, "loss": 4.16807, "time": 0.71725} +{"mode": "train", "epoch": 15, "iter": 2700, "lr": 0.09764, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.23562, "top5_acc": 0.48938, "loss_cls": 4.16349, "loss": 4.16349, "time": 0.71773} +{"mode": "train", "epoch": 15, "iter": 2800, "lr": 0.09763, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24312, "top5_acc": 0.48344, "loss_cls": 4.19175, "loss": 4.19175, "time": 0.71751} +{"mode": "train", "epoch": 15, "iter": 2900, "lr": 0.09763, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23156, "top5_acc": 0.48156, "loss_cls": 4.22906, "loss": 4.22906, "time": 0.71894} +{"mode": "train", "epoch": 15, "iter": 3000, "lr": 0.09762, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.23406, "top5_acc": 0.48984, "loss_cls": 4.14867, "loss": 4.14867, "time": 0.71869} +{"mode": "train", "epoch": 15, "iter": 3100, "lr": 0.09761, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25344, "top5_acc": 0.49703, "loss_cls": 4.13223, "loss": 4.13223, "time": 0.72097} +{"mode": "train", "epoch": 15, "iter": 3200, "lr": 0.0976, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23375, "top5_acc": 0.47688, "loss_cls": 4.23573, "loss": 4.23573, "time": 0.72135} +{"mode": "train", "epoch": 15, "iter": 3300, "lr": 0.09759, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.24469, "top5_acc": 0.48, "loss_cls": 4.17928, "loss": 4.17928, "time": 0.72064} +{"mode": "train", "epoch": 15, "iter": 3400, "lr": 0.09758, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24391, "top5_acc": 0.49188, "loss_cls": 4.18216, "loss": 4.18216, "time": 0.71812} +{"mode": "train", "epoch": 15, "iter": 3500, "lr": 0.09757, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24312, "top5_acc": 0.49703, "loss_cls": 4.15943, "loss": 4.15943, "time": 0.71743} +{"mode": "train", "epoch": 15, "iter": 3600, "lr": 0.09757, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.23703, "top5_acc": 0.49484, "loss_cls": 4.14197, "loss": 4.14197, "time": 0.71747} +{"mode": "train", "epoch": 15, "iter": 3700, "lr": 0.09756, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.24062, "top5_acc": 0.48156, "loss_cls": 4.18306, "loss": 4.18306, "time": 0.71621} +{"mode": "val", "epoch": 15, "iter": 309, "lr": 0.09755, "top1_acc": 0.14927, "top5_acc": 0.3544, "mean_class_accuracy": 0.14928} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.09754, "memory": 15990, "data_time": 1.39768, "top1_acc": 0.24125, "top5_acc": 0.50062, "loss_cls": 4.1366, "loss": 4.1366, "time": 2.11764} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.09754, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24656, "top5_acc": 0.49516, "loss_cls": 4.13008, "loss": 4.13008, "time": 0.71574} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.09753, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.25109, "top5_acc": 0.50219, "loss_cls": 4.07894, "loss": 4.07894, "time": 0.71663} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.09752, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24141, "top5_acc": 0.48844, "loss_cls": 4.16381, "loss": 4.16381, "time": 0.71673} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.09751, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23641, "top5_acc": 0.48906, "loss_cls": 4.18626, "loss": 4.18626, "time": 0.71241} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.0975, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23781, "top5_acc": 0.48625, "loss_cls": 4.197, "loss": 4.197, "time": 0.71714} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.09749, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23578, "top5_acc": 0.49125, "loss_cls": 4.18072, "loss": 4.18072, "time": 0.71296} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.09748, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24281, "top5_acc": 0.4925, "loss_cls": 4.1432, "loss": 4.1432, "time": 0.71355} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.09747, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24906, "top5_acc": 0.48875, "loss_cls": 4.12522, "loss": 4.12522, "time": 0.7135} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.09747, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24953, "top5_acc": 0.49281, "loss_cls": 4.14954, "loss": 4.14954, "time": 0.71543} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.09746, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23641, "top5_acc": 0.48625, "loss_cls": 4.20197, "loss": 4.20197, "time": 0.71767} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.09745, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23719, "top5_acc": 0.49156, "loss_cls": 4.12125, "loss": 4.12125, "time": 0.71644} +{"mode": "train", "epoch": 16, "iter": 1300, "lr": 0.09744, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23125, "top5_acc": 0.48156, "loss_cls": 4.21901, "loss": 4.21901, "time": 0.71977} +{"mode": "train", "epoch": 16, "iter": 1400, "lr": 0.09743, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24344, "top5_acc": 0.49281, "loss_cls": 4.14951, "loss": 4.14951, "time": 0.71945} +{"mode": "train", "epoch": 16, "iter": 1500, "lr": 0.09742, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24484, "top5_acc": 0.48531, "loss_cls": 4.184, "loss": 4.184, "time": 0.72109} +{"mode": "train", "epoch": 16, "iter": 1600, "lr": 0.09741, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24562, "top5_acc": 0.48438, "loss_cls": 4.16599, "loss": 4.16599, "time": 0.71735} +{"mode": "train", "epoch": 16, "iter": 1700, "lr": 0.0974, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24266, "top5_acc": 0.48156, "loss_cls": 4.19722, "loss": 4.19722, "time": 0.71842} +{"mode": "train", "epoch": 16, "iter": 1800, "lr": 0.0974, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24984, "top5_acc": 0.48656, "loss_cls": 4.18329, "loss": 4.18329, "time": 0.71491} +{"mode": "train", "epoch": 16, "iter": 1900, "lr": 0.09739, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.235, "top5_acc": 0.48938, "loss_cls": 4.18895, "loss": 4.18895, "time": 0.71675} +{"mode": "train", "epoch": 16, "iter": 2000, "lr": 0.09738, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25062, "top5_acc": 0.50125, "loss_cls": 4.15206, "loss": 4.15206, "time": 0.72067} +{"mode": "train", "epoch": 16, "iter": 2100, "lr": 0.09737, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24234, "top5_acc": 0.48016, "loss_cls": 4.18918, "loss": 4.18918, "time": 0.71611} +{"mode": "train", "epoch": 16, "iter": 2200, "lr": 0.09736, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24312, "top5_acc": 0.49062, "loss_cls": 4.15773, "loss": 4.15773, "time": 0.71692} +{"mode": "train", "epoch": 16, "iter": 2300, "lr": 0.09735, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24484, "top5_acc": 0.485, "loss_cls": 4.16214, "loss": 4.16214, "time": 0.72093} +{"mode": "train", "epoch": 16, "iter": 2400, "lr": 0.09734, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24141, "top5_acc": 0.48484, "loss_cls": 4.17994, "loss": 4.17994, "time": 0.71676} +{"mode": "train", "epoch": 16, "iter": 2500, "lr": 0.09733, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25531, "top5_acc": 0.49406, "loss_cls": 4.11684, "loss": 4.11684, "time": 0.71904} +{"mode": "train", "epoch": 16, "iter": 2600, "lr": 0.09732, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24516, "top5_acc": 0.49031, "loss_cls": 4.17299, "loss": 4.17299, "time": 0.71697} +{"mode": "train", "epoch": 16, "iter": 2700, "lr": 0.09731, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24484, "top5_acc": 0.50391, "loss_cls": 4.12864, "loss": 4.12864, "time": 0.71602} +{"mode": "train", "epoch": 16, "iter": 2800, "lr": 0.09731, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23344, "top5_acc": 0.47219, "loss_cls": 4.21848, "loss": 4.21848, "time": 0.71665} +{"mode": "train", "epoch": 16, "iter": 2900, "lr": 0.0973, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23797, "top5_acc": 0.48094, "loss_cls": 4.22993, "loss": 4.22993, "time": 0.71668} +{"mode": "train", "epoch": 16, "iter": 3000, "lr": 0.09729, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24312, "top5_acc": 0.48844, "loss_cls": 4.16357, "loss": 4.16357, "time": 0.72164} +{"mode": "train", "epoch": 16, "iter": 3100, "lr": 0.09728, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23594, "top5_acc": 0.48438, "loss_cls": 4.20549, "loss": 4.20549, "time": 0.71716} +{"mode": "train", "epoch": 16, "iter": 3200, "lr": 0.09727, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23156, "top5_acc": 0.47859, "loss_cls": 4.23152, "loss": 4.23152, "time": 0.71675} +{"mode": "train", "epoch": 16, "iter": 3300, "lr": 0.09726, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25219, "top5_acc": 0.49266, "loss_cls": 4.15345, "loss": 4.15345, "time": 0.71901} +{"mode": "train", "epoch": 16, "iter": 3400, "lr": 0.09725, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24703, "top5_acc": 0.48906, "loss_cls": 4.13915, "loss": 4.13915, "time": 0.71947} +{"mode": "train", "epoch": 16, "iter": 3500, "lr": 0.09724, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23656, "top5_acc": 0.48625, "loss_cls": 4.18967, "loss": 4.18967, "time": 0.71977} +{"mode": "train", "epoch": 16, "iter": 3600, "lr": 0.09723, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23047, "top5_acc": 0.47844, "loss_cls": 4.21412, "loss": 4.21412, "time": 0.71495} +{"mode": "train", "epoch": 16, "iter": 3700, "lr": 0.09722, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.245, "top5_acc": 0.48984, "loss_cls": 4.17557, "loss": 4.17557, "time": 0.71979} +{"mode": "val", "epoch": 16, "iter": 309, "lr": 0.09722, "top1_acc": 0.1514, "top5_acc": 0.3659, "mean_class_accuracy": 0.15127} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.09721, "memory": 15990, "data_time": 1.38222, "top1_acc": 0.24375, "top5_acc": 0.47938, "loss_cls": 4.15811, "loss": 4.15811, "time": 2.10366} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.0972, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.25609, "top5_acc": 0.49516, "loss_cls": 4.11984, "loss": 4.11984, "time": 0.71615} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.09719, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25125, "top5_acc": 0.49453, "loss_cls": 4.10827, "loss": 4.10827, "time": 0.71153} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.09718, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.24578, "top5_acc": 0.47984, "loss_cls": 4.17064, "loss": 4.17064, "time": 0.72151} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.09717, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24641, "top5_acc": 0.48672, "loss_cls": 4.16913, "loss": 4.16913, "time": 0.71447} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.09716, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24047, "top5_acc": 0.48688, "loss_cls": 4.18722, "loss": 4.18722, "time": 0.71567} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.09715, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23609, "top5_acc": 0.48031, "loss_cls": 4.18349, "loss": 4.18349, "time": 0.7156} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.09714, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24594, "top5_acc": 0.49281, "loss_cls": 4.17862, "loss": 4.17862, "time": 0.71675} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.09714, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24047, "top5_acc": 0.47766, "loss_cls": 4.2023, "loss": 4.2023, "time": 0.71437} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.09713, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25297, "top5_acc": 0.50047, "loss_cls": 4.11222, "loss": 4.11222, "time": 0.7103} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.09712, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24609, "top5_acc": 0.49375, "loss_cls": 4.13222, "loss": 4.13222, "time": 0.71846} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.09711, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25297, "top5_acc": 0.48781, "loss_cls": 4.14969, "loss": 4.14969, "time": 0.71952} +{"mode": "train", "epoch": 17, "iter": 1300, "lr": 0.0971, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24453, "top5_acc": 0.48531, "loss_cls": 4.15543, "loss": 4.15543, "time": 0.719} +{"mode": "train", "epoch": 17, "iter": 1400, "lr": 0.09709, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.23891, "top5_acc": 0.49406, "loss_cls": 4.15414, "loss": 4.15414, "time": 0.72134} +{"mode": "train", "epoch": 17, "iter": 1500, "lr": 0.09708, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24641, "top5_acc": 0.49453, "loss_cls": 4.15453, "loss": 4.15453, "time": 0.72394} +{"mode": "train", "epoch": 17, "iter": 1600, "lr": 0.09707, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24688, "top5_acc": 0.48688, "loss_cls": 4.15321, "loss": 4.15321, "time": 0.72287} +{"mode": "train", "epoch": 17, "iter": 1700, "lr": 0.09706, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24844, "top5_acc": 0.49422, "loss_cls": 4.12861, "loss": 4.12861, "time": 0.71971} +{"mode": "train", "epoch": 17, "iter": 1800, "lr": 0.09705, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24719, "top5_acc": 0.49344, "loss_cls": 4.12081, "loss": 4.12081, "time": 0.72016} +{"mode": "train", "epoch": 17, "iter": 1900, "lr": 0.09704, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24938, "top5_acc": 0.49469, "loss_cls": 4.14051, "loss": 4.14051, "time": 0.71942} +{"mode": "train", "epoch": 17, "iter": 2000, "lr": 0.09703, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25203, "top5_acc": 0.48922, "loss_cls": 4.14064, "loss": 4.14064, "time": 0.71757} +{"mode": "train", "epoch": 17, "iter": 2100, "lr": 0.09702, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24344, "top5_acc": 0.49781, "loss_cls": 4.14758, "loss": 4.14758, "time": 0.72102} +{"mode": "train", "epoch": 17, "iter": 2200, "lr": 0.09701, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23438, "top5_acc": 0.47906, "loss_cls": 4.20204, "loss": 4.20204, "time": 0.72152} +{"mode": "train", "epoch": 17, "iter": 2300, "lr": 0.097, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24562, "top5_acc": 0.495, "loss_cls": 4.15977, "loss": 4.15977, "time": 0.72028} +{"mode": "train", "epoch": 17, "iter": 2400, "lr": 0.09699, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25125, "top5_acc": 0.49938, "loss_cls": 4.08034, "loss": 4.08034, "time": 0.72009} +{"mode": "train", "epoch": 17, "iter": 2500, "lr": 0.09698, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.24109, "top5_acc": 0.49812, "loss_cls": 4.13978, "loss": 4.13978, "time": 0.72201} +{"mode": "train", "epoch": 17, "iter": 2600, "lr": 0.09697, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24297, "top5_acc": 0.48719, "loss_cls": 4.1359, "loss": 4.1359, "time": 0.72125} +{"mode": "train", "epoch": 17, "iter": 2700, "lr": 0.09697, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23547, "top5_acc": 0.47875, "loss_cls": 4.20355, "loss": 4.20355, "time": 0.71822} +{"mode": "train", "epoch": 17, "iter": 2800, "lr": 0.09696, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.24359, "top5_acc": 0.48469, "loss_cls": 4.16751, "loss": 4.16751, "time": 0.72324} +{"mode": "train", "epoch": 17, "iter": 2900, "lr": 0.09695, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23891, "top5_acc": 0.48094, "loss_cls": 4.21598, "loss": 4.21598, "time": 0.7229} +{"mode": "train", "epoch": 17, "iter": 3000, "lr": 0.09694, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24672, "top5_acc": 0.48922, "loss_cls": 4.16619, "loss": 4.16619, "time": 0.72044} +{"mode": "train", "epoch": 17, "iter": 3100, "lr": 0.09693, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25234, "top5_acc": 0.48891, "loss_cls": 4.1513, "loss": 4.1513, "time": 0.72187} +{"mode": "train", "epoch": 17, "iter": 3200, "lr": 0.09692, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23719, "top5_acc": 0.47969, "loss_cls": 4.17263, "loss": 4.17263, "time": 0.72396} +{"mode": "train", "epoch": 17, "iter": 3300, "lr": 0.09691, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.24172, "top5_acc": 0.47828, "loss_cls": 4.22157, "loss": 4.22157, "time": 0.72237} +{"mode": "train", "epoch": 17, "iter": 3400, "lr": 0.0969, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24594, "top5_acc": 0.49766, "loss_cls": 4.16865, "loss": 4.16865, "time": 0.72029} +{"mode": "train", "epoch": 17, "iter": 3500, "lr": 0.09689, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24328, "top5_acc": 0.48453, "loss_cls": 4.16085, "loss": 4.16085, "time": 0.72328} +{"mode": "train", "epoch": 17, "iter": 3600, "lr": 0.09688, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2425, "top5_acc": 0.48828, "loss_cls": 4.17147, "loss": 4.17147, "time": 0.71589} +{"mode": "train", "epoch": 17, "iter": 3700, "lr": 0.09687, "memory": 15990, "data_time": 0.00071, "top1_acc": 0.24734, "top5_acc": 0.48344, "loss_cls": 4.14183, "loss": 4.14183, "time": 0.72076} +{"mode": "val", "epoch": 17, "iter": 309, "lr": 0.09686, "top1_acc": 0.16776, "top5_acc": 0.38875, "mean_class_accuracy": 0.16775} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.09685, "memory": 15990, "data_time": 1.3944, "top1_acc": 0.24703, "top5_acc": 0.49594, "loss_cls": 4.11656, "loss": 4.11656, "time": 2.11664} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.09684, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24828, "top5_acc": 0.50109, "loss_cls": 4.10378, "loss": 4.10378, "time": 0.71323} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.09683, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24266, "top5_acc": 0.49594, "loss_cls": 4.12504, "loss": 4.12504, "time": 0.71208} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.09683, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24172, "top5_acc": 0.49172, "loss_cls": 4.16304, "loss": 4.16304, "time": 0.71445} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.09682, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24375, "top5_acc": 0.49328, "loss_cls": 4.12465, "loss": 4.12465, "time": 0.71278} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.09681, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24688, "top5_acc": 0.49422, "loss_cls": 4.13801, "loss": 4.13801, "time": 0.7142} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.0968, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23875, "top5_acc": 0.48234, "loss_cls": 4.18874, "loss": 4.18874, "time": 0.71476} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.09679, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24641, "top5_acc": 0.48812, "loss_cls": 4.16139, "loss": 4.16139, "time": 0.71819} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.09678, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24578, "top5_acc": 0.49156, "loss_cls": 4.16628, "loss": 4.16628, "time": 0.71837} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.09677, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24547, "top5_acc": 0.48656, "loss_cls": 4.15402, "loss": 4.15402, "time": 0.71395} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.09676, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24531, "top5_acc": 0.48875, "loss_cls": 4.17416, "loss": 4.17416, "time": 0.71506} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.09675, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24078, "top5_acc": 0.48719, "loss_cls": 4.17883, "loss": 4.17883, "time": 0.71809} +{"mode": "train", "epoch": 18, "iter": 1300, "lr": 0.09674, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2425, "top5_acc": 0.49125, "loss_cls": 4.18436, "loss": 4.18436, "time": 0.7179} +{"mode": "train", "epoch": 18, "iter": 1400, "lr": 0.09673, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23797, "top5_acc": 0.48328, "loss_cls": 4.15993, "loss": 4.15993, "time": 0.71606} +{"mode": "train", "epoch": 18, "iter": 1500, "lr": 0.09672, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.245, "top5_acc": 0.4975, "loss_cls": 4.12605, "loss": 4.12605, "time": 0.71585} +{"mode": "train", "epoch": 18, "iter": 1600, "lr": 0.09671, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23031, "top5_acc": 0.48891, "loss_cls": 4.17917, "loss": 4.17917, "time": 0.71684} +{"mode": "train", "epoch": 18, "iter": 1700, "lr": 0.0967, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25359, "top5_acc": 0.49875, "loss_cls": 4.12667, "loss": 4.12667, "time": 0.71666} +{"mode": "train", "epoch": 18, "iter": 1800, "lr": 0.09669, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.235, "top5_acc": 0.48469, "loss_cls": 4.15756, "loss": 4.15756, "time": 0.71582} +{"mode": "train", "epoch": 18, "iter": 1900, "lr": 0.09668, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24797, "top5_acc": 0.49406, "loss_cls": 4.12309, "loss": 4.12309, "time": 0.71534} +{"mode": "train", "epoch": 18, "iter": 2000, "lr": 0.09667, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.25203, "top5_acc": 0.49516, "loss_cls": 4.1706, "loss": 4.1706, "time": 0.71592} +{"mode": "train", "epoch": 18, "iter": 2100, "lr": 0.09666, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23922, "top5_acc": 0.48797, "loss_cls": 4.17189, "loss": 4.17189, "time": 0.719} +{"mode": "train", "epoch": 18, "iter": 2200, "lr": 0.09665, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23844, "top5_acc": 0.48562, "loss_cls": 4.1701, "loss": 4.1701, "time": 0.71659} +{"mode": "train", "epoch": 18, "iter": 2300, "lr": 0.09664, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2375, "top5_acc": 0.49172, "loss_cls": 4.15576, "loss": 4.15576, "time": 0.71555} +{"mode": "train", "epoch": 18, "iter": 2400, "lr": 0.09663, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26016, "top5_acc": 0.49922, "loss_cls": 4.10449, "loss": 4.10449, "time": 0.71511} +{"mode": "train", "epoch": 18, "iter": 2500, "lr": 0.09662, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24016, "top5_acc": 0.48891, "loss_cls": 4.16242, "loss": 4.16242, "time": 0.71475} +{"mode": "train", "epoch": 18, "iter": 2600, "lr": 0.09661, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24516, "top5_acc": 0.49609, "loss_cls": 4.13057, "loss": 4.13057, "time": 0.71462} +{"mode": "train", "epoch": 18, "iter": 2700, "lr": 0.0966, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.24766, "top5_acc": 0.49844, "loss_cls": 4.11907, "loss": 4.11907, "time": 0.71606} +{"mode": "train", "epoch": 18, "iter": 2800, "lr": 0.09659, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24938, "top5_acc": 0.48344, "loss_cls": 4.17102, "loss": 4.17102, "time": 0.71725} +{"mode": "train", "epoch": 18, "iter": 2900, "lr": 0.09658, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24609, "top5_acc": 0.49484, "loss_cls": 4.13678, "loss": 4.13678, "time": 0.71648} +{"mode": "train", "epoch": 18, "iter": 3000, "lr": 0.09657, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25156, "top5_acc": 0.49172, "loss_cls": 4.15997, "loss": 4.15997, "time": 0.71695} +{"mode": "train", "epoch": 18, "iter": 3100, "lr": 0.09656, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.255, "top5_acc": 0.49969, "loss_cls": 4.08706, "loss": 4.08706, "time": 0.7173} +{"mode": "train", "epoch": 18, "iter": 3200, "lr": 0.09654, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23328, "top5_acc": 0.49016, "loss_cls": 4.17121, "loss": 4.17121, "time": 0.7168} +{"mode": "train", "epoch": 18, "iter": 3300, "lr": 0.09653, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24531, "top5_acc": 0.49328, "loss_cls": 4.14993, "loss": 4.14993, "time": 0.71919} +{"mode": "train", "epoch": 18, "iter": 3400, "lr": 0.09652, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23484, "top5_acc": 0.48172, "loss_cls": 4.20779, "loss": 4.20779, "time": 0.71874} +{"mode": "train", "epoch": 18, "iter": 3500, "lr": 0.09651, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25703, "top5_acc": 0.49297, "loss_cls": 4.12741, "loss": 4.12741, "time": 0.71883} +{"mode": "train", "epoch": 18, "iter": 3600, "lr": 0.0965, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24797, "top5_acc": 0.49859, "loss_cls": 4.14202, "loss": 4.14202, "time": 0.71685} +{"mode": "train", "epoch": 18, "iter": 3700, "lr": 0.09649, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24625, "top5_acc": 0.49359, "loss_cls": 4.13366, "loss": 4.13366, "time": 0.71615} +{"mode": "val", "epoch": 18, "iter": 309, "lr": 0.09649, "top1_acc": 0.17743, "top5_acc": 0.39457, "mean_class_accuracy": 0.17705} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.09648, "memory": 15990, "data_time": 1.38701, "top1_acc": 0.2475, "top5_acc": 0.50359, "loss_cls": 4.10878, "loss": 4.10878, "time": 2.10544} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.09647, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25484, "top5_acc": 0.49375, "loss_cls": 4.10034, "loss": 4.10034, "time": 0.71684} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.09646, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.24766, "top5_acc": 0.49266, "loss_cls": 4.1419, "loss": 4.1419, "time": 0.71334} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.09645, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.245, "top5_acc": 0.48141, "loss_cls": 4.15983, "loss": 4.15983, "time": 0.71618} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.09644, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24391, "top5_acc": 0.49641, "loss_cls": 4.1243, "loss": 4.1243, "time": 0.71581} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.09643, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24297, "top5_acc": 0.49625, "loss_cls": 4.14704, "loss": 4.14704, "time": 0.71006} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.09642, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24969, "top5_acc": 0.49625, "loss_cls": 4.1174, "loss": 4.1174, "time": 0.71779} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.09641, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24094, "top5_acc": 0.48953, "loss_cls": 4.16683, "loss": 4.16683, "time": 0.71211} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.0964, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24047, "top5_acc": 0.49281, "loss_cls": 4.16622, "loss": 4.16622, "time": 0.71598} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.09639, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23719, "top5_acc": 0.48828, "loss_cls": 4.17152, "loss": 4.17152, "time": 0.71407} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.09637, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24, "top5_acc": 0.49562, "loss_cls": 4.14789, "loss": 4.14789, "time": 0.71702} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.09636, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24531, "top5_acc": 0.49719, "loss_cls": 4.14043, "loss": 4.14043, "time": 0.71435} +{"mode": "train", "epoch": 19, "iter": 1300, "lr": 0.09635, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.25297, "top5_acc": 0.49094, "loss_cls": 4.13591, "loss": 4.13591, "time": 0.71915} +{"mode": "train", "epoch": 19, "iter": 1400, "lr": 0.09634, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24922, "top5_acc": 0.48672, "loss_cls": 4.12891, "loss": 4.12891, "time": 0.71661} +{"mode": "train", "epoch": 19, "iter": 1500, "lr": 0.09633, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24891, "top5_acc": 0.5, "loss_cls": 4.14189, "loss": 4.14189, "time": 0.72063} +{"mode": "train", "epoch": 19, "iter": 1600, "lr": 0.09632, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23219, "top5_acc": 0.47844, "loss_cls": 4.22196, "loss": 4.22196, "time": 0.71819} +{"mode": "train", "epoch": 19, "iter": 1700, "lr": 0.09631, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24234, "top5_acc": 0.49125, "loss_cls": 4.14697, "loss": 4.14697, "time": 0.72323} +{"mode": "train", "epoch": 19, "iter": 1800, "lr": 0.0963, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24516, "top5_acc": 0.48781, "loss_cls": 4.15754, "loss": 4.15754, "time": 0.71629} +{"mode": "train", "epoch": 19, "iter": 1900, "lr": 0.09629, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24625, "top5_acc": 0.4925, "loss_cls": 4.14616, "loss": 4.14616, "time": 0.72087} +{"mode": "train", "epoch": 19, "iter": 2000, "lr": 0.09628, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24328, "top5_acc": 0.48734, "loss_cls": 4.16215, "loss": 4.16215, "time": 0.72432} +{"mode": "train", "epoch": 19, "iter": 2100, "lr": 0.09627, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.255, "top5_acc": 0.49438, "loss_cls": 4.14636, "loss": 4.14636, "time": 0.72404} +{"mode": "train", "epoch": 19, "iter": 2200, "lr": 0.09626, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24594, "top5_acc": 0.49031, "loss_cls": 4.16074, "loss": 4.16074, "time": 0.71867} +{"mode": "train", "epoch": 19, "iter": 2300, "lr": 0.09625, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26125, "top5_acc": 0.50219, "loss_cls": 4.08487, "loss": 4.08487, "time": 0.71786} +{"mode": "train", "epoch": 19, "iter": 2400, "lr": 0.09624, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23859, "top5_acc": 0.48609, "loss_cls": 4.17357, "loss": 4.17357, "time": 0.72104} +{"mode": "train", "epoch": 19, "iter": 2500, "lr": 0.09623, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24859, "top5_acc": 0.49656, "loss_cls": 4.11339, "loss": 4.11339, "time": 0.72108} +{"mode": "train", "epoch": 19, "iter": 2600, "lr": 0.09622, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23891, "top5_acc": 0.48547, "loss_cls": 4.20638, "loss": 4.20638, "time": 0.72079} +{"mode": "train", "epoch": 19, "iter": 2700, "lr": 0.09621, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.245, "top5_acc": 0.48734, "loss_cls": 4.15567, "loss": 4.15567, "time": 0.71664} +{"mode": "train", "epoch": 19, "iter": 2800, "lr": 0.0962, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23766, "top5_acc": 0.49594, "loss_cls": 4.14335, "loss": 4.14335, "time": 0.72046} +{"mode": "train", "epoch": 19, "iter": 2900, "lr": 0.09618, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24719, "top5_acc": 0.49141, "loss_cls": 4.1314, "loss": 4.1314, "time": 0.72164} +{"mode": "train", "epoch": 19, "iter": 3000, "lr": 0.09617, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25578, "top5_acc": 0.49594, "loss_cls": 4.09774, "loss": 4.09774, "time": 0.72211} +{"mode": "train", "epoch": 19, "iter": 3100, "lr": 0.09616, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24859, "top5_acc": 0.49172, "loss_cls": 4.14192, "loss": 4.14192, "time": 0.72281} +{"mode": "train", "epoch": 19, "iter": 3200, "lr": 0.09615, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24719, "top5_acc": 0.49188, "loss_cls": 4.13724, "loss": 4.13724, "time": 0.72248} +{"mode": "train", "epoch": 19, "iter": 3300, "lr": 0.09614, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24453, "top5_acc": 0.49078, "loss_cls": 4.18084, "loss": 4.18084, "time": 0.72044} +{"mode": "train", "epoch": 19, "iter": 3400, "lr": 0.09613, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23844, "top5_acc": 0.48531, "loss_cls": 4.15867, "loss": 4.15867, "time": 0.72002} +{"mode": "train", "epoch": 19, "iter": 3500, "lr": 0.09612, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24047, "top5_acc": 0.49031, "loss_cls": 4.13495, "loss": 4.13495, "time": 0.72024} +{"mode": "train", "epoch": 19, "iter": 3600, "lr": 0.09611, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.25234, "top5_acc": 0.49984, "loss_cls": 4.10828, "loss": 4.10828, "time": 0.71514} +{"mode": "train", "epoch": 19, "iter": 3700, "lr": 0.0961, "memory": 15990, "data_time": 0.00084, "top1_acc": 0.25203, "top5_acc": 0.49703, "loss_cls": 4.14891, "loss": 4.14891, "time": 0.72334} +{"mode": "val", "epoch": 19, "iter": 309, "lr": 0.09609, "top1_acc": 0.16684, "top5_acc": 0.38378, "mean_class_accuracy": 0.16674} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.09608, "memory": 15990, "data_time": 1.41575, "top1_acc": 0.24781, "top5_acc": 0.50797, "loss_cls": 4.10133, "loss": 4.10133, "time": 2.12975} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.09607, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25109, "top5_acc": 0.49578, "loss_cls": 4.09329, "loss": 4.09329, "time": 0.7173} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.09606, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.2525, "top5_acc": 0.49078, "loss_cls": 4.12512, "loss": 4.12512, "time": 0.71294} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.09605, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.24047, "top5_acc": 0.48625, "loss_cls": 4.17945, "loss": 4.17945, "time": 0.71605} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.09604, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.24656, "top5_acc": 0.49469, "loss_cls": 4.12739, "loss": 4.12739, "time": 0.71522} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.09603, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24781, "top5_acc": 0.49125, "loss_cls": 4.16269, "loss": 4.16269, "time": 0.71136} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.09602, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24953, "top5_acc": 0.49172, "loss_cls": 4.14487, "loss": 4.14487, "time": 0.71542} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.09601, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25094, "top5_acc": 0.4975, "loss_cls": 4.12218, "loss": 4.12218, "time": 0.71148} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.096, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24344, "top5_acc": 0.48797, "loss_cls": 4.14414, "loss": 4.14414, "time": 0.71615} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.09598, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24719, "top5_acc": 0.50844, "loss_cls": 4.10328, "loss": 4.10328, "time": 0.71076} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.09597, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24297, "top5_acc": 0.49172, "loss_cls": 4.14818, "loss": 4.14818, "time": 0.70985} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.09596, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24062, "top5_acc": 0.49406, "loss_cls": 4.15741, "loss": 4.15741, "time": 0.71366} +{"mode": "train", "epoch": 20, "iter": 1300, "lr": 0.09595, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24562, "top5_acc": 0.49188, "loss_cls": 4.15241, "loss": 4.15241, "time": 0.71373} +{"mode": "train", "epoch": 20, "iter": 1400, "lr": 0.09594, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25344, "top5_acc": 0.49719, "loss_cls": 4.12716, "loss": 4.12716, "time": 0.71775} +{"mode": "train", "epoch": 20, "iter": 1500, "lr": 0.09593, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.24219, "top5_acc": 0.49453, "loss_cls": 4.15234, "loss": 4.15234, "time": 0.7215} +{"mode": "train", "epoch": 20, "iter": 1600, "lr": 0.09592, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24672, "top5_acc": 0.49844, "loss_cls": 4.13415, "loss": 4.13415, "time": 0.7203} +{"mode": "train", "epoch": 20, "iter": 1700, "lr": 0.09591, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24094, "top5_acc": 0.48672, "loss_cls": 4.18652, "loss": 4.18652, "time": 0.71928} +{"mode": "train", "epoch": 20, "iter": 1800, "lr": 0.0959, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25203, "top5_acc": 0.49141, "loss_cls": 4.11397, "loss": 4.11397, "time": 0.72} +{"mode": "train", "epoch": 20, "iter": 1900, "lr": 0.09588, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23594, "top5_acc": 0.47547, "loss_cls": 4.18457, "loss": 4.18457, "time": 0.72014} +{"mode": "train", "epoch": 20, "iter": 2000, "lr": 0.09587, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24891, "top5_acc": 0.49547, "loss_cls": 4.12089, "loss": 4.12089, "time": 0.7261} +{"mode": "train", "epoch": 20, "iter": 2100, "lr": 0.09586, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24562, "top5_acc": 0.49578, "loss_cls": 4.12568, "loss": 4.12568, "time": 0.72149} +{"mode": "train", "epoch": 20, "iter": 2200, "lr": 0.09585, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25281, "top5_acc": 0.49719, "loss_cls": 4.10512, "loss": 4.10512, "time": 0.72531} +{"mode": "train", "epoch": 20, "iter": 2300, "lr": 0.09584, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24641, "top5_acc": 0.48969, "loss_cls": 4.15546, "loss": 4.15546, "time": 0.72275} +{"mode": "train", "epoch": 20, "iter": 2400, "lr": 0.09583, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24516, "top5_acc": 0.50172, "loss_cls": 4.12072, "loss": 4.12072, "time": 0.72126} +{"mode": "train", "epoch": 20, "iter": 2500, "lr": 0.09582, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24359, "top5_acc": 0.49594, "loss_cls": 4.15369, "loss": 4.15369, "time": 0.7243} +{"mode": "train", "epoch": 20, "iter": 2600, "lr": 0.09581, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25281, "top5_acc": 0.48953, "loss_cls": 4.12827, "loss": 4.12827, "time": 0.71922} +{"mode": "train", "epoch": 20, "iter": 2700, "lr": 0.0958, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25203, "top5_acc": 0.49297, "loss_cls": 4.16155, "loss": 4.16155, "time": 0.71918} +{"mode": "train", "epoch": 20, "iter": 2800, "lr": 0.09578, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26125, "top5_acc": 0.50172, "loss_cls": 4.10776, "loss": 4.10776, "time": 0.72808} +{"mode": "train", "epoch": 20, "iter": 2900, "lr": 0.09577, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25062, "top5_acc": 0.49391, "loss_cls": 4.11989, "loss": 4.11989, "time": 0.72433} +{"mode": "train", "epoch": 20, "iter": 3000, "lr": 0.09576, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24828, "top5_acc": 0.49578, "loss_cls": 4.13433, "loss": 4.13433, "time": 0.72113} +{"mode": "train", "epoch": 20, "iter": 3100, "lr": 0.09575, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25094, "top5_acc": 0.49203, "loss_cls": 4.13198, "loss": 4.13198, "time": 0.72154} +{"mode": "train", "epoch": 20, "iter": 3200, "lr": 0.09574, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24812, "top5_acc": 0.49641, "loss_cls": 4.11675, "loss": 4.11675, "time": 0.72114} +{"mode": "train", "epoch": 20, "iter": 3300, "lr": 0.09573, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24219, "top5_acc": 0.49141, "loss_cls": 4.18471, "loss": 4.18471, "time": 0.71853} +{"mode": "train", "epoch": 20, "iter": 3400, "lr": 0.09572, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24328, "top5_acc": 0.48781, "loss_cls": 4.16554, "loss": 4.16554, "time": 0.72346} +{"mode": "train", "epoch": 20, "iter": 3500, "lr": 0.09571, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.25188, "top5_acc": 0.49531, "loss_cls": 4.12634, "loss": 4.12634, "time": 0.72248} +{"mode": "train", "epoch": 20, "iter": 3600, "lr": 0.09569, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25188, "top5_acc": 0.49922, "loss_cls": 4.11077, "loss": 4.11077, "time": 0.71878} +{"mode": "train", "epoch": 20, "iter": 3700, "lr": 0.09568, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.24859, "top5_acc": 0.49266, "loss_cls": 4.13276, "loss": 4.13276, "time": 0.72174} +{"mode": "val", "epoch": 20, "iter": 309, "lr": 0.09568, "top1_acc": 0.18452, "top5_acc": 0.41042, "mean_class_accuracy": 0.18435} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.09567, "memory": 15990, "data_time": 1.388, "top1_acc": 0.24, "top5_acc": 0.48766, "loss_cls": 4.15535, "loss": 4.15535, "time": 2.10583} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.09565, "memory": 15990, "data_time": 0.00071, "top1_acc": 0.25156, "top5_acc": 0.50359, "loss_cls": 4.05226, "loss": 4.05226, "time": 0.72086} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.09564, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.25891, "top5_acc": 0.4975, "loss_cls": 4.09603, "loss": 4.09603, "time": 0.72197} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.09563, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24047, "top5_acc": 0.49469, "loss_cls": 4.12779, "loss": 4.12779, "time": 0.71991} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.09562, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24625, "top5_acc": 0.49375, "loss_cls": 4.12113, "loss": 4.12113, "time": 0.718} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.09561, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25422, "top5_acc": 0.49484, "loss_cls": 4.12662, "loss": 4.12662, "time": 0.71345} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.0956, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24688, "top5_acc": 0.50375, "loss_cls": 4.10371, "loss": 4.10371, "time": 0.71569} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.09559, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24812, "top5_acc": 0.50391, "loss_cls": 4.09521, "loss": 4.09521, "time": 0.71182} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.09557, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25125, "top5_acc": 0.49844, "loss_cls": 4.11889, "loss": 4.11889, "time": 0.72036} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.09556, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26094, "top5_acc": 0.50078, "loss_cls": 4.08642, "loss": 4.08642, "time": 0.71765} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.09555, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25828, "top5_acc": 0.49484, "loss_cls": 4.1089, "loss": 4.1089, "time": 0.71285} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.09554, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24516, "top5_acc": 0.50062, "loss_cls": 4.11874, "loss": 4.11874, "time": 0.71832} +{"mode": "train", "epoch": 21, "iter": 1300, "lr": 0.09553, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24734, "top5_acc": 0.48781, "loss_cls": 4.16242, "loss": 4.16242, "time": 0.71987} +{"mode": "train", "epoch": 21, "iter": 1400, "lr": 0.09552, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.25656, "top5_acc": 0.50266, "loss_cls": 4.12215, "loss": 4.12215, "time": 0.71902} +{"mode": "train", "epoch": 21, "iter": 1500, "lr": 0.09551, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24578, "top5_acc": 0.49828, "loss_cls": 4.11292, "loss": 4.11292, "time": 0.71727} +{"mode": "train", "epoch": 21, "iter": 1600, "lr": 0.09549, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23984, "top5_acc": 0.49578, "loss_cls": 4.15678, "loss": 4.15678, "time": 0.72212} +{"mode": "train", "epoch": 21, "iter": 1700, "lr": 0.09548, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25328, "top5_acc": 0.50125, "loss_cls": 4.12371, "loss": 4.12371, "time": 0.71685} +{"mode": "train", "epoch": 21, "iter": 1800, "lr": 0.09547, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24516, "top5_acc": 0.48281, "loss_cls": 4.19215, "loss": 4.19215, "time": 0.72118} +{"mode": "train", "epoch": 21, "iter": 1900, "lr": 0.09546, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24734, "top5_acc": 0.49641, "loss_cls": 4.12269, "loss": 4.12269, "time": 0.71598} +{"mode": "train", "epoch": 21, "iter": 2000, "lr": 0.09545, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24875, "top5_acc": 0.49562, "loss_cls": 4.12363, "loss": 4.12363, "time": 0.72149} +{"mode": "train", "epoch": 21, "iter": 2100, "lr": 0.09544, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24859, "top5_acc": 0.49828, "loss_cls": 4.11915, "loss": 4.11915, "time": 0.71667} +{"mode": "train", "epoch": 21, "iter": 2200, "lr": 0.09542, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23469, "top5_acc": 0.48828, "loss_cls": 4.15776, "loss": 4.15776, "time": 0.71905} +{"mode": "train", "epoch": 21, "iter": 2300, "lr": 0.09541, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24984, "top5_acc": 0.49797, "loss_cls": 4.12717, "loss": 4.12717, "time": 0.7181} +{"mode": "train", "epoch": 21, "iter": 2400, "lr": 0.0954, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25609, "top5_acc": 0.50406, "loss_cls": 4.08061, "loss": 4.08061, "time": 0.71595} +{"mode": "train", "epoch": 21, "iter": 2500, "lr": 0.09539, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24328, "top5_acc": 0.49484, "loss_cls": 4.15013, "loss": 4.15013, "time": 0.71908} +{"mode": "train", "epoch": 21, "iter": 2600, "lr": 0.09538, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.245, "top5_acc": 0.49141, "loss_cls": 4.16923, "loss": 4.16923, "time": 0.72291} +{"mode": "train", "epoch": 21, "iter": 2700, "lr": 0.09537, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24953, "top5_acc": 0.49047, "loss_cls": 4.15313, "loss": 4.15313, "time": 0.72281} +{"mode": "train", "epoch": 21, "iter": 2800, "lr": 0.09535, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24578, "top5_acc": 0.4925, "loss_cls": 4.17266, "loss": 4.17266, "time": 0.71882} +{"mode": "train", "epoch": 21, "iter": 2900, "lr": 0.09534, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24484, "top5_acc": 0.49781, "loss_cls": 4.15783, "loss": 4.15783, "time": 0.72126} +{"mode": "train", "epoch": 21, "iter": 3000, "lr": 0.09533, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24484, "top5_acc": 0.49891, "loss_cls": 4.14095, "loss": 4.14095, "time": 0.71779} +{"mode": "train", "epoch": 21, "iter": 3100, "lr": 0.09532, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24969, "top5_acc": 0.49062, "loss_cls": 4.15097, "loss": 4.15097, "time": 0.72003} +{"mode": "train", "epoch": 21, "iter": 3200, "lr": 0.09531, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25562, "top5_acc": 0.49922, "loss_cls": 4.13227, "loss": 4.13227, "time": 0.71318} +{"mode": "train", "epoch": 21, "iter": 3300, "lr": 0.09529, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24281, "top5_acc": 0.495, "loss_cls": 4.13945, "loss": 4.13945, "time": 0.71644} +{"mode": "train", "epoch": 21, "iter": 3400, "lr": 0.09528, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24766, "top5_acc": 0.49375, "loss_cls": 4.15, "loss": 4.15, "time": 0.71755} +{"mode": "train", "epoch": 21, "iter": 3500, "lr": 0.09527, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24016, "top5_acc": 0.48406, "loss_cls": 4.17171, "loss": 4.17171, "time": 0.71815} +{"mode": "train", "epoch": 21, "iter": 3600, "lr": 0.09526, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.24859, "top5_acc": 0.49016, "loss_cls": 4.14023, "loss": 4.14023, "time": 0.71934} +{"mode": "train", "epoch": 21, "iter": 3700, "lr": 0.09525, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.24672, "top5_acc": 0.49703, "loss_cls": 4.12239, "loss": 4.12239, "time": 0.71841} +{"mode": "val", "epoch": 21, "iter": 309, "lr": 0.09524, "top1_acc": 0.19384, "top5_acc": 0.42582, "mean_class_accuracy": 0.19364} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.09523, "memory": 15990, "data_time": 1.40579, "top1_acc": 0.24969, "top5_acc": 0.49797, "loss_cls": 4.12921, "loss": 4.12921, "time": 2.12106} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.09522, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25016, "top5_acc": 0.49656, "loss_cls": 4.11818, "loss": 4.11818, "time": 0.71955} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.09521, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.24953, "top5_acc": 0.49906, "loss_cls": 4.10864, "loss": 4.10864, "time": 0.71444} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.09519, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25078, "top5_acc": 0.49406, "loss_cls": 4.12911, "loss": 4.12911, "time": 0.71747} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.09518, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24766, "top5_acc": 0.49578, "loss_cls": 4.10677, "loss": 4.10677, "time": 0.72001} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.09517, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24984, "top5_acc": 0.50156, "loss_cls": 4.10718, "loss": 4.10718, "time": 0.71516} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.09516, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24688, "top5_acc": 0.48656, "loss_cls": 4.15633, "loss": 4.15633, "time": 0.71132} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.09515, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2475, "top5_acc": 0.49719, "loss_cls": 4.11804, "loss": 4.11804, "time": 0.71409} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.09513, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25938, "top5_acc": 0.50844, "loss_cls": 4.05805, "loss": 4.05805, "time": 0.71841} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.09512, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24266, "top5_acc": 0.49594, "loss_cls": 4.14351, "loss": 4.14351, "time": 0.71575} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.09511, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24844, "top5_acc": 0.49766, "loss_cls": 4.0997, "loss": 4.0997, "time": 0.71554} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0951, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26031, "top5_acc": 0.51562, "loss_cls": 4.05731, "loss": 4.05731, "time": 0.7145} +{"mode": "train", "epoch": 22, "iter": 1300, "lr": 0.09509, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.255, "top5_acc": 0.49625, "loss_cls": 4.12359, "loss": 4.12359, "time": 0.7127} +{"mode": "train", "epoch": 22, "iter": 1400, "lr": 0.09507, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25594, "top5_acc": 0.49266, "loss_cls": 4.11006, "loss": 4.11006, "time": 0.71566} +{"mode": "train", "epoch": 22, "iter": 1500, "lr": 0.09506, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24859, "top5_acc": 0.49672, "loss_cls": 4.14765, "loss": 4.14765, "time": 0.72057} +{"mode": "train", "epoch": 22, "iter": 1600, "lr": 0.09505, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24828, "top5_acc": 0.4975, "loss_cls": 4.09623, "loss": 4.09623, "time": 0.72142} +{"mode": "train", "epoch": 22, "iter": 1700, "lr": 0.09504, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24625, "top5_acc": 0.49156, "loss_cls": 4.15831, "loss": 4.15831, "time": 0.72141} +{"mode": "train", "epoch": 22, "iter": 1800, "lr": 0.09502, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25844, "top5_acc": 0.50094, "loss_cls": 4.09858, "loss": 4.09858, "time": 0.71604} +{"mode": "train", "epoch": 22, "iter": 1900, "lr": 0.09501, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25141, "top5_acc": 0.50094, "loss_cls": 4.12367, "loss": 4.12367, "time": 0.71835} +{"mode": "train", "epoch": 22, "iter": 2000, "lr": 0.095, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24469, "top5_acc": 0.49109, "loss_cls": 4.15804, "loss": 4.15804, "time": 0.71673} +{"mode": "train", "epoch": 22, "iter": 2100, "lr": 0.09499, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25297, "top5_acc": 0.50281, "loss_cls": 4.11108, "loss": 4.11108, "time": 0.71889} +{"mode": "train", "epoch": 22, "iter": 2200, "lr": 0.09498, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25172, "top5_acc": 0.49797, "loss_cls": 4.13175, "loss": 4.13175, "time": 0.71798} +{"mode": "train", "epoch": 22, "iter": 2300, "lr": 0.09496, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.25406, "top5_acc": 0.5025, "loss_cls": 4.08483, "loss": 4.08483, "time": 0.7173} +{"mode": "train", "epoch": 22, "iter": 2400, "lr": 0.09495, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23828, "top5_acc": 0.48328, "loss_cls": 4.18856, "loss": 4.18856, "time": 0.71849} +{"mode": "train", "epoch": 22, "iter": 2500, "lr": 0.09494, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25016, "top5_acc": 0.49812, "loss_cls": 4.12165, "loss": 4.12165, "time": 0.71487} +{"mode": "train", "epoch": 22, "iter": 2600, "lr": 0.09493, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24688, "top5_acc": 0.48859, "loss_cls": 4.15014, "loss": 4.15014, "time": 0.71752} +{"mode": "train", "epoch": 22, "iter": 2700, "lr": 0.09491, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24172, "top5_acc": 0.495, "loss_cls": 4.16081, "loss": 4.16081, "time": 0.7167} +{"mode": "train", "epoch": 22, "iter": 2800, "lr": 0.0949, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24203, "top5_acc": 0.49297, "loss_cls": 4.14588, "loss": 4.14588, "time": 0.72071} +{"mode": "train", "epoch": 22, "iter": 2900, "lr": 0.09489, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24422, "top5_acc": 0.49938, "loss_cls": 4.13948, "loss": 4.13948, "time": 0.71513} +{"mode": "train", "epoch": 22, "iter": 3000, "lr": 0.09488, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.2425, "top5_acc": 0.48719, "loss_cls": 4.15622, "loss": 4.15622, "time": 0.72019} +{"mode": "train", "epoch": 22, "iter": 3100, "lr": 0.09487, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24891, "top5_acc": 0.49703, "loss_cls": 4.13236, "loss": 4.13236, "time": 0.72148} +{"mode": "train", "epoch": 22, "iter": 3200, "lr": 0.09485, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24516, "top5_acc": 0.49422, "loss_cls": 4.13162, "loss": 4.13162, "time": 0.72477} +{"mode": "train", "epoch": 22, "iter": 3300, "lr": 0.09484, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25047, "top5_acc": 0.49484, "loss_cls": 4.17013, "loss": 4.17013, "time": 0.71675} +{"mode": "train", "epoch": 22, "iter": 3400, "lr": 0.09483, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.25484, "top5_acc": 0.50187, "loss_cls": 4.1151, "loss": 4.1151, "time": 0.71668} +{"mode": "train", "epoch": 22, "iter": 3500, "lr": 0.09482, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24859, "top5_acc": 0.49734, "loss_cls": 4.13281, "loss": 4.13281, "time": 0.71872} +{"mode": "train", "epoch": 22, "iter": 3600, "lr": 0.0948, "memory": 15990, "data_time": 0.00109, "top1_acc": 0.24266, "top5_acc": 0.49641, "loss_cls": 4.15759, "loss": 4.15759, "time": 0.71771} +{"mode": "train", "epoch": 22, "iter": 3700, "lr": 0.09479, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24625, "top5_acc": 0.49562, "loss_cls": 4.14534, "loss": 4.14534, "time": 0.71881} +{"mode": "val", "epoch": 22, "iter": 309, "lr": 0.09479, "top1_acc": 0.18999, "top5_acc": 0.41068, "mean_class_accuracy": 0.18978} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.09477, "memory": 15990, "data_time": 1.40357, "top1_acc": 0.25219, "top5_acc": 0.50531, "loss_cls": 4.07351, "loss": 4.07351, "time": 2.12404} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.09476, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24719, "top5_acc": 0.50141, "loss_cls": 4.12046, "loss": 4.12046, "time": 0.71761} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.09475, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26141, "top5_acc": 0.51281, "loss_cls": 4.03909, "loss": 4.03909, "time": 0.71715} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.09474, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24578, "top5_acc": 0.49875, "loss_cls": 4.10037, "loss": 4.10037, "time": 0.71738} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.09472, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25141, "top5_acc": 0.49312, "loss_cls": 4.14396, "loss": 4.14396, "time": 0.71538} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.09471, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26172, "top5_acc": 0.50797, "loss_cls": 4.03481, "loss": 4.03481, "time": 0.71301} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.0947, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25734, "top5_acc": 0.49656, "loss_cls": 4.09881, "loss": 4.09881, "time": 0.71447} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.09469, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25438, "top5_acc": 0.5, "loss_cls": 4.08369, "loss": 4.08369, "time": 0.71734} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.09467, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25375, "top5_acc": 0.50375, "loss_cls": 4.08382, "loss": 4.08382, "time": 0.71613} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.09466, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24281, "top5_acc": 0.48938, "loss_cls": 4.13617, "loss": 4.13617, "time": 0.71605} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.09465, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25281, "top5_acc": 0.49906, "loss_cls": 4.10689, "loss": 4.10689, "time": 0.71393} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.09464, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24672, "top5_acc": 0.49219, "loss_cls": 4.16194, "loss": 4.16194, "time": 0.71947} +{"mode": "train", "epoch": 23, "iter": 1300, "lr": 0.09462, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24719, "top5_acc": 0.49031, "loss_cls": 4.15636, "loss": 4.15636, "time": 0.71916} +{"mode": "train", "epoch": 23, "iter": 1400, "lr": 0.09461, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.25031, "top5_acc": 0.49422, "loss_cls": 4.13991, "loss": 4.13991, "time": 0.72066} +{"mode": "train", "epoch": 23, "iter": 1500, "lr": 0.0946, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24406, "top5_acc": 0.48984, "loss_cls": 4.14036, "loss": 4.14036, "time": 0.71826} +{"mode": "train", "epoch": 23, "iter": 1600, "lr": 0.09459, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25453, "top5_acc": 0.50422, "loss_cls": 4.07718, "loss": 4.07718, "time": 0.72421} +{"mode": "train", "epoch": 23, "iter": 1700, "lr": 0.09457, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23531, "top5_acc": 0.48859, "loss_cls": 4.16466, "loss": 4.16466, "time": 0.71551} +{"mode": "train", "epoch": 23, "iter": 1800, "lr": 0.09456, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25391, "top5_acc": 0.49844, "loss_cls": 4.08654, "loss": 4.08654, "time": 0.71673} +{"mode": "train", "epoch": 23, "iter": 1900, "lr": 0.09455, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24844, "top5_acc": 0.49531, "loss_cls": 4.11512, "loss": 4.11512, "time": 0.72299} +{"mode": "train", "epoch": 23, "iter": 2000, "lr": 0.09453, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24297, "top5_acc": 0.48625, "loss_cls": 4.16733, "loss": 4.16733, "time": 0.71918} +{"mode": "train", "epoch": 23, "iter": 2100, "lr": 0.09452, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25188, "top5_acc": 0.49969, "loss_cls": 4.12882, "loss": 4.12882, "time": 0.71958} +{"mode": "train", "epoch": 23, "iter": 2200, "lr": 0.09451, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24188, "top5_acc": 0.48062, "loss_cls": 4.1856, "loss": 4.1856, "time": 0.71956} +{"mode": "train", "epoch": 23, "iter": 2300, "lr": 0.0945, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25109, "top5_acc": 0.49281, "loss_cls": 4.14539, "loss": 4.14539, "time": 0.7228} +{"mode": "train", "epoch": 23, "iter": 2400, "lr": 0.09448, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25422, "top5_acc": 0.50391, "loss_cls": 4.08637, "loss": 4.08637, "time": 0.72148} +{"mode": "train", "epoch": 23, "iter": 2500, "lr": 0.09447, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.2525, "top5_acc": 0.50656, "loss_cls": 4.10293, "loss": 4.10293, "time": 0.7185} +{"mode": "train", "epoch": 23, "iter": 2600, "lr": 0.09446, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24922, "top5_acc": 0.50047, "loss_cls": 4.13046, "loss": 4.13046, "time": 0.7216} +{"mode": "train", "epoch": 23, "iter": 2700, "lr": 0.09445, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25078, "top5_acc": 0.49344, "loss_cls": 4.12885, "loss": 4.12885, "time": 0.72065} +{"mode": "train", "epoch": 23, "iter": 2800, "lr": 0.09443, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.24953, "top5_acc": 0.49625, "loss_cls": 4.12395, "loss": 4.12395, "time": 0.72124} +{"mode": "train", "epoch": 23, "iter": 2900, "lr": 0.09442, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26344, "top5_acc": 0.505, "loss_cls": 4.09626, "loss": 4.09626, "time": 0.72024} +{"mode": "train", "epoch": 23, "iter": 3000, "lr": 0.09441, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25375, "top5_acc": 0.50094, "loss_cls": 4.11912, "loss": 4.11912, "time": 0.72422} +{"mode": "train", "epoch": 23, "iter": 3100, "lr": 0.09439, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24875, "top5_acc": 0.48938, "loss_cls": 4.13006, "loss": 4.13006, "time": 0.72104} +{"mode": "train", "epoch": 23, "iter": 3200, "lr": 0.09438, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25547, "top5_acc": 0.49734, "loss_cls": 4.13018, "loss": 4.13018, "time": 0.71984} +{"mode": "train", "epoch": 23, "iter": 3300, "lr": 0.09437, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25188, "top5_acc": 0.49375, "loss_cls": 4.11725, "loss": 4.11725, "time": 0.71847} +{"mode": "train", "epoch": 23, "iter": 3400, "lr": 0.09436, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24609, "top5_acc": 0.49531, "loss_cls": 4.12502, "loss": 4.12502, "time": 0.72419} +{"mode": "train", "epoch": 23, "iter": 3500, "lr": 0.09434, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25625, "top5_acc": 0.49875, "loss_cls": 4.09887, "loss": 4.09887, "time": 0.72573} +{"mode": "train", "epoch": 23, "iter": 3600, "lr": 0.09433, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.25188, "top5_acc": 0.50125, "loss_cls": 4.09935, "loss": 4.09935, "time": 0.7215} +{"mode": "train", "epoch": 23, "iter": 3700, "lr": 0.09432, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25234, "top5_acc": 0.50422, "loss_cls": 4.09732, "loss": 4.09732, "time": 0.71922} +{"mode": "val", "epoch": 23, "iter": 309, "lr": 0.09431, "top1_acc": 0.15398, "top5_acc": 0.34888, "mean_class_accuracy": 0.15373} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.0943, "memory": 15990, "data_time": 1.42473, "top1_acc": 0.24766, "top5_acc": 0.49281, "loss_cls": 4.1298, "loss": 4.1298, "time": 2.14621} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.09428, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.26438, "top5_acc": 0.51188, "loss_cls": 4.05656, "loss": 4.05656, "time": 0.7176} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.09427, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25203, "top5_acc": 0.50719, "loss_cls": 4.08395, "loss": 4.08395, "time": 0.71765} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.09426, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26328, "top5_acc": 0.5025, "loss_cls": 4.06772, "loss": 4.06772, "time": 0.7156} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.09425, "memory": 15990, "data_time": 0.00082, "top1_acc": 0.24281, "top5_acc": 0.48594, "loss_cls": 4.15154, "loss": 4.15154, "time": 0.71273} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.09423, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.24609, "top5_acc": 0.49266, "loss_cls": 4.14619, "loss": 4.14619, "time": 0.71468} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.09422, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25922, "top5_acc": 0.51469, "loss_cls": 4.0555, "loss": 4.0555, "time": 0.71396} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.09421, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24562, "top5_acc": 0.49781, "loss_cls": 4.09954, "loss": 4.09954, "time": 0.71455} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.09419, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25875, "top5_acc": 0.50344, "loss_cls": 4.08199, "loss": 4.08199, "time": 0.71506} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.09418, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25828, "top5_acc": 0.50203, "loss_cls": 4.05852, "loss": 4.05852, "time": 0.71345} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.09417, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25625, "top5_acc": 0.4975, "loss_cls": 4.12922, "loss": 4.12922, "time": 0.7115} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.09415, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25391, "top5_acc": 0.50375, "loss_cls": 4.09008, "loss": 4.09008, "time": 0.71415} +{"mode": "train", "epoch": 24, "iter": 1300, "lr": 0.09414, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25484, "top5_acc": 0.50875, "loss_cls": 4.08601, "loss": 4.08601, "time": 0.71958} +{"mode": "train", "epoch": 24, "iter": 1400, "lr": 0.09413, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25953, "top5_acc": 0.49562, "loss_cls": 4.08953, "loss": 4.08953, "time": 0.71742} +{"mode": "train", "epoch": 24, "iter": 1500, "lr": 0.09411, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24859, "top5_acc": 0.49266, "loss_cls": 4.12754, "loss": 4.12754, "time": 0.71299} +{"mode": "train", "epoch": 24, "iter": 1600, "lr": 0.0941, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24578, "top5_acc": 0.48859, "loss_cls": 4.13422, "loss": 4.13422, "time": 0.71556} +{"mode": "train", "epoch": 24, "iter": 1700, "lr": 0.09409, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25344, "top5_acc": 0.50156, "loss_cls": 4.10052, "loss": 4.10052, "time": 0.71712} +{"mode": "train", "epoch": 24, "iter": 1800, "lr": 0.09407, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25172, "top5_acc": 0.49297, "loss_cls": 4.14665, "loss": 4.14665, "time": 0.71683} +{"mode": "train", "epoch": 24, "iter": 1900, "lr": 0.09406, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24641, "top5_acc": 0.48547, "loss_cls": 4.17269, "loss": 4.17269, "time": 0.71667} +{"mode": "train", "epoch": 24, "iter": 2000, "lr": 0.09405, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24547, "top5_acc": 0.48781, "loss_cls": 4.16753, "loss": 4.16753, "time": 0.7198} +{"mode": "train", "epoch": 24, "iter": 2100, "lr": 0.09404, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24766, "top5_acc": 0.49438, "loss_cls": 4.1478, "loss": 4.1478, "time": 0.71567} +{"mode": "train", "epoch": 24, "iter": 2200, "lr": 0.09402, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25281, "top5_acc": 0.50391, "loss_cls": 4.10752, "loss": 4.10752, "time": 0.71407} +{"mode": "train", "epoch": 24, "iter": 2300, "lr": 0.09401, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24578, "top5_acc": 0.50172, "loss_cls": 4.11294, "loss": 4.11294, "time": 0.71484} +{"mode": "train", "epoch": 24, "iter": 2400, "lr": 0.094, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24344, "top5_acc": 0.49422, "loss_cls": 4.13133, "loss": 4.13133, "time": 0.71772} +{"mode": "train", "epoch": 24, "iter": 2500, "lr": 0.09398, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26422, "top5_acc": 0.505, "loss_cls": 4.0706, "loss": 4.0706, "time": 0.7233} +{"mode": "train", "epoch": 24, "iter": 2600, "lr": 0.09397, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24922, "top5_acc": 0.49156, "loss_cls": 4.12721, "loss": 4.12721, "time": 0.71717} +{"mode": "train", "epoch": 24, "iter": 2700, "lr": 0.09396, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24672, "top5_acc": 0.49562, "loss_cls": 4.12197, "loss": 4.12197, "time": 0.71388} +{"mode": "train", "epoch": 24, "iter": 2800, "lr": 0.09394, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25312, "top5_acc": 0.50125, "loss_cls": 4.11047, "loss": 4.11047, "time": 0.71921} +{"mode": "train", "epoch": 24, "iter": 2900, "lr": 0.09393, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25469, "top5_acc": 0.50734, "loss_cls": 4.09881, "loss": 4.09881, "time": 0.71864} +{"mode": "train", "epoch": 24, "iter": 3000, "lr": 0.09392, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25891, "top5_acc": 0.49906, "loss_cls": 4.10566, "loss": 4.10566, "time": 0.71669} +{"mode": "train", "epoch": 24, "iter": 3100, "lr": 0.0939, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25047, "top5_acc": 0.49516, "loss_cls": 4.14191, "loss": 4.14191, "time": 0.71622} +{"mode": "train", "epoch": 24, "iter": 3200, "lr": 0.09389, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24828, "top5_acc": 0.49766, "loss_cls": 4.11004, "loss": 4.11004, "time": 0.71874} +{"mode": "train", "epoch": 24, "iter": 3300, "lr": 0.09388, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24703, "top5_acc": 0.49266, "loss_cls": 4.13453, "loss": 4.13453, "time": 0.71967} +{"mode": "train", "epoch": 24, "iter": 3400, "lr": 0.09386, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24891, "top5_acc": 0.49594, "loss_cls": 4.1146, "loss": 4.1146, "time": 0.71634} +{"mode": "train", "epoch": 24, "iter": 3500, "lr": 0.09385, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24625, "top5_acc": 0.49594, "loss_cls": 4.13716, "loss": 4.13716, "time": 0.71777} +{"mode": "train", "epoch": 24, "iter": 3600, "lr": 0.09384, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.24234, "top5_acc": 0.49875, "loss_cls": 4.12649, "loss": 4.12649, "time": 0.71618} +{"mode": "train", "epoch": 24, "iter": 3700, "lr": 0.09382, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25734, "top5_acc": 0.49859, "loss_cls": 4.08696, "loss": 4.08696, "time": 0.71727} +{"mode": "val", "epoch": 24, "iter": 309, "lr": 0.09382, "top1_acc": 0.17292, "top5_acc": 0.38981, "mean_class_accuracy": 0.17278} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.0938, "memory": 15990, "data_time": 1.38558, "top1_acc": 0.25766, "top5_acc": 0.50109, "loss_cls": 4.08591, "loss": 4.08591, "time": 2.10613} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.09379, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25266, "top5_acc": 0.49828, "loss_cls": 4.11604, "loss": 4.11604, "time": 0.71512} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.09378, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26656, "top5_acc": 0.50781, "loss_cls": 4.07009, "loss": 4.07009, "time": 0.71512} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.09376, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25391, "top5_acc": 0.51156, "loss_cls": 4.06846, "loss": 4.06846, "time": 0.71456} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.09375, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25609, "top5_acc": 0.50297, "loss_cls": 4.07766, "loss": 4.07766, "time": 0.70991} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.09373, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24094, "top5_acc": 0.48281, "loss_cls": 4.18872, "loss": 4.18872, "time": 0.71538} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.09372, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24984, "top5_acc": 0.505, "loss_cls": 4.10078, "loss": 4.10078, "time": 0.7172} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.09371, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25359, "top5_acc": 0.50187, "loss_cls": 4.05772, "loss": 4.05772, "time": 0.71494} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.09369, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25594, "top5_acc": 0.49859, "loss_cls": 4.09962, "loss": 4.09962, "time": 0.71248} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.09368, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.24953, "top5_acc": 0.50062, "loss_cls": 4.11534, "loss": 4.11534, "time": 0.71717} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.09367, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25078, "top5_acc": 0.49375, "loss_cls": 4.13051, "loss": 4.13051, "time": 0.71487} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.09365, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24688, "top5_acc": 0.49781, "loss_cls": 4.13143, "loss": 4.13143, "time": 0.71776} +{"mode": "train", "epoch": 25, "iter": 1300, "lr": 0.09364, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25172, "top5_acc": 0.49828, "loss_cls": 4.12406, "loss": 4.12406, "time": 0.71227} +{"mode": "train", "epoch": 25, "iter": 1400, "lr": 0.09363, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25172, "top5_acc": 0.50156, "loss_cls": 4.09282, "loss": 4.09282, "time": 0.71247} +{"mode": "train", "epoch": 25, "iter": 1500, "lr": 0.09361, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26062, "top5_acc": 0.49484, "loss_cls": 4.12301, "loss": 4.12301, "time": 0.71735} +{"mode": "train", "epoch": 25, "iter": 1600, "lr": 0.0936, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25797, "top5_acc": 0.49859, "loss_cls": 4.10045, "loss": 4.10045, "time": 0.71993} +{"mode": "train", "epoch": 25, "iter": 1700, "lr": 0.09358, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25016, "top5_acc": 0.50172, "loss_cls": 4.11426, "loss": 4.11426, "time": 0.71605} +{"mode": "train", "epoch": 25, "iter": 1800, "lr": 0.09357, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24594, "top5_acc": 0.49781, "loss_cls": 4.1172, "loss": 4.1172, "time": 0.71516} +{"mode": "train", "epoch": 25, "iter": 1900, "lr": 0.09356, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24953, "top5_acc": 0.50344, "loss_cls": 4.13724, "loss": 4.13724, "time": 0.72224} +{"mode": "train", "epoch": 25, "iter": 2000, "lr": 0.09354, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24562, "top5_acc": 0.49359, "loss_cls": 4.13085, "loss": 4.13085, "time": 0.71834} +{"mode": "train", "epoch": 25, "iter": 2100, "lr": 0.09353, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25984, "top5_acc": 0.49953, "loss_cls": 4.07767, "loss": 4.07767, "time": 0.7206} +{"mode": "train", "epoch": 25, "iter": 2200, "lr": 0.09352, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25453, "top5_acc": 0.50094, "loss_cls": 4.09884, "loss": 4.09884, "time": 0.71965} +{"mode": "train", "epoch": 25, "iter": 2300, "lr": 0.0935, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25859, "top5_acc": 0.50469, "loss_cls": 4.08043, "loss": 4.08043, "time": 0.71855} +{"mode": "train", "epoch": 25, "iter": 2400, "lr": 0.09349, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24781, "top5_acc": 0.49234, "loss_cls": 4.1502, "loss": 4.1502, "time": 0.71787} +{"mode": "train", "epoch": 25, "iter": 2500, "lr": 0.09347, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25766, "top5_acc": 0.50453, "loss_cls": 4.08075, "loss": 4.08075, "time": 0.71606} +{"mode": "train", "epoch": 25, "iter": 2600, "lr": 0.09346, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25719, "top5_acc": 0.50281, "loss_cls": 4.08974, "loss": 4.08974, "time": 0.72097} +{"mode": "train", "epoch": 25, "iter": 2700, "lr": 0.09345, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.25156, "top5_acc": 0.49781, "loss_cls": 4.09376, "loss": 4.09376, "time": 0.71917} +{"mode": "train", "epoch": 25, "iter": 2800, "lr": 0.09343, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25703, "top5_acc": 0.50094, "loss_cls": 4.12416, "loss": 4.12416, "time": 0.71629} +{"mode": "train", "epoch": 25, "iter": 2900, "lr": 0.09342, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.25141, "top5_acc": 0.50562, "loss_cls": 4.11286, "loss": 4.11286, "time": 0.71666} +{"mode": "train", "epoch": 25, "iter": 3000, "lr": 0.09341, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25422, "top5_acc": 0.49938, "loss_cls": 4.08826, "loss": 4.08826, "time": 0.71551} +{"mode": "train", "epoch": 25, "iter": 3100, "lr": 0.09339, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25078, "top5_acc": 0.49328, "loss_cls": 4.13554, "loss": 4.13554, "time": 0.71884} +{"mode": "train", "epoch": 25, "iter": 3200, "lr": 0.09338, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25422, "top5_acc": 0.4975, "loss_cls": 4.09567, "loss": 4.09567, "time": 0.71898} +{"mode": "train", "epoch": 25, "iter": 3300, "lr": 0.09336, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.255, "top5_acc": 0.49969, "loss_cls": 4.12256, "loss": 4.12256, "time": 0.71785} +{"mode": "train", "epoch": 25, "iter": 3400, "lr": 0.09335, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24938, "top5_acc": 0.49688, "loss_cls": 4.13536, "loss": 4.13536, "time": 0.71743} +{"mode": "train", "epoch": 25, "iter": 3500, "lr": 0.09334, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26156, "top5_acc": 0.50078, "loss_cls": 4.09018, "loss": 4.09018, "time": 0.72049} +{"mode": "train", "epoch": 25, "iter": 3600, "lr": 0.09332, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.24172, "top5_acc": 0.49906, "loss_cls": 4.10555, "loss": 4.10555, "time": 0.71959} +{"mode": "train", "epoch": 25, "iter": 3700, "lr": 0.09331, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26281, "top5_acc": 0.51047, "loss_cls": 4.06248, "loss": 4.06248, "time": 0.72055} +{"mode": "val", "epoch": 25, "iter": 309, "lr": 0.0933, "top1_acc": 0.17961, "top5_acc": 0.39761, "mean_class_accuracy": 0.1795} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.09329, "memory": 15990, "data_time": 1.39368, "top1_acc": 0.25641, "top5_acc": 0.50656, "loss_cls": 4.08662, "loss": 4.08662, "time": 2.10948} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.09327, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26734, "top5_acc": 0.51313, "loss_cls": 4.04875, "loss": 4.04875, "time": 0.71608} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.09326, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25844, "top5_acc": 0.49969, "loss_cls": 4.09607, "loss": 4.09607, "time": 0.71576} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.09325, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25656, "top5_acc": 0.49547, "loss_cls": 4.07495, "loss": 4.07495, "time": 0.71615} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.09323, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25531, "top5_acc": 0.50547, "loss_cls": 4.07731, "loss": 4.07731, "time": 0.71593} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.09322, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24984, "top5_acc": 0.49266, "loss_cls": 4.12989, "loss": 4.12989, "time": 0.71365} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.0932, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25344, "top5_acc": 0.51062, "loss_cls": 4.06783, "loss": 4.06783, "time": 0.71573} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.09319, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25172, "top5_acc": 0.50047, "loss_cls": 4.08319, "loss": 4.08319, "time": 0.71722} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.09318, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25312, "top5_acc": 0.50328, "loss_cls": 4.08384, "loss": 4.08384, "time": 0.71366} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.09316, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25031, "top5_acc": 0.50328, "loss_cls": 4.12095, "loss": 4.12095, "time": 0.71047} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.09315, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25656, "top5_acc": 0.50031, "loss_cls": 4.11947, "loss": 4.11947, "time": 0.71231} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.09313, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25656, "top5_acc": 0.50266, "loss_cls": 4.1087, "loss": 4.1087, "time": 0.71378} +{"mode": "train", "epoch": 26, "iter": 1300, "lr": 0.09312, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26047, "top5_acc": 0.49375, "loss_cls": 4.10277, "loss": 4.10277, "time": 0.7138} +{"mode": "train", "epoch": 26, "iter": 1400, "lr": 0.0931, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25531, "top5_acc": 0.5075, "loss_cls": 4.09804, "loss": 4.09804, "time": 0.71405} +{"mode": "train", "epoch": 26, "iter": 1500, "lr": 0.09309, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26391, "top5_acc": 0.51109, "loss_cls": 4.04969, "loss": 4.04969, "time": 0.71446} +{"mode": "train", "epoch": 26, "iter": 1600, "lr": 0.09308, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24922, "top5_acc": 0.48594, "loss_cls": 4.14537, "loss": 4.14537, "time": 0.71803} +{"mode": "train", "epoch": 26, "iter": 1700, "lr": 0.09306, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25094, "top5_acc": 0.50609, "loss_cls": 4.1143, "loss": 4.1143, "time": 0.71563} +{"mode": "train", "epoch": 26, "iter": 1800, "lr": 0.09305, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24734, "top5_acc": 0.49578, "loss_cls": 4.10608, "loss": 4.10608, "time": 0.71609} +{"mode": "train", "epoch": 26, "iter": 1900, "lr": 0.09303, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26609, "top5_acc": 0.51812, "loss_cls": 4.03359, "loss": 4.03359, "time": 0.71349} +{"mode": "train", "epoch": 26, "iter": 2000, "lr": 0.09302, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24734, "top5_acc": 0.49453, "loss_cls": 4.1233, "loss": 4.1233, "time": 0.71426} +{"mode": "train", "epoch": 26, "iter": 2100, "lr": 0.093, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24953, "top5_acc": 0.49766, "loss_cls": 4.14015, "loss": 4.14015, "time": 0.71505} +{"mode": "train", "epoch": 26, "iter": 2200, "lr": 0.09299, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25422, "top5_acc": 0.49078, "loss_cls": 4.12477, "loss": 4.12477, "time": 0.71773} +{"mode": "train", "epoch": 26, "iter": 2300, "lr": 0.09298, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24875, "top5_acc": 0.4975, "loss_cls": 4.11913, "loss": 4.11913, "time": 0.71761} +{"mode": "train", "epoch": 26, "iter": 2400, "lr": 0.09296, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24875, "top5_acc": 0.5025, "loss_cls": 4.0896, "loss": 4.0896, "time": 0.7156} +{"mode": "train", "epoch": 26, "iter": 2500, "lr": 0.09295, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25562, "top5_acc": 0.50609, "loss_cls": 4.11289, "loss": 4.11289, "time": 0.71868} +{"mode": "train", "epoch": 26, "iter": 2600, "lr": 0.09293, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25266, "top5_acc": 0.50484, "loss_cls": 4.08389, "loss": 4.08389, "time": 0.71811} +{"mode": "train", "epoch": 26, "iter": 2700, "lr": 0.09292, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25266, "top5_acc": 0.49688, "loss_cls": 4.10224, "loss": 4.10224, "time": 0.71914} +{"mode": "train", "epoch": 26, "iter": 2800, "lr": 0.0929, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24719, "top5_acc": 0.49422, "loss_cls": 4.16779, "loss": 4.16779, "time": 0.71904} +{"mode": "train", "epoch": 26, "iter": 2900, "lr": 0.09289, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25875, "top5_acc": 0.50187, "loss_cls": 4.11573, "loss": 4.11573, "time": 0.71805} +{"mode": "train", "epoch": 26, "iter": 3000, "lr": 0.09288, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25297, "top5_acc": 0.49984, "loss_cls": 4.11591, "loss": 4.11591, "time": 0.71941} +{"mode": "train", "epoch": 26, "iter": 3100, "lr": 0.09286, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25812, "top5_acc": 0.50766, "loss_cls": 4.06724, "loss": 4.06724, "time": 0.71655} +{"mode": "train", "epoch": 26, "iter": 3200, "lr": 0.09285, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25922, "top5_acc": 0.50625, "loss_cls": 4.09067, "loss": 4.09067, "time": 0.71726} +{"mode": "train", "epoch": 26, "iter": 3300, "lr": 0.09283, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25672, "top5_acc": 0.50813, "loss_cls": 4.089, "loss": 4.089, "time": 0.71883} +{"mode": "train", "epoch": 26, "iter": 3400, "lr": 0.09282, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24406, "top5_acc": 0.49719, "loss_cls": 4.14472, "loss": 4.14472, "time": 0.7177} +{"mode": "train", "epoch": 26, "iter": 3500, "lr": 0.0928, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24453, "top5_acc": 0.49719, "loss_cls": 4.12434, "loss": 4.12434, "time": 0.71657} +{"mode": "train", "epoch": 26, "iter": 3600, "lr": 0.09279, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.25906, "top5_acc": 0.50172, "loss_cls": 4.09162, "loss": 4.09162, "time": 0.71775} +{"mode": "train", "epoch": 26, "iter": 3700, "lr": 0.09278, "memory": 15990, "data_time": 0.0007, "top1_acc": 0.25812, "top5_acc": 0.50078, "loss_cls": 4.11975, "loss": 4.11975, "time": 0.71769} +{"mode": "val", "epoch": 26, "iter": 309, "lr": 0.09277, "top1_acc": 0.19121, "top5_acc": 0.4199, "mean_class_accuracy": 0.19128} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.09275, "memory": 15990, "data_time": 1.42941, "top1_acc": 0.24875, "top5_acc": 0.5, "loss_cls": 4.10204, "loss": 4.10204, "time": 2.14949} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.09274, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.25922, "top5_acc": 0.50984, "loss_cls": 4.07772, "loss": 4.07772, "time": 0.72043} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.09272, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25594, "top5_acc": 0.51109, "loss_cls": 4.06776, "loss": 4.06776, "time": 0.71706} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.09271, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.25984, "top5_acc": 0.505, "loss_cls": 4.06949, "loss": 4.06949, "time": 0.71487} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.0927, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26438, "top5_acc": 0.50859, "loss_cls": 4.02437, "loss": 4.02437, "time": 0.71365} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.09268, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.2575, "top5_acc": 0.49547, "loss_cls": 4.13315, "loss": 4.13315, "time": 0.71538} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.09267, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.25312, "top5_acc": 0.50125, "loss_cls": 4.14323, "loss": 4.14323, "time": 0.71658} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.09265, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.255, "top5_acc": 0.50094, "loss_cls": 4.0858, "loss": 4.0858, "time": 0.71636} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.09264, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.25188, "top5_acc": 0.49938, "loss_cls": 4.09572, "loss": 4.09572, "time": 0.71316} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.09262, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24828, "top5_acc": 0.5025, "loss_cls": 4.08527, "loss": 4.08527, "time": 0.71825} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.09261, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24688, "top5_acc": 0.4875, "loss_cls": 4.15976, "loss": 4.15976, "time": 0.71795} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.09259, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25484, "top5_acc": 0.50875, "loss_cls": 4.07882, "loss": 4.07882, "time": 0.71444} +{"mode": "train", "epoch": 27, "iter": 1300, "lr": 0.09258, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25156, "top5_acc": 0.48828, "loss_cls": 4.13821, "loss": 4.13821, "time": 0.71391} +{"mode": "train", "epoch": 27, "iter": 1400, "lr": 0.09256, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2575, "top5_acc": 0.50656, "loss_cls": 4.07834, "loss": 4.07834, "time": 0.72232} +{"mode": "train", "epoch": 27, "iter": 1500, "lr": 0.09255, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25125, "top5_acc": 0.50734, "loss_cls": 4.087, "loss": 4.087, "time": 0.72104} +{"mode": "train", "epoch": 27, "iter": 1600, "lr": 0.09253, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25375, "top5_acc": 0.49875, "loss_cls": 4.12975, "loss": 4.12975, "time": 0.71762} +{"mode": "train", "epoch": 27, "iter": 1700, "lr": 0.09252, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25078, "top5_acc": 0.49406, "loss_cls": 4.10608, "loss": 4.10608, "time": 0.71764} +{"mode": "train", "epoch": 27, "iter": 1800, "lr": 0.09251, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25219, "top5_acc": 0.49922, "loss_cls": 4.10987, "loss": 4.10987, "time": 0.71536} +{"mode": "train", "epoch": 27, "iter": 1900, "lr": 0.09249, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25391, "top5_acc": 0.49984, "loss_cls": 4.10912, "loss": 4.10912, "time": 0.72216} +{"mode": "train", "epoch": 27, "iter": 2000, "lr": 0.09248, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25609, "top5_acc": 0.50469, "loss_cls": 4.09431, "loss": 4.09431, "time": 0.71374} +{"mode": "train", "epoch": 27, "iter": 2100, "lr": 0.09246, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24703, "top5_acc": 0.505, "loss_cls": 4.09681, "loss": 4.09681, "time": 0.71523} +{"mode": "train", "epoch": 27, "iter": 2200, "lr": 0.09245, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.25938, "top5_acc": 0.50578, "loss_cls": 4.07524, "loss": 4.07524, "time": 0.71781} +{"mode": "train", "epoch": 27, "iter": 2300, "lr": 0.09243, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24969, "top5_acc": 0.5075, "loss_cls": 4.09366, "loss": 4.09366, "time": 0.71591} +{"mode": "train", "epoch": 27, "iter": 2400, "lr": 0.09242, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25469, "top5_acc": 0.50547, "loss_cls": 4.09863, "loss": 4.09863, "time": 0.71969} +{"mode": "train", "epoch": 27, "iter": 2500, "lr": 0.0924, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24938, "top5_acc": 0.49641, "loss_cls": 4.13629, "loss": 4.13629, "time": 0.71648} +{"mode": "train", "epoch": 27, "iter": 2600, "lr": 0.09239, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24359, "top5_acc": 0.50141, "loss_cls": 4.10096, "loss": 4.10096, "time": 0.71757} +{"mode": "train", "epoch": 27, "iter": 2700, "lr": 0.09237, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25172, "top5_acc": 0.50422, "loss_cls": 4.11286, "loss": 4.11286, "time": 0.72183} +{"mode": "train", "epoch": 27, "iter": 2800, "lr": 0.09236, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25734, "top5_acc": 0.50656, "loss_cls": 4.06806, "loss": 4.06806, "time": 0.71825} +{"mode": "train", "epoch": 27, "iter": 2900, "lr": 0.09234, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26328, "top5_acc": 0.50219, "loss_cls": 4.07972, "loss": 4.07972, "time": 0.71739} +{"mode": "train", "epoch": 27, "iter": 3000, "lr": 0.09233, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24812, "top5_acc": 0.50187, "loss_cls": 4.11646, "loss": 4.11646, "time": 0.71724} +{"mode": "train", "epoch": 27, "iter": 3100, "lr": 0.09231, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25328, "top5_acc": 0.50328, "loss_cls": 4.07144, "loss": 4.07144, "time": 0.71608} +{"mode": "train", "epoch": 27, "iter": 3200, "lr": 0.0923, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24891, "top5_acc": 0.49547, "loss_cls": 4.11715, "loss": 4.11715, "time": 0.71883} +{"mode": "train", "epoch": 27, "iter": 3300, "lr": 0.09228, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25781, "top5_acc": 0.50344, "loss_cls": 4.10576, "loss": 4.10576, "time": 0.71844} +{"mode": "train", "epoch": 27, "iter": 3400, "lr": 0.09227, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26156, "top5_acc": 0.50938, "loss_cls": 4.07244, "loss": 4.07244, "time": 0.71598} +{"mode": "train", "epoch": 27, "iter": 3500, "lr": 0.09225, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24812, "top5_acc": 0.49219, "loss_cls": 4.14684, "loss": 4.14684, "time": 0.72281} +{"mode": "train", "epoch": 27, "iter": 3600, "lr": 0.09224, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25641, "top5_acc": 0.51531, "loss_cls": 4.10194, "loss": 4.10194, "time": 0.71547} +{"mode": "train", "epoch": 27, "iter": 3700, "lr": 0.09222, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25219, "top5_acc": 0.5, "loss_cls": 4.10854, "loss": 4.10854, "time": 0.71943} +{"mode": "val", "epoch": 27, "iter": 309, "lr": 0.09222, "top1_acc": 0.17155, "top5_acc": 0.3927, "mean_class_accuracy": 0.17132} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.0922, "memory": 15990, "data_time": 1.43597, "top1_acc": 0.25844, "top5_acc": 0.51688, "loss_cls": 4.04479, "loss": 4.04479, "time": 2.15266} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.09219, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25641, "top5_acc": 0.5025, "loss_cls": 4.0722, "loss": 4.0722, "time": 0.71643} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.09217, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25391, "top5_acc": 0.50203, "loss_cls": 4.08653, "loss": 4.08653, "time": 0.71514} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.09216, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26, "top5_acc": 0.51141, "loss_cls": 4.06769, "loss": 4.06769, "time": 0.7179} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.09214, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25391, "top5_acc": 0.49594, "loss_cls": 4.11649, "loss": 4.11649, "time": 0.71745} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.09213, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25188, "top5_acc": 0.49469, "loss_cls": 4.10375, "loss": 4.10375, "time": 0.71345} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.09211, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26609, "top5_acc": 0.50875, "loss_cls": 4.01769, "loss": 4.01769, "time": 0.71638} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.0921, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25516, "top5_acc": 0.50391, "loss_cls": 4.07689, "loss": 4.07689, "time": 0.71455} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.09208, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25938, "top5_acc": 0.50859, "loss_cls": 4.07008, "loss": 4.07008, "time": 0.71861} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.09207, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24859, "top5_acc": 0.49438, "loss_cls": 4.15155, "loss": 4.15155, "time": 0.71842} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.09205, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23828, "top5_acc": 0.49906, "loss_cls": 4.13122, "loss": 4.13122, "time": 0.71359} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.09204, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25297, "top5_acc": 0.50656, "loss_cls": 4.10569, "loss": 4.10569, "time": 0.71478} +{"mode": "train", "epoch": 28, "iter": 1300, "lr": 0.09202, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25422, "top5_acc": 0.50328, "loss_cls": 4.10159, "loss": 4.10159, "time": 0.71366} +{"mode": "train", "epoch": 28, "iter": 1400, "lr": 0.09201, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26031, "top5_acc": 0.50438, "loss_cls": 4.07412, "loss": 4.07412, "time": 0.71484} +{"mode": "train", "epoch": 28, "iter": 1500, "lr": 0.09199, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24844, "top5_acc": 0.49891, "loss_cls": 4.1127, "loss": 4.1127, "time": 0.71314} +{"mode": "train", "epoch": 28, "iter": 1600, "lr": 0.09198, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25562, "top5_acc": 0.50125, "loss_cls": 4.1014, "loss": 4.1014, "time": 0.72072} +{"mode": "train", "epoch": 28, "iter": 1700, "lr": 0.09196, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25828, "top5_acc": 0.50203, "loss_cls": 4.08556, "loss": 4.08556, "time": 0.71785} +{"mode": "train", "epoch": 28, "iter": 1800, "lr": 0.09194, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25344, "top5_acc": 0.50047, "loss_cls": 4.08613, "loss": 4.08613, "time": 0.71602} +{"mode": "train", "epoch": 28, "iter": 1900, "lr": 0.09193, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24469, "top5_acc": 0.49891, "loss_cls": 4.10923, "loss": 4.10923, "time": 0.72101} +{"mode": "train", "epoch": 28, "iter": 2000, "lr": 0.09191, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25062, "top5_acc": 0.5, "loss_cls": 4.11593, "loss": 4.11593, "time": 0.71846} +{"mode": "train", "epoch": 28, "iter": 2100, "lr": 0.0919, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26172, "top5_acc": 0.51375, "loss_cls": 4.07987, "loss": 4.07987, "time": 0.72109} +{"mode": "train", "epoch": 28, "iter": 2200, "lr": 0.09188, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24031, "top5_acc": 0.49328, "loss_cls": 4.14164, "loss": 4.14164, "time": 0.71483} +{"mode": "train", "epoch": 28, "iter": 2300, "lr": 0.09187, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25812, "top5_acc": 0.49797, "loss_cls": 4.09623, "loss": 4.09623, "time": 0.72286} +{"mode": "train", "epoch": 28, "iter": 2400, "lr": 0.09185, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25719, "top5_acc": 0.51438, "loss_cls": 4.06658, "loss": 4.06658, "time": 0.71731} +{"mode": "train", "epoch": 28, "iter": 2500, "lr": 0.09184, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26344, "top5_acc": 0.50547, "loss_cls": 4.05195, "loss": 4.05195, "time": 0.72123} +{"mode": "train", "epoch": 28, "iter": 2600, "lr": 0.09182, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25031, "top5_acc": 0.50641, "loss_cls": 4.07785, "loss": 4.07785, "time": 0.71866} +{"mode": "train", "epoch": 28, "iter": 2700, "lr": 0.09181, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25734, "top5_acc": 0.50406, "loss_cls": 4.0678, "loss": 4.0678, "time": 0.72063} +{"mode": "train", "epoch": 28, "iter": 2800, "lr": 0.09179, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25234, "top5_acc": 0.49578, "loss_cls": 4.14433, "loss": 4.14433, "time": 0.71851} +{"mode": "train", "epoch": 28, "iter": 2900, "lr": 0.09178, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24766, "top5_acc": 0.50109, "loss_cls": 4.12039, "loss": 4.12039, "time": 0.72155} +{"mode": "train", "epoch": 28, "iter": 3000, "lr": 0.09176, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25562, "top5_acc": 0.50281, "loss_cls": 4.09428, "loss": 4.09428, "time": 0.72187} +{"mode": "train", "epoch": 28, "iter": 3100, "lr": 0.09175, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25234, "top5_acc": 0.50203, "loss_cls": 4.12458, "loss": 4.12458, "time": 0.72204} +{"mode": "train", "epoch": 28, "iter": 3200, "lr": 0.09173, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25312, "top5_acc": 0.49922, "loss_cls": 4.10947, "loss": 4.10947, "time": 0.72279} +{"mode": "train", "epoch": 28, "iter": 3300, "lr": 0.09172, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24828, "top5_acc": 0.49297, "loss_cls": 4.14512, "loss": 4.14512, "time": 0.7186} +{"mode": "train", "epoch": 28, "iter": 3400, "lr": 0.0917, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24922, "top5_acc": 0.50813, "loss_cls": 4.10103, "loss": 4.10103, "time": 0.71961} +{"mode": "train", "epoch": 28, "iter": 3500, "lr": 0.09168, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.25578, "top5_acc": 0.49484, "loss_cls": 4.12863, "loss": 4.12863, "time": 0.71872} +{"mode": "train", "epoch": 28, "iter": 3600, "lr": 0.09167, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25938, "top5_acc": 0.50109, "loss_cls": 4.11052, "loss": 4.11052, "time": 0.72457} +{"mode": "train", "epoch": 28, "iter": 3700, "lr": 0.09165, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26078, "top5_acc": 0.50891, "loss_cls": 4.07111, "loss": 4.07111, "time": 0.71799} +{"mode": "val", "epoch": 28, "iter": 309, "lr": 0.09165, "top1_acc": 0.18797, "top5_acc": 0.41691, "mean_class_accuracy": 0.18779} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.09163, "memory": 15990, "data_time": 1.4029, "top1_acc": 0.26125, "top5_acc": 0.51531, "loss_cls": 4.04581, "loss": 4.04581, "time": 2.12155} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.09162, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25797, "top5_acc": 0.50953, "loss_cls": 4.05034, "loss": 4.05034, "time": 0.71628} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.0916, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26016, "top5_acc": 0.50984, "loss_cls": 4.06803, "loss": 4.06803, "time": 0.71453} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.09158, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.26016, "top5_acc": 0.505, "loss_cls": 4.08146, "loss": 4.08146, "time": 0.71848} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.09157, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.24734, "top5_acc": 0.50047, "loss_cls": 4.13578, "loss": 4.13578, "time": 0.71356} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.09155, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26406, "top5_acc": 0.50797, "loss_cls": 4.05025, "loss": 4.05025, "time": 0.71339} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.09154, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25344, "top5_acc": 0.50813, "loss_cls": 4.05448, "loss": 4.05448, "time": 0.71866} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.09152, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24875, "top5_acc": 0.49734, "loss_cls": 4.11863, "loss": 4.11863, "time": 0.7159} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.09151, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25203, "top5_acc": 0.50062, "loss_cls": 4.09125, "loss": 4.09125, "time": 0.71214} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.09149, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25453, "top5_acc": 0.49688, "loss_cls": 4.10001, "loss": 4.10001, "time": 0.71656} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.09148, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25141, "top5_acc": 0.49484, "loss_cls": 4.06837, "loss": 4.06837, "time": 0.71834} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.09146, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26156, "top5_acc": 0.50656, "loss_cls": 4.07697, "loss": 4.07697, "time": 0.71293} +{"mode": "train", "epoch": 29, "iter": 1300, "lr": 0.09144, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26203, "top5_acc": 0.50406, "loss_cls": 4.05364, "loss": 4.05364, "time": 0.71405} +{"mode": "train", "epoch": 29, "iter": 1400, "lr": 0.09143, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24219, "top5_acc": 0.49641, "loss_cls": 4.1182, "loss": 4.1182, "time": 0.71983} +{"mode": "train", "epoch": 29, "iter": 1500, "lr": 0.09141, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25141, "top5_acc": 0.50281, "loss_cls": 4.08125, "loss": 4.08125, "time": 0.71166} +{"mode": "train", "epoch": 29, "iter": 1600, "lr": 0.0914, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26188, "top5_acc": 0.50469, "loss_cls": 4.07488, "loss": 4.07488, "time": 0.71707} +{"mode": "train", "epoch": 29, "iter": 1700, "lr": 0.09138, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25375, "top5_acc": 0.50203, "loss_cls": 4.09349, "loss": 4.09349, "time": 0.72303} +{"mode": "train", "epoch": 29, "iter": 1800, "lr": 0.09137, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26016, "top5_acc": 0.50109, "loss_cls": 4.09966, "loss": 4.09966, "time": 0.72026} +{"mode": "train", "epoch": 29, "iter": 1900, "lr": 0.09135, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25875, "top5_acc": 0.50031, "loss_cls": 4.07003, "loss": 4.07003, "time": 0.71925} +{"mode": "train", "epoch": 29, "iter": 2000, "lr": 0.09133, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25312, "top5_acc": 0.50094, "loss_cls": 4.09488, "loss": 4.09488, "time": 0.71734} +{"mode": "train", "epoch": 29, "iter": 2100, "lr": 0.09132, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25391, "top5_acc": 0.50219, "loss_cls": 4.08018, "loss": 4.08018, "time": 0.72112} +{"mode": "train", "epoch": 29, "iter": 2200, "lr": 0.0913, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.24125, "top5_acc": 0.49875, "loss_cls": 4.14202, "loss": 4.14202, "time": 0.71894} +{"mode": "train", "epoch": 29, "iter": 2300, "lr": 0.09129, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25562, "top5_acc": 0.50734, "loss_cls": 4.08346, "loss": 4.08346, "time": 0.72038} +{"mode": "train", "epoch": 29, "iter": 2400, "lr": 0.09127, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25125, "top5_acc": 0.50359, "loss_cls": 4.07362, "loss": 4.07362, "time": 0.7164} +{"mode": "train", "epoch": 29, "iter": 2500, "lr": 0.09126, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25188, "top5_acc": 0.50359, "loss_cls": 4.11497, "loss": 4.11497, "time": 0.71958} +{"mode": "train", "epoch": 29, "iter": 2600, "lr": 0.09124, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25219, "top5_acc": 0.49188, "loss_cls": 4.1309, "loss": 4.1309, "time": 0.71535} +{"mode": "train", "epoch": 29, "iter": 2700, "lr": 0.09122, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25016, "top5_acc": 0.48922, "loss_cls": 4.12266, "loss": 4.12266, "time": 0.72092} +{"mode": "train", "epoch": 29, "iter": 2800, "lr": 0.09121, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25859, "top5_acc": 0.50406, "loss_cls": 4.11028, "loss": 4.11028, "time": 0.72046} +{"mode": "train", "epoch": 29, "iter": 2900, "lr": 0.09119, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25203, "top5_acc": 0.50266, "loss_cls": 4.11292, "loss": 4.11292, "time": 0.72033} +{"mode": "train", "epoch": 29, "iter": 3000, "lr": 0.09118, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25234, "top5_acc": 0.50094, "loss_cls": 4.1017, "loss": 4.1017, "time": 0.72093} +{"mode": "train", "epoch": 29, "iter": 3100, "lr": 0.09116, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25297, "top5_acc": 0.50297, "loss_cls": 4.10615, "loss": 4.10615, "time": 0.71892} +{"mode": "train", "epoch": 29, "iter": 3200, "lr": 0.09114, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25172, "top5_acc": 0.49547, "loss_cls": 4.11337, "loss": 4.11337, "time": 0.715} +{"mode": "train", "epoch": 29, "iter": 3300, "lr": 0.09113, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25219, "top5_acc": 0.49953, "loss_cls": 4.14143, "loss": 4.14143, "time": 0.72113} +{"mode": "train", "epoch": 29, "iter": 3400, "lr": 0.09111, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25, "top5_acc": 0.50094, "loss_cls": 4.10741, "loss": 4.10741, "time": 0.71966} +{"mode": "train", "epoch": 29, "iter": 3500, "lr": 0.0911, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25594, "top5_acc": 0.50234, "loss_cls": 4.07975, "loss": 4.07975, "time": 0.72316} +{"mode": "train", "epoch": 29, "iter": 3600, "lr": 0.09108, "memory": 15990, "data_time": 0.00081, "top1_acc": 0.26312, "top5_acc": 0.50859, "loss_cls": 4.07133, "loss": 4.07133, "time": 0.72076} +{"mode": "train", "epoch": 29, "iter": 3700, "lr": 0.09106, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24906, "top5_acc": 0.48922, "loss_cls": 4.13676, "loss": 4.13676, "time": 0.71535} +{"mode": "val", "epoch": 29, "iter": 309, "lr": 0.09106, "top1_acc": 0.16887, "top5_acc": 0.39508, "mean_class_accuracy": 0.16871} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.09104, "memory": 15990, "data_time": 1.37131, "top1_acc": 0.26391, "top5_acc": 0.51078, "loss_cls": 4.04401, "loss": 4.04401, "time": 2.20209} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.09103, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.2675, "top5_acc": 0.50891, "loss_cls": 4.04818, "loss": 4.04818, "time": 0.82963} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.09101, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26156, "top5_acc": 0.51703, "loss_cls": 4.02372, "loss": 4.02372, "time": 0.83169} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.09099, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26312, "top5_acc": 0.51, "loss_cls": 4.04664, "loss": 4.04664, "time": 0.82501} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.09098, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.26062, "top5_acc": 0.51438, "loss_cls": 4.04414, "loss": 4.04414, "time": 0.82684} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.09096, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24828, "top5_acc": 0.49266, "loss_cls": 4.12136, "loss": 4.12136, "time": 0.82685} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.09095, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26594, "top5_acc": 0.51922, "loss_cls": 4.05788, "loss": 4.05788, "time": 0.83779} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.09093, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24812, "top5_acc": 0.49828, "loss_cls": 4.09453, "loss": 4.09453, "time": 0.83505} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.09091, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24875, "top5_acc": 0.5075, "loss_cls": 4.07754, "loss": 4.07754, "time": 0.83243} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.0909, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25656, "top5_acc": 0.4925, "loss_cls": 4.10968, "loss": 4.10968, "time": 0.83503} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.09088, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25578, "top5_acc": 0.50406, "loss_cls": 4.06962, "loss": 4.06962, "time": 0.83866} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.09087, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24406, "top5_acc": 0.49797, "loss_cls": 4.13358, "loss": 4.13358, "time": 0.83503} +{"mode": "train", "epoch": 30, "iter": 1300, "lr": 0.09085, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25234, "top5_acc": 0.49391, "loss_cls": 4.09511, "loss": 4.09511, "time": 0.83174} +{"mode": "train", "epoch": 30, "iter": 1400, "lr": 0.09083, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24703, "top5_acc": 0.49609, "loss_cls": 4.10205, "loss": 4.10205, "time": 0.83594} +{"mode": "train", "epoch": 30, "iter": 1500, "lr": 0.09082, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2525, "top5_acc": 0.505, "loss_cls": 4.07664, "loss": 4.07664, "time": 0.83083} +{"mode": "train", "epoch": 30, "iter": 1600, "lr": 0.0908, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26156, "top5_acc": 0.50719, "loss_cls": 4.08002, "loss": 4.08002, "time": 0.83623} +{"mode": "train", "epoch": 30, "iter": 1700, "lr": 0.09078, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25219, "top5_acc": 0.49625, "loss_cls": 4.12274, "loss": 4.12274, "time": 0.83595} +{"mode": "train", "epoch": 30, "iter": 1800, "lr": 0.09077, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25438, "top5_acc": 0.50281, "loss_cls": 4.0812, "loss": 4.0812, "time": 0.83565} +{"mode": "train", "epoch": 30, "iter": 1900, "lr": 0.09075, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24484, "top5_acc": 0.49234, "loss_cls": 4.12229, "loss": 4.12229, "time": 0.83919} +{"mode": "train", "epoch": 30, "iter": 2000, "lr": 0.09074, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24984, "top5_acc": 0.49531, "loss_cls": 4.11745, "loss": 4.11745, "time": 0.83791} +{"mode": "train", "epoch": 30, "iter": 2100, "lr": 0.09072, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25234, "top5_acc": 0.49828, "loss_cls": 4.1085, "loss": 4.1085, "time": 0.83623} +{"mode": "train", "epoch": 30, "iter": 2200, "lr": 0.0907, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25828, "top5_acc": 0.50516, "loss_cls": 4.07119, "loss": 4.07119, "time": 0.83408} +{"mode": "train", "epoch": 30, "iter": 2300, "lr": 0.09069, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25672, "top5_acc": 0.49797, "loss_cls": 4.09794, "loss": 4.09794, "time": 0.83878} +{"mode": "train", "epoch": 30, "iter": 2400, "lr": 0.09067, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25344, "top5_acc": 0.50984, "loss_cls": 4.09542, "loss": 4.09542, "time": 0.84089} +{"mode": "train", "epoch": 30, "iter": 2500, "lr": 0.09065, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24984, "top5_acc": 0.50453, "loss_cls": 4.09325, "loss": 4.09325, "time": 0.83299} +{"mode": "train", "epoch": 30, "iter": 2600, "lr": 0.09064, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26766, "top5_acc": 0.51109, "loss_cls": 4.05689, "loss": 4.05689, "time": 0.83676} +{"mode": "train", "epoch": 30, "iter": 2700, "lr": 0.09062, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25375, "top5_acc": 0.49359, "loss_cls": 4.10116, "loss": 4.10116, "time": 0.83833} +{"mode": "train", "epoch": 30, "iter": 2800, "lr": 0.09061, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26, "top5_acc": 0.51156, "loss_cls": 4.05887, "loss": 4.05887, "time": 0.83694} +{"mode": "train", "epoch": 30, "iter": 2900, "lr": 0.09059, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25156, "top5_acc": 0.49672, "loss_cls": 4.11381, "loss": 4.11381, "time": 0.83915} +{"mode": "train", "epoch": 30, "iter": 3000, "lr": 0.09057, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25906, "top5_acc": 0.5175, "loss_cls": 4.06076, "loss": 4.06076, "time": 0.83889} +{"mode": "train", "epoch": 30, "iter": 3100, "lr": 0.09056, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25422, "top5_acc": 0.49844, "loss_cls": 4.0978, "loss": 4.0978, "time": 0.83488} +{"mode": "train", "epoch": 30, "iter": 3200, "lr": 0.09054, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.2525, "top5_acc": 0.50844, "loss_cls": 4.07059, "loss": 4.07059, "time": 0.83915} +{"mode": "train", "epoch": 30, "iter": 3300, "lr": 0.09052, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25125, "top5_acc": 0.50281, "loss_cls": 4.06099, "loss": 4.06099, "time": 0.83899} +{"mode": "train", "epoch": 30, "iter": 3400, "lr": 0.09051, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25453, "top5_acc": 0.50516, "loss_cls": 4.09394, "loss": 4.09394, "time": 0.83996} +{"mode": "train", "epoch": 30, "iter": 3500, "lr": 0.09049, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25156, "top5_acc": 0.50594, "loss_cls": 4.10114, "loss": 4.10114, "time": 0.83733} +{"mode": "train", "epoch": 30, "iter": 3600, "lr": 0.09047, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.25688, "top5_acc": 0.50656, "loss_cls": 4.0886, "loss": 4.0886, "time": 0.8315} +{"mode": "train", "epoch": 30, "iter": 3700, "lr": 0.09046, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.25906, "top5_acc": 0.51484, "loss_cls": 4.03487, "loss": 4.03487, "time": 0.82627} +{"mode": "val", "epoch": 30, "iter": 309, "lr": 0.09045, "top1_acc": 0.18827, "top5_acc": 0.41559, "mean_class_accuracy": 0.18812} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.09043, "memory": 15990, "data_time": 1.38498, "top1_acc": 0.26641, "top5_acc": 0.52016, "loss_cls": 4.23594, "loss": 4.23594, "time": 2.42335} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.09042, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26391, "top5_acc": 0.51297, "loss_cls": 4.26209, "loss": 4.26209, "time": 0.85814} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.0904, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25531, "top5_acc": 0.50578, "loss_cls": 4.28025, "loss": 4.28025, "time": 0.85983} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.09039, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25453, "top5_acc": 0.51, "loss_cls": 4.28145, "loss": 4.28145, "time": 0.85192} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.09037, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26562, "top5_acc": 0.52531, "loss_cls": 4.18895, "loss": 4.18895, "time": 0.84961} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.09035, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26219, "top5_acc": 0.50109, "loss_cls": 4.31619, "loss": 4.31619, "time": 0.84891} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.09034, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26375, "top5_acc": 0.50547, "loss_cls": 4.27792, "loss": 4.27792, "time": 0.85283} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.09032, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25, "top5_acc": 0.50672, "loss_cls": 4.29378, "loss": 4.29378, "time": 0.84758} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0903, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25359, "top5_acc": 0.505, "loss_cls": 4.28651, "loss": 4.28651, "time": 0.84568} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.09029, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25875, "top5_acc": 0.51188, "loss_cls": 4.28813, "loss": 4.28813, "time": 0.8456} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.09027, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24797, "top5_acc": 0.49641, "loss_cls": 4.38047, "loss": 4.38047, "time": 0.84665} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.09025, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24469, "top5_acc": 0.49844, "loss_cls": 4.34371, "loss": 4.34371, "time": 0.84946} +{"mode": "train", "epoch": 31, "iter": 1300, "lr": 0.09024, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25422, "top5_acc": 0.49703, "loss_cls": 4.32591, "loss": 4.32591, "time": 0.85427} +{"mode": "train", "epoch": 31, "iter": 1400, "lr": 0.09022, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25953, "top5_acc": 0.51484, "loss_cls": 4.2834, "loss": 4.2834, "time": 0.85089} +{"mode": "train", "epoch": 31, "iter": 1500, "lr": 0.0902, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26719, "top5_acc": 0.5125, "loss_cls": 4.28533, "loss": 4.28533, "time": 0.8546} +{"mode": "train", "epoch": 31, "iter": 1600, "lr": 0.09019, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25547, "top5_acc": 0.51109, "loss_cls": 4.25864, "loss": 4.25864, "time": 0.85283} +{"mode": "train", "epoch": 31, "iter": 1700, "lr": 0.09017, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24938, "top5_acc": 0.49547, "loss_cls": 4.34565, "loss": 4.34565, "time": 0.84949} +{"mode": "train", "epoch": 31, "iter": 1800, "lr": 0.09015, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26141, "top5_acc": 0.50859, "loss_cls": 4.26577, "loss": 4.26577, "time": 0.85322} +{"mode": "train", "epoch": 31, "iter": 1900, "lr": 0.09014, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25031, "top5_acc": 0.50031, "loss_cls": 4.31032, "loss": 4.31032, "time": 0.84836} +{"mode": "train", "epoch": 31, "iter": 2000, "lr": 0.09012, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25703, "top5_acc": 0.50703, "loss_cls": 4.27847, "loss": 4.27847, "time": 0.85369} +{"mode": "train", "epoch": 31, "iter": 2100, "lr": 0.0901, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26, "top5_acc": 0.50375, "loss_cls": 4.30673, "loss": 4.30673, "time": 0.8527} +{"mode": "train", "epoch": 31, "iter": 2200, "lr": 0.09009, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26016, "top5_acc": 0.50641, "loss_cls": 4.26067, "loss": 4.26067, "time": 0.85403} +{"mode": "train", "epoch": 31, "iter": 2300, "lr": 0.09007, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25906, "top5_acc": 0.50594, "loss_cls": 4.2795, "loss": 4.2795, "time": 0.85227} +{"mode": "train", "epoch": 31, "iter": 2400, "lr": 0.09005, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25094, "top5_acc": 0.49906, "loss_cls": 4.31847, "loss": 4.31847, "time": 0.84988} +{"mode": "train", "epoch": 31, "iter": 2500, "lr": 0.09004, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25344, "top5_acc": 0.51906, "loss_cls": 4.25906, "loss": 4.25906, "time": 0.85401} +{"mode": "train", "epoch": 31, "iter": 2600, "lr": 0.09002, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.24984, "top5_acc": 0.49859, "loss_cls": 4.337, "loss": 4.337, "time": 0.8525} +{"mode": "train", "epoch": 31, "iter": 2700, "lr": 0.09, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25844, "top5_acc": 0.50422, "loss_cls": 4.31762, "loss": 4.31762, "time": 0.84933} +{"mode": "train", "epoch": 31, "iter": 2800, "lr": 0.08999, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26391, "top5_acc": 0.50547, "loss_cls": 4.27605, "loss": 4.27605, "time": 0.85268} +{"mode": "train", "epoch": 31, "iter": 2900, "lr": 0.08997, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26453, "top5_acc": 0.50172, "loss_cls": 4.29703, "loss": 4.29703, "time": 0.85426} +{"mode": "train", "epoch": 31, "iter": 3000, "lr": 0.08995, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25656, "top5_acc": 0.50688, "loss_cls": 4.29016, "loss": 4.29016, "time": 0.8503} +{"mode": "train", "epoch": 31, "iter": 3100, "lr": 0.08994, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26672, "top5_acc": 0.51172, "loss_cls": 4.28409, "loss": 4.28409, "time": 0.85517} +{"mode": "train", "epoch": 31, "iter": 3200, "lr": 0.08992, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2525, "top5_acc": 0.50281, "loss_cls": 4.30747, "loss": 4.30747, "time": 0.84936} +{"mode": "train", "epoch": 31, "iter": 3300, "lr": 0.0899, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25312, "top5_acc": 0.50984, "loss_cls": 4.30613, "loss": 4.30613, "time": 0.85769} +{"mode": "train", "epoch": 31, "iter": 3400, "lr": 0.08989, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24953, "top5_acc": 0.49922, "loss_cls": 4.30944, "loss": 4.30944, "time": 0.85498} +{"mode": "train", "epoch": 31, "iter": 3500, "lr": 0.08987, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25328, "top5_acc": 0.50875, "loss_cls": 4.29892, "loss": 4.29892, "time": 0.85464} +{"mode": "train", "epoch": 31, "iter": 3600, "lr": 0.08985, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.25219, "top5_acc": 0.49359, "loss_cls": 4.34285, "loss": 4.34285, "time": 0.85532} +{"mode": "train", "epoch": 31, "iter": 3700, "lr": 0.08983, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25734, "top5_acc": 0.50562, "loss_cls": 4.29314, "loss": 4.29314, "time": 0.84704} +{"mode": "val", "epoch": 31, "iter": 309, "lr": 0.08983, "top1_acc": 0.19369, "top5_acc": 0.41199, "mean_class_accuracy": 0.1937} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.08981, "memory": 15990, "data_time": 1.47605, "top1_acc": 0.2575, "top5_acc": 0.51031, "loss_cls": 4.2851, "loss": 4.2851, "time": 2.50446} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.08979, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26031, "top5_acc": 0.5075, "loss_cls": 4.27954, "loss": 4.27954, "time": 0.85358} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.08978, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26391, "top5_acc": 0.52156, "loss_cls": 4.23192, "loss": 4.23192, "time": 0.8607} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.08976, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25922, "top5_acc": 0.50984, "loss_cls": 4.27836, "loss": 4.27836, "time": 0.85655} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.08974, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24891, "top5_acc": 0.49516, "loss_cls": 4.31762, "loss": 4.31762, "time": 0.84928} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.08973, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25812, "top5_acc": 0.50953, "loss_cls": 4.29099, "loss": 4.29099, "time": 0.8491} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.08971, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26422, "top5_acc": 0.51984, "loss_cls": 4.22098, "loss": 4.22098, "time": 0.85274} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.08969, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26297, "top5_acc": 0.50562, "loss_cls": 4.28407, "loss": 4.28407, "time": 0.8487} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.08967, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2625, "top5_acc": 0.52, "loss_cls": 4.22891, "loss": 4.22891, "time": 0.85808} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.08966, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25734, "top5_acc": 0.50156, "loss_cls": 4.31689, "loss": 4.31689, "time": 0.8527} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.08964, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27469, "top5_acc": 0.53438, "loss_cls": 4.17843, "loss": 4.17843, "time": 0.85414} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.08962, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25312, "top5_acc": 0.50094, "loss_cls": 4.32053, "loss": 4.32053, "time": 0.85682} +{"mode": "train", "epoch": 32, "iter": 1300, "lr": 0.08961, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26531, "top5_acc": 0.51469, "loss_cls": 4.25957, "loss": 4.25957, "time": 0.85729} +{"mode": "train", "epoch": 32, "iter": 1400, "lr": 0.08959, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2625, "top5_acc": 0.50641, "loss_cls": 4.31281, "loss": 4.31281, "time": 0.85643} +{"mode": "train", "epoch": 32, "iter": 1500, "lr": 0.08957, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26375, "top5_acc": 0.51016, "loss_cls": 4.25533, "loss": 4.25533, "time": 0.85637} +{"mode": "train", "epoch": 32, "iter": 1600, "lr": 0.08955, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.25203, "top5_acc": 0.49844, "loss_cls": 4.34366, "loss": 4.34366, "time": 0.85695} +{"mode": "train", "epoch": 32, "iter": 1700, "lr": 0.08954, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26297, "top5_acc": 0.5175, "loss_cls": 4.26549, "loss": 4.26549, "time": 0.85794} +{"mode": "train", "epoch": 32, "iter": 1800, "lr": 0.08952, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25328, "top5_acc": 0.50562, "loss_cls": 4.30126, "loss": 4.30126, "time": 0.85679} +{"mode": "train", "epoch": 32, "iter": 1900, "lr": 0.0895, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24516, "top5_acc": 0.49656, "loss_cls": 4.32261, "loss": 4.32261, "time": 0.85874} +{"mode": "train", "epoch": 32, "iter": 2000, "lr": 0.08949, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25391, "top5_acc": 0.51609, "loss_cls": 4.28512, "loss": 4.28512, "time": 0.85456} +{"mode": "train", "epoch": 32, "iter": 2100, "lr": 0.08947, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25781, "top5_acc": 0.50281, "loss_cls": 4.30072, "loss": 4.30072, "time": 0.86207} +{"mode": "train", "epoch": 32, "iter": 2200, "lr": 0.08945, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26297, "top5_acc": 0.5075, "loss_cls": 4.29704, "loss": 4.29704, "time": 0.85859} +{"mode": "train", "epoch": 32, "iter": 2300, "lr": 0.08943, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25109, "top5_acc": 0.50109, "loss_cls": 4.31922, "loss": 4.31922, "time": 0.85773} +{"mode": "train", "epoch": 32, "iter": 2400, "lr": 0.08942, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25391, "top5_acc": 0.50359, "loss_cls": 4.31763, "loss": 4.31763, "time": 0.86056} +{"mode": "train", "epoch": 32, "iter": 2500, "lr": 0.0894, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25625, "top5_acc": 0.50531, "loss_cls": 4.2891, "loss": 4.2891, "time": 0.85881} +{"mode": "train", "epoch": 32, "iter": 2600, "lr": 0.08938, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25594, "top5_acc": 0.50781, "loss_cls": 4.30781, "loss": 4.30781, "time": 0.8576} +{"mode": "train", "epoch": 32, "iter": 2700, "lr": 0.08937, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24906, "top5_acc": 0.49875, "loss_cls": 4.31572, "loss": 4.31572, "time": 0.85948} +{"mode": "train", "epoch": 32, "iter": 2800, "lr": 0.08935, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25188, "top5_acc": 0.50672, "loss_cls": 4.30176, "loss": 4.30176, "time": 0.85654} +{"mode": "train", "epoch": 32, "iter": 2900, "lr": 0.08933, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.255, "top5_acc": 0.50219, "loss_cls": 4.27554, "loss": 4.27554, "time": 0.85684} +{"mode": "train", "epoch": 32, "iter": 3000, "lr": 0.08931, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26578, "top5_acc": 0.50688, "loss_cls": 4.27343, "loss": 4.27343, "time": 0.8613} +{"mode": "train", "epoch": 32, "iter": 3100, "lr": 0.0893, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26016, "top5_acc": 0.51313, "loss_cls": 4.2792, "loss": 4.2792, "time": 0.85833} +{"mode": "train", "epoch": 32, "iter": 3200, "lr": 0.08928, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24812, "top5_acc": 0.50219, "loss_cls": 4.30944, "loss": 4.30944, "time": 0.85749} +{"mode": "train", "epoch": 32, "iter": 3300, "lr": 0.08926, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26203, "top5_acc": 0.50859, "loss_cls": 4.2625, "loss": 4.2625, "time": 0.85462} +{"mode": "train", "epoch": 32, "iter": 3400, "lr": 0.08924, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26531, "top5_acc": 0.51781, "loss_cls": 4.23085, "loss": 4.23085, "time": 0.85718} +{"mode": "train", "epoch": 32, "iter": 3500, "lr": 0.08923, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25016, "top5_acc": 0.49484, "loss_cls": 4.34312, "loss": 4.34312, "time": 0.86027} +{"mode": "train", "epoch": 32, "iter": 3600, "lr": 0.08921, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26547, "top5_acc": 0.50844, "loss_cls": 4.2801, "loss": 4.2801, "time": 0.85185} +{"mode": "train", "epoch": 32, "iter": 3700, "lr": 0.08919, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.26859, "top5_acc": 0.50984, "loss_cls": 4.26532, "loss": 4.26532, "time": 0.85089} +{"mode": "val", "epoch": 32, "iter": 309, "lr": 0.08918, "top1_acc": 0.17551, "top5_acc": 0.39026, "mean_class_accuracy": 0.1751} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.08917, "memory": 15990, "data_time": 1.42533, "top1_acc": 0.26578, "top5_acc": 0.52641, "loss_cls": 4.2047, "loss": 4.2047, "time": 2.45238} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.08915, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26422, "top5_acc": 0.50813, "loss_cls": 4.25735, "loss": 4.25735, "time": 0.8532} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.08913, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26891, "top5_acc": 0.52234, "loss_cls": 4.22416, "loss": 4.22416, "time": 0.85524} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.08912, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26188, "top5_acc": 0.51313, "loss_cls": 4.25675, "loss": 4.25675, "time": 0.85501} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.0891, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.26469, "top5_acc": 0.50766, "loss_cls": 4.26973, "loss": 4.26973, "time": 0.84762} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.08908, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26328, "top5_acc": 0.50406, "loss_cls": 4.27731, "loss": 4.27731, "time": 0.84707} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.08906, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25438, "top5_acc": 0.50703, "loss_cls": 4.29051, "loss": 4.29051, "time": 0.85362} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.08905, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25375, "top5_acc": 0.50359, "loss_cls": 4.30533, "loss": 4.30533, "time": 0.85706} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.08903, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25734, "top5_acc": 0.49703, "loss_cls": 4.31405, "loss": 4.31405, "time": 0.85699} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.08901, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25281, "top5_acc": 0.51172, "loss_cls": 4.30518, "loss": 4.30518, "time": 0.85377} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.08899, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25734, "top5_acc": 0.51141, "loss_cls": 4.27602, "loss": 4.27602, "time": 0.85391} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.08898, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25328, "top5_acc": 0.50938, "loss_cls": 4.29566, "loss": 4.29566, "time": 0.85643} +{"mode": "train", "epoch": 33, "iter": 1300, "lr": 0.08896, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26234, "top5_acc": 0.51359, "loss_cls": 4.26312, "loss": 4.26312, "time": 0.85637} +{"mode": "train", "epoch": 33, "iter": 1400, "lr": 0.08894, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24578, "top5_acc": 0.49719, "loss_cls": 4.32435, "loss": 4.32435, "time": 0.85992} +{"mode": "train", "epoch": 33, "iter": 1500, "lr": 0.08892, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25859, "top5_acc": 0.51234, "loss_cls": 4.29015, "loss": 4.29015, "time": 0.8594} +{"mode": "train", "epoch": 33, "iter": 1600, "lr": 0.08891, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26391, "top5_acc": 0.51172, "loss_cls": 4.25601, "loss": 4.25601, "time": 0.85922} +{"mode": "train", "epoch": 33, "iter": 1700, "lr": 0.08889, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26047, "top5_acc": 0.50797, "loss_cls": 4.26745, "loss": 4.26745, "time": 0.86064} +{"mode": "train", "epoch": 33, "iter": 1800, "lr": 0.08887, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25609, "top5_acc": 0.50875, "loss_cls": 4.30081, "loss": 4.30081, "time": 0.85925} +{"mode": "train", "epoch": 33, "iter": 1900, "lr": 0.08885, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25875, "top5_acc": 0.50781, "loss_cls": 4.27448, "loss": 4.27448, "time": 0.8569} +{"mode": "train", "epoch": 33, "iter": 2000, "lr": 0.08884, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2625, "top5_acc": 0.50391, "loss_cls": 4.29016, "loss": 4.29016, "time": 0.85915} +{"mode": "train", "epoch": 33, "iter": 2100, "lr": 0.08882, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26484, "top5_acc": 0.51453, "loss_cls": 4.25545, "loss": 4.25545, "time": 0.85723} +{"mode": "train", "epoch": 33, "iter": 2200, "lr": 0.0888, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26094, "top5_acc": 0.50547, "loss_cls": 4.26708, "loss": 4.26708, "time": 0.8617} +{"mode": "train", "epoch": 33, "iter": 2300, "lr": 0.08878, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26, "top5_acc": 0.51016, "loss_cls": 4.26614, "loss": 4.26614, "time": 0.85587} +{"mode": "train", "epoch": 33, "iter": 2400, "lr": 0.08876, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26016, "top5_acc": 0.50562, "loss_cls": 4.27943, "loss": 4.27943, "time": 0.85926} +{"mode": "train", "epoch": 33, "iter": 2500, "lr": 0.08875, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26281, "top5_acc": 0.50672, "loss_cls": 4.29156, "loss": 4.29156, "time": 0.8593} +{"mode": "train", "epoch": 33, "iter": 2600, "lr": 0.08873, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26172, "top5_acc": 0.51562, "loss_cls": 4.24921, "loss": 4.24921, "time": 0.85539} +{"mode": "train", "epoch": 33, "iter": 2700, "lr": 0.08871, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25906, "top5_acc": 0.50141, "loss_cls": 4.28541, "loss": 4.28541, "time": 0.85837} +{"mode": "train", "epoch": 33, "iter": 2800, "lr": 0.08869, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25453, "top5_acc": 0.50313, "loss_cls": 4.30399, "loss": 4.30399, "time": 0.85951} +{"mode": "train", "epoch": 33, "iter": 2900, "lr": 0.08868, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25875, "top5_acc": 0.50781, "loss_cls": 4.30987, "loss": 4.30987, "time": 0.85788} +{"mode": "train", "epoch": 33, "iter": 3000, "lr": 0.08866, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25375, "top5_acc": 0.50266, "loss_cls": 4.30315, "loss": 4.30315, "time": 0.86076} +{"mode": "train", "epoch": 33, "iter": 3100, "lr": 0.08864, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25859, "top5_acc": 0.50703, "loss_cls": 4.28668, "loss": 4.28668, "time": 0.85706} +{"mode": "train", "epoch": 33, "iter": 3200, "lr": 0.08862, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25625, "top5_acc": 0.50172, "loss_cls": 4.30174, "loss": 4.30174, "time": 0.85683} +{"mode": "train", "epoch": 33, "iter": 3300, "lr": 0.08861, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25391, "top5_acc": 0.49953, "loss_cls": 4.32981, "loss": 4.32981, "time": 0.85867} +{"mode": "train", "epoch": 33, "iter": 3400, "lr": 0.08859, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26234, "top5_acc": 0.50781, "loss_cls": 4.28236, "loss": 4.28236, "time": 0.85846} +{"mode": "train", "epoch": 33, "iter": 3500, "lr": 0.08857, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25578, "top5_acc": 0.49531, "loss_cls": 4.3176, "loss": 4.3176, "time": 0.85068} +{"mode": "train", "epoch": 33, "iter": 3600, "lr": 0.08855, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25016, "top5_acc": 0.49516, "loss_cls": 4.34334, "loss": 4.34334, "time": 0.84642} +{"mode": "train", "epoch": 33, "iter": 3700, "lr": 0.08853, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.25656, "top5_acc": 0.50719, "loss_cls": 4.27901, "loss": 4.27901, "time": 0.84242} +{"mode": "val", "epoch": 33, "iter": 309, "lr": 0.08853, "top1_acc": 0.20539, "top5_acc": 0.43697, "mean_class_accuracy": 0.20517} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.08851, "memory": 15990, "data_time": 1.48451, "top1_acc": 0.26875, "top5_acc": 0.50969, "loss_cls": 4.25348, "loss": 4.25348, "time": 2.51635} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.08849, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25922, "top5_acc": 0.51547, "loss_cls": 4.26278, "loss": 4.26278, "time": 0.8597} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.08847, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26469, "top5_acc": 0.51594, "loss_cls": 4.25079, "loss": 4.25079, "time": 0.85916} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.08845, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26891, "top5_acc": 0.51656, "loss_cls": 4.21364, "loss": 4.21364, "time": 0.86105} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.08844, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25719, "top5_acc": 0.50828, "loss_cls": 4.29341, "loss": 4.29341, "time": 0.85901} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.08842, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26531, "top5_acc": 0.51359, "loss_cls": 4.2792, "loss": 4.2792, "time": 0.85486} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.0884, "memory": 15990, "data_time": 0.00075, "top1_acc": 0.27203, "top5_acc": 0.52078, "loss_cls": 4.22471, "loss": 4.22471, "time": 0.84996} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.08838, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26031, "top5_acc": 0.50125, "loss_cls": 4.31245, "loss": 4.31245, "time": 0.849} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.08836, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26594, "top5_acc": 0.51578, "loss_cls": 4.23094, "loss": 4.23094, "time": 0.85762} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.08835, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25719, "top5_acc": 0.50609, "loss_cls": 4.2977, "loss": 4.2977, "time": 0.85523} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.08833, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25906, "top5_acc": 0.51875, "loss_cls": 4.26705, "loss": 4.26705, "time": 0.85922} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.08831, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25375, "top5_acc": 0.50609, "loss_cls": 4.30471, "loss": 4.30471, "time": 0.85807} +{"mode": "train", "epoch": 34, "iter": 1300, "lr": 0.08829, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25766, "top5_acc": 0.50375, "loss_cls": 4.2937, "loss": 4.2937, "time": 0.85728} +{"mode": "train", "epoch": 34, "iter": 1400, "lr": 0.08828, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25859, "top5_acc": 0.49797, "loss_cls": 4.29923, "loss": 4.29923, "time": 0.85422} +{"mode": "train", "epoch": 34, "iter": 1500, "lr": 0.08826, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26125, "top5_acc": 0.51094, "loss_cls": 4.29474, "loss": 4.29474, "time": 0.85695} +{"mode": "train", "epoch": 34, "iter": 1600, "lr": 0.08824, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26203, "top5_acc": 0.51266, "loss_cls": 4.26187, "loss": 4.26187, "time": 0.85748} +{"mode": "train", "epoch": 34, "iter": 1700, "lr": 0.08822, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26016, "top5_acc": 0.50906, "loss_cls": 4.27459, "loss": 4.27459, "time": 0.8577} +{"mode": "train", "epoch": 34, "iter": 1800, "lr": 0.0882, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25938, "top5_acc": 0.51609, "loss_cls": 4.26363, "loss": 4.26363, "time": 0.85763} +{"mode": "train", "epoch": 34, "iter": 1900, "lr": 0.08819, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25656, "top5_acc": 0.50969, "loss_cls": 4.27815, "loss": 4.27815, "time": 0.85563} +{"mode": "train", "epoch": 34, "iter": 2000, "lr": 0.08817, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26016, "top5_acc": 0.51125, "loss_cls": 4.28275, "loss": 4.28275, "time": 0.85614} +{"mode": "train", "epoch": 34, "iter": 2100, "lr": 0.08815, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25828, "top5_acc": 0.50813, "loss_cls": 4.26448, "loss": 4.26448, "time": 0.85849} +{"mode": "train", "epoch": 34, "iter": 2200, "lr": 0.08813, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26453, "top5_acc": 0.50578, "loss_cls": 4.26462, "loss": 4.26462, "time": 0.85776} +{"mode": "train", "epoch": 34, "iter": 2300, "lr": 0.08811, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26297, "top5_acc": 0.51844, "loss_cls": 4.24021, "loss": 4.24021, "time": 0.85896} +{"mode": "train", "epoch": 34, "iter": 2400, "lr": 0.08809, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25906, "top5_acc": 0.49812, "loss_cls": 4.30559, "loss": 4.30559, "time": 0.856} +{"mode": "train", "epoch": 34, "iter": 2500, "lr": 0.08808, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26687, "top5_acc": 0.50344, "loss_cls": 4.26181, "loss": 4.26181, "time": 0.85489} +{"mode": "train", "epoch": 34, "iter": 2600, "lr": 0.08806, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25203, "top5_acc": 0.50797, "loss_cls": 4.32157, "loss": 4.32157, "time": 0.85667} +{"mode": "train", "epoch": 34, "iter": 2700, "lr": 0.08804, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24984, "top5_acc": 0.50469, "loss_cls": 4.30113, "loss": 4.30113, "time": 0.85725} +{"mode": "train", "epoch": 34, "iter": 2800, "lr": 0.08802, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.2475, "top5_acc": 0.50172, "loss_cls": 4.32262, "loss": 4.32262, "time": 0.85527} +{"mode": "train", "epoch": 34, "iter": 2900, "lr": 0.088, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26766, "top5_acc": 0.51359, "loss_cls": 4.24436, "loss": 4.24436, "time": 0.8606} +{"mode": "train", "epoch": 34, "iter": 3000, "lr": 0.08799, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2575, "top5_acc": 0.50391, "loss_cls": 4.31729, "loss": 4.31729, "time": 0.86148} +{"mode": "train", "epoch": 34, "iter": 3100, "lr": 0.08797, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25891, "top5_acc": 0.50359, "loss_cls": 4.30048, "loss": 4.30048, "time": 0.85806} +{"mode": "train", "epoch": 34, "iter": 3200, "lr": 0.08795, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25719, "top5_acc": 0.50531, "loss_cls": 4.28925, "loss": 4.28925, "time": 0.85545} +{"mode": "train", "epoch": 34, "iter": 3300, "lr": 0.08793, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26047, "top5_acc": 0.50328, "loss_cls": 4.29396, "loss": 4.29396, "time": 0.86032} +{"mode": "train", "epoch": 34, "iter": 3400, "lr": 0.08791, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26719, "top5_acc": 0.51109, "loss_cls": 4.25358, "loss": 4.25358, "time": 0.85539} +{"mode": "train", "epoch": 34, "iter": 3500, "lr": 0.08789, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26625, "top5_acc": 0.50516, "loss_cls": 4.26077, "loss": 4.26077, "time": 0.84942} +{"mode": "train", "epoch": 34, "iter": 3600, "lr": 0.08788, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26531, "top5_acc": 0.50109, "loss_cls": 4.24663, "loss": 4.24663, "time": 0.84106} +{"mode": "train", "epoch": 34, "iter": 3700, "lr": 0.08786, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25906, "top5_acc": 0.50297, "loss_cls": 4.31152, "loss": 4.31152, "time": 0.84893} +{"mode": "val", "epoch": 34, "iter": 309, "lr": 0.08785, "top1_acc": 0.1754, "top5_acc": 0.39037, "mean_class_accuracy": 0.17547} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.08783, "memory": 15990, "data_time": 1.43241, "top1_acc": 0.26234, "top5_acc": 0.50813, "loss_cls": 4.24577, "loss": 4.24577, "time": 2.46646} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.08781, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26031, "top5_acc": 0.51781, "loss_cls": 4.235, "loss": 4.235, "time": 0.85941} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.0878, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27031, "top5_acc": 0.51297, "loss_cls": 4.24355, "loss": 4.24355, "time": 0.86173} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.08778, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27047, "top5_acc": 0.50375, "loss_cls": 4.26876, "loss": 4.26876, "time": 0.85778} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.08776, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26844, "top5_acc": 0.51344, "loss_cls": 4.21282, "loss": 4.21282, "time": 0.85705} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.08774, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26141, "top5_acc": 0.51766, "loss_cls": 4.24819, "loss": 4.24819, "time": 0.85275} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.08772, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25766, "top5_acc": 0.50734, "loss_cls": 4.27452, "loss": 4.27452, "time": 0.84744} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.0877, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.26219, "top5_acc": 0.51094, "loss_cls": 4.25836, "loss": 4.25836, "time": 0.85479} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.08769, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26141, "top5_acc": 0.50531, "loss_cls": 4.27684, "loss": 4.27684, "time": 0.84894} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.08767, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26062, "top5_acc": 0.50766, "loss_cls": 4.25085, "loss": 4.25085, "time": 0.84749} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.08765, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26203, "top5_acc": 0.50734, "loss_cls": 4.28375, "loss": 4.28375, "time": 0.84816} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.08763, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26391, "top5_acc": 0.51406, "loss_cls": 4.23348, "loss": 4.23348, "time": 0.84562} +{"mode": "train", "epoch": 35, "iter": 1300, "lr": 0.08761, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25203, "top5_acc": 0.50469, "loss_cls": 4.28218, "loss": 4.28218, "time": 0.84528} +{"mode": "train", "epoch": 35, "iter": 1400, "lr": 0.08759, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25688, "top5_acc": 0.51641, "loss_cls": 4.28069, "loss": 4.28069, "time": 0.84967} +{"mode": "train", "epoch": 35, "iter": 1500, "lr": 0.08757, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25953, "top5_acc": 0.50031, "loss_cls": 4.30215, "loss": 4.30215, "time": 0.84693} +{"mode": "train", "epoch": 35, "iter": 1600, "lr": 0.08756, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26906, "top5_acc": 0.50234, "loss_cls": 4.25176, "loss": 4.25176, "time": 0.85047} +{"mode": "train", "epoch": 35, "iter": 1700, "lr": 0.08754, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26547, "top5_acc": 0.50953, "loss_cls": 4.27486, "loss": 4.27486, "time": 0.844} +{"mode": "train", "epoch": 35, "iter": 1800, "lr": 0.08752, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26547, "top5_acc": 0.51156, "loss_cls": 4.24766, "loss": 4.24766, "time": 0.84604} +{"mode": "train", "epoch": 35, "iter": 1900, "lr": 0.0875, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25094, "top5_acc": 0.50547, "loss_cls": 4.29256, "loss": 4.29256, "time": 0.84911} +{"mode": "train", "epoch": 35, "iter": 2000, "lr": 0.08748, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27109, "top5_acc": 0.52406, "loss_cls": 4.19953, "loss": 4.19953, "time": 0.8538} +{"mode": "train", "epoch": 35, "iter": 2100, "lr": 0.08746, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24938, "top5_acc": 0.50203, "loss_cls": 4.30847, "loss": 4.30847, "time": 0.85182} +{"mode": "train", "epoch": 35, "iter": 2200, "lr": 0.08745, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25859, "top5_acc": 0.50109, "loss_cls": 4.30146, "loss": 4.30146, "time": 0.85619} +{"mode": "train", "epoch": 35, "iter": 2300, "lr": 0.08743, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26312, "top5_acc": 0.50984, "loss_cls": 4.27499, "loss": 4.27499, "time": 0.85436} +{"mode": "train", "epoch": 35, "iter": 2400, "lr": 0.08741, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2675, "top5_acc": 0.51344, "loss_cls": 4.26109, "loss": 4.26109, "time": 0.85517} +{"mode": "train", "epoch": 35, "iter": 2500, "lr": 0.08739, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25453, "top5_acc": 0.51016, "loss_cls": 4.27707, "loss": 4.27707, "time": 0.85489} +{"mode": "train", "epoch": 35, "iter": 2600, "lr": 0.08737, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25688, "top5_acc": 0.50719, "loss_cls": 4.3064, "loss": 4.3064, "time": 0.85166} +{"mode": "train", "epoch": 35, "iter": 2700, "lr": 0.08735, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25531, "top5_acc": 0.50734, "loss_cls": 4.30302, "loss": 4.30302, "time": 0.854} +{"mode": "train", "epoch": 35, "iter": 2800, "lr": 0.08733, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26156, "top5_acc": 0.51156, "loss_cls": 4.24617, "loss": 4.24617, "time": 0.85601} +{"mode": "train", "epoch": 35, "iter": 2900, "lr": 0.08732, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25859, "top5_acc": 0.51078, "loss_cls": 4.28726, "loss": 4.28726, "time": 0.85516} +{"mode": "train", "epoch": 35, "iter": 3000, "lr": 0.0873, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26234, "top5_acc": 0.5125, "loss_cls": 4.28744, "loss": 4.28744, "time": 0.85292} +{"mode": "train", "epoch": 35, "iter": 3100, "lr": 0.08728, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26422, "top5_acc": 0.50547, "loss_cls": 4.28028, "loss": 4.28028, "time": 0.85861} +{"mode": "train", "epoch": 35, "iter": 3200, "lr": 0.08726, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25, "top5_acc": 0.5, "loss_cls": 4.31438, "loss": 4.31438, "time": 0.85722} +{"mode": "train", "epoch": 35, "iter": 3300, "lr": 0.08724, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25656, "top5_acc": 0.50219, "loss_cls": 4.30568, "loss": 4.30568, "time": 0.85759} +{"mode": "train", "epoch": 35, "iter": 3400, "lr": 0.08722, "memory": 15990, "data_time": 0.00076, "top1_acc": 0.26375, "top5_acc": 0.50797, "loss_cls": 4.27484, "loss": 4.27484, "time": 0.85218} +{"mode": "train", "epoch": 35, "iter": 3500, "lr": 0.0872, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26156, "top5_acc": 0.51219, "loss_cls": 4.25819, "loss": 4.25819, "time": 0.85426} +{"mode": "train", "epoch": 35, "iter": 3600, "lr": 0.08718, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25984, "top5_acc": 0.50625, "loss_cls": 4.2837, "loss": 4.2837, "time": 0.85117} +{"mode": "train", "epoch": 35, "iter": 3700, "lr": 0.08717, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26391, "top5_acc": 0.51047, "loss_cls": 4.24966, "loss": 4.24966, "time": 0.85292} +{"mode": "val", "epoch": 35, "iter": 309, "lr": 0.08716, "top1_acc": 0.20954, "top5_acc": 0.44689, "mean_class_accuracy": 0.2093} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.08714, "memory": 15990, "data_time": 1.41994, "top1_acc": 0.25953, "top5_acc": 0.51297, "loss_cls": 4.2475, "loss": 4.2475, "time": 2.43805} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.08712, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26859, "top5_acc": 0.52094, "loss_cls": 4.22019, "loss": 4.22019, "time": 0.85064} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.0871, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26453, "top5_acc": 0.51422, "loss_cls": 4.25643, "loss": 4.25643, "time": 0.85064} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.08708, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27016, "top5_acc": 0.51844, "loss_cls": 4.23265, "loss": 4.23265, "time": 0.84985} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.08706, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26438, "top5_acc": 0.51906, "loss_cls": 4.23728, "loss": 4.23728, "time": 0.85051} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.08704, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2575, "top5_acc": 0.51359, "loss_cls": 4.25266, "loss": 4.25266, "time": 0.85545} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.08703, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26609, "top5_acc": 0.51438, "loss_cls": 4.2467, "loss": 4.2467, "time": 0.85089} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.08701, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26719, "top5_acc": 0.51141, "loss_cls": 4.24599, "loss": 4.24599, "time": 0.84731} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.08699, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26844, "top5_acc": 0.51078, "loss_cls": 4.25276, "loss": 4.25276, "time": 0.85023} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.08697, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26672, "top5_acc": 0.51719, "loss_cls": 4.24598, "loss": 4.24598, "time": 0.84932} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.08695, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26031, "top5_acc": 0.51109, "loss_cls": 4.28923, "loss": 4.28923, "time": 0.84852} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.08693, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26281, "top5_acc": 0.51125, "loss_cls": 4.25002, "loss": 4.25002, "time": 0.8463} +{"mode": "train", "epoch": 36, "iter": 1300, "lr": 0.08691, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2675, "top5_acc": 0.51688, "loss_cls": 4.25202, "loss": 4.25202, "time": 0.85193} +{"mode": "train", "epoch": 36, "iter": 1400, "lr": 0.08689, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25188, "top5_acc": 0.49859, "loss_cls": 4.30882, "loss": 4.30882, "time": 0.84557} +{"mode": "train", "epoch": 36, "iter": 1500, "lr": 0.08688, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26656, "top5_acc": 0.51047, "loss_cls": 4.25935, "loss": 4.25935, "time": 0.84903} +{"mode": "train", "epoch": 36, "iter": 1600, "lr": 0.08686, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27047, "top5_acc": 0.51047, "loss_cls": 4.20819, "loss": 4.20819, "time": 0.85122} +{"mode": "train", "epoch": 36, "iter": 1700, "lr": 0.08684, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25562, "top5_acc": 0.50453, "loss_cls": 4.30129, "loss": 4.30129, "time": 0.85162} +{"mode": "train", "epoch": 36, "iter": 1800, "lr": 0.08682, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26109, "top5_acc": 0.50688, "loss_cls": 4.30204, "loss": 4.30204, "time": 0.84739} +{"mode": "train", "epoch": 36, "iter": 1900, "lr": 0.0868, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25609, "top5_acc": 0.50594, "loss_cls": 4.29347, "loss": 4.29347, "time": 0.84835} +{"mode": "train", "epoch": 36, "iter": 2000, "lr": 0.08678, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26422, "top5_acc": 0.51047, "loss_cls": 4.2619, "loss": 4.2619, "time": 0.84745} +{"mode": "train", "epoch": 36, "iter": 2100, "lr": 0.08676, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2625, "top5_acc": 0.51688, "loss_cls": 4.26315, "loss": 4.26315, "time": 0.85621} +{"mode": "train", "epoch": 36, "iter": 2200, "lr": 0.08674, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25656, "top5_acc": 0.50438, "loss_cls": 4.29446, "loss": 4.29446, "time": 0.85488} +{"mode": "train", "epoch": 36, "iter": 2300, "lr": 0.08672, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26578, "top5_acc": 0.52297, "loss_cls": 4.24941, "loss": 4.24941, "time": 0.85396} +{"mode": "train", "epoch": 36, "iter": 2400, "lr": 0.08671, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26578, "top5_acc": 0.51484, "loss_cls": 4.23793, "loss": 4.23793, "time": 0.85631} +{"mode": "train", "epoch": 36, "iter": 2500, "lr": 0.08669, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25297, "top5_acc": 0.50938, "loss_cls": 4.29014, "loss": 4.29014, "time": 0.85507} +{"mode": "train", "epoch": 36, "iter": 2600, "lr": 0.08667, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25719, "top5_acc": 0.49812, "loss_cls": 4.31793, "loss": 4.31793, "time": 0.85652} +{"mode": "train", "epoch": 36, "iter": 2700, "lr": 0.08665, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25484, "top5_acc": 0.51172, "loss_cls": 4.28398, "loss": 4.28398, "time": 0.85391} +{"mode": "train", "epoch": 36, "iter": 2800, "lr": 0.08663, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25656, "top5_acc": 0.51, "loss_cls": 4.28393, "loss": 4.28393, "time": 0.85332} +{"mode": "train", "epoch": 36, "iter": 2900, "lr": 0.08661, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26734, "top5_acc": 0.51938, "loss_cls": 4.24297, "loss": 4.24297, "time": 0.8564} +{"mode": "train", "epoch": 36, "iter": 3000, "lr": 0.08659, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26531, "top5_acc": 0.50703, "loss_cls": 4.25516, "loss": 4.25516, "time": 0.85207} +{"mode": "train", "epoch": 36, "iter": 3100, "lr": 0.08657, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26016, "top5_acc": 0.50531, "loss_cls": 4.26832, "loss": 4.26832, "time": 0.85645} +{"mode": "train", "epoch": 36, "iter": 3200, "lr": 0.08655, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26719, "top5_acc": 0.50766, "loss_cls": 4.2483, "loss": 4.2483, "time": 0.85535} +{"mode": "train", "epoch": 36, "iter": 3300, "lr": 0.08653, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25625, "top5_acc": 0.51109, "loss_cls": 4.27887, "loss": 4.27887, "time": 0.85917} +{"mode": "train", "epoch": 36, "iter": 3400, "lr": 0.08651, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26547, "top5_acc": 0.51047, "loss_cls": 4.26556, "loss": 4.26556, "time": 0.85206} +{"mode": "train", "epoch": 36, "iter": 3500, "lr": 0.0865, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25641, "top5_acc": 0.51188, "loss_cls": 4.25646, "loss": 4.25646, "time": 0.84471} +{"mode": "train", "epoch": 36, "iter": 3600, "lr": 0.08648, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27062, "top5_acc": 0.52, "loss_cls": 4.23094, "loss": 4.23094, "time": 0.84772} +{"mode": "train", "epoch": 36, "iter": 3700, "lr": 0.08646, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26297, "top5_acc": 0.49984, "loss_cls": 4.3104, "loss": 4.3104, "time": 0.85395} +{"mode": "val", "epoch": 36, "iter": 309, "lr": 0.08645, "top1_acc": 0.21182, "top5_acc": 0.44948, "mean_class_accuracy": 0.21157} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.08643, "memory": 15990, "data_time": 1.40425, "top1_acc": 0.2525, "top5_acc": 0.50578, "loss_cls": 4.29486, "loss": 4.29486, "time": 2.42312} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.08641, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.26, "top5_acc": 0.51422, "loss_cls": 4.24544, "loss": 4.24544, "time": 0.84334} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.08639, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26438, "top5_acc": 0.51672, "loss_cls": 4.23041, "loss": 4.23041, "time": 0.84591} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.08637, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26422, "top5_acc": 0.5175, "loss_cls": 4.23772, "loss": 4.23772, "time": 0.84805} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.08635, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26812, "top5_acc": 0.51828, "loss_cls": 4.23702, "loss": 4.23702, "time": 0.84576} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.08633, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25906, "top5_acc": 0.51047, "loss_cls": 4.26034, "loss": 4.26034, "time": 0.84651} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.08631, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25828, "top5_acc": 0.51609, "loss_cls": 4.24555, "loss": 4.24555, "time": 0.84825} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0863, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26281, "top5_acc": 0.51359, "loss_cls": 4.26111, "loss": 4.26111, "time": 0.8387} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.08628, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26078, "top5_acc": 0.51344, "loss_cls": 4.25492, "loss": 4.25492, "time": 0.84891} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.08626, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25875, "top5_acc": 0.51078, "loss_cls": 4.26455, "loss": 4.26455, "time": 0.84698} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.08624, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27062, "top5_acc": 0.52281, "loss_cls": 4.21703, "loss": 4.21703, "time": 0.84723} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.08622, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25703, "top5_acc": 0.50938, "loss_cls": 4.28481, "loss": 4.28481, "time": 0.85042} +{"mode": "train", "epoch": 37, "iter": 1300, "lr": 0.0862, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25922, "top5_acc": 0.50688, "loss_cls": 4.24347, "loss": 4.24347, "time": 0.84205} +{"mode": "train", "epoch": 37, "iter": 1400, "lr": 0.08618, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26656, "top5_acc": 0.515, "loss_cls": 4.22905, "loss": 4.22905, "time": 0.84432} +{"mode": "train", "epoch": 37, "iter": 1500, "lr": 0.08616, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26328, "top5_acc": 0.51672, "loss_cls": 4.23682, "loss": 4.23682, "time": 0.84489} +{"mode": "train", "epoch": 37, "iter": 1600, "lr": 0.08614, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26344, "top5_acc": 0.51766, "loss_cls": 4.23138, "loss": 4.23138, "time": 0.84298} +{"mode": "train", "epoch": 37, "iter": 1700, "lr": 0.08612, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26484, "top5_acc": 0.50531, "loss_cls": 4.27319, "loss": 4.27319, "time": 0.84481} +{"mode": "train", "epoch": 37, "iter": 1800, "lr": 0.0861, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26344, "top5_acc": 0.50844, "loss_cls": 4.26277, "loss": 4.26277, "time": 0.84453} +{"mode": "train", "epoch": 37, "iter": 1900, "lr": 0.08608, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26047, "top5_acc": 0.50641, "loss_cls": 4.28951, "loss": 4.28951, "time": 0.84518} +{"mode": "train", "epoch": 37, "iter": 2000, "lr": 0.08606, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25344, "top5_acc": 0.505, "loss_cls": 4.32237, "loss": 4.32237, "time": 0.84307} +{"mode": "train", "epoch": 37, "iter": 2100, "lr": 0.08604, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2725, "top5_acc": 0.51641, "loss_cls": 4.23823, "loss": 4.23823, "time": 0.84602} +{"mode": "train", "epoch": 37, "iter": 2200, "lr": 0.08602, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27437, "top5_acc": 0.52031, "loss_cls": 4.21328, "loss": 4.21328, "time": 0.85095} +{"mode": "train", "epoch": 37, "iter": 2300, "lr": 0.08601, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26078, "top5_acc": 0.50469, "loss_cls": 4.27621, "loss": 4.27621, "time": 0.85077} +{"mode": "train", "epoch": 37, "iter": 2400, "lr": 0.08599, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26641, "top5_acc": 0.51094, "loss_cls": 4.26791, "loss": 4.26791, "time": 0.85024} +{"mode": "train", "epoch": 37, "iter": 2500, "lr": 0.08597, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26188, "top5_acc": 0.50297, "loss_cls": 4.27099, "loss": 4.27099, "time": 0.84905} +{"mode": "train", "epoch": 37, "iter": 2600, "lr": 0.08595, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26359, "top5_acc": 0.50891, "loss_cls": 4.28324, "loss": 4.28324, "time": 0.8531} +{"mode": "train", "epoch": 37, "iter": 2700, "lr": 0.08593, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26297, "top5_acc": 0.51328, "loss_cls": 4.27083, "loss": 4.27083, "time": 0.85016} +{"mode": "train", "epoch": 37, "iter": 2800, "lr": 0.08591, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26016, "top5_acc": 0.49922, "loss_cls": 4.31627, "loss": 4.31627, "time": 0.85307} +{"mode": "train", "epoch": 37, "iter": 2900, "lr": 0.08589, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.275, "top5_acc": 0.5175, "loss_cls": 4.21272, "loss": 4.21272, "time": 0.8523} +{"mode": "train", "epoch": 37, "iter": 3000, "lr": 0.08587, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26328, "top5_acc": 0.50641, "loss_cls": 4.27369, "loss": 4.27369, "time": 0.85172} +{"mode": "train", "epoch": 37, "iter": 3100, "lr": 0.08585, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25969, "top5_acc": 0.50438, "loss_cls": 4.26759, "loss": 4.26759, "time": 0.85223} +{"mode": "train", "epoch": 37, "iter": 3200, "lr": 0.08583, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26344, "top5_acc": 0.50969, "loss_cls": 4.27357, "loss": 4.27357, "time": 0.84733} +{"mode": "train", "epoch": 37, "iter": 3300, "lr": 0.08581, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26266, "top5_acc": 0.51078, "loss_cls": 4.27047, "loss": 4.27047, "time": 0.85008} +{"mode": "train", "epoch": 37, "iter": 3400, "lr": 0.08579, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25328, "top5_acc": 0.50703, "loss_cls": 4.27524, "loss": 4.27524, "time": 0.84846} +{"mode": "train", "epoch": 37, "iter": 3500, "lr": 0.08577, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26109, "top5_acc": 0.5075, "loss_cls": 4.25131, "loss": 4.25131, "time": 0.84567} +{"mode": "train", "epoch": 37, "iter": 3600, "lr": 0.08575, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.25797, "top5_acc": 0.51219, "loss_cls": 4.24345, "loss": 4.24345, "time": 0.84348} +{"mode": "train", "epoch": 37, "iter": 3700, "lr": 0.08573, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25922, "top5_acc": 0.50766, "loss_cls": 4.29986, "loss": 4.29986, "time": 0.85213} +{"mode": "val", "epoch": 37, "iter": 309, "lr": 0.08572, "top1_acc": 0.18194, "top5_acc": 0.40738, "mean_class_accuracy": 0.18174} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.0857, "memory": 15990, "data_time": 1.39119, "top1_acc": 0.26672, "top5_acc": 0.52141, "loss_cls": 4.21898, "loss": 4.21898, "time": 2.42093} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.08568, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.26016, "top5_acc": 0.51391, "loss_cls": 4.25424, "loss": 4.25424, "time": 0.85105} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.08567, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26, "top5_acc": 0.51844, "loss_cls": 4.25102, "loss": 4.25102, "time": 0.84988} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.08565, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26453, "top5_acc": 0.52344, "loss_cls": 4.19707, "loss": 4.19707, "time": 0.84449} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.08563, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26937, "top5_acc": 0.52109, "loss_cls": 4.1937, "loss": 4.1937, "time": 0.84249} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.08561, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25891, "top5_acc": 0.50891, "loss_cls": 4.26081, "loss": 4.26081, "time": 0.84904} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.08559, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.26125, "top5_acc": 0.505, "loss_cls": 4.27079, "loss": 4.27079, "time": 0.84817} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.08557, "memory": 15990, "data_time": 0.00076, "top1_acc": 0.27328, "top5_acc": 0.51, "loss_cls": 4.24865, "loss": 4.24865, "time": 0.84722} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.08555, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.27016, "top5_acc": 0.51672, "loss_cls": 4.23812, "loss": 4.23812, "time": 0.84692} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.08553, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26141, "top5_acc": 0.50813, "loss_cls": 4.2833, "loss": 4.2833, "time": 0.8553} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.08551, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25969, "top5_acc": 0.52469, "loss_cls": 4.19489, "loss": 4.19489, "time": 0.85141} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.08549, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25953, "top5_acc": 0.50734, "loss_cls": 4.29579, "loss": 4.29579, "time": 0.8486} +{"mode": "train", "epoch": 38, "iter": 1300, "lr": 0.08547, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27625, "top5_acc": 0.52594, "loss_cls": 4.18903, "loss": 4.18903, "time": 0.85633} +{"mode": "train", "epoch": 38, "iter": 1400, "lr": 0.08545, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25672, "top5_acc": 0.51156, "loss_cls": 4.26493, "loss": 4.26493, "time": 0.85258} +{"mode": "train", "epoch": 38, "iter": 1500, "lr": 0.08543, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27031, "top5_acc": 0.51469, "loss_cls": 4.26325, "loss": 4.26325, "time": 0.85397} +{"mode": "train", "epoch": 38, "iter": 1600, "lr": 0.08541, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26344, "top5_acc": 0.51422, "loss_cls": 4.26743, "loss": 4.26743, "time": 0.85886} +{"mode": "train", "epoch": 38, "iter": 1700, "lr": 0.08539, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26766, "top5_acc": 0.51609, "loss_cls": 4.23694, "loss": 4.23694, "time": 0.85069} +{"mode": "train", "epoch": 38, "iter": 1800, "lr": 0.08537, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26594, "top5_acc": 0.51078, "loss_cls": 4.26012, "loss": 4.26012, "time": 0.85507} +{"mode": "train", "epoch": 38, "iter": 1900, "lr": 0.08535, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2675, "top5_acc": 0.51422, "loss_cls": 4.25531, "loss": 4.25531, "time": 0.85213} +{"mode": "train", "epoch": 38, "iter": 2000, "lr": 0.08533, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25734, "top5_acc": 0.50703, "loss_cls": 4.30967, "loss": 4.30967, "time": 0.85745} +{"mode": "train", "epoch": 38, "iter": 2100, "lr": 0.08531, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25438, "top5_acc": 0.50578, "loss_cls": 4.29049, "loss": 4.29049, "time": 0.85416} +{"mode": "train", "epoch": 38, "iter": 2200, "lr": 0.08529, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26266, "top5_acc": 0.51469, "loss_cls": 4.24226, "loss": 4.24226, "time": 0.85647} +{"mode": "train", "epoch": 38, "iter": 2300, "lr": 0.08527, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27078, "top5_acc": 0.51266, "loss_cls": 4.2513, "loss": 4.2513, "time": 0.8525} +{"mode": "train", "epoch": 38, "iter": 2400, "lr": 0.08525, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2575, "top5_acc": 0.51062, "loss_cls": 4.2353, "loss": 4.2353, "time": 0.85115} +{"mode": "train", "epoch": 38, "iter": 2500, "lr": 0.08523, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.25312, "top5_acc": 0.5, "loss_cls": 4.30544, "loss": 4.30544, "time": 0.85316} +{"mode": "train", "epoch": 38, "iter": 2600, "lr": 0.08521, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26844, "top5_acc": 0.5275, "loss_cls": 4.21083, "loss": 4.21083, "time": 0.8515} +{"mode": "train", "epoch": 38, "iter": 2700, "lr": 0.08519, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26703, "top5_acc": 0.5175, "loss_cls": 4.22401, "loss": 4.22401, "time": 0.8527} +{"mode": "train", "epoch": 38, "iter": 2800, "lr": 0.08517, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26062, "top5_acc": 0.52328, "loss_cls": 4.26332, "loss": 4.26332, "time": 0.85383} +{"mode": "train", "epoch": 38, "iter": 2900, "lr": 0.08515, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27094, "top5_acc": 0.51562, "loss_cls": 4.25312, "loss": 4.25312, "time": 0.85417} +{"mode": "train", "epoch": 38, "iter": 3000, "lr": 0.08513, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27094, "top5_acc": 0.51016, "loss_cls": 4.2541, "loss": 4.2541, "time": 0.85377} +{"mode": "train", "epoch": 38, "iter": 3100, "lr": 0.08511, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26109, "top5_acc": 0.52078, "loss_cls": 4.24557, "loss": 4.24557, "time": 0.85932} +{"mode": "train", "epoch": 38, "iter": 3200, "lr": 0.08509, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27203, "top5_acc": 0.52312, "loss_cls": 4.20067, "loss": 4.20067, "time": 0.85388} +{"mode": "train", "epoch": 38, "iter": 3300, "lr": 0.08507, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26453, "top5_acc": 0.51016, "loss_cls": 4.24914, "loss": 4.24914, "time": 0.85365} +{"mode": "train", "epoch": 38, "iter": 3400, "lr": 0.08505, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25531, "top5_acc": 0.50172, "loss_cls": 4.29428, "loss": 4.29428, "time": 0.85043} +{"mode": "train", "epoch": 38, "iter": 3500, "lr": 0.08503, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25562, "top5_acc": 0.50281, "loss_cls": 4.30207, "loss": 4.30207, "time": 0.84839} +{"mode": "train", "epoch": 38, "iter": 3600, "lr": 0.08501, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26, "top5_acc": 0.50891, "loss_cls": 4.26519, "loss": 4.26519, "time": 0.84513} +{"mode": "train", "epoch": 38, "iter": 3700, "lr": 0.08499, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25781, "top5_acc": 0.51297, "loss_cls": 4.24039, "loss": 4.24039, "time": 0.85178} +{"mode": "val", "epoch": 38, "iter": 309, "lr": 0.08498, "top1_acc": 0.17556, "top5_acc": 0.38677, "mean_class_accuracy": 0.17536} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.08496, "memory": 15990, "data_time": 1.47684, "top1_acc": 0.26844, "top5_acc": 0.51938, "loss_cls": 4.21537, "loss": 4.21537, "time": 2.49789} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.08494, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26516, "top5_acc": 0.51734, "loss_cls": 4.22615, "loss": 4.22615, "time": 0.84908} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.08492, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26906, "top5_acc": 0.51422, "loss_cls": 4.23193, "loss": 4.23193, "time": 0.85296} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.0849, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26922, "top5_acc": 0.51844, "loss_cls": 4.20242, "loss": 4.20242, "time": 0.85319} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.08488, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26219, "top5_acc": 0.51453, "loss_cls": 4.2423, "loss": 4.2423, "time": 0.85334} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.08486, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26375, "top5_acc": 0.51047, "loss_cls": 4.25342, "loss": 4.25342, "time": 0.85018} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.08484, "memory": 15990, "data_time": 0.00074, "top1_acc": 0.27078, "top5_acc": 0.52109, "loss_cls": 4.22957, "loss": 4.22957, "time": 0.84787} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.08482, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27312, "top5_acc": 0.53344, "loss_cls": 4.15639, "loss": 4.15639, "time": 0.84869} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.0848, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.25375, "top5_acc": 0.51078, "loss_cls": 4.29238, "loss": 4.29238, "time": 0.84326} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.08478, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26687, "top5_acc": 0.52719, "loss_cls": 4.20028, "loss": 4.20028, "time": 0.84529} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.08476, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27516, "top5_acc": 0.51125, "loss_cls": 4.22544, "loss": 4.22544, "time": 0.84571} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.08474, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26781, "top5_acc": 0.51109, "loss_cls": 4.25116, "loss": 4.25116, "time": 0.84498} +{"mode": "train", "epoch": 39, "iter": 1300, "lr": 0.08472, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25297, "top5_acc": 0.50438, "loss_cls": 4.33566, "loss": 4.33566, "time": 0.84688} +{"mode": "train", "epoch": 39, "iter": 1400, "lr": 0.0847, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26766, "top5_acc": 0.51578, "loss_cls": 4.24107, "loss": 4.24107, "time": 0.84745} +{"mode": "train", "epoch": 39, "iter": 1500, "lr": 0.08468, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26562, "top5_acc": 0.5, "loss_cls": 4.28774, "loss": 4.28774, "time": 0.84588} +{"mode": "train", "epoch": 39, "iter": 1600, "lr": 0.08466, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25641, "top5_acc": 0.50766, "loss_cls": 4.27017, "loss": 4.27017, "time": 0.84782} +{"mode": "train", "epoch": 39, "iter": 1700, "lr": 0.08464, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26266, "top5_acc": 0.51578, "loss_cls": 4.25073, "loss": 4.25073, "time": 0.8449} +{"mode": "train", "epoch": 39, "iter": 1800, "lr": 0.08462, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26969, "top5_acc": 0.50453, "loss_cls": 4.27237, "loss": 4.27237, "time": 0.84587} +{"mode": "train", "epoch": 39, "iter": 1900, "lr": 0.0846, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25766, "top5_acc": 0.50031, "loss_cls": 4.28815, "loss": 4.28815, "time": 0.84596} +{"mode": "train", "epoch": 39, "iter": 2000, "lr": 0.08458, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26484, "top5_acc": 0.51484, "loss_cls": 4.26174, "loss": 4.26174, "time": 0.84741} +{"mode": "train", "epoch": 39, "iter": 2100, "lr": 0.08456, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25516, "top5_acc": 0.50578, "loss_cls": 4.27624, "loss": 4.27624, "time": 0.84348} +{"mode": "train", "epoch": 39, "iter": 2200, "lr": 0.08454, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26141, "top5_acc": 0.52281, "loss_cls": 4.25469, "loss": 4.25469, "time": 0.84273} +{"mode": "train", "epoch": 39, "iter": 2300, "lr": 0.08452, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26219, "top5_acc": 0.50859, "loss_cls": 4.28122, "loss": 4.28122, "time": 0.85407} +{"mode": "train", "epoch": 39, "iter": 2400, "lr": 0.0845, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26328, "top5_acc": 0.51797, "loss_cls": 4.22936, "loss": 4.22936, "time": 0.85022} +{"mode": "train", "epoch": 39, "iter": 2500, "lr": 0.08448, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26422, "top5_acc": 0.52234, "loss_cls": 4.21429, "loss": 4.21429, "time": 0.85414} +{"mode": "train", "epoch": 39, "iter": 2600, "lr": 0.08446, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26406, "top5_acc": 0.51234, "loss_cls": 4.25751, "loss": 4.25751, "time": 0.8584} +{"mode": "train", "epoch": 39, "iter": 2700, "lr": 0.08444, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27406, "top5_acc": 0.51594, "loss_cls": 4.21408, "loss": 4.21408, "time": 0.85613} +{"mode": "train", "epoch": 39, "iter": 2800, "lr": 0.08442, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26687, "top5_acc": 0.51344, "loss_cls": 4.21499, "loss": 4.21499, "time": 0.85331} +{"mode": "train", "epoch": 39, "iter": 2900, "lr": 0.0844, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2725, "top5_acc": 0.51313, "loss_cls": 4.25389, "loss": 4.25389, "time": 0.85361} +{"mode": "train", "epoch": 39, "iter": 3000, "lr": 0.08438, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26203, "top5_acc": 0.51719, "loss_cls": 4.2285, "loss": 4.2285, "time": 0.85651} +{"mode": "train", "epoch": 39, "iter": 3100, "lr": 0.08436, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26781, "top5_acc": 0.51969, "loss_cls": 4.21851, "loss": 4.21851, "time": 0.85579} +{"mode": "train", "epoch": 39, "iter": 3200, "lr": 0.08434, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26703, "top5_acc": 0.50938, "loss_cls": 4.25568, "loss": 4.25568, "time": 0.8586} +{"mode": "train", "epoch": 39, "iter": 3300, "lr": 0.08432, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26562, "top5_acc": 0.51344, "loss_cls": 4.24448, "loss": 4.24448, "time": 0.85394} +{"mode": "train", "epoch": 39, "iter": 3400, "lr": 0.0843, "memory": 15990, "data_time": 0.00103, "top1_acc": 0.26391, "top5_acc": 0.51281, "loss_cls": 4.24376, "loss": 4.24376, "time": 0.85284} +{"mode": "train", "epoch": 39, "iter": 3500, "lr": 0.08428, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27031, "top5_acc": 0.51656, "loss_cls": 4.22723, "loss": 4.22723, "time": 0.8526} +{"mode": "train", "epoch": 39, "iter": 3600, "lr": 0.08426, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.26516, "top5_acc": 0.51391, "loss_cls": 4.24968, "loss": 4.24968, "time": 0.85114} +{"mode": "train", "epoch": 39, "iter": 3700, "lr": 0.08424, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26297, "top5_acc": 0.51922, "loss_cls": 4.22728, "loss": 4.22728, "time": 0.85438} +{"mode": "val", "epoch": 39, "iter": 309, "lr": 0.08423, "top1_acc": 0.19571, "top5_acc": 0.42167, "mean_class_accuracy": 0.1956} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.08421, "memory": 15990, "data_time": 1.41592, "top1_acc": 0.26734, "top5_acc": 0.51094, "loss_cls": 4.2303, "loss": 4.2303, "time": 2.43767} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.08419, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27203, "top5_acc": 0.52156, "loss_cls": 4.2077, "loss": 4.2077, "time": 0.84612} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.08417, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26641, "top5_acc": 0.51812, "loss_cls": 4.25374, "loss": 4.25374, "time": 0.85323} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.08415, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26875, "top5_acc": 0.525, "loss_cls": 4.21091, "loss": 4.21091, "time": 0.85079} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.08413, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26766, "top5_acc": 0.51953, "loss_cls": 4.20791, "loss": 4.20791, "time": 0.84713} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.08411, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25781, "top5_acc": 0.50875, "loss_cls": 4.28613, "loss": 4.28613, "time": 0.84708} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.08408, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26438, "top5_acc": 0.52094, "loss_cls": 4.22699, "loss": 4.22699, "time": 0.84729} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.08406, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25562, "top5_acc": 0.51031, "loss_cls": 4.2846, "loss": 4.2846, "time": 0.8481} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.08404, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26109, "top5_acc": 0.52141, "loss_cls": 4.22707, "loss": 4.22707, "time": 0.84146} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.08402, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26844, "top5_acc": 0.52172, "loss_cls": 4.21345, "loss": 4.21345, "time": 0.84759} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.084, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26047, "top5_acc": 0.5125, "loss_cls": 4.26496, "loss": 4.26496, "time": 0.85224} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.08398, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27328, "top5_acc": 0.52141, "loss_cls": 4.20914, "loss": 4.20914, "time": 0.84569} +{"mode": "train", "epoch": 40, "iter": 1300, "lr": 0.08396, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25984, "top5_acc": 0.51453, "loss_cls": 4.23185, "loss": 4.23185, "time": 0.84681} +{"mode": "train", "epoch": 40, "iter": 1400, "lr": 0.08394, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25344, "top5_acc": 0.51594, "loss_cls": 4.27234, "loss": 4.27234, "time": 0.84619} +{"mode": "train", "epoch": 40, "iter": 1500, "lr": 0.08392, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26234, "top5_acc": 0.51609, "loss_cls": 4.26812, "loss": 4.26812, "time": 0.84997} +{"mode": "train", "epoch": 40, "iter": 1600, "lr": 0.0839, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26516, "top5_acc": 0.50469, "loss_cls": 4.27824, "loss": 4.27824, "time": 0.85062} +{"mode": "train", "epoch": 40, "iter": 1700, "lr": 0.08388, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26234, "top5_acc": 0.50891, "loss_cls": 4.24195, "loss": 4.24195, "time": 0.84752} +{"mode": "train", "epoch": 40, "iter": 1800, "lr": 0.08386, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27156, "top5_acc": 0.52125, "loss_cls": 4.2243, "loss": 4.2243, "time": 0.84496} +{"mode": "train", "epoch": 40, "iter": 1900, "lr": 0.08384, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26172, "top5_acc": 0.50828, "loss_cls": 4.25351, "loss": 4.25351, "time": 0.84678} +{"mode": "train", "epoch": 40, "iter": 2000, "lr": 0.08382, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26234, "top5_acc": 0.51266, "loss_cls": 4.24931, "loss": 4.24931, "time": 0.84958} +{"mode": "train", "epoch": 40, "iter": 2100, "lr": 0.0838, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26453, "top5_acc": 0.51125, "loss_cls": 4.26537, "loss": 4.26537, "time": 0.84444} +{"mode": "train", "epoch": 40, "iter": 2200, "lr": 0.08378, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26172, "top5_acc": 0.50375, "loss_cls": 4.29401, "loss": 4.29401, "time": 0.84636} +{"mode": "train", "epoch": 40, "iter": 2300, "lr": 0.08376, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26812, "top5_acc": 0.51625, "loss_cls": 4.23425, "loss": 4.23425, "time": 0.84992} +{"mode": "train", "epoch": 40, "iter": 2400, "lr": 0.08374, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25672, "top5_acc": 0.49969, "loss_cls": 4.30297, "loss": 4.30297, "time": 0.84824} +{"mode": "train", "epoch": 40, "iter": 2500, "lr": 0.08371, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27172, "top5_acc": 0.50984, "loss_cls": 4.23686, "loss": 4.23686, "time": 0.8498} +{"mode": "train", "epoch": 40, "iter": 2600, "lr": 0.08369, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27109, "top5_acc": 0.51266, "loss_cls": 4.24502, "loss": 4.24502, "time": 0.85234} +{"mode": "train", "epoch": 40, "iter": 2700, "lr": 0.08367, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27344, "top5_acc": 0.52953, "loss_cls": 4.18537, "loss": 4.18537, "time": 0.8554} +{"mode": "train", "epoch": 40, "iter": 2800, "lr": 0.08365, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26953, "top5_acc": 0.50656, "loss_cls": 4.28061, "loss": 4.28061, "time": 0.85625} +{"mode": "train", "epoch": 40, "iter": 2900, "lr": 0.08363, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25688, "top5_acc": 0.50828, "loss_cls": 4.27024, "loss": 4.27024, "time": 0.85504} +{"mode": "train", "epoch": 40, "iter": 3000, "lr": 0.08361, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27, "top5_acc": 0.51953, "loss_cls": 4.20218, "loss": 4.20218, "time": 0.85403} +{"mode": "train", "epoch": 40, "iter": 3100, "lr": 0.08359, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26391, "top5_acc": 0.51328, "loss_cls": 4.22837, "loss": 4.22837, "time": 0.85255} +{"mode": "train", "epoch": 40, "iter": 3200, "lr": 0.08357, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26375, "top5_acc": 0.51391, "loss_cls": 4.25326, "loss": 4.25326, "time": 0.85068} +{"mode": "train", "epoch": 40, "iter": 3300, "lr": 0.08355, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27766, "top5_acc": 0.51844, "loss_cls": 4.20127, "loss": 4.20127, "time": 0.85352} +{"mode": "train", "epoch": 40, "iter": 3400, "lr": 0.08353, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25969, "top5_acc": 0.50594, "loss_cls": 4.27912, "loss": 4.27912, "time": 0.84987} +{"mode": "train", "epoch": 40, "iter": 3500, "lr": 0.08351, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26734, "top5_acc": 0.52219, "loss_cls": 4.22573, "loss": 4.22573, "time": 0.85134} +{"mode": "train", "epoch": 40, "iter": 3600, "lr": 0.08349, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27266, "top5_acc": 0.51688, "loss_cls": 4.2232, "loss": 4.2232, "time": 0.84486} +{"mode": "train", "epoch": 40, "iter": 3700, "lr": 0.08347, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26141, "top5_acc": 0.51594, "loss_cls": 4.24508, "loss": 4.24508, "time": 0.85298} +{"mode": "val", "epoch": 40, "iter": 309, "lr": 0.08346, "top1_acc": 0.19683, "top5_acc": 0.42592, "mean_class_accuracy": 0.19651} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.08344, "memory": 15990, "data_time": 1.41115, "top1_acc": 0.27422, "top5_acc": 0.52938, "loss_cls": 4.1904, "loss": 4.1904, "time": 2.43377} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.08342, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27109, "top5_acc": 0.52922, "loss_cls": 4.20891, "loss": 4.20891, "time": 0.84473} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.08339, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26766, "top5_acc": 0.52031, "loss_cls": 4.2215, "loss": 4.2215, "time": 0.84735} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.08337, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27, "top5_acc": 0.51891, "loss_cls": 4.19866, "loss": 4.19866, "time": 0.8471} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.08335, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27047, "top5_acc": 0.51906, "loss_cls": 4.21425, "loss": 4.21425, "time": 0.85084} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.08333, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26438, "top5_acc": 0.51938, "loss_cls": 4.24789, "loss": 4.24789, "time": 0.84962} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.08331, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26797, "top5_acc": 0.52125, "loss_cls": 4.2232, "loss": 4.2232, "time": 0.85323} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.08329, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26516, "top5_acc": 0.51281, "loss_cls": 4.24389, "loss": 4.24389, "time": 0.85383} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.08327, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26062, "top5_acc": 0.50641, "loss_cls": 4.22948, "loss": 4.22948, "time": 0.84561} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.08325, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26078, "top5_acc": 0.51406, "loss_cls": 4.23876, "loss": 4.23876, "time": 0.84113} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.08323, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25812, "top5_acc": 0.51719, "loss_cls": 4.24419, "loss": 4.24419, "time": 0.84793} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.08321, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26906, "top5_acc": 0.51594, "loss_cls": 4.22707, "loss": 4.22707, "time": 0.84662} +{"mode": "train", "epoch": 41, "iter": 1300, "lr": 0.08319, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25891, "top5_acc": 0.52516, "loss_cls": 4.20916, "loss": 4.20916, "time": 0.84744} +{"mode": "train", "epoch": 41, "iter": 1400, "lr": 0.08316, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26531, "top5_acc": 0.51641, "loss_cls": 4.26573, "loss": 4.26573, "time": 0.84635} +{"mode": "train", "epoch": 41, "iter": 1500, "lr": 0.08314, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27312, "top5_acc": 0.51469, "loss_cls": 4.25077, "loss": 4.25077, "time": 0.84534} +{"mode": "train", "epoch": 41, "iter": 1600, "lr": 0.08312, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26469, "top5_acc": 0.51516, "loss_cls": 4.24326, "loss": 4.24326, "time": 0.84296} +{"mode": "train", "epoch": 41, "iter": 1700, "lr": 0.0831, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27391, "top5_acc": 0.52734, "loss_cls": 4.1981, "loss": 4.1981, "time": 0.84617} +{"mode": "train", "epoch": 41, "iter": 1800, "lr": 0.08308, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26594, "top5_acc": 0.51594, "loss_cls": 4.19563, "loss": 4.19563, "time": 0.84686} +{"mode": "train", "epoch": 41, "iter": 1900, "lr": 0.08306, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27266, "top5_acc": 0.52469, "loss_cls": 4.18509, "loss": 4.18509, "time": 0.84156} +{"mode": "train", "epoch": 41, "iter": 2000, "lr": 0.08304, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25688, "top5_acc": 0.50781, "loss_cls": 4.29248, "loss": 4.29248, "time": 0.84936} +{"mode": "train", "epoch": 41, "iter": 2100, "lr": 0.08302, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27172, "top5_acc": 0.52219, "loss_cls": 4.19866, "loss": 4.19866, "time": 0.84496} +{"mode": "train", "epoch": 41, "iter": 2200, "lr": 0.083, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27562, "top5_acc": 0.51688, "loss_cls": 4.22374, "loss": 4.22374, "time": 0.84444} +{"mode": "train", "epoch": 41, "iter": 2300, "lr": 0.08298, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27469, "top5_acc": 0.52031, "loss_cls": 4.21471, "loss": 4.21471, "time": 0.84855} +{"mode": "train", "epoch": 41, "iter": 2400, "lr": 0.08296, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26531, "top5_acc": 0.515, "loss_cls": 4.26709, "loss": 4.26709, "time": 0.84686} +{"mode": "train", "epoch": 41, "iter": 2500, "lr": 0.08293, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26719, "top5_acc": 0.50984, "loss_cls": 4.22057, "loss": 4.22057, "time": 0.84877} +{"mode": "train", "epoch": 41, "iter": 2600, "lr": 0.08291, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25906, "top5_acc": 0.51, "loss_cls": 4.25083, "loss": 4.25083, "time": 0.84732} +{"mode": "train", "epoch": 41, "iter": 2700, "lr": 0.08289, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26734, "top5_acc": 0.5125, "loss_cls": 4.24719, "loss": 4.24719, "time": 0.84803} +{"mode": "train", "epoch": 41, "iter": 2800, "lr": 0.08287, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.265, "top5_acc": 0.51562, "loss_cls": 4.24931, "loss": 4.24931, "time": 0.85109} +{"mode": "train", "epoch": 41, "iter": 2900, "lr": 0.08285, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26031, "top5_acc": 0.52156, "loss_cls": 4.25154, "loss": 4.25154, "time": 0.8524} +{"mode": "train", "epoch": 41, "iter": 3000, "lr": 0.08283, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26922, "top5_acc": 0.52453, "loss_cls": 4.23024, "loss": 4.23024, "time": 0.85334} +{"mode": "train", "epoch": 41, "iter": 3100, "lr": 0.08281, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25766, "top5_acc": 0.51844, "loss_cls": 4.25728, "loss": 4.25728, "time": 0.85351} +{"mode": "train", "epoch": 41, "iter": 3200, "lr": 0.08279, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26484, "top5_acc": 0.51797, "loss_cls": 4.25679, "loss": 4.25679, "time": 0.85483} +{"mode": "train", "epoch": 41, "iter": 3300, "lr": 0.08277, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27891, "top5_acc": 0.53172, "loss_cls": 4.16142, "loss": 4.16142, "time": 0.85332} +{"mode": "train", "epoch": 41, "iter": 3400, "lr": 0.08274, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.26391, "top5_acc": 0.52188, "loss_cls": 4.22557, "loss": 4.22557, "time": 0.85244} +{"mode": "train", "epoch": 41, "iter": 3500, "lr": 0.08272, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27234, "top5_acc": 0.51219, "loss_cls": 4.2552, "loss": 4.2552, "time": 0.84754} +{"mode": "train", "epoch": 41, "iter": 3600, "lr": 0.0827, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26234, "top5_acc": 0.51734, "loss_cls": 4.22919, "loss": 4.22919, "time": 0.84615} +{"mode": "train", "epoch": 41, "iter": 3700, "lr": 0.08268, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.26016, "top5_acc": 0.50891, "loss_cls": 4.25741, "loss": 4.25741, "time": 0.85365} +{"mode": "val", "epoch": 41, "iter": 309, "lr": 0.08267, "top1_acc": 0.19222, "top5_acc": 0.4123, "mean_class_accuracy": 0.19212} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.08265, "memory": 15990, "data_time": 1.42451, "top1_acc": 0.27328, "top5_acc": 0.52969, "loss_cls": 4.1739, "loss": 4.1739, "time": 2.45677} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.08263, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.265, "top5_acc": 0.51656, "loss_cls": 4.22643, "loss": 4.22643, "time": 0.84832} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.08261, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.27031, "top5_acc": 0.51922, "loss_cls": 4.20347, "loss": 4.20347, "time": 0.85044} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.08259, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27984, "top5_acc": 0.53047, "loss_cls": 4.17741, "loss": 4.17741, "time": 0.84766} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.08257, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25328, "top5_acc": 0.51391, "loss_cls": 4.27298, "loss": 4.27298, "time": 0.85022} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.08254, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27078, "top5_acc": 0.52625, "loss_cls": 4.18779, "loss": 4.18779, "time": 0.84819} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.08252, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2675, "top5_acc": 0.52109, "loss_cls": 4.18441, "loss": 4.18441, "time": 0.84864} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.0825, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27437, "top5_acc": 0.53062, "loss_cls": 4.20544, "loss": 4.20544, "time": 0.85358} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.08248, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27172, "top5_acc": 0.52109, "loss_cls": 4.20012, "loss": 4.20012, "time": 0.85042} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.08246, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27156, "top5_acc": 0.51234, "loss_cls": 4.27334, "loss": 4.27334, "time": 0.85109} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.08244, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25422, "top5_acc": 0.50906, "loss_cls": 4.29439, "loss": 4.29439, "time": 0.85027} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.08242, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26125, "top5_acc": 0.50813, "loss_cls": 4.29446, "loss": 4.29446, "time": 0.8484} +{"mode": "train", "epoch": 42, "iter": 1300, "lr": 0.0824, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27594, "top5_acc": 0.51984, "loss_cls": 4.22019, "loss": 4.22019, "time": 0.846} +{"mode": "train", "epoch": 42, "iter": 1400, "lr": 0.08237, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25984, "top5_acc": 0.51203, "loss_cls": 4.25389, "loss": 4.25389, "time": 0.84862} +{"mode": "train", "epoch": 42, "iter": 1500, "lr": 0.08235, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27031, "top5_acc": 0.52281, "loss_cls": 4.20474, "loss": 4.20474, "time": 0.8467} +{"mode": "train", "epoch": 42, "iter": 1600, "lr": 0.08233, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26484, "top5_acc": 0.52016, "loss_cls": 4.22351, "loss": 4.22351, "time": 0.85258} +{"mode": "train", "epoch": 42, "iter": 1700, "lr": 0.08231, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2625, "top5_acc": 0.51531, "loss_cls": 4.24036, "loss": 4.24036, "time": 0.85113} +{"mode": "train", "epoch": 42, "iter": 1800, "lr": 0.08229, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26719, "top5_acc": 0.51234, "loss_cls": 4.26382, "loss": 4.26382, "time": 0.8549} +{"mode": "train", "epoch": 42, "iter": 1900, "lr": 0.08227, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26234, "top5_acc": 0.51109, "loss_cls": 4.2539, "loss": 4.2539, "time": 0.84903} +{"mode": "train", "epoch": 42, "iter": 2000, "lr": 0.08225, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26625, "top5_acc": 0.52531, "loss_cls": 4.21073, "loss": 4.21073, "time": 0.85076} +{"mode": "train", "epoch": 42, "iter": 2100, "lr": 0.08222, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25953, "top5_acc": 0.51172, "loss_cls": 4.25884, "loss": 4.25884, "time": 0.85173} +{"mode": "train", "epoch": 42, "iter": 2200, "lr": 0.0822, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26969, "top5_acc": 0.51969, "loss_cls": 4.22719, "loss": 4.22719, "time": 0.8535} +{"mode": "train", "epoch": 42, "iter": 2300, "lr": 0.08218, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27047, "top5_acc": 0.51578, "loss_cls": 4.23963, "loss": 4.23963, "time": 0.84569} +{"mode": "train", "epoch": 42, "iter": 2400, "lr": 0.08216, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26312, "top5_acc": 0.51594, "loss_cls": 4.23856, "loss": 4.23856, "time": 0.85068} +{"mode": "train", "epoch": 42, "iter": 2500, "lr": 0.08214, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26812, "top5_acc": 0.52578, "loss_cls": 4.19118, "loss": 4.19118, "time": 0.84656} +{"mode": "train", "epoch": 42, "iter": 2600, "lr": 0.08212, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26641, "top5_acc": 0.50156, "loss_cls": 4.27154, "loss": 4.27154, "time": 0.84724} +{"mode": "train", "epoch": 42, "iter": 2700, "lr": 0.0821, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27062, "top5_acc": 0.51594, "loss_cls": 4.19909, "loss": 4.19909, "time": 0.8421} +{"mode": "train", "epoch": 42, "iter": 2800, "lr": 0.08207, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27094, "top5_acc": 0.52953, "loss_cls": 4.20399, "loss": 4.20399, "time": 0.84649} +{"mode": "train", "epoch": 42, "iter": 2900, "lr": 0.08205, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26984, "top5_acc": 0.51109, "loss_cls": 4.24976, "loss": 4.24976, "time": 0.84255} +{"mode": "train", "epoch": 42, "iter": 3000, "lr": 0.08203, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26219, "top5_acc": 0.52031, "loss_cls": 4.22998, "loss": 4.22998, "time": 0.85337} +{"mode": "train", "epoch": 42, "iter": 3100, "lr": 0.08201, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27437, "top5_acc": 0.51969, "loss_cls": 4.21186, "loss": 4.21186, "time": 0.85156} +{"mode": "train", "epoch": 42, "iter": 3200, "lr": 0.08199, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27422, "top5_acc": 0.52109, "loss_cls": 4.21047, "loss": 4.21047, "time": 0.85201} +{"mode": "train", "epoch": 42, "iter": 3300, "lr": 0.08197, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27359, "top5_acc": 0.52312, "loss_cls": 4.20093, "loss": 4.20093, "time": 0.85343} +{"mode": "train", "epoch": 42, "iter": 3400, "lr": 0.08195, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26953, "top5_acc": 0.51375, "loss_cls": 4.23624, "loss": 4.23624, "time": 0.8473} +{"mode": "train", "epoch": 42, "iter": 3500, "lr": 0.08192, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26172, "top5_acc": 0.51609, "loss_cls": 4.26296, "loss": 4.26296, "time": 0.84684} +{"mode": "train", "epoch": 42, "iter": 3600, "lr": 0.0819, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.27375, "top5_acc": 0.52547, "loss_cls": 4.16934, "loss": 4.16934, "time": 0.84878} +{"mode": "train", "epoch": 42, "iter": 3700, "lr": 0.08188, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26125, "top5_acc": 0.51625, "loss_cls": 4.23776, "loss": 4.23776, "time": 0.84522} +{"mode": "val", "epoch": 42, "iter": 309, "lr": 0.08187, "top1_acc": 0.1943, "top5_acc": 0.41995, "mean_class_accuracy": 0.19405} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.08185, "memory": 15990, "data_time": 1.53872, "top1_acc": 0.27859, "top5_acc": 0.51578, "loss_cls": 4.21525, "loss": 4.21525, "time": 2.56854} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.08183, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27062, "top5_acc": 0.52078, "loss_cls": 4.20338, "loss": 4.20338, "time": 0.85281} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.08181, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25969, "top5_acc": 0.52531, "loss_cls": 4.21811, "loss": 4.21811, "time": 0.8534} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.08179, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.27234, "top5_acc": 0.53109, "loss_cls": 4.15817, "loss": 4.15817, "time": 0.84604} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.08176, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27469, "top5_acc": 0.52953, "loss_cls": 4.18272, "loss": 4.18272, "time": 0.8476} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.08174, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26531, "top5_acc": 0.51188, "loss_cls": 4.27115, "loss": 4.27115, "time": 0.84794} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.08172, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27281, "top5_acc": 0.525, "loss_cls": 4.19196, "loss": 4.19196, "time": 0.84606} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.0817, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27156, "top5_acc": 0.52094, "loss_cls": 4.23515, "loss": 4.23515, "time": 0.84672} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.08168, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26656, "top5_acc": 0.51453, "loss_cls": 4.21618, "loss": 4.21618, "time": 0.8487} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.08166, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27219, "top5_acc": 0.52484, "loss_cls": 4.21292, "loss": 4.21292, "time": 0.84401} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.08163, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.26922, "top5_acc": 0.52266, "loss_cls": 4.21732, "loss": 4.21732, "time": 0.84766} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.08161, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26312, "top5_acc": 0.52031, "loss_cls": 4.24385, "loss": 4.24385, "time": 0.845} +{"mode": "train", "epoch": 43, "iter": 1300, "lr": 0.08159, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.25094, "top5_acc": 0.51062, "loss_cls": 4.2824, "loss": 4.2824, "time": 0.85006} +{"mode": "train", "epoch": 43, "iter": 1400, "lr": 0.08157, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26625, "top5_acc": 0.52484, "loss_cls": 4.21347, "loss": 4.21347, "time": 0.84394} +{"mode": "train", "epoch": 43, "iter": 1500, "lr": 0.08155, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27594, "top5_acc": 0.51844, "loss_cls": 4.21232, "loss": 4.21232, "time": 0.84047} +{"mode": "train", "epoch": 43, "iter": 1600, "lr": 0.08153, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26422, "top5_acc": 0.51484, "loss_cls": 4.24454, "loss": 4.24454, "time": 0.84488} +{"mode": "train", "epoch": 43, "iter": 1700, "lr": 0.0815, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27359, "top5_acc": 0.52625, "loss_cls": 4.18365, "loss": 4.18365, "time": 0.84668} +{"mode": "train", "epoch": 43, "iter": 1800, "lr": 0.08148, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26781, "top5_acc": 0.52719, "loss_cls": 4.22367, "loss": 4.22367, "time": 0.84517} +{"mode": "train", "epoch": 43, "iter": 1900, "lr": 0.08146, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26922, "top5_acc": 0.51031, "loss_cls": 4.2228, "loss": 4.2228, "time": 0.84435} +{"mode": "train", "epoch": 43, "iter": 2000, "lr": 0.08144, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27031, "top5_acc": 0.51656, "loss_cls": 4.23408, "loss": 4.23408, "time": 0.84463} +{"mode": "train", "epoch": 43, "iter": 2100, "lr": 0.08142, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27406, "top5_acc": 0.52172, "loss_cls": 4.20229, "loss": 4.20229, "time": 0.84555} +{"mode": "train", "epoch": 43, "iter": 2200, "lr": 0.0814, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25641, "top5_acc": 0.51047, "loss_cls": 4.2597, "loss": 4.2597, "time": 0.85434} +{"mode": "train", "epoch": 43, "iter": 2300, "lr": 0.08137, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26359, "top5_acc": 0.51828, "loss_cls": 4.20165, "loss": 4.20165, "time": 0.84691} +{"mode": "train", "epoch": 43, "iter": 2400, "lr": 0.08135, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27406, "top5_acc": 0.52078, "loss_cls": 4.20403, "loss": 4.20403, "time": 0.85045} +{"mode": "train", "epoch": 43, "iter": 2500, "lr": 0.08133, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26453, "top5_acc": 0.51234, "loss_cls": 4.27589, "loss": 4.27589, "time": 0.84374} +{"mode": "train", "epoch": 43, "iter": 2600, "lr": 0.08131, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27781, "top5_acc": 0.53031, "loss_cls": 4.17915, "loss": 4.17915, "time": 0.8478} +{"mode": "train", "epoch": 43, "iter": 2700, "lr": 0.08129, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26969, "top5_acc": 0.52359, "loss_cls": 4.20522, "loss": 4.20522, "time": 0.85652} +{"mode": "train", "epoch": 43, "iter": 2800, "lr": 0.08126, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25734, "top5_acc": 0.51875, "loss_cls": 4.23991, "loss": 4.23991, "time": 0.85251} +{"mode": "train", "epoch": 43, "iter": 2900, "lr": 0.08124, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26188, "top5_acc": 0.50828, "loss_cls": 4.25778, "loss": 4.25778, "time": 0.85304} +{"mode": "train", "epoch": 43, "iter": 3000, "lr": 0.08122, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26438, "top5_acc": 0.50531, "loss_cls": 4.28243, "loss": 4.28243, "time": 0.84991} +{"mode": "train", "epoch": 43, "iter": 3100, "lr": 0.0812, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27, "top5_acc": 0.52891, "loss_cls": 4.20619, "loss": 4.20619, "time": 0.85761} +{"mode": "train", "epoch": 43, "iter": 3200, "lr": 0.08118, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27219, "top5_acc": 0.52234, "loss_cls": 4.1899, "loss": 4.1899, "time": 0.85567} +{"mode": "train", "epoch": 43, "iter": 3300, "lr": 0.08116, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26469, "top5_acc": 0.51406, "loss_cls": 4.24029, "loss": 4.24029, "time": 0.85476} +{"mode": "train", "epoch": 43, "iter": 3400, "lr": 0.08113, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26031, "top5_acc": 0.51328, "loss_cls": 4.24355, "loss": 4.24355, "time": 0.85442} +{"mode": "train", "epoch": 43, "iter": 3500, "lr": 0.08111, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26594, "top5_acc": 0.51859, "loss_cls": 4.20332, "loss": 4.20332, "time": 0.85428} +{"mode": "train", "epoch": 43, "iter": 3600, "lr": 0.08109, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26937, "top5_acc": 0.51516, "loss_cls": 4.22742, "loss": 4.22742, "time": 0.85744} +{"mode": "train", "epoch": 43, "iter": 3700, "lr": 0.08107, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.27187, "top5_acc": 0.52438, "loss_cls": 4.22435, "loss": 4.22435, "time": 0.85369} +{"mode": "val", "epoch": 43, "iter": 309, "lr": 0.08106, "top1_acc": 0.18204, "top5_acc": 0.41179, "mean_class_accuracy": 0.18192} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.08104, "memory": 15990, "data_time": 1.51926, "top1_acc": 0.27953, "top5_acc": 0.52578, "loss_cls": 4.15818, "loss": 4.15818, "time": 2.56085} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.08101, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.2725, "top5_acc": 0.52516, "loss_cls": 4.17696, "loss": 4.17696, "time": 0.85076} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.08099, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.27187, "top5_acc": 0.53422, "loss_cls": 4.16633, "loss": 4.16633, "time": 0.85028} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.08097, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26797, "top5_acc": 0.52109, "loss_cls": 4.22698, "loss": 4.22698, "time": 0.85246} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.08095, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.265, "top5_acc": 0.51891, "loss_cls": 4.216, "loss": 4.216, "time": 0.84992} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.08093, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27312, "top5_acc": 0.51734, "loss_cls": 4.23602, "loss": 4.23602, "time": 0.84927} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.0809, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27141, "top5_acc": 0.52844, "loss_cls": 4.19556, "loss": 4.19556, "time": 0.85057} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.08088, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.25797, "top5_acc": 0.51438, "loss_cls": 4.25196, "loss": 4.25196, "time": 0.85597} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.08086, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.27703, "top5_acc": 0.51844, "loss_cls": 4.22898, "loss": 4.22898, "time": 0.85089} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.08084, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.26047, "top5_acc": 0.51625, "loss_cls": 4.24582, "loss": 4.24582, "time": 0.84661} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.08082, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26844, "top5_acc": 0.5225, "loss_cls": 4.20695, "loss": 4.20695, "time": 0.85844} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.08079, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28031, "top5_acc": 0.53281, "loss_cls": 4.15413, "loss": 4.15413, "time": 0.85446} +{"mode": "train", "epoch": 44, "iter": 1300, "lr": 0.08077, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26703, "top5_acc": 0.51734, "loss_cls": 4.24426, "loss": 4.24426, "time": 0.85499} +{"mode": "train", "epoch": 44, "iter": 1400, "lr": 0.08075, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26266, "top5_acc": 0.52703, "loss_cls": 4.22119, "loss": 4.22119, "time": 0.8568} +{"mode": "train", "epoch": 44, "iter": 1500, "lr": 0.08073, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26094, "top5_acc": 0.50813, "loss_cls": 4.26265, "loss": 4.26265, "time": 0.84947} +{"mode": "train", "epoch": 44, "iter": 1600, "lr": 0.08071, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26844, "top5_acc": 0.51391, "loss_cls": 4.21371, "loss": 4.21371, "time": 0.85622} +{"mode": "train", "epoch": 44, "iter": 1700, "lr": 0.08068, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27578, "top5_acc": 0.51781, "loss_cls": 4.21669, "loss": 4.21669, "time": 0.85247} +{"mode": "train", "epoch": 44, "iter": 1800, "lr": 0.08066, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27484, "top5_acc": 0.52328, "loss_cls": 4.20111, "loss": 4.20111, "time": 0.85239} +{"mode": "train", "epoch": 44, "iter": 1900, "lr": 0.08064, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28297, "top5_acc": 0.53422, "loss_cls": 4.13982, "loss": 4.13982, "time": 0.85637} +{"mode": "train", "epoch": 44, "iter": 2000, "lr": 0.08062, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26516, "top5_acc": 0.51313, "loss_cls": 4.248, "loss": 4.248, "time": 0.85151} +{"mode": "train", "epoch": 44, "iter": 2100, "lr": 0.0806, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27219, "top5_acc": 0.52922, "loss_cls": 4.18753, "loss": 4.18753, "time": 0.85412} +{"mode": "train", "epoch": 44, "iter": 2200, "lr": 0.08057, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25375, "top5_acc": 0.50953, "loss_cls": 4.27683, "loss": 4.27683, "time": 0.85847} +{"mode": "train", "epoch": 44, "iter": 2300, "lr": 0.08055, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26859, "top5_acc": 0.51719, "loss_cls": 4.26118, "loss": 4.26118, "time": 0.85549} +{"mode": "train", "epoch": 44, "iter": 2400, "lr": 0.08053, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27812, "top5_acc": 0.52234, "loss_cls": 4.19459, "loss": 4.19459, "time": 0.85994} +{"mode": "train", "epoch": 44, "iter": 2500, "lr": 0.08051, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27625, "top5_acc": 0.52375, "loss_cls": 4.18981, "loss": 4.18981, "time": 0.85295} +{"mode": "train", "epoch": 44, "iter": 2600, "lr": 0.08048, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26344, "top5_acc": 0.51719, "loss_cls": 4.24838, "loss": 4.24838, "time": 0.84997} +{"mode": "train", "epoch": 44, "iter": 2700, "lr": 0.08046, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27141, "top5_acc": 0.51313, "loss_cls": 4.22209, "loss": 4.22209, "time": 0.8574} +{"mode": "train", "epoch": 44, "iter": 2800, "lr": 0.08044, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26687, "top5_acc": 0.51266, "loss_cls": 4.24115, "loss": 4.24115, "time": 0.85528} +{"mode": "train", "epoch": 44, "iter": 2900, "lr": 0.08042, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26766, "top5_acc": 0.52016, "loss_cls": 4.22332, "loss": 4.22332, "time": 0.85955} +{"mode": "train", "epoch": 44, "iter": 3000, "lr": 0.0804, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27078, "top5_acc": 0.52688, "loss_cls": 4.18491, "loss": 4.18491, "time": 0.85816} +{"mode": "train", "epoch": 44, "iter": 3100, "lr": 0.08037, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26453, "top5_acc": 0.51203, "loss_cls": 4.24671, "loss": 4.24671, "time": 0.85585} +{"mode": "train", "epoch": 44, "iter": 3200, "lr": 0.08035, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28188, "top5_acc": 0.52859, "loss_cls": 4.1517, "loss": 4.1517, "time": 0.8561} +{"mode": "train", "epoch": 44, "iter": 3300, "lr": 0.08033, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27047, "top5_acc": 0.51234, "loss_cls": 4.24084, "loss": 4.24084, "time": 0.8561} +{"mode": "train", "epoch": 44, "iter": 3400, "lr": 0.08031, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.26719, "top5_acc": 0.51766, "loss_cls": 4.24159, "loss": 4.24159, "time": 0.85241} +{"mode": "train", "epoch": 44, "iter": 3500, "lr": 0.08028, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.27828, "top5_acc": 0.5275, "loss_cls": 4.19419, "loss": 4.19419, "time": 0.85384} +{"mode": "train", "epoch": 44, "iter": 3600, "lr": 0.08026, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27125, "top5_acc": 0.52094, "loss_cls": 4.20401, "loss": 4.20401, "time": 0.85617} +{"mode": "train", "epoch": 44, "iter": 3700, "lr": 0.08024, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27797, "top5_acc": 0.52125, "loss_cls": 4.20332, "loss": 4.20332, "time": 0.86101} +{"mode": "val", "epoch": 44, "iter": 309, "lr": 0.08023, "top1_acc": 0.20017, "top5_acc": 0.42739, "mean_class_accuracy": 0.19994} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.08021, "memory": 15990, "data_time": 1.51902, "top1_acc": 0.27125, "top5_acc": 0.54062, "loss_cls": 4.1477, "loss": 4.1477, "time": 2.55641} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.08019, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.26937, "top5_acc": 0.51484, "loss_cls": 4.23067, "loss": 4.23067, "time": 0.85214} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.08016, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26969, "top5_acc": 0.52469, "loss_cls": 4.18607, "loss": 4.18607, "time": 0.84557} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.08014, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27156, "top5_acc": 0.51859, "loss_cls": 4.21551, "loss": 4.21551, "time": 0.8458} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.08012, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28234, "top5_acc": 0.52094, "loss_cls": 4.19895, "loss": 4.19895, "time": 0.84578} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.0801, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27, "top5_acc": 0.52047, "loss_cls": 4.19021, "loss": 4.19021, "time": 0.85094} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.08007, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26234, "top5_acc": 0.52859, "loss_cls": 4.2169, "loss": 4.2169, "time": 0.85528} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.08005, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26953, "top5_acc": 0.51812, "loss_cls": 4.2071, "loss": 4.2071, "time": 0.8503} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.08003, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.27406, "top5_acc": 0.52828, "loss_cls": 4.15686, "loss": 4.15686, "time": 0.85358} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.08001, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26844, "top5_acc": 0.51875, "loss_cls": 4.20533, "loss": 4.20533, "time": 0.84806} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.07998, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26453, "top5_acc": 0.52188, "loss_cls": 4.22663, "loss": 4.22663, "time": 0.844} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.07996, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27453, "top5_acc": 0.52047, "loss_cls": 4.19024, "loss": 4.19024, "time": 0.84677} +{"mode": "train", "epoch": 45, "iter": 1300, "lr": 0.07994, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27688, "top5_acc": 0.52922, "loss_cls": 4.17857, "loss": 4.17857, "time": 0.85463} +{"mode": "train", "epoch": 45, "iter": 1400, "lr": 0.07992, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27656, "top5_acc": 0.52609, "loss_cls": 4.1934, "loss": 4.1934, "time": 0.84957} +{"mode": "train", "epoch": 45, "iter": 1500, "lr": 0.0799, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2725, "top5_acc": 0.51781, "loss_cls": 4.2033, "loss": 4.2033, "time": 0.84661} +{"mode": "train", "epoch": 45, "iter": 1600, "lr": 0.07987, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26844, "top5_acc": 0.52094, "loss_cls": 4.20545, "loss": 4.20545, "time": 0.85036} +{"mode": "train", "epoch": 45, "iter": 1700, "lr": 0.07985, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27531, "top5_acc": 0.53094, "loss_cls": 4.17717, "loss": 4.17717, "time": 0.84728} +{"mode": "train", "epoch": 45, "iter": 1800, "lr": 0.07983, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26172, "top5_acc": 0.51719, "loss_cls": 4.23022, "loss": 4.23022, "time": 0.85198} +{"mode": "train", "epoch": 45, "iter": 1900, "lr": 0.07981, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26797, "top5_acc": 0.51594, "loss_cls": 4.22705, "loss": 4.22705, "time": 0.85219} +{"mode": "train", "epoch": 45, "iter": 2000, "lr": 0.07978, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27375, "top5_acc": 0.51234, "loss_cls": 4.24822, "loss": 4.24822, "time": 0.85264} +{"mode": "train", "epoch": 45, "iter": 2100, "lr": 0.07976, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27609, "top5_acc": 0.52156, "loss_cls": 4.21909, "loss": 4.21909, "time": 0.85743} +{"mode": "train", "epoch": 45, "iter": 2200, "lr": 0.07974, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27453, "top5_acc": 0.53109, "loss_cls": 4.19962, "loss": 4.19962, "time": 0.85073} +{"mode": "train", "epoch": 45, "iter": 2300, "lr": 0.07972, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26844, "top5_acc": 0.51812, "loss_cls": 4.22216, "loss": 4.22216, "time": 0.8552} +{"mode": "train", "epoch": 45, "iter": 2400, "lr": 0.07969, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27047, "top5_acc": 0.52406, "loss_cls": 4.20284, "loss": 4.20284, "time": 0.85611} +{"mode": "train", "epoch": 45, "iter": 2500, "lr": 0.07967, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27016, "top5_acc": 0.52719, "loss_cls": 4.18664, "loss": 4.18664, "time": 0.85481} +{"mode": "train", "epoch": 45, "iter": 2600, "lr": 0.07965, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27125, "top5_acc": 0.51359, "loss_cls": 4.2573, "loss": 4.2573, "time": 0.85854} +{"mode": "train", "epoch": 45, "iter": 2700, "lr": 0.07963, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27297, "top5_acc": 0.52766, "loss_cls": 4.19844, "loss": 4.19844, "time": 0.85201} +{"mode": "train", "epoch": 45, "iter": 2800, "lr": 0.0796, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27609, "top5_acc": 0.51703, "loss_cls": 4.24302, "loss": 4.24302, "time": 0.85656} +{"mode": "train", "epoch": 45, "iter": 2900, "lr": 0.07958, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27047, "top5_acc": 0.52094, "loss_cls": 4.20027, "loss": 4.20027, "time": 0.85439} +{"mode": "train", "epoch": 45, "iter": 3000, "lr": 0.07956, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28109, "top5_acc": 0.52953, "loss_cls": 4.1605, "loss": 4.1605, "time": 0.85479} +{"mode": "train", "epoch": 45, "iter": 3100, "lr": 0.07954, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27359, "top5_acc": 0.53047, "loss_cls": 4.17135, "loss": 4.17135, "time": 0.85262} +{"mode": "train", "epoch": 45, "iter": 3200, "lr": 0.07951, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27156, "top5_acc": 0.52609, "loss_cls": 4.18921, "loss": 4.18921, "time": 0.8538} +{"mode": "train", "epoch": 45, "iter": 3300, "lr": 0.07949, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27047, "top5_acc": 0.51125, "loss_cls": 4.23849, "loss": 4.23849, "time": 0.8534} +{"mode": "train", "epoch": 45, "iter": 3400, "lr": 0.07947, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.27828, "top5_acc": 0.52484, "loss_cls": 4.17334, "loss": 4.17334, "time": 0.84771} +{"mode": "train", "epoch": 45, "iter": 3500, "lr": 0.07945, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28047, "top5_acc": 0.51484, "loss_cls": 4.19536, "loss": 4.19536, "time": 0.84608} +{"mode": "train", "epoch": 45, "iter": 3600, "lr": 0.07942, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27094, "top5_acc": 0.52016, "loss_cls": 4.23513, "loss": 4.23513, "time": 0.84398} +{"mode": "train", "epoch": 45, "iter": 3700, "lr": 0.0794, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27125, "top5_acc": 0.51438, "loss_cls": 4.26914, "loss": 4.26914, "time": 0.85296} +{"mode": "val", "epoch": 45, "iter": 309, "lr": 0.07939, "top1_acc": 0.2098, "top5_acc": 0.44188, "mean_class_accuracy": 0.20951} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.07937, "memory": 15990, "data_time": 1.51849, "top1_acc": 0.27234, "top5_acc": 0.52375, "loss_cls": 4.16567, "loss": 4.16567, "time": 2.55981} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.07934, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.26828, "top5_acc": 0.52281, "loss_cls": 4.19006, "loss": 4.19006, "time": 0.85332} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.07932, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.27453, "top5_acc": 0.53, "loss_cls": 4.1866, "loss": 4.1866, "time": 0.85244} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.0793, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27281, "top5_acc": 0.52516, "loss_cls": 4.18788, "loss": 4.18788, "time": 0.85218} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.07928, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26578, "top5_acc": 0.52047, "loss_cls": 4.21013, "loss": 4.21013, "time": 0.85597} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.07925, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26969, "top5_acc": 0.51781, "loss_cls": 4.19515, "loss": 4.19515, "time": 0.85505} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.07923, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27891, "top5_acc": 0.52547, "loss_cls": 4.16471, "loss": 4.16471, "time": 0.85485} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.07921, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25969, "top5_acc": 0.50906, "loss_cls": 4.26392, "loss": 4.26392, "time": 0.85914} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.07919, "memory": 15990, "data_time": 0.0008, "top1_acc": 0.27047, "top5_acc": 0.52016, "loss_cls": 4.20889, "loss": 4.20889, "time": 0.85483} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.07916, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26172, "top5_acc": 0.51672, "loss_cls": 4.25897, "loss": 4.25897, "time": 0.8487} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.07914, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27344, "top5_acc": 0.52625, "loss_cls": 4.18, "loss": 4.18, "time": 0.84689} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.07912, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2725, "top5_acc": 0.52578, "loss_cls": 4.20412, "loss": 4.20412, "time": 0.85379} +{"mode": "train", "epoch": 46, "iter": 1300, "lr": 0.07909, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26344, "top5_acc": 0.51438, "loss_cls": 4.23116, "loss": 4.23116, "time": 0.85287} +{"mode": "train", "epoch": 46, "iter": 1400, "lr": 0.07907, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27656, "top5_acc": 0.53234, "loss_cls": 4.17602, "loss": 4.17602, "time": 0.85432} +{"mode": "train", "epoch": 46, "iter": 1500, "lr": 0.07905, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27047, "top5_acc": 0.51875, "loss_cls": 4.21807, "loss": 4.21807, "time": 0.85552} +{"mode": "train", "epoch": 46, "iter": 1600, "lr": 0.07903, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27875, "top5_acc": 0.52562, "loss_cls": 4.15619, "loss": 4.15619, "time": 0.85478} +{"mode": "train", "epoch": 46, "iter": 1700, "lr": 0.079, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26969, "top5_acc": 0.53047, "loss_cls": 4.20871, "loss": 4.20871, "time": 0.85472} +{"mode": "train", "epoch": 46, "iter": 1800, "lr": 0.07898, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26844, "top5_acc": 0.52453, "loss_cls": 4.19761, "loss": 4.19761, "time": 0.85175} +{"mode": "train", "epoch": 46, "iter": 1900, "lr": 0.07896, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26875, "top5_acc": 0.52422, "loss_cls": 4.20911, "loss": 4.20911, "time": 0.84692} +{"mode": "train", "epoch": 46, "iter": 2000, "lr": 0.07894, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27516, "top5_acc": 0.52266, "loss_cls": 4.19297, "loss": 4.19297, "time": 0.85129} +{"mode": "train", "epoch": 46, "iter": 2100, "lr": 0.07891, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27109, "top5_acc": 0.52094, "loss_cls": 4.20332, "loss": 4.20332, "time": 0.85358} +{"mode": "train", "epoch": 46, "iter": 2200, "lr": 0.07889, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27234, "top5_acc": 0.52422, "loss_cls": 4.19321, "loss": 4.19321, "time": 0.85105} +{"mode": "train", "epoch": 46, "iter": 2300, "lr": 0.07887, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27141, "top5_acc": 0.53531, "loss_cls": 4.15474, "loss": 4.15474, "time": 0.85044} +{"mode": "train", "epoch": 46, "iter": 2400, "lr": 0.07884, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26516, "top5_acc": 0.52438, "loss_cls": 4.20266, "loss": 4.20266, "time": 0.85477} +{"mode": "train", "epoch": 46, "iter": 2500, "lr": 0.07882, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27187, "top5_acc": 0.52281, "loss_cls": 4.21311, "loss": 4.21311, "time": 0.85404} +{"mode": "train", "epoch": 46, "iter": 2600, "lr": 0.0788, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27547, "top5_acc": 0.52547, "loss_cls": 4.17749, "loss": 4.17749, "time": 0.85531} +{"mode": "train", "epoch": 46, "iter": 2700, "lr": 0.07878, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27359, "top5_acc": 0.52031, "loss_cls": 4.24175, "loss": 4.24175, "time": 0.85513} +{"mode": "train", "epoch": 46, "iter": 2800, "lr": 0.07875, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27656, "top5_acc": 0.52703, "loss_cls": 4.16851, "loss": 4.16851, "time": 0.85593} +{"mode": "train", "epoch": 46, "iter": 2900, "lr": 0.07873, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.27266, "top5_acc": 0.53062, "loss_cls": 4.17749, "loss": 4.17749, "time": 0.85725} +{"mode": "train", "epoch": 46, "iter": 3000, "lr": 0.07871, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27609, "top5_acc": 0.51422, "loss_cls": 4.21467, "loss": 4.21467, "time": 0.85916} +{"mode": "train", "epoch": 46, "iter": 3100, "lr": 0.07868, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28094, "top5_acc": 0.52688, "loss_cls": 4.17122, "loss": 4.17122, "time": 0.85189} +{"mode": "train", "epoch": 46, "iter": 3200, "lr": 0.07866, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27781, "top5_acc": 0.52672, "loss_cls": 4.18359, "loss": 4.18359, "time": 0.85339} +{"mode": "train", "epoch": 46, "iter": 3300, "lr": 0.07864, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27734, "top5_acc": 0.52562, "loss_cls": 4.19528, "loss": 4.19528, "time": 0.85599} +{"mode": "train", "epoch": 46, "iter": 3400, "lr": 0.07862, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26578, "top5_acc": 0.52844, "loss_cls": 4.19429, "loss": 4.19429, "time": 0.84663} +{"mode": "train", "epoch": 46, "iter": 3500, "lr": 0.07859, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27672, "top5_acc": 0.52328, "loss_cls": 4.19696, "loss": 4.19696, "time": 0.84747} +{"mode": "train", "epoch": 46, "iter": 3600, "lr": 0.07857, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.275, "top5_acc": 0.52406, "loss_cls": 4.20248, "loss": 4.20248, "time": 0.85361} +{"mode": "train", "epoch": 46, "iter": 3700, "lr": 0.07855, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27422, "top5_acc": 0.52281, "loss_cls": 4.19917, "loss": 4.19917, "time": 0.85606} +{"mode": "val", "epoch": 46, "iter": 309, "lr": 0.07854, "top1_acc": 0.20883, "top5_acc": 0.44036, "mean_class_accuracy": 0.20857} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.07851, "memory": 15990, "data_time": 1.511, "top1_acc": 0.275, "top5_acc": 0.52281, "loss_cls": 4.18879, "loss": 4.18879, "time": 2.55236} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.07849, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27172, "top5_acc": 0.52609, "loss_cls": 4.16572, "loss": 4.16572, "time": 0.84762} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.07847, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28078, "top5_acc": 0.53141, "loss_cls": 4.15287, "loss": 4.15287, "time": 0.84945} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.07844, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27234, "top5_acc": 0.52875, "loss_cls": 4.16878, "loss": 4.16878, "time": 0.84925} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.07842, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.275, "top5_acc": 0.52688, "loss_cls": 4.18666, "loss": 4.18666, "time": 0.84801} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.0784, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26391, "top5_acc": 0.51406, "loss_cls": 4.2485, "loss": 4.2485, "time": 0.85012} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.07838, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28438, "top5_acc": 0.52812, "loss_cls": 4.18026, "loss": 4.18026, "time": 0.85031} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.07835, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28188, "top5_acc": 0.52828, "loss_cls": 4.19218, "loss": 4.19218, "time": 0.84768} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.07833, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28516, "top5_acc": 0.5275, "loss_cls": 4.16559, "loss": 4.16559, "time": 0.84958} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.07831, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27641, "top5_acc": 0.53766, "loss_cls": 4.15703, "loss": 4.15703, "time": 0.85031} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.07828, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27219, "top5_acc": 0.52484, "loss_cls": 4.19413, "loss": 4.19413, "time": 0.84413} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.07826, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.27906, "top5_acc": 0.52344, "loss_cls": 4.14593, "loss": 4.14593, "time": 0.84577} +{"mode": "train", "epoch": 47, "iter": 1300, "lr": 0.07824, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27203, "top5_acc": 0.5225, "loss_cls": 4.20214, "loss": 4.20214, "time": 0.84809} +{"mode": "train", "epoch": 47, "iter": 1400, "lr": 0.07821, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27344, "top5_acc": 0.51391, "loss_cls": 4.21197, "loss": 4.21197, "time": 0.84592} +{"mode": "train", "epoch": 47, "iter": 1500, "lr": 0.07819, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27297, "top5_acc": 0.53516, "loss_cls": 4.17154, "loss": 4.17154, "time": 0.84651} +{"mode": "train", "epoch": 47, "iter": 1600, "lr": 0.07817, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2675, "top5_acc": 0.51812, "loss_cls": 4.22031, "loss": 4.22031, "time": 0.84415} +{"mode": "train", "epoch": 47, "iter": 1700, "lr": 0.07814, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27422, "top5_acc": 0.52094, "loss_cls": 4.18838, "loss": 4.18838, "time": 0.84725} +{"mode": "train", "epoch": 47, "iter": 1800, "lr": 0.07812, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26906, "top5_acc": 0.52109, "loss_cls": 4.22184, "loss": 4.22184, "time": 0.84566} +{"mode": "train", "epoch": 47, "iter": 1900, "lr": 0.0781, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26609, "top5_acc": 0.51562, "loss_cls": 4.24353, "loss": 4.24353, "time": 0.84789} +{"mode": "train", "epoch": 47, "iter": 2000, "lr": 0.07808, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27437, "top5_acc": 0.51984, "loss_cls": 4.22807, "loss": 4.22807, "time": 0.848} +{"mode": "train", "epoch": 47, "iter": 2100, "lr": 0.07805, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27828, "top5_acc": 0.52422, "loss_cls": 4.18226, "loss": 4.18226, "time": 0.85339} +{"mode": "train", "epoch": 47, "iter": 2200, "lr": 0.07803, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27703, "top5_acc": 0.52984, "loss_cls": 4.18496, "loss": 4.18496, "time": 0.85071} +{"mode": "train", "epoch": 47, "iter": 2300, "lr": 0.07801, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28172, "top5_acc": 0.53453, "loss_cls": 4.13919, "loss": 4.13919, "time": 0.85293} +{"mode": "train", "epoch": 47, "iter": 2400, "lr": 0.07798, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26875, "top5_acc": 0.52406, "loss_cls": 4.21511, "loss": 4.21511, "time": 0.85369} +{"mode": "train", "epoch": 47, "iter": 2500, "lr": 0.07796, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27422, "top5_acc": 0.52312, "loss_cls": 4.18029, "loss": 4.18029, "time": 0.85295} +{"mode": "train", "epoch": 47, "iter": 2600, "lr": 0.07794, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26156, "top5_acc": 0.52297, "loss_cls": 4.23551, "loss": 4.23551, "time": 0.85053} +{"mode": "train", "epoch": 47, "iter": 2700, "lr": 0.07791, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26891, "top5_acc": 0.5175, "loss_cls": 4.22878, "loss": 4.22878, "time": 0.85081} +{"mode": "train", "epoch": 47, "iter": 2800, "lr": 0.07789, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26719, "top5_acc": 0.52203, "loss_cls": 4.20711, "loss": 4.20711, "time": 0.85278} +{"mode": "train", "epoch": 47, "iter": 2900, "lr": 0.07787, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28172, "top5_acc": 0.53, "loss_cls": 4.15528, "loss": 4.15528, "time": 0.84836} +{"mode": "train", "epoch": 47, "iter": 3000, "lr": 0.07784, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26344, "top5_acc": 0.52234, "loss_cls": 4.22789, "loss": 4.22789, "time": 0.85116} +{"mode": "train", "epoch": 47, "iter": 3100, "lr": 0.07782, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.27906, "top5_acc": 0.53328, "loss_cls": 4.14646, "loss": 4.14646, "time": 0.8481} +{"mode": "train", "epoch": 47, "iter": 3200, "lr": 0.0778, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26703, "top5_acc": 0.52344, "loss_cls": 4.21251, "loss": 4.21251, "time": 0.85417} +{"mode": "train", "epoch": 47, "iter": 3300, "lr": 0.07777, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28125, "top5_acc": 0.52797, "loss_cls": 4.17799, "loss": 4.17799, "time": 0.85171} +{"mode": "train", "epoch": 47, "iter": 3400, "lr": 0.07775, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26047, "top5_acc": 0.52453, "loss_cls": 4.24085, "loss": 4.24085, "time": 0.84902} +{"mode": "train", "epoch": 47, "iter": 3500, "lr": 0.07773, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.27406, "top5_acc": 0.52328, "loss_cls": 4.1697, "loss": 4.1697, "time": 0.84851} +{"mode": "train", "epoch": 47, "iter": 3600, "lr": 0.0777, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27344, "top5_acc": 0.52656, "loss_cls": 4.16633, "loss": 4.16633, "time": 0.8496} +{"mode": "train", "epoch": 47, "iter": 3700, "lr": 0.07768, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26531, "top5_acc": 0.51547, "loss_cls": 4.22295, "loss": 4.22295, "time": 0.85005} +{"mode": "val", "epoch": 47, "iter": 309, "lr": 0.07767, "top1_acc": 0.20139, "top5_acc": 0.43408, "mean_class_accuracy": 0.20105} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.07765, "memory": 15990, "data_time": 1.54678, "top1_acc": 0.27766, "top5_acc": 0.53484, "loss_cls": 4.14773, "loss": 4.14773, "time": 2.58465} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.07762, "memory": 15990, "data_time": 0.0007, "top1_acc": 0.28781, "top5_acc": 0.52859, "loss_cls": 4.11657, "loss": 4.11657, "time": 0.85688} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.0776, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.27875, "top5_acc": 0.53031, "loss_cls": 4.14346, "loss": 4.14346, "time": 0.84807} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.07758, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28906, "top5_acc": 0.53938, "loss_cls": 4.11689, "loss": 4.11689, "time": 0.85255} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.07755, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28969, "top5_acc": 0.53516, "loss_cls": 4.13551, "loss": 4.13551, "time": 0.8595} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.07753, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27078, "top5_acc": 0.52, "loss_cls": 4.18191, "loss": 4.18191, "time": 0.8553} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.07751, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26734, "top5_acc": 0.51656, "loss_cls": 4.23735, "loss": 4.23735, "time": 0.85487} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.07748, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27734, "top5_acc": 0.53109, "loss_cls": 4.14093, "loss": 4.14093, "time": 0.85708} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.07746, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27391, "top5_acc": 0.52641, "loss_cls": 4.16093, "loss": 4.16093, "time": 0.85788} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.07744, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.27156, "top5_acc": 0.52016, "loss_cls": 4.19134, "loss": 4.19134, "time": 0.85277} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.07741, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27531, "top5_acc": 0.5275, "loss_cls": 4.18236, "loss": 4.18236, "time": 0.84458} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.07739, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27422, "top5_acc": 0.51984, "loss_cls": 4.1895, "loss": 4.1895, "time": 0.84749} +{"mode": "train", "epoch": 48, "iter": 1300, "lr": 0.07737, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27578, "top5_acc": 0.52703, "loss_cls": 4.17708, "loss": 4.17708, "time": 0.84537} +{"mode": "train", "epoch": 48, "iter": 1400, "lr": 0.07734, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27078, "top5_acc": 0.52875, "loss_cls": 4.186, "loss": 4.186, "time": 0.84671} +{"mode": "train", "epoch": 48, "iter": 1500, "lr": 0.07732, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27, "top5_acc": 0.52453, "loss_cls": 4.19634, "loss": 4.19634, "time": 0.84567} +{"mode": "train", "epoch": 48, "iter": 1600, "lr": 0.0773, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27031, "top5_acc": 0.52438, "loss_cls": 4.20951, "loss": 4.20951, "time": 0.84058} +{"mode": "train", "epoch": 48, "iter": 1700, "lr": 0.07727, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27312, "top5_acc": 0.52797, "loss_cls": 4.18654, "loss": 4.18654, "time": 0.84999} +{"mode": "train", "epoch": 48, "iter": 1800, "lr": 0.07725, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27703, "top5_acc": 0.52359, "loss_cls": 4.19723, "loss": 4.19723, "time": 0.84846} +{"mode": "train", "epoch": 48, "iter": 1900, "lr": 0.07723, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26734, "top5_acc": 0.51266, "loss_cls": 4.25408, "loss": 4.25408, "time": 0.85054} +{"mode": "train", "epoch": 48, "iter": 2000, "lr": 0.0772, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.285, "top5_acc": 0.51906, "loss_cls": 4.18534, "loss": 4.18534, "time": 0.84664} +{"mode": "train", "epoch": 48, "iter": 2100, "lr": 0.07718, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27359, "top5_acc": 0.52031, "loss_cls": 4.20056, "loss": 4.20056, "time": 0.84961} +{"mode": "train", "epoch": 48, "iter": 2200, "lr": 0.07716, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2675, "top5_acc": 0.51969, "loss_cls": 4.25686, "loss": 4.25686, "time": 0.8466} +{"mode": "train", "epoch": 48, "iter": 2300, "lr": 0.07713, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26875, "top5_acc": 0.52656, "loss_cls": 4.20926, "loss": 4.20926, "time": 0.84834} +{"mode": "train", "epoch": 48, "iter": 2400, "lr": 0.07711, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27172, "top5_acc": 0.52328, "loss_cls": 4.18729, "loss": 4.18729, "time": 0.85227} +{"mode": "train", "epoch": 48, "iter": 2500, "lr": 0.07709, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27266, "top5_acc": 0.52203, "loss_cls": 4.19321, "loss": 4.19321, "time": 0.85453} +{"mode": "train", "epoch": 48, "iter": 2600, "lr": 0.07706, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28484, "top5_acc": 0.53562, "loss_cls": 4.14312, "loss": 4.14312, "time": 0.8561} +{"mode": "train", "epoch": 48, "iter": 2700, "lr": 0.07704, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27391, "top5_acc": 0.52578, "loss_cls": 4.18969, "loss": 4.18969, "time": 0.85296} +{"mode": "train", "epoch": 48, "iter": 2800, "lr": 0.07701, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27906, "top5_acc": 0.5325, "loss_cls": 4.18534, "loss": 4.18534, "time": 0.85395} +{"mode": "train", "epoch": 48, "iter": 2900, "lr": 0.07699, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27437, "top5_acc": 0.52422, "loss_cls": 4.19609, "loss": 4.19609, "time": 0.85933} +{"mode": "train", "epoch": 48, "iter": 3000, "lr": 0.07697, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.275, "top5_acc": 0.535, "loss_cls": 4.14038, "loss": 4.14038, "time": 0.8573} +{"mode": "train", "epoch": 48, "iter": 3100, "lr": 0.07694, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26937, "top5_acc": 0.51875, "loss_cls": 4.20852, "loss": 4.20852, "time": 0.85638} +{"mode": "train", "epoch": 48, "iter": 3200, "lr": 0.07692, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26953, "top5_acc": 0.53047, "loss_cls": 4.20068, "loss": 4.20068, "time": 0.85578} +{"mode": "train", "epoch": 48, "iter": 3300, "lr": 0.0769, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28719, "top5_acc": 0.53859, "loss_cls": 4.11548, "loss": 4.11548, "time": 0.8526} +{"mode": "train", "epoch": 48, "iter": 3400, "lr": 0.07687, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27922, "top5_acc": 0.52844, "loss_cls": 4.16191, "loss": 4.16191, "time": 0.85566} +{"mode": "train", "epoch": 48, "iter": 3500, "lr": 0.07685, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27109, "top5_acc": 0.52328, "loss_cls": 4.19538, "loss": 4.19538, "time": 0.85267} +{"mode": "train", "epoch": 48, "iter": 3600, "lr": 0.07683, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26719, "top5_acc": 0.51547, "loss_cls": 4.22671, "loss": 4.22671, "time": 0.85758} +{"mode": "train", "epoch": 48, "iter": 3700, "lr": 0.0768, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26578, "top5_acc": 0.52078, "loss_cls": 4.23486, "loss": 4.23486, "time": 0.85409} +{"mode": "val", "epoch": 48, "iter": 309, "lr": 0.07679, "top1_acc": 0.20807, "top5_acc": 0.43843, "mean_class_accuracy": 0.208} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.07677, "memory": 15990, "data_time": 1.55224, "top1_acc": 0.26547, "top5_acc": 0.52594, "loss_cls": 4.19565, "loss": 4.19565, "time": 2.6039} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.07674, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.27547, "top5_acc": 0.53812, "loss_cls": 4.13744, "loss": 4.13744, "time": 0.85576} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.07672, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27984, "top5_acc": 0.52359, "loss_cls": 4.17655, "loss": 4.17655, "time": 0.84723} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.0767, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27578, "top5_acc": 0.52969, "loss_cls": 4.15075, "loss": 4.15075, "time": 0.85304} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.07667, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27484, "top5_acc": 0.52641, "loss_cls": 4.16667, "loss": 4.16667, "time": 0.85617} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.07665, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28312, "top5_acc": 0.54031, "loss_cls": 4.13716, "loss": 4.13716, "time": 0.85673} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.07663, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27484, "top5_acc": 0.52672, "loss_cls": 4.17404, "loss": 4.17404, "time": 0.85648} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.0766, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27969, "top5_acc": 0.53438, "loss_cls": 4.15234, "loss": 4.15234, "time": 0.85556} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.07658, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27672, "top5_acc": 0.52828, "loss_cls": 4.17095, "loss": 4.17095, "time": 0.85266} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.07656, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26484, "top5_acc": 0.52891, "loss_cls": 4.18804, "loss": 4.18804, "time": 0.8507} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.07653, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27891, "top5_acc": 0.52922, "loss_cls": 4.18127, "loss": 4.18127, "time": 0.84743} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.07651, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27328, "top5_acc": 0.52344, "loss_cls": 4.18322, "loss": 4.18322, "time": 0.84544} +{"mode": "train", "epoch": 49, "iter": 1300, "lr": 0.07648, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27562, "top5_acc": 0.52672, "loss_cls": 4.16686, "loss": 4.16686, "time": 0.84795} +{"mode": "train", "epoch": 49, "iter": 1400, "lr": 0.07646, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27219, "top5_acc": 0.52719, "loss_cls": 4.17221, "loss": 4.17221, "time": 0.84759} +{"mode": "train", "epoch": 49, "iter": 1500, "lr": 0.07644, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26734, "top5_acc": 0.5175, "loss_cls": 4.25904, "loss": 4.25904, "time": 0.84819} +{"mode": "train", "epoch": 49, "iter": 1600, "lr": 0.07641, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28359, "top5_acc": 0.53422, "loss_cls": 4.12401, "loss": 4.12401, "time": 0.84614} +{"mode": "train", "epoch": 49, "iter": 1700, "lr": 0.07639, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28188, "top5_acc": 0.53359, "loss_cls": 4.1435, "loss": 4.1435, "time": 0.84694} +{"mode": "train", "epoch": 49, "iter": 1800, "lr": 0.07637, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27297, "top5_acc": 0.52406, "loss_cls": 4.19122, "loss": 4.19122, "time": 0.84035} +{"mode": "train", "epoch": 49, "iter": 1900, "lr": 0.07634, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27344, "top5_acc": 0.51906, "loss_cls": 4.19803, "loss": 4.19803, "time": 0.84882} +{"mode": "train", "epoch": 49, "iter": 2000, "lr": 0.07632, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26844, "top5_acc": 0.51891, "loss_cls": 4.24097, "loss": 4.24097, "time": 0.85157} +{"mode": "train", "epoch": 49, "iter": 2100, "lr": 0.07629, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29125, "top5_acc": 0.53625, "loss_cls": 4.11712, "loss": 4.11712, "time": 0.85621} +{"mode": "train", "epoch": 49, "iter": 2200, "lr": 0.07627, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27422, "top5_acc": 0.52969, "loss_cls": 4.15358, "loss": 4.15358, "time": 0.85715} +{"mode": "train", "epoch": 49, "iter": 2300, "lr": 0.07625, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27172, "top5_acc": 0.52406, "loss_cls": 4.2198, "loss": 4.2198, "time": 0.85723} +{"mode": "train", "epoch": 49, "iter": 2400, "lr": 0.07622, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27125, "top5_acc": 0.51766, "loss_cls": 4.24387, "loss": 4.24387, "time": 0.85235} +{"mode": "train", "epoch": 49, "iter": 2500, "lr": 0.0762, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26594, "top5_acc": 0.52156, "loss_cls": 4.20106, "loss": 4.20106, "time": 0.85216} +{"mode": "train", "epoch": 49, "iter": 2600, "lr": 0.07618, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27281, "top5_acc": 0.51922, "loss_cls": 4.22908, "loss": 4.22908, "time": 0.85177} +{"mode": "train", "epoch": 49, "iter": 2700, "lr": 0.07615, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28141, "top5_acc": 0.52547, "loss_cls": 4.14068, "loss": 4.14068, "time": 0.8515} +{"mode": "train", "epoch": 49, "iter": 2800, "lr": 0.07613, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28422, "top5_acc": 0.52734, "loss_cls": 4.14334, "loss": 4.14334, "time": 0.85633} +{"mode": "train", "epoch": 49, "iter": 2900, "lr": 0.0761, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27719, "top5_acc": 0.52797, "loss_cls": 4.15561, "loss": 4.15561, "time": 0.84966} +{"mode": "train", "epoch": 49, "iter": 3000, "lr": 0.07608, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27219, "top5_acc": 0.51516, "loss_cls": 4.19175, "loss": 4.19175, "time": 0.85632} +{"mode": "train", "epoch": 49, "iter": 3100, "lr": 0.07606, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28203, "top5_acc": 0.52344, "loss_cls": 4.16031, "loss": 4.16031, "time": 0.85537} +{"mode": "train", "epoch": 49, "iter": 3200, "lr": 0.07603, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27297, "top5_acc": 0.51672, "loss_cls": 4.23159, "loss": 4.23159, "time": 0.85562} +{"mode": "train", "epoch": 49, "iter": 3300, "lr": 0.07601, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.27141, "top5_acc": 0.51641, "loss_cls": 4.18774, "loss": 4.18774, "time": 0.84996} +{"mode": "train", "epoch": 49, "iter": 3400, "lr": 0.07598, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26734, "top5_acc": 0.51484, "loss_cls": 4.21758, "loss": 4.21758, "time": 0.8446} +{"mode": "train", "epoch": 49, "iter": 3500, "lr": 0.07596, "memory": 15990, "data_time": 0.00067, "top1_acc": 0.27688, "top5_acc": 0.52891, "loss_cls": 4.16723, "loss": 4.16723, "time": 0.84865} +{"mode": "train", "epoch": 49, "iter": 3600, "lr": 0.07594, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27359, "top5_acc": 0.52766, "loss_cls": 4.18396, "loss": 4.18396, "time": 0.85634} +{"mode": "train", "epoch": 49, "iter": 3700, "lr": 0.07591, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27422, "top5_acc": 0.52594, "loss_cls": 4.16266, "loss": 4.16266, "time": 0.85334} +{"mode": "val", "epoch": 49, "iter": 309, "lr": 0.0759, "top1_acc": 0.19323, "top5_acc": 0.42942, "mean_class_accuracy": 0.19323} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.07588, "memory": 15990, "data_time": 1.48936, "top1_acc": 0.285, "top5_acc": 0.54531, "loss_cls": 4.10906, "loss": 4.10906, "time": 2.53287} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.07585, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.27281, "top5_acc": 0.52328, "loss_cls": 4.16157, "loss": 4.16157, "time": 0.85902} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.07583, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28875, "top5_acc": 0.53562, "loss_cls": 4.12194, "loss": 4.12194, "time": 0.85344} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.07581, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27625, "top5_acc": 0.53031, "loss_cls": 4.14839, "loss": 4.14839, "time": 0.84502} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.07578, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27531, "top5_acc": 0.53484, "loss_cls": 4.12697, "loss": 4.12697, "time": 0.84737} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.07576, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25734, "top5_acc": 0.52203, "loss_cls": 4.23079, "loss": 4.23079, "time": 0.84884} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.07573, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27891, "top5_acc": 0.53172, "loss_cls": 4.1413, "loss": 4.1413, "time": 0.84677} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.07571, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27703, "top5_acc": 0.53312, "loss_cls": 4.16458, "loss": 4.16458, "time": 0.84793} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.07569, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26781, "top5_acc": 0.52672, "loss_cls": 4.19192, "loss": 4.19192, "time": 0.84946} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.07566, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27297, "top5_acc": 0.53062, "loss_cls": 4.16536, "loss": 4.16536, "time": 0.85088} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.07564, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26469, "top5_acc": 0.52891, "loss_cls": 4.20255, "loss": 4.20255, "time": 0.84695} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.07561, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27891, "top5_acc": 0.52797, "loss_cls": 4.16217, "loss": 4.16217, "time": 0.84737} +{"mode": "train", "epoch": 50, "iter": 1300, "lr": 0.07559, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.27016, "top5_acc": 0.52109, "loss_cls": 4.18252, "loss": 4.18252, "time": 0.84836} +{"mode": "train", "epoch": 50, "iter": 1400, "lr": 0.07557, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27938, "top5_acc": 0.52875, "loss_cls": 4.15089, "loss": 4.15089, "time": 0.84658} +{"mode": "train", "epoch": 50, "iter": 1500, "lr": 0.07554, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28016, "top5_acc": 0.52547, "loss_cls": 4.17213, "loss": 4.17213, "time": 0.85013} +{"mode": "train", "epoch": 50, "iter": 1600, "lr": 0.07552, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26641, "top5_acc": 0.52453, "loss_cls": 4.22561, "loss": 4.22561, "time": 0.84997} +{"mode": "train", "epoch": 50, "iter": 1700, "lr": 0.07549, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27391, "top5_acc": 0.52078, "loss_cls": 4.15693, "loss": 4.15693, "time": 0.85177} +{"mode": "train", "epoch": 50, "iter": 1800, "lr": 0.07547, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27969, "top5_acc": 0.52594, "loss_cls": 4.17298, "loss": 4.17298, "time": 0.84785} +{"mode": "train", "epoch": 50, "iter": 1900, "lr": 0.07545, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28203, "top5_acc": 0.52875, "loss_cls": 4.17326, "loss": 4.17326, "time": 0.84237} +{"mode": "train", "epoch": 50, "iter": 2000, "lr": 0.07542, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27547, "top5_acc": 0.51969, "loss_cls": 4.20215, "loss": 4.20215, "time": 0.84651} +{"mode": "train", "epoch": 50, "iter": 2100, "lr": 0.0754, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27984, "top5_acc": 0.52344, "loss_cls": 4.14861, "loss": 4.14861, "time": 0.84516} +{"mode": "train", "epoch": 50, "iter": 2200, "lr": 0.07537, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27344, "top5_acc": 0.52641, "loss_cls": 4.19066, "loss": 4.19066, "time": 0.84767} +{"mode": "train", "epoch": 50, "iter": 2300, "lr": 0.07535, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27406, "top5_acc": 0.525, "loss_cls": 4.17863, "loss": 4.17863, "time": 0.8478} +{"mode": "train", "epoch": 50, "iter": 2400, "lr": 0.07533, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27047, "top5_acc": 0.52453, "loss_cls": 4.20009, "loss": 4.20009, "time": 0.84668} +{"mode": "train", "epoch": 50, "iter": 2500, "lr": 0.0753, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26031, "top5_acc": 0.51875, "loss_cls": 4.23491, "loss": 4.23491, "time": 0.84569} +{"mode": "train", "epoch": 50, "iter": 2600, "lr": 0.07528, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28516, "top5_acc": 0.53094, "loss_cls": 4.14177, "loss": 4.14177, "time": 0.84764} +{"mode": "train", "epoch": 50, "iter": 2700, "lr": 0.07525, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27219, "top5_acc": 0.52922, "loss_cls": 4.18569, "loss": 4.18569, "time": 0.84983} +{"mode": "train", "epoch": 50, "iter": 2800, "lr": 0.07523, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28094, "top5_acc": 0.53859, "loss_cls": 4.13119, "loss": 4.13119, "time": 0.84937} +{"mode": "train", "epoch": 50, "iter": 2900, "lr": 0.0752, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26594, "top5_acc": 0.51906, "loss_cls": 4.22243, "loss": 4.22243, "time": 0.84893} +{"mode": "train", "epoch": 50, "iter": 3000, "lr": 0.07518, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28031, "top5_acc": 0.52703, "loss_cls": 4.1615, "loss": 4.1615, "time": 0.84977} +{"mode": "train", "epoch": 50, "iter": 3100, "lr": 0.07516, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28531, "top5_acc": 0.5375, "loss_cls": 4.1276, "loss": 4.1276, "time": 0.84727} +{"mode": "train", "epoch": 50, "iter": 3200, "lr": 0.07513, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28641, "top5_acc": 0.53438, "loss_cls": 4.12917, "loss": 4.12917, "time": 0.85073} +{"mode": "train", "epoch": 50, "iter": 3300, "lr": 0.07511, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26891, "top5_acc": 0.52438, "loss_cls": 4.2031, "loss": 4.2031, "time": 0.85259} +{"mode": "train", "epoch": 50, "iter": 3400, "lr": 0.07508, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2875, "top5_acc": 0.53219, "loss_cls": 4.13281, "loss": 4.13281, "time": 0.85213} +{"mode": "train", "epoch": 50, "iter": 3500, "lr": 0.07506, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27578, "top5_acc": 0.52141, "loss_cls": 4.19055, "loss": 4.19055, "time": 0.84271} +{"mode": "train", "epoch": 50, "iter": 3600, "lr": 0.07504, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28062, "top5_acc": 0.535, "loss_cls": 4.12025, "loss": 4.12025, "time": 0.84801} +{"mode": "train", "epoch": 50, "iter": 3700, "lr": 0.07501, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26953, "top5_acc": 0.51828, "loss_cls": 4.2287, "loss": 4.2287, "time": 0.84692} +{"mode": "val", "epoch": 50, "iter": 309, "lr": 0.075, "top1_acc": 0.2216, "top5_acc": 0.45495, "mean_class_accuracy": 0.22149} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.07498, "memory": 15990, "data_time": 1.50889, "top1_acc": 0.28625, "top5_acc": 0.53922, "loss_cls": 4.11621, "loss": 4.11621, "time": 2.53651} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.07495, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27891, "top5_acc": 0.53078, "loss_cls": 4.15558, "loss": 4.15558, "time": 0.85532} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.07493, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27953, "top5_acc": 0.52859, "loss_cls": 4.1346, "loss": 4.1346, "time": 0.85511} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.0749, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27578, "top5_acc": 0.53234, "loss_cls": 4.13865, "loss": 4.13865, "time": 0.84684} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.07488, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.27844, "top5_acc": 0.52781, "loss_cls": 4.16825, "loss": 4.16825, "time": 0.85027} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.07485, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28422, "top5_acc": 0.53359, "loss_cls": 4.11678, "loss": 4.11678, "time": 0.84355} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.07483, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27328, "top5_acc": 0.52078, "loss_cls": 4.20449, "loss": 4.20449, "time": 0.84848} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.07481, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28984, "top5_acc": 0.54047, "loss_cls": 4.12878, "loss": 4.12878, "time": 0.84959} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.07478, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28125, "top5_acc": 0.52641, "loss_cls": 4.17244, "loss": 4.17244, "time": 0.84393} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.07476, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27578, "top5_acc": 0.52719, "loss_cls": 4.16348, "loss": 4.16348, "time": 0.84722} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.07473, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27594, "top5_acc": 0.52641, "loss_cls": 4.16897, "loss": 4.16897, "time": 0.84862} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.07471, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28531, "top5_acc": 0.5275, "loss_cls": 4.15754, "loss": 4.15754, "time": 0.84563} +{"mode": "train", "epoch": 51, "iter": 1300, "lr": 0.07468, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28266, "top5_acc": 0.53328, "loss_cls": 4.1414, "loss": 4.1414, "time": 0.84704} +{"mode": "train", "epoch": 51, "iter": 1400, "lr": 0.07466, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28, "top5_acc": 0.53312, "loss_cls": 4.15991, "loss": 4.15991, "time": 0.84976} +{"mode": "train", "epoch": 51, "iter": 1500, "lr": 0.07464, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28141, "top5_acc": 0.53266, "loss_cls": 4.15454, "loss": 4.15454, "time": 0.84948} +{"mode": "train", "epoch": 51, "iter": 1600, "lr": 0.07461, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.27656, "top5_acc": 0.52859, "loss_cls": 4.18288, "loss": 4.18288, "time": 0.85661} +{"mode": "train", "epoch": 51, "iter": 1700, "lr": 0.07459, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27406, "top5_acc": 0.52344, "loss_cls": 4.1852, "loss": 4.1852, "time": 0.85187} +{"mode": "train", "epoch": 51, "iter": 1800, "lr": 0.07456, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27078, "top5_acc": 0.535, "loss_cls": 4.15011, "loss": 4.15011, "time": 0.84833} +{"mode": "train", "epoch": 51, "iter": 1900, "lr": 0.07454, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.275, "top5_acc": 0.52156, "loss_cls": 4.17684, "loss": 4.17684, "time": 0.84753} +{"mode": "train", "epoch": 51, "iter": 2000, "lr": 0.07451, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27031, "top5_acc": 0.53047, "loss_cls": 4.20558, "loss": 4.20558, "time": 0.84707} +{"mode": "train", "epoch": 51, "iter": 2100, "lr": 0.07449, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27391, "top5_acc": 0.53188, "loss_cls": 4.16097, "loss": 4.16097, "time": 0.8431} +{"mode": "train", "epoch": 51, "iter": 2200, "lr": 0.07447, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27312, "top5_acc": 0.53125, "loss_cls": 4.16454, "loss": 4.16454, "time": 0.84619} +{"mode": "train", "epoch": 51, "iter": 2300, "lr": 0.07444, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27453, "top5_acc": 0.52547, "loss_cls": 4.18014, "loss": 4.18014, "time": 0.84691} +{"mode": "train", "epoch": 51, "iter": 2400, "lr": 0.07442, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27062, "top5_acc": 0.52969, "loss_cls": 4.20179, "loss": 4.20179, "time": 0.84969} +{"mode": "train", "epoch": 51, "iter": 2500, "lr": 0.07439, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28516, "top5_acc": 0.53422, "loss_cls": 4.12134, "loss": 4.12134, "time": 0.8463} +{"mode": "train", "epoch": 51, "iter": 2600, "lr": 0.07437, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27828, "top5_acc": 0.52922, "loss_cls": 4.15996, "loss": 4.15996, "time": 0.85048} +{"mode": "train", "epoch": 51, "iter": 2700, "lr": 0.07434, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28531, "top5_acc": 0.53703, "loss_cls": 4.11677, "loss": 4.11677, "time": 0.85157} +{"mode": "train", "epoch": 51, "iter": 2800, "lr": 0.07432, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27938, "top5_acc": 0.52891, "loss_cls": 4.17398, "loss": 4.17398, "time": 0.85023} +{"mode": "train", "epoch": 51, "iter": 2900, "lr": 0.07429, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27969, "top5_acc": 0.52594, "loss_cls": 4.16756, "loss": 4.16756, "time": 0.8514} +{"mode": "train", "epoch": 51, "iter": 3000, "lr": 0.07427, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27781, "top5_acc": 0.52516, "loss_cls": 4.19836, "loss": 4.19836, "time": 0.84687} +{"mode": "train", "epoch": 51, "iter": 3100, "lr": 0.07425, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27328, "top5_acc": 0.52234, "loss_cls": 4.20831, "loss": 4.20831, "time": 0.84963} +{"mode": "train", "epoch": 51, "iter": 3200, "lr": 0.07422, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26719, "top5_acc": 0.51859, "loss_cls": 4.20537, "loss": 4.20537, "time": 0.85545} +{"mode": "train", "epoch": 51, "iter": 3300, "lr": 0.0742, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.27547, "top5_acc": 0.51641, "loss_cls": 4.18651, "loss": 4.18651, "time": 0.85427} +{"mode": "train", "epoch": 51, "iter": 3400, "lr": 0.07417, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28625, "top5_acc": 0.53234, "loss_cls": 4.15388, "loss": 4.15388, "time": 0.85237} +{"mode": "train", "epoch": 51, "iter": 3500, "lr": 0.07415, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27422, "top5_acc": 0.51547, "loss_cls": 4.19705, "loss": 4.19705, "time": 0.85167} +{"mode": "train", "epoch": 51, "iter": 3600, "lr": 0.07412, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27719, "top5_acc": 0.53, "loss_cls": 4.14648, "loss": 4.14648, "time": 0.8509} +{"mode": "train", "epoch": 51, "iter": 3700, "lr": 0.0741, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28484, "top5_acc": 0.53453, "loss_cls": 4.10464, "loss": 4.10464, "time": 0.84943} +{"mode": "val", "epoch": 51, "iter": 309, "lr": 0.07409, "top1_acc": 0.22899, "top5_acc": 0.46052, "mean_class_accuracy": 0.22872} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.07406, "memory": 15990, "data_time": 1.46927, "top1_acc": 0.28172, "top5_acc": 0.54156, "loss_cls": 4.13348, "loss": 4.13348, "time": 2.50986} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.07404, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29297, "top5_acc": 0.54406, "loss_cls": 4.08755, "loss": 4.08755, "time": 0.85018} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.07401, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29125, "top5_acc": 0.53891, "loss_cls": 4.10713, "loss": 4.10713, "time": 0.85466} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.07399, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.28, "top5_acc": 0.52719, "loss_cls": 4.17447, "loss": 4.17447, "time": 0.85257} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.07397, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28078, "top5_acc": 0.53172, "loss_cls": 4.14547, "loss": 4.14547, "time": 0.85656} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.07394, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27312, "top5_acc": 0.53172, "loss_cls": 4.18269, "loss": 4.18269, "time": 0.85666} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.07392, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27516, "top5_acc": 0.53266, "loss_cls": 4.13098, "loss": 4.13098, "time": 0.85451} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.07389, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29109, "top5_acc": 0.53688, "loss_cls": 4.10381, "loss": 4.10381, "time": 0.85797} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.07387, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28016, "top5_acc": 0.52734, "loss_cls": 4.15013, "loss": 4.15013, "time": 0.8514} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.07384, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28047, "top5_acc": 0.53891, "loss_cls": 4.15395, "loss": 4.15395, "time": 0.85508} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.07382, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27797, "top5_acc": 0.52547, "loss_cls": 4.18637, "loss": 4.18637, "time": 0.85191} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.07379, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27609, "top5_acc": 0.52484, "loss_cls": 4.1594, "loss": 4.1594, "time": 0.85416} +{"mode": "train", "epoch": 52, "iter": 1300, "lr": 0.07377, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.27734, "top5_acc": 0.53484, "loss_cls": 4.17004, "loss": 4.17004, "time": 0.84657} +{"mode": "train", "epoch": 52, "iter": 1400, "lr": 0.07374, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28078, "top5_acc": 0.53109, "loss_cls": 4.1524, "loss": 4.1524, "time": 0.84848} +{"mode": "train", "epoch": 52, "iter": 1500, "lr": 0.07372, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28266, "top5_acc": 0.53828, "loss_cls": 4.13704, "loss": 4.13704, "time": 0.84704} +{"mode": "train", "epoch": 52, "iter": 1600, "lr": 0.0737, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28, "top5_acc": 0.54438, "loss_cls": 4.1088, "loss": 4.1088, "time": 0.8508} +{"mode": "train", "epoch": 52, "iter": 1700, "lr": 0.07367, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26953, "top5_acc": 0.5275, "loss_cls": 4.17899, "loss": 4.17899, "time": 0.85366} +{"mode": "train", "epoch": 52, "iter": 1800, "lr": 0.07365, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27766, "top5_acc": 0.53375, "loss_cls": 4.1298, "loss": 4.1298, "time": 0.84591} +{"mode": "train", "epoch": 52, "iter": 1900, "lr": 0.07362, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26844, "top5_acc": 0.52391, "loss_cls": 4.16884, "loss": 4.16884, "time": 0.84922} +{"mode": "train", "epoch": 52, "iter": 2000, "lr": 0.0736, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26953, "top5_acc": 0.52266, "loss_cls": 4.20915, "loss": 4.20915, "time": 0.85237} +{"mode": "train", "epoch": 52, "iter": 2100, "lr": 0.07357, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27594, "top5_acc": 0.52906, "loss_cls": 4.16189, "loss": 4.16189, "time": 0.84681} +{"mode": "train", "epoch": 52, "iter": 2200, "lr": 0.07355, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27703, "top5_acc": 0.52406, "loss_cls": 4.20003, "loss": 4.20003, "time": 0.85073} +{"mode": "train", "epoch": 52, "iter": 2300, "lr": 0.07352, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27078, "top5_acc": 0.52203, "loss_cls": 4.21133, "loss": 4.21133, "time": 0.85348} +{"mode": "train", "epoch": 52, "iter": 2400, "lr": 0.0735, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27781, "top5_acc": 0.5225, "loss_cls": 4.1922, "loss": 4.1922, "time": 0.84956} +{"mode": "train", "epoch": 52, "iter": 2500, "lr": 0.07347, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27578, "top5_acc": 0.53219, "loss_cls": 4.15024, "loss": 4.15024, "time": 0.85338} +{"mode": "train", "epoch": 52, "iter": 2600, "lr": 0.07345, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28844, "top5_acc": 0.53891, "loss_cls": 4.13691, "loss": 4.13691, "time": 0.85112} +{"mode": "train", "epoch": 52, "iter": 2700, "lr": 0.07342, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27141, "top5_acc": 0.53375, "loss_cls": 4.13606, "loss": 4.13606, "time": 0.84926} +{"mode": "train", "epoch": 52, "iter": 2800, "lr": 0.0734, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28609, "top5_acc": 0.53281, "loss_cls": 4.12442, "loss": 4.12442, "time": 0.84922} +{"mode": "train", "epoch": 52, "iter": 2900, "lr": 0.07337, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27656, "top5_acc": 0.5325, "loss_cls": 4.15698, "loss": 4.15698, "time": 0.85275} +{"mode": "train", "epoch": 52, "iter": 3000, "lr": 0.07335, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27656, "top5_acc": 0.52594, "loss_cls": 4.15407, "loss": 4.15407, "time": 0.85205} +{"mode": "train", "epoch": 52, "iter": 3100, "lr": 0.07332, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27484, "top5_acc": 0.53078, "loss_cls": 4.16111, "loss": 4.16111, "time": 0.84882} +{"mode": "train", "epoch": 52, "iter": 3200, "lr": 0.0733, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27672, "top5_acc": 0.53484, "loss_cls": 4.14111, "loss": 4.14111, "time": 0.85333} +{"mode": "train", "epoch": 52, "iter": 3300, "lr": 0.07328, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.28406, "top5_acc": 0.53922, "loss_cls": 4.11671, "loss": 4.11671, "time": 0.84471} +{"mode": "train", "epoch": 52, "iter": 3400, "lr": 0.07325, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27516, "top5_acc": 0.52641, "loss_cls": 4.17514, "loss": 4.17514, "time": 0.84689} +{"mode": "train", "epoch": 52, "iter": 3500, "lr": 0.07323, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27734, "top5_acc": 0.53844, "loss_cls": 4.13966, "loss": 4.13966, "time": 0.84575} +{"mode": "train", "epoch": 52, "iter": 3600, "lr": 0.0732, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28156, "top5_acc": 0.52547, "loss_cls": 4.15488, "loss": 4.15488, "time": 0.8473} +{"mode": "train", "epoch": 52, "iter": 3700, "lr": 0.07318, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26578, "top5_acc": 0.52438, "loss_cls": 4.21162, "loss": 4.21162, "time": 0.84981} +{"mode": "val", "epoch": 52, "iter": 309, "lr": 0.07317, "top1_acc": 0.22286, "top5_acc": 0.46153, "mean_class_accuracy": 0.22262} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.07314, "memory": 15990, "data_time": 1.58565, "top1_acc": 0.28266, "top5_acc": 0.54547, "loss_cls": 4.09667, "loss": 4.09667, "time": 2.61074} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.07312, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27734, "top5_acc": 0.52453, "loss_cls": 4.14269, "loss": 4.14269, "time": 0.85481} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.07309, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.27703, "top5_acc": 0.53719, "loss_cls": 4.12895, "loss": 4.12895, "time": 0.8572} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.07307, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27359, "top5_acc": 0.52734, "loss_cls": 4.16806, "loss": 4.16806, "time": 0.85302} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.07304, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27969, "top5_acc": 0.53688, "loss_cls": 4.15101, "loss": 4.15101, "time": 0.85051} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.07302, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28688, "top5_acc": 0.53734, "loss_cls": 4.13182, "loss": 4.13182, "time": 0.85008} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.07299, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28391, "top5_acc": 0.53281, "loss_cls": 4.18595, "loss": 4.18595, "time": 0.85012} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.07297, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27734, "top5_acc": 0.52781, "loss_cls": 4.18063, "loss": 4.18063, "time": 0.84855} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.07294, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28125, "top5_acc": 0.53984, "loss_cls": 4.12017, "loss": 4.12017, "time": 0.84914} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.07292, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.28016, "top5_acc": 0.53484, "loss_cls": 4.15106, "loss": 4.15106, "time": 0.85316} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.07289, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.27734, "top5_acc": 0.53547, "loss_cls": 4.14141, "loss": 4.14141, "time": 0.85063} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.07287, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27375, "top5_acc": 0.53625, "loss_cls": 4.14697, "loss": 4.14697, "time": 0.84181} +{"mode": "train", "epoch": 53, "iter": 1300, "lr": 0.07284, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27766, "top5_acc": 0.53594, "loss_cls": 4.15745, "loss": 4.15745, "time": 0.84485} +{"mode": "train", "epoch": 53, "iter": 1400, "lr": 0.07282, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27984, "top5_acc": 0.52906, "loss_cls": 4.14495, "loss": 4.14495, "time": 0.84661} +{"mode": "train", "epoch": 53, "iter": 1500, "lr": 0.07279, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27719, "top5_acc": 0.52406, "loss_cls": 4.16408, "loss": 4.16408, "time": 0.84657} +{"mode": "train", "epoch": 53, "iter": 1600, "lr": 0.07277, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27906, "top5_acc": 0.52625, "loss_cls": 4.16099, "loss": 4.16099, "time": 0.84637} +{"mode": "train", "epoch": 53, "iter": 1700, "lr": 0.07274, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27266, "top5_acc": 0.52844, "loss_cls": 4.1875, "loss": 4.1875, "time": 0.84312} +{"mode": "train", "epoch": 53, "iter": 1800, "lr": 0.07272, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28219, "top5_acc": 0.52219, "loss_cls": 4.15922, "loss": 4.15922, "time": 0.8457} +{"mode": "train", "epoch": 53, "iter": 1900, "lr": 0.07269, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27781, "top5_acc": 0.53406, "loss_cls": 4.16548, "loss": 4.16548, "time": 0.84757} +{"mode": "train", "epoch": 53, "iter": 2000, "lr": 0.07267, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28203, "top5_acc": 0.53531, "loss_cls": 4.13481, "loss": 4.13481, "time": 0.84559} +{"mode": "train", "epoch": 53, "iter": 2100, "lr": 0.07264, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29188, "top5_acc": 0.54312, "loss_cls": 4.09627, "loss": 4.09627, "time": 0.84459} +{"mode": "train", "epoch": 53, "iter": 2200, "lr": 0.07262, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28422, "top5_acc": 0.53375, "loss_cls": 4.1144, "loss": 4.1144, "time": 0.84443} +{"mode": "train", "epoch": 53, "iter": 2300, "lr": 0.07259, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28438, "top5_acc": 0.53281, "loss_cls": 4.1472, "loss": 4.1472, "time": 0.84524} +{"mode": "train", "epoch": 53, "iter": 2400, "lr": 0.07257, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28219, "top5_acc": 0.53156, "loss_cls": 4.16512, "loss": 4.16512, "time": 0.84646} +{"mode": "train", "epoch": 53, "iter": 2500, "lr": 0.07254, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28391, "top5_acc": 0.53953, "loss_cls": 4.13822, "loss": 4.13822, "time": 0.84782} +{"mode": "train", "epoch": 53, "iter": 2600, "lr": 0.07252, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27422, "top5_acc": 0.53141, "loss_cls": 4.16741, "loss": 4.16741, "time": 0.84481} +{"mode": "train", "epoch": 53, "iter": 2700, "lr": 0.07249, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27906, "top5_acc": 0.53469, "loss_cls": 4.13251, "loss": 4.13251, "time": 0.85053} +{"mode": "train", "epoch": 53, "iter": 2800, "lr": 0.07247, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27203, "top5_acc": 0.52969, "loss_cls": 4.14583, "loss": 4.14583, "time": 0.84543} +{"mode": "train", "epoch": 53, "iter": 2900, "lr": 0.07244, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28, "top5_acc": 0.53609, "loss_cls": 4.11956, "loss": 4.11956, "time": 0.84506} +{"mode": "train", "epoch": 53, "iter": 3000, "lr": 0.07242, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27828, "top5_acc": 0.52812, "loss_cls": 4.17162, "loss": 4.17162, "time": 0.85022} +{"mode": "train", "epoch": 53, "iter": 3100, "lr": 0.07239, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28312, "top5_acc": 0.5325, "loss_cls": 4.11719, "loss": 4.11719, "time": 0.84999} +{"mode": "train", "epoch": 53, "iter": 3200, "lr": 0.07237, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27437, "top5_acc": 0.52625, "loss_cls": 4.16373, "loss": 4.16373, "time": 0.84726} +{"mode": "train", "epoch": 53, "iter": 3300, "lr": 0.07234, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.28469, "top5_acc": 0.53625, "loss_cls": 4.14172, "loss": 4.14172, "time": 0.85342} +{"mode": "train", "epoch": 53, "iter": 3400, "lr": 0.07232, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28156, "top5_acc": 0.53625, "loss_cls": 4.13888, "loss": 4.13888, "time": 0.84767} +{"mode": "train", "epoch": 53, "iter": 3500, "lr": 0.07229, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28688, "top5_acc": 0.53641, "loss_cls": 4.11659, "loss": 4.11659, "time": 0.843} +{"mode": "train", "epoch": 53, "iter": 3600, "lr": 0.07227, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27547, "top5_acc": 0.53016, "loss_cls": 4.17001, "loss": 4.17001, "time": 0.85107} +{"mode": "train", "epoch": 53, "iter": 3700, "lr": 0.07224, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28672, "top5_acc": 0.53344, "loss_cls": 4.12787, "loss": 4.12787, "time": 0.85714} +{"mode": "val", "epoch": 53, "iter": 309, "lr": 0.07223, "top1_acc": 0.20422, "top5_acc": 0.44213, "mean_class_accuracy": 0.20381} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.07221, "memory": 15990, "data_time": 1.57847, "top1_acc": 0.28906, "top5_acc": 0.54297, "loss_cls": 4.07098, "loss": 4.07098, "time": 2.60618} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.07218, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29141, "top5_acc": 0.53906, "loss_cls": 4.12525, "loss": 4.12525, "time": 0.85108} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.07216, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28047, "top5_acc": 0.54844, "loss_cls": 4.08574, "loss": 4.08574, "time": 0.85047} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.07213, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29281, "top5_acc": 0.54266, "loss_cls": 4.10998, "loss": 4.10998, "time": 0.84631} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.07211, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28438, "top5_acc": 0.53703, "loss_cls": 4.12152, "loss": 4.12152, "time": 0.84868} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.07208, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28203, "top5_acc": 0.53156, "loss_cls": 4.13889, "loss": 4.13889, "time": 0.84824} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.07206, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28141, "top5_acc": 0.53953, "loss_cls": 4.1164, "loss": 4.1164, "time": 0.84627} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.07203, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2775, "top5_acc": 0.52781, "loss_cls": 4.13888, "loss": 4.13888, "time": 0.84487} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.07201, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28234, "top5_acc": 0.53359, "loss_cls": 4.12299, "loss": 4.12299, "time": 0.84703} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.07198, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27469, "top5_acc": 0.53234, "loss_cls": 4.15637, "loss": 4.15637, "time": 0.84865} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.07196, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28719, "top5_acc": 0.53781, "loss_cls": 4.14048, "loss": 4.14048, "time": 0.84753} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.07193, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.2775, "top5_acc": 0.53453, "loss_cls": 4.13343, "loss": 4.13343, "time": 0.84903} +{"mode": "train", "epoch": 54, "iter": 1300, "lr": 0.07191, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27641, "top5_acc": 0.53297, "loss_cls": 4.15875, "loss": 4.15875, "time": 0.8472} +{"mode": "train", "epoch": 54, "iter": 1400, "lr": 0.07188, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29094, "top5_acc": 0.54312, "loss_cls": 4.08086, "loss": 4.08086, "time": 0.84788} +{"mode": "train", "epoch": 54, "iter": 1500, "lr": 0.07186, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27547, "top5_acc": 0.53, "loss_cls": 4.16276, "loss": 4.16276, "time": 0.84873} +{"mode": "train", "epoch": 54, "iter": 1600, "lr": 0.07183, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27594, "top5_acc": 0.53141, "loss_cls": 4.14761, "loss": 4.14761, "time": 0.84937} +{"mode": "train", "epoch": 54, "iter": 1700, "lr": 0.07181, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29, "top5_acc": 0.53203, "loss_cls": 4.16792, "loss": 4.16792, "time": 0.84845} +{"mode": "train", "epoch": 54, "iter": 1800, "lr": 0.07178, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27594, "top5_acc": 0.51953, "loss_cls": 4.19624, "loss": 4.19624, "time": 0.84701} +{"mode": "train", "epoch": 54, "iter": 1900, "lr": 0.07176, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26609, "top5_acc": 0.52922, "loss_cls": 4.15811, "loss": 4.15811, "time": 0.84498} +{"mode": "train", "epoch": 54, "iter": 2000, "lr": 0.07173, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27969, "top5_acc": 0.54297, "loss_cls": 4.13833, "loss": 4.13833, "time": 0.84719} +{"mode": "train", "epoch": 54, "iter": 2100, "lr": 0.0717, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28, "top5_acc": 0.52875, "loss_cls": 4.17792, "loss": 4.17792, "time": 0.85155} +{"mode": "train", "epoch": 54, "iter": 2200, "lr": 0.07168, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27016, "top5_acc": 0.5175, "loss_cls": 4.20421, "loss": 4.20421, "time": 0.84936} +{"mode": "train", "epoch": 54, "iter": 2300, "lr": 0.07165, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27281, "top5_acc": 0.52359, "loss_cls": 4.18888, "loss": 4.18888, "time": 0.85224} +{"mode": "train", "epoch": 54, "iter": 2400, "lr": 0.07163, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29063, "top5_acc": 0.54922, "loss_cls": 4.07118, "loss": 4.07118, "time": 0.84913} +{"mode": "train", "epoch": 54, "iter": 2500, "lr": 0.0716, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28, "top5_acc": 0.52906, "loss_cls": 4.14027, "loss": 4.14027, "time": 0.84972} +{"mode": "train", "epoch": 54, "iter": 2600, "lr": 0.07158, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27828, "top5_acc": 0.5325, "loss_cls": 4.17407, "loss": 4.17407, "time": 0.8545} +{"mode": "train", "epoch": 54, "iter": 2700, "lr": 0.07155, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27734, "top5_acc": 0.52781, "loss_cls": 4.15431, "loss": 4.15431, "time": 0.85721} +{"mode": "train", "epoch": 54, "iter": 2800, "lr": 0.07153, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27437, "top5_acc": 0.53203, "loss_cls": 4.15513, "loss": 4.15513, "time": 0.85583} +{"mode": "train", "epoch": 54, "iter": 2900, "lr": 0.0715, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27891, "top5_acc": 0.53109, "loss_cls": 4.14462, "loss": 4.14462, "time": 0.85764} +{"mode": "train", "epoch": 54, "iter": 3000, "lr": 0.07148, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28062, "top5_acc": 0.53641, "loss_cls": 4.11396, "loss": 4.11396, "time": 0.85403} +{"mode": "train", "epoch": 54, "iter": 3100, "lr": 0.07145, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27844, "top5_acc": 0.52938, "loss_cls": 4.13957, "loss": 4.13957, "time": 0.85276} +{"mode": "train", "epoch": 54, "iter": 3200, "lr": 0.07143, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28766, "top5_acc": 0.54344, "loss_cls": 4.12839, "loss": 4.12839, "time": 0.85708} +{"mode": "train", "epoch": 54, "iter": 3300, "lr": 0.0714, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28297, "top5_acc": 0.54172, "loss_cls": 4.12322, "loss": 4.12322, "time": 0.85982} +{"mode": "train", "epoch": 54, "iter": 3400, "lr": 0.07138, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29016, "top5_acc": 0.53484, "loss_cls": 4.11919, "loss": 4.11919, "time": 0.85653} +{"mode": "train", "epoch": 54, "iter": 3500, "lr": 0.07135, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27625, "top5_acc": 0.53172, "loss_cls": 4.17187, "loss": 4.17187, "time": 0.85057} +{"mode": "train", "epoch": 54, "iter": 3600, "lr": 0.07133, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27187, "top5_acc": 0.53188, "loss_cls": 4.17858, "loss": 4.17858, "time": 0.84471} +{"mode": "train", "epoch": 54, "iter": 3700, "lr": 0.0713, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27125, "top5_acc": 0.52672, "loss_cls": 4.16736, "loss": 4.16736, "time": 0.84716} +{"mode": "val", "epoch": 54, "iter": 309, "lr": 0.07129, "top1_acc": 0.2216, "top5_acc": 0.46219, "mean_class_accuracy": 0.22122} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.07126, "memory": 15990, "data_time": 1.54292, "top1_acc": 0.28391, "top5_acc": 0.54047, "loss_cls": 4.08727, "loss": 4.08727, "time": 2.57906} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.07124, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.29266, "top5_acc": 0.54906, "loss_cls": 4.049, "loss": 4.049, "time": 0.85141} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.07121, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28625, "top5_acc": 0.54188, "loss_cls": 4.09197, "loss": 4.09197, "time": 0.85162} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.07119, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28172, "top5_acc": 0.54281, "loss_cls": 4.09587, "loss": 4.09587, "time": 0.84809} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.07116, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27469, "top5_acc": 0.53422, "loss_cls": 4.13828, "loss": 4.13828, "time": 0.84396} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.07114, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28141, "top5_acc": 0.53094, "loss_cls": 4.14301, "loss": 4.14301, "time": 0.84604} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.07111, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27781, "top5_acc": 0.53625, "loss_cls": 4.14443, "loss": 4.14443, "time": 0.84675} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.07109, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29609, "top5_acc": 0.54469, "loss_cls": 4.07504, "loss": 4.07504, "time": 0.84246} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.07106, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29109, "top5_acc": 0.54531, "loss_cls": 4.07031, "loss": 4.07031, "time": 0.8453} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.07104, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27781, "top5_acc": 0.54188, "loss_cls": 4.12717, "loss": 4.12717, "time": 0.84276} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.07101, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28359, "top5_acc": 0.53125, "loss_cls": 4.14064, "loss": 4.14064, "time": 0.84812} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.07099, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26859, "top5_acc": 0.52625, "loss_cls": 4.2036, "loss": 4.2036, "time": 0.84902} +{"mode": "train", "epoch": 55, "iter": 1300, "lr": 0.07096, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27156, "top5_acc": 0.52203, "loss_cls": 4.18326, "loss": 4.18326, "time": 0.84491} +{"mode": "train", "epoch": 55, "iter": 1400, "lr": 0.07093, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28516, "top5_acc": 0.52094, "loss_cls": 4.15842, "loss": 4.15842, "time": 0.84871} +{"mode": "train", "epoch": 55, "iter": 1500, "lr": 0.07091, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2775, "top5_acc": 0.54234, "loss_cls": 4.10901, "loss": 4.10901, "time": 0.8503} +{"mode": "train", "epoch": 55, "iter": 1600, "lr": 0.07088, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27906, "top5_acc": 0.53312, "loss_cls": 4.16778, "loss": 4.16778, "time": 0.84947} +{"mode": "train", "epoch": 55, "iter": 1700, "lr": 0.07086, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28203, "top5_acc": 0.53453, "loss_cls": 4.13946, "loss": 4.13946, "time": 0.84214} +{"mode": "train", "epoch": 55, "iter": 1800, "lr": 0.07083, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28469, "top5_acc": 0.52828, "loss_cls": 4.13097, "loss": 4.13097, "time": 0.84283} +{"mode": "train", "epoch": 55, "iter": 1900, "lr": 0.07081, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28844, "top5_acc": 0.53953, "loss_cls": 4.11911, "loss": 4.11911, "time": 0.85167} +{"mode": "train", "epoch": 55, "iter": 2000, "lr": 0.07078, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27719, "top5_acc": 0.53188, "loss_cls": 4.1282, "loss": 4.1282, "time": 0.84884} +{"mode": "train", "epoch": 55, "iter": 2100, "lr": 0.07076, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28406, "top5_acc": 0.53578, "loss_cls": 4.15178, "loss": 4.15178, "time": 0.85425} +{"mode": "train", "epoch": 55, "iter": 2200, "lr": 0.07073, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27953, "top5_acc": 0.53469, "loss_cls": 4.15913, "loss": 4.15913, "time": 0.84891} +{"mode": "train", "epoch": 55, "iter": 2300, "lr": 0.07071, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27562, "top5_acc": 0.53547, "loss_cls": 4.15447, "loss": 4.15447, "time": 0.8491} +{"mode": "train", "epoch": 55, "iter": 2400, "lr": 0.07068, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.275, "top5_acc": 0.53516, "loss_cls": 4.17532, "loss": 4.17532, "time": 0.85629} +{"mode": "train", "epoch": 55, "iter": 2500, "lr": 0.07065, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28328, "top5_acc": 0.53594, "loss_cls": 4.13228, "loss": 4.13228, "time": 0.84824} +{"mode": "train", "epoch": 55, "iter": 2600, "lr": 0.07063, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28078, "top5_acc": 0.535, "loss_cls": 4.14507, "loss": 4.14507, "time": 0.85942} +{"mode": "train", "epoch": 55, "iter": 2700, "lr": 0.0706, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27922, "top5_acc": 0.53094, "loss_cls": 4.16549, "loss": 4.16549, "time": 0.85255} +{"mode": "train", "epoch": 55, "iter": 2800, "lr": 0.07058, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27656, "top5_acc": 0.53578, "loss_cls": 4.14685, "loss": 4.14685, "time": 0.85783} +{"mode": "train", "epoch": 55, "iter": 2900, "lr": 0.07055, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28344, "top5_acc": 0.54141, "loss_cls": 4.13574, "loss": 4.13574, "time": 0.85621} +{"mode": "train", "epoch": 55, "iter": 3000, "lr": 0.07053, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27641, "top5_acc": 0.53297, "loss_cls": 4.16037, "loss": 4.16037, "time": 0.85682} +{"mode": "train", "epoch": 55, "iter": 3100, "lr": 0.0705, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27781, "top5_acc": 0.53359, "loss_cls": 4.15476, "loss": 4.15476, "time": 0.86171} +{"mode": "train", "epoch": 55, "iter": 3200, "lr": 0.07048, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.28328, "top5_acc": 0.53219, "loss_cls": 4.13464, "loss": 4.13464, "time": 0.85652} +{"mode": "train", "epoch": 55, "iter": 3300, "lr": 0.07045, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.27906, "top5_acc": 0.53047, "loss_cls": 4.12729, "loss": 4.12729, "time": 0.85916} +{"mode": "train", "epoch": 55, "iter": 3400, "lr": 0.07043, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29063, "top5_acc": 0.53516, "loss_cls": 4.13495, "loss": 4.13495, "time": 0.85209} +{"mode": "train", "epoch": 55, "iter": 3500, "lr": 0.0704, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26156, "top5_acc": 0.51859, "loss_cls": 4.18747, "loss": 4.18747, "time": 0.84897} +{"mode": "train", "epoch": 55, "iter": 3600, "lr": 0.07037, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28672, "top5_acc": 0.53875, "loss_cls": 4.11034, "loss": 4.11034, "time": 0.8562} +{"mode": "train", "epoch": 55, "iter": 3700, "lr": 0.07035, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28766, "top5_acc": 0.53594, "loss_cls": 4.09837, "loss": 4.09837, "time": 0.85002} +{"mode": "val", "epoch": 55, "iter": 309, "lr": 0.07034, "top1_acc": 0.22266, "top5_acc": 0.47207, "mean_class_accuracy": 0.22257} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.07031, "memory": 15990, "data_time": 1.5374, "top1_acc": 0.28516, "top5_acc": 0.54328, "loss_cls": 4.10711, "loss": 4.10711, "time": 2.58496} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.07029, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27984, "top5_acc": 0.53234, "loss_cls": 4.14667, "loss": 4.14667, "time": 0.85704} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.07026, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28047, "top5_acc": 0.53125, "loss_cls": 4.13973, "loss": 4.13973, "time": 0.85491} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.07023, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28516, "top5_acc": 0.54188, "loss_cls": 4.09216, "loss": 4.09216, "time": 0.85174} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.07021, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28, "top5_acc": 0.535, "loss_cls": 4.11605, "loss": 4.11605, "time": 0.85566} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.07018, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28609, "top5_acc": 0.54578, "loss_cls": 4.09691, "loss": 4.09691, "time": 0.85155} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.07016, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29016, "top5_acc": 0.54562, "loss_cls": 4.08956, "loss": 4.08956, "time": 0.85461} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.07013, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27672, "top5_acc": 0.53406, "loss_cls": 4.12536, "loss": 4.12536, "time": 0.85556} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.07011, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29578, "top5_acc": 0.54094, "loss_cls": 4.08431, "loss": 4.08431, "time": 0.85381} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.07008, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28141, "top5_acc": 0.54156, "loss_cls": 4.12312, "loss": 4.12312, "time": 0.85524} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.07006, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27484, "top5_acc": 0.53453, "loss_cls": 4.13682, "loss": 4.13682, "time": 0.85575} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.07003, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28422, "top5_acc": 0.53359, "loss_cls": 4.1299, "loss": 4.1299, "time": 0.85536} +{"mode": "train", "epoch": 56, "iter": 1300, "lr": 0.07, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28062, "top5_acc": 0.53547, "loss_cls": 4.1124, "loss": 4.1124, "time": 0.85043} +{"mode": "train", "epoch": 56, "iter": 1400, "lr": 0.06998, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28203, "top5_acc": 0.54453, "loss_cls": 4.10031, "loss": 4.10031, "time": 0.84592} +{"mode": "train", "epoch": 56, "iter": 1500, "lr": 0.06995, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28734, "top5_acc": 0.52812, "loss_cls": 4.13034, "loss": 4.13034, "time": 0.84762} +{"mode": "train", "epoch": 56, "iter": 1600, "lr": 0.06993, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27266, "top5_acc": 0.53047, "loss_cls": 4.17023, "loss": 4.17023, "time": 0.84486} +{"mode": "train", "epoch": 56, "iter": 1700, "lr": 0.0699, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27156, "top5_acc": 0.52406, "loss_cls": 4.17404, "loss": 4.17404, "time": 0.85042} +{"mode": "train", "epoch": 56, "iter": 1800, "lr": 0.06988, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28469, "top5_acc": 0.54109, "loss_cls": 4.16672, "loss": 4.16672, "time": 0.85028} +{"mode": "train", "epoch": 56, "iter": 1900, "lr": 0.06985, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29156, "top5_acc": 0.53812, "loss_cls": 4.14228, "loss": 4.14228, "time": 0.85625} +{"mode": "train", "epoch": 56, "iter": 2000, "lr": 0.06983, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28813, "top5_acc": 0.53625, "loss_cls": 4.14315, "loss": 4.14315, "time": 0.85592} +{"mode": "train", "epoch": 56, "iter": 2100, "lr": 0.0698, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28, "top5_acc": 0.52297, "loss_cls": 4.16211, "loss": 4.16211, "time": 0.85566} +{"mode": "train", "epoch": 56, "iter": 2200, "lr": 0.06977, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28562, "top5_acc": 0.53641, "loss_cls": 4.13481, "loss": 4.13481, "time": 0.85009} +{"mode": "train", "epoch": 56, "iter": 2300, "lr": 0.06975, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27859, "top5_acc": 0.53109, "loss_cls": 4.1517, "loss": 4.1517, "time": 0.85393} +{"mode": "train", "epoch": 56, "iter": 2400, "lr": 0.06972, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28234, "top5_acc": 0.53797, "loss_cls": 4.14858, "loss": 4.14858, "time": 0.85696} +{"mode": "train", "epoch": 56, "iter": 2500, "lr": 0.0697, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27969, "top5_acc": 0.52938, "loss_cls": 4.13123, "loss": 4.13123, "time": 0.8546} +{"mode": "train", "epoch": 56, "iter": 2600, "lr": 0.06967, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28531, "top5_acc": 0.53547, "loss_cls": 4.11822, "loss": 4.11822, "time": 0.85613} +{"mode": "train", "epoch": 56, "iter": 2700, "lr": 0.06965, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27984, "top5_acc": 0.52516, "loss_cls": 4.15376, "loss": 4.15376, "time": 0.85222} +{"mode": "train", "epoch": 56, "iter": 2800, "lr": 0.06962, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27328, "top5_acc": 0.53219, "loss_cls": 4.1471, "loss": 4.1471, "time": 0.85548} +{"mode": "train", "epoch": 56, "iter": 2900, "lr": 0.06959, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27719, "top5_acc": 0.53594, "loss_cls": 4.1424, "loss": 4.1424, "time": 0.85728} +{"mode": "train", "epoch": 56, "iter": 3000, "lr": 0.06957, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28672, "top5_acc": 0.53922, "loss_cls": 4.11029, "loss": 4.11029, "time": 0.86027} +{"mode": "train", "epoch": 56, "iter": 3100, "lr": 0.06954, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28594, "top5_acc": 0.54016, "loss_cls": 4.11532, "loss": 4.11532, "time": 0.85415} +{"mode": "train", "epoch": 56, "iter": 3200, "lr": 0.06952, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27828, "top5_acc": 0.53391, "loss_cls": 4.13748, "loss": 4.13748, "time": 0.85218} +{"mode": "train", "epoch": 56, "iter": 3300, "lr": 0.06949, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28453, "top5_acc": 0.53188, "loss_cls": 4.12414, "loss": 4.12414, "time": 0.85419} +{"mode": "train", "epoch": 56, "iter": 3400, "lr": 0.06947, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28109, "top5_acc": 0.53656, "loss_cls": 4.13045, "loss": 4.13045, "time": 0.85384} +{"mode": "train", "epoch": 56, "iter": 3500, "lr": 0.06944, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28266, "top5_acc": 0.54297, "loss_cls": 4.10145, "loss": 4.10145, "time": 0.84948} +{"mode": "train", "epoch": 56, "iter": 3600, "lr": 0.06941, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27844, "top5_acc": 0.53703, "loss_cls": 4.13225, "loss": 4.13225, "time": 0.85483} +{"mode": "train", "epoch": 56, "iter": 3700, "lr": 0.06939, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28453, "top5_acc": 0.53984, "loss_cls": 4.0973, "loss": 4.0973, "time": 0.85681} +{"mode": "val", "epoch": 56, "iter": 309, "lr": 0.06938, "top1_acc": 0.21344, "top5_acc": 0.44841, "mean_class_accuracy": 0.21335} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.06935, "memory": 15990, "data_time": 1.55396, "top1_acc": 0.30359, "top5_acc": 0.54969, "loss_cls": 4.02088, "loss": 4.02088, "time": 2.59663} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.06932, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28391, "top5_acc": 0.54188, "loss_cls": 4.08633, "loss": 4.08633, "time": 0.85065} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.0693, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30172, "top5_acc": 0.54641, "loss_cls": 4.04552, "loss": 4.04552, "time": 0.85079} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.06927, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28547, "top5_acc": 0.54391, "loss_cls": 4.13318, "loss": 4.13318, "time": 0.84836} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.06925, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28078, "top5_acc": 0.52594, "loss_cls": 4.16488, "loss": 4.16488, "time": 0.85623} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.06922, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2825, "top5_acc": 0.54594, "loss_cls": 4.09473, "loss": 4.09473, "time": 0.86136} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.0692, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29703, "top5_acc": 0.54953, "loss_cls": 4.07335, "loss": 4.07335, "time": 0.8572} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.06917, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28438, "top5_acc": 0.53875, "loss_cls": 4.1059, "loss": 4.1059, "time": 0.85696} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.06914, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28375, "top5_acc": 0.53219, "loss_cls": 4.117, "loss": 4.117, "time": 0.86165} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.06912, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29063, "top5_acc": 0.54531, "loss_cls": 4.06837, "loss": 4.06837, "time": 0.85841} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.06909, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28109, "top5_acc": 0.52922, "loss_cls": 4.14344, "loss": 4.14344, "time": 0.85608} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.06907, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29109, "top5_acc": 0.54516, "loss_cls": 4.08235, "loss": 4.08235, "time": 0.85302} +{"mode": "train", "epoch": 57, "iter": 1300, "lr": 0.06904, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27203, "top5_acc": 0.52578, "loss_cls": 4.17875, "loss": 4.17875, "time": 0.84567} +{"mode": "train", "epoch": 57, "iter": 1400, "lr": 0.06901, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27891, "top5_acc": 0.53547, "loss_cls": 4.13188, "loss": 4.13188, "time": 0.84664} +{"mode": "train", "epoch": 57, "iter": 1500, "lr": 0.06899, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28188, "top5_acc": 0.53984, "loss_cls": 4.11633, "loss": 4.11633, "time": 0.84773} +{"mode": "train", "epoch": 57, "iter": 1600, "lr": 0.06896, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28719, "top5_acc": 0.54484, "loss_cls": 4.09227, "loss": 4.09227, "time": 0.85121} +{"mode": "train", "epoch": 57, "iter": 1700, "lr": 0.06894, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28469, "top5_acc": 0.53875, "loss_cls": 4.12094, "loss": 4.12094, "time": 0.84944} +{"mode": "train", "epoch": 57, "iter": 1800, "lr": 0.06891, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28953, "top5_acc": 0.53672, "loss_cls": 4.0976, "loss": 4.0976, "time": 0.84944} +{"mode": "train", "epoch": 57, "iter": 1900, "lr": 0.06889, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28281, "top5_acc": 0.54, "loss_cls": 4.11464, "loss": 4.11464, "time": 0.85036} +{"mode": "train", "epoch": 57, "iter": 2000, "lr": 0.06886, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28734, "top5_acc": 0.53734, "loss_cls": 4.104, "loss": 4.104, "time": 0.84673} +{"mode": "train", "epoch": 57, "iter": 2100, "lr": 0.06883, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27922, "top5_acc": 0.54141, "loss_cls": 4.11291, "loss": 4.11291, "time": 0.84797} +{"mode": "train", "epoch": 57, "iter": 2200, "lr": 0.06881, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27812, "top5_acc": 0.53234, "loss_cls": 4.13037, "loss": 4.13037, "time": 0.84619} +{"mode": "train", "epoch": 57, "iter": 2300, "lr": 0.06878, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28594, "top5_acc": 0.54453, "loss_cls": 4.07417, "loss": 4.07417, "time": 0.85185} +{"mode": "train", "epoch": 57, "iter": 2400, "lr": 0.06876, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28547, "top5_acc": 0.54281, "loss_cls": 4.12621, "loss": 4.12621, "time": 0.84824} +{"mode": "train", "epoch": 57, "iter": 2500, "lr": 0.06873, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27797, "top5_acc": 0.52906, "loss_cls": 4.147, "loss": 4.147, "time": 0.85226} +{"mode": "train", "epoch": 57, "iter": 2600, "lr": 0.0687, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28078, "top5_acc": 0.52828, "loss_cls": 4.15677, "loss": 4.15677, "time": 0.84225} +{"mode": "train", "epoch": 57, "iter": 2700, "lr": 0.06868, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28719, "top5_acc": 0.53875, "loss_cls": 4.10823, "loss": 4.10823, "time": 0.84471} +{"mode": "train", "epoch": 57, "iter": 2800, "lr": 0.06865, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28094, "top5_acc": 0.53641, "loss_cls": 4.15289, "loss": 4.15289, "time": 0.84455} +{"mode": "train", "epoch": 57, "iter": 2900, "lr": 0.06863, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27969, "top5_acc": 0.52797, "loss_cls": 4.19402, "loss": 4.19402, "time": 0.8518} +{"mode": "train", "epoch": 57, "iter": 3000, "lr": 0.0686, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28125, "top5_acc": 0.53719, "loss_cls": 4.11433, "loss": 4.11433, "time": 0.85031} +{"mode": "train", "epoch": 57, "iter": 3100, "lr": 0.06857, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29328, "top5_acc": 0.54344, "loss_cls": 4.10809, "loss": 4.10809, "time": 0.84865} +{"mode": "train", "epoch": 57, "iter": 3200, "lr": 0.06855, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28, "top5_acc": 0.53906, "loss_cls": 4.11731, "loss": 4.11731, "time": 0.84874} +{"mode": "train", "epoch": 57, "iter": 3300, "lr": 0.06852, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.275, "top5_acc": 0.5325, "loss_cls": 4.15334, "loss": 4.15334, "time": 0.84767} +{"mode": "train", "epoch": 57, "iter": 3400, "lr": 0.0685, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28078, "top5_acc": 0.54328, "loss_cls": 4.12581, "loss": 4.12581, "time": 0.84538} +{"mode": "train", "epoch": 57, "iter": 3500, "lr": 0.06847, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2925, "top5_acc": 0.54484, "loss_cls": 4.05635, "loss": 4.05635, "time": 0.8501} +{"mode": "train", "epoch": 57, "iter": 3600, "lr": 0.06844, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28109, "top5_acc": 0.53844, "loss_cls": 4.13695, "loss": 4.13695, "time": 0.85499} +{"mode": "train", "epoch": 57, "iter": 3700, "lr": 0.06842, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29484, "top5_acc": 0.54875, "loss_cls": 4.02816, "loss": 4.02816, "time": 0.85196} +{"mode": "val", "epoch": 57, "iter": 309, "lr": 0.06841, "top1_acc": 0.222, "top5_acc": 0.46497, "mean_class_accuracy": 0.22171} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.06838, "memory": 15990, "data_time": 1.53182, "top1_acc": 0.29594, "top5_acc": 0.55281, "loss_cls": 4.04882, "loss": 4.04882, "time": 2.56743} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.06835, "memory": 15990, "data_time": 0.00077, "top1_acc": 0.28172, "top5_acc": 0.54719, "loss_cls": 4.08356, "loss": 4.08356, "time": 0.85129} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.06833, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28266, "top5_acc": 0.54172, "loss_cls": 4.09902, "loss": 4.09902, "time": 0.85542} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.0683, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.295, "top5_acc": 0.55344, "loss_cls": 4.05774, "loss": 4.05774, "time": 0.84476} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.06828, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.27766, "top5_acc": 0.53281, "loss_cls": 4.13383, "loss": 4.13383, "time": 0.84818} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.06825, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28188, "top5_acc": 0.53406, "loss_cls": 4.11527, "loss": 4.11527, "time": 0.8553} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.06822, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29172, "top5_acc": 0.54719, "loss_cls": 4.05667, "loss": 4.05667, "time": 0.85034} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.0682, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28406, "top5_acc": 0.54016, "loss_cls": 4.11256, "loss": 4.11256, "time": 0.84896} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.06817, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28328, "top5_acc": 0.545, "loss_cls": 4.03996, "loss": 4.03996, "time": 0.84437} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.06815, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.27234, "top5_acc": 0.52734, "loss_cls": 4.15479, "loss": 4.15479, "time": 0.85056} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.06812, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27375, "top5_acc": 0.53016, "loss_cls": 4.14944, "loss": 4.14944, "time": 0.85808} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.06809, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.29234, "top5_acc": 0.54937, "loss_cls": 4.04485, "loss": 4.04485, "time": 0.85394} +{"mode": "train", "epoch": 58, "iter": 1300, "lr": 0.06807, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29078, "top5_acc": 0.54984, "loss_cls": 4.08622, "loss": 4.08622, "time": 0.85468} +{"mode": "train", "epoch": 58, "iter": 1400, "lr": 0.06804, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.28609, "top5_acc": 0.54203, "loss_cls": 4.09864, "loss": 4.09864, "time": 0.85439} +{"mode": "train", "epoch": 58, "iter": 1500, "lr": 0.06802, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28219, "top5_acc": 0.53703, "loss_cls": 4.13348, "loss": 4.13348, "time": 0.85019} +{"mode": "train", "epoch": 58, "iter": 1600, "lr": 0.06799, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28906, "top5_acc": 0.53922, "loss_cls": 4.0819, "loss": 4.0819, "time": 0.85117} +{"mode": "train", "epoch": 58, "iter": 1700, "lr": 0.06796, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29563, "top5_acc": 0.55, "loss_cls": 4.07359, "loss": 4.07359, "time": 0.84763} +{"mode": "train", "epoch": 58, "iter": 1800, "lr": 0.06794, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28938, "top5_acc": 0.54297, "loss_cls": 4.07778, "loss": 4.07778, "time": 0.8513} +{"mode": "train", "epoch": 58, "iter": 1900, "lr": 0.06791, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26969, "top5_acc": 0.52109, "loss_cls": 4.2115, "loss": 4.2115, "time": 0.85043} +{"mode": "train", "epoch": 58, "iter": 2000, "lr": 0.06789, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28328, "top5_acc": 0.54125, "loss_cls": 4.10977, "loss": 4.10977, "time": 0.85294} +{"mode": "train", "epoch": 58, "iter": 2100, "lr": 0.06786, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28781, "top5_acc": 0.5425, "loss_cls": 4.1035, "loss": 4.1035, "time": 0.84444} +{"mode": "train", "epoch": 58, "iter": 2200, "lr": 0.06783, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28734, "top5_acc": 0.53672, "loss_cls": 4.14368, "loss": 4.14368, "time": 0.84918} +{"mode": "train", "epoch": 58, "iter": 2300, "lr": 0.06781, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28625, "top5_acc": 0.54031, "loss_cls": 4.09769, "loss": 4.09769, "time": 0.85328} +{"mode": "train", "epoch": 58, "iter": 2400, "lr": 0.06778, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28484, "top5_acc": 0.54141, "loss_cls": 4.11768, "loss": 4.11768, "time": 0.84765} +{"mode": "train", "epoch": 58, "iter": 2500, "lr": 0.06775, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28391, "top5_acc": 0.53766, "loss_cls": 4.10702, "loss": 4.10702, "time": 0.85219} +{"mode": "train", "epoch": 58, "iter": 2600, "lr": 0.06773, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29172, "top5_acc": 0.55219, "loss_cls": 4.03886, "loss": 4.03886, "time": 0.84881} +{"mode": "train", "epoch": 58, "iter": 2700, "lr": 0.0677, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28984, "top5_acc": 0.53406, "loss_cls": 4.1277, "loss": 4.1277, "time": 0.84625} +{"mode": "train", "epoch": 58, "iter": 2800, "lr": 0.06768, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28141, "top5_acc": 0.52812, "loss_cls": 4.13035, "loss": 4.13035, "time": 0.84898} +{"mode": "train", "epoch": 58, "iter": 2900, "lr": 0.06765, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29609, "top5_acc": 0.54391, "loss_cls": 4.10386, "loss": 4.10386, "time": 0.85712} +{"mode": "train", "epoch": 58, "iter": 3000, "lr": 0.06762, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27688, "top5_acc": 0.53297, "loss_cls": 4.14872, "loss": 4.14872, "time": 0.85333} +{"mode": "train", "epoch": 58, "iter": 3100, "lr": 0.0676, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29344, "top5_acc": 0.54703, "loss_cls": 4.08763, "loss": 4.08763, "time": 0.86078} +{"mode": "train", "epoch": 58, "iter": 3200, "lr": 0.06757, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28531, "top5_acc": 0.54672, "loss_cls": 4.07925, "loss": 4.07925, "time": 0.85297} +{"mode": "train", "epoch": 58, "iter": 3300, "lr": 0.06755, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.27969, "top5_acc": 0.53094, "loss_cls": 4.18863, "loss": 4.18863, "time": 0.85586} +{"mode": "train", "epoch": 58, "iter": 3400, "lr": 0.06752, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.29063, "top5_acc": 0.54109, "loss_cls": 4.12141, "loss": 4.12141, "time": 0.84698} +{"mode": "train", "epoch": 58, "iter": 3500, "lr": 0.06749, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29375, "top5_acc": 0.53844, "loss_cls": 4.0949, "loss": 4.0949, "time": 0.84929} +{"mode": "train", "epoch": 58, "iter": 3600, "lr": 0.06747, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28469, "top5_acc": 0.54156, "loss_cls": 4.12703, "loss": 4.12703, "time": 0.84544} +{"mode": "train", "epoch": 58, "iter": 3700, "lr": 0.06744, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28375, "top5_acc": 0.54266, "loss_cls": 4.11065, "loss": 4.11065, "time": 0.85186} +{"mode": "val", "epoch": 58, "iter": 309, "lr": 0.06743, "top1_acc": 0.22833, "top5_acc": 0.47207, "mean_class_accuracy": 0.22836} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.0674, "memory": 15990, "data_time": 1.554, "top1_acc": 0.29141, "top5_acc": 0.54828, "loss_cls": 4.04567, "loss": 4.04567, "time": 2.59608} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.06738, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.29203, "top5_acc": 0.53766, "loss_cls": 4.08372, "loss": 4.08372, "time": 0.85713} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.06735, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27891, "top5_acc": 0.53359, "loss_cls": 4.10959, "loss": 4.10959, "time": 0.85305} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.06732, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29234, "top5_acc": 0.54516, "loss_cls": 4.03384, "loss": 4.03384, "time": 0.84871} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.0673, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28703, "top5_acc": 0.54266, "loss_cls": 4.10431, "loss": 4.10431, "time": 0.84743} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.06727, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27609, "top5_acc": 0.53719, "loss_cls": 4.10283, "loss": 4.10283, "time": 0.85301} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.06725, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28, "top5_acc": 0.54062, "loss_cls": 4.10334, "loss": 4.10334, "time": 0.85121} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.06722, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28281, "top5_acc": 0.53719, "loss_cls": 4.11261, "loss": 4.11261, "time": 0.84935} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.06719, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28859, "top5_acc": 0.54406, "loss_cls": 4.10649, "loss": 4.10649, "time": 0.84376} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.06717, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29594, "top5_acc": 0.54875, "loss_cls": 4.07491, "loss": 4.07491, "time": 0.85089} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.06714, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29594, "top5_acc": 0.54141, "loss_cls": 4.08743, "loss": 4.08743, "time": 0.84444} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.06711, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29188, "top5_acc": 0.54609, "loss_cls": 4.09979, "loss": 4.09979, "time": 0.84988} +{"mode": "train", "epoch": 59, "iter": 1300, "lr": 0.06709, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.27688, "top5_acc": 0.53891, "loss_cls": 4.12308, "loss": 4.12308, "time": 0.84909} +{"mode": "train", "epoch": 59, "iter": 1400, "lr": 0.06706, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.28625, "top5_acc": 0.54922, "loss_cls": 4.05169, "loss": 4.05169, "time": 0.84466} +{"mode": "train", "epoch": 59, "iter": 1500, "lr": 0.06704, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3025, "top5_acc": 0.55297, "loss_cls": 4.04706, "loss": 4.04706, "time": 0.84368} +{"mode": "train", "epoch": 59, "iter": 1600, "lr": 0.06701, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29391, "top5_acc": 0.54266, "loss_cls": 4.09075, "loss": 4.09075, "time": 0.84868} +{"mode": "train", "epoch": 59, "iter": 1700, "lr": 0.06698, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29328, "top5_acc": 0.53516, "loss_cls": 4.10609, "loss": 4.10609, "time": 0.84845} +{"mode": "train", "epoch": 59, "iter": 1800, "lr": 0.06696, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28797, "top5_acc": 0.53922, "loss_cls": 4.11739, "loss": 4.11739, "time": 0.84746} +{"mode": "train", "epoch": 59, "iter": 1900, "lr": 0.06693, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28109, "top5_acc": 0.53219, "loss_cls": 4.12367, "loss": 4.12367, "time": 0.84696} +{"mode": "train", "epoch": 59, "iter": 2000, "lr": 0.0669, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28672, "top5_acc": 0.53516, "loss_cls": 4.12958, "loss": 4.12958, "time": 0.84599} +{"mode": "train", "epoch": 59, "iter": 2100, "lr": 0.06688, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28594, "top5_acc": 0.54141, "loss_cls": 4.07933, "loss": 4.07933, "time": 0.85255} +{"mode": "train", "epoch": 59, "iter": 2200, "lr": 0.06685, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28031, "top5_acc": 0.53797, "loss_cls": 4.11233, "loss": 4.11233, "time": 0.84902} +{"mode": "train", "epoch": 59, "iter": 2300, "lr": 0.06682, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28984, "top5_acc": 0.54031, "loss_cls": 4.09669, "loss": 4.09669, "time": 0.84962} +{"mode": "train", "epoch": 59, "iter": 2400, "lr": 0.0668, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28469, "top5_acc": 0.54516, "loss_cls": 4.12229, "loss": 4.12229, "time": 0.84879} +{"mode": "train", "epoch": 59, "iter": 2500, "lr": 0.06677, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28547, "top5_acc": 0.53578, "loss_cls": 4.11652, "loss": 4.11652, "time": 0.85189} +{"mode": "train", "epoch": 59, "iter": 2600, "lr": 0.06675, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29016, "top5_acc": 0.54297, "loss_cls": 4.06168, "loss": 4.06168, "time": 0.84364} +{"mode": "train", "epoch": 59, "iter": 2700, "lr": 0.06672, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28375, "top5_acc": 0.52984, "loss_cls": 4.11439, "loss": 4.11439, "time": 0.8517} +{"mode": "train", "epoch": 59, "iter": 2800, "lr": 0.06669, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2875, "top5_acc": 0.54672, "loss_cls": 4.06631, "loss": 4.06631, "time": 0.84483} +{"mode": "train", "epoch": 59, "iter": 2900, "lr": 0.06667, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27969, "top5_acc": 0.52969, "loss_cls": 4.13362, "loss": 4.13362, "time": 0.8507} +{"mode": "train", "epoch": 59, "iter": 3000, "lr": 0.06664, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28984, "top5_acc": 0.53484, "loss_cls": 4.11116, "loss": 4.11116, "time": 0.85276} +{"mode": "train", "epoch": 59, "iter": 3100, "lr": 0.06661, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29391, "top5_acc": 0.54188, "loss_cls": 4.08842, "loss": 4.08842, "time": 0.85131} +{"mode": "train", "epoch": 59, "iter": 3200, "lr": 0.06659, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28781, "top5_acc": 0.54531, "loss_cls": 4.08515, "loss": 4.08515, "time": 0.85166} +{"mode": "train", "epoch": 59, "iter": 3300, "lr": 0.06656, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.295, "top5_acc": 0.53906, "loss_cls": 4.06826, "loss": 4.06826, "time": 0.85544} +{"mode": "train", "epoch": 59, "iter": 3400, "lr": 0.06653, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.27406, "top5_acc": 0.53359, "loss_cls": 4.14057, "loss": 4.14057, "time": 0.85109} +{"mode": "train", "epoch": 59, "iter": 3500, "lr": 0.06651, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.29047, "top5_acc": 0.54719, "loss_cls": 4.05034, "loss": 4.05034, "time": 0.8479} +{"mode": "train", "epoch": 59, "iter": 3600, "lr": 0.06648, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27922, "top5_acc": 0.53109, "loss_cls": 4.13457, "loss": 4.13457, "time": 0.84937} +{"mode": "train", "epoch": 59, "iter": 3700, "lr": 0.06646, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28141, "top5_acc": 0.53375, "loss_cls": 4.11822, "loss": 4.11822, "time": 0.84575} +{"mode": "val", "epoch": 59, "iter": 309, "lr": 0.06644, "top1_acc": 0.23071, "top5_acc": 0.46958, "mean_class_accuracy": 0.23047} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.06642, "memory": 15990, "data_time": 1.52081, "top1_acc": 0.30078, "top5_acc": 0.56625, "loss_cls": 3.99863, "loss": 3.99863, "time": 2.55614} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.06639, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29344, "top5_acc": 0.54969, "loss_cls": 4.06755, "loss": 4.06755, "time": 0.85244} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.06636, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29484, "top5_acc": 0.54953, "loss_cls": 4.05671, "loss": 4.05671, "time": 0.85163} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.06634, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28938, "top5_acc": 0.54922, "loss_cls": 4.06566, "loss": 4.06566, "time": 0.84657} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.06631, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28266, "top5_acc": 0.5375, "loss_cls": 4.10798, "loss": 4.10798, "time": 0.85254} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.06629, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28406, "top5_acc": 0.54297, "loss_cls": 4.10493, "loss": 4.10493, "time": 0.8532} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.06626, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28766, "top5_acc": 0.53516, "loss_cls": 4.10715, "loss": 4.10715, "time": 0.85377} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.06623, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29125, "top5_acc": 0.54641, "loss_cls": 4.08455, "loss": 4.08455, "time": 0.85454} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.06621, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28359, "top5_acc": 0.53859, "loss_cls": 4.11316, "loss": 4.11316, "time": 0.85458} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.06618, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30031, "top5_acc": 0.555, "loss_cls": 4.03678, "loss": 4.03678, "time": 0.84929} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.06615, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28641, "top5_acc": 0.5325, "loss_cls": 4.11631, "loss": 4.11631, "time": 0.85043} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.06613, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29344, "top5_acc": 0.54922, "loss_cls": 4.05014, "loss": 4.05014, "time": 0.85372} +{"mode": "train", "epoch": 60, "iter": 1300, "lr": 0.0661, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.28844, "top5_acc": 0.54562, "loss_cls": 4.08008, "loss": 4.08008, "time": 0.8504} +{"mode": "train", "epoch": 60, "iter": 1400, "lr": 0.06607, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28562, "top5_acc": 0.54, "loss_cls": 4.10164, "loss": 4.10164, "time": 0.84524} +{"mode": "train", "epoch": 60, "iter": 1500, "lr": 0.06605, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.27922, "top5_acc": 0.53453, "loss_cls": 4.14432, "loss": 4.14432, "time": 0.84325} +{"mode": "train", "epoch": 60, "iter": 1600, "lr": 0.06602, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28875, "top5_acc": 0.54438, "loss_cls": 4.08653, "loss": 4.08653, "time": 0.84951} +{"mode": "train", "epoch": 60, "iter": 1700, "lr": 0.06599, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28578, "top5_acc": 0.54719, "loss_cls": 4.08403, "loss": 4.08403, "time": 0.85506} +{"mode": "train", "epoch": 60, "iter": 1800, "lr": 0.06597, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28625, "top5_acc": 0.55094, "loss_cls": 4.07267, "loss": 4.07267, "time": 0.84422} +{"mode": "train", "epoch": 60, "iter": 1900, "lr": 0.06594, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29172, "top5_acc": 0.54094, "loss_cls": 4.08806, "loss": 4.08806, "time": 0.85001} +{"mode": "train", "epoch": 60, "iter": 2000, "lr": 0.06591, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29078, "top5_acc": 0.53844, "loss_cls": 4.09068, "loss": 4.09068, "time": 0.85131} +{"mode": "train", "epoch": 60, "iter": 2100, "lr": 0.06589, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28469, "top5_acc": 0.54703, "loss_cls": 4.07233, "loss": 4.07233, "time": 0.85339} +{"mode": "train", "epoch": 60, "iter": 2200, "lr": 0.06586, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28609, "top5_acc": 0.53562, "loss_cls": 4.13875, "loss": 4.13875, "time": 0.8571} +{"mode": "train", "epoch": 60, "iter": 2300, "lr": 0.06584, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28766, "top5_acc": 0.54766, "loss_cls": 4.09005, "loss": 4.09005, "time": 0.85472} +{"mode": "train", "epoch": 60, "iter": 2400, "lr": 0.06581, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28641, "top5_acc": 0.53984, "loss_cls": 4.0887, "loss": 4.0887, "time": 0.855} +{"mode": "train", "epoch": 60, "iter": 2500, "lr": 0.06578, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27359, "top5_acc": 0.52938, "loss_cls": 4.13534, "loss": 4.13534, "time": 0.85176} +{"mode": "train", "epoch": 60, "iter": 2600, "lr": 0.06576, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28875, "top5_acc": 0.54172, "loss_cls": 4.10007, "loss": 4.10007, "time": 0.85367} +{"mode": "train", "epoch": 60, "iter": 2700, "lr": 0.06573, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28312, "top5_acc": 0.54375, "loss_cls": 4.10997, "loss": 4.10997, "time": 0.85202} +{"mode": "train", "epoch": 60, "iter": 2800, "lr": 0.0657, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28141, "top5_acc": 0.53625, "loss_cls": 4.11746, "loss": 4.11746, "time": 0.85432} +{"mode": "train", "epoch": 60, "iter": 2900, "lr": 0.06568, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29266, "top5_acc": 0.55188, "loss_cls": 4.06276, "loss": 4.06276, "time": 0.85324} +{"mode": "train", "epoch": 60, "iter": 3000, "lr": 0.06565, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28641, "top5_acc": 0.54594, "loss_cls": 4.08861, "loss": 4.08861, "time": 0.85336} +{"mode": "train", "epoch": 60, "iter": 3100, "lr": 0.06562, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29203, "top5_acc": 0.54047, "loss_cls": 4.06839, "loss": 4.06839, "time": 0.85217} +{"mode": "train", "epoch": 60, "iter": 3200, "lr": 0.0656, "memory": 15990, "data_time": 0.00073, "top1_acc": 0.28406, "top5_acc": 0.53922, "loss_cls": 4.10684, "loss": 4.10684, "time": 0.85389} +{"mode": "train", "epoch": 60, "iter": 3300, "lr": 0.06557, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28359, "top5_acc": 0.54547, "loss_cls": 4.09043, "loss": 4.09043, "time": 0.85046} +{"mode": "train", "epoch": 60, "iter": 3400, "lr": 0.06554, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28844, "top5_acc": 0.53922, "loss_cls": 4.09551, "loss": 4.09551, "time": 0.85105} +{"mode": "train", "epoch": 60, "iter": 3500, "lr": 0.06552, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30109, "top5_acc": 0.55406, "loss_cls": 4.0467, "loss": 4.0467, "time": 0.85687} +{"mode": "train", "epoch": 60, "iter": 3600, "lr": 0.06549, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28203, "top5_acc": 0.52875, "loss_cls": 4.13756, "loss": 4.13756, "time": 0.85333} +{"mode": "train", "epoch": 60, "iter": 3700, "lr": 0.06546, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29156, "top5_acc": 0.54625, "loss_cls": 4.07378, "loss": 4.07378, "time": 0.8532} +{"mode": "val", "epoch": 60, "iter": 309, "lr": 0.06545, "top1_acc": 0.23492, "top5_acc": 0.4786, "mean_class_accuracy": 0.23475} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.06542, "memory": 15990, "data_time": 1.54148, "top1_acc": 0.30812, "top5_acc": 0.55734, "loss_cls": 4.02327, "loss": 4.02327, "time": 2.58391} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.0654, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.29438, "top5_acc": 0.55172, "loss_cls": 4.06657, "loss": 4.06657, "time": 0.85755} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.06537, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28859, "top5_acc": 0.55031, "loss_cls": 4.04352, "loss": 4.04352, "time": 0.8559} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.06534, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27734, "top5_acc": 0.53672, "loss_cls": 4.13242, "loss": 4.13242, "time": 0.84577} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.06532, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28625, "top5_acc": 0.55031, "loss_cls": 4.07054, "loss": 4.07054, "time": 0.84882} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.06529, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30156, "top5_acc": 0.56078, "loss_cls": 3.9953, "loss": 3.9953, "time": 0.85173} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.06526, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28781, "top5_acc": 0.5425, "loss_cls": 4.07931, "loss": 4.07931, "time": 0.8498} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.06524, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29578, "top5_acc": 0.54266, "loss_cls": 4.06875, "loss": 4.06875, "time": 0.84686} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.06521, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28109, "top5_acc": 0.545, "loss_cls": 4.10163, "loss": 4.10163, "time": 0.84989} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.06519, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29109, "top5_acc": 0.54203, "loss_cls": 4.09068, "loss": 4.09068, "time": 0.84984} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.06516, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28484, "top5_acc": 0.53797, "loss_cls": 4.14371, "loss": 4.14371, "time": 0.84725} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.06513, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30797, "top5_acc": 0.55578, "loss_cls": 3.99895, "loss": 3.99895, "time": 0.8473} +{"mode": "train", "epoch": 61, "iter": 1300, "lr": 0.06511, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28406, "top5_acc": 0.53938, "loss_cls": 4.10428, "loss": 4.10428, "time": 0.84603} +{"mode": "train", "epoch": 61, "iter": 1400, "lr": 0.06508, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29047, "top5_acc": 0.54922, "loss_cls": 4.06576, "loss": 4.06576, "time": 0.84793} +{"mode": "train", "epoch": 61, "iter": 1500, "lr": 0.06505, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28109, "top5_acc": 0.54125, "loss_cls": 4.12382, "loss": 4.12382, "time": 0.84435} +{"mode": "train", "epoch": 61, "iter": 1600, "lr": 0.06503, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29844, "top5_acc": 0.54859, "loss_cls": 4.06665, "loss": 4.06665, "time": 0.84653} +{"mode": "train", "epoch": 61, "iter": 1700, "lr": 0.065, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29391, "top5_acc": 0.55219, "loss_cls": 4.04683, "loss": 4.04683, "time": 0.84541} +{"mode": "train", "epoch": 61, "iter": 1800, "lr": 0.06497, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28281, "top5_acc": 0.55281, "loss_cls": 4.07081, "loss": 4.07081, "time": 0.84496} +{"mode": "train", "epoch": 61, "iter": 1900, "lr": 0.06495, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28688, "top5_acc": 0.54594, "loss_cls": 4.10293, "loss": 4.10293, "time": 0.84416} +{"mode": "train", "epoch": 61, "iter": 2000, "lr": 0.06492, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29234, "top5_acc": 0.54953, "loss_cls": 4.01201, "loss": 4.01201, "time": 0.84596} +{"mode": "train", "epoch": 61, "iter": 2100, "lr": 0.06489, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2875, "top5_acc": 0.53906, "loss_cls": 4.10479, "loss": 4.10479, "time": 0.84822} +{"mode": "train", "epoch": 61, "iter": 2200, "lr": 0.06487, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29797, "top5_acc": 0.54594, "loss_cls": 4.05385, "loss": 4.05385, "time": 0.84696} +{"mode": "train", "epoch": 61, "iter": 2300, "lr": 0.06484, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29594, "top5_acc": 0.55031, "loss_cls": 4.04565, "loss": 4.04565, "time": 0.84977} +{"mode": "train", "epoch": 61, "iter": 2400, "lr": 0.06481, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29156, "top5_acc": 0.54719, "loss_cls": 4.034, "loss": 4.034, "time": 0.84464} +{"mode": "train", "epoch": 61, "iter": 2500, "lr": 0.06478, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29109, "top5_acc": 0.54109, "loss_cls": 4.08391, "loss": 4.08391, "time": 0.84554} +{"mode": "train", "epoch": 61, "iter": 2600, "lr": 0.06476, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29219, "top5_acc": 0.54359, "loss_cls": 4.08829, "loss": 4.08829, "time": 0.84432} +{"mode": "train", "epoch": 61, "iter": 2700, "lr": 0.06473, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29719, "top5_acc": 0.54672, "loss_cls": 4.0701, "loss": 4.0701, "time": 0.84828} +{"mode": "train", "epoch": 61, "iter": 2800, "lr": 0.0647, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28891, "top5_acc": 0.54281, "loss_cls": 4.08569, "loss": 4.08569, "time": 0.84746} +{"mode": "train", "epoch": 61, "iter": 2900, "lr": 0.06468, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28625, "top5_acc": 0.53953, "loss_cls": 4.07958, "loss": 4.07958, "time": 0.846} +{"mode": "train", "epoch": 61, "iter": 3000, "lr": 0.06465, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29375, "top5_acc": 0.5425, "loss_cls": 4.07401, "loss": 4.07401, "time": 0.84672} +{"mode": "train", "epoch": 61, "iter": 3100, "lr": 0.06462, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29625, "top5_acc": 0.55234, "loss_cls": 4.02755, "loss": 4.02755, "time": 0.84691} +{"mode": "train", "epoch": 61, "iter": 3200, "lr": 0.0646, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.28391, "top5_acc": 0.53516, "loss_cls": 4.14173, "loss": 4.14173, "time": 0.8525} +{"mode": "train", "epoch": 61, "iter": 3300, "lr": 0.06457, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28562, "top5_acc": 0.53938, "loss_cls": 4.0809, "loss": 4.0809, "time": 0.84541} +{"mode": "train", "epoch": 61, "iter": 3400, "lr": 0.06454, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28297, "top5_acc": 0.54219, "loss_cls": 4.09156, "loss": 4.09156, "time": 0.84691} +{"mode": "train", "epoch": 61, "iter": 3500, "lr": 0.06452, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.29109, "top5_acc": 0.54344, "loss_cls": 4.08294, "loss": 4.08294, "time": 0.85504} +{"mode": "train", "epoch": 61, "iter": 3600, "lr": 0.06449, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29641, "top5_acc": 0.54156, "loss_cls": 4.08554, "loss": 4.08554, "time": 0.85096} +{"mode": "train", "epoch": 61, "iter": 3700, "lr": 0.06446, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28578, "top5_acc": 0.53734, "loss_cls": 4.1132, "loss": 4.1132, "time": 0.85646} +{"mode": "val", "epoch": 61, "iter": 309, "lr": 0.06445, "top1_acc": 0.22256, "top5_acc": 0.45682, "mean_class_accuracy": 0.22239} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.06443, "memory": 15990, "data_time": 1.48434, "top1_acc": 0.29047, "top5_acc": 0.54547, "loss_cls": 4.03582, "loss": 4.03582, "time": 2.52088} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.0644, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29734, "top5_acc": 0.55656, "loss_cls": 4.01696, "loss": 4.01696, "time": 0.85281} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.06437, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29203, "top5_acc": 0.54984, "loss_cls": 4.05751, "loss": 4.05751, "time": 0.84973} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.06434, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29031, "top5_acc": 0.55344, "loss_cls": 4.05966, "loss": 4.05966, "time": 0.84618} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.06432, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.29703, "top5_acc": 0.54641, "loss_cls": 4.05696, "loss": 4.05696, "time": 0.85053} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.06429, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29438, "top5_acc": 0.54281, "loss_cls": 4.06894, "loss": 4.06894, "time": 0.84923} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.06426, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29109, "top5_acc": 0.54156, "loss_cls": 4.087, "loss": 4.087, "time": 0.84754} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.06424, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29219, "top5_acc": 0.53828, "loss_cls": 4.08015, "loss": 4.08015, "time": 0.84767} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.06421, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29313, "top5_acc": 0.55516, "loss_cls": 4.04926, "loss": 4.04926, "time": 0.84597} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.06418, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28594, "top5_acc": 0.54734, "loss_cls": 4.10388, "loss": 4.10388, "time": 0.85073} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.06416, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28703, "top5_acc": 0.54297, "loss_cls": 4.08269, "loss": 4.08269, "time": 0.84518} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.06413, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28391, "top5_acc": 0.5375, "loss_cls": 4.08223, "loss": 4.08223, "time": 0.84807} +{"mode": "train", "epoch": 62, "iter": 1300, "lr": 0.0641, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28797, "top5_acc": 0.53984, "loss_cls": 4.08939, "loss": 4.08939, "time": 0.84412} +{"mode": "train", "epoch": 62, "iter": 1400, "lr": 0.06408, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29484, "top5_acc": 0.54328, "loss_cls": 4.06199, "loss": 4.06199, "time": 0.84846} +{"mode": "train", "epoch": 62, "iter": 1500, "lr": 0.06405, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.30094, "top5_acc": 0.54781, "loss_cls": 4.0239, "loss": 4.0239, "time": 0.84712} +{"mode": "train", "epoch": 62, "iter": 1600, "lr": 0.06402, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29359, "top5_acc": 0.54, "loss_cls": 4.07611, "loss": 4.07611, "time": 0.84833} +{"mode": "train", "epoch": 62, "iter": 1700, "lr": 0.064, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29797, "top5_acc": 0.54812, "loss_cls": 4.06278, "loss": 4.06278, "time": 0.84792} +{"mode": "train", "epoch": 62, "iter": 1800, "lr": 0.06397, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29828, "top5_acc": 0.55047, "loss_cls": 4.0556, "loss": 4.0556, "time": 0.8457} +{"mode": "train", "epoch": 62, "iter": 1900, "lr": 0.06394, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28375, "top5_acc": 0.54812, "loss_cls": 4.04182, "loss": 4.04182, "time": 0.84651} +{"mode": "train", "epoch": 62, "iter": 2000, "lr": 0.06392, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30016, "top5_acc": 0.55969, "loss_cls": 4.02343, "loss": 4.02343, "time": 0.84794} +{"mode": "train", "epoch": 62, "iter": 2100, "lr": 0.06389, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29156, "top5_acc": 0.54375, "loss_cls": 4.04997, "loss": 4.04997, "time": 0.84935} +{"mode": "train", "epoch": 62, "iter": 2200, "lr": 0.06386, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28938, "top5_acc": 0.54266, "loss_cls": 4.10374, "loss": 4.10374, "time": 0.8438} +{"mode": "train", "epoch": 62, "iter": 2300, "lr": 0.06384, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29125, "top5_acc": 0.54906, "loss_cls": 4.07573, "loss": 4.07573, "time": 0.84922} +{"mode": "train", "epoch": 62, "iter": 2400, "lr": 0.06381, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29203, "top5_acc": 0.54766, "loss_cls": 4.09929, "loss": 4.09929, "time": 0.8481} +{"mode": "train", "epoch": 62, "iter": 2500, "lr": 0.06378, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29781, "top5_acc": 0.54266, "loss_cls": 4.06773, "loss": 4.06773, "time": 0.84927} +{"mode": "train", "epoch": 62, "iter": 2600, "lr": 0.06375, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28953, "top5_acc": 0.54203, "loss_cls": 4.1005, "loss": 4.1005, "time": 0.84976} +{"mode": "train", "epoch": 62, "iter": 2700, "lr": 0.06373, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28312, "top5_acc": 0.54672, "loss_cls": 4.08995, "loss": 4.08995, "time": 0.84864} +{"mode": "train", "epoch": 62, "iter": 2800, "lr": 0.0637, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2775, "top5_acc": 0.53719, "loss_cls": 4.11331, "loss": 4.11331, "time": 0.84932} +{"mode": "train", "epoch": 62, "iter": 2900, "lr": 0.06367, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29141, "top5_acc": 0.54312, "loss_cls": 4.09833, "loss": 4.09833, "time": 0.85059} +{"mode": "train", "epoch": 62, "iter": 3000, "lr": 0.06365, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28891, "top5_acc": 0.54609, "loss_cls": 4.09302, "loss": 4.09302, "time": 0.84861} +{"mode": "train", "epoch": 62, "iter": 3100, "lr": 0.06362, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28469, "top5_acc": 0.53719, "loss_cls": 4.10138, "loss": 4.10138, "time": 0.84854} +{"mode": "train", "epoch": 62, "iter": 3200, "lr": 0.06359, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29156, "top5_acc": 0.5475, "loss_cls": 4.08377, "loss": 4.08377, "time": 0.85812} +{"mode": "train", "epoch": 62, "iter": 3300, "lr": 0.06357, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28922, "top5_acc": 0.54609, "loss_cls": 4.05272, "loss": 4.05272, "time": 0.84061} +{"mode": "train", "epoch": 62, "iter": 3400, "lr": 0.06354, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.29078, "top5_acc": 0.54656, "loss_cls": 4.05549, "loss": 4.05549, "time": 0.84931} +{"mode": "train", "epoch": 62, "iter": 3500, "lr": 0.06351, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29563, "top5_acc": 0.54547, "loss_cls": 4.05576, "loss": 4.05576, "time": 0.84886} +{"mode": "train", "epoch": 62, "iter": 3600, "lr": 0.06349, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29406, "top5_acc": 0.54094, "loss_cls": 4.08087, "loss": 4.08087, "time": 0.84983} +{"mode": "train", "epoch": 62, "iter": 3700, "lr": 0.06346, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29516, "top5_acc": 0.54531, "loss_cls": 4.0797, "loss": 4.0797, "time": 0.84745} +{"mode": "val", "epoch": 62, "iter": 309, "lr": 0.06345, "top1_acc": 0.2216, "top5_acc": 0.46614, "mean_class_accuracy": 0.22138} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.06342, "memory": 15990, "data_time": 1.48701, "top1_acc": 0.29266, "top5_acc": 0.55609, "loss_cls": 4.02284, "loss": 4.02284, "time": 2.52611} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.06339, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28984, "top5_acc": 0.54641, "loss_cls": 4.07597, "loss": 4.07597, "time": 0.84652} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.06337, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.29719, "top5_acc": 0.54875, "loss_cls": 4.04633, "loss": 4.04633, "time": 0.85125} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.06334, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.29719, "top5_acc": 0.55375, "loss_cls": 4.00682, "loss": 4.00682, "time": 0.84515} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.06331, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2925, "top5_acc": 0.54438, "loss_cls": 4.07188, "loss": 4.07188, "time": 0.84988} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.06328, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30047, "top5_acc": 0.55688, "loss_cls": 4.01231, "loss": 4.01231, "time": 0.85021} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.06326, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29031, "top5_acc": 0.54234, "loss_cls": 4.05899, "loss": 4.05899, "time": 0.8506} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.06323, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29625, "top5_acc": 0.54547, "loss_cls": 4.07289, "loss": 4.07289, "time": 0.85328} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.0632, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28625, "top5_acc": 0.53703, "loss_cls": 4.13496, "loss": 4.13496, "time": 0.84773} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.06318, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28953, "top5_acc": 0.54469, "loss_cls": 4.07444, "loss": 4.07444, "time": 0.84942} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.06315, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.29016, "top5_acc": 0.53719, "loss_cls": 4.08011, "loss": 4.08011, "time": 0.85023} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.06312, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29812, "top5_acc": 0.55844, "loss_cls": 4.00909, "loss": 4.00909, "time": 0.85031} +{"mode": "train", "epoch": 63, "iter": 1300, "lr": 0.0631, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29141, "top5_acc": 0.54766, "loss_cls": 4.05381, "loss": 4.05381, "time": 0.85203} +{"mode": "train", "epoch": 63, "iter": 1400, "lr": 0.06307, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29797, "top5_acc": 0.54844, "loss_cls": 4.03595, "loss": 4.03595, "time": 0.85237} +{"mode": "train", "epoch": 63, "iter": 1500, "lr": 0.06304, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.29625, "top5_acc": 0.55141, "loss_cls": 4.01781, "loss": 4.01781, "time": 0.84453} +{"mode": "train", "epoch": 63, "iter": 1600, "lr": 0.06301, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28875, "top5_acc": 0.53812, "loss_cls": 4.12488, "loss": 4.12488, "time": 0.84793} +{"mode": "train", "epoch": 63, "iter": 1700, "lr": 0.06299, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29172, "top5_acc": 0.55188, "loss_cls": 4.05116, "loss": 4.05116, "time": 0.85486} +{"mode": "train", "epoch": 63, "iter": 1800, "lr": 0.06296, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29344, "top5_acc": 0.55266, "loss_cls": 4.03737, "loss": 4.03737, "time": 0.85499} +{"mode": "train", "epoch": 63, "iter": 1900, "lr": 0.06293, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28578, "top5_acc": 0.53641, "loss_cls": 4.11848, "loss": 4.11848, "time": 0.85863} +{"mode": "train", "epoch": 63, "iter": 2000, "lr": 0.06291, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29141, "top5_acc": 0.54625, "loss_cls": 4.07804, "loss": 4.07804, "time": 0.85721} +{"mode": "train", "epoch": 63, "iter": 2100, "lr": 0.06288, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29109, "top5_acc": 0.545, "loss_cls": 4.06686, "loss": 4.06686, "time": 0.85306} +{"mode": "train", "epoch": 63, "iter": 2200, "lr": 0.06285, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30531, "top5_acc": 0.56063, "loss_cls": 4.00904, "loss": 4.00904, "time": 0.85923} +{"mode": "train", "epoch": 63, "iter": 2300, "lr": 0.06283, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30344, "top5_acc": 0.5575, "loss_cls": 4.00421, "loss": 4.00421, "time": 0.85413} +{"mode": "train", "epoch": 63, "iter": 2400, "lr": 0.0628, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27906, "top5_acc": 0.53047, "loss_cls": 4.12853, "loss": 4.12853, "time": 0.85763} +{"mode": "train", "epoch": 63, "iter": 2500, "lr": 0.06277, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29328, "top5_acc": 0.55078, "loss_cls": 4.05963, "loss": 4.05963, "time": 0.8565} +{"mode": "train", "epoch": 63, "iter": 2600, "lr": 0.06274, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29594, "top5_acc": 0.5475, "loss_cls": 4.07188, "loss": 4.07188, "time": 0.86089} +{"mode": "train", "epoch": 63, "iter": 2700, "lr": 0.06272, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29609, "top5_acc": 0.55266, "loss_cls": 4.04876, "loss": 4.04876, "time": 0.8525} +{"mode": "train", "epoch": 63, "iter": 2800, "lr": 0.06269, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29125, "top5_acc": 0.54156, "loss_cls": 4.09127, "loss": 4.09127, "time": 0.85442} +{"mode": "train", "epoch": 63, "iter": 2900, "lr": 0.06266, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28203, "top5_acc": 0.53672, "loss_cls": 4.11601, "loss": 4.11601, "time": 0.85306} +{"mode": "train", "epoch": 63, "iter": 3000, "lr": 0.06264, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29047, "top5_acc": 0.54391, "loss_cls": 4.08761, "loss": 4.08761, "time": 0.85417} +{"mode": "train", "epoch": 63, "iter": 3100, "lr": 0.06261, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29844, "top5_acc": 0.54672, "loss_cls": 4.05387, "loss": 4.05387, "time": 0.85705} +{"mode": "train", "epoch": 63, "iter": 3200, "lr": 0.06258, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28672, "top5_acc": 0.54594, "loss_cls": 4.07202, "loss": 4.07202, "time": 0.85444} +{"mode": "train", "epoch": 63, "iter": 3300, "lr": 0.06256, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29828, "top5_acc": 0.55844, "loss_cls": 3.98421, "loss": 3.98421, "time": 0.85146} +{"mode": "train", "epoch": 63, "iter": 3400, "lr": 0.06253, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29047, "top5_acc": 0.54, "loss_cls": 4.07174, "loss": 4.07174, "time": 0.84711} +{"mode": "train", "epoch": 63, "iter": 3500, "lr": 0.0625, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30141, "top5_acc": 0.55047, "loss_cls": 4.03516, "loss": 4.03516, "time": 0.853} +{"mode": "train", "epoch": 63, "iter": 3600, "lr": 0.06247, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.29375, "top5_acc": 0.55125, "loss_cls": 4.07092, "loss": 4.07092, "time": 0.85445} +{"mode": "train", "epoch": 63, "iter": 3700, "lr": 0.06245, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28719, "top5_acc": 0.5425, "loss_cls": 4.08737, "loss": 4.08737, "time": 0.85341} +{"mode": "val", "epoch": 63, "iter": 309, "lr": 0.06243, "top1_acc": 0.22747, "top5_acc": 0.46589, "mean_class_accuracy": 0.22712} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.06241, "memory": 15990, "data_time": 1.47474, "top1_acc": 0.30359, "top5_acc": 0.55281, "loss_cls": 4.01186, "loss": 4.01186, "time": 2.51251} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.06238, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29984, "top5_acc": 0.55297, "loss_cls": 4.02481, "loss": 4.02481, "time": 0.85797} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.06235, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.29453, "top5_acc": 0.54578, "loss_cls": 4.04216, "loss": 4.04216, "time": 0.86156} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.06233, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30172, "top5_acc": 0.55062, "loss_cls": 4.02884, "loss": 4.02884, "time": 0.85072} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.0623, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28891, "top5_acc": 0.54266, "loss_cls": 4.08207, "loss": 4.08207, "time": 0.85056} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.06227, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30141, "top5_acc": 0.55234, "loss_cls": 4.01705, "loss": 4.01705, "time": 0.85569} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.06225, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29234, "top5_acc": 0.54688, "loss_cls": 4.06033, "loss": 4.06033, "time": 0.85759} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.06222, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30453, "top5_acc": 0.55484, "loss_cls": 4.03345, "loss": 4.03345, "time": 0.85809} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.06219, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29406, "top5_acc": 0.55, "loss_cls": 4.05142, "loss": 4.05142, "time": 0.85912} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.06216, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28922, "top5_acc": 0.53844, "loss_cls": 4.09446, "loss": 4.09446, "time": 0.86009} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.06214, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29703, "top5_acc": 0.55234, "loss_cls": 4.06, "loss": 4.06, "time": 0.85791} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.06211, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.27859, "top5_acc": 0.53844, "loss_cls": 4.09706, "loss": 4.09706, "time": 0.8608} +{"mode": "train", "epoch": 64, "iter": 1300, "lr": 0.06208, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29297, "top5_acc": 0.54688, "loss_cls": 4.06434, "loss": 4.06434, "time": 0.85983} +{"mode": "train", "epoch": 64, "iter": 1400, "lr": 0.06206, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28688, "top5_acc": 0.53641, "loss_cls": 4.07723, "loss": 4.07723, "time": 0.8546} +{"mode": "train", "epoch": 64, "iter": 1500, "lr": 0.06203, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2925, "top5_acc": 0.54328, "loss_cls": 4.07283, "loss": 4.07283, "time": 0.84673} +{"mode": "train", "epoch": 64, "iter": 1600, "lr": 0.062, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28797, "top5_acc": 0.54734, "loss_cls": 4.08056, "loss": 4.08056, "time": 0.84643} +{"mode": "train", "epoch": 64, "iter": 1700, "lr": 0.06197, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.29563, "top5_acc": 0.55172, "loss_cls": 4.03422, "loss": 4.03422, "time": 0.84565} +{"mode": "train", "epoch": 64, "iter": 1800, "lr": 0.06195, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29891, "top5_acc": 0.55672, "loss_cls": 4.02855, "loss": 4.02855, "time": 0.85328} +{"mode": "train", "epoch": 64, "iter": 1900, "lr": 0.06192, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29016, "top5_acc": 0.54062, "loss_cls": 4.06428, "loss": 4.06428, "time": 0.84947} +{"mode": "train", "epoch": 64, "iter": 2000, "lr": 0.06189, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29531, "top5_acc": 0.55234, "loss_cls": 4.0382, "loss": 4.0382, "time": 0.84781} +{"mode": "train", "epoch": 64, "iter": 2100, "lr": 0.06187, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29688, "top5_acc": 0.55578, "loss_cls": 4.01321, "loss": 4.01321, "time": 0.84679} +{"mode": "train", "epoch": 64, "iter": 2200, "lr": 0.06184, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30141, "top5_acc": 0.56219, "loss_cls": 4.01354, "loss": 4.01354, "time": 0.84646} +{"mode": "train", "epoch": 64, "iter": 2300, "lr": 0.06181, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28953, "top5_acc": 0.54531, "loss_cls": 4.08584, "loss": 4.08584, "time": 0.84714} +{"mode": "train", "epoch": 64, "iter": 2400, "lr": 0.06178, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29375, "top5_acc": 0.54812, "loss_cls": 4.04633, "loss": 4.04633, "time": 0.84833} +{"mode": "train", "epoch": 64, "iter": 2500, "lr": 0.06176, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29109, "top5_acc": 0.52891, "loss_cls": 4.13527, "loss": 4.13527, "time": 0.84878} +{"mode": "train", "epoch": 64, "iter": 2600, "lr": 0.06173, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.29063, "top5_acc": 0.54406, "loss_cls": 4.06856, "loss": 4.06856, "time": 0.85101} +{"mode": "train", "epoch": 64, "iter": 2700, "lr": 0.0617, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.295, "top5_acc": 0.55156, "loss_cls": 4.0444, "loss": 4.0444, "time": 0.84825} +{"mode": "train", "epoch": 64, "iter": 2800, "lr": 0.06168, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29125, "top5_acc": 0.54609, "loss_cls": 4.06323, "loss": 4.06323, "time": 0.85003} +{"mode": "train", "epoch": 64, "iter": 2900, "lr": 0.06165, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28312, "top5_acc": 0.53969, "loss_cls": 4.09547, "loss": 4.09547, "time": 0.84906} +{"mode": "train", "epoch": 64, "iter": 3000, "lr": 0.06162, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29469, "top5_acc": 0.55484, "loss_cls": 4.01948, "loss": 4.01948, "time": 0.84933} +{"mode": "train", "epoch": 64, "iter": 3100, "lr": 0.06159, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29188, "top5_acc": 0.54109, "loss_cls": 4.07457, "loss": 4.07457, "time": 0.84916} +{"mode": "train", "epoch": 64, "iter": 3200, "lr": 0.06157, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.29125, "top5_acc": 0.55188, "loss_cls": 4.04542, "loss": 4.04542, "time": 0.84738} +{"mode": "train", "epoch": 64, "iter": 3300, "lr": 0.06154, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.30531, "top5_acc": 0.5525, "loss_cls": 4.03527, "loss": 4.03527, "time": 0.84919} +{"mode": "train", "epoch": 64, "iter": 3400, "lr": 0.06151, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30219, "top5_acc": 0.55344, "loss_cls": 4.0391, "loss": 4.0391, "time": 0.84354} +{"mode": "train", "epoch": 64, "iter": 3500, "lr": 0.06148, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29203, "top5_acc": 0.54656, "loss_cls": 4.09353, "loss": 4.09353, "time": 0.8439} +{"mode": "train", "epoch": 64, "iter": 3600, "lr": 0.06146, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29156, "top5_acc": 0.54859, "loss_cls": 4.05374, "loss": 4.05374, "time": 0.84439} +{"mode": "train", "epoch": 64, "iter": 3700, "lr": 0.06143, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29281, "top5_acc": 0.54641, "loss_cls": 4.04732, "loss": 4.04732, "time": 0.84633} +{"mode": "val", "epoch": 64, "iter": 309, "lr": 0.06142, "top1_acc": 0.20833, "top5_acc": 0.44775, "mean_class_accuracy": 0.2083} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.06139, "memory": 15990, "data_time": 1.53355, "top1_acc": 0.29359, "top5_acc": 0.55312, "loss_cls": 4.05175, "loss": 4.05175, "time": 2.56408} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.06136, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.30266, "top5_acc": 0.56, "loss_cls": 4.00807, "loss": 4.00807, "time": 0.85479} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.06134, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30703, "top5_acc": 0.55594, "loss_cls": 4.00221, "loss": 4.00221, "time": 0.85373} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.06131, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30203, "top5_acc": 0.55266, "loss_cls": 4.04908, "loss": 4.04908, "time": 0.85332} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.06128, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.29953, "top5_acc": 0.55188, "loss_cls": 4.00387, "loss": 4.00387, "time": 0.84933} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.06125, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.31141, "top5_acc": 0.56484, "loss_cls": 3.96318, "loss": 3.96318, "time": 0.85568} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.06123, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29812, "top5_acc": 0.54922, "loss_cls": 4.02969, "loss": 4.02969, "time": 0.85411} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0612, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29266, "top5_acc": 0.54625, "loss_cls": 4.04164, "loss": 4.04164, "time": 0.84907} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.06117, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29219, "top5_acc": 0.55266, "loss_cls": 4.03017, "loss": 4.03017, "time": 0.85421} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.06115, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28938, "top5_acc": 0.54594, "loss_cls": 4.04497, "loss": 4.04497, "time": 0.8554} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.06112, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30047, "top5_acc": 0.55516, "loss_cls": 4.00873, "loss": 4.00873, "time": 0.85557} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.06109, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29422, "top5_acc": 0.55578, "loss_cls": 4.00719, "loss": 4.00719, "time": 0.85195} +{"mode": "train", "epoch": 65, "iter": 1300, "lr": 0.06106, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30094, "top5_acc": 0.555, "loss_cls": 4.01009, "loss": 4.01009, "time": 0.84731} +{"mode": "train", "epoch": 65, "iter": 1400, "lr": 0.06104, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28703, "top5_acc": 0.54312, "loss_cls": 4.10549, "loss": 4.10549, "time": 0.849} +{"mode": "train", "epoch": 65, "iter": 1500, "lr": 0.06101, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28922, "top5_acc": 0.54344, "loss_cls": 4.05034, "loss": 4.05034, "time": 0.85015} +{"mode": "train", "epoch": 65, "iter": 1600, "lr": 0.06098, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.285, "top5_acc": 0.53938, "loss_cls": 4.10631, "loss": 4.10631, "time": 0.84358} +{"mode": "train", "epoch": 65, "iter": 1700, "lr": 0.06095, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29281, "top5_acc": 0.55016, "loss_cls": 4.0625, "loss": 4.0625, "time": 0.85043} +{"mode": "train", "epoch": 65, "iter": 1800, "lr": 0.06093, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30062, "top5_acc": 0.55375, "loss_cls": 4.03801, "loss": 4.03801, "time": 0.85764} +{"mode": "train", "epoch": 65, "iter": 1900, "lr": 0.0609, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29438, "top5_acc": 0.54984, "loss_cls": 4.07446, "loss": 4.07446, "time": 0.85683} +{"mode": "train", "epoch": 65, "iter": 2000, "lr": 0.06087, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28766, "top5_acc": 0.53969, "loss_cls": 4.06751, "loss": 4.06751, "time": 0.86131} +{"mode": "train", "epoch": 65, "iter": 2100, "lr": 0.06085, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.29891, "top5_acc": 0.56047, "loss_cls": 4.01792, "loss": 4.01792, "time": 0.85576} +{"mode": "train", "epoch": 65, "iter": 2200, "lr": 0.06082, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29, "top5_acc": 0.55422, "loss_cls": 4.03409, "loss": 4.03409, "time": 0.85579} +{"mode": "train", "epoch": 65, "iter": 2300, "lr": 0.06079, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29938, "top5_acc": 0.55266, "loss_cls": 4.05496, "loss": 4.05496, "time": 0.85249} +{"mode": "train", "epoch": 65, "iter": 2400, "lr": 0.06076, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29078, "top5_acc": 0.54547, "loss_cls": 4.08637, "loss": 4.08637, "time": 0.8568} +{"mode": "train", "epoch": 65, "iter": 2500, "lr": 0.06074, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29953, "top5_acc": 0.55922, "loss_cls": 4.01879, "loss": 4.01879, "time": 0.85071} +{"mode": "train", "epoch": 65, "iter": 2600, "lr": 0.06071, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30031, "top5_acc": 0.55719, "loss_cls": 4.0101, "loss": 4.0101, "time": 0.8609} +{"mode": "train", "epoch": 65, "iter": 2700, "lr": 0.06068, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.29125, "top5_acc": 0.53891, "loss_cls": 4.08724, "loss": 4.08724, "time": 0.85244} +{"mode": "train", "epoch": 65, "iter": 2800, "lr": 0.06065, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.29797, "top5_acc": 0.54469, "loss_cls": 4.07857, "loss": 4.07857, "time": 0.85839} +{"mode": "train", "epoch": 65, "iter": 2900, "lr": 0.06063, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30125, "top5_acc": 0.56719, "loss_cls": 3.99407, "loss": 3.99407, "time": 0.85616} +{"mode": "train", "epoch": 65, "iter": 3000, "lr": 0.0606, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.30016, "top5_acc": 0.54516, "loss_cls": 4.04262, "loss": 4.04262, "time": 0.8558} +{"mode": "train", "epoch": 65, "iter": 3100, "lr": 0.06057, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29922, "top5_acc": 0.54797, "loss_cls": 4.06095, "loss": 4.06095, "time": 0.85132} +{"mode": "train", "epoch": 65, "iter": 3200, "lr": 0.06055, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30016, "top5_acc": 0.54891, "loss_cls": 4.03312, "loss": 4.03312, "time": 0.85254} +{"mode": "train", "epoch": 65, "iter": 3300, "lr": 0.06052, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29047, "top5_acc": 0.54625, "loss_cls": 4.06298, "loss": 4.06298, "time": 0.8526} +{"mode": "train", "epoch": 65, "iter": 3400, "lr": 0.06049, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29313, "top5_acc": 0.54766, "loss_cls": 4.04302, "loss": 4.04302, "time": 0.84896} +{"mode": "train", "epoch": 65, "iter": 3500, "lr": 0.06046, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30844, "top5_acc": 0.55328, "loss_cls": 4.00983, "loss": 4.00983, "time": 0.84884} +{"mode": "train", "epoch": 65, "iter": 3600, "lr": 0.06044, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29469, "top5_acc": 0.53938, "loss_cls": 4.07918, "loss": 4.07918, "time": 0.84523} +{"mode": "train", "epoch": 65, "iter": 3700, "lr": 0.06041, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29063, "top5_acc": 0.54937, "loss_cls": 4.05376, "loss": 4.05376, "time": 0.84792} +{"mode": "val", "epoch": 65, "iter": 309, "lr": 0.0604, "top1_acc": 0.24434, "top5_acc": 0.48822, "mean_class_accuracy": 0.24417} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.06037, "memory": 15990, "data_time": 1.62325, "top1_acc": 0.30734, "top5_acc": 0.55219, "loss_cls": 4.00126, "loss": 4.00126, "time": 2.64826} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.06034, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30734, "top5_acc": 0.5675, "loss_cls": 3.95756, "loss": 3.95756, "time": 0.8472} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.06031, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30016, "top5_acc": 0.55734, "loss_cls": 4.00683, "loss": 4.00683, "time": 0.85083} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.06029, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.29766, "top5_acc": 0.56125, "loss_cls": 4.01229, "loss": 4.01229, "time": 0.84452} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.06026, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29625, "top5_acc": 0.54937, "loss_cls": 4.0435, "loss": 4.0435, "time": 0.8475} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.06023, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30234, "top5_acc": 0.55953, "loss_cls": 4.013, "loss": 4.013, "time": 0.84998} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.0602, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30172, "top5_acc": 0.54969, "loss_cls": 4.04117, "loss": 4.04117, "time": 0.84737} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.06018, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30328, "top5_acc": 0.55359, "loss_cls": 3.99965, "loss": 3.99965, "time": 0.84498} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.06015, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29672, "top5_acc": 0.55328, "loss_cls": 4.04427, "loss": 4.04427, "time": 0.84533} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.06012, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29422, "top5_acc": 0.55312, "loss_cls": 4.03266, "loss": 4.03266, "time": 0.847} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.06009, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30578, "top5_acc": 0.55844, "loss_cls": 4.00425, "loss": 4.00425, "time": 0.84936} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.06007, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2925, "top5_acc": 0.55391, "loss_cls": 4.04208, "loss": 4.04208, "time": 0.84671} +{"mode": "train", "epoch": 66, "iter": 1300, "lr": 0.06004, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29953, "top5_acc": 0.54594, "loss_cls": 4.03114, "loss": 4.03114, "time": 0.84805} +{"mode": "train", "epoch": 66, "iter": 1400, "lr": 0.06001, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30328, "top5_acc": 0.55281, "loss_cls": 4.03639, "loss": 4.03639, "time": 0.84594} +{"mode": "train", "epoch": 66, "iter": 1500, "lr": 0.05999, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28953, "top5_acc": 0.54438, "loss_cls": 4.07298, "loss": 4.07298, "time": 0.84646} +{"mode": "train", "epoch": 66, "iter": 1600, "lr": 0.05996, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29516, "top5_acc": 0.5525, "loss_cls": 4.02172, "loss": 4.02172, "time": 0.84759} +{"mode": "train", "epoch": 66, "iter": 1700, "lr": 0.05993, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.28875, "top5_acc": 0.54797, "loss_cls": 4.0513, "loss": 4.0513, "time": 0.84708} +{"mode": "train", "epoch": 66, "iter": 1800, "lr": 0.0599, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30172, "top5_acc": 0.55344, "loss_cls": 4.01105, "loss": 4.01105, "time": 0.84557} +{"mode": "train", "epoch": 66, "iter": 1900, "lr": 0.05988, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29703, "top5_acc": 0.55203, "loss_cls": 4.0318, "loss": 4.0318, "time": 0.84665} +{"mode": "train", "epoch": 66, "iter": 2000, "lr": 0.05985, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29719, "top5_acc": 0.5475, "loss_cls": 4.06402, "loss": 4.06402, "time": 0.84878} +{"mode": "train", "epoch": 66, "iter": 2100, "lr": 0.05982, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29203, "top5_acc": 0.54094, "loss_cls": 4.08035, "loss": 4.08035, "time": 0.85431} +{"mode": "train", "epoch": 66, "iter": 2200, "lr": 0.05979, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29453, "top5_acc": 0.54906, "loss_cls": 4.01425, "loss": 4.01425, "time": 0.85171} +{"mode": "train", "epoch": 66, "iter": 2300, "lr": 0.05977, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29875, "top5_acc": 0.55125, "loss_cls": 4.04802, "loss": 4.04802, "time": 0.84576} +{"mode": "train", "epoch": 66, "iter": 2400, "lr": 0.05974, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30469, "top5_acc": 0.56328, "loss_cls": 3.9941, "loss": 3.9941, "time": 0.84317} +{"mode": "train", "epoch": 66, "iter": 2500, "lr": 0.05971, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29906, "top5_acc": 0.55656, "loss_cls": 4.0077, "loss": 4.0077, "time": 0.85083} +{"mode": "train", "epoch": 66, "iter": 2600, "lr": 0.05968, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30047, "top5_acc": 0.55422, "loss_cls": 3.99911, "loss": 3.99911, "time": 0.85089} +{"mode": "train", "epoch": 66, "iter": 2700, "lr": 0.05966, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29688, "top5_acc": 0.55828, "loss_cls": 4.05151, "loss": 4.05151, "time": 0.85562} +{"mode": "train", "epoch": 66, "iter": 2800, "lr": 0.05963, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29609, "top5_acc": 0.55781, "loss_cls": 3.99875, "loss": 3.99875, "time": 0.8481} +{"mode": "train", "epoch": 66, "iter": 2900, "lr": 0.0596, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29375, "top5_acc": 0.54125, "loss_cls": 4.06015, "loss": 4.06015, "time": 0.85} +{"mode": "train", "epoch": 66, "iter": 3000, "lr": 0.05957, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29906, "top5_acc": 0.55609, "loss_cls": 4.01371, "loss": 4.01371, "time": 0.8472} +{"mode": "train", "epoch": 66, "iter": 3100, "lr": 0.05955, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.29656, "top5_acc": 0.55328, "loss_cls": 4.03268, "loss": 4.03268, "time": 0.85113} +{"mode": "train", "epoch": 66, "iter": 3200, "lr": 0.05952, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.29281, "top5_acc": 0.53859, "loss_cls": 4.08802, "loss": 4.08802, "time": 0.85491} +{"mode": "train", "epoch": 66, "iter": 3300, "lr": 0.05949, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29547, "top5_acc": 0.53922, "loss_cls": 4.08253, "loss": 4.08253, "time": 0.84705} +{"mode": "train", "epoch": 66, "iter": 3400, "lr": 0.05946, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29188, "top5_acc": 0.545, "loss_cls": 4.06698, "loss": 4.06698, "time": 0.84751} +{"mode": "train", "epoch": 66, "iter": 3500, "lr": 0.05944, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30094, "top5_acc": 0.54922, "loss_cls": 4.04126, "loss": 4.04126, "time": 0.84226} +{"mode": "train", "epoch": 66, "iter": 3600, "lr": 0.05941, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31234, "top5_acc": 0.56141, "loss_cls": 3.97493, "loss": 3.97493, "time": 0.84525} +{"mode": "train", "epoch": 66, "iter": 3700, "lr": 0.05938, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29922, "top5_acc": 0.55469, "loss_cls": 4.0174, "loss": 4.0174, "time": 0.84644} +{"mode": "val", "epoch": 66, "iter": 309, "lr": 0.05937, "top1_acc": 0.24378, "top5_acc": 0.49015, "mean_class_accuracy": 0.24385} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.05934, "memory": 15990, "data_time": 1.58242, "top1_acc": 0.30031, "top5_acc": 0.56422, "loss_cls": 3.99817, "loss": 3.99817, "time": 2.62368} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.05931, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.30391, "top5_acc": 0.55625, "loss_cls": 4.00468, "loss": 4.00468, "time": 0.85183} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.05929, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.30312, "top5_acc": 0.56531, "loss_cls": 3.94918, "loss": 3.94918, "time": 0.85505} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.05926, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30344, "top5_acc": 0.55031, "loss_cls": 4.02394, "loss": 4.02394, "time": 0.85507} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.05923, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29906, "top5_acc": 0.55844, "loss_cls": 4.00047, "loss": 4.00047, "time": 0.84948} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.0592, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.29891, "top5_acc": 0.55516, "loss_cls": 4.02307, "loss": 4.02307, "time": 0.85787} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.05918, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.28953, "top5_acc": 0.55875, "loss_cls": 4.00016, "loss": 4.00016, "time": 0.86199} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.05915, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29766, "top5_acc": 0.55937, "loss_cls": 4.00733, "loss": 4.00733, "time": 0.8607} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.05912, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30609, "top5_acc": 0.56, "loss_cls": 4.01854, "loss": 4.01854, "time": 0.85611} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.05909, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30703, "top5_acc": 0.55922, "loss_cls": 3.9768, "loss": 3.9768, "time": 0.85821} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.05907, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30031, "top5_acc": 0.55172, "loss_cls": 4.04328, "loss": 4.04328, "time": 0.85262} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.05904, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29313, "top5_acc": 0.54875, "loss_cls": 4.05167, "loss": 4.05167, "time": 0.85333} +{"mode": "train", "epoch": 67, "iter": 1300, "lr": 0.05901, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29703, "top5_acc": 0.54797, "loss_cls": 4.02136, "loss": 4.02136, "time": 0.85766} +{"mode": "train", "epoch": 67, "iter": 1400, "lr": 0.05898, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30016, "top5_acc": 0.55062, "loss_cls": 4.0131, "loss": 4.0131, "time": 0.85357} +{"mode": "train", "epoch": 67, "iter": 1500, "lr": 0.05896, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.29828, "top5_acc": 0.55328, "loss_cls": 4.02076, "loss": 4.02076, "time": 0.84821} +{"mode": "train", "epoch": 67, "iter": 1600, "lr": 0.05893, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29188, "top5_acc": 0.54859, "loss_cls": 4.04001, "loss": 4.04001, "time": 0.84656} +{"mode": "train", "epoch": 67, "iter": 1700, "lr": 0.0589, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.30359, "top5_acc": 0.55516, "loss_cls": 4.0268, "loss": 4.0268, "time": 0.8454} +{"mode": "train", "epoch": 67, "iter": 1800, "lr": 0.05887, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29406, "top5_acc": 0.55391, "loss_cls": 4.02512, "loss": 4.02512, "time": 0.85031} +{"mode": "train", "epoch": 67, "iter": 1900, "lr": 0.05885, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29078, "top5_acc": 0.55203, "loss_cls": 4.02665, "loss": 4.02665, "time": 0.84861} +{"mode": "train", "epoch": 67, "iter": 2000, "lr": 0.05882, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3025, "top5_acc": 0.56141, "loss_cls": 3.9815, "loss": 3.9815, "time": 0.85133} +{"mode": "train", "epoch": 67, "iter": 2100, "lr": 0.05879, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31141, "top5_acc": 0.55734, "loss_cls": 3.99567, "loss": 3.99567, "time": 0.85259} +{"mode": "train", "epoch": 67, "iter": 2200, "lr": 0.05876, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29234, "top5_acc": 0.54937, "loss_cls": 4.04721, "loss": 4.04721, "time": 0.85363} +{"mode": "train", "epoch": 67, "iter": 2300, "lr": 0.05874, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29875, "top5_acc": 0.54984, "loss_cls": 4.02131, "loss": 4.02131, "time": 0.85377} +{"mode": "train", "epoch": 67, "iter": 2400, "lr": 0.05871, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3025, "top5_acc": 0.55344, "loss_cls": 4.01214, "loss": 4.01214, "time": 0.85548} +{"mode": "train", "epoch": 67, "iter": 2500, "lr": 0.05868, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.29375, "top5_acc": 0.54984, "loss_cls": 4.04086, "loss": 4.04086, "time": 0.85032} +{"mode": "train", "epoch": 67, "iter": 2600, "lr": 0.05865, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29438, "top5_acc": 0.55141, "loss_cls": 4.0537, "loss": 4.0537, "time": 0.84995} +{"mode": "train", "epoch": 67, "iter": 2700, "lr": 0.05863, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29078, "top5_acc": 0.55328, "loss_cls": 4.06455, "loss": 4.06455, "time": 0.85779} +{"mode": "train", "epoch": 67, "iter": 2800, "lr": 0.0586, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30156, "top5_acc": 0.56109, "loss_cls": 3.99751, "loss": 3.99751, "time": 0.85531} +{"mode": "train", "epoch": 67, "iter": 2900, "lr": 0.05857, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29969, "top5_acc": 0.55797, "loss_cls": 4.00401, "loss": 4.00401, "time": 0.85833} +{"mode": "train", "epoch": 67, "iter": 3000, "lr": 0.05854, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29422, "top5_acc": 0.54922, "loss_cls": 4.04875, "loss": 4.04875, "time": 0.85343} +{"mode": "train", "epoch": 67, "iter": 3100, "lr": 0.05852, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30875, "top5_acc": 0.55562, "loss_cls": 4.00798, "loss": 4.00798, "time": 0.85916} +{"mode": "train", "epoch": 67, "iter": 3200, "lr": 0.05849, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29938, "top5_acc": 0.54375, "loss_cls": 4.0704, "loss": 4.0704, "time": 0.85117} +{"mode": "train", "epoch": 67, "iter": 3300, "lr": 0.05846, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.295, "top5_acc": 0.5575, "loss_cls": 4.01085, "loss": 4.01085, "time": 0.84824} +{"mode": "train", "epoch": 67, "iter": 3400, "lr": 0.05843, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.29031, "top5_acc": 0.55766, "loss_cls": 4.03834, "loss": 4.03834, "time": 0.8512} +{"mode": "train", "epoch": 67, "iter": 3500, "lr": 0.05841, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30203, "top5_acc": 0.55328, "loss_cls": 4.0292, "loss": 4.0292, "time": 0.85649} +{"mode": "train", "epoch": 67, "iter": 3600, "lr": 0.05838, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29734, "top5_acc": 0.54656, "loss_cls": 4.03084, "loss": 4.03084, "time": 0.85524} +{"mode": "train", "epoch": 67, "iter": 3700, "lr": 0.05835, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.3025, "top5_acc": 0.55375, "loss_cls": 4.02109, "loss": 4.02109, "time": 0.86119} +{"mode": "val", "epoch": 67, "iter": 309, "lr": 0.05834, "top1_acc": 0.23416, "top5_acc": 0.46766, "mean_class_accuracy": 0.23386} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.05831, "memory": 15990, "data_time": 1.61878, "top1_acc": 0.30578, "top5_acc": 0.55219, "loss_cls": 3.99452, "loss": 3.99452, "time": 2.66011} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.05828, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.29781, "top5_acc": 0.55547, "loss_cls": 4.00871, "loss": 4.00871, "time": 0.85509} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.05826, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30656, "top5_acc": 0.56469, "loss_cls": 3.96712, "loss": 3.96712, "time": 0.85029} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.05823, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.29766, "top5_acc": 0.55734, "loss_cls": 3.99777, "loss": 3.99777, "time": 0.85089} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.0582, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30359, "top5_acc": 0.55719, "loss_cls": 4.00461, "loss": 4.00461, "time": 0.85964} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.05817, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31281, "top5_acc": 0.56375, "loss_cls": 3.96262, "loss": 3.96262, "time": 0.85195} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.05815, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29328, "top5_acc": 0.55406, "loss_cls": 4.04106, "loss": 4.04106, "time": 0.849} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.05812, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30281, "top5_acc": 0.56016, "loss_cls": 3.98606, "loss": 3.98606, "time": 0.84775} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.05809, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29828, "top5_acc": 0.55828, "loss_cls": 4.01828, "loss": 4.01828, "time": 0.85008} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.05806, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30453, "top5_acc": 0.55672, "loss_cls": 3.99483, "loss": 3.99483, "time": 0.85073} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.05804, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29484, "top5_acc": 0.55328, "loss_cls": 4.03767, "loss": 4.03767, "time": 0.84965} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.05801, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30078, "top5_acc": 0.55, "loss_cls": 4.04678, "loss": 4.04678, "time": 0.85049} +{"mode": "train", "epoch": 68, "iter": 1300, "lr": 0.05798, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30344, "top5_acc": 0.55484, "loss_cls": 4.01316, "loss": 4.01316, "time": 0.84849} +{"mode": "train", "epoch": 68, "iter": 1400, "lr": 0.05795, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29531, "top5_acc": 0.55062, "loss_cls": 4.02829, "loss": 4.02829, "time": 0.84737} +{"mode": "train", "epoch": 68, "iter": 1500, "lr": 0.05792, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29484, "top5_acc": 0.55078, "loss_cls": 4.00606, "loss": 4.00606, "time": 0.84935} +{"mode": "train", "epoch": 68, "iter": 1600, "lr": 0.0579, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29484, "top5_acc": 0.55797, "loss_cls": 4.0125, "loss": 4.0125, "time": 0.84727} +{"mode": "train", "epoch": 68, "iter": 1700, "lr": 0.05787, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29766, "top5_acc": 0.55156, "loss_cls": 4.02028, "loss": 4.02028, "time": 0.85373} +{"mode": "train", "epoch": 68, "iter": 1800, "lr": 0.05784, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30766, "top5_acc": 0.555, "loss_cls": 4.02657, "loss": 4.02657, "time": 0.8583} +{"mode": "train", "epoch": 68, "iter": 1900, "lr": 0.05781, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29516, "top5_acc": 0.55297, "loss_cls": 4.05816, "loss": 4.05816, "time": 0.85487} +{"mode": "train", "epoch": 68, "iter": 2000, "lr": 0.05779, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29938, "top5_acc": 0.56437, "loss_cls": 3.9962, "loss": 3.9962, "time": 0.85827} +{"mode": "train", "epoch": 68, "iter": 2100, "lr": 0.05776, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29234, "top5_acc": 0.55391, "loss_cls": 4.03711, "loss": 4.03711, "time": 0.85473} +{"mode": "train", "epoch": 68, "iter": 2200, "lr": 0.05773, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28781, "top5_acc": 0.54672, "loss_cls": 4.06734, "loss": 4.06734, "time": 0.85372} +{"mode": "train", "epoch": 68, "iter": 2300, "lr": 0.0577, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.29953, "top5_acc": 0.55219, "loss_cls": 3.99725, "loss": 3.99725, "time": 0.85321} +{"mode": "train", "epoch": 68, "iter": 2400, "lr": 0.05768, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29781, "top5_acc": 0.55172, "loss_cls": 4.02821, "loss": 4.02821, "time": 0.85412} +{"mode": "train", "epoch": 68, "iter": 2500, "lr": 0.05765, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.29609, "top5_acc": 0.55375, "loss_cls": 4.00231, "loss": 4.00231, "time": 0.85123} +{"mode": "train", "epoch": 68, "iter": 2600, "lr": 0.05762, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30469, "top5_acc": 0.55156, "loss_cls": 3.99856, "loss": 3.99856, "time": 0.85244} +{"mode": "train", "epoch": 68, "iter": 2700, "lr": 0.05759, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29109, "top5_acc": 0.545, "loss_cls": 4.06251, "loss": 4.06251, "time": 0.85414} +{"mode": "train", "epoch": 68, "iter": 2800, "lr": 0.05757, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.2975, "top5_acc": 0.52844, "loss_cls": 4.09467, "loss": 4.09467, "time": 0.8549} +{"mode": "train", "epoch": 68, "iter": 2900, "lr": 0.05754, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29547, "top5_acc": 0.55547, "loss_cls": 4.02949, "loss": 4.02949, "time": 0.85077} +{"mode": "train", "epoch": 68, "iter": 3000, "lr": 0.05751, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.30422, "top5_acc": 0.56094, "loss_cls": 4.01364, "loss": 4.01364, "time": 0.85652} +{"mode": "train", "epoch": 68, "iter": 3100, "lr": 0.05748, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30641, "top5_acc": 0.5625, "loss_cls": 3.98386, "loss": 3.98386, "time": 0.85065} +{"mode": "train", "epoch": 68, "iter": 3200, "lr": 0.05746, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30453, "top5_acc": 0.55016, "loss_cls": 4.01015, "loss": 4.01015, "time": 0.84563} +{"mode": "train", "epoch": 68, "iter": 3300, "lr": 0.05743, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30047, "top5_acc": 0.55766, "loss_cls": 3.98869, "loss": 3.98869, "time": 0.84487} +{"mode": "train", "epoch": 68, "iter": 3400, "lr": 0.0574, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30781, "top5_acc": 0.56563, "loss_cls": 3.97868, "loss": 3.97868, "time": 0.85162} +{"mode": "train", "epoch": 68, "iter": 3500, "lr": 0.05737, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30422, "top5_acc": 0.56516, "loss_cls": 3.94638, "loss": 3.94638, "time": 0.85276} +{"mode": "train", "epoch": 68, "iter": 3600, "lr": 0.05734, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29609, "top5_acc": 0.55344, "loss_cls": 4.02572, "loss": 4.02572, "time": 0.85818} +{"mode": "train", "epoch": 68, "iter": 3700, "lr": 0.05732, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29891, "top5_acc": 0.55094, "loss_cls": 4.04189, "loss": 4.04189, "time": 0.86021} +{"mode": "val", "epoch": 68, "iter": 309, "lr": 0.0573, "top1_acc": 0.24895, "top5_acc": 0.48843, "mean_class_accuracy": 0.24864} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.05728, "memory": 15990, "data_time": 1.60398, "top1_acc": 0.31688, "top5_acc": 0.57781, "loss_cls": 3.89276, "loss": 3.89276, "time": 2.6326} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.05725, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.29969, "top5_acc": 0.56, "loss_cls": 3.99965, "loss": 3.99965, "time": 0.84958} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.05722, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30922, "top5_acc": 0.55875, "loss_cls": 3.95788, "loss": 3.95788, "time": 0.84815} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.05719, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29688, "top5_acc": 0.54953, "loss_cls": 3.98433, "loss": 3.98433, "time": 0.84849} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.05717, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.30375, "top5_acc": 0.56219, "loss_cls": 3.98545, "loss": 3.98545, "time": 0.8538} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.05714, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30984, "top5_acc": 0.56812, "loss_cls": 3.97198, "loss": 3.97198, "time": 0.84947} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.05711, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.29625, "top5_acc": 0.55047, "loss_cls": 4.03421, "loss": 4.03421, "time": 0.85582} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.05708, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29984, "top5_acc": 0.55672, "loss_cls": 4.01648, "loss": 4.01648, "time": 0.85136} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.05706, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30281, "top5_acc": 0.55578, "loss_cls": 4.01819, "loss": 4.01819, "time": 0.85525} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.05703, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29563, "top5_acc": 0.54812, "loss_cls": 4.04471, "loss": 4.04471, "time": 0.85932} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.057, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.31328, "top5_acc": 0.55516, "loss_cls": 3.97731, "loss": 3.97731, "time": 0.85903} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.05697, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29578, "top5_acc": 0.55891, "loss_cls": 4.00921, "loss": 4.00921, "time": 0.85595} +{"mode": "train", "epoch": 69, "iter": 1300, "lr": 0.05694, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.30156, "top5_acc": 0.55641, "loss_cls": 4.01213, "loss": 4.01213, "time": 0.85448} +{"mode": "train", "epoch": 69, "iter": 1400, "lr": 0.05692, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30078, "top5_acc": 0.55781, "loss_cls": 4.01869, "loss": 4.01869, "time": 0.84699} +{"mode": "train", "epoch": 69, "iter": 1500, "lr": 0.05689, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.29453, "top5_acc": 0.55156, "loss_cls": 4.06573, "loss": 4.06573, "time": 0.85257} +{"mode": "train", "epoch": 69, "iter": 1600, "lr": 0.05686, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31109, "top5_acc": 0.56609, "loss_cls": 3.92733, "loss": 3.92733, "time": 0.84853} +{"mode": "train", "epoch": 69, "iter": 1700, "lr": 0.05683, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.3075, "top5_acc": 0.55719, "loss_cls": 3.96791, "loss": 3.96791, "time": 0.84373} +{"mode": "train", "epoch": 69, "iter": 1800, "lr": 0.05681, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30422, "top5_acc": 0.56, "loss_cls": 3.97368, "loss": 3.97368, "time": 0.84809} +{"mode": "train", "epoch": 69, "iter": 1900, "lr": 0.05678, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30719, "top5_acc": 0.55469, "loss_cls": 4.01466, "loss": 4.01466, "time": 0.84526} +{"mode": "train", "epoch": 69, "iter": 2000, "lr": 0.05675, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.29625, "top5_acc": 0.54734, "loss_cls": 4.03383, "loss": 4.03383, "time": 0.84551} +{"mode": "train", "epoch": 69, "iter": 2100, "lr": 0.05672, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29828, "top5_acc": 0.54984, "loss_cls": 4.02337, "loss": 4.02337, "time": 0.84533} +{"mode": "train", "epoch": 69, "iter": 2200, "lr": 0.0567, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29906, "top5_acc": 0.55094, "loss_cls": 4.0389, "loss": 4.0389, "time": 0.84431} +{"mode": "train", "epoch": 69, "iter": 2300, "lr": 0.05667, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29766, "top5_acc": 0.54875, "loss_cls": 4.02596, "loss": 4.02596, "time": 0.84964} +{"mode": "train", "epoch": 69, "iter": 2400, "lr": 0.05664, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30078, "top5_acc": 0.55391, "loss_cls": 4.02844, "loss": 4.02844, "time": 0.85067} +{"mode": "train", "epoch": 69, "iter": 2500, "lr": 0.05661, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30328, "top5_acc": 0.55281, "loss_cls": 4.00149, "loss": 4.00149, "time": 0.84779} +{"mode": "train", "epoch": 69, "iter": 2600, "lr": 0.05658, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30844, "top5_acc": 0.56188, "loss_cls": 3.983, "loss": 3.983, "time": 0.85292} +{"mode": "train", "epoch": 69, "iter": 2700, "lr": 0.05656, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30641, "top5_acc": 0.57359, "loss_cls": 3.95012, "loss": 3.95012, "time": 0.86085} +{"mode": "train", "epoch": 69, "iter": 2800, "lr": 0.05653, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29484, "top5_acc": 0.54812, "loss_cls": 4.04253, "loss": 4.04253, "time": 0.8522} +{"mode": "train", "epoch": 69, "iter": 2900, "lr": 0.0565, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30891, "top5_acc": 0.5625, "loss_cls": 3.99583, "loss": 3.99583, "time": 0.85443} +{"mode": "train", "epoch": 69, "iter": 3000, "lr": 0.05647, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29234, "top5_acc": 0.54891, "loss_cls": 4.0084, "loss": 4.0084, "time": 0.85308} +{"mode": "train", "epoch": 69, "iter": 3100, "lr": 0.05645, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30016, "top5_acc": 0.56156, "loss_cls": 4.02119, "loss": 4.02119, "time": 0.85725} +{"mode": "train", "epoch": 69, "iter": 3200, "lr": 0.05642, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30828, "top5_acc": 0.56563, "loss_cls": 3.98038, "loss": 3.98038, "time": 0.84821} +{"mode": "train", "epoch": 69, "iter": 3300, "lr": 0.05639, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.3025, "top5_acc": 0.55297, "loss_cls": 3.99678, "loss": 3.99678, "time": 0.85051} +{"mode": "train", "epoch": 69, "iter": 3400, "lr": 0.05636, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3, "top5_acc": 0.55844, "loss_cls": 3.99244, "loss": 3.99244, "time": 0.85019} +{"mode": "train", "epoch": 69, "iter": 3500, "lr": 0.05634, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3, "top5_acc": 0.5625, "loss_cls": 3.97777, "loss": 3.97777, "time": 0.85491} +{"mode": "train", "epoch": 69, "iter": 3600, "lr": 0.05631, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29188, "top5_acc": 0.55359, "loss_cls": 4.04799, "loss": 4.04799, "time": 0.84861} +{"mode": "train", "epoch": 69, "iter": 3700, "lr": 0.05628, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30438, "top5_acc": 0.55328, "loss_cls": 4.00669, "loss": 4.00669, "time": 0.84957} +{"mode": "val", "epoch": 69, "iter": 309, "lr": 0.05627, "top1_acc": 0.24885, "top5_acc": 0.48868, "mean_class_accuracy": 0.24871} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.05624, "memory": 15990, "data_time": 1.59583, "top1_acc": 0.30906, "top5_acc": 0.56609, "loss_cls": 3.92523, "loss": 3.92523, "time": 2.64276} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.05621, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.31078, "top5_acc": 0.56672, "loss_cls": 3.92889, "loss": 3.92889, "time": 0.85227} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.05618, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30719, "top5_acc": 0.56125, "loss_cls": 3.99221, "loss": 3.99221, "time": 0.84893} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.05616, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.30781, "top5_acc": 0.56859, "loss_cls": 3.95214, "loss": 3.95214, "time": 0.84389} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.05613, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30688, "top5_acc": 0.56359, "loss_cls": 3.9578, "loss": 3.9578, "time": 0.84876} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.0561, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30297, "top5_acc": 0.55922, "loss_cls": 3.98108, "loss": 3.98108, "time": 0.84614} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.05607, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.30594, "top5_acc": 0.55922, "loss_cls": 3.96643, "loss": 3.96643, "time": 0.84385} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.05605, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31031, "top5_acc": 0.56969, "loss_cls": 3.94337, "loss": 3.94337, "time": 0.84645} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.05602, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30266, "top5_acc": 0.55094, "loss_cls": 4.03815, "loss": 4.03815, "time": 0.84836} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.05599, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.295, "top5_acc": 0.55266, "loss_cls": 4.03247, "loss": 4.03247, "time": 0.84609} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.05596, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29484, "top5_acc": 0.55109, "loss_cls": 3.99887, "loss": 3.99887, "time": 0.84044} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.05593, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30469, "top5_acc": 0.55828, "loss_cls": 3.97309, "loss": 3.97309, "time": 0.84748} +{"mode": "train", "epoch": 70, "iter": 1300, "lr": 0.05591, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30859, "top5_acc": 0.56141, "loss_cls": 3.9638, "loss": 3.9638, "time": 0.8494} +{"mode": "train", "epoch": 70, "iter": 1400, "lr": 0.05588, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30422, "top5_acc": 0.56219, "loss_cls": 3.97221, "loss": 3.97221, "time": 0.84638} +{"mode": "train", "epoch": 70, "iter": 1500, "lr": 0.05585, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.30594, "top5_acc": 0.56156, "loss_cls": 3.98448, "loss": 3.98448, "time": 0.84583} +{"mode": "train", "epoch": 70, "iter": 1600, "lr": 0.05582, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29563, "top5_acc": 0.54719, "loss_cls": 4.02234, "loss": 4.02234, "time": 0.85262} +{"mode": "train", "epoch": 70, "iter": 1700, "lr": 0.0558, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30312, "top5_acc": 0.55875, "loss_cls": 4.01069, "loss": 4.01069, "time": 0.84518} +{"mode": "train", "epoch": 70, "iter": 1800, "lr": 0.05577, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29922, "top5_acc": 0.55109, "loss_cls": 4.01951, "loss": 4.01951, "time": 0.84785} +{"mode": "train", "epoch": 70, "iter": 1900, "lr": 0.05574, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29781, "top5_acc": 0.55781, "loss_cls": 3.99926, "loss": 3.99926, "time": 0.85072} +{"mode": "train", "epoch": 70, "iter": 2000, "lr": 0.05571, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31672, "top5_acc": 0.55578, "loss_cls": 3.98115, "loss": 3.98115, "time": 0.853} +{"mode": "train", "epoch": 70, "iter": 2100, "lr": 0.05568, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.30781, "top5_acc": 0.56188, "loss_cls": 3.97679, "loss": 3.97679, "time": 0.85725} +{"mode": "train", "epoch": 70, "iter": 2200, "lr": 0.05566, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31547, "top5_acc": 0.57156, "loss_cls": 3.94964, "loss": 3.94964, "time": 0.85551} +{"mode": "train", "epoch": 70, "iter": 2300, "lr": 0.05563, "memory": 15990, "data_time": 0.00076, "top1_acc": 0.30094, "top5_acc": 0.55125, "loss_cls": 4.04498, "loss": 4.04498, "time": 0.85574} +{"mode": "train", "epoch": 70, "iter": 2400, "lr": 0.0556, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31391, "top5_acc": 0.56891, "loss_cls": 3.94636, "loss": 3.94636, "time": 0.85908} +{"mode": "train", "epoch": 70, "iter": 2500, "lr": 0.05557, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31203, "top5_acc": 0.56234, "loss_cls": 3.96781, "loss": 3.96781, "time": 0.85467} +{"mode": "train", "epoch": 70, "iter": 2600, "lr": 0.05555, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30953, "top5_acc": 0.5625, "loss_cls": 3.9899, "loss": 3.9899, "time": 0.85113} +{"mode": "train", "epoch": 70, "iter": 2700, "lr": 0.05552, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30203, "top5_acc": 0.55359, "loss_cls": 4.01314, "loss": 4.01314, "time": 0.85291} +{"mode": "train", "epoch": 70, "iter": 2800, "lr": 0.05549, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.29844, "top5_acc": 0.55375, "loss_cls": 4.04038, "loss": 4.04038, "time": 0.85764} +{"mode": "train", "epoch": 70, "iter": 2900, "lr": 0.05546, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28859, "top5_acc": 0.54734, "loss_cls": 4.05022, "loss": 4.05022, "time": 0.85227} +{"mode": "train", "epoch": 70, "iter": 3000, "lr": 0.05543, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.31219, "top5_acc": 0.56078, "loss_cls": 4.00643, "loss": 4.00643, "time": 0.85747} +{"mode": "train", "epoch": 70, "iter": 3100, "lr": 0.05541, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.29328, "top5_acc": 0.55719, "loss_cls": 4.01789, "loss": 4.01789, "time": 0.85552} +{"mode": "train", "epoch": 70, "iter": 3200, "lr": 0.05538, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.30141, "top5_acc": 0.56234, "loss_cls": 3.99741, "loss": 3.99741, "time": 0.85156} +{"mode": "train", "epoch": 70, "iter": 3300, "lr": 0.05535, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29406, "top5_acc": 0.55734, "loss_cls": 4.02632, "loss": 4.02632, "time": 0.85506} +{"mode": "train", "epoch": 70, "iter": 3400, "lr": 0.05532, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29297, "top5_acc": 0.56188, "loss_cls": 4.00834, "loss": 4.00834, "time": 0.84958} +{"mode": "train", "epoch": 70, "iter": 3500, "lr": 0.0553, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29969, "top5_acc": 0.55922, "loss_cls": 3.99646, "loss": 3.99646, "time": 0.85468} +{"mode": "train", "epoch": 70, "iter": 3600, "lr": 0.05527, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.30797, "top5_acc": 0.56437, "loss_cls": 3.94784, "loss": 3.94784, "time": 0.85085} +{"mode": "train", "epoch": 70, "iter": 3700, "lr": 0.05524, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30125, "top5_acc": 0.55172, "loss_cls": 4.02178, "loss": 4.02178, "time": 0.85175} +{"mode": "val", "epoch": 70, "iter": 309, "lr": 0.05523, "top1_acc": 0.23294, "top5_acc": 0.47318, "mean_class_accuracy": 0.23299} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.0552, "memory": 15990, "data_time": 1.57364, "top1_acc": 0.31453, "top5_acc": 0.57688, "loss_cls": 3.89481, "loss": 3.89481, "time": 2.60632} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.05517, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.30719, "top5_acc": 0.56578, "loss_cls": 3.95394, "loss": 3.95394, "time": 0.85166} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.05514, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.30953, "top5_acc": 0.56281, "loss_cls": 3.92573, "loss": 3.92573, "time": 0.85443} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.05512, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.29891, "top5_acc": 0.55937, "loss_cls": 3.99687, "loss": 3.99687, "time": 0.85073} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.05509, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30234, "top5_acc": 0.55578, "loss_cls": 4.00391, "loss": 4.00391, "time": 0.85806} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.05506, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29422, "top5_acc": 0.56156, "loss_cls": 4.0123, "loss": 4.0123, "time": 0.84708} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.05503, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29547, "top5_acc": 0.54688, "loss_cls": 4.0226, "loss": 4.0226, "time": 0.85334} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.055, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31438, "top5_acc": 0.56594, "loss_cls": 3.95782, "loss": 3.95782, "time": 0.85498} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.05498, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.315, "top5_acc": 0.57938, "loss_cls": 3.92617, "loss": 3.92617, "time": 0.85118} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.05495, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.305, "top5_acc": 0.56672, "loss_cls": 3.96653, "loss": 3.96653, "time": 0.85192} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.05492, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.30453, "top5_acc": 0.55094, "loss_cls": 4.00865, "loss": 4.00865, "time": 0.85579} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.05489, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30656, "top5_acc": 0.55984, "loss_cls": 3.95149, "loss": 3.95149, "time": 0.85541} +{"mode": "train", "epoch": 71, "iter": 1300, "lr": 0.05487, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31406, "top5_acc": 0.56453, "loss_cls": 3.94449, "loss": 3.94449, "time": 0.85364} +{"mode": "train", "epoch": 71, "iter": 1400, "lr": 0.05484, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.31281, "top5_acc": 0.56609, "loss_cls": 3.97998, "loss": 3.97998, "time": 0.85123} +{"mode": "train", "epoch": 71, "iter": 1500, "lr": 0.05481, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.30141, "top5_acc": 0.55688, "loss_cls": 4.0056, "loss": 4.0056, "time": 0.8531} +{"mode": "train", "epoch": 71, "iter": 1600, "lr": 0.05478, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30078, "top5_acc": 0.55531, "loss_cls": 4.02118, "loss": 4.02118, "time": 0.8515} +{"mode": "train", "epoch": 71, "iter": 1700, "lr": 0.05475, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30312, "top5_acc": 0.55969, "loss_cls": 4.01252, "loss": 4.01252, "time": 0.85085} +{"mode": "train", "epoch": 71, "iter": 1800, "lr": 0.05473, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30656, "top5_acc": 0.55937, "loss_cls": 3.98247, "loss": 3.98247, "time": 0.84369} +{"mode": "train", "epoch": 71, "iter": 1900, "lr": 0.0547, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31547, "top5_acc": 0.56672, "loss_cls": 3.95441, "loss": 3.95441, "time": 0.84548} +{"mode": "train", "epoch": 71, "iter": 2000, "lr": 0.05467, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30344, "top5_acc": 0.56188, "loss_cls": 3.96274, "loss": 3.96274, "time": 0.84349} +{"mode": "train", "epoch": 71, "iter": 2100, "lr": 0.05464, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30578, "top5_acc": 0.56453, "loss_cls": 3.95956, "loss": 3.95956, "time": 0.85096} +{"mode": "train", "epoch": 71, "iter": 2200, "lr": 0.05461, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30938, "top5_acc": 0.56859, "loss_cls": 3.93709, "loss": 3.93709, "time": 0.84568} +{"mode": "train", "epoch": 71, "iter": 2300, "lr": 0.05459, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31438, "top5_acc": 0.56969, "loss_cls": 3.94414, "loss": 3.94414, "time": 0.84993} +{"mode": "train", "epoch": 71, "iter": 2400, "lr": 0.05456, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30609, "top5_acc": 0.56781, "loss_cls": 3.95377, "loss": 3.95377, "time": 0.84608} +{"mode": "train", "epoch": 71, "iter": 2500, "lr": 0.05453, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3, "top5_acc": 0.55922, "loss_cls": 3.99282, "loss": 3.99282, "time": 0.85363} +{"mode": "train", "epoch": 71, "iter": 2600, "lr": 0.0545, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29719, "top5_acc": 0.56219, "loss_cls": 3.98188, "loss": 3.98188, "time": 0.84757} +{"mode": "train", "epoch": 71, "iter": 2700, "lr": 0.05448, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31203, "top5_acc": 0.56828, "loss_cls": 3.95108, "loss": 3.95108, "time": 0.85433} +{"mode": "train", "epoch": 71, "iter": 2800, "lr": 0.05445, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30438, "top5_acc": 0.56469, "loss_cls": 3.95028, "loss": 3.95028, "time": 0.84916} +{"mode": "train", "epoch": 71, "iter": 2900, "lr": 0.05442, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28688, "top5_acc": 0.55547, "loss_cls": 4.00928, "loss": 4.00928, "time": 0.84947} +{"mode": "train", "epoch": 71, "iter": 3000, "lr": 0.05439, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30375, "top5_acc": 0.55125, "loss_cls": 3.98841, "loss": 3.98841, "time": 0.84631} +{"mode": "train", "epoch": 71, "iter": 3100, "lr": 0.05436, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29953, "top5_acc": 0.55891, "loss_cls": 4.01725, "loss": 4.01725, "time": 0.84911} +{"mode": "train", "epoch": 71, "iter": 3200, "lr": 0.05434, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29438, "top5_acc": 0.55797, "loss_cls": 4.01052, "loss": 4.01052, "time": 0.84938} +{"mode": "train", "epoch": 71, "iter": 3300, "lr": 0.05431, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30141, "top5_acc": 0.55969, "loss_cls": 4.0103, "loss": 4.0103, "time": 0.8473} +{"mode": "train", "epoch": 71, "iter": 3400, "lr": 0.05428, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3, "top5_acc": 0.55641, "loss_cls": 4.00104, "loss": 4.00104, "time": 0.84487} +{"mode": "train", "epoch": 71, "iter": 3500, "lr": 0.05425, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29656, "top5_acc": 0.55781, "loss_cls": 3.99847, "loss": 3.99847, "time": 0.84586} +{"mode": "train", "epoch": 71, "iter": 3600, "lr": 0.05422, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30219, "top5_acc": 0.55484, "loss_cls": 3.99808, "loss": 3.99808, "time": 0.84486} +{"mode": "train", "epoch": 71, "iter": 3700, "lr": 0.0542, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29797, "top5_acc": 0.56156, "loss_cls": 3.99183, "loss": 3.99183, "time": 0.84558} +{"mode": "val", "epoch": 71, "iter": 309, "lr": 0.05418, "top1_acc": 0.24606, "top5_acc": 0.49091, "mean_class_accuracy": 0.24567} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.05416, "memory": 15990, "data_time": 1.5174, "top1_acc": 0.31406, "top5_acc": 0.57344, "loss_cls": 3.93817, "loss": 3.93817, "time": 2.55797} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.05413, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.30875, "top5_acc": 0.56922, "loss_cls": 3.927, "loss": 3.927, "time": 0.85382} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.0541, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31375, "top5_acc": 0.57812, "loss_cls": 3.92857, "loss": 3.92857, "time": 0.85063} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.05407, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30891, "top5_acc": 0.56797, "loss_cls": 3.96492, "loss": 3.96492, "time": 0.85269} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.05404, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31469, "top5_acc": 0.57594, "loss_cls": 3.92347, "loss": 3.92347, "time": 0.85188} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.05402, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30797, "top5_acc": 0.55922, "loss_cls": 3.98364, "loss": 3.98364, "time": 0.84614} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.05399, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30406, "top5_acc": 0.56328, "loss_cls": 3.96287, "loss": 3.96287, "time": 0.84876} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.05396, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30547, "top5_acc": 0.56094, "loss_cls": 3.98768, "loss": 3.98768, "time": 0.84988} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.05393, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29281, "top5_acc": 0.55906, "loss_cls": 3.97812, "loss": 3.97812, "time": 0.84904} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.05391, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30844, "top5_acc": 0.56984, "loss_cls": 3.93028, "loss": 3.93028, "time": 0.85478} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.05388, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30438, "top5_acc": 0.55891, "loss_cls": 3.98772, "loss": 3.98772, "time": 0.85181} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.05385, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30172, "top5_acc": 0.55422, "loss_cls": 4.02999, "loss": 4.02999, "time": 0.85108} +{"mode": "train", "epoch": 72, "iter": 1300, "lr": 0.05382, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29844, "top5_acc": 0.55766, "loss_cls": 3.96592, "loss": 3.96592, "time": 0.85312} +{"mode": "train", "epoch": 72, "iter": 1400, "lr": 0.05379, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31078, "top5_acc": 0.56266, "loss_cls": 3.94821, "loss": 3.94821, "time": 0.85168} +{"mode": "train", "epoch": 72, "iter": 1500, "lr": 0.05377, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30609, "top5_acc": 0.57047, "loss_cls": 3.94574, "loss": 3.94574, "time": 0.84783} +{"mode": "train", "epoch": 72, "iter": 1600, "lr": 0.05374, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28813, "top5_acc": 0.55203, "loss_cls": 4.05544, "loss": 4.05544, "time": 0.85293} +{"mode": "train", "epoch": 72, "iter": 1700, "lr": 0.05371, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30375, "top5_acc": 0.55953, "loss_cls": 3.98154, "loss": 3.98154, "time": 0.85034} +{"mode": "train", "epoch": 72, "iter": 1800, "lr": 0.05368, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30031, "top5_acc": 0.55891, "loss_cls": 3.98154, "loss": 3.98154, "time": 0.85427} +{"mode": "train", "epoch": 72, "iter": 1900, "lr": 0.05365, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30844, "top5_acc": 0.55875, "loss_cls": 3.99295, "loss": 3.99295, "time": 0.85487} +{"mode": "train", "epoch": 72, "iter": 2000, "lr": 0.05363, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30734, "top5_acc": 0.55812, "loss_cls": 3.98787, "loss": 3.98787, "time": 0.86032} +{"mode": "train", "epoch": 72, "iter": 2100, "lr": 0.0536, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31438, "top5_acc": 0.56516, "loss_cls": 3.95344, "loss": 3.95344, "time": 0.85254} +{"mode": "train", "epoch": 72, "iter": 2200, "lr": 0.05357, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30531, "top5_acc": 0.55828, "loss_cls": 3.97851, "loss": 3.97851, "time": 0.85487} +{"mode": "train", "epoch": 72, "iter": 2300, "lr": 0.05354, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30594, "top5_acc": 0.56172, "loss_cls": 4.00609, "loss": 4.00609, "time": 0.85831} +{"mode": "train", "epoch": 72, "iter": 2400, "lr": 0.05352, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30359, "top5_acc": 0.55641, "loss_cls": 3.98477, "loss": 3.98477, "time": 0.85364} +{"mode": "train", "epoch": 72, "iter": 2500, "lr": 0.05349, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30984, "top5_acc": 0.56844, "loss_cls": 3.94267, "loss": 3.94267, "time": 0.85731} +{"mode": "train", "epoch": 72, "iter": 2600, "lr": 0.05346, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30297, "top5_acc": 0.56172, "loss_cls": 4.00259, "loss": 4.00259, "time": 0.85754} +{"mode": "train", "epoch": 72, "iter": 2700, "lr": 0.05343, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30312, "top5_acc": 0.56219, "loss_cls": 3.96939, "loss": 3.96939, "time": 0.85339} +{"mode": "train", "epoch": 72, "iter": 2800, "lr": 0.0534, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.305, "top5_acc": 0.5575, "loss_cls": 3.98824, "loss": 3.98824, "time": 0.85692} +{"mode": "train", "epoch": 72, "iter": 2900, "lr": 0.05338, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30734, "top5_acc": 0.56953, "loss_cls": 3.94434, "loss": 3.94434, "time": 0.85719} +{"mode": "train", "epoch": 72, "iter": 3000, "lr": 0.05335, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.30016, "top5_acc": 0.55641, "loss_cls": 3.98898, "loss": 3.98898, "time": 0.85151} +{"mode": "train", "epoch": 72, "iter": 3100, "lr": 0.05332, "memory": 15990, "data_time": 0.00071, "top1_acc": 0.31641, "top5_acc": 0.57641, "loss_cls": 3.92486, "loss": 3.92486, "time": 0.85474} +{"mode": "train", "epoch": 72, "iter": 3200, "lr": 0.05329, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.31, "top5_acc": 0.56406, "loss_cls": 3.94767, "loss": 3.94767, "time": 0.84395} +{"mode": "train", "epoch": 72, "iter": 3300, "lr": 0.05326, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32453, "top5_acc": 0.5775, "loss_cls": 3.91758, "loss": 3.91758, "time": 0.8477} +{"mode": "train", "epoch": 72, "iter": 3400, "lr": 0.05324, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30625, "top5_acc": 0.55531, "loss_cls": 4.01741, "loss": 4.01741, "time": 0.85441} +{"mode": "train", "epoch": 72, "iter": 3500, "lr": 0.05321, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30703, "top5_acc": 0.55266, "loss_cls": 4.04147, "loss": 4.04147, "time": 0.85126} +{"mode": "train", "epoch": 72, "iter": 3600, "lr": 0.05318, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30562, "top5_acc": 0.56953, "loss_cls": 3.98151, "loss": 3.98151, "time": 0.85156} +{"mode": "train", "epoch": 72, "iter": 3700, "lr": 0.05315, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30312, "top5_acc": 0.55578, "loss_cls": 3.99052, "loss": 3.99052, "time": 0.85111} +{"mode": "val", "epoch": 72, "iter": 309, "lr": 0.05314, "top1_acc": 0.23512, "top5_acc": 0.47632, "mean_class_accuracy": 0.23493} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.05311, "memory": 15990, "data_time": 1.526, "top1_acc": 0.30766, "top5_acc": 0.5725, "loss_cls": 3.92228, "loss": 3.92228, "time": 2.56477} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.05308, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.31656, "top5_acc": 0.5675, "loss_cls": 3.92113, "loss": 3.92113, "time": 0.8522} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.05306, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31609, "top5_acc": 0.57563, "loss_cls": 3.91719, "loss": 3.91719, "time": 0.85561} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.05303, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30406, "top5_acc": 0.56734, "loss_cls": 3.93646, "loss": 3.93646, "time": 0.84547} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.053, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30484, "top5_acc": 0.56016, "loss_cls": 3.99363, "loss": 3.99363, "time": 0.85162} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.05297, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30391, "top5_acc": 0.56906, "loss_cls": 3.95192, "loss": 3.95192, "time": 0.85037} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.05294, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30875, "top5_acc": 0.5725, "loss_cls": 3.92843, "loss": 3.92843, "time": 0.84826} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.05292, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31859, "top5_acc": 0.57125, "loss_cls": 3.92031, "loss": 3.92031, "time": 0.84781} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.05289, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29938, "top5_acc": 0.555, "loss_cls": 4.00064, "loss": 4.00064, "time": 0.84782} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.05286, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30234, "top5_acc": 0.56156, "loss_cls": 3.97166, "loss": 3.97166, "time": 0.85258} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.05283, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31469, "top5_acc": 0.56375, "loss_cls": 3.96647, "loss": 3.96647, "time": 0.846} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.0528, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31734, "top5_acc": 0.57094, "loss_cls": 3.95423, "loss": 3.95423, "time": 0.84559} +{"mode": "train", "epoch": 73, "iter": 1300, "lr": 0.05278, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31156, "top5_acc": 0.56688, "loss_cls": 3.96632, "loss": 3.96632, "time": 0.85662} +{"mode": "train", "epoch": 73, "iter": 1400, "lr": 0.05275, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31062, "top5_acc": 0.56375, "loss_cls": 3.97295, "loss": 3.97295, "time": 0.84784} +{"mode": "train", "epoch": 73, "iter": 1500, "lr": 0.05272, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30688, "top5_acc": 0.56375, "loss_cls": 3.95573, "loss": 3.95573, "time": 0.84453} +{"mode": "train", "epoch": 73, "iter": 1600, "lr": 0.05269, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31281, "top5_acc": 0.56672, "loss_cls": 3.92587, "loss": 3.92587, "time": 0.85351} +{"mode": "train", "epoch": 73, "iter": 1700, "lr": 0.05267, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30844, "top5_acc": 0.56016, "loss_cls": 3.96167, "loss": 3.96167, "time": 0.84695} +{"mode": "train", "epoch": 73, "iter": 1800, "lr": 0.05264, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30984, "top5_acc": 0.56609, "loss_cls": 3.95408, "loss": 3.95408, "time": 0.85} +{"mode": "train", "epoch": 73, "iter": 1900, "lr": 0.05261, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29922, "top5_acc": 0.56734, "loss_cls": 3.95459, "loss": 3.95459, "time": 0.84696} +{"mode": "train", "epoch": 73, "iter": 2000, "lr": 0.05258, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30656, "top5_acc": 0.56812, "loss_cls": 3.92556, "loss": 3.92556, "time": 0.84383} +{"mode": "train", "epoch": 73, "iter": 2100, "lr": 0.05255, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30172, "top5_acc": 0.56047, "loss_cls": 3.98398, "loss": 3.98398, "time": 0.84654} +{"mode": "train", "epoch": 73, "iter": 2200, "lr": 0.05253, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30297, "top5_acc": 0.56312, "loss_cls": 3.98665, "loss": 3.98665, "time": 0.84577} +{"mode": "train", "epoch": 73, "iter": 2300, "lr": 0.0525, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30281, "top5_acc": 0.56641, "loss_cls": 3.98347, "loss": 3.98347, "time": 0.84435} +{"mode": "train", "epoch": 73, "iter": 2400, "lr": 0.05247, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31266, "top5_acc": 0.57297, "loss_cls": 3.94349, "loss": 3.94349, "time": 0.84432} +{"mode": "train", "epoch": 73, "iter": 2500, "lr": 0.05244, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30594, "top5_acc": 0.56437, "loss_cls": 3.95816, "loss": 3.95816, "time": 0.84712} +{"mode": "train", "epoch": 73, "iter": 2600, "lr": 0.05241, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30812, "top5_acc": 0.56047, "loss_cls": 3.95114, "loss": 3.95114, "time": 0.84176} +{"mode": "train", "epoch": 73, "iter": 2700, "lr": 0.05239, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.3025, "top5_acc": 0.55844, "loss_cls": 4.01703, "loss": 4.01703, "time": 0.84251} +{"mode": "train", "epoch": 73, "iter": 2800, "lr": 0.05236, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.3175, "top5_acc": 0.56812, "loss_cls": 3.92046, "loss": 3.92046, "time": 0.84232} +{"mode": "train", "epoch": 73, "iter": 2900, "lr": 0.05233, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30141, "top5_acc": 0.55016, "loss_cls": 4.01348, "loss": 4.01348, "time": 0.84866} +{"mode": "train", "epoch": 73, "iter": 3000, "lr": 0.0523, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30094, "top5_acc": 0.56031, "loss_cls": 3.97533, "loss": 3.97533, "time": 0.84437} +{"mode": "train", "epoch": 73, "iter": 3100, "lr": 0.05227, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31062, "top5_acc": 0.56516, "loss_cls": 3.98618, "loss": 3.98618, "time": 0.84606} +{"mode": "train", "epoch": 73, "iter": 3200, "lr": 0.05225, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30984, "top5_acc": 0.56219, "loss_cls": 3.96204, "loss": 3.96204, "time": 0.84473} +{"mode": "train", "epoch": 73, "iter": 3300, "lr": 0.05222, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30281, "top5_acc": 0.55734, "loss_cls": 3.9902, "loss": 3.9902, "time": 0.84328} +{"mode": "train", "epoch": 73, "iter": 3400, "lr": 0.05219, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.315, "top5_acc": 0.56891, "loss_cls": 3.94343, "loss": 3.94343, "time": 0.84964} +{"mode": "train", "epoch": 73, "iter": 3500, "lr": 0.05216, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30094, "top5_acc": 0.55844, "loss_cls": 3.97548, "loss": 3.97548, "time": 0.8546} +{"mode": "train", "epoch": 73, "iter": 3600, "lr": 0.05213, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.305, "top5_acc": 0.56922, "loss_cls": 3.96327, "loss": 3.96327, "time": 0.84987} +{"mode": "train", "epoch": 73, "iter": 3700, "lr": 0.05211, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30719, "top5_acc": 0.55344, "loss_cls": 3.98723, "loss": 3.98723, "time": 0.84997} +{"mode": "val", "epoch": 73, "iter": 309, "lr": 0.05209, "top1_acc": 0.24859, "top5_acc": 0.49354, "mean_class_accuracy": 0.2484} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.05207, "memory": 15990, "data_time": 1.52979, "top1_acc": 0.31234, "top5_acc": 0.56406, "loss_cls": 3.95365, "loss": 3.95365, "time": 2.57311} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.05204, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30953, "top5_acc": 0.56875, "loss_cls": 3.91374, "loss": 3.91374, "time": 0.85461} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.05201, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.32062, "top5_acc": 0.57047, "loss_cls": 3.91401, "loss": 3.91401, "time": 0.85556} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.05198, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30516, "top5_acc": 0.57156, "loss_cls": 3.95074, "loss": 3.95074, "time": 0.84734} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.05195, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31531, "top5_acc": 0.57328, "loss_cls": 3.91798, "loss": 3.91798, "time": 0.8504} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.05193, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3125, "top5_acc": 0.56437, "loss_cls": 3.91805, "loss": 3.91805, "time": 0.84922} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.0519, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3075, "top5_acc": 0.56109, "loss_cls": 3.95373, "loss": 3.95373, "time": 0.84892} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.05187, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2975, "top5_acc": 0.55375, "loss_cls": 4.00176, "loss": 4.00176, "time": 0.84714} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.05184, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31234, "top5_acc": 0.57484, "loss_cls": 3.9295, "loss": 3.9295, "time": 0.84433} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.05181, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31594, "top5_acc": 0.56297, "loss_cls": 3.9474, "loss": 3.9474, "time": 0.84884} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.05179, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31516, "top5_acc": 0.56875, "loss_cls": 3.93058, "loss": 3.93058, "time": 0.84232} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.05176, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30859, "top5_acc": 0.55844, "loss_cls": 3.96525, "loss": 3.96525, "time": 0.84533} +{"mode": "train", "epoch": 74, "iter": 1300, "lr": 0.05173, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30719, "top5_acc": 0.56656, "loss_cls": 3.94471, "loss": 3.94471, "time": 0.84957} +{"mode": "train", "epoch": 74, "iter": 1400, "lr": 0.0517, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32297, "top5_acc": 0.57938, "loss_cls": 3.88823, "loss": 3.88823, "time": 0.84595} +{"mode": "train", "epoch": 74, "iter": 1500, "lr": 0.05168, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31266, "top5_acc": 0.57031, "loss_cls": 3.92403, "loss": 3.92403, "time": 0.84777} +{"mode": "train", "epoch": 74, "iter": 1600, "lr": 0.05165, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30859, "top5_acc": 0.56656, "loss_cls": 3.94454, "loss": 3.94454, "time": 0.85184} +{"mode": "train", "epoch": 74, "iter": 1700, "lr": 0.05162, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.30594, "top5_acc": 0.56531, "loss_cls": 3.97678, "loss": 3.97678, "time": 0.84521} +{"mode": "train", "epoch": 74, "iter": 1800, "lr": 0.05159, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30797, "top5_acc": 0.56047, "loss_cls": 3.9699, "loss": 3.9699, "time": 0.84491} +{"mode": "train", "epoch": 74, "iter": 1900, "lr": 0.05156, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31812, "top5_acc": 0.56984, "loss_cls": 3.92771, "loss": 3.92771, "time": 0.84758} +{"mode": "train", "epoch": 74, "iter": 2000, "lr": 0.05154, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31906, "top5_acc": 0.57266, "loss_cls": 3.91592, "loss": 3.91592, "time": 0.84743} +{"mode": "train", "epoch": 74, "iter": 2100, "lr": 0.05151, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31328, "top5_acc": 0.56719, "loss_cls": 3.93238, "loss": 3.93238, "time": 0.84847} +{"mode": "train", "epoch": 74, "iter": 2200, "lr": 0.05148, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31062, "top5_acc": 0.57469, "loss_cls": 3.90807, "loss": 3.90807, "time": 0.84682} +{"mode": "train", "epoch": 74, "iter": 2300, "lr": 0.05145, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31766, "top5_acc": 0.56312, "loss_cls": 3.93704, "loss": 3.93704, "time": 0.84479} +{"mode": "train", "epoch": 74, "iter": 2400, "lr": 0.05142, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31141, "top5_acc": 0.56594, "loss_cls": 3.96315, "loss": 3.96315, "time": 0.84404} +{"mode": "train", "epoch": 74, "iter": 2500, "lr": 0.0514, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30812, "top5_acc": 0.56266, "loss_cls": 3.9896, "loss": 3.9896, "time": 0.84282} +{"mode": "train", "epoch": 74, "iter": 2600, "lr": 0.05137, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30578, "top5_acc": 0.56391, "loss_cls": 3.97359, "loss": 3.97359, "time": 0.84467} +{"mode": "train", "epoch": 74, "iter": 2700, "lr": 0.05134, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30984, "top5_acc": 0.56547, "loss_cls": 3.95575, "loss": 3.95575, "time": 0.84507} +{"mode": "train", "epoch": 74, "iter": 2800, "lr": 0.05131, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30516, "top5_acc": 0.56984, "loss_cls": 3.9719, "loss": 3.9719, "time": 0.84715} +{"mode": "train", "epoch": 74, "iter": 2900, "lr": 0.05128, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31875, "top5_acc": 0.57141, "loss_cls": 3.9457, "loss": 3.9457, "time": 0.85053} +{"mode": "train", "epoch": 74, "iter": 3000, "lr": 0.05126, "memory": 15990, "data_time": 0.00067, "top1_acc": 0.30438, "top5_acc": 0.56766, "loss_cls": 3.96119, "loss": 3.96119, "time": 0.84876} +{"mode": "train", "epoch": 74, "iter": 3100, "lr": 0.05123, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30312, "top5_acc": 0.56219, "loss_cls": 3.97781, "loss": 3.97781, "time": 0.84805} +{"mode": "train", "epoch": 74, "iter": 3200, "lr": 0.0512, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.30797, "top5_acc": 0.56516, "loss_cls": 3.96788, "loss": 3.96788, "time": 0.84495} +{"mode": "train", "epoch": 74, "iter": 3300, "lr": 0.05117, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31234, "top5_acc": 0.56672, "loss_cls": 3.94258, "loss": 3.94258, "time": 0.849} +{"mode": "train", "epoch": 74, "iter": 3400, "lr": 0.05114, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30812, "top5_acc": 0.56641, "loss_cls": 3.95048, "loss": 3.95048, "time": 0.84349} +{"mode": "train", "epoch": 74, "iter": 3500, "lr": 0.05112, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30609, "top5_acc": 0.55953, "loss_cls": 3.98123, "loss": 3.98123, "time": 0.84474} +{"mode": "train", "epoch": 74, "iter": 3600, "lr": 0.05109, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30391, "top5_acc": 0.55047, "loss_cls": 3.98925, "loss": 3.98925, "time": 0.84229} +{"mode": "train", "epoch": 74, "iter": 3700, "lr": 0.05106, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30938, "top5_acc": 0.56563, "loss_cls": 3.95919, "loss": 3.95919, "time": 0.83946} +{"mode": "val", "epoch": 74, "iter": 309, "lr": 0.05105, "top1_acc": 0.2605, "top5_acc": 0.50408, "mean_class_accuracy": 0.26013} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.05102, "memory": 15990, "data_time": 1.47872, "top1_acc": 0.31891, "top5_acc": 0.58531, "loss_cls": 3.86962, "loss": 3.86962, "time": 2.50711} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.05099, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31172, "top5_acc": 0.57906, "loss_cls": 3.89041, "loss": 3.89041, "time": 0.84744} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.05096, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.31875, "top5_acc": 0.57438, "loss_cls": 3.89297, "loss": 3.89297, "time": 0.85454} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.05094, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30828, "top5_acc": 0.57047, "loss_cls": 3.9357, "loss": 3.9357, "time": 0.84595} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.05091, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31797, "top5_acc": 0.56719, "loss_cls": 3.92199, "loss": 3.92199, "time": 0.84332} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.05088, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.31547, "top5_acc": 0.56969, "loss_cls": 3.90981, "loss": 3.90981, "time": 0.84471} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.05085, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30625, "top5_acc": 0.56641, "loss_cls": 3.97023, "loss": 3.97023, "time": 0.8432} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.05082, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31281, "top5_acc": 0.56688, "loss_cls": 3.9401, "loss": 3.9401, "time": 0.84519} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.0508, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.315, "top5_acc": 0.57719, "loss_cls": 3.90602, "loss": 3.90602, "time": 0.84384} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.05077, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32234, "top5_acc": 0.56312, "loss_cls": 3.94739, "loss": 3.94739, "time": 0.8462} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.05074, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32297, "top5_acc": 0.56875, "loss_cls": 3.93483, "loss": 3.93483, "time": 0.8424} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.05071, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31062, "top5_acc": 0.57938, "loss_cls": 3.90128, "loss": 3.90128, "time": 0.84872} +{"mode": "train", "epoch": 75, "iter": 1300, "lr": 0.05068, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31234, "top5_acc": 0.57109, "loss_cls": 3.92674, "loss": 3.92674, "time": 0.84249} +{"mode": "train", "epoch": 75, "iter": 1400, "lr": 0.05066, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31406, "top5_acc": 0.565, "loss_cls": 3.92878, "loss": 3.92878, "time": 0.8418} +{"mode": "train", "epoch": 75, "iter": 1500, "lr": 0.05063, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30828, "top5_acc": 0.56703, "loss_cls": 3.9372, "loss": 3.9372, "time": 0.84647} +{"mode": "train", "epoch": 75, "iter": 1600, "lr": 0.0506, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30359, "top5_acc": 0.56516, "loss_cls": 3.93145, "loss": 3.93145, "time": 0.84929} +{"mode": "train", "epoch": 75, "iter": 1700, "lr": 0.05057, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.3075, "top5_acc": 0.57688, "loss_cls": 3.91173, "loss": 3.91173, "time": 0.84627} +{"mode": "train", "epoch": 75, "iter": 1800, "lr": 0.05054, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30688, "top5_acc": 0.56484, "loss_cls": 3.97013, "loss": 3.97013, "time": 0.84504} +{"mode": "train", "epoch": 75, "iter": 1900, "lr": 0.05052, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.30984, "top5_acc": 0.56563, "loss_cls": 3.97219, "loss": 3.97219, "time": 0.84375} +{"mode": "train", "epoch": 75, "iter": 2000, "lr": 0.05049, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.3025, "top5_acc": 0.55625, "loss_cls": 3.96313, "loss": 3.96313, "time": 0.84687} +{"mode": "train", "epoch": 75, "iter": 2100, "lr": 0.05046, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31, "top5_acc": 0.56469, "loss_cls": 3.97866, "loss": 3.97866, "time": 0.84336} +{"mode": "train", "epoch": 75, "iter": 2200, "lr": 0.05043, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31453, "top5_acc": 0.56281, "loss_cls": 3.94693, "loss": 3.94693, "time": 0.84631} +{"mode": "train", "epoch": 75, "iter": 2300, "lr": 0.0504, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30328, "top5_acc": 0.56594, "loss_cls": 3.96234, "loss": 3.96234, "time": 0.84943} +{"mode": "train", "epoch": 75, "iter": 2400, "lr": 0.05038, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31109, "top5_acc": 0.57281, "loss_cls": 3.90541, "loss": 3.90541, "time": 0.84398} +{"mode": "train", "epoch": 75, "iter": 2500, "lr": 0.05035, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3025, "top5_acc": 0.56172, "loss_cls": 3.985, "loss": 3.985, "time": 0.84369} +{"mode": "train", "epoch": 75, "iter": 2600, "lr": 0.05032, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31078, "top5_acc": 0.57203, "loss_cls": 3.93523, "loss": 3.93523, "time": 0.84885} +{"mode": "train", "epoch": 75, "iter": 2700, "lr": 0.05029, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32203, "top5_acc": 0.57547, "loss_cls": 3.9099, "loss": 3.9099, "time": 0.85255} +{"mode": "train", "epoch": 75, "iter": 2800, "lr": 0.05026, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31688, "top5_acc": 0.58109, "loss_cls": 3.90902, "loss": 3.90902, "time": 0.85158} +{"mode": "train", "epoch": 75, "iter": 2900, "lr": 0.05024, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30781, "top5_acc": 0.56672, "loss_cls": 3.94621, "loss": 3.94621, "time": 0.84994} +{"mode": "train", "epoch": 75, "iter": 3000, "lr": 0.05021, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30422, "top5_acc": 0.55437, "loss_cls": 3.98504, "loss": 3.98504, "time": 0.84749} +{"mode": "train", "epoch": 75, "iter": 3100, "lr": 0.05018, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31688, "top5_acc": 0.57031, "loss_cls": 3.90787, "loss": 3.90787, "time": 0.85283} +{"mode": "train", "epoch": 75, "iter": 3200, "lr": 0.05015, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31328, "top5_acc": 0.56328, "loss_cls": 3.96176, "loss": 3.96176, "time": 0.84575} +{"mode": "train", "epoch": 75, "iter": 3300, "lr": 0.05012, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.31297, "top5_acc": 0.56734, "loss_cls": 3.939, "loss": 3.939, "time": 0.85022} +{"mode": "train", "epoch": 75, "iter": 3400, "lr": 0.0501, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30703, "top5_acc": 0.56141, "loss_cls": 3.98466, "loss": 3.98466, "time": 0.84442} +{"mode": "train", "epoch": 75, "iter": 3500, "lr": 0.05007, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30953, "top5_acc": 0.56312, "loss_cls": 3.95424, "loss": 3.95424, "time": 0.84757} +{"mode": "train", "epoch": 75, "iter": 3600, "lr": 0.05004, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31656, "top5_acc": 0.57484, "loss_cls": 3.91525, "loss": 3.91525, "time": 0.85089} +{"mode": "train", "epoch": 75, "iter": 3700, "lr": 0.05001, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31547, "top5_acc": 0.56688, "loss_cls": 3.96025, "loss": 3.96025, "time": 0.84695} +{"mode": "val", "epoch": 75, "iter": 309, "lr": 0.05, "top1_acc": 0.26627, "top5_acc": 0.50889, "mean_class_accuracy": 0.26602} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.04997, "memory": 15990, "data_time": 1.51188, "top1_acc": 0.32328, "top5_acc": 0.58203, "loss_cls": 3.85638, "loss": 3.85638, "time": 2.53589} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.04994, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32438, "top5_acc": 0.58375, "loss_cls": 3.87633, "loss": 3.87633, "time": 0.85119} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.04992, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32531, "top5_acc": 0.57531, "loss_cls": 3.90574, "loss": 3.90574, "time": 0.84732} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.04989, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32078, "top5_acc": 0.57469, "loss_cls": 3.90455, "loss": 3.90455, "time": 0.85062} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.04986, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31391, "top5_acc": 0.57063, "loss_cls": 3.92051, "loss": 3.92051, "time": 0.84682} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.04983, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30984, "top5_acc": 0.56688, "loss_cls": 3.94629, "loss": 3.94629, "time": 0.84996} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.0498, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31062, "top5_acc": 0.57406, "loss_cls": 3.91911, "loss": 3.91911, "time": 0.84717} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.04978, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31453, "top5_acc": 0.57078, "loss_cls": 3.94282, "loss": 3.94282, "time": 0.84951} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.04975, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30828, "top5_acc": 0.57125, "loss_cls": 3.94105, "loss": 3.94105, "time": 0.84514} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.04972, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32016, "top5_acc": 0.58172, "loss_cls": 3.89625, "loss": 3.89625, "time": 0.84922} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.04969, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31516, "top5_acc": 0.5775, "loss_cls": 3.90976, "loss": 3.90976, "time": 0.84577} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.04966, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31812, "top5_acc": 0.57016, "loss_cls": 3.91204, "loss": 3.91204, "time": 0.84466} +{"mode": "train", "epoch": 76, "iter": 1300, "lr": 0.04964, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31078, "top5_acc": 0.56656, "loss_cls": 3.93195, "loss": 3.93195, "time": 0.84728} +{"mode": "train", "epoch": 76, "iter": 1400, "lr": 0.04961, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31406, "top5_acc": 0.57484, "loss_cls": 3.93601, "loss": 3.93601, "time": 0.84401} +{"mode": "train", "epoch": 76, "iter": 1500, "lr": 0.04958, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32172, "top5_acc": 0.57672, "loss_cls": 3.899, "loss": 3.899, "time": 0.8512} +{"mode": "train", "epoch": 76, "iter": 1600, "lr": 0.04955, "memory": 15990, "data_time": 0.00081, "top1_acc": 0.30797, "top5_acc": 0.56703, "loss_cls": 3.94583, "loss": 3.94583, "time": 0.8429} +{"mode": "train", "epoch": 76, "iter": 1700, "lr": 0.04953, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31641, "top5_acc": 0.57406, "loss_cls": 3.9569, "loss": 3.9569, "time": 0.85096} +{"mode": "train", "epoch": 76, "iter": 1800, "lr": 0.0495, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31359, "top5_acc": 0.57359, "loss_cls": 3.90047, "loss": 3.90047, "time": 0.85032} +{"mode": "train", "epoch": 76, "iter": 1900, "lr": 0.04947, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31453, "top5_acc": 0.57797, "loss_cls": 3.90816, "loss": 3.90816, "time": 0.84478} +{"mode": "train", "epoch": 76, "iter": 2000, "lr": 0.04944, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31062, "top5_acc": 0.56625, "loss_cls": 3.95437, "loss": 3.95437, "time": 0.84588} +{"mode": "train", "epoch": 76, "iter": 2100, "lr": 0.04941, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31625, "top5_acc": 0.58266, "loss_cls": 3.86952, "loss": 3.86952, "time": 0.84487} +{"mode": "train", "epoch": 76, "iter": 2200, "lr": 0.04939, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31141, "top5_acc": 0.57203, "loss_cls": 3.92714, "loss": 3.92714, "time": 0.84635} +{"mode": "train", "epoch": 76, "iter": 2300, "lr": 0.04936, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31109, "top5_acc": 0.56625, "loss_cls": 3.94864, "loss": 3.94864, "time": 0.84351} +{"mode": "train", "epoch": 76, "iter": 2400, "lr": 0.04933, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30984, "top5_acc": 0.57047, "loss_cls": 3.93374, "loss": 3.93374, "time": 0.84521} +{"mode": "train", "epoch": 76, "iter": 2500, "lr": 0.0493, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30562, "top5_acc": 0.55922, "loss_cls": 3.96242, "loss": 3.96242, "time": 0.84719} +{"mode": "train", "epoch": 76, "iter": 2600, "lr": 0.04927, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29516, "top5_acc": 0.56125, "loss_cls": 4.0127, "loss": 4.0127, "time": 0.84466} +{"mode": "train", "epoch": 76, "iter": 2700, "lr": 0.04925, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31969, "top5_acc": 0.56688, "loss_cls": 3.93408, "loss": 3.93408, "time": 0.84272} +{"mode": "train", "epoch": 76, "iter": 2800, "lr": 0.04922, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31516, "top5_acc": 0.56219, "loss_cls": 3.95024, "loss": 3.95024, "time": 0.8483} +{"mode": "train", "epoch": 76, "iter": 2900, "lr": 0.04919, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31031, "top5_acc": 0.57391, "loss_cls": 3.92719, "loss": 3.92719, "time": 0.85157} +{"mode": "train", "epoch": 76, "iter": 3000, "lr": 0.04916, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.31281, "top5_acc": 0.57031, "loss_cls": 3.92536, "loss": 3.92536, "time": 0.84533} +{"mode": "train", "epoch": 76, "iter": 3100, "lr": 0.04913, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31922, "top5_acc": 0.57969, "loss_cls": 3.88306, "loss": 3.88306, "time": 0.84943} +{"mode": "train", "epoch": 76, "iter": 3200, "lr": 0.04911, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31688, "top5_acc": 0.57141, "loss_cls": 3.90389, "loss": 3.90389, "time": 0.85127} +{"mode": "train", "epoch": 76, "iter": 3300, "lr": 0.04908, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31672, "top5_acc": 0.57281, "loss_cls": 3.92791, "loss": 3.92791, "time": 0.84789} +{"mode": "train", "epoch": 76, "iter": 3400, "lr": 0.04905, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31625, "top5_acc": 0.56953, "loss_cls": 3.93369, "loss": 3.93369, "time": 0.84596} +{"mode": "train", "epoch": 76, "iter": 3500, "lr": 0.04902, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.315, "top5_acc": 0.56297, "loss_cls": 3.95851, "loss": 3.95851, "time": 0.84385} +{"mode": "train", "epoch": 76, "iter": 3600, "lr": 0.04899, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30438, "top5_acc": 0.56016, "loss_cls": 4.00884, "loss": 4.00884, "time": 0.8468} +{"mode": "train", "epoch": 76, "iter": 3700, "lr": 0.04897, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31297, "top5_acc": 0.56812, "loss_cls": 3.91612, "loss": 3.91612, "time": 0.84332} +{"mode": "val", "epoch": 76, "iter": 309, "lr": 0.04895, "top1_acc": 0.25933, "top5_acc": 0.50246, "mean_class_accuracy": 0.25896} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.04893, "memory": 15990, "data_time": 1.50439, "top1_acc": 0.32344, "top5_acc": 0.58359, "loss_cls": 3.86284, "loss": 3.86284, "time": 2.53769} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0489, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32297, "top5_acc": 0.58078, "loss_cls": 3.86707, "loss": 3.86707, "time": 0.85307} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.04887, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31016, "top5_acc": 0.57406, "loss_cls": 3.92056, "loss": 3.92056, "time": 0.84979} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.04884, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.32938, "top5_acc": 0.57891, "loss_cls": 3.86113, "loss": 3.86113, "time": 0.85139} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.04881, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31672, "top5_acc": 0.57703, "loss_cls": 3.91806, "loss": 3.91806, "time": 0.84455} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.04879, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31812, "top5_acc": 0.57328, "loss_cls": 3.89452, "loss": 3.89452, "time": 0.85265} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.04876, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32422, "top5_acc": 0.57719, "loss_cls": 3.88891, "loss": 3.88891, "time": 0.84786} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.04873, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31453, "top5_acc": 0.5775, "loss_cls": 3.90739, "loss": 3.90739, "time": 0.84622} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.0487, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31781, "top5_acc": 0.57453, "loss_cls": 3.89384, "loss": 3.89384, "time": 0.84755} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.04867, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31, "top5_acc": 0.57188, "loss_cls": 3.93591, "loss": 3.93591, "time": 0.8438} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.04865, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31516, "top5_acc": 0.57641, "loss_cls": 3.8995, "loss": 3.8995, "time": 0.84593} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.04862, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31891, "top5_acc": 0.57578, "loss_cls": 3.90412, "loss": 3.90412, "time": 0.84468} +{"mode": "train", "epoch": 77, "iter": 1300, "lr": 0.04859, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31109, "top5_acc": 0.57656, "loss_cls": 3.91091, "loss": 3.91091, "time": 0.84649} +{"mode": "train", "epoch": 77, "iter": 1400, "lr": 0.04856, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31672, "top5_acc": 0.575, "loss_cls": 3.91555, "loss": 3.91555, "time": 0.85161} +{"mode": "train", "epoch": 77, "iter": 1500, "lr": 0.04853, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30609, "top5_acc": 0.57063, "loss_cls": 3.96211, "loss": 3.96211, "time": 0.85173} +{"mode": "train", "epoch": 77, "iter": 1600, "lr": 0.04851, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32312, "top5_acc": 0.57359, "loss_cls": 3.91039, "loss": 3.91039, "time": 0.84555} +{"mode": "train", "epoch": 77, "iter": 1700, "lr": 0.04848, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30984, "top5_acc": 0.57484, "loss_cls": 3.91786, "loss": 3.91786, "time": 0.84424} +{"mode": "train", "epoch": 77, "iter": 1800, "lr": 0.04845, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.30969, "top5_acc": 0.57094, "loss_cls": 3.93266, "loss": 3.93266, "time": 0.84637} +{"mode": "train", "epoch": 77, "iter": 1900, "lr": 0.04842, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31297, "top5_acc": 0.56563, "loss_cls": 3.91979, "loss": 3.91979, "time": 0.85002} +{"mode": "train", "epoch": 77, "iter": 2000, "lr": 0.04839, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.31531, "top5_acc": 0.57563, "loss_cls": 3.93806, "loss": 3.93806, "time": 0.85031} +{"mode": "train", "epoch": 77, "iter": 2100, "lr": 0.04837, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3075, "top5_acc": 0.56031, "loss_cls": 3.94166, "loss": 3.94166, "time": 0.85241} +{"mode": "train", "epoch": 77, "iter": 2200, "lr": 0.04834, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31266, "top5_acc": 0.57078, "loss_cls": 3.94147, "loss": 3.94147, "time": 0.85205} +{"mode": "train", "epoch": 77, "iter": 2300, "lr": 0.04831, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31609, "top5_acc": 0.57719, "loss_cls": 3.8884, "loss": 3.8884, "time": 0.85433} +{"mode": "train", "epoch": 77, "iter": 2400, "lr": 0.04828, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31922, "top5_acc": 0.57906, "loss_cls": 3.8879, "loss": 3.8879, "time": 0.85248} +{"mode": "train", "epoch": 77, "iter": 2500, "lr": 0.04825, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32047, "top5_acc": 0.57172, "loss_cls": 3.89846, "loss": 3.89846, "time": 0.8511} +{"mode": "train", "epoch": 77, "iter": 2600, "lr": 0.04823, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31188, "top5_acc": 0.57344, "loss_cls": 3.90937, "loss": 3.90937, "time": 0.84721} +{"mode": "train", "epoch": 77, "iter": 2700, "lr": 0.0482, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31156, "top5_acc": 0.56516, "loss_cls": 3.95025, "loss": 3.95025, "time": 0.8496} +{"mode": "train", "epoch": 77, "iter": 2800, "lr": 0.04817, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30531, "top5_acc": 0.56656, "loss_cls": 3.9811, "loss": 3.9811, "time": 0.85484} +{"mode": "train", "epoch": 77, "iter": 2900, "lr": 0.04814, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32219, "top5_acc": 0.58156, "loss_cls": 3.85742, "loss": 3.85742, "time": 0.85185} +{"mode": "train", "epoch": 77, "iter": 3000, "lr": 0.04811, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.31375, "top5_acc": 0.57875, "loss_cls": 3.91137, "loss": 3.91137, "time": 0.84992} +{"mode": "train", "epoch": 77, "iter": 3100, "lr": 0.04809, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31422, "top5_acc": 0.58234, "loss_cls": 3.89467, "loss": 3.89467, "time": 0.85008} +{"mode": "train", "epoch": 77, "iter": 3200, "lr": 0.04806, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.31734, "top5_acc": 0.57516, "loss_cls": 3.93355, "loss": 3.93355, "time": 0.84723} +{"mode": "train", "epoch": 77, "iter": 3300, "lr": 0.04803, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31984, "top5_acc": 0.57141, "loss_cls": 3.92789, "loss": 3.92789, "time": 0.85051} +{"mode": "train", "epoch": 77, "iter": 3400, "lr": 0.048, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30047, "top5_acc": 0.57094, "loss_cls": 3.95569, "loss": 3.95569, "time": 0.8461} +{"mode": "train", "epoch": 77, "iter": 3500, "lr": 0.04798, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31875, "top5_acc": 0.56563, "loss_cls": 3.93369, "loss": 3.93369, "time": 0.84547} +{"mode": "train", "epoch": 77, "iter": 3600, "lr": 0.04795, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31656, "top5_acc": 0.57219, "loss_cls": 3.92814, "loss": 3.92814, "time": 0.8482} +{"mode": "train", "epoch": 77, "iter": 3700, "lr": 0.04792, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32031, "top5_acc": 0.57859, "loss_cls": 3.91458, "loss": 3.91458, "time": 0.84474} +{"mode": "val", "epoch": 77, "iter": 309, "lr": 0.04791, "top1_acc": 0.25594, "top5_acc": 0.498, "mean_class_accuracy": 0.2558} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.04788, "memory": 15990, "data_time": 1.50181, "top1_acc": 0.32859, "top5_acc": 0.58875, "loss_cls": 3.81827, "loss": 3.81827, "time": 2.52933} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.04785, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33047, "top5_acc": 0.58516, "loss_cls": 3.83113, "loss": 3.83113, "time": 0.85075} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.04782, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31984, "top5_acc": 0.58312, "loss_cls": 3.86638, "loss": 3.86638, "time": 0.84867} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.04779, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32156, "top5_acc": 0.57859, "loss_cls": 3.91169, "loss": 3.91169, "time": 0.85258} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.04777, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.31375, "top5_acc": 0.56734, "loss_cls": 3.89788, "loss": 3.89788, "time": 0.85141} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.04774, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.32188, "top5_acc": 0.56938, "loss_cls": 3.90468, "loss": 3.90468, "time": 0.84687} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.04771, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30656, "top5_acc": 0.57578, "loss_cls": 3.94203, "loss": 3.94203, "time": 0.85117} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.04768, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32062, "top5_acc": 0.56922, "loss_cls": 3.90816, "loss": 3.90816, "time": 0.85043} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.04766, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31375, "top5_acc": 0.57344, "loss_cls": 3.90318, "loss": 3.90318, "time": 0.84468} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.04763, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.3175, "top5_acc": 0.56859, "loss_cls": 3.91361, "loss": 3.91361, "time": 0.84949} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.0476, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31953, "top5_acc": 0.57234, "loss_cls": 3.92343, "loss": 3.92343, "time": 0.84668} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.04757, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32719, "top5_acc": 0.57766, "loss_cls": 3.87491, "loss": 3.87491, "time": 0.84826} +{"mode": "train", "epoch": 78, "iter": 1300, "lr": 0.04754, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31844, "top5_acc": 0.57453, "loss_cls": 3.88465, "loss": 3.88465, "time": 0.84853} +{"mode": "train", "epoch": 78, "iter": 1400, "lr": 0.04752, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32609, "top5_acc": 0.58766, "loss_cls": 3.84903, "loss": 3.84903, "time": 0.84713} +{"mode": "train", "epoch": 78, "iter": 1500, "lr": 0.04749, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31516, "top5_acc": 0.57781, "loss_cls": 3.90719, "loss": 3.90719, "time": 0.84601} +{"mode": "train", "epoch": 78, "iter": 1600, "lr": 0.04746, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32422, "top5_acc": 0.58562, "loss_cls": 3.86169, "loss": 3.86169, "time": 0.84834} +{"mode": "train", "epoch": 78, "iter": 1700, "lr": 0.04743, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.31453, "top5_acc": 0.58297, "loss_cls": 3.89348, "loss": 3.89348, "time": 0.85333} +{"mode": "train", "epoch": 78, "iter": 1800, "lr": 0.0474, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.31844, "top5_acc": 0.575, "loss_cls": 3.90587, "loss": 3.90587, "time": 0.84398} +{"mode": "train", "epoch": 78, "iter": 1900, "lr": 0.04738, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31969, "top5_acc": 0.57, "loss_cls": 3.90531, "loss": 3.90531, "time": 0.84463} +{"mode": "train", "epoch": 78, "iter": 2000, "lr": 0.04735, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31578, "top5_acc": 0.57578, "loss_cls": 3.91264, "loss": 3.91264, "time": 0.84651} +{"mode": "train", "epoch": 78, "iter": 2100, "lr": 0.04732, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31078, "top5_acc": 0.57016, "loss_cls": 3.91668, "loss": 3.91668, "time": 0.84678} +{"mode": "train", "epoch": 78, "iter": 2200, "lr": 0.04729, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31562, "top5_acc": 0.57734, "loss_cls": 3.92219, "loss": 3.92219, "time": 0.84406} +{"mode": "train", "epoch": 78, "iter": 2300, "lr": 0.04726, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31891, "top5_acc": 0.57547, "loss_cls": 3.89267, "loss": 3.89267, "time": 0.84445} +{"mode": "train", "epoch": 78, "iter": 2400, "lr": 0.04724, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33016, "top5_acc": 0.58141, "loss_cls": 3.87883, "loss": 3.87883, "time": 0.84592} +{"mode": "train", "epoch": 78, "iter": 2500, "lr": 0.04721, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31547, "top5_acc": 0.57422, "loss_cls": 3.92736, "loss": 3.92736, "time": 0.84492} +{"mode": "train", "epoch": 78, "iter": 2600, "lr": 0.04718, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30875, "top5_acc": 0.57078, "loss_cls": 3.92618, "loss": 3.92618, "time": 0.84921} +{"mode": "train", "epoch": 78, "iter": 2700, "lr": 0.04715, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30703, "top5_acc": 0.565, "loss_cls": 3.93106, "loss": 3.93106, "time": 0.84622} +{"mode": "train", "epoch": 78, "iter": 2800, "lr": 0.04712, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.325, "top5_acc": 0.57563, "loss_cls": 3.89892, "loss": 3.89892, "time": 0.84645} +{"mode": "train", "epoch": 78, "iter": 2900, "lr": 0.0471, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32062, "top5_acc": 0.57156, "loss_cls": 3.89889, "loss": 3.89889, "time": 0.8449} +{"mode": "train", "epoch": 78, "iter": 3000, "lr": 0.04707, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31953, "top5_acc": 0.58047, "loss_cls": 3.85779, "loss": 3.85779, "time": 0.84761} +{"mode": "train", "epoch": 78, "iter": 3100, "lr": 0.04704, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31797, "top5_acc": 0.57141, "loss_cls": 3.92313, "loss": 3.92313, "time": 0.84844} +{"mode": "train", "epoch": 78, "iter": 3200, "lr": 0.04701, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.32344, "top5_acc": 0.56906, "loss_cls": 3.92521, "loss": 3.92521, "time": 0.84487} +{"mode": "train", "epoch": 78, "iter": 3300, "lr": 0.04699, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.305, "top5_acc": 0.56297, "loss_cls": 3.9581, "loss": 3.9581, "time": 0.84632} +{"mode": "train", "epoch": 78, "iter": 3400, "lr": 0.04696, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.3225, "top5_acc": 0.57781, "loss_cls": 3.88057, "loss": 3.88057, "time": 0.8487} +{"mode": "train", "epoch": 78, "iter": 3500, "lr": 0.04693, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29906, "top5_acc": 0.56188, "loss_cls": 3.95873, "loss": 3.95873, "time": 0.85071} +{"mode": "train", "epoch": 78, "iter": 3600, "lr": 0.0469, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.32328, "top5_acc": 0.57844, "loss_cls": 3.88207, "loss": 3.88207, "time": 0.85529} +{"mode": "train", "epoch": 78, "iter": 3700, "lr": 0.04687, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31281, "top5_acc": 0.57188, "loss_cls": 3.89935, "loss": 3.89935, "time": 0.85433} +{"mode": "val", "epoch": 78, "iter": 309, "lr": 0.04686, "top1_acc": 0.26759, "top5_acc": 0.51157, "mean_class_accuracy": 0.26737} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.04683, "memory": 15990, "data_time": 1.52161, "top1_acc": 0.33719, "top5_acc": 0.59219, "loss_cls": 3.80678, "loss": 3.80678, "time": 2.56193} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.0468, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32516, "top5_acc": 0.58453, "loss_cls": 3.84933, "loss": 3.84933, "time": 0.85788} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.04678, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31156, "top5_acc": 0.57812, "loss_cls": 3.88423, "loss": 3.88423, "time": 0.85382} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.04675, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32125, "top5_acc": 0.57953, "loss_cls": 3.86107, "loss": 3.86107, "time": 0.84999} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.04672, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32391, "top5_acc": 0.58266, "loss_cls": 3.85263, "loss": 3.85263, "time": 0.85046} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.04669, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.33125, "top5_acc": 0.58016, "loss_cls": 3.86508, "loss": 3.86508, "time": 0.84683} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.04667, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32641, "top5_acc": 0.58328, "loss_cls": 3.84662, "loss": 3.84662, "time": 0.8467} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.04664, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31703, "top5_acc": 0.57766, "loss_cls": 3.88654, "loss": 3.88654, "time": 0.84525} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.04661, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33438, "top5_acc": 0.58469, "loss_cls": 3.82833, "loss": 3.82833, "time": 0.84669} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.04658, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32156, "top5_acc": 0.58375, "loss_cls": 3.85175, "loss": 3.85175, "time": 0.84859} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.04655, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31578, "top5_acc": 0.57781, "loss_cls": 3.85831, "loss": 3.85831, "time": 0.84421} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.04653, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31531, "top5_acc": 0.57453, "loss_cls": 3.93516, "loss": 3.93516, "time": 0.84845} +{"mode": "train", "epoch": 79, "iter": 1300, "lr": 0.0465, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31953, "top5_acc": 0.57156, "loss_cls": 3.89668, "loss": 3.89668, "time": 0.84882} +{"mode": "train", "epoch": 79, "iter": 1400, "lr": 0.04647, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32594, "top5_acc": 0.57172, "loss_cls": 3.86596, "loss": 3.86596, "time": 0.84725} +{"mode": "train", "epoch": 79, "iter": 1500, "lr": 0.04644, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32078, "top5_acc": 0.57484, "loss_cls": 3.90087, "loss": 3.90087, "time": 0.84312} +{"mode": "train", "epoch": 79, "iter": 1600, "lr": 0.04641, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32375, "top5_acc": 0.58062, "loss_cls": 3.85397, "loss": 3.85397, "time": 0.85475} +{"mode": "train", "epoch": 79, "iter": 1700, "lr": 0.04639, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30719, "top5_acc": 0.56609, "loss_cls": 3.92184, "loss": 3.92184, "time": 0.85245} +{"mode": "train", "epoch": 79, "iter": 1800, "lr": 0.04636, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32547, "top5_acc": 0.58469, "loss_cls": 3.85668, "loss": 3.85668, "time": 0.84525} +{"mode": "train", "epoch": 79, "iter": 1900, "lr": 0.04633, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31547, "top5_acc": 0.57156, "loss_cls": 3.8941, "loss": 3.8941, "time": 0.84617} +{"mode": "train", "epoch": 79, "iter": 2000, "lr": 0.0463, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31828, "top5_acc": 0.5775, "loss_cls": 3.87649, "loss": 3.87649, "time": 0.8443} +{"mode": "train", "epoch": 79, "iter": 2100, "lr": 0.04628, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32031, "top5_acc": 0.57766, "loss_cls": 3.89027, "loss": 3.89027, "time": 0.8475} +{"mode": "train", "epoch": 79, "iter": 2200, "lr": 0.04625, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33047, "top5_acc": 0.58156, "loss_cls": 3.84641, "loss": 3.84641, "time": 0.84799} +{"mode": "train", "epoch": 79, "iter": 2300, "lr": 0.04622, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31594, "top5_acc": 0.57609, "loss_cls": 3.9149, "loss": 3.9149, "time": 0.8484} +{"mode": "train", "epoch": 79, "iter": 2400, "lr": 0.04619, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32234, "top5_acc": 0.575, "loss_cls": 3.8759, "loss": 3.8759, "time": 0.84439} +{"mode": "train", "epoch": 79, "iter": 2500, "lr": 0.04616, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30781, "top5_acc": 0.56297, "loss_cls": 3.95292, "loss": 3.95292, "time": 0.84738} +{"mode": "train", "epoch": 79, "iter": 2600, "lr": 0.04614, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31078, "top5_acc": 0.57281, "loss_cls": 3.93295, "loss": 3.93295, "time": 0.84284} +{"mode": "train", "epoch": 79, "iter": 2700, "lr": 0.04611, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31594, "top5_acc": 0.57141, "loss_cls": 3.92661, "loss": 3.92661, "time": 0.84997} +{"mode": "train", "epoch": 79, "iter": 2800, "lr": 0.04608, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32078, "top5_acc": 0.57234, "loss_cls": 3.91806, "loss": 3.91806, "time": 0.8483} +{"mode": "train", "epoch": 79, "iter": 2900, "lr": 0.04605, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32156, "top5_acc": 0.58609, "loss_cls": 3.89071, "loss": 3.89071, "time": 0.84811} +{"mode": "train", "epoch": 79, "iter": 3000, "lr": 0.04602, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32094, "top5_acc": 0.57922, "loss_cls": 3.88706, "loss": 3.88706, "time": 0.85067} +{"mode": "train", "epoch": 79, "iter": 3100, "lr": 0.046, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31516, "top5_acc": 0.57219, "loss_cls": 3.88743, "loss": 3.88743, "time": 0.8502} +{"mode": "train", "epoch": 79, "iter": 3200, "lr": 0.04597, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.31984, "top5_acc": 0.57156, "loss_cls": 3.89863, "loss": 3.89863, "time": 0.84219} +{"mode": "train", "epoch": 79, "iter": 3300, "lr": 0.04594, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31078, "top5_acc": 0.57312, "loss_cls": 3.92285, "loss": 3.92285, "time": 0.84773} +{"mode": "train", "epoch": 79, "iter": 3400, "lr": 0.04591, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31062, "top5_acc": 0.57312, "loss_cls": 3.89953, "loss": 3.89953, "time": 0.84781} +{"mode": "train", "epoch": 79, "iter": 3500, "lr": 0.04588, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31, "top5_acc": 0.5625, "loss_cls": 3.95174, "loss": 3.95174, "time": 0.85068} +{"mode": "train", "epoch": 79, "iter": 3600, "lr": 0.04586, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31438, "top5_acc": 0.57594, "loss_cls": 3.93217, "loss": 3.93217, "time": 0.85104} +{"mode": "train", "epoch": 79, "iter": 3700, "lr": 0.04583, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31719, "top5_acc": 0.57125, "loss_cls": 3.91022, "loss": 3.91022, "time": 0.85172} +{"mode": "val", "epoch": 79, "iter": 309, "lr": 0.04582, "top1_acc": 0.26273, "top5_acc": 0.5096, "mean_class_accuracy": 0.26253} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.04579, "memory": 15990, "data_time": 1.54456, "top1_acc": 0.32812, "top5_acc": 0.59688, "loss_cls": 3.78979, "loss": 3.78979, "time": 2.57176} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.04576, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32641, "top5_acc": 0.58172, "loss_cls": 3.85268, "loss": 3.85268, "time": 0.85243} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.04573, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32094, "top5_acc": 0.58375, "loss_cls": 3.86706, "loss": 3.86706, "time": 0.84997} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.0457, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32484, "top5_acc": 0.57953, "loss_cls": 3.89101, "loss": 3.89101, "time": 0.84848} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.04568, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.31859, "top5_acc": 0.57797, "loss_cls": 3.87088, "loss": 3.87088, "time": 0.85151} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.04565, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.31641, "top5_acc": 0.59062, "loss_cls": 3.85677, "loss": 3.85677, "time": 0.84775} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.04562, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32406, "top5_acc": 0.58062, "loss_cls": 3.86866, "loss": 3.86866, "time": 0.85051} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.04559, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31438, "top5_acc": 0.57328, "loss_cls": 3.89926, "loss": 3.89926, "time": 0.84986} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.04557, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32516, "top5_acc": 0.57922, "loss_cls": 3.85896, "loss": 3.85896, "time": 0.84962} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.04554, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32531, "top5_acc": 0.57969, "loss_cls": 3.84253, "loss": 3.84253, "time": 0.84909} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.04551, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32203, "top5_acc": 0.57156, "loss_cls": 3.89201, "loss": 3.89201, "time": 0.84655} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.04548, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32625, "top5_acc": 0.5775, "loss_cls": 3.88575, "loss": 3.88575, "time": 0.84864} +{"mode": "train", "epoch": 80, "iter": 1300, "lr": 0.04545, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31578, "top5_acc": 0.5675, "loss_cls": 3.90958, "loss": 3.90958, "time": 0.84788} +{"mode": "train", "epoch": 80, "iter": 1400, "lr": 0.04543, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.31344, "top5_acc": 0.5725, "loss_cls": 3.90014, "loss": 3.90014, "time": 0.84751} +{"mode": "train", "epoch": 80, "iter": 1500, "lr": 0.0454, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.31703, "top5_acc": 0.57563, "loss_cls": 3.88913, "loss": 3.88913, "time": 0.84353} +{"mode": "train", "epoch": 80, "iter": 1600, "lr": 0.04537, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33094, "top5_acc": 0.57656, "loss_cls": 3.86052, "loss": 3.86052, "time": 0.84936} +{"mode": "train", "epoch": 80, "iter": 1700, "lr": 0.04534, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.31547, "top5_acc": 0.57516, "loss_cls": 3.89627, "loss": 3.89627, "time": 0.84684} +{"mode": "train", "epoch": 80, "iter": 1800, "lr": 0.04532, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33156, "top5_acc": 0.57984, "loss_cls": 3.84391, "loss": 3.84391, "time": 0.84667} +{"mode": "train", "epoch": 80, "iter": 1900, "lr": 0.04529, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32156, "top5_acc": 0.58266, "loss_cls": 3.84404, "loss": 3.84404, "time": 0.84625} +{"mode": "train", "epoch": 80, "iter": 2000, "lr": 0.04526, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31766, "top5_acc": 0.58406, "loss_cls": 3.87807, "loss": 3.87807, "time": 0.84719} +{"mode": "train", "epoch": 80, "iter": 2100, "lr": 0.04523, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.325, "top5_acc": 0.58344, "loss_cls": 3.83352, "loss": 3.83352, "time": 0.8453} +{"mode": "train", "epoch": 80, "iter": 2200, "lr": 0.0452, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32297, "top5_acc": 0.58656, "loss_cls": 3.87212, "loss": 3.87212, "time": 0.84977} +{"mode": "train", "epoch": 80, "iter": 2300, "lr": 0.04518, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31516, "top5_acc": 0.57094, "loss_cls": 3.92263, "loss": 3.92263, "time": 0.84782} +{"mode": "train", "epoch": 80, "iter": 2400, "lr": 0.04515, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32375, "top5_acc": 0.58, "loss_cls": 3.88765, "loss": 3.88765, "time": 0.84989} +{"mode": "train", "epoch": 80, "iter": 2500, "lr": 0.04512, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.32219, "top5_acc": 0.59062, "loss_cls": 3.85772, "loss": 3.85772, "time": 0.84536} +{"mode": "train", "epoch": 80, "iter": 2600, "lr": 0.04509, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31953, "top5_acc": 0.58078, "loss_cls": 3.86541, "loss": 3.86541, "time": 0.85032} +{"mode": "train", "epoch": 80, "iter": 2700, "lr": 0.04506, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33062, "top5_acc": 0.58266, "loss_cls": 3.84683, "loss": 3.84683, "time": 0.84728} +{"mode": "train", "epoch": 80, "iter": 2800, "lr": 0.04504, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31625, "top5_acc": 0.57531, "loss_cls": 3.89251, "loss": 3.89251, "time": 0.85185} +{"mode": "train", "epoch": 80, "iter": 2900, "lr": 0.04501, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31562, "top5_acc": 0.56828, "loss_cls": 3.93672, "loss": 3.93672, "time": 0.85552} +{"mode": "train", "epoch": 80, "iter": 3000, "lr": 0.04498, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.32062, "top5_acc": 0.58141, "loss_cls": 3.87997, "loss": 3.87997, "time": 0.853} +{"mode": "train", "epoch": 80, "iter": 3100, "lr": 0.04495, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31547, "top5_acc": 0.57578, "loss_cls": 3.89402, "loss": 3.89402, "time": 0.84963} +{"mode": "train", "epoch": 80, "iter": 3200, "lr": 0.04493, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.32891, "top5_acc": 0.57719, "loss_cls": 3.90594, "loss": 3.90594, "time": 0.8477} +{"mode": "train", "epoch": 80, "iter": 3300, "lr": 0.0449, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31484, "top5_acc": 0.58281, "loss_cls": 3.89338, "loss": 3.89338, "time": 0.8461} +{"mode": "train", "epoch": 80, "iter": 3400, "lr": 0.04487, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31875, "top5_acc": 0.57063, "loss_cls": 3.91622, "loss": 3.91622, "time": 0.84689} +{"mode": "train", "epoch": 80, "iter": 3500, "lr": 0.04484, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31047, "top5_acc": 0.57344, "loss_cls": 3.90675, "loss": 3.90675, "time": 0.84726} +{"mode": "train", "epoch": 80, "iter": 3600, "lr": 0.04481, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32469, "top5_acc": 0.57578, "loss_cls": 3.86729, "loss": 3.86729, "time": 0.85069} +{"mode": "train", "epoch": 80, "iter": 3700, "lr": 0.04479, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.315, "top5_acc": 0.58047, "loss_cls": 3.87289, "loss": 3.87289, "time": 0.85044} +{"mode": "val", "epoch": 80, "iter": 309, "lr": 0.04477, "top1_acc": 0.25412, "top5_acc": 0.50119, "mean_class_accuracy": 0.2541} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.04475, "memory": 15990, "data_time": 1.61008, "top1_acc": 0.33812, "top5_acc": 0.58609, "loss_cls": 3.81524, "loss": 3.81524, "time": 2.64442} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.04472, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32859, "top5_acc": 0.5825, "loss_cls": 3.83604, "loss": 3.83604, "time": 0.85304} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.04469, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32484, "top5_acc": 0.57984, "loss_cls": 3.85953, "loss": 3.85953, "time": 0.85247} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.04466, "memory": 15990, "data_time": 0.0007, "top1_acc": 0.32984, "top5_acc": 0.58547, "loss_cls": 3.82812, "loss": 3.82812, "time": 0.85148} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.04463, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33219, "top5_acc": 0.59234, "loss_cls": 3.78437, "loss": 3.78437, "time": 0.85448} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.04461, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.31844, "top5_acc": 0.58234, "loss_cls": 3.88453, "loss": 3.88453, "time": 0.84611} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.04458, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32641, "top5_acc": 0.58391, "loss_cls": 3.83323, "loss": 3.83323, "time": 0.85262} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.04455, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33312, "top5_acc": 0.5875, "loss_cls": 3.80391, "loss": 3.80391, "time": 0.8487} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.04452, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31797, "top5_acc": 0.57859, "loss_cls": 3.89317, "loss": 3.89317, "time": 0.85268} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.0445, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32672, "top5_acc": 0.57859, "loss_cls": 3.87229, "loss": 3.87229, "time": 0.84892} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.04447, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31875, "top5_acc": 0.58234, "loss_cls": 3.88877, "loss": 3.88877, "time": 0.8572} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.04444, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33266, "top5_acc": 0.59656, "loss_cls": 3.8059, "loss": 3.8059, "time": 0.85422} +{"mode": "train", "epoch": 81, "iter": 1300, "lr": 0.04441, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32375, "top5_acc": 0.57953, "loss_cls": 3.86463, "loss": 3.86463, "time": 0.84909} +{"mode": "train", "epoch": 81, "iter": 1400, "lr": 0.04438, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32406, "top5_acc": 0.57953, "loss_cls": 3.89039, "loss": 3.89039, "time": 0.85403} +{"mode": "train", "epoch": 81, "iter": 1500, "lr": 0.04436, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32828, "top5_acc": 0.58188, "loss_cls": 3.86317, "loss": 3.86317, "time": 0.85236} +{"mode": "train", "epoch": 81, "iter": 1600, "lr": 0.04433, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31547, "top5_acc": 0.57953, "loss_cls": 3.90588, "loss": 3.90588, "time": 0.85347} +{"mode": "train", "epoch": 81, "iter": 1700, "lr": 0.0443, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33016, "top5_acc": 0.58406, "loss_cls": 3.86801, "loss": 3.86801, "time": 0.84909} +{"mode": "train", "epoch": 81, "iter": 1800, "lr": 0.04427, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32828, "top5_acc": 0.58375, "loss_cls": 3.86233, "loss": 3.86233, "time": 0.84425} +{"mode": "train", "epoch": 81, "iter": 1900, "lr": 0.04425, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.31125, "top5_acc": 0.56859, "loss_cls": 3.90685, "loss": 3.90685, "time": 0.84837} +{"mode": "train", "epoch": 81, "iter": 2000, "lr": 0.04422, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.31906, "top5_acc": 0.57, "loss_cls": 3.89628, "loss": 3.89628, "time": 0.84721} +{"mode": "train", "epoch": 81, "iter": 2100, "lr": 0.04419, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31672, "top5_acc": 0.57594, "loss_cls": 3.90575, "loss": 3.90575, "time": 0.8504} +{"mode": "train", "epoch": 81, "iter": 2200, "lr": 0.04416, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32766, "top5_acc": 0.58453, "loss_cls": 3.85106, "loss": 3.85106, "time": 0.85431} +{"mode": "train", "epoch": 81, "iter": 2300, "lr": 0.04413, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31797, "top5_acc": 0.58422, "loss_cls": 3.84031, "loss": 3.84031, "time": 0.85179} +{"mode": "train", "epoch": 81, "iter": 2400, "lr": 0.04411, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32188, "top5_acc": 0.57797, "loss_cls": 3.87373, "loss": 3.87373, "time": 0.85076} +{"mode": "train", "epoch": 81, "iter": 2500, "lr": 0.04408, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33312, "top5_acc": 0.58125, "loss_cls": 3.84713, "loss": 3.84713, "time": 0.85145} +{"mode": "train", "epoch": 81, "iter": 2600, "lr": 0.04405, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31781, "top5_acc": 0.57406, "loss_cls": 3.90745, "loss": 3.90745, "time": 0.8528} +{"mode": "train", "epoch": 81, "iter": 2700, "lr": 0.04402, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32188, "top5_acc": 0.57656, "loss_cls": 3.91152, "loss": 3.91152, "time": 0.85031} +{"mode": "train", "epoch": 81, "iter": 2800, "lr": 0.044, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32922, "top5_acc": 0.58688, "loss_cls": 3.83368, "loss": 3.83368, "time": 0.84542} +{"mode": "train", "epoch": 81, "iter": 2900, "lr": 0.04397, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32641, "top5_acc": 0.57859, "loss_cls": 3.88404, "loss": 3.88404, "time": 0.84509} +{"mode": "train", "epoch": 81, "iter": 3000, "lr": 0.04394, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33047, "top5_acc": 0.58984, "loss_cls": 3.82065, "loss": 3.82065, "time": 0.8459} +{"mode": "train", "epoch": 81, "iter": 3100, "lr": 0.04391, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32156, "top5_acc": 0.58094, "loss_cls": 3.87757, "loss": 3.87757, "time": 0.84967} +{"mode": "train", "epoch": 81, "iter": 3200, "lr": 0.04389, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33109, "top5_acc": 0.58391, "loss_cls": 3.84375, "loss": 3.84375, "time": 0.84864} +{"mode": "train", "epoch": 81, "iter": 3300, "lr": 0.04386, "memory": 15990, "data_time": 0.00076, "top1_acc": 0.31875, "top5_acc": 0.57734, "loss_cls": 3.87537, "loss": 3.87537, "time": 0.84501} +{"mode": "train", "epoch": 81, "iter": 3400, "lr": 0.04383, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32312, "top5_acc": 0.58484, "loss_cls": 3.86828, "loss": 3.86828, "time": 0.84924} +{"mode": "train", "epoch": 81, "iter": 3500, "lr": 0.0438, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33016, "top5_acc": 0.58719, "loss_cls": 3.8138, "loss": 3.8138, "time": 0.85336} +{"mode": "train", "epoch": 81, "iter": 3600, "lr": 0.04377, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31094, "top5_acc": 0.57625, "loss_cls": 3.92531, "loss": 3.92531, "time": 0.84997} +{"mode": "train", "epoch": 81, "iter": 3700, "lr": 0.04375, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32078, "top5_acc": 0.57688, "loss_cls": 3.88761, "loss": 3.88761, "time": 0.84687} +{"mode": "val", "epoch": 81, "iter": 309, "lr": 0.04373, "top1_acc": 0.26597, "top5_acc": 0.51806, "mean_class_accuracy": 0.26579} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.04371, "memory": 15990, "data_time": 1.53145, "top1_acc": 0.34141, "top5_acc": 0.59609, "loss_cls": 3.79887, "loss": 3.79887, "time": 2.56464} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.04368, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33766, "top5_acc": 0.59766, "loss_cls": 3.77353, "loss": 3.77353, "time": 0.8523} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.04365, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31938, "top5_acc": 0.58031, "loss_cls": 3.85591, "loss": 3.85591, "time": 0.85366} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.04362, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33109, "top5_acc": 0.59391, "loss_cls": 3.81546, "loss": 3.81546, "time": 0.85071} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.04359, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32766, "top5_acc": 0.58609, "loss_cls": 3.81769, "loss": 3.81769, "time": 0.85393} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.04357, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.32641, "top5_acc": 0.58, "loss_cls": 3.84493, "loss": 3.84493, "time": 0.84811} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.04354, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32422, "top5_acc": 0.57969, "loss_cls": 3.86229, "loss": 3.86229, "time": 0.85133} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.04351, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32641, "top5_acc": 0.58891, "loss_cls": 3.85121, "loss": 3.85121, "time": 0.84979} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.04348, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32969, "top5_acc": 0.5775, "loss_cls": 3.87651, "loss": 3.87651, "time": 0.84735} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.04346, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34234, "top5_acc": 0.59969, "loss_cls": 3.78476, "loss": 3.78476, "time": 0.84863} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.04343, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31891, "top5_acc": 0.57969, "loss_cls": 3.85611, "loss": 3.85611, "time": 0.85086} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.0434, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31141, "top5_acc": 0.5825, "loss_cls": 3.87865, "loss": 3.87865, "time": 0.85055} +{"mode": "train", "epoch": 82, "iter": 1300, "lr": 0.04337, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32641, "top5_acc": 0.58641, "loss_cls": 3.83043, "loss": 3.83043, "time": 0.8458} +{"mode": "train", "epoch": 82, "iter": 1400, "lr": 0.04335, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33234, "top5_acc": 0.58766, "loss_cls": 3.82073, "loss": 3.82073, "time": 0.84526} +{"mode": "train", "epoch": 82, "iter": 1500, "lr": 0.04332, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32953, "top5_acc": 0.585, "loss_cls": 3.84411, "loss": 3.84411, "time": 0.8472} +{"mode": "train", "epoch": 82, "iter": 1600, "lr": 0.04329, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33188, "top5_acc": 0.57953, "loss_cls": 3.82136, "loss": 3.82136, "time": 0.84114} +{"mode": "train", "epoch": 82, "iter": 1700, "lr": 0.04326, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31344, "top5_acc": 0.57156, "loss_cls": 3.88265, "loss": 3.88265, "time": 0.84702} +{"mode": "train", "epoch": 82, "iter": 1800, "lr": 0.04323, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33, "top5_acc": 0.58219, "loss_cls": 3.82813, "loss": 3.82813, "time": 0.8485} +{"mode": "train", "epoch": 82, "iter": 1900, "lr": 0.04321, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32141, "top5_acc": 0.58234, "loss_cls": 3.88429, "loss": 3.88429, "time": 0.84327} +{"mode": "train", "epoch": 82, "iter": 2000, "lr": 0.04318, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32828, "top5_acc": 0.58812, "loss_cls": 3.81621, "loss": 3.81621, "time": 0.84665} +{"mode": "train", "epoch": 82, "iter": 2100, "lr": 0.04315, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33922, "top5_acc": 0.58781, "loss_cls": 3.78119, "loss": 3.78119, "time": 0.84608} +{"mode": "train", "epoch": 82, "iter": 2200, "lr": 0.04312, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32203, "top5_acc": 0.57812, "loss_cls": 3.88771, "loss": 3.88771, "time": 0.84464} +{"mode": "train", "epoch": 82, "iter": 2300, "lr": 0.0431, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32484, "top5_acc": 0.5825, "loss_cls": 3.84785, "loss": 3.84785, "time": 0.84386} +{"mode": "train", "epoch": 82, "iter": 2400, "lr": 0.04307, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32469, "top5_acc": 0.585, "loss_cls": 3.83537, "loss": 3.83537, "time": 0.84516} +{"mode": "train", "epoch": 82, "iter": 2500, "lr": 0.04304, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33734, "top5_acc": 0.59812, "loss_cls": 3.81168, "loss": 3.81168, "time": 0.84718} +{"mode": "train", "epoch": 82, "iter": 2600, "lr": 0.04301, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32547, "top5_acc": 0.58109, "loss_cls": 3.86229, "loss": 3.86229, "time": 0.8471} +{"mode": "train", "epoch": 82, "iter": 2700, "lr": 0.04299, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31844, "top5_acc": 0.58203, "loss_cls": 3.8702, "loss": 3.8702, "time": 0.84799} +{"mode": "train", "epoch": 82, "iter": 2800, "lr": 0.04296, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32172, "top5_acc": 0.57812, "loss_cls": 3.89544, "loss": 3.89544, "time": 0.857} +{"mode": "train", "epoch": 82, "iter": 2900, "lr": 0.04293, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32906, "top5_acc": 0.57656, "loss_cls": 3.84567, "loss": 3.84567, "time": 0.854} +{"mode": "train", "epoch": 82, "iter": 3000, "lr": 0.0429, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32172, "top5_acc": 0.58109, "loss_cls": 3.86651, "loss": 3.86651, "time": 0.85211} +{"mode": "train", "epoch": 82, "iter": 3100, "lr": 0.04287, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.32031, "top5_acc": 0.58531, "loss_cls": 3.86663, "loss": 3.86663, "time": 0.8496} +{"mode": "train", "epoch": 82, "iter": 3200, "lr": 0.04285, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.32156, "top5_acc": 0.58141, "loss_cls": 3.87815, "loss": 3.87815, "time": 0.85016} +{"mode": "train", "epoch": 82, "iter": 3300, "lr": 0.04282, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.32391, "top5_acc": 0.59359, "loss_cls": 3.84221, "loss": 3.84221, "time": 0.85244} +{"mode": "train", "epoch": 82, "iter": 3400, "lr": 0.04279, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30766, "top5_acc": 0.57625, "loss_cls": 3.91246, "loss": 3.91246, "time": 0.86057} +{"mode": "train", "epoch": 82, "iter": 3500, "lr": 0.04276, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32094, "top5_acc": 0.57875, "loss_cls": 3.88377, "loss": 3.88377, "time": 0.8541} +{"mode": "train", "epoch": 82, "iter": 3600, "lr": 0.04274, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32891, "top5_acc": 0.57328, "loss_cls": 3.84834, "loss": 3.84834, "time": 0.85471} +{"mode": "train", "epoch": 82, "iter": 3700, "lr": 0.04271, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.315, "top5_acc": 0.57469, "loss_cls": 3.91125, "loss": 3.91125, "time": 0.85589} +{"mode": "val", "epoch": 82, "iter": 309, "lr": 0.0427, "top1_acc": 0.27331, "top5_acc": 0.52312, "mean_class_accuracy": 0.27308} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.04267, "memory": 15990, "data_time": 1.52527, "top1_acc": 0.33312, "top5_acc": 0.59609, "loss_cls": 3.77006, "loss": 3.77006, "time": 2.5673} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.04264, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33578, "top5_acc": 0.58484, "loss_cls": 3.81171, "loss": 3.81171, "time": 0.86053} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.04261, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33547, "top5_acc": 0.58766, "loss_cls": 3.80502, "loss": 3.80502, "time": 0.86114} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.04259, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32875, "top5_acc": 0.58438, "loss_cls": 3.82741, "loss": 3.82741, "time": 0.85752} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.04256, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.33875, "top5_acc": 0.59562, "loss_cls": 3.79199, "loss": 3.79199, "time": 0.85245} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.04253, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33266, "top5_acc": 0.59391, "loss_cls": 3.81234, "loss": 3.81234, "time": 0.85243} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.0425, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.33281, "top5_acc": 0.58281, "loss_cls": 3.84266, "loss": 3.84266, "time": 0.85202} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.04247, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32516, "top5_acc": 0.5825, "loss_cls": 3.86309, "loss": 3.86309, "time": 0.85888} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.04245, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33328, "top5_acc": 0.59547, "loss_cls": 3.82704, "loss": 3.82704, "time": 0.85366} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.04242, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32672, "top5_acc": 0.58547, "loss_cls": 3.83873, "loss": 3.83873, "time": 0.85954} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.04239, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33094, "top5_acc": 0.58531, "loss_cls": 3.79916, "loss": 3.79916, "time": 0.85831} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.04236, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.32344, "top5_acc": 0.58562, "loss_cls": 3.84526, "loss": 3.84526, "time": 0.85886} +{"mode": "train", "epoch": 83, "iter": 1300, "lr": 0.04234, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32234, "top5_acc": 0.59047, "loss_cls": 3.82707, "loss": 3.82707, "time": 0.85478} +{"mode": "train", "epoch": 83, "iter": 1400, "lr": 0.04231, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32828, "top5_acc": 0.58828, "loss_cls": 3.84381, "loss": 3.84381, "time": 0.85752} +{"mode": "train", "epoch": 83, "iter": 1500, "lr": 0.04228, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32562, "top5_acc": 0.58469, "loss_cls": 3.85546, "loss": 3.85546, "time": 0.85908} +{"mode": "train", "epoch": 83, "iter": 1600, "lr": 0.04225, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.3275, "top5_acc": 0.58453, "loss_cls": 3.82747, "loss": 3.82747, "time": 0.86139} +{"mode": "train", "epoch": 83, "iter": 1700, "lr": 0.04223, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.32625, "top5_acc": 0.57375, "loss_cls": 3.87038, "loss": 3.87038, "time": 0.85108} +{"mode": "train", "epoch": 83, "iter": 1800, "lr": 0.0422, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33172, "top5_acc": 0.58531, "loss_cls": 3.84004, "loss": 3.84004, "time": 0.85012} +{"mode": "train", "epoch": 83, "iter": 1900, "lr": 0.04217, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31812, "top5_acc": 0.57078, "loss_cls": 3.89407, "loss": 3.89407, "time": 0.84385} +{"mode": "train", "epoch": 83, "iter": 2000, "lr": 0.04214, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.335, "top5_acc": 0.59312, "loss_cls": 3.80408, "loss": 3.80408, "time": 0.85028} +{"mode": "train", "epoch": 83, "iter": 2100, "lr": 0.04212, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32406, "top5_acc": 0.58594, "loss_cls": 3.82949, "loss": 3.82949, "time": 0.85423} +{"mode": "train", "epoch": 83, "iter": 2200, "lr": 0.04209, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33406, "top5_acc": 0.59891, "loss_cls": 3.80132, "loss": 3.80132, "time": 0.85217} +{"mode": "train", "epoch": 83, "iter": 2300, "lr": 0.04206, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32234, "top5_acc": 0.58234, "loss_cls": 3.85103, "loss": 3.85103, "time": 0.85698} +{"mode": "train", "epoch": 83, "iter": 2400, "lr": 0.04203, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32469, "top5_acc": 0.58453, "loss_cls": 3.85586, "loss": 3.85586, "time": 0.8556} +{"mode": "train", "epoch": 83, "iter": 2500, "lr": 0.04201, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32562, "top5_acc": 0.57984, "loss_cls": 3.84431, "loss": 3.84431, "time": 0.85827} +{"mode": "train", "epoch": 83, "iter": 2600, "lr": 0.04198, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33062, "top5_acc": 0.58453, "loss_cls": 3.82743, "loss": 3.82743, "time": 0.85847} +{"mode": "train", "epoch": 83, "iter": 2700, "lr": 0.04195, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33047, "top5_acc": 0.59703, "loss_cls": 3.81775, "loss": 3.81775, "time": 0.85579} +{"mode": "train", "epoch": 83, "iter": 2800, "lr": 0.04192, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32828, "top5_acc": 0.59281, "loss_cls": 3.80959, "loss": 3.80959, "time": 0.86194} +{"mode": "train", "epoch": 83, "iter": 2900, "lr": 0.0419, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32188, "top5_acc": 0.57625, "loss_cls": 3.86803, "loss": 3.86803, "time": 0.86057} +{"mode": "train", "epoch": 83, "iter": 3000, "lr": 0.04187, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33609, "top5_acc": 0.58906, "loss_cls": 3.81492, "loss": 3.81492, "time": 0.85645} +{"mode": "train", "epoch": 83, "iter": 3100, "lr": 0.04184, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32141, "top5_acc": 0.57203, "loss_cls": 3.90995, "loss": 3.90995, "time": 0.84745} +{"mode": "train", "epoch": 83, "iter": 3200, "lr": 0.04181, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.33125, "top5_acc": 0.58, "loss_cls": 3.83092, "loss": 3.83092, "time": 0.85075} +{"mode": "train", "epoch": 83, "iter": 3300, "lr": 0.04178, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.32594, "top5_acc": 0.59156, "loss_cls": 3.82775, "loss": 3.82775, "time": 0.85173} +{"mode": "train", "epoch": 83, "iter": 3400, "lr": 0.04176, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32562, "top5_acc": 0.59391, "loss_cls": 3.82424, "loss": 3.82424, "time": 0.85386} +{"mode": "train", "epoch": 83, "iter": 3500, "lr": 0.04173, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32156, "top5_acc": 0.57859, "loss_cls": 3.86916, "loss": 3.86916, "time": 0.85666} +{"mode": "train", "epoch": 83, "iter": 3600, "lr": 0.0417, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32828, "top5_acc": 0.57688, "loss_cls": 3.84294, "loss": 3.84294, "time": 0.85531} +{"mode": "train", "epoch": 83, "iter": 3700, "lr": 0.04167, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32203, "top5_acc": 0.58203, "loss_cls": 3.88204, "loss": 3.88204, "time": 0.8544} +{"mode": "val", "epoch": 83, "iter": 309, "lr": 0.04166, "top1_acc": 0.2686, "top5_acc": 0.5177, "mean_class_accuracy": 0.26822} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.04163, "memory": 15990, "data_time": 1.50216, "top1_acc": 0.34141, "top5_acc": 0.59125, "loss_cls": 3.77538, "loss": 3.77538, "time": 2.54899} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.04161, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34141, "top5_acc": 0.59766, "loss_cls": 3.78868, "loss": 3.78868, "time": 0.8543} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.04158, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32875, "top5_acc": 0.59328, "loss_cls": 3.80969, "loss": 3.80969, "time": 0.85448} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.04155, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.33734, "top5_acc": 0.58625, "loss_cls": 3.77714, "loss": 3.77714, "time": 0.85362} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.04152, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32719, "top5_acc": 0.59062, "loss_cls": 3.80893, "loss": 3.80893, "time": 0.84618} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.0415, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33047, "top5_acc": 0.58594, "loss_cls": 3.81063, "loss": 3.81063, "time": 0.84267} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.04147, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32516, "top5_acc": 0.59, "loss_cls": 3.85279, "loss": 3.85279, "time": 0.85449} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.04144, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33438, "top5_acc": 0.59203, "loss_cls": 3.79715, "loss": 3.79715, "time": 0.85328} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.04141, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33062, "top5_acc": 0.58828, "loss_cls": 3.83505, "loss": 3.83505, "time": 0.85949} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.04139, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33281, "top5_acc": 0.59172, "loss_cls": 3.7682, "loss": 3.7682, "time": 0.85282} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.04136, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33266, "top5_acc": 0.5925, "loss_cls": 3.80124, "loss": 3.80124, "time": 0.85105} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.04133, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32391, "top5_acc": 0.59266, "loss_cls": 3.81152, "loss": 3.81152, "time": 0.85396} +{"mode": "train", "epoch": 84, "iter": 1300, "lr": 0.0413, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33047, "top5_acc": 0.58703, "loss_cls": 3.82142, "loss": 3.82142, "time": 0.86101} +{"mode": "train", "epoch": 84, "iter": 1400, "lr": 0.04128, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32781, "top5_acc": 0.58797, "loss_cls": 3.82124, "loss": 3.82124, "time": 0.8561} +{"mode": "train", "epoch": 84, "iter": 1500, "lr": 0.04125, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32359, "top5_acc": 0.59219, "loss_cls": 3.81563, "loss": 3.81563, "time": 0.85683} +{"mode": "train", "epoch": 84, "iter": 1600, "lr": 0.04122, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33641, "top5_acc": 0.59062, "loss_cls": 3.80541, "loss": 3.80541, "time": 0.85834} +{"mode": "train", "epoch": 84, "iter": 1700, "lr": 0.04119, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32891, "top5_acc": 0.59, "loss_cls": 3.8055, "loss": 3.8055, "time": 0.85314} +{"mode": "train", "epoch": 84, "iter": 1800, "lr": 0.04117, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32281, "top5_acc": 0.58453, "loss_cls": 3.85848, "loss": 3.85848, "time": 0.84713} +{"mode": "train", "epoch": 84, "iter": 1900, "lr": 0.04114, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33828, "top5_acc": 0.58875, "loss_cls": 3.79604, "loss": 3.79604, "time": 0.84264} +{"mode": "train", "epoch": 84, "iter": 2000, "lr": 0.04111, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31203, "top5_acc": 0.57188, "loss_cls": 3.93186, "loss": 3.93186, "time": 0.84267} +{"mode": "train", "epoch": 84, "iter": 2100, "lr": 0.04108, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32547, "top5_acc": 0.59047, "loss_cls": 3.82258, "loss": 3.82258, "time": 0.84695} +{"mode": "train", "epoch": 84, "iter": 2200, "lr": 0.04106, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33734, "top5_acc": 0.59422, "loss_cls": 3.78222, "loss": 3.78222, "time": 0.84993} +{"mode": "train", "epoch": 84, "iter": 2300, "lr": 0.04103, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31828, "top5_acc": 0.57531, "loss_cls": 3.87816, "loss": 3.87816, "time": 0.85084} +{"mode": "train", "epoch": 84, "iter": 2400, "lr": 0.041, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32781, "top5_acc": 0.58484, "loss_cls": 3.84402, "loss": 3.84402, "time": 0.84624} +{"mode": "train", "epoch": 84, "iter": 2500, "lr": 0.04097, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32266, "top5_acc": 0.58203, "loss_cls": 3.85179, "loss": 3.85179, "time": 0.84868} +{"mode": "train", "epoch": 84, "iter": 2600, "lr": 0.04095, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33422, "top5_acc": 0.58844, "loss_cls": 3.82999, "loss": 3.82999, "time": 0.85206} +{"mode": "train", "epoch": 84, "iter": 2700, "lr": 0.04092, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33281, "top5_acc": 0.59219, "loss_cls": 3.79232, "loss": 3.79232, "time": 0.84491} +{"mode": "train", "epoch": 84, "iter": 2800, "lr": 0.04089, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32859, "top5_acc": 0.59, "loss_cls": 3.82896, "loss": 3.82896, "time": 0.84586} +{"mode": "train", "epoch": 84, "iter": 2900, "lr": 0.04086, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33812, "top5_acc": 0.59, "loss_cls": 3.79139, "loss": 3.79139, "time": 0.84442} +{"mode": "train", "epoch": 84, "iter": 3000, "lr": 0.04084, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32703, "top5_acc": 0.58516, "loss_cls": 3.82826, "loss": 3.82826, "time": 0.84555} +{"mode": "train", "epoch": 84, "iter": 3100, "lr": 0.04081, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.32484, "top5_acc": 0.57719, "loss_cls": 3.87941, "loss": 3.87941, "time": 0.85128} +{"mode": "train", "epoch": 84, "iter": 3200, "lr": 0.04078, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31578, "top5_acc": 0.58156, "loss_cls": 3.87867, "loss": 3.87867, "time": 0.84661} +{"mode": "train", "epoch": 84, "iter": 3300, "lr": 0.04075, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33688, "top5_acc": 0.58844, "loss_cls": 3.83372, "loss": 3.83372, "time": 0.84494} +{"mode": "train", "epoch": 84, "iter": 3400, "lr": 0.04073, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.3225, "top5_acc": 0.58094, "loss_cls": 3.85482, "loss": 3.85482, "time": 0.84715} +{"mode": "train", "epoch": 84, "iter": 3500, "lr": 0.0407, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32906, "top5_acc": 0.58844, "loss_cls": 3.82464, "loss": 3.82464, "time": 0.84285} +{"mode": "train", "epoch": 84, "iter": 3600, "lr": 0.04067, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32438, "top5_acc": 0.58109, "loss_cls": 3.84887, "loss": 3.84887, "time": 0.84556} +{"mode": "train", "epoch": 84, "iter": 3700, "lr": 0.04064, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32922, "top5_acc": 0.58703, "loss_cls": 3.83118, "loss": 3.83118, "time": 0.84605} +{"mode": "val", "epoch": 84, "iter": 309, "lr": 0.04063, "top1_acc": 0.27296, "top5_acc": 0.52125, "mean_class_accuracy": 0.27277} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.0406, "memory": 15990, "data_time": 1.48442, "top1_acc": 0.33562, "top5_acc": 0.59219, "loss_cls": 3.78452, "loss": 3.78452, "time": 2.51935} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.04058, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33406, "top5_acc": 0.60172, "loss_cls": 3.75427, "loss": 3.75427, "time": 0.85227} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.04055, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34344, "top5_acc": 0.60312, "loss_cls": 3.74723, "loss": 3.74723, "time": 0.85379} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.04052, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32203, "top5_acc": 0.58938, "loss_cls": 3.82491, "loss": 3.82491, "time": 0.85041} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.04049, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.33234, "top5_acc": 0.59188, "loss_cls": 3.7974, "loss": 3.7974, "time": 0.85057} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.04047, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33625, "top5_acc": 0.59375, "loss_cls": 3.81757, "loss": 3.81757, "time": 0.84639} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.04044, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.33266, "top5_acc": 0.59312, "loss_cls": 3.76986, "loss": 3.76986, "time": 0.85216} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.04041, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34031, "top5_acc": 0.60203, "loss_cls": 3.74548, "loss": 3.74548, "time": 0.84354} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.04038, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33234, "top5_acc": 0.58953, "loss_cls": 3.8271, "loss": 3.8271, "time": 0.84945} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.04036, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33844, "top5_acc": 0.59984, "loss_cls": 3.78853, "loss": 3.78853, "time": 0.84915} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.04033, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32219, "top5_acc": 0.57578, "loss_cls": 3.85736, "loss": 3.85736, "time": 0.8576} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.0403, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33109, "top5_acc": 0.57828, "loss_cls": 3.85374, "loss": 3.85374, "time": 0.8592} +{"mode": "train", "epoch": 85, "iter": 1300, "lr": 0.04027, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32766, "top5_acc": 0.58266, "loss_cls": 3.83655, "loss": 3.83655, "time": 0.85347} +{"mode": "train", "epoch": 85, "iter": 1400, "lr": 0.04025, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33641, "top5_acc": 0.59734, "loss_cls": 3.78053, "loss": 3.78053, "time": 0.85541} +{"mode": "train", "epoch": 85, "iter": 1500, "lr": 0.04022, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33359, "top5_acc": 0.59469, "loss_cls": 3.81582, "loss": 3.81582, "time": 0.85525} +{"mode": "train", "epoch": 85, "iter": 1600, "lr": 0.04019, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33828, "top5_acc": 0.59641, "loss_cls": 3.767, "loss": 3.767, "time": 0.85309} +{"mode": "train", "epoch": 85, "iter": 1700, "lr": 0.04016, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.33531, "top5_acc": 0.59766, "loss_cls": 3.78041, "loss": 3.78041, "time": 0.8525} +{"mode": "train", "epoch": 85, "iter": 1800, "lr": 0.04014, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33016, "top5_acc": 0.58781, "loss_cls": 3.82787, "loss": 3.82787, "time": 0.85296} +{"mode": "train", "epoch": 85, "iter": 1900, "lr": 0.04011, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33, "top5_acc": 0.59938, "loss_cls": 3.77919, "loss": 3.77919, "time": 0.84659} +{"mode": "train", "epoch": 85, "iter": 2000, "lr": 0.04008, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33703, "top5_acc": 0.59078, "loss_cls": 3.79061, "loss": 3.79061, "time": 0.84924} +{"mode": "train", "epoch": 85, "iter": 2100, "lr": 0.04006, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33359, "top5_acc": 0.58781, "loss_cls": 3.79771, "loss": 3.79771, "time": 0.84648} +{"mode": "train", "epoch": 85, "iter": 2200, "lr": 0.04003, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32531, "top5_acc": 0.58219, "loss_cls": 3.82926, "loss": 3.82926, "time": 0.84834} +{"mode": "train", "epoch": 85, "iter": 2300, "lr": 0.04, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33641, "top5_acc": 0.5925, "loss_cls": 3.81824, "loss": 3.81824, "time": 0.84276} +{"mode": "train", "epoch": 85, "iter": 2400, "lr": 0.03997, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33281, "top5_acc": 0.58797, "loss_cls": 3.82541, "loss": 3.82541, "time": 0.84604} +{"mode": "train", "epoch": 85, "iter": 2500, "lr": 0.03995, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32328, "top5_acc": 0.58453, "loss_cls": 3.83364, "loss": 3.83364, "time": 0.84689} +{"mode": "train", "epoch": 85, "iter": 2600, "lr": 0.03992, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32188, "top5_acc": 0.58734, "loss_cls": 3.86889, "loss": 3.86889, "time": 0.84714} +{"mode": "train", "epoch": 85, "iter": 2700, "lr": 0.03989, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33562, "top5_acc": 0.59141, "loss_cls": 3.81234, "loss": 3.81234, "time": 0.84674} +{"mode": "train", "epoch": 85, "iter": 2800, "lr": 0.03986, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33172, "top5_acc": 0.59547, "loss_cls": 3.78953, "loss": 3.78953, "time": 0.8464} +{"mode": "train", "epoch": 85, "iter": 2900, "lr": 0.03984, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32828, "top5_acc": 0.59031, "loss_cls": 3.84293, "loss": 3.84293, "time": 0.84884} +{"mode": "train", "epoch": 85, "iter": 3000, "lr": 0.03981, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33172, "top5_acc": 0.58766, "loss_cls": 3.82131, "loss": 3.82131, "time": 0.84713} +{"mode": "train", "epoch": 85, "iter": 3100, "lr": 0.03978, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33016, "top5_acc": 0.58641, "loss_cls": 3.817, "loss": 3.817, "time": 0.84682} +{"mode": "train", "epoch": 85, "iter": 3200, "lr": 0.03975, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.33719, "top5_acc": 0.59188, "loss_cls": 3.77242, "loss": 3.77242, "time": 0.84994} +{"mode": "train", "epoch": 85, "iter": 3300, "lr": 0.03973, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31828, "top5_acc": 0.58109, "loss_cls": 3.86758, "loss": 3.86758, "time": 0.84724} +{"mode": "train", "epoch": 85, "iter": 3400, "lr": 0.0397, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33172, "top5_acc": 0.59062, "loss_cls": 3.80985, "loss": 3.80985, "time": 0.84945} +{"mode": "train", "epoch": 85, "iter": 3500, "lr": 0.03967, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32188, "top5_acc": 0.58734, "loss_cls": 3.83179, "loss": 3.83179, "time": 0.8456} +{"mode": "train", "epoch": 85, "iter": 3600, "lr": 0.03964, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34719, "top5_acc": 0.60234, "loss_cls": 3.76924, "loss": 3.76924, "time": 0.8449} +{"mode": "train", "epoch": 85, "iter": 3700, "lr": 0.03962, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32547, "top5_acc": 0.58469, "loss_cls": 3.85273, "loss": 3.85273, "time": 0.84965} +{"mode": "val", "epoch": 85, "iter": 309, "lr": 0.0396, "top1_acc": 0.27088, "top5_acc": 0.52282, "mean_class_accuracy": 0.27076} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.03958, "memory": 15990, "data_time": 1.54133, "top1_acc": 0.34938, "top5_acc": 0.60047, "loss_cls": 3.7183, "loss": 3.7183, "time": 2.58159} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.03955, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33109, "top5_acc": 0.59859, "loss_cls": 3.79647, "loss": 3.79647, "time": 0.85326} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.03952, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33641, "top5_acc": 0.59203, "loss_cls": 3.81322, "loss": 3.81322, "time": 0.85652} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.0395, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3475, "top5_acc": 0.59859, "loss_cls": 3.74646, "loss": 3.74646, "time": 0.84991} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.03947, "memory": 15990, "data_time": 0.00075, "top1_acc": 0.34047, "top5_acc": 0.60016, "loss_cls": 3.75155, "loss": 3.75155, "time": 0.85385} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.03944, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32625, "top5_acc": 0.58609, "loss_cls": 3.83881, "loss": 3.83881, "time": 0.85031} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.03941, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34141, "top5_acc": 0.59641, "loss_cls": 3.77645, "loss": 3.77645, "time": 0.84869} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.03939, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32828, "top5_acc": 0.59953, "loss_cls": 3.79843, "loss": 3.79843, "time": 0.85068} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.03936, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3325, "top5_acc": 0.58953, "loss_cls": 3.82833, "loss": 3.82833, "time": 0.84876} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.03933, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33297, "top5_acc": 0.59609, "loss_cls": 3.78287, "loss": 3.78287, "time": 0.84499} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.0393, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32875, "top5_acc": 0.59984, "loss_cls": 3.78168, "loss": 3.78168, "time": 0.84918} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.03928, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33359, "top5_acc": 0.59141, "loss_cls": 3.78005, "loss": 3.78005, "time": 0.85078} +{"mode": "train", "epoch": 86, "iter": 1300, "lr": 0.03925, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33766, "top5_acc": 0.59141, "loss_cls": 3.82187, "loss": 3.82187, "time": 0.84989} +{"mode": "train", "epoch": 86, "iter": 1400, "lr": 0.03922, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32453, "top5_acc": 0.58484, "loss_cls": 3.82835, "loss": 3.82835, "time": 0.8475} +{"mode": "train", "epoch": 86, "iter": 1500, "lr": 0.03919, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31953, "top5_acc": 0.58141, "loss_cls": 3.84109, "loss": 3.84109, "time": 0.84791} +{"mode": "train", "epoch": 86, "iter": 1600, "lr": 0.03917, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33922, "top5_acc": 0.58859, "loss_cls": 3.8112, "loss": 3.8112, "time": 0.84367} +{"mode": "train", "epoch": 86, "iter": 1700, "lr": 0.03914, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.33281, "top5_acc": 0.59484, "loss_cls": 3.82233, "loss": 3.82233, "time": 0.85133} +{"mode": "train", "epoch": 86, "iter": 1800, "lr": 0.03911, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32438, "top5_acc": 0.58391, "loss_cls": 3.85064, "loss": 3.85064, "time": 0.85243} +{"mode": "train", "epoch": 86, "iter": 1900, "lr": 0.03909, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32438, "top5_acc": 0.57953, "loss_cls": 3.82937, "loss": 3.82937, "time": 0.84677} +{"mode": "train", "epoch": 86, "iter": 2000, "lr": 0.03906, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.34766, "top5_acc": 0.59375, "loss_cls": 3.76548, "loss": 3.76548, "time": 0.84883} +{"mode": "train", "epoch": 86, "iter": 2100, "lr": 0.03903, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33141, "top5_acc": 0.5825, "loss_cls": 3.82505, "loss": 3.82505, "time": 0.85104} +{"mode": "train", "epoch": 86, "iter": 2200, "lr": 0.039, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3425, "top5_acc": 0.60062, "loss_cls": 3.75043, "loss": 3.75043, "time": 0.85048} +{"mode": "train", "epoch": 86, "iter": 2300, "lr": 0.03898, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33812, "top5_acc": 0.59672, "loss_cls": 3.76185, "loss": 3.76185, "time": 0.85855} +{"mode": "train", "epoch": 86, "iter": 2400, "lr": 0.03895, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34188, "top5_acc": 0.60188, "loss_cls": 3.7733, "loss": 3.7733, "time": 0.85519} +{"mode": "train", "epoch": 86, "iter": 2500, "lr": 0.03892, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33734, "top5_acc": 0.59906, "loss_cls": 3.75541, "loss": 3.75541, "time": 0.85082} +{"mode": "train", "epoch": 86, "iter": 2600, "lr": 0.03889, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33062, "top5_acc": 0.59719, "loss_cls": 3.78447, "loss": 3.78447, "time": 0.8557} +{"mode": "train", "epoch": 86, "iter": 2700, "lr": 0.03887, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.33578, "top5_acc": 0.59953, "loss_cls": 3.76309, "loss": 3.76309, "time": 0.84949} +{"mode": "train", "epoch": 86, "iter": 2800, "lr": 0.03884, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34516, "top5_acc": 0.59844, "loss_cls": 3.73464, "loss": 3.73464, "time": 0.85396} +{"mode": "train", "epoch": 86, "iter": 2900, "lr": 0.03881, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32266, "top5_acc": 0.58359, "loss_cls": 3.84746, "loss": 3.84746, "time": 0.85133} +{"mode": "train", "epoch": 86, "iter": 3000, "lr": 0.03879, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33906, "top5_acc": 0.59719, "loss_cls": 3.80928, "loss": 3.80928, "time": 0.85253} +{"mode": "train", "epoch": 86, "iter": 3100, "lr": 0.03876, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33266, "top5_acc": 0.58625, "loss_cls": 3.8143, "loss": 3.8143, "time": 0.85617} +{"mode": "train", "epoch": 86, "iter": 3200, "lr": 0.03873, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.34141, "top5_acc": 0.59328, "loss_cls": 3.78287, "loss": 3.78287, "time": 0.84893} +{"mode": "train", "epoch": 86, "iter": 3300, "lr": 0.0387, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33609, "top5_acc": 0.60031, "loss_cls": 3.8176, "loss": 3.8176, "time": 0.84434} +{"mode": "train", "epoch": 86, "iter": 3400, "lr": 0.03868, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32438, "top5_acc": 0.58656, "loss_cls": 3.83469, "loss": 3.83469, "time": 0.84485} +{"mode": "train", "epoch": 86, "iter": 3500, "lr": 0.03865, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.32797, "top5_acc": 0.58203, "loss_cls": 3.84797, "loss": 3.84797, "time": 0.85658} +{"mode": "train", "epoch": 86, "iter": 3600, "lr": 0.03862, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32797, "top5_acc": 0.58984, "loss_cls": 3.81851, "loss": 3.81851, "time": 0.8483} +{"mode": "train", "epoch": 86, "iter": 3700, "lr": 0.0386, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33078, "top5_acc": 0.58422, "loss_cls": 3.8711, "loss": 3.8711, "time": 0.85134} +{"mode": "val", "epoch": 86, "iter": 309, "lr": 0.03858, "top1_acc": 0.27807, "top5_acc": 0.52489, "mean_class_accuracy": 0.27786} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.03856, "memory": 15990, "data_time": 1.51219, "top1_acc": 0.34953, "top5_acc": 0.60547, "loss_cls": 3.70722, "loss": 3.70722, "time": 2.55473} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.03853, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34297, "top5_acc": 0.60516, "loss_cls": 3.73441, "loss": 3.73441, "time": 0.85868} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.0385, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34, "top5_acc": 0.59875, "loss_cls": 3.75793, "loss": 3.75793, "time": 0.8608} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.03847, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.34078, "top5_acc": 0.60234, "loss_cls": 3.74962, "loss": 3.74962, "time": 0.85123} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.03845, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.345, "top5_acc": 0.59328, "loss_cls": 3.7601, "loss": 3.7601, "time": 0.85117} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.03842, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33859, "top5_acc": 0.60031, "loss_cls": 3.78941, "loss": 3.78941, "time": 0.85143} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.03839, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3375, "top5_acc": 0.59359, "loss_cls": 3.78677, "loss": 3.78677, "time": 0.84698} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.03837, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34516, "top5_acc": 0.60375, "loss_cls": 3.7303, "loss": 3.7303, "time": 0.84866} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.03834, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34828, "top5_acc": 0.595, "loss_cls": 3.75181, "loss": 3.75181, "time": 0.84612} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.03831, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34234, "top5_acc": 0.59422, "loss_cls": 3.76756, "loss": 3.76756, "time": 0.84661} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.03828, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34156, "top5_acc": 0.59922, "loss_cls": 3.7676, "loss": 3.7676, "time": 0.8471} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.03826, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33031, "top5_acc": 0.59484, "loss_cls": 3.7792, "loss": 3.7792, "time": 0.852} +{"mode": "train", "epoch": 87, "iter": 1300, "lr": 0.03823, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33828, "top5_acc": 0.595, "loss_cls": 3.77282, "loss": 3.77282, "time": 0.85088} +{"mode": "train", "epoch": 87, "iter": 1400, "lr": 0.0382, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34, "top5_acc": 0.59062, "loss_cls": 3.78098, "loss": 3.78098, "time": 0.85861} +{"mode": "train", "epoch": 87, "iter": 1500, "lr": 0.03817, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.33312, "top5_acc": 0.59484, "loss_cls": 3.75384, "loss": 3.75384, "time": 0.84695} +{"mode": "train", "epoch": 87, "iter": 1600, "lr": 0.03815, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33844, "top5_acc": 0.59641, "loss_cls": 3.77201, "loss": 3.77201, "time": 0.84891} +{"mode": "train", "epoch": 87, "iter": 1700, "lr": 0.03812, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33984, "top5_acc": 0.59438, "loss_cls": 3.78197, "loss": 3.78197, "time": 0.85204} +{"mode": "train", "epoch": 87, "iter": 1800, "lr": 0.03809, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3325, "top5_acc": 0.58484, "loss_cls": 3.8074, "loss": 3.8074, "time": 0.85158} +{"mode": "train", "epoch": 87, "iter": 1900, "lr": 0.03807, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33766, "top5_acc": 0.59484, "loss_cls": 3.78289, "loss": 3.78289, "time": 0.85208} +{"mode": "train", "epoch": 87, "iter": 2000, "lr": 0.03804, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34094, "top5_acc": 0.59969, "loss_cls": 3.77224, "loss": 3.77224, "time": 0.84681} +{"mode": "train", "epoch": 87, "iter": 2100, "lr": 0.03801, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34453, "top5_acc": 0.60812, "loss_cls": 3.72971, "loss": 3.72971, "time": 0.84686} +{"mode": "train", "epoch": 87, "iter": 2200, "lr": 0.03798, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33391, "top5_acc": 0.59719, "loss_cls": 3.79243, "loss": 3.79243, "time": 0.84796} +{"mode": "train", "epoch": 87, "iter": 2300, "lr": 0.03796, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33016, "top5_acc": 0.59984, "loss_cls": 3.78084, "loss": 3.78084, "time": 0.84729} +{"mode": "train", "epoch": 87, "iter": 2400, "lr": 0.03793, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32859, "top5_acc": 0.58641, "loss_cls": 3.82867, "loss": 3.82867, "time": 0.84214} +{"mode": "train", "epoch": 87, "iter": 2500, "lr": 0.0379, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33625, "top5_acc": 0.58703, "loss_cls": 3.82242, "loss": 3.82242, "time": 0.84653} +{"mode": "train", "epoch": 87, "iter": 2600, "lr": 0.03788, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33984, "top5_acc": 0.59438, "loss_cls": 3.76267, "loss": 3.76267, "time": 0.84734} +{"mode": "train", "epoch": 87, "iter": 2700, "lr": 0.03785, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34, "top5_acc": 0.59531, "loss_cls": 3.75961, "loss": 3.75961, "time": 0.84979} +{"mode": "train", "epoch": 87, "iter": 2800, "lr": 0.03782, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33203, "top5_acc": 0.59203, "loss_cls": 3.83497, "loss": 3.83497, "time": 0.84958} +{"mode": "train", "epoch": 87, "iter": 2900, "lr": 0.03779, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32312, "top5_acc": 0.58453, "loss_cls": 3.85595, "loss": 3.85595, "time": 0.8496} +{"mode": "train", "epoch": 87, "iter": 3000, "lr": 0.03777, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33797, "top5_acc": 0.59859, "loss_cls": 3.77545, "loss": 3.77545, "time": 0.84612} +{"mode": "train", "epoch": 87, "iter": 3100, "lr": 0.03774, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32391, "top5_acc": 0.58656, "loss_cls": 3.83016, "loss": 3.83016, "time": 0.85079} +{"mode": "train", "epoch": 87, "iter": 3200, "lr": 0.03771, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.33344, "top5_acc": 0.59125, "loss_cls": 3.77137, "loss": 3.77137, "time": 0.85194} +{"mode": "train", "epoch": 87, "iter": 3300, "lr": 0.03769, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33891, "top5_acc": 0.60016, "loss_cls": 3.76287, "loss": 3.76287, "time": 0.84926} +{"mode": "train", "epoch": 87, "iter": 3400, "lr": 0.03766, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.33062, "top5_acc": 0.57781, "loss_cls": 3.84281, "loss": 3.84281, "time": 0.84674} +{"mode": "train", "epoch": 87, "iter": 3500, "lr": 0.03763, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33266, "top5_acc": 0.58578, "loss_cls": 3.82718, "loss": 3.82718, "time": 0.8473} +{"mode": "train", "epoch": 87, "iter": 3600, "lr": 0.03761, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33297, "top5_acc": 0.59359, "loss_cls": 3.77716, "loss": 3.77716, "time": 0.84433} +{"mode": "train", "epoch": 87, "iter": 3700, "lr": 0.03758, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.325, "top5_acc": 0.58844, "loss_cls": 3.83392, "loss": 3.83392, "time": 0.84611} +{"mode": "val", "epoch": 87, "iter": 309, "lr": 0.03757, "top1_acc": 0.27767, "top5_acc": 0.53188, "mean_class_accuracy": 0.27747} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.03754, "memory": 15990, "data_time": 1.53252, "top1_acc": 0.34953, "top5_acc": 0.60703, "loss_cls": 3.70403, "loss": 3.70403, "time": 2.57103} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.03751, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.33438, "top5_acc": 0.59625, "loss_cls": 3.78722, "loss": 3.78722, "time": 0.85541} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.03748, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.335, "top5_acc": 0.60172, "loss_cls": 3.75997, "loss": 3.75997, "time": 0.8549} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.03746, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.34234, "top5_acc": 0.60766, "loss_cls": 3.72979, "loss": 3.72979, "time": 0.85183} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.03743, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33516, "top5_acc": 0.59031, "loss_cls": 3.78484, "loss": 3.78484, "time": 0.84899} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.0374, "memory": 15990, "data_time": 0.00076, "top1_acc": 0.35062, "top5_acc": 0.60406, "loss_cls": 3.72062, "loss": 3.72062, "time": 0.84859} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.03738, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34453, "top5_acc": 0.60141, "loss_cls": 3.72472, "loss": 3.72472, "time": 0.84995} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.03735, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33938, "top5_acc": 0.59547, "loss_cls": 3.76574, "loss": 3.76574, "time": 0.84882} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.03732, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34797, "top5_acc": 0.60141, "loss_cls": 3.75287, "loss": 3.75287, "time": 0.85036} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.0373, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33406, "top5_acc": 0.59719, "loss_cls": 3.77597, "loss": 3.77597, "time": 0.84871} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.03727, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33906, "top5_acc": 0.59906, "loss_cls": 3.74809, "loss": 3.74809, "time": 0.84502} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.03724, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34312, "top5_acc": 0.58781, "loss_cls": 3.76778, "loss": 3.76778, "time": 0.85042} +{"mode": "train", "epoch": 88, "iter": 1300, "lr": 0.03721, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33781, "top5_acc": 0.59188, "loss_cls": 3.82673, "loss": 3.82673, "time": 0.84865} +{"mode": "train", "epoch": 88, "iter": 1400, "lr": 0.03719, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34156, "top5_acc": 0.59984, "loss_cls": 3.74448, "loss": 3.74448, "time": 0.84991} +{"mode": "train", "epoch": 88, "iter": 1500, "lr": 0.03716, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33703, "top5_acc": 0.59969, "loss_cls": 3.75737, "loss": 3.75737, "time": 0.85019} +{"mode": "train", "epoch": 88, "iter": 1600, "lr": 0.03713, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.335, "top5_acc": 0.59766, "loss_cls": 3.78113, "loss": 3.78113, "time": 0.85107} +{"mode": "train", "epoch": 88, "iter": 1700, "lr": 0.03711, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34188, "top5_acc": 0.59859, "loss_cls": 3.74164, "loss": 3.74164, "time": 0.85088} +{"mode": "train", "epoch": 88, "iter": 1800, "lr": 0.03708, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32438, "top5_acc": 0.59438, "loss_cls": 3.77971, "loss": 3.77971, "time": 0.85104} +{"mode": "train", "epoch": 88, "iter": 1900, "lr": 0.03705, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.3325, "top5_acc": 0.59609, "loss_cls": 3.75614, "loss": 3.75614, "time": 0.85625} +{"mode": "train", "epoch": 88, "iter": 2000, "lr": 0.03703, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.34047, "top5_acc": 0.59891, "loss_cls": 3.78118, "loss": 3.78118, "time": 0.85202} +{"mode": "train", "epoch": 88, "iter": 2100, "lr": 0.037, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34, "top5_acc": 0.59359, "loss_cls": 3.77996, "loss": 3.77996, "time": 0.85956} +{"mode": "train", "epoch": 88, "iter": 2200, "lr": 0.03697, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33625, "top5_acc": 0.60047, "loss_cls": 3.74224, "loss": 3.74224, "time": 0.84947} +{"mode": "train", "epoch": 88, "iter": 2300, "lr": 0.03694, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33969, "top5_acc": 0.59672, "loss_cls": 3.75389, "loss": 3.75389, "time": 0.84886} +{"mode": "train", "epoch": 88, "iter": 2400, "lr": 0.03692, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34188, "top5_acc": 0.60328, "loss_cls": 3.74226, "loss": 3.74226, "time": 0.8515} +{"mode": "train", "epoch": 88, "iter": 2500, "lr": 0.03689, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34016, "top5_acc": 0.59641, "loss_cls": 3.76857, "loss": 3.76857, "time": 0.85494} +{"mode": "train", "epoch": 88, "iter": 2600, "lr": 0.03686, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33953, "top5_acc": 0.59547, "loss_cls": 3.80102, "loss": 3.80102, "time": 0.8489} +{"mode": "train", "epoch": 88, "iter": 2700, "lr": 0.03684, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33172, "top5_acc": 0.58766, "loss_cls": 3.80781, "loss": 3.80781, "time": 0.8496} +{"mode": "train", "epoch": 88, "iter": 2800, "lr": 0.03681, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33281, "top5_acc": 0.58797, "loss_cls": 3.82958, "loss": 3.82958, "time": 0.84997} +{"mode": "train", "epoch": 88, "iter": 2900, "lr": 0.03678, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33312, "top5_acc": 0.59391, "loss_cls": 3.81356, "loss": 3.81356, "time": 0.84688} +{"mode": "train", "epoch": 88, "iter": 3000, "lr": 0.03676, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34172, "top5_acc": 0.60359, "loss_cls": 3.75046, "loss": 3.75046, "time": 0.84768} +{"mode": "train", "epoch": 88, "iter": 3100, "lr": 0.03673, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33453, "top5_acc": 0.59422, "loss_cls": 3.79233, "loss": 3.79233, "time": 0.84702} +{"mode": "train", "epoch": 88, "iter": 3200, "lr": 0.0367, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.33797, "top5_acc": 0.58781, "loss_cls": 3.78679, "loss": 3.78679, "time": 0.84877} +{"mode": "train", "epoch": 88, "iter": 3300, "lr": 0.03667, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33266, "top5_acc": 0.58734, "loss_cls": 3.83064, "loss": 3.83064, "time": 0.84847} +{"mode": "train", "epoch": 88, "iter": 3400, "lr": 0.03665, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33938, "top5_acc": 0.59922, "loss_cls": 3.75568, "loss": 3.75568, "time": 0.84565} +{"mode": "train", "epoch": 88, "iter": 3500, "lr": 0.03662, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34156, "top5_acc": 0.59656, "loss_cls": 3.72204, "loss": 3.72204, "time": 0.84535} +{"mode": "train", "epoch": 88, "iter": 3600, "lr": 0.03659, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33359, "top5_acc": 0.59875, "loss_cls": 3.79937, "loss": 3.79937, "time": 0.84677} +{"mode": "train", "epoch": 88, "iter": 3700, "lr": 0.03657, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33062, "top5_acc": 0.59125, "loss_cls": 3.80841, "loss": 3.80841, "time": 0.84603} +{"mode": "val", "epoch": 88, "iter": 309, "lr": 0.03655, "top1_acc": 0.27696, "top5_acc": 0.52403, "mean_class_accuracy": 0.27678} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.03653, "memory": 15990, "data_time": 1.53533, "top1_acc": 0.34656, "top5_acc": 0.61266, "loss_cls": 3.7083, "loss": 3.7083, "time": 2.57641} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0365, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33812, "top5_acc": 0.60391, "loss_cls": 3.72433, "loss": 3.72433, "time": 0.84833} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.03647, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33938, "top5_acc": 0.60031, "loss_cls": 3.74609, "loss": 3.74609, "time": 0.8541} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.03645, "memory": 15990, "data_time": 0.00078, "top1_acc": 0.34281, "top5_acc": 0.61016, "loss_cls": 3.73101, "loss": 3.73101, "time": 0.85527} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.03642, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34469, "top5_acc": 0.60547, "loss_cls": 3.70379, "loss": 3.70379, "time": 0.85218} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.03639, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34094, "top5_acc": 0.59578, "loss_cls": 3.74586, "loss": 3.74586, "time": 0.84761} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.03637, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34703, "top5_acc": 0.59641, "loss_cls": 3.76284, "loss": 3.76284, "time": 0.84909} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.03634, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34078, "top5_acc": 0.60516, "loss_cls": 3.7447, "loss": 3.7447, "time": 0.85081} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.03631, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.33578, "top5_acc": 0.60766, "loss_cls": 3.73356, "loss": 3.73356, "time": 0.84957} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.03629, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34781, "top5_acc": 0.61109, "loss_cls": 3.70763, "loss": 3.70763, "time": 0.8525} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.03626, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34172, "top5_acc": 0.58734, "loss_cls": 3.79632, "loss": 3.79632, "time": 0.8506} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.03623, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34422, "top5_acc": 0.59016, "loss_cls": 3.764, "loss": 3.764, "time": 0.85149} +{"mode": "train", "epoch": 89, "iter": 1300, "lr": 0.0362, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33453, "top5_acc": 0.59297, "loss_cls": 3.80122, "loss": 3.80122, "time": 0.85035} +{"mode": "train", "epoch": 89, "iter": 1400, "lr": 0.03618, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35484, "top5_acc": 0.60578, "loss_cls": 3.69397, "loss": 3.69397, "time": 0.85261} +{"mode": "train", "epoch": 89, "iter": 1500, "lr": 0.03615, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.34078, "top5_acc": 0.60625, "loss_cls": 3.74154, "loss": 3.74154, "time": 0.84982} +{"mode": "train", "epoch": 89, "iter": 1600, "lr": 0.03612, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33844, "top5_acc": 0.59125, "loss_cls": 3.81233, "loss": 3.81233, "time": 0.84778} +{"mode": "train", "epoch": 89, "iter": 1700, "lr": 0.0361, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.34594, "top5_acc": 0.60438, "loss_cls": 3.72552, "loss": 3.72552, "time": 0.84875} +{"mode": "train", "epoch": 89, "iter": 1800, "lr": 0.03607, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3325, "top5_acc": 0.60016, "loss_cls": 3.75864, "loss": 3.75864, "time": 0.85237} +{"mode": "train", "epoch": 89, "iter": 1900, "lr": 0.03604, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34484, "top5_acc": 0.60125, "loss_cls": 3.74161, "loss": 3.74161, "time": 0.85556} +{"mode": "train", "epoch": 89, "iter": 2000, "lr": 0.03602, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33406, "top5_acc": 0.60344, "loss_cls": 3.76567, "loss": 3.76567, "time": 0.85079} +{"mode": "train", "epoch": 89, "iter": 2100, "lr": 0.03599, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.35109, "top5_acc": 0.60219, "loss_cls": 3.71732, "loss": 3.71732, "time": 0.85078} +{"mode": "train", "epoch": 89, "iter": 2200, "lr": 0.03596, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33531, "top5_acc": 0.58703, "loss_cls": 3.81199, "loss": 3.81199, "time": 0.84918} +{"mode": "train", "epoch": 89, "iter": 2300, "lr": 0.03594, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34203, "top5_acc": 0.59531, "loss_cls": 3.78081, "loss": 3.78081, "time": 0.85131} +{"mode": "train", "epoch": 89, "iter": 2400, "lr": 0.03591, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33594, "top5_acc": 0.60469, "loss_cls": 3.73801, "loss": 3.73801, "time": 0.85117} +{"mode": "train", "epoch": 89, "iter": 2500, "lr": 0.03588, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33891, "top5_acc": 0.60359, "loss_cls": 3.75809, "loss": 3.75809, "time": 0.84854} +{"mode": "train", "epoch": 89, "iter": 2600, "lr": 0.03586, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33859, "top5_acc": 0.59484, "loss_cls": 3.75328, "loss": 3.75328, "time": 0.84912} +{"mode": "train", "epoch": 89, "iter": 2700, "lr": 0.03583, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33234, "top5_acc": 0.59422, "loss_cls": 3.79695, "loss": 3.79695, "time": 0.84945} +{"mode": "train", "epoch": 89, "iter": 2800, "lr": 0.0358, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34812, "top5_acc": 0.59984, "loss_cls": 3.78441, "loss": 3.78441, "time": 0.84948} +{"mode": "train", "epoch": 89, "iter": 2900, "lr": 0.03578, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34391, "top5_acc": 0.59484, "loss_cls": 3.76416, "loss": 3.76416, "time": 0.85224} +{"mode": "train", "epoch": 89, "iter": 3000, "lr": 0.03575, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33344, "top5_acc": 0.59969, "loss_cls": 3.78365, "loss": 3.78365, "time": 0.84582} +{"mode": "train", "epoch": 89, "iter": 3100, "lr": 0.03572, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.33906, "top5_acc": 0.6, "loss_cls": 3.7574, "loss": 3.7574, "time": 0.85013} +{"mode": "train", "epoch": 89, "iter": 3200, "lr": 0.03569, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.34172, "top5_acc": 0.60234, "loss_cls": 3.75482, "loss": 3.75482, "time": 0.85183} +{"mode": "train", "epoch": 89, "iter": 3300, "lr": 0.03567, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33469, "top5_acc": 0.59484, "loss_cls": 3.76756, "loss": 3.76756, "time": 0.84935} +{"mode": "train", "epoch": 89, "iter": 3400, "lr": 0.03564, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33812, "top5_acc": 0.60219, "loss_cls": 3.79468, "loss": 3.79468, "time": 0.8477} +{"mode": "train", "epoch": 89, "iter": 3500, "lr": 0.03561, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33922, "top5_acc": 0.59531, "loss_cls": 3.77127, "loss": 3.77127, "time": 0.84878} +{"mode": "train", "epoch": 89, "iter": 3600, "lr": 0.03559, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34797, "top5_acc": 0.59578, "loss_cls": 3.75644, "loss": 3.75644, "time": 0.84614} +{"mode": "train", "epoch": 89, "iter": 3700, "lr": 0.03556, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34422, "top5_acc": 0.60609, "loss_cls": 3.7405, "loss": 3.7405, "time": 0.85226} +{"mode": "val", "epoch": 89, "iter": 309, "lr": 0.03555, "top1_acc": 0.28177, "top5_acc": 0.53741, "mean_class_accuracy": 0.28153} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.03552, "memory": 15990, "data_time": 1.51337, "top1_acc": 0.35781, "top5_acc": 0.61469, "loss_cls": 3.66356, "loss": 3.66356, "time": 2.54716} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.0355, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34062, "top5_acc": 0.61156, "loss_cls": 3.70446, "loss": 3.70446, "time": 0.85439} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.03547, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33969, "top5_acc": 0.60547, "loss_cls": 3.73422, "loss": 3.73422, "time": 0.84989} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.03544, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.34953, "top5_acc": 0.61328, "loss_cls": 3.70285, "loss": 3.70285, "time": 0.8541} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.03541, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.34203, "top5_acc": 0.59703, "loss_cls": 3.74151, "loss": 3.74151, "time": 0.85016} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.03539, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34422, "top5_acc": 0.60672, "loss_cls": 3.72483, "loss": 3.72483, "time": 0.85214} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.03536, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34797, "top5_acc": 0.60312, "loss_cls": 3.70071, "loss": 3.70071, "time": 0.84776} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.03533, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34094, "top5_acc": 0.6025, "loss_cls": 3.7462, "loss": 3.7462, "time": 0.85462} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.03531, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34766, "top5_acc": 0.60031, "loss_cls": 3.73211, "loss": 3.73211, "time": 0.85436} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.03528, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34484, "top5_acc": 0.60375, "loss_cls": 3.74219, "loss": 3.74219, "time": 0.85589} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.03525, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35078, "top5_acc": 0.59484, "loss_cls": 3.73059, "loss": 3.73059, "time": 0.8565} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.03523, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34062, "top5_acc": 0.60391, "loss_cls": 3.74651, "loss": 3.74651, "time": 0.85594} +{"mode": "train", "epoch": 90, "iter": 1300, "lr": 0.0352, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34188, "top5_acc": 0.60828, "loss_cls": 3.75205, "loss": 3.75205, "time": 0.85748} +{"mode": "train", "epoch": 90, "iter": 1400, "lr": 0.03517, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34938, "top5_acc": 0.61047, "loss_cls": 3.69558, "loss": 3.69558, "time": 0.85618} +{"mode": "train", "epoch": 90, "iter": 1500, "lr": 0.03515, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34469, "top5_acc": 0.60469, "loss_cls": 3.71461, "loss": 3.71461, "time": 0.85728} +{"mode": "train", "epoch": 90, "iter": 1600, "lr": 0.03512, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34531, "top5_acc": 0.60172, "loss_cls": 3.72959, "loss": 3.72959, "time": 0.86005} +{"mode": "train", "epoch": 90, "iter": 1700, "lr": 0.03509, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34453, "top5_acc": 0.60672, "loss_cls": 3.72925, "loss": 3.72925, "time": 0.86003} +{"mode": "train", "epoch": 90, "iter": 1800, "lr": 0.03507, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34359, "top5_acc": 0.60453, "loss_cls": 3.73634, "loss": 3.73634, "time": 0.86143} +{"mode": "train", "epoch": 90, "iter": 1900, "lr": 0.03504, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34188, "top5_acc": 0.60203, "loss_cls": 3.74997, "loss": 3.74997, "time": 0.85308} +{"mode": "train", "epoch": 90, "iter": 2000, "lr": 0.03501, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.34391, "top5_acc": 0.59469, "loss_cls": 3.73987, "loss": 3.73987, "time": 0.85325} +{"mode": "train", "epoch": 90, "iter": 2100, "lr": 0.03499, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.3525, "top5_acc": 0.60516, "loss_cls": 3.69871, "loss": 3.69871, "time": 0.855} +{"mode": "train", "epoch": 90, "iter": 2200, "lr": 0.03496, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.34234, "top5_acc": 0.59859, "loss_cls": 3.7707, "loss": 3.7707, "time": 0.85557} +{"mode": "train", "epoch": 90, "iter": 2300, "lr": 0.03493, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35, "top5_acc": 0.61047, "loss_cls": 3.70823, "loss": 3.70823, "time": 0.8537} +{"mode": "train", "epoch": 90, "iter": 2400, "lr": 0.03491, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34078, "top5_acc": 0.6025, "loss_cls": 3.75105, "loss": 3.75105, "time": 0.85385} +{"mode": "train", "epoch": 90, "iter": 2500, "lr": 0.03488, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34156, "top5_acc": 0.60547, "loss_cls": 3.76142, "loss": 3.76142, "time": 0.85591} +{"mode": "train", "epoch": 90, "iter": 2600, "lr": 0.03485, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34594, "top5_acc": 0.60641, "loss_cls": 3.75293, "loss": 3.75293, "time": 0.85483} +{"mode": "train", "epoch": 90, "iter": 2700, "lr": 0.03483, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34172, "top5_acc": 0.60203, "loss_cls": 3.73805, "loss": 3.73805, "time": 0.85548} +{"mode": "train", "epoch": 90, "iter": 2800, "lr": 0.0348, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33344, "top5_acc": 0.6025, "loss_cls": 3.75771, "loss": 3.75771, "time": 0.85107} +{"mode": "train", "epoch": 90, "iter": 2900, "lr": 0.03477, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33938, "top5_acc": 0.6, "loss_cls": 3.75077, "loss": 3.75077, "time": 0.8549} +{"mode": "train", "epoch": 90, "iter": 3000, "lr": 0.03475, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34797, "top5_acc": 0.6125, "loss_cls": 3.69423, "loss": 3.69423, "time": 0.8534} +{"mode": "train", "epoch": 90, "iter": 3100, "lr": 0.03472, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34766, "top5_acc": 0.60406, "loss_cls": 3.77117, "loss": 3.77117, "time": 0.85166} +{"mode": "train", "epoch": 90, "iter": 3200, "lr": 0.03469, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34203, "top5_acc": 0.59438, "loss_cls": 3.77619, "loss": 3.77619, "time": 0.85042} +{"mode": "train", "epoch": 90, "iter": 3300, "lr": 0.03467, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.34328, "top5_acc": 0.59359, "loss_cls": 3.79027, "loss": 3.79027, "time": 0.85029} +{"mode": "train", "epoch": 90, "iter": 3400, "lr": 0.03464, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33156, "top5_acc": 0.59484, "loss_cls": 3.80741, "loss": 3.80741, "time": 0.84557} +{"mode": "train", "epoch": 90, "iter": 3500, "lr": 0.03461, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33672, "top5_acc": 0.58562, "loss_cls": 3.80778, "loss": 3.80778, "time": 0.84629} +{"mode": "train", "epoch": 90, "iter": 3600, "lr": 0.03459, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.345, "top5_acc": 0.59781, "loss_cls": 3.73599, "loss": 3.73599, "time": 0.84953} +{"mode": "train", "epoch": 90, "iter": 3700, "lr": 0.03456, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33891, "top5_acc": 0.59531, "loss_cls": 3.76076, "loss": 3.76076, "time": 0.84879} +{"mode": "val", "epoch": 90, "iter": 309, "lr": 0.03455, "top1_acc": 0.28942, "top5_acc": 0.54667, "mean_class_accuracy": 0.2893} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.03452, "memory": 15990, "data_time": 1.54767, "top1_acc": 0.34922, "top5_acc": 0.60812, "loss_cls": 3.70682, "loss": 3.70682, "time": 2.59146} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0345, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35266, "top5_acc": 0.60906, "loss_cls": 3.68225, "loss": 3.68225, "time": 0.85402} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.03447, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34234, "top5_acc": 0.60625, "loss_cls": 3.73682, "loss": 3.73682, "time": 0.85286} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.03444, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34688, "top5_acc": 0.61312, "loss_cls": 3.67584, "loss": 3.67584, "time": 0.85176} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.03442, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35594, "top5_acc": 0.62078, "loss_cls": 3.66039, "loss": 3.66039, "time": 0.85686} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.03439, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35047, "top5_acc": 0.61359, "loss_cls": 3.698, "loss": 3.698, "time": 0.84968} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.03436, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34156, "top5_acc": 0.60406, "loss_cls": 3.73853, "loss": 3.73853, "time": 0.85441} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.03434, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34141, "top5_acc": 0.60188, "loss_cls": 3.75129, "loss": 3.75129, "time": 0.84915} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.03431, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34875, "top5_acc": 0.61781, "loss_cls": 3.71459, "loss": 3.71459, "time": 0.8478} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.03428, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35375, "top5_acc": 0.61281, "loss_cls": 3.67886, "loss": 3.67886, "time": 0.84728} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.03426, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34859, "top5_acc": 0.61469, "loss_cls": 3.67846, "loss": 3.67846, "time": 0.84343} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.03423, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33641, "top5_acc": 0.60156, "loss_cls": 3.72615, "loss": 3.72615, "time": 0.84694} +{"mode": "train", "epoch": 91, "iter": 1300, "lr": 0.0342, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34641, "top5_acc": 0.60625, "loss_cls": 3.71761, "loss": 3.71761, "time": 0.84719} +{"mode": "train", "epoch": 91, "iter": 1400, "lr": 0.03418, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34703, "top5_acc": 0.60672, "loss_cls": 3.719, "loss": 3.719, "time": 0.85444} +{"mode": "train", "epoch": 91, "iter": 1500, "lr": 0.03415, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34422, "top5_acc": 0.61062, "loss_cls": 3.70434, "loss": 3.70434, "time": 0.84781} +{"mode": "train", "epoch": 91, "iter": 1600, "lr": 0.03412, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33875, "top5_acc": 0.60031, "loss_cls": 3.73342, "loss": 3.73342, "time": 0.85073} +{"mode": "train", "epoch": 91, "iter": 1700, "lr": 0.0341, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.3375, "top5_acc": 0.60078, "loss_cls": 3.76154, "loss": 3.76154, "time": 0.84721} +{"mode": "train", "epoch": 91, "iter": 1800, "lr": 0.03407, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34484, "top5_acc": 0.6, "loss_cls": 3.71081, "loss": 3.71081, "time": 0.84737} +{"mode": "train", "epoch": 91, "iter": 1900, "lr": 0.03405, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33984, "top5_acc": 0.60859, "loss_cls": 3.73156, "loss": 3.73156, "time": 0.849} +{"mode": "train", "epoch": 91, "iter": 2000, "lr": 0.03402, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.35156, "top5_acc": 0.61906, "loss_cls": 3.66953, "loss": 3.66953, "time": 0.84239} +{"mode": "train", "epoch": 91, "iter": 2100, "lr": 0.03399, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33578, "top5_acc": 0.59953, "loss_cls": 3.72538, "loss": 3.72538, "time": 0.85242} +{"mode": "train", "epoch": 91, "iter": 2200, "lr": 0.03397, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34406, "top5_acc": 0.60375, "loss_cls": 3.71767, "loss": 3.71767, "time": 0.84274} +{"mode": "train", "epoch": 91, "iter": 2300, "lr": 0.03394, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34922, "top5_acc": 0.6075, "loss_cls": 3.69471, "loss": 3.69471, "time": 0.8472} +{"mode": "train", "epoch": 91, "iter": 2400, "lr": 0.03391, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34812, "top5_acc": 0.59281, "loss_cls": 3.75351, "loss": 3.75351, "time": 0.85274} +{"mode": "train", "epoch": 91, "iter": 2500, "lr": 0.03389, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35359, "top5_acc": 0.60391, "loss_cls": 3.74087, "loss": 3.74087, "time": 0.85335} +{"mode": "train", "epoch": 91, "iter": 2600, "lr": 0.03386, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34312, "top5_acc": 0.59875, "loss_cls": 3.7187, "loss": 3.7187, "time": 0.84748} +{"mode": "train", "epoch": 91, "iter": 2700, "lr": 0.03383, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34297, "top5_acc": 0.59625, "loss_cls": 3.76006, "loss": 3.76006, "time": 0.84082} +{"mode": "train", "epoch": 91, "iter": 2800, "lr": 0.03381, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35078, "top5_acc": 0.60344, "loss_cls": 3.72113, "loss": 3.72113, "time": 0.85136} +{"mode": "train", "epoch": 91, "iter": 2900, "lr": 0.03378, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33922, "top5_acc": 0.59766, "loss_cls": 3.7803, "loss": 3.7803, "time": 0.86227} +{"mode": "train", "epoch": 91, "iter": 3000, "lr": 0.03375, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.36031, "top5_acc": 0.61938, "loss_cls": 3.64945, "loss": 3.64945, "time": 0.86387} +{"mode": "train", "epoch": 91, "iter": 3100, "lr": 0.03373, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33734, "top5_acc": 0.59562, "loss_cls": 3.76004, "loss": 3.76004, "time": 0.85674} +{"mode": "train", "epoch": 91, "iter": 3200, "lr": 0.0337, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34422, "top5_acc": 0.60438, "loss_cls": 3.73235, "loss": 3.73235, "time": 0.85937} +{"mode": "train", "epoch": 91, "iter": 3300, "lr": 0.03367, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33391, "top5_acc": 0.6025, "loss_cls": 3.75843, "loss": 3.75843, "time": 0.85967} +{"mode": "train", "epoch": 91, "iter": 3400, "lr": 0.03365, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35, "top5_acc": 0.59891, "loss_cls": 3.72555, "loss": 3.72555, "time": 0.85643} +{"mode": "train", "epoch": 91, "iter": 3500, "lr": 0.03362, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.34109, "top5_acc": 0.59438, "loss_cls": 3.77529, "loss": 3.77529, "time": 0.86039} +{"mode": "train", "epoch": 91, "iter": 3600, "lr": 0.0336, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.3425, "top5_acc": 0.59625, "loss_cls": 3.77647, "loss": 3.77647, "time": 0.86512} +{"mode": "train", "epoch": 91, "iter": 3700, "lr": 0.03357, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.33453, "top5_acc": 0.59594, "loss_cls": 3.78616, "loss": 3.78616, "time": 0.86527} +{"mode": "val", "epoch": 91, "iter": 309, "lr": 0.03356, "top1_acc": 0.29028, "top5_acc": 0.54151, "mean_class_accuracy": 0.29008} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.03353, "memory": 15990, "data_time": 1.71817, "top1_acc": 0.36031, "top5_acc": 0.62313, "loss_cls": 3.6305, "loss": 3.6305, "time": 2.76253} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.0335, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36609, "top5_acc": 0.61984, "loss_cls": 3.63154, "loss": 3.63154, "time": 0.86156} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.03348, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35344, "top5_acc": 0.61969, "loss_cls": 3.67774, "loss": 3.67774, "time": 0.85821} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.03345, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.34297, "top5_acc": 0.61234, "loss_cls": 3.67867, "loss": 3.67867, "time": 0.85614} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.03342, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.34484, "top5_acc": 0.61578, "loss_cls": 3.67464, "loss": 3.67464, "time": 0.85848} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.0334, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.3475, "top5_acc": 0.60969, "loss_cls": 3.69929, "loss": 3.69929, "time": 0.85499} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.03337, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.34953, "top5_acc": 0.61359, "loss_cls": 3.67693, "loss": 3.67693, "time": 0.85739} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.03335, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35281, "top5_acc": 0.61781, "loss_cls": 3.70728, "loss": 3.70728, "time": 0.86089} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.03332, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.34953, "top5_acc": 0.61047, "loss_cls": 3.69909, "loss": 3.69909, "time": 0.86153} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.03329, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.35297, "top5_acc": 0.60406, "loss_cls": 3.71108, "loss": 3.71108, "time": 0.85979} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.03327, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34734, "top5_acc": 0.61516, "loss_cls": 3.66512, "loss": 3.66512, "time": 0.85691} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.03324, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34938, "top5_acc": 0.60562, "loss_cls": 3.72398, "loss": 3.72398, "time": 0.86174} +{"mode": "train", "epoch": 92, "iter": 1300, "lr": 0.03321, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.34844, "top5_acc": 0.61359, "loss_cls": 3.67786, "loss": 3.67786, "time": 0.86277} +{"mode": "train", "epoch": 92, "iter": 1400, "lr": 0.03319, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.35188, "top5_acc": 0.60578, "loss_cls": 3.72979, "loss": 3.72979, "time": 0.85978} +{"mode": "train", "epoch": 92, "iter": 1500, "lr": 0.03316, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.34906, "top5_acc": 0.60484, "loss_cls": 3.73941, "loss": 3.73941, "time": 0.85756} +{"mode": "train", "epoch": 92, "iter": 1600, "lr": 0.03314, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34438, "top5_acc": 0.61312, "loss_cls": 3.69053, "loss": 3.69053, "time": 0.85977} +{"mode": "train", "epoch": 92, "iter": 1700, "lr": 0.03311, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.34391, "top5_acc": 0.59875, "loss_cls": 3.74379, "loss": 3.74379, "time": 0.86085} +{"mode": "train", "epoch": 92, "iter": 1800, "lr": 0.03308, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.34812, "top5_acc": 0.60656, "loss_cls": 3.68398, "loss": 3.68398, "time": 0.86364} +{"mode": "train", "epoch": 92, "iter": 1900, "lr": 0.03306, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.34859, "top5_acc": 0.61234, "loss_cls": 3.70196, "loss": 3.70196, "time": 0.86395} +{"mode": "train", "epoch": 92, "iter": 2000, "lr": 0.03303, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3425, "top5_acc": 0.59406, "loss_cls": 3.7462, "loss": 3.7462, "time": 0.85281} +{"mode": "train", "epoch": 92, "iter": 2100, "lr": 0.033, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35828, "top5_acc": 0.62656, "loss_cls": 3.63137, "loss": 3.63137, "time": 0.85575} +{"mode": "train", "epoch": 92, "iter": 2200, "lr": 0.03298, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.34562, "top5_acc": 0.61047, "loss_cls": 3.71268, "loss": 3.71268, "time": 0.86061} +{"mode": "train", "epoch": 92, "iter": 2300, "lr": 0.03295, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.34828, "top5_acc": 0.5975, "loss_cls": 3.72905, "loss": 3.72905, "time": 0.85739} +{"mode": "train", "epoch": 92, "iter": 2400, "lr": 0.03292, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34141, "top5_acc": 0.59719, "loss_cls": 3.75313, "loss": 3.75313, "time": 0.85768} +{"mode": "train", "epoch": 92, "iter": 2500, "lr": 0.0329, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35312, "top5_acc": 0.60469, "loss_cls": 3.70492, "loss": 3.70492, "time": 0.85282} +{"mode": "train", "epoch": 92, "iter": 2600, "lr": 0.03287, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.34234, "top5_acc": 0.59609, "loss_cls": 3.74695, "loss": 3.74695, "time": 0.86047} +{"mode": "train", "epoch": 92, "iter": 2700, "lr": 0.03285, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35219, "top5_acc": 0.60469, "loss_cls": 3.71401, "loss": 3.71401, "time": 0.85711} +{"mode": "train", "epoch": 92, "iter": 2800, "lr": 0.03282, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.34953, "top5_acc": 0.61187, "loss_cls": 3.70352, "loss": 3.70352, "time": 0.85368} +{"mode": "train", "epoch": 92, "iter": 2900, "lr": 0.03279, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34078, "top5_acc": 0.59672, "loss_cls": 3.74407, "loss": 3.74407, "time": 0.85634} +{"mode": "train", "epoch": 92, "iter": 3000, "lr": 0.03277, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.34188, "top5_acc": 0.59609, "loss_cls": 3.76932, "loss": 3.76932, "time": 0.85913} +{"mode": "train", "epoch": 92, "iter": 3100, "lr": 0.03274, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.34172, "top5_acc": 0.60578, "loss_cls": 3.70829, "loss": 3.70829, "time": 0.86248} +{"mode": "train", "epoch": 92, "iter": 3200, "lr": 0.03271, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35156, "top5_acc": 0.60812, "loss_cls": 3.72865, "loss": 3.72865, "time": 0.86316} +{"mode": "train", "epoch": 92, "iter": 3300, "lr": 0.03269, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.35297, "top5_acc": 0.61469, "loss_cls": 3.685, "loss": 3.685, "time": 0.86083} +{"mode": "train", "epoch": 92, "iter": 3400, "lr": 0.03266, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34312, "top5_acc": 0.59688, "loss_cls": 3.72192, "loss": 3.72192, "time": 0.86137} +{"mode": "train", "epoch": 92, "iter": 3500, "lr": 0.03264, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.34797, "top5_acc": 0.60766, "loss_cls": 3.72529, "loss": 3.72529, "time": 0.86617} +{"mode": "train", "epoch": 92, "iter": 3600, "lr": 0.03261, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.33906, "top5_acc": 0.59984, "loss_cls": 3.73645, "loss": 3.73645, "time": 0.86513} +{"mode": "train", "epoch": 92, "iter": 3700, "lr": 0.03258, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33562, "top5_acc": 0.59375, "loss_cls": 3.79118, "loss": 3.79118, "time": 0.86768} +{"mode": "val", "epoch": 92, "iter": 309, "lr": 0.03257, "top1_acc": 0.28486, "top5_acc": 0.53573, "mean_class_accuracy": 0.2847} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.03255, "memory": 15990, "data_time": 1.63479, "top1_acc": 0.35859, "top5_acc": 0.62219, "loss_cls": 3.67631, "loss": 3.67631, "time": 2.69911} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.03252, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.36094, "top5_acc": 0.62266, "loss_cls": 3.62128, "loss": 3.62128, "time": 0.87214} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.03249, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.36109, "top5_acc": 0.61703, "loss_cls": 3.65781, "loss": 3.65781, "time": 0.86008} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.03247, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35484, "top5_acc": 0.61266, "loss_cls": 3.69253, "loss": 3.69253, "time": 0.86358} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.03244, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.37281, "top5_acc": 0.61875, "loss_cls": 3.64182, "loss": 3.64182, "time": 0.86055} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.03241, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.34547, "top5_acc": 0.60406, "loss_cls": 3.71596, "loss": 3.71596, "time": 0.86622} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.03239, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.35625, "top5_acc": 0.62203, "loss_cls": 3.64557, "loss": 3.64557, "time": 0.8658} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.03236, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.35734, "top5_acc": 0.61203, "loss_cls": 3.68527, "loss": 3.68527, "time": 0.8706} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.03234, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.34484, "top5_acc": 0.60391, "loss_cls": 3.73019, "loss": 3.73019, "time": 0.86823} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.03231, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.34703, "top5_acc": 0.60312, "loss_cls": 3.73636, "loss": 3.73636, "time": 0.86584} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.03228, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.35781, "top5_acc": 0.6025, "loss_cls": 3.69735, "loss": 3.69735, "time": 0.86743} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.03226, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.35531, "top5_acc": 0.61375, "loss_cls": 3.63947, "loss": 3.63947, "time": 0.86688} +{"mode": "train", "epoch": 93, "iter": 1300, "lr": 0.03223, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.34469, "top5_acc": 0.61219, "loss_cls": 3.68416, "loss": 3.68416, "time": 0.86314} +{"mode": "train", "epoch": 93, "iter": 1400, "lr": 0.03221, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34656, "top5_acc": 0.61969, "loss_cls": 3.66759, "loss": 3.66759, "time": 0.86183} +{"mode": "train", "epoch": 93, "iter": 1500, "lr": 0.03218, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.34688, "top5_acc": 0.60141, "loss_cls": 3.71356, "loss": 3.71356, "time": 0.87272} +{"mode": "train", "epoch": 93, "iter": 1600, "lr": 0.03215, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.35234, "top5_acc": 0.60953, "loss_cls": 3.66561, "loss": 3.66561, "time": 0.8674} +{"mode": "train", "epoch": 93, "iter": 1700, "lr": 0.03213, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.34516, "top5_acc": 0.59406, "loss_cls": 3.72157, "loss": 3.72157, "time": 0.86837} +{"mode": "train", "epoch": 93, "iter": 1800, "lr": 0.0321, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.35141, "top5_acc": 0.60844, "loss_cls": 3.73497, "loss": 3.73497, "time": 0.86745} +{"mode": "train", "epoch": 93, "iter": 1900, "lr": 0.03207, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.34625, "top5_acc": 0.60781, "loss_cls": 3.71318, "loss": 3.71318, "time": 0.86632} +{"mode": "train", "epoch": 93, "iter": 2000, "lr": 0.03205, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.35531, "top5_acc": 0.61422, "loss_cls": 3.67725, "loss": 3.67725, "time": 0.86935} +{"mode": "train", "epoch": 93, "iter": 2100, "lr": 0.03202, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33625, "top5_acc": 0.59734, "loss_cls": 3.75685, "loss": 3.75685, "time": 0.85864} +{"mode": "train", "epoch": 93, "iter": 2200, "lr": 0.032, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.34547, "top5_acc": 0.60719, "loss_cls": 3.72511, "loss": 3.72511, "time": 0.86785} +{"mode": "train", "epoch": 93, "iter": 2300, "lr": 0.03197, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.355, "top5_acc": 0.60844, "loss_cls": 3.69001, "loss": 3.69001, "time": 0.86065} +{"mode": "train", "epoch": 93, "iter": 2400, "lr": 0.03194, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35469, "top5_acc": 0.61438, "loss_cls": 3.67382, "loss": 3.67382, "time": 0.86296} +{"mode": "train", "epoch": 93, "iter": 2500, "lr": 0.03192, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35828, "top5_acc": 0.60812, "loss_cls": 3.69497, "loss": 3.69497, "time": 0.86285} +{"mode": "train", "epoch": 93, "iter": 2600, "lr": 0.03189, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34984, "top5_acc": 0.60891, "loss_cls": 3.70646, "loss": 3.70646, "time": 0.86788} +{"mode": "train", "epoch": 93, "iter": 2700, "lr": 0.03187, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34672, "top5_acc": 0.61031, "loss_cls": 3.67348, "loss": 3.67348, "time": 0.86205} +{"mode": "train", "epoch": 93, "iter": 2800, "lr": 0.03184, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.35188, "top5_acc": 0.61719, "loss_cls": 3.67629, "loss": 3.67629, "time": 0.86865} +{"mode": "train", "epoch": 93, "iter": 2900, "lr": 0.03181, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.35438, "top5_acc": 0.60719, "loss_cls": 3.69369, "loss": 3.69369, "time": 0.86631} +{"mode": "train", "epoch": 93, "iter": 3000, "lr": 0.03179, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.34719, "top5_acc": 0.60859, "loss_cls": 3.71454, "loss": 3.71454, "time": 0.8703} +{"mode": "train", "epoch": 93, "iter": 3100, "lr": 0.03176, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.3575, "top5_acc": 0.61438, "loss_cls": 3.64531, "loss": 3.64531, "time": 0.86556} +{"mode": "train", "epoch": 93, "iter": 3200, "lr": 0.03174, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.35188, "top5_acc": 0.60609, "loss_cls": 3.71391, "loss": 3.71391, "time": 0.86427} +{"mode": "train", "epoch": 93, "iter": 3300, "lr": 0.03171, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.34266, "top5_acc": 0.60594, "loss_cls": 3.70573, "loss": 3.70573, "time": 0.85758} +{"mode": "train", "epoch": 93, "iter": 3400, "lr": 0.03168, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35516, "top5_acc": 0.61234, "loss_cls": 3.64568, "loss": 3.64568, "time": 0.85791} +{"mode": "train", "epoch": 93, "iter": 3500, "lr": 0.03166, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35094, "top5_acc": 0.61078, "loss_cls": 3.69761, "loss": 3.69761, "time": 0.86282} +{"mode": "train", "epoch": 93, "iter": 3600, "lr": 0.03163, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.34625, "top5_acc": 0.60391, "loss_cls": 3.72627, "loss": 3.72627, "time": 0.8608} +{"mode": "train", "epoch": 93, "iter": 3700, "lr": 0.03161, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3575, "top5_acc": 0.62, "loss_cls": 3.63333, "loss": 3.63333, "time": 0.8607} +{"mode": "val", "epoch": 93, "iter": 309, "lr": 0.03159, "top1_acc": 0.29119, "top5_acc": 0.54212, "mean_class_accuracy": 0.29089} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.03157, "memory": 15990, "data_time": 1.69612, "top1_acc": 0.36422, "top5_acc": 0.61938, "loss_cls": 3.63369, "loss": 3.63369, "time": 2.74109} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.03154, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.36625, "top5_acc": 0.62547, "loss_cls": 3.60362, "loss": 3.60362, "time": 0.8584} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.03152, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35812, "top5_acc": 0.62328, "loss_cls": 3.59963, "loss": 3.59963, "time": 0.86581} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.03149, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.35078, "top5_acc": 0.61219, "loss_cls": 3.67698, "loss": 3.67698, "time": 0.86025} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.03146, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.35312, "top5_acc": 0.60391, "loss_cls": 3.66737, "loss": 3.66737, "time": 0.85996} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.03144, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.35031, "top5_acc": 0.61828, "loss_cls": 3.66157, "loss": 3.66157, "time": 0.85886} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.03141, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.365, "top5_acc": 0.62719, "loss_cls": 3.60121, "loss": 3.60121, "time": 0.8617} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.03139, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36047, "top5_acc": 0.62469, "loss_cls": 3.62252, "loss": 3.62252, "time": 0.85886} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.03136, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.345, "top5_acc": 0.60172, "loss_cls": 3.70512, "loss": 3.70512, "time": 0.86134} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.03133, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.35062, "top5_acc": 0.61234, "loss_cls": 3.68537, "loss": 3.68537, "time": 0.86265} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.03131, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34844, "top5_acc": 0.61141, "loss_cls": 3.69108, "loss": 3.69108, "time": 0.86181} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.03128, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34797, "top5_acc": 0.60875, "loss_cls": 3.69545, "loss": 3.69545, "time": 0.86787} +{"mode": "train", "epoch": 94, "iter": 1300, "lr": 0.03126, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35188, "top5_acc": 0.61031, "loss_cls": 3.68236, "loss": 3.68236, "time": 0.85967} +{"mode": "train", "epoch": 94, "iter": 1400, "lr": 0.03123, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34953, "top5_acc": 0.61016, "loss_cls": 3.67417, "loss": 3.67417, "time": 0.85038} +{"mode": "train", "epoch": 94, "iter": 1500, "lr": 0.0312, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35562, "top5_acc": 0.60391, "loss_cls": 3.71254, "loss": 3.71254, "time": 0.85359} +{"mode": "train", "epoch": 94, "iter": 1600, "lr": 0.03118, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36234, "top5_acc": 0.62406, "loss_cls": 3.63608, "loss": 3.63608, "time": 0.85133} +{"mode": "train", "epoch": 94, "iter": 1700, "lr": 0.03115, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35141, "top5_acc": 0.61422, "loss_cls": 3.67362, "loss": 3.67362, "time": 0.85384} +{"mode": "train", "epoch": 94, "iter": 1800, "lr": 0.03113, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35031, "top5_acc": 0.6125, "loss_cls": 3.71015, "loss": 3.71015, "time": 0.85561} +{"mode": "train", "epoch": 94, "iter": 1900, "lr": 0.0311, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.3525, "top5_acc": 0.62234, "loss_cls": 3.64206, "loss": 3.64206, "time": 0.8557} +{"mode": "train", "epoch": 94, "iter": 2000, "lr": 0.03108, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.34344, "top5_acc": 0.60281, "loss_cls": 3.70207, "loss": 3.70207, "time": 0.86846} +{"mode": "train", "epoch": 94, "iter": 2100, "lr": 0.03105, "memory": 15990, "data_time": 0.0007, "top1_acc": 0.35484, "top5_acc": 0.60469, "loss_cls": 3.72227, "loss": 3.72227, "time": 0.85588} +{"mode": "train", "epoch": 94, "iter": 2200, "lr": 0.03102, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.35281, "top5_acc": 0.62172, "loss_cls": 3.66452, "loss": 3.66452, "time": 0.85987} +{"mode": "train", "epoch": 94, "iter": 2300, "lr": 0.031, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.35266, "top5_acc": 0.60781, "loss_cls": 3.69068, "loss": 3.69068, "time": 0.85474} +{"mode": "train", "epoch": 94, "iter": 2400, "lr": 0.03097, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35266, "top5_acc": 0.61328, "loss_cls": 3.67884, "loss": 3.67884, "time": 0.8584} +{"mode": "train", "epoch": 94, "iter": 2500, "lr": 0.03095, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35891, "top5_acc": 0.60828, "loss_cls": 3.66786, "loss": 3.66786, "time": 0.85511} +{"mode": "train", "epoch": 94, "iter": 2600, "lr": 0.03092, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34484, "top5_acc": 0.60562, "loss_cls": 3.73901, "loss": 3.73901, "time": 0.86058} +{"mode": "train", "epoch": 94, "iter": 2700, "lr": 0.03089, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35125, "top5_acc": 0.61328, "loss_cls": 3.66858, "loss": 3.66858, "time": 0.86036} +{"mode": "train", "epoch": 94, "iter": 2800, "lr": 0.03087, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.34719, "top5_acc": 0.61344, "loss_cls": 3.69739, "loss": 3.69739, "time": 0.85757} +{"mode": "train", "epoch": 94, "iter": 2900, "lr": 0.03084, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34328, "top5_acc": 0.60297, "loss_cls": 3.73431, "loss": 3.73431, "time": 0.85293} +{"mode": "train", "epoch": 94, "iter": 3000, "lr": 0.03082, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.36219, "top5_acc": 0.61531, "loss_cls": 3.64923, "loss": 3.64923, "time": 0.86204} +{"mode": "train", "epoch": 94, "iter": 3100, "lr": 0.03079, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34219, "top5_acc": 0.59938, "loss_cls": 3.75277, "loss": 3.75277, "time": 0.8604} +{"mode": "train", "epoch": 94, "iter": 3200, "lr": 0.03077, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36188, "top5_acc": 0.61953, "loss_cls": 3.65502, "loss": 3.65502, "time": 0.85587} +{"mode": "train", "epoch": 94, "iter": 3300, "lr": 0.03074, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.34969, "top5_acc": 0.6075, "loss_cls": 3.69301, "loss": 3.69301, "time": 0.85573} +{"mode": "train", "epoch": 94, "iter": 3400, "lr": 0.03071, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35578, "top5_acc": 0.61156, "loss_cls": 3.67382, "loss": 3.67382, "time": 0.85853} +{"mode": "train", "epoch": 94, "iter": 3500, "lr": 0.03069, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34391, "top5_acc": 0.60812, "loss_cls": 3.71325, "loss": 3.71325, "time": 0.86213} +{"mode": "train", "epoch": 94, "iter": 3600, "lr": 0.03066, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35969, "top5_acc": 0.60922, "loss_cls": 3.67662, "loss": 3.67662, "time": 0.85997} +{"mode": "train", "epoch": 94, "iter": 3700, "lr": 0.03064, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.35516, "top5_acc": 0.62078, "loss_cls": 3.6508, "loss": 3.6508, "time": 0.85398} +{"mode": "val", "epoch": 94, "iter": 309, "lr": 0.03062, "top1_acc": 0.30006, "top5_acc": 0.54804, "mean_class_accuracy": 0.29985} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.0306, "memory": 15990, "data_time": 1.60954, "top1_acc": 0.35781, "top5_acc": 0.61516, "loss_cls": 3.64977, "loss": 3.64977, "time": 2.66083} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.03057, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.35328, "top5_acc": 0.62141, "loss_cls": 3.62526, "loss": 3.62526, "time": 0.86197} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.03055, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35922, "top5_acc": 0.62813, "loss_cls": 3.63446, "loss": 3.63446, "time": 0.86235} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.03052, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3625, "top5_acc": 0.61812, "loss_cls": 3.62159, "loss": 3.62159, "time": 0.85062} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.0305, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35188, "top5_acc": 0.60812, "loss_cls": 3.68685, "loss": 3.68685, "time": 0.8576} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.03047, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35562, "top5_acc": 0.61109, "loss_cls": 3.67744, "loss": 3.67744, "time": 0.85598} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.03044, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.3625, "top5_acc": 0.62031, "loss_cls": 3.63703, "loss": 3.63703, "time": 0.85366} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.03042, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3575, "top5_acc": 0.62484, "loss_cls": 3.62648, "loss": 3.62648, "time": 0.85167} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.03039, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34703, "top5_acc": 0.61516, "loss_cls": 3.65872, "loss": 3.65872, "time": 0.85798} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.03037, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35562, "top5_acc": 0.62266, "loss_cls": 3.62941, "loss": 3.62941, "time": 0.86446} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.03034, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35641, "top5_acc": 0.61547, "loss_cls": 3.65794, "loss": 3.65794, "time": 0.85875} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.03032, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.35625, "top5_acc": 0.61453, "loss_cls": 3.65024, "loss": 3.65024, "time": 0.85374} +{"mode": "train", "epoch": 95, "iter": 1300, "lr": 0.03029, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.355, "top5_acc": 0.61438, "loss_cls": 3.6637, "loss": 3.6637, "time": 0.86004} +{"mode": "train", "epoch": 95, "iter": 1400, "lr": 0.03026, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.3575, "top5_acc": 0.61672, "loss_cls": 3.66922, "loss": 3.66922, "time": 0.86706} +{"mode": "train", "epoch": 95, "iter": 1500, "lr": 0.03024, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35719, "top5_acc": 0.62156, "loss_cls": 3.65807, "loss": 3.65807, "time": 0.86268} +{"mode": "train", "epoch": 95, "iter": 1600, "lr": 0.03021, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.36328, "top5_acc": 0.625, "loss_cls": 3.61397, "loss": 3.61397, "time": 0.8638} +{"mode": "train", "epoch": 95, "iter": 1700, "lr": 0.03019, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35688, "top5_acc": 0.61719, "loss_cls": 3.64684, "loss": 3.64684, "time": 0.87051} +{"mode": "train", "epoch": 95, "iter": 1800, "lr": 0.03016, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.35219, "top5_acc": 0.61562, "loss_cls": 3.65357, "loss": 3.65357, "time": 0.87031} +{"mode": "train", "epoch": 95, "iter": 1900, "lr": 0.03014, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.35359, "top5_acc": 0.61922, "loss_cls": 3.66616, "loss": 3.66616, "time": 0.86198} +{"mode": "train", "epoch": 95, "iter": 2000, "lr": 0.03011, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36234, "top5_acc": 0.62375, "loss_cls": 3.63607, "loss": 3.63607, "time": 0.86455} +{"mode": "train", "epoch": 95, "iter": 2100, "lr": 0.03008, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35453, "top5_acc": 0.61609, "loss_cls": 3.66469, "loss": 3.66469, "time": 0.85372} +{"mode": "train", "epoch": 95, "iter": 2200, "lr": 0.03006, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35797, "top5_acc": 0.61484, "loss_cls": 3.66304, "loss": 3.66304, "time": 0.86467} +{"mode": "train", "epoch": 95, "iter": 2300, "lr": 0.03003, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35594, "top5_acc": 0.60766, "loss_cls": 3.70174, "loss": 3.70174, "time": 0.85452} +{"mode": "train", "epoch": 95, "iter": 2400, "lr": 0.03001, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35047, "top5_acc": 0.61125, "loss_cls": 3.68867, "loss": 3.68867, "time": 0.85883} +{"mode": "train", "epoch": 95, "iter": 2500, "lr": 0.02998, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36156, "top5_acc": 0.61672, "loss_cls": 3.6427, "loss": 3.6427, "time": 0.86358} +{"mode": "train", "epoch": 95, "iter": 2600, "lr": 0.02996, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35484, "top5_acc": 0.61625, "loss_cls": 3.6786, "loss": 3.6786, "time": 0.86515} +{"mode": "train", "epoch": 95, "iter": 2700, "lr": 0.02993, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35688, "top5_acc": 0.61297, "loss_cls": 3.71091, "loss": 3.71091, "time": 0.86595} +{"mode": "train", "epoch": 95, "iter": 2800, "lr": 0.02991, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35312, "top5_acc": 0.61609, "loss_cls": 3.66063, "loss": 3.66063, "time": 0.86672} +{"mode": "train", "epoch": 95, "iter": 2900, "lr": 0.02988, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34641, "top5_acc": 0.61344, "loss_cls": 3.70638, "loss": 3.70638, "time": 0.86597} +{"mode": "train", "epoch": 95, "iter": 3000, "lr": 0.02985, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.35109, "top5_acc": 0.60766, "loss_cls": 3.67234, "loss": 3.67234, "time": 0.8589} +{"mode": "train", "epoch": 95, "iter": 3100, "lr": 0.02983, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.35438, "top5_acc": 0.61359, "loss_cls": 3.6865, "loss": 3.6865, "time": 0.85507} +{"mode": "train", "epoch": 95, "iter": 3200, "lr": 0.0298, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34938, "top5_acc": 0.61406, "loss_cls": 3.68412, "loss": 3.68412, "time": 0.85449} +{"mode": "train", "epoch": 95, "iter": 3300, "lr": 0.02978, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.35594, "top5_acc": 0.61062, "loss_cls": 3.6788, "loss": 3.6788, "time": 0.85845} +{"mode": "train", "epoch": 95, "iter": 3400, "lr": 0.02975, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36219, "top5_acc": 0.61531, "loss_cls": 3.62586, "loss": 3.62586, "time": 0.85449} +{"mode": "train", "epoch": 95, "iter": 3500, "lr": 0.02973, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35828, "top5_acc": 0.61812, "loss_cls": 3.63925, "loss": 3.63925, "time": 0.85935} +{"mode": "train", "epoch": 95, "iter": 3600, "lr": 0.0297, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34719, "top5_acc": 0.61047, "loss_cls": 3.70127, "loss": 3.70127, "time": 0.85438} +{"mode": "train", "epoch": 95, "iter": 3700, "lr": 0.02968, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34375, "top5_acc": 0.60406, "loss_cls": 3.71054, "loss": 3.71054, "time": 0.85072} +{"mode": "val", "epoch": 95, "iter": 309, "lr": 0.02966, "top1_acc": 0.30502, "top5_acc": 0.56, "mean_class_accuracy": 0.3048} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.02964, "memory": 15990, "data_time": 1.57268, "top1_acc": 0.36797, "top5_acc": 0.62891, "loss_cls": 3.57147, "loss": 3.57147, "time": 2.62969} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.02961, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.35766, "top5_acc": 0.62625, "loss_cls": 3.61832, "loss": 3.61832, "time": 0.86235} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.02959, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.36172, "top5_acc": 0.61844, "loss_cls": 3.62978, "loss": 3.62978, "time": 0.85418} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.02956, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35547, "top5_acc": 0.62766, "loss_cls": 3.61335, "loss": 3.61335, "time": 0.85545} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.02954, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.36031, "top5_acc": 0.62719, "loss_cls": 3.58588, "loss": 3.58588, "time": 0.86558} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.02951, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35984, "top5_acc": 0.62891, "loss_cls": 3.5939, "loss": 3.5939, "time": 0.86488} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.02948, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35953, "top5_acc": 0.61672, "loss_cls": 3.67586, "loss": 3.67586, "time": 0.86534} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.02946, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35438, "top5_acc": 0.61859, "loss_cls": 3.63867, "loss": 3.63867, "time": 0.86546} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.02943, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.36125, "top5_acc": 0.61344, "loss_cls": 3.64725, "loss": 3.64725, "time": 0.8637} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.02941, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36125, "top5_acc": 0.62078, "loss_cls": 3.63197, "loss": 3.63197, "time": 0.86313} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.02938, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36062, "top5_acc": 0.61453, "loss_cls": 3.67592, "loss": 3.67592, "time": 0.87188} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.02936, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34891, "top5_acc": 0.61844, "loss_cls": 3.66549, "loss": 3.66549, "time": 0.86528} +{"mode": "train", "epoch": 96, "iter": 1300, "lr": 0.02933, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36062, "top5_acc": 0.62766, "loss_cls": 3.62269, "loss": 3.62269, "time": 0.87379} +{"mode": "train", "epoch": 96, "iter": 1400, "lr": 0.02931, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37172, "top5_acc": 0.63219, "loss_cls": 3.56878, "loss": 3.56878, "time": 0.86765} +{"mode": "train", "epoch": 96, "iter": 1500, "lr": 0.02928, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35172, "top5_acc": 0.61766, "loss_cls": 3.64438, "loss": 3.64438, "time": 0.8677} +{"mode": "train", "epoch": 96, "iter": 1600, "lr": 0.02926, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.3575, "top5_acc": 0.61578, "loss_cls": 3.64537, "loss": 3.64537, "time": 0.86651} +{"mode": "train", "epoch": 96, "iter": 1700, "lr": 0.02923, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36109, "top5_acc": 0.6225, "loss_cls": 3.64484, "loss": 3.64484, "time": 0.86338} +{"mode": "train", "epoch": 96, "iter": 1800, "lr": 0.0292, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36672, "top5_acc": 0.62031, "loss_cls": 3.62791, "loss": 3.62791, "time": 0.87032} +{"mode": "train", "epoch": 96, "iter": 1900, "lr": 0.02918, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35781, "top5_acc": 0.61234, "loss_cls": 3.69447, "loss": 3.69447, "time": 0.86161} +{"mode": "train", "epoch": 96, "iter": 2000, "lr": 0.02915, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.37094, "top5_acc": 0.62438, "loss_cls": 3.58799, "loss": 3.58799, "time": 0.85688} +{"mode": "train", "epoch": 96, "iter": 2100, "lr": 0.02913, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35969, "top5_acc": 0.62141, "loss_cls": 3.62875, "loss": 3.62875, "time": 0.8563} +{"mode": "train", "epoch": 96, "iter": 2200, "lr": 0.0291, "memory": 15990, "data_time": 0.00074, "top1_acc": 0.36406, "top5_acc": 0.61766, "loss_cls": 3.63996, "loss": 3.63996, "time": 0.85571} +{"mode": "train", "epoch": 96, "iter": 2300, "lr": 0.02908, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34781, "top5_acc": 0.60703, "loss_cls": 3.71812, "loss": 3.71812, "time": 0.86161} +{"mode": "train", "epoch": 96, "iter": 2400, "lr": 0.02905, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35562, "top5_acc": 0.61562, "loss_cls": 3.65996, "loss": 3.65996, "time": 0.85458} +{"mode": "train", "epoch": 96, "iter": 2500, "lr": 0.02903, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35578, "top5_acc": 0.62141, "loss_cls": 3.64525, "loss": 3.64525, "time": 0.85755} +{"mode": "train", "epoch": 96, "iter": 2600, "lr": 0.029, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35781, "top5_acc": 0.61219, "loss_cls": 3.67176, "loss": 3.67176, "time": 0.85147} +{"mode": "train", "epoch": 96, "iter": 2700, "lr": 0.02898, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36016, "top5_acc": 0.6225, "loss_cls": 3.63275, "loss": 3.63275, "time": 0.8544} +{"mode": "train", "epoch": 96, "iter": 2800, "lr": 0.02895, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35625, "top5_acc": 0.61703, "loss_cls": 3.66572, "loss": 3.66572, "time": 0.85218} +{"mode": "train", "epoch": 96, "iter": 2900, "lr": 0.02893, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34984, "top5_acc": 0.61359, "loss_cls": 3.68607, "loss": 3.68607, "time": 0.85519} +{"mode": "train", "epoch": 96, "iter": 3000, "lr": 0.0289, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35156, "top5_acc": 0.61359, "loss_cls": 3.63602, "loss": 3.63602, "time": 0.85662} +{"mode": "train", "epoch": 96, "iter": 3100, "lr": 0.02887, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35578, "top5_acc": 0.61766, "loss_cls": 3.65338, "loss": 3.65338, "time": 0.85397} +{"mode": "train", "epoch": 96, "iter": 3200, "lr": 0.02885, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.35328, "top5_acc": 0.61031, "loss_cls": 3.70501, "loss": 3.70501, "time": 0.85333} +{"mode": "train", "epoch": 96, "iter": 3300, "lr": 0.02882, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.35594, "top5_acc": 0.62172, "loss_cls": 3.63826, "loss": 3.63826, "time": 0.85341} +{"mode": "train", "epoch": 96, "iter": 3400, "lr": 0.0288, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35172, "top5_acc": 0.6075, "loss_cls": 3.67969, "loss": 3.67969, "time": 0.8528} +{"mode": "train", "epoch": 96, "iter": 3500, "lr": 0.02877, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.355, "top5_acc": 0.60734, "loss_cls": 3.70692, "loss": 3.70692, "time": 0.85612} +{"mode": "train", "epoch": 96, "iter": 3600, "lr": 0.02875, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35812, "top5_acc": 0.61656, "loss_cls": 3.64338, "loss": 3.64338, "time": 0.85341} +{"mode": "train", "epoch": 96, "iter": 3700, "lr": 0.02872, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35844, "top5_acc": 0.62562, "loss_cls": 3.61165, "loss": 3.61165, "time": 0.85057} +{"mode": "val", "epoch": 96, "iter": 309, "lr": 0.02871, "top1_acc": 0.30664, "top5_acc": 0.5604, "mean_class_accuracy": 0.30644} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.02869, "memory": 15990, "data_time": 1.51639, "top1_acc": 0.37406, "top5_acc": 0.62766, "loss_cls": 3.55251, "loss": 3.55251, "time": 2.55312} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.02866, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.36, "top5_acc": 0.62047, "loss_cls": 3.6148, "loss": 3.6148, "time": 0.85803} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.02864, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36156, "top5_acc": 0.62844, "loss_cls": 3.59676, "loss": 3.59676, "time": 0.85405} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.02861, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.375, "top5_acc": 0.635, "loss_cls": 3.54661, "loss": 3.54661, "time": 0.8551} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.02858, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36875, "top5_acc": 0.63453, "loss_cls": 3.54962, "loss": 3.54962, "time": 0.8552} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.02856, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3525, "top5_acc": 0.61344, "loss_cls": 3.65379, "loss": 3.65379, "time": 0.85501} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.02853, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35922, "top5_acc": 0.62125, "loss_cls": 3.6575, "loss": 3.6575, "time": 0.85414} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.02851, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35094, "top5_acc": 0.61359, "loss_cls": 3.67745, "loss": 3.67745, "time": 0.84791} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.02848, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35562, "top5_acc": 0.62781, "loss_cls": 3.62878, "loss": 3.62878, "time": 0.85571} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.02846, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36422, "top5_acc": 0.62234, "loss_cls": 3.62511, "loss": 3.62511, "time": 0.85099} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.02843, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35797, "top5_acc": 0.62594, "loss_cls": 3.61168, "loss": 3.61168, "time": 0.85396} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.02841, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3675, "top5_acc": 0.61797, "loss_cls": 3.63974, "loss": 3.63974, "time": 0.84975} +{"mode": "train", "epoch": 97, "iter": 1300, "lr": 0.02838, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35844, "top5_acc": 0.61484, "loss_cls": 3.65932, "loss": 3.65932, "time": 0.85212} +{"mode": "train", "epoch": 97, "iter": 1400, "lr": 0.02836, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36516, "top5_acc": 0.62078, "loss_cls": 3.61567, "loss": 3.61567, "time": 0.85327} +{"mode": "train", "epoch": 97, "iter": 1500, "lr": 0.02833, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36375, "top5_acc": 0.62219, "loss_cls": 3.60292, "loss": 3.60292, "time": 0.85838} +{"mode": "train", "epoch": 97, "iter": 1600, "lr": 0.02831, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3625, "top5_acc": 0.61641, "loss_cls": 3.62569, "loss": 3.62569, "time": 0.85378} +{"mode": "train", "epoch": 97, "iter": 1700, "lr": 0.02828, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3675, "top5_acc": 0.62484, "loss_cls": 3.59773, "loss": 3.59773, "time": 0.85389} +{"mode": "train", "epoch": 97, "iter": 1800, "lr": 0.02826, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35688, "top5_acc": 0.615, "loss_cls": 3.63922, "loss": 3.63922, "time": 0.85425} +{"mode": "train", "epoch": 97, "iter": 1900, "lr": 0.02823, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35188, "top5_acc": 0.61547, "loss_cls": 3.6692, "loss": 3.6692, "time": 0.85408} +{"mode": "train", "epoch": 97, "iter": 2000, "lr": 0.02821, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.35734, "top5_acc": 0.62, "loss_cls": 3.63189, "loss": 3.63189, "time": 0.85086} +{"mode": "train", "epoch": 97, "iter": 2100, "lr": 0.02818, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36984, "top5_acc": 0.62828, "loss_cls": 3.56093, "loss": 3.56093, "time": 0.85386} +{"mode": "train", "epoch": 97, "iter": 2200, "lr": 0.02816, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.3625, "top5_acc": 0.61641, "loss_cls": 3.63944, "loss": 3.63944, "time": 0.86096} +{"mode": "train", "epoch": 97, "iter": 2300, "lr": 0.02813, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35391, "top5_acc": 0.60984, "loss_cls": 3.67517, "loss": 3.67517, "time": 0.8666} +{"mode": "train", "epoch": 97, "iter": 2400, "lr": 0.02811, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35828, "top5_acc": 0.61859, "loss_cls": 3.62575, "loss": 3.62575, "time": 0.86608} +{"mode": "train", "epoch": 97, "iter": 2500, "lr": 0.02808, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35641, "top5_acc": 0.61484, "loss_cls": 3.66566, "loss": 3.66566, "time": 0.86243} +{"mode": "train", "epoch": 97, "iter": 2600, "lr": 0.02806, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.355, "top5_acc": 0.61812, "loss_cls": 3.67813, "loss": 3.67813, "time": 0.8644} +{"mode": "train", "epoch": 97, "iter": 2700, "lr": 0.02803, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35391, "top5_acc": 0.61141, "loss_cls": 3.65075, "loss": 3.65075, "time": 0.86152} +{"mode": "train", "epoch": 97, "iter": 2800, "lr": 0.02801, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35156, "top5_acc": 0.61312, "loss_cls": 3.65162, "loss": 3.65162, "time": 0.86149} +{"mode": "train", "epoch": 97, "iter": 2900, "lr": 0.02798, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36125, "top5_acc": 0.62328, "loss_cls": 3.60192, "loss": 3.60192, "time": 0.86495} +{"mode": "train", "epoch": 97, "iter": 3000, "lr": 0.02796, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.3675, "top5_acc": 0.63219, "loss_cls": 3.57851, "loss": 3.57851, "time": 0.851} +{"mode": "train", "epoch": 97, "iter": 3100, "lr": 0.02793, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.3675, "top5_acc": 0.63313, "loss_cls": 3.57678, "loss": 3.57678, "time": 0.86245} +{"mode": "train", "epoch": 97, "iter": 3200, "lr": 0.02791, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.35391, "top5_acc": 0.61703, "loss_cls": 3.64372, "loss": 3.64372, "time": 0.86029} +{"mode": "train", "epoch": 97, "iter": 3300, "lr": 0.02788, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.36016, "top5_acc": 0.61172, "loss_cls": 3.66183, "loss": 3.66183, "time": 0.86284} +{"mode": "train", "epoch": 97, "iter": 3400, "lr": 0.02786, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35922, "top5_acc": 0.61719, "loss_cls": 3.64642, "loss": 3.64642, "time": 0.85428} +{"mode": "train", "epoch": 97, "iter": 3500, "lr": 0.02783, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35906, "top5_acc": 0.61453, "loss_cls": 3.6683, "loss": 3.6683, "time": 0.85627} +{"mode": "train", "epoch": 97, "iter": 3600, "lr": 0.02781, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.3625, "top5_acc": 0.61766, "loss_cls": 3.64074, "loss": 3.64074, "time": 0.85869} +{"mode": "train", "epoch": 97, "iter": 3700, "lr": 0.02778, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35422, "top5_acc": 0.61531, "loss_cls": 3.64565, "loss": 3.64565, "time": 0.85288} +{"mode": "val", "epoch": 97, "iter": 309, "lr": 0.02777, "top1_acc": 0.30391, "top5_acc": 0.55777, "mean_class_accuracy": 0.30348} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.02774, "memory": 15990, "data_time": 1.55252, "top1_acc": 0.36922, "top5_acc": 0.63281, "loss_cls": 3.55167, "loss": 3.55167, "time": 2.58276} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.02772, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37641, "top5_acc": 0.63625, "loss_cls": 3.50832, "loss": 3.50832, "time": 0.86208} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.02769, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37844, "top5_acc": 0.63172, "loss_cls": 3.55233, "loss": 3.55233, "time": 0.85526} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.02767, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.365, "top5_acc": 0.61906, "loss_cls": 3.61828, "loss": 3.61828, "time": 0.85286} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.02764, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36234, "top5_acc": 0.62187, "loss_cls": 3.60682, "loss": 3.60682, "time": 0.85657} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.02762, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36203, "top5_acc": 0.62297, "loss_cls": 3.61785, "loss": 3.61785, "time": 0.85396} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.02759, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35953, "top5_acc": 0.61672, "loss_cls": 3.63912, "loss": 3.63912, "time": 0.85064} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.02757, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35469, "top5_acc": 0.61594, "loss_cls": 3.65116, "loss": 3.65116, "time": 0.85229} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.02754, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36328, "top5_acc": 0.63266, "loss_cls": 3.59583, "loss": 3.59583, "time": 0.85743} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.02752, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36609, "top5_acc": 0.62453, "loss_cls": 3.60502, "loss": 3.60502, "time": 0.85805} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.02749, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37, "top5_acc": 0.62578, "loss_cls": 3.58561, "loss": 3.58561, "time": 0.8581} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.02747, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.35953, "top5_acc": 0.62687, "loss_cls": 3.61413, "loss": 3.61413, "time": 0.85737} +{"mode": "train", "epoch": 98, "iter": 1300, "lr": 0.02744, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36531, "top5_acc": 0.63125, "loss_cls": 3.56226, "loss": 3.56226, "time": 0.856} +{"mode": "train", "epoch": 98, "iter": 1400, "lr": 0.02742, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.35984, "top5_acc": 0.62094, "loss_cls": 3.62615, "loss": 3.62615, "time": 0.85682} +{"mode": "train", "epoch": 98, "iter": 1500, "lr": 0.02739, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36156, "top5_acc": 0.61703, "loss_cls": 3.60863, "loss": 3.60863, "time": 0.85797} +{"mode": "train", "epoch": 98, "iter": 1600, "lr": 0.02737, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37188, "top5_acc": 0.63125, "loss_cls": 3.5746, "loss": 3.5746, "time": 0.85993} +{"mode": "train", "epoch": 98, "iter": 1700, "lr": 0.02734, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36, "top5_acc": 0.61547, "loss_cls": 3.63329, "loss": 3.63329, "time": 0.85584} +{"mode": "train", "epoch": 98, "iter": 1800, "lr": 0.02732, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35969, "top5_acc": 0.62031, "loss_cls": 3.64862, "loss": 3.64862, "time": 0.8506} +{"mode": "train", "epoch": 98, "iter": 1900, "lr": 0.02729, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37141, "top5_acc": 0.64062, "loss_cls": 3.54074, "loss": 3.54074, "time": 0.85192} +{"mode": "train", "epoch": 98, "iter": 2000, "lr": 0.02727, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.36984, "top5_acc": 0.62578, "loss_cls": 3.59611, "loss": 3.59611, "time": 0.85458} +{"mode": "train", "epoch": 98, "iter": 2100, "lr": 0.02724, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.37172, "top5_acc": 0.62828, "loss_cls": 3.56988, "loss": 3.56988, "time": 0.85539} +{"mode": "train", "epoch": 98, "iter": 2200, "lr": 0.02722, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.36812, "top5_acc": 0.63, "loss_cls": 3.59988, "loss": 3.59988, "time": 0.84921} +{"mode": "train", "epoch": 98, "iter": 2300, "lr": 0.02719, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.36422, "top5_acc": 0.62062, "loss_cls": 3.60893, "loss": 3.60893, "time": 0.84887} +{"mode": "train", "epoch": 98, "iter": 2400, "lr": 0.02717, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36531, "top5_acc": 0.62078, "loss_cls": 3.60628, "loss": 3.60628, "time": 0.86219} +{"mode": "train", "epoch": 98, "iter": 2500, "lr": 0.02714, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35984, "top5_acc": 0.62313, "loss_cls": 3.62242, "loss": 3.62242, "time": 0.85775} +{"mode": "train", "epoch": 98, "iter": 2600, "lr": 0.02712, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35594, "top5_acc": 0.61062, "loss_cls": 3.66721, "loss": 3.66721, "time": 0.86933} +{"mode": "train", "epoch": 98, "iter": 2700, "lr": 0.02709, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35453, "top5_acc": 0.60594, "loss_cls": 3.67671, "loss": 3.67671, "time": 0.85727} +{"mode": "train", "epoch": 98, "iter": 2800, "lr": 0.02707, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37016, "top5_acc": 0.63078, "loss_cls": 3.58065, "loss": 3.58065, "time": 0.85854} +{"mode": "train", "epoch": 98, "iter": 2900, "lr": 0.02705, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36938, "top5_acc": 0.62344, "loss_cls": 3.59899, "loss": 3.59899, "time": 0.85918} +{"mode": "train", "epoch": 98, "iter": 3000, "lr": 0.02702, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.34828, "top5_acc": 0.60844, "loss_cls": 3.69355, "loss": 3.69355, "time": 0.86154} +{"mode": "train", "epoch": 98, "iter": 3100, "lr": 0.027, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37953, "top5_acc": 0.62859, "loss_cls": 3.58254, "loss": 3.58254, "time": 0.86074} +{"mode": "train", "epoch": 98, "iter": 3200, "lr": 0.02697, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36969, "top5_acc": 0.62062, "loss_cls": 3.60582, "loss": 3.60582, "time": 0.85304} +{"mode": "train", "epoch": 98, "iter": 3300, "lr": 0.02695, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.35516, "top5_acc": 0.61938, "loss_cls": 3.64021, "loss": 3.64021, "time": 0.85401} +{"mode": "train", "epoch": 98, "iter": 3400, "lr": 0.02692, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35938, "top5_acc": 0.62047, "loss_cls": 3.61722, "loss": 3.61722, "time": 0.8658} +{"mode": "train", "epoch": 98, "iter": 3500, "lr": 0.0269, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.3575, "top5_acc": 0.61312, "loss_cls": 3.62703, "loss": 3.62703, "time": 0.86728} +{"mode": "train", "epoch": 98, "iter": 3600, "lr": 0.02687, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36703, "top5_acc": 0.62172, "loss_cls": 3.59828, "loss": 3.59828, "time": 0.86673} +{"mode": "train", "epoch": 98, "iter": 3700, "lr": 0.02685, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.355, "top5_acc": 0.61047, "loss_cls": 3.6697, "loss": 3.6697, "time": 0.86899} +{"mode": "val", "epoch": 98, "iter": 309, "lr": 0.02684, "top1_acc": 0.30917, "top5_acc": 0.56481, "mean_class_accuracy": 0.30897} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.02681, "memory": 15990, "data_time": 1.54587, "top1_acc": 0.37984, "top5_acc": 0.63, "loss_cls": 3.55476, "loss": 3.55476, "time": 2.58931} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.02679, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.35484, "top5_acc": 0.63047, "loss_cls": 3.6018, "loss": 3.6018, "time": 0.85936} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.02676, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.3675, "top5_acc": 0.63062, "loss_cls": 3.54934, "loss": 3.54934, "time": 0.85909} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.02674, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37766, "top5_acc": 0.63344, "loss_cls": 3.54448, "loss": 3.54448, "time": 0.85332} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.02671, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36953, "top5_acc": 0.62938, "loss_cls": 3.57837, "loss": 3.57837, "time": 0.85588} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.02669, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36531, "top5_acc": 0.63047, "loss_cls": 3.58568, "loss": 3.58568, "time": 0.86316} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.02666, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37062, "top5_acc": 0.62297, "loss_cls": 3.59814, "loss": 3.59814, "time": 0.85761} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.02664, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36516, "top5_acc": 0.6225, "loss_cls": 3.59536, "loss": 3.59536, "time": 0.86202} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.02661, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36938, "top5_acc": 0.63891, "loss_cls": 3.55868, "loss": 3.55868, "time": 0.86034} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.02659, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37266, "top5_acc": 0.63062, "loss_cls": 3.55513, "loss": 3.55513, "time": 0.86242} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.02656, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36359, "top5_acc": 0.63094, "loss_cls": 3.59123, "loss": 3.59123, "time": 0.86097} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.02654, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36688, "top5_acc": 0.62578, "loss_cls": 3.5891, "loss": 3.5891, "time": 0.85599} +{"mode": "train", "epoch": 99, "iter": 1300, "lr": 0.02651, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35875, "top5_acc": 0.61906, "loss_cls": 3.64479, "loss": 3.64479, "time": 0.86442} +{"mode": "train", "epoch": 99, "iter": 1400, "lr": 0.02649, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.37531, "top5_acc": 0.64031, "loss_cls": 3.55946, "loss": 3.55946, "time": 0.86468} +{"mode": "train", "epoch": 99, "iter": 1500, "lr": 0.02646, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37125, "top5_acc": 0.62797, "loss_cls": 3.57829, "loss": 3.57829, "time": 0.86444} +{"mode": "train", "epoch": 99, "iter": 1600, "lr": 0.02644, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36328, "top5_acc": 0.62078, "loss_cls": 3.61372, "loss": 3.61372, "time": 0.86209} +{"mode": "train", "epoch": 99, "iter": 1700, "lr": 0.02642, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37, "top5_acc": 0.62203, "loss_cls": 3.59731, "loss": 3.59731, "time": 0.86363} +{"mode": "train", "epoch": 99, "iter": 1800, "lr": 0.02639, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3625, "top5_acc": 0.6225, "loss_cls": 3.62993, "loss": 3.62993, "time": 0.8668} +{"mode": "train", "epoch": 99, "iter": 1900, "lr": 0.02637, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37094, "top5_acc": 0.62766, "loss_cls": 3.57653, "loss": 3.57653, "time": 0.85906} +{"mode": "train", "epoch": 99, "iter": 2000, "lr": 0.02634, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36906, "top5_acc": 0.6325, "loss_cls": 3.55044, "loss": 3.55044, "time": 0.86571} +{"mode": "train", "epoch": 99, "iter": 2100, "lr": 0.02632, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36781, "top5_acc": 0.63016, "loss_cls": 3.56772, "loss": 3.56772, "time": 0.86849} +{"mode": "train", "epoch": 99, "iter": 2200, "lr": 0.02629, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.36969, "top5_acc": 0.63328, "loss_cls": 3.57948, "loss": 3.57948, "time": 0.8604} +{"mode": "train", "epoch": 99, "iter": 2300, "lr": 0.02627, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36797, "top5_acc": 0.62734, "loss_cls": 3.5737, "loss": 3.5737, "time": 0.85134} +{"mode": "train", "epoch": 99, "iter": 2400, "lr": 0.02624, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35609, "top5_acc": 0.61625, "loss_cls": 3.66641, "loss": 3.66641, "time": 0.86063} +{"mode": "train", "epoch": 99, "iter": 2500, "lr": 0.02622, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36562, "top5_acc": 0.63328, "loss_cls": 3.58076, "loss": 3.58076, "time": 0.8606} +{"mode": "train", "epoch": 99, "iter": 2600, "lr": 0.02619, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36234, "top5_acc": 0.63078, "loss_cls": 3.60518, "loss": 3.60518, "time": 0.85763} +{"mode": "train", "epoch": 99, "iter": 2700, "lr": 0.02617, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35953, "top5_acc": 0.61562, "loss_cls": 3.64771, "loss": 3.64771, "time": 0.86052} +{"mode": "train", "epoch": 99, "iter": 2800, "lr": 0.02614, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36, "top5_acc": 0.62016, "loss_cls": 3.61254, "loss": 3.61254, "time": 0.86204} +{"mode": "train", "epoch": 99, "iter": 2900, "lr": 0.02612, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.3725, "top5_acc": 0.63109, "loss_cls": 3.57182, "loss": 3.57182, "time": 0.86344} +{"mode": "train", "epoch": 99, "iter": 3000, "lr": 0.0261, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37062, "top5_acc": 0.63594, "loss_cls": 3.54326, "loss": 3.54326, "time": 0.86176} +{"mode": "train", "epoch": 99, "iter": 3100, "lr": 0.02607, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36609, "top5_acc": 0.62094, "loss_cls": 3.57773, "loss": 3.57773, "time": 0.85627} +{"mode": "train", "epoch": 99, "iter": 3200, "lr": 0.02605, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36719, "top5_acc": 0.62859, "loss_cls": 3.56933, "loss": 3.56933, "time": 0.85065} +{"mode": "train", "epoch": 99, "iter": 3300, "lr": 0.02602, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36484, "top5_acc": 0.62844, "loss_cls": 3.59646, "loss": 3.59646, "time": 0.85719} +{"mode": "train", "epoch": 99, "iter": 3400, "lr": 0.026, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37031, "top5_acc": 0.62313, "loss_cls": 3.59809, "loss": 3.59809, "time": 0.86616} +{"mode": "train", "epoch": 99, "iter": 3500, "lr": 0.02597, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36938, "top5_acc": 0.62734, "loss_cls": 3.60206, "loss": 3.60206, "time": 0.85888} +{"mode": "train", "epoch": 99, "iter": 3600, "lr": 0.02595, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.3525, "top5_acc": 0.61844, "loss_cls": 3.65585, "loss": 3.65585, "time": 0.86339} +{"mode": "train", "epoch": 99, "iter": 3700, "lr": 0.02592, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36219, "top5_acc": 0.61125, "loss_cls": 3.65732, "loss": 3.65732, "time": 0.8625} +{"mode": "val", "epoch": 99, "iter": 309, "lr": 0.02591, "top1_acc": 0.3114, "top5_acc": 0.56729, "mean_class_accuracy": 0.31115} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.02589, "memory": 15990, "data_time": 1.56669, "top1_acc": 0.3775, "top5_acc": 0.63672, "loss_cls": 3.50348, "loss": 3.50348, "time": 2.60114} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.02586, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37719, "top5_acc": 0.63625, "loss_cls": 3.53773, "loss": 3.53773, "time": 0.85177} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.02584, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37469, "top5_acc": 0.62844, "loss_cls": 3.54953, "loss": 3.54953, "time": 0.85182} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.02581, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37109, "top5_acc": 0.64297, "loss_cls": 3.5411, "loss": 3.5411, "time": 0.85818} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.02579, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36469, "top5_acc": 0.62578, "loss_cls": 3.56643, "loss": 3.56643, "time": 0.84821} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.02577, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37703, "top5_acc": 0.62297, "loss_cls": 3.56787, "loss": 3.56787, "time": 0.85072} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.02574, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37516, "top5_acc": 0.63641, "loss_cls": 3.5642, "loss": 3.5642, "time": 0.84272} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.02572, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36781, "top5_acc": 0.62813, "loss_cls": 3.57653, "loss": 3.57653, "time": 0.8491} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.02569, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37734, "top5_acc": 0.6325, "loss_cls": 3.54135, "loss": 3.54135, "time": 0.844} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.02567, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.375, "top5_acc": 0.63203, "loss_cls": 3.57864, "loss": 3.57864, "time": 0.85336} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.02564, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.375, "top5_acc": 0.63188, "loss_cls": 3.55098, "loss": 3.55098, "time": 0.85166} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.02562, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37062, "top5_acc": 0.62953, "loss_cls": 3.5764, "loss": 3.5764, "time": 0.84895} +{"mode": "train", "epoch": 100, "iter": 1300, "lr": 0.02559, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37719, "top5_acc": 0.63313, "loss_cls": 3.51731, "loss": 3.51731, "time": 0.84954} +{"mode": "train", "epoch": 100, "iter": 1400, "lr": 0.02557, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36609, "top5_acc": 0.63359, "loss_cls": 3.55288, "loss": 3.55288, "time": 0.84554} +{"mode": "train", "epoch": 100, "iter": 1500, "lr": 0.02555, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36438, "top5_acc": 0.63359, "loss_cls": 3.56812, "loss": 3.56812, "time": 0.84904} +{"mode": "train", "epoch": 100, "iter": 1600, "lr": 0.02552, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37109, "top5_acc": 0.62562, "loss_cls": 3.59213, "loss": 3.59213, "time": 0.85113} +{"mode": "train", "epoch": 100, "iter": 1700, "lr": 0.0255, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36578, "top5_acc": 0.62281, "loss_cls": 3.60816, "loss": 3.60816, "time": 0.84624} +{"mode": "train", "epoch": 100, "iter": 1800, "lr": 0.02547, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37578, "top5_acc": 0.63359, "loss_cls": 3.55183, "loss": 3.55183, "time": 0.84566} +{"mode": "train", "epoch": 100, "iter": 1900, "lr": 0.02545, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.3725, "top5_acc": 0.63406, "loss_cls": 3.56818, "loss": 3.56818, "time": 0.85014} +{"mode": "train", "epoch": 100, "iter": 2000, "lr": 0.02542, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36891, "top5_acc": 0.62469, "loss_cls": 3.59469, "loss": 3.59469, "time": 0.8535} +{"mode": "train", "epoch": 100, "iter": 2100, "lr": 0.0254, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36422, "top5_acc": 0.62656, "loss_cls": 3.61244, "loss": 3.61244, "time": 0.84705} +{"mode": "train", "epoch": 100, "iter": 2200, "lr": 0.02538, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37422, "top5_acc": 0.62906, "loss_cls": 3.56592, "loss": 3.56592, "time": 0.85031} +{"mode": "train", "epoch": 100, "iter": 2300, "lr": 0.02535, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37547, "top5_acc": 0.63031, "loss_cls": 3.55631, "loss": 3.55631, "time": 0.84427} +{"mode": "train", "epoch": 100, "iter": 2400, "lr": 0.02533, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.36453, "top5_acc": 0.62219, "loss_cls": 3.59536, "loss": 3.59536, "time": 0.85283} +{"mode": "train", "epoch": 100, "iter": 2500, "lr": 0.0253, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37047, "top5_acc": 0.62234, "loss_cls": 3.59302, "loss": 3.59302, "time": 0.84758} +{"mode": "train", "epoch": 100, "iter": 2600, "lr": 0.02528, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35781, "top5_acc": 0.62016, "loss_cls": 3.61512, "loss": 3.61512, "time": 0.84778} +{"mode": "train", "epoch": 100, "iter": 2700, "lr": 0.02525, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37016, "top5_acc": 0.62813, "loss_cls": 3.56009, "loss": 3.56009, "time": 0.85098} +{"mode": "train", "epoch": 100, "iter": 2800, "lr": 0.02523, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37203, "top5_acc": 0.63359, "loss_cls": 3.54367, "loss": 3.54367, "time": 0.85579} +{"mode": "train", "epoch": 100, "iter": 2900, "lr": 0.02521, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37812, "top5_acc": 0.63141, "loss_cls": 3.54761, "loss": 3.54761, "time": 0.8535} +{"mode": "train", "epoch": 100, "iter": 3000, "lr": 0.02518, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.36859, "top5_acc": 0.63734, "loss_cls": 3.5575, "loss": 3.5575, "time": 0.84615} +{"mode": "train", "epoch": 100, "iter": 3100, "lr": 0.02516, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36391, "top5_acc": 0.62609, "loss_cls": 3.60594, "loss": 3.60594, "time": 0.84894} +{"mode": "train", "epoch": 100, "iter": 3200, "lr": 0.02513, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.36422, "top5_acc": 0.62391, "loss_cls": 3.6409, "loss": 3.6409, "time": 0.84389} +{"mode": "train", "epoch": 100, "iter": 3300, "lr": 0.02511, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37156, "top5_acc": 0.62344, "loss_cls": 3.60328, "loss": 3.60328, "time": 0.8517} +{"mode": "train", "epoch": 100, "iter": 3400, "lr": 0.02508, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35578, "top5_acc": 0.61922, "loss_cls": 3.63773, "loss": 3.63773, "time": 0.85336} +{"mode": "train", "epoch": 100, "iter": 3500, "lr": 0.02506, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37203, "top5_acc": 0.62422, "loss_cls": 3.59837, "loss": 3.59837, "time": 0.85651} +{"mode": "train", "epoch": 100, "iter": 3600, "lr": 0.02504, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35766, "top5_acc": 0.61703, "loss_cls": 3.6473, "loss": 3.6473, "time": 0.8532} +{"mode": "train", "epoch": 100, "iter": 3700, "lr": 0.02501, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35859, "top5_acc": 0.62328, "loss_cls": 3.62547, "loss": 3.62547, "time": 0.85339} +{"mode": "val", "epoch": 100, "iter": 309, "lr": 0.025, "top1_acc": 0.31312, "top5_acc": 0.57154, "mean_class_accuracy": 0.3129} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.02498, "memory": 15990, "data_time": 1.54019, "top1_acc": 0.37984, "top5_acc": 0.64062, "loss_cls": 3.51508, "loss": 3.51508, "time": 2.5911} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.02495, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37281, "top5_acc": 0.63219, "loss_cls": 3.58389, "loss": 3.58389, "time": 0.8579} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.02493, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38438, "top5_acc": 0.64797, "loss_cls": 3.48871, "loss": 3.48871, "time": 0.85261} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.0249, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37594, "top5_acc": 0.63938, "loss_cls": 3.51815, "loss": 3.51815, "time": 0.84854} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.02488, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38172, "top5_acc": 0.64703, "loss_cls": 3.48516, "loss": 3.48516, "time": 0.84959} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.02486, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38812, "top5_acc": 0.64438, "loss_cls": 3.47447, "loss": 3.47447, "time": 0.85037} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.02483, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.37531, "top5_acc": 0.63578, "loss_cls": 3.5321, "loss": 3.5321, "time": 0.85577} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.02481, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37078, "top5_acc": 0.63578, "loss_cls": 3.5242, "loss": 3.5242, "time": 0.85644} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.02478, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37984, "top5_acc": 0.63234, "loss_cls": 3.53805, "loss": 3.53805, "time": 0.85657} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.02476, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37891, "top5_acc": 0.64078, "loss_cls": 3.52687, "loss": 3.52687, "time": 0.86411} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.02473, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37531, "top5_acc": 0.64359, "loss_cls": 3.50243, "loss": 3.50243, "time": 0.85663} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.02471, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.37172, "top5_acc": 0.62797, "loss_cls": 3.57735, "loss": 3.57735, "time": 0.85573} +{"mode": "train", "epoch": 101, "iter": 1300, "lr": 0.02469, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36625, "top5_acc": 0.61969, "loss_cls": 3.61121, "loss": 3.61121, "time": 0.85877} +{"mode": "train", "epoch": 101, "iter": 1400, "lr": 0.02466, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37422, "top5_acc": 0.635, "loss_cls": 3.53437, "loss": 3.53437, "time": 0.85473} +{"mode": "train", "epoch": 101, "iter": 1500, "lr": 0.02464, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.36969, "top5_acc": 0.63266, "loss_cls": 3.58211, "loss": 3.58211, "time": 0.85522} +{"mode": "train", "epoch": 101, "iter": 1600, "lr": 0.02461, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.36812, "top5_acc": 0.63234, "loss_cls": 3.59162, "loss": 3.59162, "time": 0.85912} +{"mode": "train", "epoch": 101, "iter": 1700, "lr": 0.02459, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.37891, "top5_acc": 0.63266, "loss_cls": 3.53144, "loss": 3.53144, "time": 0.85955} +{"mode": "train", "epoch": 101, "iter": 1800, "lr": 0.02457, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36875, "top5_acc": 0.62281, "loss_cls": 3.62692, "loss": 3.62692, "time": 0.85396} +{"mode": "train", "epoch": 101, "iter": 1900, "lr": 0.02454, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37531, "top5_acc": 0.63313, "loss_cls": 3.57384, "loss": 3.57384, "time": 0.85398} +{"mode": "train", "epoch": 101, "iter": 2000, "lr": 0.02452, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37859, "top5_acc": 0.64594, "loss_cls": 3.49928, "loss": 3.49928, "time": 0.85919} +{"mode": "train", "epoch": 101, "iter": 2100, "lr": 0.02449, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.37312, "top5_acc": 0.63125, "loss_cls": 3.54585, "loss": 3.54585, "time": 0.85087} +{"mode": "train", "epoch": 101, "iter": 2200, "lr": 0.02447, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36125, "top5_acc": 0.63438, "loss_cls": 3.58223, "loss": 3.58223, "time": 0.8543} +{"mode": "train", "epoch": 101, "iter": 2300, "lr": 0.02445, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.37594, "top5_acc": 0.62813, "loss_cls": 3.57912, "loss": 3.57912, "time": 0.85044} +{"mode": "train", "epoch": 101, "iter": 2400, "lr": 0.02442, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36766, "top5_acc": 0.62281, "loss_cls": 3.59798, "loss": 3.59798, "time": 0.85317} +{"mode": "train", "epoch": 101, "iter": 2500, "lr": 0.0244, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37016, "top5_acc": 0.63109, "loss_cls": 3.59127, "loss": 3.59127, "time": 0.85398} +{"mode": "train", "epoch": 101, "iter": 2600, "lr": 0.02437, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38781, "top5_acc": 0.63719, "loss_cls": 3.51668, "loss": 3.51668, "time": 0.85684} +{"mode": "train", "epoch": 101, "iter": 2700, "lr": 0.02435, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.36969, "top5_acc": 0.62969, "loss_cls": 3.59376, "loss": 3.59376, "time": 0.85646} +{"mode": "train", "epoch": 101, "iter": 2800, "lr": 0.02433, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37172, "top5_acc": 0.63047, "loss_cls": 3.56528, "loss": 3.56528, "time": 0.85544} +{"mode": "train", "epoch": 101, "iter": 2900, "lr": 0.0243, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37641, "top5_acc": 0.62719, "loss_cls": 3.55409, "loss": 3.55409, "time": 0.85783} +{"mode": "train", "epoch": 101, "iter": 3000, "lr": 0.02428, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37359, "top5_acc": 0.62687, "loss_cls": 3.58145, "loss": 3.58145, "time": 0.85484} +{"mode": "train", "epoch": 101, "iter": 3100, "lr": 0.02425, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37469, "top5_acc": 0.63094, "loss_cls": 3.55667, "loss": 3.55667, "time": 0.84758} +{"mode": "train", "epoch": 101, "iter": 3200, "lr": 0.02423, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37656, "top5_acc": 0.63469, "loss_cls": 3.54507, "loss": 3.54507, "time": 0.84499} +{"mode": "train", "epoch": 101, "iter": 3300, "lr": 0.02421, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37953, "top5_acc": 0.63219, "loss_cls": 3.53983, "loss": 3.53983, "time": 0.85008} +{"mode": "train", "epoch": 101, "iter": 3400, "lr": 0.02418, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36938, "top5_acc": 0.62156, "loss_cls": 3.59822, "loss": 3.59822, "time": 0.85374} +{"mode": "train", "epoch": 101, "iter": 3500, "lr": 0.02416, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36125, "top5_acc": 0.62734, "loss_cls": 3.60056, "loss": 3.60056, "time": 0.85398} +{"mode": "train", "epoch": 101, "iter": 3600, "lr": 0.02413, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38062, "top5_acc": 0.63094, "loss_cls": 3.56777, "loss": 3.56777, "time": 0.86036} +{"mode": "train", "epoch": 101, "iter": 3700, "lr": 0.02411, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36938, "top5_acc": 0.62187, "loss_cls": 3.58618, "loss": 3.58618, "time": 0.85064} +{"mode": "val", "epoch": 101, "iter": 309, "lr": 0.0241, "top1_acc": 0.31338, "top5_acc": 0.5639, "mean_class_accuracy": 0.31303} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.02407, "memory": 15990, "data_time": 1.5649, "top1_acc": 0.37625, "top5_acc": 0.63938, "loss_cls": 3.50722, "loss": 3.50722, "time": 2.59744} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.02405, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38109, "top5_acc": 0.63844, "loss_cls": 3.53278, "loss": 3.53278, "time": 0.85687} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.02403, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38078, "top5_acc": 0.64875, "loss_cls": 3.46356, "loss": 3.46356, "time": 0.85192} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.024, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38375, "top5_acc": 0.65312, "loss_cls": 3.4659, "loss": 3.4659, "time": 0.85352} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.02398, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36719, "top5_acc": 0.63156, "loss_cls": 3.54685, "loss": 3.54685, "time": 0.84943} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.02396, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39016, "top5_acc": 0.64562, "loss_cls": 3.44852, "loss": 3.44852, "time": 0.84756} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.02393, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37609, "top5_acc": 0.63047, "loss_cls": 3.54561, "loss": 3.54561, "time": 0.85286} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.02391, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.365, "top5_acc": 0.62391, "loss_cls": 3.58684, "loss": 3.58684, "time": 0.85153} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.02388, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38156, "top5_acc": 0.63484, "loss_cls": 3.52572, "loss": 3.52572, "time": 0.8537} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.02386, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.37906, "top5_acc": 0.63547, "loss_cls": 3.55021, "loss": 3.55021, "time": 0.85649} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.02384, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.37547, "top5_acc": 0.63266, "loss_cls": 3.52957, "loss": 3.52957, "time": 0.85212} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.02381, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37562, "top5_acc": 0.63188, "loss_cls": 3.54646, "loss": 3.54646, "time": 0.85279} +{"mode": "train", "epoch": 102, "iter": 1300, "lr": 0.02379, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38016, "top5_acc": 0.63313, "loss_cls": 3.53357, "loss": 3.53357, "time": 0.85151} +{"mode": "train", "epoch": 102, "iter": 1400, "lr": 0.02376, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38188, "top5_acc": 0.64078, "loss_cls": 3.51113, "loss": 3.51113, "time": 0.8541} +{"mode": "train", "epoch": 102, "iter": 1500, "lr": 0.02374, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36656, "top5_acc": 0.63094, "loss_cls": 3.56305, "loss": 3.56305, "time": 0.84826} +{"mode": "train", "epoch": 102, "iter": 1600, "lr": 0.02372, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37062, "top5_acc": 0.63781, "loss_cls": 3.53776, "loss": 3.53776, "time": 0.85527} +{"mode": "train", "epoch": 102, "iter": 1700, "lr": 0.02369, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.38125, "top5_acc": 0.62859, "loss_cls": 3.57483, "loss": 3.57483, "time": 0.85298} +{"mode": "train", "epoch": 102, "iter": 1800, "lr": 0.02367, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36562, "top5_acc": 0.62813, "loss_cls": 3.5867, "loss": 3.5867, "time": 0.85333} +{"mode": "train", "epoch": 102, "iter": 1900, "lr": 0.02365, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37375, "top5_acc": 0.63859, "loss_cls": 3.5215, "loss": 3.5215, "time": 0.85079} +{"mode": "train", "epoch": 102, "iter": 2000, "lr": 0.02362, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.37234, "top5_acc": 0.63688, "loss_cls": 3.53168, "loss": 3.53168, "time": 0.85477} +{"mode": "train", "epoch": 102, "iter": 2100, "lr": 0.0236, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.37359, "top5_acc": 0.63, "loss_cls": 3.53962, "loss": 3.53962, "time": 0.85335} +{"mode": "train", "epoch": 102, "iter": 2200, "lr": 0.02357, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37516, "top5_acc": 0.63188, "loss_cls": 3.55222, "loss": 3.55222, "time": 0.85169} +{"mode": "train", "epoch": 102, "iter": 2300, "lr": 0.02355, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.38297, "top5_acc": 0.64047, "loss_cls": 3.51024, "loss": 3.51024, "time": 0.85} +{"mode": "train", "epoch": 102, "iter": 2400, "lr": 0.02353, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37625, "top5_acc": 0.64188, "loss_cls": 3.50755, "loss": 3.50755, "time": 0.85313} +{"mode": "train", "epoch": 102, "iter": 2500, "lr": 0.0235, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37812, "top5_acc": 0.63781, "loss_cls": 3.53214, "loss": 3.53214, "time": 0.86027} +{"mode": "train", "epoch": 102, "iter": 2600, "lr": 0.02348, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.36906, "top5_acc": 0.62922, "loss_cls": 3.58612, "loss": 3.58612, "time": 0.85459} +{"mode": "train", "epoch": 102, "iter": 2700, "lr": 0.02346, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.37328, "top5_acc": 0.63875, "loss_cls": 3.52025, "loss": 3.52025, "time": 0.85934} +{"mode": "train", "epoch": 102, "iter": 2800, "lr": 0.02343, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.37609, "top5_acc": 0.64484, "loss_cls": 3.51705, "loss": 3.51705, "time": 0.85808} +{"mode": "train", "epoch": 102, "iter": 2900, "lr": 0.02341, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36719, "top5_acc": 0.62672, "loss_cls": 3.57321, "loss": 3.57321, "time": 0.86221} +{"mode": "train", "epoch": 102, "iter": 3000, "lr": 0.02339, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37094, "top5_acc": 0.62859, "loss_cls": 3.56594, "loss": 3.56594, "time": 0.85548} +{"mode": "train", "epoch": 102, "iter": 3100, "lr": 0.02336, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36625, "top5_acc": 0.63922, "loss_cls": 3.56027, "loss": 3.56027, "time": 0.8551} +{"mode": "train", "epoch": 102, "iter": 3200, "lr": 0.02334, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38094, "top5_acc": 0.64, "loss_cls": 3.54783, "loss": 3.54783, "time": 0.84807} +{"mode": "train", "epoch": 102, "iter": 3300, "lr": 0.02331, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36688, "top5_acc": 0.62734, "loss_cls": 3.58455, "loss": 3.58455, "time": 0.85212} +{"mode": "train", "epoch": 102, "iter": 3400, "lr": 0.02329, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37156, "top5_acc": 0.63141, "loss_cls": 3.54779, "loss": 3.54779, "time": 0.85141} +{"mode": "train", "epoch": 102, "iter": 3500, "lr": 0.02327, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38484, "top5_acc": 0.64422, "loss_cls": 3.50244, "loss": 3.50244, "time": 0.85313} +{"mode": "train", "epoch": 102, "iter": 3600, "lr": 0.02324, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37516, "top5_acc": 0.63719, "loss_cls": 3.54491, "loss": 3.54491, "time": 0.85557} +{"mode": "train", "epoch": 102, "iter": 3700, "lr": 0.02322, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.36938, "top5_acc": 0.62859, "loss_cls": 3.56484, "loss": 3.56484, "time": 0.85557} +{"mode": "val", "epoch": 102, "iter": 309, "lr": 0.02321, "top1_acc": 0.31206, "top5_acc": 0.56967, "mean_class_accuracy": 0.31173} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.02319, "memory": 15990, "data_time": 1.59085, "top1_acc": 0.38328, "top5_acc": 0.65031, "loss_cls": 3.48339, "loss": 3.48339, "time": 2.6371} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.02316, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38766, "top5_acc": 0.64891, "loss_cls": 3.46714, "loss": 3.46714, "time": 0.85037} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.02314, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38797, "top5_acc": 0.64625, "loss_cls": 3.48264, "loss": 3.48264, "time": 0.85464} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.02311, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.3725, "top5_acc": 0.64312, "loss_cls": 3.53418, "loss": 3.53418, "time": 0.85213} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.02309, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38953, "top5_acc": 0.64953, "loss_cls": 3.48642, "loss": 3.48642, "time": 0.85072} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.02307, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38141, "top5_acc": 0.64281, "loss_cls": 3.49983, "loss": 3.49983, "time": 0.84973} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.02304, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38516, "top5_acc": 0.63703, "loss_cls": 3.48409, "loss": 3.48409, "time": 0.85145} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.02302, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37641, "top5_acc": 0.63844, "loss_cls": 3.54602, "loss": 3.54602, "time": 0.84488} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.023, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38047, "top5_acc": 0.63625, "loss_cls": 3.51084, "loss": 3.51084, "time": 0.8465} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.02297, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.38156, "top5_acc": 0.63313, "loss_cls": 3.50449, "loss": 3.50449, "time": 0.84792} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.02295, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36906, "top5_acc": 0.63047, "loss_cls": 3.55848, "loss": 3.55848, "time": 0.85003} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.02293, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3775, "top5_acc": 0.63891, "loss_cls": 3.52136, "loss": 3.52136, "time": 0.85289} +{"mode": "train", "epoch": 103, "iter": 1300, "lr": 0.0229, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38266, "top5_acc": 0.63828, "loss_cls": 3.55314, "loss": 3.55314, "time": 0.85061} +{"mode": "train", "epoch": 103, "iter": 1400, "lr": 0.02288, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38938, "top5_acc": 0.63781, "loss_cls": 3.50305, "loss": 3.50305, "time": 0.84931} +{"mode": "train", "epoch": 103, "iter": 1500, "lr": 0.02286, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38297, "top5_acc": 0.64375, "loss_cls": 3.46726, "loss": 3.46726, "time": 0.85329} +{"mode": "train", "epoch": 103, "iter": 1600, "lr": 0.02283, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38719, "top5_acc": 0.65547, "loss_cls": 3.46865, "loss": 3.46865, "time": 0.85518} +{"mode": "train", "epoch": 103, "iter": 1700, "lr": 0.02281, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37781, "top5_acc": 0.63719, "loss_cls": 3.53156, "loss": 3.53156, "time": 0.85257} +{"mode": "train", "epoch": 103, "iter": 1800, "lr": 0.02279, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37438, "top5_acc": 0.63016, "loss_cls": 3.56826, "loss": 3.56826, "time": 0.85298} +{"mode": "train", "epoch": 103, "iter": 1900, "lr": 0.02276, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37344, "top5_acc": 0.63797, "loss_cls": 3.51679, "loss": 3.51679, "time": 0.85542} +{"mode": "train", "epoch": 103, "iter": 2000, "lr": 0.02274, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37922, "top5_acc": 0.63188, "loss_cls": 3.53004, "loss": 3.53004, "time": 0.85586} +{"mode": "train", "epoch": 103, "iter": 2100, "lr": 0.02272, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.36922, "top5_acc": 0.63766, "loss_cls": 3.5548, "loss": 3.5548, "time": 0.85435} +{"mode": "train", "epoch": 103, "iter": 2200, "lr": 0.02269, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37031, "top5_acc": 0.63281, "loss_cls": 3.54262, "loss": 3.54262, "time": 0.85003} +{"mode": "train", "epoch": 103, "iter": 2300, "lr": 0.02267, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37859, "top5_acc": 0.64, "loss_cls": 3.52413, "loss": 3.52413, "time": 0.85051} +{"mode": "train", "epoch": 103, "iter": 2400, "lr": 0.02264, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37703, "top5_acc": 0.63266, "loss_cls": 3.52628, "loss": 3.52628, "time": 0.85293} +{"mode": "train", "epoch": 103, "iter": 2500, "lr": 0.02262, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38172, "top5_acc": 0.63281, "loss_cls": 3.52928, "loss": 3.52928, "time": 0.8471} +{"mode": "train", "epoch": 103, "iter": 2600, "lr": 0.0226, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36156, "top5_acc": 0.62953, "loss_cls": 3.57308, "loss": 3.57308, "time": 0.84754} +{"mode": "train", "epoch": 103, "iter": 2700, "lr": 0.02257, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36953, "top5_acc": 0.63484, "loss_cls": 3.56228, "loss": 3.56228, "time": 0.84793} +{"mode": "train", "epoch": 103, "iter": 2800, "lr": 0.02255, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37219, "top5_acc": 0.63047, "loss_cls": 3.52598, "loss": 3.52598, "time": 0.84297} +{"mode": "train", "epoch": 103, "iter": 2900, "lr": 0.02253, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38266, "top5_acc": 0.63906, "loss_cls": 3.51411, "loss": 3.51411, "time": 0.85221} +{"mode": "train", "epoch": 103, "iter": 3000, "lr": 0.0225, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.37, "top5_acc": 0.63953, "loss_cls": 3.53375, "loss": 3.53375, "time": 0.85034} +{"mode": "train", "epoch": 103, "iter": 3100, "lr": 0.02248, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38359, "top5_acc": 0.64734, "loss_cls": 3.48497, "loss": 3.48497, "time": 0.85457} +{"mode": "train", "epoch": 103, "iter": 3200, "lr": 0.02246, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38625, "top5_acc": 0.64484, "loss_cls": 3.48019, "loss": 3.48019, "time": 0.85142} +{"mode": "train", "epoch": 103, "iter": 3300, "lr": 0.02243, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38625, "top5_acc": 0.63328, "loss_cls": 3.54171, "loss": 3.54171, "time": 0.84826} +{"mode": "train", "epoch": 103, "iter": 3400, "lr": 0.02241, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38078, "top5_acc": 0.62953, "loss_cls": 3.54202, "loss": 3.54202, "time": 0.85452} +{"mode": "train", "epoch": 103, "iter": 3500, "lr": 0.02239, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37297, "top5_acc": 0.63, "loss_cls": 3.55019, "loss": 3.55019, "time": 0.84883} +{"mode": "train", "epoch": 103, "iter": 3600, "lr": 0.02236, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37688, "top5_acc": 0.63313, "loss_cls": 3.55852, "loss": 3.55852, "time": 0.84746} +{"mode": "train", "epoch": 103, "iter": 3700, "lr": 0.02234, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38281, "top5_acc": 0.63094, "loss_cls": 3.52671, "loss": 3.52671, "time": 0.84765} +{"mode": "val", "epoch": 103, "iter": 309, "lr": 0.02233, "top1_acc": 0.33197, "top5_acc": 0.58836, "mean_class_accuracy": 0.33166} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.02231, "memory": 15990, "data_time": 1.48355, "top1_acc": 0.39594, "top5_acc": 0.65641, "loss_cls": 3.41319, "loss": 3.41319, "time": 2.51154} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.02228, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39391, "top5_acc": 0.65516, "loss_cls": 3.43296, "loss": 3.43296, "time": 0.85168} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.02226, "memory": 15990, "data_time": 0.00083, "top1_acc": 0.39156, "top5_acc": 0.65453, "loss_cls": 3.44043, "loss": 3.44043, "time": 0.84919} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.02224, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.38953, "top5_acc": 0.64266, "loss_cls": 3.4906, "loss": 3.4906, "time": 0.84875} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.02221, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37469, "top5_acc": 0.64094, "loss_cls": 3.51384, "loss": 3.51384, "time": 0.8539} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.02219, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3875, "top5_acc": 0.65219, "loss_cls": 3.42211, "loss": 3.42211, "time": 0.85337} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.02217, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.385, "top5_acc": 0.64547, "loss_cls": 3.47703, "loss": 3.47703, "time": 0.85527} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.02214, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38078, "top5_acc": 0.64203, "loss_cls": 3.50526, "loss": 3.50526, "time": 0.85512} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.02212, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38656, "top5_acc": 0.64703, "loss_cls": 3.47403, "loss": 3.47403, "time": 0.85077} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.0221, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38797, "top5_acc": 0.64656, "loss_cls": 3.45893, "loss": 3.45893, "time": 0.85144} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.02208, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37641, "top5_acc": 0.64656, "loss_cls": 3.50503, "loss": 3.50503, "time": 0.84774} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.02205, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38203, "top5_acc": 0.64359, "loss_cls": 3.47402, "loss": 3.47402, "time": 0.8519} +{"mode": "train", "epoch": 104, "iter": 1300, "lr": 0.02203, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38016, "top5_acc": 0.64219, "loss_cls": 3.50114, "loss": 3.50114, "time": 0.84794} +{"mode": "train", "epoch": 104, "iter": 1400, "lr": 0.02201, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36891, "top5_acc": 0.63422, "loss_cls": 3.56385, "loss": 3.56385, "time": 0.84716} +{"mode": "train", "epoch": 104, "iter": 1500, "lr": 0.02198, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38219, "top5_acc": 0.64234, "loss_cls": 3.50116, "loss": 3.50116, "time": 0.85069} +{"mode": "train", "epoch": 104, "iter": 1600, "lr": 0.02196, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38266, "top5_acc": 0.64359, "loss_cls": 3.47642, "loss": 3.47642, "time": 0.85359} +{"mode": "train", "epoch": 104, "iter": 1700, "lr": 0.02194, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38484, "top5_acc": 0.63438, "loss_cls": 3.508, "loss": 3.508, "time": 0.85163} +{"mode": "train", "epoch": 104, "iter": 1800, "lr": 0.02191, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.37266, "top5_acc": 0.63078, "loss_cls": 3.54906, "loss": 3.54906, "time": 0.85626} +{"mode": "train", "epoch": 104, "iter": 1900, "lr": 0.02189, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37359, "top5_acc": 0.63578, "loss_cls": 3.51681, "loss": 3.51681, "time": 0.8544} +{"mode": "train", "epoch": 104, "iter": 2000, "lr": 0.02187, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38172, "top5_acc": 0.64344, "loss_cls": 3.49998, "loss": 3.49998, "time": 0.85559} +{"mode": "train", "epoch": 104, "iter": 2100, "lr": 0.02184, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37734, "top5_acc": 0.63594, "loss_cls": 3.54703, "loss": 3.54703, "time": 0.85536} +{"mode": "train", "epoch": 104, "iter": 2200, "lr": 0.02182, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37891, "top5_acc": 0.64641, "loss_cls": 3.49556, "loss": 3.49556, "time": 0.85243} +{"mode": "train", "epoch": 104, "iter": 2300, "lr": 0.0218, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.39328, "top5_acc": 0.64531, "loss_cls": 3.46249, "loss": 3.46249, "time": 0.84585} +{"mode": "train", "epoch": 104, "iter": 2400, "lr": 0.02177, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38516, "top5_acc": 0.65281, "loss_cls": 3.4372, "loss": 3.4372, "time": 0.84416} +{"mode": "train", "epoch": 104, "iter": 2500, "lr": 0.02175, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37359, "top5_acc": 0.63688, "loss_cls": 3.57547, "loss": 3.57547, "time": 0.84449} +{"mode": "train", "epoch": 104, "iter": 2600, "lr": 0.02173, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38438, "top5_acc": 0.63594, "loss_cls": 3.50955, "loss": 3.50955, "time": 0.84523} +{"mode": "train", "epoch": 104, "iter": 2700, "lr": 0.02171, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38844, "top5_acc": 0.64312, "loss_cls": 3.48632, "loss": 3.48632, "time": 0.84922} +{"mode": "train", "epoch": 104, "iter": 2800, "lr": 0.02168, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37156, "top5_acc": 0.62656, "loss_cls": 3.55748, "loss": 3.55748, "time": 0.84856} +{"mode": "train", "epoch": 104, "iter": 2900, "lr": 0.02166, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37344, "top5_acc": 0.63641, "loss_cls": 3.5547, "loss": 3.5547, "time": 0.85004} +{"mode": "train", "epoch": 104, "iter": 3000, "lr": 0.02164, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37453, "top5_acc": 0.63391, "loss_cls": 3.53992, "loss": 3.53992, "time": 0.84945} +{"mode": "train", "epoch": 104, "iter": 3100, "lr": 0.02161, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37984, "top5_acc": 0.64016, "loss_cls": 3.51367, "loss": 3.51367, "time": 0.8514} +{"mode": "train", "epoch": 104, "iter": 3200, "lr": 0.02159, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38094, "top5_acc": 0.63891, "loss_cls": 3.50898, "loss": 3.50898, "time": 0.84982} +{"mode": "train", "epoch": 104, "iter": 3300, "lr": 0.02157, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37328, "top5_acc": 0.64094, "loss_cls": 3.54725, "loss": 3.54725, "time": 0.85327} +{"mode": "train", "epoch": 104, "iter": 3400, "lr": 0.02154, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38094, "top5_acc": 0.64328, "loss_cls": 3.48637, "loss": 3.48637, "time": 0.85232} +{"mode": "train", "epoch": 104, "iter": 3500, "lr": 0.02152, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39312, "top5_acc": 0.65094, "loss_cls": 3.45613, "loss": 3.45613, "time": 0.8542} +{"mode": "train", "epoch": 104, "iter": 3600, "lr": 0.0215, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37844, "top5_acc": 0.6425, "loss_cls": 3.51598, "loss": 3.51598, "time": 0.85082} +{"mode": "train", "epoch": 104, "iter": 3700, "lr": 0.02148, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37469, "top5_acc": 0.63656, "loss_cls": 3.53097, "loss": 3.53097, "time": 0.84808} +{"mode": "val", "epoch": 104, "iter": 309, "lr": 0.02146, "top1_acc": 0.32518, "top5_acc": 0.58446, "mean_class_accuracy": 0.32488} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.02144, "memory": 15990, "data_time": 1.54028, "top1_acc": 0.38484, "top5_acc": 0.65484, "loss_cls": 3.42308, "loss": 3.42308, "time": 2.57375} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.02142, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.38906, "top5_acc": 0.64719, "loss_cls": 3.43409, "loss": 3.43409, "time": 0.84919} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.0214, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38953, "top5_acc": 0.64125, "loss_cls": 3.47508, "loss": 3.47508, "time": 0.85221} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.02137, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39562, "top5_acc": 0.64562, "loss_cls": 3.4473, "loss": 3.4473, "time": 0.85315} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.02135, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.39172, "top5_acc": 0.64344, "loss_cls": 3.46481, "loss": 3.46481, "time": 0.85318} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.02133, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.38672, "top5_acc": 0.64344, "loss_cls": 3.48436, "loss": 3.48436, "time": 0.85032} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.0213, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38578, "top5_acc": 0.645, "loss_cls": 3.47155, "loss": 3.47155, "time": 0.85292} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.02128, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.39266, "top5_acc": 0.6475, "loss_cls": 3.45825, "loss": 3.45825, "time": 0.8499} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.02126, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38828, "top5_acc": 0.64172, "loss_cls": 3.48384, "loss": 3.48384, "time": 0.85112} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.02124, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.39625, "top5_acc": 0.65422, "loss_cls": 3.43371, "loss": 3.43371, "time": 0.85334} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.02121, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39359, "top5_acc": 0.65094, "loss_cls": 3.41262, "loss": 3.41262, "time": 0.84491} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.02119, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37453, "top5_acc": 0.64281, "loss_cls": 3.51006, "loss": 3.51006, "time": 0.85063} +{"mode": "train", "epoch": 105, "iter": 1300, "lr": 0.02117, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37812, "top5_acc": 0.63953, "loss_cls": 3.52076, "loss": 3.52076, "time": 0.85485} +{"mode": "train", "epoch": 105, "iter": 1400, "lr": 0.02114, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.39172, "top5_acc": 0.65203, "loss_cls": 3.43768, "loss": 3.43768, "time": 0.85318} +{"mode": "train", "epoch": 105, "iter": 1500, "lr": 0.02112, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.38766, "top5_acc": 0.64219, "loss_cls": 3.48923, "loss": 3.48923, "time": 0.8491} +{"mode": "train", "epoch": 105, "iter": 1600, "lr": 0.0211, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38438, "top5_acc": 0.64781, "loss_cls": 3.47701, "loss": 3.47701, "time": 0.85206} +{"mode": "train", "epoch": 105, "iter": 1700, "lr": 0.02108, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38391, "top5_acc": 0.64297, "loss_cls": 3.50691, "loss": 3.50691, "time": 0.84921} +{"mode": "train", "epoch": 105, "iter": 1800, "lr": 0.02105, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39562, "top5_acc": 0.65156, "loss_cls": 3.41095, "loss": 3.41095, "time": 0.85358} +{"mode": "train", "epoch": 105, "iter": 1900, "lr": 0.02103, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37984, "top5_acc": 0.64297, "loss_cls": 3.51254, "loss": 3.51254, "time": 0.84874} +{"mode": "train", "epoch": 105, "iter": 2000, "lr": 0.02101, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.38078, "top5_acc": 0.63922, "loss_cls": 3.51436, "loss": 3.51436, "time": 0.8542} +{"mode": "train", "epoch": 105, "iter": 2100, "lr": 0.02098, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38406, "top5_acc": 0.64359, "loss_cls": 3.50414, "loss": 3.50414, "time": 0.85012} +{"mode": "train", "epoch": 105, "iter": 2200, "lr": 0.02096, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.37859, "top5_acc": 0.63656, "loss_cls": 3.53375, "loss": 3.53375, "time": 0.84983} +{"mode": "train", "epoch": 105, "iter": 2300, "lr": 0.02094, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.38422, "top5_acc": 0.63438, "loss_cls": 3.52009, "loss": 3.52009, "time": 0.85019} +{"mode": "train", "epoch": 105, "iter": 2400, "lr": 0.02092, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38219, "top5_acc": 0.64422, "loss_cls": 3.49424, "loss": 3.49424, "time": 0.85199} +{"mode": "train", "epoch": 105, "iter": 2500, "lr": 0.02089, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.38688, "top5_acc": 0.64828, "loss_cls": 3.48648, "loss": 3.48648, "time": 0.85521} +{"mode": "train", "epoch": 105, "iter": 2600, "lr": 0.02087, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37625, "top5_acc": 0.64359, "loss_cls": 3.50668, "loss": 3.50668, "time": 0.85432} +{"mode": "train", "epoch": 105, "iter": 2700, "lr": 0.02085, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.39172, "top5_acc": 0.6475, "loss_cls": 3.47973, "loss": 3.47973, "time": 0.85572} +{"mode": "train", "epoch": 105, "iter": 2800, "lr": 0.02083, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38891, "top5_acc": 0.6425, "loss_cls": 3.48505, "loss": 3.48505, "time": 0.85409} +{"mode": "train", "epoch": 105, "iter": 2900, "lr": 0.0208, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38406, "top5_acc": 0.63984, "loss_cls": 3.50855, "loss": 3.50855, "time": 0.85316} +{"mode": "train", "epoch": 105, "iter": 3000, "lr": 0.02078, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38641, "top5_acc": 0.64234, "loss_cls": 3.46514, "loss": 3.46514, "time": 0.85073} +{"mode": "train", "epoch": 105, "iter": 3100, "lr": 0.02076, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37641, "top5_acc": 0.6425, "loss_cls": 3.5077, "loss": 3.5077, "time": 0.85092} +{"mode": "train", "epoch": 105, "iter": 3200, "lr": 0.02073, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36969, "top5_acc": 0.64094, "loss_cls": 3.52614, "loss": 3.52614, "time": 0.85174} +{"mode": "train", "epoch": 105, "iter": 3300, "lr": 0.02071, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.38734, "top5_acc": 0.64219, "loss_cls": 3.48519, "loss": 3.48519, "time": 0.85775} +{"mode": "train", "epoch": 105, "iter": 3400, "lr": 0.02069, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.37641, "top5_acc": 0.63359, "loss_cls": 3.50962, "loss": 3.50962, "time": 0.85977} +{"mode": "train", "epoch": 105, "iter": 3500, "lr": 0.02067, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38297, "top5_acc": 0.64359, "loss_cls": 3.45714, "loss": 3.45714, "time": 0.85329} +{"mode": "train", "epoch": 105, "iter": 3600, "lr": 0.02064, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.38859, "top5_acc": 0.64438, "loss_cls": 3.45903, "loss": 3.45903, "time": 0.8589} +{"mode": "train", "epoch": 105, "iter": 3700, "lr": 0.02062, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.3675, "top5_acc": 0.63469, "loss_cls": 3.52706, "loss": 3.52706, "time": 0.85673} +{"mode": "val", "epoch": 105, "iter": 309, "lr": 0.02061, "top1_acc": 0.33126, "top5_acc": 0.58826, "mean_class_accuracy": 0.33103} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.02059, "memory": 15990, "data_time": 1.59842, "top1_acc": 0.39125, "top5_acc": 0.65734, "loss_cls": 3.40043, "loss": 3.40043, "time": 2.62974} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.02057, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39594, "top5_acc": 0.66062, "loss_cls": 3.41829, "loss": 3.41829, "time": 0.85466} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.02054, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39672, "top5_acc": 0.66484, "loss_cls": 3.39771, "loss": 3.39771, "time": 0.85} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.02052, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37984, "top5_acc": 0.64906, "loss_cls": 3.43682, "loss": 3.43682, "time": 0.85694} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.0205, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.38906, "top5_acc": 0.64203, "loss_cls": 3.47088, "loss": 3.47088, "time": 0.85341} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.02048, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39094, "top5_acc": 0.65344, "loss_cls": 3.422, "loss": 3.422, "time": 0.85063} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.02045, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39047, "top5_acc": 0.65047, "loss_cls": 3.43858, "loss": 3.43858, "time": 0.85053} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.02043, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.38062, "top5_acc": 0.64531, "loss_cls": 3.46235, "loss": 3.46235, "time": 0.85335} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.02041, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38312, "top5_acc": 0.64516, "loss_cls": 3.49318, "loss": 3.49318, "time": 0.8475} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.02039, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.40938, "top5_acc": 0.65953, "loss_cls": 3.36685, "loss": 3.36685, "time": 0.84449} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.02036, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38953, "top5_acc": 0.64875, "loss_cls": 3.44043, "loss": 3.44043, "time": 0.85492} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.02034, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38578, "top5_acc": 0.64266, "loss_cls": 3.47452, "loss": 3.47452, "time": 0.84632} +{"mode": "train", "epoch": 106, "iter": 1300, "lr": 0.02032, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39188, "top5_acc": 0.64984, "loss_cls": 3.42117, "loss": 3.42117, "time": 0.85023} +{"mode": "train", "epoch": 106, "iter": 1400, "lr": 0.0203, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38641, "top5_acc": 0.64219, "loss_cls": 3.47286, "loss": 3.47286, "time": 0.84744} +{"mode": "train", "epoch": 106, "iter": 1500, "lr": 0.02027, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38547, "top5_acc": 0.64562, "loss_cls": 3.47204, "loss": 3.47204, "time": 0.84403} +{"mode": "train", "epoch": 106, "iter": 1600, "lr": 0.02025, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40172, "top5_acc": 0.65906, "loss_cls": 3.40372, "loss": 3.40372, "time": 0.84608} +{"mode": "train", "epoch": 106, "iter": 1700, "lr": 0.02023, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37906, "top5_acc": 0.65141, "loss_cls": 3.48458, "loss": 3.48458, "time": 0.84435} +{"mode": "train", "epoch": 106, "iter": 1800, "lr": 0.02021, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38812, "top5_acc": 0.63703, "loss_cls": 3.5058, "loss": 3.5058, "time": 0.8474} +{"mode": "train", "epoch": 106, "iter": 1900, "lr": 0.02018, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38516, "top5_acc": 0.64469, "loss_cls": 3.47308, "loss": 3.47308, "time": 0.84985} +{"mode": "train", "epoch": 106, "iter": 2000, "lr": 0.02016, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38656, "top5_acc": 0.64812, "loss_cls": 3.47905, "loss": 3.47905, "time": 0.84915} +{"mode": "train", "epoch": 106, "iter": 2100, "lr": 0.02014, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38062, "top5_acc": 0.64828, "loss_cls": 3.45906, "loss": 3.45906, "time": 0.84618} +{"mode": "train", "epoch": 106, "iter": 2200, "lr": 0.02012, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.39609, "top5_acc": 0.65141, "loss_cls": 3.42017, "loss": 3.42017, "time": 0.84162} +{"mode": "train", "epoch": 106, "iter": 2300, "lr": 0.02009, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38172, "top5_acc": 0.64188, "loss_cls": 3.5034, "loss": 3.5034, "time": 0.84249} +{"mode": "train", "epoch": 106, "iter": 2400, "lr": 0.02007, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39656, "top5_acc": 0.6475, "loss_cls": 3.43401, "loss": 3.43401, "time": 0.84788} +{"mode": "train", "epoch": 106, "iter": 2500, "lr": 0.02005, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38797, "top5_acc": 0.64359, "loss_cls": 3.47071, "loss": 3.47071, "time": 0.84131} +{"mode": "train", "epoch": 106, "iter": 2600, "lr": 0.02003, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39609, "top5_acc": 0.65641, "loss_cls": 3.40852, "loss": 3.40852, "time": 0.8436} +{"mode": "train", "epoch": 106, "iter": 2700, "lr": 0.02, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38062, "top5_acc": 0.63859, "loss_cls": 3.49446, "loss": 3.49446, "time": 0.84612} +{"mode": "train", "epoch": 106, "iter": 2800, "lr": 0.01998, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38953, "top5_acc": 0.64062, "loss_cls": 3.47333, "loss": 3.47333, "time": 0.8445} +{"mode": "train", "epoch": 106, "iter": 2900, "lr": 0.01996, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37984, "top5_acc": 0.6425, "loss_cls": 3.51501, "loss": 3.51501, "time": 0.84226} +{"mode": "train", "epoch": 106, "iter": 3000, "lr": 0.01994, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39328, "top5_acc": 0.65516, "loss_cls": 3.44754, "loss": 3.44754, "time": 0.84303} +{"mode": "train", "epoch": 106, "iter": 3100, "lr": 0.01991, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38766, "top5_acc": 0.64922, "loss_cls": 3.46708, "loss": 3.46708, "time": 0.85137} +{"mode": "train", "epoch": 106, "iter": 3200, "lr": 0.01989, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.385, "top5_acc": 0.64562, "loss_cls": 3.47658, "loss": 3.47658, "time": 0.8526} +{"mode": "train", "epoch": 106, "iter": 3300, "lr": 0.01987, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37641, "top5_acc": 0.635, "loss_cls": 3.53577, "loss": 3.53577, "time": 0.85548} +{"mode": "train", "epoch": 106, "iter": 3400, "lr": 0.01985, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39, "top5_acc": 0.64328, "loss_cls": 3.47811, "loss": 3.47811, "time": 0.851} +{"mode": "train", "epoch": 106, "iter": 3500, "lr": 0.01983, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38344, "top5_acc": 0.63766, "loss_cls": 3.49953, "loss": 3.49953, "time": 0.85142} +{"mode": "train", "epoch": 106, "iter": 3600, "lr": 0.0198, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38891, "top5_acc": 0.6375, "loss_cls": 3.4937, "loss": 3.4937, "time": 0.85044} +{"mode": "train", "epoch": 106, "iter": 3700, "lr": 0.01978, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38219, "top5_acc": 0.62859, "loss_cls": 3.54195, "loss": 3.54195, "time": 0.85127} +{"mode": "val", "epoch": 106, "iter": 309, "lr": 0.01977, "top1_acc": 0.33516, "top5_acc": 0.59616, "mean_class_accuracy": 0.33498} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.01975, "memory": 15990, "data_time": 1.60817, "top1_acc": 0.39547, "top5_acc": 0.66703, "loss_cls": 3.39887, "loss": 3.39887, "time": 2.67352} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.01973, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.39453, "top5_acc": 0.66062, "loss_cls": 3.41551, "loss": 3.41551, "time": 0.85659} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.0197, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40359, "top5_acc": 0.66172, "loss_cls": 3.35271, "loss": 3.35271, "time": 0.85115} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.01968, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39156, "top5_acc": 0.65375, "loss_cls": 3.41927, "loss": 3.41927, "time": 0.84926} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.01966, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.39641, "top5_acc": 0.65453, "loss_cls": 3.40918, "loss": 3.40918, "time": 0.85418} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.01964, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39359, "top5_acc": 0.65328, "loss_cls": 3.41176, "loss": 3.41176, "time": 0.85542} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.01961, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39688, "top5_acc": 0.65438, "loss_cls": 3.41271, "loss": 3.41271, "time": 0.85326} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.01959, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.39453, "top5_acc": 0.65453, "loss_cls": 3.42558, "loss": 3.42558, "time": 0.85548} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.01957, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39, "top5_acc": 0.66, "loss_cls": 3.41185, "loss": 3.41185, "time": 0.85389} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.01955, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.39281, "top5_acc": 0.65391, "loss_cls": 3.41989, "loss": 3.41989, "time": 0.85112} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.01953, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39281, "top5_acc": 0.65891, "loss_cls": 3.40359, "loss": 3.40359, "time": 0.85311} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.0195, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38312, "top5_acc": 0.65047, "loss_cls": 3.46283, "loss": 3.46283, "time": 0.85457} +{"mode": "train", "epoch": 107, "iter": 1300, "lr": 0.01948, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39, "top5_acc": 0.65469, "loss_cls": 3.45861, "loss": 3.45861, "time": 0.85066} +{"mode": "train", "epoch": 107, "iter": 1400, "lr": 0.01946, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39719, "top5_acc": 0.64531, "loss_cls": 3.44371, "loss": 3.44371, "time": 0.84946} +{"mode": "train", "epoch": 107, "iter": 1500, "lr": 0.01944, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38688, "top5_acc": 0.64297, "loss_cls": 3.48436, "loss": 3.48436, "time": 0.85266} +{"mode": "train", "epoch": 107, "iter": 1600, "lr": 0.01942, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39203, "top5_acc": 0.64797, "loss_cls": 3.46858, "loss": 3.46858, "time": 0.84865} +{"mode": "train", "epoch": 107, "iter": 1700, "lr": 0.01939, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39641, "top5_acc": 0.65922, "loss_cls": 3.44995, "loss": 3.44995, "time": 0.85539} +{"mode": "train", "epoch": 107, "iter": 1800, "lr": 0.01937, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38984, "top5_acc": 0.63656, "loss_cls": 3.49044, "loss": 3.49044, "time": 0.85183} +{"mode": "train", "epoch": 107, "iter": 1900, "lr": 0.01935, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39125, "top5_acc": 0.64188, "loss_cls": 3.47328, "loss": 3.47328, "time": 0.8516} +{"mode": "train", "epoch": 107, "iter": 2000, "lr": 0.01933, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38562, "top5_acc": 0.65516, "loss_cls": 3.4736, "loss": 3.4736, "time": 0.85109} +{"mode": "train", "epoch": 107, "iter": 2100, "lr": 0.0193, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39578, "top5_acc": 0.64672, "loss_cls": 3.43416, "loss": 3.43416, "time": 0.84736} +{"mode": "train", "epoch": 107, "iter": 2200, "lr": 0.01928, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.38406, "top5_acc": 0.64969, "loss_cls": 3.44878, "loss": 3.44878, "time": 0.85152} +{"mode": "train", "epoch": 107, "iter": 2300, "lr": 0.01926, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37703, "top5_acc": 0.63969, "loss_cls": 3.50788, "loss": 3.50788, "time": 0.85096} +{"mode": "train", "epoch": 107, "iter": 2400, "lr": 0.01924, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38234, "top5_acc": 0.63734, "loss_cls": 3.51056, "loss": 3.51056, "time": 0.85408} +{"mode": "train", "epoch": 107, "iter": 2500, "lr": 0.01922, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39375, "top5_acc": 0.65047, "loss_cls": 3.44006, "loss": 3.44006, "time": 0.85561} +{"mode": "train", "epoch": 107, "iter": 2600, "lr": 0.01919, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3925, "top5_acc": 0.65234, "loss_cls": 3.44146, "loss": 3.44146, "time": 0.8521} +{"mode": "train", "epoch": 107, "iter": 2700, "lr": 0.01917, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40047, "top5_acc": 0.64891, "loss_cls": 3.41763, "loss": 3.41763, "time": 0.84622} +{"mode": "train", "epoch": 107, "iter": 2800, "lr": 0.01915, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39391, "top5_acc": 0.65828, "loss_cls": 3.42188, "loss": 3.42188, "time": 0.85085} +{"mode": "train", "epoch": 107, "iter": 2900, "lr": 0.01913, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39141, "top5_acc": 0.64781, "loss_cls": 3.4595, "loss": 3.4595, "time": 0.84629} +{"mode": "train", "epoch": 107, "iter": 3000, "lr": 0.01911, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40031, "top5_acc": 0.65641, "loss_cls": 3.40133, "loss": 3.40133, "time": 0.85174} +{"mode": "train", "epoch": 107, "iter": 3100, "lr": 0.01908, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39516, "top5_acc": 0.65078, "loss_cls": 3.41631, "loss": 3.41631, "time": 0.85076} +{"mode": "train", "epoch": 107, "iter": 3200, "lr": 0.01906, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.3925, "top5_acc": 0.64516, "loss_cls": 3.44049, "loss": 3.44049, "time": 0.84963} +{"mode": "train", "epoch": 107, "iter": 3300, "lr": 0.01904, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40141, "top5_acc": 0.655, "loss_cls": 3.40944, "loss": 3.40944, "time": 0.85262} +{"mode": "train", "epoch": 107, "iter": 3400, "lr": 0.01902, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38875, "top5_acc": 0.63984, "loss_cls": 3.49157, "loss": 3.49157, "time": 0.84728} +{"mode": "train", "epoch": 107, "iter": 3500, "lr": 0.019, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38438, "top5_acc": 0.64641, "loss_cls": 3.48297, "loss": 3.48297, "time": 0.85224} +{"mode": "train", "epoch": 107, "iter": 3600, "lr": 0.01897, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39141, "top5_acc": 0.64328, "loss_cls": 3.47697, "loss": 3.47697, "time": 0.84889} +{"mode": "train", "epoch": 107, "iter": 3700, "lr": 0.01895, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.38234, "top5_acc": 0.64125, "loss_cls": 3.50993, "loss": 3.50993, "time": 0.85291} +{"mode": "val", "epoch": 107, "iter": 309, "lr": 0.01894, "top1_acc": 0.33192, "top5_acc": 0.58978, "mean_class_accuracy": 0.33175} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.01892, "memory": 15990, "data_time": 1.57636, "top1_acc": 0.40641, "top5_acc": 0.67234, "loss_cls": 3.35497, "loss": 3.35497, "time": 2.60177} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0189, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40031, "top5_acc": 0.67062, "loss_cls": 3.32012, "loss": 3.32012, "time": 0.84862} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.01888, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40438, "top5_acc": 0.66031, "loss_cls": 3.36253, "loss": 3.36253, "time": 0.8493} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.01886, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40234, "top5_acc": 0.65906, "loss_cls": 3.39172, "loss": 3.39172, "time": 0.84783} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.01883, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40312, "top5_acc": 0.65453, "loss_cls": 3.42814, "loss": 3.42814, "time": 0.84992} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.01881, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39641, "top5_acc": 0.65109, "loss_cls": 3.40953, "loss": 3.40953, "time": 0.85268} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.01879, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39406, "top5_acc": 0.65078, "loss_cls": 3.43033, "loss": 3.43033, "time": 0.85247} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.01877, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39828, "top5_acc": 0.655, "loss_cls": 3.40534, "loss": 3.40534, "time": 0.85724} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.01875, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39156, "top5_acc": 0.65594, "loss_cls": 3.4122, "loss": 3.4122, "time": 0.85172} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.01872, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39594, "top5_acc": 0.65531, "loss_cls": 3.4063, "loss": 3.4063, "time": 0.85411} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.0187, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39438, "top5_acc": 0.65375, "loss_cls": 3.40997, "loss": 3.40997, "time": 0.8478} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.01868, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37875, "top5_acc": 0.64719, "loss_cls": 3.48362, "loss": 3.48362, "time": 0.85698} +{"mode": "train", "epoch": 108, "iter": 1300, "lr": 0.01866, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39219, "top5_acc": 0.65234, "loss_cls": 3.42884, "loss": 3.42884, "time": 0.84852} +{"mode": "train", "epoch": 108, "iter": 1400, "lr": 0.01864, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40516, "top5_acc": 0.66203, "loss_cls": 3.40051, "loss": 3.40051, "time": 0.85345} +{"mode": "train", "epoch": 108, "iter": 1500, "lr": 0.01862, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39969, "top5_acc": 0.65844, "loss_cls": 3.39319, "loss": 3.39319, "time": 0.84991} +{"mode": "train", "epoch": 108, "iter": 1600, "lr": 0.01859, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38891, "top5_acc": 0.65391, "loss_cls": 3.45926, "loss": 3.45926, "time": 0.8554} +{"mode": "train", "epoch": 108, "iter": 1700, "lr": 0.01857, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39609, "top5_acc": 0.65406, "loss_cls": 3.42773, "loss": 3.42773, "time": 0.84976} +{"mode": "train", "epoch": 108, "iter": 1800, "lr": 0.01855, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39172, "top5_acc": 0.65328, "loss_cls": 3.41385, "loss": 3.41385, "time": 0.85822} +{"mode": "train", "epoch": 108, "iter": 1900, "lr": 0.01853, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39656, "top5_acc": 0.65172, "loss_cls": 3.42377, "loss": 3.42377, "time": 0.85458} +{"mode": "train", "epoch": 108, "iter": 2000, "lr": 0.01851, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.38859, "top5_acc": 0.65078, "loss_cls": 3.43611, "loss": 3.43611, "time": 0.85271} +{"mode": "train", "epoch": 108, "iter": 2100, "lr": 0.01848, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.40453, "top5_acc": 0.65484, "loss_cls": 3.43082, "loss": 3.43082, "time": 0.85395} +{"mode": "train", "epoch": 108, "iter": 2200, "lr": 0.01846, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39844, "top5_acc": 0.64656, "loss_cls": 3.42876, "loss": 3.42876, "time": 0.85467} +{"mode": "train", "epoch": 108, "iter": 2300, "lr": 0.01844, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.38484, "top5_acc": 0.65297, "loss_cls": 3.43722, "loss": 3.43722, "time": 0.85271} +{"mode": "train", "epoch": 108, "iter": 2400, "lr": 0.01842, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.39188, "top5_acc": 0.64406, "loss_cls": 3.45875, "loss": 3.45875, "time": 0.84842} +{"mode": "train", "epoch": 108, "iter": 2500, "lr": 0.0184, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38656, "top5_acc": 0.64922, "loss_cls": 3.45284, "loss": 3.45284, "time": 0.85578} +{"mode": "train", "epoch": 108, "iter": 2600, "lr": 0.01838, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38531, "top5_acc": 0.65297, "loss_cls": 3.44898, "loss": 3.44898, "time": 0.85168} +{"mode": "train", "epoch": 108, "iter": 2700, "lr": 0.01835, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40766, "top5_acc": 0.66391, "loss_cls": 3.36088, "loss": 3.36088, "time": 0.8504} +{"mode": "train", "epoch": 108, "iter": 2800, "lr": 0.01833, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38125, "top5_acc": 0.64375, "loss_cls": 3.47003, "loss": 3.47003, "time": 0.85226} +{"mode": "train", "epoch": 108, "iter": 2900, "lr": 0.01831, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39859, "top5_acc": 0.65828, "loss_cls": 3.40131, "loss": 3.40131, "time": 0.85137} +{"mode": "train", "epoch": 108, "iter": 3000, "lr": 0.01829, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.38625, "top5_acc": 0.65172, "loss_cls": 3.44778, "loss": 3.44778, "time": 0.84938} +{"mode": "train", "epoch": 108, "iter": 3100, "lr": 0.01827, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.3975, "top5_acc": 0.65781, "loss_cls": 3.40039, "loss": 3.40039, "time": 0.84967} +{"mode": "train", "epoch": 108, "iter": 3200, "lr": 0.01825, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39, "top5_acc": 0.65562, "loss_cls": 3.41417, "loss": 3.41417, "time": 0.8414} +{"mode": "train", "epoch": 108, "iter": 3300, "lr": 0.01823, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38656, "top5_acc": 0.64359, "loss_cls": 3.44204, "loss": 3.44204, "time": 0.84467} +{"mode": "train", "epoch": 108, "iter": 3400, "lr": 0.0182, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39953, "top5_acc": 0.655, "loss_cls": 3.40286, "loss": 3.40286, "time": 0.84684} +{"mode": "train", "epoch": 108, "iter": 3500, "lr": 0.01818, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38906, "top5_acc": 0.64203, "loss_cls": 3.45984, "loss": 3.45984, "time": 0.84677} +{"mode": "train", "epoch": 108, "iter": 3600, "lr": 0.01816, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38688, "top5_acc": 0.64828, "loss_cls": 3.44517, "loss": 3.44517, "time": 0.85458} +{"mode": "train", "epoch": 108, "iter": 3700, "lr": 0.01814, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39031, "top5_acc": 0.65453, "loss_cls": 3.42867, "loss": 3.42867, "time": 0.85327} +{"mode": "val", "epoch": 108, "iter": 309, "lr": 0.01813, "top1_acc": 0.3389, "top5_acc": 0.59474, "mean_class_accuracy": 0.3386} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.01811, "memory": 15990, "data_time": 1.46311, "top1_acc": 0.41625, "top5_acc": 0.6675, "loss_cls": 3.32222, "loss": 3.32222, "time": 2.49844} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.01809, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39359, "top5_acc": 0.65531, "loss_cls": 3.42432, "loss": 3.42432, "time": 0.84725} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.01806, "memory": 15990, "data_time": 0.00073, "top1_acc": 0.40906, "top5_acc": 0.65906, "loss_cls": 3.36375, "loss": 3.36375, "time": 0.85817} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.01804, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39547, "top5_acc": 0.66047, "loss_cls": 3.36525, "loss": 3.36525, "time": 0.84483} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.01802, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.39625, "top5_acc": 0.65906, "loss_cls": 3.3893, "loss": 3.3893, "time": 0.84889} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.018, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40156, "top5_acc": 0.66734, "loss_cls": 3.38021, "loss": 3.38021, "time": 0.85106} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.01798, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39859, "top5_acc": 0.65125, "loss_cls": 3.41173, "loss": 3.41173, "time": 0.847} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.01796, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40344, "top5_acc": 0.66703, "loss_cls": 3.35923, "loss": 3.35923, "time": 0.85133} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.01794, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40297, "top5_acc": 0.66031, "loss_cls": 3.36644, "loss": 3.36644, "time": 0.8519} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.01791, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39172, "top5_acc": 0.66156, "loss_cls": 3.39572, "loss": 3.39572, "time": 0.8542} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.01789, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39625, "top5_acc": 0.65688, "loss_cls": 3.39649, "loss": 3.39649, "time": 0.85109} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.01787, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40328, "top5_acc": 0.65875, "loss_cls": 3.3826, "loss": 3.3826, "time": 0.85216} +{"mode": "train", "epoch": 109, "iter": 1300, "lr": 0.01785, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40078, "top5_acc": 0.66047, "loss_cls": 3.36573, "loss": 3.36573, "time": 0.84978} +{"mode": "train", "epoch": 109, "iter": 1400, "lr": 0.01783, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39969, "top5_acc": 0.66125, "loss_cls": 3.39504, "loss": 3.39504, "time": 0.85438} +{"mode": "train", "epoch": 109, "iter": 1500, "lr": 0.01781, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38672, "top5_acc": 0.64062, "loss_cls": 3.46329, "loss": 3.46329, "time": 0.85406} +{"mode": "train", "epoch": 109, "iter": 1600, "lr": 0.01779, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41219, "top5_acc": 0.67266, "loss_cls": 3.33926, "loss": 3.33926, "time": 0.85407} +{"mode": "train", "epoch": 109, "iter": 1700, "lr": 0.01776, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40547, "top5_acc": 0.65812, "loss_cls": 3.36445, "loss": 3.36445, "time": 0.84501} +{"mode": "train", "epoch": 109, "iter": 1800, "lr": 0.01774, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40797, "top5_acc": 0.65844, "loss_cls": 3.3929, "loss": 3.3929, "time": 0.85167} +{"mode": "train", "epoch": 109, "iter": 1900, "lr": 0.01772, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39547, "top5_acc": 0.65266, "loss_cls": 3.43348, "loss": 3.43348, "time": 0.85809} +{"mode": "train", "epoch": 109, "iter": 2000, "lr": 0.0177, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39375, "top5_acc": 0.65938, "loss_cls": 3.41235, "loss": 3.41235, "time": 0.85135} +{"mode": "train", "epoch": 109, "iter": 2100, "lr": 0.01768, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40234, "top5_acc": 0.65609, "loss_cls": 3.38614, "loss": 3.38614, "time": 0.85259} +{"mode": "train", "epoch": 109, "iter": 2200, "lr": 0.01766, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39547, "top5_acc": 0.65203, "loss_cls": 3.41988, "loss": 3.41988, "time": 0.85504} +{"mode": "train", "epoch": 109, "iter": 2300, "lr": 0.01764, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39016, "top5_acc": 0.64859, "loss_cls": 3.43766, "loss": 3.43766, "time": 0.85137} +{"mode": "train", "epoch": 109, "iter": 2400, "lr": 0.01761, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.38516, "top5_acc": 0.64688, "loss_cls": 3.44575, "loss": 3.44575, "time": 0.85056} +{"mode": "train", "epoch": 109, "iter": 2500, "lr": 0.01759, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39688, "top5_acc": 0.64656, "loss_cls": 3.43544, "loss": 3.43544, "time": 0.85031} +{"mode": "train", "epoch": 109, "iter": 2600, "lr": 0.01757, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39156, "top5_acc": 0.65562, "loss_cls": 3.40439, "loss": 3.40439, "time": 0.84715} +{"mode": "train", "epoch": 109, "iter": 2700, "lr": 0.01755, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38906, "top5_acc": 0.65016, "loss_cls": 3.47222, "loss": 3.47222, "time": 0.84668} +{"mode": "train", "epoch": 109, "iter": 2800, "lr": 0.01753, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38609, "top5_acc": 0.64922, "loss_cls": 3.47894, "loss": 3.47894, "time": 0.84884} +{"mode": "train", "epoch": 109, "iter": 2900, "lr": 0.01751, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38875, "top5_acc": 0.64891, "loss_cls": 3.45835, "loss": 3.45835, "time": 0.85223} +{"mode": "train", "epoch": 109, "iter": 3000, "lr": 0.01749, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.39422, "top5_acc": 0.65391, "loss_cls": 3.42818, "loss": 3.42818, "time": 0.84833} +{"mode": "train", "epoch": 109, "iter": 3100, "lr": 0.01747, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.405, "top5_acc": 0.65453, "loss_cls": 3.39296, "loss": 3.39296, "time": 0.85062} +{"mode": "train", "epoch": 109, "iter": 3200, "lr": 0.01744, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40766, "top5_acc": 0.65328, "loss_cls": 3.38476, "loss": 3.38476, "time": 0.84502} +{"mode": "train", "epoch": 109, "iter": 3300, "lr": 0.01742, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39547, "top5_acc": 0.65234, "loss_cls": 3.41188, "loss": 3.41188, "time": 0.84887} +{"mode": "train", "epoch": 109, "iter": 3400, "lr": 0.0174, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3975, "top5_acc": 0.65359, "loss_cls": 3.44077, "loss": 3.44077, "time": 0.84593} +{"mode": "train", "epoch": 109, "iter": 3500, "lr": 0.01738, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40203, "top5_acc": 0.65328, "loss_cls": 3.4087, "loss": 3.4087, "time": 0.84681} +{"mode": "train", "epoch": 109, "iter": 3600, "lr": 0.01736, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39672, "top5_acc": 0.64531, "loss_cls": 3.43681, "loss": 3.43681, "time": 0.84484} +{"mode": "train", "epoch": 109, "iter": 3700, "lr": 0.01734, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39, "top5_acc": 0.65906, "loss_cls": 3.40167, "loss": 3.40167, "time": 0.84457} +{"mode": "val", "epoch": 109, "iter": 309, "lr": 0.01733, "top1_acc": 0.34741, "top5_acc": 0.60102, "mean_class_accuracy": 0.3471} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.01731, "memory": 15990, "data_time": 1.53374, "top1_acc": 0.41875, "top5_acc": 0.67219, "loss_cls": 3.2905, "loss": 3.2905, "time": 2.57445} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.01729, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.41453, "top5_acc": 0.66422, "loss_cls": 3.32914, "loss": 3.32914, "time": 0.85507} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.01727, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41312, "top5_acc": 0.66891, "loss_cls": 3.33316, "loss": 3.33316, "time": 0.85384} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.01724, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.41766, "top5_acc": 0.66547, "loss_cls": 3.29245, "loss": 3.29245, "time": 0.84667} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.01722, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41, "top5_acc": 0.665, "loss_cls": 3.33854, "loss": 3.33854, "time": 0.84803} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.0172, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40094, "top5_acc": 0.66578, "loss_cls": 3.36005, "loss": 3.36005, "time": 0.85168} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.01718, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40344, "top5_acc": 0.65875, "loss_cls": 3.39375, "loss": 3.39375, "time": 0.85185} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.01716, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40406, "top5_acc": 0.66281, "loss_cls": 3.3418, "loss": 3.3418, "time": 0.84963} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.01714, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40703, "top5_acc": 0.67125, "loss_cls": 3.33311, "loss": 3.33311, "time": 0.85061} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.01712, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40797, "top5_acc": 0.66094, "loss_cls": 3.36626, "loss": 3.36626, "time": 0.85397} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.0171, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.4025, "top5_acc": 0.65938, "loss_cls": 3.38711, "loss": 3.38711, "time": 0.84945} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.01708, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39672, "top5_acc": 0.66484, "loss_cls": 3.39314, "loss": 3.39314, "time": 0.85185} +{"mode": "train", "epoch": 110, "iter": 1300, "lr": 0.01705, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41375, "top5_acc": 0.66391, "loss_cls": 3.32514, "loss": 3.32514, "time": 0.84904} +{"mode": "train", "epoch": 110, "iter": 1400, "lr": 0.01703, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41016, "top5_acc": 0.67172, "loss_cls": 3.33531, "loss": 3.33531, "time": 0.85011} +{"mode": "train", "epoch": 110, "iter": 1500, "lr": 0.01701, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39312, "top5_acc": 0.64172, "loss_cls": 3.46159, "loss": 3.46159, "time": 0.84699} +{"mode": "train", "epoch": 110, "iter": 1600, "lr": 0.01699, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38734, "top5_acc": 0.65203, "loss_cls": 3.42514, "loss": 3.42514, "time": 0.85067} +{"mode": "train", "epoch": 110, "iter": 1700, "lr": 0.01697, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39953, "top5_acc": 0.65938, "loss_cls": 3.37869, "loss": 3.37869, "time": 0.85225} +{"mode": "train", "epoch": 110, "iter": 1800, "lr": 0.01695, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39281, "top5_acc": 0.64969, "loss_cls": 3.39786, "loss": 3.39786, "time": 0.85029} +{"mode": "train", "epoch": 110, "iter": 1900, "lr": 0.01693, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39516, "top5_acc": 0.65781, "loss_cls": 3.39965, "loss": 3.39965, "time": 0.85066} +{"mode": "train", "epoch": 110, "iter": 2000, "lr": 0.01691, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40766, "top5_acc": 0.66469, "loss_cls": 3.36499, "loss": 3.36499, "time": 0.85262} +{"mode": "train", "epoch": 110, "iter": 2100, "lr": 0.01689, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39641, "top5_acc": 0.66016, "loss_cls": 3.40931, "loss": 3.40931, "time": 0.85522} +{"mode": "train", "epoch": 110, "iter": 2200, "lr": 0.01687, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39609, "top5_acc": 0.65906, "loss_cls": 3.43075, "loss": 3.43075, "time": 0.85314} +{"mode": "train", "epoch": 110, "iter": 2300, "lr": 0.01685, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.40016, "top5_acc": 0.65016, "loss_cls": 3.37817, "loss": 3.37817, "time": 0.85026} +{"mode": "train", "epoch": 110, "iter": 2400, "lr": 0.01682, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38734, "top5_acc": 0.65766, "loss_cls": 3.4457, "loss": 3.4457, "time": 0.84772} +{"mode": "train", "epoch": 110, "iter": 2500, "lr": 0.0168, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40359, "top5_acc": 0.66625, "loss_cls": 3.37499, "loss": 3.37499, "time": 0.84849} +{"mode": "train", "epoch": 110, "iter": 2600, "lr": 0.01678, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.4, "top5_acc": 0.65609, "loss_cls": 3.40903, "loss": 3.40903, "time": 0.8437} +{"mode": "train", "epoch": 110, "iter": 2700, "lr": 0.01676, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40406, "top5_acc": 0.66578, "loss_cls": 3.38952, "loss": 3.38952, "time": 0.85101} +{"mode": "train", "epoch": 110, "iter": 2800, "lr": 0.01674, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.38953, "top5_acc": 0.64859, "loss_cls": 3.44665, "loss": 3.44665, "time": 0.85027} +{"mode": "train", "epoch": 110, "iter": 2900, "lr": 0.01672, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.3875, "top5_acc": 0.6475, "loss_cls": 3.44132, "loss": 3.44132, "time": 0.84961} +{"mode": "train", "epoch": 110, "iter": 3000, "lr": 0.0167, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40156, "top5_acc": 0.65734, "loss_cls": 3.37806, "loss": 3.37806, "time": 0.84354} +{"mode": "train", "epoch": 110, "iter": 3100, "lr": 0.01668, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40422, "top5_acc": 0.65625, "loss_cls": 3.39194, "loss": 3.39194, "time": 0.85234} +{"mode": "train", "epoch": 110, "iter": 3200, "lr": 0.01666, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40266, "top5_acc": 0.65859, "loss_cls": 3.40387, "loss": 3.40387, "time": 0.84448} +{"mode": "train", "epoch": 110, "iter": 3300, "lr": 0.01664, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39703, "top5_acc": 0.65672, "loss_cls": 3.40587, "loss": 3.40587, "time": 0.84555} +{"mode": "train", "epoch": 110, "iter": 3400, "lr": 0.01662, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39594, "top5_acc": 0.6575, "loss_cls": 3.34708, "loss": 3.34708, "time": 0.84195} +{"mode": "train", "epoch": 110, "iter": 3500, "lr": 0.01659, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40234, "top5_acc": 0.66547, "loss_cls": 3.36785, "loss": 3.36785, "time": 0.84337} +{"mode": "train", "epoch": 110, "iter": 3600, "lr": 0.01657, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39172, "top5_acc": 0.65094, "loss_cls": 3.44648, "loss": 3.44648, "time": 0.84632} +{"mode": "train", "epoch": 110, "iter": 3700, "lr": 0.01655, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40484, "top5_acc": 0.66109, "loss_cls": 3.37541, "loss": 3.37541, "time": 0.84727} +{"mode": "val", "epoch": 110, "iter": 309, "lr": 0.01654, "top1_acc": 0.33865, "top5_acc": 0.5956, "mean_class_accuracy": 0.33835} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.01652, "memory": 15990, "data_time": 1.58464, "top1_acc": 0.41125, "top5_acc": 0.67344, "loss_cls": 3.28736, "loss": 3.28736, "time": 2.61234} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.0165, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41891, "top5_acc": 0.67891, "loss_cls": 3.28864, "loss": 3.28864, "time": 0.85185} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.01648, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41, "top5_acc": 0.67422, "loss_cls": 3.32521, "loss": 3.32521, "time": 0.85101} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.01646, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40891, "top5_acc": 0.65812, "loss_cls": 3.35841, "loss": 3.35841, "time": 0.85433} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.01644, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42, "top5_acc": 0.66594, "loss_cls": 3.33, "loss": 3.33, "time": 0.85246} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.01642, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41547, "top5_acc": 0.66766, "loss_cls": 3.3212, "loss": 3.3212, "time": 0.84546} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.0164, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41219, "top5_acc": 0.67078, "loss_cls": 3.33566, "loss": 3.33566, "time": 0.85186} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.01638, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41547, "top5_acc": 0.67969, "loss_cls": 3.27818, "loss": 3.27818, "time": 0.84926} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.01636, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39594, "top5_acc": 0.66484, "loss_cls": 3.36009, "loss": 3.36009, "time": 0.85071} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.01634, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39562, "top5_acc": 0.65359, "loss_cls": 3.4074, "loss": 3.4074, "time": 0.84855} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.01632, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.405, "top5_acc": 0.67062, "loss_cls": 3.36077, "loss": 3.36077, "time": 0.84741} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.0163, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40641, "top5_acc": 0.67219, "loss_cls": 3.32661, "loss": 3.32661, "time": 0.84646} +{"mode": "train", "epoch": 111, "iter": 1300, "lr": 0.01627, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41, "top5_acc": 0.67234, "loss_cls": 3.32519, "loss": 3.32519, "time": 0.8491} +{"mode": "train", "epoch": 111, "iter": 1400, "lr": 0.01625, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40344, "top5_acc": 0.66625, "loss_cls": 3.38289, "loss": 3.38289, "time": 0.84734} +{"mode": "train", "epoch": 111, "iter": 1500, "lr": 0.01623, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39859, "top5_acc": 0.65469, "loss_cls": 3.36269, "loss": 3.36269, "time": 0.85019} +{"mode": "train", "epoch": 111, "iter": 1600, "lr": 0.01621, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41328, "top5_acc": 0.66359, "loss_cls": 3.33754, "loss": 3.33754, "time": 0.84831} +{"mode": "train", "epoch": 111, "iter": 1700, "lr": 0.01619, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41156, "top5_acc": 0.66266, "loss_cls": 3.34785, "loss": 3.34785, "time": 0.84569} +{"mode": "train", "epoch": 111, "iter": 1800, "lr": 0.01617, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40438, "top5_acc": 0.66219, "loss_cls": 3.37633, "loss": 3.37633, "time": 0.84521} +{"mode": "train", "epoch": 111, "iter": 1900, "lr": 0.01615, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41047, "top5_acc": 0.66219, "loss_cls": 3.36486, "loss": 3.36486, "time": 0.85032} +{"mode": "train", "epoch": 111, "iter": 2000, "lr": 0.01613, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39422, "top5_acc": 0.66109, "loss_cls": 3.38933, "loss": 3.38933, "time": 0.85002} +{"mode": "train", "epoch": 111, "iter": 2100, "lr": 0.01611, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40031, "top5_acc": 0.6625, "loss_cls": 3.39555, "loss": 3.39555, "time": 0.85432} +{"mode": "train", "epoch": 111, "iter": 2200, "lr": 0.01609, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.40281, "top5_acc": 0.66906, "loss_cls": 3.3226, "loss": 3.3226, "time": 0.85148} +{"mode": "train", "epoch": 111, "iter": 2300, "lr": 0.01607, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.40359, "top5_acc": 0.66469, "loss_cls": 3.35745, "loss": 3.35745, "time": 0.85457} +{"mode": "train", "epoch": 111, "iter": 2400, "lr": 0.01605, "memory": 15990, "data_time": 0.00067, "top1_acc": 0.40375, "top5_acc": 0.66688, "loss_cls": 3.35435, "loss": 3.35435, "time": 0.85582} +{"mode": "train", "epoch": 111, "iter": 2500, "lr": 0.01603, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.4075, "top5_acc": 0.66188, "loss_cls": 3.35235, "loss": 3.35235, "time": 0.85128} +{"mode": "train", "epoch": 111, "iter": 2600, "lr": 0.01601, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40031, "top5_acc": 0.66375, "loss_cls": 3.35766, "loss": 3.35766, "time": 0.84863} +{"mode": "train", "epoch": 111, "iter": 2700, "lr": 0.01599, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.41078, "top5_acc": 0.65234, "loss_cls": 3.39437, "loss": 3.39437, "time": 0.84892} +{"mode": "train", "epoch": 111, "iter": 2800, "lr": 0.01597, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.39672, "top5_acc": 0.66344, "loss_cls": 3.41285, "loss": 3.41285, "time": 0.85064} +{"mode": "train", "epoch": 111, "iter": 2900, "lr": 0.01595, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40734, "top5_acc": 0.66484, "loss_cls": 3.37974, "loss": 3.37974, "time": 0.84867} +{"mode": "train", "epoch": 111, "iter": 3000, "lr": 0.01593, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41734, "top5_acc": 0.65844, "loss_cls": 3.35746, "loss": 3.35746, "time": 0.84731} +{"mode": "train", "epoch": 111, "iter": 3100, "lr": 0.0159, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39547, "top5_acc": 0.65438, "loss_cls": 3.38953, "loss": 3.38953, "time": 0.85109} +{"mode": "train", "epoch": 111, "iter": 3200, "lr": 0.01588, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40266, "top5_acc": 0.65906, "loss_cls": 3.38497, "loss": 3.38497, "time": 0.85242} +{"mode": "train", "epoch": 111, "iter": 3300, "lr": 0.01586, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40844, "top5_acc": 0.66266, "loss_cls": 3.35434, "loss": 3.35434, "time": 0.84829} +{"mode": "train", "epoch": 111, "iter": 3400, "lr": 0.01584, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40156, "top5_acc": 0.65766, "loss_cls": 3.37818, "loss": 3.37818, "time": 0.84815} +{"mode": "train", "epoch": 111, "iter": 3500, "lr": 0.01582, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40953, "top5_acc": 0.665, "loss_cls": 3.3583, "loss": 3.3583, "time": 0.85064} +{"mode": "train", "epoch": 111, "iter": 3600, "lr": 0.0158, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.4075, "top5_acc": 0.66516, "loss_cls": 3.33593, "loss": 3.33593, "time": 0.84784} +{"mode": "train", "epoch": 111, "iter": 3700, "lr": 0.01578, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40016, "top5_acc": 0.66766, "loss_cls": 3.36218, "loss": 3.36218, "time": 0.8497} +{"mode": "val", "epoch": 111, "iter": 309, "lr": 0.01577, "top1_acc": 0.34772, "top5_acc": 0.60229, "mean_class_accuracy": 0.34741} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.01575, "memory": 15990, "data_time": 1.59479, "top1_acc": 0.41688, "top5_acc": 0.67047, "loss_cls": 3.29677, "loss": 3.29677, "time": 2.62244} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.01573, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.42219, "top5_acc": 0.67734, "loss_cls": 3.2722, "loss": 3.2722, "time": 0.85311} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.01571, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41672, "top5_acc": 0.6775, "loss_cls": 3.28697, "loss": 3.28697, "time": 0.85115} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.01569, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.42234, "top5_acc": 0.66844, "loss_cls": 3.31315, "loss": 3.31315, "time": 0.85139} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.01567, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.43266, "top5_acc": 0.68969, "loss_cls": 3.20591, "loss": 3.20591, "time": 0.84842} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.01565, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39953, "top5_acc": 0.66438, "loss_cls": 3.35396, "loss": 3.35396, "time": 0.84828} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.01563, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41797, "top5_acc": 0.67688, "loss_cls": 3.26987, "loss": 3.26987, "time": 0.84825} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.01561, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40516, "top5_acc": 0.66359, "loss_cls": 3.34518, "loss": 3.34518, "time": 0.84963} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.01559, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40953, "top5_acc": 0.66641, "loss_cls": 3.3275, "loss": 3.3275, "time": 0.84657} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.01557, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41047, "top5_acc": 0.67578, "loss_cls": 3.28944, "loss": 3.28944, "time": 0.84397} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.01555, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41406, "top5_acc": 0.67484, "loss_cls": 3.3117, "loss": 3.3117, "time": 0.84587} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.01553, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40609, "top5_acc": 0.65984, "loss_cls": 3.35422, "loss": 3.35422, "time": 0.84735} +{"mode": "train", "epoch": 112, "iter": 1300, "lr": 0.01551, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41172, "top5_acc": 0.66891, "loss_cls": 3.32842, "loss": 3.32842, "time": 0.85064} +{"mode": "train", "epoch": 112, "iter": 1400, "lr": 0.01549, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41734, "top5_acc": 0.66172, "loss_cls": 3.32707, "loss": 3.32707, "time": 0.84577} +{"mode": "train", "epoch": 112, "iter": 1500, "lr": 0.01547, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40781, "top5_acc": 0.66859, "loss_cls": 3.31911, "loss": 3.31911, "time": 0.85223} +{"mode": "train", "epoch": 112, "iter": 1600, "lr": 0.01545, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40938, "top5_acc": 0.66453, "loss_cls": 3.3243, "loss": 3.3243, "time": 0.84795} +{"mode": "train", "epoch": 112, "iter": 1700, "lr": 0.01543, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42062, "top5_acc": 0.67469, "loss_cls": 3.29836, "loss": 3.29836, "time": 0.84959} +{"mode": "train", "epoch": 112, "iter": 1800, "lr": 0.01541, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41172, "top5_acc": 0.65984, "loss_cls": 3.35033, "loss": 3.35033, "time": 0.8473} +{"mode": "train", "epoch": 112, "iter": 1900, "lr": 0.01539, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.4, "top5_acc": 0.66188, "loss_cls": 3.3716, "loss": 3.3716, "time": 0.84933} +{"mode": "train", "epoch": 112, "iter": 2000, "lr": 0.01537, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40297, "top5_acc": 0.66344, "loss_cls": 3.35004, "loss": 3.35004, "time": 0.84646} +{"mode": "train", "epoch": 112, "iter": 2100, "lr": 0.01535, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40969, "top5_acc": 0.66766, "loss_cls": 3.3259, "loss": 3.3259, "time": 0.84897} +{"mode": "train", "epoch": 112, "iter": 2200, "lr": 0.01533, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38875, "top5_acc": 0.66, "loss_cls": 3.39308, "loss": 3.39308, "time": 0.84417} +{"mode": "train", "epoch": 112, "iter": 2300, "lr": 0.01531, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40016, "top5_acc": 0.67016, "loss_cls": 3.34841, "loss": 3.34841, "time": 0.85} +{"mode": "train", "epoch": 112, "iter": 2400, "lr": 0.01529, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.41594, "top5_acc": 0.66641, "loss_cls": 3.31974, "loss": 3.31974, "time": 0.85657} +{"mode": "train", "epoch": 112, "iter": 2500, "lr": 0.01527, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40234, "top5_acc": 0.66422, "loss_cls": 3.35506, "loss": 3.35506, "time": 0.85283} +{"mode": "train", "epoch": 112, "iter": 2600, "lr": 0.01525, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41141, "top5_acc": 0.67094, "loss_cls": 3.31655, "loss": 3.31655, "time": 0.85137} +{"mode": "train", "epoch": 112, "iter": 2700, "lr": 0.01523, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40734, "top5_acc": 0.66922, "loss_cls": 3.33345, "loss": 3.33345, "time": 0.85048} +{"mode": "train", "epoch": 112, "iter": 2800, "lr": 0.01521, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.40312, "top5_acc": 0.66203, "loss_cls": 3.35806, "loss": 3.35806, "time": 0.85044} +{"mode": "train", "epoch": 112, "iter": 2900, "lr": 0.01519, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3925, "top5_acc": 0.65625, "loss_cls": 3.39668, "loss": 3.39668, "time": 0.85479} +{"mode": "train", "epoch": 112, "iter": 3000, "lr": 0.01517, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.4075, "top5_acc": 0.65453, "loss_cls": 3.38483, "loss": 3.38483, "time": 0.85689} +{"mode": "train", "epoch": 112, "iter": 3100, "lr": 0.01515, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40891, "top5_acc": 0.66297, "loss_cls": 3.34815, "loss": 3.34815, "time": 0.85156} +{"mode": "train", "epoch": 112, "iter": 3200, "lr": 0.01513, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.41641, "top5_acc": 0.67281, "loss_cls": 3.32808, "loss": 3.32808, "time": 0.84459} +{"mode": "train", "epoch": 112, "iter": 3300, "lr": 0.01511, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40641, "top5_acc": 0.66672, "loss_cls": 3.36694, "loss": 3.36694, "time": 0.8469} +{"mode": "train", "epoch": 112, "iter": 3400, "lr": 0.01509, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.4075, "top5_acc": 0.66469, "loss_cls": 3.36208, "loss": 3.36208, "time": 0.84673} +{"mode": "train", "epoch": 112, "iter": 3500, "lr": 0.01507, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40922, "top5_acc": 0.65641, "loss_cls": 3.36268, "loss": 3.36268, "time": 0.84584} +{"mode": "train", "epoch": 112, "iter": 3600, "lr": 0.01505, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.4025, "top5_acc": 0.66219, "loss_cls": 3.37921, "loss": 3.37921, "time": 0.8475} +{"mode": "train", "epoch": 112, "iter": 3700, "lr": 0.01503, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39547, "top5_acc": 0.65562, "loss_cls": 3.39124, "loss": 3.39124, "time": 0.84641} +{"mode": "val", "epoch": 112, "iter": 309, "lr": 0.01502, "top1_acc": 0.35055, "top5_acc": 0.60735, "mean_class_accuracy": 0.35038} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.015, "memory": 15990, "data_time": 1.60673, "top1_acc": 0.42844, "top5_acc": 0.68062, "loss_cls": 3.25021, "loss": 3.25021, "time": 2.63692} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.01498, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.41906, "top5_acc": 0.68281, "loss_cls": 3.23485, "loss": 3.23485, "time": 0.85729} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.01496, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41578, "top5_acc": 0.67641, "loss_cls": 3.2877, "loss": 3.2877, "time": 0.85018} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.01494, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41203, "top5_acc": 0.67828, "loss_cls": 3.31023, "loss": 3.31023, "time": 0.8519} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.01492, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.41641, "top5_acc": 0.67734, "loss_cls": 3.26788, "loss": 3.26788, "time": 0.8555} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.0149, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.42078, "top5_acc": 0.67859, "loss_cls": 3.25992, "loss": 3.25992, "time": 0.84944} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.01488, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41359, "top5_acc": 0.67391, "loss_cls": 3.31874, "loss": 3.31874, "time": 0.84731} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.01486, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42422, "top5_acc": 0.67688, "loss_cls": 3.2523, "loss": 3.2523, "time": 0.85026} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.01484, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41922, "top5_acc": 0.67031, "loss_cls": 3.30696, "loss": 3.30696, "time": 0.84728} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.01482, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40844, "top5_acc": 0.67312, "loss_cls": 3.34015, "loss": 3.34015, "time": 0.84675} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0148, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41578, "top5_acc": 0.67703, "loss_cls": 3.27475, "loss": 3.27475, "time": 0.85106} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.01478, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42938, "top5_acc": 0.68391, "loss_cls": 3.23661, "loss": 3.23661, "time": 0.84923} +{"mode": "train", "epoch": 113, "iter": 1300, "lr": 0.01476, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.4175, "top5_acc": 0.67531, "loss_cls": 3.31241, "loss": 3.31241, "time": 0.85237} +{"mode": "train", "epoch": 113, "iter": 1400, "lr": 0.01474, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41281, "top5_acc": 0.67047, "loss_cls": 3.29277, "loss": 3.29277, "time": 0.85408} +{"mode": "train", "epoch": 113, "iter": 1500, "lr": 0.01472, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41578, "top5_acc": 0.67359, "loss_cls": 3.31477, "loss": 3.31477, "time": 0.85105} +{"mode": "train", "epoch": 113, "iter": 1600, "lr": 0.0147, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.41406, "top5_acc": 0.67578, "loss_cls": 3.28733, "loss": 3.28733, "time": 0.84755} +{"mode": "train", "epoch": 113, "iter": 1700, "lr": 0.01468, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41219, "top5_acc": 0.66375, "loss_cls": 3.3714, "loss": 3.3714, "time": 0.85286} +{"mode": "train", "epoch": 113, "iter": 1800, "lr": 0.01466, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40281, "top5_acc": 0.66391, "loss_cls": 3.38146, "loss": 3.38146, "time": 0.85443} +{"mode": "train", "epoch": 113, "iter": 1900, "lr": 0.01464, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41234, "top5_acc": 0.67031, "loss_cls": 3.29965, "loss": 3.29965, "time": 0.848} +{"mode": "train", "epoch": 113, "iter": 2000, "lr": 0.01462, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41578, "top5_acc": 0.68109, "loss_cls": 3.26513, "loss": 3.26513, "time": 0.8434} +{"mode": "train", "epoch": 113, "iter": 2100, "lr": 0.0146, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40766, "top5_acc": 0.67047, "loss_cls": 3.33553, "loss": 3.33553, "time": 0.85629} +{"mode": "train", "epoch": 113, "iter": 2200, "lr": 0.01458, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.41328, "top5_acc": 0.67062, "loss_cls": 3.3039, "loss": 3.3039, "time": 0.8506} +{"mode": "train", "epoch": 113, "iter": 2300, "lr": 0.01456, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41531, "top5_acc": 0.65797, "loss_cls": 3.35394, "loss": 3.35394, "time": 0.8547} +{"mode": "train", "epoch": 113, "iter": 2400, "lr": 0.01454, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.40031, "top5_acc": 0.66344, "loss_cls": 3.36257, "loss": 3.36257, "time": 0.8506} +{"mode": "train", "epoch": 113, "iter": 2500, "lr": 0.01452, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40969, "top5_acc": 0.66875, "loss_cls": 3.33187, "loss": 3.33187, "time": 0.85272} +{"mode": "train", "epoch": 113, "iter": 2600, "lr": 0.0145, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40469, "top5_acc": 0.65797, "loss_cls": 3.37928, "loss": 3.37928, "time": 0.84982} +{"mode": "train", "epoch": 113, "iter": 2700, "lr": 0.01448, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42391, "top5_acc": 0.68, "loss_cls": 3.27152, "loss": 3.27152, "time": 0.85113} +{"mode": "train", "epoch": 113, "iter": 2800, "lr": 0.01446, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.4125, "top5_acc": 0.66984, "loss_cls": 3.35299, "loss": 3.35299, "time": 0.85103} +{"mode": "train", "epoch": 113, "iter": 2900, "lr": 0.01444, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40953, "top5_acc": 0.6625, "loss_cls": 3.34276, "loss": 3.34276, "time": 0.84941} +{"mode": "train", "epoch": 113, "iter": 3000, "lr": 0.01442, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.39812, "top5_acc": 0.65438, "loss_cls": 3.3707, "loss": 3.3707, "time": 0.85026} +{"mode": "train", "epoch": 113, "iter": 3100, "lr": 0.0144, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39125, "top5_acc": 0.65734, "loss_cls": 3.42532, "loss": 3.42532, "time": 0.85078} +{"mode": "train", "epoch": 113, "iter": 3200, "lr": 0.01438, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40891, "top5_acc": 0.67016, "loss_cls": 3.30831, "loss": 3.30831, "time": 0.8496} +{"mode": "train", "epoch": 113, "iter": 3300, "lr": 0.01436, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.40969, "top5_acc": 0.6675, "loss_cls": 3.33488, "loss": 3.33488, "time": 0.84749} +{"mode": "train", "epoch": 113, "iter": 3400, "lr": 0.01434, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40469, "top5_acc": 0.66391, "loss_cls": 3.37488, "loss": 3.37488, "time": 0.85179} +{"mode": "train", "epoch": 113, "iter": 3500, "lr": 0.01432, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41031, "top5_acc": 0.66469, "loss_cls": 3.33266, "loss": 3.33266, "time": 0.85657} +{"mode": "train", "epoch": 113, "iter": 3600, "lr": 0.01431, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40844, "top5_acc": 0.66922, "loss_cls": 3.34105, "loss": 3.34105, "time": 0.84727} +{"mode": "train", "epoch": 113, "iter": 3700, "lr": 0.01429, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40797, "top5_acc": 0.66109, "loss_cls": 3.36579, "loss": 3.36579, "time": 0.85368} +{"mode": "val", "epoch": 113, "iter": 309, "lr": 0.01428, "top1_acc": 0.35152, "top5_acc": 0.60492, "mean_class_accuracy": 0.35112} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.01426, "memory": 15990, "data_time": 1.55601, "top1_acc": 0.43172, "top5_acc": 0.68109, "loss_cls": 3.21339, "loss": 3.21339, "time": 2.60996} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.01424, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.42953, "top5_acc": 0.67891, "loss_cls": 3.24822, "loss": 3.24822, "time": 0.85674} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.01422, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41688, "top5_acc": 0.67891, "loss_cls": 3.27902, "loss": 3.27902, "time": 0.85099} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.0142, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.43016, "top5_acc": 0.69203, "loss_cls": 3.21308, "loss": 3.21308, "time": 0.85905} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.01418, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.42625, "top5_acc": 0.67906, "loss_cls": 3.23775, "loss": 3.23775, "time": 0.86049} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.01416, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.41906, "top5_acc": 0.68438, "loss_cls": 3.24973, "loss": 3.24973, "time": 0.8618} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.01414, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.42734, "top5_acc": 0.67781, "loss_cls": 3.22964, "loss": 3.22964, "time": 0.86165} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.01412, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.41938, "top5_acc": 0.68078, "loss_cls": 3.2614, "loss": 3.2614, "time": 0.85845} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.0141, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.41984, "top5_acc": 0.67969, "loss_cls": 3.25174, "loss": 3.25174, "time": 0.85687} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.01408, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.4125, "top5_acc": 0.67203, "loss_cls": 3.3201, "loss": 3.3201, "time": 0.85964} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.01406, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42109, "top5_acc": 0.67062, "loss_cls": 3.30903, "loss": 3.30903, "time": 0.85541} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.01404, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41672, "top5_acc": 0.67828, "loss_cls": 3.2684, "loss": 3.2684, "time": 0.85771} +{"mode": "train", "epoch": 114, "iter": 1300, "lr": 0.01402, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.41812, "top5_acc": 0.67797, "loss_cls": 3.28789, "loss": 3.28789, "time": 0.85654} +{"mode": "train", "epoch": 114, "iter": 1400, "lr": 0.014, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41672, "top5_acc": 0.6725, "loss_cls": 3.27952, "loss": 3.27952, "time": 0.85445} +{"mode": "train", "epoch": 114, "iter": 1500, "lr": 0.01398, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.41719, "top5_acc": 0.67969, "loss_cls": 3.25245, "loss": 3.25245, "time": 0.85591} +{"mode": "train", "epoch": 114, "iter": 1600, "lr": 0.01397, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.40391, "top5_acc": 0.66609, "loss_cls": 3.3336, "loss": 3.3336, "time": 0.8572} +{"mode": "train", "epoch": 114, "iter": 1700, "lr": 0.01395, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.41125, "top5_acc": 0.66375, "loss_cls": 3.35302, "loss": 3.35302, "time": 0.85645} +{"mode": "train", "epoch": 114, "iter": 1800, "lr": 0.01393, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.41078, "top5_acc": 0.66891, "loss_cls": 3.34462, "loss": 3.34462, "time": 0.85726} +{"mode": "train", "epoch": 114, "iter": 1900, "lr": 0.01391, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42, "top5_acc": 0.68578, "loss_cls": 3.25796, "loss": 3.25796, "time": 0.85308} +{"mode": "train", "epoch": 114, "iter": 2000, "lr": 0.01389, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.41062, "top5_acc": 0.675, "loss_cls": 3.32645, "loss": 3.32645, "time": 0.85538} +{"mode": "train", "epoch": 114, "iter": 2100, "lr": 0.01387, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42672, "top5_acc": 0.67703, "loss_cls": 3.22895, "loss": 3.22895, "time": 0.85535} +{"mode": "train", "epoch": 114, "iter": 2200, "lr": 0.01385, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41688, "top5_acc": 0.66984, "loss_cls": 3.30181, "loss": 3.30181, "time": 0.85729} +{"mode": "train", "epoch": 114, "iter": 2300, "lr": 0.01383, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41203, "top5_acc": 0.67344, "loss_cls": 3.31321, "loss": 3.31321, "time": 0.85148} +{"mode": "train", "epoch": 114, "iter": 2400, "lr": 0.01381, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40703, "top5_acc": 0.66844, "loss_cls": 3.32544, "loss": 3.32544, "time": 0.84896} +{"mode": "train", "epoch": 114, "iter": 2500, "lr": 0.01379, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41797, "top5_acc": 0.68141, "loss_cls": 3.27057, "loss": 3.27057, "time": 0.84607} +{"mode": "train", "epoch": 114, "iter": 2600, "lr": 0.01377, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.41078, "top5_acc": 0.66922, "loss_cls": 3.34719, "loss": 3.34719, "time": 0.84731} +{"mode": "train", "epoch": 114, "iter": 2700, "lr": 0.01375, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.41109, "top5_acc": 0.67156, "loss_cls": 3.3129, "loss": 3.3129, "time": 0.84983} +{"mode": "train", "epoch": 114, "iter": 2800, "lr": 0.01373, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.41266, "top5_acc": 0.66484, "loss_cls": 3.32654, "loss": 3.32654, "time": 0.85096} +{"mode": "train", "epoch": 114, "iter": 2900, "lr": 0.01371, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41906, "top5_acc": 0.67031, "loss_cls": 3.28985, "loss": 3.28985, "time": 0.84961} +{"mode": "train", "epoch": 114, "iter": 3000, "lr": 0.01369, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41438, "top5_acc": 0.67594, "loss_cls": 3.3169, "loss": 3.3169, "time": 0.85113} +{"mode": "train", "epoch": 114, "iter": 3100, "lr": 0.01368, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41672, "top5_acc": 0.67281, "loss_cls": 3.30306, "loss": 3.30306, "time": 0.84662} +{"mode": "train", "epoch": 114, "iter": 3200, "lr": 0.01366, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41641, "top5_acc": 0.67812, "loss_cls": 3.3039, "loss": 3.3039, "time": 0.847} +{"mode": "train", "epoch": 114, "iter": 3300, "lr": 0.01364, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41594, "top5_acc": 0.67828, "loss_cls": 3.29151, "loss": 3.29151, "time": 0.8463} +{"mode": "train", "epoch": 114, "iter": 3400, "lr": 0.01362, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.42281, "top5_acc": 0.67609, "loss_cls": 3.27057, "loss": 3.27057, "time": 0.84547} +{"mode": "train", "epoch": 114, "iter": 3500, "lr": 0.0136, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40953, "top5_acc": 0.66297, "loss_cls": 3.33463, "loss": 3.33463, "time": 0.84686} +{"mode": "train", "epoch": 114, "iter": 3600, "lr": 0.01358, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41203, "top5_acc": 0.67391, "loss_cls": 3.30193, "loss": 3.30193, "time": 0.84795} +{"mode": "train", "epoch": 114, "iter": 3700, "lr": 0.01356, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.41281, "top5_acc": 0.66719, "loss_cls": 3.31201, "loss": 3.31201, "time": 0.8513} +{"mode": "val", "epoch": 114, "iter": 309, "lr": 0.01355, "top1_acc": 0.35704, "top5_acc": 0.6107, "mean_class_accuracy": 0.35677} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.01353, "memory": 15990, "data_time": 1.63991, "top1_acc": 0.43812, "top5_acc": 0.69891, "loss_cls": 3.15458, "loss": 3.15458, "time": 2.67406} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.01351, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.42609, "top5_acc": 0.68781, "loss_cls": 3.23118, "loss": 3.23118, "time": 0.85533} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.01349, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.43, "top5_acc": 0.68469, "loss_cls": 3.20809, "loss": 3.20809, "time": 0.85165} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.01348, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.425, "top5_acc": 0.68656, "loss_cls": 3.21476, "loss": 3.21476, "time": 0.85032} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.01346, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.41828, "top5_acc": 0.67, "loss_cls": 3.30059, "loss": 3.30059, "time": 0.85535} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.01344, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42016, "top5_acc": 0.68609, "loss_cls": 3.26525, "loss": 3.26525, "time": 0.85725} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.01342, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42469, "top5_acc": 0.68, "loss_cls": 3.23725, "loss": 3.23725, "time": 0.85466} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.0134, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.42609, "top5_acc": 0.67984, "loss_cls": 3.23722, "loss": 3.23722, "time": 0.85329} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.01338, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.41516, "top5_acc": 0.66328, "loss_cls": 3.32328, "loss": 3.32328, "time": 0.84838} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.01336, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42203, "top5_acc": 0.67688, "loss_cls": 3.29369, "loss": 3.29369, "time": 0.85438} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.01334, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42375, "top5_acc": 0.69062, "loss_cls": 3.2407, "loss": 3.2407, "time": 0.84951} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.01332, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.41234, "top5_acc": 0.67234, "loss_cls": 3.31757, "loss": 3.31757, "time": 0.8546} +{"mode": "train", "epoch": 115, "iter": 1300, "lr": 0.0133, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.41672, "top5_acc": 0.67875, "loss_cls": 3.29135, "loss": 3.29135, "time": 0.85555} +{"mode": "train", "epoch": 115, "iter": 1400, "lr": 0.01328, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.42812, "top5_acc": 0.68219, "loss_cls": 3.22292, "loss": 3.22292, "time": 0.8532} +{"mode": "train", "epoch": 115, "iter": 1500, "lr": 0.01327, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41578, "top5_acc": 0.67656, "loss_cls": 3.30836, "loss": 3.30836, "time": 0.85404} +{"mode": "train", "epoch": 115, "iter": 1600, "lr": 0.01325, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42375, "top5_acc": 0.67703, "loss_cls": 3.23822, "loss": 3.23822, "time": 0.85244} +{"mode": "train", "epoch": 115, "iter": 1700, "lr": 0.01323, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42328, "top5_acc": 0.68656, "loss_cls": 3.27382, "loss": 3.27382, "time": 0.85458} +{"mode": "train", "epoch": 115, "iter": 1800, "lr": 0.01321, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41922, "top5_acc": 0.67188, "loss_cls": 3.27554, "loss": 3.27554, "time": 0.85356} +{"mode": "train", "epoch": 115, "iter": 1900, "lr": 0.01319, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.42219, "top5_acc": 0.67406, "loss_cls": 3.24899, "loss": 3.24899, "time": 0.854} +{"mode": "train", "epoch": 115, "iter": 2000, "lr": 0.01317, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.415, "top5_acc": 0.68141, "loss_cls": 3.27431, "loss": 3.27431, "time": 0.8492} +{"mode": "train", "epoch": 115, "iter": 2100, "lr": 0.01315, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42234, "top5_acc": 0.67672, "loss_cls": 3.26974, "loss": 3.26974, "time": 0.84913} +{"mode": "train", "epoch": 115, "iter": 2200, "lr": 0.01313, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41188, "top5_acc": 0.66719, "loss_cls": 3.33034, "loss": 3.33034, "time": 0.85045} +{"mode": "train", "epoch": 115, "iter": 2300, "lr": 0.01311, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.41734, "top5_acc": 0.67656, "loss_cls": 3.28932, "loss": 3.28932, "time": 0.84861} +{"mode": "train", "epoch": 115, "iter": 2400, "lr": 0.0131, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42703, "top5_acc": 0.67578, "loss_cls": 3.2413, "loss": 3.2413, "time": 0.84765} +{"mode": "train", "epoch": 115, "iter": 2500, "lr": 0.01308, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41609, "top5_acc": 0.66828, "loss_cls": 3.27105, "loss": 3.27105, "time": 0.85183} +{"mode": "train", "epoch": 115, "iter": 2600, "lr": 0.01306, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.41891, "top5_acc": 0.68172, "loss_cls": 3.25307, "loss": 3.25307, "time": 0.85696} +{"mode": "train", "epoch": 115, "iter": 2700, "lr": 0.01304, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.42609, "top5_acc": 0.69219, "loss_cls": 3.22504, "loss": 3.22504, "time": 0.85235} +{"mode": "train", "epoch": 115, "iter": 2800, "lr": 0.01302, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42281, "top5_acc": 0.67641, "loss_cls": 3.28249, "loss": 3.28249, "time": 0.84835} +{"mode": "train", "epoch": 115, "iter": 2900, "lr": 0.013, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.40703, "top5_acc": 0.67781, "loss_cls": 3.28924, "loss": 3.28924, "time": 0.85389} +{"mode": "train", "epoch": 115, "iter": 3000, "lr": 0.01298, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42062, "top5_acc": 0.68016, "loss_cls": 3.26597, "loss": 3.26597, "time": 0.85161} +{"mode": "train", "epoch": 115, "iter": 3100, "lr": 0.01296, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.41562, "top5_acc": 0.67875, "loss_cls": 3.25887, "loss": 3.25887, "time": 0.84865} +{"mode": "train", "epoch": 115, "iter": 3200, "lr": 0.01295, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.40844, "top5_acc": 0.67734, "loss_cls": 3.30253, "loss": 3.30253, "time": 0.84892} +{"mode": "train", "epoch": 115, "iter": 3300, "lr": 0.01293, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42219, "top5_acc": 0.67141, "loss_cls": 3.29327, "loss": 3.29327, "time": 0.85143} +{"mode": "train", "epoch": 115, "iter": 3400, "lr": 0.01291, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.41438, "top5_acc": 0.66859, "loss_cls": 3.32078, "loss": 3.32078, "time": 0.84927} +{"mode": "train", "epoch": 115, "iter": 3500, "lr": 0.01289, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.42219, "top5_acc": 0.68203, "loss_cls": 3.25278, "loss": 3.25278, "time": 0.85097} +{"mode": "train", "epoch": 115, "iter": 3600, "lr": 0.01287, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41906, "top5_acc": 0.67766, "loss_cls": 3.26473, "loss": 3.26473, "time": 0.84677} +{"mode": "train", "epoch": 115, "iter": 3700, "lr": 0.01285, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41484, "top5_acc": 0.67047, "loss_cls": 3.33632, "loss": 3.33632, "time": 0.8487} +{"mode": "val", "epoch": 115, "iter": 309, "lr": 0.01284, "top1_acc": 0.35835, "top5_acc": 0.61875, "mean_class_accuracy": 0.35803} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.01282, "memory": 15990, "data_time": 1.58775, "top1_acc": 0.43359, "top5_acc": 0.69359, "loss_cls": 3.17555, "loss": 3.17555, "time": 2.61901} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.01281, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.43328, "top5_acc": 0.69312, "loss_cls": 3.19861, "loss": 3.19861, "time": 0.84961} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.01279, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.43047, "top5_acc": 0.68531, "loss_cls": 3.21982, "loss": 3.21982, "time": 0.84928} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.01277, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44781, "top5_acc": 0.69984, "loss_cls": 3.13439, "loss": 3.13439, "time": 0.85007} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.01275, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.43891, "top5_acc": 0.69219, "loss_cls": 3.16918, "loss": 3.16918, "time": 0.85147} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.01273, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.43344, "top5_acc": 0.68625, "loss_cls": 3.20958, "loss": 3.20958, "time": 0.85688} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.01271, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.43094, "top5_acc": 0.68922, "loss_cls": 3.20644, "loss": 3.20644, "time": 0.85182} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.01269, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44703, "top5_acc": 0.69625, "loss_cls": 3.15758, "loss": 3.15758, "time": 0.85112} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.01268, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42969, "top5_acc": 0.68922, "loss_cls": 3.22241, "loss": 3.22241, "time": 0.84879} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.01266, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.42094, "top5_acc": 0.67766, "loss_cls": 3.24251, "loss": 3.24251, "time": 0.85211} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.01264, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42672, "top5_acc": 0.68, "loss_cls": 3.23998, "loss": 3.23998, "time": 0.85607} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.01262, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41719, "top5_acc": 0.67047, "loss_cls": 3.27121, "loss": 3.27121, "time": 0.85131} +{"mode": "train", "epoch": 116, "iter": 1300, "lr": 0.0126, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42156, "top5_acc": 0.67641, "loss_cls": 3.2457, "loss": 3.2457, "time": 0.85702} +{"mode": "train", "epoch": 116, "iter": 1400, "lr": 0.01258, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41953, "top5_acc": 0.67781, "loss_cls": 3.26556, "loss": 3.26556, "time": 0.84981} +{"mode": "train", "epoch": 116, "iter": 1500, "lr": 0.01256, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42219, "top5_acc": 0.67734, "loss_cls": 3.26099, "loss": 3.26099, "time": 0.84968} +{"mode": "train", "epoch": 116, "iter": 1600, "lr": 0.01255, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40922, "top5_acc": 0.67609, "loss_cls": 3.29024, "loss": 3.29024, "time": 0.84874} +{"mode": "train", "epoch": 116, "iter": 1700, "lr": 0.01253, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42391, "top5_acc": 0.68266, "loss_cls": 3.23258, "loss": 3.23258, "time": 0.85033} +{"mode": "train", "epoch": 116, "iter": 1800, "lr": 0.01251, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.41672, "top5_acc": 0.66875, "loss_cls": 3.30466, "loss": 3.30466, "time": 0.85227} +{"mode": "train", "epoch": 116, "iter": 1900, "lr": 0.01249, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.42469, "top5_acc": 0.68578, "loss_cls": 3.22605, "loss": 3.22605, "time": 0.85122} +{"mode": "train", "epoch": 116, "iter": 2000, "lr": 0.01247, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42984, "top5_acc": 0.68578, "loss_cls": 3.20643, "loss": 3.20643, "time": 0.85382} +{"mode": "train", "epoch": 116, "iter": 2100, "lr": 0.01245, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41297, "top5_acc": 0.67516, "loss_cls": 3.29572, "loss": 3.29572, "time": 0.85052} +{"mode": "train", "epoch": 116, "iter": 2200, "lr": 0.01243, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.4325, "top5_acc": 0.68797, "loss_cls": 3.21952, "loss": 3.21952, "time": 0.85509} +{"mode": "train", "epoch": 116, "iter": 2300, "lr": 0.01242, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43141, "top5_acc": 0.685, "loss_cls": 3.19147, "loss": 3.19147, "time": 0.84816} +{"mode": "train", "epoch": 116, "iter": 2400, "lr": 0.0124, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42, "top5_acc": 0.67688, "loss_cls": 3.2521, "loss": 3.2521, "time": 0.84954} +{"mode": "train", "epoch": 116, "iter": 2500, "lr": 0.01238, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41578, "top5_acc": 0.67609, "loss_cls": 3.30355, "loss": 3.30355, "time": 0.85038} +{"mode": "train", "epoch": 116, "iter": 2600, "lr": 0.01236, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.425, "top5_acc": 0.67562, "loss_cls": 3.28896, "loss": 3.28896, "time": 0.84563} +{"mode": "train", "epoch": 116, "iter": 2700, "lr": 0.01234, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41656, "top5_acc": 0.67609, "loss_cls": 3.27125, "loss": 3.27125, "time": 0.85196} +{"mode": "train", "epoch": 116, "iter": 2800, "lr": 0.01232, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41656, "top5_acc": 0.65922, "loss_cls": 3.3502, "loss": 3.3502, "time": 0.84735} +{"mode": "train", "epoch": 116, "iter": 2900, "lr": 0.01231, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.41969, "top5_acc": 0.68094, "loss_cls": 3.25228, "loss": 3.25228, "time": 0.84642} +{"mode": "train", "epoch": 116, "iter": 3000, "lr": 0.01229, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41766, "top5_acc": 0.68344, "loss_cls": 3.24683, "loss": 3.24683, "time": 0.84536} +{"mode": "train", "epoch": 116, "iter": 3100, "lr": 0.01227, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42438, "top5_acc": 0.68281, "loss_cls": 3.23255, "loss": 3.23255, "time": 0.85067} +{"mode": "train", "epoch": 116, "iter": 3200, "lr": 0.01225, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.41875, "top5_acc": 0.67969, "loss_cls": 3.27035, "loss": 3.27035, "time": 0.85356} +{"mode": "train", "epoch": 116, "iter": 3300, "lr": 0.01223, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.415, "top5_acc": 0.67484, "loss_cls": 3.31397, "loss": 3.31397, "time": 0.84835} +{"mode": "train", "epoch": 116, "iter": 3400, "lr": 0.01221, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.42344, "top5_acc": 0.67375, "loss_cls": 3.26369, "loss": 3.26369, "time": 0.85009} +{"mode": "train", "epoch": 116, "iter": 3500, "lr": 0.0122, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.425, "top5_acc": 0.68391, "loss_cls": 3.24028, "loss": 3.24028, "time": 0.85387} +{"mode": "train", "epoch": 116, "iter": 3600, "lr": 0.01218, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42766, "top5_acc": 0.67859, "loss_cls": 3.25427, "loss": 3.25427, "time": 0.85211} +{"mode": "train", "epoch": 116, "iter": 3700, "lr": 0.01216, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.43, "top5_acc": 0.67828, "loss_cls": 3.24428, "loss": 3.24428, "time": 0.85682} +{"mode": "val", "epoch": 116, "iter": 309, "lr": 0.01215, "top1_acc": 0.34281, "top5_acc": 0.59525, "mean_class_accuracy": 0.34257} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.01213, "memory": 15990, "data_time": 1.5452, "top1_acc": 0.43312, "top5_acc": 0.69641, "loss_cls": 3.17306, "loss": 3.17306, "time": 2.57674} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.01211, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42484, "top5_acc": 0.68703, "loss_cls": 3.21718, "loss": 3.21718, "time": 0.845} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.0121, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43312, "top5_acc": 0.69203, "loss_cls": 3.20524, "loss": 3.20524, "time": 0.84789} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.01208, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42219, "top5_acc": 0.68672, "loss_cls": 3.22063, "loss": 3.22063, "time": 0.84719} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.01206, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42812, "top5_acc": 0.68781, "loss_cls": 3.22127, "loss": 3.22127, "time": 0.8483} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.01204, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.445, "top5_acc": 0.6925, "loss_cls": 3.15591, "loss": 3.15591, "time": 0.85452} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.01202, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44312, "top5_acc": 0.69125, "loss_cls": 3.14225, "loss": 3.14225, "time": 0.85266} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.012, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42594, "top5_acc": 0.68547, "loss_cls": 3.22403, "loss": 3.22403, "time": 0.85003} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.01199, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43297, "top5_acc": 0.68625, "loss_cls": 3.17895, "loss": 3.17895, "time": 0.85633} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.01197, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43266, "top5_acc": 0.69094, "loss_cls": 3.21263, "loss": 3.21263, "time": 0.85263} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.01195, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43094, "top5_acc": 0.68609, "loss_cls": 3.20167, "loss": 3.20167, "time": 0.85465} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.01193, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.44047, "top5_acc": 0.69859, "loss_cls": 3.16362, "loss": 3.16362, "time": 0.8546} +{"mode": "train", "epoch": 117, "iter": 1300, "lr": 0.01191, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42109, "top5_acc": 0.67656, "loss_cls": 3.28507, "loss": 3.28507, "time": 0.86179} +{"mode": "train", "epoch": 117, "iter": 1400, "lr": 0.0119, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42641, "top5_acc": 0.69312, "loss_cls": 3.19646, "loss": 3.19646, "time": 0.85547} +{"mode": "train", "epoch": 117, "iter": 1500, "lr": 0.01188, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.43219, "top5_acc": 0.68719, "loss_cls": 3.19889, "loss": 3.19889, "time": 0.86267} +{"mode": "train", "epoch": 117, "iter": 1600, "lr": 0.01186, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.41375, "top5_acc": 0.67484, "loss_cls": 3.26267, "loss": 3.26267, "time": 0.85783} +{"mode": "train", "epoch": 117, "iter": 1700, "lr": 0.01184, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42438, "top5_acc": 0.68844, "loss_cls": 3.21455, "loss": 3.21455, "time": 0.86069} +{"mode": "train", "epoch": 117, "iter": 1800, "lr": 0.01182, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.42938, "top5_acc": 0.68734, "loss_cls": 3.17148, "loss": 3.17148, "time": 0.86548} +{"mode": "train", "epoch": 117, "iter": 1900, "lr": 0.01181, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.43312, "top5_acc": 0.68438, "loss_cls": 3.22698, "loss": 3.22698, "time": 0.86016} +{"mode": "train", "epoch": 117, "iter": 2000, "lr": 0.01179, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.42359, "top5_acc": 0.68469, "loss_cls": 3.20844, "loss": 3.20844, "time": 0.86069} +{"mode": "train", "epoch": 117, "iter": 2100, "lr": 0.01177, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.4275, "top5_acc": 0.68594, "loss_cls": 3.2063, "loss": 3.2063, "time": 0.86454} +{"mode": "train", "epoch": 117, "iter": 2200, "lr": 0.01175, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.43594, "top5_acc": 0.69438, "loss_cls": 3.18125, "loss": 3.18125, "time": 0.8583} +{"mode": "train", "epoch": 117, "iter": 2300, "lr": 0.01173, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42516, "top5_acc": 0.68359, "loss_cls": 3.2357, "loss": 3.2357, "time": 0.85787} +{"mode": "train", "epoch": 117, "iter": 2400, "lr": 0.01172, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42875, "top5_acc": 0.68859, "loss_cls": 3.21619, "loss": 3.21619, "time": 0.85456} +{"mode": "train", "epoch": 117, "iter": 2500, "lr": 0.0117, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42938, "top5_acc": 0.68094, "loss_cls": 3.22831, "loss": 3.22831, "time": 0.85728} +{"mode": "train", "epoch": 117, "iter": 2600, "lr": 0.01168, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42234, "top5_acc": 0.68078, "loss_cls": 3.24613, "loss": 3.24613, "time": 0.85176} +{"mode": "train", "epoch": 117, "iter": 2700, "lr": 0.01166, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.42625, "top5_acc": 0.68203, "loss_cls": 3.2563, "loss": 3.2563, "time": 0.85692} +{"mode": "train", "epoch": 117, "iter": 2800, "lr": 0.01164, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.43031, "top5_acc": 0.6825, "loss_cls": 3.21087, "loss": 3.21087, "time": 0.85164} +{"mode": "train", "epoch": 117, "iter": 2900, "lr": 0.01163, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42016, "top5_acc": 0.68328, "loss_cls": 3.24621, "loss": 3.24621, "time": 0.85082} +{"mode": "train", "epoch": 117, "iter": 3000, "lr": 0.01161, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43672, "top5_acc": 0.69312, "loss_cls": 3.19392, "loss": 3.19392, "time": 0.8507} +{"mode": "train", "epoch": 117, "iter": 3100, "lr": 0.01159, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41, "top5_acc": 0.66875, "loss_cls": 3.31595, "loss": 3.31595, "time": 0.8499} +{"mode": "train", "epoch": 117, "iter": 3200, "lr": 0.01157, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42828, "top5_acc": 0.68938, "loss_cls": 3.18618, "loss": 3.18618, "time": 0.85089} +{"mode": "train", "epoch": 117, "iter": 3300, "lr": 0.01155, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43, "top5_acc": 0.68547, "loss_cls": 3.21042, "loss": 3.21042, "time": 0.84885} +{"mode": "train", "epoch": 117, "iter": 3400, "lr": 0.01154, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42078, "top5_acc": 0.67891, "loss_cls": 3.26939, "loss": 3.26939, "time": 0.85357} +{"mode": "train", "epoch": 117, "iter": 3500, "lr": 0.01152, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43172, "top5_acc": 0.68641, "loss_cls": 3.23452, "loss": 3.23452, "time": 0.84952} +{"mode": "train", "epoch": 117, "iter": 3600, "lr": 0.0115, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42578, "top5_acc": 0.67422, "loss_cls": 3.25658, "loss": 3.25658, "time": 0.84789} +{"mode": "train", "epoch": 117, "iter": 3700, "lr": 0.01148, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41812, "top5_acc": 0.67562, "loss_cls": 3.26983, "loss": 3.26983, "time": 0.84841} +{"mode": "val", "epoch": 117, "iter": 309, "lr": 0.01147, "top1_acc": 0.36033, "top5_acc": 0.60913, "mean_class_accuracy": 0.36004} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.01146, "memory": 15990, "data_time": 1.48311, "top1_acc": 0.44453, "top5_acc": 0.70047, "loss_cls": 3.12376, "loss": 3.12376, "time": 2.51686} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.01144, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45359, "top5_acc": 0.71, "loss_cls": 3.08366, "loss": 3.08366, "time": 0.8502} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.01142, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45172, "top5_acc": 0.70688, "loss_cls": 3.08662, "loss": 3.08662, "time": 0.85283} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.0114, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43922, "top5_acc": 0.68953, "loss_cls": 3.16742, "loss": 3.16742, "time": 0.85426} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.01139, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43312, "top5_acc": 0.70031, "loss_cls": 3.13818, "loss": 3.13818, "time": 0.85486} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.01137, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45656, "top5_acc": 0.70969, "loss_cls": 3.08632, "loss": 3.08632, "time": 0.85248} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.01135, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43719, "top5_acc": 0.68766, "loss_cls": 3.19493, "loss": 3.19493, "time": 0.85164} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.01133, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43328, "top5_acc": 0.69047, "loss_cls": 3.19688, "loss": 3.19688, "time": 0.85031} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.01131, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43375, "top5_acc": 0.68953, "loss_cls": 3.16731, "loss": 3.16731, "time": 0.84955} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.0113, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43266, "top5_acc": 0.68469, "loss_cls": 3.19885, "loss": 3.19885, "time": 0.8552} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.01128, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43359, "top5_acc": 0.69516, "loss_cls": 3.14695, "loss": 3.14695, "time": 0.85287} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.01126, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42719, "top5_acc": 0.68281, "loss_cls": 3.21157, "loss": 3.21157, "time": 0.85458} +{"mode": "train", "epoch": 118, "iter": 1300, "lr": 0.01124, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44094, "top5_acc": 0.69359, "loss_cls": 3.16511, "loss": 3.16511, "time": 0.85324} +{"mode": "train", "epoch": 118, "iter": 1400, "lr": 0.01123, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43141, "top5_acc": 0.68812, "loss_cls": 3.20392, "loss": 3.20392, "time": 0.8565} +{"mode": "train", "epoch": 118, "iter": 1500, "lr": 0.01121, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43234, "top5_acc": 0.69156, "loss_cls": 3.21629, "loss": 3.21629, "time": 0.8571} +{"mode": "train", "epoch": 118, "iter": 1600, "lr": 0.01119, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43906, "top5_acc": 0.69156, "loss_cls": 3.18091, "loss": 3.18091, "time": 0.85296} +{"mode": "train", "epoch": 118, "iter": 1700, "lr": 0.01117, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.43531, "top5_acc": 0.69344, "loss_cls": 3.18521, "loss": 3.18521, "time": 0.85899} +{"mode": "train", "epoch": 118, "iter": 1800, "lr": 0.01116, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.43125, "top5_acc": 0.69094, "loss_cls": 3.20742, "loss": 3.20742, "time": 0.85581} +{"mode": "train", "epoch": 118, "iter": 1900, "lr": 0.01114, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42688, "top5_acc": 0.68781, "loss_cls": 3.19642, "loss": 3.19642, "time": 0.85727} +{"mode": "train", "epoch": 118, "iter": 2000, "lr": 0.01112, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42688, "top5_acc": 0.69172, "loss_cls": 3.19822, "loss": 3.19822, "time": 0.85485} +{"mode": "train", "epoch": 118, "iter": 2100, "lr": 0.0111, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42547, "top5_acc": 0.68484, "loss_cls": 3.23387, "loss": 3.23387, "time": 0.85607} +{"mode": "train", "epoch": 118, "iter": 2200, "lr": 0.01109, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44766, "top5_acc": 0.68922, "loss_cls": 3.17836, "loss": 3.17836, "time": 0.85832} +{"mode": "train", "epoch": 118, "iter": 2300, "lr": 0.01107, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42172, "top5_acc": 0.68688, "loss_cls": 3.24132, "loss": 3.24132, "time": 0.85676} +{"mode": "train", "epoch": 118, "iter": 2400, "lr": 0.01105, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.43078, "top5_acc": 0.68359, "loss_cls": 3.20668, "loss": 3.20668, "time": 0.85041} +{"mode": "train", "epoch": 118, "iter": 2500, "lr": 0.01103, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.42953, "top5_acc": 0.68844, "loss_cls": 3.18006, "loss": 3.18006, "time": 0.84755} +{"mode": "train", "epoch": 118, "iter": 2600, "lr": 0.01102, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.42359, "top5_acc": 0.68109, "loss_cls": 3.2409, "loss": 3.2409, "time": 0.85062} +{"mode": "train", "epoch": 118, "iter": 2700, "lr": 0.011, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42688, "top5_acc": 0.68484, "loss_cls": 3.21489, "loss": 3.21489, "time": 0.85538} +{"mode": "train", "epoch": 118, "iter": 2800, "lr": 0.01098, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42734, "top5_acc": 0.67203, "loss_cls": 3.28368, "loss": 3.28368, "time": 0.84759} +{"mode": "train", "epoch": 118, "iter": 2900, "lr": 0.01096, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42875, "top5_acc": 0.67859, "loss_cls": 3.23082, "loss": 3.23082, "time": 0.84846} +{"mode": "train", "epoch": 118, "iter": 3000, "lr": 0.01095, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43812, "top5_acc": 0.68172, "loss_cls": 3.20868, "loss": 3.20868, "time": 0.8454} +{"mode": "train", "epoch": 118, "iter": 3100, "lr": 0.01093, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42859, "top5_acc": 0.68641, "loss_cls": 3.19512, "loss": 3.19512, "time": 0.8485} +{"mode": "train", "epoch": 118, "iter": 3200, "lr": 0.01091, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42328, "top5_acc": 0.69078, "loss_cls": 3.21919, "loss": 3.21919, "time": 0.85448} +{"mode": "train", "epoch": 118, "iter": 3300, "lr": 0.01089, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42766, "top5_acc": 0.69156, "loss_cls": 3.17892, "loss": 3.17892, "time": 0.84778} +{"mode": "train", "epoch": 118, "iter": 3400, "lr": 0.01088, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42906, "top5_acc": 0.68188, "loss_cls": 3.24751, "loss": 3.24751, "time": 0.84663} +{"mode": "train", "epoch": 118, "iter": 3500, "lr": 0.01086, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42781, "top5_acc": 0.68016, "loss_cls": 3.22061, "loss": 3.22061, "time": 0.84857} +{"mode": "train", "epoch": 118, "iter": 3600, "lr": 0.01084, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.4375, "top5_acc": 0.68562, "loss_cls": 3.20027, "loss": 3.20027, "time": 0.85308} +{"mode": "train", "epoch": 118, "iter": 3700, "lr": 0.01082, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42875, "top5_acc": 0.6825, "loss_cls": 3.23388, "loss": 3.23388, "time": 0.85099} +{"mode": "val", "epoch": 118, "iter": 309, "lr": 0.01082, "top1_acc": 0.36484, "top5_acc": 0.62007, "mean_class_accuracy": 0.36458} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.0108, "memory": 15990, "data_time": 1.54489, "top1_acc": 0.45141, "top5_acc": 0.70109, "loss_cls": 3.10934, "loss": 3.10934, "time": 2.57454} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.01078, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44578, "top5_acc": 0.71156, "loss_cls": 3.08713, "loss": 3.08713, "time": 0.85549} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.01076, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.43141, "top5_acc": 0.69922, "loss_cls": 3.15644, "loss": 3.15644, "time": 0.85826} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.01075, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43812, "top5_acc": 0.69656, "loss_cls": 3.14664, "loss": 3.14664, "time": 0.84888} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.01073, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43781, "top5_acc": 0.69156, "loss_cls": 3.17183, "loss": 3.17183, "time": 0.84944} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.01071, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44438, "top5_acc": 0.69906, "loss_cls": 3.1202, "loss": 3.1202, "time": 0.849} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.01069, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44328, "top5_acc": 0.70719, "loss_cls": 3.13091, "loss": 3.13091, "time": 0.85162} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.01068, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44625, "top5_acc": 0.70281, "loss_cls": 3.11245, "loss": 3.11245, "time": 0.84826} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.01066, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44734, "top5_acc": 0.70062, "loss_cls": 3.11161, "loss": 3.11161, "time": 0.84797} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.01064, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44344, "top5_acc": 0.69719, "loss_cls": 3.11822, "loss": 3.11822, "time": 0.85493} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.01063, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.44156, "top5_acc": 0.69641, "loss_cls": 3.14829, "loss": 3.14829, "time": 0.85696} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.01061, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.43875, "top5_acc": 0.69859, "loss_cls": 3.15994, "loss": 3.15994, "time": 0.85328} +{"mode": "train", "epoch": 119, "iter": 1300, "lr": 0.01059, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43812, "top5_acc": 0.69719, "loss_cls": 3.15657, "loss": 3.15657, "time": 0.85359} +{"mode": "train", "epoch": 119, "iter": 1400, "lr": 0.01057, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42516, "top5_acc": 0.69391, "loss_cls": 3.18263, "loss": 3.18263, "time": 0.84908} +{"mode": "train", "epoch": 119, "iter": 1500, "lr": 0.01056, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43203, "top5_acc": 0.68703, "loss_cls": 3.2045, "loss": 3.2045, "time": 0.84927} +{"mode": "train", "epoch": 119, "iter": 1600, "lr": 0.01054, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44781, "top5_acc": 0.69141, "loss_cls": 3.14109, "loss": 3.14109, "time": 0.84676} +{"mode": "train", "epoch": 119, "iter": 1700, "lr": 0.01052, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43781, "top5_acc": 0.6825, "loss_cls": 3.18585, "loss": 3.18585, "time": 0.84786} +{"mode": "train", "epoch": 119, "iter": 1800, "lr": 0.0105, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43953, "top5_acc": 0.69484, "loss_cls": 3.14507, "loss": 3.14507, "time": 0.84878} +{"mode": "train", "epoch": 119, "iter": 1900, "lr": 0.01049, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44297, "top5_acc": 0.69875, "loss_cls": 3.12443, "loss": 3.12443, "time": 0.85059} +{"mode": "train", "epoch": 119, "iter": 2000, "lr": 0.01047, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43453, "top5_acc": 0.69609, "loss_cls": 3.17711, "loss": 3.17711, "time": 0.84768} +{"mode": "train", "epoch": 119, "iter": 2100, "lr": 0.01045, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43016, "top5_acc": 0.68906, "loss_cls": 3.20747, "loss": 3.20747, "time": 0.8483} +{"mode": "train", "epoch": 119, "iter": 2200, "lr": 0.01044, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42984, "top5_acc": 0.68375, "loss_cls": 3.20521, "loss": 3.20521, "time": 0.85018} +{"mode": "train", "epoch": 119, "iter": 2300, "lr": 0.01042, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.43203, "top5_acc": 0.68672, "loss_cls": 3.21842, "loss": 3.21842, "time": 0.85542} +{"mode": "train", "epoch": 119, "iter": 2400, "lr": 0.0104, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.43, "top5_acc": 0.68406, "loss_cls": 3.2144, "loss": 3.2144, "time": 0.85165} +{"mode": "train", "epoch": 119, "iter": 2500, "lr": 0.01039, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43516, "top5_acc": 0.68297, "loss_cls": 3.20129, "loss": 3.20129, "time": 0.84933} +{"mode": "train", "epoch": 119, "iter": 2600, "lr": 0.01037, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43875, "top5_acc": 0.68391, "loss_cls": 3.19485, "loss": 3.19485, "time": 0.85224} +{"mode": "train", "epoch": 119, "iter": 2700, "lr": 0.01035, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44531, "top5_acc": 0.69266, "loss_cls": 3.14572, "loss": 3.14572, "time": 0.85341} +{"mode": "train", "epoch": 119, "iter": 2800, "lr": 0.01033, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.44031, "top5_acc": 0.69844, "loss_cls": 3.15841, "loss": 3.15841, "time": 0.84835} +{"mode": "train", "epoch": 119, "iter": 2900, "lr": 0.01032, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.42859, "top5_acc": 0.68906, "loss_cls": 3.19512, "loss": 3.19512, "time": 0.84876} +{"mode": "train", "epoch": 119, "iter": 3000, "lr": 0.0103, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44016, "top5_acc": 0.69484, "loss_cls": 3.1543, "loss": 3.1543, "time": 0.85157} +{"mode": "train", "epoch": 119, "iter": 3100, "lr": 0.01028, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44141, "top5_acc": 0.69641, "loss_cls": 3.15104, "loss": 3.15104, "time": 0.85853} +{"mode": "train", "epoch": 119, "iter": 3200, "lr": 0.01027, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.43562, "top5_acc": 0.69406, "loss_cls": 3.15867, "loss": 3.15867, "time": 0.85495} +{"mode": "train", "epoch": 119, "iter": 3300, "lr": 0.01025, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42438, "top5_acc": 0.69688, "loss_cls": 3.20815, "loss": 3.20815, "time": 0.8554} +{"mode": "train", "epoch": 119, "iter": 3400, "lr": 0.01023, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.44453, "top5_acc": 0.68938, "loss_cls": 3.16024, "loss": 3.16024, "time": 0.85669} +{"mode": "train", "epoch": 119, "iter": 3500, "lr": 0.01022, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.43938, "top5_acc": 0.69578, "loss_cls": 3.18298, "loss": 3.18298, "time": 0.8589} +{"mode": "train", "epoch": 119, "iter": 3600, "lr": 0.0102, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43375, "top5_acc": 0.69062, "loss_cls": 3.20596, "loss": 3.20596, "time": 0.85946} +{"mode": "train", "epoch": 119, "iter": 3700, "lr": 0.01018, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42031, "top5_acc": 0.67766, "loss_cls": 3.24457, "loss": 3.24457, "time": 0.85793} +{"mode": "val", "epoch": 119, "iter": 309, "lr": 0.01017, "top1_acc": 0.37456, "top5_acc": 0.63025, "mean_class_accuracy": 0.3743} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.01016, "memory": 15990, "data_time": 1.55179, "top1_acc": 0.4575, "top5_acc": 0.71516, "loss_cls": 3.04818, "loss": 3.04818, "time": 2.58805} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.01014, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45156, "top5_acc": 0.70547, "loss_cls": 3.08992, "loss": 3.08992, "time": 0.85384} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.01012, "memory": 15990, "data_time": 0.00075, "top1_acc": 0.45406, "top5_acc": 0.70953, "loss_cls": 3.07388, "loss": 3.07388, "time": 0.84873} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.01011, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.45594, "top5_acc": 0.70562, "loss_cls": 3.06212, "loss": 3.06212, "time": 0.85154} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.01009, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44922, "top5_acc": 0.69781, "loss_cls": 3.1236, "loss": 3.1236, "time": 0.85072} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.01007, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.45422, "top5_acc": 0.70172, "loss_cls": 3.09781, "loss": 3.09781, "time": 0.85204} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.01006, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44047, "top5_acc": 0.69656, "loss_cls": 3.13332, "loss": 3.13332, "time": 0.85294} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.01004, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44234, "top5_acc": 0.69578, "loss_cls": 3.13827, "loss": 3.13827, "time": 0.84589} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.01002, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45703, "top5_acc": 0.69906, "loss_cls": 3.10767, "loss": 3.10767, "time": 0.85648} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.01001, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45234, "top5_acc": 0.70594, "loss_cls": 3.11434, "loss": 3.11434, "time": 0.85072} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00999, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43781, "top5_acc": 0.69812, "loss_cls": 3.14873, "loss": 3.14873, "time": 0.85308} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.00997, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44016, "top5_acc": 0.69219, "loss_cls": 3.15654, "loss": 3.15654, "time": 0.85421} +{"mode": "train", "epoch": 120, "iter": 1300, "lr": 0.00996, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.44828, "top5_acc": 0.69656, "loss_cls": 3.13207, "loss": 3.13207, "time": 0.85576} +{"mode": "train", "epoch": 120, "iter": 1400, "lr": 0.00994, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.44422, "top5_acc": 0.69797, "loss_cls": 3.14591, "loss": 3.14591, "time": 0.85109} +{"mode": "train", "epoch": 120, "iter": 1500, "lr": 0.00992, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44844, "top5_acc": 0.7, "loss_cls": 3.10045, "loss": 3.10045, "time": 0.84977} +{"mode": "train", "epoch": 120, "iter": 1600, "lr": 0.0099, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44594, "top5_acc": 0.69906, "loss_cls": 3.14051, "loss": 3.14051, "time": 0.84832} +{"mode": "train", "epoch": 120, "iter": 1700, "lr": 0.00989, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43969, "top5_acc": 0.7075, "loss_cls": 3.13425, "loss": 3.13425, "time": 0.84718} +{"mode": "train", "epoch": 120, "iter": 1800, "lr": 0.00987, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.4325, "top5_acc": 0.69281, "loss_cls": 3.17715, "loss": 3.17715, "time": 0.85482} +{"mode": "train", "epoch": 120, "iter": 1900, "lr": 0.00985, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43859, "top5_acc": 0.69125, "loss_cls": 3.15897, "loss": 3.15897, "time": 0.85121} +{"mode": "train", "epoch": 120, "iter": 2000, "lr": 0.00984, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.43953, "top5_acc": 0.70281, "loss_cls": 3.10203, "loss": 3.10203, "time": 0.85057} +{"mode": "train", "epoch": 120, "iter": 2100, "lr": 0.00982, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43422, "top5_acc": 0.695, "loss_cls": 3.16626, "loss": 3.16626, "time": 0.85572} +{"mode": "train", "epoch": 120, "iter": 2200, "lr": 0.0098, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43875, "top5_acc": 0.69375, "loss_cls": 3.16314, "loss": 3.16314, "time": 0.85651} +{"mode": "train", "epoch": 120, "iter": 2300, "lr": 0.00979, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.42375, "top5_acc": 0.68656, "loss_cls": 3.19519, "loss": 3.19519, "time": 0.85089} +{"mode": "train", "epoch": 120, "iter": 2400, "lr": 0.00977, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.44594, "top5_acc": 0.68938, "loss_cls": 3.15933, "loss": 3.15933, "time": 0.85128} +{"mode": "train", "epoch": 120, "iter": 2500, "lr": 0.00976, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45344, "top5_acc": 0.69984, "loss_cls": 3.13161, "loss": 3.13161, "time": 0.84695} +{"mode": "train", "epoch": 120, "iter": 2600, "lr": 0.00974, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.43219, "top5_acc": 0.69797, "loss_cls": 3.16032, "loss": 3.16032, "time": 0.85722} +{"mode": "train", "epoch": 120, "iter": 2700, "lr": 0.00972, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43516, "top5_acc": 0.70234, "loss_cls": 3.1587, "loss": 3.1587, "time": 0.85312} +{"mode": "train", "epoch": 120, "iter": 2800, "lr": 0.00971, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.43812, "top5_acc": 0.69781, "loss_cls": 3.15229, "loss": 3.15229, "time": 0.84727} +{"mode": "train", "epoch": 120, "iter": 2900, "lr": 0.00969, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.44203, "top5_acc": 0.69953, "loss_cls": 3.12886, "loss": 3.12886, "time": 0.84755} +{"mode": "train", "epoch": 120, "iter": 3000, "lr": 0.00967, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43469, "top5_acc": 0.68828, "loss_cls": 3.19766, "loss": 3.19766, "time": 0.8486} +{"mode": "train", "epoch": 120, "iter": 3100, "lr": 0.00966, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44031, "top5_acc": 0.69188, "loss_cls": 3.16106, "loss": 3.16106, "time": 0.84526} +{"mode": "train", "epoch": 120, "iter": 3200, "lr": 0.00964, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44344, "top5_acc": 0.69438, "loss_cls": 3.14709, "loss": 3.14709, "time": 0.84846} +{"mode": "train", "epoch": 120, "iter": 3300, "lr": 0.00962, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44172, "top5_acc": 0.69438, "loss_cls": 3.14297, "loss": 3.14297, "time": 0.85161} +{"mode": "train", "epoch": 120, "iter": 3400, "lr": 0.00961, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44312, "top5_acc": 0.70016, "loss_cls": 3.12952, "loss": 3.12952, "time": 0.84498} +{"mode": "train", "epoch": 120, "iter": 3500, "lr": 0.00959, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44469, "top5_acc": 0.69594, "loss_cls": 3.15312, "loss": 3.15312, "time": 0.84959} +{"mode": "train", "epoch": 120, "iter": 3600, "lr": 0.00957, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44203, "top5_acc": 0.69125, "loss_cls": 3.18201, "loss": 3.18201, "time": 0.8533} +{"mode": "train", "epoch": 120, "iter": 3700, "lr": 0.00956, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42344, "top5_acc": 0.69078, "loss_cls": 3.20864, "loss": 3.20864, "time": 0.8546} +{"mode": "val", "epoch": 120, "iter": 309, "lr": 0.00955, "top1_acc": 0.37006, "top5_acc": 0.62974, "mean_class_accuracy": 0.36989} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00953, "memory": 15990, "data_time": 1.50467, "top1_acc": 0.45047, "top5_acc": 0.70391, "loss_cls": 3.09555, "loss": 3.09555, "time": 2.54123} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00952, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.45844, "top5_acc": 0.71312, "loss_cls": 3.0436, "loss": 3.0436, "time": 0.8512} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.0095, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45984, "top5_acc": 0.71391, "loss_cls": 3.02449, "loss": 3.02449, "time": 0.85} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00948, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45797, "top5_acc": 0.70453, "loss_cls": 3.07344, "loss": 3.07344, "time": 0.84502} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00947, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44797, "top5_acc": 0.70375, "loss_cls": 3.0901, "loss": 3.0901, "time": 0.84956} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00945, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.45875, "top5_acc": 0.7075, "loss_cls": 3.05402, "loss": 3.05402, "time": 0.84681} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.00943, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45562, "top5_acc": 0.70594, "loss_cls": 3.07273, "loss": 3.07273, "time": 0.84762} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00942, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44984, "top5_acc": 0.70656, "loss_cls": 3.09453, "loss": 3.09453, "time": 0.84954} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.0094, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44109, "top5_acc": 0.70812, "loss_cls": 3.08202, "loss": 3.08202, "time": 0.85424} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00939, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46062, "top5_acc": 0.70719, "loss_cls": 3.04691, "loss": 3.04691, "time": 0.84882} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00937, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44109, "top5_acc": 0.70109, "loss_cls": 3.12522, "loss": 3.12522, "time": 0.85267} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00935, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44422, "top5_acc": 0.69297, "loss_cls": 3.1675, "loss": 3.1675, "time": 0.85053} +{"mode": "train", "epoch": 121, "iter": 1300, "lr": 0.00934, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43781, "top5_acc": 0.70094, "loss_cls": 3.14444, "loss": 3.14444, "time": 0.85304} +{"mode": "train", "epoch": 121, "iter": 1400, "lr": 0.00932, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44859, "top5_acc": 0.70406, "loss_cls": 3.1211, "loss": 3.1211, "time": 0.85183} +{"mode": "train", "epoch": 121, "iter": 1500, "lr": 0.0093, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44875, "top5_acc": 0.70641, "loss_cls": 3.10072, "loss": 3.10072, "time": 0.85159} +{"mode": "train", "epoch": 121, "iter": 1600, "lr": 0.00929, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44797, "top5_acc": 0.69875, "loss_cls": 3.13676, "loss": 3.13676, "time": 0.84931} +{"mode": "train", "epoch": 121, "iter": 1700, "lr": 0.00927, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44094, "top5_acc": 0.69766, "loss_cls": 3.15483, "loss": 3.15483, "time": 0.85288} +{"mode": "train", "epoch": 121, "iter": 1800, "lr": 0.00926, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44391, "top5_acc": 0.7, "loss_cls": 3.10804, "loss": 3.10804, "time": 0.85693} +{"mode": "train", "epoch": 121, "iter": 1900, "lr": 0.00924, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43203, "top5_acc": 0.68688, "loss_cls": 3.19195, "loss": 3.19195, "time": 0.85515} +{"mode": "train", "epoch": 121, "iter": 2000, "lr": 0.00922, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43812, "top5_acc": 0.69516, "loss_cls": 3.14985, "loss": 3.14985, "time": 0.85084} +{"mode": "train", "epoch": 121, "iter": 2100, "lr": 0.00921, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.44375, "top5_acc": 0.69719, "loss_cls": 3.11484, "loss": 3.11484, "time": 0.85003} +{"mode": "train", "epoch": 121, "iter": 2200, "lr": 0.00919, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.455, "top5_acc": 0.71359, "loss_cls": 3.0725, "loss": 3.0725, "time": 0.85668} +{"mode": "train", "epoch": 121, "iter": 2300, "lr": 0.00917, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44734, "top5_acc": 0.7025, "loss_cls": 3.10522, "loss": 3.10522, "time": 0.84743} +{"mode": "train", "epoch": 121, "iter": 2400, "lr": 0.00916, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44297, "top5_acc": 0.69641, "loss_cls": 3.13917, "loss": 3.13917, "time": 0.84955} +{"mode": "train", "epoch": 121, "iter": 2500, "lr": 0.00914, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.4475, "top5_acc": 0.70375, "loss_cls": 3.11415, "loss": 3.11415, "time": 0.84582} +{"mode": "train", "epoch": 121, "iter": 2600, "lr": 0.00913, "memory": 15990, "data_time": 0.001, "top1_acc": 0.44047, "top5_acc": 0.69344, "loss_cls": 3.13289, "loss": 3.13289, "time": 0.85035} +{"mode": "train", "epoch": 121, "iter": 2700, "lr": 0.00911, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43109, "top5_acc": 0.69672, "loss_cls": 3.15584, "loss": 3.15584, "time": 0.84807} +{"mode": "train", "epoch": 121, "iter": 2800, "lr": 0.00909, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.43844, "top5_acc": 0.6825, "loss_cls": 3.19734, "loss": 3.19734, "time": 0.84231} +{"mode": "train", "epoch": 121, "iter": 2900, "lr": 0.00908, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43859, "top5_acc": 0.70656, "loss_cls": 3.11727, "loss": 3.11727, "time": 0.84589} +{"mode": "train", "epoch": 121, "iter": 3000, "lr": 0.00906, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43094, "top5_acc": 0.69609, "loss_cls": 3.14832, "loss": 3.14832, "time": 0.84682} +{"mode": "train", "epoch": 121, "iter": 3100, "lr": 0.00905, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44984, "top5_acc": 0.70125, "loss_cls": 3.10646, "loss": 3.10646, "time": 0.85229} +{"mode": "train", "epoch": 121, "iter": 3200, "lr": 0.00903, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45438, "top5_acc": 0.70438, "loss_cls": 3.08888, "loss": 3.08888, "time": 0.84741} +{"mode": "train", "epoch": 121, "iter": 3300, "lr": 0.00901, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45703, "top5_acc": 0.71891, "loss_cls": 3.02469, "loss": 3.02469, "time": 0.851} +{"mode": "train", "epoch": 121, "iter": 3400, "lr": 0.009, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44031, "top5_acc": 0.69656, "loss_cls": 3.15235, "loss": 3.15235, "time": 0.84736} +{"mode": "train", "epoch": 121, "iter": 3500, "lr": 0.00898, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.44969, "top5_acc": 0.70641, "loss_cls": 3.10092, "loss": 3.10092, "time": 0.85133} +{"mode": "train", "epoch": 121, "iter": 3600, "lr": 0.00897, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.44609, "top5_acc": 0.7125, "loss_cls": 3.08093, "loss": 3.08093, "time": 0.8542} +{"mode": "train", "epoch": 121, "iter": 3700, "lr": 0.00895, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.44297, "top5_acc": 0.70266, "loss_cls": 3.13183, "loss": 3.13183, "time": 0.84805} +{"mode": "val", "epoch": 121, "iter": 309, "lr": 0.00894, "top1_acc": 0.38398, "top5_acc": 0.63623, "mean_class_accuracy": 0.38368} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00893, "memory": 15990, "data_time": 1.52371, "top1_acc": 0.46812, "top5_acc": 0.7225, "loss_cls": 3.00136, "loss": 3.00136, "time": 2.58354} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00891, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.45969, "top5_acc": 0.71969, "loss_cls": 3.01802, "loss": 3.01802, "time": 0.85243} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.00889, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.46688, "top5_acc": 0.71562, "loss_cls": 3.02127, "loss": 3.02127, "time": 0.85597} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00888, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.44469, "top5_acc": 0.71172, "loss_cls": 3.04652, "loss": 3.04652, "time": 0.84949} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00886, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.45922, "top5_acc": 0.71625, "loss_cls": 3.03808, "loss": 3.03808, "time": 0.8554} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00885, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.45609, "top5_acc": 0.71203, "loss_cls": 3.07995, "loss": 3.07995, "time": 0.85335} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00883, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.4625, "top5_acc": 0.71641, "loss_cls": 3.05044, "loss": 3.05044, "time": 0.85403} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00882, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.45156, "top5_acc": 0.70453, "loss_cls": 3.07651, "loss": 3.07651, "time": 0.85527} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.0088, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.45609, "top5_acc": 0.71531, "loss_cls": 3.05135, "loss": 3.05135, "time": 0.8569} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00878, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.46578, "top5_acc": 0.71703, "loss_cls": 3.03244, "loss": 3.03244, "time": 0.8533} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00877, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.44969, "top5_acc": 0.70703, "loss_cls": 3.06443, "loss": 3.06443, "time": 0.85546} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.00875, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.44984, "top5_acc": 0.69578, "loss_cls": 3.06613, "loss": 3.06613, "time": 0.85682} +{"mode": "train", "epoch": 122, "iter": 1300, "lr": 0.00874, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44938, "top5_acc": 0.69656, "loss_cls": 3.0921, "loss": 3.0921, "time": 0.85339} +{"mode": "train", "epoch": 122, "iter": 1400, "lr": 0.00872, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.44703, "top5_acc": 0.70828, "loss_cls": 3.07785, "loss": 3.07785, "time": 0.8588} +{"mode": "train", "epoch": 122, "iter": 1500, "lr": 0.0087, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44844, "top5_acc": 0.70688, "loss_cls": 3.06331, "loss": 3.06331, "time": 0.85291} +{"mode": "train", "epoch": 122, "iter": 1600, "lr": 0.00869, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45312, "top5_acc": 0.71203, "loss_cls": 3.05765, "loss": 3.05765, "time": 0.8508} +{"mode": "train", "epoch": 122, "iter": 1700, "lr": 0.00867, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44406, "top5_acc": 0.70281, "loss_cls": 3.12586, "loss": 3.12586, "time": 0.85024} +{"mode": "train", "epoch": 122, "iter": 1800, "lr": 0.00866, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.44156, "top5_acc": 0.68859, "loss_cls": 3.15863, "loss": 3.15863, "time": 0.8553} +{"mode": "train", "epoch": 122, "iter": 1900, "lr": 0.00864, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45203, "top5_acc": 0.70547, "loss_cls": 3.08986, "loss": 3.08986, "time": 0.85133} +{"mode": "train", "epoch": 122, "iter": 2000, "lr": 0.00863, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.455, "top5_acc": 0.70953, "loss_cls": 3.05446, "loss": 3.05446, "time": 0.85071} +{"mode": "train", "epoch": 122, "iter": 2100, "lr": 0.00861, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45156, "top5_acc": 0.7075, "loss_cls": 3.08311, "loss": 3.08311, "time": 0.85414} +{"mode": "train", "epoch": 122, "iter": 2200, "lr": 0.00859, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.43922, "top5_acc": 0.69922, "loss_cls": 3.1532, "loss": 3.1532, "time": 0.85062} +{"mode": "train", "epoch": 122, "iter": 2300, "lr": 0.00858, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.45203, "top5_acc": 0.70781, "loss_cls": 3.08028, "loss": 3.08028, "time": 0.85299} +{"mode": "train", "epoch": 122, "iter": 2400, "lr": 0.00856, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44578, "top5_acc": 0.70375, "loss_cls": 3.08897, "loss": 3.08897, "time": 0.8525} +{"mode": "train", "epoch": 122, "iter": 2500, "lr": 0.00855, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45094, "top5_acc": 0.70641, "loss_cls": 3.09809, "loss": 3.09809, "time": 0.84787} +{"mode": "train", "epoch": 122, "iter": 2600, "lr": 0.00853, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.44422, "top5_acc": 0.70344, "loss_cls": 3.10778, "loss": 3.10778, "time": 0.85317} +{"mode": "train", "epoch": 122, "iter": 2700, "lr": 0.00852, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44891, "top5_acc": 0.69938, "loss_cls": 3.12072, "loss": 3.12072, "time": 0.84558} +{"mode": "train", "epoch": 122, "iter": 2800, "lr": 0.0085, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44828, "top5_acc": 0.70453, "loss_cls": 3.10845, "loss": 3.10845, "time": 0.84675} +{"mode": "train", "epoch": 122, "iter": 2900, "lr": 0.00849, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.4475, "top5_acc": 0.69922, "loss_cls": 3.12909, "loss": 3.12909, "time": 0.84608} +{"mode": "train", "epoch": 122, "iter": 3000, "lr": 0.00847, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44766, "top5_acc": 0.70766, "loss_cls": 3.05595, "loss": 3.05595, "time": 0.85164} +{"mode": "train", "epoch": 122, "iter": 3100, "lr": 0.00845, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44859, "top5_acc": 0.715, "loss_cls": 3.07572, "loss": 3.07572, "time": 0.84935} +{"mode": "train", "epoch": 122, "iter": 3200, "lr": 0.00844, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45375, "top5_acc": 0.70609, "loss_cls": 3.07325, "loss": 3.07325, "time": 0.84969} +{"mode": "train", "epoch": 122, "iter": 3300, "lr": 0.00842, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44922, "top5_acc": 0.70406, "loss_cls": 3.10386, "loss": 3.10386, "time": 0.84317} +{"mode": "train", "epoch": 122, "iter": 3400, "lr": 0.00841, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45719, "top5_acc": 0.70156, "loss_cls": 3.11174, "loss": 3.11174, "time": 0.84207} +{"mode": "train", "epoch": 122, "iter": 3500, "lr": 0.00839, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.4475, "top5_acc": 0.70672, "loss_cls": 3.1041, "loss": 3.1041, "time": 0.84659} +{"mode": "train", "epoch": 122, "iter": 3600, "lr": 0.00838, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45047, "top5_acc": 0.70391, "loss_cls": 3.09306, "loss": 3.09306, "time": 0.84202} +{"mode": "train", "epoch": 122, "iter": 3700, "lr": 0.00836, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45172, "top5_acc": 0.70188, "loss_cls": 3.12192, "loss": 3.12192, "time": 0.85033} +{"mode": "val", "epoch": 122, "iter": 309, "lr": 0.00835, "top1_acc": 0.37598, "top5_acc": 0.63374, "mean_class_accuracy": 0.3758} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00834, "memory": 15990, "data_time": 1.54032, "top1_acc": 0.46484, "top5_acc": 0.72453, "loss_cls": 2.98005, "loss": 2.98005, "time": 2.57835} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00832, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45625, "top5_acc": 0.71125, "loss_cls": 3.05107, "loss": 3.05107, "time": 0.851} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00831, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.45969, "top5_acc": 0.71562, "loss_cls": 3.02482, "loss": 3.02482, "time": 0.84685} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00829, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46344, "top5_acc": 0.71422, "loss_cls": 3.04421, "loss": 3.04421, "time": 0.85252} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00828, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46, "top5_acc": 0.72453, "loss_cls": 2.96873, "loss": 2.96873, "time": 0.84539} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00826, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.46719, "top5_acc": 0.71938, "loss_cls": 3.0213, "loss": 3.0213, "time": 0.85221} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00825, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.4575, "top5_acc": 0.71469, "loss_cls": 3.0386, "loss": 3.0386, "time": 0.84714} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.00823, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.45188, "top5_acc": 0.71938, "loss_cls": 3.0366, "loss": 3.0366, "time": 0.85543} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00822, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.45891, "top5_acc": 0.72453, "loss_cls": 3.00388, "loss": 3.00388, "time": 0.85181} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.0082, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.45219, "top5_acc": 0.71531, "loss_cls": 3.06211, "loss": 3.06211, "time": 0.85299} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00818, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44906, "top5_acc": 0.70828, "loss_cls": 3.07431, "loss": 3.07431, "time": 0.85227} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00817, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45359, "top5_acc": 0.71266, "loss_cls": 3.06643, "loss": 3.06643, "time": 0.85301} +{"mode": "train", "epoch": 123, "iter": 1300, "lr": 0.00815, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44266, "top5_acc": 0.70844, "loss_cls": 3.06729, "loss": 3.06729, "time": 0.84886} +{"mode": "train", "epoch": 123, "iter": 1400, "lr": 0.00814, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45953, "top5_acc": 0.70766, "loss_cls": 3.06073, "loss": 3.06073, "time": 0.85236} +{"mode": "train", "epoch": 123, "iter": 1500, "lr": 0.00812, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45969, "top5_acc": 0.70812, "loss_cls": 3.0552, "loss": 3.0552, "time": 0.85138} +{"mode": "train", "epoch": 123, "iter": 1600, "lr": 0.00811, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.46281, "top5_acc": 0.71172, "loss_cls": 3.03644, "loss": 3.03644, "time": 0.85377} +{"mode": "train", "epoch": 123, "iter": 1700, "lr": 0.00809, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45266, "top5_acc": 0.70766, "loss_cls": 3.08428, "loss": 3.08428, "time": 0.8503} +{"mode": "train", "epoch": 123, "iter": 1800, "lr": 0.00808, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46359, "top5_acc": 0.71688, "loss_cls": 3.02402, "loss": 3.02402, "time": 0.84703} +{"mode": "train", "epoch": 123, "iter": 1900, "lr": 0.00806, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45562, "top5_acc": 0.71438, "loss_cls": 3.05024, "loss": 3.05024, "time": 0.85516} +{"mode": "train", "epoch": 123, "iter": 2000, "lr": 0.00805, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.4525, "top5_acc": 0.70922, "loss_cls": 3.071, "loss": 3.071, "time": 0.85016} +{"mode": "train", "epoch": 123, "iter": 2100, "lr": 0.00803, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45562, "top5_acc": 0.71047, "loss_cls": 3.03829, "loss": 3.03829, "time": 0.8474} +{"mode": "train", "epoch": 123, "iter": 2200, "lr": 0.00802, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.45312, "top5_acc": 0.70578, "loss_cls": 3.05943, "loss": 3.05943, "time": 0.85374} +{"mode": "train", "epoch": 123, "iter": 2300, "lr": 0.008, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.45312, "top5_acc": 0.71406, "loss_cls": 3.0521, "loss": 3.0521, "time": 0.85049} +{"mode": "train", "epoch": 123, "iter": 2400, "lr": 0.00799, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.45516, "top5_acc": 0.70609, "loss_cls": 3.07438, "loss": 3.07438, "time": 0.84834} +{"mode": "train", "epoch": 123, "iter": 2500, "lr": 0.00797, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45562, "top5_acc": 0.72078, "loss_cls": 3.02767, "loss": 3.02767, "time": 0.84549} +{"mode": "train", "epoch": 123, "iter": 2600, "lr": 0.00796, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.45109, "top5_acc": 0.695, "loss_cls": 3.09961, "loss": 3.09961, "time": 0.85} +{"mode": "train", "epoch": 123, "iter": 2700, "lr": 0.00794, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44828, "top5_acc": 0.70062, "loss_cls": 3.1209, "loss": 3.1209, "time": 0.84887} +{"mode": "train", "epoch": 123, "iter": 2800, "lr": 0.00793, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.455, "top5_acc": 0.71094, "loss_cls": 3.05583, "loss": 3.05583, "time": 0.84783} +{"mode": "train", "epoch": 123, "iter": 2900, "lr": 0.00791, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.44891, "top5_acc": 0.70141, "loss_cls": 3.08999, "loss": 3.08999, "time": 0.84983} +{"mode": "train", "epoch": 123, "iter": 3000, "lr": 0.0079, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45203, "top5_acc": 0.70422, "loss_cls": 3.09808, "loss": 3.09808, "time": 0.85676} +{"mode": "train", "epoch": 123, "iter": 3100, "lr": 0.00788, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45578, "top5_acc": 0.70922, "loss_cls": 3.0654, "loss": 3.0654, "time": 0.85617} +{"mode": "train", "epoch": 123, "iter": 3200, "lr": 0.00787, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46953, "top5_acc": 0.72547, "loss_cls": 2.99635, "loss": 2.99635, "time": 0.85004} +{"mode": "train", "epoch": 123, "iter": 3300, "lr": 0.00785, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44422, "top5_acc": 0.69938, "loss_cls": 3.13226, "loss": 3.13226, "time": 0.85421} +{"mode": "train", "epoch": 123, "iter": 3400, "lr": 0.00784, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.46375, "top5_acc": 0.71953, "loss_cls": 3.03396, "loss": 3.03396, "time": 0.85436} +{"mode": "train", "epoch": 123, "iter": 3500, "lr": 0.00782, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45078, "top5_acc": 0.70469, "loss_cls": 3.06622, "loss": 3.06622, "time": 0.8571} +{"mode": "train", "epoch": 123, "iter": 3600, "lr": 0.00781, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45641, "top5_acc": 0.70656, "loss_cls": 3.08162, "loss": 3.08162, "time": 0.85377} +{"mode": "train", "epoch": 123, "iter": 3700, "lr": 0.00779, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.44938, "top5_acc": 0.71172, "loss_cls": 3.09365, "loss": 3.09365, "time": 0.85491} +{"mode": "val", "epoch": 123, "iter": 309, "lr": 0.00778, "top1_acc": 0.38768, "top5_acc": 0.63911, "mean_class_accuracy": 0.38747} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00777, "memory": 15990, "data_time": 1.50252, "top1_acc": 0.47359, "top5_acc": 0.7275, "loss_cls": 2.94017, "loss": 2.94017, "time": 2.52845} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00775, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47016, "top5_acc": 0.72172, "loss_cls": 2.98543, "loss": 2.98543, "time": 0.84894} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00774, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.46172, "top5_acc": 0.71125, "loss_cls": 3.01043, "loss": 3.01043, "time": 0.85205} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.00772, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.47516, "top5_acc": 0.73203, "loss_cls": 2.9189, "loss": 2.9189, "time": 0.845} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00771, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.46469, "top5_acc": 0.72281, "loss_cls": 3.00779, "loss": 3.00779, "time": 0.84726} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00769, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47359, "top5_acc": 0.72984, "loss_cls": 2.95432, "loss": 2.95432, "time": 0.84744} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00768, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47578, "top5_acc": 0.72734, "loss_cls": 2.9654, "loss": 2.9654, "time": 0.8435} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00766, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.46922, "top5_acc": 0.72047, "loss_cls": 2.97709, "loss": 2.97709, "time": 0.84525} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00765, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46891, "top5_acc": 0.7225, "loss_cls": 2.9961, "loss": 2.9961, "time": 0.84809} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00763, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46641, "top5_acc": 0.72656, "loss_cls": 2.98719, "loss": 2.98719, "time": 0.85174} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00762, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.4625, "top5_acc": 0.71, "loss_cls": 3.05193, "loss": 3.05193, "time": 0.85454} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.0076, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45453, "top5_acc": 0.71109, "loss_cls": 3.05474, "loss": 3.05474, "time": 0.84823} +{"mode": "train", "epoch": 124, "iter": 1300, "lr": 0.00759, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45547, "top5_acc": 0.70547, "loss_cls": 3.06808, "loss": 3.06808, "time": 0.85063} +{"mode": "train", "epoch": 124, "iter": 1400, "lr": 0.00758, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.46094, "top5_acc": 0.70797, "loss_cls": 3.03359, "loss": 3.03359, "time": 0.84282} +{"mode": "train", "epoch": 124, "iter": 1500, "lr": 0.00756, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45812, "top5_acc": 0.7125, "loss_cls": 3.03422, "loss": 3.03422, "time": 0.85051} +{"mode": "train", "epoch": 124, "iter": 1600, "lr": 0.00755, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46047, "top5_acc": 0.72, "loss_cls": 3.04048, "loss": 3.04048, "time": 0.84855} +{"mode": "train", "epoch": 124, "iter": 1700, "lr": 0.00753, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46203, "top5_acc": 0.71641, "loss_cls": 3.01708, "loss": 3.01708, "time": 0.8428} +{"mode": "train", "epoch": 124, "iter": 1800, "lr": 0.00752, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.4625, "top5_acc": 0.72172, "loss_cls": 3.0154, "loss": 3.0154, "time": 0.85} +{"mode": "train", "epoch": 124, "iter": 1900, "lr": 0.0075, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.46516, "top5_acc": 0.71391, "loss_cls": 2.99885, "loss": 2.99885, "time": 0.8483} +{"mode": "train", "epoch": 124, "iter": 2000, "lr": 0.00749, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45281, "top5_acc": 0.72109, "loss_cls": 3.03877, "loss": 3.03877, "time": 0.84945} +{"mode": "train", "epoch": 124, "iter": 2100, "lr": 0.00747, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45547, "top5_acc": 0.71828, "loss_cls": 3.02559, "loss": 3.02559, "time": 0.84718} +{"mode": "train", "epoch": 124, "iter": 2200, "lr": 0.00746, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.46125, "top5_acc": 0.71859, "loss_cls": 3.01275, "loss": 3.01275, "time": 0.8482} +{"mode": "train", "epoch": 124, "iter": 2300, "lr": 0.00744, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45812, "top5_acc": 0.71656, "loss_cls": 3.01703, "loss": 3.01703, "time": 0.85007} +{"mode": "train", "epoch": 124, "iter": 2400, "lr": 0.00743, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.47344, "top5_acc": 0.72078, "loss_cls": 2.96995, "loss": 2.96995, "time": 0.85105} +{"mode": "train", "epoch": 124, "iter": 2500, "lr": 0.00741, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.46203, "top5_acc": 0.71766, "loss_cls": 3.01095, "loss": 3.01095, "time": 0.8455} +{"mode": "train", "epoch": 124, "iter": 2600, "lr": 0.0074, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44766, "top5_acc": 0.71234, "loss_cls": 3.06034, "loss": 3.06034, "time": 0.8491} +{"mode": "train", "epoch": 124, "iter": 2700, "lr": 0.00738, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45406, "top5_acc": 0.70953, "loss_cls": 3.08207, "loss": 3.08207, "time": 0.84257} +{"mode": "train", "epoch": 124, "iter": 2800, "lr": 0.00737, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.45391, "top5_acc": 0.70797, "loss_cls": 3.05312, "loss": 3.05312, "time": 0.84596} +{"mode": "train", "epoch": 124, "iter": 2900, "lr": 0.00735, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46156, "top5_acc": 0.70984, "loss_cls": 3.05288, "loss": 3.05288, "time": 0.84307} +{"mode": "train", "epoch": 124, "iter": 3000, "lr": 0.00734, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44969, "top5_acc": 0.70766, "loss_cls": 3.08194, "loss": 3.08194, "time": 0.84631} +{"mode": "train", "epoch": 124, "iter": 3100, "lr": 0.00733, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45781, "top5_acc": 0.69781, "loss_cls": 3.09902, "loss": 3.09902, "time": 0.8456} +{"mode": "train", "epoch": 124, "iter": 3200, "lr": 0.00731, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.45188, "top5_acc": 0.70766, "loss_cls": 3.05197, "loss": 3.05197, "time": 0.84897} +{"mode": "train", "epoch": 124, "iter": 3300, "lr": 0.0073, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45281, "top5_acc": 0.71453, "loss_cls": 3.047, "loss": 3.047, "time": 0.84582} +{"mode": "train", "epoch": 124, "iter": 3400, "lr": 0.00728, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46438, "top5_acc": 0.71062, "loss_cls": 3.03826, "loss": 3.03826, "time": 0.84805} +{"mode": "train", "epoch": 124, "iter": 3500, "lr": 0.00727, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46047, "top5_acc": 0.71328, "loss_cls": 3.00238, "loss": 3.00238, "time": 0.84539} +{"mode": "train", "epoch": 124, "iter": 3600, "lr": 0.00725, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46438, "top5_acc": 0.71781, "loss_cls": 3.01886, "loss": 3.01886, "time": 0.84783} +{"mode": "train", "epoch": 124, "iter": 3700, "lr": 0.00724, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45203, "top5_acc": 0.70625, "loss_cls": 3.08196, "loss": 3.08196, "time": 0.84313} +{"mode": "val", "epoch": 124, "iter": 309, "lr": 0.00723, "top1_acc": 0.38429, "top5_acc": 0.64139, "mean_class_accuracy": 0.38416} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.00722, "memory": 15990, "data_time": 1.48865, "top1_acc": 0.48562, "top5_acc": 0.73953, "loss_cls": 2.89266, "loss": 2.89266, "time": 2.51575} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.0072, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46859, "top5_acc": 0.72812, "loss_cls": 2.93766, "loss": 2.93766, "time": 0.85045} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00719, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47422, "top5_acc": 0.72406, "loss_cls": 2.95927, "loss": 2.95927, "time": 0.85003} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00717, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47453, "top5_acc": 0.72656, "loss_cls": 2.94006, "loss": 2.94006, "time": 0.84413} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00716, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48188, "top5_acc": 0.72578, "loss_cls": 2.94664, "loss": 2.94664, "time": 0.84975} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00715, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46375, "top5_acc": 0.71688, "loss_cls": 2.99358, "loss": 2.99358, "time": 0.84612} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00713, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47609, "top5_acc": 0.72328, "loss_cls": 2.94267, "loss": 2.94267, "time": 0.84752} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00712, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.47281, "top5_acc": 0.71938, "loss_cls": 2.96741, "loss": 2.96741, "time": 0.84941} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.0071, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46422, "top5_acc": 0.71031, "loss_cls": 3.02097, "loss": 3.02097, "time": 0.84414} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.00709, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46828, "top5_acc": 0.72328, "loss_cls": 2.98856, "loss": 2.98856, "time": 0.84707} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00707, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47234, "top5_acc": 0.72953, "loss_cls": 2.9506, "loss": 2.9506, "time": 0.8442} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00706, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46766, "top5_acc": 0.72438, "loss_cls": 3.00011, "loss": 3.00011, "time": 0.84888} +{"mode": "train", "epoch": 125, "iter": 1300, "lr": 0.00704, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.46844, "top5_acc": 0.72797, "loss_cls": 2.956, "loss": 2.956, "time": 0.84689} +{"mode": "train", "epoch": 125, "iter": 1400, "lr": 0.00703, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46266, "top5_acc": 0.72344, "loss_cls": 2.97491, "loss": 2.97491, "time": 0.84944} +{"mode": "train", "epoch": 125, "iter": 1500, "lr": 0.00702, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46875, "top5_acc": 0.71734, "loss_cls": 2.98766, "loss": 2.98766, "time": 0.84645} +{"mode": "train", "epoch": 125, "iter": 1600, "lr": 0.007, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.47734, "top5_acc": 0.72984, "loss_cls": 2.94467, "loss": 2.94467, "time": 0.84875} +{"mode": "train", "epoch": 125, "iter": 1700, "lr": 0.00699, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46609, "top5_acc": 0.71422, "loss_cls": 3.00557, "loss": 3.00557, "time": 0.84318} +{"mode": "train", "epoch": 125, "iter": 1800, "lr": 0.00697, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46234, "top5_acc": 0.71344, "loss_cls": 3.01851, "loss": 3.01851, "time": 0.84739} +{"mode": "train", "epoch": 125, "iter": 1900, "lr": 0.00696, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46922, "top5_acc": 0.71719, "loss_cls": 3.0044, "loss": 3.0044, "time": 0.84547} +{"mode": "train", "epoch": 125, "iter": 2000, "lr": 0.00694, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.46766, "top5_acc": 0.72234, "loss_cls": 3.01982, "loss": 3.01982, "time": 0.84668} +{"mode": "train", "epoch": 125, "iter": 2100, "lr": 0.00693, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47234, "top5_acc": 0.72375, "loss_cls": 2.95672, "loss": 2.95672, "time": 0.84531} +{"mode": "train", "epoch": 125, "iter": 2200, "lr": 0.00692, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45844, "top5_acc": 0.71328, "loss_cls": 3.04292, "loss": 3.04292, "time": 0.84384} +{"mode": "train", "epoch": 125, "iter": 2300, "lr": 0.0069, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46328, "top5_acc": 0.71469, "loss_cls": 3.02328, "loss": 3.02328, "time": 0.84652} +{"mode": "train", "epoch": 125, "iter": 2400, "lr": 0.00689, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.46625, "top5_acc": 0.72203, "loss_cls": 2.99982, "loss": 2.99982, "time": 0.84402} +{"mode": "train", "epoch": 125, "iter": 2500, "lr": 0.00687, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.46984, "top5_acc": 0.72297, "loss_cls": 2.98095, "loss": 2.98095, "time": 0.84802} +{"mode": "train", "epoch": 125, "iter": 2600, "lr": 0.00686, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.46719, "top5_acc": 0.72453, "loss_cls": 2.99982, "loss": 2.99982, "time": 0.85077} +{"mode": "train", "epoch": 125, "iter": 2700, "lr": 0.00685, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.46031, "top5_acc": 0.71125, "loss_cls": 3.03628, "loss": 3.03628, "time": 0.8471} +{"mode": "train", "epoch": 125, "iter": 2800, "lr": 0.00683, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.46938, "top5_acc": 0.72266, "loss_cls": 2.97183, "loss": 2.97183, "time": 0.84748} +{"mode": "train", "epoch": 125, "iter": 2900, "lr": 0.00682, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47062, "top5_acc": 0.71922, "loss_cls": 3.00853, "loss": 3.00853, "time": 0.84935} +{"mode": "train", "epoch": 125, "iter": 3000, "lr": 0.0068, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45812, "top5_acc": 0.71156, "loss_cls": 3.05195, "loss": 3.05195, "time": 0.84732} +{"mode": "train", "epoch": 125, "iter": 3100, "lr": 0.00679, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.46938, "top5_acc": 0.72078, "loss_cls": 2.99246, "loss": 2.99246, "time": 0.85064} +{"mode": "train", "epoch": 125, "iter": 3200, "lr": 0.00678, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47219, "top5_acc": 0.71875, "loss_cls": 2.99842, "loss": 2.99842, "time": 0.84851} +{"mode": "train", "epoch": 125, "iter": 3300, "lr": 0.00676, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45609, "top5_acc": 0.70906, "loss_cls": 3.08284, "loss": 3.08284, "time": 0.84949} +{"mode": "train", "epoch": 125, "iter": 3400, "lr": 0.00675, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45688, "top5_acc": 0.71188, "loss_cls": 3.04805, "loss": 3.04805, "time": 0.84565} +{"mode": "train", "epoch": 125, "iter": 3500, "lr": 0.00673, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46422, "top5_acc": 0.71469, "loss_cls": 3.02121, "loss": 3.02121, "time": 0.84512} +{"mode": "train", "epoch": 125, "iter": 3600, "lr": 0.00672, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46188, "top5_acc": 0.71156, "loss_cls": 3.01437, "loss": 3.01437, "time": 0.84805} +{"mode": "train", "epoch": 125, "iter": 3700, "lr": 0.00671, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47141, "top5_acc": 0.72281, "loss_cls": 2.98496, "loss": 2.98496, "time": 0.84546} +{"mode": "val", "epoch": 125, "iter": 309, "lr": 0.0067, "top1_acc": 0.39639, "top5_acc": 0.65507, "mean_class_accuracy": 0.39617} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00668, "memory": 15990, "data_time": 1.47913, "top1_acc": 0.47547, "top5_acc": 0.71844, "loss_cls": 2.95966, "loss": 2.95966, "time": 2.51065} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00667, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48438, "top5_acc": 0.73453, "loss_cls": 2.87907, "loss": 2.87907, "time": 0.84911} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00666, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48594, "top5_acc": 0.72922, "loss_cls": 2.89611, "loss": 2.89611, "time": 0.8496} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00664, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.47188, "top5_acc": 0.72328, "loss_cls": 2.9528, "loss": 2.9528, "time": 0.84681} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00663, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.47641, "top5_acc": 0.72625, "loss_cls": 2.94161, "loss": 2.94161, "time": 0.8516} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00662, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47422, "top5_acc": 0.72984, "loss_cls": 2.95234, "loss": 2.95234, "time": 0.84882} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0066, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46828, "top5_acc": 0.72172, "loss_cls": 2.975, "loss": 2.975, "time": 0.84889} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00659, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47781, "top5_acc": 0.73484, "loss_cls": 2.92669, "loss": 2.92669, "time": 0.84505} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00657, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47594, "top5_acc": 0.73234, "loss_cls": 2.91096, "loss": 2.91096, "time": 0.85051} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00656, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.47859, "top5_acc": 0.73, "loss_cls": 2.9389, "loss": 2.9389, "time": 0.85067} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00655, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47453, "top5_acc": 0.73172, "loss_cls": 2.93955, "loss": 2.93955, "time": 0.84872} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00653, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47828, "top5_acc": 0.72969, "loss_cls": 2.92723, "loss": 2.92723, "time": 0.84981} +{"mode": "train", "epoch": 126, "iter": 1300, "lr": 0.00652, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.47281, "top5_acc": 0.73016, "loss_cls": 2.93609, "loss": 2.93609, "time": 0.84632} +{"mode": "train", "epoch": 126, "iter": 1400, "lr": 0.0065, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.46703, "top5_acc": 0.72344, "loss_cls": 2.98882, "loss": 2.98882, "time": 0.84742} +{"mode": "train", "epoch": 126, "iter": 1500, "lr": 0.00649, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47766, "top5_acc": 0.73594, "loss_cls": 2.92878, "loss": 2.92878, "time": 0.8499} +{"mode": "train", "epoch": 126, "iter": 1600, "lr": 0.00648, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47969, "top5_acc": 0.73375, "loss_cls": 2.91167, "loss": 2.91167, "time": 0.84621} +{"mode": "train", "epoch": 126, "iter": 1700, "lr": 0.00646, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47469, "top5_acc": 0.72359, "loss_cls": 2.95554, "loss": 2.95554, "time": 0.84744} +{"mode": "train", "epoch": 126, "iter": 1800, "lr": 0.00645, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48344, "top5_acc": 0.72672, "loss_cls": 2.93826, "loss": 2.93826, "time": 0.8496} +{"mode": "train", "epoch": 126, "iter": 1900, "lr": 0.00644, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47016, "top5_acc": 0.72703, "loss_cls": 2.96778, "loss": 2.96778, "time": 0.84688} +{"mode": "train", "epoch": 126, "iter": 2000, "lr": 0.00642, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47469, "top5_acc": 0.72922, "loss_cls": 2.93414, "loss": 2.93414, "time": 0.84978} +{"mode": "train", "epoch": 126, "iter": 2100, "lr": 0.00641, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.47219, "top5_acc": 0.72625, "loss_cls": 2.96976, "loss": 2.96976, "time": 0.84622} +{"mode": "train", "epoch": 126, "iter": 2200, "lr": 0.00639, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46641, "top5_acc": 0.71906, "loss_cls": 2.9934, "loss": 2.9934, "time": 0.84835} +{"mode": "train", "epoch": 126, "iter": 2300, "lr": 0.00638, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47875, "top5_acc": 0.72469, "loss_cls": 2.96727, "loss": 2.96727, "time": 0.8476} +{"mode": "train", "epoch": 126, "iter": 2400, "lr": 0.00637, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47781, "top5_acc": 0.72641, "loss_cls": 2.94019, "loss": 2.94019, "time": 0.84904} +{"mode": "train", "epoch": 126, "iter": 2500, "lr": 0.00635, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.47312, "top5_acc": 0.72781, "loss_cls": 2.95167, "loss": 2.95167, "time": 0.84711} +{"mode": "train", "epoch": 126, "iter": 2600, "lr": 0.00634, "memory": 15990, "data_time": 0.0007, "top1_acc": 0.45875, "top5_acc": 0.71188, "loss_cls": 3.03314, "loss": 3.03314, "time": 0.84647} +{"mode": "train", "epoch": 126, "iter": 2700, "lr": 0.00633, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.47922, "top5_acc": 0.72875, "loss_cls": 2.93732, "loss": 2.93732, "time": 0.84844} +{"mode": "train", "epoch": 126, "iter": 2800, "lr": 0.00631, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.4775, "top5_acc": 0.72344, "loss_cls": 2.96877, "loss": 2.96877, "time": 0.84307} +{"mode": "train", "epoch": 126, "iter": 2900, "lr": 0.0063, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46375, "top5_acc": 0.72391, "loss_cls": 2.98847, "loss": 2.98847, "time": 0.85434} +{"mode": "train", "epoch": 126, "iter": 3000, "lr": 0.00629, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46109, "top5_acc": 0.71984, "loss_cls": 3.00731, "loss": 3.00731, "time": 0.84524} +{"mode": "train", "epoch": 126, "iter": 3100, "lr": 0.00627, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46672, "top5_acc": 0.72297, "loss_cls": 2.98762, "loss": 2.98762, "time": 0.84853} +{"mode": "train", "epoch": 126, "iter": 3200, "lr": 0.00626, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46312, "top5_acc": 0.71516, "loss_cls": 3.03104, "loss": 3.03104, "time": 0.84812} +{"mode": "train", "epoch": 126, "iter": 3300, "lr": 0.00625, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47609, "top5_acc": 0.71812, "loss_cls": 2.97161, "loss": 2.97161, "time": 0.84997} +{"mode": "train", "epoch": 126, "iter": 3400, "lr": 0.00623, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.47188, "top5_acc": 0.71422, "loss_cls": 2.97989, "loss": 2.97989, "time": 0.85029} +{"mode": "train", "epoch": 126, "iter": 3500, "lr": 0.00622, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46578, "top5_acc": 0.71969, "loss_cls": 2.99317, "loss": 2.99317, "time": 0.85168} +{"mode": "train", "epoch": 126, "iter": 3600, "lr": 0.0062, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.47172, "top5_acc": 0.71578, "loss_cls": 2.99035, "loss": 2.99035, "time": 0.85049} +{"mode": "train", "epoch": 126, "iter": 3700, "lr": 0.00619, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45844, "top5_acc": 0.71766, "loss_cls": 3.01381, "loss": 3.01381, "time": 0.85434} +{"mode": "val", "epoch": 126, "iter": 309, "lr": 0.00618, "top1_acc": 0.39346, "top5_acc": 0.64945, "mean_class_accuracy": 0.39323} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00617, "memory": 15990, "data_time": 1.5304, "top1_acc": 0.47953, "top5_acc": 0.73125, "loss_cls": 2.89657, "loss": 2.89657, "time": 2.55558} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00616, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47875, "top5_acc": 0.73531, "loss_cls": 2.90162, "loss": 2.90162, "time": 0.85247} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00614, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.48766, "top5_acc": 0.74031, "loss_cls": 2.87137, "loss": 2.87137, "time": 0.84944} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00613, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.48484, "top5_acc": 0.73609, "loss_cls": 2.9054, "loss": 2.9054, "time": 0.84013} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.00612, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.49453, "top5_acc": 0.73859, "loss_cls": 2.88005, "loss": 2.88005, "time": 0.84208} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.0061, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48156, "top5_acc": 0.73438, "loss_cls": 2.91421, "loss": 2.91421, "time": 0.84654} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00609, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.48891, "top5_acc": 0.73844, "loss_cls": 2.86117, "loss": 2.86117, "time": 0.84621} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00608, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.48812, "top5_acc": 0.73438, "loss_cls": 2.89741, "loss": 2.89741, "time": 0.84634} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00606, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.485, "top5_acc": 0.73266, "loss_cls": 2.89463, "loss": 2.89463, "time": 0.84886} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00605, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.49062, "top5_acc": 0.73547, "loss_cls": 2.87833, "loss": 2.87833, "time": 0.85116} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00604, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.4825, "top5_acc": 0.73297, "loss_cls": 2.90641, "loss": 2.90641, "time": 0.84949} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00602, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.4725, "top5_acc": 0.73141, "loss_cls": 2.97184, "loss": 2.97184, "time": 0.84446} +{"mode": "train", "epoch": 127, "iter": 1300, "lr": 0.00601, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47219, "top5_acc": 0.73531, "loss_cls": 2.92375, "loss": 2.92375, "time": 0.85059} +{"mode": "train", "epoch": 127, "iter": 1400, "lr": 0.006, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48312, "top5_acc": 0.73109, "loss_cls": 2.92129, "loss": 2.92129, "time": 0.85115} +{"mode": "train", "epoch": 127, "iter": 1500, "lr": 0.00598, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.48078, "top5_acc": 0.7375, "loss_cls": 2.89322, "loss": 2.89322, "time": 0.84582} +{"mode": "train", "epoch": 127, "iter": 1600, "lr": 0.00597, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.47172, "top5_acc": 0.72609, "loss_cls": 2.93078, "loss": 2.93078, "time": 0.84523} +{"mode": "train", "epoch": 127, "iter": 1700, "lr": 0.00596, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.48234, "top5_acc": 0.73219, "loss_cls": 2.89989, "loss": 2.89989, "time": 0.84927} +{"mode": "train", "epoch": 127, "iter": 1800, "lr": 0.00594, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.4775, "top5_acc": 0.72828, "loss_cls": 2.9393, "loss": 2.9393, "time": 0.84679} +{"mode": "train", "epoch": 127, "iter": 1900, "lr": 0.00593, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48016, "top5_acc": 0.73234, "loss_cls": 2.92654, "loss": 2.92654, "time": 0.84763} +{"mode": "train", "epoch": 127, "iter": 2000, "lr": 0.00592, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.47406, "top5_acc": 0.73312, "loss_cls": 2.94516, "loss": 2.94516, "time": 0.84482} +{"mode": "train", "epoch": 127, "iter": 2100, "lr": 0.00591, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47672, "top5_acc": 0.72734, "loss_cls": 2.97384, "loss": 2.97384, "time": 0.84838} +{"mode": "train", "epoch": 127, "iter": 2200, "lr": 0.00589, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47953, "top5_acc": 0.73656, "loss_cls": 2.89186, "loss": 2.89186, "time": 0.84636} +{"mode": "train", "epoch": 127, "iter": 2300, "lr": 0.00588, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47734, "top5_acc": 0.72688, "loss_cls": 2.96025, "loss": 2.96025, "time": 0.84794} +{"mode": "train", "epoch": 127, "iter": 2400, "lr": 0.00587, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.47578, "top5_acc": 0.72172, "loss_cls": 2.96465, "loss": 2.96465, "time": 0.84915} +{"mode": "train", "epoch": 127, "iter": 2500, "lr": 0.00585, "memory": 15990, "data_time": 0.00084, "top1_acc": 0.48281, "top5_acc": 0.73359, "loss_cls": 2.90575, "loss": 2.90575, "time": 0.85439} +{"mode": "train", "epoch": 127, "iter": 2600, "lr": 0.00584, "memory": 15990, "data_time": 0.00083, "top1_acc": 0.475, "top5_acc": 0.73156, "loss_cls": 2.92678, "loss": 2.92678, "time": 0.85449} +{"mode": "train", "epoch": 127, "iter": 2700, "lr": 0.00583, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.47609, "top5_acc": 0.72922, "loss_cls": 2.9435, "loss": 2.9435, "time": 0.84795} +{"mode": "train", "epoch": 127, "iter": 2800, "lr": 0.00581, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47859, "top5_acc": 0.7275, "loss_cls": 2.93899, "loss": 2.93899, "time": 0.84571} +{"mode": "train", "epoch": 127, "iter": 2900, "lr": 0.0058, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47406, "top5_acc": 0.73062, "loss_cls": 2.93614, "loss": 2.93614, "time": 0.84291} +{"mode": "train", "epoch": 127, "iter": 3000, "lr": 0.00579, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47406, "top5_acc": 0.72578, "loss_cls": 2.95537, "loss": 2.95537, "time": 0.84412} +{"mode": "train", "epoch": 127, "iter": 3100, "lr": 0.00577, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.47078, "top5_acc": 0.72688, "loss_cls": 2.9488, "loss": 2.9488, "time": 0.84629} +{"mode": "train", "epoch": 127, "iter": 3200, "lr": 0.00576, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47562, "top5_acc": 0.72906, "loss_cls": 2.91658, "loss": 2.91658, "time": 0.84559} +{"mode": "train", "epoch": 127, "iter": 3300, "lr": 0.00575, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47453, "top5_acc": 0.7275, "loss_cls": 2.92165, "loss": 2.92165, "time": 0.8473} +{"mode": "train", "epoch": 127, "iter": 3400, "lr": 0.00573, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.46891, "top5_acc": 0.71969, "loss_cls": 2.96076, "loss": 2.96076, "time": 0.8487} +{"mode": "train", "epoch": 127, "iter": 3500, "lr": 0.00572, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.46828, "top5_acc": 0.71812, "loss_cls": 3.01718, "loss": 3.01718, "time": 0.85384} +{"mode": "train", "epoch": 127, "iter": 3600, "lr": 0.00571, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47, "top5_acc": 0.72266, "loss_cls": 2.98423, "loss": 2.98423, "time": 0.84493} +{"mode": "train", "epoch": 127, "iter": 3700, "lr": 0.0057, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47172, "top5_acc": 0.72875, "loss_cls": 2.94187, "loss": 2.94187, "time": 0.84989} +{"mode": "val", "epoch": 127, "iter": 309, "lr": 0.00569, "top1_acc": 0.40475, "top5_acc": 0.65507, "mean_class_accuracy": 0.40443} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00568, "memory": 15990, "data_time": 1.5444, "top1_acc": 0.49938, "top5_acc": 0.74422, "loss_cls": 2.82043, "loss": 2.82043, "time": 2.57458} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.00566, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51313, "top5_acc": 0.75141, "loss_cls": 2.78237, "loss": 2.78237, "time": 0.85205} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00565, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49859, "top5_acc": 0.75141, "loss_cls": 2.78879, "loss": 2.78879, "time": 0.85183} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00564, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48625, "top5_acc": 0.73172, "loss_cls": 2.89521, "loss": 2.89521, "time": 0.84969} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00563, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.49625, "top5_acc": 0.74078, "loss_cls": 2.85266, "loss": 2.85266, "time": 0.84876} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00561, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.49562, "top5_acc": 0.74938, "loss_cls": 2.84364, "loss": 2.84364, "time": 0.84868} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.0056, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.485, "top5_acc": 0.74312, "loss_cls": 2.86918, "loss": 2.86918, "time": 0.8474} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00559, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49641, "top5_acc": 0.73859, "loss_cls": 2.87168, "loss": 2.87168, "time": 0.84611} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00557, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.48359, "top5_acc": 0.73953, "loss_cls": 2.89246, "loss": 2.89246, "time": 0.84813} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00556, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49062, "top5_acc": 0.73625, "loss_cls": 2.90428, "loss": 2.90428, "time": 0.85001} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00555, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.48938, "top5_acc": 0.73594, "loss_cls": 2.87005, "loss": 2.87005, "time": 0.8443} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00554, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47906, "top5_acc": 0.73281, "loss_cls": 2.91856, "loss": 2.91856, "time": 0.84634} +{"mode": "train", "epoch": 128, "iter": 1300, "lr": 0.00552, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.47844, "top5_acc": 0.74016, "loss_cls": 2.88181, "loss": 2.88181, "time": 0.85098} +{"mode": "train", "epoch": 128, "iter": 1400, "lr": 0.00551, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48297, "top5_acc": 0.74078, "loss_cls": 2.88409, "loss": 2.88409, "time": 0.84803} +{"mode": "train", "epoch": 128, "iter": 1500, "lr": 0.0055, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48781, "top5_acc": 0.74, "loss_cls": 2.88399, "loss": 2.88399, "time": 0.85382} +{"mode": "train", "epoch": 128, "iter": 1600, "lr": 0.00548, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.47438, "top5_acc": 0.73312, "loss_cls": 2.92878, "loss": 2.92878, "time": 0.85326} +{"mode": "train", "epoch": 128, "iter": 1700, "lr": 0.00547, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.49078, "top5_acc": 0.73188, "loss_cls": 2.90825, "loss": 2.90825, "time": 0.84904} +{"mode": "train", "epoch": 128, "iter": 1800, "lr": 0.00546, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.49234, "top5_acc": 0.73891, "loss_cls": 2.87369, "loss": 2.87369, "time": 0.84539} +{"mode": "train", "epoch": 128, "iter": 1900, "lr": 0.00545, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.47984, "top5_acc": 0.73266, "loss_cls": 2.89045, "loss": 2.89045, "time": 0.84835} +{"mode": "train", "epoch": 128, "iter": 2000, "lr": 0.00543, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48172, "top5_acc": 0.73453, "loss_cls": 2.8786, "loss": 2.8786, "time": 0.84898} +{"mode": "train", "epoch": 128, "iter": 2100, "lr": 0.00542, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48734, "top5_acc": 0.73844, "loss_cls": 2.85408, "loss": 2.85408, "time": 0.84758} +{"mode": "train", "epoch": 128, "iter": 2200, "lr": 0.00541, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.47656, "top5_acc": 0.72938, "loss_cls": 2.92041, "loss": 2.92041, "time": 0.84886} +{"mode": "train", "epoch": 128, "iter": 2300, "lr": 0.0054, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.48547, "top5_acc": 0.73453, "loss_cls": 2.90318, "loss": 2.90318, "time": 0.85301} +{"mode": "train", "epoch": 128, "iter": 2400, "lr": 0.00538, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.48078, "top5_acc": 0.73422, "loss_cls": 2.91649, "loss": 2.91649, "time": 0.8498} +{"mode": "train", "epoch": 128, "iter": 2500, "lr": 0.00537, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.48469, "top5_acc": 0.72906, "loss_cls": 2.90861, "loss": 2.90861, "time": 0.85241} +{"mode": "train", "epoch": 128, "iter": 2600, "lr": 0.00536, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.48484, "top5_acc": 0.72406, "loss_cls": 2.90849, "loss": 2.90849, "time": 0.84711} +{"mode": "train", "epoch": 128, "iter": 2700, "lr": 0.00535, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.47141, "top5_acc": 0.72219, "loss_cls": 2.97929, "loss": 2.97929, "time": 0.84828} +{"mode": "train", "epoch": 128, "iter": 2800, "lr": 0.00533, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48344, "top5_acc": 0.72594, "loss_cls": 2.91937, "loss": 2.91937, "time": 0.8473} +{"mode": "train", "epoch": 128, "iter": 2900, "lr": 0.00532, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.48125, "top5_acc": 0.73906, "loss_cls": 2.90434, "loss": 2.90434, "time": 0.84534} +{"mode": "train", "epoch": 128, "iter": 3000, "lr": 0.00531, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.4775, "top5_acc": 0.73734, "loss_cls": 2.91484, "loss": 2.91484, "time": 0.85056} +{"mode": "train", "epoch": 128, "iter": 3100, "lr": 0.0053, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47391, "top5_acc": 0.72828, "loss_cls": 2.93303, "loss": 2.93303, "time": 0.84369} +{"mode": "train", "epoch": 128, "iter": 3200, "lr": 0.00528, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47656, "top5_acc": 0.72156, "loss_cls": 2.94167, "loss": 2.94167, "time": 0.84776} +{"mode": "train", "epoch": 128, "iter": 3300, "lr": 0.00527, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.48391, "top5_acc": 0.73469, "loss_cls": 2.89547, "loss": 2.89547, "time": 0.84487} +{"mode": "train", "epoch": 128, "iter": 3400, "lr": 0.00526, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48297, "top5_acc": 0.735, "loss_cls": 2.8825, "loss": 2.8825, "time": 0.84469} +{"mode": "train", "epoch": 128, "iter": 3500, "lr": 0.00525, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48703, "top5_acc": 0.74156, "loss_cls": 2.8729, "loss": 2.8729, "time": 0.84314} +{"mode": "train", "epoch": 128, "iter": 3600, "lr": 0.00523, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.47828, "top5_acc": 0.73047, "loss_cls": 2.92116, "loss": 2.92116, "time": 0.85325} +{"mode": "train", "epoch": 128, "iter": 3700, "lr": 0.00522, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48719, "top5_acc": 0.73578, "loss_cls": 2.89887, "loss": 2.89887, "time": 0.8466} +{"mode": "val", "epoch": 128, "iter": 309, "lr": 0.00521, "top1_acc": 0.40566, "top5_acc": 0.65704, "mean_class_accuracy": 0.40553} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.0052, "memory": 15990, "data_time": 1.51214, "top1_acc": 0.49484, "top5_acc": 0.74109, "loss_cls": 2.85054, "loss": 2.85054, "time": 2.53269} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00519, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.49734, "top5_acc": 0.74594, "loss_cls": 2.82124, "loss": 2.82124, "time": 0.84979} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00518, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.49641, "top5_acc": 0.74734, "loss_cls": 2.81215, "loss": 2.81215, "time": 0.85113} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00516, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.50016, "top5_acc": 0.75641, "loss_cls": 2.77938, "loss": 2.77938, "time": 0.84899} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00515, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49328, "top5_acc": 0.74359, "loss_cls": 2.83299, "loss": 2.83299, "time": 0.84722} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00514, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.4975, "top5_acc": 0.74875, "loss_cls": 2.81285, "loss": 2.81285, "time": 0.84841} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00513, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.51203, "top5_acc": 0.7525, "loss_cls": 2.77967, "loss": 2.77967, "time": 0.84449} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00512, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.50031, "top5_acc": 0.74594, "loss_cls": 2.80988, "loss": 2.80988, "time": 0.84712} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.0051, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48953, "top5_acc": 0.74453, "loss_cls": 2.84768, "loss": 2.84768, "time": 0.84493} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00509, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49984, "top5_acc": 0.74578, "loss_cls": 2.83285, "loss": 2.83285, "time": 0.84559} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00508, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.49703, "top5_acc": 0.75156, "loss_cls": 2.79465, "loss": 2.79465, "time": 0.84622} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.00507, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.48516, "top5_acc": 0.73844, "loss_cls": 2.89475, "loss": 2.89475, "time": 0.85088} +{"mode": "train", "epoch": 129, "iter": 1300, "lr": 0.00505, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49797, "top5_acc": 0.75391, "loss_cls": 2.79264, "loss": 2.79264, "time": 0.84912} +{"mode": "train", "epoch": 129, "iter": 1400, "lr": 0.00504, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.49297, "top5_acc": 0.73859, "loss_cls": 2.87225, "loss": 2.87225, "time": 0.84639} +{"mode": "train", "epoch": 129, "iter": 1500, "lr": 0.00503, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48375, "top5_acc": 0.73609, "loss_cls": 2.87016, "loss": 2.87016, "time": 0.85726} +{"mode": "train", "epoch": 129, "iter": 1600, "lr": 0.00502, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.48734, "top5_acc": 0.73547, "loss_cls": 2.9059, "loss": 2.9059, "time": 0.86381} +{"mode": "train", "epoch": 129, "iter": 1700, "lr": 0.00501, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.50047, "top5_acc": 0.74484, "loss_cls": 2.81388, "loss": 2.81388, "time": 0.85636} +{"mode": "train", "epoch": 129, "iter": 1800, "lr": 0.00499, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49578, "top5_acc": 0.74672, "loss_cls": 2.84155, "loss": 2.84155, "time": 0.85755} +{"mode": "train", "epoch": 129, "iter": 1900, "lr": 0.00498, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.48609, "top5_acc": 0.73125, "loss_cls": 2.8952, "loss": 2.8952, "time": 0.85955} +{"mode": "train", "epoch": 129, "iter": 2000, "lr": 0.00497, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.47359, "top5_acc": 0.73359, "loss_cls": 2.90592, "loss": 2.90592, "time": 0.8603} +{"mode": "train", "epoch": 129, "iter": 2100, "lr": 0.00496, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.50078, "top5_acc": 0.73812, "loss_cls": 2.851, "loss": 2.851, "time": 0.86501} +{"mode": "train", "epoch": 129, "iter": 2200, "lr": 0.00494, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.48047, "top5_acc": 0.73734, "loss_cls": 2.89358, "loss": 2.89358, "time": 0.86253} +{"mode": "train", "epoch": 129, "iter": 2300, "lr": 0.00493, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.48547, "top5_acc": 0.73266, "loss_cls": 2.89289, "loss": 2.89289, "time": 0.86497} +{"mode": "train", "epoch": 129, "iter": 2400, "lr": 0.00492, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.49812, "top5_acc": 0.74188, "loss_cls": 2.83833, "loss": 2.83833, "time": 0.86091} +{"mode": "train", "epoch": 129, "iter": 2500, "lr": 0.00491, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.47703, "top5_acc": 0.73891, "loss_cls": 2.88046, "loss": 2.88046, "time": 0.85994} +{"mode": "train", "epoch": 129, "iter": 2600, "lr": 0.0049, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.48953, "top5_acc": 0.73875, "loss_cls": 2.86353, "loss": 2.86353, "time": 0.84761} +{"mode": "train", "epoch": 129, "iter": 2700, "lr": 0.00488, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.48688, "top5_acc": 0.73547, "loss_cls": 2.86546, "loss": 2.86546, "time": 0.85605} +{"mode": "train", "epoch": 129, "iter": 2800, "lr": 0.00487, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.48141, "top5_acc": 0.74141, "loss_cls": 2.88197, "loss": 2.88197, "time": 0.85819} +{"mode": "train", "epoch": 129, "iter": 2900, "lr": 0.00486, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.485, "top5_acc": 0.74141, "loss_cls": 2.87965, "loss": 2.87965, "time": 0.8566} +{"mode": "train", "epoch": 129, "iter": 3000, "lr": 0.00485, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49406, "top5_acc": 0.74391, "loss_cls": 2.85896, "loss": 2.85896, "time": 0.85564} +{"mode": "train", "epoch": 129, "iter": 3100, "lr": 0.00484, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.48859, "top5_acc": 0.74766, "loss_cls": 2.85553, "loss": 2.85553, "time": 0.85815} +{"mode": "train", "epoch": 129, "iter": 3200, "lr": 0.00482, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49766, "top5_acc": 0.73812, "loss_cls": 2.83518, "loss": 2.83518, "time": 0.85767} +{"mode": "train", "epoch": 129, "iter": 3300, "lr": 0.00481, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.48578, "top5_acc": 0.73547, "loss_cls": 2.89501, "loss": 2.89501, "time": 0.86076} +{"mode": "train", "epoch": 129, "iter": 3400, "lr": 0.0048, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.48156, "top5_acc": 0.73156, "loss_cls": 2.90568, "loss": 2.90568, "time": 0.85531} +{"mode": "train", "epoch": 129, "iter": 3500, "lr": 0.00479, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.48062, "top5_acc": 0.72734, "loss_cls": 2.9095, "loss": 2.9095, "time": 0.85802} +{"mode": "train", "epoch": 129, "iter": 3600, "lr": 0.00478, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.49047, "top5_acc": 0.74156, "loss_cls": 2.85288, "loss": 2.85288, "time": 0.8564} +{"mode": "train", "epoch": 129, "iter": 3700, "lr": 0.00476, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.485, "top5_acc": 0.73656, "loss_cls": 2.88336, "loss": 2.88336, "time": 0.8558} +{"mode": "val", "epoch": 129, "iter": 309, "lr": 0.00476, "top1_acc": 0.40744, "top5_acc": 0.66251, "mean_class_accuracy": 0.40722} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00475, "memory": 15990, "data_time": 1.5412, "top1_acc": 0.50953, "top5_acc": 0.75156, "loss_cls": 2.75491, "loss": 2.75491, "time": 2.60489} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00473, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.50594, "top5_acc": 0.75656, "loss_cls": 2.76113, "loss": 2.76113, "time": 0.85948} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00472, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.50578, "top5_acc": 0.74734, "loss_cls": 2.77947, "loss": 2.77947, "time": 0.85802} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00471, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.51, "top5_acc": 0.75891, "loss_cls": 2.75388, "loss": 2.75388, "time": 0.8618} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.0047, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.50047, "top5_acc": 0.75156, "loss_cls": 2.79007, "loss": 2.79007, "time": 0.85427} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00469, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.49281, "top5_acc": 0.74625, "loss_cls": 2.82057, "loss": 2.82057, "time": 0.85912} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00468, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.5025, "top5_acc": 0.75547, "loss_cls": 2.76892, "loss": 2.76892, "time": 0.86357} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00466, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.50344, "top5_acc": 0.75234, "loss_cls": 2.77025, "loss": 2.77025, "time": 0.86143} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00465, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.50953, "top5_acc": 0.74672, "loss_cls": 2.76494, "loss": 2.76494, "time": 0.86761} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.00464, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50609, "top5_acc": 0.75219, "loss_cls": 2.78362, "loss": 2.78362, "time": 0.8612} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.00463, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.49297, "top5_acc": 0.74766, "loss_cls": 2.84076, "loss": 2.84076, "time": 0.86779} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00462, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.48859, "top5_acc": 0.73953, "loss_cls": 2.85741, "loss": 2.85741, "time": 0.86751} +{"mode": "train", "epoch": 130, "iter": 1300, "lr": 0.00461, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.495, "top5_acc": 0.75172, "loss_cls": 2.8069, "loss": 2.8069, "time": 0.8652} +{"mode": "train", "epoch": 130, "iter": 1400, "lr": 0.00459, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50328, "top5_acc": 0.75359, "loss_cls": 2.80077, "loss": 2.80077, "time": 0.86133} +{"mode": "train", "epoch": 130, "iter": 1500, "lr": 0.00458, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.50719, "top5_acc": 0.75297, "loss_cls": 2.77667, "loss": 2.77667, "time": 0.8703} +{"mode": "train", "epoch": 130, "iter": 1600, "lr": 0.00457, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.50125, "top5_acc": 0.75062, "loss_cls": 2.81037, "loss": 2.81037, "time": 0.86496} +{"mode": "train", "epoch": 130, "iter": 1700, "lr": 0.00456, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.49484, "top5_acc": 0.73641, "loss_cls": 2.84293, "loss": 2.84293, "time": 0.86725} +{"mode": "train", "epoch": 130, "iter": 1800, "lr": 0.00455, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.50187, "top5_acc": 0.74781, "loss_cls": 2.82048, "loss": 2.82048, "time": 0.87233} +{"mode": "train", "epoch": 130, "iter": 1900, "lr": 0.00454, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.49688, "top5_acc": 0.74891, "loss_cls": 2.80619, "loss": 2.80619, "time": 0.8756} +{"mode": "train", "epoch": 130, "iter": 2000, "lr": 0.00452, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.50688, "top5_acc": 0.75203, "loss_cls": 2.80453, "loss": 2.80453, "time": 0.87177} +{"mode": "train", "epoch": 130, "iter": 2100, "lr": 0.00451, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.48828, "top5_acc": 0.74406, "loss_cls": 2.84969, "loss": 2.84969, "time": 0.87229} +{"mode": "train", "epoch": 130, "iter": 2200, "lr": 0.0045, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.49594, "top5_acc": 0.74219, "loss_cls": 2.86348, "loss": 2.86348, "time": 0.87642} +{"mode": "train", "epoch": 130, "iter": 2300, "lr": 0.00449, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.49875, "top5_acc": 0.74672, "loss_cls": 2.79212, "loss": 2.79212, "time": 0.87508} +{"mode": "train", "epoch": 130, "iter": 2400, "lr": 0.00448, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.49641, "top5_acc": 0.74328, "loss_cls": 2.84054, "loss": 2.84054, "time": 0.8621} +{"mode": "train", "epoch": 130, "iter": 2500, "lr": 0.00447, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.49547, "top5_acc": 0.74625, "loss_cls": 2.81006, "loss": 2.81006, "time": 0.86649} +{"mode": "train", "epoch": 130, "iter": 2600, "lr": 0.00445, "memory": 15990, "data_time": 0.0009, "top1_acc": 0.49438, "top5_acc": 0.73672, "loss_cls": 2.85445, "loss": 2.85445, "time": 0.85505} +{"mode": "train", "epoch": 130, "iter": 2700, "lr": 0.00444, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.48641, "top5_acc": 0.73609, "loss_cls": 2.88959, "loss": 2.88959, "time": 0.86429} +{"mode": "train", "epoch": 130, "iter": 2800, "lr": 0.00443, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.48516, "top5_acc": 0.73594, "loss_cls": 2.86118, "loss": 2.86118, "time": 0.86288} +{"mode": "train", "epoch": 130, "iter": 2900, "lr": 0.00442, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.49703, "top5_acc": 0.74547, "loss_cls": 2.82292, "loss": 2.82292, "time": 0.86503} +{"mode": "train", "epoch": 130, "iter": 3000, "lr": 0.00441, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.48891, "top5_acc": 0.74031, "loss_cls": 2.84726, "loss": 2.84726, "time": 0.86324} +{"mode": "train", "epoch": 130, "iter": 3100, "lr": 0.0044, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49594, "top5_acc": 0.74031, "loss_cls": 2.83992, "loss": 2.83992, "time": 0.86283} +{"mode": "train", "epoch": 130, "iter": 3200, "lr": 0.00439, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.49875, "top5_acc": 0.75297, "loss_cls": 2.79561, "loss": 2.79561, "time": 0.8639} +{"mode": "train", "epoch": 130, "iter": 3300, "lr": 0.00437, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50156, "top5_acc": 0.74719, "loss_cls": 2.81286, "loss": 2.81286, "time": 0.87133} +{"mode": "train", "epoch": 130, "iter": 3400, "lr": 0.00436, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.48938, "top5_acc": 0.73672, "loss_cls": 2.86156, "loss": 2.86156, "time": 0.86155} +{"mode": "train", "epoch": 130, "iter": 3500, "lr": 0.00435, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.50391, "top5_acc": 0.7425, "loss_cls": 2.83036, "loss": 2.83036, "time": 0.86332} +{"mode": "train", "epoch": 130, "iter": 3600, "lr": 0.00434, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.49125, "top5_acc": 0.74953, "loss_cls": 2.8347, "loss": 2.8347, "time": 0.86083} +{"mode": "train", "epoch": 130, "iter": 3700, "lr": 0.00433, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.49672, "top5_acc": 0.74688, "loss_cls": 2.8208, "loss": 2.8208, "time": 0.85957} +{"mode": "val", "epoch": 130, "iter": 309, "lr": 0.00432, "top1_acc": 0.41306, "top5_acc": 0.66464, "mean_class_accuracy": 0.41272} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00431, "memory": 15990, "data_time": 1.5139, "top1_acc": 0.50906, "top5_acc": 0.76453, "loss_cls": 2.71024, "loss": 2.71024, "time": 2.55793} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.0043, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.51812, "top5_acc": 0.76312, "loss_cls": 2.69942, "loss": 2.69942, "time": 0.86263} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00429, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51625, "top5_acc": 0.76031, "loss_cls": 2.71996, "loss": 2.71996, "time": 0.86557} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00428, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.51078, "top5_acc": 0.75641, "loss_cls": 2.73928, "loss": 2.73928, "time": 0.85815} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00427, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.49688, "top5_acc": 0.74375, "loss_cls": 2.82975, "loss": 2.82975, "time": 0.86377} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00425, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.51922, "top5_acc": 0.76766, "loss_cls": 2.69758, "loss": 2.69758, "time": 0.8525} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00424, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.50797, "top5_acc": 0.75547, "loss_cls": 2.76245, "loss": 2.76245, "time": 0.85735} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00423, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.51375, "top5_acc": 0.75797, "loss_cls": 2.7329, "loss": 2.7329, "time": 0.86618} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00422, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.51062, "top5_acc": 0.75453, "loss_cls": 2.74879, "loss": 2.74879, "time": 0.86441} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.00421, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.50891, "top5_acc": 0.75406, "loss_cls": 2.74233, "loss": 2.74233, "time": 0.86536} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.0042, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.51969, "top5_acc": 0.75797, "loss_cls": 2.73899, "loss": 2.73899, "time": 0.86897} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00419, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.51438, "top5_acc": 0.75484, "loss_cls": 2.76436, "loss": 2.76436, "time": 0.87109} +{"mode": "train", "epoch": 131, "iter": 1300, "lr": 0.00418, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.50406, "top5_acc": 0.7625, "loss_cls": 2.7534, "loss": 2.7534, "time": 0.86403} +{"mode": "train", "epoch": 131, "iter": 1400, "lr": 0.00417, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.50922, "top5_acc": 0.7575, "loss_cls": 2.77572, "loss": 2.77572, "time": 0.86779} +{"mode": "train", "epoch": 131, "iter": 1500, "lr": 0.00415, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.51172, "top5_acc": 0.75969, "loss_cls": 2.739, "loss": 2.739, "time": 0.86746} +{"mode": "train", "epoch": 131, "iter": 1600, "lr": 0.00414, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.50531, "top5_acc": 0.75672, "loss_cls": 2.76709, "loss": 2.76709, "time": 0.87159} +{"mode": "train", "epoch": 131, "iter": 1700, "lr": 0.00413, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.51484, "top5_acc": 0.75391, "loss_cls": 2.77969, "loss": 2.77969, "time": 0.87168} +{"mode": "train", "epoch": 131, "iter": 1800, "lr": 0.00412, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.49984, "top5_acc": 0.73766, "loss_cls": 2.84608, "loss": 2.84608, "time": 0.86918} +{"mode": "train", "epoch": 131, "iter": 1900, "lr": 0.00411, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.49859, "top5_acc": 0.75031, "loss_cls": 2.82139, "loss": 2.82139, "time": 0.87208} +{"mode": "train", "epoch": 131, "iter": 2000, "lr": 0.0041, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.50094, "top5_acc": 0.75266, "loss_cls": 2.78731, "loss": 2.78731, "time": 0.8702} +{"mode": "train", "epoch": 131, "iter": 2100, "lr": 0.00409, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.49719, "top5_acc": 0.74453, "loss_cls": 2.83117, "loss": 2.83117, "time": 0.86909} +{"mode": "train", "epoch": 131, "iter": 2200, "lr": 0.00408, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.50656, "top5_acc": 0.74578, "loss_cls": 2.78974, "loss": 2.78974, "time": 0.86887} +{"mode": "train", "epoch": 131, "iter": 2300, "lr": 0.00407, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.49688, "top5_acc": 0.74703, "loss_cls": 2.79346, "loss": 2.79346, "time": 0.86583} +{"mode": "train", "epoch": 131, "iter": 2400, "lr": 0.00405, "memory": 15990, "data_time": 0.00088, "top1_acc": 0.50297, "top5_acc": 0.74719, "loss_cls": 2.82221, "loss": 2.82221, "time": 0.86056} +{"mode": "train", "epoch": 131, "iter": 2500, "lr": 0.00404, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49906, "top5_acc": 0.74594, "loss_cls": 2.81083, "loss": 2.81083, "time": 0.85867} +{"mode": "train", "epoch": 131, "iter": 2600, "lr": 0.00403, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.50219, "top5_acc": 0.74812, "loss_cls": 2.79841, "loss": 2.79841, "time": 0.84714} +{"mode": "train", "epoch": 131, "iter": 2700, "lr": 0.00402, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.50281, "top5_acc": 0.74594, "loss_cls": 2.81964, "loss": 2.81964, "time": 0.86668} +{"mode": "train", "epoch": 131, "iter": 2800, "lr": 0.00401, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.49609, "top5_acc": 0.74859, "loss_cls": 2.82789, "loss": 2.82789, "time": 0.8628} +{"mode": "train", "epoch": 131, "iter": 2900, "lr": 0.004, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.50797, "top5_acc": 0.75078, "loss_cls": 2.79546, "loss": 2.79546, "time": 0.8675} +{"mode": "train", "epoch": 131, "iter": 3000, "lr": 0.00399, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.4975, "top5_acc": 0.745, "loss_cls": 2.80009, "loss": 2.80009, "time": 0.8596} +{"mode": "train", "epoch": 131, "iter": 3100, "lr": 0.00398, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.49969, "top5_acc": 0.74656, "loss_cls": 2.81672, "loss": 2.81672, "time": 0.86435} +{"mode": "train", "epoch": 131, "iter": 3200, "lr": 0.00397, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.50391, "top5_acc": 0.75781, "loss_cls": 2.76959, "loss": 2.76959, "time": 0.86507} +{"mode": "train", "epoch": 131, "iter": 3300, "lr": 0.00396, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.49781, "top5_acc": 0.74734, "loss_cls": 2.81983, "loss": 2.81983, "time": 0.85994} +{"mode": "train", "epoch": 131, "iter": 3400, "lr": 0.00394, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.4975, "top5_acc": 0.74938, "loss_cls": 2.80983, "loss": 2.80983, "time": 0.86119} +{"mode": "train", "epoch": 131, "iter": 3500, "lr": 0.00393, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.49953, "top5_acc": 0.74828, "loss_cls": 2.81381, "loss": 2.81381, "time": 0.86977} +{"mode": "train", "epoch": 131, "iter": 3600, "lr": 0.00392, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.50047, "top5_acc": 0.75031, "loss_cls": 2.79338, "loss": 2.79338, "time": 0.87249} +{"mode": "train", "epoch": 131, "iter": 3700, "lr": 0.00391, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.5075, "top5_acc": 0.75203, "loss_cls": 2.78899, "loss": 2.78899, "time": 0.86853} +{"mode": "val", "epoch": 131, "iter": 309, "lr": 0.00391, "top1_acc": 0.4204, "top5_acc": 0.67031, "mean_class_accuracy": 0.42002} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.0039, "memory": 15990, "data_time": 1.53679, "top1_acc": 0.53062, "top5_acc": 0.78156, "loss_cls": 2.61115, "loss": 2.61115, "time": 2.60916} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00389, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.52391, "top5_acc": 0.77281, "loss_cls": 2.64813, "loss": 2.64813, "time": 0.87135} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00387, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.51641, "top5_acc": 0.75922, "loss_cls": 2.7314, "loss": 2.7314, "time": 0.87089} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00386, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.51922, "top5_acc": 0.76641, "loss_cls": 2.67526, "loss": 2.67526, "time": 0.87614} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00385, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.51844, "top5_acc": 0.76016, "loss_cls": 2.70455, "loss": 2.70455, "time": 0.85736} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00384, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.52047, "top5_acc": 0.76359, "loss_cls": 2.69345, "loss": 2.69345, "time": 0.85528} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00383, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51594, "top5_acc": 0.76766, "loss_cls": 2.69695, "loss": 2.69695, "time": 0.85799} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00382, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.51672, "top5_acc": 0.76453, "loss_cls": 2.70756, "loss": 2.70756, "time": 0.86302} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00381, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51547, "top5_acc": 0.76125, "loss_cls": 2.71641, "loss": 2.71641, "time": 0.86266} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0038, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.51656, "top5_acc": 0.765, "loss_cls": 2.69651, "loss": 2.69651, "time": 0.86807} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00379, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.51031, "top5_acc": 0.74531, "loss_cls": 2.78763, "loss": 2.78763, "time": 0.87029} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00378, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.50547, "top5_acc": 0.75734, "loss_cls": 2.74322, "loss": 2.74322, "time": 0.86718} +{"mode": "train", "epoch": 132, "iter": 1300, "lr": 0.00377, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.50594, "top5_acc": 0.75156, "loss_cls": 2.7712, "loss": 2.7712, "time": 0.87054} +{"mode": "train", "epoch": 132, "iter": 1400, "lr": 0.00376, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.50781, "top5_acc": 0.75141, "loss_cls": 2.73461, "loss": 2.73461, "time": 0.86546} +{"mode": "train", "epoch": 132, "iter": 1500, "lr": 0.00375, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51891, "top5_acc": 0.76281, "loss_cls": 2.71849, "loss": 2.71849, "time": 0.86612} +{"mode": "train", "epoch": 132, "iter": 1600, "lr": 0.00374, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.51469, "top5_acc": 0.75125, "loss_cls": 2.74727, "loss": 2.74727, "time": 0.87295} +{"mode": "train", "epoch": 132, "iter": 1700, "lr": 0.00372, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.51125, "top5_acc": 0.75859, "loss_cls": 2.7522, "loss": 2.7522, "time": 0.86584} +{"mode": "train", "epoch": 132, "iter": 1800, "lr": 0.00371, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.505, "top5_acc": 0.75719, "loss_cls": 2.74012, "loss": 2.74012, "time": 0.86519} +{"mode": "train", "epoch": 132, "iter": 1900, "lr": 0.0037, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.51234, "top5_acc": 0.75828, "loss_cls": 2.75474, "loss": 2.75474, "time": 0.8652} +{"mode": "train", "epoch": 132, "iter": 2000, "lr": 0.00369, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50734, "top5_acc": 0.755, "loss_cls": 2.74845, "loss": 2.74845, "time": 0.86169} +{"mode": "train", "epoch": 132, "iter": 2100, "lr": 0.00368, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.49969, "top5_acc": 0.74859, "loss_cls": 2.80371, "loss": 2.80371, "time": 0.86616} +{"mode": "train", "epoch": 132, "iter": 2200, "lr": 0.00367, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.50594, "top5_acc": 0.75578, "loss_cls": 2.7618, "loss": 2.7618, "time": 0.87312} +{"mode": "train", "epoch": 132, "iter": 2300, "lr": 0.00366, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.50187, "top5_acc": 0.75375, "loss_cls": 2.78915, "loss": 2.78915, "time": 0.86925} +{"mode": "train", "epoch": 132, "iter": 2400, "lr": 0.00365, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.5125, "top5_acc": 0.7525, "loss_cls": 2.76347, "loss": 2.76347, "time": 0.86194} +{"mode": "train", "epoch": 132, "iter": 2500, "lr": 0.00364, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.52109, "top5_acc": 0.76406, "loss_cls": 2.68618, "loss": 2.68618, "time": 0.86014} +{"mode": "train", "epoch": 132, "iter": 2600, "lr": 0.00363, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.50844, "top5_acc": 0.7525, "loss_cls": 2.73662, "loss": 2.73662, "time": 0.85829} +{"mode": "train", "epoch": 132, "iter": 2700, "lr": 0.00362, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.51516, "top5_acc": 0.76062, "loss_cls": 2.72386, "loss": 2.72386, "time": 0.86329} +{"mode": "train", "epoch": 132, "iter": 2800, "lr": 0.00361, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.50094, "top5_acc": 0.745, "loss_cls": 2.79556, "loss": 2.79556, "time": 0.86878} +{"mode": "train", "epoch": 132, "iter": 2900, "lr": 0.0036, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.50734, "top5_acc": 0.75359, "loss_cls": 2.76069, "loss": 2.76069, "time": 0.8666} +{"mode": "train", "epoch": 132, "iter": 3000, "lr": 0.00359, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.51219, "top5_acc": 0.76516, "loss_cls": 2.73189, "loss": 2.73189, "time": 0.86505} +{"mode": "train", "epoch": 132, "iter": 3100, "lr": 0.00358, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.50609, "top5_acc": 0.74734, "loss_cls": 2.80354, "loss": 2.80354, "time": 0.8729} +{"mode": "train", "epoch": 132, "iter": 3200, "lr": 0.00357, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.50656, "top5_acc": 0.75188, "loss_cls": 2.7689, "loss": 2.7689, "time": 0.86296} +{"mode": "train", "epoch": 132, "iter": 3300, "lr": 0.00356, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51125, "top5_acc": 0.76688, "loss_cls": 2.69899, "loss": 2.69899, "time": 0.86787} +{"mode": "train", "epoch": 132, "iter": 3400, "lr": 0.00355, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.52, "top5_acc": 0.75906, "loss_cls": 2.71684, "loss": 2.71684, "time": 0.8624} +{"mode": "train", "epoch": 132, "iter": 3500, "lr": 0.00354, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50844, "top5_acc": 0.75516, "loss_cls": 2.75161, "loss": 2.75161, "time": 0.867} +{"mode": "train", "epoch": 132, "iter": 3600, "lr": 0.00353, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.49547, "top5_acc": 0.74875, "loss_cls": 2.80036, "loss": 2.80036, "time": 0.86934} +{"mode": "train", "epoch": 132, "iter": 3700, "lr": 0.00352, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.51297, "top5_acc": 0.75859, "loss_cls": 2.75375, "loss": 2.75375, "time": 0.8641} +{"mode": "val", "epoch": 132, "iter": 309, "lr": 0.00351, "top1_acc": 0.41888, "top5_acc": 0.67406, "mean_class_accuracy": 0.41862} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.0035, "memory": 15990, "data_time": 1.57207, "top1_acc": 0.52781, "top5_acc": 0.77047, "loss_cls": 2.67112, "loss": 2.67112, "time": 2.63544} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00349, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.52906, "top5_acc": 0.76328, "loss_cls": 2.65843, "loss": 2.65843, "time": 0.86926} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00348, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.54172, "top5_acc": 0.78422, "loss_cls": 2.57518, "loss": 2.57518, "time": 0.87037} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00347, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.52281, "top5_acc": 0.77312, "loss_cls": 2.64509, "loss": 2.64509, "time": 0.87188} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00346, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.53594, "top5_acc": 0.76734, "loss_cls": 2.66742, "loss": 2.66742, "time": 0.85731} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00345, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.53375, "top5_acc": 0.775, "loss_cls": 2.6244, "loss": 2.6244, "time": 0.85573} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00344, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.52266, "top5_acc": 0.76578, "loss_cls": 2.68518, "loss": 2.68518, "time": 0.85113} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00343, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.52156, "top5_acc": 0.77328, "loss_cls": 2.65386, "loss": 2.65386, "time": 0.85881} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00342, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.52766, "top5_acc": 0.75969, "loss_cls": 2.68147, "loss": 2.68147, "time": 0.86666} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.00341, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.52156, "top5_acc": 0.76797, "loss_cls": 2.6751, "loss": 2.6751, "time": 0.86894} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0034, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.52141, "top5_acc": 0.76203, "loss_cls": 2.70616, "loss": 2.70616, "time": 0.86452} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00339, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.52578, "top5_acc": 0.76312, "loss_cls": 2.66233, "loss": 2.66233, "time": 0.8656} +{"mode": "train", "epoch": 133, "iter": 1300, "lr": 0.00338, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.52156, "top5_acc": 0.75953, "loss_cls": 2.68991, "loss": 2.68991, "time": 0.87241} +{"mode": "train", "epoch": 133, "iter": 1400, "lr": 0.00337, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.50688, "top5_acc": 0.76438, "loss_cls": 2.71656, "loss": 2.71656, "time": 0.86967} +{"mode": "train", "epoch": 133, "iter": 1500, "lr": 0.00336, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.50906, "top5_acc": 0.765, "loss_cls": 2.69527, "loss": 2.69527, "time": 0.86885} +{"mode": "train", "epoch": 133, "iter": 1600, "lr": 0.00335, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51891, "top5_acc": 0.77078, "loss_cls": 2.70788, "loss": 2.70788, "time": 0.86704} +{"mode": "train", "epoch": 133, "iter": 1700, "lr": 0.00334, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51562, "top5_acc": 0.76328, "loss_cls": 2.7282, "loss": 2.7282, "time": 0.87138} +{"mode": "train", "epoch": 133, "iter": 1800, "lr": 0.00333, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.52328, "top5_acc": 0.76281, "loss_cls": 2.69581, "loss": 2.69581, "time": 0.86671} +{"mode": "train", "epoch": 133, "iter": 1900, "lr": 0.00332, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.50922, "top5_acc": 0.76047, "loss_cls": 2.71197, "loss": 2.71197, "time": 0.87053} +{"mode": "train", "epoch": 133, "iter": 2000, "lr": 0.00331, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.51859, "top5_acc": 0.76312, "loss_cls": 2.69022, "loss": 2.69022, "time": 0.8738} +{"mode": "train", "epoch": 133, "iter": 2100, "lr": 0.0033, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.52141, "top5_acc": 0.76219, "loss_cls": 2.69623, "loss": 2.69623, "time": 0.86748} +{"mode": "train", "epoch": 133, "iter": 2200, "lr": 0.00329, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.51344, "top5_acc": 0.76078, "loss_cls": 2.73841, "loss": 2.73841, "time": 0.86737} +{"mode": "train", "epoch": 133, "iter": 2300, "lr": 0.00328, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.52156, "top5_acc": 0.76812, "loss_cls": 2.68495, "loss": 2.68495, "time": 0.86671} +{"mode": "train", "epoch": 133, "iter": 2400, "lr": 0.00327, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.52094, "top5_acc": 0.76297, "loss_cls": 2.69047, "loss": 2.69047, "time": 0.86658} +{"mode": "train", "epoch": 133, "iter": 2500, "lr": 0.00326, "memory": 15990, "data_time": 0.0007, "top1_acc": 0.50187, "top5_acc": 0.75641, "loss_cls": 2.76742, "loss": 2.76742, "time": 0.85937} +{"mode": "train", "epoch": 133, "iter": 2600, "lr": 0.00325, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.50938, "top5_acc": 0.76047, "loss_cls": 2.73145, "loss": 2.73145, "time": 0.85715} +{"mode": "train", "epoch": 133, "iter": 2700, "lr": 0.00324, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.51922, "top5_acc": 0.76656, "loss_cls": 2.68602, "loss": 2.68602, "time": 0.85359} +{"mode": "train", "epoch": 133, "iter": 2800, "lr": 0.00323, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51859, "top5_acc": 0.76188, "loss_cls": 2.70754, "loss": 2.70754, "time": 0.8544} +{"mode": "train", "epoch": 133, "iter": 2900, "lr": 0.00322, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.50062, "top5_acc": 0.75266, "loss_cls": 2.78059, "loss": 2.78059, "time": 0.85624} +{"mode": "train", "epoch": 133, "iter": 3000, "lr": 0.00321, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.50906, "top5_acc": 0.76391, "loss_cls": 2.72172, "loss": 2.72172, "time": 0.86145} +{"mode": "train", "epoch": 133, "iter": 3100, "lr": 0.0032, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.52375, "top5_acc": 0.76938, "loss_cls": 2.65212, "loss": 2.65212, "time": 0.86149} +{"mode": "train", "epoch": 133, "iter": 3200, "lr": 0.00319, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.52609, "top5_acc": 0.76656, "loss_cls": 2.6833, "loss": 2.6833, "time": 0.86431} +{"mode": "train", "epoch": 133, "iter": 3300, "lr": 0.00318, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.50438, "top5_acc": 0.75516, "loss_cls": 2.74935, "loss": 2.74935, "time": 0.86387} +{"mode": "train", "epoch": 133, "iter": 3400, "lr": 0.00317, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51938, "top5_acc": 0.76312, "loss_cls": 2.69452, "loss": 2.69452, "time": 0.86835} +{"mode": "train", "epoch": 133, "iter": 3500, "lr": 0.00316, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.50234, "top5_acc": 0.75062, "loss_cls": 2.74671, "loss": 2.74671, "time": 0.86157} +{"mode": "train", "epoch": 133, "iter": 3600, "lr": 0.00315, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.5075, "top5_acc": 0.75906, "loss_cls": 2.75073, "loss": 2.75073, "time": 0.86852} +{"mode": "train", "epoch": 133, "iter": 3700, "lr": 0.00314, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51797, "top5_acc": 0.76906, "loss_cls": 2.69461, "loss": 2.69461, "time": 0.86345} +{"mode": "val", "epoch": 133, "iter": 309, "lr": 0.00314, "top1_acc": 0.42324, "top5_acc": 0.67563, "mean_class_accuracy": 0.42303} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00313, "memory": 15990, "data_time": 1.56321, "top1_acc": 0.54906, "top5_acc": 0.78703, "loss_cls": 2.53715, "loss": 2.53715, "time": 2.63298} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00312, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.52344, "top5_acc": 0.77438, "loss_cls": 2.62849, "loss": 2.62849, "time": 0.8722} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00311, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.52703, "top5_acc": 0.77344, "loss_cls": 2.66075, "loss": 2.66075, "time": 0.87015} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.0031, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.53484, "top5_acc": 0.77766, "loss_cls": 2.62935, "loss": 2.62935, "time": 0.8763} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00309, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.53438, "top5_acc": 0.78297, "loss_cls": 2.6091, "loss": 2.6091, "time": 0.87535} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00308, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.53391, "top5_acc": 0.77109, "loss_cls": 2.64274, "loss": 2.64274, "time": 0.86073} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00307, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.53422, "top5_acc": 0.78922, "loss_cls": 2.5938, "loss": 2.5938, "time": 0.85325} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00306, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.53297, "top5_acc": 0.77203, "loss_cls": 2.61766, "loss": 2.61766, "time": 0.85282} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00305, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.53656, "top5_acc": 0.77375, "loss_cls": 2.64548, "loss": 2.64548, "time": 0.86063} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00304, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.53656, "top5_acc": 0.77672, "loss_cls": 2.61371, "loss": 2.61371, "time": 0.86668} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00303, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.53203, "top5_acc": 0.77719, "loss_cls": 2.6143, "loss": 2.6143, "time": 0.86133} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.00302, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.5175, "top5_acc": 0.76422, "loss_cls": 2.68582, "loss": 2.68582, "time": 0.8647} +{"mode": "train", "epoch": 134, "iter": 1300, "lr": 0.00301, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.54406, "top5_acc": 0.78016, "loss_cls": 2.59647, "loss": 2.59647, "time": 0.86696} +{"mode": "train", "epoch": 134, "iter": 1400, "lr": 0.003, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.52469, "top5_acc": 0.76547, "loss_cls": 2.67238, "loss": 2.67238, "time": 0.86621} +{"mode": "train", "epoch": 134, "iter": 1500, "lr": 0.00299, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.53062, "top5_acc": 0.77219, "loss_cls": 2.64443, "loss": 2.64443, "time": 0.86276} +{"mode": "train", "epoch": 134, "iter": 1600, "lr": 0.00298, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53266, "top5_acc": 0.77375, "loss_cls": 2.65266, "loss": 2.65266, "time": 0.86945} +{"mode": "train", "epoch": 134, "iter": 1700, "lr": 0.00297, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.53219, "top5_acc": 0.76469, "loss_cls": 2.66043, "loss": 2.66043, "time": 0.86311} +{"mode": "train", "epoch": 134, "iter": 1800, "lr": 0.00296, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.51984, "top5_acc": 0.76156, "loss_cls": 2.73165, "loss": 2.73165, "time": 0.8646} +{"mode": "train", "epoch": 134, "iter": 1900, "lr": 0.00295, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.53453, "top5_acc": 0.77328, "loss_cls": 2.62664, "loss": 2.62664, "time": 0.86192} +{"mode": "train", "epoch": 134, "iter": 2000, "lr": 0.00294, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.52984, "top5_acc": 0.76875, "loss_cls": 2.66793, "loss": 2.66793, "time": 0.86326} +{"mode": "train", "epoch": 134, "iter": 2100, "lr": 0.00293, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53078, "top5_acc": 0.77531, "loss_cls": 2.62241, "loss": 2.62241, "time": 0.85885} +{"mode": "train", "epoch": 134, "iter": 2200, "lr": 0.00293, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.52562, "top5_acc": 0.76562, "loss_cls": 2.65267, "loss": 2.65267, "time": 0.86264} +{"mode": "train", "epoch": 134, "iter": 2300, "lr": 0.00292, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.53047, "top5_acc": 0.77516, "loss_cls": 2.62121, "loss": 2.62121, "time": 0.86607} +{"mode": "train", "epoch": 134, "iter": 2400, "lr": 0.00291, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.52141, "top5_acc": 0.75578, "loss_cls": 2.69421, "loss": 2.69421, "time": 0.86006} +{"mode": "train", "epoch": 134, "iter": 2500, "lr": 0.0029, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.53281, "top5_acc": 0.77094, "loss_cls": 2.65902, "loss": 2.65902, "time": 0.85773} +{"mode": "train", "epoch": 134, "iter": 2600, "lr": 0.00289, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.52719, "top5_acc": 0.76984, "loss_cls": 2.65814, "loss": 2.65814, "time": 0.85808} +{"mode": "train", "epoch": 134, "iter": 2700, "lr": 0.00288, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53188, "top5_acc": 0.77719, "loss_cls": 2.63472, "loss": 2.63472, "time": 0.85779} +{"mode": "train", "epoch": 134, "iter": 2800, "lr": 0.00287, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.52609, "top5_acc": 0.77469, "loss_cls": 2.66977, "loss": 2.66977, "time": 0.86333} +{"mode": "train", "epoch": 134, "iter": 2900, "lr": 0.00286, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.51625, "top5_acc": 0.76469, "loss_cls": 2.7061, "loss": 2.7061, "time": 0.86689} +{"mode": "train", "epoch": 134, "iter": 3000, "lr": 0.00285, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.52422, "top5_acc": 0.76359, "loss_cls": 2.69776, "loss": 2.69776, "time": 0.85909} +{"mode": "train", "epoch": 134, "iter": 3100, "lr": 0.00284, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.52203, "top5_acc": 0.76391, "loss_cls": 2.70724, "loss": 2.70724, "time": 0.86513} +{"mode": "train", "epoch": 134, "iter": 3200, "lr": 0.00283, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.51953, "top5_acc": 0.7675, "loss_cls": 2.67332, "loss": 2.67332, "time": 0.86716} +{"mode": "train", "epoch": 134, "iter": 3300, "lr": 0.00282, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.52891, "top5_acc": 0.77875, "loss_cls": 2.61024, "loss": 2.61024, "time": 0.86313} +{"mode": "train", "epoch": 134, "iter": 3400, "lr": 0.00281, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.52297, "top5_acc": 0.77484, "loss_cls": 2.67234, "loss": 2.67234, "time": 0.86368} +{"mode": "train", "epoch": 134, "iter": 3500, "lr": 0.0028, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.52766, "top5_acc": 0.7725, "loss_cls": 2.62241, "loss": 2.62241, "time": 0.8678} +{"mode": "train", "epoch": 134, "iter": 3600, "lr": 0.00279, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.5275, "top5_acc": 0.76516, "loss_cls": 2.66225, "loss": 2.66225, "time": 0.86317} +{"mode": "train", "epoch": 134, "iter": 3700, "lr": 0.00279, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.52188, "top5_acc": 0.76188, "loss_cls": 2.70893, "loss": 2.70893, "time": 0.86654} +{"mode": "val", "epoch": 134, "iter": 309, "lr": 0.00278, "top1_acc": 0.42263, "top5_acc": 0.67528, "mean_class_accuracy": 0.42242} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00277, "memory": 15990, "data_time": 1.58456, "top1_acc": 0.55172, "top5_acc": 0.78812, "loss_cls": 2.52821, "loss": 2.52821, "time": 2.63765} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00276, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.54812, "top5_acc": 0.78906, "loss_cls": 2.5191, "loss": 2.5191, "time": 0.87402} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00275, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.53797, "top5_acc": 0.78156, "loss_cls": 2.57762, "loss": 2.57762, "time": 0.86933} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00274, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.54438, "top5_acc": 0.79094, "loss_cls": 2.53846, "loss": 2.53846, "time": 0.86252} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00274, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.54484, "top5_acc": 0.78266, "loss_cls": 2.57541, "loss": 2.57541, "time": 0.86925} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00273, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.54516, "top5_acc": 0.77797, "loss_cls": 2.57579, "loss": 2.57579, "time": 0.86552} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00272, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.54469, "top5_acc": 0.775, "loss_cls": 2.60521, "loss": 2.60521, "time": 0.85673} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00271, "memory": 15990, "data_time": 0.00077, "top1_acc": 0.53781, "top5_acc": 0.78016, "loss_cls": 2.60103, "loss": 2.60103, "time": 0.86105} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.0027, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.53531, "top5_acc": 0.77797, "loss_cls": 2.60603, "loss": 2.60603, "time": 0.85527} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00269, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.54891, "top5_acc": 0.78594, "loss_cls": 2.5455, "loss": 2.5455, "time": 0.86363} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00268, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.54625, "top5_acc": 0.78828, "loss_cls": 2.554, "loss": 2.554, "time": 0.86399} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00267, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.53531, "top5_acc": 0.77703, "loss_cls": 2.60278, "loss": 2.60278, "time": 0.86586} +{"mode": "train", "epoch": 135, "iter": 1300, "lr": 0.00266, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.53531, "top5_acc": 0.77578, "loss_cls": 2.61771, "loss": 2.61771, "time": 0.86531} +{"mode": "train", "epoch": 135, "iter": 1400, "lr": 0.00265, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.54, "top5_acc": 0.77188, "loss_cls": 2.60238, "loss": 2.60238, "time": 0.86666} +{"mode": "train", "epoch": 135, "iter": 1500, "lr": 0.00265, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.53766, "top5_acc": 0.77391, "loss_cls": 2.60559, "loss": 2.60559, "time": 0.86577} +{"mode": "train", "epoch": 135, "iter": 1600, "lr": 0.00264, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.53391, "top5_acc": 0.77297, "loss_cls": 2.63928, "loss": 2.63928, "time": 0.86465} +{"mode": "train", "epoch": 135, "iter": 1700, "lr": 0.00263, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.52047, "top5_acc": 0.77, "loss_cls": 2.66747, "loss": 2.66747, "time": 0.86715} +{"mode": "train", "epoch": 135, "iter": 1800, "lr": 0.00262, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.52531, "top5_acc": 0.7775, "loss_cls": 2.61329, "loss": 2.61329, "time": 0.86843} +{"mode": "train", "epoch": 135, "iter": 1900, "lr": 0.00261, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.53391, "top5_acc": 0.77344, "loss_cls": 2.61153, "loss": 2.61153, "time": 0.86892} +{"mode": "train", "epoch": 135, "iter": 2000, "lr": 0.0026, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53844, "top5_acc": 0.77938, "loss_cls": 2.61316, "loss": 2.61316, "time": 0.86949} +{"mode": "train", "epoch": 135, "iter": 2100, "lr": 0.00259, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.54234, "top5_acc": 0.77672, "loss_cls": 2.60197, "loss": 2.60197, "time": 0.87194} +{"mode": "train", "epoch": 135, "iter": 2200, "lr": 0.00258, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.53812, "top5_acc": 0.77703, "loss_cls": 2.60378, "loss": 2.60378, "time": 0.86629} +{"mode": "train", "epoch": 135, "iter": 2300, "lr": 0.00257, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.53375, "top5_acc": 0.77484, "loss_cls": 2.61363, "loss": 2.61363, "time": 0.86466} +{"mode": "train", "epoch": 135, "iter": 2400, "lr": 0.00256, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.53844, "top5_acc": 0.78375, "loss_cls": 2.61211, "loss": 2.61211, "time": 0.85927} +{"mode": "train", "epoch": 135, "iter": 2500, "lr": 0.00256, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.53594, "top5_acc": 0.77, "loss_cls": 2.62336, "loss": 2.62336, "time": 0.85721} +{"mode": "train", "epoch": 135, "iter": 2600, "lr": 0.00255, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.53109, "top5_acc": 0.76828, "loss_cls": 2.63645, "loss": 2.63645, "time": 0.85442} +{"mode": "train", "epoch": 135, "iter": 2700, "lr": 0.00254, "memory": 15990, "data_time": 0.00073, "top1_acc": 0.52172, "top5_acc": 0.77031, "loss_cls": 2.63575, "loss": 2.63575, "time": 0.85312} +{"mode": "train", "epoch": 135, "iter": 2800, "lr": 0.00253, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.53797, "top5_acc": 0.77844, "loss_cls": 2.59926, "loss": 2.59926, "time": 0.86749} +{"mode": "train", "epoch": 135, "iter": 2900, "lr": 0.00252, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51938, "top5_acc": 0.77266, "loss_cls": 2.6456, "loss": 2.6456, "time": 0.86581} +{"mode": "train", "epoch": 135, "iter": 3000, "lr": 0.00251, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.53328, "top5_acc": 0.77781, "loss_cls": 2.60114, "loss": 2.60114, "time": 0.8655} +{"mode": "train", "epoch": 135, "iter": 3100, "lr": 0.0025, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.52578, "top5_acc": 0.76984, "loss_cls": 2.66538, "loss": 2.66538, "time": 0.86816} +{"mode": "train", "epoch": 135, "iter": 3200, "lr": 0.00249, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.54766, "top5_acc": 0.77656, "loss_cls": 2.57083, "loss": 2.57083, "time": 0.867} +{"mode": "train", "epoch": 135, "iter": 3300, "lr": 0.00249, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.52734, "top5_acc": 0.77219, "loss_cls": 2.65595, "loss": 2.65595, "time": 0.86947} +{"mode": "train", "epoch": 135, "iter": 3400, "lr": 0.00248, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53406, "top5_acc": 0.78156, "loss_cls": 2.59377, "loss": 2.59377, "time": 0.87229} +{"mode": "train", "epoch": 135, "iter": 3500, "lr": 0.00247, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.52625, "top5_acc": 0.76422, "loss_cls": 2.65627, "loss": 2.65627, "time": 0.86803} +{"mode": "train", "epoch": 135, "iter": 3600, "lr": 0.00246, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.52562, "top5_acc": 0.76703, "loss_cls": 2.65326, "loss": 2.65326, "time": 0.87299} +{"mode": "train", "epoch": 135, "iter": 3700, "lr": 0.00245, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.53328, "top5_acc": 0.77672, "loss_cls": 2.60802, "loss": 2.60802, "time": 0.87186} +{"mode": "val", "epoch": 135, "iter": 309, "lr": 0.00245, "top1_acc": 0.42932, "top5_acc": 0.68232, "mean_class_accuracy": 0.42904} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00244, "memory": 15990, "data_time": 1.6034, "top1_acc": 0.55141, "top5_acc": 0.78797, "loss_cls": 2.52453, "loss": 2.52453, "time": 2.68003} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.00243, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.55437, "top5_acc": 0.79469, "loss_cls": 2.50087, "loss": 2.50087, "time": 0.87656} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00242, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.55531, "top5_acc": 0.79062, "loss_cls": 2.50726, "loss": 2.50726, "time": 0.87318} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00241, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.555, "top5_acc": 0.80125, "loss_cls": 2.49179, "loss": 2.49179, "time": 0.87621} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.0024, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.54828, "top5_acc": 0.78938, "loss_cls": 2.56296, "loss": 2.56296, "time": 0.87384} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.0024, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55391, "top5_acc": 0.78891, "loss_cls": 2.52919, "loss": 2.52919, "time": 0.87259} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00239, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.55156, "top5_acc": 0.79297, "loss_cls": 2.51305, "loss": 2.51305, "time": 0.85637} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00238, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.55844, "top5_acc": 0.7975, "loss_cls": 2.48963, "loss": 2.48963, "time": 0.85776} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00237, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.54922, "top5_acc": 0.78453, "loss_cls": 2.54962, "loss": 2.54962, "time": 0.85464} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00236, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.55484, "top5_acc": 0.78969, "loss_cls": 2.52575, "loss": 2.52575, "time": 0.86371} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00235, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.53375, "top5_acc": 0.77375, "loss_cls": 2.61452, "loss": 2.61452, "time": 0.86615} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00234, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.54359, "top5_acc": 0.79172, "loss_cls": 2.5411, "loss": 2.5411, "time": 0.86754} +{"mode": "train", "epoch": 136, "iter": 1300, "lr": 0.00234, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.54234, "top5_acc": 0.78766, "loss_cls": 2.57268, "loss": 2.57268, "time": 0.87367} +{"mode": "train", "epoch": 136, "iter": 1400, "lr": 0.00233, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.53219, "top5_acc": 0.77562, "loss_cls": 2.61189, "loss": 2.61189, "time": 0.87373} +{"mode": "train", "epoch": 136, "iter": 1500, "lr": 0.00232, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56063, "top5_acc": 0.80172, "loss_cls": 2.48914, "loss": 2.48914, "time": 0.86749} +{"mode": "train", "epoch": 136, "iter": 1600, "lr": 0.00231, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.53719, "top5_acc": 0.77984, "loss_cls": 2.56692, "loss": 2.56692, "time": 0.86705} +{"mode": "train", "epoch": 136, "iter": 1700, "lr": 0.0023, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.54406, "top5_acc": 0.78516, "loss_cls": 2.54431, "loss": 2.54431, "time": 0.86735} +{"mode": "train", "epoch": 136, "iter": 1800, "lr": 0.00229, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.53609, "top5_acc": 0.78016, "loss_cls": 2.58528, "loss": 2.58528, "time": 0.87037} +{"mode": "train", "epoch": 136, "iter": 1900, "lr": 0.00229, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.54234, "top5_acc": 0.78766, "loss_cls": 2.55456, "loss": 2.55456, "time": 0.87156} +{"mode": "train", "epoch": 136, "iter": 2000, "lr": 0.00228, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.53516, "top5_acc": 0.78875, "loss_cls": 2.55987, "loss": 2.55987, "time": 0.87104} +{"mode": "train", "epoch": 136, "iter": 2100, "lr": 0.00227, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.54078, "top5_acc": 0.78516, "loss_cls": 2.5657, "loss": 2.5657, "time": 0.87083} +{"mode": "train", "epoch": 136, "iter": 2200, "lr": 0.00226, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.53281, "top5_acc": 0.77422, "loss_cls": 2.5988, "loss": 2.5988, "time": 0.86806} +{"mode": "train", "epoch": 136, "iter": 2300, "lr": 0.00225, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.54625, "top5_acc": 0.78234, "loss_cls": 2.55916, "loss": 2.55916, "time": 0.85826} +{"mode": "train", "epoch": 136, "iter": 2400, "lr": 0.00224, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.54453, "top5_acc": 0.78859, "loss_cls": 2.52666, "loss": 2.52666, "time": 0.85856} +{"mode": "train", "epoch": 136, "iter": 2500, "lr": 0.00224, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55234, "top5_acc": 0.78453, "loss_cls": 2.54127, "loss": 2.54127, "time": 0.85035} +{"mode": "train", "epoch": 136, "iter": 2600, "lr": 0.00223, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.54016, "top5_acc": 0.785, "loss_cls": 2.58437, "loss": 2.58437, "time": 0.85559} +{"mode": "train", "epoch": 136, "iter": 2700, "lr": 0.00222, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.53641, "top5_acc": 0.77562, "loss_cls": 2.60512, "loss": 2.60512, "time": 0.84885} +{"mode": "train", "epoch": 136, "iter": 2800, "lr": 0.00221, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.53969, "top5_acc": 0.77625, "loss_cls": 2.57721, "loss": 2.57721, "time": 0.85715} +{"mode": "train", "epoch": 136, "iter": 2900, "lr": 0.0022, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.53922, "top5_acc": 0.78812, "loss_cls": 2.56525, "loss": 2.56525, "time": 0.85981} +{"mode": "train", "epoch": 136, "iter": 3000, "lr": 0.00219, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53, "top5_acc": 0.7625, "loss_cls": 2.64832, "loss": 2.64832, "time": 0.85753} +{"mode": "train", "epoch": 136, "iter": 3100, "lr": 0.00219, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.54297, "top5_acc": 0.78828, "loss_cls": 2.5779, "loss": 2.5779, "time": 0.85234} +{"mode": "train", "epoch": 136, "iter": 3200, "lr": 0.00218, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.54547, "top5_acc": 0.77891, "loss_cls": 2.57148, "loss": 2.57148, "time": 0.85563} +{"mode": "train", "epoch": 136, "iter": 3300, "lr": 0.00217, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.54156, "top5_acc": 0.78328, "loss_cls": 2.57449, "loss": 2.57449, "time": 0.85456} +{"mode": "train", "epoch": 136, "iter": 3400, "lr": 0.00216, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.53766, "top5_acc": 0.78359, "loss_cls": 2.58708, "loss": 2.58708, "time": 0.85834} +{"mode": "train", "epoch": 136, "iter": 3500, "lr": 0.00215, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.54641, "top5_acc": 0.78609, "loss_cls": 2.5359, "loss": 2.5359, "time": 0.85425} +{"mode": "train", "epoch": 136, "iter": 3600, "lr": 0.00215, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.53438, "top5_acc": 0.77203, "loss_cls": 2.60555, "loss": 2.60555, "time": 0.85225} +{"mode": "train", "epoch": 136, "iter": 3700, "lr": 0.00214, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.53656, "top5_acc": 0.77562, "loss_cls": 2.61341, "loss": 2.61341, "time": 0.85249} +{"mode": "val", "epoch": 136, "iter": 309, "lr": 0.00213, "top1_acc": 0.42962, "top5_acc": 0.68313, "mean_class_accuracy": 0.42932} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00213, "memory": 15990, "data_time": 1.56417, "top1_acc": 0.56391, "top5_acc": 0.80312, "loss_cls": 2.44261, "loss": 2.44261, "time": 2.64631} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00212, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.56172, "top5_acc": 0.80672, "loss_cls": 2.45798, "loss": 2.45798, "time": 0.85887} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00211, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.56672, "top5_acc": 0.80016, "loss_cls": 2.44735, "loss": 2.44735, "time": 0.86648} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.0021, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.5575, "top5_acc": 0.79734, "loss_cls": 2.48171, "loss": 2.48171, "time": 0.86189} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.00209, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.55734, "top5_acc": 0.80094, "loss_cls": 2.47277, "loss": 2.47277, "time": 0.86415} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.00209, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.55922, "top5_acc": 0.79812, "loss_cls": 2.49417, "loss": 2.49417, "time": 0.86393} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00208, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.55328, "top5_acc": 0.78953, "loss_cls": 2.51217, "loss": 2.51217, "time": 0.85558} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00207, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.55328, "top5_acc": 0.79938, "loss_cls": 2.49779, "loss": 2.49779, "time": 0.86695} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00206, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.56156, "top5_acc": 0.79703, "loss_cls": 2.47932, "loss": 2.47932, "time": 0.85921} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00205, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.55266, "top5_acc": 0.78828, "loss_cls": 2.51709, "loss": 2.51709, "time": 0.86539} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00205, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.55406, "top5_acc": 0.79594, "loss_cls": 2.50208, "loss": 2.50208, "time": 0.87455} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00204, "memory": 15990, "data_time": 0.0007, "top1_acc": 0.56578, "top5_acc": 0.79484, "loss_cls": 2.46281, "loss": 2.46281, "time": 0.87924} +{"mode": "train", "epoch": 137, "iter": 1300, "lr": 0.00203, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.55156, "top5_acc": 0.78359, "loss_cls": 2.5426, "loss": 2.5426, "time": 0.86859} +{"mode": "train", "epoch": 137, "iter": 1400, "lr": 0.00202, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.55406, "top5_acc": 0.79156, "loss_cls": 2.48906, "loss": 2.48906, "time": 0.86756} +{"mode": "train", "epoch": 137, "iter": 1500, "lr": 0.00201, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.55125, "top5_acc": 0.78422, "loss_cls": 2.53077, "loss": 2.53077, "time": 0.87207} +{"mode": "train", "epoch": 137, "iter": 1600, "lr": 0.00201, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.54484, "top5_acc": 0.79562, "loss_cls": 2.50132, "loss": 2.50132, "time": 0.87315} +{"mode": "train", "epoch": 137, "iter": 1700, "lr": 0.002, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.54641, "top5_acc": 0.78625, "loss_cls": 2.53374, "loss": 2.53374, "time": 0.87424} +{"mode": "train", "epoch": 137, "iter": 1800, "lr": 0.00199, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.55141, "top5_acc": 0.78641, "loss_cls": 2.52236, "loss": 2.52236, "time": 0.87547} +{"mode": "train", "epoch": 137, "iter": 1900, "lr": 0.00198, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.55609, "top5_acc": 0.79234, "loss_cls": 2.51577, "loss": 2.51577, "time": 0.8757} +{"mode": "train", "epoch": 137, "iter": 2000, "lr": 0.00198, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.55312, "top5_acc": 0.78812, "loss_cls": 2.5366, "loss": 2.5366, "time": 0.87687} +{"mode": "train", "epoch": 137, "iter": 2100, "lr": 0.00197, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.5525, "top5_acc": 0.79547, "loss_cls": 2.49531, "loss": 2.49531, "time": 0.87574} +{"mode": "train", "epoch": 137, "iter": 2200, "lr": 0.00196, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.56141, "top5_acc": 0.79703, "loss_cls": 2.48128, "loss": 2.48128, "time": 0.86467} +{"mode": "train", "epoch": 137, "iter": 2300, "lr": 0.00195, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.5475, "top5_acc": 0.78781, "loss_cls": 2.5271, "loss": 2.5271, "time": 0.86498} +{"mode": "train", "epoch": 137, "iter": 2400, "lr": 0.00194, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.54594, "top5_acc": 0.78453, "loss_cls": 2.57579, "loss": 2.57579, "time": 0.86142} +{"mode": "train", "epoch": 137, "iter": 2500, "lr": 0.00194, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.55188, "top5_acc": 0.79703, "loss_cls": 2.49942, "loss": 2.49942, "time": 0.86225} +{"mode": "train", "epoch": 137, "iter": 2600, "lr": 0.00193, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.54375, "top5_acc": 0.78844, "loss_cls": 2.54754, "loss": 2.54754, "time": 0.85755} +{"mode": "train", "epoch": 137, "iter": 2700, "lr": 0.00192, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.54156, "top5_acc": 0.78641, "loss_cls": 2.5555, "loss": 2.5555, "time": 0.85842} +{"mode": "train", "epoch": 137, "iter": 2800, "lr": 0.00191, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.54391, "top5_acc": 0.78531, "loss_cls": 2.56374, "loss": 2.56374, "time": 0.86648} +{"mode": "train", "epoch": 137, "iter": 2900, "lr": 0.00191, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.5575, "top5_acc": 0.79109, "loss_cls": 2.48192, "loss": 2.48192, "time": 0.87722} +{"mode": "train", "epoch": 137, "iter": 3000, "lr": 0.0019, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.55625, "top5_acc": 0.78875, "loss_cls": 2.52246, "loss": 2.52246, "time": 0.88094} +{"mode": "train", "epoch": 137, "iter": 3100, "lr": 0.00189, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.56, "top5_acc": 0.79219, "loss_cls": 2.49791, "loss": 2.49791, "time": 0.88105} +{"mode": "train", "epoch": 137, "iter": 3200, "lr": 0.00188, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.55109, "top5_acc": 0.78812, "loss_cls": 2.52795, "loss": 2.52795, "time": 0.88247} +{"mode": "train", "epoch": 137, "iter": 3300, "lr": 0.00188, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.55125, "top5_acc": 0.78719, "loss_cls": 2.53248, "loss": 2.53248, "time": 0.88027} +{"mode": "train", "epoch": 137, "iter": 3400, "lr": 0.00187, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.56453, "top5_acc": 0.79688, "loss_cls": 2.47965, "loss": 2.47965, "time": 0.87762} +{"mode": "train", "epoch": 137, "iter": 3500, "lr": 0.00186, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.55344, "top5_acc": 0.78672, "loss_cls": 2.51144, "loss": 2.51144, "time": 0.88063} +{"mode": "train", "epoch": 137, "iter": 3600, "lr": 0.00185, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.54703, "top5_acc": 0.78578, "loss_cls": 2.52649, "loss": 2.52649, "time": 0.88192} +{"mode": "train", "epoch": 137, "iter": 3700, "lr": 0.00185, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.54531, "top5_acc": 0.79234, "loss_cls": 2.52469, "loss": 2.52469, "time": 0.88005} +{"mode": "val", "epoch": 137, "iter": 309, "lr": 0.00184, "top1_acc": 0.43312, "top5_acc": 0.68586, "mean_class_accuracy": 0.43286} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00183, "memory": 15990, "data_time": 1.58461, "top1_acc": 0.56297, "top5_acc": 0.80281, "loss_cls": 2.44823, "loss": 2.44823, "time": 2.62882} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00183, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.57563, "top5_acc": 0.80609, "loss_cls": 2.40068, "loss": 2.40068, "time": 0.85087} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00182, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56281, "top5_acc": 0.80734, "loss_cls": 2.42835, "loss": 2.42835, "time": 0.85289} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00181, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57016, "top5_acc": 0.80859, "loss_cls": 2.42568, "loss": 2.42568, "time": 0.84805} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.0018, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57328, "top5_acc": 0.80234, "loss_cls": 2.44119, "loss": 2.44119, "time": 0.85} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.0018, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55937, "top5_acc": 0.80266, "loss_cls": 2.44458, "loss": 2.44458, "time": 0.85108} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00179, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.56844, "top5_acc": 0.79875, "loss_cls": 2.43284, "loss": 2.43284, "time": 0.85355} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00178, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56422, "top5_acc": 0.79516, "loss_cls": 2.44689, "loss": 2.44689, "time": 0.85075} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00177, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.57922, "top5_acc": 0.79828, "loss_cls": 2.41289, "loss": 2.41289, "time": 0.84951} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00177, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.56734, "top5_acc": 0.79797, "loss_cls": 2.47149, "loss": 2.47149, "time": 0.84647} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.00176, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.57063, "top5_acc": 0.80234, "loss_cls": 2.43069, "loss": 2.43069, "time": 0.84723} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.00175, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.56844, "top5_acc": 0.80641, "loss_cls": 2.41781, "loss": 2.41781, "time": 0.85402} +{"mode": "train", "epoch": 138, "iter": 1300, "lr": 0.00175, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57078, "top5_acc": 0.80625, "loss_cls": 2.42478, "loss": 2.42478, "time": 0.85073} +{"mode": "train", "epoch": 138, "iter": 1400, "lr": 0.00174, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.57125, "top5_acc": 0.79969, "loss_cls": 2.42546, "loss": 2.42546, "time": 0.85298} +{"mode": "train", "epoch": 138, "iter": 1500, "lr": 0.00173, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.57766, "top5_acc": 0.80047, "loss_cls": 2.42912, "loss": 2.42912, "time": 0.85265} +{"mode": "train", "epoch": 138, "iter": 1600, "lr": 0.00172, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57094, "top5_acc": 0.79438, "loss_cls": 2.46895, "loss": 2.46895, "time": 0.8497} +{"mode": "train", "epoch": 138, "iter": 1700, "lr": 0.00172, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56031, "top5_acc": 0.79922, "loss_cls": 2.47678, "loss": 2.47678, "time": 0.84939} +{"mode": "train", "epoch": 138, "iter": 1800, "lr": 0.00171, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.56219, "top5_acc": 0.79375, "loss_cls": 2.49406, "loss": 2.49406, "time": 0.85257} +{"mode": "train", "epoch": 138, "iter": 1900, "lr": 0.0017, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55984, "top5_acc": 0.79406, "loss_cls": 2.49177, "loss": 2.49177, "time": 0.84915} +{"mode": "train", "epoch": 138, "iter": 2000, "lr": 0.00169, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55266, "top5_acc": 0.79219, "loss_cls": 2.51024, "loss": 2.51024, "time": 0.85158} +{"mode": "train", "epoch": 138, "iter": 2100, "lr": 0.00169, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.56047, "top5_acc": 0.79828, "loss_cls": 2.47752, "loss": 2.47752, "time": 0.85055} +{"mode": "train", "epoch": 138, "iter": 2200, "lr": 0.00168, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.56516, "top5_acc": 0.79703, "loss_cls": 2.44289, "loss": 2.44289, "time": 0.85251} +{"mode": "train", "epoch": 138, "iter": 2300, "lr": 0.00167, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55969, "top5_acc": 0.79859, "loss_cls": 2.47107, "loss": 2.47107, "time": 0.8475} +{"mode": "train", "epoch": 138, "iter": 2400, "lr": 0.00167, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.57094, "top5_acc": 0.80188, "loss_cls": 2.41809, "loss": 2.41809, "time": 0.84833} +{"mode": "train", "epoch": 138, "iter": 2500, "lr": 0.00166, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.555, "top5_acc": 0.79734, "loss_cls": 2.50136, "loss": 2.50136, "time": 0.85155} +{"mode": "train", "epoch": 138, "iter": 2600, "lr": 0.00165, "memory": 15990, "data_time": 0.00068, "top1_acc": 0.56453, "top5_acc": 0.80344, "loss_cls": 2.43883, "loss": 2.43883, "time": 0.85263} +{"mode": "train", "epoch": 138, "iter": 2700, "lr": 0.00164, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.55891, "top5_acc": 0.79469, "loss_cls": 2.46197, "loss": 2.46197, "time": 0.84468} +{"mode": "train", "epoch": 138, "iter": 2800, "lr": 0.00164, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.54844, "top5_acc": 0.79297, "loss_cls": 2.52168, "loss": 2.52168, "time": 0.85476} +{"mode": "train", "epoch": 138, "iter": 2900, "lr": 0.00163, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56516, "top5_acc": 0.80281, "loss_cls": 2.45595, "loss": 2.45595, "time": 0.84899} +{"mode": "train", "epoch": 138, "iter": 3000, "lr": 0.00162, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.55422, "top5_acc": 0.79422, "loss_cls": 2.49665, "loss": 2.49665, "time": 0.85073} +{"mode": "train", "epoch": 138, "iter": 3100, "lr": 0.00162, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.56188, "top5_acc": 0.79766, "loss_cls": 2.49215, "loss": 2.49215, "time": 0.84478} +{"mode": "train", "epoch": 138, "iter": 3200, "lr": 0.00161, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.55562, "top5_acc": 0.79, "loss_cls": 2.50693, "loss": 2.50693, "time": 0.84812} +{"mode": "train", "epoch": 138, "iter": 3300, "lr": 0.0016, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.57141, "top5_acc": 0.79812, "loss_cls": 2.44182, "loss": 2.44182, "time": 0.84862} +{"mode": "train", "epoch": 138, "iter": 3400, "lr": 0.0016, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.56109, "top5_acc": 0.80141, "loss_cls": 2.47405, "loss": 2.47405, "time": 0.84625} +{"mode": "train", "epoch": 138, "iter": 3500, "lr": 0.00159, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57766, "top5_acc": 0.80406, "loss_cls": 2.42143, "loss": 2.42143, "time": 0.85111} +{"mode": "train", "epoch": 138, "iter": 3600, "lr": 0.00158, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.54969, "top5_acc": 0.79, "loss_cls": 2.50283, "loss": 2.50283, "time": 0.84595} +{"mode": "train", "epoch": 138, "iter": 3700, "lr": 0.00157, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55875, "top5_acc": 0.80203, "loss_cls": 2.45514, "loss": 2.45514, "time": 0.84589} +{"mode": "val", "epoch": 138, "iter": 309, "lr": 0.00157, "top1_acc": 0.44041, "top5_acc": 0.68617, "mean_class_accuracy": 0.44016} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00156, "memory": 15990, "data_time": 1.53443, "top1_acc": 0.58844, "top5_acc": 0.80344, "loss_cls": 2.36043, "loss": 2.36043, "time": 2.57178} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00156, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.58016, "top5_acc": 0.81688, "loss_cls": 2.36174, "loss": 2.36174, "time": 0.84781} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00155, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.59234, "top5_acc": 0.81625, "loss_cls": 2.32016, "loss": 2.32016, "time": 0.84996} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00154, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.57359, "top5_acc": 0.80422, "loss_cls": 2.42446, "loss": 2.42446, "time": 0.85117} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00154, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.57594, "top5_acc": 0.80922, "loss_cls": 2.39582, "loss": 2.39582, "time": 0.85494} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00153, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.58219, "top5_acc": 0.81594, "loss_cls": 2.37081, "loss": 2.37081, "time": 0.85231} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00152, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.58641, "top5_acc": 0.81922, "loss_cls": 2.32251, "loss": 2.32251, "time": 0.85131} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00152, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.57297, "top5_acc": 0.81109, "loss_cls": 2.3764, "loss": 2.3764, "time": 0.84965} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00151, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.58719, "top5_acc": 0.81812, "loss_cls": 2.3423, "loss": 2.3423, "time": 0.85536} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.0015, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.57672, "top5_acc": 0.81406, "loss_cls": 2.37302, "loss": 2.37302, "time": 0.85251} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.0015, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.58594, "top5_acc": 0.80828, "loss_cls": 2.37107, "loss": 2.37107, "time": 0.84868} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00149, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.56453, "top5_acc": 0.80562, "loss_cls": 2.42793, "loss": 2.42793, "time": 0.84656} +{"mode": "train", "epoch": 139, "iter": 1300, "lr": 0.00148, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.57188, "top5_acc": 0.8075, "loss_cls": 2.39068, "loss": 2.39068, "time": 0.85418} +{"mode": "train", "epoch": 139, "iter": 1400, "lr": 0.00148, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.5825, "top5_acc": 0.81297, "loss_cls": 2.36841, "loss": 2.36841, "time": 0.8463} +{"mode": "train", "epoch": 139, "iter": 1500, "lr": 0.00147, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.56844, "top5_acc": 0.80703, "loss_cls": 2.40551, "loss": 2.40551, "time": 0.84619} +{"mode": "train", "epoch": 139, "iter": 1600, "lr": 0.00146, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.57234, "top5_acc": 0.80219, "loss_cls": 2.40306, "loss": 2.40306, "time": 0.84746} +{"mode": "train", "epoch": 139, "iter": 1700, "lr": 0.00145, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.56953, "top5_acc": 0.80516, "loss_cls": 2.41045, "loss": 2.41045, "time": 0.8455} +{"mode": "train", "epoch": 139, "iter": 1800, "lr": 0.00145, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.55906, "top5_acc": 0.79922, "loss_cls": 2.4525, "loss": 2.4525, "time": 0.84745} +{"mode": "train", "epoch": 139, "iter": 1900, "lr": 0.00144, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.57563, "top5_acc": 0.81625, "loss_cls": 2.37149, "loss": 2.37149, "time": 0.84588} +{"mode": "train", "epoch": 139, "iter": 2000, "lr": 0.00143, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.57234, "top5_acc": 0.79984, "loss_cls": 2.42716, "loss": 2.42716, "time": 0.85243} +{"mode": "train", "epoch": 139, "iter": 2100, "lr": 0.00143, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.57172, "top5_acc": 0.79766, "loss_cls": 2.42227, "loss": 2.42227, "time": 0.84809} +{"mode": "train", "epoch": 139, "iter": 2200, "lr": 0.00142, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.56859, "top5_acc": 0.80484, "loss_cls": 2.40307, "loss": 2.40307, "time": 0.85074} +{"mode": "train", "epoch": 139, "iter": 2300, "lr": 0.00142, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.57953, "top5_acc": 0.80953, "loss_cls": 2.38916, "loss": 2.38916, "time": 0.84176} +{"mode": "train", "epoch": 139, "iter": 2400, "lr": 0.00141, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.57172, "top5_acc": 0.80469, "loss_cls": 2.41183, "loss": 2.41183, "time": 0.84802} +{"mode": "train", "epoch": 139, "iter": 2500, "lr": 0.0014, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.57219, "top5_acc": 0.80391, "loss_cls": 2.42234, "loss": 2.42234, "time": 0.85512} +{"mode": "train", "epoch": 139, "iter": 2600, "lr": 0.0014, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.56016, "top5_acc": 0.79594, "loss_cls": 2.45206, "loss": 2.45206, "time": 0.84615} +{"mode": "train", "epoch": 139, "iter": 2700, "lr": 0.00139, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.56688, "top5_acc": 0.79875, "loss_cls": 2.43443, "loss": 2.43443, "time": 0.83929} +{"mode": "train", "epoch": 139, "iter": 2800, "lr": 0.00138, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.57453, "top5_acc": 0.81375, "loss_cls": 2.38778, "loss": 2.38778, "time": 0.84255} +{"mode": "train", "epoch": 139, "iter": 2900, "lr": 0.00138, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.57453, "top5_acc": 0.80766, "loss_cls": 2.39225, "loss": 2.39225, "time": 0.85325} +{"mode": "train", "epoch": 139, "iter": 3000, "lr": 0.00137, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.57516, "top5_acc": 0.80938, "loss_cls": 2.42708, "loss": 2.42708, "time": 0.85676} +{"mode": "train", "epoch": 139, "iter": 3100, "lr": 0.00136, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.55969, "top5_acc": 0.80734, "loss_cls": 2.45636, "loss": 2.45636, "time": 0.86209} +{"mode": "train", "epoch": 139, "iter": 3200, "lr": 0.00136, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.56563, "top5_acc": 0.80375, "loss_cls": 2.43949, "loss": 2.43949, "time": 0.85809} +{"mode": "train", "epoch": 139, "iter": 3300, "lr": 0.00135, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.57203, "top5_acc": 0.81375, "loss_cls": 2.41805, "loss": 2.41805, "time": 0.85712} +{"mode": "train", "epoch": 139, "iter": 3400, "lr": 0.00134, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.56391, "top5_acc": 0.8025, "loss_cls": 2.44317, "loss": 2.44317, "time": 0.85152} +{"mode": "train", "epoch": 139, "iter": 3500, "lr": 0.00134, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.57359, "top5_acc": 0.80172, "loss_cls": 2.4156, "loss": 2.4156, "time": 0.84952} +{"mode": "train", "epoch": 139, "iter": 3600, "lr": 0.00133, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.56109, "top5_acc": 0.79016, "loss_cls": 2.48124, "loss": 2.48124, "time": 0.85816} +{"mode": "train", "epoch": 139, "iter": 3700, "lr": 0.00132, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.55594, "top5_acc": 0.80688, "loss_cls": 2.43552, "loss": 2.43552, "time": 0.85369} +{"mode": "val", "epoch": 139, "iter": 309, "lr": 0.00132, "top1_acc": 0.43661, "top5_acc": 0.68612, "mean_class_accuracy": 0.43635} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00131, "memory": 15990, "data_time": 1.56768, "top1_acc": 0.59656, "top5_acc": 0.82422, "loss_cls": 2.30401, "loss": 2.30401, "time": 2.59792} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00131, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59438, "top5_acc": 0.82828, "loss_cls": 2.30273, "loss": 2.30273, "time": 0.84511} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.0013, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58922, "top5_acc": 0.81422, "loss_cls": 2.33474, "loss": 2.33474, "time": 0.85406} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.0013, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59297, "top5_acc": 0.82, "loss_cls": 2.3017, "loss": 2.3017, "time": 0.85104} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00129, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.59844, "top5_acc": 0.82828, "loss_cls": 2.268, "loss": 2.268, "time": 0.85319} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.00128, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.59422, "top5_acc": 0.81688, "loss_cls": 2.31352, "loss": 2.31352, "time": 0.8579} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.00128, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.59141, "top5_acc": 0.82609, "loss_cls": 2.30794, "loss": 2.30794, "time": 0.85403} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00127, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.59562, "top5_acc": 0.82156, "loss_cls": 2.2959, "loss": 2.2959, "time": 0.85267} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00126, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.58547, "top5_acc": 0.81219, "loss_cls": 2.35498, "loss": 2.35498, "time": 0.85307} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00126, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.57406, "top5_acc": 0.80875, "loss_cls": 2.39925, "loss": 2.39925, "time": 0.85844} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00125, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59328, "top5_acc": 0.81609, "loss_cls": 2.3201, "loss": 2.3201, "time": 0.85837} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00125, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.58859, "top5_acc": 0.81359, "loss_cls": 2.32634, "loss": 2.32634, "time": 0.85683} +{"mode": "train", "epoch": 140, "iter": 1300, "lr": 0.00124, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.58422, "top5_acc": 0.81906, "loss_cls": 2.34139, "loss": 2.34139, "time": 0.8577} +{"mode": "train", "epoch": 140, "iter": 1400, "lr": 0.00123, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.59266, "top5_acc": 0.82078, "loss_cls": 2.31736, "loss": 2.31736, "time": 0.85565} +{"mode": "train", "epoch": 140, "iter": 1500, "lr": 0.00123, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.59719, "top5_acc": 0.82406, "loss_cls": 2.29305, "loss": 2.29305, "time": 0.85692} +{"mode": "train", "epoch": 140, "iter": 1600, "lr": 0.00122, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.57906, "top5_acc": 0.81438, "loss_cls": 2.37261, "loss": 2.37261, "time": 0.85497} +{"mode": "train", "epoch": 140, "iter": 1700, "lr": 0.00121, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.59109, "top5_acc": 0.81672, "loss_cls": 2.32339, "loss": 2.32339, "time": 0.85249} +{"mode": "train", "epoch": 140, "iter": 1800, "lr": 0.00121, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.58156, "top5_acc": 0.81672, "loss_cls": 2.33652, "loss": 2.33652, "time": 0.8508} +{"mode": "train", "epoch": 140, "iter": 1900, "lr": 0.0012, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.58078, "top5_acc": 0.81438, "loss_cls": 2.34023, "loss": 2.34023, "time": 0.85689} +{"mode": "train", "epoch": 140, "iter": 2000, "lr": 0.0012, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.58281, "top5_acc": 0.81, "loss_cls": 2.37133, "loss": 2.37133, "time": 0.85102} +{"mode": "train", "epoch": 140, "iter": 2100, "lr": 0.00119, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.57859, "top5_acc": 0.81031, "loss_cls": 2.38109, "loss": 2.38109, "time": 0.85415} +{"mode": "train", "epoch": 140, "iter": 2200, "lr": 0.00118, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.58359, "top5_acc": 0.81516, "loss_cls": 2.35457, "loss": 2.35457, "time": 0.85072} +{"mode": "train", "epoch": 140, "iter": 2300, "lr": 0.00118, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58406, "top5_acc": 0.81719, "loss_cls": 2.33999, "loss": 2.33999, "time": 0.84591} +{"mode": "train", "epoch": 140, "iter": 2400, "lr": 0.00117, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.57641, "top5_acc": 0.80969, "loss_cls": 2.37344, "loss": 2.37344, "time": 0.84775} +{"mode": "train", "epoch": 140, "iter": 2500, "lr": 0.00117, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58969, "top5_acc": 0.81484, "loss_cls": 2.35521, "loss": 2.35521, "time": 0.84982} +{"mode": "train", "epoch": 140, "iter": 2600, "lr": 0.00116, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.58359, "top5_acc": 0.81453, "loss_cls": 2.35145, "loss": 2.35145, "time": 0.84699} +{"mode": "train", "epoch": 140, "iter": 2700, "lr": 0.00115, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.58188, "top5_acc": 0.80719, "loss_cls": 2.38302, "loss": 2.38302, "time": 0.85361} +{"mode": "train", "epoch": 140, "iter": 2800, "lr": 0.00115, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.57281, "top5_acc": 0.80734, "loss_cls": 2.38592, "loss": 2.38592, "time": 0.84927} +{"mode": "train", "epoch": 140, "iter": 2900, "lr": 0.00114, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.57469, "top5_acc": 0.81219, "loss_cls": 2.35982, "loss": 2.35982, "time": 0.84498} +{"mode": "train", "epoch": 140, "iter": 3000, "lr": 0.00114, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.57828, "top5_acc": 0.81047, "loss_cls": 2.38074, "loss": 2.38074, "time": 0.84965} +{"mode": "train", "epoch": 140, "iter": 3100, "lr": 0.00113, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57391, "top5_acc": 0.81062, "loss_cls": 2.40164, "loss": 2.40164, "time": 0.85177} +{"mode": "train", "epoch": 140, "iter": 3200, "lr": 0.00112, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58188, "top5_acc": 0.81422, "loss_cls": 2.3768, "loss": 2.3768, "time": 0.84692} +{"mode": "train", "epoch": 140, "iter": 3300, "lr": 0.00112, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.58938, "top5_acc": 0.81375, "loss_cls": 2.34627, "loss": 2.34627, "time": 0.84971} +{"mode": "train", "epoch": 140, "iter": 3400, "lr": 0.00111, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58766, "top5_acc": 0.81391, "loss_cls": 2.36078, "loss": 2.36078, "time": 0.8461} +{"mode": "train", "epoch": 140, "iter": 3500, "lr": 0.00111, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.575, "top5_acc": 0.81547, "loss_cls": 2.3643, "loss": 2.3643, "time": 0.84466} +{"mode": "train", "epoch": 140, "iter": 3600, "lr": 0.0011, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57375, "top5_acc": 0.805, "loss_cls": 2.40623, "loss": 2.40623, "time": 0.84845} +{"mode": "train", "epoch": 140, "iter": 3700, "lr": 0.0011, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.58094, "top5_acc": 0.80844, "loss_cls": 2.36093, "loss": 2.36093, "time": 0.8496} +{"mode": "val", "epoch": 140, "iter": 309, "lr": 0.00109, "top1_acc": 0.44107, "top5_acc": 0.68951, "mean_class_accuracy": 0.44087} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00109, "memory": 15990, "data_time": 1.62385, "top1_acc": 0.60906, "top5_acc": 0.83547, "loss_cls": 2.21931, "loss": 2.21931, "time": 2.66946} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00108, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.61172, "top5_acc": 0.82859, "loss_cls": 2.21485, "loss": 2.21485, "time": 0.86596} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00108, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.60125, "top5_acc": 0.83, "loss_cls": 2.27511, "loss": 2.27511, "time": 0.86224} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00107, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.59891, "top5_acc": 0.82812, "loss_cls": 2.25778, "loss": 2.25778, "time": 0.86156} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00106, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.59344, "top5_acc": 0.82156, "loss_cls": 2.30232, "loss": 2.30232, "time": 0.85817} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00106, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.60344, "top5_acc": 0.82422, "loss_cls": 2.27045, "loss": 2.27045, "time": 0.85574} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00105, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.59531, "top5_acc": 0.82438, "loss_cls": 2.31942, "loss": 2.31942, "time": 0.8528} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00105, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.61016, "top5_acc": 0.83172, "loss_cls": 2.22394, "loss": 2.22394, "time": 0.84932} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00104, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.6025, "top5_acc": 0.82422, "loss_cls": 2.29302, "loss": 2.29302, "time": 0.85255} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00104, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.59219, "top5_acc": 0.82234, "loss_cls": 2.29123, "loss": 2.29123, "time": 0.85217} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00103, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.60141, "top5_acc": 0.83391, "loss_cls": 2.24948, "loss": 2.24948, "time": 0.85478} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00102, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.59453, "top5_acc": 0.82906, "loss_cls": 2.27554, "loss": 2.27554, "time": 0.85339} +{"mode": "train", "epoch": 141, "iter": 1300, "lr": 0.00102, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.60078, "top5_acc": 0.82656, "loss_cls": 2.26516, "loss": 2.26516, "time": 0.85084} +{"mode": "train", "epoch": 141, "iter": 1400, "lr": 0.00101, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.60031, "top5_acc": 0.81938, "loss_cls": 2.27556, "loss": 2.27556, "time": 0.85254} +{"mode": "train", "epoch": 141, "iter": 1500, "lr": 0.00101, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.58703, "top5_acc": 0.81922, "loss_cls": 2.30315, "loss": 2.30315, "time": 0.84869} +{"mode": "train", "epoch": 141, "iter": 1600, "lr": 0.001, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.59047, "top5_acc": 0.82219, "loss_cls": 2.30986, "loss": 2.30986, "time": 0.84702} +{"mode": "train", "epoch": 141, "iter": 1700, "lr": 0.001, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.59812, "top5_acc": 0.8225, "loss_cls": 2.28712, "loss": 2.28712, "time": 0.84605} +{"mode": "train", "epoch": 141, "iter": 1800, "lr": 0.00099, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59875, "top5_acc": 0.82297, "loss_cls": 2.29186, "loss": 2.29186, "time": 0.85265} +{"mode": "train", "epoch": 141, "iter": 1900, "lr": 0.00099, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.59922, "top5_acc": 0.82984, "loss_cls": 2.26098, "loss": 2.26098, "time": 0.85179} +{"mode": "train", "epoch": 141, "iter": 2000, "lr": 0.00098, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.59438, "top5_acc": 0.82438, "loss_cls": 2.27273, "loss": 2.27273, "time": 0.84814} +{"mode": "train", "epoch": 141, "iter": 2100, "lr": 0.00097, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59656, "top5_acc": 0.82812, "loss_cls": 2.28379, "loss": 2.28379, "time": 0.85054} +{"mode": "train", "epoch": 141, "iter": 2200, "lr": 0.00097, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.58703, "top5_acc": 0.82078, "loss_cls": 2.30642, "loss": 2.30642, "time": 0.85153} +{"mode": "train", "epoch": 141, "iter": 2300, "lr": 0.00096, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.59609, "top5_acc": 0.82188, "loss_cls": 2.26144, "loss": 2.26144, "time": 0.84991} +{"mode": "train", "epoch": 141, "iter": 2400, "lr": 0.00096, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.59281, "top5_acc": 0.82266, "loss_cls": 2.30789, "loss": 2.30789, "time": 0.84552} +{"mode": "train", "epoch": 141, "iter": 2500, "lr": 0.00095, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58484, "top5_acc": 0.82016, "loss_cls": 2.32414, "loss": 2.32414, "time": 0.85132} +{"mode": "train", "epoch": 141, "iter": 2600, "lr": 0.00095, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.58781, "top5_acc": 0.82125, "loss_cls": 2.33478, "loss": 2.33478, "time": 0.85031} +{"mode": "train", "epoch": 141, "iter": 2700, "lr": 0.00094, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.60391, "top5_acc": 0.82891, "loss_cls": 2.2648, "loss": 2.2648, "time": 0.84709} +{"mode": "train", "epoch": 141, "iter": 2800, "lr": 0.00094, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.60469, "top5_acc": 0.82984, "loss_cls": 2.25076, "loss": 2.25076, "time": 0.84195} +{"mode": "train", "epoch": 141, "iter": 2900, "lr": 0.00093, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.59703, "top5_acc": 0.82719, "loss_cls": 2.28164, "loss": 2.28164, "time": 0.85119} +{"mode": "train", "epoch": 141, "iter": 3000, "lr": 0.00093, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58688, "top5_acc": 0.81734, "loss_cls": 2.31186, "loss": 2.31186, "time": 0.84712} +{"mode": "train", "epoch": 141, "iter": 3100, "lr": 0.00092, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.58203, "top5_acc": 0.80844, "loss_cls": 2.37137, "loss": 2.37137, "time": 0.85173} +{"mode": "train", "epoch": 141, "iter": 3200, "lr": 0.00091, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59844, "top5_acc": 0.81875, "loss_cls": 2.29622, "loss": 2.29622, "time": 0.8494} +{"mode": "train", "epoch": 141, "iter": 3300, "lr": 0.00091, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.59812, "top5_acc": 0.82375, "loss_cls": 2.29868, "loss": 2.29868, "time": 0.85255} +{"mode": "train", "epoch": 141, "iter": 3400, "lr": 0.0009, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.59641, "top5_acc": 0.81531, "loss_cls": 2.30324, "loss": 2.30324, "time": 0.84654} +{"mode": "train", "epoch": 141, "iter": 3500, "lr": 0.0009, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58812, "top5_acc": 0.81922, "loss_cls": 2.31543, "loss": 2.31543, "time": 0.84445} +{"mode": "train", "epoch": 141, "iter": 3600, "lr": 0.00089, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.59672, "top5_acc": 0.81906, "loss_cls": 2.29576, "loss": 2.29576, "time": 0.84894} +{"mode": "train", "epoch": 141, "iter": 3700, "lr": 0.00089, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.59719, "top5_acc": 0.82203, "loss_cls": 2.30879, "loss": 2.30879, "time": 0.85401} +{"mode": "val", "epoch": 141, "iter": 309, "lr": 0.00089, "top1_acc": 0.44512, "top5_acc": 0.69128, "mean_class_accuracy": 0.44482} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00088, "memory": 15990, "data_time": 1.60075, "top1_acc": 0.61234, "top5_acc": 0.84422, "loss_cls": 2.15982, "loss": 2.15982, "time": 2.63872} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00088, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.62031, "top5_acc": 0.845, "loss_cls": 2.16732, "loss": 2.16732, "time": 0.85575} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00087, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.61859, "top5_acc": 0.83953, "loss_cls": 2.19258, "loss": 2.19258, "time": 0.85349} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00086, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.61641, "top5_acc": 0.83938, "loss_cls": 2.19206, "loss": 2.19206, "time": 0.84901} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.00086, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.61828, "top5_acc": 0.84141, "loss_cls": 2.18231, "loss": 2.18231, "time": 0.84822} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.00085, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.60047, "top5_acc": 0.835, "loss_cls": 2.24735, "loss": 2.24735, "time": 0.85026} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.00085, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.60531, "top5_acc": 0.82422, "loss_cls": 2.24118, "loss": 2.24118, "time": 0.85878} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00084, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.61, "top5_acc": 0.82938, "loss_cls": 2.23324, "loss": 2.23324, "time": 0.84773} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00084, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.61594, "top5_acc": 0.83969, "loss_cls": 2.18458, "loss": 2.18458, "time": 0.84972} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00083, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.61062, "top5_acc": 0.83984, "loss_cls": 2.1857, "loss": 2.1857, "time": 0.84505} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00083, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.61031, "top5_acc": 0.83734, "loss_cls": 2.21147, "loss": 2.21147, "time": 0.85093} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00082, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.61062, "top5_acc": 0.82969, "loss_cls": 2.23988, "loss": 2.23988, "time": 0.85026} +{"mode": "train", "epoch": 142, "iter": 1300, "lr": 0.00082, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.61187, "top5_acc": 0.84234, "loss_cls": 2.19336, "loss": 2.19336, "time": 0.85004} +{"mode": "train", "epoch": 142, "iter": 1400, "lr": 0.00081, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.61187, "top5_acc": 0.83797, "loss_cls": 2.18876, "loss": 2.18876, "time": 0.85304} +{"mode": "train", "epoch": 142, "iter": 1500, "lr": 0.00081, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.60766, "top5_acc": 0.83891, "loss_cls": 2.21519, "loss": 2.21519, "time": 0.86284} +{"mode": "train", "epoch": 142, "iter": 1600, "lr": 0.0008, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.60891, "top5_acc": 0.82875, "loss_cls": 2.22319, "loss": 2.22319, "time": 0.85733} +{"mode": "train", "epoch": 142, "iter": 1700, "lr": 0.0008, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.60844, "top5_acc": 0.83672, "loss_cls": 2.21743, "loss": 2.21743, "time": 0.86302} +{"mode": "train", "epoch": 142, "iter": 1800, "lr": 0.00079, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.61375, "top5_acc": 0.82594, "loss_cls": 2.24241, "loss": 2.24241, "time": 0.85336} +{"mode": "train", "epoch": 142, "iter": 1900, "lr": 0.00079, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.60016, "top5_acc": 0.8225, "loss_cls": 2.2703, "loss": 2.2703, "time": 0.86199} +{"mode": "train", "epoch": 142, "iter": 2000, "lr": 0.00078, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.60266, "top5_acc": 0.82453, "loss_cls": 2.27912, "loss": 2.27912, "time": 0.85491} +{"mode": "train", "epoch": 142, "iter": 2100, "lr": 0.00078, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.60141, "top5_acc": 0.83453, "loss_cls": 2.25101, "loss": 2.25101, "time": 0.86307} +{"mode": "train", "epoch": 142, "iter": 2200, "lr": 0.00077, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.61562, "top5_acc": 0.83312, "loss_cls": 2.21724, "loss": 2.21724, "time": 0.85648} +{"mode": "train", "epoch": 142, "iter": 2300, "lr": 0.00077, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.60656, "top5_acc": 0.83344, "loss_cls": 2.23124, "loss": 2.23124, "time": 0.85873} +{"mode": "train", "epoch": 142, "iter": 2400, "lr": 0.00076, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.62078, "top5_acc": 0.83469, "loss_cls": 2.19759, "loss": 2.19759, "time": 0.85348} +{"mode": "train", "epoch": 142, "iter": 2500, "lr": 0.00076, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59594, "top5_acc": 0.825, "loss_cls": 2.25952, "loss": 2.25952, "time": 0.85281} +{"mode": "train", "epoch": 142, "iter": 2600, "lr": 0.00075, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.60625, "top5_acc": 0.82875, "loss_cls": 2.25257, "loss": 2.25257, "time": 0.85277} +{"mode": "train", "epoch": 142, "iter": 2700, "lr": 0.00075, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.61281, "top5_acc": 0.83688, "loss_cls": 2.20447, "loss": 2.20447, "time": 0.84934} +{"mode": "train", "epoch": 142, "iter": 2800, "lr": 0.00075, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.59016, "top5_acc": 0.8225, "loss_cls": 2.28632, "loss": 2.28632, "time": 0.84479} +{"mode": "train", "epoch": 142, "iter": 2900, "lr": 0.00074, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.60484, "top5_acc": 0.83078, "loss_cls": 2.22163, "loss": 2.22163, "time": 0.84675} +{"mode": "train", "epoch": 142, "iter": 3000, "lr": 0.00074, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.61422, "top5_acc": 0.84344, "loss_cls": 2.17392, "loss": 2.17392, "time": 0.84738} +{"mode": "train", "epoch": 142, "iter": 3100, "lr": 0.00073, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.5925, "top5_acc": 0.83078, "loss_cls": 2.26659, "loss": 2.26659, "time": 0.85435} +{"mode": "train", "epoch": 142, "iter": 3200, "lr": 0.00073, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.60516, "top5_acc": 0.83719, "loss_cls": 2.22649, "loss": 2.22649, "time": 0.85801} +{"mode": "train", "epoch": 142, "iter": 3300, "lr": 0.00072, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.59672, "top5_acc": 0.81984, "loss_cls": 2.27546, "loss": 2.27546, "time": 0.85239} +{"mode": "train", "epoch": 142, "iter": 3400, "lr": 0.00072, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.5925, "top5_acc": 0.82766, "loss_cls": 2.28026, "loss": 2.28026, "time": 0.85694} +{"mode": "train", "epoch": 142, "iter": 3500, "lr": 0.00071, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.60359, "top5_acc": 0.82906, "loss_cls": 2.26113, "loss": 2.26113, "time": 0.85334} +{"mode": "train", "epoch": 142, "iter": 3600, "lr": 0.00071, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.61391, "top5_acc": 0.83156, "loss_cls": 2.22351, "loss": 2.22351, "time": 0.85809} +{"mode": "train", "epoch": 142, "iter": 3700, "lr": 0.0007, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.60547, "top5_acc": 0.83047, "loss_cls": 2.24222, "loss": 2.24222, "time": 0.84852} +{"mode": "val", "epoch": 142, "iter": 309, "lr": 0.0007, "top1_acc": 0.44644, "top5_acc": 0.69285, "mean_class_accuracy": 0.44619} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.0007, "memory": 15990, "data_time": 1.54627, "top1_acc": 0.63344, "top5_acc": 0.84906, "loss_cls": 2.09949, "loss": 2.09949, "time": 2.57496} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00069, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.62625, "top5_acc": 0.84328, "loss_cls": 2.15022, "loss": 2.15022, "time": 0.85552} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00069, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.61594, "top5_acc": 0.83375, "loss_cls": 2.18014, "loss": 2.18014, "time": 0.85755} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00068, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.63313, "top5_acc": 0.84703, "loss_cls": 2.11806, "loss": 2.11806, "time": 0.85704} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00068, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.62031, "top5_acc": 0.83922, "loss_cls": 2.15794, "loss": 2.15794, "time": 0.86035} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00067, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.62578, "top5_acc": 0.84062, "loss_cls": 2.15495, "loss": 2.15495, "time": 0.85776} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00067, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.62641, "top5_acc": 0.84484, "loss_cls": 2.13216, "loss": 2.13216, "time": 0.85823} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00066, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.6225, "top5_acc": 0.84062, "loss_cls": 2.15868, "loss": 2.15868, "time": 0.85099} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00066, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.62484, "top5_acc": 0.84, "loss_cls": 2.16498, "loss": 2.16498, "time": 0.85446} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00065, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.61391, "top5_acc": 0.83984, "loss_cls": 2.18135, "loss": 2.18135, "time": 0.84952} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00065, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.62687, "top5_acc": 0.84328, "loss_cls": 2.14729, "loss": 2.14729, "time": 0.84712} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00065, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.62344, "top5_acc": 0.84219, "loss_cls": 2.16075, "loss": 2.16075, "time": 0.85512} +{"mode": "train", "epoch": 143, "iter": 1300, "lr": 0.00064, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.61766, "top5_acc": 0.83922, "loss_cls": 2.17726, "loss": 2.17726, "time": 0.84971} +{"mode": "train", "epoch": 143, "iter": 1400, "lr": 0.00064, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.61719, "top5_acc": 0.83656, "loss_cls": 2.1822, "loss": 2.1822, "time": 0.84946} +{"mode": "train", "epoch": 143, "iter": 1500, "lr": 0.00063, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.6225, "top5_acc": 0.83438, "loss_cls": 2.17845, "loss": 2.17845, "time": 0.84723} +{"mode": "train", "epoch": 143, "iter": 1600, "lr": 0.00063, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.62531, "top5_acc": 0.83828, "loss_cls": 2.15092, "loss": 2.15092, "time": 0.8476} +{"mode": "train", "epoch": 143, "iter": 1700, "lr": 0.00062, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.62078, "top5_acc": 0.84516, "loss_cls": 2.14389, "loss": 2.14389, "time": 0.84937} +{"mode": "train", "epoch": 143, "iter": 1800, "lr": 0.00062, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.60953, "top5_acc": 0.84156, "loss_cls": 2.16524, "loss": 2.16524, "time": 0.84999} +{"mode": "train", "epoch": 143, "iter": 1900, "lr": 0.00061, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.61141, "top5_acc": 0.83969, "loss_cls": 2.17086, "loss": 2.17086, "time": 0.84858} +{"mode": "train", "epoch": 143, "iter": 2000, "lr": 0.00061, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.62359, "top5_acc": 0.84453, "loss_cls": 2.14566, "loss": 2.14566, "time": 0.8489} +{"mode": "train", "epoch": 143, "iter": 2100, "lr": 0.00061, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.62125, "top5_acc": 0.84312, "loss_cls": 2.16105, "loss": 2.16105, "time": 0.85213} +{"mode": "train", "epoch": 143, "iter": 2200, "lr": 0.0006, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.61344, "top5_acc": 0.83562, "loss_cls": 2.20088, "loss": 2.20088, "time": 0.85576} +{"mode": "train", "epoch": 143, "iter": 2300, "lr": 0.0006, "memory": 15990, "data_time": 0.00084, "top1_acc": 0.62641, "top5_acc": 0.83734, "loss_cls": 2.16994, "loss": 2.16994, "time": 0.84958} +{"mode": "train", "epoch": 143, "iter": 2400, "lr": 0.00059, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.63156, "top5_acc": 0.84438, "loss_cls": 2.13299, "loss": 2.13299, "time": 0.84914} +{"mode": "train", "epoch": 143, "iter": 2500, "lr": 0.00059, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.61328, "top5_acc": 0.84469, "loss_cls": 2.18935, "loss": 2.18935, "time": 0.84682} +{"mode": "train", "epoch": 143, "iter": 2600, "lr": 0.00058, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.61734, "top5_acc": 0.84, "loss_cls": 2.16062, "loss": 2.16062, "time": 0.84936} +{"mode": "train", "epoch": 143, "iter": 2700, "lr": 0.00058, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.60984, "top5_acc": 0.83719, "loss_cls": 2.19039, "loss": 2.19039, "time": 0.851} +{"mode": "train", "epoch": 143, "iter": 2800, "lr": 0.00058, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.61156, "top5_acc": 0.83359, "loss_cls": 2.20272, "loss": 2.20272, "time": 0.84808} +{"mode": "train", "epoch": 143, "iter": 2900, "lr": 0.00057, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.62766, "top5_acc": 0.84062, "loss_cls": 2.16207, "loss": 2.16207, "time": 0.85117} +{"mode": "train", "epoch": 143, "iter": 3000, "lr": 0.00057, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.62, "top5_acc": 0.84297, "loss_cls": 2.16881, "loss": 2.16881, "time": 0.8508} +{"mode": "train", "epoch": 143, "iter": 3100, "lr": 0.00056, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.60766, "top5_acc": 0.83672, "loss_cls": 2.21334, "loss": 2.21334, "time": 0.84637} +{"mode": "train", "epoch": 143, "iter": 3200, "lr": 0.00056, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.61438, "top5_acc": 0.83234, "loss_cls": 2.20445, "loss": 2.20445, "time": 0.85382} +{"mode": "train", "epoch": 143, "iter": 3300, "lr": 0.00055, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.61703, "top5_acc": 0.83531, "loss_cls": 2.18577, "loss": 2.18577, "time": 0.8544} +{"mode": "train", "epoch": 143, "iter": 3400, "lr": 0.00055, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.62578, "top5_acc": 0.84516, "loss_cls": 2.14379, "loss": 2.14379, "time": 0.85484} +{"mode": "train", "epoch": 143, "iter": 3500, "lr": 0.00055, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.60781, "top5_acc": 0.83484, "loss_cls": 2.20166, "loss": 2.20166, "time": 0.85283} +{"mode": "train", "epoch": 143, "iter": 3600, "lr": 0.00054, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.62578, "top5_acc": 0.84469, "loss_cls": 2.14829, "loss": 2.14829, "time": 0.84833} +{"mode": "train", "epoch": 143, "iter": 3700, "lr": 0.00054, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.61344, "top5_acc": 0.8325, "loss_cls": 2.18896, "loss": 2.18896, "time": 0.85758} +{"mode": "val", "epoch": 143, "iter": 309, "lr": 0.00054, "top1_acc": 0.44411, "top5_acc": 0.69331, "mean_class_accuracy": 0.44386} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00053, "memory": 15990, "data_time": 1.57892, "top1_acc": 0.63313, "top5_acc": 0.85453, "loss_cls": 2.09307, "loss": 2.09307, "time": 2.62034} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00053, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.63906, "top5_acc": 0.85578, "loss_cls": 2.06581, "loss": 2.06581, "time": 0.85667} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00052, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.64234, "top5_acc": 0.86031, "loss_cls": 2.04105, "loss": 2.04105, "time": 0.86162} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00052, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.64531, "top5_acc": 0.85172, "loss_cls": 2.06804, "loss": 2.06804, "time": 0.85699} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00052, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.63141, "top5_acc": 0.84547, "loss_cls": 2.10129, "loss": 2.10129, "time": 0.86066} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00051, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.63094, "top5_acc": 0.84641, "loss_cls": 2.10895, "loss": 2.10895, "time": 0.85562} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00051, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.63531, "top5_acc": 0.84641, "loss_cls": 2.09271, "loss": 2.09271, "time": 0.85437} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.0005, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.63656, "top5_acc": 0.85, "loss_cls": 2.09761, "loss": 2.09761, "time": 0.84755} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.0005, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.63203, "top5_acc": 0.85109, "loss_cls": 2.10105, "loss": 2.10105, "time": 0.85937} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.0005, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.6325, "top5_acc": 0.84781, "loss_cls": 2.12021, "loss": 2.12021, "time": 0.8566} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.00049, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.62906, "top5_acc": 0.85047, "loss_cls": 2.11799, "loss": 2.11799, "time": 0.856} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.00049, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.63391, "top5_acc": 0.84609, "loss_cls": 2.11077, "loss": 2.11077, "time": 0.8605} +{"mode": "train", "epoch": 144, "iter": 1300, "lr": 0.00048, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.635, "top5_acc": 0.85, "loss_cls": 2.11592, "loss": 2.11592, "time": 0.85899} +{"mode": "train", "epoch": 144, "iter": 1400, "lr": 0.00048, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.63422, "top5_acc": 0.85078, "loss_cls": 2.12079, "loss": 2.12079, "time": 0.85394} +{"mode": "train", "epoch": 144, "iter": 1500, "lr": 0.00048, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.63344, "top5_acc": 0.85125, "loss_cls": 2.08838, "loss": 2.08838, "time": 0.85088} +{"mode": "train", "epoch": 144, "iter": 1600, "lr": 0.00047, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.62266, "top5_acc": 0.8475, "loss_cls": 2.1435, "loss": 2.1435, "time": 0.85761} +{"mode": "train", "epoch": 144, "iter": 1700, "lr": 0.00047, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.63016, "top5_acc": 0.85156, "loss_cls": 2.11516, "loss": 2.11516, "time": 0.85273} +{"mode": "train", "epoch": 144, "iter": 1800, "lr": 0.00047, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.64312, "top5_acc": 0.84672, "loss_cls": 2.09151, "loss": 2.09151, "time": 0.85785} +{"mode": "train", "epoch": 144, "iter": 1900, "lr": 0.00046, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.63156, "top5_acc": 0.84547, "loss_cls": 2.11271, "loss": 2.11271, "time": 0.85712} +{"mode": "train", "epoch": 144, "iter": 2000, "lr": 0.00046, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.62781, "top5_acc": 0.84797, "loss_cls": 2.11733, "loss": 2.11733, "time": 0.86331} +{"mode": "train", "epoch": 144, "iter": 2100, "lr": 0.00045, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.62187, "top5_acc": 0.84859, "loss_cls": 2.14404, "loss": 2.14404, "time": 0.85539} +{"mode": "train", "epoch": 144, "iter": 2200, "lr": 0.00045, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.62828, "top5_acc": 0.84656, "loss_cls": 2.12251, "loss": 2.12251, "time": 0.85381} +{"mode": "train", "epoch": 144, "iter": 2300, "lr": 0.00045, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.63156, "top5_acc": 0.84734, "loss_cls": 2.1195, "loss": 2.1195, "time": 0.84806} +{"mode": "train", "epoch": 144, "iter": 2400, "lr": 0.00044, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.63641, "top5_acc": 0.84562, "loss_cls": 2.11354, "loss": 2.11354, "time": 0.84828} +{"mode": "train", "epoch": 144, "iter": 2500, "lr": 0.00044, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.63094, "top5_acc": 0.84812, "loss_cls": 2.10862, "loss": 2.10862, "time": 0.85201} +{"mode": "train", "epoch": 144, "iter": 2600, "lr": 0.00044, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.62516, "top5_acc": 0.84625, "loss_cls": 2.09803, "loss": 2.09803, "time": 0.8543} +{"mode": "train", "epoch": 144, "iter": 2700, "lr": 0.00043, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.63531, "top5_acc": 0.85109, "loss_cls": 2.09938, "loss": 2.09938, "time": 0.84932} +{"mode": "train", "epoch": 144, "iter": 2800, "lr": 0.00043, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.61812, "top5_acc": 0.84484, "loss_cls": 2.16954, "loss": 2.16954, "time": 0.84848} +{"mode": "train", "epoch": 144, "iter": 2900, "lr": 0.00042, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.62047, "top5_acc": 0.84094, "loss_cls": 2.14226, "loss": 2.14226, "time": 0.84892} +{"mode": "train", "epoch": 144, "iter": 3000, "lr": 0.00042, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.63375, "top5_acc": 0.84359, "loss_cls": 2.10854, "loss": 2.10854, "time": 0.84651} +{"mode": "train", "epoch": 144, "iter": 3100, "lr": 0.00042, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.63422, "top5_acc": 0.84703, "loss_cls": 2.10224, "loss": 2.10224, "time": 0.85151} +{"mode": "train", "epoch": 144, "iter": 3200, "lr": 0.00041, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.63656, "top5_acc": 0.84844, "loss_cls": 2.10187, "loss": 2.10187, "time": 0.85401} +{"mode": "train", "epoch": 144, "iter": 3300, "lr": 0.00041, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.62906, "top5_acc": 0.84984, "loss_cls": 2.10719, "loss": 2.10719, "time": 0.84222} +{"mode": "train", "epoch": 144, "iter": 3400, "lr": 0.00041, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.62391, "top5_acc": 0.83578, "loss_cls": 2.16224, "loss": 2.16224, "time": 0.86352} +{"mode": "train", "epoch": 144, "iter": 3500, "lr": 0.0004, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.63297, "top5_acc": 0.84297, "loss_cls": 2.12018, "loss": 2.12018, "time": 0.8577} +{"mode": "train", "epoch": 144, "iter": 3600, "lr": 0.0004, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.62641, "top5_acc": 0.84078, "loss_cls": 2.18372, "loss": 2.18372, "time": 0.85775} +{"mode": "train", "epoch": 144, "iter": 3700, "lr": 0.0004, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.62484, "top5_acc": 0.84172, "loss_cls": 2.13499, "loss": 2.13499, "time": 0.85773} +{"mode": "val", "epoch": 144, "iter": 309, "lr": 0.00039, "top1_acc": 0.44988, "top5_acc": 0.69701, "mean_class_accuracy": 0.44968} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.00039, "memory": 15990, "data_time": 1.57737, "top1_acc": 0.65469, "top5_acc": 0.86422, "loss_cls": 2.00954, "loss": 2.00954, "time": 2.6113} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 0.00039, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.65406, "top5_acc": 0.86531, "loss_cls": 2.00359, "loss": 2.00359, "time": 0.86041} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 0.00038, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.64344, "top5_acc": 0.85781, "loss_cls": 2.0695, "loss": 2.0695, "time": 0.8539} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 0.00038, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.63625, "top5_acc": 0.85391, "loss_cls": 2.0757, "loss": 2.0757, "time": 0.85671} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 0.00038, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.64109, "top5_acc": 0.85125, "loss_cls": 2.07266, "loss": 2.07266, "time": 0.85527} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 0.00037, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.64047, "top5_acc": 0.85781, "loss_cls": 2.06852, "loss": 2.06852, "time": 0.8569} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 0.00037, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65453, "top5_acc": 0.85781, "loss_cls": 2.01847, "loss": 2.01847, "time": 0.85548} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 0.00037, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.63953, "top5_acc": 0.85188, "loss_cls": 2.08192, "loss": 2.08192, "time": 0.8512} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 0.00036, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.64922, "top5_acc": 0.86094, "loss_cls": 2.03871, "loss": 2.03871, "time": 0.85293} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 0.00036, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65359, "top5_acc": 0.86312, "loss_cls": 2.0218, "loss": 2.0218, "time": 0.84741} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 0.00036, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.62828, "top5_acc": 0.85047, "loss_cls": 2.11741, "loss": 2.11741, "time": 0.85305} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 0.00035, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.64391, "top5_acc": 0.85797, "loss_cls": 2.04175, "loss": 2.04175, "time": 0.84925} +{"mode": "train", "epoch": 145, "iter": 1300, "lr": 0.00035, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65375, "top5_acc": 0.86156, "loss_cls": 2.01125, "loss": 2.01125, "time": 0.84776} +{"mode": "train", "epoch": 145, "iter": 1400, "lr": 0.00035, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64516, "top5_acc": 0.85484, "loss_cls": 2.0523, "loss": 2.0523, "time": 0.84719} +{"mode": "train", "epoch": 145, "iter": 1500, "lr": 0.00034, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.63203, "top5_acc": 0.85391, "loss_cls": 2.07601, "loss": 2.07601, "time": 0.85188} +{"mode": "train", "epoch": 145, "iter": 1600, "lr": 0.00034, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.64719, "top5_acc": 0.85531, "loss_cls": 2.05264, "loss": 2.05264, "time": 0.85048} +{"mode": "train", "epoch": 145, "iter": 1700, "lr": 0.00034, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.64, "top5_acc": 0.85062, "loss_cls": 2.07857, "loss": 2.07857, "time": 0.84818} +{"mode": "train", "epoch": 145, "iter": 1800, "lr": 0.00033, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.64, "top5_acc": 0.84812, "loss_cls": 2.05593, "loss": 2.05593, "time": 0.84725} +{"mode": "train", "epoch": 145, "iter": 1900, "lr": 0.00033, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.64125, "top5_acc": 0.85156, "loss_cls": 2.06595, "loss": 2.06595, "time": 0.84916} +{"mode": "train", "epoch": 145, "iter": 2000, "lr": 0.00033, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.65062, "top5_acc": 0.85953, "loss_cls": 2.0245, "loss": 2.0245, "time": 0.84608} +{"mode": "train", "epoch": 145, "iter": 2100, "lr": 0.00032, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.64641, "top5_acc": 0.85766, "loss_cls": 2.03987, "loss": 2.03987, "time": 0.85626} +{"mode": "train", "epoch": 145, "iter": 2200, "lr": 0.00032, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.64109, "top5_acc": 0.85422, "loss_cls": 2.07222, "loss": 2.07222, "time": 0.85518} +{"mode": "train", "epoch": 145, "iter": 2300, "lr": 0.00032, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.64062, "top5_acc": 0.85312, "loss_cls": 2.07387, "loss": 2.07387, "time": 0.84954} +{"mode": "train", "epoch": 145, "iter": 2400, "lr": 0.00031, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.62922, "top5_acc": 0.84891, "loss_cls": 2.12102, "loss": 2.12102, "time": 0.85229} +{"mode": "train", "epoch": 145, "iter": 2500, "lr": 0.00031, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.64375, "top5_acc": 0.85234, "loss_cls": 2.08199, "loss": 2.08199, "time": 0.85436} +{"mode": "train", "epoch": 145, "iter": 2600, "lr": 0.00031, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.62984, "top5_acc": 0.85, "loss_cls": 2.10267, "loss": 2.10267, "time": 0.85311} +{"mode": "train", "epoch": 145, "iter": 2700, "lr": 0.00031, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.6475, "top5_acc": 0.85547, "loss_cls": 2.04581, "loss": 2.04581, "time": 0.85463} +{"mode": "train", "epoch": 145, "iter": 2800, "lr": 0.0003, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.64547, "top5_acc": 0.85922, "loss_cls": 2.02778, "loss": 2.02778, "time": 0.85086} +{"mode": "train", "epoch": 145, "iter": 2900, "lr": 0.0003, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.63906, "top5_acc": 0.85719, "loss_cls": 2.07421, "loss": 2.07421, "time": 0.84846} +{"mode": "train", "epoch": 145, "iter": 3000, "lr": 0.0003, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.64625, "top5_acc": 0.85016, "loss_cls": 2.07261, "loss": 2.07261, "time": 0.85324} +{"mode": "train", "epoch": 145, "iter": 3100, "lr": 0.00029, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.64594, "top5_acc": 0.85297, "loss_cls": 2.05433, "loss": 2.05433, "time": 0.84991} +{"mode": "train", "epoch": 145, "iter": 3200, "lr": 0.00029, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65203, "top5_acc": 0.85875, "loss_cls": 2.01843, "loss": 2.01843, "time": 0.85211} +{"mode": "train", "epoch": 145, "iter": 3300, "lr": 0.00029, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.63359, "top5_acc": 0.85234, "loss_cls": 2.08527, "loss": 2.08527, "time": 0.85108} +{"mode": "train", "epoch": 145, "iter": 3400, "lr": 0.00028, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64547, "top5_acc": 0.85719, "loss_cls": 2.04482, "loss": 2.04482, "time": 0.84955} +{"mode": "train", "epoch": 145, "iter": 3500, "lr": 0.00028, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.64406, "top5_acc": 0.85016, "loss_cls": 2.06947, "loss": 2.06947, "time": 0.84401} +{"mode": "train", "epoch": 145, "iter": 3600, "lr": 0.00028, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.63594, "top5_acc": 0.85125, "loss_cls": 2.10114, "loss": 2.10114, "time": 0.84729} +{"mode": "train", "epoch": 145, "iter": 3700, "lr": 0.00028, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.63781, "top5_acc": 0.85422, "loss_cls": 2.06592, "loss": 2.06592, "time": 0.8463} +{"mode": "val", "epoch": 145, "iter": 309, "lr": 0.00027, "top1_acc": 0.45044, "top5_acc": 0.69574, "mean_class_accuracy": 0.45019} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 0.00027, "memory": 15990, "data_time": 1.46338, "top1_acc": 0.66188, "top5_acc": 0.86953, "loss_cls": 1.98423, "loss": 1.98423, "time": 2.4808} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 0.00027, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.65516, "top5_acc": 0.86203, "loss_cls": 1.99816, "loss": 1.99816, "time": 0.85079} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 0.00027, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65984, "top5_acc": 0.86766, "loss_cls": 1.96139, "loss": 1.96139, "time": 0.84883} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 0.00026, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.66859, "top5_acc": 0.87, "loss_cls": 1.95683, "loss": 1.95683, "time": 0.84869} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 0.00026, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.65141, "top5_acc": 0.86125, "loss_cls": 2.03176, "loss": 2.03176, "time": 0.85499} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 0.00026, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.65703, "top5_acc": 0.86016, "loss_cls": 2.02366, "loss": 2.02366, "time": 0.85011} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 0.00025, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.65031, "top5_acc": 0.86344, "loss_cls": 2.00822, "loss": 2.00822, "time": 0.8515} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 0.00025, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65172, "top5_acc": 0.86, "loss_cls": 2.00379, "loss": 2.00379, "time": 0.84778} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 0.00025, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.64609, "top5_acc": 0.8625, "loss_cls": 2.03489, "loss": 2.03489, "time": 0.84286} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 0.00025, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65297, "top5_acc": 0.86531, "loss_cls": 1.99754, "loss": 1.99754, "time": 0.84645} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 0.00024, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65672, "top5_acc": 0.86391, "loss_cls": 1.98749, "loss": 1.98749, "time": 0.84551} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 0.00024, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65297, "top5_acc": 0.85562, "loss_cls": 2.01012, "loss": 2.01012, "time": 0.85144} +{"mode": "train", "epoch": 146, "iter": 1300, "lr": 0.00024, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.65078, "top5_acc": 0.86234, "loss_cls": 2.00102, "loss": 2.00102, "time": 0.8458} +{"mode": "train", "epoch": 146, "iter": 1400, "lr": 0.00023, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65188, "top5_acc": 0.85734, "loss_cls": 2.02589, "loss": 2.02589, "time": 0.84651} +{"mode": "train", "epoch": 146, "iter": 1500, "lr": 0.00023, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65578, "top5_acc": 0.85562, "loss_cls": 2.03504, "loss": 2.03504, "time": 0.85052} +{"mode": "train", "epoch": 146, "iter": 1600, "lr": 0.00023, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.65, "top5_acc": 0.86328, "loss_cls": 2.00578, "loss": 2.00578, "time": 0.84625} +{"mode": "train", "epoch": 146, "iter": 1700, "lr": 0.00023, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.65688, "top5_acc": 0.86094, "loss_cls": 1.99821, "loss": 1.99821, "time": 0.84768} +{"mode": "train", "epoch": 146, "iter": 1800, "lr": 0.00022, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65578, "top5_acc": 0.86359, "loss_cls": 2.00545, "loss": 2.00545, "time": 0.84817} +{"mode": "train", "epoch": 146, "iter": 1900, "lr": 0.00022, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.665, "top5_acc": 0.86422, "loss_cls": 1.96741, "loss": 1.96741, "time": 0.85105} +{"mode": "train", "epoch": 146, "iter": 2000, "lr": 0.00022, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65391, "top5_acc": 0.86266, "loss_cls": 2.00511, "loss": 2.00511, "time": 0.84936} +{"mode": "train", "epoch": 146, "iter": 2100, "lr": 0.00022, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64578, "top5_acc": 0.86031, "loss_cls": 2.0139, "loss": 2.0139, "time": 0.85258} +{"mode": "train", "epoch": 146, "iter": 2200, "lr": 0.00021, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.6425, "top5_acc": 0.86031, "loss_cls": 2.02764, "loss": 2.02764, "time": 0.85346} +{"mode": "train", "epoch": 146, "iter": 2300, "lr": 0.00021, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.65438, "top5_acc": 0.86453, "loss_cls": 1.97827, "loss": 1.97827, "time": 0.84756} +{"mode": "train", "epoch": 146, "iter": 2400, "lr": 0.00021, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.64922, "top5_acc": 0.8575, "loss_cls": 2.03302, "loss": 2.03302, "time": 0.85089} +{"mode": "train", "epoch": 146, "iter": 2500, "lr": 0.00021, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.65141, "top5_acc": 0.85656, "loss_cls": 2.03062, "loss": 2.03062, "time": 0.8476} +{"mode": "train", "epoch": 146, "iter": 2600, "lr": 0.0002, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.65328, "top5_acc": 0.86156, "loss_cls": 2.00755, "loss": 2.00755, "time": 0.84796} +{"mode": "train", "epoch": 146, "iter": 2700, "lr": 0.0002, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.64797, "top5_acc": 0.85641, "loss_cls": 2.03844, "loss": 2.03844, "time": 0.84796} +{"mode": "train", "epoch": 146, "iter": 2800, "lr": 0.0002, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.64719, "top5_acc": 0.85969, "loss_cls": 2.04633, "loss": 2.04633, "time": 0.84855} +{"mode": "train", "epoch": 146, "iter": 2900, "lr": 0.0002, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.66156, "top5_acc": 0.86609, "loss_cls": 1.96697, "loss": 1.96697, "time": 0.85026} +{"mode": "train", "epoch": 146, "iter": 3000, "lr": 0.00019, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65219, "top5_acc": 0.85906, "loss_cls": 2.01312, "loss": 2.01312, "time": 0.84373} +{"mode": "train", "epoch": 146, "iter": 3100, "lr": 0.00019, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65469, "top5_acc": 0.85312, "loss_cls": 2.01786, "loss": 2.01786, "time": 0.85135} +{"mode": "train", "epoch": 146, "iter": 3200, "lr": 0.00019, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64312, "top5_acc": 0.85781, "loss_cls": 2.05474, "loss": 2.05474, "time": 0.84718} +{"mode": "train", "epoch": 146, "iter": 3300, "lr": 0.00019, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.64875, "top5_acc": 0.86203, "loss_cls": 2.01639, "loss": 2.01639, "time": 0.84793} +{"mode": "train", "epoch": 146, "iter": 3400, "lr": 0.00018, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64891, "top5_acc": 0.85859, "loss_cls": 2.03075, "loss": 2.03075, "time": 0.84538} +{"mode": "train", "epoch": 146, "iter": 3500, "lr": 0.00018, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.64875, "top5_acc": 0.86531, "loss_cls": 2.00481, "loss": 2.00481, "time": 0.85099} +{"mode": "train", "epoch": 146, "iter": 3600, "lr": 0.00018, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64859, "top5_acc": 0.86219, "loss_cls": 2.02415, "loss": 2.02415, "time": 0.84702} +{"mode": "train", "epoch": 146, "iter": 3700, "lr": 0.00018, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65531, "top5_acc": 0.86266, "loss_cls": 2.01558, "loss": 2.01558, "time": 0.84984} +{"mode": "val", "epoch": 146, "iter": 309, "lr": 0.00018, "top1_acc": 0.4471, "top5_acc": 0.69625, "mean_class_accuracy": 0.44686} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 0.00017, "memory": 15990, "data_time": 1.47882, "top1_acc": 0.66344, "top5_acc": 0.8725, "loss_cls": 1.96549, "loss": 1.96549, "time": 2.50885} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 0.00017, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.66641, "top5_acc": 0.87219, "loss_cls": 1.95525, "loss": 1.95525, "time": 0.8545} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 0.00017, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.66094, "top5_acc": 0.86734, "loss_cls": 1.98049, "loss": 1.98049, "time": 0.84974} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 0.00017, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.66469, "top5_acc": 0.87203, "loss_cls": 1.93201, "loss": 1.93201, "time": 0.85028} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 0.00016, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.66016, "top5_acc": 0.87031, "loss_cls": 1.96676, "loss": 1.96676, "time": 0.84724} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 0.00016, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.64781, "top5_acc": 0.86156, "loss_cls": 2.0202, "loss": 2.0202, "time": 0.84994} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 0.00016, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.66375, "top5_acc": 0.87125, "loss_cls": 1.95381, "loss": 1.95381, "time": 0.85549} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 0.00016, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66438, "top5_acc": 0.86797, "loss_cls": 1.96881, "loss": 1.96881, "time": 0.84793} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 0.00015, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65578, "top5_acc": 0.86125, "loss_cls": 1.98822, "loss": 1.98822, "time": 0.85253} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 0.00015, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.66453, "top5_acc": 0.87047, "loss_cls": 1.95506, "loss": 1.95506, "time": 0.85356} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 0.00015, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.67297, "top5_acc": 0.86875, "loss_cls": 1.93497, "loss": 1.93497, "time": 0.85201} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 0.00015, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.66141, "top5_acc": 0.86656, "loss_cls": 1.96988, "loss": 1.96988, "time": 0.85562} +{"mode": "train", "epoch": 147, "iter": 1300, "lr": 0.00015, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.6525, "top5_acc": 0.86094, "loss_cls": 2.00251, "loss": 2.00251, "time": 0.8515} +{"mode": "train", "epoch": 147, "iter": 1400, "lr": 0.00014, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66, "top5_acc": 0.86781, "loss_cls": 1.97644, "loss": 1.97644, "time": 0.85208} +{"mode": "train", "epoch": 147, "iter": 1500, "lr": 0.00014, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67016, "top5_acc": 0.8675, "loss_cls": 1.94055, "loss": 1.94055, "time": 0.85366} +{"mode": "train", "epoch": 147, "iter": 1600, "lr": 0.00014, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.66016, "top5_acc": 0.86906, "loss_cls": 1.98863, "loss": 1.98863, "time": 0.85584} +{"mode": "train", "epoch": 147, "iter": 1700, "lr": 0.00014, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65719, "top5_acc": 0.86438, "loss_cls": 1.9871, "loss": 1.9871, "time": 0.85541} +{"mode": "train", "epoch": 147, "iter": 1800, "lr": 0.00014, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.66562, "top5_acc": 0.86984, "loss_cls": 1.96252, "loss": 1.96252, "time": 0.85859} +{"mode": "train", "epoch": 147, "iter": 1900, "lr": 0.00013, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65781, "top5_acc": 0.86922, "loss_cls": 1.94806, "loss": 1.94806, "time": 0.85907} +{"mode": "train", "epoch": 147, "iter": 2000, "lr": 0.00013, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65938, "top5_acc": 0.87234, "loss_cls": 1.96325, "loss": 1.96325, "time": 0.85405} +{"mode": "train", "epoch": 147, "iter": 2100, "lr": 0.00013, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.6625, "top5_acc": 0.87094, "loss_cls": 1.97799, "loss": 1.97799, "time": 0.85185} +{"mode": "train", "epoch": 147, "iter": 2200, "lr": 0.00013, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.66625, "top5_acc": 0.87328, "loss_cls": 1.95098, "loss": 1.95098, "time": 0.84742} +{"mode": "train", "epoch": 147, "iter": 2300, "lr": 0.00013, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66234, "top5_acc": 0.86156, "loss_cls": 1.97968, "loss": 1.97968, "time": 0.84903} +{"mode": "train", "epoch": 147, "iter": 2400, "lr": 0.00012, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66375, "top5_acc": 0.87516, "loss_cls": 1.92807, "loss": 1.92807, "time": 0.84326} +{"mode": "train", "epoch": 147, "iter": 2500, "lr": 0.00012, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65359, "top5_acc": 0.86109, "loss_cls": 1.98765, "loss": 1.98765, "time": 0.85118} +{"mode": "train", "epoch": 147, "iter": 2600, "lr": 0.00012, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65094, "top5_acc": 0.86484, "loss_cls": 1.98897, "loss": 1.98897, "time": 0.84823} +{"mode": "train", "epoch": 147, "iter": 2700, "lr": 0.00012, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66188, "top5_acc": 0.86359, "loss_cls": 1.96017, "loss": 1.96017, "time": 0.84987} +{"mode": "train", "epoch": 147, "iter": 2800, "lr": 0.00012, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.65438, "top5_acc": 0.86297, "loss_cls": 2.01039, "loss": 2.01039, "time": 0.85006} +{"mode": "train", "epoch": 147, "iter": 2900, "lr": 0.00011, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.66125, "top5_acc": 0.85922, "loss_cls": 1.9827, "loss": 1.9827, "time": 0.84392} +{"mode": "train", "epoch": 147, "iter": 3000, "lr": 0.00011, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.64828, "top5_acc": 0.85656, "loss_cls": 2.02402, "loss": 2.02402, "time": 0.8453} +{"mode": "train", "epoch": 147, "iter": 3100, "lr": 0.00011, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66469, "top5_acc": 0.86828, "loss_cls": 1.9572, "loss": 1.9572, "time": 0.85024} +{"mode": "train", "epoch": 147, "iter": 3200, "lr": 0.00011, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.66391, "top5_acc": 0.86984, "loss_cls": 1.96403, "loss": 1.96403, "time": 0.84507} +{"mode": "train", "epoch": 147, "iter": 3300, "lr": 0.00011, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65969, "top5_acc": 0.865, "loss_cls": 1.97343, "loss": 1.97343, "time": 0.84813} +{"mode": "train", "epoch": 147, "iter": 3400, "lr": 0.0001, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.66562, "top5_acc": 0.86906, "loss_cls": 1.95074, "loss": 1.95074, "time": 0.84784} +{"mode": "train", "epoch": 147, "iter": 3500, "lr": 0.0001, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.66156, "top5_acc": 0.86016, "loss_cls": 1.99393, "loss": 1.99393, "time": 0.84874} +{"mode": "train", "epoch": 147, "iter": 3600, "lr": 0.0001, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65469, "top5_acc": 0.86844, "loss_cls": 1.97298, "loss": 1.97298, "time": 0.84651} +{"mode": "train", "epoch": 147, "iter": 3700, "lr": 0.0001, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.66172, "top5_acc": 0.86484, "loss_cls": 1.97867, "loss": 1.97867, "time": 0.85367} +{"mode": "val", "epoch": 147, "iter": 309, "lr": 0.0001, "top1_acc": 0.44968, "top5_acc": 0.69615, "mean_class_accuracy": 0.44949} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 0.0001, "memory": 15990, "data_time": 1.5063, "top1_acc": 0.67125, "top5_acc": 0.87469, "loss_cls": 1.91482, "loss": 1.91482, "time": 2.54612} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 0.0001, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.6775, "top5_acc": 0.87578, "loss_cls": 1.91826, "loss": 1.91826, "time": 0.85389} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 9e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.67109, "top5_acc": 0.87672, "loss_cls": 1.91364, "loss": 1.91364, "time": 0.85108} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 9e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66156, "top5_acc": 0.87016, "loss_cls": 1.95492, "loss": 1.95492, "time": 0.84897} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 9e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66922, "top5_acc": 0.87141, "loss_cls": 1.93477, "loss": 1.93477, "time": 0.85252} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 9e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.67422, "top5_acc": 0.86875, "loss_cls": 1.92218, "loss": 1.92218, "time": 0.852} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 9e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.66469, "top5_acc": 0.86438, "loss_cls": 1.97034, "loss": 1.97034, "time": 0.85472} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 9e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.66781, "top5_acc": 0.87078, "loss_cls": 1.92789, "loss": 1.92789, "time": 0.84923} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 8e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66562, "top5_acc": 0.87234, "loss_cls": 1.93156, "loss": 1.93156, "time": 0.84818} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 8e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66969, "top5_acc": 0.87688, "loss_cls": 1.92669, "loss": 1.92669, "time": 0.85069} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 8e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.66219, "top5_acc": 0.87172, "loss_cls": 1.94534, "loss": 1.94534, "time": 0.85488} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 8e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.67094, "top5_acc": 0.87094, "loss_cls": 1.94405, "loss": 1.94405, "time": 0.85347} +{"mode": "train", "epoch": 148, "iter": 1300, "lr": 8e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.66719, "top5_acc": 0.87406, "loss_cls": 1.91461, "loss": 1.91461, "time": 0.85379} +{"mode": "train", "epoch": 148, "iter": 1400, "lr": 8e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.67094, "top5_acc": 0.87359, "loss_cls": 1.91428, "loss": 1.91428, "time": 0.8563} +{"mode": "train", "epoch": 148, "iter": 1500, "lr": 7e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.67266, "top5_acc": 0.86672, "loss_cls": 1.94588, "loss": 1.94588, "time": 0.85589} +{"mode": "train", "epoch": 148, "iter": 1600, "lr": 7e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.67734, "top5_acc": 0.87125, "loss_cls": 1.92279, "loss": 1.92279, "time": 0.86055} +{"mode": "train", "epoch": 148, "iter": 1700, "lr": 7e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.66344, "top5_acc": 0.87172, "loss_cls": 1.93842, "loss": 1.93842, "time": 0.855} +{"mode": "train", "epoch": 148, "iter": 1800, "lr": 7e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67, "top5_acc": 0.87156, "loss_cls": 1.94192, "loss": 1.94192, "time": 0.85371} +{"mode": "train", "epoch": 148, "iter": 1900, "lr": 7e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.67172, "top5_acc": 0.86828, "loss_cls": 1.93424, "loss": 1.93424, "time": 0.85326} +{"mode": "train", "epoch": 148, "iter": 2000, "lr": 7e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.65859, "top5_acc": 0.86703, "loss_cls": 1.96767, "loss": 1.96767, "time": 0.85495} +{"mode": "train", "epoch": 148, "iter": 2100, "lr": 7e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66812, "top5_acc": 0.87719, "loss_cls": 1.93596, "loss": 1.93596, "time": 0.85287} +{"mode": "train", "epoch": 148, "iter": 2200, "lr": 6e-05, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.65906, "top5_acc": 0.8775, "loss_cls": 1.93683, "loss": 1.93683, "time": 0.8437} +{"mode": "train", "epoch": 148, "iter": 2300, "lr": 6e-05, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.67688, "top5_acc": 0.87406, "loss_cls": 1.92173, "loss": 1.92173, "time": 0.85025} +{"mode": "train", "epoch": 148, "iter": 2400, "lr": 6e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.65844, "top5_acc": 0.85938, "loss_cls": 1.98544, "loss": 1.98544, "time": 0.85552} +{"mode": "train", "epoch": 148, "iter": 2500, "lr": 6e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.67938, "top5_acc": 0.87328, "loss_cls": 1.91007, "loss": 1.91007, "time": 0.8587} +{"mode": "train", "epoch": 148, "iter": 2600, "lr": 6e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67109, "top5_acc": 0.87562, "loss_cls": 1.92054, "loss": 1.92054, "time": 0.85347} +{"mode": "train", "epoch": 148, "iter": 2700, "lr": 6e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.67141, "top5_acc": 0.87141, "loss_cls": 1.94773, "loss": 1.94773, "time": 0.85302} +{"mode": "train", "epoch": 148, "iter": 2800, "lr": 6e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67406, "top5_acc": 0.87406, "loss_cls": 1.91671, "loss": 1.91671, "time": 0.84919} +{"mode": "train", "epoch": 148, "iter": 2900, "lr": 5e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.66625, "top5_acc": 0.86922, "loss_cls": 1.94613, "loss": 1.94613, "time": 0.8442} +{"mode": "train", "epoch": 148, "iter": 3000, "lr": 5e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66516, "top5_acc": 0.87234, "loss_cls": 1.93747, "loss": 1.93747, "time": 0.84699} +{"mode": "train", "epoch": 148, "iter": 3100, "lr": 5e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66172, "top5_acc": 0.86875, "loss_cls": 1.9802, "loss": 1.9802, "time": 0.85063} +{"mode": "train", "epoch": 148, "iter": 3200, "lr": 5e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67797, "top5_acc": 0.88016, "loss_cls": 1.89842, "loss": 1.89842, "time": 0.84665} +{"mode": "train", "epoch": 148, "iter": 3300, "lr": 5e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.68109, "top5_acc": 0.87531, "loss_cls": 1.92439, "loss": 1.92439, "time": 0.85188} +{"mode": "train", "epoch": 148, "iter": 3400, "lr": 5e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.68422, "top5_acc": 0.87969, "loss_cls": 1.87732, "loss": 1.87732, "time": 0.85219} +{"mode": "train", "epoch": 148, "iter": 3500, "lr": 5e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.6575, "top5_acc": 0.87172, "loss_cls": 1.9682, "loss": 1.9682, "time": 0.8499} +{"mode": "train", "epoch": 148, "iter": 3600, "lr": 5e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.66, "top5_acc": 0.86297, "loss_cls": 1.96301, "loss": 1.96301, "time": 0.84742} +{"mode": "train", "epoch": 148, "iter": 3700, "lr": 4e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65188, "top5_acc": 0.86719, "loss_cls": 1.98972, "loss": 1.98972, "time": 0.84787} +{"mode": "val", "epoch": 148, "iter": 309, "lr": 4e-05, "top1_acc": 0.4478, "top5_acc": 0.69665, "mean_class_accuracy": 0.44755} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 4e-05, "memory": 15990, "data_time": 1.46294, "top1_acc": 0.67344, "top5_acc": 0.87906, "loss_cls": 1.90222, "loss": 1.90222, "time": 2.48483} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 4e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.66719, "top5_acc": 0.86891, "loss_cls": 1.96111, "loss": 1.96111, "time": 0.85293} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 4e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.67125, "top5_acc": 0.8725, "loss_cls": 1.94013, "loss": 1.94013, "time": 0.84742} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 4e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.66594, "top5_acc": 0.86797, "loss_cls": 1.93818, "loss": 1.93818, "time": 0.85184} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 4e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.67547, "top5_acc": 0.86594, "loss_cls": 1.94015, "loss": 1.94015, "time": 0.8465} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 4e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.67406, "top5_acc": 0.87234, "loss_cls": 1.91845, "loss": 1.91845, "time": 0.84792} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 4e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.67578, "top5_acc": 0.87578, "loss_cls": 1.92096, "loss": 1.92096, "time": 0.84373} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 4e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66984, "top5_acc": 0.87328, "loss_cls": 1.9342, "loss": 1.9342, "time": 0.8503} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 3e-05, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.67406, "top5_acc": 0.87688, "loss_cls": 1.90577, "loss": 1.90577, "time": 0.84396} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 3e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.66938, "top5_acc": 0.87188, "loss_cls": 1.94821, "loss": 1.94821, "time": 0.84714} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 3e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67406, "top5_acc": 0.87625, "loss_cls": 1.8969, "loss": 1.8969, "time": 0.84151} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 3e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67594, "top5_acc": 0.87734, "loss_cls": 1.91111, "loss": 1.91111, "time": 0.84611} +{"mode": "train", "epoch": 149, "iter": 1300, "lr": 3e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.67094, "top5_acc": 0.87453, "loss_cls": 1.94162, "loss": 1.94162, "time": 0.84823} +{"mode": "train", "epoch": 149, "iter": 1400, "lr": 3e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.67516, "top5_acc": 0.86281, "loss_cls": 1.94371, "loss": 1.94371, "time": 0.84817} +{"mode": "train", "epoch": 149, "iter": 1500, "lr": 3e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.67328, "top5_acc": 0.86922, "loss_cls": 1.91955, "loss": 1.91955, "time": 0.84499} +{"mode": "train", "epoch": 149, "iter": 1600, "lr": 3e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.68297, "top5_acc": 0.88344, "loss_cls": 1.86269, "loss": 1.86269, "time": 0.84448} +{"mode": "train", "epoch": 149, "iter": 1700, "lr": 3e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.67531, "top5_acc": 0.87453, "loss_cls": 1.88976, "loss": 1.88976, "time": 0.84364} +{"mode": "train", "epoch": 149, "iter": 1800, "lr": 3e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.68078, "top5_acc": 0.87297, "loss_cls": 1.93684, "loss": 1.93684, "time": 0.84811} +{"mode": "train", "epoch": 149, "iter": 1900, "lr": 2e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.68234, "top5_acc": 0.87266, "loss_cls": 1.90692, "loss": 1.90692, "time": 0.84399} +{"mode": "train", "epoch": 149, "iter": 2000, "lr": 2e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.67219, "top5_acc": 0.87844, "loss_cls": 1.92172, "loss": 1.92172, "time": 0.84897} +{"mode": "train", "epoch": 149, "iter": 2100, "lr": 2e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.66734, "top5_acc": 0.87188, "loss_cls": 1.94733, "loss": 1.94733, "time": 0.84896} +{"mode": "train", "epoch": 149, "iter": 2200, "lr": 2e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.66766, "top5_acc": 0.87094, "loss_cls": 1.92962, "loss": 1.92962, "time": 0.8543} +{"mode": "train", "epoch": 149, "iter": 2300, "lr": 2e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.67172, "top5_acc": 0.87266, "loss_cls": 1.92187, "loss": 1.92187, "time": 0.84957} +{"mode": "train", "epoch": 149, "iter": 2400, "lr": 2e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.66906, "top5_acc": 0.87312, "loss_cls": 1.9215, "loss": 1.9215, "time": 0.84871} +{"mode": "train", "epoch": 149, "iter": 2500, "lr": 2e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.66078, "top5_acc": 0.8725, "loss_cls": 1.96394, "loss": 1.96394, "time": 0.84699} +{"mode": "train", "epoch": 149, "iter": 2600, "lr": 2e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66984, "top5_acc": 0.88, "loss_cls": 1.90483, "loss": 1.90483, "time": 0.84786} +{"mode": "train", "epoch": 149, "iter": 2700, "lr": 2e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66969, "top5_acc": 0.87312, "loss_cls": 1.93169, "loss": 1.93169, "time": 0.84677} +{"mode": "train", "epoch": 149, "iter": 2800, "lr": 2e-05, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.67562, "top5_acc": 0.87438, "loss_cls": 1.89068, "loss": 1.89068, "time": 0.85443} +{"mode": "train", "epoch": 149, "iter": 2900, "lr": 2e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.67578, "top5_acc": 0.87844, "loss_cls": 1.90058, "loss": 1.90058, "time": 0.85565} +{"mode": "train", "epoch": 149, "iter": 3000, "lr": 2e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.68141, "top5_acc": 0.87828, "loss_cls": 1.88505, "loss": 1.88505, "time": 0.85062} +{"mode": "train", "epoch": 149, "iter": 3100, "lr": 2e-05, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.67906, "top5_acc": 0.87891, "loss_cls": 1.88236, "loss": 1.88236, "time": 0.84964} +{"mode": "train", "epoch": 149, "iter": 3200, "lr": 1e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.66719, "top5_acc": 0.87578, "loss_cls": 1.91555, "loss": 1.91555, "time": 0.84987} +{"mode": "train", "epoch": 149, "iter": 3300, "lr": 1e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.6825, "top5_acc": 0.88422, "loss_cls": 1.87693, "loss": 1.87693, "time": 0.85167} +{"mode": "train", "epoch": 149, "iter": 3400, "lr": 1e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.66516, "top5_acc": 0.86625, "loss_cls": 1.95753, "loss": 1.95753, "time": 0.85013} +{"mode": "train", "epoch": 149, "iter": 3500, "lr": 1e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.67516, "top5_acc": 0.87797, "loss_cls": 1.91326, "loss": 1.91326, "time": 0.84426} +{"mode": "train", "epoch": 149, "iter": 3600, "lr": 1e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.66391, "top5_acc": 0.875, "loss_cls": 1.9586, "loss": 1.9586, "time": 0.85133} +{"mode": "train", "epoch": 149, "iter": 3700, "lr": 1e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.675, "top5_acc": 0.87719, "loss_cls": 1.929, "loss": 1.929, "time": 0.84991} +{"mode": "val", "epoch": 149, "iter": 309, "lr": 1e-05, "top1_acc": 0.44791, "top5_acc": 0.69569, "mean_class_accuracy": 0.44768} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 1e-05, "memory": 15990, "data_time": 1.52488, "top1_acc": 0.67344, "top5_acc": 0.87734, "loss_cls": 1.92405, "loss": 1.92405, "time": 2.56064} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 1e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.67328, "top5_acc": 0.8775, "loss_cls": 1.90329, "loss": 1.90329, "time": 0.85179} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 1e-05, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.68797, "top5_acc": 0.88422, "loss_cls": 1.85417, "loss": 1.85417, "time": 0.85228} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 1e-05, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.67812, "top5_acc": 0.87641, "loss_cls": 1.90208, "loss": 1.90208, "time": 0.85192} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 1e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.68172, "top5_acc": 0.88016, "loss_cls": 1.8837, "loss": 1.8837, "time": 0.85383} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 1e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.67094, "top5_acc": 0.87562, "loss_cls": 1.91416, "loss": 1.91416, "time": 0.85365} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 1e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.67328, "top5_acc": 0.87781, "loss_cls": 1.90535, "loss": 1.90535, "time": 0.85351} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 1e-05, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.66672, "top5_acc": 0.87234, "loss_cls": 1.92076, "loss": 1.92076, "time": 0.84974} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 1e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.68266, "top5_acc": 0.88, "loss_cls": 1.88315, "loss": 1.88315, "time": 0.84759} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 1e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.68172, "top5_acc": 0.885, "loss_cls": 1.85405, "loss": 1.85405, "time": 0.85069} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 1e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67484, "top5_acc": 0.87594, "loss_cls": 1.92592, "loss": 1.92592, "time": 0.84585} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 1e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.68156, "top5_acc": 0.87625, "loss_cls": 1.90644, "loss": 1.90644, "time": 0.85043} +{"mode": "train", "epoch": 150, "iter": 1300, "lr": 0.0, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.67344, "top5_acc": 0.87234, "loss_cls": 1.92794, "loss": 1.92794, "time": 0.84828} +{"mode": "train", "epoch": 150, "iter": 1400, "lr": 0.0, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.67891, "top5_acc": 0.87797, "loss_cls": 1.89119, "loss": 1.89119, "time": 0.85233} +{"mode": "train", "epoch": 150, "iter": 1500, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66391, "top5_acc": 0.86766, "loss_cls": 1.94175, "loss": 1.94175, "time": 0.84959} +{"mode": "train", "epoch": 150, "iter": 1600, "lr": 0.0, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.67406, "top5_acc": 0.86984, "loss_cls": 1.9338, "loss": 1.9338, "time": 0.84918} +{"mode": "train", "epoch": 150, "iter": 1700, "lr": 0.0, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.67922, "top5_acc": 0.87547, "loss_cls": 1.89379, "loss": 1.89379, "time": 0.8489} +{"mode": "train", "epoch": 150, "iter": 1800, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.68062, "top5_acc": 0.875, "loss_cls": 1.90998, "loss": 1.90998, "time": 0.85001} +{"mode": "train", "epoch": 150, "iter": 1900, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.6675, "top5_acc": 0.87516, "loss_cls": 1.94168, "loss": 1.94168, "time": 0.85194} +{"mode": "train", "epoch": 150, "iter": 2000, "lr": 0.0, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67375, "top5_acc": 0.87531, "loss_cls": 1.90469, "loss": 1.90469, "time": 0.84591} +{"mode": "train", "epoch": 150, "iter": 2100, "lr": 0.0, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.68078, "top5_acc": 0.88344, "loss_cls": 1.89403, "loss": 1.89403, "time": 0.8533} +{"mode": "train", "epoch": 150, "iter": 2200, "lr": 0.0, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.67297, "top5_acc": 0.87312, "loss_cls": 1.92246, "loss": 1.92246, "time": 0.84697} +{"mode": "train", "epoch": 150, "iter": 2300, "lr": 0.0, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.67266, "top5_acc": 0.87844, "loss_cls": 1.88424, "loss": 1.88424, "time": 0.85219} +{"mode": "train", "epoch": 150, "iter": 2400, "lr": 0.0, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.675, "top5_acc": 0.87719, "loss_cls": 1.90727, "loss": 1.90727, "time": 0.85018} +{"mode": "train", "epoch": 150, "iter": 2500, "lr": 0.0, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.66125, "top5_acc": 0.86828, "loss_cls": 1.94506, "loss": 1.94506, "time": 0.85289} +{"mode": "train", "epoch": 150, "iter": 2600, "lr": 0.0, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.68109, "top5_acc": 0.87641, "loss_cls": 1.89129, "loss": 1.89129, "time": 0.8489} +{"mode": "train", "epoch": 150, "iter": 2700, "lr": 0.0, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.67625, "top5_acc": 0.87391, "loss_cls": 1.90527, "loss": 1.90527, "time": 0.85526} +{"mode": "train", "epoch": 150, "iter": 2800, "lr": 0.0, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.67875, "top5_acc": 0.87703, "loss_cls": 1.89911, "loss": 1.89911, "time": 0.85291} +{"mode": "train", "epoch": 150, "iter": 2900, "lr": 0.0, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66203, "top5_acc": 0.86578, "loss_cls": 1.9619, "loss": 1.9619, "time": 0.85826} +{"mode": "train", "epoch": 150, "iter": 3000, "lr": 0.0, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.67359, "top5_acc": 0.87734, "loss_cls": 1.91954, "loss": 1.91954, "time": 0.84376} +{"mode": "train", "epoch": 150, "iter": 3100, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67703, "top5_acc": 0.87469, "loss_cls": 1.88805, "loss": 1.88805, "time": 0.84624} +{"mode": "train", "epoch": 150, "iter": 3200, "lr": 0.0, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.67438, "top5_acc": 0.87219, "loss_cls": 1.93362, "loss": 1.93362, "time": 0.85009} +{"mode": "train", "epoch": 150, "iter": 3300, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.67469, "top5_acc": 0.87188, "loss_cls": 1.93789, "loss": 1.93789, "time": 0.84496} +{"mode": "train", "epoch": 150, "iter": 3400, "lr": 0.0, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.67875, "top5_acc": 0.88266, "loss_cls": 1.89014, "loss": 1.89014, "time": 0.84601} +{"mode": "train", "epoch": 150, "iter": 3500, "lr": 0.0, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.6725, "top5_acc": 0.87531, "loss_cls": 1.9129, "loss": 1.9129, "time": 0.84746} +{"mode": "train", "epoch": 150, "iter": 3600, "lr": 0.0, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.67578, "top5_acc": 0.87953, "loss_cls": 1.90182, "loss": 1.90182, "time": 0.84834} +{"mode": "train", "epoch": 150, "iter": 3700, "lr": 0.0, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.66641, "top5_acc": 0.87469, "loss_cls": 1.92585, "loss": 1.92585, "time": 0.8485} +{"mode": "val", "epoch": 150, "iter": 309, "lr": 0.0, "top1_acc": 0.44831, "top5_acc": 0.69382, "mean_class_accuracy": 0.44807} diff --git a/k400/k_2/best_pred.pkl b/k400/k_2/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..ac2ef81b1b4a8de65276df0aa3774a9e02921b15 --- /dev/null +++ b/k400/k_2/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:82fa5e277e76cb536a09e289bdcfe16e67a0f05ba7d4412a5d53eac537743f97 +size 44889386 diff --git a/k400/k_2/best_top1_acc_epoch_150.pth b/k400/k_2/best_top1_acc_epoch_150.pth new file mode 100644 index 0000000000000000000000000000000000000000..f3d943b231d1c6b61f7322c5ba4a98daa785934b --- /dev/null +++ b/k400/k_2/best_top1_acc_epoch_150.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7ffbf480dc347d044bd772db542221414cf0f90f07d6842d48c0aab4aeca5566 +size 32931889 diff --git a/k400/k_2/k_2.py b/k400/k_2/k_2.py new file mode 100644 index 0000000000000000000000000000000000000000..c9f99a5d05d131799de888762e1d8b6608bdb033 --- /dev/null +++ b/k400/k_2/k_2.py @@ -0,0 +1,133 @@ +modality = 'k' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/k_2' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/k400/k_3/20241226_015027.log b/k400/k_3/20241226_015027.log new file mode 100644 index 0000000000000000000000000000000000000000..74820f122d4b4a01361955d92605c973b5150550 --- /dev/null +++ b/k400/k_3/20241226_015027.log @@ -0,0 +1,7334 @@ +2024-12-26 01:50:27,649 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2024-12-26 01:50:27,983 - pyskl - INFO - Config: modality = 'k' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/k_3' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2024-12-26 01:50:27,983 - pyskl - INFO - Set random seed to 1625270613, deterministic: False +2024-12-26 01:50:38,495 - pyskl - INFO - 239737 videos remain after valid thresholding +2024-12-26 01:50:52,596 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-12-26 01:50:52,598 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3 +2024-12-26 01:50:52,604 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2024-12-26 01:50:52,628 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2024-12-26 01:50:52,633 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3 by HardDiskBackend. +2024-12-26 01:54:27,745 - pyskl - INFO - Epoch [1][100/3746] lr: 1.000e-01, eta: 13 days, 23:40:42, time: 2.151, data_time: 1.425, memory: 15990, top1_acc: 0.0059, top5_acc: 0.0339, loss_cls: 6.4181, loss: 6.4181 +2024-12-26 01:55:39,676 - pyskl - INFO - Epoch [1][200/3746] lr: 1.000e-01, eta: 9 days, 7:55:30, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.0122, top5_acc: 0.0495, loss_cls: 6.3498, loss: 6.3498 +2024-12-26 01:56:51,101 - pyskl - INFO - Epoch [1][300/3746] lr: 1.000e-01, eta: 7 days, 18:23:49, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0166, top5_acc: 0.0703, loss_cls: 6.1820, loss: 6.1820 +2024-12-26 01:58:02,562 - pyskl - INFO - Epoch [1][400/3746] lr: 1.000e-01, eta: 6 days, 23:38:13, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0233, top5_acc: 0.0970, loss_cls: 6.0202, loss: 6.0202 +2024-12-26 01:59:14,468 - pyskl - INFO - Epoch [1][500/3746] lr: 1.000e-01, eta: 6 days, 12:30:44, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.0258, top5_acc: 0.0994, loss_cls: 5.9421, loss: 5.9421 +2024-12-26 02:00:25,853 - pyskl - INFO - Epoch [1][600/3746] lr: 1.000e-01, eta: 6 days, 4:57:12, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0345, top5_acc: 0.1125, loss_cls: 5.8778, loss: 5.8778 +2024-12-26 02:01:37,445 - pyskl - INFO - Epoch [1][700/3746] lr: 1.000e-01, eta: 5 days, 23:35:41, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.0306, top5_acc: 0.1145, loss_cls: 5.8128, loss: 5.8128 +2024-12-26 02:02:48,931 - pyskl - INFO - Epoch [1][800/3746] lr: 1.000e-01, eta: 5 days, 19:33:00, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0364, top5_acc: 0.1298, loss_cls: 5.7632, loss: 5.7632 +2024-12-26 02:04:00,569 - pyskl - INFO - Epoch [1][900/3746] lr: 1.000e-01, eta: 5 days, 16:25:34, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.0394, top5_acc: 0.1305, loss_cls: 5.7623, loss: 5.7623 +2024-12-26 02:05:12,878 - pyskl - INFO - Epoch [1][1000/3746] lr: 1.000e-01, eta: 5 days, 14:01:39, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.0419, top5_acc: 0.1431, loss_cls: 5.6974, loss: 5.6974 +2024-12-26 02:06:25,111 - pyskl - INFO - Epoch [1][1100/3746] lr: 1.000e-01, eta: 5 days, 12:03:03, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.0489, top5_acc: 0.1611, loss_cls: 5.6280, loss: 5.6280 +2024-12-26 02:07:37,114 - pyskl - INFO - Epoch [1][1200/3746] lr: 1.000e-01, eta: 5 days, 10:22:13, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.0475, top5_acc: 0.1630, loss_cls: 5.6365, loss: 5.6365 +2024-12-26 02:08:49,003 - pyskl - INFO - Epoch [1][1300/3746] lr: 1.000e-01, eta: 5 days, 8:55:53, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.0492, top5_acc: 0.1689, loss_cls: 5.6140, loss: 5.6140 +2024-12-26 02:10:01,054 - pyskl - INFO - Epoch [1][1400/3746] lr: 1.000e-01, eta: 5 days, 7:42:48, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.0542, top5_acc: 0.1777, loss_cls: 5.5618, loss: 5.5618 +2024-12-26 02:11:12,957 - pyskl - INFO - Epoch [1][1500/3746] lr: 1.000e-01, eta: 5 days, 6:38:23, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.0578, top5_acc: 0.1795, loss_cls: 5.5511, loss: 5.5511 +2024-12-26 02:12:25,058 - pyskl - INFO - Epoch [1][1600/3746] lr: 1.000e-01, eta: 5 days, 5:43:01, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.0572, top5_acc: 0.1831, loss_cls: 5.5020, loss: 5.5020 +2024-12-26 02:13:37,275 - pyskl - INFO - Epoch [1][1700/3746] lr: 1.000e-01, eta: 5 days, 4:54:40, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.0592, top5_acc: 0.1977, loss_cls: 5.4732, loss: 5.4732 +2024-12-26 02:14:49,212 - pyskl - INFO - Epoch [1][1800/3746] lr: 1.000e-01, eta: 5 days, 4:10:05, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.0612, top5_acc: 0.2016, loss_cls: 5.4602, loss: 5.4602 +2024-12-26 02:16:01,286 - pyskl - INFO - Epoch [1][1900/3746] lr: 1.000e-01, eta: 5 days, 3:30:46, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.0727, top5_acc: 0.2022, loss_cls: 5.4392, loss: 5.4392 +2024-12-26 02:17:13,716 - pyskl - INFO - Epoch [1][2000/3746] lr: 1.000e-01, eta: 5 days, 2:56:54, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.0684, top5_acc: 0.2095, loss_cls: 5.4263, loss: 5.4263 +2024-12-26 02:18:25,890 - pyskl - INFO - Epoch [1][2100/3746] lr: 1.000e-01, eta: 5 days, 2:25:02, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.0725, top5_acc: 0.2102, loss_cls: 5.4040, loss: 5.4040 +2024-12-26 02:19:37,827 - pyskl - INFO - Epoch [1][2200/3746] lr: 1.000e-01, eta: 5 days, 1:54:56, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.0703, top5_acc: 0.2164, loss_cls: 5.3821, loss: 5.3821 +2024-12-26 02:20:49,720 - pyskl - INFO - Epoch [1][2300/3746] lr: 1.000e-01, eta: 5 days, 1:27:10, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.0663, top5_acc: 0.2056, loss_cls: 5.4432, loss: 5.4432 +2024-12-26 02:22:01,762 - pyskl - INFO - Epoch [1][2400/3746] lr: 1.000e-01, eta: 5 days, 1:02:11, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.0733, top5_acc: 0.2266, loss_cls: 5.3904, loss: 5.3904 +2024-12-26 02:23:13,972 - pyskl - INFO - Epoch [1][2500/3746] lr: 1.000e-01, eta: 5 days, 0:39:45, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.0788, top5_acc: 0.2291, loss_cls: 5.3289, loss: 5.3289 +2024-12-26 02:24:26,186 - pyskl - INFO - Epoch [1][2600/3746] lr: 9.999e-02, eta: 5 days, 0:18:57, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.0817, top5_acc: 0.2342, loss_cls: 5.3100, loss: 5.3100 +2024-12-26 02:25:38,028 - pyskl - INFO - Epoch [1][2700/3746] lr: 9.999e-02, eta: 4 days, 23:58:19, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.0792, top5_acc: 0.2278, loss_cls: 5.3368, loss: 5.3368 +2024-12-26 02:26:50,042 - pyskl - INFO - Epoch [1][2800/3746] lr: 9.999e-02, eta: 4 days, 23:39:39, time: 0.720, data_time: 0.001, memory: 15990, top1_acc: 0.0766, top5_acc: 0.2359, loss_cls: 5.3369, loss: 5.3369 +2024-12-26 02:28:01,784 - pyskl - INFO - Epoch [1][2900/3746] lr: 9.999e-02, eta: 4 days, 23:21:19, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.0748, top5_acc: 0.2408, loss_cls: 5.3181, loss: 5.3181 +2024-12-26 02:29:13,722 - pyskl - INFO - Epoch [1][3000/3746] lr: 9.999e-02, eta: 4 days, 23:04:44, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.0867, top5_acc: 0.2400, loss_cls: 5.2971, loss: 5.2971 +2024-12-26 02:30:25,713 - pyskl - INFO - Epoch [1][3100/3746] lr: 9.999e-02, eta: 4 days, 22:49:18, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.0877, top5_acc: 0.2469, loss_cls: 5.2407, loss: 5.2407 +2024-12-26 02:31:37,671 - pyskl - INFO - Epoch [1][3200/3746] lr: 9.999e-02, eta: 4 days, 22:34:39, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.0842, top5_acc: 0.2433, loss_cls: 5.2749, loss: 5.2749 +2024-12-26 02:32:49,775 - pyskl - INFO - Epoch [1][3300/3746] lr: 9.999e-02, eta: 4 days, 22:21:15, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.0877, top5_acc: 0.2558, loss_cls: 5.2298, loss: 5.2298 +2024-12-26 02:34:01,598 - pyskl - INFO - Epoch [1][3400/3746] lr: 9.999e-02, eta: 4 days, 22:07:47, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.0881, top5_acc: 0.2534, loss_cls: 5.2646, loss: 5.2646 +2024-12-26 02:35:13,275 - pyskl - INFO - Epoch [1][3500/3746] lr: 9.999e-02, eta: 4 days, 21:54:38, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.0958, top5_acc: 0.2564, loss_cls: 5.2154, loss: 5.2154 +2024-12-26 02:36:24,717 - pyskl - INFO - Epoch [1][3600/3746] lr: 9.999e-02, eta: 4 days, 21:41:32, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0997, top5_acc: 0.2669, loss_cls: 5.1982, loss: 5.1982 +2024-12-26 02:37:36,129 - pyskl - INFO - Epoch [1][3700/3746] lr: 9.999e-02, eta: 4 days, 21:29:00, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0941, top5_acc: 0.2656, loss_cls: 5.2178, loss: 5.2178 +2024-12-26 02:38:11,227 - pyskl - INFO - Saving checkpoint at 1 epochs +2024-12-26 02:40:08,500 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 02:40:09,262 - pyskl - INFO - +top1_acc 0.0592 +top5_acc 0.1752 +2024-12-26 02:40:09,262 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 02:40:09,297 - pyskl - INFO - +mean_acc 0.0593 +2024-12-26 02:40:09,687 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2024-12-26 02:40:09,687 - pyskl - INFO - Best top1_acc is 0.0592 at 1 epoch. +2024-12-26 02:40:09,700 - pyskl - INFO - Epoch(val) [1][309] top1_acc: 0.0592, top5_acc: 0.1752, mean_class_accuracy: 0.0593 +2024-12-26 02:43:42,298 - pyskl - INFO - Epoch [2][100/3746] lr: 9.999e-02, eta: 5 days, 1:33:45, time: 2.126, data_time: 1.409, memory: 15990, top1_acc: 0.0970, top5_acc: 0.2589, loss_cls: 5.1853, loss: 5.1853 +2024-12-26 02:44:54,203 - pyskl - INFO - Epoch [2][200/3746] lr: 9.999e-02, eta: 5 days, 1:17:06, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.0983, top5_acc: 0.2737, loss_cls: 5.1468, loss: 5.1468 +2024-12-26 02:46:05,671 - pyskl - INFO - Epoch [2][300/3746] lr: 9.999e-02, eta: 5 days, 1:00:11, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1013, top5_acc: 0.2730, loss_cls: 5.1503, loss: 5.1503 +2024-12-26 02:47:17,592 - pyskl - INFO - Epoch [2][400/3746] lr: 9.999e-02, eta: 5 days, 0:45:04, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1019, top5_acc: 0.2767, loss_cls: 5.1553, loss: 5.1553 +2024-12-26 02:48:29,046 - pyskl - INFO - Epoch [2][500/3746] lr: 9.999e-02, eta: 5 days, 0:29:34, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.0998, top5_acc: 0.2872, loss_cls: 5.1392, loss: 5.1392 +2024-12-26 02:49:40,470 - pyskl - INFO - Epoch [2][600/3746] lr: 9.999e-02, eta: 5 days, 0:14:40, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.0970, top5_acc: 0.2762, loss_cls: 5.1195, loss: 5.1195 +2024-12-26 02:50:52,248 - pyskl - INFO - Epoch [2][700/3746] lr: 9.998e-02, eta: 5 days, 0:01:07, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1117, top5_acc: 0.2867, loss_cls: 5.1191, loss: 5.1191 +2024-12-26 02:52:04,045 - pyskl - INFO - Epoch [2][800/3746] lr: 9.998e-02, eta: 4 days, 23:48:09, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1070, top5_acc: 0.2827, loss_cls: 5.1159, loss: 5.1159 +2024-12-26 02:53:15,628 - pyskl - INFO - Epoch [2][900/3746] lr: 9.998e-02, eta: 4 days, 23:35:16, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1091, top5_acc: 0.2873, loss_cls: 5.1195, loss: 5.1195 +2024-12-26 02:54:27,967 - pyskl - INFO - Epoch [2][1000/3746] lr: 9.998e-02, eta: 4 days, 23:24:22, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1109, top5_acc: 0.2950, loss_cls: 5.0472, loss: 5.0472 +2024-12-26 02:55:39,968 - pyskl - INFO - Epoch [2][1100/3746] lr: 9.998e-02, eta: 4 days, 23:13:12, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1103, top5_acc: 0.2923, loss_cls: 5.0650, loss: 5.0650 +2024-12-26 02:56:52,309 - pyskl - INFO - Epoch [2][1200/3746] lr: 9.998e-02, eta: 4 days, 23:03:05, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1200, top5_acc: 0.3038, loss_cls: 5.0300, loss: 5.0300 +2024-12-26 02:58:04,490 - pyskl - INFO - Epoch [2][1300/3746] lr: 9.998e-02, eta: 4 days, 22:53:01, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1172, top5_acc: 0.2995, loss_cls: 5.0210, loss: 5.0210 +2024-12-26 02:59:16,522 - pyskl - INFO - Epoch [2][1400/3746] lr: 9.998e-02, eta: 4 days, 22:43:02, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1191, top5_acc: 0.3002, loss_cls: 5.0173, loss: 5.0173 +2024-12-26 03:00:28,598 - pyskl - INFO - Epoch [2][1500/3746] lr: 9.998e-02, eta: 4 days, 22:33:28, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1147, top5_acc: 0.3014, loss_cls: 5.0309, loss: 5.0309 +2024-12-26 03:01:40,680 - pyskl - INFO - Epoch [2][1600/3746] lr: 9.998e-02, eta: 4 days, 22:24:13, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1192, top5_acc: 0.3084, loss_cls: 5.0131, loss: 5.0131 +2024-12-26 03:02:52,939 - pyskl - INFO - Epoch [2][1700/3746] lr: 9.998e-02, eta: 4 days, 22:15:34, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1189, top5_acc: 0.3045, loss_cls: 4.9916, loss: 4.9916 +2024-12-26 03:04:05,084 - pyskl - INFO - Epoch [2][1800/3746] lr: 9.998e-02, eta: 4 days, 22:07:00, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1212, top5_acc: 0.3050, loss_cls: 5.0094, loss: 5.0094 +2024-12-26 03:05:17,202 - pyskl - INFO - Epoch [2][1900/3746] lr: 9.998e-02, eta: 4 days, 21:58:38, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1231, top5_acc: 0.3127, loss_cls: 4.9949, loss: 4.9949 +2024-12-26 03:06:29,168 - pyskl - INFO - Epoch [2][2000/3746] lr: 9.997e-02, eta: 4 days, 21:50:17, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1269, top5_acc: 0.3261, loss_cls: 4.9284, loss: 4.9284 +2024-12-26 03:07:41,204 - pyskl - INFO - Epoch [2][2100/3746] lr: 9.997e-02, eta: 4 days, 21:42:17, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1263, top5_acc: 0.3203, loss_cls: 4.9556, loss: 4.9556 +2024-12-26 03:08:53,457 - pyskl - INFO - Epoch [2][2200/3746] lr: 9.997e-02, eta: 4 days, 21:34:52, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1363, top5_acc: 0.3320, loss_cls: 4.9009, loss: 4.9009 +2024-12-26 03:10:05,183 - pyskl - INFO - Epoch [2][2300/3746] lr: 9.997e-02, eta: 4 days, 21:26:50, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1378, top5_acc: 0.3337, loss_cls: 4.9186, loss: 4.9186 +2024-12-26 03:11:17,491 - pyskl - INFO - Epoch [2][2400/3746] lr: 9.997e-02, eta: 4 days, 21:19:54, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1298, top5_acc: 0.3334, loss_cls: 4.9197, loss: 4.9197 +2024-12-26 03:12:29,485 - pyskl - INFO - Epoch [2][2500/3746] lr: 9.997e-02, eta: 4 days, 21:12:41, time: 0.720, data_time: 0.001, memory: 15990, top1_acc: 0.1342, top5_acc: 0.3248, loss_cls: 4.9151, loss: 4.9151 +2024-12-26 03:13:41,757 - pyskl - INFO - Epoch [2][2600/3746] lr: 9.997e-02, eta: 4 days, 21:06:04, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1475, top5_acc: 0.3436, loss_cls: 4.8476, loss: 4.8476 +2024-12-26 03:14:53,692 - pyskl - INFO - Epoch [2][2700/3746] lr: 9.997e-02, eta: 4 days, 20:59:08, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1405, top5_acc: 0.3420, loss_cls: 4.8593, loss: 4.8593 +2024-12-26 03:16:05,655 - pyskl - INFO - Epoch [2][2800/3746] lr: 9.997e-02, eta: 4 days, 20:52:25, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1327, top5_acc: 0.3352, loss_cls: 4.8663, loss: 4.8663 +2024-12-26 03:17:17,821 - pyskl - INFO - Epoch [2][2900/3746] lr: 9.997e-02, eta: 4 days, 20:46:08, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1391, top5_acc: 0.3416, loss_cls: 4.8638, loss: 4.8638 +2024-12-26 03:18:29,510 - pyskl - INFO - Epoch [2][3000/3746] lr: 9.996e-02, eta: 4 days, 20:39:22, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1434, top5_acc: 0.3544, loss_cls: 4.8231, loss: 4.8231 +2024-12-26 03:19:41,802 - pyskl - INFO - Epoch [2][3100/3746] lr: 9.996e-02, eta: 4 days, 20:33:34, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1381, top5_acc: 0.3461, loss_cls: 4.8341, loss: 4.8341 +2024-12-26 03:20:54,098 - pyskl - INFO - Epoch [2][3200/3746] lr: 9.996e-02, eta: 4 days, 20:27:54, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1475, top5_acc: 0.3530, loss_cls: 4.8460, loss: 4.8460 +2024-12-26 03:22:06,235 - pyskl - INFO - Epoch [2][3300/3746] lr: 9.996e-02, eta: 4 days, 20:22:10, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1480, top5_acc: 0.3528, loss_cls: 4.8272, loss: 4.8272 +2024-12-26 03:23:18,149 - pyskl - INFO - Epoch [2][3400/3746] lr: 9.996e-02, eta: 4 days, 20:16:16, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1511, top5_acc: 0.3636, loss_cls: 4.7589, loss: 4.7589 +2024-12-26 03:24:29,510 - pyskl - INFO - Epoch [2][3500/3746] lr: 9.996e-02, eta: 4 days, 20:09:47, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1481, top5_acc: 0.3508, loss_cls: 4.7766, loss: 4.7766 +2024-12-26 03:25:41,254 - pyskl - INFO - Epoch [2][3600/3746] lr: 9.996e-02, eta: 4 days, 20:03:56, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1473, top5_acc: 0.3619, loss_cls: 4.7888, loss: 4.7888 +2024-12-26 03:26:52,957 - pyskl - INFO - Epoch [2][3700/3746] lr: 9.996e-02, eta: 4 days, 19:58:09, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1561, top5_acc: 0.3666, loss_cls: 4.7630, loss: 4.7630 +2024-12-26 03:27:28,241 - pyskl - INFO - Saving checkpoint at 2 epochs +2024-12-26 03:29:25,290 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 03:29:25,970 - pyskl - INFO - +top1_acc 0.0973 +top5_acc 0.2590 +2024-12-26 03:29:25,970 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 03:29:26,005 - pyskl - INFO - +mean_acc 0.0974 +2024-12-26 03:29:26,010 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_1.pth was removed +2024-12-26 03:29:26,248 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2024-12-26 03:29:26,249 - pyskl - INFO - Best top1_acc is 0.0973 at 2 epoch. +2024-12-26 03:29:26,258 - pyskl - INFO - Epoch(val) [2][309] top1_acc: 0.0973, top5_acc: 0.2590, mean_class_accuracy: 0.0974 +2024-12-26 03:32:58,442 - pyskl - INFO - Epoch [3][100/3746] lr: 9.995e-02, eta: 4 days, 22:00:44, time: 2.122, data_time: 1.403, memory: 15990, top1_acc: 0.1525, top5_acc: 0.3677, loss_cls: 4.7278, loss: 4.7278 +2024-12-26 03:34:10,041 - pyskl - INFO - Epoch [3][200/3746] lr: 9.995e-02, eta: 4 days, 21:53:24, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1547, top5_acc: 0.3667, loss_cls: 4.7202, loss: 4.7202 +2024-12-26 03:35:21,248 - pyskl - INFO - Epoch [3][300/3746] lr: 9.995e-02, eta: 4 days, 21:45:45, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1609, top5_acc: 0.3742, loss_cls: 4.7151, loss: 4.7151 +2024-12-26 03:36:32,955 - pyskl - INFO - Epoch [3][400/3746] lr: 9.995e-02, eta: 4 days, 21:38:51, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1548, top5_acc: 0.3697, loss_cls: 4.7203, loss: 4.7203 +2024-12-26 03:37:44,274 - pyskl - INFO - Epoch [3][500/3746] lr: 9.995e-02, eta: 4 days, 21:31:39, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1578, top5_acc: 0.3705, loss_cls: 4.7248, loss: 4.7248 +2024-12-26 03:38:55,653 - pyskl - INFO - Epoch [3][600/3746] lr: 9.995e-02, eta: 4 days, 21:24:40, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1486, top5_acc: 0.3584, loss_cls: 4.7529, loss: 4.7529 +2024-12-26 03:40:07,319 - pyskl - INFO - Epoch [3][700/3746] lr: 9.995e-02, eta: 4 days, 21:18:09, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1556, top5_acc: 0.3627, loss_cls: 4.7461, loss: 4.7461 +2024-12-26 03:41:18,781 - pyskl - INFO - Epoch [3][800/3746] lr: 9.995e-02, eta: 4 days, 21:11:32, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1609, top5_acc: 0.3689, loss_cls: 4.7111, loss: 4.7111 +2024-12-26 03:42:30,575 - pyskl - INFO - Epoch [3][900/3746] lr: 9.994e-02, eta: 4 days, 21:05:25, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1575, top5_acc: 0.3803, loss_cls: 4.6809, loss: 4.6809 +2024-12-26 03:43:42,078 - pyskl - INFO - Epoch [3][1000/3746] lr: 9.994e-02, eta: 4 days, 20:59:05, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1556, top5_acc: 0.3658, loss_cls: 4.7129, loss: 4.7129 +2024-12-26 03:44:53,926 - pyskl - INFO - Epoch [3][1100/3746] lr: 9.994e-02, eta: 4 days, 20:53:15, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1655, top5_acc: 0.3789, loss_cls: 4.7024, loss: 4.7024 +2024-12-26 03:46:05,738 - pyskl - INFO - Epoch [3][1200/3746] lr: 9.994e-02, eta: 4 days, 20:47:29, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1573, top5_acc: 0.3730, loss_cls: 4.7101, loss: 4.7101 +2024-12-26 03:47:17,763 - pyskl - INFO - Epoch [3][1300/3746] lr: 9.994e-02, eta: 4 days, 20:42:03, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1588, top5_acc: 0.3734, loss_cls: 4.7148, loss: 4.7148 +2024-12-26 03:48:29,472 - pyskl - INFO - Epoch [3][1400/3746] lr: 9.994e-02, eta: 4 days, 20:36:23, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1634, top5_acc: 0.3833, loss_cls: 4.6659, loss: 4.6659 +2024-12-26 03:49:41,516 - pyskl - INFO - Epoch [3][1500/3746] lr: 9.994e-02, eta: 4 days, 20:31:09, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1638, top5_acc: 0.3842, loss_cls: 4.6684, loss: 4.6684 +2024-12-26 03:50:53,344 - pyskl - INFO - Epoch [3][1600/3746] lr: 9.994e-02, eta: 4 days, 20:25:48, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1736, top5_acc: 0.3895, loss_cls: 4.6652, loss: 4.6652 +2024-12-26 03:52:04,818 - pyskl - INFO - Epoch [3][1700/3746] lr: 9.993e-02, eta: 4 days, 20:20:10, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1673, top5_acc: 0.3919, loss_cls: 4.6439, loss: 4.6439 +2024-12-26 03:53:17,051 - pyskl - INFO - Epoch [3][1800/3746] lr: 9.993e-02, eta: 4 days, 20:15:24, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1689, top5_acc: 0.3922, loss_cls: 4.6357, loss: 4.6357 +2024-12-26 03:54:29,024 - pyskl - INFO - Epoch [3][1900/3746] lr: 9.993e-02, eta: 4 days, 20:10:27, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1766, top5_acc: 0.3986, loss_cls: 4.5982, loss: 4.5982 +2024-12-26 03:55:41,150 - pyskl - INFO - Epoch [3][2000/3746] lr: 9.993e-02, eta: 4 days, 20:05:43, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1772, top5_acc: 0.4022, loss_cls: 4.6016, loss: 4.6016 +2024-12-26 03:56:53,522 - pyskl - INFO - Epoch [3][2100/3746] lr: 9.993e-02, eta: 4 days, 20:01:18, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.1708, top5_acc: 0.3964, loss_cls: 4.6100, loss: 4.6100 +2024-12-26 03:58:05,711 - pyskl - INFO - Epoch [3][2200/3746] lr: 9.993e-02, eta: 4 days, 19:56:47, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1837, top5_acc: 0.3933, loss_cls: 4.6161, loss: 4.6161 +2024-12-26 03:59:17,616 - pyskl - INFO - Epoch [3][2300/3746] lr: 9.993e-02, eta: 4 days, 19:52:03, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1747, top5_acc: 0.4028, loss_cls: 4.5738, loss: 4.5738 +2024-12-26 04:00:29,302 - pyskl - INFO - Epoch [3][2400/3746] lr: 9.992e-02, eta: 4 days, 19:47:12, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1848, top5_acc: 0.4131, loss_cls: 4.5583, loss: 4.5583 +2024-12-26 04:01:41,214 - pyskl - INFO - Epoch [3][2500/3746] lr: 9.992e-02, eta: 4 days, 19:42:38, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1794, top5_acc: 0.3994, loss_cls: 4.5937, loss: 4.5937 +2024-12-26 04:02:53,060 - pyskl - INFO - Epoch [3][2600/3746] lr: 9.992e-02, eta: 4 days, 19:38:04, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1864, top5_acc: 0.4058, loss_cls: 4.5747, loss: 4.5747 +2024-12-26 04:04:05,202 - pyskl - INFO - Epoch [3][2700/3746] lr: 9.992e-02, eta: 4 days, 19:33:50, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1778, top5_acc: 0.4009, loss_cls: 4.5760, loss: 4.5760 +2024-12-26 04:05:17,395 - pyskl - INFO - Epoch [3][2800/3746] lr: 9.992e-02, eta: 4 days, 19:29:42, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1723, top5_acc: 0.4031, loss_cls: 4.5964, loss: 4.5964 +2024-12-26 04:06:29,500 - pyskl - INFO - Epoch [3][2900/3746] lr: 9.992e-02, eta: 4 days, 19:25:33, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1780, top5_acc: 0.4105, loss_cls: 4.5410, loss: 4.5410 +2024-12-26 04:07:41,327 - pyskl - INFO - Epoch [3][3000/3746] lr: 9.991e-02, eta: 4 days, 19:21:13, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1780, top5_acc: 0.4033, loss_cls: 4.5504, loss: 4.5504 +2024-12-26 04:08:53,278 - pyskl - INFO - Epoch [3][3100/3746] lr: 9.991e-02, eta: 4 days, 19:17:02, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1950, top5_acc: 0.4197, loss_cls: 4.4809, loss: 4.4809 +2024-12-26 04:10:05,474 - pyskl - INFO - Epoch [3][3200/3746] lr: 9.991e-02, eta: 4 days, 19:13:08, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1787, top5_acc: 0.4030, loss_cls: 4.5787, loss: 4.5787 +2024-12-26 04:11:17,445 - pyskl - INFO - Epoch [3][3300/3746] lr: 9.991e-02, eta: 4 days, 19:09:05, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1916, top5_acc: 0.4198, loss_cls: 4.4991, loss: 4.4991 +2024-12-26 04:12:29,285 - pyskl - INFO - Epoch [3][3400/3746] lr: 9.991e-02, eta: 4 days, 19:04:59, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1802, top5_acc: 0.4122, loss_cls: 4.5687, loss: 4.5687 +2024-12-26 04:13:40,914 - pyskl - INFO - Epoch [3][3500/3746] lr: 9.991e-02, eta: 4 days, 19:00:45, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1855, top5_acc: 0.4167, loss_cls: 4.5309, loss: 4.5309 +2024-12-26 04:14:52,510 - pyskl - INFO - Epoch [3][3600/3746] lr: 9.990e-02, eta: 4 days, 18:56:33, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1819, top5_acc: 0.4062, loss_cls: 4.5603, loss: 4.5603 +2024-12-26 04:16:04,399 - pyskl - INFO - Epoch [3][3700/3746] lr: 9.990e-02, eta: 4 days, 18:52:39, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1888, top5_acc: 0.4141, loss_cls: 4.5232, loss: 4.5232 +2024-12-26 04:16:39,352 - pyskl - INFO - Saving checkpoint at 3 epochs +2024-12-26 04:18:35,112 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 04:18:35,800 - pyskl - INFO - +top1_acc 0.1159 +top5_acc 0.2997 +2024-12-26 04:18:35,800 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 04:18:35,836 - pyskl - INFO - +mean_acc 0.1159 +2024-12-26 04:18:35,841 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_2.pth was removed +2024-12-26 04:18:36,072 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2024-12-26 04:18:36,072 - pyskl - INFO - Best top1_acc is 0.1159 at 3 epoch. +2024-12-26 04:18:36,083 - pyskl - INFO - Epoch(val) [3][309] top1_acc: 0.1159, top5_acc: 0.2997, mean_class_accuracy: 0.1159 +2024-12-26 04:22:08,566 - pyskl - INFO - Epoch [4][100/3746] lr: 9.990e-02, eta: 4 days, 20:14:03, time: 2.125, data_time: 1.405, memory: 15990, top1_acc: 0.1866, top5_acc: 0.4244, loss_cls: 4.5041, loss: 4.5041 +2024-12-26 04:23:20,326 - pyskl - INFO - Epoch [4][200/3746] lr: 9.990e-02, eta: 4 days, 20:09:22, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1886, top5_acc: 0.4158, loss_cls: 4.5282, loss: 4.5282 +2024-12-26 04:24:31,705 - pyskl - INFO - Epoch [4][300/3746] lr: 9.990e-02, eta: 4 days, 20:04:28, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1984, top5_acc: 0.4273, loss_cls: 4.4797, loss: 4.4797 +2024-12-26 04:25:42,784 - pyskl - INFO - Epoch [4][400/3746] lr: 9.989e-02, eta: 4 days, 19:59:23, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1916, top5_acc: 0.4303, loss_cls: 4.4738, loss: 4.4738 +2024-12-26 04:26:54,171 - pyskl - INFO - Epoch [4][500/3746] lr: 9.989e-02, eta: 4 days, 19:54:36, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.1977, top5_acc: 0.4186, loss_cls: 4.5027, loss: 4.5027 +2024-12-26 04:28:05,930 - pyskl - INFO - Epoch [4][600/3746] lr: 9.989e-02, eta: 4 days, 19:50:10, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1942, top5_acc: 0.4213, loss_cls: 4.4923, loss: 4.4923 +2024-12-26 04:29:17,560 - pyskl - INFO - Epoch [4][700/3746] lr: 9.989e-02, eta: 4 days, 19:45:42, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1963, top5_acc: 0.4191, loss_cls: 4.4852, loss: 4.4852 +2024-12-26 04:30:28,684 - pyskl - INFO - Epoch [4][800/3746] lr: 9.989e-02, eta: 4 days, 19:40:53, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1955, top5_acc: 0.4209, loss_cls: 4.5159, loss: 4.5159 +2024-12-26 04:31:40,033 - pyskl - INFO - Epoch [4][900/3746] lr: 9.988e-02, eta: 4 days, 19:36:19, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1888, top5_acc: 0.4216, loss_cls: 4.4838, loss: 4.4838 +2024-12-26 04:32:51,371 - pyskl - INFO - Epoch [4][1000/3746] lr: 9.988e-02, eta: 4 days, 19:31:47, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1942, top5_acc: 0.4242, loss_cls: 4.4750, loss: 4.4750 +2024-12-26 04:34:02,830 - pyskl - INFO - Epoch [4][1100/3746] lr: 9.988e-02, eta: 4 days, 19:27:24, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4272, loss_cls: 4.4742, loss: 4.4742 +2024-12-26 04:35:14,160 - pyskl - INFO - Epoch [4][1200/3746] lr: 9.988e-02, eta: 4 days, 19:22:58, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4458, loss_cls: 4.4150, loss: 4.4150 +2024-12-26 04:36:25,459 - pyskl - INFO - Epoch [4][1300/3746] lr: 9.988e-02, eta: 4 days, 19:18:34, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1837, top5_acc: 0.4208, loss_cls: 4.5274, loss: 4.5274 +2024-12-26 04:37:36,729 - pyskl - INFO - Epoch [4][1400/3746] lr: 9.988e-02, eta: 4 days, 19:14:12, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1944, top5_acc: 0.4295, loss_cls: 4.4588, loss: 4.4588 +2024-12-26 04:38:48,250 - pyskl - INFO - Epoch [4][1500/3746] lr: 9.987e-02, eta: 4 days, 19:10:04, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1823, top5_acc: 0.4181, loss_cls: 4.5141, loss: 4.5141 +2024-12-26 04:39:59,550 - pyskl - INFO - Epoch [4][1600/3746] lr: 9.987e-02, eta: 4 days, 19:05:49, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4356, loss_cls: 4.4466, loss: 4.4466 +2024-12-26 04:41:11,308 - pyskl - INFO - Epoch [4][1700/3746] lr: 9.987e-02, eta: 4 days, 19:01:56, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1927, top5_acc: 0.4191, loss_cls: 4.4893, loss: 4.4893 +2024-12-26 04:42:23,019 - pyskl - INFO - Epoch [4][1800/3746] lr: 9.987e-02, eta: 4 days, 18:58:03, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2092, top5_acc: 0.4389, loss_cls: 4.4165, loss: 4.4165 +2024-12-26 04:43:34,957 - pyskl - INFO - Epoch [4][1900/3746] lr: 9.987e-02, eta: 4 days, 18:54:23, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4331, loss_cls: 4.4321, loss: 4.4321 +2024-12-26 04:44:47,298 - pyskl - INFO - Epoch [4][2000/3746] lr: 9.986e-02, eta: 4 days, 18:51:02, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1945, top5_acc: 0.4288, loss_cls: 4.4800, loss: 4.4800 +2024-12-26 04:45:59,168 - pyskl - INFO - Epoch [4][2100/3746] lr: 9.986e-02, eta: 4 days, 18:47:23, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2033, top5_acc: 0.4412, loss_cls: 4.4296, loss: 4.4296 +2024-12-26 04:47:11,072 - pyskl - INFO - Epoch [4][2200/3746] lr: 9.986e-02, eta: 4 days, 18:43:47, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4250, loss_cls: 4.4654, loss: 4.4654 +2024-12-26 04:48:22,764 - pyskl - INFO - Epoch [4][2300/3746] lr: 9.986e-02, eta: 4 days, 18:40:06, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1970, top5_acc: 0.4311, loss_cls: 4.4292, loss: 4.4292 +2024-12-26 04:49:34,524 - pyskl - INFO - Epoch [4][2400/3746] lr: 9.985e-02, eta: 4 days, 18:36:29, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4261, loss_cls: 4.4228, loss: 4.4228 +2024-12-26 04:50:46,651 - pyskl - INFO - Epoch [4][2500/3746] lr: 9.985e-02, eta: 4 days, 18:33:09, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.1978, top5_acc: 0.4300, loss_cls: 4.4321, loss: 4.4321 +2024-12-26 04:51:58,635 - pyskl - INFO - Epoch [4][2600/3746] lr: 9.985e-02, eta: 4 days, 18:29:45, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2009, top5_acc: 0.4213, loss_cls: 4.4838, loss: 4.4838 +2024-12-26 04:53:10,955 - pyskl - INFO - Epoch [4][2700/3746] lr: 9.985e-02, eta: 4 days, 18:26:36, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.1966, top5_acc: 0.4295, loss_cls: 4.4536, loss: 4.4536 +2024-12-26 04:54:23,055 - pyskl - INFO - Epoch [4][2800/3746] lr: 9.985e-02, eta: 4 days, 18:23:20, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4339, loss_cls: 4.4312, loss: 4.4312 +2024-12-26 04:55:35,042 - pyskl - INFO - Epoch [4][2900/3746] lr: 9.984e-02, eta: 4 days, 18:20:02, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.1967, top5_acc: 0.4334, loss_cls: 4.4614, loss: 4.4614 +2024-12-26 04:56:47,217 - pyskl - INFO - Epoch [4][3000/3746] lr: 9.984e-02, eta: 4 days, 18:16:53, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4339, loss_cls: 4.4412, loss: 4.4412 +2024-12-26 04:57:59,177 - pyskl - INFO - Epoch [4][3100/3746] lr: 9.984e-02, eta: 4 days, 18:13:37, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4398, loss_cls: 4.4260, loss: 4.4260 +2024-12-26 04:59:11,144 - pyskl - INFO - Epoch [4][3200/3746] lr: 9.984e-02, eta: 4 days, 18:10:23, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4386, loss_cls: 4.4137, loss: 4.4137 +2024-12-26 05:00:22,949 - pyskl - INFO - Epoch [4][3300/3746] lr: 9.983e-02, eta: 4 days, 18:07:04, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.1945, top5_acc: 0.4311, loss_cls: 4.4747, loss: 4.4747 +2024-12-26 05:01:34,509 - pyskl - INFO - Epoch [4][3400/3746] lr: 9.983e-02, eta: 4 days, 18:03:39, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4352, loss_cls: 4.4227, loss: 4.4227 +2024-12-26 05:02:46,003 - pyskl - INFO - Epoch [4][3500/3746] lr: 9.983e-02, eta: 4 days, 18:00:12, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4425, loss_cls: 4.3653, loss: 4.3653 +2024-12-26 05:03:57,559 - pyskl - INFO - Epoch [4][3600/3746] lr: 9.983e-02, eta: 4 days, 17:56:50, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.1989, top5_acc: 0.4372, loss_cls: 4.4550, loss: 4.4550 +2024-12-26 05:05:09,594 - pyskl - INFO - Epoch [4][3700/3746] lr: 9.983e-02, eta: 4 days, 17:53:47, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2047, top5_acc: 0.4384, loss_cls: 4.4070, loss: 4.4070 +2024-12-26 05:05:44,937 - pyskl - INFO - Saving checkpoint at 4 epochs +2024-12-26 05:07:41,009 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 05:07:41,782 - pyskl - INFO - +top1_acc 0.1219 +top5_acc 0.3183 +2024-12-26 05:07:41,782 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 05:07:41,823 - pyskl - INFO - +mean_acc 0.1218 +2024-12-26 05:07:41,827 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_3.pth was removed +2024-12-26 05:07:42,068 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2024-12-26 05:07:42,069 - pyskl - INFO - Best top1_acc is 0.1219 at 4 epoch. +2024-12-26 05:07:42,085 - pyskl - INFO - Epoch(val) [4][309] top1_acc: 0.1219, top5_acc: 0.3183, mean_class_accuracy: 0.1218 +2024-12-26 05:11:17,377 - pyskl - INFO - Epoch [5][100/3746] lr: 9.982e-02, eta: 4 days, 18:55:54, time: 2.153, data_time: 1.437, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4377, loss_cls: 4.4191, loss: 4.4191 +2024-12-26 05:12:29,014 - pyskl - INFO - Epoch [5][200/3746] lr: 9.982e-02, eta: 4 days, 18:52:13, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4394, loss_cls: 4.4283, loss: 4.4283 +2024-12-26 05:13:41,037 - pyskl - INFO - Epoch [5][300/3746] lr: 9.982e-02, eta: 4 days, 18:48:48, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4472, loss_cls: 4.3820, loss: 4.3820 +2024-12-26 05:14:52,603 - pyskl - INFO - Epoch [5][400/3746] lr: 9.982e-02, eta: 4 days, 18:45:09, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4452, loss_cls: 4.3883, loss: 4.3883 +2024-12-26 05:16:04,348 - pyskl - INFO - Epoch [5][500/3746] lr: 9.981e-02, eta: 4 days, 18:41:37, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1973, top5_acc: 0.4292, loss_cls: 4.4511, loss: 4.4511 +2024-12-26 05:17:15,978 - pyskl - INFO - Epoch [5][600/3746] lr: 9.981e-02, eta: 4 days, 18:38:04, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4447, loss_cls: 4.3985, loss: 4.3985 +2024-12-26 05:18:27,567 - pyskl - INFO - Epoch [5][700/3746] lr: 9.981e-02, eta: 4 days, 18:34:31, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4686, loss_cls: 4.3147, loss: 4.3147 +2024-12-26 05:19:39,148 - pyskl - INFO - Epoch [5][800/3746] lr: 9.981e-02, eta: 4 days, 18:30:59, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4492, loss_cls: 4.3745, loss: 4.3745 +2024-12-26 05:20:50,557 - pyskl - INFO - Epoch [5][900/3746] lr: 9.980e-02, eta: 4 days, 18:27:23, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4459, loss_cls: 4.3542, loss: 4.3542 +2024-12-26 05:22:02,053 - pyskl - INFO - Epoch [5][1000/3746] lr: 9.980e-02, eta: 4 days, 18:23:52, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4462, loss_cls: 4.3795, loss: 4.3795 +2024-12-26 05:23:13,489 - pyskl - INFO - Epoch [5][1100/3746] lr: 9.980e-02, eta: 4 days, 18:20:21, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4553, loss_cls: 4.3819, loss: 4.3819 +2024-12-26 05:24:24,905 - pyskl - INFO - Epoch [5][1200/3746] lr: 9.980e-02, eta: 4 days, 18:16:51, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4458, loss_cls: 4.3781, loss: 4.3781 +2024-12-26 05:25:36,699 - pyskl - INFO - Epoch [5][1300/3746] lr: 9.979e-02, eta: 4 days, 18:13:35, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4508, loss_cls: 4.3285, loss: 4.3285 +2024-12-26 05:26:48,593 - pyskl - INFO - Epoch [5][1400/3746] lr: 9.979e-02, eta: 4 days, 18:10:24, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.1992, top5_acc: 0.4344, loss_cls: 4.4470, loss: 4.4470 +2024-12-26 05:28:00,482 - pyskl - INFO - Epoch [5][1500/3746] lr: 9.979e-02, eta: 4 days, 18:07:14, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4459, loss_cls: 4.3979, loss: 4.3979 +2024-12-26 05:29:12,564 - pyskl - INFO - Epoch [5][1600/3746] lr: 9.979e-02, eta: 4 days, 18:04:12, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4467, loss_cls: 4.3754, loss: 4.3754 +2024-12-26 05:30:24,375 - pyskl - INFO - Epoch [5][1700/3746] lr: 9.978e-02, eta: 4 days, 18:01:03, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4445, loss_cls: 4.3649, loss: 4.3649 +2024-12-26 05:31:36,010 - pyskl - INFO - Epoch [5][1800/3746] lr: 9.978e-02, eta: 4 days, 17:57:49, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4478, loss_cls: 4.3646, loss: 4.3646 +2024-12-26 05:32:47,968 - pyskl - INFO - Epoch [5][1900/3746] lr: 9.978e-02, eta: 4 days, 17:54:47, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4592, loss_cls: 4.3203, loss: 4.3203 +2024-12-26 05:33:59,643 - pyskl - INFO - Epoch [5][2000/3746] lr: 9.977e-02, eta: 4 days, 17:51:37, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2059, top5_acc: 0.4500, loss_cls: 4.3579, loss: 4.3579 +2024-12-26 05:35:11,961 - pyskl - INFO - Epoch [5][2100/3746] lr: 9.977e-02, eta: 4 days, 17:48:49, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4541, loss_cls: 4.3421, loss: 4.3421 +2024-12-26 05:36:23,903 - pyskl - INFO - Epoch [5][2200/3746] lr: 9.977e-02, eta: 4 days, 17:45:50, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4567, loss_cls: 4.3416, loss: 4.3416 +2024-12-26 05:37:35,965 - pyskl - INFO - Epoch [5][2300/3746] lr: 9.977e-02, eta: 4 days, 17:42:57, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4617, loss_cls: 4.3305, loss: 4.3305 +2024-12-26 05:38:48,040 - pyskl - INFO - Epoch [5][2400/3746] lr: 9.976e-02, eta: 4 days, 17:40:05, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4423, loss_cls: 4.3843, loss: 4.3843 +2024-12-26 05:40:00,368 - pyskl - INFO - Epoch [5][2500/3746] lr: 9.976e-02, eta: 4 days, 17:37:22, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4573, loss_cls: 4.3249, loss: 4.3249 +2024-12-26 05:41:12,121 - pyskl - INFO - Epoch [5][2600/3746] lr: 9.976e-02, eta: 4 days, 17:34:22, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4509, loss_cls: 4.3548, loss: 4.3548 +2024-12-26 05:42:24,292 - pyskl - INFO - Epoch [5][2700/3746] lr: 9.976e-02, eta: 4 days, 17:31:36, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4583, loss_cls: 4.2807, loss: 4.2807 +2024-12-26 05:43:36,697 - pyskl - INFO - Epoch [5][2800/3746] lr: 9.975e-02, eta: 4 days, 17:28:59, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4505, loss_cls: 4.3592, loss: 4.3592 +2024-12-26 05:44:48,605 - pyskl - INFO - Epoch [5][2900/3746] lr: 9.975e-02, eta: 4 days, 17:26:07, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4519, loss_cls: 4.3538, loss: 4.3538 +2024-12-26 05:46:00,764 - pyskl - INFO - Epoch [5][3000/3746] lr: 9.975e-02, eta: 4 days, 17:23:24, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4522, loss_cls: 4.3487, loss: 4.3487 +2024-12-26 05:47:13,132 - pyskl - INFO - Epoch [5][3100/3746] lr: 9.974e-02, eta: 4 days, 17:20:48, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4442, loss_cls: 4.3763, loss: 4.3763 +2024-12-26 05:48:25,101 - pyskl - INFO - Epoch [5][3200/3746] lr: 9.974e-02, eta: 4 days, 17:18:01, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4502, loss_cls: 4.3425, loss: 4.3425 +2024-12-26 05:49:36,863 - pyskl - INFO - Epoch [5][3300/3746] lr: 9.974e-02, eta: 4 days, 17:15:10, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4534, loss_cls: 4.3455, loss: 4.3455 +2024-12-26 05:50:48,486 - pyskl - INFO - Epoch [5][3400/3746] lr: 9.974e-02, eta: 4 days, 17:12:15, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4544, loss_cls: 4.3618, loss: 4.3618 +2024-12-26 05:52:00,167 - pyskl - INFO - Epoch [5][3500/3746] lr: 9.973e-02, eta: 4 days, 17:09:23, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4553, loss_cls: 4.3179, loss: 4.3179 +2024-12-26 05:53:11,769 - pyskl - INFO - Epoch [5][3600/3746] lr: 9.973e-02, eta: 4 days, 17:06:29, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4512, loss_cls: 4.3588, loss: 4.3588 +2024-12-26 05:54:23,334 - pyskl - INFO - Epoch [5][3700/3746] lr: 9.973e-02, eta: 4 days, 17:03:36, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4520, loss_cls: 4.3286, loss: 4.3286 +2024-12-26 05:54:58,362 - pyskl - INFO - Saving checkpoint at 5 epochs +2024-12-26 05:56:55,948 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 05:56:56,678 - pyskl - INFO - +top1_acc 0.1612 +top5_acc 0.3770 +2024-12-26 05:56:56,678 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 05:56:56,728 - pyskl - INFO - +mean_acc 0.1609 +2024-12-26 05:56:56,736 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_4.pth was removed +2024-12-26 05:56:57,079 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_5.pth. +2024-12-26 05:56:57,079 - pyskl - INFO - Best top1_acc is 0.1612 at 5 epoch. +2024-12-26 05:56:57,092 - pyskl - INFO - Epoch(val) [5][309] top1_acc: 0.1612, top5_acc: 0.3770, mean_class_accuracy: 0.1609 +2024-12-26 06:00:35,714 - pyskl - INFO - Epoch [6][100/3746] lr: 9.972e-02, eta: 4 days, 17:54:17, time: 2.186, data_time: 1.470, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4723, loss_cls: 4.2640, loss: 4.2640 +2024-12-26 06:01:47,053 - pyskl - INFO - Epoch [6][200/3746] lr: 9.972e-02, eta: 4 days, 17:51:02, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4606, loss_cls: 4.3030, loss: 4.3030 +2024-12-26 06:02:58,716 - pyskl - INFO - Epoch [6][300/3746] lr: 9.972e-02, eta: 4 days, 17:47:57, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4614, loss_cls: 4.2985, loss: 4.2985 +2024-12-26 06:04:10,371 - pyskl - INFO - Epoch [6][400/3746] lr: 9.971e-02, eta: 4 days, 17:44:53, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4688, loss_cls: 4.2896, loss: 4.2896 +2024-12-26 06:05:21,755 - pyskl - INFO - Epoch [6][500/3746] lr: 9.971e-02, eta: 4 days, 17:41:43, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4541, loss_cls: 4.3362, loss: 4.3362 +2024-12-26 06:06:33,617 - pyskl - INFO - Epoch [6][600/3746] lr: 9.971e-02, eta: 4 days, 17:38:48, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4597, loss_cls: 4.3385, loss: 4.3385 +2024-12-26 06:07:45,414 - pyskl - INFO - Epoch [6][700/3746] lr: 9.971e-02, eta: 4 days, 17:35:52, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4544, loss_cls: 4.3539, loss: 4.3539 +2024-12-26 06:08:56,950 - pyskl - INFO - Epoch [6][800/3746] lr: 9.970e-02, eta: 4 days, 17:32:49, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4498, loss_cls: 4.3307, loss: 4.3307 +2024-12-26 06:10:08,979 - pyskl - INFO - Epoch [6][900/3746] lr: 9.970e-02, eta: 4 days, 17:30:02, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4664, loss_cls: 4.2526, loss: 4.2526 +2024-12-26 06:11:20,947 - pyskl - INFO - Epoch [6][1000/3746] lr: 9.970e-02, eta: 4 days, 17:27:13, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4398, loss_cls: 4.3889, loss: 4.3889 +2024-12-26 06:12:32,857 - pyskl - INFO - Epoch [6][1100/3746] lr: 9.969e-02, eta: 4 days, 17:24:25, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4667, loss_cls: 4.3094, loss: 4.3094 +2024-12-26 06:13:44,284 - pyskl - INFO - Epoch [6][1200/3746] lr: 9.969e-02, eta: 4 days, 17:21:23, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4672, loss_cls: 4.2964, loss: 4.2964 +2024-12-26 06:14:56,301 - pyskl - INFO - Epoch [6][1300/3746] lr: 9.969e-02, eta: 4 days, 17:18:39, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4589, loss_cls: 4.2985, loss: 4.2985 +2024-12-26 06:16:08,192 - pyskl - INFO - Epoch [6][1400/3746] lr: 9.968e-02, eta: 4 days, 17:15:53, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4595, loss_cls: 4.3474, loss: 4.3474 +2024-12-26 06:17:20,162 - pyskl - INFO - Epoch [6][1500/3746] lr: 9.968e-02, eta: 4 days, 17:13:09, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4603, loss_cls: 4.3373, loss: 4.3373 +2024-12-26 06:18:32,147 - pyskl - INFO - Epoch [6][1600/3746] lr: 9.968e-02, eta: 4 days, 17:10:27, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4686, loss_cls: 4.2873, loss: 4.2873 +2024-12-26 06:19:44,376 - pyskl - INFO - Epoch [6][1700/3746] lr: 9.967e-02, eta: 4 days, 17:07:52, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4570, loss_cls: 4.3006, loss: 4.3006 +2024-12-26 06:20:56,454 - pyskl - INFO - Epoch [6][1800/3746] lr: 9.967e-02, eta: 4 days, 17:05:14, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4561, loss_cls: 4.3237, loss: 4.3237 +2024-12-26 06:22:08,585 - pyskl - INFO - Epoch [6][1900/3746] lr: 9.967e-02, eta: 4 days, 17:02:39, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4659, loss_cls: 4.2909, loss: 4.2909 +2024-12-26 06:23:21,130 - pyskl - INFO - Epoch [6][2000/3746] lr: 9.966e-02, eta: 4 days, 17:00:14, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4670, loss_cls: 4.2645, loss: 4.2645 +2024-12-26 06:24:33,153 - pyskl - INFO - Epoch [6][2100/3746] lr: 9.966e-02, eta: 4 days, 16:57:37, time: 0.720, data_time: 0.001, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4537, loss_cls: 4.3329, loss: 4.3329 +2024-12-26 06:25:45,302 - pyskl - INFO - Epoch [6][2200/3746] lr: 9.966e-02, eta: 4 days, 16:55:04, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4706, loss_cls: 4.2833, loss: 4.2833 +2024-12-26 06:26:57,606 - pyskl - INFO - Epoch [6][2300/3746] lr: 9.965e-02, eta: 4 days, 16:52:36, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4652, loss_cls: 4.2974, loss: 4.2974 +2024-12-26 06:28:09,807 - pyskl - INFO - Epoch [6][2400/3746] lr: 9.965e-02, eta: 4 days, 16:50:06, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4683, loss_cls: 4.2643, loss: 4.2643 +2024-12-26 06:29:21,860 - pyskl - INFO - Epoch [6][2500/3746] lr: 9.965e-02, eta: 4 days, 16:47:33, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4672, loss_cls: 4.2885, loss: 4.2885 +2024-12-26 06:30:34,036 - pyskl - INFO - Epoch [6][2600/3746] lr: 9.964e-02, eta: 4 days, 16:45:04, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4711, loss_cls: 4.2922, loss: 4.2922 +2024-12-26 06:31:46,547 - pyskl - INFO - Epoch [6][2700/3746] lr: 9.964e-02, eta: 4 days, 16:42:43, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4555, loss_cls: 4.3263, loss: 4.3263 +2024-12-26 06:32:58,798 - pyskl - INFO - Epoch [6][2800/3746] lr: 9.964e-02, eta: 4 days, 16:40:17, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4605, loss_cls: 4.2951, loss: 4.2951 +2024-12-26 06:34:10,738 - pyskl - INFO - Epoch [6][2900/3746] lr: 9.963e-02, eta: 4 days, 16:37:44, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4741, loss_cls: 4.2547, loss: 4.2547 +2024-12-26 06:35:22,909 - pyskl - INFO - Epoch [6][3000/3746] lr: 9.963e-02, eta: 4 days, 16:35:18, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4533, loss_cls: 4.3091, loss: 4.3091 +2024-12-26 06:36:35,689 - pyskl - INFO - Epoch [6][3100/3746] lr: 9.963e-02, eta: 4 days, 16:33:07, time: 0.728, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4603, loss_cls: 4.3033, loss: 4.3033 +2024-12-26 06:37:47,866 - pyskl - INFO - Epoch [6][3200/3746] lr: 9.962e-02, eta: 4 days, 16:30:42, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4630, loss_cls: 4.3108, loss: 4.3108 +2024-12-26 06:39:00,166 - pyskl - INFO - Epoch [6][3300/3746] lr: 9.962e-02, eta: 4 days, 16:28:20, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4603, loss_cls: 4.3142, loss: 4.3142 +2024-12-26 06:40:12,099 - pyskl - INFO - Epoch [6][3400/3746] lr: 9.962e-02, eta: 4 days, 16:25:50, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4753, loss_cls: 4.2840, loss: 4.2840 +2024-12-26 06:41:23,718 - pyskl - INFO - Epoch [6][3500/3746] lr: 9.961e-02, eta: 4 days, 16:23:14, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4416, loss_cls: 4.3660, loss: 4.3660 +2024-12-26 06:42:35,184 - pyskl - INFO - Epoch [6][3600/3746] lr: 9.961e-02, eta: 4 days, 16:20:34, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4669, loss_cls: 4.3076, loss: 4.3076 +2024-12-26 06:43:46,914 - pyskl - INFO - Epoch [6][3700/3746] lr: 9.961e-02, eta: 4 days, 16:18:01, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4578, loss_cls: 4.3240, loss: 4.3240 +2024-12-26 06:44:22,062 - pyskl - INFO - Saving checkpoint at 6 epochs +2024-12-26 06:46:19,959 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 06:46:20,720 - pyskl - INFO - +top1_acc 0.1182 +top5_acc 0.3121 +2024-12-26 06:46:20,721 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 06:46:20,756 - pyskl - INFO - +mean_acc 0.1181 +2024-12-26 06:46:20,766 - pyskl - INFO - Epoch(val) [6][309] top1_acc: 0.1182, top5_acc: 0.3121, mean_class_accuracy: 0.1181 +2024-12-26 06:50:02,907 - pyskl - INFO - Epoch [7][100/3746] lr: 9.960e-02, eta: 4 days, 17:01:05, time: 2.221, data_time: 1.507, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4706, loss_cls: 4.2518, loss: 4.2518 +2024-12-26 06:51:14,675 - pyskl - INFO - Epoch [7][200/3746] lr: 9.960e-02, eta: 4 days, 16:58:22, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4622, loss_cls: 4.2801, loss: 4.2801 +2024-12-26 06:52:26,365 - pyskl - INFO - Epoch [7][300/3746] lr: 9.960e-02, eta: 4 days, 16:55:38, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4603, loss_cls: 4.2882, loss: 4.2882 +2024-12-26 06:53:37,894 - pyskl - INFO - Epoch [7][400/3746] lr: 9.959e-02, eta: 4 days, 16:52:51, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4652, loss_cls: 4.2813, loss: 4.2813 +2024-12-26 06:54:49,192 - pyskl - INFO - Epoch [7][500/3746] lr: 9.959e-02, eta: 4 days, 16:50:00, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4694, loss_cls: 4.2855, loss: 4.2855 +2024-12-26 06:56:00,507 - pyskl - INFO - Epoch [7][600/3746] lr: 9.958e-02, eta: 4 days, 16:47:10, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4759, loss_cls: 4.2574, loss: 4.2574 +2024-12-26 06:57:12,565 - pyskl - INFO - Epoch [7][700/3746] lr: 9.958e-02, eta: 4 days, 16:44:38, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4731, loss_cls: 4.2608, loss: 4.2608 +2024-12-26 06:58:24,471 - pyskl - INFO - Epoch [7][800/3746] lr: 9.958e-02, eta: 4 days, 16:42:03, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4708, loss_cls: 4.2518, loss: 4.2518 +2024-12-26 06:59:36,245 - pyskl - INFO - Epoch [7][900/3746] lr: 9.957e-02, eta: 4 days, 16:39:26, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4747, loss_cls: 4.2548, loss: 4.2548 +2024-12-26 07:00:47,829 - pyskl - INFO - Epoch [7][1000/3746] lr: 9.957e-02, eta: 4 days, 16:36:45, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4705, loss_cls: 4.2789, loss: 4.2789 +2024-12-26 07:01:59,615 - pyskl - INFO - Epoch [7][1100/3746] lr: 9.957e-02, eta: 4 days, 16:34:10, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4708, loss_cls: 4.2658, loss: 4.2658 +2024-12-26 07:03:11,666 - pyskl - INFO - Epoch [7][1200/3746] lr: 9.956e-02, eta: 4 days, 16:31:41, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4716, loss_cls: 4.2812, loss: 4.2812 +2024-12-26 07:04:23,866 - pyskl - INFO - Epoch [7][1300/3746] lr: 9.956e-02, eta: 4 days, 16:29:16, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4772, loss_cls: 4.2503, loss: 4.2503 +2024-12-26 07:05:35,956 - pyskl - INFO - Epoch [7][1400/3746] lr: 9.956e-02, eta: 4 days, 16:26:50, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4673, loss_cls: 4.2937, loss: 4.2937 +2024-12-26 07:06:48,247 - pyskl - INFO - Epoch [7][1500/3746] lr: 9.955e-02, eta: 4 days, 16:24:28, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4612, loss_cls: 4.2916, loss: 4.2916 +2024-12-26 07:08:00,148 - pyskl - INFO - Epoch [7][1600/3746] lr: 9.955e-02, eta: 4 days, 16:21:59, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4666, loss_cls: 4.2482, loss: 4.2482 +2024-12-26 07:09:12,414 - pyskl - INFO - Epoch [7][1700/3746] lr: 9.954e-02, eta: 4 days, 16:19:38, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4653, loss_cls: 4.2661, loss: 4.2661 +2024-12-26 07:10:24,133 - pyskl - INFO - Epoch [7][1800/3746] lr: 9.954e-02, eta: 4 days, 16:17:05, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4686, loss_cls: 4.2759, loss: 4.2759 +2024-12-26 07:11:36,073 - pyskl - INFO - Epoch [7][1900/3746] lr: 9.954e-02, eta: 4 days, 16:14:38, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4647, loss_cls: 4.2671, loss: 4.2671 +2024-12-26 07:12:48,565 - pyskl - INFO - Epoch [7][2000/3746] lr: 9.953e-02, eta: 4 days, 16:12:24, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4661, loss_cls: 4.2882, loss: 4.2882 +2024-12-26 07:14:00,473 - pyskl - INFO - Epoch [7][2100/3746] lr: 9.953e-02, eta: 4 days, 16:09:58, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4656, loss_cls: 4.2905, loss: 4.2905 +2024-12-26 07:15:12,248 - pyskl - INFO - Epoch [7][2200/3746] lr: 9.952e-02, eta: 4 days, 16:07:29, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4639, loss_cls: 4.2559, loss: 4.2559 +2024-12-26 07:16:24,380 - pyskl - INFO - Epoch [7][2300/3746] lr: 9.952e-02, eta: 4 days, 16:05:09, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4659, loss_cls: 4.2534, loss: 4.2534 +2024-12-26 07:17:36,791 - pyskl - INFO - Epoch [7][2400/3746] lr: 9.952e-02, eta: 4 days, 16:02:55, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4672, loss_cls: 4.2857, loss: 4.2857 +2024-12-26 07:18:48,668 - pyskl - INFO - Epoch [7][2500/3746] lr: 9.951e-02, eta: 4 days, 16:00:31, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4637, loss_cls: 4.2926, loss: 4.2926 +2024-12-26 07:20:00,862 - pyskl - INFO - Epoch [7][2600/3746] lr: 9.951e-02, eta: 4 days, 15:58:13, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4784, loss_cls: 4.2450, loss: 4.2450 +2024-12-26 07:21:13,116 - pyskl - INFO - Epoch [7][2700/3746] lr: 9.951e-02, eta: 4 days, 15:55:58, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4800, loss_cls: 4.2130, loss: 4.2130 +2024-12-26 07:22:25,464 - pyskl - INFO - Epoch [7][2800/3746] lr: 9.950e-02, eta: 4 days, 15:53:45, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4736, loss_cls: 4.2397, loss: 4.2397 +2024-12-26 07:23:37,711 - pyskl - INFO - Epoch [7][2900/3746] lr: 9.950e-02, eta: 4 days, 15:51:30, time: 0.722, data_time: 0.001, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4773, loss_cls: 4.2277, loss: 4.2277 +2024-12-26 07:24:49,872 - pyskl - INFO - Epoch [7][3000/3746] lr: 9.949e-02, eta: 4 days, 15:49:14, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4678, loss_cls: 4.2740, loss: 4.2740 +2024-12-26 07:26:02,205 - pyskl - INFO - Epoch [7][3100/3746] lr: 9.949e-02, eta: 4 days, 15:47:02, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4697, loss_cls: 4.2651, loss: 4.2651 +2024-12-26 07:27:14,588 - pyskl - INFO - Epoch [7][3200/3746] lr: 9.949e-02, eta: 4 days, 15:44:52, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4636, loss_cls: 4.2542, loss: 4.2542 +2024-12-26 07:28:26,553 - pyskl - INFO - Epoch [7][3300/3746] lr: 9.948e-02, eta: 4 days, 15:42:33, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4633, loss_cls: 4.2633, loss: 4.2633 +2024-12-26 07:29:38,319 - pyskl - INFO - Epoch [7][3400/3746] lr: 9.948e-02, eta: 4 days, 15:40:11, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4666, loss_cls: 4.2975, loss: 4.2975 +2024-12-26 07:30:49,914 - pyskl - INFO - Epoch [7][3500/3746] lr: 9.947e-02, eta: 4 days, 15:37:45, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4747, loss_cls: 4.2654, loss: 4.2654 +2024-12-26 07:32:01,854 - pyskl - INFO - Epoch [7][3600/3746] lr: 9.947e-02, eta: 4 days, 15:35:28, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4667, loss_cls: 4.2911, loss: 4.2911 +2024-12-26 07:33:13,559 - pyskl - INFO - Epoch [7][3700/3746] lr: 9.947e-02, eta: 4 days, 15:33:06, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4817, loss_cls: 4.2121, loss: 4.2121 +2024-12-26 07:33:48,547 - pyskl - INFO - Saving checkpoint at 7 epochs +2024-12-26 07:35:42,690 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 07:35:43,392 - pyskl - INFO - +top1_acc 0.1620 +top5_acc 0.3771 +2024-12-26 07:35:43,392 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 07:35:43,432 - pyskl - INFO - +mean_acc 0.1618 +2024-12-26 07:35:43,437 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_5.pth was removed +2024-12-26 07:35:43,669 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2024-12-26 07:35:43,669 - pyskl - INFO - Best top1_acc is 0.1620 at 7 epoch. +2024-12-26 07:35:43,679 - pyskl - INFO - Epoch(val) [7][309] top1_acc: 0.1620, top5_acc: 0.3771, mean_class_accuracy: 0.1618 +2024-12-26 07:39:22,325 - pyskl - INFO - Epoch [8][100/3746] lr: 9.946e-02, eta: 4 days, 16:08:18, time: 2.186, data_time: 1.471, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4750, loss_cls: 4.2261, loss: 4.2261 +2024-12-26 07:40:33,537 - pyskl - INFO - Epoch [8][200/3746] lr: 9.946e-02, eta: 4 days, 16:05:39, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4820, loss_cls: 4.2181, loss: 4.2181 +2024-12-26 07:41:45,433 - pyskl - INFO - Epoch [8][300/3746] lr: 9.945e-02, eta: 4 days, 16:03:13, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4683, loss_cls: 4.2423, loss: 4.2423 +2024-12-26 07:42:56,714 - pyskl - INFO - Epoch [8][400/3746] lr: 9.945e-02, eta: 4 days, 16:00:36, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4675, loss_cls: 4.2846, loss: 4.2846 +2024-12-26 07:44:08,205 - pyskl - INFO - Epoch [8][500/3746] lr: 9.944e-02, eta: 4 days, 15:58:04, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4794, loss_cls: 4.1963, loss: 4.1963 +2024-12-26 07:45:19,776 - pyskl - INFO - Epoch [8][600/3746] lr: 9.944e-02, eta: 4 days, 15:55:34, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4647, loss_cls: 4.2572, loss: 4.2572 +2024-12-26 07:46:31,330 - pyskl - INFO - Epoch [8][700/3746] lr: 9.943e-02, eta: 4 days, 15:53:04, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4792, loss_cls: 4.2105, loss: 4.2105 +2024-12-26 07:47:42,935 - pyskl - INFO - Epoch [8][800/3746] lr: 9.943e-02, eta: 4 days, 15:50:36, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4877, loss_cls: 4.2134, loss: 4.2134 +2024-12-26 07:48:54,452 - pyskl - INFO - Epoch [8][900/3746] lr: 9.943e-02, eta: 4 days, 15:48:06, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4819, loss_cls: 4.2306, loss: 4.2306 +2024-12-26 07:50:05,950 - pyskl - INFO - Epoch [8][1000/3746] lr: 9.942e-02, eta: 4 days, 15:45:37, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4720, loss_cls: 4.2609, loss: 4.2609 +2024-12-26 07:51:17,460 - pyskl - INFO - Epoch [8][1100/3746] lr: 9.942e-02, eta: 4 days, 15:43:09, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4691, loss_cls: 4.2510, loss: 4.2510 +2024-12-26 07:52:29,385 - pyskl - INFO - Epoch [8][1200/3746] lr: 9.941e-02, eta: 4 days, 15:40:49, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4839, loss_cls: 4.2132, loss: 4.2132 +2024-12-26 07:53:41,107 - pyskl - INFO - Epoch [8][1300/3746] lr: 9.941e-02, eta: 4 days, 15:38:26, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4830, loss_cls: 4.1796, loss: 4.1796 +2024-12-26 07:54:52,851 - pyskl - INFO - Epoch [8][1400/3746] lr: 9.940e-02, eta: 4 days, 15:36:03, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4833, loss_cls: 4.2015, loss: 4.2015 +2024-12-26 07:56:04,805 - pyskl - INFO - Epoch [8][1500/3746] lr: 9.940e-02, eta: 4 days, 15:33:46, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4830, loss_cls: 4.2021, loss: 4.2021 +2024-12-26 07:57:17,148 - pyskl - INFO - Epoch [8][1600/3746] lr: 9.940e-02, eta: 4 days, 15:31:36, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4677, loss_cls: 4.2558, loss: 4.2558 +2024-12-26 07:58:29,371 - pyskl - INFO - Epoch [8][1700/3746] lr: 9.939e-02, eta: 4 days, 15:29:24, time: 0.722, data_time: 0.001, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4777, loss_cls: 4.2421, loss: 4.2421 +2024-12-26 07:59:41,540 - pyskl - INFO - Epoch [8][1800/3746] lr: 9.939e-02, eta: 4 days, 15:27:12, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4872, loss_cls: 4.1776, loss: 4.1776 +2024-12-26 08:00:53,447 - pyskl - INFO - Epoch [8][1900/3746] lr: 9.938e-02, eta: 4 days, 15:24:55, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4831, loss_cls: 4.2129, loss: 4.2129 +2024-12-26 08:02:05,567 - pyskl - INFO - Epoch [8][2000/3746] lr: 9.938e-02, eta: 4 days, 15:22:43, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4670, loss_cls: 4.2819, loss: 4.2819 +2024-12-26 08:03:17,622 - pyskl - INFO - Epoch [8][2100/3746] lr: 9.937e-02, eta: 4 days, 15:20:30, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4753, loss_cls: 4.2193, loss: 4.2193 +2024-12-26 08:04:29,637 - pyskl - INFO - Epoch [8][2200/3746] lr: 9.937e-02, eta: 4 days, 15:18:16, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4672, loss_cls: 4.2840, loss: 4.2840 +2024-12-26 08:05:41,494 - pyskl - INFO - Epoch [8][2300/3746] lr: 9.937e-02, eta: 4 days, 15:16:00, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4731, loss_cls: 4.2322, loss: 4.2322 +2024-12-26 08:06:53,295 - pyskl - INFO - Epoch [8][2400/3746] lr: 9.936e-02, eta: 4 days, 15:13:44, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4759, loss_cls: 4.2424, loss: 4.2424 +2024-12-26 08:08:05,295 - pyskl - INFO - Epoch [8][2500/3746] lr: 9.936e-02, eta: 4 days, 15:11:31, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4750, loss_cls: 4.2573, loss: 4.2573 +2024-12-26 08:09:17,359 - pyskl - INFO - Epoch [8][2600/3746] lr: 9.935e-02, eta: 4 days, 15:09:20, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4752, loss_cls: 4.2545, loss: 4.2545 +2024-12-26 08:10:29,621 - pyskl - INFO - Epoch [8][2700/3746] lr: 9.935e-02, eta: 4 days, 15:07:14, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4634, loss_cls: 4.2648, loss: 4.2648 +2024-12-26 08:11:41,909 - pyskl - INFO - Epoch [8][2800/3746] lr: 9.934e-02, eta: 4 days, 15:05:08, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4650, loss_cls: 4.2493, loss: 4.2493 +2024-12-26 08:12:54,366 - pyskl - INFO - Epoch [8][2900/3746] lr: 9.934e-02, eta: 4 days, 15:03:05, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4703, loss_cls: 4.2430, loss: 4.2430 +2024-12-26 08:14:06,354 - pyskl - INFO - Epoch [8][3000/3746] lr: 9.933e-02, eta: 4 days, 15:00:55, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4700, loss_cls: 4.2320, loss: 4.2320 +2024-12-26 08:15:18,703 - pyskl - INFO - Epoch [8][3100/3746] lr: 9.933e-02, eta: 4 days, 14:58:51, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4670, loss_cls: 4.2564, loss: 4.2564 +2024-12-26 08:16:30,681 - pyskl - INFO - Epoch [8][3200/3746] lr: 9.933e-02, eta: 4 days, 14:56:41, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4708, loss_cls: 4.2683, loss: 4.2683 +2024-12-26 08:17:42,733 - pyskl - INFO - Epoch [8][3300/3746] lr: 9.932e-02, eta: 4 days, 14:54:32, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4873, loss_cls: 4.1919, loss: 4.1919 +2024-12-26 08:18:54,354 - pyskl - INFO - Epoch [8][3400/3746] lr: 9.932e-02, eta: 4 days, 14:52:17, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4672, loss_cls: 4.2448, loss: 4.2448 +2024-12-26 08:20:05,829 - pyskl - INFO - Epoch [8][3500/3746] lr: 9.931e-02, eta: 4 days, 14:49:59, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4808, loss_cls: 4.2273, loss: 4.2273 +2024-12-26 08:21:17,374 - pyskl - INFO - Epoch [8][3600/3746] lr: 9.931e-02, eta: 4 days, 14:47:43, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4773, loss_cls: 4.2470, loss: 4.2470 +2024-12-26 08:22:28,851 - pyskl - INFO - Epoch [8][3700/3746] lr: 9.930e-02, eta: 4 days, 14:45:26, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4708, loss_cls: 4.2442, loss: 4.2442 +2024-12-26 08:23:03,736 - pyskl - INFO - Saving checkpoint at 8 epochs +2024-12-26 08:24:55,632 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 08:24:56,378 - pyskl - INFO - +top1_acc 0.1681 +top5_acc 0.3875 +2024-12-26 08:24:56,378 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 08:24:56,429 - pyskl - INFO - +mean_acc 0.1683 +2024-12-26 08:24:56,436 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_7.pth was removed +2024-12-26 08:24:56,728 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_8.pth. +2024-12-26 08:24:56,728 - pyskl - INFO - Best top1_acc is 0.1681 at 8 epoch. +2024-12-26 08:24:56,744 - pyskl - INFO - Epoch(val) [8][309] top1_acc: 0.1681, top5_acc: 0.3875, mean_class_accuracy: 0.1683 +2024-12-26 08:28:34,572 - pyskl - INFO - Epoch [9][100/3746] lr: 9.930e-02, eta: 4 days, 15:15:33, time: 2.178, data_time: 1.464, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4903, loss_cls: 4.1752, loss: 4.1752 +2024-12-26 08:29:45,640 - pyskl - INFO - Epoch [9][200/3746] lr: 9.929e-02, eta: 4 days, 15:13:03, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4788, loss_cls: 4.2105, loss: 4.2105 +2024-12-26 08:30:57,187 - pyskl - INFO - Epoch [9][300/3746] lr: 9.929e-02, eta: 4 days, 15:10:42, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4734, loss_cls: 4.2330, loss: 4.2330 +2024-12-26 08:32:08,863 - pyskl - INFO - Epoch [9][400/3746] lr: 9.928e-02, eta: 4 days, 15:08:24, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4780, loss_cls: 4.2233, loss: 4.2233 +2024-12-26 08:33:20,509 - pyskl - INFO - Epoch [9][500/3746] lr: 9.928e-02, eta: 4 days, 15:06:05, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4838, loss_cls: 4.1865, loss: 4.1865 +2024-12-26 08:34:32,132 - pyskl - INFO - Epoch [9][600/3746] lr: 9.927e-02, eta: 4 days, 15:03:47, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4791, loss_cls: 4.2060, loss: 4.2060 +2024-12-26 08:35:43,725 - pyskl - INFO - Epoch [9][700/3746] lr: 9.927e-02, eta: 4 days, 15:01:28, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4844, loss_cls: 4.2025, loss: 4.2025 +2024-12-26 08:36:55,310 - pyskl - INFO - Epoch [9][800/3746] lr: 9.926e-02, eta: 4 days, 14:59:10, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4742, loss_cls: 4.2336, loss: 4.2336 +2024-12-26 08:38:06,890 - pyskl - INFO - Epoch [9][900/3746] lr: 9.926e-02, eta: 4 days, 14:56:52, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4831, loss_cls: 4.1943, loss: 4.1943 +2024-12-26 08:39:18,476 - pyskl - INFO - Epoch [9][1000/3746] lr: 9.925e-02, eta: 4 days, 14:54:34, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4786, loss_cls: 4.2096, loss: 4.2096 +2024-12-26 08:40:29,917 - pyskl - INFO - Epoch [9][1100/3746] lr: 9.925e-02, eta: 4 days, 14:52:15, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4783, loss_cls: 4.2325, loss: 4.2325 +2024-12-26 08:41:41,411 - pyskl - INFO - Epoch [9][1200/3746] lr: 9.924e-02, eta: 4 days, 14:49:57, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4681, loss_cls: 4.2520, loss: 4.2520 +2024-12-26 08:42:52,517 - pyskl - INFO - Epoch [9][1300/3746] lr: 9.924e-02, eta: 4 days, 14:47:32, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4711, loss_cls: 4.2279, loss: 4.2279 +2024-12-26 08:44:03,687 - pyskl - INFO - Epoch [9][1400/3746] lr: 9.923e-02, eta: 4 days, 14:45:09, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4880, loss_cls: 4.1752, loss: 4.1752 +2024-12-26 08:45:15,465 - pyskl - INFO - Epoch [9][1500/3746] lr: 9.923e-02, eta: 4 days, 14:42:57, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4780, loss_cls: 4.1953, loss: 4.1953 +2024-12-26 08:46:27,240 - pyskl - INFO - Epoch [9][1600/3746] lr: 9.922e-02, eta: 4 days, 14:40:46, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4783, loss_cls: 4.2442, loss: 4.2442 +2024-12-26 08:47:39,173 - pyskl - INFO - Epoch [9][1700/3746] lr: 9.922e-02, eta: 4 days, 14:38:37, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4795, loss_cls: 4.1845, loss: 4.1845 +2024-12-26 08:48:50,969 - pyskl - INFO - Epoch [9][1800/3746] lr: 9.921e-02, eta: 4 days, 14:36:26, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4786, loss_cls: 4.2082, loss: 4.2082 +2024-12-26 08:50:03,129 - pyskl - INFO - Epoch [9][1900/3746] lr: 9.921e-02, eta: 4 days, 14:34:22, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4975, loss_cls: 4.1470, loss: 4.1470 +2024-12-26 08:51:15,377 - pyskl - INFO - Epoch [9][2000/3746] lr: 9.920e-02, eta: 4 days, 14:32:20, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4770, loss_cls: 4.2339, loss: 4.2339 +2024-12-26 08:52:27,576 - pyskl - INFO - Epoch [9][2100/3746] lr: 9.920e-02, eta: 4 days, 14:30:17, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4803, loss_cls: 4.2330, loss: 4.2330 +2024-12-26 08:53:39,673 - pyskl - INFO - Epoch [9][2200/3746] lr: 9.919e-02, eta: 4 days, 14:28:13, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4661, loss_cls: 4.2342, loss: 4.2342 +2024-12-26 08:54:51,988 - pyskl - INFO - Epoch [9][2300/3746] lr: 9.919e-02, eta: 4 days, 14:26:12, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4825, loss_cls: 4.2135, loss: 4.2135 +2024-12-26 08:56:03,925 - pyskl - INFO - Epoch [9][2400/3746] lr: 9.918e-02, eta: 4 days, 14:24:06, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4761, loss_cls: 4.2499, loss: 4.2499 +2024-12-26 08:57:15,905 - pyskl - INFO - Epoch [9][2500/3746] lr: 9.918e-02, eta: 4 days, 14:22:01, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4863, loss_cls: 4.1861, loss: 4.1861 +2024-12-26 08:58:28,135 - pyskl - INFO - Epoch [9][2600/3746] lr: 9.917e-02, eta: 4 days, 14:20:00, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4892, loss_cls: 4.1820, loss: 4.1820 +2024-12-26 08:59:40,495 - pyskl - INFO - Epoch [9][2700/3746] lr: 9.917e-02, eta: 4 days, 14:18:01, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4838, loss_cls: 4.2318, loss: 4.2318 +2024-12-26 09:00:52,352 - pyskl - INFO - Epoch [9][2800/3746] lr: 9.916e-02, eta: 4 days, 14:15:55, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4784, loss_cls: 4.2255, loss: 4.2255 +2024-12-26 09:02:04,339 - pyskl - INFO - Epoch [9][2900/3746] lr: 9.916e-02, eta: 4 days, 14:13:51, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4763, loss_cls: 4.2388, loss: 4.2388 +2024-12-26 09:03:16,276 - pyskl - INFO - Epoch [9][3000/3746] lr: 9.915e-02, eta: 4 days, 14:11:47, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4909, loss_cls: 4.1814, loss: 4.1814 +2024-12-26 09:04:28,190 - pyskl - INFO - Epoch [9][3100/3746] lr: 9.915e-02, eta: 4 days, 14:09:42, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4872, loss_cls: 4.2087, loss: 4.2087 +2024-12-26 09:05:40,553 - pyskl - INFO - Epoch [9][3200/3746] lr: 9.914e-02, eta: 4 days, 14:07:45, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4769, loss_cls: 4.2114, loss: 4.2114 +2024-12-26 09:06:52,481 - pyskl - INFO - Epoch [9][3300/3746] lr: 9.914e-02, eta: 4 days, 14:05:42, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4781, loss_cls: 4.2208, loss: 4.2208 +2024-12-26 09:08:04,357 - pyskl - INFO - Epoch [9][3400/3746] lr: 9.913e-02, eta: 4 days, 14:03:38, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4892, loss_cls: 4.1872, loss: 4.1872 +2024-12-26 09:09:15,703 - pyskl - INFO - Epoch [9][3500/3746] lr: 9.913e-02, eta: 4 days, 14:01:26, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4792, loss_cls: 4.2422, loss: 4.2422 +2024-12-26 09:10:27,456 - pyskl - INFO - Epoch [9][3600/3746] lr: 9.912e-02, eta: 4 days, 13:59:20, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4742, loss_cls: 4.2160, loss: 4.2160 +2024-12-26 09:11:39,235 - pyskl - INFO - Epoch [9][3700/3746] lr: 9.912e-02, eta: 4 days, 13:57:16, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4905, loss_cls: 4.1623, loss: 4.1623 +2024-12-26 09:12:13,937 - pyskl - INFO - Saving checkpoint at 9 epochs +2024-12-26 09:14:05,411 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 09:14:06,328 - pyskl - INFO - +top1_acc 0.1608 +top5_acc 0.3819 +2024-12-26 09:14:06,329 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 09:14:06,367 - pyskl - INFO - +mean_acc 0.1607 +2024-12-26 09:14:06,378 - pyskl - INFO - Epoch(val) [9][309] top1_acc: 0.1608, top5_acc: 0.3819, mean_class_accuracy: 0.1607 +2024-12-26 09:17:45,770 - pyskl - INFO - Epoch [10][100/3746] lr: 9.911e-02, eta: 4 days, 14:24:04, time: 2.194, data_time: 1.478, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4955, loss_cls: 4.1437, loss: 4.1437 +2024-12-26 09:18:57,514 - pyskl - INFO - Epoch [10][200/3746] lr: 9.910e-02, eta: 4 days, 14:21:54, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4902, loss_cls: 4.1692, loss: 4.1692 +2024-12-26 09:20:08,890 - pyskl - INFO - Epoch [10][300/3746] lr: 9.910e-02, eta: 4 days, 14:19:38, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4805, loss_cls: 4.2019, loss: 4.2019 +2024-12-26 09:21:20,210 - pyskl - INFO - Epoch [10][400/3746] lr: 9.909e-02, eta: 4 days, 14:17:22, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4778, loss_cls: 4.1647, loss: 4.1647 +2024-12-26 09:22:31,737 - pyskl - INFO - Epoch [10][500/3746] lr: 9.909e-02, eta: 4 days, 14:15:10, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4788, loss_cls: 4.1703, loss: 4.1703 +2024-12-26 09:23:43,034 - pyskl - INFO - Epoch [10][600/3746] lr: 9.908e-02, eta: 4 days, 14:12:55, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4839, loss_cls: 4.1911, loss: 4.1911 +2024-12-26 09:24:54,395 - pyskl - INFO - Epoch [10][700/3746] lr: 9.908e-02, eta: 4 days, 14:10:41, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4695, loss_cls: 4.2112, loss: 4.2112 +2024-12-26 09:26:05,700 - pyskl - INFO - Epoch [10][800/3746] lr: 9.907e-02, eta: 4 days, 14:08:26, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4822, loss_cls: 4.1859, loss: 4.1859 +2024-12-26 09:27:16,932 - pyskl - INFO - Epoch [10][900/3746] lr: 9.907e-02, eta: 4 days, 14:06:10, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4919, loss_cls: 4.1509, loss: 4.1509 +2024-12-26 09:28:28,046 - pyskl - INFO - Epoch [10][1000/3746] lr: 9.906e-02, eta: 4 days, 14:03:54, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4778, loss_cls: 4.2044, loss: 4.2044 +2024-12-26 09:29:39,313 - pyskl - INFO - Epoch [10][1100/3746] lr: 9.906e-02, eta: 4 days, 14:01:40, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4836, loss_cls: 4.1908, loss: 4.1908 +2024-12-26 09:30:50,495 - pyskl - INFO - Epoch [10][1200/3746] lr: 9.905e-02, eta: 4 days, 13:59:25, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4778, loss_cls: 4.2278, loss: 4.2278 +2024-12-26 09:32:02,070 - pyskl - INFO - Epoch [10][1300/3746] lr: 9.905e-02, eta: 4 days, 13:57:16, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4822, loss_cls: 4.2009, loss: 4.2009 +2024-12-26 09:33:13,387 - pyskl - INFO - Epoch [10][1400/3746] lr: 9.904e-02, eta: 4 days, 13:55:03, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4834, loss_cls: 4.1636, loss: 4.1636 +2024-12-26 09:34:24,787 - pyskl - INFO - Epoch [10][1500/3746] lr: 9.903e-02, eta: 4 days, 13:52:53, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4911, loss_cls: 4.1920, loss: 4.1920 +2024-12-26 09:35:36,305 - pyskl - INFO - Epoch [10][1600/3746] lr: 9.903e-02, eta: 4 days, 13:50:44, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4827, loss_cls: 4.1874, loss: 4.1874 +2024-12-26 09:36:48,068 - pyskl - INFO - Epoch [10][1700/3746] lr: 9.902e-02, eta: 4 days, 13:48:39, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4911, loss_cls: 4.1537, loss: 4.1537 +2024-12-26 09:38:00,076 - pyskl - INFO - Epoch [10][1800/3746] lr: 9.902e-02, eta: 4 days, 13:46:39, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4798, loss_cls: 4.1997, loss: 4.1997 +2024-12-26 09:39:12,072 - pyskl - INFO - Epoch [10][1900/3746] lr: 9.901e-02, eta: 4 days, 13:44:38, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4869, loss_cls: 4.1953, loss: 4.1953 +2024-12-26 09:40:24,015 - pyskl - INFO - Epoch [10][2000/3746] lr: 9.901e-02, eta: 4 days, 13:42:37, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4858, loss_cls: 4.1994, loss: 4.1994 +2024-12-26 09:41:35,870 - pyskl - INFO - Epoch [10][2100/3746] lr: 9.900e-02, eta: 4 days, 13:40:35, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4808, loss_cls: 4.1867, loss: 4.1867 +2024-12-26 09:42:48,280 - pyskl - INFO - Epoch [10][2200/3746] lr: 9.900e-02, eta: 4 days, 13:38:41, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4852, loss_cls: 4.1732, loss: 4.1732 +2024-12-26 09:44:00,427 - pyskl - INFO - Epoch [10][2300/3746] lr: 9.899e-02, eta: 4 days, 13:36:44, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4908, loss_cls: 4.1852, loss: 4.1852 +2024-12-26 09:45:12,513 - pyskl - INFO - Epoch [10][2400/3746] lr: 9.898e-02, eta: 4 days, 13:34:46, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4956, loss_cls: 4.1320, loss: 4.1320 +2024-12-26 09:46:24,355 - pyskl - INFO - Epoch [10][2500/3746] lr: 9.898e-02, eta: 4 days, 13:32:44, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2297, top5_acc: 0.4839, loss_cls: 4.2138, loss: 4.2138 +2024-12-26 09:47:36,459 - pyskl - INFO - Epoch [10][2600/3746] lr: 9.897e-02, eta: 4 days, 13:30:47, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4877, loss_cls: 4.1982, loss: 4.1982 +2024-12-26 09:48:48,639 - pyskl - INFO - Epoch [10][2700/3746] lr: 9.897e-02, eta: 4 days, 13:28:51, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4786, loss_cls: 4.2291, loss: 4.2291 +2024-12-26 09:50:00,846 - pyskl - INFO - Epoch [10][2800/3746] lr: 9.896e-02, eta: 4 days, 13:26:56, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4800, loss_cls: 4.2385, loss: 4.2385 +2024-12-26 09:51:12,854 - pyskl - INFO - Epoch [10][2900/3746] lr: 9.896e-02, eta: 4 days, 13:24:58, time: 0.720, data_time: 0.001, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4816, loss_cls: 4.2281, loss: 4.2281 +2024-12-26 09:52:25,112 - pyskl - INFO - Epoch [10][3000/3746] lr: 9.895e-02, eta: 4 days, 13:23:04, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4678, loss_cls: 4.2478, loss: 4.2478 +2024-12-26 09:53:36,942 - pyskl - INFO - Epoch [10][3100/3746] lr: 9.894e-02, eta: 4 days, 13:21:04, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4825, loss_cls: 4.1982, loss: 4.1982 +2024-12-26 09:54:48,847 - pyskl - INFO - Epoch [10][3200/3746] lr: 9.894e-02, eta: 4 days, 13:19:05, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4748, loss_cls: 4.2046, loss: 4.2046 +2024-12-26 09:56:00,867 - pyskl - INFO - Epoch [10][3300/3746] lr: 9.893e-02, eta: 4 days, 13:17:09, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4952, loss_cls: 4.1411, loss: 4.1411 +2024-12-26 09:57:12,919 - pyskl - INFO - Epoch [10][3400/3746] lr: 9.893e-02, eta: 4 days, 13:15:13, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4916, loss_cls: 4.1723, loss: 4.1723 +2024-12-26 09:58:24,438 - pyskl - INFO - Epoch [10][3500/3746] lr: 9.892e-02, eta: 4 days, 13:13:09, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4803, loss_cls: 4.2023, loss: 4.2023 +2024-12-26 09:59:36,127 - pyskl - INFO - Epoch [10][3600/3746] lr: 9.892e-02, eta: 4 days, 13:11:09, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4855, loss_cls: 4.1853, loss: 4.1853 +2024-12-26 10:00:47,741 - pyskl - INFO - Epoch [10][3700/3746] lr: 9.891e-02, eta: 4 days, 13:09:07, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4827, loss_cls: 4.2176, loss: 4.2176 +2024-12-26 10:01:22,640 - pyskl - INFO - Saving checkpoint at 10 epochs +2024-12-26 10:03:20,062 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 10:03:20,745 - pyskl - INFO - +top1_acc 0.1747 +top5_acc 0.3990 +2024-12-26 10:03:20,746 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 10:03:20,786 - pyskl - INFO - +mean_acc 0.1745 +2024-12-26 10:03:20,792 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_8.pth was removed +2024-12-26 10:03:21,050 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_10.pth. +2024-12-26 10:03:21,050 - pyskl - INFO - Best top1_acc is 0.1747 at 10 epoch. +2024-12-26 10:03:21,061 - pyskl - INFO - Epoch(val) [10][309] top1_acc: 0.1747, top5_acc: 0.3990, mean_class_accuracy: 0.1745 +2024-12-26 10:07:02,568 - pyskl - INFO - Epoch [11][100/3746] lr: 9.890e-02, eta: 4 days, 13:33:23, time: 2.215, data_time: 1.495, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4863, loss_cls: 4.1732, loss: 4.1732 +2024-12-26 10:08:14,388 - pyskl - INFO - Epoch [11][200/3746] lr: 9.890e-02, eta: 4 days, 13:31:21, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4813, loss_cls: 4.1892, loss: 4.1892 +2024-12-26 10:09:26,068 - pyskl - INFO - Epoch [11][300/3746] lr: 9.889e-02, eta: 4 days, 13:29:16, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4906, loss_cls: 4.1324, loss: 4.1324 +2024-12-26 10:10:37,517 - pyskl - INFO - Epoch [11][400/3746] lr: 9.888e-02, eta: 4 days, 13:27:09, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4852, loss_cls: 4.1763, loss: 4.1763 +2024-12-26 10:11:49,226 - pyskl - INFO - Epoch [11][500/3746] lr: 9.888e-02, eta: 4 days, 13:25:06, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4875, loss_cls: 4.1558, loss: 4.1558 +2024-12-26 10:13:01,274 - pyskl - INFO - Epoch [11][600/3746] lr: 9.887e-02, eta: 4 days, 13:23:08, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4939, loss_cls: 4.1440, loss: 4.1440 +2024-12-26 10:14:13,236 - pyskl - INFO - Epoch [11][700/3746] lr: 9.887e-02, eta: 4 days, 13:21:08, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4736, loss_cls: 4.2190, loss: 4.2190 +2024-12-26 10:15:25,325 - pyskl - INFO - Epoch [11][800/3746] lr: 9.886e-02, eta: 4 days, 13:19:11, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4841, loss_cls: 4.2147, loss: 4.2147 +2024-12-26 10:16:37,453 - pyskl - INFO - Epoch [11][900/3746] lr: 9.885e-02, eta: 4 days, 13:17:15, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4823, loss_cls: 4.2265, loss: 4.2265 +2024-12-26 10:17:49,383 - pyskl - INFO - Epoch [11][1000/3746] lr: 9.885e-02, eta: 4 days, 13:15:16, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4944, loss_cls: 4.1456, loss: 4.1456 +2024-12-26 10:19:01,946 - pyskl - INFO - Epoch [11][1100/3746] lr: 9.884e-02, eta: 4 days, 13:13:25, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4795, loss_cls: 4.2217, loss: 4.2217 +2024-12-26 10:20:14,105 - pyskl - INFO - Epoch [11][1200/3746] lr: 9.884e-02, eta: 4 days, 13:11:30, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4902, loss_cls: 4.1569, loss: 4.1569 +2024-12-26 10:21:26,237 - pyskl - INFO - Epoch [11][1300/3746] lr: 9.883e-02, eta: 4 days, 13:09:34, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4873, loss_cls: 4.1783, loss: 4.1783 +2024-12-26 10:22:38,314 - pyskl - INFO - Epoch [11][1400/3746] lr: 9.882e-02, eta: 4 days, 13:07:38, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4863, loss_cls: 4.1541, loss: 4.1541 +2024-12-26 10:23:50,767 - pyskl - INFO - Epoch [11][1500/3746] lr: 9.882e-02, eta: 4 days, 13:05:48, time: 0.725, data_time: 0.001, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4872, loss_cls: 4.1827, loss: 4.1827 +2024-12-26 10:25:03,357 - pyskl - INFO - Epoch [11][1600/3746] lr: 9.881e-02, eta: 4 days, 13:03:59, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4861, loss_cls: 4.1879, loss: 4.1879 +2024-12-26 10:26:15,439 - pyskl - INFO - Epoch [11][1700/3746] lr: 9.881e-02, eta: 4 days, 13:02:03, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4895, loss_cls: 4.1696, loss: 4.1696 +2024-12-26 10:27:27,744 - pyskl - INFO - Epoch [11][1800/3746] lr: 9.880e-02, eta: 4 days, 13:00:11, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4950, loss_cls: 4.1704, loss: 4.1704 +2024-12-26 10:28:40,523 - pyskl - INFO - Epoch [11][1900/3746] lr: 9.879e-02, eta: 4 days, 12:58:25, time: 0.728, data_time: 0.001, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4872, loss_cls: 4.1713, loss: 4.1713 +2024-12-26 10:29:52,510 - pyskl - INFO - Epoch [11][2000/3746] lr: 9.879e-02, eta: 4 days, 12:56:30, time: 0.720, data_time: 0.001, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4772, loss_cls: 4.2018, loss: 4.2018 +2024-12-26 10:31:05,146 - pyskl - INFO - Epoch [11][2100/3746] lr: 9.878e-02, eta: 4 days, 12:54:42, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4831, loss_cls: 4.1941, loss: 4.1941 +2024-12-26 10:32:17,417 - pyskl - INFO - Epoch [11][2200/3746] lr: 9.878e-02, eta: 4 days, 12:52:50, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4869, loss_cls: 4.1904, loss: 4.1904 +2024-12-26 10:33:29,790 - pyskl - INFO - Epoch [11][2300/3746] lr: 9.877e-02, eta: 4 days, 12:51:00, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4969, loss_cls: 4.1844, loss: 4.1844 +2024-12-26 10:34:42,181 - pyskl - INFO - Epoch [11][2400/3746] lr: 9.876e-02, eta: 4 days, 12:49:10, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4889, loss_cls: 4.1542, loss: 4.1542 +2024-12-26 10:35:54,224 - pyskl - INFO - Epoch [11][2500/3746] lr: 9.876e-02, eta: 4 days, 12:47:16, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4889, loss_cls: 4.1743, loss: 4.1743 +2024-12-26 10:37:06,423 - pyskl - INFO - Epoch [11][2600/3746] lr: 9.875e-02, eta: 4 days, 12:45:24, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4928, loss_cls: 4.1621, loss: 4.1621 +2024-12-26 10:38:18,893 - pyskl - INFO - Epoch [11][2700/3746] lr: 9.874e-02, eta: 4 days, 12:43:36, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5008, loss_cls: 4.1354, loss: 4.1354 +2024-12-26 10:39:31,177 - pyskl - INFO - Epoch [11][2800/3746] lr: 9.874e-02, eta: 4 days, 12:41:45, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4881, loss_cls: 4.1887, loss: 4.1887 +2024-12-26 10:40:43,581 - pyskl - INFO - Epoch [11][2900/3746] lr: 9.873e-02, eta: 4 days, 12:39:57, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4805, loss_cls: 4.1888, loss: 4.1888 +2024-12-26 10:41:56,029 - pyskl - INFO - Epoch [11][3000/3746] lr: 9.873e-02, eta: 4 days, 12:38:08, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4870, loss_cls: 4.1582, loss: 4.1582 +2024-12-26 10:43:08,163 - pyskl - INFO - Epoch [11][3100/3746] lr: 9.872e-02, eta: 4 days, 12:36:17, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5016, loss_cls: 4.0948, loss: 4.0948 +2024-12-26 10:44:20,194 - pyskl - INFO - Epoch [11][3200/3746] lr: 9.871e-02, eta: 4 days, 12:34:24, time: 0.720, data_time: 0.001, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4856, loss_cls: 4.1947, loss: 4.1947 +2024-12-26 10:45:32,624 - pyskl - INFO - Epoch [11][3300/3746] lr: 9.871e-02, eta: 4 days, 12:32:36, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4894, loss_cls: 4.1552, loss: 4.1552 +2024-12-26 10:46:44,542 - pyskl - INFO - Epoch [11][3400/3746] lr: 9.870e-02, eta: 4 days, 12:30:42, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4856, loss_cls: 4.2124, loss: 4.2124 +2024-12-26 10:47:56,577 - pyskl - INFO - Epoch [11][3500/3746] lr: 9.869e-02, eta: 4 days, 12:28:49, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4945, loss_cls: 4.1529, loss: 4.1529 +2024-12-26 10:49:08,219 - pyskl - INFO - Epoch [11][3600/3746] lr: 9.869e-02, eta: 4 days, 12:26:52, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4791, loss_cls: 4.1994, loss: 4.1994 +2024-12-26 10:50:20,067 - pyskl - INFO - Epoch [11][3700/3746] lr: 9.868e-02, eta: 4 days, 12:24:58, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4944, loss_cls: 4.1546, loss: 4.1546 +2024-12-26 10:50:55,092 - pyskl - INFO - Saving checkpoint at 11 epochs +2024-12-26 10:52:52,554 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 10:52:53,412 - pyskl - INFO - +top1_acc 0.1640 +top5_acc 0.3829 +2024-12-26 10:52:53,412 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 10:52:53,451 - pyskl - INFO - +mean_acc 0.1637 +2024-12-26 10:52:53,461 - pyskl - INFO - Epoch(val) [11][309] top1_acc: 0.1640, top5_acc: 0.3829, mean_class_accuracy: 0.1637 +2024-12-26 10:56:35,671 - pyskl - INFO - Epoch [12][100/3746] lr: 9.867e-02, eta: 4 days, 12:46:50, time: 2.222, data_time: 1.499, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4872, loss_cls: 4.1549, loss: 4.1549 +2024-12-26 10:57:48,006 - pyskl - INFO - Epoch [12][200/3746] lr: 9.867e-02, eta: 4 days, 12:44:58, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4895, loss_cls: 4.1583, loss: 4.1583 +2024-12-26 10:59:00,032 - pyskl - INFO - Epoch [12][300/3746] lr: 9.866e-02, eta: 4 days, 12:43:03, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4939, loss_cls: 4.1230, loss: 4.1230 +2024-12-26 11:00:11,607 - pyskl - INFO - Epoch [12][400/3746] lr: 9.865e-02, eta: 4 days, 12:41:02, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4948, loss_cls: 4.1179, loss: 4.1179 +2024-12-26 11:01:22,932 - pyskl - INFO - Epoch [12][500/3746] lr: 9.865e-02, eta: 4 days, 12:38:59, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4883, loss_cls: 4.1477, loss: 4.1477 +2024-12-26 11:02:34,735 - pyskl - INFO - Epoch [12][600/3746] lr: 9.864e-02, eta: 4 days, 12:37:02, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4939, loss_cls: 4.1481, loss: 4.1481 +2024-12-26 11:03:46,896 - pyskl - INFO - Epoch [12][700/3746] lr: 9.863e-02, eta: 4 days, 12:35:09, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4906, loss_cls: 4.1583, loss: 4.1583 +2024-12-26 11:04:58,933 - pyskl - INFO - Epoch [12][800/3746] lr: 9.863e-02, eta: 4 days, 12:33:15, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4902, loss_cls: 4.1779, loss: 4.1779 +2024-12-26 11:06:11,545 - pyskl - INFO - Epoch [12][900/3746] lr: 9.862e-02, eta: 4 days, 12:31:28, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4867, loss_cls: 4.1591, loss: 4.1591 +2024-12-26 11:07:23,349 - pyskl - INFO - Epoch [12][1000/3746] lr: 9.861e-02, eta: 4 days, 12:29:31, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4825, loss_cls: 4.1750, loss: 4.1750 +2024-12-26 11:08:35,748 - pyskl - INFO - Epoch [12][1100/3746] lr: 9.861e-02, eta: 4 days, 12:27:42, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4905, loss_cls: 4.1685, loss: 4.1685 +2024-12-26 11:09:47,823 - pyskl - INFO - Epoch [12][1200/3746] lr: 9.860e-02, eta: 4 days, 12:25:50, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4913, loss_cls: 4.1372, loss: 4.1372 +2024-12-26 11:11:00,005 - pyskl - INFO - Epoch [12][1300/3746] lr: 9.859e-02, eta: 4 days, 12:23:58, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4869, loss_cls: 4.1552, loss: 4.1552 +2024-12-26 11:12:12,252 - pyskl - INFO - Epoch [12][1400/3746] lr: 9.859e-02, eta: 4 days, 12:22:08, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4784, loss_cls: 4.1986, loss: 4.1986 +2024-12-26 11:13:24,234 - pyskl - INFO - Epoch [12][1500/3746] lr: 9.858e-02, eta: 4 days, 12:20:15, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4842, loss_cls: 4.1805, loss: 4.1805 +2024-12-26 11:14:36,162 - pyskl - INFO - Epoch [12][1600/3746] lr: 9.857e-02, eta: 4 days, 12:18:21, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4813, loss_cls: 4.1691, loss: 4.1691 +2024-12-26 11:15:48,539 - pyskl - INFO - Epoch [12][1700/3746] lr: 9.857e-02, eta: 4 days, 12:16:33, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4931, loss_cls: 4.1904, loss: 4.1904 +2024-12-26 11:17:00,493 - pyskl - INFO - Epoch [12][1800/3746] lr: 9.856e-02, eta: 4 days, 12:14:39, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4920, loss_cls: 4.1784, loss: 4.1784 +2024-12-26 11:18:12,878 - pyskl - INFO - Epoch [12][1900/3746] lr: 9.855e-02, eta: 4 days, 12:12:52, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4964, loss_cls: 4.1311, loss: 4.1311 +2024-12-26 11:19:25,388 - pyskl - INFO - Epoch [12][2000/3746] lr: 9.855e-02, eta: 4 days, 12:11:06, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4964, loss_cls: 4.1775, loss: 4.1775 +2024-12-26 11:20:37,669 - pyskl - INFO - Epoch [12][2100/3746] lr: 9.854e-02, eta: 4 days, 12:09:17, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4841, loss_cls: 4.1836, loss: 4.1836 +2024-12-26 11:21:49,953 - pyskl - INFO - Epoch [12][2200/3746] lr: 9.853e-02, eta: 4 days, 12:07:28, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4706, loss_cls: 4.2264, loss: 4.2264 +2024-12-26 11:23:02,194 - pyskl - INFO - Epoch [12][2300/3746] lr: 9.853e-02, eta: 4 days, 12:05:40, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4948, loss_cls: 4.1469, loss: 4.1469 +2024-12-26 11:24:14,218 - pyskl - INFO - Epoch [12][2400/3746] lr: 9.852e-02, eta: 4 days, 12:03:48, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.4897, loss_cls: 4.1706, loss: 4.1706 +2024-12-26 11:25:26,699 - pyskl - INFO - Epoch [12][2500/3746] lr: 9.851e-02, eta: 4 days, 12:02:03, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4856, loss_cls: 4.1807, loss: 4.1807 +2024-12-26 11:26:39,011 - pyskl - INFO - Epoch [12][2600/3746] lr: 9.851e-02, eta: 4 days, 12:00:15, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4978, loss_cls: 4.1348, loss: 4.1348 +2024-12-26 11:27:51,606 - pyskl - INFO - Epoch [12][2700/3746] lr: 9.850e-02, eta: 4 days, 11:58:31, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4892, loss_cls: 4.1381, loss: 4.1381 +2024-12-26 11:29:03,414 - pyskl - INFO - Epoch [12][2800/3746] lr: 9.849e-02, eta: 4 days, 11:56:38, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4869, loss_cls: 4.1596, loss: 4.1596 +2024-12-26 11:30:15,717 - pyskl - INFO - Epoch [12][2900/3746] lr: 9.849e-02, eta: 4 days, 11:54:51, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4939, loss_cls: 4.1259, loss: 4.1259 +2024-12-26 11:31:28,010 - pyskl - INFO - Epoch [12][3000/3746] lr: 9.848e-02, eta: 4 days, 11:53:04, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4838, loss_cls: 4.1897, loss: 4.1897 +2024-12-26 11:32:40,410 - pyskl - INFO - Epoch [12][3100/3746] lr: 9.847e-02, eta: 4 days, 11:51:18, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4867, loss_cls: 4.1671, loss: 4.1671 +2024-12-26 11:33:52,864 - pyskl - INFO - Epoch [12][3200/3746] lr: 9.847e-02, eta: 4 days, 11:49:33, time: 0.725, data_time: 0.001, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4863, loss_cls: 4.1695, loss: 4.1695 +2024-12-26 11:35:05,305 - pyskl - INFO - Epoch [12][3300/3746] lr: 9.846e-02, eta: 4 days, 11:47:48, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5000, loss_cls: 4.1343, loss: 4.1343 +2024-12-26 11:36:17,058 - pyskl - INFO - Epoch [12][3400/3746] lr: 9.845e-02, eta: 4 days, 11:45:56, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4897, loss_cls: 4.1575, loss: 4.1575 +2024-12-26 11:37:28,961 - pyskl - INFO - Epoch [12][3500/3746] lr: 9.845e-02, eta: 4 days, 11:44:05, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4903, loss_cls: 4.1663, loss: 4.1663 +2024-12-26 11:38:40,770 - pyskl - INFO - Epoch [12][3600/3746] lr: 9.844e-02, eta: 4 days, 11:42:13, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4895, loss_cls: 4.1892, loss: 4.1892 +2024-12-26 11:39:52,280 - pyskl - INFO - Epoch [12][3700/3746] lr: 9.843e-02, eta: 4 days, 11:40:18, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4908, loss_cls: 4.1610, loss: 4.1610 +2024-12-26 11:40:27,276 - pyskl - INFO - Saving checkpoint at 12 epochs +2024-12-26 11:42:24,943 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 11:42:25,632 - pyskl - INFO - +top1_acc 0.1779 +top5_acc 0.3923 +2024-12-26 11:42:25,632 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 11:42:25,667 - pyskl - INFO - +mean_acc 0.1779 +2024-12-26 11:42:25,672 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_10.pth was removed +2024-12-26 11:42:25,913 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_12.pth. +2024-12-26 11:42:25,914 - pyskl - INFO - Best top1_acc is 0.1779 at 12 epoch. +2024-12-26 11:42:25,924 - pyskl - INFO - Epoch(val) [12][309] top1_acc: 0.1779, top5_acc: 0.3923, mean_class_accuracy: 0.1779 +2024-12-26 11:46:07,806 - pyskl - INFO - Epoch [13][100/3746] lr: 9.842e-02, eta: 4 days, 11:59:58, time: 2.219, data_time: 1.497, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4925, loss_cls: 4.1400, loss: 4.1400 +2024-12-26 11:47:19,867 - pyskl - INFO - Epoch [13][200/3746] lr: 9.842e-02, eta: 4 days, 11:58:07, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4997, loss_cls: 4.1242, loss: 4.1242 +2024-12-26 11:48:31,954 - pyskl - INFO - Epoch [13][300/3746] lr: 9.841e-02, eta: 4 days, 11:56:16, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4830, loss_cls: 4.1856, loss: 4.1856 +2024-12-26 11:49:43,532 - pyskl - INFO - Epoch [13][400/3746] lr: 9.840e-02, eta: 4 days, 11:54:19, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4859, loss_cls: 4.1586, loss: 4.1586 +2024-12-26 11:50:55,174 - pyskl - INFO - Epoch [13][500/3746] lr: 9.839e-02, eta: 4 days, 11:52:24, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4869, loss_cls: 4.1509, loss: 4.1509 +2024-12-26 11:52:06,936 - pyskl - INFO - Epoch [13][600/3746] lr: 9.839e-02, eta: 4 days, 11:50:29, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.5022, loss_cls: 4.1282, loss: 4.1282 +2024-12-26 11:53:19,079 - pyskl - INFO - Epoch [13][700/3746] lr: 9.838e-02, eta: 4 days, 11:48:40, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4855, loss_cls: 4.1929, loss: 4.1929 +2024-12-26 11:54:31,309 - pyskl - INFO - Epoch [13][800/3746] lr: 9.837e-02, eta: 4 days, 11:46:51, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4917, loss_cls: 4.1589, loss: 4.1589 +2024-12-26 11:55:43,540 - pyskl - INFO - Epoch [13][900/3746] lr: 9.837e-02, eta: 4 days, 11:45:03, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4859, loss_cls: 4.1776, loss: 4.1776 +2024-12-26 11:56:55,953 - pyskl - INFO - Epoch [13][1000/3746] lr: 9.836e-02, eta: 4 days, 11:43:17, time: 0.724, data_time: 0.001, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4867, loss_cls: 4.1719, loss: 4.1719 +2024-12-26 11:58:08,157 - pyskl - INFO - Epoch [13][1100/3746] lr: 9.835e-02, eta: 4 days, 11:41:29, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5067, loss_cls: 4.1120, loss: 4.1120 +2024-12-26 11:59:20,320 - pyskl - INFO - Epoch [13][1200/3746] lr: 9.834e-02, eta: 4 days, 11:39:40, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4972, loss_cls: 4.1455, loss: 4.1455 +2024-12-26 12:00:32,471 - pyskl - INFO - Epoch [13][1300/3746] lr: 9.834e-02, eta: 4 days, 11:37:51, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.4916, loss_cls: 4.1736, loss: 4.1736 +2024-12-26 12:01:44,694 - pyskl - INFO - Epoch [13][1400/3746] lr: 9.833e-02, eta: 4 days, 11:36:04, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4975, loss_cls: 4.1426, loss: 4.1426 +2024-12-26 12:02:56,597 - pyskl - INFO - Epoch [13][1500/3746] lr: 9.832e-02, eta: 4 days, 11:34:13, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4841, loss_cls: 4.1901, loss: 4.1901 +2024-12-26 12:04:09,001 - pyskl - INFO - Epoch [13][1600/3746] lr: 9.832e-02, eta: 4 days, 11:32:27, time: 0.724, data_time: 0.001, memory: 15990, top1_acc: 0.2573, top5_acc: 0.4866, loss_cls: 4.1436, loss: 4.1436 +2024-12-26 12:05:21,314 - pyskl - INFO - Epoch [13][1700/3746] lr: 9.831e-02, eta: 4 days, 11:30:41, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4986, loss_cls: 4.1397, loss: 4.1397 +2024-12-26 12:06:33,456 - pyskl - INFO - Epoch [13][1800/3746] lr: 9.830e-02, eta: 4 days, 11:28:53, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4978, loss_cls: 4.1305, loss: 4.1305 +2024-12-26 12:07:45,550 - pyskl - INFO - Epoch [13][1900/3746] lr: 9.829e-02, eta: 4 days, 11:27:05, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4872, loss_cls: 4.1802, loss: 4.1802 +2024-12-26 12:08:57,553 - pyskl - INFO - Epoch [13][2000/3746] lr: 9.829e-02, eta: 4 days, 11:25:16, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4970, loss_cls: 4.1206, loss: 4.1206 +2024-12-26 12:10:09,862 - pyskl - INFO - Epoch [13][2100/3746] lr: 9.828e-02, eta: 4 days, 11:23:30, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4928, loss_cls: 4.1571, loss: 4.1571 +2024-12-26 12:11:22,541 - pyskl - INFO - Epoch [13][2200/3746] lr: 9.827e-02, eta: 4 days, 11:21:49, time: 0.727, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4902, loss_cls: 4.1384, loss: 4.1384 +2024-12-26 12:12:34,914 - pyskl - INFO - Epoch [13][2300/3746] lr: 9.827e-02, eta: 4 days, 11:20:04, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4950, loss_cls: 4.1230, loss: 4.1230 +2024-12-26 12:13:47,386 - pyskl - INFO - Epoch [13][2400/3746] lr: 9.826e-02, eta: 4 days, 11:18:21, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4942, loss_cls: 4.1456, loss: 4.1456 +2024-12-26 12:14:59,916 - pyskl - INFO - Epoch [13][2500/3746] lr: 9.825e-02, eta: 4 days, 11:16:38, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4900, loss_cls: 4.1589, loss: 4.1589 +2024-12-26 12:16:12,096 - pyskl - INFO - Epoch [13][2600/3746] lr: 9.824e-02, eta: 4 days, 11:14:52, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4931, loss_cls: 4.1470, loss: 4.1470 +2024-12-26 12:17:24,181 - pyskl - INFO - Epoch [13][2700/3746] lr: 9.824e-02, eta: 4 days, 11:13:04, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4966, loss_cls: 4.1472, loss: 4.1472 +2024-12-26 12:18:36,474 - pyskl - INFO - Epoch [13][2800/3746] lr: 9.823e-02, eta: 4 days, 11:11:20, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5033, loss_cls: 4.1339, loss: 4.1339 +2024-12-26 12:19:48,694 - pyskl - INFO - Epoch [13][2900/3746] lr: 9.822e-02, eta: 4 days, 11:09:34, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4755, loss_cls: 4.2062, loss: 4.2062 +2024-12-26 12:21:00,468 - pyskl - INFO - Epoch [13][3000/3746] lr: 9.821e-02, eta: 4 days, 11:07:44, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4850, loss_cls: 4.1625, loss: 4.1625 +2024-12-26 12:22:12,617 - pyskl - INFO - Epoch [13][3100/3746] lr: 9.821e-02, eta: 4 days, 11:05:58, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4913, loss_cls: 4.1494, loss: 4.1494 +2024-12-26 12:23:24,923 - pyskl - INFO - Epoch [13][3200/3746] lr: 9.820e-02, eta: 4 days, 11:04:14, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4872, loss_cls: 4.1945, loss: 4.1945 +2024-12-26 12:24:36,764 - pyskl - INFO - Epoch [13][3300/3746] lr: 9.819e-02, eta: 4 days, 11:02:25, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5012, loss_cls: 4.1377, loss: 4.1377 +2024-12-26 12:25:48,478 - pyskl - INFO - Epoch [13][3400/3746] lr: 9.818e-02, eta: 4 days, 11:00:35, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4888, loss_cls: 4.1389, loss: 4.1389 +2024-12-26 12:27:00,219 - pyskl - INFO - Epoch [13][3500/3746] lr: 9.818e-02, eta: 4 days, 10:58:45, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.4930, loss_cls: 4.1085, loss: 4.1085 +2024-12-26 12:28:11,826 - pyskl - INFO - Epoch [13][3600/3746] lr: 9.817e-02, eta: 4 days, 10:56:54, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4934, loss_cls: 4.1482, loss: 4.1482 +2024-12-26 12:29:23,268 - pyskl - INFO - Epoch [13][3700/3746] lr: 9.816e-02, eta: 4 days, 10:55:01, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4761, loss_cls: 4.1927, loss: 4.1927 +2024-12-26 12:29:58,307 - pyskl - INFO - Saving checkpoint at 13 epochs +2024-12-26 12:31:57,162 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 12:31:57,899 - pyskl - INFO - +top1_acc 0.1777 +top5_acc 0.4020 +2024-12-26 12:31:57,899 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 12:31:57,948 - pyskl - INFO - +mean_acc 0.1775 +2024-12-26 12:31:57,961 - pyskl - INFO - Epoch(val) [13][309] top1_acc: 0.1777, top5_acc: 0.4020, mean_class_accuracy: 0.1775 +2024-12-26 12:35:38,625 - pyskl - INFO - Epoch [14][100/3746] lr: 9.815e-02, eta: 4 days, 11:12:41, time: 2.207, data_time: 1.487, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4994, loss_cls: 4.1122, loss: 4.1122 +2024-12-26 12:36:50,465 - pyskl - INFO - Epoch [14][200/3746] lr: 9.814e-02, eta: 4 days, 11:10:50, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4891, loss_cls: 4.1590, loss: 4.1590 +2024-12-26 12:38:02,542 - pyskl - INFO - Epoch [14][300/3746] lr: 9.814e-02, eta: 4 days, 11:09:02, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.5009, loss_cls: 4.0881, loss: 4.0881 +2024-12-26 12:39:14,220 - pyskl - INFO - Epoch [14][400/3746] lr: 9.813e-02, eta: 4 days, 11:07:10, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4861, loss_cls: 4.1408, loss: 4.1408 +2024-12-26 12:40:25,881 - pyskl - INFO - Epoch [14][500/3746] lr: 9.812e-02, eta: 4 days, 11:05:18, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.4905, loss_cls: 4.1317, loss: 4.1317 +2024-12-26 12:41:37,835 - pyskl - INFO - Epoch [14][600/3746] lr: 9.811e-02, eta: 4 days, 11:03:29, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4953, loss_cls: 4.1371, loss: 4.1371 +2024-12-26 12:42:50,236 - pyskl - INFO - Epoch [14][700/3746] lr: 9.811e-02, eta: 4 days, 11:01:45, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4894, loss_cls: 4.1707, loss: 4.1707 +2024-12-26 12:44:02,873 - pyskl - INFO - Epoch [14][800/3746] lr: 9.810e-02, eta: 4 days, 11:00:03, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4995, loss_cls: 4.1236, loss: 4.1236 +2024-12-26 12:45:15,404 - pyskl - INFO - Epoch [14][900/3746] lr: 9.809e-02, eta: 4 days, 10:58:21, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4905, loss_cls: 4.1472, loss: 4.1472 +2024-12-26 12:46:27,828 - pyskl - INFO - Epoch [14][1000/3746] lr: 9.808e-02, eta: 4 days, 10:56:37, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4881, loss_cls: 4.1672, loss: 4.1672 +2024-12-26 12:47:39,936 - pyskl - INFO - Epoch [14][1100/3746] lr: 9.807e-02, eta: 4 days, 10:54:51, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4991, loss_cls: 4.1163, loss: 4.1163 +2024-12-26 12:48:52,220 - pyskl - INFO - Epoch [14][1200/3746] lr: 9.807e-02, eta: 4 days, 10:53:06, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5016, loss_cls: 4.1043, loss: 4.1043 +2024-12-26 12:50:04,379 - pyskl - INFO - Epoch [14][1300/3746] lr: 9.806e-02, eta: 4 days, 10:51:20, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4866, loss_cls: 4.1775, loss: 4.1775 +2024-12-26 12:51:16,636 - pyskl - INFO - Epoch [14][1400/3746] lr: 9.805e-02, eta: 4 days, 10:49:36, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.5016, loss_cls: 4.1033, loss: 4.1033 +2024-12-26 12:52:28,621 - pyskl - INFO - Epoch [14][1500/3746] lr: 9.804e-02, eta: 4 days, 10:47:48, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4809, loss_cls: 4.1899, loss: 4.1899 +2024-12-26 12:53:40,553 - pyskl - INFO - Epoch [14][1600/3746] lr: 9.804e-02, eta: 4 days, 10:46:00, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4883, loss_cls: 4.1481, loss: 4.1481 +2024-12-26 12:54:52,698 - pyskl - INFO - Epoch [14][1700/3746] lr: 9.803e-02, eta: 4 days, 10:44:15, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4941, loss_cls: 4.1409, loss: 4.1409 +2024-12-26 12:56:05,065 - pyskl - INFO - Epoch [14][1800/3746] lr: 9.802e-02, eta: 4 days, 10:42:32, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4922, loss_cls: 4.1561, loss: 4.1561 +2024-12-26 12:57:17,026 - pyskl - INFO - Epoch [14][1900/3746] lr: 9.801e-02, eta: 4 days, 10:40:45, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4850, loss_cls: 4.1763, loss: 4.1763 +2024-12-26 12:58:29,280 - pyskl - INFO - Epoch [14][2000/3746] lr: 9.800e-02, eta: 4 days, 10:39:01, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4903, loss_cls: 4.1266, loss: 4.1266 +2024-12-26 12:59:41,650 - pyskl - INFO - Epoch [14][2100/3746] lr: 9.800e-02, eta: 4 days, 10:37:18, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4878, loss_cls: 4.1432, loss: 4.1432 +2024-12-26 13:00:53,772 - pyskl - INFO - Epoch [14][2200/3746] lr: 9.799e-02, eta: 4 days, 10:35:33, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4897, loss_cls: 4.1591, loss: 4.1591 +2024-12-26 13:02:05,921 - pyskl - INFO - Epoch [14][2300/3746] lr: 9.798e-02, eta: 4 days, 10:33:49, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4992, loss_cls: 4.1096, loss: 4.1096 +2024-12-26 13:03:18,026 - pyskl - INFO - Epoch [14][2400/3746] lr: 9.797e-02, eta: 4 days, 10:32:04, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5025, loss_cls: 4.1208, loss: 4.1208 +2024-12-26 13:04:30,538 - pyskl - INFO - Epoch [14][2500/3746] lr: 9.797e-02, eta: 4 days, 10:30:23, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4966, loss_cls: 4.1303, loss: 4.1303 +2024-12-26 13:05:42,800 - pyskl - INFO - Epoch [14][2600/3746] lr: 9.796e-02, eta: 4 days, 10:28:40, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4936, loss_cls: 4.1228, loss: 4.1228 +2024-12-26 13:06:54,725 - pyskl - INFO - Epoch [14][2700/3746] lr: 9.795e-02, eta: 4 days, 10:26:54, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4841, loss_cls: 4.1722, loss: 4.1722 +2024-12-26 13:08:07,171 - pyskl - INFO - Epoch [14][2800/3746] lr: 9.794e-02, eta: 4 days, 10:25:13, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4942, loss_cls: 4.1623, loss: 4.1623 +2024-12-26 13:09:19,403 - pyskl - INFO - Epoch [14][2900/3746] lr: 9.793e-02, eta: 4 days, 10:23:30, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4973, loss_cls: 4.1347, loss: 4.1347 +2024-12-26 13:10:31,486 - pyskl - INFO - Epoch [14][3000/3746] lr: 9.793e-02, eta: 4 days, 10:21:45, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4956, loss_cls: 4.1373, loss: 4.1373 +2024-12-26 13:11:44,101 - pyskl - INFO - Epoch [14][3100/3746] lr: 9.792e-02, eta: 4 days, 10:20:06, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4939, loss_cls: 4.1269, loss: 4.1269 +2024-12-26 13:12:56,347 - pyskl - INFO - Epoch [14][3200/3746] lr: 9.791e-02, eta: 4 days, 10:18:24, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4798, loss_cls: 4.2029, loss: 4.2029 +2024-12-26 13:14:08,181 - pyskl - INFO - Epoch [14][3300/3746] lr: 9.790e-02, eta: 4 days, 10:16:37, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4934, loss_cls: 4.1220, loss: 4.1220 +2024-12-26 13:15:20,207 - pyskl - INFO - Epoch [14][3400/3746] lr: 9.789e-02, eta: 4 days, 10:14:53, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4908, loss_cls: 4.1554, loss: 4.1554 +2024-12-26 13:16:32,217 - pyskl - INFO - Epoch [14][3500/3746] lr: 9.789e-02, eta: 4 days, 10:13:08, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4856, loss_cls: 4.2011, loss: 4.2011 +2024-12-26 13:17:43,986 - pyskl - INFO - Epoch [14][3600/3746] lr: 9.788e-02, eta: 4 days, 10:11:21, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.4941, loss_cls: 4.1353, loss: 4.1353 +2024-12-26 13:18:55,459 - pyskl - INFO - Epoch [14][3700/3746] lr: 9.787e-02, eta: 4 days, 10:09:32, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.5019, loss_cls: 4.1389, loss: 4.1389 +2024-12-26 13:19:30,777 - pyskl - INFO - Saving checkpoint at 14 epochs +2024-12-26 13:21:29,250 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 13:21:30,206 - pyskl - INFO - +top1_acc 0.1880 +top5_acc 0.4146 +2024-12-26 13:21:30,206 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 13:21:30,245 - pyskl - INFO - +mean_acc 0.1879 +2024-12-26 13:21:30,250 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_12.pth was removed +2024-12-26 13:21:30,548 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_14.pth. +2024-12-26 13:21:30,549 - pyskl - INFO - Best top1_acc is 0.1880 at 14 epoch. +2024-12-26 13:21:30,563 - pyskl - INFO - Epoch(val) [14][309] top1_acc: 0.1880, top5_acc: 0.4146, mean_class_accuracy: 0.1879 +2024-12-26 13:25:11,446 - pyskl - INFO - Epoch [15][100/3746] lr: 9.786e-02, eta: 4 days, 10:25:42, time: 2.209, data_time: 1.486, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5012, loss_cls: 4.0824, loss: 4.0824 +2024-12-26 13:26:23,001 - pyskl - INFO - Epoch [15][200/3746] lr: 9.785e-02, eta: 4 days, 10:23:51, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5097, loss_cls: 4.0634, loss: 4.0634 +2024-12-26 13:27:34,818 - pyskl - INFO - Epoch [15][300/3746] lr: 9.784e-02, eta: 4 days, 10:22:03, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4955, loss_cls: 4.1464, loss: 4.1464 +2024-12-26 13:28:46,831 - pyskl - INFO - Epoch [15][400/3746] lr: 9.783e-02, eta: 4 days, 10:20:17, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4964, loss_cls: 4.1228, loss: 4.1228 +2024-12-26 13:29:58,812 - pyskl - INFO - Epoch [15][500/3746] lr: 9.783e-02, eta: 4 days, 10:18:31, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4791, loss_cls: 4.2096, loss: 4.2096 +2024-12-26 13:31:10,205 - pyskl - INFO - Epoch [15][600/3746] lr: 9.782e-02, eta: 4 days, 10:16:39, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4933, loss_cls: 4.1369, loss: 4.1369 +2024-12-26 13:32:22,179 - pyskl - INFO - Epoch [15][700/3746] lr: 9.781e-02, eta: 4 days, 10:14:53, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4986, loss_cls: 4.1081, loss: 4.1081 +2024-12-26 13:33:34,204 - pyskl - INFO - Epoch [15][800/3746] lr: 9.780e-02, eta: 4 days, 10:13:08, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4863, loss_cls: 4.1499, loss: 4.1499 +2024-12-26 13:34:46,126 - pyskl - INFO - Epoch [15][900/3746] lr: 9.779e-02, eta: 4 days, 10:11:22, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4941, loss_cls: 4.1263, loss: 4.1263 +2024-12-26 13:35:58,672 - pyskl - INFO - Epoch [15][1000/3746] lr: 9.778e-02, eta: 4 days, 10:09:42, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4930, loss_cls: 4.1421, loss: 4.1421 +2024-12-26 13:37:10,805 - pyskl - INFO - Epoch [15][1100/3746] lr: 9.778e-02, eta: 4 days, 10:07:58, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.5019, loss_cls: 4.1182, loss: 4.1182 +2024-12-26 13:38:22,803 - pyskl - INFO - Epoch [15][1200/3746] lr: 9.777e-02, eta: 4 days, 10:06:13, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5047, loss_cls: 4.1218, loss: 4.1218 +2024-12-26 13:39:34,906 - pyskl - INFO - Epoch [15][1300/3746] lr: 9.776e-02, eta: 4 days, 10:04:29, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4961, loss_cls: 4.1295, loss: 4.1295 +2024-12-26 13:40:47,128 - pyskl - INFO - Epoch [15][1400/3746] lr: 9.775e-02, eta: 4 days, 10:02:46, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.4980, loss_cls: 4.1080, loss: 4.1080 +2024-12-26 13:41:59,319 - pyskl - INFO - Epoch [15][1500/3746] lr: 9.774e-02, eta: 4 days, 10:01:03, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4967, loss_cls: 4.1724, loss: 4.1724 +2024-12-26 13:43:11,866 - pyskl - INFO - Epoch [15][1600/3746] lr: 9.773e-02, eta: 4 days, 9:59:23, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4892, loss_cls: 4.1469, loss: 4.1469 +2024-12-26 13:44:23,814 - pyskl - INFO - Epoch [15][1700/3746] lr: 9.773e-02, eta: 4 days, 9:57:38, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4875, loss_cls: 4.1311, loss: 4.1311 +2024-12-26 13:45:36,149 - pyskl - INFO - Epoch [15][1800/3746] lr: 9.772e-02, eta: 4 days, 9:55:57, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4892, loss_cls: 4.1555, loss: 4.1555 +2024-12-26 13:46:48,211 - pyskl - INFO - Epoch [15][1900/3746] lr: 9.771e-02, eta: 4 days, 9:54:13, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4884, loss_cls: 4.1337, loss: 4.1337 +2024-12-26 13:48:00,755 - pyskl - INFO - Epoch [15][2000/3746] lr: 9.770e-02, eta: 4 days, 9:52:34, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4819, loss_cls: 4.1745, loss: 4.1745 +2024-12-26 13:49:13,053 - pyskl - INFO - Epoch [15][2100/3746] lr: 9.769e-02, eta: 4 days, 9:50:53, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5020, loss_cls: 4.1106, loss: 4.1106 +2024-12-26 13:50:25,446 - pyskl - INFO - Epoch [15][2200/3746] lr: 9.768e-02, eta: 4 days, 9:49:13, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4875, loss_cls: 4.1905, loss: 4.1905 +2024-12-26 13:51:37,487 - pyskl - INFO - Epoch [15][2300/3746] lr: 9.768e-02, eta: 4 days, 9:47:29, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4920, loss_cls: 4.1538, loss: 4.1538 +2024-12-26 13:52:49,659 - pyskl - INFO - Epoch [15][2400/3746] lr: 9.767e-02, eta: 4 days, 9:45:47, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4919, loss_cls: 4.1613, loss: 4.1613 +2024-12-26 13:54:01,872 - pyskl - INFO - Epoch [15][2500/3746] lr: 9.766e-02, eta: 4 days, 9:44:05, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4867, loss_cls: 4.1613, loss: 4.1613 +2024-12-26 13:55:13,855 - pyskl - INFO - Epoch [15][2600/3746] lr: 9.765e-02, eta: 4 days, 9:42:22, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4898, loss_cls: 4.1555, loss: 4.1555 +2024-12-26 13:56:25,995 - pyskl - INFO - Epoch [15][2700/3746] lr: 9.764e-02, eta: 4 days, 9:40:40, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4986, loss_cls: 4.1225, loss: 4.1225 +2024-12-26 13:57:37,809 - pyskl - INFO - Epoch [15][2800/3746] lr: 9.763e-02, eta: 4 days, 9:38:55, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5041, loss_cls: 4.1074, loss: 4.1074 +2024-12-26 13:58:50,103 - pyskl - INFO - Epoch [15][2900/3746] lr: 9.763e-02, eta: 4 days, 9:37:14, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5027, loss_cls: 4.0959, loss: 4.0959 +2024-12-26 14:00:02,078 - pyskl - INFO - Epoch [15][3000/3746] lr: 9.762e-02, eta: 4 days, 9:35:31, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4908, loss_cls: 4.1606, loss: 4.1606 +2024-12-26 14:01:14,396 - pyskl - INFO - Epoch [15][3100/3746] lr: 9.761e-02, eta: 4 days, 9:33:51, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5012, loss_cls: 4.1099, loss: 4.1099 +2024-12-26 14:02:26,387 - pyskl - INFO - Epoch [15][3200/3746] lr: 9.760e-02, eta: 4 days, 9:32:08, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4903, loss_cls: 4.1433, loss: 4.1433 +2024-12-26 14:03:38,065 - pyskl - INFO - Epoch [15][3300/3746] lr: 9.759e-02, eta: 4 days, 9:30:22, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4941, loss_cls: 4.1374, loss: 4.1374 +2024-12-26 14:04:49,784 - pyskl - INFO - Epoch [15][3400/3746] lr: 9.758e-02, eta: 4 days, 9:28:37, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4902, loss_cls: 4.1497, loss: 4.1497 +2024-12-26 14:06:02,043 - pyskl - INFO - Epoch [15][3500/3746] lr: 9.757e-02, eta: 4 days, 9:26:57, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4884, loss_cls: 4.1529, loss: 4.1529 +2024-12-26 14:07:14,199 - pyskl - INFO - Epoch [15][3600/3746] lr: 9.757e-02, eta: 4 days, 9:25:16, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4941, loss_cls: 4.1066, loss: 4.1066 +2024-12-26 14:08:25,842 - pyskl - INFO - Epoch [15][3700/3746] lr: 9.756e-02, eta: 4 days, 9:23:31, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4892, loss_cls: 4.1562, loss: 4.1562 +2024-12-26 14:09:01,248 - pyskl - INFO - Saving checkpoint at 15 epochs +2024-12-26 14:10:59,438 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 14:11:00,329 - pyskl - INFO - +top1_acc 0.1896 +top5_acc 0.4179 +2024-12-26 14:11:00,329 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 14:11:00,379 - pyskl - INFO - +mean_acc 0.1895 +2024-12-26 14:11:00,388 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_14.pth was removed +2024-12-26 14:11:00,695 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_15.pth. +2024-12-26 14:11:00,696 - pyskl - INFO - Best top1_acc is 0.1896 at 15 epoch. +2024-12-26 14:11:00,706 - pyskl - INFO - Epoch(val) [15][309] top1_acc: 0.1896, top5_acc: 0.4179, mean_class_accuracy: 0.1895 +2024-12-26 14:14:43,576 - pyskl - INFO - Epoch [16][100/3746] lr: 9.754e-02, eta: 4 days, 9:38:39, time: 2.229, data_time: 1.508, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5042, loss_cls: 4.0886, loss: 4.0886 +2024-12-26 14:15:55,258 - pyskl - INFO - Epoch [16][200/3746] lr: 9.754e-02, eta: 4 days, 9:36:52, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5073, loss_cls: 4.0962, loss: 4.0962 +2024-12-26 14:17:07,002 - pyskl - INFO - Epoch [16][300/3746] lr: 9.753e-02, eta: 4 days, 9:35:06, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4955, loss_cls: 4.1015, loss: 4.1015 +2024-12-26 14:18:18,606 - pyskl - INFO - Epoch [16][400/3746] lr: 9.752e-02, eta: 4 days, 9:33:18, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4939, loss_cls: 4.1279, loss: 4.1279 +2024-12-26 14:19:30,091 - pyskl - INFO - Epoch [16][500/3746] lr: 9.751e-02, eta: 4 days, 9:31:30, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5000, loss_cls: 4.1351, loss: 4.1351 +2024-12-26 14:20:41,881 - pyskl - INFO - Epoch [16][600/3746] lr: 9.750e-02, eta: 4 days, 9:29:45, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4984, loss_cls: 4.1201, loss: 4.1201 +2024-12-26 14:21:54,140 - pyskl - INFO - Epoch [16][700/3746] lr: 9.749e-02, eta: 4 days, 9:28:03, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4908, loss_cls: 4.1428, loss: 4.1428 +2024-12-26 14:23:06,376 - pyskl - INFO - Epoch [16][800/3746] lr: 9.748e-02, eta: 4 days, 9:26:22, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.4969, loss_cls: 4.1045, loss: 4.1045 +2024-12-26 14:24:18,793 - pyskl - INFO - Epoch [16][900/3746] lr: 9.747e-02, eta: 4 days, 9:24:42, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4903, loss_cls: 4.1071, loss: 4.1071 +2024-12-26 14:25:30,705 - pyskl - INFO - Epoch [16][1000/3746] lr: 9.747e-02, eta: 4 days, 9:22:59, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.5009, loss_cls: 4.1338, loss: 4.1338 +2024-12-26 14:26:43,126 - pyskl - INFO - Epoch [16][1100/3746] lr: 9.746e-02, eta: 4 days, 9:21:19, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4875, loss_cls: 4.1290, loss: 4.1290 +2024-12-26 14:27:55,075 - pyskl - INFO - Epoch [16][1200/3746] lr: 9.745e-02, eta: 4 days, 9:19:36, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4913, loss_cls: 4.1653, loss: 4.1653 +2024-12-26 14:29:07,427 - pyskl - INFO - Epoch [16][1300/3746] lr: 9.744e-02, eta: 4 days, 9:17:56, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5070, loss_cls: 4.0817, loss: 4.0817 +2024-12-26 14:30:19,625 - pyskl - INFO - Epoch [16][1400/3746] lr: 9.743e-02, eta: 4 days, 9:16:15, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5014, loss_cls: 4.1113, loss: 4.1113 +2024-12-26 14:31:31,713 - pyskl - INFO - Epoch [16][1500/3746] lr: 9.742e-02, eta: 4 days, 9:14:33, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4866, loss_cls: 4.1734, loss: 4.1734 +2024-12-26 14:32:43,959 - pyskl - INFO - Epoch [16][1600/3746] lr: 9.741e-02, eta: 4 days, 9:12:53, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5086, loss_cls: 4.0889, loss: 4.0889 +2024-12-26 14:33:56,453 - pyskl - INFO - Epoch [16][1700/3746] lr: 9.740e-02, eta: 4 days, 9:11:14, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4823, loss_cls: 4.1665, loss: 4.1665 +2024-12-26 14:35:08,722 - pyskl - INFO - Epoch [16][1800/3746] lr: 9.740e-02, eta: 4 days, 9:09:34, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4908, loss_cls: 4.1138, loss: 4.1138 +2024-12-26 14:36:20,935 - pyskl - INFO - Epoch [16][1900/3746] lr: 9.739e-02, eta: 4 days, 9:07:54, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.4933, loss_cls: 4.1041, loss: 4.1041 +2024-12-26 14:37:33,065 - pyskl - INFO - Epoch [16][2000/3746] lr: 9.738e-02, eta: 4 days, 9:06:13, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4970, loss_cls: 4.1278, loss: 4.1278 +2024-12-26 14:38:45,061 - pyskl - INFO - Epoch [16][2100/3746] lr: 9.737e-02, eta: 4 days, 9:04:31, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4975, loss_cls: 4.1494, loss: 4.1494 +2024-12-26 14:39:57,208 - pyskl - INFO - Epoch [16][2200/3746] lr: 9.736e-02, eta: 4 days, 9:02:50, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5000, loss_cls: 4.1275, loss: 4.1275 +2024-12-26 14:41:09,346 - pyskl - INFO - Epoch [16][2300/3746] lr: 9.735e-02, eta: 4 days, 9:01:09, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4988, loss_cls: 4.1152, loss: 4.1152 +2024-12-26 14:42:21,424 - pyskl - INFO - Epoch [16][2400/3746] lr: 9.734e-02, eta: 4 days, 8:59:28, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4891, loss_cls: 4.1479, loss: 4.1479 +2024-12-26 14:43:33,465 - pyskl - INFO - Epoch [16][2500/3746] lr: 9.733e-02, eta: 4 days, 8:57:47, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5097, loss_cls: 4.0878, loss: 4.0878 +2024-12-26 14:44:45,836 - pyskl - INFO - Epoch [16][2600/3746] lr: 9.732e-02, eta: 4 days, 8:56:09, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4916, loss_cls: 4.1834, loss: 4.1834 +2024-12-26 14:45:58,238 - pyskl - INFO - Epoch [16][2700/3746] lr: 9.731e-02, eta: 4 days, 8:54:31, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.4917, loss_cls: 4.1163, loss: 4.1163 +2024-12-26 14:47:10,360 - pyskl - INFO - Epoch [16][2800/3746] lr: 9.731e-02, eta: 4 days, 8:52:50, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.4930, loss_cls: 4.1136, loss: 4.1136 +2024-12-26 14:48:22,586 - pyskl - INFO - Epoch [16][2900/3746] lr: 9.730e-02, eta: 4 days, 8:51:11, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5017, loss_cls: 4.1033, loss: 4.1033 +2024-12-26 14:49:35,101 - pyskl - INFO - Epoch [16][3000/3746] lr: 9.729e-02, eta: 4 days, 8:49:34, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4936, loss_cls: 4.1185, loss: 4.1185 +2024-12-26 14:50:47,371 - pyskl - INFO - Epoch [16][3100/3746] lr: 9.728e-02, eta: 4 days, 8:47:55, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4941, loss_cls: 4.1459, loss: 4.1459 +2024-12-26 14:51:59,015 - pyskl - INFO - Epoch [16][3200/3746] lr: 9.727e-02, eta: 4 days, 8:46:11, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4942, loss_cls: 4.1281, loss: 4.1281 +2024-12-26 14:53:10,957 - pyskl - INFO - Epoch [16][3300/3746] lr: 9.726e-02, eta: 4 days, 8:44:30, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4942, loss_cls: 4.1476, loss: 4.1476 +2024-12-26 14:54:22,883 - pyskl - INFO - Epoch [16][3400/3746] lr: 9.725e-02, eta: 4 days, 8:42:49, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4928, loss_cls: 4.1499, loss: 4.1499 +2024-12-26 14:55:34,575 - pyskl - INFO - Epoch [16][3500/3746] lr: 9.724e-02, eta: 4 days, 8:41:05, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4905, loss_cls: 4.1600, loss: 4.1600 +2024-12-26 14:56:46,422 - pyskl - INFO - Epoch [16][3600/3746] lr: 9.723e-02, eta: 4 days, 8:39:23, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4861, loss_cls: 4.1616, loss: 4.1616 +2024-12-26 14:57:57,910 - pyskl - INFO - Epoch [16][3700/3746] lr: 9.722e-02, eta: 4 days, 8:37:39, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4870, loss_cls: 4.1569, loss: 4.1569 +2024-12-26 14:58:33,067 - pyskl - INFO - Saving checkpoint at 16 epochs +2024-12-26 15:00:31,089 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 15:00:32,206 - pyskl - INFO - +top1_acc 0.1943 +top5_acc 0.4211 +2024-12-26 15:00:32,206 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 15:00:32,245 - pyskl - INFO - +mean_acc 0.1941 +2024-12-26 15:00:32,249 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_15.pth was removed +2024-12-26 15:00:32,532 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_16.pth. +2024-12-26 15:00:32,533 - pyskl - INFO - Best top1_acc is 0.1943 at 16 epoch. +2024-12-26 15:00:32,547 - pyskl - INFO - Epoch(val) [16][309] top1_acc: 0.1943, top5_acc: 0.4211, mean_class_accuracy: 0.1941 +2024-12-26 15:04:12,561 - pyskl - INFO - Epoch [17][100/3746] lr: 9.721e-02, eta: 4 days, 8:51:12, time: 2.200, data_time: 1.479, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5130, loss_cls: 4.0393, loss: 4.0393 +2024-12-26 15:05:24,397 - pyskl - INFO - Epoch [17][200/3746] lr: 9.720e-02, eta: 4 days, 8:49:29, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4975, loss_cls: 4.1137, loss: 4.1137 +2024-12-26 15:06:35,963 - pyskl - INFO - Epoch [17][300/3746] lr: 9.719e-02, eta: 4 days, 8:47:43, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5055, loss_cls: 4.0634, loss: 4.0634 +2024-12-26 15:07:47,741 - pyskl - INFO - Epoch [17][400/3746] lr: 9.718e-02, eta: 4 days, 8:46:00, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4978, loss_cls: 4.1198, loss: 4.1198 +2024-12-26 15:08:59,472 - pyskl - INFO - Epoch [17][500/3746] lr: 9.717e-02, eta: 4 days, 8:44:16, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4984, loss_cls: 4.1025, loss: 4.1025 +2024-12-26 15:10:10,608 - pyskl - INFO - Epoch [17][600/3746] lr: 9.716e-02, eta: 4 days, 8:42:27, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4959, loss_cls: 4.1218, loss: 4.1218 +2024-12-26 15:11:22,243 - pyskl - INFO - Epoch [17][700/3746] lr: 9.715e-02, eta: 4 days, 8:40:43, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4844, loss_cls: 4.1554, loss: 4.1554 +2024-12-26 15:12:34,019 - pyskl - INFO - Epoch [17][800/3746] lr: 9.714e-02, eta: 4 days, 8:38:59, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5016, loss_cls: 4.1006, loss: 4.1006 +2024-12-26 15:13:46,000 - pyskl - INFO - Epoch [17][900/3746] lr: 9.714e-02, eta: 4 days, 8:37:18, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5059, loss_cls: 4.0876, loss: 4.0876 +2024-12-26 15:14:58,323 - pyskl - INFO - Epoch [17][1000/3746] lr: 9.713e-02, eta: 4 days, 8:35:39, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.4953, loss_cls: 4.1380, loss: 4.1380 +2024-12-26 15:16:10,828 - pyskl - INFO - Epoch [17][1100/3746] lr: 9.712e-02, eta: 4 days, 8:34:02, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5009, loss_cls: 4.0719, loss: 4.0719 +2024-12-26 15:17:23,028 - pyskl - INFO - Epoch [17][1200/3746] lr: 9.711e-02, eta: 4 days, 8:32:23, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4934, loss_cls: 4.1702, loss: 4.1702 +2024-12-26 15:18:35,222 - pyskl - INFO - Epoch [17][1300/3746] lr: 9.710e-02, eta: 4 days, 8:30:43, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4931, loss_cls: 4.1448, loss: 4.1448 +2024-12-26 15:19:47,630 - pyskl - INFO - Epoch [17][1400/3746] lr: 9.709e-02, eta: 4 days, 8:29:06, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4942, loss_cls: 4.1243, loss: 4.1243 +2024-12-26 15:21:00,014 - pyskl - INFO - Epoch [17][1500/3746] lr: 9.708e-02, eta: 4 days, 8:27:28, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4944, loss_cls: 4.1397, loss: 4.1397 +2024-12-26 15:22:11,941 - pyskl - INFO - Epoch [17][1600/3746] lr: 9.707e-02, eta: 4 days, 8:25:47, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5089, loss_cls: 4.0711, loss: 4.0711 +2024-12-26 15:23:24,165 - pyskl - INFO - Epoch [17][1700/3746] lr: 9.706e-02, eta: 4 days, 8:24:08, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.4952, loss_cls: 4.1209, loss: 4.1209 +2024-12-26 15:24:36,275 - pyskl - INFO - Epoch [17][1800/3746] lr: 9.705e-02, eta: 4 days, 8:22:29, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4863, loss_cls: 4.1746, loss: 4.1746 +2024-12-26 15:25:48,379 - pyskl - INFO - Epoch [17][1900/3746] lr: 9.704e-02, eta: 4 days, 8:20:49, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4995, loss_cls: 4.1238, loss: 4.1238 +2024-12-26 15:27:00,454 - pyskl - INFO - Epoch [17][2000/3746] lr: 9.703e-02, eta: 4 days, 8:19:09, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5097, loss_cls: 4.0692, loss: 4.0692 +2024-12-26 15:28:12,660 - pyskl - INFO - Epoch [17][2100/3746] lr: 9.702e-02, eta: 4 days, 8:17:31, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4947, loss_cls: 4.1115, loss: 4.1115 +2024-12-26 15:29:24,931 - pyskl - INFO - Epoch [17][2200/3746] lr: 9.701e-02, eta: 4 days, 8:15:53, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5088, loss_cls: 4.0889, loss: 4.0889 +2024-12-26 15:30:36,911 - pyskl - INFO - Epoch [17][2300/3746] lr: 9.700e-02, eta: 4 days, 8:14:13, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.4964, loss_cls: 4.1014, loss: 4.1014 +2024-12-26 15:31:49,429 - pyskl - INFO - Epoch [17][2400/3746] lr: 9.699e-02, eta: 4 days, 8:12:37, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4956, loss_cls: 4.1589, loss: 4.1589 +2024-12-26 15:33:01,665 - pyskl - INFO - Epoch [17][2500/3746] lr: 9.698e-02, eta: 4 days, 8:10:59, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4903, loss_cls: 4.1423, loss: 4.1423 +2024-12-26 15:34:13,770 - pyskl - INFO - Epoch [17][2600/3746] lr: 9.697e-02, eta: 4 days, 8:09:20, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4873, loss_cls: 4.1767, loss: 4.1767 +2024-12-26 15:35:25,840 - pyskl - INFO - Epoch [17][2700/3746] lr: 9.697e-02, eta: 4 days, 8:07:41, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.4989, loss_cls: 4.0950, loss: 4.0950 +2024-12-26 15:36:37,928 - pyskl - INFO - Epoch [17][2800/3746] lr: 9.696e-02, eta: 4 days, 8:06:02, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4963, loss_cls: 4.1013, loss: 4.1013 +2024-12-26 15:37:49,838 - pyskl - INFO - Epoch [17][2900/3746] lr: 9.695e-02, eta: 4 days, 8:04:22, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4958, loss_cls: 4.1252, loss: 4.1252 +2024-12-26 15:39:02,241 - pyskl - INFO - Epoch [17][3000/3746] lr: 9.694e-02, eta: 4 days, 8:02:45, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4884, loss_cls: 4.1561, loss: 4.1561 +2024-12-26 15:40:14,199 - pyskl - INFO - Epoch [17][3100/3746] lr: 9.693e-02, eta: 4 days, 8:01:06, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4967, loss_cls: 4.1438, loss: 4.1438 +2024-12-26 15:41:26,311 - pyskl - INFO - Epoch [17][3200/3746] lr: 9.692e-02, eta: 4 days, 7:59:27, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.5022, loss_cls: 4.1077, loss: 4.1077 +2024-12-26 15:42:37,741 - pyskl - INFO - Epoch [17][3300/3746] lr: 9.691e-02, eta: 4 days, 7:57:43, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4867, loss_cls: 4.1815, loss: 4.1815 +2024-12-26 15:43:49,452 - pyskl - INFO - Epoch [17][3400/3746] lr: 9.690e-02, eta: 4 days, 7:56:02, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.4972, loss_cls: 4.1382, loss: 4.1382 +2024-12-26 15:45:01,229 - pyskl - INFO - Epoch [17][3500/3746] lr: 9.689e-02, eta: 4 days, 7:54:21, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.5003, loss_cls: 4.1075, loss: 4.1075 +2024-12-26 15:46:13,038 - pyskl - INFO - Epoch [17][3600/3746] lr: 9.688e-02, eta: 4 days, 7:52:41, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4986, loss_cls: 4.1186, loss: 4.1186 +2024-12-26 15:47:24,351 - pyskl - INFO - Epoch [17][3700/3746] lr: 9.687e-02, eta: 4 days, 7:50:57, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5030, loss_cls: 4.0751, loss: 4.0751 +2024-12-26 15:47:59,668 - pyskl - INFO - Saving checkpoint at 17 epochs +2024-12-26 15:49:58,391 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 15:49:59,364 - pyskl - INFO - +top1_acc 0.1918 +top5_acc 0.4250 +2024-12-26 15:49:59,364 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 15:49:59,411 - pyskl - INFO - +mean_acc 0.1916 +2024-12-26 15:49:59,421 - pyskl - INFO - Epoch(val) [17][309] top1_acc: 0.1918, top5_acc: 0.4250, mean_class_accuracy: 0.1916 +2024-12-26 15:53:39,624 - pyskl - INFO - Epoch [18][100/3746] lr: 9.685e-02, eta: 4 days, 8:03:31, time: 2.202, data_time: 1.485, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5094, loss_cls: 4.0413, loss: 4.0413 +2024-12-26 15:54:51,273 - pyskl - INFO - Epoch [18][200/3746] lr: 9.684e-02, eta: 4 days, 8:01:48, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.5017, loss_cls: 4.1166, loss: 4.1166 +2024-12-26 15:56:03,013 - pyskl - INFO - Epoch [18][300/3746] lr: 9.683e-02, eta: 4 days, 8:00:06, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5075, loss_cls: 4.0824, loss: 4.0824 +2024-12-26 15:57:14,618 - pyskl - INFO - Epoch [18][400/3746] lr: 9.683e-02, eta: 4 days, 7:58:23, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5042, loss_cls: 4.1064, loss: 4.1064 +2024-12-26 15:58:26,874 - pyskl - INFO - Epoch [18][500/3746] lr: 9.682e-02, eta: 4 days, 7:56:45, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4864, loss_cls: 4.1567, loss: 4.1567 +2024-12-26 15:59:38,742 - pyskl - INFO - Epoch [18][600/3746] lr: 9.681e-02, eta: 4 days, 7:55:04, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4984, loss_cls: 4.1422, loss: 4.1422 +2024-12-26 16:00:50,549 - pyskl - INFO - Epoch [18][700/3746] lr: 9.680e-02, eta: 4 days, 7:53:23, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4914, loss_cls: 4.1538, loss: 4.1538 +2024-12-26 16:02:02,655 - pyskl - INFO - Epoch [18][800/3746] lr: 9.679e-02, eta: 4 days, 7:51:44, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4941, loss_cls: 4.1234, loss: 4.1234 +2024-12-26 16:03:14,926 - pyskl - INFO - Epoch [18][900/3746] lr: 9.678e-02, eta: 4 days, 7:50:07, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5105, loss_cls: 4.0651, loss: 4.0651 +2024-12-26 16:04:27,388 - pyskl - INFO - Epoch [18][1000/3746] lr: 9.677e-02, eta: 4 days, 7:48:31, time: 0.725, data_time: 0.001, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5091, loss_cls: 4.0514, loss: 4.0514 +2024-12-26 16:05:39,435 - pyskl - INFO - Epoch [18][1100/3746] lr: 9.676e-02, eta: 4 days, 7:46:52, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.4997, loss_cls: 4.0921, loss: 4.0921 +2024-12-26 16:06:51,505 - pyskl - INFO - Epoch [18][1200/3746] lr: 9.675e-02, eta: 4 days, 7:45:13, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4922, loss_cls: 4.1304, loss: 4.1304 +2024-12-26 16:08:03,535 - pyskl - INFO - Epoch [18][1300/3746] lr: 9.674e-02, eta: 4 days, 7:43:34, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4975, loss_cls: 4.1283, loss: 4.1283 +2024-12-26 16:09:15,505 - pyskl - INFO - Epoch [18][1400/3746] lr: 9.673e-02, eta: 4 days, 7:41:54, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4905, loss_cls: 4.1499, loss: 4.1499 +2024-12-26 16:10:27,241 - pyskl - INFO - Epoch [18][1500/3746] lr: 9.672e-02, eta: 4 days, 7:40:13, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4875, loss_cls: 4.1376, loss: 4.1376 +2024-12-26 16:11:39,379 - pyskl - INFO - Epoch [18][1600/3746] lr: 9.671e-02, eta: 4 days, 7:38:35, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5005, loss_cls: 4.1044, loss: 4.1044 +2024-12-26 16:12:51,714 - pyskl - INFO - Epoch [18][1700/3746] lr: 9.670e-02, eta: 4 days, 7:36:59, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5064, loss_cls: 4.0584, loss: 4.0584 +2024-12-26 16:14:03,790 - pyskl - INFO - Epoch [18][1800/3746] lr: 9.669e-02, eta: 4 days, 7:35:21, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5055, loss_cls: 4.1109, loss: 4.1109 +2024-12-26 16:15:16,175 - pyskl - INFO - Epoch [18][1900/3746] lr: 9.668e-02, eta: 4 days, 7:33:45, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4995, loss_cls: 4.1161, loss: 4.1161 +2024-12-26 16:16:28,510 - pyskl - INFO - Epoch [18][2000/3746] lr: 9.667e-02, eta: 4 days, 7:32:09, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4902, loss_cls: 4.1447, loss: 4.1447 +2024-12-26 16:17:40,743 - pyskl - INFO - Epoch [18][2100/3746] lr: 9.666e-02, eta: 4 days, 7:30:32, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4975, loss_cls: 4.1327, loss: 4.1327 +2024-12-26 16:18:52,778 - pyskl - INFO - Epoch [18][2200/3746] lr: 9.665e-02, eta: 4 days, 7:28:54, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5000, loss_cls: 4.1185, loss: 4.1185 +2024-12-26 16:20:04,442 - pyskl - INFO - Epoch [18][2300/3746] lr: 9.664e-02, eta: 4 days, 7:27:13, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4922, loss_cls: 4.1638, loss: 4.1638 +2024-12-26 16:21:16,831 - pyskl - INFO - Epoch [18][2400/3746] lr: 9.663e-02, eta: 4 days, 7:25:37, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4839, loss_cls: 4.1403, loss: 4.1403 +2024-12-26 16:22:28,831 - pyskl - INFO - Epoch [18][2500/3746] lr: 9.662e-02, eta: 4 days, 7:23:59, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4920, loss_cls: 4.1700, loss: 4.1700 +2024-12-26 16:23:41,090 - pyskl - INFO - Epoch [18][2600/3746] lr: 9.661e-02, eta: 4 days, 7:22:23, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5086, loss_cls: 4.0818, loss: 4.0818 +2024-12-26 16:24:53,514 - pyskl - INFO - Epoch [18][2700/3746] lr: 9.660e-02, eta: 4 days, 7:20:48, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5005, loss_cls: 4.1064, loss: 4.1064 +2024-12-26 16:26:05,850 - pyskl - INFO - Epoch [18][2800/3746] lr: 9.659e-02, eta: 4 days, 7:19:12, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5084, loss_cls: 4.0574, loss: 4.0574 +2024-12-26 16:27:18,135 - pyskl - INFO - Epoch [18][2900/3746] lr: 9.658e-02, eta: 4 days, 7:17:36, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4963, loss_cls: 4.1191, loss: 4.1191 +2024-12-26 16:28:30,356 - pyskl - INFO - Epoch [18][3000/3746] lr: 9.657e-02, eta: 4 days, 7:16:00, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4917, loss_cls: 4.1580, loss: 4.1580 +2024-12-26 16:29:42,446 - pyskl - INFO - Epoch [18][3100/3746] lr: 9.656e-02, eta: 4 days, 7:14:23, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5130, loss_cls: 4.0689, loss: 4.0689 +2024-12-26 16:30:54,493 - pyskl - INFO - Epoch [18][3200/3746] lr: 9.654e-02, eta: 4 days, 7:12:45, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5050, loss_cls: 4.0977, loss: 4.0977 +2024-12-26 16:32:06,142 - pyskl - INFO - Epoch [18][3300/3746] lr: 9.653e-02, eta: 4 days, 7:11:05, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4931, loss_cls: 4.1272, loss: 4.1272 +2024-12-26 16:33:17,758 - pyskl - INFO - Epoch [18][3400/3746] lr: 9.652e-02, eta: 4 days, 7:09:25, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5042, loss_cls: 4.1008, loss: 4.1008 +2024-12-26 16:34:29,419 - pyskl - INFO - Epoch [18][3500/3746] lr: 9.651e-02, eta: 4 days, 7:07:45, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4917, loss_cls: 4.1095, loss: 4.1095 +2024-12-26 16:35:40,970 - pyskl - INFO - Epoch [18][3600/3746] lr: 9.650e-02, eta: 4 days, 7:06:04, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4928, loss_cls: 4.1280, loss: 4.1280 +2024-12-26 16:36:52,504 - pyskl - INFO - Epoch [18][3700/3746] lr: 9.649e-02, eta: 4 days, 7:04:23, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5023, loss_cls: 4.1409, loss: 4.1409 +2024-12-26 16:37:27,807 - pyskl - INFO - Saving checkpoint at 18 epochs +2024-12-26 16:39:26,923 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 16:39:27,787 - pyskl - INFO - +top1_acc 0.1890 +top5_acc 0.4094 +2024-12-26 16:39:27,787 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 16:39:27,845 - pyskl - INFO - +mean_acc 0.1887 +2024-12-26 16:39:27,855 - pyskl - INFO - Epoch(val) [18][309] top1_acc: 0.1890, top5_acc: 0.4094, mean_class_accuracy: 0.1887 +2024-12-26 16:43:06,962 - pyskl - INFO - Epoch [19][100/3746] lr: 9.648e-02, eta: 4 days, 7:15:56, time: 2.191, data_time: 1.468, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5042, loss_cls: 4.0720, loss: 4.0720 +2024-12-26 16:44:18,487 - pyskl - INFO - Epoch [19][200/3746] lr: 9.647e-02, eta: 4 days, 7:14:13, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5130, loss_cls: 4.0556, loss: 4.0556 +2024-12-26 16:45:30,259 - pyskl - INFO - Epoch [19][300/3746] lr: 9.646e-02, eta: 4 days, 7:12:33, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5119, loss_cls: 4.0568, loss: 4.0568 +2024-12-26 16:46:41,925 - pyskl - INFO - Epoch [19][400/3746] lr: 9.645e-02, eta: 4 days, 7:10:52, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4997, loss_cls: 4.1170, loss: 4.1170 +2024-12-26 16:47:53,264 - pyskl - INFO - Epoch [19][500/3746] lr: 9.644e-02, eta: 4 days, 7:09:09, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5036, loss_cls: 4.0879, loss: 4.0879 +2024-12-26 16:49:04,830 - pyskl - INFO - Epoch [19][600/3746] lr: 9.643e-02, eta: 4 days, 7:07:28, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5038, loss_cls: 4.0540, loss: 4.0540 +2024-12-26 16:50:16,176 - pyskl - INFO - Epoch [19][700/3746] lr: 9.642e-02, eta: 4 days, 7:05:45, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.4964, loss_cls: 4.1081, loss: 4.1081 +2024-12-26 16:51:28,076 - pyskl - INFO - Epoch [19][800/3746] lr: 9.641e-02, eta: 4 days, 7:04:06, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5009, loss_cls: 4.0959, loss: 4.0959 +2024-12-26 16:52:39,902 - pyskl - INFO - Epoch [19][900/3746] lr: 9.640e-02, eta: 4 days, 7:02:27, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5023, loss_cls: 4.1006, loss: 4.1006 +2024-12-26 16:53:51,772 - pyskl - INFO - Epoch [19][1000/3746] lr: 9.639e-02, eta: 4 days, 7:00:48, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5055, loss_cls: 4.0500, loss: 4.0500 +2024-12-26 16:55:04,101 - pyskl - INFO - Epoch [19][1100/3746] lr: 9.637e-02, eta: 4 days, 6:59:13, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4913, loss_cls: 4.1855, loss: 4.1855 +2024-12-26 16:56:16,350 - pyskl - INFO - Epoch [19][1200/3746] lr: 9.636e-02, eta: 4 days, 6:57:37, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5089, loss_cls: 4.0784, loss: 4.0784 +2024-12-26 16:57:28,877 - pyskl - INFO - Epoch [19][1300/3746] lr: 9.635e-02, eta: 4 days, 6:56:03, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4922, loss_cls: 4.1330, loss: 4.1330 +2024-12-26 16:58:41,170 - pyskl - INFO - Epoch [19][1400/3746] lr: 9.634e-02, eta: 4 days, 6:54:27, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4886, loss_cls: 4.1504, loss: 4.1504 +2024-12-26 16:59:53,365 - pyskl - INFO - Epoch [19][1500/3746] lr: 9.633e-02, eta: 4 days, 6:52:51, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5023, loss_cls: 4.0945, loss: 4.0945 +2024-12-26 17:01:05,648 - pyskl - INFO - Epoch [19][1600/3746] lr: 9.632e-02, eta: 4 days, 6:51:16, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4994, loss_cls: 4.1231, loss: 4.1231 +2024-12-26 17:02:17,879 - pyskl - INFO - Epoch [19][1700/3746] lr: 9.631e-02, eta: 4 days, 6:49:40, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5006, loss_cls: 4.1240, loss: 4.1240 +2024-12-26 17:03:29,986 - pyskl - INFO - Epoch [19][1800/3746] lr: 9.630e-02, eta: 4 days, 6:48:03, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4913, loss_cls: 4.1298, loss: 4.1298 +2024-12-26 17:04:42,269 - pyskl - INFO - Epoch [19][1900/3746] lr: 9.629e-02, eta: 4 days, 6:46:28, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4945, loss_cls: 4.1276, loss: 4.1276 +2024-12-26 17:05:54,438 - pyskl - INFO - Epoch [19][2000/3746] lr: 9.628e-02, eta: 4 days, 6:44:52, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5011, loss_cls: 4.1350, loss: 4.1350 +2024-12-26 17:07:06,505 - pyskl - INFO - Epoch [19][2100/3746] lr: 9.627e-02, eta: 4 days, 6:43:15, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5005, loss_cls: 4.1044, loss: 4.1044 +2024-12-26 17:08:18,796 - pyskl - INFO - Epoch [19][2200/3746] lr: 9.626e-02, eta: 4 days, 6:41:40, time: 0.723, data_time: 0.001, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5011, loss_cls: 4.0968, loss: 4.0968 +2024-12-26 17:09:31,083 - pyskl - INFO - Epoch [19][2300/3746] lr: 9.625e-02, eta: 4 days, 6:40:05, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4950, loss_cls: 4.1432, loss: 4.1432 +2024-12-26 17:10:43,170 - pyskl - INFO - Epoch [19][2400/3746] lr: 9.624e-02, eta: 4 days, 6:38:29, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5022, loss_cls: 4.1093, loss: 4.1093 +2024-12-26 17:11:55,619 - pyskl - INFO - Epoch [19][2500/3746] lr: 9.623e-02, eta: 4 days, 6:36:55, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4995, loss_cls: 4.1347, loss: 4.1347 +2024-12-26 17:13:07,684 - pyskl - INFO - Epoch [19][2600/3746] lr: 9.622e-02, eta: 4 days, 6:35:19, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4933, loss_cls: 4.1394, loss: 4.1394 +2024-12-26 17:14:19,903 - pyskl - INFO - Epoch [19][2700/3746] lr: 9.621e-02, eta: 4 days, 6:33:44, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.4956, loss_cls: 4.0919, loss: 4.0919 +2024-12-26 17:15:32,161 - pyskl - INFO - Epoch [19][2800/3746] lr: 9.620e-02, eta: 4 days, 6:32:09, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.4981, loss_cls: 4.1084, loss: 4.1084 +2024-12-26 17:16:44,574 - pyskl - INFO - Epoch [19][2900/3746] lr: 9.618e-02, eta: 4 days, 6:30:35, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4983, loss_cls: 4.1056, loss: 4.1056 +2024-12-26 17:17:57,087 - pyskl - INFO - Epoch [19][3000/3746] lr: 9.617e-02, eta: 4 days, 6:29:02, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4880, loss_cls: 4.1685, loss: 4.1685 +2024-12-26 17:19:09,026 - pyskl - INFO - Epoch [19][3100/3746] lr: 9.616e-02, eta: 4 days, 6:27:25, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4970, loss_cls: 4.1232, loss: 4.1232 +2024-12-26 17:20:21,060 - pyskl - INFO - Epoch [19][3200/3746] lr: 9.615e-02, eta: 4 days, 6:25:49, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4908, loss_cls: 4.1460, loss: 4.1460 +2024-12-26 17:21:32,606 - pyskl - INFO - Epoch [19][3300/3746] lr: 9.614e-02, eta: 4 days, 6:24:10, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4866, loss_cls: 4.1251, loss: 4.1251 +2024-12-26 17:22:44,460 - pyskl - INFO - Epoch [19][3400/3746] lr: 9.613e-02, eta: 4 days, 6:22:32, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.5034, loss_cls: 4.1165, loss: 4.1165 +2024-12-26 17:23:56,286 - pyskl - INFO - Epoch [19][3500/3746] lr: 9.612e-02, eta: 4 days, 6:20:55, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5080, loss_cls: 4.0770, loss: 4.0770 +2024-12-26 17:25:07,879 - pyskl - INFO - Epoch [19][3600/3746] lr: 9.611e-02, eta: 4 days, 6:19:16, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4970, loss_cls: 4.1244, loss: 4.1244 +2024-12-26 17:26:19,515 - pyskl - INFO - Epoch [19][3700/3746] lr: 9.610e-02, eta: 4 days, 6:17:38, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4983, loss_cls: 4.1331, loss: 4.1331 +2024-12-26 17:26:54,936 - pyskl - INFO - Saving checkpoint at 19 epochs +2024-12-26 17:28:54,890 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 17:28:55,584 - pyskl - INFO - +top1_acc 0.1928 +top5_acc 0.4219 +2024-12-26 17:28:55,584 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 17:28:55,630 - pyskl - INFO - +mean_acc 0.1925 +2024-12-26 17:28:55,641 - pyskl - INFO - Epoch(val) [19][309] top1_acc: 0.1928, top5_acc: 0.4219, mean_class_accuracy: 0.1925 +2024-12-26 17:32:39,832 - pyskl - INFO - Epoch [20][100/3746] lr: 9.608e-02, eta: 4 days, 6:28:57, time: 2.242, data_time: 1.523, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5162, loss_cls: 4.0157, loss: 4.0157 +2024-12-26 17:33:51,781 - pyskl - INFO - Epoch [20][200/3746] lr: 9.607e-02, eta: 4 days, 6:27:20, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5027, loss_cls: 4.0872, loss: 4.0872 +2024-12-26 17:35:03,782 - pyskl - INFO - Epoch [20][300/3746] lr: 9.606e-02, eta: 4 days, 6:25:42, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5030, loss_cls: 4.1134, loss: 4.1134 +2024-12-26 17:36:15,692 - pyskl - INFO - Epoch [20][400/3746] lr: 9.605e-02, eta: 4 days, 6:24:05, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4927, loss_cls: 4.1280, loss: 4.1280 +2024-12-26 17:37:27,237 - pyskl - INFO - Epoch [20][500/3746] lr: 9.604e-02, eta: 4 days, 6:22:25, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5130, loss_cls: 4.0601, loss: 4.0601 +2024-12-26 17:38:38,990 - pyskl - INFO - Epoch [20][600/3746] lr: 9.603e-02, eta: 4 days, 6:20:46, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4938, loss_cls: 4.1216, loss: 4.1216 +2024-12-26 17:39:50,819 - pyskl - INFO - Epoch [20][700/3746] lr: 9.602e-02, eta: 4 days, 6:19:08, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5086, loss_cls: 4.1070, loss: 4.1070 +2024-12-26 17:41:02,693 - pyskl - INFO - Epoch [20][800/3746] lr: 9.601e-02, eta: 4 days, 6:17:31, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5061, loss_cls: 4.0999, loss: 4.0999 +2024-12-26 17:42:14,931 - pyskl - INFO - Epoch [20][900/3746] lr: 9.600e-02, eta: 4 days, 6:15:56, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4997, loss_cls: 4.1070, loss: 4.1070 +2024-12-26 17:43:27,143 - pyskl - INFO - Epoch [20][1000/3746] lr: 9.598e-02, eta: 4 days, 6:14:20, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.4997, loss_cls: 4.1073, loss: 4.1073 +2024-12-26 17:44:39,324 - pyskl - INFO - Epoch [20][1100/3746] lr: 9.597e-02, eta: 4 days, 6:12:45, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.4995, loss_cls: 4.0983, loss: 4.0983 +2024-12-26 17:45:51,660 - pyskl - INFO - Epoch [20][1200/3746] lr: 9.596e-02, eta: 4 days, 6:11:11, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5034, loss_cls: 4.0860, loss: 4.0860 +2024-12-26 17:47:03,836 - pyskl - INFO - Epoch [20][1300/3746] lr: 9.595e-02, eta: 4 days, 6:09:36, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.4995, loss_cls: 4.1095, loss: 4.1095 +2024-12-26 17:48:15,900 - pyskl - INFO - Epoch [20][1400/3746] lr: 9.594e-02, eta: 4 days, 6:08:00, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4998, loss_cls: 4.1037, loss: 4.1037 +2024-12-26 17:49:28,359 - pyskl - INFO - Epoch [20][1500/3746] lr: 9.593e-02, eta: 4 days, 6:06:26, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4988, loss_cls: 4.1082, loss: 4.1082 +2024-12-26 17:50:40,573 - pyskl - INFO - Epoch [20][1600/3746] lr: 9.592e-02, eta: 4 days, 6:04:52, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4947, loss_cls: 4.1178, loss: 4.1178 +2024-12-26 17:51:53,107 - pyskl - INFO - Epoch [20][1700/3746] lr: 9.591e-02, eta: 4 days, 6:03:19, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4984, loss_cls: 4.1352, loss: 4.1352 +2024-12-26 17:53:05,302 - pyskl - INFO - Epoch [20][1800/3746] lr: 9.590e-02, eta: 4 days, 6:01:44, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5014, loss_cls: 4.1041, loss: 4.1041 +2024-12-26 17:54:17,578 - pyskl - INFO - Epoch [20][1900/3746] lr: 9.588e-02, eta: 4 days, 6:00:10, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4953, loss_cls: 4.1182, loss: 4.1182 +2024-12-26 17:55:29,964 - pyskl - INFO - Epoch [20][2000/3746] lr: 9.587e-02, eta: 4 days, 5:58:37, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5003, loss_cls: 4.1151, loss: 4.1151 +2024-12-26 17:56:42,191 - pyskl - INFO - Epoch [20][2100/3746] lr: 9.586e-02, eta: 4 days, 5:57:02, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4969, loss_cls: 4.1271, loss: 4.1271 +2024-12-26 17:57:53,979 - pyskl - INFO - Epoch [20][2200/3746] lr: 9.585e-02, eta: 4 days, 5:55:25, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5066, loss_cls: 4.0917, loss: 4.0917 +2024-12-26 17:59:06,291 - pyskl - INFO - Epoch [20][2300/3746] lr: 9.584e-02, eta: 4 days, 5:53:51, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4989, loss_cls: 4.1018, loss: 4.1018 +2024-12-26 18:00:18,956 - pyskl - INFO - Epoch [20][2400/3746] lr: 9.583e-02, eta: 4 days, 5:52:20, time: 0.727, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4948, loss_cls: 4.1421, loss: 4.1421 +2024-12-26 18:01:31,076 - pyskl - INFO - Epoch [20][2500/3746] lr: 9.582e-02, eta: 4 days, 5:50:45, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5058, loss_cls: 4.0610, loss: 4.0610 +2024-12-26 18:02:43,212 - pyskl - INFO - Epoch [20][2600/3746] lr: 9.581e-02, eta: 4 days, 5:49:10, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5039, loss_cls: 4.1127, loss: 4.1127 +2024-12-26 18:03:55,337 - pyskl - INFO - Epoch [20][2700/3746] lr: 9.580e-02, eta: 4 days, 5:47:36, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2523, top5_acc: 0.4977, loss_cls: 4.1346, loss: 4.1346 +2024-12-26 18:05:07,367 - pyskl - INFO - Epoch [20][2800/3746] lr: 9.578e-02, eta: 4 days, 5:46:00, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.4972, loss_cls: 4.1065, loss: 4.1065 +2024-12-26 18:06:19,634 - pyskl - INFO - Epoch [20][2900/3746] lr: 9.577e-02, eta: 4 days, 5:44:27, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.4953, loss_cls: 4.0934, loss: 4.0934 +2024-12-26 18:07:31,786 - pyskl - INFO - Epoch [20][3000/3746] lr: 9.576e-02, eta: 4 days, 5:42:52, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5048, loss_cls: 4.0855, loss: 4.0855 +2024-12-26 18:08:44,129 - pyskl - INFO - Epoch [20][3100/3746] lr: 9.575e-02, eta: 4 days, 5:41:19, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.4989, loss_cls: 4.0848, loss: 4.0848 +2024-12-26 18:09:56,105 - pyskl - INFO - Epoch [20][3200/3746] lr: 9.574e-02, eta: 4 days, 5:39:44, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5120, loss_cls: 4.0387, loss: 4.0387 +2024-12-26 18:11:07,631 - pyskl - INFO - Epoch [20][3300/3746] lr: 9.573e-02, eta: 4 days, 5:38:06, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.5081, loss_cls: 4.0952, loss: 4.0952 +2024-12-26 18:12:19,165 - pyskl - INFO - Epoch [20][3400/3746] lr: 9.572e-02, eta: 4 days, 5:36:28, time: 0.715, data_time: 0.001, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4930, loss_cls: 4.1323, loss: 4.1323 +2024-12-26 18:13:31,117 - pyskl - INFO - Epoch [20][3500/3746] lr: 9.571e-02, eta: 4 days, 5:34:52, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4958, loss_cls: 4.1116, loss: 4.1116 +2024-12-26 18:14:42,649 - pyskl - INFO - Epoch [20][3600/3746] lr: 9.569e-02, eta: 4 days, 5:33:14, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5017, loss_cls: 4.1099, loss: 4.1099 +2024-12-26 18:15:54,418 - pyskl - INFO - Epoch [20][3700/3746] lr: 9.568e-02, eta: 4 days, 5:31:38, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4920, loss_cls: 4.1563, loss: 4.1563 +2024-12-26 18:16:29,612 - pyskl - INFO - Saving checkpoint at 20 epochs +2024-12-26 18:18:29,546 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 18:18:30,343 - pyskl - INFO - +top1_acc 0.1209 +top5_acc 0.3060 +2024-12-26 18:18:30,344 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 18:18:30,400 - pyskl - INFO - +mean_acc 0.1209 +2024-12-26 18:18:30,413 - pyskl - INFO - Epoch(val) [20][309] top1_acc: 0.1209, top5_acc: 0.3060, mean_class_accuracy: 0.1209 +2024-12-26 18:22:11,370 - pyskl - INFO - Epoch [21][100/3746] lr: 9.567e-02, eta: 4 days, 5:41:51, time: 2.209, data_time: 1.493, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5072, loss_cls: 4.0633, loss: 4.0633 +2024-12-26 18:23:23,203 - pyskl - INFO - Epoch [21][200/3746] lr: 9.565e-02, eta: 4 days, 5:40:14, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5122, loss_cls: 4.0557, loss: 4.0557 +2024-12-26 18:24:35,046 - pyskl - INFO - Epoch [21][300/3746] lr: 9.564e-02, eta: 4 days, 5:38:37, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5039, loss_cls: 4.0684, loss: 4.0684 +2024-12-26 18:25:46,883 - pyskl - INFO - Epoch [21][400/3746] lr: 9.563e-02, eta: 4 days, 5:37:00, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.4978, loss_cls: 4.0682, loss: 4.0682 +2024-12-26 18:26:58,815 - pyskl - INFO - Epoch [21][500/3746] lr: 9.562e-02, eta: 4 days, 5:35:24, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5089, loss_cls: 4.0706, loss: 4.0706 +2024-12-26 18:28:10,731 - pyskl - INFO - Epoch [21][600/3746] lr: 9.561e-02, eta: 4 days, 5:33:48, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5084, loss_cls: 4.0779, loss: 4.0779 +2024-12-26 18:29:22,457 - pyskl - INFO - Epoch [21][700/3746] lr: 9.560e-02, eta: 4 days, 5:32:11, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4952, loss_cls: 4.0959, loss: 4.0959 +2024-12-26 18:30:34,135 - pyskl - INFO - Epoch [21][800/3746] lr: 9.559e-02, eta: 4 days, 5:30:33, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4980, loss_cls: 4.1006, loss: 4.1006 +2024-12-26 18:31:46,269 - pyskl - INFO - Epoch [21][900/3746] lr: 9.557e-02, eta: 4 days, 5:28:59, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5041, loss_cls: 4.0953, loss: 4.0953 +2024-12-26 18:32:58,265 - pyskl - INFO - Epoch [21][1000/3746] lr: 9.556e-02, eta: 4 days, 5:27:23, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4989, loss_cls: 4.0968, loss: 4.0968 +2024-12-26 18:34:10,153 - pyskl - INFO - Epoch [21][1100/3746] lr: 9.555e-02, eta: 4 days, 5:25:47, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5056, loss_cls: 4.0589, loss: 4.0589 +2024-12-26 18:35:22,476 - pyskl - INFO - Epoch [21][1200/3746] lr: 9.554e-02, eta: 4 days, 5:24:14, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5030, loss_cls: 4.0813, loss: 4.0813 +2024-12-26 18:36:34,711 - pyskl - INFO - Epoch [21][1300/3746] lr: 9.553e-02, eta: 4 days, 5:22:40, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5005, loss_cls: 4.0731, loss: 4.0731 +2024-12-26 18:37:47,360 - pyskl - INFO - Epoch [21][1400/3746] lr: 9.552e-02, eta: 4 days, 5:21:09, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4994, loss_cls: 4.1144, loss: 4.1144 +2024-12-26 18:38:59,376 - pyskl - INFO - Epoch [21][1500/3746] lr: 9.551e-02, eta: 4 days, 5:19:34, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5081, loss_cls: 4.1092, loss: 4.1092 +2024-12-26 18:40:11,652 - pyskl - INFO - Epoch [21][1600/3746] lr: 9.549e-02, eta: 4 days, 5:18:01, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.4986, loss_cls: 4.1116, loss: 4.1116 +2024-12-26 18:41:23,936 - pyskl - INFO - Epoch [21][1700/3746] lr: 9.548e-02, eta: 4 days, 5:16:28, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5017, loss_cls: 4.0962, loss: 4.0962 +2024-12-26 18:42:35,989 - pyskl - INFO - Epoch [21][1800/3746] lr: 9.547e-02, eta: 4 days, 5:14:53, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5130, loss_cls: 4.0726, loss: 4.0726 +2024-12-26 18:43:48,361 - pyskl - INFO - Epoch [21][1900/3746] lr: 9.546e-02, eta: 4 days, 5:13:21, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4984, loss_cls: 4.1086, loss: 4.1086 +2024-12-26 18:45:00,390 - pyskl - INFO - Epoch [21][2000/3746] lr: 9.545e-02, eta: 4 days, 5:11:46, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4961, loss_cls: 4.1271, loss: 4.1271 +2024-12-26 18:46:12,702 - pyskl - INFO - Epoch [21][2100/3746] lr: 9.544e-02, eta: 4 days, 5:10:13, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.4991, loss_cls: 4.0807, loss: 4.0807 +2024-12-26 18:47:24,843 - pyskl - INFO - Epoch [21][2200/3746] lr: 9.542e-02, eta: 4 days, 5:08:39, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5008, loss_cls: 4.1286, loss: 4.1286 +2024-12-26 18:48:37,326 - pyskl - INFO - Epoch [21][2300/3746] lr: 9.541e-02, eta: 4 days, 5:07:08, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.4966, loss_cls: 4.1013, loss: 4.1013 +2024-12-26 18:49:49,603 - pyskl - INFO - Epoch [21][2400/3746] lr: 9.540e-02, eta: 4 days, 5:05:35, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.4959, loss_cls: 4.1112, loss: 4.1112 +2024-12-26 18:51:02,163 - pyskl - INFO - Epoch [21][2500/3746] lr: 9.539e-02, eta: 4 days, 5:04:04, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4966, loss_cls: 4.1450, loss: 4.1450 +2024-12-26 18:52:14,431 - pyskl - INFO - Epoch [21][2600/3746] lr: 9.538e-02, eta: 4 days, 5:02:31, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5059, loss_cls: 4.0730, loss: 4.0730 +2024-12-26 18:53:26,589 - pyskl - INFO - Epoch [21][2700/3746] lr: 9.537e-02, eta: 4 days, 5:00:58, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4950, loss_cls: 4.1203, loss: 4.1203 +2024-12-26 18:54:38,593 - pyskl - INFO - Epoch [21][2800/3746] lr: 9.535e-02, eta: 4 days, 4:59:23, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5031, loss_cls: 4.1077, loss: 4.1077 +2024-12-26 18:55:50,677 - pyskl - INFO - Epoch [21][2900/3746] lr: 9.534e-02, eta: 4 days, 4:57:49, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5006, loss_cls: 4.1205, loss: 4.1205 +2024-12-26 18:57:02,995 - pyskl - INFO - Epoch [21][3000/3746] lr: 9.533e-02, eta: 4 days, 4:56:17, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4888, loss_cls: 4.1577, loss: 4.1577 +2024-12-26 18:58:15,057 - pyskl - INFO - Epoch [21][3100/3746] lr: 9.532e-02, eta: 4 days, 4:54:43, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.4991, loss_cls: 4.1226, loss: 4.1226 +2024-12-26 18:59:26,759 - pyskl - INFO - Epoch [21][3200/3746] lr: 9.531e-02, eta: 4 days, 4:53:07, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.5052, loss_cls: 4.1153, loss: 4.1153 +2024-12-26 19:00:38,773 - pyskl - INFO - Epoch [21][3300/3746] lr: 9.529e-02, eta: 4 days, 4:51:33, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5073, loss_cls: 4.0815, loss: 4.0815 +2024-12-26 19:01:50,425 - pyskl - INFO - Epoch [21][3400/3746] lr: 9.528e-02, eta: 4 days, 4:49:57, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4863, loss_cls: 4.1495, loss: 4.1495 +2024-12-26 19:03:02,070 - pyskl - INFO - Epoch [21][3500/3746] lr: 9.527e-02, eta: 4 days, 4:48:21, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.4950, loss_cls: 4.0865, loss: 4.0865 +2024-12-26 19:04:13,840 - pyskl - INFO - Epoch [21][3600/3746] lr: 9.526e-02, eta: 4 days, 4:46:46, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.5036, loss_cls: 4.1094, loss: 4.1094 +2024-12-26 19:05:25,659 - pyskl - INFO - Epoch [21][3700/3746] lr: 9.525e-02, eta: 4 days, 4:45:11, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4958, loss_cls: 4.1335, loss: 4.1335 +2024-12-26 19:06:00,800 - pyskl - INFO - Saving checkpoint at 21 epochs +2024-12-26 19:07:59,558 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 19:08:00,446 - pyskl - INFO - +top1_acc 0.2084 +top5_acc 0.4457 +2024-12-26 19:08:00,446 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 19:08:00,498 - pyskl - INFO - +mean_acc 0.2082 +2024-12-26 19:08:00,503 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_16.pth was removed +2024-12-26 19:08:00,789 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_21.pth. +2024-12-26 19:08:00,789 - pyskl - INFO - Best top1_acc is 0.2084 at 21 epoch. +2024-12-26 19:08:00,799 - pyskl - INFO - Epoch(val) [21][309] top1_acc: 0.2084, top5_acc: 0.4457, mean_class_accuracy: 0.2082 +2024-12-26 19:11:44,736 - pyskl - INFO - Epoch [22][100/3746] lr: 9.523e-02, eta: 4 days, 4:55:03, time: 2.239, data_time: 1.514, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5112, loss_cls: 4.0739, loss: 4.0739 +2024-12-26 19:12:56,495 - pyskl - INFO - Epoch [22][200/3746] lr: 9.522e-02, eta: 4 days, 4:53:26, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5086, loss_cls: 4.0899, loss: 4.0899 +2024-12-26 19:14:08,446 - pyskl - INFO - Epoch [22][300/3746] lr: 9.521e-02, eta: 4 days, 4:51:51, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5086, loss_cls: 4.0561, loss: 4.0561 +2024-12-26 19:15:20,398 - pyskl - INFO - Epoch [22][400/3746] lr: 9.519e-02, eta: 4 days, 4:50:16, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5147, loss_cls: 4.0731, loss: 4.0731 +2024-12-26 19:16:32,212 - pyskl - INFO - Epoch [22][500/3746] lr: 9.518e-02, eta: 4 days, 4:48:41, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5003, loss_cls: 4.0803, loss: 4.0803 +2024-12-26 19:17:43,763 - pyskl - INFO - Epoch [22][600/3746] lr: 9.517e-02, eta: 4 days, 4:47:03, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.5075, loss_cls: 4.0981, loss: 4.0981 +2024-12-26 19:18:55,645 - pyskl - INFO - Epoch [22][700/3746] lr: 9.516e-02, eta: 4 days, 4:45:28, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5075, loss_cls: 4.0625, loss: 4.0625 +2024-12-26 19:20:07,701 - pyskl - INFO - Epoch [22][800/3746] lr: 9.515e-02, eta: 4 days, 4:43:54, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4995, loss_cls: 4.1010, loss: 4.1010 +2024-12-26 19:21:19,876 - pyskl - INFO - Epoch [22][900/3746] lr: 9.513e-02, eta: 4 days, 4:42:21, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5028, loss_cls: 4.1000, loss: 4.1000 +2024-12-26 19:22:32,210 - pyskl - INFO - Epoch [22][1000/3746] lr: 9.512e-02, eta: 4 days, 4:40:48, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.4892, loss_cls: 4.1210, loss: 4.1210 +2024-12-26 19:23:44,333 - pyskl - INFO - Epoch [22][1100/3746] lr: 9.511e-02, eta: 4 days, 4:39:15, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5042, loss_cls: 4.0688, loss: 4.0688 +2024-12-26 19:24:56,566 - pyskl - INFO - Epoch [22][1200/3746] lr: 9.510e-02, eta: 4 days, 4:37:42, time: 0.722, data_time: 0.001, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4980, loss_cls: 4.1110, loss: 4.1110 +2024-12-26 19:26:08,526 - pyskl - INFO - Epoch [22][1300/3746] lr: 9.509e-02, eta: 4 days, 4:36:08, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5036, loss_cls: 4.1030, loss: 4.1030 +2024-12-26 19:27:20,544 - pyskl - INFO - Epoch [22][1400/3746] lr: 9.507e-02, eta: 4 days, 4:34:34, time: 0.720, data_time: 0.001, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5028, loss_cls: 4.0811, loss: 4.0811 +2024-12-26 19:28:32,661 - pyskl - INFO - Epoch [22][1500/3746] lr: 9.506e-02, eta: 4 days, 4:33:00, time: 0.721, data_time: 0.001, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4933, loss_cls: 4.1015, loss: 4.1015 +2024-12-26 19:29:45,010 - pyskl - INFO - Epoch [22][1600/3746] lr: 9.505e-02, eta: 4 days, 4:31:28, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5017, loss_cls: 4.1051, loss: 4.1051 +2024-12-26 19:30:57,390 - pyskl - INFO - Epoch [22][1700/3746] lr: 9.504e-02, eta: 4 days, 4:29:57, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5070, loss_cls: 4.0647, loss: 4.0647 +2024-12-26 19:32:09,742 - pyskl - INFO - Epoch [22][1800/3746] lr: 9.502e-02, eta: 4 days, 4:28:25, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4967, loss_cls: 4.1446, loss: 4.1446 +2024-12-26 19:33:22,010 - pyskl - INFO - Epoch [22][1900/3746] lr: 9.501e-02, eta: 4 days, 4:26:53, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5044, loss_cls: 4.1169, loss: 4.1169 +2024-12-26 19:34:34,368 - pyskl - INFO - Epoch [22][2000/3746] lr: 9.500e-02, eta: 4 days, 4:25:21, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5066, loss_cls: 4.0692, loss: 4.0692 +2024-12-26 19:35:46,479 - pyskl - INFO - Epoch [22][2100/3746] lr: 9.499e-02, eta: 4 days, 4:23:48, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5194, loss_cls: 4.0371, loss: 4.0371 +2024-12-26 19:36:58,468 - pyskl - INFO - Epoch [22][2200/3746] lr: 9.498e-02, eta: 4 days, 4:22:14, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5031, loss_cls: 4.0712, loss: 4.0712 +2024-12-26 19:38:10,506 - pyskl - INFO - Epoch [22][2300/3746] lr: 9.496e-02, eta: 4 days, 4:20:41, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5073, loss_cls: 4.0563, loss: 4.0563 +2024-12-26 19:39:23,153 - pyskl - INFO - Epoch [22][2400/3746] lr: 9.495e-02, eta: 4 days, 4:19:11, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5041, loss_cls: 4.0853, loss: 4.0853 +2024-12-26 19:40:35,383 - pyskl - INFO - Epoch [22][2500/3746] lr: 9.494e-02, eta: 4 days, 4:17:39, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5012, loss_cls: 4.1003, loss: 4.1003 +2024-12-26 19:41:47,857 - pyskl - INFO - Epoch [22][2600/3746] lr: 9.493e-02, eta: 4 days, 4:16:08, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.4961, loss_cls: 4.1503, loss: 4.1503 +2024-12-26 19:43:00,316 - pyskl - INFO - Epoch [22][2700/3746] lr: 9.491e-02, eta: 4 days, 4:14:37, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5047, loss_cls: 4.0695, loss: 4.0695 +2024-12-26 19:44:12,636 - pyskl - INFO - Epoch [22][2800/3746] lr: 9.490e-02, eta: 4 days, 4:13:06, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4977, loss_cls: 4.1362, loss: 4.1362 +2024-12-26 19:45:24,753 - pyskl - INFO - Epoch [22][2900/3746] lr: 9.489e-02, eta: 4 days, 4:11:33, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5153, loss_cls: 4.0501, loss: 4.0501 +2024-12-26 19:46:36,928 - pyskl - INFO - Epoch [22][3000/3746] lr: 9.488e-02, eta: 4 days, 4:10:01, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.5016, loss_cls: 4.1205, loss: 4.1205 +2024-12-26 19:47:48,992 - pyskl - INFO - Epoch [22][3100/3746] lr: 9.487e-02, eta: 4 days, 4:08:28, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5080, loss_cls: 4.0720, loss: 4.0720 +2024-12-26 19:49:00,736 - pyskl - INFO - Epoch [22][3200/3746] lr: 9.485e-02, eta: 4 days, 4:06:53, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4903, loss_cls: 4.1484, loss: 4.1484 +2024-12-26 19:50:12,382 - pyskl - INFO - Epoch [22][3300/3746] lr: 9.484e-02, eta: 4 days, 4:05:18, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.5033, loss_cls: 4.1061, loss: 4.1061 +2024-12-26 19:51:24,196 - pyskl - INFO - Epoch [22][3400/3746] lr: 9.483e-02, eta: 4 days, 4:03:44, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4994, loss_cls: 4.0820, loss: 4.0820 +2024-12-26 19:52:35,924 - pyskl - INFO - Epoch [22][3500/3746] lr: 9.482e-02, eta: 4 days, 4:02:10, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4983, loss_cls: 4.1457, loss: 4.1457 +2024-12-26 19:53:47,294 - pyskl - INFO - Epoch [22][3600/3746] lr: 9.480e-02, eta: 4 days, 4:00:33, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5028, loss_cls: 4.0707, loss: 4.0707 +2024-12-26 19:54:59,619 - pyskl - INFO - Epoch [22][3700/3746] lr: 9.479e-02, eta: 4 days, 3:59:02, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.4988, loss_cls: 4.1053, loss: 4.1053 +2024-12-26 19:55:35,028 - pyskl - INFO - Saving checkpoint at 22 epochs +2024-12-26 19:57:34,508 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 19:57:35,287 - pyskl - INFO - +top1_acc 0.1984 +top5_acc 0.4202 +2024-12-26 19:57:35,287 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 19:57:35,326 - pyskl - INFO - +mean_acc 0.1982 +2024-12-26 19:57:35,342 - pyskl - INFO - Epoch(val) [22][309] top1_acc: 0.1984, top5_acc: 0.4202, mean_class_accuracy: 0.1982 +2024-12-26 20:01:13,559 - pyskl - INFO - Epoch [23][100/3746] lr: 9.477e-02, eta: 4 days, 4:07:43, time: 2.182, data_time: 1.463, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5003, loss_cls: 4.0841, loss: 4.0841 +2024-12-26 20:02:25,485 - pyskl - INFO - Epoch [23][200/3746] lr: 9.476e-02, eta: 4 days, 4:06:09, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5136, loss_cls: 4.0392, loss: 4.0392 +2024-12-26 20:03:37,393 - pyskl - INFO - Epoch [23][300/3746] lr: 9.475e-02, eta: 4 days, 4:04:35, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5061, loss_cls: 4.0808, loss: 4.0808 +2024-12-26 20:04:48,982 - pyskl - INFO - Epoch [23][400/3746] lr: 9.474e-02, eta: 4 days, 4:02:59, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.5042, loss_cls: 4.0820, loss: 4.0820 +2024-12-26 20:06:00,728 - pyskl - INFO - Epoch [23][500/3746] lr: 9.472e-02, eta: 4 days, 4:01:24, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5008, loss_cls: 4.0503, loss: 4.0503 +2024-12-26 20:07:12,453 - pyskl - INFO - Epoch [23][600/3746] lr: 9.471e-02, eta: 4 days, 3:59:49, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.4939, loss_cls: 4.0960, loss: 4.0960 +2024-12-26 20:08:24,849 - pyskl - INFO - Epoch [23][700/3746] lr: 9.470e-02, eta: 4 days, 3:58:18, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5148, loss_cls: 4.0737, loss: 4.0737 +2024-12-26 20:09:37,078 - pyskl - INFO - Epoch [23][800/3746] lr: 9.469e-02, eta: 4 days, 3:56:46, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5084, loss_cls: 4.0458, loss: 4.0458 +2024-12-26 20:10:49,170 - pyskl - INFO - Epoch [23][900/3746] lr: 9.467e-02, eta: 4 days, 3:55:13, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5147, loss_cls: 4.0344, loss: 4.0344 +2024-12-26 20:12:01,295 - pyskl - INFO - Epoch [23][1000/3746] lr: 9.466e-02, eta: 4 days, 3:53:40, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4964, loss_cls: 4.1153, loss: 4.1153 +2024-12-26 20:13:13,414 - pyskl - INFO - Epoch [23][1100/3746] lr: 9.465e-02, eta: 4 days, 3:52:08, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5034, loss_cls: 4.0636, loss: 4.0636 +2024-12-26 20:14:25,963 - pyskl - INFO - Epoch [23][1200/3746] lr: 9.464e-02, eta: 4 days, 3:50:38, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4978, loss_cls: 4.1237, loss: 4.1237 +2024-12-26 20:15:38,158 - pyskl - INFO - Epoch [23][1300/3746] lr: 9.462e-02, eta: 4 days, 3:49:06, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5103, loss_cls: 4.0731, loss: 4.0731 +2024-12-26 20:16:50,333 - pyskl - INFO - Epoch [23][1400/3746] lr: 9.461e-02, eta: 4 days, 3:47:33, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.5045, loss_cls: 4.1018, loss: 4.1018 +2024-12-26 20:18:02,487 - pyskl - INFO - Epoch [23][1500/3746] lr: 9.460e-02, eta: 4 days, 3:46:01, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5039, loss_cls: 4.0803, loss: 4.0803 +2024-12-26 20:19:14,815 - pyskl - INFO - Epoch [23][1600/3746] lr: 9.459e-02, eta: 4 days, 3:44:30, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5027, loss_cls: 4.0955, loss: 4.0955 +2024-12-26 20:20:27,343 - pyskl - INFO - Epoch [23][1700/3746] lr: 9.457e-02, eta: 4 days, 3:43:00, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.5034, loss_cls: 4.0885, loss: 4.0885 +2024-12-26 20:21:39,780 - pyskl - INFO - Epoch [23][1800/3746] lr: 9.456e-02, eta: 4 days, 3:41:30, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5077, loss_cls: 4.0647, loss: 4.0647 +2024-12-26 20:22:51,590 - pyskl - INFO - Epoch [23][1900/3746] lr: 9.455e-02, eta: 4 days, 3:39:56, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4958, loss_cls: 4.0910, loss: 4.0910 +2024-12-26 20:24:03,705 - pyskl - INFO - Epoch [23][2000/3746] lr: 9.453e-02, eta: 4 days, 3:38:24, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5036, loss_cls: 4.0829, loss: 4.0829 +2024-12-26 20:25:16,042 - pyskl - INFO - Epoch [23][2100/3746] lr: 9.452e-02, eta: 4 days, 3:36:53, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5036, loss_cls: 4.0745, loss: 4.0745 +2024-12-26 20:26:28,230 - pyskl - INFO - Epoch [23][2200/3746] lr: 9.451e-02, eta: 4 days, 3:35:21, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5042, loss_cls: 4.0817, loss: 4.0817 +2024-12-26 20:27:40,504 - pyskl - INFO - Epoch [23][2300/3746] lr: 9.450e-02, eta: 4 days, 3:33:50, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5084, loss_cls: 4.0791, loss: 4.0791 +2024-12-26 20:28:52,942 - pyskl - INFO - Epoch [23][2400/3746] lr: 9.448e-02, eta: 4 days, 3:32:20, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5039, loss_cls: 4.0941, loss: 4.0941 +2024-12-26 20:30:05,125 - pyskl - INFO - Epoch [23][2500/3746] lr: 9.447e-02, eta: 4 days, 3:30:48, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5042, loss_cls: 4.0864, loss: 4.0864 +2024-12-26 20:31:17,487 - pyskl - INFO - Epoch [23][2600/3746] lr: 9.446e-02, eta: 4 days, 3:29:18, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.5027, loss_cls: 4.1008, loss: 4.1008 +2024-12-26 20:32:29,948 - pyskl - INFO - Epoch [23][2700/3746] lr: 9.445e-02, eta: 4 days, 3:27:48, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4970, loss_cls: 4.1184, loss: 4.1184 +2024-12-26 20:33:42,335 - pyskl - INFO - Epoch [23][2800/3746] lr: 9.443e-02, eta: 4 days, 3:26:17, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4975, loss_cls: 4.1396, loss: 4.1396 +2024-12-26 20:34:54,754 - pyskl - INFO - Epoch [23][2900/3746] lr: 9.442e-02, eta: 4 days, 3:24:47, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4950, loss_cls: 4.1347, loss: 4.1347 +2024-12-26 20:36:07,406 - pyskl - INFO - Epoch [23][3000/3746] lr: 9.441e-02, eta: 4 days, 3:23:19, time: 0.727, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5038, loss_cls: 4.0942, loss: 4.0942 +2024-12-26 20:37:19,031 - pyskl - INFO - Epoch [23][3100/3746] lr: 9.439e-02, eta: 4 days, 3:21:44, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4919, loss_cls: 4.1426, loss: 4.1426 +2024-12-26 20:38:30,883 - pyskl - INFO - Epoch [23][3200/3746] lr: 9.438e-02, eta: 4 days, 3:20:11, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5033, loss_cls: 4.0888, loss: 4.0888 +2024-12-26 20:39:42,394 - pyskl - INFO - Epoch [23][3300/3746] lr: 9.437e-02, eta: 4 days, 3:18:36, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5006, loss_cls: 4.1016, loss: 4.1016 +2024-12-26 20:40:54,152 - pyskl - INFO - Epoch [23][3400/3746] lr: 9.436e-02, eta: 4 days, 3:17:03, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4886, loss_cls: 4.1417, loss: 4.1417 +2024-12-26 20:42:05,745 - pyskl - INFO - Epoch [23][3500/3746] lr: 9.434e-02, eta: 4 days, 3:15:28, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4903, loss_cls: 4.1437, loss: 4.1437 +2024-12-26 20:43:17,318 - pyskl - INFO - Epoch [23][3600/3746] lr: 9.433e-02, eta: 4 days, 3:13:54, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4998, loss_cls: 4.0921, loss: 4.0921 +2024-12-26 20:44:29,505 - pyskl - INFO - Epoch [23][3700/3746] lr: 9.432e-02, eta: 4 days, 3:12:23, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5075, loss_cls: 4.0961, loss: 4.0961 +2024-12-26 20:45:04,703 - pyskl - INFO - Saving checkpoint at 23 epochs +2024-12-26 20:47:03,043 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 20:47:03,895 - pyskl - INFO - +top1_acc 0.1928 +top5_acc 0.4215 +2024-12-26 20:47:03,896 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 20:47:03,936 - pyskl - INFO - +mean_acc 0.1925 +2024-12-26 20:47:03,945 - pyskl - INFO - Epoch(val) [23][309] top1_acc: 0.1928, top5_acc: 0.4215, mean_class_accuracy: 0.1925 +2024-12-26 20:50:45,421 - pyskl - INFO - Epoch [24][100/3746] lr: 9.430e-02, eta: 4 days, 3:20:50, time: 2.215, data_time: 1.494, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4936, loss_cls: 4.1051, loss: 4.1051 +2024-12-26 20:51:57,073 - pyskl - INFO - Epoch [24][200/3746] lr: 9.428e-02, eta: 4 days, 3:19:16, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5112, loss_cls: 4.0828, loss: 4.0828 +2024-12-26 20:53:08,837 - pyskl - INFO - Epoch [24][300/3746] lr: 9.427e-02, eta: 4 days, 3:17:42, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5083, loss_cls: 4.0675, loss: 4.0675 +2024-12-26 20:54:20,589 - pyskl - INFO - Epoch [24][400/3746] lr: 9.426e-02, eta: 4 days, 3:16:07, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.5041, loss_cls: 4.0947, loss: 4.0947 +2024-12-26 20:55:32,020 - pyskl - INFO - Epoch [24][500/3746] lr: 9.425e-02, eta: 4 days, 3:14:32, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.5025, loss_cls: 4.1036, loss: 4.1036 +2024-12-26 20:56:43,645 - pyskl - INFO - Epoch [24][600/3746] lr: 9.423e-02, eta: 4 days, 3:12:57, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5011, loss_cls: 4.0679, loss: 4.0679 +2024-12-26 20:57:55,123 - pyskl - INFO - Epoch [24][700/3746] lr: 9.422e-02, eta: 4 days, 3:11:22, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5058, loss_cls: 4.0616, loss: 4.0616 +2024-12-26 20:59:06,825 - pyskl - INFO - Epoch [24][800/3746] lr: 9.421e-02, eta: 4 days, 3:09:48, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5006, loss_cls: 4.0915, loss: 4.0915 +2024-12-26 21:00:18,919 - pyskl - INFO - Epoch [24][900/3746] lr: 9.419e-02, eta: 4 days, 3:08:16, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5081, loss_cls: 4.0452, loss: 4.0452 +2024-12-26 21:01:31,015 - pyskl - INFO - Epoch [24][1000/3746] lr: 9.418e-02, eta: 4 days, 3:06:44, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5047, loss_cls: 4.0828, loss: 4.0828 +2024-12-26 21:02:43,431 - pyskl - INFO - Epoch [24][1100/3746] lr: 9.417e-02, eta: 4 days, 3:05:14, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5112, loss_cls: 4.0883, loss: 4.0883 +2024-12-26 21:03:55,772 - pyskl - INFO - Epoch [24][1200/3746] lr: 9.415e-02, eta: 4 days, 3:03:43, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5073, loss_cls: 4.0890, loss: 4.0890 +2024-12-26 21:05:07,525 - pyskl - INFO - Epoch [24][1300/3746] lr: 9.414e-02, eta: 4 days, 3:02:10, time: 0.718, data_time: 0.001, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4975, loss_cls: 4.1034, loss: 4.1034 +2024-12-26 21:06:19,855 - pyskl - INFO - Epoch [24][1400/3746] lr: 9.413e-02, eta: 4 days, 3:00:39, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5064, loss_cls: 4.1034, loss: 4.1034 +2024-12-26 21:07:31,670 - pyskl - INFO - Epoch [24][1500/3746] lr: 9.411e-02, eta: 4 days, 2:59:06, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5092, loss_cls: 4.0635, loss: 4.0635 +2024-12-26 21:08:43,832 - pyskl - INFO - Epoch [24][1600/3746] lr: 9.410e-02, eta: 4 days, 2:57:35, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.4998, loss_cls: 4.0697, loss: 4.0697 +2024-12-26 21:09:56,164 - pyskl - INFO - Epoch [24][1700/3746] lr: 9.409e-02, eta: 4 days, 2:56:05, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5094, loss_cls: 4.0630, loss: 4.0630 +2024-12-26 21:11:08,333 - pyskl - INFO - Epoch [24][1800/3746] lr: 9.407e-02, eta: 4 days, 2:54:33, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5083, loss_cls: 4.0448, loss: 4.0448 +2024-12-26 21:12:20,492 - pyskl - INFO - Epoch [24][1900/3746] lr: 9.406e-02, eta: 4 days, 2:53:02, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5017, loss_cls: 4.0759, loss: 4.0759 +2024-12-26 21:13:33,325 - pyskl - INFO - Epoch [24][2000/3746] lr: 9.405e-02, eta: 4 days, 2:51:35, time: 0.728, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5030, loss_cls: 4.1011, loss: 4.1011 +2024-12-26 21:14:45,468 - pyskl - INFO - Epoch [24][2100/3746] lr: 9.404e-02, eta: 4 days, 2:50:04, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5031, loss_cls: 4.0934, loss: 4.0934 +2024-12-26 21:15:57,560 - pyskl - INFO - Epoch [24][2200/3746] lr: 9.402e-02, eta: 4 days, 2:48:32, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5011, loss_cls: 4.0995, loss: 4.0995 +2024-12-26 21:17:10,057 - pyskl - INFO - Epoch [24][2300/3746] lr: 9.401e-02, eta: 4 days, 2:47:03, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4927, loss_cls: 4.1402, loss: 4.1402 +2024-12-26 21:18:22,230 - pyskl - INFO - Epoch [24][2400/3746] lr: 9.400e-02, eta: 4 days, 2:45:32, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5022, loss_cls: 4.1004, loss: 4.1004 +2024-12-26 21:19:34,662 - pyskl - INFO - Epoch [24][2500/3746] lr: 9.398e-02, eta: 4 days, 2:44:03, time: 0.724, data_time: 0.001, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5125, loss_cls: 4.0413, loss: 4.0413 +2024-12-26 21:20:46,877 - pyskl - INFO - Epoch [24][2600/3746] lr: 9.397e-02, eta: 4 days, 2:42:32, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5022, loss_cls: 4.0989, loss: 4.0989 +2024-12-26 21:21:59,022 - pyskl - INFO - Epoch [24][2700/3746] lr: 9.396e-02, eta: 4 days, 2:41:02, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.4991, loss_cls: 4.1078, loss: 4.1078 +2024-12-26 21:23:11,291 - pyskl - INFO - Epoch [24][2800/3746] lr: 9.394e-02, eta: 4 days, 2:39:31, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4941, loss_cls: 4.1287, loss: 4.1287 +2024-12-26 21:24:23,255 - pyskl - INFO - Epoch [24][2900/3746] lr: 9.393e-02, eta: 4 days, 2:38:00, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5120, loss_cls: 4.0178, loss: 4.0178 +2024-12-26 21:25:35,548 - pyskl - INFO - Epoch [24][3000/3746] lr: 9.392e-02, eta: 4 days, 2:36:30, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.5008, loss_cls: 4.1020, loss: 4.1020 +2024-12-26 21:26:47,539 - pyskl - INFO - Epoch [24][3100/3746] lr: 9.390e-02, eta: 4 days, 2:34:58, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5041, loss_cls: 4.0813, loss: 4.0813 +2024-12-26 21:27:59,085 - pyskl - INFO - Epoch [24][3200/3746] lr: 9.389e-02, eta: 4 days, 2:33:24, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5152, loss_cls: 4.0576, loss: 4.0576 +2024-12-26 21:29:11,050 - pyskl - INFO - Epoch [24][3300/3746] lr: 9.388e-02, eta: 4 days, 2:31:53, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.4967, loss_cls: 4.1040, loss: 4.1040 +2024-12-26 21:30:23,035 - pyskl - INFO - Epoch [24][3400/3746] lr: 9.386e-02, eta: 4 days, 2:30:21, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5002, loss_cls: 4.1046, loss: 4.1046 +2024-12-26 21:31:34,481 - pyskl - INFO - Epoch [24][3500/3746] lr: 9.385e-02, eta: 4 days, 2:28:47, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.4970, loss_cls: 4.1031, loss: 4.1031 +2024-12-26 21:32:46,219 - pyskl - INFO - Epoch [24][3600/3746] lr: 9.384e-02, eta: 4 days, 2:27:15, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.4986, loss_cls: 4.1220, loss: 4.1220 +2024-12-26 21:33:57,860 - pyskl - INFO - Epoch [24][3700/3746] lr: 9.382e-02, eta: 4 days, 2:25:41, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4994, loss_cls: 4.1096, loss: 4.1096 +2024-12-26 21:34:32,992 - pyskl - INFO - Saving checkpoint at 24 epochs +2024-12-26 21:36:32,135 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 21:36:33,091 - pyskl - INFO - +top1_acc 0.1754 +top5_acc 0.4057 +2024-12-26 21:36:33,092 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 21:36:33,146 - pyskl - INFO - +mean_acc 0.1753 +2024-12-26 21:36:33,165 - pyskl - INFO - Epoch(val) [24][309] top1_acc: 0.1754, top5_acc: 0.4057, mean_class_accuracy: 0.1753 +2024-12-26 21:40:13,597 - pyskl - INFO - Epoch [25][100/3746] lr: 9.380e-02, eta: 4 days, 2:33:33, time: 2.204, data_time: 1.482, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5086, loss_cls: 4.0599, loss: 4.0599 +2024-12-26 21:41:25,331 - pyskl - INFO - Epoch [25][200/3746] lr: 9.379e-02, eta: 4 days, 2:32:00, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5153, loss_cls: 4.0272, loss: 4.0272 +2024-12-26 21:42:36,774 - pyskl - INFO - Epoch [25][300/3746] lr: 9.378e-02, eta: 4 days, 2:30:25, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5064, loss_cls: 4.0832, loss: 4.0832 +2024-12-26 21:43:48,420 - pyskl - INFO - Epoch [25][400/3746] lr: 9.376e-02, eta: 4 days, 2:28:51, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5172, loss_cls: 4.0587, loss: 4.0587 +2024-12-26 21:45:00,393 - pyskl - INFO - Epoch [25][500/3746] lr: 9.375e-02, eta: 4 days, 2:27:19, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5116, loss_cls: 4.0379, loss: 4.0379 +2024-12-26 21:46:11,847 - pyskl - INFO - Epoch [25][600/3746] lr: 9.373e-02, eta: 4 days, 2:25:45, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5078, loss_cls: 4.0538, loss: 4.0538 +2024-12-26 21:47:23,083 - pyskl - INFO - Epoch [25][700/3746] lr: 9.372e-02, eta: 4 days, 2:24:09, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4934, loss_cls: 4.1423, loss: 4.1423 +2024-12-26 21:48:34,580 - pyskl - INFO - Epoch [25][800/3746] lr: 9.371e-02, eta: 4 days, 2:22:35, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5036, loss_cls: 4.0993, loss: 4.0993 +2024-12-26 21:49:46,372 - pyskl - INFO - Epoch [25][900/3746] lr: 9.369e-02, eta: 4 days, 2:21:02, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5066, loss_cls: 4.0461, loss: 4.0461 +2024-12-26 21:50:58,464 - pyskl - INFO - Epoch [25][1000/3746] lr: 9.368e-02, eta: 4 days, 2:19:31, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5080, loss_cls: 4.0868, loss: 4.0868 +2024-12-26 21:52:10,944 - pyskl - INFO - Epoch [25][1100/3746] lr: 9.367e-02, eta: 4 days, 2:18:02, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5017, loss_cls: 4.0985, loss: 4.0985 +2024-12-26 21:53:23,118 - pyskl - INFO - Epoch [25][1200/3746] lr: 9.365e-02, eta: 4 days, 2:16:32, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.4989, loss_cls: 4.0931, loss: 4.0931 +2024-12-26 21:54:35,557 - pyskl - INFO - Epoch [25][1300/3746] lr: 9.364e-02, eta: 4 days, 2:15:03, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5027, loss_cls: 4.0810, loss: 4.0810 +2024-12-26 21:55:47,877 - pyskl - INFO - Epoch [25][1400/3746] lr: 9.363e-02, eta: 4 days, 2:13:33, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5100, loss_cls: 4.0624, loss: 4.0624 +2024-12-26 21:56:59,944 - pyskl - INFO - Epoch [25][1500/3746] lr: 9.361e-02, eta: 4 days, 2:12:02, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5120, loss_cls: 4.0393, loss: 4.0393 +2024-12-26 21:58:12,003 - pyskl - INFO - Epoch [25][1600/3746] lr: 9.360e-02, eta: 4 days, 2:10:31, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5067, loss_cls: 4.0996, loss: 4.0996 +2024-12-26 21:59:24,197 - pyskl - INFO - Epoch [25][1700/3746] lr: 9.358e-02, eta: 4 days, 2:09:01, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5027, loss_cls: 4.0875, loss: 4.0875 +2024-12-26 22:00:36,393 - pyskl - INFO - Epoch [25][1800/3746] lr: 9.357e-02, eta: 4 days, 2:07:30, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5172, loss_cls: 4.0234, loss: 4.0234 +2024-12-26 22:01:49,108 - pyskl - INFO - Epoch [25][1900/3746] lr: 9.356e-02, eta: 4 days, 2:06:03, time: 0.727, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5078, loss_cls: 4.0390, loss: 4.0390 +2024-12-26 22:03:00,975 - pyskl - INFO - Epoch [25][2000/3746] lr: 9.354e-02, eta: 4 days, 2:04:31, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5145, loss_cls: 4.0373, loss: 4.0373 +2024-12-26 22:04:13,251 - pyskl - INFO - Epoch [25][2100/3746] lr: 9.353e-02, eta: 4 days, 2:03:01, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4936, loss_cls: 4.1392, loss: 4.1392 +2024-12-26 22:05:25,639 - pyskl - INFO - Epoch [25][2200/3746] lr: 9.352e-02, eta: 4 days, 2:01:32, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5134, loss_cls: 4.0466, loss: 4.0466 +2024-12-26 22:06:37,940 - pyskl - INFO - Epoch [25][2300/3746] lr: 9.350e-02, eta: 4 days, 2:00:03, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5009, loss_cls: 4.0853, loss: 4.0853 +2024-12-26 22:07:50,231 - pyskl - INFO - Epoch [25][2400/3746] lr: 9.349e-02, eta: 4 days, 1:58:33, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5067, loss_cls: 4.0796, loss: 4.0796 +2024-12-26 22:09:02,357 - pyskl - INFO - Epoch [25][2500/3746] lr: 9.347e-02, eta: 4 days, 1:57:03, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5142, loss_cls: 4.0547, loss: 4.0547 +2024-12-26 22:10:14,756 - pyskl - INFO - Epoch [25][2600/3746] lr: 9.346e-02, eta: 4 days, 1:55:34, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4983, loss_cls: 4.1095, loss: 4.1095 +2024-12-26 22:11:26,872 - pyskl - INFO - Epoch [25][2700/3746] lr: 9.345e-02, eta: 4 days, 1:54:04, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4988, loss_cls: 4.1006, loss: 4.1006 +2024-12-26 22:12:39,018 - pyskl - INFO - Epoch [25][2800/3746] lr: 9.343e-02, eta: 4 days, 1:52:34, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5130, loss_cls: 4.0714, loss: 4.0714 +2024-12-26 22:13:51,269 - pyskl - INFO - Epoch [25][2900/3746] lr: 9.342e-02, eta: 4 days, 1:51:05, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5025, loss_cls: 4.1081, loss: 4.1081 +2024-12-26 22:15:03,169 - pyskl - INFO - Epoch [25][3000/3746] lr: 9.341e-02, eta: 4 days, 1:49:33, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5173, loss_cls: 4.0376, loss: 4.0376 +2024-12-26 22:16:14,940 - pyskl - INFO - Epoch [25][3100/3746] lr: 9.339e-02, eta: 4 days, 1:48:01, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5014, loss_cls: 4.0831, loss: 4.0831 +2024-12-26 22:17:26,396 - pyskl - INFO - Epoch [25][3200/3746] lr: 9.338e-02, eta: 4 days, 1:46:28, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5047, loss_cls: 4.1164, loss: 4.1164 +2024-12-26 22:18:38,049 - pyskl - INFO - Epoch [25][3300/3746] lr: 9.336e-02, eta: 4 days, 1:44:56, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.5011, loss_cls: 4.1038, loss: 4.1038 +2024-12-26 22:19:49,706 - pyskl - INFO - Epoch [25][3400/3746] lr: 9.335e-02, eta: 4 days, 1:43:23, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5103, loss_cls: 4.0685, loss: 4.0685 +2024-12-26 22:21:01,240 - pyskl - INFO - Epoch [25][3500/3746] lr: 9.334e-02, eta: 4 days, 1:41:51, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5053, loss_cls: 4.1183, loss: 4.1183 +2024-12-26 22:22:12,966 - pyskl - INFO - Epoch [25][3600/3746] lr: 9.332e-02, eta: 4 days, 1:40:19, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5114, loss_cls: 4.0942, loss: 4.0942 +2024-12-26 22:23:24,596 - pyskl - INFO - Epoch [25][3700/3746] lr: 9.331e-02, eta: 4 days, 1:38:47, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5033, loss_cls: 4.0741, loss: 4.0741 +2024-12-26 22:24:00,030 - pyskl - INFO - Saving checkpoint at 25 epochs +2024-12-26 22:25:59,007 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 22:25:59,897 - pyskl - INFO - +top1_acc 0.2034 +top5_acc 0.4273 +2024-12-26 22:25:59,897 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 22:25:59,942 - pyskl - INFO - +mean_acc 0.2031 +2024-12-26 22:25:59,963 - pyskl - INFO - Epoch(val) [25][309] top1_acc: 0.2034, top5_acc: 0.4273, mean_class_accuracy: 0.2031 +2024-12-26 22:29:42,422 - pyskl - INFO - Epoch [26][100/3746] lr: 9.329e-02, eta: 4 days, 1:46:20, time: 2.224, data_time: 1.509, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5233, loss_cls: 4.0175, loss: 4.0175 +2024-12-26 22:30:54,454 - pyskl - INFO - Epoch [26][200/3746] lr: 9.327e-02, eta: 4 days, 1:44:50, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5094, loss_cls: 4.0341, loss: 4.0341 +2024-12-26 22:32:06,050 - pyskl - INFO - Epoch [26][300/3746] lr: 9.326e-02, eta: 4 days, 1:43:16, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5061, loss_cls: 4.0652, loss: 4.0652 +2024-12-26 22:33:17,823 - pyskl - INFO - Epoch [26][400/3746] lr: 9.325e-02, eta: 4 days, 1:41:44, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5159, loss_cls: 4.0285, loss: 4.0285 +2024-12-26 22:34:29,268 - pyskl - INFO - Epoch [26][500/3746] lr: 9.323e-02, eta: 4 days, 1:40:11, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5258, loss_cls: 4.0086, loss: 4.0086 +2024-12-26 22:35:40,669 - pyskl - INFO - Epoch [26][600/3746] lr: 9.322e-02, eta: 4 days, 1:38:37, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5066, loss_cls: 4.0588, loss: 4.0588 +2024-12-26 22:36:52,290 - pyskl - INFO - Epoch [26][700/3746] lr: 9.320e-02, eta: 4 days, 1:37:04, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5120, loss_cls: 4.0781, loss: 4.0781 +2024-12-26 22:38:04,012 - pyskl - INFO - Epoch [26][800/3746] lr: 9.319e-02, eta: 4 days, 1:35:32, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5108, loss_cls: 4.0476, loss: 4.0476 +2024-12-26 22:39:16,010 - pyskl - INFO - Epoch [26][900/3746] lr: 9.318e-02, eta: 4 days, 1:34:01, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5020, loss_cls: 4.0550, loss: 4.0550 +2024-12-26 22:40:27,934 - pyskl - INFO - Epoch [26][1000/3746] lr: 9.316e-02, eta: 4 days, 1:32:30, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.4989, loss_cls: 4.0926, loss: 4.0926 +2024-12-26 22:41:39,867 - pyskl - INFO - Epoch [26][1100/3746] lr: 9.315e-02, eta: 4 days, 1:30:59, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4977, loss_cls: 4.1018, loss: 4.1018 +2024-12-26 22:42:51,949 - pyskl - INFO - Epoch [26][1200/3746] lr: 9.313e-02, eta: 4 days, 1:29:29, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5130, loss_cls: 4.0187, loss: 4.0187 +2024-12-26 22:44:04,074 - pyskl - INFO - Epoch [26][1300/3746] lr: 9.312e-02, eta: 4 days, 1:27:59, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5023, loss_cls: 4.1025, loss: 4.1025 +2024-12-26 22:45:16,427 - pyskl - INFO - Epoch [26][1400/3746] lr: 9.310e-02, eta: 4 days, 1:26:30, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.4994, loss_cls: 4.0534, loss: 4.0534 +2024-12-26 22:46:28,198 - pyskl - INFO - Epoch [26][1500/3746] lr: 9.309e-02, eta: 4 days, 1:24:58, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5058, loss_cls: 4.0629, loss: 4.0629 +2024-12-26 22:47:40,679 - pyskl - INFO - Epoch [26][1600/3746] lr: 9.308e-02, eta: 4 days, 1:23:30, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4930, loss_cls: 4.1441, loss: 4.1441 +2024-12-26 22:48:52,705 - pyskl - INFO - Epoch [26][1700/3746] lr: 9.306e-02, eta: 4 days, 1:22:00, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5048, loss_cls: 4.0798, loss: 4.0798 +2024-12-26 22:50:04,746 - pyskl - INFO - Epoch [26][1800/3746] lr: 9.305e-02, eta: 4 days, 1:20:29, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.4948, loss_cls: 4.0897, loss: 4.0897 +2024-12-26 22:51:17,035 - pyskl - INFO - Epoch [26][1900/3746] lr: 9.303e-02, eta: 4 days, 1:19:00, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5100, loss_cls: 4.0410, loss: 4.0410 +2024-12-26 22:52:29,476 - pyskl - INFO - Epoch [26][2000/3746] lr: 9.302e-02, eta: 4 days, 1:17:32, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5052, loss_cls: 4.0906, loss: 4.0906 +2024-12-26 22:53:41,341 - pyskl - INFO - Epoch [26][2100/3746] lr: 9.300e-02, eta: 4 days, 1:16:01, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5047, loss_cls: 4.1068, loss: 4.1068 +2024-12-26 22:54:53,371 - pyskl - INFO - Epoch [26][2200/3746] lr: 9.299e-02, eta: 4 days, 1:14:31, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.4983, loss_cls: 4.0880, loss: 4.0880 +2024-12-26 22:56:05,530 - pyskl - INFO - Epoch [26][2300/3746] lr: 9.298e-02, eta: 4 days, 1:13:02, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5083, loss_cls: 4.0709, loss: 4.0709 +2024-12-26 22:57:17,648 - pyskl - INFO - Epoch [26][2400/3746] lr: 9.296e-02, eta: 4 days, 1:11:32, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5069, loss_cls: 4.0747, loss: 4.0747 +2024-12-26 22:58:29,730 - pyskl - INFO - Epoch [26][2500/3746] lr: 9.295e-02, eta: 4 days, 1:10:02, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5106, loss_cls: 4.0349, loss: 4.0349 +2024-12-26 22:59:41,932 - pyskl - INFO - Epoch [26][2600/3746] lr: 9.293e-02, eta: 4 days, 1:08:33, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5150, loss_cls: 4.0395, loss: 4.0395 +2024-12-26 23:00:54,442 - pyskl - INFO - Epoch [26][2700/3746] lr: 9.292e-02, eta: 4 days, 1:07:05, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5114, loss_cls: 4.0479, loss: 4.0479 +2024-12-26 23:02:06,466 - pyskl - INFO - Epoch [26][2800/3746] lr: 9.290e-02, eta: 4 days, 1:05:35, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5130, loss_cls: 4.0425, loss: 4.0425 +2024-12-26 23:03:18,856 - pyskl - INFO - Epoch [26][2900/3746] lr: 9.289e-02, eta: 4 days, 1:04:07, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.4978, loss_cls: 4.1017, loss: 4.1017 +2024-12-26 23:04:30,867 - pyskl - INFO - Epoch [26][3000/3746] lr: 9.288e-02, eta: 4 days, 1:02:37, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.4984, loss_cls: 4.1062, loss: 4.1062 +2024-12-26 23:05:42,637 - pyskl - INFO - Epoch [26][3100/3746] lr: 9.286e-02, eta: 4 days, 1:01:06, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5030, loss_cls: 4.1292, loss: 4.1292 +2024-12-26 23:06:54,352 - pyskl - INFO - Epoch [26][3200/3746] lr: 9.285e-02, eta: 4 days, 0:59:35, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5056, loss_cls: 4.0631, loss: 4.0631 +2024-12-26 23:08:06,023 - pyskl - INFO - Epoch [26][3300/3746] lr: 9.283e-02, eta: 4 days, 0:58:04, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5036, loss_cls: 4.0873, loss: 4.0873 +2024-12-26 23:09:17,766 - pyskl - INFO - Epoch [26][3400/3746] lr: 9.282e-02, eta: 4 days, 0:56:32, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5019, loss_cls: 4.0909, loss: 4.0909 +2024-12-26 23:10:29,628 - pyskl - INFO - Epoch [26][3500/3746] lr: 9.280e-02, eta: 4 days, 0:55:02, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5102, loss_cls: 4.0544, loss: 4.0544 +2024-12-26 23:11:41,884 - pyskl - INFO - Epoch [26][3600/3746] lr: 9.279e-02, eta: 4 days, 0:53:33, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5059, loss_cls: 4.0837, loss: 4.0837 +2024-12-26 23:12:54,367 - pyskl - INFO - Epoch [26][3700/3746] lr: 9.278e-02, eta: 4 days, 0:52:06, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4978, loss_cls: 4.1196, loss: 4.1196 +2024-12-26 23:13:29,967 - pyskl - INFO - Saving checkpoint at 26 epochs +2024-12-26 23:15:29,761 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-26 23:15:30,610 - pyskl - INFO - +top1_acc 0.1737 +top5_acc 0.3892 +2024-12-26 23:15:30,610 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-26 23:15:30,659 - pyskl - INFO - +mean_acc 0.1735 +2024-12-26 23:15:30,676 - pyskl - INFO - Epoch(val) [26][309] top1_acc: 0.1737, top5_acc: 0.3892, mean_class_accuracy: 0.1735 +2024-12-26 23:19:09,828 - pyskl - INFO - Epoch [27][100/3746] lr: 9.275e-02, eta: 4 days, 0:58:58, time: 2.191, data_time: 1.470, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5106, loss_cls: 4.0587, loss: 4.0587 +2024-12-26 23:20:21,684 - pyskl - INFO - Epoch [27][200/3746] lr: 9.274e-02, eta: 4 days, 0:57:27, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5167, loss_cls: 4.0157, loss: 4.0157 +2024-12-26 23:21:33,667 - pyskl - INFO - Epoch [27][300/3746] lr: 9.272e-02, eta: 4 days, 0:55:57, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5064, loss_cls: 4.0943, loss: 4.0943 +2024-12-26 23:22:45,696 - pyskl - INFO - Epoch [27][400/3746] lr: 9.271e-02, eta: 4 days, 0:54:27, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5094, loss_cls: 4.0781, loss: 4.0781 +2024-12-26 23:23:57,200 - pyskl - INFO - Epoch [27][500/3746] lr: 9.270e-02, eta: 4 days, 0:52:54, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5148, loss_cls: 4.0591, loss: 4.0591 +2024-12-26 23:25:09,175 - pyskl - INFO - Epoch [27][600/3746] lr: 9.268e-02, eta: 4 days, 0:51:24, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5050, loss_cls: 4.0857, loss: 4.0857 +2024-12-26 23:26:21,722 - pyskl - INFO - Epoch [27][700/3746] lr: 9.267e-02, eta: 4 days, 0:49:56, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5261, loss_cls: 4.0042, loss: 4.0042 +2024-12-26 23:27:33,840 - pyskl - INFO - Epoch [27][800/3746] lr: 9.265e-02, eta: 4 days, 0:48:27, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5047, loss_cls: 4.0663, loss: 4.0663 +2024-12-26 23:28:46,092 - pyskl - INFO - Epoch [27][900/3746] lr: 9.264e-02, eta: 4 days, 0:46:58, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.5027, loss_cls: 4.0995, loss: 4.0995 +2024-12-26 23:29:58,178 - pyskl - INFO - Epoch [27][1000/3746] lr: 9.262e-02, eta: 4 days, 0:45:28, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5050, loss_cls: 4.0738, loss: 4.0738 +2024-12-26 23:31:10,250 - pyskl - INFO - Epoch [27][1100/3746] lr: 9.261e-02, eta: 4 days, 0:43:59, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5102, loss_cls: 4.0469, loss: 4.0469 +2024-12-26 23:32:22,611 - pyskl - INFO - Epoch [27][1200/3746] lr: 9.259e-02, eta: 4 days, 0:42:30, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5094, loss_cls: 4.0658, loss: 4.0658 +2024-12-26 23:33:34,821 - pyskl - INFO - Epoch [27][1300/3746] lr: 9.258e-02, eta: 4 days, 0:41:02, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5153, loss_cls: 4.0353, loss: 4.0353 +2024-12-26 23:34:47,035 - pyskl - INFO - Epoch [27][1400/3746] lr: 9.256e-02, eta: 4 days, 0:39:33, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5230, loss_cls: 3.9830, loss: 3.9830 +2024-12-26 23:35:59,348 - pyskl - INFO - Epoch [27][1500/3746] lr: 9.255e-02, eta: 4 days, 0:38:04, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5150, loss_cls: 4.0492, loss: 4.0492 +2024-12-26 23:37:11,717 - pyskl - INFO - Epoch [27][1600/3746] lr: 9.253e-02, eta: 4 days, 0:36:36, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5036, loss_cls: 4.0963, loss: 4.0963 +2024-12-26 23:38:24,179 - pyskl - INFO - Epoch [27][1700/3746] lr: 9.252e-02, eta: 4 days, 0:35:09, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5058, loss_cls: 4.0666, loss: 4.0666 +2024-12-26 23:39:36,571 - pyskl - INFO - Epoch [27][1800/3746] lr: 9.251e-02, eta: 4 days, 0:33:41, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5105, loss_cls: 4.0572, loss: 4.0572 +2024-12-26 23:40:48,855 - pyskl - INFO - Epoch [27][1900/3746] lr: 9.249e-02, eta: 4 days, 0:32:12, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5125, loss_cls: 4.0359, loss: 4.0359 +2024-12-26 23:42:01,456 - pyskl - INFO - Epoch [27][2000/3746] lr: 9.248e-02, eta: 4 days, 0:30:45, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5072, loss_cls: 4.0582, loss: 4.0582 +2024-12-26 23:43:13,993 - pyskl - INFO - Epoch [27][2100/3746] lr: 9.246e-02, eta: 4 days, 0:29:18, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5092, loss_cls: 4.0389, loss: 4.0389 +2024-12-26 23:44:26,327 - pyskl - INFO - Epoch [27][2200/3746] lr: 9.245e-02, eta: 4 days, 0:27:50, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.4952, loss_cls: 4.1132, loss: 4.1132 +2024-12-26 23:45:38,506 - pyskl - INFO - Epoch [27][2300/3746] lr: 9.243e-02, eta: 4 days, 0:26:21, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5056, loss_cls: 4.0920, loss: 4.0920 +2024-12-26 23:46:50,805 - pyskl - INFO - Epoch [27][2400/3746] lr: 9.242e-02, eta: 4 days, 0:24:53, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5080, loss_cls: 4.0520, loss: 4.0520 +2024-12-26 23:48:02,783 - pyskl - INFO - Epoch [27][2500/3746] lr: 9.240e-02, eta: 4 days, 0:23:24, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5086, loss_cls: 4.1041, loss: 4.1041 +2024-12-26 23:49:15,284 - pyskl - INFO - Epoch [27][2600/3746] lr: 9.239e-02, eta: 4 days, 0:21:57, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5078, loss_cls: 4.0672, loss: 4.0672 +2024-12-26 23:50:27,790 - pyskl - INFO - Epoch [27][2700/3746] lr: 9.237e-02, eta: 4 days, 0:20:29, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5094, loss_cls: 4.0503, loss: 4.0503 +2024-12-26 23:51:40,170 - pyskl - INFO - Epoch [27][2800/3746] lr: 9.236e-02, eta: 4 days, 0:19:02, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5092, loss_cls: 4.0823, loss: 4.0823 +2024-12-26 23:52:52,335 - pyskl - INFO - Epoch [27][2900/3746] lr: 9.234e-02, eta: 4 days, 0:17:33, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5039, loss_cls: 4.0787, loss: 4.0787 +2024-12-26 23:54:04,076 - pyskl - INFO - Epoch [27][3000/3746] lr: 9.233e-02, eta: 4 days, 0:16:03, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5055, loss_cls: 4.0752, loss: 4.0752 +2024-12-26 23:55:15,654 - pyskl - INFO - Epoch [27][3100/3746] lr: 9.231e-02, eta: 4 days, 0:14:31, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5080, loss_cls: 4.0715, loss: 4.0715 +2024-12-26 23:56:27,370 - pyskl - INFO - Epoch [27][3200/3746] lr: 9.230e-02, eta: 4 days, 0:13:01, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5028, loss_cls: 4.1280, loss: 4.1280 +2024-12-26 23:57:39,124 - pyskl - INFO - Epoch [27][3300/3746] lr: 9.228e-02, eta: 4 days, 0:11:30, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5102, loss_cls: 4.0746, loss: 4.0746 +2024-12-26 23:58:50,782 - pyskl - INFO - Epoch [27][3400/3746] lr: 9.227e-02, eta: 4 days, 0:10:00, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5042, loss_cls: 4.0736, loss: 4.0736 +2024-12-27 00:00:02,635 - pyskl - INFO - Epoch [27][3500/3746] lr: 9.225e-02, eta: 4 days, 0:08:30, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4977, loss_cls: 4.1237, loss: 4.1237 +2024-12-27 00:01:14,400 - pyskl - INFO - Epoch [27][3600/3746] lr: 9.224e-02, eta: 4 days, 0:07:00, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4972, loss_cls: 4.1169, loss: 4.1169 +2024-12-27 00:02:25,935 - pyskl - INFO - Epoch [27][3700/3746] lr: 9.222e-02, eta: 4 days, 0:05:28, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5081, loss_cls: 4.0790, loss: 4.0790 +2024-12-27 00:03:01,077 - pyskl - INFO - Saving checkpoint at 27 epochs +2024-12-27 00:05:00,358 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 00:05:01,103 - pyskl - INFO - +top1_acc 0.1859 +top5_acc 0.4166 +2024-12-27 00:05:01,104 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 00:05:01,172 - pyskl - INFO - +mean_acc 0.1858 +2024-12-27 00:05:01,189 - pyskl - INFO - Epoch(val) [27][309] top1_acc: 0.1859, top5_acc: 0.4166, mean_class_accuracy: 0.1858 +2024-12-27 00:08:41,046 - pyskl - INFO - Epoch [28][100/3746] lr: 9.220e-02, eta: 4 days, 0:12:00, time: 2.198, data_time: 1.481, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5134, loss_cls: 4.0116, loss: 4.0116 +2024-12-27 00:09:53,130 - pyskl - INFO - Epoch [28][200/3746] lr: 9.219e-02, eta: 4 days, 0:10:31, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5183, loss_cls: 4.0508, loss: 4.0508 +2024-12-27 00:11:04,781 - pyskl - INFO - Epoch [28][300/3746] lr: 9.217e-02, eta: 4 days, 0:09:00, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5172, loss_cls: 4.0414, loss: 4.0414 +2024-12-27 00:12:16,597 - pyskl - INFO - Epoch [28][400/3746] lr: 9.216e-02, eta: 4 days, 0:07:30, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5073, loss_cls: 4.0602, loss: 4.0602 +2024-12-27 00:13:28,463 - pyskl - INFO - Epoch [28][500/3746] lr: 9.214e-02, eta: 4 days, 0:05:59, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5081, loss_cls: 4.0457, loss: 4.0457 +2024-12-27 00:14:40,394 - pyskl - INFO - Epoch [28][600/3746] lr: 9.213e-02, eta: 4 days, 0:04:30, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4963, loss_cls: 4.1123, loss: 4.1123 +2024-12-27 00:15:51,957 - pyskl - INFO - Epoch [28][700/3746] lr: 9.211e-02, eta: 4 days, 0:02:58, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5166, loss_cls: 4.0237, loss: 4.0237 +2024-12-27 00:17:03,437 - pyskl - INFO - Epoch [28][800/3746] lr: 9.210e-02, eta: 4 days, 0:01:26, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5027, loss_cls: 4.0926, loss: 4.0926 +2024-12-27 00:18:15,202 - pyskl - INFO - Epoch [28][900/3746] lr: 9.208e-02, eta: 3 days, 23:59:56, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5053, loss_cls: 4.0845, loss: 4.0845 +2024-12-27 00:19:27,292 - pyskl - INFO - Epoch [28][1000/3746] lr: 9.207e-02, eta: 3 days, 23:58:27, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.5088, loss_cls: 4.1127, loss: 4.1127 +2024-12-27 00:20:39,305 - pyskl - INFO - Epoch [28][1100/3746] lr: 9.205e-02, eta: 3 days, 23:56:58, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.4955, loss_cls: 4.0856, loss: 4.0856 +2024-12-27 00:21:51,341 - pyskl - INFO - Epoch [28][1200/3746] lr: 9.204e-02, eta: 3 days, 23:55:29, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.5047, loss_cls: 4.0852, loss: 4.0852 +2024-12-27 00:23:03,670 - pyskl - INFO - Epoch [28][1300/3746] lr: 9.202e-02, eta: 3 days, 23:54:01, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4969, loss_cls: 4.0885, loss: 4.0885 +2024-12-27 00:24:15,768 - pyskl - INFO - Epoch [28][1400/3746] lr: 9.201e-02, eta: 3 days, 23:52:32, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5055, loss_cls: 4.0631, loss: 4.0631 +2024-12-27 00:25:27,753 - pyskl - INFO - Epoch [28][1500/3746] lr: 9.199e-02, eta: 3 days, 23:51:03, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5027, loss_cls: 4.0902, loss: 4.0902 +2024-12-27 00:26:39,899 - pyskl - INFO - Epoch [28][1600/3746] lr: 9.198e-02, eta: 3 days, 23:49:34, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5052, loss_cls: 4.0701, loss: 4.0701 +2024-12-27 00:27:52,605 - pyskl - INFO - Epoch [28][1700/3746] lr: 9.196e-02, eta: 3 days, 23:48:08, time: 0.727, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5114, loss_cls: 4.0832, loss: 4.0832 +2024-12-27 00:29:05,135 - pyskl - INFO - Epoch [28][1800/3746] lr: 9.194e-02, eta: 3 days, 23:46:42, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5150, loss_cls: 4.0470, loss: 4.0470 +2024-12-27 00:30:17,595 - pyskl - INFO - Epoch [28][1900/3746] lr: 9.193e-02, eta: 3 days, 23:45:15, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5144, loss_cls: 4.0366, loss: 4.0366 +2024-12-27 00:31:30,001 - pyskl - INFO - Epoch [28][2000/3746] lr: 9.191e-02, eta: 3 days, 23:43:47, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5052, loss_cls: 4.0736, loss: 4.0736 +2024-12-27 00:32:42,106 - pyskl - INFO - Epoch [28][2100/3746] lr: 9.190e-02, eta: 3 days, 23:42:19, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5044, loss_cls: 4.0794, loss: 4.0794 +2024-12-27 00:33:54,311 - pyskl - INFO - Epoch [28][2200/3746] lr: 9.188e-02, eta: 3 days, 23:40:51, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4998, loss_cls: 4.1012, loss: 4.1012 +2024-12-27 00:35:06,710 - pyskl - INFO - Epoch [28][2300/3746] lr: 9.187e-02, eta: 3 days, 23:39:24, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5169, loss_cls: 4.0419, loss: 4.0419 +2024-12-27 00:36:19,091 - pyskl - INFO - Epoch [28][2400/3746] lr: 9.185e-02, eta: 3 days, 23:37:56, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5003, loss_cls: 4.1302, loss: 4.1302 +2024-12-27 00:37:31,561 - pyskl - INFO - Epoch [28][2500/3746] lr: 9.184e-02, eta: 3 days, 23:36:30, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4939, loss_cls: 4.1251, loss: 4.1251 +2024-12-27 00:38:43,911 - pyskl - INFO - Epoch [28][2600/3746] lr: 9.182e-02, eta: 3 days, 23:35:02, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5252, loss_cls: 4.0352, loss: 4.0352 +2024-12-27 00:39:56,151 - pyskl - INFO - Epoch [28][2700/3746] lr: 9.181e-02, eta: 3 days, 23:33:35, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5106, loss_cls: 4.0285, loss: 4.0285 +2024-12-27 00:41:08,589 - pyskl - INFO - Epoch [28][2800/3746] lr: 9.179e-02, eta: 3 days, 23:32:08, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4956, loss_cls: 4.1040, loss: 4.1040 +2024-12-27 00:42:20,936 - pyskl - INFO - Epoch [28][2900/3746] lr: 9.178e-02, eta: 3 days, 23:30:41, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5095, loss_cls: 4.1101, loss: 4.1101 +2024-12-27 00:43:32,640 - pyskl - INFO - Epoch [28][3000/3746] lr: 9.176e-02, eta: 3 days, 23:29:11, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5052, loss_cls: 4.0672, loss: 4.0672 +2024-12-27 00:44:44,442 - pyskl - INFO - Epoch [28][3100/3746] lr: 9.175e-02, eta: 3 days, 23:27:41, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4986, loss_cls: 4.0943, loss: 4.0943 +2024-12-27 00:45:56,043 - pyskl - INFO - Epoch [28][3200/3746] lr: 9.173e-02, eta: 3 days, 23:26:11, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5056, loss_cls: 4.0650, loss: 4.0650 +2024-12-27 00:47:07,443 - pyskl - INFO - Epoch [28][3300/3746] lr: 9.172e-02, eta: 3 days, 23:24:39, time: 0.714, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5164, loss_cls: 4.0275, loss: 4.0275 +2024-12-27 00:48:19,070 - pyskl - INFO - Epoch [28][3400/3746] lr: 9.170e-02, eta: 3 days, 23:23:09, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5130, loss_cls: 4.0603, loss: 4.0603 +2024-12-27 00:49:30,871 - pyskl - INFO - Epoch [28][3500/3746] lr: 9.168e-02, eta: 3 days, 23:21:40, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5086, loss_cls: 4.0587, loss: 4.0587 +2024-12-27 00:50:42,811 - pyskl - INFO - Epoch [28][3600/3746] lr: 9.167e-02, eta: 3 days, 23:20:11, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5153, loss_cls: 4.0350, loss: 4.0350 +2024-12-27 00:51:54,557 - pyskl - INFO - Epoch [28][3700/3746] lr: 9.165e-02, eta: 3 days, 23:18:41, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.5045, loss_cls: 4.0883, loss: 4.0883 +2024-12-27 00:52:29,641 - pyskl - INFO - Saving checkpoint at 28 epochs +2024-12-27 00:54:29,087 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 00:54:29,826 - pyskl - INFO - +top1_acc 0.1849 +top5_acc 0.4082 +2024-12-27 00:54:29,826 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 00:54:29,871 - pyskl - INFO - +mean_acc 0.1848 +2024-12-27 00:54:29,883 - pyskl - INFO - Epoch(val) [28][309] top1_acc: 0.1849, top5_acc: 0.4082, mean_class_accuracy: 0.1848 +2024-12-27 00:58:13,325 - pyskl - INFO - Epoch [29][100/3746] lr: 9.163e-02, eta: 3 days, 23:25:07, time: 2.234, data_time: 1.518, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5247, loss_cls: 3.9807, loss: 3.9807 +2024-12-27 00:59:25,017 - pyskl - INFO - Epoch [29][200/3746] lr: 9.162e-02, eta: 3 days, 23:23:37, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5116, loss_cls: 4.0402, loss: 4.0402 +2024-12-27 01:00:36,514 - pyskl - INFO - Epoch [29][300/3746] lr: 9.160e-02, eta: 3 days, 23:22:06, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5144, loss_cls: 4.0048, loss: 4.0048 +2024-12-27 01:01:48,516 - pyskl - INFO - Epoch [29][400/3746] lr: 9.158e-02, eta: 3 days, 23:20:37, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5077, loss_cls: 4.0529, loss: 4.0529 +2024-12-27 01:03:00,066 - pyskl - INFO - Epoch [29][500/3746] lr: 9.157e-02, eta: 3 days, 23:19:06, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5033, loss_cls: 4.0796, loss: 4.0796 +2024-12-27 01:04:11,661 - pyskl - INFO - Epoch [29][600/3746] lr: 9.155e-02, eta: 3 days, 23:17:35, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5200, loss_cls: 4.0141, loss: 4.0141 +2024-12-27 01:05:23,778 - pyskl - INFO - Epoch [29][700/3746] lr: 9.154e-02, eta: 3 days, 23:16:07, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5128, loss_cls: 4.0443, loss: 4.0443 +2024-12-27 01:06:35,407 - pyskl - INFO - Epoch [29][800/3746] lr: 9.152e-02, eta: 3 days, 23:14:36, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.4992, loss_cls: 4.1008, loss: 4.1008 +2024-12-27 01:07:47,280 - pyskl - INFO - Epoch [29][900/3746] lr: 9.151e-02, eta: 3 days, 23:13:07, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5041, loss_cls: 4.0719, loss: 4.0719 +2024-12-27 01:08:59,706 - pyskl - INFO - Epoch [29][1000/3746] lr: 9.149e-02, eta: 3 days, 23:11:40, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5080, loss_cls: 4.0650, loss: 4.0650 +2024-12-27 01:10:12,351 - pyskl - INFO - Epoch [29][1100/3746] lr: 9.148e-02, eta: 3 days, 23:10:14, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5114, loss_cls: 4.0647, loss: 4.0647 +2024-12-27 01:11:24,408 - pyskl - INFO - Epoch [29][1200/3746] lr: 9.146e-02, eta: 3 days, 23:08:46, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5080, loss_cls: 4.0458, loss: 4.0458 +2024-12-27 01:12:36,495 - pyskl - INFO - Epoch [29][1300/3746] lr: 9.144e-02, eta: 3 days, 23:07:18, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5086, loss_cls: 4.0511, loss: 4.0511 +2024-12-27 01:13:48,538 - pyskl - INFO - Epoch [29][1400/3746] lr: 9.143e-02, eta: 3 days, 23:05:49, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5039, loss_cls: 4.1040, loss: 4.1040 +2024-12-27 01:15:00,911 - pyskl - INFO - Epoch [29][1500/3746] lr: 9.141e-02, eta: 3 days, 23:04:22, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.4944, loss_cls: 4.1261, loss: 4.1261 +2024-12-27 01:16:12,864 - pyskl - INFO - Epoch [29][1600/3746] lr: 9.140e-02, eta: 3 days, 23:02:53, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4956, loss_cls: 4.0979, loss: 4.0979 +2024-12-27 01:17:25,127 - pyskl - INFO - Epoch [29][1700/3746] lr: 9.138e-02, eta: 3 days, 23:01:26, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4978, loss_cls: 4.1009, loss: 4.1009 +2024-12-27 01:18:37,584 - pyskl - INFO - Epoch [29][1800/3746] lr: 9.137e-02, eta: 3 days, 22:59:59, time: 0.725, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5022, loss_cls: 4.0408, loss: 4.0408 +2024-12-27 01:19:49,609 - pyskl - INFO - Epoch [29][1900/3746] lr: 9.135e-02, eta: 3 days, 22:58:31, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5069, loss_cls: 4.0688, loss: 4.0688 +2024-12-27 01:21:01,620 - pyskl - INFO - Epoch [29][2000/3746] lr: 9.133e-02, eta: 3 days, 22:57:03, time: 0.720, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5053, loss_cls: 4.0729, loss: 4.0729 +2024-12-27 01:22:13,862 - pyskl - INFO - Epoch [29][2100/3746] lr: 9.132e-02, eta: 3 days, 22:55:35, time: 0.722, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5155, loss_cls: 4.0615, loss: 4.0615 +2024-12-27 01:23:26,305 - pyskl - INFO - Epoch [29][2200/3746] lr: 9.130e-02, eta: 3 days, 22:54:09, time: 0.724, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5142, loss_cls: 4.0837, loss: 4.0837 +2024-12-27 01:24:38,623 - pyskl - INFO - Epoch [29][2300/3746] lr: 9.129e-02, eta: 3 days, 22:52:42, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5094, loss_cls: 4.0872, loss: 4.0872 +2024-12-27 01:25:51,267 - pyskl - INFO - Epoch [29][2400/3746] lr: 9.127e-02, eta: 3 days, 22:51:16, time: 0.726, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5038, loss_cls: 4.1121, loss: 4.1121 +2024-12-27 01:27:03,056 - pyskl - INFO - Epoch [29][2500/3746] lr: 9.126e-02, eta: 3 days, 22:49:47, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5023, loss_cls: 4.0556, loss: 4.0556 +2024-12-27 01:28:15,408 - pyskl - INFO - Epoch [29][2600/3746] lr: 9.124e-02, eta: 3 days, 22:48:20, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5120, loss_cls: 4.0478, loss: 4.0478 +2024-12-27 01:29:27,693 - pyskl - INFO - Epoch [29][2700/3746] lr: 9.122e-02, eta: 3 days, 22:46:53, time: 0.723, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5142, loss_cls: 4.0546, loss: 4.0546 +2024-12-27 01:30:39,827 - pyskl - INFO - Epoch [29][2800/3746] lr: 9.121e-02, eta: 3 days, 22:45:25, time: 0.721, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5027, loss_cls: 4.0627, loss: 4.0627 +2024-12-27 01:31:51,533 - pyskl - INFO - Epoch [29][2900/3746] lr: 9.119e-02, eta: 3 days, 22:43:56, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5075, loss_cls: 4.0581, loss: 4.0581 +2024-12-27 01:33:03,316 - pyskl - INFO - Epoch [29][3000/3746] lr: 9.118e-02, eta: 3 days, 22:42:27, time: 0.718, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5027, loss_cls: 4.1127, loss: 4.1127 +2024-12-27 01:34:15,217 - pyskl - INFO - Epoch [29][3100/3746] lr: 9.116e-02, eta: 3 days, 22:40:58, time: 0.719, data_time: 0.001, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5039, loss_cls: 4.0757, loss: 4.0757 +2024-12-27 01:35:26,861 - pyskl - INFO - Epoch [29][3200/3746] lr: 9.114e-02, eta: 3 days, 22:39:29, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5072, loss_cls: 4.0645, loss: 4.0645 +2024-12-27 01:36:38,762 - pyskl - INFO - Epoch [29][3300/3746] lr: 9.113e-02, eta: 3 days, 22:38:00, time: 0.719, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5022, loss_cls: 4.1190, loss: 4.1190 +2024-12-27 01:37:50,253 - pyskl - INFO - Epoch [29][3400/3746] lr: 9.111e-02, eta: 3 days, 22:36:30, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.4992, loss_cls: 4.0958, loss: 4.0958 +2024-12-27 01:39:01,782 - pyskl - INFO - Epoch [29][3500/3746] lr: 9.110e-02, eta: 3 days, 22:35:00, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5106, loss_cls: 4.0514, loss: 4.0514 +2024-12-27 01:40:13,410 - pyskl - INFO - Epoch [29][3600/3746] lr: 9.108e-02, eta: 3 days, 22:33:31, time: 0.716, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5025, loss_cls: 4.0886, loss: 4.0886 +2024-12-27 01:41:25,106 - pyskl - INFO - Epoch [29][3700/3746] lr: 9.106e-02, eta: 3 days, 22:32:02, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5119, loss_cls: 4.0605, loss: 4.0605 +2024-12-27 01:42:00,206 - pyskl - INFO - Saving checkpoint at 29 epochs +2024-12-27 01:44:01,368 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 01:44:02,181 - pyskl - INFO - +top1_acc 0.1462 +top5_acc 0.3596 +2024-12-27 01:44:02,181 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 01:44:02,239 - pyskl - INFO - +mean_acc 0.1461 +2024-12-27 01:44:02,254 - pyskl - INFO - Epoch(val) [29][309] top1_acc: 0.1462, top5_acc: 0.3596, mean_class_accuracy: 0.1461 +2024-12-27 01:48:09,075 - pyskl - INFO - Epoch [30][100/3746] lr: 9.104e-02, eta: 3 days, 22:39:44, time: 2.468, data_time: 1.627, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5214, loss_cls: 3.9767, loss: 3.9767 +2024-12-27 01:49:32,884 - pyskl - INFO - Epoch [30][200/3746] lr: 9.103e-02, eta: 3 days, 22:39:04, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5088, loss_cls: 4.0626, loss: 4.0626 +2024-12-27 01:50:57,282 - pyskl - INFO - Epoch [30][300/3746] lr: 9.101e-02, eta: 3 days, 22:38:27, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5148, loss_cls: 4.0323, loss: 4.0323 +2024-12-27 01:52:21,507 - pyskl - INFO - Epoch [30][400/3746] lr: 9.099e-02, eta: 3 days, 22:37:50, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5181, loss_cls: 4.0539, loss: 4.0539 +2024-12-27 01:53:45,328 - pyskl - INFO - Epoch [30][500/3746] lr: 9.098e-02, eta: 3 days, 22:37:10, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5109, loss_cls: 4.0584, loss: 4.0584 +2024-12-27 01:55:10,083 - pyskl - INFO - Epoch [30][600/3746] lr: 9.096e-02, eta: 3 days, 22:36:34, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5005, loss_cls: 4.0883, loss: 4.0883 +2024-12-27 01:56:34,011 - pyskl - INFO - Epoch [30][700/3746] lr: 9.095e-02, eta: 3 days, 22:35:55, time: 0.839, data_time: 0.001, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5189, loss_cls: 4.0351, loss: 4.0351 +2024-12-27 01:57:58,055 - pyskl - INFO - Epoch [30][800/3746] lr: 9.093e-02, eta: 3 days, 22:35:16, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5183, loss_cls: 4.0537, loss: 4.0537 +2024-12-27 01:59:22,087 - pyskl - INFO - Epoch [30][900/3746] lr: 9.091e-02, eta: 3 days, 22:34:37, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5056, loss_cls: 4.0782, loss: 4.0782 +2024-12-27 02:00:44,944 - pyskl - INFO - Epoch [30][1000/3746] lr: 9.090e-02, eta: 3 days, 22:33:53, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5144, loss_cls: 4.0431, loss: 4.0431 +2024-12-27 02:02:09,162 - pyskl - INFO - Epoch [30][1100/3746] lr: 9.088e-02, eta: 3 days, 22:33:15, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5178, loss_cls: 4.0409, loss: 4.0409 +2024-12-27 02:03:33,284 - pyskl - INFO - Epoch [30][1200/3746] lr: 9.087e-02, eta: 3 days, 22:32:36, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5067, loss_cls: 4.0680, loss: 4.0680 +2024-12-27 02:04:57,308 - pyskl - INFO - Epoch [30][1300/3746] lr: 9.085e-02, eta: 3 days, 22:31:56, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5075, loss_cls: 4.0880, loss: 4.0880 +2024-12-27 02:06:20,991 - pyskl - INFO - Epoch [30][1400/3746] lr: 9.083e-02, eta: 3 days, 22:31:16, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5159, loss_cls: 4.0377, loss: 4.0377 +2024-12-27 02:07:45,023 - pyskl - INFO - Epoch [30][1500/3746] lr: 9.082e-02, eta: 3 days, 22:30:36, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5042, loss_cls: 4.0843, loss: 4.0843 +2024-12-27 02:09:08,492 - pyskl - INFO - Epoch [30][1600/3746] lr: 9.080e-02, eta: 3 days, 22:29:54, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5064, loss_cls: 4.0450, loss: 4.0450 +2024-12-27 02:10:33,075 - pyskl - INFO - Epoch [30][1700/3746] lr: 9.078e-02, eta: 3 days, 22:29:17, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5180, loss_cls: 4.0197, loss: 4.0197 +2024-12-27 02:11:56,944 - pyskl - INFO - Epoch [30][1800/3746] lr: 9.077e-02, eta: 3 days, 22:28:36, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5164, loss_cls: 4.0417, loss: 4.0417 +2024-12-27 02:13:20,622 - pyskl - INFO - Epoch [30][1900/3746] lr: 9.075e-02, eta: 3 days, 22:27:55, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5102, loss_cls: 4.0417, loss: 4.0417 +2024-12-27 02:14:44,594 - pyskl - INFO - Epoch [30][2000/3746] lr: 9.074e-02, eta: 3 days, 22:27:15, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5031, loss_cls: 4.0759, loss: 4.0759 +2024-12-27 02:16:08,817 - pyskl - INFO - Epoch [30][2100/3746] lr: 9.072e-02, eta: 3 days, 22:26:36, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5175, loss_cls: 4.0134, loss: 4.0134 +2024-12-27 02:17:32,660 - pyskl - INFO - Epoch [30][2200/3746] lr: 9.070e-02, eta: 3 days, 22:25:55, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5047, loss_cls: 4.0805, loss: 4.0805 +2024-12-27 02:18:57,001 - pyskl - INFO - Epoch [30][2300/3746] lr: 9.069e-02, eta: 3 days, 22:25:16, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5123, loss_cls: 4.0791, loss: 4.0791 +2024-12-27 02:20:20,989 - pyskl - INFO - Epoch [30][2400/3746] lr: 9.067e-02, eta: 3 days, 22:24:35, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5041, loss_cls: 4.0716, loss: 4.0716 +2024-12-27 02:21:44,913 - pyskl - INFO - Epoch [30][2500/3746] lr: 9.065e-02, eta: 3 days, 22:23:55, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5131, loss_cls: 4.0633, loss: 4.0633 +2024-12-27 02:23:09,175 - pyskl - INFO - Epoch [30][2600/3746] lr: 9.064e-02, eta: 3 days, 22:23:15, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5208, loss_cls: 4.0137, loss: 4.0137 +2024-12-27 02:24:33,240 - pyskl - INFO - Epoch [30][2700/3746] lr: 9.062e-02, eta: 3 days, 22:22:35, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.5086, loss_cls: 4.0884, loss: 4.0884 +2024-12-27 02:25:56,802 - pyskl - INFO - Epoch [30][2800/3746] lr: 9.061e-02, eta: 3 days, 22:21:52, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5044, loss_cls: 4.0533, loss: 4.0533 +2024-12-27 02:27:20,512 - pyskl - INFO - Epoch [30][2900/3746] lr: 9.059e-02, eta: 3 days, 22:21:11, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5188, loss_cls: 4.0422, loss: 4.0422 +2024-12-27 02:28:43,448 - pyskl - INFO - Epoch [30][3000/3746] lr: 9.057e-02, eta: 3 days, 22:20:25, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5139, loss_cls: 4.0330, loss: 4.0330 +2024-12-27 02:30:05,869 - pyskl - INFO - Epoch [30][3100/3746] lr: 9.056e-02, eta: 3 days, 22:19:38, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4998, loss_cls: 4.0933, loss: 4.0933 +2024-12-27 02:31:28,528 - pyskl - INFO - Epoch [30][3200/3746] lr: 9.054e-02, eta: 3 days, 22:18:52, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5212, loss_cls: 3.9806, loss: 3.9806 +2024-12-27 02:32:51,211 - pyskl - INFO - Epoch [30][3300/3746] lr: 9.052e-02, eta: 3 days, 22:18:06, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4978, loss_cls: 4.1145, loss: 4.1145 +2024-12-27 02:34:14,552 - pyskl - INFO - Epoch [30][3400/3746] lr: 9.051e-02, eta: 3 days, 22:17:22, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5081, loss_cls: 4.0483, loss: 4.0483 +2024-12-27 02:35:38,102 - pyskl - INFO - Epoch [30][3500/3746] lr: 9.049e-02, eta: 3 days, 22:16:39, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5016, loss_cls: 4.1018, loss: 4.1018 +2024-12-27 02:37:01,628 - pyskl - INFO - Epoch [30][3600/3746] lr: 9.047e-02, eta: 3 days, 22:15:56, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5142, loss_cls: 4.0779, loss: 4.0779 +2024-12-27 02:38:25,250 - pyskl - INFO - Epoch [30][3700/3746] lr: 9.046e-02, eta: 3 days, 22:15:13, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5117, loss_cls: 4.0387, loss: 4.0387 +2024-12-27 02:39:06,398 - pyskl - INFO - Saving checkpoint at 30 epochs +2024-12-27 02:41:06,661 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 02:41:07,501 - pyskl - INFO - +top1_acc 0.1887 +top5_acc 0.4147 +2024-12-27 02:41:07,501 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 02:41:07,565 - pyskl - INFO - +mean_acc 0.1886 +2024-12-27 02:41:07,577 - pyskl - INFO - Epoch(val) [30][309] top1_acc: 0.1887, top5_acc: 0.4147, mean_class_accuracy: 0.1886 +2024-12-27 02:45:27,795 - pyskl - INFO - Epoch [31][100/3746] lr: 9.043e-02, eta: 3 days, 22:23:22, time: 2.602, data_time: 1.570, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5219, loss_cls: 4.2104, loss: 4.2104 +2024-12-27 02:46:53,126 - pyskl - INFO - Epoch [31][200/3746] lr: 9.042e-02, eta: 3 days, 22:22:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5178, loss_cls: 4.2571, loss: 4.2571 +2024-12-27 02:48:18,401 - pyskl - INFO - Epoch [31][300/3746] lr: 9.040e-02, eta: 3 days, 22:22:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5200, loss_cls: 4.2448, loss: 4.2448 +2024-12-27 02:49:44,124 - pyskl - INFO - Epoch [31][400/3746] lr: 9.039e-02, eta: 3 days, 22:21:33, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5061, loss_cls: 4.2776, loss: 4.2776 +2024-12-27 02:51:09,958 - pyskl - INFO - Epoch [31][500/3746] lr: 9.037e-02, eta: 3 days, 22:20:58, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5114, loss_cls: 4.2894, loss: 4.2894 +2024-12-27 02:52:35,741 - pyskl - INFO - Epoch [31][600/3746] lr: 9.035e-02, eta: 3 days, 22:20:22, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5009, loss_cls: 4.3186, loss: 4.3186 +2024-12-27 02:54:01,763 - pyskl - INFO - Epoch [31][700/3746] lr: 9.034e-02, eta: 3 days, 22:19:48, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.5011, loss_cls: 4.3510, loss: 4.3510 +2024-12-27 02:55:27,217 - pyskl - INFO - Epoch [31][800/3746] lr: 9.032e-02, eta: 3 days, 22:19:11, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5136, loss_cls: 4.2926, loss: 4.2926 +2024-12-27 02:56:52,889 - pyskl - INFO - Epoch [31][900/3746] lr: 9.030e-02, eta: 3 days, 22:18:35, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5058, loss_cls: 4.3175, loss: 4.3175 +2024-12-27 02:58:18,944 - pyskl - INFO - Epoch [31][1000/3746] lr: 9.029e-02, eta: 3 days, 22:18:00, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5023, loss_cls: 4.3203, loss: 4.3203 +2024-12-27 02:59:44,413 - pyskl - INFO - Epoch [31][1100/3746] lr: 9.027e-02, eta: 3 days, 22:17:23, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5109, loss_cls: 4.2824, loss: 4.2824 +2024-12-27 03:01:10,432 - pyskl - INFO - Epoch [31][1200/3746] lr: 9.025e-02, eta: 3 days, 22:16:48, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5066, loss_cls: 4.2946, loss: 4.2946 +2024-12-27 03:02:36,303 - pyskl - INFO - Epoch [31][1300/3746] lr: 9.024e-02, eta: 3 days, 22:16:12, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.5052, loss_cls: 4.2962, loss: 4.2962 +2024-12-27 03:04:02,088 - pyskl - INFO - Epoch [31][1400/3746] lr: 9.022e-02, eta: 3 days, 22:15:36, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.5130, loss_cls: 4.2685, loss: 4.2685 +2024-12-27 03:05:28,137 - pyskl - INFO - Epoch [31][1500/3746] lr: 9.020e-02, eta: 3 days, 22:15:01, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4950, loss_cls: 4.3556, loss: 4.3556 +2024-12-27 03:06:54,293 - pyskl - INFO - Epoch [31][1600/3746] lr: 9.019e-02, eta: 3 days, 22:14:27, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5233, loss_cls: 4.2212, loss: 4.2212 +2024-12-27 03:08:20,564 - pyskl - INFO - Epoch [31][1700/3746] lr: 9.017e-02, eta: 3 days, 22:13:52, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5120, loss_cls: 4.2966, loss: 4.2966 +2024-12-27 03:09:47,046 - pyskl - INFO - Epoch [31][1800/3746] lr: 9.015e-02, eta: 3 days, 22:13:18, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5166, loss_cls: 4.2758, loss: 4.2758 +2024-12-27 03:11:13,038 - pyskl - INFO - Epoch [31][1900/3746] lr: 9.014e-02, eta: 3 days, 22:12:43, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5073, loss_cls: 4.3038, loss: 4.3038 +2024-12-27 03:12:39,034 - pyskl - INFO - Epoch [31][2000/3746] lr: 9.012e-02, eta: 3 days, 22:12:07, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5228, loss_cls: 4.2366, loss: 4.2366 +2024-12-27 03:14:05,904 - pyskl - INFO - Epoch [31][2100/3746] lr: 9.010e-02, eta: 3 days, 22:11:35, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5241, loss_cls: 4.2465, loss: 4.2465 +2024-12-27 03:15:32,175 - pyskl - INFO - Epoch [31][2200/3746] lr: 9.009e-02, eta: 3 days, 22:11:00, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5175, loss_cls: 4.2358, loss: 4.2358 +2024-12-27 03:16:58,367 - pyskl - INFO - Epoch [31][2300/3746] lr: 9.007e-02, eta: 3 days, 22:10:24, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5139, loss_cls: 4.2603, loss: 4.2603 +2024-12-27 03:18:24,221 - pyskl - INFO - Epoch [31][2400/3746] lr: 9.005e-02, eta: 3 days, 22:09:48, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5081, loss_cls: 4.3103, loss: 4.3103 +2024-12-27 03:19:50,403 - pyskl - INFO - Epoch [31][2500/3746] lr: 9.004e-02, eta: 3 days, 22:09:12, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5012, loss_cls: 4.3054, loss: 4.3054 +2024-12-27 03:21:16,440 - pyskl - INFO - Epoch [31][2600/3746] lr: 9.002e-02, eta: 3 days, 22:08:36, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5069, loss_cls: 4.3147, loss: 4.3147 +2024-12-27 03:22:42,476 - pyskl - INFO - Epoch [31][2700/3746] lr: 9.000e-02, eta: 3 days, 22:08:00, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5119, loss_cls: 4.2317, loss: 4.2317 +2024-12-27 03:24:08,711 - pyskl - INFO - Epoch [31][2800/3746] lr: 8.999e-02, eta: 3 days, 22:07:24, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5070, loss_cls: 4.2965, loss: 4.2965 +2024-12-27 03:25:34,120 - pyskl - INFO - Epoch [31][2900/3746] lr: 8.997e-02, eta: 3 days, 22:06:45, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5061, loss_cls: 4.2881, loss: 4.2881 +2024-12-27 03:26:59,247 - pyskl - INFO - Epoch [31][3000/3746] lr: 8.995e-02, eta: 3 days, 22:06:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5066, loss_cls: 4.2730, loss: 4.2730 +2024-12-27 03:28:24,422 - pyskl - INFO - Epoch [31][3100/3746] lr: 8.994e-02, eta: 3 days, 22:05:26, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5028, loss_cls: 4.3152, loss: 4.3152 +2024-12-27 03:29:50,904 - pyskl - INFO - Epoch [31][3200/3746] lr: 8.992e-02, eta: 3 days, 22:04:51, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5008, loss_cls: 4.3347, loss: 4.3347 +2024-12-27 03:31:16,133 - pyskl - INFO - Epoch [31][3300/3746] lr: 8.990e-02, eta: 3 days, 22:04:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5083, loss_cls: 4.2678, loss: 4.2678 +2024-12-27 03:32:41,573 - pyskl - INFO - Epoch [31][3400/3746] lr: 8.989e-02, eta: 3 days, 22:03:32, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5092, loss_cls: 4.2928, loss: 4.2928 +2024-12-27 03:34:06,919 - pyskl - INFO - Epoch [31][3500/3746] lr: 8.987e-02, eta: 3 days, 22:02:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5106, loss_cls: 4.2845, loss: 4.2845 +2024-12-27 03:35:32,623 - pyskl - INFO - Epoch [31][3600/3746] lr: 8.985e-02, eta: 3 days, 22:02:14, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5184, loss_cls: 4.2804, loss: 4.2804 +2024-12-27 03:36:58,187 - pyskl - INFO - Epoch [31][3700/3746] lr: 8.983e-02, eta: 3 days, 22:01:35, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.5020, loss_cls: 4.3352, loss: 4.3352 +2024-12-27 03:37:39,721 - pyskl - INFO - Saving checkpoint at 31 epochs +2024-12-27 03:39:38,222 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 03:39:38,986 - pyskl - INFO - +top1_acc 0.1783 +top5_acc 0.4010 +2024-12-27 03:39:38,986 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 03:39:39,037 - pyskl - INFO - +mean_acc 0.1781 +2024-12-27 03:39:39,057 - pyskl - INFO - Epoch(val) [31][309] top1_acc: 0.1783, top5_acc: 0.4010, mean_class_accuracy: 0.1781 +2024-12-27 03:44:02,238 - pyskl - INFO - Epoch [32][100/3746] lr: 8.981e-02, eta: 3 days, 22:09:28, time: 2.632, data_time: 1.593, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5164, loss_cls: 4.2061, loss: 4.2061 +2024-12-27 03:45:28,562 - pyskl - INFO - Epoch [32][200/3746] lr: 8.979e-02, eta: 3 days, 22:08:51, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5058, loss_cls: 4.2848, loss: 4.2848 +2024-12-27 03:46:54,284 - pyskl - INFO - Epoch [32][300/3746] lr: 8.978e-02, eta: 3 days, 22:08:12, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5303, loss_cls: 4.1940, loss: 4.1940 +2024-12-27 03:48:20,208 - pyskl - INFO - Epoch [32][400/3746] lr: 8.976e-02, eta: 3 days, 22:07:34, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5184, loss_cls: 4.2259, loss: 4.2259 +2024-12-27 03:49:46,420 - pyskl - INFO - Epoch [32][500/3746] lr: 8.974e-02, eta: 3 days, 22:06:56, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5300, loss_cls: 4.2216, loss: 4.2216 +2024-12-27 03:51:12,797 - pyskl - INFO - Epoch [32][600/3746] lr: 8.973e-02, eta: 3 days, 22:06:20, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5175, loss_cls: 4.2680, loss: 4.2680 +2024-12-27 03:52:38,983 - pyskl - INFO - Epoch [32][700/3746] lr: 8.971e-02, eta: 3 days, 22:05:42, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5208, loss_cls: 4.2333, loss: 4.2333 +2024-12-27 03:54:04,817 - pyskl - INFO - Epoch [32][800/3746] lr: 8.969e-02, eta: 3 days, 22:05:03, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5053, loss_cls: 4.2898, loss: 4.2898 +2024-12-27 03:55:30,776 - pyskl - INFO - Epoch [32][900/3746] lr: 8.967e-02, eta: 3 days, 22:04:24, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.5008, loss_cls: 4.3069, loss: 4.3069 +2024-12-27 03:56:57,163 - pyskl - INFO - Epoch [32][1000/3746] lr: 8.966e-02, eta: 3 days, 22:03:47, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5142, loss_cls: 4.2575, loss: 4.2575 +2024-12-27 03:58:23,651 - pyskl - INFO - Epoch [32][1100/3746] lr: 8.964e-02, eta: 3 days, 22:03:10, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5156, loss_cls: 4.2347, loss: 4.2347 +2024-12-27 03:59:49,748 - pyskl - INFO - Epoch [32][1200/3746] lr: 8.962e-02, eta: 3 days, 22:02:32, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5164, loss_cls: 4.2687, loss: 4.2687 +2024-12-27 04:01:15,733 - pyskl - INFO - Epoch [32][1300/3746] lr: 8.961e-02, eta: 3 days, 22:01:53, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5194, loss_cls: 4.2481, loss: 4.2481 +2024-12-27 04:02:41,843 - pyskl - INFO - Epoch [32][1400/3746] lr: 8.959e-02, eta: 3 days, 22:01:14, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5181, loss_cls: 4.2195, loss: 4.2195 +2024-12-27 04:04:07,775 - pyskl - INFO - Epoch [32][1500/3746] lr: 8.957e-02, eta: 3 days, 22:00:35, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5155, loss_cls: 4.2484, loss: 4.2484 +2024-12-27 04:05:33,397 - pyskl - INFO - Epoch [32][1600/3746] lr: 8.955e-02, eta: 3 days, 21:59:54, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5061, loss_cls: 4.3036, loss: 4.3036 +2024-12-27 04:06:59,553 - pyskl - INFO - Epoch [32][1700/3746] lr: 8.954e-02, eta: 3 days, 21:59:16, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5150, loss_cls: 4.2306, loss: 4.2306 +2024-12-27 04:08:25,909 - pyskl - INFO - Epoch [32][1800/3746] lr: 8.952e-02, eta: 3 days, 21:58:38, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5136, loss_cls: 4.2804, loss: 4.2804 +2024-12-27 04:09:52,178 - pyskl - INFO - Epoch [32][1900/3746] lr: 8.950e-02, eta: 3 days, 21:57:59, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.4994, loss_cls: 4.2659, loss: 4.2659 +2024-12-27 04:11:18,742 - pyskl - INFO - Epoch [32][2000/3746] lr: 8.949e-02, eta: 3 days, 21:57:22, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4989, loss_cls: 4.3524, loss: 4.3524 +2024-12-27 04:12:45,195 - pyskl - INFO - Epoch [32][2100/3746] lr: 8.947e-02, eta: 3 days, 21:56:44, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5122, loss_cls: 4.3121, loss: 4.3121 +2024-12-27 04:14:11,200 - pyskl - INFO - Epoch [32][2200/3746] lr: 8.945e-02, eta: 3 days, 21:56:05, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4988, loss_cls: 4.3307, loss: 4.3307 +2024-12-27 04:15:37,496 - pyskl - INFO - Epoch [32][2300/3746] lr: 8.943e-02, eta: 3 days, 21:55:26, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5136, loss_cls: 4.2412, loss: 4.2412 +2024-12-27 04:17:03,950 - pyskl - INFO - Epoch [32][2400/3746] lr: 8.942e-02, eta: 3 days, 21:54:48, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5072, loss_cls: 4.2927, loss: 4.2927 +2024-12-27 04:18:30,783 - pyskl - INFO - Epoch [32][2500/3746] lr: 8.940e-02, eta: 3 days, 21:54:11, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4998, loss_cls: 4.3177, loss: 4.3177 +2024-12-27 04:19:57,466 - pyskl - INFO - Epoch [32][2600/3746] lr: 8.938e-02, eta: 3 days, 21:53:34, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5156, loss_cls: 4.2448, loss: 4.2448 +2024-12-27 04:21:23,914 - pyskl - INFO - Epoch [32][2700/3746] lr: 8.937e-02, eta: 3 days, 21:52:56, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5091, loss_cls: 4.2845, loss: 4.2845 +2024-12-27 04:22:50,214 - pyskl - INFO - Epoch [32][2800/3746] lr: 8.935e-02, eta: 3 days, 21:52:17, time: 0.863, data_time: 0.001, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5078, loss_cls: 4.3108, loss: 4.3108 +2024-12-27 04:24:15,971 - pyskl - INFO - Epoch [32][2900/3746] lr: 8.933e-02, eta: 3 days, 21:51:36, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.5009, loss_cls: 4.3265, loss: 4.3265 +2024-12-27 04:25:41,144 - pyskl - INFO - Epoch [32][3000/3746] lr: 8.931e-02, eta: 3 days, 21:50:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5278, loss_cls: 4.2052, loss: 4.2052 +2024-12-27 04:27:06,392 - pyskl - INFO - Epoch [32][3100/3746] lr: 8.930e-02, eta: 3 days, 21:50:09, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5106, loss_cls: 4.2728, loss: 4.2728 +2024-12-27 04:28:31,438 - pyskl - INFO - Epoch [32][3200/3746] lr: 8.928e-02, eta: 3 days, 21:49:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5061, loss_cls: 4.2789, loss: 4.2789 +2024-12-27 04:29:56,667 - pyskl - INFO - Epoch [32][3300/3746] lr: 8.926e-02, eta: 3 days, 21:48:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5030, loss_cls: 4.2933, loss: 4.2933 +2024-12-27 04:31:21,806 - pyskl - INFO - Epoch [32][3400/3746] lr: 8.924e-02, eta: 3 days, 21:47:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5119, loss_cls: 4.2706, loss: 4.2706 +2024-12-27 04:32:47,878 - pyskl - INFO - Epoch [32][3500/3746] lr: 8.923e-02, eta: 3 days, 21:47:18, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5144, loss_cls: 4.2283, loss: 4.2283 +2024-12-27 04:34:13,809 - pyskl - INFO - Epoch [32][3600/3746] lr: 8.921e-02, eta: 3 days, 21:46:37, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5091, loss_cls: 4.2771, loss: 4.2771 +2024-12-27 04:35:39,506 - pyskl - INFO - Epoch [32][3700/3746] lr: 8.919e-02, eta: 3 days, 21:45:55, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5202, loss_cls: 4.2631, loss: 4.2631 +2024-12-27 04:36:21,492 - pyskl - INFO - Saving checkpoint at 32 epochs +2024-12-27 04:38:19,240 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 04:38:20,059 - pyskl - INFO - +top1_acc 0.1903 +top5_acc 0.4176 +2024-12-27 04:38:20,059 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 04:38:20,100 - pyskl - INFO - +mean_acc 0.1901 +2024-12-27 04:38:20,112 - pyskl - INFO - Epoch(val) [32][309] top1_acc: 0.1903, top5_acc: 0.4176, mean_class_accuracy: 0.1901 +2024-12-27 04:42:48,671 - pyskl - INFO - Epoch [33][100/3746] lr: 8.917e-02, eta: 3 days, 21:53:42, time: 2.685, data_time: 1.645, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5162, loss_cls: 4.2341, loss: 4.2341 +2024-12-27 04:44:15,066 - pyskl - INFO - Epoch [33][200/3746] lr: 8.915e-02, eta: 3 days, 21:53:02, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5080, loss_cls: 4.2863, loss: 4.2863 +2024-12-27 04:45:41,511 - pyskl - INFO - Epoch [33][300/3746] lr: 8.913e-02, eta: 3 days, 21:52:22, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5058, loss_cls: 4.2916, loss: 4.2916 +2024-12-27 04:47:08,415 - pyskl - INFO - Epoch [33][400/3746] lr: 8.912e-02, eta: 3 days, 21:51:44, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5092, loss_cls: 4.2942, loss: 4.2942 +2024-12-27 04:48:35,217 - pyskl - INFO - Epoch [33][500/3746] lr: 8.910e-02, eta: 3 days, 21:51:05, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5133, loss_cls: 4.2368, loss: 4.2368 +2024-12-27 04:50:01,806 - pyskl - INFO - Epoch [33][600/3746] lr: 8.908e-02, eta: 3 days, 21:50:25, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5070, loss_cls: 4.3125, loss: 4.3125 +2024-12-27 04:51:27,855 - pyskl - INFO - Epoch [33][700/3746] lr: 8.906e-02, eta: 3 days, 21:49:44, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5255, loss_cls: 4.2191, loss: 4.2191 +2024-12-27 04:52:53,595 - pyskl - INFO - Epoch [33][800/3746] lr: 8.905e-02, eta: 3 days, 21:49:01, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5131, loss_cls: 4.2811, loss: 4.2811 +2024-12-27 04:54:19,807 - pyskl - INFO - Epoch [33][900/3746] lr: 8.903e-02, eta: 3 days, 21:48:20, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5050, loss_cls: 4.2923, loss: 4.2923 +2024-12-27 04:55:46,109 - pyskl - INFO - Epoch [33][1000/3746] lr: 8.901e-02, eta: 3 days, 21:47:39, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5011, loss_cls: 4.3303, loss: 4.3303 +2024-12-27 04:57:12,693 - pyskl - INFO - Epoch [33][1100/3746] lr: 8.899e-02, eta: 3 days, 21:46:59, time: 0.866, data_time: 0.001, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5162, loss_cls: 4.2244, loss: 4.2244 +2024-12-27 04:58:38,984 - pyskl - INFO - Epoch [33][1200/3746] lr: 8.898e-02, eta: 3 days, 21:46:17, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5125, loss_cls: 4.2722, loss: 4.2722 +2024-12-27 05:00:04,670 - pyskl - INFO - Epoch [33][1300/3746] lr: 8.896e-02, eta: 3 days, 21:45:34, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5047, loss_cls: 4.2853, loss: 4.2853 +2024-12-27 05:01:30,690 - pyskl - INFO - Epoch [33][1400/3746] lr: 8.894e-02, eta: 3 days, 21:44:52, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5141, loss_cls: 4.2667, loss: 4.2667 +2024-12-27 05:02:57,302 - pyskl - INFO - Epoch [33][1500/3746] lr: 8.892e-02, eta: 3 days, 21:44:11, time: 0.866, data_time: 0.001, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5217, loss_cls: 4.2460, loss: 4.2460 +2024-12-27 05:04:23,355 - pyskl - INFO - Epoch [33][1600/3746] lr: 8.891e-02, eta: 3 days, 21:43:29, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5050, loss_cls: 4.2793, loss: 4.2793 +2024-12-27 05:05:49,320 - pyskl - INFO - Epoch [33][1700/3746] lr: 8.889e-02, eta: 3 days, 21:42:46, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5075, loss_cls: 4.2863, loss: 4.2863 +2024-12-27 05:07:15,619 - pyskl - INFO - Epoch [33][1800/3746] lr: 8.887e-02, eta: 3 days, 21:42:05, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5117, loss_cls: 4.2330, loss: 4.2330 +2024-12-27 05:08:42,175 - pyskl - INFO - Epoch [33][1900/3746] lr: 8.885e-02, eta: 3 days, 21:41:24, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5097, loss_cls: 4.2654, loss: 4.2654 +2024-12-27 05:10:08,806 - pyskl - INFO - Epoch [33][2000/3746] lr: 8.884e-02, eta: 3 days, 21:40:43, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5195, loss_cls: 4.2483, loss: 4.2483 +2024-12-27 05:11:34,961 - pyskl - INFO - Epoch [33][2100/3746] lr: 8.882e-02, eta: 3 days, 21:40:01, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.5012, loss_cls: 4.3028, loss: 4.3028 +2024-12-27 05:13:01,539 - pyskl - INFO - Epoch [33][2200/3746] lr: 8.880e-02, eta: 3 days, 21:39:20, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2634, top5_acc: 0.5131, loss_cls: 4.2620, loss: 4.2620 +2024-12-27 05:14:28,052 - pyskl - INFO - Epoch [33][2300/3746] lr: 8.878e-02, eta: 3 days, 21:38:39, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5138, loss_cls: 4.2686, loss: 4.2686 +2024-12-27 05:15:54,067 - pyskl - INFO - Epoch [33][2400/3746] lr: 8.876e-02, eta: 3 days, 21:37:56, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5173, loss_cls: 4.2727, loss: 4.2727 +2024-12-27 05:17:20,369 - pyskl - INFO - Epoch [33][2500/3746] lr: 8.875e-02, eta: 3 days, 21:37:14, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5044, loss_cls: 4.3054, loss: 4.3054 +2024-12-27 05:18:46,751 - pyskl - INFO - Epoch [33][2600/3746] lr: 8.873e-02, eta: 3 days, 21:36:32, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5078, loss_cls: 4.2936, loss: 4.2936 +2024-12-27 05:20:12,575 - pyskl - INFO - Epoch [33][2700/3746] lr: 8.871e-02, eta: 3 days, 21:35:48, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5270, loss_cls: 4.2017, loss: 4.2017 +2024-12-27 05:21:37,273 - pyskl - INFO - Epoch [33][2800/3746] lr: 8.869e-02, eta: 3 days, 21:35:00, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.4913, loss_cls: 4.3356, loss: 4.3356 +2024-12-27 05:23:01,566 - pyskl - INFO - Epoch [33][2900/3746] lr: 8.868e-02, eta: 3 days, 21:34:11, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5169, loss_cls: 4.2449, loss: 4.2449 +2024-12-27 05:24:27,026 - pyskl - INFO - Epoch [33][3000/3746] lr: 8.866e-02, eta: 3 days, 21:33:25, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5100, loss_cls: 4.2716, loss: 4.2716 +2024-12-27 05:25:51,878 - pyskl - INFO - Epoch [33][3100/3746] lr: 8.864e-02, eta: 3 days, 21:32:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5067, loss_cls: 4.2584, loss: 4.2584 +2024-12-27 05:27:16,626 - pyskl - INFO - Epoch [33][3200/3746] lr: 8.862e-02, eta: 3 days, 21:31:49, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5162, loss_cls: 4.2774, loss: 4.2774 +2024-12-27 05:28:41,417 - pyskl - INFO - Epoch [33][3300/3746] lr: 8.861e-02, eta: 3 days, 21:31:01, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5219, loss_cls: 4.2379, loss: 4.2379 +2024-12-27 05:30:07,421 - pyskl - INFO - Epoch [33][3400/3746] lr: 8.859e-02, eta: 3 days, 21:30:17, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5066, loss_cls: 4.2813, loss: 4.2813 +2024-12-27 05:31:33,094 - pyskl - INFO - Epoch [33][3500/3746] lr: 8.857e-02, eta: 3 days, 21:29:32, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5170, loss_cls: 4.2715, loss: 4.2715 +2024-12-27 05:32:58,993 - pyskl - INFO - Epoch [33][3600/3746] lr: 8.855e-02, eta: 3 days, 21:28:48, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.4972, loss_cls: 4.3117, loss: 4.3117 +2024-12-27 05:34:25,128 - pyskl - INFO - Epoch [33][3700/3746] lr: 8.853e-02, eta: 3 days, 21:28:05, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5077, loss_cls: 4.2943, loss: 4.2943 +2024-12-27 05:35:07,367 - pyskl - INFO - Saving checkpoint at 33 epochs +2024-12-27 05:37:07,794 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 05:37:08,499 - pyskl - INFO - +top1_acc 0.1772 +top5_acc 0.3948 +2024-12-27 05:37:08,500 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 05:37:08,556 - pyskl - INFO - +mean_acc 0.1767 +2024-12-27 05:37:08,573 - pyskl - INFO - Epoch(val) [33][309] top1_acc: 0.1772, top5_acc: 0.3948, mean_class_accuracy: 0.1767 +2024-12-27 05:41:33,122 - pyskl - INFO - Epoch [34][100/3746] lr: 8.851e-02, eta: 3 days, 21:35:13, time: 2.645, data_time: 1.601, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5114, loss_cls: 4.2560, loss: 4.2560 +2024-12-27 05:43:00,315 - pyskl - INFO - Epoch [34][200/3746] lr: 8.849e-02, eta: 3 days, 21:34:32, time: 0.872, data_time: 0.001, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5097, loss_cls: 4.2631, loss: 4.2631 +2024-12-27 05:44:26,952 - pyskl - INFO - Epoch [34][300/3746] lr: 8.847e-02, eta: 3 days, 21:33:50, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5233, loss_cls: 4.2111, loss: 4.2111 +2024-12-27 05:45:53,319 - pyskl - INFO - Epoch [34][400/3746] lr: 8.845e-02, eta: 3 days, 21:33:06, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5139, loss_cls: 4.2391, loss: 4.2391 +2024-12-27 05:47:19,525 - pyskl - INFO - Epoch [34][500/3746] lr: 8.844e-02, eta: 3 days, 21:32:22, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5266, loss_cls: 4.2117, loss: 4.2117 +2024-12-27 05:48:45,998 - pyskl - INFO - Epoch [34][600/3746] lr: 8.842e-02, eta: 3 days, 21:31:39, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5130, loss_cls: 4.2515, loss: 4.2515 +2024-12-27 05:50:11,735 - pyskl - INFO - Epoch [34][700/3746] lr: 8.840e-02, eta: 3 days, 21:30:53, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5192, loss_cls: 4.2223, loss: 4.2223 +2024-12-27 05:51:37,815 - pyskl - INFO - Epoch [34][800/3746] lr: 8.838e-02, eta: 3 days, 21:30:08, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5131, loss_cls: 4.2676, loss: 4.2676 +2024-12-27 05:53:03,564 - pyskl - INFO - Epoch [34][900/3746] lr: 8.836e-02, eta: 3 days, 21:29:22, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5030, loss_cls: 4.3122, loss: 4.3122 +2024-12-27 05:54:29,649 - pyskl - INFO - Epoch [34][1000/3746] lr: 8.835e-02, eta: 3 days, 21:28:37, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5094, loss_cls: 4.2782, loss: 4.2782 +2024-12-27 05:55:55,394 - pyskl - INFO - Epoch [34][1100/3746] lr: 8.833e-02, eta: 3 days, 21:27:51, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5102, loss_cls: 4.2731, loss: 4.2731 +2024-12-27 05:57:21,973 - pyskl - INFO - Epoch [34][1200/3746] lr: 8.831e-02, eta: 3 days, 21:27:08, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5152, loss_cls: 4.2545, loss: 4.2545 +2024-12-27 05:58:48,519 - pyskl - INFO - Epoch [34][1300/3746] lr: 8.829e-02, eta: 3 days, 21:26:24, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5106, loss_cls: 4.2799, loss: 4.2799 +2024-12-27 06:00:14,374 - pyskl - INFO - Epoch [34][1400/3746] lr: 8.828e-02, eta: 3 days, 21:25:38, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5227, loss_cls: 4.2139, loss: 4.2139 +2024-12-27 06:01:40,591 - pyskl - INFO - Epoch [34][1500/3746] lr: 8.826e-02, eta: 3 days, 21:24:53, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5122, loss_cls: 4.2689, loss: 4.2689 +2024-12-27 06:03:07,015 - pyskl - INFO - Epoch [34][1600/3746] lr: 8.824e-02, eta: 3 days, 21:24:09, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5166, loss_cls: 4.2343, loss: 4.2343 +2024-12-27 06:04:33,427 - pyskl - INFO - Epoch [34][1700/3746] lr: 8.822e-02, eta: 3 days, 21:23:25, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5275, loss_cls: 4.2177, loss: 4.2177 +2024-12-27 06:05:59,717 - pyskl - INFO - Epoch [34][1800/3746] lr: 8.820e-02, eta: 3 days, 21:22:40, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.4981, loss_cls: 4.3156, loss: 4.3156 +2024-12-27 06:07:25,934 - pyskl - INFO - Epoch [34][1900/3746] lr: 8.819e-02, eta: 3 days, 21:21:55, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5197, loss_cls: 4.2386, loss: 4.2386 +2024-12-27 06:08:51,493 - pyskl - INFO - Epoch [34][2000/3746] lr: 8.817e-02, eta: 3 days, 21:21:08, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.4966, loss_cls: 4.2936, loss: 4.2936 +2024-12-27 06:10:17,719 - pyskl - INFO - Epoch [34][2100/3746] lr: 8.815e-02, eta: 3 days, 21:20:23, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5183, loss_cls: 4.2138, loss: 4.2138 +2024-12-27 06:11:43,666 - pyskl - INFO - Epoch [34][2200/3746] lr: 8.813e-02, eta: 3 days, 21:19:37, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5256, loss_cls: 4.2150, loss: 4.2150 +2024-12-27 06:13:09,482 - pyskl - INFO - Epoch [34][2300/3746] lr: 8.811e-02, eta: 3 days, 21:18:50, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5177, loss_cls: 4.2540, loss: 4.2540 +2024-12-27 06:14:36,209 - pyskl - INFO - Epoch [34][2400/3746] lr: 8.809e-02, eta: 3 days, 21:18:06, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5091, loss_cls: 4.2808, loss: 4.2808 +2024-12-27 06:16:02,352 - pyskl - INFO - Epoch [34][2500/3746] lr: 8.808e-02, eta: 3 days, 21:17:21, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5200, loss_cls: 4.1943, loss: 4.1943 +2024-12-27 06:17:28,517 - pyskl - INFO - Epoch [34][2600/3746] lr: 8.806e-02, eta: 3 days, 21:16:35, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5058, loss_cls: 4.2994, loss: 4.2994 +2024-12-27 06:18:53,181 - pyskl - INFO - Epoch [34][2700/3746] lr: 8.804e-02, eta: 3 days, 21:15:44, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5177, loss_cls: 4.2317, loss: 4.2317 +2024-12-27 06:20:18,072 - pyskl - INFO - Epoch [34][2800/3746] lr: 8.802e-02, eta: 3 days, 21:14:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5023, loss_cls: 4.2772, loss: 4.2772 +2024-12-27 06:21:42,868 - pyskl - INFO - Epoch [34][2900/3746] lr: 8.800e-02, eta: 3 days, 21:14:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.5130, loss_cls: 4.3152, loss: 4.3152 +2024-12-27 06:23:07,782 - pyskl - INFO - Epoch [34][3000/3746] lr: 8.799e-02, eta: 3 days, 21:13:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5038, loss_cls: 4.2808, loss: 4.2808 +2024-12-27 06:24:33,297 - pyskl - INFO - Epoch [34][3100/3746] lr: 8.797e-02, eta: 3 days, 21:12:25, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.5028, loss_cls: 4.3225, loss: 4.3225 +2024-12-27 06:25:58,293 - pyskl - INFO - Epoch [34][3200/3746] lr: 8.795e-02, eta: 3 days, 21:11:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5089, loss_cls: 4.2777, loss: 4.2777 +2024-12-27 06:27:23,640 - pyskl - INFO - Epoch [34][3300/3746] lr: 8.793e-02, eta: 3 days, 21:10:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5067, loss_cls: 4.2637, loss: 4.2637 +2024-12-27 06:28:49,607 - pyskl - INFO - Epoch [34][3400/3746] lr: 8.791e-02, eta: 3 days, 21:09:59, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5144, loss_cls: 4.2687, loss: 4.2687 +2024-12-27 06:30:15,636 - pyskl - INFO - Epoch [34][3500/3746] lr: 8.789e-02, eta: 3 days, 21:09:13, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5144, loss_cls: 4.2530, loss: 4.2530 +2024-12-27 06:31:41,692 - pyskl - INFO - Epoch [34][3600/3746] lr: 8.788e-02, eta: 3 days, 21:08:26, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5078, loss_cls: 4.2981, loss: 4.2981 +2024-12-27 06:33:07,634 - pyskl - INFO - Epoch [34][3700/3746] lr: 8.786e-02, eta: 3 days, 21:07:39, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5142, loss_cls: 4.2889, loss: 4.2889 +2024-12-27 06:33:49,256 - pyskl - INFO - Saving checkpoint at 34 epochs +2024-12-27 06:35:49,569 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 06:35:50,283 - pyskl - INFO - +top1_acc 0.2051 +top5_acc 0.4376 +2024-12-27 06:35:50,284 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 06:35:50,345 - pyskl - INFO - +mean_acc 0.2050 +2024-12-27 06:35:50,362 - pyskl - INFO - Epoch(val) [34][309] top1_acc: 0.2051, top5_acc: 0.4376, mean_class_accuracy: 0.2050 +2024-12-27 06:40:08,712 - pyskl - INFO - Epoch [35][100/3746] lr: 8.783e-02, eta: 3 days, 21:14:03, time: 2.583, data_time: 1.557, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5084, loss_cls: 4.2662, loss: 4.2662 +2024-12-27 06:41:34,308 - pyskl - INFO - Epoch [35][200/3746] lr: 8.781e-02, eta: 3 days, 21:13:14, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5128, loss_cls: 4.2607, loss: 4.2607 +2024-12-27 06:42:59,880 - pyskl - INFO - Epoch [35][300/3746] lr: 8.780e-02, eta: 3 days, 21:12:25, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5222, loss_cls: 4.2211, loss: 4.2211 +2024-12-27 06:44:25,379 - pyskl - INFO - Epoch [35][400/3746] lr: 8.778e-02, eta: 3 days, 21:11:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5075, loss_cls: 4.2615, loss: 4.2615 +2024-12-27 06:45:51,050 - pyskl - INFO - Epoch [35][500/3746] lr: 8.776e-02, eta: 3 days, 21:10:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5208, loss_cls: 4.2066, loss: 4.2066 +2024-12-27 06:47:16,709 - pyskl - INFO - Epoch [35][600/3746] lr: 8.774e-02, eta: 3 days, 21:09:58, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5086, loss_cls: 4.2653, loss: 4.2653 +2024-12-27 06:48:42,313 - pyskl - INFO - Epoch [35][700/3746] lr: 8.772e-02, eta: 3 days, 21:09:09, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5164, loss_cls: 4.2542, loss: 4.2542 +2024-12-27 06:50:07,888 - pyskl - INFO - Epoch [35][800/3746] lr: 8.770e-02, eta: 3 days, 21:08:20, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5173, loss_cls: 4.1951, loss: 4.1951 +2024-12-27 06:51:33,316 - pyskl - INFO - Epoch [35][900/3746] lr: 8.769e-02, eta: 3 days, 21:07:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5092, loss_cls: 4.2683, loss: 4.2683 +2024-12-27 06:52:58,646 - pyskl - INFO - Epoch [35][1000/3746] lr: 8.767e-02, eta: 3 days, 21:06:39, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5003, loss_cls: 4.3164, loss: 4.3164 +2024-12-27 06:54:23,980 - pyskl - INFO - Epoch [35][1100/3746] lr: 8.765e-02, eta: 3 days, 21:05:49, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5138, loss_cls: 4.2846, loss: 4.2846 +2024-12-27 06:55:49,545 - pyskl - INFO - Epoch [35][1200/3746] lr: 8.763e-02, eta: 3 days, 21:05:00, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5166, loss_cls: 4.2402, loss: 4.2402 +2024-12-27 06:57:15,497 - pyskl - INFO - Epoch [35][1300/3746] lr: 8.761e-02, eta: 3 days, 21:04:11, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5297, loss_cls: 4.1957, loss: 4.1957 +2024-12-27 06:58:41,088 - pyskl - INFO - Epoch [35][1400/3746] lr: 8.759e-02, eta: 3 days, 21:03:22, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.5075, loss_cls: 4.2984, loss: 4.2984 +2024-12-27 07:00:06,482 - pyskl - INFO - Epoch [35][1500/3746] lr: 8.757e-02, eta: 3 days, 21:02:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5189, loss_cls: 4.2206, loss: 4.2206 +2024-12-27 07:01:32,087 - pyskl - INFO - Epoch [35][1600/3746] lr: 8.756e-02, eta: 3 days, 21:01:42, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5139, loss_cls: 4.2407, loss: 4.2407 +2024-12-27 07:02:57,432 - pyskl - INFO - Epoch [35][1700/3746] lr: 8.754e-02, eta: 3 days, 21:00:51, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5119, loss_cls: 4.2588, loss: 4.2588 +2024-12-27 07:04:23,075 - pyskl - INFO - Epoch [35][1800/3746] lr: 8.752e-02, eta: 3 days, 21:00:02, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5200, loss_cls: 4.1932, loss: 4.1932 +2024-12-27 07:05:48,689 - pyskl - INFO - Epoch [35][1900/3746] lr: 8.750e-02, eta: 3 days, 20:59:12, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5181, loss_cls: 4.2007, loss: 4.2007 +2024-12-27 07:07:14,605 - pyskl - INFO - Epoch [35][2000/3746] lr: 8.748e-02, eta: 3 days, 20:58:23, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5239, loss_cls: 4.2443, loss: 4.2443 +2024-12-27 07:08:40,736 - pyskl - INFO - Epoch [35][2100/3746] lr: 8.746e-02, eta: 3 days, 20:57:35, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5089, loss_cls: 4.2737, loss: 4.2737 +2024-12-27 07:10:06,375 - pyskl - INFO - Epoch [35][2200/3746] lr: 8.745e-02, eta: 3 days, 20:56:45, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5133, loss_cls: 4.2631, loss: 4.2631 +2024-12-27 07:11:32,150 - pyskl - INFO - Epoch [35][2300/3746] lr: 8.743e-02, eta: 3 days, 20:55:56, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5164, loss_cls: 4.2352, loss: 4.2352 +2024-12-27 07:12:57,969 - pyskl - INFO - Epoch [35][2400/3746] lr: 8.741e-02, eta: 3 days, 20:55:06, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5106, loss_cls: 4.2597, loss: 4.2597 +2024-12-27 07:14:24,018 - pyskl - INFO - Epoch [35][2500/3746] lr: 8.739e-02, eta: 3 days, 20:54:18, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5162, loss_cls: 4.2493, loss: 4.2493 +2024-12-27 07:15:49,142 - pyskl - INFO - Epoch [35][2600/3746] lr: 8.737e-02, eta: 3 days, 20:53:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5039, loss_cls: 4.2967, loss: 4.2967 +2024-12-27 07:17:14,635 - pyskl - INFO - Epoch [35][2700/3746] lr: 8.735e-02, eta: 3 days, 20:52:35, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5112, loss_cls: 4.2798, loss: 4.2798 +2024-12-27 07:18:38,837 - pyskl - INFO - Epoch [35][2800/3746] lr: 8.733e-02, eta: 3 days, 20:51:40, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5239, loss_cls: 4.2311, loss: 4.2311 +2024-12-27 07:20:04,045 - pyskl - INFO - Epoch [35][2900/3746] lr: 8.732e-02, eta: 3 days, 20:50:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5098, loss_cls: 4.3336, loss: 4.3336 +2024-12-27 07:21:28,626 - pyskl - INFO - Epoch [35][3000/3746] lr: 8.730e-02, eta: 3 days, 20:49:55, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5097, loss_cls: 4.2527, loss: 4.2527 +2024-12-27 07:22:53,048 - pyskl - INFO - Epoch [35][3100/3746] lr: 8.728e-02, eta: 3 days, 20:49:00, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5192, loss_cls: 4.2536, loss: 4.2536 +2024-12-27 07:24:18,482 - pyskl - INFO - Epoch [35][3200/3746] lr: 8.726e-02, eta: 3 days, 20:48:09, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5081, loss_cls: 4.2606, loss: 4.2606 +2024-12-27 07:25:43,508 - pyskl - INFO - Epoch [35][3300/3746] lr: 8.724e-02, eta: 3 days, 20:47:17, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5058, loss_cls: 4.3143, loss: 4.3143 +2024-12-27 07:27:09,018 - pyskl - INFO - Epoch [35][3400/3746] lr: 8.722e-02, eta: 3 days, 20:46:26, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5150, loss_cls: 4.2549, loss: 4.2549 +2024-12-27 07:28:33,264 - pyskl - INFO - Epoch [35][3500/3746] lr: 8.720e-02, eta: 3 days, 20:45:31, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5100, loss_cls: 4.2512, loss: 4.2512 +2024-12-27 07:29:58,136 - pyskl - INFO - Epoch [35][3600/3746] lr: 8.718e-02, eta: 3 days, 20:44:38, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5053, loss_cls: 4.2716, loss: 4.2716 +2024-12-27 07:31:22,595 - pyskl - INFO - Epoch [35][3700/3746] lr: 8.717e-02, eta: 3 days, 20:43:43, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5175, loss_cls: 4.2762, loss: 4.2762 +2024-12-27 07:32:03,851 - pyskl - INFO - Saving checkpoint at 35 epochs +2024-12-27 07:34:04,680 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 07:34:05,453 - pyskl - INFO - +top1_acc 0.1642 +top5_acc 0.3803 +2024-12-27 07:34:05,454 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 07:34:05,505 - pyskl - INFO - +mean_acc 0.1642 +2024-12-27 07:34:05,518 - pyskl - INFO - Epoch(val) [35][309] top1_acc: 0.1642, top5_acc: 0.3803, mean_class_accuracy: 0.1642 +2024-12-27 07:38:19,998 - pyskl - INFO - Epoch [36][100/3746] lr: 8.714e-02, eta: 3 days, 20:49:34, time: 2.545, data_time: 1.529, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5139, loss_cls: 4.1714, loss: 4.1714 +2024-12-27 07:39:44,916 - pyskl - INFO - Epoch [36][200/3746] lr: 8.712e-02, eta: 3 days, 20:48:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5238, loss_cls: 4.2076, loss: 4.2076 +2024-12-27 07:41:09,839 - pyskl - INFO - Epoch [36][300/3746] lr: 8.710e-02, eta: 3 days, 20:47:47, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5198, loss_cls: 4.2333, loss: 4.2333 +2024-12-27 07:42:34,486 - pyskl - INFO - Epoch [36][400/3746] lr: 8.708e-02, eta: 3 days, 20:46:52, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5225, loss_cls: 4.2335, loss: 4.2335 +2024-12-27 07:43:59,397 - pyskl - INFO - Epoch [36][500/3746] lr: 8.706e-02, eta: 3 days, 20:45:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5227, loss_cls: 4.2341, loss: 4.2341 +2024-12-27 07:45:24,404 - pyskl - INFO - Epoch [36][600/3746] lr: 8.704e-02, eta: 3 days, 20:45:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5186, loss_cls: 4.2025, loss: 4.2025 +2024-12-27 07:46:49,740 - pyskl - INFO - Epoch [36][700/3746] lr: 8.703e-02, eta: 3 days, 20:44:13, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5216, loss_cls: 4.2156, loss: 4.2156 +2024-12-27 07:48:15,124 - pyskl - INFO - Epoch [36][800/3746] lr: 8.701e-02, eta: 3 days, 20:43:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5072, loss_cls: 4.2609, loss: 4.2609 +2024-12-27 07:49:40,043 - pyskl - INFO - Epoch [36][900/3746] lr: 8.699e-02, eta: 3 days, 20:42:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5108, loss_cls: 4.2571, loss: 4.2571 +2024-12-27 07:51:05,105 - pyskl - INFO - Epoch [36][1000/3746] lr: 8.697e-02, eta: 3 days, 20:41:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.5123, loss_cls: 4.2869, loss: 4.2869 +2024-12-27 07:52:30,441 - pyskl - INFO - Epoch [36][1100/3746] lr: 8.695e-02, eta: 3 days, 20:40:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5166, loss_cls: 4.2203, loss: 4.2203 +2024-12-27 07:53:56,135 - pyskl - INFO - Epoch [36][1200/3746] lr: 8.693e-02, eta: 3 days, 20:39:49, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5064, loss_cls: 4.2770, loss: 4.2770 +2024-12-27 07:55:21,533 - pyskl - INFO - Epoch [36][1300/3746] lr: 8.691e-02, eta: 3 days, 20:38:56, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5247, loss_cls: 4.2060, loss: 4.2060 +2024-12-27 07:56:47,572 - pyskl - INFO - Epoch [36][1400/3746] lr: 8.689e-02, eta: 3 days, 20:38:06, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5164, loss_cls: 4.2240, loss: 4.2240 +2024-12-27 07:58:13,682 - pyskl - INFO - Epoch [36][1500/3746] lr: 8.688e-02, eta: 3 days, 20:37:15, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.5156, loss_cls: 4.2974, loss: 4.2974 +2024-12-27 07:59:39,373 - pyskl - INFO - Epoch [36][1600/3746] lr: 8.686e-02, eta: 3 days, 20:36:24, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5058, loss_cls: 4.2918, loss: 4.2918 +2024-12-27 08:01:05,310 - pyskl - INFO - Epoch [36][1700/3746] lr: 8.684e-02, eta: 3 days, 20:35:33, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5111, loss_cls: 4.2639, loss: 4.2639 +2024-12-27 08:02:30,819 - pyskl - INFO - Epoch [36][1800/3746] lr: 8.682e-02, eta: 3 days, 20:34:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5231, loss_cls: 4.1894, loss: 4.1894 +2024-12-27 08:03:56,455 - pyskl - INFO - Epoch [36][1900/3746] lr: 8.680e-02, eta: 3 days, 20:33:48, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5081, loss_cls: 4.2775, loss: 4.2775 +2024-12-27 08:05:22,273 - pyskl - INFO - Epoch [36][2000/3746] lr: 8.678e-02, eta: 3 days, 20:32:56, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5234, loss_cls: 4.2244, loss: 4.2244 +2024-12-27 08:06:47,778 - pyskl - INFO - Epoch [36][2100/3746] lr: 8.676e-02, eta: 3 days, 20:32:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5127, loss_cls: 4.2440, loss: 4.2440 +2024-12-27 08:08:13,792 - pyskl - INFO - Epoch [36][2200/3746] lr: 8.674e-02, eta: 3 days, 20:31:13, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5184, loss_cls: 4.2384, loss: 4.2384 +2024-12-27 08:09:39,697 - pyskl - INFO - Epoch [36][2300/3746] lr: 8.672e-02, eta: 3 days, 20:30:21, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5061, loss_cls: 4.2887, loss: 4.2887 +2024-12-27 08:11:05,663 - pyskl - INFO - Epoch [36][2400/3746] lr: 8.671e-02, eta: 3 days, 20:29:30, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5134, loss_cls: 4.2428, loss: 4.2428 +2024-12-27 08:12:31,003 - pyskl - INFO - Epoch [36][2500/3746] lr: 8.669e-02, eta: 3 days, 20:28:37, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5228, loss_cls: 4.2225, loss: 4.2225 +2024-12-27 08:13:55,670 - pyskl - INFO - Epoch [36][2600/3746] lr: 8.667e-02, eta: 3 days, 20:27:41, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5166, loss_cls: 4.2247, loss: 4.2247 +2024-12-27 08:15:20,547 - pyskl - INFO - Epoch [36][2700/3746] lr: 8.665e-02, eta: 3 days, 20:26:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5045, loss_cls: 4.2991, loss: 4.2991 +2024-12-27 08:16:45,303 - pyskl - INFO - Epoch [36][2800/3746] lr: 8.663e-02, eta: 3 days, 20:25:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5209, loss_cls: 4.2432, loss: 4.2432 +2024-12-27 08:18:11,091 - pyskl - INFO - Epoch [36][2900/3746] lr: 8.661e-02, eta: 3 days, 20:24:59, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5227, loss_cls: 4.2131, loss: 4.2131 +2024-12-27 08:19:36,249 - pyskl - INFO - Epoch [36][3000/3746] lr: 8.659e-02, eta: 3 days, 20:24:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5111, loss_cls: 4.2526, loss: 4.2526 +2024-12-27 08:21:00,892 - pyskl - INFO - Epoch [36][3100/3746] lr: 8.657e-02, eta: 3 days, 20:23:09, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5094, loss_cls: 4.2553, loss: 4.2553 +2024-12-27 08:22:25,974 - pyskl - INFO - Epoch [36][3200/3746] lr: 8.655e-02, eta: 3 days, 20:22:15, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5242, loss_cls: 4.1991, loss: 4.1991 +2024-12-27 08:23:50,844 - pyskl - INFO - Epoch [36][3300/3746] lr: 8.653e-02, eta: 3 days, 20:21:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5100, loss_cls: 4.2345, loss: 4.2345 +2024-12-27 08:25:15,839 - pyskl - INFO - Epoch [36][3400/3746] lr: 8.651e-02, eta: 3 days, 20:20:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5150, loss_cls: 4.2292, loss: 4.2292 +2024-12-27 08:26:40,446 - pyskl - INFO - Epoch [36][3500/3746] lr: 8.650e-02, eta: 3 days, 20:19:28, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5192, loss_cls: 4.2357, loss: 4.2357 +2024-12-27 08:28:05,360 - pyskl - INFO - Epoch [36][3600/3746] lr: 8.648e-02, eta: 3 days, 20:18:33, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5016, loss_cls: 4.3086, loss: 4.3086 +2024-12-27 08:29:30,705 - pyskl - INFO - Epoch [36][3700/3746] lr: 8.646e-02, eta: 3 days, 20:17:39, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5095, loss_cls: 4.2891, loss: 4.2891 +2024-12-27 08:30:12,239 - pyskl - INFO - Saving checkpoint at 36 epochs +2024-12-27 08:32:12,712 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 08:32:13,492 - pyskl - INFO - +top1_acc 0.2026 +top5_acc 0.4277 +2024-12-27 08:32:13,492 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 08:32:13,528 - pyskl - INFO - +mean_acc 0.2024 +2024-12-27 08:32:13,538 - pyskl - INFO - Epoch(val) [36][309] top1_acc: 0.2026, top5_acc: 0.4277, mean_class_accuracy: 0.2024 +2024-12-27 08:36:35,140 - pyskl - INFO - Epoch [37][100/3746] lr: 8.643e-02, eta: 3 days, 20:23:34, time: 2.616, data_time: 1.581, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5319, loss_cls: 4.1703, loss: 4.1703 +2024-12-27 08:38:00,732 - pyskl - INFO - Epoch [37][200/3746] lr: 8.641e-02, eta: 3 days, 20:22:40, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5259, loss_cls: 4.1962, loss: 4.1962 +2024-12-27 08:39:26,203 - pyskl - INFO - Epoch [37][300/3746] lr: 8.639e-02, eta: 3 days, 20:21:46, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5205, loss_cls: 4.2225, loss: 4.2225 +2024-12-27 08:40:51,713 - pyskl - INFO - Epoch [37][400/3746] lr: 8.637e-02, eta: 3 days, 20:20:52, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5150, loss_cls: 4.2310, loss: 4.2310 +2024-12-27 08:42:17,275 - pyskl - INFO - Epoch [37][500/3746] lr: 8.635e-02, eta: 3 days, 20:19:59, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5197, loss_cls: 4.2154, loss: 4.2154 +2024-12-27 08:43:42,445 - pyskl - INFO - Epoch [37][600/3746] lr: 8.633e-02, eta: 3 days, 20:19:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5144, loss_cls: 4.2339, loss: 4.2339 +2024-12-27 08:45:07,933 - pyskl - INFO - Epoch [37][700/3746] lr: 8.631e-02, eta: 3 days, 20:18:09, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5066, loss_cls: 4.2712, loss: 4.2712 +2024-12-27 08:46:33,376 - pyskl - INFO - Epoch [37][800/3746] lr: 8.630e-02, eta: 3 days, 20:17:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5314, loss_cls: 4.1938, loss: 4.1938 +2024-12-27 08:47:58,560 - pyskl - INFO - Epoch [37][900/3746] lr: 8.628e-02, eta: 3 days, 20:16:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5220, loss_cls: 4.2123, loss: 4.2123 +2024-12-27 08:49:24,137 - pyskl - INFO - Epoch [37][1000/3746] lr: 8.626e-02, eta: 3 days, 20:15:26, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5241, loss_cls: 4.2157, loss: 4.2157 +2024-12-27 08:50:49,270 - pyskl - INFO - Epoch [37][1100/3746] lr: 8.624e-02, eta: 3 days, 20:14:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5191, loss_cls: 4.2072, loss: 4.2072 +2024-12-27 08:52:14,825 - pyskl - INFO - Epoch [37][1200/3746] lr: 8.622e-02, eta: 3 days, 20:13:36, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5227, loss_cls: 4.2158, loss: 4.2158 +2024-12-27 08:53:40,297 - pyskl - INFO - Epoch [37][1300/3746] lr: 8.620e-02, eta: 3 days, 20:12:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5178, loss_cls: 4.2176, loss: 4.2176 +2024-12-27 08:55:06,293 - pyskl - INFO - Epoch [37][1400/3746] lr: 8.618e-02, eta: 3 days, 20:11:49, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5178, loss_cls: 4.2280, loss: 4.2280 +2024-12-27 08:56:32,289 - pyskl - INFO - Epoch [37][1500/3746] lr: 8.616e-02, eta: 3 days, 20:10:56, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5155, loss_cls: 4.2369, loss: 4.2369 +2024-12-27 08:57:57,682 - pyskl - INFO - Epoch [37][1600/3746] lr: 8.614e-02, eta: 3 days, 20:10:01, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5106, loss_cls: 4.2798, loss: 4.2798 +2024-12-27 08:59:22,831 - pyskl - INFO - Epoch [37][1700/3746] lr: 8.612e-02, eta: 3 days, 20:09:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5183, loss_cls: 4.2606, loss: 4.2606 +2024-12-27 09:00:48,706 - pyskl - INFO - Epoch [37][1800/3746] lr: 8.610e-02, eta: 3 days, 20:08:12, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5064, loss_cls: 4.2549, loss: 4.2549 +2024-12-27 09:02:14,256 - pyskl - INFO - Epoch [37][1900/3746] lr: 8.608e-02, eta: 3 days, 20:07:17, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5206, loss_cls: 4.2216, loss: 4.2216 +2024-12-27 09:03:40,069 - pyskl - INFO - Epoch [37][2000/3746] lr: 8.606e-02, eta: 3 days, 20:06:23, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5172, loss_cls: 4.2268, loss: 4.2268 +2024-12-27 09:05:06,294 - pyskl - INFO - Epoch [37][2100/3746] lr: 8.604e-02, eta: 3 days, 20:05:31, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5081, loss_cls: 4.2678, loss: 4.2678 +2024-12-27 09:06:31,756 - pyskl - INFO - Epoch [37][2200/3746] lr: 8.602e-02, eta: 3 days, 20:04:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5222, loss_cls: 4.2183, loss: 4.2183 +2024-12-27 09:07:57,540 - pyskl - INFO - Epoch [37][2300/3746] lr: 8.601e-02, eta: 3 days, 20:03:42, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5111, loss_cls: 4.2542, loss: 4.2542 +2024-12-27 09:09:23,140 - pyskl - INFO - Epoch [37][2400/3746] lr: 8.599e-02, eta: 3 days, 20:02:47, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5189, loss_cls: 4.2458, loss: 4.2458 +2024-12-27 09:10:48,451 - pyskl - INFO - Epoch [37][2500/3746] lr: 8.597e-02, eta: 3 days, 20:01:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5108, loss_cls: 4.2566, loss: 4.2566 +2024-12-27 09:12:13,039 - pyskl - INFO - Epoch [37][2600/3746] lr: 8.595e-02, eta: 3 days, 20:00:54, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5117, loss_cls: 4.2573, loss: 4.2573 +2024-12-27 09:13:37,669 - pyskl - INFO - Epoch [37][2700/3746] lr: 8.593e-02, eta: 3 days, 19:59:56, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5141, loss_cls: 4.2527, loss: 4.2527 +2024-12-27 09:15:02,673 - pyskl - INFO - Epoch [37][2800/3746] lr: 8.591e-02, eta: 3 days, 19:58:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5180, loss_cls: 4.2468, loss: 4.2468 +2024-12-27 09:16:27,741 - pyskl - INFO - Epoch [37][2900/3746] lr: 8.589e-02, eta: 3 days, 19:58:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5273, loss_cls: 4.1843, loss: 4.1843 +2024-12-27 09:17:53,062 - pyskl - INFO - Epoch [37][3000/3746] lr: 8.587e-02, eta: 3 days, 19:57:07, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5108, loss_cls: 4.2547, loss: 4.2547 +2024-12-27 09:19:17,699 - pyskl - INFO - Epoch [37][3100/3746] lr: 8.585e-02, eta: 3 days, 19:56:09, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5041, loss_cls: 4.2729, loss: 4.2729 +2024-12-27 09:20:42,920 - pyskl - INFO - Epoch [37][3200/3746] lr: 8.583e-02, eta: 3 days, 19:55:13, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5148, loss_cls: 4.2611, loss: 4.2611 +2024-12-27 09:22:07,731 - pyskl - INFO - Epoch [37][3300/3746] lr: 8.581e-02, eta: 3 days, 19:54:16, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5227, loss_cls: 4.2260, loss: 4.2260 +2024-12-27 09:23:32,671 - pyskl - INFO - Epoch [37][3400/3746] lr: 8.579e-02, eta: 3 days, 19:53:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5156, loss_cls: 4.2490, loss: 4.2490 +2024-12-27 09:24:57,021 - pyskl - INFO - Epoch [37][3500/3746] lr: 8.577e-02, eta: 3 days, 19:52:20, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5161, loss_cls: 4.2183, loss: 4.2183 +2024-12-27 09:26:21,453 - pyskl - INFO - Epoch [37][3600/3746] lr: 8.575e-02, eta: 3 days, 19:51:21, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5181, loss_cls: 4.2704, loss: 4.2704 +2024-12-27 09:27:46,432 - pyskl - INFO - Epoch [37][3700/3746] lr: 8.573e-02, eta: 3 days, 19:50:24, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5100, loss_cls: 4.2846, loss: 4.2846 +2024-12-27 09:28:27,801 - pyskl - INFO - Saving checkpoint at 37 epochs +2024-12-27 09:30:28,547 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 09:30:29,276 - pyskl - INFO - +top1_acc 0.1947 +top5_acc 0.4235 +2024-12-27 09:30:29,276 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 09:30:29,327 - pyskl - INFO - +mean_acc 0.1945 +2024-12-27 09:30:29,342 - pyskl - INFO - Epoch(val) [37][309] top1_acc: 0.1947, top5_acc: 0.4235, mean_class_accuracy: 0.1945 +2024-12-27 09:34:48,412 - pyskl - INFO - Epoch [38][100/3746] lr: 8.570e-02, eta: 3 days, 19:55:53, time: 2.591, data_time: 1.548, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5241, loss_cls: 4.1880, loss: 4.1880 +2024-12-27 09:36:14,943 - pyskl - INFO - Epoch [38][200/3746] lr: 8.568e-02, eta: 3 days, 19:55:00, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5142, loss_cls: 4.1960, loss: 4.1960 +2024-12-27 09:37:41,048 - pyskl - INFO - Epoch [38][300/3746] lr: 8.567e-02, eta: 3 days, 19:54:06, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5289, loss_cls: 4.1759, loss: 4.1759 +2024-12-27 09:39:07,320 - pyskl - INFO - Epoch [38][400/3746] lr: 8.565e-02, eta: 3 days, 19:53:12, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5145, loss_cls: 4.2220, loss: 4.2220 +2024-12-27 09:40:32,793 - pyskl - INFO - Epoch [38][500/3746] lr: 8.563e-02, eta: 3 days, 19:52:16, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5277, loss_cls: 4.1982, loss: 4.1982 +2024-12-27 09:41:58,688 - pyskl - INFO - Epoch [38][600/3746] lr: 8.561e-02, eta: 3 days, 19:51:21, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5089, loss_cls: 4.2320, loss: 4.2320 +2024-12-27 09:43:24,510 - pyskl - INFO - Epoch [38][700/3746] lr: 8.559e-02, eta: 3 days, 19:50:26, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5186, loss_cls: 4.2306, loss: 4.2306 +2024-12-27 09:44:49,966 - pyskl - INFO - Epoch [38][800/3746] lr: 8.557e-02, eta: 3 days, 19:49:29, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5236, loss_cls: 4.2165, loss: 4.2165 +2024-12-27 09:46:15,930 - pyskl - INFO - Epoch [38][900/3746] lr: 8.555e-02, eta: 3 days, 19:48:34, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5214, loss_cls: 4.2153, loss: 4.2153 +2024-12-27 09:47:41,672 - pyskl - INFO - Epoch [38][1000/3746] lr: 8.553e-02, eta: 3 days, 19:47:39, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5128, loss_cls: 4.2487, loss: 4.2487 +2024-12-27 09:49:07,959 - pyskl - INFO - Epoch [38][1100/3746] lr: 8.551e-02, eta: 3 days, 19:46:45, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5280, loss_cls: 4.2136, loss: 4.2136 +2024-12-27 09:50:33,952 - pyskl - INFO - Epoch [38][1200/3746] lr: 8.549e-02, eta: 3 days, 19:45:50, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5233, loss_cls: 4.2007, loss: 4.2007 +2024-12-27 09:51:59,528 - pyskl - INFO - Epoch [38][1300/3746] lr: 8.547e-02, eta: 3 days, 19:44:54, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5136, loss_cls: 4.2516, loss: 4.2516 +2024-12-27 09:53:25,141 - pyskl - INFO - Epoch [38][1400/3746] lr: 8.545e-02, eta: 3 days, 19:43:58, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5202, loss_cls: 4.2629, loss: 4.2629 +2024-12-27 09:54:51,115 - pyskl - INFO - Epoch [38][1500/3746] lr: 8.543e-02, eta: 3 days, 19:43:02, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5200, loss_cls: 4.2382, loss: 4.2382 +2024-12-27 09:56:17,288 - pyskl - INFO - Epoch [38][1600/3746] lr: 8.541e-02, eta: 3 days, 19:42:08, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5197, loss_cls: 4.2347, loss: 4.2347 +2024-12-27 09:57:42,902 - pyskl - INFO - Epoch [38][1700/3746] lr: 8.539e-02, eta: 3 days, 19:41:12, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5166, loss_cls: 4.2481, loss: 4.2481 +2024-12-27 09:59:08,846 - pyskl - INFO - Epoch [38][1800/3746] lr: 8.537e-02, eta: 3 days, 19:40:16, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5195, loss_cls: 4.2155, loss: 4.2155 +2024-12-27 10:00:34,529 - pyskl - INFO - Epoch [38][1900/3746] lr: 8.535e-02, eta: 3 days, 19:39:20, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5186, loss_cls: 4.2004, loss: 4.2004 +2024-12-27 10:02:00,470 - pyskl - INFO - Epoch [38][2000/3746] lr: 8.533e-02, eta: 3 days, 19:38:25, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5178, loss_cls: 4.2316, loss: 4.2316 +2024-12-27 10:03:26,232 - pyskl - INFO - Epoch [38][2100/3746] lr: 8.531e-02, eta: 3 days, 19:37:29, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5244, loss_cls: 4.2032, loss: 4.2032 +2024-12-27 10:04:52,076 - pyskl - INFO - Epoch [38][2200/3746] lr: 8.529e-02, eta: 3 days, 19:36:33, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5095, loss_cls: 4.2577, loss: 4.2577 +2024-12-27 10:06:18,243 - pyskl - INFO - Epoch [38][2300/3746] lr: 8.527e-02, eta: 3 days, 19:35:38, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5234, loss_cls: 4.1861, loss: 4.1861 +2024-12-27 10:07:44,008 - pyskl - INFO - Epoch [38][2400/3746] lr: 8.525e-02, eta: 3 days, 19:34:42, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5119, loss_cls: 4.2741, loss: 4.2741 +2024-12-27 10:09:09,667 - pyskl - INFO - Epoch [38][2500/3746] lr: 8.523e-02, eta: 3 days, 19:33:46, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5116, loss_cls: 4.2417, loss: 4.2417 +2024-12-27 10:10:34,314 - pyskl - INFO - Epoch [38][2600/3746] lr: 8.521e-02, eta: 3 days, 19:32:46, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5173, loss_cls: 4.2222, loss: 4.2222 +2024-12-27 10:11:59,943 - pyskl - INFO - Epoch [38][2700/3746] lr: 8.519e-02, eta: 3 days, 19:31:49, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5172, loss_cls: 4.2217, loss: 4.2217 +2024-12-27 10:13:25,212 - pyskl - INFO - Epoch [38][2800/3746] lr: 8.517e-02, eta: 3 days, 19:30:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5203, loss_cls: 4.2286, loss: 4.2286 +2024-12-27 10:14:50,050 - pyskl - INFO - Epoch [38][2900/3746] lr: 8.515e-02, eta: 3 days, 19:29:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5203, loss_cls: 4.2267, loss: 4.2267 +2024-12-27 10:16:15,117 - pyskl - INFO - Epoch [38][3000/3746] lr: 8.513e-02, eta: 3 days, 19:28:54, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5119, loss_cls: 4.2885, loss: 4.2885 +2024-12-27 10:17:40,012 - pyskl - INFO - Epoch [38][3100/3746] lr: 8.511e-02, eta: 3 days, 19:27:55, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5139, loss_cls: 4.2553, loss: 4.2553 +2024-12-27 10:19:05,195 - pyskl - INFO - Epoch [38][3200/3746] lr: 8.509e-02, eta: 3 days, 19:26:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5186, loss_cls: 4.2273, loss: 4.2273 +2024-12-27 10:20:30,097 - pyskl - INFO - Epoch [38][3300/3746] lr: 8.507e-02, eta: 3 days, 19:25:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5262, loss_cls: 4.2179, loss: 4.2179 +2024-12-27 10:21:55,922 - pyskl - INFO - Epoch [38][3400/3746] lr: 8.505e-02, eta: 3 days, 19:25:02, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5061, loss_cls: 4.2823, loss: 4.2823 +2024-12-27 10:23:21,226 - pyskl - INFO - Epoch [38][3500/3746] lr: 8.503e-02, eta: 3 days, 19:24:04, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5134, loss_cls: 4.2609, loss: 4.2609 +2024-12-27 10:24:47,100 - pyskl - INFO - Epoch [38][3600/3746] lr: 8.501e-02, eta: 3 days, 19:23:07, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5192, loss_cls: 4.2336, loss: 4.2336 +2024-12-27 10:26:12,513 - pyskl - INFO - Epoch [38][3700/3746] lr: 8.499e-02, eta: 3 days, 19:22:10, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5205, loss_cls: 4.2081, loss: 4.2081 +2024-12-27 10:26:54,228 - pyskl - INFO - Saving checkpoint at 38 epochs +2024-12-27 10:28:53,517 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 10:28:54,253 - pyskl - INFO - +top1_acc 0.1995 +top5_acc 0.4360 +2024-12-27 10:28:54,253 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 10:28:54,294 - pyskl - INFO - +mean_acc 0.1993 +2024-12-27 10:28:54,306 - pyskl - INFO - Epoch(val) [38][309] top1_acc: 0.1995, top5_acc: 0.4360, mean_class_accuracy: 0.1993 +2024-12-27 10:33:14,705 - pyskl - INFO - Epoch [39][100/3746] lr: 8.496e-02, eta: 3 days, 19:27:25, time: 2.604, data_time: 1.569, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5162, loss_cls: 4.2022, loss: 4.2022 +2024-12-27 10:34:40,117 - pyskl - INFO - Epoch [39][200/3746] lr: 8.494e-02, eta: 3 days, 19:26:27, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5208, loss_cls: 4.2214, loss: 4.2214 +2024-12-27 10:36:05,091 - pyskl - INFO - Epoch [39][300/3746] lr: 8.492e-02, eta: 3 days, 19:25:27, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5231, loss_cls: 4.2050, loss: 4.2050 +2024-12-27 10:37:30,210 - pyskl - INFO - Epoch [39][400/3746] lr: 8.490e-02, eta: 3 days, 19:24:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5225, loss_cls: 4.2082, loss: 4.2082 +2024-12-27 10:38:55,439 - pyskl - INFO - Epoch [39][500/3746] lr: 8.488e-02, eta: 3 days, 19:23:30, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5253, loss_cls: 4.2018, loss: 4.2018 +2024-12-27 10:40:20,389 - pyskl - INFO - Epoch [39][600/3746] lr: 8.486e-02, eta: 3 days, 19:22:30, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5083, loss_cls: 4.2425, loss: 4.2425 +2024-12-27 10:41:45,716 - pyskl - INFO - Epoch [39][700/3746] lr: 8.484e-02, eta: 3 days, 19:21:31, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5116, loss_cls: 4.2534, loss: 4.2534 +2024-12-27 10:43:10,623 - pyskl - INFO - Epoch [39][800/3746] lr: 8.482e-02, eta: 3 days, 19:20:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5264, loss_cls: 4.2005, loss: 4.2005 +2024-12-27 10:44:35,448 - pyskl - INFO - Epoch [39][900/3746] lr: 8.480e-02, eta: 3 days, 19:19:31, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5233, loss_cls: 4.2081, loss: 4.2081 +2024-12-27 10:46:00,199 - pyskl - INFO - Epoch [39][1000/3746] lr: 8.478e-02, eta: 3 days, 19:18:31, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5255, loss_cls: 4.1856, loss: 4.1856 +2024-12-27 10:47:24,895 - pyskl - INFO - Epoch [39][1100/3746] lr: 8.476e-02, eta: 3 days, 19:17:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5238, loss_cls: 4.1775, loss: 4.1775 +2024-12-27 10:48:50,112 - pyskl - INFO - Epoch [39][1200/3746] lr: 8.474e-02, eta: 3 days, 19:16:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5169, loss_cls: 4.2122, loss: 4.2122 +2024-12-27 10:50:15,549 - pyskl - INFO - Epoch [39][1300/3746] lr: 8.472e-02, eta: 3 days, 19:15:33, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5214, loss_cls: 4.2426, loss: 4.2426 +2024-12-27 10:51:41,325 - pyskl - INFO - Epoch [39][1400/3746] lr: 8.470e-02, eta: 3 days, 19:14:35, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5159, loss_cls: 4.2492, loss: 4.2492 +2024-12-27 10:53:06,965 - pyskl - INFO - Epoch [39][1500/3746] lr: 8.468e-02, eta: 3 days, 19:13:37, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5195, loss_cls: 4.2383, loss: 4.2383 +2024-12-27 10:54:32,725 - pyskl - INFO - Epoch [39][1600/3746] lr: 8.466e-02, eta: 3 days, 19:12:40, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5309, loss_cls: 4.1699, loss: 4.1699 +2024-12-27 10:55:58,714 - pyskl - INFO - Epoch [39][1700/3746] lr: 8.464e-02, eta: 3 days, 19:11:43, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5119, loss_cls: 4.2599, loss: 4.2599 +2024-12-27 10:57:24,422 - pyskl - INFO - Epoch [39][1800/3746] lr: 8.462e-02, eta: 3 days, 19:10:45, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5222, loss_cls: 4.2430, loss: 4.2430 +2024-12-27 10:58:49,913 - pyskl - INFO - Epoch [39][1900/3746] lr: 8.460e-02, eta: 3 days, 19:09:46, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5288, loss_cls: 4.1653, loss: 4.1653 +2024-12-27 11:00:15,291 - pyskl - INFO - Epoch [39][2000/3746] lr: 8.458e-02, eta: 3 days, 19:08:47, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5262, loss_cls: 4.1699, loss: 4.1699 +2024-12-27 11:01:41,111 - pyskl - INFO - Epoch [39][2100/3746] lr: 8.456e-02, eta: 3 days, 19:07:50, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5295, loss_cls: 4.1804, loss: 4.1804 +2024-12-27 11:03:06,852 - pyskl - INFO - Epoch [39][2200/3746] lr: 8.454e-02, eta: 3 days, 19:06:52, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5102, loss_cls: 4.2454, loss: 4.2454 +2024-12-27 11:04:32,307 - pyskl - INFO - Epoch [39][2300/3746] lr: 8.452e-02, eta: 3 days, 19:05:53, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5141, loss_cls: 4.2393, loss: 4.2393 +2024-12-27 11:05:57,663 - pyskl - INFO - Epoch [39][2400/3746] lr: 8.450e-02, eta: 3 days, 19:04:54, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5164, loss_cls: 4.2161, loss: 4.2161 +2024-12-27 11:07:22,281 - pyskl - INFO - Epoch [39][2500/3746] lr: 8.448e-02, eta: 3 days, 19:03:52, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5184, loss_cls: 4.2384, loss: 4.2384 +2024-12-27 11:08:47,270 - pyskl - INFO - Epoch [39][2600/3746] lr: 8.446e-02, eta: 3 days, 19:02:52, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5219, loss_cls: 4.2034, loss: 4.2034 +2024-12-27 11:10:12,595 - pyskl - INFO - Epoch [39][2700/3746] lr: 8.444e-02, eta: 3 days, 19:01:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5239, loss_cls: 4.1920, loss: 4.1920 +2024-12-27 11:11:38,091 - pyskl - INFO - Epoch [39][2800/3746] lr: 8.442e-02, eta: 3 days, 19:00:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5284, loss_cls: 4.2014, loss: 4.2014 +2024-12-27 11:13:02,506 - pyskl - INFO - Epoch [39][2900/3746] lr: 8.440e-02, eta: 3 days, 18:59:52, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5098, loss_cls: 4.2842, loss: 4.2842 +2024-12-27 11:14:27,047 - pyskl - INFO - Epoch [39][3000/3746] lr: 8.438e-02, eta: 3 days, 18:58:50, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5167, loss_cls: 4.2296, loss: 4.2296 +2024-12-27 11:15:51,912 - pyskl - INFO - Epoch [39][3100/3746] lr: 8.436e-02, eta: 3 days, 18:57:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5059, loss_cls: 4.2705, loss: 4.2705 +2024-12-27 11:17:16,919 - pyskl - INFO - Epoch [39][3200/3746] lr: 8.434e-02, eta: 3 days, 18:56:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5144, loss_cls: 4.2430, loss: 4.2430 +2024-12-27 11:18:42,274 - pyskl - INFO - Epoch [39][3300/3746] lr: 8.432e-02, eta: 3 days, 18:55:50, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5250, loss_cls: 4.2121, loss: 4.2121 +2024-12-27 11:20:07,623 - pyskl - INFO - Epoch [39][3400/3746] lr: 8.430e-02, eta: 3 days, 18:54:50, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5162, loss_cls: 4.2290, loss: 4.2290 +2024-12-27 11:21:32,798 - pyskl - INFO - Epoch [39][3500/3746] lr: 8.428e-02, eta: 3 days, 18:53:50, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5267, loss_cls: 4.1707, loss: 4.1707 +2024-12-27 11:22:57,767 - pyskl - INFO - Epoch [39][3600/3746] lr: 8.426e-02, eta: 3 days, 18:52:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5239, loss_cls: 4.2260, loss: 4.2260 +2024-12-27 11:24:22,677 - pyskl - INFO - Epoch [39][3700/3746] lr: 8.424e-02, eta: 3 days, 18:51:49, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5239, loss_cls: 4.2137, loss: 4.2137 +2024-12-27 11:25:03,417 - pyskl - INFO - Saving checkpoint at 39 epochs +2024-12-27 11:27:04,708 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 11:27:05,504 - pyskl - INFO - +top1_acc 0.1987 +top5_acc 0.4304 +2024-12-27 11:27:05,505 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 11:27:05,557 - pyskl - INFO - +mean_acc 0.1987 +2024-12-27 11:27:05,580 - pyskl - INFO - Epoch(val) [39][309] top1_acc: 0.1987, top5_acc: 0.4304, mean_class_accuracy: 0.1987 +2024-12-27 11:31:25,001 - pyskl - INFO - Epoch [40][100/3746] lr: 8.421e-02, eta: 3 days, 18:56:45, time: 2.594, data_time: 1.555, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5337, loss_cls: 4.1800, loss: 4.1800 +2024-12-27 11:32:50,544 - pyskl - INFO - Epoch [40][200/3746] lr: 8.419e-02, eta: 3 days, 18:55:45, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5245, loss_cls: 4.1681, loss: 4.1681 +2024-12-27 11:34:15,699 - pyskl - INFO - Epoch [40][300/3746] lr: 8.417e-02, eta: 3 days, 18:54:45, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5183, loss_cls: 4.2058, loss: 4.2058 +2024-12-27 11:35:40,776 - pyskl - INFO - Epoch [40][400/3746] lr: 8.415e-02, eta: 3 days, 18:53:44, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5259, loss_cls: 4.1748, loss: 4.1748 +2024-12-27 11:37:05,718 - pyskl - INFO - Epoch [40][500/3746] lr: 8.413e-02, eta: 3 days, 18:52:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5155, loss_cls: 4.2081, loss: 4.2081 +2024-12-27 11:38:30,718 - pyskl - INFO - Epoch [40][600/3746] lr: 8.411e-02, eta: 3 days, 18:51:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5312, loss_cls: 4.1576, loss: 4.1576 +2024-12-27 11:39:55,790 - pyskl - INFO - Epoch [40][700/3746] lr: 8.408e-02, eta: 3 days, 18:50:41, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5147, loss_cls: 4.2237, loss: 4.2237 +2024-12-27 11:41:20,763 - pyskl - INFO - Epoch [40][800/3746] lr: 8.406e-02, eta: 3 days, 18:49:39, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5270, loss_cls: 4.1761, loss: 4.1761 +2024-12-27 11:42:46,080 - pyskl - INFO - Epoch [40][900/3746] lr: 8.404e-02, eta: 3 days, 18:48:39, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5225, loss_cls: 4.1832, loss: 4.1832 +2024-12-27 11:44:11,155 - pyskl - INFO - Epoch [40][1000/3746] lr: 8.402e-02, eta: 3 days, 18:47:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5225, loss_cls: 4.2123, loss: 4.2123 +2024-12-27 11:45:35,898 - pyskl - INFO - Epoch [40][1100/3746] lr: 8.400e-02, eta: 3 days, 18:46:36, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5159, loss_cls: 4.2267, loss: 4.2267 +2024-12-27 11:47:00,857 - pyskl - INFO - Epoch [40][1200/3746] lr: 8.398e-02, eta: 3 days, 18:45:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5183, loss_cls: 4.2109, loss: 4.2109 +2024-12-27 11:48:25,551 - pyskl - INFO - Epoch [40][1300/3746] lr: 8.396e-02, eta: 3 days, 18:44:32, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5084, loss_cls: 4.2620, loss: 4.2620 +2024-12-27 11:49:50,820 - pyskl - INFO - Epoch [40][1400/3746] lr: 8.394e-02, eta: 3 days, 18:43:31, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2720, top5_acc: 0.5258, loss_cls: 4.2246, loss: 4.2246 +2024-12-27 11:51:15,956 - pyskl - INFO - Epoch [40][1500/3746] lr: 8.392e-02, eta: 3 days, 18:42:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5052, loss_cls: 4.3009, loss: 4.3009 +2024-12-27 11:52:40,904 - pyskl - INFO - Epoch [40][1600/3746] lr: 8.390e-02, eta: 3 days, 18:41:29, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5202, loss_cls: 4.2150, loss: 4.2150 +2024-12-27 11:54:06,450 - pyskl - INFO - Epoch [40][1700/3746] lr: 8.388e-02, eta: 3 days, 18:40:29, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5158, loss_cls: 4.2266, loss: 4.2266 +2024-12-27 11:55:32,123 - pyskl - INFO - Epoch [40][1800/3746] lr: 8.386e-02, eta: 3 days, 18:39:29, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5192, loss_cls: 4.2212, loss: 4.2212 +2024-12-27 11:56:58,470 - pyskl - INFO - Epoch [40][1900/3746] lr: 8.384e-02, eta: 3 days, 18:38:31, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5197, loss_cls: 4.2060, loss: 4.2060 +2024-12-27 11:58:24,001 - pyskl - INFO - Epoch [40][2000/3746] lr: 8.382e-02, eta: 3 days, 18:37:31, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5138, loss_cls: 4.2577, loss: 4.2577 +2024-12-27 11:59:49,363 - pyskl - INFO - Epoch [40][2100/3746] lr: 8.380e-02, eta: 3 days, 18:36:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5209, loss_cls: 4.2080, loss: 4.2080 +2024-12-27 12:01:15,042 - pyskl - INFO - Epoch [40][2200/3746] lr: 8.378e-02, eta: 3 days, 18:35:31, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5197, loss_cls: 4.2487, loss: 4.2487 +2024-12-27 12:02:40,786 - pyskl - INFO - Epoch [40][2300/3746] lr: 8.376e-02, eta: 3 days, 18:34:31, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5133, loss_cls: 4.2410, loss: 4.2410 +2024-12-27 12:04:05,786 - pyskl - INFO - Epoch [40][2400/3746] lr: 8.374e-02, eta: 3 days, 18:33:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5238, loss_cls: 4.2158, loss: 4.2158 +2024-12-27 12:05:30,427 - pyskl - INFO - Epoch [40][2500/3746] lr: 8.371e-02, eta: 3 days, 18:32:26, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5236, loss_cls: 4.2198, loss: 4.2198 +2024-12-27 12:06:55,314 - pyskl - INFO - Epoch [40][2600/3746] lr: 8.369e-02, eta: 3 days, 18:31:24, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5259, loss_cls: 4.2057, loss: 4.2057 +2024-12-27 12:08:20,247 - pyskl - INFO - Epoch [40][2700/3746] lr: 8.367e-02, eta: 3 days, 18:30:22, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5228, loss_cls: 4.2278, loss: 4.2278 +2024-12-27 12:09:45,126 - pyskl - INFO - Epoch [40][2800/3746] lr: 8.365e-02, eta: 3 days, 18:29:20, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5259, loss_cls: 4.1986, loss: 4.1986 +2024-12-27 12:11:09,579 - pyskl - INFO - Epoch [40][2900/3746] lr: 8.363e-02, eta: 3 days, 18:28:17, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5242, loss_cls: 4.2081, loss: 4.2081 +2024-12-27 12:12:34,560 - pyskl - INFO - Epoch [40][3000/3746] lr: 8.361e-02, eta: 3 days, 18:27:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5186, loss_cls: 4.1962, loss: 4.1962 +2024-12-27 12:13:59,747 - pyskl - INFO - Epoch [40][3100/3746] lr: 8.359e-02, eta: 3 days, 18:26:13, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5266, loss_cls: 4.1851, loss: 4.1851 +2024-12-27 12:15:24,985 - pyskl - INFO - Epoch [40][3200/3746] lr: 8.357e-02, eta: 3 days, 18:25:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5194, loss_cls: 4.2089, loss: 4.2089 +2024-12-27 12:16:50,314 - pyskl - INFO - Epoch [40][3300/3746] lr: 8.355e-02, eta: 3 days, 18:24:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5288, loss_cls: 4.1494, loss: 4.1494 +2024-12-27 12:18:15,829 - pyskl - INFO - Epoch [40][3400/3746] lr: 8.353e-02, eta: 3 days, 18:23:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5048, loss_cls: 4.3131, loss: 4.3131 +2024-12-27 12:19:40,505 - pyskl - INFO - Epoch [40][3500/3746] lr: 8.351e-02, eta: 3 days, 18:22:07, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5172, loss_cls: 4.2135, loss: 4.2135 +2024-12-27 12:21:04,319 - pyskl - INFO - Epoch [40][3600/3746] lr: 8.349e-02, eta: 3 days, 18:21:02, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5170, loss_cls: 4.2121, loss: 4.2121 +2024-12-27 12:22:28,466 - pyskl - INFO - Epoch [40][3700/3746] lr: 8.347e-02, eta: 3 days, 18:19:57, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5133, loss_cls: 4.2368, loss: 4.2368 +2024-12-27 12:23:09,547 - pyskl - INFO - Saving checkpoint at 40 epochs +2024-12-27 12:25:08,624 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 12:25:09,391 - pyskl - INFO - +top1_acc 0.1973 +top5_acc 0.4315 +2024-12-27 12:25:09,391 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 12:25:09,432 - pyskl - INFO - +mean_acc 0.1971 +2024-12-27 12:25:09,443 - pyskl - INFO - Epoch(val) [40][309] top1_acc: 0.1973, top5_acc: 0.4315, mean_class_accuracy: 0.1971 +2024-12-27 12:29:21,781 - pyskl - INFO - Epoch [41][100/3746] lr: 8.344e-02, eta: 3 days, 18:24:19, time: 2.523, data_time: 1.506, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5333, loss_cls: 4.1454, loss: 4.1454 +2024-12-27 12:30:46,645 - pyskl - INFO - Epoch [41][200/3746] lr: 8.342e-02, eta: 3 days, 18:23:16, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5219, loss_cls: 4.1668, loss: 4.1668 +2024-12-27 12:32:11,059 - pyskl - INFO - Epoch [41][300/3746] lr: 8.339e-02, eta: 3 days, 18:22:12, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5308, loss_cls: 4.1340, loss: 4.1340 +2024-12-27 12:33:35,766 - pyskl - INFO - Epoch [41][400/3746] lr: 8.337e-02, eta: 3 days, 18:21:08, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5228, loss_cls: 4.2062, loss: 4.2062 +2024-12-27 12:35:00,476 - pyskl - INFO - Epoch [41][500/3746] lr: 8.335e-02, eta: 3 days, 18:20:05, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5170, loss_cls: 4.2389, loss: 4.2389 +2024-12-27 12:36:25,315 - pyskl - INFO - Epoch [41][600/3746] lr: 8.333e-02, eta: 3 days, 18:19:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5273, loss_cls: 4.1725, loss: 4.1725 +2024-12-27 12:37:49,618 - pyskl - INFO - Epoch [41][700/3746] lr: 8.331e-02, eta: 3 days, 18:17:57, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5219, loss_cls: 4.2039, loss: 4.2039 +2024-12-27 12:39:14,249 - pyskl - INFO - Epoch [41][800/3746] lr: 8.329e-02, eta: 3 days, 18:16:53, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5228, loss_cls: 4.2051, loss: 4.2051 +2024-12-27 12:40:39,725 - pyskl - INFO - Epoch [41][900/3746] lr: 8.327e-02, eta: 3 days, 18:15:52, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5366, loss_cls: 4.1312, loss: 4.1312 +2024-12-27 12:42:04,395 - pyskl - INFO - Epoch [41][1000/3746] lr: 8.325e-02, eta: 3 days, 18:14:48, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5231, loss_cls: 4.2179, loss: 4.2179 +2024-12-27 12:43:29,112 - pyskl - INFO - Epoch [41][1100/3746] lr: 8.323e-02, eta: 3 days, 18:13:45, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5181, loss_cls: 4.2184, loss: 4.2184 +2024-12-27 12:44:54,093 - pyskl - INFO - Epoch [41][1200/3746] lr: 8.321e-02, eta: 3 days, 18:12:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5311, loss_cls: 4.1806, loss: 4.1806 +2024-12-27 12:46:19,285 - pyskl - INFO - Epoch [41][1300/3746] lr: 8.319e-02, eta: 3 days, 18:11:39, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5294, loss_cls: 4.1793, loss: 4.1793 +2024-12-27 12:47:44,798 - pyskl - INFO - Epoch [41][1400/3746] lr: 8.316e-02, eta: 3 days, 18:10:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5162, loss_cls: 4.2497, loss: 4.2497 +2024-12-27 12:49:09,449 - pyskl - INFO - Epoch [41][1500/3746] lr: 8.314e-02, eta: 3 days, 18:09:34, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5120, loss_cls: 4.2261, loss: 4.2261 +2024-12-27 12:50:34,677 - pyskl - INFO - Epoch [41][1600/3746] lr: 8.312e-02, eta: 3 days, 18:08:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5200, loss_cls: 4.2149, loss: 4.2149 +2024-12-27 12:51:59,258 - pyskl - INFO - Epoch [41][1700/3746] lr: 8.310e-02, eta: 3 days, 18:07:28, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5041, loss_cls: 4.2831, loss: 4.2831 +2024-12-27 12:53:24,531 - pyskl - INFO - Epoch [41][1800/3746] lr: 8.308e-02, eta: 3 days, 18:06:25, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5181, loss_cls: 4.2378, loss: 4.2378 +2024-12-27 12:54:49,638 - pyskl - INFO - Epoch [41][1900/3746] lr: 8.306e-02, eta: 3 days, 18:05:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5258, loss_cls: 4.1856, loss: 4.1856 +2024-12-27 12:56:14,868 - pyskl - INFO - Epoch [41][2000/3746] lr: 8.304e-02, eta: 3 days, 18:04:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5194, loss_cls: 4.1978, loss: 4.1978 +2024-12-27 12:57:40,326 - pyskl - INFO - Epoch [41][2100/3746] lr: 8.302e-02, eta: 3 days, 18:03:18, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5203, loss_cls: 4.2166, loss: 4.2166 +2024-12-27 12:59:05,740 - pyskl - INFO - Epoch [41][2200/3746] lr: 8.300e-02, eta: 3 days, 18:02:16, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5155, loss_cls: 4.2261, loss: 4.2261 +2024-12-27 13:00:31,345 - pyskl - INFO - Epoch [41][2300/3746] lr: 8.298e-02, eta: 3 days, 18:01:15, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5194, loss_cls: 4.2231, loss: 4.2231 +2024-12-27 13:01:56,205 - pyskl - INFO - Epoch [41][2400/3746] lr: 8.296e-02, eta: 3 days, 18:00:11, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5191, loss_cls: 4.2173, loss: 4.2173 +2024-12-27 13:03:20,685 - pyskl - INFO - Epoch [41][2500/3746] lr: 8.293e-02, eta: 3 days, 17:59:07, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5169, loss_cls: 4.2296, loss: 4.2296 +2024-12-27 13:04:45,456 - pyskl - INFO - Epoch [41][2600/3746] lr: 8.291e-02, eta: 3 days, 17:58:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5078, loss_cls: 4.2498, loss: 4.2498 +2024-12-27 13:06:10,878 - pyskl - INFO - Epoch [41][2700/3746] lr: 8.289e-02, eta: 3 days, 17:57:01, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5212, loss_cls: 4.2193, loss: 4.2193 +2024-12-27 13:07:35,928 - pyskl - INFO - Epoch [41][2800/3746] lr: 8.287e-02, eta: 3 days, 17:55:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5186, loss_cls: 4.2147, loss: 4.2147 +2024-12-27 13:09:01,037 - pyskl - INFO - Epoch [41][2900/3746] lr: 8.285e-02, eta: 3 days, 17:54:54, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5211, loss_cls: 4.2203, loss: 4.2203 +2024-12-27 13:10:25,684 - pyskl - INFO - Epoch [41][3000/3746] lr: 8.283e-02, eta: 3 days, 17:53:50, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5233, loss_cls: 4.1949, loss: 4.1949 +2024-12-27 13:11:50,818 - pyskl - INFO - Epoch [41][3100/3746] lr: 8.281e-02, eta: 3 days, 17:52:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5233, loss_cls: 4.2118, loss: 4.2118 +2024-12-27 13:13:15,780 - pyskl - INFO - Epoch [41][3200/3746] lr: 8.279e-02, eta: 3 days, 17:51:44, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5278, loss_cls: 4.2072, loss: 4.2072 +2024-12-27 13:14:41,004 - pyskl - INFO - Epoch [41][3300/3746] lr: 8.277e-02, eta: 3 days, 17:50:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5241, loss_cls: 4.1950, loss: 4.1950 +2024-12-27 13:16:06,300 - pyskl - INFO - Epoch [41][3400/3746] lr: 8.274e-02, eta: 3 days, 17:49:38, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5073, loss_cls: 4.2537, loss: 4.2537 +2024-12-27 13:17:31,794 - pyskl - INFO - Epoch [41][3500/3746] lr: 8.272e-02, eta: 3 days, 17:48:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5177, loss_cls: 4.2184, loss: 4.2184 +2024-12-27 13:18:56,746 - pyskl - INFO - Epoch [41][3600/3746] lr: 8.270e-02, eta: 3 days, 17:47:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5256, loss_cls: 4.1974, loss: 4.1974 +2024-12-27 13:20:22,003 - pyskl - INFO - Epoch [41][3700/3746] lr: 8.268e-02, eta: 3 days, 17:46:29, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5238, loss_cls: 4.1884, loss: 4.1884 +2024-12-27 13:21:03,603 - pyskl - INFO - Saving checkpoint at 41 epochs +2024-12-27 13:23:03,071 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 13:23:03,781 - pyskl - INFO - +top1_acc 0.2011 +top5_acc 0.4270 +2024-12-27 13:23:03,781 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 13:23:03,828 - pyskl - INFO - +mean_acc 0.2011 +2024-12-27 13:23:03,842 - pyskl - INFO - Epoch(val) [41][309] top1_acc: 0.2011, top5_acc: 0.4270, mean_class_accuracy: 0.2011 +2024-12-27 13:27:19,833 - pyskl - INFO - Epoch [42][100/3746] lr: 8.265e-02, eta: 3 days, 17:50:47, time: 2.560, data_time: 1.520, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5319, loss_cls: 4.1655, loss: 4.1655 +2024-12-27 13:28:45,005 - pyskl - INFO - Epoch [42][200/3746] lr: 8.263e-02, eta: 3 days, 17:49:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5370, loss_cls: 4.1537, loss: 4.1537 +2024-12-27 13:30:10,693 - pyskl - INFO - Epoch [42][300/3746] lr: 8.261e-02, eta: 3 days, 17:48:41, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5219, loss_cls: 4.2033, loss: 4.2033 +2024-12-27 13:31:36,066 - pyskl - INFO - Epoch [42][400/3746] lr: 8.259e-02, eta: 3 days, 17:47:38, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5281, loss_cls: 4.1850, loss: 4.1850 +2024-12-27 13:33:01,113 - pyskl - INFO - Epoch [42][500/3746] lr: 8.257e-02, eta: 3 days, 17:46:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5188, loss_cls: 4.2068, loss: 4.2068 +2024-12-27 13:34:26,260 - pyskl - INFO - Epoch [42][600/3746] lr: 8.254e-02, eta: 3 days, 17:45:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5169, loss_cls: 4.2267, loss: 4.2267 +2024-12-27 13:35:51,648 - pyskl - INFO - Epoch [42][700/3746] lr: 8.252e-02, eta: 3 days, 17:44:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5253, loss_cls: 4.2124, loss: 4.2124 +2024-12-27 13:37:17,105 - pyskl - INFO - Epoch [42][800/3746] lr: 8.250e-02, eta: 3 days, 17:43:25, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5270, loss_cls: 4.1878, loss: 4.1878 +2024-12-27 13:38:42,339 - pyskl - INFO - Epoch [42][900/3746] lr: 8.248e-02, eta: 3 days, 17:42:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5192, loss_cls: 4.2007, loss: 4.2007 +2024-12-27 13:40:07,509 - pyskl - INFO - Epoch [42][1000/3746] lr: 8.246e-02, eta: 3 days, 17:41:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5212, loss_cls: 4.2126, loss: 4.2126 +2024-12-27 13:41:33,555 - pyskl - INFO - Epoch [42][1100/3746] lr: 8.244e-02, eta: 3 days, 17:40:16, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5247, loss_cls: 4.1761, loss: 4.1761 +2024-12-27 13:42:59,402 - pyskl - INFO - Epoch [42][1200/3746] lr: 8.242e-02, eta: 3 days, 17:39:14, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5131, loss_cls: 4.2400, loss: 4.2400 +2024-12-27 13:44:25,269 - pyskl - INFO - Epoch [42][1300/3746] lr: 8.240e-02, eta: 3 days, 17:38:12, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5267, loss_cls: 4.1777, loss: 4.1777 +2024-12-27 13:45:50,794 - pyskl - INFO - Epoch [42][1400/3746] lr: 8.237e-02, eta: 3 days, 17:37:09, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5181, loss_cls: 4.2269, loss: 4.2269 +2024-12-27 13:47:16,008 - pyskl - INFO - Epoch [42][1500/3746] lr: 8.235e-02, eta: 3 days, 17:36:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5216, loss_cls: 4.2041, loss: 4.2041 +2024-12-27 13:48:41,155 - pyskl - INFO - Epoch [42][1600/3746] lr: 8.233e-02, eta: 3 days, 17:35:01, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5173, loss_cls: 4.2166, loss: 4.2166 +2024-12-27 13:50:06,814 - pyskl - INFO - Epoch [42][1700/3746] lr: 8.231e-02, eta: 3 days, 17:33:59, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5209, loss_cls: 4.1595, loss: 4.1595 +2024-12-27 13:51:32,591 - pyskl - INFO - Epoch [42][1800/3746] lr: 8.229e-02, eta: 3 days, 17:32:56, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5164, loss_cls: 4.2097, loss: 4.2097 +2024-12-27 13:52:58,582 - pyskl - INFO - Epoch [42][1900/3746] lr: 8.227e-02, eta: 3 days, 17:31:55, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5325, loss_cls: 4.1618, loss: 4.1618 +2024-12-27 13:54:25,201 - pyskl - INFO - Epoch [42][2000/3746] lr: 8.225e-02, eta: 3 days, 17:30:54, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5138, loss_cls: 4.2635, loss: 4.2635 +2024-12-27 13:55:50,890 - pyskl - INFO - Epoch [42][2100/3746] lr: 8.222e-02, eta: 3 days, 17:29:52, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5184, loss_cls: 4.2300, loss: 4.2300 +2024-12-27 13:57:17,162 - pyskl - INFO - Epoch [42][2200/3746] lr: 8.220e-02, eta: 3 days, 17:28:51, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5278, loss_cls: 4.2144, loss: 4.2144 +2024-12-27 13:58:42,786 - pyskl - INFO - Epoch [42][2300/3746] lr: 8.218e-02, eta: 3 days, 17:27:48, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5234, loss_cls: 4.2090, loss: 4.2090 +2024-12-27 14:00:07,999 - pyskl - INFO - Epoch [42][2400/3746] lr: 8.216e-02, eta: 3 days, 17:26:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5261, loss_cls: 4.1729, loss: 4.1729 +2024-12-27 14:01:32,809 - pyskl - INFO - Epoch [42][2500/3746] lr: 8.214e-02, eta: 3 days, 17:25:39, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5194, loss_cls: 4.2101, loss: 4.2101 +2024-12-27 14:02:57,641 - pyskl - INFO - Epoch [42][2600/3746] lr: 8.212e-02, eta: 3 days, 17:24:33, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5288, loss_cls: 4.1833, loss: 4.1833 +2024-12-27 14:04:22,906 - pyskl - INFO - Epoch [42][2700/3746] lr: 8.210e-02, eta: 3 days, 17:23:30, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5248, loss_cls: 4.1872, loss: 4.1872 +2024-12-27 14:05:48,343 - pyskl - INFO - Epoch [42][2800/3746] lr: 8.207e-02, eta: 3 days, 17:22:26, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5144, loss_cls: 4.2471, loss: 4.2471 +2024-12-27 14:07:12,861 - pyskl - INFO - Epoch [42][2900/3746] lr: 8.205e-02, eta: 3 days, 17:21:20, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5142, loss_cls: 4.2223, loss: 4.2223 +2024-12-27 14:08:37,773 - pyskl - INFO - Epoch [42][3000/3746] lr: 8.203e-02, eta: 3 days, 17:20:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5183, loss_cls: 4.2095, loss: 4.2095 +2024-12-27 14:10:02,696 - pyskl - INFO - Epoch [42][3100/3746] lr: 8.201e-02, eta: 3 days, 17:19:10, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5275, loss_cls: 4.2059, loss: 4.2059 +2024-12-27 14:11:27,942 - pyskl - INFO - Epoch [42][3200/3746] lr: 8.199e-02, eta: 3 days, 17:18:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5269, loss_cls: 4.2093, loss: 4.2093 +2024-12-27 14:12:52,357 - pyskl - INFO - Epoch [42][3300/3746] lr: 8.197e-02, eta: 3 days, 17:17:00, time: 0.844, data_time: 0.001, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5253, loss_cls: 4.1659, loss: 4.1659 +2024-12-27 14:14:17,762 - pyskl - INFO - Epoch [42][3400/3746] lr: 8.195e-02, eta: 3 days, 17:15:56, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5127, loss_cls: 4.2236, loss: 4.2236 +2024-12-27 14:15:42,840 - pyskl - INFO - Epoch [42][3500/3746] lr: 8.192e-02, eta: 3 days, 17:14:51, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5194, loss_cls: 4.2138, loss: 4.2138 +2024-12-27 14:17:08,267 - pyskl - INFO - Epoch [42][3600/3746] lr: 8.190e-02, eta: 3 days, 17:13:48, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5194, loss_cls: 4.2276, loss: 4.2276 +2024-12-27 14:18:33,534 - pyskl - INFO - Epoch [42][3700/3746] lr: 8.188e-02, eta: 3 days, 17:12:43, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5294, loss_cls: 4.1744, loss: 4.1744 +2024-12-27 14:19:14,838 - pyskl - INFO - Saving checkpoint at 42 epochs +2024-12-27 14:21:13,920 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 14:21:14,651 - pyskl - INFO - +top1_acc 0.1813 +top5_acc 0.4056 +2024-12-27 14:21:14,652 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 14:21:14,714 - pyskl - INFO - +mean_acc 0.1810 +2024-12-27 14:21:14,727 - pyskl - INFO - Epoch(val) [42][309] top1_acc: 0.1813, top5_acc: 0.4056, mean_class_accuracy: 0.1810 +2024-12-27 14:25:42,471 - pyskl - INFO - Epoch [43][100/3746] lr: 8.185e-02, eta: 3 days, 17:17:18, time: 2.677, data_time: 1.617, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5248, loss_cls: 4.1958, loss: 4.1958 +2024-12-27 14:27:07,895 - pyskl - INFO - Epoch [43][200/3746] lr: 8.183e-02, eta: 3 days, 17:16:13, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5342, loss_cls: 4.1509, loss: 4.1509 +2024-12-27 14:28:33,705 - pyskl - INFO - Epoch [43][300/3746] lr: 8.181e-02, eta: 3 days, 17:15:10, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5306, loss_cls: 4.1313, loss: 4.1313 +2024-12-27 14:29:59,416 - pyskl - INFO - Epoch [43][400/3746] lr: 8.179e-02, eta: 3 days, 17:14:07, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5209, loss_cls: 4.2414, loss: 4.2414 +2024-12-27 14:31:24,833 - pyskl - INFO - Epoch [43][500/3746] lr: 8.176e-02, eta: 3 days, 17:13:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5200, loss_cls: 4.1905, loss: 4.1905 +2024-12-27 14:32:50,544 - pyskl - INFO - Epoch [43][600/3746] lr: 8.174e-02, eta: 3 days, 17:11:59, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5197, loss_cls: 4.2448, loss: 4.2448 +2024-12-27 14:34:16,130 - pyskl - INFO - Epoch [43][700/3746] lr: 8.172e-02, eta: 3 days, 17:10:55, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5112, loss_cls: 4.2145, loss: 4.2145 +2024-12-27 14:35:41,466 - pyskl - INFO - Epoch [43][800/3746] lr: 8.170e-02, eta: 3 days, 17:09:50, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5309, loss_cls: 4.1832, loss: 4.1832 +2024-12-27 14:37:07,161 - pyskl - INFO - Epoch [43][900/3746] lr: 8.168e-02, eta: 3 days, 17:08:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5305, loss_cls: 4.1803, loss: 4.1803 +2024-12-27 14:38:32,909 - pyskl - INFO - Epoch [43][1000/3746] lr: 8.166e-02, eta: 3 days, 17:07:43, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5253, loss_cls: 4.2036, loss: 4.2036 +2024-12-27 14:39:58,737 - pyskl - INFO - Epoch [43][1100/3746] lr: 8.163e-02, eta: 3 days, 17:06:40, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5128, loss_cls: 4.2059, loss: 4.2059 +2024-12-27 14:41:24,256 - pyskl - INFO - Epoch [43][1200/3746] lr: 8.161e-02, eta: 3 days, 17:05:35, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5175, loss_cls: 4.1645, loss: 4.1645 +2024-12-27 14:42:49,980 - pyskl - INFO - Epoch [43][1300/3746] lr: 8.159e-02, eta: 3 days, 17:04:32, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5223, loss_cls: 4.1858, loss: 4.1858 +2024-12-27 14:44:15,316 - pyskl - INFO - Epoch [43][1400/3746] lr: 8.157e-02, eta: 3 days, 17:03:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5194, loss_cls: 4.1951, loss: 4.1951 +2024-12-27 14:45:41,258 - pyskl - INFO - Epoch [43][1500/3746] lr: 8.155e-02, eta: 3 days, 17:02:24, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5367, loss_cls: 4.1359, loss: 4.1359 +2024-12-27 14:47:06,490 - pyskl - INFO - Epoch [43][1600/3746] lr: 8.153e-02, eta: 3 days, 17:01:19, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5278, loss_cls: 4.1714, loss: 4.1714 +2024-12-27 14:48:32,330 - pyskl - INFO - Epoch [43][1700/3746] lr: 8.150e-02, eta: 3 days, 17:00:15, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5238, loss_cls: 4.1869, loss: 4.1869 +2024-12-27 14:49:57,394 - pyskl - INFO - Epoch [43][1800/3746] lr: 8.148e-02, eta: 3 days, 16:59:09, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5294, loss_cls: 4.1886, loss: 4.1886 +2024-12-27 14:51:22,573 - pyskl - INFO - Epoch [43][1900/3746] lr: 8.146e-02, eta: 3 days, 16:58:04, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5200, loss_cls: 4.2251, loss: 4.2251 +2024-12-27 14:52:47,865 - pyskl - INFO - Epoch [43][2000/3746] lr: 8.144e-02, eta: 3 days, 16:56:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5123, loss_cls: 4.2456, loss: 4.2456 +2024-12-27 14:54:13,435 - pyskl - INFO - Epoch [43][2100/3746] lr: 8.142e-02, eta: 3 days, 16:55:55, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5178, loss_cls: 4.2044, loss: 4.2044 +2024-12-27 14:55:38,696 - pyskl - INFO - Epoch [43][2200/3746] lr: 8.140e-02, eta: 3 days, 16:54:50, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5241, loss_cls: 4.1876, loss: 4.1876 +2024-12-27 14:57:04,142 - pyskl - INFO - Epoch [43][2300/3746] lr: 8.137e-02, eta: 3 days, 16:53:45, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5336, loss_cls: 4.1724, loss: 4.1724 +2024-12-27 14:58:28,879 - pyskl - INFO - Epoch [43][2400/3746] lr: 8.135e-02, eta: 3 days, 16:52:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5289, loss_cls: 4.1656, loss: 4.1656 +2024-12-27 14:59:53,655 - pyskl - INFO - Epoch [43][2500/3746] lr: 8.133e-02, eta: 3 days, 16:51:32, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5184, loss_cls: 4.2096, loss: 4.2096 +2024-12-27 15:01:18,589 - pyskl - INFO - Epoch [43][2600/3746] lr: 8.131e-02, eta: 3 days, 16:50:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5142, loss_cls: 4.2105, loss: 4.2105 +2024-12-27 15:02:44,061 - pyskl - INFO - Epoch [43][2700/3746] lr: 8.129e-02, eta: 3 days, 16:49:21, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5139, loss_cls: 4.2357, loss: 4.2357 +2024-12-27 15:04:09,384 - pyskl - INFO - Epoch [43][2800/3746] lr: 8.126e-02, eta: 3 days, 16:48:16, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5202, loss_cls: 4.1991, loss: 4.1991 +2024-12-27 15:05:34,634 - pyskl - INFO - Epoch [43][2900/3746] lr: 8.124e-02, eta: 3 days, 16:47:11, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5350, loss_cls: 4.1836, loss: 4.1836 +2024-12-27 15:06:59,938 - pyskl - INFO - Epoch [43][3000/3746] lr: 8.122e-02, eta: 3 days, 16:46:06, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5239, loss_cls: 4.1931, loss: 4.1931 +2024-12-27 15:08:25,277 - pyskl - INFO - Epoch [43][3100/3746] lr: 8.120e-02, eta: 3 days, 16:45:00, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5312, loss_cls: 4.1742, loss: 4.1742 +2024-12-27 15:09:50,405 - pyskl - INFO - Epoch [43][3200/3746] lr: 8.118e-02, eta: 3 days, 16:43:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5131, loss_cls: 4.2167, loss: 4.2167 +2024-12-27 15:11:15,301 - pyskl - INFO - Epoch [43][3300/3746] lr: 8.116e-02, eta: 3 days, 16:42:48, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5131, loss_cls: 4.1997, loss: 4.1997 +2024-12-27 15:12:40,292 - pyskl - INFO - Epoch [43][3400/3746] lr: 8.113e-02, eta: 3 days, 16:41:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5180, loss_cls: 4.1880, loss: 4.1880 +2024-12-27 15:14:06,164 - pyskl - INFO - Epoch [43][3500/3746] lr: 8.111e-02, eta: 3 days, 16:40:38, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5294, loss_cls: 4.1797, loss: 4.1797 +2024-12-27 15:15:31,656 - pyskl - INFO - Epoch [43][3600/3746] lr: 8.109e-02, eta: 3 days, 16:39:33, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5200, loss_cls: 4.2022, loss: 4.2022 +2024-12-27 15:16:57,016 - pyskl - INFO - Epoch [43][3700/3746] lr: 8.107e-02, eta: 3 days, 16:38:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5189, loss_cls: 4.2212, loss: 4.2212 +2024-12-27 15:17:38,494 - pyskl - INFO - Saving checkpoint at 43 epochs +2024-12-27 15:19:37,899 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 15:19:38,728 - pyskl - INFO - +top1_acc 0.2068 +top5_acc 0.4363 +2024-12-27 15:19:38,728 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 15:19:38,770 - pyskl - INFO - +mean_acc 0.2066 +2024-12-27 15:19:38,783 - pyskl - INFO - Epoch(val) [43][309] top1_acc: 0.2068, top5_acc: 0.4363, mean_class_accuracy: 0.2066 +2024-12-27 15:24:03,522 - pyskl - INFO - Epoch [44][100/3746] lr: 8.104e-02, eta: 3 days, 16:42:41, time: 2.647, data_time: 1.600, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5356, loss_cls: 4.1550, loss: 4.1550 +2024-12-27 15:25:29,511 - pyskl - INFO - Epoch [44][200/3746] lr: 8.101e-02, eta: 3 days, 16:41:37, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5302, loss_cls: 4.1386, loss: 4.1386 +2024-12-27 15:26:55,747 - pyskl - INFO - Epoch [44][300/3746] lr: 8.099e-02, eta: 3 days, 16:40:34, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5347, loss_cls: 4.1548, loss: 4.1548 +2024-12-27 15:28:22,078 - pyskl - INFO - Epoch [44][400/3746] lr: 8.097e-02, eta: 3 days, 16:39:30, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5267, loss_cls: 4.1457, loss: 4.1457 +2024-12-27 15:29:48,177 - pyskl - INFO - Epoch [44][500/3746] lr: 8.095e-02, eta: 3 days, 16:38:26, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5253, loss_cls: 4.1564, loss: 4.1564 +2024-12-27 15:31:14,520 - pyskl - INFO - Epoch [44][600/3746] lr: 8.093e-02, eta: 3 days, 16:37:23, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5147, loss_cls: 4.2309, loss: 4.2309 +2024-12-27 15:32:40,809 - pyskl - INFO - Epoch [44][700/3746] lr: 8.090e-02, eta: 3 days, 16:36:20, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5234, loss_cls: 4.1933, loss: 4.1933 +2024-12-27 15:34:06,874 - pyskl - INFO - Epoch [44][800/3746] lr: 8.088e-02, eta: 3 days, 16:35:16, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5289, loss_cls: 4.1930, loss: 4.1930 +2024-12-27 15:35:33,004 - pyskl - INFO - Epoch [44][900/3746] lr: 8.086e-02, eta: 3 days, 16:34:12, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5317, loss_cls: 4.1502, loss: 4.1502 +2024-12-27 15:36:58,945 - pyskl - INFO - Epoch [44][1000/3746] lr: 8.084e-02, eta: 3 days, 16:33:07, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5242, loss_cls: 4.1918, loss: 4.1918 +2024-12-27 15:38:24,683 - pyskl - INFO - Epoch [44][1100/3746] lr: 8.082e-02, eta: 3 days, 16:32:02, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5397, loss_cls: 4.1136, loss: 4.1136 +2024-12-27 15:39:50,327 - pyskl - INFO - Epoch [44][1200/3746] lr: 8.079e-02, eta: 3 days, 16:30:57, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5297, loss_cls: 4.1614, loss: 4.1614 +2024-12-27 15:41:16,343 - pyskl - INFO - Epoch [44][1300/3746] lr: 8.077e-02, eta: 3 days, 16:29:53, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5231, loss_cls: 4.2009, loss: 4.2009 +2024-12-27 15:42:42,516 - pyskl - INFO - Epoch [44][1400/3746] lr: 8.075e-02, eta: 3 days, 16:28:49, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5286, loss_cls: 4.2022, loss: 4.2022 +2024-12-27 15:44:08,739 - pyskl - INFO - Epoch [44][1500/3746] lr: 8.073e-02, eta: 3 days, 16:27:45, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5330, loss_cls: 4.1544, loss: 4.1544 +2024-12-27 15:45:34,902 - pyskl - INFO - Epoch [44][1600/3746] lr: 8.071e-02, eta: 3 days, 16:26:41, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5347, loss_cls: 4.1833, loss: 4.1833 +2024-12-27 15:47:01,013 - pyskl - INFO - Epoch [44][1700/3746] lr: 8.068e-02, eta: 3 days, 16:25:37, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5319, loss_cls: 4.1590, loss: 4.1590 +2024-12-27 15:48:27,400 - pyskl - INFO - Epoch [44][1800/3746] lr: 8.066e-02, eta: 3 days, 16:24:33, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5200, loss_cls: 4.1932, loss: 4.1932 +2024-12-27 15:49:53,788 - pyskl - INFO - Epoch [44][1900/3746] lr: 8.064e-02, eta: 3 days, 16:23:30, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5161, loss_cls: 4.2201, loss: 4.2201 +2024-12-27 15:51:20,210 - pyskl - INFO - Epoch [44][2000/3746] lr: 8.062e-02, eta: 3 days, 16:22:26, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5278, loss_cls: 4.1942, loss: 4.1942 +2024-12-27 15:52:46,945 - pyskl - INFO - Epoch [44][2100/3746] lr: 8.060e-02, eta: 3 days, 16:21:23, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5158, loss_cls: 4.2338, loss: 4.2338 +2024-12-27 15:54:13,107 - pyskl - INFO - Epoch [44][2200/3746] lr: 8.057e-02, eta: 3 days, 16:20:19, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5162, loss_cls: 4.2335, loss: 4.2335 +2024-12-27 15:55:37,767 - pyskl - INFO - Epoch [44][2300/3746] lr: 8.055e-02, eta: 3 days, 16:19:11, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5280, loss_cls: 4.2016, loss: 4.2016 +2024-12-27 15:57:02,558 - pyskl - INFO - Epoch [44][2400/3746] lr: 8.053e-02, eta: 3 days, 16:18:04, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5252, loss_cls: 4.1744, loss: 4.1744 +2024-12-27 15:58:27,774 - pyskl - INFO - Epoch [44][2500/3746] lr: 8.051e-02, eta: 3 days, 16:16:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5203, loss_cls: 4.2606, loss: 4.2606 +2024-12-27 15:59:53,641 - pyskl - INFO - Epoch [44][2600/3746] lr: 8.048e-02, eta: 3 days, 16:15:52, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5242, loss_cls: 4.1857, loss: 4.1857 +2024-12-27 16:01:18,618 - pyskl - INFO - Epoch [44][2700/3746] lr: 8.046e-02, eta: 3 days, 16:14:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5166, loss_cls: 4.2170, loss: 4.2170 +2024-12-27 16:02:43,228 - pyskl - INFO - Epoch [44][2800/3746] lr: 8.044e-02, eta: 3 days, 16:13:37, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5294, loss_cls: 4.1327, loss: 4.1327 +2024-12-27 16:04:07,852 - pyskl - INFO - Epoch [44][2900/3746] lr: 8.042e-02, eta: 3 days, 16:12:29, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5223, loss_cls: 4.2030, loss: 4.2030 +2024-12-27 16:05:33,397 - pyskl - INFO - Epoch [44][3000/3746] lr: 8.040e-02, eta: 3 days, 16:11:23, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5211, loss_cls: 4.1866, loss: 4.1866 +2024-12-27 16:06:58,528 - pyskl - INFO - Epoch [44][3100/3746] lr: 8.037e-02, eta: 3 days, 16:10:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5205, loss_cls: 4.2064, loss: 4.2064 +2024-12-27 16:08:23,179 - pyskl - INFO - Epoch [44][3200/3746] lr: 8.035e-02, eta: 3 days, 16:09:08, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5256, loss_cls: 4.1858, loss: 4.1858 +2024-12-27 16:09:48,123 - pyskl - INFO - Epoch [44][3300/3746] lr: 8.033e-02, eta: 3 days, 16:08:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5258, loss_cls: 4.1832, loss: 4.1832 +2024-12-27 16:11:13,282 - pyskl - INFO - Epoch [44][3400/3746] lr: 8.031e-02, eta: 3 days, 16:06:54, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5244, loss_cls: 4.1978, loss: 4.1978 +2024-12-27 16:12:38,316 - pyskl - INFO - Epoch [44][3500/3746] lr: 8.028e-02, eta: 3 days, 16:05:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5142, loss_cls: 4.2366, loss: 4.2366 +2024-12-27 16:14:03,683 - pyskl - INFO - Epoch [44][3600/3746] lr: 8.026e-02, eta: 3 days, 16:04:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5177, loss_cls: 4.2206, loss: 4.2206 +2024-12-27 16:15:28,757 - pyskl - INFO - Epoch [44][3700/3746] lr: 8.024e-02, eta: 3 days, 16:03:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5280, loss_cls: 4.1812, loss: 4.1812 +2024-12-27 16:16:10,312 - pyskl - INFO - Saving checkpoint at 44 epochs +2024-12-27 16:18:12,447 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 16:18:13,190 - pyskl - INFO - +top1_acc 0.2263 +top5_acc 0.4663 +2024-12-27 16:18:13,190 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 16:18:13,261 - pyskl - INFO - +mean_acc 0.2259 +2024-12-27 16:18:13,269 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_21.pth was removed +2024-12-27 16:18:13,679 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_44.pth. +2024-12-27 16:18:13,680 - pyskl - INFO - Best top1_acc is 0.2263 at 44 epoch. +2024-12-27 16:18:13,695 - pyskl - INFO - Epoch(val) [44][309] top1_acc: 0.2263, top5_acc: 0.4663, mean_class_accuracy: 0.2259 +2024-12-27 16:22:32,473 - pyskl - INFO - Epoch [45][100/3746] lr: 8.021e-02, eta: 3 days, 16:07:19, time: 2.588, data_time: 1.548, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5228, loss_cls: 4.1748, loss: 4.1748 +2024-12-27 16:23:58,163 - pyskl - INFO - Epoch [45][200/3746] lr: 8.019e-02, eta: 3 days, 16:06:13, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5272, loss_cls: 4.1827, loss: 4.1827 +2024-12-27 16:25:23,092 - pyskl - INFO - Epoch [45][300/3746] lr: 8.016e-02, eta: 3 days, 16:05:05, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5300, loss_cls: 4.1581, loss: 4.1581 +2024-12-27 16:26:48,344 - pyskl - INFO - Epoch [45][400/3746] lr: 8.014e-02, eta: 3 days, 16:03:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5294, loss_cls: 4.1686, loss: 4.1686 +2024-12-27 16:28:14,214 - pyskl - INFO - Epoch [45][500/3746] lr: 8.012e-02, eta: 3 days, 16:02:52, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5247, loss_cls: 4.2017, loss: 4.2017 +2024-12-27 16:29:39,942 - pyskl - INFO - Epoch [45][600/3746] lr: 8.010e-02, eta: 3 days, 16:01:46, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5397, loss_cls: 4.1223, loss: 4.1223 +2024-12-27 16:31:05,970 - pyskl - INFO - Epoch [45][700/3746] lr: 8.007e-02, eta: 3 days, 16:00:41, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5416, loss_cls: 4.1414, loss: 4.1414 +2024-12-27 16:32:32,310 - pyskl - INFO - Epoch [45][800/3746] lr: 8.005e-02, eta: 3 days, 15:59:36, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5342, loss_cls: 4.1611, loss: 4.1611 +2024-12-27 16:33:58,132 - pyskl - INFO - Epoch [45][900/3746] lr: 8.003e-02, eta: 3 days, 15:58:30, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5267, loss_cls: 4.1532, loss: 4.1532 +2024-12-27 16:35:23,846 - pyskl - INFO - Epoch [45][1000/3746] lr: 8.001e-02, eta: 3 days, 15:57:24, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5295, loss_cls: 4.1473, loss: 4.1473 +2024-12-27 16:36:49,781 - pyskl - INFO - Epoch [45][1100/3746] lr: 7.998e-02, eta: 3 days, 15:56:18, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5241, loss_cls: 4.1881, loss: 4.1881 +2024-12-27 16:38:15,236 - pyskl - INFO - Epoch [45][1200/3746] lr: 7.996e-02, eta: 3 days, 15:55:11, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5306, loss_cls: 4.2005, loss: 4.2005 +2024-12-27 16:39:40,848 - pyskl - INFO - Epoch [45][1300/3746] lr: 7.994e-02, eta: 3 days, 15:54:05, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5284, loss_cls: 4.1839, loss: 4.1839 +2024-12-27 16:41:06,555 - pyskl - INFO - Epoch [45][1400/3746] lr: 7.992e-02, eta: 3 days, 15:52:59, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5209, loss_cls: 4.2153, loss: 4.2153 +2024-12-27 16:42:32,785 - pyskl - INFO - Epoch [45][1500/3746] lr: 7.990e-02, eta: 3 days, 15:51:54, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5222, loss_cls: 4.1973, loss: 4.1973 +2024-12-27 16:43:59,178 - pyskl - INFO - Epoch [45][1600/3746] lr: 7.987e-02, eta: 3 days, 15:50:49, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5239, loss_cls: 4.1927, loss: 4.1927 +2024-12-27 16:45:25,216 - pyskl - INFO - Epoch [45][1700/3746] lr: 7.985e-02, eta: 3 days, 15:49:43, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5253, loss_cls: 4.1757, loss: 4.1757 +2024-12-27 16:46:51,130 - pyskl - INFO - Epoch [45][1800/3746] lr: 7.983e-02, eta: 3 days, 15:48:38, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5211, loss_cls: 4.1918, loss: 4.1918 +2024-12-27 16:48:17,048 - pyskl - INFO - Epoch [45][1900/3746] lr: 7.981e-02, eta: 3 days, 15:47:32, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5150, loss_cls: 4.2282, loss: 4.2282 +2024-12-27 16:49:42,836 - pyskl - INFO - Epoch [45][2000/3746] lr: 7.978e-02, eta: 3 days, 15:46:25, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5216, loss_cls: 4.1865, loss: 4.1865 +2024-12-27 16:51:09,304 - pyskl - INFO - Epoch [45][2100/3746] lr: 7.976e-02, eta: 3 days, 15:45:21, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5250, loss_cls: 4.2187, loss: 4.2187 +2024-12-27 16:52:35,096 - pyskl - INFO - Epoch [45][2200/3746] lr: 7.974e-02, eta: 3 days, 15:44:15, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5292, loss_cls: 4.1764, loss: 4.1764 +2024-12-27 16:54:00,349 - pyskl - INFO - Epoch [45][2300/3746] lr: 7.972e-02, eta: 3 days, 15:43:07, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5258, loss_cls: 4.2146, loss: 4.2146 +2024-12-27 16:55:24,688 - pyskl - INFO - Epoch [45][2400/3746] lr: 7.969e-02, eta: 3 days, 15:41:57, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5319, loss_cls: 4.1515, loss: 4.1515 +2024-12-27 16:56:50,336 - pyskl - INFO - Epoch [45][2500/3746] lr: 7.967e-02, eta: 3 days, 15:40:51, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5250, loss_cls: 4.1891, loss: 4.1891 +2024-12-27 16:58:16,431 - pyskl - INFO - Epoch [45][2600/3746] lr: 7.965e-02, eta: 3 days, 15:39:45, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5244, loss_cls: 4.1825, loss: 4.1825 +2024-12-27 16:59:41,445 - pyskl - INFO - Epoch [45][2700/3746] lr: 7.963e-02, eta: 3 days, 15:38:37, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5284, loss_cls: 4.1836, loss: 4.1836 +2024-12-27 17:01:06,294 - pyskl - INFO - Epoch [45][2800/3746] lr: 7.960e-02, eta: 3 days, 15:37:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5308, loss_cls: 4.1545, loss: 4.1545 +2024-12-27 17:02:30,858 - pyskl - INFO - Epoch [45][2900/3746] lr: 7.958e-02, eta: 3 days, 15:36:19, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5314, loss_cls: 4.1575, loss: 4.1575 +2024-12-27 17:03:57,022 - pyskl - INFO - Epoch [45][3000/3746] lr: 7.956e-02, eta: 3 days, 15:35:13, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5250, loss_cls: 4.1877, loss: 4.1877 +2024-12-27 17:05:22,098 - pyskl - INFO - Epoch [45][3100/3746] lr: 7.954e-02, eta: 3 days, 15:34:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5225, loss_cls: 4.2394, loss: 4.2394 +2024-12-27 17:06:47,157 - pyskl - INFO - Epoch [45][3200/3746] lr: 7.951e-02, eta: 3 days, 15:32:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5291, loss_cls: 4.1457, loss: 4.1457 +2024-12-27 17:08:13,016 - pyskl - INFO - Epoch [45][3300/3746] lr: 7.949e-02, eta: 3 days, 15:31:51, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5314, loss_cls: 4.1566, loss: 4.1566 +2024-12-27 17:09:38,374 - pyskl - INFO - Epoch [45][3400/3746] lr: 7.947e-02, eta: 3 days, 15:30:43, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5161, loss_cls: 4.2213, loss: 4.2213 +2024-12-27 17:11:04,048 - pyskl - INFO - Epoch [45][3500/3746] lr: 7.945e-02, eta: 3 days, 15:29:36, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5203, loss_cls: 4.1856, loss: 4.1856 +2024-12-27 17:12:29,995 - pyskl - INFO - Epoch [45][3600/3746] lr: 7.942e-02, eta: 3 days, 15:28:30, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5295, loss_cls: 4.1772, loss: 4.1772 +2024-12-27 17:13:55,809 - pyskl - INFO - Epoch [45][3700/3746] lr: 7.940e-02, eta: 3 days, 15:27:23, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5223, loss_cls: 4.2069, loss: 4.2069 +2024-12-27 17:14:37,525 - pyskl - INFO - Saving checkpoint at 45 epochs +2024-12-27 17:16:37,294 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 17:16:38,127 - pyskl - INFO - +top1_acc 0.2146 +top5_acc 0.4484 +2024-12-27 17:16:38,128 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 17:16:38,179 - pyskl - INFO - +mean_acc 0.2143 +2024-12-27 17:16:38,197 - pyskl - INFO - Epoch(val) [45][309] top1_acc: 0.2146, top5_acc: 0.4484, mean_class_accuracy: 0.2143 +2024-12-27 17:21:00,028 - pyskl - INFO - Epoch [46][100/3746] lr: 7.937e-02, eta: 3 days, 15:31:05, time: 2.618, data_time: 1.574, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5375, loss_cls: 4.1544, loss: 4.1544 +2024-12-27 17:22:26,136 - pyskl - INFO - Epoch [46][200/3746] lr: 7.934e-02, eta: 3 days, 15:29:58, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5397, loss_cls: 4.1219, loss: 4.1219 +2024-12-27 17:23:52,342 - pyskl - INFO - Epoch [46][300/3746] lr: 7.932e-02, eta: 3 days, 15:28:52, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5417, loss_cls: 4.0939, loss: 4.0939 +2024-12-27 17:25:18,554 - pyskl - INFO - Epoch [46][400/3746] lr: 7.930e-02, eta: 3 days, 15:27:46, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5186, loss_cls: 4.1997, loss: 4.1997 +2024-12-27 17:26:44,282 - pyskl - INFO - Epoch [46][500/3746] lr: 7.928e-02, eta: 3 days, 15:26:39, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5314, loss_cls: 4.1412, loss: 4.1412 +2024-12-27 17:28:09,905 - pyskl - INFO - Epoch [46][600/3746] lr: 7.925e-02, eta: 3 days, 15:25:32, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5298, loss_cls: 4.1764, loss: 4.1764 +2024-12-27 17:29:35,747 - pyskl - INFO - Epoch [46][700/3746] lr: 7.923e-02, eta: 3 days, 15:24:25, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5250, loss_cls: 4.2207, loss: 4.2207 +2024-12-27 17:31:02,041 - pyskl - INFO - Epoch [46][800/3746] lr: 7.921e-02, eta: 3 days, 15:23:19, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5117, loss_cls: 4.2284, loss: 4.2284 +2024-12-27 17:32:28,327 - pyskl - INFO - Epoch [46][900/3746] lr: 7.919e-02, eta: 3 days, 15:22:13, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5238, loss_cls: 4.1756, loss: 4.1756 +2024-12-27 17:33:54,601 - pyskl - INFO - Epoch [46][1000/3746] lr: 7.916e-02, eta: 3 days, 15:21:07, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5153, loss_cls: 4.2169, loss: 4.2169 +2024-12-27 17:35:20,590 - pyskl - INFO - Epoch [46][1100/3746] lr: 7.914e-02, eta: 3 days, 15:20:01, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5252, loss_cls: 4.1797, loss: 4.1797 +2024-12-27 17:36:46,391 - pyskl - INFO - Epoch [46][1200/3746] lr: 7.912e-02, eta: 3 days, 15:18:53, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5298, loss_cls: 4.1713, loss: 4.1713 +2024-12-27 17:38:12,727 - pyskl - INFO - Epoch [46][1300/3746] lr: 7.909e-02, eta: 3 days, 15:17:47, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5347, loss_cls: 4.1202, loss: 4.1202 +2024-12-27 17:39:39,550 - pyskl - INFO - Epoch [46][1400/3746] lr: 7.907e-02, eta: 3 days, 15:16:43, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5219, loss_cls: 4.1937, loss: 4.1937 +2024-12-27 17:41:05,944 - pyskl - INFO - Epoch [46][1500/3746] lr: 7.905e-02, eta: 3 days, 15:15:37, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5209, loss_cls: 4.1961, loss: 4.1961 +2024-12-27 17:42:32,416 - pyskl - INFO - Epoch [46][1600/3746] lr: 7.903e-02, eta: 3 days, 15:14:31, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5363, loss_cls: 4.1083, loss: 4.1083 +2024-12-27 17:43:58,880 - pyskl - INFO - Epoch [46][1700/3746] lr: 7.900e-02, eta: 3 days, 15:13:25, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5373, loss_cls: 4.1281, loss: 4.1281 +2024-12-27 17:45:25,593 - pyskl - INFO - Epoch [46][1800/3746] lr: 7.898e-02, eta: 3 days, 15:12:20, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5234, loss_cls: 4.2093, loss: 4.2093 +2024-12-27 17:46:51,896 - pyskl - INFO - Epoch [46][1900/3746] lr: 7.896e-02, eta: 3 days, 15:11:14, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5253, loss_cls: 4.1681, loss: 4.1681 +2024-12-27 17:48:18,717 - pyskl - INFO - Epoch [46][2000/3746] lr: 7.894e-02, eta: 3 days, 15:10:09, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5253, loss_cls: 4.1667, loss: 4.1667 +2024-12-27 17:49:44,623 - pyskl - INFO - Epoch [46][2100/3746] lr: 7.891e-02, eta: 3 days, 15:09:02, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5198, loss_cls: 4.1998, loss: 4.1998 +2024-12-27 17:51:09,299 - pyskl - INFO - Epoch [46][2200/3746] lr: 7.889e-02, eta: 3 days, 15:07:52, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5217, loss_cls: 4.1893, loss: 4.1893 +2024-12-27 17:52:34,145 - pyskl - INFO - Epoch [46][2300/3746] lr: 7.887e-02, eta: 3 days, 15:06:43, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5270, loss_cls: 4.1691, loss: 4.1691 +2024-12-27 17:53:58,977 - pyskl - INFO - Epoch [46][2400/3746] lr: 7.884e-02, eta: 3 days, 15:05:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5261, loss_cls: 4.1778, loss: 4.1778 +2024-12-27 17:55:24,724 - pyskl - INFO - Epoch [46][2500/3746] lr: 7.882e-02, eta: 3 days, 15:04:25, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5148, loss_cls: 4.2472, loss: 4.2472 +2024-12-27 17:56:49,968 - pyskl - INFO - Epoch [46][2600/3746] lr: 7.880e-02, eta: 3 days, 15:03:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5162, loss_cls: 4.2019, loss: 4.2019 +2024-12-27 17:58:14,968 - pyskl - INFO - Epoch [46][2700/3746] lr: 7.878e-02, eta: 3 days, 15:02:07, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5230, loss_cls: 4.2004, loss: 4.2004 +2024-12-27 17:59:39,783 - pyskl - INFO - Epoch [46][2800/3746] lr: 7.875e-02, eta: 3 days, 15:00:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5131, loss_cls: 4.2412, loss: 4.2412 +2024-12-27 18:01:04,868 - pyskl - INFO - Epoch [46][2900/3746] lr: 7.873e-02, eta: 3 days, 14:59:49, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5181, loss_cls: 4.2339, loss: 4.2339 +2024-12-27 18:02:30,986 - pyskl - INFO - Epoch [46][3000/3746] lr: 7.871e-02, eta: 3 days, 14:58:42, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5267, loss_cls: 4.1845, loss: 4.1845 +2024-12-27 18:03:56,147 - pyskl - INFO - Epoch [46][3100/3746] lr: 7.868e-02, eta: 3 days, 14:57:33, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5402, loss_cls: 4.0990, loss: 4.0990 +2024-12-27 18:05:21,125 - pyskl - INFO - Epoch [46][3200/3746] lr: 7.866e-02, eta: 3 days, 14:56:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5216, loss_cls: 4.1820, loss: 4.1820 +2024-12-27 18:06:46,489 - pyskl - INFO - Epoch [46][3300/3746] lr: 7.864e-02, eta: 3 days, 14:55:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5239, loss_cls: 4.1962, loss: 4.1962 +2024-12-27 18:08:11,548 - pyskl - INFO - Epoch [46][3400/3746] lr: 7.862e-02, eta: 3 days, 14:54:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5308, loss_cls: 4.1837, loss: 4.1837 +2024-12-27 18:09:36,495 - pyskl - INFO - Epoch [46][3500/3746] lr: 7.859e-02, eta: 3 days, 14:52:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5361, loss_cls: 4.1442, loss: 4.1442 +2024-12-27 18:11:01,533 - pyskl - INFO - Epoch [46][3600/3746] lr: 7.857e-02, eta: 3 days, 14:51:47, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5242, loss_cls: 4.1630, loss: 4.1630 +2024-12-27 18:12:27,188 - pyskl - INFO - Epoch [46][3700/3746] lr: 7.855e-02, eta: 3 days, 14:50:39, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5233, loss_cls: 4.1679, loss: 4.1679 +2024-12-27 18:13:08,581 - pyskl - INFO - Saving checkpoint at 46 epochs +2024-12-27 18:15:10,346 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 18:15:11,107 - pyskl - INFO - +top1_acc 0.1916 +top5_acc 0.4194 +2024-12-27 18:15:11,107 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 18:15:11,146 - pyskl - INFO - +mean_acc 0.1914 +2024-12-27 18:15:11,157 - pyskl - INFO - Epoch(val) [46][309] top1_acc: 0.1916, top5_acc: 0.4194, mean_class_accuracy: 0.1914 +2024-12-27 18:19:39,883 - pyskl - INFO - Epoch [47][100/3746] lr: 7.851e-02, eta: 3 days, 14:54:24, time: 2.687, data_time: 1.635, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5430, loss_cls: 4.1076, loss: 4.1076 +2024-12-27 18:21:05,362 - pyskl - INFO - Epoch [47][200/3746] lr: 7.849e-02, eta: 3 days, 14:53:15, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5270, loss_cls: 4.1721, loss: 4.1721 +2024-12-27 18:22:31,278 - pyskl - INFO - Epoch [47][300/3746] lr: 7.847e-02, eta: 3 days, 14:52:07, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5345, loss_cls: 4.1145, loss: 4.1145 +2024-12-27 18:23:56,565 - pyskl - INFO - Epoch [47][400/3746] lr: 7.844e-02, eta: 3 days, 14:50:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5288, loss_cls: 4.1529, loss: 4.1529 +2024-12-27 18:25:22,047 - pyskl - INFO - Epoch [47][500/3746] lr: 7.842e-02, eta: 3 days, 14:49:50, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5333, loss_cls: 4.1522, loss: 4.1522 +2024-12-27 18:26:47,397 - pyskl - INFO - Epoch [47][600/3746] lr: 7.840e-02, eta: 3 days, 14:48:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5266, loss_cls: 4.1769, loss: 4.1769 +2024-12-27 18:28:13,146 - pyskl - INFO - Epoch [47][700/3746] lr: 7.838e-02, eta: 3 days, 14:47:32, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5211, loss_cls: 4.2038, loss: 4.2038 +2024-12-27 18:29:38,875 - pyskl - INFO - Epoch [47][800/3746] lr: 7.835e-02, eta: 3 days, 14:46:24, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5267, loss_cls: 4.1990, loss: 4.1990 +2024-12-27 18:31:04,623 - pyskl - INFO - Epoch [47][900/3746] lr: 7.833e-02, eta: 3 days, 14:45:16, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5277, loss_cls: 4.1610, loss: 4.1610 +2024-12-27 18:32:30,359 - pyskl - INFO - Epoch [47][1000/3746] lr: 7.831e-02, eta: 3 days, 14:44:07, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5270, loss_cls: 4.1712, loss: 4.1712 +2024-12-27 18:33:56,356 - pyskl - INFO - Epoch [47][1100/3746] lr: 7.828e-02, eta: 3 days, 14:43:00, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5303, loss_cls: 4.1442, loss: 4.1442 +2024-12-27 18:35:21,974 - pyskl - INFO - Epoch [47][1200/3746] lr: 7.826e-02, eta: 3 days, 14:41:51, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5342, loss_cls: 4.1245, loss: 4.1245 +2024-12-27 18:36:47,569 - pyskl - INFO - Epoch [47][1300/3746] lr: 7.824e-02, eta: 3 days, 14:40:42, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5298, loss_cls: 4.1954, loss: 4.1954 +2024-12-27 18:38:13,074 - pyskl - INFO - Epoch [47][1400/3746] lr: 7.821e-02, eta: 3 days, 14:39:34, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5245, loss_cls: 4.2122, loss: 4.2122 +2024-12-27 18:39:38,240 - pyskl - INFO - Epoch [47][1500/3746] lr: 7.819e-02, eta: 3 days, 14:38:24, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5372, loss_cls: 4.1385, loss: 4.1385 +2024-12-27 18:41:03,651 - pyskl - INFO - Epoch [47][1600/3746] lr: 7.817e-02, eta: 3 days, 14:37:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5333, loss_cls: 4.1393, loss: 4.1393 +2024-12-27 18:42:28,708 - pyskl - INFO - Epoch [47][1700/3746] lr: 7.814e-02, eta: 3 days, 14:36:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5223, loss_cls: 4.2058, loss: 4.2058 +2024-12-27 18:43:54,556 - pyskl - INFO - Epoch [47][1800/3746] lr: 7.812e-02, eta: 3 days, 14:34:57, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5289, loss_cls: 4.1650, loss: 4.1650 +2024-12-27 18:45:20,764 - pyskl - INFO - Epoch [47][1900/3746] lr: 7.810e-02, eta: 3 days, 14:33:49, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5302, loss_cls: 4.1715, loss: 4.1715 +2024-12-27 18:46:46,823 - pyskl - INFO - Epoch [47][2000/3746] lr: 7.808e-02, eta: 3 days, 14:32:42, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5194, loss_cls: 4.2111, loss: 4.2111 +2024-12-27 18:48:12,510 - pyskl - INFO - Epoch [47][2100/3746] lr: 7.805e-02, eta: 3 days, 14:31:33, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5253, loss_cls: 4.2083, loss: 4.2083 +2024-12-27 18:49:37,980 - pyskl - INFO - Epoch [47][2200/3746] lr: 7.803e-02, eta: 3 days, 14:30:24, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5303, loss_cls: 4.1758, loss: 4.1758 +2024-12-27 18:51:03,048 - pyskl - INFO - Epoch [47][2300/3746] lr: 7.801e-02, eta: 3 days, 14:29:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5239, loss_cls: 4.1780, loss: 4.1780 +2024-12-27 18:52:28,484 - pyskl - INFO - Epoch [47][2400/3746] lr: 7.798e-02, eta: 3 days, 14:28:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5203, loss_cls: 4.2126, loss: 4.2126 +2024-12-27 18:53:54,353 - pyskl - INFO - Epoch [47][2500/3746] lr: 7.796e-02, eta: 3 days, 14:26:56, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5302, loss_cls: 4.1771, loss: 4.1771 +2024-12-27 18:55:19,567 - pyskl - INFO - Epoch [47][2600/3746] lr: 7.794e-02, eta: 3 days, 14:25:47, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5262, loss_cls: 4.1572, loss: 4.1572 +2024-12-27 18:56:45,046 - pyskl - INFO - Epoch [47][2700/3746] lr: 7.791e-02, eta: 3 days, 14:24:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2722, top5_acc: 0.5184, loss_cls: 4.2207, loss: 4.2207 +2024-12-27 18:58:10,292 - pyskl - INFO - Epoch [47][2800/3746] lr: 7.789e-02, eta: 3 days, 14:23:28, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5255, loss_cls: 4.1734, loss: 4.1734 +2024-12-27 18:59:36,603 - pyskl - INFO - Epoch [47][2900/3746] lr: 7.787e-02, eta: 3 days, 14:22:20, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5336, loss_cls: 4.1557, loss: 4.1557 +2024-12-27 19:01:02,252 - pyskl - INFO - Epoch [47][3000/3746] lr: 7.784e-02, eta: 3 days, 14:21:11, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5255, loss_cls: 4.1916, loss: 4.1916 +2024-12-27 19:02:27,773 - pyskl - INFO - Epoch [47][3100/3746] lr: 7.782e-02, eta: 3 days, 14:20:02, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5270, loss_cls: 4.1668, loss: 4.1668 +2024-12-27 19:03:52,957 - pyskl - INFO - Epoch [47][3200/3746] lr: 7.780e-02, eta: 3 days, 14:18:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5216, loss_cls: 4.1755, loss: 4.1755 +2024-12-27 19:05:19,191 - pyskl - INFO - Epoch [47][3300/3746] lr: 7.777e-02, eta: 3 days, 14:17:45, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5327, loss_cls: 4.1457, loss: 4.1457 +2024-12-27 19:06:44,767 - pyskl - INFO - Epoch [47][3400/3746] lr: 7.775e-02, eta: 3 days, 14:16:36, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5272, loss_cls: 4.1678, loss: 4.1678 +2024-12-27 19:08:10,878 - pyskl - INFO - Epoch [47][3500/3746] lr: 7.773e-02, eta: 3 days, 14:15:28, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5211, loss_cls: 4.2375, loss: 4.2375 +2024-12-27 19:09:37,515 - pyskl - INFO - Epoch [47][3600/3746] lr: 7.770e-02, eta: 3 days, 14:14:21, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5267, loss_cls: 4.1784, loss: 4.1784 +2024-12-27 19:11:03,424 - pyskl - INFO - Epoch [47][3700/3746] lr: 7.768e-02, eta: 3 days, 14:13:12, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5366, loss_cls: 4.1416, loss: 4.1416 +2024-12-27 19:11:44,977 - pyskl - INFO - Saving checkpoint at 47 epochs +2024-12-27 19:13:43,459 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 19:13:44,236 - pyskl - INFO - +top1_acc 0.2149 +top5_acc 0.4564 +2024-12-27 19:13:44,236 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 19:13:44,295 - pyskl - INFO - +mean_acc 0.2147 +2024-12-27 19:13:44,309 - pyskl - INFO - Epoch(val) [47][309] top1_acc: 0.2149, top5_acc: 0.4564, mean_class_accuracy: 0.2147 +2024-12-27 19:18:07,077 - pyskl - INFO - Epoch [48][100/3746] lr: 7.765e-02, eta: 3 days, 14:16:33, time: 2.628, data_time: 1.588, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5359, loss_cls: 4.1315, loss: 4.1315 +2024-12-27 19:19:32,211 - pyskl - INFO - Epoch [48][200/3746] lr: 7.762e-02, eta: 3 days, 14:15:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5416, loss_cls: 4.0824, loss: 4.0824 +2024-12-27 19:20:57,580 - pyskl - INFO - Epoch [48][300/3746] lr: 7.760e-02, eta: 3 days, 14:14:13, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5298, loss_cls: 4.1757, loss: 4.1757 +2024-12-27 19:22:22,735 - pyskl - INFO - Epoch [48][400/3746] lr: 7.758e-02, eta: 3 days, 14:13:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5245, loss_cls: 4.1691, loss: 4.1691 +2024-12-27 19:23:48,165 - pyskl - INFO - Epoch [48][500/3746] lr: 7.755e-02, eta: 3 days, 14:11:52, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5367, loss_cls: 4.1114, loss: 4.1114 +2024-12-27 19:25:13,939 - pyskl - INFO - Epoch [48][600/3746] lr: 7.753e-02, eta: 3 days, 14:10:43, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5288, loss_cls: 4.1378, loss: 4.1378 +2024-12-27 19:26:39,788 - pyskl - INFO - Epoch [48][700/3746] lr: 7.751e-02, eta: 3 days, 14:09:34, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5394, loss_cls: 4.1146, loss: 4.1146 +2024-12-27 19:28:05,985 - pyskl - INFO - Epoch [48][800/3746] lr: 7.748e-02, eta: 3 days, 14:08:26, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5169, loss_cls: 4.2065, loss: 4.2065 +2024-12-27 19:29:31,457 - pyskl - INFO - Epoch [48][900/3746] lr: 7.746e-02, eta: 3 days, 14:07:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5323, loss_cls: 4.1728, loss: 4.1728 +2024-12-27 19:30:56,903 - pyskl - INFO - Epoch [48][1000/3746] lr: 7.744e-02, eta: 3 days, 14:06:07, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5208, loss_cls: 4.1718, loss: 4.1718 +2024-12-27 19:32:23,091 - pyskl - INFO - Epoch [48][1100/3746] lr: 7.741e-02, eta: 3 days, 14:04:58, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5311, loss_cls: 4.1485, loss: 4.1485 +2024-12-27 19:33:49,628 - pyskl - INFO - Epoch [48][1200/3746] lr: 7.739e-02, eta: 3 days, 14:03:51, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5347, loss_cls: 4.1316, loss: 4.1316 +2024-12-27 19:35:15,928 - pyskl - INFO - Epoch [48][1300/3746] lr: 7.737e-02, eta: 3 days, 14:02:43, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5303, loss_cls: 4.1740, loss: 4.1740 +2024-12-27 19:36:42,148 - pyskl - INFO - Epoch [48][1400/3746] lr: 7.734e-02, eta: 3 days, 14:01:34, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5325, loss_cls: 4.1604, loss: 4.1604 +2024-12-27 19:38:08,463 - pyskl - INFO - Epoch [48][1500/3746] lr: 7.732e-02, eta: 3 days, 14:00:26, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5262, loss_cls: 4.1897, loss: 4.1897 +2024-12-27 19:39:34,468 - pyskl - INFO - Epoch [48][1600/3746] lr: 7.730e-02, eta: 3 days, 13:59:17, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5331, loss_cls: 4.1484, loss: 4.1484 +2024-12-27 19:41:00,971 - pyskl - INFO - Epoch [48][1700/3746] lr: 7.727e-02, eta: 3 days, 13:58:10, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5292, loss_cls: 4.1497, loss: 4.1497 +2024-12-27 19:42:27,280 - pyskl - INFO - Epoch [48][1800/3746] lr: 7.725e-02, eta: 3 days, 13:57:01, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5327, loss_cls: 4.1777, loss: 4.1777 +2024-12-27 19:43:53,697 - pyskl - INFO - Epoch [48][1900/3746] lr: 7.723e-02, eta: 3 days, 13:55:54, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5262, loss_cls: 4.1924, loss: 4.1924 +2024-12-27 19:45:19,960 - pyskl - INFO - Epoch [48][2000/3746] lr: 7.720e-02, eta: 3 days, 13:54:45, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5239, loss_cls: 4.1936, loss: 4.1936 +2024-12-27 19:46:45,625 - pyskl - INFO - Epoch [48][2100/3746] lr: 7.718e-02, eta: 3 days, 13:53:36, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2683, top5_acc: 0.5269, loss_cls: 4.2120, loss: 4.2120 +2024-12-27 19:48:11,119 - pyskl - INFO - Epoch [48][2200/3746] lr: 7.716e-02, eta: 3 days, 13:52:26, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5322, loss_cls: 4.1596, loss: 4.1596 +2024-12-27 19:49:36,892 - pyskl - INFO - Epoch [48][2300/3746] lr: 7.713e-02, eta: 3 days, 13:51:16, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5298, loss_cls: 4.1711, loss: 4.1711 +2024-12-27 19:51:02,562 - pyskl - INFO - Epoch [48][2400/3746] lr: 7.711e-02, eta: 3 days, 13:50:06, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5247, loss_cls: 4.1986, loss: 4.1986 +2024-12-27 19:52:28,278 - pyskl - INFO - Epoch [48][2500/3746] lr: 7.709e-02, eta: 3 days, 13:48:57, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5286, loss_cls: 4.1554, loss: 4.1554 +2024-12-27 19:53:53,880 - pyskl - INFO - Epoch [48][2600/3746] lr: 7.706e-02, eta: 3 days, 13:47:47, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5253, loss_cls: 4.1688, loss: 4.1688 +2024-12-27 19:55:18,960 - pyskl - INFO - Epoch [48][2700/3746] lr: 7.704e-02, eta: 3 days, 13:46:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5255, loss_cls: 4.1674, loss: 4.1674 +2024-12-27 19:56:44,031 - pyskl - INFO - Epoch [48][2800/3746] lr: 7.701e-02, eta: 3 days, 13:45:25, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5292, loss_cls: 4.1506, loss: 4.1506 +2024-12-27 19:58:09,372 - pyskl - INFO - Epoch [48][2900/3746] lr: 7.699e-02, eta: 3 days, 13:44:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5288, loss_cls: 4.1816, loss: 4.1816 +2024-12-27 19:59:34,930 - pyskl - INFO - Epoch [48][3000/3746] lr: 7.697e-02, eta: 3 days, 13:43:05, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5297, loss_cls: 4.1760, loss: 4.1760 +2024-12-27 20:00:59,965 - pyskl - INFO - Epoch [48][3100/3746] lr: 7.694e-02, eta: 3 days, 13:41:53, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5297, loss_cls: 4.1402, loss: 4.1402 +2024-12-27 20:02:25,212 - pyskl - INFO - Epoch [48][3200/3746] lr: 7.692e-02, eta: 3 days, 13:40:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5278, loss_cls: 4.1761, loss: 4.1761 +2024-12-27 20:03:50,775 - pyskl - INFO - Epoch [48][3300/3746] lr: 7.690e-02, eta: 3 days, 13:39:33, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5294, loss_cls: 4.1749, loss: 4.1749 +2024-12-27 20:05:16,379 - pyskl - INFO - Epoch [48][3400/3746] lr: 7.687e-02, eta: 3 days, 13:38:23, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5347, loss_cls: 4.1383, loss: 4.1383 +2024-12-27 20:06:42,376 - pyskl - INFO - Epoch [48][3500/3746] lr: 7.685e-02, eta: 3 days, 13:37:14, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5298, loss_cls: 4.1656, loss: 4.1656 +2024-12-27 20:08:07,914 - pyskl - INFO - Epoch [48][3600/3746] lr: 7.683e-02, eta: 3 days, 13:36:03, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5281, loss_cls: 4.1587, loss: 4.1587 +2024-12-27 20:09:33,680 - pyskl - INFO - Epoch [48][3700/3746] lr: 7.680e-02, eta: 3 days, 13:34:54, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5333, loss_cls: 4.1384, loss: 4.1384 +2024-12-27 20:10:15,399 - pyskl - INFO - Saving checkpoint at 48 epochs +2024-12-27 20:12:15,703 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 20:12:16,499 - pyskl - INFO - +top1_acc 0.1789 +top5_acc 0.4011 +2024-12-27 20:12:16,499 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 20:12:16,550 - pyskl - INFO - +mean_acc 0.1788 +2024-12-27 20:12:16,564 - pyskl - INFO - Epoch(val) [48][309] top1_acc: 0.1789, top5_acc: 0.4011, mean_class_accuracy: 0.1788 +2024-12-27 20:16:38,048 - pyskl - INFO - Epoch [49][100/3746] lr: 7.677e-02, eta: 3 days, 13:38:01, time: 2.615, data_time: 1.575, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5397, loss_cls: 4.1311, loss: 4.1311 +2024-12-27 20:18:03,967 - pyskl - INFO - Epoch [49][200/3746] lr: 7.674e-02, eta: 3 days, 13:36:52, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5378, loss_cls: 4.1414, loss: 4.1414 +2024-12-27 20:19:28,894 - pyskl - INFO - Epoch [49][300/3746] lr: 7.672e-02, eta: 3 days, 13:35:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5306, loss_cls: 4.1590, loss: 4.1590 +2024-12-27 20:20:54,257 - pyskl - INFO - Epoch [49][400/3746] lr: 7.670e-02, eta: 3 days, 13:34:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5306, loss_cls: 4.1509, loss: 4.1509 +2024-12-27 20:22:19,249 - pyskl - INFO - Epoch [49][500/3746] lr: 7.667e-02, eta: 3 days, 13:33:17, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5311, loss_cls: 4.1319, loss: 4.1319 +2024-12-27 20:23:44,235 - pyskl - INFO - Epoch [49][600/3746] lr: 7.665e-02, eta: 3 days, 13:32:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5386, loss_cls: 4.1547, loss: 4.1547 +2024-12-27 20:25:09,697 - pyskl - INFO - Epoch [49][700/3746] lr: 7.663e-02, eta: 3 days, 13:30:55, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5356, loss_cls: 4.1338, loss: 4.1338 +2024-12-27 20:26:35,651 - pyskl - INFO - Epoch [49][800/3746] lr: 7.660e-02, eta: 3 days, 13:29:45, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5355, loss_cls: 4.1274, loss: 4.1274 +2024-12-27 20:28:01,202 - pyskl - INFO - Epoch [49][900/3746] lr: 7.658e-02, eta: 3 days, 13:28:35, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5169, loss_cls: 4.1935, loss: 4.1935 +2024-12-27 20:29:26,977 - pyskl - INFO - Epoch [49][1000/3746] lr: 7.656e-02, eta: 3 days, 13:27:25, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5383, loss_cls: 4.1266, loss: 4.1266 +2024-12-27 20:30:52,586 - pyskl - INFO - Epoch [49][1100/3746] lr: 7.653e-02, eta: 3 days, 13:26:14, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5281, loss_cls: 4.1718, loss: 4.1718 +2024-12-27 20:32:18,489 - pyskl - INFO - Epoch [49][1200/3746] lr: 7.651e-02, eta: 3 days, 13:25:04, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5258, loss_cls: 4.1631, loss: 4.1631 +2024-12-27 20:33:44,037 - pyskl - INFO - Epoch [49][1300/3746] lr: 7.648e-02, eta: 3 days, 13:23:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5241, loss_cls: 4.1860, loss: 4.1860 +2024-12-27 20:35:10,156 - pyskl - INFO - Epoch [49][1400/3746] lr: 7.646e-02, eta: 3 days, 13:22:44, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5245, loss_cls: 4.1752, loss: 4.1752 +2024-12-27 20:36:35,922 - pyskl - INFO - Epoch [49][1500/3746] lr: 7.644e-02, eta: 3 days, 13:21:34, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5325, loss_cls: 4.1664, loss: 4.1664 +2024-12-27 20:38:01,763 - pyskl - INFO - Epoch [49][1600/3746] lr: 7.641e-02, eta: 3 days, 13:20:24, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5353, loss_cls: 4.1429, loss: 4.1429 +2024-12-27 20:39:28,282 - pyskl - INFO - Epoch [49][1700/3746] lr: 7.639e-02, eta: 3 days, 13:19:15, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5275, loss_cls: 4.2055, loss: 4.2055 +2024-12-27 20:40:53,993 - pyskl - INFO - Epoch [49][1800/3746] lr: 7.637e-02, eta: 3 days, 13:18:05, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5344, loss_cls: 4.1300, loss: 4.1300 +2024-12-27 20:42:19,650 - pyskl - INFO - Epoch [49][1900/3746] lr: 7.634e-02, eta: 3 days, 13:16:55, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5348, loss_cls: 4.1169, loss: 4.1169 +2024-12-27 20:43:44,306 - pyskl - INFO - Epoch [49][2000/3746] lr: 7.632e-02, eta: 3 days, 13:15:42, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5302, loss_cls: 4.1741, loss: 4.1741 +2024-12-27 20:45:09,237 - pyskl - INFO - Epoch [49][2100/3746] lr: 7.629e-02, eta: 3 days, 13:14:30, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5323, loss_cls: 4.1745, loss: 4.1745 +2024-12-27 20:46:34,356 - pyskl - INFO - Epoch [49][2200/3746] lr: 7.627e-02, eta: 3 days, 13:13:18, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5227, loss_cls: 4.1961, loss: 4.1961 +2024-12-27 20:47:59,345 - pyskl - INFO - Epoch [49][2300/3746] lr: 7.625e-02, eta: 3 days, 13:12:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5291, loss_cls: 4.1658, loss: 4.1658 +2024-12-27 20:49:24,924 - pyskl - INFO - Epoch [49][2400/3746] lr: 7.622e-02, eta: 3 days, 13:10:56, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5277, loss_cls: 4.1350, loss: 4.1350 +2024-12-27 20:50:50,300 - pyskl - INFO - Epoch [49][2500/3746] lr: 7.620e-02, eta: 3 days, 13:09:45, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5275, loss_cls: 4.1893, loss: 4.1893 +2024-12-27 20:52:15,195 - pyskl - INFO - Epoch [49][2600/3746] lr: 7.618e-02, eta: 3 days, 13:08:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5298, loss_cls: 4.1181, loss: 4.1181 +2024-12-27 20:53:39,996 - pyskl - INFO - Epoch [49][2700/3746] lr: 7.615e-02, eta: 3 days, 13:07:20, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5231, loss_cls: 4.1428, loss: 4.1428 +2024-12-27 20:55:05,150 - pyskl - INFO - Epoch [49][2800/3746] lr: 7.613e-02, eta: 3 days, 13:06:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5208, loss_cls: 4.2003, loss: 4.2003 +2024-12-27 20:56:30,885 - pyskl - INFO - Epoch [49][2900/3746] lr: 7.610e-02, eta: 3 days, 13:04:58, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5347, loss_cls: 4.1446, loss: 4.1446 +2024-12-27 20:57:56,821 - pyskl - INFO - Epoch [49][3000/3746] lr: 7.608e-02, eta: 3 days, 13:03:48, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5142, loss_cls: 4.2147, loss: 4.2147 +2024-12-27 20:59:21,917 - pyskl - INFO - Epoch [49][3100/3746] lr: 7.606e-02, eta: 3 days, 13:02:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5281, loss_cls: 4.1492, loss: 4.1492 +2024-12-27 21:00:46,777 - pyskl - INFO - Epoch [49][3200/3746] lr: 7.603e-02, eta: 3 days, 13:01:24, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5333, loss_cls: 4.1465, loss: 4.1465 +2024-12-27 21:02:12,063 - pyskl - INFO - Epoch [49][3300/3746] lr: 7.601e-02, eta: 3 days, 13:00:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5348, loss_cls: 4.1407, loss: 4.1407 +2024-12-27 21:03:36,737 - pyskl - INFO - Epoch [49][3400/3746] lr: 7.598e-02, eta: 3 days, 12:58:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5311, loss_cls: 4.1158, loss: 4.1158 +2024-12-27 21:05:01,572 - pyskl - INFO - Epoch [49][3500/3746] lr: 7.596e-02, eta: 3 days, 12:57:47, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5463, loss_cls: 4.1264, loss: 4.1264 +2024-12-27 21:06:26,504 - pyskl - INFO - Epoch [49][3600/3746] lr: 7.594e-02, eta: 3 days, 12:56:35, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5355, loss_cls: 4.1658, loss: 4.1658 +2024-12-27 21:07:51,600 - pyskl - INFO - Epoch [49][3700/3746] lr: 7.591e-02, eta: 3 days, 12:55:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5288, loss_cls: 4.1820, loss: 4.1820 +2024-12-27 21:08:32,747 - pyskl - INFO - Saving checkpoint at 49 epochs +2024-12-27 21:10:34,182 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 21:10:35,051 - pyskl - INFO - +top1_acc 0.2068 +top5_acc 0.4435 +2024-12-27 21:10:35,051 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 21:10:35,097 - pyskl - INFO - +mean_acc 0.2067 +2024-12-27 21:10:35,115 - pyskl - INFO - Epoch(val) [49][309] top1_acc: 0.2068, top5_acc: 0.4435, mean_class_accuracy: 0.2067 +2024-12-27 21:15:01,873 - pyskl - INFO - Epoch [50][100/3746] lr: 7.588e-02, eta: 3 days, 12:58:31, time: 2.667, data_time: 1.627, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5333, loss_cls: 4.1508, loss: 4.1508 +2024-12-27 21:16:27,508 - pyskl - INFO - Epoch [50][200/3746] lr: 7.585e-02, eta: 3 days, 12:57:20, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5341, loss_cls: 4.1144, loss: 4.1144 +2024-12-27 21:17:53,007 - pyskl - INFO - Epoch [50][300/3746] lr: 7.583e-02, eta: 3 days, 12:56:09, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5317, loss_cls: 4.1472, loss: 4.1472 +2024-12-27 21:19:18,248 - pyskl - INFO - Epoch [50][400/3746] lr: 7.581e-02, eta: 3 days, 12:54:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5311, loss_cls: 4.1277, loss: 4.1277 +2024-12-27 21:20:43,705 - pyskl - INFO - Epoch [50][500/3746] lr: 7.578e-02, eta: 3 days, 12:53:45, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5280, loss_cls: 4.1613, loss: 4.1613 +2024-12-27 21:22:09,141 - pyskl - INFO - Epoch [50][600/3746] lr: 7.576e-02, eta: 3 days, 12:52:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5350, loss_cls: 4.0979, loss: 4.0979 +2024-12-27 21:23:35,096 - pyskl - INFO - Epoch [50][700/3746] lr: 7.573e-02, eta: 3 days, 12:51:23, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5408, loss_cls: 4.1073, loss: 4.1073 +2024-12-27 21:25:00,649 - pyskl - INFO - Epoch [50][800/3746] lr: 7.571e-02, eta: 3 days, 12:50:12, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5288, loss_cls: 4.1334, loss: 4.1334 +2024-12-27 21:26:26,473 - pyskl - INFO - Epoch [50][900/3746] lr: 7.569e-02, eta: 3 days, 12:49:01, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5234, loss_cls: 4.1918, loss: 4.1918 +2024-12-27 21:27:52,099 - pyskl - INFO - Epoch [50][1000/3746] lr: 7.566e-02, eta: 3 days, 12:47:50, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5288, loss_cls: 4.1826, loss: 4.1826 +2024-12-27 21:29:17,117 - pyskl - INFO - Epoch [50][1100/3746] lr: 7.564e-02, eta: 3 days, 12:46:37, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5342, loss_cls: 4.1337, loss: 4.1337 +2024-12-27 21:30:42,120 - pyskl - INFO - Epoch [50][1200/3746] lr: 7.561e-02, eta: 3 days, 12:45:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5361, loss_cls: 4.1195, loss: 4.1195 +2024-12-27 21:32:07,842 - pyskl - INFO - Epoch [50][1300/3746] lr: 7.559e-02, eta: 3 days, 12:44:14, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5344, loss_cls: 4.1110, loss: 4.1110 +2024-12-27 21:33:33,805 - pyskl - INFO - Epoch [50][1400/3746] lr: 7.557e-02, eta: 3 days, 12:43:03, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5336, loss_cls: 4.1417, loss: 4.1417 +2024-12-27 21:34:58,827 - pyskl - INFO - Epoch [50][1500/3746] lr: 7.554e-02, eta: 3 days, 12:41:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5353, loss_cls: 4.1415, loss: 4.1415 +2024-12-27 21:36:24,026 - pyskl - INFO - Epoch [50][1600/3746] lr: 7.552e-02, eta: 3 days, 12:40:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5270, loss_cls: 4.1568, loss: 4.1568 +2024-12-27 21:37:49,494 - pyskl - INFO - Epoch [50][1700/3746] lr: 7.549e-02, eta: 3 days, 12:39:27, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5400, loss_cls: 4.0793, loss: 4.0793 +2024-12-27 21:39:15,401 - pyskl - INFO - Epoch [50][1800/3746] lr: 7.547e-02, eta: 3 days, 12:38:16, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5337, loss_cls: 4.1351, loss: 4.1351 +2024-12-27 21:40:41,063 - pyskl - INFO - Epoch [50][1900/3746] lr: 7.545e-02, eta: 3 days, 12:37:05, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5330, loss_cls: 4.1512, loss: 4.1512 +2024-12-27 21:42:06,526 - pyskl - INFO - Epoch [50][2000/3746] lr: 7.542e-02, eta: 3 days, 12:35:53, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5212, loss_cls: 4.1830, loss: 4.1830 +2024-12-27 21:43:31,330 - pyskl - INFO - Epoch [50][2100/3746] lr: 7.540e-02, eta: 3 days, 12:34:40, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5142, loss_cls: 4.2141, loss: 4.2141 +2024-12-27 21:44:55,793 - pyskl - INFO - Epoch [50][2200/3746] lr: 7.537e-02, eta: 3 days, 12:33:26, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5330, loss_cls: 4.1398, loss: 4.1398 +2024-12-27 21:46:20,733 - pyskl - INFO - Epoch [50][2300/3746] lr: 7.535e-02, eta: 3 days, 12:32:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5223, loss_cls: 4.1926, loss: 4.1926 +2024-12-27 21:47:45,794 - pyskl - INFO - Epoch [50][2400/3746] lr: 7.533e-02, eta: 3 days, 12:31:01, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5355, loss_cls: 4.1563, loss: 4.1563 +2024-12-27 21:49:10,326 - pyskl - INFO - Epoch [50][2500/3746] lr: 7.530e-02, eta: 3 days, 12:29:47, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5308, loss_cls: 4.1636, loss: 4.1636 +2024-12-27 21:50:35,353 - pyskl - INFO - Epoch [50][2600/3746] lr: 7.528e-02, eta: 3 days, 12:28:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5345, loss_cls: 4.1134, loss: 4.1134 +2024-12-27 21:52:00,769 - pyskl - INFO - Epoch [50][2700/3746] lr: 7.525e-02, eta: 3 days, 12:27:22, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5305, loss_cls: 4.1299, loss: 4.1299 +2024-12-27 21:53:25,872 - pyskl - INFO - Epoch [50][2800/3746] lr: 7.523e-02, eta: 3 days, 12:26:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5181, loss_cls: 4.1953, loss: 4.1953 +2024-12-27 21:54:50,727 - pyskl - INFO - Epoch [50][2900/3746] lr: 7.520e-02, eta: 3 days, 12:24:57, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5234, loss_cls: 4.1895, loss: 4.1895 +2024-12-27 21:56:15,938 - pyskl - INFO - Epoch [50][3000/3746] lr: 7.518e-02, eta: 3 days, 12:23:45, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5300, loss_cls: 4.1716, loss: 4.1716 +2024-12-27 21:57:40,963 - pyskl - INFO - Epoch [50][3100/3746] lr: 7.516e-02, eta: 3 days, 12:22:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5369, loss_cls: 4.1233, loss: 4.1233 +2024-12-27 21:59:05,624 - pyskl - INFO - Epoch [50][3200/3746] lr: 7.513e-02, eta: 3 days, 12:21:18, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5298, loss_cls: 4.1252, loss: 4.1252 +2024-12-27 22:00:30,630 - pyskl - INFO - Epoch [50][3300/3746] lr: 7.511e-02, eta: 3 days, 12:20:05, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5298, loss_cls: 4.1768, loss: 4.1768 +2024-12-27 22:01:55,822 - pyskl - INFO - Epoch [50][3400/3746] lr: 7.508e-02, eta: 3 days, 12:18:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5342, loss_cls: 4.1366, loss: 4.1366 +2024-12-27 22:03:20,857 - pyskl - INFO - Epoch [50][3500/3746] lr: 7.506e-02, eta: 3 days, 12:17:40, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5448, loss_cls: 4.0812, loss: 4.0812 +2024-12-27 22:04:46,128 - pyskl - INFO - Epoch [50][3600/3746] lr: 7.504e-02, eta: 3 days, 12:16:28, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5372, loss_cls: 4.1222, loss: 4.1222 +2024-12-27 22:06:11,537 - pyskl - INFO - Epoch [50][3700/3746] lr: 7.501e-02, eta: 3 days, 12:15:16, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5189, loss_cls: 4.1905, loss: 4.1905 +2024-12-27 22:06:52,627 - pyskl - INFO - Saving checkpoint at 50 epochs +2024-12-27 22:08:55,275 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 22:08:56,109 - pyskl - INFO - +top1_acc 0.2221 +top5_acc 0.4658 +2024-12-27 22:08:56,109 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 22:08:56,162 - pyskl - INFO - +mean_acc 0.2218 +2024-12-27 22:08:56,190 - pyskl - INFO - Epoch(val) [50][309] top1_acc: 0.2221, top5_acc: 0.4658, mean_class_accuracy: 0.2218 +2024-12-27 22:13:20,488 - pyskl - INFO - Epoch [51][100/3746] lr: 7.498e-02, eta: 3 days, 12:18:10, time: 2.643, data_time: 1.590, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5433, loss_cls: 4.0758, loss: 4.0758 +2024-12-27 22:14:45,698 - pyskl - INFO - Epoch [51][200/3746] lr: 7.495e-02, eta: 3 days, 12:16:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5353, loss_cls: 4.1265, loss: 4.1265 +2024-12-27 22:16:11,899 - pyskl - INFO - Epoch [51][300/3746] lr: 7.493e-02, eta: 3 days, 12:15:46, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5266, loss_cls: 4.1693, loss: 4.1693 +2024-12-27 22:17:37,299 - pyskl - INFO - Epoch [51][400/3746] lr: 7.490e-02, eta: 3 days, 12:14:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5364, loss_cls: 4.1257, loss: 4.1257 +2024-12-27 22:19:03,219 - pyskl - INFO - Epoch [51][500/3746] lr: 7.488e-02, eta: 3 days, 12:13:22, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5214, loss_cls: 4.1363, loss: 4.1363 +2024-12-27 22:20:29,108 - pyskl - INFO - Epoch [51][600/3746] lr: 7.485e-02, eta: 3 days, 12:12:11, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5316, loss_cls: 4.1359, loss: 4.1359 +2024-12-27 22:21:54,551 - pyskl - INFO - Epoch [51][700/3746] lr: 7.483e-02, eta: 3 days, 12:10:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5298, loss_cls: 4.1593, loss: 4.1593 +2024-12-27 22:23:20,113 - pyskl - INFO - Epoch [51][800/3746] lr: 7.481e-02, eta: 3 days, 12:09:47, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5242, loss_cls: 4.1805, loss: 4.1805 +2024-12-27 22:24:45,554 - pyskl - INFO - Epoch [51][900/3746] lr: 7.478e-02, eta: 3 days, 12:08:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5328, loss_cls: 4.1302, loss: 4.1302 +2024-12-27 22:26:10,124 - pyskl - INFO - Epoch [51][1000/3746] lr: 7.476e-02, eta: 3 days, 12:07:20, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5189, loss_cls: 4.2204, loss: 4.2204 +2024-12-27 22:27:35,232 - pyskl - INFO - Epoch [51][1100/3746] lr: 7.473e-02, eta: 3 days, 12:06:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5367, loss_cls: 4.1425, loss: 4.1425 +2024-12-27 22:29:00,768 - pyskl - INFO - Epoch [51][1200/3746] lr: 7.471e-02, eta: 3 days, 12:04:55, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5436, loss_cls: 4.1110, loss: 4.1110 +2024-12-27 22:30:26,155 - pyskl - INFO - Epoch [51][1300/3746] lr: 7.468e-02, eta: 3 days, 12:03:42, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5330, loss_cls: 4.1148, loss: 4.1148 +2024-12-27 22:31:51,419 - pyskl - INFO - Epoch [51][1400/3746] lr: 7.466e-02, eta: 3 days, 12:02:30, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5278, loss_cls: 4.1650, loss: 4.1650 +2024-12-27 22:33:16,639 - pyskl - INFO - Epoch [51][1500/3746] lr: 7.464e-02, eta: 3 days, 12:01:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5333, loss_cls: 4.1425, loss: 4.1425 +2024-12-27 22:34:42,357 - pyskl - INFO - Epoch [51][1600/3746] lr: 7.461e-02, eta: 3 days, 12:00:05, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5347, loss_cls: 4.1121, loss: 4.1121 +2024-12-27 22:36:07,633 - pyskl - INFO - Epoch [51][1700/3746] lr: 7.459e-02, eta: 3 days, 11:58:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5348, loss_cls: 4.1286, loss: 4.1286 +2024-12-27 22:37:33,045 - pyskl - INFO - Epoch [51][1800/3746] lr: 7.456e-02, eta: 3 days, 11:57:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5334, loss_cls: 4.1690, loss: 4.1690 +2024-12-27 22:38:58,722 - pyskl - INFO - Epoch [51][1900/3746] lr: 7.454e-02, eta: 3 days, 11:56:28, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5347, loss_cls: 4.1306, loss: 4.1306 +2024-12-27 22:40:24,246 - pyskl - INFO - Epoch [51][2000/3746] lr: 7.451e-02, eta: 3 days, 11:55:15, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5344, loss_cls: 4.1433, loss: 4.1433 +2024-12-27 22:41:49,771 - pyskl - INFO - Epoch [51][2100/3746] lr: 7.449e-02, eta: 3 days, 11:54:03, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5375, loss_cls: 4.1434, loss: 4.1434 +2024-12-27 22:43:15,521 - pyskl - INFO - Epoch [51][2200/3746] lr: 7.447e-02, eta: 3 days, 11:52:51, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5391, loss_cls: 4.1381, loss: 4.1381 +2024-12-27 22:44:41,107 - pyskl - INFO - Epoch [51][2300/3746] lr: 7.444e-02, eta: 3 days, 11:51:39, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5353, loss_cls: 4.1184, loss: 4.1184 +2024-12-27 22:46:07,011 - pyskl - INFO - Epoch [51][2400/3746] lr: 7.442e-02, eta: 3 days, 11:50:27, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5255, loss_cls: 4.1592, loss: 4.1592 +2024-12-27 22:47:32,747 - pyskl - INFO - Epoch [51][2500/3746] lr: 7.439e-02, eta: 3 days, 11:49:15, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5252, loss_cls: 4.1776, loss: 4.1776 +2024-12-27 22:48:57,953 - pyskl - INFO - Epoch [51][2600/3746] lr: 7.437e-02, eta: 3 days, 11:48:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5262, loss_cls: 4.1630, loss: 4.1630 +2024-12-27 22:50:22,962 - pyskl - INFO - Epoch [51][2700/3746] lr: 7.434e-02, eta: 3 days, 11:46:49, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5320, loss_cls: 4.1447, loss: 4.1447 +2024-12-27 22:51:48,483 - pyskl - INFO - Epoch [51][2800/3746] lr: 7.432e-02, eta: 3 days, 11:45:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5269, loss_cls: 4.1825, loss: 4.1825 +2024-12-27 22:53:13,969 - pyskl - INFO - Epoch [51][2900/3746] lr: 7.429e-02, eta: 3 days, 11:44:24, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5217, loss_cls: 4.1765, loss: 4.1765 +2024-12-27 22:54:39,044 - pyskl - INFO - Epoch [51][3000/3746] lr: 7.427e-02, eta: 3 days, 11:43:10, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5408, loss_cls: 4.1286, loss: 4.1286 +2024-12-27 22:56:04,049 - pyskl - INFO - Epoch [51][3100/3746] lr: 7.425e-02, eta: 3 days, 11:41:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5397, loss_cls: 4.1107, loss: 4.1107 +2024-12-27 22:57:29,360 - pyskl - INFO - Epoch [51][3200/3746] lr: 7.422e-02, eta: 3 days, 11:40:44, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5306, loss_cls: 4.1548, loss: 4.1548 +2024-12-27 22:58:55,007 - pyskl - INFO - Epoch [51][3300/3746] lr: 7.420e-02, eta: 3 days, 11:39:32, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5369, loss_cls: 4.1318, loss: 4.1318 +2024-12-27 23:00:20,613 - pyskl - INFO - Epoch [51][3400/3746] lr: 7.417e-02, eta: 3 days, 11:38:19, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5395, loss_cls: 4.1128, loss: 4.1128 +2024-12-27 23:01:46,408 - pyskl - INFO - Epoch [51][3500/3746] lr: 7.415e-02, eta: 3 days, 11:37:07, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5344, loss_cls: 4.1487, loss: 4.1487 +2024-12-27 23:03:12,006 - pyskl - INFO - Epoch [51][3600/3746] lr: 7.412e-02, eta: 3 days, 11:35:55, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5319, loss_cls: 4.1506, loss: 4.1506 +2024-12-27 23:04:37,742 - pyskl - INFO - Epoch [51][3700/3746] lr: 7.410e-02, eta: 3 days, 11:34:43, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5300, loss_cls: 4.1737, loss: 4.1737 +2024-12-27 23:05:19,453 - pyskl - INFO - Saving checkpoint at 51 epochs +2024-12-27 23:07:18,816 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-27 23:07:19,909 - pyskl - INFO - +top1_acc 0.2096 +top5_acc 0.4525 +2024-12-27 23:07:19,909 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-27 23:07:19,971 - pyskl - INFO - +mean_acc 0.2094 +2024-12-27 23:07:19,988 - pyskl - INFO - Epoch(val) [51][309] top1_acc: 0.2096, top5_acc: 0.4525, mean_class_accuracy: 0.2094 +2024-12-27 23:11:40,060 - pyskl - INFO - Epoch [52][100/3746] lr: 7.406e-02, eta: 3 days, 11:37:19, time: 2.601, data_time: 1.570, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5555, loss_cls: 4.0318, loss: 4.0318 +2024-12-27 23:13:05,448 - pyskl - INFO - Epoch [52][200/3746] lr: 7.404e-02, eta: 3 days, 11:36:06, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5409, loss_cls: 4.1312, loss: 4.1312 +2024-12-27 23:14:30,819 - pyskl - INFO - Epoch [52][300/3746] lr: 7.401e-02, eta: 3 days, 11:34:53, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5373, loss_cls: 4.1251, loss: 4.1251 +2024-12-27 23:15:56,335 - pyskl - INFO - Epoch [52][400/3746] lr: 7.399e-02, eta: 3 days, 11:33:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5389, loss_cls: 4.0779, loss: 4.0779 +2024-12-27 23:17:21,244 - pyskl - INFO - Epoch [52][500/3746] lr: 7.397e-02, eta: 3 days, 11:32:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5392, loss_cls: 4.1119, loss: 4.1119 +2024-12-27 23:18:46,551 - pyskl - INFO - Epoch [52][600/3746] lr: 7.394e-02, eta: 3 days, 11:31:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5295, loss_cls: 4.1576, loss: 4.1576 +2024-12-27 23:20:12,339 - pyskl - INFO - Epoch [52][700/3746] lr: 7.392e-02, eta: 3 days, 11:30:00, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5253, loss_cls: 4.1367, loss: 4.1367 +2024-12-27 23:21:38,167 - pyskl - INFO - Epoch [52][800/3746] lr: 7.389e-02, eta: 3 days, 11:28:48, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5414, loss_cls: 4.1011, loss: 4.1011 +2024-12-27 23:23:04,069 - pyskl - INFO - Epoch [52][900/3746] lr: 7.387e-02, eta: 3 days, 11:27:36, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5270, loss_cls: 4.1432, loss: 4.1432 +2024-12-27 23:24:29,550 - pyskl - INFO - Epoch [52][1000/3746] lr: 7.384e-02, eta: 3 days, 11:26:23, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5303, loss_cls: 4.1633, loss: 4.1633 +2024-12-27 23:25:55,374 - pyskl - INFO - Epoch [52][1100/3746] lr: 7.382e-02, eta: 3 days, 11:25:10, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5455, loss_cls: 4.0831, loss: 4.0831 +2024-12-27 23:27:21,072 - pyskl - INFO - Epoch [52][1200/3746] lr: 7.379e-02, eta: 3 days, 11:23:58, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5358, loss_cls: 4.1025, loss: 4.1025 +2024-12-27 23:28:46,943 - pyskl - INFO - Epoch [52][1300/3746] lr: 7.377e-02, eta: 3 days, 11:22:45, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5322, loss_cls: 4.1425, loss: 4.1425 +2024-12-27 23:30:12,785 - pyskl - INFO - Epoch [52][1400/3746] lr: 7.374e-02, eta: 3 days, 11:21:33, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5316, loss_cls: 4.1440, loss: 4.1440 +2024-12-27 23:31:38,173 - pyskl - INFO - Epoch [52][1500/3746] lr: 7.372e-02, eta: 3 days, 11:20:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5311, loss_cls: 4.1690, loss: 4.1690 +2024-12-27 23:33:04,166 - pyskl - INFO - Epoch [52][1600/3746] lr: 7.370e-02, eta: 3 days, 11:19:08, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5294, loss_cls: 4.1782, loss: 4.1782 +2024-12-27 23:34:29,874 - pyskl - INFO - Epoch [52][1700/3746] lr: 7.367e-02, eta: 3 days, 11:17:55, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5300, loss_cls: 4.1570, loss: 4.1570 +2024-12-27 23:35:55,744 - pyskl - INFO - Epoch [52][1800/3746] lr: 7.365e-02, eta: 3 days, 11:16:43, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5464, loss_cls: 4.0948, loss: 4.0948 +2024-12-27 23:37:21,733 - pyskl - INFO - Epoch [52][1900/3746] lr: 7.362e-02, eta: 3 days, 11:15:30, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5264, loss_cls: 4.1937, loss: 4.1937 +2024-12-27 23:38:47,203 - pyskl - INFO - Epoch [52][2000/3746] lr: 7.360e-02, eta: 3 days, 11:14:17, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5267, loss_cls: 4.1694, loss: 4.1694 +2024-12-27 23:40:12,622 - pyskl - INFO - Epoch [52][2100/3746] lr: 7.357e-02, eta: 3 days, 11:13:04, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5289, loss_cls: 4.1363, loss: 4.1363 +2024-12-27 23:41:37,630 - pyskl - INFO - Epoch [52][2200/3746] lr: 7.355e-02, eta: 3 days, 11:11:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5339, loss_cls: 4.1166, loss: 4.1166 +2024-12-27 23:43:03,009 - pyskl - INFO - Epoch [52][2300/3746] lr: 7.352e-02, eta: 3 days, 11:10:37, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5359, loss_cls: 4.1336, loss: 4.1336 +2024-12-27 23:44:28,600 - pyskl - INFO - Epoch [52][2400/3746] lr: 7.350e-02, eta: 3 days, 11:09:24, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5286, loss_cls: 4.1480, loss: 4.1480 +2024-12-27 23:45:54,380 - pyskl - INFO - Epoch [52][2500/3746] lr: 7.347e-02, eta: 3 days, 11:08:11, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5192, loss_cls: 4.1897, loss: 4.1897 +2024-12-27 23:47:19,615 - pyskl - INFO - Epoch [52][2600/3746] lr: 7.345e-02, eta: 3 days, 11:06:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5331, loss_cls: 4.1297, loss: 4.1297 +2024-12-27 23:48:44,675 - pyskl - INFO - Epoch [52][2700/3746] lr: 7.342e-02, eta: 3 days, 11:05:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5292, loss_cls: 4.1761, loss: 4.1761 +2024-12-27 23:50:10,034 - pyskl - INFO - Epoch [52][2800/3746] lr: 7.340e-02, eta: 3 days, 11:04:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5352, loss_cls: 4.1294, loss: 4.1294 +2024-12-27 23:51:35,272 - pyskl - INFO - Epoch [52][2900/3746] lr: 7.337e-02, eta: 3 days, 11:03:16, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5236, loss_cls: 4.1867, loss: 4.1867 +2024-12-27 23:53:00,541 - pyskl - INFO - Epoch [52][3000/3746] lr: 7.335e-02, eta: 3 days, 11:02:02, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5452, loss_cls: 4.0864, loss: 4.0864 +2024-12-27 23:54:25,674 - pyskl - INFO - Epoch [52][3100/3746] lr: 7.332e-02, eta: 3 days, 11:00:49, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5281, loss_cls: 4.1861, loss: 4.1861 +2024-12-27 23:55:51,009 - pyskl - INFO - Epoch [52][3200/3746] lr: 7.330e-02, eta: 3 days, 10:59:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5255, loss_cls: 4.1649, loss: 4.1649 +2024-12-27 23:57:15,696 - pyskl - INFO - Epoch [52][3300/3746] lr: 7.328e-02, eta: 3 days, 10:58:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5331, loss_cls: 4.1246, loss: 4.1246 +2024-12-27 23:58:40,573 - pyskl - INFO - Epoch [52][3400/3746] lr: 7.325e-02, eta: 3 days, 10:57:06, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5345, loss_cls: 4.1587, loss: 4.1587 +2024-12-28 00:00:05,611 - pyskl - INFO - Epoch [52][3500/3746] lr: 7.323e-02, eta: 3 days, 10:55:52, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5295, loss_cls: 4.1804, loss: 4.1804 +2024-12-28 00:01:30,625 - pyskl - INFO - Epoch [52][3600/3746] lr: 7.320e-02, eta: 3 days, 10:54:37, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5319, loss_cls: 4.1413, loss: 4.1413 +2024-12-28 00:02:55,755 - pyskl - INFO - Epoch [52][3700/3746] lr: 7.318e-02, eta: 3 days, 10:53:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5356, loss_cls: 4.1354, loss: 4.1354 +2024-12-28 00:03:37,065 - pyskl - INFO - Saving checkpoint at 52 epochs +2024-12-28 00:05:36,340 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 00:05:37,063 - pyskl - INFO - +top1_acc 0.2273 +top5_acc 0.4667 +2024-12-28 00:05:37,063 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 00:05:37,119 - pyskl - INFO - +mean_acc 0.2272 +2024-12-28 00:05:37,124 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_44.pth was removed +2024-12-28 00:05:37,382 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_52.pth. +2024-12-28 00:05:37,383 - pyskl - INFO - Best top1_acc is 0.2273 at 52 epoch. +2024-12-28 00:05:37,396 - pyskl - INFO - Epoch(val) [52][309] top1_acc: 0.2273, top5_acc: 0.4667, mean_class_accuracy: 0.2272 +2024-12-28 00:10:00,728 - pyskl - INFO - Epoch [53][100/3746] lr: 7.314e-02, eta: 3 days, 10:55:57, time: 2.633, data_time: 1.603, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5398, loss_cls: 4.0920, loss: 4.0920 +2024-12-28 00:11:26,417 - pyskl - INFO - Epoch [53][200/3746] lr: 7.312e-02, eta: 3 days, 10:54:44, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5458, loss_cls: 4.0492, loss: 4.0492 +2024-12-28 00:12:52,189 - pyskl - INFO - Epoch [53][300/3746] lr: 7.309e-02, eta: 3 days, 10:53:31, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5309, loss_cls: 4.1445, loss: 4.1445 +2024-12-28 00:14:17,845 - pyskl - INFO - Epoch [53][400/3746] lr: 7.307e-02, eta: 3 days, 10:52:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5500, loss_cls: 4.0802, loss: 4.0802 +2024-12-28 00:15:43,494 - pyskl - INFO - Epoch [53][500/3746] lr: 7.304e-02, eta: 3 days, 10:51:04, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5380, loss_cls: 4.0849, loss: 4.0849 +2024-12-28 00:17:08,789 - pyskl - INFO - Epoch [53][600/3746] lr: 7.302e-02, eta: 3 days, 10:49:50, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5280, loss_cls: 4.1444, loss: 4.1444 +2024-12-28 00:18:34,389 - pyskl - INFO - Epoch [53][700/3746] lr: 7.299e-02, eta: 3 days, 10:48:37, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5377, loss_cls: 4.1047, loss: 4.1047 +2024-12-28 00:19:59,915 - pyskl - INFO - Epoch [53][800/3746] lr: 7.297e-02, eta: 3 days, 10:47:23, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5297, loss_cls: 4.1404, loss: 4.1404 +2024-12-28 00:21:25,639 - pyskl - INFO - Epoch [53][900/3746] lr: 7.294e-02, eta: 3 days, 10:46:10, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5238, loss_cls: 4.1819, loss: 4.1819 +2024-12-28 00:22:51,764 - pyskl - INFO - Epoch [53][1000/3746] lr: 7.292e-02, eta: 3 days, 10:44:58, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5433, loss_cls: 4.0774, loss: 4.0774 +2024-12-28 00:24:17,431 - pyskl - INFO - Epoch [53][1100/3746] lr: 7.289e-02, eta: 3 days, 10:43:44, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5341, loss_cls: 4.1253, loss: 4.1253 +2024-12-28 00:25:43,075 - pyskl - INFO - Epoch [53][1200/3746] lr: 7.287e-02, eta: 3 days, 10:42:31, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5336, loss_cls: 4.1240, loss: 4.1240 +2024-12-28 00:27:08,091 - pyskl - INFO - Epoch [53][1300/3746] lr: 7.284e-02, eta: 3 days, 10:41:16, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5513, loss_cls: 4.0511, loss: 4.0511 +2024-12-28 00:28:32,883 - pyskl - INFO - Epoch [53][1400/3746] lr: 7.282e-02, eta: 3 days, 10:40:01, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5253, loss_cls: 4.1339, loss: 4.1339 +2024-12-28 00:29:58,202 - pyskl - INFO - Epoch [53][1500/3746] lr: 7.279e-02, eta: 3 days, 10:38:47, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5377, loss_cls: 4.1011, loss: 4.1011 +2024-12-28 00:31:23,774 - pyskl - INFO - Epoch [53][1600/3746] lr: 7.277e-02, eta: 3 days, 10:37:34, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5433, loss_cls: 4.0980, loss: 4.0980 +2024-12-28 00:32:49,094 - pyskl - INFO - Epoch [53][1700/3746] lr: 7.274e-02, eta: 3 days, 10:36:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5475, loss_cls: 4.0870, loss: 4.0870 +2024-12-28 00:34:14,492 - pyskl - INFO - Epoch [53][1800/3746] lr: 7.272e-02, eta: 3 days, 10:35:06, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5433, loss_cls: 4.1121, loss: 4.1121 +2024-12-28 00:35:39,561 - pyskl - INFO - Epoch [53][1900/3746] lr: 7.269e-02, eta: 3 days, 10:33:51, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5295, loss_cls: 4.1611, loss: 4.1611 +2024-12-28 00:37:04,500 - pyskl - INFO - Epoch [53][2000/3746] lr: 7.267e-02, eta: 3 days, 10:32:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5255, loss_cls: 4.1666, loss: 4.1666 +2024-12-28 00:38:29,920 - pyskl - INFO - Epoch [53][2100/3746] lr: 7.264e-02, eta: 3 days, 10:31:23, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5364, loss_cls: 4.1151, loss: 4.1151 +2024-12-28 00:39:55,600 - pyskl - INFO - Epoch [53][2200/3746] lr: 7.262e-02, eta: 3 days, 10:30:09, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5406, loss_cls: 4.0992, loss: 4.0992 +2024-12-28 00:41:21,783 - pyskl - INFO - Epoch [53][2300/3746] lr: 7.259e-02, eta: 3 days, 10:28:57, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5333, loss_cls: 4.1331, loss: 4.1331 +2024-12-28 00:42:47,309 - pyskl - INFO - Epoch [53][2400/3746] lr: 7.257e-02, eta: 3 days, 10:27:43, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5252, loss_cls: 4.1660, loss: 4.1660 +2024-12-28 00:44:12,301 - pyskl - INFO - Epoch [53][2500/3746] lr: 7.254e-02, eta: 3 days, 10:26:28, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5233, loss_cls: 4.1573, loss: 4.1573 +2024-12-28 00:45:37,406 - pyskl - INFO - Epoch [53][2600/3746] lr: 7.252e-02, eta: 3 days, 10:25:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5344, loss_cls: 4.1742, loss: 4.1742 +2024-12-28 00:47:02,434 - pyskl - INFO - Epoch [53][2700/3746] lr: 7.249e-02, eta: 3 days, 10:23:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5395, loss_cls: 4.1293, loss: 4.1293 +2024-12-28 00:48:27,252 - pyskl - INFO - Epoch [53][2800/3746] lr: 7.247e-02, eta: 3 days, 10:22:44, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5289, loss_cls: 4.1466, loss: 4.1466 +2024-12-28 00:49:52,467 - pyskl - INFO - Epoch [53][2900/3746] lr: 7.244e-02, eta: 3 days, 10:21:29, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5302, loss_cls: 4.1671, loss: 4.1671 +2024-12-28 00:51:17,721 - pyskl - INFO - Epoch [53][3000/3746] lr: 7.242e-02, eta: 3 days, 10:20:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5297, loss_cls: 4.1304, loss: 4.1304 +2024-12-28 00:52:42,699 - pyskl - INFO - Epoch [53][3100/3746] lr: 7.239e-02, eta: 3 days, 10:19:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5327, loss_cls: 4.1414, loss: 4.1414 +2024-12-28 00:54:07,646 - pyskl - INFO - Epoch [53][3200/3746] lr: 7.237e-02, eta: 3 days, 10:17:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5363, loss_cls: 4.1548, loss: 4.1548 +2024-12-28 00:55:33,468 - pyskl - INFO - Epoch [53][3300/3746] lr: 7.234e-02, eta: 3 days, 10:16:32, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5377, loss_cls: 4.1311, loss: 4.1311 +2024-12-28 00:56:58,346 - pyskl - INFO - Epoch [53][3400/3746] lr: 7.232e-02, eta: 3 days, 10:15:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5334, loss_cls: 4.1201, loss: 4.1201 +2024-12-28 00:58:23,958 - pyskl - INFO - Epoch [53][3500/3746] lr: 7.229e-02, eta: 3 days, 10:14:03, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5255, loss_cls: 4.1645, loss: 4.1645 +2024-12-28 00:59:49,284 - pyskl - INFO - Epoch [53][3600/3746] lr: 7.227e-02, eta: 3 days, 10:12:49, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5350, loss_cls: 4.1658, loss: 4.1658 +2024-12-28 01:01:14,535 - pyskl - INFO - Epoch [53][3700/3746] lr: 7.224e-02, eta: 3 days, 10:11:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5345, loss_cls: 4.1368, loss: 4.1368 +2024-12-28 01:01:55,808 - pyskl - INFO - Saving checkpoint at 53 epochs +2024-12-28 01:03:57,178 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 01:03:57,944 - pyskl - INFO - +top1_acc 0.2210 +top5_acc 0.4638 +2024-12-28 01:03:57,944 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 01:03:58,000 - pyskl - INFO - +mean_acc 0.2208 +2024-12-28 01:03:58,023 - pyskl - INFO - Epoch(val) [53][309] top1_acc: 0.2210, top5_acc: 0.4638, mean_class_accuracy: 0.2208 +2024-12-28 01:08:22,847 - pyskl - INFO - Epoch [54][100/3746] lr: 7.221e-02, eta: 3 days, 10:14:02, time: 2.648, data_time: 1.612, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5469, loss_cls: 4.0886, loss: 4.0886 +2024-12-28 01:09:48,722 - pyskl - INFO - Epoch [54][200/3746] lr: 7.218e-02, eta: 3 days, 10:12:49, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5414, loss_cls: 4.0858, loss: 4.0858 +2024-12-28 01:11:14,175 - pyskl - INFO - Epoch [54][300/3746] lr: 7.216e-02, eta: 3 days, 10:11:35, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5392, loss_cls: 4.1418, loss: 4.1418 +2024-12-28 01:12:39,673 - pyskl - INFO - Epoch [54][400/3746] lr: 7.213e-02, eta: 3 days, 10:10:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5387, loss_cls: 4.0850, loss: 4.0850 +2024-12-28 01:14:05,473 - pyskl - INFO - Epoch [54][500/3746] lr: 7.211e-02, eta: 3 days, 10:09:07, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5370, loss_cls: 4.1123, loss: 4.1123 +2024-12-28 01:15:31,120 - pyskl - INFO - Epoch [54][600/3746] lr: 7.208e-02, eta: 3 days, 10:07:53, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5309, loss_cls: 4.1605, loss: 4.1605 +2024-12-28 01:16:57,523 - pyskl - INFO - Epoch [54][700/3746] lr: 7.206e-02, eta: 3 days, 10:06:40, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5386, loss_cls: 4.1080, loss: 4.1080 +2024-12-28 01:18:23,823 - pyskl - INFO - Epoch [54][800/3746] lr: 7.203e-02, eta: 3 days, 10:05:27, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5413, loss_cls: 4.0792, loss: 4.0792 +2024-12-28 01:19:49,471 - pyskl - INFO - Epoch [54][900/3746] lr: 7.201e-02, eta: 3 days, 10:04:13, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5377, loss_cls: 4.0915, loss: 4.0915 +2024-12-28 01:21:15,165 - pyskl - INFO - Epoch [54][1000/3746] lr: 7.198e-02, eta: 3 days, 10:02:59, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5334, loss_cls: 4.1155, loss: 4.1155 +2024-12-28 01:22:40,943 - pyskl - INFO - Epoch [54][1100/3746] lr: 7.196e-02, eta: 3 days, 10:01:46, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5328, loss_cls: 4.1417, loss: 4.1417 +2024-12-28 01:24:06,689 - pyskl - INFO - Epoch [54][1200/3746] lr: 7.193e-02, eta: 3 days, 10:00:32, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5406, loss_cls: 4.0787, loss: 4.0787 +2024-12-28 01:25:32,097 - pyskl - INFO - Epoch [54][1300/3746] lr: 7.191e-02, eta: 3 days, 9:59:17, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5281, loss_cls: 4.1362, loss: 4.1362 +2024-12-28 01:26:57,681 - pyskl - INFO - Epoch [54][1400/3746] lr: 7.188e-02, eta: 3 days, 9:58:03, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5303, loss_cls: 4.1567, loss: 4.1567 +2024-12-28 01:28:22,763 - pyskl - INFO - Epoch [54][1500/3746] lr: 7.186e-02, eta: 3 days, 9:56:48, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5295, loss_cls: 4.1420, loss: 4.1420 +2024-12-28 01:29:47,776 - pyskl - INFO - Epoch [54][1600/3746] lr: 7.183e-02, eta: 3 days, 9:55:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5473, loss_cls: 4.0381, loss: 4.0381 +2024-12-28 01:31:13,110 - pyskl - INFO - Epoch [54][1700/3746] lr: 7.181e-02, eta: 3 days, 9:54:18, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5411, loss_cls: 4.1001, loss: 4.1001 +2024-12-28 01:32:38,289 - pyskl - INFO - Epoch [54][1800/3746] lr: 7.178e-02, eta: 3 days, 9:53:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5473, loss_cls: 4.0706, loss: 4.0706 +2024-12-28 01:34:03,738 - pyskl - INFO - Epoch [54][1900/3746] lr: 7.176e-02, eta: 3 days, 9:51:49, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5422, loss_cls: 4.0895, loss: 4.0895 +2024-12-28 01:35:28,584 - pyskl - INFO - Epoch [54][2000/3746] lr: 7.173e-02, eta: 3 days, 9:50:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5444, loss_cls: 4.0889, loss: 4.0889 +2024-12-28 01:36:53,768 - pyskl - INFO - Epoch [54][2100/3746] lr: 7.170e-02, eta: 3 days, 9:49:18, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5295, loss_cls: 4.1551, loss: 4.1551 +2024-12-28 01:38:18,492 - pyskl - INFO - Epoch [54][2200/3746] lr: 7.168e-02, eta: 3 days, 9:48:03, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5434, loss_cls: 4.0935, loss: 4.0935 +2024-12-28 01:39:43,950 - pyskl - INFO - Epoch [54][2300/3746] lr: 7.165e-02, eta: 3 days, 9:46:48, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5327, loss_cls: 4.1300, loss: 4.1300 +2024-12-28 01:41:08,948 - pyskl - INFO - Epoch [54][2400/3746] lr: 7.163e-02, eta: 3 days, 9:45:33, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5316, loss_cls: 4.1158, loss: 4.1158 +2024-12-28 01:42:34,057 - pyskl - INFO - Epoch [54][2500/3746] lr: 7.160e-02, eta: 3 days, 9:44:18, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5353, loss_cls: 4.1397, loss: 4.1397 +2024-12-28 01:43:59,616 - pyskl - INFO - Epoch [54][2600/3746] lr: 7.158e-02, eta: 3 days, 9:43:03, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5372, loss_cls: 4.1312, loss: 4.1312 +2024-12-28 01:45:25,234 - pyskl - INFO - Epoch [54][2700/3746] lr: 7.155e-02, eta: 3 days, 9:41:49, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5411, loss_cls: 4.0917, loss: 4.0917 +2024-12-28 01:46:50,194 - pyskl - INFO - Epoch [54][2800/3746] lr: 7.153e-02, eta: 3 days, 9:40:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5369, loss_cls: 4.1130, loss: 4.1130 +2024-12-28 01:48:15,522 - pyskl - INFO - Epoch [54][2900/3746] lr: 7.150e-02, eta: 3 days, 9:39:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5295, loss_cls: 4.1282, loss: 4.1282 +2024-12-28 01:49:40,984 - pyskl - INFO - Epoch [54][3000/3746] lr: 7.148e-02, eta: 3 days, 9:38:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5380, loss_cls: 4.1105, loss: 4.1105 +2024-12-28 01:51:06,593 - pyskl - INFO - Epoch [54][3100/3746] lr: 7.145e-02, eta: 3 days, 9:36:50, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5364, loss_cls: 4.1488, loss: 4.1488 +2024-12-28 01:52:31,580 - pyskl - INFO - Epoch [54][3200/3746] lr: 7.143e-02, eta: 3 days, 9:35:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5273, loss_cls: 4.1325, loss: 4.1325 +2024-12-28 01:53:57,010 - pyskl - INFO - Epoch [54][3300/3746] lr: 7.140e-02, eta: 3 days, 9:34:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5291, loss_cls: 4.1690, loss: 4.1690 +2024-12-28 01:55:22,730 - pyskl - INFO - Epoch [54][3400/3746] lr: 7.138e-02, eta: 3 days, 9:33:06, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5336, loss_cls: 4.1413, loss: 4.1413 +2024-12-28 01:56:47,829 - pyskl - INFO - Epoch [54][3500/3746] lr: 7.135e-02, eta: 3 days, 9:31:50, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5341, loss_cls: 4.1437, loss: 4.1437 +2024-12-28 01:58:13,229 - pyskl - INFO - Epoch [54][3600/3746] lr: 7.133e-02, eta: 3 days, 9:30:36, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5355, loss_cls: 4.1455, loss: 4.1455 +2024-12-28 01:59:38,666 - pyskl - INFO - Epoch [54][3700/3746] lr: 7.130e-02, eta: 3 days, 9:29:21, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5222, loss_cls: 4.1777, loss: 4.1777 +2024-12-28 02:00:20,214 - pyskl - INFO - Saving checkpoint at 54 epochs +2024-12-28 02:02:17,979 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 02:02:18,662 - pyskl - INFO - +top1_acc 0.2195 +top5_acc 0.4623 +2024-12-28 02:02:18,662 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 02:02:18,704 - pyskl - INFO - +mean_acc 0.2194 +2024-12-28 02:02:18,717 - pyskl - INFO - Epoch(val) [54][309] top1_acc: 0.2195, top5_acc: 0.4623, mean_class_accuracy: 0.2194 +2024-12-28 02:06:35,947 - pyskl - INFO - Epoch [55][100/3746] lr: 7.126e-02, eta: 3 days, 9:31:27, time: 2.572, data_time: 1.538, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5350, loss_cls: 4.0946, loss: 4.0946 +2024-12-28 02:08:01,790 - pyskl - INFO - Epoch [55][200/3746] lr: 7.124e-02, eta: 3 days, 9:30:13, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5391, loss_cls: 4.0689, loss: 4.0689 +2024-12-28 02:09:27,077 - pyskl - INFO - Epoch [55][300/3746] lr: 7.121e-02, eta: 3 days, 9:28:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5473, loss_cls: 4.0693, loss: 4.0693 +2024-12-28 02:10:52,432 - pyskl - INFO - Epoch [55][400/3746] lr: 7.119e-02, eta: 3 days, 9:27:43, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5370, loss_cls: 4.1013, loss: 4.1013 +2024-12-28 02:12:18,956 - pyskl - INFO - Epoch [55][500/3746] lr: 7.116e-02, eta: 3 days, 9:26:30, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5423, loss_cls: 4.0635, loss: 4.0635 +2024-12-28 02:13:45,107 - pyskl - INFO - Epoch [55][600/3746] lr: 7.114e-02, eta: 3 days, 9:25:16, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5369, loss_cls: 4.1105, loss: 4.1105 +2024-12-28 02:15:11,319 - pyskl - INFO - Epoch [55][700/3746] lr: 7.111e-02, eta: 3 days, 9:24:03, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5317, loss_cls: 4.1301, loss: 4.1301 +2024-12-28 02:16:37,361 - pyskl - INFO - Epoch [55][800/3746] lr: 7.109e-02, eta: 3 days, 9:22:49, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5322, loss_cls: 4.1450, loss: 4.1450 +2024-12-28 02:18:03,418 - pyskl - INFO - Epoch [55][900/3746] lr: 7.106e-02, eta: 3 days, 9:21:35, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5394, loss_cls: 4.1160, loss: 4.1160 +2024-12-28 02:19:29,907 - pyskl - INFO - Epoch [55][1000/3746] lr: 7.104e-02, eta: 3 days, 9:20:22, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5370, loss_cls: 4.1091, loss: 4.1091 +2024-12-28 02:20:56,001 - pyskl - INFO - Epoch [55][1100/3746] lr: 7.101e-02, eta: 3 days, 9:19:08, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5337, loss_cls: 4.1133, loss: 4.1133 +2024-12-28 02:22:22,162 - pyskl - INFO - Epoch [55][1200/3746] lr: 7.099e-02, eta: 3 days, 9:17:54, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5386, loss_cls: 4.1154, loss: 4.1154 +2024-12-28 02:23:47,816 - pyskl - INFO - Epoch [55][1300/3746] lr: 7.096e-02, eta: 3 days, 9:16:40, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5480, loss_cls: 4.0762, loss: 4.0762 +2024-12-28 02:25:13,653 - pyskl - INFO - Epoch [55][1400/3746] lr: 7.093e-02, eta: 3 days, 9:15:25, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5297, loss_cls: 4.1573, loss: 4.1573 +2024-12-28 02:26:40,181 - pyskl - INFO - Epoch [55][1500/3746] lr: 7.091e-02, eta: 3 days, 9:14:12, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5455, loss_cls: 4.0725, loss: 4.0725 +2024-12-28 02:28:06,177 - pyskl - INFO - Epoch [55][1600/3746] lr: 7.088e-02, eta: 3 days, 9:12:58, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5363, loss_cls: 4.1208, loss: 4.1208 +2024-12-28 02:29:31,960 - pyskl - INFO - Epoch [55][1700/3746] lr: 7.086e-02, eta: 3 days, 9:11:44, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5361, loss_cls: 4.0982, loss: 4.0982 +2024-12-28 02:30:57,471 - pyskl - INFO - Epoch [55][1800/3746] lr: 7.083e-02, eta: 3 days, 9:10:29, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5472, loss_cls: 4.0907, loss: 4.0907 +2024-12-28 02:32:22,829 - pyskl - INFO - Epoch [55][1900/3746] lr: 7.081e-02, eta: 3 days, 9:09:14, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5352, loss_cls: 4.1105, loss: 4.1105 +2024-12-28 02:33:48,205 - pyskl - INFO - Epoch [55][2000/3746] lr: 7.078e-02, eta: 3 days, 9:07:58, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5384, loss_cls: 4.1026, loss: 4.1026 +2024-12-28 02:35:13,510 - pyskl - INFO - Epoch [55][2100/3746] lr: 7.076e-02, eta: 3 days, 9:06:43, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5366, loss_cls: 4.1430, loss: 4.1430 +2024-12-28 02:36:39,519 - pyskl - INFO - Epoch [55][2200/3746] lr: 7.073e-02, eta: 3 days, 9:05:29, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5300, loss_cls: 4.1232, loss: 4.1232 +2024-12-28 02:38:05,392 - pyskl - INFO - Epoch [55][2300/3746] lr: 7.071e-02, eta: 3 days, 9:04:15, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5369, loss_cls: 4.1285, loss: 4.1285 +2024-12-28 02:39:30,465 - pyskl - INFO - Epoch [55][2400/3746] lr: 7.068e-02, eta: 3 days, 9:02:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5316, loss_cls: 4.1412, loss: 4.1412 +2024-12-28 02:40:55,699 - pyskl - INFO - Epoch [55][2500/3746] lr: 7.065e-02, eta: 3 days, 9:01:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5409, loss_cls: 4.0835, loss: 4.0835 +2024-12-28 02:42:21,432 - pyskl - INFO - Epoch [55][2600/3746] lr: 7.063e-02, eta: 3 days, 9:00:29, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5297, loss_cls: 4.1486, loss: 4.1486 +2024-12-28 02:43:47,996 - pyskl - INFO - Epoch [55][2700/3746] lr: 7.060e-02, eta: 3 days, 8:59:15, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5502, loss_cls: 4.0555, loss: 4.0555 +2024-12-28 02:45:14,245 - pyskl - INFO - Epoch [55][2800/3746] lr: 7.058e-02, eta: 3 days, 8:58:02, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5381, loss_cls: 4.1290, loss: 4.1290 +2024-12-28 02:46:39,496 - pyskl - INFO - Epoch [55][2900/3746] lr: 7.055e-02, eta: 3 days, 8:56:46, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5306, loss_cls: 4.1752, loss: 4.1752 +2024-12-28 02:48:04,731 - pyskl - INFO - Epoch [55][3000/3746] lr: 7.053e-02, eta: 3 days, 8:55:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5442, loss_cls: 4.0687, loss: 4.0687 +2024-12-28 02:49:30,475 - pyskl - INFO - Epoch [55][3100/3746] lr: 7.050e-02, eta: 3 days, 8:54:16, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5370, loss_cls: 4.1196, loss: 4.1196 +2024-12-28 02:50:56,543 - pyskl - INFO - Epoch [55][3200/3746] lr: 7.048e-02, eta: 3 days, 8:53:02, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5333, loss_cls: 4.1307, loss: 4.1307 +2024-12-28 02:52:22,566 - pyskl - INFO - Epoch [55][3300/3746] lr: 7.045e-02, eta: 3 days, 8:51:48, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5378, loss_cls: 4.1517, loss: 4.1517 +2024-12-28 02:53:48,540 - pyskl - INFO - Epoch [55][3400/3746] lr: 7.043e-02, eta: 3 days, 8:50:33, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5395, loss_cls: 4.1159, loss: 4.1159 +2024-12-28 02:55:14,402 - pyskl - INFO - Epoch [55][3500/3746] lr: 7.040e-02, eta: 3 days, 8:49:19, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5380, loss_cls: 4.1297, loss: 4.1297 +2024-12-28 02:56:40,658 - pyskl - INFO - Epoch [55][3600/3746] lr: 7.037e-02, eta: 3 days, 8:48:05, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5392, loss_cls: 4.1122, loss: 4.1122 +2024-12-28 02:58:07,129 - pyskl - INFO - Epoch [55][3700/3746] lr: 7.035e-02, eta: 3 days, 8:46:52, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5423, loss_cls: 4.1174, loss: 4.1174 +2024-12-28 02:58:48,793 - pyskl - INFO - Saving checkpoint at 55 epochs +2024-12-28 03:00:50,433 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 03:00:51,330 - pyskl - INFO - +top1_acc 0.2313 +top5_acc 0.4661 +2024-12-28 03:00:51,331 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 03:00:51,400 - pyskl - INFO - +mean_acc 0.2312 +2024-12-28 03:00:51,409 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_52.pth was removed +2024-12-28 03:00:51,704 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_55.pth. +2024-12-28 03:00:51,704 - pyskl - INFO - Best top1_acc is 0.2313 at 55 epoch. +2024-12-28 03:00:51,722 - pyskl - INFO - Epoch(val) [55][309] top1_acc: 0.2313, top5_acc: 0.4661, mean_class_accuracy: 0.2312 +2024-12-28 03:05:12,840 - pyskl - INFO - Epoch [56][100/3746] lr: 7.031e-02, eta: 3 days, 8:48:57, time: 2.611, data_time: 1.571, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5498, loss_cls: 4.0388, loss: 4.0388 +2024-12-28 03:06:39,174 - pyskl - INFO - Epoch [56][200/3746] lr: 7.029e-02, eta: 3 days, 8:47:43, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5425, loss_cls: 4.0638, loss: 4.0638 +2024-12-28 03:08:05,294 - pyskl - INFO - Epoch [56][300/3746] lr: 7.026e-02, eta: 3 days, 8:46:29, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5500, loss_cls: 4.0447, loss: 4.0447 +2024-12-28 03:09:31,045 - pyskl - INFO - Epoch [56][400/3746] lr: 7.023e-02, eta: 3 days, 8:45:14, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5425, loss_cls: 4.1053, loss: 4.1053 +2024-12-28 03:10:56,424 - pyskl - INFO - Epoch [56][500/3746] lr: 7.021e-02, eta: 3 days, 8:43:58, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5430, loss_cls: 4.1269, loss: 4.1269 +2024-12-28 03:12:22,091 - pyskl - INFO - Epoch [56][600/3746] lr: 7.018e-02, eta: 3 days, 8:42:43, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5369, loss_cls: 4.1001, loss: 4.1001 +2024-12-28 03:13:47,741 - pyskl - INFO - Epoch [56][700/3746] lr: 7.016e-02, eta: 3 days, 8:41:28, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5419, loss_cls: 4.0671, loss: 4.0671 +2024-12-28 03:15:13,312 - pyskl - INFO - Epoch [56][800/3746] lr: 7.013e-02, eta: 3 days, 8:40:13, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5622, loss_cls: 4.0177, loss: 4.0177 +2024-12-28 03:16:39,561 - pyskl - INFO - Epoch [56][900/3746] lr: 7.011e-02, eta: 3 days, 8:38:59, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5453, loss_cls: 4.0794, loss: 4.0794 +2024-12-28 03:18:05,519 - pyskl - INFO - Epoch [56][1000/3746] lr: 7.008e-02, eta: 3 days, 8:37:44, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5275, loss_cls: 4.1145, loss: 4.1145 +2024-12-28 03:19:31,498 - pyskl - INFO - Epoch [56][1100/3746] lr: 7.006e-02, eta: 3 days, 8:36:29, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5344, loss_cls: 4.1224, loss: 4.1224 +2024-12-28 03:20:57,635 - pyskl - INFO - Epoch [56][1200/3746] lr: 7.003e-02, eta: 3 days, 8:35:15, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5370, loss_cls: 4.1150, loss: 4.1150 +2024-12-28 03:22:24,007 - pyskl - INFO - Epoch [56][1300/3746] lr: 7.000e-02, eta: 3 days, 8:34:01, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5319, loss_cls: 4.1572, loss: 4.1572 +2024-12-28 03:23:49,978 - pyskl - INFO - Epoch [56][1400/3746] lr: 6.998e-02, eta: 3 days, 8:32:46, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5481, loss_cls: 4.0593, loss: 4.0593 +2024-12-28 03:25:16,113 - pyskl - INFO - Epoch [56][1500/3746] lr: 6.995e-02, eta: 3 days, 8:31:32, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5427, loss_cls: 4.1139, loss: 4.1139 +2024-12-28 03:26:41,992 - pyskl - INFO - Epoch [56][1600/3746] lr: 6.993e-02, eta: 3 days, 8:30:17, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5492, loss_cls: 4.0466, loss: 4.0466 +2024-12-28 03:28:07,594 - pyskl - INFO - Epoch [56][1700/3746] lr: 6.990e-02, eta: 3 days, 8:29:02, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5437, loss_cls: 4.0751, loss: 4.0751 +2024-12-28 03:29:32,731 - pyskl - INFO - Epoch [56][1800/3746] lr: 6.988e-02, eta: 3 days, 8:27:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5419, loss_cls: 4.1188, loss: 4.1188 +2024-12-28 03:30:57,728 - pyskl - INFO - Epoch [56][1900/3746] lr: 6.985e-02, eta: 3 days, 8:26:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5373, loss_cls: 4.1070, loss: 4.1070 +2024-12-28 03:32:22,470 - pyskl - INFO - Epoch [56][2000/3746] lr: 6.983e-02, eta: 3 days, 8:25:12, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5439, loss_cls: 4.0896, loss: 4.0896 +2024-12-28 03:33:48,018 - pyskl - INFO - Epoch [56][2100/3746] lr: 6.980e-02, eta: 3 days, 8:23:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5477, loss_cls: 4.0707, loss: 4.0707 +2024-12-28 03:35:14,442 - pyskl - INFO - Epoch [56][2200/3746] lr: 6.977e-02, eta: 3 days, 8:22:43, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5245, loss_cls: 4.1826, loss: 4.1826 +2024-12-28 03:36:39,602 - pyskl - INFO - Epoch [56][2300/3746] lr: 6.975e-02, eta: 3 days, 8:21:27, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5270, loss_cls: 4.1333, loss: 4.1333 +2024-12-28 03:38:04,943 - pyskl - INFO - Epoch [56][2400/3746] lr: 6.972e-02, eta: 3 days, 8:20:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5398, loss_cls: 4.0720, loss: 4.0720 +2024-12-28 03:39:29,845 - pyskl - INFO - Epoch [56][2500/3746] lr: 6.970e-02, eta: 3 days, 8:18:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5477, loss_cls: 4.0482, loss: 4.0482 +2024-12-28 03:40:55,484 - pyskl - INFO - Epoch [56][2600/3746] lr: 6.967e-02, eta: 3 days, 8:17:39, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5413, loss_cls: 4.0560, loss: 4.0560 +2024-12-28 03:42:21,409 - pyskl - INFO - Epoch [56][2700/3746] lr: 6.965e-02, eta: 3 days, 8:16:24, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5373, loss_cls: 4.1136, loss: 4.1136 +2024-12-28 03:43:46,677 - pyskl - INFO - Epoch [56][2800/3746] lr: 6.962e-02, eta: 3 days, 8:15:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5244, loss_cls: 4.1665, loss: 4.1665 +2024-12-28 03:45:11,679 - pyskl - INFO - Epoch [56][2900/3746] lr: 6.959e-02, eta: 3 days, 8:13:52, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5359, loss_cls: 4.1297, loss: 4.1297 +2024-12-28 03:46:36,655 - pyskl - INFO - Epoch [56][3000/3746] lr: 6.957e-02, eta: 3 days, 8:12:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5331, loss_cls: 4.1330, loss: 4.1330 +2024-12-28 03:48:02,922 - pyskl - INFO - Epoch [56][3100/3746] lr: 6.954e-02, eta: 3 days, 8:11:21, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5433, loss_cls: 4.0793, loss: 4.0793 +2024-12-28 03:49:28,291 - pyskl - INFO - Epoch [56][3200/3746] lr: 6.952e-02, eta: 3 days, 8:10:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5322, loss_cls: 4.1233, loss: 4.1233 +2024-12-28 03:50:54,323 - pyskl - INFO - Epoch [56][3300/3746] lr: 6.949e-02, eta: 3 days, 8:08:50, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5477, loss_cls: 4.0925, loss: 4.0925 +2024-12-28 03:52:20,694 - pyskl - INFO - Epoch [56][3400/3746] lr: 6.947e-02, eta: 3 days, 8:07:36, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5372, loss_cls: 4.0795, loss: 4.0795 +2024-12-28 03:53:46,426 - pyskl - INFO - Epoch [56][3500/3746] lr: 6.944e-02, eta: 3 days, 8:06:21, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5339, loss_cls: 4.1450, loss: 4.1450 +2024-12-28 03:55:12,291 - pyskl - INFO - Epoch [56][3600/3746] lr: 6.941e-02, eta: 3 days, 8:05:05, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5334, loss_cls: 4.1426, loss: 4.1426 +2024-12-28 03:56:38,257 - pyskl - INFO - Epoch [56][3700/3746] lr: 6.939e-02, eta: 3 days, 8:03:51, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5389, loss_cls: 4.0958, loss: 4.0958 +2024-12-28 03:57:19,905 - pyskl - INFO - Saving checkpoint at 56 epochs +2024-12-28 03:59:21,626 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 03:59:22,427 - pyskl - INFO - +top1_acc 0.2097 +top5_acc 0.4459 +2024-12-28 03:59:22,428 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 03:59:22,496 - pyskl - INFO - +mean_acc 0.2095 +2024-12-28 03:59:22,521 - pyskl - INFO - Epoch(val) [56][309] top1_acc: 0.2097, top5_acc: 0.4459, mean_class_accuracy: 0.2095 +2024-12-28 04:03:42,344 - pyskl - INFO - Epoch [57][100/3746] lr: 6.935e-02, eta: 3 days, 8:05:46, time: 2.598, data_time: 1.562, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5519, loss_cls: 4.0405, loss: 4.0405 +2024-12-28 04:05:08,153 - pyskl - INFO - Epoch [57][200/3746] lr: 6.932e-02, eta: 3 days, 8:04:31, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5430, loss_cls: 4.0882, loss: 4.0882 +2024-12-28 04:06:33,872 - pyskl - INFO - Epoch [57][300/3746] lr: 6.930e-02, eta: 3 days, 8:03:15, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5377, loss_cls: 4.0993, loss: 4.0993 +2024-12-28 04:07:59,680 - pyskl - INFO - Epoch [57][400/3746] lr: 6.927e-02, eta: 3 days, 8:02:00, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5553, loss_cls: 4.0244, loss: 4.0244 +2024-12-28 04:09:25,223 - pyskl - INFO - Epoch [57][500/3746] lr: 6.925e-02, eta: 3 days, 8:00:44, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5517, loss_cls: 4.0491, loss: 4.0491 +2024-12-28 04:10:51,155 - pyskl - INFO - Epoch [57][600/3746] lr: 6.922e-02, eta: 3 days, 7:59:29, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5389, loss_cls: 4.0766, loss: 4.0766 +2024-12-28 04:12:16,477 - pyskl - INFO - Epoch [57][700/3746] lr: 6.920e-02, eta: 3 days, 7:58:13, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5475, loss_cls: 4.0831, loss: 4.0831 +2024-12-28 04:13:41,926 - pyskl - INFO - Epoch [57][800/3746] lr: 6.917e-02, eta: 3 days, 7:56:57, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5441, loss_cls: 4.0809, loss: 4.0809 +2024-12-28 04:15:07,689 - pyskl - INFO - Epoch [57][900/3746] lr: 6.914e-02, eta: 3 days, 7:55:41, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5472, loss_cls: 4.0964, loss: 4.0964 +2024-12-28 04:16:33,247 - pyskl - INFO - Epoch [57][1000/3746] lr: 6.912e-02, eta: 3 days, 7:54:25, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5483, loss_cls: 4.0547, loss: 4.0547 +2024-12-28 04:17:58,744 - pyskl - INFO - Epoch [57][1100/3746] lr: 6.909e-02, eta: 3 days, 7:53:09, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5559, loss_cls: 4.0684, loss: 4.0684 +2024-12-28 04:19:24,639 - pyskl - INFO - Epoch [57][1200/3746] lr: 6.907e-02, eta: 3 days, 7:51:54, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5431, loss_cls: 4.1002, loss: 4.1002 +2024-12-28 04:20:50,193 - pyskl - INFO - Epoch [57][1300/3746] lr: 6.904e-02, eta: 3 days, 7:50:38, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5384, loss_cls: 4.1250, loss: 4.1250 +2024-12-28 04:22:15,543 - pyskl - INFO - Epoch [57][1400/3746] lr: 6.901e-02, eta: 3 days, 7:49:22, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5408, loss_cls: 4.0761, loss: 4.0761 +2024-12-28 04:23:40,754 - pyskl - INFO - Epoch [57][1500/3746] lr: 6.899e-02, eta: 3 days, 7:48:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5355, loss_cls: 4.1342, loss: 4.1342 +2024-12-28 04:25:06,033 - pyskl - INFO - Epoch [57][1600/3746] lr: 6.896e-02, eta: 3 days, 7:46:49, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5361, loss_cls: 4.1253, loss: 4.1253 +2024-12-28 04:26:30,751 - pyskl - INFO - Epoch [57][1700/3746] lr: 6.894e-02, eta: 3 days, 7:45:32, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5539, loss_cls: 4.0691, loss: 4.0691 +2024-12-28 04:27:55,487 - pyskl - INFO - Epoch [57][1800/3746] lr: 6.891e-02, eta: 3 days, 7:44:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5450, loss_cls: 4.0795, loss: 4.0795 +2024-12-28 04:29:20,493 - pyskl - INFO - Epoch [57][1900/3746] lr: 6.889e-02, eta: 3 days, 7:42:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5317, loss_cls: 4.1265, loss: 4.1265 +2024-12-28 04:30:45,407 - pyskl - INFO - Epoch [57][2000/3746] lr: 6.886e-02, eta: 3 days, 7:41:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5398, loss_cls: 4.1007, loss: 4.1007 +2024-12-28 04:32:11,030 - pyskl - INFO - Epoch [57][2100/3746] lr: 6.883e-02, eta: 3 days, 7:40:25, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5381, loss_cls: 4.0918, loss: 4.0918 +2024-12-28 04:33:36,791 - pyskl - INFO - Epoch [57][2200/3746] lr: 6.881e-02, eta: 3 days, 7:39:09, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5245, loss_cls: 4.1495, loss: 4.1495 +2024-12-28 04:35:02,839 - pyskl - INFO - Epoch [57][2300/3746] lr: 6.878e-02, eta: 3 days, 7:37:54, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5483, loss_cls: 4.0521, loss: 4.0521 +2024-12-28 04:36:27,444 - pyskl - INFO - Epoch [57][2400/3746] lr: 6.876e-02, eta: 3 days, 7:36:36, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5506, loss_cls: 4.0579, loss: 4.0579 +2024-12-28 04:37:52,475 - pyskl - INFO - Epoch [57][2500/3746] lr: 6.873e-02, eta: 3 days, 7:35:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5378, loss_cls: 4.1108, loss: 4.1108 +2024-12-28 04:39:18,376 - pyskl - INFO - Epoch [57][2600/3746] lr: 6.870e-02, eta: 3 days, 7:34:04, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5455, loss_cls: 4.0706, loss: 4.0706 +2024-12-28 04:40:43,930 - pyskl - INFO - Epoch [57][2700/3746] lr: 6.868e-02, eta: 3 days, 7:32:48, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5419, loss_cls: 4.1263, loss: 4.1263 +2024-12-28 04:42:08,809 - pyskl - INFO - Epoch [57][2800/3746] lr: 6.865e-02, eta: 3 days, 7:31:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5420, loss_cls: 4.0975, loss: 4.0975 +2024-12-28 04:43:33,679 - pyskl - INFO - Epoch [57][2900/3746] lr: 6.863e-02, eta: 3 days, 7:30:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5369, loss_cls: 4.0854, loss: 4.0854 +2024-12-28 04:44:58,682 - pyskl - INFO - Epoch [57][3000/3746] lr: 6.860e-02, eta: 3 days, 7:28:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5428, loss_cls: 4.0936, loss: 4.0936 +2024-12-28 04:46:23,135 - pyskl - INFO - Epoch [57][3100/3746] lr: 6.857e-02, eta: 3 days, 7:27:39, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5402, loss_cls: 4.1132, loss: 4.1132 +2024-12-28 04:47:48,399 - pyskl - INFO - Epoch [57][3200/3746] lr: 6.855e-02, eta: 3 days, 7:26:22, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5364, loss_cls: 4.1098, loss: 4.1098 +2024-12-28 04:49:13,146 - pyskl - INFO - Epoch [57][3300/3746] lr: 6.852e-02, eta: 3 days, 7:25:05, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5405, loss_cls: 4.1172, loss: 4.1172 +2024-12-28 04:50:38,147 - pyskl - INFO - Epoch [57][3400/3746] lr: 6.850e-02, eta: 3 days, 7:23:48, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5495, loss_cls: 4.0940, loss: 4.0940 +2024-12-28 04:52:02,865 - pyskl - INFO - Epoch [57][3500/3746] lr: 6.847e-02, eta: 3 days, 7:22:30, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5370, loss_cls: 4.1146, loss: 4.1146 +2024-12-28 04:53:28,017 - pyskl - INFO - Epoch [57][3600/3746] lr: 6.844e-02, eta: 3 days, 7:21:13, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5378, loss_cls: 4.0863, loss: 4.0863 +2024-12-28 04:54:52,952 - pyskl - INFO - Epoch [57][3700/3746] lr: 6.842e-02, eta: 3 days, 7:19:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2787, top5_acc: 0.5378, loss_cls: 4.1400, loss: 4.1400 +2024-12-28 04:55:34,625 - pyskl - INFO - Saving checkpoint at 57 epochs +2024-12-28 04:57:36,207 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 04:57:36,934 - pyskl - INFO - +top1_acc 0.2281 +top5_acc 0.4610 +2024-12-28 04:57:36,934 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 04:57:37,010 - pyskl - INFO - +mean_acc 0.2279 +2024-12-28 04:57:37,039 - pyskl - INFO - Epoch(val) [57][309] top1_acc: 0.2281, top5_acc: 0.4610, mean_class_accuracy: 0.2279 +2024-12-28 05:01:57,132 - pyskl - INFO - Epoch [58][100/3746] lr: 6.838e-02, eta: 3 days, 7:21:46, time: 2.601, data_time: 1.569, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5461, loss_cls: 4.0620, loss: 4.0620 +2024-12-28 05:03:22,359 - pyskl - INFO - Epoch [58][200/3746] lr: 6.835e-02, eta: 3 days, 7:20:29, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5497, loss_cls: 4.0497, loss: 4.0497 +2024-12-28 05:04:47,900 - pyskl - INFO - Epoch [58][300/3746] lr: 6.833e-02, eta: 3 days, 7:19:12, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5484, loss_cls: 4.0651, loss: 4.0651 +2024-12-28 05:06:13,694 - pyskl - INFO - Epoch [58][400/3746] lr: 6.830e-02, eta: 3 days, 7:17:56, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5467, loss_cls: 4.0859, loss: 4.0859 +2024-12-28 05:07:39,719 - pyskl - INFO - Epoch [58][500/3746] lr: 6.828e-02, eta: 3 days, 7:16:41, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5547, loss_cls: 4.0255, loss: 4.0255 +2024-12-28 05:09:05,597 - pyskl - INFO - Epoch [58][600/3746] lr: 6.825e-02, eta: 3 days, 7:15:25, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5328, loss_cls: 4.1497, loss: 4.1497 +2024-12-28 05:10:31,605 - pyskl - INFO - Epoch [58][700/3746] lr: 6.822e-02, eta: 3 days, 7:14:09, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5447, loss_cls: 4.0868, loss: 4.0868 +2024-12-28 05:11:57,715 - pyskl - INFO - Epoch [58][800/3746] lr: 6.820e-02, eta: 3 days, 7:12:54, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5437, loss_cls: 4.0721, loss: 4.0721 +2024-12-28 05:13:23,411 - pyskl - INFO - Epoch [58][900/3746] lr: 6.817e-02, eta: 3 days, 7:11:38, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5531, loss_cls: 4.0251, loss: 4.0251 +2024-12-28 05:14:49,034 - pyskl - INFO - Epoch [58][1000/3746] lr: 6.815e-02, eta: 3 days, 7:10:22, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5402, loss_cls: 4.0961, loss: 4.0961 +2024-12-28 05:16:14,449 - pyskl - INFO - Epoch [58][1100/3746] lr: 6.812e-02, eta: 3 days, 7:09:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5528, loss_cls: 4.0290, loss: 4.0290 +2024-12-28 05:17:40,614 - pyskl - INFO - Epoch [58][1200/3746] lr: 6.809e-02, eta: 3 days, 7:07:50, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5420, loss_cls: 4.1245, loss: 4.1245 +2024-12-28 05:19:06,310 - pyskl - INFO - Epoch [58][1300/3746] lr: 6.807e-02, eta: 3 days, 7:06:33, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5405, loss_cls: 4.1029, loss: 4.1029 +2024-12-28 05:20:31,661 - pyskl - INFO - Epoch [58][1400/3746] lr: 6.804e-02, eta: 3 days, 7:05:17, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5428, loss_cls: 4.0854, loss: 4.0854 +2024-12-28 05:21:56,932 - pyskl - INFO - Epoch [58][1500/3746] lr: 6.802e-02, eta: 3 days, 7:04:00, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5363, loss_cls: 4.0968, loss: 4.0968 +2024-12-28 05:23:22,459 - pyskl - INFO - Epoch [58][1600/3746] lr: 6.799e-02, eta: 3 days, 7:02:43, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5367, loss_cls: 4.0747, loss: 4.0747 +2024-12-28 05:24:48,333 - pyskl - INFO - Epoch [58][1700/3746] lr: 6.796e-02, eta: 3 days, 7:01:27, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5442, loss_cls: 4.0768, loss: 4.0768 +2024-12-28 05:26:13,234 - pyskl - INFO - Epoch [58][1800/3746] lr: 6.794e-02, eta: 3 days, 7:00:10, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5555, loss_cls: 4.0064, loss: 4.0064 +2024-12-28 05:27:37,720 - pyskl - INFO - Epoch [58][1900/3746] lr: 6.791e-02, eta: 3 days, 6:58:52, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5394, loss_cls: 4.0943, loss: 4.0943 +2024-12-28 05:29:03,090 - pyskl - INFO - Epoch [58][2000/3746] lr: 6.789e-02, eta: 3 days, 6:57:35, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5370, loss_cls: 4.1183, loss: 4.1183 +2024-12-28 05:30:28,594 - pyskl - INFO - Epoch [58][2100/3746] lr: 6.786e-02, eta: 3 days, 6:56:19, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5503, loss_cls: 4.0847, loss: 4.0847 +2024-12-28 05:31:53,951 - pyskl - INFO - Epoch [58][2200/3746] lr: 6.783e-02, eta: 3 days, 6:55:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5389, loss_cls: 4.1063, loss: 4.1063 +2024-12-28 05:33:19,896 - pyskl - INFO - Epoch [58][2300/3746] lr: 6.781e-02, eta: 3 days, 6:53:46, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5511, loss_cls: 4.0377, loss: 4.0377 +2024-12-28 05:34:45,107 - pyskl - INFO - Epoch [58][2400/3746] lr: 6.778e-02, eta: 3 days, 6:52:29, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5417, loss_cls: 4.1276, loss: 4.1276 +2024-12-28 05:36:10,124 - pyskl - INFO - Epoch [58][2500/3746] lr: 6.775e-02, eta: 3 days, 6:51:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5498, loss_cls: 4.0477, loss: 4.0477 +2024-12-28 05:37:34,891 - pyskl - INFO - Epoch [58][2600/3746] lr: 6.773e-02, eta: 3 days, 6:49:54, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5427, loss_cls: 4.0835, loss: 4.0835 +2024-12-28 05:39:00,018 - pyskl - INFO - Epoch [58][2700/3746] lr: 6.770e-02, eta: 3 days, 6:48:37, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5467, loss_cls: 4.0781, loss: 4.0781 +2024-12-28 05:40:25,081 - pyskl - INFO - Epoch [58][2800/3746] lr: 6.768e-02, eta: 3 days, 6:47:19, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5553, loss_cls: 4.0508, loss: 4.0508 +2024-12-28 05:41:49,998 - pyskl - INFO - Epoch [58][2900/3746] lr: 6.765e-02, eta: 3 days, 6:46:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5331, loss_cls: 4.1480, loss: 4.1480 +2024-12-28 05:43:15,432 - pyskl - INFO - Epoch [58][3000/3746] lr: 6.762e-02, eta: 3 days, 6:44:45, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5473, loss_cls: 4.0452, loss: 4.0452 +2024-12-28 05:44:40,525 - pyskl - INFO - Epoch [58][3100/3746] lr: 6.760e-02, eta: 3 days, 6:43:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5262, loss_cls: 4.1811, loss: 4.1811 +2024-12-28 05:46:06,185 - pyskl - INFO - Epoch [58][3200/3746] lr: 6.757e-02, eta: 3 days, 6:42:11, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5398, loss_cls: 4.0826, loss: 4.0826 +2024-12-28 05:47:31,471 - pyskl - INFO - Epoch [58][3300/3746] lr: 6.755e-02, eta: 3 days, 6:40:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5350, loss_cls: 4.1314, loss: 4.1314 +2024-12-28 05:48:56,673 - pyskl - INFO - Epoch [58][3400/3746] lr: 6.752e-02, eta: 3 days, 6:39:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5394, loss_cls: 4.0720, loss: 4.0720 +2024-12-28 05:50:21,392 - pyskl - INFO - Epoch [58][3500/3746] lr: 6.749e-02, eta: 3 days, 6:38:19, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5413, loss_cls: 4.1018, loss: 4.1018 +2024-12-28 05:51:46,495 - pyskl - INFO - Epoch [58][3600/3746] lr: 6.747e-02, eta: 3 days, 6:37:02, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5439, loss_cls: 4.1184, loss: 4.1184 +2024-12-28 05:53:11,039 - pyskl - INFO - Epoch [58][3700/3746] lr: 6.744e-02, eta: 3 days, 6:35:44, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5561, loss_cls: 4.0317, loss: 4.0317 +2024-12-28 05:53:51,956 - pyskl - INFO - Saving checkpoint at 58 epochs +2024-12-28 05:55:50,216 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 05:55:50,950 - pyskl - INFO - +top1_acc 0.2210 +top5_acc 0.4643 +2024-12-28 05:55:50,951 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 05:55:51,001 - pyskl - INFO - +mean_acc 0.2208 +2024-12-28 05:55:51,017 - pyskl - INFO - Epoch(val) [58][309] top1_acc: 0.2210, top5_acc: 0.4643, mean_class_accuracy: 0.2208 +2024-12-28 06:00:12,674 - pyskl - INFO - Epoch [59][100/3746] lr: 6.740e-02, eta: 3 days, 6:37:29, time: 2.616, data_time: 1.581, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5572, loss_cls: 4.0245, loss: 4.0245 +2024-12-28 06:01:38,063 - pyskl - INFO - Epoch [59][200/3746] lr: 6.738e-02, eta: 3 days, 6:36:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5486, loss_cls: 4.0450, loss: 4.0450 +2024-12-28 06:03:03,351 - pyskl - INFO - Epoch [59][300/3746] lr: 6.735e-02, eta: 3 days, 6:34:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5405, loss_cls: 4.0887, loss: 4.0887 +2024-12-28 06:04:29,213 - pyskl - INFO - Epoch [59][400/3746] lr: 6.732e-02, eta: 3 days, 6:33:38, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5531, loss_cls: 4.0406, loss: 4.0406 +2024-12-28 06:05:54,807 - pyskl - INFO - Epoch [59][500/3746] lr: 6.730e-02, eta: 3 days, 6:32:21, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5453, loss_cls: 4.0501, loss: 4.0501 +2024-12-28 06:07:20,751 - pyskl - INFO - Epoch [59][600/3746] lr: 6.727e-02, eta: 3 days, 6:31:05, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5559, loss_cls: 4.0346, loss: 4.0346 +2024-12-28 06:08:46,153 - pyskl - INFO - Epoch [59][700/3746] lr: 6.725e-02, eta: 3 days, 6:29:48, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5542, loss_cls: 4.0275, loss: 4.0275 +2024-12-28 06:10:11,937 - pyskl - INFO - Epoch [59][800/3746] lr: 6.722e-02, eta: 3 days, 6:28:32, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5416, loss_cls: 4.0892, loss: 4.0892 +2024-12-28 06:11:37,198 - pyskl - INFO - Epoch [59][900/3746] lr: 6.719e-02, eta: 3 days, 6:27:14, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5437, loss_cls: 4.1003, loss: 4.1003 +2024-12-28 06:13:03,162 - pyskl - INFO - Epoch [59][1000/3746] lr: 6.717e-02, eta: 3 days, 6:25:58, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5195, loss_cls: 4.1673, loss: 4.1673 +2024-12-28 06:14:28,471 - pyskl - INFO - Epoch [59][1100/3746] lr: 6.714e-02, eta: 3 days, 6:24:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5500, loss_cls: 4.0518, loss: 4.0518 +2024-12-28 06:15:53,076 - pyskl - INFO - Epoch [59][1200/3746] lr: 6.711e-02, eta: 3 days, 6:23:23, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5430, loss_cls: 4.0985, loss: 4.0985 +2024-12-28 06:17:18,418 - pyskl - INFO - Epoch [59][1300/3746] lr: 6.709e-02, eta: 3 days, 6:22:05, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5614, loss_cls: 3.9951, loss: 3.9951 +2024-12-28 06:18:44,090 - pyskl - INFO - Epoch [59][1400/3746] lr: 6.706e-02, eta: 3 days, 6:20:49, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5333, loss_cls: 4.1505, loss: 4.1505 +2024-12-28 06:20:09,685 - pyskl - INFO - Epoch [59][1500/3746] lr: 6.704e-02, eta: 3 days, 6:19:32, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5481, loss_cls: 4.1030, loss: 4.1030 +2024-12-28 06:21:35,538 - pyskl - INFO - Epoch [59][1600/3746] lr: 6.701e-02, eta: 3 days, 6:18:16, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5458, loss_cls: 4.0702, loss: 4.0702 +2024-12-28 06:23:00,096 - pyskl - INFO - Epoch [59][1700/3746] lr: 6.698e-02, eta: 3 days, 6:16:57, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5553, loss_cls: 4.0297, loss: 4.0297 +2024-12-28 06:24:24,984 - pyskl - INFO - Epoch [59][1800/3746] lr: 6.696e-02, eta: 3 days, 6:15:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5311, loss_cls: 4.1128, loss: 4.1128 +2024-12-28 06:25:50,083 - pyskl - INFO - Epoch [59][1900/3746] lr: 6.693e-02, eta: 3 days, 6:14:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5400, loss_cls: 4.0756, loss: 4.0756 +2024-12-28 06:27:15,047 - pyskl - INFO - Epoch [59][2000/3746] lr: 6.690e-02, eta: 3 days, 6:13:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5444, loss_cls: 4.0927, loss: 4.0927 +2024-12-28 06:28:40,519 - pyskl - INFO - Epoch [59][2100/3746] lr: 6.688e-02, eta: 3 days, 6:11:47, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5492, loss_cls: 4.0707, loss: 4.0707 +2024-12-28 06:30:05,980 - pyskl - INFO - Epoch [59][2200/3746] lr: 6.685e-02, eta: 3 days, 6:10:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5450, loss_cls: 4.0794, loss: 4.0794 +2024-12-28 06:31:31,506 - pyskl - INFO - Epoch [59][2300/3746] lr: 6.682e-02, eta: 3 days, 6:09:13, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5464, loss_cls: 4.0842, loss: 4.0842 +2024-12-28 06:32:56,876 - pyskl - INFO - Epoch [59][2400/3746] lr: 6.680e-02, eta: 3 days, 6:07:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5581, loss_cls: 4.0265, loss: 4.0265 +2024-12-28 06:34:22,292 - pyskl - INFO - Epoch [59][2500/3746] lr: 6.677e-02, eta: 3 days, 6:06:38, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5439, loss_cls: 4.0860, loss: 4.0860 +2024-12-28 06:35:47,393 - pyskl - INFO - Epoch [59][2600/3746] lr: 6.675e-02, eta: 3 days, 6:05:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5381, loss_cls: 4.1093, loss: 4.1093 +2024-12-28 06:37:12,769 - pyskl - INFO - Epoch [59][2700/3746] lr: 6.672e-02, eta: 3 days, 6:04:03, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5480, loss_cls: 4.0617, loss: 4.0617 +2024-12-28 06:38:37,868 - pyskl - INFO - Epoch [59][2800/3746] lr: 6.669e-02, eta: 3 days, 6:02:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5408, loss_cls: 4.0892, loss: 4.0892 +2024-12-28 06:40:02,966 - pyskl - INFO - Epoch [59][2900/3746] lr: 6.667e-02, eta: 3 days, 6:01:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5391, loss_cls: 4.1035, loss: 4.1035 +2024-12-28 06:41:28,289 - pyskl - INFO - Epoch [59][3000/3746] lr: 6.664e-02, eta: 3 days, 6:00:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5463, loss_cls: 4.0747, loss: 4.0747 +2024-12-28 06:42:54,036 - pyskl - INFO - Epoch [59][3100/3746] lr: 6.661e-02, eta: 3 days, 5:58:54, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5352, loss_cls: 4.1050, loss: 4.1050 +2024-12-28 06:44:19,855 - pyskl - INFO - Epoch [59][3200/3746] lr: 6.659e-02, eta: 3 days, 5:57:37, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5398, loss_cls: 4.1057, loss: 4.1057 +2024-12-28 06:45:45,636 - pyskl - INFO - Epoch [59][3300/3746] lr: 6.656e-02, eta: 3 days, 5:56:21, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5445, loss_cls: 4.0659, loss: 4.0659 +2024-12-28 06:47:11,157 - pyskl - INFO - Epoch [59][3400/3746] lr: 6.653e-02, eta: 3 days, 5:55:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5453, loss_cls: 4.0661, loss: 4.0661 +2024-12-28 06:48:37,434 - pyskl - INFO - Epoch [59][3500/3746] lr: 6.651e-02, eta: 3 days, 5:53:48, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5402, loss_cls: 4.0870, loss: 4.0870 +2024-12-28 06:50:03,292 - pyskl - INFO - Epoch [59][3600/3746] lr: 6.648e-02, eta: 3 days, 5:52:31, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5447, loss_cls: 4.0837, loss: 4.0837 +2024-12-28 06:51:28,887 - pyskl - INFO - Epoch [59][3700/3746] lr: 6.646e-02, eta: 3 days, 5:51:14, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5417, loss_cls: 4.1029, loss: 4.1029 +2024-12-28 06:52:10,363 - pyskl - INFO - Saving checkpoint at 59 epochs +2024-12-28 06:54:12,086 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 06:54:12,867 - pyskl - INFO - +top1_acc 0.2334 +top5_acc 0.4740 +2024-12-28 06:54:12,867 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 06:54:12,917 - pyskl - INFO - +mean_acc 0.2332 +2024-12-28 06:54:12,924 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_55.pth was removed +2024-12-28 06:54:13,271 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_59.pth. +2024-12-28 06:54:13,272 - pyskl - INFO - Best top1_acc is 0.2334 at 59 epoch. +2024-12-28 06:54:13,289 - pyskl - INFO - Epoch(val) [59][309] top1_acc: 0.2334, top5_acc: 0.4740, mean_class_accuracy: 0.2332 +2024-12-28 06:58:33,019 - pyskl - INFO - Epoch [60][100/3746] lr: 6.642e-02, eta: 3 days, 5:52:49, time: 2.597, data_time: 1.561, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5487, loss_cls: 4.0373, loss: 4.0373 +2024-12-28 06:59:58,642 - pyskl - INFO - Epoch [60][200/3746] lr: 6.639e-02, eta: 3 days, 5:51:32, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5463, loss_cls: 4.0394, loss: 4.0394 +2024-12-28 07:01:24,804 - pyskl - INFO - Epoch [60][300/3746] lr: 6.636e-02, eta: 3 days, 5:50:16, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5573, loss_cls: 3.9945, loss: 3.9945 +2024-12-28 07:02:50,792 - pyskl - INFO - Epoch [60][400/3746] lr: 6.634e-02, eta: 3 days, 5:48:59, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5469, loss_cls: 4.0558, loss: 4.0558 +2024-12-28 07:04:16,925 - pyskl - INFO - Epoch [60][500/3746] lr: 6.631e-02, eta: 3 days, 5:47:43, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5534, loss_cls: 4.0314, loss: 4.0314 +2024-12-28 07:05:43,583 - pyskl - INFO - Epoch [60][600/3746] lr: 6.629e-02, eta: 3 days, 5:46:27, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5584, loss_cls: 4.0188, loss: 4.0188 +2024-12-28 07:07:09,245 - pyskl - INFO - Epoch [60][700/3746] lr: 6.626e-02, eta: 3 days, 5:45:10, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5447, loss_cls: 4.0558, loss: 4.0558 +2024-12-28 07:08:35,175 - pyskl - INFO - Epoch [60][800/3746] lr: 6.623e-02, eta: 3 days, 5:43:54, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5500, loss_cls: 4.0264, loss: 4.0264 +2024-12-28 07:10:01,062 - pyskl - INFO - Epoch [60][900/3746] lr: 6.621e-02, eta: 3 days, 5:42:37, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5455, loss_cls: 4.0273, loss: 4.0273 +2024-12-28 07:11:27,476 - pyskl - INFO - Epoch [60][1000/3746] lr: 6.618e-02, eta: 3 days, 5:41:21, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5464, loss_cls: 4.0952, loss: 4.0952 +2024-12-28 07:12:53,296 - pyskl - INFO - Epoch [60][1100/3746] lr: 6.615e-02, eta: 3 days, 5:40:04, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5469, loss_cls: 4.0437, loss: 4.0437 +2024-12-28 07:14:19,389 - pyskl - INFO - Epoch [60][1200/3746] lr: 6.613e-02, eta: 3 days, 5:38:48, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5483, loss_cls: 4.0810, loss: 4.0810 +2024-12-28 07:15:46,153 - pyskl - INFO - Epoch [60][1300/3746] lr: 6.610e-02, eta: 3 days, 5:37:32, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5419, loss_cls: 4.0571, loss: 4.0571 +2024-12-28 07:17:11,962 - pyskl - INFO - Epoch [60][1400/3746] lr: 6.607e-02, eta: 3 days, 5:36:15, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5414, loss_cls: 4.1076, loss: 4.1076 +2024-12-28 07:18:38,072 - pyskl - INFO - Epoch [60][1500/3746] lr: 6.605e-02, eta: 3 days, 5:34:59, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5483, loss_cls: 4.0520, loss: 4.0520 +2024-12-28 07:20:03,414 - pyskl - INFO - Epoch [60][1600/3746] lr: 6.602e-02, eta: 3 days, 5:33:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5423, loss_cls: 4.0956, loss: 4.0956 +2024-12-28 07:21:28,517 - pyskl - INFO - Epoch [60][1700/3746] lr: 6.599e-02, eta: 3 days, 5:32:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5525, loss_cls: 4.0813, loss: 4.0813 +2024-12-28 07:22:53,602 - pyskl - INFO - Epoch [60][1800/3746] lr: 6.597e-02, eta: 3 days, 5:31:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5478, loss_cls: 4.0481, loss: 4.0481 +2024-12-28 07:24:19,772 - pyskl - INFO - Epoch [60][1900/3746] lr: 6.594e-02, eta: 3 days, 5:29:49, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5520, loss_cls: 4.0408, loss: 4.0408 +2024-12-28 07:25:45,668 - pyskl - INFO - Epoch [60][2000/3746] lr: 6.591e-02, eta: 3 days, 5:28:32, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5394, loss_cls: 4.0862, loss: 4.0862 +2024-12-28 07:27:11,632 - pyskl - INFO - Epoch [60][2100/3746] lr: 6.589e-02, eta: 3 days, 5:27:15, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5370, loss_cls: 4.1015, loss: 4.1015 +2024-12-28 07:28:37,039 - pyskl - INFO - Epoch [60][2200/3746] lr: 6.586e-02, eta: 3 days, 5:25:57, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5409, loss_cls: 4.0875, loss: 4.0875 +2024-12-28 07:30:02,360 - pyskl - INFO - Epoch [60][2300/3746] lr: 6.584e-02, eta: 3 days, 5:24:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5325, loss_cls: 4.0983, loss: 4.0983 +2024-12-28 07:31:27,662 - pyskl - INFO - Epoch [60][2400/3746] lr: 6.581e-02, eta: 3 days, 5:23:22, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5448, loss_cls: 4.0708, loss: 4.0708 +2024-12-28 07:32:52,908 - pyskl - INFO - Epoch [60][2500/3746] lr: 6.578e-02, eta: 3 days, 5:22:04, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5464, loss_cls: 4.0733, loss: 4.0733 +2024-12-28 07:34:18,751 - pyskl - INFO - Epoch [60][2600/3746] lr: 6.576e-02, eta: 3 days, 5:20:47, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5467, loss_cls: 4.0362, loss: 4.0362 +2024-12-28 07:35:44,644 - pyskl - INFO - Epoch [60][2700/3746] lr: 6.573e-02, eta: 3 days, 5:19:30, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5492, loss_cls: 4.0495, loss: 4.0495 +2024-12-28 07:37:09,651 - pyskl - INFO - Epoch [60][2800/3746] lr: 6.570e-02, eta: 3 days, 5:18:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5503, loss_cls: 4.0748, loss: 4.0748 +2024-12-28 07:38:34,870 - pyskl - INFO - Epoch [60][2900/3746] lr: 6.568e-02, eta: 3 days, 5:16:54, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5478, loss_cls: 4.0766, loss: 4.0766 +2024-12-28 07:39:59,690 - pyskl - INFO - Epoch [60][3000/3746] lr: 6.565e-02, eta: 3 days, 5:15:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5450, loss_cls: 4.0788, loss: 4.0788 +2024-12-28 07:41:25,199 - pyskl - INFO - Epoch [60][3100/3746] lr: 6.562e-02, eta: 3 days, 5:14:18, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2878, top5_acc: 0.5409, loss_cls: 4.1131, loss: 4.1131 +2024-12-28 07:42:50,061 - pyskl - INFO - Epoch [60][3200/3746] lr: 6.560e-02, eta: 3 days, 5:12:59, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5480, loss_cls: 4.0917, loss: 4.0917 +2024-12-28 07:44:14,864 - pyskl - INFO - Epoch [60][3300/3746] lr: 6.557e-02, eta: 3 days, 5:11:41, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5498, loss_cls: 4.0571, loss: 4.0571 +2024-12-28 07:45:39,670 - pyskl - INFO - Epoch [60][3400/3746] lr: 6.554e-02, eta: 3 days, 5:10:22, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5361, loss_cls: 4.1044, loss: 4.1044 +2024-12-28 07:47:04,735 - pyskl - INFO - Epoch [60][3500/3746] lr: 6.552e-02, eta: 3 days, 5:09:04, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5473, loss_cls: 4.0712, loss: 4.0712 +2024-12-28 07:48:30,433 - pyskl - INFO - Epoch [60][3600/3746] lr: 6.549e-02, eta: 3 days, 5:07:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5403, loss_cls: 4.1015, loss: 4.1015 +2024-12-28 07:49:55,770 - pyskl - INFO - Epoch [60][3700/3746] lr: 6.546e-02, eta: 3 days, 5:06:29, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5444, loss_cls: 4.0735, loss: 4.0735 +2024-12-28 07:50:37,296 - pyskl - INFO - Saving checkpoint at 60 epochs +2024-12-28 07:52:38,191 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 07:52:38,931 - pyskl - INFO - +top1_acc 0.2338 +top5_acc 0.4761 +2024-12-28 07:52:38,931 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 07:52:38,992 - pyskl - INFO - +mean_acc 0.2336 +2024-12-28 07:52:39,002 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_59.pth was removed +2024-12-28 07:52:39,354 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_60.pth. +2024-12-28 07:52:39,355 - pyskl - INFO - Best top1_acc is 0.2338 at 60 epoch. +2024-12-28 07:52:39,373 - pyskl - INFO - Epoch(val) [60][309] top1_acc: 0.2338, top5_acc: 0.4761, mean_class_accuracy: 0.2336 +2024-12-28 07:57:01,681 - pyskl - INFO - Epoch [61][100/3746] lr: 6.542e-02, eta: 3 days, 5:08:02, time: 2.623, data_time: 1.588, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5505, loss_cls: 4.0261, loss: 4.0261 +2024-12-28 07:58:27,311 - pyskl - INFO - Epoch [61][200/3746] lr: 6.540e-02, eta: 3 days, 5:06:44, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5548, loss_cls: 4.0122, loss: 4.0122 +2024-12-28 07:59:52,685 - pyskl - INFO - Epoch [61][300/3746] lr: 6.537e-02, eta: 3 days, 5:05:26, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5781, loss_cls: 3.9299, loss: 3.9299 +2024-12-28 08:01:18,284 - pyskl - INFO - Epoch [61][400/3746] lr: 6.534e-02, eta: 3 days, 5:04:09, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5458, loss_cls: 4.0708, loss: 4.0708 +2024-12-28 08:02:44,110 - pyskl - INFO - Epoch [61][500/3746] lr: 6.532e-02, eta: 3 days, 5:02:51, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5519, loss_cls: 4.0148, loss: 4.0148 +2024-12-28 08:04:09,607 - pyskl - INFO - Epoch [61][600/3746] lr: 6.529e-02, eta: 3 days, 5:01:34, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5536, loss_cls: 4.0283, loss: 4.0283 +2024-12-28 08:05:35,300 - pyskl - INFO - Epoch [61][700/3746] lr: 6.526e-02, eta: 3 days, 5:00:16, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5420, loss_cls: 4.0861, loss: 4.0861 +2024-12-28 08:07:00,868 - pyskl - INFO - Epoch [61][800/3746] lr: 6.524e-02, eta: 3 days, 4:58:59, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5400, loss_cls: 4.0833, loss: 4.0833 +2024-12-28 08:08:26,348 - pyskl - INFO - Epoch [61][900/3746] lr: 6.521e-02, eta: 3 days, 4:57:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5444, loss_cls: 4.0720, loss: 4.0720 +2024-12-28 08:09:51,891 - pyskl - INFO - Epoch [61][1000/3746] lr: 6.519e-02, eta: 3 days, 4:56:23, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5428, loss_cls: 4.0884, loss: 4.0884 +2024-12-28 08:11:17,022 - pyskl - INFO - Epoch [61][1100/3746] lr: 6.516e-02, eta: 3 days, 4:55:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5469, loss_cls: 4.0559, loss: 4.0559 +2024-12-28 08:12:41,822 - pyskl - INFO - Epoch [61][1200/3746] lr: 6.513e-02, eta: 3 days, 4:53:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2916, top5_acc: 0.5406, loss_cls: 4.0523, loss: 4.0523 +2024-12-28 08:14:06,895 - pyskl - INFO - Epoch [61][1300/3746] lr: 6.511e-02, eta: 3 days, 4:52:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5537, loss_cls: 4.0248, loss: 4.0248 +2024-12-28 08:15:31,989 - pyskl - INFO - Epoch [61][1400/3746] lr: 6.508e-02, eta: 3 days, 4:51:09, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5464, loss_cls: 4.0761, loss: 4.0761 +2024-12-28 08:16:57,414 - pyskl - INFO - Epoch [61][1500/3746] lr: 6.505e-02, eta: 3 days, 4:49:51, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5536, loss_cls: 4.0273, loss: 4.0273 +2024-12-28 08:18:22,177 - pyskl - INFO - Epoch [61][1600/3746] lr: 6.503e-02, eta: 3 days, 4:48:32, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5413, loss_cls: 4.0682, loss: 4.0682 +2024-12-28 08:19:47,336 - pyskl - INFO - Epoch [61][1700/3746] lr: 6.500e-02, eta: 3 days, 4:47:14, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5475, loss_cls: 4.0672, loss: 4.0672 +2024-12-28 08:21:11,840 - pyskl - INFO - Epoch [61][1800/3746] lr: 6.497e-02, eta: 3 days, 4:45:55, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5547, loss_cls: 4.0424, loss: 4.0424 +2024-12-28 08:22:37,211 - pyskl - INFO - Epoch [61][1900/3746] lr: 6.495e-02, eta: 3 days, 4:44:36, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5484, loss_cls: 4.0629, loss: 4.0629 +2024-12-28 08:24:02,894 - pyskl - INFO - Epoch [61][2000/3746] lr: 6.492e-02, eta: 3 days, 4:43:19, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5573, loss_cls: 4.0348, loss: 4.0348 +2024-12-28 08:25:28,235 - pyskl - INFO - Epoch [61][2100/3746] lr: 6.489e-02, eta: 3 days, 4:42:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5522, loss_cls: 4.0392, loss: 4.0392 +2024-12-28 08:26:53,430 - pyskl - INFO - Epoch [61][2200/3746] lr: 6.487e-02, eta: 3 days, 4:40:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5464, loss_cls: 4.0373, loss: 4.0373 +2024-12-28 08:28:18,545 - pyskl - INFO - Epoch [61][2300/3746] lr: 6.484e-02, eta: 3 days, 4:39:24, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5578, loss_cls: 4.0270, loss: 4.0270 +2024-12-28 08:29:43,394 - pyskl - INFO - Epoch [61][2400/3746] lr: 6.481e-02, eta: 3 days, 4:38:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5502, loss_cls: 4.0736, loss: 4.0736 +2024-12-28 08:31:08,644 - pyskl - INFO - Epoch [61][2500/3746] lr: 6.478e-02, eta: 3 days, 4:36:47, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5419, loss_cls: 4.1101, loss: 4.1101 +2024-12-28 08:32:34,252 - pyskl - INFO - Epoch [61][2600/3746] lr: 6.476e-02, eta: 3 days, 4:35:29, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5425, loss_cls: 4.0897, loss: 4.0897 +2024-12-28 08:33:59,799 - pyskl - INFO - Epoch [61][2700/3746] lr: 6.473e-02, eta: 3 days, 4:34:11, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5456, loss_cls: 4.0465, loss: 4.0465 +2024-12-28 08:35:24,938 - pyskl - INFO - Epoch [61][2800/3746] lr: 6.470e-02, eta: 3 days, 4:32:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5497, loss_cls: 4.0791, loss: 4.0791 +2024-12-28 08:36:49,903 - pyskl - INFO - Epoch [61][2900/3746] lr: 6.468e-02, eta: 3 days, 4:31:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5491, loss_cls: 4.0508, loss: 4.0508 +2024-12-28 08:38:14,651 - pyskl - INFO - Epoch [61][3000/3746] lr: 6.465e-02, eta: 3 days, 4:30:15, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5363, loss_cls: 4.0918, loss: 4.0918 +2024-12-28 08:39:39,316 - pyskl - INFO - Epoch [61][3100/3746] lr: 6.462e-02, eta: 3 days, 4:28:56, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5464, loss_cls: 4.0778, loss: 4.0778 +2024-12-28 08:41:04,139 - pyskl - INFO - Epoch [61][3200/3746] lr: 6.460e-02, eta: 3 days, 4:27:37, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5419, loss_cls: 4.0532, loss: 4.0532 +2024-12-28 08:42:29,116 - pyskl - INFO - Epoch [61][3300/3746] lr: 6.457e-02, eta: 3 days, 4:26:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5448, loss_cls: 4.1020, loss: 4.1020 +2024-12-28 08:43:54,230 - pyskl - INFO - Epoch [61][3400/3746] lr: 6.454e-02, eta: 3 days, 4:25:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5461, loss_cls: 4.0440, loss: 4.0440 +2024-12-28 08:45:18,856 - pyskl - INFO - Epoch [61][3500/3746] lr: 6.452e-02, eta: 3 days, 4:23:41, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2853, top5_acc: 0.5384, loss_cls: 4.0903, loss: 4.0903 +2024-12-28 08:46:44,283 - pyskl - INFO - Epoch [61][3600/3746] lr: 6.449e-02, eta: 3 days, 4:22:23, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5425, loss_cls: 4.0906, loss: 4.0906 +2024-12-28 08:48:09,309 - pyskl - INFO - Epoch [61][3700/3746] lr: 6.446e-02, eta: 3 days, 4:21:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5398, loss_cls: 4.0607, loss: 4.0607 +2024-12-28 08:48:50,742 - pyskl - INFO - Saving checkpoint at 61 epochs +2024-12-28 08:50:51,334 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 08:50:52,106 - pyskl - INFO - +top1_acc 0.2300 +top5_acc 0.4689 +2024-12-28 08:50:52,106 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 08:50:52,145 - pyskl - INFO - +mean_acc 0.2298 +2024-12-28 08:50:52,157 - pyskl - INFO - Epoch(val) [61][309] top1_acc: 0.2300, top5_acc: 0.4689, mean_class_accuracy: 0.2298 +2024-12-28 08:55:13,535 - pyskl - INFO - Epoch [62][100/3746] lr: 6.443e-02, eta: 3 days, 4:22:29, time: 2.614, data_time: 1.585, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5616, loss_cls: 3.9924, loss: 3.9924 +2024-12-28 08:56:39,335 - pyskl - INFO - Epoch [62][200/3746] lr: 6.440e-02, eta: 3 days, 4:21:12, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5548, loss_cls: 4.0230, loss: 4.0230 +2024-12-28 08:58:04,672 - pyskl - INFO - Epoch [62][300/3746] lr: 6.437e-02, eta: 3 days, 4:19:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5473, loss_cls: 4.0576, loss: 4.0576 +2024-12-28 08:59:29,889 - pyskl - INFO - Epoch [62][400/3746] lr: 6.434e-02, eta: 3 days, 4:18:35, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5525, loss_cls: 4.0206, loss: 4.0206 +2024-12-28 09:00:55,673 - pyskl - INFO - Epoch [62][500/3746] lr: 6.432e-02, eta: 3 days, 4:17:17, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5609, loss_cls: 3.9968, loss: 3.9968 +2024-12-28 09:02:21,941 - pyskl - INFO - Epoch [62][600/3746] lr: 6.429e-02, eta: 3 days, 4:16:00, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5452, loss_cls: 4.0505, loss: 4.0505 +2024-12-28 09:03:47,685 - pyskl - INFO - Epoch [62][700/3746] lr: 6.426e-02, eta: 3 days, 4:14:42, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5556, loss_cls: 4.0367, loss: 4.0367 +2024-12-28 09:05:13,693 - pyskl - INFO - Epoch [62][800/3746] lr: 6.424e-02, eta: 3 days, 4:13:25, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5377, loss_cls: 4.0780, loss: 4.0780 +2024-12-28 09:06:39,034 - pyskl - INFO - Epoch [62][900/3746] lr: 6.421e-02, eta: 3 days, 4:12:06, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5530, loss_cls: 4.0435, loss: 4.0435 +2024-12-28 09:08:04,485 - pyskl - INFO - Epoch [62][1000/3746] lr: 6.418e-02, eta: 3 days, 4:10:48, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5478, loss_cls: 4.0372, loss: 4.0372 +2024-12-28 09:09:30,248 - pyskl - INFO - Epoch [62][1100/3746] lr: 6.416e-02, eta: 3 days, 4:09:30, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5548, loss_cls: 4.0417, loss: 4.0417 +2024-12-28 09:10:56,222 - pyskl - INFO - Epoch [62][1200/3746] lr: 6.413e-02, eta: 3 days, 4:08:13, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5420, loss_cls: 4.0573, loss: 4.0573 +2024-12-28 09:12:21,846 - pyskl - INFO - Epoch [62][1300/3746] lr: 6.410e-02, eta: 3 days, 4:06:55, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5463, loss_cls: 4.0365, loss: 4.0365 +2024-12-28 09:13:46,995 - pyskl - INFO - Epoch [62][1400/3746] lr: 6.408e-02, eta: 3 days, 4:05:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5587, loss_cls: 3.9913, loss: 3.9913 +2024-12-28 09:15:12,226 - pyskl - INFO - Epoch [62][1500/3746] lr: 6.405e-02, eta: 3 days, 4:04:18, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5552, loss_cls: 4.0196, loss: 4.0196 +2024-12-28 09:16:37,812 - pyskl - INFO - Epoch [62][1600/3746] lr: 6.402e-02, eta: 3 days, 4:02:59, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5472, loss_cls: 4.0526, loss: 4.0526 +2024-12-28 09:18:02,696 - pyskl - INFO - Epoch [62][1700/3746] lr: 6.400e-02, eta: 3 days, 4:01:40, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5384, loss_cls: 4.1225, loss: 4.1225 +2024-12-28 09:19:27,749 - pyskl - INFO - Epoch [62][1800/3746] lr: 6.397e-02, eta: 3 days, 4:00:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5559, loss_cls: 4.0318, loss: 4.0318 +2024-12-28 09:20:53,223 - pyskl - INFO - Epoch [62][1900/3746] lr: 6.394e-02, eta: 3 days, 3:59:03, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5437, loss_cls: 4.1036, loss: 4.1036 +2024-12-28 09:22:18,781 - pyskl - INFO - Epoch [62][2000/3746] lr: 6.392e-02, eta: 3 days, 3:57:45, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5456, loss_cls: 4.0458, loss: 4.0458 +2024-12-28 09:23:44,084 - pyskl - INFO - Epoch [62][2100/3746] lr: 6.389e-02, eta: 3 days, 3:56:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5442, loss_cls: 4.0483, loss: 4.0483 +2024-12-28 09:25:09,470 - pyskl - INFO - Epoch [62][2200/3746] lr: 6.386e-02, eta: 3 days, 3:55:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5561, loss_cls: 4.0356, loss: 4.0356 +2024-12-28 09:26:34,941 - pyskl - INFO - Epoch [62][2300/3746] lr: 6.384e-02, eta: 3 days, 3:53:50, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5437, loss_cls: 4.1001, loss: 4.1001 +2024-12-28 09:27:59,828 - pyskl - INFO - Epoch [62][2400/3746] lr: 6.381e-02, eta: 3 days, 3:52:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5380, loss_cls: 4.0953, loss: 4.0953 +2024-12-28 09:29:25,053 - pyskl - INFO - Epoch [62][2500/3746] lr: 6.378e-02, eta: 3 days, 3:51:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5453, loss_cls: 4.0571, loss: 4.0571 +2024-12-28 09:30:50,423 - pyskl - INFO - Epoch [62][2600/3746] lr: 6.375e-02, eta: 3 days, 3:49:54, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5578, loss_cls: 4.0169, loss: 4.0169 +2024-12-28 09:32:15,351 - pyskl - INFO - Epoch [62][2700/3746] lr: 6.373e-02, eta: 3 days, 3:48:35, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5534, loss_cls: 4.0399, loss: 4.0399 +2024-12-28 09:33:40,849 - pyskl - INFO - Epoch [62][2800/3746] lr: 6.370e-02, eta: 3 days, 3:47:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5405, loss_cls: 4.0599, loss: 4.0599 +2024-12-28 09:35:06,065 - pyskl - INFO - Epoch [62][2900/3746] lr: 6.367e-02, eta: 3 days, 3:45:58, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5408, loss_cls: 4.0924, loss: 4.0924 +2024-12-28 09:36:31,240 - pyskl - INFO - Epoch [62][3000/3746] lr: 6.365e-02, eta: 3 days, 3:44:39, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5520, loss_cls: 4.0380, loss: 4.0380 +2024-12-28 09:37:56,525 - pyskl - INFO - Epoch [62][3100/3746] lr: 6.362e-02, eta: 3 days, 3:43:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5425, loss_cls: 4.0593, loss: 4.0593 +2024-12-28 09:39:21,766 - pyskl - INFO - Epoch [62][3200/3746] lr: 6.359e-02, eta: 3 days, 3:42:01, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5583, loss_cls: 4.0004, loss: 4.0004 +2024-12-28 09:40:46,560 - pyskl - INFO - Epoch [62][3300/3746] lr: 6.357e-02, eta: 3 days, 3:40:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5464, loss_cls: 4.0682, loss: 4.0682 +2024-12-28 09:42:11,560 - pyskl - INFO - Epoch [62][3400/3746] lr: 6.354e-02, eta: 3 days, 3:39:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5575, loss_cls: 4.0048, loss: 4.0048 +2024-12-28 09:43:36,595 - pyskl - INFO - Epoch [62][3500/3746] lr: 6.351e-02, eta: 3 days, 3:38:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5578, loss_cls: 4.0299, loss: 4.0299 +2024-12-28 09:45:02,535 - pyskl - INFO - Epoch [62][3600/3746] lr: 6.349e-02, eta: 3 days, 3:36:46, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5513, loss_cls: 3.9996, loss: 3.9996 +2024-12-28 09:46:28,209 - pyskl - INFO - Epoch [62][3700/3746] lr: 6.346e-02, eta: 3 days, 3:35:28, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5402, loss_cls: 4.0861, loss: 4.0861 +2024-12-28 09:47:09,680 - pyskl - INFO - Saving checkpoint at 62 epochs +2024-12-28 09:49:09,348 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 09:49:10,330 - pyskl - INFO - +top1_acc 0.2478 +top5_acc 0.4819 +2024-12-28 09:49:10,330 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 09:49:10,379 - pyskl - INFO - +mean_acc 0.2477 +2024-12-28 09:49:10,384 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_60.pth was removed +2024-12-28 09:49:10,644 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_62.pth. +2024-12-28 09:49:10,645 - pyskl - INFO - Best top1_acc is 0.2478 at 62 epoch. +2024-12-28 09:49:10,666 - pyskl - INFO - Epoch(val) [62][309] top1_acc: 0.2478, top5_acc: 0.4819, mean_class_accuracy: 0.2477 +2024-12-28 09:53:38,716 - pyskl - INFO - Epoch [63][100/3746] lr: 6.342e-02, eta: 3 days, 3:36:57, time: 2.680, data_time: 1.630, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5648, loss_cls: 3.9655, loss: 3.9655 +2024-12-28 09:55:05,269 - pyskl - INFO - Epoch [63][200/3746] lr: 6.339e-02, eta: 3 days, 3:35:40, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5536, loss_cls: 4.0068, loss: 4.0068 +2024-12-28 09:56:32,277 - pyskl - INFO - Epoch [63][300/3746] lr: 6.337e-02, eta: 3 days, 3:34:24, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5608, loss_cls: 4.0047, loss: 4.0047 +2024-12-28 09:57:59,510 - pyskl - INFO - Epoch [63][400/3746] lr: 6.334e-02, eta: 3 days, 3:33:08, time: 0.872, data_time: 0.001, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5473, loss_cls: 4.0557, loss: 4.0557 +2024-12-28 09:59:26,076 - pyskl - INFO - Epoch [63][500/3746] lr: 6.331e-02, eta: 3 days, 3:31:51, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5514, loss_cls: 4.0460, loss: 4.0460 +2024-12-28 10:00:52,572 - pyskl - INFO - Epoch [63][600/3746] lr: 6.328e-02, eta: 3 days, 3:30:34, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5572, loss_cls: 4.0245, loss: 4.0245 +2024-12-28 10:02:19,378 - pyskl - INFO - Epoch [63][700/3746] lr: 6.326e-02, eta: 3 days, 3:29:17, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5606, loss_cls: 4.0283, loss: 4.0283 +2024-12-28 10:03:45,535 - pyskl - INFO - Epoch [63][800/3746] lr: 6.323e-02, eta: 3 days, 3:27:59, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5453, loss_cls: 4.0509, loss: 4.0509 +2024-12-28 10:05:12,528 - pyskl - INFO - Epoch [63][900/3746] lr: 6.320e-02, eta: 3 days, 3:26:43, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5428, loss_cls: 4.0735, loss: 4.0735 +2024-12-28 10:06:38,526 - pyskl - INFO - Epoch [63][1000/3746] lr: 6.318e-02, eta: 3 days, 3:25:25, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5669, loss_cls: 3.9668, loss: 3.9668 +2024-12-28 10:08:04,868 - pyskl - INFO - Epoch [63][1100/3746] lr: 6.315e-02, eta: 3 days, 3:24:08, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5491, loss_cls: 4.0536, loss: 4.0536 +2024-12-28 10:09:30,794 - pyskl - INFO - Epoch [63][1200/3746] lr: 6.312e-02, eta: 3 days, 3:22:50, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5450, loss_cls: 4.0693, loss: 4.0693 +2024-12-28 10:10:57,015 - pyskl - INFO - Epoch [63][1300/3746] lr: 6.310e-02, eta: 3 days, 3:21:32, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5502, loss_cls: 4.0603, loss: 4.0603 +2024-12-28 10:12:22,936 - pyskl - INFO - Epoch [63][1400/3746] lr: 6.307e-02, eta: 3 days, 3:20:14, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5547, loss_cls: 4.0235, loss: 4.0235 +2024-12-28 10:13:48,422 - pyskl - INFO - Epoch [63][1500/3746] lr: 6.304e-02, eta: 3 days, 3:18:55, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5522, loss_cls: 4.0227, loss: 4.0227 +2024-12-28 10:15:13,817 - pyskl - INFO - Epoch [63][1600/3746] lr: 6.301e-02, eta: 3 days, 3:17:37, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5537, loss_cls: 4.0428, loss: 4.0428 +2024-12-28 10:16:38,526 - pyskl - INFO - Epoch [63][1700/3746] lr: 6.299e-02, eta: 3 days, 3:16:17, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5544, loss_cls: 4.0135, loss: 4.0135 +2024-12-28 10:18:03,653 - pyskl - INFO - Epoch [63][1800/3746] lr: 6.296e-02, eta: 3 days, 3:14:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5573, loss_cls: 4.0178, loss: 4.0178 +2024-12-28 10:19:29,050 - pyskl - INFO - Epoch [63][1900/3746] lr: 6.293e-02, eta: 3 days, 3:13:39, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5414, loss_cls: 4.0654, loss: 4.0654 +2024-12-28 10:20:55,373 - pyskl - INFO - Epoch [63][2000/3746] lr: 6.291e-02, eta: 3 days, 3:12:22, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5433, loss_cls: 4.0565, loss: 4.0565 +2024-12-28 10:22:21,349 - pyskl - INFO - Epoch [63][2100/3746] lr: 6.288e-02, eta: 3 days, 3:11:04, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5491, loss_cls: 4.0771, loss: 4.0771 +2024-12-28 10:23:46,668 - pyskl - INFO - Epoch [63][2200/3746] lr: 6.285e-02, eta: 3 days, 3:09:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5414, loss_cls: 4.0834, loss: 4.0834 +2024-12-28 10:25:11,421 - pyskl - INFO - Epoch [63][2300/3746] lr: 6.283e-02, eta: 3 days, 3:08:25, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5534, loss_cls: 4.0015, loss: 4.0015 +2024-12-28 10:26:36,447 - pyskl - INFO - Epoch [63][2400/3746] lr: 6.280e-02, eta: 3 days, 3:07:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5450, loss_cls: 4.0739, loss: 4.0739 +2024-12-28 10:28:02,270 - pyskl - INFO - Epoch [63][2500/3746] lr: 6.277e-02, eta: 3 days, 3:05:48, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5536, loss_cls: 4.0118, loss: 4.0118 +2024-12-28 10:29:27,966 - pyskl - INFO - Epoch [63][2600/3746] lr: 6.274e-02, eta: 3 days, 3:04:29, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5580, loss_cls: 4.0135, loss: 4.0135 +2024-12-28 10:30:53,618 - pyskl - INFO - Epoch [63][2700/3746] lr: 6.272e-02, eta: 3 days, 3:03:11, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5436, loss_cls: 4.0721, loss: 4.0721 +2024-12-28 10:32:18,499 - pyskl - INFO - Epoch [63][2800/3746] lr: 6.269e-02, eta: 3 days, 3:01:51, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5450, loss_cls: 4.0580, loss: 4.0580 +2024-12-28 10:33:43,677 - pyskl - INFO - Epoch [63][2900/3746] lr: 6.266e-02, eta: 3 days, 3:00:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5653, loss_cls: 4.0023, loss: 4.0023 +2024-12-28 10:35:09,329 - pyskl - INFO - Epoch [63][3000/3746] lr: 6.264e-02, eta: 3 days, 2:59:14, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5509, loss_cls: 4.0555, loss: 4.0555 +2024-12-28 10:36:34,885 - pyskl - INFO - Epoch [63][3100/3746] lr: 6.261e-02, eta: 3 days, 2:57:55, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5405, loss_cls: 4.0746, loss: 4.0746 +2024-12-28 10:38:01,488 - pyskl - INFO - Epoch [63][3200/3746] lr: 6.258e-02, eta: 3 days, 2:56:38, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5556, loss_cls: 4.0278, loss: 4.0278 +2024-12-28 10:39:27,156 - pyskl - INFO - Epoch [63][3300/3746] lr: 6.256e-02, eta: 3 days, 2:55:19, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5600, loss_cls: 3.9906, loss: 3.9906 +2024-12-28 10:40:53,439 - pyskl - INFO - Epoch [63][3400/3746] lr: 6.253e-02, eta: 3 days, 2:54:02, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5611, loss_cls: 4.0305, loss: 4.0305 +2024-12-28 10:42:19,320 - pyskl - INFO - Epoch [63][3500/3746] lr: 6.250e-02, eta: 3 days, 2:52:44, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5452, loss_cls: 4.0834, loss: 4.0834 +2024-12-28 10:43:45,626 - pyskl - INFO - Epoch [63][3600/3746] lr: 6.247e-02, eta: 3 days, 2:51:26, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2884, top5_acc: 0.5494, loss_cls: 4.0572, loss: 4.0572 +2024-12-28 10:45:11,455 - pyskl - INFO - Epoch [63][3700/3746] lr: 6.245e-02, eta: 3 days, 2:50:08, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5513, loss_cls: 4.0108, loss: 4.0108 +2024-12-28 10:45:53,017 - pyskl - INFO - Saving checkpoint at 63 epochs +2024-12-28 10:47:54,491 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 10:47:55,348 - pyskl - INFO - +top1_acc 0.2440 +top5_acc 0.4838 +2024-12-28 10:47:55,348 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 10:47:55,396 - pyskl - INFO - +mean_acc 0.2437 +2024-12-28 10:47:55,415 - pyskl - INFO - Epoch(val) [63][309] top1_acc: 0.2440, top5_acc: 0.4838, mean_class_accuracy: 0.2437 +2024-12-28 10:52:20,175 - pyskl - INFO - Epoch [64][100/3746] lr: 6.241e-02, eta: 3 days, 2:51:26, time: 2.647, data_time: 1.616, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5566, loss_cls: 3.9993, loss: 3.9993 +2024-12-28 10:53:45,581 - pyskl - INFO - Epoch [64][200/3746] lr: 6.238e-02, eta: 3 days, 2:50:07, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5491, loss_cls: 4.0363, loss: 4.0363 +2024-12-28 10:55:11,119 - pyskl - INFO - Epoch [64][300/3746] lr: 6.235e-02, eta: 3 days, 2:48:48, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5553, loss_cls: 4.0093, loss: 4.0093 +2024-12-28 10:56:36,056 - pyskl - INFO - Epoch [64][400/3746] lr: 6.233e-02, eta: 3 days, 2:47:28, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5623, loss_cls: 3.9962, loss: 3.9962 +2024-12-28 10:58:01,642 - pyskl - INFO - Epoch [64][500/3746] lr: 6.230e-02, eta: 3 days, 2:46:10, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5628, loss_cls: 3.9764, loss: 3.9764 +2024-12-28 10:59:27,614 - pyskl - INFO - Epoch [64][600/3746] lr: 6.227e-02, eta: 3 days, 2:44:51, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5520, loss_cls: 4.0122, loss: 4.0122 +2024-12-28 11:00:53,067 - pyskl - INFO - Epoch [64][700/3746] lr: 6.225e-02, eta: 3 days, 2:43:32, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5545, loss_cls: 4.0360, loss: 4.0360 +2024-12-28 11:02:18,492 - pyskl - INFO - Epoch [64][800/3746] lr: 6.222e-02, eta: 3 days, 2:42:13, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5598, loss_cls: 4.0159, loss: 4.0159 +2024-12-28 11:03:44,220 - pyskl - INFO - Epoch [64][900/3746] lr: 6.219e-02, eta: 3 days, 2:40:55, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5617, loss_cls: 3.9798, loss: 3.9798 +2024-12-28 11:05:09,721 - pyskl - INFO - Epoch [64][1000/3746] lr: 6.216e-02, eta: 3 days, 2:39:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5475, loss_cls: 4.0606, loss: 4.0606 +2024-12-28 11:06:35,178 - pyskl - INFO - Epoch [64][1100/3746] lr: 6.214e-02, eta: 3 days, 2:38:17, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5511, loss_cls: 4.0226, loss: 4.0226 +2024-12-28 11:08:01,039 - pyskl - INFO - Epoch [64][1200/3746] lr: 6.211e-02, eta: 3 days, 2:36:58, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5539, loss_cls: 4.0341, loss: 4.0341 +2024-12-28 11:09:26,938 - pyskl - INFO - Epoch [64][1300/3746] lr: 6.208e-02, eta: 3 days, 2:35:40, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5508, loss_cls: 3.9875, loss: 3.9875 +2024-12-28 11:10:52,247 - pyskl - INFO - Epoch [64][1400/3746] lr: 6.206e-02, eta: 3 days, 2:34:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5491, loss_cls: 4.0359, loss: 4.0359 +2024-12-28 11:12:17,233 - pyskl - INFO - Epoch [64][1500/3746] lr: 6.203e-02, eta: 3 days, 2:33:01, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5544, loss_cls: 4.0133, loss: 4.0133 +2024-12-28 11:13:42,542 - pyskl - INFO - Epoch [64][1600/3746] lr: 6.200e-02, eta: 3 days, 2:31:42, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5491, loss_cls: 4.0733, loss: 4.0733 +2024-12-28 11:15:07,597 - pyskl - INFO - Epoch [64][1700/3746] lr: 6.197e-02, eta: 3 days, 2:30:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5533, loss_cls: 4.0238, loss: 4.0238 +2024-12-28 11:16:32,348 - pyskl - INFO - Epoch [64][1800/3746] lr: 6.195e-02, eta: 3 days, 2:29:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5469, loss_cls: 4.0459, loss: 4.0459 +2024-12-28 11:17:57,186 - pyskl - INFO - Epoch [64][1900/3746] lr: 6.192e-02, eta: 3 days, 2:27:43, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5487, loss_cls: 4.0292, loss: 4.0292 +2024-12-28 11:19:22,106 - pyskl - INFO - Epoch [64][2000/3746] lr: 6.189e-02, eta: 3 days, 2:26:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5458, loss_cls: 4.0539, loss: 4.0539 +2024-12-28 11:20:47,643 - pyskl - INFO - Epoch [64][2100/3746] lr: 6.187e-02, eta: 3 days, 2:25:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5595, loss_cls: 4.0310, loss: 4.0310 +2024-12-28 11:22:12,529 - pyskl - INFO - Epoch [64][2200/3746] lr: 6.184e-02, eta: 3 days, 2:23:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5591, loss_cls: 3.9902, loss: 3.9902 +2024-12-28 11:23:37,551 - pyskl - INFO - Epoch [64][2300/3746] lr: 6.181e-02, eta: 3 days, 2:22:24, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5506, loss_cls: 4.0276, loss: 4.0276 +2024-12-28 11:25:02,762 - pyskl - INFO - Epoch [64][2400/3746] lr: 6.178e-02, eta: 3 days, 2:21:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5400, loss_cls: 4.0671, loss: 4.0671 +2024-12-28 11:26:27,869 - pyskl - INFO - Epoch [64][2500/3746] lr: 6.176e-02, eta: 3 days, 2:19:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5569, loss_cls: 4.0257, loss: 4.0257 +2024-12-28 11:27:52,887 - pyskl - INFO - Epoch [64][2600/3746] lr: 6.173e-02, eta: 3 days, 2:18:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5422, loss_cls: 4.0583, loss: 4.0583 +2024-12-28 11:29:18,155 - pyskl - INFO - Epoch [64][2700/3746] lr: 6.170e-02, eta: 3 days, 2:17:06, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5614, loss_cls: 3.9907, loss: 3.9907 +2024-12-28 11:30:43,576 - pyskl - INFO - Epoch [64][2800/3746] lr: 6.168e-02, eta: 3 days, 2:15:47, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5481, loss_cls: 4.0394, loss: 4.0394 +2024-12-28 11:32:08,312 - pyskl - INFO - Epoch [64][2900/3746] lr: 6.165e-02, eta: 3 days, 2:14:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5506, loss_cls: 4.0515, loss: 4.0515 +2024-12-28 11:33:33,525 - pyskl - INFO - Epoch [64][3000/3746] lr: 6.162e-02, eta: 3 days, 2:13:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5527, loss_cls: 4.0246, loss: 4.0246 +2024-12-28 11:34:59,147 - pyskl - INFO - Epoch [64][3100/3746] lr: 6.159e-02, eta: 3 days, 2:11:49, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5537, loss_cls: 4.0472, loss: 4.0472 +2024-12-28 11:36:23,911 - pyskl - INFO - Epoch [64][3200/3746] lr: 6.157e-02, eta: 3 days, 2:10:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5581, loss_cls: 4.0103, loss: 4.0103 +2024-12-28 11:37:49,300 - pyskl - INFO - Epoch [64][3300/3746] lr: 6.154e-02, eta: 3 days, 2:09:10, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5592, loss_cls: 4.0258, loss: 4.0258 +2024-12-28 11:39:14,281 - pyskl - INFO - Epoch [64][3400/3746] lr: 6.151e-02, eta: 3 days, 2:07:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2939, top5_acc: 0.5522, loss_cls: 4.0442, loss: 4.0442 +2024-12-28 11:40:39,654 - pyskl - INFO - Epoch [64][3500/3746] lr: 6.148e-02, eta: 3 days, 2:06:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5403, loss_cls: 4.0831, loss: 4.0831 +2024-12-28 11:42:04,870 - pyskl - INFO - Epoch [64][3600/3746] lr: 6.146e-02, eta: 3 days, 2:05:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5613, loss_cls: 4.0406, loss: 4.0406 +2024-12-28 11:43:29,588 - pyskl - INFO - Epoch [64][3700/3746] lr: 6.143e-02, eta: 3 days, 2:03:51, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5556, loss_cls: 4.0305, loss: 4.0305 +2024-12-28 11:44:10,840 - pyskl - INFO - Saving checkpoint at 64 epochs +2024-12-28 11:46:09,972 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 11:46:10,728 - pyskl - INFO - +top1_acc 0.2310 +top5_acc 0.4665 +2024-12-28 11:46:10,728 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 11:46:10,793 - pyskl - INFO - +mean_acc 0.2308 +2024-12-28 11:46:10,816 - pyskl - INFO - Epoch(val) [64][309] top1_acc: 0.2310, top5_acc: 0.4665, mean_class_accuracy: 0.2308 +2024-12-28 11:50:36,672 - pyskl - INFO - Epoch [65][100/3746] lr: 6.139e-02, eta: 3 days, 2:05:05, time: 2.658, data_time: 1.605, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5669, loss_cls: 3.9453, loss: 3.9453 +2024-12-28 11:52:02,435 - pyskl - INFO - Epoch [65][200/3746] lr: 6.136e-02, eta: 3 days, 2:03:46, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5577, loss_cls: 4.0025, loss: 4.0025 +2024-12-28 11:53:28,489 - pyskl - INFO - Epoch [65][300/3746] lr: 6.134e-02, eta: 3 days, 2:02:28, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5550, loss_cls: 4.0079, loss: 4.0079 +2024-12-28 11:54:54,854 - pyskl - INFO - Epoch [65][400/3746] lr: 6.131e-02, eta: 3 days, 2:01:10, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5578, loss_cls: 3.9986, loss: 3.9986 +2024-12-28 11:56:20,861 - pyskl - INFO - Epoch [65][500/3746] lr: 6.128e-02, eta: 3 days, 1:59:51, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5630, loss_cls: 4.0050, loss: 4.0050 +2024-12-28 11:57:47,013 - pyskl - INFO - Epoch [65][600/3746] lr: 6.125e-02, eta: 3 days, 1:58:33, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5584, loss_cls: 3.9810, loss: 3.9810 +2024-12-28 11:59:13,190 - pyskl - INFO - Epoch [65][700/3746] lr: 6.123e-02, eta: 3 days, 1:57:15, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5452, loss_cls: 4.0630, loss: 4.0630 +2024-12-28 12:00:39,811 - pyskl - INFO - Epoch [65][800/3746] lr: 6.120e-02, eta: 3 days, 1:55:57, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5566, loss_cls: 4.0205, loss: 4.0205 +2024-12-28 12:02:06,471 - pyskl - INFO - Epoch [65][900/3746] lr: 6.117e-02, eta: 3 days, 1:54:39, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5548, loss_cls: 4.0053, loss: 4.0053 +2024-12-28 12:03:32,987 - pyskl - INFO - Epoch [65][1000/3746] lr: 6.115e-02, eta: 3 days, 1:53:21, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5661, loss_cls: 3.9360, loss: 3.9360 +2024-12-28 12:04:58,992 - pyskl - INFO - Epoch [65][1100/3746] lr: 6.112e-02, eta: 3 days, 1:52:03, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5553, loss_cls: 4.0122, loss: 4.0122 +2024-12-28 12:06:24,977 - pyskl - INFO - Epoch [65][1200/3746] lr: 6.109e-02, eta: 3 days, 1:50:44, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5519, loss_cls: 4.0140, loss: 4.0140 +2024-12-28 12:07:51,568 - pyskl - INFO - Epoch [65][1300/3746] lr: 6.106e-02, eta: 3 days, 1:49:26, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5487, loss_cls: 4.0530, loss: 4.0530 +2024-12-28 12:09:17,711 - pyskl - INFO - Epoch [65][1400/3746] lr: 6.104e-02, eta: 3 days, 1:48:08, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5603, loss_cls: 4.0072, loss: 4.0072 +2024-12-28 12:10:43,211 - pyskl - INFO - Epoch [65][1500/3746] lr: 6.101e-02, eta: 3 days, 1:46:48, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5408, loss_cls: 4.0774, loss: 4.0774 +2024-12-28 12:12:08,586 - pyskl - INFO - Epoch [65][1600/3746] lr: 6.098e-02, eta: 3 days, 1:45:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5587, loss_cls: 4.0244, loss: 4.0244 +2024-12-28 12:13:33,608 - pyskl - INFO - Epoch [65][1700/3746] lr: 6.095e-02, eta: 3 days, 1:44:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5558, loss_cls: 4.0283, loss: 4.0283 +2024-12-28 12:14:58,407 - pyskl - INFO - Epoch [65][1800/3746] lr: 6.093e-02, eta: 3 days, 1:42:49, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5553, loss_cls: 4.0127, loss: 4.0127 +2024-12-28 12:16:23,540 - pyskl - INFO - Epoch [65][1900/3746] lr: 6.090e-02, eta: 3 days, 1:41:29, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5453, loss_cls: 4.0662, loss: 4.0662 +2024-12-28 12:17:48,313 - pyskl - INFO - Epoch [65][2000/3746] lr: 6.087e-02, eta: 3 days, 1:40:09, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5583, loss_cls: 3.9978, loss: 3.9978 +2024-12-28 12:19:13,425 - pyskl - INFO - Epoch [65][2100/3746] lr: 6.085e-02, eta: 3 days, 1:38:49, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5553, loss_cls: 4.0522, loss: 4.0522 +2024-12-28 12:20:38,510 - pyskl - INFO - Epoch [65][2200/3746] lr: 6.082e-02, eta: 3 days, 1:37:29, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5609, loss_cls: 4.0089, loss: 4.0089 +2024-12-28 12:22:03,900 - pyskl - INFO - Epoch [65][2300/3746] lr: 6.079e-02, eta: 3 days, 1:36:10, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5563, loss_cls: 4.0149, loss: 4.0149 +2024-12-28 12:23:28,953 - pyskl - INFO - Epoch [65][2400/3746] lr: 6.076e-02, eta: 3 days, 1:34:50, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5484, loss_cls: 4.0305, loss: 4.0305 +2024-12-28 12:24:54,007 - pyskl - INFO - Epoch [65][2500/3746] lr: 6.074e-02, eta: 3 days, 1:33:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5580, loss_cls: 4.0013, loss: 4.0013 +2024-12-28 12:26:19,483 - pyskl - INFO - Epoch [65][2600/3746] lr: 6.071e-02, eta: 3 days, 1:32:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5525, loss_cls: 4.0443, loss: 4.0443 +2024-12-28 12:27:44,816 - pyskl - INFO - Epoch [65][2700/3746] lr: 6.068e-02, eta: 3 days, 1:30:51, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5533, loss_cls: 4.0424, loss: 4.0424 +2024-12-28 12:29:09,755 - pyskl - INFO - Epoch [65][2800/3746] lr: 6.065e-02, eta: 3 days, 1:29:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5617, loss_cls: 4.0000, loss: 4.0000 +2024-12-28 12:30:34,630 - pyskl - INFO - Epoch [65][2900/3746] lr: 6.063e-02, eta: 3 days, 1:28:11, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5445, loss_cls: 4.0692, loss: 4.0692 +2024-12-28 12:31:59,878 - pyskl - INFO - Epoch [65][3000/3746] lr: 6.060e-02, eta: 3 days, 1:26:51, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5486, loss_cls: 4.0543, loss: 4.0543 +2024-12-28 12:33:24,735 - pyskl - INFO - Epoch [65][3100/3746] lr: 6.057e-02, eta: 3 days, 1:25:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5498, loss_cls: 4.0342, loss: 4.0342 +2024-12-28 12:34:49,563 - pyskl - INFO - Epoch [65][3200/3746] lr: 6.055e-02, eta: 3 days, 1:24:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5541, loss_cls: 4.0083, loss: 4.0083 +2024-12-28 12:36:14,976 - pyskl - INFO - Epoch [65][3300/3746] lr: 6.052e-02, eta: 3 days, 1:22:51, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5528, loss_cls: 4.0308, loss: 4.0308 +2024-12-28 12:37:40,064 - pyskl - INFO - Epoch [65][3400/3746] lr: 6.049e-02, eta: 3 days, 1:21:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5553, loss_cls: 4.0435, loss: 4.0435 +2024-12-28 12:39:05,314 - pyskl - INFO - Epoch [65][3500/3746] lr: 6.046e-02, eta: 3 days, 1:20:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5584, loss_cls: 4.0134, loss: 4.0134 +2024-12-28 12:40:30,788 - pyskl - INFO - Epoch [65][3600/3746] lr: 6.044e-02, eta: 3 days, 1:18:52, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5600, loss_cls: 3.9924, loss: 3.9924 +2024-12-28 12:41:55,753 - pyskl - INFO - Epoch [65][3700/3746] lr: 6.041e-02, eta: 3 days, 1:17:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5486, loss_cls: 4.0385, loss: 4.0385 +2024-12-28 12:42:36,474 - pyskl - INFO - Saving checkpoint at 65 epochs +2024-12-28 12:44:34,681 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 12:44:35,460 - pyskl - INFO - +top1_acc 0.2169 +top5_acc 0.4465 +2024-12-28 12:44:35,460 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 12:44:35,506 - pyskl - INFO - +mean_acc 0.2167 +2024-12-28 12:44:35,524 - pyskl - INFO - Epoch(val) [65][309] top1_acc: 0.2169, top5_acc: 0.4465, mean_class_accuracy: 0.2167 +2024-12-28 12:48:57,754 - pyskl - INFO - Epoch [66][100/3746] lr: 6.037e-02, eta: 3 days, 1:18:35, time: 2.622, data_time: 1.566, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5667, loss_cls: 3.9626, loss: 3.9626 +2024-12-28 12:50:23,264 - pyskl - INFO - Epoch [66][200/3746] lr: 6.034e-02, eta: 3 days, 1:17:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5594, loss_cls: 3.9811, loss: 3.9811 +2024-12-28 12:51:48,912 - pyskl - INFO - Epoch [66][300/3746] lr: 6.031e-02, eta: 3 days, 1:15:57, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5678, loss_cls: 3.9294, loss: 3.9294 +2024-12-28 12:53:14,518 - pyskl - INFO - Epoch [66][400/3746] lr: 6.029e-02, eta: 3 days, 1:14:37, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5513, loss_cls: 4.0066, loss: 4.0066 +2024-12-28 12:54:40,661 - pyskl - INFO - Epoch [66][500/3746] lr: 6.026e-02, eta: 3 days, 1:13:18, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5709, loss_cls: 3.9417, loss: 3.9417 +2024-12-28 12:56:06,908 - pyskl - INFO - Epoch [66][600/3746] lr: 6.023e-02, eta: 3 days, 1:12:00, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5516, loss_cls: 4.0105, loss: 4.0105 +2024-12-28 12:57:32,631 - pyskl - INFO - Epoch [66][700/3746] lr: 6.020e-02, eta: 3 days, 1:10:41, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5516, loss_cls: 4.0138, loss: 4.0138 +2024-12-28 12:58:58,092 - pyskl - INFO - Epoch [66][800/3746] lr: 6.018e-02, eta: 3 days, 1:09:21, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5652, loss_cls: 3.9613, loss: 3.9613 +2024-12-28 13:00:24,087 - pyskl - INFO - Epoch [66][900/3746] lr: 6.015e-02, eta: 3 days, 1:08:02, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5625, loss_cls: 4.0134, loss: 4.0134 +2024-12-28 13:01:49,668 - pyskl - INFO - Epoch [66][1000/3746] lr: 6.012e-02, eta: 3 days, 1:06:43, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5598, loss_cls: 3.9852, loss: 3.9852 +2024-12-28 13:03:15,197 - pyskl - INFO - Epoch [66][1100/3746] lr: 6.009e-02, eta: 3 days, 1:05:23, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5614, loss_cls: 4.0006, loss: 4.0006 +2024-12-28 13:04:40,678 - pyskl - INFO - Epoch [66][1200/3746] lr: 6.007e-02, eta: 3 days, 1:04:03, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5464, loss_cls: 4.0656, loss: 4.0656 +2024-12-28 13:06:06,151 - pyskl - INFO - Epoch [66][1300/3746] lr: 6.004e-02, eta: 3 days, 1:02:44, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5550, loss_cls: 4.0267, loss: 4.0267 +2024-12-28 13:07:31,292 - pyskl - INFO - Epoch [66][1400/3746] lr: 6.001e-02, eta: 3 days, 1:01:24, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5517, loss_cls: 4.0083, loss: 4.0083 +2024-12-28 13:08:56,426 - pyskl - INFO - Epoch [66][1500/3746] lr: 5.999e-02, eta: 3 days, 1:00:04, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5483, loss_cls: 4.0340, loss: 4.0340 +2024-12-28 13:10:21,436 - pyskl - INFO - Epoch [66][1600/3746] lr: 5.996e-02, eta: 3 days, 0:58:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5531, loss_cls: 4.0420, loss: 4.0420 +2024-12-28 13:11:46,555 - pyskl - INFO - Epoch [66][1700/3746] lr: 5.993e-02, eta: 3 days, 0:57:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5489, loss_cls: 4.0631, loss: 4.0631 +2024-12-28 13:13:11,865 - pyskl - INFO - Epoch [66][1800/3746] lr: 5.990e-02, eta: 3 days, 0:56:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5586, loss_cls: 4.0008, loss: 4.0008 +2024-12-28 13:14:36,562 - pyskl - INFO - Epoch [66][1900/3746] lr: 5.988e-02, eta: 3 days, 0:54:43, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5542, loss_cls: 4.0113, loss: 4.0113 +2024-12-28 13:16:01,564 - pyskl - INFO - Epoch [66][2000/3746] lr: 5.985e-02, eta: 3 days, 0:53:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5556, loss_cls: 4.0000, loss: 4.0000 +2024-12-28 13:17:27,345 - pyskl - INFO - Epoch [66][2100/3746] lr: 5.982e-02, eta: 3 days, 0:52:03, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5534, loss_cls: 4.0127, loss: 4.0127 +2024-12-28 13:18:52,773 - pyskl - INFO - Epoch [66][2200/3746] lr: 5.979e-02, eta: 3 days, 0:50:44, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2892, top5_acc: 0.5550, loss_cls: 4.0485, loss: 4.0485 +2024-12-28 13:20:18,065 - pyskl - INFO - Epoch [66][2300/3746] lr: 5.977e-02, eta: 3 days, 0:49:24, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5578, loss_cls: 4.0286, loss: 4.0286 +2024-12-28 13:21:43,491 - pyskl - INFO - Epoch [66][2400/3746] lr: 5.974e-02, eta: 3 days, 0:48:04, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5463, loss_cls: 4.0440, loss: 4.0440 +2024-12-28 13:23:08,667 - pyskl - INFO - Epoch [66][2500/3746] lr: 5.971e-02, eta: 3 days, 0:46:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5533, loss_cls: 4.0280, loss: 4.0280 +2024-12-28 13:24:34,247 - pyskl - INFO - Epoch [66][2600/3746] lr: 5.968e-02, eta: 3 days, 0:45:24, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5539, loss_cls: 4.0385, loss: 4.0385 +2024-12-28 13:25:59,587 - pyskl - INFO - Epoch [66][2700/3746] lr: 5.966e-02, eta: 3 days, 0:44:04, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5542, loss_cls: 4.0283, loss: 4.0283 +2024-12-28 13:27:24,826 - pyskl - INFO - Epoch [66][2800/3746] lr: 5.963e-02, eta: 3 days, 0:42:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5442, loss_cls: 4.0739, loss: 4.0739 +2024-12-28 13:28:50,364 - pyskl - INFO - Epoch [66][2900/3746] lr: 5.960e-02, eta: 3 days, 0:41:25, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5602, loss_cls: 4.0283, loss: 4.0283 +2024-12-28 13:30:15,908 - pyskl - INFO - Epoch [66][3000/3746] lr: 5.957e-02, eta: 3 days, 0:40:05, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5530, loss_cls: 4.0014, loss: 4.0014 +2024-12-28 13:31:41,316 - pyskl - INFO - Epoch [66][3100/3746] lr: 5.955e-02, eta: 3 days, 0:38:45, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5616, loss_cls: 3.9903, loss: 3.9903 +2024-12-28 13:33:06,708 - pyskl - INFO - Epoch [66][3200/3746] lr: 5.952e-02, eta: 3 days, 0:37:26, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5630, loss_cls: 3.9654, loss: 3.9654 +2024-12-28 13:34:32,007 - pyskl - INFO - Epoch [66][3300/3746] lr: 5.949e-02, eta: 3 days, 0:36:06, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5563, loss_cls: 4.0155, loss: 4.0155 +2024-12-28 13:35:57,738 - pyskl - INFO - Epoch [66][3400/3746] lr: 5.946e-02, eta: 3 days, 0:34:46, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5480, loss_cls: 4.0245, loss: 4.0245 +2024-12-28 13:37:23,238 - pyskl - INFO - Epoch [66][3500/3746] lr: 5.944e-02, eta: 3 days, 0:33:27, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5609, loss_cls: 3.9894, loss: 3.9894 +2024-12-28 13:38:48,714 - pyskl - INFO - Epoch [66][3600/3746] lr: 5.941e-02, eta: 3 days, 0:32:07, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5552, loss_cls: 4.0128, loss: 4.0128 +2024-12-28 13:40:14,448 - pyskl - INFO - Epoch [66][3700/3746] lr: 5.938e-02, eta: 3 days, 0:30:48, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5541, loss_cls: 3.9856, loss: 3.9856 +2024-12-28 13:40:55,794 - pyskl - INFO - Saving checkpoint at 66 epochs +2024-12-28 13:42:55,391 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 13:42:56,254 - pyskl - INFO - +top1_acc 0.2491 +top5_acc 0.4964 +2024-12-28 13:42:56,254 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 13:42:56,305 - pyskl - INFO - +mean_acc 0.2488 +2024-12-28 13:42:56,310 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_62.pth was removed +2024-12-28 13:42:56,567 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_66.pth. +2024-12-28 13:42:56,568 - pyskl - INFO - Best top1_acc is 0.2491 at 66 epoch. +2024-12-28 13:42:56,583 - pyskl - INFO - Epoch(val) [66][309] top1_acc: 0.2491, top5_acc: 0.4964, mean_class_accuracy: 0.2488 +2024-12-28 13:47:11,295 - pyskl - INFO - Epoch [67][100/3746] lr: 5.934e-02, eta: 3 days, 0:31:36, time: 2.547, data_time: 1.513, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5592, loss_cls: 4.0207, loss: 4.0207 +2024-12-28 13:48:37,211 - pyskl - INFO - Epoch [67][200/3746] lr: 5.931e-02, eta: 3 days, 0:30:17, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5698, loss_cls: 3.9473, loss: 3.9473 +2024-12-28 13:50:03,063 - pyskl - INFO - Epoch [67][300/3746] lr: 5.929e-02, eta: 3 days, 0:28:58, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5587, loss_cls: 4.0068, loss: 4.0068 +2024-12-28 13:51:29,263 - pyskl - INFO - Epoch [67][400/3746] lr: 5.926e-02, eta: 3 days, 0:27:39, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5534, loss_cls: 4.0273, loss: 4.0273 +2024-12-28 13:52:54,940 - pyskl - INFO - Epoch [67][500/3746] lr: 5.923e-02, eta: 3 days, 0:26:19, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5548, loss_cls: 4.0082, loss: 4.0082 +2024-12-28 13:54:20,313 - pyskl - INFO - Epoch [67][600/3746] lr: 5.920e-02, eta: 3 days, 0:24:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5594, loss_cls: 3.9850, loss: 3.9850 +2024-12-28 13:55:45,809 - pyskl - INFO - Epoch [67][700/3746] lr: 5.918e-02, eta: 3 days, 0:23:39, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5567, loss_cls: 4.0001, loss: 4.0001 +2024-12-28 13:57:11,618 - pyskl - INFO - Epoch [67][800/3746] lr: 5.915e-02, eta: 3 days, 0:22:20, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5544, loss_cls: 4.0117, loss: 4.0117 +2024-12-28 13:58:37,405 - pyskl - INFO - Epoch [67][900/3746] lr: 5.912e-02, eta: 3 days, 0:21:00, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5573, loss_cls: 3.9631, loss: 3.9631 +2024-12-28 14:00:02,897 - pyskl - INFO - Epoch [67][1000/3746] lr: 5.909e-02, eta: 3 days, 0:19:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5509, loss_cls: 4.0321, loss: 4.0321 +2024-12-28 14:01:28,607 - pyskl - INFO - Epoch [67][1100/3746] lr: 5.907e-02, eta: 3 days, 0:18:21, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5559, loss_cls: 4.0045, loss: 4.0045 +2024-12-28 14:02:54,227 - pyskl - INFO - Epoch [67][1200/3746] lr: 5.904e-02, eta: 3 days, 0:17:01, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5481, loss_cls: 4.0369, loss: 4.0369 +2024-12-28 14:04:19,862 - pyskl - INFO - Epoch [67][1300/3746] lr: 5.901e-02, eta: 3 days, 0:15:41, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5595, loss_cls: 3.9869, loss: 3.9869 +2024-12-28 14:05:45,161 - pyskl - INFO - Epoch [67][1400/3746] lr: 5.898e-02, eta: 3 days, 0:14:21, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5623, loss_cls: 3.9726, loss: 3.9726 +2024-12-28 14:07:09,982 - pyskl - INFO - Epoch [67][1500/3746] lr: 5.896e-02, eta: 3 days, 0:13:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5664, loss_cls: 3.9626, loss: 3.9626 +2024-12-28 14:08:34,897 - pyskl - INFO - Epoch [67][1600/3746] lr: 5.893e-02, eta: 3 days, 0:11:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5633, loss_cls: 3.9952, loss: 3.9952 +2024-12-28 14:10:00,403 - pyskl - INFO - Epoch [67][1700/3746] lr: 5.890e-02, eta: 3 days, 0:10:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5587, loss_cls: 4.0050, loss: 4.0050 +2024-12-28 14:11:25,767 - pyskl - INFO - Epoch [67][1800/3746] lr: 5.887e-02, eta: 3 days, 0:09:00, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5667, loss_cls: 3.9664, loss: 3.9664 +2024-12-28 14:12:51,558 - pyskl - INFO - Epoch [67][1900/3746] lr: 5.885e-02, eta: 3 days, 0:07:40, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5587, loss_cls: 4.0143, loss: 4.0143 +2024-12-28 14:14:17,036 - pyskl - INFO - Epoch [67][2000/3746] lr: 5.882e-02, eta: 3 days, 0:06:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5555, loss_cls: 4.0297, loss: 4.0297 +2024-12-28 14:15:41,912 - pyskl - INFO - Epoch [67][2100/3746] lr: 5.879e-02, eta: 3 days, 0:05:00, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5491, loss_cls: 4.0404, loss: 4.0404 +2024-12-28 14:17:06,767 - pyskl - INFO - Epoch [67][2200/3746] lr: 5.876e-02, eta: 3 days, 0:03:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5542, loss_cls: 4.0153, loss: 4.0153 +2024-12-28 14:18:31,869 - pyskl - INFO - Epoch [67][2300/3746] lr: 5.874e-02, eta: 3 days, 0:02:18, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5602, loss_cls: 3.9838, loss: 3.9838 +2024-12-28 14:19:57,172 - pyskl - INFO - Epoch [67][2400/3746] lr: 5.871e-02, eta: 3 days, 0:00:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5614, loss_cls: 3.9721, loss: 3.9721 +2024-12-28 14:21:22,338 - pyskl - INFO - Epoch [67][2500/3746] lr: 5.868e-02, eta: 2 days, 23:59:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5492, loss_cls: 4.0580, loss: 4.0580 +2024-12-28 14:22:47,472 - pyskl - INFO - Epoch [67][2600/3746] lr: 5.865e-02, eta: 2 days, 23:58:18, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5548, loss_cls: 4.0218, loss: 4.0218 +2024-12-28 14:24:13,152 - pyskl - INFO - Epoch [67][2700/3746] lr: 5.863e-02, eta: 2 days, 23:56:58, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5642, loss_cls: 3.9757, loss: 3.9757 +2024-12-28 14:25:37,916 - pyskl - INFO - Epoch [67][2800/3746] lr: 5.860e-02, eta: 2 days, 23:55:37, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5584, loss_cls: 3.9967, loss: 3.9967 +2024-12-28 14:27:03,090 - pyskl - INFO - Epoch [67][2900/3746] lr: 5.857e-02, eta: 2 days, 23:54:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5420, loss_cls: 4.0523, loss: 4.0523 +2024-12-28 14:28:28,084 - pyskl - INFO - Epoch [67][3000/3746] lr: 5.854e-02, eta: 2 days, 23:52:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5733, loss_cls: 3.9534, loss: 3.9534 +2024-12-28 14:29:53,450 - pyskl - INFO - Epoch [67][3100/3746] lr: 5.852e-02, eta: 2 days, 23:51:36, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5539, loss_cls: 3.9923, loss: 3.9923 +2024-12-28 14:31:18,346 - pyskl - INFO - Epoch [67][3200/3746] lr: 5.849e-02, eta: 2 days, 23:50:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5633, loss_cls: 3.9627, loss: 3.9627 +2024-12-28 14:32:43,483 - pyskl - INFO - Epoch [67][3300/3746] lr: 5.846e-02, eta: 2 days, 23:48:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5603, loss_cls: 3.9634, loss: 3.9634 +2024-12-28 14:34:08,012 - pyskl - INFO - Epoch [67][3400/3746] lr: 5.843e-02, eta: 2 days, 23:47:34, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5561, loss_cls: 4.0024, loss: 4.0024 +2024-12-28 14:35:33,099 - pyskl - INFO - Epoch [67][3500/3746] lr: 5.841e-02, eta: 2 days, 23:46:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5473, loss_cls: 4.0587, loss: 4.0587 +2024-12-28 14:36:59,113 - pyskl - INFO - Epoch [67][3600/3746] lr: 5.838e-02, eta: 2 days, 23:44:54, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5631, loss_cls: 4.0066, loss: 4.0066 +2024-12-28 14:38:24,653 - pyskl - INFO - Epoch [67][3700/3746] lr: 5.835e-02, eta: 2 days, 23:43:34, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5558, loss_cls: 4.0200, loss: 4.0200 +2024-12-28 14:39:05,918 - pyskl - INFO - Saving checkpoint at 67 epochs +2024-12-28 14:41:05,037 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 14:41:05,714 - pyskl - INFO - +top1_acc 0.2544 +top5_acc 0.4992 +2024-12-28 14:41:05,714 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 14:41:05,760 - pyskl - INFO - +mean_acc 0.2542 +2024-12-28 14:41:05,765 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_66.pth was removed +2024-12-28 14:41:06,067 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_67.pth. +2024-12-28 14:41:06,068 - pyskl - INFO - Best top1_acc is 0.2544 at 67 epoch. +2024-12-28 14:41:06,088 - pyskl - INFO - Epoch(val) [67][309] top1_acc: 0.2544, top5_acc: 0.4992, mean_class_accuracy: 0.2542 +2024-12-28 14:45:23,307 - pyskl - INFO - Epoch [68][100/3746] lr: 5.831e-02, eta: 2 days, 23:44:21, time: 2.572, data_time: 1.533, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5708, loss_cls: 3.9286, loss: 3.9286 +2024-12-28 14:46:49,461 - pyskl - INFO - Epoch [68][200/3746] lr: 5.828e-02, eta: 2 days, 23:43:02, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5648, loss_cls: 3.9601, loss: 3.9601 +2024-12-28 14:48:15,340 - pyskl - INFO - Epoch [68][300/3746] lr: 5.826e-02, eta: 2 days, 23:41:42, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5570, loss_cls: 4.0037, loss: 4.0037 +2024-12-28 14:49:41,663 - pyskl - INFO - Epoch [68][400/3746] lr: 5.823e-02, eta: 2 days, 23:40:23, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5448, loss_cls: 4.0497, loss: 4.0497 +2024-12-28 14:51:08,015 - pyskl - INFO - Epoch [68][500/3746] lr: 5.820e-02, eta: 2 days, 23:39:04, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5670, loss_cls: 3.9447, loss: 3.9447 +2024-12-28 14:52:34,480 - pyskl - INFO - Epoch [68][600/3746] lr: 5.817e-02, eta: 2 days, 23:37:45, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5552, loss_cls: 4.0104, loss: 4.0104 +2024-12-28 14:54:00,607 - pyskl - INFO - Epoch [68][700/3746] lr: 5.815e-02, eta: 2 days, 23:36:25, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5591, loss_cls: 3.9978, loss: 3.9978 +2024-12-28 14:55:27,461 - pyskl - INFO - Epoch [68][800/3746] lr: 5.812e-02, eta: 2 days, 23:35:07, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5525, loss_cls: 3.9747, loss: 3.9747 +2024-12-28 14:56:53,876 - pyskl - INFO - Epoch [68][900/3746] lr: 5.809e-02, eta: 2 days, 23:33:48, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5655, loss_cls: 3.9766, loss: 3.9766 +2024-12-28 14:58:20,293 - pyskl - INFO - Epoch [68][1000/3746] lr: 5.806e-02, eta: 2 days, 23:32:29, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5642, loss_cls: 3.9667, loss: 3.9667 +2024-12-28 14:59:46,861 - pyskl - INFO - Epoch [68][1100/3746] lr: 5.804e-02, eta: 2 days, 23:31:10, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5647, loss_cls: 3.9595, loss: 3.9595 +2024-12-28 15:01:13,703 - pyskl - INFO - Epoch [68][1200/3746] lr: 5.801e-02, eta: 2 days, 23:29:52, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3013, top5_acc: 0.5567, loss_cls: 4.0101, loss: 4.0101 +2024-12-28 15:02:39,914 - pyskl - INFO - Epoch [68][1300/3746] lr: 5.798e-02, eta: 2 days, 23:28:32, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5572, loss_cls: 4.0228, loss: 4.0228 +2024-12-28 15:04:05,574 - pyskl - INFO - Epoch [68][1400/3746] lr: 5.795e-02, eta: 2 days, 23:27:12, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5509, loss_cls: 4.0110, loss: 4.0110 +2024-12-28 15:05:30,729 - pyskl - INFO - Epoch [68][1500/3746] lr: 5.792e-02, eta: 2 days, 23:25:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5691, loss_cls: 3.9646, loss: 3.9646 +2024-12-28 15:06:56,094 - pyskl - INFO - Epoch [68][1600/3746] lr: 5.790e-02, eta: 2 days, 23:24:31, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5553, loss_cls: 3.9870, loss: 3.9870 +2024-12-28 15:08:21,890 - pyskl - INFO - Epoch [68][1700/3746] lr: 5.787e-02, eta: 2 days, 23:23:12, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5492, loss_cls: 4.0165, loss: 4.0165 +2024-12-28 15:09:47,516 - pyskl - INFO - Epoch [68][1800/3746] lr: 5.784e-02, eta: 2 days, 23:21:52, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5609, loss_cls: 3.9687, loss: 3.9687 +2024-12-28 15:11:12,949 - pyskl - INFO - Epoch [68][1900/3746] lr: 5.781e-02, eta: 2 days, 23:20:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5563, loss_cls: 3.9916, loss: 3.9916 +2024-12-28 15:12:38,801 - pyskl - INFO - Epoch [68][2000/3746] lr: 5.779e-02, eta: 2 days, 23:19:11, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5614, loss_cls: 3.9622, loss: 3.9622 +2024-12-28 15:14:03,990 - pyskl - INFO - Epoch [68][2100/3746] lr: 5.776e-02, eta: 2 days, 23:17:51, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5716, loss_cls: 3.9142, loss: 3.9142 +2024-12-28 15:15:28,801 - pyskl - INFO - Epoch [68][2200/3746] lr: 5.773e-02, eta: 2 days, 23:16:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5627, loss_cls: 3.9554, loss: 3.9554 +2024-12-28 15:16:54,169 - pyskl - INFO - Epoch [68][2300/3746] lr: 5.770e-02, eta: 2 days, 23:15:09, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5648, loss_cls: 3.9632, loss: 3.9632 +2024-12-28 15:18:19,661 - pyskl - INFO - Epoch [68][2400/3746] lr: 5.768e-02, eta: 2 days, 23:13:49, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5608, loss_cls: 3.9936, loss: 3.9936 +2024-12-28 15:19:44,882 - pyskl - INFO - Epoch [68][2500/3746] lr: 5.765e-02, eta: 2 days, 23:12:29, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5581, loss_cls: 3.9968, loss: 3.9968 +2024-12-28 15:21:10,560 - pyskl - INFO - Epoch [68][2600/3746] lr: 5.762e-02, eta: 2 days, 23:11:09, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5678, loss_cls: 3.9732, loss: 3.9732 +2024-12-28 15:22:35,889 - pyskl - INFO - Epoch [68][2700/3746] lr: 5.759e-02, eta: 2 days, 23:09:48, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5580, loss_cls: 4.0077, loss: 4.0077 +2024-12-28 15:24:01,336 - pyskl - INFO - Epoch [68][2800/3746] lr: 5.757e-02, eta: 2 days, 23:08:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5548, loss_cls: 4.0172, loss: 4.0172 +2024-12-28 15:25:26,394 - pyskl - INFO - Epoch [68][2900/3746] lr: 5.754e-02, eta: 2 days, 23:07:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5595, loss_cls: 3.9888, loss: 3.9888 +2024-12-28 15:26:51,542 - pyskl - INFO - Epoch [68][3000/3746] lr: 5.751e-02, eta: 2 days, 23:05:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5489, loss_cls: 4.0325, loss: 4.0325 +2024-12-28 15:28:17,182 - pyskl - INFO - Epoch [68][3100/3746] lr: 5.748e-02, eta: 2 days, 23:04:26, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5609, loss_cls: 4.0244, loss: 4.0244 +2024-12-28 15:29:42,171 - pyskl - INFO - Epoch [68][3200/3746] lr: 5.746e-02, eta: 2 days, 23:03:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5450, loss_cls: 4.0458, loss: 4.0458 +2024-12-28 15:31:07,374 - pyskl - INFO - Epoch [68][3300/3746] lr: 5.743e-02, eta: 2 days, 23:01:45, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5464, loss_cls: 4.0390, loss: 4.0390 +2024-12-28 15:32:32,694 - pyskl - INFO - Epoch [68][3400/3746] lr: 5.740e-02, eta: 2 days, 23:00:25, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5675, loss_cls: 3.9950, loss: 3.9950 +2024-12-28 15:33:58,040 - pyskl - INFO - Epoch [68][3500/3746] lr: 5.737e-02, eta: 2 days, 22:59:04, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5611, loss_cls: 3.9948, loss: 3.9948 +2024-12-28 15:35:23,593 - pyskl - INFO - Epoch [68][3600/3746] lr: 5.734e-02, eta: 2 days, 22:57:44, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5602, loss_cls: 4.0091, loss: 4.0091 +2024-12-28 15:36:48,543 - pyskl - INFO - Epoch [68][3700/3746] lr: 5.732e-02, eta: 2 days, 22:56:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5520, loss_cls: 4.0321, loss: 4.0321 +2024-12-28 15:37:29,778 - pyskl - INFO - Saving checkpoint at 68 epochs +2024-12-28 15:39:28,615 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 15:39:29,451 - pyskl - INFO - +top1_acc 0.2401 +top5_acc 0.4871 +2024-12-28 15:39:29,452 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 15:39:29,509 - pyskl - INFO - +mean_acc 0.2399 +2024-12-28 15:39:29,526 - pyskl - INFO - Epoch(val) [68][309] top1_acc: 0.2401, top5_acc: 0.4871, mean_class_accuracy: 0.2399 +2024-12-28 15:43:52,822 - pyskl - INFO - Epoch [69][100/3746] lr: 5.728e-02, eta: 2 days, 22:57:13, time: 2.633, data_time: 1.602, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5725, loss_cls: 3.9451, loss: 3.9451 +2024-12-28 15:45:18,277 - pyskl - INFO - Epoch [69][200/3746] lr: 5.725e-02, eta: 2 days, 22:55:52, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5672, loss_cls: 3.9509, loss: 3.9509 +2024-12-28 15:46:44,052 - pyskl - INFO - Epoch [69][300/3746] lr: 5.722e-02, eta: 2 days, 22:54:32, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5587, loss_cls: 3.9765, loss: 3.9765 +2024-12-28 15:48:09,963 - pyskl - INFO - Epoch [69][400/3746] lr: 5.719e-02, eta: 2 days, 22:53:12, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5658, loss_cls: 3.9747, loss: 3.9747 +2024-12-28 15:49:35,659 - pyskl - INFO - Epoch [69][500/3746] lr: 5.717e-02, eta: 2 days, 22:51:52, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5619, loss_cls: 3.9492, loss: 3.9492 +2024-12-28 15:51:01,168 - pyskl - INFO - Epoch [69][600/3746] lr: 5.714e-02, eta: 2 days, 22:50:32, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5633, loss_cls: 3.9410, loss: 3.9410 +2024-12-28 15:52:26,871 - pyskl - INFO - Epoch [69][700/3746] lr: 5.711e-02, eta: 2 days, 22:49:11, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5587, loss_cls: 3.9821, loss: 3.9821 +2024-12-28 15:53:52,485 - pyskl - INFO - Epoch [69][800/3746] lr: 5.708e-02, eta: 2 days, 22:47:51, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5653, loss_cls: 3.9770, loss: 3.9770 +2024-12-28 15:55:18,002 - pyskl - INFO - Epoch [69][900/3746] lr: 5.706e-02, eta: 2 days, 22:46:31, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5556, loss_cls: 3.9905, loss: 3.9905 +2024-12-28 15:56:43,769 - pyskl - INFO - Epoch [69][1000/3746] lr: 5.703e-02, eta: 2 days, 22:45:11, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5670, loss_cls: 3.9360, loss: 3.9360 +2024-12-28 15:58:09,568 - pyskl - INFO - Epoch [69][1100/3746] lr: 5.700e-02, eta: 2 days, 22:43:51, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5573, loss_cls: 3.9722, loss: 3.9722 +2024-12-28 15:59:35,406 - pyskl - INFO - Epoch [69][1200/3746] lr: 5.697e-02, eta: 2 days, 22:42:30, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5597, loss_cls: 4.0103, loss: 4.0103 +2024-12-28 16:01:00,586 - pyskl - INFO - Epoch [69][1300/3746] lr: 5.694e-02, eta: 2 days, 22:41:10, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5737, loss_cls: 3.9094, loss: 3.9094 +2024-12-28 16:02:26,602 - pyskl - INFO - Epoch [69][1400/3746] lr: 5.692e-02, eta: 2 days, 22:39:50, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5644, loss_cls: 3.9398, loss: 3.9398 +2024-12-28 16:03:52,006 - pyskl - INFO - Epoch [69][1500/3746] lr: 5.689e-02, eta: 2 days, 22:38:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5558, loss_cls: 3.9962, loss: 3.9962 +2024-12-28 16:05:17,188 - pyskl - INFO - Epoch [69][1600/3746] lr: 5.686e-02, eta: 2 days, 22:37:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5587, loss_cls: 3.9676, loss: 3.9676 +2024-12-28 16:06:42,517 - pyskl - INFO - Epoch [69][1700/3746] lr: 5.683e-02, eta: 2 days, 22:35:48, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5572, loss_cls: 4.0067, loss: 4.0067 +2024-12-28 16:08:07,703 - pyskl - INFO - Epoch [69][1800/3746] lr: 5.681e-02, eta: 2 days, 22:34:27, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5548, loss_cls: 3.9976, loss: 3.9976 +2024-12-28 16:09:32,349 - pyskl - INFO - Epoch [69][1900/3746] lr: 5.678e-02, eta: 2 days, 22:33:05, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5498, loss_cls: 4.0341, loss: 4.0341 +2024-12-28 16:10:57,260 - pyskl - INFO - Epoch [69][2000/3746] lr: 5.675e-02, eta: 2 days, 22:31:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5573, loss_cls: 4.0050, loss: 4.0050 +2024-12-28 16:12:22,458 - pyskl - INFO - Epoch [69][2100/3746] lr: 5.672e-02, eta: 2 days, 22:30:23, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5611, loss_cls: 3.9819, loss: 3.9819 +2024-12-28 16:13:47,649 - pyskl - INFO - Epoch [69][2200/3746] lr: 5.670e-02, eta: 2 days, 22:29:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5670, loss_cls: 3.9887, loss: 3.9887 +2024-12-28 16:15:13,139 - pyskl - INFO - Epoch [69][2300/3746] lr: 5.667e-02, eta: 2 days, 22:27:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5616, loss_cls: 3.9971, loss: 3.9971 +2024-12-28 16:16:39,543 - pyskl - INFO - Epoch [69][2400/3746] lr: 5.664e-02, eta: 2 days, 22:26:23, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5598, loss_cls: 3.9792, loss: 3.9792 +2024-12-28 16:18:05,132 - pyskl - INFO - Epoch [69][2500/3746] lr: 5.661e-02, eta: 2 days, 22:25:02, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5552, loss_cls: 4.0036, loss: 4.0036 +2024-12-28 16:19:30,069 - pyskl - INFO - Epoch [69][2600/3746] lr: 5.658e-02, eta: 2 days, 22:23:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5592, loss_cls: 4.0196, loss: 4.0196 +2024-12-28 16:20:54,765 - pyskl - INFO - Epoch [69][2700/3746] lr: 5.656e-02, eta: 2 days, 22:22:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5541, loss_cls: 4.0373, loss: 4.0373 +2024-12-28 16:22:19,845 - pyskl - INFO - Epoch [69][2800/3746] lr: 5.653e-02, eta: 2 days, 22:20:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5664, loss_cls: 3.9570, loss: 3.9570 +2024-12-28 16:23:44,675 - pyskl - INFO - Epoch [69][2900/3746] lr: 5.650e-02, eta: 2 days, 22:19:37, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5498, loss_cls: 4.0258, loss: 4.0258 +2024-12-28 16:25:09,671 - pyskl - INFO - Epoch [69][3000/3746] lr: 5.647e-02, eta: 2 days, 22:18:16, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5634, loss_cls: 3.9845, loss: 3.9845 +2024-12-28 16:26:34,500 - pyskl - INFO - Epoch [69][3100/3746] lr: 5.645e-02, eta: 2 days, 22:16:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5625, loss_cls: 3.9596, loss: 3.9596 +2024-12-28 16:28:00,137 - pyskl - INFO - Epoch [69][3200/3746] lr: 5.642e-02, eta: 2 days, 22:15:35, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5634, loss_cls: 3.9811, loss: 3.9811 +2024-12-28 16:29:24,968 - pyskl - INFO - Epoch [69][3300/3746] lr: 5.639e-02, eta: 2 days, 22:14:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5609, loss_cls: 3.9784, loss: 3.9784 +2024-12-28 16:30:50,454 - pyskl - INFO - Epoch [69][3400/3746] lr: 5.636e-02, eta: 2 days, 22:12:53, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5509, loss_cls: 4.0135, loss: 4.0135 +2024-12-28 16:32:15,452 - pyskl - INFO - Epoch [69][3500/3746] lr: 5.634e-02, eta: 2 days, 22:11:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5628, loss_cls: 3.9613, loss: 3.9613 +2024-12-28 16:33:40,599 - pyskl - INFO - Epoch [69][3600/3746] lr: 5.631e-02, eta: 2 days, 22:10:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5613, loss_cls: 3.9633, loss: 3.9633 +2024-12-28 16:35:05,384 - pyskl - INFO - Epoch [69][3700/3746] lr: 5.628e-02, eta: 2 days, 22:08:49, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5661, loss_cls: 3.9373, loss: 3.9373 +2024-12-28 16:35:46,288 - pyskl - INFO - Saving checkpoint at 69 epochs +2024-12-28 16:37:48,290 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 16:37:49,182 - pyskl - INFO - +top1_acc 0.2408 +top5_acc 0.4779 +2024-12-28 16:37:49,182 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 16:37:49,243 - pyskl - INFO - +mean_acc 0.2407 +2024-12-28 16:37:49,260 - pyskl - INFO - Epoch(val) [69][309] top1_acc: 0.2408, top5_acc: 0.4779, mean_class_accuracy: 0.2407 +2024-12-28 16:42:12,661 - pyskl - INFO - Epoch [70][100/3746] lr: 5.624e-02, eta: 2 days, 22:09:34, time: 2.634, data_time: 1.596, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5736, loss_cls: 3.9194, loss: 3.9194 +2024-12-28 16:43:38,079 - pyskl - INFO - Epoch [70][200/3746] lr: 5.621e-02, eta: 2 days, 22:08:14, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5603, loss_cls: 3.9420, loss: 3.9420 +2024-12-28 16:45:03,273 - pyskl - INFO - Epoch [70][300/3746] lr: 5.618e-02, eta: 2 days, 22:06:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5794, loss_cls: 3.9110, loss: 3.9110 +2024-12-28 16:46:28,850 - pyskl - INFO - Epoch [70][400/3746] lr: 5.616e-02, eta: 2 days, 22:05:32, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5658, loss_cls: 3.9465, loss: 3.9465 +2024-12-28 16:47:54,281 - pyskl - INFO - Epoch [70][500/3746] lr: 5.613e-02, eta: 2 days, 22:04:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5609, loss_cls: 3.9593, loss: 3.9593 +2024-12-28 16:49:19,211 - pyskl - INFO - Epoch [70][600/3746] lr: 5.610e-02, eta: 2 days, 22:02:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5677, loss_cls: 3.9237, loss: 3.9237 +2024-12-28 16:50:44,364 - pyskl - INFO - Epoch [70][700/3746] lr: 5.607e-02, eta: 2 days, 22:01:29, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3066, top5_acc: 0.5623, loss_cls: 3.9719, loss: 3.9719 +2024-12-28 16:52:09,373 - pyskl - INFO - Epoch [70][800/3746] lr: 5.605e-02, eta: 2 days, 22:00:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5592, loss_cls: 3.9683, loss: 3.9683 +2024-12-28 16:53:34,191 - pyskl - INFO - Epoch [70][900/3746] lr: 5.602e-02, eta: 2 days, 21:58:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5680, loss_cls: 3.9342, loss: 3.9342 +2024-12-28 16:54:59,531 - pyskl - INFO - Epoch [70][1000/3746] lr: 5.599e-02, eta: 2 days, 21:57:25, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5686, loss_cls: 3.9710, loss: 3.9710 +2024-12-28 16:56:24,580 - pyskl - INFO - Epoch [70][1100/3746] lr: 5.596e-02, eta: 2 days, 21:56:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5608, loss_cls: 3.9633, loss: 3.9633 +2024-12-28 16:57:50,195 - pyskl - INFO - Epoch [70][1200/3746] lr: 5.593e-02, eta: 2 days, 21:54:44, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5714, loss_cls: 3.9439, loss: 3.9439 +2024-12-28 16:59:15,369 - pyskl - INFO - Epoch [70][1300/3746] lr: 5.591e-02, eta: 2 days, 21:53:23, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5559, loss_cls: 4.0340, loss: 4.0340 +2024-12-28 17:00:40,241 - pyskl - INFO - Epoch [70][1400/3746] lr: 5.588e-02, eta: 2 days, 21:52:01, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5605, loss_cls: 3.9775, loss: 3.9775 +2024-12-28 17:02:05,113 - pyskl - INFO - Epoch [70][1500/3746] lr: 5.585e-02, eta: 2 days, 21:50:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5598, loss_cls: 4.0130, loss: 4.0130 +2024-12-28 17:03:30,549 - pyskl - INFO - Epoch [70][1600/3746] lr: 5.582e-02, eta: 2 days, 21:49:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5536, loss_cls: 3.9990, loss: 3.9990 +2024-12-28 17:04:55,519 - pyskl - INFO - Epoch [70][1700/3746] lr: 5.580e-02, eta: 2 days, 21:47:58, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5647, loss_cls: 3.9894, loss: 3.9894 +2024-12-28 17:06:20,907 - pyskl - INFO - Epoch [70][1800/3746] lr: 5.577e-02, eta: 2 days, 21:46:37, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5614, loss_cls: 3.9978, loss: 3.9978 +2024-12-28 17:07:45,820 - pyskl - INFO - Epoch [70][1900/3746] lr: 5.574e-02, eta: 2 days, 21:45:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5697, loss_cls: 3.9499, loss: 3.9499 +2024-12-28 17:09:11,738 - pyskl - INFO - Epoch [70][2000/3746] lr: 5.571e-02, eta: 2 days, 21:43:55, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5564, loss_cls: 3.9938, loss: 3.9938 +2024-12-28 17:10:37,368 - pyskl - INFO - Epoch [70][2100/3746] lr: 5.568e-02, eta: 2 days, 21:42:35, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5677, loss_cls: 3.9454, loss: 3.9454 +2024-12-28 17:12:02,478 - pyskl - INFO - Epoch [70][2200/3746] lr: 5.566e-02, eta: 2 days, 21:41:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5748, loss_cls: 3.9139, loss: 3.9139 +2024-12-28 17:13:27,560 - pyskl - INFO - Epoch [70][2300/3746] lr: 5.563e-02, eta: 2 days, 21:39:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5511, loss_cls: 4.0054, loss: 4.0054 +2024-12-28 17:14:52,348 - pyskl - INFO - Epoch [70][2400/3746] lr: 5.560e-02, eta: 2 days, 21:38:31, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5602, loss_cls: 3.9903, loss: 3.9903 +2024-12-28 17:16:17,784 - pyskl - INFO - Epoch [70][2500/3746] lr: 5.557e-02, eta: 2 days, 21:37:10, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5491, loss_cls: 4.0423, loss: 4.0423 +2024-12-28 17:17:43,209 - pyskl - INFO - Epoch [70][2600/3746] lr: 5.555e-02, eta: 2 days, 21:35:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5673, loss_cls: 3.9592, loss: 3.9592 +2024-12-28 17:19:08,905 - pyskl - INFO - Epoch [70][2700/3746] lr: 5.552e-02, eta: 2 days, 21:34:28, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5575, loss_cls: 4.0016, loss: 4.0016 +2024-12-28 17:20:34,581 - pyskl - INFO - Epoch [70][2800/3746] lr: 5.549e-02, eta: 2 days, 21:33:08, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5511, loss_cls: 4.0086, loss: 4.0086 +2024-12-28 17:22:00,604 - pyskl - INFO - Epoch [70][2900/3746] lr: 5.546e-02, eta: 2 days, 21:31:48, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5598, loss_cls: 4.0121, loss: 4.0121 +2024-12-28 17:23:26,614 - pyskl - INFO - Epoch [70][3000/3746] lr: 5.543e-02, eta: 2 days, 21:30:28, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5655, loss_cls: 3.9535, loss: 3.9535 +2024-12-28 17:24:52,524 - pyskl - INFO - Epoch [70][3100/3746] lr: 5.541e-02, eta: 2 days, 21:29:07, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5520, loss_cls: 4.0291, loss: 4.0291 +2024-12-28 17:26:18,362 - pyskl - INFO - Epoch [70][3200/3746] lr: 5.538e-02, eta: 2 days, 21:27:47, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5683, loss_cls: 3.9422, loss: 3.9422 +2024-12-28 17:27:43,758 - pyskl - INFO - Epoch [70][3300/3746] lr: 5.535e-02, eta: 2 days, 21:26:26, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5567, loss_cls: 3.9799, loss: 3.9799 +2024-12-28 17:29:09,818 - pyskl - INFO - Epoch [70][3400/3746] lr: 5.532e-02, eta: 2 days, 21:25:06, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5617, loss_cls: 3.9742, loss: 3.9742 +2024-12-28 17:30:36,435 - pyskl - INFO - Epoch [70][3500/3746] lr: 5.530e-02, eta: 2 days, 21:23:46, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5642, loss_cls: 3.9380, loss: 3.9380 +2024-12-28 17:32:03,651 - pyskl - INFO - Epoch [70][3600/3746] lr: 5.527e-02, eta: 2 days, 21:22:28, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5598, loss_cls: 3.9796, loss: 3.9796 +2024-12-28 17:33:29,829 - pyskl - INFO - Epoch [70][3700/3746] lr: 5.524e-02, eta: 2 days, 21:21:08, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5627, loss_cls: 3.9741, loss: 3.9741 +2024-12-28 17:34:11,469 - pyskl - INFO - Saving checkpoint at 70 epochs +2024-12-28 17:36:11,070 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 17:36:11,796 - pyskl - INFO - +top1_acc 0.2388 +top5_acc 0.4757 +2024-12-28 17:36:11,796 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 17:36:11,851 - pyskl - INFO - +mean_acc 0.2385 +2024-12-28 17:36:11,864 - pyskl - INFO - Epoch(val) [70][309] top1_acc: 0.2388, top5_acc: 0.4757, mean_class_accuracy: 0.2385 +2024-12-28 17:40:32,866 - pyskl - INFO - Epoch [71][100/3746] lr: 5.520e-02, eta: 2 days, 21:21:45, time: 2.610, data_time: 1.557, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5728, loss_cls: 3.9144, loss: 3.9144 +2024-12-28 17:41:58,309 - pyskl - INFO - Epoch [71][200/3746] lr: 5.517e-02, eta: 2 days, 21:20:24, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5634, loss_cls: 3.9485, loss: 3.9485 +2024-12-28 17:43:23,878 - pyskl - INFO - Epoch [71][300/3746] lr: 5.514e-02, eta: 2 days, 21:19:03, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5719, loss_cls: 3.9381, loss: 3.9381 +2024-12-28 17:44:49,535 - pyskl - INFO - Epoch [71][400/3746] lr: 5.512e-02, eta: 2 days, 21:17:43, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5669, loss_cls: 3.9885, loss: 3.9885 +2024-12-28 17:46:15,378 - pyskl - INFO - Epoch [71][500/3746] lr: 5.509e-02, eta: 2 days, 21:16:22, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5614, loss_cls: 3.9568, loss: 3.9568 +2024-12-28 17:47:41,448 - pyskl - INFO - Epoch [71][600/3746] lr: 5.506e-02, eta: 2 days, 21:15:02, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5650, loss_cls: 3.9239, loss: 3.9239 +2024-12-28 17:49:07,668 - pyskl - INFO - Epoch [71][700/3746] lr: 5.503e-02, eta: 2 days, 21:13:42, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5658, loss_cls: 3.9741, loss: 3.9741 +2024-12-28 17:50:33,510 - pyskl - INFO - Epoch [71][800/3746] lr: 5.500e-02, eta: 2 days, 21:12:21, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5677, loss_cls: 3.9182, loss: 3.9182 +2024-12-28 17:51:58,872 - pyskl - INFO - Epoch [71][900/3746] lr: 5.498e-02, eta: 2 days, 21:11:00, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5580, loss_cls: 4.0034, loss: 4.0034 +2024-12-28 17:53:24,433 - pyskl - INFO - Epoch [71][1000/3746] lr: 5.495e-02, eta: 2 days, 21:09:39, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5569, loss_cls: 3.9908, loss: 3.9908 +2024-12-28 17:54:50,461 - pyskl - INFO - Epoch [71][1100/3746] lr: 5.492e-02, eta: 2 days, 21:08:19, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5711, loss_cls: 3.9301, loss: 3.9301 +2024-12-28 17:56:16,254 - pyskl - INFO - Epoch [71][1200/3746] lr: 5.489e-02, eta: 2 days, 21:06:58, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5656, loss_cls: 3.9349, loss: 3.9349 +2024-12-28 17:57:41,932 - pyskl - INFO - Epoch [71][1300/3746] lr: 5.487e-02, eta: 2 days, 21:05:38, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3116, top5_acc: 0.5652, loss_cls: 3.9437, loss: 3.9437 +2024-12-28 17:59:07,709 - pyskl - INFO - Epoch [71][1400/3746] lr: 5.484e-02, eta: 2 days, 21:04:17, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5639, loss_cls: 3.9450, loss: 3.9450 +2024-12-28 18:00:32,423 - pyskl - INFO - Epoch [71][1500/3746] lr: 5.481e-02, eta: 2 days, 21:02:55, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5609, loss_cls: 3.9787, loss: 3.9787 +2024-12-28 18:01:57,582 - pyskl - INFO - Epoch [71][1600/3746] lr: 5.478e-02, eta: 2 days, 21:01:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5575, loss_cls: 4.0040, loss: 4.0040 +2024-12-28 18:03:22,640 - pyskl - INFO - Epoch [71][1700/3746] lr: 5.475e-02, eta: 2 days, 21:00:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5702, loss_cls: 3.9689, loss: 3.9689 +2024-12-28 18:04:47,668 - pyskl - INFO - Epoch [71][1800/3746] lr: 5.473e-02, eta: 2 days, 20:58:51, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5687, loss_cls: 3.9269, loss: 3.9269 +2024-12-28 18:06:13,088 - pyskl - INFO - Epoch [71][1900/3746] lr: 5.470e-02, eta: 2 days, 20:57:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5736, loss_cls: 3.9170, loss: 3.9170 +2024-12-28 18:07:38,551 - pyskl - INFO - Epoch [71][2000/3746] lr: 5.467e-02, eta: 2 days, 20:56:09, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5725, loss_cls: 3.9209, loss: 3.9209 +2024-12-28 18:09:04,295 - pyskl - INFO - Epoch [71][2100/3746] lr: 5.464e-02, eta: 2 days, 20:54:48, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5625, loss_cls: 4.0029, loss: 4.0029 +2024-12-28 18:10:29,362 - pyskl - INFO - Epoch [71][2200/3746] lr: 5.461e-02, eta: 2 days, 20:53:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5498, loss_cls: 4.0276, loss: 4.0276 +2024-12-28 18:11:54,579 - pyskl - INFO - Epoch [71][2300/3746] lr: 5.459e-02, eta: 2 days, 20:52:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5559, loss_cls: 3.9984, loss: 3.9984 +2024-12-28 18:13:19,524 - pyskl - INFO - Epoch [71][2400/3746] lr: 5.456e-02, eta: 2 days, 20:50:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5611, loss_cls: 3.9692, loss: 3.9692 +2024-12-28 18:14:45,179 - pyskl - INFO - Epoch [71][2500/3746] lr: 5.453e-02, eta: 2 days, 20:49:23, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5700, loss_cls: 3.9458, loss: 3.9458 +2024-12-28 18:16:10,132 - pyskl - INFO - Epoch [71][2600/3746] lr: 5.450e-02, eta: 2 days, 20:48:01, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5778, loss_cls: 3.9210, loss: 3.9210 +2024-12-28 18:17:35,228 - pyskl - INFO - Epoch [71][2700/3746] lr: 5.448e-02, eta: 2 days, 20:46:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5633, loss_cls: 3.9479, loss: 3.9479 +2024-12-28 18:19:00,592 - pyskl - INFO - Epoch [71][2800/3746] lr: 5.445e-02, eta: 2 days, 20:45:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5591, loss_cls: 3.9830, loss: 3.9830 +2024-12-28 18:20:25,546 - pyskl - INFO - Epoch [71][2900/3746] lr: 5.442e-02, eta: 2 days, 20:43:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5642, loss_cls: 3.9624, loss: 3.9624 +2024-12-28 18:21:51,103 - pyskl - INFO - Epoch [71][3000/3746] lr: 5.439e-02, eta: 2 days, 20:42:36, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5736, loss_cls: 3.9338, loss: 3.9338 +2024-12-28 18:23:16,121 - pyskl - INFO - Epoch [71][3100/3746] lr: 5.436e-02, eta: 2 days, 20:41:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5653, loss_cls: 3.9682, loss: 3.9682 +2024-12-28 18:24:41,384 - pyskl - INFO - Epoch [71][3200/3746] lr: 5.434e-02, eta: 2 days, 20:39:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5602, loss_cls: 3.9528, loss: 3.9528 +2024-12-28 18:26:06,683 - pyskl - INFO - Epoch [71][3300/3746] lr: 5.431e-02, eta: 2 days, 20:38:32, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5669, loss_cls: 3.9612, loss: 3.9612 +2024-12-28 18:27:33,027 - pyskl - INFO - Epoch [71][3400/3746] lr: 5.428e-02, eta: 2 days, 20:37:12, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5669, loss_cls: 3.9730, loss: 3.9730 +2024-12-28 18:28:58,423 - pyskl - INFO - Epoch [71][3500/3746] lr: 5.425e-02, eta: 2 days, 20:35:51, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5645, loss_cls: 3.9578, loss: 3.9578 +2024-12-28 18:30:23,748 - pyskl - INFO - Epoch [71][3600/3746] lr: 5.422e-02, eta: 2 days, 20:34:30, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5570, loss_cls: 3.9816, loss: 3.9816 +2024-12-28 18:31:49,019 - pyskl - INFO - Epoch [71][3700/3746] lr: 5.420e-02, eta: 2 days, 20:33:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3020, top5_acc: 0.5716, loss_cls: 3.9775, loss: 3.9775 +2024-12-28 18:32:30,971 - pyskl - INFO - Saving checkpoint at 71 epochs +2024-12-28 18:34:30,001 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 18:34:30,714 - pyskl - INFO - +top1_acc 0.2537 +top5_acc 0.4938 +2024-12-28 18:34:30,714 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 18:34:30,764 - pyskl - INFO - +mean_acc 0.2534 +2024-12-28 18:34:30,788 - pyskl - INFO - Epoch(val) [71][309] top1_acc: 0.2537, top5_acc: 0.4938, mean_class_accuracy: 0.2534 +2024-12-28 18:38:55,323 - pyskl - INFO - Epoch [72][100/3746] lr: 5.416e-02, eta: 2 days, 20:33:45, time: 2.645, data_time: 1.591, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5842, loss_cls: 3.8657, loss: 3.8657 +2024-12-28 18:40:21,301 - pyskl - INFO - Epoch [72][200/3746] lr: 5.413e-02, eta: 2 days, 20:32:25, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5622, loss_cls: 3.9598, loss: 3.9598 +2024-12-28 18:41:47,528 - pyskl - INFO - Epoch [72][300/3746] lr: 5.410e-02, eta: 2 days, 20:31:05, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5647, loss_cls: 3.9784, loss: 3.9784 +2024-12-28 18:43:13,934 - pyskl - INFO - Epoch [72][400/3746] lr: 5.407e-02, eta: 2 days, 20:29:44, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5687, loss_cls: 3.9232, loss: 3.9232 +2024-12-28 18:44:39,959 - pyskl - INFO - Epoch [72][500/3746] lr: 5.404e-02, eta: 2 days, 20:28:24, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5611, loss_cls: 3.9686, loss: 3.9686 +2024-12-28 18:46:05,697 - pyskl - INFO - Epoch [72][600/3746] lr: 5.402e-02, eta: 2 days, 20:27:03, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5648, loss_cls: 3.9352, loss: 3.9352 +2024-12-28 18:47:32,212 - pyskl - INFO - Epoch [72][700/3746] lr: 5.399e-02, eta: 2 days, 20:25:43, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5689, loss_cls: 3.9162, loss: 3.9162 +2024-12-28 18:48:58,353 - pyskl - INFO - Epoch [72][800/3746] lr: 5.396e-02, eta: 2 days, 20:24:22, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.2995, top5_acc: 0.5625, loss_cls: 4.0152, loss: 4.0152 +2024-12-28 18:50:24,636 - pyskl - INFO - Epoch [72][900/3746] lr: 5.393e-02, eta: 2 days, 20:23:02, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5673, loss_cls: 3.9435, loss: 3.9435 +2024-12-28 18:51:51,055 - pyskl - INFO - Epoch [72][1000/3746] lr: 5.391e-02, eta: 2 days, 20:21:42, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5644, loss_cls: 3.9513, loss: 3.9513 +2024-12-28 18:53:17,624 - pyskl - INFO - Epoch [72][1100/3746] lr: 5.388e-02, eta: 2 days, 20:20:22, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5730, loss_cls: 3.9428, loss: 3.9428 +2024-12-28 18:54:43,109 - pyskl - INFO - Epoch [72][1200/3746] lr: 5.385e-02, eta: 2 days, 20:19:01, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5673, loss_cls: 3.9440, loss: 3.9440 +2024-12-28 18:56:08,761 - pyskl - INFO - Epoch [72][1300/3746] lr: 5.382e-02, eta: 2 days, 20:17:40, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5636, loss_cls: 3.9658, loss: 3.9658 +2024-12-28 18:57:33,414 - pyskl - INFO - Epoch [72][1400/3746] lr: 5.379e-02, eta: 2 days, 20:16:18, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5655, loss_cls: 3.9285, loss: 3.9285 +2024-12-28 18:58:59,337 - pyskl - INFO - Epoch [72][1500/3746] lr: 5.377e-02, eta: 2 days, 20:14:57, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5625, loss_cls: 3.9697, loss: 3.9697 +2024-12-28 19:00:25,528 - pyskl - INFO - Epoch [72][1600/3746] lr: 5.374e-02, eta: 2 days, 20:13:37, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5650, loss_cls: 3.9348, loss: 3.9348 +2024-12-28 19:01:50,754 - pyskl - INFO - Epoch [72][1700/3746] lr: 5.371e-02, eta: 2 days, 20:12:15, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5797, loss_cls: 3.8324, loss: 3.8324 +2024-12-28 19:03:16,778 - pyskl - INFO - Epoch [72][1800/3746] lr: 5.368e-02, eta: 2 days, 20:10:54, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5616, loss_cls: 3.9727, loss: 3.9727 +2024-12-28 19:04:42,624 - pyskl - INFO - Epoch [72][1900/3746] lr: 5.365e-02, eta: 2 days, 20:09:34, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5752, loss_cls: 3.9262, loss: 3.9262 +2024-12-28 19:06:08,069 - pyskl - INFO - Epoch [72][2000/3746] lr: 5.363e-02, eta: 2 days, 20:08:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5798, loss_cls: 3.8703, loss: 3.8703 +2024-12-28 19:07:33,174 - pyskl - INFO - Epoch [72][2100/3746] lr: 5.360e-02, eta: 2 days, 20:06:51, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5598, loss_cls: 3.9922, loss: 3.9922 +2024-12-28 19:08:58,079 - pyskl - INFO - Epoch [72][2200/3746] lr: 5.357e-02, eta: 2 days, 20:05:29, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3036, top5_acc: 0.5577, loss_cls: 4.0017, loss: 4.0017 +2024-12-28 19:10:23,418 - pyskl - INFO - Epoch [72][2300/3746] lr: 5.354e-02, eta: 2 days, 20:04:07, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5695, loss_cls: 3.9335, loss: 3.9335 +2024-12-28 19:11:48,237 - pyskl - INFO - Epoch [72][2400/3746] lr: 5.352e-02, eta: 2 days, 20:02:45, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5603, loss_cls: 3.9777, loss: 3.9777 +2024-12-28 19:13:13,816 - pyskl - INFO - Epoch [72][2500/3746] lr: 5.349e-02, eta: 2 days, 20:01:24, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5692, loss_cls: 3.9367, loss: 3.9367 +2024-12-28 19:14:39,160 - pyskl - INFO - Epoch [72][2600/3746] lr: 5.346e-02, eta: 2 days, 20:00:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5664, loss_cls: 3.9631, loss: 3.9631 +2024-12-28 19:16:04,565 - pyskl - INFO - Epoch [72][2700/3746] lr: 5.343e-02, eta: 2 days, 19:58:42, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5648, loss_cls: 3.9643, loss: 3.9643 +2024-12-28 19:17:29,843 - pyskl - INFO - Epoch [72][2800/3746] lr: 5.340e-02, eta: 2 days, 19:57:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5587, loss_cls: 3.9971, loss: 3.9971 +2024-12-28 19:18:54,887 - pyskl - INFO - Epoch [72][2900/3746] lr: 5.338e-02, eta: 2 days, 19:55:58, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5614, loss_cls: 3.9776, loss: 3.9776 +2024-12-28 19:20:20,099 - pyskl - INFO - Epoch [72][3000/3746] lr: 5.335e-02, eta: 2 days, 19:54:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5773, loss_cls: 3.9079, loss: 3.9079 +2024-12-28 19:21:45,380 - pyskl - INFO - Epoch [72][3100/3746] lr: 5.332e-02, eta: 2 days, 19:53:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5541, loss_cls: 3.9961, loss: 3.9961 +2024-12-28 19:23:10,701 - pyskl - INFO - Epoch [72][3200/3746] lr: 5.329e-02, eta: 2 days, 19:51:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5716, loss_cls: 3.9506, loss: 3.9506 +2024-12-28 19:24:35,720 - pyskl - INFO - Epoch [72][3300/3746] lr: 5.326e-02, eta: 2 days, 19:50:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5673, loss_cls: 3.9472, loss: 3.9472 +2024-12-28 19:26:01,040 - pyskl - INFO - Epoch [72][3400/3746] lr: 5.324e-02, eta: 2 days, 19:49:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3056, top5_acc: 0.5672, loss_cls: 3.9519, loss: 3.9519 +2024-12-28 19:27:26,256 - pyskl - INFO - Epoch [72][3500/3746] lr: 5.321e-02, eta: 2 days, 19:47:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5552, loss_cls: 3.9802, loss: 3.9802 +2024-12-28 19:28:51,703 - pyskl - INFO - Epoch [72][3600/3746] lr: 5.318e-02, eta: 2 days, 19:46:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5559, loss_cls: 3.9901, loss: 3.9901 +2024-12-28 19:30:16,792 - pyskl - INFO - Epoch [72][3700/3746] lr: 5.315e-02, eta: 2 days, 19:45:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5684, loss_cls: 3.9389, loss: 3.9389 +2024-12-28 19:30:57,856 - pyskl - INFO - Saving checkpoint at 72 epochs +2024-12-28 19:32:56,849 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 19:32:57,538 - pyskl - INFO - +top1_acc 0.2459 +top5_acc 0.4943 +2024-12-28 19:32:57,538 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 19:32:57,585 - pyskl - INFO - +mean_acc 0.2458 +2024-12-28 19:32:57,601 - pyskl - INFO - Epoch(val) [72][309] top1_acc: 0.2459, top5_acc: 0.4943, mean_class_accuracy: 0.2458 +2024-12-28 19:37:14,435 - pyskl - INFO - Epoch [73][100/3746] lr: 5.311e-02, eta: 2 days, 19:45:30, time: 2.568, data_time: 1.534, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5777, loss_cls: 3.9062, loss: 3.9062 +2024-12-28 19:38:40,228 - pyskl - INFO - Epoch [73][200/3746] lr: 5.308e-02, eta: 2 days, 19:44:09, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5697, loss_cls: 3.9307, loss: 3.9307 +2024-12-28 19:40:06,042 - pyskl - INFO - Epoch [73][300/3746] lr: 5.306e-02, eta: 2 days, 19:42:48, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5742, loss_cls: 3.9048, loss: 3.9048 +2024-12-28 19:41:31,856 - pyskl - INFO - Epoch [73][400/3746] lr: 5.303e-02, eta: 2 days, 19:41:27, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5620, loss_cls: 3.9434, loss: 3.9434 +2024-12-28 19:42:58,040 - pyskl - INFO - Epoch [73][500/3746] lr: 5.300e-02, eta: 2 days, 19:40:07, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5687, loss_cls: 3.9613, loss: 3.9613 +2024-12-28 19:44:23,763 - pyskl - INFO - Epoch [73][600/3746] lr: 5.297e-02, eta: 2 days, 19:38:46, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5714, loss_cls: 3.9082, loss: 3.9082 +2024-12-28 19:45:49,371 - pyskl - INFO - Epoch [73][700/3746] lr: 5.294e-02, eta: 2 days, 19:37:24, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5627, loss_cls: 3.9580, loss: 3.9580 +2024-12-28 19:47:15,331 - pyskl - INFO - Epoch [73][800/3746] lr: 5.292e-02, eta: 2 days, 19:36:03, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5744, loss_cls: 3.9125, loss: 3.9125 +2024-12-28 19:48:41,082 - pyskl - INFO - Epoch [73][900/3746] lr: 5.289e-02, eta: 2 days, 19:34:42, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5575, loss_cls: 3.9743, loss: 3.9743 +2024-12-28 19:50:06,407 - pyskl - INFO - Epoch [73][1000/3746] lr: 5.286e-02, eta: 2 days, 19:33:21, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5797, loss_cls: 3.9005, loss: 3.9005 +2024-12-28 19:51:31,863 - pyskl - INFO - Epoch [73][1100/3746] lr: 5.283e-02, eta: 2 days, 19:31:59, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5742, loss_cls: 3.9239, loss: 3.9239 +2024-12-28 19:52:56,971 - pyskl - INFO - Epoch [73][1200/3746] lr: 5.280e-02, eta: 2 days, 19:30:37, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5666, loss_cls: 3.9520, loss: 3.9520 +2024-12-28 19:54:22,522 - pyskl - INFO - Epoch [73][1300/3746] lr: 5.278e-02, eta: 2 days, 19:29:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5737, loss_cls: 3.9313, loss: 3.9313 +2024-12-28 19:55:47,875 - pyskl - INFO - Epoch [73][1400/3746] lr: 5.275e-02, eta: 2 days, 19:27:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5766, loss_cls: 3.8880, loss: 3.8880 +2024-12-28 19:57:13,802 - pyskl - INFO - Epoch [73][1500/3746] lr: 5.272e-02, eta: 2 days, 19:26:34, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5594, loss_cls: 3.9816, loss: 3.9816 +2024-12-28 19:58:39,207 - pyskl - INFO - Epoch [73][1600/3746] lr: 5.269e-02, eta: 2 days, 19:25:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5608, loss_cls: 3.9871, loss: 3.9871 +2024-12-28 20:00:05,193 - pyskl - INFO - Epoch [73][1700/3746] lr: 5.267e-02, eta: 2 days, 19:23:51, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5769, loss_cls: 3.8953, loss: 3.8953 +2024-12-28 20:01:30,720 - pyskl - INFO - Epoch [73][1800/3746] lr: 5.264e-02, eta: 2 days, 19:22:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5705, loss_cls: 3.9333, loss: 3.9333 +2024-12-28 20:02:56,029 - pyskl - INFO - Epoch [73][1900/3746] lr: 5.261e-02, eta: 2 days, 19:21:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5667, loss_cls: 3.9634, loss: 3.9634 +2024-12-28 20:04:21,578 - pyskl - INFO - Epoch [73][2000/3746] lr: 5.258e-02, eta: 2 days, 19:19:47, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5652, loss_cls: 3.9556, loss: 3.9556 +2024-12-28 20:05:46,994 - pyskl - INFO - Epoch [73][2100/3746] lr: 5.255e-02, eta: 2 days, 19:18:25, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5553, loss_cls: 3.9714, loss: 3.9714 +2024-12-28 20:07:12,934 - pyskl - INFO - Epoch [73][2200/3746] lr: 5.253e-02, eta: 2 days, 19:17:04, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5613, loss_cls: 3.9544, loss: 3.9544 +2024-12-28 20:08:38,985 - pyskl - INFO - Epoch [73][2300/3746] lr: 5.250e-02, eta: 2 days, 19:15:43, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5736, loss_cls: 3.9396, loss: 3.9396 +2024-12-28 20:10:04,719 - pyskl - INFO - Epoch [73][2400/3746] lr: 5.247e-02, eta: 2 days, 19:14:22, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5569, loss_cls: 4.0056, loss: 4.0056 +2024-12-28 20:11:29,607 - pyskl - INFO - Epoch [73][2500/3746] lr: 5.244e-02, eta: 2 days, 19:13:00, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3033, top5_acc: 0.5659, loss_cls: 3.9621, loss: 3.9621 +2024-12-28 20:12:54,658 - pyskl - INFO - Epoch [73][2600/3746] lr: 5.241e-02, eta: 2 days, 19:11:38, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5639, loss_cls: 3.9670, loss: 3.9670 +2024-12-28 20:14:19,413 - pyskl - INFO - Epoch [73][2700/3746] lr: 5.239e-02, eta: 2 days, 19:10:16, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5673, loss_cls: 3.9509, loss: 3.9509 +2024-12-28 20:15:44,295 - pyskl - INFO - Epoch [73][2800/3746] lr: 5.236e-02, eta: 2 days, 19:08:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5614, loss_cls: 3.9380, loss: 3.9380 +2024-12-28 20:17:09,673 - pyskl - INFO - Epoch [73][2900/3746] lr: 5.233e-02, eta: 2 days, 19:07:32, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5695, loss_cls: 3.9534, loss: 3.9534 +2024-12-28 20:18:34,625 - pyskl - INFO - Epoch [73][3000/3746] lr: 5.230e-02, eta: 2 days, 19:06:10, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5689, loss_cls: 3.9293, loss: 3.9293 +2024-12-28 20:19:59,477 - pyskl - INFO - Epoch [73][3100/3746] lr: 5.227e-02, eta: 2 days, 19:04:48, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5708, loss_cls: 3.9390, loss: 3.9390 +2024-12-28 20:21:24,603 - pyskl - INFO - Epoch [73][3200/3746] lr: 5.225e-02, eta: 2 days, 19:03:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5734, loss_cls: 3.9155, loss: 3.9155 +2024-12-28 20:22:49,951 - pyskl - INFO - Epoch [73][3300/3746] lr: 5.222e-02, eta: 2 days, 19:02:05, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5753, loss_cls: 3.8891, loss: 3.8891 +2024-12-28 20:24:14,699 - pyskl - INFO - Epoch [73][3400/3746] lr: 5.219e-02, eta: 2 days, 19:00:42, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5606, loss_cls: 3.9916, loss: 3.9916 +2024-12-28 20:25:39,684 - pyskl - INFO - Epoch [73][3500/3746] lr: 5.216e-02, eta: 2 days, 18:59:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5725, loss_cls: 3.9295, loss: 3.9295 +2024-12-28 20:27:04,794 - pyskl - INFO - Epoch [73][3600/3746] lr: 5.213e-02, eta: 2 days, 18:57:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5486, loss_cls: 4.0121, loss: 4.0121 +2024-12-28 20:28:29,678 - pyskl - INFO - Epoch [73][3700/3746] lr: 5.211e-02, eta: 2 days, 18:56:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5828, loss_cls: 3.8714, loss: 3.8714 +2024-12-28 20:29:10,721 - pyskl - INFO - Saving checkpoint at 73 epochs +2024-12-28 20:31:10,205 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 20:31:11,076 - pyskl - INFO - +top1_acc 0.2327 +top5_acc 0.4753 +2024-12-28 20:31:11,076 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 20:31:11,134 - pyskl - INFO - +mean_acc 0.2324 +2024-12-28 20:31:11,151 - pyskl - INFO - Epoch(val) [73][309] top1_acc: 0.2327, top5_acc: 0.4753, mean_class_accuracy: 0.2324 +2024-12-28 20:35:18,240 - pyskl - INFO - Epoch [74][100/3746] lr: 5.207e-02, eta: 2 days, 18:56:46, time: 2.471, data_time: 1.443, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5819, loss_cls: 3.9111, loss: 3.9111 +2024-12-28 20:36:43,627 - pyskl - INFO - Epoch [74][200/3746] lr: 5.204e-02, eta: 2 days, 18:55:25, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5755, loss_cls: 3.9195, loss: 3.9195 +2024-12-28 20:38:08,835 - pyskl - INFO - Epoch [74][300/3746] lr: 5.201e-02, eta: 2 days, 18:54:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5642, loss_cls: 3.9248, loss: 3.9248 +2024-12-28 20:39:33,733 - pyskl - INFO - Epoch [74][400/3746] lr: 5.198e-02, eta: 2 days, 18:52:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5784, loss_cls: 3.8926, loss: 3.8926 +2024-12-28 20:40:58,976 - pyskl - INFO - Epoch [74][500/3746] lr: 5.195e-02, eta: 2 days, 18:51:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3067, top5_acc: 0.5745, loss_cls: 3.9149, loss: 3.9149 +2024-12-28 20:42:23,716 - pyskl - INFO - Epoch [74][600/3746] lr: 5.193e-02, eta: 2 days, 18:49:56, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5764, loss_cls: 3.9122, loss: 3.9122 +2024-12-28 20:43:48,577 - pyskl - INFO - Epoch [74][700/3746] lr: 5.190e-02, eta: 2 days, 18:48:34, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5748, loss_cls: 3.8890, loss: 3.8890 +2024-12-28 20:45:13,543 - pyskl - INFO - Epoch [74][800/3746] lr: 5.187e-02, eta: 2 days, 18:47:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5675, loss_cls: 3.9232, loss: 3.9232 +2024-12-28 20:46:38,751 - pyskl - INFO - Epoch [74][900/3746] lr: 5.184e-02, eta: 2 days, 18:45:50, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5725, loss_cls: 3.9046, loss: 3.9046 +2024-12-28 20:48:04,151 - pyskl - INFO - Epoch [74][1000/3746] lr: 5.181e-02, eta: 2 days, 18:44:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5775, loss_cls: 3.8880, loss: 3.8880 +2024-12-28 20:49:29,476 - pyskl - INFO - Epoch [74][1100/3746] lr: 5.179e-02, eta: 2 days, 18:43:07, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5741, loss_cls: 3.8983, loss: 3.8983 +2024-12-28 20:50:54,807 - pyskl - INFO - Epoch [74][1200/3746] lr: 5.176e-02, eta: 2 days, 18:41:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5689, loss_cls: 3.9652, loss: 3.9652 +2024-12-28 20:52:19,959 - pyskl - INFO - Epoch [74][1300/3746] lr: 5.173e-02, eta: 2 days, 18:40:23, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5652, loss_cls: 3.9129, loss: 3.9129 +2024-12-28 20:53:44,530 - pyskl - INFO - Epoch [74][1400/3746] lr: 5.170e-02, eta: 2 days, 18:39:00, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5689, loss_cls: 3.9309, loss: 3.9309 +2024-12-28 20:55:09,650 - pyskl - INFO - Epoch [74][1500/3746] lr: 5.168e-02, eta: 2 days, 18:37:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5656, loss_cls: 3.9576, loss: 3.9576 +2024-12-28 20:56:34,745 - pyskl - INFO - Epoch [74][1600/3746] lr: 5.165e-02, eta: 2 days, 18:36:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5639, loss_cls: 3.9507, loss: 3.9507 +2024-12-28 20:57:59,584 - pyskl - INFO - Epoch [74][1700/3746] lr: 5.162e-02, eta: 2 days, 18:34:54, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5764, loss_cls: 3.8647, loss: 3.8647 +2024-12-28 20:59:24,660 - pyskl - INFO - Epoch [74][1800/3746] lr: 5.159e-02, eta: 2 days, 18:33:32, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5727, loss_cls: 3.9102, loss: 3.9102 +2024-12-28 21:00:49,536 - pyskl - INFO - Epoch [74][1900/3746] lr: 5.156e-02, eta: 2 days, 18:32:10, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5702, loss_cls: 3.9393, loss: 3.9393 +2024-12-28 21:02:14,714 - pyskl - INFO - Epoch [74][2000/3746] lr: 5.154e-02, eta: 2 days, 18:30:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5658, loss_cls: 3.9471, loss: 3.9471 +2024-12-28 21:03:39,578 - pyskl - INFO - Epoch [74][2100/3746] lr: 5.151e-02, eta: 2 days, 18:29:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5686, loss_cls: 3.9478, loss: 3.9478 +2024-12-28 21:05:04,651 - pyskl - INFO - Epoch [74][2200/3746] lr: 5.148e-02, eta: 2 days, 18:28:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5763, loss_cls: 3.8805, loss: 3.8805 +2024-12-28 21:06:29,528 - pyskl - INFO - Epoch [74][2300/3746] lr: 5.145e-02, eta: 2 days, 18:26:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5573, loss_cls: 3.9760, loss: 3.9760 +2024-12-28 21:07:54,450 - pyskl - INFO - Epoch [74][2400/3746] lr: 5.142e-02, eta: 2 days, 18:25:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5709, loss_cls: 3.9103, loss: 3.9103 +2024-12-28 21:09:19,612 - pyskl - INFO - Epoch [74][2500/3746] lr: 5.140e-02, eta: 2 days, 18:23:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5659, loss_cls: 3.9577, loss: 3.9577 +2024-12-28 21:10:44,546 - pyskl - INFO - Epoch [74][2600/3746] lr: 5.137e-02, eta: 2 days, 18:22:35, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5617, loss_cls: 3.9640, loss: 3.9640 +2024-12-28 21:12:09,364 - pyskl - INFO - Epoch [74][2700/3746] lr: 5.134e-02, eta: 2 days, 18:21:12, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5694, loss_cls: 3.9485, loss: 3.9485 +2024-12-28 21:13:34,231 - pyskl - INFO - Epoch [74][2800/3746] lr: 5.131e-02, eta: 2 days, 18:19:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5614, loss_cls: 3.9828, loss: 3.9828 +2024-12-28 21:14:59,009 - pyskl - INFO - Epoch [74][2900/3746] lr: 5.128e-02, eta: 2 days, 18:18:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5719, loss_cls: 3.9551, loss: 3.9551 +2024-12-28 21:16:23,712 - pyskl - INFO - Epoch [74][3000/3746] lr: 5.126e-02, eta: 2 days, 18:17:05, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5631, loss_cls: 3.9525, loss: 3.9525 +2024-12-28 21:17:48,771 - pyskl - INFO - Epoch [74][3100/3746] lr: 5.123e-02, eta: 2 days, 18:15:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5717, loss_cls: 3.9227, loss: 3.9227 +2024-12-28 21:19:13,882 - pyskl - INFO - Epoch [74][3200/3746] lr: 5.120e-02, eta: 2 days, 18:14:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5783, loss_cls: 3.9145, loss: 3.9145 +2024-12-28 21:20:38,622 - pyskl - INFO - Epoch [74][3300/3746] lr: 5.117e-02, eta: 2 days, 18:12:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5684, loss_cls: 3.9457, loss: 3.9457 +2024-12-28 21:22:03,863 - pyskl - INFO - Epoch [74][3400/3746] lr: 5.114e-02, eta: 2 days, 18:11:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5750, loss_cls: 3.9099, loss: 3.9099 +2024-12-28 21:23:28,575 - pyskl - INFO - Epoch [74][3500/3746] lr: 5.112e-02, eta: 2 days, 18:10:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5586, loss_cls: 3.9745, loss: 3.9745 +2024-12-28 21:24:53,646 - pyskl - INFO - Epoch [74][3600/3746] lr: 5.109e-02, eta: 2 days, 18:08:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5764, loss_cls: 3.8798, loss: 3.8798 +2024-12-28 21:26:18,549 - pyskl - INFO - Epoch [74][3700/3746] lr: 5.106e-02, eta: 2 days, 18:07:30, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5789, loss_cls: 3.8979, loss: 3.8979 +2024-12-28 21:26:59,385 - pyskl - INFO - Saving checkpoint at 74 epochs +2024-12-28 21:28:59,130 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 21:28:59,812 - pyskl - INFO - +top1_acc 0.2534 +top5_acc 0.4996 +2024-12-28 21:28:59,813 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 21:28:59,861 - pyskl - INFO - +mean_acc 0.2532 +2024-12-28 21:28:59,875 - pyskl - INFO - Epoch(val) [74][309] top1_acc: 0.2534, top5_acc: 0.4996, mean_class_accuracy: 0.2532 +2024-12-28 21:33:10,809 - pyskl - INFO - Epoch [75][100/3746] lr: 5.102e-02, eta: 2 days, 18:07:40, time: 2.509, data_time: 1.475, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5833, loss_cls: 3.8392, loss: 3.8392 +2024-12-28 21:34:35,891 - pyskl - INFO - Epoch [75][200/3746] lr: 5.099e-02, eta: 2 days, 18:06:18, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5817, loss_cls: 3.8812, loss: 3.8812 +2024-12-28 21:36:00,802 - pyskl - INFO - Epoch [75][300/3746] lr: 5.096e-02, eta: 2 days, 18:04:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5769, loss_cls: 3.8844, loss: 3.8844 +2024-12-28 21:37:25,619 - pyskl - INFO - Epoch [75][400/3746] lr: 5.094e-02, eta: 2 days, 18:03:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5872, loss_cls: 3.8317, loss: 3.8317 +2024-12-28 21:38:50,490 - pyskl - INFO - Epoch [75][500/3746] lr: 5.091e-02, eta: 2 days, 18:02:11, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5766, loss_cls: 3.9242, loss: 3.9242 +2024-12-28 21:40:15,466 - pyskl - INFO - Epoch [75][600/3746] lr: 5.088e-02, eta: 2 days, 18:00:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5716, loss_cls: 3.9290, loss: 3.9290 +2024-12-28 21:41:40,345 - pyskl - INFO - Epoch [75][700/3746] lr: 5.085e-02, eta: 2 days, 17:59:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5752, loss_cls: 3.9278, loss: 3.9278 +2024-12-28 21:43:05,366 - pyskl - INFO - Epoch [75][800/3746] lr: 5.082e-02, eta: 2 days, 17:58:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5639, loss_cls: 3.9197, loss: 3.9197 +2024-12-28 21:44:30,285 - pyskl - INFO - Epoch [75][900/3746] lr: 5.080e-02, eta: 2 days, 17:56:42, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5792, loss_cls: 3.8749, loss: 3.8749 +2024-12-28 21:45:55,088 - pyskl - INFO - Epoch [75][1000/3746] lr: 5.077e-02, eta: 2 days, 17:55:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5834, loss_cls: 3.8467, loss: 3.8467 +2024-12-28 21:47:19,731 - pyskl - INFO - Epoch [75][1100/3746] lr: 5.074e-02, eta: 2 days, 17:53:56, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5773, loss_cls: 3.9606, loss: 3.9606 +2024-12-28 21:48:44,933 - pyskl - INFO - Epoch [75][1200/3746] lr: 5.071e-02, eta: 2 days, 17:52:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5653, loss_cls: 3.9441, loss: 3.9441 +2024-12-28 21:50:09,136 - pyskl - INFO - Epoch [75][1300/3746] lr: 5.068e-02, eta: 2 days, 17:51:11, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5764, loss_cls: 3.9262, loss: 3.9262 +2024-12-28 21:51:34,094 - pyskl - INFO - Epoch [75][1400/3746] lr: 5.066e-02, eta: 2 days, 17:49:49, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5739, loss_cls: 3.9169, loss: 3.9169 +2024-12-28 21:52:59,361 - pyskl - INFO - Epoch [75][1500/3746] lr: 5.063e-02, eta: 2 days, 17:48:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5713, loss_cls: 3.8897, loss: 3.8897 +2024-12-28 21:54:24,371 - pyskl - INFO - Epoch [75][1600/3746] lr: 5.060e-02, eta: 2 days, 17:47:05, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5678, loss_cls: 3.9485, loss: 3.9485 +2024-12-28 21:55:49,260 - pyskl - INFO - Epoch [75][1700/3746] lr: 5.057e-02, eta: 2 days, 17:45:42, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5689, loss_cls: 3.9252, loss: 3.9252 +2024-12-28 21:57:14,765 - pyskl - INFO - Epoch [75][1800/3746] lr: 5.054e-02, eta: 2 days, 17:44:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5713, loss_cls: 3.9093, loss: 3.9093 +2024-12-28 21:58:39,598 - pyskl - INFO - Epoch [75][1900/3746] lr: 5.052e-02, eta: 2 days, 17:42:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5837, loss_cls: 3.8985, loss: 3.8985 +2024-12-28 22:00:04,958 - pyskl - INFO - Epoch [75][2000/3746] lr: 5.049e-02, eta: 2 days, 17:41:36, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5661, loss_cls: 3.9161, loss: 3.9161 +2024-12-28 22:01:30,260 - pyskl - INFO - Epoch [75][2100/3746] lr: 5.046e-02, eta: 2 days, 17:40:14, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5722, loss_cls: 3.9108, loss: 3.9108 +2024-12-28 22:02:55,120 - pyskl - INFO - Epoch [75][2200/3746] lr: 5.043e-02, eta: 2 days, 17:38:52, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5623, loss_cls: 3.9505, loss: 3.9505 +2024-12-28 22:04:20,005 - pyskl - INFO - Epoch [75][2300/3746] lr: 5.040e-02, eta: 2 days, 17:37:29, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5783, loss_cls: 3.8782, loss: 3.8782 +2024-12-28 22:05:44,536 - pyskl - INFO - Epoch [75][2400/3746] lr: 5.038e-02, eta: 2 days, 17:36:06, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5781, loss_cls: 3.8979, loss: 3.8979 +2024-12-28 22:07:08,926 - pyskl - INFO - Epoch [75][2500/3746] lr: 5.035e-02, eta: 2 days, 17:34:43, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5772, loss_cls: 3.9133, loss: 3.9133 +2024-12-28 22:08:33,606 - pyskl - INFO - Epoch [75][2600/3746] lr: 5.032e-02, eta: 2 days, 17:33:21, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5527, loss_cls: 3.9908, loss: 3.9908 +2024-12-28 22:09:58,706 - pyskl - INFO - Epoch [75][2700/3746] lr: 5.029e-02, eta: 2 days, 17:31:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5678, loss_cls: 3.9324, loss: 3.9324 +2024-12-28 22:11:23,437 - pyskl - INFO - Epoch [75][2800/3746] lr: 5.026e-02, eta: 2 days, 17:30:36, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5678, loss_cls: 3.9266, loss: 3.9266 +2024-12-28 22:12:48,595 - pyskl - INFO - Epoch [75][2900/3746] lr: 5.024e-02, eta: 2 days, 17:29:14, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5623, loss_cls: 3.9433, loss: 3.9433 +2024-12-28 22:14:13,497 - pyskl - INFO - Epoch [75][3000/3746] lr: 5.021e-02, eta: 2 days, 17:27:51, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5737, loss_cls: 3.9192, loss: 3.9192 +2024-12-28 22:15:38,463 - pyskl - INFO - Epoch [75][3100/3746] lr: 5.018e-02, eta: 2 days, 17:26:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5577, loss_cls: 3.9633, loss: 3.9633 +2024-12-28 22:17:03,097 - pyskl - INFO - Epoch [75][3200/3746] lr: 5.015e-02, eta: 2 days, 17:25:06, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5720, loss_cls: 3.9252, loss: 3.9252 +2024-12-28 22:18:28,029 - pyskl - INFO - Epoch [75][3300/3746] lr: 5.012e-02, eta: 2 days, 17:23:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5705, loss_cls: 3.9042, loss: 3.9042 +2024-12-28 22:19:52,960 - pyskl - INFO - Epoch [75][3400/3746] lr: 5.010e-02, eta: 2 days, 17:22:22, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5686, loss_cls: 3.9494, loss: 3.9494 +2024-12-28 22:21:17,382 - pyskl - INFO - Epoch [75][3500/3746] lr: 5.007e-02, eta: 2 days, 17:20:59, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3128, top5_acc: 0.5684, loss_cls: 3.9437, loss: 3.9437 +2024-12-28 22:22:42,046 - pyskl - INFO - Epoch [75][3600/3746] lr: 5.004e-02, eta: 2 days, 17:19:36, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5581, loss_cls: 3.9675, loss: 3.9675 +2024-12-28 22:24:07,215 - pyskl - INFO - Epoch [75][3700/3746] lr: 5.001e-02, eta: 2 days, 17:18:14, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5713, loss_cls: 3.9217, loss: 3.9217 +2024-12-28 22:24:47,894 - pyskl - INFO - Saving checkpoint at 75 epochs +2024-12-28 22:26:47,502 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 22:26:48,177 - pyskl - INFO - +top1_acc 0.2535 +top5_acc 0.5017 +2024-12-28 22:26:48,177 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 22:26:48,224 - pyskl - INFO - +mean_acc 0.2532 +2024-12-28 22:26:48,242 - pyskl - INFO - Epoch(val) [75][309] top1_acc: 0.2535, top5_acc: 0.5017, mean_class_accuracy: 0.2532 +2024-12-28 22:30:58,821 - pyskl - INFO - Epoch [76][100/3746] lr: 4.997e-02, eta: 2 days, 17:18:20, time: 2.506, data_time: 1.474, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5870, loss_cls: 3.8745, loss: 3.8745 +2024-12-28 22:32:23,935 - pyskl - INFO - Epoch [76][200/3746] lr: 4.994e-02, eta: 2 days, 17:16:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5731, loss_cls: 3.9014, loss: 3.9014 +2024-12-28 22:33:49,041 - pyskl - INFO - Epoch [76][300/3746] lr: 4.992e-02, eta: 2 days, 17:15:35, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5773, loss_cls: 3.8527, loss: 3.8527 +2024-12-28 22:35:14,118 - pyskl - INFO - Epoch [76][400/3746] lr: 4.989e-02, eta: 2 days, 17:14:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5750, loss_cls: 3.8977, loss: 3.8977 +2024-12-28 22:36:39,602 - pyskl - INFO - Epoch [76][500/3746] lr: 4.986e-02, eta: 2 days, 17:12:51, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5794, loss_cls: 3.8801, loss: 3.8801 +2024-12-28 22:38:05,093 - pyskl - INFO - Epoch [76][600/3746] lr: 4.983e-02, eta: 2 days, 17:11:29, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5789, loss_cls: 3.8926, loss: 3.8926 +2024-12-28 22:39:30,640 - pyskl - INFO - Epoch [76][700/3746] lr: 4.980e-02, eta: 2 days, 17:10:07, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5822, loss_cls: 3.8814, loss: 3.8814 +2024-12-28 22:40:55,765 - pyskl - INFO - Epoch [76][800/3746] lr: 4.978e-02, eta: 2 days, 17:08:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5741, loss_cls: 3.8754, loss: 3.8754 +2024-12-28 22:42:21,023 - pyskl - INFO - Epoch [76][900/3746] lr: 4.975e-02, eta: 2 days, 17:07:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5755, loss_cls: 3.8853, loss: 3.8853 +2024-12-28 22:43:46,588 - pyskl - INFO - Epoch [76][1000/3746] lr: 4.972e-02, eta: 2 days, 17:06:01, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5767, loss_cls: 3.9030, loss: 3.9030 +2024-12-28 22:45:11,951 - pyskl - INFO - Epoch [76][1100/3746] lr: 4.969e-02, eta: 2 days, 17:04:39, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5653, loss_cls: 3.9517, loss: 3.9517 +2024-12-28 22:46:37,093 - pyskl - INFO - Epoch [76][1200/3746] lr: 4.966e-02, eta: 2 days, 17:03:17, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5745, loss_cls: 3.8781, loss: 3.8781 +2024-12-28 22:48:01,984 - pyskl - INFO - Epoch [76][1300/3746] lr: 4.964e-02, eta: 2 days, 17:01:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5867, loss_cls: 3.8473, loss: 3.8473 +2024-12-28 22:49:26,729 - pyskl - INFO - Epoch [76][1400/3746] lr: 4.961e-02, eta: 2 days, 17:00:31, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5772, loss_cls: 3.8910, loss: 3.8910 +2024-12-28 22:50:51,887 - pyskl - INFO - Epoch [76][1500/3746] lr: 4.958e-02, eta: 2 days, 16:59:09, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5697, loss_cls: 3.9077, loss: 3.9077 +2024-12-28 22:52:17,288 - pyskl - INFO - Epoch [76][1600/3746] lr: 4.955e-02, eta: 2 days, 16:57:47, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5678, loss_cls: 3.9087, loss: 3.9087 +2024-12-28 22:53:42,065 - pyskl - INFO - Epoch [76][1700/3746] lr: 4.953e-02, eta: 2 days, 16:56:24, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5716, loss_cls: 3.9061, loss: 3.9061 +2024-12-28 22:55:07,101 - pyskl - INFO - Epoch [76][1800/3746] lr: 4.950e-02, eta: 2 days, 16:55:02, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5802, loss_cls: 3.8453, loss: 3.8453 +2024-12-28 22:56:31,770 - pyskl - INFO - Epoch [76][1900/3746] lr: 4.947e-02, eta: 2 days, 16:53:39, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5744, loss_cls: 3.8926, loss: 3.8926 +2024-12-28 22:57:57,210 - pyskl - INFO - Epoch [76][2000/3746] lr: 4.944e-02, eta: 2 days, 16:52:17, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5737, loss_cls: 3.9273, loss: 3.9273 +2024-12-28 22:59:22,204 - pyskl - INFO - Epoch [76][2100/3746] lr: 4.941e-02, eta: 2 days, 16:50:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5747, loss_cls: 3.9195, loss: 3.9195 +2024-12-28 23:00:47,189 - pyskl - INFO - Epoch [76][2200/3746] lr: 4.939e-02, eta: 2 days, 16:49:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5705, loss_cls: 3.9061, loss: 3.9061 +2024-12-28 23:02:12,763 - pyskl - INFO - Epoch [76][2300/3746] lr: 4.936e-02, eta: 2 days, 16:48:10, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5700, loss_cls: 3.8915, loss: 3.8915 +2024-12-28 23:03:38,100 - pyskl - INFO - Epoch [76][2400/3746] lr: 4.933e-02, eta: 2 days, 16:46:48, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5670, loss_cls: 3.9294, loss: 3.9294 +2024-12-28 23:05:03,197 - pyskl - INFO - Epoch [76][2500/3746] lr: 4.930e-02, eta: 2 days, 16:45:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5686, loss_cls: 3.9215, loss: 3.9215 +2024-12-28 23:06:28,346 - pyskl - INFO - Epoch [76][2600/3746] lr: 4.927e-02, eta: 2 days, 16:44:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5673, loss_cls: 3.9317, loss: 3.9317 +2024-12-28 23:07:53,374 - pyskl - INFO - Epoch [76][2700/3746] lr: 4.925e-02, eta: 2 days, 16:42:41, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5689, loss_cls: 3.9149, loss: 3.9149 +2024-12-28 23:09:18,336 - pyskl - INFO - Epoch [76][2800/3746] lr: 4.922e-02, eta: 2 days, 16:41:18, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5714, loss_cls: 3.8932, loss: 3.8932 +2024-12-28 23:10:43,296 - pyskl - INFO - Epoch [76][2900/3746] lr: 4.919e-02, eta: 2 days, 16:39:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3089, top5_acc: 0.5605, loss_cls: 3.9815, loss: 3.9815 +2024-12-28 23:12:08,364 - pyskl - INFO - Epoch [76][3000/3746] lr: 4.916e-02, eta: 2 days, 16:38:34, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5631, loss_cls: 3.9150, loss: 3.9150 +2024-12-28 23:13:33,288 - pyskl - INFO - Epoch [76][3100/3746] lr: 4.913e-02, eta: 2 days, 16:37:11, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5828, loss_cls: 3.8918, loss: 3.8918 +2024-12-28 23:14:58,442 - pyskl - INFO - Epoch [76][3200/3746] lr: 4.911e-02, eta: 2 days, 16:35:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5736, loss_cls: 3.9110, loss: 3.9110 +2024-12-28 23:16:23,066 - pyskl - INFO - Epoch [76][3300/3746] lr: 4.908e-02, eta: 2 days, 16:34:26, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5703, loss_cls: 3.8966, loss: 3.8966 +2024-12-28 23:17:48,280 - pyskl - INFO - Epoch [76][3400/3746] lr: 4.905e-02, eta: 2 days, 16:33:04, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5803, loss_cls: 3.8947, loss: 3.8947 +2024-12-28 23:19:13,281 - pyskl - INFO - Epoch [76][3500/3746] lr: 4.902e-02, eta: 2 days, 16:31:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5722, loss_cls: 3.9429, loss: 3.9429 +2024-12-28 23:20:38,800 - pyskl - INFO - Epoch [76][3600/3746] lr: 4.899e-02, eta: 2 days, 16:30:19, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5684, loss_cls: 3.9191, loss: 3.9191 +2024-12-28 23:22:04,208 - pyskl - INFO - Epoch [76][3700/3746] lr: 4.897e-02, eta: 2 days, 16:28:57, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5747, loss_cls: 3.9015, loss: 3.9015 +2024-12-28 23:22:45,208 - pyskl - INFO - Saving checkpoint at 76 epochs +2024-12-28 23:24:44,370 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-28 23:24:45,263 - pyskl - INFO - +top1_acc 0.2609 +top5_acc 0.5084 +2024-12-28 23:24:45,263 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-28 23:24:45,321 - pyskl - INFO - +mean_acc 0.2603 +2024-12-28 23:24:45,330 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_67.pth was removed +2024-12-28 23:24:45,647 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_76.pth. +2024-12-28 23:24:45,648 - pyskl - INFO - Best top1_acc is 0.2609 at 76 epoch. +2024-12-28 23:24:45,661 - pyskl - INFO - Epoch(val) [76][309] top1_acc: 0.2609, top5_acc: 0.5084, mean_class_accuracy: 0.2603 +2024-12-28 23:28:57,476 - pyskl - INFO - Epoch [77][100/3746] lr: 4.893e-02, eta: 2 days, 16:29:01, time: 2.518, data_time: 1.483, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5886, loss_cls: 3.8065, loss: 3.8065 +2024-12-28 23:30:23,351 - pyskl - INFO - Epoch [77][200/3746] lr: 4.890e-02, eta: 2 days, 16:27:39, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5752, loss_cls: 3.9035, loss: 3.9035 +2024-12-28 23:31:48,843 - pyskl - INFO - Epoch [77][300/3746] lr: 4.887e-02, eta: 2 days, 16:26:17, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5811, loss_cls: 3.8633, loss: 3.8633 +2024-12-28 23:33:14,649 - pyskl - INFO - Epoch [77][400/3746] lr: 4.884e-02, eta: 2 days, 16:24:55, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5842, loss_cls: 3.8244, loss: 3.8244 +2024-12-28 23:34:40,400 - pyskl - INFO - Epoch [77][500/3746] lr: 4.881e-02, eta: 2 days, 16:23:33, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5720, loss_cls: 3.8874, loss: 3.8874 +2024-12-28 23:36:06,568 - pyskl - INFO - Epoch [77][600/3746] lr: 4.879e-02, eta: 2 days, 16:22:12, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5836, loss_cls: 3.8610, loss: 3.8610 +2024-12-28 23:37:32,236 - pyskl - INFO - Epoch [77][700/3746] lr: 4.876e-02, eta: 2 days, 16:20:50, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5783, loss_cls: 3.8603, loss: 3.8603 +2024-12-28 23:38:57,888 - pyskl - INFO - Epoch [77][800/3746] lr: 4.873e-02, eta: 2 days, 16:19:28, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5756, loss_cls: 3.8827, loss: 3.8827 +2024-12-28 23:40:23,837 - pyskl - INFO - Epoch [77][900/3746] lr: 4.870e-02, eta: 2 days, 16:18:06, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5877, loss_cls: 3.8632, loss: 3.8632 +2024-12-28 23:41:50,108 - pyskl - INFO - Epoch [77][1000/3746] lr: 4.867e-02, eta: 2 days, 16:16:45, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5794, loss_cls: 3.9175, loss: 3.9175 +2024-12-28 23:43:15,802 - pyskl - INFO - Epoch [77][1100/3746] lr: 4.865e-02, eta: 2 days, 16:15:23, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5703, loss_cls: 3.9226, loss: 3.9226 +2024-12-28 23:44:40,937 - pyskl - INFO - Epoch [77][1200/3746] lr: 4.862e-02, eta: 2 days, 16:14:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5794, loss_cls: 3.8658, loss: 3.8658 +2024-12-28 23:46:05,735 - pyskl - INFO - Epoch [77][1300/3746] lr: 4.859e-02, eta: 2 days, 16:12:38, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5756, loss_cls: 3.9226, loss: 3.9226 +2024-12-28 23:47:30,657 - pyskl - INFO - Epoch [77][1400/3746] lr: 4.856e-02, eta: 2 days, 16:11:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5784, loss_cls: 3.8456, loss: 3.8456 +2024-12-28 23:48:55,549 - pyskl - INFO - Epoch [77][1500/3746] lr: 4.853e-02, eta: 2 days, 16:09:52, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5691, loss_cls: 3.9206, loss: 3.9206 +2024-12-28 23:50:20,393 - pyskl - INFO - Epoch [77][1600/3746] lr: 4.851e-02, eta: 2 days, 16:08:29, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5720, loss_cls: 3.8945, loss: 3.8945 +2024-12-28 23:51:45,186 - pyskl - INFO - Epoch [77][1700/3746] lr: 4.848e-02, eta: 2 days, 16:07:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5742, loss_cls: 3.8911, loss: 3.8911 +2024-12-28 23:53:10,066 - pyskl - INFO - Epoch [77][1800/3746] lr: 4.845e-02, eta: 2 days, 16:05:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5684, loss_cls: 3.9175, loss: 3.9175 +2024-12-28 23:54:35,120 - pyskl - INFO - Epoch [77][1900/3746] lr: 4.842e-02, eta: 2 days, 16:04:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5755, loss_cls: 3.9146, loss: 3.9146 +2024-12-28 23:56:00,445 - pyskl - INFO - Epoch [77][2000/3746] lr: 4.839e-02, eta: 2 days, 16:02:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5825, loss_cls: 3.8479, loss: 3.8479 +2024-12-28 23:57:25,081 - pyskl - INFO - Epoch [77][2100/3746] lr: 4.837e-02, eta: 2 days, 16:01:36, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5708, loss_cls: 3.8929, loss: 3.8929 +2024-12-28 23:58:49,438 - pyskl - INFO - Epoch [77][2200/3746] lr: 4.834e-02, eta: 2 days, 16:00:13, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5702, loss_cls: 3.9060, loss: 3.9060 +2024-12-29 00:00:14,432 - pyskl - INFO - Epoch [77][2300/3746] lr: 4.831e-02, eta: 2 days, 15:58:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5687, loss_cls: 3.9526, loss: 3.9526 +2024-12-29 00:01:39,243 - pyskl - INFO - Epoch [77][2400/3746] lr: 4.828e-02, eta: 2 days, 15:57:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5691, loss_cls: 3.9080, loss: 3.9080 +2024-12-29 00:03:04,485 - pyskl - INFO - Epoch [77][2500/3746] lr: 4.825e-02, eta: 2 days, 15:56:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5713, loss_cls: 3.8880, loss: 3.8880 +2024-12-29 00:04:29,473 - pyskl - INFO - Epoch [77][2600/3746] lr: 4.823e-02, eta: 2 days, 15:54:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5723, loss_cls: 3.8882, loss: 3.8882 +2024-12-29 00:05:54,286 - pyskl - INFO - Epoch [77][2700/3746] lr: 4.820e-02, eta: 2 days, 15:53:20, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5850, loss_cls: 3.8414, loss: 3.8414 +2024-12-29 00:07:18,816 - pyskl - INFO - Epoch [77][2800/3746] lr: 4.817e-02, eta: 2 days, 15:51:56, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5769, loss_cls: 3.9113, loss: 3.9113 +2024-12-29 00:08:44,083 - pyskl - INFO - Epoch [77][2900/3746] lr: 4.814e-02, eta: 2 days, 15:50:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5731, loss_cls: 3.9124, loss: 3.9124 +2024-12-29 00:10:09,075 - pyskl - INFO - Epoch [77][3000/3746] lr: 4.811e-02, eta: 2 days, 15:49:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5670, loss_cls: 3.9437, loss: 3.9437 +2024-12-29 00:11:33,649 - pyskl - INFO - Epoch [77][3100/3746] lr: 4.809e-02, eta: 2 days, 15:47:48, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5687, loss_cls: 3.8972, loss: 3.8972 +2024-12-29 00:12:58,546 - pyskl - INFO - Epoch [77][3200/3746] lr: 4.806e-02, eta: 2 days, 15:46:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5789, loss_cls: 3.8628, loss: 3.8628 +2024-12-29 00:14:23,647 - pyskl - INFO - Epoch [77][3300/3746] lr: 4.803e-02, eta: 2 days, 15:45:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5692, loss_cls: 3.9306, loss: 3.9306 +2024-12-29 00:15:48,569 - pyskl - INFO - Epoch [77][3400/3746] lr: 4.800e-02, eta: 2 days, 15:43:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3205, top5_acc: 0.5753, loss_cls: 3.8795, loss: 3.8795 +2024-12-29 00:17:13,470 - pyskl - INFO - Epoch [77][3500/3746] lr: 4.798e-02, eta: 2 days, 15:42:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5642, loss_cls: 3.9633, loss: 3.9633 +2024-12-29 00:18:38,520 - pyskl - INFO - Epoch [77][3600/3746] lr: 4.795e-02, eta: 2 days, 15:40:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5723, loss_cls: 3.8746, loss: 3.8746 +2024-12-29 00:20:03,325 - pyskl - INFO - Epoch [77][3700/3746] lr: 4.792e-02, eta: 2 days, 15:39:32, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5755, loss_cls: 3.8933, loss: 3.8933 +2024-12-29 00:20:44,029 - pyskl - INFO - Saving checkpoint at 77 epochs +2024-12-29 00:22:42,907 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 00:22:43,621 - pyskl - INFO - +top1_acc 0.2667 +top5_acc 0.5118 +2024-12-29 00:22:43,621 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 00:22:43,671 - pyskl - INFO - +mean_acc 0.2664 +2024-12-29 00:22:43,675 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_76.pth was removed +2024-12-29 00:22:43,937 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_77.pth. +2024-12-29 00:22:43,938 - pyskl - INFO - Best top1_acc is 0.2667 at 77 epoch. +2024-12-29 00:22:43,950 - pyskl - INFO - Epoch(val) [77][309] top1_acc: 0.2667, top5_acc: 0.5118, mean_class_accuracy: 0.2664 +2024-12-29 00:26:57,727 - pyskl - INFO - Epoch [78][100/3746] lr: 4.788e-02, eta: 2 days, 15:39:34, time: 2.538, data_time: 1.512, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5995, loss_cls: 3.8103, loss: 3.8103 +2024-12-29 00:28:22,955 - pyskl - INFO - Epoch [78][200/3746] lr: 4.785e-02, eta: 2 days, 15:38:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5903, loss_cls: 3.8295, loss: 3.8295 +2024-12-29 00:29:48,429 - pyskl - INFO - Epoch [78][300/3746] lr: 4.782e-02, eta: 2 days, 15:36:50, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5813, loss_cls: 3.9056, loss: 3.9056 +2024-12-29 00:31:13,605 - pyskl - INFO - Epoch [78][400/3746] lr: 4.779e-02, eta: 2 days, 15:35:27, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5697, loss_cls: 3.8696, loss: 3.8696 +2024-12-29 00:32:38,874 - pyskl - INFO - Epoch [78][500/3746] lr: 4.777e-02, eta: 2 days, 15:34:05, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5855, loss_cls: 3.8538, loss: 3.8538 +2024-12-29 00:34:04,048 - pyskl - INFO - Epoch [78][600/3746] lr: 4.774e-02, eta: 2 days, 15:32:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5673, loss_cls: 3.9545, loss: 3.9545 +2024-12-29 00:35:29,293 - pyskl - INFO - Epoch [78][700/3746] lr: 4.771e-02, eta: 2 days, 15:31:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5861, loss_cls: 3.8150, loss: 3.8150 +2024-12-29 00:36:54,295 - pyskl - INFO - Epoch [78][800/3746] lr: 4.768e-02, eta: 2 days, 15:29:57, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5778, loss_cls: 3.8851, loss: 3.8851 +2024-12-29 00:38:20,093 - pyskl - INFO - Epoch [78][900/3746] lr: 4.766e-02, eta: 2 days, 15:28:35, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5873, loss_cls: 3.8264, loss: 3.8264 +2024-12-29 00:39:45,134 - pyskl - INFO - Epoch [78][1000/3746] lr: 4.763e-02, eta: 2 days, 15:27:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5723, loss_cls: 3.8823, loss: 3.8823 +2024-12-29 00:41:10,287 - pyskl - INFO - Epoch [78][1100/3746] lr: 4.760e-02, eta: 2 days, 15:25:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5805, loss_cls: 3.8919, loss: 3.8919 +2024-12-29 00:42:35,255 - pyskl - INFO - Epoch [78][1200/3746] lr: 4.757e-02, eta: 2 days, 15:24:27, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5773, loss_cls: 3.8832, loss: 3.8832 +2024-12-29 00:43:59,897 - pyskl - INFO - Epoch [78][1300/3746] lr: 4.754e-02, eta: 2 days, 15:23:04, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5825, loss_cls: 3.8462, loss: 3.8462 +2024-12-29 00:45:25,162 - pyskl - INFO - Epoch [78][1400/3746] lr: 4.752e-02, eta: 2 days, 15:21:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5747, loss_cls: 3.8882, loss: 3.8882 +2024-12-29 00:46:50,032 - pyskl - INFO - Epoch [78][1500/3746] lr: 4.749e-02, eta: 2 days, 15:20:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5872, loss_cls: 3.8647, loss: 3.8647 +2024-12-29 00:48:15,636 - pyskl - INFO - Epoch [78][1600/3746] lr: 4.746e-02, eta: 2 days, 15:18:56, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5691, loss_cls: 3.9029, loss: 3.9029 +2024-12-29 00:49:41,099 - pyskl - INFO - Epoch [78][1700/3746] lr: 4.743e-02, eta: 2 days, 15:17:34, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5700, loss_cls: 3.9021, loss: 3.9021 +2024-12-29 00:51:07,110 - pyskl - INFO - Epoch [78][1800/3746] lr: 4.740e-02, eta: 2 days, 15:16:12, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5730, loss_cls: 3.9121, loss: 3.9121 +2024-12-29 00:52:32,618 - pyskl - INFO - Epoch [78][1900/3746] lr: 4.738e-02, eta: 2 days, 15:14:50, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5787, loss_cls: 3.8692, loss: 3.8692 +2024-12-29 00:53:58,088 - pyskl - INFO - Epoch [78][2000/3746] lr: 4.735e-02, eta: 2 days, 15:13:27, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5794, loss_cls: 3.8998, loss: 3.8998 +2024-12-29 00:55:23,063 - pyskl - INFO - Epoch [78][2100/3746] lr: 4.732e-02, eta: 2 days, 15:12:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5848, loss_cls: 3.8601, loss: 3.8601 +2024-12-29 00:56:47,995 - pyskl - INFO - Epoch [78][2200/3746] lr: 4.729e-02, eta: 2 days, 15:10:42, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5864, loss_cls: 3.8206, loss: 3.8206 +2024-12-29 00:58:13,422 - pyskl - INFO - Epoch [78][2300/3746] lr: 4.726e-02, eta: 2 days, 15:09:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5805, loss_cls: 3.8715, loss: 3.8715 +2024-12-29 00:59:39,386 - pyskl - INFO - Epoch [78][2400/3746] lr: 4.724e-02, eta: 2 days, 15:07:57, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5794, loss_cls: 3.8438, loss: 3.8438 +2024-12-29 01:01:04,796 - pyskl - INFO - Epoch [78][2500/3746] lr: 4.721e-02, eta: 2 days, 15:06:35, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5716, loss_cls: 3.9061, loss: 3.9061 +2024-12-29 01:02:30,329 - pyskl - INFO - Epoch [78][2600/3746] lr: 4.718e-02, eta: 2 days, 15:05:13, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5842, loss_cls: 3.8297, loss: 3.8297 +2024-12-29 01:03:55,368 - pyskl - INFO - Epoch [78][2700/3746] lr: 4.715e-02, eta: 2 days, 15:03:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5775, loss_cls: 3.8975, loss: 3.8975 +2024-12-29 01:05:19,979 - pyskl - INFO - Epoch [78][2800/3746] lr: 4.712e-02, eta: 2 days, 15:02:27, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5723, loss_cls: 3.9033, loss: 3.9033 +2024-12-29 01:06:44,987 - pyskl - INFO - Epoch [78][2900/3746] lr: 4.710e-02, eta: 2 days, 15:01:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5811, loss_cls: 3.8786, loss: 3.8786 +2024-12-29 01:08:10,276 - pyskl - INFO - Epoch [78][3000/3746] lr: 4.707e-02, eta: 2 days, 14:59:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5748, loss_cls: 3.8838, loss: 3.8838 +2024-12-29 01:09:35,188 - pyskl - INFO - Epoch [78][3100/3746] lr: 4.704e-02, eta: 2 days, 14:58:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5806, loss_cls: 3.8717, loss: 3.8717 +2024-12-29 01:11:00,141 - pyskl - INFO - Epoch [78][3200/3746] lr: 4.701e-02, eta: 2 days, 14:56:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5775, loss_cls: 3.8830, loss: 3.8830 +2024-12-29 01:12:25,220 - pyskl - INFO - Epoch [78][3300/3746] lr: 4.699e-02, eta: 2 days, 14:55:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5728, loss_cls: 3.8861, loss: 3.8861 +2024-12-29 01:13:50,423 - pyskl - INFO - Epoch [78][3400/3746] lr: 4.696e-02, eta: 2 days, 14:54:10, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5702, loss_cls: 3.9126, loss: 3.9126 +2024-12-29 01:15:16,003 - pyskl - INFO - Epoch [78][3500/3746] lr: 4.693e-02, eta: 2 days, 14:52:48, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5741, loss_cls: 3.8944, loss: 3.8944 +2024-12-29 01:16:41,109 - pyskl - INFO - Epoch [78][3600/3746] lr: 4.690e-02, eta: 2 days, 14:51:25, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5798, loss_cls: 3.8978, loss: 3.8978 +2024-12-29 01:18:06,047 - pyskl - INFO - Epoch [78][3700/3746] lr: 4.687e-02, eta: 2 days, 14:50:03, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5691, loss_cls: 3.9124, loss: 3.9124 +2024-12-29 01:18:47,303 - pyskl - INFO - Saving checkpoint at 78 epochs +2024-12-29 01:20:46,882 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 01:20:47,840 - pyskl - INFO - +top1_acc 0.2489 +top5_acc 0.4976 +2024-12-29 01:20:47,840 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 01:20:47,888 - pyskl - INFO - +mean_acc 0.2487 +2024-12-29 01:20:47,902 - pyskl - INFO - Epoch(val) [78][309] top1_acc: 0.2489, top5_acc: 0.4976, mean_class_accuracy: 0.2487 +2024-12-29 01:25:00,677 - pyskl - INFO - Epoch [79][100/3746] lr: 4.683e-02, eta: 2 days, 14:50:00, time: 2.528, data_time: 1.495, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5808, loss_cls: 3.8412, loss: 3.8412 +2024-12-29 01:26:25,952 - pyskl - INFO - Epoch [79][200/3746] lr: 4.680e-02, eta: 2 days, 14:48:38, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5863, loss_cls: 3.7987, loss: 3.7987 +2024-12-29 01:27:51,949 - pyskl - INFO - Epoch [79][300/3746] lr: 4.678e-02, eta: 2 days, 14:47:16, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5819, loss_cls: 3.8221, loss: 3.8221 +2024-12-29 01:29:17,080 - pyskl - INFO - Epoch [79][400/3746] lr: 4.675e-02, eta: 2 days, 14:45:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5864, loss_cls: 3.8482, loss: 3.8482 +2024-12-29 01:30:42,183 - pyskl - INFO - Epoch [79][500/3746] lr: 4.672e-02, eta: 2 days, 14:44:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5803, loss_cls: 3.8459, loss: 3.8459 +2024-12-29 01:32:07,631 - pyskl - INFO - Epoch [79][600/3746] lr: 4.669e-02, eta: 2 days, 14:43:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5758, loss_cls: 3.9067, loss: 3.9067 +2024-12-29 01:33:32,783 - pyskl - INFO - Epoch [79][700/3746] lr: 4.667e-02, eta: 2 days, 14:41:45, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3169, top5_acc: 0.5794, loss_cls: 3.8825, loss: 3.8825 +2024-12-29 01:34:57,899 - pyskl - INFO - Epoch [79][800/3746] lr: 4.664e-02, eta: 2 days, 14:40:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5875, loss_cls: 3.8438, loss: 3.8438 +2024-12-29 01:36:22,692 - pyskl - INFO - Epoch [79][900/3746] lr: 4.661e-02, eta: 2 days, 14:38:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5842, loss_cls: 3.8671, loss: 3.8671 +2024-12-29 01:37:47,863 - pyskl - INFO - Epoch [79][1000/3746] lr: 4.658e-02, eta: 2 days, 14:37:36, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5706, loss_cls: 3.8934, loss: 3.8934 +2024-12-29 01:39:13,306 - pyskl - INFO - Epoch [79][1100/3746] lr: 4.655e-02, eta: 2 days, 14:36:14, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5816, loss_cls: 3.8789, loss: 3.8789 +2024-12-29 01:40:38,515 - pyskl - INFO - Epoch [79][1200/3746] lr: 4.653e-02, eta: 2 days, 14:34:51, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5884, loss_cls: 3.8355, loss: 3.8355 +2024-12-29 01:42:03,197 - pyskl - INFO - Epoch [79][1300/3746] lr: 4.650e-02, eta: 2 days, 14:33:28, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5969, loss_cls: 3.8192, loss: 3.8192 +2024-12-29 01:43:28,287 - pyskl - INFO - Epoch [79][1400/3746] lr: 4.647e-02, eta: 2 days, 14:32:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5742, loss_cls: 3.9053, loss: 3.9053 +2024-12-29 01:44:53,441 - pyskl - INFO - Epoch [79][1500/3746] lr: 4.644e-02, eta: 2 days, 14:30:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5753, loss_cls: 3.8971, loss: 3.8971 +2024-12-29 01:46:19,000 - pyskl - INFO - Epoch [79][1600/3746] lr: 4.641e-02, eta: 2 days, 14:29:20, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5919, loss_cls: 3.8288, loss: 3.8288 +2024-12-29 01:47:44,773 - pyskl - INFO - Epoch [79][1700/3746] lr: 4.639e-02, eta: 2 days, 14:27:58, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5720, loss_cls: 3.8956, loss: 3.8956 +2024-12-29 01:49:09,607 - pyskl - INFO - Epoch [79][1800/3746] lr: 4.636e-02, eta: 2 days, 14:26:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5731, loss_cls: 3.8843, loss: 3.8843 +2024-12-29 01:50:34,759 - pyskl - INFO - Epoch [79][1900/3746] lr: 4.633e-02, eta: 2 days, 14:25:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5902, loss_cls: 3.8445, loss: 3.8445 +2024-12-29 01:52:00,600 - pyskl - INFO - Epoch [79][2000/3746] lr: 4.630e-02, eta: 2 days, 14:23:50, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5727, loss_cls: 3.8942, loss: 3.8942 +2024-12-29 01:53:25,598 - pyskl - INFO - Epoch [79][2100/3746] lr: 4.628e-02, eta: 2 days, 14:22:27, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3159, top5_acc: 0.5737, loss_cls: 3.8837, loss: 3.8837 +2024-12-29 01:54:50,591 - pyskl - INFO - Epoch [79][2200/3746] lr: 4.625e-02, eta: 2 days, 14:21:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5800, loss_cls: 3.8705, loss: 3.8705 +2024-12-29 01:56:15,676 - pyskl - INFO - Epoch [79][2300/3746] lr: 4.622e-02, eta: 2 days, 14:19:41, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5759, loss_cls: 3.8702, loss: 3.8702 +2024-12-29 01:57:40,600 - pyskl - INFO - Epoch [79][2400/3746] lr: 4.619e-02, eta: 2 days, 14:18:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5792, loss_cls: 3.8490, loss: 3.8490 +2024-12-29 01:59:06,335 - pyskl - INFO - Epoch [79][2500/3746] lr: 4.616e-02, eta: 2 days, 14:16:56, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5881, loss_cls: 3.8041, loss: 3.8041 +2024-12-29 02:00:31,474 - pyskl - INFO - Epoch [79][2600/3746] lr: 4.614e-02, eta: 2 days, 14:15:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5667, loss_cls: 3.9276, loss: 3.9276 +2024-12-29 02:01:56,851 - pyskl - INFO - Epoch [79][2700/3746] lr: 4.611e-02, eta: 2 days, 14:14:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5744, loss_cls: 3.8944, loss: 3.8944 +2024-12-29 02:03:21,779 - pyskl - INFO - Epoch [79][2800/3746] lr: 4.608e-02, eta: 2 days, 14:12:48, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5773, loss_cls: 3.8775, loss: 3.8775 +2024-12-29 02:04:46,790 - pyskl - INFO - Epoch [79][2900/3746] lr: 4.605e-02, eta: 2 days, 14:11:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5837, loss_cls: 3.8704, loss: 3.8704 +2024-12-29 02:06:11,865 - pyskl - INFO - Epoch [79][3000/3746] lr: 4.602e-02, eta: 2 days, 14:10:02, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5852, loss_cls: 3.8617, loss: 3.8617 +2024-12-29 02:07:36,348 - pyskl - INFO - Epoch [79][3100/3746] lr: 4.600e-02, eta: 2 days, 14:08:38, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5741, loss_cls: 3.8908, loss: 3.8908 +2024-12-29 02:09:01,244 - pyskl - INFO - Epoch [79][3200/3746] lr: 4.597e-02, eta: 2 days, 14:07:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5805, loss_cls: 3.8837, loss: 3.8837 +2024-12-29 02:10:26,218 - pyskl - INFO - Epoch [79][3300/3746] lr: 4.594e-02, eta: 2 days, 14:05:52, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5839, loss_cls: 3.8613, loss: 3.8613 +2024-12-29 02:11:51,906 - pyskl - INFO - Epoch [79][3400/3746] lr: 4.591e-02, eta: 2 days, 14:04:30, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5783, loss_cls: 3.8647, loss: 3.8647 +2024-12-29 02:13:17,173 - pyskl - INFO - Epoch [79][3500/3746] lr: 4.588e-02, eta: 2 days, 14:03:07, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5830, loss_cls: 3.8566, loss: 3.8566 +2024-12-29 02:14:42,414 - pyskl - INFO - Epoch [79][3600/3746] lr: 4.586e-02, eta: 2 days, 14:01:45, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5794, loss_cls: 3.8691, loss: 3.8691 +2024-12-29 02:16:07,791 - pyskl - INFO - Epoch [79][3700/3746] lr: 4.583e-02, eta: 2 days, 14:00:22, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5848, loss_cls: 3.8709, loss: 3.8709 +2024-12-29 02:16:48,960 - pyskl - INFO - Saving checkpoint at 79 epochs +2024-12-29 02:18:47,842 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 02:18:48,536 - pyskl - INFO - +top1_acc 0.2620 +top5_acc 0.5041 +2024-12-29 02:18:48,536 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 02:18:48,593 - pyskl - INFO - +mean_acc 0.2617 +2024-12-29 02:18:48,609 - pyskl - INFO - Epoch(val) [79][309] top1_acc: 0.2620, top5_acc: 0.5041, mean_class_accuracy: 0.2617 +2024-12-29 02:23:04,859 - pyskl - INFO - Epoch [80][100/3746] lr: 4.579e-02, eta: 2 days, 14:00:20, time: 2.562, data_time: 1.512, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5877, loss_cls: 3.8072, loss: 3.8072 +2024-12-29 02:24:30,796 - pyskl - INFO - Epoch [80][200/3746] lr: 4.576e-02, eta: 2 days, 13:58:57, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5841, loss_cls: 3.8280, loss: 3.8280 +2024-12-29 02:25:56,594 - pyskl - INFO - Epoch [80][300/3746] lr: 4.573e-02, eta: 2 days, 13:57:35, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5983, loss_cls: 3.7736, loss: 3.7736 +2024-12-29 02:27:22,585 - pyskl - INFO - Epoch [80][400/3746] lr: 4.570e-02, eta: 2 days, 13:56:13, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5869, loss_cls: 3.8285, loss: 3.8285 +2024-12-29 02:28:48,485 - pyskl - INFO - Epoch [80][500/3746] lr: 4.568e-02, eta: 2 days, 13:54:51, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5820, loss_cls: 3.8667, loss: 3.8667 +2024-12-29 02:30:13,935 - pyskl - INFO - Epoch [80][600/3746] lr: 4.565e-02, eta: 2 days, 13:53:28, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5800, loss_cls: 3.8433, loss: 3.8433 +2024-12-29 02:31:39,132 - pyskl - INFO - Epoch [80][700/3746] lr: 4.562e-02, eta: 2 days, 13:52:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.5930, loss_cls: 3.7951, loss: 3.7951 +2024-12-29 02:33:04,379 - pyskl - INFO - Epoch [80][800/3746] lr: 4.559e-02, eta: 2 days, 13:50:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5858, loss_cls: 3.8151, loss: 3.8151 +2024-12-29 02:34:29,900 - pyskl - INFO - Epoch [80][900/3746] lr: 4.557e-02, eta: 2 days, 13:49:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5962, loss_cls: 3.8161, loss: 3.8161 +2024-12-29 02:35:55,260 - pyskl - INFO - Epoch [80][1000/3746] lr: 4.554e-02, eta: 2 days, 13:47:57, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5872, loss_cls: 3.8828, loss: 3.8828 +2024-12-29 02:37:20,489 - pyskl - INFO - Epoch [80][1100/3746] lr: 4.551e-02, eta: 2 days, 13:46:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3083, top5_acc: 0.5789, loss_cls: 3.8905, loss: 3.8905 +2024-12-29 02:38:45,098 - pyskl - INFO - Epoch [80][1200/3746] lr: 4.548e-02, eta: 2 days, 13:45:11, time: 0.846, data_time: 0.001, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5787, loss_cls: 3.8665, loss: 3.8665 +2024-12-29 02:40:10,108 - pyskl - INFO - Epoch [80][1300/3746] lr: 4.545e-02, eta: 2 days, 13:43:48, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5930, loss_cls: 3.8096, loss: 3.8096 +2024-12-29 02:41:35,380 - pyskl - INFO - Epoch [80][1400/3746] lr: 4.543e-02, eta: 2 days, 13:42:25, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5861, loss_cls: 3.8663, loss: 3.8663 +2024-12-29 02:43:00,567 - pyskl - INFO - Epoch [80][1500/3746] lr: 4.540e-02, eta: 2 days, 13:41:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5867, loss_cls: 3.8099, loss: 3.8099 +2024-12-29 02:44:25,944 - pyskl - INFO - Epoch [80][1600/3746] lr: 4.537e-02, eta: 2 days, 13:39:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5791, loss_cls: 3.8552, loss: 3.8552 +2024-12-29 02:45:51,575 - pyskl - INFO - Epoch [80][1700/3746] lr: 4.534e-02, eta: 2 days, 13:38:17, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5841, loss_cls: 3.8650, loss: 3.8650 +2024-12-29 02:47:16,790 - pyskl - INFO - Epoch [80][1800/3746] lr: 4.532e-02, eta: 2 days, 13:36:54, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5845, loss_cls: 3.8523, loss: 3.8523 +2024-12-29 02:48:42,389 - pyskl - INFO - Epoch [80][1900/3746] lr: 4.529e-02, eta: 2 days, 13:35:32, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5770, loss_cls: 3.8880, loss: 3.8880 +2024-12-29 02:50:07,178 - pyskl - INFO - Epoch [80][2000/3746] lr: 4.526e-02, eta: 2 days, 13:34:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5722, loss_cls: 3.8580, loss: 3.8580 +2024-12-29 02:51:32,342 - pyskl - INFO - Epoch [80][2100/3746] lr: 4.523e-02, eta: 2 days, 13:32:45, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5903, loss_cls: 3.8130, loss: 3.8130 +2024-12-29 02:52:57,224 - pyskl - INFO - Epoch [80][2200/3746] lr: 4.520e-02, eta: 2 days, 13:31:22, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5756, loss_cls: 3.9006, loss: 3.9006 +2024-12-29 02:54:22,276 - pyskl - INFO - Epoch [80][2300/3746] lr: 4.518e-02, eta: 2 days, 13:29:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3156, top5_acc: 0.5830, loss_cls: 3.8774, loss: 3.8774 +2024-12-29 02:55:47,412 - pyskl - INFO - Epoch [80][2400/3746] lr: 4.515e-02, eta: 2 days, 13:28:36, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5711, loss_cls: 3.8801, loss: 3.8801 +2024-12-29 02:57:12,867 - pyskl - INFO - Epoch [80][2500/3746] lr: 4.512e-02, eta: 2 days, 13:27:14, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5770, loss_cls: 3.8612, loss: 3.8612 +2024-12-29 02:58:38,160 - pyskl - INFO - Epoch [80][2600/3746] lr: 4.509e-02, eta: 2 days, 13:25:51, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5922, loss_cls: 3.7973, loss: 3.7973 +2024-12-29 03:00:03,806 - pyskl - INFO - Epoch [80][2700/3746] lr: 4.506e-02, eta: 2 days, 13:24:28, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5845, loss_cls: 3.8544, loss: 3.8544 +2024-12-29 03:01:29,009 - pyskl - INFO - Epoch [80][2800/3746] lr: 4.504e-02, eta: 2 days, 13:23:06, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5795, loss_cls: 3.8709, loss: 3.8709 +2024-12-29 03:02:54,194 - pyskl - INFO - Epoch [80][2900/3746] lr: 4.501e-02, eta: 2 days, 13:21:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3145, top5_acc: 0.5758, loss_cls: 3.8951, loss: 3.8951 +2024-12-29 03:04:19,756 - pyskl - INFO - Epoch [80][3000/3746] lr: 4.498e-02, eta: 2 days, 13:20:20, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5841, loss_cls: 3.8465, loss: 3.8465 +2024-12-29 03:05:45,249 - pyskl - INFO - Epoch [80][3100/3746] lr: 4.495e-02, eta: 2 days, 13:18:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5933, loss_cls: 3.8367, loss: 3.8367 +2024-12-29 03:07:10,713 - pyskl - INFO - Epoch [80][3200/3746] lr: 4.493e-02, eta: 2 days, 13:17:35, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5861, loss_cls: 3.8679, loss: 3.8679 +2024-12-29 03:08:36,566 - pyskl - INFO - Epoch [80][3300/3746] lr: 4.490e-02, eta: 2 days, 13:16:12, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5736, loss_cls: 3.8665, loss: 3.8665 +2024-12-29 03:10:01,911 - pyskl - INFO - Epoch [80][3400/3746] lr: 4.487e-02, eta: 2 days, 13:14:50, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5806, loss_cls: 3.8547, loss: 3.8547 +2024-12-29 03:11:27,564 - pyskl - INFO - Epoch [80][3500/3746] lr: 4.484e-02, eta: 2 days, 13:13:27, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5844, loss_cls: 3.8850, loss: 3.8850 +2024-12-29 03:12:53,021 - pyskl - INFO - Epoch [80][3600/3746] lr: 4.481e-02, eta: 2 days, 13:12:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5831, loss_cls: 3.9005, loss: 3.9005 +2024-12-29 03:14:18,776 - pyskl - INFO - Epoch [80][3700/3746] lr: 4.479e-02, eta: 2 days, 13:10:42, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3141, top5_acc: 0.5759, loss_cls: 3.8914, loss: 3.8914 +2024-12-29 03:15:00,249 - pyskl - INFO - Saving checkpoint at 80 epochs +2024-12-29 03:16:59,849 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 03:17:00,670 - pyskl - INFO - +top1_acc 0.2650 +top5_acc 0.5152 +2024-12-29 03:17:00,671 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 03:17:00,716 - pyskl - INFO - +mean_acc 0.2648 +2024-12-29 03:17:00,737 - pyskl - INFO - Epoch(val) [80][309] top1_acc: 0.2650, top5_acc: 0.5152, mean_class_accuracy: 0.2648 +2024-12-29 03:21:18,068 - pyskl - INFO - Epoch [81][100/3746] lr: 4.475e-02, eta: 2 days, 13:10:37, time: 2.573, data_time: 1.541, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5872, loss_cls: 3.8199, loss: 3.8199 +2024-12-29 03:22:43,522 - pyskl - INFO - Epoch [81][200/3746] lr: 4.472e-02, eta: 2 days, 13:09:14, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5905, loss_cls: 3.8163, loss: 3.8163 +2024-12-29 03:24:09,123 - pyskl - INFO - Epoch [81][300/3746] lr: 4.469e-02, eta: 2 days, 13:07:52, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5811, loss_cls: 3.8265, loss: 3.8265 +2024-12-29 03:25:34,500 - pyskl - INFO - Epoch [81][400/3746] lr: 4.466e-02, eta: 2 days, 13:06:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5917, loss_cls: 3.7863, loss: 3.7863 +2024-12-29 03:26:59,759 - pyskl - INFO - Epoch [81][500/3746] lr: 4.463e-02, eta: 2 days, 13:05:06, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5884, loss_cls: 3.8161, loss: 3.8161 +2024-12-29 03:28:25,082 - pyskl - INFO - Epoch [81][600/3746] lr: 4.461e-02, eta: 2 days, 13:03:43, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3192, top5_acc: 0.5778, loss_cls: 3.8972, loss: 3.8972 +2024-12-29 03:29:50,680 - pyskl - INFO - Epoch [81][700/3746] lr: 4.458e-02, eta: 2 days, 13:02:20, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5847, loss_cls: 3.8240, loss: 3.8240 +2024-12-29 03:31:15,963 - pyskl - INFO - Epoch [81][800/3746] lr: 4.455e-02, eta: 2 days, 13:00:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5905, loss_cls: 3.8046, loss: 3.8046 +2024-12-29 03:32:41,576 - pyskl - INFO - Epoch [81][900/3746] lr: 4.452e-02, eta: 2 days, 12:59:35, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5878, loss_cls: 3.8194, loss: 3.8194 +2024-12-29 03:34:06,968 - pyskl - INFO - Epoch [81][1000/3746] lr: 4.450e-02, eta: 2 days, 12:58:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5941, loss_cls: 3.8048, loss: 3.8048 +2024-12-29 03:35:31,697 - pyskl - INFO - Epoch [81][1100/3746] lr: 4.447e-02, eta: 2 days, 12:56:49, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5863, loss_cls: 3.8696, loss: 3.8696 +2024-12-29 03:36:56,708 - pyskl - INFO - Epoch [81][1200/3746] lr: 4.444e-02, eta: 2 days, 12:55:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5764, loss_cls: 3.8842, loss: 3.8842 +2024-12-29 03:38:21,267 - pyskl - INFO - Epoch [81][1300/3746] lr: 4.441e-02, eta: 2 days, 12:54:02, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5927, loss_cls: 3.7804, loss: 3.7804 +2024-12-29 03:39:46,071 - pyskl - INFO - Epoch [81][1400/3746] lr: 4.438e-02, eta: 2 days, 12:52:39, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5797, loss_cls: 3.8550, loss: 3.8550 +2024-12-29 03:41:10,675 - pyskl - INFO - Epoch [81][1500/3746] lr: 4.436e-02, eta: 2 days, 12:51:15, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5828, loss_cls: 3.8794, loss: 3.8794 +2024-12-29 03:42:35,798 - pyskl - INFO - Epoch [81][1600/3746] lr: 4.433e-02, eta: 2 days, 12:49:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5773, loss_cls: 3.8831, loss: 3.8831 +2024-12-29 03:44:01,003 - pyskl - INFO - Epoch [81][1700/3746] lr: 4.430e-02, eta: 2 days, 12:48:29, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3244, top5_acc: 0.5742, loss_cls: 3.8772, loss: 3.8772 +2024-12-29 03:45:26,269 - pyskl - INFO - Epoch [81][1800/3746] lr: 4.427e-02, eta: 2 days, 12:47:06, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5878, loss_cls: 3.8297, loss: 3.8297 +2024-12-29 03:46:51,902 - pyskl - INFO - Epoch [81][1900/3746] lr: 4.425e-02, eta: 2 days, 12:45:43, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5728, loss_cls: 3.8947, loss: 3.8947 +2024-12-29 03:48:16,974 - pyskl - INFO - Epoch [81][2000/3746] lr: 4.422e-02, eta: 2 days, 12:44:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5906, loss_cls: 3.8339, loss: 3.8339 +2024-12-29 03:49:42,113 - pyskl - INFO - Epoch [81][2100/3746] lr: 4.419e-02, eta: 2 days, 12:42:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5953, loss_cls: 3.8059, loss: 3.8059 +2024-12-29 03:51:07,772 - pyskl - INFO - Epoch [81][2200/3746] lr: 4.416e-02, eta: 2 days, 12:41:34, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5859, loss_cls: 3.8183, loss: 3.8183 +2024-12-29 03:52:33,025 - pyskl - INFO - Epoch [81][2300/3746] lr: 4.413e-02, eta: 2 days, 12:40:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5886, loss_cls: 3.8195, loss: 3.8195 +2024-12-29 03:53:58,011 - pyskl - INFO - Epoch [81][2400/3746] lr: 4.411e-02, eta: 2 days, 12:38:48, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5913, loss_cls: 3.8434, loss: 3.8434 +2024-12-29 03:55:22,759 - pyskl - INFO - Epoch [81][2500/3746] lr: 4.408e-02, eta: 2 days, 12:37:25, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5831, loss_cls: 3.8555, loss: 3.8555 +2024-12-29 03:56:48,336 - pyskl - INFO - Epoch [81][2600/3746] lr: 4.405e-02, eta: 2 days, 12:36:02, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5794, loss_cls: 3.8385, loss: 3.8385 +2024-12-29 03:58:13,667 - pyskl - INFO - Epoch [81][2700/3746] lr: 4.402e-02, eta: 2 days, 12:34:39, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5858, loss_cls: 3.8771, loss: 3.8771 +2024-12-29 03:59:38,990 - pyskl - INFO - Epoch [81][2800/3746] lr: 4.400e-02, eta: 2 days, 12:33:16, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5830, loss_cls: 3.8575, loss: 3.8575 +2024-12-29 04:01:04,229 - pyskl - INFO - Epoch [81][2900/3746] lr: 4.397e-02, eta: 2 days, 12:31:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5889, loss_cls: 3.8273, loss: 3.8273 +2024-12-29 04:02:29,872 - pyskl - INFO - Epoch [81][3000/3746] lr: 4.394e-02, eta: 2 days, 12:30:31, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3250, top5_acc: 0.5864, loss_cls: 3.8444, loss: 3.8444 +2024-12-29 04:03:55,258 - pyskl - INFO - Epoch [81][3100/3746] lr: 4.391e-02, eta: 2 days, 12:29:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5873, loss_cls: 3.8149, loss: 3.8149 +2024-12-29 04:05:20,374 - pyskl - INFO - Epoch [81][3200/3746] lr: 4.389e-02, eta: 2 days, 12:27:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5848, loss_cls: 3.8275, loss: 3.8275 +2024-12-29 04:06:45,652 - pyskl - INFO - Epoch [81][3300/3746] lr: 4.386e-02, eta: 2 days, 12:26:22, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5883, loss_cls: 3.8276, loss: 3.8276 +2024-12-29 04:08:11,171 - pyskl - INFO - Epoch [81][3400/3746] lr: 4.383e-02, eta: 2 days, 12:24:59, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5903, loss_cls: 3.8212, loss: 3.8212 +2024-12-29 04:09:36,657 - pyskl - INFO - Epoch [81][3500/3746] lr: 4.380e-02, eta: 2 days, 12:23:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5772, loss_cls: 3.8530, loss: 3.8530 +2024-12-29 04:11:01,946 - pyskl - INFO - Epoch [81][3600/3746] lr: 4.377e-02, eta: 2 days, 12:22:13, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5844, loss_cls: 3.8647, loss: 3.8647 +2024-12-29 04:12:27,537 - pyskl - INFO - Epoch [81][3700/3746] lr: 4.375e-02, eta: 2 days, 12:20:50, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5780, loss_cls: 3.8549, loss: 3.8549 +2024-12-29 04:13:08,446 - pyskl - INFO - Saving checkpoint at 81 epochs +2024-12-29 04:15:07,043 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 04:15:07,775 - pyskl - INFO - +top1_acc 0.2812 +top5_acc 0.5234 +2024-12-29 04:15:07,775 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 04:15:07,822 - pyskl - INFO - +mean_acc 0.2810 +2024-12-29 04:15:07,827 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_77.pth was removed +2024-12-29 04:15:08,145 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_81.pth. +2024-12-29 04:15:08,146 - pyskl - INFO - Best top1_acc is 0.2812 at 81 epoch. +2024-12-29 04:15:08,162 - pyskl - INFO - Epoch(val) [81][309] top1_acc: 0.2812, top5_acc: 0.5234, mean_class_accuracy: 0.2810 +2024-12-29 04:19:24,928 - pyskl - INFO - Epoch [82][100/3746] lr: 4.371e-02, eta: 2 days, 12:20:42, time: 2.568, data_time: 1.524, memory: 15990, top1_acc: 0.3398, top5_acc: 0.5994, loss_cls: 3.7534, loss: 3.7534 +2024-12-29 04:20:50,201 - pyskl - INFO - Epoch [82][200/3746] lr: 4.368e-02, eta: 2 days, 12:19:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5959, loss_cls: 3.8031, loss: 3.8031 +2024-12-29 04:22:15,451 - pyskl - INFO - Epoch [82][300/3746] lr: 4.365e-02, eta: 2 days, 12:17:56, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5881, loss_cls: 3.7940, loss: 3.7940 +2024-12-29 04:23:41,429 - pyskl - INFO - Epoch [82][400/3746] lr: 4.362e-02, eta: 2 days, 12:16:33, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3417, top5_acc: 0.5959, loss_cls: 3.7365, loss: 3.7365 +2024-12-29 04:25:06,902 - pyskl - INFO - Epoch [82][500/3746] lr: 4.359e-02, eta: 2 days, 12:15:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.5881, loss_cls: 3.7884, loss: 3.7884 +2024-12-29 04:26:32,452 - pyskl - INFO - Epoch [82][600/3746] lr: 4.357e-02, eta: 2 days, 12:13:48, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5947, loss_cls: 3.7715, loss: 3.7715 +2024-12-29 04:27:57,592 - pyskl - INFO - Epoch [82][700/3746] lr: 4.354e-02, eta: 2 days, 12:12:24, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.5917, loss_cls: 3.7889, loss: 3.7889 +2024-12-29 04:29:23,775 - pyskl - INFO - Epoch [82][800/3746] lr: 4.351e-02, eta: 2 days, 12:11:02, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5895, loss_cls: 3.8055, loss: 3.8055 +2024-12-29 04:30:49,781 - pyskl - INFO - Epoch [82][900/3746] lr: 4.348e-02, eta: 2 days, 12:09:40, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5867, loss_cls: 3.8302, loss: 3.8302 +2024-12-29 04:32:15,502 - pyskl - INFO - Epoch [82][1000/3746] lr: 4.346e-02, eta: 2 days, 12:08:17, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3230, top5_acc: 0.5880, loss_cls: 3.8602, loss: 3.8602 +2024-12-29 04:33:41,001 - pyskl - INFO - Epoch [82][1100/3746] lr: 4.343e-02, eta: 2 days, 12:06:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5942, loss_cls: 3.8112, loss: 3.8112 +2024-12-29 04:35:06,195 - pyskl - INFO - Epoch [82][1200/3746] lr: 4.340e-02, eta: 2 days, 12:05:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5925, loss_cls: 3.8248, loss: 3.8248 +2024-12-29 04:36:31,484 - pyskl - INFO - Epoch [82][1300/3746] lr: 4.337e-02, eta: 2 days, 12:04:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5891, loss_cls: 3.8530, loss: 3.8530 +2024-12-29 04:37:57,343 - pyskl - INFO - Epoch [82][1400/3746] lr: 4.335e-02, eta: 2 days, 12:02:45, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5898, loss_cls: 3.8126, loss: 3.8126 +2024-12-29 04:39:23,304 - pyskl - INFO - Epoch [82][1500/3746] lr: 4.332e-02, eta: 2 days, 12:01:23, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5953, loss_cls: 3.8009, loss: 3.8009 +2024-12-29 04:40:49,561 - pyskl - INFO - Epoch [82][1600/3746] lr: 4.329e-02, eta: 2 days, 12:00:00, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5820, loss_cls: 3.8552, loss: 3.8552 +2024-12-29 04:42:15,389 - pyskl - INFO - Epoch [82][1700/3746] lr: 4.326e-02, eta: 2 days, 11:58:38, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5917, loss_cls: 3.8130, loss: 3.8130 +2024-12-29 04:43:40,969 - pyskl - INFO - Epoch [82][1800/3746] lr: 4.323e-02, eta: 2 days, 11:57:15, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5864, loss_cls: 3.8369, loss: 3.8369 +2024-12-29 04:45:06,529 - pyskl - INFO - Epoch [82][1900/3746] lr: 4.321e-02, eta: 2 days, 11:55:52, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5694, loss_cls: 3.8896, loss: 3.8896 +2024-12-29 04:46:32,163 - pyskl - INFO - Epoch [82][2000/3746] lr: 4.318e-02, eta: 2 days, 11:54:29, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5773, loss_cls: 3.8709, loss: 3.8709 +2024-12-29 04:47:57,254 - pyskl - INFO - Epoch [82][2100/3746] lr: 4.315e-02, eta: 2 days, 11:53:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5827, loss_cls: 3.8103, loss: 3.8103 +2024-12-29 04:49:22,779 - pyskl - INFO - Epoch [82][2200/3746] lr: 4.312e-02, eta: 2 days, 11:51:43, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5825, loss_cls: 3.8466, loss: 3.8466 +2024-12-29 04:50:48,464 - pyskl - INFO - Epoch [82][2300/3746] lr: 4.310e-02, eta: 2 days, 11:50:20, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5784, loss_cls: 3.8686, loss: 3.8686 +2024-12-29 04:52:14,423 - pyskl - INFO - Epoch [82][2400/3746] lr: 4.307e-02, eta: 2 days, 11:48:58, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6078, loss_cls: 3.7640, loss: 3.7640 +2024-12-29 04:53:40,124 - pyskl - INFO - Epoch [82][2500/3746] lr: 4.304e-02, eta: 2 days, 11:47:35, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5794, loss_cls: 3.8486, loss: 3.8486 +2024-12-29 04:55:05,711 - pyskl - INFO - Epoch [82][2600/3746] lr: 4.301e-02, eta: 2 days, 11:46:12, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5858, loss_cls: 3.8232, loss: 3.8232 +2024-12-29 04:56:31,198 - pyskl - INFO - Epoch [82][2700/3746] lr: 4.299e-02, eta: 2 days, 11:44:49, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5875, loss_cls: 3.8124, loss: 3.8124 +2024-12-29 04:57:56,245 - pyskl - INFO - Epoch [82][2800/3746] lr: 4.296e-02, eta: 2 days, 11:43:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5894, loss_cls: 3.8528, loss: 3.8528 +2024-12-29 04:59:22,142 - pyskl - INFO - Epoch [82][2900/3746] lr: 4.293e-02, eta: 2 days, 11:42:03, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5870, loss_cls: 3.8260, loss: 3.8260 +2024-12-29 05:00:47,972 - pyskl - INFO - Epoch [82][3000/3746] lr: 4.290e-02, eta: 2 days, 11:40:41, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5858, loss_cls: 3.8501, loss: 3.8501 +2024-12-29 05:02:13,781 - pyskl - INFO - Epoch [82][3100/3746] lr: 4.287e-02, eta: 2 days, 11:39:18, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5872, loss_cls: 3.8248, loss: 3.8248 +2024-12-29 05:03:39,419 - pyskl - INFO - Epoch [82][3200/3746] lr: 4.285e-02, eta: 2 days, 11:37:55, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5811, loss_cls: 3.9038, loss: 3.9038 +2024-12-29 05:05:05,102 - pyskl - INFO - Epoch [82][3300/3746] lr: 4.282e-02, eta: 2 days, 11:36:33, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5841, loss_cls: 3.8487, loss: 3.8487 +2024-12-29 05:06:30,919 - pyskl - INFO - Epoch [82][3400/3746] lr: 4.279e-02, eta: 2 days, 11:35:10, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5819, loss_cls: 3.8620, loss: 3.8620 +2024-12-29 05:07:56,949 - pyskl - INFO - Epoch [82][3500/3746] lr: 4.276e-02, eta: 2 days, 11:33:47, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5837, loss_cls: 3.8506, loss: 3.8506 +2024-12-29 05:09:22,532 - pyskl - INFO - Epoch [82][3600/3746] lr: 4.274e-02, eta: 2 days, 11:32:25, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5867, loss_cls: 3.8373, loss: 3.8373 +2024-12-29 05:10:48,541 - pyskl - INFO - Epoch [82][3700/3746] lr: 4.271e-02, eta: 2 days, 11:31:02, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5859, loss_cls: 3.8470, loss: 3.8470 +2024-12-29 05:11:30,284 - pyskl - INFO - Saving checkpoint at 82 epochs +2024-12-29 05:13:29,019 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 05:13:29,843 - pyskl - INFO - +top1_acc 0.2719 +top5_acc 0.5201 +2024-12-29 05:13:29,843 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 05:13:29,886 - pyskl - INFO - +mean_acc 0.2719 +2024-12-29 05:13:29,901 - pyskl - INFO - Epoch(val) [82][309] top1_acc: 0.2719, top5_acc: 0.5201, mean_class_accuracy: 0.2719 +2024-12-29 05:17:47,876 - pyskl - INFO - Epoch [83][100/3746] lr: 4.267e-02, eta: 2 days, 11:30:51, time: 2.580, data_time: 1.527, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6050, loss_cls: 3.7203, loss: 3.7203 +2024-12-29 05:19:14,614 - pyskl - INFO - Epoch [83][200/3746] lr: 4.264e-02, eta: 2 days, 11:29:29, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6020, loss_cls: 3.7594, loss: 3.7594 +2024-12-29 05:20:41,568 - pyskl - INFO - Epoch [83][300/3746] lr: 4.261e-02, eta: 2 days, 11:28:07, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5880, loss_cls: 3.8032, loss: 3.8032 +2024-12-29 05:22:08,293 - pyskl - INFO - Epoch [83][400/3746] lr: 4.259e-02, eta: 2 days, 11:26:45, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5837, loss_cls: 3.8147, loss: 3.8147 +2024-12-29 05:23:35,056 - pyskl - INFO - Epoch [83][500/3746] lr: 4.256e-02, eta: 2 days, 11:25:23, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5837, loss_cls: 3.8557, loss: 3.8557 +2024-12-29 05:25:01,370 - pyskl - INFO - Epoch [83][600/3746] lr: 4.253e-02, eta: 2 days, 11:24:01, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5850, loss_cls: 3.7966, loss: 3.7966 +2024-12-29 05:26:27,839 - pyskl - INFO - Epoch [83][700/3746] lr: 4.250e-02, eta: 2 days, 11:22:39, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5809, loss_cls: 3.8600, loss: 3.8600 +2024-12-29 05:27:53,802 - pyskl - INFO - Epoch [83][800/3746] lr: 4.247e-02, eta: 2 days, 11:21:16, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5895, loss_cls: 3.8000, loss: 3.8000 +2024-12-29 05:29:20,061 - pyskl - INFO - Epoch [83][900/3746] lr: 4.245e-02, eta: 2 days, 11:19:54, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5836, loss_cls: 3.8234, loss: 3.8234 +2024-12-29 05:30:45,640 - pyskl - INFO - Epoch [83][1000/3746] lr: 4.242e-02, eta: 2 days, 11:18:31, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.5981, loss_cls: 3.7722, loss: 3.7722 +2024-12-29 05:32:10,554 - pyskl - INFO - Epoch [83][1100/3746] lr: 4.239e-02, eta: 2 days, 11:17:07, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.5916, loss_cls: 3.7810, loss: 3.7810 +2024-12-29 05:33:35,851 - pyskl - INFO - Epoch [83][1200/3746] lr: 4.236e-02, eta: 2 days, 11:15:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5859, loss_cls: 3.8115, loss: 3.8115 +2024-12-29 05:35:00,515 - pyskl - INFO - Epoch [83][1300/3746] lr: 4.234e-02, eta: 2 days, 11:14:20, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5944, loss_cls: 3.7908, loss: 3.7908 +2024-12-29 05:36:26,037 - pyskl - INFO - Epoch [83][1400/3746] lr: 4.231e-02, eta: 2 days, 11:12:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5917, loss_cls: 3.8144, loss: 3.8144 +2024-12-29 05:37:51,944 - pyskl - INFO - Epoch [83][1500/3746] lr: 4.228e-02, eta: 2 days, 11:11:35, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5884, loss_cls: 3.8209, loss: 3.8209 +2024-12-29 05:39:18,013 - pyskl - INFO - Epoch [83][1600/3746] lr: 4.225e-02, eta: 2 days, 11:10:12, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5875, loss_cls: 3.8343, loss: 3.8343 +2024-12-29 05:40:43,924 - pyskl - INFO - Epoch [83][1700/3746] lr: 4.223e-02, eta: 2 days, 11:08:49, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5906, loss_cls: 3.8027, loss: 3.8027 +2024-12-29 05:42:09,895 - pyskl - INFO - Epoch [83][1800/3746] lr: 4.220e-02, eta: 2 days, 11:07:27, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5825, loss_cls: 3.8327, loss: 3.8327 +2024-12-29 05:43:35,074 - pyskl - INFO - Epoch [83][1900/3746] lr: 4.217e-02, eta: 2 days, 11:06:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5822, loss_cls: 3.8234, loss: 3.8234 +2024-12-29 05:45:00,409 - pyskl - INFO - Epoch [83][2000/3746] lr: 4.214e-02, eta: 2 days, 11:04:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5941, loss_cls: 3.7834, loss: 3.7834 +2024-12-29 05:46:25,519 - pyskl - INFO - Epoch [83][2100/3746] lr: 4.212e-02, eta: 2 days, 11:03:17, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.5991, loss_cls: 3.7803, loss: 3.7803 +2024-12-29 05:47:51,385 - pyskl - INFO - Epoch [83][2200/3746] lr: 4.209e-02, eta: 2 days, 11:01:54, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.5978, loss_cls: 3.7908, loss: 3.7908 +2024-12-29 05:49:16,980 - pyskl - INFO - Epoch [83][2300/3746] lr: 4.206e-02, eta: 2 days, 11:00:31, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5828, loss_cls: 3.8544, loss: 3.8544 +2024-12-29 05:50:43,072 - pyskl - INFO - Epoch [83][2400/3746] lr: 4.203e-02, eta: 2 days, 10:59:08, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5800, loss_cls: 3.8505, loss: 3.8505 +2024-12-29 05:52:08,661 - pyskl - INFO - Epoch [83][2500/3746] lr: 4.201e-02, eta: 2 days, 10:57:45, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5822, loss_cls: 3.8331, loss: 3.8331 +2024-12-29 05:53:33,325 - pyskl - INFO - Epoch [83][2600/3746] lr: 4.198e-02, eta: 2 days, 10:56:22, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3302, top5_acc: 0.5903, loss_cls: 3.8080, loss: 3.8080 +2024-12-29 05:54:58,765 - pyskl - INFO - Epoch [83][2700/3746] lr: 4.195e-02, eta: 2 days, 10:54:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5867, loss_cls: 3.8243, loss: 3.8243 +2024-12-29 05:56:23,880 - pyskl - INFO - Epoch [83][2800/3746] lr: 4.192e-02, eta: 2 days, 10:53:35, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5927, loss_cls: 3.8058, loss: 3.8058 +2024-12-29 05:57:49,696 - pyskl - INFO - Epoch [83][2900/3746] lr: 4.190e-02, eta: 2 days, 10:52:12, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5895, loss_cls: 3.7812, loss: 3.7812 +2024-12-29 05:59:16,106 - pyskl - INFO - Epoch [83][3000/3746] lr: 4.187e-02, eta: 2 days, 10:50:50, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5723, loss_cls: 3.9016, loss: 3.9016 +2024-12-29 06:00:42,083 - pyskl - INFO - Epoch [83][3100/3746] lr: 4.184e-02, eta: 2 days, 10:49:27, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5786, loss_cls: 3.8397, loss: 3.8397 +2024-12-29 06:02:08,084 - pyskl - INFO - Epoch [83][3200/3746] lr: 4.181e-02, eta: 2 days, 10:48:05, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5934, loss_cls: 3.8009, loss: 3.8009 +2024-12-29 06:03:34,205 - pyskl - INFO - Epoch [83][3300/3746] lr: 4.178e-02, eta: 2 days, 10:46:42, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5755, loss_cls: 3.8689, loss: 3.8689 +2024-12-29 06:05:00,005 - pyskl - INFO - Epoch [83][3400/3746] lr: 4.176e-02, eta: 2 days, 10:45:19, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5891, loss_cls: 3.8000, loss: 3.8000 +2024-12-29 06:06:25,848 - pyskl - INFO - Epoch [83][3500/3746] lr: 4.173e-02, eta: 2 days, 10:43:57, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5877, loss_cls: 3.8016, loss: 3.8016 +2024-12-29 06:07:51,303 - pyskl - INFO - Epoch [83][3600/3746] lr: 4.170e-02, eta: 2 days, 10:42:33, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3236, top5_acc: 0.5808, loss_cls: 3.8465, loss: 3.8465 +2024-12-29 06:09:17,421 - pyskl - INFO - Epoch [83][3700/3746] lr: 4.167e-02, eta: 2 days, 10:41:11, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5777, loss_cls: 3.8674, loss: 3.8674 +2024-12-29 06:09:58,319 - pyskl - INFO - Saving checkpoint at 83 epochs +2024-12-29 06:11:58,338 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 06:11:59,074 - pyskl - INFO - +top1_acc 0.2703 +top5_acc 0.5199 +2024-12-29 06:11:59,074 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 06:11:59,131 - pyskl - INFO - +mean_acc 0.2700 +2024-12-29 06:11:59,151 - pyskl - INFO - Epoch(val) [83][309] top1_acc: 0.2703, top5_acc: 0.5199, mean_class_accuracy: 0.2700 +2024-12-29 06:16:20,654 - pyskl - INFO - Epoch [84][100/3746] lr: 4.163e-02, eta: 2 days, 10:41:00, time: 2.615, data_time: 1.573, memory: 15990, top1_acc: 0.3423, top5_acc: 0.6106, loss_cls: 3.7104, loss: 3.7104 +2024-12-29 06:17:45,484 - pyskl - INFO - Epoch [84][200/3746] lr: 4.161e-02, eta: 2 days, 10:39:36, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5884, loss_cls: 3.7939, loss: 3.7939 +2024-12-29 06:19:10,963 - pyskl - INFO - Epoch [84][300/3746] lr: 4.158e-02, eta: 2 days, 10:38:13, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.5983, loss_cls: 3.7270, loss: 3.7270 +2024-12-29 06:20:36,341 - pyskl - INFO - Epoch [84][400/3746] lr: 4.155e-02, eta: 2 days, 10:36:50, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6064, loss_cls: 3.7125, loss: 3.7125 +2024-12-29 06:22:01,463 - pyskl - INFO - Epoch [84][500/3746] lr: 4.152e-02, eta: 2 days, 10:35:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5928, loss_cls: 3.7919, loss: 3.7919 +2024-12-29 06:23:26,701 - pyskl - INFO - Epoch [84][600/3746] lr: 4.150e-02, eta: 2 days, 10:34:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5906, loss_cls: 3.7844, loss: 3.7844 +2024-12-29 06:24:52,066 - pyskl - INFO - Epoch [84][700/3746] lr: 4.147e-02, eta: 2 days, 10:32:39, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6012, loss_cls: 3.7496, loss: 3.7496 +2024-12-29 06:26:17,555 - pyskl - INFO - Epoch [84][800/3746] lr: 4.144e-02, eta: 2 days, 10:31:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5870, loss_cls: 3.8189, loss: 3.8189 +2024-12-29 06:27:42,968 - pyskl - INFO - Epoch [84][900/3746] lr: 4.141e-02, eta: 2 days, 10:29:53, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5856, loss_cls: 3.8169, loss: 3.8169 +2024-12-29 06:29:08,041 - pyskl - INFO - Epoch [84][1000/3746] lr: 4.139e-02, eta: 2 days, 10:28:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5927, loss_cls: 3.7951, loss: 3.7951 +2024-12-29 06:30:33,431 - pyskl - INFO - Epoch [84][1100/3746] lr: 4.136e-02, eta: 2 days, 10:27:06, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5969, loss_cls: 3.7950, loss: 3.7950 +2024-12-29 06:31:59,225 - pyskl - INFO - Epoch [84][1200/3746] lr: 4.133e-02, eta: 2 days, 10:25:43, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5878, loss_cls: 3.7942, loss: 3.7942 +2024-12-29 06:33:24,236 - pyskl - INFO - Epoch [84][1300/3746] lr: 4.130e-02, eta: 2 days, 10:24:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3316, top5_acc: 0.5859, loss_cls: 3.8130, loss: 3.8130 +2024-12-29 06:34:49,856 - pyskl - INFO - Epoch [84][1400/3746] lr: 4.128e-02, eta: 2 days, 10:22:57, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5841, loss_cls: 3.8609, loss: 3.8609 +2024-12-29 06:36:14,920 - pyskl - INFO - Epoch [84][1500/3746] lr: 4.125e-02, eta: 2 days, 10:21:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5894, loss_cls: 3.7962, loss: 3.7962 +2024-12-29 06:37:39,808 - pyskl - INFO - Epoch [84][1600/3746] lr: 4.122e-02, eta: 2 days, 10:20:09, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.6008, loss_cls: 3.7501, loss: 3.7501 +2024-12-29 06:39:04,491 - pyskl - INFO - Epoch [84][1700/3746] lr: 4.119e-02, eta: 2 days, 10:18:46, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5845, loss_cls: 3.8355, loss: 3.8355 +2024-12-29 06:40:29,633 - pyskl - INFO - Epoch [84][1800/3746] lr: 4.117e-02, eta: 2 days, 10:17:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5852, loss_cls: 3.8527, loss: 3.8527 +2024-12-29 06:41:54,842 - pyskl - INFO - Epoch [84][1900/3746] lr: 4.114e-02, eta: 2 days, 10:15:59, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5867, loss_cls: 3.8319, loss: 3.8319 +2024-12-29 06:43:19,721 - pyskl - INFO - Epoch [84][2000/3746] lr: 4.111e-02, eta: 2 days, 10:14:35, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5827, loss_cls: 3.8495, loss: 3.8495 +2024-12-29 06:44:44,901 - pyskl - INFO - Epoch [84][2100/3746] lr: 4.108e-02, eta: 2 days, 10:13:12, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5944, loss_cls: 3.7918, loss: 3.7918 +2024-12-29 06:46:10,386 - pyskl - INFO - Epoch [84][2200/3746] lr: 4.106e-02, eta: 2 days, 10:11:48, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5878, loss_cls: 3.8201, loss: 3.8201 +2024-12-29 06:47:35,828 - pyskl - INFO - Epoch [84][2300/3746] lr: 4.103e-02, eta: 2 days, 10:10:25, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5913, loss_cls: 3.8168, loss: 3.8168 +2024-12-29 06:49:01,223 - pyskl - INFO - Epoch [84][2400/3746] lr: 4.100e-02, eta: 2 days, 10:09:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5936, loss_cls: 3.8042, loss: 3.8042 +2024-12-29 06:50:26,406 - pyskl - INFO - Epoch [84][2500/3746] lr: 4.097e-02, eta: 2 days, 10:07:38, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5931, loss_cls: 3.7977, loss: 3.7977 +2024-12-29 06:51:51,327 - pyskl - INFO - Epoch [84][2600/3746] lr: 4.095e-02, eta: 2 days, 10:06:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5859, loss_cls: 3.8405, loss: 3.8405 +2024-12-29 06:53:16,555 - pyskl - INFO - Epoch [84][2700/3746] lr: 4.092e-02, eta: 2 days, 10:04:51, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.5936, loss_cls: 3.7841, loss: 3.7841 +2024-12-29 06:54:41,814 - pyskl - INFO - Epoch [84][2800/3746] lr: 4.089e-02, eta: 2 days, 10:03:28, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5922, loss_cls: 3.7718, loss: 3.7718 +2024-12-29 06:56:06,511 - pyskl - INFO - Epoch [84][2900/3746] lr: 4.086e-02, eta: 2 days, 10:02:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5930, loss_cls: 3.8189, loss: 3.8189 +2024-12-29 06:57:31,680 - pyskl - INFO - Epoch [84][3000/3746] lr: 4.084e-02, eta: 2 days, 10:00:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5931, loss_cls: 3.8037, loss: 3.8037 +2024-12-29 06:58:56,882 - pyskl - INFO - Epoch [84][3100/3746] lr: 4.081e-02, eta: 2 days, 9:59:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3280, top5_acc: 0.5969, loss_cls: 3.8134, loss: 3.8134 +2024-12-29 07:00:22,184 - pyskl - INFO - Epoch [84][3200/3746] lr: 4.078e-02, eta: 2 days, 9:57:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.5883, loss_cls: 3.7917, loss: 3.7917 +2024-12-29 07:01:47,562 - pyskl - INFO - Epoch [84][3300/3746] lr: 4.075e-02, eta: 2 days, 9:56:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5845, loss_cls: 3.8247, loss: 3.8247 +2024-12-29 07:03:13,068 - pyskl - INFO - Epoch [84][3400/3746] lr: 4.073e-02, eta: 2 days, 9:55:07, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5823, loss_cls: 3.8247, loss: 3.8247 +2024-12-29 07:04:38,480 - pyskl - INFO - Epoch [84][3500/3746] lr: 4.070e-02, eta: 2 days, 9:53:44, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.5920, loss_cls: 3.7941, loss: 3.7941 +2024-12-29 07:06:03,589 - pyskl - INFO - Epoch [84][3600/3746] lr: 4.067e-02, eta: 2 days, 9:52:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5817, loss_cls: 3.8496, loss: 3.8496 +2024-12-29 07:07:28,753 - pyskl - INFO - Epoch [84][3700/3746] lr: 4.064e-02, eta: 2 days, 9:50:57, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5892, loss_cls: 3.8064, loss: 3.8064 +2024-12-29 07:08:10,027 - pyskl - INFO - Saving checkpoint at 84 epochs +2024-12-29 07:10:10,032 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 07:10:10,736 - pyskl - INFO - +top1_acc 0.2734 +top5_acc 0.5255 +2024-12-29 07:10:10,737 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 07:10:10,783 - pyskl - INFO - +mean_acc 0.2731 +2024-12-29 07:10:10,797 - pyskl - INFO - Epoch(val) [84][309] top1_acc: 0.2734, top5_acc: 0.5255, mean_class_accuracy: 0.2731 +2024-12-29 07:14:28,679 - pyskl - INFO - Epoch [85][100/3746] lr: 4.060e-02, eta: 2 days, 9:50:40, time: 2.579, data_time: 1.536, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5941, loss_cls: 3.7810, loss: 3.7810 +2024-12-29 07:15:53,948 - pyskl - INFO - Epoch [85][200/3746] lr: 4.058e-02, eta: 2 days, 9:49:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6017, loss_cls: 3.7354, loss: 3.7354 +2024-12-29 07:17:19,498 - pyskl - INFO - Epoch [85][300/3746] lr: 4.055e-02, eta: 2 days, 9:47:53, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.6006, loss_cls: 3.7498, loss: 3.7498 +2024-12-29 07:18:44,684 - pyskl - INFO - Epoch [85][400/3746] lr: 4.052e-02, eta: 2 days, 9:46:30, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5930, loss_cls: 3.7855, loss: 3.7855 +2024-12-29 07:20:10,247 - pyskl - INFO - Epoch [85][500/3746] lr: 4.049e-02, eta: 2 days, 9:45:07, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3377, top5_acc: 0.6027, loss_cls: 3.7514, loss: 3.7514 +2024-12-29 07:21:35,269 - pyskl - INFO - Epoch [85][600/3746] lr: 4.047e-02, eta: 2 days, 9:43:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.5923, loss_cls: 3.7415, loss: 3.7415 +2024-12-29 07:23:00,982 - pyskl - INFO - Epoch [85][700/3746] lr: 4.044e-02, eta: 2 days, 9:42:20, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5995, loss_cls: 3.7948, loss: 3.7948 +2024-12-29 07:24:26,475 - pyskl - INFO - Epoch [85][800/3746] lr: 4.041e-02, eta: 2 days, 9:40:56, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5923, loss_cls: 3.7701, loss: 3.7701 +2024-12-29 07:25:52,333 - pyskl - INFO - Epoch [85][900/3746] lr: 4.038e-02, eta: 2 days, 9:39:33, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5942, loss_cls: 3.7945, loss: 3.7945 +2024-12-29 07:27:17,721 - pyskl - INFO - Epoch [85][1000/3746] lr: 4.036e-02, eta: 2 days, 9:38:10, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.5967, loss_cls: 3.7776, loss: 3.7776 +2024-12-29 07:28:42,816 - pyskl - INFO - Epoch [85][1100/3746] lr: 4.033e-02, eta: 2 days, 9:36:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.6038, loss_cls: 3.7367, loss: 3.7367 +2024-12-29 07:30:07,824 - pyskl - INFO - Epoch [85][1200/3746] lr: 4.030e-02, eta: 2 days, 9:35:23, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5977, loss_cls: 3.7605, loss: 3.7605 +2024-12-29 07:31:32,516 - pyskl - INFO - Epoch [85][1300/3746] lr: 4.027e-02, eta: 2 days, 9:33:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5988, loss_cls: 3.7812, loss: 3.7812 +2024-12-29 07:32:57,883 - pyskl - INFO - Epoch [85][1400/3746] lr: 4.025e-02, eta: 2 days, 9:32:35, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5972, loss_cls: 3.7950, loss: 3.7950 +2024-12-29 07:34:23,384 - pyskl - INFO - Epoch [85][1500/3746] lr: 4.022e-02, eta: 2 days, 9:31:12, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5991, loss_cls: 3.7631, loss: 3.7631 +2024-12-29 07:35:48,828 - pyskl - INFO - Epoch [85][1600/3746] lr: 4.019e-02, eta: 2 days, 9:29:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.5897, loss_cls: 3.7836, loss: 3.7836 +2024-12-29 07:37:14,300 - pyskl - INFO - Epoch [85][1700/3746] lr: 4.016e-02, eta: 2 days, 9:28:25, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5830, loss_cls: 3.8453, loss: 3.8453 +2024-12-29 07:38:40,100 - pyskl - INFO - Epoch [85][1800/3746] lr: 4.014e-02, eta: 2 days, 9:27:02, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5900, loss_cls: 3.8034, loss: 3.8034 +2024-12-29 07:40:05,049 - pyskl - INFO - Epoch [85][1900/3746] lr: 4.011e-02, eta: 2 days, 9:25:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5909, loss_cls: 3.8054, loss: 3.8054 +2024-12-29 07:41:29,827 - pyskl - INFO - Epoch [85][2000/3746] lr: 4.008e-02, eta: 2 days, 9:24:15, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3356, top5_acc: 0.5887, loss_cls: 3.7860, loss: 3.7860 +2024-12-29 07:42:55,481 - pyskl - INFO - Epoch [85][2100/3746] lr: 4.006e-02, eta: 2 days, 9:22:51, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5867, loss_cls: 3.8310, loss: 3.8310 +2024-12-29 07:44:20,927 - pyskl - INFO - Epoch [85][2200/3746] lr: 4.003e-02, eta: 2 days, 9:21:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5881, loss_cls: 3.7997, loss: 3.7997 +2024-12-29 07:45:46,255 - pyskl - INFO - Epoch [85][2300/3746] lr: 4.000e-02, eta: 2 days, 9:20:05, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5892, loss_cls: 3.7973, loss: 3.7973 +2024-12-29 07:47:11,474 - pyskl - INFO - Epoch [85][2400/3746] lr: 3.997e-02, eta: 2 days, 9:18:41, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5848, loss_cls: 3.8302, loss: 3.8302 +2024-12-29 07:48:37,130 - pyskl - INFO - Epoch [85][2500/3746] lr: 3.995e-02, eta: 2 days, 9:17:18, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5981, loss_cls: 3.7754, loss: 3.7754 +2024-12-29 07:50:02,324 - pyskl - INFO - Epoch [85][2600/3746] lr: 3.992e-02, eta: 2 days, 9:15:54, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5898, loss_cls: 3.8224, loss: 3.8224 +2024-12-29 07:51:27,543 - pyskl - INFO - Epoch [85][2700/3746] lr: 3.989e-02, eta: 2 days, 9:14:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5858, loss_cls: 3.8277, loss: 3.8277 +2024-12-29 07:52:52,956 - pyskl - INFO - Epoch [85][2800/3746] lr: 3.986e-02, eta: 2 days, 9:13:07, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5914, loss_cls: 3.8006, loss: 3.8006 +2024-12-29 07:54:17,850 - pyskl - INFO - Epoch [85][2900/3746] lr: 3.984e-02, eta: 2 days, 9:11:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5828, loss_cls: 3.8184, loss: 3.8184 +2024-12-29 07:55:42,896 - pyskl - INFO - Epoch [85][3000/3746] lr: 3.981e-02, eta: 2 days, 9:10:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6028, loss_cls: 3.7465, loss: 3.7465 +2024-12-29 07:57:08,075 - pyskl - INFO - Epoch [85][3100/3746] lr: 3.978e-02, eta: 2 days, 9:08:56, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5956, loss_cls: 3.7822, loss: 3.7822 +2024-12-29 07:58:33,210 - pyskl - INFO - Epoch [85][3200/3746] lr: 3.975e-02, eta: 2 days, 9:07:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5916, loss_cls: 3.7915, loss: 3.7915 +2024-12-29 07:59:58,413 - pyskl - INFO - Epoch [85][3300/3746] lr: 3.973e-02, eta: 2 days, 9:06:09, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5848, loss_cls: 3.8009, loss: 3.8009 +2024-12-29 08:01:23,784 - pyskl - INFO - Epoch [85][3400/3746] lr: 3.970e-02, eta: 2 days, 9:04:46, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.6000, loss_cls: 3.7925, loss: 3.7925 +2024-12-29 08:02:49,415 - pyskl - INFO - Epoch [85][3500/3746] lr: 3.967e-02, eta: 2 days, 9:03:23, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3308, top5_acc: 0.5873, loss_cls: 3.7940, loss: 3.7940 +2024-12-29 08:04:14,819 - pyskl - INFO - Epoch [85][3600/3746] lr: 3.964e-02, eta: 2 days, 9:01:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5877, loss_cls: 3.8186, loss: 3.8186 +2024-12-29 08:05:40,278 - pyskl - INFO - Epoch [85][3700/3746] lr: 3.962e-02, eta: 2 days, 9:00:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5911, loss_cls: 3.7898, loss: 3.7898 +2024-12-29 08:06:21,349 - pyskl - INFO - Saving checkpoint at 85 epochs +2024-12-29 08:08:22,777 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 08:08:23,597 - pyskl - INFO - +top1_acc 0.2801 +top5_acc 0.5281 +2024-12-29 08:08:23,597 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 08:08:23,642 - pyskl - INFO - +mean_acc 0.2798 +2024-12-29 08:08:23,656 - pyskl - INFO - Epoch(val) [85][309] top1_acc: 0.2801, top5_acc: 0.5281, mean_class_accuracy: 0.2798 +2024-12-29 08:12:41,864 - pyskl - INFO - Epoch [86][100/3746] lr: 3.958e-02, eta: 2 days, 9:00:16, time: 2.582, data_time: 1.533, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5994, loss_cls: 3.7624, loss: 3.7624 +2024-12-29 08:14:07,855 - pyskl - INFO - Epoch [86][200/3746] lr: 3.955e-02, eta: 2 days, 8:58:53, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.6031, loss_cls: 3.7375, loss: 3.7375 +2024-12-29 08:15:33,850 - pyskl - INFO - Epoch [86][300/3746] lr: 3.952e-02, eta: 2 days, 8:57:30, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6003, loss_cls: 3.7365, loss: 3.7365 +2024-12-29 08:16:59,860 - pyskl - INFO - Epoch [86][400/3746] lr: 3.950e-02, eta: 2 days, 8:56:07, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6020, loss_cls: 3.7340, loss: 3.7340 +2024-12-29 08:18:26,291 - pyskl - INFO - Epoch [86][500/3746] lr: 3.947e-02, eta: 2 days, 8:54:44, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3327, top5_acc: 0.5911, loss_cls: 3.7980, loss: 3.7980 +2024-12-29 08:19:51,955 - pyskl - INFO - Epoch [86][600/3746] lr: 3.944e-02, eta: 2 days, 8:53:21, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5984, loss_cls: 3.7629, loss: 3.7629 +2024-12-29 08:21:18,007 - pyskl - INFO - Epoch [86][700/3746] lr: 3.941e-02, eta: 2 days, 8:51:58, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5887, loss_cls: 3.7805, loss: 3.7805 +2024-12-29 08:22:44,192 - pyskl - INFO - Epoch [86][800/3746] lr: 3.939e-02, eta: 2 days, 8:50:35, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6053, loss_cls: 3.7448, loss: 3.7448 +2024-12-29 08:24:10,879 - pyskl - INFO - Epoch [86][900/3746] lr: 3.936e-02, eta: 2 days, 8:49:12, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.5927, loss_cls: 3.7680, loss: 3.7680 +2024-12-29 08:25:36,847 - pyskl - INFO - Epoch [86][1000/3746] lr: 3.933e-02, eta: 2 days, 8:47:49, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5962, loss_cls: 3.7974, loss: 3.7974 +2024-12-29 08:27:02,571 - pyskl - INFO - Epoch [86][1100/3746] lr: 3.930e-02, eta: 2 days, 8:46:26, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6017, loss_cls: 3.7487, loss: 3.7487 +2024-12-29 08:28:27,928 - pyskl - INFO - Epoch [86][1200/3746] lr: 3.928e-02, eta: 2 days, 8:45:03, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6084, loss_cls: 3.6978, loss: 3.6978 +2024-12-29 08:29:53,315 - pyskl - INFO - Epoch [86][1300/3746] lr: 3.925e-02, eta: 2 days, 8:43:39, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6030, loss_cls: 3.7759, loss: 3.7759 +2024-12-29 08:31:18,813 - pyskl - INFO - Epoch [86][1400/3746] lr: 3.922e-02, eta: 2 days, 8:42:16, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3348, top5_acc: 0.5969, loss_cls: 3.7797, loss: 3.7797 +2024-12-29 08:32:44,412 - pyskl - INFO - Epoch [86][1500/3746] lr: 3.919e-02, eta: 2 days, 8:40:52, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5870, loss_cls: 3.8070, loss: 3.8070 +2024-12-29 08:34:10,579 - pyskl - INFO - Epoch [86][1600/3746] lr: 3.917e-02, eta: 2 days, 8:39:29, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.6012, loss_cls: 3.7578, loss: 3.7578 +2024-12-29 08:35:36,890 - pyskl - INFO - Epoch [86][1700/3746] lr: 3.914e-02, eta: 2 days, 8:38:07, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.5988, loss_cls: 3.7626, loss: 3.7626 +2024-12-29 08:37:02,460 - pyskl - INFO - Epoch [86][1800/3746] lr: 3.911e-02, eta: 2 days, 8:36:43, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.5962, loss_cls: 3.7530, loss: 3.7530 +2024-12-29 08:38:27,863 - pyskl - INFO - Epoch [86][1900/3746] lr: 3.909e-02, eta: 2 days, 8:35:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6044, loss_cls: 3.7531, loss: 3.7531 +2024-12-29 08:39:53,416 - pyskl - INFO - Epoch [86][2000/3746] lr: 3.906e-02, eta: 2 days, 8:33:56, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5950, loss_cls: 3.7534, loss: 3.7534 +2024-12-29 08:41:19,548 - pyskl - INFO - Epoch [86][2100/3746] lr: 3.903e-02, eta: 2 days, 8:32:33, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5906, loss_cls: 3.8335, loss: 3.8335 +2024-12-29 08:42:46,278 - pyskl - INFO - Epoch [86][2200/3746] lr: 3.900e-02, eta: 2 days, 8:31:11, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5917, loss_cls: 3.7751, loss: 3.7751 +2024-12-29 08:44:12,425 - pyskl - INFO - Epoch [86][2300/3746] lr: 3.898e-02, eta: 2 days, 8:29:48, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5819, loss_cls: 3.8394, loss: 3.8394 +2024-12-29 08:45:38,056 - pyskl - INFO - Epoch [86][2400/3746] lr: 3.895e-02, eta: 2 days, 8:28:24, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5844, loss_cls: 3.8209, loss: 3.8209 +2024-12-29 08:47:03,494 - pyskl - INFO - Epoch [86][2500/3746] lr: 3.892e-02, eta: 2 days, 8:27:01, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.5917, loss_cls: 3.7798, loss: 3.7798 +2024-12-29 08:48:28,798 - pyskl - INFO - Epoch [86][2600/3746] lr: 3.889e-02, eta: 2 days, 8:25:37, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6044, loss_cls: 3.7527, loss: 3.7527 +2024-12-29 08:49:54,664 - pyskl - INFO - Epoch [86][2700/3746] lr: 3.887e-02, eta: 2 days, 8:24:14, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5977, loss_cls: 3.7847, loss: 3.7847 +2024-12-29 08:51:20,049 - pyskl - INFO - Epoch [86][2800/3746] lr: 3.884e-02, eta: 2 days, 8:22:51, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6017, loss_cls: 3.7339, loss: 3.7339 +2024-12-29 08:52:45,376 - pyskl - INFO - Epoch [86][2900/3746] lr: 3.881e-02, eta: 2 days, 8:21:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6002, loss_cls: 3.7482, loss: 3.7482 +2024-12-29 08:54:10,615 - pyskl - INFO - Epoch [86][3000/3746] lr: 3.879e-02, eta: 2 days, 8:20:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5842, loss_cls: 3.8186, loss: 3.8186 +2024-12-29 08:55:36,402 - pyskl - INFO - Epoch [86][3100/3746] lr: 3.876e-02, eta: 2 days, 8:18:40, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.3333, top5_acc: 0.5989, loss_cls: 3.7574, loss: 3.7574 +2024-12-29 08:57:01,750 - pyskl - INFO - Epoch [86][3200/3746] lr: 3.873e-02, eta: 2 days, 8:17:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5927, loss_cls: 3.8066, loss: 3.8066 +2024-12-29 08:58:27,429 - pyskl - INFO - Epoch [86][3300/3746] lr: 3.870e-02, eta: 2 days, 8:15:53, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3317, top5_acc: 0.5873, loss_cls: 3.7941, loss: 3.7941 +2024-12-29 08:59:53,759 - pyskl - INFO - Epoch [86][3400/3746] lr: 3.868e-02, eta: 2 days, 8:14:30, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5905, loss_cls: 3.7755, loss: 3.7755 +2024-12-29 09:01:19,468 - pyskl - INFO - Epoch [86][3500/3746] lr: 3.865e-02, eta: 2 days, 8:13:07, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5961, loss_cls: 3.7901, loss: 3.7901 +2024-12-29 09:02:44,679 - pyskl - INFO - Epoch [86][3600/3746] lr: 3.862e-02, eta: 2 days, 8:11:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.6031, loss_cls: 3.7442, loss: 3.7442 +2024-12-29 09:04:10,528 - pyskl - INFO - Epoch [86][3700/3746] lr: 3.860e-02, eta: 2 days, 8:10:20, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5994, loss_cls: 3.7604, loss: 3.7604 +2024-12-29 09:04:51,946 - pyskl - INFO - Saving checkpoint at 86 epochs +2024-12-29 09:06:52,352 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 09:06:53,060 - pyskl - INFO - +top1_acc 0.2793 +top5_acc 0.5321 +2024-12-29 09:06:53,060 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 09:06:53,115 - pyskl - INFO - +mean_acc 0.2792 +2024-12-29 09:06:53,134 - pyskl - INFO - Epoch(val) [86][309] top1_acc: 0.2793, top5_acc: 0.5321, mean_class_accuracy: 0.2792 +2024-12-29 09:11:16,053 - pyskl - INFO - Epoch [87][100/3746] lr: 3.856e-02, eta: 2 days, 8:10:01, time: 2.629, data_time: 1.589, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6078, loss_cls: 3.7079, loss: 3.7079 +2024-12-29 09:12:41,585 - pyskl - INFO - Epoch [87][200/3746] lr: 3.853e-02, eta: 2 days, 8:08:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6041, loss_cls: 3.7145, loss: 3.7145 +2024-12-29 09:14:07,067 - pyskl - INFO - Epoch [87][300/3746] lr: 3.850e-02, eta: 2 days, 8:07:14, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6119, loss_cls: 3.6738, loss: 3.6738 +2024-12-29 09:15:32,576 - pyskl - INFO - Epoch [87][400/3746] lr: 3.847e-02, eta: 2 days, 8:05:50, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6016, loss_cls: 3.7586, loss: 3.7586 +2024-12-29 09:16:58,253 - pyskl - INFO - Epoch [87][500/3746] lr: 3.845e-02, eta: 2 days, 8:04:27, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3456, top5_acc: 0.6077, loss_cls: 3.6955, loss: 3.6955 +2024-12-29 09:18:24,155 - pyskl - INFO - Epoch [87][600/3746] lr: 3.842e-02, eta: 2 days, 8:03:04, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.5966, loss_cls: 3.7772, loss: 3.7772 +2024-12-29 09:19:49,789 - pyskl - INFO - Epoch [87][700/3746] lr: 3.839e-02, eta: 2 days, 8:01:40, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5995, loss_cls: 3.7369, loss: 3.7369 +2024-12-29 09:21:15,604 - pyskl - INFO - Epoch [87][800/3746] lr: 3.837e-02, eta: 2 days, 8:00:17, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6077, loss_cls: 3.7125, loss: 3.7125 +2024-12-29 09:22:40,976 - pyskl - INFO - Epoch [87][900/3746] lr: 3.834e-02, eta: 2 days, 7:58:53, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.5913, loss_cls: 3.7720, loss: 3.7720 +2024-12-29 09:24:06,459 - pyskl - INFO - Epoch [87][1000/3746] lr: 3.831e-02, eta: 2 days, 7:57:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5852, loss_cls: 3.8145, loss: 3.8145 +2024-12-29 09:25:31,246 - pyskl - INFO - Epoch [87][1100/3746] lr: 3.828e-02, eta: 2 days, 7:56:06, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3331, top5_acc: 0.5875, loss_cls: 3.8205, loss: 3.8205 +2024-12-29 09:26:56,312 - pyskl - INFO - Epoch [87][1200/3746] lr: 3.826e-02, eta: 2 days, 7:54:42, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5886, loss_cls: 3.8056, loss: 3.8056 +2024-12-29 09:28:21,457 - pyskl - INFO - Epoch [87][1300/3746] lr: 3.823e-02, eta: 2 days, 7:53:18, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5917, loss_cls: 3.7810, loss: 3.7810 +2024-12-29 09:29:46,714 - pyskl - INFO - Epoch [87][1400/3746] lr: 3.820e-02, eta: 2 days, 7:51:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5950, loss_cls: 3.7774, loss: 3.7774 +2024-12-29 09:31:12,174 - pyskl - INFO - Epoch [87][1500/3746] lr: 3.817e-02, eta: 2 days, 7:50:31, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6139, loss_cls: 3.6830, loss: 3.6830 +2024-12-29 09:32:37,977 - pyskl - INFO - Epoch [87][1600/3746] lr: 3.815e-02, eta: 2 days, 7:49:07, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3305, top5_acc: 0.5942, loss_cls: 3.7691, loss: 3.7691 +2024-12-29 09:34:03,440 - pyskl - INFO - Epoch [87][1700/3746] lr: 3.812e-02, eta: 2 days, 7:47:44, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.5998, loss_cls: 3.7560, loss: 3.7560 +2024-12-29 09:35:29,124 - pyskl - INFO - Epoch [87][1800/3746] lr: 3.809e-02, eta: 2 days, 7:46:20, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5936, loss_cls: 3.7745, loss: 3.7745 +2024-12-29 09:36:54,557 - pyskl - INFO - Epoch [87][1900/3746] lr: 3.807e-02, eta: 2 days, 7:44:57, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5958, loss_cls: 3.7973, loss: 3.7973 +2024-12-29 09:38:19,943 - pyskl - INFO - Epoch [87][2000/3746] lr: 3.804e-02, eta: 2 days, 7:43:33, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6081, loss_cls: 3.7214, loss: 3.7214 +2024-12-29 09:39:46,501 - pyskl - INFO - Epoch [87][2100/3746] lr: 3.801e-02, eta: 2 days, 7:42:10, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5947, loss_cls: 3.7592, loss: 3.7592 +2024-12-29 09:41:11,955 - pyskl - INFO - Epoch [87][2200/3746] lr: 3.798e-02, eta: 2 days, 7:40:47, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.6005, loss_cls: 3.7709, loss: 3.7709 +2024-12-29 09:42:37,750 - pyskl - INFO - Epoch [87][2300/3746] lr: 3.796e-02, eta: 2 days, 7:39:23, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5984, loss_cls: 3.7795, loss: 3.7795 +2024-12-29 09:44:03,685 - pyskl - INFO - Epoch [87][2400/3746] lr: 3.793e-02, eta: 2 days, 7:38:00, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.5917, loss_cls: 3.7855, loss: 3.7855 +2024-12-29 09:45:29,085 - pyskl - INFO - Epoch [87][2500/3746] lr: 3.790e-02, eta: 2 days, 7:36:37, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.5967, loss_cls: 3.7715, loss: 3.7715 +2024-12-29 09:46:54,346 - pyskl - INFO - Epoch [87][2600/3746] lr: 3.788e-02, eta: 2 days, 7:35:13, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6036, loss_cls: 3.7271, loss: 3.7271 +2024-12-29 09:48:19,367 - pyskl - INFO - Epoch [87][2700/3746] lr: 3.785e-02, eta: 2 days, 7:33:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5948, loss_cls: 3.7714, loss: 3.7714 +2024-12-29 09:49:44,365 - pyskl - INFO - Epoch [87][2800/3746] lr: 3.782e-02, eta: 2 days, 7:32:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3452, top5_acc: 0.5920, loss_cls: 3.7608, loss: 3.7608 +2024-12-29 09:51:09,180 - pyskl - INFO - Epoch [87][2900/3746] lr: 3.779e-02, eta: 2 days, 7:31:01, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.6036, loss_cls: 3.7564, loss: 3.7564 +2024-12-29 09:52:34,280 - pyskl - INFO - Epoch [87][3000/3746] lr: 3.777e-02, eta: 2 days, 7:29:37, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.5938, loss_cls: 3.7823, loss: 3.7823 +2024-12-29 09:53:59,728 - pyskl - INFO - Epoch [87][3100/3746] lr: 3.774e-02, eta: 2 days, 7:28:13, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5856, loss_cls: 3.8252, loss: 3.8252 +2024-12-29 09:55:24,885 - pyskl - INFO - Epoch [87][3200/3746] lr: 3.771e-02, eta: 2 days, 7:26:50, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5997, loss_cls: 3.7607, loss: 3.7607 +2024-12-29 09:56:50,321 - pyskl - INFO - Epoch [87][3300/3746] lr: 3.769e-02, eta: 2 days, 7:25:26, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5964, loss_cls: 3.7654, loss: 3.7654 +2024-12-29 09:58:15,033 - pyskl - INFO - Epoch [87][3400/3746] lr: 3.766e-02, eta: 2 days, 7:24:02, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3345, top5_acc: 0.5883, loss_cls: 3.7749, loss: 3.7749 +2024-12-29 09:59:40,164 - pyskl - INFO - Epoch [87][3500/3746] lr: 3.763e-02, eta: 2 days, 7:22:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6033, loss_cls: 3.7363, loss: 3.7363 +2024-12-29 10:01:05,512 - pyskl - INFO - Epoch [87][3600/3746] lr: 3.761e-02, eta: 2 days, 7:21:14, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5942, loss_cls: 3.7797, loss: 3.7797 +2024-12-29 10:02:30,398 - pyskl - INFO - Epoch [87][3700/3746] lr: 3.758e-02, eta: 2 days, 7:19:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.6066, loss_cls: 3.7513, loss: 3.7513 +2024-12-29 10:03:11,690 - pyskl - INFO - Saving checkpoint at 87 epochs +2024-12-29 10:05:12,747 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 10:05:13,506 - pyskl - INFO - +top1_acc 0.2728 +top5_acc 0.5289 +2024-12-29 10:05:13,506 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 10:05:13,553 - pyskl - INFO - +mean_acc 0.2727 +2024-12-29 10:05:13,566 - pyskl - INFO - Epoch(val) [87][309] top1_acc: 0.2728, top5_acc: 0.5289, mean_class_accuracy: 0.2727 +2024-12-29 10:09:36,645 - pyskl - INFO - Epoch [88][100/3746] lr: 3.754e-02, eta: 2 days, 7:19:28, time: 2.631, data_time: 1.583, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6069, loss_cls: 3.7140, loss: 3.7140 +2024-12-29 10:11:02,245 - pyskl - INFO - Epoch [88][200/3746] lr: 3.751e-02, eta: 2 days, 7:18:05, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6083, loss_cls: 3.7009, loss: 3.7009 +2024-12-29 10:12:27,528 - pyskl - INFO - Epoch [88][300/3746] lr: 3.748e-02, eta: 2 days, 7:16:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6172, loss_cls: 3.6711, loss: 3.6711 +2024-12-29 10:13:53,164 - pyskl - INFO - Epoch [88][400/3746] lr: 3.746e-02, eta: 2 days, 7:15:17, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.5981, loss_cls: 3.7460, loss: 3.7460 +2024-12-29 10:15:18,634 - pyskl - INFO - Epoch [88][500/3746] lr: 3.743e-02, eta: 2 days, 7:13:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5991, loss_cls: 3.7256, loss: 3.7256 +2024-12-29 10:16:43,973 - pyskl - INFO - Epoch [88][600/3746] lr: 3.740e-02, eta: 2 days, 7:12:30, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.6120, loss_cls: 3.7209, loss: 3.7209 +2024-12-29 10:18:09,745 - pyskl - INFO - Epoch [88][700/3746] lr: 3.738e-02, eta: 2 days, 7:11:06, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6142, loss_cls: 3.6676, loss: 3.6676 +2024-12-29 10:19:34,936 - pyskl - INFO - Epoch [88][800/3746] lr: 3.735e-02, eta: 2 days, 7:09:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6006, loss_cls: 3.7414, loss: 3.7414 +2024-12-29 10:21:00,076 - pyskl - INFO - Epoch [88][900/3746] lr: 3.732e-02, eta: 2 days, 7:08:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5969, loss_cls: 3.7542, loss: 3.7542 +2024-12-29 10:22:25,854 - pyskl - INFO - Epoch [88][1000/3746] lr: 3.730e-02, eta: 2 days, 7:06:55, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.5953, loss_cls: 3.7522, loss: 3.7522 +2024-12-29 10:23:50,866 - pyskl - INFO - Epoch [88][1100/3746] lr: 3.727e-02, eta: 2 days, 7:05:31, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6048, loss_cls: 3.7070, loss: 3.7070 +2024-12-29 10:25:15,681 - pyskl - INFO - Epoch [88][1200/3746] lr: 3.724e-02, eta: 2 days, 7:04:07, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.6028, loss_cls: 3.7343, loss: 3.7343 +2024-12-29 10:26:40,663 - pyskl - INFO - Epoch [88][1300/3746] lr: 3.721e-02, eta: 2 days, 7:02:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3323, top5_acc: 0.5916, loss_cls: 3.7804, loss: 3.7804 +2024-12-29 10:28:05,545 - pyskl - INFO - Epoch [88][1400/3746] lr: 3.719e-02, eta: 2 days, 7:01:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.5998, loss_cls: 3.7451, loss: 3.7451 +2024-12-29 10:29:31,195 - pyskl - INFO - Epoch [88][1500/3746] lr: 3.716e-02, eta: 2 days, 6:59:55, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5936, loss_cls: 3.7976, loss: 3.7976 +2024-12-29 10:30:56,354 - pyskl - INFO - Epoch [88][1600/3746] lr: 3.713e-02, eta: 2 days, 6:58:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.5977, loss_cls: 3.7698, loss: 3.7698 +2024-12-29 10:32:21,546 - pyskl - INFO - Epoch [88][1700/3746] lr: 3.711e-02, eta: 2 days, 6:57:07, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.6006, loss_cls: 3.7455, loss: 3.7455 +2024-12-29 10:33:46,669 - pyskl - INFO - Epoch [88][1800/3746] lr: 3.708e-02, eta: 2 days, 6:55:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5941, loss_cls: 3.7673, loss: 3.7673 +2024-12-29 10:35:12,216 - pyskl - INFO - Epoch [88][1900/3746] lr: 3.705e-02, eta: 2 days, 6:54:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.5973, loss_cls: 3.7496, loss: 3.7496 +2024-12-29 10:36:37,245 - pyskl - INFO - Epoch [88][2000/3746] lr: 3.703e-02, eta: 2 days, 6:52:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5944, loss_cls: 3.7419, loss: 3.7419 +2024-12-29 10:38:02,339 - pyskl - INFO - Epoch [88][2100/3746] lr: 3.700e-02, eta: 2 days, 6:51:32, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6020, loss_cls: 3.7488, loss: 3.7488 +2024-12-29 10:39:27,050 - pyskl - INFO - Epoch [88][2200/3746] lr: 3.697e-02, eta: 2 days, 6:50:08, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.5970, loss_cls: 3.7698, loss: 3.7698 +2024-12-29 10:40:52,239 - pyskl - INFO - Epoch [88][2300/3746] lr: 3.694e-02, eta: 2 days, 6:48:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.6044, loss_cls: 3.7234, loss: 3.7234 +2024-12-29 10:42:17,391 - pyskl - INFO - Epoch [88][2400/3746] lr: 3.692e-02, eta: 2 days, 6:47:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6072, loss_cls: 3.7226, loss: 3.7226 +2024-12-29 10:43:42,708 - pyskl - INFO - Epoch [88][2500/3746] lr: 3.689e-02, eta: 2 days, 6:45:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5994, loss_cls: 3.7464, loss: 3.7464 +2024-12-29 10:45:07,867 - pyskl - INFO - Epoch [88][2600/3746] lr: 3.686e-02, eta: 2 days, 6:44:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5927, loss_cls: 3.7863, loss: 3.7863 +2024-12-29 10:46:32,750 - pyskl - INFO - Epoch [88][2700/3746] lr: 3.684e-02, eta: 2 days, 6:43:08, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6092, loss_cls: 3.6958, loss: 3.6958 +2024-12-29 10:47:57,421 - pyskl - INFO - Epoch [88][2800/3746] lr: 3.681e-02, eta: 2 days, 6:41:44, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5859, loss_cls: 3.8291, loss: 3.8291 +2024-12-29 10:49:22,843 - pyskl - INFO - Epoch [88][2900/3746] lr: 3.678e-02, eta: 2 days, 6:40:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.6000, loss_cls: 3.7651, loss: 3.7651 +2024-12-29 10:50:47,879 - pyskl - INFO - Epoch [88][3000/3746] lr: 3.676e-02, eta: 2 days, 6:38:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6083, loss_cls: 3.7336, loss: 3.7336 +2024-12-29 10:52:13,150 - pyskl - INFO - Epoch [88][3100/3746] lr: 3.673e-02, eta: 2 days, 6:37:32, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.5872, loss_cls: 3.8011, loss: 3.8011 +2024-12-29 10:53:38,158 - pyskl - INFO - Epoch [88][3200/3746] lr: 3.670e-02, eta: 2 days, 6:36:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5942, loss_cls: 3.7711, loss: 3.7711 +2024-12-29 10:55:03,447 - pyskl - INFO - Epoch [88][3300/3746] lr: 3.667e-02, eta: 2 days, 6:34:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6034, loss_cls: 3.7325, loss: 3.7325 +2024-12-29 10:56:28,330 - pyskl - INFO - Epoch [88][3400/3746] lr: 3.665e-02, eta: 2 days, 6:33:20, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.5966, loss_cls: 3.7392, loss: 3.7392 +2024-12-29 10:57:53,441 - pyskl - INFO - Epoch [88][3500/3746] lr: 3.662e-02, eta: 2 days, 6:31:56, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.5978, loss_cls: 3.7898, loss: 3.7898 +2024-12-29 10:59:18,312 - pyskl - INFO - Epoch [88][3600/3746] lr: 3.659e-02, eta: 2 days, 6:30:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3320, top5_acc: 0.5852, loss_cls: 3.7838, loss: 3.7838 +2024-12-29 11:00:43,701 - pyskl - INFO - Epoch [88][3700/3746] lr: 3.657e-02, eta: 2 days, 6:29:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5872, loss_cls: 3.7634, loss: 3.7634 +2024-12-29 11:01:25,317 - pyskl - INFO - Saving checkpoint at 88 epochs +2024-12-29 11:03:25,535 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 11:03:26,330 - pyskl - INFO - +top1_acc 0.2813 +top5_acc 0.5300 +2024-12-29 11:03:26,330 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 11:03:26,372 - pyskl - INFO - +mean_acc 0.2811 +2024-12-29 11:03:26,377 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_81.pth was removed +2024-12-29 11:03:26,647 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_88.pth. +2024-12-29 11:03:26,648 - pyskl - INFO - Best top1_acc is 0.2813 at 88 epoch. +2024-12-29 11:03:26,664 - pyskl - INFO - Epoch(val) [88][309] top1_acc: 0.2813, top5_acc: 0.5300, mean_class_accuracy: 0.2811 +2024-12-29 11:07:51,012 - pyskl - INFO - Epoch [89][100/3746] lr: 3.653e-02, eta: 2 days, 6:28:44, time: 2.643, data_time: 1.608, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6134, loss_cls: 3.6522, loss: 3.6522 +2024-12-29 11:09:16,929 - pyskl - INFO - Epoch [89][200/3746] lr: 3.650e-02, eta: 2 days, 6:27:21, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6123, loss_cls: 3.6646, loss: 3.6646 +2024-12-29 11:10:42,843 - pyskl - INFO - Epoch [89][300/3746] lr: 3.647e-02, eta: 2 days, 6:25:57, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6092, loss_cls: 3.6966, loss: 3.6966 +2024-12-29 11:12:08,905 - pyskl - INFO - Epoch [89][400/3746] lr: 3.645e-02, eta: 2 days, 6:24:34, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6064, loss_cls: 3.6666, loss: 3.6666 +2024-12-29 11:13:34,583 - pyskl - INFO - Epoch [89][500/3746] lr: 3.642e-02, eta: 2 days, 6:23:10, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6112, loss_cls: 3.7037, loss: 3.7037 +2024-12-29 11:15:00,341 - pyskl - INFO - Epoch [89][600/3746] lr: 3.639e-02, eta: 2 days, 6:21:47, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.5958, loss_cls: 3.7738, loss: 3.7738 +2024-12-29 11:16:25,945 - pyskl - INFO - Epoch [89][700/3746] lr: 3.637e-02, eta: 2 days, 6:20:23, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.5988, loss_cls: 3.7352, loss: 3.7352 +2024-12-29 11:17:51,298 - pyskl - INFO - Epoch [89][800/3746] lr: 3.634e-02, eta: 2 days, 6:18:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6178, loss_cls: 3.6783, loss: 3.6783 +2024-12-29 11:19:17,072 - pyskl - INFO - Epoch [89][900/3746] lr: 3.631e-02, eta: 2 days, 6:17:35, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6045, loss_cls: 3.7273, loss: 3.7273 +2024-12-29 11:20:42,355 - pyskl - INFO - Epoch [89][1000/3746] lr: 3.629e-02, eta: 2 days, 6:16:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6000, loss_cls: 3.7422, loss: 3.7422 +2024-12-29 11:22:07,836 - pyskl - INFO - Epoch [89][1100/3746] lr: 3.626e-02, eta: 2 days, 6:14:48, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6028, loss_cls: 3.7429, loss: 3.7429 +2024-12-29 11:23:33,506 - pyskl - INFO - Epoch [89][1200/3746] lr: 3.623e-02, eta: 2 days, 6:13:24, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6128, loss_cls: 3.7029, loss: 3.7029 +2024-12-29 11:24:59,382 - pyskl - INFO - Epoch [89][1300/3746] lr: 3.620e-02, eta: 2 days, 6:12:01, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6041, loss_cls: 3.7481, loss: 3.7481 +2024-12-29 11:26:24,937 - pyskl - INFO - Epoch [89][1400/3746] lr: 3.618e-02, eta: 2 days, 6:10:37, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6088, loss_cls: 3.7088, loss: 3.7088 +2024-12-29 11:27:50,203 - pyskl - INFO - Epoch [89][1500/3746] lr: 3.615e-02, eta: 2 days, 6:09:13, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.5975, loss_cls: 3.7394, loss: 3.7394 +2024-12-29 11:29:15,358 - pyskl - INFO - Epoch [89][1600/3746] lr: 3.612e-02, eta: 2 days, 6:07:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5947, loss_cls: 3.7745, loss: 3.7745 +2024-12-29 11:30:41,149 - pyskl - INFO - Epoch [89][1700/3746] lr: 3.610e-02, eta: 2 days, 6:06:25, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6011, loss_cls: 3.7374, loss: 3.7374 +2024-12-29 11:32:06,500 - pyskl - INFO - Epoch [89][1800/3746] lr: 3.607e-02, eta: 2 days, 6:05:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5858, loss_cls: 3.7807, loss: 3.7807 +2024-12-29 11:33:32,048 - pyskl - INFO - Epoch [89][1900/3746] lr: 3.604e-02, eta: 2 days, 6:03:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.5975, loss_cls: 3.7621, loss: 3.7621 +2024-12-29 11:34:57,590 - pyskl - INFO - Epoch [89][2000/3746] lr: 3.602e-02, eta: 2 days, 6:02:14, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5981, loss_cls: 3.7207, loss: 3.7207 +2024-12-29 11:36:23,189 - pyskl - INFO - Epoch [89][2100/3746] lr: 3.599e-02, eta: 2 days, 6:00:50, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.5953, loss_cls: 3.7574, loss: 3.7574 +2024-12-29 11:37:48,603 - pyskl - INFO - Epoch [89][2200/3746] lr: 3.596e-02, eta: 2 days, 5:59:26, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5953, loss_cls: 3.7947, loss: 3.7947 +2024-12-29 11:39:13,965 - pyskl - INFO - Epoch [89][2300/3746] lr: 3.594e-02, eta: 2 days, 5:58:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6023, loss_cls: 3.7285, loss: 3.7285 +2024-12-29 11:40:39,214 - pyskl - INFO - Epoch [89][2400/3746] lr: 3.591e-02, eta: 2 days, 5:56:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.5995, loss_cls: 3.7542, loss: 3.7542 +2024-12-29 11:42:04,662 - pyskl - INFO - Epoch [89][2500/3746] lr: 3.588e-02, eta: 2 days, 5:55:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6077, loss_cls: 3.6913, loss: 3.6913 +2024-12-29 11:43:30,343 - pyskl - INFO - Epoch [89][2600/3746] lr: 3.586e-02, eta: 2 days, 5:53:51, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3270, top5_acc: 0.5959, loss_cls: 3.7959, loss: 3.7959 +2024-12-29 11:44:55,742 - pyskl - INFO - Epoch [89][2700/3746] lr: 3.583e-02, eta: 2 days, 5:52:27, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5925, loss_cls: 3.7919, loss: 3.7919 +2024-12-29 11:46:21,092 - pyskl - INFO - Epoch [89][2800/3746] lr: 3.580e-02, eta: 2 days, 5:51:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.6017, loss_cls: 3.7439, loss: 3.7439 +2024-12-29 11:47:46,324 - pyskl - INFO - Epoch [89][2900/3746] lr: 3.578e-02, eta: 2 days, 5:49:39, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.5966, loss_cls: 3.7432, loss: 3.7432 +2024-12-29 11:49:11,740 - pyskl - INFO - Epoch [89][3000/3746] lr: 3.575e-02, eta: 2 days, 5:48:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6034, loss_cls: 3.7214, loss: 3.7214 +2024-12-29 11:50:37,377 - pyskl - INFO - Epoch [89][3100/3746] lr: 3.572e-02, eta: 2 days, 5:46:52, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3391, top5_acc: 0.5948, loss_cls: 3.7537, loss: 3.7537 +2024-12-29 11:52:03,156 - pyskl - INFO - Epoch [89][3200/3746] lr: 3.569e-02, eta: 2 days, 5:45:28, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6005, loss_cls: 3.7406, loss: 3.7406 +2024-12-29 11:53:28,844 - pyskl - INFO - Epoch [89][3300/3746] lr: 3.567e-02, eta: 2 days, 5:44:04, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.5948, loss_cls: 3.7409, loss: 3.7409 +2024-12-29 11:54:55,151 - pyskl - INFO - Epoch [89][3400/3746] lr: 3.564e-02, eta: 2 days, 5:42:41, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.5994, loss_cls: 3.7496, loss: 3.7496 +2024-12-29 11:56:21,182 - pyskl - INFO - Epoch [89][3500/3746] lr: 3.561e-02, eta: 2 days, 5:41:18, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6031, loss_cls: 3.7289, loss: 3.7289 +2024-12-29 11:57:47,489 - pyskl - INFO - Epoch [89][3600/3746] lr: 3.559e-02, eta: 2 days, 5:39:54, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3434, top5_acc: 0.5995, loss_cls: 3.7477, loss: 3.7477 +2024-12-29 11:59:13,689 - pyskl - INFO - Epoch [89][3700/3746] lr: 3.556e-02, eta: 2 days, 5:38:31, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6072, loss_cls: 3.7103, loss: 3.7103 +2024-12-29 11:59:55,334 - pyskl - INFO - Saving checkpoint at 89 epochs +2024-12-29 12:01:56,525 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 12:01:57,319 - pyskl - INFO - +top1_acc 0.2901 +top5_acc 0.5397 +2024-12-29 12:01:57,319 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 12:01:57,378 - pyskl - INFO - +mean_acc 0.2898 +2024-12-29 12:01:57,383 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_88.pth was removed +2024-12-29 12:01:57,735 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_89.pth. +2024-12-29 12:01:57,736 - pyskl - INFO - Best top1_acc is 0.2901 at 89 epoch. +2024-12-29 12:01:57,750 - pyskl - INFO - Epoch(val) [89][309] top1_acc: 0.2901, top5_acc: 0.5397, mean_class_accuracy: 0.2898 +2024-12-29 12:06:20,203 - pyskl - INFO - Epoch [90][100/3746] lr: 3.552e-02, eta: 2 days, 5:38:03, time: 2.624, data_time: 1.562, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6120, loss_cls: 3.6666, loss: 3.6666 +2024-12-29 12:07:47,010 - pyskl - INFO - Epoch [90][200/3746] lr: 3.550e-02, eta: 2 days, 5:36:40, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.6067, loss_cls: 3.6802, loss: 3.6802 +2024-12-29 12:09:12,674 - pyskl - INFO - Epoch [90][300/3746] lr: 3.547e-02, eta: 2 days, 5:35:16, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6166, loss_cls: 3.6583, loss: 3.6583 +2024-12-29 12:10:38,766 - pyskl - INFO - Epoch [90][400/3746] lr: 3.544e-02, eta: 2 days, 5:33:53, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6083, loss_cls: 3.6952, loss: 3.6952 +2024-12-29 12:12:05,251 - pyskl - INFO - Epoch [90][500/3746] lr: 3.541e-02, eta: 2 days, 5:32:30, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6053, loss_cls: 3.7227, loss: 3.7227 +2024-12-29 12:13:31,716 - pyskl - INFO - Epoch [90][600/3746] lr: 3.539e-02, eta: 2 days, 5:31:06, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6161, loss_cls: 3.6493, loss: 3.6493 +2024-12-29 12:14:57,487 - pyskl - INFO - Epoch [90][700/3746] lr: 3.536e-02, eta: 2 days, 5:29:43, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6041, loss_cls: 3.7086, loss: 3.7086 +2024-12-29 12:16:23,833 - pyskl - INFO - Epoch [90][800/3746] lr: 3.533e-02, eta: 2 days, 5:28:19, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6073, loss_cls: 3.7049, loss: 3.7049 +2024-12-29 12:17:49,229 - pyskl - INFO - Epoch [90][900/3746] lr: 3.531e-02, eta: 2 days, 5:26:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6014, loss_cls: 3.7285, loss: 3.7285 +2024-12-29 12:19:13,974 - pyskl - INFO - Epoch [90][1000/3746] lr: 3.528e-02, eta: 2 days, 5:25:31, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6059, loss_cls: 3.6918, loss: 3.6918 +2024-12-29 12:20:38,355 - pyskl - INFO - Epoch [90][1100/3746] lr: 3.525e-02, eta: 2 days, 5:24:06, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6133, loss_cls: 3.6441, loss: 3.6441 +2024-12-29 12:22:03,211 - pyskl - INFO - Epoch [90][1200/3746] lr: 3.523e-02, eta: 2 days, 5:22:42, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.5911, loss_cls: 3.7620, loss: 3.7620 +2024-12-29 12:23:28,470 - pyskl - INFO - Epoch [90][1300/3746] lr: 3.520e-02, eta: 2 days, 5:21:18, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3389, top5_acc: 0.6000, loss_cls: 3.7476, loss: 3.7476 +2024-12-29 12:24:53,812 - pyskl - INFO - Epoch [90][1400/3746] lr: 3.517e-02, eta: 2 days, 5:19:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6050, loss_cls: 3.6991, loss: 3.6991 +2024-12-29 12:26:18,927 - pyskl - INFO - Epoch [90][1500/3746] lr: 3.515e-02, eta: 2 days, 5:18:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6045, loss_cls: 3.7370, loss: 3.7370 +2024-12-29 12:27:44,105 - pyskl - INFO - Epoch [90][1600/3746] lr: 3.512e-02, eta: 2 days, 5:17:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6030, loss_cls: 3.7364, loss: 3.7364 +2024-12-29 12:29:09,260 - pyskl - INFO - Epoch [90][1700/3746] lr: 3.509e-02, eta: 2 days, 5:15:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6062, loss_cls: 3.7091, loss: 3.7091 +2024-12-29 12:30:34,199 - pyskl - INFO - Epoch [90][1800/3746] lr: 3.507e-02, eta: 2 days, 5:14:17, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.6023, loss_cls: 3.7338, loss: 3.7338 +2024-12-29 12:31:59,413 - pyskl - INFO - Epoch [90][1900/3746] lr: 3.504e-02, eta: 2 days, 5:12:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.5955, loss_cls: 3.7271, loss: 3.7271 +2024-12-29 12:33:25,142 - pyskl - INFO - Epoch [90][2000/3746] lr: 3.501e-02, eta: 2 days, 5:11:30, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6081, loss_cls: 3.6975, loss: 3.6975 +2024-12-29 12:34:50,705 - pyskl - INFO - Epoch [90][2100/3746] lr: 3.499e-02, eta: 2 days, 5:10:06, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6014, loss_cls: 3.7060, loss: 3.7060 +2024-12-29 12:36:15,738 - pyskl - INFO - Epoch [90][2200/3746] lr: 3.496e-02, eta: 2 days, 5:08:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5984, loss_cls: 3.7487, loss: 3.7487 +2024-12-29 12:37:40,977 - pyskl - INFO - Epoch [90][2300/3746] lr: 3.493e-02, eta: 2 days, 5:07:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6042, loss_cls: 3.7312, loss: 3.7312 +2024-12-29 12:39:06,291 - pyskl - INFO - Epoch [90][2400/3746] lr: 3.491e-02, eta: 2 days, 5:05:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3478, top5_acc: 0.6047, loss_cls: 3.7290, loss: 3.7290 +2024-12-29 12:40:30,983 - pyskl - INFO - Epoch [90][2500/3746] lr: 3.488e-02, eta: 2 days, 5:04:29, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6064, loss_cls: 3.7076, loss: 3.7076 +2024-12-29 12:41:56,173 - pyskl - INFO - Epoch [90][2600/3746] lr: 3.485e-02, eta: 2 days, 5:03:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3408, top5_acc: 0.5942, loss_cls: 3.7483, loss: 3.7483 +2024-12-29 12:43:21,135 - pyskl - INFO - Epoch [90][2700/3746] lr: 3.483e-02, eta: 2 days, 5:01:41, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5939, loss_cls: 3.7810, loss: 3.7810 +2024-12-29 12:44:46,226 - pyskl - INFO - Epoch [90][2800/3746] lr: 3.480e-02, eta: 2 days, 5:00:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6092, loss_cls: 3.7236, loss: 3.7236 +2024-12-29 12:46:10,922 - pyskl - INFO - Epoch [90][2900/3746] lr: 3.477e-02, eta: 2 days, 4:58:52, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3541, top5_acc: 0.6173, loss_cls: 3.6601, loss: 3.6601 +2024-12-29 12:47:35,963 - pyskl - INFO - Epoch [90][3000/3746] lr: 3.475e-02, eta: 2 days, 4:57:28, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6095, loss_cls: 3.7381, loss: 3.7381 +2024-12-29 12:49:01,136 - pyskl - INFO - Epoch [90][3100/3746] lr: 3.472e-02, eta: 2 days, 4:56:04, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5989, loss_cls: 3.7490, loss: 3.7490 +2024-12-29 12:50:26,374 - pyskl - INFO - Epoch [90][3200/3746] lr: 3.469e-02, eta: 2 days, 4:54:40, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6014, loss_cls: 3.7231, loss: 3.7231 +2024-12-29 12:51:51,295 - pyskl - INFO - Epoch [90][3300/3746] lr: 3.467e-02, eta: 2 days, 4:53:15, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6003, loss_cls: 3.7478, loss: 3.7478 +2024-12-29 12:53:16,241 - pyskl - INFO - Epoch [90][3400/3746] lr: 3.464e-02, eta: 2 days, 4:51:51, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6048, loss_cls: 3.6964, loss: 3.6964 +2024-12-29 12:54:41,242 - pyskl - INFO - Epoch [90][3500/3746] lr: 3.461e-02, eta: 2 days, 4:50:27, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6059, loss_cls: 3.7124, loss: 3.7124 +2024-12-29 12:56:06,037 - pyskl - INFO - Epoch [90][3600/3746] lr: 3.459e-02, eta: 2 days, 4:49:02, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5827, loss_cls: 3.8084, loss: 3.8084 +2024-12-29 12:57:31,028 - pyskl - INFO - Epoch [90][3700/3746] lr: 3.456e-02, eta: 2 days, 4:47:38, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.6011, loss_cls: 3.7600, loss: 3.7600 +2024-12-29 12:58:12,048 - pyskl - INFO - Saving checkpoint at 90 epochs +2024-12-29 13:00:12,508 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 13:00:13,246 - pyskl - INFO - +top1_acc 0.2923 +top5_acc 0.5395 +2024-12-29 13:00:13,246 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 13:00:13,286 - pyskl - INFO - +mean_acc 0.2920 +2024-12-29 13:00:13,292 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_89.pth was removed +2024-12-29 13:00:13,635 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_90.pth. +2024-12-29 13:00:13,636 - pyskl - INFO - Best top1_acc is 0.2923 at 90 epoch. +2024-12-29 13:00:13,657 - pyskl - INFO - Epoch(val) [90][309] top1_acc: 0.2923, top5_acc: 0.5395, mean_class_accuracy: 0.2920 +2024-12-29 13:04:35,570 - pyskl - INFO - Epoch [91][100/3746] lr: 3.452e-02, eta: 2 days, 4:47:07, time: 2.619, data_time: 1.586, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6155, loss_cls: 3.6210, loss: 3.6210 +2024-12-29 13:06:00,988 - pyskl - INFO - Epoch [91][200/3746] lr: 3.450e-02, eta: 2 days, 4:45:43, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6119, loss_cls: 3.6804, loss: 3.6804 +2024-12-29 13:07:26,434 - pyskl - INFO - Epoch [91][300/3746] lr: 3.447e-02, eta: 2 days, 4:44:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6189, loss_cls: 3.6295, loss: 3.6295 +2024-12-29 13:08:52,017 - pyskl - INFO - Epoch [91][400/3746] lr: 3.444e-02, eta: 2 days, 4:42:55, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6228, loss_cls: 3.6133, loss: 3.6133 +2024-12-29 13:10:17,183 - pyskl - INFO - Epoch [91][500/3746] lr: 3.442e-02, eta: 2 days, 4:41:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6066, loss_cls: 3.6889, loss: 3.6889 +2024-12-29 13:11:42,775 - pyskl - INFO - Epoch [91][600/3746] lr: 3.439e-02, eta: 2 days, 4:40:07, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6134, loss_cls: 3.6595, loss: 3.6595 +2024-12-29 13:13:08,525 - pyskl - INFO - Epoch [91][700/3746] lr: 3.436e-02, eta: 2 days, 4:38:43, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6125, loss_cls: 3.6860, loss: 3.6860 +2024-12-29 13:14:34,587 - pyskl - INFO - Epoch [91][800/3746] lr: 3.434e-02, eta: 2 days, 4:37:19, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3405, top5_acc: 0.6127, loss_cls: 3.7084, loss: 3.7084 +2024-12-29 13:16:00,023 - pyskl - INFO - Epoch [91][900/3746] lr: 3.431e-02, eta: 2 days, 4:35:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6205, loss_cls: 3.6261, loss: 3.6261 +2024-12-29 13:17:26,221 - pyskl - INFO - Epoch [91][1000/3746] lr: 3.428e-02, eta: 2 days, 4:34:32, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.3420, top5_acc: 0.6008, loss_cls: 3.7352, loss: 3.7352 +2024-12-29 13:18:51,567 - pyskl - INFO - Epoch [91][1100/3746] lr: 3.426e-02, eta: 2 days, 4:33:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6086, loss_cls: 3.7088, loss: 3.7088 +2024-12-29 13:20:16,864 - pyskl - INFO - Epoch [91][1200/3746] lr: 3.423e-02, eta: 2 days, 4:31:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6098, loss_cls: 3.6989, loss: 3.6989 +2024-12-29 13:21:42,169 - pyskl - INFO - Epoch [91][1300/3746] lr: 3.420e-02, eta: 2 days, 4:30:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.6086, loss_cls: 3.6984, loss: 3.6984 +2024-12-29 13:23:07,542 - pyskl - INFO - Epoch [91][1400/3746] lr: 3.418e-02, eta: 2 days, 4:28:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6103, loss_cls: 3.6881, loss: 3.6881 +2024-12-29 13:24:33,868 - pyskl - INFO - Epoch [91][1500/3746] lr: 3.415e-02, eta: 2 days, 4:27:32, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6100, loss_cls: 3.6945, loss: 3.6945 +2024-12-29 13:25:59,503 - pyskl - INFO - Epoch [91][1600/3746] lr: 3.412e-02, eta: 2 days, 4:26:08, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6006, loss_cls: 3.7140, loss: 3.7140 +2024-12-29 13:27:24,772 - pyskl - INFO - Epoch [91][1700/3746] lr: 3.410e-02, eta: 2 days, 4:24:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6092, loss_cls: 3.6975, loss: 3.6975 +2024-12-29 13:28:50,542 - pyskl - INFO - Epoch [91][1800/3746] lr: 3.407e-02, eta: 2 days, 4:23:20, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.5997, loss_cls: 3.7531, loss: 3.7531 +2024-12-29 13:30:16,696 - pyskl - INFO - Epoch [91][1900/3746] lr: 3.405e-02, eta: 2 days, 4:21:57, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.3423, top5_acc: 0.6112, loss_cls: 3.7014, loss: 3.7014 +2024-12-29 13:31:42,777 - pyskl - INFO - Epoch [91][2000/3746] lr: 3.402e-02, eta: 2 days, 4:20:33, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6116, loss_cls: 3.7019, loss: 3.7019 +2024-12-29 13:33:09,144 - pyskl - INFO - Epoch [91][2100/3746] lr: 3.399e-02, eta: 2 days, 4:19:10, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6072, loss_cls: 3.7091, loss: 3.7091 +2024-12-29 13:34:35,425 - pyskl - INFO - Epoch [91][2200/3746] lr: 3.397e-02, eta: 2 days, 4:17:46, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5927, loss_cls: 3.7911, loss: 3.7911 +2024-12-29 13:36:00,892 - pyskl - INFO - Epoch [91][2300/3746] lr: 3.394e-02, eta: 2 days, 4:16:22, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.5998, loss_cls: 3.7463, loss: 3.7463 +2024-12-29 13:37:26,853 - pyskl - INFO - Epoch [91][2400/3746] lr: 3.391e-02, eta: 2 days, 4:14:58, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.5952, loss_cls: 3.7545, loss: 3.7545 +2024-12-29 13:38:52,631 - pyskl - INFO - Epoch [91][2500/3746] lr: 3.389e-02, eta: 2 days, 4:13:35, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.6105, loss_cls: 3.6787, loss: 3.6787 +2024-12-29 13:40:18,766 - pyskl - INFO - Epoch [91][2600/3746] lr: 3.386e-02, eta: 2 days, 4:12:11, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6000, loss_cls: 3.7696, loss: 3.7696 +2024-12-29 13:41:44,294 - pyskl - INFO - Epoch [91][2700/3746] lr: 3.383e-02, eta: 2 days, 4:10:47, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.6039, loss_cls: 3.7187, loss: 3.7187 +2024-12-29 13:43:10,752 - pyskl - INFO - Epoch [91][2800/3746] lr: 3.381e-02, eta: 2 days, 4:09:24, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6127, loss_cls: 3.6974, loss: 3.6974 +2024-12-29 13:44:36,831 - pyskl - INFO - Epoch [91][2900/3746] lr: 3.378e-02, eta: 2 days, 4:08:00, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6094, loss_cls: 3.6868, loss: 3.6868 +2024-12-29 13:46:02,335 - pyskl - INFO - Epoch [91][3000/3746] lr: 3.375e-02, eta: 2 days, 4:06:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6127, loss_cls: 3.7184, loss: 3.7184 +2024-12-29 13:47:28,003 - pyskl - INFO - Epoch [91][3100/3746] lr: 3.373e-02, eta: 2 days, 4:05:12, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6077, loss_cls: 3.7214, loss: 3.7214 +2024-12-29 13:48:53,922 - pyskl - INFO - Epoch [91][3200/3746] lr: 3.370e-02, eta: 2 days, 4:03:48, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6058, loss_cls: 3.6987, loss: 3.6987 +2024-12-29 13:50:19,955 - pyskl - INFO - Epoch [91][3300/3746] lr: 3.367e-02, eta: 2 days, 4:02:25, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3406, top5_acc: 0.5948, loss_cls: 3.7702, loss: 3.7702 +2024-12-29 13:51:45,931 - pyskl - INFO - Epoch [91][3400/3746] lr: 3.365e-02, eta: 2 days, 4:01:01, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.5991, loss_cls: 3.7281, loss: 3.7281 +2024-12-29 13:53:11,641 - pyskl - INFO - Epoch [91][3500/3746] lr: 3.362e-02, eta: 2 days, 3:59:37, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6075, loss_cls: 3.6911, loss: 3.6911 +2024-12-29 13:54:37,251 - pyskl - INFO - Epoch [91][3600/3746] lr: 3.360e-02, eta: 2 days, 3:58:13, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6114, loss_cls: 3.6895, loss: 3.6895 +2024-12-29 13:56:03,390 - pyskl - INFO - Epoch [91][3700/3746] lr: 3.357e-02, eta: 2 days, 3:56:50, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6078, loss_cls: 3.7020, loss: 3.7020 +2024-12-29 13:56:45,137 - pyskl - INFO - Saving checkpoint at 91 epochs +2024-12-29 13:58:44,721 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 13:58:45,413 - pyskl - INFO - +top1_acc 0.2975 +top5_acc 0.5460 +2024-12-29 13:58:45,413 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 13:58:45,478 - pyskl - INFO - +mean_acc 0.2973 +2024-12-29 13:58:45,485 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_90.pth was removed +2024-12-29 13:58:46,061 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_91.pth. +2024-12-29 13:58:46,063 - pyskl - INFO - Best top1_acc is 0.2975 at 91 epoch. +2024-12-29 13:58:46,076 - pyskl - INFO - Epoch(val) [91][309] top1_acc: 0.2975, top5_acc: 0.5460, mean_class_accuracy: 0.2973 +2024-12-29 14:03:12,962 - pyskl - INFO - Epoch [92][100/3746] lr: 3.353e-02, eta: 2 days, 3:56:19, time: 2.669, data_time: 1.620, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6183, loss_cls: 3.6557, loss: 3.6557 +2024-12-29 14:04:39,559 - pyskl - INFO - Epoch [92][200/3746] lr: 3.350e-02, eta: 2 days, 3:54:56, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6314, loss_cls: 3.5656, loss: 3.5656 +2024-12-29 14:06:05,640 - pyskl - INFO - Epoch [92][300/3746] lr: 3.348e-02, eta: 2 days, 3:53:32, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6095, loss_cls: 3.6497, loss: 3.6497 +2024-12-29 14:07:32,999 - pyskl - INFO - Epoch [92][400/3746] lr: 3.345e-02, eta: 2 days, 3:52:09, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6141, loss_cls: 3.6509, loss: 3.6509 +2024-12-29 14:09:00,522 - pyskl - INFO - Epoch [92][500/3746] lr: 3.342e-02, eta: 2 days, 3:50:46, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6228, loss_cls: 3.6372, loss: 3.6372 +2024-12-29 14:10:27,722 - pyskl - INFO - Epoch [92][600/3746] lr: 3.340e-02, eta: 2 days, 3:49:23, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6112, loss_cls: 3.6565, loss: 3.6565 +2024-12-29 14:11:55,118 - pyskl - INFO - Epoch [92][700/3746] lr: 3.337e-02, eta: 2 days, 3:48:00, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6270, loss_cls: 3.6233, loss: 3.6233 +2024-12-29 14:13:22,467 - pyskl - INFO - Epoch [92][800/3746] lr: 3.335e-02, eta: 2 days, 3:46:37, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6044, loss_cls: 3.7024, loss: 3.7024 +2024-12-29 14:14:49,669 - pyskl - INFO - Epoch [92][900/3746] lr: 3.332e-02, eta: 2 days, 3:45:14, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6111, loss_cls: 3.6875, loss: 3.6875 +2024-12-29 14:16:16,550 - pyskl - INFO - Epoch [92][1000/3746] lr: 3.329e-02, eta: 2 days, 3:43:51, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6155, loss_cls: 3.6538, loss: 3.6538 +2024-12-29 14:17:42,557 - pyskl - INFO - Epoch [92][1100/3746] lr: 3.327e-02, eta: 2 days, 3:42:28, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6088, loss_cls: 3.7115, loss: 3.7115 +2024-12-29 14:19:08,853 - pyskl - INFO - Epoch [92][1200/3746] lr: 3.324e-02, eta: 2 days, 3:41:04, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.6056, loss_cls: 3.7020, loss: 3.7020 +2024-12-29 14:20:34,901 - pyskl - INFO - Epoch [92][1300/3746] lr: 3.321e-02, eta: 2 days, 3:39:40, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6078, loss_cls: 3.6892, loss: 3.6892 +2024-12-29 14:22:00,525 - pyskl - INFO - Epoch [92][1400/3746] lr: 3.319e-02, eta: 2 days, 3:38:16, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6098, loss_cls: 3.7305, loss: 3.7305 +2024-12-29 14:23:26,826 - pyskl - INFO - Epoch [92][1500/3746] lr: 3.316e-02, eta: 2 days, 3:36:53, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6080, loss_cls: 3.7196, loss: 3.7196 +2024-12-29 14:24:53,731 - pyskl - INFO - Epoch [92][1600/3746] lr: 3.314e-02, eta: 2 days, 3:35:29, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.6241, loss_cls: 3.6423, loss: 3.6423 +2024-12-29 14:26:20,198 - pyskl - INFO - Epoch [92][1700/3746] lr: 3.311e-02, eta: 2 days, 3:34:06, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6084, loss_cls: 3.6612, loss: 3.6612 +2024-12-29 14:27:46,323 - pyskl - INFO - Epoch [92][1800/3746] lr: 3.308e-02, eta: 2 days, 3:32:42, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.6014, loss_cls: 3.7523, loss: 3.7523 +2024-12-29 14:29:12,825 - pyskl - INFO - Epoch [92][1900/3746] lr: 3.306e-02, eta: 2 days, 3:31:19, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.6102, loss_cls: 3.7203, loss: 3.7203 +2024-12-29 14:30:39,193 - pyskl - INFO - Epoch [92][2000/3746] lr: 3.303e-02, eta: 2 days, 3:29:55, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6095, loss_cls: 3.7130, loss: 3.7130 +2024-12-29 14:32:05,745 - pyskl - INFO - Epoch [92][2100/3746] lr: 3.300e-02, eta: 2 days, 3:28:32, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6139, loss_cls: 3.6948, loss: 3.6948 +2024-12-29 14:33:31,595 - pyskl - INFO - Epoch [92][2200/3746] lr: 3.298e-02, eta: 2 days, 3:27:08, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6152, loss_cls: 3.6746, loss: 3.6746 +2024-12-29 14:34:57,133 - pyskl - INFO - Epoch [92][2300/3746] lr: 3.295e-02, eta: 2 days, 3:25:44, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3455, top5_acc: 0.6023, loss_cls: 3.7095, loss: 3.7095 +2024-12-29 14:36:22,827 - pyskl - INFO - Epoch [92][2400/3746] lr: 3.292e-02, eta: 2 days, 3:24:20, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6048, loss_cls: 3.7033, loss: 3.7033 +2024-12-29 14:37:48,700 - pyskl - INFO - Epoch [92][2500/3746] lr: 3.290e-02, eta: 2 days, 3:22:56, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6081, loss_cls: 3.7100, loss: 3.7100 +2024-12-29 14:39:14,579 - pyskl - INFO - Epoch [92][2600/3746] lr: 3.287e-02, eta: 2 days, 3:21:32, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6062, loss_cls: 3.6711, loss: 3.6711 +2024-12-29 14:40:40,691 - pyskl - INFO - Epoch [92][2700/3746] lr: 3.285e-02, eta: 2 days, 3:20:08, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.5945, loss_cls: 3.7333, loss: 3.7333 +2024-12-29 14:42:06,816 - pyskl - INFO - Epoch [92][2800/3746] lr: 3.282e-02, eta: 2 days, 3:18:45, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6059, loss_cls: 3.6881, loss: 3.6881 +2024-12-29 14:43:33,557 - pyskl - INFO - Epoch [92][2900/3746] lr: 3.279e-02, eta: 2 days, 3:17:21, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6055, loss_cls: 3.6992, loss: 3.6992 +2024-12-29 14:45:00,382 - pyskl - INFO - Epoch [92][3000/3746] lr: 3.277e-02, eta: 2 days, 3:15:58, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6103, loss_cls: 3.6837, loss: 3.6837 +2024-12-29 14:46:26,435 - pyskl - INFO - Epoch [92][3100/3746] lr: 3.274e-02, eta: 2 days, 3:14:34, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6014, loss_cls: 3.7039, loss: 3.7039 +2024-12-29 14:47:53,221 - pyskl - INFO - Epoch [92][3200/3746] lr: 3.271e-02, eta: 2 days, 3:13:11, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6022, loss_cls: 3.7333, loss: 3.7333 +2024-12-29 14:49:20,417 - pyskl - INFO - Epoch [92][3300/3746] lr: 3.269e-02, eta: 2 days, 3:11:48, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6025, loss_cls: 3.6967, loss: 3.6967 +2024-12-29 14:50:46,996 - pyskl - INFO - Epoch [92][3400/3746] lr: 3.266e-02, eta: 2 days, 3:10:24, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.6088, loss_cls: 3.7105, loss: 3.7105 +2024-12-29 14:52:13,714 - pyskl - INFO - Epoch [92][3500/3746] lr: 3.264e-02, eta: 2 days, 3:09:01, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6083, loss_cls: 3.6958, loss: 3.6958 +2024-12-29 14:53:40,626 - pyskl - INFO - Epoch [92][3600/3746] lr: 3.261e-02, eta: 2 days, 3:07:38, time: 0.869, data_time: 0.001, memory: 15990, top1_acc: 0.3434, top5_acc: 0.5998, loss_cls: 3.7572, loss: 3.7572 +2024-12-29 14:55:06,961 - pyskl - INFO - Epoch [92][3700/3746] lr: 3.258e-02, eta: 2 days, 3:06:14, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6084, loss_cls: 3.6836, loss: 3.6836 +2024-12-29 14:55:48,971 - pyskl - INFO - Saving checkpoint at 92 epochs +2024-12-29 14:57:47,348 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 14:57:48,228 - pyskl - INFO - +top1_acc 0.2970 +top5_acc 0.5495 +2024-12-29 14:57:48,228 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 14:57:48,304 - pyskl - INFO - +mean_acc 0.2968 +2024-12-29 14:57:48,349 - pyskl - INFO - Epoch(val) [92][309] top1_acc: 0.2970, top5_acc: 0.5495, mean_class_accuracy: 0.2968 +2024-12-29 15:02:20,721 - pyskl - INFO - Epoch [93][100/3746] lr: 3.255e-02, eta: 2 days, 3:05:44, time: 2.724, data_time: 1.663, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6222, loss_cls: 3.6172, loss: 3.6172 +2024-12-29 15:03:47,604 - pyskl - INFO - Epoch [93][200/3746] lr: 3.252e-02, eta: 2 days, 3:04:21, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6255, loss_cls: 3.5995, loss: 3.5995 +2024-12-29 15:05:14,521 - pyskl - INFO - Epoch [93][300/3746] lr: 3.249e-02, eta: 2 days, 3:02:58, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6116, loss_cls: 3.6732, loss: 3.6732 +2024-12-29 15:06:41,470 - pyskl - INFO - Epoch [93][400/3746] lr: 3.247e-02, eta: 2 days, 3:01:34, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6169, loss_cls: 3.6101, loss: 3.6101 +2024-12-29 15:08:08,289 - pyskl - INFO - Epoch [93][500/3746] lr: 3.244e-02, eta: 2 days, 3:00:11, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6134, loss_cls: 3.6323, loss: 3.6323 +2024-12-29 15:09:34,668 - pyskl - INFO - Epoch [93][600/3746] lr: 3.241e-02, eta: 2 days, 2:58:47, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6209, loss_cls: 3.6426, loss: 3.6426 +2024-12-29 15:11:01,652 - pyskl - INFO - Epoch [93][700/3746] lr: 3.239e-02, eta: 2 days, 2:57:24, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6136, loss_cls: 3.6622, loss: 3.6622 +2024-12-29 15:12:28,168 - pyskl - INFO - Epoch [93][800/3746] lr: 3.236e-02, eta: 2 days, 2:56:00, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6150, loss_cls: 3.6561, loss: 3.6561 +2024-12-29 15:13:55,191 - pyskl - INFO - Epoch [93][900/3746] lr: 3.234e-02, eta: 2 days, 2:54:37, time: 0.870, data_time: 0.001, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6044, loss_cls: 3.6789, loss: 3.6789 +2024-12-29 15:15:21,483 - pyskl - INFO - Epoch [93][1000/3746] lr: 3.231e-02, eta: 2 days, 2:53:14, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6103, loss_cls: 3.6879, loss: 3.6879 +2024-12-29 15:16:47,257 - pyskl - INFO - Epoch [93][1100/3746] lr: 3.228e-02, eta: 2 days, 2:51:49, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6142, loss_cls: 3.6851, loss: 3.6851 +2024-12-29 15:18:12,958 - pyskl - INFO - Epoch [93][1200/3746] lr: 3.226e-02, eta: 2 days, 2:50:25, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3427, top5_acc: 0.5991, loss_cls: 3.7450, loss: 3.7450 +2024-12-29 15:19:38,459 - pyskl - INFO - Epoch [93][1300/3746] lr: 3.223e-02, eta: 2 days, 2:49:01, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6100, loss_cls: 3.7041, loss: 3.7041 +2024-12-29 15:21:04,136 - pyskl - INFO - Epoch [93][1400/3746] lr: 3.221e-02, eta: 2 days, 2:47:37, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6062, loss_cls: 3.6980, loss: 3.6980 +2024-12-29 15:22:30,741 - pyskl - INFO - Epoch [93][1500/3746] lr: 3.218e-02, eta: 2 days, 2:46:14, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6184, loss_cls: 3.6510, loss: 3.6510 +2024-12-29 15:23:56,669 - pyskl - INFO - Epoch [93][1600/3746] lr: 3.215e-02, eta: 2 days, 2:44:50, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6178, loss_cls: 3.6565, loss: 3.6565 +2024-12-29 15:25:23,455 - pyskl - INFO - Epoch [93][1700/3746] lr: 3.213e-02, eta: 2 days, 2:43:26, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6120, loss_cls: 3.6827, loss: 3.6827 +2024-12-29 15:26:50,111 - pyskl - INFO - Epoch [93][1800/3746] lr: 3.210e-02, eta: 2 days, 2:42:03, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6128, loss_cls: 3.6673, loss: 3.6673 +2024-12-29 15:28:16,212 - pyskl - INFO - Epoch [93][1900/3746] lr: 3.207e-02, eta: 2 days, 2:40:39, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6194, loss_cls: 3.6382, loss: 3.6382 +2024-12-29 15:29:42,481 - pyskl - INFO - Epoch [93][2000/3746] lr: 3.205e-02, eta: 2 days, 2:39:15, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6172, loss_cls: 3.6554, loss: 3.6554 +2024-12-29 15:31:08,077 - pyskl - INFO - Epoch [93][2100/3746] lr: 3.202e-02, eta: 2 days, 2:37:51, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6162, loss_cls: 3.6659, loss: 3.6659 +2024-12-29 15:32:33,396 - pyskl - INFO - Epoch [93][2200/3746] lr: 3.200e-02, eta: 2 days, 2:36:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6020, loss_cls: 3.6993, loss: 3.6993 +2024-12-29 15:33:59,192 - pyskl - INFO - Epoch [93][2300/3746] lr: 3.197e-02, eta: 2 days, 2:35:03, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.6081, loss_cls: 3.6941, loss: 3.6941 +2024-12-29 15:35:24,584 - pyskl - INFO - Epoch [93][2400/3746] lr: 3.194e-02, eta: 2 days, 2:33:38, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6066, loss_cls: 3.6713, loss: 3.6713 +2024-12-29 15:36:51,507 - pyskl - INFO - Epoch [93][2500/3746] lr: 3.192e-02, eta: 2 days, 2:32:15, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6142, loss_cls: 3.6911, loss: 3.6911 +2024-12-29 15:38:18,212 - pyskl - INFO - Epoch [93][2600/3746] lr: 3.189e-02, eta: 2 days, 2:30:51, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6069, loss_cls: 3.6811, loss: 3.6811 +2024-12-29 15:39:44,560 - pyskl - INFO - Epoch [93][2700/3746] lr: 3.187e-02, eta: 2 days, 2:29:28, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6191, loss_cls: 3.6589, loss: 3.6589 +2024-12-29 15:41:10,042 - pyskl - INFO - Epoch [93][2800/3746] lr: 3.184e-02, eta: 2 days, 2:28:03, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6172, loss_cls: 3.6077, loss: 3.6077 +2024-12-29 15:42:35,938 - pyskl - INFO - Epoch [93][2900/3746] lr: 3.181e-02, eta: 2 days, 2:26:40, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6098, loss_cls: 3.6512, loss: 3.6512 +2024-12-29 15:44:01,919 - pyskl - INFO - Epoch [93][3000/3746] lr: 3.179e-02, eta: 2 days, 2:25:16, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6069, loss_cls: 3.7294, loss: 3.7294 +2024-12-29 15:45:27,848 - pyskl - INFO - Epoch [93][3100/3746] lr: 3.176e-02, eta: 2 days, 2:23:52, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6009, loss_cls: 3.7453, loss: 3.7453 +2024-12-29 15:46:53,967 - pyskl - INFO - Epoch [93][3200/3746] lr: 3.174e-02, eta: 2 days, 2:22:28, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3588, top5_acc: 0.6106, loss_cls: 3.6620, loss: 3.6620 +2024-12-29 15:48:19,501 - pyskl - INFO - Epoch [93][3300/3746] lr: 3.171e-02, eta: 2 days, 2:21:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6122, loss_cls: 3.6829, loss: 3.6829 +2024-12-29 15:49:44,889 - pyskl - INFO - Epoch [93][3400/3746] lr: 3.168e-02, eta: 2 days, 2:19:39, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6014, loss_cls: 3.6965, loss: 3.6965 +2024-12-29 15:51:11,210 - pyskl - INFO - Epoch [93][3500/3746] lr: 3.166e-02, eta: 2 days, 2:18:16, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3492, top5_acc: 0.6120, loss_cls: 3.6899, loss: 3.6899 +2024-12-29 15:52:37,751 - pyskl - INFO - Epoch [93][3600/3746] lr: 3.163e-02, eta: 2 days, 2:16:52, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6084, loss_cls: 3.6660, loss: 3.6660 +2024-12-29 15:54:04,449 - pyskl - INFO - Epoch [93][3700/3746] lr: 3.161e-02, eta: 2 days, 2:15:28, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6120, loss_cls: 3.6776, loss: 3.6776 +2024-12-29 15:54:46,359 - pyskl - INFO - Saving checkpoint at 93 epochs +2024-12-29 15:56:46,761 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 15:56:47,711 - pyskl - INFO - +top1_acc 0.2978 +top5_acc 0.5426 +2024-12-29 15:56:47,712 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 15:56:47,766 - pyskl - INFO - +mean_acc 0.2976 +2024-12-29 15:56:47,773 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_91.pth was removed +2024-12-29 15:56:48,079 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_93.pth. +2024-12-29 15:56:48,080 - pyskl - INFO - Best top1_acc is 0.2978 at 93 epoch. +2024-12-29 15:56:48,098 - pyskl - INFO - Epoch(val) [93][309] top1_acc: 0.2978, top5_acc: 0.5426, mean_class_accuracy: 0.2976 +2024-12-29 16:01:20,972 - pyskl - INFO - Epoch [94][100/3746] lr: 3.157e-02, eta: 2 days, 2:14:56, time: 2.729, data_time: 1.666, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6156, loss_cls: 3.6588, loss: 3.6588 +2024-12-29 16:02:47,481 - pyskl - INFO - Epoch [94][200/3746] lr: 3.154e-02, eta: 2 days, 2:13:32, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6378, loss_cls: 3.5576, loss: 3.5576 +2024-12-29 16:04:13,755 - pyskl - INFO - Epoch [94][300/3746] lr: 3.152e-02, eta: 2 days, 2:12:09, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6134, loss_cls: 3.6149, loss: 3.6149 +2024-12-29 16:05:40,313 - pyskl - INFO - Epoch [94][400/3746] lr: 3.149e-02, eta: 2 days, 2:10:45, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6158, loss_cls: 3.6407, loss: 3.6407 +2024-12-29 16:07:06,915 - pyskl - INFO - Epoch [94][500/3746] lr: 3.146e-02, eta: 2 days, 2:09:21, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6272, loss_cls: 3.6008, loss: 3.6008 +2024-12-29 16:08:33,868 - pyskl - INFO - Epoch [94][600/3746] lr: 3.144e-02, eta: 2 days, 2:07:58, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6095, loss_cls: 3.6545, loss: 3.6545 +2024-12-29 16:10:00,501 - pyskl - INFO - Epoch [94][700/3746] lr: 3.141e-02, eta: 2 days, 2:06:34, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6197, loss_cls: 3.6137, loss: 3.6137 +2024-12-29 16:11:26,884 - pyskl - INFO - Epoch [94][800/3746] lr: 3.139e-02, eta: 2 days, 2:05:10, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6019, loss_cls: 3.7234, loss: 3.7234 +2024-12-29 16:12:53,910 - pyskl - INFO - Epoch [94][900/3746] lr: 3.136e-02, eta: 2 days, 2:03:47, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6078, loss_cls: 3.6911, loss: 3.6911 +2024-12-29 16:14:20,337 - pyskl - INFO - Epoch [94][1000/3746] lr: 3.133e-02, eta: 2 days, 2:02:23, time: 0.864, data_time: 0.001, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6125, loss_cls: 3.6679, loss: 3.6679 +2024-12-29 16:15:46,389 - pyskl - INFO - Epoch [94][1100/3746] lr: 3.131e-02, eta: 2 days, 2:00:59, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6194, loss_cls: 3.6538, loss: 3.6538 +2024-12-29 16:17:12,509 - pyskl - INFO - Epoch [94][1200/3746] lr: 3.128e-02, eta: 2 days, 1:59:35, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6097, loss_cls: 3.6553, loss: 3.6553 +2024-12-29 16:18:39,352 - pyskl - INFO - Epoch [94][1300/3746] lr: 3.126e-02, eta: 2 days, 1:58:12, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6123, loss_cls: 3.6343, loss: 3.6343 +2024-12-29 16:20:05,952 - pyskl - INFO - Epoch [94][1400/3746] lr: 3.123e-02, eta: 2 days, 1:56:48, time: 0.866, data_time: 0.001, memory: 15990, top1_acc: 0.3541, top5_acc: 0.6178, loss_cls: 3.6354, loss: 3.6354 +2024-12-29 16:21:32,372 - pyskl - INFO - Epoch [94][1500/3746] lr: 3.120e-02, eta: 2 days, 1:55:24, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.6025, loss_cls: 3.7378, loss: 3.7378 +2024-12-29 16:22:58,566 - pyskl - INFO - Epoch [94][1600/3746] lr: 3.118e-02, eta: 2 days, 1:54:01, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6145, loss_cls: 3.6780, loss: 3.6780 +2024-12-29 16:24:25,319 - pyskl - INFO - Epoch [94][1700/3746] lr: 3.115e-02, eta: 2 days, 1:52:37, time: 0.867, data_time: 0.001, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6130, loss_cls: 3.6541, loss: 3.6541 +2024-12-29 16:25:50,800 - pyskl - INFO - Epoch [94][1800/3746] lr: 3.113e-02, eta: 2 days, 1:51:13, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6131, loss_cls: 3.6557, loss: 3.6557 +2024-12-29 16:27:16,497 - pyskl - INFO - Epoch [94][1900/3746] lr: 3.110e-02, eta: 2 days, 1:49:48, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6164, loss_cls: 3.6566, loss: 3.6566 +2024-12-29 16:28:41,757 - pyskl - INFO - Epoch [94][2000/3746] lr: 3.108e-02, eta: 2 days, 1:48:24, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6183, loss_cls: 3.6327, loss: 3.6327 +2024-12-29 16:30:07,385 - pyskl - INFO - Epoch [94][2100/3746] lr: 3.105e-02, eta: 2 days, 1:47:00, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6183, loss_cls: 3.6088, loss: 3.6088 +2024-12-29 16:31:33,392 - pyskl - INFO - Epoch [94][2200/3746] lr: 3.102e-02, eta: 2 days, 1:45:36, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6117, loss_cls: 3.6657, loss: 3.6657 +2024-12-29 16:32:59,194 - pyskl - INFO - Epoch [94][2300/3746] lr: 3.100e-02, eta: 2 days, 1:44:12, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6175, loss_cls: 3.6512, loss: 3.6512 +2024-12-29 16:34:25,123 - pyskl - INFO - Epoch [94][2400/3746] lr: 3.097e-02, eta: 2 days, 1:42:47, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6112, loss_cls: 3.6953, loss: 3.6953 +2024-12-29 16:35:51,213 - pyskl - INFO - Epoch [94][2500/3746] lr: 3.095e-02, eta: 2 days, 1:41:23, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6152, loss_cls: 3.6819, loss: 3.6819 +2024-12-29 16:37:16,663 - pyskl - INFO - Epoch [94][2600/3746] lr: 3.092e-02, eta: 2 days, 1:39:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6125, loss_cls: 3.6575, loss: 3.6575 +2024-12-29 16:38:42,765 - pyskl - INFO - Epoch [94][2700/3746] lr: 3.089e-02, eta: 2 days, 1:38:35, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6125, loss_cls: 3.6665, loss: 3.6665 +2024-12-29 16:40:08,501 - pyskl - INFO - Epoch [94][2800/3746] lr: 3.087e-02, eta: 2 days, 1:37:11, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3659, top5_acc: 0.6216, loss_cls: 3.6260, loss: 3.6260 +2024-12-29 16:41:34,688 - pyskl - INFO - Epoch [94][2900/3746] lr: 3.084e-02, eta: 2 days, 1:35:47, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6166, loss_cls: 3.6504, loss: 3.6504 +2024-12-29 16:43:00,772 - pyskl - INFO - Epoch [94][3000/3746] lr: 3.082e-02, eta: 2 days, 1:34:23, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6084, loss_cls: 3.6851, loss: 3.6851 +2024-12-29 16:44:27,510 - pyskl - INFO - Epoch [94][3100/3746] lr: 3.079e-02, eta: 2 days, 1:32:59, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6155, loss_cls: 3.6418, loss: 3.6418 +2024-12-29 16:45:54,534 - pyskl - INFO - Epoch [94][3200/3746] lr: 3.077e-02, eta: 2 days, 1:31:36, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6145, loss_cls: 3.6513, loss: 3.6513 +2024-12-29 16:47:20,910 - pyskl - INFO - Epoch [94][3300/3746] lr: 3.074e-02, eta: 2 days, 1:30:12, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6058, loss_cls: 3.7036, loss: 3.7036 +2024-12-29 16:48:47,212 - pyskl - INFO - Epoch [94][3400/3746] lr: 3.071e-02, eta: 2 days, 1:28:48, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6219, loss_cls: 3.6298, loss: 3.6298 +2024-12-29 16:50:13,885 - pyskl - INFO - Epoch [94][3500/3746] lr: 3.069e-02, eta: 2 days, 1:27:25, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6136, loss_cls: 3.6512, loss: 3.6512 +2024-12-29 16:51:40,636 - pyskl - INFO - Epoch [94][3600/3746] lr: 3.066e-02, eta: 2 days, 1:26:01, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6136, loss_cls: 3.6963, loss: 3.6963 +2024-12-29 16:53:07,274 - pyskl - INFO - Epoch [94][3700/3746] lr: 3.064e-02, eta: 2 days, 1:24:37, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6094, loss_cls: 3.6965, loss: 3.6965 +2024-12-29 16:53:49,494 - pyskl - INFO - Saving checkpoint at 94 epochs +2024-12-29 16:55:47,692 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 16:55:48,482 - pyskl - INFO - +top1_acc 0.2923 +top5_acc 0.5501 +2024-12-29 16:55:48,483 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 16:55:48,542 - pyskl - INFO - +mean_acc 0.2921 +2024-12-29 16:55:48,562 - pyskl - INFO - Epoch(val) [94][309] top1_acc: 0.2923, top5_acc: 0.5501, mean_class_accuracy: 0.2921 +2024-12-29 17:00:20,630 - pyskl - INFO - Epoch [95][100/3746] lr: 3.060e-02, eta: 2 days, 1:24:02, time: 2.721, data_time: 1.650, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6233, loss_cls: 3.5703, loss: 3.5703 +2024-12-29 17:01:48,076 - pyskl - INFO - Epoch [95][200/3746] lr: 3.057e-02, eta: 2 days, 1:22:39, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6200, loss_cls: 3.6110, loss: 3.6110 +2024-12-29 17:03:16,000 - pyskl - INFO - Epoch [95][300/3746] lr: 3.055e-02, eta: 2 days, 1:21:16, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6241, loss_cls: 3.6150, loss: 3.6150 +2024-12-29 17:04:44,331 - pyskl - INFO - Epoch [95][400/3746] lr: 3.052e-02, eta: 2 days, 1:19:53, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6242, loss_cls: 3.6122, loss: 3.6122 +2024-12-29 17:06:12,670 - pyskl - INFO - Epoch [95][500/3746] lr: 3.050e-02, eta: 2 days, 1:18:30, time: 0.883, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6131, loss_cls: 3.6410, loss: 3.6410 +2024-12-29 17:07:41,097 - pyskl - INFO - Epoch [95][600/3746] lr: 3.047e-02, eta: 2 days, 1:17:08, time: 0.884, data_time: 0.001, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6242, loss_cls: 3.6206, loss: 3.6206 +2024-12-29 17:09:09,708 - pyskl - INFO - Epoch [95][700/3746] lr: 3.044e-02, eta: 2 days, 1:15:45, time: 0.886, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6197, loss_cls: 3.6188, loss: 3.6188 +2024-12-29 17:10:36,276 - pyskl - INFO - Epoch [95][800/3746] lr: 3.042e-02, eta: 2 days, 1:14:21, time: 0.866, data_time: 0.001, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6181, loss_cls: 3.6374, loss: 3.6374 +2024-12-29 17:12:02,668 - pyskl - INFO - Epoch [95][900/3746] lr: 3.039e-02, eta: 2 days, 1:12:57, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6133, loss_cls: 3.6367, loss: 3.6367 +2024-12-29 17:13:28,584 - pyskl - INFO - Epoch [95][1000/3746] lr: 3.037e-02, eta: 2 days, 1:11:33, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6272, loss_cls: 3.6100, loss: 3.6100 +2024-12-29 17:14:54,834 - pyskl - INFO - Epoch [95][1100/3746] lr: 3.034e-02, eta: 2 days, 1:10:09, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6186, loss_cls: 3.6609, loss: 3.6609 +2024-12-29 17:16:21,600 - pyskl - INFO - Epoch [95][1200/3746] lr: 3.032e-02, eta: 2 days, 1:08:45, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6241, loss_cls: 3.5790, loss: 3.5790 +2024-12-29 17:17:48,402 - pyskl - INFO - Epoch [95][1300/3746] lr: 3.029e-02, eta: 2 days, 1:07:22, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6173, loss_cls: 3.6140, loss: 3.6140 +2024-12-29 17:19:14,893 - pyskl - INFO - Epoch [95][1400/3746] lr: 3.026e-02, eta: 2 days, 1:05:58, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6144, loss_cls: 3.6368, loss: 3.6368 +2024-12-29 17:20:41,612 - pyskl - INFO - Epoch [95][1500/3746] lr: 3.024e-02, eta: 2 days, 1:04:34, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6177, loss_cls: 3.6253, loss: 3.6253 +2024-12-29 17:22:08,072 - pyskl - INFO - Epoch [95][1600/3746] lr: 3.021e-02, eta: 2 days, 1:03:10, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6306, loss_cls: 3.5971, loss: 3.5971 +2024-12-29 17:23:34,558 - pyskl - INFO - Epoch [95][1700/3746] lr: 3.019e-02, eta: 2 days, 1:01:46, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6144, loss_cls: 3.6777, loss: 3.6777 +2024-12-29 17:25:00,886 - pyskl - INFO - Epoch [95][1800/3746] lr: 3.016e-02, eta: 2 days, 1:00:23, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6134, loss_cls: 3.6708, loss: 3.6708 +2024-12-29 17:26:28,586 - pyskl - INFO - Epoch [95][1900/3746] lr: 3.014e-02, eta: 2 days, 0:58:59, time: 0.877, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6083, loss_cls: 3.6670, loss: 3.6670 +2024-12-29 17:27:56,029 - pyskl - INFO - Epoch [95][2000/3746] lr: 3.011e-02, eta: 2 days, 0:57:36, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6095, loss_cls: 3.6546, loss: 3.6546 +2024-12-29 17:29:22,217 - pyskl - INFO - Epoch [95][2100/3746] lr: 3.008e-02, eta: 2 days, 0:56:12, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.3572, top5_acc: 0.6261, loss_cls: 3.6220, loss: 3.6220 +2024-12-29 17:30:49,094 - pyskl - INFO - Epoch [95][2200/3746] lr: 3.006e-02, eta: 2 days, 0:54:48, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3573, top5_acc: 0.6116, loss_cls: 3.6235, loss: 3.6235 +2024-12-29 17:32:15,592 - pyskl - INFO - Epoch [95][2300/3746] lr: 3.003e-02, eta: 2 days, 0:53:25, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6134, loss_cls: 3.6476, loss: 3.6476 +2024-12-29 17:33:43,423 - pyskl - INFO - Epoch [95][2400/3746] lr: 3.001e-02, eta: 2 days, 0:52:02, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6112, loss_cls: 3.6720, loss: 3.6720 +2024-12-29 17:35:11,148 - pyskl - INFO - Epoch [95][2500/3746] lr: 2.998e-02, eta: 2 days, 0:50:38, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.3488, top5_acc: 0.6195, loss_cls: 3.6569, loss: 3.6569 +2024-12-29 17:36:38,867 - pyskl - INFO - Epoch [95][2600/3746] lr: 2.996e-02, eta: 2 days, 0:49:15, time: 0.877, data_time: 0.000, memory: 15990, top1_acc: 0.3563, top5_acc: 0.6098, loss_cls: 3.6851, loss: 3.6851 +2024-12-29 17:38:06,719 - pyskl - INFO - Epoch [95][2700/3746] lr: 2.993e-02, eta: 2 days, 0:47:52, time: 0.878, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6253, loss_cls: 3.6242, loss: 3.6242 +2024-12-29 17:39:35,128 - pyskl - INFO - Epoch [95][2800/3746] lr: 2.991e-02, eta: 2 days, 0:46:29, time: 0.884, data_time: 0.001, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6259, loss_cls: 3.6132, loss: 3.6132 +2024-12-29 17:41:03,457 - pyskl - INFO - Epoch [95][2900/3746] lr: 2.988e-02, eta: 2 days, 0:45:07, time: 0.883, data_time: 0.001, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6075, loss_cls: 3.6741, loss: 3.6741 +2024-12-29 17:42:31,557 - pyskl - INFO - Epoch [95][3000/3746] lr: 2.985e-02, eta: 2 days, 0:43:44, time: 0.881, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6248, loss_cls: 3.6329, loss: 3.6329 +2024-12-29 17:43:59,574 - pyskl - INFO - Epoch [95][3100/3746] lr: 2.983e-02, eta: 2 days, 0:42:21, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6198, loss_cls: 3.6637, loss: 3.6637 +2024-12-29 17:45:27,239 - pyskl - INFO - Epoch [95][3200/3746] lr: 2.980e-02, eta: 2 days, 0:40:57, time: 0.877, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6088, loss_cls: 3.6894, loss: 3.6894 +2024-12-29 17:46:55,346 - pyskl - INFO - Epoch [95][3300/3746] lr: 2.978e-02, eta: 2 days, 0:39:35, time: 0.881, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6009, loss_cls: 3.7127, loss: 3.7127 +2024-12-29 17:48:23,733 - pyskl - INFO - Epoch [95][3400/3746] lr: 2.975e-02, eta: 2 days, 0:38:12, time: 0.884, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6109, loss_cls: 3.6768, loss: 3.6768 +2024-12-29 17:49:52,299 - pyskl - INFO - Epoch [95][3500/3746] lr: 2.973e-02, eta: 2 days, 0:36:49, time: 0.886, data_time: 0.001, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6223, loss_cls: 3.6357, loss: 3.6357 +2024-12-29 17:51:20,177 - pyskl - INFO - Epoch [95][3600/3746] lr: 2.970e-02, eta: 2 days, 0:35:26, time: 0.879, data_time: 0.001, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6202, loss_cls: 3.6248, loss: 3.6248 +2024-12-29 17:52:48,552 - pyskl - INFO - Epoch [95][3700/3746] lr: 2.968e-02, eta: 2 days, 0:34:03, time: 0.884, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6103, loss_cls: 3.6747, loss: 3.6747 +2024-12-29 17:53:31,065 - pyskl - INFO - Saving checkpoint at 95 epochs +2024-12-29 17:55:33,712 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 17:55:34,399 - pyskl - INFO - +top1_acc 0.2990 +top5_acc 0.5526 +2024-12-29 17:55:34,399 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 17:55:34,440 - pyskl - INFO - +mean_acc 0.2986 +2024-12-29 17:55:34,446 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_93.pth was removed +2024-12-29 17:55:34,684 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_95.pth. +2024-12-29 17:55:34,685 - pyskl - INFO - Best top1_acc is 0.2990 at 95 epoch. +2024-12-29 17:55:34,697 - pyskl - INFO - Epoch(val) [95][309] top1_acc: 0.2990, top5_acc: 0.5526, mean_class_accuracy: 0.2986 +2024-12-29 18:00:10,685 - pyskl - INFO - Epoch [96][100/3746] lr: 2.964e-02, eta: 2 days, 0:33:27, time: 2.760, data_time: 1.686, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6244, loss_cls: 3.5951, loss: 3.5951 +2024-12-29 18:01:37,829 - pyskl - INFO - Epoch [96][200/3746] lr: 2.961e-02, eta: 2 days, 0:32:04, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6208, loss_cls: 3.6355, loss: 3.6355 +2024-12-29 18:03:04,803 - pyskl - INFO - Epoch [96][300/3746] lr: 2.959e-02, eta: 2 days, 0:30:40, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6261, loss_cls: 3.5775, loss: 3.5775 +2024-12-29 18:04:31,915 - pyskl - INFO - Epoch [96][400/3746] lr: 2.956e-02, eta: 2 days, 0:29:16, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.3691, top5_acc: 0.6208, loss_cls: 3.5914, loss: 3.5914 +2024-12-29 18:05:58,734 - pyskl - INFO - Epoch [96][500/3746] lr: 2.954e-02, eta: 2 days, 0:27:53, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6303, loss_cls: 3.5842, loss: 3.5842 +2024-12-29 18:07:26,173 - pyskl - INFO - Epoch [96][600/3746] lr: 2.951e-02, eta: 2 days, 0:26:29, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6231, loss_cls: 3.5847, loss: 3.5847 +2024-12-29 18:08:52,798 - pyskl - INFO - Epoch [96][700/3746] lr: 2.948e-02, eta: 2 days, 0:25:05, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6161, loss_cls: 3.6220, loss: 3.6220 +2024-12-29 18:10:19,158 - pyskl - INFO - Epoch [96][800/3746] lr: 2.946e-02, eta: 2 days, 0:23:41, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6191, loss_cls: 3.6220, loss: 3.6220 +2024-12-29 18:11:45,773 - pyskl - INFO - Epoch [96][900/3746] lr: 2.943e-02, eta: 2 days, 0:22:17, time: 0.866, data_time: 0.001, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6323, loss_cls: 3.5985, loss: 3.5985 +2024-12-29 18:13:11,900 - pyskl - INFO - Epoch [96][1000/3746] lr: 2.941e-02, eta: 2 days, 0:20:53, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6194, loss_cls: 3.6367, loss: 3.6367 +2024-12-29 18:14:38,172 - pyskl - INFO - Epoch [96][1100/3746] lr: 2.938e-02, eta: 2 days, 0:19:29, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6348, loss_cls: 3.5559, loss: 3.5559 +2024-12-29 18:16:04,338 - pyskl - INFO - Epoch [96][1200/3746] lr: 2.936e-02, eta: 2 days, 0:18:05, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6239, loss_cls: 3.6016, loss: 3.6016 +2024-12-29 18:17:30,171 - pyskl - INFO - Epoch [96][1300/3746] lr: 2.933e-02, eta: 2 days, 0:16:41, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6184, loss_cls: 3.6200, loss: 3.6200 +2024-12-29 18:18:55,995 - pyskl - INFO - Epoch [96][1400/3746] lr: 2.931e-02, eta: 2 days, 0:15:16, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6269, loss_cls: 3.6147, loss: 3.6147 +2024-12-29 18:20:21,676 - pyskl - INFO - Epoch [96][1500/3746] lr: 2.928e-02, eta: 2 days, 0:13:52, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6136, loss_cls: 3.6509, loss: 3.6509 +2024-12-29 18:21:48,125 - pyskl - INFO - Epoch [96][1600/3746] lr: 2.926e-02, eta: 2 days, 0:12:28, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3567, top5_acc: 0.6170, loss_cls: 3.6338, loss: 3.6338 +2024-12-29 18:23:13,301 - pyskl - INFO - Epoch [96][1700/3746] lr: 2.923e-02, eta: 2 days, 0:11:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6102, loss_cls: 3.6701, loss: 3.6701 +2024-12-29 18:24:39,007 - pyskl - INFO - Epoch [96][1800/3746] lr: 2.920e-02, eta: 2 days, 0:09:39, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6167, loss_cls: 3.6148, loss: 3.6148 +2024-12-29 18:26:05,021 - pyskl - INFO - Epoch [96][1900/3746] lr: 2.918e-02, eta: 2 days, 0:08:14, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6119, loss_cls: 3.6693, loss: 3.6693 +2024-12-29 18:27:31,270 - pyskl - INFO - Epoch [96][2000/3746] lr: 2.915e-02, eta: 2 days, 0:06:50, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6153, loss_cls: 3.6556, loss: 3.6556 +2024-12-29 18:28:57,024 - pyskl - INFO - Epoch [96][2100/3746] lr: 2.913e-02, eta: 2 days, 0:05:26, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6145, loss_cls: 3.6784, loss: 3.6784 +2024-12-29 18:30:23,017 - pyskl - INFO - Epoch [96][2200/3746] lr: 2.910e-02, eta: 2 days, 0:04:02, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6169, loss_cls: 3.6399, loss: 3.6399 +2024-12-29 18:31:48,839 - pyskl - INFO - Epoch [96][2300/3746] lr: 2.908e-02, eta: 2 days, 0:02:37, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6212, loss_cls: 3.5989, loss: 3.5989 +2024-12-29 18:33:14,871 - pyskl - INFO - Epoch [96][2400/3746] lr: 2.905e-02, eta: 2 days, 0:01:13, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6106, loss_cls: 3.6433, loss: 3.6433 +2024-12-29 18:34:40,556 - pyskl - INFO - Epoch [96][2500/3746] lr: 2.903e-02, eta: 1 day, 23:59:48, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6211, loss_cls: 3.6279, loss: 3.6279 +2024-12-29 18:36:07,253 - pyskl - INFO - Epoch [96][2600/3746] lr: 2.900e-02, eta: 1 day, 23:58:25, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6222, loss_cls: 3.6347, loss: 3.6347 +2024-12-29 18:37:33,070 - pyskl - INFO - Epoch [96][2700/3746] lr: 2.898e-02, eta: 1 day, 23:57:00, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6316, loss_cls: 3.5516, loss: 3.5516 +2024-12-29 18:38:58,993 - pyskl - INFO - Epoch [96][2800/3746] lr: 2.895e-02, eta: 1 day, 23:55:36, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6166, loss_cls: 3.6106, loss: 3.6106 +2024-12-29 18:40:24,900 - pyskl - INFO - Epoch [96][2900/3746] lr: 2.893e-02, eta: 1 day, 23:54:12, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6136, loss_cls: 3.6500, loss: 3.6500 +2024-12-29 18:41:51,114 - pyskl - INFO - Epoch [96][3000/3746] lr: 2.890e-02, eta: 1 day, 23:52:47, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6122, loss_cls: 3.6606, loss: 3.6606 +2024-12-29 18:43:16,832 - pyskl - INFO - Epoch [96][3100/3746] lr: 2.887e-02, eta: 1 day, 23:51:23, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6191, loss_cls: 3.6380, loss: 3.6380 +2024-12-29 18:44:42,576 - pyskl - INFO - Epoch [96][3200/3746] lr: 2.885e-02, eta: 1 day, 23:49:59, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6331, loss_cls: 3.6015, loss: 3.6015 +2024-12-29 18:46:07,782 - pyskl - INFO - Epoch [96][3300/3746] lr: 2.882e-02, eta: 1 day, 23:48:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6286, loss_cls: 3.6249, loss: 3.6249 +2024-12-29 18:47:33,338 - pyskl - INFO - Epoch [96][3400/3746] lr: 2.880e-02, eta: 1 day, 23:47:09, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3514, top5_acc: 0.6123, loss_cls: 3.6729, loss: 3.6729 +2024-12-29 18:48:59,789 - pyskl - INFO - Epoch [96][3500/3746] lr: 2.877e-02, eta: 1 day, 23:45:45, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6188, loss_cls: 3.6281, loss: 3.6281 +2024-12-29 18:50:26,292 - pyskl - INFO - Epoch [96][3600/3746] lr: 2.875e-02, eta: 1 day, 23:44:21, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6209, loss_cls: 3.6066, loss: 3.6066 +2024-12-29 18:51:52,255 - pyskl - INFO - Epoch [96][3700/3746] lr: 2.872e-02, eta: 1 day, 23:42:57, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6238, loss_cls: 3.6283, loss: 3.6283 +2024-12-29 18:52:33,953 - pyskl - INFO - Saving checkpoint at 96 epochs +2024-12-29 18:54:34,795 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 18:54:35,646 - pyskl - INFO - +top1_acc 0.2943 +top5_acc 0.5417 +2024-12-29 18:54:35,646 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 18:54:35,696 - pyskl - INFO - +mean_acc 0.2940 +2024-12-29 18:54:35,715 - pyskl - INFO - Epoch(val) [96][309] top1_acc: 0.2943, top5_acc: 0.5417, mean_class_accuracy: 0.2940 +2024-12-29 18:59:11,267 - pyskl - INFO - Epoch [97][100/3746] lr: 2.869e-02, eta: 1 day, 23:42:18, time: 2.755, data_time: 1.691, memory: 15990, top1_acc: 0.3720, top5_acc: 0.6253, loss_cls: 3.5657, loss: 3.5657 +2024-12-29 19:00:38,765 - pyskl - INFO - Epoch [97][200/3746] lr: 2.866e-02, eta: 1 day, 23:40:55, time: 0.875, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6408, loss_cls: 3.5303, loss: 3.5303 +2024-12-29 19:02:06,463 - pyskl - INFO - Epoch [97][300/3746] lr: 2.864e-02, eta: 1 day, 23:39:31, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6222, loss_cls: 3.5591, loss: 3.5591 +2024-12-29 19:03:34,361 - pyskl - INFO - Epoch [97][400/3746] lr: 2.861e-02, eta: 1 day, 23:38:08, time: 0.879, data_time: 0.001, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6403, loss_cls: 3.5512, loss: 3.5512 +2024-12-29 19:05:02,053 - pyskl - INFO - Epoch [97][500/3746] lr: 2.858e-02, eta: 1 day, 23:36:45, time: 0.877, data_time: 0.001, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6317, loss_cls: 3.5813, loss: 3.5813 +2024-12-29 19:06:29,930 - pyskl - INFO - Epoch [97][600/3746] lr: 2.856e-02, eta: 1 day, 23:35:21, time: 0.879, data_time: 0.001, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6217, loss_cls: 3.6111, loss: 3.6111 +2024-12-29 19:07:56,868 - pyskl - INFO - Epoch [97][700/3746] lr: 2.853e-02, eta: 1 day, 23:33:58, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6230, loss_cls: 3.6321, loss: 3.6321 +2024-12-29 19:09:23,385 - pyskl - INFO - Epoch [97][800/3746] lr: 2.851e-02, eta: 1 day, 23:32:33, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6183, loss_cls: 3.5983, loss: 3.5983 +2024-12-29 19:10:50,046 - pyskl - INFO - Epoch [97][900/3746] lr: 2.848e-02, eta: 1 day, 23:31:09, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6159, loss_cls: 3.6251, loss: 3.6251 +2024-12-29 19:12:16,660 - pyskl - INFO - Epoch [97][1000/3746] lr: 2.846e-02, eta: 1 day, 23:29:45, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3655, top5_acc: 0.6225, loss_cls: 3.6095, loss: 3.6095 +2024-12-29 19:13:43,120 - pyskl - INFO - Epoch [97][1100/3746] lr: 2.843e-02, eta: 1 day, 23:28:21, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6166, loss_cls: 3.6171, loss: 3.6171 +2024-12-29 19:15:08,680 - pyskl - INFO - Epoch [97][1200/3746] lr: 2.841e-02, eta: 1 day, 23:26:57, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6197, loss_cls: 3.6208, loss: 3.6208 +2024-12-29 19:16:34,465 - pyskl - INFO - Epoch [97][1300/3746] lr: 2.838e-02, eta: 1 day, 23:25:32, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3588, top5_acc: 0.6245, loss_cls: 3.6425, loss: 3.6425 +2024-12-29 19:18:00,774 - pyskl - INFO - Epoch [97][1400/3746] lr: 2.836e-02, eta: 1 day, 23:24:08, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6205, loss_cls: 3.6269, loss: 3.6269 +2024-12-29 19:19:27,109 - pyskl - INFO - Epoch [97][1500/3746] lr: 2.833e-02, eta: 1 day, 23:22:44, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3762, top5_acc: 0.6347, loss_cls: 3.5559, loss: 3.5559 +2024-12-29 19:20:53,083 - pyskl - INFO - Epoch [97][1600/3746] lr: 2.831e-02, eta: 1 day, 23:21:19, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6175, loss_cls: 3.6057, loss: 3.6057 +2024-12-29 19:22:18,791 - pyskl - INFO - Epoch [97][1700/3746] lr: 2.828e-02, eta: 1 day, 23:19:55, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6250, loss_cls: 3.5898, loss: 3.5898 +2024-12-29 19:23:44,527 - pyskl - INFO - Epoch [97][1800/3746] lr: 2.826e-02, eta: 1 day, 23:18:30, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6186, loss_cls: 3.6135, loss: 3.6135 +2024-12-29 19:25:10,711 - pyskl - INFO - Epoch [97][1900/3746] lr: 2.823e-02, eta: 1 day, 23:17:06, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6239, loss_cls: 3.5931, loss: 3.5931 +2024-12-29 19:26:36,707 - pyskl - INFO - Epoch [97][2000/3746] lr: 2.821e-02, eta: 1 day, 23:15:42, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6136, loss_cls: 3.6488, loss: 3.6488 +2024-12-29 19:28:02,072 - pyskl - INFO - Epoch [97][2100/3746] lr: 2.818e-02, eta: 1 day, 23:14:17, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6289, loss_cls: 3.5888, loss: 3.5888 +2024-12-29 19:29:27,864 - pyskl - INFO - Epoch [97][2200/3746] lr: 2.816e-02, eta: 1 day, 23:12:53, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6111, loss_cls: 3.6357, loss: 3.6357 +2024-12-29 19:30:53,340 - pyskl - INFO - Epoch [97][2300/3746] lr: 2.813e-02, eta: 1 day, 23:11:28, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6272, loss_cls: 3.5751, loss: 3.5751 +2024-12-29 19:32:19,042 - pyskl - INFO - Epoch [97][2400/3746] lr: 2.811e-02, eta: 1 day, 23:10:03, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6175, loss_cls: 3.6098, loss: 3.6098 +2024-12-29 19:33:45,060 - pyskl - INFO - Epoch [97][2500/3746] lr: 2.808e-02, eta: 1 day, 23:08:39, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6178, loss_cls: 3.6502, loss: 3.6502 +2024-12-29 19:35:11,029 - pyskl - INFO - Epoch [97][2600/3746] lr: 2.806e-02, eta: 1 day, 23:07:15, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6252, loss_cls: 3.5841, loss: 3.5841 +2024-12-29 19:36:36,424 - pyskl - INFO - Epoch [97][2700/3746] lr: 2.803e-02, eta: 1 day, 23:05:50, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6225, loss_cls: 3.6295, loss: 3.6295 +2024-12-29 19:38:02,426 - pyskl - INFO - Epoch [97][2800/3746] lr: 2.801e-02, eta: 1 day, 23:04:26, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6184, loss_cls: 3.6562, loss: 3.6562 +2024-12-29 19:39:28,231 - pyskl - INFO - Epoch [97][2900/3746] lr: 2.798e-02, eta: 1 day, 23:03:01, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6317, loss_cls: 3.5728, loss: 3.5728 +2024-12-29 19:40:53,962 - pyskl - INFO - Epoch [97][3000/3746] lr: 2.796e-02, eta: 1 day, 23:01:37, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6247, loss_cls: 3.6102, loss: 3.6102 +2024-12-29 19:42:19,991 - pyskl - INFO - Epoch [97][3100/3746] lr: 2.793e-02, eta: 1 day, 23:00:12, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6262, loss_cls: 3.6180, loss: 3.6180 +2024-12-29 19:43:45,910 - pyskl - INFO - Epoch [97][3200/3746] lr: 2.791e-02, eta: 1 day, 22:58:48, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6261, loss_cls: 3.6013, loss: 3.6013 +2024-12-29 19:45:11,738 - pyskl - INFO - Epoch [97][3300/3746] lr: 2.788e-02, eta: 1 day, 22:57:23, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.6095, loss_cls: 3.6698, loss: 3.6698 +2024-12-29 19:46:37,278 - pyskl - INFO - Epoch [97][3400/3746] lr: 2.786e-02, eta: 1 day, 22:55:59, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6158, loss_cls: 3.6378, loss: 3.6378 +2024-12-29 19:48:03,299 - pyskl - INFO - Epoch [97][3500/3746] lr: 2.783e-02, eta: 1 day, 22:54:34, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6195, loss_cls: 3.6188, loss: 3.6188 +2024-12-29 19:49:29,621 - pyskl - INFO - Epoch [97][3600/3746] lr: 2.781e-02, eta: 1 day, 22:53:10, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6286, loss_cls: 3.6145, loss: 3.6145 +2024-12-29 19:50:55,091 - pyskl - INFO - Epoch [97][3700/3746] lr: 2.778e-02, eta: 1 day, 22:51:45, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6300, loss_cls: 3.5696, loss: 3.5696 +2024-12-29 19:51:36,588 - pyskl - INFO - Saving checkpoint at 97 epochs +2024-12-29 19:53:39,949 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 19:53:40,641 - pyskl - INFO - +top1_acc 0.3077 +top5_acc 0.5595 +2024-12-29 19:53:40,642 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 19:53:40,687 - pyskl - INFO - +mean_acc 0.3074 +2024-12-29 19:53:40,692 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_95.pth was removed +2024-12-29 19:53:40,991 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_97.pth. +2024-12-29 19:53:40,992 - pyskl - INFO - Best top1_acc is 0.3077 at 97 epoch. +2024-12-29 19:53:41,007 - pyskl - INFO - Epoch(val) [97][309] top1_acc: 0.3077, top5_acc: 0.5595, mean_class_accuracy: 0.3074 +2024-12-29 19:58:03,850 - pyskl - INFO - Epoch [98][100/3746] lr: 2.774e-02, eta: 1 day, 22:50:57, time: 2.628, data_time: 1.564, memory: 15990, top1_acc: 0.3717, top5_acc: 0.6330, loss_cls: 3.5460, loss: 3.5460 +2024-12-29 19:59:31,777 - pyskl - INFO - Epoch [98][200/3746] lr: 2.772e-02, eta: 1 day, 22:49:34, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6400, loss_cls: 3.5073, loss: 3.5073 +2024-12-29 20:00:59,499 - pyskl - INFO - Epoch [98][300/3746] lr: 2.769e-02, eta: 1 day, 22:48:10, time: 0.877, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6331, loss_cls: 3.5151, loss: 3.5151 +2024-12-29 20:02:26,577 - pyskl - INFO - Epoch [98][400/3746] lr: 2.767e-02, eta: 1 day, 22:46:46, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6153, loss_cls: 3.6374, loss: 3.6374 +2024-12-29 20:03:52,898 - pyskl - INFO - Epoch [98][500/3746] lr: 2.764e-02, eta: 1 day, 22:45:22, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6334, loss_cls: 3.5908, loss: 3.5908 +2024-12-29 20:05:20,372 - pyskl - INFO - Epoch [98][600/3746] lr: 2.762e-02, eta: 1 day, 22:43:58, time: 0.875, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6297, loss_cls: 3.5765, loss: 3.5765 +2024-12-29 20:06:46,957 - pyskl - INFO - Epoch [98][700/3746] lr: 2.759e-02, eta: 1 day, 22:42:34, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6355, loss_cls: 3.5317, loss: 3.5317 +2024-12-29 20:08:13,257 - pyskl - INFO - Epoch [98][800/3746] lr: 2.757e-02, eta: 1 day, 22:41:10, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6319, loss_cls: 3.5547, loss: 3.5547 +2024-12-29 20:09:38,943 - pyskl - INFO - Epoch [98][900/3746] lr: 2.754e-02, eta: 1 day, 22:39:45, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6194, loss_cls: 3.6232, loss: 3.6232 +2024-12-29 20:11:06,254 - pyskl - INFO - Epoch [98][1000/3746] lr: 2.752e-02, eta: 1 day, 22:38:22, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6302, loss_cls: 3.5699, loss: 3.5699 +2024-12-29 20:12:33,719 - pyskl - INFO - Epoch [98][1100/3746] lr: 2.749e-02, eta: 1 day, 22:36:58, time: 0.875, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6225, loss_cls: 3.5934, loss: 3.5934 +2024-12-29 20:14:01,102 - pyskl - INFO - Epoch [98][1200/3746] lr: 2.747e-02, eta: 1 day, 22:35:34, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.3556, top5_acc: 0.6225, loss_cls: 3.6055, loss: 3.6055 +2024-12-29 20:15:28,180 - pyskl - INFO - Epoch [98][1300/3746] lr: 2.744e-02, eta: 1 day, 22:34:10, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6267, loss_cls: 3.5570, loss: 3.5570 +2024-12-29 20:16:55,604 - pyskl - INFO - Epoch [98][1400/3746] lr: 2.742e-02, eta: 1 day, 22:32:47, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6164, loss_cls: 3.6480, loss: 3.6480 +2024-12-29 20:18:21,795 - pyskl - INFO - Epoch [98][1500/3746] lr: 2.739e-02, eta: 1 day, 22:31:22, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3739, top5_acc: 0.6319, loss_cls: 3.5593, loss: 3.5593 +2024-12-29 20:19:47,623 - pyskl - INFO - Epoch [98][1600/3746] lr: 2.737e-02, eta: 1 day, 22:29:58, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6295, loss_cls: 3.5838, loss: 3.5838 +2024-12-29 20:21:13,154 - pyskl - INFO - Epoch [98][1700/3746] lr: 2.734e-02, eta: 1 day, 22:28:33, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6291, loss_cls: 3.5780, loss: 3.5780 +2024-12-29 20:22:39,554 - pyskl - INFO - Epoch [98][1800/3746] lr: 2.732e-02, eta: 1 day, 22:27:09, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6138, loss_cls: 3.6346, loss: 3.6346 +2024-12-29 20:24:05,309 - pyskl - INFO - Epoch [98][1900/3746] lr: 2.729e-02, eta: 1 day, 22:25:44, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3720, top5_acc: 0.6223, loss_cls: 3.5943, loss: 3.5943 +2024-12-29 20:25:31,675 - pyskl - INFO - Epoch [98][2000/3746] lr: 2.727e-02, eta: 1 day, 22:24:20, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6297, loss_cls: 3.5908, loss: 3.5908 +2024-12-29 20:26:57,442 - pyskl - INFO - Epoch [98][2100/3746] lr: 2.724e-02, eta: 1 day, 22:22:55, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6239, loss_cls: 3.6250, loss: 3.6250 +2024-12-29 20:28:23,443 - pyskl - INFO - Epoch [98][2200/3746] lr: 2.722e-02, eta: 1 day, 22:21:31, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6322, loss_cls: 3.5688, loss: 3.5688 +2024-12-29 20:29:49,534 - pyskl - INFO - Epoch [98][2300/3746] lr: 2.719e-02, eta: 1 day, 22:20:07, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6255, loss_cls: 3.6163, loss: 3.6163 +2024-12-29 20:31:16,350 - pyskl - INFO - Epoch [98][2400/3746] lr: 2.717e-02, eta: 1 day, 22:18:43, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6145, loss_cls: 3.6449, loss: 3.6449 +2024-12-29 20:32:42,486 - pyskl - INFO - Epoch [98][2500/3746] lr: 2.714e-02, eta: 1 day, 22:17:18, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6294, loss_cls: 3.5574, loss: 3.5574 +2024-12-29 20:34:08,729 - pyskl - INFO - Epoch [98][2600/3746] lr: 2.712e-02, eta: 1 day, 22:15:54, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6322, loss_cls: 3.5372, loss: 3.5372 +2024-12-29 20:35:35,302 - pyskl - INFO - Epoch [98][2700/3746] lr: 2.709e-02, eta: 1 day, 22:14:30, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6242, loss_cls: 3.5788, loss: 3.5788 +2024-12-29 20:37:01,975 - pyskl - INFO - Epoch [98][2800/3746] lr: 2.707e-02, eta: 1 day, 22:13:06, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6109, loss_cls: 3.6429, loss: 3.6429 +2024-12-29 20:38:28,139 - pyskl - INFO - Epoch [98][2900/3746] lr: 2.705e-02, eta: 1 day, 22:11:41, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6233, loss_cls: 3.6198, loss: 3.6198 +2024-12-29 20:39:54,436 - pyskl - INFO - Epoch [98][3000/3746] lr: 2.702e-02, eta: 1 day, 22:10:17, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3594, top5_acc: 0.6244, loss_cls: 3.6278, loss: 3.6278 +2024-12-29 20:41:21,177 - pyskl - INFO - Epoch [98][3100/3746] lr: 2.700e-02, eta: 1 day, 22:08:53, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6162, loss_cls: 3.6420, loss: 3.6420 +2024-12-29 20:42:47,817 - pyskl - INFO - Epoch [98][3200/3746] lr: 2.697e-02, eta: 1 day, 22:07:29, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6303, loss_cls: 3.5782, loss: 3.5782 +2024-12-29 20:44:14,098 - pyskl - INFO - Epoch [98][3300/3746] lr: 2.695e-02, eta: 1 day, 22:06:04, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3598, top5_acc: 0.6205, loss_cls: 3.6464, loss: 3.6464 +2024-12-29 20:45:40,922 - pyskl - INFO - Epoch [98][3400/3746] lr: 2.692e-02, eta: 1 day, 22:04:40, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6247, loss_cls: 3.5881, loss: 3.5881 +2024-12-29 20:47:07,407 - pyskl - INFO - Epoch [98][3500/3746] lr: 2.690e-02, eta: 1 day, 22:03:16, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6208, loss_cls: 3.5787, loss: 3.5787 +2024-12-29 20:48:34,227 - pyskl - INFO - Epoch [98][3600/3746] lr: 2.687e-02, eta: 1 day, 22:01:52, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6175, loss_cls: 3.6256, loss: 3.6256 +2024-12-29 20:50:00,753 - pyskl - INFO - Epoch [98][3700/3746] lr: 2.685e-02, eta: 1 day, 22:00:28, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6250, loss_cls: 3.6089, loss: 3.6089 +2024-12-29 20:50:42,519 - pyskl - INFO - Saving checkpoint at 98 epochs +2024-12-29 20:52:43,617 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 20:52:44,462 - pyskl - INFO - +top1_acc 0.3041 +top5_acc 0.5566 +2024-12-29 20:52:44,463 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 20:52:44,509 - pyskl - INFO - +mean_acc 0.3039 +2024-12-29 20:52:44,525 - pyskl - INFO - Epoch(val) [98][309] top1_acc: 0.3041, top5_acc: 0.5566, mean_class_accuracy: 0.3039 +2024-12-29 20:57:04,468 - pyskl - INFO - Epoch [99][100/3746] lr: 2.681e-02, eta: 1 day, 21:59:36, time: 2.599, data_time: 1.547, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6428, loss_cls: 3.4755, loss: 3.4755 +2024-12-29 20:58:31,165 - pyskl - INFO - Epoch [99][200/3746] lr: 2.679e-02, eta: 1 day, 21:58:11, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6417, loss_cls: 3.5133, loss: 3.5133 +2024-12-29 20:59:58,123 - pyskl - INFO - Epoch [99][300/3746] lr: 2.676e-02, eta: 1 day, 21:56:47, time: 0.870, data_time: 0.001, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6319, loss_cls: 3.5465, loss: 3.5465 +2024-12-29 21:01:25,309 - pyskl - INFO - Epoch [99][400/3746] lr: 2.674e-02, eta: 1 day, 21:55:24, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6275, loss_cls: 3.5915, loss: 3.5915 +2024-12-29 21:02:52,777 - pyskl - INFO - Epoch [99][500/3746] lr: 2.671e-02, eta: 1 day, 21:54:00, time: 0.875, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6241, loss_cls: 3.5903, loss: 3.5903 +2024-12-29 21:04:20,224 - pyskl - INFO - Epoch [99][600/3746] lr: 2.669e-02, eta: 1 day, 21:52:36, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6294, loss_cls: 3.5847, loss: 3.5847 +2024-12-29 21:05:46,316 - pyskl - INFO - Epoch [99][700/3746] lr: 2.666e-02, eta: 1 day, 21:51:11, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6369, loss_cls: 3.5288, loss: 3.5288 +2024-12-29 21:07:12,279 - pyskl - INFO - Epoch [99][800/3746] lr: 2.664e-02, eta: 1 day, 21:49:47, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6402, loss_cls: 3.5443, loss: 3.5443 +2024-12-29 21:08:37,489 - pyskl - INFO - Epoch [99][900/3746] lr: 2.661e-02, eta: 1 day, 21:48:22, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6331, loss_cls: 3.5477, loss: 3.5477 +2024-12-29 21:10:03,732 - pyskl - INFO - Epoch [99][1000/3746] lr: 2.659e-02, eta: 1 day, 21:46:58, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6302, loss_cls: 3.5316, loss: 3.5316 +2024-12-29 21:11:30,081 - pyskl - INFO - Epoch [99][1100/3746] lr: 2.656e-02, eta: 1 day, 21:45:33, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6242, loss_cls: 3.6418, loss: 3.6418 +2024-12-29 21:12:56,531 - pyskl - INFO - Epoch [99][1200/3746] lr: 2.654e-02, eta: 1 day, 21:44:09, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3691, top5_acc: 0.6358, loss_cls: 3.5687, loss: 3.5687 +2024-12-29 21:14:23,538 - pyskl - INFO - Epoch [99][1300/3746] lr: 2.651e-02, eta: 1 day, 21:42:45, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6153, loss_cls: 3.6570, loss: 3.6570 +2024-12-29 21:15:49,613 - pyskl - INFO - Epoch [99][1400/3746] lr: 2.649e-02, eta: 1 day, 21:41:20, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6344, loss_cls: 3.5804, loss: 3.5804 +2024-12-29 21:17:15,633 - pyskl - INFO - Epoch [99][1500/3746] lr: 2.646e-02, eta: 1 day, 21:39:56, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6383, loss_cls: 3.5102, loss: 3.5102 +2024-12-29 21:18:41,341 - pyskl - INFO - Epoch [99][1600/3746] lr: 2.644e-02, eta: 1 day, 21:38:31, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6364, loss_cls: 3.5540, loss: 3.5540 +2024-12-29 21:20:06,341 - pyskl - INFO - Epoch [99][1700/3746] lr: 2.642e-02, eta: 1 day, 21:37:06, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3739, top5_acc: 0.6177, loss_cls: 3.6124, loss: 3.6124 +2024-12-29 21:21:31,660 - pyskl - INFO - Epoch [99][1800/3746] lr: 2.639e-02, eta: 1 day, 21:35:41, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3723, top5_acc: 0.6314, loss_cls: 3.5549, loss: 3.5549 +2024-12-29 21:22:56,980 - pyskl - INFO - Epoch [99][1900/3746] lr: 2.637e-02, eta: 1 day, 21:34:16, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6375, loss_cls: 3.5605, loss: 3.5605 +2024-12-29 21:24:22,386 - pyskl - INFO - Epoch [99][2000/3746] lr: 2.634e-02, eta: 1 day, 21:32:51, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6270, loss_cls: 3.5900, loss: 3.5900 +2024-12-29 21:25:47,704 - pyskl - INFO - Epoch [99][2100/3746] lr: 2.632e-02, eta: 1 day, 21:31:26, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6206, loss_cls: 3.6014, loss: 3.6014 +2024-12-29 21:27:13,453 - pyskl - INFO - Epoch [99][2200/3746] lr: 2.629e-02, eta: 1 day, 21:30:02, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6273, loss_cls: 3.5790, loss: 3.5790 +2024-12-29 21:28:39,222 - pyskl - INFO - Epoch [99][2300/3746] lr: 2.627e-02, eta: 1 day, 21:28:37, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3631, top5_acc: 0.6225, loss_cls: 3.6199, loss: 3.6199 +2024-12-29 21:30:04,964 - pyskl - INFO - Epoch [99][2400/3746] lr: 2.624e-02, eta: 1 day, 21:27:12, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6286, loss_cls: 3.5728, loss: 3.5728 +2024-12-29 21:31:30,168 - pyskl - INFO - Epoch [99][2500/3746] lr: 2.622e-02, eta: 1 day, 21:25:47, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6372, loss_cls: 3.5503, loss: 3.5503 +2024-12-29 21:32:55,817 - pyskl - INFO - Epoch [99][2600/3746] lr: 2.619e-02, eta: 1 day, 21:24:23, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6247, loss_cls: 3.6282, loss: 3.6282 +2024-12-29 21:34:20,893 - pyskl - INFO - Epoch [99][2700/3746] lr: 2.617e-02, eta: 1 day, 21:22:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6250, loss_cls: 3.6066, loss: 3.6066 +2024-12-29 21:35:46,328 - pyskl - INFO - Epoch [99][2800/3746] lr: 2.614e-02, eta: 1 day, 21:21:33, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6377, loss_cls: 3.5618, loss: 3.5618 +2024-12-29 21:37:11,442 - pyskl - INFO - Epoch [99][2900/3746] lr: 2.612e-02, eta: 1 day, 21:20:08, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6281, loss_cls: 3.6034, loss: 3.6034 +2024-12-29 21:38:36,615 - pyskl - INFO - Epoch [99][3000/3746] lr: 2.610e-02, eta: 1 day, 21:18:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6269, loss_cls: 3.5755, loss: 3.5755 +2024-12-29 21:40:02,130 - pyskl - INFO - Epoch [99][3100/3746] lr: 2.607e-02, eta: 1 day, 21:17:18, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6155, loss_cls: 3.6424, loss: 3.6424 +2024-12-29 21:41:27,497 - pyskl - INFO - Epoch [99][3200/3746] lr: 2.605e-02, eta: 1 day, 21:15:53, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6230, loss_cls: 3.6210, loss: 3.6210 +2024-12-29 21:42:53,026 - pyskl - INFO - Epoch [99][3300/3746] lr: 2.602e-02, eta: 1 day, 21:14:28, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6258, loss_cls: 3.6074, loss: 3.6074 +2024-12-29 21:44:18,284 - pyskl - INFO - Epoch [99][3400/3746] lr: 2.600e-02, eta: 1 day, 21:13:03, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6359, loss_cls: 3.5568, loss: 3.5568 +2024-12-29 21:45:43,558 - pyskl - INFO - Epoch [99][3500/3746] lr: 2.597e-02, eta: 1 day, 21:11:38, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6256, loss_cls: 3.6047, loss: 3.6047 +2024-12-29 21:47:09,573 - pyskl - INFO - Epoch [99][3600/3746] lr: 2.595e-02, eta: 1 day, 21:10:14, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6322, loss_cls: 3.5662, loss: 3.5662 +2024-12-29 21:48:35,244 - pyskl - INFO - Epoch [99][3700/3746] lr: 2.592e-02, eta: 1 day, 21:08:49, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3667, top5_acc: 0.6372, loss_cls: 3.5672, loss: 3.5672 +2024-12-29 21:49:16,132 - pyskl - INFO - Saving checkpoint at 99 epochs +2024-12-29 21:51:16,100 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 21:51:16,776 - pyskl - INFO - +top1_acc 0.3137 +top5_acc 0.5652 +2024-12-29 21:51:16,776 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 21:51:16,824 - pyskl - INFO - +mean_acc 0.3132 +2024-12-29 21:51:16,829 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_97.pth was removed +2024-12-29 21:51:17,110 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_99.pth. +2024-12-29 21:51:17,111 - pyskl - INFO - Best top1_acc is 0.3137 at 99 epoch. +2024-12-29 21:51:17,128 - pyskl - INFO - Epoch(val) [99][309] top1_acc: 0.3137, top5_acc: 0.5652, mean_class_accuracy: 0.3132 +2024-12-29 21:55:27,750 - pyskl - INFO - Epoch [100][100/3746] lr: 2.589e-02, eta: 1 day, 21:07:50, time: 2.506, data_time: 1.466, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6422, loss_cls: 3.5070, loss: 3.5070 +2024-12-29 21:56:53,582 - pyskl - INFO - Epoch [100][200/3746] lr: 2.586e-02, eta: 1 day, 21:06:25, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6369, loss_cls: 3.5177, loss: 3.5177 +2024-12-29 21:58:19,409 - pyskl - INFO - Epoch [100][300/3746] lr: 2.584e-02, eta: 1 day, 21:05:01, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6439, loss_cls: 3.4958, loss: 3.4958 +2024-12-29 21:59:45,179 - pyskl - INFO - Epoch [100][400/3746] lr: 2.581e-02, eta: 1 day, 21:03:36, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6361, loss_cls: 3.5346, loss: 3.5346 +2024-12-29 22:01:11,067 - pyskl - INFO - Epoch [100][500/3746] lr: 2.579e-02, eta: 1 day, 21:02:11, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6305, loss_cls: 3.5487, loss: 3.5487 +2024-12-29 22:02:37,023 - pyskl - INFO - Epoch [100][600/3746] lr: 2.577e-02, eta: 1 day, 21:00:46, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6212, loss_cls: 3.6200, loss: 3.6200 +2024-12-29 22:04:02,231 - pyskl - INFO - Epoch [100][700/3746] lr: 2.574e-02, eta: 1 day, 20:59:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6284, loss_cls: 3.5526, loss: 3.5526 +2024-12-29 22:05:27,418 - pyskl - INFO - Epoch [100][800/3746] lr: 2.572e-02, eta: 1 day, 20:57:56, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3762, top5_acc: 0.6267, loss_cls: 3.5647, loss: 3.5647 +2024-12-29 22:06:52,743 - pyskl - INFO - Epoch [100][900/3746] lr: 2.569e-02, eta: 1 day, 20:56:31, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6353, loss_cls: 3.5215, loss: 3.5215 +2024-12-29 22:08:18,607 - pyskl - INFO - Epoch [100][1000/3746] lr: 2.567e-02, eta: 1 day, 20:55:07, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6253, loss_cls: 3.5593, loss: 3.5593 +2024-12-29 22:09:44,254 - pyskl - INFO - Epoch [100][1100/3746] lr: 2.564e-02, eta: 1 day, 20:53:42, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6308, loss_cls: 3.5895, loss: 3.5895 +2024-12-29 22:11:09,605 - pyskl - INFO - Epoch [100][1200/3746] lr: 2.562e-02, eta: 1 day, 20:52:17, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6288, loss_cls: 3.5457, loss: 3.5457 +2024-12-29 22:12:35,155 - pyskl - INFO - Epoch [100][1300/3746] lr: 2.559e-02, eta: 1 day, 20:50:52, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6375, loss_cls: 3.5111, loss: 3.5111 +2024-12-29 22:14:00,714 - pyskl - INFO - Epoch [100][1400/3746] lr: 2.557e-02, eta: 1 day, 20:49:27, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6316, loss_cls: 3.5466, loss: 3.5466 +2024-12-29 22:15:26,188 - pyskl - INFO - Epoch [100][1500/3746] lr: 2.555e-02, eta: 1 day, 20:48:02, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6283, loss_cls: 3.5776, loss: 3.5776 +2024-12-29 22:16:51,385 - pyskl - INFO - Epoch [100][1600/3746] lr: 2.552e-02, eta: 1 day, 20:46:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6255, loss_cls: 3.5944, loss: 3.5944 +2024-12-29 22:18:16,447 - pyskl - INFO - Epoch [100][1700/3746] lr: 2.550e-02, eta: 1 day, 20:45:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6412, loss_cls: 3.5405, loss: 3.5405 +2024-12-29 22:19:42,279 - pyskl - INFO - Epoch [100][1800/3746] lr: 2.547e-02, eta: 1 day, 20:43:48, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3622, top5_acc: 0.6281, loss_cls: 3.5901, loss: 3.5901 +2024-12-29 22:21:08,006 - pyskl - INFO - Epoch [100][1900/3746] lr: 2.545e-02, eta: 1 day, 20:42:23, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6459, loss_cls: 3.4704, loss: 3.4704 +2024-12-29 22:22:33,507 - pyskl - INFO - Epoch [100][2000/3746] lr: 2.542e-02, eta: 1 day, 20:40:58, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6297, loss_cls: 3.5484, loss: 3.5484 +2024-12-29 22:23:58,585 - pyskl - INFO - Epoch [100][2100/3746] lr: 2.540e-02, eta: 1 day, 20:39:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6352, loss_cls: 3.5699, loss: 3.5699 +2024-12-29 22:25:23,971 - pyskl - INFO - Epoch [100][2200/3746] lr: 2.538e-02, eta: 1 day, 20:38:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6319, loss_cls: 3.5537, loss: 3.5537 +2024-12-29 22:26:49,093 - pyskl - INFO - Epoch [100][2300/3746] lr: 2.535e-02, eta: 1 day, 20:36:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6305, loss_cls: 3.5684, loss: 3.5684 +2024-12-29 22:28:14,675 - pyskl - INFO - Epoch [100][2400/3746] lr: 2.533e-02, eta: 1 day, 20:35:18, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6258, loss_cls: 3.5705, loss: 3.5705 +2024-12-29 22:29:40,257 - pyskl - INFO - Epoch [100][2500/3746] lr: 2.530e-02, eta: 1 day, 20:33:53, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6347, loss_cls: 3.5390, loss: 3.5390 +2024-12-29 22:31:06,081 - pyskl - INFO - Epoch [100][2600/3746] lr: 2.528e-02, eta: 1 day, 20:32:28, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6327, loss_cls: 3.5530, loss: 3.5530 +2024-12-29 22:32:31,653 - pyskl - INFO - Epoch [100][2700/3746] lr: 2.525e-02, eta: 1 day, 20:31:04, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6167, loss_cls: 3.6199, loss: 3.6199 +2024-12-29 22:33:57,265 - pyskl - INFO - Epoch [100][2800/3746] lr: 2.523e-02, eta: 1 day, 20:29:39, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6352, loss_cls: 3.5363, loss: 3.5363 +2024-12-29 22:35:22,957 - pyskl - INFO - Epoch [100][2900/3746] lr: 2.521e-02, eta: 1 day, 20:28:14, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6367, loss_cls: 3.5287, loss: 3.5287 +2024-12-29 22:36:49,109 - pyskl - INFO - Epoch [100][3000/3746] lr: 2.518e-02, eta: 1 day, 20:26:49, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6192, loss_cls: 3.6242, loss: 3.6242 +2024-12-29 22:38:15,056 - pyskl - INFO - Epoch [100][3100/3746] lr: 2.516e-02, eta: 1 day, 20:25:25, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6294, loss_cls: 3.5512, loss: 3.5512 +2024-12-29 22:39:40,497 - pyskl - INFO - Epoch [100][3200/3746] lr: 2.513e-02, eta: 1 day, 20:24:00, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6312, loss_cls: 3.5565, loss: 3.5565 +2024-12-29 22:41:06,268 - pyskl - INFO - Epoch [100][3300/3746] lr: 2.511e-02, eta: 1 day, 20:22:35, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6172, loss_cls: 3.6260, loss: 3.6260 +2024-12-29 22:42:32,249 - pyskl - INFO - Epoch [100][3400/3746] lr: 2.508e-02, eta: 1 day, 20:21:10, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6405, loss_cls: 3.5221, loss: 3.5221 +2024-12-29 22:43:58,042 - pyskl - INFO - Epoch [100][3500/3746] lr: 2.506e-02, eta: 1 day, 20:19:46, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6284, loss_cls: 3.5690, loss: 3.5690 +2024-12-29 22:45:23,896 - pyskl - INFO - Epoch [100][3600/3746] lr: 2.504e-02, eta: 1 day, 20:18:21, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6131, loss_cls: 3.6570, loss: 3.6570 +2024-12-29 22:46:50,012 - pyskl - INFO - Epoch [100][3700/3746] lr: 2.501e-02, eta: 1 day, 20:16:56, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3691, top5_acc: 0.6236, loss_cls: 3.6302, loss: 3.6302 +2024-12-29 22:47:31,452 - pyskl - INFO - Saving checkpoint at 100 epochs +2024-12-29 22:49:30,649 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 22:49:31,475 - pyskl - INFO - +top1_acc 0.3150 +top5_acc 0.5679 +2024-12-29 22:49:31,475 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 22:49:31,531 - pyskl - INFO - +mean_acc 0.3148 +2024-12-29 22:49:31,537 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_99.pth was removed +2024-12-29 22:49:31,805 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_100.pth. +2024-12-29 22:49:31,806 - pyskl - INFO - Best top1_acc is 0.3150 at 100 epoch. +2024-12-29 22:49:31,824 - pyskl - INFO - Epoch(val) [100][309] top1_acc: 0.3150, top5_acc: 0.5679, mean_class_accuracy: 0.3148 +2024-12-29 22:53:46,016 - pyskl - INFO - Epoch [101][100/3746] lr: 2.498e-02, eta: 1 day, 20:15:57, time: 2.542, data_time: 1.498, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6433, loss_cls: 3.4501, loss: 3.4501 +2024-12-29 22:55:11,835 - pyskl - INFO - Epoch [101][200/3746] lr: 2.495e-02, eta: 1 day, 20:14:32, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6423, loss_cls: 3.4994, loss: 3.4994 +2024-12-29 22:56:38,302 - pyskl - INFO - Epoch [101][300/3746] lr: 2.493e-02, eta: 1 day, 20:13:08, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.3762, top5_acc: 0.6384, loss_cls: 3.5187, loss: 3.5187 +2024-12-29 22:58:04,489 - pyskl - INFO - Epoch [101][400/3746] lr: 2.490e-02, eta: 1 day, 20:11:43, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6275, loss_cls: 3.5586, loss: 3.5586 +2024-12-29 22:59:30,713 - pyskl - INFO - Epoch [101][500/3746] lr: 2.488e-02, eta: 1 day, 20:10:19, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6403, loss_cls: 3.5520, loss: 3.5520 +2024-12-29 23:00:56,535 - pyskl - INFO - Epoch [101][600/3746] lr: 2.486e-02, eta: 1 day, 20:08:54, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6325, loss_cls: 3.5310, loss: 3.5310 +2024-12-29 23:02:21,954 - pyskl - INFO - Epoch [101][700/3746] lr: 2.483e-02, eta: 1 day, 20:07:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6261, loss_cls: 3.5384, loss: 3.5384 +2024-12-29 23:03:47,105 - pyskl - INFO - Epoch [101][800/3746] lr: 2.481e-02, eta: 1 day, 20:06:04, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6375, loss_cls: 3.5404, loss: 3.5404 +2024-12-29 23:05:12,312 - pyskl - INFO - Epoch [101][900/3746] lr: 2.478e-02, eta: 1 day, 20:04:39, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6373, loss_cls: 3.5442, loss: 3.5442 +2024-12-29 23:06:38,579 - pyskl - INFO - Epoch [101][1000/3746] lr: 2.476e-02, eta: 1 day, 20:03:14, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3731, top5_acc: 0.6356, loss_cls: 3.5543, loss: 3.5543 +2024-12-29 23:08:04,390 - pyskl - INFO - Epoch [101][1100/3746] lr: 2.473e-02, eta: 1 day, 20:01:49, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6377, loss_cls: 3.5371, loss: 3.5371 +2024-12-29 23:09:30,580 - pyskl - INFO - Epoch [101][1200/3746] lr: 2.471e-02, eta: 1 day, 20:00:25, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6367, loss_cls: 3.5518, loss: 3.5518 +2024-12-29 23:10:56,727 - pyskl - INFO - Epoch [101][1300/3746] lr: 2.469e-02, eta: 1 day, 19:59:00, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6352, loss_cls: 3.5314, loss: 3.5314 +2024-12-29 23:12:22,026 - pyskl - INFO - Epoch [101][1400/3746] lr: 2.466e-02, eta: 1 day, 19:57:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6384, loss_cls: 3.5294, loss: 3.5294 +2024-12-29 23:13:47,047 - pyskl - INFO - Epoch [101][1500/3746] lr: 2.464e-02, eta: 1 day, 19:56:10, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3733, top5_acc: 0.6400, loss_cls: 3.5511, loss: 3.5511 +2024-12-29 23:15:12,328 - pyskl - INFO - Epoch [101][1600/3746] lr: 2.461e-02, eta: 1 day, 19:54:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6400, loss_cls: 3.5233, loss: 3.5233 +2024-12-29 23:16:37,512 - pyskl - INFO - Epoch [101][1700/3746] lr: 2.459e-02, eta: 1 day, 19:53:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3720, top5_acc: 0.6331, loss_cls: 3.5411, loss: 3.5411 +2024-12-29 23:18:02,235 - pyskl - INFO - Epoch [101][1800/3746] lr: 2.457e-02, eta: 1 day, 19:51:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3705, top5_acc: 0.6305, loss_cls: 3.5462, loss: 3.5462 +2024-12-29 23:19:27,459 - pyskl - INFO - Epoch [101][1900/3746] lr: 2.454e-02, eta: 1 day, 19:50:29, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6141, loss_cls: 3.6192, loss: 3.6192 +2024-12-29 23:20:52,387 - pyskl - INFO - Epoch [101][2000/3746] lr: 2.452e-02, eta: 1 day, 19:49:04, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6338, loss_cls: 3.5531, loss: 3.5531 +2024-12-29 23:22:17,132 - pyskl - INFO - Epoch [101][2100/3746] lr: 2.449e-02, eta: 1 day, 19:47:39, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6261, loss_cls: 3.5793, loss: 3.5793 +2024-12-29 23:23:41,898 - pyskl - INFO - Epoch [101][2200/3746] lr: 2.447e-02, eta: 1 day, 19:46:13, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3691, top5_acc: 0.6289, loss_cls: 3.5647, loss: 3.5647 +2024-12-29 23:25:06,647 - pyskl - INFO - Epoch [101][2300/3746] lr: 2.445e-02, eta: 1 day, 19:44:48, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6267, loss_cls: 3.5752, loss: 3.5752 +2024-12-29 23:26:31,850 - pyskl - INFO - Epoch [101][2400/3746] lr: 2.442e-02, eta: 1 day, 19:43:23, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3559, top5_acc: 0.6195, loss_cls: 3.6230, loss: 3.6230 +2024-12-29 23:27:57,188 - pyskl - INFO - Epoch [101][2500/3746] lr: 2.440e-02, eta: 1 day, 19:41:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6416, loss_cls: 3.5096, loss: 3.5096 +2024-12-29 23:29:21,772 - pyskl - INFO - Epoch [101][2600/3746] lr: 2.437e-02, eta: 1 day, 19:40:33, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6381, loss_cls: 3.5357, loss: 3.5357 +2024-12-29 23:30:46,902 - pyskl - INFO - Epoch [101][2700/3746] lr: 2.435e-02, eta: 1 day, 19:39:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6319, loss_cls: 3.5847, loss: 3.5847 +2024-12-29 23:32:11,965 - pyskl - INFO - Epoch [101][2800/3746] lr: 2.433e-02, eta: 1 day, 19:37:42, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6403, loss_cls: 3.5198, loss: 3.5198 +2024-12-29 23:33:37,281 - pyskl - INFO - Epoch [101][2900/3746] lr: 2.430e-02, eta: 1 day, 19:36:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6356, loss_cls: 3.4769, loss: 3.4769 +2024-12-29 23:35:02,552 - pyskl - INFO - Epoch [101][3000/3746] lr: 2.428e-02, eta: 1 day, 19:34:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6289, loss_cls: 3.5663, loss: 3.5663 +2024-12-29 23:36:27,588 - pyskl - INFO - Epoch [101][3100/3746] lr: 2.425e-02, eta: 1 day, 19:33:27, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6355, loss_cls: 3.5488, loss: 3.5488 +2024-12-29 23:37:52,853 - pyskl - INFO - Epoch [101][3200/3746] lr: 2.423e-02, eta: 1 day, 19:32:02, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6327, loss_cls: 3.5087, loss: 3.5087 +2024-12-29 23:39:17,612 - pyskl - INFO - Epoch [101][3300/3746] lr: 2.421e-02, eta: 1 day, 19:30:37, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3709, top5_acc: 0.6212, loss_cls: 3.5752, loss: 3.5752 +2024-12-29 23:40:42,608 - pyskl - INFO - Epoch [101][3400/3746] lr: 2.418e-02, eta: 1 day, 19:29:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6264, loss_cls: 3.5851, loss: 3.5851 +2024-12-29 23:42:07,522 - pyskl - INFO - Epoch [101][3500/3746] lr: 2.416e-02, eta: 1 day, 19:27:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6342, loss_cls: 3.5640, loss: 3.5640 +2024-12-29 23:43:32,435 - pyskl - INFO - Epoch [101][3600/3746] lr: 2.413e-02, eta: 1 day, 19:26:21, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6359, loss_cls: 3.5136, loss: 3.5136 +2024-12-29 23:44:57,236 - pyskl - INFO - Epoch [101][3700/3746] lr: 2.411e-02, eta: 1 day, 19:24:56, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6323, loss_cls: 3.5817, loss: 3.5817 +2024-12-29 23:45:37,914 - pyskl - INFO - Saving checkpoint at 101 epochs +2024-12-29 23:47:39,319 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-29 23:47:40,095 - pyskl - INFO - +top1_acc 0.3158 +top5_acc 0.5693 +2024-12-29 23:47:40,095 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-29 23:47:40,150 - pyskl - INFO - +mean_acc 0.3155 +2024-12-29 23:47:40,155 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_100.pth was removed +2024-12-29 23:47:40,424 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_101.pth. +2024-12-29 23:47:40,425 - pyskl - INFO - Best top1_acc is 0.3158 at 101 epoch. +2024-12-29 23:47:40,442 - pyskl - INFO - Epoch(val) [101][309] top1_acc: 0.3158, top5_acc: 0.5693, mean_class_accuracy: 0.3155 +2024-12-29 23:51:55,668 - pyskl - INFO - Epoch [102][100/3746] lr: 2.407e-02, eta: 1 day, 19:23:55, time: 2.552, data_time: 1.514, memory: 15990, top1_acc: 0.3859, top5_acc: 0.6402, loss_cls: 3.4743, loss: 3.4743 +2024-12-29 23:53:21,434 - pyskl - INFO - Epoch [102][200/3746] lr: 2.405e-02, eta: 1 day, 19:22:30, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6384, loss_cls: 3.5177, loss: 3.5177 +2024-12-29 23:54:47,412 - pyskl - INFO - Epoch [102][300/3746] lr: 2.403e-02, eta: 1 day, 19:21:05, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6273, loss_cls: 3.5505, loss: 3.5505 +2024-12-29 23:56:13,636 - pyskl - INFO - Epoch [102][400/3746] lr: 2.400e-02, eta: 1 day, 19:19:41, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3859, top5_acc: 0.6456, loss_cls: 3.4817, loss: 3.4817 +2024-12-29 23:57:39,768 - pyskl - INFO - Epoch [102][500/3746] lr: 2.398e-02, eta: 1 day, 19:18:16, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6273, loss_cls: 3.5474, loss: 3.5474 +2024-12-29 23:59:06,163 - pyskl - INFO - Epoch [102][600/3746] lr: 2.396e-02, eta: 1 day, 19:16:51, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6417, loss_cls: 3.5023, loss: 3.5023 +2024-12-30 00:00:31,596 - pyskl - INFO - Epoch [102][700/3746] lr: 2.393e-02, eta: 1 day, 19:15:26, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3752, top5_acc: 0.6358, loss_cls: 3.5305, loss: 3.5305 +2024-12-30 00:01:56,491 - pyskl - INFO - Epoch [102][800/3746] lr: 2.391e-02, eta: 1 day, 19:14:01, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3808, top5_acc: 0.6355, loss_cls: 3.5256, loss: 3.5256 +2024-12-30 00:03:22,036 - pyskl - INFO - Epoch [102][900/3746] lr: 2.388e-02, eta: 1 day, 19:12:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6312, loss_cls: 3.5384, loss: 3.5384 +2024-12-30 00:04:47,896 - pyskl - INFO - Epoch [102][1000/3746] lr: 2.386e-02, eta: 1 day, 19:11:11, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6350, loss_cls: 3.5308, loss: 3.5308 +2024-12-30 00:06:13,965 - pyskl - INFO - Epoch [102][1100/3746] lr: 2.384e-02, eta: 1 day, 19:09:46, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6397, loss_cls: 3.5209, loss: 3.5209 +2024-12-30 00:07:40,327 - pyskl - INFO - Epoch [102][1200/3746] lr: 2.381e-02, eta: 1 day, 19:08:22, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6367, loss_cls: 3.5124, loss: 3.5124 +2024-12-30 00:09:05,966 - pyskl - INFO - Epoch [102][1300/3746] lr: 2.379e-02, eta: 1 day, 19:06:57, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6448, loss_cls: 3.4685, loss: 3.4685 +2024-12-30 00:10:32,294 - pyskl - INFO - Epoch [102][1400/3746] lr: 2.376e-02, eta: 1 day, 19:05:32, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6355, loss_cls: 3.5390, loss: 3.5390 +2024-12-30 00:11:58,374 - pyskl - INFO - Epoch [102][1500/3746] lr: 2.374e-02, eta: 1 day, 19:04:08, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3722, top5_acc: 0.6267, loss_cls: 3.5815, loss: 3.5815 +2024-12-30 00:13:23,153 - pyskl - INFO - Epoch [102][1600/3746] lr: 2.372e-02, eta: 1 day, 19:02:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6394, loss_cls: 3.5415, loss: 3.5415 +2024-12-30 00:14:48,311 - pyskl - INFO - Epoch [102][1700/3746] lr: 2.369e-02, eta: 1 day, 19:01:17, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6456, loss_cls: 3.4791, loss: 3.4791 +2024-12-30 00:16:14,319 - pyskl - INFO - Epoch [102][1800/3746] lr: 2.367e-02, eta: 1 day, 18:59:52, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6391, loss_cls: 3.5073, loss: 3.5073 +2024-12-30 00:17:39,644 - pyskl - INFO - Epoch [102][1900/3746] lr: 2.365e-02, eta: 1 day, 18:58:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3708, top5_acc: 0.6356, loss_cls: 3.5519, loss: 3.5519 +2024-12-30 00:19:04,513 - pyskl - INFO - Epoch [102][2000/3746] lr: 2.362e-02, eta: 1 day, 18:57:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6258, loss_cls: 3.5425, loss: 3.5425 +2024-12-30 00:20:29,435 - pyskl - INFO - Epoch [102][2100/3746] lr: 2.360e-02, eta: 1 day, 18:55:37, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6380, loss_cls: 3.5048, loss: 3.5048 +2024-12-30 00:21:54,645 - pyskl - INFO - Epoch [102][2200/3746] lr: 2.357e-02, eta: 1 day, 18:54:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6392, loss_cls: 3.5088, loss: 3.5088 +2024-12-30 00:23:19,462 - pyskl - INFO - Epoch [102][2300/3746] lr: 2.355e-02, eta: 1 day, 18:52:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6286, loss_cls: 3.5404, loss: 3.5404 +2024-12-30 00:24:44,965 - pyskl - INFO - Epoch [102][2400/3746] lr: 2.353e-02, eta: 1 day, 18:51:21, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3812, top5_acc: 0.6359, loss_cls: 3.5241, loss: 3.5241 +2024-12-30 00:26:09,925 - pyskl - INFO - Epoch [102][2500/3746] lr: 2.350e-02, eta: 1 day, 18:49:56, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6391, loss_cls: 3.5128, loss: 3.5128 +2024-12-30 00:27:34,772 - pyskl - INFO - Epoch [102][2600/3746] lr: 2.348e-02, eta: 1 day, 18:48:30, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6431, loss_cls: 3.5404, loss: 3.5404 +2024-12-30 00:28:59,557 - pyskl - INFO - Epoch [102][2700/3746] lr: 2.346e-02, eta: 1 day, 18:47:05, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6319, loss_cls: 3.5757, loss: 3.5757 +2024-12-30 00:30:24,650 - pyskl - INFO - Epoch [102][2800/3746] lr: 2.343e-02, eta: 1 day, 18:45:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6408, loss_cls: 3.4969, loss: 3.4969 +2024-12-30 00:31:50,000 - pyskl - INFO - Epoch [102][2900/3746] lr: 2.341e-02, eta: 1 day, 18:44:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6358, loss_cls: 3.5380, loss: 3.5380 +2024-12-30 00:33:14,883 - pyskl - INFO - Epoch [102][3000/3746] lr: 2.339e-02, eta: 1 day, 18:42:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6436, loss_cls: 3.4908, loss: 3.4908 +2024-12-30 00:34:40,441 - pyskl - INFO - Epoch [102][3100/3746] lr: 2.336e-02, eta: 1 day, 18:41:25, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6384, loss_cls: 3.5331, loss: 3.5331 +2024-12-30 00:36:06,366 - pyskl - INFO - Epoch [102][3200/3746] lr: 2.334e-02, eta: 1 day, 18:40:00, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6378, loss_cls: 3.5019, loss: 3.5019 +2024-12-30 00:37:31,343 - pyskl - INFO - Epoch [102][3300/3746] lr: 2.331e-02, eta: 1 day, 18:38:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6277, loss_cls: 3.5458, loss: 3.5458 +2024-12-30 00:38:56,636 - pyskl - INFO - Epoch [102][3400/3746] lr: 2.329e-02, eta: 1 day, 18:37:09, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3730, top5_acc: 0.6291, loss_cls: 3.5926, loss: 3.5926 +2024-12-30 00:40:22,569 - pyskl - INFO - Epoch [102][3500/3746] lr: 2.327e-02, eta: 1 day, 18:35:45, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3708, top5_acc: 0.6338, loss_cls: 3.5440, loss: 3.5440 +2024-12-30 00:41:48,220 - pyskl - INFO - Epoch [102][3600/3746] lr: 2.324e-02, eta: 1 day, 18:34:20, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6336, loss_cls: 3.5678, loss: 3.5678 +2024-12-30 00:43:13,816 - pyskl - INFO - Epoch [102][3700/3746] lr: 2.322e-02, eta: 1 day, 18:32:55, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6264, loss_cls: 3.5578, loss: 3.5578 +2024-12-30 00:43:55,143 - pyskl - INFO - Saving checkpoint at 102 epochs +2024-12-30 00:45:56,545 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 00:45:57,276 - pyskl - INFO - +top1_acc 0.3242 +top5_acc 0.5773 +2024-12-30 00:45:57,276 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 00:45:57,338 - pyskl - INFO - +mean_acc 0.3240 +2024-12-30 00:45:57,343 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_101.pth was removed +2024-12-30 00:45:57,622 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_102.pth. +2024-12-30 00:45:57,623 - pyskl - INFO - Best top1_acc is 0.3242 at 102 epoch. +2024-12-30 00:45:57,640 - pyskl - INFO - Epoch(val) [102][309] top1_acc: 0.3242, top5_acc: 0.5773, mean_class_accuracy: 0.3240 +2024-12-30 00:50:20,590 - pyskl - INFO - Epoch [103][100/3746] lr: 2.319e-02, eta: 1 day, 18:31:56, time: 2.629, data_time: 1.589, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6589, loss_cls: 3.4387, loss: 3.4387 +2024-12-30 00:51:46,530 - pyskl - INFO - Epoch [103][200/3746] lr: 2.316e-02, eta: 1 day, 18:30:31, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6414, loss_cls: 3.4757, loss: 3.4757 +2024-12-30 00:53:11,930 - pyskl - INFO - Epoch [103][300/3746] lr: 2.314e-02, eta: 1 day, 18:29:06, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6536, loss_cls: 3.4417, loss: 3.4417 +2024-12-30 00:54:37,408 - pyskl - INFO - Epoch [103][400/3746] lr: 2.311e-02, eta: 1 day, 18:27:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3909, top5_acc: 0.6483, loss_cls: 3.4623, loss: 3.4623 +2024-12-30 00:56:02,755 - pyskl - INFO - Epoch [103][500/3746] lr: 2.309e-02, eta: 1 day, 18:26:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6481, loss_cls: 3.4904, loss: 3.4904 +2024-12-30 00:57:28,321 - pyskl - INFO - Epoch [103][600/3746] lr: 2.307e-02, eta: 1 day, 18:24:50, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6444, loss_cls: 3.4940, loss: 3.4940 +2024-12-30 00:58:53,722 - pyskl - INFO - Epoch [103][700/3746] lr: 2.304e-02, eta: 1 day, 18:23:25, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6377, loss_cls: 3.5229, loss: 3.5229 +2024-12-30 01:00:19,258 - pyskl - INFO - Epoch [103][800/3746] lr: 2.302e-02, eta: 1 day, 18:22:00, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6366, loss_cls: 3.5434, loss: 3.5434 +2024-12-30 01:01:45,136 - pyskl - INFO - Epoch [103][900/3746] lr: 2.300e-02, eta: 1 day, 18:20:35, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6412, loss_cls: 3.5010, loss: 3.5010 +2024-12-30 01:03:10,623 - pyskl - INFO - Epoch [103][1000/3746] lr: 2.297e-02, eta: 1 day, 18:19:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6422, loss_cls: 3.4935, loss: 3.4935 +2024-12-30 01:04:36,269 - pyskl - INFO - Epoch [103][1100/3746] lr: 2.295e-02, eta: 1 day, 18:17:45, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6383, loss_cls: 3.5084, loss: 3.5084 +2024-12-30 01:06:01,542 - pyskl - INFO - Epoch [103][1200/3746] lr: 2.293e-02, eta: 1 day, 18:16:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4005, top5_acc: 0.6613, loss_cls: 3.3839, loss: 3.3839 +2024-12-30 01:07:26,813 - pyskl - INFO - Epoch [103][1300/3746] lr: 2.290e-02, eta: 1 day, 18:14:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6339, loss_cls: 3.4921, loss: 3.4921 +2024-12-30 01:08:52,267 - pyskl - INFO - Epoch [103][1400/3746] lr: 2.288e-02, eta: 1 day, 18:13:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6353, loss_cls: 3.4951, loss: 3.4951 +2024-12-30 01:10:17,808 - pyskl - INFO - Epoch [103][1500/3746] lr: 2.286e-02, eta: 1 day, 18:12:05, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6417, loss_cls: 3.5025, loss: 3.5025 +2024-12-30 01:11:43,465 - pyskl - INFO - Epoch [103][1600/3746] lr: 2.283e-02, eta: 1 day, 18:10:40, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6391, loss_cls: 3.5043, loss: 3.5043 +2024-12-30 01:13:08,731 - pyskl - INFO - Epoch [103][1700/3746] lr: 2.281e-02, eta: 1 day, 18:09:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6295, loss_cls: 3.5418, loss: 3.5418 +2024-12-30 01:14:33,901 - pyskl - INFO - Epoch [103][1800/3746] lr: 2.279e-02, eta: 1 day, 18:07:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3672, top5_acc: 0.6342, loss_cls: 3.5432, loss: 3.5432 +2024-12-30 01:15:59,509 - pyskl - INFO - Epoch [103][1900/3746] lr: 2.276e-02, eta: 1 day, 18:06:24, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6436, loss_cls: 3.4587, loss: 3.4587 +2024-12-30 01:17:25,175 - pyskl - INFO - Epoch [103][2000/3746] lr: 2.274e-02, eta: 1 day, 18:04:59, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6428, loss_cls: 3.5175, loss: 3.5175 +2024-12-30 01:18:51,285 - pyskl - INFO - Epoch [103][2100/3746] lr: 2.272e-02, eta: 1 day, 18:03:35, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6516, loss_cls: 3.4796, loss: 3.4796 +2024-12-30 01:20:17,308 - pyskl - INFO - Epoch [103][2200/3746] lr: 2.269e-02, eta: 1 day, 18:02:10, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6414, loss_cls: 3.5114, loss: 3.5114 +2024-12-30 01:21:43,095 - pyskl - INFO - Epoch [103][2300/3746] lr: 2.267e-02, eta: 1 day, 18:00:45, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6306, loss_cls: 3.5553, loss: 3.5553 +2024-12-30 01:23:09,199 - pyskl - INFO - Epoch [103][2400/3746] lr: 2.264e-02, eta: 1 day, 17:59:20, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6339, loss_cls: 3.5192, loss: 3.5192 +2024-12-30 01:24:35,428 - pyskl - INFO - Epoch [103][2500/3746] lr: 2.262e-02, eta: 1 day, 17:57:55, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6320, loss_cls: 3.5578, loss: 3.5578 +2024-12-30 01:26:01,114 - pyskl - INFO - Epoch [103][2600/3746] lr: 2.260e-02, eta: 1 day, 17:56:30, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6358, loss_cls: 3.4796, loss: 3.4796 +2024-12-30 01:27:27,295 - pyskl - INFO - Epoch [103][2700/3746] lr: 2.257e-02, eta: 1 day, 17:55:06, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6334, loss_cls: 3.5337, loss: 3.5337 +2024-12-30 01:28:52,723 - pyskl - INFO - Epoch [103][2800/3746] lr: 2.255e-02, eta: 1 day, 17:53:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6355, loss_cls: 3.5400, loss: 3.5400 +2024-12-30 01:30:18,747 - pyskl - INFO - Epoch [103][2900/3746] lr: 2.253e-02, eta: 1 day, 17:52:16, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6395, loss_cls: 3.5006, loss: 3.5006 +2024-12-30 01:31:44,770 - pyskl - INFO - Epoch [103][3000/3746] lr: 2.250e-02, eta: 1 day, 17:50:51, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6438, loss_cls: 3.5094, loss: 3.5094 +2024-12-30 01:33:10,192 - pyskl - INFO - Epoch [103][3100/3746] lr: 2.248e-02, eta: 1 day, 17:49:26, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6402, loss_cls: 3.5079, loss: 3.5079 +2024-12-30 01:34:35,776 - pyskl - INFO - Epoch [103][3200/3746] lr: 2.246e-02, eta: 1 day, 17:48:01, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3725, top5_acc: 0.6300, loss_cls: 3.5233, loss: 3.5233 +2024-12-30 01:36:01,088 - pyskl - INFO - Epoch [103][3300/3746] lr: 2.243e-02, eta: 1 day, 17:46:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6359, loss_cls: 3.5314, loss: 3.5314 +2024-12-30 01:37:27,270 - pyskl - INFO - Epoch [103][3400/3746] lr: 2.241e-02, eta: 1 day, 17:45:11, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3731, top5_acc: 0.6347, loss_cls: 3.5726, loss: 3.5726 +2024-12-30 01:38:52,662 - pyskl - INFO - Epoch [103][3500/3746] lr: 2.239e-02, eta: 1 day, 17:43:46, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6211, loss_cls: 3.6127, loss: 3.6127 +2024-12-30 01:40:17,933 - pyskl - INFO - Epoch [103][3600/3746] lr: 2.236e-02, eta: 1 day, 17:42:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6495, loss_cls: 3.4640, loss: 3.4640 +2024-12-30 01:41:43,909 - pyskl - INFO - Epoch [103][3700/3746] lr: 2.234e-02, eta: 1 day, 17:40:56, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3713, top5_acc: 0.6302, loss_cls: 3.5457, loss: 3.5457 +2024-12-30 01:42:25,295 - pyskl - INFO - Saving checkpoint at 103 epochs +2024-12-30 01:44:26,664 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 01:44:27,402 - pyskl - INFO - +top1_acc 0.3308 +top5_acc 0.5812 +2024-12-30 01:44:27,402 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 01:44:27,462 - pyskl - INFO - +mean_acc 0.3304 +2024-12-30 01:44:27,467 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_102.pth was removed +2024-12-30 01:44:27,758 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_103.pth. +2024-12-30 01:44:27,759 - pyskl - INFO - Best top1_acc is 0.3308 at 103 epoch. +2024-12-30 01:44:27,772 - pyskl - INFO - Epoch(val) [103][309] top1_acc: 0.3308, top5_acc: 0.5812, mean_class_accuracy: 0.3304 +2024-12-30 01:48:44,608 - pyskl - INFO - Epoch [104][100/3746] lr: 2.231e-02, eta: 1 day, 17:39:52, time: 2.568, data_time: 1.538, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6595, loss_cls: 3.4128, loss: 3.4128 +2024-12-30 01:50:09,441 - pyskl - INFO - Epoch [104][200/3746] lr: 2.228e-02, eta: 1 day, 17:38:26, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6587, loss_cls: 3.4016, loss: 3.4016 +2024-12-30 01:51:34,897 - pyskl - INFO - Epoch [104][300/3746] lr: 2.226e-02, eta: 1 day, 17:37:01, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6395, loss_cls: 3.4906, loss: 3.4906 +2024-12-30 01:53:00,074 - pyskl - INFO - Epoch [104][400/3746] lr: 2.224e-02, eta: 1 day, 17:35:36, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3828, top5_acc: 0.6567, loss_cls: 3.4296, loss: 3.4296 +2024-12-30 01:54:25,495 - pyskl - INFO - Epoch [104][500/3746] lr: 2.221e-02, eta: 1 day, 17:34:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6477, loss_cls: 3.4555, loss: 3.4555 +2024-12-30 01:55:50,955 - pyskl - INFO - Epoch [104][600/3746] lr: 2.219e-02, eta: 1 day, 17:32:45, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3786, top5_acc: 0.6384, loss_cls: 3.5214, loss: 3.5214 +2024-12-30 01:57:15,692 - pyskl - INFO - Epoch [104][700/3746] lr: 2.217e-02, eta: 1 day, 17:31:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6502, loss_cls: 3.4479, loss: 3.4479 +2024-12-30 01:58:40,679 - pyskl - INFO - Epoch [104][800/3746] lr: 2.214e-02, eta: 1 day, 17:29:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6420, loss_cls: 3.4424, loss: 3.4424 +2024-12-30 02:00:06,276 - pyskl - INFO - Epoch [104][900/3746] lr: 2.212e-02, eta: 1 day, 17:28:29, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6417, loss_cls: 3.4781, loss: 3.4781 +2024-12-30 02:01:31,660 - pyskl - INFO - Epoch [104][1000/3746] lr: 2.210e-02, eta: 1 day, 17:27:04, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6469, loss_cls: 3.5003, loss: 3.5003 +2024-12-30 02:02:57,349 - pyskl - INFO - Epoch [104][1100/3746] lr: 2.208e-02, eta: 1 day, 17:25:39, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6483, loss_cls: 3.4741, loss: 3.4741 +2024-12-30 02:04:22,555 - pyskl - INFO - Epoch [104][1200/3746] lr: 2.205e-02, eta: 1 day, 17:24:14, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6461, loss_cls: 3.5151, loss: 3.5151 +2024-12-30 02:05:47,523 - pyskl - INFO - Epoch [104][1300/3746] lr: 2.203e-02, eta: 1 day, 17:22:49, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3856, top5_acc: 0.6436, loss_cls: 3.4814, loss: 3.4814 +2024-12-30 02:07:12,925 - pyskl - INFO - Epoch [104][1400/3746] lr: 2.201e-02, eta: 1 day, 17:21:23, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6434, loss_cls: 3.4948, loss: 3.4948 +2024-12-30 02:08:37,987 - pyskl - INFO - Epoch [104][1500/3746] lr: 2.198e-02, eta: 1 day, 17:19:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6412, loss_cls: 3.4652, loss: 3.4652 +2024-12-30 02:10:03,090 - pyskl - INFO - Epoch [104][1600/3746] lr: 2.196e-02, eta: 1 day, 17:18:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6464, loss_cls: 3.4543, loss: 3.4543 +2024-12-30 02:11:28,356 - pyskl - INFO - Epoch [104][1700/3746] lr: 2.194e-02, eta: 1 day, 17:17:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3825, top5_acc: 0.6391, loss_cls: 3.4876, loss: 3.4876 +2024-12-30 02:12:53,759 - pyskl - INFO - Epoch [104][1800/3746] lr: 2.191e-02, eta: 1 day, 17:15:42, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6500, loss_cls: 3.4698, loss: 3.4698 +2024-12-30 02:14:18,783 - pyskl - INFO - Epoch [104][1900/3746] lr: 2.189e-02, eta: 1 day, 17:14:17, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3703, top5_acc: 0.6314, loss_cls: 3.5461, loss: 3.5461 +2024-12-30 02:15:43,648 - pyskl - INFO - Epoch [104][2000/3746] lr: 2.187e-02, eta: 1 day, 17:12:52, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6389, loss_cls: 3.4974, loss: 3.4974 +2024-12-30 02:17:08,807 - pyskl - INFO - Epoch [104][2100/3746] lr: 2.184e-02, eta: 1 day, 17:11:26, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6417, loss_cls: 3.5038, loss: 3.5038 +2024-12-30 02:18:34,172 - pyskl - INFO - Epoch [104][2200/3746] lr: 2.182e-02, eta: 1 day, 17:10:01, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6303, loss_cls: 3.5363, loss: 3.5363 +2024-12-30 02:19:59,134 - pyskl - INFO - Epoch [104][2300/3746] lr: 2.180e-02, eta: 1 day, 17:08:36, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6320, loss_cls: 3.5421, loss: 3.5421 +2024-12-30 02:21:24,083 - pyskl - INFO - Epoch [104][2400/3746] lr: 2.177e-02, eta: 1 day, 17:07:10, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6288, loss_cls: 3.5688, loss: 3.5688 +2024-12-30 02:22:49,000 - pyskl - INFO - Epoch [104][2500/3746] lr: 2.175e-02, eta: 1 day, 17:05:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6452, loss_cls: 3.5102, loss: 3.5102 +2024-12-30 02:24:14,592 - pyskl - INFO - Epoch [104][2600/3746] lr: 2.173e-02, eta: 1 day, 17:04:20, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6355, loss_cls: 3.5198, loss: 3.5198 +2024-12-30 02:25:39,418 - pyskl - INFO - Epoch [104][2700/3746] lr: 2.171e-02, eta: 1 day, 17:02:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6481, loss_cls: 3.4629, loss: 3.4629 +2024-12-30 02:27:05,284 - pyskl - INFO - Epoch [104][2800/3746] lr: 2.168e-02, eta: 1 day, 17:01:30, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6438, loss_cls: 3.4867, loss: 3.4867 +2024-12-30 02:28:30,562 - pyskl - INFO - Epoch [104][2900/3746] lr: 2.166e-02, eta: 1 day, 17:00:04, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6380, loss_cls: 3.4839, loss: 3.4839 +2024-12-30 02:29:55,934 - pyskl - INFO - Epoch [104][3000/3746] lr: 2.164e-02, eta: 1 day, 16:58:39, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6267, loss_cls: 3.5573, loss: 3.5573 +2024-12-30 02:31:21,511 - pyskl - INFO - Epoch [104][3100/3746] lr: 2.161e-02, eta: 1 day, 16:57:14, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6269, loss_cls: 3.5614, loss: 3.5614 +2024-12-30 02:32:46,955 - pyskl - INFO - Epoch [104][3200/3746] lr: 2.159e-02, eta: 1 day, 16:55:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3750, top5_acc: 0.6359, loss_cls: 3.5048, loss: 3.5048 +2024-12-30 02:34:12,175 - pyskl - INFO - Epoch [104][3300/3746] lr: 2.157e-02, eta: 1 day, 16:54:24, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6434, loss_cls: 3.4836, loss: 3.4836 +2024-12-30 02:35:37,769 - pyskl - INFO - Epoch [104][3400/3746] lr: 2.154e-02, eta: 1 day, 16:52:59, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6458, loss_cls: 3.4562, loss: 3.4562 +2024-12-30 02:37:03,501 - pyskl - INFO - Epoch [104][3500/3746] lr: 2.152e-02, eta: 1 day, 16:51:34, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3844, top5_acc: 0.6352, loss_cls: 3.5013, loss: 3.5013 +2024-12-30 02:38:28,868 - pyskl - INFO - Epoch [104][3600/3746] lr: 2.150e-02, eta: 1 day, 16:50:08, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6461, loss_cls: 3.4734, loss: 3.4734 +2024-12-30 02:39:54,118 - pyskl - INFO - Epoch [104][3700/3746] lr: 2.148e-02, eta: 1 day, 16:48:43, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6397, loss_cls: 3.5413, loss: 3.5413 +2024-12-30 02:40:35,591 - pyskl - INFO - Saving checkpoint at 104 epochs +2024-12-30 02:42:35,348 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 02:42:36,032 - pyskl - INFO - +top1_acc 0.3159 +top5_acc 0.5770 +2024-12-30 02:42:36,032 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 02:42:36,074 - pyskl - INFO - +mean_acc 0.3155 +2024-12-30 02:42:36,086 - pyskl - INFO - Epoch(val) [104][309] top1_acc: 0.3159, top5_acc: 0.5770, mean_class_accuracy: 0.3155 +2024-12-30 02:46:52,417 - pyskl - INFO - Epoch [105][100/3746] lr: 2.144e-02, eta: 1 day, 16:47:37, time: 2.563, data_time: 1.520, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6575, loss_cls: 3.4088, loss: 3.4088 +2024-12-30 02:48:18,370 - pyskl - INFO - Epoch [105][200/3746] lr: 2.142e-02, eta: 1 day, 16:46:12, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3872, top5_acc: 0.6564, loss_cls: 3.4215, loss: 3.4215 +2024-12-30 02:49:44,739 - pyskl - INFO - Epoch [105][300/3746] lr: 2.140e-02, eta: 1 day, 16:44:47, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.3945, top5_acc: 0.6553, loss_cls: 3.4418, loss: 3.4418 +2024-12-30 02:51:10,983 - pyskl - INFO - Epoch [105][400/3746] lr: 2.137e-02, eta: 1 day, 16:43:22, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6594, loss_cls: 3.4259, loss: 3.4259 +2024-12-30 02:52:36,936 - pyskl - INFO - Epoch [105][500/3746] lr: 2.135e-02, eta: 1 day, 16:41:57, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6403, loss_cls: 3.4810, loss: 3.4810 +2024-12-30 02:54:02,336 - pyskl - INFO - Epoch [105][600/3746] lr: 2.133e-02, eta: 1 day, 16:40:32, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6523, loss_cls: 3.4257, loss: 3.4257 +2024-12-30 02:55:27,452 - pyskl - INFO - Epoch [105][700/3746] lr: 2.130e-02, eta: 1 day, 16:39:07, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6489, loss_cls: 3.4497, loss: 3.4497 +2024-12-30 02:56:52,851 - pyskl - INFO - Epoch [105][800/3746] lr: 2.128e-02, eta: 1 day, 16:37:42, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6517, loss_cls: 3.4367, loss: 3.4367 +2024-12-30 02:58:18,549 - pyskl - INFO - Epoch [105][900/3746] lr: 2.126e-02, eta: 1 day, 16:36:17, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6527, loss_cls: 3.4432, loss: 3.4432 +2024-12-30 02:59:44,778 - pyskl - INFO - Epoch [105][1000/3746] lr: 2.124e-02, eta: 1 day, 16:34:52, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6436, loss_cls: 3.4741, loss: 3.4741 +2024-12-30 03:01:10,587 - pyskl - INFO - Epoch [105][1100/3746] lr: 2.121e-02, eta: 1 day, 16:33:27, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6564, loss_cls: 3.4408, loss: 3.4408 +2024-12-30 03:02:36,265 - pyskl - INFO - Epoch [105][1200/3746] lr: 2.119e-02, eta: 1 day, 16:32:02, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6438, loss_cls: 3.4670, loss: 3.4670 +2024-12-30 03:04:01,786 - pyskl - INFO - Epoch [105][1300/3746] lr: 2.117e-02, eta: 1 day, 16:30:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6553, loss_cls: 3.4466, loss: 3.4466 +2024-12-30 03:05:27,374 - pyskl - INFO - Epoch [105][1400/3746] lr: 2.114e-02, eta: 1 day, 16:29:11, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3817, top5_acc: 0.6425, loss_cls: 3.4861, loss: 3.4861 +2024-12-30 03:06:52,307 - pyskl - INFO - Epoch [105][1500/3746] lr: 2.112e-02, eta: 1 day, 16:27:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6394, loss_cls: 3.5118, loss: 3.5118 +2024-12-30 03:08:17,915 - pyskl - INFO - Epoch [105][1600/3746] lr: 2.110e-02, eta: 1 day, 16:26:21, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6461, loss_cls: 3.4811, loss: 3.4811 +2024-12-30 03:09:43,229 - pyskl - INFO - Epoch [105][1700/3746] lr: 2.108e-02, eta: 1 day, 16:24:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3748, top5_acc: 0.6230, loss_cls: 3.5508, loss: 3.5508 +2024-12-30 03:11:08,087 - pyskl - INFO - Epoch [105][1800/3746] lr: 2.105e-02, eta: 1 day, 16:23:30, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3769, top5_acc: 0.6306, loss_cls: 3.5159, loss: 3.5159 +2024-12-30 03:12:33,499 - pyskl - INFO - Epoch [105][1900/3746] lr: 2.103e-02, eta: 1 day, 16:22:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6459, loss_cls: 3.4283, loss: 3.4283 +2024-12-30 03:13:58,563 - pyskl - INFO - Epoch [105][2000/3746] lr: 2.101e-02, eta: 1 day, 16:20:39, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6377, loss_cls: 3.5095, loss: 3.5095 +2024-12-30 03:15:23,599 - pyskl - INFO - Epoch [105][2100/3746] lr: 2.098e-02, eta: 1 day, 16:19:14, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3852, top5_acc: 0.6438, loss_cls: 3.4754, loss: 3.4754 +2024-12-30 03:16:48,763 - pyskl - INFO - Epoch [105][2200/3746] lr: 2.096e-02, eta: 1 day, 16:17:49, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6512, loss_cls: 3.4478, loss: 3.4478 +2024-12-30 03:18:13,694 - pyskl - INFO - Epoch [105][2300/3746] lr: 2.094e-02, eta: 1 day, 16:16:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6530, loss_cls: 3.4545, loss: 3.4545 +2024-12-30 03:19:38,657 - pyskl - INFO - Epoch [105][2400/3746] lr: 2.092e-02, eta: 1 day, 16:14:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3816, top5_acc: 0.6434, loss_cls: 3.4748, loss: 3.4748 +2024-12-30 03:21:03,807 - pyskl - INFO - Epoch [105][2500/3746] lr: 2.089e-02, eta: 1 day, 16:13:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6403, loss_cls: 3.4750, loss: 3.4750 +2024-12-30 03:22:29,322 - pyskl - INFO - Epoch [105][2600/3746] lr: 2.087e-02, eta: 1 day, 16:12:07, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6431, loss_cls: 3.4578, loss: 3.4578 +2024-12-30 03:23:54,767 - pyskl - INFO - Epoch [105][2700/3746] lr: 2.085e-02, eta: 1 day, 16:10:42, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6325, loss_cls: 3.5353, loss: 3.5353 +2024-12-30 03:25:19,767 - pyskl - INFO - Epoch [105][2800/3746] lr: 2.083e-02, eta: 1 day, 16:09:17, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6347, loss_cls: 3.5140, loss: 3.5140 +2024-12-30 03:26:44,566 - pyskl - INFO - Epoch [105][2900/3746] lr: 2.080e-02, eta: 1 day, 16:07:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3831, top5_acc: 0.6420, loss_cls: 3.4624, loss: 3.4624 +2024-12-30 03:28:09,363 - pyskl - INFO - Epoch [105][3000/3746] lr: 2.078e-02, eta: 1 day, 16:06:26, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6428, loss_cls: 3.5167, loss: 3.5167 +2024-12-30 03:29:34,588 - pyskl - INFO - Epoch [105][3100/3746] lr: 2.076e-02, eta: 1 day, 16:05:00, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3831, top5_acc: 0.6447, loss_cls: 3.4747, loss: 3.4747 +2024-12-30 03:30:59,926 - pyskl - INFO - Epoch [105][3200/3746] lr: 2.073e-02, eta: 1 day, 16:03:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6484, loss_cls: 3.4538, loss: 3.4538 +2024-12-30 03:32:24,996 - pyskl - INFO - Epoch [105][3300/3746] lr: 2.071e-02, eta: 1 day, 16:02:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6425, loss_cls: 3.4743, loss: 3.4743 +2024-12-30 03:33:50,073 - pyskl - INFO - Epoch [105][3400/3746] lr: 2.069e-02, eta: 1 day, 16:00:44, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3831, top5_acc: 0.6331, loss_cls: 3.5228, loss: 3.5228 +2024-12-30 03:35:15,564 - pyskl - INFO - Epoch [105][3500/3746] lr: 2.067e-02, eta: 1 day, 15:59:19, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3780, top5_acc: 0.6480, loss_cls: 3.4968, loss: 3.4968 +2024-12-30 03:36:40,968 - pyskl - INFO - Epoch [105][3600/3746] lr: 2.064e-02, eta: 1 day, 15:57:54, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3784, top5_acc: 0.6411, loss_cls: 3.5118, loss: 3.5118 +2024-12-30 03:38:06,697 - pyskl - INFO - Epoch [105][3700/3746] lr: 2.062e-02, eta: 1 day, 15:56:29, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3819, top5_acc: 0.6355, loss_cls: 3.4870, loss: 3.4870 +2024-12-30 03:38:47,754 - pyskl - INFO - Saving checkpoint at 105 epochs +2024-12-30 03:40:46,187 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 03:40:46,879 - pyskl - INFO - +top1_acc 0.3212 +top5_acc 0.5797 +2024-12-30 03:40:46,879 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 03:40:46,932 - pyskl - INFO - +mean_acc 0.3209 +2024-12-30 03:40:46,948 - pyskl - INFO - Epoch(val) [105][309] top1_acc: 0.3212, top5_acc: 0.5797, mean_class_accuracy: 0.3209 +2024-12-30 03:44:56,198 - pyskl - INFO - Epoch [106][100/3746] lr: 2.059e-02, eta: 1 day, 15:55:18, time: 2.492, data_time: 1.460, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6581, loss_cls: 3.3589, loss: 3.3589 +2024-12-30 03:46:21,632 - pyskl - INFO - Epoch [106][200/3746] lr: 2.057e-02, eta: 1 day, 15:53:53, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6581, loss_cls: 3.4160, loss: 3.4160 +2024-12-30 03:47:46,843 - pyskl - INFO - Epoch [106][300/3746] lr: 2.054e-02, eta: 1 day, 15:52:27, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6573, loss_cls: 3.3982, loss: 3.3982 +2024-12-30 03:49:12,533 - pyskl - INFO - Epoch [106][400/3746] lr: 2.052e-02, eta: 1 day, 15:51:02, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4091, top5_acc: 0.6644, loss_cls: 3.3441, loss: 3.3441 +2024-12-30 03:50:37,770 - pyskl - INFO - Epoch [106][500/3746] lr: 2.050e-02, eta: 1 day, 15:49:37, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6489, loss_cls: 3.4640, loss: 3.4640 +2024-12-30 03:52:02,672 - pyskl - INFO - Epoch [106][600/3746] lr: 2.048e-02, eta: 1 day, 15:48:11, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3859, top5_acc: 0.6486, loss_cls: 3.4516, loss: 3.4516 +2024-12-30 03:53:27,055 - pyskl - INFO - Epoch [106][700/3746] lr: 2.045e-02, eta: 1 day, 15:46:46, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6500, loss_cls: 3.4535, loss: 3.4535 +2024-12-30 03:54:52,465 - pyskl - INFO - Epoch [106][800/3746] lr: 2.043e-02, eta: 1 day, 15:45:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6481, loss_cls: 3.4198, loss: 3.4198 +2024-12-30 03:56:18,199 - pyskl - INFO - Epoch [106][900/3746] lr: 2.041e-02, eta: 1 day, 15:43:55, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3848, top5_acc: 0.6450, loss_cls: 3.4783, loss: 3.4783 +2024-12-30 03:57:43,851 - pyskl - INFO - Epoch [106][1000/3746] lr: 2.039e-02, eta: 1 day, 15:42:30, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6416, loss_cls: 3.4485, loss: 3.4485 +2024-12-30 03:59:09,690 - pyskl - INFO - Epoch [106][1100/3746] lr: 2.036e-02, eta: 1 day, 15:41:05, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6431, loss_cls: 3.4506, loss: 3.4506 +2024-12-30 04:00:34,975 - pyskl - INFO - Epoch [106][1200/3746] lr: 2.034e-02, eta: 1 day, 15:39:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6589, loss_cls: 3.4105, loss: 3.4105 +2024-12-30 04:02:00,565 - pyskl - INFO - Epoch [106][1300/3746] lr: 2.032e-02, eta: 1 day, 15:38:14, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6506, loss_cls: 3.4275, loss: 3.4275 +2024-12-30 04:03:25,883 - pyskl - INFO - Epoch [106][1400/3746] lr: 2.030e-02, eta: 1 day, 15:36:49, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6562, loss_cls: 3.4340, loss: 3.4340 +2024-12-30 04:04:50,894 - pyskl - INFO - Epoch [106][1500/3746] lr: 2.027e-02, eta: 1 day, 15:35:24, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3859, top5_acc: 0.6530, loss_cls: 3.4433, loss: 3.4433 +2024-12-30 04:06:16,538 - pyskl - INFO - Epoch [106][1600/3746] lr: 2.025e-02, eta: 1 day, 15:33:59, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6419, loss_cls: 3.4785, loss: 3.4785 +2024-12-30 04:07:42,601 - pyskl - INFO - Epoch [106][1700/3746] lr: 2.023e-02, eta: 1 day, 15:32:34, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6436, loss_cls: 3.4640, loss: 3.4640 +2024-12-30 04:09:08,445 - pyskl - INFO - Epoch [106][1800/3746] lr: 2.021e-02, eta: 1 day, 15:31:08, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6464, loss_cls: 3.4522, loss: 3.4522 +2024-12-30 04:10:33,970 - pyskl - INFO - Epoch [106][1900/3746] lr: 2.018e-02, eta: 1 day, 15:29:43, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3923, top5_acc: 0.6578, loss_cls: 3.4386, loss: 3.4386 +2024-12-30 04:11:59,044 - pyskl - INFO - Epoch [106][2000/3746] lr: 2.016e-02, eta: 1 day, 15:28:18, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6345, loss_cls: 3.5246, loss: 3.5246 +2024-12-30 04:13:24,167 - pyskl - INFO - Epoch [106][2100/3746] lr: 2.014e-02, eta: 1 day, 15:26:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6428, loss_cls: 3.4719, loss: 3.4719 +2024-12-30 04:14:49,084 - pyskl - INFO - Epoch [106][2200/3746] lr: 2.012e-02, eta: 1 day, 15:25:27, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3869, top5_acc: 0.6542, loss_cls: 3.4592, loss: 3.4592 +2024-12-30 04:16:14,282 - pyskl - INFO - Epoch [106][2300/3746] lr: 2.009e-02, eta: 1 day, 15:24:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3805, top5_acc: 0.6434, loss_cls: 3.5002, loss: 3.5002 +2024-12-30 04:17:39,249 - pyskl - INFO - Epoch [106][2400/3746] lr: 2.007e-02, eta: 1 day, 15:22:36, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6467, loss_cls: 3.4150, loss: 3.4150 +2024-12-30 04:19:04,288 - pyskl - INFO - Epoch [106][2500/3746] lr: 2.005e-02, eta: 1 day, 15:21:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3844, top5_acc: 0.6469, loss_cls: 3.4766, loss: 3.4766 +2024-12-30 04:20:28,980 - pyskl - INFO - Epoch [106][2600/3746] lr: 2.003e-02, eta: 1 day, 15:19:45, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3947, top5_acc: 0.6412, loss_cls: 3.4603, loss: 3.4603 +2024-12-30 04:21:54,275 - pyskl - INFO - Epoch [106][2700/3746] lr: 2.000e-02, eta: 1 day, 15:18:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6547, loss_cls: 3.4462, loss: 3.4462 +2024-12-30 04:23:19,529 - pyskl - INFO - Epoch [106][2800/3746] lr: 1.998e-02, eta: 1 day, 15:16:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3872, top5_acc: 0.6414, loss_cls: 3.4796, loss: 3.4796 +2024-12-30 04:24:44,639 - pyskl - INFO - Epoch [106][2900/3746] lr: 1.996e-02, eta: 1 day, 15:15:29, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6402, loss_cls: 3.4828, loss: 3.4828 +2024-12-30 04:26:09,983 - pyskl - INFO - Epoch [106][3000/3746] lr: 1.994e-02, eta: 1 day, 15:14:04, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6431, loss_cls: 3.4556, loss: 3.4556 +2024-12-30 04:27:34,726 - pyskl - INFO - Epoch [106][3100/3746] lr: 1.991e-02, eta: 1 day, 15:12:38, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6444, loss_cls: 3.5199, loss: 3.5199 +2024-12-30 04:28:59,822 - pyskl - INFO - Epoch [106][3200/3746] lr: 1.989e-02, eta: 1 day, 15:11:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3878, top5_acc: 0.6478, loss_cls: 3.4663, loss: 3.4663 +2024-12-30 04:30:25,435 - pyskl - INFO - Epoch [106][3300/3746] lr: 1.987e-02, eta: 1 day, 15:09:48, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6441, loss_cls: 3.4532, loss: 3.4532 +2024-12-30 04:31:51,012 - pyskl - INFO - Epoch [106][3400/3746] lr: 1.985e-02, eta: 1 day, 15:08:23, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6388, loss_cls: 3.5259, loss: 3.5259 +2024-12-30 04:33:16,089 - pyskl - INFO - Epoch [106][3500/3746] lr: 1.983e-02, eta: 1 day, 15:06:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3830, top5_acc: 0.6417, loss_cls: 3.4677, loss: 3.4677 +2024-12-30 04:34:40,960 - pyskl - INFO - Epoch [106][3600/3746] lr: 1.980e-02, eta: 1 day, 15:05:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6491, loss_cls: 3.4342, loss: 3.4342 +2024-12-30 04:36:06,179 - pyskl - INFO - Epoch [106][3700/3746] lr: 1.978e-02, eta: 1 day, 15:04:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6477, loss_cls: 3.4459, loss: 3.4459 +2024-12-30 04:36:46,970 - pyskl - INFO - Saving checkpoint at 106 epochs +2024-12-30 04:38:45,823 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 04:38:46,581 - pyskl - INFO - +top1_acc 0.3280 +top5_acc 0.5821 +2024-12-30 04:38:46,581 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 04:38:46,634 - pyskl - INFO - +mean_acc 0.3277 +2024-12-30 04:38:46,655 - pyskl - INFO - Epoch(val) [106][309] top1_acc: 0.3280, top5_acc: 0.5821, mean_class_accuracy: 0.3277 +2024-12-30 04:42:57,423 - pyskl - INFO - Epoch [107][100/3746] lr: 1.975e-02, eta: 1 day, 15:02:54, time: 2.508, data_time: 1.477, memory: 15990, top1_acc: 0.3964, top5_acc: 0.6561, loss_cls: 3.3663, loss: 3.3663 +2024-12-30 04:44:22,951 - pyskl - INFO - Epoch [107][200/3746] lr: 1.973e-02, eta: 1 day, 15:01:29, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4147, top5_acc: 0.6641, loss_cls: 3.3497, loss: 3.3497 +2024-12-30 04:45:47,944 - pyskl - INFO - Epoch [107][300/3746] lr: 1.970e-02, eta: 1 day, 15:00:03, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6644, loss_cls: 3.3944, loss: 3.3944 +2024-12-30 04:47:13,297 - pyskl - INFO - Epoch [107][400/3746] lr: 1.968e-02, eta: 1 day, 14:58:38, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.3986, top5_acc: 0.6575, loss_cls: 3.3993, loss: 3.3993 +2024-12-30 04:48:38,781 - pyskl - INFO - Epoch [107][500/3746] lr: 1.966e-02, eta: 1 day, 14:57:13, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6567, loss_cls: 3.4161, loss: 3.4161 +2024-12-30 04:50:03,967 - pyskl - INFO - Epoch [107][600/3746] lr: 1.964e-02, eta: 1 day, 14:55:47, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.3844, top5_acc: 0.6503, loss_cls: 3.4387, loss: 3.4387 +2024-12-30 04:51:29,146 - pyskl - INFO - Epoch [107][700/3746] lr: 1.961e-02, eta: 1 day, 14:54:22, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6445, loss_cls: 3.4748, loss: 3.4748 +2024-12-30 04:52:54,019 - pyskl - INFO - Epoch [107][800/3746] lr: 1.959e-02, eta: 1 day, 14:52:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6522, loss_cls: 3.4115, loss: 3.4115 +2024-12-30 04:54:19,404 - pyskl - INFO - Epoch [107][900/3746] lr: 1.957e-02, eta: 1 day, 14:51:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6483, loss_cls: 3.4207, loss: 3.4207 +2024-12-30 04:55:44,886 - pyskl - INFO - Epoch [107][1000/3746] lr: 1.955e-02, eta: 1 day, 14:50:06, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3972, top5_acc: 0.6575, loss_cls: 3.3762, loss: 3.3762 +2024-12-30 04:57:10,310 - pyskl - INFO - Epoch [107][1100/3746] lr: 1.953e-02, eta: 1 day, 14:48:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6500, loss_cls: 3.4307, loss: 3.4307 +2024-12-30 04:58:35,564 - pyskl - INFO - Epoch [107][1200/3746] lr: 1.950e-02, eta: 1 day, 14:47:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6491, loss_cls: 3.4262, loss: 3.4262 +2024-12-30 05:00:00,979 - pyskl - INFO - Epoch [107][1300/3746] lr: 1.948e-02, eta: 1 day, 14:45:50, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6531, loss_cls: 3.4340, loss: 3.4340 +2024-12-30 05:01:25,787 - pyskl - INFO - Epoch [107][1400/3746] lr: 1.946e-02, eta: 1 day, 14:44:24, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6644, loss_cls: 3.3665, loss: 3.3665 +2024-12-30 05:02:51,242 - pyskl - INFO - Epoch [107][1500/3746] lr: 1.944e-02, eta: 1 day, 14:42:59, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3858, top5_acc: 0.6428, loss_cls: 3.4855, loss: 3.4855 +2024-12-30 05:04:16,209 - pyskl - INFO - Epoch [107][1600/3746] lr: 1.942e-02, eta: 1 day, 14:41:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6461, loss_cls: 3.4509, loss: 3.4509 +2024-12-30 05:05:41,003 - pyskl - INFO - Epoch [107][1700/3746] lr: 1.939e-02, eta: 1 day, 14:40:08, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6566, loss_cls: 3.4343, loss: 3.4343 +2024-12-30 05:07:05,808 - pyskl - INFO - Epoch [107][1800/3746] lr: 1.937e-02, eta: 1 day, 14:38:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6445, loss_cls: 3.4648, loss: 3.4648 +2024-12-30 05:08:31,239 - pyskl - INFO - Epoch [107][1900/3746] lr: 1.935e-02, eta: 1 day, 14:37:17, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3981, top5_acc: 0.6600, loss_cls: 3.3900, loss: 3.3900 +2024-12-30 05:09:56,422 - pyskl - INFO - Epoch [107][2000/3746] lr: 1.933e-02, eta: 1 day, 14:35:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3859, top5_acc: 0.6472, loss_cls: 3.4674, loss: 3.4674 +2024-12-30 05:11:21,251 - pyskl - INFO - Epoch [107][2100/3746] lr: 1.930e-02, eta: 1 day, 14:34:26, time: 0.848, data_time: 0.001, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6500, loss_cls: 3.4423, loss: 3.4423 +2024-12-30 05:12:46,279 - pyskl - INFO - Epoch [107][2200/3746] lr: 1.928e-02, eta: 1 day, 14:33:01, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3844, top5_acc: 0.6577, loss_cls: 3.4506, loss: 3.4506 +2024-12-30 05:14:11,092 - pyskl - INFO - Epoch [107][2300/3746] lr: 1.926e-02, eta: 1 day, 14:31:35, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6473, loss_cls: 3.4607, loss: 3.4607 +2024-12-30 05:15:35,882 - pyskl - INFO - Epoch [107][2400/3746] lr: 1.924e-02, eta: 1 day, 14:30:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6469, loss_cls: 3.4581, loss: 3.4581 +2024-12-30 05:17:00,848 - pyskl - INFO - Epoch [107][2500/3746] lr: 1.922e-02, eta: 1 day, 14:28:44, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6517, loss_cls: 3.4328, loss: 3.4328 +2024-12-30 05:18:26,050 - pyskl - INFO - Epoch [107][2600/3746] lr: 1.919e-02, eta: 1 day, 14:27:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3916, top5_acc: 0.6509, loss_cls: 3.4430, loss: 3.4430 +2024-12-30 05:19:50,826 - pyskl - INFO - Epoch [107][2700/3746] lr: 1.917e-02, eta: 1 day, 14:25:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6472, loss_cls: 3.4584, loss: 3.4584 +2024-12-30 05:21:15,966 - pyskl - INFO - Epoch [107][2800/3746] lr: 1.915e-02, eta: 1 day, 14:24:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6481, loss_cls: 3.4871, loss: 3.4871 +2024-12-30 05:22:40,925 - pyskl - INFO - Epoch [107][2900/3746] lr: 1.913e-02, eta: 1 day, 14:23:02, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6561, loss_cls: 3.3953, loss: 3.3953 +2024-12-30 05:24:06,418 - pyskl - INFO - Epoch [107][3000/3746] lr: 1.911e-02, eta: 1 day, 14:21:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6470, loss_cls: 3.4604, loss: 3.4604 +2024-12-30 05:25:31,828 - pyskl - INFO - Epoch [107][3100/3746] lr: 1.908e-02, eta: 1 day, 14:20:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6438, loss_cls: 3.4466, loss: 3.4466 +2024-12-30 05:26:57,110 - pyskl - INFO - Epoch [107][3200/3746] lr: 1.906e-02, eta: 1 day, 14:18:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6416, loss_cls: 3.4908, loss: 3.4908 +2024-12-30 05:28:22,634 - pyskl - INFO - Epoch [107][3300/3746] lr: 1.904e-02, eta: 1 day, 14:17:21, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6494, loss_cls: 3.4391, loss: 3.4391 +2024-12-30 05:29:47,613 - pyskl - INFO - Epoch [107][3400/3746] lr: 1.902e-02, eta: 1 day, 14:15:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6600, loss_cls: 3.4164, loss: 3.4164 +2024-12-30 05:31:12,518 - pyskl - INFO - Epoch [107][3500/3746] lr: 1.900e-02, eta: 1 day, 14:14:30, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6383, loss_cls: 3.5477, loss: 3.5477 +2024-12-30 05:32:37,529 - pyskl - INFO - Epoch [107][3600/3746] lr: 1.897e-02, eta: 1 day, 14:13:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6498, loss_cls: 3.4582, loss: 3.4582 +2024-12-30 05:34:03,152 - pyskl - INFO - Epoch [107][3700/3746] lr: 1.895e-02, eta: 1 day, 14:11:39, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6527, loss_cls: 3.3998, loss: 3.3998 +2024-12-30 05:34:43,945 - pyskl - INFO - Saving checkpoint at 107 epochs +2024-12-30 05:36:43,468 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 05:36:44,458 - pyskl - INFO - +top1_acc 0.3325 +top5_acc 0.5891 +2024-12-30 05:36:44,458 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 05:36:44,514 - pyskl - INFO - +mean_acc 0.3324 +2024-12-30 05:36:44,519 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_103.pth was removed +2024-12-30 05:36:44,880 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_107.pth. +2024-12-30 05:36:44,881 - pyskl - INFO - Best top1_acc is 0.3325 at 107 epoch. +2024-12-30 05:36:44,897 - pyskl - INFO - Epoch(val) [107][309] top1_acc: 0.3325, top5_acc: 0.5891, mean_class_accuracy: 0.3324 +2024-12-30 05:41:02,565 - pyskl - INFO - Epoch [108][100/3746] lr: 1.892e-02, eta: 1 day, 14:10:28, time: 2.577, data_time: 1.544, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6594, loss_cls: 3.3596, loss: 3.3596 +2024-12-30 05:42:28,479 - pyskl - INFO - Epoch [108][200/3746] lr: 1.890e-02, eta: 1 day, 14:09:03, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6603, loss_cls: 3.4074, loss: 3.4074 +2024-12-30 05:43:54,379 - pyskl - INFO - Epoch [108][300/3746] lr: 1.888e-02, eta: 1 day, 14:07:38, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6561, loss_cls: 3.3810, loss: 3.3810 +2024-12-30 05:45:20,475 - pyskl - INFO - Epoch [108][400/3746] lr: 1.886e-02, eta: 1 day, 14:06:13, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6644, loss_cls: 3.3555, loss: 3.3555 +2024-12-30 05:46:45,593 - pyskl - INFO - Epoch [108][500/3746] lr: 1.883e-02, eta: 1 day, 14:04:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6573, loss_cls: 3.4095, loss: 3.4095 +2024-12-30 05:48:10,869 - pyskl - INFO - Epoch [108][600/3746] lr: 1.881e-02, eta: 1 day, 14:03:22, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6648, loss_cls: 3.3756, loss: 3.3756 +2024-12-30 05:49:36,102 - pyskl - INFO - Epoch [108][700/3746] lr: 1.879e-02, eta: 1 day, 14:01:56, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4070, top5_acc: 0.6633, loss_cls: 3.3422, loss: 3.3422 +2024-12-30 05:51:01,597 - pyskl - INFO - Epoch [108][800/3746] lr: 1.877e-02, eta: 1 day, 14:00:31, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6575, loss_cls: 3.3818, loss: 3.3818 +2024-12-30 05:52:27,305 - pyskl - INFO - Epoch [108][900/3746] lr: 1.875e-02, eta: 1 day, 13:59:06, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6466, loss_cls: 3.4353, loss: 3.4353 +2024-12-30 05:53:52,628 - pyskl - INFO - Epoch [108][1000/3746] lr: 1.872e-02, eta: 1 day, 13:57:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3994, top5_acc: 0.6508, loss_cls: 3.4216, loss: 3.4216 +2024-12-30 05:55:18,250 - pyskl - INFO - Epoch [108][1100/3746] lr: 1.870e-02, eta: 1 day, 13:56:15, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6481, loss_cls: 3.4309, loss: 3.4309 +2024-12-30 05:56:44,059 - pyskl - INFO - Epoch [108][1200/3746] lr: 1.868e-02, eta: 1 day, 13:54:50, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6567, loss_cls: 3.3919, loss: 3.3919 +2024-12-30 05:58:09,730 - pyskl - INFO - Epoch [108][1300/3746] lr: 1.866e-02, eta: 1 day, 13:53:25, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6597, loss_cls: 3.3902, loss: 3.3902 +2024-12-30 05:59:35,411 - pyskl - INFO - Epoch [108][1400/3746] lr: 1.864e-02, eta: 1 day, 13:51:59, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4025, top5_acc: 0.6619, loss_cls: 3.3771, loss: 3.3771 +2024-12-30 06:01:00,682 - pyskl - INFO - Epoch [108][1500/3746] lr: 1.862e-02, eta: 1 day, 13:50:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6367, loss_cls: 3.4844, loss: 3.4844 +2024-12-30 06:02:26,072 - pyskl - INFO - Epoch [108][1600/3746] lr: 1.859e-02, eta: 1 day, 13:49:09, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.3936, top5_acc: 0.6508, loss_cls: 3.4237, loss: 3.4237 +2024-12-30 06:03:52,363 - pyskl - INFO - Epoch [108][1700/3746] lr: 1.857e-02, eta: 1 day, 13:47:44, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3966, top5_acc: 0.6606, loss_cls: 3.3809, loss: 3.3809 +2024-12-30 06:05:18,702 - pyskl - INFO - Epoch [108][1800/3746] lr: 1.855e-02, eta: 1 day, 13:46:19, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3927, top5_acc: 0.6577, loss_cls: 3.4075, loss: 3.4075 +2024-12-30 06:06:43,884 - pyskl - INFO - Epoch [108][1900/3746] lr: 1.853e-02, eta: 1 day, 13:44:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6522, loss_cls: 3.4190, loss: 3.4190 +2024-12-30 06:08:09,305 - pyskl - INFO - Epoch [108][2000/3746] lr: 1.851e-02, eta: 1 day, 13:43:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3947, top5_acc: 0.6502, loss_cls: 3.4304, loss: 3.4304 +2024-12-30 06:09:35,017 - pyskl - INFO - Epoch [108][2100/3746] lr: 1.848e-02, eta: 1 day, 13:42:03, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6641, loss_cls: 3.3528, loss: 3.3528 +2024-12-30 06:11:01,162 - pyskl - INFO - Epoch [108][2200/3746] lr: 1.846e-02, eta: 1 day, 13:40:38, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6528, loss_cls: 3.4397, loss: 3.4397 +2024-12-30 06:12:26,779 - pyskl - INFO - Epoch [108][2300/3746] lr: 1.844e-02, eta: 1 day, 13:39:12, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6458, loss_cls: 3.4432, loss: 3.4432 +2024-12-30 06:13:52,022 - pyskl - INFO - Epoch [108][2400/3746] lr: 1.842e-02, eta: 1 day, 13:37:47, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6577, loss_cls: 3.4035, loss: 3.4035 +2024-12-30 06:15:17,679 - pyskl - INFO - Epoch [108][2500/3746] lr: 1.840e-02, eta: 1 day, 13:36:22, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3862, top5_acc: 0.6509, loss_cls: 3.4376, loss: 3.4376 +2024-12-30 06:16:43,962 - pyskl - INFO - Epoch [108][2600/3746] lr: 1.838e-02, eta: 1 day, 13:34:57, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6470, loss_cls: 3.4150, loss: 3.4150 +2024-12-30 06:18:10,050 - pyskl - INFO - Epoch [108][2700/3746] lr: 1.835e-02, eta: 1 day, 13:33:31, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.3827, top5_acc: 0.6384, loss_cls: 3.4898, loss: 3.4898 +2024-12-30 06:19:35,955 - pyskl - INFO - Epoch [108][2800/3746] lr: 1.833e-02, eta: 1 day, 13:32:06, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6605, loss_cls: 3.4030, loss: 3.4030 +2024-12-30 06:21:01,710 - pyskl - INFO - Epoch [108][2900/3746] lr: 1.831e-02, eta: 1 day, 13:30:41, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6509, loss_cls: 3.4302, loss: 3.4302 +2024-12-30 06:22:27,508 - pyskl - INFO - Epoch [108][3000/3746] lr: 1.829e-02, eta: 1 day, 13:29:16, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6541, loss_cls: 3.4337, loss: 3.4337 +2024-12-30 06:23:53,549 - pyskl - INFO - Epoch [108][3100/3746] lr: 1.827e-02, eta: 1 day, 13:27:51, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3997, top5_acc: 0.6561, loss_cls: 3.3890, loss: 3.3890 +2024-12-30 06:25:19,670 - pyskl - INFO - Epoch [108][3200/3746] lr: 1.825e-02, eta: 1 day, 13:26:26, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6486, loss_cls: 3.4529, loss: 3.4529 +2024-12-30 06:26:45,519 - pyskl - INFO - Epoch [108][3300/3746] lr: 1.823e-02, eta: 1 day, 13:25:01, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6511, loss_cls: 3.4283, loss: 3.4283 +2024-12-30 06:28:11,684 - pyskl - INFO - Epoch [108][3400/3746] lr: 1.820e-02, eta: 1 day, 13:23:35, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3820, top5_acc: 0.6452, loss_cls: 3.4656, loss: 3.4656 +2024-12-30 06:29:37,392 - pyskl - INFO - Epoch [108][3500/3746] lr: 1.818e-02, eta: 1 day, 13:22:10, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4014, top5_acc: 0.6555, loss_cls: 3.3928, loss: 3.3928 +2024-12-30 06:31:03,425 - pyskl - INFO - Epoch [108][3600/3746] lr: 1.816e-02, eta: 1 day, 13:20:45, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6516, loss_cls: 3.4273, loss: 3.4273 +2024-12-30 06:32:28,921 - pyskl - INFO - Epoch [108][3700/3746] lr: 1.814e-02, eta: 1 day, 13:19:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6533, loss_cls: 3.4013, loss: 3.4013 +2024-12-30 06:33:10,520 - pyskl - INFO - Saving checkpoint at 108 epochs +2024-12-30 06:35:12,293 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 06:35:13,053 - pyskl - INFO - +top1_acc 0.3390 +top5_acc 0.5917 +2024-12-30 06:35:13,054 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 06:35:13,121 - pyskl - INFO - +mean_acc 0.3387 +2024-12-30 06:35:13,126 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_107.pth was removed +2024-12-30 06:35:13,482 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_108.pth. +2024-12-30 06:35:13,483 - pyskl - INFO - Best top1_acc is 0.3390 at 108 epoch. +2024-12-30 06:35:13,503 - pyskl - INFO - Epoch(val) [108][309] top1_acc: 0.3390, top5_acc: 0.5917, mean_class_accuracy: 0.3387 +2024-12-30 06:39:36,575 - pyskl - INFO - Epoch [109][100/3746] lr: 1.811e-02, eta: 1 day, 13:18:09, time: 2.631, data_time: 1.585, memory: 15990, top1_acc: 0.4070, top5_acc: 0.6683, loss_cls: 3.3126, loss: 3.3126 +2024-12-30 06:41:02,317 - pyskl - INFO - Epoch [109][200/3746] lr: 1.809e-02, eta: 1 day, 13:16:44, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4189, top5_acc: 0.6750, loss_cls: 3.2797, loss: 3.2797 +2024-12-30 06:42:28,284 - pyskl - INFO - Epoch [109][300/3746] lr: 1.806e-02, eta: 1 day, 13:15:18, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4006, top5_acc: 0.6539, loss_cls: 3.3778, loss: 3.3778 +2024-12-30 06:43:53,814 - pyskl - INFO - Epoch [109][400/3746] lr: 1.804e-02, eta: 1 day, 13:13:53, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6617, loss_cls: 3.3710, loss: 3.3710 +2024-12-30 06:45:19,213 - pyskl - INFO - Epoch [109][500/3746] lr: 1.802e-02, eta: 1 day, 13:12:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6680, loss_cls: 3.3566, loss: 3.3566 +2024-12-30 06:46:44,446 - pyskl - INFO - Epoch [109][600/3746] lr: 1.800e-02, eta: 1 day, 13:11:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6569, loss_cls: 3.3841, loss: 3.3841 +2024-12-30 06:48:09,666 - pyskl - INFO - Epoch [109][700/3746] lr: 1.798e-02, eta: 1 day, 13:09:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6580, loss_cls: 3.3865, loss: 3.3865 +2024-12-30 06:49:35,248 - pyskl - INFO - Epoch [109][800/3746] lr: 1.796e-02, eta: 1 day, 13:08:11, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4109, top5_acc: 0.6716, loss_cls: 3.3309, loss: 3.3309 +2024-12-30 06:51:00,483 - pyskl - INFO - Epoch [109][900/3746] lr: 1.794e-02, eta: 1 day, 13:06:46, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6575, loss_cls: 3.3862, loss: 3.3862 +2024-12-30 06:52:26,195 - pyskl - INFO - Epoch [109][1000/3746] lr: 1.791e-02, eta: 1 day, 13:05:21, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3900, top5_acc: 0.6531, loss_cls: 3.3995, loss: 3.3995 +2024-12-30 06:53:51,886 - pyskl - INFO - Epoch [109][1100/3746] lr: 1.789e-02, eta: 1 day, 13:03:55, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6673, loss_cls: 3.3652, loss: 3.3652 +2024-12-30 06:55:17,432 - pyskl - INFO - Epoch [109][1200/3746] lr: 1.787e-02, eta: 1 day, 13:02:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6533, loss_cls: 3.3953, loss: 3.3953 +2024-12-30 06:56:42,452 - pyskl - INFO - Epoch [109][1300/3746] lr: 1.785e-02, eta: 1 day, 13:01:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6541, loss_cls: 3.4267, loss: 3.4267 +2024-12-30 06:58:08,114 - pyskl - INFO - Epoch [109][1400/3746] lr: 1.783e-02, eta: 1 day, 12:59:39, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4033, top5_acc: 0.6702, loss_cls: 3.3528, loss: 3.3528 +2024-12-30 06:59:33,774 - pyskl - INFO - Epoch [109][1500/3746] lr: 1.781e-02, eta: 1 day, 12:58:14, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4044, top5_acc: 0.6595, loss_cls: 3.3739, loss: 3.3739 +2024-12-30 07:00:59,407 - pyskl - INFO - Epoch [109][1600/3746] lr: 1.779e-02, eta: 1 day, 12:56:48, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6567, loss_cls: 3.4197, loss: 3.4197 +2024-12-30 07:02:24,919 - pyskl - INFO - Epoch [109][1700/3746] lr: 1.776e-02, eta: 1 day, 12:55:23, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6491, loss_cls: 3.4340, loss: 3.4340 +2024-12-30 07:03:50,039 - pyskl - INFO - Epoch [109][1800/3746] lr: 1.774e-02, eta: 1 day, 12:53:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3997, top5_acc: 0.6664, loss_cls: 3.3731, loss: 3.3731 +2024-12-30 07:05:15,860 - pyskl - INFO - Epoch [109][1900/3746] lr: 1.772e-02, eta: 1 day, 12:52:32, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6619, loss_cls: 3.3722, loss: 3.3722 +2024-12-30 07:06:41,072 - pyskl - INFO - Epoch [109][2000/3746] lr: 1.770e-02, eta: 1 day, 12:51:07, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6556, loss_cls: 3.4113, loss: 3.4113 +2024-12-30 07:08:06,691 - pyskl - INFO - Epoch [109][2100/3746] lr: 1.768e-02, eta: 1 day, 12:49:41, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6587, loss_cls: 3.3743, loss: 3.3743 +2024-12-30 07:09:32,071 - pyskl - INFO - Epoch [109][2200/3746] lr: 1.766e-02, eta: 1 day, 12:48:16, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6509, loss_cls: 3.4303, loss: 3.4303 +2024-12-30 07:10:57,216 - pyskl - INFO - Epoch [109][2300/3746] lr: 1.764e-02, eta: 1 day, 12:46:51, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6448, loss_cls: 3.4568, loss: 3.4568 +2024-12-30 07:12:23,049 - pyskl - INFO - Epoch [109][2400/3746] lr: 1.761e-02, eta: 1 day, 12:45:25, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3889, top5_acc: 0.6498, loss_cls: 3.4358, loss: 3.4358 +2024-12-30 07:13:48,403 - pyskl - INFO - Epoch [109][2500/3746] lr: 1.759e-02, eta: 1 day, 12:44:00, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3872, top5_acc: 0.6458, loss_cls: 3.4444, loss: 3.4444 +2024-12-30 07:15:13,738 - pyskl - INFO - Epoch [109][2600/3746] lr: 1.757e-02, eta: 1 day, 12:42:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4002, top5_acc: 0.6562, loss_cls: 3.3809, loss: 3.3809 +2024-12-30 07:16:38,804 - pyskl - INFO - Epoch [109][2700/3746] lr: 1.755e-02, eta: 1 day, 12:41:09, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6608, loss_cls: 3.3631, loss: 3.3631 +2024-12-30 07:18:04,353 - pyskl - INFO - Epoch [109][2800/3746] lr: 1.753e-02, eta: 1 day, 12:39:44, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4081, top5_acc: 0.6609, loss_cls: 3.3835, loss: 3.3835 +2024-12-30 07:19:28,972 - pyskl - INFO - Epoch [109][2900/3746] lr: 1.751e-02, eta: 1 day, 12:38:18, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6497, loss_cls: 3.4405, loss: 3.4405 +2024-12-30 07:20:54,180 - pyskl - INFO - Epoch [109][3000/3746] lr: 1.749e-02, eta: 1 day, 12:36:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6545, loss_cls: 3.3904, loss: 3.3904 +2024-12-30 07:22:19,102 - pyskl - INFO - Epoch [109][3100/3746] lr: 1.747e-02, eta: 1 day, 12:35:27, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3920, top5_acc: 0.6536, loss_cls: 3.4129, loss: 3.4129 +2024-12-30 07:23:44,737 - pyskl - INFO - Epoch [109][3200/3746] lr: 1.744e-02, eta: 1 day, 12:34:01, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6489, loss_cls: 3.4420, loss: 3.4420 +2024-12-30 07:25:09,749 - pyskl - INFO - Epoch [109][3300/3746] lr: 1.742e-02, eta: 1 day, 12:32:36, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3891, top5_acc: 0.6406, loss_cls: 3.4595, loss: 3.4595 +2024-12-30 07:26:34,551 - pyskl - INFO - Epoch [109][3400/3746] lr: 1.740e-02, eta: 1 day, 12:31:10, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6536, loss_cls: 3.3944, loss: 3.3944 +2024-12-30 07:27:58,855 - pyskl - INFO - Epoch [109][3500/3746] lr: 1.738e-02, eta: 1 day, 12:29:44, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6511, loss_cls: 3.4371, loss: 3.4371 +2024-12-30 07:29:24,198 - pyskl - INFO - Epoch [109][3600/3746] lr: 1.736e-02, eta: 1 day, 12:28:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3972, top5_acc: 0.6572, loss_cls: 3.4100, loss: 3.4100 +2024-12-30 07:30:49,245 - pyskl - INFO - Epoch [109][3700/3746] lr: 1.734e-02, eta: 1 day, 12:26:53, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4091, top5_acc: 0.6656, loss_cls: 3.3725, loss: 3.3725 +2024-12-30 07:31:30,521 - pyskl - INFO - Saving checkpoint at 109 epochs +2024-12-30 07:33:31,450 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 07:33:32,187 - pyskl - INFO - +top1_acc 0.3420 +top5_acc 0.5988 +2024-12-30 07:33:32,188 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 07:33:32,257 - pyskl - INFO - +mean_acc 0.3419 +2024-12-30 07:33:32,262 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_108.pth was removed +2024-12-30 07:33:32,535 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_109.pth. +2024-12-30 07:33:32,536 - pyskl - INFO - Best top1_acc is 0.3420 at 109 epoch. +2024-12-30 07:33:32,547 - pyskl - INFO - Epoch(val) [109][309] top1_acc: 0.3420, top5_acc: 0.5988, mean_class_accuracy: 0.3419 +2024-12-30 07:37:57,780 - pyskl - INFO - Epoch [110][100/3746] lr: 1.731e-02, eta: 1 day, 12:25:42, time: 2.652, data_time: 1.598, memory: 15990, top1_acc: 0.4170, top5_acc: 0.6706, loss_cls: 3.3057, loss: 3.3057 +2024-12-30 07:39:23,262 - pyskl - INFO - Epoch [110][200/3746] lr: 1.729e-02, eta: 1 day, 12:24:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6661, loss_cls: 3.3435, loss: 3.3435 +2024-12-30 07:40:48,935 - pyskl - INFO - Epoch [110][300/3746] lr: 1.727e-02, eta: 1 day, 12:22:51, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4073, top5_acc: 0.6703, loss_cls: 3.2669, loss: 3.2669 +2024-12-30 07:42:13,771 - pyskl - INFO - Epoch [110][400/3746] lr: 1.724e-02, eta: 1 day, 12:21:25, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4119, top5_acc: 0.6719, loss_cls: 3.3073, loss: 3.3073 +2024-12-30 07:43:39,242 - pyskl - INFO - Epoch [110][500/3746] lr: 1.722e-02, eta: 1 day, 12:20:00, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6669, loss_cls: 3.3416, loss: 3.3416 +2024-12-30 07:45:04,331 - pyskl - INFO - Epoch [110][600/3746] lr: 1.720e-02, eta: 1 day, 12:18:34, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6619, loss_cls: 3.3499, loss: 3.3499 +2024-12-30 07:46:29,518 - pyskl - INFO - Epoch [110][700/3746] lr: 1.718e-02, eta: 1 day, 12:17:09, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6591, loss_cls: 3.3620, loss: 3.3620 +2024-12-30 07:47:54,343 - pyskl - INFO - Epoch [110][800/3746] lr: 1.716e-02, eta: 1 day, 12:15:43, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6595, loss_cls: 3.3730, loss: 3.3730 +2024-12-30 07:49:19,175 - pyskl - INFO - Epoch [110][900/3746] lr: 1.714e-02, eta: 1 day, 12:14:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4070, top5_acc: 0.6658, loss_cls: 3.3278, loss: 3.3278 +2024-12-30 07:50:44,748 - pyskl - INFO - Epoch [110][1000/3746] lr: 1.712e-02, eta: 1 day, 12:12:52, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6555, loss_cls: 3.3818, loss: 3.3818 +2024-12-30 07:52:09,775 - pyskl - INFO - Epoch [110][1100/3746] lr: 1.710e-02, eta: 1 day, 12:11:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.3987, top5_acc: 0.6553, loss_cls: 3.3958, loss: 3.3958 +2024-12-30 07:53:34,925 - pyskl - INFO - Epoch [110][1200/3746] lr: 1.708e-02, eta: 1 day, 12:10:01, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6652, loss_cls: 3.3638, loss: 3.3638 +2024-12-30 07:55:00,589 - pyskl - INFO - Epoch [110][1300/3746] lr: 1.705e-02, eta: 1 day, 12:08:35, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6622, loss_cls: 3.3679, loss: 3.3679 +2024-12-30 07:56:25,676 - pyskl - INFO - Epoch [110][1400/3746] lr: 1.703e-02, eta: 1 day, 12:07:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6594, loss_cls: 3.3943, loss: 3.3943 +2024-12-30 07:57:50,436 - pyskl - INFO - Epoch [110][1500/3746] lr: 1.701e-02, eta: 1 day, 12:05:44, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6656, loss_cls: 3.3505, loss: 3.3505 +2024-12-30 07:59:15,670 - pyskl - INFO - Epoch [110][1600/3746] lr: 1.699e-02, eta: 1 day, 12:04:18, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6577, loss_cls: 3.3897, loss: 3.3897 +2024-12-30 08:00:41,230 - pyskl - INFO - Epoch [110][1700/3746] lr: 1.697e-02, eta: 1 day, 12:02:53, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4044, top5_acc: 0.6603, loss_cls: 3.3514, loss: 3.3514 +2024-12-30 08:02:06,334 - pyskl - INFO - Epoch [110][1800/3746] lr: 1.695e-02, eta: 1 day, 12:01:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6577, loss_cls: 3.3800, loss: 3.3800 +2024-12-30 08:03:31,402 - pyskl - INFO - Epoch [110][1900/3746] lr: 1.693e-02, eta: 1 day, 12:00:02, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6478, loss_cls: 3.4176, loss: 3.4176 +2024-12-30 08:04:56,122 - pyskl - INFO - Epoch [110][2000/3746] lr: 1.691e-02, eta: 1 day, 11:58:36, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4066, top5_acc: 0.6614, loss_cls: 3.3758, loss: 3.3758 +2024-12-30 08:06:21,303 - pyskl - INFO - Epoch [110][2100/3746] lr: 1.689e-02, eta: 1 day, 11:57:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6531, loss_cls: 3.4223, loss: 3.4223 +2024-12-30 08:07:46,150 - pyskl - INFO - Epoch [110][2200/3746] lr: 1.687e-02, eta: 1 day, 11:55:45, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6600, loss_cls: 3.3895, loss: 3.3895 +2024-12-30 08:09:11,304 - pyskl - INFO - Epoch [110][2300/3746] lr: 1.685e-02, eta: 1 day, 11:54:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6589, loss_cls: 3.3670, loss: 3.3670 +2024-12-30 08:10:36,054 - pyskl - INFO - Epoch [110][2400/3746] lr: 1.682e-02, eta: 1 day, 11:52:54, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6552, loss_cls: 3.3655, loss: 3.3655 +2024-12-30 08:12:00,608 - pyskl - INFO - Epoch [110][2500/3746] lr: 1.680e-02, eta: 1 day, 11:51:28, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6584, loss_cls: 3.3937, loss: 3.3937 +2024-12-30 08:13:25,471 - pyskl - INFO - Epoch [110][2600/3746] lr: 1.678e-02, eta: 1 day, 11:50:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6564, loss_cls: 3.3832, loss: 3.3832 +2024-12-30 08:14:50,586 - pyskl - INFO - Epoch [110][2700/3746] lr: 1.676e-02, eta: 1 day, 11:48:37, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6545, loss_cls: 3.4135, loss: 3.4135 +2024-12-30 08:16:15,483 - pyskl - INFO - Epoch [110][2800/3746] lr: 1.674e-02, eta: 1 day, 11:47:11, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6531, loss_cls: 3.4414, loss: 3.4414 +2024-12-30 08:17:41,301 - pyskl - INFO - Epoch [110][2900/3746] lr: 1.672e-02, eta: 1 day, 11:45:46, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.3948, top5_acc: 0.6473, loss_cls: 3.4072, loss: 3.4072 +2024-12-30 08:19:06,386 - pyskl - INFO - Epoch [110][3000/3746] lr: 1.670e-02, eta: 1 day, 11:44:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3980, top5_acc: 0.6491, loss_cls: 3.4528, loss: 3.4528 +2024-12-30 08:20:31,663 - pyskl - INFO - Epoch [110][3100/3746] lr: 1.668e-02, eta: 1 day, 11:42:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4073, top5_acc: 0.6620, loss_cls: 3.3740, loss: 3.3740 +2024-12-30 08:21:56,618 - pyskl - INFO - Epoch [110][3200/3746] lr: 1.666e-02, eta: 1 day, 11:41:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6708, loss_cls: 3.3443, loss: 3.3443 +2024-12-30 08:23:21,286 - pyskl - INFO - Epoch [110][3300/3746] lr: 1.664e-02, eta: 1 day, 11:40:03, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6608, loss_cls: 3.4169, loss: 3.4169 +2024-12-30 08:24:46,966 - pyskl - INFO - Epoch [110][3400/3746] lr: 1.662e-02, eta: 1 day, 11:38:38, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6617, loss_cls: 3.3515, loss: 3.3515 +2024-12-30 08:26:12,363 - pyskl - INFO - Epoch [110][3500/3746] lr: 1.659e-02, eta: 1 day, 11:37:13, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3977, top5_acc: 0.6509, loss_cls: 3.4057, loss: 3.4057 +2024-12-30 08:27:37,741 - pyskl - INFO - Epoch [110][3600/3746] lr: 1.657e-02, eta: 1 day, 11:35:47, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.3909, top5_acc: 0.6541, loss_cls: 3.4063, loss: 3.4063 +2024-12-30 08:29:02,828 - pyskl - INFO - Epoch [110][3700/3746] lr: 1.655e-02, eta: 1 day, 11:34:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3914, top5_acc: 0.6611, loss_cls: 3.4118, loss: 3.4118 +2024-12-30 08:29:43,992 - pyskl - INFO - Saving checkpoint at 110 epochs +2024-12-30 08:31:45,165 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 08:31:45,905 - pyskl - INFO - +top1_acc 0.3394 +top5_acc 0.5987 +2024-12-30 08:31:45,905 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 08:31:45,970 - pyskl - INFO - +mean_acc 0.3392 +2024-12-30 08:31:45,984 - pyskl - INFO - Epoch(val) [110][309] top1_acc: 0.3394, top5_acc: 0.5987, mean_class_accuracy: 0.3392 +2024-12-30 08:36:03,694 - pyskl - INFO - Epoch [111][100/3746] lr: 1.652e-02, eta: 1 day, 11:33:05, time: 2.577, data_time: 1.546, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6805, loss_cls: 3.2760, loss: 3.2760 +2024-12-30 08:37:29,069 - pyskl - INFO - Epoch [111][200/3746] lr: 1.650e-02, eta: 1 day, 11:31:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6767, loss_cls: 3.2978, loss: 3.2978 +2024-12-30 08:38:54,870 - pyskl - INFO - Epoch [111][300/3746] lr: 1.648e-02, eta: 1 day, 11:30:14, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6713, loss_cls: 3.3590, loss: 3.3590 +2024-12-30 08:40:20,265 - pyskl - INFO - Epoch [111][400/3746] lr: 1.646e-02, eta: 1 day, 11:28:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6623, loss_cls: 3.3534, loss: 3.3534 +2024-12-30 08:41:45,167 - pyskl - INFO - Epoch [111][500/3746] lr: 1.644e-02, eta: 1 day, 11:27:23, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4139, top5_acc: 0.6659, loss_cls: 3.3298, loss: 3.3298 +2024-12-30 08:43:10,246 - pyskl - INFO - Epoch [111][600/3746] lr: 1.642e-02, eta: 1 day, 11:25:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6773, loss_cls: 3.3104, loss: 3.3104 +2024-12-30 08:44:35,361 - pyskl - INFO - Epoch [111][700/3746] lr: 1.640e-02, eta: 1 day, 11:24:32, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4042, top5_acc: 0.6617, loss_cls: 3.3407, loss: 3.3407 +2024-12-30 08:46:00,291 - pyskl - INFO - Epoch [111][800/3746] lr: 1.638e-02, eta: 1 day, 11:23:06, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6698, loss_cls: 3.3136, loss: 3.3136 +2024-12-30 08:47:25,662 - pyskl - INFO - Epoch [111][900/3746] lr: 1.636e-02, eta: 1 day, 11:21:41, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4016, top5_acc: 0.6533, loss_cls: 3.3819, loss: 3.3819 +2024-12-30 08:48:50,719 - pyskl - INFO - Epoch [111][1000/3746] lr: 1.634e-02, eta: 1 day, 11:20:15, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4116, top5_acc: 0.6653, loss_cls: 3.3257, loss: 3.3257 +2024-12-30 08:50:15,775 - pyskl - INFO - Epoch [111][1100/3746] lr: 1.632e-02, eta: 1 day, 11:18:50, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6637, loss_cls: 3.3693, loss: 3.3693 +2024-12-30 08:51:41,068 - pyskl - INFO - Epoch [111][1200/3746] lr: 1.630e-02, eta: 1 day, 11:17:24, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6641, loss_cls: 3.3637, loss: 3.3637 +2024-12-30 08:53:06,056 - pyskl - INFO - Epoch [111][1300/3746] lr: 1.627e-02, eta: 1 day, 11:15:58, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6687, loss_cls: 3.3537, loss: 3.3537 +2024-12-30 08:54:31,789 - pyskl - INFO - Epoch [111][1400/3746] lr: 1.625e-02, eta: 1 day, 11:14:33, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6592, loss_cls: 3.3513, loss: 3.3513 +2024-12-30 08:55:57,051 - pyskl - INFO - Epoch [111][1500/3746] lr: 1.623e-02, eta: 1 day, 11:13:07, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6636, loss_cls: 3.3550, loss: 3.3550 +2024-12-30 08:57:21,859 - pyskl - INFO - Epoch [111][1600/3746] lr: 1.621e-02, eta: 1 day, 11:11:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4092, top5_acc: 0.6669, loss_cls: 3.3362, loss: 3.3362 +2024-12-30 08:58:47,096 - pyskl - INFO - Epoch [111][1700/3746] lr: 1.619e-02, eta: 1 day, 11:10:16, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6567, loss_cls: 3.3980, loss: 3.3980 +2024-12-30 09:00:12,423 - pyskl - INFO - Epoch [111][1800/3746] lr: 1.617e-02, eta: 1 day, 11:08:51, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4091, top5_acc: 0.6652, loss_cls: 3.3339, loss: 3.3339 +2024-12-30 09:01:37,468 - pyskl - INFO - Epoch [111][1900/3746] lr: 1.615e-02, eta: 1 day, 11:07:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6652, loss_cls: 3.3346, loss: 3.3346 +2024-12-30 09:03:02,578 - pyskl - INFO - Epoch [111][2000/3746] lr: 1.613e-02, eta: 1 day, 11:05:59, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4188, top5_acc: 0.6748, loss_cls: 3.2995, loss: 3.2995 +2024-12-30 09:04:28,114 - pyskl - INFO - Epoch [111][2100/3746] lr: 1.611e-02, eta: 1 day, 11:04:34, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6553, loss_cls: 3.4114, loss: 3.4114 +2024-12-30 09:05:53,348 - pyskl - INFO - Epoch [111][2200/3746] lr: 1.609e-02, eta: 1 day, 11:03:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6725, loss_cls: 3.3057, loss: 3.3057 +2024-12-30 09:07:18,623 - pyskl - INFO - Epoch [111][2300/3746] lr: 1.607e-02, eta: 1 day, 11:01:43, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4034, top5_acc: 0.6702, loss_cls: 3.3460, loss: 3.3460 +2024-12-30 09:08:43,721 - pyskl - INFO - Epoch [111][2400/3746] lr: 1.605e-02, eta: 1 day, 11:00:17, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6587, loss_cls: 3.3801, loss: 3.3801 +2024-12-30 09:10:08,997 - pyskl - INFO - Epoch [111][2500/3746] lr: 1.603e-02, eta: 1 day, 10:58:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6567, loss_cls: 3.3886, loss: 3.3886 +2024-12-30 09:11:33,965 - pyskl - INFO - Epoch [111][2600/3746] lr: 1.601e-02, eta: 1 day, 10:57:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6577, loss_cls: 3.3422, loss: 3.3422 +2024-12-30 09:12:59,300 - pyskl - INFO - Epoch [111][2700/3746] lr: 1.599e-02, eta: 1 day, 10:56:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6541, loss_cls: 3.4181, loss: 3.4181 +2024-12-30 09:14:24,757 - pyskl - INFO - Epoch [111][2800/3746] lr: 1.597e-02, eta: 1 day, 10:54:35, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6670, loss_cls: 3.3156, loss: 3.3156 +2024-12-30 09:15:49,969 - pyskl - INFO - Epoch [111][2900/3746] lr: 1.595e-02, eta: 1 day, 10:53:09, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3855, top5_acc: 0.6492, loss_cls: 3.4160, loss: 3.4160 +2024-12-30 09:17:15,565 - pyskl - INFO - Epoch [111][3000/3746] lr: 1.593e-02, eta: 1 day, 10:51:44, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6498, loss_cls: 3.4123, loss: 3.4123 +2024-12-30 09:18:40,699 - pyskl - INFO - Epoch [111][3100/3746] lr: 1.590e-02, eta: 1 day, 10:50:18, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6683, loss_cls: 3.3212, loss: 3.3212 +2024-12-30 09:20:06,265 - pyskl - INFO - Epoch [111][3200/3746] lr: 1.588e-02, eta: 1 day, 10:48:53, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6642, loss_cls: 3.3219, loss: 3.3219 +2024-12-30 09:21:32,081 - pyskl - INFO - Epoch [111][3300/3746] lr: 1.586e-02, eta: 1 day, 10:47:28, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6577, loss_cls: 3.3897, loss: 3.3897 +2024-12-30 09:22:57,546 - pyskl - INFO - Epoch [111][3400/3746] lr: 1.584e-02, eta: 1 day, 10:46:02, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4113, top5_acc: 0.6733, loss_cls: 3.3190, loss: 3.3190 +2024-12-30 09:24:22,725 - pyskl - INFO - Epoch [111][3500/3746] lr: 1.582e-02, eta: 1 day, 10:44:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.3997, top5_acc: 0.6666, loss_cls: 3.3730, loss: 3.3730 +2024-12-30 09:25:47,606 - pyskl - INFO - Epoch [111][3600/3746] lr: 1.580e-02, eta: 1 day, 10:43:11, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6534, loss_cls: 3.4003, loss: 3.4003 +2024-12-30 09:27:12,682 - pyskl - INFO - Epoch [111][3700/3746] lr: 1.578e-02, eta: 1 day, 10:41:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6605, loss_cls: 3.3903, loss: 3.3903 +2024-12-30 09:27:54,053 - pyskl - INFO - Saving checkpoint at 111 epochs +2024-12-30 09:29:53,607 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 09:29:54,444 - pyskl - INFO - +top1_acc 0.3494 +top5_acc 0.6059 +2024-12-30 09:29:54,445 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 09:29:54,495 - pyskl - INFO - +mean_acc 0.3491 +2024-12-30 09:29:54,499 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_109.pth was removed +2024-12-30 09:29:54,765 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_111.pth. +2024-12-30 09:29:54,765 - pyskl - INFO - Best top1_acc is 0.3494 at 111 epoch. +2024-12-30 09:29:54,779 - pyskl - INFO - Epoch(val) [111][309] top1_acc: 0.3494, top5_acc: 0.6059, mean_class_accuracy: 0.3491 +2024-12-30 09:34:16,398 - pyskl - INFO - Epoch [112][100/3746] lr: 1.575e-02, eta: 1 day, 10:40:29, time: 2.616, data_time: 1.569, memory: 15990, top1_acc: 0.4264, top5_acc: 0.6958, loss_cls: 3.2045, loss: 3.2045 +2024-12-30 09:35:41,905 - pyskl - INFO - Epoch [112][200/3746] lr: 1.573e-02, eta: 1 day, 10:39:03, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4130, top5_acc: 0.6745, loss_cls: 3.2738, loss: 3.2738 +2024-12-30 09:37:07,378 - pyskl - INFO - Epoch [112][300/3746] lr: 1.571e-02, eta: 1 day, 10:37:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6770, loss_cls: 3.2720, loss: 3.2720 +2024-12-30 09:38:32,691 - pyskl - INFO - Epoch [112][400/3746] lr: 1.569e-02, eta: 1 day, 10:36:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4214, top5_acc: 0.6686, loss_cls: 3.2852, loss: 3.2852 +2024-12-30 09:39:58,323 - pyskl - INFO - Epoch [112][500/3746] lr: 1.567e-02, eta: 1 day, 10:34:47, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4241, top5_acc: 0.6736, loss_cls: 3.2715, loss: 3.2715 +2024-12-30 09:41:24,363 - pyskl - INFO - Epoch [112][600/3746] lr: 1.565e-02, eta: 1 day, 10:33:21, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6719, loss_cls: 3.2861, loss: 3.2861 +2024-12-30 09:42:50,491 - pyskl - INFO - Epoch [112][700/3746] lr: 1.563e-02, eta: 1 day, 10:31:56, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4052, top5_acc: 0.6636, loss_cls: 3.3522, loss: 3.3522 +2024-12-30 09:44:15,884 - pyskl - INFO - Epoch [112][800/3746] lr: 1.561e-02, eta: 1 day, 10:30:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6652, loss_cls: 3.3250, loss: 3.3250 +2024-12-30 09:45:41,366 - pyskl - INFO - Epoch [112][900/3746] lr: 1.559e-02, eta: 1 day, 10:29:05, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4086, top5_acc: 0.6694, loss_cls: 3.3395, loss: 3.3395 +2024-12-30 09:47:06,305 - pyskl - INFO - Epoch [112][1000/3746] lr: 1.557e-02, eta: 1 day, 10:27:39, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4125, top5_acc: 0.6661, loss_cls: 3.2935, loss: 3.2935 +2024-12-30 09:48:31,467 - pyskl - INFO - Epoch [112][1100/3746] lr: 1.555e-02, eta: 1 day, 10:26:14, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6681, loss_cls: 3.3177, loss: 3.3177 +2024-12-30 09:49:57,237 - pyskl - INFO - Epoch [112][1200/3746] lr: 1.553e-02, eta: 1 day, 10:24:48, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6702, loss_cls: 3.3296, loss: 3.3296 +2024-12-30 09:51:23,496 - pyskl - INFO - Epoch [112][1300/3746] lr: 1.551e-02, eta: 1 day, 10:23:23, time: 0.863, data_time: 0.001, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6803, loss_cls: 3.2775, loss: 3.2775 +2024-12-30 09:52:49,386 - pyskl - INFO - Epoch [112][1400/3746] lr: 1.549e-02, eta: 1 day, 10:21:58, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.4125, top5_acc: 0.6747, loss_cls: 3.3109, loss: 3.3109 +2024-12-30 09:54:15,363 - pyskl - INFO - Epoch [112][1500/3746] lr: 1.547e-02, eta: 1 day, 10:20:32, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4116, top5_acc: 0.6703, loss_cls: 3.3116, loss: 3.3116 +2024-12-30 09:55:41,572 - pyskl - INFO - Epoch [112][1600/3746] lr: 1.545e-02, eta: 1 day, 10:19:07, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4170, top5_acc: 0.6692, loss_cls: 3.3141, loss: 3.3141 +2024-12-30 09:57:06,932 - pyskl - INFO - Epoch [112][1700/3746] lr: 1.543e-02, eta: 1 day, 10:17:41, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6620, loss_cls: 3.3781, loss: 3.3781 +2024-12-30 09:58:32,370 - pyskl - INFO - Epoch [112][1800/3746] lr: 1.541e-02, eta: 1 day, 10:16:16, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4091, top5_acc: 0.6661, loss_cls: 3.3442, loss: 3.3442 +2024-12-30 09:59:57,586 - pyskl - INFO - Epoch [112][1900/3746] lr: 1.539e-02, eta: 1 day, 10:14:50, time: 0.852, data_time: 0.001, memory: 15990, top1_acc: 0.4066, top5_acc: 0.6736, loss_cls: 3.3195, loss: 3.3195 +2024-12-30 10:01:22,476 - pyskl - INFO - Epoch [112][2000/3746] lr: 1.537e-02, eta: 1 day, 10:13:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4006, top5_acc: 0.6577, loss_cls: 3.3720, loss: 3.3720 +2024-12-30 10:02:47,745 - pyskl - INFO - Epoch [112][2100/3746] lr: 1.535e-02, eta: 1 day, 10:11:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6733, loss_cls: 3.3062, loss: 3.3062 +2024-12-30 10:04:13,094 - pyskl - INFO - Epoch [112][2200/3746] lr: 1.533e-02, eta: 1 day, 10:10:33, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.3977, top5_acc: 0.6559, loss_cls: 3.3650, loss: 3.3650 +2024-12-30 10:05:37,786 - pyskl - INFO - Epoch [112][2300/3746] lr: 1.531e-02, eta: 1 day, 10:09:08, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6592, loss_cls: 3.3647, loss: 3.3647 +2024-12-30 10:07:03,493 - pyskl - INFO - Epoch [112][2400/3746] lr: 1.529e-02, eta: 1 day, 10:07:42, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.3970, top5_acc: 0.6531, loss_cls: 3.4204, loss: 3.4204 +2024-12-30 10:08:28,973 - pyskl - INFO - Epoch [112][2500/3746] lr: 1.527e-02, eta: 1 day, 10:06:17, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6581, loss_cls: 3.3585, loss: 3.3585 +2024-12-30 10:09:54,642 - pyskl - INFO - Epoch [112][2600/3746] lr: 1.525e-02, eta: 1 day, 10:04:51, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4020, top5_acc: 0.6641, loss_cls: 3.3572, loss: 3.3572 +2024-12-30 10:11:20,229 - pyskl - INFO - Epoch [112][2700/3746] lr: 1.523e-02, eta: 1 day, 10:03:26, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6730, loss_cls: 3.3366, loss: 3.3366 +2024-12-30 10:12:45,315 - pyskl - INFO - Epoch [112][2800/3746] lr: 1.521e-02, eta: 1 day, 10:02:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6644, loss_cls: 3.3376, loss: 3.3376 +2024-12-30 10:14:10,644 - pyskl - INFO - Epoch [112][2900/3746] lr: 1.519e-02, eta: 1 day, 10:00:35, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4098, top5_acc: 0.6608, loss_cls: 3.3581, loss: 3.3581 +2024-12-30 10:15:36,267 - pyskl - INFO - Epoch [112][3000/3746] lr: 1.517e-02, eta: 1 day, 9:59:09, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4066, top5_acc: 0.6675, loss_cls: 3.3522, loss: 3.3522 +2024-12-30 10:17:01,916 - pyskl - INFO - Epoch [112][3100/3746] lr: 1.515e-02, eta: 1 day, 9:57:44, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6550, loss_cls: 3.3951, loss: 3.3951 +2024-12-30 10:18:27,599 - pyskl - INFO - Epoch [112][3200/3746] lr: 1.513e-02, eta: 1 day, 9:56:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6648, loss_cls: 3.3624, loss: 3.3624 +2024-12-30 10:19:53,146 - pyskl - INFO - Epoch [112][3300/3746] lr: 1.511e-02, eta: 1 day, 9:54:53, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4155, top5_acc: 0.6675, loss_cls: 3.3455, loss: 3.3455 +2024-12-30 10:21:18,576 - pyskl - INFO - Epoch [112][3400/3746] lr: 1.509e-02, eta: 1 day, 9:53:27, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4091, top5_acc: 0.6617, loss_cls: 3.3477, loss: 3.3477 +2024-12-30 10:22:43,991 - pyskl - INFO - Epoch [112][3500/3746] lr: 1.507e-02, eta: 1 day, 9:52:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6747, loss_cls: 3.2960, loss: 3.2960 +2024-12-30 10:24:09,743 - pyskl - INFO - Epoch [112][3600/3746] lr: 1.505e-02, eta: 1 day, 9:50:36, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6686, loss_cls: 3.3263, loss: 3.3263 +2024-12-30 10:25:35,811 - pyskl - INFO - Epoch [112][3700/3746] lr: 1.503e-02, eta: 1 day, 9:49:11, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4156, top5_acc: 0.6675, loss_cls: 3.3186, loss: 3.3186 +2024-12-30 10:26:16,918 - pyskl - INFO - Saving checkpoint at 112 epochs +2024-12-30 10:28:17,010 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 10:28:17,705 - pyskl - INFO - +top1_acc 0.3442 +top5_acc 0.5990 +2024-12-30 10:28:17,706 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 10:28:17,752 - pyskl - INFO - +mean_acc 0.3440 +2024-12-30 10:28:17,764 - pyskl - INFO - Epoch(val) [112][309] top1_acc: 0.3442, top5_acc: 0.5990, mean_class_accuracy: 0.3440 +2024-12-30 10:32:38,964 - pyskl - INFO - Epoch [113][100/3746] lr: 1.500e-02, eta: 1 day, 9:47:52, time: 2.612, data_time: 1.571, memory: 15990, top1_acc: 0.4341, top5_acc: 0.6773, loss_cls: 3.2359, loss: 3.2359 +2024-12-30 10:34:04,303 - pyskl - INFO - Epoch [113][200/3746] lr: 1.498e-02, eta: 1 day, 9:46:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6783, loss_cls: 3.2418, loss: 3.2418 +2024-12-30 10:35:29,851 - pyskl - INFO - Epoch [113][300/3746] lr: 1.496e-02, eta: 1 day, 9:45:01, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6834, loss_cls: 3.2487, loss: 3.2487 +2024-12-30 10:36:55,192 - pyskl - INFO - Epoch [113][400/3746] lr: 1.494e-02, eta: 1 day, 9:43:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6855, loss_cls: 3.2612, loss: 3.2612 +2024-12-30 10:38:20,780 - pyskl - INFO - Epoch [113][500/3746] lr: 1.492e-02, eta: 1 day, 9:42:10, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6837, loss_cls: 3.2510, loss: 3.2510 +2024-12-30 10:39:46,129 - pyskl - INFO - Epoch [113][600/3746] lr: 1.490e-02, eta: 1 day, 9:40:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6781, loss_cls: 3.2602, loss: 3.2602 +2024-12-30 10:41:11,835 - pyskl - INFO - Epoch [113][700/3746] lr: 1.488e-02, eta: 1 day, 9:39:19, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6731, loss_cls: 3.2740, loss: 3.2740 +2024-12-30 10:42:37,591 - pyskl - INFO - Epoch [113][800/3746] lr: 1.486e-02, eta: 1 day, 9:37:54, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4114, top5_acc: 0.6792, loss_cls: 3.3071, loss: 3.3071 +2024-12-30 10:44:03,455 - pyskl - INFO - Epoch [113][900/3746] lr: 1.484e-02, eta: 1 day, 9:36:28, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6655, loss_cls: 3.3021, loss: 3.3021 +2024-12-30 10:45:29,225 - pyskl - INFO - Epoch [113][1000/3746] lr: 1.482e-02, eta: 1 day, 9:35:03, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4109, top5_acc: 0.6798, loss_cls: 3.2900, loss: 3.2900 +2024-12-30 10:46:54,698 - pyskl - INFO - Epoch [113][1100/3746] lr: 1.480e-02, eta: 1 day, 9:33:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4228, top5_acc: 0.6705, loss_cls: 3.2865, loss: 3.2865 +2024-12-30 10:48:20,404 - pyskl - INFO - Epoch [113][1200/3746] lr: 1.478e-02, eta: 1 day, 9:32:12, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4139, top5_acc: 0.6664, loss_cls: 3.3236, loss: 3.3236 +2024-12-30 10:49:45,802 - pyskl - INFO - Epoch [113][1300/3746] lr: 1.476e-02, eta: 1 day, 9:30:46, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4025, top5_acc: 0.6736, loss_cls: 3.3071, loss: 3.3071 +2024-12-30 10:51:11,568 - pyskl - INFO - Epoch [113][1400/3746] lr: 1.474e-02, eta: 1 day, 9:29:21, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6745, loss_cls: 3.2962, loss: 3.2962 +2024-12-30 10:52:37,737 - pyskl - INFO - Epoch [113][1500/3746] lr: 1.472e-02, eta: 1 day, 9:27:55, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6622, loss_cls: 3.3455, loss: 3.3455 +2024-12-30 10:54:03,890 - pyskl - INFO - Epoch [113][1600/3746] lr: 1.470e-02, eta: 1 day, 9:26:30, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4084, top5_acc: 0.6695, loss_cls: 3.3195, loss: 3.3195 +2024-12-30 10:55:29,851 - pyskl - INFO - Epoch [113][1700/3746] lr: 1.468e-02, eta: 1 day, 9:25:05, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4017, top5_acc: 0.6694, loss_cls: 3.3345, loss: 3.3345 +2024-12-30 10:56:55,385 - pyskl - INFO - Epoch [113][1800/3746] lr: 1.466e-02, eta: 1 day, 9:23:39, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4072, top5_acc: 0.6702, loss_cls: 3.3322, loss: 3.3322 +2024-12-30 10:58:20,404 - pyskl - INFO - Epoch [113][1900/3746] lr: 1.464e-02, eta: 1 day, 9:22:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6630, loss_cls: 3.3262, loss: 3.3262 +2024-12-30 10:59:45,545 - pyskl - INFO - Epoch [113][2000/3746] lr: 1.462e-02, eta: 1 day, 9:20:48, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6706, loss_cls: 3.3285, loss: 3.3285 +2024-12-30 11:01:11,016 - pyskl - INFO - Epoch [113][2100/3746] lr: 1.460e-02, eta: 1 day, 9:19:22, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6736, loss_cls: 3.3230, loss: 3.3230 +2024-12-30 11:02:36,720 - pyskl - INFO - Epoch [113][2200/3746] lr: 1.458e-02, eta: 1 day, 9:17:57, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6706, loss_cls: 3.3413, loss: 3.3413 +2024-12-30 11:04:02,356 - pyskl - INFO - Epoch [113][2300/3746] lr: 1.456e-02, eta: 1 day, 9:16:31, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4183, top5_acc: 0.6728, loss_cls: 3.2906, loss: 3.2906 +2024-12-30 11:05:28,152 - pyskl - INFO - Epoch [113][2400/3746] lr: 1.454e-02, eta: 1 day, 9:15:06, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4078, top5_acc: 0.6658, loss_cls: 3.3430, loss: 3.3430 +2024-12-30 11:06:54,436 - pyskl - INFO - Epoch [113][2500/3746] lr: 1.452e-02, eta: 1 day, 9:13:40, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4123, top5_acc: 0.6720, loss_cls: 3.3188, loss: 3.3188 +2024-12-30 11:08:20,345 - pyskl - INFO - Epoch [113][2600/3746] lr: 1.450e-02, eta: 1 day, 9:12:15, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4083, top5_acc: 0.6684, loss_cls: 3.3490, loss: 3.3490 +2024-12-30 11:09:46,204 - pyskl - INFO - Epoch [113][2700/3746] lr: 1.448e-02, eta: 1 day, 9:10:49, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6655, loss_cls: 3.3325, loss: 3.3325 +2024-12-30 11:11:11,867 - pyskl - INFO - Epoch [113][2800/3746] lr: 1.446e-02, eta: 1 day, 9:09:24, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6750, loss_cls: 3.2599, loss: 3.2599 +2024-12-30 11:12:37,565 - pyskl - INFO - Epoch [113][2900/3746] lr: 1.444e-02, eta: 1 day, 9:07:58, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6611, loss_cls: 3.3537, loss: 3.3537 +2024-12-30 11:14:03,603 - pyskl - INFO - Epoch [113][3000/3746] lr: 1.442e-02, eta: 1 day, 9:06:33, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6736, loss_cls: 3.3026, loss: 3.3026 +2024-12-30 11:15:29,370 - pyskl - INFO - Epoch [113][3100/3746] lr: 1.440e-02, eta: 1 day, 9:05:08, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4017, top5_acc: 0.6658, loss_cls: 3.3377, loss: 3.3377 +2024-12-30 11:16:55,612 - pyskl - INFO - Epoch [113][3200/3746] lr: 1.438e-02, eta: 1 day, 9:03:42, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6722, loss_cls: 3.3021, loss: 3.3021 +2024-12-30 11:18:21,859 - pyskl - INFO - Epoch [113][3300/3746] lr: 1.436e-02, eta: 1 day, 9:02:17, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4169, top5_acc: 0.6620, loss_cls: 3.3340, loss: 3.3340 +2024-12-30 11:19:47,951 - pyskl - INFO - Epoch [113][3400/3746] lr: 1.434e-02, eta: 1 day, 9:00:52, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6683, loss_cls: 3.3241, loss: 3.3241 +2024-12-30 11:21:13,609 - pyskl - INFO - Epoch [113][3500/3746] lr: 1.432e-02, eta: 1 day, 8:59:26, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6680, loss_cls: 3.3348, loss: 3.3348 +2024-12-30 11:22:39,336 - pyskl - INFO - Epoch [113][3600/3746] lr: 1.431e-02, eta: 1 day, 8:58:01, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4053, top5_acc: 0.6591, loss_cls: 3.3548, loss: 3.3548 +2024-12-30 11:24:05,537 - pyskl - INFO - Epoch [113][3700/3746] lr: 1.429e-02, eta: 1 day, 8:56:35, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.3986, top5_acc: 0.6667, loss_cls: 3.3403, loss: 3.3403 +2024-12-30 11:24:46,844 - pyskl - INFO - Saving checkpoint at 113 epochs +2024-12-30 11:26:47,498 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 11:26:48,407 - pyskl - INFO - +top1_acc 0.3438 +top5_acc 0.6079 +2024-12-30 11:26:48,407 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 11:26:48,454 - pyskl - INFO - +mean_acc 0.3436 +2024-12-30 11:26:48,468 - pyskl - INFO - Epoch(val) [113][309] top1_acc: 0.3438, top5_acc: 0.6079, mean_class_accuracy: 0.3436 +2024-12-30 11:31:08,332 - pyskl - INFO - Epoch [114][100/3746] lr: 1.426e-02, eta: 1 day, 8:55:15, time: 2.599, data_time: 1.552, memory: 15990, top1_acc: 0.4328, top5_acc: 0.6875, loss_cls: 3.2060, loss: 3.2060 +2024-12-30 11:32:33,707 - pyskl - INFO - Epoch [114][200/3746] lr: 1.424e-02, eta: 1 day, 8:53:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4173, top5_acc: 0.6686, loss_cls: 3.3028, loss: 3.3028 +2024-12-30 11:33:59,066 - pyskl - INFO - Epoch [114][300/3746] lr: 1.422e-02, eta: 1 day, 8:52:23, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4269, top5_acc: 0.6850, loss_cls: 3.2316, loss: 3.2316 +2024-12-30 11:35:24,524 - pyskl - INFO - Epoch [114][400/3746] lr: 1.420e-02, eta: 1 day, 8:50:58, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4227, top5_acc: 0.6728, loss_cls: 3.2725, loss: 3.2725 +2024-12-30 11:36:49,468 - pyskl - INFO - Epoch [114][500/3746] lr: 1.418e-02, eta: 1 day, 8:49:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4145, top5_acc: 0.6736, loss_cls: 3.2806, loss: 3.2806 +2024-12-30 11:38:14,753 - pyskl - INFO - Epoch [114][600/3746] lr: 1.416e-02, eta: 1 day, 8:48:06, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4252, top5_acc: 0.6880, loss_cls: 3.2179, loss: 3.2179 +2024-12-30 11:39:40,174 - pyskl - INFO - Epoch [114][700/3746] lr: 1.414e-02, eta: 1 day, 8:46:41, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4219, top5_acc: 0.6813, loss_cls: 3.2576, loss: 3.2576 +2024-12-30 11:41:05,534 - pyskl - INFO - Epoch [114][800/3746] lr: 1.412e-02, eta: 1 day, 8:45:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6791, loss_cls: 3.2719, loss: 3.2719 +2024-12-30 11:42:30,921 - pyskl - INFO - Epoch [114][900/3746] lr: 1.410e-02, eta: 1 day, 8:43:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6716, loss_cls: 3.3281, loss: 3.3281 +2024-12-30 11:43:56,372 - pyskl - INFO - Epoch [114][1000/3746] lr: 1.408e-02, eta: 1 day, 8:42:24, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6802, loss_cls: 3.2337, loss: 3.2337 +2024-12-30 11:45:21,763 - pyskl - INFO - Epoch [114][1100/3746] lr: 1.406e-02, eta: 1 day, 8:40:58, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4147, top5_acc: 0.6756, loss_cls: 3.2526, loss: 3.2526 +2024-12-30 11:46:47,034 - pyskl - INFO - Epoch [114][1200/3746] lr: 1.404e-02, eta: 1 day, 8:39:33, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4075, top5_acc: 0.6709, loss_cls: 3.2981, loss: 3.2981 +2024-12-30 11:48:12,608 - pyskl - INFO - Epoch [114][1300/3746] lr: 1.402e-02, eta: 1 day, 8:38:07, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4098, top5_acc: 0.6786, loss_cls: 3.2817, loss: 3.2817 +2024-12-30 11:49:37,699 - pyskl - INFO - Epoch [114][1400/3746] lr: 1.400e-02, eta: 1 day, 8:36:41, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4152, top5_acc: 0.6694, loss_cls: 3.2596, loss: 3.2596 +2024-12-30 11:51:02,664 - pyskl - INFO - Epoch [114][1500/3746] lr: 1.398e-02, eta: 1 day, 8:35:16, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4214, top5_acc: 0.6805, loss_cls: 3.2456, loss: 3.2456 +2024-12-30 11:52:27,983 - pyskl - INFO - Epoch [114][1600/3746] lr: 1.397e-02, eta: 1 day, 8:33:50, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6820, loss_cls: 3.3052, loss: 3.3052 +2024-12-30 11:53:53,702 - pyskl - INFO - Epoch [114][1700/3746] lr: 1.395e-02, eta: 1 day, 8:32:24, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6737, loss_cls: 3.3209, loss: 3.3209 +2024-12-30 11:55:19,176 - pyskl - INFO - Epoch [114][1800/3746] lr: 1.393e-02, eta: 1 day, 8:30:59, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6778, loss_cls: 3.2859, loss: 3.2859 +2024-12-30 11:56:44,273 - pyskl - INFO - Epoch [114][1900/3746] lr: 1.391e-02, eta: 1 day, 8:29:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4125, top5_acc: 0.6597, loss_cls: 3.3269, loss: 3.3269 +2024-12-30 11:58:09,601 - pyskl - INFO - Epoch [114][2000/3746] lr: 1.389e-02, eta: 1 day, 8:28:07, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6822, loss_cls: 3.2597, loss: 3.2597 +2024-12-30 11:59:34,645 - pyskl - INFO - Epoch [114][2100/3746] lr: 1.387e-02, eta: 1 day, 8:26:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6753, loss_cls: 3.2542, loss: 3.2542 +2024-12-30 12:01:00,429 - pyskl - INFO - Epoch [114][2200/3746] lr: 1.385e-02, eta: 1 day, 8:25:16, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6781, loss_cls: 3.2600, loss: 3.2600 +2024-12-30 12:02:25,840 - pyskl - INFO - Epoch [114][2300/3746] lr: 1.383e-02, eta: 1 day, 8:23:51, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4089, top5_acc: 0.6708, loss_cls: 3.3376, loss: 3.3376 +2024-12-30 12:03:51,204 - pyskl - INFO - Epoch [114][2400/3746] lr: 1.381e-02, eta: 1 day, 8:22:25, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6827, loss_cls: 3.2563, loss: 3.2563 +2024-12-30 12:05:16,223 - pyskl - INFO - Epoch [114][2500/3746] lr: 1.379e-02, eta: 1 day, 8:20:59, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4061, top5_acc: 0.6641, loss_cls: 3.3300, loss: 3.3300 +2024-12-30 12:06:41,319 - pyskl - INFO - Epoch [114][2600/3746] lr: 1.377e-02, eta: 1 day, 8:19:33, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6803, loss_cls: 3.2856, loss: 3.2856 +2024-12-30 12:08:06,477 - pyskl - INFO - Epoch [114][2700/3746] lr: 1.375e-02, eta: 1 day, 8:18:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6564, loss_cls: 3.3703, loss: 3.3703 +2024-12-30 12:09:31,687 - pyskl - INFO - Epoch [114][2800/3746] lr: 1.373e-02, eta: 1 day, 8:16:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6739, loss_cls: 3.2826, loss: 3.2826 +2024-12-30 12:10:56,792 - pyskl - INFO - Epoch [114][2900/3746] lr: 1.371e-02, eta: 1 day, 8:15:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4145, top5_acc: 0.6758, loss_cls: 3.2751, loss: 3.2751 +2024-12-30 12:12:22,649 - pyskl - INFO - Epoch [114][3000/3746] lr: 1.369e-02, eta: 1 day, 8:13:51, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4170, top5_acc: 0.6842, loss_cls: 3.2692, loss: 3.2692 +2024-12-30 12:13:48,211 - pyskl - INFO - Epoch [114][3100/3746] lr: 1.368e-02, eta: 1 day, 8:12:25, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4178, top5_acc: 0.6750, loss_cls: 3.2937, loss: 3.2937 +2024-12-30 12:15:13,566 - pyskl - INFO - Epoch [114][3200/3746] lr: 1.366e-02, eta: 1 day, 8:11:00, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4103, top5_acc: 0.6773, loss_cls: 3.3123, loss: 3.3123 +2024-12-30 12:16:39,052 - pyskl - INFO - Epoch [114][3300/3746] lr: 1.364e-02, eta: 1 day, 8:09:34, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6645, loss_cls: 3.3204, loss: 3.3204 +2024-12-30 12:18:05,129 - pyskl - INFO - Epoch [114][3400/3746] lr: 1.362e-02, eta: 1 day, 8:08:09, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4109, top5_acc: 0.6767, loss_cls: 3.2823, loss: 3.2823 +2024-12-30 12:19:31,017 - pyskl - INFO - Epoch [114][3500/3746] lr: 1.360e-02, eta: 1 day, 8:06:43, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6641, loss_cls: 3.3414, loss: 3.3414 +2024-12-30 12:20:56,496 - pyskl - INFO - Epoch [114][3600/3746] lr: 1.358e-02, eta: 1 day, 8:05:18, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6673, loss_cls: 3.2916, loss: 3.2916 +2024-12-30 12:22:22,135 - pyskl - INFO - Epoch [114][3700/3746] lr: 1.356e-02, eta: 1 day, 8:03:52, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6664, loss_cls: 3.3686, loss: 3.3686 +2024-12-30 12:23:03,539 - pyskl - INFO - Saving checkpoint at 114 epochs +2024-12-30 12:25:02,472 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 12:25:03,213 - pyskl - INFO - +top1_acc 0.3534 +top5_acc 0.6095 +2024-12-30 12:25:03,213 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 12:25:03,277 - pyskl - INFO - +mean_acc 0.3532 +2024-12-30 12:25:03,285 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_111.pth was removed +2024-12-30 12:25:03,568 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_114.pth. +2024-12-30 12:25:03,569 - pyskl - INFO - Best top1_acc is 0.3534 at 114 epoch. +2024-12-30 12:25:03,588 - pyskl - INFO - Epoch(val) [114][309] top1_acc: 0.3534, top5_acc: 0.6095, mean_class_accuracy: 0.3532 +2024-12-30 12:29:19,430 - pyskl - INFO - Epoch [115][100/3746] lr: 1.353e-02, eta: 1 day, 8:02:28, time: 2.558, data_time: 1.514, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6817, loss_cls: 3.2045, loss: 3.2045 +2024-12-30 12:30:44,572 - pyskl - INFO - Epoch [115][200/3746] lr: 1.351e-02, eta: 1 day, 8:01:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4153, top5_acc: 0.6767, loss_cls: 3.2554, loss: 3.2554 +2024-12-30 12:32:10,136 - pyskl - INFO - Epoch [115][300/3746] lr: 1.349e-02, eta: 1 day, 7:59:37, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4381, top5_acc: 0.6880, loss_cls: 3.1890, loss: 3.1890 +2024-12-30 12:33:35,281 - pyskl - INFO - Epoch [115][400/3746] lr: 1.348e-02, eta: 1 day, 7:58:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6825, loss_cls: 3.2464, loss: 3.2464 +2024-12-30 12:35:00,725 - pyskl - INFO - Epoch [115][500/3746] lr: 1.346e-02, eta: 1 day, 7:56:46, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4302, top5_acc: 0.6802, loss_cls: 3.2248, loss: 3.2248 +2024-12-30 12:36:26,026 - pyskl - INFO - Epoch [115][600/3746] lr: 1.344e-02, eta: 1 day, 7:55:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4273, top5_acc: 0.6789, loss_cls: 3.2368, loss: 3.2368 +2024-12-30 12:37:51,250 - pyskl - INFO - Epoch [115][700/3746] lr: 1.342e-02, eta: 1 day, 7:53:54, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4161, top5_acc: 0.6744, loss_cls: 3.2807, loss: 3.2807 +2024-12-30 12:39:16,603 - pyskl - INFO - Epoch [115][800/3746] lr: 1.340e-02, eta: 1 day, 7:52:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6808, loss_cls: 3.2350, loss: 3.2350 +2024-12-30 12:40:41,360 - pyskl - INFO - Epoch [115][900/3746] lr: 1.338e-02, eta: 1 day, 7:51:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4252, top5_acc: 0.6792, loss_cls: 3.2636, loss: 3.2636 +2024-12-30 12:42:06,559 - pyskl - INFO - Epoch [115][1000/3746] lr: 1.336e-02, eta: 1 day, 7:49:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6814, loss_cls: 3.2317, loss: 3.2317 +2024-12-30 12:43:31,982 - pyskl - INFO - Epoch [115][1100/3746] lr: 1.334e-02, eta: 1 day, 7:48:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4170, top5_acc: 0.6741, loss_cls: 3.2859, loss: 3.2859 +2024-12-30 12:44:56,875 - pyskl - INFO - Epoch [115][1200/3746] lr: 1.332e-02, eta: 1 day, 7:46:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6769, loss_cls: 3.2439, loss: 3.2439 +2024-12-30 12:46:22,591 - pyskl - INFO - Epoch [115][1300/3746] lr: 1.330e-02, eta: 1 day, 7:45:20, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6731, loss_cls: 3.2792, loss: 3.2792 +2024-12-30 12:47:47,623 - pyskl - INFO - Epoch [115][1400/3746] lr: 1.328e-02, eta: 1 day, 7:43:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6753, loss_cls: 3.2514, loss: 3.2514 +2024-12-30 12:49:13,134 - pyskl - INFO - Epoch [115][1500/3746] lr: 1.327e-02, eta: 1 day, 7:42:29, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6822, loss_cls: 3.2480, loss: 3.2480 +2024-12-30 12:50:39,031 - pyskl - INFO - Epoch [115][1600/3746] lr: 1.325e-02, eta: 1 day, 7:41:03, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.4220, top5_acc: 0.6813, loss_cls: 3.2450, loss: 3.2450 +2024-12-30 12:52:04,445 - pyskl - INFO - Epoch [115][1700/3746] lr: 1.323e-02, eta: 1 day, 7:39:37, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4114, top5_acc: 0.6733, loss_cls: 3.3042, loss: 3.3042 +2024-12-30 12:53:29,284 - pyskl - INFO - Epoch [115][1800/3746] lr: 1.321e-02, eta: 1 day, 7:38:12, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6797, loss_cls: 3.2502, loss: 3.2502 +2024-12-30 12:54:54,157 - pyskl - INFO - Epoch [115][1900/3746] lr: 1.319e-02, eta: 1 day, 7:36:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6747, loss_cls: 3.2848, loss: 3.2848 +2024-12-30 12:56:19,310 - pyskl - INFO - Epoch [115][2000/3746] lr: 1.317e-02, eta: 1 day, 7:35:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6791, loss_cls: 3.2541, loss: 3.2541 +2024-12-30 12:57:44,575 - pyskl - INFO - Epoch [115][2100/3746] lr: 1.315e-02, eta: 1 day, 7:33:54, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.4113, top5_acc: 0.6783, loss_cls: 3.3091, loss: 3.3091 +2024-12-30 12:59:09,979 - pyskl - INFO - Epoch [115][2200/3746] lr: 1.313e-02, eta: 1 day, 7:32:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6777, loss_cls: 3.2579, loss: 3.2579 +2024-12-30 13:00:35,064 - pyskl - INFO - Epoch [115][2300/3746] lr: 1.311e-02, eta: 1 day, 7:31:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6663, loss_cls: 3.3114, loss: 3.3114 +2024-12-30 13:02:00,289 - pyskl - INFO - Epoch [115][2400/3746] lr: 1.310e-02, eta: 1 day, 7:29:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6773, loss_cls: 3.2645, loss: 3.2645 +2024-12-30 13:03:25,329 - pyskl - INFO - Epoch [115][2500/3746] lr: 1.308e-02, eta: 1 day, 7:28:11, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6723, loss_cls: 3.2812, loss: 3.2812 +2024-12-30 13:04:50,421 - pyskl - INFO - Epoch [115][2600/3746] lr: 1.306e-02, eta: 1 day, 7:26:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4228, top5_acc: 0.6717, loss_cls: 3.2679, loss: 3.2679 +2024-12-30 13:06:15,968 - pyskl - INFO - Epoch [115][2700/3746] lr: 1.304e-02, eta: 1 day, 7:25:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4214, top5_acc: 0.6705, loss_cls: 3.2791, loss: 3.2791 +2024-12-30 13:07:41,339 - pyskl - INFO - Epoch [115][2800/3746] lr: 1.302e-02, eta: 1 day, 7:23:54, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4195, top5_acc: 0.6692, loss_cls: 3.2983, loss: 3.2983 +2024-12-30 13:09:06,839 - pyskl - INFO - Epoch [115][2900/3746] lr: 1.300e-02, eta: 1 day, 7:22:29, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4195, top5_acc: 0.6703, loss_cls: 3.3044, loss: 3.3044 +2024-12-30 13:10:31,929 - pyskl - INFO - Epoch [115][3000/3746] lr: 1.298e-02, eta: 1 day, 7:21:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4106, top5_acc: 0.6797, loss_cls: 3.2990, loss: 3.2990 +2024-12-30 13:11:57,191 - pyskl - INFO - Epoch [115][3100/3746] lr: 1.296e-02, eta: 1 day, 7:19:37, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4209, top5_acc: 0.6748, loss_cls: 3.2748, loss: 3.2748 +2024-12-30 13:13:22,608 - pyskl - INFO - Epoch [115][3200/3746] lr: 1.295e-02, eta: 1 day, 7:18:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4220, top5_acc: 0.6775, loss_cls: 3.2593, loss: 3.2593 +2024-12-30 13:14:47,873 - pyskl - INFO - Epoch [115][3300/3746] lr: 1.293e-02, eta: 1 day, 7:16:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6725, loss_cls: 3.2821, loss: 3.2821 +2024-12-30 13:16:12,890 - pyskl - INFO - Epoch [115][3400/3746] lr: 1.291e-02, eta: 1 day, 7:15:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4109, top5_acc: 0.6673, loss_cls: 3.3082, loss: 3.3082 +2024-12-30 13:17:38,007 - pyskl - INFO - Epoch [115][3500/3746] lr: 1.289e-02, eta: 1 day, 7:13:54, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6772, loss_cls: 3.2932, loss: 3.2932 +2024-12-30 13:19:02,991 - pyskl - INFO - Epoch [115][3600/3746] lr: 1.287e-02, eta: 1 day, 7:12:29, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6784, loss_cls: 3.2493, loss: 3.2493 +2024-12-30 13:20:28,066 - pyskl - INFO - Epoch [115][3700/3746] lr: 1.285e-02, eta: 1 day, 7:11:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6784, loss_cls: 3.2616, loss: 3.2616 +2024-12-30 13:21:08,945 - pyskl - INFO - Saving checkpoint at 115 epochs +2024-12-30 13:23:08,207 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 13:23:08,946 - pyskl - INFO - +top1_acc 0.3533 +top5_acc 0.6053 +2024-12-30 13:23:08,946 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 13:23:08,997 - pyskl - INFO - +mean_acc 0.3532 +2024-12-30 13:23:09,014 - pyskl - INFO - Epoch(val) [115][309] top1_acc: 0.3533, top5_acc: 0.6053, mean_class_accuracy: 0.3532 +2024-12-30 13:27:25,181 - pyskl - INFO - Epoch [116][100/3746] lr: 1.282e-02, eta: 1 day, 7:09:38, time: 2.562, data_time: 1.525, memory: 15990, top1_acc: 0.4448, top5_acc: 0.7006, loss_cls: 3.1068, loss: 3.1068 +2024-12-30 13:28:50,298 - pyskl - INFO - Epoch [116][200/3746] lr: 1.281e-02, eta: 1 day, 7:08:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6967, loss_cls: 3.1694, loss: 3.1694 +2024-12-30 13:30:15,994 - pyskl - INFO - Epoch [116][300/3746] lr: 1.279e-02, eta: 1 day, 7:06:46, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4356, top5_acc: 0.6914, loss_cls: 3.1822, loss: 3.1822 +2024-12-30 13:31:41,843 - pyskl - INFO - Epoch [116][400/3746] lr: 1.277e-02, eta: 1 day, 7:05:21, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6869, loss_cls: 3.2164, loss: 3.2164 +2024-12-30 13:33:07,683 - pyskl - INFO - Epoch [116][500/3746] lr: 1.275e-02, eta: 1 day, 7:03:55, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4295, top5_acc: 0.6814, loss_cls: 3.1983, loss: 3.1983 +2024-12-30 13:34:33,216 - pyskl - INFO - Epoch [116][600/3746] lr: 1.273e-02, eta: 1 day, 7:02:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4309, top5_acc: 0.6861, loss_cls: 3.2165, loss: 3.2165 +2024-12-30 13:35:58,735 - pyskl - INFO - Epoch [116][700/3746] lr: 1.271e-02, eta: 1 day, 7:01:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4277, top5_acc: 0.6939, loss_cls: 3.1718, loss: 3.1718 +2024-12-30 13:37:23,978 - pyskl - INFO - Epoch [116][800/3746] lr: 1.269e-02, eta: 1 day, 6:59:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4295, top5_acc: 0.6878, loss_cls: 3.2119, loss: 3.2119 +2024-12-30 13:38:49,528 - pyskl - INFO - Epoch [116][900/3746] lr: 1.268e-02, eta: 1 day, 6:58:13, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6773, loss_cls: 3.2666, loss: 3.2666 +2024-12-30 13:40:15,004 - pyskl - INFO - Epoch [116][1000/3746] lr: 1.266e-02, eta: 1 day, 6:56:47, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4148, top5_acc: 0.6803, loss_cls: 3.2724, loss: 3.2724 +2024-12-30 13:41:40,181 - pyskl - INFO - Epoch [116][1100/3746] lr: 1.264e-02, eta: 1 day, 6:55:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6769, loss_cls: 3.2322, loss: 3.2322 +2024-12-30 13:43:05,814 - pyskl - INFO - Epoch [116][1200/3746] lr: 1.262e-02, eta: 1 day, 6:53:56, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6863, loss_cls: 3.2261, loss: 3.2261 +2024-12-30 13:44:31,203 - pyskl - INFO - Epoch [116][1300/3746] lr: 1.260e-02, eta: 1 day, 6:52:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6834, loss_cls: 3.2465, loss: 3.2465 +2024-12-30 13:45:56,359 - pyskl - INFO - Epoch [116][1400/3746] lr: 1.258e-02, eta: 1 day, 6:51:04, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6781, loss_cls: 3.2582, loss: 3.2582 +2024-12-30 13:47:21,612 - pyskl - INFO - Epoch [116][1500/3746] lr: 1.256e-02, eta: 1 day, 6:49:38, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4369, top5_acc: 0.6919, loss_cls: 3.1987, loss: 3.1987 +2024-12-30 13:48:46,575 - pyskl - INFO - Epoch [116][1600/3746] lr: 1.255e-02, eta: 1 day, 6:48:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4188, top5_acc: 0.6816, loss_cls: 3.2626, loss: 3.2626 +2024-12-30 13:50:11,648 - pyskl - INFO - Epoch [116][1700/3746] lr: 1.253e-02, eta: 1 day, 6:46:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4133, top5_acc: 0.6800, loss_cls: 3.2815, loss: 3.2815 +2024-12-30 13:51:36,641 - pyskl - INFO - Epoch [116][1800/3746] lr: 1.251e-02, eta: 1 day, 6:45:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6763, loss_cls: 3.2638, loss: 3.2638 +2024-12-30 13:53:01,832 - pyskl - INFO - Epoch [116][1900/3746] lr: 1.249e-02, eta: 1 day, 6:43:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6814, loss_cls: 3.2387, loss: 3.2387 +2024-12-30 13:54:27,120 - pyskl - INFO - Epoch [116][2000/3746] lr: 1.247e-02, eta: 1 day, 6:42:29, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6770, loss_cls: 3.2463, loss: 3.2463 +2024-12-30 13:55:52,651 - pyskl - INFO - Epoch [116][2100/3746] lr: 1.245e-02, eta: 1 day, 6:41:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4258, top5_acc: 0.6898, loss_cls: 3.2069, loss: 3.2069 +2024-12-30 13:57:17,722 - pyskl - INFO - Epoch [116][2200/3746] lr: 1.243e-02, eta: 1 day, 6:39:38, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6808, loss_cls: 3.2491, loss: 3.2491 +2024-12-30 13:58:42,780 - pyskl - INFO - Epoch [116][2300/3746] lr: 1.242e-02, eta: 1 day, 6:38:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4189, top5_acc: 0.6747, loss_cls: 3.2808, loss: 3.2808 +2024-12-30 14:00:08,021 - pyskl - INFO - Epoch [116][2400/3746] lr: 1.240e-02, eta: 1 day, 6:36:46, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6733, loss_cls: 3.2815, loss: 3.2815 +2024-12-30 14:01:33,230 - pyskl - INFO - Epoch [116][2500/3746] lr: 1.238e-02, eta: 1 day, 6:35:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6850, loss_cls: 3.2468, loss: 3.2468 +2024-12-30 14:02:58,425 - pyskl - INFO - Epoch [116][2600/3746] lr: 1.236e-02, eta: 1 day, 6:33:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6813, loss_cls: 3.2382, loss: 3.2382 +2024-12-30 14:04:23,764 - pyskl - INFO - Epoch [116][2700/3746] lr: 1.234e-02, eta: 1 day, 6:32:29, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4198, top5_acc: 0.6759, loss_cls: 3.2906, loss: 3.2906 +2024-12-30 14:05:49,249 - pyskl - INFO - Epoch [116][2800/3746] lr: 1.232e-02, eta: 1 day, 6:31:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4230, top5_acc: 0.6753, loss_cls: 3.2646, loss: 3.2646 +2024-12-30 14:07:14,618 - pyskl - INFO - Epoch [116][2900/3746] lr: 1.231e-02, eta: 1 day, 6:29:38, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4202, top5_acc: 0.6864, loss_cls: 3.2132, loss: 3.2132 +2024-12-30 14:08:40,142 - pyskl - INFO - Epoch [116][3000/3746] lr: 1.229e-02, eta: 1 day, 6:28:12, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6725, loss_cls: 3.3075, loss: 3.3075 +2024-12-30 14:10:05,474 - pyskl - INFO - Epoch [116][3100/3746] lr: 1.227e-02, eta: 1 day, 6:26:47, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4156, top5_acc: 0.6798, loss_cls: 3.2842, loss: 3.2842 +2024-12-30 14:11:31,177 - pyskl - INFO - Epoch [116][3200/3746] lr: 1.225e-02, eta: 1 day, 6:25:21, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4156, top5_acc: 0.6709, loss_cls: 3.2619, loss: 3.2619 +2024-12-30 14:12:57,040 - pyskl - INFO - Epoch [116][3300/3746] lr: 1.223e-02, eta: 1 day, 6:23:55, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4317, top5_acc: 0.6777, loss_cls: 3.2344, loss: 3.2344 +2024-12-30 14:14:22,278 - pyskl - INFO - Epoch [116][3400/3746] lr: 1.221e-02, eta: 1 day, 6:22:30, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4130, top5_acc: 0.6678, loss_cls: 3.2994, loss: 3.2994 +2024-12-30 14:15:47,794 - pyskl - INFO - Epoch [116][3500/3746] lr: 1.220e-02, eta: 1 day, 6:21:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4311, top5_acc: 0.6891, loss_cls: 3.2114, loss: 3.2114 +2024-12-30 14:17:13,820 - pyskl - INFO - Epoch [116][3600/3746] lr: 1.218e-02, eta: 1 day, 6:19:39, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4219, top5_acc: 0.6713, loss_cls: 3.2578, loss: 3.2578 +2024-12-30 14:18:39,384 - pyskl - INFO - Epoch [116][3700/3746] lr: 1.216e-02, eta: 1 day, 6:18:13, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4289, top5_acc: 0.6789, loss_cls: 3.2525, loss: 3.2525 +2024-12-30 14:19:20,593 - pyskl - INFO - Saving checkpoint at 116 epochs +2024-12-30 14:21:21,550 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 14:21:22,255 - pyskl - INFO - +top1_acc 0.3584 +top5_acc 0.6114 +2024-12-30 14:21:22,255 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 14:21:22,312 - pyskl - INFO - +mean_acc 0.3581 +2024-12-30 14:21:22,319 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_114.pth was removed +2024-12-30 14:21:22,667 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_116.pth. +2024-12-30 14:21:22,668 - pyskl - INFO - Best top1_acc is 0.3584 at 116 epoch. +2024-12-30 14:21:22,691 - pyskl - INFO - Epoch(val) [116][309] top1_acc: 0.3584, top5_acc: 0.6114, mean_class_accuracy: 0.3581 +2024-12-30 14:25:46,224 - pyskl - INFO - Epoch [117][100/3746] lr: 1.213e-02, eta: 1 day, 6:16:48, time: 2.635, data_time: 1.598, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6916, loss_cls: 3.1497, loss: 3.1497 +2024-12-30 14:27:11,669 - pyskl - INFO - Epoch [117][200/3746] lr: 1.211e-02, eta: 1 day, 6:15:23, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6894, loss_cls: 3.1931, loss: 3.1931 +2024-12-30 14:28:37,283 - pyskl - INFO - Epoch [117][300/3746] lr: 1.210e-02, eta: 1 day, 6:13:57, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.6937, loss_cls: 3.1620, loss: 3.1620 +2024-12-30 14:30:02,778 - pyskl - INFO - Epoch [117][400/3746] lr: 1.208e-02, eta: 1 day, 6:12:31, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4402, top5_acc: 0.6994, loss_cls: 3.1446, loss: 3.1446 +2024-12-30 14:31:28,867 - pyskl - INFO - Epoch [117][500/3746] lr: 1.206e-02, eta: 1 day, 6:11:06, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.6913, loss_cls: 3.1696, loss: 3.1696 +2024-12-30 14:32:54,419 - pyskl - INFO - Epoch [117][600/3746] lr: 1.204e-02, eta: 1 day, 6:09:40, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4280, top5_acc: 0.6880, loss_cls: 3.1777, loss: 3.1777 +2024-12-30 14:34:19,577 - pyskl - INFO - Epoch [117][700/3746] lr: 1.202e-02, eta: 1 day, 6:08:14, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4350, top5_acc: 0.6931, loss_cls: 3.1885, loss: 3.1885 +2024-12-30 14:35:44,569 - pyskl - INFO - Epoch [117][800/3746] lr: 1.200e-02, eta: 1 day, 6:06:48, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4258, top5_acc: 0.6867, loss_cls: 3.2585, loss: 3.2585 +2024-12-30 14:37:09,634 - pyskl - INFO - Epoch [117][900/3746] lr: 1.199e-02, eta: 1 day, 6:05:23, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4247, top5_acc: 0.6866, loss_cls: 3.2122, loss: 3.2122 +2024-12-30 14:38:35,734 - pyskl - INFO - Epoch [117][1000/3746] lr: 1.197e-02, eta: 1 day, 6:03:57, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6853, loss_cls: 3.2444, loss: 3.2444 +2024-12-30 14:40:00,613 - pyskl - INFO - Epoch [117][1100/3746] lr: 1.195e-02, eta: 1 day, 6:02:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4205, top5_acc: 0.6870, loss_cls: 3.2252, loss: 3.2252 +2024-12-30 14:41:26,309 - pyskl - INFO - Epoch [117][1200/3746] lr: 1.193e-02, eta: 1 day, 6:01:06, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4398, top5_acc: 0.6937, loss_cls: 3.1747, loss: 3.1747 +2024-12-30 14:42:51,797 - pyskl - INFO - Epoch [117][1300/3746] lr: 1.191e-02, eta: 1 day, 5:59:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4291, top5_acc: 0.6934, loss_cls: 3.1899, loss: 3.1899 +2024-12-30 14:44:16,541 - pyskl - INFO - Epoch [117][1400/3746] lr: 1.190e-02, eta: 1 day, 5:58:14, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4328, top5_acc: 0.6856, loss_cls: 3.2053, loss: 3.2053 +2024-12-30 14:45:41,774 - pyskl - INFO - Epoch [117][1500/3746] lr: 1.188e-02, eta: 1 day, 5:56:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4297, top5_acc: 0.6934, loss_cls: 3.1675, loss: 3.1675 +2024-12-30 14:47:07,177 - pyskl - INFO - Epoch [117][1600/3746] lr: 1.186e-02, eta: 1 day, 5:55:22, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4347, top5_acc: 0.6945, loss_cls: 3.1728, loss: 3.1728 +2024-12-30 14:48:31,707 - pyskl - INFO - Epoch [117][1700/3746] lr: 1.184e-02, eta: 1 day, 5:53:56, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4245, top5_acc: 0.6820, loss_cls: 3.2279, loss: 3.2279 +2024-12-30 14:49:56,645 - pyskl - INFO - Epoch [117][1800/3746] lr: 1.182e-02, eta: 1 day, 5:52:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6877, loss_cls: 3.2110, loss: 3.2110 +2024-12-30 14:51:21,854 - pyskl - INFO - Epoch [117][1900/3746] lr: 1.181e-02, eta: 1 day, 5:51:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4322, top5_acc: 0.6850, loss_cls: 3.2014, loss: 3.2014 +2024-12-30 14:52:47,507 - pyskl - INFO - Epoch [117][2000/3746] lr: 1.179e-02, eta: 1 day, 5:49:39, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6775, loss_cls: 3.2341, loss: 3.2341 +2024-12-30 14:54:13,557 - pyskl - INFO - Epoch [117][2100/3746] lr: 1.177e-02, eta: 1 day, 5:48:14, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6872, loss_cls: 3.2440, loss: 3.2440 +2024-12-30 14:55:39,292 - pyskl - INFO - Epoch [117][2200/3746] lr: 1.175e-02, eta: 1 day, 5:46:48, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4222, top5_acc: 0.6733, loss_cls: 3.2589, loss: 3.2589 +2024-12-30 14:57:04,879 - pyskl - INFO - Epoch [117][2300/3746] lr: 1.173e-02, eta: 1 day, 5:45:22, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4248, top5_acc: 0.6839, loss_cls: 3.2088, loss: 3.2088 +2024-12-30 14:58:30,194 - pyskl - INFO - Epoch [117][2400/3746] lr: 1.172e-02, eta: 1 day, 5:43:57, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6733, loss_cls: 3.2653, loss: 3.2653 +2024-12-30 14:59:56,379 - pyskl - INFO - Epoch [117][2500/3746] lr: 1.170e-02, eta: 1 day, 5:42:31, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4252, top5_acc: 0.6811, loss_cls: 3.2478, loss: 3.2478 +2024-12-30 15:01:22,460 - pyskl - INFO - Epoch [117][2600/3746] lr: 1.168e-02, eta: 1 day, 5:41:06, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4259, top5_acc: 0.6841, loss_cls: 3.2324, loss: 3.2324 +2024-12-30 15:02:48,404 - pyskl - INFO - Epoch [117][2700/3746] lr: 1.166e-02, eta: 1 day, 5:39:40, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.4327, top5_acc: 0.6869, loss_cls: 3.1772, loss: 3.1772 +2024-12-30 15:04:14,505 - pyskl - INFO - Epoch [117][2800/3746] lr: 1.164e-02, eta: 1 day, 5:38:14, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6817, loss_cls: 3.2554, loss: 3.2554 +2024-12-30 15:05:40,089 - pyskl - INFO - Epoch [117][2900/3746] lr: 1.163e-02, eta: 1 day, 5:36:49, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4252, top5_acc: 0.6814, loss_cls: 3.2200, loss: 3.2200 +2024-12-30 15:07:05,689 - pyskl - INFO - Epoch [117][3000/3746] lr: 1.161e-02, eta: 1 day, 5:35:23, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4269, top5_acc: 0.6800, loss_cls: 3.2092, loss: 3.2092 +2024-12-30 15:08:31,305 - pyskl - INFO - Epoch [117][3100/3746] lr: 1.159e-02, eta: 1 day, 5:33:57, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6783, loss_cls: 3.2640, loss: 3.2640 +2024-12-30 15:09:56,964 - pyskl - INFO - Epoch [117][3200/3746] lr: 1.157e-02, eta: 1 day, 5:32:32, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4308, top5_acc: 0.6841, loss_cls: 3.2138, loss: 3.2138 +2024-12-30 15:11:23,194 - pyskl - INFO - Epoch [117][3300/3746] lr: 1.155e-02, eta: 1 day, 5:31:06, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4323, top5_acc: 0.6773, loss_cls: 3.2438, loss: 3.2438 +2024-12-30 15:12:48,338 - pyskl - INFO - Epoch [117][3400/3746] lr: 1.154e-02, eta: 1 day, 5:29:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4161, top5_acc: 0.6816, loss_cls: 3.2387, loss: 3.2387 +2024-12-30 15:14:13,743 - pyskl - INFO - Epoch [117][3500/3746] lr: 1.152e-02, eta: 1 day, 5:28:15, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4241, top5_acc: 0.6772, loss_cls: 3.2423, loss: 3.2423 +2024-12-30 15:15:39,466 - pyskl - INFO - Epoch [117][3600/3746] lr: 1.150e-02, eta: 1 day, 5:26:49, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4298, top5_acc: 0.6869, loss_cls: 3.2257, loss: 3.2257 +2024-12-30 15:17:05,461 - pyskl - INFO - Epoch [117][3700/3746] lr: 1.148e-02, eta: 1 day, 5:25:24, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4283, top5_acc: 0.6792, loss_cls: 3.2472, loss: 3.2472 +2024-12-30 15:17:47,362 - pyskl - INFO - Saving checkpoint at 117 epochs +2024-12-30 15:19:48,637 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 15:19:49,358 - pyskl - INFO - +top1_acc 0.3646 +top5_acc 0.6149 +2024-12-30 15:19:49,359 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 15:19:49,404 - pyskl - INFO - +mean_acc 0.3644 +2024-12-30 15:19:49,408 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_116.pth was removed +2024-12-30 15:19:49,700 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_117.pth. +2024-12-30 15:19:49,701 - pyskl - INFO - Best top1_acc is 0.3646 at 117 epoch. +2024-12-30 15:19:49,718 - pyskl - INFO - Epoch(val) [117][309] top1_acc: 0.3646, top5_acc: 0.6149, mean_class_accuracy: 0.3644 +2024-12-30 15:24:09,546 - pyskl - INFO - Epoch [118][100/3746] lr: 1.146e-02, eta: 1 day, 5:23:56, time: 2.598, data_time: 1.553, memory: 15990, top1_acc: 0.4431, top5_acc: 0.7045, loss_cls: 3.0984, loss: 3.0984 +2024-12-30 15:25:35,138 - pyskl - INFO - Epoch [118][200/3746] lr: 1.144e-02, eta: 1 day, 5:22:31, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4372, top5_acc: 0.7020, loss_cls: 3.1551, loss: 3.1551 +2024-12-30 15:27:00,534 - pyskl - INFO - Epoch [118][300/3746] lr: 1.142e-02, eta: 1 day, 5:21:05, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4489, top5_acc: 0.6983, loss_cls: 3.1243, loss: 3.1243 +2024-12-30 15:28:25,744 - pyskl - INFO - Epoch [118][400/3746] lr: 1.140e-02, eta: 1 day, 5:19:39, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4309, top5_acc: 0.6991, loss_cls: 3.1665, loss: 3.1665 +2024-12-30 15:29:51,223 - pyskl - INFO - Epoch [118][500/3746] lr: 1.139e-02, eta: 1 day, 5:18:13, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4330, top5_acc: 0.6945, loss_cls: 3.1863, loss: 3.1863 +2024-12-30 15:31:16,949 - pyskl - INFO - Epoch [118][600/3746] lr: 1.137e-02, eta: 1 day, 5:16:48, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4359, top5_acc: 0.6997, loss_cls: 3.1474, loss: 3.1474 +2024-12-30 15:32:42,662 - pyskl - INFO - Epoch [118][700/3746] lr: 1.135e-02, eta: 1 day, 5:15:22, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4356, top5_acc: 0.6998, loss_cls: 3.1704, loss: 3.1704 +2024-12-30 15:34:08,274 - pyskl - INFO - Epoch [118][800/3746] lr: 1.133e-02, eta: 1 day, 5:13:56, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4381, top5_acc: 0.6948, loss_cls: 3.1514, loss: 3.1514 +2024-12-30 15:35:33,574 - pyskl - INFO - Epoch [118][900/3746] lr: 1.131e-02, eta: 1 day, 5:12:31, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4306, top5_acc: 0.6942, loss_cls: 3.1735, loss: 3.1735 +2024-12-30 15:36:59,294 - pyskl - INFO - Epoch [118][1000/3746] lr: 1.130e-02, eta: 1 day, 5:11:05, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4347, top5_acc: 0.6897, loss_cls: 3.1855, loss: 3.1855 +2024-12-30 15:38:25,178 - pyskl - INFO - Epoch [118][1100/3746] lr: 1.128e-02, eta: 1 day, 5:09:39, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4394, top5_acc: 0.6945, loss_cls: 3.1700, loss: 3.1700 +2024-12-30 15:39:51,189 - pyskl - INFO - Epoch [118][1200/3746] lr: 1.126e-02, eta: 1 day, 5:08:14, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4334, top5_acc: 0.6928, loss_cls: 3.1666, loss: 3.1666 +2024-12-30 15:41:16,339 - pyskl - INFO - Epoch [118][1300/3746] lr: 1.124e-02, eta: 1 day, 5:06:48, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4355, top5_acc: 0.6895, loss_cls: 3.1897, loss: 3.1897 +2024-12-30 15:42:41,520 - pyskl - INFO - Epoch [118][1400/3746] lr: 1.123e-02, eta: 1 day, 5:05:22, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4369, top5_acc: 0.6934, loss_cls: 3.1708, loss: 3.1708 +2024-12-30 15:44:07,201 - pyskl - INFO - Epoch [118][1500/3746] lr: 1.121e-02, eta: 1 day, 5:03:56, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4408, top5_acc: 0.6950, loss_cls: 3.1283, loss: 3.1283 +2024-12-30 15:45:32,825 - pyskl - INFO - Epoch [118][1600/3746] lr: 1.119e-02, eta: 1 day, 5:02:31, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4358, top5_acc: 0.6945, loss_cls: 3.1788, loss: 3.1788 +2024-12-30 15:46:58,033 - pyskl - INFO - Epoch [118][1700/3746] lr: 1.117e-02, eta: 1 day, 5:01:05, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4317, top5_acc: 0.6970, loss_cls: 3.1701, loss: 3.1701 +2024-12-30 15:48:23,083 - pyskl - INFO - Epoch [118][1800/3746] lr: 1.116e-02, eta: 1 day, 4:59:39, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6828, loss_cls: 3.2224, loss: 3.2224 +2024-12-30 15:49:48,481 - pyskl - INFO - Epoch [118][1900/3746] lr: 1.114e-02, eta: 1 day, 4:58:13, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4283, top5_acc: 0.6800, loss_cls: 3.2149, loss: 3.2149 +2024-12-30 15:51:13,562 - pyskl - INFO - Epoch [118][2000/3746] lr: 1.112e-02, eta: 1 day, 4:56:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6914, loss_cls: 3.1869, loss: 3.1869 +2024-12-30 15:52:38,741 - pyskl - INFO - Epoch [118][2100/3746] lr: 1.110e-02, eta: 1 day, 4:55:22, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4295, top5_acc: 0.6836, loss_cls: 3.2042, loss: 3.2042 +2024-12-30 15:54:04,064 - pyskl - INFO - Epoch [118][2200/3746] lr: 1.109e-02, eta: 1 day, 4:53:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4381, top5_acc: 0.6947, loss_cls: 3.1684, loss: 3.1684 +2024-12-30 15:55:29,442 - pyskl - INFO - Epoch [118][2300/3746] lr: 1.107e-02, eta: 1 day, 4:52:30, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4470, top5_acc: 0.6980, loss_cls: 3.1350, loss: 3.1350 +2024-12-30 15:56:54,418 - pyskl - INFO - Epoch [118][2400/3746] lr: 1.105e-02, eta: 1 day, 4:51:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6813, loss_cls: 3.2086, loss: 3.2086 +2024-12-30 15:58:19,889 - pyskl - INFO - Epoch [118][2500/3746] lr: 1.103e-02, eta: 1 day, 4:49:38, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4266, top5_acc: 0.6842, loss_cls: 3.2077, loss: 3.2077 +2024-12-30 15:59:45,341 - pyskl - INFO - Epoch [118][2600/3746] lr: 1.102e-02, eta: 1 day, 4:48:13, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4286, top5_acc: 0.6809, loss_cls: 3.2141, loss: 3.2141 +2024-12-30 16:01:10,716 - pyskl - INFO - Epoch [118][2700/3746] lr: 1.100e-02, eta: 1 day, 4:46:47, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4370, top5_acc: 0.6870, loss_cls: 3.2022, loss: 3.2022 +2024-12-30 16:02:35,913 - pyskl - INFO - Epoch [118][2800/3746] lr: 1.098e-02, eta: 1 day, 4:45:21, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4278, top5_acc: 0.6884, loss_cls: 3.2113, loss: 3.2113 +2024-12-30 16:04:01,107 - pyskl - INFO - Epoch [118][2900/3746] lr: 1.096e-02, eta: 1 day, 4:43:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4327, top5_acc: 0.6864, loss_cls: 3.1961, loss: 3.1961 +2024-12-30 16:05:26,866 - pyskl - INFO - Epoch [118][3000/3746] lr: 1.095e-02, eta: 1 day, 4:42:30, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.6909, loss_cls: 3.1765, loss: 3.1765 +2024-12-30 16:06:52,255 - pyskl - INFO - Epoch [118][3100/3746] lr: 1.093e-02, eta: 1 day, 4:41:04, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4327, top5_acc: 0.6864, loss_cls: 3.2143, loss: 3.2143 +2024-12-30 16:08:17,157 - pyskl - INFO - Epoch [118][3200/3746] lr: 1.091e-02, eta: 1 day, 4:39:38, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4334, top5_acc: 0.6831, loss_cls: 3.2136, loss: 3.2136 +2024-12-30 16:09:42,141 - pyskl - INFO - Epoch [118][3300/3746] lr: 1.089e-02, eta: 1 day, 4:38:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4339, top5_acc: 0.6784, loss_cls: 3.2389, loss: 3.2389 +2024-12-30 16:11:07,567 - pyskl - INFO - Epoch [118][3400/3746] lr: 1.088e-02, eta: 1 day, 4:36:46, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6816, loss_cls: 3.2302, loss: 3.2302 +2024-12-30 16:12:33,660 - pyskl - INFO - Epoch [118][3500/3746] lr: 1.086e-02, eta: 1 day, 4:35:21, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6795, loss_cls: 3.2310, loss: 3.2310 +2024-12-30 16:13:59,305 - pyskl - INFO - Epoch [118][3600/3746] lr: 1.084e-02, eta: 1 day, 4:33:55, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6817, loss_cls: 3.2585, loss: 3.2585 +2024-12-30 16:15:25,153 - pyskl - INFO - Epoch [118][3700/3746] lr: 1.082e-02, eta: 1 day, 4:32:29, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6800, loss_cls: 3.2566, loss: 3.2566 +2024-12-30 16:16:06,374 - pyskl - INFO - Saving checkpoint at 118 epochs +2024-12-30 16:18:05,742 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 16:18:06,423 - pyskl - INFO - +top1_acc 0.3692 +top5_acc 0.6242 +2024-12-30 16:18:06,423 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 16:18:06,471 - pyskl - INFO - +mean_acc 0.3689 +2024-12-30 16:18:06,475 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_117.pth was removed +2024-12-30 16:18:06,735 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2024-12-30 16:18:06,735 - pyskl - INFO - Best top1_acc is 0.3692 at 118 epoch. +2024-12-30 16:18:06,752 - pyskl - INFO - Epoch(val) [118][309] top1_acc: 0.3692, top5_acc: 0.6242, mean_class_accuracy: 0.3689 +2024-12-30 16:22:22,904 - pyskl - INFO - Epoch [119][100/3746] lr: 1.080e-02, eta: 1 day, 4:31:00, time: 2.561, data_time: 1.532, memory: 15990, top1_acc: 0.4469, top5_acc: 0.7005, loss_cls: 3.0950, loss: 3.0950 +2024-12-30 16:23:48,110 - pyskl - INFO - Epoch [119][200/3746] lr: 1.078e-02, eta: 1 day, 4:29:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4363, top5_acc: 0.6953, loss_cls: 3.1317, loss: 3.1317 +2024-12-30 16:25:13,597 - pyskl - INFO - Epoch [119][300/3746] lr: 1.076e-02, eta: 1 day, 4:28:08, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4384, top5_acc: 0.7036, loss_cls: 3.1135, loss: 3.1135 +2024-12-30 16:26:39,161 - pyskl - INFO - Epoch [119][400/3746] lr: 1.075e-02, eta: 1 day, 4:26:42, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.6984, loss_cls: 3.1560, loss: 3.1560 +2024-12-30 16:28:04,722 - pyskl - INFO - Epoch [119][500/3746] lr: 1.073e-02, eta: 1 day, 4:25:17, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4450, top5_acc: 0.7123, loss_cls: 3.0950, loss: 3.0950 +2024-12-30 16:29:30,314 - pyskl - INFO - Epoch [119][600/3746] lr: 1.071e-02, eta: 1 day, 4:23:51, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4441, top5_acc: 0.7013, loss_cls: 3.1093, loss: 3.1093 +2024-12-30 16:30:55,861 - pyskl - INFO - Epoch [119][700/3746] lr: 1.069e-02, eta: 1 day, 4:22:25, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4355, top5_acc: 0.6981, loss_cls: 3.1436, loss: 3.1436 +2024-12-30 16:32:21,591 - pyskl - INFO - Epoch [119][800/3746] lr: 1.068e-02, eta: 1 day, 4:20:59, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4350, top5_acc: 0.7022, loss_cls: 3.1466, loss: 3.1466 +2024-12-30 16:33:46,921 - pyskl - INFO - Epoch [119][900/3746] lr: 1.066e-02, eta: 1 day, 4:19:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.7064, loss_cls: 3.1056, loss: 3.1056 +2024-12-30 16:35:12,265 - pyskl - INFO - Epoch [119][1000/3746] lr: 1.064e-02, eta: 1 day, 4:18:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4416, top5_acc: 0.6953, loss_cls: 3.1633, loss: 3.1633 +2024-12-30 16:36:37,775 - pyskl - INFO - Epoch [119][1100/3746] lr: 1.063e-02, eta: 1 day, 4:16:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6934, loss_cls: 3.1727, loss: 3.1727 +2024-12-30 16:38:03,375 - pyskl - INFO - Epoch [119][1200/3746] lr: 1.061e-02, eta: 1 day, 4:15:16, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4417, top5_acc: 0.6986, loss_cls: 3.1227, loss: 3.1227 +2024-12-30 16:39:29,031 - pyskl - INFO - Epoch [119][1300/3746] lr: 1.059e-02, eta: 1 day, 4:13:51, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4384, top5_acc: 0.7023, loss_cls: 3.1372, loss: 3.1372 +2024-12-30 16:40:54,282 - pyskl - INFO - Epoch [119][1400/3746] lr: 1.057e-02, eta: 1 day, 4:12:25, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6891, loss_cls: 3.2219, loss: 3.2219 +2024-12-30 16:42:19,087 - pyskl - INFO - Epoch [119][1500/3746] lr: 1.056e-02, eta: 1 day, 4:10:59, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6884, loss_cls: 3.1736, loss: 3.1736 +2024-12-30 16:43:44,509 - pyskl - INFO - Epoch [119][1600/3746] lr: 1.054e-02, eta: 1 day, 4:09:33, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4259, top5_acc: 0.6864, loss_cls: 3.1958, loss: 3.1958 +2024-12-30 16:45:09,841 - pyskl - INFO - Epoch [119][1700/3746] lr: 1.052e-02, eta: 1 day, 4:08:07, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6858, loss_cls: 3.2289, loss: 3.2289 +2024-12-30 16:46:35,203 - pyskl - INFO - Epoch [119][1800/3746] lr: 1.050e-02, eta: 1 day, 4:06:41, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.6937, loss_cls: 3.1303, loss: 3.1303 +2024-12-30 16:48:00,533 - pyskl - INFO - Epoch [119][1900/3746] lr: 1.049e-02, eta: 1 day, 4:05:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4323, top5_acc: 0.6858, loss_cls: 3.2180, loss: 3.2180 +2024-12-30 16:49:25,756 - pyskl - INFO - Epoch [119][2000/3746] lr: 1.047e-02, eta: 1 day, 4:03:50, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6936, loss_cls: 3.1579, loss: 3.1579 +2024-12-30 16:50:50,822 - pyskl - INFO - Epoch [119][2100/3746] lr: 1.045e-02, eta: 1 day, 4:02:24, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4373, top5_acc: 0.6903, loss_cls: 3.1729, loss: 3.1729 +2024-12-30 16:52:15,958 - pyskl - INFO - Epoch [119][2200/3746] lr: 1.044e-02, eta: 1 day, 4:00:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4480, top5_acc: 0.6983, loss_cls: 3.1277, loss: 3.1277 +2024-12-30 16:53:41,206 - pyskl - INFO - Epoch [119][2300/3746] lr: 1.042e-02, eta: 1 day, 3:59:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4470, top5_acc: 0.7027, loss_cls: 3.1355, loss: 3.1355 +2024-12-30 16:55:06,187 - pyskl - INFO - Epoch [119][2400/3746] lr: 1.040e-02, eta: 1 day, 3:58:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4372, top5_acc: 0.6989, loss_cls: 3.1511, loss: 3.1511 +2024-12-30 16:56:31,607 - pyskl - INFO - Epoch [119][2500/3746] lr: 1.039e-02, eta: 1 day, 3:56:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6816, loss_cls: 3.2378, loss: 3.2378 +2024-12-30 16:57:57,343 - pyskl - INFO - Epoch [119][2600/3746] lr: 1.037e-02, eta: 1 day, 3:55:15, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4366, top5_acc: 0.6950, loss_cls: 3.1682, loss: 3.1682 +2024-12-30 16:59:22,759 - pyskl - INFO - Epoch [119][2700/3746] lr: 1.035e-02, eta: 1 day, 3:53:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6955, loss_cls: 3.1743, loss: 3.1743 +2024-12-30 17:00:48,025 - pyskl - INFO - Epoch [119][2800/3746] lr: 1.033e-02, eta: 1 day, 3:52:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4414, top5_acc: 0.6906, loss_cls: 3.1869, loss: 3.1869 +2024-12-30 17:02:13,138 - pyskl - INFO - Epoch [119][2900/3746] lr: 1.032e-02, eta: 1 day, 3:50:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4273, top5_acc: 0.6914, loss_cls: 3.2438, loss: 3.2438 +2024-12-30 17:03:38,220 - pyskl - INFO - Epoch [119][3000/3746] lr: 1.030e-02, eta: 1 day, 3:49:31, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4425, top5_acc: 0.6883, loss_cls: 3.1813, loss: 3.1813 +2024-12-30 17:05:03,872 - pyskl - INFO - Epoch [119][3100/3746] lr: 1.028e-02, eta: 1 day, 3:48:06, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4436, top5_acc: 0.6967, loss_cls: 3.1295, loss: 3.1295 +2024-12-30 17:06:29,262 - pyskl - INFO - Epoch [119][3200/3746] lr: 1.027e-02, eta: 1 day, 3:46:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6897, loss_cls: 3.2121, loss: 3.2121 +2024-12-30 17:07:54,330 - pyskl - INFO - Epoch [119][3300/3746] lr: 1.025e-02, eta: 1 day, 3:45:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.6819, loss_cls: 3.2122, loss: 3.2122 +2024-12-30 17:09:19,547 - pyskl - INFO - Epoch [119][3400/3746] lr: 1.023e-02, eta: 1 day, 3:43:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4398, top5_acc: 0.6942, loss_cls: 3.1216, loss: 3.1216 +2024-12-30 17:10:44,602 - pyskl - INFO - Epoch [119][3500/3746] lr: 1.022e-02, eta: 1 day, 3:42:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4202, top5_acc: 0.6772, loss_cls: 3.2620, loss: 3.2620 +2024-12-30 17:12:10,244 - pyskl - INFO - Epoch [119][3600/3746] lr: 1.020e-02, eta: 1 day, 3:40:56, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4338, top5_acc: 0.6853, loss_cls: 3.2271, loss: 3.2271 +2024-12-30 17:13:35,681 - pyskl - INFO - Epoch [119][3700/3746] lr: 1.018e-02, eta: 1 day, 3:39:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4453, top5_acc: 0.6972, loss_cls: 3.1378, loss: 3.1378 +2024-12-30 17:14:16,929 - pyskl - INFO - Saving checkpoint at 119 epochs +2024-12-30 17:16:14,948 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 17:16:15,651 - pyskl - INFO - +top1_acc 0.3697 +top5_acc 0.6236 +2024-12-30 17:16:15,651 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 17:16:15,698 - pyskl - INFO - +mean_acc 0.3694 +2024-12-30 17:16:15,703 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_118.pth was removed +2024-12-30 17:16:15,986 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_119.pth. +2024-12-30 17:16:15,987 - pyskl - INFO - Best top1_acc is 0.3697 at 119 epoch. +2024-12-30 17:16:16,004 - pyskl - INFO - Epoch(val) [119][309] top1_acc: 0.3697, top5_acc: 0.6236, mean_class_accuracy: 0.3694 +2024-12-30 17:20:32,328 - pyskl - INFO - Epoch [120][100/3746] lr: 1.016e-02, eta: 1 day, 3:38:00, time: 2.563, data_time: 1.533, memory: 15990, top1_acc: 0.4398, top5_acc: 0.7059, loss_cls: 3.1153, loss: 3.1153 +2024-12-30 17:21:58,122 - pyskl - INFO - Epoch [120][200/3746] lr: 1.014e-02, eta: 1 day, 3:36:34, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.4620, top5_acc: 0.7109, loss_cls: 3.0660, loss: 3.0660 +2024-12-30 17:23:23,757 - pyskl - INFO - Epoch [120][300/3746] lr: 1.012e-02, eta: 1 day, 3:35:08, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4489, top5_acc: 0.7078, loss_cls: 3.0857, loss: 3.0857 +2024-12-30 17:24:49,206 - pyskl - INFO - Epoch [120][400/3746] lr: 1.011e-02, eta: 1 day, 3:33:42, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4495, top5_acc: 0.7047, loss_cls: 3.1013, loss: 3.1013 +2024-12-30 17:26:14,714 - pyskl - INFO - Epoch [120][500/3746] lr: 1.009e-02, eta: 1 day, 3:32:17, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7089, loss_cls: 3.0779, loss: 3.0779 +2024-12-30 17:27:39,833 - pyskl - INFO - Epoch [120][600/3746] lr: 1.007e-02, eta: 1 day, 3:30:51, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4583, top5_acc: 0.7022, loss_cls: 3.0829, loss: 3.0829 +2024-12-30 17:29:05,011 - pyskl - INFO - Epoch [120][700/3746] lr: 1.006e-02, eta: 1 day, 3:29:25, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4486, top5_acc: 0.6987, loss_cls: 3.1031, loss: 3.1031 +2024-12-30 17:30:31,086 - pyskl - INFO - Epoch [120][800/3746] lr: 1.004e-02, eta: 1 day, 3:27:59, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4439, top5_acc: 0.7033, loss_cls: 3.1224, loss: 3.1224 +2024-12-30 17:31:57,055 - pyskl - INFO - Epoch [120][900/3746] lr: 1.002e-02, eta: 1 day, 3:26:33, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4319, top5_acc: 0.6898, loss_cls: 3.1521, loss: 3.1521 +2024-12-30 17:33:22,873 - pyskl - INFO - Epoch [120][1000/3746] lr: 1.001e-02, eta: 1 day, 3:25:08, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4441, top5_acc: 0.7005, loss_cls: 3.1151, loss: 3.1151 +2024-12-30 17:34:48,667 - pyskl - INFO - Epoch [120][1100/3746] lr: 9.989e-03, eta: 1 day, 3:23:42, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4417, top5_acc: 0.7041, loss_cls: 3.1370, loss: 3.1370 +2024-12-30 17:36:14,428 - pyskl - INFO - Epoch [120][1200/3746] lr: 9.972e-03, eta: 1 day, 3:22:16, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.6958, loss_cls: 3.1709, loss: 3.1709 +2024-12-30 17:37:40,064 - pyskl - INFO - Epoch [120][1300/3746] lr: 9.955e-03, eta: 1 day, 3:20:51, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.7008, loss_cls: 3.1396, loss: 3.1396 +2024-12-30 17:39:05,882 - pyskl - INFO - Epoch [120][1400/3746] lr: 9.938e-03, eta: 1 day, 3:19:25, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4445, top5_acc: 0.7127, loss_cls: 3.0925, loss: 3.0925 +2024-12-30 17:40:31,222 - pyskl - INFO - Epoch [120][1500/3746] lr: 9.922e-03, eta: 1 day, 3:17:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4412, top5_acc: 0.6933, loss_cls: 3.1572, loss: 3.1572 +2024-12-30 17:41:57,405 - pyskl - INFO - Epoch [120][1600/3746] lr: 9.905e-03, eta: 1 day, 3:16:33, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4309, top5_acc: 0.7003, loss_cls: 3.1405, loss: 3.1405 +2024-12-30 17:43:22,729 - pyskl - INFO - Epoch [120][1700/3746] lr: 9.888e-03, eta: 1 day, 3:15:07, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4373, top5_acc: 0.7053, loss_cls: 3.1150, loss: 3.1150 +2024-12-30 17:44:48,264 - pyskl - INFO - Epoch [120][1800/3746] lr: 9.871e-03, eta: 1 day, 3:13:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.7030, loss_cls: 3.1259, loss: 3.1259 +2024-12-30 17:46:13,550 - pyskl - INFO - Epoch [120][1900/3746] lr: 9.855e-03, eta: 1 day, 3:12:16, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4345, top5_acc: 0.6936, loss_cls: 3.1704, loss: 3.1704 +2024-12-30 17:47:39,045 - pyskl - INFO - Epoch [120][2000/3746] lr: 9.838e-03, eta: 1 day, 3:10:50, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4386, top5_acc: 0.6884, loss_cls: 3.1829, loss: 3.1829 +2024-12-30 17:49:04,648 - pyskl - INFO - Epoch [120][2100/3746] lr: 9.821e-03, eta: 1 day, 3:09:24, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4434, top5_acc: 0.7075, loss_cls: 3.1080, loss: 3.1080 +2024-12-30 17:50:29,928 - pyskl - INFO - Epoch [120][2200/3746] lr: 9.805e-03, eta: 1 day, 3:07:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4280, top5_acc: 0.6848, loss_cls: 3.2245, loss: 3.2245 +2024-12-30 17:51:55,588 - pyskl - INFO - Epoch [120][2300/3746] lr: 9.788e-03, eta: 1 day, 3:06:33, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4355, top5_acc: 0.6941, loss_cls: 3.1890, loss: 3.1890 +2024-12-30 17:53:21,048 - pyskl - INFO - Epoch [120][2400/3746] lr: 9.772e-03, eta: 1 day, 3:05:07, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4461, top5_acc: 0.7009, loss_cls: 3.1028, loss: 3.1028 +2024-12-30 17:54:46,593 - pyskl - INFO - Epoch [120][2500/3746] lr: 9.755e-03, eta: 1 day, 3:03:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4313, top5_acc: 0.6969, loss_cls: 3.1340, loss: 3.1340 +2024-12-30 17:56:12,623 - pyskl - INFO - Epoch [120][2600/3746] lr: 9.738e-03, eta: 1 day, 3:02:15, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4398, top5_acc: 0.6777, loss_cls: 3.2124, loss: 3.2124 +2024-12-30 17:57:38,684 - pyskl - INFO - Epoch [120][2700/3746] lr: 9.722e-03, eta: 1 day, 3:00:50, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4503, top5_acc: 0.7055, loss_cls: 3.0930, loss: 3.0930 +2024-12-30 17:59:04,719 - pyskl - INFO - Epoch [120][2800/3746] lr: 9.705e-03, eta: 1 day, 2:59:24, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4456, top5_acc: 0.6950, loss_cls: 3.1436, loss: 3.1436 +2024-12-30 18:00:30,885 - pyskl - INFO - Epoch [120][2900/3746] lr: 9.689e-03, eta: 1 day, 2:57:58, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.6945, loss_cls: 3.1303, loss: 3.1303 +2024-12-30 18:01:57,307 - pyskl - INFO - Epoch [120][3000/3746] lr: 9.672e-03, eta: 1 day, 2:56:33, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.4256, top5_acc: 0.6948, loss_cls: 3.1608, loss: 3.1608 +2024-12-30 18:03:23,501 - pyskl - INFO - Epoch [120][3100/3746] lr: 9.656e-03, eta: 1 day, 2:55:07, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4366, top5_acc: 0.6948, loss_cls: 3.1317, loss: 3.1317 +2024-12-30 18:04:48,974 - pyskl - INFO - Epoch [120][3200/3746] lr: 9.639e-03, eta: 1 day, 2:53:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4459, top5_acc: 0.6994, loss_cls: 3.1351, loss: 3.1351 +2024-12-30 18:06:14,529 - pyskl - INFO - Epoch [120][3300/3746] lr: 9.623e-03, eta: 1 day, 2:52:16, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6878, loss_cls: 3.1875, loss: 3.1875 +2024-12-30 18:07:40,405 - pyskl - INFO - Epoch [120][3400/3746] lr: 9.606e-03, eta: 1 day, 2:50:50, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4305, top5_acc: 0.6845, loss_cls: 3.2040, loss: 3.2040 +2024-12-30 18:09:06,348 - pyskl - INFO - Epoch [120][3500/3746] lr: 9.590e-03, eta: 1 day, 2:49:24, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4303, top5_acc: 0.6870, loss_cls: 3.1957, loss: 3.1957 +2024-12-30 18:10:32,576 - pyskl - INFO - Epoch [120][3600/3746] lr: 9.573e-03, eta: 1 day, 2:47:59, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.6952, loss_cls: 3.1450, loss: 3.1450 +2024-12-30 18:11:58,437 - pyskl - INFO - Epoch [120][3700/3746] lr: 9.557e-03, eta: 1 day, 2:46:33, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4412, top5_acc: 0.7002, loss_cls: 3.1025, loss: 3.1025 +2024-12-30 18:12:39,425 - pyskl - INFO - Saving checkpoint at 120 epochs +2024-12-30 18:14:39,745 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 18:14:40,576 - pyskl - INFO - +top1_acc 0.3763 +top5_acc 0.6302 +2024-12-30 18:14:40,577 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 18:14:40,630 - pyskl - INFO - +mean_acc 0.3762 +2024-12-30 18:14:40,635 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_119.pth was removed +2024-12-30 18:14:40,914 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_120.pth. +2024-12-30 18:14:40,914 - pyskl - INFO - Best top1_acc is 0.3763 at 120 epoch. +2024-12-30 18:14:40,931 - pyskl - INFO - Epoch(val) [120][309] top1_acc: 0.3763, top5_acc: 0.6302, mean_class_accuracy: 0.3762 +2024-12-30 18:19:01,120 - pyskl - INFO - Epoch [121][100/3746] lr: 9.533e-03, eta: 1 day, 2:45:01, time: 2.602, data_time: 1.575, memory: 15990, top1_acc: 0.4594, top5_acc: 0.7122, loss_cls: 3.0180, loss: 3.0180 +2024-12-30 18:20:26,075 - pyskl - INFO - Epoch [121][200/3746] lr: 9.516e-03, eta: 1 day, 2:43:35, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.4534, top5_acc: 0.7020, loss_cls: 3.0623, loss: 3.0623 +2024-12-30 18:21:51,597 - pyskl - INFO - Epoch [121][300/3746] lr: 9.500e-03, eta: 1 day, 2:42:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4564, top5_acc: 0.7087, loss_cls: 3.0620, loss: 3.0620 +2024-12-30 18:23:17,495 - pyskl - INFO - Epoch [121][400/3746] lr: 9.484e-03, eta: 1 day, 2:40:44, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4400, top5_acc: 0.7050, loss_cls: 3.1265, loss: 3.1265 +2024-12-30 18:24:43,398 - pyskl - INFO - Epoch [121][500/3746] lr: 9.467e-03, eta: 1 day, 2:39:18, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4539, top5_acc: 0.7108, loss_cls: 3.0796, loss: 3.0796 +2024-12-30 18:26:08,713 - pyskl - INFO - Epoch [121][600/3746] lr: 9.451e-03, eta: 1 day, 2:37:52, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4622, top5_acc: 0.7077, loss_cls: 3.0472, loss: 3.0472 +2024-12-30 18:27:34,804 - pyskl - INFO - Epoch [121][700/3746] lr: 9.435e-03, eta: 1 day, 2:36:27, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.7019, loss_cls: 3.1152, loss: 3.1152 +2024-12-30 18:29:00,367 - pyskl - INFO - Epoch [121][800/3746] lr: 9.418e-03, eta: 1 day, 2:35:01, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.7097, loss_cls: 3.0691, loss: 3.0691 +2024-12-30 18:30:26,185 - pyskl - INFO - Epoch [121][900/3746] lr: 9.402e-03, eta: 1 day, 2:33:35, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4383, top5_acc: 0.7013, loss_cls: 3.1204, loss: 3.1204 +2024-12-30 18:31:51,778 - pyskl - INFO - Epoch [121][1000/3746] lr: 9.386e-03, eta: 1 day, 2:32:09, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7087, loss_cls: 3.0755, loss: 3.0755 +2024-12-30 18:33:17,392 - pyskl - INFO - Epoch [121][1100/3746] lr: 9.369e-03, eta: 1 day, 2:30:43, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4484, top5_acc: 0.7009, loss_cls: 3.1112, loss: 3.1112 +2024-12-30 18:34:43,095 - pyskl - INFO - Epoch [121][1200/3746] lr: 9.353e-03, eta: 1 day, 2:29:18, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.6967, loss_cls: 3.1522, loss: 3.1522 +2024-12-30 18:36:09,171 - pyskl - INFO - Epoch [121][1300/3746] lr: 9.337e-03, eta: 1 day, 2:27:52, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4458, top5_acc: 0.6963, loss_cls: 3.1117, loss: 3.1117 +2024-12-30 18:37:34,157 - pyskl - INFO - Epoch [121][1400/3746] lr: 9.321e-03, eta: 1 day, 2:26:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4553, top5_acc: 0.7136, loss_cls: 3.0407, loss: 3.0407 +2024-12-30 18:38:59,069 - pyskl - INFO - Epoch [121][1500/3746] lr: 9.304e-03, eta: 1 day, 2:25:00, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.7023, loss_cls: 3.1031, loss: 3.1031 +2024-12-30 18:40:24,411 - pyskl - INFO - Epoch [121][1600/3746] lr: 9.288e-03, eta: 1 day, 2:23:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4433, top5_acc: 0.6997, loss_cls: 3.1331, loss: 3.1331 +2024-12-30 18:41:49,703 - pyskl - INFO - Epoch [121][1700/3746] lr: 9.272e-03, eta: 1 day, 2:22:08, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4434, top5_acc: 0.7037, loss_cls: 3.1219, loss: 3.1219 +2024-12-30 18:43:14,840 - pyskl - INFO - Epoch [121][1800/3746] lr: 9.256e-03, eta: 1 day, 2:20:42, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4434, top5_acc: 0.6956, loss_cls: 3.1105, loss: 3.1105 +2024-12-30 18:44:39,924 - pyskl - INFO - Epoch [121][1900/3746] lr: 9.239e-03, eta: 1 day, 2:19:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4308, top5_acc: 0.7000, loss_cls: 3.1543, loss: 3.1543 +2024-12-30 18:46:04,512 - pyskl - INFO - Epoch [121][2000/3746] lr: 9.223e-03, eta: 1 day, 2:17:50, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4452, top5_acc: 0.6955, loss_cls: 3.1329, loss: 3.1329 +2024-12-30 18:47:29,134 - pyskl - INFO - Epoch [121][2100/3746] lr: 9.207e-03, eta: 1 day, 2:16:24, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4372, top5_acc: 0.7083, loss_cls: 3.1034, loss: 3.1034 +2024-12-30 18:48:54,434 - pyskl - INFO - Epoch [121][2200/3746] lr: 9.191e-03, eta: 1 day, 2:14:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.6909, loss_cls: 3.1455, loss: 3.1455 +2024-12-30 18:50:20,126 - pyskl - INFO - Epoch [121][2300/3746] lr: 9.175e-03, eta: 1 day, 2:13:33, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4511, top5_acc: 0.7120, loss_cls: 3.0502, loss: 3.0502 +2024-12-30 18:51:45,278 - pyskl - INFO - Epoch [121][2400/3746] lr: 9.159e-03, eta: 1 day, 2:12:07, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4431, top5_acc: 0.7005, loss_cls: 3.1306, loss: 3.1306 +2024-12-30 18:53:09,903 - pyskl - INFO - Epoch [121][2500/3746] lr: 9.142e-03, eta: 1 day, 2:10:41, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.6948, loss_cls: 3.1343, loss: 3.1343 +2024-12-30 18:54:34,903 - pyskl - INFO - Epoch [121][2600/3746] lr: 9.126e-03, eta: 1 day, 2:09:15, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.7002, loss_cls: 3.1306, loss: 3.1306 +2024-12-30 18:56:00,044 - pyskl - INFO - Epoch [121][2700/3746] lr: 9.110e-03, eta: 1 day, 2:07:49, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4466, top5_acc: 0.7027, loss_cls: 3.1202, loss: 3.1202 +2024-12-30 18:57:25,388 - pyskl - INFO - Epoch [121][2800/3746] lr: 9.094e-03, eta: 1 day, 2:06:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4392, top5_acc: 0.7013, loss_cls: 3.1620, loss: 3.1620 +2024-12-30 18:58:51,148 - pyskl - INFO - Epoch [121][2900/3746] lr: 9.078e-03, eta: 1 day, 2:04:57, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4277, top5_acc: 0.6864, loss_cls: 3.1830, loss: 3.1830 +2024-12-30 19:00:15,843 - pyskl - INFO - Epoch [121][3000/3746] lr: 9.062e-03, eta: 1 day, 2:03:31, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4422, top5_acc: 0.7020, loss_cls: 3.1113, loss: 3.1113 +2024-12-30 19:01:40,914 - pyskl - INFO - Epoch [121][3100/3746] lr: 9.046e-03, eta: 1 day, 2:02:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4433, top5_acc: 0.6995, loss_cls: 3.1233, loss: 3.1233 +2024-12-30 19:03:06,823 - pyskl - INFO - Epoch [121][3200/3746] lr: 9.030e-03, eta: 1 day, 2:00:39, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4398, top5_acc: 0.6981, loss_cls: 3.1463, loss: 3.1463 +2024-12-30 19:04:32,156 - pyskl - INFO - Epoch [121][3300/3746] lr: 9.014e-03, eta: 1 day, 1:59:14, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4422, top5_acc: 0.7087, loss_cls: 3.0988, loss: 3.0988 +2024-12-30 19:05:57,177 - pyskl - INFO - Epoch [121][3400/3746] lr: 8.998e-03, eta: 1 day, 1:57:48, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4344, top5_acc: 0.6920, loss_cls: 3.1606, loss: 3.1606 +2024-12-30 19:07:22,252 - pyskl - INFO - Epoch [121][3500/3746] lr: 8.982e-03, eta: 1 day, 1:56:22, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4363, top5_acc: 0.6998, loss_cls: 3.1370, loss: 3.1370 +2024-12-30 19:08:47,639 - pyskl - INFO - Epoch [121][3600/3746] lr: 8.966e-03, eta: 1 day, 1:54:56, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.4462, top5_acc: 0.6933, loss_cls: 3.1167, loss: 3.1167 +2024-12-30 19:10:13,217 - pyskl - INFO - Epoch [121][3700/3746] lr: 8.950e-03, eta: 1 day, 1:53:30, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6928, loss_cls: 3.1715, loss: 3.1715 +2024-12-30 19:10:54,187 - pyskl - INFO - Saving checkpoint at 121 epochs +2024-12-30 19:12:51,850 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 19:12:52,550 - pyskl - INFO - +top1_acc 0.3786 +top5_acc 0.6347 +2024-12-30 19:12:52,550 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 19:12:52,593 - pyskl - INFO - +mean_acc 0.3784 +2024-12-30 19:12:52,598 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_120.pth was removed +2024-12-30 19:12:52,982 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_121.pth. +2024-12-30 19:12:52,983 - pyskl - INFO - Best top1_acc is 0.3786 at 121 epoch. +2024-12-30 19:12:53,001 - pyskl - INFO - Epoch(val) [121][309] top1_acc: 0.3786, top5_acc: 0.6347, mean_class_accuracy: 0.3784 +2024-12-30 19:17:14,640 - pyskl - INFO - Epoch [122][100/3746] lr: 8.927e-03, eta: 1 day, 1:51:57, time: 2.616, data_time: 1.587, memory: 15990, top1_acc: 0.4614, top5_acc: 0.7200, loss_cls: 3.0032, loss: 3.0032 +2024-12-30 19:18:39,501 - pyskl - INFO - Epoch [122][200/3746] lr: 8.911e-03, eta: 1 day, 1:50:31, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4636, top5_acc: 0.7198, loss_cls: 3.0081, loss: 3.0081 +2024-12-30 19:20:04,754 - pyskl - INFO - Epoch [122][300/3746] lr: 8.895e-03, eta: 1 day, 1:49:05, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4520, top5_acc: 0.7081, loss_cls: 3.0832, loss: 3.0832 +2024-12-30 19:21:30,282 - pyskl - INFO - Epoch [122][400/3746] lr: 8.879e-03, eta: 1 day, 1:47:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4533, top5_acc: 0.7072, loss_cls: 3.0409, loss: 3.0409 +2024-12-30 19:22:55,781 - pyskl - INFO - Epoch [122][500/3746] lr: 8.863e-03, eta: 1 day, 1:46:14, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4475, top5_acc: 0.7123, loss_cls: 3.0572, loss: 3.0572 +2024-12-30 19:24:21,041 - pyskl - INFO - Epoch [122][600/3746] lr: 8.847e-03, eta: 1 day, 1:44:48, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4537, top5_acc: 0.7127, loss_cls: 3.0298, loss: 3.0298 +2024-12-30 19:25:46,729 - pyskl - INFO - Epoch [122][700/3746] lr: 8.831e-03, eta: 1 day, 1:43:22, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7066, loss_cls: 3.0451, loss: 3.0451 +2024-12-30 19:27:12,420 - pyskl - INFO - Epoch [122][800/3746] lr: 8.815e-03, eta: 1 day, 1:41:56, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4555, top5_acc: 0.7142, loss_cls: 3.0399, loss: 3.0399 +2024-12-30 19:28:37,766 - pyskl - INFO - Epoch [122][900/3746] lr: 8.800e-03, eta: 1 day, 1:40:30, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.7052, loss_cls: 3.1161, loss: 3.1161 +2024-12-30 19:30:03,438 - pyskl - INFO - Epoch [122][1000/3746] lr: 8.784e-03, eta: 1 day, 1:39:04, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4511, top5_acc: 0.7114, loss_cls: 3.0402, loss: 3.0402 +2024-12-30 19:31:28,700 - pyskl - INFO - Epoch [122][1100/3746] lr: 8.768e-03, eta: 1 day, 1:37:39, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4484, top5_acc: 0.7072, loss_cls: 3.0696, loss: 3.0696 +2024-12-30 19:32:53,704 - pyskl - INFO - Epoch [122][1200/3746] lr: 8.752e-03, eta: 1 day, 1:36:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4609, top5_acc: 0.7066, loss_cls: 3.0580, loss: 3.0580 +2024-12-30 19:34:18,775 - pyskl - INFO - Epoch [122][1300/3746] lr: 8.736e-03, eta: 1 day, 1:34:47, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4417, top5_acc: 0.7048, loss_cls: 3.0879, loss: 3.0879 +2024-12-30 19:35:43,925 - pyskl - INFO - Epoch [122][1400/3746] lr: 8.721e-03, eta: 1 day, 1:33:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4525, top5_acc: 0.7073, loss_cls: 3.0755, loss: 3.0755 +2024-12-30 19:37:09,358 - pyskl - INFO - Epoch [122][1500/3746] lr: 8.705e-03, eta: 1 day, 1:31:55, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4573, top5_acc: 0.7106, loss_cls: 3.0571, loss: 3.0571 +2024-12-30 19:38:35,297 - pyskl - INFO - Epoch [122][1600/3746] lr: 8.689e-03, eta: 1 day, 1:30:29, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.7058, loss_cls: 3.0832, loss: 3.0832 +2024-12-30 19:40:00,992 - pyskl - INFO - Epoch [122][1700/3746] lr: 8.673e-03, eta: 1 day, 1:29:03, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4356, top5_acc: 0.6955, loss_cls: 3.1315, loss: 3.1315 +2024-12-30 19:41:25,980 - pyskl - INFO - Epoch [122][1800/3746] lr: 8.658e-03, eta: 1 day, 1:27:37, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4500, top5_acc: 0.7047, loss_cls: 3.0801, loss: 3.0801 +2024-12-30 19:42:51,901 - pyskl - INFO - Epoch [122][1900/3746] lr: 8.642e-03, eta: 1 day, 1:26:11, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.7031, loss_cls: 3.0893, loss: 3.0893 +2024-12-30 19:44:16,619 - pyskl - INFO - Epoch [122][2000/3746] lr: 8.626e-03, eta: 1 day, 1:24:45, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4406, top5_acc: 0.7006, loss_cls: 3.1336, loss: 3.1336 +2024-12-30 19:45:41,731 - pyskl - INFO - Epoch [122][2100/3746] lr: 8.610e-03, eta: 1 day, 1:23:19, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4502, top5_acc: 0.6973, loss_cls: 3.1258, loss: 3.1258 +2024-12-30 19:47:06,999 - pyskl - INFO - Epoch [122][2200/3746] lr: 8.595e-03, eta: 1 day, 1:21:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4589, top5_acc: 0.7092, loss_cls: 3.0675, loss: 3.0675 +2024-12-30 19:48:32,146 - pyskl - INFO - Epoch [122][2300/3746] lr: 8.579e-03, eta: 1 day, 1:20:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4447, top5_acc: 0.7047, loss_cls: 3.1130, loss: 3.1130 +2024-12-30 19:49:57,436 - pyskl - INFO - Epoch [122][2400/3746] lr: 8.563e-03, eta: 1 day, 1:19:02, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4506, top5_acc: 0.7017, loss_cls: 3.1026, loss: 3.1026 +2024-12-30 19:51:22,991 - pyskl - INFO - Epoch [122][2500/3746] lr: 8.548e-03, eta: 1 day, 1:17:36, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4395, top5_acc: 0.6897, loss_cls: 3.1686, loss: 3.1686 +2024-12-30 19:52:48,116 - pyskl - INFO - Epoch [122][2600/3746] lr: 8.532e-03, eta: 1 day, 1:16:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.7050, loss_cls: 3.1080, loss: 3.1080 +2024-12-30 19:54:13,350 - pyskl - INFO - Epoch [122][2700/3746] lr: 8.517e-03, eta: 1 day, 1:14:44, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4483, top5_acc: 0.7003, loss_cls: 3.1293, loss: 3.1293 +2024-12-30 19:55:38,492 - pyskl - INFO - Epoch [122][2800/3746] lr: 8.501e-03, eta: 1 day, 1:13:18, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4450, top5_acc: 0.6933, loss_cls: 3.1389, loss: 3.1389 +2024-12-30 19:57:03,501 - pyskl - INFO - Epoch [122][2900/3746] lr: 8.485e-03, eta: 1 day, 1:11:52, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4484, top5_acc: 0.7119, loss_cls: 3.0894, loss: 3.0894 +2024-12-30 19:58:28,779 - pyskl - INFO - Epoch [122][3000/3746] lr: 8.470e-03, eta: 1 day, 1:10:26, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4547, top5_acc: 0.7058, loss_cls: 3.0648, loss: 3.0648 +2024-12-30 19:59:53,827 - pyskl - INFO - Epoch [122][3100/3746] lr: 8.454e-03, eta: 1 day, 1:09:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4534, top5_acc: 0.7014, loss_cls: 3.0875, loss: 3.0875 +2024-12-30 20:01:19,095 - pyskl - INFO - Epoch [122][3200/3746] lr: 8.439e-03, eta: 1 day, 1:07:34, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4452, top5_acc: 0.6997, loss_cls: 3.1030, loss: 3.1030 +2024-12-30 20:02:44,003 - pyskl - INFO - Epoch [122][3300/3746] lr: 8.423e-03, eta: 1 day, 1:06:08, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.7122, loss_cls: 3.0706, loss: 3.0706 +2024-12-30 20:04:09,170 - pyskl - INFO - Epoch [122][3400/3746] lr: 8.408e-03, eta: 1 day, 1:04:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4517, top5_acc: 0.7002, loss_cls: 3.1149, loss: 3.1149 +2024-12-30 20:05:34,561 - pyskl - INFO - Epoch [122][3500/3746] lr: 8.392e-03, eta: 1 day, 1:03:16, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4473, top5_acc: 0.7042, loss_cls: 3.0908, loss: 3.0908 +2024-12-30 20:07:00,022 - pyskl - INFO - Epoch [122][3600/3746] lr: 8.377e-03, eta: 1 day, 1:01:51, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4483, top5_acc: 0.7013, loss_cls: 3.0954, loss: 3.0954 +2024-12-30 20:08:25,261 - pyskl - INFO - Epoch [122][3700/3746] lr: 8.361e-03, eta: 1 day, 1:00:25, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4414, top5_acc: 0.6897, loss_cls: 3.1709, loss: 3.1709 +2024-12-30 20:09:06,351 - pyskl - INFO - Saving checkpoint at 122 epochs +2024-12-30 20:11:04,825 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 20:11:05,562 - pyskl - INFO - +top1_acc 0.3817 +top5_acc 0.6352 +2024-12-30 20:11:05,562 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 20:11:05,613 - pyskl - INFO - +mean_acc 0.3813 +2024-12-30 20:11:05,618 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_121.pth was removed +2024-12-30 20:11:05,939 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_122.pth. +2024-12-30 20:11:05,940 - pyskl - INFO - Best top1_acc is 0.3817 at 122 epoch. +2024-12-30 20:11:05,953 - pyskl - INFO - Epoch(val) [122][309] top1_acc: 0.3817, top5_acc: 0.6352, mean_class_accuracy: 0.3813 +2024-12-30 20:15:19,429 - pyskl - INFO - Epoch [123][100/3746] lr: 8.339e-03, eta: 1 day, 0:58:49, time: 2.535, data_time: 1.506, memory: 15990, top1_acc: 0.4662, top5_acc: 0.7336, loss_cls: 2.9394, loss: 2.9394 +2024-12-30 20:16:45,494 - pyskl - INFO - Epoch [123][200/3746] lr: 8.323e-03, eta: 1 day, 0:57:23, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4542, top5_acc: 0.7142, loss_cls: 3.0570, loss: 3.0570 +2024-12-30 20:18:11,549 - pyskl - INFO - Epoch [123][300/3746] lr: 8.308e-03, eta: 1 day, 0:55:57, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7150, loss_cls: 3.0039, loss: 3.0039 +2024-12-30 20:19:37,224 - pyskl - INFO - Epoch [123][400/3746] lr: 8.292e-03, eta: 1 day, 0:54:31, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4548, top5_acc: 0.7105, loss_cls: 3.0638, loss: 3.0638 +2024-12-30 20:21:03,131 - pyskl - INFO - Epoch [123][500/3746] lr: 8.277e-03, eta: 1 day, 0:53:06, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4717, top5_acc: 0.7206, loss_cls: 3.0001, loss: 3.0001 +2024-12-30 20:22:29,409 - pyskl - INFO - Epoch [123][600/3746] lr: 8.262e-03, eta: 1 day, 0:51:40, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4694, top5_acc: 0.7228, loss_cls: 2.9691, loss: 2.9691 +2024-12-30 20:23:55,394 - pyskl - INFO - Epoch [123][700/3746] lr: 8.246e-03, eta: 1 day, 0:50:14, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7163, loss_cls: 3.0085, loss: 3.0085 +2024-12-30 20:25:21,676 - pyskl - INFO - Epoch [123][800/3746] lr: 8.231e-03, eta: 1 day, 0:48:48, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.7086, loss_cls: 3.0800, loss: 3.0800 +2024-12-30 20:26:47,849 - pyskl - INFO - Epoch [123][900/3746] lr: 8.215e-03, eta: 1 day, 0:47:23, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.4555, top5_acc: 0.7144, loss_cls: 3.0795, loss: 3.0795 +2024-12-30 20:28:13,884 - pyskl - INFO - Epoch [123][1000/3746] lr: 8.200e-03, eta: 1 day, 0:45:57, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4537, top5_acc: 0.7148, loss_cls: 3.0385, loss: 3.0385 +2024-12-30 20:29:40,292 - pyskl - INFO - Epoch [123][1100/3746] lr: 8.185e-03, eta: 1 day, 0:44:31, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.4503, top5_acc: 0.7047, loss_cls: 3.0859, loss: 3.0859 +2024-12-30 20:31:06,033 - pyskl - INFO - Epoch [123][1200/3746] lr: 8.169e-03, eta: 1 day, 0:43:05, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4511, top5_acc: 0.7048, loss_cls: 3.0713, loss: 3.0713 +2024-12-30 20:32:31,155 - pyskl - INFO - Epoch [123][1300/3746] lr: 8.154e-03, eta: 1 day, 0:41:39, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4439, top5_acc: 0.6997, loss_cls: 3.1143, loss: 3.1143 +2024-12-30 20:33:56,209 - pyskl - INFO - Epoch [123][1400/3746] lr: 8.139e-03, eta: 1 day, 0:40:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4559, top5_acc: 0.7114, loss_cls: 3.0428, loss: 3.0428 +2024-12-30 20:35:21,203 - pyskl - INFO - Epoch [123][1500/3746] lr: 8.124e-03, eta: 1 day, 0:38:47, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4527, top5_acc: 0.7042, loss_cls: 3.0760, loss: 3.0760 +2024-12-30 20:36:46,656 - pyskl - INFO - Epoch [123][1600/3746] lr: 8.108e-03, eta: 1 day, 0:37:21, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4586, top5_acc: 0.7148, loss_cls: 3.0303, loss: 3.0303 +2024-12-30 20:38:11,455 - pyskl - INFO - Epoch [123][1700/3746] lr: 8.093e-03, eta: 1 day, 0:35:55, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.7092, loss_cls: 3.0694, loss: 3.0694 +2024-12-30 20:39:36,826 - pyskl - INFO - Epoch [123][1800/3746] lr: 8.078e-03, eta: 1 day, 0:34:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4480, top5_acc: 0.7009, loss_cls: 3.1093, loss: 3.1093 +2024-12-30 20:41:01,551 - pyskl - INFO - Epoch [123][1900/3746] lr: 8.063e-03, eta: 1 day, 0:33:03, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4602, top5_acc: 0.7105, loss_cls: 3.0263, loss: 3.0263 +2024-12-30 20:42:26,609 - pyskl - INFO - Epoch [123][2000/3746] lr: 8.047e-03, eta: 1 day, 0:31:37, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4547, top5_acc: 0.7056, loss_cls: 3.0523, loss: 3.0523 +2024-12-30 20:43:51,946 - pyskl - INFO - Epoch [123][2100/3746] lr: 8.032e-03, eta: 1 day, 0:30:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4542, top5_acc: 0.7180, loss_cls: 3.0329, loss: 3.0329 +2024-12-30 20:45:17,057 - pyskl - INFO - Epoch [123][2200/3746] lr: 8.017e-03, eta: 1 day, 0:28:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4569, top5_acc: 0.7089, loss_cls: 3.0371, loss: 3.0371 +2024-12-30 20:46:42,283 - pyskl - INFO - Epoch [123][2300/3746] lr: 8.002e-03, eta: 1 day, 0:27:20, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4586, top5_acc: 0.7186, loss_cls: 3.0149, loss: 3.0149 +2024-12-30 20:48:07,199 - pyskl - INFO - Epoch [123][2400/3746] lr: 7.987e-03, eta: 1 day, 0:25:54, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4491, top5_acc: 0.7086, loss_cls: 3.0833, loss: 3.0833 +2024-12-30 20:49:32,496 - pyskl - INFO - Epoch [123][2500/3746] lr: 7.971e-03, eta: 1 day, 0:24:28, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4592, top5_acc: 0.7077, loss_cls: 3.0857, loss: 3.0857 +2024-12-30 20:50:57,610 - pyskl - INFO - Epoch [123][2600/3746] lr: 7.956e-03, eta: 1 day, 0:23:02, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4520, top5_acc: 0.7009, loss_cls: 3.0786, loss: 3.0786 +2024-12-30 20:52:23,084 - pyskl - INFO - Epoch [123][2700/3746] lr: 7.941e-03, eta: 1 day, 0:21:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4608, top5_acc: 0.7045, loss_cls: 3.0707, loss: 3.0707 +2024-12-30 20:53:48,426 - pyskl - INFO - Epoch [123][2800/3746] lr: 7.926e-03, eta: 1 day, 0:20:10, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.7122, loss_cls: 3.0642, loss: 3.0642 +2024-12-30 20:55:13,320 - pyskl - INFO - Epoch [123][2900/3746] lr: 7.911e-03, eta: 1 day, 0:18:44, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4495, top5_acc: 0.7045, loss_cls: 3.0838, loss: 3.0838 +2024-12-30 20:56:38,283 - pyskl - INFO - Epoch [123][3000/3746] lr: 7.896e-03, eta: 1 day, 0:17:18, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4477, top5_acc: 0.7073, loss_cls: 3.0647, loss: 3.0647 +2024-12-30 20:58:03,409 - pyskl - INFO - Epoch [123][3100/3746] lr: 7.881e-03, eta: 1 day, 0:15:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4527, top5_acc: 0.7048, loss_cls: 3.0735, loss: 3.0735 +2024-12-30 20:59:28,473 - pyskl - INFO - Epoch [123][3200/3746] lr: 7.866e-03, eta: 1 day, 0:14:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4542, top5_acc: 0.7167, loss_cls: 3.0134, loss: 3.0134 +2024-12-30 21:00:53,327 - pyskl - INFO - Epoch [123][3300/3746] lr: 7.851e-03, eta: 1 day, 0:13:00, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.6953, loss_cls: 3.1453, loss: 3.1453 +2024-12-30 21:02:18,442 - pyskl - INFO - Epoch [123][3400/3746] lr: 7.836e-03, eta: 1 day, 0:11:34, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4534, top5_acc: 0.7027, loss_cls: 3.0664, loss: 3.0664 +2024-12-30 21:03:42,926 - pyskl - INFO - Epoch [123][3500/3746] lr: 7.821e-03, eta: 1 day, 0:10:08, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4577, top5_acc: 0.7142, loss_cls: 3.0485, loss: 3.0485 +2024-12-30 21:05:08,026 - pyskl - INFO - Epoch [123][3600/3746] lr: 7.806e-03, eta: 1 day, 0:08:42, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4544, top5_acc: 0.6997, loss_cls: 3.0915, loss: 3.0915 +2024-12-30 21:06:32,965 - pyskl - INFO - Epoch [123][3700/3746] lr: 7.791e-03, eta: 1 day, 0:07:16, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4445, top5_acc: 0.7011, loss_cls: 3.1115, loss: 3.1115 +2024-12-30 21:07:14,099 - pyskl - INFO - Saving checkpoint at 123 epochs +2024-12-30 21:09:12,158 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 21:09:12,889 - pyskl - INFO - +top1_acc 0.3862 +top5_acc 0.6378 +2024-12-30 21:09:12,890 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 21:09:12,954 - pyskl - INFO - +mean_acc 0.3859 +2024-12-30 21:09:12,960 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_122.pth was removed +2024-12-30 21:09:13,271 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_123.pth. +2024-12-30 21:09:13,271 - pyskl - INFO - Best top1_acc is 0.3862 at 123 epoch. +2024-12-30 21:09:13,294 - pyskl - INFO - Epoch(val) [123][309] top1_acc: 0.3862, top5_acc: 0.6378, mean_class_accuracy: 0.3859 +2024-12-30 21:13:26,754 - pyskl - INFO - Epoch [124][100/3746] lr: 7.769e-03, eta: 1 day, 0:05:39, time: 2.534, data_time: 1.509, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7266, loss_cls: 2.9741, loss: 2.9741 +2024-12-30 21:14:51,484 - pyskl - INFO - Epoch [124][200/3746] lr: 7.754e-03, eta: 1 day, 0:04:12, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4641, top5_acc: 0.7195, loss_cls: 2.9607, loss: 2.9607 +2024-12-30 21:16:16,446 - pyskl - INFO - Epoch [124][300/3746] lr: 7.739e-03, eta: 1 day, 0:02:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4688, top5_acc: 0.7286, loss_cls: 2.9642, loss: 2.9642 +2024-12-30 21:17:41,253 - pyskl - INFO - Epoch [124][400/3746] lr: 7.724e-03, eta: 1 day, 0:01:20, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4708, top5_acc: 0.7197, loss_cls: 3.0016, loss: 3.0016 +2024-12-30 21:19:06,248 - pyskl - INFO - Epoch [124][500/3746] lr: 7.709e-03, eta: 23:59:54, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4606, top5_acc: 0.7158, loss_cls: 3.0229, loss: 3.0229 +2024-12-30 21:20:31,061 - pyskl - INFO - Epoch [124][600/3746] lr: 7.694e-03, eta: 23:58:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4608, top5_acc: 0.7134, loss_cls: 3.0147, loss: 3.0147 +2024-12-30 21:21:56,288 - pyskl - INFO - Epoch [124][700/3746] lr: 7.679e-03, eta: 23:57:02, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4600, top5_acc: 0.7197, loss_cls: 2.9880, loss: 2.9880 +2024-12-30 21:23:21,172 - pyskl - INFO - Epoch [124][800/3746] lr: 7.664e-03, eta: 23:55:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4644, top5_acc: 0.7255, loss_cls: 3.0051, loss: 3.0051 +2024-12-30 21:24:46,502 - pyskl - INFO - Epoch [124][900/3746] lr: 7.649e-03, eta: 23:54:10, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4570, top5_acc: 0.7208, loss_cls: 3.0176, loss: 3.0176 +2024-12-30 21:26:11,891 - pyskl - INFO - Epoch [124][1000/3746] lr: 7.635e-03, eta: 23:52:44, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4537, top5_acc: 0.7100, loss_cls: 3.0433, loss: 3.0433 +2024-12-30 21:27:36,964 - pyskl - INFO - Epoch [124][1100/3746] lr: 7.620e-03, eta: 23:51:18, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4678, top5_acc: 0.7087, loss_cls: 3.0332, loss: 3.0332 +2024-12-30 21:29:01,919 - pyskl - INFO - Epoch [124][1200/3746] lr: 7.605e-03, eta: 23:49:52, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.4586, top5_acc: 0.7158, loss_cls: 3.0238, loss: 3.0238 +2024-12-30 21:30:26,782 - pyskl - INFO - Epoch [124][1300/3746] lr: 7.590e-03, eta: 23:48:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4653, top5_acc: 0.7230, loss_cls: 2.9740, loss: 2.9740 +2024-12-30 21:31:51,596 - pyskl - INFO - Epoch [124][1400/3746] lr: 7.575e-03, eta: 23:47:00, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4628, top5_acc: 0.7147, loss_cls: 3.0260, loss: 3.0260 +2024-12-30 21:33:16,741 - pyskl - INFO - Epoch [124][1500/3746] lr: 7.561e-03, eta: 23:45:34, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4577, top5_acc: 0.7077, loss_cls: 3.0396, loss: 3.0396 +2024-12-30 21:34:41,989 - pyskl - INFO - Epoch [124][1600/3746] lr: 7.546e-03, eta: 23:44:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.7016, loss_cls: 3.0909, loss: 3.0909 +2024-12-30 21:36:07,240 - pyskl - INFO - Epoch [124][1700/3746] lr: 7.531e-03, eta: 23:42:42, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4519, top5_acc: 0.7089, loss_cls: 3.0524, loss: 3.0524 +2024-12-30 21:37:32,554 - pyskl - INFO - Epoch [124][1800/3746] lr: 7.516e-03, eta: 23:41:16, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4503, top5_acc: 0.7102, loss_cls: 3.0641, loss: 3.0641 +2024-12-30 21:38:58,279 - pyskl - INFO - Epoch [124][1900/3746] lr: 7.502e-03, eta: 23:39:50, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4586, top5_acc: 0.7055, loss_cls: 3.0692, loss: 3.0692 +2024-12-30 21:40:23,686 - pyskl - INFO - Epoch [124][2000/3746] lr: 7.487e-03, eta: 23:38:24, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4575, top5_acc: 0.7181, loss_cls: 3.0339, loss: 3.0339 +2024-12-30 21:41:48,427 - pyskl - INFO - Epoch [124][2100/3746] lr: 7.472e-03, eta: 23:36:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4614, top5_acc: 0.7141, loss_cls: 3.0341, loss: 3.0341 +2024-12-30 21:43:13,385 - pyskl - INFO - Epoch [124][2200/3746] lr: 7.457e-03, eta: 23:35:32, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7178, loss_cls: 3.0294, loss: 3.0294 +2024-12-30 21:44:38,568 - pyskl - INFO - Epoch [124][2300/3746] lr: 7.443e-03, eta: 23:34:06, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4530, top5_acc: 0.7073, loss_cls: 3.0479, loss: 3.0479 +2024-12-30 21:46:03,879 - pyskl - INFO - Epoch [124][2400/3746] lr: 7.428e-03, eta: 23:32:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4570, top5_acc: 0.7083, loss_cls: 3.0674, loss: 3.0674 +2024-12-30 21:47:28,979 - pyskl - INFO - Epoch [124][2500/3746] lr: 7.413e-03, eta: 23:31:14, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4622, top5_acc: 0.7177, loss_cls: 3.0133, loss: 3.0133 +2024-12-30 21:48:53,946 - pyskl - INFO - Epoch [124][2600/3746] lr: 7.399e-03, eta: 23:29:48, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4636, top5_acc: 0.7189, loss_cls: 2.9695, loss: 2.9695 +2024-12-30 21:50:18,743 - pyskl - INFO - Epoch [124][2700/3746] lr: 7.384e-03, eta: 23:28:22, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4703, top5_acc: 0.7216, loss_cls: 2.9931, loss: 2.9931 +2024-12-30 21:51:43,623 - pyskl - INFO - Epoch [124][2800/3746] lr: 7.370e-03, eta: 23:26:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4528, top5_acc: 0.7091, loss_cls: 3.0669, loss: 3.0669 +2024-12-30 21:53:08,855 - pyskl - INFO - Epoch [124][2900/3746] lr: 7.355e-03, eta: 23:25:30, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4594, top5_acc: 0.7116, loss_cls: 3.0435, loss: 3.0435 +2024-12-30 21:54:33,587 - pyskl - INFO - Epoch [124][3000/3746] lr: 7.340e-03, eta: 23:24:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4508, top5_acc: 0.7083, loss_cls: 3.0788, loss: 3.0788 +2024-12-30 21:55:58,173 - pyskl - INFO - Epoch [124][3100/3746] lr: 7.326e-03, eta: 23:22:38, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4577, top5_acc: 0.7083, loss_cls: 3.0526, loss: 3.0526 +2024-12-30 21:57:23,164 - pyskl - INFO - Epoch [124][3200/3746] lr: 7.311e-03, eta: 23:21:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4645, top5_acc: 0.7173, loss_cls: 3.0056, loss: 3.0056 +2024-12-30 21:58:48,241 - pyskl - INFO - Epoch [124][3300/3746] lr: 7.297e-03, eta: 23:19:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4528, top5_acc: 0.7087, loss_cls: 3.0721, loss: 3.0721 +2024-12-30 22:00:13,253 - pyskl - INFO - Epoch [124][3400/3746] lr: 7.282e-03, eta: 23:18:20, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.7106, loss_cls: 3.0426, loss: 3.0426 +2024-12-30 22:01:38,444 - pyskl - INFO - Epoch [124][3500/3746] lr: 7.268e-03, eta: 23:16:54, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4581, top5_acc: 0.7073, loss_cls: 3.0602, loss: 3.0602 +2024-12-30 22:03:03,255 - pyskl - INFO - Epoch [124][3600/3746] lr: 7.253e-03, eta: 23:15:28, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4505, top5_acc: 0.7031, loss_cls: 3.0746, loss: 3.0746 +2024-12-30 22:04:28,124 - pyskl - INFO - Epoch [124][3700/3746] lr: 7.239e-03, eta: 23:14:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7153, loss_cls: 2.9971, loss: 2.9971 +2024-12-30 22:05:08,946 - pyskl - INFO - Saving checkpoint at 124 epochs +2024-12-30 22:07:07,607 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 22:07:08,435 - pyskl - INFO - +top1_acc 0.3896 +top5_acc 0.6455 +2024-12-30 22:07:08,435 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 22:07:08,478 - pyskl - INFO - +mean_acc 0.3893 +2024-12-30 22:07:08,483 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_123.pth was removed +2024-12-30 22:07:08,728 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_124.pth. +2024-12-30 22:07:08,729 - pyskl - INFO - Best top1_acc is 0.3896 at 124 epoch. +2024-12-30 22:07:08,742 - pyskl - INFO - Epoch(val) [124][309] top1_acc: 0.3896, top5_acc: 0.6455, mean_class_accuracy: 0.3893 +2024-12-30 22:11:17,111 - pyskl - INFO - Epoch [125][100/3746] lr: 7.217e-03, eta: 23:12:22, time: 2.484, data_time: 1.462, memory: 15990, top1_acc: 0.4744, top5_acc: 0.7328, loss_cls: 2.9278, loss: 2.9278 +2024-12-30 22:12:42,280 - pyskl - INFO - Epoch [125][200/3746] lr: 7.203e-03, eta: 23:10:56, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4730, top5_acc: 0.7278, loss_cls: 2.9447, loss: 2.9447 +2024-12-30 22:14:07,362 - pyskl - INFO - Epoch [125][300/3746] lr: 7.189e-03, eta: 23:09:30, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4664, top5_acc: 0.7222, loss_cls: 2.9963, loss: 2.9963 +2024-12-30 22:15:32,333 - pyskl - INFO - Epoch [125][400/3746] lr: 7.174e-03, eta: 23:08:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4753, top5_acc: 0.7295, loss_cls: 2.9234, loss: 2.9234 +2024-12-30 22:16:57,190 - pyskl - INFO - Epoch [125][500/3746] lr: 7.160e-03, eta: 23:06:38, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4723, top5_acc: 0.7233, loss_cls: 2.9823, loss: 2.9823 +2024-12-30 22:18:23,012 - pyskl - INFO - Epoch [125][600/3746] lr: 7.145e-03, eta: 23:05:12, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4697, top5_acc: 0.7283, loss_cls: 2.9469, loss: 2.9469 +2024-12-30 22:19:48,773 - pyskl - INFO - Epoch [125][700/3746] lr: 7.131e-03, eta: 23:03:47, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4730, top5_acc: 0.7234, loss_cls: 2.9555, loss: 2.9555 +2024-12-30 22:21:14,156 - pyskl - INFO - Epoch [125][800/3746] lr: 7.117e-03, eta: 23:02:21, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4655, top5_acc: 0.7220, loss_cls: 2.9911, loss: 2.9911 +2024-12-30 22:22:39,362 - pyskl - INFO - Epoch [125][900/3746] lr: 7.102e-03, eta: 23:00:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4711, top5_acc: 0.7212, loss_cls: 2.9839, loss: 2.9839 +2024-12-30 22:24:04,591 - pyskl - INFO - Epoch [125][1000/3746] lr: 7.088e-03, eta: 22:59:29, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4734, top5_acc: 0.7244, loss_cls: 2.9604, loss: 2.9604 +2024-12-30 22:25:29,809 - pyskl - INFO - Epoch [125][1100/3746] lr: 7.073e-03, eta: 22:58:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4706, top5_acc: 0.7231, loss_cls: 2.9644, loss: 2.9644 +2024-12-30 22:26:55,649 - pyskl - INFO - Epoch [125][1200/3746] lr: 7.059e-03, eta: 22:56:37, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4745, top5_acc: 0.7211, loss_cls: 2.9724, loss: 2.9724 +2024-12-30 22:28:20,447 - pyskl - INFO - Epoch [125][1300/3746] lr: 7.045e-03, eta: 22:55:11, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7122, loss_cls: 3.0352, loss: 3.0352 +2024-12-30 22:29:45,350 - pyskl - INFO - Epoch [125][1400/3746] lr: 7.031e-03, eta: 22:53:45, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4698, top5_acc: 0.7212, loss_cls: 2.9752, loss: 2.9752 +2024-12-30 22:31:10,223 - pyskl - INFO - Epoch [125][1500/3746] lr: 7.016e-03, eta: 22:52:18, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4469, top5_acc: 0.7127, loss_cls: 3.0638, loss: 3.0638 +2024-12-30 22:32:35,344 - pyskl - INFO - Epoch [125][1600/3746] lr: 7.002e-03, eta: 22:50:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7188, loss_cls: 2.9795, loss: 2.9795 +2024-12-30 22:34:00,401 - pyskl - INFO - Epoch [125][1700/3746] lr: 6.988e-03, eta: 22:49:26, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4572, top5_acc: 0.7178, loss_cls: 3.0233, loss: 3.0233 +2024-12-30 22:35:25,418 - pyskl - INFO - Epoch [125][1800/3746] lr: 6.973e-03, eta: 22:48:00, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4652, top5_acc: 0.7167, loss_cls: 2.9894, loss: 2.9894 +2024-12-30 22:36:50,116 - pyskl - INFO - Epoch [125][1900/3746] lr: 6.959e-03, eta: 22:46:34, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4733, top5_acc: 0.7214, loss_cls: 2.9897, loss: 2.9897 +2024-12-30 22:38:15,019 - pyskl - INFO - Epoch [125][2000/3746] lr: 6.945e-03, eta: 22:45:08, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4608, top5_acc: 0.7139, loss_cls: 3.0297, loss: 3.0297 +2024-12-30 22:39:39,803 - pyskl - INFO - Epoch [125][2100/3746] lr: 6.931e-03, eta: 22:43:42, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4622, top5_acc: 0.7145, loss_cls: 3.0204, loss: 3.0204 +2024-12-30 22:41:04,901 - pyskl - INFO - Epoch [125][2200/3746] lr: 6.917e-03, eta: 22:42:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4598, top5_acc: 0.7186, loss_cls: 3.0101, loss: 3.0101 +2024-12-30 22:42:29,710 - pyskl - INFO - Epoch [125][2300/3746] lr: 6.902e-03, eta: 22:40:50, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4614, top5_acc: 0.7128, loss_cls: 3.0350, loss: 3.0350 +2024-12-30 22:43:54,309 - pyskl - INFO - Epoch [125][2400/3746] lr: 6.888e-03, eta: 22:39:24, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4558, top5_acc: 0.7137, loss_cls: 3.0205, loss: 3.0205 +2024-12-30 22:45:19,081 - pyskl - INFO - Epoch [125][2500/3746] lr: 6.874e-03, eta: 22:37:58, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4694, top5_acc: 0.7188, loss_cls: 2.9865, loss: 2.9865 +2024-12-30 22:46:44,562 - pyskl - INFO - Epoch [125][2600/3746] lr: 6.860e-03, eta: 22:36:32, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4569, top5_acc: 0.7131, loss_cls: 3.0245, loss: 3.0245 +2024-12-30 22:48:09,539 - pyskl - INFO - Epoch [125][2700/3746] lr: 6.846e-03, eta: 22:35:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4698, top5_acc: 0.7269, loss_cls: 2.9728, loss: 2.9728 +2024-12-30 22:49:34,433 - pyskl - INFO - Epoch [125][2800/3746] lr: 6.832e-03, eta: 22:33:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4589, top5_acc: 0.7142, loss_cls: 3.0188, loss: 3.0188 +2024-12-30 22:50:59,272 - pyskl - INFO - Epoch [125][2900/3746] lr: 6.818e-03, eta: 22:32:14, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4580, top5_acc: 0.7209, loss_cls: 2.9952, loss: 2.9952 +2024-12-30 22:52:24,260 - pyskl - INFO - Epoch [125][3000/3746] lr: 6.804e-03, eta: 22:30:48, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4583, top5_acc: 0.7245, loss_cls: 3.0048, loss: 3.0048 +2024-12-30 22:53:48,891 - pyskl - INFO - Epoch [125][3100/3746] lr: 6.789e-03, eta: 22:29:22, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4656, top5_acc: 0.7198, loss_cls: 3.0030, loss: 3.0030 +2024-12-30 22:55:13,722 - pyskl - INFO - Epoch [125][3200/3746] lr: 6.775e-03, eta: 22:27:56, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4583, top5_acc: 0.7144, loss_cls: 3.0468, loss: 3.0468 +2024-12-30 22:56:38,580 - pyskl - INFO - Epoch [125][3300/3746] lr: 6.761e-03, eta: 22:26:30, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4625, top5_acc: 0.7211, loss_cls: 3.0102, loss: 3.0102 +2024-12-30 22:58:03,494 - pyskl - INFO - Epoch [125][3400/3746] lr: 6.747e-03, eta: 22:25:03, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4598, top5_acc: 0.7113, loss_cls: 3.0262, loss: 3.0262 +2024-12-30 22:59:28,695 - pyskl - INFO - Epoch [125][3500/3746] lr: 6.733e-03, eta: 22:23:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4686, top5_acc: 0.7100, loss_cls: 3.0113, loss: 3.0113 +2024-12-30 23:00:53,904 - pyskl - INFO - Epoch [125][3600/3746] lr: 6.719e-03, eta: 22:22:11, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4577, top5_acc: 0.7141, loss_cls: 3.0553, loss: 3.0553 +2024-12-30 23:02:18,938 - pyskl - INFO - Epoch [125][3700/3746] lr: 6.705e-03, eta: 22:20:45, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4664, top5_acc: 0.7194, loss_cls: 3.0101, loss: 3.0101 +2024-12-30 23:02:59,958 - pyskl - INFO - Saving checkpoint at 125 epochs +2024-12-30 23:04:58,778 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-30 23:04:59,522 - pyskl - INFO - +top1_acc 0.3910 +top5_acc 0.6474 +2024-12-30 23:04:59,522 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-30 23:04:59,570 - pyskl - INFO - +mean_acc 0.3907 +2024-12-30 23:04:59,574 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_124.pth was removed +2024-12-30 23:04:59,865 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_125.pth. +2024-12-30 23:04:59,866 - pyskl - INFO - Best top1_acc is 0.3910 at 125 epoch. +2024-12-30 23:04:59,884 - pyskl - INFO - Epoch(val) [125][309] top1_acc: 0.3910, top5_acc: 0.6474, mean_class_accuracy: 0.3907 +2024-12-30 23:09:06,235 - pyskl - INFO - Epoch [126][100/3746] lr: 6.685e-03, eta: 22:19:04, time: 2.463, data_time: 1.441, memory: 15990, top1_acc: 0.4803, top5_acc: 0.7334, loss_cls: 2.9185, loss: 2.9185 +2024-12-30 23:10:31,060 - pyskl - INFO - Epoch [126][200/3746] lr: 6.671e-03, eta: 22:17:38, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4900, top5_acc: 0.7381, loss_cls: 2.8738, loss: 2.8738 +2024-12-30 23:11:56,377 - pyskl - INFO - Epoch [126][300/3746] lr: 6.657e-03, eta: 22:16:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4825, top5_acc: 0.7308, loss_cls: 2.8944, loss: 2.8944 +2024-12-30 23:13:21,476 - pyskl - INFO - Epoch [126][400/3746] lr: 6.643e-03, eta: 22:14:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4777, top5_acc: 0.7352, loss_cls: 2.9022, loss: 2.9022 +2024-12-30 23:14:46,281 - pyskl - INFO - Epoch [126][500/3746] lr: 6.629e-03, eta: 22:13:20, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4692, top5_acc: 0.7259, loss_cls: 2.9347, loss: 2.9347 +2024-12-30 23:16:11,793 - pyskl - INFO - Epoch [126][600/3746] lr: 6.615e-03, eta: 22:11:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4750, top5_acc: 0.7247, loss_cls: 2.9609, loss: 2.9609 +2024-12-30 23:17:37,736 - pyskl - INFO - Epoch [126][700/3746] lr: 6.601e-03, eta: 22:10:28, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4636, top5_acc: 0.7252, loss_cls: 2.9567, loss: 2.9567 +2024-12-30 23:19:03,171 - pyskl - INFO - Epoch [126][800/3746] lr: 6.587e-03, eta: 22:09:02, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4670, top5_acc: 0.7197, loss_cls: 2.9730, loss: 2.9730 +2024-12-30 23:20:28,610 - pyskl - INFO - Epoch [126][900/3746] lr: 6.574e-03, eta: 22:07:36, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4794, top5_acc: 0.7362, loss_cls: 2.9212, loss: 2.9212 +2024-12-30 23:21:54,220 - pyskl - INFO - Epoch [126][1000/3746] lr: 6.560e-03, eta: 22:06:10, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4686, top5_acc: 0.7228, loss_cls: 2.9588, loss: 2.9588 +2024-12-30 23:23:19,547 - pyskl - INFO - Epoch [126][1100/3746] lr: 6.546e-03, eta: 22:04:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4753, top5_acc: 0.7320, loss_cls: 2.9377, loss: 2.9377 +2024-12-30 23:24:44,913 - pyskl - INFO - Epoch [126][1200/3746] lr: 6.532e-03, eta: 22:03:18, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.4719, top5_acc: 0.7227, loss_cls: 2.9422, loss: 2.9422 +2024-12-30 23:26:09,367 - pyskl - INFO - Epoch [126][1300/3746] lr: 6.518e-03, eta: 22:01:52, time: 0.845, data_time: 0.001, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7117, loss_cls: 3.0078, loss: 3.0078 +2024-12-30 23:27:34,354 - pyskl - INFO - Epoch [126][1400/3746] lr: 6.505e-03, eta: 22:00:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4713, top5_acc: 0.7214, loss_cls: 2.9815, loss: 2.9815 +2024-12-30 23:28:59,476 - pyskl - INFO - Epoch [126][1500/3746] lr: 6.491e-03, eta: 21:59:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4667, top5_acc: 0.7281, loss_cls: 2.9454, loss: 2.9454 +2024-12-30 23:30:24,328 - pyskl - INFO - Epoch [126][1600/3746] lr: 6.477e-03, eta: 21:57:34, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.4781, top5_acc: 0.7286, loss_cls: 2.9568, loss: 2.9568 +2024-12-30 23:31:49,488 - pyskl - INFO - Epoch [126][1700/3746] lr: 6.463e-03, eta: 21:56:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4747, top5_acc: 0.7306, loss_cls: 2.9407, loss: 2.9407 +2024-12-30 23:33:14,560 - pyskl - INFO - Epoch [126][1800/3746] lr: 6.449e-03, eta: 21:54:42, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4648, top5_acc: 0.7166, loss_cls: 3.0009, loss: 3.0009 +2024-12-30 23:34:39,599 - pyskl - INFO - Epoch [126][1900/3746] lr: 6.436e-03, eta: 21:53:16, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4869, top5_acc: 0.7248, loss_cls: 2.9524, loss: 2.9524 +2024-12-30 23:36:05,178 - pyskl - INFO - Epoch [126][2000/3746] lr: 6.422e-03, eta: 21:51:50, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7292, loss_cls: 2.9624, loss: 2.9624 +2024-12-30 23:37:30,304 - pyskl - INFO - Epoch [126][2100/3746] lr: 6.408e-03, eta: 21:50:24, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4662, top5_acc: 0.7225, loss_cls: 2.9782, loss: 2.9782 +2024-12-30 23:38:55,447 - pyskl - INFO - Epoch [126][2200/3746] lr: 6.395e-03, eta: 21:48:58, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4711, top5_acc: 0.7214, loss_cls: 2.9490, loss: 2.9490 +2024-12-30 23:40:21,158 - pyskl - INFO - Epoch [126][2300/3746] lr: 6.381e-03, eta: 21:47:32, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4669, top5_acc: 0.7209, loss_cls: 2.9874, loss: 2.9874 +2024-12-30 23:41:45,991 - pyskl - INFO - Epoch [126][2400/3746] lr: 6.367e-03, eta: 21:46:06, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4630, top5_acc: 0.7203, loss_cls: 3.0023, loss: 3.0023 +2024-12-30 23:43:10,413 - pyskl - INFO - Epoch [126][2500/3746] lr: 6.354e-03, eta: 21:44:40, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4756, top5_acc: 0.7200, loss_cls: 2.9752, loss: 2.9752 +2024-12-30 23:44:35,214 - pyskl - INFO - Epoch [126][2600/3746] lr: 6.340e-03, eta: 21:43:14, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4595, top5_acc: 0.7111, loss_cls: 3.0117, loss: 3.0117 +2024-12-30 23:46:00,457 - pyskl - INFO - Epoch [126][2700/3746] lr: 6.326e-03, eta: 21:41:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4605, top5_acc: 0.7109, loss_cls: 3.0216, loss: 3.0216 +2024-12-30 23:47:25,763 - pyskl - INFO - Epoch [126][2800/3746] lr: 6.313e-03, eta: 21:40:22, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4741, top5_acc: 0.7219, loss_cls: 2.9689, loss: 2.9689 +2024-12-30 23:48:51,051 - pyskl - INFO - Epoch [126][2900/3746] lr: 6.299e-03, eta: 21:38:56, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4706, top5_acc: 0.7248, loss_cls: 2.9796, loss: 2.9796 +2024-12-30 23:50:16,306 - pyskl - INFO - Epoch [126][3000/3746] lr: 6.286e-03, eta: 21:37:30, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4711, top5_acc: 0.7258, loss_cls: 2.9610, loss: 2.9610 +2024-12-30 23:51:41,319 - pyskl - INFO - Epoch [126][3100/3746] lr: 6.272e-03, eta: 21:36:04, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4520, top5_acc: 0.7166, loss_cls: 3.0391, loss: 3.0391 +2024-12-30 23:53:06,523 - pyskl - INFO - Epoch [126][3200/3746] lr: 6.259e-03, eta: 21:34:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4709, top5_acc: 0.7309, loss_cls: 2.9545, loss: 2.9545 +2024-12-30 23:54:31,474 - pyskl - INFO - Epoch [126][3300/3746] lr: 6.245e-03, eta: 21:33:12, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4684, top5_acc: 0.7131, loss_cls: 3.0174, loss: 3.0174 +2024-12-30 23:55:56,554 - pyskl - INFO - Epoch [126][3400/3746] lr: 6.231e-03, eta: 21:31:46, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4670, top5_acc: 0.7214, loss_cls: 2.9884, loss: 2.9884 +2024-12-30 23:57:21,437 - pyskl - INFO - Epoch [126][3500/3746] lr: 6.218e-03, eta: 21:30:19, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4725, top5_acc: 0.7248, loss_cls: 2.9664, loss: 2.9664 +2024-12-30 23:58:46,526 - pyskl - INFO - Epoch [126][3600/3746] lr: 6.204e-03, eta: 21:28:53, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4602, top5_acc: 0.7133, loss_cls: 3.0401, loss: 3.0401 +2024-12-31 00:00:11,378 - pyskl - INFO - Epoch [126][3700/3746] lr: 6.191e-03, eta: 21:27:27, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4631, top5_acc: 0.7211, loss_cls: 3.0166, loss: 3.0166 +2024-12-31 00:00:52,013 - pyskl - INFO - Saving checkpoint at 126 epochs +2024-12-31 00:02:50,679 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 00:02:51,383 - pyskl - INFO - +top1_acc 0.3933 +top5_acc 0.6492 +2024-12-31 00:02:51,383 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 00:02:51,424 - pyskl - INFO - +mean_acc 0.3930 +2024-12-31 00:02:51,429 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_125.pth was removed +2024-12-31 00:02:51,679 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_126.pth. +2024-12-31 00:02:51,680 - pyskl - INFO - Best top1_acc is 0.3933 at 126 epoch. +2024-12-31 00:02:51,692 - pyskl - INFO - Epoch(val) [126][309] top1_acc: 0.3933, top5_acc: 0.6492, mean_class_accuracy: 0.3930 +2024-12-31 00:07:01,102 - pyskl - INFO - Epoch [127][100/3746] lr: 6.171e-03, eta: 21:25:46, time: 2.494, data_time: 1.469, memory: 15990, top1_acc: 0.5014, top5_acc: 0.7448, loss_cls: 2.8156, loss: 2.8156 +2024-12-31 00:08:26,399 - pyskl - INFO - Epoch [127][200/3746] lr: 6.158e-03, eta: 21:24:20, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4842, top5_acc: 0.7475, loss_cls: 2.8462, loss: 2.8462 +2024-12-31 00:09:51,249 - pyskl - INFO - Epoch [127][300/3746] lr: 6.144e-03, eta: 21:22:53, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4844, top5_acc: 0.7389, loss_cls: 2.8835, loss: 2.8835 +2024-12-31 00:11:16,303 - pyskl - INFO - Epoch [127][400/3746] lr: 6.131e-03, eta: 21:21:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4906, top5_acc: 0.7416, loss_cls: 2.8541, loss: 2.8541 +2024-12-31 00:12:40,784 - pyskl - INFO - Epoch [127][500/3746] lr: 6.118e-03, eta: 21:20:01, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4778, top5_acc: 0.7327, loss_cls: 2.9178, loss: 2.9178 +2024-12-31 00:14:05,280 - pyskl - INFO - Epoch [127][600/3746] lr: 6.104e-03, eta: 21:18:35, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.4733, top5_acc: 0.7297, loss_cls: 2.9418, loss: 2.9418 +2024-12-31 00:15:30,463 - pyskl - INFO - Epoch [127][700/3746] lr: 6.091e-03, eta: 21:17:09, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4831, top5_acc: 0.7328, loss_cls: 2.9115, loss: 2.9115 +2024-12-31 00:16:55,347 - pyskl - INFO - Epoch [127][800/3746] lr: 6.077e-03, eta: 21:15:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4778, top5_acc: 0.7341, loss_cls: 2.9165, loss: 2.9165 +2024-12-31 00:18:20,156 - pyskl - INFO - Epoch [127][900/3746] lr: 6.064e-03, eta: 21:14:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4664, top5_acc: 0.7219, loss_cls: 2.9678, loss: 2.9678 +2024-12-31 00:19:45,291 - pyskl - INFO - Epoch [127][1000/3746] lr: 6.051e-03, eta: 21:12:51, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4778, top5_acc: 0.7198, loss_cls: 2.9424, loss: 2.9424 +2024-12-31 00:21:10,224 - pyskl - INFO - Epoch [127][1100/3746] lr: 6.037e-03, eta: 21:11:25, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4672, top5_acc: 0.7250, loss_cls: 2.9553, loss: 2.9553 +2024-12-31 00:22:35,759 - pyskl - INFO - Epoch [127][1200/3746] lr: 6.024e-03, eta: 21:09:59, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.4773, top5_acc: 0.7314, loss_cls: 2.9276, loss: 2.9276 +2024-12-31 00:24:00,754 - pyskl - INFO - Epoch [127][1300/3746] lr: 6.011e-03, eta: 21:08:33, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4761, top5_acc: 0.7331, loss_cls: 2.8928, loss: 2.8928 +2024-12-31 00:25:25,983 - pyskl - INFO - Epoch [127][1400/3746] lr: 5.998e-03, eta: 21:07:07, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4691, top5_acc: 0.7283, loss_cls: 2.9615, loss: 2.9615 +2024-12-31 00:26:50,720 - pyskl - INFO - Epoch [127][1500/3746] lr: 5.984e-03, eta: 21:05:40, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4727, top5_acc: 0.7238, loss_cls: 2.9508, loss: 2.9508 +2024-12-31 00:28:16,195 - pyskl - INFO - Epoch [127][1600/3746] lr: 5.971e-03, eta: 21:04:14, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4695, top5_acc: 0.7269, loss_cls: 2.9277, loss: 2.9277 +2024-12-31 00:29:41,421 - pyskl - INFO - Epoch [127][1700/3746] lr: 5.958e-03, eta: 21:02:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4747, top5_acc: 0.7325, loss_cls: 2.9085, loss: 2.9085 +2024-12-31 00:31:06,369 - pyskl - INFO - Epoch [127][1800/3746] lr: 5.945e-03, eta: 21:01:22, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4853, top5_acc: 0.7370, loss_cls: 2.9016, loss: 2.9016 +2024-12-31 00:32:31,274 - pyskl - INFO - Epoch [127][1900/3746] lr: 5.931e-03, eta: 20:59:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4642, top5_acc: 0.7147, loss_cls: 2.9995, loss: 2.9995 +2024-12-31 00:33:56,198 - pyskl - INFO - Epoch [127][2000/3746] lr: 5.918e-03, eta: 20:58:30, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4833, top5_acc: 0.7186, loss_cls: 2.9553, loss: 2.9553 +2024-12-31 00:35:21,070 - pyskl - INFO - Epoch [127][2100/3746] lr: 5.905e-03, eta: 20:57:04, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4755, top5_acc: 0.7280, loss_cls: 2.9372, loss: 2.9372 +2024-12-31 00:36:46,435 - pyskl - INFO - Epoch [127][2200/3746] lr: 5.892e-03, eta: 20:55:38, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4713, top5_acc: 0.7206, loss_cls: 2.9781, loss: 2.9781 +2024-12-31 00:38:11,580 - pyskl - INFO - Epoch [127][2300/3746] lr: 5.879e-03, eta: 20:54:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4755, top5_acc: 0.7288, loss_cls: 2.9266, loss: 2.9266 +2024-12-31 00:39:36,374 - pyskl - INFO - Epoch [127][2400/3746] lr: 5.866e-03, eta: 20:52:46, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4780, top5_acc: 0.7256, loss_cls: 2.9500, loss: 2.9500 +2024-12-31 00:41:01,077 - pyskl - INFO - Epoch [127][2500/3746] lr: 5.852e-03, eta: 20:51:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4623, top5_acc: 0.7159, loss_cls: 2.9939, loss: 2.9939 +2024-12-31 00:42:26,613 - pyskl - INFO - Epoch [127][2600/3746] lr: 5.839e-03, eta: 20:49:54, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4717, top5_acc: 0.7286, loss_cls: 2.9731, loss: 2.9731 +2024-12-31 00:43:51,040 - pyskl - INFO - Epoch [127][2700/3746] lr: 5.826e-03, eta: 20:48:28, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7255, loss_cls: 2.9809, loss: 2.9809 +2024-12-31 00:45:15,937 - pyskl - INFO - Epoch [127][2800/3746] lr: 5.813e-03, eta: 20:47:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4805, top5_acc: 0.7281, loss_cls: 2.9243, loss: 2.9243 +2024-12-31 00:46:41,167 - pyskl - INFO - Epoch [127][2900/3746] lr: 5.800e-03, eta: 20:45:36, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4869, top5_acc: 0.7331, loss_cls: 2.8925, loss: 2.8925 +2024-12-31 00:48:06,158 - pyskl - INFO - Epoch [127][3000/3746] lr: 5.787e-03, eta: 20:44:10, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4639, top5_acc: 0.7152, loss_cls: 3.0185, loss: 3.0185 +2024-12-31 00:49:31,171 - pyskl - INFO - Epoch [127][3100/3746] lr: 5.774e-03, eta: 20:42:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4652, top5_acc: 0.7233, loss_cls: 2.9712, loss: 2.9712 +2024-12-31 00:50:55,618 - pyskl - INFO - Epoch [127][3200/3746] lr: 5.761e-03, eta: 20:41:17, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.4688, top5_acc: 0.7252, loss_cls: 2.9476, loss: 2.9476 +2024-12-31 00:52:20,386 - pyskl - INFO - Epoch [127][3300/3746] lr: 5.748e-03, eta: 20:39:51, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4833, top5_acc: 0.7266, loss_cls: 2.8967, loss: 2.8967 +2024-12-31 00:53:45,434 - pyskl - INFO - Epoch [127][3400/3746] lr: 5.735e-03, eta: 20:38:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4633, top5_acc: 0.7203, loss_cls: 2.9864, loss: 2.9864 +2024-12-31 00:55:10,089 - pyskl - INFO - Epoch [127][3500/3746] lr: 5.722e-03, eta: 20:36:59, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4755, top5_acc: 0.7255, loss_cls: 2.9114, loss: 2.9114 +2024-12-31 00:56:34,877 - pyskl - INFO - Epoch [127][3600/3746] lr: 5.709e-03, eta: 20:35:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4777, top5_acc: 0.7227, loss_cls: 2.9230, loss: 2.9230 +2024-12-31 00:58:00,626 - pyskl - INFO - Epoch [127][3700/3746] lr: 5.696e-03, eta: 20:34:07, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4705, top5_acc: 0.7266, loss_cls: 2.9323, loss: 2.9323 +2024-12-31 00:58:41,297 - pyskl - INFO - Saving checkpoint at 127 epochs +2024-12-31 01:00:39,243 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 01:00:39,928 - pyskl - INFO - +top1_acc 0.4018 +top5_acc 0.6545 +2024-12-31 01:00:39,928 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 01:00:39,973 - pyskl - INFO - +mean_acc 0.4014 +2024-12-31 01:00:39,978 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_126.pth was removed +2024-12-31 01:00:40,236 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_127.pth. +2024-12-31 01:00:40,237 - pyskl - INFO - Best top1_acc is 0.4018 at 127 epoch. +2024-12-31 01:00:40,250 - pyskl - INFO - Epoch(val) [127][309] top1_acc: 0.4018, top5_acc: 0.6545, mean_class_accuracy: 0.4014 +2024-12-31 01:04:48,427 - pyskl - INFO - Epoch [128][100/3746] lr: 5.677e-03, eta: 20:32:24, time: 2.482, data_time: 1.464, memory: 15990, top1_acc: 0.4956, top5_acc: 0.7478, loss_cls: 2.8154, loss: 2.8154 +2024-12-31 01:06:13,631 - pyskl - INFO - Epoch [128][200/3746] lr: 5.664e-03, eta: 20:30:58, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7342, loss_cls: 2.8987, loss: 2.8987 +2024-12-31 01:07:38,936 - pyskl - INFO - Epoch [128][300/3746] lr: 5.651e-03, eta: 20:29:32, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5016, top5_acc: 0.7420, loss_cls: 2.8202, loss: 2.8202 +2024-12-31 01:09:03,833 - pyskl - INFO - Epoch [128][400/3746] lr: 5.638e-03, eta: 20:28:06, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7423, loss_cls: 2.8630, loss: 2.8630 +2024-12-31 01:10:29,228 - pyskl - INFO - Epoch [128][500/3746] lr: 5.625e-03, eta: 20:26:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4970, top5_acc: 0.7403, loss_cls: 2.8505, loss: 2.8505 +2024-12-31 01:11:54,478 - pyskl - INFO - Epoch [128][600/3746] lr: 5.612e-03, eta: 20:25:13, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.4891, top5_acc: 0.7458, loss_cls: 2.8313, loss: 2.8313 +2024-12-31 01:13:19,504 - pyskl - INFO - Epoch [128][700/3746] lr: 5.600e-03, eta: 20:23:47, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4861, top5_acc: 0.7347, loss_cls: 2.8712, loss: 2.8712 +2024-12-31 01:14:44,491 - pyskl - INFO - Epoch [128][800/3746] lr: 5.587e-03, eta: 20:22:21, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4817, top5_acc: 0.7331, loss_cls: 2.9102, loss: 2.9102 +2024-12-31 01:16:09,590 - pyskl - INFO - Epoch [128][900/3746] lr: 5.574e-03, eta: 20:20:55, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4834, top5_acc: 0.7412, loss_cls: 2.8837, loss: 2.8837 +2024-12-31 01:17:34,971 - pyskl - INFO - Epoch [128][1000/3746] lr: 5.561e-03, eta: 20:19:29, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4923, top5_acc: 0.7395, loss_cls: 2.8466, loss: 2.8466 +2024-12-31 01:18:59,815 - pyskl - INFO - Epoch [128][1100/3746] lr: 5.548e-03, eta: 20:18:03, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4927, top5_acc: 0.7356, loss_cls: 2.8716, loss: 2.8716 +2024-12-31 01:20:25,160 - pyskl - INFO - Epoch [128][1200/3746] lr: 5.536e-03, eta: 20:16:37, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4831, top5_acc: 0.7298, loss_cls: 2.9312, loss: 2.9312 +2024-12-31 01:21:50,289 - pyskl - INFO - Epoch [128][1300/3746] lr: 5.523e-03, eta: 20:15:11, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4744, top5_acc: 0.7278, loss_cls: 2.9407, loss: 2.9407 +2024-12-31 01:23:15,754 - pyskl - INFO - Epoch [128][1400/3746] lr: 5.510e-03, eta: 20:13:45, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.4844, top5_acc: 0.7378, loss_cls: 2.8744, loss: 2.8744 +2024-12-31 01:24:40,571 - pyskl - INFO - Epoch [128][1500/3746] lr: 5.497e-03, eta: 20:12:19, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4763, top5_acc: 0.7277, loss_cls: 2.9310, loss: 2.9310 +2024-12-31 01:26:05,580 - pyskl - INFO - Epoch [128][1600/3746] lr: 5.485e-03, eta: 20:10:53, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4942, top5_acc: 0.7364, loss_cls: 2.8373, loss: 2.8373 +2024-12-31 01:27:30,383 - pyskl - INFO - Epoch [128][1700/3746] lr: 5.472e-03, eta: 20:09:27, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.4922, top5_acc: 0.7395, loss_cls: 2.8651, loss: 2.8651 +2024-12-31 01:28:55,781 - pyskl - INFO - Epoch [128][1800/3746] lr: 5.459e-03, eta: 20:08:01, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4811, top5_acc: 0.7369, loss_cls: 2.9105, loss: 2.9105 +2024-12-31 01:30:20,637 - pyskl - INFO - Epoch [128][1900/3746] lr: 5.446e-03, eta: 20:06:35, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4806, top5_acc: 0.7250, loss_cls: 2.9222, loss: 2.9222 +2024-12-31 01:31:45,260 - pyskl - INFO - Epoch [128][2000/3746] lr: 5.434e-03, eta: 20:05:08, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.4725, top5_acc: 0.7277, loss_cls: 2.9606, loss: 2.9606 +2024-12-31 01:33:10,248 - pyskl - INFO - Epoch [128][2100/3746] lr: 5.421e-03, eta: 20:03:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.4755, top5_acc: 0.7286, loss_cls: 2.9334, loss: 2.9334 +2024-12-31 01:34:35,170 - pyskl - INFO - Epoch [128][2200/3746] lr: 5.408e-03, eta: 20:02:16, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4788, top5_acc: 0.7320, loss_cls: 2.9081, loss: 2.9081 +2024-12-31 01:35:59,876 - pyskl - INFO - Epoch [128][2300/3746] lr: 5.396e-03, eta: 20:00:50, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4813, top5_acc: 0.7244, loss_cls: 2.9319, loss: 2.9319 +2024-12-31 01:37:24,991 - pyskl - INFO - Epoch [128][2400/3746] lr: 5.383e-03, eta: 19:59:24, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7297, loss_cls: 2.9258, loss: 2.9258 +2024-12-31 01:38:49,694 - pyskl - INFO - Epoch [128][2500/3746] lr: 5.370e-03, eta: 19:57:58, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.4747, top5_acc: 0.7316, loss_cls: 2.9103, loss: 2.9103 +2024-12-31 01:40:14,946 - pyskl - INFO - Epoch [128][2600/3746] lr: 5.358e-03, eta: 19:56:32, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.4825, top5_acc: 0.7412, loss_cls: 2.8828, loss: 2.8828 +2024-12-31 01:41:40,422 - pyskl - INFO - Epoch [128][2700/3746] lr: 5.345e-03, eta: 19:55:06, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4800, top5_acc: 0.7372, loss_cls: 2.8964, loss: 2.8964 +2024-12-31 01:43:05,317 - pyskl - INFO - Epoch [128][2800/3746] lr: 5.333e-03, eta: 19:53:40, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.4753, top5_acc: 0.7247, loss_cls: 2.9396, loss: 2.9396 +2024-12-31 01:44:30,714 - pyskl - INFO - Epoch [128][2900/3746] lr: 5.320e-03, eta: 19:52:14, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4786, top5_acc: 0.7328, loss_cls: 2.9123, loss: 2.9123 +2024-12-31 01:45:56,388 - pyskl - INFO - Epoch [128][3000/3746] lr: 5.308e-03, eta: 19:50:48, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4758, top5_acc: 0.7250, loss_cls: 2.9408, loss: 2.9408 +2024-12-31 01:47:22,460 - pyskl - INFO - Epoch [128][3100/3746] lr: 5.295e-03, eta: 19:49:22, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4834, top5_acc: 0.7264, loss_cls: 2.9218, loss: 2.9218 +2024-12-31 01:48:48,398 - pyskl - INFO - Epoch [128][3200/3746] lr: 5.283e-03, eta: 19:47:56, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4800, top5_acc: 0.7234, loss_cls: 2.9312, loss: 2.9312 +2024-12-31 01:50:13,864 - pyskl - INFO - Epoch [128][3300/3746] lr: 5.270e-03, eta: 19:46:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.4767, top5_acc: 0.7342, loss_cls: 2.8984, loss: 2.8984 +2024-12-31 01:51:39,504 - pyskl - INFO - Epoch [128][3400/3746] lr: 5.258e-03, eta: 19:45:04, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4713, top5_acc: 0.7269, loss_cls: 2.9835, loss: 2.9835 +2024-12-31 01:53:05,494 - pyskl - INFO - Epoch [128][3500/3746] lr: 5.245e-03, eta: 19:43:38, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4825, top5_acc: 0.7238, loss_cls: 2.9283, loss: 2.9283 +2024-12-31 01:54:31,195 - pyskl - INFO - Epoch [128][3600/3746] lr: 5.233e-03, eta: 19:42:12, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4798, top5_acc: 0.7316, loss_cls: 2.9243, loss: 2.9243 +2024-12-31 01:55:56,589 - pyskl - INFO - Epoch [128][3700/3746] lr: 5.220e-03, eta: 19:40:46, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4672, top5_acc: 0.7238, loss_cls: 2.9722, loss: 2.9722 +2024-12-31 01:56:37,475 - pyskl - INFO - Saving checkpoint at 128 epochs +2024-12-31 01:58:37,112 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 01:58:37,936 - pyskl - INFO - +top1_acc 0.4027 +top5_acc 0.6499 +2024-12-31 01:58:37,937 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 01:58:37,983 - pyskl - INFO - +mean_acc 0.4025 +2024-12-31 01:58:37,987 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_127.pth was removed +2024-12-31 01:58:38,353 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_128.pth. +2024-12-31 01:58:38,353 - pyskl - INFO - Best top1_acc is 0.4027 at 128 epoch. +2024-12-31 01:58:38,367 - pyskl - INFO - Epoch(val) [128][309] top1_acc: 0.4027, top5_acc: 0.6499, mean_class_accuracy: 0.4025 +2024-12-31 02:02:52,512 - pyskl - INFO - Epoch [129][100/3746] lr: 5.202e-03, eta: 19:39:03, time: 2.541, data_time: 1.507, memory: 15990, top1_acc: 0.4984, top5_acc: 0.7527, loss_cls: 2.8033, loss: 2.8033 +2024-12-31 02:04:18,864 - pyskl - INFO - Epoch [129][200/3746] lr: 5.190e-03, eta: 19:37:37, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5000, top5_acc: 0.7559, loss_cls: 2.7941, loss: 2.7941 +2024-12-31 02:05:45,089 - pyskl - INFO - Epoch [129][300/3746] lr: 5.177e-03, eta: 19:36:11, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4967, top5_acc: 0.7419, loss_cls: 2.8381, loss: 2.8381 +2024-12-31 02:07:11,778 - pyskl - INFO - Epoch [129][400/3746] lr: 5.165e-03, eta: 19:34:45, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.4920, top5_acc: 0.7444, loss_cls: 2.8222, loss: 2.8222 +2024-12-31 02:08:37,960 - pyskl - INFO - Epoch [129][500/3746] lr: 5.153e-03, eta: 19:33:19, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4855, top5_acc: 0.7430, loss_cls: 2.8227, loss: 2.8227 +2024-12-31 02:10:03,532 - pyskl - INFO - Epoch [129][600/3746] lr: 5.140e-03, eta: 19:31:53, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4806, top5_acc: 0.7412, loss_cls: 2.8744, loss: 2.8744 +2024-12-31 02:11:29,751 - pyskl - INFO - Epoch [129][700/3746] lr: 5.128e-03, eta: 19:30:27, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4892, top5_acc: 0.7456, loss_cls: 2.8290, loss: 2.8290 +2024-12-31 02:12:55,866 - pyskl - INFO - Epoch [129][800/3746] lr: 5.116e-03, eta: 19:29:02, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7336, loss_cls: 2.8877, loss: 2.8877 +2024-12-31 02:14:21,643 - pyskl - INFO - Epoch [129][900/3746] lr: 5.103e-03, eta: 19:27:36, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4853, top5_acc: 0.7416, loss_cls: 2.8638, loss: 2.8638 +2024-12-31 02:15:47,716 - pyskl - INFO - Epoch [129][1000/3746] lr: 5.091e-03, eta: 19:26:10, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5027, top5_acc: 0.7450, loss_cls: 2.8421, loss: 2.8421 +2024-12-31 02:17:14,454 - pyskl - INFO - Epoch [129][1100/3746] lr: 5.079e-03, eta: 19:24:44, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7430, loss_cls: 2.8384, loss: 2.8384 +2024-12-31 02:18:40,780 - pyskl - INFO - Epoch [129][1200/3746] lr: 5.066e-03, eta: 19:23:18, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4967, top5_acc: 0.7508, loss_cls: 2.8075, loss: 2.8075 +2024-12-31 02:20:06,904 - pyskl - INFO - Epoch [129][1300/3746] lr: 5.054e-03, eta: 19:21:52, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4923, top5_acc: 0.7442, loss_cls: 2.8453, loss: 2.8453 +2024-12-31 02:21:32,369 - pyskl - INFO - Epoch [129][1400/3746] lr: 5.042e-03, eta: 19:20:26, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.4956, top5_acc: 0.7439, loss_cls: 2.8164, loss: 2.8164 +2024-12-31 02:22:58,423 - pyskl - INFO - Epoch [129][1500/3746] lr: 5.030e-03, eta: 19:19:00, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4838, top5_acc: 0.7361, loss_cls: 2.8859, loss: 2.8859 +2024-12-31 02:24:24,394 - pyskl - INFO - Epoch [129][1600/3746] lr: 5.017e-03, eta: 19:17:34, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4766, top5_acc: 0.7394, loss_cls: 2.8836, loss: 2.8836 +2024-12-31 02:25:50,231 - pyskl - INFO - Epoch [129][1700/3746] lr: 5.005e-03, eta: 19:16:08, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7425, loss_cls: 2.8305, loss: 2.8305 +2024-12-31 02:27:16,581 - pyskl - INFO - Epoch [129][1800/3746] lr: 4.993e-03, eta: 19:14:42, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4842, top5_acc: 0.7381, loss_cls: 2.8640, loss: 2.8640 +2024-12-31 02:28:42,269 - pyskl - INFO - Epoch [129][1900/3746] lr: 4.981e-03, eta: 19:13:16, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4889, top5_acc: 0.7366, loss_cls: 2.8876, loss: 2.8876 +2024-12-31 02:30:08,417 - pyskl - INFO - Epoch [129][2000/3746] lr: 4.969e-03, eta: 19:11:50, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4875, top5_acc: 0.7405, loss_cls: 2.8451, loss: 2.8451 +2024-12-31 02:31:35,110 - pyskl - INFO - Epoch [129][2100/3746] lr: 4.957e-03, eta: 19:10:25, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.4900, top5_acc: 0.7467, loss_cls: 2.8339, loss: 2.8339 +2024-12-31 02:33:01,632 - pyskl - INFO - Epoch [129][2200/3746] lr: 4.944e-03, eta: 19:08:59, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.4864, top5_acc: 0.7416, loss_cls: 2.8734, loss: 2.8734 +2024-12-31 02:34:27,581 - pyskl - INFO - Epoch [129][2300/3746] lr: 4.932e-03, eta: 19:07:33, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7339, loss_cls: 2.9021, loss: 2.9021 +2024-12-31 02:35:53,654 - pyskl - INFO - Epoch [129][2400/3746] lr: 4.920e-03, eta: 19:06:07, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.4859, top5_acc: 0.7402, loss_cls: 2.8628, loss: 2.8628 +2024-12-31 02:37:20,362 - pyskl - INFO - Epoch [129][2500/3746] lr: 4.908e-03, eta: 19:04:41, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.4895, top5_acc: 0.7386, loss_cls: 2.8763, loss: 2.8763 +2024-12-31 02:38:46,583 - pyskl - INFO - Epoch [129][2600/3746] lr: 4.896e-03, eta: 19:03:15, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4845, top5_acc: 0.7355, loss_cls: 2.9052, loss: 2.9052 +2024-12-31 02:40:12,459 - pyskl - INFO - Epoch [129][2700/3746] lr: 4.884e-03, eta: 19:01:49, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.4861, top5_acc: 0.7422, loss_cls: 2.8669, loss: 2.8669 +2024-12-31 02:41:38,099 - pyskl - INFO - Epoch [129][2800/3746] lr: 4.872e-03, eta: 19:00:23, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4789, top5_acc: 0.7253, loss_cls: 2.9056, loss: 2.9056 +2024-12-31 02:43:04,789 - pyskl - INFO - Epoch [129][2900/3746] lr: 4.860e-03, eta: 18:58:57, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7369, loss_cls: 2.8695, loss: 2.8695 +2024-12-31 02:44:31,636 - pyskl - INFO - Epoch [129][3000/3746] lr: 4.848e-03, eta: 18:57:32, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.4866, top5_acc: 0.7352, loss_cls: 2.8685, loss: 2.8685 +2024-12-31 02:45:58,259 - pyskl - INFO - Epoch [129][3100/3746] lr: 4.836e-03, eta: 18:56:06, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.4823, top5_acc: 0.7377, loss_cls: 2.8846, loss: 2.8846 +2024-12-31 02:47:25,314 - pyskl - INFO - Epoch [129][3200/3746] lr: 4.824e-03, eta: 18:54:40, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.4806, top5_acc: 0.7222, loss_cls: 2.9336, loss: 2.9336 +2024-12-31 02:48:52,197 - pyskl - INFO - Epoch [129][3300/3746] lr: 4.812e-03, eta: 18:53:14, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.4748, top5_acc: 0.7269, loss_cls: 2.9307, loss: 2.9307 +2024-12-31 02:50:19,610 - pyskl - INFO - Epoch [129][3400/3746] lr: 4.800e-03, eta: 18:51:48, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.4803, top5_acc: 0.7297, loss_cls: 2.9107, loss: 2.9107 +2024-12-31 02:51:46,810 - pyskl - INFO - Epoch [129][3500/3746] lr: 4.788e-03, eta: 18:50:23, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7367, loss_cls: 2.8692, loss: 2.8692 +2024-12-31 02:53:13,645 - pyskl - INFO - Epoch [129][3600/3746] lr: 4.776e-03, eta: 18:48:57, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7333, loss_cls: 2.8962, loss: 2.8962 +2024-12-31 02:54:40,997 - pyskl - INFO - Epoch [129][3700/3746] lr: 4.764e-03, eta: 18:47:31, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.4875, top5_acc: 0.7330, loss_cls: 2.8966, loss: 2.8966 +2024-12-31 02:55:23,006 - pyskl - INFO - Saving checkpoint at 129 epochs +2024-12-31 02:57:21,975 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 02:57:22,893 - pyskl - INFO - +top1_acc 0.4115 +top5_acc 0.6626 +2024-12-31 02:57:22,893 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 02:57:22,935 - pyskl - INFO - +mean_acc 0.4111 +2024-12-31 02:57:22,939 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_128.pth was removed +2024-12-31 02:57:23,212 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_129.pth. +2024-12-31 02:57:23,213 - pyskl - INFO - Best top1_acc is 0.4115 at 129 epoch. +2024-12-31 02:57:23,225 - pyskl - INFO - Epoch(val) [129][309] top1_acc: 0.4115, top5_acc: 0.6626, mean_class_accuracy: 0.4111 +2024-12-31 03:01:40,886 - pyskl - INFO - Epoch [130][100/3746] lr: 4.747e-03, eta: 18:45:47, time: 2.576, data_time: 1.517, memory: 15990, top1_acc: 0.5088, top5_acc: 0.7608, loss_cls: 2.7315, loss: 2.7315 +2024-12-31 03:03:07,534 - pyskl - INFO - Epoch [130][200/3746] lr: 4.735e-03, eta: 18:44:21, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5102, top5_acc: 0.7544, loss_cls: 2.7377, loss: 2.7377 +2024-12-31 03:04:34,512 - pyskl - INFO - Epoch [130][300/3746] lr: 4.723e-03, eta: 18:42:55, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5033, top5_acc: 0.7633, loss_cls: 2.7437, loss: 2.7437 +2024-12-31 03:06:01,350 - pyskl - INFO - Epoch [130][400/3746] lr: 4.711e-03, eta: 18:41:30, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5080, top5_acc: 0.7436, loss_cls: 2.8159, loss: 2.8159 +2024-12-31 03:07:28,237 - pyskl - INFO - Epoch [130][500/3746] lr: 4.699e-03, eta: 18:40:04, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.4955, top5_acc: 0.7508, loss_cls: 2.8280, loss: 2.8280 +2024-12-31 03:08:55,621 - pyskl - INFO - Epoch [130][600/3746] lr: 4.688e-03, eta: 18:38:38, time: 0.874, data_time: 0.001, memory: 15990, top1_acc: 0.4905, top5_acc: 0.7436, loss_cls: 2.8273, loss: 2.8273 +2024-12-31 03:10:23,157 - pyskl - INFO - Epoch [130][700/3746] lr: 4.676e-03, eta: 18:37:12, time: 0.875, data_time: 0.000, memory: 15990, top1_acc: 0.5055, top5_acc: 0.7559, loss_cls: 2.7766, loss: 2.7766 +2024-12-31 03:11:50,069 - pyskl - INFO - Epoch [130][800/3746] lr: 4.664e-03, eta: 18:35:46, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5058, top5_acc: 0.7512, loss_cls: 2.7836, loss: 2.7836 +2024-12-31 03:13:17,498 - pyskl - INFO - Epoch [130][900/3746] lr: 4.652e-03, eta: 18:34:21, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.4948, top5_acc: 0.7416, loss_cls: 2.8407, loss: 2.8407 +2024-12-31 03:14:45,056 - pyskl - INFO - Epoch [130][1000/3746] lr: 4.640e-03, eta: 18:32:55, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.4888, top5_acc: 0.7356, loss_cls: 2.8752, loss: 2.8752 +2024-12-31 03:16:12,044 - pyskl - INFO - Epoch [130][1100/3746] lr: 4.629e-03, eta: 18:31:29, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5002, top5_acc: 0.7508, loss_cls: 2.8157, loss: 2.8157 +2024-12-31 03:17:38,239 - pyskl - INFO - Epoch [130][1200/3746] lr: 4.617e-03, eta: 18:30:03, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4995, top5_acc: 0.7438, loss_cls: 2.8129, loss: 2.8129 +2024-12-31 03:19:03,669 - pyskl - INFO - Epoch [130][1300/3746] lr: 4.605e-03, eta: 18:28:37, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4963, top5_acc: 0.7442, loss_cls: 2.8204, loss: 2.8204 +2024-12-31 03:20:29,695 - pyskl - INFO - Epoch [130][1400/3746] lr: 4.594e-03, eta: 18:27:11, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.4950, top5_acc: 0.7473, loss_cls: 2.8030, loss: 2.8030 +2024-12-31 03:21:55,111 - pyskl - INFO - Epoch [130][1500/3746] lr: 4.582e-03, eta: 18:25:45, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.4955, top5_acc: 0.7442, loss_cls: 2.8354, loss: 2.8354 +2024-12-31 03:23:20,848 - pyskl - INFO - Epoch [130][1600/3746] lr: 4.570e-03, eta: 18:24:19, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4877, top5_acc: 0.7405, loss_cls: 2.8391, loss: 2.8391 +2024-12-31 03:24:46,519 - pyskl - INFO - Epoch [130][1700/3746] lr: 4.558e-03, eta: 18:22:53, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7448, loss_cls: 2.8276, loss: 2.8276 +2024-12-31 03:26:12,316 - pyskl - INFO - Epoch [130][1800/3746] lr: 4.547e-03, eta: 18:21:27, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4888, top5_acc: 0.7417, loss_cls: 2.8636, loss: 2.8636 +2024-12-31 03:27:38,539 - pyskl - INFO - Epoch [130][1900/3746] lr: 4.535e-03, eta: 18:20:01, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5092, top5_acc: 0.7580, loss_cls: 2.7725, loss: 2.7725 +2024-12-31 03:29:04,154 - pyskl - INFO - Epoch [130][2000/3746] lr: 4.524e-03, eta: 18:18:35, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.4822, top5_acc: 0.7434, loss_cls: 2.8674, loss: 2.8674 +2024-12-31 03:30:29,951 - pyskl - INFO - Epoch [130][2100/3746] lr: 4.512e-03, eta: 18:17:09, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4950, top5_acc: 0.7394, loss_cls: 2.8459, loss: 2.8459 +2024-12-31 03:31:56,725 - pyskl - INFO - Epoch [130][2200/3746] lr: 4.500e-03, eta: 18:15:43, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.4877, top5_acc: 0.7348, loss_cls: 2.8517, loss: 2.8517 +2024-12-31 03:33:23,430 - pyskl - INFO - Epoch [130][2300/3746] lr: 4.489e-03, eta: 18:14:17, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.4928, top5_acc: 0.7453, loss_cls: 2.8249, loss: 2.8249 +2024-12-31 03:34:49,825 - pyskl - INFO - Epoch [130][2400/3746] lr: 4.477e-03, eta: 18:12:51, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.4927, top5_acc: 0.7425, loss_cls: 2.8633, loss: 2.8633 +2024-12-31 03:36:16,807 - pyskl - INFO - Epoch [130][2500/3746] lr: 4.466e-03, eta: 18:11:26, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.4873, top5_acc: 0.7473, loss_cls: 2.8491, loss: 2.8491 +2024-12-31 03:37:43,174 - pyskl - INFO - Epoch [130][2600/3746] lr: 4.454e-03, eta: 18:10:00, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.4913, top5_acc: 0.7400, loss_cls: 2.8425, loss: 2.8425 +2024-12-31 03:39:10,350 - pyskl - INFO - Epoch [130][2700/3746] lr: 4.443e-03, eta: 18:08:34, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.4920, top5_acc: 0.7420, loss_cls: 2.8511, loss: 2.8511 +2024-12-31 03:40:37,357 - pyskl - INFO - Epoch [130][2800/3746] lr: 4.431e-03, eta: 18:07:08, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.4886, top5_acc: 0.7388, loss_cls: 2.8671, loss: 2.8671 +2024-12-31 03:42:05,126 - pyskl - INFO - Epoch [130][2900/3746] lr: 4.420e-03, eta: 18:05:42, time: 0.878, data_time: 0.000, memory: 15990, top1_acc: 0.4791, top5_acc: 0.7362, loss_cls: 2.8898, loss: 2.8898 +2024-12-31 03:43:32,724 - pyskl - INFO - Epoch [130][3000/3746] lr: 4.408e-03, eta: 18:04:17, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.4909, top5_acc: 0.7289, loss_cls: 2.8745, loss: 2.8745 +2024-12-31 03:45:00,765 - pyskl - INFO - Epoch [130][3100/3746] lr: 4.397e-03, eta: 18:02:51, time: 0.880, data_time: 0.000, memory: 15990, top1_acc: 0.4875, top5_acc: 0.7380, loss_cls: 2.8474, loss: 2.8474 +2024-12-31 03:46:28,581 - pyskl - INFO - Epoch [130][3200/3746] lr: 4.385e-03, eta: 18:01:25, time: 0.878, data_time: 0.000, memory: 15990, top1_acc: 0.4906, top5_acc: 0.7375, loss_cls: 2.8256, loss: 2.8256 +2024-12-31 03:47:56,484 - pyskl - INFO - Epoch [130][3300/3746] lr: 4.374e-03, eta: 18:00:00, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.5103, top5_acc: 0.7450, loss_cls: 2.8090, loss: 2.8090 +2024-12-31 03:49:23,810 - pyskl - INFO - Epoch [130][3400/3746] lr: 4.362e-03, eta: 17:58:34, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.4934, top5_acc: 0.7469, loss_cls: 2.8213, loss: 2.8213 +2024-12-31 03:50:51,364 - pyskl - INFO - Epoch [130][3500/3746] lr: 4.351e-03, eta: 17:57:08, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.4875, top5_acc: 0.7450, loss_cls: 2.8786, loss: 2.8786 +2024-12-31 03:52:18,296 - pyskl - INFO - Epoch [130][3600/3746] lr: 4.339e-03, eta: 17:55:42, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7405, loss_cls: 2.8379, loss: 2.8379 +2024-12-31 03:53:46,397 - pyskl - INFO - Epoch [130][3700/3746] lr: 4.328e-03, eta: 17:54:17, time: 0.881, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7402, loss_cls: 2.8710, loss: 2.8710 +2024-12-31 03:54:27,782 - pyskl - INFO - Saving checkpoint at 130 epochs +2024-12-31 03:56:27,116 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 03:56:27,828 - pyskl - INFO - +top1_acc 0.4084 +top5_acc 0.6625 +2024-12-31 03:56:27,829 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 03:56:27,882 - pyskl - INFO - +mean_acc 0.4082 +2024-12-31 03:56:27,895 - pyskl - INFO - Epoch(val) [130][309] top1_acc: 0.4084, top5_acc: 0.6625, mean_class_accuracy: 0.4082 +2024-12-31 04:00:45,978 - pyskl - INFO - Epoch [131][100/3746] lr: 4.311e-03, eta: 17:52:32, time: 2.581, data_time: 1.522, memory: 15990, top1_acc: 0.5139, top5_acc: 0.7617, loss_cls: 2.7314, loss: 2.7314 +2024-12-31 04:02:13,409 - pyskl - INFO - Epoch [131][200/3746] lr: 4.300e-03, eta: 17:51:06, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.5108, top5_acc: 0.7559, loss_cls: 2.7413, loss: 2.7413 +2024-12-31 04:03:40,481 - pyskl - INFO - Epoch [131][300/3746] lr: 4.289e-03, eta: 17:49:40, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5161, top5_acc: 0.7616, loss_cls: 2.7001, loss: 2.7001 +2024-12-31 04:05:07,608 - pyskl - INFO - Epoch [131][400/3746] lr: 4.277e-03, eta: 17:48:14, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5067, top5_acc: 0.7478, loss_cls: 2.7646, loss: 2.7646 +2024-12-31 04:06:34,909 - pyskl - INFO - Epoch [131][500/3746] lr: 4.266e-03, eta: 17:46:48, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.5155, top5_acc: 0.7664, loss_cls: 2.7194, loss: 2.7194 +2024-12-31 04:08:02,010 - pyskl - INFO - Epoch [131][600/3746] lr: 4.255e-03, eta: 17:45:22, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.4972, top5_acc: 0.7556, loss_cls: 2.7906, loss: 2.7906 +2024-12-31 04:09:28,974 - pyskl - INFO - Epoch [131][700/3746] lr: 4.244e-03, eta: 17:43:57, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5088, top5_acc: 0.7508, loss_cls: 2.7767, loss: 2.7767 +2024-12-31 04:10:56,419 - pyskl - INFO - Epoch [131][800/3746] lr: 4.232e-03, eta: 17:42:31, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.4981, top5_acc: 0.7489, loss_cls: 2.8042, loss: 2.8042 +2024-12-31 04:12:24,299 - pyskl - INFO - Epoch [131][900/3746] lr: 4.221e-03, eta: 17:41:05, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.4969, top5_acc: 0.7427, loss_cls: 2.7929, loss: 2.7929 +2024-12-31 04:13:52,344 - pyskl - INFO - Epoch [131][1000/3746] lr: 4.210e-03, eta: 17:39:39, time: 0.880, data_time: 0.000, memory: 15990, top1_acc: 0.5077, top5_acc: 0.7541, loss_cls: 2.7642, loss: 2.7642 +2024-12-31 04:15:19,431 - pyskl - INFO - Epoch [131][1100/3746] lr: 4.199e-03, eta: 17:38:13, time: 0.871, data_time: 0.001, memory: 15990, top1_acc: 0.5097, top5_acc: 0.7538, loss_cls: 2.7512, loss: 2.7512 +2024-12-31 04:16:45,729 - pyskl - INFO - Epoch [131][1200/3746] lr: 4.187e-03, eta: 17:36:47, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5080, top5_acc: 0.7553, loss_cls: 2.7707, loss: 2.7707 +2024-12-31 04:18:11,191 - pyskl - INFO - Epoch [131][1300/3746] lr: 4.176e-03, eta: 17:35:21, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5058, top5_acc: 0.7483, loss_cls: 2.7876, loss: 2.7876 +2024-12-31 04:19:37,829 - pyskl - INFO - Epoch [131][1400/3746] lr: 4.165e-03, eta: 17:33:55, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5033, top5_acc: 0.7572, loss_cls: 2.7473, loss: 2.7473 +2024-12-31 04:21:05,064 - pyskl - INFO - Epoch [131][1500/3746] lr: 4.154e-03, eta: 17:32:30, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5036, top5_acc: 0.7558, loss_cls: 2.7694, loss: 2.7694 +2024-12-31 04:22:31,824 - pyskl - INFO - Epoch [131][1600/3746] lr: 4.143e-03, eta: 17:31:04, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.4970, top5_acc: 0.7441, loss_cls: 2.8265, loss: 2.8265 +2024-12-31 04:23:58,683 - pyskl - INFO - Epoch [131][1700/3746] lr: 4.132e-03, eta: 17:29:38, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5025, top5_acc: 0.7594, loss_cls: 2.7932, loss: 2.7932 +2024-12-31 04:25:25,215 - pyskl - INFO - Epoch [131][1800/3746] lr: 4.120e-03, eta: 17:28:12, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.4964, top5_acc: 0.7448, loss_cls: 2.8178, loss: 2.8178 +2024-12-31 04:26:50,993 - pyskl - INFO - Epoch [131][1900/3746] lr: 4.109e-03, eta: 17:26:46, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4959, top5_acc: 0.7491, loss_cls: 2.8244, loss: 2.8244 +2024-12-31 04:28:16,572 - pyskl - INFO - Epoch [131][2000/3746] lr: 4.098e-03, eta: 17:25:20, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5039, top5_acc: 0.7542, loss_cls: 2.7596, loss: 2.7596 +2024-12-31 04:29:42,337 - pyskl - INFO - Epoch [131][2100/3746] lr: 4.087e-03, eta: 17:23:54, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4950, top5_acc: 0.7434, loss_cls: 2.8131, loss: 2.8131 +2024-12-31 04:31:08,674 - pyskl - INFO - Epoch [131][2200/3746] lr: 4.076e-03, eta: 17:22:28, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.4989, top5_acc: 0.7530, loss_cls: 2.7924, loss: 2.7924 +2024-12-31 04:32:35,976 - pyskl - INFO - Epoch [131][2300/3746] lr: 4.065e-03, eta: 17:21:02, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.4872, top5_acc: 0.7384, loss_cls: 2.8621, loss: 2.8621 +2024-12-31 04:34:02,735 - pyskl - INFO - Epoch [131][2400/3746] lr: 4.054e-03, eta: 17:19:36, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.4964, top5_acc: 0.7414, loss_cls: 2.8013, loss: 2.8013 +2024-12-31 04:35:30,197 - pyskl - INFO - Epoch [131][2500/3746] lr: 4.043e-03, eta: 17:18:10, time: 0.875, data_time: 0.000, memory: 15990, top1_acc: 0.4955, top5_acc: 0.7517, loss_cls: 2.8267, loss: 2.8267 +2024-12-31 04:36:57,188 - pyskl - INFO - Epoch [131][2600/3746] lr: 4.032e-03, eta: 17:16:44, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.4989, top5_acc: 0.7517, loss_cls: 2.7905, loss: 2.7905 +2024-12-31 04:38:24,094 - pyskl - INFO - Epoch [131][2700/3746] lr: 4.021e-03, eta: 17:15:18, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.4922, top5_acc: 0.7478, loss_cls: 2.8277, loss: 2.8277 +2024-12-31 04:39:50,712 - pyskl - INFO - Epoch [131][2800/3746] lr: 4.010e-03, eta: 17:13:53, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.4997, top5_acc: 0.7525, loss_cls: 2.7824, loss: 2.7824 +2024-12-31 04:41:17,509 - pyskl - INFO - Epoch [131][2900/3746] lr: 3.999e-03, eta: 17:12:27, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5028, top5_acc: 0.7489, loss_cls: 2.8031, loss: 2.8031 +2024-12-31 04:42:44,344 - pyskl - INFO - Epoch [131][3000/3746] lr: 3.988e-03, eta: 17:11:01, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.4830, top5_acc: 0.7433, loss_cls: 2.8575, loss: 2.8575 +2024-12-31 04:44:11,423 - pyskl - INFO - Epoch [131][3100/3746] lr: 3.977e-03, eta: 17:09:35, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.4855, top5_acc: 0.7431, loss_cls: 2.8466, loss: 2.8466 +2024-12-31 04:45:38,187 - pyskl - INFO - Epoch [131][3200/3746] lr: 3.966e-03, eta: 17:08:09, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.4848, top5_acc: 0.7422, loss_cls: 2.8446, loss: 2.8446 +2024-12-31 04:47:05,084 - pyskl - INFO - Epoch [131][3300/3746] lr: 3.955e-03, eta: 17:06:43, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.4998, top5_acc: 0.7448, loss_cls: 2.8142, loss: 2.8142 +2024-12-31 04:48:31,909 - pyskl - INFO - Epoch [131][3400/3746] lr: 3.945e-03, eta: 17:05:17, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.4972, top5_acc: 0.7466, loss_cls: 2.8037, loss: 2.8037 +2024-12-31 04:49:59,227 - pyskl - INFO - Epoch [131][3500/3746] lr: 3.934e-03, eta: 17:03:51, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.4984, top5_acc: 0.7419, loss_cls: 2.8379, loss: 2.8379 +2024-12-31 04:51:26,195 - pyskl - INFO - Epoch [131][3600/3746] lr: 3.923e-03, eta: 17:02:26, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.4908, top5_acc: 0.7431, loss_cls: 2.8543, loss: 2.8543 +2024-12-31 04:52:53,340 - pyskl - INFO - Epoch [131][3700/3746] lr: 3.912e-03, eta: 17:01:00, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.4992, top5_acc: 0.7514, loss_cls: 2.7818, loss: 2.7818 +2024-12-31 04:53:35,174 - pyskl - INFO - Saving checkpoint at 131 epochs +2024-12-31 04:55:34,665 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 04:55:35,401 - pyskl - INFO - +top1_acc 0.4120 +top5_acc 0.6619 +2024-12-31 04:55:35,401 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 04:55:35,454 - pyskl - INFO - +mean_acc 0.4120 +2024-12-31 04:55:35,459 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_129.pth was removed +2024-12-31 04:55:35,759 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_131.pth. +2024-12-31 04:55:35,760 - pyskl - INFO - Best top1_acc is 0.4120 at 131 epoch. +2024-12-31 04:55:35,776 - pyskl - INFO - Epoch(val) [131][309] top1_acc: 0.4120, top5_acc: 0.6619, mean_class_accuracy: 0.4120 +2024-12-31 04:59:56,547 - pyskl - INFO - Epoch [132][100/3746] lr: 3.896e-03, eta: 16:59:14, time: 2.608, data_time: 1.530, memory: 15990, top1_acc: 0.5311, top5_acc: 0.7611, loss_cls: 2.6745, loss: 2.6745 +2024-12-31 05:01:24,520 - pyskl - INFO - Epoch [132][200/3746] lr: 3.885e-03, eta: 16:57:48, time: 0.880, data_time: 0.000, memory: 15990, top1_acc: 0.5119, top5_acc: 0.7608, loss_cls: 2.7287, loss: 2.7287 +2024-12-31 05:02:52,358 - pyskl - INFO - Epoch [132][300/3746] lr: 3.875e-03, eta: 16:56:22, time: 0.878, data_time: 0.000, memory: 15990, top1_acc: 0.5198, top5_acc: 0.7609, loss_cls: 2.7203, loss: 2.7203 +2024-12-31 05:04:20,455 - pyskl - INFO - Epoch [132][400/3746] lr: 3.864e-03, eta: 16:54:56, time: 0.881, data_time: 0.000, memory: 15990, top1_acc: 0.5130, top5_acc: 0.7620, loss_cls: 2.7210, loss: 2.7210 +2024-12-31 05:05:48,624 - pyskl - INFO - Epoch [132][500/3746] lr: 3.853e-03, eta: 16:53:31, time: 0.882, data_time: 0.001, memory: 15990, top1_acc: 0.5073, top5_acc: 0.7547, loss_cls: 2.7479, loss: 2.7479 +2024-12-31 05:07:15,989 - pyskl - INFO - Epoch [132][600/3746] lr: 3.842e-03, eta: 16:52:05, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.5255, top5_acc: 0.7653, loss_cls: 2.6697, loss: 2.6697 +2024-12-31 05:08:43,845 - pyskl - INFO - Epoch [132][700/3746] lr: 3.831e-03, eta: 16:50:39, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.5230, top5_acc: 0.7673, loss_cls: 2.6975, loss: 2.6975 +2024-12-31 05:10:11,581 - pyskl - INFO - Epoch [132][800/3746] lr: 3.821e-03, eta: 16:49:13, time: 0.877, data_time: 0.000, memory: 15990, top1_acc: 0.5145, top5_acc: 0.7567, loss_cls: 2.7388, loss: 2.7388 +2024-12-31 05:11:38,978 - pyskl - INFO - Epoch [132][900/3746] lr: 3.810e-03, eta: 16:47:47, time: 0.874, data_time: 0.001, memory: 15990, top1_acc: 0.5119, top5_acc: 0.7575, loss_cls: 2.7202, loss: 2.7202 +2024-12-31 05:13:06,729 - pyskl - INFO - Epoch [132][1000/3746] lr: 3.799e-03, eta: 16:46:22, time: 0.878, data_time: 0.001, memory: 15990, top1_acc: 0.4952, top5_acc: 0.7422, loss_cls: 2.8234, loss: 2.8234 +2024-12-31 05:14:33,951 - pyskl - INFO - Epoch [132][1100/3746] lr: 3.789e-03, eta: 16:44:56, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5109, top5_acc: 0.7511, loss_cls: 2.7616, loss: 2.7616 +2024-12-31 05:15:59,788 - pyskl - INFO - Epoch [132][1200/3746] lr: 3.778e-03, eta: 16:43:30, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5216, top5_acc: 0.7589, loss_cls: 2.6938, loss: 2.6938 +2024-12-31 05:17:26,074 - pyskl - INFO - Epoch [132][1300/3746] lr: 3.767e-03, eta: 16:42:04, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5048, top5_acc: 0.7603, loss_cls: 2.7217, loss: 2.7217 +2024-12-31 05:18:53,421 - pyskl - INFO - Epoch [132][1400/3746] lr: 3.757e-03, eta: 16:40:38, time: 0.873, data_time: 0.001, memory: 15990, top1_acc: 0.5086, top5_acc: 0.7567, loss_cls: 2.7342, loss: 2.7342 +2024-12-31 05:20:20,745 - pyskl - INFO - Epoch [132][1500/3746] lr: 3.746e-03, eta: 16:39:12, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.5062, top5_acc: 0.7558, loss_cls: 2.7473, loss: 2.7473 +2024-12-31 05:21:47,475 - pyskl - INFO - Epoch [132][1600/3746] lr: 3.735e-03, eta: 16:37:46, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5125, top5_acc: 0.7548, loss_cls: 2.7319, loss: 2.7319 +2024-12-31 05:23:14,793 - pyskl - INFO - Epoch [132][1700/3746] lr: 3.725e-03, eta: 16:36:20, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.5056, top5_acc: 0.7555, loss_cls: 2.7513, loss: 2.7513 +2024-12-31 05:24:41,564 - pyskl - INFO - Epoch [132][1800/3746] lr: 3.714e-03, eta: 16:34:54, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5105, top5_acc: 0.7544, loss_cls: 2.7331, loss: 2.7331 +2024-12-31 05:26:07,720 - pyskl - INFO - Epoch [132][1900/3746] lr: 3.704e-03, eta: 16:33:28, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.4994, top5_acc: 0.7531, loss_cls: 2.7787, loss: 2.7787 +2024-12-31 05:27:33,287 - pyskl - INFO - Epoch [132][2000/3746] lr: 3.693e-03, eta: 16:32:02, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5166, top5_acc: 0.7578, loss_cls: 2.7069, loss: 2.7069 +2024-12-31 05:28:59,119 - pyskl - INFO - Epoch [132][2100/3746] lr: 3.683e-03, eta: 16:30:36, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.4986, top5_acc: 0.7536, loss_cls: 2.7786, loss: 2.7786 +2024-12-31 05:30:25,611 - pyskl - INFO - Epoch [132][2200/3746] lr: 3.672e-03, eta: 16:29:10, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.5073, top5_acc: 0.7606, loss_cls: 2.7483, loss: 2.7483 +2024-12-31 05:31:52,474 - pyskl - INFO - Epoch [132][2300/3746] lr: 3.662e-03, eta: 16:27:44, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5048, top5_acc: 0.7555, loss_cls: 2.7776, loss: 2.7776 +2024-12-31 05:33:19,444 - pyskl - INFO - Epoch [132][2400/3746] lr: 3.651e-03, eta: 16:26:18, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5034, top5_acc: 0.7500, loss_cls: 2.7765, loss: 2.7765 +2024-12-31 05:34:46,599 - pyskl - INFO - Epoch [132][2500/3746] lr: 3.641e-03, eta: 16:24:52, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.4930, top5_acc: 0.7477, loss_cls: 2.7853, loss: 2.7853 +2024-12-31 05:36:13,530 - pyskl - INFO - Epoch [132][2600/3746] lr: 3.630e-03, eta: 16:23:26, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5106, top5_acc: 0.7473, loss_cls: 2.7801, loss: 2.7801 +2024-12-31 05:37:40,679 - pyskl - INFO - Epoch [132][2700/3746] lr: 3.620e-03, eta: 16:22:00, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.4948, top5_acc: 0.7505, loss_cls: 2.7963, loss: 2.7963 +2024-12-31 05:39:07,800 - pyskl - INFO - Epoch [132][2800/3746] lr: 3.609e-03, eta: 16:20:34, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.4936, top5_acc: 0.7452, loss_cls: 2.8070, loss: 2.8070 +2024-12-31 05:40:35,565 - pyskl - INFO - Epoch [132][2900/3746] lr: 3.599e-03, eta: 16:19:09, time: 0.878, data_time: 0.000, memory: 15990, top1_acc: 0.5066, top5_acc: 0.7512, loss_cls: 2.7630, loss: 2.7630 +2024-12-31 05:42:02,725 - pyskl - INFO - Epoch [132][3000/3746] lr: 3.588e-03, eta: 16:17:43, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5031, top5_acc: 0.7539, loss_cls: 2.7541, loss: 2.7541 +2024-12-31 05:43:30,093 - pyskl - INFO - Epoch [132][3100/3746] lr: 3.578e-03, eta: 16:16:17, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.5069, top5_acc: 0.7542, loss_cls: 2.7562, loss: 2.7562 +2024-12-31 05:44:57,407 - pyskl - INFO - Epoch [132][3200/3746] lr: 3.568e-03, eta: 16:14:51, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.5045, top5_acc: 0.7556, loss_cls: 2.7609, loss: 2.7609 +2024-12-31 05:46:25,295 - pyskl - INFO - Epoch [132][3300/3746] lr: 3.557e-03, eta: 16:13:25, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.5031, top5_acc: 0.7447, loss_cls: 2.8224, loss: 2.8224 +2024-12-31 05:47:52,280 - pyskl - INFO - Epoch [132][3400/3746] lr: 3.547e-03, eta: 16:11:59, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.4998, top5_acc: 0.7500, loss_cls: 2.7743, loss: 2.7743 +2024-12-31 05:49:19,796 - pyskl - INFO - Epoch [132][3500/3746] lr: 3.537e-03, eta: 16:10:33, time: 0.875, data_time: 0.000, memory: 15990, top1_acc: 0.4930, top5_acc: 0.7450, loss_cls: 2.8226, loss: 2.8226 +2024-12-31 05:50:47,044 - pyskl - INFO - Epoch [132][3600/3746] lr: 3.526e-03, eta: 16:09:07, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5039, top5_acc: 0.7567, loss_cls: 2.7786, loss: 2.7786 +2024-12-31 05:52:14,489 - pyskl - INFO - Epoch [132][3700/3746] lr: 3.516e-03, eta: 16:07:42, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.4927, top5_acc: 0.7411, loss_cls: 2.8425, loss: 2.8425 +2024-12-31 05:52:56,748 - pyskl - INFO - Saving checkpoint at 132 epochs +2024-12-31 05:54:55,870 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 05:54:56,611 - pyskl - INFO - +top1_acc 0.4146 +top5_acc 0.6615 +2024-12-31 05:54:56,611 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 05:54:56,658 - pyskl - INFO - +mean_acc 0.4144 +2024-12-31 05:54:56,662 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_131.pth was removed +2024-12-31 05:54:56,958 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_132.pth. +2024-12-31 05:54:56,959 - pyskl - INFO - Best top1_acc is 0.4146 at 132 epoch. +2024-12-31 05:54:56,974 - pyskl - INFO - Epoch(val) [132][309] top1_acc: 0.4146, top5_acc: 0.6615, mean_class_accuracy: 0.4144 +2024-12-31 05:59:15,497 - pyskl - INFO - Epoch [133][100/3746] lr: 3.501e-03, eta: 16:05:54, time: 2.585, data_time: 1.527, memory: 15990, top1_acc: 0.5130, top5_acc: 0.7641, loss_cls: 2.7224, loss: 2.7224 +2024-12-31 06:00:42,891 - pyskl - INFO - Epoch [133][200/3746] lr: 3.491e-03, eta: 16:04:28, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.5280, top5_acc: 0.7744, loss_cls: 2.6359, loss: 2.6359 +2024-12-31 06:02:10,691 - pyskl - INFO - Epoch [133][300/3746] lr: 3.480e-03, eta: 16:03:02, time: 0.878, data_time: 0.000, memory: 15990, top1_acc: 0.5172, top5_acc: 0.7669, loss_cls: 2.6703, loss: 2.6703 +2024-12-31 06:03:39,157 - pyskl - INFO - Epoch [133][400/3746] lr: 3.470e-03, eta: 16:01:37, time: 0.885, data_time: 0.000, memory: 15990, top1_acc: 0.5242, top5_acc: 0.7762, loss_cls: 2.6411, loss: 2.6411 +2024-12-31 06:05:07,074 - pyskl - INFO - Epoch [133][500/3746] lr: 3.460e-03, eta: 16:00:11, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.5181, top5_acc: 0.7594, loss_cls: 2.7091, loss: 2.7091 +2024-12-31 06:06:35,145 - pyskl - INFO - Epoch [133][600/3746] lr: 3.450e-03, eta: 15:58:45, time: 0.881, data_time: 0.000, memory: 15990, top1_acc: 0.5117, top5_acc: 0.7620, loss_cls: 2.7208, loss: 2.7208 +2024-12-31 06:08:03,063 - pyskl - INFO - Epoch [133][700/3746] lr: 3.440e-03, eta: 15:57:19, time: 0.879, data_time: 0.001, memory: 15990, top1_acc: 0.5081, top5_acc: 0.7592, loss_cls: 2.7065, loss: 2.7065 +2024-12-31 06:09:30,988 - pyskl - INFO - Epoch [133][800/3746] lr: 3.429e-03, eta: 15:55:53, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.5103, top5_acc: 0.7533, loss_cls: 2.7492, loss: 2.7492 +2024-12-31 06:10:58,583 - pyskl - INFO - Epoch [133][900/3746] lr: 3.419e-03, eta: 15:54:27, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.5173, top5_acc: 0.7638, loss_cls: 2.7120, loss: 2.7120 +2024-12-31 06:12:26,138 - pyskl - INFO - Epoch [133][1000/3746] lr: 3.409e-03, eta: 15:53:02, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.5214, top5_acc: 0.7600, loss_cls: 2.7076, loss: 2.7076 +2024-12-31 06:13:52,890 - pyskl - INFO - Epoch [133][1100/3746] lr: 3.399e-03, eta: 15:51:36, time: 0.868, data_time: 0.001, memory: 15990, top1_acc: 0.5259, top5_acc: 0.7645, loss_cls: 2.6745, loss: 2.6745 +2024-12-31 06:15:18,890 - pyskl - INFO - Epoch [133][1200/3746] lr: 3.389e-03, eta: 15:50:09, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5167, top5_acc: 0.7719, loss_cls: 2.6854, loss: 2.6854 +2024-12-31 06:16:45,061 - pyskl - INFO - Epoch [133][1300/3746] lr: 3.379e-03, eta: 15:48:43, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5145, top5_acc: 0.7675, loss_cls: 2.6932, loss: 2.6932 +2024-12-31 06:18:12,414 - pyskl - INFO - Epoch [133][1400/3746] lr: 3.369e-03, eta: 15:47:17, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.5125, top5_acc: 0.7539, loss_cls: 2.7453, loss: 2.7453 +2024-12-31 06:19:39,178 - pyskl - INFO - Epoch [133][1500/3746] lr: 3.359e-03, eta: 15:45:51, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5178, top5_acc: 0.7555, loss_cls: 2.6920, loss: 2.6920 +2024-12-31 06:21:06,213 - pyskl - INFO - Epoch [133][1600/3746] lr: 3.348e-03, eta: 15:44:26, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5209, top5_acc: 0.7611, loss_cls: 2.6909, loss: 2.6909 +2024-12-31 06:22:33,442 - pyskl - INFO - Epoch [133][1700/3746] lr: 3.338e-03, eta: 15:43:00, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5188, top5_acc: 0.7581, loss_cls: 2.7006, loss: 2.7006 +2024-12-31 06:24:00,243 - pyskl - INFO - Epoch [133][1800/3746] lr: 3.328e-03, eta: 15:41:34, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5097, top5_acc: 0.7542, loss_cls: 2.7392, loss: 2.7392 +2024-12-31 06:25:26,855 - pyskl - INFO - Epoch [133][1900/3746] lr: 3.318e-03, eta: 15:40:08, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5159, top5_acc: 0.7595, loss_cls: 2.7120, loss: 2.7120 +2024-12-31 06:26:53,310 - pyskl - INFO - Epoch [133][2000/3746] lr: 3.308e-03, eta: 15:38:42, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.5194, top5_acc: 0.7681, loss_cls: 2.7097, loss: 2.7097 +2024-12-31 06:28:19,310 - pyskl - INFO - Epoch [133][2100/3746] lr: 3.298e-03, eta: 15:37:15, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5133, top5_acc: 0.7580, loss_cls: 2.7263, loss: 2.7263 +2024-12-31 06:29:45,457 - pyskl - INFO - Epoch [133][2200/3746] lr: 3.288e-03, eta: 15:35:49, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5256, top5_acc: 0.7661, loss_cls: 2.6792, loss: 2.6792 +2024-12-31 06:31:11,990 - pyskl - INFO - Epoch [133][2300/3746] lr: 3.278e-03, eta: 15:34:23, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5077, top5_acc: 0.7506, loss_cls: 2.7453, loss: 2.7453 +2024-12-31 06:32:38,962 - pyskl - INFO - Epoch [133][2400/3746] lr: 3.268e-03, eta: 15:32:57, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5125, top5_acc: 0.7592, loss_cls: 2.7315, loss: 2.7315 +2024-12-31 06:34:05,814 - pyskl - INFO - Epoch [133][2500/3746] lr: 3.259e-03, eta: 15:31:31, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5119, top5_acc: 0.7573, loss_cls: 2.7257, loss: 2.7257 +2024-12-31 06:35:32,920 - pyskl - INFO - Epoch [133][2600/3746] lr: 3.249e-03, eta: 15:30:05, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5120, top5_acc: 0.7586, loss_cls: 2.7221, loss: 2.7221 +2024-12-31 06:37:00,112 - pyskl - INFO - Epoch [133][2700/3746] lr: 3.239e-03, eta: 15:28:40, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5166, top5_acc: 0.7566, loss_cls: 2.7085, loss: 2.7085 +2024-12-31 06:38:26,619 - pyskl - INFO - Epoch [133][2800/3746] lr: 3.229e-03, eta: 15:27:13, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5150, top5_acc: 0.7589, loss_cls: 2.7329, loss: 2.7329 +2024-12-31 06:39:53,389 - pyskl - INFO - Epoch [133][2900/3746] lr: 3.219e-03, eta: 15:25:47, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5078, top5_acc: 0.7525, loss_cls: 2.7360, loss: 2.7360 +2024-12-31 06:41:20,639 - pyskl - INFO - Epoch [133][3000/3746] lr: 3.209e-03, eta: 15:24:22, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5097, top5_acc: 0.7569, loss_cls: 2.7544, loss: 2.7544 +2024-12-31 06:42:47,750 - pyskl - INFO - Epoch [133][3100/3746] lr: 3.199e-03, eta: 15:22:56, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5134, top5_acc: 0.7600, loss_cls: 2.6948, loss: 2.6948 +2024-12-31 06:44:14,789 - pyskl - INFO - Epoch [133][3200/3746] lr: 3.189e-03, eta: 15:21:30, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5217, top5_acc: 0.7661, loss_cls: 2.7121, loss: 2.7121 +2024-12-31 06:45:41,871 - pyskl - INFO - Epoch [133][3300/3746] lr: 3.180e-03, eta: 15:20:04, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5078, top5_acc: 0.7556, loss_cls: 2.7686, loss: 2.7686 +2024-12-31 06:47:08,771 - pyskl - INFO - Epoch [133][3400/3746] lr: 3.170e-03, eta: 15:18:38, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5072, top5_acc: 0.7545, loss_cls: 2.7517, loss: 2.7517 +2024-12-31 06:48:35,577 - pyskl - INFO - Epoch [133][3500/3746] lr: 3.160e-03, eta: 15:17:12, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5117, top5_acc: 0.7605, loss_cls: 2.7319, loss: 2.7319 +2024-12-31 06:50:02,780 - pyskl - INFO - Epoch [133][3600/3746] lr: 3.150e-03, eta: 15:15:46, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5211, top5_acc: 0.7630, loss_cls: 2.7085, loss: 2.7085 +2024-12-31 06:51:29,972 - pyskl - INFO - Epoch [133][3700/3746] lr: 3.140e-03, eta: 15:14:20, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.4966, top5_acc: 0.7552, loss_cls: 2.7796, loss: 2.7796 +2024-12-31 06:52:11,255 - pyskl - INFO - Saving checkpoint at 133 epochs +2024-12-31 06:54:09,942 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 06:54:10,715 - pyskl - INFO - +top1_acc 0.4188 +top5_acc 0.6694 +2024-12-31 06:54:10,716 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 06:54:10,768 - pyskl - INFO - +mean_acc 0.4186 +2024-12-31 06:54:10,775 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_132.pth was removed +2024-12-31 06:54:11,035 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_133.pth. +2024-12-31 06:54:11,037 - pyskl - INFO - Best top1_acc is 0.4188 at 133 epoch. +2024-12-31 06:54:11,054 - pyskl - INFO - Epoch(val) [133][309] top1_acc: 0.4188, top5_acc: 0.6694, mean_class_accuracy: 0.4186 +2024-12-31 06:58:34,431 - pyskl - INFO - Epoch [134][100/3746] lr: 3.126e-03, eta: 15:12:32, time: 2.634, data_time: 1.570, memory: 15990, top1_acc: 0.5238, top5_acc: 0.7784, loss_cls: 2.6373, loss: 2.6373 +2024-12-31 07:00:02,371 - pyskl - INFO - Epoch [134][200/3746] lr: 3.117e-03, eta: 15:11:06, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.5252, top5_acc: 0.7714, loss_cls: 2.6674, loss: 2.6674 +2024-12-31 07:01:29,895 - pyskl - INFO - Epoch [134][300/3746] lr: 3.107e-03, eta: 15:09:40, time: 0.875, data_time: 0.000, memory: 15990, top1_acc: 0.5294, top5_acc: 0.7770, loss_cls: 2.6321, loss: 2.6321 +2024-12-31 07:02:57,604 - pyskl - INFO - Epoch [134][400/3746] lr: 3.097e-03, eta: 15:08:14, time: 0.877, data_time: 0.000, memory: 15990, top1_acc: 0.5342, top5_acc: 0.7688, loss_cls: 2.6267, loss: 2.6267 +2024-12-31 07:04:25,615 - pyskl - INFO - Epoch [134][500/3746] lr: 3.087e-03, eta: 15:06:48, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.5250, top5_acc: 0.7708, loss_cls: 2.6351, loss: 2.6351 +2024-12-31 07:05:53,183 - pyskl - INFO - Epoch [134][600/3746] lr: 3.078e-03, eta: 15:05:22, time: 0.876, data_time: 0.000, memory: 15990, top1_acc: 0.5138, top5_acc: 0.7733, loss_cls: 2.6590, loss: 2.6590 +2024-12-31 07:07:20,999 - pyskl - INFO - Epoch [134][700/3746] lr: 3.068e-03, eta: 15:03:56, time: 0.878, data_time: 0.000, memory: 15990, top1_acc: 0.5331, top5_acc: 0.7722, loss_cls: 2.6229, loss: 2.6229 +2024-12-31 07:08:48,638 - pyskl - INFO - Epoch [134][800/3746] lr: 3.059e-03, eta: 15:02:30, time: 0.876, data_time: 0.001, memory: 15990, top1_acc: 0.5297, top5_acc: 0.7700, loss_cls: 2.6594, loss: 2.6594 +2024-12-31 07:10:16,079 - pyskl - INFO - Epoch [134][900/3746] lr: 3.049e-03, eta: 15:01:04, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.5164, top5_acc: 0.7659, loss_cls: 2.6789, loss: 2.6789 +2024-12-31 07:11:44,048 - pyskl - INFO - Epoch [134][1000/3746] lr: 3.039e-03, eta: 14:59:39, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.5261, top5_acc: 0.7734, loss_cls: 2.6242, loss: 2.6242 +2024-12-31 07:13:10,378 - pyskl - INFO - Epoch [134][1100/3746] lr: 3.030e-03, eta: 14:58:12, time: 0.863, data_time: 0.001, memory: 15990, top1_acc: 0.5211, top5_acc: 0.7642, loss_cls: 2.6707, loss: 2.6707 +2024-12-31 07:14:36,340 - pyskl - INFO - Epoch [134][1200/3746] lr: 3.020e-03, eta: 14:56:46, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5244, top5_acc: 0.7675, loss_cls: 2.6591, loss: 2.6591 +2024-12-31 07:16:01,754 - pyskl - INFO - Epoch [134][1300/3746] lr: 3.011e-03, eta: 14:55:20, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5278, top5_acc: 0.7658, loss_cls: 2.6488, loss: 2.6488 +2024-12-31 07:17:28,036 - pyskl - INFO - Epoch [134][1400/3746] lr: 3.001e-03, eta: 14:53:54, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5175, top5_acc: 0.7708, loss_cls: 2.6703, loss: 2.6703 +2024-12-31 07:18:54,622 - pyskl - INFO - Epoch [134][1500/3746] lr: 2.991e-03, eta: 14:52:28, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5123, top5_acc: 0.7556, loss_cls: 2.7272, loss: 2.7272 +2024-12-31 07:20:21,586 - pyskl - INFO - Epoch [134][1600/3746] lr: 2.982e-03, eta: 14:51:02, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5323, top5_acc: 0.7694, loss_cls: 2.6355, loss: 2.6355 +2024-12-31 07:21:48,206 - pyskl - INFO - Epoch [134][1700/3746] lr: 2.972e-03, eta: 14:49:36, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5241, top5_acc: 0.7758, loss_cls: 2.6295, loss: 2.6295 +2024-12-31 07:23:15,364 - pyskl - INFO - Epoch [134][1800/3746] lr: 2.963e-03, eta: 14:48:10, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5138, top5_acc: 0.7614, loss_cls: 2.6949, loss: 2.6949 +2024-12-31 07:24:42,813 - pyskl - INFO - Epoch [134][1900/3746] lr: 2.953e-03, eta: 14:46:44, time: 0.874, data_time: 0.000, memory: 15990, top1_acc: 0.5281, top5_acc: 0.7681, loss_cls: 2.6554, loss: 2.6554 +2024-12-31 07:26:09,211 - pyskl - INFO - Epoch [134][2000/3746] lr: 2.944e-03, eta: 14:45:18, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5238, top5_acc: 0.7656, loss_cls: 2.6740, loss: 2.6740 +2024-12-31 07:27:35,013 - pyskl - INFO - Epoch [134][2100/3746] lr: 2.935e-03, eta: 14:43:52, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.5184, top5_acc: 0.7678, loss_cls: 2.6765, loss: 2.6765 +2024-12-31 07:29:00,570 - pyskl - INFO - Epoch [134][2200/3746] lr: 2.925e-03, eta: 14:42:26, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5295, top5_acc: 0.7716, loss_cls: 2.6444, loss: 2.6444 +2024-12-31 07:30:26,624 - pyskl - INFO - Epoch [134][2300/3746] lr: 2.916e-03, eta: 14:40:59, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5166, top5_acc: 0.7667, loss_cls: 2.6896, loss: 2.6896 +2024-12-31 07:31:53,206 - pyskl - INFO - Epoch [134][2400/3746] lr: 2.906e-03, eta: 14:39:33, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5208, top5_acc: 0.7608, loss_cls: 2.6935, loss: 2.6935 +2024-12-31 07:33:20,075 - pyskl - INFO - Epoch [134][2500/3746] lr: 2.897e-03, eta: 14:38:07, time: 0.869, data_time: 0.000, memory: 15990, top1_acc: 0.5256, top5_acc: 0.7659, loss_cls: 2.6806, loss: 2.6806 +2024-12-31 07:34:47,082 - pyskl - INFO - Epoch [134][2600/3746] lr: 2.888e-03, eta: 14:36:41, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5169, top5_acc: 0.7614, loss_cls: 2.6768, loss: 2.6768 +2024-12-31 07:36:14,205 - pyskl - INFO - Epoch [134][2700/3746] lr: 2.878e-03, eta: 14:35:15, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5206, top5_acc: 0.7684, loss_cls: 2.6749, loss: 2.6749 +2024-12-31 07:37:41,394 - pyskl - INFO - Epoch [134][2800/3746] lr: 2.869e-03, eta: 14:33:49, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5227, top5_acc: 0.7645, loss_cls: 2.7033, loss: 2.7033 +2024-12-31 07:39:08,400 - pyskl - INFO - Epoch [134][2900/3746] lr: 2.860e-03, eta: 14:32:23, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5209, top5_acc: 0.7761, loss_cls: 2.6579, loss: 2.6579 +2024-12-31 07:40:35,461 - pyskl - INFO - Epoch [134][3000/3746] lr: 2.850e-03, eta: 14:30:57, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5125, top5_acc: 0.7609, loss_cls: 2.7187, loss: 2.7187 +2024-12-31 07:42:02,113 - pyskl - INFO - Epoch [134][3100/3746] lr: 2.841e-03, eta: 14:29:31, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5248, top5_acc: 0.7681, loss_cls: 2.6639, loss: 2.6639 +2024-12-31 07:43:28,296 - pyskl - INFO - Epoch [134][3200/3746] lr: 2.832e-03, eta: 14:28:05, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5264, top5_acc: 0.7750, loss_cls: 2.6341, loss: 2.6341 +2024-12-31 07:44:55,003 - pyskl - INFO - Epoch [134][3300/3746] lr: 2.822e-03, eta: 14:26:39, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5142, top5_acc: 0.7648, loss_cls: 2.7023, loss: 2.7023 +2024-12-31 07:46:21,831 - pyskl - INFO - Epoch [134][3400/3746] lr: 2.813e-03, eta: 14:25:13, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5181, top5_acc: 0.7645, loss_cls: 2.7294, loss: 2.7294 +2024-12-31 07:47:48,907 - pyskl - INFO - Epoch [134][3500/3746] lr: 2.804e-03, eta: 14:23:47, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5148, top5_acc: 0.7591, loss_cls: 2.7243, loss: 2.7243 +2024-12-31 07:49:15,657 - pyskl - INFO - Epoch [134][3600/3746] lr: 2.795e-03, eta: 14:22:21, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5186, top5_acc: 0.7612, loss_cls: 2.7029, loss: 2.7029 +2024-12-31 07:50:41,788 - pyskl - INFO - Epoch [134][3700/3746] lr: 2.786e-03, eta: 14:20:55, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.5164, top5_acc: 0.7642, loss_cls: 2.7115, loss: 2.7115 +2024-12-31 07:51:23,011 - pyskl - INFO - Saving checkpoint at 134 epochs +2024-12-31 07:53:23,342 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 07:53:24,112 - pyskl - INFO - +top1_acc 0.4189 +top5_acc 0.6721 +2024-12-31 07:53:24,113 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 07:53:24,158 - pyskl - INFO - +mean_acc 0.4186 +2024-12-31 07:53:24,163 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_133.pth was removed +2024-12-31 07:53:24,427 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_134.pth. +2024-12-31 07:53:24,428 - pyskl - INFO - Best top1_acc is 0.4189 at 134 epoch. +2024-12-31 07:53:24,444 - pyskl - INFO - Epoch(val) [134][309] top1_acc: 0.4189, top5_acc: 0.6721, mean_class_accuracy: 0.4186 +2024-12-31 07:57:51,787 - pyskl - INFO - Epoch [135][100/3746] lr: 2.772e-03, eta: 14:19:06, time: 2.673, data_time: 1.606, memory: 15990, top1_acc: 0.5466, top5_acc: 0.7889, loss_cls: 2.5424, loss: 2.5424 +2024-12-31 07:59:19,990 - pyskl - INFO - Epoch [135][200/3746] lr: 2.763e-03, eta: 14:17:40, time: 0.882, data_time: 0.001, memory: 15990, top1_acc: 0.5384, top5_acc: 0.7789, loss_cls: 2.5802, loss: 2.5802 +2024-12-31 08:00:48,448 - pyskl - INFO - Epoch [135][300/3746] lr: 2.754e-03, eta: 14:16:14, time: 0.885, data_time: 0.000, memory: 15990, top1_acc: 0.5447, top5_acc: 0.7850, loss_cls: 2.5387, loss: 2.5387 +2024-12-31 08:02:16,968 - pyskl - INFO - Epoch [135][400/3746] lr: 2.745e-03, eta: 14:14:48, time: 0.885, data_time: 0.000, memory: 15990, top1_acc: 0.5337, top5_acc: 0.7839, loss_cls: 2.5983, loss: 2.5983 +2024-12-31 08:03:45,857 - pyskl - INFO - Epoch [135][500/3746] lr: 2.735e-03, eta: 14:13:23, time: 0.889, data_time: 0.001, memory: 15990, top1_acc: 0.5384, top5_acc: 0.7727, loss_cls: 2.5941, loss: 2.5941 +2024-12-31 08:05:14,392 - pyskl - INFO - Epoch [135][600/3746] lr: 2.726e-03, eta: 14:11:57, time: 0.885, data_time: 0.000, memory: 15990, top1_acc: 0.5391, top5_acc: 0.7720, loss_cls: 2.6200, loss: 2.6200 +2024-12-31 08:06:43,067 - pyskl - INFO - Epoch [135][700/3746] lr: 2.717e-03, eta: 14:10:31, time: 0.887, data_time: 0.000, memory: 15990, top1_acc: 0.5363, top5_acc: 0.7831, loss_cls: 2.5982, loss: 2.5982 +2024-12-31 08:08:11,884 - pyskl - INFO - Epoch [135][800/3746] lr: 2.708e-03, eta: 14:09:05, time: 0.888, data_time: 0.001, memory: 15990, top1_acc: 0.5325, top5_acc: 0.7684, loss_cls: 2.6558, loss: 2.6558 +2024-12-31 08:09:41,236 - pyskl - INFO - Epoch [135][900/3746] lr: 2.699e-03, eta: 14:07:39, time: 0.894, data_time: 0.001, memory: 15990, top1_acc: 0.5264, top5_acc: 0.7756, loss_cls: 2.6138, loss: 2.6138 +2024-12-31 08:11:08,778 - pyskl - INFO - Epoch [135][1000/3746] lr: 2.690e-03, eta: 14:06:13, time: 0.875, data_time: 0.001, memory: 15990, top1_acc: 0.5312, top5_acc: 0.7750, loss_cls: 2.6206, loss: 2.6206 +2024-12-31 08:12:34,957 - pyskl - INFO - Epoch [135][1100/3746] lr: 2.681e-03, eta: 14:04:47, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5384, top5_acc: 0.7880, loss_cls: 2.5717, loss: 2.5717 +2024-12-31 08:14:00,781 - pyskl - INFO - Epoch [135][1200/3746] lr: 2.672e-03, eta: 14:03:21, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5370, top5_acc: 0.7722, loss_cls: 2.6119, loss: 2.6119 +2024-12-31 08:15:27,671 - pyskl - INFO - Epoch [135][1300/3746] lr: 2.663e-03, eta: 14:01:55, time: 0.869, data_time: 0.001, memory: 15990, top1_acc: 0.5355, top5_acc: 0.7809, loss_cls: 2.6047, loss: 2.6047 +2024-12-31 08:16:55,719 - pyskl - INFO - Epoch [135][1400/3746] lr: 2.654e-03, eta: 14:00:29, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.5261, top5_acc: 0.7739, loss_cls: 2.6441, loss: 2.6441 +2024-12-31 08:18:24,144 - pyskl - INFO - Epoch [135][1500/3746] lr: 2.645e-03, eta: 13:59:03, time: 0.884, data_time: 0.001, memory: 15990, top1_acc: 0.5325, top5_acc: 0.7700, loss_cls: 2.6432, loss: 2.6432 +2024-12-31 08:19:52,124 - pyskl - INFO - Epoch [135][1600/3746] lr: 2.636e-03, eta: 13:57:37, time: 0.880, data_time: 0.001, memory: 15990, top1_acc: 0.5189, top5_acc: 0.7702, loss_cls: 2.6337, loss: 2.6337 +2024-12-31 08:21:20,222 - pyskl - INFO - Epoch [135][1700/3746] lr: 2.627e-03, eta: 13:56:11, time: 0.881, data_time: 0.000, memory: 15990, top1_acc: 0.5300, top5_acc: 0.7720, loss_cls: 2.6200, loss: 2.6200 +2024-12-31 08:22:48,297 - pyskl - INFO - Epoch [135][1800/3746] lr: 2.618e-03, eta: 13:54:45, time: 0.881, data_time: 0.001, memory: 15990, top1_acc: 0.5284, top5_acc: 0.7731, loss_cls: 2.6348, loss: 2.6348 +2024-12-31 08:24:16,590 - pyskl - INFO - Epoch [135][1900/3746] lr: 2.609e-03, eta: 13:53:19, time: 0.883, data_time: 0.000, memory: 15990, top1_acc: 0.5280, top5_acc: 0.7656, loss_cls: 2.6608, loss: 2.6608 +2024-12-31 08:25:43,687 - pyskl - INFO - Epoch [135][2000/3746] lr: 2.600e-03, eta: 13:51:53, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5305, top5_acc: 0.7675, loss_cls: 2.6176, loss: 2.6176 +2024-12-31 08:27:10,027 - pyskl - INFO - Epoch [135][2100/3746] lr: 2.591e-03, eta: 13:50:27, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5319, top5_acc: 0.7677, loss_cls: 2.6281, loss: 2.6281 +2024-12-31 08:28:36,619 - pyskl - INFO - Epoch [135][2200/3746] lr: 2.583e-03, eta: 13:49:01, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5300, top5_acc: 0.7714, loss_cls: 2.6348, loss: 2.6348 +2024-12-31 08:30:03,869 - pyskl - INFO - Epoch [135][2300/3746] lr: 2.574e-03, eta: 13:47:35, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5309, top5_acc: 0.7794, loss_cls: 2.6141, loss: 2.6141 +2024-12-31 08:31:31,811 - pyskl - INFO - Epoch [135][2400/3746] lr: 2.565e-03, eta: 13:46:09, time: 0.879, data_time: 0.000, memory: 15990, top1_acc: 0.5241, top5_acc: 0.7659, loss_cls: 2.6683, loss: 2.6683 +2024-12-31 08:33:00,205 - pyskl - INFO - Epoch [135][2500/3746] lr: 2.556e-03, eta: 13:44:43, time: 0.884, data_time: 0.001, memory: 15990, top1_acc: 0.5261, top5_acc: 0.7759, loss_cls: 2.6396, loss: 2.6396 +2024-12-31 08:34:29,338 - pyskl - INFO - Epoch [135][2600/3746] lr: 2.547e-03, eta: 13:43:17, time: 0.891, data_time: 0.000, memory: 15990, top1_acc: 0.5242, top5_acc: 0.7662, loss_cls: 2.6805, loss: 2.6805 +2024-12-31 08:35:58,003 - pyskl - INFO - Epoch [135][2700/3746] lr: 2.538e-03, eta: 13:41:52, time: 0.887, data_time: 0.001, memory: 15990, top1_acc: 0.5320, top5_acc: 0.7712, loss_cls: 2.6375, loss: 2.6375 +2024-12-31 08:37:26,177 - pyskl - INFO - Epoch [135][2800/3746] lr: 2.530e-03, eta: 13:40:26, time: 0.882, data_time: 0.000, memory: 15990, top1_acc: 0.5228, top5_acc: 0.7583, loss_cls: 2.6748, loss: 2.6748 +2024-12-31 08:38:54,798 - pyskl - INFO - Epoch [135][2900/3746] lr: 2.521e-03, eta: 13:39:00, time: 0.886, data_time: 0.001, memory: 15990, top1_acc: 0.5161, top5_acc: 0.7698, loss_cls: 2.6427, loss: 2.6427 +2024-12-31 08:40:23,393 - pyskl - INFO - Epoch [135][3000/3746] lr: 2.512e-03, eta: 13:37:34, time: 0.886, data_time: 0.001, memory: 15990, top1_acc: 0.5191, top5_acc: 0.7697, loss_cls: 2.6456, loss: 2.6456 +2024-12-31 08:41:52,210 - pyskl - INFO - Epoch [135][3100/3746] lr: 2.503e-03, eta: 13:36:08, time: 0.888, data_time: 0.001, memory: 15990, top1_acc: 0.5184, top5_acc: 0.7684, loss_cls: 2.6714, loss: 2.6714 +2024-12-31 08:43:20,313 - pyskl - INFO - Epoch [135][3200/3746] lr: 2.495e-03, eta: 13:34:42, time: 0.881, data_time: 0.001, memory: 15990, top1_acc: 0.5305, top5_acc: 0.7719, loss_cls: 2.6238, loss: 2.6238 +2024-12-31 08:44:48,808 - pyskl - INFO - Epoch [135][3300/3746] lr: 2.486e-03, eta: 13:33:16, time: 0.885, data_time: 0.000, memory: 15990, top1_acc: 0.5253, top5_acc: 0.7659, loss_cls: 2.6602, loss: 2.6602 +2024-12-31 08:46:16,747 - pyskl - INFO - Epoch [135][3400/3746] lr: 2.477e-03, eta: 13:31:50, time: 0.879, data_time: 0.001, memory: 15990, top1_acc: 0.5250, top5_acc: 0.7645, loss_cls: 2.6730, loss: 2.6730 +2024-12-31 08:47:45,947 - pyskl - INFO - Epoch [135][3500/3746] lr: 2.469e-03, eta: 13:30:24, time: 0.892, data_time: 0.001, memory: 15990, top1_acc: 0.5200, top5_acc: 0.7639, loss_cls: 2.6830, loss: 2.6830 +2024-12-31 08:49:11,465 - pyskl - INFO - Epoch [135][3600/3746] lr: 2.460e-03, eta: 13:28:58, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5189, top5_acc: 0.7695, loss_cls: 2.6664, loss: 2.6664 +2024-12-31 08:50:36,676 - pyskl - INFO - Epoch [135][3700/3746] lr: 2.451e-03, eta: 13:27:32, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5289, top5_acc: 0.7675, loss_cls: 2.6731, loss: 2.6731 +2024-12-31 08:51:17,957 - pyskl - INFO - Saving checkpoint at 135 epochs +2024-12-31 08:53:22,332 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 08:53:23,038 - pyskl - INFO - +top1_acc 0.4232 +top5_acc 0.6777 +2024-12-31 08:53:23,038 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 08:53:23,077 - pyskl - INFO - +mean_acc 0.4229 +2024-12-31 08:53:23,082 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_134.pth was removed +2024-12-31 08:53:23,437 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_135.pth. +2024-12-31 08:53:23,438 - pyskl - INFO - Best top1_acc is 0.4232 at 135 epoch. +2024-12-31 08:53:23,480 - pyskl - INFO - Epoch(val) [135][309] top1_acc: 0.4232, top5_acc: 0.6777, mean_class_accuracy: 0.4229 +2024-12-31 08:57:57,613 - pyskl - INFO - Epoch [136][100/3746] lr: 2.439e-03, eta: 13:25:43, time: 2.741, data_time: 1.680, memory: 15990, top1_acc: 0.5408, top5_acc: 0.7814, loss_cls: 2.5566, loss: 2.5566 +2024-12-31 08:59:23,908 - pyskl - INFO - Epoch [136][200/3746] lr: 2.430e-03, eta: 13:24:16, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5470, top5_acc: 0.7916, loss_cls: 2.5224, loss: 2.5224 +2024-12-31 09:00:50,769 - pyskl - INFO - Epoch [136][300/3746] lr: 2.421e-03, eta: 13:22:50, time: 0.869, data_time: 0.001, memory: 15990, top1_acc: 0.5516, top5_acc: 0.7937, loss_cls: 2.5307, loss: 2.5307 +2024-12-31 09:02:18,050 - pyskl - INFO - Epoch [136][400/3746] lr: 2.413e-03, eta: 13:21:24, time: 0.873, data_time: 0.000, memory: 15990, top1_acc: 0.5402, top5_acc: 0.7794, loss_cls: 2.5638, loss: 2.5638 +2024-12-31 09:03:44,604 - pyskl - INFO - Epoch [136][500/3746] lr: 2.404e-03, eta: 13:19:58, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5442, top5_acc: 0.7908, loss_cls: 2.5195, loss: 2.5195 +2024-12-31 09:05:11,419 - pyskl - INFO - Epoch [136][600/3746] lr: 2.396e-03, eta: 13:18:32, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5472, top5_acc: 0.7880, loss_cls: 2.5532, loss: 2.5532 +2024-12-31 09:06:38,491 - pyskl - INFO - Epoch [136][700/3746] lr: 2.387e-03, eta: 13:17:06, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5398, top5_acc: 0.7773, loss_cls: 2.5829, loss: 2.5829 +2024-12-31 09:08:04,896 - pyskl - INFO - Epoch [136][800/3746] lr: 2.379e-03, eta: 13:15:40, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5436, top5_acc: 0.7855, loss_cls: 2.5434, loss: 2.5434 +2024-12-31 09:09:32,960 - pyskl - INFO - Epoch [136][900/3746] lr: 2.370e-03, eta: 13:14:14, time: 0.881, data_time: 0.000, memory: 15990, top1_acc: 0.5414, top5_acc: 0.7791, loss_cls: 2.5699, loss: 2.5699 +2024-12-31 09:11:00,284 - pyskl - INFO - Epoch [136][1000/3746] lr: 2.362e-03, eta: 13:12:48, time: 0.873, data_time: 0.001, memory: 15990, top1_acc: 0.5384, top5_acc: 0.7866, loss_cls: 2.5713, loss: 2.5713 +2024-12-31 09:12:26,937 - pyskl - INFO - Epoch [136][1100/3746] lr: 2.353e-03, eta: 13:11:21, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5397, top5_acc: 0.7789, loss_cls: 2.5922, loss: 2.5922 +2024-12-31 09:13:53,465 - pyskl - INFO - Epoch [136][1200/3746] lr: 2.345e-03, eta: 13:09:55, time: 0.865, data_time: 0.001, memory: 15990, top1_acc: 0.5342, top5_acc: 0.7797, loss_cls: 2.5724, loss: 2.5724 +2024-12-31 09:15:20,485 - pyskl - INFO - Epoch [136][1300/3746] lr: 2.336e-03, eta: 13:08:29, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5333, top5_acc: 0.7847, loss_cls: 2.5755, loss: 2.5755 +2024-12-31 09:16:47,013 - pyskl - INFO - Epoch [136][1400/3746] lr: 2.328e-03, eta: 13:07:03, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5400, top5_acc: 0.7798, loss_cls: 2.5910, loss: 2.5910 +2024-12-31 09:18:13,776 - pyskl - INFO - Epoch [136][1500/3746] lr: 2.319e-03, eta: 13:05:37, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5414, top5_acc: 0.7736, loss_cls: 2.5936, loss: 2.5936 +2024-12-31 09:19:39,656 - pyskl - INFO - Epoch [136][1600/3746] lr: 2.311e-03, eta: 13:04:11, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5364, top5_acc: 0.7841, loss_cls: 2.5833, loss: 2.5833 +2024-12-31 09:21:06,308 - pyskl - INFO - Epoch [136][1700/3746] lr: 2.303e-03, eta: 13:02:45, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5369, top5_acc: 0.7739, loss_cls: 2.5828, loss: 2.5828 +2024-12-31 09:22:32,473 - pyskl - INFO - Epoch [136][1800/3746] lr: 2.294e-03, eta: 13:01:18, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5500, top5_acc: 0.7897, loss_cls: 2.5104, loss: 2.5104 +2024-12-31 09:23:58,584 - pyskl - INFO - Epoch [136][1900/3746] lr: 2.286e-03, eta: 12:59:52, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5236, top5_acc: 0.7684, loss_cls: 2.6619, loss: 2.6619 +2024-12-31 09:25:25,294 - pyskl - INFO - Epoch [136][2000/3746] lr: 2.277e-03, eta: 12:58:26, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5470, top5_acc: 0.7827, loss_cls: 2.5645, loss: 2.5645 +2024-12-31 09:26:51,336 - pyskl - INFO - Epoch [136][2100/3746] lr: 2.269e-03, eta: 12:57:00, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5295, top5_acc: 0.7761, loss_cls: 2.6215, loss: 2.6215 +2024-12-31 09:28:16,768 - pyskl - INFO - Epoch [136][2200/3746] lr: 2.261e-03, eta: 12:55:34, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5341, top5_acc: 0.7716, loss_cls: 2.6252, loss: 2.6252 +2024-12-31 09:29:43,077 - pyskl - INFO - Epoch [136][2300/3746] lr: 2.253e-03, eta: 12:54:07, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5417, top5_acc: 0.7805, loss_cls: 2.5782, loss: 2.5782 +2024-12-31 09:31:10,262 - pyskl - INFO - Epoch [136][2400/3746] lr: 2.244e-03, eta: 12:52:41, time: 0.872, data_time: 0.000, memory: 15990, top1_acc: 0.5389, top5_acc: 0.7798, loss_cls: 2.5951, loss: 2.5951 +2024-12-31 09:32:36,996 - pyskl - INFO - Epoch [136][2500/3746] lr: 2.236e-03, eta: 12:51:15, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5516, top5_acc: 0.7809, loss_cls: 2.5511, loss: 2.5511 +2024-12-31 09:34:03,830 - pyskl - INFO - Epoch [136][2600/3746] lr: 2.228e-03, eta: 12:49:49, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.5414, top5_acc: 0.7730, loss_cls: 2.5996, loss: 2.5996 +2024-12-31 09:35:30,521 - pyskl - INFO - Epoch [136][2700/3746] lr: 2.219e-03, eta: 12:48:23, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5394, top5_acc: 0.7789, loss_cls: 2.5871, loss: 2.5871 +2024-12-31 09:36:56,872 - pyskl - INFO - Epoch [136][2800/3746] lr: 2.211e-03, eta: 12:46:57, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5250, top5_acc: 0.7727, loss_cls: 2.6378, loss: 2.6378 +2024-12-31 09:38:23,454 - pyskl - INFO - Epoch [136][2900/3746] lr: 2.203e-03, eta: 12:45:31, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5517, top5_acc: 0.7913, loss_cls: 2.5235, loss: 2.5235 +2024-12-31 09:39:49,958 - pyskl - INFO - Epoch [136][3000/3746] lr: 2.195e-03, eta: 12:44:04, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5333, top5_acc: 0.7722, loss_cls: 2.6107, loss: 2.6107 +2024-12-31 09:41:16,151 - pyskl - INFO - Epoch [136][3100/3746] lr: 2.187e-03, eta: 12:42:38, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5358, top5_acc: 0.7767, loss_cls: 2.5975, loss: 2.5975 +2024-12-31 09:42:42,604 - pyskl - INFO - Epoch [136][3200/3746] lr: 2.178e-03, eta: 12:41:12, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5334, top5_acc: 0.7791, loss_cls: 2.6075, loss: 2.6075 +2024-12-31 09:44:09,054 - pyskl - INFO - Epoch [136][3300/3746] lr: 2.170e-03, eta: 12:39:46, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5297, top5_acc: 0.7773, loss_cls: 2.6118, loss: 2.6118 +2024-12-31 09:45:36,186 - pyskl - INFO - Epoch [136][3400/3746] lr: 2.162e-03, eta: 12:38:20, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5305, top5_acc: 0.7734, loss_cls: 2.6207, loss: 2.6207 +2024-12-31 09:47:02,281 - pyskl - INFO - Epoch [136][3500/3746] lr: 2.154e-03, eta: 12:36:54, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5305, top5_acc: 0.7794, loss_cls: 2.5778, loss: 2.5778 +2024-12-31 09:48:27,747 - pyskl - INFO - Epoch [136][3600/3746] lr: 2.146e-03, eta: 12:35:27, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5284, top5_acc: 0.7670, loss_cls: 2.6251, loss: 2.6251 +2024-12-31 09:49:53,203 - pyskl - INFO - Epoch [136][3700/3746] lr: 2.138e-03, eta: 12:34:01, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5248, top5_acc: 0.7739, loss_cls: 2.6266, loss: 2.6266 +2024-12-31 09:50:34,518 - pyskl - INFO - Saving checkpoint at 136 epochs +2024-12-31 09:52:36,294 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 09:52:37,081 - pyskl - INFO - +top1_acc 0.4295 +top5_acc 0.6764 +2024-12-31 09:52:37,081 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 09:52:37,122 - pyskl - INFO - +mean_acc 0.4293 +2024-12-31 09:52:37,130 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_135.pth was removed +2024-12-31 09:52:37,371 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_136.pth. +2024-12-31 09:52:37,372 - pyskl - INFO - Best top1_acc is 0.4295 at 136 epoch. +2024-12-31 09:52:37,386 - pyskl - INFO - Epoch(val) [136][309] top1_acc: 0.4295, top5_acc: 0.6764, mean_class_accuracy: 0.4293 +2024-12-31 09:57:08,984 - pyskl - INFO - Epoch [137][100/3746] lr: 2.126e-03, eta: 12:32:10, time: 2.716, data_time: 1.651, memory: 15990, top1_acc: 0.5636, top5_acc: 0.8030, loss_cls: 2.4437, loss: 2.4437 +2024-12-31 09:58:37,807 - pyskl - INFO - Epoch [137][200/3746] lr: 2.118e-03, eta: 12:30:44, time: 0.888, data_time: 0.000, memory: 15990, top1_acc: 0.5441, top5_acc: 0.7963, loss_cls: 2.5058, loss: 2.5058 +2024-12-31 10:00:06,345 - pyskl - INFO - Epoch [137][300/3746] lr: 2.110e-03, eta: 12:29:18, time: 0.885, data_time: 0.001, memory: 15990, top1_acc: 0.5567, top5_acc: 0.8009, loss_cls: 2.4580, loss: 2.4580 +2024-12-31 10:01:35,163 - pyskl - INFO - Epoch [137][400/3746] lr: 2.102e-03, eta: 12:27:52, time: 0.888, data_time: 0.001, memory: 15990, top1_acc: 0.5466, top5_acc: 0.7873, loss_cls: 2.5191, loss: 2.5191 +2024-12-31 10:03:03,731 - pyskl - INFO - Epoch [137][500/3746] lr: 2.094e-03, eta: 12:26:26, time: 0.886, data_time: 0.001, memory: 15990, top1_acc: 0.5550, top5_acc: 0.7991, loss_cls: 2.4948, loss: 2.4948 +2024-12-31 10:04:31,833 - pyskl - INFO - Epoch [137][600/3746] lr: 2.086e-03, eta: 12:25:00, time: 0.881, data_time: 0.000, memory: 15990, top1_acc: 0.5475, top5_acc: 0.7906, loss_cls: 2.5068, loss: 2.5068 +2024-12-31 10:06:00,612 - pyskl - INFO - Epoch [137][700/3746] lr: 2.078e-03, eta: 12:23:34, time: 0.888, data_time: 0.001, memory: 15990, top1_acc: 0.5466, top5_acc: 0.7847, loss_cls: 2.5361, loss: 2.5361 +2024-12-31 10:07:29,333 - pyskl - INFO - Epoch [137][800/3746] lr: 2.070e-03, eta: 12:22:08, time: 0.887, data_time: 0.001, memory: 15990, top1_acc: 0.5473, top5_acc: 0.7872, loss_cls: 2.5276, loss: 2.5276 +2024-12-31 10:08:57,919 - pyskl - INFO - Epoch [137][900/3746] lr: 2.062e-03, eta: 12:20:42, time: 0.886, data_time: 0.001, memory: 15990, top1_acc: 0.5570, top5_acc: 0.7928, loss_cls: 2.4961, loss: 2.4961 +2024-12-31 10:10:24,874 - pyskl - INFO - Epoch [137][1000/3746] lr: 2.054e-03, eta: 12:19:16, time: 0.870, data_time: 0.000, memory: 15990, top1_acc: 0.5591, top5_acc: 0.7920, loss_cls: 2.4817, loss: 2.4817 +2024-12-31 10:11:51,392 - pyskl - INFO - Epoch [137][1100/3746] lr: 2.046e-03, eta: 12:17:50, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5519, top5_acc: 0.7913, loss_cls: 2.5155, loss: 2.5155 +2024-12-31 10:13:18,127 - pyskl - INFO - Epoch [137][1200/3746] lr: 2.038e-03, eta: 12:16:24, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.5420, top5_acc: 0.7803, loss_cls: 2.5586, loss: 2.5586 +2024-12-31 10:14:45,195 - pyskl - INFO - Epoch [137][1300/3746] lr: 2.030e-03, eta: 12:14:58, time: 0.871, data_time: 0.000, memory: 15990, top1_acc: 0.5397, top5_acc: 0.7808, loss_cls: 2.5724, loss: 2.5724 +2024-12-31 10:16:11,165 - pyskl - INFO - Epoch [137][1400/3746] lr: 2.022e-03, eta: 12:13:31, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5500, top5_acc: 0.7839, loss_cls: 2.5246, loss: 2.5246 +2024-12-31 10:17:36,702 - pyskl - INFO - Epoch [137][1500/3746] lr: 2.015e-03, eta: 12:12:05, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5398, top5_acc: 0.7870, loss_cls: 2.5482, loss: 2.5482 +2024-12-31 10:19:02,046 - pyskl - INFO - Epoch [137][1600/3746] lr: 2.007e-03, eta: 12:10:39, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5480, top5_acc: 0.7897, loss_cls: 2.5078, loss: 2.5078 +2024-12-31 10:20:27,488 - pyskl - INFO - Epoch [137][1700/3746] lr: 1.999e-03, eta: 12:09:13, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5506, top5_acc: 0.7805, loss_cls: 2.5365, loss: 2.5365 +2024-12-31 10:21:52,754 - pyskl - INFO - Epoch [137][1800/3746] lr: 1.991e-03, eta: 12:07:46, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5450, top5_acc: 0.7827, loss_cls: 2.5119, loss: 2.5119 +2024-12-31 10:23:17,840 - pyskl - INFO - Epoch [137][1900/3746] lr: 1.983e-03, eta: 12:06:20, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.5500, top5_acc: 0.7863, loss_cls: 2.5213, loss: 2.5213 +2024-12-31 10:24:43,200 - pyskl - INFO - Epoch [137][2000/3746] lr: 1.976e-03, eta: 12:04:54, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5489, top5_acc: 0.7866, loss_cls: 2.5272, loss: 2.5272 +2024-12-31 10:26:08,813 - pyskl - INFO - Epoch [137][2100/3746] lr: 1.968e-03, eta: 12:03:27, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5469, top5_acc: 0.7908, loss_cls: 2.5179, loss: 2.5179 +2024-12-31 10:27:33,913 - pyskl - INFO - Epoch [137][2200/3746] lr: 1.960e-03, eta: 12:02:01, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5411, top5_acc: 0.7887, loss_cls: 2.5399, loss: 2.5399 +2024-12-31 10:28:59,276 - pyskl - INFO - Epoch [137][2300/3746] lr: 1.952e-03, eta: 12:00:35, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5347, top5_acc: 0.7745, loss_cls: 2.5738, loss: 2.5738 +2024-12-31 10:30:24,478 - pyskl - INFO - Epoch [137][2400/3746] lr: 1.944e-03, eta: 11:59:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5380, top5_acc: 0.7830, loss_cls: 2.5532, loss: 2.5532 +2024-12-31 10:31:49,586 - pyskl - INFO - Epoch [137][2500/3746] lr: 1.937e-03, eta: 11:57:42, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5416, top5_acc: 0.7772, loss_cls: 2.5791, loss: 2.5791 +2024-12-31 10:33:14,686 - pyskl - INFO - Epoch [137][2600/3746] lr: 1.929e-03, eta: 11:56:16, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5489, top5_acc: 0.7844, loss_cls: 2.5504, loss: 2.5504 +2024-12-31 10:34:40,099 - pyskl - INFO - Epoch [137][2700/3746] lr: 1.921e-03, eta: 11:54:49, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5514, top5_acc: 0.7891, loss_cls: 2.5119, loss: 2.5119 +2024-12-31 10:36:05,288 - pyskl - INFO - Epoch [137][2800/3746] lr: 1.914e-03, eta: 11:53:23, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5439, top5_acc: 0.7856, loss_cls: 2.5294, loss: 2.5294 +2024-12-31 10:37:31,179 - pyskl - INFO - Epoch [137][2900/3746] lr: 1.906e-03, eta: 11:51:57, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5323, top5_acc: 0.7744, loss_cls: 2.6147, loss: 2.6147 +2024-12-31 10:38:56,785 - pyskl - INFO - Epoch [137][3000/3746] lr: 1.898e-03, eta: 11:50:31, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5527, top5_acc: 0.7975, loss_cls: 2.5160, loss: 2.5160 +2024-12-31 10:40:22,096 - pyskl - INFO - Epoch [137][3100/3746] lr: 1.891e-03, eta: 11:49:04, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5288, top5_acc: 0.7725, loss_cls: 2.6027, loss: 2.6027 +2024-12-31 10:41:47,381 - pyskl - INFO - Epoch [137][3200/3746] lr: 1.883e-03, eta: 11:47:38, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5536, top5_acc: 0.7859, loss_cls: 2.5252, loss: 2.5252 +2024-12-31 10:43:12,852 - pyskl - INFO - Epoch [137][3300/3746] lr: 1.876e-03, eta: 11:46:12, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5372, top5_acc: 0.7745, loss_cls: 2.6009, loss: 2.6009 +2024-12-31 10:44:38,862 - pyskl - INFO - Epoch [137][3400/3746] lr: 1.868e-03, eta: 11:44:45, time: 0.860, data_time: 0.001, memory: 15990, top1_acc: 0.5422, top5_acc: 0.7741, loss_cls: 2.5803, loss: 2.5803 +2024-12-31 10:46:04,548 - pyskl - INFO - Epoch [137][3500/3746] lr: 1.860e-03, eta: 11:43:19, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5306, top5_acc: 0.7748, loss_cls: 2.6037, loss: 2.6037 +2024-12-31 10:47:29,846 - pyskl - INFO - Epoch [137][3600/3746] lr: 1.853e-03, eta: 11:41:53, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5459, top5_acc: 0.7906, loss_cls: 2.5328, loss: 2.5328 +2024-12-31 10:48:54,792 - pyskl - INFO - Epoch [137][3700/3746] lr: 1.845e-03, eta: 11:40:26, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.5361, top5_acc: 0.7786, loss_cls: 2.5664, loss: 2.5664 +2024-12-31 10:49:36,312 - pyskl - INFO - Saving checkpoint at 137 epochs +2024-12-31 10:51:36,376 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 10:51:37,154 - pyskl - INFO - +top1_acc 0.4346 +top5_acc 0.6785 +2024-12-31 10:51:37,154 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 10:51:37,203 - pyskl - INFO - +mean_acc 0.4344 +2024-12-31 10:51:37,207 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_136.pth was removed +2024-12-31 10:51:37,571 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_137.pth. +2024-12-31 10:51:37,572 - pyskl - INFO - Best top1_acc is 0.4346 at 137 epoch. +2024-12-31 10:51:37,588 - pyskl - INFO - Epoch(val) [137][309] top1_acc: 0.4346, top5_acc: 0.6785, mean_class_accuracy: 0.4344 +2024-12-31 10:55:58,267 - pyskl - INFO - Epoch [138][100/3746] lr: 1.834e-03, eta: 11:38:33, time: 2.607, data_time: 1.571, memory: 15990, top1_acc: 0.5572, top5_acc: 0.7989, loss_cls: 2.4673, loss: 2.4673 +2024-12-31 10:57:24,157 - pyskl - INFO - Epoch [138][200/3746] lr: 1.827e-03, eta: 11:37:07, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5777, top5_acc: 0.8058, loss_cls: 2.3978, loss: 2.3978 +2024-12-31 10:58:50,028 - pyskl - INFO - Epoch [138][300/3746] lr: 1.819e-03, eta: 11:35:41, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5647, top5_acc: 0.8047, loss_cls: 2.4278, loss: 2.4278 +2024-12-31 11:00:16,006 - pyskl - INFO - Epoch [138][400/3746] lr: 1.812e-03, eta: 11:34:15, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5684, top5_acc: 0.8058, loss_cls: 2.3916, loss: 2.3916 +2024-12-31 11:01:42,365 - pyskl - INFO - Epoch [138][500/3746] lr: 1.805e-03, eta: 11:32:48, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5542, top5_acc: 0.7973, loss_cls: 2.4726, loss: 2.4726 +2024-12-31 11:03:08,240 - pyskl - INFO - Epoch [138][600/3746] lr: 1.797e-03, eta: 11:31:22, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5541, top5_acc: 0.7952, loss_cls: 2.4740, loss: 2.4740 +2024-12-31 11:04:34,838 - pyskl - INFO - Epoch [138][700/3746] lr: 1.790e-03, eta: 11:29:56, time: 0.866, data_time: 0.000, memory: 15990, top1_acc: 0.5605, top5_acc: 0.7927, loss_cls: 2.4535, loss: 2.4535 +2024-12-31 11:06:00,855 - pyskl - INFO - Epoch [138][800/3746] lr: 1.782e-03, eta: 11:28:30, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5727, top5_acc: 0.8102, loss_cls: 2.4063, loss: 2.4063 +2024-12-31 11:07:26,522 - pyskl - INFO - Epoch [138][900/3746] lr: 1.775e-03, eta: 11:27:03, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5555, top5_acc: 0.7913, loss_cls: 2.4893, loss: 2.4893 +2024-12-31 11:08:52,602 - pyskl - INFO - Epoch [138][1000/3746] lr: 1.768e-03, eta: 11:25:37, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5475, top5_acc: 0.7870, loss_cls: 2.5237, loss: 2.5237 +2024-12-31 11:10:17,938 - pyskl - INFO - Epoch [138][1100/3746] lr: 1.760e-03, eta: 11:24:11, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5513, top5_acc: 0.7953, loss_cls: 2.4779, loss: 2.4779 +2024-12-31 11:11:43,432 - pyskl - INFO - Epoch [138][1200/3746] lr: 1.753e-03, eta: 11:22:44, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5655, top5_acc: 0.7956, loss_cls: 2.4601, loss: 2.4601 +2024-12-31 11:13:08,794 - pyskl - INFO - Epoch [138][1300/3746] lr: 1.745e-03, eta: 11:21:18, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5598, top5_acc: 0.7925, loss_cls: 2.4761, loss: 2.4761 +2024-12-31 11:14:34,384 - pyskl - INFO - Epoch [138][1400/3746] lr: 1.738e-03, eta: 11:19:52, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5572, top5_acc: 0.7983, loss_cls: 2.4751, loss: 2.4751 +2024-12-31 11:15:59,693 - pyskl - INFO - Epoch [138][1500/3746] lr: 1.731e-03, eta: 11:18:25, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5614, top5_acc: 0.7986, loss_cls: 2.4575, loss: 2.4575 +2024-12-31 11:17:25,154 - pyskl - INFO - Epoch [138][1600/3746] lr: 1.724e-03, eta: 11:16:59, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5477, top5_acc: 0.7873, loss_cls: 2.5296, loss: 2.5296 +2024-12-31 11:18:50,911 - pyskl - INFO - Epoch [138][1700/3746] lr: 1.716e-03, eta: 11:15:33, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5555, top5_acc: 0.7886, loss_cls: 2.5022, loss: 2.5022 +2024-12-31 11:20:16,239 - pyskl - INFO - Epoch [138][1800/3746] lr: 1.709e-03, eta: 11:14:06, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5537, top5_acc: 0.7983, loss_cls: 2.4805, loss: 2.4805 +2024-12-31 11:21:41,582 - pyskl - INFO - Epoch [138][1900/3746] lr: 1.702e-03, eta: 11:12:40, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5580, top5_acc: 0.7953, loss_cls: 2.4864, loss: 2.4864 +2024-12-31 11:23:06,520 - pyskl - INFO - Epoch [138][2000/3746] lr: 1.695e-03, eta: 11:11:14, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5636, top5_acc: 0.7931, loss_cls: 2.4713, loss: 2.4713 +2024-12-31 11:24:31,468 - pyskl - INFO - Epoch [138][2100/3746] lr: 1.687e-03, eta: 11:09:47, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5527, top5_acc: 0.7973, loss_cls: 2.4932, loss: 2.4932 +2024-12-31 11:25:56,588 - pyskl - INFO - Epoch [138][2200/3746] lr: 1.680e-03, eta: 11:08:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5494, top5_acc: 0.7975, loss_cls: 2.4908, loss: 2.4908 +2024-12-31 11:27:21,555 - pyskl - INFO - Epoch [138][2300/3746] lr: 1.673e-03, eta: 11:06:55, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5563, top5_acc: 0.7863, loss_cls: 2.4902, loss: 2.4902 +2024-12-31 11:28:46,959 - pyskl - INFO - Epoch [138][2400/3746] lr: 1.666e-03, eta: 11:05:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5505, top5_acc: 0.7855, loss_cls: 2.5080, loss: 2.5080 +2024-12-31 11:30:12,543 - pyskl - INFO - Epoch [138][2500/3746] lr: 1.659e-03, eta: 11:04:02, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5622, top5_acc: 0.7920, loss_cls: 2.4804, loss: 2.4804 +2024-12-31 11:31:37,826 - pyskl - INFO - Epoch [138][2600/3746] lr: 1.652e-03, eta: 11:02:36, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5530, top5_acc: 0.7916, loss_cls: 2.4897, loss: 2.4897 +2024-12-31 11:33:02,557 - pyskl - INFO - Epoch [138][2700/3746] lr: 1.644e-03, eta: 11:01:09, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5559, top5_acc: 0.7934, loss_cls: 2.4759, loss: 2.4759 +2024-12-31 11:34:28,004 - pyskl - INFO - Epoch [138][2800/3746] lr: 1.637e-03, eta: 10:59:43, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5545, top5_acc: 0.7908, loss_cls: 2.4841, loss: 2.4841 +2024-12-31 11:35:52,790 - pyskl - INFO - Epoch [138][2900/3746] lr: 1.630e-03, eta: 10:58:17, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5559, top5_acc: 0.7911, loss_cls: 2.4933, loss: 2.4933 +2024-12-31 11:37:17,807 - pyskl - INFO - Epoch [138][3000/3746] lr: 1.623e-03, eta: 10:56:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5517, top5_acc: 0.7867, loss_cls: 2.5128, loss: 2.5128 +2024-12-31 11:38:43,039 - pyskl - INFO - Epoch [138][3100/3746] lr: 1.616e-03, eta: 10:55:24, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5539, top5_acc: 0.7925, loss_cls: 2.4988, loss: 2.4988 +2024-12-31 11:40:07,895 - pyskl - INFO - Epoch [138][3200/3746] lr: 1.609e-03, eta: 10:53:58, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5523, top5_acc: 0.7933, loss_cls: 2.5013, loss: 2.5013 +2024-12-31 11:41:32,847 - pyskl - INFO - Epoch [138][3300/3746] lr: 1.602e-03, eta: 10:52:31, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5441, top5_acc: 0.7808, loss_cls: 2.5468, loss: 2.5468 +2024-12-31 11:42:57,727 - pyskl - INFO - Epoch [138][3400/3746] lr: 1.595e-03, eta: 10:51:05, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.5556, top5_acc: 0.7905, loss_cls: 2.4909, loss: 2.4909 +2024-12-31 11:44:22,461 - pyskl - INFO - Epoch [138][3500/3746] lr: 1.588e-03, eta: 10:49:39, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5425, top5_acc: 0.7927, loss_cls: 2.5097, loss: 2.5097 +2024-12-31 11:45:47,430 - pyskl - INFO - Epoch [138][3600/3746] lr: 1.581e-03, eta: 10:48:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5506, top5_acc: 0.7844, loss_cls: 2.5479, loss: 2.5479 +2024-12-31 11:47:12,970 - pyskl - INFO - Epoch [138][3700/3746] lr: 1.574e-03, eta: 10:46:46, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5437, top5_acc: 0.7808, loss_cls: 2.5515, loss: 2.5515 +2024-12-31 11:47:54,135 - pyskl - INFO - Saving checkpoint at 138 epochs +2024-12-31 11:49:53,076 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 11:49:54,138 - pyskl - INFO - +top1_acc 0.4299 +top5_acc 0.6801 +2024-12-31 11:49:54,139 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 11:49:54,197 - pyskl - INFO - +mean_acc 0.4296 +2024-12-31 11:49:54,216 - pyskl - INFO - Epoch(val) [138][309] top1_acc: 0.4299, top5_acc: 0.6801, mean_class_accuracy: 0.4296 +2024-12-31 11:54:12,107 - pyskl - INFO - Epoch [139][100/3746] lr: 1.564e-03, eta: 10:44:51, time: 2.579, data_time: 1.535, memory: 15990, top1_acc: 0.5823, top5_acc: 0.8059, loss_cls: 2.3591, loss: 2.3591 +2024-12-31 11:55:38,616 - pyskl - INFO - Epoch [139][200/3746] lr: 1.557e-03, eta: 10:43:25, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.5705, top5_acc: 0.8030, loss_cls: 2.4078, loss: 2.4078 +2024-12-31 11:57:04,682 - pyskl - INFO - Epoch [139][300/3746] lr: 1.550e-03, eta: 10:41:59, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5728, top5_acc: 0.7997, loss_cls: 2.4265, loss: 2.4265 +2024-12-31 11:58:30,539 - pyskl - INFO - Epoch [139][400/3746] lr: 1.543e-03, eta: 10:40:33, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5711, top5_acc: 0.8020, loss_cls: 2.4230, loss: 2.4230 +2024-12-31 11:59:56,329 - pyskl - INFO - Epoch [139][500/3746] lr: 1.536e-03, eta: 10:39:06, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5684, top5_acc: 0.7980, loss_cls: 2.4269, loss: 2.4269 +2024-12-31 12:01:22,180 - pyskl - INFO - Epoch [139][600/3746] lr: 1.529e-03, eta: 10:37:40, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5698, top5_acc: 0.8111, loss_cls: 2.4082, loss: 2.4082 +2024-12-31 12:02:48,203 - pyskl - INFO - Epoch [139][700/3746] lr: 1.523e-03, eta: 10:36:14, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5795, top5_acc: 0.8058, loss_cls: 2.3841, loss: 2.3841 +2024-12-31 12:04:14,125 - pyskl - INFO - Epoch [139][800/3746] lr: 1.516e-03, eta: 10:34:47, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5648, top5_acc: 0.8111, loss_cls: 2.3915, loss: 2.3915 +2024-12-31 12:05:39,687 - pyskl - INFO - Epoch [139][900/3746] lr: 1.509e-03, eta: 10:33:21, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5709, top5_acc: 0.7994, loss_cls: 2.4054, loss: 2.4054 +2024-12-31 12:07:04,904 - pyskl - INFO - Epoch [139][1000/3746] lr: 1.502e-03, eta: 10:31:55, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5600, top5_acc: 0.7959, loss_cls: 2.4464, loss: 2.4464 +2024-12-31 12:08:29,932 - pyskl - INFO - Epoch [139][1100/3746] lr: 1.495e-03, eta: 10:30:28, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5695, top5_acc: 0.8002, loss_cls: 2.4317, loss: 2.4317 +2024-12-31 12:09:55,235 - pyskl - INFO - Epoch [139][1200/3746] lr: 1.489e-03, eta: 10:29:02, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5617, top5_acc: 0.8052, loss_cls: 2.4369, loss: 2.4369 +2024-12-31 12:11:20,137 - pyskl - INFO - Epoch [139][1300/3746] lr: 1.482e-03, eta: 10:27:36, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5745, top5_acc: 0.8080, loss_cls: 2.3863, loss: 2.3863 +2024-12-31 12:12:45,770 - pyskl - INFO - Epoch [139][1400/3746] lr: 1.475e-03, eta: 10:26:09, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5672, top5_acc: 0.8106, loss_cls: 2.4090, loss: 2.4090 +2024-12-31 12:14:10,903 - pyskl - INFO - Epoch [139][1500/3746] lr: 1.468e-03, eta: 10:24:43, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5769, top5_acc: 0.8103, loss_cls: 2.3671, loss: 2.3671 +2024-12-31 12:15:36,196 - pyskl - INFO - Epoch [139][1600/3746] lr: 1.462e-03, eta: 10:23:17, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5663, top5_acc: 0.8028, loss_cls: 2.4176, loss: 2.4176 +2024-12-31 12:17:01,532 - pyskl - INFO - Epoch [139][1700/3746] lr: 1.455e-03, eta: 10:21:50, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5664, top5_acc: 0.7916, loss_cls: 2.4327, loss: 2.4327 +2024-12-31 12:18:27,332 - pyskl - INFO - Epoch [139][1800/3746] lr: 1.448e-03, eta: 10:20:24, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5505, top5_acc: 0.8027, loss_cls: 2.4489, loss: 2.4489 +2024-12-31 12:19:53,192 - pyskl - INFO - Epoch [139][1900/3746] lr: 1.442e-03, eta: 10:18:58, time: 0.859, data_time: 0.001, memory: 15990, top1_acc: 0.5584, top5_acc: 0.8003, loss_cls: 2.4555, loss: 2.4555 +2024-12-31 12:21:18,629 - pyskl - INFO - Epoch [139][2000/3746] lr: 1.435e-03, eta: 10:17:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5487, top5_acc: 0.7911, loss_cls: 2.4757, loss: 2.4757 +2024-12-31 12:22:44,153 - pyskl - INFO - Epoch [139][2100/3746] lr: 1.428e-03, eta: 10:16:05, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5581, top5_acc: 0.7891, loss_cls: 2.4719, loss: 2.4719 +2024-12-31 12:24:09,709 - pyskl - INFO - Epoch [139][2200/3746] lr: 1.422e-03, eta: 10:14:39, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5630, top5_acc: 0.7958, loss_cls: 2.4458, loss: 2.4458 +2024-12-31 12:25:35,031 - pyskl - INFO - Epoch [139][2300/3746] lr: 1.415e-03, eta: 10:13:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5617, top5_acc: 0.7939, loss_cls: 2.4545, loss: 2.4545 +2024-12-31 12:27:00,796 - pyskl - INFO - Epoch [139][2400/3746] lr: 1.408e-03, eta: 10:11:46, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5577, top5_acc: 0.7989, loss_cls: 2.4525, loss: 2.4525 +2024-12-31 12:28:25,918 - pyskl - INFO - Epoch [139][2500/3746] lr: 1.402e-03, eta: 10:10:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5592, top5_acc: 0.7983, loss_cls: 2.4643, loss: 2.4643 +2024-12-31 12:29:51,282 - pyskl - INFO - Epoch [139][2600/3746] lr: 1.395e-03, eta: 10:08:53, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5617, top5_acc: 0.7998, loss_cls: 2.4529, loss: 2.4529 +2024-12-31 12:31:16,817 - pyskl - INFO - Epoch [139][2700/3746] lr: 1.389e-03, eta: 10:07:27, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5730, top5_acc: 0.8017, loss_cls: 2.4238, loss: 2.4238 +2024-12-31 12:32:42,123 - pyskl - INFO - Epoch [139][2800/3746] lr: 1.382e-03, eta: 10:06:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5692, top5_acc: 0.7973, loss_cls: 2.4318, loss: 2.4318 +2024-12-31 12:34:07,306 - pyskl - INFO - Epoch [139][2900/3746] lr: 1.376e-03, eta: 10:04:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5631, top5_acc: 0.8006, loss_cls: 2.4165, loss: 2.4165 +2024-12-31 12:35:32,463 - pyskl - INFO - Epoch [139][3000/3746] lr: 1.369e-03, eta: 10:03:08, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5530, top5_acc: 0.7970, loss_cls: 2.4646, loss: 2.4646 +2024-12-31 12:36:57,726 - pyskl - INFO - Epoch [139][3100/3746] lr: 1.363e-03, eta: 10:01:42, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5577, top5_acc: 0.7964, loss_cls: 2.4434, loss: 2.4434 +2024-12-31 12:38:22,981 - pyskl - INFO - Epoch [139][3200/3746] lr: 1.356e-03, eta: 10:00:15, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5589, top5_acc: 0.7878, loss_cls: 2.4867, loss: 2.4867 +2024-12-31 12:39:47,911 - pyskl - INFO - Epoch [139][3300/3746] lr: 1.350e-03, eta: 9:58:49, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5614, top5_acc: 0.7964, loss_cls: 2.4671, loss: 2.4671 +2024-12-31 12:41:13,222 - pyskl - INFO - Epoch [139][3400/3746] lr: 1.343e-03, eta: 9:57:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5625, top5_acc: 0.8008, loss_cls: 2.4300, loss: 2.4300 +2024-12-31 12:42:38,073 - pyskl - INFO - Epoch [139][3500/3746] lr: 1.337e-03, eta: 9:55:56, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5659, top5_acc: 0.8045, loss_cls: 2.4030, loss: 2.4030 +2024-12-31 12:44:02,646 - pyskl - INFO - Epoch [139][3600/3746] lr: 1.330e-03, eta: 9:54:30, time: 0.846, data_time: 0.000, memory: 15990, top1_acc: 0.5509, top5_acc: 0.7933, loss_cls: 2.4834, loss: 2.4834 +2024-12-31 12:45:28,000 - pyskl - INFO - Epoch [139][3700/3746] lr: 1.324e-03, eta: 9:53:03, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5492, top5_acc: 0.7900, loss_cls: 2.4903, loss: 2.4903 +2024-12-31 12:46:08,899 - pyskl - INFO - Saving checkpoint at 139 epochs +2024-12-31 12:48:08,215 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 12:48:08,914 - pyskl - INFO - +top1_acc 0.4379 +top5_acc 0.6855 +2024-12-31 12:48:08,914 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 12:48:08,963 - pyskl - INFO - +mean_acc 0.4375 +2024-12-31 12:48:08,968 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_137.pth was removed +2024-12-31 12:48:09,226 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_139.pth. +2024-12-31 12:48:09,227 - pyskl - INFO - Best top1_acc is 0.4379 at 139 epoch. +2024-12-31 12:48:09,241 - pyskl - INFO - Epoch(val) [139][309] top1_acc: 0.4379, top5_acc: 0.6855, mean_class_accuracy: 0.4375 +2024-12-31 12:52:20,807 - pyskl - INFO - Epoch [140][100/3746] lr: 1.315e-03, eta: 9:51:07, time: 2.516, data_time: 1.474, memory: 15990, top1_acc: 0.5833, top5_acc: 0.8178, loss_cls: 2.3220, loss: 2.3220 +2024-12-31 12:53:46,326 - pyskl - INFO - Epoch [140][200/3746] lr: 1.308e-03, eta: 9:49:41, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5681, top5_acc: 0.8127, loss_cls: 2.3612, loss: 2.3612 +2024-12-31 12:55:11,823 - pyskl - INFO - Epoch [140][300/3746] lr: 1.302e-03, eta: 9:48:15, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5702, top5_acc: 0.8125, loss_cls: 2.3871, loss: 2.3871 +2024-12-31 12:56:37,038 - pyskl - INFO - Epoch [140][400/3746] lr: 1.296e-03, eta: 9:46:48, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5741, top5_acc: 0.8173, loss_cls: 2.3421, loss: 2.3421 +2024-12-31 12:58:02,429 - pyskl - INFO - Epoch [140][500/3746] lr: 1.289e-03, eta: 9:45:22, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5809, top5_acc: 0.8002, loss_cls: 2.3717, loss: 2.3717 +2024-12-31 12:59:28,071 - pyskl - INFO - Epoch [140][600/3746] lr: 1.283e-03, eta: 9:43:56, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5855, top5_acc: 0.8139, loss_cls: 2.3312, loss: 2.3312 +2024-12-31 13:00:53,603 - pyskl - INFO - Epoch [140][700/3746] lr: 1.277e-03, eta: 9:42:29, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5747, top5_acc: 0.8008, loss_cls: 2.3901, loss: 2.3901 +2024-12-31 13:02:18,749 - pyskl - INFO - Epoch [140][800/3746] lr: 1.271e-03, eta: 9:41:03, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5728, top5_acc: 0.8044, loss_cls: 2.3986, loss: 2.3986 +2024-12-31 13:03:43,913 - pyskl - INFO - Epoch [140][900/3746] lr: 1.264e-03, eta: 9:39:37, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5922, top5_acc: 0.8173, loss_cls: 2.3189, loss: 2.3189 +2024-12-31 13:05:09,427 - pyskl - INFO - Epoch [140][1000/3746] lr: 1.258e-03, eta: 9:38:10, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5730, top5_acc: 0.8061, loss_cls: 2.3921, loss: 2.3921 +2024-12-31 13:06:34,504 - pyskl - INFO - Epoch [140][1100/3746] lr: 1.252e-03, eta: 9:36:44, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5906, top5_acc: 0.8142, loss_cls: 2.3412, loss: 2.3412 +2024-12-31 13:07:59,769 - pyskl - INFO - Epoch [140][1200/3746] lr: 1.246e-03, eta: 9:35:18, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5752, top5_acc: 0.8091, loss_cls: 2.3998, loss: 2.3998 +2024-12-31 13:09:25,871 - pyskl - INFO - Epoch [140][1300/3746] lr: 1.239e-03, eta: 9:33:51, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5731, top5_acc: 0.8078, loss_cls: 2.4040, loss: 2.4040 +2024-12-31 13:10:51,599 - pyskl - INFO - Epoch [140][1400/3746] lr: 1.233e-03, eta: 9:32:25, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5716, top5_acc: 0.8050, loss_cls: 2.3956, loss: 2.3956 +2024-12-31 13:12:17,343 - pyskl - INFO - Epoch [140][1500/3746] lr: 1.227e-03, eta: 9:30:59, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5783, top5_acc: 0.8119, loss_cls: 2.3536, loss: 2.3536 +2024-12-31 13:13:43,033 - pyskl - INFO - Epoch [140][1600/3746] lr: 1.221e-03, eta: 9:29:32, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5772, top5_acc: 0.8128, loss_cls: 2.3609, loss: 2.3609 +2024-12-31 13:15:08,572 - pyskl - INFO - Epoch [140][1700/3746] lr: 1.215e-03, eta: 9:28:06, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5727, top5_acc: 0.8039, loss_cls: 2.3846, loss: 2.3846 +2024-12-31 13:16:34,219 - pyskl - INFO - Epoch [140][1800/3746] lr: 1.209e-03, eta: 9:26:40, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5708, top5_acc: 0.8092, loss_cls: 2.4091, loss: 2.4091 +2024-12-31 13:17:59,178 - pyskl - INFO - Epoch [140][1900/3746] lr: 1.203e-03, eta: 9:25:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5744, top5_acc: 0.8070, loss_cls: 2.3981, loss: 2.3981 +2024-12-31 13:19:23,882 - pyskl - INFO - Epoch [140][2000/3746] lr: 1.196e-03, eta: 9:23:47, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.5642, top5_acc: 0.8052, loss_cls: 2.4119, loss: 2.4119 +2024-12-31 13:20:48,634 - pyskl - INFO - Epoch [140][2100/3746] lr: 1.190e-03, eta: 9:22:20, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5758, top5_acc: 0.8055, loss_cls: 2.3829, loss: 2.3829 +2024-12-31 13:22:13,394 - pyskl - INFO - Epoch [140][2200/3746] lr: 1.184e-03, eta: 9:20:54, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5725, top5_acc: 0.8067, loss_cls: 2.4035, loss: 2.4035 +2024-12-31 13:23:38,480 - pyskl - INFO - Epoch [140][2300/3746] lr: 1.178e-03, eta: 9:19:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5783, top5_acc: 0.8095, loss_cls: 2.3637, loss: 2.3637 +2024-12-31 13:25:03,518 - pyskl - INFO - Epoch [140][2400/3746] lr: 1.172e-03, eta: 9:18:01, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5753, top5_acc: 0.8087, loss_cls: 2.3843, loss: 2.3843 +2024-12-31 13:26:28,663 - pyskl - INFO - Epoch [140][2500/3746] lr: 1.166e-03, eta: 9:16:35, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5858, top5_acc: 0.8147, loss_cls: 2.3445, loss: 2.3445 +2024-12-31 13:27:54,131 - pyskl - INFO - Epoch [140][2600/3746] lr: 1.160e-03, eta: 9:15:09, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5752, top5_acc: 0.8073, loss_cls: 2.3748, loss: 2.3748 +2024-12-31 13:29:19,620 - pyskl - INFO - Epoch [140][2700/3746] lr: 1.154e-03, eta: 9:13:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5819, top5_acc: 0.8147, loss_cls: 2.3502, loss: 2.3502 +2024-12-31 13:30:45,287 - pyskl - INFO - Epoch [140][2800/3746] lr: 1.148e-03, eta: 9:12:16, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5594, top5_acc: 0.8003, loss_cls: 2.4349, loss: 2.4349 +2024-12-31 13:32:10,621 - pyskl - INFO - Epoch [140][2900/3746] lr: 1.142e-03, eta: 9:10:50, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5663, top5_acc: 0.8041, loss_cls: 2.4075, loss: 2.4075 +2024-12-31 13:33:36,565 - pyskl - INFO - Epoch [140][3000/3746] lr: 1.136e-03, eta: 9:09:23, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5691, top5_acc: 0.8036, loss_cls: 2.4076, loss: 2.4076 +2024-12-31 13:35:01,875 - pyskl - INFO - Epoch [140][3100/3746] lr: 1.131e-03, eta: 9:07:57, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5663, top5_acc: 0.7995, loss_cls: 2.4359, loss: 2.4359 +2024-12-31 13:36:27,244 - pyskl - INFO - Epoch [140][3200/3746] lr: 1.125e-03, eta: 9:06:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5709, top5_acc: 0.8134, loss_cls: 2.3721, loss: 2.3721 +2024-12-31 13:37:52,720 - pyskl - INFO - Epoch [140][3300/3746] lr: 1.119e-03, eta: 9:05:04, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5750, top5_acc: 0.8098, loss_cls: 2.3690, loss: 2.3690 +2024-12-31 13:39:17,733 - pyskl - INFO - Epoch [140][3400/3746] lr: 1.113e-03, eta: 9:03:38, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.5664, top5_acc: 0.8053, loss_cls: 2.3867, loss: 2.3867 +2024-12-31 13:40:43,017 - pyskl - INFO - Epoch [140][3500/3746] lr: 1.107e-03, eta: 9:02:12, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5681, top5_acc: 0.7989, loss_cls: 2.4303, loss: 2.4303 +2024-12-31 13:42:08,358 - pyskl - INFO - Epoch [140][3600/3746] lr: 1.101e-03, eta: 9:00:45, time: 0.853, data_time: 0.001, memory: 15990, top1_acc: 0.5708, top5_acc: 0.8048, loss_cls: 2.4010, loss: 2.4010 +2024-12-31 13:43:33,314 - pyskl - INFO - Epoch [140][3700/3746] lr: 1.095e-03, eta: 8:59:19, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5681, top5_acc: 0.8048, loss_cls: 2.4047, loss: 2.4047 +2024-12-31 13:44:14,150 - pyskl - INFO - Saving checkpoint at 140 epochs +2024-12-31 13:46:12,071 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 13:46:12,813 - pyskl - INFO - +top1_acc 0.4386 +top5_acc 0.6823 +2024-12-31 13:46:12,814 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 13:46:12,859 - pyskl - INFO - +mean_acc 0.4384 +2024-12-31 13:46:12,865 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_139.pth was removed +2024-12-31 13:46:13,200 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_140.pth. +2024-12-31 13:46:13,201 - pyskl - INFO - Best top1_acc is 0.4386 at 140 epoch. +2024-12-31 13:46:13,213 - pyskl - INFO - Epoch(val) [140][309] top1_acc: 0.4386, top5_acc: 0.6823, mean_class_accuracy: 0.4384 +2024-12-31 13:50:34,046 - pyskl - INFO - Epoch [141][100/3746] lr: 1.087e-03, eta: 8:57:22, time: 2.608, data_time: 1.565, memory: 15990, top1_acc: 0.5938, top5_acc: 0.8211, loss_cls: 2.2895, loss: 2.2895 +2024-12-31 13:52:00,033 - pyskl - INFO - Epoch [141][200/3746] lr: 1.081e-03, eta: 8:55:56, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6064, top5_acc: 0.8186, loss_cls: 2.2725, loss: 2.2725 +2024-12-31 13:53:25,912 - pyskl - INFO - Epoch [141][300/3746] lr: 1.075e-03, eta: 8:54:30, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5947, top5_acc: 0.8164, loss_cls: 2.2923, loss: 2.2923 +2024-12-31 13:54:52,100 - pyskl - INFO - Epoch [141][400/3746] lr: 1.070e-03, eta: 8:53:03, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5939, top5_acc: 0.8213, loss_cls: 2.2716, loss: 2.2716 +2024-12-31 13:56:18,437 - pyskl - INFO - Epoch [141][500/3746] lr: 1.064e-03, eta: 8:51:37, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5802, top5_acc: 0.8131, loss_cls: 2.3553, loss: 2.3553 +2024-12-31 13:57:44,451 - pyskl - INFO - Epoch [141][600/3746] lr: 1.058e-03, eta: 8:50:11, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5777, top5_acc: 0.8178, loss_cls: 2.3394, loss: 2.3394 +2024-12-31 13:59:09,773 - pyskl - INFO - Epoch [141][700/3746] lr: 1.052e-03, eta: 8:48:44, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5809, top5_acc: 0.8208, loss_cls: 2.3297, loss: 2.3297 +2024-12-31 14:00:35,604 - pyskl - INFO - Epoch [141][800/3746] lr: 1.047e-03, eta: 8:47:18, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5752, top5_acc: 0.8134, loss_cls: 2.3689, loss: 2.3689 +2024-12-31 14:02:01,394 - pyskl - INFO - Epoch [141][900/3746] lr: 1.041e-03, eta: 8:45:52, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5942, top5_acc: 0.8263, loss_cls: 2.2556, loss: 2.2556 +2024-12-31 14:03:27,633 - pyskl - INFO - Epoch [141][1000/3746] lr: 1.035e-03, eta: 8:44:25, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5914, top5_acc: 0.8170, loss_cls: 2.2947, loss: 2.2947 +2024-12-31 14:04:52,951 - pyskl - INFO - Epoch [141][1100/3746] lr: 1.030e-03, eta: 8:42:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5870, top5_acc: 0.8208, loss_cls: 2.3041, loss: 2.3041 +2024-12-31 14:06:19,378 - pyskl - INFO - Epoch [141][1200/3746] lr: 1.024e-03, eta: 8:41:33, time: 0.864, data_time: 0.000, memory: 15990, top1_acc: 0.5853, top5_acc: 0.8231, loss_cls: 2.2911, loss: 2.2911 +2024-12-31 14:07:45,351 - pyskl - INFO - Epoch [141][1300/3746] lr: 1.018e-03, eta: 8:40:07, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5908, top5_acc: 0.8161, loss_cls: 2.3164, loss: 2.3164 +2024-12-31 14:09:10,870 - pyskl - INFO - Epoch [141][1400/3746] lr: 1.013e-03, eta: 8:38:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5902, top5_acc: 0.8234, loss_cls: 2.2959, loss: 2.2959 +2024-12-31 14:10:36,972 - pyskl - INFO - Epoch [141][1500/3746] lr: 1.007e-03, eta: 8:37:14, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5861, top5_acc: 0.8186, loss_cls: 2.3239, loss: 2.3239 +2024-12-31 14:12:02,984 - pyskl - INFO - Epoch [141][1600/3746] lr: 1.002e-03, eta: 8:35:48, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5875, top5_acc: 0.8183, loss_cls: 2.3143, loss: 2.3143 +2024-12-31 14:13:29,253 - pyskl - INFO - Epoch [141][1700/3746] lr: 9.961e-04, eta: 8:34:21, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5713, top5_acc: 0.8134, loss_cls: 2.3859, loss: 2.3859 +2024-12-31 14:14:54,949 - pyskl - INFO - Epoch [141][1800/3746] lr: 9.905e-04, eta: 8:32:55, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5875, top5_acc: 0.8133, loss_cls: 2.3146, loss: 2.3146 +2024-12-31 14:16:20,948 - pyskl - INFO - Epoch [141][1900/3746] lr: 9.850e-04, eta: 8:31:29, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5808, top5_acc: 0.8091, loss_cls: 2.3415, loss: 2.3415 +2024-12-31 14:17:46,847 - pyskl - INFO - Epoch [141][2000/3746] lr: 9.795e-04, eta: 8:30:02, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5805, top5_acc: 0.8127, loss_cls: 2.3441, loss: 2.3441 +2024-12-31 14:19:12,466 - pyskl - INFO - Epoch [141][2100/3746] lr: 9.740e-04, eta: 8:28:36, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5911, top5_acc: 0.8180, loss_cls: 2.2955, loss: 2.2955 +2024-12-31 14:20:37,459 - pyskl - INFO - Epoch [141][2200/3746] lr: 9.685e-04, eta: 8:27:09, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5759, top5_acc: 0.8123, loss_cls: 2.3638, loss: 2.3638 +2024-12-31 14:22:02,395 - pyskl - INFO - Epoch [141][2300/3746] lr: 9.630e-04, eta: 8:25:43, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.5830, top5_acc: 0.8086, loss_cls: 2.3603, loss: 2.3603 +2024-12-31 14:23:27,407 - pyskl - INFO - Epoch [141][2400/3746] lr: 9.576e-04, eta: 8:24:17, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5894, top5_acc: 0.8155, loss_cls: 2.3255, loss: 2.3255 +2024-12-31 14:24:51,824 - pyskl - INFO - Epoch [141][2500/3746] lr: 9.522e-04, eta: 8:22:50, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.5913, top5_acc: 0.8220, loss_cls: 2.2876, loss: 2.2876 +2024-12-31 14:26:16,797 - pyskl - INFO - Epoch [141][2600/3746] lr: 9.467e-04, eta: 8:21:24, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.5764, top5_acc: 0.8133, loss_cls: 2.3498, loss: 2.3498 +2024-12-31 14:27:42,143 - pyskl - INFO - Epoch [141][2700/3746] lr: 9.413e-04, eta: 8:19:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5808, top5_acc: 0.8066, loss_cls: 2.3827, loss: 2.3827 +2024-12-31 14:29:07,602 - pyskl - INFO - Epoch [141][2800/3746] lr: 9.359e-04, eta: 8:18:31, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5781, top5_acc: 0.8153, loss_cls: 2.3407, loss: 2.3407 +2024-12-31 14:30:32,690 - pyskl - INFO - Epoch [141][2900/3746] lr: 9.306e-04, eta: 8:17:05, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5867, top5_acc: 0.8163, loss_cls: 2.3253, loss: 2.3253 +2024-12-31 14:31:57,920 - pyskl - INFO - Epoch [141][3000/3746] lr: 9.252e-04, eta: 8:15:38, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5703, top5_acc: 0.8081, loss_cls: 2.3847, loss: 2.3847 +2024-12-31 14:33:23,290 - pyskl - INFO - Epoch [141][3100/3746] lr: 9.199e-04, eta: 8:14:12, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5794, top5_acc: 0.8123, loss_cls: 2.3596, loss: 2.3596 +2024-12-31 14:34:48,891 - pyskl - INFO - Epoch [141][3200/3746] lr: 9.145e-04, eta: 8:12:46, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5766, top5_acc: 0.8091, loss_cls: 2.3739, loss: 2.3739 +2024-12-31 14:36:14,169 - pyskl - INFO - Epoch [141][3300/3746] lr: 9.092e-04, eta: 8:11:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5773, top5_acc: 0.8103, loss_cls: 2.3576, loss: 2.3576 +2024-12-31 14:37:39,349 - pyskl - INFO - Epoch [141][3400/3746] lr: 9.039e-04, eta: 8:09:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5841, top5_acc: 0.8175, loss_cls: 2.3453, loss: 2.3453 +2024-12-31 14:39:04,245 - pyskl - INFO - Epoch [141][3500/3746] lr: 8.986e-04, eta: 8:08:27, time: 0.849, data_time: 0.001, memory: 15990, top1_acc: 0.5841, top5_acc: 0.8161, loss_cls: 2.3270, loss: 2.3270 +2024-12-31 14:40:29,344 - pyskl - INFO - Epoch [141][3600/3746] lr: 8.934e-04, eta: 8:07:00, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.5923, top5_acc: 0.8202, loss_cls: 2.2921, loss: 2.2921 +2024-12-31 14:41:54,535 - pyskl - INFO - Epoch [141][3700/3746] lr: 8.881e-04, eta: 8:05:34, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5852, top5_acc: 0.8141, loss_cls: 2.3205, loss: 2.3205 +2024-12-31 14:42:35,791 - pyskl - INFO - Saving checkpoint at 141 epochs +2024-12-31 14:44:33,716 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 14:44:34,405 - pyskl - INFO - +top1_acc 0.4386 +top5_acc 0.6876 +2024-12-31 14:44:34,405 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 14:44:34,457 - pyskl - INFO - +mean_acc 0.4385 +2024-12-31 14:44:34,478 - pyskl - INFO - Epoch(val) [141][309] top1_acc: 0.4386, top5_acc: 0.6876, mean_class_accuracy: 0.4385 +2024-12-31 14:48:59,410 - pyskl - INFO - Epoch [142][100/3746] lr: 8.805e-04, eta: 8:03:37, time: 2.649, data_time: 1.616, memory: 15990, top1_acc: 0.6020, top5_acc: 0.8236, loss_cls: 2.2520, loss: 2.2520 +2024-12-31 14:50:25,341 - pyskl - INFO - Epoch [142][200/3746] lr: 8.752e-04, eta: 8:02:10, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.5983, top5_acc: 0.8278, loss_cls: 2.2509, loss: 2.2509 +2024-12-31 14:51:51,488 - pyskl - INFO - Epoch [142][300/3746] lr: 8.700e-04, eta: 8:00:44, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.5952, top5_acc: 0.8255, loss_cls: 2.2572, loss: 2.2572 +2024-12-31 14:53:17,405 - pyskl - INFO - Epoch [142][400/3746] lr: 8.649e-04, eta: 7:59:18, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6003, top5_acc: 0.8281, loss_cls: 2.2591, loss: 2.2591 +2024-12-31 14:54:43,231 - pyskl - INFO - Epoch [142][500/3746] lr: 8.597e-04, eta: 7:57:51, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5916, top5_acc: 0.8261, loss_cls: 2.2898, loss: 2.2898 +2024-12-31 14:56:09,249 - pyskl - INFO - Epoch [142][600/3746] lr: 8.545e-04, eta: 7:56:25, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5941, top5_acc: 0.8247, loss_cls: 2.2742, loss: 2.2742 +2024-12-31 14:57:34,987 - pyskl - INFO - Epoch [142][700/3746] lr: 8.494e-04, eta: 7:54:59, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5995, top5_acc: 0.8263, loss_cls: 2.2481, loss: 2.2481 +2024-12-31 14:59:01,323 - pyskl - INFO - Epoch [142][800/3746] lr: 8.443e-04, eta: 7:53:32, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.6030, top5_acc: 0.8261, loss_cls: 2.2390, loss: 2.2390 +2024-12-31 15:00:27,091 - pyskl - INFO - Epoch [142][900/3746] lr: 8.392e-04, eta: 7:52:06, time: 0.858, data_time: 0.001, memory: 15990, top1_acc: 0.5981, top5_acc: 0.8245, loss_cls: 2.2692, loss: 2.2692 +2024-12-31 15:01:53,233 - pyskl - INFO - Epoch [142][1000/3746] lr: 8.341e-04, eta: 7:50:40, time: 0.861, data_time: 0.001, memory: 15990, top1_acc: 0.5983, top5_acc: 0.8291, loss_cls: 2.2661, loss: 2.2661 +2024-12-31 15:03:19,033 - pyskl - INFO - Epoch [142][1100/3746] lr: 8.290e-04, eta: 7:49:13, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5913, top5_acc: 0.8156, loss_cls: 2.3078, loss: 2.3078 +2024-12-31 15:04:44,694 - pyskl - INFO - Epoch [142][1200/3746] lr: 8.239e-04, eta: 7:47:47, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6012, top5_acc: 0.8245, loss_cls: 2.2664, loss: 2.2664 +2024-12-31 15:06:10,207 - pyskl - INFO - Epoch [142][1300/3746] lr: 8.189e-04, eta: 7:46:20, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5884, top5_acc: 0.8217, loss_cls: 2.2830, loss: 2.2830 +2024-12-31 15:07:35,531 - pyskl - INFO - Epoch [142][1400/3746] lr: 8.139e-04, eta: 7:44:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6083, top5_acc: 0.8272, loss_cls: 2.2426, loss: 2.2426 +2024-12-31 15:09:01,333 - pyskl - INFO - Epoch [142][1500/3746] lr: 8.088e-04, eta: 7:43:28, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5994, top5_acc: 0.8227, loss_cls: 2.2512, loss: 2.2512 +2024-12-31 15:10:26,588 - pyskl - INFO - Epoch [142][1600/3746] lr: 8.038e-04, eta: 7:42:01, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5856, top5_acc: 0.8187, loss_cls: 2.3037, loss: 2.3037 +2024-12-31 15:11:52,301 - pyskl - INFO - Epoch [142][1700/3746] lr: 7.989e-04, eta: 7:40:35, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6033, top5_acc: 0.8166, loss_cls: 2.2679, loss: 2.2679 +2024-12-31 15:13:17,899 - pyskl - INFO - Epoch [142][1800/3746] lr: 7.939e-04, eta: 7:39:09, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5892, top5_acc: 0.8195, loss_cls: 2.2853, loss: 2.2853 +2024-12-31 15:14:43,987 - pyskl - INFO - Epoch [142][1900/3746] lr: 7.889e-04, eta: 7:37:42, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.6045, top5_acc: 0.8220, loss_cls: 2.2557, loss: 2.2557 +2024-12-31 15:16:09,220 - pyskl - INFO - Epoch [142][2000/3746] lr: 7.840e-04, eta: 7:36:16, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5848, top5_acc: 0.8223, loss_cls: 2.2849, loss: 2.2849 +2024-12-31 15:17:34,609 - pyskl - INFO - Epoch [142][2100/3746] lr: 7.791e-04, eta: 7:34:50, time: 0.854, data_time: 0.001, memory: 15990, top1_acc: 0.5920, top5_acc: 0.8300, loss_cls: 2.2479, loss: 2.2479 +2024-12-31 15:18:59,952 - pyskl - INFO - Epoch [142][2200/3746] lr: 7.742e-04, eta: 7:33:23, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5962, top5_acc: 0.8328, loss_cls: 2.2494, loss: 2.2494 +2024-12-31 15:20:25,600 - pyskl - INFO - Epoch [142][2300/3746] lr: 7.693e-04, eta: 7:31:57, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5931, top5_acc: 0.8128, loss_cls: 2.2868, loss: 2.2868 +2024-12-31 15:21:51,585 - pyskl - INFO - Epoch [142][2400/3746] lr: 7.644e-04, eta: 7:30:30, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6028, top5_acc: 0.8223, loss_cls: 2.2787, loss: 2.2787 +2024-12-31 15:23:16,998 - pyskl - INFO - Epoch [142][2500/3746] lr: 7.595e-04, eta: 7:29:04, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5950, top5_acc: 0.8242, loss_cls: 2.2791, loss: 2.2791 +2024-12-31 15:24:42,743 - pyskl - INFO - Epoch [142][2600/3746] lr: 7.547e-04, eta: 7:27:38, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5945, top5_acc: 0.8250, loss_cls: 2.2752, loss: 2.2752 +2024-12-31 15:26:08,941 - pyskl - INFO - Epoch [142][2700/3746] lr: 7.499e-04, eta: 7:26:11, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.5861, top5_acc: 0.8127, loss_cls: 2.3180, loss: 2.3180 +2024-12-31 15:27:34,973 - pyskl - INFO - Epoch [142][2800/3746] lr: 7.450e-04, eta: 7:24:45, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6070, top5_acc: 0.8267, loss_cls: 2.2440, loss: 2.2440 +2024-12-31 15:29:00,370 - pyskl - INFO - Epoch [142][2900/3746] lr: 7.402e-04, eta: 7:23:19, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5920, top5_acc: 0.8278, loss_cls: 2.2778, loss: 2.2778 +2024-12-31 15:30:26,083 - pyskl - INFO - Epoch [142][3000/3746] lr: 7.355e-04, eta: 7:21:52, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.5922, top5_acc: 0.8228, loss_cls: 2.2884, loss: 2.2884 +2024-12-31 15:31:51,329 - pyskl - INFO - Epoch [142][3100/3746] lr: 7.307e-04, eta: 7:20:26, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.5934, top5_acc: 0.8264, loss_cls: 2.2728, loss: 2.2728 +2024-12-31 15:33:16,616 - pyskl - INFO - Epoch [142][3200/3746] lr: 7.259e-04, eta: 7:19:00, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5873, top5_acc: 0.8180, loss_cls: 2.3034, loss: 2.3034 +2024-12-31 15:34:42,376 - pyskl - INFO - Epoch [142][3300/3746] lr: 7.212e-04, eta: 7:17:33, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.5947, top5_acc: 0.8298, loss_cls: 2.2559, loss: 2.2559 +2024-12-31 15:36:07,849 - pyskl - INFO - Epoch [142][3400/3746] lr: 7.165e-04, eta: 7:16:07, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.5942, top5_acc: 0.8134, loss_cls: 2.2985, loss: 2.2985 +2024-12-31 15:37:33,251 - pyskl - INFO - Epoch [142][3500/3746] lr: 7.118e-04, eta: 7:14:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5919, top5_acc: 0.8209, loss_cls: 2.3103, loss: 2.3103 +2024-12-31 15:38:58,558 - pyskl - INFO - Epoch [142][3600/3746] lr: 7.071e-04, eta: 7:13:14, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.5805, top5_acc: 0.8178, loss_cls: 2.3327, loss: 2.3327 +2024-12-31 15:40:24,067 - pyskl - INFO - Epoch [142][3700/3746] lr: 7.024e-04, eta: 7:11:48, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5989, top5_acc: 0.8225, loss_cls: 2.2612, loss: 2.2612 +2024-12-31 15:41:05,175 - pyskl - INFO - Saving checkpoint at 142 epochs +2024-12-31 15:43:04,029 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 15:43:04,708 - pyskl - INFO - +top1_acc 0.4439 +top5_acc 0.6899 +2024-12-31 15:43:04,709 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 15:43:04,750 - pyskl - INFO - +mean_acc 0.4436 +2024-12-31 15:43:04,755 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_140.pth was removed +2024-12-31 15:43:05,011 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_142.pth. +2024-12-31 15:43:05,012 - pyskl - INFO - Best top1_acc is 0.4439 at 142 epoch. +2024-12-31 15:43:05,024 - pyskl - INFO - Epoch(val) [142][309] top1_acc: 0.4439, top5_acc: 0.6899, mean_class_accuracy: 0.4436 +2024-12-31 15:47:16,516 - pyskl - INFO - Epoch [143][100/3746] lr: 6.956e-04, eta: 7:09:49, time: 2.515, data_time: 1.482, memory: 15990, top1_acc: 0.6150, top5_acc: 0.8419, loss_cls: 2.1739, loss: 2.1739 +2024-12-31 15:48:42,611 - pyskl - INFO - Epoch [143][200/3746] lr: 6.910e-04, eta: 7:08:22, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.6162, top5_acc: 0.8280, loss_cls: 2.1999, loss: 2.1999 +2024-12-31 15:50:08,727 - pyskl - INFO - Epoch [143][300/3746] lr: 6.863e-04, eta: 7:06:56, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.6127, top5_acc: 0.8378, loss_cls: 2.1926, loss: 2.1926 +2024-12-31 15:51:34,746 - pyskl - INFO - Epoch [143][400/3746] lr: 6.817e-04, eta: 7:05:30, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6231, top5_acc: 0.8428, loss_cls: 2.1554, loss: 2.1554 +2024-12-31 15:53:01,511 - pyskl - INFO - Epoch [143][500/3746] lr: 6.771e-04, eta: 7:04:03, time: 0.868, data_time: 0.000, memory: 15990, top1_acc: 0.6105, top5_acc: 0.8284, loss_cls: 2.2346, loss: 2.2346 +2024-12-31 15:54:27,002 - pyskl - INFO - Epoch [143][600/3746] lr: 6.725e-04, eta: 7:02:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6062, top5_acc: 0.8248, loss_cls: 2.2390, loss: 2.2390 +2024-12-31 15:55:52,800 - pyskl - INFO - Epoch [143][700/3746] lr: 6.680e-04, eta: 7:01:11, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6091, top5_acc: 0.8308, loss_cls: 2.2166, loss: 2.2166 +2024-12-31 15:57:18,424 - pyskl - INFO - Epoch [143][800/3746] lr: 6.634e-04, eta: 6:59:44, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6069, top5_acc: 0.8325, loss_cls: 2.2223, loss: 2.2223 +2024-12-31 15:58:43,849 - pyskl - INFO - Epoch [143][900/3746] lr: 6.589e-04, eta: 6:58:18, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6170, top5_acc: 0.8411, loss_cls: 2.1273, loss: 2.1273 +2024-12-31 16:00:09,189 - pyskl - INFO - Epoch [143][1000/3746] lr: 6.544e-04, eta: 6:56:51, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6053, top5_acc: 0.8272, loss_cls: 2.2177, loss: 2.2177 +2024-12-31 16:01:34,332 - pyskl - INFO - Epoch [143][1100/3746] lr: 6.499e-04, eta: 6:55:25, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6138, top5_acc: 0.8355, loss_cls: 2.1826, loss: 2.1826 +2024-12-31 16:03:00,072 - pyskl - INFO - Epoch [143][1200/3746] lr: 6.454e-04, eta: 6:53:59, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.6125, top5_acc: 0.8358, loss_cls: 2.2017, loss: 2.2017 +2024-12-31 16:04:26,077 - pyskl - INFO - Epoch [143][1300/3746] lr: 6.409e-04, eta: 6:52:32, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6083, top5_acc: 0.8320, loss_cls: 2.2119, loss: 2.2119 +2024-12-31 16:05:51,762 - pyskl - INFO - Epoch [143][1400/3746] lr: 6.365e-04, eta: 6:51:06, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6080, top5_acc: 0.8280, loss_cls: 2.2316, loss: 2.2316 +2024-12-31 16:07:17,812 - pyskl - INFO - Epoch [143][1500/3746] lr: 6.320e-04, eta: 6:49:39, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6125, top5_acc: 0.8253, loss_cls: 2.2210, loss: 2.2210 +2024-12-31 16:08:43,487 - pyskl - INFO - Epoch [143][1600/3746] lr: 6.276e-04, eta: 6:48:13, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6102, top5_acc: 0.8259, loss_cls: 2.2347, loss: 2.2347 +2024-12-31 16:10:09,777 - pyskl - INFO - Epoch [143][1700/3746] lr: 6.232e-04, eta: 6:46:47, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.5931, top5_acc: 0.8269, loss_cls: 2.2550, loss: 2.2550 +2024-12-31 16:11:35,459 - pyskl - INFO - Epoch [143][1800/3746] lr: 6.188e-04, eta: 6:45:20, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6134, top5_acc: 0.8297, loss_cls: 2.2178, loss: 2.2178 +2024-12-31 16:13:01,081 - pyskl - INFO - Epoch [143][1900/3746] lr: 6.144e-04, eta: 6:43:54, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.5988, top5_acc: 0.8236, loss_cls: 2.2649, loss: 2.2649 +2024-12-31 16:14:26,194 - pyskl - INFO - Epoch [143][2000/3746] lr: 6.101e-04, eta: 6:42:28, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6098, top5_acc: 0.8339, loss_cls: 2.2101, loss: 2.2101 +2024-12-31 16:15:51,805 - pyskl - INFO - Epoch [143][2100/3746] lr: 6.057e-04, eta: 6:41:01, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6039, top5_acc: 0.8244, loss_cls: 2.2331, loss: 2.2331 +2024-12-31 16:17:17,463 - pyskl - INFO - Epoch [143][2200/3746] lr: 6.014e-04, eta: 6:39:35, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6183, top5_acc: 0.8420, loss_cls: 2.1743, loss: 2.1743 +2024-12-31 16:18:43,442 - pyskl - INFO - Epoch [143][2300/3746] lr: 5.971e-04, eta: 6:38:08, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5920, top5_acc: 0.8241, loss_cls: 2.2873, loss: 2.2873 +2024-12-31 16:20:09,599 - pyskl - INFO - Epoch [143][2400/3746] lr: 5.928e-04, eta: 6:36:42, time: 0.862, data_time: 0.001, memory: 15990, top1_acc: 0.6064, top5_acc: 0.8337, loss_cls: 2.2137, loss: 2.2137 +2024-12-31 16:21:35,660 - pyskl - INFO - Epoch [143][2500/3746] lr: 5.885e-04, eta: 6:35:16, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.6039, top5_acc: 0.8308, loss_cls: 2.2189, loss: 2.2189 +2024-12-31 16:23:01,734 - pyskl - INFO - Epoch [143][2600/3746] lr: 5.842e-04, eta: 6:33:49, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.6005, top5_acc: 0.8261, loss_cls: 2.2387, loss: 2.2387 +2024-12-31 16:24:27,752 - pyskl - INFO - Epoch [143][2700/3746] lr: 5.800e-04, eta: 6:32:23, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.5887, top5_acc: 0.8230, loss_cls: 2.2841, loss: 2.2841 +2024-12-31 16:25:53,257 - pyskl - INFO - Epoch [143][2800/3746] lr: 5.757e-04, eta: 6:30:57, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.5994, top5_acc: 0.8313, loss_cls: 2.2332, loss: 2.2332 +2024-12-31 16:27:19,029 - pyskl - INFO - Epoch [143][2900/3746] lr: 5.715e-04, eta: 6:29:30, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6023, top5_acc: 0.8331, loss_cls: 2.2298, loss: 2.2298 +2024-12-31 16:28:45,266 - pyskl - INFO - Epoch [143][3000/3746] lr: 5.673e-04, eta: 6:28:04, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.6038, top5_acc: 0.8331, loss_cls: 2.2197, loss: 2.2197 +2024-12-31 16:30:11,218 - pyskl - INFO - Epoch [143][3100/3746] lr: 5.631e-04, eta: 6:26:38, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6036, top5_acc: 0.8339, loss_cls: 2.2164, loss: 2.2164 +2024-12-31 16:31:36,996 - pyskl - INFO - Epoch [143][3200/3746] lr: 5.590e-04, eta: 6:25:11, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6117, top5_acc: 0.8336, loss_cls: 2.1996, loss: 2.1996 +2024-12-31 16:33:02,897 - pyskl - INFO - Epoch [143][3300/3746] lr: 5.548e-04, eta: 6:23:45, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6025, top5_acc: 0.8289, loss_cls: 2.2136, loss: 2.2136 +2024-12-31 16:34:28,773 - pyskl - INFO - Epoch [143][3400/3746] lr: 5.506e-04, eta: 6:22:18, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6044, top5_acc: 0.8334, loss_cls: 2.2085, loss: 2.2085 +2024-12-31 16:35:53,623 - pyskl - INFO - Epoch [143][3500/3746] lr: 5.465e-04, eta: 6:20:52, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.5930, top5_acc: 0.8170, loss_cls: 2.2801, loss: 2.2801 +2024-12-31 16:37:18,504 - pyskl - INFO - Epoch [143][3600/3746] lr: 5.424e-04, eta: 6:19:26, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6022, top5_acc: 0.8270, loss_cls: 2.2489, loss: 2.2489 +2024-12-31 16:38:43,873 - pyskl - INFO - Epoch [143][3700/3746] lr: 5.383e-04, eta: 6:17:59, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.5911, top5_acc: 0.8269, loss_cls: 2.2698, loss: 2.2698 +2024-12-31 16:39:25,115 - pyskl - INFO - Saving checkpoint at 143 epochs +2024-12-31 16:41:23,447 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 16:41:24,159 - pyskl - INFO - +top1_acc 0.4418 +top5_acc 0.6911 +2024-12-31 16:41:24,159 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 16:41:24,217 - pyskl - INFO - +mean_acc 0.4416 +2024-12-31 16:41:24,235 - pyskl - INFO - Epoch(val) [143][309] top1_acc: 0.4418, top5_acc: 0.6911, mean_class_accuracy: 0.4416 +2024-12-31 16:45:48,106 - pyskl - INFO - Epoch [144][100/3746] lr: 5.323e-04, eta: 6:16:00, time: 2.639, data_time: 1.595, memory: 15990, top1_acc: 0.6275, top5_acc: 0.8498, loss_cls: 2.1267, loss: 2.1267 +2024-12-31 16:47:13,747 - pyskl - INFO - Epoch [144][200/3746] lr: 5.283e-04, eta: 6:14:34, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6258, top5_acc: 0.8456, loss_cls: 2.1332, loss: 2.1332 +2024-12-31 16:48:39,349 - pyskl - INFO - Epoch [144][300/3746] lr: 5.242e-04, eta: 6:13:07, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6172, top5_acc: 0.8433, loss_cls: 2.1627, loss: 2.1627 +2024-12-31 16:50:04,233 - pyskl - INFO - Epoch [144][400/3746] lr: 5.202e-04, eta: 6:11:41, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6166, top5_acc: 0.8350, loss_cls: 2.1772, loss: 2.1772 +2024-12-31 16:51:29,551 - pyskl - INFO - Epoch [144][500/3746] lr: 5.162e-04, eta: 6:10:14, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6166, top5_acc: 0.8405, loss_cls: 2.1771, loss: 2.1771 +2024-12-31 16:52:54,579 - pyskl - INFO - Epoch [144][600/3746] lr: 5.122e-04, eta: 6:08:48, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6200, top5_acc: 0.8328, loss_cls: 2.1566, loss: 2.1566 +2024-12-31 16:54:19,677 - pyskl - INFO - Epoch [144][700/3746] lr: 5.082e-04, eta: 6:07:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6170, top5_acc: 0.8469, loss_cls: 2.1328, loss: 2.1328 +2024-12-31 16:55:44,679 - pyskl - INFO - Epoch [144][800/3746] lr: 5.042e-04, eta: 6:05:55, time: 0.850, data_time: 0.001, memory: 15990, top1_acc: 0.6209, top5_acc: 0.8353, loss_cls: 2.1622, loss: 2.1622 +2024-12-31 16:57:10,275 - pyskl - INFO - Epoch [144][900/3746] lr: 5.003e-04, eta: 6:04:29, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6109, top5_acc: 0.8409, loss_cls: 2.1681, loss: 2.1681 +2024-12-31 16:58:35,734 - pyskl - INFO - Epoch [144][1000/3746] lr: 4.964e-04, eta: 6:03:02, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6128, top5_acc: 0.8358, loss_cls: 2.1679, loss: 2.1679 +2024-12-31 17:00:01,090 - pyskl - INFO - Epoch [144][1100/3746] lr: 4.924e-04, eta: 6:01:36, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6145, top5_acc: 0.8394, loss_cls: 2.1554, loss: 2.1554 +2024-12-31 17:01:26,040 - pyskl - INFO - Epoch [144][1200/3746] lr: 4.885e-04, eta: 6:00:09, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6083, top5_acc: 0.8369, loss_cls: 2.2001, loss: 2.2001 +2024-12-31 17:02:51,086 - pyskl - INFO - Epoch [144][1300/3746] lr: 4.846e-04, eta: 5:58:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6127, top5_acc: 0.8336, loss_cls: 2.1870, loss: 2.1870 +2024-12-31 17:04:16,084 - pyskl - INFO - Epoch [144][1400/3746] lr: 4.808e-04, eta: 5:57:17, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6203, top5_acc: 0.8373, loss_cls: 2.1248, loss: 2.1248 +2024-12-31 17:05:41,250 - pyskl - INFO - Epoch [144][1500/3746] lr: 4.769e-04, eta: 5:55:50, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6212, top5_acc: 0.8427, loss_cls: 2.1444, loss: 2.1444 +2024-12-31 17:07:05,975 - pyskl - INFO - Epoch [144][1600/3746] lr: 4.731e-04, eta: 5:54:24, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6142, top5_acc: 0.8378, loss_cls: 2.1677, loss: 2.1677 +2024-12-31 17:08:31,355 - pyskl - INFO - Epoch [144][1700/3746] lr: 4.692e-04, eta: 5:52:57, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6041, top5_acc: 0.8367, loss_cls: 2.1978, loss: 2.1978 +2024-12-31 17:09:56,761 - pyskl - INFO - Epoch [144][1800/3746] lr: 4.654e-04, eta: 5:51:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6155, top5_acc: 0.8387, loss_cls: 2.2137, loss: 2.2137 +2024-12-31 17:11:22,320 - pyskl - INFO - Epoch [144][1900/3746] lr: 4.616e-04, eta: 5:50:04, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6094, top5_acc: 0.8336, loss_cls: 2.1771, loss: 2.1771 +2024-12-31 17:12:47,606 - pyskl - INFO - Epoch [144][2000/3746] lr: 4.578e-04, eta: 5:48:38, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6156, top5_acc: 0.8413, loss_cls: 2.1795, loss: 2.1795 +2024-12-31 17:14:13,184 - pyskl - INFO - Epoch [144][2100/3746] lr: 4.541e-04, eta: 5:47:12, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6203, top5_acc: 0.8358, loss_cls: 2.1724, loss: 2.1724 +2024-12-31 17:15:38,684 - pyskl - INFO - Epoch [144][2200/3746] lr: 4.503e-04, eta: 5:45:45, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6166, top5_acc: 0.8342, loss_cls: 2.1663, loss: 2.1663 +2024-12-31 17:17:04,182 - pyskl - INFO - Epoch [144][2300/3746] lr: 4.466e-04, eta: 5:44:19, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6150, top5_acc: 0.8348, loss_cls: 2.1986, loss: 2.1986 +2024-12-31 17:18:29,723 - pyskl - INFO - Epoch [144][2400/3746] lr: 4.429e-04, eta: 5:42:52, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6206, top5_acc: 0.8373, loss_cls: 2.1664, loss: 2.1664 +2024-12-31 17:19:54,889 - pyskl - INFO - Epoch [144][2500/3746] lr: 4.392e-04, eta: 5:41:26, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6122, top5_acc: 0.8320, loss_cls: 2.1950, loss: 2.1950 +2024-12-31 17:21:20,521 - pyskl - INFO - Epoch [144][2600/3746] lr: 4.355e-04, eta: 5:40:00, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6092, top5_acc: 0.8303, loss_cls: 2.2036, loss: 2.2036 +2024-12-31 17:22:45,982 - pyskl - INFO - Epoch [144][2700/3746] lr: 4.318e-04, eta: 5:38:33, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6105, top5_acc: 0.8356, loss_cls: 2.1844, loss: 2.1844 +2024-12-31 17:24:11,355 - pyskl - INFO - Epoch [144][2800/3746] lr: 4.281e-04, eta: 5:37:07, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6178, top5_acc: 0.8370, loss_cls: 2.1528, loss: 2.1528 +2024-12-31 17:25:37,173 - pyskl - INFO - Epoch [144][2900/3746] lr: 4.245e-04, eta: 5:35:40, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6044, top5_acc: 0.8325, loss_cls: 2.2090, loss: 2.2090 +2024-12-31 17:27:02,970 - pyskl - INFO - Epoch [144][3000/3746] lr: 4.209e-04, eta: 5:34:14, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6173, top5_acc: 0.8330, loss_cls: 2.1979, loss: 2.1979 +2024-12-31 17:28:28,247 - pyskl - INFO - Epoch [144][3100/3746] lr: 4.173e-04, eta: 5:32:48, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6131, top5_acc: 0.8316, loss_cls: 2.1923, loss: 2.1923 +2024-12-31 17:29:53,379 - pyskl - INFO - Epoch [144][3200/3746] lr: 4.137e-04, eta: 5:31:21, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6236, top5_acc: 0.8383, loss_cls: 2.1466, loss: 2.1466 +2024-12-31 17:31:18,520 - pyskl - INFO - Epoch [144][3300/3746] lr: 4.101e-04, eta: 5:29:55, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.6084, top5_acc: 0.8411, loss_cls: 2.1940, loss: 2.1940 +2024-12-31 17:32:44,041 - pyskl - INFO - Epoch [144][3400/3746] lr: 4.065e-04, eta: 5:28:28, time: 0.855, data_time: 0.001, memory: 15990, top1_acc: 0.6164, top5_acc: 0.8420, loss_cls: 2.1529, loss: 2.1529 +2024-12-31 17:34:08,734 - pyskl - INFO - Epoch [144][3500/3746] lr: 4.030e-04, eta: 5:27:02, time: 0.847, data_time: 0.001, memory: 15990, top1_acc: 0.6123, top5_acc: 0.8305, loss_cls: 2.2035, loss: 2.2035 +2024-12-31 17:35:34,486 - pyskl - INFO - Epoch [144][3600/3746] lr: 3.994e-04, eta: 5:25:36, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6184, top5_acc: 0.8447, loss_cls: 2.1474, loss: 2.1474 +2024-12-31 17:37:00,321 - pyskl - INFO - Epoch [144][3700/3746] lr: 3.959e-04, eta: 5:24:09, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6200, top5_acc: 0.8391, loss_cls: 2.1565, loss: 2.1565 +2024-12-31 17:37:41,408 - pyskl - INFO - Saving checkpoint at 144 epochs +2024-12-31 17:39:40,948 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 17:39:42,109 - pyskl - INFO - +top1_acc 0.4460 +top5_acc 0.6909 +2024-12-31 17:39:42,110 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 17:39:42,158 - pyskl - INFO - +mean_acc 0.4458 +2024-12-31 17:39:42,163 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_142.pth was removed +2024-12-31 17:39:42,427 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_144.pth. +2024-12-31 17:39:42,428 - pyskl - INFO - Best top1_acc is 0.4460 at 144 epoch. +2024-12-31 17:39:42,441 - pyskl - INFO - Epoch(val) [144][309] top1_acc: 0.4460, top5_acc: 0.6909, mean_class_accuracy: 0.4458 +2024-12-31 17:44:01,505 - pyskl - INFO - Epoch [145][100/3746] lr: 3.908e-04, eta: 5:22:09, time: 2.590, data_time: 1.556, memory: 15990, top1_acc: 0.6342, top5_acc: 0.8555, loss_cls: 2.0859, loss: 2.0859 +2024-12-31 17:45:26,996 - pyskl - INFO - Epoch [145][200/3746] lr: 3.873e-04, eta: 5:20:42, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6206, top5_acc: 0.8445, loss_cls: 2.1373, loss: 2.1373 +2024-12-31 17:46:52,517 - pyskl - INFO - Epoch [145][300/3746] lr: 3.839e-04, eta: 5:19:16, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6238, top5_acc: 0.8386, loss_cls: 2.1516, loss: 2.1516 +2024-12-31 17:48:17,623 - pyskl - INFO - Epoch [145][400/3746] lr: 3.804e-04, eta: 5:17:49, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6283, top5_acc: 0.8527, loss_cls: 2.1132, loss: 2.1132 +2024-12-31 17:49:42,975 - pyskl - INFO - Epoch [145][500/3746] lr: 3.770e-04, eta: 5:16:23, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6288, top5_acc: 0.8480, loss_cls: 2.0863, loss: 2.0863 +2024-12-31 17:51:08,919 - pyskl - INFO - Epoch [145][600/3746] lr: 3.736e-04, eta: 5:14:57, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6289, top5_acc: 0.8448, loss_cls: 2.1077, loss: 2.1077 +2024-12-31 17:52:34,883 - pyskl - INFO - Epoch [145][700/3746] lr: 3.702e-04, eta: 5:13:30, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6372, top5_acc: 0.8503, loss_cls: 2.0776, loss: 2.0776 +2024-12-31 17:54:00,485 - pyskl - INFO - Epoch [145][800/3746] lr: 3.668e-04, eta: 5:12:04, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6220, top5_acc: 0.8462, loss_cls: 2.1310, loss: 2.1310 +2024-12-31 17:55:25,601 - pyskl - INFO - Epoch [145][900/3746] lr: 3.634e-04, eta: 5:10:37, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6336, top5_acc: 0.8514, loss_cls: 2.0666, loss: 2.0666 +2024-12-31 17:56:51,692 - pyskl - INFO - Epoch [145][1000/3746] lr: 3.600e-04, eta: 5:09:11, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.6302, top5_acc: 0.8434, loss_cls: 2.1290, loss: 2.1290 +2024-12-31 17:58:16,977 - pyskl - INFO - Epoch [145][1100/3746] lr: 3.567e-04, eta: 5:07:45, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6225, top5_acc: 0.8409, loss_cls: 2.1431, loss: 2.1431 +2024-12-31 17:59:42,449 - pyskl - INFO - Epoch [145][1200/3746] lr: 3.534e-04, eta: 5:06:18, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6120, top5_acc: 0.8402, loss_cls: 2.1728, loss: 2.1728 +2024-12-31 18:01:08,110 - pyskl - INFO - Epoch [145][1300/3746] lr: 3.501e-04, eta: 5:04:52, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6241, top5_acc: 0.8433, loss_cls: 2.1341, loss: 2.1341 +2024-12-31 18:02:33,323 - pyskl - INFO - Epoch [145][1400/3746] lr: 3.468e-04, eta: 5:03:25, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6377, top5_acc: 0.8478, loss_cls: 2.0899, loss: 2.0899 +2024-12-31 18:03:58,848 - pyskl - INFO - Epoch [145][1500/3746] lr: 3.435e-04, eta: 5:01:59, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6217, top5_acc: 0.8408, loss_cls: 2.1242, loss: 2.1242 +2024-12-31 18:05:24,749 - pyskl - INFO - Epoch [145][1600/3746] lr: 3.402e-04, eta: 5:00:32, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6222, top5_acc: 0.8402, loss_cls: 2.1431, loss: 2.1431 +2024-12-31 18:06:49,736 - pyskl - INFO - Epoch [145][1700/3746] lr: 3.370e-04, eta: 4:59:06, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6325, top5_acc: 0.8489, loss_cls: 2.0752, loss: 2.0752 +2024-12-31 18:08:15,096 - pyskl - INFO - Epoch [145][1800/3746] lr: 3.337e-04, eta: 4:57:40, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6300, top5_acc: 0.8427, loss_cls: 2.1121, loss: 2.1121 +2024-12-31 18:09:40,153 - pyskl - INFO - Epoch [145][1900/3746] lr: 3.305e-04, eta: 4:56:13, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6316, top5_acc: 0.8448, loss_cls: 2.1119, loss: 2.1119 +2024-12-31 18:11:05,127 - pyskl - INFO - Epoch [145][2000/3746] lr: 3.273e-04, eta: 4:54:47, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6148, top5_acc: 0.8355, loss_cls: 2.1683, loss: 2.1683 +2024-12-31 18:12:30,243 - pyskl - INFO - Epoch [145][2100/3746] lr: 3.241e-04, eta: 4:53:20, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6253, top5_acc: 0.8527, loss_cls: 2.1075, loss: 2.1075 +2024-12-31 18:13:55,318 - pyskl - INFO - Epoch [145][2200/3746] lr: 3.210e-04, eta: 4:51:54, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6309, top5_acc: 0.8477, loss_cls: 2.0823, loss: 2.0823 +2024-12-31 18:15:20,403 - pyskl - INFO - Epoch [145][2300/3746] lr: 3.178e-04, eta: 4:50:27, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6289, top5_acc: 0.8494, loss_cls: 2.0753, loss: 2.0753 +2024-12-31 18:16:45,639 - pyskl - INFO - Epoch [145][2400/3746] lr: 3.147e-04, eta: 4:49:01, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6238, top5_acc: 0.8469, loss_cls: 2.1285, loss: 2.1285 +2024-12-31 18:18:10,601 - pyskl - INFO - Epoch [145][2500/3746] lr: 3.116e-04, eta: 4:47:35, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6309, top5_acc: 0.8466, loss_cls: 2.1172, loss: 2.1172 +2024-12-31 18:19:35,612 - pyskl - INFO - Epoch [145][2600/3746] lr: 3.084e-04, eta: 4:46:08, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6252, top5_acc: 0.8347, loss_cls: 2.1527, loss: 2.1527 +2024-12-31 18:21:00,632 - pyskl - INFO - Epoch [145][2700/3746] lr: 3.054e-04, eta: 4:44:42, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6167, top5_acc: 0.8373, loss_cls: 2.1710, loss: 2.1710 +2024-12-31 18:22:25,877 - pyskl - INFO - Epoch [145][2800/3746] lr: 3.023e-04, eta: 4:43:15, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6358, top5_acc: 0.8502, loss_cls: 2.0922, loss: 2.0922 +2024-12-31 18:23:50,939 - pyskl - INFO - Epoch [145][2900/3746] lr: 2.992e-04, eta: 4:41:49, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6170, top5_acc: 0.8339, loss_cls: 2.1677, loss: 2.1677 +2024-12-31 18:25:16,854 - pyskl - INFO - Epoch [145][3000/3746] lr: 2.962e-04, eta: 4:40:22, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6239, top5_acc: 0.8414, loss_cls: 2.1592, loss: 2.1592 +2024-12-31 18:26:42,271 - pyskl - INFO - Epoch [145][3100/3746] lr: 2.931e-04, eta: 4:38:56, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6153, top5_acc: 0.8392, loss_cls: 2.1573, loss: 2.1573 +2024-12-31 18:28:07,785 - pyskl - INFO - Epoch [145][3200/3746] lr: 2.901e-04, eta: 4:37:30, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6302, top5_acc: 0.8422, loss_cls: 2.1093, loss: 2.1093 +2024-12-31 18:29:32,954 - pyskl - INFO - Epoch [145][3300/3746] lr: 2.871e-04, eta: 4:36:03, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6189, top5_acc: 0.8364, loss_cls: 2.1604, loss: 2.1604 +2024-12-31 18:30:57,505 - pyskl - INFO - Epoch [145][3400/3746] lr: 2.841e-04, eta: 4:34:37, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6256, top5_acc: 0.8431, loss_cls: 2.1083, loss: 2.1083 +2024-12-31 18:32:22,481 - pyskl - INFO - Epoch [145][3500/3746] lr: 2.812e-04, eta: 4:33:10, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6258, top5_acc: 0.8392, loss_cls: 2.1445, loss: 2.1445 +2024-12-31 18:33:47,599 - pyskl - INFO - Epoch [145][3600/3746] lr: 2.782e-04, eta: 4:31:44, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6219, top5_acc: 0.8462, loss_cls: 2.1157, loss: 2.1157 +2024-12-31 18:35:12,815 - pyskl - INFO - Epoch [145][3700/3746] lr: 2.753e-04, eta: 4:30:18, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6161, top5_acc: 0.8391, loss_cls: 2.1632, loss: 2.1632 +2024-12-31 18:35:53,847 - pyskl - INFO - Saving checkpoint at 145 epochs +2024-12-31 18:37:55,701 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 18:37:56,412 - pyskl - INFO - +top1_acc 0.4489 +top5_acc 0.6939 +2024-12-31 18:37:56,412 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 18:37:56,471 - pyskl - INFO - +mean_acc 0.4486 +2024-12-31 18:37:56,476 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_144.pth was removed +2024-12-31 18:37:56,840 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_145.pth. +2024-12-31 18:37:56,841 - pyskl - INFO - Best top1_acc is 0.4489 at 145 epoch. +2024-12-31 18:37:56,854 - pyskl - INFO - Epoch(val) [145][309] top1_acc: 0.4489, top5_acc: 0.6939, mean_class_accuracy: 0.4486 +2024-12-31 18:42:18,170 - pyskl - INFO - Epoch [146][100/3746] lr: 2.710e-04, eta: 4:28:16, time: 2.613, data_time: 1.579, memory: 15990, top1_acc: 0.6352, top5_acc: 0.8483, loss_cls: 2.0792, loss: 2.0792 +2024-12-31 18:43:44,035 - pyskl - INFO - Epoch [146][200/3746] lr: 2.681e-04, eta: 4:26:50, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6336, top5_acc: 0.8520, loss_cls: 2.0992, loss: 2.0992 +2024-12-31 18:45:10,003 - pyskl - INFO - Epoch [146][300/3746] lr: 2.652e-04, eta: 4:25:23, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6348, top5_acc: 0.8494, loss_cls: 2.1060, loss: 2.1060 +2024-12-31 18:46:35,710 - pyskl - INFO - Epoch [146][400/3746] lr: 2.624e-04, eta: 4:23:57, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6314, top5_acc: 0.8425, loss_cls: 2.1054, loss: 2.1054 +2024-12-31 18:48:01,570 - pyskl - INFO - Epoch [146][500/3746] lr: 2.595e-04, eta: 4:22:30, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6373, top5_acc: 0.8452, loss_cls: 2.0961, loss: 2.0961 +2024-12-31 18:49:27,362 - pyskl - INFO - Epoch [146][600/3746] lr: 2.567e-04, eta: 4:21:04, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6312, top5_acc: 0.8500, loss_cls: 2.0799, loss: 2.0799 +2024-12-31 18:50:53,010 - pyskl - INFO - Epoch [146][700/3746] lr: 2.539e-04, eta: 4:19:38, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6350, top5_acc: 0.8509, loss_cls: 2.1046, loss: 2.1046 +2024-12-31 18:52:18,512 - pyskl - INFO - Epoch [146][800/3746] lr: 2.511e-04, eta: 4:18:11, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6370, top5_acc: 0.8534, loss_cls: 2.0474, loss: 2.0474 +2024-12-31 18:53:43,886 - pyskl - INFO - Epoch [146][900/3746] lr: 2.483e-04, eta: 4:16:45, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6275, top5_acc: 0.8452, loss_cls: 2.0930, loss: 2.0930 +2024-12-31 18:55:09,525 - pyskl - INFO - Epoch [146][1000/3746] lr: 2.455e-04, eta: 4:15:18, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6425, top5_acc: 0.8536, loss_cls: 2.0350, loss: 2.0350 +2024-12-31 18:56:34,759 - pyskl - INFO - Epoch [146][1100/3746] lr: 2.427e-04, eta: 4:13:52, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6405, top5_acc: 0.8494, loss_cls: 2.0409, loss: 2.0409 +2024-12-31 18:57:59,860 - pyskl - INFO - Epoch [146][1200/3746] lr: 2.400e-04, eta: 4:12:25, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6358, top5_acc: 0.8494, loss_cls: 2.0765, loss: 2.0765 +2024-12-31 18:59:25,150 - pyskl - INFO - Epoch [146][1300/3746] lr: 2.373e-04, eta: 4:10:59, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6308, top5_acc: 0.8456, loss_cls: 2.0870, loss: 2.0870 +2024-12-31 19:00:50,058 - pyskl - INFO - Epoch [146][1400/3746] lr: 2.345e-04, eta: 4:09:32, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6381, top5_acc: 0.8578, loss_cls: 2.0451, loss: 2.0451 +2024-12-31 19:02:15,185 - pyskl - INFO - Epoch [146][1500/3746] lr: 2.318e-04, eta: 4:08:06, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6502, top5_acc: 0.8570, loss_cls: 2.0225, loss: 2.0225 +2024-12-31 19:03:40,682 - pyskl - INFO - Epoch [146][1600/3746] lr: 2.292e-04, eta: 4:06:40, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6328, top5_acc: 0.8439, loss_cls: 2.0858, loss: 2.0858 +2024-12-31 19:05:06,057 - pyskl - INFO - Epoch [146][1700/3746] lr: 2.265e-04, eta: 4:05:13, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6331, top5_acc: 0.8536, loss_cls: 2.0786, loss: 2.0786 +2024-12-31 19:06:31,603 - pyskl - INFO - Epoch [146][1800/3746] lr: 2.239e-04, eta: 4:03:47, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6395, top5_acc: 0.8538, loss_cls: 2.0516, loss: 2.0516 +2024-12-31 19:07:56,332 - pyskl - INFO - Epoch [146][1900/3746] lr: 2.212e-04, eta: 4:02:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6320, top5_acc: 0.8480, loss_cls: 2.0903, loss: 2.0903 +2024-12-31 19:09:21,608 - pyskl - INFO - Epoch [146][2000/3746] lr: 2.186e-04, eta: 4:00:54, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6303, top5_acc: 0.8395, loss_cls: 2.0961, loss: 2.0961 +2024-12-31 19:10:47,033 - pyskl - INFO - Epoch [146][2100/3746] lr: 2.160e-04, eta: 3:59:27, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6339, top5_acc: 0.8533, loss_cls: 2.0783, loss: 2.0783 +2024-12-31 19:12:12,993 - pyskl - INFO - Epoch [146][2200/3746] lr: 2.134e-04, eta: 3:58:01, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6342, top5_acc: 0.8486, loss_cls: 2.0847, loss: 2.0847 +2024-12-31 19:13:38,517 - pyskl - INFO - Epoch [146][2300/3746] lr: 2.108e-04, eta: 3:56:35, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6341, top5_acc: 0.8508, loss_cls: 2.0772, loss: 2.0772 +2024-12-31 19:15:04,589 - pyskl - INFO - Epoch [146][2400/3746] lr: 2.083e-04, eta: 3:55:08, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.6358, top5_acc: 0.8484, loss_cls: 2.0704, loss: 2.0704 +2024-12-31 19:16:30,506 - pyskl - INFO - Epoch [146][2500/3746] lr: 2.057e-04, eta: 3:53:42, time: 0.859, data_time: 0.000, memory: 15990, top1_acc: 0.6305, top5_acc: 0.8528, loss_cls: 2.0565, loss: 2.0565 +2024-12-31 19:17:56,066 - pyskl - INFO - Epoch [146][2600/3746] lr: 2.032e-04, eta: 3:52:15, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6317, top5_acc: 0.8500, loss_cls: 2.0933, loss: 2.0933 +2024-12-31 19:19:21,524 - pyskl - INFO - Epoch [146][2700/3746] lr: 2.007e-04, eta: 3:50:49, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6261, top5_acc: 0.8422, loss_cls: 2.1102, loss: 2.1102 +2024-12-31 19:20:47,349 - pyskl - INFO - Epoch [146][2800/3746] lr: 1.982e-04, eta: 3:49:23, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6402, top5_acc: 0.8450, loss_cls: 2.0748, loss: 2.0748 +2024-12-31 19:22:12,998 - pyskl - INFO - Epoch [146][2900/3746] lr: 1.957e-04, eta: 3:47:56, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6339, top5_acc: 0.8545, loss_cls: 2.0764, loss: 2.0764 +2024-12-31 19:23:38,789 - pyskl - INFO - Epoch [146][3000/3746] lr: 1.933e-04, eta: 3:46:30, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6364, top5_acc: 0.8503, loss_cls: 2.0604, loss: 2.0604 +2024-12-31 19:25:04,492 - pyskl - INFO - Epoch [146][3100/3746] lr: 1.908e-04, eta: 3:45:03, time: 0.857, data_time: 0.001, memory: 15990, top1_acc: 0.6331, top5_acc: 0.8508, loss_cls: 2.0941, loss: 2.0941 +2024-12-31 19:26:29,957 - pyskl - INFO - Epoch [146][3200/3746] lr: 1.884e-04, eta: 3:43:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6266, top5_acc: 0.8423, loss_cls: 2.1060, loss: 2.1060 +2024-12-31 19:27:55,085 - pyskl - INFO - Epoch [146][3300/3746] lr: 1.860e-04, eta: 3:42:10, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6356, top5_acc: 0.8528, loss_cls: 2.0637, loss: 2.0637 +2024-12-31 19:29:20,857 - pyskl - INFO - Epoch [146][3400/3746] lr: 1.836e-04, eta: 3:40:44, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6392, top5_acc: 0.8502, loss_cls: 2.0691, loss: 2.0691 +2024-12-31 19:30:46,158 - pyskl - INFO - Epoch [146][3500/3746] lr: 1.812e-04, eta: 3:39:18, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6419, top5_acc: 0.8591, loss_cls: 2.0660, loss: 2.0660 +2024-12-31 19:32:11,366 - pyskl - INFO - Epoch [146][3600/3746] lr: 1.788e-04, eta: 3:37:51, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6381, top5_acc: 0.8516, loss_cls: 2.0687, loss: 2.0687 +2024-12-31 19:33:36,797 - pyskl - INFO - Epoch [146][3700/3746] lr: 1.765e-04, eta: 3:36:25, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6317, top5_acc: 0.8512, loss_cls: 2.0960, loss: 2.0960 +2024-12-31 19:34:18,532 - pyskl - INFO - Saving checkpoint at 146 epochs +2024-12-31 19:36:19,969 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 19:36:20,700 - pyskl - INFO - +top1_acc 0.4483 +top5_acc 0.6941 +2024-12-31 19:36:20,700 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 19:36:20,764 - pyskl - INFO - +mean_acc 0.4480 +2024-12-31 19:36:20,783 - pyskl - INFO - Epoch(val) [146][309] top1_acc: 0.4483, top5_acc: 0.6941, mean_class_accuracy: 0.4480 +2024-12-31 19:40:42,246 - pyskl - INFO - Epoch [147][100/3746] lr: 1.730e-04, eta: 3:34:22, time: 2.614, data_time: 1.568, memory: 15990, top1_acc: 0.6489, top5_acc: 0.8545, loss_cls: 2.0223, loss: 2.0223 +2024-12-31 19:42:07,918 - pyskl - INFO - Epoch [147][200/3746] lr: 1.707e-04, eta: 3:32:56, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6494, top5_acc: 0.8569, loss_cls: 2.0094, loss: 2.0094 +2024-12-31 19:43:33,764 - pyskl - INFO - Epoch [147][300/3746] lr: 1.684e-04, eta: 3:31:29, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6519, top5_acc: 0.8612, loss_cls: 2.0061, loss: 2.0061 +2024-12-31 19:44:59,312 - pyskl - INFO - Epoch [147][400/3746] lr: 1.661e-04, eta: 3:30:03, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6480, top5_acc: 0.8567, loss_cls: 2.0114, loss: 2.0114 +2024-12-31 19:46:24,782 - pyskl - INFO - Epoch [147][500/3746] lr: 1.639e-04, eta: 3:28:37, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6445, top5_acc: 0.8534, loss_cls: 2.0430, loss: 2.0430 +2024-12-31 19:47:50,610 - pyskl - INFO - Epoch [147][600/3746] lr: 1.616e-04, eta: 3:27:10, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6486, top5_acc: 0.8600, loss_cls: 2.0150, loss: 2.0150 +2024-12-31 19:49:16,308 - pyskl - INFO - Epoch [147][700/3746] lr: 1.594e-04, eta: 3:25:44, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6498, top5_acc: 0.8566, loss_cls: 2.0308, loss: 2.0308 +2024-12-31 19:50:41,851 - pyskl - INFO - Epoch [147][800/3746] lr: 1.572e-04, eta: 3:24:17, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6447, top5_acc: 0.8580, loss_cls: 2.0329, loss: 2.0329 +2024-12-31 19:52:07,336 - pyskl - INFO - Epoch [147][900/3746] lr: 1.550e-04, eta: 3:22:51, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6388, top5_acc: 0.8559, loss_cls: 2.0384, loss: 2.0384 +2024-12-31 19:53:33,030 - pyskl - INFO - Epoch [147][1000/3746] lr: 1.528e-04, eta: 3:21:24, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6470, top5_acc: 0.8566, loss_cls: 2.0261, loss: 2.0261 +2024-12-31 19:54:58,377 - pyskl - INFO - Epoch [147][1100/3746] lr: 1.506e-04, eta: 3:19:58, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6412, top5_acc: 0.8566, loss_cls: 2.0579, loss: 2.0579 +2024-12-31 19:56:23,540 - pyskl - INFO - Epoch [147][1200/3746] lr: 1.484e-04, eta: 3:18:31, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6336, top5_acc: 0.8539, loss_cls: 2.0679, loss: 2.0679 +2024-12-31 19:57:49,015 - pyskl - INFO - Epoch [147][1300/3746] lr: 1.463e-04, eta: 3:17:05, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6480, top5_acc: 0.8497, loss_cls: 2.0243, loss: 2.0243 +2024-12-31 19:59:14,479 - pyskl - INFO - Epoch [147][1400/3746] lr: 1.442e-04, eta: 3:15:39, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6373, top5_acc: 0.8534, loss_cls: 2.0469, loss: 2.0469 +2024-12-31 20:00:39,447 - pyskl - INFO - Epoch [147][1500/3746] lr: 1.420e-04, eta: 3:14:12, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6527, top5_acc: 0.8619, loss_cls: 1.9893, loss: 1.9893 +2024-12-31 20:02:04,470 - pyskl - INFO - Epoch [147][1600/3746] lr: 1.399e-04, eta: 3:12:46, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6380, top5_acc: 0.8572, loss_cls: 2.0435, loss: 2.0435 +2024-12-31 20:03:29,629 - pyskl - INFO - Epoch [147][1700/3746] lr: 1.379e-04, eta: 3:11:19, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6300, top5_acc: 0.8538, loss_cls: 2.0748, loss: 2.0748 +2024-12-31 20:04:55,275 - pyskl - INFO - Epoch [147][1800/3746] lr: 1.358e-04, eta: 3:09:53, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6327, top5_acc: 0.8481, loss_cls: 2.0870, loss: 2.0870 +2024-12-31 20:06:20,856 - pyskl - INFO - Epoch [147][1900/3746] lr: 1.337e-04, eta: 3:08:26, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6373, top5_acc: 0.8480, loss_cls: 2.0698, loss: 2.0698 +2024-12-31 20:07:46,411 - pyskl - INFO - Epoch [147][2000/3746] lr: 1.317e-04, eta: 3:07:00, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6442, top5_acc: 0.8602, loss_cls: 2.0208, loss: 2.0208 +2024-12-31 20:09:12,211 - pyskl - INFO - Epoch [147][2100/3746] lr: 1.297e-04, eta: 3:05:33, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6239, top5_acc: 0.8445, loss_cls: 2.1196, loss: 2.1196 +2024-12-31 20:10:38,380 - pyskl - INFO - Epoch [147][2200/3746] lr: 1.277e-04, eta: 3:04:07, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.6458, top5_acc: 0.8552, loss_cls: 2.0407, loss: 2.0407 +2024-12-31 20:12:04,508 - pyskl - INFO - Epoch [147][2300/3746] lr: 1.257e-04, eta: 3:02:41, time: 0.861, data_time: 0.000, memory: 15990, top1_acc: 0.6442, top5_acc: 0.8595, loss_cls: 2.0383, loss: 2.0383 +2024-12-31 20:13:30,669 - pyskl - INFO - Epoch [147][2400/3746] lr: 1.237e-04, eta: 3:01:14, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.6502, top5_acc: 0.8617, loss_cls: 2.0165, loss: 2.0165 +2024-12-31 20:14:56,660 - pyskl - INFO - Epoch [147][2500/3746] lr: 1.218e-04, eta: 2:59:48, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6461, top5_acc: 0.8538, loss_cls: 2.0315, loss: 2.0315 +2024-12-31 20:16:22,946 - pyskl - INFO - Epoch [147][2600/3746] lr: 1.198e-04, eta: 2:58:21, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.6455, top5_acc: 0.8548, loss_cls: 2.0284, loss: 2.0284 +2024-12-31 20:17:48,933 - pyskl - INFO - Epoch [147][2700/3746] lr: 1.179e-04, eta: 2:56:55, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6358, top5_acc: 0.8512, loss_cls: 2.0464, loss: 2.0464 +2024-12-31 20:19:15,671 - pyskl - INFO - Epoch [147][2800/3746] lr: 1.160e-04, eta: 2:55:28, time: 0.867, data_time: 0.000, memory: 15990, top1_acc: 0.6366, top5_acc: 0.8561, loss_cls: 2.0421, loss: 2.0421 +2024-12-31 20:20:41,959 - pyskl - INFO - Epoch [147][2900/3746] lr: 1.141e-04, eta: 2:54:02, time: 0.863, data_time: 0.000, memory: 15990, top1_acc: 0.6327, top5_acc: 0.8475, loss_cls: 2.1028, loss: 2.1028 +2024-12-31 20:22:07,974 - pyskl - INFO - Epoch [147][3000/3746] lr: 1.122e-04, eta: 2:52:36, time: 0.860, data_time: 0.000, memory: 15990, top1_acc: 0.6417, top5_acc: 0.8597, loss_cls: 2.0458, loss: 2.0458 +2024-12-31 20:23:34,441 - pyskl - INFO - Epoch [147][3100/3746] lr: 1.103e-04, eta: 2:51:09, time: 0.865, data_time: 0.000, memory: 15990, top1_acc: 0.6364, top5_acc: 0.8550, loss_cls: 2.0306, loss: 2.0306 +2024-12-31 20:24:59,431 - pyskl - INFO - Epoch [147][3200/3746] lr: 1.085e-04, eta: 2:49:43, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6298, top5_acc: 0.8444, loss_cls: 2.1029, loss: 2.1029 +2024-12-31 20:26:24,086 - pyskl - INFO - Epoch [147][3300/3746] lr: 1.067e-04, eta: 2:48:16, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6355, top5_acc: 0.8514, loss_cls: 2.0903, loss: 2.0903 +2024-12-31 20:27:48,984 - pyskl - INFO - Epoch [147][3400/3746] lr: 1.048e-04, eta: 2:46:50, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6477, top5_acc: 0.8598, loss_cls: 2.0106, loss: 2.0106 +2024-12-31 20:29:14,738 - pyskl - INFO - Epoch [147][3500/3746] lr: 1.030e-04, eta: 2:45:23, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6428, top5_acc: 0.8597, loss_cls: 2.0410, loss: 2.0410 +2024-12-31 20:30:40,064 - pyskl - INFO - Epoch [147][3600/3746] lr: 1.013e-04, eta: 2:43:57, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6466, top5_acc: 0.8523, loss_cls: 2.0358, loss: 2.0358 +2024-12-31 20:32:05,661 - pyskl - INFO - Epoch [147][3700/3746] lr: 9.949e-05, eta: 2:42:31, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6445, top5_acc: 0.8536, loss_cls: 2.0445, loss: 2.0445 +2024-12-31 20:32:47,137 - pyskl - INFO - Saving checkpoint at 147 epochs +2024-12-31 20:34:48,227 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 20:34:48,928 - pyskl - INFO - +top1_acc 0.4488 +top5_acc 0.6932 +2024-12-31 20:34:48,928 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 20:34:48,966 - pyskl - INFO - +mean_acc 0.4486 +2024-12-31 20:34:48,977 - pyskl - INFO - Epoch(val) [147][309] top1_acc: 0.4488, top5_acc: 0.6932, mean_class_accuracy: 0.4486 +2024-12-31 20:39:10,292 - pyskl - INFO - Epoch [148][100/3746] lr: 9.693e-05, eta: 2:40:27, time: 2.613, data_time: 1.582, memory: 15990, top1_acc: 0.6577, top5_acc: 0.8641, loss_cls: 1.9900, loss: 1.9900 +2024-12-31 20:40:36,017 - pyskl - INFO - Epoch [148][200/3746] lr: 9.520e-05, eta: 2:39:01, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6534, top5_acc: 0.8630, loss_cls: 1.9932, loss: 1.9932 +2024-12-31 20:42:01,679 - pyskl - INFO - Epoch [148][300/3746] lr: 9.348e-05, eta: 2:37:34, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6558, top5_acc: 0.8667, loss_cls: 1.9702, loss: 1.9702 +2024-12-31 20:43:27,523 - pyskl - INFO - Epoch [148][400/3746] lr: 9.178e-05, eta: 2:36:08, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6444, top5_acc: 0.8556, loss_cls: 2.0431, loss: 2.0431 +2024-12-31 20:44:53,201 - pyskl - INFO - Epoch [148][500/3746] lr: 9.010e-05, eta: 2:34:41, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6611, top5_acc: 0.8611, loss_cls: 1.9825, loss: 1.9825 +2024-12-31 20:46:18,989 - pyskl - INFO - Epoch [148][600/3746] lr: 8.843e-05, eta: 2:33:15, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6478, top5_acc: 0.8634, loss_cls: 1.9963, loss: 1.9963 +2024-12-31 20:47:44,409 - pyskl - INFO - Epoch [148][700/3746] lr: 8.678e-05, eta: 2:31:48, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6494, top5_acc: 0.8500, loss_cls: 2.0193, loss: 2.0193 +2024-12-31 20:49:10,014 - pyskl - INFO - Epoch [148][800/3746] lr: 8.514e-05, eta: 2:30:22, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6527, top5_acc: 0.8581, loss_cls: 2.0029, loss: 2.0029 +2024-12-31 20:50:35,552 - pyskl - INFO - Epoch [148][900/3746] lr: 8.351e-05, eta: 2:28:56, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6461, top5_acc: 0.8612, loss_cls: 2.0081, loss: 2.0081 +2024-12-31 20:52:01,136 - pyskl - INFO - Epoch [148][1000/3746] lr: 8.191e-05, eta: 2:27:29, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6509, top5_acc: 0.8580, loss_cls: 2.0212, loss: 2.0212 +2024-12-31 20:53:26,918 - pyskl - INFO - Epoch [148][1100/3746] lr: 8.031e-05, eta: 2:26:03, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6461, top5_acc: 0.8608, loss_cls: 2.0105, loss: 2.0105 +2024-12-31 20:54:52,600 - pyskl - INFO - Epoch [148][1200/3746] lr: 7.874e-05, eta: 2:24:36, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6445, top5_acc: 0.8588, loss_cls: 2.0337, loss: 2.0337 +2024-12-31 20:56:17,847 - pyskl - INFO - Epoch [148][1300/3746] lr: 7.718e-05, eta: 2:23:10, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6450, top5_acc: 0.8539, loss_cls: 2.0280, loss: 2.0280 +2024-12-31 20:57:43,255 - pyskl - INFO - Epoch [148][1400/3746] lr: 7.563e-05, eta: 2:21:43, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6467, top5_acc: 0.8639, loss_cls: 2.0094, loss: 2.0094 +2024-12-31 20:59:08,641 - pyskl - INFO - Epoch [148][1500/3746] lr: 7.410e-05, eta: 2:20:17, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6408, top5_acc: 0.8536, loss_cls: 2.0225, loss: 2.0225 +2024-12-31 21:00:33,665 - pyskl - INFO - Epoch [148][1600/3746] lr: 7.259e-05, eta: 2:18:50, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6328, top5_acc: 0.8472, loss_cls: 2.0896, loss: 2.0896 +2024-12-31 21:01:58,567 - pyskl - INFO - Epoch [148][1700/3746] lr: 7.109e-05, eta: 2:17:24, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6475, top5_acc: 0.8575, loss_cls: 2.0284, loss: 2.0284 +2024-12-31 21:03:23,504 - pyskl - INFO - Epoch [148][1800/3746] lr: 6.961e-05, eta: 2:15:57, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6367, top5_acc: 0.8562, loss_cls: 2.0357, loss: 2.0357 +2024-12-31 21:04:48,901 - pyskl - INFO - Epoch [148][1900/3746] lr: 6.814e-05, eta: 2:14:31, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6381, top5_acc: 0.8533, loss_cls: 2.0456, loss: 2.0456 +2024-12-31 21:06:13,604 - pyskl - INFO - Epoch [148][2000/3746] lr: 6.669e-05, eta: 2:13:04, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6412, top5_acc: 0.8584, loss_cls: 2.0292, loss: 2.0292 +2024-12-31 21:07:39,259 - pyskl - INFO - Epoch [148][2100/3746] lr: 6.526e-05, eta: 2:11:38, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6353, top5_acc: 0.8527, loss_cls: 2.0596, loss: 2.0596 +2024-12-31 21:09:04,356 - pyskl - INFO - Epoch [148][2200/3746] lr: 6.384e-05, eta: 2:10:12, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6514, top5_acc: 0.8617, loss_cls: 2.0066, loss: 2.0066 +2024-12-31 21:10:29,908 - pyskl - INFO - Epoch [148][2300/3746] lr: 6.243e-05, eta: 2:08:45, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6512, top5_acc: 0.8594, loss_cls: 2.0268, loss: 2.0268 +2024-12-31 21:11:55,252 - pyskl - INFO - Epoch [148][2400/3746] lr: 6.104e-05, eta: 2:07:19, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6494, top5_acc: 0.8578, loss_cls: 2.0119, loss: 2.0119 +2024-12-31 21:13:20,230 - pyskl - INFO - Epoch [148][2500/3746] lr: 5.967e-05, eta: 2:05:52, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6598, top5_acc: 0.8648, loss_cls: 1.9625, loss: 1.9625 +2024-12-31 21:14:45,193 - pyskl - INFO - Epoch [148][2600/3746] lr: 5.831e-05, eta: 2:04:26, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6534, top5_acc: 0.8636, loss_cls: 1.9643, loss: 1.9643 +2024-12-31 21:16:10,401 - pyskl - INFO - Epoch [148][2700/3746] lr: 5.697e-05, eta: 2:02:59, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6481, top5_acc: 0.8594, loss_cls: 2.0101, loss: 2.0101 +2024-12-31 21:17:35,210 - pyskl - INFO - Epoch [148][2800/3746] lr: 5.564e-05, eta: 2:01:33, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6477, top5_acc: 0.8548, loss_cls: 2.0418, loss: 2.0418 +2024-12-31 21:19:00,667 - pyskl - INFO - Epoch [148][2900/3746] lr: 5.433e-05, eta: 2:00:06, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6430, top5_acc: 0.8564, loss_cls: 2.0317, loss: 2.0317 +2024-12-31 21:20:25,738 - pyskl - INFO - Epoch [148][3000/3746] lr: 5.304e-05, eta: 1:58:40, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6439, top5_acc: 0.8573, loss_cls: 2.0355, loss: 2.0355 +2024-12-31 21:21:50,730 - pyskl - INFO - Epoch [148][3100/3746] lr: 5.176e-05, eta: 1:57:13, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6505, top5_acc: 0.8559, loss_cls: 2.0127, loss: 2.0127 +2024-12-31 21:23:15,633 - pyskl - INFO - Epoch [148][3200/3746] lr: 5.050e-05, eta: 1:55:47, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6456, top5_acc: 0.8569, loss_cls: 2.0343, loss: 2.0343 +2024-12-31 21:24:41,096 - pyskl - INFO - Epoch [148][3300/3746] lr: 4.925e-05, eta: 1:54:21, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6461, top5_acc: 0.8531, loss_cls: 2.0212, loss: 2.0212 +2024-12-31 21:26:06,672 - pyskl - INFO - Epoch [148][3400/3746] lr: 4.801e-05, eta: 1:52:54, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6462, top5_acc: 0.8584, loss_cls: 2.0150, loss: 2.0150 +2024-12-31 21:27:32,031 - pyskl - INFO - Epoch [148][3500/3746] lr: 4.680e-05, eta: 1:51:28, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6469, top5_acc: 0.8655, loss_cls: 1.9993, loss: 1.9993 +2024-12-31 21:28:56,791 - pyskl - INFO - Epoch [148][3600/3746] lr: 4.560e-05, eta: 1:50:01, time: 0.848, data_time: 0.000, memory: 15990, top1_acc: 0.6552, top5_acc: 0.8622, loss_cls: 2.0044, loss: 2.0044 +2024-12-31 21:30:21,926 - pyskl - INFO - Epoch [148][3700/3746] lr: 4.441e-05, eta: 1:48:35, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6420, top5_acc: 0.8539, loss_cls: 2.0366, loss: 2.0366 +2024-12-31 21:31:03,080 - pyskl - INFO - Saving checkpoint at 148 epochs +2024-12-31 21:33:05,099 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 21:33:05,834 - pyskl - INFO - +top1_acc 0.4491 +top5_acc 0.6955 +2024-12-31 21:33:05,834 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 21:33:05,879 - pyskl - INFO - +mean_acc 0.4488 +2024-12-31 21:33:05,884 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_145.pth was removed +2024-12-31 21:33:06,247 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_148.pth. +2024-12-31 21:33:06,249 - pyskl - INFO - Best top1_acc is 0.4491 at 148 epoch. +2024-12-31 21:33:06,269 - pyskl - INFO - Epoch(val) [148][309] top1_acc: 0.4491, top5_acc: 0.6955, mean_class_accuracy: 0.4488 +2024-12-31 21:37:31,520 - pyskl - INFO - Epoch [149][100/3746] lr: 4.271e-05, eta: 1:46:30, time: 2.652, data_time: 1.618, memory: 15990, top1_acc: 0.6614, top5_acc: 0.8608, loss_cls: 1.9718, loss: 1.9718 +2024-12-31 21:38:56,902 - pyskl - INFO - Epoch [149][200/3746] lr: 4.156e-05, eta: 1:45:04, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6447, top5_acc: 0.8553, loss_cls: 2.0395, loss: 2.0395 +2024-12-31 21:40:22,675 - pyskl - INFO - Epoch [149][300/3746] lr: 4.043e-05, eta: 1:43:37, time: 0.858, data_time: 0.000, memory: 15990, top1_acc: 0.6456, top5_acc: 0.8595, loss_cls: 2.0100, loss: 2.0100 +2024-12-31 21:41:48,065 - pyskl - INFO - Epoch [149][400/3746] lr: 3.931e-05, eta: 1:42:11, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6538, top5_acc: 0.8609, loss_cls: 1.9865, loss: 1.9865 +2024-12-31 21:43:13,147 - pyskl - INFO - Epoch [149][500/3746] lr: 3.821e-05, eta: 1:40:45, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6459, top5_acc: 0.8652, loss_cls: 1.9800, loss: 1.9800 +2024-12-31 21:44:38,522 - pyskl - INFO - Epoch [149][600/3746] lr: 3.713e-05, eta: 1:39:18, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6550, top5_acc: 0.8619, loss_cls: 1.9846, loss: 1.9846 +2024-12-31 21:46:03,660 - pyskl - INFO - Epoch [149][700/3746] lr: 3.606e-05, eta: 1:37:52, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6533, top5_acc: 0.8648, loss_cls: 1.9579, loss: 1.9579 +2024-12-31 21:47:28,615 - pyskl - INFO - Epoch [149][800/3746] lr: 3.500e-05, eta: 1:36:25, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6480, top5_acc: 0.8538, loss_cls: 2.0384, loss: 2.0384 +2024-12-31 21:48:54,173 - pyskl - INFO - Epoch [149][900/3746] lr: 3.397e-05, eta: 1:34:59, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6425, top5_acc: 0.8584, loss_cls: 2.0377, loss: 2.0377 +2024-12-31 21:50:19,300 - pyskl - INFO - Epoch [149][1000/3746] lr: 3.294e-05, eta: 1:33:32, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6414, top5_acc: 0.8584, loss_cls: 2.0303, loss: 2.0303 +2024-12-31 21:51:44,215 - pyskl - INFO - Epoch [149][1100/3746] lr: 3.194e-05, eta: 1:32:06, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6506, top5_acc: 0.8569, loss_cls: 2.0109, loss: 2.0109 +2024-12-31 21:53:08,960 - pyskl - INFO - Epoch [149][1200/3746] lr: 3.095e-05, eta: 1:30:39, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6597, top5_acc: 0.8681, loss_cls: 1.9464, loss: 1.9464 +2024-12-31 21:54:34,170 - pyskl - INFO - Epoch [149][1300/3746] lr: 2.997e-05, eta: 1:29:13, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6558, top5_acc: 0.8697, loss_cls: 1.9661, loss: 1.9661 +2024-12-31 21:55:59,690 - pyskl - INFO - Epoch [149][1400/3746] lr: 2.901e-05, eta: 1:27:46, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6461, top5_acc: 0.8561, loss_cls: 2.0149, loss: 2.0149 +2024-12-31 21:57:25,273 - pyskl - INFO - Epoch [149][1500/3746] lr: 2.807e-05, eta: 1:26:20, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6589, top5_acc: 0.8700, loss_cls: 1.9788, loss: 1.9788 +2024-12-31 21:58:50,853 - pyskl - INFO - Epoch [149][1600/3746] lr: 2.714e-05, eta: 1:24:53, time: 0.856, data_time: 0.001, memory: 15990, top1_acc: 0.6552, top5_acc: 0.8602, loss_cls: 1.9972, loss: 1.9972 +2024-12-31 22:00:16,170 - pyskl - INFO - Epoch [149][1700/3746] lr: 2.622e-05, eta: 1:23:27, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6584, top5_acc: 0.8659, loss_cls: 1.9595, loss: 1.9595 +2024-12-31 22:01:41,450 - pyskl - INFO - Epoch [149][1800/3746] lr: 2.533e-05, eta: 1:22:00, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6550, top5_acc: 0.8608, loss_cls: 2.0051, loss: 2.0051 +2024-12-31 22:03:06,501 - pyskl - INFO - Epoch [149][1900/3746] lr: 2.444e-05, eta: 1:20:34, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6483, top5_acc: 0.8522, loss_cls: 2.0313, loss: 2.0313 +2024-12-31 22:04:32,166 - pyskl - INFO - Epoch [149][2000/3746] lr: 2.358e-05, eta: 1:19:08, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6533, top5_acc: 0.8592, loss_cls: 1.9877, loss: 1.9877 +2024-12-31 22:05:57,566 - pyskl - INFO - Epoch [149][2100/3746] lr: 2.273e-05, eta: 1:17:41, time: 0.854, data_time: 0.000, memory: 15990, top1_acc: 0.6511, top5_acc: 0.8595, loss_cls: 2.0193, loss: 2.0193 +2024-12-31 22:07:23,047 - pyskl - INFO - Epoch [149][2200/3746] lr: 2.189e-05, eta: 1:16:15, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6447, top5_acc: 0.8527, loss_cls: 2.0583, loss: 2.0583 +2024-12-31 22:08:48,629 - pyskl - INFO - Epoch [149][2300/3746] lr: 2.107e-05, eta: 1:14:48, time: 0.856, data_time: 0.000, memory: 15990, top1_acc: 0.6600, top5_acc: 0.8659, loss_cls: 1.9595, loss: 1.9595 +2024-12-31 22:10:13,861 - pyskl - INFO - Epoch [149][2400/3746] lr: 2.027e-05, eta: 1:13:22, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6517, top5_acc: 0.8662, loss_cls: 1.9869, loss: 1.9869 +2024-12-31 22:11:39,116 - pyskl - INFO - Epoch [149][2500/3746] lr: 1.948e-05, eta: 1:11:55, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6613, top5_acc: 0.8631, loss_cls: 1.9754, loss: 1.9754 +2024-12-31 22:13:04,614 - pyskl - INFO - Epoch [149][2600/3746] lr: 1.871e-05, eta: 1:10:29, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6392, top5_acc: 0.8550, loss_cls: 2.0346, loss: 2.0346 +2024-12-31 22:14:30,313 - pyskl - INFO - Epoch [149][2700/3746] lr: 1.795e-05, eta: 1:09:02, time: 0.857, data_time: 0.000, memory: 15990, top1_acc: 0.6509, top5_acc: 0.8580, loss_cls: 2.0097, loss: 2.0097 +2024-12-31 22:15:55,843 - pyskl - INFO - Epoch [149][2800/3746] lr: 1.721e-05, eta: 1:07:36, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6642, top5_acc: 0.8634, loss_cls: 1.9763, loss: 1.9763 +2024-12-31 22:17:22,055 - pyskl - INFO - Epoch [149][2900/3746] lr: 1.649e-05, eta: 1:06:09, time: 0.862, data_time: 0.000, memory: 15990, top1_acc: 0.6625, top5_acc: 0.8614, loss_cls: 1.9838, loss: 1.9838 +2024-12-31 22:18:47,528 - pyskl - INFO - Epoch [149][3000/3746] lr: 1.578e-05, eta: 1:04:43, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6581, top5_acc: 0.8620, loss_cls: 1.9905, loss: 1.9905 +2024-12-31 22:20:12,458 - pyskl - INFO - Epoch [149][3100/3746] lr: 1.508e-05, eta: 1:03:16, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6473, top5_acc: 0.8555, loss_cls: 1.9920, loss: 1.9920 +2024-12-31 22:21:37,766 - pyskl - INFO - Epoch [149][3200/3746] lr: 1.440e-05, eta: 1:01:50, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6523, top5_acc: 0.8544, loss_cls: 1.9991, loss: 1.9991 +2024-12-31 22:23:02,725 - pyskl - INFO - Epoch [149][3300/3746] lr: 1.374e-05, eta: 1:00:24, time: 0.850, data_time: 0.000, memory: 15990, top1_acc: 0.6522, top5_acc: 0.8631, loss_cls: 2.0076, loss: 2.0076 +2024-12-31 22:24:27,832 - pyskl - INFO - Epoch [149][3400/3746] lr: 1.309e-05, eta: 0:58:57, time: 0.851, data_time: 0.000, memory: 15990, top1_acc: 0.6508, top5_acc: 0.8611, loss_cls: 2.0101, loss: 2.0101 +2024-12-31 22:25:53,339 - pyskl - INFO - Epoch [149][3500/3746] lr: 1.246e-05, eta: 0:57:31, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6586, top5_acc: 0.8572, loss_cls: 2.0099, loss: 2.0099 +2024-12-31 22:27:18,651 - pyskl - INFO - Epoch [149][3600/3746] lr: 1.184e-05, eta: 0:56:04, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6506, top5_acc: 0.8592, loss_cls: 1.9973, loss: 1.9973 +2024-12-31 22:28:43,912 - pyskl - INFO - Epoch [149][3700/3746] lr: 1.124e-05, eta: 0:54:38, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6558, top5_acc: 0.8698, loss_cls: 1.9637, loss: 1.9637 +2024-12-31 22:29:25,351 - pyskl - INFO - Saving checkpoint at 149 epochs +2024-12-31 22:31:25,879 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 22:31:26,824 - pyskl - INFO - +top1_acc 0.4473 +top5_acc 0.6925 +2024-12-31 22:31:26,824 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 22:31:26,894 - pyskl - INFO - +mean_acc 0.4471 +2024-12-31 22:31:26,911 - pyskl - INFO - Epoch(val) [149][309] top1_acc: 0.4473, top5_acc: 0.6925, mean_class_accuracy: 0.4471 +2024-12-31 22:35:49,547 - pyskl - INFO - Epoch [150][100/3746] lr: 1.039e-05, eta: 0:52:32, time: 2.626, data_time: 1.594, memory: 15990, top1_acc: 0.6495, top5_acc: 0.8664, loss_cls: 1.9675, loss: 1.9675 +2024-12-31 22:37:14,860 - pyskl - INFO - Epoch [150][200/3746] lr: 9.832e-06, eta: 0:51:06, time: 0.853, data_time: 0.000, memory: 15990, top1_acc: 0.6481, top5_acc: 0.8577, loss_cls: 2.0150, loss: 2.0150 +2024-12-31 22:38:40,379 - pyskl - INFO - Epoch [150][300/3746] lr: 9.285e-06, eta: 0:49:39, time: 0.855, data_time: 0.000, memory: 15990, top1_acc: 0.6581, top5_acc: 0.8602, loss_cls: 1.9895, loss: 1.9895 +2024-12-31 22:40:05,258 - pyskl - INFO - Epoch [150][400/3746] lr: 8.754e-06, eta: 0:48:13, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6616, top5_acc: 0.8684, loss_cls: 1.9491, loss: 1.9491 +2024-12-31 22:41:30,206 - pyskl - INFO - Epoch [150][500/3746] lr: 8.239e-06, eta: 0:46:46, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6445, top5_acc: 0.8581, loss_cls: 2.0306, loss: 2.0306 +2024-12-31 22:42:54,906 - pyskl - INFO - Epoch [150][600/3746] lr: 7.739e-06, eta: 0:45:20, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6550, top5_acc: 0.8633, loss_cls: 1.9818, loss: 1.9818 +2024-12-31 22:44:20,132 - pyskl - INFO - Epoch [150][700/3746] lr: 7.255e-06, eta: 0:43:53, time: 0.852, data_time: 0.000, memory: 15990, top1_acc: 0.6578, top5_acc: 0.8631, loss_cls: 1.9894, loss: 1.9894 +2024-12-31 22:45:44,851 - pyskl - INFO - Epoch [150][800/3746] lr: 6.787e-06, eta: 0:42:27, time: 0.847, data_time: 0.000, memory: 15990, top1_acc: 0.6573, top5_acc: 0.8541, loss_cls: 2.0085, loss: 2.0085 +2024-12-31 22:47:09,958 - pyskl - INFO - Epoch [150][900/3746] lr: 6.334e-06, eta: 0:41:01, time: 0.851, data_time: 0.001, memory: 15990, top1_acc: 0.6564, top5_acc: 0.8592, loss_cls: 1.9948, loss: 1.9948 +2024-12-31 22:48:34,191 - pyskl - INFO - Epoch [150][1000/3746] lr: 5.897e-06, eta: 0:39:34, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6622, top5_acc: 0.8675, loss_cls: 1.9521, loss: 1.9521 +2024-12-31 22:49:58,580 - pyskl - INFO - Epoch [150][1100/3746] lr: 5.475e-06, eta: 0:38:08, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6505, top5_acc: 0.8552, loss_cls: 2.0049, loss: 2.0049 +2024-12-31 22:51:22,612 - pyskl - INFO - Epoch [150][1200/3746] lr: 5.070e-06, eta: 0:36:41, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.6558, top5_acc: 0.8645, loss_cls: 1.9729, loss: 1.9729 +2024-12-31 22:52:46,628 - pyskl - INFO - Epoch [150][1300/3746] lr: 4.679e-06, eta: 0:35:15, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.6523, top5_acc: 0.8652, loss_cls: 1.9901, loss: 1.9901 +2024-12-31 22:54:11,138 - pyskl - INFO - Epoch [150][1400/3746] lr: 4.305e-06, eta: 0:33:48, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6509, top5_acc: 0.8669, loss_cls: 1.9779, loss: 1.9779 +2024-12-31 22:55:36,012 - pyskl - INFO - Epoch [150][1500/3746] lr: 3.946e-06, eta: 0:32:22, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6533, top5_acc: 0.8602, loss_cls: 1.9880, loss: 1.9880 +2024-12-31 22:56:59,950 - pyskl - INFO - Epoch [150][1600/3746] lr: 3.602e-06, eta: 0:30:55, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.6570, top5_acc: 0.8603, loss_cls: 1.9968, loss: 1.9968 +2024-12-31 22:58:24,410 - pyskl - INFO - Epoch [150][1700/3746] lr: 3.275e-06, eta: 0:29:29, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.6569, top5_acc: 0.8561, loss_cls: 1.9908, loss: 1.9908 +2024-12-31 22:59:49,341 - pyskl - INFO - Epoch [150][1800/3746] lr: 2.962e-06, eta: 0:28:02, time: 0.849, data_time: 0.000, memory: 15990, top1_acc: 0.6511, top5_acc: 0.8602, loss_cls: 2.0134, loss: 2.0134 +2024-12-31 23:01:13,789 - pyskl - INFO - Epoch [150][1900/3746] lr: 2.666e-06, eta: 0:26:36, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6622, top5_acc: 0.8661, loss_cls: 1.9512, loss: 1.9512 +2024-12-31 23:02:38,107 - pyskl - INFO - Epoch [150][2000/3746] lr: 2.385e-06, eta: 0:25:09, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.6561, top5_acc: 0.8650, loss_cls: 1.9640, loss: 1.9640 +2024-12-31 23:04:02,109 - pyskl - INFO - Epoch [150][2100/3746] lr: 2.120e-06, eta: 0:23:43, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.6622, top5_acc: 0.8623, loss_cls: 1.9832, loss: 1.9832 +2024-12-31 23:05:26,541 - pyskl - INFO - Epoch [150][2200/3746] lr: 1.870e-06, eta: 0:22:16, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6523, top5_acc: 0.8655, loss_cls: 1.9809, loss: 1.9809 +2024-12-31 23:06:50,223 - pyskl - INFO - Epoch [150][2300/3746] lr: 1.636e-06, eta: 0:20:50, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.6511, top5_acc: 0.8638, loss_cls: 1.9860, loss: 1.9860 +2024-12-31 23:08:14,412 - pyskl - INFO - Epoch [150][2400/3746] lr: 1.418e-06, eta: 0:19:23, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6525, top5_acc: 0.8678, loss_cls: 1.9890, loss: 1.9890 +2024-12-31 23:09:38,831 - pyskl - INFO - Epoch [150][2500/3746] lr: 1.215e-06, eta: 0:17:57, time: 0.844, data_time: 0.000, memory: 15990, top1_acc: 0.6475, top5_acc: 0.8598, loss_cls: 1.9984, loss: 1.9984 +2024-12-31 23:11:02,467 - pyskl - INFO - Epoch [150][2600/3746] lr: 1.028e-06, eta: 0:16:30, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.6522, top5_acc: 0.8622, loss_cls: 1.9943, loss: 1.9943 +2024-12-31 23:12:26,564 - pyskl - INFO - Epoch [150][2700/3746] lr: 8.567e-07, eta: 0:15:04, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.6491, top5_acc: 0.8555, loss_cls: 2.0101, loss: 2.0101 +2024-12-31 23:13:50,566 - pyskl - INFO - Epoch [150][2800/3746] lr: 7.008e-07, eta: 0:13:37, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.6470, top5_acc: 0.8544, loss_cls: 2.0353, loss: 2.0353 +2024-12-31 23:15:13,862 - pyskl - INFO - Epoch [150][2900/3746] lr: 5.606e-07, eta: 0:12:11, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.6445, top5_acc: 0.8534, loss_cls: 2.0302, loss: 2.0302 +2024-12-31 23:16:37,152 - pyskl - INFO - Epoch [150][3000/3746] lr: 4.361e-07, eta: 0:10:45, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.6491, top5_acc: 0.8589, loss_cls: 2.0306, loss: 2.0306 +2024-12-31 23:18:00,579 - pyskl - INFO - Epoch [150][3100/3746] lr: 3.271e-07, eta: 0:09:18, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.6536, top5_acc: 0.8589, loss_cls: 2.0046, loss: 2.0046 +2024-12-31 23:19:24,350 - pyskl - INFO - Epoch [150][3200/3746] lr: 2.338e-07, eta: 0:07:52, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.6567, top5_acc: 0.8628, loss_cls: 1.9771, loss: 1.9771 +2024-12-31 23:20:48,036 - pyskl - INFO - Epoch [150][3300/3746] lr: 1.561e-07, eta: 0:06:25, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.6684, top5_acc: 0.8723, loss_cls: 1.9209, loss: 1.9209 +2024-12-31 23:22:12,259 - pyskl - INFO - Epoch [150][3400/3746] lr: 9.410e-08, eta: 0:04:59, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.6573, top5_acc: 0.8578, loss_cls: 1.9854, loss: 1.9854 +2024-12-31 23:23:35,560 - pyskl - INFO - Epoch [150][3500/3746] lr: 4.768e-08, eta: 0:03:32, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.6512, top5_acc: 0.8567, loss_cls: 1.9887, loss: 1.9887 +2024-12-31 23:24:59,531 - pyskl - INFO - Epoch [150][3600/3746] lr: 1.689e-08, eta: 0:02:06, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.6689, top5_acc: 0.8725, loss_cls: 1.9246, loss: 1.9246 +2024-12-31 23:26:23,465 - pyskl - INFO - Epoch [150][3700/3746] lr: 1.726e-09, eta: 0:00:39, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.6478, top5_acc: 0.8572, loss_cls: 2.0360, loss: 2.0360 +2024-12-31 23:27:03,835 - pyskl - INFO - Saving checkpoint at 150 epochs +2024-12-31 23:28:57,965 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-12-31 23:28:58,689 - pyskl - INFO - +top1_acc 0.4488 +top5_acc 0.6924 +2024-12-31 23:28:58,690 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-12-31 23:28:58,736 - pyskl - INFO - +mean_acc 0.4486 +2024-12-31 23:28:58,749 - pyskl - INFO - Epoch(val) [150][309] top1_acc: 0.4488, top5_acc: 0.6924, mean_class_accuracy: 0.4486 +2024-12-31 23:29:14,354 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-12-31 23:41:39,562 - pyskl - INFO - Testing results of the last checkpoint +2024-12-31 23:41:39,562 - pyskl - INFO - top1_acc: 0.4572 +2024-12-31 23:41:39,562 - pyskl - INFO - top5_acc: 0.7039 +2024-12-31 23:41:39,562 - pyskl - INFO - mean_class_accuracy: 0.4570 +2024-12-31 23:41:39,563 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/k400/k_3/best_top1_acc_epoch_148.pth +2024-12-31 23:53:51,314 - pyskl - INFO - Testing results of the best checkpoint +2024-12-31 23:53:51,314 - pyskl - INFO - top1_acc: 0.4567 +2024-12-31 23:53:51,314 - pyskl - INFO - top5_acc: 0.7051 +2024-12-31 23:53:51,314 - pyskl - INFO - mean_class_accuracy: 0.4565 diff --git a/k400/k_3/20241226_015027.log.json b/k400/k_3/20241226_015027.log.json new file mode 100644 index 0000000000000000000000000000000000000000..14fe99ff0631b4c68a64c186ee4581293c1d935f --- /dev/null +++ b/k400/k_3/20241226_015027.log.json @@ -0,0 +1,5701 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 1625270613, "config_name": "k_3.py", "work_dir": "k_3", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.1, "memory": 15990, "data_time": 1.42547, "top1_acc": 0.00594, "top5_acc": 0.03391, "loss_cls": 6.41806, "loss": 6.41806, "time": 2.15102} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.1, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.01219, "top5_acc": 0.04953, "loss_cls": 6.34978, "loss": 6.34978, "time": 0.7193} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.1, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.01656, "top5_acc": 0.07031, "loss_cls": 6.18201, "loss": 6.18201, "time": 0.71423} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.1, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.02328, "top5_acc": 0.09703, "loss_cls": 6.02015, "loss": 6.02015, "time": 0.7146} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.1, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.02578, "top5_acc": 0.09938, "loss_cls": 5.94213, "loss": 5.94213, "time": 0.71905} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.1, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.03453, "top5_acc": 0.1125, "loss_cls": 5.87778, "loss": 5.87778, "time": 0.71384} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.1, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.03062, "top5_acc": 0.11453, "loss_cls": 5.81283, "loss": 5.81283, "time": 0.71591} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.1, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.03641, "top5_acc": 0.12984, "loss_cls": 5.76324, "loss": 5.76324, "time": 0.71485} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.1, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.03938, "top5_acc": 0.13047, "loss_cls": 5.76231, "loss": 5.76231, "time": 0.71637} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.1, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.04188, "top5_acc": 0.14312, "loss_cls": 5.69739, "loss": 5.69739, "time": 0.72308} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.1, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.04891, "top5_acc": 0.16109, "loss_cls": 5.62804, "loss": 5.62804, "time": 0.72232} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.1, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.0475, "top5_acc": 0.16297, "loss_cls": 5.63654, "loss": 5.63654, "time": 0.72002} +{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.1, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.04922, "top5_acc": 0.16891, "loss_cls": 5.61402, "loss": 5.61402, "time": 0.71887} +{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.1, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.05422, "top5_acc": 0.17766, "loss_cls": 5.56176, "loss": 5.56176, "time": 0.72051} +{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.1, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.05781, "top5_acc": 0.17953, "loss_cls": 5.55111, "loss": 5.55111, "time": 0.71902} +{"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.1, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.05719, "top5_acc": 0.18312, "loss_cls": 5.50199, "loss": 5.50199, "time": 0.721} +{"mode": "train", "epoch": 1, "iter": 1700, "lr": 0.1, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.05922, "top5_acc": 0.19766, "loss_cls": 5.47323, "loss": 5.47323, "time": 0.72217} +{"mode": "train", "epoch": 1, "iter": 1800, "lr": 0.1, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.06125, "top5_acc": 0.20156, "loss_cls": 5.46018, "loss": 5.46018, "time": 0.71935} +{"mode": "train", "epoch": 1, "iter": 1900, "lr": 0.1, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.07266, "top5_acc": 0.20219, "loss_cls": 5.43918, "loss": 5.43918, "time": 0.72073} +{"mode": "train", "epoch": 1, "iter": 2000, "lr": 0.1, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.06844, "top5_acc": 0.20953, "loss_cls": 5.42627, "loss": 5.42627, "time": 0.72429} +{"mode": "train", "epoch": 1, "iter": 2100, "lr": 0.1, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.0725, "top5_acc": 0.21016, "loss_cls": 5.40401, "loss": 5.40401, "time": 0.72173} +{"mode": "train", "epoch": 1, "iter": 2200, "lr": 0.1, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.07031, "top5_acc": 0.21641, "loss_cls": 5.38211, "loss": 5.38211, "time": 0.71936} +{"mode": "train", "epoch": 1, "iter": 2300, "lr": 0.1, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.06625, "top5_acc": 0.20562, "loss_cls": 5.44315, "loss": 5.44315, "time": 0.71892} +{"mode": "train", "epoch": 1, "iter": 2400, "lr": 0.1, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.07328, "top5_acc": 0.22656, "loss_cls": 5.39036, "loss": 5.39036, "time": 0.72039} +{"mode": "train", "epoch": 1, "iter": 2500, "lr": 0.1, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.07875, "top5_acc": 0.22906, "loss_cls": 5.32895, "loss": 5.32895, "time": 0.72209} +{"mode": "train", "epoch": 1, "iter": 2600, "lr": 0.09999, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.08172, "top5_acc": 0.23422, "loss_cls": 5.31001, "loss": 5.31001, "time": 0.72213} +{"mode": "train", "epoch": 1, "iter": 2700, "lr": 0.09999, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.07922, "top5_acc": 0.22781, "loss_cls": 5.33683, "loss": 5.33683, "time": 0.71838} +{"mode": "train", "epoch": 1, "iter": 2800, "lr": 0.09999, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.07656, "top5_acc": 0.23594, "loss_cls": 5.33692, "loss": 5.33692, "time": 0.72014} +{"mode": "train", "epoch": 1, "iter": 2900, "lr": 0.09999, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.07484, "top5_acc": 0.24078, "loss_cls": 5.3181, "loss": 5.3181, "time": 0.71741} +{"mode": "train", "epoch": 1, "iter": 3000, "lr": 0.09999, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.08672, "top5_acc": 0.24, "loss_cls": 5.29711, "loss": 5.29711, "time": 0.71939} +{"mode": "train", "epoch": 1, "iter": 3100, "lr": 0.09999, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.08766, "top5_acc": 0.24688, "loss_cls": 5.24073, "loss": 5.24073, "time": 0.7199} +{"mode": "train", "epoch": 1, "iter": 3200, "lr": 0.09999, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.08422, "top5_acc": 0.24328, "loss_cls": 5.27494, "loss": 5.27494, "time": 0.71957} +{"mode": "train", "epoch": 1, "iter": 3300, "lr": 0.09999, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.08766, "top5_acc": 0.25578, "loss_cls": 5.22982, "loss": 5.22982, "time": 0.72102} +{"mode": "train", "epoch": 1, "iter": 3400, "lr": 0.09999, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.08812, "top5_acc": 0.25344, "loss_cls": 5.26456, "loss": 5.26456, "time": 0.71822} +{"mode": "train", "epoch": 1, "iter": 3500, "lr": 0.09999, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.09578, "top5_acc": 0.25641, "loss_cls": 5.21541, "loss": 5.21541, "time": 0.71677} +{"mode": "train", "epoch": 1, "iter": 3600, "lr": 0.09999, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.09969, "top5_acc": 0.26687, "loss_cls": 5.19817, "loss": 5.19817, "time": 0.71441} +{"mode": "train", "epoch": 1, "iter": 3700, "lr": 0.09999, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.09406, "top5_acc": 0.26562, "loss_cls": 5.21778, "loss": 5.21778, "time": 0.71411} +{"mode": "val", "epoch": 1, "iter": 309, "lr": 0.09999, "top1_acc": 0.05921, "top5_acc": 0.1752, "mean_class_accuracy": 0.05926} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.09999, "memory": 15990, "data_time": 1.40907, "top1_acc": 0.09703, "top5_acc": 0.25891, "loss_cls": 5.1853, "loss": 5.1853, "time": 2.1259} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.09999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.09828, "top5_acc": 0.27375, "loss_cls": 5.14678, "loss": 5.14678, "time": 0.71905} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.09999, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.10125, "top5_acc": 0.27297, "loss_cls": 5.15035, "loss": 5.15035, "time": 0.71466} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.09999, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.10188, "top5_acc": 0.27672, "loss_cls": 5.15533, "loss": 5.15533, "time": 0.7192} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.09999, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.09984, "top5_acc": 0.28719, "loss_cls": 5.13922, "loss": 5.13922, "time": 0.71453} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.09999, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.09703, "top5_acc": 0.27625, "loss_cls": 5.11949, "loss": 5.11949, "time": 0.71423} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.09998, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.11172, "top5_acc": 0.28672, "loss_cls": 5.11911, "loss": 5.11911, "time": 0.71776} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.09998, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.10703, "top5_acc": 0.28266, "loss_cls": 5.11585, "loss": 5.11585, "time": 0.71797} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.09998, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.10906, "top5_acc": 0.28734, "loss_cls": 5.11951, "loss": 5.11951, "time": 0.71581} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.09998, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.11094, "top5_acc": 0.295, "loss_cls": 5.04721, "loss": 5.04721, "time": 0.72338} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.09998, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.11031, "top5_acc": 0.29234, "loss_cls": 5.065, "loss": 5.065, "time": 0.71998} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.09998, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.12, "top5_acc": 0.30375, "loss_cls": 5.02997, "loss": 5.02997, "time": 0.7234} +{"mode": "train", "epoch": 2, "iter": 1300, "lr": 0.09998, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.11719, "top5_acc": 0.29953, "loss_cls": 5.02101, "loss": 5.02101, "time": 0.72181} +{"mode": "train", "epoch": 2, "iter": 1400, "lr": 0.09998, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.11906, "top5_acc": 0.30016, "loss_cls": 5.01731, "loss": 5.01731, "time": 0.72031} +{"mode": "train", "epoch": 2, "iter": 1500, "lr": 0.09998, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.11469, "top5_acc": 0.30141, "loss_cls": 5.0309, "loss": 5.0309, "time": 0.72074} +{"mode": "train", "epoch": 2, "iter": 1600, "lr": 0.09998, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.11922, "top5_acc": 0.30844, "loss_cls": 5.01306, "loss": 5.01306, "time": 0.72081} +{"mode": "train", "epoch": 2, "iter": 1700, "lr": 0.09998, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.11891, "top5_acc": 0.30453, "loss_cls": 4.99159, "loss": 4.99159, "time": 0.72258} +{"mode": "train", "epoch": 2, "iter": 1800, "lr": 0.09998, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.12125, "top5_acc": 0.305, "loss_cls": 5.00941, "loss": 5.00941, "time": 0.72144} +{"mode": "train", "epoch": 2, "iter": 1900, "lr": 0.09998, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.12312, "top5_acc": 0.31266, "loss_cls": 4.99488, "loss": 4.99488, "time": 0.72116} +{"mode": "train", "epoch": 2, "iter": 2000, "lr": 0.09997, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.12687, "top5_acc": 0.32609, "loss_cls": 4.92838, "loss": 4.92838, "time": 0.71964} +{"mode": "train", "epoch": 2, "iter": 2100, "lr": 0.09997, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.12625, "top5_acc": 0.32031, "loss_cls": 4.95563, "loss": 4.95563, "time": 0.72035} +{"mode": "train", "epoch": 2, "iter": 2200, "lr": 0.09997, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.13625, "top5_acc": 0.33203, "loss_cls": 4.90093, "loss": 4.90093, "time": 0.72252} +{"mode": "train", "epoch": 2, "iter": 2300, "lr": 0.09997, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.13781, "top5_acc": 0.33375, "loss_cls": 4.91859, "loss": 4.91859, "time": 0.71724} +{"mode": "train", "epoch": 2, "iter": 2400, "lr": 0.09997, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.12984, "top5_acc": 0.33344, "loss_cls": 4.91965, "loss": 4.91965, "time": 0.72306} +{"mode": "train", "epoch": 2, "iter": 2500, "lr": 0.09997, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.13422, "top5_acc": 0.32484, "loss_cls": 4.91513, "loss": 4.91513, "time": 0.71991} +{"mode": "train", "epoch": 2, "iter": 2600, "lr": 0.09997, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.1475, "top5_acc": 0.34359, "loss_cls": 4.84758, "loss": 4.84758, "time": 0.72274} +{"mode": "train", "epoch": 2, "iter": 2700, "lr": 0.09997, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.14047, "top5_acc": 0.34203, "loss_cls": 4.85929, "loss": 4.85929, "time": 0.71933} +{"mode": "train", "epoch": 2, "iter": 2800, "lr": 0.09997, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.13266, "top5_acc": 0.33516, "loss_cls": 4.86633, "loss": 4.86633, "time": 0.71962} +{"mode": "train", "epoch": 2, "iter": 2900, "lr": 0.09997, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.13906, "top5_acc": 0.34156, "loss_cls": 4.86376, "loss": 4.86376, "time": 0.72164} +{"mode": "train", "epoch": 2, "iter": 3000, "lr": 0.09996, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.14344, "top5_acc": 0.35438, "loss_cls": 4.82312, "loss": 4.82312, "time": 0.71688} +{"mode": "train", "epoch": 2, "iter": 3100, "lr": 0.09996, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.13812, "top5_acc": 0.34609, "loss_cls": 4.83412, "loss": 4.83412, "time": 0.72291} +{"mode": "train", "epoch": 2, "iter": 3200, "lr": 0.09996, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.1475, "top5_acc": 0.35297, "loss_cls": 4.84596, "loss": 4.84596, "time": 0.72295} +{"mode": "train", "epoch": 2, "iter": 3300, "lr": 0.09996, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.14797, "top5_acc": 0.35281, "loss_cls": 4.82718, "loss": 4.82718, "time": 0.72136} +{"mode": "train", "epoch": 2, "iter": 3400, "lr": 0.09996, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.15109, "top5_acc": 0.36359, "loss_cls": 4.75887, "loss": 4.75887, "time": 0.71913} +{"mode": "train", "epoch": 2, "iter": 3500, "lr": 0.09996, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.14812, "top5_acc": 0.35078, "loss_cls": 4.77663, "loss": 4.77663, "time": 0.7136} +{"mode": "train", "epoch": 2, "iter": 3600, "lr": 0.09996, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.14734, "top5_acc": 0.36188, "loss_cls": 4.78878, "loss": 4.78878, "time": 0.71743} +{"mode": "train", "epoch": 2, "iter": 3700, "lr": 0.09996, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.15609, "top5_acc": 0.36656, "loss_cls": 4.76295, "loss": 4.76295, "time": 0.71702} +{"mode": "val", "epoch": 2, "iter": 309, "lr": 0.09996, "top1_acc": 0.0973, "top5_acc": 0.25903, "mean_class_accuracy": 0.09738} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.09995, "memory": 15990, "data_time": 1.40276, "top1_acc": 0.1525, "top5_acc": 0.36766, "loss_cls": 4.72782, "loss": 4.72782, "time": 2.12176} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.09995, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.15469, "top5_acc": 0.36672, "loss_cls": 4.72018, "loss": 4.72018, "time": 0.71597} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.09995, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.16094, "top5_acc": 0.37422, "loss_cls": 4.71512, "loss": 4.71512, "time": 0.71206} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.09995, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.15484, "top5_acc": 0.36969, "loss_cls": 4.72026, "loss": 4.72026, "time": 0.71706} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.09995, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.15781, "top5_acc": 0.37047, "loss_cls": 4.72481, "loss": 4.72481, "time": 0.71318} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.09995, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.14859, "top5_acc": 0.35844, "loss_cls": 4.75288, "loss": 4.75288, "time": 0.71379} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.09995, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.15563, "top5_acc": 0.36266, "loss_cls": 4.74612, "loss": 4.74612, "time": 0.71664} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.09995, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.16094, "top5_acc": 0.36891, "loss_cls": 4.71108, "loss": 4.71108, "time": 0.71462} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.09994, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.1575, "top5_acc": 0.38031, "loss_cls": 4.68088, "loss": 4.68088, "time": 0.71792} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.09994, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.15563, "top5_acc": 0.36578, "loss_cls": 4.71285, "loss": 4.71285, "time": 0.71502} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.09994, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.16547, "top5_acc": 0.37891, "loss_cls": 4.70242, "loss": 4.70242, "time": 0.71848} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.09994, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.15734, "top5_acc": 0.37297, "loss_cls": 4.71012, "loss": 4.71012, "time": 0.71811} +{"mode": "train", "epoch": 3, "iter": 1300, "lr": 0.09994, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.15875, "top5_acc": 0.37344, "loss_cls": 4.71483, "loss": 4.71483, "time": 0.72024} +{"mode": "train", "epoch": 3, "iter": 1400, "lr": 0.09994, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.16344, "top5_acc": 0.38328, "loss_cls": 4.66591, "loss": 4.66591, "time": 0.71708} +{"mode": "train", "epoch": 3, "iter": 1500, "lr": 0.09994, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.16375, "top5_acc": 0.38422, "loss_cls": 4.66843, "loss": 4.66843, "time": 0.72043} +{"mode": "train", "epoch": 3, "iter": 1600, "lr": 0.09994, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.17359, "top5_acc": 0.38953, "loss_cls": 4.66515, "loss": 4.66515, "time": 0.71826} +{"mode": "train", "epoch": 3, "iter": 1700, "lr": 0.09993, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.16734, "top5_acc": 0.39188, "loss_cls": 4.64388, "loss": 4.64388, "time": 0.71472} +{"mode": "train", "epoch": 3, "iter": 1800, "lr": 0.09993, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.16891, "top5_acc": 0.39219, "loss_cls": 4.63569, "loss": 4.63569, "time": 0.72232} +{"mode": "train", "epoch": 3, "iter": 1900, "lr": 0.09993, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.17656, "top5_acc": 0.39859, "loss_cls": 4.59822, "loss": 4.59822, "time": 0.71972} +{"mode": "train", "epoch": 3, "iter": 2000, "lr": 0.09993, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.17719, "top5_acc": 0.40219, "loss_cls": 4.60159, "loss": 4.60159, "time": 0.72125} +{"mode": "train", "epoch": 3, "iter": 2100, "lr": 0.09993, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.17078, "top5_acc": 0.39641, "loss_cls": 4.60996, "loss": 4.60996, "time": 0.72371} +{"mode": "train", "epoch": 3, "iter": 2200, "lr": 0.09993, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.18375, "top5_acc": 0.39328, "loss_cls": 4.61607, "loss": 4.61607, "time": 0.72187} +{"mode": "train", "epoch": 3, "iter": 2300, "lr": 0.09993, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.17469, "top5_acc": 0.40281, "loss_cls": 4.57383, "loss": 4.57383, "time": 0.71902} +{"mode": "train", "epoch": 3, "iter": 2400, "lr": 0.09992, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.18484, "top5_acc": 0.41312, "loss_cls": 4.55827, "loss": 4.55827, "time": 0.71687} +{"mode": "train", "epoch": 3, "iter": 2500, "lr": 0.09992, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.17938, "top5_acc": 0.39938, "loss_cls": 4.59372, "loss": 4.59372, "time": 0.71912} +{"mode": "train", "epoch": 3, "iter": 2600, "lr": 0.09992, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.18641, "top5_acc": 0.40578, "loss_cls": 4.57474, "loss": 4.57474, "time": 0.71844} +{"mode": "train", "epoch": 3, "iter": 2700, "lr": 0.09992, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.17781, "top5_acc": 0.40094, "loss_cls": 4.57595, "loss": 4.57595, "time": 0.72141} +{"mode": "train", "epoch": 3, "iter": 2800, "lr": 0.09992, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.17234, "top5_acc": 0.40312, "loss_cls": 4.5964, "loss": 4.5964, "time": 0.72191} +{"mode": "train", "epoch": 3, "iter": 2900, "lr": 0.09992, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.17797, "top5_acc": 0.41047, "loss_cls": 4.54097, "loss": 4.54097, "time": 0.72103} +{"mode": "train", "epoch": 3, "iter": 3000, "lr": 0.09991, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.17797, "top5_acc": 0.40328, "loss_cls": 4.55038, "loss": 4.55038, "time": 0.71827} +{"mode": "train", "epoch": 3, "iter": 3100, "lr": 0.09991, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.195, "top5_acc": 0.41969, "loss_cls": 4.48086, "loss": 4.48086, "time": 0.7195} +{"mode": "train", "epoch": 3, "iter": 3200, "lr": 0.09991, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.17875, "top5_acc": 0.40297, "loss_cls": 4.57872, "loss": 4.57872, "time": 0.72195} +{"mode": "train", "epoch": 3, "iter": 3300, "lr": 0.09991, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.19156, "top5_acc": 0.41984, "loss_cls": 4.49913, "loss": 4.49913, "time": 0.7197} +{"mode": "train", "epoch": 3, "iter": 3400, "lr": 0.09991, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.18016, "top5_acc": 0.41219, "loss_cls": 4.56871, "loss": 4.56871, "time": 0.71838} +{"mode": "train", "epoch": 3, "iter": 3500, "lr": 0.09991, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.18547, "top5_acc": 0.41672, "loss_cls": 4.53095, "loss": 4.53095, "time": 0.71628} +{"mode": "train", "epoch": 3, "iter": 3600, "lr": 0.0999, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.18188, "top5_acc": 0.40625, "loss_cls": 4.56035, "loss": 4.56035, "time": 0.71595} +{"mode": "train", "epoch": 3, "iter": 3700, "lr": 0.0999, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.18875, "top5_acc": 0.41406, "loss_cls": 4.52317, "loss": 4.52317, "time": 0.71888} +{"mode": "val", "epoch": 3, "iter": 309, "lr": 0.0999, "top1_acc": 0.11589, "top5_acc": 0.2997, "mean_class_accuracy": 0.11589} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.0999, "memory": 15990, "data_time": 1.40461, "top1_acc": 0.18656, "top5_acc": 0.42438, "loss_cls": 4.50414, "loss": 4.50414, "time": 2.12475} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.0999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18859, "top5_acc": 0.41578, "loss_cls": 4.52817, "loss": 4.52817, "time": 0.71759} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.0999, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19844, "top5_acc": 0.42734, "loss_cls": 4.47973, "loss": 4.47973, "time": 0.71378} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.09989, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19156, "top5_acc": 0.43031, "loss_cls": 4.4738, "loss": 4.4738, "time": 0.71079} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.09989, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19766, "top5_acc": 0.41859, "loss_cls": 4.50266, "loss": 4.50266, "time": 0.71386} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.09989, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19422, "top5_acc": 0.42125, "loss_cls": 4.49233, "loss": 4.49233, "time": 0.71758} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.09989, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19625, "top5_acc": 0.41906, "loss_cls": 4.48518, "loss": 4.48518, "time": 0.71628} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.09989, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19547, "top5_acc": 0.42094, "loss_cls": 4.51586, "loss": 4.51586, "time": 0.71124} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.09988, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18875, "top5_acc": 0.42156, "loss_cls": 4.48376, "loss": 4.48376, "time": 0.71348} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.09988, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19422, "top5_acc": 0.42422, "loss_cls": 4.47499, "loss": 4.47499, "time": 0.71336} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.09988, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20219, "top5_acc": 0.42719, "loss_cls": 4.47423, "loss": 4.47423, "time": 0.71458} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.09988, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21359, "top5_acc": 0.44578, "loss_cls": 4.41496, "loss": 4.41496, "time": 0.71329} +{"mode": "train", "epoch": 4, "iter": 1300, "lr": 0.09988, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.18375, "top5_acc": 0.42078, "loss_cls": 4.52742, "loss": 4.52742, "time": 0.71298} +{"mode": "train", "epoch": 4, "iter": 1400, "lr": 0.09988, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19438, "top5_acc": 0.42953, "loss_cls": 4.45879, "loss": 4.45879, "time": 0.71269} +{"mode": "train", "epoch": 4, "iter": 1500, "lr": 0.09987, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.18234, "top5_acc": 0.41812, "loss_cls": 4.51413, "loss": 4.51413, "time": 0.7152} +{"mode": "train", "epoch": 4, "iter": 1600, "lr": 0.09987, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20906, "top5_acc": 0.43562, "loss_cls": 4.4466, "loss": 4.4466, "time": 0.713} +{"mode": "train", "epoch": 4, "iter": 1700, "lr": 0.09987, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19266, "top5_acc": 0.41906, "loss_cls": 4.48928, "loss": 4.48928, "time": 0.71757} +{"mode": "train", "epoch": 4, "iter": 1800, "lr": 0.09987, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20922, "top5_acc": 0.43891, "loss_cls": 4.41648, "loss": 4.41648, "time": 0.71709} +{"mode": "train", "epoch": 4, "iter": 1900, "lr": 0.09987, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.19875, "top5_acc": 0.43312, "loss_cls": 4.43211, "loss": 4.43211, "time": 0.71937} +{"mode": "train", "epoch": 4, "iter": 2000, "lr": 0.09986, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.19453, "top5_acc": 0.42875, "loss_cls": 4.48002, "loss": 4.48002, "time": 0.7234} +{"mode": "train", "epoch": 4, "iter": 2100, "lr": 0.09986, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20328, "top5_acc": 0.44125, "loss_cls": 4.42965, "loss": 4.42965, "time": 0.71868} +{"mode": "train", "epoch": 4, "iter": 2200, "lr": 0.09986, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20203, "top5_acc": 0.425, "loss_cls": 4.46544, "loss": 4.46544, "time": 0.71904} +{"mode": "train", "epoch": 4, "iter": 2300, "lr": 0.09986, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.19703, "top5_acc": 0.43109, "loss_cls": 4.42917, "loss": 4.42917, "time": 0.71691} +{"mode": "train", "epoch": 4, "iter": 2400, "lr": 0.09985, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20203, "top5_acc": 0.42609, "loss_cls": 4.42284, "loss": 4.42284, "time": 0.71758} +{"mode": "train", "epoch": 4, "iter": 2500, "lr": 0.09985, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.19781, "top5_acc": 0.43, "loss_cls": 4.43206, "loss": 4.43206, "time": 0.72127} +{"mode": "train", "epoch": 4, "iter": 2600, "lr": 0.09985, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20094, "top5_acc": 0.42125, "loss_cls": 4.48383, "loss": 4.48383, "time": 0.71982} +{"mode": "train", "epoch": 4, "iter": 2700, "lr": 0.09985, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.19656, "top5_acc": 0.42953, "loss_cls": 4.45358, "loss": 4.45358, "time": 0.72318} +{"mode": "train", "epoch": 4, "iter": 2800, "lr": 0.09985, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20984, "top5_acc": 0.43391, "loss_cls": 4.43119, "loss": 4.43119, "time": 0.721} +{"mode": "train", "epoch": 4, "iter": 2900, "lr": 0.09984, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.19672, "top5_acc": 0.43344, "loss_cls": 4.4614, "loss": 4.4614, "time": 0.71983} +{"mode": "train", "epoch": 4, "iter": 3000, "lr": 0.09984, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.19797, "top5_acc": 0.43391, "loss_cls": 4.44117, "loss": 4.44117, "time": 0.72176} +{"mode": "train", "epoch": 4, "iter": 3100, "lr": 0.09984, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20031, "top5_acc": 0.43984, "loss_cls": 4.42603, "loss": 4.42603, "time": 0.71959} +{"mode": "train", "epoch": 4, "iter": 3200, "lr": 0.09984, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20859, "top5_acc": 0.43859, "loss_cls": 4.41373, "loss": 4.41373, "time": 0.71965} +{"mode": "train", "epoch": 4, "iter": 3300, "lr": 0.09983, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.19453, "top5_acc": 0.43109, "loss_cls": 4.47466, "loss": 4.47466, "time": 0.71804} +{"mode": "train", "epoch": 4, "iter": 3400, "lr": 0.09983, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20672, "top5_acc": 0.43516, "loss_cls": 4.42271, "loss": 4.42271, "time": 0.71559} +{"mode": "train", "epoch": 4, "iter": 3500, "lr": 0.09983, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.22094, "top5_acc": 0.4425, "loss_cls": 4.36533, "loss": 4.36533, "time": 0.71492} +{"mode": "train", "epoch": 4, "iter": 3600, "lr": 0.09983, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.19891, "top5_acc": 0.43719, "loss_cls": 4.45498, "loss": 4.45498, "time": 0.71556} +{"mode": "train", "epoch": 4, "iter": 3700, "lr": 0.09983, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20469, "top5_acc": 0.43844, "loss_cls": 4.40699, "loss": 4.40699, "time": 0.72034} +{"mode": "val", "epoch": 4, "iter": 309, "lr": 0.09982, "top1_acc": 0.12192, "top5_acc": 0.31829, "mean_class_accuracy": 0.12184} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.09982, "memory": 15990, "data_time": 1.43654, "top1_acc": 0.20156, "top5_acc": 0.43766, "loss_cls": 4.41913, "loss": 4.41913, "time": 2.15284} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.09982, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20203, "top5_acc": 0.43938, "loss_cls": 4.42831, "loss": 4.42831, "time": 0.71636} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.09982, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20641, "top5_acc": 0.44719, "loss_cls": 4.38198, "loss": 4.38198, "time": 0.72022} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.09982, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21016, "top5_acc": 0.44516, "loss_cls": 4.38834, "loss": 4.38834, "time": 0.71565} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.09981, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.19734, "top5_acc": 0.42922, "loss_cls": 4.45107, "loss": 4.45107, "time": 0.71744} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.09981, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20812, "top5_acc": 0.44469, "loss_cls": 4.39845, "loss": 4.39845, "time": 0.71629} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.09981, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21812, "top5_acc": 0.46859, "loss_cls": 4.31466, "loss": 4.31466, "time": 0.71588} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.09981, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20844, "top5_acc": 0.44922, "loss_cls": 4.37454, "loss": 4.37454, "time": 0.71581} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.0998, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21203, "top5_acc": 0.44594, "loss_cls": 4.35423, "loss": 4.35423, "time": 0.71408} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.0998, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21203, "top5_acc": 0.44625, "loss_cls": 4.3795, "loss": 4.3795, "time": 0.71495} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.0998, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21047, "top5_acc": 0.45531, "loss_cls": 4.38193, "loss": 4.38193, "time": 0.71436} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.0998, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21797, "top5_acc": 0.44578, "loss_cls": 4.37809, "loss": 4.37809, "time": 0.71415} +{"mode": "train", "epoch": 5, "iter": 1300, "lr": 0.09979, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21641, "top5_acc": 0.45078, "loss_cls": 4.32852, "loss": 4.32852, "time": 0.71793} +{"mode": "train", "epoch": 5, "iter": 1400, "lr": 0.09979, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19922, "top5_acc": 0.43438, "loss_cls": 4.44701, "loss": 4.44701, "time": 0.71893} +{"mode": "train", "epoch": 5, "iter": 1500, "lr": 0.09979, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21172, "top5_acc": 0.44594, "loss_cls": 4.39793, "loss": 4.39793, "time": 0.71888} +{"mode": "train", "epoch": 5, "iter": 1600, "lr": 0.09979, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21156, "top5_acc": 0.44672, "loss_cls": 4.37542, "loss": 4.37542, "time": 0.72081} +{"mode": "train", "epoch": 5, "iter": 1700, "lr": 0.09978, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20891, "top5_acc": 0.44453, "loss_cls": 4.36486, "loss": 4.36486, "time": 0.7181} +{"mode": "train", "epoch": 5, "iter": 1800, "lr": 0.09978, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21062, "top5_acc": 0.44781, "loss_cls": 4.36461, "loss": 4.36461, "time": 0.71633} +{"mode": "train", "epoch": 5, "iter": 1900, "lr": 0.09978, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21438, "top5_acc": 0.45922, "loss_cls": 4.32032, "loss": 4.32032, "time": 0.71958} +{"mode": "train", "epoch": 5, "iter": 2000, "lr": 0.09977, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20594, "top5_acc": 0.45, "loss_cls": 4.35793, "loss": 4.35793, "time": 0.71672} +{"mode": "train", "epoch": 5, "iter": 2100, "lr": 0.09977, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21562, "top5_acc": 0.45406, "loss_cls": 4.34211, "loss": 4.34211, "time": 0.72316} +{"mode": "train", "epoch": 5, "iter": 2200, "lr": 0.09977, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21328, "top5_acc": 0.45672, "loss_cls": 4.34159, "loss": 4.34159, "time": 0.71943} +{"mode": "train", "epoch": 5, "iter": 2300, "lr": 0.09977, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21016, "top5_acc": 0.46172, "loss_cls": 4.33048, "loss": 4.33048, "time": 0.72061} +{"mode": "train", "epoch": 5, "iter": 2400, "lr": 0.09976, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21109, "top5_acc": 0.44234, "loss_cls": 4.3843, "loss": 4.3843, "time": 0.72074} +{"mode": "train", "epoch": 5, "iter": 2500, "lr": 0.09976, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.21953, "top5_acc": 0.45734, "loss_cls": 4.32491, "loss": 4.32491, "time": 0.72327} +{"mode": "train", "epoch": 5, "iter": 2600, "lr": 0.09976, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20875, "top5_acc": 0.45094, "loss_cls": 4.3548, "loss": 4.3548, "time": 0.71752} +{"mode": "train", "epoch": 5, "iter": 2700, "lr": 0.09976, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22328, "top5_acc": 0.45828, "loss_cls": 4.28069, "loss": 4.28069, "time": 0.72169} +{"mode": "train", "epoch": 5, "iter": 2800, "lr": 0.09975, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21281, "top5_acc": 0.45047, "loss_cls": 4.35924, "loss": 4.35924, "time": 0.72404} +{"mode": "train", "epoch": 5, "iter": 2900, "lr": 0.09975, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21297, "top5_acc": 0.45188, "loss_cls": 4.35379, "loss": 4.35379, "time": 0.71906} +{"mode": "train", "epoch": 5, "iter": 3000, "lr": 0.09975, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.20891, "top5_acc": 0.45219, "loss_cls": 4.34871, "loss": 4.34871, "time": 0.72158} +{"mode": "train", "epoch": 5, "iter": 3100, "lr": 0.09974, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20891, "top5_acc": 0.44422, "loss_cls": 4.37626, "loss": 4.37626, "time": 0.72366} +{"mode": "train", "epoch": 5, "iter": 3200, "lr": 0.09974, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21281, "top5_acc": 0.45016, "loss_cls": 4.34245, "loss": 4.34245, "time": 0.71969} +{"mode": "train", "epoch": 5, "iter": 3300, "lr": 0.09974, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.21594, "top5_acc": 0.45344, "loss_cls": 4.34547, "loss": 4.34547, "time": 0.71761} +{"mode": "train", "epoch": 5, "iter": 3400, "lr": 0.09974, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21953, "top5_acc": 0.45438, "loss_cls": 4.36181, "loss": 4.36181, "time": 0.71621} +{"mode": "train", "epoch": 5, "iter": 3500, "lr": 0.09973, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21984, "top5_acc": 0.45531, "loss_cls": 4.3179, "loss": 4.3179, "time": 0.71681} +{"mode": "train", "epoch": 5, "iter": 3600, "lr": 0.09973, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21328, "top5_acc": 0.45125, "loss_cls": 4.35877, "loss": 4.35877, "time": 0.71601} +{"mode": "train", "epoch": 5, "iter": 3700, "lr": 0.09973, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21328, "top5_acc": 0.45203, "loss_cls": 4.32862, "loss": 4.32862, "time": 0.71563} +{"mode": "val", "epoch": 5, "iter": 309, "lr": 0.09973, "top1_acc": 0.16117, "top5_acc": 0.37699, "mean_class_accuracy": 0.16095} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.09972, "memory": 15990, "data_time": 1.47046, "top1_acc": 0.22875, "top5_acc": 0.47234, "loss_cls": 4.26395, "loss": 4.26395, "time": 2.18612} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.09972, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22281, "top5_acc": 0.46062, "loss_cls": 4.30299, "loss": 4.30299, "time": 0.71338} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.09972, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22375, "top5_acc": 0.46141, "loss_cls": 4.29847, "loss": 4.29847, "time": 0.71662} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.09971, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22234, "top5_acc": 0.46875, "loss_cls": 4.28956, "loss": 4.28956, "time": 0.71654} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.09971, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22266, "top5_acc": 0.45406, "loss_cls": 4.33616, "loss": 4.33616, "time": 0.71383} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.09971, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21922, "top5_acc": 0.45969, "loss_cls": 4.33847, "loss": 4.33847, "time": 0.71861} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.09971, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21156, "top5_acc": 0.45438, "loss_cls": 4.35387, "loss": 4.35387, "time": 0.71796} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.0997, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21672, "top5_acc": 0.44984, "loss_cls": 4.33068, "loss": 4.33068, "time": 0.71535} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.0997, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22453, "top5_acc": 0.46641, "loss_cls": 4.2526, "loss": 4.2526, "time": 0.72028} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.0997, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21234, "top5_acc": 0.43984, "loss_cls": 4.38892, "loss": 4.38892, "time": 0.71966} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.09969, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22094, "top5_acc": 0.46672, "loss_cls": 4.30939, "loss": 4.30939, "time": 0.7191} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.09969, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22312, "top5_acc": 0.46719, "loss_cls": 4.29639, "loss": 4.29639, "time": 0.71425} +{"mode": "train", "epoch": 6, "iter": 1300, "lr": 0.09969, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21906, "top5_acc": 0.45891, "loss_cls": 4.2985, "loss": 4.2985, "time": 0.72016} +{"mode": "train", "epoch": 6, "iter": 1400, "lr": 0.09968, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21672, "top5_acc": 0.45953, "loss_cls": 4.34744, "loss": 4.34744, "time": 0.7189} +{"mode": "train", "epoch": 6, "iter": 1500, "lr": 0.09968, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21344, "top5_acc": 0.46031, "loss_cls": 4.33732, "loss": 4.33732, "time": 0.71968} +{"mode": "train", "epoch": 6, "iter": 1600, "lr": 0.09968, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22141, "top5_acc": 0.46859, "loss_cls": 4.28725, "loss": 4.28725, "time": 0.71984} +{"mode": "train", "epoch": 6, "iter": 1700, "lr": 0.09967, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22703, "top5_acc": 0.45703, "loss_cls": 4.30063, "loss": 4.30063, "time": 0.72227} +{"mode": "train", "epoch": 6, "iter": 1800, "lr": 0.09967, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22078, "top5_acc": 0.45609, "loss_cls": 4.32373, "loss": 4.32373, "time": 0.72078} +{"mode": "train", "epoch": 6, "iter": 1900, "lr": 0.09967, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23062, "top5_acc": 0.46594, "loss_cls": 4.29092, "loss": 4.29092, "time": 0.72128} +{"mode": "train", "epoch": 6, "iter": 2000, "lr": 0.09966, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23047, "top5_acc": 0.46703, "loss_cls": 4.2645, "loss": 4.2645, "time": 0.72544} +{"mode": "train", "epoch": 6, "iter": 2100, "lr": 0.09966, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.21109, "top5_acc": 0.45375, "loss_cls": 4.33292, "loss": 4.33292, "time": 0.72022} +{"mode": "train", "epoch": 6, "iter": 2200, "lr": 0.09966, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22875, "top5_acc": 0.47062, "loss_cls": 4.28334, "loss": 4.28334, "time": 0.72148} +{"mode": "train", "epoch": 6, "iter": 2300, "lr": 0.09965, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22844, "top5_acc": 0.46516, "loss_cls": 4.29738, "loss": 4.29738, "time": 0.72303} +{"mode": "train", "epoch": 6, "iter": 2400, "lr": 0.09965, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22516, "top5_acc": 0.46828, "loss_cls": 4.26432, "loss": 4.26432, "time": 0.722} +{"mode": "train", "epoch": 6, "iter": 2500, "lr": 0.09965, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22453, "top5_acc": 0.46719, "loss_cls": 4.28851, "loss": 4.28851, "time": 0.72051} +{"mode": "train", "epoch": 6, "iter": 2600, "lr": 0.09964, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.2275, "top5_acc": 0.47109, "loss_cls": 4.29218, "loss": 4.29218, "time": 0.72176} +{"mode": "train", "epoch": 6, "iter": 2700, "lr": 0.09964, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21453, "top5_acc": 0.45547, "loss_cls": 4.32631, "loss": 4.32631, "time": 0.72508} +{"mode": "train", "epoch": 6, "iter": 2800, "lr": 0.09964, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22828, "top5_acc": 0.46047, "loss_cls": 4.29511, "loss": 4.29511, "time": 0.72251} +{"mode": "train", "epoch": 6, "iter": 2900, "lr": 0.09963, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23703, "top5_acc": 0.47406, "loss_cls": 4.25468, "loss": 4.25468, "time": 0.71939} +{"mode": "train", "epoch": 6, "iter": 3000, "lr": 0.09963, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.22172, "top5_acc": 0.45328, "loss_cls": 4.30906, "loss": 4.30906, "time": 0.72168} +{"mode": "train", "epoch": 6, "iter": 3100, "lr": 0.09963, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22641, "top5_acc": 0.46031, "loss_cls": 4.30327, "loss": 4.30327, "time": 0.7278} +{"mode": "train", "epoch": 6, "iter": 3200, "lr": 0.09962, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22312, "top5_acc": 0.46297, "loss_cls": 4.31077, "loss": 4.31077, "time": 0.72176} +{"mode": "train", "epoch": 6, "iter": 3300, "lr": 0.09962, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.21688, "top5_acc": 0.46031, "loss_cls": 4.31418, "loss": 4.31418, "time": 0.72297} +{"mode": "train", "epoch": 6, "iter": 3400, "lr": 0.09962, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22484, "top5_acc": 0.47531, "loss_cls": 4.28397, "loss": 4.28397, "time": 0.71933} +{"mode": "train", "epoch": 6, "iter": 3500, "lr": 0.09961, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21422, "top5_acc": 0.44156, "loss_cls": 4.36595, "loss": 4.36595, "time": 0.71618} +{"mode": "train", "epoch": 6, "iter": 3600, "lr": 0.09961, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2175, "top5_acc": 0.46688, "loss_cls": 4.30761, "loss": 4.30761, "time": 0.71465} +{"mode": "train", "epoch": 6, "iter": 3700, "lr": 0.09961, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21953, "top5_acc": 0.45781, "loss_cls": 4.32405, "loss": 4.32405, "time": 0.71728} +{"mode": "val", "epoch": 6, "iter": 309, "lr": 0.09961, "top1_acc": 0.11822, "top5_acc": 0.31206, "mean_class_accuracy": 0.11813} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0996, "memory": 15990, "data_time": 1.50692, "top1_acc": 0.22516, "top5_acc": 0.47062, "loss_cls": 4.25178, "loss": 4.25178, "time": 2.22132} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0996, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22312, "top5_acc": 0.46219, "loss_cls": 4.28015, "loss": 4.28015, "time": 0.71767} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.0996, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22984, "top5_acc": 0.46031, "loss_cls": 4.28822, "loss": 4.28822, "time": 0.71688} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.09959, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22281, "top5_acc": 0.46516, "loss_cls": 4.28133, "loss": 4.28133, "time": 0.71528} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.09959, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22031, "top5_acc": 0.46938, "loss_cls": 4.28554, "loss": 4.28554, "time": 0.71297} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.09958, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22406, "top5_acc": 0.47594, "loss_cls": 4.25744, "loss": 4.25744, "time": 0.71314} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.09958, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23797, "top5_acc": 0.47312, "loss_cls": 4.26081, "loss": 4.26081, "time": 0.72058} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.09958, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23188, "top5_acc": 0.47078, "loss_cls": 4.25183, "loss": 4.25183, "time": 0.71904} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.09957, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22922, "top5_acc": 0.47469, "loss_cls": 4.25479, "loss": 4.25479, "time": 0.71773} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.09957, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22719, "top5_acc": 0.47047, "loss_cls": 4.27894, "loss": 4.27894, "time": 0.71583} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.09957, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22531, "top5_acc": 0.47078, "loss_cls": 4.26584, "loss": 4.26584, "time": 0.71785} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.09956, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22578, "top5_acc": 0.47156, "loss_cls": 4.28119, "loss": 4.28119, "time": 0.7205} +{"mode": "train", "epoch": 7, "iter": 1300, "lr": 0.09956, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22453, "top5_acc": 0.47719, "loss_cls": 4.2503, "loss": 4.2503, "time": 0.72199} +{"mode": "train", "epoch": 7, "iter": 1400, "lr": 0.09956, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21984, "top5_acc": 0.46734, "loss_cls": 4.29373, "loss": 4.29373, "time": 0.72089} +{"mode": "train", "epoch": 7, "iter": 1500, "lr": 0.09955, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22141, "top5_acc": 0.46125, "loss_cls": 4.29163, "loss": 4.29163, "time": 0.7229} +{"mode": "train", "epoch": 7, "iter": 1600, "lr": 0.09955, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22188, "top5_acc": 0.46656, "loss_cls": 4.24817, "loss": 4.24817, "time": 0.71899} +{"mode": "train", "epoch": 7, "iter": 1700, "lr": 0.09954, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22641, "top5_acc": 0.46531, "loss_cls": 4.26614, "loss": 4.26614, "time": 0.72265} +{"mode": "train", "epoch": 7, "iter": 1800, "lr": 0.09954, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23016, "top5_acc": 0.46859, "loss_cls": 4.27589, "loss": 4.27589, "time": 0.71714} +{"mode": "train", "epoch": 7, "iter": 1900, "lr": 0.09954, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22984, "top5_acc": 0.46469, "loss_cls": 4.26711, "loss": 4.26711, "time": 0.71938} +{"mode": "train", "epoch": 7, "iter": 2000, "lr": 0.09953, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22859, "top5_acc": 0.46609, "loss_cls": 4.28818, "loss": 4.28818, "time": 0.72489} +{"mode": "train", "epoch": 7, "iter": 2100, "lr": 0.09953, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22406, "top5_acc": 0.46562, "loss_cls": 4.29049, "loss": 4.29049, "time": 0.71909} +{"mode": "train", "epoch": 7, "iter": 2200, "lr": 0.09952, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23141, "top5_acc": 0.46391, "loss_cls": 4.2559, "loss": 4.2559, "time": 0.71773} +{"mode": "train", "epoch": 7, "iter": 2300, "lr": 0.09952, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.23, "top5_acc": 0.46594, "loss_cls": 4.25337, "loss": 4.25337, "time": 0.72132} +{"mode": "train", "epoch": 7, "iter": 2400, "lr": 0.09952, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22531, "top5_acc": 0.46719, "loss_cls": 4.28571, "loss": 4.28571, "time": 0.72409} +{"mode": "train", "epoch": 7, "iter": 2500, "lr": 0.09951, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22156, "top5_acc": 0.46375, "loss_cls": 4.29262, "loss": 4.29262, "time": 0.71876} +{"mode": "train", "epoch": 7, "iter": 2600, "lr": 0.09951, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22203, "top5_acc": 0.47844, "loss_cls": 4.245, "loss": 4.245, "time": 0.72193} +{"mode": "train", "epoch": 7, "iter": 2700, "lr": 0.09951, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23859, "top5_acc": 0.48, "loss_cls": 4.21296, "loss": 4.21296, "time": 0.72253} +{"mode": "train", "epoch": 7, "iter": 2800, "lr": 0.0995, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23172, "top5_acc": 0.47359, "loss_cls": 4.23966, "loss": 4.23966, "time": 0.72344} +{"mode": "train", "epoch": 7, "iter": 2900, "lr": 0.0995, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.23938, "top5_acc": 0.47734, "loss_cls": 4.22765, "loss": 4.22765, "time": 0.72249} +{"mode": "train", "epoch": 7, "iter": 3000, "lr": 0.09949, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22672, "top5_acc": 0.46781, "loss_cls": 4.27402, "loss": 4.27402, "time": 0.7216} +{"mode": "train", "epoch": 7, "iter": 3100, "lr": 0.09949, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22609, "top5_acc": 0.46969, "loss_cls": 4.26505, "loss": 4.26505, "time": 0.7233} +{"mode": "train", "epoch": 7, "iter": 3200, "lr": 0.09949, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.22688, "top5_acc": 0.46359, "loss_cls": 4.25421, "loss": 4.25421, "time": 0.72382} +{"mode": "train", "epoch": 7, "iter": 3300, "lr": 0.09948, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2325, "top5_acc": 0.46328, "loss_cls": 4.26329, "loss": 4.26329, "time": 0.71964} +{"mode": "train", "epoch": 7, "iter": 3400, "lr": 0.09948, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21641, "top5_acc": 0.46656, "loss_cls": 4.29749, "loss": 4.29749, "time": 0.71766} +{"mode": "train", "epoch": 7, "iter": 3500, "lr": 0.09947, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23547, "top5_acc": 0.47469, "loss_cls": 4.26537, "loss": 4.26537, "time": 0.71593} +{"mode": "train", "epoch": 7, "iter": 3600, "lr": 0.09947, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.22172, "top5_acc": 0.46672, "loss_cls": 4.29113, "loss": 4.29113, "time": 0.71938} +{"mode": "train", "epoch": 7, "iter": 3700, "lr": 0.09947, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2375, "top5_acc": 0.48172, "loss_cls": 4.21213, "loss": 4.21213, "time": 0.71705} +{"mode": "val", "epoch": 7, "iter": 309, "lr": 0.09946, "top1_acc": 0.16198, "top5_acc": 0.37715, "mean_class_accuracy": 0.16178} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.09946, "memory": 15990, "data_time": 1.47078, "top1_acc": 0.23562, "top5_acc": 0.475, "loss_cls": 4.22612, "loss": 4.22612, "time": 2.18638} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.09946, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23078, "top5_acc": 0.48203, "loss_cls": 4.21813, "loss": 4.21813, "time": 0.71211} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.09945, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22719, "top5_acc": 0.46828, "loss_cls": 4.2423, "loss": 4.2423, "time": 0.71895} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.09945, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22312, "top5_acc": 0.4675, "loss_cls": 4.28465, "loss": 4.28465, "time": 0.7128} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.09944, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23125, "top5_acc": 0.47938, "loss_cls": 4.19628, "loss": 4.19628, "time": 0.7149} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.09944, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22422, "top5_acc": 0.46469, "loss_cls": 4.2572, "loss": 4.2572, "time": 0.7157} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.09943, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23938, "top5_acc": 0.47922, "loss_cls": 4.21051, "loss": 4.21051, "time": 0.71554} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.09943, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23266, "top5_acc": 0.48766, "loss_cls": 4.21343, "loss": 4.21343, "time": 0.71603} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.09943, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23562, "top5_acc": 0.48188, "loss_cls": 4.23063, "loss": 4.23063, "time": 0.71517} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.09942, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22938, "top5_acc": 0.47203, "loss_cls": 4.26086, "loss": 4.26086, "time": 0.71497} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.09942, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23438, "top5_acc": 0.46906, "loss_cls": 4.25095, "loss": 4.25095, "time": 0.71509} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.09941, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23547, "top5_acc": 0.48391, "loss_cls": 4.2132, "loss": 4.2132, "time": 0.71924} +{"mode": "train", "epoch": 8, "iter": 1300, "lr": 0.09941, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23891, "top5_acc": 0.48297, "loss_cls": 4.17965, "loss": 4.17965, "time": 0.71721} +{"mode": "train", "epoch": 8, "iter": 1400, "lr": 0.0994, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24203, "top5_acc": 0.48328, "loss_cls": 4.20152, "loss": 4.20152, "time": 0.71743} +{"mode": "train", "epoch": 8, "iter": 1500, "lr": 0.0994, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23422, "top5_acc": 0.48297, "loss_cls": 4.20212, "loss": 4.20212, "time": 0.71954} +{"mode": "train", "epoch": 8, "iter": 1600, "lr": 0.0994, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22938, "top5_acc": 0.46766, "loss_cls": 4.25576, "loss": 4.25576, "time": 0.72342} +{"mode": "train", "epoch": 8, "iter": 1700, "lr": 0.09939, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.23219, "top5_acc": 0.47766, "loss_cls": 4.24213, "loss": 4.24213, "time": 0.7222} +{"mode": "train", "epoch": 8, "iter": 1800, "lr": 0.09939, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23578, "top5_acc": 0.48719, "loss_cls": 4.17756, "loss": 4.17756, "time": 0.72167} +{"mode": "train", "epoch": 8, "iter": 1900, "lr": 0.09938, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23094, "top5_acc": 0.48312, "loss_cls": 4.21285, "loss": 4.21285, "time": 0.71907} +{"mode": "train", "epoch": 8, "iter": 2000, "lr": 0.09938, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.21891, "top5_acc": 0.46703, "loss_cls": 4.2819, "loss": 4.2819, "time": 0.72118} +{"mode": "train", "epoch": 8, "iter": 2100, "lr": 0.09937, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.22984, "top5_acc": 0.47531, "loss_cls": 4.21933, "loss": 4.21933, "time": 0.72054} +{"mode": "train", "epoch": 8, "iter": 2200, "lr": 0.09937, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22266, "top5_acc": 0.46719, "loss_cls": 4.28396, "loss": 4.28396, "time": 0.72014} +{"mode": "train", "epoch": 8, "iter": 2300, "lr": 0.09937, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23422, "top5_acc": 0.47312, "loss_cls": 4.23216, "loss": 4.23216, "time": 0.71855} +{"mode": "train", "epoch": 8, "iter": 2400, "lr": 0.09936, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.22141, "top5_acc": 0.47594, "loss_cls": 4.24235, "loss": 4.24235, "time": 0.71801} +{"mode": "train", "epoch": 8, "iter": 2500, "lr": 0.09936, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23062, "top5_acc": 0.475, "loss_cls": 4.25733, "loss": 4.25733, "time": 0.71999} +{"mode": "train", "epoch": 8, "iter": 2600, "lr": 0.09935, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22344, "top5_acc": 0.47516, "loss_cls": 4.25452, "loss": 4.25452, "time": 0.72061} +{"mode": "train", "epoch": 8, "iter": 2700, "lr": 0.09935, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23141, "top5_acc": 0.46344, "loss_cls": 4.26485, "loss": 4.26485, "time": 0.72262} +{"mode": "train", "epoch": 8, "iter": 2800, "lr": 0.09934, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22781, "top5_acc": 0.465, "loss_cls": 4.24928, "loss": 4.24928, "time": 0.72287} +{"mode": "train", "epoch": 8, "iter": 2900, "lr": 0.09934, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23688, "top5_acc": 0.47031, "loss_cls": 4.24302, "loss": 4.24302, "time": 0.72456} +{"mode": "train", "epoch": 8, "iter": 3000, "lr": 0.09933, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22734, "top5_acc": 0.47, "loss_cls": 4.232, "loss": 4.232, "time": 0.71986} +{"mode": "train", "epoch": 8, "iter": 3100, "lr": 0.09933, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23469, "top5_acc": 0.46703, "loss_cls": 4.25637, "loss": 4.25637, "time": 0.72347} +{"mode": "train", "epoch": 8, "iter": 3200, "lr": 0.09933, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24031, "top5_acc": 0.47078, "loss_cls": 4.26828, "loss": 4.26828, "time": 0.71978} +{"mode": "train", "epoch": 8, "iter": 3300, "lr": 0.09932, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23938, "top5_acc": 0.48734, "loss_cls": 4.19192, "loss": 4.19192, "time": 0.7205} +{"mode": "train", "epoch": 8, "iter": 3400, "lr": 0.09932, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22531, "top5_acc": 0.46719, "loss_cls": 4.24481, "loss": 4.24481, "time": 0.7162} +{"mode": "train", "epoch": 8, "iter": 3500, "lr": 0.09931, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22766, "top5_acc": 0.48078, "loss_cls": 4.22734, "loss": 4.22734, "time": 0.71475} +{"mode": "train", "epoch": 8, "iter": 3600, "lr": 0.09931, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23641, "top5_acc": 0.47734, "loss_cls": 4.24696, "loss": 4.24696, "time": 0.71544} +{"mode": "train", "epoch": 8, "iter": 3700, "lr": 0.0993, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23109, "top5_acc": 0.47078, "loss_cls": 4.2442, "loss": 4.2442, "time": 0.71476} +{"mode": "val", "epoch": 8, "iter": 309, "lr": 0.0993, "top1_acc": 0.16811, "top5_acc": 0.38753, "mean_class_accuracy": 0.16831} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.0993, "memory": 15990, "data_time": 1.46363, "top1_acc": 0.24312, "top5_acc": 0.49031, "loss_cls": 4.17515, "loss": 4.17515, "time": 2.1782} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.09929, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23188, "top5_acc": 0.47875, "loss_cls": 4.21047, "loss": 4.21047, "time": 0.71066} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.09929, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23688, "top5_acc": 0.47344, "loss_cls": 4.23304, "loss": 4.23304, "time": 0.71547} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.09928, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23734, "top5_acc": 0.47797, "loss_cls": 4.22325, "loss": 4.22325, "time": 0.71675} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.09928, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23172, "top5_acc": 0.48375, "loss_cls": 4.18652, "loss": 4.18652, "time": 0.71645} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.09927, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23656, "top5_acc": 0.47906, "loss_cls": 4.20604, "loss": 4.20604, "time": 0.71621} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.09927, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23766, "top5_acc": 0.48438, "loss_cls": 4.20255, "loss": 4.20255, "time": 0.71592} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.09926, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23078, "top5_acc": 0.47422, "loss_cls": 4.23355, "loss": 4.23355, "time": 0.71585} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.09926, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23875, "top5_acc": 0.48312, "loss_cls": 4.19427, "loss": 4.19427, "time": 0.71579} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.09925, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.23375, "top5_acc": 0.47859, "loss_cls": 4.20957, "loss": 4.20957, "time": 0.71584} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.09925, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23031, "top5_acc": 0.47828, "loss_cls": 4.23252, "loss": 4.23252, "time": 0.71441} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.09924, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22484, "top5_acc": 0.46812, "loss_cls": 4.25197, "loss": 4.25197, "time": 0.71493} +{"mode": "train", "epoch": 9, "iter": 1300, "lr": 0.09924, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22688, "top5_acc": 0.47109, "loss_cls": 4.22794, "loss": 4.22794, "time": 0.71104} +{"mode": "train", "epoch": 9, "iter": 1400, "lr": 0.09923, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24625, "top5_acc": 0.48797, "loss_cls": 4.17516, "loss": 4.17516, "time": 0.7117} +{"mode": "train", "epoch": 9, "iter": 1500, "lr": 0.09923, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23594, "top5_acc": 0.47797, "loss_cls": 4.1953, "loss": 4.1953, "time": 0.71777} +{"mode": "train", "epoch": 9, "iter": 1600, "lr": 0.09922, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.235, "top5_acc": 0.47828, "loss_cls": 4.24422, "loss": 4.24422, "time": 0.71774} +{"mode": "train", "epoch": 9, "iter": 1700, "lr": 0.09922, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.24312, "top5_acc": 0.47953, "loss_cls": 4.18451, "loss": 4.18451, "time": 0.71931} +{"mode": "train", "epoch": 9, "iter": 1800, "lr": 0.09921, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23547, "top5_acc": 0.47859, "loss_cls": 4.20822, "loss": 4.20822, "time": 0.71794} +{"mode": "train", "epoch": 9, "iter": 1900, "lr": 0.09921, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24, "top5_acc": 0.4975, "loss_cls": 4.14697, "loss": 4.14697, "time": 0.72159} +{"mode": "train", "epoch": 9, "iter": 2000, "lr": 0.0992, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23625, "top5_acc": 0.47703, "loss_cls": 4.23385, "loss": 4.23385, "time": 0.72248} +{"mode": "train", "epoch": 9, "iter": 2100, "lr": 0.0992, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23516, "top5_acc": 0.48031, "loss_cls": 4.23302, "loss": 4.23302, "time": 0.72197} +{"mode": "train", "epoch": 9, "iter": 2200, "lr": 0.09919, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22938, "top5_acc": 0.46609, "loss_cls": 4.23416, "loss": 4.23416, "time": 0.72096} +{"mode": "train", "epoch": 9, "iter": 2300, "lr": 0.09919, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24156, "top5_acc": 0.4825, "loss_cls": 4.21351, "loss": 4.21351, "time": 0.72314} +{"mode": "train", "epoch": 9, "iter": 2400, "lr": 0.09918, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.22516, "top5_acc": 0.47609, "loss_cls": 4.24988, "loss": 4.24988, "time": 0.71934} +{"mode": "train", "epoch": 9, "iter": 2500, "lr": 0.09918, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.24172, "top5_acc": 0.48625, "loss_cls": 4.18609, "loss": 4.18609, "time": 0.71978} +{"mode": "train", "epoch": 9, "iter": 2600, "lr": 0.09917, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23516, "top5_acc": 0.48922, "loss_cls": 4.18203, "loss": 4.18203, "time": 0.72231} +{"mode": "train", "epoch": 9, "iter": 2700, "lr": 0.09917, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22906, "top5_acc": 0.48375, "loss_cls": 4.23179, "loss": 4.23179, "time": 0.72357} +{"mode": "train", "epoch": 9, "iter": 2800, "lr": 0.09916, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23266, "top5_acc": 0.47844, "loss_cls": 4.22554, "loss": 4.22554, "time": 0.71856} +{"mode": "train", "epoch": 9, "iter": 2900, "lr": 0.09916, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23672, "top5_acc": 0.47625, "loss_cls": 4.2388, "loss": 4.2388, "time": 0.71986} +{"mode": "train", "epoch": 9, "iter": 3000, "lr": 0.09915, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23922, "top5_acc": 0.49094, "loss_cls": 4.18139, "loss": 4.18139, "time": 0.71935} +{"mode": "train", "epoch": 9, "iter": 3100, "lr": 0.09915, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23734, "top5_acc": 0.48719, "loss_cls": 4.2087, "loss": 4.2087, "time": 0.71913} +{"mode": "train", "epoch": 9, "iter": 3200, "lr": 0.09914, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24031, "top5_acc": 0.47688, "loss_cls": 4.21138, "loss": 4.21138, "time": 0.72362} +{"mode": "train", "epoch": 9, "iter": 3300, "lr": 0.09914, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23688, "top5_acc": 0.47812, "loss_cls": 4.22079, "loss": 4.22079, "time": 0.71927} +{"mode": "train", "epoch": 9, "iter": 3400, "lr": 0.09913, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24234, "top5_acc": 0.48922, "loss_cls": 4.18723, "loss": 4.18723, "time": 0.71874} +{"mode": "train", "epoch": 9, "iter": 3500, "lr": 0.09913, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23578, "top5_acc": 0.47922, "loss_cls": 4.24221, "loss": 4.24221, "time": 0.71345} +{"mode": "train", "epoch": 9, "iter": 3600, "lr": 0.09912, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22484, "top5_acc": 0.47422, "loss_cls": 4.21605, "loss": 4.21605, "time": 0.71751} +{"mode": "train", "epoch": 9, "iter": 3700, "lr": 0.09912, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24109, "top5_acc": 0.49047, "loss_cls": 4.16229, "loss": 4.16229, "time": 0.71779} +{"mode": "val", "epoch": 9, "iter": 309, "lr": 0.09911, "top1_acc": 0.16082, "top5_acc": 0.38186, "mean_class_accuracy": 0.16072} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.09911, "memory": 15990, "data_time": 1.47826, "top1_acc": 0.24922, "top5_acc": 0.49547, "loss_cls": 4.14369, "loss": 4.14369, "time": 2.19384} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.0991, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24922, "top5_acc": 0.49016, "loss_cls": 4.16921, "loss": 4.16921, "time": 0.71743} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.0991, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23109, "top5_acc": 0.48047, "loss_cls": 4.20195, "loss": 4.20195, "time": 0.71375} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.09909, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23453, "top5_acc": 0.47781, "loss_cls": 4.16472, "loss": 4.16472, "time": 0.71319} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.09909, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24547, "top5_acc": 0.47875, "loss_cls": 4.17033, "loss": 4.17033, "time": 0.71526} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.09908, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23391, "top5_acc": 0.48391, "loss_cls": 4.19111, "loss": 4.19111, "time": 0.71295} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.09908, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23875, "top5_acc": 0.46953, "loss_cls": 4.21116, "loss": 4.21116, "time": 0.7136} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.09907, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24031, "top5_acc": 0.48219, "loss_cls": 4.18593, "loss": 4.18593, "time": 0.71304} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.09907, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23875, "top5_acc": 0.49188, "loss_cls": 4.15095, "loss": 4.15095, "time": 0.71231} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.09906, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23109, "top5_acc": 0.47781, "loss_cls": 4.20445, "loss": 4.20445, "time": 0.71113} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.09906, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25, "top5_acc": 0.48359, "loss_cls": 4.19076, "loss": 4.19076, "time": 0.71267} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.09905, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23453, "top5_acc": 0.47781, "loss_cls": 4.22785, "loss": 4.22785, "time": 0.7118} +{"mode": "train", "epoch": 10, "iter": 1300, "lr": 0.09905, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23719, "top5_acc": 0.48219, "loss_cls": 4.20087, "loss": 4.20087, "time": 0.71575} +{"mode": "train", "epoch": 10, "iter": 1400, "lr": 0.09904, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24062, "top5_acc": 0.48344, "loss_cls": 4.16357, "loss": 4.16357, "time": 0.71316} +{"mode": "train", "epoch": 10, "iter": 1500, "lr": 0.09903, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24156, "top5_acc": 0.49109, "loss_cls": 4.19201, "loss": 4.19201, "time": 0.71399} +{"mode": "train", "epoch": 10, "iter": 1600, "lr": 0.09903, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23969, "top5_acc": 0.48266, "loss_cls": 4.18735, "loss": 4.18735, "time": 0.71517} +{"mode": "train", "epoch": 10, "iter": 1700, "lr": 0.09902, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23859, "top5_acc": 0.49109, "loss_cls": 4.15372, "loss": 4.15372, "time": 0.71762} +{"mode": "train", "epoch": 10, "iter": 1800, "lr": 0.09902, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23578, "top5_acc": 0.47984, "loss_cls": 4.19971, "loss": 4.19971, "time": 0.72007} +{"mode": "train", "epoch": 10, "iter": 1900, "lr": 0.09901, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23766, "top5_acc": 0.48688, "loss_cls": 4.19532, "loss": 4.19532, "time": 0.71994} +{"mode": "train", "epoch": 10, "iter": 2000, "lr": 0.09901, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.23641, "top5_acc": 0.48578, "loss_cls": 4.19942, "loss": 4.19942, "time": 0.7194} +{"mode": "train", "epoch": 10, "iter": 2100, "lr": 0.099, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23469, "top5_acc": 0.48078, "loss_cls": 4.18665, "loss": 4.18665, "time": 0.71855} +{"mode": "train", "epoch": 10, "iter": 2200, "lr": 0.099, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23891, "top5_acc": 0.48516, "loss_cls": 4.1732, "loss": 4.1732, "time": 0.72409} +{"mode": "train", "epoch": 10, "iter": 2300, "lr": 0.09899, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23969, "top5_acc": 0.49078, "loss_cls": 4.18519, "loss": 4.18519, "time": 0.72146} +{"mode": "train", "epoch": 10, "iter": 2400, "lr": 0.09898, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.23906, "top5_acc": 0.49562, "loss_cls": 4.13202, "loss": 4.13202, "time": 0.72084} +{"mode": "train", "epoch": 10, "iter": 2500, "lr": 0.09898, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22969, "top5_acc": 0.48391, "loss_cls": 4.21383, "loss": 4.21383, "time": 0.71839} +{"mode": "train", "epoch": 10, "iter": 2600, "lr": 0.09897, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24266, "top5_acc": 0.48766, "loss_cls": 4.19823, "loss": 4.19823, "time": 0.72104} +{"mode": "train", "epoch": 10, "iter": 2700, "lr": 0.09897, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23078, "top5_acc": 0.47859, "loss_cls": 4.22914, "loss": 4.22914, "time": 0.72179} +{"mode": "train", "epoch": 10, "iter": 2800, "lr": 0.09896, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23156, "top5_acc": 0.48, "loss_cls": 4.23855, "loss": 4.23855, "time": 0.72205} +{"mode": "train", "epoch": 10, "iter": 2900, "lr": 0.09896, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.23219, "top5_acc": 0.48156, "loss_cls": 4.22812, "loss": 4.22812, "time": 0.72007} +{"mode": "train", "epoch": 10, "iter": 3000, "lr": 0.09895, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.22609, "top5_acc": 0.46781, "loss_cls": 4.24779, "loss": 4.24779, "time": 0.72256} +{"mode": "train", "epoch": 10, "iter": 3100, "lr": 0.09894, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24016, "top5_acc": 0.4825, "loss_cls": 4.19819, "loss": 4.19819, "time": 0.71826} +{"mode": "train", "epoch": 10, "iter": 3200, "lr": 0.09894, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.23766, "top5_acc": 0.47484, "loss_cls": 4.20461, "loss": 4.20461, "time": 0.71907} +{"mode": "train", "epoch": 10, "iter": 3300, "lr": 0.09893, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25016, "top5_acc": 0.49516, "loss_cls": 4.1411, "loss": 4.1411, "time": 0.72019} +{"mode": "train", "epoch": 10, "iter": 3400, "lr": 0.09893, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.24922, "top5_acc": 0.49156, "loss_cls": 4.17226, "loss": 4.17226, "time": 0.72051} +{"mode": "train", "epoch": 10, "iter": 3500, "lr": 0.09892, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23812, "top5_acc": 0.48031, "loss_cls": 4.20228, "loss": 4.20228, "time": 0.71518} +{"mode": "train", "epoch": 10, "iter": 3600, "lr": 0.09892, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24734, "top5_acc": 0.48547, "loss_cls": 4.18531, "loss": 4.18531, "time": 0.71687} +{"mode": "train", "epoch": 10, "iter": 3700, "lr": 0.09891, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24312, "top5_acc": 0.48266, "loss_cls": 4.21764, "loss": 4.21764, "time": 0.71614} +{"mode": "val", "epoch": 10, "iter": 309, "lr": 0.09891, "top1_acc": 0.17475, "top5_acc": 0.39898, "mean_class_accuracy": 0.17454} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.0989, "memory": 15990, "data_time": 1.4947, "top1_acc": 0.2475, "top5_acc": 0.48625, "loss_cls": 4.17317, "loss": 4.17317, "time": 2.21495} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.0989, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23281, "top5_acc": 0.48125, "loss_cls": 4.18919, "loss": 4.18919, "time": 0.71818} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.09889, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24219, "top5_acc": 0.49062, "loss_cls": 4.13245, "loss": 4.13245, "time": 0.71679} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.09888, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23469, "top5_acc": 0.48516, "loss_cls": 4.17628, "loss": 4.17628, "time": 0.71446} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.09888, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24281, "top5_acc": 0.4875, "loss_cls": 4.15577, "loss": 4.15577, "time": 0.71709} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.09887, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24266, "top5_acc": 0.49391, "loss_cls": 4.14396, "loss": 4.14396, "time": 0.72047} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.09887, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24016, "top5_acc": 0.47359, "loss_cls": 4.219, "loss": 4.219, "time": 0.7196} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.09886, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23141, "top5_acc": 0.48406, "loss_cls": 4.21473, "loss": 4.21473, "time": 0.72088} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.09885, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.22609, "top5_acc": 0.48234, "loss_cls": 4.22654, "loss": 4.22654, "time": 0.72126} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.09885, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24844, "top5_acc": 0.49438, "loss_cls": 4.14563, "loss": 4.14563, "time": 0.71928} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.09884, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23516, "top5_acc": 0.47953, "loss_cls": 4.22165, "loss": 4.22165, "time": 0.72562} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.09884, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24469, "top5_acc": 0.49016, "loss_cls": 4.15691, "loss": 4.15691, "time": 0.72156} +{"mode": "train", "epoch": 11, "iter": 1300, "lr": 0.09883, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23734, "top5_acc": 0.48734, "loss_cls": 4.17834, "loss": 4.17834, "time": 0.72131} +{"mode": "train", "epoch": 11, "iter": 1400, "lr": 0.09882, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.24562, "top5_acc": 0.48625, "loss_cls": 4.1541, "loss": 4.1541, "time": 0.72073} +{"mode": "train", "epoch": 11, "iter": 1500, "lr": 0.09882, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.23562, "top5_acc": 0.48719, "loss_cls": 4.18266, "loss": 4.18266, "time": 0.72455} +{"mode": "train", "epoch": 11, "iter": 1600, "lr": 0.09881, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24234, "top5_acc": 0.48609, "loss_cls": 4.18791, "loss": 4.18791, "time": 0.72588} +{"mode": "train", "epoch": 11, "iter": 1700, "lr": 0.09881, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24125, "top5_acc": 0.48953, "loss_cls": 4.16963, "loss": 4.16963, "time": 0.72081} +{"mode": "train", "epoch": 11, "iter": 1800, "lr": 0.0988, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24641, "top5_acc": 0.495, "loss_cls": 4.1704, "loss": 4.1704, "time": 0.72303} +{"mode": "train", "epoch": 11, "iter": 1900, "lr": 0.09879, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.24375, "top5_acc": 0.48719, "loss_cls": 4.17131, "loss": 4.17131, "time": 0.72773} +{"mode": "train", "epoch": 11, "iter": 2000, "lr": 0.09879, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.24031, "top5_acc": 0.47719, "loss_cls": 4.20184, "loss": 4.20184, "time": 0.7199} +{"mode": "train", "epoch": 11, "iter": 2100, "lr": 0.09878, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23016, "top5_acc": 0.48312, "loss_cls": 4.1941, "loss": 4.1941, "time": 0.72632} +{"mode": "train", "epoch": 11, "iter": 2200, "lr": 0.09878, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24062, "top5_acc": 0.48688, "loss_cls": 4.19042, "loss": 4.19042, "time": 0.72271} +{"mode": "train", "epoch": 11, "iter": 2300, "lr": 0.09877, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23641, "top5_acc": 0.49688, "loss_cls": 4.18442, "loss": 4.18442, "time": 0.7237} +{"mode": "train", "epoch": 11, "iter": 2400, "lr": 0.09876, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.24281, "top5_acc": 0.48891, "loss_cls": 4.15416, "loss": 4.15416, "time": 0.72391} +{"mode": "train", "epoch": 11, "iter": 2500, "lr": 0.09876, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24594, "top5_acc": 0.48891, "loss_cls": 4.17431, "loss": 4.17431, "time": 0.72042} +{"mode": "train", "epoch": 11, "iter": 2600, "lr": 0.09875, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24938, "top5_acc": 0.49281, "loss_cls": 4.16212, "loss": 4.16212, "time": 0.72196} +{"mode": "train", "epoch": 11, "iter": 2700, "lr": 0.09874, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25047, "top5_acc": 0.50078, "loss_cls": 4.13542, "loss": 4.13542, "time": 0.72468} +{"mode": "train", "epoch": 11, "iter": 2800, "lr": 0.09874, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.23781, "top5_acc": 0.48812, "loss_cls": 4.18873, "loss": 4.18873, "time": 0.72283} +{"mode": "train", "epoch": 11, "iter": 2900, "lr": 0.09873, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.235, "top5_acc": 0.48047, "loss_cls": 4.18878, "loss": 4.18878, "time": 0.72405} +{"mode": "train", "epoch": 11, "iter": 3000, "lr": 0.09873, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24406, "top5_acc": 0.48703, "loss_cls": 4.15821, "loss": 4.15821, "time": 0.72445} +{"mode": "train", "epoch": 11, "iter": 3100, "lr": 0.09872, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25719, "top5_acc": 0.50156, "loss_cls": 4.09478, "loss": 4.09478, "time": 0.72135} +{"mode": "train", "epoch": 11, "iter": 3200, "lr": 0.09871, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.24812, "top5_acc": 0.48562, "loss_cls": 4.19465, "loss": 4.19465, "time": 0.72029} +{"mode": "train", "epoch": 11, "iter": 3300, "lr": 0.09871, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24, "top5_acc": 0.48938, "loss_cls": 4.15524, "loss": 4.15524, "time": 0.72429} +{"mode": "train", "epoch": 11, "iter": 3400, "lr": 0.0987, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23578, "top5_acc": 0.48562, "loss_cls": 4.2124, "loss": 4.2124, "time": 0.71916} +{"mode": "train", "epoch": 11, "iter": 3500, "lr": 0.09869, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24688, "top5_acc": 0.49453, "loss_cls": 4.15291, "loss": 4.15291, "time": 0.72034} +{"mode": "train", "epoch": 11, "iter": 3600, "lr": 0.09869, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23734, "top5_acc": 0.47906, "loss_cls": 4.19943, "loss": 4.19943, "time": 0.71641} +{"mode": "train", "epoch": 11, "iter": 3700, "lr": 0.09868, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25219, "top5_acc": 0.49438, "loss_cls": 4.15463, "loss": 4.15463, "time": 0.71847} +{"mode": "val", "epoch": 11, "iter": 309, "lr": 0.09868, "top1_acc": 0.16401, "top5_acc": 0.38292, "mean_class_accuracy": 0.16368} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.09867, "memory": 15990, "data_time": 1.49906, "top1_acc": 0.2425, "top5_acc": 0.48719, "loss_cls": 4.15491, "loss": 4.15491, "time": 2.22201} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.09867, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24844, "top5_acc": 0.48953, "loss_cls": 4.15834, "loss": 4.15834, "time": 0.72333} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.09866, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25031, "top5_acc": 0.49391, "loss_cls": 4.12301, "loss": 4.12301, "time": 0.72025} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.09865, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25297, "top5_acc": 0.49484, "loss_cls": 4.11794, "loss": 4.11794, "time": 0.71573} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.09865, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24609, "top5_acc": 0.48828, "loss_cls": 4.14772, "loss": 4.14772, "time": 0.71324} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.09864, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24719, "top5_acc": 0.49391, "loss_cls": 4.14812, "loss": 4.14812, "time": 0.71802} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.09863, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24578, "top5_acc": 0.49062, "loss_cls": 4.15825, "loss": 4.15825, "time": 0.72159} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.09863, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24453, "top5_acc": 0.49016, "loss_cls": 4.17795, "loss": 4.17795, "time": 0.72037} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.09862, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24609, "top5_acc": 0.48672, "loss_cls": 4.15913, "loss": 4.15913, "time": 0.7261} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.09861, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23656, "top5_acc": 0.4825, "loss_cls": 4.17502, "loss": 4.17502, "time": 0.71802} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.09861, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23812, "top5_acc": 0.49047, "loss_cls": 4.16847, "loss": 4.16847, "time": 0.72398} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.0986, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24078, "top5_acc": 0.49125, "loss_cls": 4.13716, "loss": 4.13716, "time": 0.7207} +{"mode": "train", "epoch": 12, "iter": 1300, "lr": 0.09859, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24609, "top5_acc": 0.48688, "loss_cls": 4.15525, "loss": 4.15525, "time": 0.72181} +{"mode": "train", "epoch": 12, "iter": 1400, "lr": 0.09859, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.24609, "top5_acc": 0.47844, "loss_cls": 4.19861, "loss": 4.19861, "time": 0.72247} +{"mode": "train", "epoch": 12, "iter": 1500, "lr": 0.09858, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24031, "top5_acc": 0.48422, "loss_cls": 4.1805, "loss": 4.1805, "time": 0.7198} +{"mode": "train", "epoch": 12, "iter": 1600, "lr": 0.09857, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.24562, "top5_acc": 0.48125, "loss_cls": 4.16912, "loss": 4.16912, "time": 0.71926} +{"mode": "train", "epoch": 12, "iter": 1700, "lr": 0.09857, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24516, "top5_acc": 0.49312, "loss_cls": 4.19044, "loss": 4.19044, "time": 0.72376} +{"mode": "train", "epoch": 12, "iter": 1800, "lr": 0.09856, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23781, "top5_acc": 0.49203, "loss_cls": 4.17844, "loss": 4.17844, "time": 0.71953} +{"mode": "train", "epoch": 12, "iter": 1900, "lr": 0.09855, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24859, "top5_acc": 0.49641, "loss_cls": 4.13109, "loss": 4.13109, "time": 0.72384} +{"mode": "train", "epoch": 12, "iter": 2000, "lr": 0.09855, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24047, "top5_acc": 0.49641, "loss_cls": 4.17746, "loss": 4.17746, "time": 0.72509} +{"mode": "train", "epoch": 12, "iter": 2100, "lr": 0.09854, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.24438, "top5_acc": 0.48406, "loss_cls": 4.1836, "loss": 4.1836, "time": 0.7228} +{"mode": "train", "epoch": 12, "iter": 2200, "lr": 0.09853, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.23797, "top5_acc": 0.47062, "loss_cls": 4.22641, "loss": 4.22641, "time": 0.72281} +{"mode": "train", "epoch": 12, "iter": 2300, "lr": 0.09853, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24703, "top5_acc": 0.49484, "loss_cls": 4.14687, "loss": 4.14687, "time": 0.72241} +{"mode": "train", "epoch": 12, "iter": 2400, "lr": 0.09852, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25438, "top5_acc": 0.48969, "loss_cls": 4.17056, "loss": 4.17056, "time": 0.72022} +{"mode": "train", "epoch": 12, "iter": 2500, "lr": 0.09851, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23516, "top5_acc": 0.48562, "loss_cls": 4.18073, "loss": 4.18073, "time": 0.7248} +{"mode": "train", "epoch": 12, "iter": 2600, "lr": 0.09851, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24906, "top5_acc": 0.49781, "loss_cls": 4.13485, "loss": 4.13485, "time": 0.72311} +{"mode": "train", "epoch": 12, "iter": 2700, "lr": 0.0985, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2525, "top5_acc": 0.48922, "loss_cls": 4.13815, "loss": 4.13815, "time": 0.72589} +{"mode": "train", "epoch": 12, "iter": 2800, "lr": 0.09849, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.24312, "top5_acc": 0.48688, "loss_cls": 4.15956, "loss": 4.15956, "time": 0.7181} +{"mode": "train", "epoch": 12, "iter": 2900, "lr": 0.09849, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24953, "top5_acc": 0.49391, "loss_cls": 4.12593, "loss": 4.12593, "time": 0.72303} +{"mode": "train", "epoch": 12, "iter": 3000, "lr": 0.09848, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24438, "top5_acc": 0.48375, "loss_cls": 4.18972, "loss": 4.18972, "time": 0.72291} +{"mode": "train", "epoch": 12, "iter": 3100, "lr": 0.09847, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.23719, "top5_acc": 0.48672, "loss_cls": 4.16706, "loss": 4.16706, "time": 0.72398} +{"mode": "train", "epoch": 12, "iter": 3200, "lr": 0.09847, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.24375, "top5_acc": 0.48625, "loss_cls": 4.16955, "loss": 4.16955, "time": 0.72454} +{"mode": "train", "epoch": 12, "iter": 3300, "lr": 0.09846, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2525, "top5_acc": 0.5, "loss_cls": 4.13432, "loss": 4.13432, "time": 0.72439} +{"mode": "train", "epoch": 12, "iter": 3400, "lr": 0.09845, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24484, "top5_acc": 0.48969, "loss_cls": 4.15749, "loss": 4.15749, "time": 0.71751} +{"mode": "train", "epoch": 12, "iter": 3500, "lr": 0.09845, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24969, "top5_acc": 0.49031, "loss_cls": 4.16626, "loss": 4.16626, "time": 0.71903} +{"mode": "train", "epoch": 12, "iter": 3600, "lr": 0.09844, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24375, "top5_acc": 0.48953, "loss_cls": 4.18923, "loss": 4.18923, "time": 0.71808} +{"mode": "train", "epoch": 12, "iter": 3700, "lr": 0.09843, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2425, "top5_acc": 0.49078, "loss_cls": 4.16099, "loss": 4.16099, "time": 0.7151} +{"mode": "val", "epoch": 12, "iter": 309, "lr": 0.09843, "top1_acc": 0.17794, "top5_acc": 0.39234, "mean_class_accuracy": 0.17792} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.09842, "memory": 15990, "data_time": 1.49681, "top1_acc": 0.24922, "top5_acc": 0.4925, "loss_cls": 4.14004, "loss": 4.14004, "time": 2.21872} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.09842, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24766, "top5_acc": 0.49969, "loss_cls": 4.12423, "loss": 4.12423, "time": 0.7206} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.09841, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24422, "top5_acc": 0.48297, "loss_cls": 4.1856, "loss": 4.1856, "time": 0.72086} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.0984, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24375, "top5_acc": 0.48594, "loss_cls": 4.15862, "loss": 4.15862, "time": 0.71576} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.09839, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24281, "top5_acc": 0.48688, "loss_cls": 4.15095, "loss": 4.15095, "time": 0.7164} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.09839, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24719, "top5_acc": 0.50219, "loss_cls": 4.12818, "loss": 4.12818, "time": 0.7176} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.09838, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.235, "top5_acc": 0.48547, "loss_cls": 4.19291, "loss": 4.19291, "time": 0.72142} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.09837, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24844, "top5_acc": 0.49172, "loss_cls": 4.15892, "loss": 4.15892, "time": 0.72229} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.09837, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24094, "top5_acc": 0.48594, "loss_cls": 4.17762, "loss": 4.17762, "time": 0.7223} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.09836, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.23797, "top5_acc": 0.48672, "loss_cls": 4.17191, "loss": 4.17191, "time": 0.72412} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.09835, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25203, "top5_acc": 0.50672, "loss_cls": 4.11204, "loss": 4.11204, "time": 0.72203} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.09834, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25141, "top5_acc": 0.49719, "loss_cls": 4.14547, "loss": 4.14547, "time": 0.72162} +{"mode": "train", "epoch": 13, "iter": 1300, "lr": 0.09834, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25281, "top5_acc": 0.49156, "loss_cls": 4.17364, "loss": 4.17364, "time": 0.72149} +{"mode": "train", "epoch": 13, "iter": 1400, "lr": 0.09833, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24484, "top5_acc": 0.4975, "loss_cls": 4.1426, "loss": 4.1426, "time": 0.72222} +{"mode": "train", "epoch": 13, "iter": 1500, "lr": 0.09832, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.23875, "top5_acc": 0.48406, "loss_cls": 4.19009, "loss": 4.19009, "time": 0.71901} +{"mode": "train", "epoch": 13, "iter": 1600, "lr": 0.09832, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.25734, "top5_acc": 0.48656, "loss_cls": 4.14357, "loss": 4.14357, "time": 0.72403} +{"mode": "train", "epoch": 13, "iter": 1700, "lr": 0.09831, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2425, "top5_acc": 0.49859, "loss_cls": 4.13974, "loss": 4.13974, "time": 0.72312} +{"mode": "train", "epoch": 13, "iter": 1800, "lr": 0.0983, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25, "top5_acc": 0.49781, "loss_cls": 4.13055, "loss": 4.13055, "time": 0.7214} +{"mode": "train", "epoch": 13, "iter": 1900, "lr": 0.09829, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24328, "top5_acc": 0.48719, "loss_cls": 4.18023, "loss": 4.18023, "time": 0.72093} +{"mode": "train", "epoch": 13, "iter": 2000, "lr": 0.09829, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25266, "top5_acc": 0.49703, "loss_cls": 4.12062, "loss": 4.12062, "time": 0.72002} +{"mode": "train", "epoch": 13, "iter": 2100, "lr": 0.09828, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25203, "top5_acc": 0.49281, "loss_cls": 4.15713, "loss": 4.15713, "time": 0.72308} +{"mode": "train", "epoch": 13, "iter": 2200, "lr": 0.09827, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24703, "top5_acc": 0.49016, "loss_cls": 4.13837, "loss": 4.13837, "time": 0.72678} +{"mode": "train", "epoch": 13, "iter": 2300, "lr": 0.09827, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24359, "top5_acc": 0.495, "loss_cls": 4.12296, "loss": 4.12296, "time": 0.72372} +{"mode": "train", "epoch": 13, "iter": 2400, "lr": 0.09826, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24672, "top5_acc": 0.49422, "loss_cls": 4.14563, "loss": 4.14563, "time": 0.72468} +{"mode": "train", "epoch": 13, "iter": 2500, "lr": 0.09825, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23719, "top5_acc": 0.49, "loss_cls": 4.15885, "loss": 4.15885, "time": 0.72532} +{"mode": "train", "epoch": 13, "iter": 2600, "lr": 0.09824, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24484, "top5_acc": 0.49312, "loss_cls": 4.147, "loss": 4.147, "time": 0.72178} +{"mode": "train", "epoch": 13, "iter": 2700, "lr": 0.09824, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24656, "top5_acc": 0.49656, "loss_cls": 4.1472, "loss": 4.1472, "time": 0.72084} +{"mode": "train", "epoch": 13, "iter": 2800, "lr": 0.09823, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25312, "top5_acc": 0.50328, "loss_cls": 4.13391, "loss": 4.13391, "time": 0.72292} +{"mode": "train", "epoch": 13, "iter": 2900, "lr": 0.09822, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.23719, "top5_acc": 0.47547, "loss_cls": 4.20621, "loss": 4.20621, "time": 0.72219} +{"mode": "train", "epoch": 13, "iter": 3000, "lr": 0.09821, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24609, "top5_acc": 0.485, "loss_cls": 4.16249, "loss": 4.16249, "time": 0.71772} +{"mode": "train", "epoch": 13, "iter": 3100, "lr": 0.09821, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24672, "top5_acc": 0.49125, "loss_cls": 4.14937, "loss": 4.14937, "time": 0.72149} +{"mode": "train", "epoch": 13, "iter": 3200, "lr": 0.0982, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.23594, "top5_acc": 0.48719, "loss_cls": 4.19447, "loss": 4.19447, "time": 0.72304} +{"mode": "train", "epoch": 13, "iter": 3300, "lr": 0.09819, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25672, "top5_acc": 0.50125, "loss_cls": 4.13775, "loss": 4.13775, "time": 0.7184} +{"mode": "train", "epoch": 13, "iter": 3400, "lr": 0.09818, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24969, "top5_acc": 0.48875, "loss_cls": 4.1389, "loss": 4.1389, "time": 0.71713} +{"mode": "train", "epoch": 13, "iter": 3500, "lr": 0.09818, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25562, "top5_acc": 0.49297, "loss_cls": 4.10846, "loss": 4.10846, "time": 0.7174} +{"mode": "train", "epoch": 13, "iter": 3600, "lr": 0.09817, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24609, "top5_acc": 0.49344, "loss_cls": 4.14823, "loss": 4.14823, "time": 0.71605} +{"mode": "train", "epoch": 13, "iter": 3700, "lr": 0.09816, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23781, "top5_acc": 0.47609, "loss_cls": 4.19275, "loss": 4.19275, "time": 0.71442} +{"mode": "val", "epoch": 13, "iter": 309, "lr": 0.09816, "top1_acc": 0.17773, "top5_acc": 0.40197, "mean_class_accuracy": 0.17753} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.09815, "memory": 15990, "data_time": 1.48718, "top1_acc": 0.24984, "top5_acc": 0.49938, "loss_cls": 4.11222, "loss": 4.11222, "time": 2.20654} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.09814, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24531, "top5_acc": 0.48906, "loss_cls": 4.159, "loss": 4.159, "time": 0.71838} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.09814, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.245, "top5_acc": 0.50094, "loss_cls": 4.08808, "loss": 4.08808, "time": 0.72076} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.09813, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24656, "top5_acc": 0.48609, "loss_cls": 4.14085, "loss": 4.14085, "time": 0.71675} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.09812, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25562, "top5_acc": 0.49047, "loss_cls": 4.13166, "loss": 4.13166, "time": 0.71662} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.09811, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24625, "top5_acc": 0.49531, "loss_cls": 4.13709, "loss": 4.13709, "time": 0.71952} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.09811, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24016, "top5_acc": 0.48938, "loss_cls": 4.17068, "loss": 4.17068, "time": 0.724} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.0981, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.2475, "top5_acc": 0.49953, "loss_cls": 4.1236, "loss": 4.1236, "time": 0.72633} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.09809, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.24562, "top5_acc": 0.49047, "loss_cls": 4.14718, "loss": 4.14718, "time": 0.72533} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.09808, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.23859, "top5_acc": 0.48812, "loss_cls": 4.16721, "loss": 4.16721, "time": 0.72422} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.09807, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25094, "top5_acc": 0.49906, "loss_cls": 4.11629, "loss": 4.11629, "time": 0.72107} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.09807, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25312, "top5_acc": 0.50156, "loss_cls": 4.10435, "loss": 4.10435, "time": 0.72283} +{"mode": "train", "epoch": 14, "iter": 1300, "lr": 0.09806, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24156, "top5_acc": 0.48656, "loss_cls": 4.17749, "loss": 4.17749, "time": 0.72157} +{"mode": "train", "epoch": 14, "iter": 1400, "lr": 0.09805, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2475, "top5_acc": 0.50156, "loss_cls": 4.1033, "loss": 4.1033, "time": 0.72257} +{"mode": "train", "epoch": 14, "iter": 1500, "lr": 0.09804, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23562, "top5_acc": 0.48094, "loss_cls": 4.18989, "loss": 4.18989, "time": 0.71982} +{"mode": "train", "epoch": 14, "iter": 1600, "lr": 0.09804, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.2475, "top5_acc": 0.48828, "loss_cls": 4.14813, "loss": 4.14813, "time": 0.71931} +{"mode": "train", "epoch": 14, "iter": 1700, "lr": 0.09803, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24906, "top5_acc": 0.49406, "loss_cls": 4.14089, "loss": 4.14089, "time": 0.72143} +{"mode": "train", "epoch": 14, "iter": 1800, "lr": 0.09802, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24109, "top5_acc": 0.49219, "loss_cls": 4.15613, "loss": 4.15613, "time": 0.72363} +{"mode": "train", "epoch": 14, "iter": 1900, "lr": 0.09801, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.24109, "top5_acc": 0.485, "loss_cls": 4.17627, "loss": 4.17627, "time": 0.71963} +{"mode": "train", "epoch": 14, "iter": 2000, "lr": 0.098, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25156, "top5_acc": 0.49031, "loss_cls": 4.12663, "loss": 4.12663, "time": 0.72253} +{"mode": "train", "epoch": 14, "iter": 2100, "lr": 0.098, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.2425, "top5_acc": 0.48781, "loss_cls": 4.14319, "loss": 4.14319, "time": 0.72369} +{"mode": "train", "epoch": 14, "iter": 2200, "lr": 0.09799, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.24719, "top5_acc": 0.48969, "loss_cls": 4.15909, "loss": 4.15909, "time": 0.7212} +{"mode": "train", "epoch": 14, "iter": 2300, "lr": 0.09798, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24531, "top5_acc": 0.49922, "loss_cls": 4.1096, "loss": 4.1096, "time": 0.72144} +{"mode": "train", "epoch": 14, "iter": 2400, "lr": 0.09797, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25344, "top5_acc": 0.5025, "loss_cls": 4.12077, "loss": 4.12077, "time": 0.72107} +{"mode": "train", "epoch": 14, "iter": 2500, "lr": 0.09797, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24203, "top5_acc": 0.49656, "loss_cls": 4.13026, "loss": 4.13026, "time": 0.72511} +{"mode": "train", "epoch": 14, "iter": 2600, "lr": 0.09796, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25094, "top5_acc": 0.49359, "loss_cls": 4.1228, "loss": 4.1228, "time": 0.72259} +{"mode": "train", "epoch": 14, "iter": 2700, "lr": 0.09795, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24781, "top5_acc": 0.48406, "loss_cls": 4.17217, "loss": 4.17217, "time": 0.71925} +{"mode": "train", "epoch": 14, "iter": 2800, "lr": 0.09794, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24969, "top5_acc": 0.49422, "loss_cls": 4.16234, "loss": 4.16234, "time": 0.72443} +{"mode": "train", "epoch": 14, "iter": 2900, "lr": 0.09793, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25188, "top5_acc": 0.49734, "loss_cls": 4.13471, "loss": 4.13471, "time": 0.72232} +{"mode": "train", "epoch": 14, "iter": 3000, "lr": 0.09793, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24531, "top5_acc": 0.49562, "loss_cls": 4.13732, "loss": 4.13732, "time": 0.72082} +{"mode": "train", "epoch": 14, "iter": 3100, "lr": 0.09792, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24438, "top5_acc": 0.49391, "loss_cls": 4.12686, "loss": 4.12686, "time": 0.72614} +{"mode": "train", "epoch": 14, "iter": 3200, "lr": 0.09791, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.24031, "top5_acc": 0.47984, "loss_cls": 4.20292, "loss": 4.20292, "time": 0.72244} +{"mode": "train", "epoch": 14, "iter": 3300, "lr": 0.0979, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25078, "top5_acc": 0.49344, "loss_cls": 4.12201, "loss": 4.12201, "time": 0.71834} +{"mode": "train", "epoch": 14, "iter": 3400, "lr": 0.09789, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24719, "top5_acc": 0.49078, "loss_cls": 4.15544, "loss": 4.15544, "time": 0.72025} +{"mode": "train", "epoch": 14, "iter": 3500, "lr": 0.09789, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24828, "top5_acc": 0.48562, "loss_cls": 4.2011, "loss": 4.2011, "time": 0.72009} +{"mode": "train", "epoch": 14, "iter": 3600, "lr": 0.09788, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25672, "top5_acc": 0.49406, "loss_cls": 4.13533, "loss": 4.13533, "time": 0.71768} +{"mode": "train", "epoch": 14, "iter": 3700, "lr": 0.09787, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24, "top5_acc": 0.50187, "loss_cls": 4.13889, "loss": 4.13889, "time": 0.71473} +{"mode": "val", "epoch": 14, "iter": 309, "lr": 0.09787, "top1_acc": 0.18797, "top5_acc": 0.41458, "mean_class_accuracy": 0.18794} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.09786, "memory": 15990, "data_time": 1.48568, "top1_acc": 0.25172, "top5_acc": 0.50125, "loss_cls": 4.08244, "loss": 4.08244, "time": 2.20873} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.09785, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25594, "top5_acc": 0.50969, "loss_cls": 4.06343, "loss": 4.06343, "time": 0.71554} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.09784, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24562, "top5_acc": 0.49547, "loss_cls": 4.14637, "loss": 4.14637, "time": 0.71815} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.09783, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24891, "top5_acc": 0.49641, "loss_cls": 4.12276, "loss": 4.12276, "time": 0.72012} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.09783, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23562, "top5_acc": 0.47906, "loss_cls": 4.20959, "loss": 4.20959, "time": 0.71981} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.09782, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24922, "top5_acc": 0.49328, "loss_cls": 4.13688, "loss": 4.13688, "time": 0.71392} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.09781, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24812, "top5_acc": 0.49859, "loss_cls": 4.10811, "loss": 4.10811, "time": 0.71973} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.0978, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24922, "top5_acc": 0.48625, "loss_cls": 4.14993, "loss": 4.14993, "time": 0.72024} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.09779, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25406, "top5_acc": 0.49406, "loss_cls": 4.12627, "loss": 4.12627, "time": 0.7192} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.09778, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24797, "top5_acc": 0.49297, "loss_cls": 4.14213, "loss": 4.14213, "time": 0.72546} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.09778, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23984, "top5_acc": 0.50187, "loss_cls": 4.11816, "loss": 4.11816, "time": 0.72132} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.09777, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.255, "top5_acc": 0.50469, "loss_cls": 4.12181, "loss": 4.12181, "time": 0.71995} +{"mode": "train", "epoch": 15, "iter": 1300, "lr": 0.09776, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.24719, "top5_acc": 0.49609, "loss_cls": 4.12947, "loss": 4.12947, "time": 0.72102} +{"mode": "train", "epoch": 15, "iter": 1400, "lr": 0.09775, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25453, "top5_acc": 0.49797, "loss_cls": 4.10803, "loss": 4.10803, "time": 0.72221} +{"mode": "train", "epoch": 15, "iter": 1500, "lr": 0.09774, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.24703, "top5_acc": 0.49672, "loss_cls": 4.17245, "loss": 4.17245, "time": 0.72186} +{"mode": "train", "epoch": 15, "iter": 1600, "lr": 0.09773, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24688, "top5_acc": 0.48922, "loss_cls": 4.14687, "loss": 4.14687, "time": 0.72549} +{"mode": "train", "epoch": 15, "iter": 1700, "lr": 0.09773, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24297, "top5_acc": 0.4875, "loss_cls": 4.13114, "loss": 4.13114, "time": 0.71947} +{"mode": "train", "epoch": 15, "iter": 1800, "lr": 0.09772, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24781, "top5_acc": 0.48922, "loss_cls": 4.15549, "loss": 4.15549, "time": 0.72334} +{"mode": "train", "epoch": 15, "iter": 1900, "lr": 0.09771, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.24766, "top5_acc": 0.48844, "loss_cls": 4.13373, "loss": 4.13373, "time": 0.72061} +{"mode": "train", "epoch": 15, "iter": 2000, "lr": 0.0977, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24312, "top5_acc": 0.48188, "loss_cls": 4.17448, "loss": 4.17448, "time": 0.72542} +{"mode": "train", "epoch": 15, "iter": 2100, "lr": 0.09769, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.25828, "top5_acc": 0.50203, "loss_cls": 4.11064, "loss": 4.11064, "time": 0.72297} +{"mode": "train", "epoch": 15, "iter": 2200, "lr": 0.09768, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24188, "top5_acc": 0.4875, "loss_cls": 4.19053, "loss": 4.19053, "time": 0.72393} +{"mode": "train", "epoch": 15, "iter": 2300, "lr": 0.09768, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24953, "top5_acc": 0.49203, "loss_cls": 4.15384, "loss": 4.15384, "time": 0.7204} +{"mode": "train", "epoch": 15, "iter": 2400, "lr": 0.09767, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24188, "top5_acc": 0.49188, "loss_cls": 4.16132, "loss": 4.16132, "time": 0.7217} +{"mode": "train", "epoch": 15, "iter": 2500, "lr": 0.09766, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23719, "top5_acc": 0.48672, "loss_cls": 4.16134, "loss": 4.16134, "time": 0.72211} +{"mode": "train", "epoch": 15, "iter": 2600, "lr": 0.09765, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24781, "top5_acc": 0.48984, "loss_cls": 4.15548, "loss": 4.15548, "time": 0.71982} +{"mode": "train", "epoch": 15, "iter": 2700, "lr": 0.09764, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24812, "top5_acc": 0.49859, "loss_cls": 4.12254, "loss": 4.12254, "time": 0.7214} +{"mode": "train", "epoch": 15, "iter": 2800, "lr": 0.09763, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25484, "top5_acc": 0.50406, "loss_cls": 4.10736, "loss": 4.10736, "time": 0.71812} +{"mode": "train", "epoch": 15, "iter": 2900, "lr": 0.09763, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25359, "top5_acc": 0.50266, "loss_cls": 4.0959, "loss": 4.0959, "time": 0.72293} +{"mode": "train", "epoch": 15, "iter": 3000, "lr": 0.09762, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23844, "top5_acc": 0.49078, "loss_cls": 4.16064, "loss": 4.16064, "time": 0.71973} +{"mode": "train", "epoch": 15, "iter": 3100, "lr": 0.09761, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25062, "top5_acc": 0.50125, "loss_cls": 4.10994, "loss": 4.10994, "time": 0.72317} +{"mode": "train", "epoch": 15, "iter": 3200, "lr": 0.0976, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25016, "top5_acc": 0.49031, "loss_cls": 4.14325, "loss": 4.14325, "time": 0.7199} +{"mode": "train", "epoch": 15, "iter": 3300, "lr": 0.09759, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.2525, "top5_acc": 0.49406, "loss_cls": 4.13742, "loss": 4.13742, "time": 0.71677} +{"mode": "train", "epoch": 15, "iter": 3400, "lr": 0.09758, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24953, "top5_acc": 0.49016, "loss_cls": 4.14971, "loss": 4.14971, "time": 0.71717} +{"mode": "train", "epoch": 15, "iter": 3500, "lr": 0.09757, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24031, "top5_acc": 0.48844, "loss_cls": 4.15292, "loss": 4.15292, "time": 0.72257} +{"mode": "train", "epoch": 15, "iter": 3600, "lr": 0.09757, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25062, "top5_acc": 0.49406, "loss_cls": 4.10662, "loss": 4.10662, "time": 0.72156} +{"mode": "train", "epoch": 15, "iter": 3700, "lr": 0.09756, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24109, "top5_acc": 0.48922, "loss_cls": 4.15623, "loss": 4.15623, "time": 0.71642} +{"mode": "val", "epoch": 15, "iter": 309, "lr": 0.09755, "top1_acc": 0.18964, "top5_acc": 0.41787, "mean_class_accuracy": 0.18953} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.09754, "memory": 15990, "data_time": 1.50782, "top1_acc": 0.25641, "top5_acc": 0.50422, "loss_cls": 4.08864, "loss": 4.08864, "time": 2.2286} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.09754, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25062, "top5_acc": 0.50734, "loss_cls": 4.09625, "loss": 4.09625, "time": 0.71681} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.09753, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25109, "top5_acc": 0.49547, "loss_cls": 4.10151, "loss": 4.10151, "time": 0.71743} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.09752, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25219, "top5_acc": 0.49391, "loss_cls": 4.12795, "loss": 4.12795, "time": 0.71603} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.09751, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25047, "top5_acc": 0.5, "loss_cls": 4.13506, "loss": 4.13506, "time": 0.71484} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.0975, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25031, "top5_acc": 0.49844, "loss_cls": 4.12005, "loss": 4.12005, "time": 0.71789} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.09749, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24438, "top5_acc": 0.49078, "loss_cls": 4.14281, "loss": 4.14281, "time": 0.72257} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.09748, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2575, "top5_acc": 0.49688, "loss_cls": 4.10451, "loss": 4.10451, "time": 0.72235} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.09747, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2525, "top5_acc": 0.49031, "loss_cls": 4.10708, "loss": 4.10708, "time": 0.72416} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.09747, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24453, "top5_acc": 0.50094, "loss_cls": 4.13378, "loss": 4.13378, "time": 0.7191} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.09746, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24969, "top5_acc": 0.4875, "loss_cls": 4.12904, "loss": 4.12904, "time": 0.7242} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.09745, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.23547, "top5_acc": 0.49125, "loss_cls": 4.16533, "loss": 4.16533, "time": 0.71947} +{"mode": "train", "epoch": 16, "iter": 1300, "lr": 0.09744, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25562, "top5_acc": 0.50703, "loss_cls": 4.08174, "loss": 4.08174, "time": 0.72351} +{"mode": "train", "epoch": 16, "iter": 1400, "lr": 0.09743, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24953, "top5_acc": 0.50141, "loss_cls": 4.11134, "loss": 4.11134, "time": 0.72197} +{"mode": "train", "epoch": 16, "iter": 1500, "lr": 0.09742, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23844, "top5_acc": 0.48656, "loss_cls": 4.1734, "loss": 4.1734, "time": 0.72087} +{"mode": "train", "epoch": 16, "iter": 1600, "lr": 0.09741, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25797, "top5_acc": 0.50859, "loss_cls": 4.08889, "loss": 4.08889, "time": 0.72244} +{"mode": "train", "epoch": 16, "iter": 1700, "lr": 0.0974, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25219, "top5_acc": 0.48234, "loss_cls": 4.16648, "loss": 4.16648, "time": 0.72492} +{"mode": "train", "epoch": 16, "iter": 1800, "lr": 0.0974, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24906, "top5_acc": 0.49078, "loss_cls": 4.11379, "loss": 4.11379, "time": 0.72268} +{"mode": "train", "epoch": 16, "iter": 1900, "lr": 0.09739, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25484, "top5_acc": 0.49328, "loss_cls": 4.10411, "loss": 4.10411, "time": 0.7221} +{"mode": "train", "epoch": 16, "iter": 2000, "lr": 0.09738, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25188, "top5_acc": 0.49703, "loss_cls": 4.12783, "loss": 4.12783, "time": 0.72129} +{"mode": "train", "epoch": 16, "iter": 2100, "lr": 0.09737, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24844, "top5_acc": 0.4975, "loss_cls": 4.14935, "loss": 4.14935, "time": 0.71995} +{"mode": "train", "epoch": 16, "iter": 2200, "lr": 0.09736, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25469, "top5_acc": 0.5, "loss_cls": 4.12746, "loss": 4.12746, "time": 0.72145} +{"mode": "train", "epoch": 16, "iter": 2300, "lr": 0.09735, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24516, "top5_acc": 0.49875, "loss_cls": 4.11522, "loss": 4.11522, "time": 0.72137} +{"mode": "train", "epoch": 16, "iter": 2400, "lr": 0.09734, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23938, "top5_acc": 0.48906, "loss_cls": 4.14786, "loss": 4.14786, "time": 0.72076} +{"mode": "train", "epoch": 16, "iter": 2500, "lr": 0.09733, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.255, "top5_acc": 0.50969, "loss_cls": 4.08778, "loss": 4.08778, "time": 0.72038} +{"mode": "train", "epoch": 16, "iter": 2600, "lr": 0.09732, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24188, "top5_acc": 0.49156, "loss_cls": 4.18343, "loss": 4.18343, "time": 0.72372} +{"mode": "train", "epoch": 16, "iter": 2700, "lr": 0.09731, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25672, "top5_acc": 0.49172, "loss_cls": 4.11631, "loss": 4.11631, "time": 0.724} +{"mode": "train", "epoch": 16, "iter": 2800, "lr": 0.09731, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25656, "top5_acc": 0.49297, "loss_cls": 4.11361, "loss": 4.11361, "time": 0.72121} +{"mode": "train", "epoch": 16, "iter": 2900, "lr": 0.0973, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.255, "top5_acc": 0.50172, "loss_cls": 4.10329, "loss": 4.10329, "time": 0.72225} +{"mode": "train", "epoch": 16, "iter": 3000, "lr": 0.09729, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24859, "top5_acc": 0.49359, "loss_cls": 4.11854, "loss": 4.11854, "time": 0.72513} +{"mode": "train", "epoch": 16, "iter": 3100, "lr": 0.09728, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24484, "top5_acc": 0.49406, "loss_cls": 4.14593, "loss": 4.14593, "time": 0.72266} +{"mode": "train", "epoch": 16, "iter": 3200, "lr": 0.09727, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24484, "top5_acc": 0.49422, "loss_cls": 4.1281, "loss": 4.1281, "time": 0.71646} +{"mode": "train", "epoch": 16, "iter": 3300, "lr": 0.09726, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24188, "top5_acc": 0.49422, "loss_cls": 4.14764, "loss": 4.14764, "time": 0.71941} +{"mode": "train", "epoch": 16, "iter": 3400, "lr": 0.09725, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24141, "top5_acc": 0.49281, "loss_cls": 4.14993, "loss": 4.14993, "time": 0.71924} +{"mode": "train", "epoch": 16, "iter": 3500, "lr": 0.09724, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.24875, "top5_acc": 0.49047, "loss_cls": 4.16002, "loss": 4.16002, "time": 0.7169} +{"mode": "train", "epoch": 16, "iter": 3600, "lr": 0.09723, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25344, "top5_acc": 0.48609, "loss_cls": 4.16156, "loss": 4.16156, "time": 0.71847} +{"mode": "train", "epoch": 16, "iter": 3700, "lr": 0.09722, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25078, "top5_acc": 0.48703, "loss_cls": 4.15688, "loss": 4.15688, "time": 0.71487} +{"mode": "val", "epoch": 16, "iter": 309, "lr": 0.09722, "top1_acc": 0.19435, "top5_acc": 0.42111, "mean_class_accuracy": 0.1941} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.09721, "memory": 15990, "data_time": 1.4792, "top1_acc": 0.26312, "top5_acc": 0.51297, "loss_cls": 4.03929, "loss": 4.03929, "time": 2.20003} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.0972, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25609, "top5_acc": 0.4975, "loss_cls": 4.11366, "loss": 4.11366, "time": 0.71834} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.09719, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.265, "top5_acc": 0.50547, "loss_cls": 4.06341, "loss": 4.06341, "time": 0.71565} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.09718, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24938, "top5_acc": 0.49781, "loss_cls": 4.1198, "loss": 4.1198, "time": 0.71776} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.09717, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.25047, "top5_acc": 0.49844, "loss_cls": 4.10248, "loss": 4.10248, "time": 0.7173} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.09716, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.245, "top5_acc": 0.49594, "loss_cls": 4.12183, "loss": 4.12183, "time": 0.71135} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.09715, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24875, "top5_acc": 0.48438, "loss_cls": 4.15543, "loss": 4.15543, "time": 0.71633} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.09714, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25516, "top5_acc": 0.50156, "loss_cls": 4.10059, "loss": 4.10059, "time": 0.71774} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.09714, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.25141, "top5_acc": 0.50594, "loss_cls": 4.08764, "loss": 4.08764, "time": 0.7198} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.09713, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25469, "top5_acc": 0.49531, "loss_cls": 4.13797, "loss": 4.13797, "time": 0.72322} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.09712, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2575, "top5_acc": 0.50094, "loss_cls": 4.07188, "loss": 4.07188, "time": 0.72504} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.09711, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.23938, "top5_acc": 0.49344, "loss_cls": 4.17022, "loss": 4.17022, "time": 0.72199} +{"mode": "train", "epoch": 17, "iter": 1300, "lr": 0.0971, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24328, "top5_acc": 0.49312, "loss_cls": 4.14483, "loss": 4.14483, "time": 0.72193} +{"mode": "train", "epoch": 17, "iter": 1400, "lr": 0.09709, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.2475, "top5_acc": 0.49422, "loss_cls": 4.12427, "loss": 4.12427, "time": 0.72406} +{"mode": "train", "epoch": 17, "iter": 1500, "lr": 0.09708, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24844, "top5_acc": 0.49438, "loss_cls": 4.13971, "loss": 4.13971, "time": 0.72383} +{"mode": "train", "epoch": 17, "iter": 1600, "lr": 0.09707, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25781, "top5_acc": 0.50891, "loss_cls": 4.0711, "loss": 4.0711, "time": 0.71926} +{"mode": "train", "epoch": 17, "iter": 1700, "lr": 0.09706, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25938, "top5_acc": 0.49516, "loss_cls": 4.12088, "loss": 4.12088, "time": 0.72222} +{"mode": "train", "epoch": 17, "iter": 1800, "lr": 0.09705, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24312, "top5_acc": 0.48625, "loss_cls": 4.17464, "loss": 4.17464, "time": 0.72108} +{"mode": "train", "epoch": 17, "iter": 1900, "lr": 0.09704, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24688, "top5_acc": 0.49953, "loss_cls": 4.1238, "loss": 4.1238, "time": 0.72104} +{"mode": "train", "epoch": 17, "iter": 2000, "lr": 0.09703, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25797, "top5_acc": 0.50969, "loss_cls": 4.06918, "loss": 4.06918, "time": 0.72073} +{"mode": "train", "epoch": 17, "iter": 2100, "lr": 0.09702, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25062, "top5_acc": 0.49469, "loss_cls": 4.11146, "loss": 4.11146, "time": 0.72205} +{"mode": "train", "epoch": 17, "iter": 2200, "lr": 0.09701, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25844, "top5_acc": 0.50875, "loss_cls": 4.08891, "loss": 4.08891, "time": 0.7227} +{"mode": "train", "epoch": 17, "iter": 2300, "lr": 0.097, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25844, "top5_acc": 0.49641, "loss_cls": 4.10139, "loss": 4.10139, "time": 0.71978} +{"mode": "train", "epoch": 17, "iter": 2400, "lr": 0.09699, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.24875, "top5_acc": 0.49562, "loss_cls": 4.15888, "loss": 4.15888, "time": 0.72515} +{"mode": "train", "epoch": 17, "iter": 2500, "lr": 0.09698, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.24297, "top5_acc": 0.49031, "loss_cls": 4.14233, "loss": 4.14233, "time": 0.72237} +{"mode": "train", "epoch": 17, "iter": 2600, "lr": 0.09697, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24422, "top5_acc": 0.48734, "loss_cls": 4.1767, "loss": 4.1767, "time": 0.72104} +{"mode": "train", "epoch": 17, "iter": 2700, "lr": 0.09697, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25359, "top5_acc": 0.49891, "loss_cls": 4.09498, "loss": 4.09498, "time": 0.72069} +{"mode": "train", "epoch": 17, "iter": 2800, "lr": 0.09696, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25109, "top5_acc": 0.49625, "loss_cls": 4.10128, "loss": 4.10128, "time": 0.72087} +{"mode": "train", "epoch": 17, "iter": 2900, "lr": 0.09695, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25422, "top5_acc": 0.49578, "loss_cls": 4.12523, "loss": 4.12523, "time": 0.71907} +{"mode": "train", "epoch": 17, "iter": 3000, "lr": 0.09694, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24172, "top5_acc": 0.48844, "loss_cls": 4.15609, "loss": 4.15609, "time": 0.72402} +{"mode": "train", "epoch": 17, "iter": 3100, "lr": 0.09693, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24719, "top5_acc": 0.49672, "loss_cls": 4.1438, "loss": 4.1438, "time": 0.71958} +{"mode": "train", "epoch": 17, "iter": 3200, "lr": 0.09692, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24797, "top5_acc": 0.50219, "loss_cls": 4.10769, "loss": 4.10769, "time": 0.7211} +{"mode": "train", "epoch": 17, "iter": 3300, "lr": 0.09691, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.23797, "top5_acc": 0.48672, "loss_cls": 4.18155, "loss": 4.18155, "time": 0.71428} +{"mode": "train", "epoch": 17, "iter": 3400, "lr": 0.0969, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25688, "top5_acc": 0.49719, "loss_cls": 4.13819, "loss": 4.13819, "time": 0.7171} +{"mode": "train", "epoch": 17, "iter": 3500, "lr": 0.09689, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24672, "top5_acc": 0.50031, "loss_cls": 4.10746, "loss": 4.10746, "time": 0.71776} +{"mode": "train", "epoch": 17, "iter": 3600, "lr": 0.09688, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24938, "top5_acc": 0.49859, "loss_cls": 4.11863, "loss": 4.11863, "time": 0.71808} +{"mode": "train", "epoch": 17, "iter": 3700, "lr": 0.09687, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25922, "top5_acc": 0.50297, "loss_cls": 4.07511, "loss": 4.07511, "time": 0.71312} +{"mode": "val", "epoch": 17, "iter": 309, "lr": 0.09686, "top1_acc": 0.19176, "top5_acc": 0.42496, "mean_class_accuracy": 0.19155} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.09685, "memory": 15990, "data_time": 1.48485, "top1_acc": 0.26562, "top5_acc": 0.50938, "loss_cls": 4.04126, "loss": 4.04126, "time": 2.2019} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.09684, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24672, "top5_acc": 0.50172, "loss_cls": 4.11664, "loss": 4.11664, "time": 0.71646} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.09683, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25766, "top5_acc": 0.5075, "loss_cls": 4.08242, "loss": 4.08242, "time": 0.7174} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.09683, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25906, "top5_acc": 0.50422, "loss_cls": 4.10641, "loss": 4.10641, "time": 0.71604} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.09682, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24172, "top5_acc": 0.48641, "loss_cls": 4.15671, "loss": 4.15671, "time": 0.72254} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.09681, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24469, "top5_acc": 0.49844, "loss_cls": 4.14216, "loss": 4.14216, "time": 0.71867} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.0968, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24516, "top5_acc": 0.49141, "loss_cls": 4.15383, "loss": 4.15383, "time": 0.71806} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.09679, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24812, "top5_acc": 0.49406, "loss_cls": 4.12345, "loss": 4.12345, "time": 0.72105} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.09678, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26062, "top5_acc": 0.51047, "loss_cls": 4.0651, "loss": 4.0651, "time": 0.72269} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.09677, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.26219, "top5_acc": 0.50906, "loss_cls": 4.05145, "loss": 4.05145, "time": 0.72459} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.09676, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25688, "top5_acc": 0.49969, "loss_cls": 4.09213, "loss": 4.09213, "time": 0.72048} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.09675, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24953, "top5_acc": 0.49219, "loss_cls": 4.13038, "loss": 4.13038, "time": 0.72068} +{"mode": "train", "epoch": 18, "iter": 1300, "lr": 0.09674, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24969, "top5_acc": 0.4975, "loss_cls": 4.12835, "loss": 4.12835, "time": 0.72029} +{"mode": "train", "epoch": 18, "iter": 1400, "lr": 0.09673, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24266, "top5_acc": 0.49047, "loss_cls": 4.14991, "loss": 4.14991, "time": 0.71968} +{"mode": "train", "epoch": 18, "iter": 1500, "lr": 0.09672, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25125, "top5_acc": 0.4875, "loss_cls": 4.13762, "loss": 4.13762, "time": 0.71736} +{"mode": "train", "epoch": 18, "iter": 1600, "lr": 0.09671, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25688, "top5_acc": 0.50047, "loss_cls": 4.1044, "loss": 4.1044, "time": 0.72136} +{"mode": "train", "epoch": 18, "iter": 1700, "lr": 0.0967, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25391, "top5_acc": 0.50641, "loss_cls": 4.05839, "loss": 4.05839, "time": 0.72333} +{"mode": "train", "epoch": 18, "iter": 1800, "lr": 0.09669, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25547, "top5_acc": 0.50547, "loss_cls": 4.11093, "loss": 4.11093, "time": 0.72076} +{"mode": "train", "epoch": 18, "iter": 1900, "lr": 0.09668, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.24688, "top5_acc": 0.49953, "loss_cls": 4.11613, "loss": 4.11613, "time": 0.72384} +{"mode": "train", "epoch": 18, "iter": 2000, "lr": 0.09667, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24672, "top5_acc": 0.49016, "loss_cls": 4.14468, "loss": 4.14468, "time": 0.72334} +{"mode": "train", "epoch": 18, "iter": 2100, "lr": 0.09666, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25109, "top5_acc": 0.4975, "loss_cls": 4.13274, "loss": 4.13274, "time": 0.72231} +{"mode": "train", "epoch": 18, "iter": 2200, "lr": 0.09665, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24984, "top5_acc": 0.5, "loss_cls": 4.1185, "loss": 4.1185, "time": 0.72033} +{"mode": "train", "epoch": 18, "iter": 2300, "lr": 0.09664, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23812, "top5_acc": 0.49219, "loss_cls": 4.16377, "loss": 4.16377, "time": 0.71663} +{"mode": "train", "epoch": 18, "iter": 2400, "lr": 0.09663, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24469, "top5_acc": 0.48391, "loss_cls": 4.14034, "loss": 4.14034, "time": 0.72387} +{"mode": "train", "epoch": 18, "iter": 2500, "lr": 0.09662, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24453, "top5_acc": 0.49203, "loss_cls": 4.16995, "loss": 4.16995, "time": 0.71998} +{"mode": "train", "epoch": 18, "iter": 2600, "lr": 0.09661, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.25562, "top5_acc": 0.50859, "loss_cls": 4.08182, "loss": 4.08182, "time": 0.72258} +{"mode": "train", "epoch": 18, "iter": 2700, "lr": 0.0966, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26203, "top5_acc": 0.50047, "loss_cls": 4.10639, "loss": 4.10639, "time": 0.72423} +{"mode": "train", "epoch": 18, "iter": 2800, "lr": 0.09659, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26609, "top5_acc": 0.50844, "loss_cls": 4.0574, "loss": 4.0574, "time": 0.72335} +{"mode": "train", "epoch": 18, "iter": 2900, "lr": 0.09658, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.25062, "top5_acc": 0.49625, "loss_cls": 4.11908, "loss": 4.11908, "time": 0.72284} +{"mode": "train", "epoch": 18, "iter": 3000, "lr": 0.09657, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24672, "top5_acc": 0.49172, "loss_cls": 4.158, "loss": 4.158, "time": 0.72219} +{"mode": "train", "epoch": 18, "iter": 3100, "lr": 0.09656, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25906, "top5_acc": 0.51297, "loss_cls": 4.06891, "loss": 4.06891, "time": 0.7209} +{"mode": "train", "epoch": 18, "iter": 3200, "lr": 0.09654, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25469, "top5_acc": 0.505, "loss_cls": 4.09766, "loss": 4.09766, "time": 0.72045} +{"mode": "train", "epoch": 18, "iter": 3300, "lr": 0.09653, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25109, "top5_acc": 0.49312, "loss_cls": 4.12717, "loss": 4.12717, "time": 0.71649} +{"mode": "train", "epoch": 18, "iter": 3400, "lr": 0.09652, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26219, "top5_acc": 0.50422, "loss_cls": 4.10084, "loss": 4.10084, "time": 0.71614} +{"mode": "train", "epoch": 18, "iter": 3500, "lr": 0.09651, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24641, "top5_acc": 0.49172, "loss_cls": 4.10949, "loss": 4.10949, "time": 0.7166} +{"mode": "train", "epoch": 18, "iter": 3600, "lr": 0.0965, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25078, "top5_acc": 0.49281, "loss_cls": 4.12801, "loss": 4.12801, "time": 0.71549} +{"mode": "train", "epoch": 18, "iter": 3700, "lr": 0.09649, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24969, "top5_acc": 0.50234, "loss_cls": 4.14088, "loss": 4.14088, "time": 0.71534} +{"mode": "val", "epoch": 18, "iter": 309, "lr": 0.09649, "top1_acc": 0.18898, "top5_acc": 0.40936, "mean_class_accuracy": 0.18874} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.09648, "memory": 15990, "data_time": 1.46793, "top1_acc": 0.25312, "top5_acc": 0.50422, "loss_cls": 4.07197, "loss": 4.07197, "time": 2.19094} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.09647, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25781, "top5_acc": 0.51297, "loss_cls": 4.05564, "loss": 4.05564, "time": 0.71524} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.09646, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2625, "top5_acc": 0.51188, "loss_cls": 4.05681, "loss": 4.05681, "time": 0.7177} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.09645, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25344, "top5_acc": 0.49969, "loss_cls": 4.11697, "loss": 4.11697, "time": 0.71663} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.09644, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26156, "top5_acc": 0.50359, "loss_cls": 4.08786, "loss": 4.08786, "time": 0.71339} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.09643, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26625, "top5_acc": 0.50375, "loss_cls": 4.05404, "loss": 4.05404, "time": 0.71565} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.09642, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25438, "top5_acc": 0.49641, "loss_cls": 4.1081, "loss": 4.1081, "time": 0.71344} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.09641, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25078, "top5_acc": 0.50094, "loss_cls": 4.0959, "loss": 4.0959, "time": 0.71899} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.0964, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26094, "top5_acc": 0.50234, "loss_cls": 4.10059, "loss": 4.10059, "time": 0.71825} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.09639, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26234, "top5_acc": 0.50547, "loss_cls": 4.05004, "loss": 4.05004, "time": 0.71869} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.09637, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24078, "top5_acc": 0.49125, "loss_cls": 4.18545, "loss": 4.18545, "time": 0.72327} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.09636, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25422, "top5_acc": 0.50891, "loss_cls": 4.07839, "loss": 4.07839, "time": 0.72248} +{"mode": "train", "epoch": 19, "iter": 1300, "lr": 0.09635, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.24656, "top5_acc": 0.49219, "loss_cls": 4.13301, "loss": 4.13301, "time": 0.72526} +{"mode": "train", "epoch": 19, "iter": 1400, "lr": 0.09634, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24797, "top5_acc": 0.48859, "loss_cls": 4.15036, "loss": 4.15036, "time": 0.7229} +{"mode": "train", "epoch": 19, "iter": 1500, "lr": 0.09633, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25516, "top5_acc": 0.50234, "loss_cls": 4.09445, "loss": 4.09445, "time": 0.72196} +{"mode": "train", "epoch": 19, "iter": 1600, "lr": 0.09632, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.25203, "top5_acc": 0.49938, "loss_cls": 4.12309, "loss": 4.12309, "time": 0.72281} +{"mode": "train", "epoch": 19, "iter": 1700, "lr": 0.09631, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25391, "top5_acc": 0.50062, "loss_cls": 4.12402, "loss": 4.12402, "time": 0.7223} +{"mode": "train", "epoch": 19, "iter": 1800, "lr": 0.0963, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.24688, "top5_acc": 0.49125, "loss_cls": 4.12979, "loss": 4.12979, "time": 0.72105} +{"mode": "train", "epoch": 19, "iter": 1900, "lr": 0.09629, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25266, "top5_acc": 0.49453, "loss_cls": 4.12756, "loss": 4.12756, "time": 0.72278} +{"mode": "train", "epoch": 19, "iter": 2000, "lr": 0.09628, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.25109, "top5_acc": 0.50109, "loss_cls": 4.13504, "loss": 4.13504, "time": 0.72171} +{"mode": "train", "epoch": 19, "iter": 2100, "lr": 0.09627, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25516, "top5_acc": 0.50047, "loss_cls": 4.10443, "loss": 4.10443, "time": 0.72065} +{"mode": "train", "epoch": 19, "iter": 2200, "lr": 0.09626, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.24969, "top5_acc": 0.50109, "loss_cls": 4.09684, "loss": 4.09684, "time": 0.7229} +{"mode": "train", "epoch": 19, "iter": 2300, "lr": 0.09625, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24359, "top5_acc": 0.495, "loss_cls": 4.1432, "loss": 4.1432, "time": 0.72284} +{"mode": "train", "epoch": 19, "iter": 2400, "lr": 0.09624, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25141, "top5_acc": 0.50219, "loss_cls": 4.10927, "loss": 4.10927, "time": 0.72087} +{"mode": "train", "epoch": 19, "iter": 2500, "lr": 0.09623, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24312, "top5_acc": 0.49953, "loss_cls": 4.13466, "loss": 4.13466, "time": 0.72448} +{"mode": "train", "epoch": 19, "iter": 2600, "lr": 0.09622, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.25109, "top5_acc": 0.49328, "loss_cls": 4.13942, "loss": 4.13942, "time": 0.72064} +{"mode": "train", "epoch": 19, "iter": 2700, "lr": 0.09621, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26125, "top5_acc": 0.49562, "loss_cls": 4.0919, "loss": 4.0919, "time": 0.72217} +{"mode": "train", "epoch": 19, "iter": 2800, "lr": 0.0962, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25953, "top5_acc": 0.49812, "loss_cls": 4.10842, "loss": 4.10842, "time": 0.72257} +{"mode": "train", "epoch": 19, "iter": 2900, "lr": 0.09618, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25203, "top5_acc": 0.49828, "loss_cls": 4.10558, "loss": 4.10558, "time": 0.7241} +{"mode": "train", "epoch": 19, "iter": 3000, "lr": 0.09617, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.23469, "top5_acc": 0.48797, "loss_cls": 4.16846, "loss": 4.16846, "time": 0.72514} +{"mode": "train", "epoch": 19, "iter": 3100, "lr": 0.09616, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24938, "top5_acc": 0.49703, "loss_cls": 4.12322, "loss": 4.12322, "time": 0.71936} +{"mode": "train", "epoch": 19, "iter": 3200, "lr": 0.09615, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24078, "top5_acc": 0.49078, "loss_cls": 4.14598, "loss": 4.14598, "time": 0.72035} +{"mode": "train", "epoch": 19, "iter": 3300, "lr": 0.09614, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24438, "top5_acc": 0.48656, "loss_cls": 4.12509, "loss": 4.12509, "time": 0.71544} +{"mode": "train", "epoch": 19, "iter": 3400, "lr": 0.09613, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.2475, "top5_acc": 0.50344, "loss_cls": 4.11646, "loss": 4.11646, "time": 0.71853} +{"mode": "train", "epoch": 19, "iter": 3500, "lr": 0.09612, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25625, "top5_acc": 0.50797, "loss_cls": 4.07704, "loss": 4.07704, "time": 0.71825} +{"mode": "train", "epoch": 19, "iter": 3600, "lr": 0.09611, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25422, "top5_acc": 0.49703, "loss_cls": 4.12437, "loss": 4.12437, "time": 0.71592} +{"mode": "train", "epoch": 19, "iter": 3700, "lr": 0.0961, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24781, "top5_acc": 0.49828, "loss_cls": 4.13315, "loss": 4.13315, "time": 0.71635} +{"mode": "val", "epoch": 19, "iter": 309, "lr": 0.09609, "top1_acc": 0.19278, "top5_acc": 0.42192, "mean_class_accuracy": 0.19246} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.09608, "memory": 15990, "data_time": 1.5234, "top1_acc": 0.26391, "top5_acc": 0.51625, "loss_cls": 4.0157, "loss": 4.0157, "time": 2.24182} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.09607, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26703, "top5_acc": 0.50266, "loss_cls": 4.08717, "loss": 4.08717, "time": 0.71947} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.09606, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25094, "top5_acc": 0.50297, "loss_cls": 4.11342, "loss": 4.11342, "time": 0.71997} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.09605, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25016, "top5_acc": 0.49266, "loss_cls": 4.12797, "loss": 4.12797, "time": 0.71911} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.09604, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25812, "top5_acc": 0.51297, "loss_cls": 4.06007, "loss": 4.06007, "time": 0.71544} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.09603, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24625, "top5_acc": 0.49375, "loss_cls": 4.12162, "loss": 4.12162, "time": 0.71752} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.09602, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25438, "top5_acc": 0.50859, "loss_cls": 4.10697, "loss": 4.10697, "time": 0.71828} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.09601, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25406, "top5_acc": 0.50609, "loss_cls": 4.09993, "loss": 4.09993, "time": 0.71869} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.096, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25406, "top5_acc": 0.49969, "loss_cls": 4.107, "loss": 4.107, "time": 0.7224} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.09598, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25641, "top5_acc": 0.49969, "loss_cls": 4.10727, "loss": 4.10727, "time": 0.72209} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.09597, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25438, "top5_acc": 0.49953, "loss_cls": 4.09833, "loss": 4.09833, "time": 0.72182} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.09596, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25312, "top5_acc": 0.50344, "loss_cls": 4.08601, "loss": 4.08601, "time": 0.72335} +{"mode": "train", "epoch": 20, "iter": 1300, "lr": 0.09595, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25625, "top5_acc": 0.49953, "loss_cls": 4.10947, "loss": 4.10947, "time": 0.72174} +{"mode": "train", "epoch": 20, "iter": 1400, "lr": 0.09594, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25141, "top5_acc": 0.49984, "loss_cls": 4.10373, "loss": 4.10373, "time": 0.72063} +{"mode": "train", "epoch": 20, "iter": 1500, "lr": 0.09593, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.2475, "top5_acc": 0.49875, "loss_cls": 4.10821, "loss": 4.10821, "time": 0.72456} +{"mode": "train", "epoch": 20, "iter": 1600, "lr": 0.09592, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24875, "top5_acc": 0.49469, "loss_cls": 4.11783, "loss": 4.11783, "time": 0.72215} +{"mode": "train", "epoch": 20, "iter": 1700, "lr": 0.09591, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25156, "top5_acc": 0.49844, "loss_cls": 4.1352, "loss": 4.1352, "time": 0.72532} +{"mode": "train", "epoch": 20, "iter": 1800, "lr": 0.0959, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25375, "top5_acc": 0.50141, "loss_cls": 4.10408, "loss": 4.10408, "time": 0.72193} +{"mode": "train", "epoch": 20, "iter": 1900, "lr": 0.09588, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24656, "top5_acc": 0.49531, "loss_cls": 4.11822, "loss": 4.11822, "time": 0.72275} +{"mode": "train", "epoch": 20, "iter": 2000, "lr": 0.09587, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25578, "top5_acc": 0.50031, "loss_cls": 4.11511, "loss": 4.11511, "time": 0.72385} +{"mode": "train", "epoch": 20, "iter": 2100, "lr": 0.09586, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24781, "top5_acc": 0.49688, "loss_cls": 4.12714, "loss": 4.12714, "time": 0.72226} +{"mode": "train", "epoch": 20, "iter": 2200, "lr": 0.09585, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25422, "top5_acc": 0.50656, "loss_cls": 4.09168, "loss": 4.09168, "time": 0.71786} +{"mode": "train", "epoch": 20, "iter": 2300, "lr": 0.09584, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24859, "top5_acc": 0.49891, "loss_cls": 4.10175, "loss": 4.10175, "time": 0.72311} +{"mode": "train", "epoch": 20, "iter": 2400, "lr": 0.09583, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24438, "top5_acc": 0.49484, "loss_cls": 4.14211, "loss": 4.14211, "time": 0.72664} +{"mode": "train", "epoch": 20, "iter": 2500, "lr": 0.09582, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.26188, "top5_acc": 0.50578, "loss_cls": 4.06099, "loss": 4.06099, "time": 0.72119} +{"mode": "train", "epoch": 20, "iter": 2600, "lr": 0.09581, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25719, "top5_acc": 0.50391, "loss_cls": 4.1127, "loss": 4.1127, "time": 0.72134} +{"mode": "train", "epoch": 20, "iter": 2700, "lr": 0.0958, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.25234, "top5_acc": 0.49766, "loss_cls": 4.13464, "loss": 4.13464, "time": 0.72122} +{"mode": "train", "epoch": 20, "iter": 2800, "lr": 0.09578, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25891, "top5_acc": 0.49719, "loss_cls": 4.10648, "loss": 4.10648, "time": 0.7203} +{"mode": "train", "epoch": 20, "iter": 2900, "lr": 0.09577, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25703, "top5_acc": 0.49531, "loss_cls": 4.09335, "loss": 4.09335, "time": 0.72266} +{"mode": "train", "epoch": 20, "iter": 3000, "lr": 0.09576, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25766, "top5_acc": 0.50484, "loss_cls": 4.08555, "loss": 4.08555, "time": 0.7215} +{"mode": "train", "epoch": 20, "iter": 3100, "lr": 0.09575, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25828, "top5_acc": 0.49891, "loss_cls": 4.08481, "loss": 4.08481, "time": 0.72342} +{"mode": "train", "epoch": 20, "iter": 3200, "lr": 0.09574, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26094, "top5_acc": 0.51203, "loss_cls": 4.03872, "loss": 4.03872, "time": 0.71974} +{"mode": "train", "epoch": 20, "iter": 3300, "lr": 0.09573, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24578, "top5_acc": 0.50813, "loss_cls": 4.09521, "loss": 4.09521, "time": 0.71525} +{"mode": "train", "epoch": 20, "iter": 3400, "lr": 0.09572, "memory": 15990, "data_time": 0.00079, "top1_acc": 0.24562, "top5_acc": 0.49297, "loss_cls": 4.13233, "loss": 4.13233, "time": 0.71533} +{"mode": "train", "epoch": 20, "iter": 3500, "lr": 0.09571, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25078, "top5_acc": 0.49578, "loss_cls": 4.11159, "loss": 4.11159, "time": 0.71952} +{"mode": "train", "epoch": 20, "iter": 3600, "lr": 0.09569, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25078, "top5_acc": 0.50172, "loss_cls": 4.10988, "loss": 4.10988, "time": 0.71531} +{"mode": "train", "epoch": 20, "iter": 3700, "lr": 0.09568, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24422, "top5_acc": 0.49203, "loss_cls": 4.15633, "loss": 4.15633, "time": 0.71769} +{"mode": "val", "epoch": 20, "iter": 309, "lr": 0.09568, "top1_acc": 0.1209, "top5_acc": 0.30603, "mean_class_accuracy": 0.12089} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.09567, "memory": 15990, "data_time": 1.49287, "top1_acc": 0.25859, "top5_acc": 0.50719, "loss_cls": 4.06327, "loss": 4.06327, "time": 2.20947} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.09565, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26141, "top5_acc": 0.51219, "loss_cls": 4.0557, "loss": 4.0557, "time": 0.71832} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.09564, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.255, "top5_acc": 0.50391, "loss_cls": 4.06839, "loss": 4.06839, "time": 0.7184} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.09563, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25531, "top5_acc": 0.49781, "loss_cls": 4.06819, "loss": 4.06819, "time": 0.71837} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.09562, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25125, "top5_acc": 0.50891, "loss_cls": 4.07065, "loss": 4.07065, "time": 0.7193} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.09561, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25016, "top5_acc": 0.50844, "loss_cls": 4.07787, "loss": 4.07787, "time": 0.71916} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.0956, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25094, "top5_acc": 0.49516, "loss_cls": 4.09588, "loss": 4.09588, "time": 0.71724} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.09559, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25047, "top5_acc": 0.49797, "loss_cls": 4.10056, "loss": 4.10056, "time": 0.71677} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.09557, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25984, "top5_acc": 0.50406, "loss_cls": 4.09532, "loss": 4.09532, "time": 0.72133} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.09556, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.255, "top5_acc": 0.49891, "loss_cls": 4.0968, "loss": 4.0968, "time": 0.71995} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.09555, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.255, "top5_acc": 0.50562, "loss_cls": 4.05894, "loss": 4.05894, "time": 0.71886} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.09554, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.25203, "top5_acc": 0.50297, "loss_cls": 4.08132, "loss": 4.08132, "time": 0.72322} +{"mode": "train", "epoch": 21, "iter": 1300, "lr": 0.09553, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25625, "top5_acc": 0.50047, "loss_cls": 4.07311, "loss": 4.07311, "time": 0.72234} +{"mode": "train", "epoch": 21, "iter": 1400, "lr": 0.09552, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24828, "top5_acc": 0.49938, "loss_cls": 4.11438, "loss": 4.11438, "time": 0.72644} +{"mode": "train", "epoch": 21, "iter": 1500, "lr": 0.09551, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.25891, "top5_acc": 0.50813, "loss_cls": 4.10917, "loss": 4.10917, "time": 0.72018} +{"mode": "train", "epoch": 21, "iter": 1600, "lr": 0.09549, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25281, "top5_acc": 0.49859, "loss_cls": 4.11165, "loss": 4.11165, "time": 0.72275} +{"mode": "train", "epoch": 21, "iter": 1700, "lr": 0.09548, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.2575, "top5_acc": 0.50172, "loss_cls": 4.09625, "loss": 4.09625, "time": 0.72277} +{"mode": "train", "epoch": 21, "iter": 1800, "lr": 0.09547, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.26297, "top5_acc": 0.51297, "loss_cls": 4.07262, "loss": 4.07262, "time": 0.72057} +{"mode": "train", "epoch": 21, "iter": 1900, "lr": 0.09546, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25047, "top5_acc": 0.49844, "loss_cls": 4.10858, "loss": 4.10858, "time": 0.72368} +{"mode": "train", "epoch": 21, "iter": 2000, "lr": 0.09545, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24734, "top5_acc": 0.49609, "loss_cls": 4.12707, "loss": 4.12707, "time": 0.72027} +{"mode": "train", "epoch": 21, "iter": 2100, "lr": 0.09544, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26266, "top5_acc": 0.49906, "loss_cls": 4.08072, "loss": 4.08072, "time": 0.72311} +{"mode": "train", "epoch": 21, "iter": 2200, "lr": 0.09542, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25016, "top5_acc": 0.50078, "loss_cls": 4.12855, "loss": 4.12855, "time": 0.72139} +{"mode": "train", "epoch": 21, "iter": 2300, "lr": 0.09541, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25781, "top5_acc": 0.49656, "loss_cls": 4.10127, "loss": 4.10127, "time": 0.72482} +{"mode": "train", "epoch": 21, "iter": 2400, "lr": 0.0954, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25766, "top5_acc": 0.49594, "loss_cls": 4.11119, "loss": 4.11119, "time": 0.72276} +{"mode": "train", "epoch": 21, "iter": 2500, "lr": 0.09539, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24578, "top5_acc": 0.49656, "loss_cls": 4.14503, "loss": 4.14503, "time": 0.72557} +{"mode": "train", "epoch": 21, "iter": 2600, "lr": 0.09538, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26094, "top5_acc": 0.50594, "loss_cls": 4.07295, "loss": 4.07295, "time": 0.72269} +{"mode": "train", "epoch": 21, "iter": 2700, "lr": 0.09537, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25188, "top5_acc": 0.495, "loss_cls": 4.12031, "loss": 4.12031, "time": 0.72152} +{"mode": "train", "epoch": 21, "iter": 2800, "lr": 0.09535, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25766, "top5_acc": 0.50313, "loss_cls": 4.10772, "loss": 4.10772, "time": 0.72001} +{"mode": "train", "epoch": 21, "iter": 2900, "lr": 0.09534, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25234, "top5_acc": 0.50062, "loss_cls": 4.12053, "loss": 4.12053, "time": 0.72083} +{"mode": "train", "epoch": 21, "iter": 3000, "lr": 0.09533, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25031, "top5_acc": 0.48875, "loss_cls": 4.15767, "loss": 4.15767, "time": 0.72317} +{"mode": "train", "epoch": 21, "iter": 3100, "lr": 0.09532, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25234, "top5_acc": 0.49906, "loss_cls": 4.12258, "loss": 4.12258, "time": 0.72061} +{"mode": "train", "epoch": 21, "iter": 3200, "lr": 0.09531, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24594, "top5_acc": 0.50516, "loss_cls": 4.11529, "loss": 4.11529, "time": 0.71702} +{"mode": "train", "epoch": 21, "iter": 3300, "lr": 0.09529, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25938, "top5_acc": 0.50734, "loss_cls": 4.08153, "loss": 4.08153, "time": 0.72013} +{"mode": "train", "epoch": 21, "iter": 3400, "lr": 0.09528, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24031, "top5_acc": 0.48625, "loss_cls": 4.1495, "loss": 4.1495, "time": 0.71651} +{"mode": "train", "epoch": 21, "iter": 3500, "lr": 0.09527, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25312, "top5_acc": 0.495, "loss_cls": 4.08654, "loss": 4.08654, "time": 0.71644} +{"mode": "train", "epoch": 21, "iter": 3600, "lr": 0.09526, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24688, "top5_acc": 0.50359, "loss_cls": 4.10937, "loss": 4.10937, "time": 0.71769} +{"mode": "train", "epoch": 21, "iter": 3700, "lr": 0.09525, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25188, "top5_acc": 0.49578, "loss_cls": 4.13355, "loss": 4.13355, "time": 0.71817} +{"mode": "val", "epoch": 21, "iter": 309, "lr": 0.09524, "top1_acc": 0.20843, "top5_acc": 0.44573, "mean_class_accuracy": 0.20821} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.09523, "memory": 15990, "data_time": 1.51429, "top1_acc": 0.26047, "top5_acc": 0.51125, "loss_cls": 4.07394, "loss": 4.07394, "time": 2.23926} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.09522, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25188, "top5_acc": 0.50859, "loss_cls": 4.08994, "loss": 4.08994, "time": 0.71756} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.09521, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26344, "top5_acc": 0.50859, "loss_cls": 4.05613, "loss": 4.05613, "time": 0.7195} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.09519, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25797, "top5_acc": 0.51469, "loss_cls": 4.07308, "loss": 4.07308, "time": 0.71951} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.09518, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25703, "top5_acc": 0.50031, "loss_cls": 4.08031, "loss": 4.08031, "time": 0.71811} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.09517, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24375, "top5_acc": 0.5075, "loss_cls": 4.0981, "loss": 4.0981, "time": 0.71551} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.09516, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25156, "top5_acc": 0.5075, "loss_cls": 4.06245, "loss": 4.06245, "time": 0.71881} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.09515, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24062, "top5_acc": 0.49953, "loss_cls": 4.10099, "loss": 4.10099, "time": 0.72054} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.09513, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25766, "top5_acc": 0.50281, "loss_cls": 4.09996, "loss": 4.09996, "time": 0.72173} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.09512, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25562, "top5_acc": 0.48922, "loss_cls": 4.12098, "loss": 4.12098, "time": 0.72333} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.09511, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26312, "top5_acc": 0.50422, "loss_cls": 4.06875, "loss": 4.06875, "time": 0.72119} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0951, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.25219, "top5_acc": 0.49797, "loss_cls": 4.11104, "loss": 4.11104, "time": 0.72234} +{"mode": "train", "epoch": 22, "iter": 1300, "lr": 0.09509, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25375, "top5_acc": 0.50359, "loss_cls": 4.10297, "loss": 4.10297, "time": 0.71953} +{"mode": "train", "epoch": 22, "iter": 1400, "lr": 0.09507, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.25016, "top5_acc": 0.50281, "loss_cls": 4.08114, "loss": 4.08114, "time": 0.72019} +{"mode": "train", "epoch": 22, "iter": 1500, "lr": 0.09506, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.25422, "top5_acc": 0.49328, "loss_cls": 4.1015, "loss": 4.1015, "time": 0.72117} +{"mode": "train", "epoch": 22, "iter": 1600, "lr": 0.09505, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25969, "top5_acc": 0.50172, "loss_cls": 4.10506, "loss": 4.10506, "time": 0.72348} +{"mode": "train", "epoch": 22, "iter": 1700, "lr": 0.09504, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25641, "top5_acc": 0.50703, "loss_cls": 4.06471, "loss": 4.06471, "time": 0.72379} +{"mode": "train", "epoch": 22, "iter": 1800, "lr": 0.09502, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24547, "top5_acc": 0.49672, "loss_cls": 4.14463, "loss": 4.14463, "time": 0.7235} +{"mode": "train", "epoch": 22, "iter": 1900, "lr": 0.09501, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25469, "top5_acc": 0.50438, "loss_cls": 4.11688, "loss": 4.11688, "time": 0.72266} +{"mode": "train", "epoch": 22, "iter": 2000, "lr": 0.095, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26125, "top5_acc": 0.50656, "loss_cls": 4.06925, "loss": 4.06925, "time": 0.72356} +{"mode": "train", "epoch": 22, "iter": 2100, "lr": 0.09499, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26562, "top5_acc": 0.51938, "loss_cls": 4.0371, "loss": 4.0371, "time": 0.72111} +{"mode": "train", "epoch": 22, "iter": 2200, "lr": 0.09498, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25656, "top5_acc": 0.50313, "loss_cls": 4.0712, "loss": 4.0712, "time": 0.71984} +{"mode": "train", "epoch": 22, "iter": 2300, "lr": 0.09496, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26953, "top5_acc": 0.50734, "loss_cls": 4.05626, "loss": 4.05626, "time": 0.7204} +{"mode": "train", "epoch": 22, "iter": 2400, "lr": 0.09495, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25266, "top5_acc": 0.50406, "loss_cls": 4.08534, "loss": 4.08534, "time": 0.72644} +{"mode": "train", "epoch": 22, "iter": 2500, "lr": 0.09494, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25422, "top5_acc": 0.50125, "loss_cls": 4.10029, "loss": 4.10029, "time": 0.72229} +{"mode": "train", "epoch": 22, "iter": 2600, "lr": 0.09493, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25312, "top5_acc": 0.49609, "loss_cls": 4.15026, "loss": 4.15026, "time": 0.72473} +{"mode": "train", "epoch": 22, "iter": 2700, "lr": 0.09491, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.25844, "top5_acc": 0.50469, "loss_cls": 4.06946, "loss": 4.06946, "time": 0.72457} +{"mode": "train", "epoch": 22, "iter": 2800, "lr": 0.0949, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24797, "top5_acc": 0.49766, "loss_cls": 4.13621, "loss": 4.13621, "time": 0.72318} +{"mode": "train", "epoch": 22, "iter": 2900, "lr": 0.09489, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25953, "top5_acc": 0.51531, "loss_cls": 4.05013, "loss": 4.05013, "time": 0.72116} +{"mode": "train", "epoch": 22, "iter": 3000, "lr": 0.09488, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24484, "top5_acc": 0.50156, "loss_cls": 4.12054, "loss": 4.12054, "time": 0.72173} +{"mode": "train", "epoch": 22, "iter": 3100, "lr": 0.09487, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25812, "top5_acc": 0.50797, "loss_cls": 4.07202, "loss": 4.07202, "time": 0.72063} +{"mode": "train", "epoch": 22, "iter": 3200, "lr": 0.09485, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24281, "top5_acc": 0.49031, "loss_cls": 4.14836, "loss": 4.14836, "time": 0.71742} +{"mode": "train", "epoch": 22, "iter": 3300, "lr": 0.09484, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24703, "top5_acc": 0.50328, "loss_cls": 4.10609, "loss": 4.10609, "time": 0.71643} +{"mode": "train", "epoch": 22, "iter": 3400, "lr": 0.09483, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25375, "top5_acc": 0.49938, "loss_cls": 4.08204, "loss": 4.08204, "time": 0.71815} +{"mode": "train", "epoch": 22, "iter": 3500, "lr": 0.09482, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25094, "top5_acc": 0.49828, "loss_cls": 4.14573, "loss": 4.14573, "time": 0.71728} +{"mode": "train", "epoch": 22, "iter": 3600, "lr": 0.0948, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25406, "top5_acc": 0.50281, "loss_cls": 4.07071, "loss": 4.07071, "time": 0.71369} +{"mode": "train", "epoch": 22, "iter": 3700, "lr": 0.09479, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25656, "top5_acc": 0.49875, "loss_cls": 4.10526, "loss": 4.10526, "time": 0.72324} +{"mode": "val", "epoch": 22, "iter": 309, "lr": 0.09479, "top1_acc": 0.1984, "top5_acc": 0.4202, "mean_class_accuracy": 0.19817} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.09477, "memory": 15990, "data_time": 1.46337, "top1_acc": 0.26328, "top5_acc": 0.50031, "loss_cls": 4.08405, "loss": 4.08405, "time": 2.18209} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.09476, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26188, "top5_acc": 0.51359, "loss_cls": 4.0392, "loss": 4.0392, "time": 0.71923} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.09475, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25328, "top5_acc": 0.50609, "loss_cls": 4.08081, "loss": 4.08081, "time": 0.71908} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.09474, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24766, "top5_acc": 0.50422, "loss_cls": 4.08202, "loss": 4.08202, "time": 0.71588} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.09472, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26484, "top5_acc": 0.50078, "loss_cls": 4.05035, "loss": 4.05035, "time": 0.71744} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.09471, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25906, "top5_acc": 0.49391, "loss_cls": 4.09602, "loss": 4.09602, "time": 0.71722} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.0947, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26109, "top5_acc": 0.51484, "loss_cls": 4.07368, "loss": 4.07368, "time": 0.72396} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.09469, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25734, "top5_acc": 0.50844, "loss_cls": 4.04576, "loss": 4.04576, "time": 0.72226} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.09467, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26, "top5_acc": 0.51469, "loss_cls": 4.03439, "loss": 4.03439, "time": 0.72092} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.09466, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.24609, "top5_acc": 0.49641, "loss_cls": 4.11531, "loss": 4.11531, "time": 0.72124} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.09465, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25703, "top5_acc": 0.50344, "loss_cls": 4.06362, "loss": 4.06362, "time": 0.72117} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.09464, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24375, "top5_acc": 0.49781, "loss_cls": 4.12366, "loss": 4.12366, "time": 0.72547} +{"mode": "train", "epoch": 23, "iter": 1300, "lr": 0.09462, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25109, "top5_acc": 0.51031, "loss_cls": 4.07305, "loss": 4.07305, "time": 0.72194} +{"mode": "train", "epoch": 23, "iter": 1400, "lr": 0.09461, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24766, "top5_acc": 0.50453, "loss_cls": 4.10181, "loss": 4.10181, "time": 0.72173} +{"mode": "train", "epoch": 23, "iter": 1500, "lr": 0.0946, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2525, "top5_acc": 0.50391, "loss_cls": 4.08027, "loss": 4.08027, "time": 0.72151} +{"mode": "train", "epoch": 23, "iter": 1600, "lr": 0.09459, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25281, "top5_acc": 0.50266, "loss_cls": 4.09553, "loss": 4.09553, "time": 0.72328} +{"mode": "train", "epoch": 23, "iter": 1700, "lr": 0.09457, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24875, "top5_acc": 0.50344, "loss_cls": 4.08851, "loss": 4.08851, "time": 0.72527} +{"mode": "train", "epoch": 23, "iter": 1800, "lr": 0.09456, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25469, "top5_acc": 0.50766, "loss_cls": 4.06465, "loss": 4.06465, "time": 0.72436} +{"mode": "train", "epoch": 23, "iter": 1900, "lr": 0.09455, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25109, "top5_acc": 0.49578, "loss_cls": 4.09101, "loss": 4.09101, "time": 0.71807} +{"mode": "train", "epoch": 23, "iter": 2000, "lr": 0.09453, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26047, "top5_acc": 0.50359, "loss_cls": 4.08289, "loss": 4.08289, "time": 0.72116} +{"mode": "train", "epoch": 23, "iter": 2100, "lr": 0.09452, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26125, "top5_acc": 0.50359, "loss_cls": 4.07445, "loss": 4.07445, "time": 0.72336} +{"mode": "train", "epoch": 23, "iter": 2200, "lr": 0.09451, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.2575, "top5_acc": 0.50422, "loss_cls": 4.08172, "loss": 4.08172, "time": 0.72186} +{"mode": "train", "epoch": 23, "iter": 2300, "lr": 0.0945, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.25688, "top5_acc": 0.50844, "loss_cls": 4.07907, "loss": 4.07907, "time": 0.72273} +{"mode": "train", "epoch": 23, "iter": 2400, "lr": 0.09448, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25375, "top5_acc": 0.50391, "loss_cls": 4.09407, "loss": 4.09407, "time": 0.72436} +{"mode": "train", "epoch": 23, "iter": 2500, "lr": 0.09447, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25719, "top5_acc": 0.50422, "loss_cls": 4.08639, "loss": 4.08639, "time": 0.72182} +{"mode": "train", "epoch": 23, "iter": 2600, "lr": 0.09446, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24844, "top5_acc": 0.50266, "loss_cls": 4.10076, "loss": 4.10076, "time": 0.72361} +{"mode": "train", "epoch": 23, "iter": 2700, "lr": 0.09445, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24719, "top5_acc": 0.49703, "loss_cls": 4.11841, "loss": 4.11841, "time": 0.7246} +{"mode": "train", "epoch": 23, "iter": 2800, "lr": 0.09443, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.24578, "top5_acc": 0.4975, "loss_cls": 4.13964, "loss": 4.13964, "time": 0.72385} +{"mode": "train", "epoch": 23, "iter": 2900, "lr": 0.09442, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24859, "top5_acc": 0.495, "loss_cls": 4.13475, "loss": 4.13475, "time": 0.72418} +{"mode": "train", "epoch": 23, "iter": 3000, "lr": 0.09441, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25922, "top5_acc": 0.50375, "loss_cls": 4.09415, "loss": 4.09415, "time": 0.7265} +{"mode": "train", "epoch": 23, "iter": 3100, "lr": 0.09439, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24547, "top5_acc": 0.49188, "loss_cls": 4.14257, "loss": 4.14257, "time": 0.71624} +{"mode": "train", "epoch": 23, "iter": 3200, "lr": 0.09438, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25797, "top5_acc": 0.50328, "loss_cls": 4.08882, "loss": 4.08882, "time": 0.71851} +{"mode": "train", "epoch": 23, "iter": 3300, "lr": 0.09437, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24984, "top5_acc": 0.50062, "loss_cls": 4.1016, "loss": 4.1016, "time": 0.71511} +{"mode": "train", "epoch": 23, "iter": 3400, "lr": 0.09436, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.24734, "top5_acc": 0.48859, "loss_cls": 4.14172, "loss": 4.14172, "time": 0.71756} +{"mode": "train", "epoch": 23, "iter": 3500, "lr": 0.09434, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25, "top5_acc": 0.49031, "loss_cls": 4.14375, "loss": 4.14375, "time": 0.71593} +{"mode": "train", "epoch": 23, "iter": 3600, "lr": 0.09433, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25156, "top5_acc": 0.49984, "loss_cls": 4.09206, "loss": 4.09206, "time": 0.71572} +{"mode": "train", "epoch": 23, "iter": 3700, "lr": 0.09432, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25734, "top5_acc": 0.5075, "loss_cls": 4.09614, "loss": 4.09614, "time": 0.72187} +{"mode": "val", "epoch": 23, "iter": 309, "lr": 0.09431, "top1_acc": 0.19278, "top5_acc": 0.42152, "mean_class_accuracy": 0.19249} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.0943, "memory": 15990, "data_time": 1.49398, "top1_acc": 0.24938, "top5_acc": 0.49359, "loss_cls": 4.10506, "loss": 4.10506, "time": 2.21467} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.09428, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25969, "top5_acc": 0.51125, "loss_cls": 4.08277, "loss": 4.08277, "time": 0.71651} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.09427, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25156, "top5_acc": 0.50828, "loss_cls": 4.06746, "loss": 4.06746, "time": 0.71761} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.09426, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25078, "top5_acc": 0.50406, "loss_cls": 4.09467, "loss": 4.09467, "time": 0.71752} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.09425, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24516, "top5_acc": 0.5025, "loss_cls": 4.10363, "loss": 4.10363, "time": 0.71429} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.09423, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25609, "top5_acc": 0.50109, "loss_cls": 4.06786, "loss": 4.06786, "time": 0.71625} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.09422, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25688, "top5_acc": 0.50578, "loss_cls": 4.06162, "loss": 4.06162, "time": 0.71471} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.09421, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25234, "top5_acc": 0.50062, "loss_cls": 4.09152, "loss": 4.09152, "time": 0.71702} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.09419, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26156, "top5_acc": 0.50813, "loss_cls": 4.04519, "loss": 4.04519, "time": 0.72092} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.09418, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25453, "top5_acc": 0.50469, "loss_cls": 4.08276, "loss": 4.08276, "time": 0.72095} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.09417, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25922, "top5_acc": 0.51125, "loss_cls": 4.08828, "loss": 4.08828, "time": 0.72415} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.09415, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25438, "top5_acc": 0.50734, "loss_cls": 4.08896, "loss": 4.08896, "time": 0.72333} +{"mode": "train", "epoch": 24, "iter": 1300, "lr": 0.09414, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.24953, "top5_acc": 0.4975, "loss_cls": 4.10343, "loss": 4.10343, "time": 0.71758} +{"mode": "train", "epoch": 24, "iter": 1400, "lr": 0.09413, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25062, "top5_acc": 0.50641, "loss_cls": 4.10342, "loss": 4.10342, "time": 0.72328} +{"mode": "train", "epoch": 24, "iter": 1500, "lr": 0.09411, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26141, "top5_acc": 0.50922, "loss_cls": 4.06347, "loss": 4.06347, "time": 0.71814} +{"mode": "train", "epoch": 24, "iter": 1600, "lr": 0.0941, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.25469, "top5_acc": 0.49984, "loss_cls": 4.06966, "loss": 4.06966, "time": 0.7216} +{"mode": "train", "epoch": 24, "iter": 1700, "lr": 0.09409, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26172, "top5_acc": 0.50938, "loss_cls": 4.06297, "loss": 4.06297, "time": 0.7233} +{"mode": "train", "epoch": 24, "iter": 1800, "lr": 0.09407, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26687, "top5_acc": 0.50828, "loss_cls": 4.04484, "loss": 4.04484, "time": 0.72168} +{"mode": "train", "epoch": 24, "iter": 1900, "lr": 0.09406, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26266, "top5_acc": 0.50172, "loss_cls": 4.07586, "loss": 4.07586, "time": 0.72157} +{"mode": "train", "epoch": 24, "iter": 2000, "lr": 0.09405, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25703, "top5_acc": 0.50297, "loss_cls": 4.10107, "loss": 4.10107, "time": 0.72833} +{"mode": "train", "epoch": 24, "iter": 2100, "lr": 0.09404, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25766, "top5_acc": 0.50313, "loss_cls": 4.09341, "loss": 4.09341, "time": 0.72141} +{"mode": "train", "epoch": 24, "iter": 2200, "lr": 0.09402, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25203, "top5_acc": 0.50109, "loss_cls": 4.09951, "loss": 4.09951, "time": 0.7209} +{"mode": "train", "epoch": 24, "iter": 2300, "lr": 0.09401, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25109, "top5_acc": 0.49266, "loss_cls": 4.14022, "loss": 4.14022, "time": 0.72497} +{"mode": "train", "epoch": 24, "iter": 2400, "lr": 0.094, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24781, "top5_acc": 0.50219, "loss_cls": 4.10036, "loss": 4.10036, "time": 0.72169} +{"mode": "train", "epoch": 24, "iter": 2500, "lr": 0.09398, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.26859, "top5_acc": 0.5125, "loss_cls": 4.04132, "loss": 4.04132, "time": 0.72433} +{"mode": "train", "epoch": 24, "iter": 2600, "lr": 0.09397, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25984, "top5_acc": 0.50219, "loss_cls": 4.09891, "loss": 4.09891, "time": 0.72214} +{"mode": "train", "epoch": 24, "iter": 2700, "lr": 0.09396, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25953, "top5_acc": 0.49906, "loss_cls": 4.10782, "loss": 4.10782, "time": 0.72143} +{"mode": "train", "epoch": 24, "iter": 2800, "lr": 0.09394, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24719, "top5_acc": 0.49406, "loss_cls": 4.12874, "loss": 4.12874, "time": 0.72267} +{"mode": "train", "epoch": 24, "iter": 2900, "lr": 0.09393, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2625, "top5_acc": 0.51203, "loss_cls": 4.01776, "loss": 4.01776, "time": 0.71963} +{"mode": "train", "epoch": 24, "iter": 3000, "lr": 0.09392, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25094, "top5_acc": 0.50078, "loss_cls": 4.10201, "loss": 4.10201, "time": 0.72291} +{"mode": "train", "epoch": 24, "iter": 3100, "lr": 0.0939, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26172, "top5_acc": 0.50406, "loss_cls": 4.08135, "loss": 4.08135, "time": 0.7199} +{"mode": "train", "epoch": 24, "iter": 3200, "lr": 0.09389, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26484, "top5_acc": 0.51516, "loss_cls": 4.05761, "loss": 4.05761, "time": 0.71545} +{"mode": "train", "epoch": 24, "iter": 3300, "lr": 0.09388, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25312, "top5_acc": 0.49672, "loss_cls": 4.10398, "loss": 4.10398, "time": 0.71965} +{"mode": "train", "epoch": 24, "iter": 3400, "lr": 0.09386, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25344, "top5_acc": 0.50016, "loss_cls": 4.1046, "loss": 4.1046, "time": 0.71983} +{"mode": "train", "epoch": 24, "iter": 3500, "lr": 0.09385, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26266, "top5_acc": 0.49703, "loss_cls": 4.10305, "loss": 4.10305, "time": 0.71446} +{"mode": "train", "epoch": 24, "iter": 3600, "lr": 0.09384, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25656, "top5_acc": 0.49859, "loss_cls": 4.12201, "loss": 4.12201, "time": 0.71736} +{"mode": "train", "epoch": 24, "iter": 3700, "lr": 0.09382, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24891, "top5_acc": 0.49938, "loss_cls": 4.1096, "loss": 4.1096, "time": 0.71642} +{"mode": "val", "epoch": 24, "iter": 309, "lr": 0.09382, "top1_acc": 0.1754, "top5_acc": 0.40566, "mean_class_accuracy": 0.1753} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.0938, "memory": 15990, "data_time": 1.48175, "top1_acc": 0.25531, "top5_acc": 0.50859, "loss_cls": 4.05986, "loss": 4.05986, "time": 2.20417} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.09379, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26062, "top5_acc": 0.51531, "loss_cls": 4.02722, "loss": 4.02722, "time": 0.71733} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.09378, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26453, "top5_acc": 0.50641, "loss_cls": 4.08324, "loss": 4.08324, "time": 0.71441} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.09376, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26219, "top5_acc": 0.51719, "loss_cls": 4.05869, "loss": 4.05869, "time": 0.71645} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.09375, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26375, "top5_acc": 0.51156, "loss_cls": 4.03789, "loss": 4.03789, "time": 0.71972} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.09373, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26203, "top5_acc": 0.50781, "loss_cls": 4.05376, "loss": 4.05376, "time": 0.71452} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.09372, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24734, "top5_acc": 0.49344, "loss_cls": 4.14228, "loss": 4.14228, "time": 0.71234} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.09371, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25203, "top5_acc": 0.50359, "loss_cls": 4.09932, "loss": 4.09932, "time": 0.71496} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.09369, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25891, "top5_acc": 0.50656, "loss_cls": 4.04611, "loss": 4.04611, "time": 0.7179} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.09368, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25531, "top5_acc": 0.50797, "loss_cls": 4.08682, "loss": 4.08682, "time": 0.72091} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.09367, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26203, "top5_acc": 0.50172, "loss_cls": 4.09846, "loss": 4.09846, "time": 0.72479} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.09365, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25672, "top5_acc": 0.49891, "loss_cls": 4.09305, "loss": 4.09305, "time": 0.72167} +{"mode": "train", "epoch": 25, "iter": 1300, "lr": 0.09364, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26047, "top5_acc": 0.50266, "loss_cls": 4.08103, "loss": 4.08103, "time": 0.72444} +{"mode": "train", "epoch": 25, "iter": 1400, "lr": 0.09363, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.26203, "top5_acc": 0.51, "loss_cls": 4.06238, "loss": 4.06238, "time": 0.72318} +{"mode": "train", "epoch": 25, "iter": 1500, "lr": 0.09361, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25344, "top5_acc": 0.51203, "loss_cls": 4.03934, "loss": 4.03934, "time": 0.72065} +{"mode": "train", "epoch": 25, "iter": 1600, "lr": 0.0936, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25141, "top5_acc": 0.50672, "loss_cls": 4.09958, "loss": 4.09958, "time": 0.72059} +{"mode": "train", "epoch": 25, "iter": 1700, "lr": 0.09358, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24984, "top5_acc": 0.50266, "loss_cls": 4.08746, "loss": 4.08746, "time": 0.72193} +{"mode": "train", "epoch": 25, "iter": 1800, "lr": 0.09357, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25844, "top5_acc": 0.51719, "loss_cls": 4.02344, "loss": 4.02344, "time": 0.72193} +{"mode": "train", "epoch": 25, "iter": 1900, "lr": 0.09356, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26156, "top5_acc": 0.50781, "loss_cls": 4.03897, "loss": 4.03897, "time": 0.72714} +{"mode": "train", "epoch": 25, "iter": 2000, "lr": 0.09354, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26375, "top5_acc": 0.51453, "loss_cls": 4.0373, "loss": 4.0373, "time": 0.71866} +{"mode": "train", "epoch": 25, "iter": 2100, "lr": 0.09353, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.24062, "top5_acc": 0.49359, "loss_cls": 4.13915, "loss": 4.13915, "time": 0.72275} +{"mode": "train", "epoch": 25, "iter": 2200, "lr": 0.09352, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25938, "top5_acc": 0.51344, "loss_cls": 4.04661, "loss": 4.04661, "time": 0.72386} +{"mode": "train", "epoch": 25, "iter": 2300, "lr": 0.0935, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26578, "top5_acc": 0.50094, "loss_cls": 4.08527, "loss": 4.08527, "time": 0.723} +{"mode": "train", "epoch": 25, "iter": 2400, "lr": 0.09349, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25219, "top5_acc": 0.50672, "loss_cls": 4.07964, "loss": 4.07964, "time": 0.72289} +{"mode": "train", "epoch": 25, "iter": 2500, "lr": 0.09347, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26047, "top5_acc": 0.51422, "loss_cls": 4.05473, "loss": 4.05473, "time": 0.72125} +{"mode": "train", "epoch": 25, "iter": 2600, "lr": 0.09346, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25328, "top5_acc": 0.49828, "loss_cls": 4.1095, "loss": 4.1095, "time": 0.72398} +{"mode": "train", "epoch": 25, "iter": 2700, "lr": 0.09345, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25125, "top5_acc": 0.49875, "loss_cls": 4.10056, "loss": 4.10056, "time": 0.72114} +{"mode": "train", "epoch": 25, "iter": 2800, "lr": 0.09343, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26422, "top5_acc": 0.51297, "loss_cls": 4.07139, "loss": 4.07139, "time": 0.72145} +{"mode": "train", "epoch": 25, "iter": 2900, "lr": 0.09342, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25297, "top5_acc": 0.5025, "loss_cls": 4.10814, "loss": 4.10814, "time": 0.7225} +{"mode": "train", "epoch": 25, "iter": 3000, "lr": 0.09341, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26188, "top5_acc": 0.51734, "loss_cls": 4.0376, "loss": 4.0376, "time": 0.71899} +{"mode": "train", "epoch": 25, "iter": 3100, "lr": 0.09339, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25844, "top5_acc": 0.50141, "loss_cls": 4.0831, "loss": 4.0831, "time": 0.71769} +{"mode": "train", "epoch": 25, "iter": 3200, "lr": 0.09338, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25375, "top5_acc": 0.50469, "loss_cls": 4.11643, "loss": 4.11643, "time": 0.71456} +{"mode": "train", "epoch": 25, "iter": 3300, "lr": 0.09336, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.24828, "top5_acc": 0.50109, "loss_cls": 4.1038, "loss": 4.1038, "time": 0.71652} +{"mode": "train", "epoch": 25, "iter": 3400, "lr": 0.09335, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.25938, "top5_acc": 0.51031, "loss_cls": 4.06851, "loss": 4.06851, "time": 0.71654} +{"mode": "train", "epoch": 25, "iter": 3500, "lr": 0.09334, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2575, "top5_acc": 0.50531, "loss_cls": 4.11831, "loss": 4.11831, "time": 0.71534} +{"mode": "train", "epoch": 25, "iter": 3600, "lr": 0.09332, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2575, "top5_acc": 0.51141, "loss_cls": 4.09418, "loss": 4.09418, "time": 0.71726} +{"mode": "train", "epoch": 25, "iter": 3700, "lr": 0.09331, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25266, "top5_acc": 0.50328, "loss_cls": 4.07406, "loss": 4.07406, "time": 0.71629} +{"mode": "val", "epoch": 25, "iter": 309, "lr": 0.0933, "top1_acc": 0.20341, "top5_acc": 0.42734, "mean_class_accuracy": 0.20309} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.09329, "memory": 15990, "data_time": 1.5087, "top1_acc": 0.26875, "top5_acc": 0.52328, "loss_cls": 4.01749, "loss": 4.01749, "time": 2.22447} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.09327, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25766, "top5_acc": 0.50938, "loss_cls": 4.0341, "loss": 4.0341, "time": 0.72031} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.09326, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25109, "top5_acc": 0.50609, "loss_cls": 4.06519, "loss": 4.06519, "time": 0.71594} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.09325, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26516, "top5_acc": 0.51594, "loss_cls": 4.02855, "loss": 4.02855, "time": 0.71772} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.09323, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26906, "top5_acc": 0.52578, "loss_cls": 4.00858, "loss": 4.00858, "time": 0.71444} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.09322, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.26266, "top5_acc": 0.50656, "loss_cls": 4.05876, "loss": 4.05876, "time": 0.71399} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.0932, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25484, "top5_acc": 0.51203, "loss_cls": 4.07807, "loss": 4.07807, "time": 0.7162} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.09319, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26125, "top5_acc": 0.51078, "loss_cls": 4.04759, "loss": 4.04759, "time": 0.71719} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.09318, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26234, "top5_acc": 0.50203, "loss_cls": 4.05498, "loss": 4.05498, "time": 0.71998} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.09316, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25656, "top5_acc": 0.49891, "loss_cls": 4.09262, "loss": 4.09262, "time": 0.71922} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.09315, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25344, "top5_acc": 0.49766, "loss_cls": 4.1018, "loss": 4.1018, "time": 0.71932} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.09313, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26109, "top5_acc": 0.51297, "loss_cls": 4.01866, "loss": 4.01866, "time": 0.72081} +{"mode": "train", "epoch": 26, "iter": 1300, "lr": 0.09312, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26172, "top5_acc": 0.50234, "loss_cls": 4.10247, "loss": 4.10247, "time": 0.72123} +{"mode": "train", "epoch": 26, "iter": 1400, "lr": 0.0931, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26281, "top5_acc": 0.49938, "loss_cls": 4.05342, "loss": 4.05342, "time": 0.72352} +{"mode": "train", "epoch": 26, "iter": 1500, "lr": 0.09309, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25734, "top5_acc": 0.50578, "loss_cls": 4.06291, "loss": 4.06291, "time": 0.7177} +{"mode": "train", "epoch": 26, "iter": 1600, "lr": 0.09308, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.24219, "top5_acc": 0.49297, "loss_cls": 4.14411, "loss": 4.14411, "time": 0.7248} +{"mode": "train", "epoch": 26, "iter": 1700, "lr": 0.09306, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25484, "top5_acc": 0.50484, "loss_cls": 4.07983, "loss": 4.07983, "time": 0.72024} +{"mode": "train", "epoch": 26, "iter": 1800, "lr": 0.09305, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25641, "top5_acc": 0.49484, "loss_cls": 4.08971, "loss": 4.08971, "time": 0.7204} +{"mode": "train", "epoch": 26, "iter": 1900, "lr": 0.09303, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26469, "top5_acc": 0.51, "loss_cls": 4.04103, "loss": 4.04103, "time": 0.72287} +{"mode": "train", "epoch": 26, "iter": 2000, "lr": 0.09302, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25672, "top5_acc": 0.50516, "loss_cls": 4.09057, "loss": 4.09057, "time": 0.72439} +{"mode": "train", "epoch": 26, "iter": 2100, "lr": 0.093, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25219, "top5_acc": 0.50469, "loss_cls": 4.10676, "loss": 4.10676, "time": 0.71864} +{"mode": "train", "epoch": 26, "iter": 2200, "lr": 0.09299, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25922, "top5_acc": 0.49828, "loss_cls": 4.08796, "loss": 4.08796, "time": 0.7203} +{"mode": "train", "epoch": 26, "iter": 2300, "lr": 0.09298, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25844, "top5_acc": 0.50828, "loss_cls": 4.0709, "loss": 4.0709, "time": 0.72157} +{"mode": "train", "epoch": 26, "iter": 2400, "lr": 0.09296, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26391, "top5_acc": 0.50688, "loss_cls": 4.07472, "loss": 4.07472, "time": 0.72117} +{"mode": "train", "epoch": 26, "iter": 2500, "lr": 0.09295, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26344, "top5_acc": 0.51062, "loss_cls": 4.03492, "loss": 4.03492, "time": 0.72082} +{"mode": "train", "epoch": 26, "iter": 2600, "lr": 0.09293, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26812, "top5_acc": 0.515, "loss_cls": 4.03955, "loss": 4.03955, "time": 0.722} +{"mode": "train", "epoch": 26, "iter": 2700, "lr": 0.09292, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26484, "top5_acc": 0.51141, "loss_cls": 4.04795, "loss": 4.04795, "time": 0.72505} +{"mode": "train", "epoch": 26, "iter": 2800, "lr": 0.0929, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26547, "top5_acc": 0.51297, "loss_cls": 4.04253, "loss": 4.04253, "time": 0.72026} +{"mode": "train", "epoch": 26, "iter": 2900, "lr": 0.09289, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25719, "top5_acc": 0.49781, "loss_cls": 4.10166, "loss": 4.10166, "time": 0.72389} +{"mode": "train", "epoch": 26, "iter": 3000, "lr": 0.09288, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25625, "top5_acc": 0.49844, "loss_cls": 4.1062, "loss": 4.1062, "time": 0.7201} +{"mode": "train", "epoch": 26, "iter": 3100, "lr": 0.09286, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25312, "top5_acc": 0.50297, "loss_cls": 4.12917, "loss": 4.12917, "time": 0.71768} +{"mode": "train", "epoch": 26, "iter": 3200, "lr": 0.09285, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25484, "top5_acc": 0.50562, "loss_cls": 4.06308, "loss": 4.06308, "time": 0.71714} +{"mode": "train", "epoch": 26, "iter": 3300, "lr": 0.09283, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24969, "top5_acc": 0.50359, "loss_cls": 4.08732, "loss": 4.08732, "time": 0.7167} +{"mode": "train", "epoch": 26, "iter": 3400, "lr": 0.09282, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25594, "top5_acc": 0.50187, "loss_cls": 4.09094, "loss": 4.09094, "time": 0.71742} +{"mode": "train", "epoch": 26, "iter": 3500, "lr": 0.0928, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26, "top5_acc": 0.51016, "loss_cls": 4.05438, "loss": 4.05438, "time": 0.71861} +{"mode": "train", "epoch": 26, "iter": 3600, "lr": 0.09279, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25828, "top5_acc": 0.50594, "loss_cls": 4.08375, "loss": 4.08375, "time": 0.72255} +{"mode": "train", "epoch": 26, "iter": 3700, "lr": 0.09278, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24875, "top5_acc": 0.49781, "loss_cls": 4.1196, "loss": 4.1196, "time": 0.72482} +{"mode": "val", "epoch": 26, "iter": 309, "lr": 0.09277, "top1_acc": 0.17368, "top5_acc": 0.3892, "mean_class_accuracy": 0.17354} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.09275, "memory": 15990, "data_time": 1.46976, "top1_acc": 0.255, "top5_acc": 0.51062, "loss_cls": 4.05873, "loss": 4.05873, "time": 2.19136} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.09274, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26328, "top5_acc": 0.51672, "loss_cls": 4.01571, "loss": 4.01571, "time": 0.71853} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.09272, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25406, "top5_acc": 0.50641, "loss_cls": 4.0943, "loss": 4.0943, "time": 0.71984} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.09271, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25859, "top5_acc": 0.50938, "loss_cls": 4.07809, "loss": 4.07809, "time": 0.72027} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.0927, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2575, "top5_acc": 0.51484, "loss_cls": 4.05907, "loss": 4.05907, "time": 0.71501} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.09268, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25469, "top5_acc": 0.505, "loss_cls": 4.08575, "loss": 4.08575, "time": 0.71975} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.09267, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27062, "top5_acc": 0.52609, "loss_cls": 4.00425, "loss": 4.00425, "time": 0.72546} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.09265, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26016, "top5_acc": 0.50469, "loss_cls": 4.06634, "loss": 4.06634, "time": 0.72115} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.09264, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25406, "top5_acc": 0.50266, "loss_cls": 4.0995, "loss": 4.0995, "time": 0.72251} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.09262, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25938, "top5_acc": 0.505, "loss_cls": 4.0738, "loss": 4.0738, "time": 0.72086} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.09261, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26719, "top5_acc": 0.51016, "loss_cls": 4.04688, "loss": 4.04688, "time": 0.7207} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.09259, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2625, "top5_acc": 0.50938, "loss_cls": 4.06581, "loss": 4.06581, "time": 0.72359} +{"mode": "train", "epoch": 27, "iter": 1300, "lr": 0.09258, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26375, "top5_acc": 0.51531, "loss_cls": 4.03531, "loss": 4.03531, "time": 0.72206} +{"mode": "train", "epoch": 27, "iter": 1400, "lr": 0.09256, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27312, "top5_acc": 0.52297, "loss_cls": 3.98301, "loss": 3.98301, "time": 0.72216} +{"mode": "train", "epoch": 27, "iter": 1500, "lr": 0.09255, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26234, "top5_acc": 0.515, "loss_cls": 4.04919, "loss": 4.04919, "time": 0.72312} +{"mode": "train", "epoch": 27, "iter": 1600, "lr": 0.09253, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25156, "top5_acc": 0.50359, "loss_cls": 4.09632, "loss": 4.09632, "time": 0.72367} +{"mode": "train", "epoch": 27, "iter": 1700, "lr": 0.09252, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25734, "top5_acc": 0.50578, "loss_cls": 4.06664, "loss": 4.06664, "time": 0.7246} +{"mode": "train", "epoch": 27, "iter": 1800, "lr": 0.09251, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.25797, "top5_acc": 0.51047, "loss_cls": 4.05724, "loss": 4.05724, "time": 0.72391} +{"mode": "train", "epoch": 27, "iter": 1900, "lr": 0.09249, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25922, "top5_acc": 0.5125, "loss_cls": 4.03587, "loss": 4.03587, "time": 0.72282} +{"mode": "train", "epoch": 27, "iter": 2000, "lr": 0.09248, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25906, "top5_acc": 0.50719, "loss_cls": 4.05818, "loss": 4.05818, "time": 0.72598} +{"mode": "train", "epoch": 27, "iter": 2100, "lr": 0.09246, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26109, "top5_acc": 0.50922, "loss_cls": 4.03888, "loss": 4.03888, "time": 0.72537} +{"mode": "train", "epoch": 27, "iter": 2200, "lr": 0.09245, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.25547, "top5_acc": 0.49516, "loss_cls": 4.11323, "loss": 4.11323, "time": 0.72333} +{"mode": "train", "epoch": 27, "iter": 2300, "lr": 0.09243, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25641, "top5_acc": 0.50562, "loss_cls": 4.09198, "loss": 4.09198, "time": 0.72177} +{"mode": "train", "epoch": 27, "iter": 2400, "lr": 0.09242, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26562, "top5_acc": 0.50797, "loss_cls": 4.05196, "loss": 4.05196, "time": 0.72298} +{"mode": "train", "epoch": 27, "iter": 2500, "lr": 0.0924, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25328, "top5_acc": 0.50859, "loss_cls": 4.10407, "loss": 4.10407, "time": 0.71978} +{"mode": "train", "epoch": 27, "iter": 2600, "lr": 0.09239, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25859, "top5_acc": 0.50781, "loss_cls": 4.06723, "loss": 4.06723, "time": 0.72499} +{"mode": "train", "epoch": 27, "iter": 2700, "lr": 0.09237, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25875, "top5_acc": 0.50938, "loss_cls": 4.05034, "loss": 4.05034, "time": 0.72501} +{"mode": "train", "epoch": 27, "iter": 2800, "lr": 0.09236, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.24953, "top5_acc": 0.50922, "loss_cls": 4.08229, "loss": 4.08229, "time": 0.72378} +{"mode": "train", "epoch": 27, "iter": 2900, "lr": 0.09234, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26125, "top5_acc": 0.50391, "loss_cls": 4.07873, "loss": 4.07873, "time": 0.72164} +{"mode": "train", "epoch": 27, "iter": 3000, "lr": 0.09233, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25703, "top5_acc": 0.50547, "loss_cls": 4.07522, "loss": 4.07522, "time": 0.71741} +{"mode": "train", "epoch": 27, "iter": 3100, "lr": 0.09231, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25969, "top5_acc": 0.50797, "loss_cls": 4.07147, "loss": 4.07147, "time": 0.71576} +{"mode": "train", "epoch": 27, "iter": 3200, "lr": 0.0923, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25297, "top5_acc": 0.50281, "loss_cls": 4.128, "loss": 4.128, "time": 0.71716} +{"mode": "train", "epoch": 27, "iter": 3300, "lr": 0.09228, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26375, "top5_acc": 0.51016, "loss_cls": 4.07463, "loss": 4.07463, "time": 0.71753} +{"mode": "train", "epoch": 27, "iter": 3400, "lr": 0.09227, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25891, "top5_acc": 0.50422, "loss_cls": 4.0736, "loss": 4.0736, "time": 0.71657} +{"mode": "train", "epoch": 27, "iter": 3500, "lr": 0.09225, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25156, "top5_acc": 0.49766, "loss_cls": 4.12366, "loss": 4.12366, "time": 0.71851} +{"mode": "train", "epoch": 27, "iter": 3600, "lr": 0.09224, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25016, "top5_acc": 0.49719, "loss_cls": 4.11688, "loss": 4.11688, "time": 0.71764} +{"mode": "train", "epoch": 27, "iter": 3700, "lr": 0.09222, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26062, "top5_acc": 0.50813, "loss_cls": 4.07899, "loss": 4.07899, "time": 0.71534} +{"mode": "val", "epoch": 27, "iter": 309, "lr": 0.09222, "top1_acc": 0.18594, "top5_acc": 0.4166, "mean_class_accuracy": 0.18575} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.0922, "memory": 15990, "data_time": 1.48107, "top1_acc": 0.26422, "top5_acc": 0.51344, "loss_cls": 4.01158, "loss": 4.01158, "time": 2.19845} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.09219, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25531, "top5_acc": 0.51828, "loss_cls": 4.05077, "loss": 4.05077, "time": 0.72082} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.09217, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26328, "top5_acc": 0.51719, "loss_cls": 4.04141, "loss": 4.04141, "time": 0.7165} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.09216, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25641, "top5_acc": 0.50734, "loss_cls": 4.06018, "loss": 4.06018, "time": 0.71815} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.09214, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25109, "top5_acc": 0.50813, "loss_cls": 4.04575, "loss": 4.04575, "time": 0.71864} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.09213, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.255, "top5_acc": 0.49625, "loss_cls": 4.11225, "loss": 4.11225, "time": 0.7193} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.09211, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25891, "top5_acc": 0.51656, "loss_cls": 4.02368, "loss": 4.02368, "time": 0.71563} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.0921, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26094, "top5_acc": 0.50266, "loss_cls": 4.09261, "loss": 4.09261, "time": 0.71478} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.09208, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25656, "top5_acc": 0.50531, "loss_cls": 4.08452, "loss": 4.08452, "time": 0.71764} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.09207, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24969, "top5_acc": 0.50875, "loss_cls": 4.11271, "loss": 4.11271, "time": 0.72088} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.09205, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25922, "top5_acc": 0.49547, "loss_cls": 4.08556, "loss": 4.08556, "time": 0.72013} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.09204, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24562, "top5_acc": 0.50469, "loss_cls": 4.0852, "loss": 4.0852, "time": 0.72034} +{"mode": "train", "epoch": 28, "iter": 1300, "lr": 0.09202, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25047, "top5_acc": 0.49688, "loss_cls": 4.08846, "loss": 4.08846, "time": 0.72328} +{"mode": "train", "epoch": 28, "iter": 1400, "lr": 0.09201, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25625, "top5_acc": 0.50547, "loss_cls": 4.06313, "loss": 4.06313, "time": 0.72097} +{"mode": "train", "epoch": 28, "iter": 1500, "lr": 0.09199, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25688, "top5_acc": 0.50266, "loss_cls": 4.0902, "loss": 4.0902, "time": 0.71983} +{"mode": "train", "epoch": 28, "iter": 1600, "lr": 0.09198, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25812, "top5_acc": 0.50516, "loss_cls": 4.07013, "loss": 4.07013, "time": 0.72145} +{"mode": "train", "epoch": 28, "iter": 1700, "lr": 0.09196, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25438, "top5_acc": 0.51141, "loss_cls": 4.08317, "loss": 4.08317, "time": 0.72705} +{"mode": "train", "epoch": 28, "iter": 1800, "lr": 0.09194, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26281, "top5_acc": 0.515, "loss_cls": 4.04701, "loss": 4.04701, "time": 0.72528} +{"mode": "train", "epoch": 28, "iter": 1900, "lr": 0.09193, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26031, "top5_acc": 0.51438, "loss_cls": 4.03663, "loss": 4.03663, "time": 0.72458} +{"mode": "train", "epoch": 28, "iter": 2000, "lr": 0.09191, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25734, "top5_acc": 0.50516, "loss_cls": 4.07364, "loss": 4.07364, "time": 0.72406} +{"mode": "train", "epoch": 28, "iter": 2100, "lr": 0.0919, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25828, "top5_acc": 0.50438, "loss_cls": 4.07943, "loss": 4.07943, "time": 0.72104} +{"mode": "train", "epoch": 28, "iter": 2200, "lr": 0.09188, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.255, "top5_acc": 0.49984, "loss_cls": 4.10119, "loss": 4.10119, "time": 0.72201} +{"mode": "train", "epoch": 28, "iter": 2300, "lr": 0.09187, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26297, "top5_acc": 0.51688, "loss_cls": 4.04193, "loss": 4.04193, "time": 0.724} +{"mode": "train", "epoch": 28, "iter": 2400, "lr": 0.09185, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25531, "top5_acc": 0.50031, "loss_cls": 4.13024, "loss": 4.13024, "time": 0.72378} +{"mode": "train", "epoch": 28, "iter": 2500, "lr": 0.09184, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.24562, "top5_acc": 0.49391, "loss_cls": 4.12509, "loss": 4.12509, "time": 0.72471} +{"mode": "train", "epoch": 28, "iter": 2600, "lr": 0.09182, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26797, "top5_acc": 0.52516, "loss_cls": 4.03518, "loss": 4.03518, "time": 0.72349} +{"mode": "train", "epoch": 28, "iter": 2700, "lr": 0.09181, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27016, "top5_acc": 0.51062, "loss_cls": 4.02846, "loss": 4.02846, "time": 0.72239} +{"mode": "train", "epoch": 28, "iter": 2800, "lr": 0.09179, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25609, "top5_acc": 0.49562, "loss_cls": 4.10399, "loss": 4.10399, "time": 0.72435} +{"mode": "train", "epoch": 28, "iter": 2900, "lr": 0.09178, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25281, "top5_acc": 0.50953, "loss_cls": 4.11009, "loss": 4.11009, "time": 0.72349} +{"mode": "train", "epoch": 28, "iter": 3000, "lr": 0.09176, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.2525, "top5_acc": 0.50516, "loss_cls": 4.06725, "loss": 4.06725, "time": 0.71702} +{"mode": "train", "epoch": 28, "iter": 3100, "lr": 0.09175, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25391, "top5_acc": 0.49859, "loss_cls": 4.0943, "loss": 4.0943, "time": 0.71801} +{"mode": "train", "epoch": 28, "iter": 3200, "lr": 0.09173, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26094, "top5_acc": 0.50562, "loss_cls": 4.06499, "loss": 4.06499, "time": 0.716} +{"mode": "train", "epoch": 28, "iter": 3300, "lr": 0.09172, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.265, "top5_acc": 0.51641, "loss_cls": 4.02754, "loss": 4.02754, "time": 0.71399} +{"mode": "train", "epoch": 28, "iter": 3400, "lr": 0.0917, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26562, "top5_acc": 0.51297, "loss_cls": 4.06029, "loss": 4.06029, "time": 0.71626} +{"mode": "train", "epoch": 28, "iter": 3500, "lr": 0.09168, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25859, "top5_acc": 0.50859, "loss_cls": 4.05874, "loss": 4.05874, "time": 0.71799} +{"mode": "train", "epoch": 28, "iter": 3600, "lr": 0.09167, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27016, "top5_acc": 0.51531, "loss_cls": 4.03504, "loss": 4.03504, "time": 0.71939} +{"mode": "train", "epoch": 28, "iter": 3700, "lr": 0.09165, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24984, "top5_acc": 0.50453, "loss_cls": 4.08831, "loss": 4.08831, "time": 0.71746} +{"mode": "val", "epoch": 28, "iter": 309, "lr": 0.09165, "top1_acc": 0.18488, "top5_acc": 0.40825, "mean_class_accuracy": 0.18478} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.09163, "memory": 15990, "data_time": 1.51752, "top1_acc": 0.27531, "top5_acc": 0.52469, "loss_cls": 3.98067, "loss": 3.98067, "time": 2.2343} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.09162, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26609, "top5_acc": 0.51156, "loss_cls": 4.04018, "loss": 4.04018, "time": 0.71691} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.0916, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.2725, "top5_acc": 0.51438, "loss_cls": 4.00484, "loss": 4.00484, "time": 0.71495} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.09158, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25719, "top5_acc": 0.50766, "loss_cls": 4.05293, "loss": 4.05293, "time": 0.72001} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.09157, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25953, "top5_acc": 0.50328, "loss_cls": 4.07956, "loss": 4.07956, "time": 0.71548} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.09155, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26469, "top5_acc": 0.52, "loss_cls": 4.01409, "loss": 4.01409, "time": 0.71594} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.09154, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25766, "top5_acc": 0.51281, "loss_cls": 4.04427, "loss": 4.04427, "time": 0.72116} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.09152, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25953, "top5_acc": 0.49922, "loss_cls": 4.10082, "loss": 4.10082, "time": 0.71628} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.09151, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25703, "top5_acc": 0.50406, "loss_cls": 4.07193, "loss": 4.07193, "time": 0.71871} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.09149, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.265, "top5_acc": 0.50797, "loss_cls": 4.06501, "loss": 4.06501, "time": 0.72425} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.09148, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25969, "top5_acc": 0.51141, "loss_cls": 4.0647, "loss": 4.0647, "time": 0.72642} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.09146, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25859, "top5_acc": 0.50797, "loss_cls": 4.04579, "loss": 4.04579, "time": 0.72059} +{"mode": "train", "epoch": 29, "iter": 1300, "lr": 0.09144, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25859, "top5_acc": 0.50859, "loss_cls": 4.05115, "loss": 4.05115, "time": 0.72083} +{"mode": "train", "epoch": 29, "iter": 1400, "lr": 0.09143, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26234, "top5_acc": 0.50391, "loss_cls": 4.10403, "loss": 4.10403, "time": 0.72043} +{"mode": "train", "epoch": 29, "iter": 1500, "lr": 0.09141, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25766, "top5_acc": 0.49438, "loss_cls": 4.12606, "loss": 4.12606, "time": 0.72372} +{"mode": "train", "epoch": 29, "iter": 1600, "lr": 0.0914, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25078, "top5_acc": 0.49562, "loss_cls": 4.09791, "loss": 4.09791, "time": 0.71948} +{"mode": "train", "epoch": 29, "iter": 1700, "lr": 0.09138, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.24984, "top5_acc": 0.49781, "loss_cls": 4.10085, "loss": 4.10085, "time": 0.72266} +{"mode": "train", "epoch": 29, "iter": 1800, "lr": 0.09137, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25891, "top5_acc": 0.50219, "loss_cls": 4.04082, "loss": 4.04082, "time": 0.72456} +{"mode": "train", "epoch": 29, "iter": 1900, "lr": 0.09135, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25766, "top5_acc": 0.50688, "loss_cls": 4.06877, "loss": 4.06877, "time": 0.72023} +{"mode": "train", "epoch": 29, "iter": 2000, "lr": 0.09133, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26031, "top5_acc": 0.50531, "loss_cls": 4.07289, "loss": 4.07289, "time": 0.72009} +{"mode": "train", "epoch": 29, "iter": 2100, "lr": 0.09132, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26109, "top5_acc": 0.51547, "loss_cls": 4.06155, "loss": 4.06155, "time": 0.72241} +{"mode": "train", "epoch": 29, "iter": 2200, "lr": 0.0913, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25484, "top5_acc": 0.51422, "loss_cls": 4.08372, "loss": 4.08372, "time": 0.72442} +{"mode": "train", "epoch": 29, "iter": 2300, "lr": 0.09129, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25844, "top5_acc": 0.50938, "loss_cls": 4.0872, "loss": 4.0872, "time": 0.72316} +{"mode": "train", "epoch": 29, "iter": 2400, "lr": 0.09127, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25391, "top5_acc": 0.50375, "loss_cls": 4.11208, "loss": 4.11208, "time": 0.72643} +{"mode": "train", "epoch": 29, "iter": 2500, "lr": 0.09126, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26328, "top5_acc": 0.50234, "loss_cls": 4.05558, "loss": 4.05558, "time": 0.71788} +{"mode": "train", "epoch": 29, "iter": 2600, "lr": 0.09124, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26422, "top5_acc": 0.51203, "loss_cls": 4.04781, "loss": 4.04781, "time": 0.72349} +{"mode": "train", "epoch": 29, "iter": 2700, "lr": 0.09122, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25203, "top5_acc": 0.51422, "loss_cls": 4.05457, "loss": 4.05457, "time": 0.72282} +{"mode": "train", "epoch": 29, "iter": 2800, "lr": 0.09121, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.2575, "top5_acc": 0.50266, "loss_cls": 4.06269, "loss": 4.06269, "time": 0.72136} +{"mode": "train", "epoch": 29, "iter": 2900, "lr": 0.09119, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26375, "top5_acc": 0.5075, "loss_cls": 4.05809, "loss": 4.05809, "time": 0.71704} +{"mode": "train", "epoch": 29, "iter": 3000, "lr": 0.09118, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25172, "top5_acc": 0.50266, "loss_cls": 4.1127, "loss": 4.1127, "time": 0.71782} +{"mode": "train", "epoch": 29, "iter": 3100, "lr": 0.09116, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.26094, "top5_acc": 0.50391, "loss_cls": 4.07571, "loss": 4.07571, "time": 0.719} +{"mode": "train", "epoch": 29, "iter": 3200, "lr": 0.09114, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26, "top5_acc": 0.50719, "loss_cls": 4.06454, "loss": 4.06454, "time": 0.71644} +{"mode": "train", "epoch": 29, "iter": 3300, "lr": 0.09113, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25203, "top5_acc": 0.50219, "loss_cls": 4.11896, "loss": 4.11896, "time": 0.719} +{"mode": "train", "epoch": 29, "iter": 3400, "lr": 0.09111, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25531, "top5_acc": 0.49922, "loss_cls": 4.09582, "loss": 4.09582, "time": 0.7149} +{"mode": "train", "epoch": 29, "iter": 3500, "lr": 0.0911, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26188, "top5_acc": 0.51062, "loss_cls": 4.05137, "loss": 4.05137, "time": 0.71527} +{"mode": "train", "epoch": 29, "iter": 3600, "lr": 0.09108, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25453, "top5_acc": 0.5025, "loss_cls": 4.08862, "loss": 4.08862, "time": 0.71627} +{"mode": "train", "epoch": 29, "iter": 3700, "lr": 0.09106, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25719, "top5_acc": 0.51188, "loss_cls": 4.06048, "loss": 4.06048, "time": 0.71695} +{"mode": "val", "epoch": 29, "iter": 309, "lr": 0.09106, "top1_acc": 0.14623, "top5_acc": 0.35957, "mean_class_accuracy": 0.14608} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.09104, "memory": 15990, "data_time": 1.62668, "top1_acc": 0.26969, "top5_acc": 0.52141, "loss_cls": 3.97667, "loss": 3.97667, "time": 2.4681} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.09103, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25625, "top5_acc": 0.50875, "loss_cls": 4.06264, "loss": 4.06264, "time": 0.83807} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.09101, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26656, "top5_acc": 0.51484, "loss_cls": 4.03228, "loss": 4.03228, "time": 0.84396} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.09099, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25625, "top5_acc": 0.51812, "loss_cls": 4.0539, "loss": 4.0539, "time": 0.84223} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.09098, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26438, "top5_acc": 0.51094, "loss_cls": 4.05836, "loss": 4.05836, "time": 0.83816} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.09096, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.25672, "top5_acc": 0.50047, "loss_cls": 4.08828, "loss": 4.08828, "time": 0.84757} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.09095, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.26203, "top5_acc": 0.51891, "loss_cls": 4.03514, "loss": 4.03514, "time": 0.83927} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.09093, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26125, "top5_acc": 0.51828, "loss_cls": 4.05366, "loss": 4.05366, "time": 0.84042} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.09091, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26344, "top5_acc": 0.50562, "loss_cls": 4.0782, "loss": 4.0782, "time": 0.8403} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.0909, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26297, "top5_acc": 0.51438, "loss_cls": 4.04306, "loss": 4.04306, "time": 0.82856} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.09088, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26469, "top5_acc": 0.51781, "loss_cls": 4.04095, "loss": 4.04095, "time": 0.84217} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.09087, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26016, "top5_acc": 0.50672, "loss_cls": 4.06796, "loss": 4.06796, "time": 0.84119} +{"mode": "train", "epoch": 30, "iter": 1300, "lr": 0.09085, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25562, "top5_acc": 0.5075, "loss_cls": 4.08803, "loss": 4.08803, "time": 0.84023} +{"mode": "train", "epoch": 30, "iter": 1400, "lr": 0.09083, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26547, "top5_acc": 0.51594, "loss_cls": 4.03769, "loss": 4.03769, "time": 0.83682} +{"mode": "train", "epoch": 30, "iter": 1500, "lr": 0.09082, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25609, "top5_acc": 0.50422, "loss_cls": 4.08431, "loss": 4.08431, "time": 0.84031} +{"mode": "train", "epoch": 30, "iter": 1600, "lr": 0.0908, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26625, "top5_acc": 0.50641, "loss_cls": 4.045, "loss": 4.045, "time": 0.83469} +{"mode": "train", "epoch": 30, "iter": 1700, "lr": 0.09078, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26516, "top5_acc": 0.51797, "loss_cls": 4.01965, "loss": 4.01965, "time": 0.84581} +{"mode": "train", "epoch": 30, "iter": 1800, "lr": 0.09077, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.265, "top5_acc": 0.51641, "loss_cls": 4.04171, "loss": 4.04171, "time": 0.83868} +{"mode": "train", "epoch": 30, "iter": 1900, "lr": 0.09075, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26453, "top5_acc": 0.51016, "loss_cls": 4.04175, "loss": 4.04175, "time": 0.83676} +{"mode": "train", "epoch": 30, "iter": 2000, "lr": 0.09074, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25766, "top5_acc": 0.50313, "loss_cls": 4.07592, "loss": 4.07592, "time": 0.83971} +{"mode": "train", "epoch": 30, "iter": 2100, "lr": 0.09072, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27234, "top5_acc": 0.5175, "loss_cls": 4.01345, "loss": 4.01345, "time": 0.84221} +{"mode": "train", "epoch": 30, "iter": 2200, "lr": 0.0907, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25547, "top5_acc": 0.50469, "loss_cls": 4.08045, "loss": 4.08045, "time": 0.83841} +{"mode": "train", "epoch": 30, "iter": 2300, "lr": 0.09069, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.25641, "top5_acc": 0.51234, "loss_cls": 4.0791, "loss": 4.0791, "time": 0.84341} +{"mode": "train", "epoch": 30, "iter": 2400, "lr": 0.09067, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26016, "top5_acc": 0.50406, "loss_cls": 4.07159, "loss": 4.07159, "time": 0.83986} +{"mode": "train", "epoch": 30, "iter": 2500, "lr": 0.09065, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.26062, "top5_acc": 0.51313, "loss_cls": 4.06333, "loss": 4.06333, "time": 0.83923} +{"mode": "train", "epoch": 30, "iter": 2600, "lr": 0.09064, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26234, "top5_acc": 0.52078, "loss_cls": 4.01374, "loss": 4.01374, "time": 0.8426} +{"mode": "train", "epoch": 30, "iter": 2700, "lr": 0.09062, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24844, "top5_acc": 0.50859, "loss_cls": 4.08836, "loss": 4.08836, "time": 0.84061} +{"mode": "train", "epoch": 30, "iter": 2800, "lr": 0.09061, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26172, "top5_acc": 0.50438, "loss_cls": 4.05333, "loss": 4.05333, "time": 0.8356} +{"mode": "train", "epoch": 30, "iter": 2900, "lr": 0.09059, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.25797, "top5_acc": 0.51875, "loss_cls": 4.04217, "loss": 4.04217, "time": 0.83707} +{"mode": "train", "epoch": 30, "iter": 3000, "lr": 0.09057, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2625, "top5_acc": 0.51391, "loss_cls": 4.03296, "loss": 4.03296, "time": 0.82937} +{"mode": "train", "epoch": 30, "iter": 3100, "lr": 0.09056, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25609, "top5_acc": 0.49984, "loss_cls": 4.09333, "loss": 4.09333, "time": 0.8242} +{"mode": "train", "epoch": 30, "iter": 3200, "lr": 0.09054, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27328, "top5_acc": 0.52125, "loss_cls": 3.9806, "loss": 3.9806, "time": 0.82656} +{"mode": "train", "epoch": 30, "iter": 3300, "lr": 0.09052, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25172, "top5_acc": 0.49781, "loss_cls": 4.11447, "loss": 4.11447, "time": 0.82683} +{"mode": "train", "epoch": 30, "iter": 3400, "lr": 0.09051, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26391, "top5_acc": 0.50813, "loss_cls": 4.04828, "loss": 4.04828, "time": 0.8334} +{"mode": "train", "epoch": 30, "iter": 3500, "lr": 0.09049, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26141, "top5_acc": 0.50156, "loss_cls": 4.10179, "loss": 4.10179, "time": 0.83549} +{"mode": "train", "epoch": 30, "iter": 3600, "lr": 0.09047, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25766, "top5_acc": 0.51422, "loss_cls": 4.0779, "loss": 4.0779, "time": 0.83525} +{"mode": "train", "epoch": 30, "iter": 3700, "lr": 0.09046, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26609, "top5_acc": 0.51172, "loss_cls": 4.03873, "loss": 4.03873, "time": 0.83621} +{"mode": "val", "epoch": 30, "iter": 309, "lr": 0.09045, "top1_acc": 0.18867, "top5_acc": 0.41468, "mean_class_accuracy": 0.18858} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.09043, "memory": 15990, "data_time": 1.56952, "top1_acc": 0.27484, "top5_acc": 0.52188, "loss_cls": 4.21038, "loss": 4.21038, "time": 2.60208} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.09042, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26484, "top5_acc": 0.51781, "loss_cls": 4.25712, "loss": 4.25712, "time": 0.8533} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.0904, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27203, "top5_acc": 0.52, "loss_cls": 4.24481, "loss": 4.24481, "time": 0.85273} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.09039, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26594, "top5_acc": 0.50609, "loss_cls": 4.27758, "loss": 4.27758, "time": 0.85722} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.09037, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25609, "top5_acc": 0.51141, "loss_cls": 4.28939, "loss": 4.28939, "time": 0.85832} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.09035, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25719, "top5_acc": 0.50094, "loss_cls": 4.31857, "loss": 4.31857, "time": 0.85781} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.09034, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24422, "top5_acc": 0.50109, "loss_cls": 4.35098, "loss": 4.35098, "time": 0.86021} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.09032, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25562, "top5_acc": 0.51359, "loss_cls": 4.2926, "loss": 4.2926, "time": 0.85453} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0903, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26031, "top5_acc": 0.50578, "loss_cls": 4.31751, "loss": 4.31751, "time": 0.8567} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.09029, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26109, "top5_acc": 0.50234, "loss_cls": 4.32035, "loss": 4.32035, "time": 0.86054} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.09027, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26703, "top5_acc": 0.51094, "loss_cls": 4.28236, "loss": 4.28236, "time": 0.85468} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.09025, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26156, "top5_acc": 0.50656, "loss_cls": 4.29456, "loss": 4.29456, "time": 0.86018} +{"mode": "train", "epoch": 31, "iter": 1300, "lr": 0.09024, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25547, "top5_acc": 0.50516, "loss_cls": 4.2962, "loss": 4.2962, "time": 0.85869} +{"mode": "train", "epoch": 31, "iter": 1400, "lr": 0.09022, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25172, "top5_acc": 0.51297, "loss_cls": 4.26853, "loss": 4.26853, "time": 0.85783} +{"mode": "train", "epoch": 31, "iter": 1500, "lr": 0.0902, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24391, "top5_acc": 0.495, "loss_cls": 4.35558, "loss": 4.35558, "time": 0.86048} +{"mode": "train", "epoch": 31, "iter": 1600, "lr": 0.09019, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27109, "top5_acc": 0.52328, "loss_cls": 4.22116, "loss": 4.22116, "time": 0.86154} +{"mode": "train", "epoch": 31, "iter": 1700, "lr": 0.09017, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26141, "top5_acc": 0.51203, "loss_cls": 4.29659, "loss": 4.29659, "time": 0.8627} +{"mode": "train", "epoch": 31, "iter": 1800, "lr": 0.09015, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25781, "top5_acc": 0.51656, "loss_cls": 4.27576, "loss": 4.27576, "time": 0.86481} +{"mode": "train", "epoch": 31, "iter": 1900, "lr": 0.09014, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26125, "top5_acc": 0.50734, "loss_cls": 4.30379, "loss": 4.30379, "time": 0.8599} +{"mode": "train", "epoch": 31, "iter": 2000, "lr": 0.09012, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26297, "top5_acc": 0.52281, "loss_cls": 4.23664, "loss": 4.23664, "time": 0.85993} +{"mode": "train", "epoch": 31, "iter": 2100, "lr": 0.0901, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26453, "top5_acc": 0.52406, "loss_cls": 4.24649, "loss": 4.24649, "time": 0.86871} +{"mode": "train", "epoch": 31, "iter": 2200, "lr": 0.09009, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26594, "top5_acc": 0.5175, "loss_cls": 4.2358, "loss": 4.2358, "time": 0.86269} +{"mode": "train", "epoch": 31, "iter": 2300, "lr": 0.09007, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26344, "top5_acc": 0.51391, "loss_cls": 4.26029, "loss": 4.26029, "time": 0.86191} +{"mode": "train", "epoch": 31, "iter": 2400, "lr": 0.09005, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25594, "top5_acc": 0.50813, "loss_cls": 4.31028, "loss": 4.31028, "time": 0.85852} +{"mode": "train", "epoch": 31, "iter": 2500, "lr": 0.09004, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26344, "top5_acc": 0.50125, "loss_cls": 4.30542, "loss": 4.30542, "time": 0.86181} +{"mode": "train", "epoch": 31, "iter": 2600, "lr": 0.09002, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26359, "top5_acc": 0.50688, "loss_cls": 4.31467, "loss": 4.31467, "time": 0.86036} +{"mode": "train", "epoch": 31, "iter": 2700, "lr": 0.09, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26859, "top5_acc": 0.51188, "loss_cls": 4.23171, "loss": 4.23171, "time": 0.86034} +{"mode": "train", "epoch": 31, "iter": 2800, "lr": 0.08999, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.26328, "top5_acc": 0.50703, "loss_cls": 4.29651, "loss": 4.29651, "time": 0.86232} +{"mode": "train", "epoch": 31, "iter": 2900, "lr": 0.08997, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26469, "top5_acc": 0.50609, "loss_cls": 4.28809, "loss": 4.28809, "time": 0.85408} +{"mode": "train", "epoch": 31, "iter": 3000, "lr": 0.08995, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26484, "top5_acc": 0.50656, "loss_cls": 4.27298, "loss": 4.27298, "time": 0.85127} +{"mode": "train", "epoch": 31, "iter": 3100, "lr": 0.08994, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.26281, "top5_acc": 0.50281, "loss_cls": 4.31516, "loss": 4.31516, "time": 0.85174} +{"mode": "train", "epoch": 31, "iter": 3200, "lr": 0.08992, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.25672, "top5_acc": 0.50078, "loss_cls": 4.33467, "loss": 4.33467, "time": 0.86482} +{"mode": "train", "epoch": 31, "iter": 3300, "lr": 0.0899, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26375, "top5_acc": 0.50828, "loss_cls": 4.26783, "loss": 4.26783, "time": 0.85228} +{"mode": "train", "epoch": 31, "iter": 3400, "lr": 0.08989, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25156, "top5_acc": 0.50922, "loss_cls": 4.29275, "loss": 4.29275, "time": 0.85439} +{"mode": "train", "epoch": 31, "iter": 3500, "lr": 0.08987, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26031, "top5_acc": 0.51062, "loss_cls": 4.28447, "loss": 4.28447, "time": 0.85345} +{"mode": "train", "epoch": 31, "iter": 3600, "lr": 0.08985, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25484, "top5_acc": 0.51844, "loss_cls": 4.28038, "loss": 4.28038, "time": 0.85703} +{"mode": "train", "epoch": 31, "iter": 3700, "lr": 0.08983, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25484, "top5_acc": 0.50203, "loss_cls": 4.33525, "loss": 4.33525, "time": 0.85563} +{"mode": "val", "epoch": 31, "iter": 309, "lr": 0.08983, "top1_acc": 0.17834, "top5_acc": 0.40095, "mean_class_accuracy": 0.1781} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.08981, "memory": 15990, "data_time": 1.59316, "top1_acc": 0.27016, "top5_acc": 0.51641, "loss_cls": 4.20611, "loss": 4.20611, "time": 2.63171} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.08979, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26156, "top5_acc": 0.50578, "loss_cls": 4.28476, "loss": 4.28476, "time": 0.86321} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.08978, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26859, "top5_acc": 0.53031, "loss_cls": 4.194, "loss": 4.194, "time": 0.85722} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.08976, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27406, "top5_acc": 0.51844, "loss_cls": 4.22585, "loss": 4.22585, "time": 0.85922} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.08974, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26703, "top5_acc": 0.53, "loss_cls": 4.22162, "loss": 4.22162, "time": 0.8621} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.08973, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26359, "top5_acc": 0.5175, "loss_cls": 4.26796, "loss": 4.26796, "time": 0.86375} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.08971, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27187, "top5_acc": 0.52078, "loss_cls": 4.23327, "loss": 4.23327, "time": 0.86185} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.08969, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26375, "top5_acc": 0.50531, "loss_cls": 4.28984, "loss": 4.28984, "time": 0.85833} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.08967, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.255, "top5_acc": 0.50078, "loss_cls": 4.30689, "loss": 4.30689, "time": 0.85957} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.08966, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26438, "top5_acc": 0.51422, "loss_cls": 4.2575, "loss": 4.2575, "time": 0.86385} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.08964, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26625, "top5_acc": 0.51562, "loss_cls": 4.23469, "loss": 4.23469, "time": 0.86487} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.08962, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26172, "top5_acc": 0.51641, "loss_cls": 4.26872, "loss": 4.26872, "time": 0.86096} +{"mode": "train", "epoch": 32, "iter": 1300, "lr": 0.08961, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26891, "top5_acc": 0.51938, "loss_cls": 4.24815, "loss": 4.24815, "time": 0.85983} +{"mode": "train", "epoch": 32, "iter": 1400, "lr": 0.08959, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.27391, "top5_acc": 0.51812, "loss_cls": 4.21954, "loss": 4.21954, "time": 0.86109} +{"mode": "train", "epoch": 32, "iter": 1500, "lr": 0.08957, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26578, "top5_acc": 0.51547, "loss_cls": 4.24839, "loss": 4.24839, "time": 0.8593} +{"mode": "train", "epoch": 32, "iter": 1600, "lr": 0.08955, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25609, "top5_acc": 0.50609, "loss_cls": 4.30357, "loss": 4.30357, "time": 0.85621} +{"mode": "train", "epoch": 32, "iter": 1700, "lr": 0.08954, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26094, "top5_acc": 0.515, "loss_cls": 4.23063, "loss": 4.23063, "time": 0.86154} +{"mode": "train", "epoch": 32, "iter": 1800, "lr": 0.08952, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26641, "top5_acc": 0.51359, "loss_cls": 4.28035, "loss": 4.28035, "time": 0.86353} +{"mode": "train", "epoch": 32, "iter": 1900, "lr": 0.0895, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26359, "top5_acc": 0.49938, "loss_cls": 4.26585, "loss": 4.26585, "time": 0.86266} +{"mode": "train", "epoch": 32, "iter": 2000, "lr": 0.08949, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.24625, "top5_acc": 0.49891, "loss_cls": 4.35243, "loss": 4.35243, "time": 0.86565} +{"mode": "train", "epoch": 32, "iter": 2100, "lr": 0.08947, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25453, "top5_acc": 0.51219, "loss_cls": 4.31208, "loss": 4.31208, "time": 0.8645} +{"mode": "train", "epoch": 32, "iter": 2200, "lr": 0.08945, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25391, "top5_acc": 0.49875, "loss_cls": 4.33069, "loss": 4.33069, "time": 0.86004} +{"mode": "train", "epoch": 32, "iter": 2300, "lr": 0.08943, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.26656, "top5_acc": 0.51359, "loss_cls": 4.24121, "loss": 4.24121, "time": 0.86295} +{"mode": "train", "epoch": 32, "iter": 2400, "lr": 0.08942, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26297, "top5_acc": 0.50719, "loss_cls": 4.29268, "loss": 4.29268, "time": 0.86452} +{"mode": "train", "epoch": 32, "iter": 2500, "lr": 0.0894, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.24766, "top5_acc": 0.49984, "loss_cls": 4.31766, "loss": 4.31766, "time": 0.86831} +{"mode": "train", "epoch": 32, "iter": 2600, "lr": 0.08938, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26469, "top5_acc": 0.51562, "loss_cls": 4.24479, "loss": 4.24479, "time": 0.86682} +{"mode": "train", "epoch": 32, "iter": 2700, "lr": 0.08937, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25797, "top5_acc": 0.50906, "loss_cls": 4.28448, "loss": 4.28448, "time": 0.86442} +{"mode": "train", "epoch": 32, "iter": 2800, "lr": 0.08935, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.26188, "top5_acc": 0.50781, "loss_cls": 4.31079, "loss": 4.31079, "time": 0.86303} +{"mode": "train", "epoch": 32, "iter": 2900, "lr": 0.08933, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.24781, "top5_acc": 0.50094, "loss_cls": 4.32654, "loss": 4.32654, "time": 0.85754} +{"mode": "train", "epoch": 32, "iter": 3000, "lr": 0.08931, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.26406, "top5_acc": 0.52781, "loss_cls": 4.20523, "loss": 4.20523, "time": 0.85173} +{"mode": "train", "epoch": 32, "iter": 3100, "lr": 0.0893, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26719, "top5_acc": 0.51062, "loss_cls": 4.27278, "loss": 4.27278, "time": 0.85246} +{"mode": "train", "epoch": 32, "iter": 3200, "lr": 0.08928, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25844, "top5_acc": 0.50609, "loss_cls": 4.27888, "loss": 4.27888, "time": 0.85045} +{"mode": "train", "epoch": 32, "iter": 3300, "lr": 0.08926, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25844, "top5_acc": 0.50297, "loss_cls": 4.29325, "loss": 4.29325, "time": 0.85228} +{"mode": "train", "epoch": 32, "iter": 3400, "lr": 0.08924, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25906, "top5_acc": 0.51188, "loss_cls": 4.2706, "loss": 4.2706, "time": 0.85138} +{"mode": "train", "epoch": 32, "iter": 3500, "lr": 0.08923, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.275, "top5_acc": 0.51438, "loss_cls": 4.22834, "loss": 4.22834, "time": 0.86071} +{"mode": "train", "epoch": 32, "iter": 3600, "lr": 0.08921, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25281, "top5_acc": 0.50906, "loss_cls": 4.27708, "loss": 4.27708, "time": 0.8593} +{"mode": "train", "epoch": 32, "iter": 3700, "lr": 0.08919, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27, "top5_acc": 0.52016, "loss_cls": 4.2631, "loss": 4.2631, "time": 0.85696} +{"mode": "val", "epoch": 32, "iter": 309, "lr": 0.08918, "top1_acc": 0.19035, "top5_acc": 0.41762, "mean_class_accuracy": 0.19013} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.08917, "memory": 15990, "data_time": 1.64538, "top1_acc": 0.26953, "top5_acc": 0.51625, "loss_cls": 4.23412, "loss": 4.23412, "time": 2.68544} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.08915, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26641, "top5_acc": 0.50797, "loss_cls": 4.28628, "loss": 4.28628, "time": 0.86392} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.08913, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25859, "top5_acc": 0.50578, "loss_cls": 4.2916, "loss": 4.2916, "time": 0.86443} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.08912, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.2625, "top5_acc": 0.50922, "loss_cls": 4.29423, "loss": 4.29423, "time": 0.86902} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.0891, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26891, "top5_acc": 0.51328, "loss_cls": 4.23676, "loss": 4.23676, "time": 0.86801} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.08908, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25906, "top5_acc": 0.50703, "loss_cls": 4.31247, "loss": 4.31247, "time": 0.86586} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.08906, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27125, "top5_acc": 0.52547, "loss_cls": 4.21905, "loss": 4.21905, "time": 0.86048} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.08905, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2575, "top5_acc": 0.51313, "loss_cls": 4.28105, "loss": 4.28105, "time": 0.85739} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.08903, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25797, "top5_acc": 0.505, "loss_cls": 4.29234, "loss": 4.29234, "time": 0.86211} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.08901, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25828, "top5_acc": 0.50109, "loss_cls": 4.33031, "loss": 4.33031, "time": 0.86299} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.08899, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.26344, "top5_acc": 0.51625, "loss_cls": 4.2244, "loss": 4.2244, "time": 0.8658} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.08898, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26375, "top5_acc": 0.5125, "loss_cls": 4.27217, "loss": 4.27217, "time": 0.86293} +{"mode": "train", "epoch": 33, "iter": 1300, "lr": 0.08896, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25031, "top5_acc": 0.50469, "loss_cls": 4.28532, "loss": 4.28532, "time": 0.85685} +{"mode": "train", "epoch": 33, "iter": 1400, "lr": 0.08894, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26031, "top5_acc": 0.51406, "loss_cls": 4.2667, "loss": 4.2667, "time": 0.86016} +{"mode": "train", "epoch": 33, "iter": 1500, "lr": 0.08892, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.26859, "top5_acc": 0.52172, "loss_cls": 4.24602, "loss": 4.24602, "time": 0.86613} +{"mode": "train", "epoch": 33, "iter": 1600, "lr": 0.08891, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25516, "top5_acc": 0.505, "loss_cls": 4.27928, "loss": 4.27928, "time": 0.86051} +{"mode": "train", "epoch": 33, "iter": 1700, "lr": 0.08889, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25938, "top5_acc": 0.5075, "loss_cls": 4.28631, "loss": 4.28631, "time": 0.85965} +{"mode": "train", "epoch": 33, "iter": 1800, "lr": 0.08887, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26, "top5_acc": 0.51172, "loss_cls": 4.233, "loss": 4.233, "time": 0.86295} +{"mode": "train", "epoch": 33, "iter": 1900, "lr": 0.08885, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.26469, "top5_acc": 0.50969, "loss_cls": 4.26537, "loss": 4.26537, "time": 0.86557} +{"mode": "train", "epoch": 33, "iter": 2000, "lr": 0.08884, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26984, "top5_acc": 0.51953, "loss_cls": 4.24832, "loss": 4.24832, "time": 0.86629} +{"mode": "train", "epoch": 33, "iter": 2100, "lr": 0.08882, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25875, "top5_acc": 0.50125, "loss_cls": 4.30281, "loss": 4.30281, "time": 0.86155} +{"mode": "train", "epoch": 33, "iter": 2200, "lr": 0.0888, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26344, "top5_acc": 0.51313, "loss_cls": 4.26196, "loss": 4.26196, "time": 0.86576} +{"mode": "train", "epoch": 33, "iter": 2300, "lr": 0.08878, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.25672, "top5_acc": 0.51375, "loss_cls": 4.2686, "loss": 4.2686, "time": 0.86512} +{"mode": "train", "epoch": 33, "iter": 2400, "lr": 0.08876, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26234, "top5_acc": 0.51734, "loss_cls": 4.27269, "loss": 4.27269, "time": 0.86013} +{"mode": "train", "epoch": 33, "iter": 2500, "lr": 0.08875, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25844, "top5_acc": 0.50438, "loss_cls": 4.30536, "loss": 4.30536, "time": 0.86303} +{"mode": "train", "epoch": 33, "iter": 2600, "lr": 0.08873, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26062, "top5_acc": 0.50781, "loss_cls": 4.29358, "loss": 4.29358, "time": 0.8638} +{"mode": "train", "epoch": 33, "iter": 2700, "lr": 0.08871, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27375, "top5_acc": 0.52703, "loss_cls": 4.20165, "loss": 4.20165, "time": 0.85823} +{"mode": "train", "epoch": 33, "iter": 2800, "lr": 0.08869, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25281, "top5_acc": 0.49125, "loss_cls": 4.3356, "loss": 4.3356, "time": 0.84697} +{"mode": "train", "epoch": 33, "iter": 2900, "lr": 0.08868, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26734, "top5_acc": 0.51688, "loss_cls": 4.24487, "loss": 4.24487, "time": 0.84291} +{"mode": "train", "epoch": 33, "iter": 3000, "lr": 0.08866, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26219, "top5_acc": 0.51, "loss_cls": 4.27156, "loss": 4.27156, "time": 0.85459} +{"mode": "train", "epoch": 33, "iter": 3100, "lr": 0.08864, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27266, "top5_acc": 0.50672, "loss_cls": 4.25835, "loss": 4.25835, "time": 0.84851} +{"mode": "train", "epoch": 33, "iter": 3200, "lr": 0.08862, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26438, "top5_acc": 0.51625, "loss_cls": 4.27736, "loss": 4.27736, "time": 0.84747} +{"mode": "train", "epoch": 33, "iter": 3300, "lr": 0.08861, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26656, "top5_acc": 0.52188, "loss_cls": 4.23795, "loss": 4.23795, "time": 0.8479} +{"mode": "train", "epoch": 33, "iter": 3400, "lr": 0.08859, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26297, "top5_acc": 0.50656, "loss_cls": 4.28134, "loss": 4.28134, "time": 0.86002} +{"mode": "train", "epoch": 33, "iter": 3500, "lr": 0.08857, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26469, "top5_acc": 0.51703, "loss_cls": 4.27154, "loss": 4.27154, "time": 0.8567} +{"mode": "train", "epoch": 33, "iter": 3600, "lr": 0.08855, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25875, "top5_acc": 0.49719, "loss_cls": 4.31167, "loss": 4.31167, "time": 0.859} +{"mode": "train", "epoch": 33, "iter": 3700, "lr": 0.08853, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25438, "top5_acc": 0.50766, "loss_cls": 4.29434, "loss": 4.29434, "time": 0.86133} +{"mode": "val", "epoch": 33, "iter": 309, "lr": 0.08853, "top1_acc": 0.17723, "top5_acc": 0.39482, "mean_class_accuracy": 0.17672} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.08851, "memory": 15990, "data_time": 1.60053, "top1_acc": 0.26359, "top5_acc": 0.51141, "loss_cls": 4.25599, "loss": 4.25599, "time": 2.64531} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.08849, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.26391, "top5_acc": 0.50969, "loss_cls": 4.26306, "loss": 4.26306, "time": 0.87192} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.08847, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.27359, "top5_acc": 0.52328, "loss_cls": 4.2111, "loss": 4.2111, "time": 0.86634} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.08845, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2775, "top5_acc": 0.51391, "loss_cls": 4.23914, "loss": 4.23914, "time": 0.86366} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.08844, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26266, "top5_acc": 0.52656, "loss_cls": 4.21175, "loss": 4.21175, "time": 0.86204} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.08842, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.26219, "top5_acc": 0.51297, "loss_cls": 4.25148, "loss": 4.25148, "time": 0.86471} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.0884, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26953, "top5_acc": 0.51922, "loss_cls": 4.22225, "loss": 4.22225, "time": 0.85736} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.08838, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27031, "top5_acc": 0.51313, "loss_cls": 4.26764, "loss": 4.26764, "time": 0.86078} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.08836, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25609, "top5_acc": 0.50297, "loss_cls": 4.31216, "loss": 4.31216, "time": 0.85747} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.08835, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26312, "top5_acc": 0.50938, "loss_cls": 4.27817, "loss": 4.27817, "time": 0.8608} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.08833, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.26516, "top5_acc": 0.51016, "loss_cls": 4.27307, "loss": 4.27307, "time": 0.85748} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.08831, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26516, "top5_acc": 0.51516, "loss_cls": 4.25447, "loss": 4.25447, "time": 0.86577} +{"mode": "train", "epoch": 34, "iter": 1300, "lr": 0.08829, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25781, "top5_acc": 0.51062, "loss_cls": 4.27995, "loss": 4.27995, "time": 0.86545} +{"mode": "train", "epoch": 34, "iter": 1400, "lr": 0.08828, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26812, "top5_acc": 0.52266, "loss_cls": 4.21394, "loss": 4.21394, "time": 0.85852} +{"mode": "train", "epoch": 34, "iter": 1500, "lr": 0.08826, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26359, "top5_acc": 0.51219, "loss_cls": 4.26887, "loss": 4.26887, "time": 0.86217} +{"mode": "train", "epoch": 34, "iter": 1600, "lr": 0.08824, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25938, "top5_acc": 0.51656, "loss_cls": 4.23429, "loss": 4.23429, "time": 0.86423} +{"mode": "train", "epoch": 34, "iter": 1700, "lr": 0.08822, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26969, "top5_acc": 0.5275, "loss_cls": 4.21767, "loss": 4.21767, "time": 0.8641} +{"mode": "train", "epoch": 34, "iter": 1800, "lr": 0.0882, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25547, "top5_acc": 0.49812, "loss_cls": 4.31562, "loss": 4.31562, "time": 0.86288} +{"mode": "train", "epoch": 34, "iter": 1900, "lr": 0.08819, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27094, "top5_acc": 0.51969, "loss_cls": 4.23864, "loss": 4.23864, "time": 0.86215} +{"mode": "train", "epoch": 34, "iter": 2000, "lr": 0.08817, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25922, "top5_acc": 0.49656, "loss_cls": 4.2936, "loss": 4.2936, "time": 0.85557} +{"mode": "train", "epoch": 34, "iter": 2100, "lr": 0.08815, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.27016, "top5_acc": 0.51828, "loss_cls": 4.21379, "loss": 4.21379, "time": 0.86226} +{"mode": "train", "epoch": 34, "iter": 2200, "lr": 0.08813, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26703, "top5_acc": 0.52562, "loss_cls": 4.21501, "loss": 4.21501, "time": 0.85945} +{"mode": "train", "epoch": 34, "iter": 2300, "lr": 0.08811, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26469, "top5_acc": 0.51766, "loss_cls": 4.254, "loss": 4.254, "time": 0.85815} +{"mode": "train", "epoch": 34, "iter": 2400, "lr": 0.08809, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.25984, "top5_acc": 0.50906, "loss_cls": 4.28082, "loss": 4.28082, "time": 0.86726} +{"mode": "train", "epoch": 34, "iter": 2500, "lr": 0.08808, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27, "top5_acc": 0.52, "loss_cls": 4.19428, "loss": 4.19428, "time": 0.86141} +{"mode": "train", "epoch": 34, "iter": 2600, "lr": 0.08806, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26188, "top5_acc": 0.50578, "loss_cls": 4.29941, "loss": 4.29941, "time": 0.86164} +{"mode": "train", "epoch": 34, "iter": 2700, "lr": 0.08804, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26703, "top5_acc": 0.51766, "loss_cls": 4.23174, "loss": 4.23174, "time": 0.84662} +{"mode": "train", "epoch": 34, "iter": 2800, "lr": 0.08802, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26125, "top5_acc": 0.50234, "loss_cls": 4.27724, "loss": 4.27724, "time": 0.84888} +{"mode": "train", "epoch": 34, "iter": 2900, "lr": 0.088, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25141, "top5_acc": 0.51297, "loss_cls": 4.31517, "loss": 4.31517, "time": 0.84796} +{"mode": "train", "epoch": 34, "iter": 3000, "lr": 0.08799, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26156, "top5_acc": 0.50375, "loss_cls": 4.28077, "loss": 4.28077, "time": 0.84913} +{"mode": "train", "epoch": 34, "iter": 3100, "lr": 0.08797, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25391, "top5_acc": 0.50281, "loss_cls": 4.32248, "loss": 4.32248, "time": 0.85515} +{"mode": "train", "epoch": 34, "iter": 3200, "lr": 0.08795, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26438, "top5_acc": 0.50891, "loss_cls": 4.27768, "loss": 4.27768, "time": 0.84995} +{"mode": "train", "epoch": 34, "iter": 3300, "lr": 0.08793, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25828, "top5_acc": 0.50672, "loss_cls": 4.26372, "loss": 4.26372, "time": 0.85345} +{"mode": "train", "epoch": 34, "iter": 3400, "lr": 0.08791, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26406, "top5_acc": 0.51438, "loss_cls": 4.26872, "loss": 4.26872, "time": 0.85967} +{"mode": "train", "epoch": 34, "iter": 3500, "lr": 0.08789, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27031, "top5_acc": 0.51438, "loss_cls": 4.25303, "loss": 4.25303, "time": 0.86028} +{"mode": "train", "epoch": 34, "iter": 3600, "lr": 0.08788, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25609, "top5_acc": 0.50781, "loss_cls": 4.29814, "loss": 4.29814, "time": 0.86053} +{"mode": "train", "epoch": 34, "iter": 3700, "lr": 0.08786, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25641, "top5_acc": 0.51422, "loss_cls": 4.28891, "loss": 4.28891, "time": 0.8594} +{"mode": "val", "epoch": 34, "iter": 309, "lr": 0.08785, "top1_acc": 0.20509, "top5_acc": 0.43757, "mean_class_accuracy": 0.20497} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.08783, "memory": 15990, "data_time": 1.55719, "top1_acc": 0.25781, "top5_acc": 0.50844, "loss_cls": 4.26622, "loss": 4.26622, "time": 2.5834} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.08781, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26531, "top5_acc": 0.51281, "loss_cls": 4.26067, "loss": 4.26067, "time": 0.85594} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.0878, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26562, "top5_acc": 0.52219, "loss_cls": 4.22106, "loss": 4.22106, "time": 0.8557} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.08778, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2575, "top5_acc": 0.5075, "loss_cls": 4.26149, "loss": 4.26149, "time": 0.85498} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.08776, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27047, "top5_acc": 0.52078, "loss_cls": 4.20661, "loss": 4.20661, "time": 0.85669} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.08774, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26547, "top5_acc": 0.50859, "loss_cls": 4.26533, "loss": 4.26533, "time": 0.85658} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.08772, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26094, "top5_acc": 0.51641, "loss_cls": 4.25418, "loss": 4.25418, "time": 0.85602} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.0877, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27688, "top5_acc": 0.51734, "loss_cls": 4.19509, "loss": 4.19509, "time": 0.85573} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.08769, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25609, "top5_acc": 0.50922, "loss_cls": 4.2683, "loss": 4.2683, "time": 0.85427} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.08767, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25969, "top5_acc": 0.50031, "loss_cls": 4.31643, "loss": 4.31643, "time": 0.85329} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.08765, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26516, "top5_acc": 0.51375, "loss_cls": 4.28463, "loss": 4.28463, "time": 0.85333} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.08763, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26687, "top5_acc": 0.51656, "loss_cls": 4.24025, "loss": 4.24025, "time": 0.85562} +{"mode": "train", "epoch": 35, "iter": 1300, "lr": 0.08761, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26687, "top5_acc": 0.52969, "loss_cls": 4.19567, "loss": 4.19567, "time": 0.85952} +{"mode": "train", "epoch": 35, "iter": 1400, "lr": 0.08759, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25297, "top5_acc": 0.5075, "loss_cls": 4.29842, "loss": 4.29842, "time": 0.8559} +{"mode": "train", "epoch": 35, "iter": 1500, "lr": 0.08757, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26687, "top5_acc": 0.51891, "loss_cls": 4.22055, "loss": 4.22055, "time": 0.85392} +{"mode": "train", "epoch": 35, "iter": 1600, "lr": 0.08756, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26812, "top5_acc": 0.51391, "loss_cls": 4.24072, "loss": 4.24072, "time": 0.85603} +{"mode": "train", "epoch": 35, "iter": 1700, "lr": 0.08754, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27109, "top5_acc": 0.51188, "loss_cls": 4.25881, "loss": 4.25881, "time": 0.85343} +{"mode": "train", "epoch": 35, "iter": 1800, "lr": 0.08752, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28328, "top5_acc": 0.52, "loss_cls": 4.19324, "loss": 4.19324, "time": 0.85643} +{"mode": "train", "epoch": 35, "iter": 1900, "lr": 0.0875, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26844, "top5_acc": 0.51812, "loss_cls": 4.20072, "loss": 4.20072, "time": 0.85611} +{"mode": "train", "epoch": 35, "iter": 2000, "lr": 0.08748, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26406, "top5_acc": 0.52391, "loss_cls": 4.24426, "loss": 4.24426, "time": 0.85915} +{"mode": "train", "epoch": 35, "iter": 2100, "lr": 0.08746, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25953, "top5_acc": 0.50891, "loss_cls": 4.27367, "loss": 4.27367, "time": 0.8613} +{"mode": "train", "epoch": 35, "iter": 2200, "lr": 0.08745, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26469, "top5_acc": 0.51328, "loss_cls": 4.26312, "loss": 4.26312, "time": 0.85638} +{"mode": "train", "epoch": 35, "iter": 2300, "lr": 0.08743, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26719, "top5_acc": 0.51641, "loss_cls": 4.23524, "loss": 4.23524, "time": 0.85774} +{"mode": "train", "epoch": 35, "iter": 2400, "lr": 0.08741, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26031, "top5_acc": 0.51062, "loss_cls": 4.25966, "loss": 4.25966, "time": 0.85818} +{"mode": "train", "epoch": 35, "iter": 2500, "lr": 0.08739, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26984, "top5_acc": 0.51625, "loss_cls": 4.24925, "loss": 4.24925, "time": 0.86048} +{"mode": "train", "epoch": 35, "iter": 2600, "lr": 0.08737, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26, "top5_acc": 0.50391, "loss_cls": 4.29675, "loss": 4.29675, "time": 0.85123} +{"mode": "train", "epoch": 35, "iter": 2700, "lr": 0.08735, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26547, "top5_acc": 0.51125, "loss_cls": 4.27985, "loss": 4.27985, "time": 0.85491} +{"mode": "train", "epoch": 35, "iter": 2800, "lr": 0.08733, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27078, "top5_acc": 0.52391, "loss_cls": 4.23111, "loss": 4.23111, "time": 0.84201} +{"mode": "train", "epoch": 35, "iter": 2900, "lr": 0.08732, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25734, "top5_acc": 0.50984, "loss_cls": 4.33364, "loss": 4.33364, "time": 0.85206} +{"mode": "train", "epoch": 35, "iter": 3000, "lr": 0.0873, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26484, "top5_acc": 0.50969, "loss_cls": 4.25275, "loss": 4.25275, "time": 0.8458} +{"mode": "train", "epoch": 35, "iter": 3100, "lr": 0.08728, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26266, "top5_acc": 0.51922, "loss_cls": 4.2536, "loss": 4.2536, "time": 0.84422} +{"mode": "train", "epoch": 35, "iter": 3200, "lr": 0.08726, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26188, "top5_acc": 0.50813, "loss_cls": 4.26057, "loss": 4.26057, "time": 0.85432} +{"mode": "train", "epoch": 35, "iter": 3300, "lr": 0.08724, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26562, "top5_acc": 0.50578, "loss_cls": 4.31434, "loss": 4.31434, "time": 0.85025} +{"mode": "train", "epoch": 35, "iter": 3400, "lr": 0.08722, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26969, "top5_acc": 0.515, "loss_cls": 4.25489, "loss": 4.25489, "time": 0.85509} +{"mode": "train", "epoch": 35, "iter": 3500, "lr": 0.0872, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26609, "top5_acc": 0.51, "loss_cls": 4.25123, "loss": 4.25123, "time": 0.84245} +{"mode": "train", "epoch": 35, "iter": 3600, "lr": 0.08718, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25906, "top5_acc": 0.50531, "loss_cls": 4.27158, "loss": 4.27158, "time": 0.84871} +{"mode": "train", "epoch": 35, "iter": 3700, "lr": 0.08717, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25984, "top5_acc": 0.5175, "loss_cls": 4.27622, "loss": 4.27622, "time": 0.84457} +{"mode": "val", "epoch": 35, "iter": 309, "lr": 0.08716, "top1_acc": 0.16421, "top5_acc": 0.38034, "mean_class_accuracy": 0.16418} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.08714, "memory": 15990, "data_time": 1.52893, "top1_acc": 0.27719, "top5_acc": 0.51391, "loss_cls": 4.17144, "loss": 4.17144, "time": 2.54468} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.08712, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27578, "top5_acc": 0.52375, "loss_cls": 4.20764, "loss": 4.20764, "time": 0.84916} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.0871, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27172, "top5_acc": 0.51984, "loss_cls": 4.23334, "loss": 4.23334, "time": 0.84921} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.08708, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27062, "top5_acc": 0.5225, "loss_cls": 4.23352, "loss": 4.23352, "time": 0.84645} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.08706, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26906, "top5_acc": 0.52266, "loss_cls": 4.23409, "loss": 4.23409, "time": 0.8491} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.08704, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27203, "top5_acc": 0.51859, "loss_cls": 4.20249, "loss": 4.20249, "time": 0.85006} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.08703, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.265, "top5_acc": 0.52156, "loss_cls": 4.21558, "loss": 4.21558, "time": 0.85336} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.08701, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25562, "top5_acc": 0.50719, "loss_cls": 4.2609, "loss": 4.2609, "time": 0.85383} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.08699, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26687, "top5_acc": 0.51078, "loss_cls": 4.2571, "loss": 4.2571, "time": 0.84918} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.08697, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.25156, "top5_acc": 0.51234, "loss_cls": 4.28692, "loss": 4.28692, "time": 0.8506} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.08695, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26656, "top5_acc": 0.51656, "loss_cls": 4.2203, "loss": 4.2203, "time": 0.85335} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.08693, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26156, "top5_acc": 0.50641, "loss_cls": 4.27699, "loss": 4.27699, "time": 0.85692} +{"mode": "train", "epoch": 36, "iter": 1300, "lr": 0.08691, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27547, "top5_acc": 0.52469, "loss_cls": 4.20602, "loss": 4.20602, "time": 0.85396} +{"mode": "train", "epoch": 36, "iter": 1400, "lr": 0.08689, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26438, "top5_acc": 0.51641, "loss_cls": 4.22395, "loss": 4.22395, "time": 0.86038} +{"mode": "train", "epoch": 36, "iter": 1500, "lr": 0.08688, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25531, "top5_acc": 0.51562, "loss_cls": 4.29742, "loss": 4.29742, "time": 0.86108} +{"mode": "train", "epoch": 36, "iter": 1600, "lr": 0.08686, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.25469, "top5_acc": 0.50578, "loss_cls": 4.29179, "loss": 4.29179, "time": 0.85691} +{"mode": "train", "epoch": 36, "iter": 1700, "lr": 0.08684, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26516, "top5_acc": 0.51109, "loss_cls": 4.26389, "loss": 4.26389, "time": 0.85936} +{"mode": "train", "epoch": 36, "iter": 1800, "lr": 0.08682, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2775, "top5_acc": 0.52312, "loss_cls": 4.18937, "loss": 4.18937, "time": 0.85508} +{"mode": "train", "epoch": 36, "iter": 1900, "lr": 0.0868, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26109, "top5_acc": 0.50813, "loss_cls": 4.27745, "loss": 4.27745, "time": 0.85634} +{"mode": "train", "epoch": 36, "iter": 2000, "lr": 0.08678, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26641, "top5_acc": 0.52344, "loss_cls": 4.22443, "loss": 4.22443, "time": 0.85815} +{"mode": "train", "epoch": 36, "iter": 2100, "lr": 0.08676, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26312, "top5_acc": 0.51266, "loss_cls": 4.24397, "loss": 4.24397, "time": 0.85504} +{"mode": "train", "epoch": 36, "iter": 2200, "lr": 0.08674, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26906, "top5_acc": 0.51844, "loss_cls": 4.23838, "loss": 4.23838, "time": 0.86013} +{"mode": "train", "epoch": 36, "iter": 2300, "lr": 0.08672, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25328, "top5_acc": 0.50609, "loss_cls": 4.28871, "loss": 4.28871, "time": 0.85903} +{"mode": "train", "epoch": 36, "iter": 2400, "lr": 0.08671, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26844, "top5_acc": 0.51344, "loss_cls": 4.2428, "loss": 4.2428, "time": 0.85965} +{"mode": "train", "epoch": 36, "iter": 2500, "lr": 0.08669, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27531, "top5_acc": 0.52281, "loss_cls": 4.22251, "loss": 4.22251, "time": 0.85339} +{"mode": "train", "epoch": 36, "iter": 2600, "lr": 0.08667, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27562, "top5_acc": 0.51656, "loss_cls": 4.22475, "loss": 4.22475, "time": 0.84667} +{"mode": "train", "epoch": 36, "iter": 2700, "lr": 0.08665, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25969, "top5_acc": 0.50453, "loss_cls": 4.29914, "loss": 4.29914, "time": 0.84876} +{"mode": "train", "epoch": 36, "iter": 2800, "lr": 0.08663, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26016, "top5_acc": 0.52094, "loss_cls": 4.24316, "loss": 4.24316, "time": 0.84755} +{"mode": "train", "epoch": 36, "iter": 2900, "lr": 0.08661, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26547, "top5_acc": 0.52266, "loss_cls": 4.21308, "loss": 4.21308, "time": 0.85786} +{"mode": "train", "epoch": 36, "iter": 3000, "lr": 0.08659, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26703, "top5_acc": 0.51109, "loss_cls": 4.25257, "loss": 4.25257, "time": 0.85157} +{"mode": "train", "epoch": 36, "iter": 3100, "lr": 0.08657, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26172, "top5_acc": 0.50938, "loss_cls": 4.25528, "loss": 4.25528, "time": 0.84641} +{"mode": "train", "epoch": 36, "iter": 3200, "lr": 0.08655, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27109, "top5_acc": 0.52422, "loss_cls": 4.19905, "loss": 4.19905, "time": 0.85082} +{"mode": "train", "epoch": 36, "iter": 3300, "lr": 0.08653, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27109, "top5_acc": 0.51, "loss_cls": 4.23452, "loss": 4.23452, "time": 0.84868} +{"mode": "train", "epoch": 36, "iter": 3400, "lr": 0.08651, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26609, "top5_acc": 0.515, "loss_cls": 4.22921, "loss": 4.22921, "time": 0.84994} +{"mode": "train", "epoch": 36, "iter": 3500, "lr": 0.0865, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26391, "top5_acc": 0.51922, "loss_cls": 4.23568, "loss": 4.23568, "time": 0.84605} +{"mode": "train", "epoch": 36, "iter": 3600, "lr": 0.08648, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25891, "top5_acc": 0.50156, "loss_cls": 4.30856, "loss": 4.30856, "time": 0.84914} +{"mode": "train", "epoch": 36, "iter": 3700, "lr": 0.08646, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.25828, "top5_acc": 0.50953, "loss_cls": 4.28911, "loss": 4.28911, "time": 0.85344} +{"mode": "val", "epoch": 36, "iter": 309, "lr": 0.08645, "top1_acc": 0.20255, "top5_acc": 0.4277, "mean_class_accuracy": 0.20238} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.08643, "memory": 15990, "data_time": 1.581, "top1_acc": 0.27469, "top5_acc": 0.53188, "loss_cls": 4.17032, "loss": 4.17032, "time": 2.61591} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.08641, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27594, "top5_acc": 0.52594, "loss_cls": 4.19622, "loss": 4.19622, "time": 0.8559} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.08639, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27062, "top5_acc": 0.52047, "loss_cls": 4.22253, "loss": 4.22253, "time": 0.85468} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.08637, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26922, "top5_acc": 0.515, "loss_cls": 4.23097, "loss": 4.23097, "time": 0.8551} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.08635, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27359, "top5_acc": 0.51969, "loss_cls": 4.21537, "loss": 4.21537, "time": 0.8556} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.08633, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27203, "top5_acc": 0.51438, "loss_cls": 4.23389, "loss": 4.23389, "time": 0.85168} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.08631, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25938, "top5_acc": 0.50656, "loss_cls": 4.27122, "loss": 4.27122, "time": 0.85487} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0863, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26375, "top5_acc": 0.53141, "loss_cls": 4.19376, "loss": 4.19376, "time": 0.85442} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.08628, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27109, "top5_acc": 0.52203, "loss_cls": 4.21229, "loss": 4.21229, "time": 0.85183} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.08626, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26859, "top5_acc": 0.52406, "loss_cls": 4.21572, "loss": 4.21572, "time": 0.85575} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.08624, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27641, "top5_acc": 0.51906, "loss_cls": 4.20717, "loss": 4.20717, "time": 0.85132} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.08622, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26094, "top5_acc": 0.52266, "loss_cls": 4.21582, "loss": 4.21582, "time": 0.85554} +{"mode": "train", "epoch": 37, "iter": 1300, "lr": 0.0862, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26438, "top5_acc": 0.51781, "loss_cls": 4.21761, "loss": 4.21761, "time": 0.85471} +{"mode": "train", "epoch": 37, "iter": 1400, "lr": 0.08618, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26531, "top5_acc": 0.51781, "loss_cls": 4.22801, "loss": 4.22801, "time": 0.85995} +{"mode": "train", "epoch": 37, "iter": 1500, "lr": 0.08616, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26375, "top5_acc": 0.51547, "loss_cls": 4.2369, "loss": 4.2369, "time": 0.85994} +{"mode": "train", "epoch": 37, "iter": 1600, "lr": 0.08614, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26109, "top5_acc": 0.51062, "loss_cls": 4.27977, "loss": 4.27977, "time": 0.85392} +{"mode": "train", "epoch": 37, "iter": 1700, "lr": 0.08612, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25672, "top5_acc": 0.51828, "loss_cls": 4.26061, "loss": 4.26061, "time": 0.85148} +{"mode": "train", "epoch": 37, "iter": 1800, "lr": 0.0861, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26828, "top5_acc": 0.50641, "loss_cls": 4.25489, "loss": 4.25489, "time": 0.85873} +{"mode": "train", "epoch": 37, "iter": 1900, "lr": 0.08608, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27094, "top5_acc": 0.52062, "loss_cls": 4.22159, "loss": 4.22159, "time": 0.85548} +{"mode": "train", "epoch": 37, "iter": 2000, "lr": 0.08606, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26906, "top5_acc": 0.51719, "loss_cls": 4.22676, "loss": 4.22676, "time": 0.85813} +{"mode": "train", "epoch": 37, "iter": 2100, "lr": 0.08604, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26297, "top5_acc": 0.50813, "loss_cls": 4.26784, "loss": 4.26784, "time": 0.86223} +{"mode": "train", "epoch": 37, "iter": 2200, "lr": 0.08602, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26484, "top5_acc": 0.52219, "loss_cls": 4.21834, "loss": 4.21834, "time": 0.85461} +{"mode": "train", "epoch": 37, "iter": 2300, "lr": 0.08601, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26922, "top5_acc": 0.51109, "loss_cls": 4.25424, "loss": 4.25424, "time": 0.85782} +{"mode": "train", "epoch": 37, "iter": 2400, "lr": 0.08599, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27109, "top5_acc": 0.51891, "loss_cls": 4.24582, "loss": 4.24582, "time": 0.85599} +{"mode": "train", "epoch": 37, "iter": 2500, "lr": 0.08597, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25922, "top5_acc": 0.51078, "loss_cls": 4.25657, "loss": 4.25657, "time": 0.85309} +{"mode": "train", "epoch": 37, "iter": 2600, "lr": 0.08595, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2675, "top5_acc": 0.51172, "loss_cls": 4.25729, "loss": 4.25729, "time": 0.84588} +{"mode": "train", "epoch": 37, "iter": 2700, "lr": 0.08593, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27297, "top5_acc": 0.51406, "loss_cls": 4.25267, "loss": 4.25267, "time": 0.84629} +{"mode": "train", "epoch": 37, "iter": 2800, "lr": 0.08591, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26281, "top5_acc": 0.51797, "loss_cls": 4.24678, "loss": 4.24678, "time": 0.85001} +{"mode": "train", "epoch": 37, "iter": 2900, "lr": 0.08589, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27562, "top5_acc": 0.52734, "loss_cls": 4.1843, "loss": 4.1843, "time": 0.85068} +{"mode": "train", "epoch": 37, "iter": 3000, "lr": 0.08587, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26188, "top5_acc": 0.51078, "loss_cls": 4.25473, "loss": 4.25473, "time": 0.85318} +{"mode": "train", "epoch": 37, "iter": 3100, "lr": 0.08585, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26828, "top5_acc": 0.50406, "loss_cls": 4.27289, "loss": 4.27289, "time": 0.84638} +{"mode": "train", "epoch": 37, "iter": 3200, "lr": 0.08583, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26312, "top5_acc": 0.51484, "loss_cls": 4.2611, "loss": 4.2611, "time": 0.8522} +{"mode": "train", "epoch": 37, "iter": 3300, "lr": 0.08581, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26359, "top5_acc": 0.52266, "loss_cls": 4.22603, "loss": 4.22603, "time": 0.8481} +{"mode": "train", "epoch": 37, "iter": 3400, "lr": 0.08579, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26937, "top5_acc": 0.51562, "loss_cls": 4.24897, "loss": 4.24897, "time": 0.84939} +{"mode": "train", "epoch": 37, "iter": 3500, "lr": 0.08577, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26172, "top5_acc": 0.51609, "loss_cls": 4.21831, "loss": 4.21831, "time": 0.84349} +{"mode": "train", "epoch": 37, "iter": 3600, "lr": 0.08575, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26094, "top5_acc": 0.51812, "loss_cls": 4.2704, "loss": 4.2704, "time": 0.8443} +{"mode": "train", "epoch": 37, "iter": 3700, "lr": 0.08573, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26156, "top5_acc": 0.51, "loss_cls": 4.28461, "loss": 4.28461, "time": 0.84978} +{"mode": "val", "epoch": 37, "iter": 309, "lr": 0.08572, "top1_acc": 0.1947, "top5_acc": 0.42349, "mean_class_accuracy": 0.19454} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.0857, "memory": 15990, "data_time": 1.54773, "top1_acc": 0.26703, "top5_acc": 0.52406, "loss_cls": 4.188, "loss": 4.188, "time": 2.59059} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.08568, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27109, "top5_acc": 0.51422, "loss_cls": 4.19599, "loss": 4.19599, "time": 0.86529} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.08567, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26922, "top5_acc": 0.52891, "loss_cls": 4.17591, "loss": 4.17591, "time": 0.86102} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.08565, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26109, "top5_acc": 0.51453, "loss_cls": 4.22198, "loss": 4.22198, "time": 0.86272} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.08563, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.27187, "top5_acc": 0.52766, "loss_cls": 4.1982, "loss": 4.1982, "time": 0.85471} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.08561, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26438, "top5_acc": 0.50891, "loss_cls": 4.23199, "loss": 4.23199, "time": 0.85893} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.08559, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26297, "top5_acc": 0.51859, "loss_cls": 4.23058, "loss": 4.23058, "time": 0.85822} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.08557, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27484, "top5_acc": 0.52359, "loss_cls": 4.21648, "loss": 4.21648, "time": 0.85453} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.08555, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27141, "top5_acc": 0.52141, "loss_cls": 4.21532, "loss": 4.21532, "time": 0.85963} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.08553, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26453, "top5_acc": 0.51281, "loss_cls": 4.24869, "loss": 4.24869, "time": 0.8574} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.08551, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.27437, "top5_acc": 0.52797, "loss_cls": 4.21357, "loss": 4.21357, "time": 0.86284} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.08549, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.275, "top5_acc": 0.52328, "loss_cls": 4.20067, "loss": 4.20067, "time": 0.85993} +{"mode": "train", "epoch": 38, "iter": 1300, "lr": 0.08547, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25891, "top5_acc": 0.51359, "loss_cls": 4.25155, "loss": 4.25155, "time": 0.85575} +{"mode": "train", "epoch": 38, "iter": 1400, "lr": 0.08545, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26156, "top5_acc": 0.52016, "loss_cls": 4.26295, "loss": 4.26295, "time": 0.85613} +{"mode": "train", "epoch": 38, "iter": 1500, "lr": 0.08543, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27047, "top5_acc": 0.52, "loss_cls": 4.23818, "loss": 4.23818, "time": 0.85972} +{"mode": "train", "epoch": 38, "iter": 1600, "lr": 0.08541, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26969, "top5_acc": 0.51969, "loss_cls": 4.23465, "loss": 4.23465, "time": 0.86171} +{"mode": "train", "epoch": 38, "iter": 1700, "lr": 0.08539, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26531, "top5_acc": 0.51656, "loss_cls": 4.24806, "loss": 4.24806, "time": 0.85612} +{"mode": "train", "epoch": 38, "iter": 1800, "lr": 0.08537, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27094, "top5_acc": 0.51953, "loss_cls": 4.21553, "loss": 4.21553, "time": 0.85942} +{"mode": "train", "epoch": 38, "iter": 1900, "lr": 0.08535, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27625, "top5_acc": 0.51859, "loss_cls": 4.20043, "loss": 4.20043, "time": 0.85682} +{"mode": "train", "epoch": 38, "iter": 2000, "lr": 0.08533, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.2725, "top5_acc": 0.51781, "loss_cls": 4.23162, "loss": 4.23162, "time": 0.85939} +{"mode": "train", "epoch": 38, "iter": 2100, "lr": 0.08531, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27391, "top5_acc": 0.52438, "loss_cls": 4.20315, "loss": 4.20315, "time": 0.85761} +{"mode": "train", "epoch": 38, "iter": 2200, "lr": 0.08529, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26016, "top5_acc": 0.50953, "loss_cls": 4.25766, "loss": 4.25766, "time": 0.85843} +{"mode": "train", "epoch": 38, "iter": 2300, "lr": 0.08527, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27672, "top5_acc": 0.52344, "loss_cls": 4.18614, "loss": 4.18614, "time": 0.86166} +{"mode": "train", "epoch": 38, "iter": 2400, "lr": 0.08525, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26594, "top5_acc": 0.51188, "loss_cls": 4.2741, "loss": 4.2741, "time": 0.85761} +{"mode": "train", "epoch": 38, "iter": 2500, "lr": 0.08523, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26625, "top5_acc": 0.51156, "loss_cls": 4.24173, "loss": 4.24173, "time": 0.8566} +{"mode": "train", "epoch": 38, "iter": 2600, "lr": 0.08521, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26875, "top5_acc": 0.51734, "loss_cls": 4.22223, "loss": 4.22223, "time": 0.84646} +{"mode": "train", "epoch": 38, "iter": 2700, "lr": 0.08519, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26047, "top5_acc": 0.51719, "loss_cls": 4.2217, "loss": 4.2217, "time": 0.85628} +{"mode": "train", "epoch": 38, "iter": 2800, "lr": 0.08517, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27, "top5_acc": 0.52031, "loss_cls": 4.22861, "loss": 4.22861, "time": 0.85267} +{"mode": "train", "epoch": 38, "iter": 2900, "lr": 0.08515, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26734, "top5_acc": 0.52031, "loss_cls": 4.22672, "loss": 4.22672, "time": 0.84837} +{"mode": "train", "epoch": 38, "iter": 3000, "lr": 0.08513, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25281, "top5_acc": 0.51188, "loss_cls": 4.28853, "loss": 4.28853, "time": 0.85067} +{"mode": "train", "epoch": 38, "iter": 3100, "lr": 0.08511, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26391, "top5_acc": 0.51391, "loss_cls": 4.25531, "loss": 4.25531, "time": 0.84894} +{"mode": "train", "epoch": 38, "iter": 3200, "lr": 0.08509, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26906, "top5_acc": 0.51859, "loss_cls": 4.22728, "loss": 4.22728, "time": 0.85181} +{"mode": "train", "epoch": 38, "iter": 3300, "lr": 0.08507, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27219, "top5_acc": 0.52625, "loss_cls": 4.21788, "loss": 4.21788, "time": 0.84901} +{"mode": "train", "epoch": 38, "iter": 3400, "lr": 0.08505, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26219, "top5_acc": 0.50609, "loss_cls": 4.28231, "loss": 4.28231, "time": 0.85824} +{"mode": "train", "epoch": 38, "iter": 3500, "lr": 0.08503, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26375, "top5_acc": 0.51344, "loss_cls": 4.26087, "loss": 4.26087, "time": 0.85302} +{"mode": "train", "epoch": 38, "iter": 3600, "lr": 0.08501, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27375, "top5_acc": 0.51922, "loss_cls": 4.23361, "loss": 4.23361, "time": 0.85873} +{"mode": "train", "epoch": 38, "iter": 3700, "lr": 0.08499, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.27234, "top5_acc": 0.52047, "loss_cls": 4.20809, "loss": 4.20809, "time": 0.85412} +{"mode": "val", "epoch": 38, "iter": 309, "lr": 0.08498, "top1_acc": 0.19951, "top5_acc": 0.436, "mean_class_accuracy": 0.19934} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.08496, "memory": 15990, "data_time": 1.56855, "top1_acc": 0.27453, "top5_acc": 0.51625, "loss_cls": 4.2022, "loss": 4.2022, "time": 2.60389} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.08494, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26937, "top5_acc": 0.52078, "loss_cls": 4.22137, "loss": 4.22137, "time": 0.85409} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.08492, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26969, "top5_acc": 0.52312, "loss_cls": 4.20501, "loss": 4.20501, "time": 0.84973} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.0849, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27547, "top5_acc": 0.5225, "loss_cls": 4.20816, "loss": 4.20816, "time": 0.85118} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.08488, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26891, "top5_acc": 0.52531, "loss_cls": 4.20179, "loss": 4.20179, "time": 0.85227} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.08486, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26188, "top5_acc": 0.50828, "loss_cls": 4.24249, "loss": 4.24249, "time": 0.84949} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.08484, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26703, "top5_acc": 0.51156, "loss_cls": 4.25338, "loss": 4.25338, "time": 0.85326} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.08482, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27156, "top5_acc": 0.52641, "loss_cls": 4.20053, "loss": 4.20053, "time": 0.84906} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.0848, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26844, "top5_acc": 0.52328, "loss_cls": 4.20808, "loss": 4.20808, "time": 0.84822} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.08478, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27578, "top5_acc": 0.52547, "loss_cls": 4.18555, "loss": 4.18555, "time": 0.84751} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.08476, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27859, "top5_acc": 0.52375, "loss_cls": 4.17752, "loss": 4.17752, "time": 0.84696} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.08474, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27172, "top5_acc": 0.51688, "loss_cls": 4.21224, "loss": 4.21224, "time": 0.85215} +{"mode": "train", "epoch": 39, "iter": 1300, "lr": 0.08472, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26781, "top5_acc": 0.52141, "loss_cls": 4.24264, "loss": 4.24264, "time": 0.85436} +{"mode": "train", "epoch": 39, "iter": 1400, "lr": 0.0847, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26062, "top5_acc": 0.51594, "loss_cls": 4.24916, "loss": 4.24916, "time": 0.85775} +{"mode": "train", "epoch": 39, "iter": 1500, "lr": 0.08468, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27016, "top5_acc": 0.51953, "loss_cls": 4.23826, "loss": 4.23826, "time": 0.85638} +{"mode": "train", "epoch": 39, "iter": 1600, "lr": 0.08466, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26578, "top5_acc": 0.53094, "loss_cls": 4.16991, "loss": 4.16991, "time": 0.85759} +{"mode": "train", "epoch": 39, "iter": 1700, "lr": 0.08464, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.25984, "top5_acc": 0.51188, "loss_cls": 4.25987, "loss": 4.25987, "time": 0.85987} +{"mode": "train", "epoch": 39, "iter": 1800, "lr": 0.08462, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27016, "top5_acc": 0.52219, "loss_cls": 4.24304, "loss": 4.24304, "time": 0.85706} +{"mode": "train", "epoch": 39, "iter": 1900, "lr": 0.0846, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27812, "top5_acc": 0.52875, "loss_cls": 4.16533, "loss": 4.16533, "time": 0.8549} +{"mode": "train", "epoch": 39, "iter": 2000, "lr": 0.08458, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27719, "top5_acc": 0.52625, "loss_cls": 4.16988, "loss": 4.16988, "time": 0.85377} +{"mode": "train", "epoch": 39, "iter": 2100, "lr": 0.08456, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28266, "top5_acc": 0.52953, "loss_cls": 4.18038, "loss": 4.18038, "time": 0.85819} +{"mode": "train", "epoch": 39, "iter": 2200, "lr": 0.08454, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26469, "top5_acc": 0.51016, "loss_cls": 4.24536, "loss": 4.24536, "time": 0.85738} +{"mode": "train", "epoch": 39, "iter": 2300, "lr": 0.08452, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.26625, "top5_acc": 0.51406, "loss_cls": 4.23933, "loss": 4.23933, "time": 0.85456} +{"mode": "train", "epoch": 39, "iter": 2400, "lr": 0.0845, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27469, "top5_acc": 0.51641, "loss_cls": 4.2161, "loss": 4.2161, "time": 0.85355} +{"mode": "train", "epoch": 39, "iter": 2500, "lr": 0.08448, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2625, "top5_acc": 0.51844, "loss_cls": 4.23839, "loss": 4.23839, "time": 0.84617} +{"mode": "train", "epoch": 39, "iter": 2600, "lr": 0.08446, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27688, "top5_acc": 0.52188, "loss_cls": 4.20336, "loss": 4.20336, "time": 0.84987} +{"mode": "train", "epoch": 39, "iter": 2700, "lr": 0.08444, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27516, "top5_acc": 0.52391, "loss_cls": 4.19196, "loss": 4.19196, "time": 0.85323} +{"mode": "train", "epoch": 39, "iter": 2800, "lr": 0.08442, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26891, "top5_acc": 0.52844, "loss_cls": 4.20135, "loss": 4.20135, "time": 0.85495} +{"mode": "train", "epoch": 39, "iter": 2900, "lr": 0.0844, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2625, "top5_acc": 0.50984, "loss_cls": 4.28424, "loss": 4.28424, "time": 0.84415} +{"mode": "train", "epoch": 39, "iter": 3000, "lr": 0.08438, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25797, "top5_acc": 0.51672, "loss_cls": 4.22961, "loss": 4.22961, "time": 0.84539} +{"mode": "train", "epoch": 39, "iter": 3100, "lr": 0.08436, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26156, "top5_acc": 0.50594, "loss_cls": 4.27047, "loss": 4.27047, "time": 0.84863} +{"mode": "train", "epoch": 39, "iter": 3200, "lr": 0.08434, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27312, "top5_acc": 0.51438, "loss_cls": 4.24295, "loss": 4.24295, "time": 0.85007} +{"mode": "train", "epoch": 39, "iter": 3300, "lr": 0.08432, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27062, "top5_acc": 0.525, "loss_cls": 4.21206, "loss": 4.21206, "time": 0.85354} +{"mode": "train", "epoch": 39, "iter": 3400, "lr": 0.0843, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26687, "top5_acc": 0.51625, "loss_cls": 4.229, "loss": 4.229, "time": 0.85347} +{"mode": "train", "epoch": 39, "iter": 3500, "lr": 0.08428, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27359, "top5_acc": 0.52672, "loss_cls": 4.17071, "loss": 4.17071, "time": 0.85174} +{"mode": "train", "epoch": 39, "iter": 3600, "lr": 0.08426, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26859, "top5_acc": 0.52391, "loss_cls": 4.22601, "loss": 4.22601, "time": 0.84968} +{"mode": "train", "epoch": 39, "iter": 3700, "lr": 0.08424, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26937, "top5_acc": 0.52391, "loss_cls": 4.21366, "loss": 4.21366, "time": 0.84908} +{"mode": "val", "epoch": 39, "iter": 309, "lr": 0.08423, "top1_acc": 0.1987, "top5_acc": 0.43043, "mean_class_accuracy": 0.19866} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.08421, "memory": 15990, "data_time": 1.55498, "top1_acc": 0.26625, "top5_acc": 0.53375, "loss_cls": 4.18005, "loss": 4.18005, "time": 2.59408} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.08419, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27391, "top5_acc": 0.52453, "loss_cls": 4.16813, "loss": 4.16813, "time": 0.85541} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.08417, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27, "top5_acc": 0.51828, "loss_cls": 4.20578, "loss": 4.20578, "time": 0.85153} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.08415, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26797, "top5_acc": 0.52594, "loss_cls": 4.17479, "loss": 4.17479, "time": 0.85076} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.08413, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27062, "top5_acc": 0.51547, "loss_cls": 4.20809, "loss": 4.20809, "time": 0.8494} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.08411, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27609, "top5_acc": 0.53125, "loss_cls": 4.15765, "loss": 4.15765, "time": 0.85} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.08408, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27344, "top5_acc": 0.51469, "loss_cls": 4.22365, "loss": 4.22365, "time": 0.8507} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.08406, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27328, "top5_acc": 0.52703, "loss_cls": 4.1761, "loss": 4.1761, "time": 0.84972} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.08404, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27078, "top5_acc": 0.5225, "loss_cls": 4.18318, "loss": 4.18318, "time": 0.85316} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.08402, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27609, "top5_acc": 0.5225, "loss_cls": 4.21225, "loss": 4.21225, "time": 0.85074} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.084, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27172, "top5_acc": 0.51594, "loss_cls": 4.2267, "loss": 4.2267, "time": 0.84742} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.08398, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26875, "top5_acc": 0.51828, "loss_cls": 4.21091, "loss": 4.21091, "time": 0.84958} +{"mode": "train", "epoch": 40, "iter": 1300, "lr": 0.08396, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26484, "top5_acc": 0.50844, "loss_cls": 4.26196, "loss": 4.26196, "time": 0.84692} +{"mode": "train", "epoch": 40, "iter": 1400, "lr": 0.08394, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27203, "top5_acc": 0.52578, "loss_cls": 4.22459, "loss": 4.22459, "time": 0.85268} +{"mode": "train", "epoch": 40, "iter": 1500, "lr": 0.08392, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26062, "top5_acc": 0.50516, "loss_cls": 4.30088, "loss": 4.30088, "time": 0.85135} +{"mode": "train", "epoch": 40, "iter": 1600, "lr": 0.0839, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27391, "top5_acc": 0.52016, "loss_cls": 4.21499, "loss": 4.21499, "time": 0.84948} +{"mode": "train", "epoch": 40, "iter": 1700, "lr": 0.08388, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.26922, "top5_acc": 0.51578, "loss_cls": 4.22661, "loss": 4.22661, "time": 0.85544} +{"mode": "train", "epoch": 40, "iter": 1800, "lr": 0.08386, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27, "top5_acc": 0.51922, "loss_cls": 4.22119, "loss": 4.22119, "time": 0.85672} +{"mode": "train", "epoch": 40, "iter": 1900, "lr": 0.08384, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27406, "top5_acc": 0.51969, "loss_cls": 4.20602, "loss": 4.20602, "time": 0.86346} +{"mode": "train", "epoch": 40, "iter": 2000, "lr": 0.08382, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26047, "top5_acc": 0.51375, "loss_cls": 4.25765, "loss": 4.25765, "time": 0.8553} +{"mode": "train", "epoch": 40, "iter": 2100, "lr": 0.0838, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27016, "top5_acc": 0.52094, "loss_cls": 4.208, "loss": 4.208, "time": 0.85358} +{"mode": "train", "epoch": 40, "iter": 2200, "lr": 0.08378, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26672, "top5_acc": 0.51969, "loss_cls": 4.24875, "loss": 4.24875, "time": 0.85679} +{"mode": "train", "epoch": 40, "iter": 2300, "lr": 0.08376, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.265, "top5_acc": 0.51328, "loss_cls": 4.24101, "loss": 4.24101, "time": 0.85743} +{"mode": "train", "epoch": 40, "iter": 2400, "lr": 0.08374, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26922, "top5_acc": 0.52375, "loss_cls": 4.2158, "loss": 4.2158, "time": 0.84999} +{"mode": "train", "epoch": 40, "iter": 2500, "lr": 0.08371, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2675, "top5_acc": 0.52359, "loss_cls": 4.21977, "loss": 4.21977, "time": 0.8464} +{"mode": "train", "epoch": 40, "iter": 2600, "lr": 0.08369, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.26687, "top5_acc": 0.52594, "loss_cls": 4.20565, "loss": 4.20565, "time": 0.84887} +{"mode": "train", "epoch": 40, "iter": 2700, "lr": 0.08367, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26922, "top5_acc": 0.52281, "loss_cls": 4.2278, "loss": 4.2278, "time": 0.84931} +{"mode": "train", "epoch": 40, "iter": 2800, "lr": 0.08365, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27734, "top5_acc": 0.52594, "loss_cls": 4.19863, "loss": 4.19863, "time": 0.84879} +{"mode": "train", "epoch": 40, "iter": 2900, "lr": 0.08363, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2675, "top5_acc": 0.52422, "loss_cls": 4.20807, "loss": 4.20807, "time": 0.84451} +{"mode": "train", "epoch": 40, "iter": 3000, "lr": 0.08361, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26531, "top5_acc": 0.51859, "loss_cls": 4.19615, "loss": 4.19615, "time": 0.8498} +{"mode": "train", "epoch": 40, "iter": 3100, "lr": 0.08359, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27812, "top5_acc": 0.52656, "loss_cls": 4.1851, "loss": 4.1851, "time": 0.85186} +{"mode": "train", "epoch": 40, "iter": 3200, "lr": 0.08357, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27391, "top5_acc": 0.51938, "loss_cls": 4.20887, "loss": 4.20887, "time": 0.85237} +{"mode": "train", "epoch": 40, "iter": 3300, "lr": 0.08355, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28359, "top5_acc": 0.52875, "loss_cls": 4.14937, "loss": 4.14937, "time": 0.85328} +{"mode": "train", "epoch": 40, "iter": 3400, "lr": 0.08353, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26562, "top5_acc": 0.50484, "loss_cls": 4.31308, "loss": 4.31308, "time": 0.85515} +{"mode": "train", "epoch": 40, "iter": 3500, "lr": 0.08351, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27437, "top5_acc": 0.51719, "loss_cls": 4.21349, "loss": 4.21349, "time": 0.84675} +{"mode": "train", "epoch": 40, "iter": 3600, "lr": 0.08349, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27766, "top5_acc": 0.51703, "loss_cls": 4.21208, "loss": 4.21208, "time": 0.83814} +{"mode": "train", "epoch": 40, "iter": 3700, "lr": 0.08347, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27062, "top5_acc": 0.51328, "loss_cls": 4.23675, "loss": 4.23675, "time": 0.84145} +{"mode": "val", "epoch": 40, "iter": 309, "lr": 0.08346, "top1_acc": 0.19729, "top5_acc": 0.43149, "mean_class_accuracy": 0.19714} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.08344, "memory": 15990, "data_time": 1.5059, "top1_acc": 0.28391, "top5_acc": 0.53328, "loss_cls": 4.14537, "loss": 4.14537, "time": 2.52325} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.08342, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27391, "top5_acc": 0.52188, "loss_cls": 4.16676, "loss": 4.16676, "time": 0.84862} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.08339, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28078, "top5_acc": 0.53078, "loss_cls": 4.13404, "loss": 4.13404, "time": 0.84413} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.08337, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26937, "top5_acc": 0.52281, "loss_cls": 4.20623, "loss": 4.20623, "time": 0.84705} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.08335, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26937, "top5_acc": 0.51703, "loss_cls": 4.23887, "loss": 4.23887, "time": 0.84709} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.08333, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26875, "top5_acc": 0.52734, "loss_cls": 4.1725, "loss": 4.1725, "time": 0.84837} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.08331, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26719, "top5_acc": 0.52188, "loss_cls": 4.20385, "loss": 4.20385, "time": 0.84302} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.08329, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27703, "top5_acc": 0.52281, "loss_cls": 4.20511, "loss": 4.20511, "time": 0.8463} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.08327, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2825, "top5_acc": 0.53656, "loss_cls": 4.13122, "loss": 4.13122, "time": 0.85475} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.08325, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26766, "top5_acc": 0.52312, "loss_cls": 4.21786, "loss": 4.21786, "time": 0.84667} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.08323, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27234, "top5_acc": 0.51812, "loss_cls": 4.21837, "loss": 4.21837, "time": 0.84717} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.08321, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27016, "top5_acc": 0.53109, "loss_cls": 4.18056, "loss": 4.18056, "time": 0.8498} +{"mode": "train", "epoch": 41, "iter": 1300, "lr": 0.08319, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27422, "top5_acc": 0.52938, "loss_cls": 4.17929, "loss": 4.17929, "time": 0.85191} +{"mode": "train", "epoch": 41, "iter": 1400, "lr": 0.08316, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27219, "top5_acc": 0.51625, "loss_cls": 4.24967, "loss": 4.24967, "time": 0.85512} +{"mode": "train", "epoch": 41, "iter": 1500, "lr": 0.08314, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26844, "top5_acc": 0.51203, "loss_cls": 4.22605, "loss": 4.22605, "time": 0.84649} +{"mode": "train", "epoch": 41, "iter": 1600, "lr": 0.08312, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26687, "top5_acc": 0.52, "loss_cls": 4.21491, "loss": 4.21491, "time": 0.85227} +{"mode": "train", "epoch": 41, "iter": 1700, "lr": 0.0831, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2575, "top5_acc": 0.50406, "loss_cls": 4.28306, "loss": 4.28306, "time": 0.8458} +{"mode": "train", "epoch": 41, "iter": 1800, "lr": 0.08308, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27, "top5_acc": 0.51812, "loss_cls": 4.23778, "loss": 4.23778, "time": 0.85272} +{"mode": "train", "epoch": 41, "iter": 1900, "lr": 0.08306, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27547, "top5_acc": 0.52578, "loss_cls": 4.18564, "loss": 4.18564, "time": 0.85106} +{"mode": "train", "epoch": 41, "iter": 2000, "lr": 0.08304, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26859, "top5_acc": 0.51938, "loss_cls": 4.19783, "loss": 4.19783, "time": 0.85227} +{"mode": "train", "epoch": 41, "iter": 2100, "lr": 0.08302, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26672, "top5_acc": 0.52031, "loss_cls": 4.21662, "loss": 4.21662, "time": 0.85458} +{"mode": "train", "epoch": 41, "iter": 2200, "lr": 0.083, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27469, "top5_acc": 0.51547, "loss_cls": 4.22613, "loss": 4.22613, "time": 0.85414} +{"mode": "train", "epoch": 41, "iter": 2300, "lr": 0.08298, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27406, "top5_acc": 0.51938, "loss_cls": 4.22311, "loss": 4.22311, "time": 0.85604} +{"mode": "train", "epoch": 41, "iter": 2400, "lr": 0.08296, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27328, "top5_acc": 0.51906, "loss_cls": 4.21732, "loss": 4.21732, "time": 0.84859} +{"mode": "train", "epoch": 41, "iter": 2500, "lr": 0.08293, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27281, "top5_acc": 0.51688, "loss_cls": 4.22962, "loss": 4.22962, "time": 0.84479} +{"mode": "train", "epoch": 41, "iter": 2600, "lr": 0.08291, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26422, "top5_acc": 0.50781, "loss_cls": 4.24983, "loss": 4.24983, "time": 0.84769} +{"mode": "train", "epoch": 41, "iter": 2700, "lr": 0.08289, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26641, "top5_acc": 0.52125, "loss_cls": 4.21928, "loss": 4.21928, "time": 0.8542} +{"mode": "train", "epoch": 41, "iter": 2800, "lr": 0.08287, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2675, "top5_acc": 0.51859, "loss_cls": 4.21474, "loss": 4.21474, "time": 0.85049} +{"mode": "train", "epoch": 41, "iter": 2900, "lr": 0.08285, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26844, "top5_acc": 0.52109, "loss_cls": 4.22027, "loss": 4.22027, "time": 0.85107} +{"mode": "train", "epoch": 41, "iter": 3000, "lr": 0.08283, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.27906, "top5_acc": 0.52328, "loss_cls": 4.19494, "loss": 4.19494, "time": 0.84646} +{"mode": "train", "epoch": 41, "iter": 3100, "lr": 0.08281, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27094, "top5_acc": 0.52328, "loss_cls": 4.21181, "loss": 4.21181, "time": 0.85133} +{"mode": "train", "epoch": 41, "iter": 3200, "lr": 0.08279, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27734, "top5_acc": 0.52781, "loss_cls": 4.20719, "loss": 4.20719, "time": 0.84961} +{"mode": "train", "epoch": 41, "iter": 3300, "lr": 0.08277, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27422, "top5_acc": 0.52406, "loss_cls": 4.19496, "loss": 4.19496, "time": 0.85222} +{"mode": "train", "epoch": 41, "iter": 3400, "lr": 0.08274, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2675, "top5_acc": 0.50734, "loss_cls": 4.25374, "loss": 4.25374, "time": 0.85295} +{"mode": "train", "epoch": 41, "iter": 3500, "lr": 0.08272, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27516, "top5_acc": 0.51766, "loss_cls": 4.2184, "loss": 4.2184, "time": 0.85492} +{"mode": "train", "epoch": 41, "iter": 3600, "lr": 0.0827, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27141, "top5_acc": 0.52562, "loss_cls": 4.19735, "loss": 4.19735, "time": 0.84951} +{"mode": "train", "epoch": 41, "iter": 3700, "lr": 0.08268, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26906, "top5_acc": 0.52375, "loss_cls": 4.18836, "loss": 4.18836, "time": 0.85256} +{"mode": "val", "epoch": 41, "iter": 309, "lr": 0.08267, "top1_acc": 0.20108, "top5_acc": 0.42699, "mean_class_accuracy": 0.20108} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.08265, "memory": 15990, "data_time": 1.52045, "top1_acc": 0.27312, "top5_acc": 0.53188, "loss_cls": 4.16547, "loss": 4.16547, "time": 2.55982} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.08263, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27703, "top5_acc": 0.53703, "loss_cls": 4.15373, "loss": 4.15373, "time": 0.8517} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.08261, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27047, "top5_acc": 0.52188, "loss_cls": 4.20334, "loss": 4.20334, "time": 0.85686} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.08259, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27703, "top5_acc": 0.52812, "loss_cls": 4.18498, "loss": 4.18498, "time": 0.85371} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.08257, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26625, "top5_acc": 0.51875, "loss_cls": 4.20683, "loss": 4.20683, "time": 0.85045} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.08254, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26391, "top5_acc": 0.51688, "loss_cls": 4.22671, "loss": 4.22671, "time": 0.85144} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.08252, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27391, "top5_acc": 0.52531, "loss_cls": 4.21244, "loss": 4.21244, "time": 0.85387} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.0825, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26937, "top5_acc": 0.52703, "loss_cls": 4.18783, "loss": 4.18783, "time": 0.85456} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.08248, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26937, "top5_acc": 0.51922, "loss_cls": 4.20066, "loss": 4.20066, "time": 0.85232} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.08246, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26859, "top5_acc": 0.52125, "loss_cls": 4.21257, "loss": 4.21257, "time": 0.85169} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.08244, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28125, "top5_acc": 0.52469, "loss_cls": 4.17607, "loss": 4.17607, "time": 0.86044} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.08242, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26859, "top5_acc": 0.51313, "loss_cls": 4.24002, "loss": 4.24002, "time": 0.85846} +{"mode": "train", "epoch": 42, "iter": 1300, "lr": 0.0824, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27125, "top5_acc": 0.52672, "loss_cls": 4.17769, "loss": 4.17769, "time": 0.85866} +{"mode": "train", "epoch": 42, "iter": 1400, "lr": 0.08237, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26531, "top5_acc": 0.51812, "loss_cls": 4.22688, "loss": 4.22688, "time": 0.85525} +{"mode": "train", "epoch": 42, "iter": 1500, "lr": 0.08235, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27125, "top5_acc": 0.52156, "loss_cls": 4.20414, "loss": 4.20414, "time": 0.85212} +{"mode": "train", "epoch": 42, "iter": 1600, "lr": 0.08233, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2725, "top5_acc": 0.51734, "loss_cls": 4.2166, "loss": 4.2166, "time": 0.85146} +{"mode": "train", "epoch": 42, "iter": 1700, "lr": 0.08231, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27844, "top5_acc": 0.52094, "loss_cls": 4.15952, "loss": 4.15952, "time": 0.85657} +{"mode": "train", "epoch": 42, "iter": 1800, "lr": 0.08229, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26875, "top5_acc": 0.51641, "loss_cls": 4.20973, "loss": 4.20973, "time": 0.85776} +{"mode": "train", "epoch": 42, "iter": 1900, "lr": 0.08227, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27984, "top5_acc": 0.5325, "loss_cls": 4.16179, "loss": 4.16179, "time": 0.85988} +{"mode": "train", "epoch": 42, "iter": 2000, "lr": 0.08225, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.25906, "top5_acc": 0.51375, "loss_cls": 4.26352, "loss": 4.26352, "time": 0.86619} +{"mode": "train", "epoch": 42, "iter": 2100, "lr": 0.08222, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27047, "top5_acc": 0.51844, "loss_cls": 4.23001, "loss": 4.23001, "time": 0.85688} +{"mode": "train", "epoch": 42, "iter": 2200, "lr": 0.0822, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26719, "top5_acc": 0.52781, "loss_cls": 4.21436, "loss": 4.21436, "time": 0.86271} +{"mode": "train", "epoch": 42, "iter": 2300, "lr": 0.08218, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.26922, "top5_acc": 0.52344, "loss_cls": 4.20904, "loss": 4.20904, "time": 0.85621} +{"mode": "train", "epoch": 42, "iter": 2400, "lr": 0.08216, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27359, "top5_acc": 0.52609, "loss_cls": 4.17285, "loss": 4.17285, "time": 0.85213} +{"mode": "train", "epoch": 42, "iter": 2500, "lr": 0.08214, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27359, "top5_acc": 0.51938, "loss_cls": 4.21006, "loss": 4.21006, "time": 0.84808} +{"mode": "train", "epoch": 42, "iter": 2600, "lr": 0.08212, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.28125, "top5_acc": 0.52875, "loss_cls": 4.18328, "loss": 4.18328, "time": 0.84831} +{"mode": "train", "epoch": 42, "iter": 2700, "lr": 0.0821, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.27656, "top5_acc": 0.52484, "loss_cls": 4.18719, "loss": 4.18719, "time": 0.85265} +{"mode": "train", "epoch": 42, "iter": 2800, "lr": 0.08207, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.27156, "top5_acc": 0.51438, "loss_cls": 4.24709, "loss": 4.24709, "time": 0.85436} +{"mode": "train", "epoch": 42, "iter": 2900, "lr": 0.08205, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26906, "top5_acc": 0.51422, "loss_cls": 4.22228, "loss": 4.22228, "time": 0.84518} +{"mode": "train", "epoch": 42, "iter": 3000, "lr": 0.08203, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27078, "top5_acc": 0.51828, "loss_cls": 4.20946, "loss": 4.20946, "time": 0.8491} +{"mode": "train", "epoch": 42, "iter": 3100, "lr": 0.08201, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.27469, "top5_acc": 0.5275, "loss_cls": 4.20587, "loss": 4.20587, "time": 0.84922} +{"mode": "train", "epoch": 42, "iter": 3200, "lr": 0.08199, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27453, "top5_acc": 0.52688, "loss_cls": 4.20929, "loss": 4.20929, "time": 0.85245} +{"mode": "train", "epoch": 42, "iter": 3300, "lr": 0.08197, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.26953, "top5_acc": 0.52531, "loss_cls": 4.16588, "loss": 4.16588, "time": 0.84414} +{"mode": "train", "epoch": 42, "iter": 3400, "lr": 0.08195, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26438, "top5_acc": 0.51266, "loss_cls": 4.22364, "loss": 4.22364, "time": 0.85403} +{"mode": "train", "epoch": 42, "iter": 3500, "lr": 0.08192, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26828, "top5_acc": 0.51938, "loss_cls": 4.21377, "loss": 4.21377, "time": 0.85077} +{"mode": "train", "epoch": 42, "iter": 3600, "lr": 0.0819, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27109, "top5_acc": 0.51938, "loss_cls": 4.22759, "loss": 4.22759, "time": 0.85426} +{"mode": "train", "epoch": 42, "iter": 3700, "lr": 0.08188, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27266, "top5_acc": 0.52938, "loss_cls": 4.17435, "loss": 4.17435, "time": 0.85266} +{"mode": "val", "epoch": 42, "iter": 309, "lr": 0.08187, "top1_acc": 0.18128, "top5_acc": 0.40556, "mean_class_accuracy": 0.18102} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.08185, "memory": 15990, "data_time": 1.61724, "top1_acc": 0.27187, "top5_acc": 0.52484, "loss_cls": 4.19582, "loss": 4.19582, "time": 2.67727} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.08183, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27953, "top5_acc": 0.53422, "loss_cls": 4.15091, "loss": 4.15091, "time": 0.85422} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.08181, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27938, "top5_acc": 0.53062, "loss_cls": 4.13133, "loss": 4.13133, "time": 0.85808} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.08179, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26375, "top5_acc": 0.52094, "loss_cls": 4.24142, "loss": 4.24142, "time": 0.85709} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.08176, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26953, "top5_acc": 0.52, "loss_cls": 4.19049, "loss": 4.19049, "time": 0.85416} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.08174, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26359, "top5_acc": 0.51969, "loss_cls": 4.24483, "loss": 4.24483, "time": 0.8571} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.08172, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.26562, "top5_acc": 0.51125, "loss_cls": 4.2145, "loss": 4.2145, "time": 0.85584} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.0817, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27766, "top5_acc": 0.53094, "loss_cls": 4.18324, "loss": 4.18324, "time": 0.85335} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.08168, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.27859, "top5_acc": 0.53047, "loss_cls": 4.18025, "loss": 4.18025, "time": 0.85694} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.08166, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27219, "top5_acc": 0.52531, "loss_cls": 4.20361, "loss": 4.20361, "time": 0.85746} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.08163, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27141, "top5_acc": 0.51281, "loss_cls": 4.20588, "loss": 4.20588, "time": 0.85826} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.08161, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26969, "top5_acc": 0.5175, "loss_cls": 4.16447, "loss": 4.16447, "time": 0.85518} +{"mode": "train", "epoch": 43, "iter": 1300, "lr": 0.08159, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26797, "top5_acc": 0.52234, "loss_cls": 4.18577, "loss": 4.18577, "time": 0.85723} +{"mode": "train", "epoch": 43, "iter": 1400, "lr": 0.08157, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.275, "top5_acc": 0.51938, "loss_cls": 4.19511, "loss": 4.19511, "time": 0.85335} +{"mode": "train", "epoch": 43, "iter": 1500, "lr": 0.08155, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28234, "top5_acc": 0.53672, "loss_cls": 4.13592, "loss": 4.13592, "time": 0.8594} +{"mode": "train", "epoch": 43, "iter": 1600, "lr": 0.08153, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.27656, "top5_acc": 0.52781, "loss_cls": 4.17139, "loss": 4.17139, "time": 0.85231} +{"mode": "train", "epoch": 43, "iter": 1700, "lr": 0.0815, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27156, "top5_acc": 0.52375, "loss_cls": 4.18688, "loss": 4.18688, "time": 0.85839} +{"mode": "train", "epoch": 43, "iter": 1800, "lr": 0.08148, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27266, "top5_acc": 0.52938, "loss_cls": 4.18859, "loss": 4.18859, "time": 0.85063} +{"mode": "train", "epoch": 43, "iter": 1900, "lr": 0.08146, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26953, "top5_acc": 0.52, "loss_cls": 4.22508, "loss": 4.22508, "time": 0.85178} +{"mode": "train", "epoch": 43, "iter": 2000, "lr": 0.08144, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26875, "top5_acc": 0.51234, "loss_cls": 4.24555, "loss": 4.24555, "time": 0.85289} +{"mode": "train", "epoch": 43, "iter": 2100, "lr": 0.08142, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2725, "top5_acc": 0.51781, "loss_cls": 4.20436, "loss": 4.20436, "time": 0.8557} +{"mode": "train", "epoch": 43, "iter": 2200, "lr": 0.0814, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27234, "top5_acc": 0.52406, "loss_cls": 4.18765, "loss": 4.18765, "time": 0.85261} +{"mode": "train", "epoch": 43, "iter": 2300, "lr": 0.08137, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.27359, "top5_acc": 0.53359, "loss_cls": 4.17242, "loss": 4.17242, "time": 0.85445} +{"mode": "train", "epoch": 43, "iter": 2400, "lr": 0.08135, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27984, "top5_acc": 0.52891, "loss_cls": 4.16562, "loss": 4.16562, "time": 0.84735} +{"mode": "train", "epoch": 43, "iter": 2500, "lr": 0.08133, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27547, "top5_acc": 0.51844, "loss_cls": 4.20959, "loss": 4.20959, "time": 0.84775} +{"mode": "train", "epoch": 43, "iter": 2600, "lr": 0.08131, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26781, "top5_acc": 0.51422, "loss_cls": 4.21054, "loss": 4.21054, "time": 0.84933} +{"mode": "train", "epoch": 43, "iter": 2700, "lr": 0.08129, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26578, "top5_acc": 0.51391, "loss_cls": 4.23574, "loss": 4.23574, "time": 0.8547} +{"mode": "train", "epoch": 43, "iter": 2800, "lr": 0.08126, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27031, "top5_acc": 0.52016, "loss_cls": 4.19908, "loss": 4.19908, "time": 0.85322} +{"mode": "train", "epoch": 43, "iter": 2900, "lr": 0.08124, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.27328, "top5_acc": 0.535, "loss_cls": 4.18364, "loss": 4.18364, "time": 0.85248} +{"mode": "train", "epoch": 43, "iter": 3000, "lr": 0.08122, "memory": 15990, "data_time": 0.00068, "top1_acc": 0.28, "top5_acc": 0.52391, "loss_cls": 4.19315, "loss": 4.19315, "time": 0.85303} +{"mode": "train", "epoch": 43, "iter": 3100, "lr": 0.0812, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27875, "top5_acc": 0.53125, "loss_cls": 4.17421, "loss": 4.17421, "time": 0.85338} +{"mode": "train", "epoch": 43, "iter": 3200, "lr": 0.08118, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27047, "top5_acc": 0.51313, "loss_cls": 4.21672, "loss": 4.21672, "time": 0.85127} +{"mode": "train", "epoch": 43, "iter": 3300, "lr": 0.08116, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.27437, "top5_acc": 0.51313, "loss_cls": 4.19967, "loss": 4.19967, "time": 0.84895} +{"mode": "train", "epoch": 43, "iter": 3400, "lr": 0.08113, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27891, "top5_acc": 0.51797, "loss_cls": 4.18802, "loss": 4.18802, "time": 0.84989} +{"mode": "train", "epoch": 43, "iter": 3500, "lr": 0.08111, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27484, "top5_acc": 0.52938, "loss_cls": 4.17968, "loss": 4.17968, "time": 0.85871} +{"mode": "train", "epoch": 43, "iter": 3600, "lr": 0.08109, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27688, "top5_acc": 0.52, "loss_cls": 4.20217, "loss": 4.20217, "time": 0.85491} +{"mode": "train", "epoch": 43, "iter": 3700, "lr": 0.08107, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27578, "top5_acc": 0.51891, "loss_cls": 4.22116, "loss": 4.22116, "time": 0.85359} +{"mode": "val", "epoch": 43, "iter": 309, "lr": 0.08106, "top1_acc": 0.20676, "top5_acc": 0.43626, "mean_class_accuracy": 0.20655} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.08104, "memory": 15990, "data_time": 1.60036, "top1_acc": 0.28562, "top5_acc": 0.53562, "loss_cls": 4.15498, "loss": 4.15498, "time": 2.64729} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.08101, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27641, "top5_acc": 0.53016, "loss_cls": 4.13861, "loss": 4.13861, "time": 0.85987} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.08099, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.285, "top5_acc": 0.53469, "loss_cls": 4.15476, "loss": 4.15476, "time": 0.86235} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.08097, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27906, "top5_acc": 0.52672, "loss_cls": 4.14575, "loss": 4.14575, "time": 0.86328} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.08095, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27719, "top5_acc": 0.52531, "loss_cls": 4.15641, "loss": 4.15641, "time": 0.86099} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.08093, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26328, "top5_acc": 0.51469, "loss_cls": 4.23095, "loss": 4.23095, "time": 0.86342} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.0809, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26828, "top5_acc": 0.52344, "loss_cls": 4.19335, "loss": 4.19335, "time": 0.86287} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.08088, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27109, "top5_acc": 0.52891, "loss_cls": 4.19305, "loss": 4.19305, "time": 0.86064} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.08086, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28078, "top5_acc": 0.53172, "loss_cls": 4.15023, "loss": 4.15023, "time": 0.86128} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.08084, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27187, "top5_acc": 0.52422, "loss_cls": 4.19184, "loss": 4.19184, "time": 0.8594} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.08082, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27484, "top5_acc": 0.53969, "loss_cls": 4.11363, "loss": 4.11363, "time": 0.85736} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.08079, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27437, "top5_acc": 0.52969, "loss_cls": 4.16143, "loss": 4.16143, "time": 0.85643} +{"mode": "train", "epoch": 44, "iter": 1300, "lr": 0.08077, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27625, "top5_acc": 0.52312, "loss_cls": 4.20086, "loss": 4.20086, "time": 0.86013} +{"mode": "train", "epoch": 44, "iter": 1400, "lr": 0.08075, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27047, "top5_acc": 0.52859, "loss_cls": 4.2022, "loss": 4.2022, "time": 0.86174} +{"mode": "train", "epoch": 44, "iter": 1500, "lr": 0.08073, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27891, "top5_acc": 0.53297, "loss_cls": 4.15436, "loss": 4.15436, "time": 0.86221} +{"mode": "train", "epoch": 44, "iter": 1600, "lr": 0.08071, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27672, "top5_acc": 0.53469, "loss_cls": 4.18326, "loss": 4.18326, "time": 0.86162} +{"mode": "train", "epoch": 44, "iter": 1700, "lr": 0.08068, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27547, "top5_acc": 0.53188, "loss_cls": 4.15903, "loss": 4.15903, "time": 0.86109} +{"mode": "train", "epoch": 44, "iter": 1800, "lr": 0.08066, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.27094, "top5_acc": 0.52, "loss_cls": 4.19323, "loss": 4.19323, "time": 0.86385} +{"mode": "train", "epoch": 44, "iter": 1900, "lr": 0.08064, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26453, "top5_acc": 0.51609, "loss_cls": 4.22014, "loss": 4.22014, "time": 0.86387} +{"mode": "train", "epoch": 44, "iter": 2000, "lr": 0.08062, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.275, "top5_acc": 0.52781, "loss_cls": 4.19416, "loss": 4.19416, "time": 0.8642} +{"mode": "train", "epoch": 44, "iter": 2100, "lr": 0.0806, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26812, "top5_acc": 0.51578, "loss_cls": 4.2338, "loss": 4.2338, "time": 0.86733} +{"mode": "train", "epoch": 44, "iter": 2200, "lr": 0.08057, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26766, "top5_acc": 0.51625, "loss_cls": 4.23345, "loss": 4.23345, "time": 0.86162} +{"mode": "train", "epoch": 44, "iter": 2300, "lr": 0.08055, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27, "top5_acc": 0.52797, "loss_cls": 4.20163, "loss": 4.20163, "time": 0.84658} +{"mode": "train", "epoch": 44, "iter": 2400, "lr": 0.08053, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27062, "top5_acc": 0.52516, "loss_cls": 4.17439, "loss": 4.17439, "time": 0.8479} +{"mode": "train", "epoch": 44, "iter": 2500, "lr": 0.08051, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.25766, "top5_acc": 0.52031, "loss_cls": 4.26058, "loss": 4.26058, "time": 0.85215} +{"mode": "train", "epoch": 44, "iter": 2600, "lr": 0.08048, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27391, "top5_acc": 0.52422, "loss_cls": 4.18565, "loss": 4.18565, "time": 0.85864} +{"mode": "train", "epoch": 44, "iter": 2700, "lr": 0.08046, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26984, "top5_acc": 0.51656, "loss_cls": 4.21702, "loss": 4.21702, "time": 0.84978} +{"mode": "train", "epoch": 44, "iter": 2800, "lr": 0.08044, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28703, "top5_acc": 0.52938, "loss_cls": 4.13275, "loss": 4.13275, "time": 0.84609} +{"mode": "train", "epoch": 44, "iter": 2900, "lr": 0.08042, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27359, "top5_acc": 0.52234, "loss_cls": 4.20296, "loss": 4.20296, "time": 0.84623} +{"mode": "train", "epoch": 44, "iter": 3000, "lr": 0.0804, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27422, "top5_acc": 0.52109, "loss_cls": 4.18661, "loss": 4.18661, "time": 0.85543} +{"mode": "train", "epoch": 44, "iter": 3100, "lr": 0.08037, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27062, "top5_acc": 0.52047, "loss_cls": 4.20644, "loss": 4.20644, "time": 0.85131} +{"mode": "train", "epoch": 44, "iter": 3200, "lr": 0.08035, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27656, "top5_acc": 0.52562, "loss_cls": 4.18575, "loss": 4.18575, "time": 0.8465} +{"mode": "train", "epoch": 44, "iter": 3300, "lr": 0.08033, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27875, "top5_acc": 0.52578, "loss_cls": 4.1832, "loss": 4.1832, "time": 0.84943} +{"mode": "train", "epoch": 44, "iter": 3400, "lr": 0.08031, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27328, "top5_acc": 0.52438, "loss_cls": 4.19782, "loss": 4.19782, "time": 0.85158} +{"mode": "train", "epoch": 44, "iter": 3500, "lr": 0.08028, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26734, "top5_acc": 0.51422, "loss_cls": 4.23656, "loss": 4.23656, "time": 0.85032} +{"mode": "train", "epoch": 44, "iter": 3600, "lr": 0.08026, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26812, "top5_acc": 0.51766, "loss_cls": 4.22058, "loss": 4.22058, "time": 0.85366} +{"mode": "train", "epoch": 44, "iter": 3700, "lr": 0.08024, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28203, "top5_acc": 0.52797, "loss_cls": 4.18125, "loss": 4.18125, "time": 0.85074} +{"mode": "val", "epoch": 44, "iter": 309, "lr": 0.08023, "top1_acc": 0.22631, "top5_acc": 0.46634, "mean_class_accuracy": 0.22589} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.08021, "memory": 15990, "data_time": 1.5477, "top1_acc": 0.28484, "top5_acc": 0.52281, "loss_cls": 4.17481, "loss": 4.17481, "time": 2.58766} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.08019, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27766, "top5_acc": 0.52719, "loss_cls": 4.18274, "loss": 4.18274, "time": 0.85688} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.08016, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27266, "top5_acc": 0.53, "loss_cls": 4.15812, "loss": 4.15812, "time": 0.84927} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.08014, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27734, "top5_acc": 0.52938, "loss_cls": 4.16858, "loss": 4.16858, "time": 0.85251} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.08012, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26766, "top5_acc": 0.52469, "loss_cls": 4.20167, "loss": 4.20167, "time": 0.85868} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.0801, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28062, "top5_acc": 0.53969, "loss_cls": 4.12225, "loss": 4.12225, "time": 0.85727} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.08007, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28844, "top5_acc": 0.54156, "loss_cls": 4.14139, "loss": 4.14139, "time": 0.86027} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.08005, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27094, "top5_acc": 0.53422, "loss_cls": 4.16111, "loss": 4.16111, "time": 0.86339} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.08003, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27484, "top5_acc": 0.52672, "loss_cls": 4.15324, "loss": 4.15324, "time": 0.8582} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.08001, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.27594, "top5_acc": 0.52953, "loss_cls": 4.1473, "loss": 4.1473, "time": 0.85713} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.07998, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27375, "top5_acc": 0.52406, "loss_cls": 4.18814, "loss": 4.18814, "time": 0.8593} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.07996, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.275, "top5_acc": 0.53062, "loss_cls": 4.20048, "loss": 4.20048, "time": 0.85457} +{"mode": "train", "epoch": 45, "iter": 1300, "lr": 0.07994, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27516, "top5_acc": 0.52844, "loss_cls": 4.18393, "loss": 4.18393, "time": 0.85611} +{"mode": "train", "epoch": 45, "iter": 1400, "lr": 0.07992, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27359, "top5_acc": 0.52094, "loss_cls": 4.2153, "loss": 4.2153, "time": 0.85706} +{"mode": "train", "epoch": 45, "iter": 1500, "lr": 0.0799, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26969, "top5_acc": 0.52219, "loss_cls": 4.19733, "loss": 4.19733, "time": 0.86228} +{"mode": "train", "epoch": 45, "iter": 1600, "lr": 0.07987, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27703, "top5_acc": 0.52391, "loss_cls": 4.19269, "loss": 4.19269, "time": 0.86393} +{"mode": "train", "epoch": 45, "iter": 1700, "lr": 0.07985, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27719, "top5_acc": 0.52531, "loss_cls": 4.17575, "loss": 4.17575, "time": 0.86036} +{"mode": "train", "epoch": 45, "iter": 1800, "lr": 0.07983, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27266, "top5_acc": 0.52109, "loss_cls": 4.19176, "loss": 4.19176, "time": 0.85912} +{"mode": "train", "epoch": 45, "iter": 1900, "lr": 0.07981, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27125, "top5_acc": 0.515, "loss_cls": 4.22819, "loss": 4.22819, "time": 0.85917} +{"mode": "train", "epoch": 45, "iter": 2000, "lr": 0.07978, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27469, "top5_acc": 0.52156, "loss_cls": 4.18655, "loss": 4.18655, "time": 0.85786} +{"mode": "train", "epoch": 45, "iter": 2100, "lr": 0.07976, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27578, "top5_acc": 0.525, "loss_cls": 4.21871, "loss": 4.21871, "time": 0.86467} +{"mode": "train", "epoch": 45, "iter": 2200, "lr": 0.07974, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27484, "top5_acc": 0.52922, "loss_cls": 4.17643, "loss": 4.17643, "time": 0.85791} +{"mode": "train", "epoch": 45, "iter": 2300, "lr": 0.07972, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26766, "top5_acc": 0.52578, "loss_cls": 4.21461, "loss": 4.21461, "time": 0.85253} +{"mode": "train", "epoch": 45, "iter": 2400, "lr": 0.07969, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27703, "top5_acc": 0.53188, "loss_cls": 4.15154, "loss": 4.15154, "time": 0.84338} +{"mode": "train", "epoch": 45, "iter": 2500, "lr": 0.07967, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27672, "top5_acc": 0.525, "loss_cls": 4.18909, "loss": 4.18909, "time": 0.85638} +{"mode": "train", "epoch": 45, "iter": 2600, "lr": 0.07965, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27766, "top5_acc": 0.52438, "loss_cls": 4.18246, "loss": 4.18246, "time": 0.86094} +{"mode": "train", "epoch": 45, "iter": 2700, "lr": 0.07963, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28016, "top5_acc": 0.52844, "loss_cls": 4.1836, "loss": 4.1836, "time": 0.85013} +{"mode": "train", "epoch": 45, "iter": 2800, "lr": 0.0796, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28266, "top5_acc": 0.53078, "loss_cls": 4.15445, "loss": 4.15445, "time": 0.84848} +{"mode": "train", "epoch": 45, "iter": 2900, "lr": 0.07958, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26875, "top5_acc": 0.53141, "loss_cls": 4.15753, "loss": 4.15753, "time": 0.84563} +{"mode": "train", "epoch": 45, "iter": 3000, "lr": 0.07956, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27156, "top5_acc": 0.525, "loss_cls": 4.18771, "loss": 4.18771, "time": 0.86162} +{"mode": "train", "epoch": 45, "iter": 3100, "lr": 0.07954, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26578, "top5_acc": 0.5225, "loss_cls": 4.2394, "loss": 4.2394, "time": 0.85076} +{"mode": "train", "epoch": 45, "iter": 3200, "lr": 0.07951, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27484, "top5_acc": 0.52906, "loss_cls": 4.1457, "loss": 4.1457, "time": 0.85057} +{"mode": "train", "epoch": 45, "iter": 3300, "lr": 0.07949, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.28078, "top5_acc": 0.53141, "loss_cls": 4.15665, "loss": 4.15665, "time": 0.85857} +{"mode": "train", "epoch": 45, "iter": 3400, "lr": 0.07947, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27187, "top5_acc": 0.51609, "loss_cls": 4.22131, "loss": 4.22131, "time": 0.85357} +{"mode": "train", "epoch": 45, "iter": 3500, "lr": 0.07945, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27266, "top5_acc": 0.52031, "loss_cls": 4.18563, "loss": 4.18563, "time": 0.85673} +{"mode": "train", "epoch": 45, "iter": 3600, "lr": 0.07942, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27453, "top5_acc": 0.52953, "loss_cls": 4.1772, "loss": 4.1772, "time": 0.85947} +{"mode": "train", "epoch": 45, "iter": 3700, "lr": 0.0794, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26312, "top5_acc": 0.52234, "loss_cls": 4.20686, "loss": 4.20686, "time": 0.85813} +{"mode": "val", "epoch": 45, "iter": 309, "lr": 0.07939, "top1_acc": 0.21456, "top5_acc": 0.44841, "mean_class_accuracy": 0.21432} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.07937, "memory": 15990, "data_time": 1.57378, "top1_acc": 0.28047, "top5_acc": 0.5375, "loss_cls": 4.15443, "loss": 4.15443, "time": 2.61821} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.07934, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28859, "top5_acc": 0.53969, "loss_cls": 4.12193, "loss": 4.12193, "time": 0.86106} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.07932, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28125, "top5_acc": 0.54172, "loss_cls": 4.09393, "loss": 4.09393, "time": 0.86205} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.0793, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26844, "top5_acc": 0.51859, "loss_cls": 4.19965, "loss": 4.19965, "time": 0.8621} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.07928, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27891, "top5_acc": 0.53141, "loss_cls": 4.14122, "loss": 4.14122, "time": 0.85726} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.07925, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28078, "top5_acc": 0.52984, "loss_cls": 4.17644, "loss": 4.17644, "time": 0.85622} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.07923, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.26562, "top5_acc": 0.525, "loss_cls": 4.22073, "loss": 4.22073, "time": 0.85841} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.07921, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26828, "top5_acc": 0.51172, "loss_cls": 4.22844, "loss": 4.22844, "time": 0.86293} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.07919, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27859, "top5_acc": 0.52375, "loss_cls": 4.17564, "loss": 4.17564, "time": 0.86284} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.07916, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27047, "top5_acc": 0.51531, "loss_cls": 4.21687, "loss": 4.21687, "time": 0.86269} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.07914, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27094, "top5_acc": 0.52516, "loss_cls": 4.17972, "loss": 4.17972, "time": 0.85992} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.07912, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27156, "top5_acc": 0.52984, "loss_cls": 4.17127, "loss": 4.17127, "time": 0.85799} +{"mode": "train", "epoch": 46, "iter": 1300, "lr": 0.07909, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28281, "top5_acc": 0.53469, "loss_cls": 4.12022, "loss": 4.12022, "time": 0.86335} +{"mode": "train", "epoch": 46, "iter": 1400, "lr": 0.07907, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.26687, "top5_acc": 0.52188, "loss_cls": 4.1937, "loss": 4.1937, "time": 0.86821} +{"mode": "train", "epoch": 46, "iter": 1500, "lr": 0.07905, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27375, "top5_acc": 0.52094, "loss_cls": 4.19614, "loss": 4.19614, "time": 0.86393} +{"mode": "train", "epoch": 46, "iter": 1600, "lr": 0.07903, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29047, "top5_acc": 0.53625, "loss_cls": 4.1083, "loss": 4.1083, "time": 0.86471} +{"mode": "train", "epoch": 46, "iter": 1700, "lr": 0.079, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28781, "top5_acc": 0.53734, "loss_cls": 4.12808, "loss": 4.12808, "time": 0.86463} +{"mode": "train", "epoch": 46, "iter": 1800, "lr": 0.07898, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.27656, "top5_acc": 0.52344, "loss_cls": 4.20932, "loss": 4.20932, "time": 0.86708} +{"mode": "train", "epoch": 46, "iter": 1900, "lr": 0.07896, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.27859, "top5_acc": 0.52531, "loss_cls": 4.16814, "loss": 4.16814, "time": 0.86305} +{"mode": "train", "epoch": 46, "iter": 2000, "lr": 0.07894, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27734, "top5_acc": 0.52531, "loss_cls": 4.16671, "loss": 4.16671, "time": 0.86819} +{"mode": "train", "epoch": 46, "iter": 2100, "lr": 0.07891, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.27469, "top5_acc": 0.51984, "loss_cls": 4.19979, "loss": 4.19979, "time": 0.85905} +{"mode": "train", "epoch": 46, "iter": 2200, "lr": 0.07889, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27281, "top5_acc": 0.52172, "loss_cls": 4.18932, "loss": 4.18932, "time": 0.84675} +{"mode": "train", "epoch": 46, "iter": 2300, "lr": 0.07887, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27422, "top5_acc": 0.52703, "loss_cls": 4.1691, "loss": 4.1691, "time": 0.84845} +{"mode": "train", "epoch": 46, "iter": 2400, "lr": 0.07884, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2725, "top5_acc": 0.52609, "loss_cls": 4.17783, "loss": 4.17783, "time": 0.84831} +{"mode": "train", "epoch": 46, "iter": 2500, "lr": 0.07882, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26672, "top5_acc": 0.51484, "loss_cls": 4.2472, "loss": 4.2472, "time": 0.85742} +{"mode": "train", "epoch": 46, "iter": 2600, "lr": 0.0788, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26891, "top5_acc": 0.51625, "loss_cls": 4.20186, "loss": 4.20186, "time": 0.85247} +{"mode": "train", "epoch": 46, "iter": 2700, "lr": 0.07878, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27422, "top5_acc": 0.52297, "loss_cls": 4.20036, "loss": 4.20036, "time": 0.84999} +{"mode": "train", "epoch": 46, "iter": 2800, "lr": 0.07875, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26062, "top5_acc": 0.51313, "loss_cls": 4.24122, "loss": 4.24122, "time": 0.84814} +{"mode": "train", "epoch": 46, "iter": 2900, "lr": 0.07873, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.2675, "top5_acc": 0.51812, "loss_cls": 4.23389, "loss": 4.23389, "time": 0.85082} +{"mode": "train", "epoch": 46, "iter": 3000, "lr": 0.07871, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26906, "top5_acc": 0.52672, "loss_cls": 4.1845, "loss": 4.1845, "time": 0.86118} +{"mode": "train", "epoch": 46, "iter": 3100, "lr": 0.07868, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28188, "top5_acc": 0.54016, "loss_cls": 4.09895, "loss": 4.09895, "time": 0.8516} +{"mode": "train", "epoch": 46, "iter": 3200, "lr": 0.07866, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27391, "top5_acc": 0.52156, "loss_cls": 4.18205, "loss": 4.18205, "time": 0.84977} +{"mode": "train", "epoch": 46, "iter": 3300, "lr": 0.07864, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27391, "top5_acc": 0.52391, "loss_cls": 4.19623, "loss": 4.19623, "time": 0.85363} +{"mode": "train", "epoch": 46, "iter": 3400, "lr": 0.07862, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28125, "top5_acc": 0.53078, "loss_cls": 4.18365, "loss": 4.18365, "time": 0.85058} +{"mode": "train", "epoch": 46, "iter": 3500, "lr": 0.07859, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28172, "top5_acc": 0.53609, "loss_cls": 4.14424, "loss": 4.14424, "time": 0.84946} +{"mode": "train", "epoch": 46, "iter": 3600, "lr": 0.07857, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.26984, "top5_acc": 0.52422, "loss_cls": 4.163, "loss": 4.163, "time": 0.85036} +{"mode": "train", "epoch": 46, "iter": 3700, "lr": 0.07855, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27406, "top5_acc": 0.52328, "loss_cls": 4.16795, "loss": 4.16795, "time": 0.85654} +{"mode": "val", "epoch": 46, "iter": 309, "lr": 0.07854, "top1_acc": 0.19161, "top5_acc": 0.41939, "mean_class_accuracy": 0.19135} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.07851, "memory": 15990, "data_time": 1.63543, "top1_acc": 0.28469, "top5_acc": 0.54297, "loss_cls": 4.10762, "loss": 4.10762, "time": 2.68712} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.07849, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27453, "top5_acc": 0.52703, "loss_cls": 4.17209, "loss": 4.17209, "time": 0.8548} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.07847, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28203, "top5_acc": 0.53453, "loss_cls": 4.11454, "loss": 4.11454, "time": 0.85914} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.07844, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27484, "top5_acc": 0.52875, "loss_cls": 4.15291, "loss": 4.15291, "time": 0.85283} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.07842, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28297, "top5_acc": 0.53328, "loss_cls": 4.15221, "loss": 4.15221, "time": 0.85483} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.0784, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28406, "top5_acc": 0.52656, "loss_cls": 4.17687, "loss": 4.17687, "time": 0.85348} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.07838, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27062, "top5_acc": 0.52109, "loss_cls": 4.20381, "loss": 4.20381, "time": 0.85748} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.07835, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.2725, "top5_acc": 0.52672, "loss_cls": 4.19897, "loss": 4.19897, "time": 0.85727} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.07833, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27375, "top5_acc": 0.52766, "loss_cls": 4.161, "loss": 4.161, "time": 0.85746} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.07831, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28141, "top5_acc": 0.52703, "loss_cls": 4.17122, "loss": 4.17122, "time": 0.85735} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.07828, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27609, "top5_acc": 0.53031, "loss_cls": 4.14418, "loss": 4.14418, "time": 0.85996} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.07826, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28766, "top5_acc": 0.53422, "loss_cls": 4.1245, "loss": 4.1245, "time": 0.85615} +{"mode": "train", "epoch": 47, "iter": 1300, "lr": 0.07824, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26719, "top5_acc": 0.52984, "loss_cls": 4.19541, "loss": 4.19541, "time": 0.85595} +{"mode": "train", "epoch": 47, "iter": 1400, "lr": 0.07821, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.26562, "top5_acc": 0.52453, "loss_cls": 4.21218, "loss": 4.21218, "time": 0.85503} +{"mode": "train", "epoch": 47, "iter": 1500, "lr": 0.07819, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27016, "top5_acc": 0.53719, "loss_cls": 4.13851, "loss": 4.13851, "time": 0.85165} +{"mode": "train", "epoch": 47, "iter": 1600, "lr": 0.07817, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28578, "top5_acc": 0.53328, "loss_cls": 4.13933, "loss": 4.13933, "time": 0.85411} +{"mode": "train", "epoch": 47, "iter": 1700, "lr": 0.07814, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27406, "top5_acc": 0.52234, "loss_cls": 4.20578, "loss": 4.20578, "time": 0.85055} +{"mode": "train", "epoch": 47, "iter": 1800, "lr": 0.07812, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.27531, "top5_acc": 0.52891, "loss_cls": 4.16497, "loss": 4.16497, "time": 0.85847} +{"mode": "train", "epoch": 47, "iter": 1900, "lr": 0.0781, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27437, "top5_acc": 0.53016, "loss_cls": 4.17146, "loss": 4.17146, "time": 0.86204} +{"mode": "train", "epoch": 47, "iter": 2000, "lr": 0.07808, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26578, "top5_acc": 0.51938, "loss_cls": 4.21111, "loss": 4.21111, "time": 0.86061} +{"mode": "train", "epoch": 47, "iter": 2100, "lr": 0.07805, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27141, "top5_acc": 0.52531, "loss_cls": 4.20827, "loss": 4.20827, "time": 0.85685} +{"mode": "train", "epoch": 47, "iter": 2200, "lr": 0.07803, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27719, "top5_acc": 0.53031, "loss_cls": 4.17582, "loss": 4.17582, "time": 0.85469} +{"mode": "train", "epoch": 47, "iter": 2300, "lr": 0.07801, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27359, "top5_acc": 0.52391, "loss_cls": 4.17799, "loss": 4.17799, "time": 0.85065} +{"mode": "train", "epoch": 47, "iter": 2400, "lr": 0.07798, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.26469, "top5_acc": 0.52031, "loss_cls": 4.21265, "loss": 4.21265, "time": 0.85436} +{"mode": "train", "epoch": 47, "iter": 2500, "lr": 0.07796, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26969, "top5_acc": 0.53016, "loss_cls": 4.17713, "loss": 4.17713, "time": 0.85868} +{"mode": "train", "epoch": 47, "iter": 2600, "lr": 0.07794, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2825, "top5_acc": 0.52625, "loss_cls": 4.15722, "loss": 4.15722, "time": 0.85212} +{"mode": "train", "epoch": 47, "iter": 2700, "lr": 0.07791, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27219, "top5_acc": 0.51844, "loss_cls": 4.22073, "loss": 4.22073, "time": 0.85477} +{"mode": "train", "epoch": 47, "iter": 2800, "lr": 0.07789, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27688, "top5_acc": 0.52547, "loss_cls": 4.17345, "loss": 4.17345, "time": 0.85245} +{"mode": "train", "epoch": 47, "iter": 2900, "lr": 0.07787, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27031, "top5_acc": 0.53359, "loss_cls": 4.15567, "loss": 4.15567, "time": 0.8631} +{"mode": "train", "epoch": 47, "iter": 3000, "lr": 0.07784, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27406, "top5_acc": 0.52547, "loss_cls": 4.19156, "loss": 4.19156, "time": 0.85648} +{"mode": "train", "epoch": 47, "iter": 3100, "lr": 0.07782, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28203, "top5_acc": 0.52703, "loss_cls": 4.1668, "loss": 4.1668, "time": 0.8552} +{"mode": "train", "epoch": 47, "iter": 3200, "lr": 0.0778, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27938, "top5_acc": 0.52156, "loss_cls": 4.1755, "loss": 4.1755, "time": 0.85183} +{"mode": "train", "epoch": 47, "iter": 3300, "lr": 0.07777, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27437, "top5_acc": 0.53266, "loss_cls": 4.14567, "loss": 4.14567, "time": 0.86232} +{"mode": "train", "epoch": 47, "iter": 3400, "lr": 0.07775, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27453, "top5_acc": 0.52719, "loss_cls": 4.16779, "loss": 4.16779, "time": 0.85575} +{"mode": "train", "epoch": 47, "iter": 3500, "lr": 0.07773, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.26922, "top5_acc": 0.52109, "loss_cls": 4.23745, "loss": 4.23745, "time": 0.8611} +{"mode": "train", "epoch": 47, "iter": 3600, "lr": 0.0777, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27531, "top5_acc": 0.52672, "loss_cls": 4.1784, "loss": 4.1784, "time": 0.86635} +{"mode": "train", "epoch": 47, "iter": 3700, "lr": 0.07768, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28781, "top5_acc": 0.53656, "loss_cls": 4.14164, "loss": 4.14164, "time": 0.85908} +{"mode": "val", "epoch": 47, "iter": 309, "lr": 0.07767, "top1_acc": 0.21486, "top5_acc": 0.45641, "mean_class_accuracy": 0.21474} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.07765, "memory": 15990, "data_time": 1.58755, "top1_acc": 0.28266, "top5_acc": 0.53594, "loss_cls": 4.13147, "loss": 4.13147, "time": 2.62753} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.07762, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29656, "top5_acc": 0.54156, "loss_cls": 4.08239, "loss": 4.08239, "time": 0.85133} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.0776, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27922, "top5_acc": 0.52984, "loss_cls": 4.17568, "loss": 4.17568, "time": 0.85367} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.07758, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.28594, "top5_acc": 0.52453, "loss_cls": 4.16913, "loss": 4.16913, "time": 0.85154} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.07755, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28344, "top5_acc": 0.53672, "loss_cls": 4.11139, "loss": 4.11139, "time": 0.85428} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.07753, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28359, "top5_acc": 0.52875, "loss_cls": 4.1378, "loss": 4.1378, "time": 0.8577} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.07751, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.2975, "top5_acc": 0.53938, "loss_cls": 4.11459, "loss": 4.11459, "time": 0.85849} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.07748, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.27469, "top5_acc": 0.51688, "loss_cls": 4.20647, "loss": 4.20647, "time": 0.86194} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.07746, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27625, "top5_acc": 0.53234, "loss_cls": 4.17277, "loss": 4.17277, "time": 0.85474} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.07744, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28203, "top5_acc": 0.52078, "loss_cls": 4.17175, "loss": 4.17175, "time": 0.85445} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.07741, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27562, "top5_acc": 0.53109, "loss_cls": 4.14845, "loss": 4.14845, "time": 0.86186} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.07739, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28547, "top5_acc": 0.53469, "loss_cls": 4.13164, "loss": 4.13164, "time": 0.86537} +{"mode": "train", "epoch": 48, "iter": 1300, "lr": 0.07737, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27953, "top5_acc": 0.53031, "loss_cls": 4.174, "loss": 4.174, "time": 0.86298} +{"mode": "train", "epoch": 48, "iter": 1400, "lr": 0.07734, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27281, "top5_acc": 0.5325, "loss_cls": 4.16039, "loss": 4.16039, "time": 0.86219} +{"mode": "train", "epoch": 48, "iter": 1500, "lr": 0.07732, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.26312, "top5_acc": 0.52625, "loss_cls": 4.18971, "loss": 4.18971, "time": 0.86313} +{"mode": "train", "epoch": 48, "iter": 1600, "lr": 0.0773, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27844, "top5_acc": 0.53312, "loss_cls": 4.14843, "loss": 4.14843, "time": 0.86001} +{"mode": "train", "epoch": 48, "iter": 1700, "lr": 0.07727, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.27359, "top5_acc": 0.52922, "loss_cls": 4.14968, "loss": 4.14968, "time": 0.86504} +{"mode": "train", "epoch": 48, "iter": 1800, "lr": 0.07725, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27187, "top5_acc": 0.53266, "loss_cls": 4.17773, "loss": 4.17773, "time": 0.86306} +{"mode": "train", "epoch": 48, "iter": 1900, "lr": 0.07723, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27078, "top5_acc": 0.52625, "loss_cls": 4.19241, "loss": 4.19241, "time": 0.86417} +{"mode": "train", "epoch": 48, "iter": 2000, "lr": 0.0772, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27234, "top5_acc": 0.52391, "loss_cls": 4.19361, "loss": 4.19361, "time": 0.86261} +{"mode": "train", "epoch": 48, "iter": 2100, "lr": 0.07718, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26828, "top5_acc": 0.52688, "loss_cls": 4.21203, "loss": 4.21203, "time": 0.85663} +{"mode": "train", "epoch": 48, "iter": 2200, "lr": 0.07716, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28125, "top5_acc": 0.53219, "loss_cls": 4.1596, "loss": 4.1596, "time": 0.85492} +{"mode": "train", "epoch": 48, "iter": 2300, "lr": 0.07713, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26953, "top5_acc": 0.52984, "loss_cls": 4.17111, "loss": 4.17111, "time": 0.85773} +{"mode": "train", "epoch": 48, "iter": 2400, "lr": 0.07711, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27047, "top5_acc": 0.52469, "loss_cls": 4.19858, "loss": 4.19858, "time": 0.85669} +{"mode": "train", "epoch": 48, "iter": 2500, "lr": 0.07709, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27625, "top5_acc": 0.52859, "loss_cls": 4.15537, "loss": 4.15537, "time": 0.85714} +{"mode": "train", "epoch": 48, "iter": 2600, "lr": 0.07706, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.27656, "top5_acc": 0.52531, "loss_cls": 4.16881, "loss": 4.16881, "time": 0.85602} +{"mode": "train", "epoch": 48, "iter": 2700, "lr": 0.07704, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27797, "top5_acc": 0.52547, "loss_cls": 4.16741, "loss": 4.16741, "time": 0.85078} +{"mode": "train", "epoch": 48, "iter": 2800, "lr": 0.07701, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28703, "top5_acc": 0.52922, "loss_cls": 4.15062, "loss": 4.15062, "time": 0.85071} +{"mode": "train", "epoch": 48, "iter": 2900, "lr": 0.07699, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27297, "top5_acc": 0.52875, "loss_cls": 4.18159, "loss": 4.18159, "time": 0.8534} +{"mode": "train", "epoch": 48, "iter": 3000, "lr": 0.07697, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27422, "top5_acc": 0.52969, "loss_cls": 4.17597, "loss": 4.17597, "time": 0.85557} +{"mode": "train", "epoch": 48, "iter": 3100, "lr": 0.07694, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28297, "top5_acc": 0.52969, "loss_cls": 4.14025, "loss": 4.14025, "time": 0.85034} +{"mode": "train", "epoch": 48, "iter": 3200, "lr": 0.07692, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27578, "top5_acc": 0.52781, "loss_cls": 4.1761, "loss": 4.1761, "time": 0.85246} +{"mode": "train", "epoch": 48, "iter": 3300, "lr": 0.0769, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.275, "top5_acc": 0.52938, "loss_cls": 4.17488, "loss": 4.17488, "time": 0.85561} +{"mode": "train", "epoch": 48, "iter": 3400, "lr": 0.07687, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28328, "top5_acc": 0.53469, "loss_cls": 4.13828, "loss": 4.13828, "time": 0.85603} +{"mode": "train", "epoch": 48, "iter": 3500, "lr": 0.07685, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28047, "top5_acc": 0.52984, "loss_cls": 4.16561, "loss": 4.16561, "time": 0.85996} +{"mode": "train", "epoch": 48, "iter": 3600, "lr": 0.07683, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.27859, "top5_acc": 0.52812, "loss_cls": 4.15874, "loss": 4.15874, "time": 0.85535} +{"mode": "train", "epoch": 48, "iter": 3700, "lr": 0.0768, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28422, "top5_acc": 0.53328, "loss_cls": 4.13841, "loss": 4.13841, "time": 0.85767} +{"mode": "val", "epoch": 48, "iter": 309, "lr": 0.07679, "top1_acc": 0.17895, "top5_acc": 0.4011, "mean_class_accuracy": 0.17883} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.07677, "memory": 15990, "data_time": 1.57529, "top1_acc": 0.27922, "top5_acc": 0.53969, "loss_cls": 4.13115, "loss": 4.13115, "time": 2.61474} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.07674, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27594, "top5_acc": 0.53781, "loss_cls": 4.14137, "loss": 4.14137, "time": 0.85917} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.07672, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27938, "top5_acc": 0.53062, "loss_cls": 4.15904, "loss": 4.15904, "time": 0.84926} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.0767, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27984, "top5_acc": 0.53062, "loss_cls": 4.15087, "loss": 4.15087, "time": 0.85361} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.07667, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28203, "top5_acc": 0.53109, "loss_cls": 4.1319, "loss": 4.1319, "time": 0.84992} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.07665, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27812, "top5_acc": 0.53859, "loss_cls": 4.15466, "loss": 4.15466, "time": 0.84985} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.07663, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28422, "top5_acc": 0.53562, "loss_cls": 4.13385, "loss": 4.13385, "time": 0.85461} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.0766, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28141, "top5_acc": 0.53547, "loss_cls": 4.12743, "loss": 4.12743, "time": 0.85952} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.07658, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27391, "top5_acc": 0.51688, "loss_cls": 4.19345, "loss": 4.19345, "time": 0.85549} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.07656, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28422, "top5_acc": 0.53828, "loss_cls": 4.12663, "loss": 4.12663, "time": 0.85774} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.07653, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27734, "top5_acc": 0.52812, "loss_cls": 4.17177, "loss": 4.17177, "time": 0.85607} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.07651, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27828, "top5_acc": 0.52578, "loss_cls": 4.16311, "loss": 4.16311, "time": 0.85903} +{"mode": "train", "epoch": 49, "iter": 1300, "lr": 0.07648, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27906, "top5_acc": 0.52406, "loss_cls": 4.18598, "loss": 4.18598, "time": 0.85546} +{"mode": "train", "epoch": 49, "iter": 1400, "lr": 0.07646, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27609, "top5_acc": 0.52453, "loss_cls": 4.1752, "loss": 4.1752, "time": 0.86118} +{"mode": "train", "epoch": 49, "iter": 1500, "lr": 0.07644, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.285, "top5_acc": 0.5325, "loss_cls": 4.1664, "loss": 4.1664, "time": 0.85765} +{"mode": "train", "epoch": 49, "iter": 1600, "lr": 0.07641, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27719, "top5_acc": 0.53531, "loss_cls": 4.14292, "loss": 4.14292, "time": 0.85839} +{"mode": "train", "epoch": 49, "iter": 1700, "lr": 0.07639, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27031, "top5_acc": 0.5275, "loss_cls": 4.20554, "loss": 4.20554, "time": 0.86519} +{"mode": "train", "epoch": 49, "iter": 1800, "lr": 0.07637, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27984, "top5_acc": 0.53438, "loss_cls": 4.12996, "loss": 4.12996, "time": 0.8571} +{"mode": "train", "epoch": 49, "iter": 1900, "lr": 0.07634, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28609, "top5_acc": 0.53484, "loss_cls": 4.1169, "loss": 4.1169, "time": 0.85654} +{"mode": "train", "epoch": 49, "iter": 2000, "lr": 0.07632, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28, "top5_acc": 0.53016, "loss_cls": 4.17405, "loss": 4.17405, "time": 0.84655} +{"mode": "train", "epoch": 49, "iter": 2100, "lr": 0.07629, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27391, "top5_acc": 0.53234, "loss_cls": 4.17455, "loss": 4.17455, "time": 0.8493} +{"mode": "train", "epoch": 49, "iter": 2200, "lr": 0.07627, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27031, "top5_acc": 0.52266, "loss_cls": 4.19614, "loss": 4.19614, "time": 0.85119} +{"mode": "train", "epoch": 49, "iter": 2300, "lr": 0.07625, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27656, "top5_acc": 0.52906, "loss_cls": 4.16585, "loss": 4.16585, "time": 0.84988} +{"mode": "train", "epoch": 49, "iter": 2400, "lr": 0.07622, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28, "top5_acc": 0.52766, "loss_cls": 4.13497, "loss": 4.13497, "time": 0.85577} +{"mode": "train", "epoch": 49, "iter": 2500, "lr": 0.0762, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27281, "top5_acc": 0.5275, "loss_cls": 4.18927, "loss": 4.18927, "time": 0.85375} +{"mode": "train", "epoch": 49, "iter": 2600, "lr": 0.07618, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28359, "top5_acc": 0.52984, "loss_cls": 4.11808, "loss": 4.11808, "time": 0.84892} +{"mode": "train", "epoch": 49, "iter": 2700, "lr": 0.07615, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28312, "top5_acc": 0.52312, "loss_cls": 4.1428, "loss": 4.1428, "time": 0.84802} +{"mode": "train", "epoch": 49, "iter": 2800, "lr": 0.07613, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27625, "top5_acc": 0.52078, "loss_cls": 4.20031, "loss": 4.20031, "time": 0.85153} +{"mode": "train", "epoch": 49, "iter": 2900, "lr": 0.0761, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28203, "top5_acc": 0.53469, "loss_cls": 4.14461, "loss": 4.14461, "time": 0.85734} +{"mode": "train", "epoch": 49, "iter": 3000, "lr": 0.07608, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26516, "top5_acc": 0.51422, "loss_cls": 4.21475, "loss": 4.21475, "time": 0.85933} +{"mode": "train", "epoch": 49, "iter": 3100, "lr": 0.07606, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27484, "top5_acc": 0.52812, "loss_cls": 4.14917, "loss": 4.14917, "time": 0.85096} +{"mode": "train", "epoch": 49, "iter": 3200, "lr": 0.07603, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28609, "top5_acc": 0.53328, "loss_cls": 4.14645, "loss": 4.14645, "time": 0.84859} +{"mode": "train", "epoch": 49, "iter": 3300, "lr": 0.07601, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27703, "top5_acc": 0.53484, "loss_cls": 4.14073, "loss": 4.14073, "time": 0.85285} +{"mode": "train", "epoch": 49, "iter": 3400, "lr": 0.07598, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28828, "top5_acc": 0.53109, "loss_cls": 4.11579, "loss": 4.11579, "time": 0.84673} +{"mode": "train", "epoch": 49, "iter": 3500, "lr": 0.07596, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28078, "top5_acc": 0.54625, "loss_cls": 4.12637, "loss": 4.12637, "time": 0.84833} +{"mode": "train", "epoch": 49, "iter": 3600, "lr": 0.07594, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27844, "top5_acc": 0.53547, "loss_cls": 4.16575, "loss": 4.16575, "time": 0.84931} +{"mode": "train", "epoch": 49, "iter": 3700, "lr": 0.07591, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27562, "top5_acc": 0.52875, "loss_cls": 4.18204, "loss": 4.18204, "time": 0.85096} +{"mode": "val", "epoch": 49, "iter": 309, "lr": 0.0759, "top1_acc": 0.20676, "top5_acc": 0.44355, "mean_class_accuracy": 0.20669} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.07588, "memory": 15990, "data_time": 1.62676, "top1_acc": 0.27469, "top5_acc": 0.53328, "loss_cls": 4.1508, "loss": 4.1508, "time": 2.66746} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.07585, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28625, "top5_acc": 0.53406, "loss_cls": 4.11437, "loss": 4.11437, "time": 0.85634} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.07583, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28438, "top5_acc": 0.53172, "loss_cls": 4.14723, "loss": 4.14723, "time": 0.85496} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.07581, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27953, "top5_acc": 0.53109, "loss_cls": 4.12769, "loss": 4.12769, "time": 0.8524} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.07578, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27875, "top5_acc": 0.52797, "loss_cls": 4.16128, "loss": 4.16128, "time": 0.85456} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.07576, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29063, "top5_acc": 0.535, "loss_cls": 4.09795, "loss": 4.09795, "time": 0.85434} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.07573, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2875, "top5_acc": 0.54078, "loss_cls": 4.10734, "loss": 4.10734, "time": 0.85954} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.07571, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28359, "top5_acc": 0.52875, "loss_cls": 4.13341, "loss": 4.13341, "time": 0.85551} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.07569, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27562, "top5_acc": 0.52344, "loss_cls": 4.19176, "loss": 4.19176, "time": 0.85822} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.07566, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.27281, "top5_acc": 0.52875, "loss_cls": 4.18265, "loss": 4.18265, "time": 0.85624} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.07564, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2825, "top5_acc": 0.53422, "loss_cls": 4.13373, "loss": 4.13373, "time": 0.85018} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.07561, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27469, "top5_acc": 0.53609, "loss_cls": 4.11949, "loss": 4.11949, "time": 0.84999} +{"mode": "train", "epoch": 50, "iter": 1300, "lr": 0.07559, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28578, "top5_acc": 0.53438, "loss_cls": 4.11098, "loss": 4.11098, "time": 0.85723} +{"mode": "train", "epoch": 50, "iter": 1400, "lr": 0.07557, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27734, "top5_acc": 0.53359, "loss_cls": 4.14174, "loss": 4.14174, "time": 0.85961} +{"mode": "train", "epoch": 50, "iter": 1500, "lr": 0.07554, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.27953, "top5_acc": 0.53531, "loss_cls": 4.14154, "loss": 4.14154, "time": 0.85022} +{"mode": "train", "epoch": 50, "iter": 1600, "lr": 0.07552, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27797, "top5_acc": 0.52703, "loss_cls": 4.15676, "loss": 4.15676, "time": 0.85197} +{"mode": "train", "epoch": 50, "iter": 1700, "lr": 0.07549, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28109, "top5_acc": 0.54, "loss_cls": 4.07926, "loss": 4.07926, "time": 0.85468} +{"mode": "train", "epoch": 50, "iter": 1800, "lr": 0.07547, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28188, "top5_acc": 0.53375, "loss_cls": 4.13515, "loss": 4.13515, "time": 0.85905} +{"mode": "train", "epoch": 50, "iter": 1900, "lr": 0.07545, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27422, "top5_acc": 0.53297, "loss_cls": 4.15119, "loss": 4.15119, "time": 0.85661} +{"mode": "train", "epoch": 50, "iter": 2000, "lr": 0.07542, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27609, "top5_acc": 0.52125, "loss_cls": 4.18301, "loss": 4.18301, "time": 0.85462} +{"mode": "train", "epoch": 50, "iter": 2100, "lr": 0.0754, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26937, "top5_acc": 0.51422, "loss_cls": 4.21408, "loss": 4.21408, "time": 0.84803} +{"mode": "train", "epoch": 50, "iter": 2200, "lr": 0.07537, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28625, "top5_acc": 0.53297, "loss_cls": 4.13984, "loss": 4.13984, "time": 0.84462} +{"mode": "train", "epoch": 50, "iter": 2300, "lr": 0.07535, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27547, "top5_acc": 0.52234, "loss_cls": 4.19265, "loss": 4.19265, "time": 0.84938} +{"mode": "train", "epoch": 50, "iter": 2400, "lr": 0.07533, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27672, "top5_acc": 0.53547, "loss_cls": 4.1563, "loss": 4.1563, "time": 0.8506} +{"mode": "train", "epoch": 50, "iter": 2500, "lr": 0.0753, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27828, "top5_acc": 0.53078, "loss_cls": 4.16362, "loss": 4.16362, "time": 0.8453} +{"mode": "train", "epoch": 50, "iter": 2600, "lr": 0.07528, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28188, "top5_acc": 0.53453, "loss_cls": 4.11343, "loss": 4.11343, "time": 0.85027} +{"mode": "train", "epoch": 50, "iter": 2700, "lr": 0.07525, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27938, "top5_acc": 0.53047, "loss_cls": 4.12991, "loss": 4.12991, "time": 0.85415} +{"mode": "train", "epoch": 50, "iter": 2800, "lr": 0.07523, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26672, "top5_acc": 0.51812, "loss_cls": 4.19534, "loss": 4.19534, "time": 0.851} +{"mode": "train", "epoch": 50, "iter": 2900, "lr": 0.0752, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28188, "top5_acc": 0.52344, "loss_cls": 4.18949, "loss": 4.18949, "time": 0.84855} +{"mode": "train", "epoch": 50, "iter": 3000, "lr": 0.07518, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.27516, "top5_acc": 0.53, "loss_cls": 4.17156, "loss": 4.17156, "time": 0.85209} +{"mode": "train", "epoch": 50, "iter": 3100, "lr": 0.07516, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2875, "top5_acc": 0.53688, "loss_cls": 4.12332, "loss": 4.12332, "time": 0.85024} +{"mode": "train", "epoch": 50, "iter": 3200, "lr": 0.07513, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28625, "top5_acc": 0.52984, "loss_cls": 4.12516, "loss": 4.12516, "time": 0.84662} +{"mode": "train", "epoch": 50, "iter": 3300, "lr": 0.07511, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.28578, "top5_acc": 0.52984, "loss_cls": 4.17681, "loss": 4.17681, "time": 0.85004} +{"mode": "train", "epoch": 50, "iter": 3400, "lr": 0.07508, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28359, "top5_acc": 0.53422, "loss_cls": 4.13662, "loss": 4.13662, "time": 0.8519} +{"mode": "train", "epoch": 50, "iter": 3500, "lr": 0.07506, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28844, "top5_acc": 0.54484, "loss_cls": 4.08119, "loss": 4.08119, "time": 0.85034} +{"mode": "train", "epoch": 50, "iter": 3600, "lr": 0.07504, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28141, "top5_acc": 0.53719, "loss_cls": 4.12219, "loss": 4.12219, "time": 0.8527} +{"mode": "train", "epoch": 50, "iter": 3700, "lr": 0.07501, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.26937, "top5_acc": 0.51891, "loss_cls": 4.19051, "loss": 4.19051, "time": 0.85409} +{"mode": "val", "epoch": 50, "iter": 309, "lr": 0.075, "top1_acc": 0.22205, "top5_acc": 0.46579, "mean_class_accuracy": 0.22183} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.07498, "memory": 15990, "data_time": 1.59016, "top1_acc": 0.28766, "top5_acc": 0.54328, "loss_cls": 4.07583, "loss": 4.07583, "time": 2.64287} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.07495, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28344, "top5_acc": 0.53531, "loss_cls": 4.12653, "loss": 4.12653, "time": 0.85209} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.07493, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27266, "top5_acc": 0.52656, "loss_cls": 4.16932, "loss": 4.16932, "time": 0.86199} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.0749, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27875, "top5_acc": 0.53641, "loss_cls": 4.12573, "loss": 4.12573, "time": 0.85399} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.07488, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28453, "top5_acc": 0.52141, "loss_cls": 4.13634, "loss": 4.13634, "time": 0.85918} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.07485, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28156, "top5_acc": 0.53156, "loss_cls": 4.13593, "loss": 4.13593, "time": 0.85888} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.07483, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28156, "top5_acc": 0.52984, "loss_cls": 4.15929, "loss": 4.15929, "time": 0.85441} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.07481, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27516, "top5_acc": 0.52422, "loss_cls": 4.18048, "loss": 4.18048, "time": 0.85562} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.07478, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28328, "top5_acc": 0.53281, "loss_cls": 4.13022, "loss": 4.13022, "time": 0.85438} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.07476, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27359, "top5_acc": 0.51891, "loss_cls": 4.2204, "loss": 4.2204, "time": 0.8457} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.07473, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27656, "top5_acc": 0.53672, "loss_cls": 4.14252, "loss": 4.14252, "time": 0.85105} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.07471, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28094, "top5_acc": 0.54359, "loss_cls": 4.11099, "loss": 4.11099, "time": 0.85535} +{"mode": "train", "epoch": 51, "iter": 1300, "lr": 0.07468, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28688, "top5_acc": 0.53297, "loss_cls": 4.11482, "loss": 4.11482, "time": 0.85387} +{"mode": "train", "epoch": 51, "iter": 1400, "lr": 0.07466, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27406, "top5_acc": 0.52781, "loss_cls": 4.16504, "loss": 4.16504, "time": 0.85263} +{"mode": "train", "epoch": 51, "iter": 1500, "lr": 0.07464, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27984, "top5_acc": 0.53328, "loss_cls": 4.14253, "loss": 4.14253, "time": 0.85219} +{"mode": "train", "epoch": 51, "iter": 1600, "lr": 0.07461, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29516, "top5_acc": 0.53469, "loss_cls": 4.11205, "loss": 4.11205, "time": 0.85716} +{"mode": "train", "epoch": 51, "iter": 1700, "lr": 0.07459, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27609, "top5_acc": 0.53484, "loss_cls": 4.12859, "loss": 4.12859, "time": 0.85276} +{"mode": "train", "epoch": 51, "iter": 1800, "lr": 0.07456, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28062, "top5_acc": 0.53344, "loss_cls": 4.16898, "loss": 4.16898, "time": 0.8541} +{"mode": "train", "epoch": 51, "iter": 1900, "lr": 0.07454, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28562, "top5_acc": 0.53469, "loss_cls": 4.13059, "loss": 4.13059, "time": 0.85675} +{"mode": "train", "epoch": 51, "iter": 2000, "lr": 0.07451, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28031, "top5_acc": 0.53438, "loss_cls": 4.1433, "loss": 4.1433, "time": 0.85523} +{"mode": "train", "epoch": 51, "iter": 2100, "lr": 0.07449, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28016, "top5_acc": 0.5375, "loss_cls": 4.14337, "loss": 4.14337, "time": 0.85523} +{"mode": "train", "epoch": 51, "iter": 2200, "lr": 0.07447, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27859, "top5_acc": 0.53906, "loss_cls": 4.13806, "loss": 4.13806, "time": 0.85749} +{"mode": "train", "epoch": 51, "iter": 2300, "lr": 0.07444, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29047, "top5_acc": 0.53531, "loss_cls": 4.11836, "loss": 4.11836, "time": 0.85584} +{"mode": "train", "epoch": 51, "iter": 2400, "lr": 0.07442, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27375, "top5_acc": 0.52547, "loss_cls": 4.15917, "loss": 4.15917, "time": 0.85903} +{"mode": "train", "epoch": 51, "iter": 2500, "lr": 0.07439, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27797, "top5_acc": 0.52516, "loss_cls": 4.17765, "loss": 4.17765, "time": 0.85735} +{"mode": "train", "epoch": 51, "iter": 2600, "lr": 0.07437, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27688, "top5_acc": 0.52625, "loss_cls": 4.16299, "loss": 4.16299, "time": 0.85204} +{"mode": "train", "epoch": 51, "iter": 2700, "lr": 0.07434, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.27906, "top5_acc": 0.53203, "loss_cls": 4.1447, "loss": 4.1447, "time": 0.85008} +{"mode": "train", "epoch": 51, "iter": 2800, "lr": 0.07432, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27609, "top5_acc": 0.52688, "loss_cls": 4.18246, "loss": 4.18246, "time": 0.8552} +{"mode": "train", "epoch": 51, "iter": 2900, "lr": 0.07429, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28, "top5_acc": 0.52172, "loss_cls": 4.17652, "loss": 4.17652, "time": 0.85485} +{"mode": "train", "epoch": 51, "iter": 3000, "lr": 0.07427, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.28172, "top5_acc": 0.54078, "loss_cls": 4.12858, "loss": 4.12858, "time": 0.85074} +{"mode": "train", "epoch": 51, "iter": 3100, "lr": 0.07425, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28156, "top5_acc": 0.53969, "loss_cls": 4.11074, "loss": 4.11074, "time": 0.85003} +{"mode": "train", "epoch": 51, "iter": 3200, "lr": 0.07422, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.27859, "top5_acc": 0.53062, "loss_cls": 4.15476, "loss": 4.15476, "time": 0.85311} +{"mode": "train", "epoch": 51, "iter": 3300, "lr": 0.0742, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29031, "top5_acc": 0.53688, "loss_cls": 4.13183, "loss": 4.13183, "time": 0.85647} +{"mode": "train", "epoch": 51, "iter": 3400, "lr": 0.07417, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28969, "top5_acc": 0.53953, "loss_cls": 4.11279, "loss": 4.11279, "time": 0.85604} +{"mode": "train", "epoch": 51, "iter": 3500, "lr": 0.07415, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28578, "top5_acc": 0.53438, "loss_cls": 4.14868, "loss": 4.14868, "time": 0.85795} +{"mode": "train", "epoch": 51, "iter": 3600, "lr": 0.07412, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27422, "top5_acc": 0.53188, "loss_cls": 4.15063, "loss": 4.15063, "time": 0.85596} +{"mode": "train", "epoch": 51, "iter": 3700, "lr": 0.0741, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2825, "top5_acc": 0.53, "loss_cls": 4.17369, "loss": 4.17369, "time": 0.85736} +{"mode": "val", "epoch": 51, "iter": 309, "lr": 0.07409, "top1_acc": 0.20959, "top5_acc": 0.45251, "mean_class_accuracy": 0.2094} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.07406, "memory": 15990, "data_time": 1.5704, "top1_acc": 0.29594, "top5_acc": 0.55547, "loss_cls": 4.03178, "loss": 4.03178, "time": 2.60061} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.07404, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27859, "top5_acc": 0.54094, "loss_cls": 4.13124, "loss": 4.13124, "time": 0.85387} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.07401, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27656, "top5_acc": 0.53734, "loss_cls": 4.12508, "loss": 4.12508, "time": 0.8537} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.07399, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28453, "top5_acc": 0.53891, "loss_cls": 4.07794, "loss": 4.07794, "time": 0.85515} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.07397, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29047, "top5_acc": 0.53922, "loss_cls": 4.11187, "loss": 4.11187, "time": 0.84907} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.07394, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28047, "top5_acc": 0.52953, "loss_cls": 4.1576, "loss": 4.1576, "time": 0.85306} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.07392, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28125, "top5_acc": 0.52531, "loss_cls": 4.13667, "loss": 4.13667, "time": 0.85787} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.07389, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28422, "top5_acc": 0.54141, "loss_cls": 4.10108, "loss": 4.10108, "time": 0.85826} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.07387, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28125, "top5_acc": 0.52703, "loss_cls": 4.14318, "loss": 4.14318, "time": 0.859} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.07384, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26844, "top5_acc": 0.53031, "loss_cls": 4.16331, "loss": 4.16331, "time": 0.8548} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.07382, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28719, "top5_acc": 0.54547, "loss_cls": 4.08313, "loss": 4.08313, "time": 0.85823} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.07379, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28813, "top5_acc": 0.53578, "loss_cls": 4.10248, "loss": 4.10248, "time": 0.85697} +{"mode": "train", "epoch": 52, "iter": 1300, "lr": 0.07377, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28, "top5_acc": 0.53219, "loss_cls": 4.14247, "loss": 4.14247, "time": 0.85869} +{"mode": "train", "epoch": 52, "iter": 1400, "lr": 0.07374, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28172, "top5_acc": 0.53156, "loss_cls": 4.14397, "loss": 4.14397, "time": 0.85841} +{"mode": "train", "epoch": 52, "iter": 1500, "lr": 0.07372, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27234, "top5_acc": 0.53109, "loss_cls": 4.16896, "loss": 4.16896, "time": 0.85387} +{"mode": "train", "epoch": 52, "iter": 1600, "lr": 0.0737, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27625, "top5_acc": 0.52938, "loss_cls": 4.17815, "loss": 4.17815, "time": 0.85992} +{"mode": "train", "epoch": 52, "iter": 1700, "lr": 0.07367, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2775, "top5_acc": 0.53, "loss_cls": 4.15702, "loss": 4.15702, "time": 0.85707} +{"mode": "train", "epoch": 52, "iter": 1800, "lr": 0.07365, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28188, "top5_acc": 0.54641, "loss_cls": 4.09482, "loss": 4.09482, "time": 0.85868} +{"mode": "train", "epoch": 52, "iter": 1900, "lr": 0.07362, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.2725, "top5_acc": 0.52641, "loss_cls": 4.19372, "loss": 4.19372, "time": 0.85987} +{"mode": "train", "epoch": 52, "iter": 2000, "lr": 0.0736, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.28453, "top5_acc": 0.52672, "loss_cls": 4.16945, "loss": 4.16945, "time": 0.85469} +{"mode": "train", "epoch": 52, "iter": 2100, "lr": 0.07357, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28438, "top5_acc": 0.52891, "loss_cls": 4.13626, "loss": 4.13626, "time": 0.85418} +{"mode": "train", "epoch": 52, "iter": 2200, "lr": 0.07355, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28813, "top5_acc": 0.53391, "loss_cls": 4.11658, "loss": 4.11658, "time": 0.85007} +{"mode": "train", "epoch": 52, "iter": 2300, "lr": 0.07352, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28797, "top5_acc": 0.53594, "loss_cls": 4.13359, "loss": 4.13359, "time": 0.85376} +{"mode": "train", "epoch": 52, "iter": 2400, "lr": 0.0735, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.27766, "top5_acc": 0.52859, "loss_cls": 4.14797, "loss": 4.14797, "time": 0.8559} +{"mode": "train", "epoch": 52, "iter": 2500, "lr": 0.07347, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27875, "top5_acc": 0.51922, "loss_cls": 4.18975, "loss": 4.18975, "time": 0.85779} +{"mode": "train", "epoch": 52, "iter": 2600, "lr": 0.07345, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.285, "top5_acc": 0.53312, "loss_cls": 4.1297, "loss": 4.1297, "time": 0.85235} +{"mode": "train", "epoch": 52, "iter": 2700, "lr": 0.07342, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.27641, "top5_acc": 0.52922, "loss_cls": 4.17607, "loss": 4.17607, "time": 0.85057} +{"mode": "train", "epoch": 52, "iter": 2800, "lr": 0.0734, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28266, "top5_acc": 0.53516, "loss_cls": 4.12937, "loss": 4.12937, "time": 0.85359} +{"mode": "train", "epoch": 52, "iter": 2900, "lr": 0.07337, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.27078, "top5_acc": 0.52359, "loss_cls": 4.18674, "loss": 4.18674, "time": 0.85236} +{"mode": "train", "epoch": 52, "iter": 3000, "lr": 0.07335, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.29047, "top5_acc": 0.54516, "loss_cls": 4.08638, "loss": 4.08638, "time": 0.85269} +{"mode": "train", "epoch": 52, "iter": 3100, "lr": 0.07332, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.27922, "top5_acc": 0.52812, "loss_cls": 4.18614, "loss": 4.18614, "time": 0.85131} +{"mode": "train", "epoch": 52, "iter": 3200, "lr": 0.0733, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28719, "top5_acc": 0.52547, "loss_cls": 4.16495, "loss": 4.16495, "time": 0.85334} +{"mode": "train", "epoch": 52, "iter": 3300, "lr": 0.07328, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29219, "top5_acc": 0.53312, "loss_cls": 4.12459, "loss": 4.12459, "time": 0.84686} +{"mode": "train", "epoch": 52, "iter": 3400, "lr": 0.07325, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26953, "top5_acc": 0.53453, "loss_cls": 4.15873, "loss": 4.15873, "time": 0.84876} +{"mode": "train", "epoch": 52, "iter": 3500, "lr": 0.07323, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27531, "top5_acc": 0.52953, "loss_cls": 4.18044, "loss": 4.18044, "time": 0.85037} +{"mode": "train", "epoch": 52, "iter": 3600, "lr": 0.0732, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28156, "top5_acc": 0.53188, "loss_cls": 4.14128, "loss": 4.14128, "time": 0.85013} +{"mode": "train", "epoch": 52, "iter": 3700, "lr": 0.07318, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28406, "top5_acc": 0.53562, "loss_cls": 4.13542, "loss": 4.13542, "time": 0.85129} +{"mode": "val", "epoch": 52, "iter": 309, "lr": 0.07317, "top1_acc": 0.22727, "top5_acc": 0.46675, "mean_class_accuracy": 0.22716} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.07314, "memory": 15990, "data_time": 1.6033, "top1_acc": 0.29547, "top5_acc": 0.53984, "loss_cls": 4.09199, "loss": 4.09199, "time": 2.6332} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.07312, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29609, "top5_acc": 0.54578, "loss_cls": 4.0492, "loss": 4.0492, "time": 0.85687} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.07309, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27953, "top5_acc": 0.53094, "loss_cls": 4.14446, "loss": 4.14446, "time": 0.85773} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.07307, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29734, "top5_acc": 0.55, "loss_cls": 4.08019, "loss": 4.08019, "time": 0.85655} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.07304, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29125, "top5_acc": 0.53797, "loss_cls": 4.08486, "loss": 4.08486, "time": 0.85647} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.07302, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28547, "top5_acc": 0.52797, "loss_cls": 4.14438, "loss": 4.14438, "time": 0.85292} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.07299, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28453, "top5_acc": 0.53766, "loss_cls": 4.10467, "loss": 4.10467, "time": 0.856} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.07297, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28, "top5_acc": 0.52969, "loss_cls": 4.14039, "loss": 4.14039, "time": 0.85524} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.07294, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2775, "top5_acc": 0.52375, "loss_cls": 4.18192, "loss": 4.18192, "time": 0.85723} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.07292, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28813, "top5_acc": 0.54328, "loss_cls": 4.0774, "loss": 4.0774, "time": 0.86123} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.07289, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.27469, "top5_acc": 0.53406, "loss_cls": 4.12531, "loss": 4.12531, "time": 0.85666} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.07287, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29031, "top5_acc": 0.53359, "loss_cls": 4.12403, "loss": 4.12403, "time": 0.85642} +{"mode": "train", "epoch": 53, "iter": 1300, "lr": 0.07284, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30266, "top5_acc": 0.55125, "loss_cls": 4.05107, "loss": 4.05107, "time": 0.85014} +{"mode": "train", "epoch": 53, "iter": 1400, "lr": 0.07282, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28469, "top5_acc": 0.52531, "loss_cls": 4.13392, "loss": 4.13392, "time": 0.84791} +{"mode": "train", "epoch": 53, "iter": 1500, "lr": 0.07279, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28734, "top5_acc": 0.53766, "loss_cls": 4.10108, "loss": 4.10108, "time": 0.85318} +{"mode": "train", "epoch": 53, "iter": 1600, "lr": 0.07277, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28516, "top5_acc": 0.54328, "loss_cls": 4.098, "loss": 4.098, "time": 0.8557} +{"mode": "train", "epoch": 53, "iter": 1700, "lr": 0.07274, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28797, "top5_acc": 0.5475, "loss_cls": 4.08703, "loss": 4.08703, "time": 0.85319} +{"mode": "train", "epoch": 53, "iter": 1800, "lr": 0.07272, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28562, "top5_acc": 0.54328, "loss_cls": 4.11207, "loss": 4.11207, "time": 0.85396} +{"mode": "train", "epoch": 53, "iter": 1900, "lr": 0.07269, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26609, "top5_acc": 0.52953, "loss_cls": 4.16113, "loss": 4.16113, "time": 0.85066} +{"mode": "train", "epoch": 53, "iter": 2000, "lr": 0.07267, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.26969, "top5_acc": 0.52547, "loss_cls": 4.16665, "loss": 4.16665, "time": 0.84939} +{"mode": "train", "epoch": 53, "iter": 2100, "lr": 0.07264, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28609, "top5_acc": 0.53641, "loss_cls": 4.11509, "loss": 4.11509, "time": 0.85418} +{"mode": "train", "epoch": 53, "iter": 2200, "lr": 0.07262, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.2925, "top5_acc": 0.54062, "loss_cls": 4.0992, "loss": 4.0992, "time": 0.85678} +{"mode": "train", "epoch": 53, "iter": 2300, "lr": 0.07259, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28453, "top5_acc": 0.53328, "loss_cls": 4.13314, "loss": 4.13314, "time": 0.86184} +{"mode": "train", "epoch": 53, "iter": 2400, "lr": 0.07257, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27312, "top5_acc": 0.52516, "loss_cls": 4.16597, "loss": 4.16597, "time": 0.85524} +{"mode": "train", "epoch": 53, "iter": 2500, "lr": 0.07254, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28344, "top5_acc": 0.52328, "loss_cls": 4.15725, "loss": 4.15725, "time": 0.84992} +{"mode": "train", "epoch": 53, "iter": 2600, "lr": 0.07252, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26766, "top5_acc": 0.53438, "loss_cls": 4.17415, "loss": 4.17415, "time": 0.85104} +{"mode": "train", "epoch": 53, "iter": 2700, "lr": 0.07249, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2775, "top5_acc": 0.53953, "loss_cls": 4.12926, "loss": 4.12926, "time": 0.85027} +{"mode": "train", "epoch": 53, "iter": 2800, "lr": 0.07247, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.28359, "top5_acc": 0.52891, "loss_cls": 4.1466, "loss": 4.1466, "time": 0.84816} +{"mode": "train", "epoch": 53, "iter": 2900, "lr": 0.07244, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27672, "top5_acc": 0.53016, "loss_cls": 4.16712, "loss": 4.16712, "time": 0.85215} +{"mode": "train", "epoch": 53, "iter": 3000, "lr": 0.07242, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27797, "top5_acc": 0.52969, "loss_cls": 4.13042, "loss": 4.13042, "time": 0.85253} +{"mode": "train", "epoch": 53, "iter": 3100, "lr": 0.07239, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28688, "top5_acc": 0.53266, "loss_cls": 4.14143, "loss": 4.14143, "time": 0.84976} +{"mode": "train", "epoch": 53, "iter": 3200, "lr": 0.07237, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27922, "top5_acc": 0.53625, "loss_cls": 4.15485, "loss": 4.15485, "time": 0.84946} +{"mode": "train", "epoch": 53, "iter": 3300, "lr": 0.07234, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28531, "top5_acc": 0.53766, "loss_cls": 4.13111, "loss": 4.13111, "time": 0.85822} +{"mode": "train", "epoch": 53, "iter": 3400, "lr": 0.07232, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28188, "top5_acc": 0.53344, "loss_cls": 4.12014, "loss": 4.12014, "time": 0.84875} +{"mode": "train", "epoch": 53, "iter": 3500, "lr": 0.07229, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2825, "top5_acc": 0.52547, "loss_cls": 4.16452, "loss": 4.16452, "time": 0.85611} +{"mode": "train", "epoch": 53, "iter": 3600, "lr": 0.07227, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27375, "top5_acc": 0.535, "loss_cls": 4.16579, "loss": 4.16579, "time": 0.85325} +{"mode": "train", "epoch": 53, "iter": 3700, "lr": 0.07224, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28391, "top5_acc": 0.53453, "loss_cls": 4.1368, "loss": 4.1368, "time": 0.8525} +{"mode": "val", "epoch": 53, "iter": 309, "lr": 0.07223, "top1_acc": 0.22104, "top5_acc": 0.46376, "mean_class_accuracy": 0.2208} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.07221, "memory": 15990, "data_time": 1.61211, "top1_acc": 0.28469, "top5_acc": 0.54688, "loss_cls": 4.08858, "loss": 4.08858, "time": 2.64813} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.07218, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29344, "top5_acc": 0.54141, "loss_cls": 4.0858, "loss": 4.0858, "time": 0.85874} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.07216, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27938, "top5_acc": 0.53922, "loss_cls": 4.14182, "loss": 4.14182, "time": 0.85451} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.07213, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28844, "top5_acc": 0.53875, "loss_cls": 4.085, "loss": 4.085, "time": 0.85495} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.07211, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29141, "top5_acc": 0.53703, "loss_cls": 4.11234, "loss": 4.11234, "time": 0.85799} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.07208, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.27625, "top5_acc": 0.53094, "loss_cls": 4.16053, "loss": 4.16053, "time": 0.85643} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.07206, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.28375, "top5_acc": 0.53859, "loss_cls": 4.10797, "loss": 4.10797, "time": 0.86404} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.07203, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28062, "top5_acc": 0.54125, "loss_cls": 4.07922, "loss": 4.07922, "time": 0.86298} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.07201, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29531, "top5_acc": 0.53766, "loss_cls": 4.09148, "loss": 4.09148, "time": 0.85647} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.07198, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28266, "top5_acc": 0.53344, "loss_cls": 4.11551, "loss": 4.11551, "time": 0.85693} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.07196, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28813, "top5_acc": 0.53281, "loss_cls": 4.14174, "loss": 4.14174, "time": 0.85775} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.07193, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28609, "top5_acc": 0.54062, "loss_cls": 4.0787, "loss": 4.0787, "time": 0.85745} +{"mode": "train", "epoch": 54, "iter": 1300, "lr": 0.07191, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28312, "top5_acc": 0.52812, "loss_cls": 4.13619, "loss": 4.13619, "time": 0.85407} +{"mode": "train", "epoch": 54, "iter": 1400, "lr": 0.07188, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28297, "top5_acc": 0.53031, "loss_cls": 4.15669, "loss": 4.15669, "time": 0.85582} +{"mode": "train", "epoch": 54, "iter": 1500, "lr": 0.07186, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27969, "top5_acc": 0.52953, "loss_cls": 4.14199, "loss": 4.14199, "time": 0.85081} +{"mode": "train", "epoch": 54, "iter": 1600, "lr": 0.07183, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29922, "top5_acc": 0.54734, "loss_cls": 4.0381, "loss": 4.0381, "time": 0.85012} +{"mode": "train", "epoch": 54, "iter": 1700, "lr": 0.07181, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29047, "top5_acc": 0.54109, "loss_cls": 4.10007, "loss": 4.10007, "time": 0.85333} +{"mode": "train", "epoch": 54, "iter": 1800, "lr": 0.07178, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29516, "top5_acc": 0.54734, "loss_cls": 4.07065, "loss": 4.07065, "time": 0.85178} +{"mode": "train", "epoch": 54, "iter": 1900, "lr": 0.07176, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.28219, "top5_acc": 0.54219, "loss_cls": 4.08953, "loss": 4.08953, "time": 0.85448} +{"mode": "train", "epoch": 54, "iter": 2000, "lr": 0.07173, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28469, "top5_acc": 0.54438, "loss_cls": 4.08892, "loss": 4.08892, "time": 0.84845} +{"mode": "train", "epoch": 54, "iter": 2100, "lr": 0.0717, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28078, "top5_acc": 0.52953, "loss_cls": 4.1551, "loss": 4.1551, "time": 0.85182} +{"mode": "train", "epoch": 54, "iter": 2200, "lr": 0.07168, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28625, "top5_acc": 0.54344, "loss_cls": 4.09353, "loss": 4.09353, "time": 0.84723} +{"mode": "train", "epoch": 54, "iter": 2300, "lr": 0.07165, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28922, "top5_acc": 0.53266, "loss_cls": 4.13002, "loss": 4.13002, "time": 0.85457} +{"mode": "train", "epoch": 54, "iter": 2400, "lr": 0.07163, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.28797, "top5_acc": 0.53156, "loss_cls": 4.11582, "loss": 4.11582, "time": 0.84997} +{"mode": "train", "epoch": 54, "iter": 2500, "lr": 0.0716, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.27453, "top5_acc": 0.53531, "loss_cls": 4.13967, "loss": 4.13967, "time": 0.85108} +{"mode": "train", "epoch": 54, "iter": 2600, "lr": 0.07158, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28, "top5_acc": 0.53719, "loss_cls": 4.13121, "loss": 4.13121, "time": 0.85559} +{"mode": "train", "epoch": 54, "iter": 2700, "lr": 0.07155, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28375, "top5_acc": 0.54109, "loss_cls": 4.0917, "loss": 4.0917, "time": 0.85617} +{"mode": "train", "epoch": 54, "iter": 2800, "lr": 0.07153, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.28531, "top5_acc": 0.53688, "loss_cls": 4.11303, "loss": 4.11303, "time": 0.84959} +{"mode": "train", "epoch": 54, "iter": 2900, "lr": 0.0715, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28, "top5_acc": 0.52953, "loss_cls": 4.12817, "loss": 4.12817, "time": 0.85327} +{"mode": "train", "epoch": 54, "iter": 3000, "lr": 0.07148, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28062, "top5_acc": 0.53797, "loss_cls": 4.11055, "loss": 4.11055, "time": 0.85461} +{"mode": "train", "epoch": 54, "iter": 3100, "lr": 0.07145, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28344, "top5_acc": 0.53641, "loss_cls": 4.14881, "loss": 4.14881, "time": 0.85608} +{"mode": "train", "epoch": 54, "iter": 3200, "lr": 0.07143, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27734, "top5_acc": 0.52734, "loss_cls": 4.13251, "loss": 4.13251, "time": 0.84986} +{"mode": "train", "epoch": 54, "iter": 3300, "lr": 0.0714, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27594, "top5_acc": 0.52906, "loss_cls": 4.16902, "loss": 4.16902, "time": 0.85429} +{"mode": "train", "epoch": 54, "iter": 3400, "lr": 0.07138, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28719, "top5_acc": 0.53359, "loss_cls": 4.14135, "loss": 4.14135, "time": 0.85717} +{"mode": "train", "epoch": 54, "iter": 3500, "lr": 0.07135, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28219, "top5_acc": 0.53406, "loss_cls": 4.14373, "loss": 4.14373, "time": 0.85099} +{"mode": "train", "epoch": 54, "iter": 3600, "lr": 0.07133, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28781, "top5_acc": 0.53547, "loss_cls": 4.14546, "loss": 4.14546, "time": 0.854} +{"mode": "train", "epoch": 54, "iter": 3700, "lr": 0.0713, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27812, "top5_acc": 0.52219, "loss_cls": 4.17766, "loss": 4.17766, "time": 0.85435} +{"mode": "val", "epoch": 54, "iter": 309, "lr": 0.07129, "top1_acc": 0.21952, "top5_acc": 0.46229, "mean_class_accuracy": 0.21941} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.07126, "memory": 15990, "data_time": 1.53763, "top1_acc": 0.28516, "top5_acc": 0.535, "loss_cls": 4.09457, "loss": 4.09457, "time": 2.57219} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.07124, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.28703, "top5_acc": 0.53906, "loss_cls": 4.06886, "loss": 4.06886, "time": 0.85842} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.07121, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29594, "top5_acc": 0.54734, "loss_cls": 4.06928, "loss": 4.06928, "time": 0.85285} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.07119, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28531, "top5_acc": 0.53703, "loss_cls": 4.10125, "loss": 4.10125, "time": 0.85353} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.07116, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30062, "top5_acc": 0.54234, "loss_cls": 4.06347, "loss": 4.06347, "time": 0.86523} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.07114, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28516, "top5_acc": 0.53688, "loss_cls": 4.11048, "loss": 4.11048, "time": 0.86148} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.07111, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.28312, "top5_acc": 0.53172, "loss_cls": 4.13014, "loss": 4.13014, "time": 0.86211} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.07109, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27516, "top5_acc": 0.53219, "loss_cls": 4.14503, "loss": 4.14503, "time": 0.86041} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.07106, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28375, "top5_acc": 0.53938, "loss_cls": 4.11603, "loss": 4.11603, "time": 0.86054} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.07104, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28531, "top5_acc": 0.53703, "loss_cls": 4.10908, "loss": 4.10908, "time": 0.86489} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.07101, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28328, "top5_acc": 0.53375, "loss_cls": 4.11333, "loss": 4.11333, "time": 0.86092} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.07099, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28453, "top5_acc": 0.53859, "loss_cls": 4.11541, "loss": 4.11541, "time": 0.86159} +{"mode": "train", "epoch": 55, "iter": 1300, "lr": 0.07096, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29188, "top5_acc": 0.54797, "loss_cls": 4.07621, "loss": 4.07621, "time": 0.85652} +{"mode": "train", "epoch": 55, "iter": 1400, "lr": 0.07093, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2825, "top5_acc": 0.52969, "loss_cls": 4.15728, "loss": 4.15728, "time": 0.85834} +{"mode": "train", "epoch": 55, "iter": 1500, "lr": 0.07091, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.29156, "top5_acc": 0.54547, "loss_cls": 4.0725, "loss": 4.0725, "time": 0.86527} +{"mode": "train", "epoch": 55, "iter": 1600, "lr": 0.07088, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28641, "top5_acc": 0.53625, "loss_cls": 4.12079, "loss": 4.12079, "time": 0.85992} +{"mode": "train", "epoch": 55, "iter": 1700, "lr": 0.07086, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.29266, "top5_acc": 0.53609, "loss_cls": 4.09824, "loss": 4.09824, "time": 0.85785} +{"mode": "train", "epoch": 55, "iter": 1800, "lr": 0.07083, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28672, "top5_acc": 0.54719, "loss_cls": 4.09074, "loss": 4.09074, "time": 0.85508} +{"mode": "train", "epoch": 55, "iter": 1900, "lr": 0.07081, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29375, "top5_acc": 0.53516, "loss_cls": 4.1105, "loss": 4.1105, "time": 0.85357} +{"mode": "train", "epoch": 55, "iter": 2000, "lr": 0.07078, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28969, "top5_acc": 0.53844, "loss_cls": 4.10261, "loss": 4.10261, "time": 0.85374} +{"mode": "train", "epoch": 55, "iter": 2100, "lr": 0.07076, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28125, "top5_acc": 0.53656, "loss_cls": 4.14302, "loss": 4.14302, "time": 0.85304} +{"mode": "train", "epoch": 55, "iter": 2200, "lr": 0.07073, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28625, "top5_acc": 0.53, "loss_cls": 4.12318, "loss": 4.12318, "time": 0.86008} +{"mode": "train", "epoch": 55, "iter": 2300, "lr": 0.07071, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.28312, "top5_acc": 0.53688, "loss_cls": 4.12854, "loss": 4.12854, "time": 0.85872} +{"mode": "train", "epoch": 55, "iter": 2400, "lr": 0.07068, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28125, "top5_acc": 0.53156, "loss_cls": 4.14124, "loss": 4.14124, "time": 0.85072} +{"mode": "train", "epoch": 55, "iter": 2500, "lr": 0.07065, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29125, "top5_acc": 0.54094, "loss_cls": 4.0835, "loss": 4.0835, "time": 0.85234} +{"mode": "train", "epoch": 55, "iter": 2600, "lr": 0.07063, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28562, "top5_acc": 0.52969, "loss_cls": 4.14855, "loss": 4.14855, "time": 0.85731} +{"mode": "train", "epoch": 55, "iter": 2700, "lr": 0.0706, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.30125, "top5_acc": 0.55016, "loss_cls": 4.05551, "loss": 4.05551, "time": 0.86562} +{"mode": "train", "epoch": 55, "iter": 2800, "lr": 0.07058, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28, "top5_acc": 0.53812, "loss_cls": 4.12895, "loss": 4.12895, "time": 0.86248} +{"mode": "train", "epoch": 55, "iter": 2900, "lr": 0.07055, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27609, "top5_acc": 0.53062, "loss_cls": 4.17522, "loss": 4.17522, "time": 0.8525} +{"mode": "train", "epoch": 55, "iter": 3000, "lr": 0.07053, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28781, "top5_acc": 0.54422, "loss_cls": 4.06873, "loss": 4.06873, "time": 0.85234} +{"mode": "train", "epoch": 55, "iter": 3100, "lr": 0.0705, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28766, "top5_acc": 0.53703, "loss_cls": 4.11961, "loss": 4.11961, "time": 0.85742} +{"mode": "train", "epoch": 55, "iter": 3200, "lr": 0.07048, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28078, "top5_acc": 0.53328, "loss_cls": 4.13069, "loss": 4.13069, "time": 0.86066} +{"mode": "train", "epoch": 55, "iter": 3300, "lr": 0.07045, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28328, "top5_acc": 0.53781, "loss_cls": 4.15172, "loss": 4.15172, "time": 0.86022} +{"mode": "train", "epoch": 55, "iter": 3400, "lr": 0.07043, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.2925, "top5_acc": 0.53953, "loss_cls": 4.1159, "loss": 4.1159, "time": 0.85973} +{"mode": "train", "epoch": 55, "iter": 3500, "lr": 0.0704, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28922, "top5_acc": 0.53797, "loss_cls": 4.12974, "loss": 4.12974, "time": 0.85861} +{"mode": "train", "epoch": 55, "iter": 3600, "lr": 0.07037, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28188, "top5_acc": 0.53922, "loss_cls": 4.11225, "loss": 4.11225, "time": 0.86254} +{"mode": "train", "epoch": 55, "iter": 3700, "lr": 0.07035, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28141, "top5_acc": 0.54234, "loss_cls": 4.11738, "loss": 4.11738, "time": 0.8647} +{"mode": "val", "epoch": 55, "iter": 309, "lr": 0.07034, "top1_acc": 0.23127, "top5_acc": 0.46614, "mean_class_accuracy": 0.23115} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.07031, "memory": 15990, "data_time": 1.57144, "top1_acc": 0.29391, "top5_acc": 0.54984, "loss_cls": 4.03882, "loss": 4.03882, "time": 2.61106} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.07029, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.29156, "top5_acc": 0.5425, "loss_cls": 4.06381, "loss": 4.06381, "time": 0.86333} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.07026, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29859, "top5_acc": 0.55, "loss_cls": 4.04469, "loss": 4.04469, "time": 0.86118} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.07023, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.2875, "top5_acc": 0.5425, "loss_cls": 4.10526, "loss": 4.10526, "time": 0.8575} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.07021, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28156, "top5_acc": 0.54297, "loss_cls": 4.12688, "loss": 4.12688, "time": 0.85377} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.07018, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29344, "top5_acc": 0.53688, "loss_cls": 4.10007, "loss": 4.10007, "time": 0.85664} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.07016, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29063, "top5_acc": 0.54188, "loss_cls": 4.06706, "loss": 4.06706, "time": 0.85649} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.07013, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29578, "top5_acc": 0.56219, "loss_cls": 4.01774, "loss": 4.01774, "time": 0.85569} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.07011, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28578, "top5_acc": 0.54531, "loss_cls": 4.07941, "loss": 4.07941, "time": 0.86247} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.07008, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28125, "top5_acc": 0.5275, "loss_cls": 4.11448, "loss": 4.11448, "time": 0.85956} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.07006, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.285, "top5_acc": 0.53438, "loss_cls": 4.12241, "loss": 4.12241, "time": 0.85979} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.07003, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28516, "top5_acc": 0.53703, "loss_cls": 4.11502, "loss": 4.11502, "time": 0.86135} +{"mode": "train", "epoch": 56, "iter": 1300, "lr": 0.07, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28328, "top5_acc": 0.53188, "loss_cls": 4.15725, "loss": 4.15725, "time": 0.8637} +{"mode": "train", "epoch": 56, "iter": 1400, "lr": 0.06998, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29688, "top5_acc": 0.54812, "loss_cls": 4.05932, "loss": 4.05932, "time": 0.8597} +{"mode": "train", "epoch": 56, "iter": 1500, "lr": 0.06995, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28578, "top5_acc": 0.54266, "loss_cls": 4.11386, "loss": 4.11386, "time": 0.86133} +{"mode": "train", "epoch": 56, "iter": 1600, "lr": 0.06993, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29766, "top5_acc": 0.54922, "loss_cls": 4.04661, "loss": 4.04661, "time": 0.85877} +{"mode": "train", "epoch": 56, "iter": 1700, "lr": 0.0699, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30203, "top5_acc": 0.54375, "loss_cls": 4.07507, "loss": 4.07507, "time": 0.85601} +{"mode": "train", "epoch": 56, "iter": 1800, "lr": 0.06988, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28594, "top5_acc": 0.54188, "loss_cls": 4.11878, "loss": 4.11878, "time": 0.85135} +{"mode": "train", "epoch": 56, "iter": 1900, "lr": 0.06985, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28922, "top5_acc": 0.53734, "loss_cls": 4.10704, "loss": 4.10704, "time": 0.84996} +{"mode": "train", "epoch": 56, "iter": 2000, "lr": 0.06983, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28953, "top5_acc": 0.54391, "loss_cls": 4.08964, "loss": 4.08964, "time": 0.8474} +{"mode": "train", "epoch": 56, "iter": 2100, "lr": 0.0698, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29297, "top5_acc": 0.54766, "loss_cls": 4.07072, "loss": 4.07072, "time": 0.85546} +{"mode": "train", "epoch": 56, "iter": 2200, "lr": 0.06977, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.27625, "top5_acc": 0.52453, "loss_cls": 4.18263, "loss": 4.18263, "time": 0.86421} +{"mode": "train", "epoch": 56, "iter": 2300, "lr": 0.06975, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.27844, "top5_acc": 0.52703, "loss_cls": 4.13334, "loss": 4.13334, "time": 0.85161} +{"mode": "train", "epoch": 56, "iter": 2400, "lr": 0.06972, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28234, "top5_acc": 0.53984, "loss_cls": 4.07202, "loss": 4.07202, "time": 0.85339} +{"mode": "train", "epoch": 56, "iter": 2500, "lr": 0.0697, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29875, "top5_acc": 0.54766, "loss_cls": 4.04818, "loss": 4.04818, "time": 0.84901} +{"mode": "train", "epoch": 56, "iter": 2600, "lr": 0.06967, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29078, "top5_acc": 0.54125, "loss_cls": 4.05596, "loss": 4.05596, "time": 0.85637} +{"mode": "train", "epoch": 56, "iter": 2700, "lr": 0.06965, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28906, "top5_acc": 0.53734, "loss_cls": 4.11363, "loss": 4.11363, "time": 0.85924} +{"mode": "train", "epoch": 56, "iter": 2800, "lr": 0.06962, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.27547, "top5_acc": 0.52438, "loss_cls": 4.16649, "loss": 4.16649, "time": 0.85268} +{"mode": "train", "epoch": 56, "iter": 2900, "lr": 0.06959, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.27906, "top5_acc": 0.53594, "loss_cls": 4.12971, "loss": 4.12971, "time": 0.85} +{"mode": "train", "epoch": 56, "iter": 3000, "lr": 0.06957, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28125, "top5_acc": 0.53312, "loss_cls": 4.13302, "loss": 4.13302, "time": 0.84975} +{"mode": "train", "epoch": 56, "iter": 3100, "lr": 0.06954, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.2875, "top5_acc": 0.54328, "loss_cls": 4.07929, "loss": 4.07929, "time": 0.86266} +{"mode": "train", "epoch": 56, "iter": 3200, "lr": 0.06952, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28281, "top5_acc": 0.53219, "loss_cls": 4.12331, "loss": 4.12331, "time": 0.85367} +{"mode": "train", "epoch": 56, "iter": 3300, "lr": 0.06949, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28656, "top5_acc": 0.54766, "loss_cls": 4.09251, "loss": 4.09251, "time": 0.86031} +{"mode": "train", "epoch": 56, "iter": 3400, "lr": 0.06947, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29078, "top5_acc": 0.53719, "loss_cls": 4.07951, "loss": 4.07951, "time": 0.86369} +{"mode": "train", "epoch": 56, "iter": 3500, "lr": 0.06944, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28391, "top5_acc": 0.53391, "loss_cls": 4.14499, "loss": 4.14499, "time": 0.85731} +{"mode": "train", "epoch": 56, "iter": 3600, "lr": 0.06941, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27641, "top5_acc": 0.53344, "loss_cls": 4.14262, "loss": 4.14262, "time": 0.85863} +{"mode": "train", "epoch": 56, "iter": 3700, "lr": 0.06939, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28688, "top5_acc": 0.53891, "loss_cls": 4.09578, "loss": 4.09578, "time": 0.85966} +{"mode": "val", "epoch": 56, "iter": 309, "lr": 0.06938, "top1_acc": 0.20969, "top5_acc": 0.44588, "mean_class_accuracy": 0.20947} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.06935, "memory": 15990, "data_time": 1.56157, "top1_acc": 0.29063, "top5_acc": 0.55188, "loss_cls": 4.04049, "loss": 4.04049, "time": 2.59807} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.06932, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29219, "top5_acc": 0.54297, "loss_cls": 4.08823, "loss": 4.08823, "time": 0.85807} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.0693, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29031, "top5_acc": 0.53766, "loss_cls": 4.09928, "loss": 4.09928, "time": 0.85718} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.06927, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29844, "top5_acc": 0.55531, "loss_cls": 4.02442, "loss": 4.02442, "time": 0.85806} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.06925, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29203, "top5_acc": 0.55172, "loss_cls": 4.04912, "loss": 4.04912, "time": 0.85541} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.06922, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29031, "top5_acc": 0.53891, "loss_cls": 4.07664, "loss": 4.07664, "time": 0.85931} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.0692, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28781, "top5_acc": 0.5475, "loss_cls": 4.08309, "loss": 4.08309, "time": 0.8532} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.06917, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28656, "top5_acc": 0.54406, "loss_cls": 4.08087, "loss": 4.08087, "time": 0.85448} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.06914, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28297, "top5_acc": 0.54719, "loss_cls": 4.09638, "loss": 4.09638, "time": 0.85762} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.06912, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29891, "top5_acc": 0.54828, "loss_cls": 4.05472, "loss": 4.05472, "time": 0.85557} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.06909, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28781, "top5_acc": 0.55594, "loss_cls": 4.06837, "loss": 4.06837, "time": 0.85496} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.06907, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28234, "top5_acc": 0.54312, "loss_cls": 4.10017, "loss": 4.10017, "time": 0.85893} +{"mode": "train", "epoch": 57, "iter": 1300, "lr": 0.06904, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28312, "top5_acc": 0.53844, "loss_cls": 4.12497, "loss": 4.12497, "time": 0.85553} +{"mode": "train", "epoch": 57, "iter": 1400, "lr": 0.06901, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28484, "top5_acc": 0.54078, "loss_cls": 4.07609, "loss": 4.07609, "time": 0.85348} +{"mode": "train", "epoch": 57, "iter": 1500, "lr": 0.06899, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28016, "top5_acc": 0.53547, "loss_cls": 4.13423, "loss": 4.13423, "time": 0.85211} +{"mode": "train", "epoch": 57, "iter": 1600, "lr": 0.06896, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28234, "top5_acc": 0.53609, "loss_cls": 4.12531, "loss": 4.12531, "time": 0.85277} +{"mode": "train", "epoch": 57, "iter": 1700, "lr": 0.06894, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29125, "top5_acc": 0.55391, "loss_cls": 4.06906, "loss": 4.06906, "time": 0.84717} +{"mode": "train", "epoch": 57, "iter": 1800, "lr": 0.06891, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29125, "top5_acc": 0.545, "loss_cls": 4.07949, "loss": 4.07949, "time": 0.84737} +{"mode": "train", "epoch": 57, "iter": 1900, "lr": 0.06889, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28438, "top5_acc": 0.53172, "loss_cls": 4.12653, "loss": 4.12653, "time": 0.85004} +{"mode": "train", "epoch": 57, "iter": 2000, "lr": 0.06886, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28203, "top5_acc": 0.53984, "loss_cls": 4.10069, "loss": 4.10069, "time": 0.84913} +{"mode": "train", "epoch": 57, "iter": 2100, "lr": 0.06883, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29531, "top5_acc": 0.53812, "loss_cls": 4.09183, "loss": 4.09183, "time": 0.8562} +{"mode": "train", "epoch": 57, "iter": 2200, "lr": 0.06881, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28062, "top5_acc": 0.52453, "loss_cls": 4.14955, "loss": 4.14955, "time": 0.85761} +{"mode": "train", "epoch": 57, "iter": 2300, "lr": 0.06878, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30109, "top5_acc": 0.54828, "loss_cls": 4.05211, "loss": 4.05211, "time": 0.86047} +{"mode": "train", "epoch": 57, "iter": 2400, "lr": 0.06876, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29125, "top5_acc": 0.55062, "loss_cls": 4.05793, "loss": 4.05793, "time": 0.84603} +{"mode": "train", "epoch": 57, "iter": 2500, "lr": 0.06873, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29141, "top5_acc": 0.53781, "loss_cls": 4.11082, "loss": 4.11082, "time": 0.8503} +{"mode": "train", "epoch": 57, "iter": 2600, "lr": 0.0687, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27891, "top5_acc": 0.54547, "loss_cls": 4.07063, "loss": 4.07063, "time": 0.8589} +{"mode": "train", "epoch": 57, "iter": 2700, "lr": 0.06868, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.28219, "top5_acc": 0.54188, "loss_cls": 4.12632, "loss": 4.12632, "time": 0.85563} +{"mode": "train", "epoch": 57, "iter": 2800, "lr": 0.06865, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29078, "top5_acc": 0.54203, "loss_cls": 4.09752, "loss": 4.09752, "time": 0.84878} +{"mode": "train", "epoch": 57, "iter": 2900, "lr": 0.06863, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29313, "top5_acc": 0.53688, "loss_cls": 4.08537, "loss": 4.08537, "time": 0.84869} +{"mode": "train", "epoch": 57, "iter": 3000, "lr": 0.0686, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28438, "top5_acc": 0.54281, "loss_cls": 4.09362, "loss": 4.09362, "time": 0.85001} +{"mode": "train", "epoch": 57, "iter": 3100, "lr": 0.06857, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28516, "top5_acc": 0.54016, "loss_cls": 4.11321, "loss": 4.11321, "time": 0.84451} +{"mode": "train", "epoch": 57, "iter": 3200, "lr": 0.06855, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.27969, "top5_acc": 0.53641, "loss_cls": 4.10979, "loss": 4.10979, "time": 0.85264} +{"mode": "train", "epoch": 57, "iter": 3300, "lr": 0.06852, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28234, "top5_acc": 0.54047, "loss_cls": 4.11725, "loss": 4.11725, "time": 0.84745} +{"mode": "train", "epoch": 57, "iter": 3400, "lr": 0.0685, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28672, "top5_acc": 0.54953, "loss_cls": 4.09404, "loss": 4.09404, "time": 0.85} +{"mode": "train", "epoch": 57, "iter": 3500, "lr": 0.06847, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28531, "top5_acc": 0.53703, "loss_cls": 4.11456, "loss": 4.11456, "time": 0.84718} +{"mode": "train", "epoch": 57, "iter": 3600, "lr": 0.06844, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29609, "top5_acc": 0.53781, "loss_cls": 4.08635, "loss": 4.08635, "time": 0.8515} +{"mode": "train", "epoch": 57, "iter": 3700, "lr": 0.06842, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.27875, "top5_acc": 0.53781, "loss_cls": 4.13995, "loss": 4.13995, "time": 0.84935} +{"mode": "val", "epoch": 57, "iter": 309, "lr": 0.06841, "top1_acc": 0.22813, "top5_acc": 0.46102, "mean_class_accuracy": 0.2279} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.06838, "memory": 15990, "data_time": 1.56877, "top1_acc": 0.29141, "top5_acc": 0.54609, "loss_cls": 4.06201, "loss": 4.06201, "time": 2.60082} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.06835, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28859, "top5_acc": 0.54969, "loss_cls": 4.04972, "loss": 4.04972, "time": 0.85225} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.06833, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28813, "top5_acc": 0.54844, "loss_cls": 4.06507, "loss": 4.06507, "time": 0.8554} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.0683, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28781, "top5_acc": 0.54672, "loss_cls": 4.08593, "loss": 4.08593, "time": 0.85792} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.06828, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.30688, "top5_acc": 0.55469, "loss_cls": 4.02547, "loss": 4.02547, "time": 0.86022} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.06825, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28188, "top5_acc": 0.53281, "loss_cls": 4.14974, "loss": 4.14974, "time": 0.85877} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.06822, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29219, "top5_acc": 0.54469, "loss_cls": 4.08676, "loss": 4.08676, "time": 0.86005} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.0682, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28766, "top5_acc": 0.54375, "loss_cls": 4.07207, "loss": 4.07207, "time": 0.86109} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.06817, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30281, "top5_acc": 0.55312, "loss_cls": 4.02505, "loss": 4.02505, "time": 0.85695} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.06815, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29063, "top5_acc": 0.54016, "loss_cls": 4.09606, "loss": 4.09606, "time": 0.85622} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.06812, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28672, "top5_acc": 0.55281, "loss_cls": 4.02896, "loss": 4.02896, "time": 0.85413} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.06809, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28469, "top5_acc": 0.54203, "loss_cls": 4.12454, "loss": 4.12454, "time": 0.86164} +{"mode": "train", "epoch": 58, "iter": 1300, "lr": 0.06807, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29125, "top5_acc": 0.54047, "loss_cls": 4.10291, "loss": 4.10291, "time": 0.85694} +{"mode": "train", "epoch": 58, "iter": 1400, "lr": 0.06804, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28844, "top5_acc": 0.54281, "loss_cls": 4.08539, "loss": 4.08539, "time": 0.8535} +{"mode": "train", "epoch": 58, "iter": 1500, "lr": 0.06802, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.285, "top5_acc": 0.53625, "loss_cls": 4.09678, "loss": 4.09678, "time": 0.85269} +{"mode": "train", "epoch": 58, "iter": 1600, "lr": 0.06799, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29469, "top5_acc": 0.53672, "loss_cls": 4.07468, "loss": 4.07468, "time": 0.85525} +{"mode": "train", "epoch": 58, "iter": 1700, "lr": 0.06796, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28813, "top5_acc": 0.54422, "loss_cls": 4.07683, "loss": 4.07683, "time": 0.85873} +{"mode": "train", "epoch": 58, "iter": 1800, "lr": 0.06794, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3, "top5_acc": 0.55547, "loss_cls": 4.00638, "loss": 4.00638, "time": 0.849} +{"mode": "train", "epoch": 58, "iter": 1900, "lr": 0.06791, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28547, "top5_acc": 0.53938, "loss_cls": 4.09428, "loss": 4.09428, "time": 0.84485} +{"mode": "train", "epoch": 58, "iter": 2000, "lr": 0.06789, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28078, "top5_acc": 0.53703, "loss_cls": 4.11826, "loss": 4.11826, "time": 0.8537} +{"mode": "train", "epoch": 58, "iter": 2100, "lr": 0.06786, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29156, "top5_acc": 0.55031, "loss_cls": 4.08472, "loss": 4.08472, "time": 0.85503} +{"mode": "train", "epoch": 58, "iter": 2200, "lr": 0.06783, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28828, "top5_acc": 0.53891, "loss_cls": 4.10631, "loss": 4.10631, "time": 0.85355} +{"mode": "train", "epoch": 58, "iter": 2300, "lr": 0.06781, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29672, "top5_acc": 0.55109, "loss_cls": 4.03771, "loss": 4.03771, "time": 0.85944} +{"mode": "train", "epoch": 58, "iter": 2400, "lr": 0.06778, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.27594, "top5_acc": 0.54172, "loss_cls": 4.12761, "loss": 4.12761, "time": 0.8521} +{"mode": "train", "epoch": 58, "iter": 2500, "lr": 0.06775, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29719, "top5_acc": 0.54984, "loss_cls": 4.04767, "loss": 4.04767, "time": 0.85016} +{"mode": "train", "epoch": 58, "iter": 2600, "lr": 0.06773, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.29, "top5_acc": 0.54266, "loss_cls": 4.0835, "loss": 4.0835, "time": 0.84766} +{"mode": "train", "epoch": 58, "iter": 2700, "lr": 0.0677, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2875, "top5_acc": 0.54672, "loss_cls": 4.07809, "loss": 4.07809, "time": 0.85126} +{"mode": "train", "epoch": 58, "iter": 2800, "lr": 0.06768, "memory": 15990, "data_time": 0.00071, "top1_acc": 0.29422, "top5_acc": 0.55531, "loss_cls": 4.05077, "loss": 4.05077, "time": 0.85062} +{"mode": "train", "epoch": 58, "iter": 2900, "lr": 0.06765, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28406, "top5_acc": 0.53312, "loss_cls": 4.14804, "loss": 4.14804, "time": 0.84916} +{"mode": "train", "epoch": 58, "iter": 3000, "lr": 0.06762, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.29891, "top5_acc": 0.54734, "loss_cls": 4.0452, "loss": 4.0452, "time": 0.85433} +{"mode": "train", "epoch": 58, "iter": 3100, "lr": 0.0676, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27, "top5_acc": 0.52625, "loss_cls": 4.18107, "loss": 4.18107, "time": 0.85092} +{"mode": "train", "epoch": 58, "iter": 3200, "lr": 0.06757, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29391, "top5_acc": 0.53984, "loss_cls": 4.08264, "loss": 4.08264, "time": 0.85658} +{"mode": "train", "epoch": 58, "iter": 3300, "lr": 0.06755, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28719, "top5_acc": 0.535, "loss_cls": 4.13144, "loss": 4.13144, "time": 0.85286} +{"mode": "train", "epoch": 58, "iter": 3400, "lr": 0.06752, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29281, "top5_acc": 0.53938, "loss_cls": 4.07205, "loss": 4.07205, "time": 0.85201} +{"mode": "train", "epoch": 58, "iter": 3500, "lr": 0.06749, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29094, "top5_acc": 0.54125, "loss_cls": 4.10178, "loss": 4.10178, "time": 0.84717} +{"mode": "train", "epoch": 58, "iter": 3600, "lr": 0.06747, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.28813, "top5_acc": 0.54391, "loss_cls": 4.11836, "loss": 4.11836, "time": 0.85102} +{"mode": "train", "epoch": 58, "iter": 3700, "lr": 0.06744, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29375, "top5_acc": 0.55609, "loss_cls": 4.03173, "loss": 4.03173, "time": 0.84544} +{"mode": "val", "epoch": 58, "iter": 309, "lr": 0.06743, "top1_acc": 0.22104, "top5_acc": 0.46427, "mean_class_accuracy": 0.2208} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.0674, "memory": 15990, "data_time": 1.58123, "top1_acc": 0.29625, "top5_acc": 0.55719, "loss_cls": 4.02447, "loss": 4.02447, "time": 2.61642} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.06738, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29391, "top5_acc": 0.54859, "loss_cls": 4.04498, "loss": 4.04498, "time": 0.85386} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.06735, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28656, "top5_acc": 0.54047, "loss_cls": 4.08867, "loss": 4.08867, "time": 0.85286} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.06732, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29516, "top5_acc": 0.55312, "loss_cls": 4.04056, "loss": 4.04056, "time": 0.8586} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.0673, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28578, "top5_acc": 0.54531, "loss_cls": 4.05012, "loss": 4.05012, "time": 0.85593} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.06727, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29547, "top5_acc": 0.55594, "loss_cls": 4.03457, "loss": 4.03457, "time": 0.85943} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.06725, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30094, "top5_acc": 0.55422, "loss_cls": 4.0275, "loss": 4.0275, "time": 0.85399} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.06722, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29234, "top5_acc": 0.54156, "loss_cls": 4.08924, "loss": 4.08924, "time": 0.85784} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.06719, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.29203, "top5_acc": 0.54375, "loss_cls": 4.10032, "loss": 4.10032, "time": 0.85259} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.06717, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.26969, "top5_acc": 0.51953, "loss_cls": 4.16726, "loss": 4.16726, "time": 0.85962} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.06714, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29984, "top5_acc": 0.55, "loss_cls": 4.05178, "loss": 4.05178, "time": 0.85307} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.06711, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.28312, "top5_acc": 0.54297, "loss_cls": 4.09847, "loss": 4.09847, "time": 0.84604} +{"mode": "train", "epoch": 59, "iter": 1300, "lr": 0.06709, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30359, "top5_acc": 0.56141, "loss_cls": 3.99511, "loss": 3.99511, "time": 0.8534} +{"mode": "train", "epoch": 59, "iter": 1400, "lr": 0.06706, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28031, "top5_acc": 0.53328, "loss_cls": 4.15049, "loss": 4.15049, "time": 0.85671} +{"mode": "train", "epoch": 59, "iter": 1500, "lr": 0.06704, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29172, "top5_acc": 0.54812, "loss_cls": 4.10297, "loss": 4.10297, "time": 0.85594} +{"mode": "train", "epoch": 59, "iter": 1600, "lr": 0.06701, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28766, "top5_acc": 0.54578, "loss_cls": 4.07021, "loss": 4.07021, "time": 0.85851} +{"mode": "train", "epoch": 59, "iter": 1700, "lr": 0.06698, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29438, "top5_acc": 0.55531, "loss_cls": 4.02971, "loss": 4.02971, "time": 0.84557} +{"mode": "train", "epoch": 59, "iter": 1800, "lr": 0.06696, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28, "top5_acc": 0.53109, "loss_cls": 4.11284, "loss": 4.11284, "time": 0.84888} +{"mode": "train", "epoch": 59, "iter": 1900, "lr": 0.06693, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28672, "top5_acc": 0.54, "loss_cls": 4.07555, "loss": 4.07555, "time": 0.85096} +{"mode": "train", "epoch": 59, "iter": 2000, "lr": 0.0669, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29156, "top5_acc": 0.54438, "loss_cls": 4.09273, "loss": 4.09273, "time": 0.84963} +{"mode": "train", "epoch": 59, "iter": 2100, "lr": 0.06688, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28969, "top5_acc": 0.54922, "loss_cls": 4.07069, "loss": 4.07069, "time": 0.85471} +{"mode": "train", "epoch": 59, "iter": 2200, "lr": 0.06685, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29344, "top5_acc": 0.545, "loss_cls": 4.07945, "loss": 4.07945, "time": 0.85458} +{"mode": "train", "epoch": 59, "iter": 2300, "lr": 0.06682, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.2875, "top5_acc": 0.54641, "loss_cls": 4.08424, "loss": 4.08424, "time": 0.85527} +{"mode": "train", "epoch": 59, "iter": 2400, "lr": 0.0668, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.30125, "top5_acc": 0.55812, "loss_cls": 4.02655, "loss": 4.02655, "time": 0.85369} +{"mode": "train", "epoch": 59, "iter": 2500, "lr": 0.06677, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29125, "top5_acc": 0.54391, "loss_cls": 4.08601, "loss": 4.08601, "time": 0.85415} +{"mode": "train", "epoch": 59, "iter": 2600, "lr": 0.06675, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28484, "top5_acc": 0.53812, "loss_cls": 4.10932, "loss": 4.10932, "time": 0.85098} +{"mode": "train", "epoch": 59, "iter": 2700, "lr": 0.06672, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29063, "top5_acc": 0.54797, "loss_cls": 4.06167, "loss": 4.06167, "time": 0.85377} +{"mode": "train", "epoch": 59, "iter": 2800, "lr": 0.06669, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.295, "top5_acc": 0.54078, "loss_cls": 4.08921, "loss": 4.08921, "time": 0.85097} +{"mode": "train", "epoch": 59, "iter": 2900, "lr": 0.06667, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29016, "top5_acc": 0.53906, "loss_cls": 4.10355, "loss": 4.10355, "time": 0.85098} +{"mode": "train", "epoch": 59, "iter": 3000, "lr": 0.06664, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28656, "top5_acc": 0.54625, "loss_cls": 4.07468, "loss": 4.07468, "time": 0.85321} +{"mode": "train", "epoch": 59, "iter": 3100, "lr": 0.06661, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.28359, "top5_acc": 0.53516, "loss_cls": 4.10503, "loss": 4.10503, "time": 0.85745} +{"mode": "train", "epoch": 59, "iter": 3200, "lr": 0.06659, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28875, "top5_acc": 0.53984, "loss_cls": 4.10571, "loss": 4.10571, "time": 0.85819} +{"mode": "train", "epoch": 59, "iter": 3300, "lr": 0.06656, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28594, "top5_acc": 0.54453, "loss_cls": 4.06585, "loss": 4.06585, "time": 0.85778} +{"mode": "train", "epoch": 59, "iter": 3400, "lr": 0.06653, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29547, "top5_acc": 0.54531, "loss_cls": 4.06614, "loss": 4.06614, "time": 0.85519} +{"mode": "train", "epoch": 59, "iter": 3500, "lr": 0.06651, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.28906, "top5_acc": 0.54016, "loss_cls": 4.08697, "loss": 4.08697, "time": 0.86278} +{"mode": "train", "epoch": 59, "iter": 3600, "lr": 0.06648, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28984, "top5_acc": 0.54469, "loss_cls": 4.08371, "loss": 4.08371, "time": 0.85857} +{"mode": "train", "epoch": 59, "iter": 3700, "lr": 0.06646, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29266, "top5_acc": 0.54172, "loss_cls": 4.10286, "loss": 4.10286, "time": 0.85594} +{"mode": "val", "epoch": 59, "iter": 309, "lr": 0.06644, "top1_acc": 0.2334, "top5_acc": 0.47399, "mean_class_accuracy": 0.2332} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.06642, "memory": 15990, "data_time": 1.56114, "top1_acc": 0.29812, "top5_acc": 0.54875, "loss_cls": 4.03728, "loss": 4.03728, "time": 2.59718} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.06639, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29313, "top5_acc": 0.54625, "loss_cls": 4.03942, "loss": 4.03942, "time": 0.85622} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.06636, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29984, "top5_acc": 0.55734, "loss_cls": 3.99453, "loss": 3.99453, "time": 0.8616} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.06634, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29531, "top5_acc": 0.54688, "loss_cls": 4.05584, "loss": 4.05584, "time": 0.85986} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.06631, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29672, "top5_acc": 0.55344, "loss_cls": 4.03141, "loss": 4.03141, "time": 0.86131} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.06629, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30219, "top5_acc": 0.55844, "loss_cls": 4.01885, "loss": 4.01885, "time": 0.86657} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.06626, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29063, "top5_acc": 0.54469, "loss_cls": 4.05583, "loss": 4.05583, "time": 0.85661} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.06623, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.30141, "top5_acc": 0.55, "loss_cls": 4.0264, "loss": 4.0264, "time": 0.85928} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.06621, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28734, "top5_acc": 0.54547, "loss_cls": 4.02731, "loss": 4.02731, "time": 0.85885} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.06618, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29297, "top5_acc": 0.54641, "loss_cls": 4.09521, "loss": 4.09521, "time": 0.86412} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.06615, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29328, "top5_acc": 0.54688, "loss_cls": 4.04373, "loss": 4.04373, "time": 0.85818} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.06613, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29422, "top5_acc": 0.54828, "loss_cls": 4.08105, "loss": 4.08105, "time": 0.8609} +{"mode": "train", "epoch": 60, "iter": 1300, "lr": 0.0661, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29922, "top5_acc": 0.54188, "loss_cls": 4.05709, "loss": 4.05709, "time": 0.86764} +{"mode": "train", "epoch": 60, "iter": 1400, "lr": 0.06607, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28359, "top5_acc": 0.54141, "loss_cls": 4.10757, "loss": 4.10757, "time": 0.85806} +{"mode": "train", "epoch": 60, "iter": 1500, "lr": 0.06605, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28953, "top5_acc": 0.54828, "loss_cls": 4.05199, "loss": 4.05199, "time": 0.86109} +{"mode": "train", "epoch": 60, "iter": 1600, "lr": 0.06602, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28703, "top5_acc": 0.54234, "loss_cls": 4.09563, "loss": 4.09563, "time": 0.85341} +{"mode": "train", "epoch": 60, "iter": 1700, "lr": 0.06599, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28359, "top5_acc": 0.5525, "loss_cls": 4.08131, "loss": 4.08131, "time": 0.85101} +{"mode": "train", "epoch": 60, "iter": 1800, "lr": 0.06597, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29406, "top5_acc": 0.54781, "loss_cls": 4.04813, "loss": 4.04813, "time": 0.85084} +{"mode": "train", "epoch": 60, "iter": 1900, "lr": 0.06594, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.30375, "top5_acc": 0.55203, "loss_cls": 4.04079, "loss": 4.04079, "time": 0.86168} +{"mode": "train", "epoch": 60, "iter": 2000, "lr": 0.06591, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29, "top5_acc": 0.53938, "loss_cls": 4.0862, "loss": 4.0862, "time": 0.85895} +{"mode": "train", "epoch": 60, "iter": 2100, "lr": 0.06589, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.28406, "top5_acc": 0.53703, "loss_cls": 4.10149, "loss": 4.10149, "time": 0.85962} +{"mode": "train", "epoch": 60, "iter": 2200, "lr": 0.06586, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29281, "top5_acc": 0.54094, "loss_cls": 4.08745, "loss": 4.08745, "time": 0.85407} +{"mode": "train", "epoch": 60, "iter": 2300, "lr": 0.06584, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.28422, "top5_acc": 0.5325, "loss_cls": 4.09833, "loss": 4.09833, "time": 0.8532} +{"mode": "train", "epoch": 60, "iter": 2400, "lr": 0.06581, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29281, "top5_acc": 0.54484, "loss_cls": 4.07077, "loss": 4.07077, "time": 0.853} +{"mode": "train", "epoch": 60, "iter": 2500, "lr": 0.06578, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29141, "top5_acc": 0.54641, "loss_cls": 4.07333, "loss": 4.07333, "time": 0.85243} +{"mode": "train", "epoch": 60, "iter": 2600, "lr": 0.06576, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.29297, "top5_acc": 0.54672, "loss_cls": 4.03619, "loss": 4.03619, "time": 0.85844} +{"mode": "train", "epoch": 60, "iter": 2700, "lr": 0.06573, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30141, "top5_acc": 0.54922, "loss_cls": 4.04946, "loss": 4.04946, "time": 0.8589} +{"mode": "train", "epoch": 60, "iter": 2800, "lr": 0.0657, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28781, "top5_acc": 0.55031, "loss_cls": 4.07478, "loss": 4.07478, "time": 0.85008} +{"mode": "train", "epoch": 60, "iter": 2900, "lr": 0.06568, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29203, "top5_acc": 0.54781, "loss_cls": 4.07663, "loss": 4.07663, "time": 0.85217} +{"mode": "train", "epoch": 60, "iter": 3000, "lr": 0.06565, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29375, "top5_acc": 0.545, "loss_cls": 4.07876, "loss": 4.07876, "time": 0.84818} +{"mode": "train", "epoch": 60, "iter": 3100, "lr": 0.06562, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28781, "top5_acc": 0.54094, "loss_cls": 4.11305, "loss": 4.11305, "time": 0.85509} +{"mode": "train", "epoch": 60, "iter": 3200, "lr": 0.0656, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28203, "top5_acc": 0.54797, "loss_cls": 4.09166, "loss": 4.09166, "time": 0.8486} +{"mode": "train", "epoch": 60, "iter": 3300, "lr": 0.06557, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29328, "top5_acc": 0.54984, "loss_cls": 4.05708, "loss": 4.05708, "time": 0.84802} +{"mode": "train", "epoch": 60, "iter": 3400, "lr": 0.06554, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29125, "top5_acc": 0.53609, "loss_cls": 4.10443, "loss": 4.10443, "time": 0.84805} +{"mode": "train", "epoch": 60, "iter": 3500, "lr": 0.06552, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29, "top5_acc": 0.54734, "loss_cls": 4.07123, "loss": 4.07123, "time": 0.85063} +{"mode": "train", "epoch": 60, "iter": 3600, "lr": 0.06549, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28672, "top5_acc": 0.54031, "loss_cls": 4.10153, "loss": 4.10153, "time": 0.85698} +{"mode": "train", "epoch": 60, "iter": 3700, "lr": 0.06546, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.2925, "top5_acc": 0.54438, "loss_cls": 4.07349, "loss": 4.07349, "time": 0.85335} +{"mode": "val", "epoch": 60, "iter": 309, "lr": 0.06545, "top1_acc": 0.2338, "top5_acc": 0.47612, "mean_class_accuracy": 0.23355} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.06542, "memory": 15990, "data_time": 1.5884, "top1_acc": 0.30328, "top5_acc": 0.55047, "loss_cls": 4.02606, "loss": 4.02606, "time": 2.62294} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.0654, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29125, "top5_acc": 0.55484, "loss_cls": 4.01224, "loss": 4.01224, "time": 0.85629} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.06537, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31922, "top5_acc": 0.57812, "loss_cls": 3.92986, "loss": 3.92986, "time": 0.85372} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.06534, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28844, "top5_acc": 0.54578, "loss_cls": 4.07079, "loss": 4.07079, "time": 0.85597} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.06532, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29672, "top5_acc": 0.55188, "loss_cls": 4.01478, "loss": 4.01478, "time": 0.85825} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.06529, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29969, "top5_acc": 0.55359, "loss_cls": 4.02832, "loss": 4.02832, "time": 0.85495} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.06526, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28266, "top5_acc": 0.54203, "loss_cls": 4.08606, "loss": 4.08606, "time": 0.85692} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.06524, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28375, "top5_acc": 0.54, "loss_cls": 4.08328, "loss": 4.08328, "time": 0.85566} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.06521, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29641, "top5_acc": 0.54438, "loss_cls": 4.07199, "loss": 4.07199, "time": 0.85478} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.06519, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28328, "top5_acc": 0.54281, "loss_cls": 4.08841, "loss": 4.08841, "time": 0.85542} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.06516, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.295, "top5_acc": 0.54688, "loss_cls": 4.05587, "loss": 4.05587, "time": 0.8513} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.06513, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29156, "top5_acc": 0.54062, "loss_cls": 4.05229, "loss": 4.05229, "time": 0.848} +{"mode": "train", "epoch": 61, "iter": 1300, "lr": 0.06511, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29375, "top5_acc": 0.55375, "loss_cls": 4.02478, "loss": 4.02478, "time": 0.85072} +{"mode": "train", "epoch": 61, "iter": 1400, "lr": 0.06508, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28953, "top5_acc": 0.54641, "loss_cls": 4.07612, "loss": 4.07612, "time": 0.85093} +{"mode": "train", "epoch": 61, "iter": 1500, "lr": 0.06505, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.295, "top5_acc": 0.55359, "loss_cls": 4.02733, "loss": 4.02733, "time": 0.85424} +{"mode": "train", "epoch": 61, "iter": 1600, "lr": 0.06503, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29297, "top5_acc": 0.54125, "loss_cls": 4.06815, "loss": 4.06815, "time": 0.84762} +{"mode": "train", "epoch": 61, "iter": 1700, "lr": 0.065, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30109, "top5_acc": 0.5475, "loss_cls": 4.06717, "loss": 4.06717, "time": 0.85157} +{"mode": "train", "epoch": 61, "iter": 1800, "lr": 0.06497, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28922, "top5_acc": 0.55469, "loss_cls": 4.04243, "loss": 4.04243, "time": 0.84503} +{"mode": "train", "epoch": 61, "iter": 1900, "lr": 0.06495, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29313, "top5_acc": 0.54844, "loss_cls": 4.0629, "loss": 4.0629, "time": 0.85368} +{"mode": "train", "epoch": 61, "iter": 2000, "lr": 0.06492, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29016, "top5_acc": 0.55734, "loss_cls": 4.0348, "loss": 4.0348, "time": 0.85684} +{"mode": "train", "epoch": 61, "iter": 2100, "lr": 0.06489, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29906, "top5_acc": 0.55219, "loss_cls": 4.03923, "loss": 4.03923, "time": 0.85338} +{"mode": "train", "epoch": 61, "iter": 2200, "lr": 0.06487, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29094, "top5_acc": 0.54641, "loss_cls": 4.03731, "loss": 4.03731, "time": 0.85195} +{"mode": "train", "epoch": 61, "iter": 2300, "lr": 0.06484, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3025, "top5_acc": 0.55781, "loss_cls": 4.02702, "loss": 4.02702, "time": 0.85114} +{"mode": "train", "epoch": 61, "iter": 2400, "lr": 0.06481, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29484, "top5_acc": 0.55016, "loss_cls": 4.07355, "loss": 4.07355, "time": 0.84848} +{"mode": "train", "epoch": 61, "iter": 2500, "lr": 0.06478, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29203, "top5_acc": 0.54188, "loss_cls": 4.11012, "loss": 4.11012, "time": 0.85248} +{"mode": "train", "epoch": 61, "iter": 2600, "lr": 0.06476, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2875, "top5_acc": 0.5425, "loss_cls": 4.08967, "loss": 4.08967, "time": 0.85608} +{"mode": "train", "epoch": 61, "iter": 2700, "lr": 0.06473, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.29688, "top5_acc": 0.54562, "loss_cls": 4.04652, "loss": 4.04652, "time": 0.85545} +{"mode": "train", "epoch": 61, "iter": 2800, "lr": 0.0647, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29109, "top5_acc": 0.54969, "loss_cls": 4.07908, "loss": 4.07908, "time": 0.85137} +{"mode": "train", "epoch": 61, "iter": 2900, "lr": 0.06468, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.29344, "top5_acc": 0.54906, "loss_cls": 4.05081, "loss": 4.05081, "time": 0.84964} +{"mode": "train", "epoch": 61, "iter": 3000, "lr": 0.06465, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28344, "top5_acc": 0.53625, "loss_cls": 4.09176, "loss": 4.09176, "time": 0.84747} +{"mode": "train", "epoch": 61, "iter": 3100, "lr": 0.06462, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28766, "top5_acc": 0.54641, "loss_cls": 4.07783, "loss": 4.07783, "time": 0.84664} +{"mode": "train", "epoch": 61, "iter": 3200, "lr": 0.0646, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29656, "top5_acc": 0.54188, "loss_cls": 4.0532, "loss": 4.0532, "time": 0.84822} +{"mode": "train", "epoch": 61, "iter": 3300, "lr": 0.06457, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29375, "top5_acc": 0.54484, "loss_cls": 4.10196, "loss": 4.10196, "time": 0.84976} +{"mode": "train", "epoch": 61, "iter": 3400, "lr": 0.06454, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30125, "top5_acc": 0.54609, "loss_cls": 4.04403, "loss": 4.04403, "time": 0.85112} +{"mode": "train", "epoch": 61, "iter": 3500, "lr": 0.06452, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28531, "top5_acc": 0.53844, "loss_cls": 4.0903, "loss": 4.0903, "time": 0.84624} +{"mode": "train", "epoch": 61, "iter": 3600, "lr": 0.06449, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28859, "top5_acc": 0.5425, "loss_cls": 4.09056, "loss": 4.09056, "time": 0.85426} +{"mode": "train", "epoch": 61, "iter": 3700, "lr": 0.06446, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29281, "top5_acc": 0.53984, "loss_cls": 4.0607, "loss": 4.0607, "time": 0.85025} +{"mode": "val", "epoch": 61, "iter": 309, "lr": 0.06445, "top1_acc": 0.23001, "top5_acc": 0.46893, "mean_class_accuracy": 0.22982} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.06443, "memory": 15990, "data_time": 1.58459, "top1_acc": 0.30688, "top5_acc": 0.56156, "loss_cls": 3.99243, "loss": 3.99243, "time": 2.61368} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.0644, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29563, "top5_acc": 0.55484, "loss_cls": 4.02299, "loss": 4.02299, "time": 0.85796} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.06437, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28938, "top5_acc": 0.54734, "loss_cls": 4.05763, "loss": 4.05763, "time": 0.85338} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.06434, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28969, "top5_acc": 0.5525, "loss_cls": 4.02062, "loss": 4.02062, "time": 0.85215} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.06432, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30094, "top5_acc": 0.56094, "loss_cls": 3.99676, "loss": 3.99676, "time": 0.85783} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.06429, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.29188, "top5_acc": 0.54516, "loss_cls": 4.05047, "loss": 4.05047, "time": 0.86267} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.06426, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29734, "top5_acc": 0.55562, "loss_cls": 4.03675, "loss": 4.03675, "time": 0.85742} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.06424, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28922, "top5_acc": 0.53766, "loss_cls": 4.07797, "loss": 4.07797, "time": 0.86007} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.06421, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30141, "top5_acc": 0.55297, "loss_cls": 4.04345, "loss": 4.04345, "time": 0.8534} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.06418, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29984, "top5_acc": 0.54781, "loss_cls": 4.03721, "loss": 4.03721, "time": 0.85449} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.06416, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.29547, "top5_acc": 0.55484, "loss_cls": 4.04173, "loss": 4.04173, "time": 0.85762} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.06413, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2975, "top5_acc": 0.54203, "loss_cls": 4.05727, "loss": 4.05727, "time": 0.85972} +{"mode": "train", "epoch": 62, "iter": 1300, "lr": 0.0641, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29688, "top5_acc": 0.54625, "loss_cls": 4.03654, "loss": 4.03654, "time": 0.85624} +{"mode": "train", "epoch": 62, "iter": 1400, "lr": 0.06408, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3075, "top5_acc": 0.55875, "loss_cls": 3.99133, "loss": 3.99133, "time": 0.85147} +{"mode": "train", "epoch": 62, "iter": 1500, "lr": 0.06405, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29266, "top5_acc": 0.55516, "loss_cls": 4.01961, "loss": 4.01961, "time": 0.8523} +{"mode": "train", "epoch": 62, "iter": 1600, "lr": 0.06402, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29359, "top5_acc": 0.54719, "loss_cls": 4.05257, "loss": 4.05257, "time": 0.85585} +{"mode": "train", "epoch": 62, "iter": 1700, "lr": 0.064, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.27781, "top5_acc": 0.53844, "loss_cls": 4.12247, "loss": 4.12247, "time": 0.84882} +{"mode": "train", "epoch": 62, "iter": 1800, "lr": 0.06397, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29547, "top5_acc": 0.55594, "loss_cls": 4.03176, "loss": 4.03176, "time": 0.85052} +{"mode": "train", "epoch": 62, "iter": 1900, "lr": 0.06394, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28266, "top5_acc": 0.54375, "loss_cls": 4.10358, "loss": 4.10358, "time": 0.85473} +{"mode": "train", "epoch": 62, "iter": 2000, "lr": 0.06392, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29359, "top5_acc": 0.54562, "loss_cls": 4.04579, "loss": 4.04579, "time": 0.85557} +{"mode": "train", "epoch": 62, "iter": 2100, "lr": 0.06389, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29453, "top5_acc": 0.54422, "loss_cls": 4.04828, "loss": 4.04828, "time": 0.85301} +{"mode": "train", "epoch": 62, "iter": 2200, "lr": 0.06386, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29094, "top5_acc": 0.55609, "loss_cls": 4.03557, "loss": 4.03557, "time": 0.85385} +{"mode": "train", "epoch": 62, "iter": 2300, "lr": 0.06384, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28656, "top5_acc": 0.54375, "loss_cls": 4.10006, "loss": 4.10006, "time": 0.8547} +{"mode": "train", "epoch": 62, "iter": 2400, "lr": 0.06381, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.28516, "top5_acc": 0.53797, "loss_cls": 4.09534, "loss": 4.09534, "time": 0.84886} +{"mode": "train", "epoch": 62, "iter": 2500, "lr": 0.06378, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.30062, "top5_acc": 0.54531, "loss_cls": 4.05707, "loss": 4.05707, "time": 0.85224} +{"mode": "train", "epoch": 62, "iter": 2600, "lr": 0.06375, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30125, "top5_acc": 0.55781, "loss_cls": 4.0169, "loss": 4.0169, "time": 0.85369} +{"mode": "train", "epoch": 62, "iter": 2700, "lr": 0.06373, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29594, "top5_acc": 0.55344, "loss_cls": 4.03992, "loss": 4.03992, "time": 0.84927} +{"mode": "train", "epoch": 62, "iter": 2800, "lr": 0.0637, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.3, "top5_acc": 0.54047, "loss_cls": 4.05993, "loss": 4.05993, "time": 0.85496} +{"mode": "train", "epoch": 62, "iter": 2900, "lr": 0.06367, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29266, "top5_acc": 0.54078, "loss_cls": 4.09244, "loss": 4.09244, "time": 0.85215} +{"mode": "train", "epoch": 62, "iter": 3000, "lr": 0.06365, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29953, "top5_acc": 0.55203, "loss_cls": 4.038, "loss": 4.038, "time": 0.85172} +{"mode": "train", "epoch": 62, "iter": 3100, "lr": 0.06362, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29484, "top5_acc": 0.5425, "loss_cls": 4.05934, "loss": 4.05934, "time": 0.85285} +{"mode": "train", "epoch": 62, "iter": 3200, "lr": 0.06359, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30594, "top5_acc": 0.55828, "loss_cls": 4.00038, "loss": 4.00038, "time": 0.8524} +{"mode": "train", "epoch": 62, "iter": 3300, "lr": 0.06357, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29313, "top5_acc": 0.54641, "loss_cls": 4.06825, "loss": 4.06825, "time": 0.84792} +{"mode": "train", "epoch": 62, "iter": 3400, "lr": 0.06354, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3, "top5_acc": 0.5575, "loss_cls": 4.00482, "loss": 4.00482, "time": 0.84999} +{"mode": "train", "epoch": 62, "iter": 3500, "lr": 0.06351, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29625, "top5_acc": 0.55781, "loss_cls": 4.02991, "loss": 4.02991, "time": 0.85034} +{"mode": "train", "epoch": 62, "iter": 3600, "lr": 0.06349, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30609, "top5_acc": 0.55125, "loss_cls": 3.99959, "loss": 3.99959, "time": 0.85939} +{"mode": "train", "epoch": 62, "iter": 3700, "lr": 0.06346, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28453, "top5_acc": 0.54016, "loss_cls": 4.08613, "loss": 4.08613, "time": 0.85669} +{"mode": "val", "epoch": 62, "iter": 309, "lr": 0.06345, "top1_acc": 0.24783, "top5_acc": 0.48189, "mean_class_accuracy": 0.24771} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.06342, "memory": 15990, "data_time": 1.62954, "top1_acc": 0.30531, "top5_acc": 0.56484, "loss_cls": 3.96552, "loss": 3.96552, "time": 2.68037} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.06339, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29812, "top5_acc": 0.55359, "loss_cls": 4.0068, "loss": 4.0068, "time": 0.86551} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.06337, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.30156, "top5_acc": 0.56078, "loss_cls": 4.00467, "loss": 4.00467, "time": 0.87003} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.06334, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.29609, "top5_acc": 0.54734, "loss_cls": 4.05573, "loss": 4.05573, "time": 0.87233} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.06331, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29328, "top5_acc": 0.55141, "loss_cls": 4.04601, "loss": 4.04601, "time": 0.86562} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.06328, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.29328, "top5_acc": 0.55719, "loss_cls": 4.02455, "loss": 4.02455, "time": 0.86497} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.06326, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.30172, "top5_acc": 0.56063, "loss_cls": 4.02827, "loss": 4.02827, "time": 0.86805} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.06323, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29859, "top5_acc": 0.54531, "loss_cls": 4.05093, "loss": 4.05093, "time": 0.86154} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.0632, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.28922, "top5_acc": 0.54281, "loss_cls": 4.0735, "loss": 4.0735, "time": 0.8699} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.06318, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30828, "top5_acc": 0.56688, "loss_cls": 3.96682, "loss": 3.96682, "time": 0.85995} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.06315, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29641, "top5_acc": 0.54906, "loss_cls": 4.0536, "loss": 4.0536, "time": 0.86344} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.06312, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29094, "top5_acc": 0.545, "loss_cls": 4.06929, "loss": 4.06929, "time": 0.85924} +{"mode": "train", "epoch": 63, "iter": 1300, "lr": 0.0631, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.29688, "top5_acc": 0.55016, "loss_cls": 4.06027, "loss": 4.06027, "time": 0.86221} +{"mode": "train", "epoch": 63, "iter": 1400, "lr": 0.06307, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29406, "top5_acc": 0.55469, "loss_cls": 4.02346, "loss": 4.02346, "time": 0.85919} +{"mode": "train", "epoch": 63, "iter": 1500, "lr": 0.06304, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.29828, "top5_acc": 0.55219, "loss_cls": 4.02272, "loss": 4.02272, "time": 0.85484} +{"mode": "train", "epoch": 63, "iter": 1600, "lr": 0.06301, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.30094, "top5_acc": 0.55375, "loss_cls": 4.04283, "loss": 4.04283, "time": 0.85395} +{"mode": "train", "epoch": 63, "iter": 1700, "lr": 0.06299, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30562, "top5_acc": 0.55437, "loss_cls": 4.01348, "loss": 4.01348, "time": 0.84707} +{"mode": "train", "epoch": 63, "iter": 1800, "lr": 0.06296, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29953, "top5_acc": 0.55734, "loss_cls": 4.01784, "loss": 4.01784, "time": 0.85127} +{"mode": "train", "epoch": 63, "iter": 1900, "lr": 0.06293, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29547, "top5_acc": 0.54141, "loss_cls": 4.06536, "loss": 4.06536, "time": 0.85395} +{"mode": "train", "epoch": 63, "iter": 2000, "lr": 0.06291, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28734, "top5_acc": 0.54328, "loss_cls": 4.05655, "loss": 4.05655, "time": 0.86317} +{"mode": "train", "epoch": 63, "iter": 2100, "lr": 0.06288, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.29359, "top5_acc": 0.54906, "loss_cls": 4.07711, "loss": 4.07711, "time": 0.85979} +{"mode": "train", "epoch": 63, "iter": 2200, "lr": 0.06285, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29578, "top5_acc": 0.54141, "loss_cls": 4.08342, "loss": 4.08342, "time": 0.85318} +{"mode": "train", "epoch": 63, "iter": 2300, "lr": 0.06283, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29375, "top5_acc": 0.55344, "loss_cls": 4.00152, "loss": 4.00152, "time": 0.84752} +{"mode": "train", "epoch": 63, "iter": 2400, "lr": 0.0628, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.28906, "top5_acc": 0.545, "loss_cls": 4.07386, "loss": 4.07386, "time": 0.85024} +{"mode": "train", "epoch": 63, "iter": 2500, "lr": 0.06277, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30188, "top5_acc": 0.55359, "loss_cls": 4.01183, "loss": 4.01183, "time": 0.85822} +{"mode": "train", "epoch": 63, "iter": 2600, "lr": 0.06274, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.30094, "top5_acc": 0.55797, "loss_cls": 4.0135, "loss": 4.0135, "time": 0.85695} +{"mode": "train", "epoch": 63, "iter": 2700, "lr": 0.06272, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29109, "top5_acc": 0.54359, "loss_cls": 4.07212, "loss": 4.07212, "time": 0.85649} +{"mode": "train", "epoch": 63, "iter": 2800, "lr": 0.06269, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.3, "top5_acc": 0.545, "loss_cls": 4.05804, "loss": 4.05804, "time": 0.84881} +{"mode": "train", "epoch": 63, "iter": 2900, "lr": 0.06266, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29188, "top5_acc": 0.56531, "loss_cls": 4.00226, "loss": 4.00226, "time": 0.85176} +{"mode": "train", "epoch": 63, "iter": 3000, "lr": 0.06264, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.28953, "top5_acc": 0.55094, "loss_cls": 4.05549, "loss": 4.05549, "time": 0.8565} +{"mode": "train", "epoch": 63, "iter": 3100, "lr": 0.06261, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28938, "top5_acc": 0.54047, "loss_cls": 4.07458, "loss": 4.07458, "time": 0.85554} +{"mode": "train", "epoch": 63, "iter": 3200, "lr": 0.06258, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.29906, "top5_acc": 0.55562, "loss_cls": 4.02783, "loss": 4.02783, "time": 0.86603} +{"mode": "train", "epoch": 63, "iter": 3300, "lr": 0.06256, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29688, "top5_acc": 0.56, "loss_cls": 3.9906, "loss": 3.9906, "time": 0.85667} +{"mode": "train", "epoch": 63, "iter": 3400, "lr": 0.06253, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29594, "top5_acc": 0.56109, "loss_cls": 4.03054, "loss": 4.03054, "time": 0.86281} +{"mode": "train", "epoch": 63, "iter": 3500, "lr": 0.0625, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.28969, "top5_acc": 0.54516, "loss_cls": 4.08337, "loss": 4.08337, "time": 0.85881} +{"mode": "train", "epoch": 63, "iter": 3600, "lr": 0.06247, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.28844, "top5_acc": 0.54937, "loss_cls": 4.05721, "loss": 4.05721, "time": 0.86304} +{"mode": "train", "epoch": 63, "iter": 3700, "lr": 0.06245, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.29844, "top5_acc": 0.55125, "loss_cls": 4.01082, "loss": 4.01082, "time": 0.85827} +{"mode": "val", "epoch": 63, "iter": 309, "lr": 0.06243, "top1_acc": 0.24399, "top5_acc": 0.48382, "mean_class_accuracy": 0.24372} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.06241, "memory": 15990, "data_time": 1.61568, "top1_acc": 0.30141, "top5_acc": 0.55656, "loss_cls": 3.99934, "loss": 3.99934, "time": 2.64747} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.06238, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29953, "top5_acc": 0.54906, "loss_cls": 4.03633, "loss": 4.03633, "time": 0.85404} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.06235, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30297, "top5_acc": 0.55531, "loss_cls": 4.0093, "loss": 4.0093, "time": 0.85537} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.06233, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.30547, "top5_acc": 0.56234, "loss_cls": 3.99623, "loss": 3.99623, "time": 0.84935} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.0623, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30781, "top5_acc": 0.56281, "loss_cls": 3.97643, "loss": 3.97643, "time": 0.85585} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.06227, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29625, "top5_acc": 0.55203, "loss_cls": 4.01217, "loss": 4.01217, "time": 0.8597} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.06225, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29438, "top5_acc": 0.55453, "loss_cls": 4.036, "loss": 4.036, "time": 0.85452} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.06222, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30328, "top5_acc": 0.55984, "loss_cls": 4.01585, "loss": 4.01585, "time": 0.85423} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.06219, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31266, "top5_acc": 0.56172, "loss_cls": 3.97981, "loss": 3.97981, "time": 0.85726} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.06216, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29641, "top5_acc": 0.5475, "loss_cls": 4.06064, "loss": 4.06064, "time": 0.85501} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.06214, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29234, "top5_acc": 0.55109, "loss_cls": 4.02256, "loss": 4.02256, "time": 0.85456} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.06211, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.28953, "top5_acc": 0.55391, "loss_cls": 4.03413, "loss": 4.03413, "time": 0.85858} +{"mode": "train", "epoch": 64, "iter": 1300, "lr": 0.06208, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.30656, "top5_acc": 0.55078, "loss_cls": 3.98745, "loss": 3.98745, "time": 0.85899} +{"mode": "train", "epoch": 64, "iter": 1400, "lr": 0.06206, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29469, "top5_acc": 0.54906, "loss_cls": 4.03588, "loss": 4.03588, "time": 0.85308} +{"mode": "train", "epoch": 64, "iter": 1500, "lr": 0.06203, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.29922, "top5_acc": 0.55437, "loss_cls": 4.01327, "loss": 4.01327, "time": 0.84984} +{"mode": "train", "epoch": 64, "iter": 1600, "lr": 0.062, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30016, "top5_acc": 0.54906, "loss_cls": 4.07331, "loss": 4.07331, "time": 0.85307} +{"mode": "train", "epoch": 64, "iter": 1700, "lr": 0.06197, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30062, "top5_acc": 0.55328, "loss_cls": 4.02384, "loss": 4.02384, "time": 0.85054} +{"mode": "train", "epoch": 64, "iter": 1800, "lr": 0.06195, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30172, "top5_acc": 0.54688, "loss_cls": 4.04594, "loss": 4.04594, "time": 0.8475} +{"mode": "train", "epoch": 64, "iter": 1900, "lr": 0.06192, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30062, "top5_acc": 0.54875, "loss_cls": 4.02915, "loss": 4.02915, "time": 0.84837} +{"mode": "train", "epoch": 64, "iter": 2000, "lr": 0.06189, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29094, "top5_acc": 0.54578, "loss_cls": 4.05394, "loss": 4.05394, "time": 0.84918} +{"mode": "train", "epoch": 64, "iter": 2100, "lr": 0.06187, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29375, "top5_acc": 0.55953, "loss_cls": 4.03097, "loss": 4.03097, "time": 0.85537} +{"mode": "train", "epoch": 64, "iter": 2200, "lr": 0.06184, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30344, "top5_acc": 0.55906, "loss_cls": 3.99021, "loss": 3.99021, "time": 0.84885} +{"mode": "train", "epoch": 64, "iter": 2300, "lr": 0.06181, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.28562, "top5_acc": 0.55062, "loss_cls": 4.02763, "loss": 4.02763, "time": 0.85021} +{"mode": "train", "epoch": 64, "iter": 2400, "lr": 0.06178, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.28609, "top5_acc": 0.54, "loss_cls": 4.06706, "loss": 4.06706, "time": 0.85209} +{"mode": "train", "epoch": 64, "iter": 2500, "lr": 0.06176, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29703, "top5_acc": 0.55688, "loss_cls": 4.02567, "loss": 4.02567, "time": 0.85106} +{"mode": "train", "epoch": 64, "iter": 2600, "lr": 0.06173, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29422, "top5_acc": 0.54219, "loss_cls": 4.05827, "loss": 4.05827, "time": 0.85018} +{"mode": "train", "epoch": 64, "iter": 2700, "lr": 0.0617, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30703, "top5_acc": 0.56141, "loss_cls": 3.99069, "loss": 3.99069, "time": 0.85266} +{"mode": "train", "epoch": 64, "iter": 2800, "lr": 0.06168, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29906, "top5_acc": 0.54812, "loss_cls": 4.03943, "loss": 4.03943, "time": 0.8542} +{"mode": "train", "epoch": 64, "iter": 2900, "lr": 0.06165, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29547, "top5_acc": 0.55062, "loss_cls": 4.05145, "loss": 4.05145, "time": 0.84735} +{"mode": "train", "epoch": 64, "iter": 3000, "lr": 0.06162, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2925, "top5_acc": 0.55266, "loss_cls": 4.02463, "loss": 4.02463, "time": 0.85212} +{"mode": "train", "epoch": 64, "iter": 3100, "lr": 0.06159, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29953, "top5_acc": 0.55375, "loss_cls": 4.0472, "loss": 4.0472, "time": 0.85621} +{"mode": "train", "epoch": 64, "iter": 3200, "lr": 0.06157, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29438, "top5_acc": 0.55812, "loss_cls": 4.0103, "loss": 4.0103, "time": 0.84763} +{"mode": "train", "epoch": 64, "iter": 3300, "lr": 0.06154, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29938, "top5_acc": 0.55922, "loss_cls": 4.0258, "loss": 4.0258, "time": 0.85387} +{"mode": "train", "epoch": 64, "iter": 3400, "lr": 0.06151, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29391, "top5_acc": 0.55219, "loss_cls": 4.0442, "loss": 4.0442, "time": 0.8498} +{"mode": "train", "epoch": 64, "iter": 3500, "lr": 0.06148, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30031, "top5_acc": 0.54031, "loss_cls": 4.0831, "loss": 4.0831, "time": 0.85373} +{"mode": "train", "epoch": 64, "iter": 3600, "lr": 0.06146, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29656, "top5_acc": 0.56125, "loss_cls": 4.04061, "loss": 4.04061, "time": 0.85213} +{"mode": "train", "epoch": 64, "iter": 3700, "lr": 0.06143, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29422, "top5_acc": 0.55562, "loss_cls": 4.03054, "loss": 4.03054, "time": 0.84718} +{"mode": "val", "epoch": 64, "iter": 309, "lr": 0.06142, "top1_acc": 0.23097, "top5_acc": 0.46655, "mean_class_accuracy": 0.23077} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.06139, "memory": 15990, "data_time": 1.60493, "top1_acc": 0.30438, "top5_acc": 0.56688, "loss_cls": 3.94527, "loss": 3.94527, "time": 2.65844} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.06136, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30812, "top5_acc": 0.55766, "loss_cls": 4.00249, "loss": 4.00249, "time": 0.8576} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.06134, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.3, "top5_acc": 0.555, "loss_cls": 4.00789, "loss": 4.00789, "time": 0.86054} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.06131, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30672, "top5_acc": 0.55781, "loss_cls": 3.9986, "loss": 3.9986, "time": 0.86364} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.06128, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.29781, "top5_acc": 0.56297, "loss_cls": 4.005, "loss": 4.005, "time": 0.86004} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.06125, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30688, "top5_acc": 0.55844, "loss_cls": 3.98096, "loss": 3.98096, "time": 0.86151} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.06123, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29438, "top5_acc": 0.54516, "loss_cls": 4.06302, "loss": 4.06302, "time": 0.86176} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0612, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30031, "top5_acc": 0.55656, "loss_cls": 4.02053, "loss": 4.02053, "time": 0.86619} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.06117, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29531, "top5_acc": 0.55484, "loss_cls": 4.00532, "loss": 4.00532, "time": 0.86659} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.06115, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31094, "top5_acc": 0.56609, "loss_cls": 3.93602, "loss": 3.93602, "time": 0.86515} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.06112, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29984, "top5_acc": 0.55531, "loss_cls": 4.01224, "loss": 4.01224, "time": 0.86004} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.06109, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29875, "top5_acc": 0.55188, "loss_cls": 4.014, "loss": 4.014, "time": 0.85983} +{"mode": "train", "epoch": 65, "iter": 1300, "lr": 0.06106, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30391, "top5_acc": 0.54875, "loss_cls": 4.05298, "loss": 4.05298, "time": 0.8659} +{"mode": "train", "epoch": 65, "iter": 1400, "lr": 0.06104, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.28906, "top5_acc": 0.56031, "loss_cls": 4.00717, "loss": 4.00717, "time": 0.86141} +{"mode": "train", "epoch": 65, "iter": 1500, "lr": 0.06101, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29234, "top5_acc": 0.54078, "loss_cls": 4.07736, "loss": 4.07736, "time": 0.855} +{"mode": "train", "epoch": 65, "iter": 1600, "lr": 0.06098, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29531, "top5_acc": 0.55875, "loss_cls": 4.02439, "loss": 4.02439, "time": 0.85374} +{"mode": "train", "epoch": 65, "iter": 1700, "lr": 0.06095, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29516, "top5_acc": 0.55578, "loss_cls": 4.02834, "loss": 4.02834, "time": 0.85021} +{"mode": "train", "epoch": 65, "iter": 1800, "lr": 0.06093, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30625, "top5_acc": 0.55531, "loss_cls": 4.01273, "loss": 4.01273, "time": 0.84798} +{"mode": "train", "epoch": 65, "iter": 1900, "lr": 0.0609, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29078, "top5_acc": 0.54531, "loss_cls": 4.06616, "loss": 4.06616, "time": 0.85132} +{"mode": "train", "epoch": 65, "iter": 2000, "lr": 0.06087, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.3025, "top5_acc": 0.55828, "loss_cls": 3.99783, "loss": 3.99783, "time": 0.84772} +{"mode": "train", "epoch": 65, "iter": 2100, "lr": 0.06085, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30312, "top5_acc": 0.55531, "loss_cls": 4.05222, "loss": 4.05222, "time": 0.85111} +{"mode": "train", "epoch": 65, "iter": 2200, "lr": 0.06082, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30344, "top5_acc": 0.56094, "loss_cls": 4.00887, "loss": 4.00887, "time": 0.85084} +{"mode": "train", "epoch": 65, "iter": 2300, "lr": 0.06079, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.30156, "top5_acc": 0.55625, "loss_cls": 4.01486, "loss": 4.01486, "time": 0.8539} +{"mode": "train", "epoch": 65, "iter": 2400, "lr": 0.06076, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29313, "top5_acc": 0.54844, "loss_cls": 4.03048, "loss": 4.03048, "time": 0.85052} +{"mode": "train", "epoch": 65, "iter": 2500, "lr": 0.06074, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30422, "top5_acc": 0.55797, "loss_cls": 4.00135, "loss": 4.00135, "time": 0.85053} +{"mode": "train", "epoch": 65, "iter": 2600, "lr": 0.06071, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29703, "top5_acc": 0.5525, "loss_cls": 4.04429, "loss": 4.04429, "time": 0.85475} +{"mode": "train", "epoch": 65, "iter": 2700, "lr": 0.06068, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.30391, "top5_acc": 0.55328, "loss_cls": 4.04235, "loss": 4.04235, "time": 0.85332} +{"mode": "train", "epoch": 65, "iter": 2800, "lr": 0.06065, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.29828, "top5_acc": 0.56172, "loss_cls": 4.00001, "loss": 4.00001, "time": 0.84937} +{"mode": "train", "epoch": 65, "iter": 2900, "lr": 0.06063, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29406, "top5_acc": 0.54453, "loss_cls": 4.06925, "loss": 4.06925, "time": 0.84874} +{"mode": "train", "epoch": 65, "iter": 3000, "lr": 0.0606, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29234, "top5_acc": 0.54859, "loss_cls": 4.05426, "loss": 4.05426, "time": 0.85247} +{"mode": "train", "epoch": 65, "iter": 3100, "lr": 0.06057, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.2975, "top5_acc": 0.54984, "loss_cls": 4.03416, "loss": 4.03416, "time": 0.84856} +{"mode": "train", "epoch": 65, "iter": 3200, "lr": 0.06055, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30609, "top5_acc": 0.55406, "loss_cls": 4.00833, "loss": 4.00833, "time": 0.84827} +{"mode": "train", "epoch": 65, "iter": 3300, "lr": 0.06052, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29344, "top5_acc": 0.55281, "loss_cls": 4.03081, "loss": 4.03081, "time": 0.85413} +{"mode": "train", "epoch": 65, "iter": 3400, "lr": 0.06049, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29203, "top5_acc": 0.55531, "loss_cls": 4.04352, "loss": 4.04352, "time": 0.85086} +{"mode": "train", "epoch": 65, "iter": 3500, "lr": 0.06046, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30125, "top5_acc": 0.55844, "loss_cls": 4.01343, "loss": 4.01343, "time": 0.85249} +{"mode": "train", "epoch": 65, "iter": 3600, "lr": 0.06044, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29906, "top5_acc": 0.56, "loss_cls": 3.99242, "loss": 3.99242, "time": 0.85473} +{"mode": "train", "epoch": 65, "iter": 3700, "lr": 0.06041, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30047, "top5_acc": 0.54859, "loss_cls": 4.03846, "loss": 4.03846, "time": 0.84965} +{"mode": "val", "epoch": 65, "iter": 309, "lr": 0.0604, "top1_acc": 0.21689, "top5_acc": 0.44654, "mean_class_accuracy": 0.21665} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.06037, "memory": 15990, "data_time": 1.56579, "top1_acc": 0.30062, "top5_acc": 0.56672, "loss_cls": 3.96263, "loss": 3.96263, "time": 2.62214} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.06034, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29922, "top5_acc": 0.55937, "loss_cls": 3.98107, "loss": 3.98107, "time": 0.85509} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.06031, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30438, "top5_acc": 0.56781, "loss_cls": 3.92942, "loss": 3.92942, "time": 0.85646} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.06029, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29563, "top5_acc": 0.55125, "loss_cls": 4.0066, "loss": 4.0066, "time": 0.85605} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.06026, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30828, "top5_acc": 0.57094, "loss_cls": 3.94165, "loss": 3.94165, "time": 0.86142} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.06023, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29766, "top5_acc": 0.55156, "loss_cls": 4.01051, "loss": 4.01051, "time": 0.86245} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.0602, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29984, "top5_acc": 0.55156, "loss_cls": 4.01385, "loss": 4.01385, "time": 0.85722} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.06018, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31094, "top5_acc": 0.56516, "loss_cls": 3.96128, "loss": 3.96128, "time": 0.85459} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.06015, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30672, "top5_acc": 0.5625, "loss_cls": 4.01338, "loss": 4.01338, "time": 0.85994} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.06012, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30812, "top5_acc": 0.55984, "loss_cls": 3.98516, "loss": 3.98516, "time": 0.8558} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.06009, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30812, "top5_acc": 0.56141, "loss_cls": 4.00065, "loss": 4.00065, "time": 0.85527} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.06007, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29469, "top5_acc": 0.54641, "loss_cls": 4.06565, "loss": 4.06565, "time": 0.85481} +{"mode": "train", "epoch": 66, "iter": 1300, "lr": 0.06004, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29563, "top5_acc": 0.555, "loss_cls": 4.0267, "loss": 4.0267, "time": 0.85468} +{"mode": "train", "epoch": 66, "iter": 1400, "lr": 0.06001, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.30516, "top5_acc": 0.55172, "loss_cls": 4.00831, "loss": 4.00831, "time": 0.85143} +{"mode": "train", "epoch": 66, "iter": 1500, "lr": 0.05999, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29781, "top5_acc": 0.54828, "loss_cls": 4.03403, "loss": 4.03403, "time": 0.85132} +{"mode": "train", "epoch": 66, "iter": 1600, "lr": 0.05996, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.29984, "top5_acc": 0.55312, "loss_cls": 4.04199, "loss": 4.04199, "time": 0.85007} +{"mode": "train", "epoch": 66, "iter": 1700, "lr": 0.05993, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29688, "top5_acc": 0.54891, "loss_cls": 4.06306, "loss": 4.06306, "time": 0.85119} +{"mode": "train", "epoch": 66, "iter": 1800, "lr": 0.0599, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30422, "top5_acc": 0.55859, "loss_cls": 4.00085, "loss": 4.00085, "time": 0.8531} +{"mode": "train", "epoch": 66, "iter": 1900, "lr": 0.05988, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29563, "top5_acc": 0.55422, "loss_cls": 4.0113, "loss": 4.0113, "time": 0.84695} +{"mode": "train", "epoch": 66, "iter": 2000, "lr": 0.05985, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30094, "top5_acc": 0.55562, "loss_cls": 3.99999, "loss": 3.99999, "time": 0.85001} +{"mode": "train", "epoch": 66, "iter": 2100, "lr": 0.05982, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29828, "top5_acc": 0.55344, "loss_cls": 4.01265, "loss": 4.01265, "time": 0.85781} +{"mode": "train", "epoch": 66, "iter": 2200, "lr": 0.05979, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.28922, "top5_acc": 0.555, "loss_cls": 4.0485, "loss": 4.0485, "time": 0.85426} +{"mode": "train", "epoch": 66, "iter": 2300, "lr": 0.05977, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.29625, "top5_acc": 0.55781, "loss_cls": 4.02864, "loss": 4.02864, "time": 0.85291} +{"mode": "train", "epoch": 66, "iter": 2400, "lr": 0.05974, "memory": 15990, "data_time": 0.00074, "top1_acc": 0.28641, "top5_acc": 0.54625, "loss_cls": 4.04404, "loss": 4.04404, "time": 0.85425} +{"mode": "train", "epoch": 66, "iter": 2500, "lr": 0.05971, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.2875, "top5_acc": 0.55328, "loss_cls": 4.02798, "loss": 4.02798, "time": 0.85175} +{"mode": "train", "epoch": 66, "iter": 2600, "lr": 0.05968, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29016, "top5_acc": 0.55391, "loss_cls": 4.03847, "loss": 4.03847, "time": 0.85579} +{"mode": "train", "epoch": 66, "iter": 2700, "lr": 0.05966, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29938, "top5_acc": 0.55422, "loss_cls": 4.02834, "loss": 4.02834, "time": 0.85339} +{"mode": "train", "epoch": 66, "iter": 2800, "lr": 0.05963, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.28938, "top5_acc": 0.54422, "loss_cls": 4.0739, "loss": 4.0739, "time": 0.85239} +{"mode": "train", "epoch": 66, "iter": 2900, "lr": 0.0596, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29656, "top5_acc": 0.56016, "loss_cls": 4.02832, "loss": 4.02832, "time": 0.85537} +{"mode": "train", "epoch": 66, "iter": 3000, "lr": 0.05957, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30047, "top5_acc": 0.55297, "loss_cls": 4.00137, "loss": 4.00137, "time": 0.85542} +{"mode": "train", "epoch": 66, "iter": 3100, "lr": 0.05955, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30734, "top5_acc": 0.56156, "loss_cls": 3.99027, "loss": 3.99027, "time": 0.85407} +{"mode": "train", "epoch": 66, "iter": 3200, "lr": 0.05952, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30969, "top5_acc": 0.56297, "loss_cls": 3.96536, "loss": 3.96536, "time": 0.85391} +{"mode": "train", "epoch": 66, "iter": 3300, "lr": 0.05949, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.295, "top5_acc": 0.55625, "loss_cls": 4.01551, "loss": 4.01551, "time": 0.85298} +{"mode": "train", "epoch": 66, "iter": 3400, "lr": 0.05946, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30094, "top5_acc": 0.54797, "loss_cls": 4.02453, "loss": 4.02453, "time": 0.8573} +{"mode": "train", "epoch": 66, "iter": 3500, "lr": 0.05944, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31062, "top5_acc": 0.56094, "loss_cls": 3.98937, "loss": 3.98937, "time": 0.85499} +{"mode": "train", "epoch": 66, "iter": 3600, "lr": 0.05941, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30141, "top5_acc": 0.55516, "loss_cls": 4.01281, "loss": 4.01281, "time": 0.85475} +{"mode": "train", "epoch": 66, "iter": 3700, "lr": 0.05938, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31297, "top5_acc": 0.55406, "loss_cls": 3.98564, "loss": 3.98564, "time": 0.85733} +{"mode": "val", "epoch": 66, "iter": 309, "lr": 0.05937, "top1_acc": 0.2491, "top5_acc": 0.49643, "mean_class_accuracy": 0.24883} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.05934, "memory": 15990, "data_time": 1.51275, "top1_acc": 0.29828, "top5_acc": 0.55922, "loss_cls": 4.02074, "loss": 4.02074, "time": 2.54702} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.05931, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31, "top5_acc": 0.56984, "loss_cls": 3.94729, "loss": 3.94729, "time": 0.85914} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.05929, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29609, "top5_acc": 0.55875, "loss_cls": 4.00678, "loss": 4.00678, "time": 0.8585} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.05926, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.29922, "top5_acc": 0.55344, "loss_cls": 4.02727, "loss": 4.02727, "time": 0.86199} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.05923, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29938, "top5_acc": 0.55484, "loss_cls": 4.0082, "loss": 4.0082, "time": 0.85671} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.0592, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30484, "top5_acc": 0.55937, "loss_cls": 3.98501, "loss": 3.98501, "time": 0.85377} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.05918, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30016, "top5_acc": 0.55672, "loss_cls": 4.00007, "loss": 4.00007, "time": 0.85495} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.05915, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29609, "top5_acc": 0.55437, "loss_cls": 4.01167, "loss": 4.01167, "time": 0.85808} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.05912, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30562, "top5_acc": 0.55734, "loss_cls": 3.96308, "loss": 3.96308, "time": 0.85786} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.05909, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29547, "top5_acc": 0.55094, "loss_cls": 4.03205, "loss": 4.03205, "time": 0.8549} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.05907, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30156, "top5_acc": 0.55594, "loss_cls": 4.00455, "loss": 4.00455, "time": 0.85709} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.05904, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30172, "top5_acc": 0.54812, "loss_cls": 4.0369, "loss": 4.0369, "time": 0.85619} +{"mode": "train", "epoch": 67, "iter": 1300, "lr": 0.05901, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30688, "top5_acc": 0.55953, "loss_cls": 3.98694, "loss": 3.98694, "time": 0.85632} +{"mode": "train", "epoch": 67, "iter": 1400, "lr": 0.05898, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.30266, "top5_acc": 0.56234, "loss_cls": 3.97262, "loss": 3.97262, "time": 0.85299} +{"mode": "train", "epoch": 67, "iter": 1500, "lr": 0.05896, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30781, "top5_acc": 0.56641, "loss_cls": 3.96257, "loss": 3.96257, "time": 0.84819} +{"mode": "train", "epoch": 67, "iter": 1600, "lr": 0.05893, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30703, "top5_acc": 0.56328, "loss_cls": 3.99519, "loss": 3.99519, "time": 0.84915} +{"mode": "train", "epoch": 67, "iter": 1700, "lr": 0.0589, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30219, "top5_acc": 0.55875, "loss_cls": 4.005, "loss": 4.005, "time": 0.85505} +{"mode": "train", "epoch": 67, "iter": 1800, "lr": 0.05887, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30641, "top5_acc": 0.56672, "loss_cls": 3.96639, "loss": 3.96639, "time": 0.85362} +{"mode": "train", "epoch": 67, "iter": 1900, "lr": 0.05885, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29594, "top5_acc": 0.55875, "loss_cls": 4.01433, "loss": 4.01433, "time": 0.8579} +{"mode": "train", "epoch": 67, "iter": 2000, "lr": 0.05882, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30297, "top5_acc": 0.55547, "loss_cls": 4.02968, "loss": 4.02968, "time": 0.85477} +{"mode": "train", "epoch": 67, "iter": 2100, "lr": 0.05879, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29344, "top5_acc": 0.54906, "loss_cls": 4.04044, "loss": 4.04044, "time": 0.84876} +{"mode": "train", "epoch": 67, "iter": 2200, "lr": 0.05876, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30328, "top5_acc": 0.55422, "loss_cls": 4.01531, "loss": 4.01531, "time": 0.84854} +{"mode": "train", "epoch": 67, "iter": 2300, "lr": 0.05874, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30172, "top5_acc": 0.56016, "loss_cls": 3.9838, "loss": 3.9838, "time": 0.851} +{"mode": "train", "epoch": 67, "iter": 2400, "lr": 0.05871, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30281, "top5_acc": 0.56141, "loss_cls": 3.97212, "loss": 3.97212, "time": 0.85302} +{"mode": "train", "epoch": 67, "iter": 2500, "lr": 0.05868, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29188, "top5_acc": 0.54922, "loss_cls": 4.05796, "loss": 4.05796, "time": 0.85165} +{"mode": "train", "epoch": 67, "iter": 2600, "lr": 0.05865, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.29734, "top5_acc": 0.55484, "loss_cls": 4.02181, "loss": 4.02181, "time": 0.85132} +{"mode": "train", "epoch": 67, "iter": 2700, "lr": 0.05863, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30859, "top5_acc": 0.56422, "loss_cls": 3.9757, "loss": 3.9757, "time": 0.85679} +{"mode": "train", "epoch": 67, "iter": 2800, "lr": 0.0586, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30609, "top5_acc": 0.55844, "loss_cls": 3.99673, "loss": 3.99673, "time": 0.84763} +{"mode": "train", "epoch": 67, "iter": 2900, "lr": 0.05857, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29516, "top5_acc": 0.54203, "loss_cls": 4.05232, "loss": 4.05232, "time": 0.85174} +{"mode": "train", "epoch": 67, "iter": 3000, "lr": 0.05854, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31328, "top5_acc": 0.57328, "loss_cls": 3.95342, "loss": 3.95342, "time": 0.84992} +{"mode": "train", "epoch": 67, "iter": 3100, "lr": 0.05852, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.305, "top5_acc": 0.55391, "loss_cls": 3.99225, "loss": 3.99225, "time": 0.85365} +{"mode": "train", "epoch": 67, "iter": 3200, "lr": 0.05849, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31016, "top5_acc": 0.56328, "loss_cls": 3.96272, "loss": 3.96272, "time": 0.84895} +{"mode": "train", "epoch": 67, "iter": 3300, "lr": 0.05846, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31, "top5_acc": 0.56031, "loss_cls": 3.96337, "loss": 3.96337, "time": 0.85136} +{"mode": "train", "epoch": 67, "iter": 3400, "lr": 0.05843, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30125, "top5_acc": 0.55609, "loss_cls": 4.00237, "loss": 4.00237, "time": 0.84528} +{"mode": "train", "epoch": 67, "iter": 3500, "lr": 0.05841, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29281, "top5_acc": 0.54734, "loss_cls": 4.0587, "loss": 4.0587, "time": 0.85086} +{"mode": "train", "epoch": 67, "iter": 3600, "lr": 0.05838, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30078, "top5_acc": 0.56312, "loss_cls": 4.00662, "loss": 4.00662, "time": 0.86012} +{"mode": "train", "epoch": 67, "iter": 3700, "lr": 0.05835, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.305, "top5_acc": 0.55578, "loss_cls": 4.01998, "loss": 4.01998, "time": 0.85539} +{"mode": "val", "epoch": 67, "iter": 309, "lr": 0.05834, "top1_acc": 0.25437, "top5_acc": 0.49921, "mean_class_accuracy": 0.2542} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.05831, "memory": 15990, "data_time": 1.53341, "top1_acc": 0.31828, "top5_acc": 0.57078, "loss_cls": 3.92859, "loss": 3.92859, "time": 2.57205} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.05828, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30875, "top5_acc": 0.56484, "loss_cls": 3.96013, "loss": 3.96013, "time": 0.86152} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.05826, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.3, "top5_acc": 0.55703, "loss_cls": 4.00366, "loss": 4.00366, "time": 0.85877} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.05823, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29188, "top5_acc": 0.54484, "loss_cls": 4.04969, "loss": 4.04969, "time": 0.86321} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.0582, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31641, "top5_acc": 0.56703, "loss_cls": 3.94472, "loss": 3.94472, "time": 0.86349} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.05817, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29344, "top5_acc": 0.55516, "loss_cls": 4.0104, "loss": 4.0104, "time": 0.86465} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.05815, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30328, "top5_acc": 0.55906, "loss_cls": 3.99781, "loss": 3.99781, "time": 0.86125} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.05812, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29828, "top5_acc": 0.5525, "loss_cls": 3.97471, "loss": 3.97471, "time": 0.86853} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.05809, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31125, "top5_acc": 0.56547, "loss_cls": 3.97661, "loss": 3.97661, "time": 0.86413} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.05806, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30547, "top5_acc": 0.56422, "loss_cls": 3.96666, "loss": 3.96666, "time": 0.86416} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.05804, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31047, "top5_acc": 0.56469, "loss_cls": 3.95954, "loss": 3.95954, "time": 0.86567} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.05801, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30125, "top5_acc": 0.55672, "loss_cls": 4.01005, "loss": 4.01005, "time": 0.86839} +{"mode": "train", "epoch": 68, "iter": 1300, "lr": 0.05798, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.2975, "top5_acc": 0.55719, "loss_cls": 4.02278, "loss": 4.02278, "time": 0.8621} +{"mode": "train", "epoch": 68, "iter": 1400, "lr": 0.05795, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29812, "top5_acc": 0.55094, "loss_cls": 4.01098, "loss": 4.01098, "time": 0.85659} +{"mode": "train", "epoch": 68, "iter": 1500, "lr": 0.05792, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30859, "top5_acc": 0.56906, "loss_cls": 3.96461, "loss": 3.96461, "time": 0.85154} +{"mode": "train", "epoch": 68, "iter": 1600, "lr": 0.0579, "memory": 15990, "data_time": 0.00079, "top1_acc": 0.29766, "top5_acc": 0.55531, "loss_cls": 3.98697, "loss": 3.98697, "time": 0.85364} +{"mode": "train", "epoch": 68, "iter": 1700, "lr": 0.05787, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29891, "top5_acc": 0.54922, "loss_cls": 4.01653, "loss": 4.01653, "time": 0.85794} +{"mode": "train", "epoch": 68, "iter": 1800, "lr": 0.05784, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31109, "top5_acc": 0.56094, "loss_cls": 3.96871, "loss": 3.96871, "time": 0.85625} +{"mode": "train", "epoch": 68, "iter": 1900, "lr": 0.05781, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30469, "top5_acc": 0.55625, "loss_cls": 3.99155, "loss": 3.99155, "time": 0.85431} +{"mode": "train", "epoch": 68, "iter": 2000, "lr": 0.05779, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31078, "top5_acc": 0.56141, "loss_cls": 3.9622, "loss": 3.9622, "time": 0.85852} +{"mode": "train", "epoch": 68, "iter": 2100, "lr": 0.05776, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31734, "top5_acc": 0.57156, "loss_cls": 3.91416, "loss": 3.91416, "time": 0.85187} +{"mode": "train", "epoch": 68, "iter": 2200, "lr": 0.05773, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30109, "top5_acc": 0.56266, "loss_cls": 3.95537, "loss": 3.95537, "time": 0.8481} +{"mode": "train", "epoch": 68, "iter": 2300, "lr": 0.0577, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30766, "top5_acc": 0.56484, "loss_cls": 3.96317, "loss": 3.96317, "time": 0.85366} +{"mode": "train", "epoch": 68, "iter": 2400, "lr": 0.05768, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30281, "top5_acc": 0.56078, "loss_cls": 3.99361, "loss": 3.99361, "time": 0.8549} +{"mode": "train", "epoch": 68, "iter": 2500, "lr": 0.05765, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30516, "top5_acc": 0.55812, "loss_cls": 3.99683, "loss": 3.99683, "time": 0.85221} +{"mode": "train", "epoch": 68, "iter": 2600, "lr": 0.05762, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.30969, "top5_acc": 0.56781, "loss_cls": 3.97322, "loss": 3.97322, "time": 0.85677} +{"mode": "train", "epoch": 68, "iter": 2700, "lr": 0.05759, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.29922, "top5_acc": 0.55797, "loss_cls": 4.00767, "loss": 4.00767, "time": 0.85328} +{"mode": "train", "epoch": 68, "iter": 2800, "lr": 0.05757, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29328, "top5_acc": 0.55484, "loss_cls": 4.01722, "loss": 4.01722, "time": 0.85447} +{"mode": "train", "epoch": 68, "iter": 2900, "lr": 0.05754, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31031, "top5_acc": 0.55953, "loss_cls": 3.98884, "loss": 3.98884, "time": 0.85057} +{"mode": "train", "epoch": 68, "iter": 3000, "lr": 0.05751, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30188, "top5_acc": 0.54891, "loss_cls": 4.03254, "loss": 4.03254, "time": 0.85146} +{"mode": "train", "epoch": 68, "iter": 3100, "lr": 0.05748, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30469, "top5_acc": 0.56094, "loss_cls": 4.02439, "loss": 4.02439, "time": 0.85638} +{"mode": "train", "epoch": 68, "iter": 3200, "lr": 0.05746, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29516, "top5_acc": 0.545, "loss_cls": 4.04577, "loss": 4.04577, "time": 0.84989} +{"mode": "train", "epoch": 68, "iter": 3300, "lr": 0.05743, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29344, "top5_acc": 0.54641, "loss_cls": 4.03904, "loss": 4.03904, "time": 0.85201} +{"mode": "train", "epoch": 68, "iter": 3400, "lr": 0.0574, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30797, "top5_acc": 0.5675, "loss_cls": 3.99499, "loss": 3.99499, "time": 0.85319} +{"mode": "train", "epoch": 68, "iter": 3500, "lr": 0.05737, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30703, "top5_acc": 0.56109, "loss_cls": 3.99479, "loss": 3.99479, "time": 0.85345} +{"mode": "train", "epoch": 68, "iter": 3600, "lr": 0.05734, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30312, "top5_acc": 0.56016, "loss_cls": 4.00905, "loss": 4.00905, "time": 0.85552} +{"mode": "train", "epoch": 68, "iter": 3700, "lr": 0.05732, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29281, "top5_acc": 0.55203, "loss_cls": 4.03211, "loss": 4.03211, "time": 0.84949} +{"mode": "val", "epoch": 68, "iter": 309, "lr": 0.0573, "top1_acc": 0.24009, "top5_acc": 0.48706, "mean_class_accuracy": 0.23995} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.05728, "memory": 15990, "data_time": 1.60189, "top1_acc": 0.30438, "top5_acc": 0.5725, "loss_cls": 3.94515, "loss": 3.94515, "time": 2.63283} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.05725, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31203, "top5_acc": 0.56719, "loss_cls": 3.95093, "loss": 3.95093, "time": 0.85453} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.05722, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30781, "top5_acc": 0.55875, "loss_cls": 3.97655, "loss": 3.97655, "time": 0.85773} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.05719, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30281, "top5_acc": 0.56578, "loss_cls": 3.97475, "loss": 3.97475, "time": 0.85909} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.05717, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31062, "top5_acc": 0.56188, "loss_cls": 3.94922, "loss": 3.94922, "time": 0.85695} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.05714, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.31062, "top5_acc": 0.56328, "loss_cls": 3.941, "loss": 3.941, "time": 0.85507} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.05711, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30547, "top5_acc": 0.55875, "loss_cls": 3.98212, "loss": 3.98212, "time": 0.85701} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.05708, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30359, "top5_acc": 0.56531, "loss_cls": 3.97698, "loss": 3.97698, "time": 0.85613} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.05706, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.30516, "top5_acc": 0.55562, "loss_cls": 3.99049, "loss": 3.99049, "time": 0.85516} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.05703, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30984, "top5_acc": 0.56703, "loss_cls": 3.93603, "loss": 3.93603, "time": 0.85766} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.057, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30984, "top5_acc": 0.55734, "loss_cls": 3.97218, "loss": 3.97218, "time": 0.85797} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.05697, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30203, "top5_acc": 0.55969, "loss_cls": 4.01032, "loss": 4.01032, "time": 0.85836} +{"mode": "train", "epoch": 69, "iter": 1300, "lr": 0.05694, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32047, "top5_acc": 0.57375, "loss_cls": 3.9094, "loss": 3.9094, "time": 0.85179} +{"mode": "train", "epoch": 69, "iter": 1400, "lr": 0.05692, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31266, "top5_acc": 0.56437, "loss_cls": 3.93984, "loss": 3.93984, "time": 0.86015} +{"mode": "train", "epoch": 69, "iter": 1500, "lr": 0.05689, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30281, "top5_acc": 0.55578, "loss_cls": 3.99621, "loss": 3.99621, "time": 0.85403} +{"mode": "train", "epoch": 69, "iter": 1600, "lr": 0.05686, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31141, "top5_acc": 0.55875, "loss_cls": 3.96755, "loss": 3.96755, "time": 0.8518} +{"mode": "train", "epoch": 69, "iter": 1700, "lr": 0.05683, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30578, "top5_acc": 0.55719, "loss_cls": 4.00667, "loss": 4.00667, "time": 0.85329} +{"mode": "train", "epoch": 69, "iter": 1800, "lr": 0.05681, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30812, "top5_acc": 0.55484, "loss_cls": 3.99762, "loss": 3.99762, "time": 0.85185} +{"mode": "train", "epoch": 69, "iter": 1900, "lr": 0.05678, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29969, "top5_acc": 0.54984, "loss_cls": 4.0341, "loss": 4.0341, "time": 0.84645} +{"mode": "train", "epoch": 69, "iter": 2000, "lr": 0.05675, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30562, "top5_acc": 0.55734, "loss_cls": 4.00499, "loss": 4.00499, "time": 0.84909} +{"mode": "train", "epoch": 69, "iter": 2100, "lr": 0.05672, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31797, "top5_acc": 0.56109, "loss_cls": 3.98188, "loss": 3.98188, "time": 0.85198} +{"mode": "train", "epoch": 69, "iter": 2200, "lr": 0.0567, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30641, "top5_acc": 0.56703, "loss_cls": 3.98868, "loss": 3.98868, "time": 0.85189} +{"mode": "train", "epoch": 69, "iter": 2300, "lr": 0.05667, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30625, "top5_acc": 0.56156, "loss_cls": 3.9971, "loss": 3.9971, "time": 0.85488} +{"mode": "train", "epoch": 69, "iter": 2400, "lr": 0.05664, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.30641, "top5_acc": 0.55984, "loss_cls": 3.97917, "loss": 3.97917, "time": 0.864} +{"mode": "train", "epoch": 69, "iter": 2500, "lr": 0.05661, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.29266, "top5_acc": 0.55516, "loss_cls": 4.00361, "loss": 4.00361, "time": 0.85591} +{"mode": "train", "epoch": 69, "iter": 2600, "lr": 0.05658, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30609, "top5_acc": 0.55922, "loss_cls": 4.0196, "loss": 4.0196, "time": 0.84937} +{"mode": "train", "epoch": 69, "iter": 2700, "lr": 0.05656, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29891, "top5_acc": 0.55406, "loss_cls": 4.03726, "loss": 4.03726, "time": 0.84694} +{"mode": "train", "epoch": 69, "iter": 2800, "lr": 0.05653, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30922, "top5_acc": 0.56641, "loss_cls": 3.957, "loss": 3.957, "time": 0.85079} +{"mode": "train", "epoch": 69, "iter": 2900, "lr": 0.0565, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.29938, "top5_acc": 0.54984, "loss_cls": 4.0258, "loss": 4.0258, "time": 0.84829} +{"mode": "train", "epoch": 69, "iter": 3000, "lr": 0.05647, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30453, "top5_acc": 0.56344, "loss_cls": 3.98453, "loss": 3.98453, "time": 0.84995} +{"mode": "train", "epoch": 69, "iter": 3100, "lr": 0.05645, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30578, "top5_acc": 0.5625, "loss_cls": 3.95955, "loss": 3.95955, "time": 0.84828} +{"mode": "train", "epoch": 69, "iter": 3200, "lr": 0.05642, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.30297, "top5_acc": 0.56344, "loss_cls": 3.98106, "loss": 3.98106, "time": 0.85635} +{"mode": "train", "epoch": 69, "iter": 3300, "lr": 0.05639, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30797, "top5_acc": 0.56094, "loss_cls": 3.97842, "loss": 3.97842, "time": 0.8483} +{"mode": "train", "epoch": 69, "iter": 3400, "lr": 0.05636, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29625, "top5_acc": 0.55094, "loss_cls": 4.01347, "loss": 4.01347, "time": 0.85485} +{"mode": "train", "epoch": 69, "iter": 3500, "lr": 0.05634, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30484, "top5_acc": 0.56281, "loss_cls": 3.96129, "loss": 3.96129, "time": 0.84998} +{"mode": "train", "epoch": 69, "iter": 3600, "lr": 0.05631, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31375, "top5_acc": 0.56125, "loss_cls": 3.96334, "loss": 3.96334, "time": 0.85146} +{"mode": "train", "epoch": 69, "iter": 3700, "lr": 0.05628, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31344, "top5_acc": 0.56609, "loss_cls": 3.9373, "loss": 3.9373, "time": 0.84784} +{"mode": "val", "epoch": 69, "iter": 309, "lr": 0.05627, "top1_acc": 0.24084, "top5_acc": 0.47794, "mean_class_accuracy": 0.24073} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.05624, "memory": 15990, "data_time": 1.59587, "top1_acc": 0.31531, "top5_acc": 0.57359, "loss_cls": 3.91944, "loss": 3.91944, "time": 2.63391} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.05621, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30672, "top5_acc": 0.56031, "loss_cls": 3.94202, "loss": 3.94202, "time": 0.85416} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.05618, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31797, "top5_acc": 0.57938, "loss_cls": 3.91099, "loss": 3.91099, "time": 0.85192} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.05616, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30984, "top5_acc": 0.56578, "loss_cls": 3.94652, "loss": 3.94652, "time": 0.85576} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.05613, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30781, "top5_acc": 0.56094, "loss_cls": 3.95926, "loss": 3.95926, "time": 0.85429} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.0561, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31719, "top5_acc": 0.56766, "loss_cls": 3.92369, "loss": 3.92369, "time": 0.8493} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.05607, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30656, "top5_acc": 0.56234, "loss_cls": 3.97187, "loss": 3.97187, "time": 0.85152} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.05605, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30766, "top5_acc": 0.55922, "loss_cls": 3.96834, "loss": 3.96834, "time": 0.85008} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.05602, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31094, "top5_acc": 0.56797, "loss_cls": 3.93423, "loss": 3.93423, "time": 0.84816} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.05599, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30297, "top5_acc": 0.56859, "loss_cls": 3.97096, "loss": 3.97096, "time": 0.85339} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.05596, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31031, "top5_acc": 0.56078, "loss_cls": 3.9633, "loss": 3.9633, "time": 0.85048} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.05593, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31484, "top5_acc": 0.57141, "loss_cls": 3.94386, "loss": 3.94386, "time": 0.85614} +{"mode": "train", "epoch": 70, "iter": 1300, "lr": 0.05591, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.29219, "top5_acc": 0.55594, "loss_cls": 4.03401, "loss": 4.03401, "time": 0.85172} +{"mode": "train", "epoch": 70, "iter": 1400, "lr": 0.05588, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.30578, "top5_acc": 0.56047, "loss_cls": 3.97754, "loss": 3.97754, "time": 0.84871} +{"mode": "train", "epoch": 70, "iter": 1500, "lr": 0.05585, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.30109, "top5_acc": 0.55984, "loss_cls": 4.01301, "loss": 4.01301, "time": 0.84871} +{"mode": "train", "epoch": 70, "iter": 1600, "lr": 0.05582, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30078, "top5_acc": 0.55359, "loss_cls": 3.999, "loss": 3.999, "time": 0.85435} +{"mode": "train", "epoch": 70, "iter": 1700, "lr": 0.0558, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30109, "top5_acc": 0.56469, "loss_cls": 3.98939, "loss": 3.98939, "time": 0.84968} +{"mode": "train", "epoch": 70, "iter": 1800, "lr": 0.05577, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.29906, "top5_acc": 0.56141, "loss_cls": 3.99781, "loss": 3.99781, "time": 0.85387} +{"mode": "train", "epoch": 70, "iter": 1900, "lr": 0.05574, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31562, "top5_acc": 0.56969, "loss_cls": 3.94989, "loss": 3.94989, "time": 0.84911} +{"mode": "train", "epoch": 70, "iter": 2000, "lr": 0.05571, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29828, "top5_acc": 0.55641, "loss_cls": 3.9938, "loss": 3.9938, "time": 0.85918} +{"mode": "train", "epoch": 70, "iter": 2100, "lr": 0.05568, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.30797, "top5_acc": 0.56766, "loss_cls": 3.94545, "loss": 3.94545, "time": 0.85628} +{"mode": "train", "epoch": 70, "iter": 2200, "lr": 0.05566, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.31266, "top5_acc": 0.57484, "loss_cls": 3.91392, "loss": 3.91392, "time": 0.85109} +{"mode": "train", "epoch": 70, "iter": 2300, "lr": 0.05563, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29953, "top5_acc": 0.55109, "loss_cls": 4.00537, "loss": 4.00537, "time": 0.85081} +{"mode": "train", "epoch": 70, "iter": 2400, "lr": 0.0556, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30234, "top5_acc": 0.56016, "loss_cls": 3.99032, "loss": 3.99032, "time": 0.84787} +{"mode": "train", "epoch": 70, "iter": 2500, "lr": 0.05557, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.29297, "top5_acc": 0.54906, "loss_cls": 4.04234, "loss": 4.04234, "time": 0.85435} +{"mode": "train", "epoch": 70, "iter": 2600, "lr": 0.05555, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30875, "top5_acc": 0.56734, "loss_cls": 3.95924, "loss": 3.95924, "time": 0.85424} +{"mode": "train", "epoch": 70, "iter": 2700, "lr": 0.05552, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30172, "top5_acc": 0.5575, "loss_cls": 4.00157, "loss": 4.00157, "time": 0.85695} +{"mode": "train", "epoch": 70, "iter": 2800, "lr": 0.05549, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30188, "top5_acc": 0.55109, "loss_cls": 4.00865, "loss": 4.00865, "time": 0.85674} +{"mode": "train", "epoch": 70, "iter": 2900, "lr": 0.05546, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.30344, "top5_acc": 0.55984, "loss_cls": 4.01207, "loss": 4.01207, "time": 0.86022} +{"mode": "train", "epoch": 70, "iter": 3000, "lr": 0.05543, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30969, "top5_acc": 0.56547, "loss_cls": 3.95352, "loss": 3.95352, "time": 0.86008} +{"mode": "train", "epoch": 70, "iter": 3100, "lr": 0.05541, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.30109, "top5_acc": 0.55203, "loss_cls": 4.02907, "loss": 4.02907, "time": 0.85908} +{"mode": "train", "epoch": 70, "iter": 3200, "lr": 0.05538, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.31594, "top5_acc": 0.56828, "loss_cls": 3.94222, "loss": 3.94222, "time": 0.85839} +{"mode": "train", "epoch": 70, "iter": 3300, "lr": 0.05535, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30609, "top5_acc": 0.55672, "loss_cls": 3.97989, "loss": 3.97989, "time": 0.85395} +{"mode": "train", "epoch": 70, "iter": 3400, "lr": 0.05532, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30297, "top5_acc": 0.56172, "loss_cls": 3.97422, "loss": 3.97422, "time": 0.86058} +{"mode": "train", "epoch": 70, "iter": 3500, "lr": 0.0553, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31922, "top5_acc": 0.56422, "loss_cls": 3.93804, "loss": 3.93804, "time": 0.86616} +{"mode": "train", "epoch": 70, "iter": 3600, "lr": 0.05527, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.29781, "top5_acc": 0.55984, "loss_cls": 3.97964, "loss": 3.97964, "time": 0.87214} +{"mode": "train", "epoch": 70, "iter": 3700, "lr": 0.05524, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30328, "top5_acc": 0.56266, "loss_cls": 3.97406, "loss": 3.97406, "time": 0.86176} +{"mode": "val", "epoch": 70, "iter": 309, "lr": 0.05523, "top1_acc": 0.23882, "top5_acc": 0.47566, "mean_class_accuracy": 0.23854} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.0552, "memory": 15990, "data_time": 1.55704, "top1_acc": 0.31312, "top5_acc": 0.57281, "loss_cls": 3.9144, "loss": 3.9144, "time": 2.60987} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.05517, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30406, "top5_acc": 0.56344, "loss_cls": 3.94851, "loss": 3.94851, "time": 0.85441} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.05514, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30594, "top5_acc": 0.57188, "loss_cls": 3.93806, "loss": 3.93806, "time": 0.85567} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.05512, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30609, "top5_acc": 0.56688, "loss_cls": 3.98847, "loss": 3.98847, "time": 0.85656} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.05509, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31156, "top5_acc": 0.56141, "loss_cls": 3.95685, "loss": 3.95685, "time": 0.85841} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.05506, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32078, "top5_acc": 0.565, "loss_cls": 3.92394, "loss": 3.92394, "time": 0.86067} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.05503, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30578, "top5_acc": 0.56578, "loss_cls": 3.97412, "loss": 3.97412, "time": 0.86219} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.055, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.30953, "top5_acc": 0.56766, "loss_cls": 3.91816, "loss": 3.91816, "time": 0.85841} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.05498, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30266, "top5_acc": 0.55797, "loss_cls": 4.00339, "loss": 4.00339, "time": 0.85361} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.05495, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30172, "top5_acc": 0.55688, "loss_cls": 3.99076, "loss": 3.99076, "time": 0.85559} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.05492, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31484, "top5_acc": 0.57109, "loss_cls": 3.93009, "loss": 3.93009, "time": 0.86027} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.05489, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.31234, "top5_acc": 0.56563, "loss_cls": 3.93494, "loss": 3.93494, "time": 0.85792} +{"mode": "train", "epoch": 71, "iter": 1300, "lr": 0.05487, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31156, "top5_acc": 0.56516, "loss_cls": 3.94366, "loss": 3.94366, "time": 0.85677} +{"mode": "train", "epoch": 71, "iter": 1400, "lr": 0.05484, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.315, "top5_acc": 0.56391, "loss_cls": 3.94503, "loss": 3.94503, "time": 0.85776} +{"mode": "train", "epoch": 71, "iter": 1500, "lr": 0.05481, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30688, "top5_acc": 0.56094, "loss_cls": 3.97874, "loss": 3.97874, "time": 0.84713} +{"mode": "train", "epoch": 71, "iter": 1600, "lr": 0.05478, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.29891, "top5_acc": 0.5575, "loss_cls": 4.00399, "loss": 4.00399, "time": 0.85157} +{"mode": "train", "epoch": 71, "iter": 1700, "lr": 0.05475, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29812, "top5_acc": 0.57016, "loss_cls": 3.96888, "loss": 3.96888, "time": 0.85058} +{"mode": "train", "epoch": 71, "iter": 1800, "lr": 0.05473, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31906, "top5_acc": 0.56875, "loss_cls": 3.92692, "loss": 3.92692, "time": 0.85026} +{"mode": "train", "epoch": 71, "iter": 1900, "lr": 0.0547, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30906, "top5_acc": 0.57359, "loss_cls": 3.91703, "loss": 3.91703, "time": 0.8542} +{"mode": "train", "epoch": 71, "iter": 2000, "lr": 0.05467, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30984, "top5_acc": 0.5725, "loss_cls": 3.92094, "loss": 3.92094, "time": 0.85461} +{"mode": "train", "epoch": 71, "iter": 2100, "lr": 0.05464, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.30047, "top5_acc": 0.5625, "loss_cls": 4.00291, "loss": 4.00291, "time": 0.85744} +{"mode": "train", "epoch": 71, "iter": 2200, "lr": 0.05461, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30469, "top5_acc": 0.54984, "loss_cls": 4.02763, "loss": 4.02763, "time": 0.85066} +{"mode": "train", "epoch": 71, "iter": 2300, "lr": 0.05459, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.305, "top5_acc": 0.55594, "loss_cls": 3.99841, "loss": 3.99841, "time": 0.85216} +{"mode": "train", "epoch": 71, "iter": 2400, "lr": 0.05456, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29797, "top5_acc": 0.56109, "loss_cls": 3.96925, "loss": 3.96925, "time": 0.84944} +{"mode": "train", "epoch": 71, "iter": 2500, "lr": 0.05453, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.30641, "top5_acc": 0.57, "loss_cls": 3.94585, "loss": 3.94585, "time": 0.85654} +{"mode": "train", "epoch": 71, "iter": 2600, "lr": 0.0545, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31, "top5_acc": 0.57781, "loss_cls": 3.92097, "loss": 3.92097, "time": 0.84953} +{"mode": "train", "epoch": 71, "iter": 2700, "lr": 0.05448, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31109, "top5_acc": 0.56328, "loss_cls": 3.94789, "loss": 3.94789, "time": 0.85094} +{"mode": "train", "epoch": 71, "iter": 2800, "lr": 0.05445, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31188, "top5_acc": 0.55906, "loss_cls": 3.98304, "loss": 3.98304, "time": 0.85364} +{"mode": "train", "epoch": 71, "iter": 2900, "lr": 0.05442, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31641, "top5_acc": 0.56422, "loss_cls": 3.96243, "loss": 3.96243, "time": 0.84952} +{"mode": "train", "epoch": 71, "iter": 3000, "lr": 0.05439, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31297, "top5_acc": 0.57359, "loss_cls": 3.93377, "loss": 3.93377, "time": 0.85556} +{"mode": "train", "epoch": 71, "iter": 3100, "lr": 0.05436, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30203, "top5_acc": 0.56531, "loss_cls": 3.96821, "loss": 3.96821, "time": 0.85017} +{"mode": "train", "epoch": 71, "iter": 3200, "lr": 0.05434, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31438, "top5_acc": 0.56016, "loss_cls": 3.95276, "loss": 3.95276, "time": 0.85262} +{"mode": "train", "epoch": 71, "iter": 3300, "lr": 0.05431, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31406, "top5_acc": 0.56688, "loss_cls": 3.96121, "loss": 3.96121, "time": 0.85297} +{"mode": "train", "epoch": 71, "iter": 3400, "lr": 0.05428, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31219, "top5_acc": 0.56688, "loss_cls": 3.97297, "loss": 3.97297, "time": 0.86343} +{"mode": "train", "epoch": 71, "iter": 3500, "lr": 0.05425, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31, "top5_acc": 0.56453, "loss_cls": 3.95784, "loss": 3.95784, "time": 0.85394} +{"mode": "train", "epoch": 71, "iter": 3600, "lr": 0.05422, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30516, "top5_acc": 0.55703, "loss_cls": 3.98158, "loss": 3.98158, "time": 0.85325} +{"mode": "train", "epoch": 71, "iter": 3700, "lr": 0.0542, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30203, "top5_acc": 0.57156, "loss_cls": 3.97751, "loss": 3.97751, "time": 0.8527} +{"mode": "val", "epoch": 71, "iter": 309, "lr": 0.05418, "top1_acc": 0.25371, "top5_acc": 0.4938, "mean_class_accuracy": 0.2534} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.05416, "memory": 15990, "data_time": 1.59058, "top1_acc": 0.32469, "top5_acc": 0.58422, "loss_cls": 3.86567, "loss": 3.86567, "time": 2.64522} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.05413, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30562, "top5_acc": 0.56219, "loss_cls": 3.95976, "loss": 3.95976, "time": 0.85977} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.0541, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.29625, "top5_acc": 0.56469, "loss_cls": 3.97843, "loss": 3.97843, "time": 0.86224} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.05407, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30891, "top5_acc": 0.56875, "loss_cls": 3.9232, "loss": 3.9232, "time": 0.86405} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.05404, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.30625, "top5_acc": 0.56109, "loss_cls": 3.96858, "loss": 3.96858, "time": 0.86023} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.05402, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.31453, "top5_acc": 0.56484, "loss_cls": 3.93516, "loss": 3.93516, "time": 0.85736} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.05399, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32234, "top5_acc": 0.56891, "loss_cls": 3.91623, "loss": 3.91623, "time": 0.86513} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.05396, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29953, "top5_acc": 0.5625, "loss_cls": 4.01522, "loss": 4.01522, "time": 0.8614} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.05393, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31203, "top5_acc": 0.56734, "loss_cls": 3.94353, "loss": 3.94353, "time": 0.86282} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.05391, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31453, "top5_acc": 0.56437, "loss_cls": 3.95135, "loss": 3.95135, "time": 0.86417} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.05388, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32031, "top5_acc": 0.57297, "loss_cls": 3.94284, "loss": 3.94284, "time": 0.86566} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.05385, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31219, "top5_acc": 0.56734, "loss_cls": 3.94396, "loss": 3.94396, "time": 0.85485} +{"mode": "train", "epoch": 72, "iter": 1300, "lr": 0.05382, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30047, "top5_acc": 0.56359, "loss_cls": 3.96578, "loss": 3.96578, "time": 0.85651} +{"mode": "train", "epoch": 72, "iter": 1400, "lr": 0.05379, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31391, "top5_acc": 0.56547, "loss_cls": 3.92849, "loss": 3.92849, "time": 0.84652} +{"mode": "train", "epoch": 72, "iter": 1500, "lr": 0.05377, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30953, "top5_acc": 0.5625, "loss_cls": 3.96972, "loss": 3.96972, "time": 0.8592} +{"mode": "train", "epoch": 72, "iter": 1600, "lr": 0.05374, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3175, "top5_acc": 0.565, "loss_cls": 3.93481, "loss": 3.93481, "time": 0.86192} +{"mode": "train", "epoch": 72, "iter": 1700, "lr": 0.05371, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33047, "top5_acc": 0.57969, "loss_cls": 3.83238, "loss": 3.83238, "time": 0.85224} +{"mode": "train", "epoch": 72, "iter": 1800, "lr": 0.05368, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31203, "top5_acc": 0.56156, "loss_cls": 3.9727, "loss": 3.9727, "time": 0.86023} +{"mode": "train", "epoch": 72, "iter": 1900, "lr": 0.05365, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30859, "top5_acc": 0.57516, "loss_cls": 3.92616, "loss": 3.92616, "time": 0.85845} +{"mode": "train", "epoch": 72, "iter": 2000, "lr": 0.05363, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.32844, "top5_acc": 0.57984, "loss_cls": 3.87035, "loss": 3.87035, "time": 0.85444} +{"mode": "train", "epoch": 72, "iter": 2100, "lr": 0.0536, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30312, "top5_acc": 0.55984, "loss_cls": 3.99219, "loss": 3.99219, "time": 0.85104} +{"mode": "train", "epoch": 72, "iter": 2200, "lr": 0.05357, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30359, "top5_acc": 0.55766, "loss_cls": 4.00167, "loss": 4.00167, "time": 0.84904} +{"mode": "train", "epoch": 72, "iter": 2300, "lr": 0.05354, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31625, "top5_acc": 0.56953, "loss_cls": 3.93354, "loss": 3.93354, "time": 0.85337} +{"mode": "train", "epoch": 72, "iter": 2400, "lr": 0.05352, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31094, "top5_acc": 0.56031, "loss_cls": 3.97768, "loss": 3.97768, "time": 0.84819} +{"mode": "train", "epoch": 72, "iter": 2500, "lr": 0.05349, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.30641, "top5_acc": 0.56922, "loss_cls": 3.93673, "loss": 3.93673, "time": 0.85578} +{"mode": "train", "epoch": 72, "iter": 2600, "lr": 0.05346, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30094, "top5_acc": 0.56641, "loss_cls": 3.96314, "loss": 3.96314, "time": 0.85343} +{"mode": "train", "epoch": 72, "iter": 2700, "lr": 0.05343, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31078, "top5_acc": 0.56484, "loss_cls": 3.96431, "loss": 3.96431, "time": 0.85404} +{"mode": "train", "epoch": 72, "iter": 2800, "lr": 0.0534, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30641, "top5_acc": 0.55875, "loss_cls": 3.99711, "loss": 3.99711, "time": 0.85278} +{"mode": "train", "epoch": 72, "iter": 2900, "lr": 0.05338, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30328, "top5_acc": 0.56141, "loss_cls": 3.9776, "loss": 3.9776, "time": 0.85042} +{"mode": "train", "epoch": 72, "iter": 3000, "lr": 0.05335, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31391, "top5_acc": 0.57734, "loss_cls": 3.9079, "loss": 3.9079, "time": 0.85212} +{"mode": "train", "epoch": 72, "iter": 3100, "lr": 0.05332, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30406, "top5_acc": 0.55406, "loss_cls": 3.99605, "loss": 3.99605, "time": 0.85279} +{"mode": "train", "epoch": 72, "iter": 3200, "lr": 0.05329, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30531, "top5_acc": 0.57156, "loss_cls": 3.95059, "loss": 3.95059, "time": 0.85321} +{"mode": "train", "epoch": 72, "iter": 3300, "lr": 0.05326, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31047, "top5_acc": 0.56734, "loss_cls": 3.94723, "loss": 3.94723, "time": 0.85017} +{"mode": "train", "epoch": 72, "iter": 3400, "lr": 0.05324, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30562, "top5_acc": 0.56719, "loss_cls": 3.95193, "loss": 3.95193, "time": 0.85318} +{"mode": "train", "epoch": 72, "iter": 3500, "lr": 0.05321, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3, "top5_acc": 0.55516, "loss_cls": 3.98021, "loss": 3.98021, "time": 0.85216} +{"mode": "train", "epoch": 72, "iter": 3600, "lr": 0.05318, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30328, "top5_acc": 0.55594, "loss_cls": 3.99012, "loss": 3.99012, "time": 0.85444} +{"mode": "train", "epoch": 72, "iter": 3700, "lr": 0.05315, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30875, "top5_acc": 0.56844, "loss_cls": 3.93892, "loss": 3.93892, "time": 0.8509} +{"mode": "val", "epoch": 72, "iter": 309, "lr": 0.05314, "top1_acc": 0.24586, "top5_acc": 0.49425, "mean_class_accuracy": 0.24584} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.05311, "memory": 15990, "data_time": 1.53377, "top1_acc": 0.32125, "top5_acc": 0.57766, "loss_cls": 3.90617, "loss": 3.90617, "time": 2.56821} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.05308, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30578, "top5_acc": 0.56969, "loss_cls": 3.93068, "loss": 3.93068, "time": 0.85791} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.05306, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31797, "top5_acc": 0.57422, "loss_cls": 3.90477, "loss": 3.90477, "time": 0.85812} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.05303, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.31234, "top5_acc": 0.56203, "loss_cls": 3.94341, "loss": 3.94341, "time": 0.85812} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.053, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31375, "top5_acc": 0.56875, "loss_cls": 3.96132, "loss": 3.96132, "time": 0.86182} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.05297, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31906, "top5_acc": 0.57141, "loss_cls": 3.90819, "loss": 3.90819, "time": 0.85721} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.05294, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30719, "top5_acc": 0.56266, "loss_cls": 3.95804, "loss": 3.95804, "time": 0.85605} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.05292, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31312, "top5_acc": 0.57438, "loss_cls": 3.91248, "loss": 3.91248, "time": 0.85959} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.05289, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30781, "top5_acc": 0.5575, "loss_cls": 3.97427, "loss": 3.97427, "time": 0.85751} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.05286, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31656, "top5_acc": 0.57969, "loss_cls": 3.90053, "loss": 3.90053, "time": 0.85323} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.05283, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30766, "top5_acc": 0.57422, "loss_cls": 3.92387, "loss": 3.92387, "time": 0.85455} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.0528, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3125, "top5_acc": 0.56656, "loss_cls": 3.95202, "loss": 3.95202, "time": 0.85107} +{"mode": "train", "epoch": 73, "iter": 1300, "lr": 0.05278, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31453, "top5_acc": 0.57375, "loss_cls": 3.93125, "loss": 3.93125, "time": 0.85549} +{"mode": "train", "epoch": 73, "iter": 1400, "lr": 0.05275, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.315, "top5_acc": 0.57656, "loss_cls": 3.88796, "loss": 3.88796, "time": 0.85351} +{"mode": "train", "epoch": 73, "iter": 1500, "lr": 0.05272, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30094, "top5_acc": 0.55937, "loss_cls": 3.98155, "loss": 3.98155, "time": 0.85928} +{"mode": "train", "epoch": 73, "iter": 1600, "lr": 0.05269, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30297, "top5_acc": 0.56078, "loss_cls": 3.98713, "loss": 3.98713, "time": 0.85402} +{"mode": "train", "epoch": 73, "iter": 1700, "lr": 0.05267, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31953, "top5_acc": 0.57688, "loss_cls": 3.89529, "loss": 3.89529, "time": 0.85986} +{"mode": "train", "epoch": 73, "iter": 1800, "lr": 0.05264, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.30781, "top5_acc": 0.57047, "loss_cls": 3.93329, "loss": 3.93329, "time": 0.85526} +{"mode": "train", "epoch": 73, "iter": 1900, "lr": 0.05261, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31, "top5_acc": 0.56672, "loss_cls": 3.96342, "loss": 3.96342, "time": 0.85307} +{"mode": "train", "epoch": 73, "iter": 2000, "lr": 0.05258, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.30547, "top5_acc": 0.56516, "loss_cls": 3.95559, "loss": 3.95559, "time": 0.85549} +{"mode": "train", "epoch": 73, "iter": 2100, "lr": 0.05255, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.3075, "top5_acc": 0.55531, "loss_cls": 3.97144, "loss": 3.97144, "time": 0.85415} +{"mode": "train", "epoch": 73, "iter": 2200, "lr": 0.05253, "memory": 15990, "data_time": 0.00097, "top1_acc": 0.31031, "top5_acc": 0.56125, "loss_cls": 3.9544, "loss": 3.9544, "time": 0.85938} +{"mode": "train", "epoch": 73, "iter": 2300, "lr": 0.0525, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30812, "top5_acc": 0.57359, "loss_cls": 3.9396, "loss": 3.9396, "time": 0.8605} +{"mode": "train", "epoch": 73, "iter": 2400, "lr": 0.05247, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3075, "top5_acc": 0.55688, "loss_cls": 4.00563, "loss": 4.00563, "time": 0.85733} +{"mode": "train", "epoch": 73, "iter": 2500, "lr": 0.05244, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30328, "top5_acc": 0.56594, "loss_cls": 3.96207, "loss": 3.96207, "time": 0.84888} +{"mode": "train", "epoch": 73, "iter": 2600, "lr": 0.05241, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30828, "top5_acc": 0.56391, "loss_cls": 3.96697, "loss": 3.96697, "time": 0.85049} +{"mode": "train", "epoch": 73, "iter": 2700, "lr": 0.05239, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31266, "top5_acc": 0.56734, "loss_cls": 3.9509, "loss": 3.9509, "time": 0.84754} +{"mode": "train", "epoch": 73, "iter": 2800, "lr": 0.05236, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31, "top5_acc": 0.56141, "loss_cls": 3.93799, "loss": 3.93799, "time": 0.8488} +{"mode": "train", "epoch": 73, "iter": 2900, "lr": 0.05233, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31109, "top5_acc": 0.56953, "loss_cls": 3.95345, "loss": 3.95345, "time": 0.85377} +{"mode": "train", "epoch": 73, "iter": 3000, "lr": 0.0523, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.30875, "top5_acc": 0.56891, "loss_cls": 3.9293, "loss": 3.9293, "time": 0.84951} +{"mode": "train", "epoch": 73, "iter": 3100, "lr": 0.05227, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31219, "top5_acc": 0.57078, "loss_cls": 3.93897, "loss": 3.93897, "time": 0.8485} +{"mode": "train", "epoch": 73, "iter": 3200, "lr": 0.05225, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.315, "top5_acc": 0.57344, "loss_cls": 3.91552, "loss": 3.91552, "time": 0.85125} +{"mode": "train", "epoch": 73, "iter": 3300, "lr": 0.05222, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32281, "top5_acc": 0.57531, "loss_cls": 3.8891, "loss": 3.8891, "time": 0.85347} +{"mode": "train", "epoch": 73, "iter": 3400, "lr": 0.05219, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29922, "top5_acc": 0.56063, "loss_cls": 3.99158, "loss": 3.99158, "time": 0.84742} +{"mode": "train", "epoch": 73, "iter": 3500, "lr": 0.05216, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31484, "top5_acc": 0.5725, "loss_cls": 3.92947, "loss": 3.92947, "time": 0.84984} +{"mode": "train", "epoch": 73, "iter": 3600, "lr": 0.05213, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3, "top5_acc": 0.54859, "loss_cls": 4.01215, "loss": 4.01215, "time": 0.85109} +{"mode": "train", "epoch": 73, "iter": 3700, "lr": 0.05211, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.325, "top5_acc": 0.58281, "loss_cls": 3.87135, "loss": 3.87135, "time": 0.84883} +{"mode": "val", "epoch": 73, "iter": 309, "lr": 0.05209, "top1_acc": 0.23274, "top5_acc": 0.47531, "mean_class_accuracy": 0.23239} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.05207, "memory": 15990, "data_time": 1.44301, "top1_acc": 0.31781, "top5_acc": 0.58188, "loss_cls": 3.91106, "loss": 3.91106, "time": 2.47078} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.05204, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30781, "top5_acc": 0.57547, "loss_cls": 3.91946, "loss": 3.91946, "time": 0.85384} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.05201, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31281, "top5_acc": 0.56422, "loss_cls": 3.92478, "loss": 3.92478, "time": 0.85207} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.05198, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32141, "top5_acc": 0.57844, "loss_cls": 3.89264, "loss": 3.89264, "time": 0.84897} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.05195, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30672, "top5_acc": 0.57453, "loss_cls": 3.91488, "loss": 3.91488, "time": 0.85241} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.05193, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31469, "top5_acc": 0.57641, "loss_cls": 3.91224, "loss": 3.91224, "time": 0.84738} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.0519, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31594, "top5_acc": 0.57484, "loss_cls": 3.88904, "loss": 3.88904, "time": 0.8486} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.05187, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31297, "top5_acc": 0.5675, "loss_cls": 3.92319, "loss": 3.92319, "time": 0.84964} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.05184, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32203, "top5_acc": 0.5725, "loss_cls": 3.90462, "loss": 3.90462, "time": 0.85208} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.05181, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31766, "top5_acc": 0.5775, "loss_cls": 3.88797, "loss": 3.88797, "time": 0.85398} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.05179, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31578, "top5_acc": 0.57406, "loss_cls": 3.89827, "loss": 3.89827, "time": 0.85324} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.05176, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30828, "top5_acc": 0.56891, "loss_cls": 3.96522, "loss": 3.96522, "time": 0.8533} +{"mode": "train", "epoch": 74, "iter": 1300, "lr": 0.05173, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31172, "top5_acc": 0.56516, "loss_cls": 3.91292, "loss": 3.91292, "time": 0.85151} +{"mode": "train", "epoch": 74, "iter": 1400, "lr": 0.0517, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30734, "top5_acc": 0.56891, "loss_cls": 3.93086, "loss": 3.93086, "time": 0.84571} +{"mode": "train", "epoch": 74, "iter": 1500, "lr": 0.05168, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30625, "top5_acc": 0.56563, "loss_cls": 3.95759, "loss": 3.95759, "time": 0.85118} +{"mode": "train", "epoch": 74, "iter": 1600, "lr": 0.05165, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31562, "top5_acc": 0.56391, "loss_cls": 3.95067, "loss": 3.95067, "time": 0.85095} +{"mode": "train", "epoch": 74, "iter": 1700, "lr": 0.05162, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32609, "top5_acc": 0.57641, "loss_cls": 3.86465, "loss": 3.86465, "time": 0.84837} +{"mode": "train", "epoch": 74, "iter": 1800, "lr": 0.05159, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31875, "top5_acc": 0.57266, "loss_cls": 3.91018, "loss": 3.91018, "time": 0.85075} +{"mode": "train", "epoch": 74, "iter": 1900, "lr": 0.05156, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31203, "top5_acc": 0.57016, "loss_cls": 3.93934, "loss": 3.93934, "time": 0.84875} +{"mode": "train", "epoch": 74, "iter": 2000, "lr": 0.05154, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30578, "top5_acc": 0.56578, "loss_cls": 3.94714, "loss": 3.94714, "time": 0.85177} +{"mode": "train", "epoch": 74, "iter": 2100, "lr": 0.05151, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30828, "top5_acc": 0.56859, "loss_cls": 3.94783, "loss": 3.94783, "time": 0.84863} +{"mode": "train", "epoch": 74, "iter": 2200, "lr": 0.05148, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.325, "top5_acc": 0.57625, "loss_cls": 3.8805, "loss": 3.8805, "time": 0.85073} +{"mode": "train", "epoch": 74, "iter": 2300, "lr": 0.05145, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30594, "top5_acc": 0.55734, "loss_cls": 3.976, "loss": 3.976, "time": 0.84875} +{"mode": "train", "epoch": 74, "iter": 2400, "lr": 0.05142, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32062, "top5_acc": 0.57094, "loss_cls": 3.91033, "loss": 3.91033, "time": 0.84921} +{"mode": "train", "epoch": 74, "iter": 2500, "lr": 0.0514, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31125, "top5_acc": 0.56594, "loss_cls": 3.95771, "loss": 3.95771, "time": 0.85161} +{"mode": "train", "epoch": 74, "iter": 2600, "lr": 0.05137, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.30891, "top5_acc": 0.56172, "loss_cls": 3.96396, "loss": 3.96396, "time": 0.84933} +{"mode": "train", "epoch": 74, "iter": 2700, "lr": 0.05134, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30734, "top5_acc": 0.56938, "loss_cls": 3.94848, "loss": 3.94848, "time": 0.84817} +{"mode": "train", "epoch": 74, "iter": 2800, "lr": 0.05131, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30719, "top5_acc": 0.56141, "loss_cls": 3.98275, "loss": 3.98275, "time": 0.84866} +{"mode": "train", "epoch": 74, "iter": 2900, "lr": 0.05128, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31625, "top5_acc": 0.57188, "loss_cls": 3.9551, "loss": 3.9551, "time": 0.84777} +{"mode": "train", "epoch": 74, "iter": 3000, "lr": 0.05126, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31047, "top5_acc": 0.56312, "loss_cls": 3.95255, "loss": 3.95255, "time": 0.84701} +{"mode": "train", "epoch": 74, "iter": 3100, "lr": 0.05123, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32016, "top5_acc": 0.57172, "loss_cls": 3.9227, "loss": 3.9227, "time": 0.85059} +{"mode": "train", "epoch": 74, "iter": 3200, "lr": 0.0512, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30875, "top5_acc": 0.57828, "loss_cls": 3.9145, "loss": 3.9145, "time": 0.8511} +{"mode": "train", "epoch": 74, "iter": 3300, "lr": 0.05117, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31594, "top5_acc": 0.56844, "loss_cls": 3.94567, "loss": 3.94567, "time": 0.84739} +{"mode": "train", "epoch": 74, "iter": 3400, "lr": 0.05114, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31359, "top5_acc": 0.575, "loss_cls": 3.90989, "loss": 3.90989, "time": 0.85239} +{"mode": "train", "epoch": 74, "iter": 3500, "lr": 0.05112, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30547, "top5_acc": 0.55859, "loss_cls": 3.97454, "loss": 3.97454, "time": 0.8471} +{"mode": "train", "epoch": 74, "iter": 3600, "lr": 0.05109, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31969, "top5_acc": 0.57641, "loss_cls": 3.87977, "loss": 3.87977, "time": 0.85071} +{"mode": "train", "epoch": 74, "iter": 3700, "lr": 0.05106, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31516, "top5_acc": 0.57891, "loss_cls": 3.8979, "loss": 3.8979, "time": 0.84902} +{"mode": "val", "epoch": 74, "iter": 309, "lr": 0.05105, "top1_acc": 0.25341, "top5_acc": 0.49962, "mean_class_accuracy": 0.25316} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.05102, "memory": 15990, "data_time": 1.47526, "top1_acc": 0.32734, "top5_acc": 0.58328, "loss_cls": 3.8392, "loss": 3.8392, "time": 2.50923} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.05099, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31797, "top5_acc": 0.58172, "loss_cls": 3.88121, "loss": 3.88121, "time": 0.85081} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.05096, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31609, "top5_acc": 0.57688, "loss_cls": 3.88444, "loss": 3.88444, "time": 0.8491} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.05094, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31781, "top5_acc": 0.58719, "loss_cls": 3.8317, "loss": 3.8317, "time": 0.84815} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.05091, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31406, "top5_acc": 0.57656, "loss_cls": 3.9242, "loss": 3.9242, "time": 0.84869} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.05088, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31312, "top5_acc": 0.57156, "loss_cls": 3.929, "loss": 3.929, "time": 0.84975} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.05085, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31422, "top5_acc": 0.57516, "loss_cls": 3.92778, "loss": 3.92778, "time": 0.84878} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.05082, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31531, "top5_acc": 0.56391, "loss_cls": 3.91969, "loss": 3.91969, "time": 0.85019} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.0508, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32422, "top5_acc": 0.57922, "loss_cls": 3.87486, "loss": 3.87486, "time": 0.84918} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.05077, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31859, "top5_acc": 0.58344, "loss_cls": 3.84671, "loss": 3.84671, "time": 0.84801} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.05074, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.30906, "top5_acc": 0.57734, "loss_cls": 3.96064, "loss": 3.96064, "time": 0.84642} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.05071, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.30891, "top5_acc": 0.56531, "loss_cls": 3.94408, "loss": 3.94408, "time": 0.85201} +{"mode": "train", "epoch": 75, "iter": 1300, "lr": 0.05068, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30938, "top5_acc": 0.57641, "loss_cls": 3.9262, "loss": 3.9262, "time": 0.84201} +{"mode": "train", "epoch": 75, "iter": 1400, "lr": 0.05066, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.31578, "top5_acc": 0.57391, "loss_cls": 3.91694, "loss": 3.91694, "time": 0.84958} +{"mode": "train", "epoch": 75, "iter": 1500, "lr": 0.05063, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31781, "top5_acc": 0.57125, "loss_cls": 3.88969, "loss": 3.88969, "time": 0.85266} +{"mode": "train", "epoch": 75, "iter": 1600, "lr": 0.0506, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.30719, "top5_acc": 0.56781, "loss_cls": 3.9485, "loss": 3.9485, "time": 0.85009} +{"mode": "train", "epoch": 75, "iter": 1700, "lr": 0.05057, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31094, "top5_acc": 0.56891, "loss_cls": 3.92515, "loss": 3.92515, "time": 0.84887} +{"mode": "train", "epoch": 75, "iter": 1800, "lr": 0.05054, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31047, "top5_acc": 0.57125, "loss_cls": 3.90935, "loss": 3.90935, "time": 0.85504} +{"mode": "train", "epoch": 75, "iter": 1900, "lr": 0.05052, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31844, "top5_acc": 0.58375, "loss_cls": 3.89848, "loss": 3.89848, "time": 0.84832} +{"mode": "train", "epoch": 75, "iter": 2000, "lr": 0.05049, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31625, "top5_acc": 0.56609, "loss_cls": 3.91613, "loss": 3.91613, "time": 0.85359} +{"mode": "train", "epoch": 75, "iter": 2100, "lr": 0.05046, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.31922, "top5_acc": 0.57219, "loss_cls": 3.91084, "loss": 3.91084, "time": 0.85302} +{"mode": "train", "epoch": 75, "iter": 2200, "lr": 0.05043, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30828, "top5_acc": 0.56234, "loss_cls": 3.95053, "loss": 3.95053, "time": 0.84858} +{"mode": "train", "epoch": 75, "iter": 2300, "lr": 0.0504, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31828, "top5_acc": 0.57828, "loss_cls": 3.87822, "loss": 3.87822, "time": 0.84885} +{"mode": "train", "epoch": 75, "iter": 2400, "lr": 0.05038, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32094, "top5_acc": 0.57812, "loss_cls": 3.8979, "loss": 3.8979, "time": 0.84529} +{"mode": "train", "epoch": 75, "iter": 2500, "lr": 0.05035, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31766, "top5_acc": 0.57719, "loss_cls": 3.91335, "loss": 3.91335, "time": 0.84389} +{"mode": "train", "epoch": 75, "iter": 2600, "lr": 0.05032, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30703, "top5_acc": 0.55266, "loss_cls": 3.99083, "loss": 3.99083, "time": 0.84679} +{"mode": "train", "epoch": 75, "iter": 2700, "lr": 0.05029, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31359, "top5_acc": 0.56781, "loss_cls": 3.93245, "loss": 3.93245, "time": 0.85099} +{"mode": "train", "epoch": 75, "iter": 2800, "lr": 0.05026, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31094, "top5_acc": 0.56781, "loss_cls": 3.92659, "loss": 3.92659, "time": 0.8473} +{"mode": "train", "epoch": 75, "iter": 2900, "lr": 0.05024, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31797, "top5_acc": 0.56234, "loss_cls": 3.9433, "loss": 3.9433, "time": 0.85156} +{"mode": "train", "epoch": 75, "iter": 3000, "lr": 0.05021, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31781, "top5_acc": 0.57375, "loss_cls": 3.91921, "loss": 3.91921, "time": 0.84901} +{"mode": "train", "epoch": 75, "iter": 3100, "lr": 0.05018, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30406, "top5_acc": 0.55766, "loss_cls": 3.96332, "loss": 3.96332, "time": 0.84966} +{"mode": "train", "epoch": 75, "iter": 3200, "lr": 0.05015, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31781, "top5_acc": 0.57203, "loss_cls": 3.92522, "loss": 3.92522, "time": 0.84631} +{"mode": "train", "epoch": 75, "iter": 3300, "lr": 0.05012, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31484, "top5_acc": 0.57047, "loss_cls": 3.90416, "loss": 3.90416, "time": 0.84932} +{"mode": "train", "epoch": 75, "iter": 3400, "lr": 0.0501, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30891, "top5_acc": 0.56859, "loss_cls": 3.94939, "loss": 3.94939, "time": 0.8493} +{"mode": "train", "epoch": 75, "iter": 3500, "lr": 0.05007, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31281, "top5_acc": 0.56844, "loss_cls": 3.94367, "loss": 3.94367, "time": 0.84421} +{"mode": "train", "epoch": 75, "iter": 3600, "lr": 0.05004, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.31578, "top5_acc": 0.55812, "loss_cls": 3.96752, "loss": 3.96752, "time": 0.84664} +{"mode": "train", "epoch": 75, "iter": 3700, "lr": 0.05001, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30812, "top5_acc": 0.57125, "loss_cls": 3.92166, "loss": 3.92166, "time": 0.85168} +{"mode": "val", "epoch": 75, "iter": 309, "lr": 0.05, "top1_acc": 0.25346, "top5_acc": 0.5017, "mean_class_accuracy": 0.25324} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.04997, "memory": 15990, "data_time": 1.47362, "top1_acc": 0.31719, "top5_acc": 0.58703, "loss_cls": 3.87453, "loss": 3.87453, "time": 2.50568} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.04994, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31141, "top5_acc": 0.57312, "loss_cls": 3.90138, "loss": 3.90138, "time": 0.85112} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.04992, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32094, "top5_acc": 0.57734, "loss_cls": 3.85272, "loss": 3.85272, "time": 0.85105} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.04989, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.315, "top5_acc": 0.575, "loss_cls": 3.89769, "loss": 3.89769, "time": 0.85075} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.04986, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32328, "top5_acc": 0.57938, "loss_cls": 3.8801, "loss": 3.8801, "time": 0.85483} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.04983, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31234, "top5_acc": 0.57891, "loss_cls": 3.89256, "loss": 3.89256, "time": 0.85489} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.0498, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32203, "top5_acc": 0.58219, "loss_cls": 3.88143, "loss": 3.88143, "time": 0.85546} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.04978, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31891, "top5_acc": 0.57406, "loss_cls": 3.87535, "loss": 3.87535, "time": 0.85124} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.04975, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31922, "top5_acc": 0.57547, "loss_cls": 3.88525, "loss": 3.88525, "time": 0.85256} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.04972, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32297, "top5_acc": 0.57672, "loss_cls": 3.90301, "loss": 3.90301, "time": 0.85562} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.04969, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30609, "top5_acc": 0.56531, "loss_cls": 3.95166, "loss": 3.95166, "time": 0.85364} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.04966, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32141, "top5_acc": 0.57453, "loss_cls": 3.87806, "loss": 3.87806, "time": 0.85141} +{"mode": "train", "epoch": 76, "iter": 1300, "lr": 0.04964, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32672, "top5_acc": 0.58672, "loss_cls": 3.84729, "loss": 3.84729, "time": 0.84889} +{"mode": "train", "epoch": 76, "iter": 1400, "lr": 0.04961, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.31906, "top5_acc": 0.57719, "loss_cls": 3.89097, "loss": 3.89097, "time": 0.84743} +{"mode": "train", "epoch": 76, "iter": 1500, "lr": 0.04958, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31531, "top5_acc": 0.56969, "loss_cls": 3.9077, "loss": 3.9077, "time": 0.85158} +{"mode": "train", "epoch": 76, "iter": 1600, "lr": 0.04955, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.315, "top5_acc": 0.56781, "loss_cls": 3.90865, "loss": 3.90865, "time": 0.854} +{"mode": "train", "epoch": 76, "iter": 1700, "lr": 0.04953, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31188, "top5_acc": 0.57156, "loss_cls": 3.90605, "loss": 3.90605, "time": 0.84775} +{"mode": "train", "epoch": 76, "iter": 1800, "lr": 0.0495, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32391, "top5_acc": 0.58016, "loss_cls": 3.84532, "loss": 3.84532, "time": 0.85035} +{"mode": "train", "epoch": 76, "iter": 1900, "lr": 0.04947, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31812, "top5_acc": 0.57438, "loss_cls": 3.8926, "loss": 3.8926, "time": 0.84668} +{"mode": "train", "epoch": 76, "iter": 2000, "lr": 0.04944, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31219, "top5_acc": 0.57375, "loss_cls": 3.92728, "loss": 3.92728, "time": 0.85439} +{"mode": "train", "epoch": 76, "iter": 2100, "lr": 0.04941, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31578, "top5_acc": 0.57469, "loss_cls": 3.91952, "loss": 3.91952, "time": 0.84993} +{"mode": "train", "epoch": 76, "iter": 2200, "lr": 0.04939, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31828, "top5_acc": 0.57047, "loss_cls": 3.90609, "loss": 3.90609, "time": 0.84983} +{"mode": "train", "epoch": 76, "iter": 2300, "lr": 0.04936, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31891, "top5_acc": 0.57, "loss_cls": 3.89149, "loss": 3.89149, "time": 0.85574} +{"mode": "train", "epoch": 76, "iter": 2400, "lr": 0.04933, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30641, "top5_acc": 0.56703, "loss_cls": 3.92943, "loss": 3.92943, "time": 0.85335} +{"mode": "train", "epoch": 76, "iter": 2500, "lr": 0.0493, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31328, "top5_acc": 0.56859, "loss_cls": 3.92151, "loss": 3.92151, "time": 0.85096} +{"mode": "train", "epoch": 76, "iter": 2600, "lr": 0.04927, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30781, "top5_acc": 0.56734, "loss_cls": 3.93168, "loss": 3.93168, "time": 0.85148} +{"mode": "train", "epoch": 76, "iter": 2700, "lr": 0.04925, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.31562, "top5_acc": 0.56891, "loss_cls": 3.91492, "loss": 3.91492, "time": 0.85026} +{"mode": "train", "epoch": 76, "iter": 2800, "lr": 0.04922, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.32312, "top5_acc": 0.57141, "loss_cls": 3.89324, "loss": 3.89324, "time": 0.84961} +{"mode": "train", "epoch": 76, "iter": 2900, "lr": 0.04919, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30891, "top5_acc": 0.56047, "loss_cls": 3.98154, "loss": 3.98154, "time": 0.84959} +{"mode": "train", "epoch": 76, "iter": 3000, "lr": 0.04916, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31906, "top5_acc": 0.56312, "loss_cls": 3.91501, "loss": 3.91501, "time": 0.85067} +{"mode": "train", "epoch": 76, "iter": 3100, "lr": 0.04913, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31875, "top5_acc": 0.58281, "loss_cls": 3.8918, "loss": 3.8918, "time": 0.84922} +{"mode": "train", "epoch": 76, "iter": 3200, "lr": 0.04911, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32281, "top5_acc": 0.57359, "loss_cls": 3.91095, "loss": 3.91095, "time": 0.85153} +{"mode": "train", "epoch": 76, "iter": 3300, "lr": 0.04908, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31859, "top5_acc": 0.57031, "loss_cls": 3.89659, "loss": 3.89659, "time": 0.84624} +{"mode": "train", "epoch": 76, "iter": 3400, "lr": 0.04905, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31969, "top5_acc": 0.58031, "loss_cls": 3.89474, "loss": 3.89474, "time": 0.85213} +{"mode": "train", "epoch": 76, "iter": 3500, "lr": 0.04902, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31344, "top5_acc": 0.57219, "loss_cls": 3.94291, "loss": 3.94291, "time": 0.85} +{"mode": "train", "epoch": 76, "iter": 3600, "lr": 0.04899, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32594, "top5_acc": 0.56844, "loss_cls": 3.91909, "loss": 3.91909, "time": 0.85517} +{"mode": "train", "epoch": 76, "iter": 3700, "lr": 0.04897, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31422, "top5_acc": 0.57469, "loss_cls": 3.90151, "loss": 3.90151, "time": 0.85407} +{"mode": "val", "epoch": 76, "iter": 309, "lr": 0.04895, "top1_acc": 0.2609, "top5_acc": 0.50838, "mean_class_accuracy": 0.26033} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.04893, "memory": 15990, "data_time": 1.4831, "top1_acc": 0.33234, "top5_acc": 0.58859, "loss_cls": 3.80647, "loss": 3.80647, "time": 2.51805} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0489, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32391, "top5_acc": 0.57516, "loss_cls": 3.90345, "loss": 3.90345, "time": 0.85874} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.04887, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31969, "top5_acc": 0.58109, "loss_cls": 3.86329, "loss": 3.86329, "time": 0.8549} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.04884, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3225, "top5_acc": 0.58422, "loss_cls": 3.82439, "loss": 3.82439, "time": 0.85805} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.04881, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31953, "top5_acc": 0.57203, "loss_cls": 3.88742, "loss": 3.88742, "time": 0.85749} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.04879, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32656, "top5_acc": 0.58359, "loss_cls": 3.86097, "loss": 3.86097, "time": 0.86167} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.04876, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32656, "top5_acc": 0.57828, "loss_cls": 3.86033, "loss": 3.86033, "time": 0.85666} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.04873, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31672, "top5_acc": 0.57563, "loss_cls": 3.88272, "loss": 3.88272, "time": 0.85651} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.0487, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31562, "top5_acc": 0.58766, "loss_cls": 3.86322, "loss": 3.86322, "time": 0.85947} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.04867, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31219, "top5_acc": 0.57938, "loss_cls": 3.91749, "loss": 3.91749, "time": 0.8627} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.04865, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30984, "top5_acc": 0.57031, "loss_cls": 3.92256, "loss": 3.92256, "time": 0.85693} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.04862, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33188, "top5_acc": 0.57938, "loss_cls": 3.8658, "loss": 3.8658, "time": 0.85134} +{"mode": "train", "epoch": 77, "iter": 1300, "lr": 0.04859, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31547, "top5_acc": 0.57563, "loss_cls": 3.92259, "loss": 3.92259, "time": 0.84796} +{"mode": "train", "epoch": 77, "iter": 1400, "lr": 0.04856, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32375, "top5_acc": 0.57844, "loss_cls": 3.84561, "loss": 3.84561, "time": 0.84922} +{"mode": "train", "epoch": 77, "iter": 1500, "lr": 0.04853, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30844, "top5_acc": 0.56906, "loss_cls": 3.92057, "loss": 3.92057, "time": 0.84889} +{"mode": "train", "epoch": 77, "iter": 1600, "lr": 0.04851, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31719, "top5_acc": 0.57203, "loss_cls": 3.89453, "loss": 3.89453, "time": 0.84844} +{"mode": "train", "epoch": 77, "iter": 1700, "lr": 0.04848, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32281, "top5_acc": 0.57422, "loss_cls": 3.89112, "loss": 3.89112, "time": 0.84791} +{"mode": "train", "epoch": 77, "iter": 1800, "lr": 0.04845, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30422, "top5_acc": 0.56844, "loss_cls": 3.91754, "loss": 3.91754, "time": 0.84878} +{"mode": "train", "epoch": 77, "iter": 1900, "lr": 0.04842, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31219, "top5_acc": 0.57547, "loss_cls": 3.91457, "loss": 3.91457, "time": 0.85054} +{"mode": "train", "epoch": 77, "iter": 2000, "lr": 0.04839, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32625, "top5_acc": 0.5825, "loss_cls": 3.84789, "loss": 3.84789, "time": 0.85324} +{"mode": "train", "epoch": 77, "iter": 2100, "lr": 0.04837, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31984, "top5_acc": 0.57078, "loss_cls": 3.89291, "loss": 3.89291, "time": 0.84635} +{"mode": "train", "epoch": 77, "iter": 2200, "lr": 0.04834, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31953, "top5_acc": 0.57016, "loss_cls": 3.90596, "loss": 3.90596, "time": 0.84357} +{"mode": "train", "epoch": 77, "iter": 2300, "lr": 0.04831, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31188, "top5_acc": 0.56875, "loss_cls": 3.95259, "loss": 3.95259, "time": 0.84993} +{"mode": "train", "epoch": 77, "iter": 2400, "lr": 0.04828, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31609, "top5_acc": 0.56906, "loss_cls": 3.90804, "loss": 3.90804, "time": 0.8481} +{"mode": "train", "epoch": 77, "iter": 2500, "lr": 0.04825, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31781, "top5_acc": 0.57125, "loss_cls": 3.88804, "loss": 3.88804, "time": 0.85241} +{"mode": "train", "epoch": 77, "iter": 2600, "lr": 0.04823, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32406, "top5_acc": 0.57234, "loss_cls": 3.88817, "loss": 3.88817, "time": 0.84988} +{"mode": "train", "epoch": 77, "iter": 2700, "lr": 0.0482, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32406, "top5_acc": 0.585, "loss_cls": 3.84138, "loss": 3.84138, "time": 0.84812} +{"mode": "train", "epoch": 77, "iter": 2800, "lr": 0.04817, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31609, "top5_acc": 0.57688, "loss_cls": 3.91132, "loss": 3.91132, "time": 0.84528} +{"mode": "train", "epoch": 77, "iter": 2900, "lr": 0.04814, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.31844, "top5_acc": 0.57312, "loss_cls": 3.9124, "loss": 3.9124, "time": 0.85266} +{"mode": "train", "epoch": 77, "iter": 3000, "lr": 0.04811, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.3075, "top5_acc": 0.56703, "loss_cls": 3.94373, "loss": 3.94373, "time": 0.84991} +{"mode": "train", "epoch": 77, "iter": 3100, "lr": 0.04809, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32453, "top5_acc": 0.56875, "loss_cls": 3.89722, "loss": 3.89722, "time": 0.84572} +{"mode": "train", "epoch": 77, "iter": 3200, "lr": 0.04806, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32, "top5_acc": 0.57891, "loss_cls": 3.86285, "loss": 3.86285, "time": 0.84896} +{"mode": "train", "epoch": 77, "iter": 3300, "lr": 0.04803, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31531, "top5_acc": 0.56922, "loss_cls": 3.93059, "loss": 3.93059, "time": 0.85101} +{"mode": "train", "epoch": 77, "iter": 3400, "lr": 0.048, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32047, "top5_acc": 0.57531, "loss_cls": 3.87947, "loss": 3.87947, "time": 0.8492} +{"mode": "train", "epoch": 77, "iter": 3500, "lr": 0.04798, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31516, "top5_acc": 0.56422, "loss_cls": 3.96327, "loss": 3.96327, "time": 0.849} +{"mode": "train", "epoch": 77, "iter": 3600, "lr": 0.04795, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32062, "top5_acc": 0.57234, "loss_cls": 3.87464, "loss": 3.87464, "time": 0.85049} +{"mode": "train", "epoch": 77, "iter": 3700, "lr": 0.04792, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32203, "top5_acc": 0.57547, "loss_cls": 3.89327, "loss": 3.89327, "time": 0.84804} +{"mode": "val", "epoch": 77, "iter": 309, "lr": 0.04791, "top1_acc": 0.26673, "top5_acc": 0.51183, "mean_class_accuracy": 0.26639} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.04788, "memory": 15990, "data_time": 1.51214, "top1_acc": 0.32969, "top5_acc": 0.59953, "loss_cls": 3.81033, "loss": 3.81033, "time": 2.53768} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.04785, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33281, "top5_acc": 0.59031, "loss_cls": 3.82954, "loss": 3.82954, "time": 0.85226} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.04782, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31703, "top5_acc": 0.58125, "loss_cls": 3.90564, "loss": 3.90564, "time": 0.85472} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.04779, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31984, "top5_acc": 0.56969, "loss_cls": 3.86957, "loss": 3.86957, "time": 0.85174} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.04777, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32672, "top5_acc": 0.58547, "loss_cls": 3.8538, "loss": 3.8538, "time": 0.85269} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.04774, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30797, "top5_acc": 0.56734, "loss_cls": 3.95448, "loss": 3.95448, "time": 0.85173} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.04771, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32703, "top5_acc": 0.58609, "loss_cls": 3.81503, "loss": 3.81503, "time": 0.85244} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.04768, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31781, "top5_acc": 0.57781, "loss_cls": 3.88513, "loss": 3.88513, "time": 0.85} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.04766, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32719, "top5_acc": 0.58734, "loss_cls": 3.82638, "loss": 3.82638, "time": 0.85798} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.04763, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32203, "top5_acc": 0.57234, "loss_cls": 3.88231, "loss": 3.88231, "time": 0.85039} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.0476, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31609, "top5_acc": 0.58047, "loss_cls": 3.89187, "loss": 3.89187, "time": 0.85153} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.04757, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31516, "top5_acc": 0.57734, "loss_cls": 3.88315, "loss": 3.88315, "time": 0.84966} +{"mode": "train", "epoch": 78, "iter": 1300, "lr": 0.04754, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.3275, "top5_acc": 0.5825, "loss_cls": 3.84616, "loss": 3.84616, "time": 0.84641} +{"mode": "train", "epoch": 78, "iter": 1400, "lr": 0.04752, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32594, "top5_acc": 0.57469, "loss_cls": 3.88821, "loss": 3.88821, "time": 0.85265} +{"mode": "train", "epoch": 78, "iter": 1500, "lr": 0.04749, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32469, "top5_acc": 0.58719, "loss_cls": 3.86474, "loss": 3.86474, "time": 0.84869} +{"mode": "train", "epoch": 78, "iter": 1600, "lr": 0.04746, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31938, "top5_acc": 0.56906, "loss_cls": 3.90289, "loss": 3.90289, "time": 0.85603} +{"mode": "train", "epoch": 78, "iter": 1700, "lr": 0.04743, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31078, "top5_acc": 0.57, "loss_cls": 3.90207, "loss": 3.90207, "time": 0.85462} +{"mode": "train", "epoch": 78, "iter": 1800, "lr": 0.0474, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31188, "top5_acc": 0.57297, "loss_cls": 3.9121, "loss": 3.9121, "time": 0.8601} +{"mode": "train", "epoch": 78, "iter": 1900, "lr": 0.04738, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32156, "top5_acc": 0.57875, "loss_cls": 3.86917, "loss": 3.86917, "time": 0.85507} +{"mode": "train", "epoch": 78, "iter": 2000, "lr": 0.04735, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.31406, "top5_acc": 0.57938, "loss_cls": 3.89981, "loss": 3.89981, "time": 0.85469} +{"mode": "train", "epoch": 78, "iter": 2100, "lr": 0.04732, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32609, "top5_acc": 0.58484, "loss_cls": 3.86007, "loss": 3.86007, "time": 0.84973} +{"mode": "train", "epoch": 78, "iter": 2200, "lr": 0.04729, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33688, "top5_acc": 0.58641, "loss_cls": 3.82063, "loss": 3.82063, "time": 0.8493} +{"mode": "train", "epoch": 78, "iter": 2300, "lr": 0.04726, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32297, "top5_acc": 0.58047, "loss_cls": 3.8715, "loss": 3.8715, "time": 0.85428} +{"mode": "train", "epoch": 78, "iter": 2400, "lr": 0.04724, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32594, "top5_acc": 0.57938, "loss_cls": 3.84375, "loss": 3.84375, "time": 0.85961} +{"mode": "train", "epoch": 78, "iter": 2500, "lr": 0.04721, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31781, "top5_acc": 0.57156, "loss_cls": 3.9061, "loss": 3.9061, "time": 0.85409} +{"mode": "train", "epoch": 78, "iter": 2600, "lr": 0.04718, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.33328, "top5_acc": 0.58422, "loss_cls": 3.82972, "loss": 3.82972, "time": 0.85532} +{"mode": "train", "epoch": 78, "iter": 2700, "lr": 0.04715, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31531, "top5_acc": 0.5775, "loss_cls": 3.89755, "loss": 3.89755, "time": 0.85038} +{"mode": "train", "epoch": 78, "iter": 2800, "lr": 0.04712, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31266, "top5_acc": 0.57234, "loss_cls": 3.90327, "loss": 3.90327, "time": 0.8461} +{"mode": "train", "epoch": 78, "iter": 2900, "lr": 0.0471, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31781, "top5_acc": 0.58109, "loss_cls": 3.87857, "loss": 3.87857, "time": 0.85007} +{"mode": "train", "epoch": 78, "iter": 3000, "lr": 0.04707, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32016, "top5_acc": 0.57484, "loss_cls": 3.88379, "loss": 3.88379, "time": 0.85288} +{"mode": "train", "epoch": 78, "iter": 3100, "lr": 0.04704, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31656, "top5_acc": 0.58062, "loss_cls": 3.87168, "loss": 3.87168, "time": 0.8491} +{"mode": "train", "epoch": 78, "iter": 3200, "lr": 0.04701, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32359, "top5_acc": 0.5775, "loss_cls": 3.88302, "loss": 3.88302, "time": 0.84953} +{"mode": "train", "epoch": 78, "iter": 3300, "lr": 0.04699, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31703, "top5_acc": 0.57281, "loss_cls": 3.88608, "loss": 3.88608, "time": 0.85077} +{"mode": "train", "epoch": 78, "iter": 3400, "lr": 0.04696, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31766, "top5_acc": 0.57016, "loss_cls": 3.91261, "loss": 3.91261, "time": 0.85202} +{"mode": "train", "epoch": 78, "iter": 3500, "lr": 0.04693, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32, "top5_acc": 0.57406, "loss_cls": 3.89444, "loss": 3.89444, "time": 0.8558} +{"mode": "train", "epoch": 78, "iter": 3600, "lr": 0.0469, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31609, "top5_acc": 0.57984, "loss_cls": 3.89782, "loss": 3.89782, "time": 0.85104} +{"mode": "train", "epoch": 78, "iter": 3700, "lr": 0.04687, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.31188, "top5_acc": 0.56906, "loss_cls": 3.91244, "loss": 3.91244, "time": 0.84937} +{"mode": "val", "epoch": 78, "iter": 309, "lr": 0.04686, "top1_acc": 0.2489, "top5_acc": 0.49764, "mean_class_accuracy": 0.2487} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.04683, "memory": 15990, "data_time": 1.4954, "top1_acc": 0.32531, "top5_acc": 0.58078, "loss_cls": 3.84117, "loss": 3.84117, "time": 2.52765} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.0468, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32719, "top5_acc": 0.58625, "loss_cls": 3.7987, "loss": 3.7987, "time": 0.85273} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.04678, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32328, "top5_acc": 0.58188, "loss_cls": 3.82211, "loss": 3.82211, "time": 0.85996} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.04675, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33, "top5_acc": 0.58641, "loss_cls": 3.84824, "loss": 3.84824, "time": 0.85129} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.04672, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33297, "top5_acc": 0.58031, "loss_cls": 3.84588, "loss": 3.84588, "time": 0.85102} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.04669, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32375, "top5_acc": 0.57578, "loss_cls": 3.90666, "loss": 3.90666, "time": 0.85447} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.04667, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31688, "top5_acc": 0.57938, "loss_cls": 3.88246, "loss": 3.88246, "time": 0.85151} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.04664, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32875, "top5_acc": 0.5875, "loss_cls": 3.84381, "loss": 3.84381, "time": 0.85115} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.04661, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32188, "top5_acc": 0.58422, "loss_cls": 3.8671, "loss": 3.8671, "time": 0.84792} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.04658, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32062, "top5_acc": 0.57063, "loss_cls": 3.89337, "loss": 3.89337, "time": 0.8517} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.04655, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.31125, "top5_acc": 0.58156, "loss_cls": 3.87889, "loss": 3.87889, "time": 0.85442} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.04653, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.32562, "top5_acc": 0.58844, "loss_cls": 3.83546, "loss": 3.83546, "time": 0.85206} +{"mode": "train", "epoch": 79, "iter": 1300, "lr": 0.0465, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.325, "top5_acc": 0.59688, "loss_cls": 3.8192, "loss": 3.8192, "time": 0.84684} +{"mode": "train", "epoch": 79, "iter": 1400, "lr": 0.04647, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3225, "top5_acc": 0.57422, "loss_cls": 3.90529, "loss": 3.90529, "time": 0.85088} +{"mode": "train", "epoch": 79, "iter": 1500, "lr": 0.04644, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31797, "top5_acc": 0.57531, "loss_cls": 3.89713, "loss": 3.89713, "time": 0.85152} +{"mode": "train", "epoch": 79, "iter": 1600, "lr": 0.04641, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33609, "top5_acc": 0.59188, "loss_cls": 3.82877, "loss": 3.82877, "time": 0.85559} +{"mode": "train", "epoch": 79, "iter": 1700, "lr": 0.04639, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31422, "top5_acc": 0.57203, "loss_cls": 3.89556, "loss": 3.89556, "time": 0.85771} +{"mode": "train", "epoch": 79, "iter": 1800, "lr": 0.04636, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3225, "top5_acc": 0.57312, "loss_cls": 3.88426, "loss": 3.88426, "time": 0.84834} +{"mode": "train", "epoch": 79, "iter": 1900, "lr": 0.04633, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32672, "top5_acc": 0.59016, "loss_cls": 3.84446, "loss": 3.84446, "time": 0.8515} +{"mode": "train", "epoch": 79, "iter": 2000, "lr": 0.0463, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31766, "top5_acc": 0.57266, "loss_cls": 3.89417, "loss": 3.89417, "time": 0.85839} +{"mode": "train", "epoch": 79, "iter": 2100, "lr": 0.04628, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31594, "top5_acc": 0.57375, "loss_cls": 3.88367, "loss": 3.88367, "time": 0.84997} +{"mode": "train", "epoch": 79, "iter": 2200, "lr": 0.04625, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32359, "top5_acc": 0.58, "loss_cls": 3.87052, "loss": 3.87052, "time": 0.84991} +{"mode": "train", "epoch": 79, "iter": 2300, "lr": 0.04622, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32906, "top5_acc": 0.57594, "loss_cls": 3.87016, "loss": 3.87016, "time": 0.85086} +{"mode": "train", "epoch": 79, "iter": 2400, "lr": 0.04619, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33125, "top5_acc": 0.57922, "loss_cls": 3.849, "loss": 3.849, "time": 0.84922} +{"mode": "train", "epoch": 79, "iter": 2500, "lr": 0.04616, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32938, "top5_acc": 0.58812, "loss_cls": 3.8041, "loss": 3.8041, "time": 0.85734} +{"mode": "train", "epoch": 79, "iter": 2600, "lr": 0.04614, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.3225, "top5_acc": 0.56672, "loss_cls": 3.92758, "loss": 3.92758, "time": 0.85138} +{"mode": "train", "epoch": 79, "iter": 2700, "lr": 0.04611, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31422, "top5_acc": 0.57438, "loss_cls": 3.89435, "loss": 3.89435, "time": 0.85376} +{"mode": "train", "epoch": 79, "iter": 2800, "lr": 0.04608, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32469, "top5_acc": 0.57734, "loss_cls": 3.87755, "loss": 3.87755, "time": 0.84927} +{"mode": "train", "epoch": 79, "iter": 2900, "lr": 0.04605, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31984, "top5_acc": 0.58375, "loss_cls": 3.87039, "loss": 3.87039, "time": 0.8501} +{"mode": "train", "epoch": 79, "iter": 3000, "lr": 0.04602, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32375, "top5_acc": 0.58516, "loss_cls": 3.86175, "loss": 3.86175, "time": 0.85074} +{"mode": "train", "epoch": 79, "iter": 3100, "lr": 0.046, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.32562, "top5_acc": 0.57406, "loss_cls": 3.89077, "loss": 3.89077, "time": 0.84482} +{"mode": "train", "epoch": 79, "iter": 3200, "lr": 0.04597, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32266, "top5_acc": 0.58047, "loss_cls": 3.88366, "loss": 3.88366, "time": 0.84893} +{"mode": "train", "epoch": 79, "iter": 3300, "lr": 0.04594, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32578, "top5_acc": 0.58391, "loss_cls": 3.86127, "loss": 3.86127, "time": 0.84975} +{"mode": "train", "epoch": 79, "iter": 3400, "lr": 0.04591, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32062, "top5_acc": 0.57828, "loss_cls": 3.8647, "loss": 3.8647, "time": 0.85687} +{"mode": "train", "epoch": 79, "iter": 3500, "lr": 0.04588, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32688, "top5_acc": 0.58297, "loss_cls": 3.85656, "loss": 3.85656, "time": 0.85266} +{"mode": "train", "epoch": 79, "iter": 3600, "lr": 0.04586, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32406, "top5_acc": 0.57938, "loss_cls": 3.86906, "loss": 3.86906, "time": 0.8524} +{"mode": "train", "epoch": 79, "iter": 3700, "lr": 0.04583, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31953, "top5_acc": 0.58484, "loss_cls": 3.87088, "loss": 3.87088, "time": 0.85376} +{"mode": "val", "epoch": 79, "iter": 309, "lr": 0.04582, "top1_acc": 0.26197, "top5_acc": 0.50408, "mean_class_accuracy": 0.26169} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.04579, "memory": 15990, "data_time": 1.51173, "top1_acc": 0.32906, "top5_acc": 0.58766, "loss_cls": 3.80722, "loss": 3.80722, "time": 2.56236} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.04576, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32125, "top5_acc": 0.58406, "loss_cls": 3.82796, "loss": 3.82796, "time": 0.85936} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.04573, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33344, "top5_acc": 0.59828, "loss_cls": 3.77358, "loss": 3.77358, "time": 0.85795} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.0457, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32078, "top5_acc": 0.58688, "loss_cls": 3.82849, "loss": 3.82849, "time": 0.85991} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.04568, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32359, "top5_acc": 0.58203, "loss_cls": 3.86666, "loss": 3.86666, "time": 0.85898} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.04565, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.32234, "top5_acc": 0.58, "loss_cls": 3.84328, "loss": 3.84328, "time": 0.85449} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.04562, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33984, "top5_acc": 0.59297, "loss_cls": 3.79512, "loss": 3.79512, "time": 0.85196} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.04559, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33438, "top5_acc": 0.58578, "loss_cls": 3.8151, "loss": 3.8151, "time": 0.85245} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.04557, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32891, "top5_acc": 0.59625, "loss_cls": 3.81614, "loss": 3.81614, "time": 0.8552} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.04554, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.31859, "top5_acc": 0.58719, "loss_cls": 3.88282, "loss": 3.88282, "time": 0.85359} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.04551, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.30828, "top5_acc": 0.57891, "loss_cls": 3.89053, "loss": 3.89053, "time": 0.85227} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.04548, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.32141, "top5_acc": 0.57875, "loss_cls": 3.86647, "loss": 3.86647, "time": 0.84608} +{"mode": "train", "epoch": 80, "iter": 1300, "lr": 0.04545, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.3275, "top5_acc": 0.59297, "loss_cls": 3.80958, "loss": 3.80958, "time": 0.85009} +{"mode": "train", "epoch": 80, "iter": 1400, "lr": 0.04543, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32594, "top5_acc": 0.58609, "loss_cls": 3.86626, "loss": 3.86626, "time": 0.85271} +{"mode": "train", "epoch": 80, "iter": 1500, "lr": 0.0454, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32625, "top5_acc": 0.58672, "loss_cls": 3.80994, "loss": 3.80994, "time": 0.85186} +{"mode": "train", "epoch": 80, "iter": 1600, "lr": 0.04537, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32266, "top5_acc": 0.57906, "loss_cls": 3.85518, "loss": 3.85518, "time": 0.85375} +{"mode": "train", "epoch": 80, "iter": 1700, "lr": 0.04534, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31891, "top5_acc": 0.58406, "loss_cls": 3.86501, "loss": 3.86501, "time": 0.8563} +{"mode": "train", "epoch": 80, "iter": 1800, "lr": 0.04532, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32375, "top5_acc": 0.58453, "loss_cls": 3.85231, "loss": 3.85231, "time": 0.85212} +{"mode": "train", "epoch": 80, "iter": 1900, "lr": 0.04529, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.31516, "top5_acc": 0.57703, "loss_cls": 3.88799, "loss": 3.88799, "time": 0.856} +{"mode": "train", "epoch": 80, "iter": 2000, "lr": 0.04526, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32172, "top5_acc": 0.57219, "loss_cls": 3.85801, "loss": 3.85801, "time": 0.84787} +{"mode": "train", "epoch": 80, "iter": 2100, "lr": 0.04523, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32328, "top5_acc": 0.59031, "loss_cls": 3.81304, "loss": 3.81304, "time": 0.85162} +{"mode": "train", "epoch": 80, "iter": 2200, "lr": 0.0452, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31781, "top5_acc": 0.57563, "loss_cls": 3.90063, "loss": 3.90063, "time": 0.84882} +{"mode": "train", "epoch": 80, "iter": 2300, "lr": 0.04518, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.31562, "top5_acc": 0.58297, "loss_cls": 3.87737, "loss": 3.87737, "time": 0.85051} +{"mode": "train", "epoch": 80, "iter": 2400, "lr": 0.04515, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32031, "top5_acc": 0.57109, "loss_cls": 3.8801, "loss": 3.8801, "time": 0.85135} +{"mode": "train", "epoch": 80, "iter": 2500, "lr": 0.04512, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32703, "top5_acc": 0.57703, "loss_cls": 3.86117, "loss": 3.86117, "time": 0.85454} +{"mode": "train", "epoch": 80, "iter": 2600, "lr": 0.04509, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.33422, "top5_acc": 0.59219, "loss_cls": 3.79732, "loss": 3.79732, "time": 0.85291} +{"mode": "train", "epoch": 80, "iter": 2700, "lr": 0.04506, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32484, "top5_acc": 0.58453, "loss_cls": 3.85439, "loss": 3.85439, "time": 0.85646} +{"mode": "train", "epoch": 80, "iter": 2800, "lr": 0.04504, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.33, "top5_acc": 0.57953, "loss_cls": 3.87091, "loss": 3.87091, "time": 0.85201} +{"mode": "train", "epoch": 80, "iter": 2900, "lr": 0.04501, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31453, "top5_acc": 0.57578, "loss_cls": 3.89507, "loss": 3.89507, "time": 0.85185} +{"mode": "train", "epoch": 80, "iter": 3000, "lr": 0.04498, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32688, "top5_acc": 0.58406, "loss_cls": 3.84652, "loss": 3.84652, "time": 0.85561} +{"mode": "train", "epoch": 80, "iter": 3100, "lr": 0.04495, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32938, "top5_acc": 0.59328, "loss_cls": 3.83671, "loss": 3.83671, "time": 0.8549} +{"mode": "train", "epoch": 80, "iter": 3200, "lr": 0.04493, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32422, "top5_acc": 0.58609, "loss_cls": 3.86787, "loss": 3.86787, "time": 0.85465} +{"mode": "train", "epoch": 80, "iter": 3300, "lr": 0.0449, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32312, "top5_acc": 0.57359, "loss_cls": 3.86653, "loss": 3.86653, "time": 0.85851} +{"mode": "train", "epoch": 80, "iter": 3400, "lr": 0.04487, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33062, "top5_acc": 0.58062, "loss_cls": 3.85473, "loss": 3.85473, "time": 0.85344} +{"mode": "train", "epoch": 80, "iter": 3500, "lr": 0.04484, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32078, "top5_acc": 0.58438, "loss_cls": 3.885, "loss": 3.885, "time": 0.85652} +{"mode": "train", "epoch": 80, "iter": 3600, "lr": 0.04481, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.3225, "top5_acc": 0.58312, "loss_cls": 3.90048, "loss": 3.90048, "time": 0.85454} +{"mode": "train", "epoch": 80, "iter": 3700, "lr": 0.04479, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31406, "top5_acc": 0.57594, "loss_cls": 3.89136, "loss": 3.89136, "time": 0.85756} +{"mode": "val", "epoch": 80, "iter": 309, "lr": 0.04477, "top1_acc": 0.26501, "top5_acc": 0.51517, "mean_class_accuracy": 0.26477} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.04475, "memory": 15990, "data_time": 1.54142, "top1_acc": 0.33047, "top5_acc": 0.58719, "loss_cls": 3.81985, "loss": 3.81985, "time": 2.57316} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.04472, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33281, "top5_acc": 0.59047, "loss_cls": 3.81634, "loss": 3.81634, "time": 0.85452} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.04469, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.3325, "top5_acc": 0.58109, "loss_cls": 3.82648, "loss": 3.82648, "time": 0.85599} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.04466, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33719, "top5_acc": 0.59172, "loss_cls": 3.78626, "loss": 3.78626, "time": 0.85377} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.04463, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33156, "top5_acc": 0.58844, "loss_cls": 3.81613, "loss": 3.81613, "time": 0.85257} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.04461, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31922, "top5_acc": 0.57781, "loss_cls": 3.8972, "loss": 3.8972, "time": 0.85322} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.04458, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32828, "top5_acc": 0.58469, "loss_cls": 3.82396, "loss": 3.82396, "time": 0.85598} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.04455, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33281, "top5_acc": 0.59047, "loss_cls": 3.80458, "loss": 3.80458, "time": 0.85281} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.04452, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32703, "top5_acc": 0.58781, "loss_cls": 3.8194, "loss": 3.8194, "time": 0.85613} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.0445, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33797, "top5_acc": 0.59406, "loss_cls": 3.80477, "loss": 3.80477, "time": 0.8539} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.04447, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32, "top5_acc": 0.58625, "loss_cls": 3.86956, "loss": 3.86956, "time": 0.84728} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.04444, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3225, "top5_acc": 0.57641, "loss_cls": 3.88423, "loss": 3.88423, "time": 0.85011} +{"mode": "train", "epoch": 81, "iter": 1300, "lr": 0.04441, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33625, "top5_acc": 0.59266, "loss_cls": 3.78037, "loss": 3.78037, "time": 0.84558} +{"mode": "train", "epoch": 81, "iter": 1400, "lr": 0.04438, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32359, "top5_acc": 0.57969, "loss_cls": 3.85499, "loss": 3.85499, "time": 0.84802} +{"mode": "train", "epoch": 81, "iter": 1500, "lr": 0.04436, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32547, "top5_acc": 0.58281, "loss_cls": 3.87939, "loss": 3.87939, "time": 0.84603} +{"mode": "train", "epoch": 81, "iter": 1600, "lr": 0.04433, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32281, "top5_acc": 0.57734, "loss_cls": 3.88312, "loss": 3.88312, "time": 0.85122} +{"mode": "train", "epoch": 81, "iter": 1700, "lr": 0.0443, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32438, "top5_acc": 0.57422, "loss_cls": 3.87722, "loss": 3.87722, "time": 0.85204} +{"mode": "train", "epoch": 81, "iter": 1800, "lr": 0.04427, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32406, "top5_acc": 0.58781, "loss_cls": 3.82967, "loss": 3.82967, "time": 0.85265} +{"mode": "train", "epoch": 81, "iter": 1900, "lr": 0.04425, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32281, "top5_acc": 0.57281, "loss_cls": 3.89467, "loss": 3.89467, "time": 0.85632} +{"mode": "train", "epoch": 81, "iter": 2000, "lr": 0.04422, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32391, "top5_acc": 0.59062, "loss_cls": 3.8339, "loss": 3.8339, "time": 0.8507} +{"mode": "train", "epoch": 81, "iter": 2100, "lr": 0.04419, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32875, "top5_acc": 0.59531, "loss_cls": 3.80587, "loss": 3.80587, "time": 0.85138} +{"mode": "train", "epoch": 81, "iter": 2200, "lr": 0.04416, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32625, "top5_acc": 0.58594, "loss_cls": 3.81831, "loss": 3.81831, "time": 0.85658} +{"mode": "train", "epoch": 81, "iter": 2300, "lr": 0.04413, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33328, "top5_acc": 0.58859, "loss_cls": 3.81946, "loss": 3.81946, "time": 0.85253} +{"mode": "train", "epoch": 81, "iter": 2400, "lr": 0.04411, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32297, "top5_acc": 0.59125, "loss_cls": 3.84339, "loss": 3.84339, "time": 0.84984} +{"mode": "train", "epoch": 81, "iter": 2500, "lr": 0.04408, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32328, "top5_acc": 0.58312, "loss_cls": 3.85548, "loss": 3.85548, "time": 0.84748} +{"mode": "train", "epoch": 81, "iter": 2600, "lr": 0.04405, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.32516, "top5_acc": 0.57938, "loss_cls": 3.83852, "loss": 3.83852, "time": 0.85576} +{"mode": "train", "epoch": 81, "iter": 2700, "lr": 0.04402, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31844, "top5_acc": 0.58578, "loss_cls": 3.8771, "loss": 3.8771, "time": 0.8533} +{"mode": "train", "epoch": 81, "iter": 2800, "lr": 0.044, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32812, "top5_acc": 0.58297, "loss_cls": 3.85746, "loss": 3.85746, "time": 0.85321} +{"mode": "train", "epoch": 81, "iter": 2900, "lr": 0.04397, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33438, "top5_acc": 0.58891, "loss_cls": 3.82728, "loss": 3.82728, "time": 0.85238} +{"mode": "train", "epoch": 81, "iter": 3000, "lr": 0.04394, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.325, "top5_acc": 0.58641, "loss_cls": 3.84441, "loss": 3.84441, "time": 0.85642} +{"mode": "train", "epoch": 81, "iter": 3100, "lr": 0.04391, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33266, "top5_acc": 0.58734, "loss_cls": 3.81489, "loss": 3.81489, "time": 0.85385} +{"mode": "train", "epoch": 81, "iter": 3200, "lr": 0.04389, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32922, "top5_acc": 0.58484, "loss_cls": 3.82747, "loss": 3.82747, "time": 0.85115} +{"mode": "train", "epoch": 81, "iter": 3300, "lr": 0.04386, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33297, "top5_acc": 0.58828, "loss_cls": 3.82757, "loss": 3.82757, "time": 0.85276} +{"mode": "train", "epoch": 81, "iter": 3400, "lr": 0.04383, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32844, "top5_acc": 0.59031, "loss_cls": 3.82123, "loss": 3.82123, "time": 0.85518} +{"mode": "train", "epoch": 81, "iter": 3500, "lr": 0.0438, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32312, "top5_acc": 0.57719, "loss_cls": 3.85304, "loss": 3.85304, "time": 0.85484} +{"mode": "train", "epoch": 81, "iter": 3600, "lr": 0.04377, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32922, "top5_acc": 0.58438, "loss_cls": 3.86473, "loss": 3.86473, "time": 0.85288} +{"mode": "train", "epoch": 81, "iter": 3700, "lr": 0.04375, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32828, "top5_acc": 0.57797, "loss_cls": 3.85485, "loss": 3.85485, "time": 0.85591} +{"mode": "val", "epoch": 81, "iter": 309, "lr": 0.04373, "top1_acc": 0.28121, "top5_acc": 0.52343, "mean_class_accuracy": 0.28098} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.04371, "memory": 15990, "data_time": 1.52368, "top1_acc": 0.33984, "top5_acc": 0.59938, "loss_cls": 3.75336, "loss": 3.75336, "time": 2.5675} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.04368, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33031, "top5_acc": 0.59594, "loss_cls": 3.80311, "loss": 3.80311, "time": 0.85273} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.04365, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33109, "top5_acc": 0.58812, "loss_cls": 3.79404, "loss": 3.79404, "time": 0.85248} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.04362, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34172, "top5_acc": 0.59594, "loss_cls": 3.73651, "loss": 3.73651, "time": 0.85976} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.04359, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33984, "top5_acc": 0.58812, "loss_cls": 3.78841, "loss": 3.78841, "time": 0.85471} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.04357, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33281, "top5_acc": 0.59469, "loss_cls": 3.77152, "loss": 3.77152, "time": 0.85549} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.04354, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33859, "top5_acc": 0.59172, "loss_cls": 3.78895, "loss": 3.78895, "time": 0.85137} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.04351, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33422, "top5_acc": 0.58953, "loss_cls": 3.80549, "loss": 3.80549, "time": 0.8618} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.04348, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.31703, "top5_acc": 0.58672, "loss_cls": 3.83023, "loss": 3.83023, "time": 0.86005} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.04346, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.32297, "top5_acc": 0.58797, "loss_cls": 3.8602, "loss": 3.8602, "time": 0.85719} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.04343, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.33625, "top5_acc": 0.59422, "loss_cls": 3.81118, "loss": 3.81118, "time": 0.85499} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.0434, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32344, "top5_acc": 0.5925, "loss_cls": 3.82476, "loss": 3.82476, "time": 0.85192} +{"mode": "train", "epoch": 82, "iter": 1300, "lr": 0.04337, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33016, "top5_acc": 0.58906, "loss_cls": 3.85299, "loss": 3.85299, "time": 0.85288} +{"mode": "train", "epoch": 82, "iter": 1400, "lr": 0.04335, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32906, "top5_acc": 0.58984, "loss_cls": 3.8126, "loss": 3.8126, "time": 0.85857} +{"mode": "train", "epoch": 82, "iter": 1500, "lr": 0.04332, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33453, "top5_acc": 0.59531, "loss_cls": 3.80091, "loss": 3.80091, "time": 0.85959} +{"mode": "train", "epoch": 82, "iter": 1600, "lr": 0.04329, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32188, "top5_acc": 0.58203, "loss_cls": 3.85517, "loss": 3.85517, "time": 0.86257} +{"mode": "train", "epoch": 82, "iter": 1700, "lr": 0.04326, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33406, "top5_acc": 0.59172, "loss_cls": 3.81303, "loss": 3.81303, "time": 0.85826} +{"mode": "train", "epoch": 82, "iter": 1800, "lr": 0.04323, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32797, "top5_acc": 0.58641, "loss_cls": 3.83689, "loss": 3.83689, "time": 0.85579} +{"mode": "train", "epoch": 82, "iter": 1900, "lr": 0.04321, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.32203, "top5_acc": 0.56938, "loss_cls": 3.88957, "loss": 3.88957, "time": 0.85559} +{"mode": "train", "epoch": 82, "iter": 2000, "lr": 0.04318, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.32375, "top5_acc": 0.57734, "loss_cls": 3.87089, "loss": 3.87089, "time": 0.85633} +{"mode": "train", "epoch": 82, "iter": 2100, "lr": 0.04315, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33422, "top5_acc": 0.58266, "loss_cls": 3.81033, "loss": 3.81033, "time": 0.8509} +{"mode": "train", "epoch": 82, "iter": 2200, "lr": 0.04312, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32594, "top5_acc": 0.5825, "loss_cls": 3.84663, "loss": 3.84663, "time": 0.85525} +{"mode": "train", "epoch": 82, "iter": 2300, "lr": 0.0431, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31859, "top5_acc": 0.57844, "loss_cls": 3.86856, "loss": 3.86856, "time": 0.85683} +{"mode": "train", "epoch": 82, "iter": 2400, "lr": 0.04307, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.34359, "top5_acc": 0.60781, "loss_cls": 3.76401, "loss": 3.76401, "time": 0.85958} +{"mode": "train", "epoch": 82, "iter": 2500, "lr": 0.04304, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3275, "top5_acc": 0.57938, "loss_cls": 3.84856, "loss": 3.84856, "time": 0.857} +{"mode": "train", "epoch": 82, "iter": 2600, "lr": 0.04301, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33266, "top5_acc": 0.58578, "loss_cls": 3.82315, "loss": 3.82315, "time": 0.85585} +{"mode": "train", "epoch": 82, "iter": 2700, "lr": 0.04299, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3375, "top5_acc": 0.5875, "loss_cls": 3.81236, "loss": 3.81236, "time": 0.85485} +{"mode": "train", "epoch": 82, "iter": 2800, "lr": 0.04296, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.32891, "top5_acc": 0.58938, "loss_cls": 3.85279, "loss": 3.85279, "time": 0.85048} +{"mode": "train", "epoch": 82, "iter": 2900, "lr": 0.04293, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32906, "top5_acc": 0.58703, "loss_cls": 3.82604, "loss": 3.82604, "time": 0.85896} +{"mode": "train", "epoch": 82, "iter": 3000, "lr": 0.0429, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33031, "top5_acc": 0.58578, "loss_cls": 3.85013, "loss": 3.85013, "time": 0.85827} +{"mode": "train", "epoch": 82, "iter": 3100, "lr": 0.04287, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32875, "top5_acc": 0.58719, "loss_cls": 3.82475, "loss": 3.82475, "time": 0.85809} +{"mode": "train", "epoch": 82, "iter": 3200, "lr": 0.04285, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32875, "top5_acc": 0.58109, "loss_cls": 3.90377, "loss": 3.90377, "time": 0.85636} +{"mode": "train", "epoch": 82, "iter": 3300, "lr": 0.04282, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32406, "top5_acc": 0.58406, "loss_cls": 3.84874, "loss": 3.84874, "time": 0.85682} +{"mode": "train", "epoch": 82, "iter": 3400, "lr": 0.04279, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32562, "top5_acc": 0.58188, "loss_cls": 3.86203, "loss": 3.86203, "time": 0.85815} +{"mode": "train", "epoch": 82, "iter": 3500, "lr": 0.04276, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32547, "top5_acc": 0.58375, "loss_cls": 3.85062, "loss": 3.85062, "time": 0.86029} +{"mode": "train", "epoch": 82, "iter": 3600, "lr": 0.04274, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32828, "top5_acc": 0.58672, "loss_cls": 3.83729, "loss": 3.83729, "time": 0.85582} +{"mode": "train", "epoch": 82, "iter": 3700, "lr": 0.04271, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32531, "top5_acc": 0.58594, "loss_cls": 3.84702, "loss": 3.84702, "time": 0.86008} +{"mode": "val", "epoch": 82, "iter": 309, "lr": 0.0427, "top1_acc": 0.27189, "top5_acc": 0.52008, "mean_class_accuracy": 0.27189} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.04267, "memory": 15990, "data_time": 1.52661, "top1_acc": 0.34016, "top5_acc": 0.605, "loss_cls": 3.72032, "loss": 3.72032, "time": 2.57962} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.04264, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.34047, "top5_acc": 0.60203, "loss_cls": 3.75937, "loss": 3.75937, "time": 0.86734} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.04261, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.33016, "top5_acc": 0.58797, "loss_cls": 3.80317, "loss": 3.80317, "time": 0.86953} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.04259, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.32922, "top5_acc": 0.58375, "loss_cls": 3.81467, "loss": 3.81467, "time": 0.86723} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.04256, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.335, "top5_acc": 0.58375, "loss_cls": 3.85566, "loss": 3.85566, "time": 0.8676} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.04253, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.32984, "top5_acc": 0.585, "loss_cls": 3.79662, "loss": 3.79662, "time": 0.86313} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.0425, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32266, "top5_acc": 0.58094, "loss_cls": 3.85997, "loss": 3.85997, "time": 0.86467} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.04247, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.32906, "top5_acc": 0.58953, "loss_cls": 3.79998, "loss": 3.79998, "time": 0.85961} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.04245, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33141, "top5_acc": 0.58359, "loss_cls": 3.82336, "loss": 3.82336, "time": 0.86256} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.04242, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33547, "top5_acc": 0.59812, "loss_cls": 3.77223, "loss": 3.77223, "time": 0.8558} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.04239, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34078, "top5_acc": 0.59156, "loss_cls": 3.78102, "loss": 3.78102, "time": 0.8491} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.04236, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33141, "top5_acc": 0.58594, "loss_cls": 3.81149, "loss": 3.81149, "time": 0.85299} +{"mode": "train", "epoch": 83, "iter": 1300, "lr": 0.04234, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.32828, "top5_acc": 0.59438, "loss_cls": 3.79084, "loss": 3.79084, "time": 0.84663} +{"mode": "train", "epoch": 83, "iter": 1400, "lr": 0.04231, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32812, "top5_acc": 0.59172, "loss_cls": 3.8144, "loss": 3.8144, "time": 0.8552} +{"mode": "train", "epoch": 83, "iter": 1500, "lr": 0.04228, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32578, "top5_acc": 0.58844, "loss_cls": 3.8209, "loss": 3.8209, "time": 0.85904} +{"mode": "train", "epoch": 83, "iter": 1600, "lr": 0.04225, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32938, "top5_acc": 0.5875, "loss_cls": 3.83428, "loss": 3.83428, "time": 0.8607} +{"mode": "train", "epoch": 83, "iter": 1700, "lr": 0.04223, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32969, "top5_acc": 0.59062, "loss_cls": 3.8027, "loss": 3.8027, "time": 0.85909} +{"mode": "train", "epoch": 83, "iter": 1800, "lr": 0.0422, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33422, "top5_acc": 0.5825, "loss_cls": 3.83267, "loss": 3.83267, "time": 0.85971} +{"mode": "train", "epoch": 83, "iter": 1900, "lr": 0.04217, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33562, "top5_acc": 0.58219, "loss_cls": 3.8234, "loss": 3.8234, "time": 0.85177} +{"mode": "train", "epoch": 83, "iter": 2000, "lr": 0.04214, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33062, "top5_acc": 0.59406, "loss_cls": 3.78339, "loss": 3.78339, "time": 0.85334} +{"mode": "train", "epoch": 83, "iter": 2100, "lr": 0.04212, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34359, "top5_acc": 0.59906, "loss_cls": 3.78032, "loss": 3.78032, "time": 0.85108} +{"mode": "train", "epoch": 83, "iter": 2200, "lr": 0.04209, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34594, "top5_acc": 0.59781, "loss_cls": 3.7908, "loss": 3.7908, "time": 0.85866} +{"mode": "train", "epoch": 83, "iter": 2300, "lr": 0.04206, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.31906, "top5_acc": 0.58281, "loss_cls": 3.85437, "loss": 3.85437, "time": 0.85593} +{"mode": "train", "epoch": 83, "iter": 2400, "lr": 0.04203, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32375, "top5_acc": 0.58, "loss_cls": 3.85055, "loss": 3.85055, "time": 0.86089} +{"mode": "train", "epoch": 83, "iter": 2500, "lr": 0.04201, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32344, "top5_acc": 0.58219, "loss_cls": 3.83312, "loss": 3.83312, "time": 0.85588} +{"mode": "train", "epoch": 83, "iter": 2600, "lr": 0.04198, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33016, "top5_acc": 0.59031, "loss_cls": 3.80796, "loss": 3.80796, "time": 0.84664} +{"mode": "train", "epoch": 83, "iter": 2700, "lr": 0.04195, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32953, "top5_acc": 0.58672, "loss_cls": 3.82433, "loss": 3.82433, "time": 0.85438} +{"mode": "train", "epoch": 83, "iter": 2800, "lr": 0.04192, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33109, "top5_acc": 0.59266, "loss_cls": 3.80578, "loss": 3.80578, "time": 0.85113} +{"mode": "train", "epoch": 83, "iter": 2900, "lr": 0.0419, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34109, "top5_acc": 0.58953, "loss_cls": 3.78116, "loss": 3.78116, "time": 0.85812} +{"mode": "train", "epoch": 83, "iter": 3000, "lr": 0.04187, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.31875, "top5_acc": 0.57234, "loss_cls": 3.9016, "loss": 3.9016, "time": 0.86412} +{"mode": "train", "epoch": 83, "iter": 3100, "lr": 0.04184, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.32219, "top5_acc": 0.57859, "loss_cls": 3.8397, "loss": 3.8397, "time": 0.85976} +{"mode": "train", "epoch": 83, "iter": 3200, "lr": 0.04181, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.33438, "top5_acc": 0.59344, "loss_cls": 3.80091, "loss": 3.80091, "time": 0.85999} +{"mode": "train", "epoch": 83, "iter": 3300, "lr": 0.04178, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32328, "top5_acc": 0.57547, "loss_cls": 3.86886, "loss": 3.86886, "time": 0.8612} +{"mode": "train", "epoch": 83, "iter": 3400, "lr": 0.04176, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33391, "top5_acc": 0.58906, "loss_cls": 3.79997, "loss": 3.79997, "time": 0.85799} +{"mode": "train", "epoch": 83, "iter": 3500, "lr": 0.04173, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33828, "top5_acc": 0.58766, "loss_cls": 3.80161, "loss": 3.80161, "time": 0.85842} +{"mode": "train", "epoch": 83, "iter": 3600, "lr": 0.0417, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32359, "top5_acc": 0.58078, "loss_cls": 3.8465, "loss": 3.8465, "time": 0.85453} +{"mode": "train", "epoch": 83, "iter": 3700, "lr": 0.04167, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.31812, "top5_acc": 0.57766, "loss_cls": 3.86743, "loss": 3.86743, "time": 0.86116} +{"mode": "val", "epoch": 83, "iter": 309, "lr": 0.04166, "top1_acc": 0.27032, "top5_acc": 0.51993, "mean_class_accuracy": 0.27002} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.04163, "memory": 15990, "data_time": 1.57283, "top1_acc": 0.34234, "top5_acc": 0.61062, "loss_cls": 3.71043, "loss": 3.71043, "time": 2.6149} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.04161, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33875, "top5_acc": 0.58844, "loss_cls": 3.79388, "loss": 3.79388, "time": 0.84827} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.04158, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34359, "top5_acc": 0.59828, "loss_cls": 3.72698, "loss": 3.72698, "time": 0.85479} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.04155, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34594, "top5_acc": 0.60641, "loss_cls": 3.71248, "loss": 3.71248, "time": 0.85377} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.04152, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.32625, "top5_acc": 0.59281, "loss_cls": 3.79193, "loss": 3.79193, "time": 0.8512} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.0415, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33344, "top5_acc": 0.59062, "loss_cls": 3.78436, "loss": 3.78436, "time": 0.85237} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.04147, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34484, "top5_acc": 0.60125, "loss_cls": 3.74961, "loss": 3.74961, "time": 0.85364} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.04144, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.32891, "top5_acc": 0.58703, "loss_cls": 3.81894, "loss": 3.81894, "time": 0.85487} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.04141, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32719, "top5_acc": 0.58562, "loss_cls": 3.81687, "loss": 3.81687, "time": 0.85412} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.04139, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33047, "top5_acc": 0.59266, "loss_cls": 3.79512, "loss": 3.79512, "time": 0.85072} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.04136, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.33047, "top5_acc": 0.59688, "loss_cls": 3.79497, "loss": 3.79497, "time": 0.85389} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.04133, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33359, "top5_acc": 0.58781, "loss_cls": 3.79422, "loss": 3.79422, "time": 0.85792} +{"mode": "train", "epoch": 84, "iter": 1300, "lr": 0.0413, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33156, "top5_acc": 0.58594, "loss_cls": 3.81297, "loss": 3.81297, "time": 0.8501} +{"mode": "train", "epoch": 84, "iter": 1400, "lr": 0.04128, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.32422, "top5_acc": 0.58406, "loss_cls": 3.8609, "loss": 3.8609, "time": 0.85619} +{"mode": "train", "epoch": 84, "iter": 1500, "lr": 0.04125, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33703, "top5_acc": 0.58938, "loss_cls": 3.79615, "loss": 3.79615, "time": 0.85062} +{"mode": "train", "epoch": 84, "iter": 1600, "lr": 0.04122, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33969, "top5_acc": 0.60078, "loss_cls": 3.75011, "loss": 3.75011, "time": 0.84887} +{"mode": "train", "epoch": 84, "iter": 1700, "lr": 0.04119, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32375, "top5_acc": 0.58453, "loss_cls": 3.83554, "loss": 3.83554, "time": 0.84681} +{"mode": "train", "epoch": 84, "iter": 1800, "lr": 0.04117, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32781, "top5_acc": 0.58516, "loss_cls": 3.85274, "loss": 3.85274, "time": 0.85141} +{"mode": "train", "epoch": 84, "iter": 1900, "lr": 0.04114, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32094, "top5_acc": 0.58672, "loss_cls": 3.83188, "loss": 3.83188, "time": 0.85208} +{"mode": "train", "epoch": 84, "iter": 2000, "lr": 0.04111, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.32906, "top5_acc": 0.58266, "loss_cls": 3.84952, "loss": 3.84952, "time": 0.84878} +{"mode": "train", "epoch": 84, "iter": 2100, "lr": 0.04108, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33203, "top5_acc": 0.59438, "loss_cls": 3.79178, "loss": 3.79178, "time": 0.85179} +{"mode": "train", "epoch": 84, "iter": 2200, "lr": 0.04106, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33375, "top5_acc": 0.58781, "loss_cls": 3.82013, "loss": 3.82013, "time": 0.85484} +{"mode": "train", "epoch": 84, "iter": 2300, "lr": 0.04103, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32938, "top5_acc": 0.59125, "loss_cls": 3.8168, "loss": 3.8168, "time": 0.85441} +{"mode": "train", "epoch": 84, "iter": 2400, "lr": 0.041, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33688, "top5_acc": 0.59359, "loss_cls": 3.80418, "loss": 3.80418, "time": 0.85393} +{"mode": "train", "epoch": 84, "iter": 2500, "lr": 0.04097, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.33359, "top5_acc": 0.59312, "loss_cls": 3.79773, "loss": 3.79773, "time": 0.85183} +{"mode": "train", "epoch": 84, "iter": 2600, "lr": 0.04095, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32656, "top5_acc": 0.58594, "loss_cls": 3.8405, "loss": 3.8405, "time": 0.8492} +{"mode": "train", "epoch": 84, "iter": 2700, "lr": 0.04092, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33766, "top5_acc": 0.59359, "loss_cls": 3.78408, "loss": 3.78408, "time": 0.85227} +{"mode": "train", "epoch": 84, "iter": 2800, "lr": 0.04089, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33875, "top5_acc": 0.59219, "loss_cls": 3.77183, "loss": 3.77183, "time": 0.85258} +{"mode": "train", "epoch": 84, "iter": 2900, "lr": 0.04086, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33203, "top5_acc": 0.59297, "loss_cls": 3.81894, "loss": 3.81894, "time": 0.84696} +{"mode": "train", "epoch": 84, "iter": 3000, "lr": 0.04084, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32938, "top5_acc": 0.59312, "loss_cls": 3.8037, "loss": 3.8037, "time": 0.85168} +{"mode": "train", "epoch": 84, "iter": 3100, "lr": 0.04081, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.32797, "top5_acc": 0.59688, "loss_cls": 3.81344, "loss": 3.81344, "time": 0.852} +{"mode": "train", "epoch": 84, "iter": 3200, "lr": 0.04078, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33656, "top5_acc": 0.58828, "loss_cls": 3.7917, "loss": 3.7917, "time": 0.85301} +{"mode": "train", "epoch": 84, "iter": 3300, "lr": 0.04075, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33109, "top5_acc": 0.58453, "loss_cls": 3.82466, "loss": 3.82466, "time": 0.85376} +{"mode": "train", "epoch": 84, "iter": 3400, "lr": 0.04073, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.3325, "top5_acc": 0.58234, "loss_cls": 3.82472, "loss": 3.82472, "time": 0.85505} +{"mode": "train", "epoch": 84, "iter": 3500, "lr": 0.0407, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33953, "top5_acc": 0.59203, "loss_cls": 3.79407, "loss": 3.79407, "time": 0.85412} +{"mode": "train", "epoch": 84, "iter": 3600, "lr": 0.04067, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33234, "top5_acc": 0.58172, "loss_cls": 3.84963, "loss": 3.84963, "time": 0.85108} +{"mode": "train", "epoch": 84, "iter": 3700, "lr": 0.04064, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32625, "top5_acc": 0.58922, "loss_cls": 3.80642, "loss": 3.80642, "time": 0.85163} +{"mode": "val", "epoch": 84, "iter": 309, "lr": 0.04063, "top1_acc": 0.27341, "top5_acc": 0.5255, "mean_class_accuracy": 0.27314} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.0406, "memory": 15990, "data_time": 1.53579, "top1_acc": 0.33562, "top5_acc": 0.59406, "loss_cls": 3.78097, "loss": 3.78097, "time": 2.57869} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.04058, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34266, "top5_acc": 0.60172, "loss_cls": 3.73543, "loss": 3.73543, "time": 0.85267} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.04055, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33453, "top5_acc": 0.60062, "loss_cls": 3.74976, "loss": 3.74976, "time": 0.85548} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.04052, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33047, "top5_acc": 0.59297, "loss_cls": 3.78548, "loss": 3.78548, "time": 0.85185} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.04049, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33766, "top5_acc": 0.60266, "loss_cls": 3.75142, "loss": 3.75142, "time": 0.85562} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.04047, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34469, "top5_acc": 0.59234, "loss_cls": 3.74147, "loss": 3.74147, "time": 0.8502} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.04044, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33141, "top5_acc": 0.59953, "loss_cls": 3.79482, "loss": 3.79482, "time": 0.85712} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.04041, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33031, "top5_acc": 0.59234, "loss_cls": 3.77006, "loss": 3.77006, "time": 0.85492} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.04038, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.3325, "top5_acc": 0.59422, "loss_cls": 3.79445, "loss": 3.79445, "time": 0.85856} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.04036, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34359, "top5_acc": 0.59672, "loss_cls": 3.77758, "loss": 3.77758, "time": 0.85387} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.04033, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.33672, "top5_acc": 0.60375, "loss_cls": 3.7367, "loss": 3.7367, "time": 0.85094} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.0403, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33609, "top5_acc": 0.59766, "loss_cls": 3.76045, "loss": 3.76045, "time": 0.85006} +{"mode": "train", "epoch": 85, "iter": 1300, "lr": 0.04027, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33609, "top5_acc": 0.59875, "loss_cls": 3.78125, "loss": 3.78125, "time": 0.84691} +{"mode": "train", "epoch": 85, "iter": 1400, "lr": 0.04025, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33062, "top5_acc": 0.59719, "loss_cls": 3.79495, "loss": 3.79495, "time": 0.85366} +{"mode": "train", "epoch": 85, "iter": 1500, "lr": 0.04022, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33641, "top5_acc": 0.59906, "loss_cls": 3.76308, "loss": 3.76308, "time": 0.85499} +{"mode": "train", "epoch": 85, "iter": 1600, "lr": 0.04019, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33859, "top5_acc": 0.58969, "loss_cls": 3.78356, "loss": 3.78356, "time": 0.85443} +{"mode": "train", "epoch": 85, "iter": 1700, "lr": 0.04016, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32562, "top5_acc": 0.58297, "loss_cls": 3.84535, "loss": 3.84535, "time": 0.85471} +{"mode": "train", "epoch": 85, "iter": 1800, "lr": 0.04014, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33578, "top5_acc": 0.59, "loss_cls": 3.80342, "loss": 3.80342, "time": 0.85799} +{"mode": "train", "epoch": 85, "iter": 1900, "lr": 0.04011, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33344, "top5_acc": 0.59094, "loss_cls": 3.80537, "loss": 3.80537, "time": 0.84948} +{"mode": "train", "epoch": 85, "iter": 2000, "lr": 0.04008, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33562, "top5_acc": 0.58875, "loss_cls": 3.78599, "loss": 3.78599, "time": 0.84777} +{"mode": "train", "epoch": 85, "iter": 2100, "lr": 0.04006, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33094, "top5_acc": 0.58672, "loss_cls": 3.83101, "loss": 3.83101, "time": 0.85653} +{"mode": "train", "epoch": 85, "iter": 2200, "lr": 0.04003, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32812, "top5_acc": 0.58812, "loss_cls": 3.79971, "loss": 3.79971, "time": 0.85445} +{"mode": "train", "epoch": 85, "iter": 2300, "lr": 0.04, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32938, "top5_acc": 0.58922, "loss_cls": 3.79733, "loss": 3.79733, "time": 0.85327} +{"mode": "train", "epoch": 85, "iter": 2400, "lr": 0.03997, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.32391, "top5_acc": 0.58484, "loss_cls": 3.83023, "loss": 3.83023, "time": 0.85218} +{"mode": "train", "epoch": 85, "iter": 2500, "lr": 0.03995, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.33797, "top5_acc": 0.59812, "loss_cls": 3.77542, "loss": 3.77542, "time": 0.85655} +{"mode": "train", "epoch": 85, "iter": 2600, "lr": 0.03992, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33203, "top5_acc": 0.58984, "loss_cls": 3.82237, "loss": 3.82237, "time": 0.85192} +{"mode": "train", "epoch": 85, "iter": 2700, "lr": 0.03989, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32562, "top5_acc": 0.58578, "loss_cls": 3.82767, "loss": 3.82767, "time": 0.85218} +{"mode": "train", "epoch": 85, "iter": 2800, "lr": 0.03986, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33453, "top5_acc": 0.59141, "loss_cls": 3.8006, "loss": 3.8006, "time": 0.85412} +{"mode": "train", "epoch": 85, "iter": 2900, "lr": 0.03984, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33531, "top5_acc": 0.58281, "loss_cls": 3.81837, "loss": 3.81837, "time": 0.84893} +{"mode": "train", "epoch": 85, "iter": 3000, "lr": 0.03981, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.345, "top5_acc": 0.60281, "loss_cls": 3.74647, "loss": 3.74647, "time": 0.85044} +{"mode": "train", "epoch": 85, "iter": 3100, "lr": 0.03978, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33406, "top5_acc": 0.59562, "loss_cls": 3.78222, "loss": 3.78222, "time": 0.85179} +{"mode": "train", "epoch": 85, "iter": 3200, "lr": 0.03975, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33281, "top5_acc": 0.59156, "loss_cls": 3.79146, "loss": 3.79146, "time": 0.85133} +{"mode": "train", "epoch": 85, "iter": 3300, "lr": 0.03973, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.33047, "top5_acc": 0.58484, "loss_cls": 3.80089, "loss": 3.80089, "time": 0.85202} +{"mode": "train", "epoch": 85, "iter": 3400, "lr": 0.0397, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.3275, "top5_acc": 0.6, "loss_cls": 3.79253, "loss": 3.79253, "time": 0.85369} +{"mode": "train", "epoch": 85, "iter": 3500, "lr": 0.03967, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33078, "top5_acc": 0.58734, "loss_cls": 3.79403, "loss": 3.79403, "time": 0.8563} +{"mode": "train", "epoch": 85, "iter": 3600, "lr": 0.03964, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32641, "top5_acc": 0.58766, "loss_cls": 3.81863, "loss": 3.81863, "time": 0.85404} +{"mode": "train", "epoch": 85, "iter": 3700, "lr": 0.03962, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33469, "top5_acc": 0.59109, "loss_cls": 3.78985, "loss": 3.78985, "time": 0.85457} +{"mode": "val", "epoch": 85, "iter": 309, "lr": 0.0396, "top1_acc": 0.2801, "top5_acc": 0.52809, "mean_class_accuracy": 0.27977} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.03958, "memory": 15990, "data_time": 1.53282, "top1_acc": 0.34141, "top5_acc": 0.59938, "loss_cls": 3.7624, "loss": 3.7624, "time": 2.58197} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.03955, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33812, "top5_acc": 0.60312, "loss_cls": 3.73747, "loss": 3.73747, "time": 0.85989} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.03952, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34453, "top5_acc": 0.60031, "loss_cls": 3.73647, "loss": 3.73647, "time": 0.85992} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.0395, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34375, "top5_acc": 0.60203, "loss_cls": 3.73397, "loss": 3.73397, "time": 0.86009} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.03947, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33266, "top5_acc": 0.59109, "loss_cls": 3.79795, "loss": 3.79795, "time": 0.86429} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.03944, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33672, "top5_acc": 0.59844, "loss_cls": 3.76286, "loss": 3.76286, "time": 0.85661} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.03941, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32781, "top5_acc": 0.58875, "loss_cls": 3.78045, "loss": 3.78045, "time": 0.86052} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.03939, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.34016, "top5_acc": 0.60531, "loss_cls": 3.74482, "loss": 3.74482, "time": 0.86183} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.03936, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.34016, "top5_acc": 0.59266, "loss_cls": 3.76804, "loss": 3.76804, "time": 0.86685} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.03933, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33484, "top5_acc": 0.59625, "loss_cls": 3.79736, "loss": 3.79736, "time": 0.85966} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.0393, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.34281, "top5_acc": 0.60172, "loss_cls": 3.74865, "loss": 3.74865, "time": 0.85721} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.03928, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35156, "top5_acc": 0.60844, "loss_cls": 3.69785, "loss": 3.69785, "time": 0.85358} +{"mode": "train", "epoch": 86, "iter": 1300, "lr": 0.03925, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.34016, "top5_acc": 0.60297, "loss_cls": 3.77587, "loss": 3.77587, "time": 0.85385} +{"mode": "train", "epoch": 86, "iter": 1400, "lr": 0.03922, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.33484, "top5_acc": 0.59688, "loss_cls": 3.77972, "loss": 3.77972, "time": 0.85497} +{"mode": "train", "epoch": 86, "iter": 1500, "lr": 0.03919, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33047, "top5_acc": 0.58703, "loss_cls": 3.80695, "loss": 3.80695, "time": 0.85597} +{"mode": "train", "epoch": 86, "iter": 1600, "lr": 0.03917, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34094, "top5_acc": 0.60125, "loss_cls": 3.75784, "loss": 3.75784, "time": 0.86166} +{"mode": "train", "epoch": 86, "iter": 1700, "lr": 0.03914, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.34094, "top5_acc": 0.59875, "loss_cls": 3.76256, "loss": 3.76256, "time": 0.86309} +{"mode": "train", "epoch": 86, "iter": 1800, "lr": 0.03911, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.34625, "top5_acc": 0.59625, "loss_cls": 3.75303, "loss": 3.75303, "time": 0.85569} +{"mode": "train", "epoch": 86, "iter": 1900, "lr": 0.03909, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34, "top5_acc": 0.60438, "loss_cls": 3.75314, "loss": 3.75314, "time": 0.85402} +{"mode": "train", "epoch": 86, "iter": 2000, "lr": 0.03906, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33203, "top5_acc": 0.595, "loss_cls": 3.75337, "loss": 3.75337, "time": 0.85552} +{"mode": "train", "epoch": 86, "iter": 2100, "lr": 0.03903, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32391, "top5_acc": 0.59062, "loss_cls": 3.83354, "loss": 3.83354, "time": 0.8613} +{"mode": "train", "epoch": 86, "iter": 2200, "lr": 0.039, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.32766, "top5_acc": 0.59172, "loss_cls": 3.77509, "loss": 3.77509, "time": 0.86729} +{"mode": "train", "epoch": 86, "iter": 2300, "lr": 0.03898, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.32625, "top5_acc": 0.58188, "loss_cls": 3.83944, "loss": 3.83944, "time": 0.86146} +{"mode": "train", "epoch": 86, "iter": 2400, "lr": 0.03895, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32672, "top5_acc": 0.58438, "loss_cls": 3.82092, "loss": 3.82092, "time": 0.85628} +{"mode": "train", "epoch": 86, "iter": 2500, "lr": 0.03892, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33844, "top5_acc": 0.59172, "loss_cls": 3.77979, "loss": 3.77979, "time": 0.85437} +{"mode": "train", "epoch": 86, "iter": 2600, "lr": 0.03889, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34625, "top5_acc": 0.60438, "loss_cls": 3.75271, "loss": 3.75271, "time": 0.85302} +{"mode": "train", "epoch": 86, "iter": 2700, "lr": 0.03887, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33297, "top5_acc": 0.59766, "loss_cls": 3.78474, "loss": 3.78474, "time": 0.85866} +{"mode": "train", "epoch": 86, "iter": 2800, "lr": 0.03884, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34047, "top5_acc": 0.60172, "loss_cls": 3.73393, "loss": 3.73393, "time": 0.85383} +{"mode": "train", "epoch": 86, "iter": 2900, "lr": 0.03881, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34609, "top5_acc": 0.60016, "loss_cls": 3.74818, "loss": 3.74818, "time": 0.85326} +{"mode": "train", "epoch": 86, "iter": 3000, "lr": 0.03879, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.32922, "top5_acc": 0.58422, "loss_cls": 3.81858, "loss": 3.81858, "time": 0.85238} +{"mode": "train", "epoch": 86, "iter": 3100, "lr": 0.03876, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.33328, "top5_acc": 0.59891, "loss_cls": 3.75739, "loss": 3.75739, "time": 0.85785} +{"mode": "train", "epoch": 86, "iter": 3200, "lr": 0.03873, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33094, "top5_acc": 0.59266, "loss_cls": 3.80657, "loss": 3.80657, "time": 0.85347} +{"mode": "train", "epoch": 86, "iter": 3300, "lr": 0.0387, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.33172, "top5_acc": 0.58734, "loss_cls": 3.79414, "loss": 3.79414, "time": 0.85677} +{"mode": "train", "epoch": 86, "iter": 3400, "lr": 0.03868, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33828, "top5_acc": 0.59047, "loss_cls": 3.77547, "loss": 3.77547, "time": 0.86329} +{"mode": "train", "epoch": 86, "iter": 3500, "lr": 0.03865, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33125, "top5_acc": 0.59609, "loss_cls": 3.79007, "loss": 3.79007, "time": 0.85708} +{"mode": "train", "epoch": 86, "iter": 3600, "lr": 0.03862, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33391, "top5_acc": 0.60312, "loss_cls": 3.7442, "loss": 3.7442, "time": 0.85209} +{"mode": "train", "epoch": 86, "iter": 3700, "lr": 0.0386, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33812, "top5_acc": 0.59938, "loss_cls": 3.76036, "loss": 3.76036, "time": 0.85849} +{"mode": "val", "epoch": 86, "iter": 309, "lr": 0.03858, "top1_acc": 0.27934, "top5_acc": 0.53209, "mean_class_accuracy": 0.27916} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.03856, "memory": 15990, "data_time": 1.5892, "top1_acc": 0.34562, "top5_acc": 0.60781, "loss_cls": 3.70791, "loss": 3.70791, "time": 2.62908} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.03853, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34328, "top5_acc": 0.60406, "loss_cls": 3.71453, "loss": 3.71453, "time": 0.85529} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.0385, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.355, "top5_acc": 0.61187, "loss_cls": 3.6738, "loss": 3.6738, "time": 0.85481} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.03847, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34328, "top5_acc": 0.60156, "loss_cls": 3.75859, "loss": 3.75859, "time": 0.85507} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.03845, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34562, "top5_acc": 0.60766, "loss_cls": 3.69547, "loss": 3.69547, "time": 0.85677} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.03842, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33922, "top5_acc": 0.59656, "loss_cls": 3.7772, "loss": 3.7772, "time": 0.85899} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.03839, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33906, "top5_acc": 0.59953, "loss_cls": 3.73693, "loss": 3.73693, "time": 0.85632} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.03837, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34719, "top5_acc": 0.60766, "loss_cls": 3.71249, "loss": 3.71249, "time": 0.85814} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.03834, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34016, "top5_acc": 0.59125, "loss_cls": 3.77197, "loss": 3.77197, "time": 0.85371} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.03831, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.32812, "top5_acc": 0.58516, "loss_cls": 3.81446, "loss": 3.81446, "time": 0.85481} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.03828, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.33312, "top5_acc": 0.5875, "loss_cls": 3.82048, "loss": 3.82048, "time": 0.84786} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.03826, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32891, "top5_acc": 0.58859, "loss_cls": 3.80559, "loss": 3.80559, "time": 0.85064} +{"mode": "train", "epoch": 87, "iter": 1300, "lr": 0.03823, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32953, "top5_acc": 0.59172, "loss_cls": 3.78096, "loss": 3.78096, "time": 0.85145} +{"mode": "train", "epoch": 87, "iter": 1400, "lr": 0.0382, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33422, "top5_acc": 0.595, "loss_cls": 3.77744, "loss": 3.77744, "time": 0.85255} +{"mode": "train", "epoch": 87, "iter": 1500, "lr": 0.03817, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35672, "top5_acc": 0.61391, "loss_cls": 3.68296, "loss": 3.68296, "time": 0.85458} +{"mode": "train", "epoch": 87, "iter": 1600, "lr": 0.03815, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33047, "top5_acc": 0.59422, "loss_cls": 3.76905, "loss": 3.76905, "time": 0.85804} +{"mode": "train", "epoch": 87, "iter": 1700, "lr": 0.03812, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33781, "top5_acc": 0.59984, "loss_cls": 3.75599, "loss": 3.75599, "time": 0.85461} +{"mode": "train", "epoch": 87, "iter": 1800, "lr": 0.03809, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.335, "top5_acc": 0.59359, "loss_cls": 3.77448, "loss": 3.77448, "time": 0.85683} +{"mode": "train", "epoch": 87, "iter": 1900, "lr": 0.03807, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.33375, "top5_acc": 0.59578, "loss_cls": 3.79732, "loss": 3.79732, "time": 0.85431} +{"mode": "train", "epoch": 87, "iter": 2000, "lr": 0.03804, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35359, "top5_acc": 0.60812, "loss_cls": 3.72141, "loss": 3.72141, "time": 0.85385} +{"mode": "train", "epoch": 87, "iter": 2100, "lr": 0.03801, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34047, "top5_acc": 0.59469, "loss_cls": 3.75917, "loss": 3.75917, "time": 0.86556} +{"mode": "train", "epoch": 87, "iter": 2200, "lr": 0.03798, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34141, "top5_acc": 0.60047, "loss_cls": 3.77093, "loss": 3.77093, "time": 0.85453} +{"mode": "train", "epoch": 87, "iter": 2300, "lr": 0.03796, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33719, "top5_acc": 0.59844, "loss_cls": 3.77951, "loss": 3.77951, "time": 0.85792} +{"mode": "train", "epoch": 87, "iter": 2400, "lr": 0.03793, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.33547, "top5_acc": 0.59172, "loss_cls": 3.78545, "loss": 3.78545, "time": 0.85936} +{"mode": "train", "epoch": 87, "iter": 2500, "lr": 0.0379, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34156, "top5_acc": 0.59672, "loss_cls": 3.77153, "loss": 3.77153, "time": 0.85398} +{"mode": "train", "epoch": 87, "iter": 2600, "lr": 0.03788, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34422, "top5_acc": 0.60359, "loss_cls": 3.72706, "loss": 3.72706, "time": 0.8526} +{"mode": "train", "epoch": 87, "iter": 2700, "lr": 0.03785, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33828, "top5_acc": 0.59484, "loss_cls": 3.77137, "loss": 3.77137, "time": 0.8502} +{"mode": "train", "epoch": 87, "iter": 2800, "lr": 0.03782, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34516, "top5_acc": 0.59203, "loss_cls": 3.76084, "loss": 3.76084, "time": 0.84996} +{"mode": "train", "epoch": 87, "iter": 2900, "lr": 0.03779, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33906, "top5_acc": 0.60359, "loss_cls": 3.75642, "loss": 3.75642, "time": 0.84815} +{"mode": "train", "epoch": 87, "iter": 3000, "lr": 0.03777, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34094, "top5_acc": 0.59375, "loss_cls": 3.78227, "loss": 3.78227, "time": 0.85099} +{"mode": "train", "epoch": 87, "iter": 3100, "lr": 0.03774, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33031, "top5_acc": 0.58562, "loss_cls": 3.82522, "loss": 3.82522, "time": 0.85447} +{"mode": "train", "epoch": 87, "iter": 3200, "lr": 0.03771, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33672, "top5_acc": 0.59969, "loss_cls": 3.76072, "loss": 3.76072, "time": 0.85155} +{"mode": "train", "epoch": 87, "iter": 3300, "lr": 0.03769, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33375, "top5_acc": 0.59641, "loss_cls": 3.76543, "loss": 3.76543, "time": 0.85435} +{"mode": "train", "epoch": 87, "iter": 3400, "lr": 0.03766, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33453, "top5_acc": 0.58828, "loss_cls": 3.77485, "loss": 3.77485, "time": 0.8471} +{"mode": "train", "epoch": 87, "iter": 3500, "lr": 0.03763, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34391, "top5_acc": 0.60328, "loss_cls": 3.73626, "loss": 3.73626, "time": 0.8513} +{"mode": "train", "epoch": 87, "iter": 3600, "lr": 0.03761, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32922, "top5_acc": 0.59422, "loss_cls": 3.77968, "loss": 3.77968, "time": 0.85347} +{"mode": "train", "epoch": 87, "iter": 3700, "lr": 0.03758, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32828, "top5_acc": 0.60656, "loss_cls": 3.7513, "loss": 3.7513, "time": 0.84885} +{"mode": "val", "epoch": 87, "iter": 309, "lr": 0.03757, "top1_acc": 0.27275, "top5_acc": 0.5289, "mean_class_accuracy": 0.27269} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.03754, "memory": 15990, "data_time": 1.58341, "top1_acc": 0.34391, "top5_acc": 0.60688, "loss_cls": 3.71401, "loss": 3.71401, "time": 2.63067} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.03751, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35094, "top5_acc": 0.60828, "loss_cls": 3.7009, "loss": 3.7009, "time": 0.85598} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.03748, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35391, "top5_acc": 0.61719, "loss_cls": 3.67111, "loss": 3.67111, "time": 0.85281} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.03746, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34219, "top5_acc": 0.59812, "loss_cls": 3.74598, "loss": 3.74598, "time": 0.85634} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.03743, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34141, "top5_acc": 0.59906, "loss_cls": 3.72562, "loss": 3.72562, "time": 0.85469} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.0374, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33688, "top5_acc": 0.61203, "loss_cls": 3.7209, "loss": 3.7209, "time": 0.85337} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.03738, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35109, "top5_acc": 0.61422, "loss_cls": 3.6676, "loss": 3.6676, "time": 0.8577} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.03735, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34203, "top5_acc": 0.60062, "loss_cls": 3.74136, "loss": 3.74136, "time": 0.85191} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.03732, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33641, "top5_acc": 0.59688, "loss_cls": 3.7542, "loss": 3.7542, "time": 0.85138} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.0373, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.33812, "top5_acc": 0.59531, "loss_cls": 3.75225, "loss": 3.75225, "time": 0.85778} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.03727, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34594, "top5_acc": 0.60484, "loss_cls": 3.707, "loss": 3.707, "time": 0.8501} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.03724, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34125, "top5_acc": 0.60281, "loss_cls": 3.7343, "loss": 3.7343, "time": 0.84813} +{"mode": "train", "epoch": 88, "iter": 1300, "lr": 0.03721, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33234, "top5_acc": 0.59156, "loss_cls": 3.78044, "loss": 3.78044, "time": 0.84983} +{"mode": "train", "epoch": 88, "iter": 1400, "lr": 0.03719, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34203, "top5_acc": 0.59984, "loss_cls": 3.74508, "loss": 3.74508, "time": 0.84881} +{"mode": "train", "epoch": 88, "iter": 1500, "lr": 0.03716, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33469, "top5_acc": 0.59359, "loss_cls": 3.79758, "loss": 3.79758, "time": 0.85649} +{"mode": "train", "epoch": 88, "iter": 1600, "lr": 0.03713, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.34047, "top5_acc": 0.59766, "loss_cls": 3.76985, "loss": 3.76985, "time": 0.85158} +{"mode": "train", "epoch": 88, "iter": 1700, "lr": 0.03711, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33953, "top5_acc": 0.60062, "loss_cls": 3.74546, "loss": 3.74546, "time": 0.85191} +{"mode": "train", "epoch": 88, "iter": 1800, "lr": 0.03708, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33906, "top5_acc": 0.59406, "loss_cls": 3.76735, "loss": 3.76735, "time": 0.85123} +{"mode": "train", "epoch": 88, "iter": 1900, "lr": 0.03705, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3375, "top5_acc": 0.59734, "loss_cls": 3.74961, "loss": 3.74961, "time": 0.85545} +{"mode": "train", "epoch": 88, "iter": 2000, "lr": 0.03703, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34188, "top5_acc": 0.59438, "loss_cls": 3.74187, "loss": 3.74187, "time": 0.85028} +{"mode": "train", "epoch": 88, "iter": 2100, "lr": 0.037, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34922, "top5_acc": 0.60203, "loss_cls": 3.74875, "loss": 3.74875, "time": 0.85093} +{"mode": "train", "epoch": 88, "iter": 2200, "lr": 0.03697, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34, "top5_acc": 0.59703, "loss_cls": 3.7698, "loss": 3.7698, "time": 0.8471} +{"mode": "train", "epoch": 88, "iter": 2300, "lr": 0.03694, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33391, "top5_acc": 0.60438, "loss_cls": 3.72337, "loss": 3.72337, "time": 0.85187} +{"mode": "train", "epoch": 88, "iter": 2400, "lr": 0.03692, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34766, "top5_acc": 0.60719, "loss_cls": 3.72255, "loss": 3.72255, "time": 0.85152} +{"mode": "train", "epoch": 88, "iter": 2500, "lr": 0.03689, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33828, "top5_acc": 0.59938, "loss_cls": 3.74636, "loss": 3.74636, "time": 0.85314} +{"mode": "train", "epoch": 88, "iter": 2600, "lr": 0.03686, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33281, "top5_acc": 0.59266, "loss_cls": 3.78632, "loss": 3.78632, "time": 0.85159} +{"mode": "train", "epoch": 88, "iter": 2700, "lr": 0.03684, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35328, "top5_acc": 0.60922, "loss_cls": 3.69576, "loss": 3.69576, "time": 0.84881} +{"mode": "train", "epoch": 88, "iter": 2800, "lr": 0.03681, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31938, "top5_acc": 0.58594, "loss_cls": 3.82914, "loss": 3.82914, "time": 0.84669} +{"mode": "train", "epoch": 88, "iter": 2900, "lr": 0.03678, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33625, "top5_acc": 0.6, "loss_cls": 3.76506, "loss": 3.76506, "time": 0.85421} +{"mode": "train", "epoch": 88, "iter": 3000, "lr": 0.03676, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34578, "top5_acc": 0.60828, "loss_cls": 3.73358, "loss": 3.73358, "time": 0.85036} +{"mode": "train", "epoch": 88, "iter": 3100, "lr": 0.03673, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33938, "top5_acc": 0.58719, "loss_cls": 3.80107, "loss": 3.80107, "time": 0.8527} +{"mode": "train", "epoch": 88, "iter": 3200, "lr": 0.0367, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33641, "top5_acc": 0.59422, "loss_cls": 3.77108, "loss": 3.77108, "time": 0.85006} +{"mode": "train", "epoch": 88, "iter": 3300, "lr": 0.03667, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34781, "top5_acc": 0.60344, "loss_cls": 3.73251, "loss": 3.73251, "time": 0.85288} +{"mode": "train", "epoch": 88, "iter": 3400, "lr": 0.03665, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34453, "top5_acc": 0.59656, "loss_cls": 3.73919, "loss": 3.73919, "time": 0.84882} +{"mode": "train", "epoch": 88, "iter": 3500, "lr": 0.03662, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34094, "top5_acc": 0.59781, "loss_cls": 3.78984, "loss": 3.78984, "time": 0.85108} +{"mode": "train", "epoch": 88, "iter": 3600, "lr": 0.03659, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33203, "top5_acc": 0.58516, "loss_cls": 3.78376, "loss": 3.78376, "time": 0.84871} +{"mode": "train", "epoch": 88, "iter": 3700, "lr": 0.03657, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34109, "top5_acc": 0.58719, "loss_cls": 3.76343, "loss": 3.76343, "time": 0.85387} +{"mode": "val", "epoch": 88, "iter": 309, "lr": 0.03655, "top1_acc": 0.28131, "top5_acc": 0.53001, "mean_class_accuracy": 0.2811} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.03653, "memory": 15990, "data_time": 1.60832, "top1_acc": 0.35828, "top5_acc": 0.61344, "loss_cls": 3.65217, "loss": 3.65217, "time": 2.64337} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0365, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35016, "top5_acc": 0.61234, "loss_cls": 3.66458, "loss": 3.66458, "time": 0.85915} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.03647, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.34891, "top5_acc": 0.60922, "loss_cls": 3.69658, "loss": 3.69658, "time": 0.85913} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.03645, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35219, "top5_acc": 0.60641, "loss_cls": 3.66659, "loss": 3.66659, "time": 0.8606} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.03642, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34703, "top5_acc": 0.61125, "loss_cls": 3.70372, "loss": 3.70372, "time": 0.85677} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.03639, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.33859, "top5_acc": 0.59578, "loss_cls": 3.77378, "loss": 3.77378, "time": 0.85757} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.03637, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33969, "top5_acc": 0.59875, "loss_cls": 3.73523, "loss": 3.73523, "time": 0.85603} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.03634, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34812, "top5_acc": 0.61781, "loss_cls": 3.67828, "loss": 3.67828, "time": 0.85351} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.03631, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34703, "top5_acc": 0.60453, "loss_cls": 3.72727, "loss": 3.72727, "time": 0.85772} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.03629, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34281, "top5_acc": 0.6, "loss_cls": 3.74224, "loss": 3.74224, "time": 0.8528} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.03626, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33891, "top5_acc": 0.60281, "loss_cls": 3.74287, "loss": 3.74287, "time": 0.85482} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.03623, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34656, "top5_acc": 0.61281, "loss_cls": 3.70294, "loss": 3.70294, "time": 0.85668} +{"mode": "train", "epoch": 89, "iter": 1300, "lr": 0.0362, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34109, "top5_acc": 0.60406, "loss_cls": 3.74813, "loss": 3.74813, "time": 0.85875} +{"mode": "train", "epoch": 89, "iter": 1400, "lr": 0.03618, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34375, "top5_acc": 0.60875, "loss_cls": 3.70884, "loss": 3.70884, "time": 0.85553} +{"mode": "train", "epoch": 89, "iter": 1500, "lr": 0.03615, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34094, "top5_acc": 0.5975, "loss_cls": 3.73945, "loss": 3.73945, "time": 0.85265} +{"mode": "train", "epoch": 89, "iter": 1600, "lr": 0.03612, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33672, "top5_acc": 0.59469, "loss_cls": 3.77446, "loss": 3.77446, "time": 0.85154} +{"mode": "train", "epoch": 89, "iter": 1700, "lr": 0.0361, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34406, "top5_acc": 0.60109, "loss_cls": 3.73737, "loss": 3.73737, "time": 0.8579} +{"mode": "train", "epoch": 89, "iter": 1800, "lr": 0.03607, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33641, "top5_acc": 0.58578, "loss_cls": 3.78072, "loss": 3.78072, "time": 0.8535} +{"mode": "train", "epoch": 89, "iter": 1900, "lr": 0.03604, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33938, "top5_acc": 0.5975, "loss_cls": 3.76214, "loss": 3.76214, "time": 0.85547} +{"mode": "train", "epoch": 89, "iter": 2000, "lr": 0.03602, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34031, "top5_acc": 0.59812, "loss_cls": 3.72073, "loss": 3.72073, "time": 0.85541} +{"mode": "train", "epoch": 89, "iter": 2100, "lr": 0.03599, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33984, "top5_acc": 0.59531, "loss_cls": 3.75736, "loss": 3.75736, "time": 0.85596} +{"mode": "train", "epoch": 89, "iter": 2200, "lr": 0.03596, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33344, "top5_acc": 0.59531, "loss_cls": 3.79465, "loss": 3.79465, "time": 0.85414} +{"mode": "train", "epoch": 89, "iter": 2300, "lr": 0.03594, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34, "top5_acc": 0.60234, "loss_cls": 3.72852, "loss": 3.72852, "time": 0.85361} +{"mode": "train", "epoch": 89, "iter": 2400, "lr": 0.03591, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34406, "top5_acc": 0.59953, "loss_cls": 3.75424, "loss": 3.75424, "time": 0.85248} +{"mode": "train", "epoch": 89, "iter": 2500, "lr": 0.03588, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35547, "top5_acc": 0.60766, "loss_cls": 3.69126, "loss": 3.69126, "time": 0.85447} +{"mode": "train", "epoch": 89, "iter": 2600, "lr": 0.03586, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.32703, "top5_acc": 0.59594, "loss_cls": 3.79585, "loss": 3.79585, "time": 0.85679} +{"mode": "train", "epoch": 89, "iter": 2700, "lr": 0.03583, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.33672, "top5_acc": 0.5925, "loss_cls": 3.79194, "loss": 3.79194, "time": 0.85398} +{"mode": "train", "epoch": 89, "iter": 2800, "lr": 0.0358, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33844, "top5_acc": 0.60172, "loss_cls": 3.74387, "loss": 3.74387, "time": 0.8535} +{"mode": "train", "epoch": 89, "iter": 2900, "lr": 0.03578, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34094, "top5_acc": 0.59656, "loss_cls": 3.74324, "loss": 3.74324, "time": 0.85231} +{"mode": "train", "epoch": 89, "iter": 3000, "lr": 0.03575, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34766, "top5_acc": 0.60344, "loss_cls": 3.72139, "loss": 3.72139, "time": 0.85415} +{"mode": "train", "epoch": 89, "iter": 3100, "lr": 0.03572, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33906, "top5_acc": 0.59484, "loss_cls": 3.75375, "loss": 3.75375, "time": 0.85636} +{"mode": "train", "epoch": 89, "iter": 3200, "lr": 0.03569, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.34312, "top5_acc": 0.60047, "loss_cls": 3.74057, "loss": 3.74057, "time": 0.85778} +{"mode": "train", "epoch": 89, "iter": 3300, "lr": 0.03567, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34766, "top5_acc": 0.59484, "loss_cls": 3.74094, "loss": 3.74094, "time": 0.85686} +{"mode": "train", "epoch": 89, "iter": 3400, "lr": 0.03564, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.34062, "top5_acc": 0.59938, "loss_cls": 3.74961, "loss": 3.74961, "time": 0.86305} +{"mode": "train", "epoch": 89, "iter": 3500, "lr": 0.03561, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34531, "top5_acc": 0.60312, "loss_cls": 3.72892, "loss": 3.72892, "time": 0.8603} +{"mode": "train", "epoch": 89, "iter": 3600, "lr": 0.03559, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.34344, "top5_acc": 0.59953, "loss_cls": 3.7477, "loss": 3.7477, "time": 0.86306} +{"mode": "train", "epoch": 89, "iter": 3700, "lr": 0.03556, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.345, "top5_acc": 0.60719, "loss_cls": 3.71034, "loss": 3.71034, "time": 0.86198} +{"mode": "val", "epoch": 89, "iter": 309, "lr": 0.03555, "top1_acc": 0.29013, "top5_acc": 0.53968, "mean_class_accuracy": 0.28979} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.03552, "memory": 15990, "data_time": 1.56218, "top1_acc": 0.35422, "top5_acc": 0.61203, "loss_cls": 3.66659, "loss": 3.66659, "time": 2.62442} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.0355, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34141, "top5_acc": 0.60672, "loss_cls": 3.68025, "loss": 3.68025, "time": 0.86805} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.03547, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.35953, "top5_acc": 0.61656, "loss_cls": 3.65829, "loss": 3.65829, "time": 0.85662} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.03544, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35078, "top5_acc": 0.60828, "loss_cls": 3.69523, "loss": 3.69523, "time": 0.8609} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.03541, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.35219, "top5_acc": 0.60531, "loss_cls": 3.72268, "loss": 3.72268, "time": 0.86484} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.03539, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35531, "top5_acc": 0.61609, "loss_cls": 3.64926, "loss": 3.64926, "time": 0.86463} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.03536, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.34781, "top5_acc": 0.60406, "loss_cls": 3.70858, "loss": 3.70858, "time": 0.85769} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.03533, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35016, "top5_acc": 0.60734, "loss_cls": 3.70489, "loss": 3.70489, "time": 0.86346} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.03531, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34328, "top5_acc": 0.60141, "loss_cls": 3.72852, "loss": 3.72852, "time": 0.85394} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.03528, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34812, "top5_acc": 0.60594, "loss_cls": 3.6918, "loss": 3.6918, "time": 0.84743} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.03525, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35641, "top5_acc": 0.61328, "loss_cls": 3.64409, "loss": 3.64409, "time": 0.84379} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.03523, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33859, "top5_acc": 0.59109, "loss_cls": 3.76201, "loss": 3.76201, "time": 0.84855} +{"mode": "train", "epoch": 90, "iter": 1300, "lr": 0.0352, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.33891, "top5_acc": 0.6, "loss_cls": 3.7476, "loss": 3.7476, "time": 0.85259} +{"mode": "train", "epoch": 90, "iter": 1400, "lr": 0.03517, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35281, "top5_acc": 0.605, "loss_cls": 3.69911, "loss": 3.69911, "time": 0.85341} +{"mode": "train", "epoch": 90, "iter": 1500, "lr": 0.03515, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34375, "top5_acc": 0.60453, "loss_cls": 3.73701, "loss": 3.73701, "time": 0.85112} +{"mode": "train", "epoch": 90, "iter": 1600, "lr": 0.03512, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35156, "top5_acc": 0.60297, "loss_cls": 3.73643, "loss": 3.73643, "time": 0.85177} +{"mode": "train", "epoch": 90, "iter": 1700, "lr": 0.03509, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35562, "top5_acc": 0.60625, "loss_cls": 3.70914, "loss": 3.70914, "time": 0.85154} +{"mode": "train", "epoch": 90, "iter": 1800, "lr": 0.03507, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34078, "top5_acc": 0.60234, "loss_cls": 3.73383, "loss": 3.73383, "time": 0.84938} +{"mode": "train", "epoch": 90, "iter": 1900, "lr": 0.03504, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34453, "top5_acc": 0.59547, "loss_cls": 3.72714, "loss": 3.72714, "time": 0.85212} +{"mode": "train", "epoch": 90, "iter": 2000, "lr": 0.03501, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34734, "top5_acc": 0.60812, "loss_cls": 3.69752, "loss": 3.69752, "time": 0.8573} +{"mode": "train", "epoch": 90, "iter": 2100, "lr": 0.03499, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34266, "top5_acc": 0.60141, "loss_cls": 3.70605, "loss": 3.70605, "time": 0.85558} +{"mode": "train", "epoch": 90, "iter": 2200, "lr": 0.03496, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33828, "top5_acc": 0.59844, "loss_cls": 3.74871, "loss": 3.74871, "time": 0.85035} +{"mode": "train", "epoch": 90, "iter": 2300, "lr": 0.03493, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35281, "top5_acc": 0.60422, "loss_cls": 3.73117, "loss": 3.73117, "time": 0.85239} +{"mode": "train", "epoch": 90, "iter": 2400, "lr": 0.03491, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34781, "top5_acc": 0.60469, "loss_cls": 3.72897, "loss": 3.72897, "time": 0.85312} +{"mode": "train", "epoch": 90, "iter": 2500, "lr": 0.03488, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34953, "top5_acc": 0.60641, "loss_cls": 3.70759, "loss": 3.70759, "time": 0.84691} +{"mode": "train", "epoch": 90, "iter": 2600, "lr": 0.03485, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34078, "top5_acc": 0.59422, "loss_cls": 3.74832, "loss": 3.74832, "time": 0.85188} +{"mode": "train", "epoch": 90, "iter": 2700, "lr": 0.03483, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33672, "top5_acc": 0.59391, "loss_cls": 3.78102, "loss": 3.78102, "time": 0.84962} +{"mode": "train", "epoch": 90, "iter": 2800, "lr": 0.0348, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34359, "top5_acc": 0.60922, "loss_cls": 3.72355, "loss": 3.72355, "time": 0.85089} +{"mode": "train", "epoch": 90, "iter": 2900, "lr": 0.03477, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35406, "top5_acc": 0.61734, "loss_cls": 3.66008, "loss": 3.66008, "time": 0.84694} +{"mode": "train", "epoch": 90, "iter": 3000, "lr": 0.03475, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34016, "top5_acc": 0.60953, "loss_cls": 3.73813, "loss": 3.73813, "time": 0.8504} +{"mode": "train", "epoch": 90, "iter": 3100, "lr": 0.03472, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33703, "top5_acc": 0.59891, "loss_cls": 3.74904, "loss": 3.74904, "time": 0.85172} +{"mode": "train", "epoch": 90, "iter": 3200, "lr": 0.03469, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34328, "top5_acc": 0.60141, "loss_cls": 3.7231, "loss": 3.7231, "time": 0.85238} +{"mode": "train", "epoch": 90, "iter": 3300, "lr": 0.03467, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34297, "top5_acc": 0.60031, "loss_cls": 3.74784, "loss": 3.74784, "time": 0.84919} +{"mode": "train", "epoch": 90, "iter": 3400, "lr": 0.03464, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35344, "top5_acc": 0.60484, "loss_cls": 3.69643, "loss": 3.69643, "time": 0.84945} +{"mode": "train", "epoch": 90, "iter": 3500, "lr": 0.03461, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34641, "top5_acc": 0.60594, "loss_cls": 3.71243, "loss": 3.71243, "time": 0.85} +{"mode": "train", "epoch": 90, "iter": 3600, "lr": 0.03459, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33359, "top5_acc": 0.58266, "loss_cls": 3.80837, "loss": 3.80837, "time": 0.84795} +{"mode": "train", "epoch": 90, "iter": 3700, "lr": 0.03456, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33859, "top5_acc": 0.60109, "loss_cls": 3.76001, "loss": 3.76001, "time": 0.8499} +{"mode": "val", "epoch": 90, "iter": 309, "lr": 0.03455, "top1_acc": 0.29231, "top5_acc": 0.53953, "mean_class_accuracy": 0.29198} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.03452, "memory": 15990, "data_time": 1.58638, "top1_acc": 0.36312, "top5_acc": 0.61547, "loss_cls": 3.62097, "loss": 3.62097, "time": 2.61898} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0345, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35484, "top5_acc": 0.61187, "loss_cls": 3.68038, "loss": 3.68038, "time": 0.85417} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.03447, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36453, "top5_acc": 0.61891, "loss_cls": 3.62952, "loss": 3.62952, "time": 0.85444} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.03444, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36156, "top5_acc": 0.62281, "loss_cls": 3.61329, "loss": 3.61329, "time": 0.85582} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.03442, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35047, "top5_acc": 0.60656, "loss_cls": 3.68888, "loss": 3.68888, "time": 0.85164} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.03439, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35797, "top5_acc": 0.61344, "loss_cls": 3.65954, "loss": 3.65954, "time": 0.85591} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.03436, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.35516, "top5_acc": 0.6125, "loss_cls": 3.68603, "loss": 3.68603, "time": 0.85749} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.03434, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34047, "top5_acc": 0.61266, "loss_cls": 3.70842, "loss": 3.70842, "time": 0.86061} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.03431, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36547, "top5_acc": 0.62047, "loss_cls": 3.62611, "loss": 3.62611, "time": 0.85434} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.03428, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.34203, "top5_acc": 0.60078, "loss_cls": 3.73524, "loss": 3.73524, "time": 0.86197} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.03426, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3475, "top5_acc": 0.60859, "loss_cls": 3.70885, "loss": 3.70885, "time": 0.85345} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.03423, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34375, "top5_acc": 0.60984, "loss_cls": 3.69888, "loss": 3.69888, "time": 0.85295} +{"mode": "train", "epoch": 91, "iter": 1300, "lr": 0.0342, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34266, "top5_acc": 0.60859, "loss_cls": 3.69839, "loss": 3.69839, "time": 0.85304} +{"mode": "train", "epoch": 91, "iter": 1400, "lr": 0.03418, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35328, "top5_acc": 0.61031, "loss_cls": 3.68806, "loss": 3.68806, "time": 0.85372} +{"mode": "train", "epoch": 91, "iter": 1500, "lr": 0.03415, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35031, "top5_acc": 0.61, "loss_cls": 3.69452, "loss": 3.69452, "time": 0.86325} +{"mode": "train", "epoch": 91, "iter": 1600, "lr": 0.03412, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34812, "top5_acc": 0.60062, "loss_cls": 3.714, "loss": 3.714, "time": 0.85634} +{"mode": "train", "epoch": 91, "iter": 1700, "lr": 0.0341, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.34594, "top5_acc": 0.60922, "loss_cls": 3.6975, "loss": 3.6975, "time": 0.85268} +{"mode": "train", "epoch": 91, "iter": 1800, "lr": 0.03407, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34375, "top5_acc": 0.59969, "loss_cls": 3.75308, "loss": 3.75308, "time": 0.85769} +{"mode": "train", "epoch": 91, "iter": 1900, "lr": 0.03405, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.34234, "top5_acc": 0.61125, "loss_cls": 3.70136, "loss": 3.70136, "time": 0.86152} +{"mode": "train", "epoch": 91, "iter": 2000, "lr": 0.03402, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34859, "top5_acc": 0.61156, "loss_cls": 3.70187, "loss": 3.70187, "time": 0.86081} +{"mode": "train", "epoch": 91, "iter": 2100, "lr": 0.03399, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34797, "top5_acc": 0.60719, "loss_cls": 3.70909, "loss": 3.70909, "time": 0.86366} +{"mode": "train", "epoch": 91, "iter": 2200, "lr": 0.03397, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.33344, "top5_acc": 0.59266, "loss_cls": 3.79114, "loss": 3.79114, "time": 0.8628} +{"mode": "train", "epoch": 91, "iter": 2300, "lr": 0.03394, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34297, "top5_acc": 0.59984, "loss_cls": 3.74626, "loss": 3.74626, "time": 0.85466} +{"mode": "train", "epoch": 91, "iter": 2400, "lr": 0.03391, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34734, "top5_acc": 0.59516, "loss_cls": 3.75452, "loss": 3.75452, "time": 0.85959} +{"mode": "train", "epoch": 91, "iter": 2500, "lr": 0.03389, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.345, "top5_acc": 0.61047, "loss_cls": 3.67869, "loss": 3.67869, "time": 0.85777} +{"mode": "train", "epoch": 91, "iter": 2600, "lr": 0.03386, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34531, "top5_acc": 0.6, "loss_cls": 3.76962, "loss": 3.76962, "time": 0.86134} +{"mode": "train", "epoch": 91, "iter": 2700, "lr": 0.03383, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33922, "top5_acc": 0.60391, "loss_cls": 3.71866, "loss": 3.71866, "time": 0.85527} +{"mode": "train", "epoch": 91, "iter": 2800, "lr": 0.03381, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34812, "top5_acc": 0.61266, "loss_cls": 3.69741, "loss": 3.69741, "time": 0.86456} +{"mode": "train", "epoch": 91, "iter": 2900, "lr": 0.03378, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.35391, "top5_acc": 0.60938, "loss_cls": 3.68678, "loss": 3.68678, "time": 0.86078} +{"mode": "train", "epoch": 91, "iter": 3000, "lr": 0.03375, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35016, "top5_acc": 0.61266, "loss_cls": 3.71844, "loss": 3.71844, "time": 0.85503} +{"mode": "train", "epoch": 91, "iter": 3100, "lr": 0.03373, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.34328, "top5_acc": 0.60766, "loss_cls": 3.72145, "loss": 3.72145, "time": 0.85667} +{"mode": "train", "epoch": 91, "iter": 3200, "lr": 0.0337, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35641, "top5_acc": 0.60578, "loss_cls": 3.69866, "loss": 3.69866, "time": 0.85918} +{"mode": "train", "epoch": 91, "iter": 3300, "lr": 0.03367, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34062, "top5_acc": 0.59484, "loss_cls": 3.77023, "loss": 3.77023, "time": 0.86031} +{"mode": "train", "epoch": 91, "iter": 3400, "lr": 0.03365, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35016, "top5_acc": 0.59906, "loss_cls": 3.72807, "loss": 3.72807, "time": 0.85976} +{"mode": "train", "epoch": 91, "iter": 3500, "lr": 0.03362, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34891, "top5_acc": 0.6075, "loss_cls": 3.6911, "loss": 3.6911, "time": 0.85707} +{"mode": "train", "epoch": 91, "iter": 3600, "lr": 0.0336, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.35656, "top5_acc": 0.61141, "loss_cls": 3.68947, "loss": 3.68947, "time": 0.8561} +{"mode": "train", "epoch": 91, "iter": 3700, "lr": 0.03357, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35391, "top5_acc": 0.60781, "loss_cls": 3.70204, "loss": 3.70204, "time": 0.86137} +{"mode": "val", "epoch": 91, "iter": 309, "lr": 0.03356, "top1_acc": 0.29752, "top5_acc": 0.54597, "mean_class_accuracy": 0.29735} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.03353, "memory": 15990, "data_time": 1.61956, "top1_acc": 0.35469, "top5_acc": 0.61828, "loss_cls": 3.65566, "loss": 3.65566, "time": 2.66866} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.0335, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37141, "top5_acc": 0.63141, "loss_cls": 3.5656, "loss": 3.5656, "time": 0.86596} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.03348, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35734, "top5_acc": 0.60953, "loss_cls": 3.64966, "loss": 3.64966, "time": 0.86079} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.03345, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.35562, "top5_acc": 0.61406, "loss_cls": 3.65091, "loss": 3.65091, "time": 0.87357} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.03342, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.35844, "top5_acc": 0.62281, "loss_cls": 3.63723, "loss": 3.63723, "time": 0.87521} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.0334, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.36062, "top5_acc": 0.61125, "loss_cls": 3.65649, "loss": 3.65649, "time": 0.87197} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.03337, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.36125, "top5_acc": 0.62703, "loss_cls": 3.62327, "loss": 3.62327, "time": 0.87394} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.03335, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.35078, "top5_acc": 0.60438, "loss_cls": 3.70235, "loss": 3.70235, "time": 0.87348} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.03332, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35141, "top5_acc": 0.61109, "loss_cls": 3.68753, "loss": 3.68753, "time": 0.87198} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.03329, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.35812, "top5_acc": 0.61547, "loss_cls": 3.65383, "loss": 3.65383, "time": 0.86881} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.03327, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.35375, "top5_acc": 0.60875, "loss_cls": 3.71153, "loss": 3.71153, "time": 0.86006} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.03324, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34219, "top5_acc": 0.60562, "loss_cls": 3.70204, "loss": 3.70204, "time": 0.86295} +{"mode": "train", "epoch": 92, "iter": 1300, "lr": 0.03321, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.35531, "top5_acc": 0.60781, "loss_cls": 3.68924, "loss": 3.68924, "time": 0.86046} +{"mode": "train", "epoch": 92, "iter": 1400, "lr": 0.03319, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.34281, "top5_acc": 0.60984, "loss_cls": 3.73051, "loss": 3.73051, "time": 0.85623} +{"mode": "train", "epoch": 92, "iter": 1500, "lr": 0.03316, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34734, "top5_acc": 0.60797, "loss_cls": 3.71964, "loss": 3.71964, "time": 0.86301} +{"mode": "train", "epoch": 92, "iter": 1600, "lr": 0.03314, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34578, "top5_acc": 0.62406, "loss_cls": 3.64226, "loss": 3.64226, "time": 0.86903} +{"mode": "train", "epoch": 92, "iter": 1700, "lr": 0.03311, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.35344, "top5_acc": 0.60844, "loss_cls": 3.66116, "loss": 3.66116, "time": 0.86465} +{"mode": "train", "epoch": 92, "iter": 1800, "lr": 0.03308, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33938, "top5_acc": 0.60141, "loss_cls": 3.75234, "loss": 3.75234, "time": 0.86125} +{"mode": "train", "epoch": 92, "iter": 1900, "lr": 0.03306, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.33984, "top5_acc": 0.61016, "loss_cls": 3.7203, "loss": 3.7203, "time": 0.865} +{"mode": "train", "epoch": 92, "iter": 2000, "lr": 0.03303, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.34641, "top5_acc": 0.60953, "loss_cls": 3.71304, "loss": 3.71304, "time": 0.86367} +{"mode": "train", "epoch": 92, "iter": 2100, "lr": 0.033, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34812, "top5_acc": 0.61391, "loss_cls": 3.69481, "loss": 3.69481, "time": 0.86549} +{"mode": "train", "epoch": 92, "iter": 2200, "lr": 0.03298, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35312, "top5_acc": 0.61516, "loss_cls": 3.67459, "loss": 3.67459, "time": 0.85848} +{"mode": "train", "epoch": 92, "iter": 2300, "lr": 0.03295, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.34547, "top5_acc": 0.60234, "loss_cls": 3.70948, "loss": 3.70948, "time": 0.85538} +{"mode": "train", "epoch": 92, "iter": 2400, "lr": 0.03292, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34328, "top5_acc": 0.60484, "loss_cls": 3.70333, "loss": 3.70333, "time": 0.85693} +{"mode": "train", "epoch": 92, "iter": 2500, "lr": 0.0329, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35594, "top5_acc": 0.60812, "loss_cls": 3.70997, "loss": 3.70997, "time": 0.85873} +{"mode": "train", "epoch": 92, "iter": 2600, "lr": 0.03287, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35266, "top5_acc": 0.60625, "loss_cls": 3.67105, "loss": 3.67105, "time": 0.85877} +{"mode": "train", "epoch": 92, "iter": 2700, "lr": 0.03285, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.34016, "top5_acc": 0.59453, "loss_cls": 3.73327, "loss": 3.73327, "time": 0.86111} +{"mode": "train", "epoch": 92, "iter": 2800, "lr": 0.03282, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35484, "top5_acc": 0.60594, "loss_cls": 3.68811, "loss": 3.68811, "time": 0.86123} +{"mode": "train", "epoch": 92, "iter": 2900, "lr": 0.03279, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.34297, "top5_acc": 0.60547, "loss_cls": 3.69921, "loss": 3.69921, "time": 0.8674} +{"mode": "train", "epoch": 92, "iter": 3000, "lr": 0.03277, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35203, "top5_acc": 0.61031, "loss_cls": 3.68367, "loss": 3.68367, "time": 0.86824} +{"mode": "train", "epoch": 92, "iter": 3100, "lr": 0.03274, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3475, "top5_acc": 0.60141, "loss_cls": 3.70389, "loss": 3.70389, "time": 0.86051} +{"mode": "train", "epoch": 92, "iter": 3200, "lr": 0.03271, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34891, "top5_acc": 0.60219, "loss_cls": 3.73327, "loss": 3.73327, "time": 0.86785} +{"mode": "train", "epoch": 92, "iter": 3300, "lr": 0.03269, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35953, "top5_acc": 0.6025, "loss_cls": 3.69674, "loss": 3.69674, "time": 0.87196} +{"mode": "train", "epoch": 92, "iter": 3400, "lr": 0.03266, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34156, "top5_acc": 0.60875, "loss_cls": 3.71051, "loss": 3.71051, "time": 0.86578} +{"mode": "train", "epoch": 92, "iter": 3500, "lr": 0.03264, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35156, "top5_acc": 0.60828, "loss_cls": 3.69582, "loss": 3.69582, "time": 0.86716} +{"mode": "train", "epoch": 92, "iter": 3600, "lr": 0.03261, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.34344, "top5_acc": 0.59984, "loss_cls": 3.75721, "loss": 3.75721, "time": 0.86911} +{"mode": "train", "epoch": 92, "iter": 3700, "lr": 0.03258, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.35156, "top5_acc": 0.60844, "loss_cls": 3.68363, "loss": 3.68363, "time": 0.86334} +{"mode": "val", "epoch": 92, "iter": 309, "lr": 0.03257, "top1_acc": 0.29702, "top5_acc": 0.54951, "mean_class_accuracy": 0.29675} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.03255, "memory": 15990, "data_time": 1.66328, "top1_acc": 0.36297, "top5_acc": 0.62219, "loss_cls": 3.61719, "loss": 3.61719, "time": 2.7235} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.03252, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.35969, "top5_acc": 0.62547, "loss_cls": 3.59954, "loss": 3.59954, "time": 0.86882} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.03249, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34875, "top5_acc": 0.61156, "loss_cls": 3.67315, "loss": 3.67315, "time": 0.86916} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.03247, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36047, "top5_acc": 0.61687, "loss_cls": 3.61012, "loss": 3.61012, "time": 0.86947} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.03244, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36656, "top5_acc": 0.61344, "loss_cls": 3.63226, "loss": 3.63226, "time": 0.86817} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.03241, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.34797, "top5_acc": 0.62094, "loss_cls": 3.64256, "loss": 3.64256, "time": 0.86379} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.03239, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35891, "top5_acc": 0.61359, "loss_cls": 3.66215, "loss": 3.66215, "time": 0.86982} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.03236, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35766, "top5_acc": 0.615, "loss_cls": 3.65613, "loss": 3.65613, "time": 0.86514} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.03234, "memory": 15990, "data_time": 0.00067, "top1_acc": 0.35516, "top5_acc": 0.60438, "loss_cls": 3.6789, "loss": 3.6789, "time": 0.87022} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.03231, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34906, "top5_acc": 0.61031, "loss_cls": 3.68795, "loss": 3.68795, "time": 0.8629} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.03228, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35375, "top5_acc": 0.61422, "loss_cls": 3.68515, "loss": 3.68515, "time": 0.85774} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.03226, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34266, "top5_acc": 0.59906, "loss_cls": 3.74498, "loss": 3.74498, "time": 0.85699} +{"mode": "train", "epoch": 93, "iter": 1300, "lr": 0.03223, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.34812, "top5_acc": 0.61, "loss_cls": 3.7041, "loss": 3.7041, "time": 0.855} +{"mode": "train", "epoch": 93, "iter": 1400, "lr": 0.03221, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34688, "top5_acc": 0.60625, "loss_cls": 3.69804, "loss": 3.69804, "time": 0.85675} +{"mode": "train", "epoch": 93, "iter": 1500, "lr": 0.03218, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36281, "top5_acc": 0.61844, "loss_cls": 3.65101, "loss": 3.65101, "time": 0.86604} +{"mode": "train", "epoch": 93, "iter": 1600, "lr": 0.03215, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.35344, "top5_acc": 0.61781, "loss_cls": 3.65647, "loss": 3.65647, "time": 0.85927} +{"mode": "train", "epoch": 93, "iter": 1700, "lr": 0.03213, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34688, "top5_acc": 0.61203, "loss_cls": 3.68274, "loss": 3.68274, "time": 0.86784} +{"mode": "train", "epoch": 93, "iter": 1800, "lr": 0.0321, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35625, "top5_acc": 0.61281, "loss_cls": 3.66732, "loss": 3.66732, "time": 0.86655} +{"mode": "train", "epoch": 93, "iter": 1900, "lr": 0.03207, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.35438, "top5_acc": 0.61938, "loss_cls": 3.63821, "loss": 3.63821, "time": 0.861} +{"mode": "train", "epoch": 93, "iter": 2000, "lr": 0.03205, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35391, "top5_acc": 0.61719, "loss_cls": 3.6554, "loss": 3.6554, "time": 0.86268} +{"mode": "train", "epoch": 93, "iter": 2100, "lr": 0.03202, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.35234, "top5_acc": 0.61625, "loss_cls": 3.6659, "loss": 3.6659, "time": 0.85594} +{"mode": "train", "epoch": 93, "iter": 2200, "lr": 0.032, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3475, "top5_acc": 0.60203, "loss_cls": 3.6993, "loss": 3.6993, "time": 0.85318} +{"mode": "train", "epoch": 93, "iter": 2300, "lr": 0.03197, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34234, "top5_acc": 0.60812, "loss_cls": 3.69407, "loss": 3.69407, "time": 0.85795} +{"mode": "train", "epoch": 93, "iter": 2400, "lr": 0.03194, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35594, "top5_acc": 0.60656, "loss_cls": 3.67127, "loss": 3.67127, "time": 0.85391} +{"mode": "train", "epoch": 93, "iter": 2500, "lr": 0.03192, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.34797, "top5_acc": 0.61422, "loss_cls": 3.69108, "loss": 3.69108, "time": 0.86921} +{"mode": "train", "epoch": 93, "iter": 2600, "lr": 0.03189, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35531, "top5_acc": 0.60688, "loss_cls": 3.68106, "loss": 3.68106, "time": 0.86705} +{"mode": "train", "epoch": 93, "iter": 2700, "lr": 0.03187, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.35703, "top5_acc": 0.61906, "loss_cls": 3.65888, "loss": 3.65888, "time": 0.86345} +{"mode": "train", "epoch": 93, "iter": 2800, "lr": 0.03184, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36641, "top5_acc": 0.61719, "loss_cls": 3.60767, "loss": 3.60767, "time": 0.85482} +{"mode": "train", "epoch": 93, "iter": 2900, "lr": 0.03181, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35609, "top5_acc": 0.60984, "loss_cls": 3.65118, "loss": 3.65118, "time": 0.85895} +{"mode": "train", "epoch": 93, "iter": 3000, "lr": 0.03179, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34469, "top5_acc": 0.60688, "loss_cls": 3.72942, "loss": 3.72942, "time": 0.85979} +{"mode": "train", "epoch": 93, "iter": 3100, "lr": 0.03176, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34359, "top5_acc": 0.60094, "loss_cls": 3.74525, "loss": 3.74525, "time": 0.85928} +{"mode": "train", "epoch": 93, "iter": 3200, "lr": 0.03174, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35875, "top5_acc": 0.61062, "loss_cls": 3.66203, "loss": 3.66203, "time": 0.86117} +{"mode": "train", "epoch": 93, "iter": 3300, "lr": 0.03171, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.35703, "top5_acc": 0.61219, "loss_cls": 3.68292, "loss": 3.68292, "time": 0.85535} +{"mode": "train", "epoch": 93, "iter": 3400, "lr": 0.03168, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35281, "top5_acc": 0.60141, "loss_cls": 3.69645, "loss": 3.69645, "time": 0.85386} +{"mode": "train", "epoch": 93, "iter": 3500, "lr": 0.03166, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.34922, "top5_acc": 0.61203, "loss_cls": 3.68994, "loss": 3.68994, "time": 0.86319} +{"mode": "train", "epoch": 93, "iter": 3600, "lr": 0.03163, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.355, "top5_acc": 0.60844, "loss_cls": 3.666, "loss": 3.666, "time": 0.86541} +{"mode": "train", "epoch": 93, "iter": 3700, "lr": 0.03161, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35438, "top5_acc": 0.61203, "loss_cls": 3.67755, "loss": 3.67755, "time": 0.86697} +{"mode": "val", "epoch": 93, "iter": 309, "lr": 0.03159, "top1_acc": 0.29783, "top5_acc": 0.54257, "mean_class_accuracy": 0.29764} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.03157, "memory": 15990, "data_time": 1.66593, "top1_acc": 0.35141, "top5_acc": 0.61562, "loss_cls": 3.65879, "loss": 3.65879, "time": 2.7286} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.03154, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37578, "top5_acc": 0.63781, "loss_cls": 3.55763, "loss": 3.55763, "time": 0.86509} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.03152, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36094, "top5_acc": 0.61344, "loss_cls": 3.61487, "loss": 3.61487, "time": 0.86272} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.03149, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35609, "top5_acc": 0.61578, "loss_cls": 3.64072, "loss": 3.64072, "time": 0.86556} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.03146, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36266, "top5_acc": 0.62719, "loss_cls": 3.60079, "loss": 3.60079, "time": 0.866} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.03144, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.35328, "top5_acc": 0.60953, "loss_cls": 3.65454, "loss": 3.65454, "time": 0.86952} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.03141, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.36062, "top5_acc": 0.61969, "loss_cls": 3.61368, "loss": 3.61368, "time": 0.86632} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.03139, "memory": 15990, "data_time": 0.00071, "top1_acc": 0.34328, "top5_acc": 0.60188, "loss_cls": 3.72342, "loss": 3.72342, "time": 0.8638} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.03136, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.3475, "top5_acc": 0.60781, "loss_cls": 3.69111, "loss": 3.69111, "time": 0.87025} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.03133, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.35844, "top5_acc": 0.6125, "loss_cls": 3.66794, "loss": 3.66794, "time": 0.86426} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.03131, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35297, "top5_acc": 0.61938, "loss_cls": 3.65376, "loss": 3.65376, "time": 0.86051} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.03128, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35016, "top5_acc": 0.60969, "loss_cls": 3.65532, "loss": 3.65532, "time": 0.86119} +{"mode": "train", "epoch": 94, "iter": 1300, "lr": 0.03126, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35797, "top5_acc": 0.61234, "loss_cls": 3.63433, "loss": 3.63433, "time": 0.86841} +{"mode": "train", "epoch": 94, "iter": 1400, "lr": 0.03123, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.35406, "top5_acc": 0.61781, "loss_cls": 3.63543, "loss": 3.63543, "time": 0.86598} +{"mode": "train", "epoch": 94, "iter": 1500, "lr": 0.0312, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.33781, "top5_acc": 0.6025, "loss_cls": 3.73785, "loss": 3.73785, "time": 0.86418} +{"mode": "train", "epoch": 94, "iter": 1600, "lr": 0.03118, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.34656, "top5_acc": 0.61453, "loss_cls": 3.67805, "loss": 3.67805, "time": 0.86193} +{"mode": "train", "epoch": 94, "iter": 1700, "lr": 0.03115, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.35719, "top5_acc": 0.61297, "loss_cls": 3.65407, "loss": 3.65407, "time": 0.86749} +{"mode": "train", "epoch": 94, "iter": 1800, "lr": 0.03113, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.36156, "top5_acc": 0.61312, "loss_cls": 3.65565, "loss": 3.65565, "time": 0.85483} +{"mode": "train", "epoch": 94, "iter": 1900, "lr": 0.0311, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35609, "top5_acc": 0.61641, "loss_cls": 3.65661, "loss": 3.65661, "time": 0.85695} +{"mode": "train", "epoch": 94, "iter": 2000, "lr": 0.03108, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37125, "top5_acc": 0.61828, "loss_cls": 3.63272, "loss": 3.63272, "time": 0.85259} +{"mode": "train", "epoch": 94, "iter": 2100, "lr": 0.03105, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.36281, "top5_acc": 0.61828, "loss_cls": 3.60883, "loss": 3.60883, "time": 0.85627} +{"mode": "train", "epoch": 94, "iter": 2200, "lr": 0.03102, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35609, "top5_acc": 0.61172, "loss_cls": 3.66565, "loss": 3.66565, "time": 0.86006} +{"mode": "train", "epoch": 94, "iter": 2300, "lr": 0.031, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35547, "top5_acc": 0.6175, "loss_cls": 3.65116, "loss": 3.65116, "time": 0.85802} +{"mode": "train", "epoch": 94, "iter": 2400, "lr": 0.03097, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35078, "top5_acc": 0.61125, "loss_cls": 3.69533, "loss": 3.69533, "time": 0.85927} +{"mode": "train", "epoch": 94, "iter": 2500, "lr": 0.03095, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3525, "top5_acc": 0.61516, "loss_cls": 3.68194, "loss": 3.68194, "time": 0.86089} +{"mode": "train", "epoch": 94, "iter": 2600, "lr": 0.03092, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35609, "top5_acc": 0.6125, "loss_cls": 3.65746, "loss": 3.65746, "time": 0.8544} +{"mode": "train", "epoch": 94, "iter": 2700, "lr": 0.03089, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35016, "top5_acc": 0.6125, "loss_cls": 3.66647, "loss": 3.66647, "time": 0.86094} +{"mode": "train", "epoch": 94, "iter": 2800, "lr": 0.03087, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.36594, "top5_acc": 0.62156, "loss_cls": 3.62603, "loss": 3.62603, "time": 0.85735} +{"mode": "train", "epoch": 94, "iter": 2900, "lr": 0.03084, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35641, "top5_acc": 0.61656, "loss_cls": 3.65038, "loss": 3.65038, "time": 0.86187} +{"mode": "train", "epoch": 94, "iter": 3000, "lr": 0.03082, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35453, "top5_acc": 0.60844, "loss_cls": 3.68514, "loss": 3.68514, "time": 0.86083} +{"mode": "train", "epoch": 94, "iter": 3100, "lr": 0.03079, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36234, "top5_acc": 0.61547, "loss_cls": 3.64185, "loss": 3.64185, "time": 0.86737} +{"mode": "train", "epoch": 94, "iter": 3200, "lr": 0.03077, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35672, "top5_acc": 0.61453, "loss_cls": 3.65133, "loss": 3.65133, "time": 0.87023} +{"mode": "train", "epoch": 94, "iter": 3300, "lr": 0.03074, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.34188, "top5_acc": 0.60578, "loss_cls": 3.70362, "loss": 3.70362, "time": 0.86375} +{"mode": "train", "epoch": 94, "iter": 3400, "lr": 0.03071, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.35297, "top5_acc": 0.62187, "loss_cls": 3.62981, "loss": 3.62981, "time": 0.86298} +{"mode": "train", "epoch": 94, "iter": 3500, "lr": 0.03069, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.35203, "top5_acc": 0.61359, "loss_cls": 3.65123, "loss": 3.65123, "time": 0.86673} +{"mode": "train", "epoch": 94, "iter": 3600, "lr": 0.03066, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.34297, "top5_acc": 0.61359, "loss_cls": 3.69628, "loss": 3.69628, "time": 0.8675} +{"mode": "train", "epoch": 94, "iter": 3700, "lr": 0.03064, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35547, "top5_acc": 0.60938, "loss_cls": 3.69652, "loss": 3.69652, "time": 0.86637} +{"mode": "val", "epoch": 94, "iter": 309, "lr": 0.03062, "top1_acc": 0.29231, "top5_acc": 0.55012, "mean_class_accuracy": 0.29213} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.0306, "memory": 15990, "data_time": 1.65005, "top1_acc": 0.37922, "top5_acc": 0.62328, "loss_cls": 3.57026, "loss": 3.57026, "time": 2.72056} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.03057, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35672, "top5_acc": 0.62, "loss_cls": 3.61103, "loss": 3.61103, "time": 0.87444} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.03055, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.36094, "top5_acc": 0.62406, "loss_cls": 3.61496, "loss": 3.61496, "time": 0.8792} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.03052, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.36156, "top5_acc": 0.62422, "loss_cls": 3.61223, "loss": 3.61223, "time": 0.8833} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.0305, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.35422, "top5_acc": 0.61312, "loss_cls": 3.64095, "loss": 3.64095, "time": 0.88336} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.03047, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.35516, "top5_acc": 0.62422, "loss_cls": 3.62056, "loss": 3.62056, "time": 0.88424} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.03044, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.35516, "top5_acc": 0.61969, "loss_cls": 3.61878, "loss": 3.61878, "time": 0.88609} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.03042, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.35297, "top5_acc": 0.61812, "loss_cls": 3.63736, "loss": 3.63736, "time": 0.86566} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.03039, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.35859, "top5_acc": 0.61328, "loss_cls": 3.63667, "loss": 3.63667, "time": 0.86388} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.03037, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.36422, "top5_acc": 0.62719, "loss_cls": 3.60996, "loss": 3.60996, "time": 0.85917} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.03034, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36031, "top5_acc": 0.61859, "loss_cls": 3.66087, "loss": 3.66087, "time": 0.86248} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.03032, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.35906, "top5_acc": 0.62406, "loss_cls": 3.57905, "loss": 3.57905, "time": 0.86765} +{"mode": "train", "epoch": 95, "iter": 1300, "lr": 0.03029, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35859, "top5_acc": 0.61734, "loss_cls": 3.61395, "loss": 3.61395, "time": 0.868} +{"mode": "train", "epoch": 95, "iter": 1400, "lr": 0.03026, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.35609, "top5_acc": 0.61438, "loss_cls": 3.6368, "loss": 3.6368, "time": 0.8649} +{"mode": "train", "epoch": 95, "iter": 1500, "lr": 0.03024, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36188, "top5_acc": 0.61766, "loss_cls": 3.62529, "loss": 3.62529, "time": 0.86717} +{"mode": "train", "epoch": 95, "iter": 1600, "lr": 0.03021, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36453, "top5_acc": 0.63062, "loss_cls": 3.59713, "loss": 3.59713, "time": 0.86457} +{"mode": "train", "epoch": 95, "iter": 1700, "lr": 0.03019, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.35531, "top5_acc": 0.61438, "loss_cls": 3.67773, "loss": 3.67773, "time": 0.86484} +{"mode": "train", "epoch": 95, "iter": 1800, "lr": 0.03016, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35016, "top5_acc": 0.61344, "loss_cls": 3.67077, "loss": 3.67077, "time": 0.86329} +{"mode": "train", "epoch": 95, "iter": 1900, "lr": 0.03014, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.35656, "top5_acc": 0.60828, "loss_cls": 3.66698, "loss": 3.66698, "time": 0.87698} +{"mode": "train", "epoch": 95, "iter": 2000, "lr": 0.03011, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.36641, "top5_acc": 0.60953, "loss_cls": 3.6546, "loss": 3.6546, "time": 0.87441} +{"mode": "train", "epoch": 95, "iter": 2100, "lr": 0.03008, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.35719, "top5_acc": 0.62609, "loss_cls": 3.62203, "loss": 3.62203, "time": 0.86186} +{"mode": "train", "epoch": 95, "iter": 2200, "lr": 0.03006, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35734, "top5_acc": 0.61156, "loss_cls": 3.62353, "loss": 3.62353, "time": 0.86877} +{"mode": "train", "epoch": 95, "iter": 2300, "lr": 0.03003, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.36, "top5_acc": 0.61344, "loss_cls": 3.64757, "loss": 3.64757, "time": 0.86496} +{"mode": "train", "epoch": 95, "iter": 2400, "lr": 0.03001, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.35312, "top5_acc": 0.61125, "loss_cls": 3.67199, "loss": 3.67199, "time": 0.87829} +{"mode": "train", "epoch": 95, "iter": 2500, "lr": 0.02998, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.34875, "top5_acc": 0.61953, "loss_cls": 3.65694, "loss": 3.65694, "time": 0.87724} +{"mode": "train", "epoch": 95, "iter": 2600, "lr": 0.02996, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.35625, "top5_acc": 0.60984, "loss_cls": 3.68513, "loss": 3.68513, "time": 0.87716} +{"mode": "train", "epoch": 95, "iter": 2700, "lr": 0.02993, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.36281, "top5_acc": 0.62531, "loss_cls": 3.62424, "loss": 3.62424, "time": 0.87832} +{"mode": "train", "epoch": 95, "iter": 2800, "lr": 0.02991, "memory": 15990, "data_time": 0.00076, "top1_acc": 0.36547, "top5_acc": 0.62594, "loss_cls": 3.6132, "loss": 3.6132, "time": 0.88428} +{"mode": "train", "epoch": 95, "iter": 2900, "lr": 0.02988, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.34688, "top5_acc": 0.6075, "loss_cls": 3.67414, "loss": 3.67414, "time": 0.88327} +{"mode": "train", "epoch": 95, "iter": 3000, "lr": 0.02985, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.35453, "top5_acc": 0.62484, "loss_cls": 3.63289, "loss": 3.63289, "time": 0.88097} +{"mode": "train", "epoch": 95, "iter": 3100, "lr": 0.02983, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.35125, "top5_acc": 0.61984, "loss_cls": 3.66374, "loss": 3.66374, "time": 0.88016} +{"mode": "train", "epoch": 95, "iter": 3200, "lr": 0.0298, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.35891, "top5_acc": 0.60875, "loss_cls": 3.68943, "loss": 3.68943, "time": 0.87664} +{"mode": "train", "epoch": 95, "iter": 3300, "lr": 0.02978, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.34891, "top5_acc": 0.60094, "loss_cls": 3.71266, "loss": 3.71266, "time": 0.88103} +{"mode": "train", "epoch": 95, "iter": 3400, "lr": 0.02975, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.35547, "top5_acc": 0.61094, "loss_cls": 3.6768, "loss": 3.6768, "time": 0.88388} +{"mode": "train", "epoch": 95, "iter": 3500, "lr": 0.02973, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.36703, "top5_acc": 0.62234, "loss_cls": 3.63571, "loss": 3.63571, "time": 0.88564} +{"mode": "train", "epoch": 95, "iter": 3600, "lr": 0.0297, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.35469, "top5_acc": 0.62016, "loss_cls": 3.62481, "loss": 3.62481, "time": 0.87876} +{"mode": "train", "epoch": 95, "iter": 3700, "lr": 0.02968, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.35453, "top5_acc": 0.61031, "loss_cls": 3.67471, "loss": 3.67471, "time": 0.88372} +{"mode": "val", "epoch": 95, "iter": 309, "lr": 0.02966, "top1_acc": 0.29904, "top5_acc": 0.55255, "mean_class_accuracy": 0.29863} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.02964, "memory": 15990, "data_time": 1.68618, "top1_acc": 0.36531, "top5_acc": 0.62438, "loss_cls": 3.59509, "loss": 3.59509, "time": 2.75978} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.02961, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36141, "top5_acc": 0.62078, "loss_cls": 3.63554, "loss": 3.63554, "time": 0.87142} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.02959, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36453, "top5_acc": 0.62609, "loss_cls": 3.57753, "loss": 3.57753, "time": 0.86957} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.02956, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36906, "top5_acc": 0.62078, "loss_cls": 3.5914, "loss": 3.5914, "time": 0.8711} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.02954, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.36516, "top5_acc": 0.63031, "loss_cls": 3.58424, "loss": 3.58424, "time": 0.86817} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.02951, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.36156, "top5_acc": 0.62313, "loss_cls": 3.58469, "loss": 3.58469, "time": 0.87436} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.02948, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36875, "top5_acc": 0.61609, "loss_cls": 3.62201, "loss": 3.62201, "time": 0.86624} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.02946, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36281, "top5_acc": 0.61906, "loss_cls": 3.62197, "loss": 3.62197, "time": 0.86359} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.02943, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.36891, "top5_acc": 0.63234, "loss_cls": 3.59854, "loss": 3.59854, "time": 0.86615} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.02941, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36391, "top5_acc": 0.61938, "loss_cls": 3.63669, "loss": 3.63669, "time": 0.86126} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.02938, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36344, "top5_acc": 0.63484, "loss_cls": 3.55593, "loss": 3.55593, "time": 0.8627} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.02936, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.36094, "top5_acc": 0.62391, "loss_cls": 3.60156, "loss": 3.60156, "time": 0.86165} +{"mode": "train", "epoch": 96, "iter": 1300, "lr": 0.02933, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36609, "top5_acc": 0.61844, "loss_cls": 3.62005, "loss": 3.62005, "time": 0.85831} +{"mode": "train", "epoch": 96, "iter": 1400, "lr": 0.02931, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.35672, "top5_acc": 0.62687, "loss_cls": 3.6147, "loss": 3.6147, "time": 0.85824} +{"mode": "train", "epoch": 96, "iter": 1500, "lr": 0.02928, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35125, "top5_acc": 0.61359, "loss_cls": 3.65095, "loss": 3.65095, "time": 0.85679} +{"mode": "train", "epoch": 96, "iter": 1600, "lr": 0.02926, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.35672, "top5_acc": 0.61703, "loss_cls": 3.63379, "loss": 3.63379, "time": 0.86449} +{"mode": "train", "epoch": 96, "iter": 1700, "lr": 0.02923, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35859, "top5_acc": 0.61016, "loss_cls": 3.67008, "loss": 3.67008, "time": 0.85174} +{"mode": "train", "epoch": 96, "iter": 1800, "lr": 0.0292, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.36703, "top5_acc": 0.61672, "loss_cls": 3.61481, "loss": 3.61481, "time": 0.85705} +{"mode": "train", "epoch": 96, "iter": 1900, "lr": 0.02918, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35266, "top5_acc": 0.61187, "loss_cls": 3.66933, "loss": 3.66933, "time": 0.86012} +{"mode": "train", "epoch": 96, "iter": 2000, "lr": 0.02915, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35969, "top5_acc": 0.61531, "loss_cls": 3.65561, "loss": 3.65561, "time": 0.86248} +{"mode": "train", "epoch": 96, "iter": 2100, "lr": 0.02913, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35422, "top5_acc": 0.61453, "loss_cls": 3.67845, "loss": 3.67845, "time": 0.85753} +{"mode": "train", "epoch": 96, "iter": 2200, "lr": 0.0291, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35641, "top5_acc": 0.61687, "loss_cls": 3.63992, "loss": 3.63992, "time": 0.85993} +{"mode": "train", "epoch": 96, "iter": 2300, "lr": 0.02908, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36422, "top5_acc": 0.62125, "loss_cls": 3.59893, "loss": 3.59893, "time": 0.85821} +{"mode": "train", "epoch": 96, "iter": 2400, "lr": 0.02905, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36328, "top5_acc": 0.61062, "loss_cls": 3.6433, "loss": 3.6433, "time": 0.86031} +{"mode": "train", "epoch": 96, "iter": 2500, "lr": 0.02903, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36672, "top5_acc": 0.62109, "loss_cls": 3.62787, "loss": 3.62787, "time": 0.85684} +{"mode": "train", "epoch": 96, "iter": 2600, "lr": 0.029, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36016, "top5_acc": 0.62219, "loss_cls": 3.63469, "loss": 3.63469, "time": 0.86696} +{"mode": "train", "epoch": 96, "iter": 2700, "lr": 0.02898, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37188, "top5_acc": 0.63156, "loss_cls": 3.55163, "loss": 3.55163, "time": 0.85816} +{"mode": "train", "epoch": 96, "iter": 2800, "lr": 0.02895, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36547, "top5_acc": 0.61656, "loss_cls": 3.61059, "loss": 3.61059, "time": 0.85922} +{"mode": "train", "epoch": 96, "iter": 2900, "lr": 0.02893, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36203, "top5_acc": 0.61359, "loss_cls": 3.65003, "loss": 3.65003, "time": 0.85904} +{"mode": "train", "epoch": 96, "iter": 3000, "lr": 0.0289, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35531, "top5_acc": 0.61219, "loss_cls": 3.66061, "loss": 3.66061, "time": 0.86213} +{"mode": "train", "epoch": 96, "iter": 3100, "lr": 0.02887, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36297, "top5_acc": 0.61906, "loss_cls": 3.63801, "loss": 3.63801, "time": 0.85717} +{"mode": "train", "epoch": 96, "iter": 3200, "lr": 0.02885, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36125, "top5_acc": 0.63313, "loss_cls": 3.60151, "loss": 3.60151, "time": 0.85742} +{"mode": "train", "epoch": 96, "iter": 3300, "lr": 0.02882, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36156, "top5_acc": 0.62859, "loss_cls": 3.62494, "loss": 3.62494, "time": 0.85205} +{"mode": "train", "epoch": 96, "iter": 3400, "lr": 0.0288, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35141, "top5_acc": 0.61234, "loss_cls": 3.67288, "loss": 3.67288, "time": 0.85555} +{"mode": "train", "epoch": 96, "iter": 3500, "lr": 0.02877, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.36109, "top5_acc": 0.61875, "loss_cls": 3.62806, "loss": 3.62806, "time": 0.86449} +{"mode": "train", "epoch": 96, "iter": 3600, "lr": 0.02875, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.36375, "top5_acc": 0.62094, "loss_cls": 3.60659, "loss": 3.60659, "time": 0.86501} +{"mode": "train", "epoch": 96, "iter": 3700, "lr": 0.02872, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.36297, "top5_acc": 0.62375, "loss_cls": 3.62828, "loss": 3.62828, "time": 0.85963} +{"mode": "val", "epoch": 96, "iter": 309, "lr": 0.02871, "top1_acc": 0.29428, "top5_acc": 0.54171, "mean_class_accuracy": 0.294} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.02869, "memory": 15990, "data_time": 1.69069, "top1_acc": 0.37203, "top5_acc": 0.62531, "loss_cls": 3.56567, "loss": 3.56567, "time": 2.75537} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.02866, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.37297, "top5_acc": 0.64078, "loss_cls": 3.53031, "loss": 3.53031, "time": 0.87495} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.02864, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.37141, "top5_acc": 0.62219, "loss_cls": 3.55911, "loss": 3.55911, "time": 0.87697} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.02861, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.36703, "top5_acc": 0.64031, "loss_cls": 3.5512, "loss": 3.5512, "time": 0.87896} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.02858, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.36734, "top5_acc": 0.63172, "loss_cls": 3.58134, "loss": 3.58134, "time": 0.87689} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.02856, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.36234, "top5_acc": 0.62172, "loss_cls": 3.61114, "loss": 3.61114, "time": 0.87876} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.02853, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.36188, "top5_acc": 0.62297, "loss_cls": 3.63215, "loss": 3.63215, "time": 0.86936} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.02851, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.3675, "top5_acc": 0.61828, "loss_cls": 3.5983, "loss": 3.5983, "time": 0.86516} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.02848, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.36438, "top5_acc": 0.61594, "loss_cls": 3.62506, "loss": 3.62506, "time": 0.86659} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.02846, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.36547, "top5_acc": 0.6225, "loss_cls": 3.60947, "loss": 3.60947, "time": 0.86613} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.02843, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36453, "top5_acc": 0.61656, "loss_cls": 3.61713, "loss": 3.61713, "time": 0.86458} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.02841, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.36141, "top5_acc": 0.61969, "loss_cls": 3.62079, "loss": 3.62079, "time": 0.85559} +{"mode": "train", "epoch": 97, "iter": 1300, "lr": 0.02838, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35875, "top5_acc": 0.62453, "loss_cls": 3.64249, "loss": 3.64249, "time": 0.85785} +{"mode": "train", "epoch": 97, "iter": 1400, "lr": 0.02836, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35766, "top5_acc": 0.62047, "loss_cls": 3.62686, "loss": 3.62686, "time": 0.86307} +{"mode": "train", "epoch": 97, "iter": 1500, "lr": 0.02833, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.37625, "top5_acc": 0.63469, "loss_cls": 3.5559, "loss": 3.5559, "time": 0.86334} +{"mode": "train", "epoch": 97, "iter": 1600, "lr": 0.02831, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36453, "top5_acc": 0.6175, "loss_cls": 3.60569, "loss": 3.60569, "time": 0.85973} +{"mode": "train", "epoch": 97, "iter": 1700, "lr": 0.02828, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.37375, "top5_acc": 0.625, "loss_cls": 3.58979, "loss": 3.58979, "time": 0.85706} +{"mode": "train", "epoch": 97, "iter": 1800, "lr": 0.02826, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36516, "top5_acc": 0.61859, "loss_cls": 3.61353, "loss": 3.61353, "time": 0.85736} +{"mode": "train", "epoch": 97, "iter": 1900, "lr": 0.02823, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35922, "top5_acc": 0.62391, "loss_cls": 3.59313, "loss": 3.59313, "time": 0.86182} +{"mode": "train", "epoch": 97, "iter": 2000, "lr": 0.02821, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36453, "top5_acc": 0.61359, "loss_cls": 3.64881, "loss": 3.64881, "time": 0.85995} +{"mode": "train", "epoch": 97, "iter": 2100, "lr": 0.02818, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37188, "top5_acc": 0.62891, "loss_cls": 3.58881, "loss": 3.58881, "time": 0.85364} +{"mode": "train", "epoch": 97, "iter": 2200, "lr": 0.02816, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36141, "top5_acc": 0.61109, "loss_cls": 3.6357, "loss": 3.6357, "time": 0.8579} +{"mode": "train", "epoch": 97, "iter": 2300, "lr": 0.02813, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37156, "top5_acc": 0.62719, "loss_cls": 3.57513, "loss": 3.57513, "time": 0.85477} +{"mode": "train", "epoch": 97, "iter": 2400, "lr": 0.02811, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36969, "top5_acc": 0.6175, "loss_cls": 3.60983, "loss": 3.60983, "time": 0.857} +{"mode": "train", "epoch": 97, "iter": 2500, "lr": 0.02808, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.35922, "top5_acc": 0.61781, "loss_cls": 3.65019, "loss": 3.65019, "time": 0.86016} +{"mode": "train", "epoch": 97, "iter": 2600, "lr": 0.02806, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35641, "top5_acc": 0.62516, "loss_cls": 3.58409, "loss": 3.58409, "time": 0.85968} +{"mode": "train", "epoch": 97, "iter": 2700, "lr": 0.02803, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35391, "top5_acc": 0.6225, "loss_cls": 3.62951, "loss": 3.62951, "time": 0.85393} +{"mode": "train", "epoch": 97, "iter": 2800, "lr": 0.02801, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.355, "top5_acc": 0.61844, "loss_cls": 3.65616, "loss": 3.65616, "time": 0.86002} +{"mode": "train", "epoch": 97, "iter": 2900, "lr": 0.02798, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36719, "top5_acc": 0.63172, "loss_cls": 3.57276, "loss": 3.57276, "time": 0.85804} +{"mode": "train", "epoch": 97, "iter": 3000, "lr": 0.02796, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36812, "top5_acc": 0.62469, "loss_cls": 3.61016, "loss": 3.61016, "time": 0.8573} +{"mode": "train", "epoch": 97, "iter": 3100, "lr": 0.02793, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.35609, "top5_acc": 0.62625, "loss_cls": 3.61799, "loss": 3.61799, "time": 0.86028} +{"mode": "train", "epoch": 97, "iter": 3200, "lr": 0.02791, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36969, "top5_acc": 0.62609, "loss_cls": 3.60133, "loss": 3.60133, "time": 0.85918} +{"mode": "train", "epoch": 97, "iter": 3300, "lr": 0.02788, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.34703, "top5_acc": 0.60953, "loss_cls": 3.66977, "loss": 3.66977, "time": 0.85824} +{"mode": "train", "epoch": 97, "iter": 3400, "lr": 0.02786, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.35094, "top5_acc": 0.61578, "loss_cls": 3.6378, "loss": 3.6378, "time": 0.85541} +{"mode": "train", "epoch": 97, "iter": 3500, "lr": 0.02783, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36234, "top5_acc": 0.61953, "loss_cls": 3.61876, "loss": 3.61876, "time": 0.86019} +{"mode": "train", "epoch": 97, "iter": 3600, "lr": 0.02781, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36156, "top5_acc": 0.62859, "loss_cls": 3.61446, "loss": 3.61446, "time": 0.8632} +{"mode": "train", "epoch": 97, "iter": 3700, "lr": 0.02778, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36828, "top5_acc": 0.63, "loss_cls": 3.56965, "loss": 3.56965, "time": 0.8547} +{"mode": "val", "epoch": 97, "iter": 309, "lr": 0.02777, "top1_acc": 0.30765, "top5_acc": 0.55949, "mean_class_accuracy": 0.30739} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.02774, "memory": 15990, "data_time": 1.5635, "top1_acc": 0.37172, "top5_acc": 0.63297, "loss_cls": 3.54599, "loss": 3.54599, "time": 2.6283} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.02772, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.3825, "top5_acc": 0.64, "loss_cls": 3.50732, "loss": 3.50732, "time": 0.87922} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.02769, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.3775, "top5_acc": 0.63313, "loss_cls": 3.51507, "loss": 3.51507, "time": 0.87722} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.02767, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.35547, "top5_acc": 0.61531, "loss_cls": 3.63736, "loss": 3.63736, "time": 0.87075} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.02764, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.36125, "top5_acc": 0.63344, "loss_cls": 3.59079, "loss": 3.59079, "time": 0.86319} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.02762, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36625, "top5_acc": 0.62969, "loss_cls": 3.57648, "loss": 3.57648, "time": 0.87474} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.02759, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.36953, "top5_acc": 0.63547, "loss_cls": 3.53173, "loss": 3.53173, "time": 0.86583} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.02757, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.37562, "top5_acc": 0.63188, "loss_cls": 3.55469, "loss": 3.55469, "time": 0.86299} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.02754, "memory": 15990, "data_time": 0.00068, "top1_acc": 0.35953, "top5_acc": 0.61938, "loss_cls": 3.6232, "loss": 3.6232, "time": 0.85684} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.02752, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37109, "top5_acc": 0.63016, "loss_cls": 3.56993, "loss": 3.56993, "time": 0.87309} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.02749, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36469, "top5_acc": 0.6225, "loss_cls": 3.59341, "loss": 3.59341, "time": 0.87464} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.02747, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35562, "top5_acc": 0.6225, "loss_cls": 3.60546, "loss": 3.60546, "time": 0.8738} +{"mode": "train", "epoch": 98, "iter": 1300, "lr": 0.02744, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36688, "top5_acc": 0.62672, "loss_cls": 3.55698, "loss": 3.55698, "time": 0.87079} +{"mode": "train", "epoch": 98, "iter": 1400, "lr": 0.02742, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.35359, "top5_acc": 0.61641, "loss_cls": 3.64798, "loss": 3.64798, "time": 0.87423} +{"mode": "train", "epoch": 98, "iter": 1500, "lr": 0.02739, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.37391, "top5_acc": 0.63188, "loss_cls": 3.55927, "loss": 3.55927, "time": 0.86189} +{"mode": "train", "epoch": 98, "iter": 1600, "lr": 0.02737, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36578, "top5_acc": 0.62953, "loss_cls": 3.58375, "loss": 3.58375, "time": 0.85826} +{"mode": "train", "epoch": 98, "iter": 1700, "lr": 0.02734, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.36203, "top5_acc": 0.62906, "loss_cls": 3.57796, "loss": 3.57796, "time": 0.85531} +{"mode": "train", "epoch": 98, "iter": 1800, "lr": 0.02732, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35453, "top5_acc": 0.61375, "loss_cls": 3.63464, "loss": 3.63464, "time": 0.86399} +{"mode": "train", "epoch": 98, "iter": 1900, "lr": 0.02729, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37203, "top5_acc": 0.62234, "loss_cls": 3.59429, "loss": 3.59429, "time": 0.85754} +{"mode": "train", "epoch": 98, "iter": 2000, "lr": 0.02727, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36656, "top5_acc": 0.62969, "loss_cls": 3.5908, "loss": 3.5908, "time": 0.86365} +{"mode": "train", "epoch": 98, "iter": 2100, "lr": 0.02724, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36969, "top5_acc": 0.62391, "loss_cls": 3.62505, "loss": 3.62505, "time": 0.85765} +{"mode": "train", "epoch": 98, "iter": 2200, "lr": 0.02722, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37156, "top5_acc": 0.63219, "loss_cls": 3.56879, "loss": 3.56879, "time": 0.86} +{"mode": "train", "epoch": 98, "iter": 2300, "lr": 0.02719, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36062, "top5_acc": 0.62547, "loss_cls": 3.61628, "loss": 3.61628, "time": 0.8609} +{"mode": "train", "epoch": 98, "iter": 2400, "lr": 0.02717, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35047, "top5_acc": 0.61453, "loss_cls": 3.64492, "loss": 3.64492, "time": 0.86814} +{"mode": "train", "epoch": 98, "iter": 2500, "lr": 0.02714, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.37328, "top5_acc": 0.62938, "loss_cls": 3.55742, "loss": 3.55742, "time": 0.86135} +{"mode": "train", "epoch": 98, "iter": 2600, "lr": 0.02712, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37219, "top5_acc": 0.63219, "loss_cls": 3.53716, "loss": 3.53716, "time": 0.86242} +{"mode": "train", "epoch": 98, "iter": 2700, "lr": 0.02709, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36062, "top5_acc": 0.62422, "loss_cls": 3.57876, "loss": 3.57876, "time": 0.86572} +{"mode": "train", "epoch": 98, "iter": 2800, "lr": 0.02707, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36453, "top5_acc": 0.61094, "loss_cls": 3.64289, "loss": 3.64289, "time": 0.86672} +{"mode": "train", "epoch": 98, "iter": 2900, "lr": 0.02705, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35984, "top5_acc": 0.62328, "loss_cls": 3.61979, "loss": 3.61979, "time": 0.86163} +{"mode": "train", "epoch": 98, "iter": 3000, "lr": 0.02702, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.35938, "top5_acc": 0.62438, "loss_cls": 3.62776, "loss": 3.62776, "time": 0.86293} +{"mode": "train", "epoch": 98, "iter": 3100, "lr": 0.027, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.35047, "top5_acc": 0.61625, "loss_cls": 3.64203, "loss": 3.64203, "time": 0.86743} +{"mode": "train", "epoch": 98, "iter": 3200, "lr": 0.02697, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36844, "top5_acc": 0.63031, "loss_cls": 3.57824, "loss": 3.57824, "time": 0.86638} +{"mode": "train", "epoch": 98, "iter": 3300, "lr": 0.02695, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35984, "top5_acc": 0.62047, "loss_cls": 3.6464, "loss": 3.6464, "time": 0.86281} +{"mode": "train", "epoch": 98, "iter": 3400, "lr": 0.02692, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36703, "top5_acc": 0.62469, "loss_cls": 3.5881, "loss": 3.5881, "time": 0.86822} +{"mode": "train", "epoch": 98, "iter": 3500, "lr": 0.0269, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36609, "top5_acc": 0.62078, "loss_cls": 3.57874, "loss": 3.57874, "time": 0.86484} +{"mode": "train", "epoch": 98, "iter": 3600, "lr": 0.02687, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.35594, "top5_acc": 0.6175, "loss_cls": 3.62555, "loss": 3.62555, "time": 0.86818} +{"mode": "train", "epoch": 98, "iter": 3700, "lr": 0.02685, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36516, "top5_acc": 0.625, "loss_cls": 3.60887, "loss": 3.60887, "time": 0.86526} +{"mode": "val", "epoch": 98, "iter": 309, "lr": 0.02684, "top1_acc": 0.30406, "top5_acc": 0.55655, "mean_class_accuracy": 0.3039} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.02681, "memory": 15990, "data_time": 1.54652, "top1_acc": 0.38766, "top5_acc": 0.64281, "loss_cls": 3.47553, "loss": 3.47553, "time": 2.5993} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.02679, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.37844, "top5_acc": 0.64172, "loss_cls": 3.51327, "loss": 3.51327, "time": 0.86684} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.02676, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.37031, "top5_acc": 0.63188, "loss_cls": 3.54654, "loss": 3.54654, "time": 0.86966} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.02674, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36938, "top5_acc": 0.6275, "loss_cls": 3.5915, "loss": 3.5915, "time": 0.87184} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.02671, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.36578, "top5_acc": 0.62406, "loss_cls": 3.59029, "loss": 3.59029, "time": 0.87467} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.02669, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37109, "top5_acc": 0.62938, "loss_cls": 3.58468, "loss": 3.58468, "time": 0.87444} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.02666, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37281, "top5_acc": 0.63688, "loss_cls": 3.52877, "loss": 3.52877, "time": 0.86092} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.02664, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37219, "top5_acc": 0.64016, "loss_cls": 3.54427, "loss": 3.54427, "time": 0.85961} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.02661, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37297, "top5_acc": 0.63313, "loss_cls": 3.54765, "loss": 3.54765, "time": 0.85209} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.02659, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.37562, "top5_acc": 0.63016, "loss_cls": 3.53165, "loss": 3.53165, "time": 0.86241} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.02656, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35484, "top5_acc": 0.62422, "loss_cls": 3.64176, "loss": 3.64176, "time": 0.86349} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.02654, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36906, "top5_acc": 0.63578, "loss_cls": 3.56872, "loss": 3.56872, "time": 0.86448} +{"mode": "train", "epoch": 99, "iter": 1300, "lr": 0.02651, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.35422, "top5_acc": 0.61531, "loss_cls": 3.65701, "loss": 3.65701, "time": 0.87006} +{"mode": "train", "epoch": 99, "iter": 1400, "lr": 0.02649, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36469, "top5_acc": 0.63438, "loss_cls": 3.58039, "loss": 3.58039, "time": 0.86075} +{"mode": "train", "epoch": 99, "iter": 1500, "lr": 0.02646, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37891, "top5_acc": 0.63828, "loss_cls": 3.51017, "loss": 3.51017, "time": 0.86018} +{"mode": "train", "epoch": 99, "iter": 1600, "lr": 0.02644, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.37266, "top5_acc": 0.63641, "loss_cls": 3.55398, "loss": 3.55398, "time": 0.85707} +{"mode": "train", "epoch": 99, "iter": 1700, "lr": 0.02642, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.37391, "top5_acc": 0.61766, "loss_cls": 3.61241, "loss": 3.61241, "time": 0.84997} +{"mode": "train", "epoch": 99, "iter": 1800, "lr": 0.02639, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37234, "top5_acc": 0.63141, "loss_cls": 3.55485, "loss": 3.55485, "time": 0.85318} +{"mode": "train", "epoch": 99, "iter": 1900, "lr": 0.02637, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.3725, "top5_acc": 0.6375, "loss_cls": 3.56051, "loss": 3.56051, "time": 0.85319} +{"mode": "train", "epoch": 99, "iter": 2000, "lr": 0.02634, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36125, "top5_acc": 0.62703, "loss_cls": 3.59001, "loss": 3.59001, "time": 0.85405} +{"mode": "train", "epoch": 99, "iter": 2100, "lr": 0.02632, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36891, "top5_acc": 0.62062, "loss_cls": 3.6014, "loss": 3.6014, "time": 0.85317} +{"mode": "train", "epoch": 99, "iter": 2200, "lr": 0.02629, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37031, "top5_acc": 0.62734, "loss_cls": 3.57902, "loss": 3.57902, "time": 0.85748} +{"mode": "train", "epoch": 99, "iter": 2300, "lr": 0.02627, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36312, "top5_acc": 0.6225, "loss_cls": 3.61993, "loss": 3.61993, "time": 0.85768} +{"mode": "train", "epoch": 99, "iter": 2400, "lr": 0.02624, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36766, "top5_acc": 0.62859, "loss_cls": 3.57277, "loss": 3.57277, "time": 0.8574} +{"mode": "train", "epoch": 99, "iter": 2500, "lr": 0.02622, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36734, "top5_acc": 0.63719, "loss_cls": 3.55034, "loss": 3.55034, "time": 0.85203} +{"mode": "train", "epoch": 99, "iter": 2600, "lr": 0.02619, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36125, "top5_acc": 0.62469, "loss_cls": 3.62821, "loss": 3.62821, "time": 0.85648} +{"mode": "train", "epoch": 99, "iter": 2700, "lr": 0.02617, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36438, "top5_acc": 0.625, "loss_cls": 3.60656, "loss": 3.60656, "time": 0.85074} +{"mode": "train", "epoch": 99, "iter": 2800, "lr": 0.02614, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37031, "top5_acc": 0.63766, "loss_cls": 3.56175, "loss": 3.56175, "time": 0.85435} +{"mode": "train", "epoch": 99, "iter": 2900, "lr": 0.02612, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36344, "top5_acc": 0.62813, "loss_cls": 3.60337, "loss": 3.60337, "time": 0.85113} +{"mode": "train", "epoch": 99, "iter": 3000, "lr": 0.0261, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.36984, "top5_acc": 0.62687, "loss_cls": 3.57548, "loss": 3.57548, "time": 0.85172} +{"mode": "train", "epoch": 99, "iter": 3100, "lr": 0.02607, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35469, "top5_acc": 0.61547, "loss_cls": 3.64244, "loss": 3.64244, "time": 0.85514} +{"mode": "train", "epoch": 99, "iter": 3200, "lr": 0.02605, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.36781, "top5_acc": 0.62297, "loss_cls": 3.62104, "loss": 3.62104, "time": 0.85366} +{"mode": "train", "epoch": 99, "iter": 3300, "lr": 0.02602, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36578, "top5_acc": 0.62578, "loss_cls": 3.60737, "loss": 3.60737, "time": 0.85528} +{"mode": "train", "epoch": 99, "iter": 3400, "lr": 0.026, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37438, "top5_acc": 0.63594, "loss_cls": 3.55679, "loss": 3.55679, "time": 0.85256} +{"mode": "train", "epoch": 99, "iter": 3500, "lr": 0.02597, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35906, "top5_acc": 0.62562, "loss_cls": 3.60468, "loss": 3.60468, "time": 0.85274} +{"mode": "train", "epoch": 99, "iter": 3600, "lr": 0.02595, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36422, "top5_acc": 0.63219, "loss_cls": 3.56616, "loss": 3.56616, "time": 0.86014} +{"mode": "train", "epoch": 99, "iter": 3700, "lr": 0.02592, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36672, "top5_acc": 0.63719, "loss_cls": 3.56725, "loss": 3.56725, "time": 0.8567} +{"mode": "val", "epoch": 99, "iter": 309, "lr": 0.02591, "top1_acc": 0.31368, "top5_acc": 0.56521, "mean_class_accuracy": 0.31324} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.02589, "memory": 15990, "data_time": 1.46599, "top1_acc": 0.37953, "top5_acc": 0.64219, "loss_cls": 3.50699, "loss": 3.50699, "time": 2.50609} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.02586, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37438, "top5_acc": 0.63688, "loss_cls": 3.51773, "loss": 3.51773, "time": 0.8583} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.02584, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37859, "top5_acc": 0.64391, "loss_cls": 3.49582, "loss": 3.49582, "time": 0.85824} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.02581, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37438, "top5_acc": 0.63609, "loss_cls": 3.53465, "loss": 3.53465, "time": 0.85769} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.02579, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.36984, "top5_acc": 0.63047, "loss_cls": 3.54868, "loss": 3.54868, "time": 0.85887} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.02577, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36531, "top5_acc": 0.62125, "loss_cls": 3.61995, "loss": 3.61995, "time": 0.85953} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.02574, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37156, "top5_acc": 0.62844, "loss_cls": 3.5526, "loss": 3.5526, "time": 0.85207} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.02572, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37625, "top5_acc": 0.62672, "loss_cls": 3.56472, "loss": 3.56472, "time": 0.85186} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.02569, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.37875, "top5_acc": 0.63531, "loss_cls": 3.52149, "loss": 3.52149, "time": 0.85323} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.02567, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37703, "top5_acc": 0.62531, "loss_cls": 3.55929, "loss": 3.55929, "time": 0.85864} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.02564, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.35469, "top5_acc": 0.63078, "loss_cls": 3.58954, "loss": 3.58954, "time": 0.85645} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.02562, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37438, "top5_acc": 0.62875, "loss_cls": 3.54567, "loss": 3.54567, "time": 0.8535} +{"mode": "train", "epoch": 100, "iter": 1300, "lr": 0.02559, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38016, "top5_acc": 0.6375, "loss_cls": 3.51109, "loss": 3.51109, "time": 0.85549} +{"mode": "train", "epoch": 100, "iter": 1400, "lr": 0.02557, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37594, "top5_acc": 0.63156, "loss_cls": 3.54661, "loss": 3.54661, "time": 0.85558} +{"mode": "train", "epoch": 100, "iter": 1500, "lr": 0.02555, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36984, "top5_acc": 0.62828, "loss_cls": 3.57756, "loss": 3.57756, "time": 0.85473} +{"mode": "train", "epoch": 100, "iter": 1600, "lr": 0.02552, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36344, "top5_acc": 0.62547, "loss_cls": 3.59441, "loss": 3.59441, "time": 0.85196} +{"mode": "train", "epoch": 100, "iter": 1700, "lr": 0.0255, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.37109, "top5_acc": 0.64125, "loss_cls": 3.54049, "loss": 3.54049, "time": 0.85061} +{"mode": "train", "epoch": 100, "iter": 1800, "lr": 0.02547, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36219, "top5_acc": 0.62813, "loss_cls": 3.59007, "loss": 3.59007, "time": 0.8583} +{"mode": "train", "epoch": 100, "iter": 1900, "lr": 0.02545, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38625, "top5_acc": 0.64594, "loss_cls": 3.47039, "loss": 3.47039, "time": 0.85727} +{"mode": "train", "epoch": 100, "iter": 2000, "lr": 0.02542, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.36781, "top5_acc": 0.62969, "loss_cls": 3.54835, "loss": 3.54835, "time": 0.855} +{"mode": "train", "epoch": 100, "iter": 2100, "lr": 0.0254, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36406, "top5_acc": 0.63516, "loss_cls": 3.5699, "loss": 3.5699, "time": 0.85076} +{"mode": "train", "epoch": 100, "iter": 2200, "lr": 0.02538, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37328, "top5_acc": 0.63188, "loss_cls": 3.55375, "loss": 3.55375, "time": 0.85386} +{"mode": "train", "epoch": 100, "iter": 2300, "lr": 0.02535, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36719, "top5_acc": 0.63047, "loss_cls": 3.56839, "loss": 3.56839, "time": 0.85121} +{"mode": "train", "epoch": 100, "iter": 2400, "lr": 0.02533, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3725, "top5_acc": 0.62578, "loss_cls": 3.57048, "loss": 3.57048, "time": 0.8558} +{"mode": "train", "epoch": 100, "iter": 2500, "lr": 0.0253, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37578, "top5_acc": 0.63469, "loss_cls": 3.53896, "loss": 3.53896, "time": 0.85581} +{"mode": "train", "epoch": 100, "iter": 2600, "lr": 0.02528, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.3775, "top5_acc": 0.63266, "loss_cls": 3.55299, "loss": 3.55299, "time": 0.85823} +{"mode": "train", "epoch": 100, "iter": 2700, "lr": 0.02525, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36203, "top5_acc": 0.61672, "loss_cls": 3.61987, "loss": 3.61987, "time": 0.8557} +{"mode": "train", "epoch": 100, "iter": 2800, "lr": 0.02523, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38031, "top5_acc": 0.63516, "loss_cls": 3.53625, "loss": 3.53625, "time": 0.85612} +{"mode": "train", "epoch": 100, "iter": 2900, "lr": 0.02521, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37781, "top5_acc": 0.63672, "loss_cls": 3.52869, "loss": 3.52869, "time": 0.85691} +{"mode": "train", "epoch": 100, "iter": 3000, "lr": 0.02518, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36625, "top5_acc": 0.61922, "loss_cls": 3.62423, "loss": 3.62423, "time": 0.8615} +{"mode": "train", "epoch": 100, "iter": 3100, "lr": 0.02516, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.3775, "top5_acc": 0.62938, "loss_cls": 3.55118, "loss": 3.55118, "time": 0.85947} +{"mode": "train", "epoch": 100, "iter": 3200, "lr": 0.02513, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38031, "top5_acc": 0.63125, "loss_cls": 3.55653, "loss": 3.55653, "time": 0.8544} +{"mode": "train", "epoch": 100, "iter": 3300, "lr": 0.02511, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36047, "top5_acc": 0.61719, "loss_cls": 3.62601, "loss": 3.62601, "time": 0.8577} +{"mode": "train", "epoch": 100, "iter": 3400, "lr": 0.02508, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37812, "top5_acc": 0.64047, "loss_cls": 3.52209, "loss": 3.52209, "time": 0.85979} +{"mode": "train", "epoch": 100, "iter": 3500, "lr": 0.02506, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36703, "top5_acc": 0.62844, "loss_cls": 3.56898, "loss": 3.56898, "time": 0.85792} +{"mode": "train", "epoch": 100, "iter": 3600, "lr": 0.02504, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36172, "top5_acc": 0.61312, "loss_cls": 3.657, "loss": 3.657, "time": 0.85853} +{"mode": "train", "epoch": 100, "iter": 3700, "lr": 0.02501, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36906, "top5_acc": 0.62359, "loss_cls": 3.63019, "loss": 3.63019, "time": 0.86114} +{"mode": "val", "epoch": 100, "iter": 309, "lr": 0.025, "top1_acc": 0.31505, "top5_acc": 0.5679, "mean_class_accuracy": 0.31477} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.02498, "memory": 15990, "data_time": 1.49814, "top1_acc": 0.38938, "top5_acc": 0.64328, "loss_cls": 3.45009, "loss": 3.45009, "time": 2.5418} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.02495, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37844, "top5_acc": 0.64234, "loss_cls": 3.49942, "loss": 3.49942, "time": 0.85816} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.02493, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37625, "top5_acc": 0.63844, "loss_cls": 3.51867, "loss": 3.51867, "time": 0.86466} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.0249, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37547, "top5_acc": 0.6275, "loss_cls": 3.5586, "loss": 3.5586, "time": 0.8618} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.02488, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.37641, "top5_acc": 0.64031, "loss_cls": 3.55198, "loss": 3.55198, "time": 0.86224} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.02486, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37641, "top5_acc": 0.6325, "loss_cls": 3.53103, "loss": 3.53103, "time": 0.85821} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.02483, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.36297, "top5_acc": 0.62609, "loss_cls": 3.53839, "loss": 3.53839, "time": 0.85418} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.02481, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.375, "top5_acc": 0.6375, "loss_cls": 3.54039, "loss": 3.54039, "time": 0.85149} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.02478, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37438, "top5_acc": 0.63734, "loss_cls": 3.54419, "loss": 3.54419, "time": 0.85205} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.02476, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37312, "top5_acc": 0.63562, "loss_cls": 3.55434, "loss": 3.55434, "time": 0.86264} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.02473, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.37141, "top5_acc": 0.63766, "loss_cls": 3.53708, "loss": 3.53708, "time": 0.85811} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.02471, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.36953, "top5_acc": 0.63672, "loss_cls": 3.55185, "loss": 3.55185, "time": 0.86188} +{"mode": "train", "epoch": 101, "iter": 1300, "lr": 0.02469, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.37469, "top5_acc": 0.63516, "loss_cls": 3.53137, "loss": 3.53137, "time": 0.86146} +{"mode": "train", "epoch": 101, "iter": 1400, "lr": 0.02466, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37938, "top5_acc": 0.63844, "loss_cls": 3.52944, "loss": 3.52944, "time": 0.85296} +{"mode": "train", "epoch": 101, "iter": 1500, "lr": 0.02464, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37328, "top5_acc": 0.64, "loss_cls": 3.5511, "loss": 3.5511, "time": 0.8502} +{"mode": "train", "epoch": 101, "iter": 1600, "lr": 0.02461, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38609, "top5_acc": 0.64, "loss_cls": 3.52333, "loss": 3.52333, "time": 0.8528} +{"mode": "train", "epoch": 101, "iter": 1700, "lr": 0.02459, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37203, "top5_acc": 0.63313, "loss_cls": 3.54115, "loss": 3.54115, "time": 0.85182} +{"mode": "train", "epoch": 101, "iter": 1800, "lr": 0.02457, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37047, "top5_acc": 0.63047, "loss_cls": 3.54621, "loss": 3.54621, "time": 0.84722} +{"mode": "train", "epoch": 101, "iter": 1900, "lr": 0.02454, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3625, "top5_acc": 0.61406, "loss_cls": 3.61922, "loss": 3.61922, "time": 0.85223} +{"mode": "train", "epoch": 101, "iter": 2000, "lr": 0.02452, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.37578, "top5_acc": 0.63375, "loss_cls": 3.55312, "loss": 3.55312, "time": 0.84925} +{"mode": "train", "epoch": 101, "iter": 2100, "lr": 0.02449, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.36234, "top5_acc": 0.62609, "loss_cls": 3.57927, "loss": 3.57927, "time": 0.84745} +{"mode": "train", "epoch": 101, "iter": 2200, "lr": 0.02447, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.36906, "top5_acc": 0.62891, "loss_cls": 3.56466, "loss": 3.56466, "time": 0.84764} +{"mode": "train", "epoch": 101, "iter": 2300, "lr": 0.02445, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36703, "top5_acc": 0.62672, "loss_cls": 3.57515, "loss": 3.57515, "time": 0.84748} +{"mode": "train", "epoch": 101, "iter": 2400, "lr": 0.02442, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35594, "top5_acc": 0.61953, "loss_cls": 3.62299, "loss": 3.62299, "time": 0.85202} +{"mode": "train", "epoch": 101, "iter": 2500, "lr": 0.0244, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3725, "top5_acc": 0.64156, "loss_cls": 3.50964, "loss": 3.50964, "time": 0.85338} +{"mode": "train", "epoch": 101, "iter": 2600, "lr": 0.02437, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37812, "top5_acc": 0.63813, "loss_cls": 3.53573, "loss": 3.53573, "time": 0.84583} +{"mode": "train", "epoch": 101, "iter": 2700, "lr": 0.02435, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37438, "top5_acc": 0.63188, "loss_cls": 3.58466, "loss": 3.58466, "time": 0.85128} +{"mode": "train", "epoch": 101, "iter": 2800, "lr": 0.02433, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38359, "top5_acc": 0.64031, "loss_cls": 3.51981, "loss": 3.51981, "time": 0.85062} +{"mode": "train", "epoch": 101, "iter": 2900, "lr": 0.0243, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38641, "top5_acc": 0.63562, "loss_cls": 3.47689, "loss": 3.47689, "time": 0.85315} +{"mode": "train", "epoch": 101, "iter": 3000, "lr": 0.02428, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.36969, "top5_acc": 0.62891, "loss_cls": 3.56634, "loss": 3.56634, "time": 0.8527} +{"mode": "train", "epoch": 101, "iter": 3100, "lr": 0.02425, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36875, "top5_acc": 0.63547, "loss_cls": 3.54879, "loss": 3.54879, "time": 0.85035} +{"mode": "train", "epoch": 101, "iter": 3200, "lr": 0.02423, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37344, "top5_acc": 0.63266, "loss_cls": 3.50871, "loss": 3.50871, "time": 0.85264} +{"mode": "train", "epoch": 101, "iter": 3300, "lr": 0.02421, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37094, "top5_acc": 0.62125, "loss_cls": 3.57521, "loss": 3.57521, "time": 0.84758} +{"mode": "train", "epoch": 101, "iter": 3400, "lr": 0.02418, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37016, "top5_acc": 0.62641, "loss_cls": 3.58511, "loss": 3.58511, "time": 0.84994} +{"mode": "train", "epoch": 101, "iter": 3500, "lr": 0.02416, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37953, "top5_acc": 0.63422, "loss_cls": 3.56402, "loss": 3.56402, "time": 0.84913} +{"mode": "train", "epoch": 101, "iter": 3600, "lr": 0.02413, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37641, "top5_acc": 0.63594, "loss_cls": 3.51362, "loss": 3.51362, "time": 0.84912} +{"mode": "train", "epoch": 101, "iter": 3700, "lr": 0.02411, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37609, "top5_acc": 0.63234, "loss_cls": 3.58172, "loss": 3.58172, "time": 0.84799} +{"mode": "val", "epoch": 101, "iter": 309, "lr": 0.0241, "top1_acc": 0.31581, "top5_acc": 0.56932, "mean_class_accuracy": 0.31553} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.02407, "memory": 15990, "data_time": 1.51371, "top1_acc": 0.38594, "top5_acc": 0.64016, "loss_cls": 3.4743, "loss": 3.4743, "time": 2.55214} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.02405, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.37906, "top5_acc": 0.63844, "loss_cls": 3.51774, "loss": 3.51774, "time": 0.85764} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.02403, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.37875, "top5_acc": 0.62734, "loss_cls": 3.55047, "loss": 3.55047, "time": 0.85978} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.024, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.38594, "top5_acc": 0.64562, "loss_cls": 3.48165, "loss": 3.48165, "time": 0.86222} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.02398, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.37281, "top5_acc": 0.62734, "loss_cls": 3.54736, "loss": 3.54736, "time": 0.86131} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.02396, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.38031, "top5_acc": 0.64172, "loss_cls": 3.50227, "loss": 3.50227, "time": 0.86392} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.02393, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37516, "top5_acc": 0.63578, "loss_cls": 3.53047, "loss": 3.53047, "time": 0.85433} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.02391, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38078, "top5_acc": 0.63547, "loss_cls": 3.5256, "loss": 3.5256, "time": 0.84893} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.02388, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.37156, "top5_acc": 0.63125, "loss_cls": 3.53835, "loss": 3.53835, "time": 0.85544} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.02386, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37547, "top5_acc": 0.635, "loss_cls": 3.53076, "loss": 3.53076, "time": 0.85858} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.02384, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.37188, "top5_acc": 0.63969, "loss_cls": 3.52093, "loss": 3.52093, "time": 0.86068} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.02381, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37844, "top5_acc": 0.63672, "loss_cls": 3.5124, "loss": 3.5124, "time": 0.8636} +{"mode": "train", "epoch": 102, "iter": 1300, "lr": 0.02379, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38266, "top5_acc": 0.64484, "loss_cls": 3.46852, "loss": 3.46852, "time": 0.85638} +{"mode": "train", "epoch": 102, "iter": 1400, "lr": 0.02376, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.36844, "top5_acc": 0.63547, "loss_cls": 3.53903, "loss": 3.53903, "time": 0.86327} +{"mode": "train", "epoch": 102, "iter": 1500, "lr": 0.02374, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37219, "top5_acc": 0.62672, "loss_cls": 3.58149, "loss": 3.58149, "time": 0.8608} +{"mode": "train", "epoch": 102, "iter": 1600, "lr": 0.02372, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.37438, "top5_acc": 0.63938, "loss_cls": 3.54146, "loss": 3.54146, "time": 0.84778} +{"mode": "train", "epoch": 102, "iter": 1700, "lr": 0.02369, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37891, "top5_acc": 0.64562, "loss_cls": 3.47906, "loss": 3.47906, "time": 0.85156} +{"mode": "train", "epoch": 102, "iter": 1800, "lr": 0.02367, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37938, "top5_acc": 0.63906, "loss_cls": 3.50729, "loss": 3.50729, "time": 0.86005} +{"mode": "train", "epoch": 102, "iter": 1900, "lr": 0.02365, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37078, "top5_acc": 0.63562, "loss_cls": 3.5519, "loss": 3.5519, "time": 0.85324} +{"mode": "train", "epoch": 102, "iter": 2000, "lr": 0.02362, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37531, "top5_acc": 0.62578, "loss_cls": 3.54253, "loss": 3.54253, "time": 0.84867} +{"mode": "train", "epoch": 102, "iter": 2100, "lr": 0.0236, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38047, "top5_acc": 0.63797, "loss_cls": 3.50476, "loss": 3.50476, "time": 0.84921} +{"mode": "train", "epoch": 102, "iter": 2200, "lr": 0.02357, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38891, "top5_acc": 0.63922, "loss_cls": 3.50876, "loss": 3.50876, "time": 0.85209} +{"mode": "train", "epoch": 102, "iter": 2300, "lr": 0.02355, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37266, "top5_acc": 0.62859, "loss_cls": 3.54036, "loss": 3.54036, "time": 0.84816} +{"mode": "train", "epoch": 102, "iter": 2400, "lr": 0.02353, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38125, "top5_acc": 0.63594, "loss_cls": 3.52405, "loss": 3.52405, "time": 0.85502} +{"mode": "train", "epoch": 102, "iter": 2500, "lr": 0.0235, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38531, "top5_acc": 0.63906, "loss_cls": 3.51282, "loss": 3.51282, "time": 0.8496} +{"mode": "train", "epoch": 102, "iter": 2600, "lr": 0.02348, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.37422, "top5_acc": 0.64312, "loss_cls": 3.54037, "loss": 3.54037, "time": 0.84846} +{"mode": "train", "epoch": 102, "iter": 2700, "lr": 0.02346, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.37922, "top5_acc": 0.63188, "loss_cls": 3.5757, "loss": 3.5757, "time": 0.84782} +{"mode": "train", "epoch": 102, "iter": 2800, "lr": 0.02343, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38344, "top5_acc": 0.64078, "loss_cls": 3.49687, "loss": 3.49687, "time": 0.85092} +{"mode": "train", "epoch": 102, "iter": 2900, "lr": 0.02341, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37703, "top5_acc": 0.63578, "loss_cls": 3.53795, "loss": 3.53795, "time": 0.85349} +{"mode": "train", "epoch": 102, "iter": 3000, "lr": 0.02339, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3825, "top5_acc": 0.64359, "loss_cls": 3.49075, "loss": 3.49075, "time": 0.84882} +{"mode": "train", "epoch": 102, "iter": 3100, "lr": 0.02336, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37359, "top5_acc": 0.63844, "loss_cls": 3.53305, "loss": 3.53305, "time": 0.85557} +{"mode": "train", "epoch": 102, "iter": 3200, "lr": 0.02334, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37812, "top5_acc": 0.63781, "loss_cls": 3.50191, "loss": 3.50191, "time": 0.85924} +{"mode": "train", "epoch": 102, "iter": 3300, "lr": 0.02331, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37844, "top5_acc": 0.62766, "loss_cls": 3.54575, "loss": 3.54575, "time": 0.84975} +{"mode": "train", "epoch": 102, "iter": 3400, "lr": 0.02329, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.37297, "top5_acc": 0.62906, "loss_cls": 3.59264, "loss": 3.59264, "time": 0.85291} +{"mode": "train", "epoch": 102, "iter": 3500, "lr": 0.02327, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37078, "top5_acc": 0.63375, "loss_cls": 3.54395, "loss": 3.54395, "time": 0.85932} +{"mode": "train", "epoch": 102, "iter": 3600, "lr": 0.02324, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36969, "top5_acc": 0.63359, "loss_cls": 3.56778, "loss": 3.56778, "time": 0.85651} +{"mode": "train", "epoch": 102, "iter": 3700, "lr": 0.02322, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.36391, "top5_acc": 0.62641, "loss_cls": 3.5578, "loss": 3.5578, "time": 0.85594} +{"mode": "val", "epoch": 102, "iter": 309, "lr": 0.02321, "top1_acc": 0.32422, "top5_acc": 0.57732, "mean_class_accuracy": 0.32401} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.02319, "memory": 15990, "data_time": 1.58892, "top1_acc": 0.39062, "top5_acc": 0.65891, "loss_cls": 3.43867, "loss": 3.43867, "time": 2.62938} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.02316, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38531, "top5_acc": 0.64141, "loss_cls": 3.47567, "loss": 3.47567, "time": 0.85937} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.02314, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.38469, "top5_acc": 0.65359, "loss_cls": 3.44172, "loss": 3.44172, "time": 0.85399} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.02311, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39094, "top5_acc": 0.64828, "loss_cls": 3.46229, "loss": 3.46229, "time": 0.85476} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.02309, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38359, "top5_acc": 0.64812, "loss_cls": 3.49037, "loss": 3.49037, "time": 0.85346} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.02307, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.38469, "top5_acc": 0.64438, "loss_cls": 3.494, "loss": 3.494, "time": 0.85563} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.02304, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.38109, "top5_acc": 0.63766, "loss_cls": 3.5229, "loss": 3.5229, "time": 0.85401} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.02302, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.37469, "top5_acc": 0.63656, "loss_cls": 3.54343, "loss": 3.54343, "time": 0.85534} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.023, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.37531, "top5_acc": 0.64125, "loss_cls": 3.50097, "loss": 3.50097, "time": 0.85875} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.02297, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38625, "top5_acc": 0.64219, "loss_cls": 3.49347, "loss": 3.49347, "time": 0.85488} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.02295, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38234, "top5_acc": 0.63828, "loss_cls": 3.50842, "loss": 3.50842, "time": 0.85644} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.02293, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.40047, "top5_acc": 0.66125, "loss_cls": 3.38393, "loss": 3.38393, "time": 0.8527} +{"mode": "train", "epoch": 103, "iter": 1300, "lr": 0.0229, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.385, "top5_acc": 0.63391, "loss_cls": 3.49208, "loss": 3.49208, "time": 0.85269} +{"mode": "train", "epoch": 103, "iter": 1400, "lr": 0.02288, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.38328, "top5_acc": 0.63531, "loss_cls": 3.49509, "loss": 3.49509, "time": 0.85453} +{"mode": "train", "epoch": 103, "iter": 1500, "lr": 0.02286, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38406, "top5_acc": 0.64172, "loss_cls": 3.50255, "loss": 3.50255, "time": 0.85538} +{"mode": "train", "epoch": 103, "iter": 1600, "lr": 0.02283, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38156, "top5_acc": 0.63906, "loss_cls": 3.50434, "loss": 3.50434, "time": 0.85658} +{"mode": "train", "epoch": 103, "iter": 1700, "lr": 0.02281, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37922, "top5_acc": 0.62953, "loss_cls": 3.54181, "loss": 3.54181, "time": 0.85265} +{"mode": "train", "epoch": 103, "iter": 1800, "lr": 0.02279, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.36719, "top5_acc": 0.63422, "loss_cls": 3.54316, "loss": 3.54316, "time": 0.85169} +{"mode": "train", "epoch": 103, "iter": 1900, "lr": 0.02276, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38906, "top5_acc": 0.64359, "loss_cls": 3.45869, "loss": 3.45869, "time": 0.85605} +{"mode": "train", "epoch": 103, "iter": 2000, "lr": 0.02274, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38297, "top5_acc": 0.64281, "loss_cls": 3.51747, "loss": 3.51747, "time": 0.85666} +{"mode": "train", "epoch": 103, "iter": 2100, "lr": 0.02272, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38016, "top5_acc": 0.65156, "loss_cls": 3.47961, "loss": 3.47961, "time": 0.86107} +{"mode": "train", "epoch": 103, "iter": 2200, "lr": 0.02269, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.38203, "top5_acc": 0.64141, "loss_cls": 3.51142, "loss": 3.51142, "time": 0.86023} +{"mode": "train", "epoch": 103, "iter": 2300, "lr": 0.02267, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36953, "top5_acc": 0.63062, "loss_cls": 3.55533, "loss": 3.55533, "time": 0.85786} +{"mode": "train", "epoch": 103, "iter": 2400, "lr": 0.02264, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37375, "top5_acc": 0.63391, "loss_cls": 3.51924, "loss": 3.51924, "time": 0.86102} +{"mode": "train", "epoch": 103, "iter": 2500, "lr": 0.02262, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36891, "top5_acc": 0.63203, "loss_cls": 3.55782, "loss": 3.55782, "time": 0.86226} +{"mode": "train", "epoch": 103, "iter": 2600, "lr": 0.0226, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37938, "top5_acc": 0.63578, "loss_cls": 3.47964, "loss": 3.47964, "time": 0.85687} +{"mode": "train", "epoch": 103, "iter": 2700, "lr": 0.02257, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37469, "top5_acc": 0.63344, "loss_cls": 3.53373, "loss": 3.53373, "time": 0.8618} +{"mode": "train", "epoch": 103, "iter": 2800, "lr": 0.02255, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.37016, "top5_acc": 0.63547, "loss_cls": 3.54001, "loss": 3.54001, "time": 0.85426} +{"mode": "train", "epoch": 103, "iter": 2900, "lr": 0.02253, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37781, "top5_acc": 0.63953, "loss_cls": 3.50065, "loss": 3.50065, "time": 0.86023} +{"mode": "train", "epoch": 103, "iter": 3000, "lr": 0.0225, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.375, "top5_acc": 0.64375, "loss_cls": 3.50943, "loss": 3.50943, "time": 0.86022} +{"mode": "train", "epoch": 103, "iter": 3100, "lr": 0.02248, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38234, "top5_acc": 0.64016, "loss_cls": 3.50788, "loss": 3.50788, "time": 0.85421} +{"mode": "train", "epoch": 103, "iter": 3200, "lr": 0.02246, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3725, "top5_acc": 0.63, "loss_cls": 3.52331, "loss": 3.52331, "time": 0.8558} +{"mode": "train", "epoch": 103, "iter": 3300, "lr": 0.02243, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.37656, "top5_acc": 0.63594, "loss_cls": 3.53145, "loss": 3.53145, "time": 0.85314} +{"mode": "train", "epoch": 103, "iter": 3400, "lr": 0.02241, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37312, "top5_acc": 0.63469, "loss_cls": 3.5726, "loss": 3.5726, "time": 0.86181} +{"mode": "train", "epoch": 103, "iter": 3500, "lr": 0.02239, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37016, "top5_acc": 0.62109, "loss_cls": 3.61265, "loss": 3.61265, "time": 0.8539} +{"mode": "train", "epoch": 103, "iter": 3600, "lr": 0.02236, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.38844, "top5_acc": 0.64953, "loss_cls": 3.46401, "loss": 3.46401, "time": 0.8527} +{"mode": "train", "epoch": 103, "iter": 3700, "lr": 0.02234, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37125, "top5_acc": 0.63016, "loss_cls": 3.54572, "loss": 3.54572, "time": 0.85975} +{"mode": "val", "epoch": 103, "iter": 309, "lr": 0.02233, "top1_acc": 0.3308, "top5_acc": 0.58117, "mean_class_accuracy": 0.33036} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.02231, "memory": 15990, "data_time": 1.53822, "top1_acc": 0.39438, "top5_acc": 0.65953, "loss_cls": 3.41283, "loss": 3.41283, "time": 2.56826} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.02228, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39312, "top5_acc": 0.65875, "loss_cls": 3.40165, "loss": 3.40165, "time": 0.84831} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.02226, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37719, "top5_acc": 0.63953, "loss_cls": 3.49059, "loss": 3.49059, "time": 0.85453} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.02224, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38281, "top5_acc": 0.65672, "loss_cls": 3.42961, "loss": 3.42961, "time": 0.85177} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.02221, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38625, "top5_acc": 0.64766, "loss_cls": 3.45552, "loss": 3.45552, "time": 0.85419} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.02219, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.37859, "top5_acc": 0.63844, "loss_cls": 3.52137, "loss": 3.52137, "time": 0.85458} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.02217, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39641, "top5_acc": 0.65016, "loss_cls": 3.44795, "loss": 3.44795, "time": 0.84737} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.02214, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38625, "top5_acc": 0.64203, "loss_cls": 3.44241, "loss": 3.44241, "time": 0.84986} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.02212, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38516, "top5_acc": 0.64172, "loss_cls": 3.47813, "loss": 3.47813, "time": 0.85595} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.0221, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38391, "top5_acc": 0.64688, "loss_cls": 3.50027, "loss": 3.50027, "time": 0.85382} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.02208, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38891, "top5_acc": 0.64828, "loss_cls": 3.47409, "loss": 3.47409, "time": 0.85689} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.02205, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37578, "top5_acc": 0.64609, "loss_cls": 3.51513, "loss": 3.51513, "time": 0.85205} +{"mode": "train", "epoch": 104, "iter": 1300, "lr": 0.02203, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38562, "top5_acc": 0.64359, "loss_cls": 3.48141, "loss": 3.48141, "time": 0.84967} +{"mode": "train", "epoch": 104, "iter": 1400, "lr": 0.02201, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38172, "top5_acc": 0.64344, "loss_cls": 3.49476, "loss": 3.49476, "time": 0.85391} +{"mode": "train", "epoch": 104, "iter": 1500, "lr": 0.02198, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.38766, "top5_acc": 0.64125, "loss_cls": 3.46524, "loss": 3.46524, "time": 0.85071} +{"mode": "train", "epoch": 104, "iter": 1600, "lr": 0.02196, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39, "top5_acc": 0.64641, "loss_cls": 3.45428, "loss": 3.45428, "time": 0.85102} +{"mode": "train", "epoch": 104, "iter": 1700, "lr": 0.02194, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.3825, "top5_acc": 0.63906, "loss_cls": 3.48755, "loss": 3.48755, "time": 0.85265} +{"mode": "train", "epoch": 104, "iter": 1800, "lr": 0.02191, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.37766, "top5_acc": 0.65, "loss_cls": 3.46977, "loss": 3.46977, "time": 0.85402} +{"mode": "train", "epoch": 104, "iter": 1900, "lr": 0.02189, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.37031, "top5_acc": 0.63141, "loss_cls": 3.54608, "loss": 3.54608, "time": 0.85023} +{"mode": "train", "epoch": 104, "iter": 2000, "lr": 0.02187, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36953, "top5_acc": 0.63891, "loss_cls": 3.4974, "loss": 3.4974, "time": 0.84863} +{"mode": "train", "epoch": 104, "iter": 2100, "lr": 0.02184, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.37766, "top5_acc": 0.64172, "loss_cls": 3.50379, "loss": 3.50379, "time": 0.85158} +{"mode": "train", "epoch": 104, "iter": 2200, "lr": 0.02182, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37375, "top5_acc": 0.63031, "loss_cls": 3.53631, "loss": 3.53631, "time": 0.85364} +{"mode": "train", "epoch": 104, "iter": 2300, "lr": 0.0218, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38328, "top5_acc": 0.63203, "loss_cls": 3.54214, "loss": 3.54214, "time": 0.84962} +{"mode": "train", "epoch": 104, "iter": 2400, "lr": 0.02177, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37375, "top5_acc": 0.62875, "loss_cls": 3.5688, "loss": 3.5688, "time": 0.84948} +{"mode": "train", "epoch": 104, "iter": 2500, "lr": 0.02175, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38031, "top5_acc": 0.64516, "loss_cls": 3.51018, "loss": 3.51018, "time": 0.84916} +{"mode": "train", "epoch": 104, "iter": 2600, "lr": 0.02173, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37438, "top5_acc": 0.63547, "loss_cls": 3.51978, "loss": 3.51978, "time": 0.8559} +{"mode": "train", "epoch": 104, "iter": 2700, "lr": 0.02171, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38922, "top5_acc": 0.64812, "loss_cls": 3.46292, "loss": 3.46292, "time": 0.84825} +{"mode": "train", "epoch": 104, "iter": 2800, "lr": 0.02168, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38844, "top5_acc": 0.64375, "loss_cls": 3.48666, "loss": 3.48666, "time": 0.85864} +{"mode": "train", "epoch": 104, "iter": 2900, "lr": 0.02166, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38516, "top5_acc": 0.63797, "loss_cls": 3.4839, "loss": 3.4839, "time": 0.85277} +{"mode": "train", "epoch": 104, "iter": 3000, "lr": 0.02164, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.36922, "top5_acc": 0.62672, "loss_cls": 3.55727, "loss": 3.55727, "time": 0.85371} +{"mode": "train", "epoch": 104, "iter": 3100, "lr": 0.02161, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37984, "top5_acc": 0.62687, "loss_cls": 3.56143, "loss": 3.56143, "time": 0.85576} +{"mode": "train", "epoch": 104, "iter": 3200, "lr": 0.02159, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.375, "top5_acc": 0.63594, "loss_cls": 3.50483, "loss": 3.50483, "time": 0.85443} +{"mode": "train", "epoch": 104, "iter": 3300, "lr": 0.02157, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38703, "top5_acc": 0.64344, "loss_cls": 3.48361, "loss": 3.48361, "time": 0.85218} +{"mode": "train", "epoch": 104, "iter": 3400, "lr": 0.02154, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.39156, "top5_acc": 0.64578, "loss_cls": 3.4562, "loss": 3.4562, "time": 0.85593} +{"mode": "train", "epoch": 104, "iter": 3500, "lr": 0.02152, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38438, "top5_acc": 0.63516, "loss_cls": 3.50127, "loss": 3.50127, "time": 0.8573} +{"mode": "train", "epoch": 104, "iter": 3600, "lr": 0.0215, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38391, "top5_acc": 0.64609, "loss_cls": 3.4734, "loss": 3.4734, "time": 0.85366} +{"mode": "train", "epoch": 104, "iter": 3700, "lr": 0.02148, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.37422, "top5_acc": 0.63969, "loss_cls": 3.5413, "loss": 3.5413, "time": 0.85249} +{"mode": "val", "epoch": 104, "iter": 309, "lr": 0.02146, "top1_acc": 0.31591, "top5_acc": 0.57696, "mean_class_accuracy": 0.31554} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.02144, "memory": 15990, "data_time": 1.52, "top1_acc": 0.395, "top5_acc": 0.6575, "loss_cls": 3.40875, "loss": 3.40875, "time": 2.56321} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.02142, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38719, "top5_acc": 0.65641, "loss_cls": 3.42152, "loss": 3.42152, "time": 0.8595} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.0214, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39453, "top5_acc": 0.65531, "loss_cls": 3.44181, "loss": 3.44181, "time": 0.86368} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.02137, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.39359, "top5_acc": 0.65938, "loss_cls": 3.42595, "loss": 3.42595, "time": 0.86241} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.02135, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38625, "top5_acc": 0.64031, "loss_cls": 3.48099, "loss": 3.48099, "time": 0.85952} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.02133, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.38812, "top5_acc": 0.65234, "loss_cls": 3.42565, "loss": 3.42565, "time": 0.85398} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.0213, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39016, "top5_acc": 0.64891, "loss_cls": 3.44966, "loss": 3.44966, "time": 0.85116} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.02128, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38422, "top5_acc": 0.65172, "loss_cls": 3.43667, "loss": 3.43667, "time": 0.85398} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.02126, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38531, "top5_acc": 0.65266, "loss_cls": 3.44323, "loss": 3.44323, "time": 0.85695} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.02124, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39281, "top5_acc": 0.64359, "loss_cls": 3.4741, "loss": 3.4741, "time": 0.86229} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.02121, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38844, "top5_acc": 0.65641, "loss_cls": 3.44081, "loss": 3.44081, "time": 0.85808} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.02119, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38875, "top5_acc": 0.64375, "loss_cls": 3.46696, "loss": 3.46696, "time": 0.85677} +{"mode": "train", "epoch": 105, "iter": 1300, "lr": 0.02117, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38109, "top5_acc": 0.65531, "loss_cls": 3.4466, "loss": 3.4466, "time": 0.85519} +{"mode": "train", "epoch": 105, "iter": 1400, "lr": 0.02114, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38172, "top5_acc": 0.6425, "loss_cls": 3.48612, "loss": 3.48612, "time": 0.85587} +{"mode": "train", "epoch": 105, "iter": 1500, "lr": 0.02112, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37969, "top5_acc": 0.63938, "loss_cls": 3.5118, "loss": 3.5118, "time": 0.84931} +{"mode": "train", "epoch": 105, "iter": 1600, "lr": 0.0211, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.37891, "top5_acc": 0.64609, "loss_cls": 3.48107, "loss": 3.48107, "time": 0.85607} +{"mode": "train", "epoch": 105, "iter": 1700, "lr": 0.02108, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.37484, "top5_acc": 0.62297, "loss_cls": 3.55084, "loss": 3.55084, "time": 0.85314} +{"mode": "train", "epoch": 105, "iter": 1800, "lr": 0.02105, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.37688, "top5_acc": 0.63062, "loss_cls": 3.51591, "loss": 3.51591, "time": 0.84857} +{"mode": "train", "epoch": 105, "iter": 1900, "lr": 0.02103, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38625, "top5_acc": 0.64594, "loss_cls": 3.42831, "loss": 3.42831, "time": 0.85411} +{"mode": "train", "epoch": 105, "iter": 2000, "lr": 0.02101, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37906, "top5_acc": 0.63766, "loss_cls": 3.50947, "loss": 3.50947, "time": 0.85063} +{"mode": "train", "epoch": 105, "iter": 2100, "lr": 0.02098, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38516, "top5_acc": 0.64375, "loss_cls": 3.47539, "loss": 3.47539, "time": 0.85036} +{"mode": "train", "epoch": 105, "iter": 2200, "lr": 0.02096, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39125, "top5_acc": 0.65125, "loss_cls": 3.4478, "loss": 3.4478, "time": 0.85163} +{"mode": "train", "epoch": 105, "iter": 2300, "lr": 0.02094, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39547, "top5_acc": 0.65297, "loss_cls": 3.45445, "loss": 3.45445, "time": 0.84929} +{"mode": "train", "epoch": 105, "iter": 2400, "lr": 0.02092, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38156, "top5_acc": 0.64344, "loss_cls": 3.47477, "loss": 3.47477, "time": 0.84947} +{"mode": "train", "epoch": 105, "iter": 2500, "lr": 0.02089, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.38609, "top5_acc": 0.64031, "loss_cls": 3.475, "loss": 3.475, "time": 0.85165} +{"mode": "train", "epoch": 105, "iter": 2600, "lr": 0.02087, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38859, "top5_acc": 0.64312, "loss_cls": 3.45778, "loss": 3.45778, "time": 0.85513} +{"mode": "train", "epoch": 105, "iter": 2700, "lr": 0.02085, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37906, "top5_acc": 0.6325, "loss_cls": 3.53529, "loss": 3.53529, "time": 0.85445} +{"mode": "train", "epoch": 105, "iter": 2800, "lr": 0.02083, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38203, "top5_acc": 0.63469, "loss_cls": 3.51401, "loss": 3.51401, "time": 0.84999} +{"mode": "train", "epoch": 105, "iter": 2900, "lr": 0.0208, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38312, "top5_acc": 0.64203, "loss_cls": 3.46238, "loss": 3.46238, "time": 0.84797} +{"mode": "train", "epoch": 105, "iter": 3000, "lr": 0.02078, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38422, "top5_acc": 0.64281, "loss_cls": 3.51667, "loss": 3.51667, "time": 0.84797} +{"mode": "train", "epoch": 105, "iter": 3100, "lr": 0.02076, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38312, "top5_acc": 0.64469, "loss_cls": 3.47466, "loss": 3.47466, "time": 0.85223} +{"mode": "train", "epoch": 105, "iter": 3200, "lr": 0.02073, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39516, "top5_acc": 0.64844, "loss_cls": 3.45377, "loss": 3.45377, "time": 0.85338} +{"mode": "train", "epoch": 105, "iter": 3300, "lr": 0.02071, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.38469, "top5_acc": 0.6425, "loss_cls": 3.47426, "loss": 3.47426, "time": 0.85068} +{"mode": "train", "epoch": 105, "iter": 3400, "lr": 0.02069, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38312, "top5_acc": 0.63313, "loss_cls": 3.5228, "loss": 3.5228, "time": 0.85076} +{"mode": "train", "epoch": 105, "iter": 3500, "lr": 0.02067, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.37797, "top5_acc": 0.64797, "loss_cls": 3.49677, "loss": 3.49677, "time": 0.85489} +{"mode": "train", "epoch": 105, "iter": 3600, "lr": 0.02064, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37844, "top5_acc": 0.64109, "loss_cls": 3.5118, "loss": 3.5118, "time": 0.85403} +{"mode": "train", "epoch": 105, "iter": 3700, "lr": 0.02062, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38188, "top5_acc": 0.63547, "loss_cls": 3.48697, "loss": 3.48697, "time": 0.85729} +{"mode": "val", "epoch": 105, "iter": 309, "lr": 0.02061, "top1_acc": 0.32123, "top5_acc": 0.5797, "mean_class_accuracy": 0.32087} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.02059, "memory": 15990, "data_time": 1.4601, "top1_acc": 0.41078, "top5_acc": 0.65812, "loss_cls": 3.35886, "loss": 3.35886, "time": 2.49237} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.02057, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39547, "top5_acc": 0.65812, "loss_cls": 3.41597, "loss": 3.41597, "time": 0.85432} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.02054, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39625, "top5_acc": 0.65734, "loss_cls": 3.39821, "loss": 3.39821, "time": 0.8521} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.02052, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40906, "top5_acc": 0.66438, "loss_cls": 3.34408, "loss": 3.34408, "time": 0.85689} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.0205, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.38531, "top5_acc": 0.64891, "loss_cls": 3.46396, "loss": 3.46396, "time": 0.85236} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.02048, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38594, "top5_acc": 0.64859, "loss_cls": 3.4516, "loss": 3.4516, "time": 0.84901} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.02045, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.38422, "top5_acc": 0.65, "loss_cls": 3.45346, "loss": 3.45346, "time": 0.84382} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.02043, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.38922, "top5_acc": 0.64812, "loss_cls": 3.41982, "loss": 3.41982, "time": 0.85408} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.02041, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38484, "top5_acc": 0.645, "loss_cls": 3.47834, "loss": 3.47834, "time": 0.85734} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.02039, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39609, "top5_acc": 0.64156, "loss_cls": 3.44847, "loss": 3.44847, "time": 0.85651} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.02036, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38391, "top5_acc": 0.64312, "loss_cls": 3.45056, "loss": 3.45056, "time": 0.85839} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.02034, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39812, "top5_acc": 0.65891, "loss_cls": 3.41049, "loss": 3.41049, "time": 0.85283} +{"mode": "train", "epoch": 106, "iter": 1300, "lr": 0.02032, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38844, "top5_acc": 0.65062, "loss_cls": 3.42751, "loss": 3.42751, "time": 0.85589} +{"mode": "train", "epoch": 106, "iter": 1400, "lr": 0.0203, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38266, "top5_acc": 0.65625, "loss_cls": 3.43397, "loss": 3.43397, "time": 0.85316} +{"mode": "train", "epoch": 106, "iter": 1500, "lr": 0.02027, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38594, "top5_acc": 0.65297, "loss_cls": 3.44329, "loss": 3.44329, "time": 0.85011} +{"mode": "train", "epoch": 106, "iter": 1600, "lr": 0.02025, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.38844, "top5_acc": 0.64188, "loss_cls": 3.47855, "loss": 3.47855, "time": 0.85642} +{"mode": "train", "epoch": 106, "iter": 1700, "lr": 0.02023, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38391, "top5_acc": 0.64359, "loss_cls": 3.46402, "loss": 3.46402, "time": 0.86063} +{"mode": "train", "epoch": 106, "iter": 1800, "lr": 0.02021, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39422, "top5_acc": 0.64641, "loss_cls": 3.45225, "loss": 3.45225, "time": 0.85843} +{"mode": "train", "epoch": 106, "iter": 1900, "lr": 0.02018, "memory": 15990, "data_time": 0.00073, "top1_acc": 0.39234, "top5_acc": 0.65781, "loss_cls": 3.4386, "loss": 3.4386, "time": 0.85524} +{"mode": "train", "epoch": 106, "iter": 2000, "lr": 0.02016, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.395, "top5_acc": 0.63453, "loss_cls": 3.52464, "loss": 3.52464, "time": 0.85074} +{"mode": "train", "epoch": 106, "iter": 2100, "lr": 0.02014, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.38781, "top5_acc": 0.64281, "loss_cls": 3.47194, "loss": 3.47194, "time": 0.85121} +{"mode": "train", "epoch": 106, "iter": 2200, "lr": 0.02012, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.38688, "top5_acc": 0.65422, "loss_cls": 3.45922, "loss": 3.45922, "time": 0.84917} +{"mode": "train", "epoch": 106, "iter": 2300, "lr": 0.02009, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38047, "top5_acc": 0.64344, "loss_cls": 3.50018, "loss": 3.50018, "time": 0.85197} +{"mode": "train", "epoch": 106, "iter": 2400, "lr": 0.02007, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.3925, "top5_acc": 0.64672, "loss_cls": 3.41498, "loss": 3.41498, "time": 0.84965} +{"mode": "train", "epoch": 106, "iter": 2500, "lr": 0.02005, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38438, "top5_acc": 0.64688, "loss_cls": 3.4766, "loss": 3.4766, "time": 0.85039} +{"mode": "train", "epoch": 106, "iter": 2600, "lr": 0.02003, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39469, "top5_acc": 0.64125, "loss_cls": 3.46027, "loss": 3.46027, "time": 0.8469} +{"mode": "train", "epoch": 106, "iter": 2700, "lr": 0.02, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38969, "top5_acc": 0.65469, "loss_cls": 3.44617, "loss": 3.44617, "time": 0.85293} +{"mode": "train", "epoch": 106, "iter": 2800, "lr": 0.01998, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38719, "top5_acc": 0.64141, "loss_cls": 3.47963, "loss": 3.47963, "time": 0.85254} +{"mode": "train", "epoch": 106, "iter": 2900, "lr": 0.01996, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38828, "top5_acc": 0.64016, "loss_cls": 3.48284, "loss": 3.48284, "time": 0.85109} +{"mode": "train", "epoch": 106, "iter": 3000, "lr": 0.01994, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38641, "top5_acc": 0.64312, "loss_cls": 3.45563, "loss": 3.45563, "time": 0.85343} +{"mode": "train", "epoch": 106, "iter": 3100, "lr": 0.01991, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.37641, "top5_acc": 0.64438, "loss_cls": 3.51986, "loss": 3.51986, "time": 0.84742} +{"mode": "train", "epoch": 106, "iter": 3200, "lr": 0.01989, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38781, "top5_acc": 0.64781, "loss_cls": 3.46628, "loss": 3.46628, "time": 0.85095} +{"mode": "train", "epoch": 106, "iter": 3300, "lr": 0.01987, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39172, "top5_acc": 0.64406, "loss_cls": 3.45315, "loss": 3.45315, "time": 0.85611} +{"mode": "train", "epoch": 106, "iter": 3400, "lr": 0.01985, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.37609, "top5_acc": 0.63875, "loss_cls": 3.52593, "loss": 3.52593, "time": 0.85575} +{"mode": "train", "epoch": 106, "iter": 3500, "lr": 0.01983, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38297, "top5_acc": 0.64172, "loss_cls": 3.46768, "loss": 3.46768, "time": 0.85078} +{"mode": "train", "epoch": 106, "iter": 3600, "lr": 0.0198, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38672, "top5_acc": 0.64906, "loss_cls": 3.4342, "loss": 3.4342, "time": 0.84869} +{"mode": "train", "epoch": 106, "iter": 3700, "lr": 0.01978, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39672, "top5_acc": 0.64766, "loss_cls": 3.44593, "loss": 3.44593, "time": 0.85218} +{"mode": "val", "epoch": 106, "iter": 309, "lr": 0.01977, "top1_acc": 0.32796, "top5_acc": 0.58208, "mean_class_accuracy": 0.32766} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.01975, "memory": 15990, "data_time": 1.47736, "top1_acc": 0.39641, "top5_acc": 0.65609, "loss_cls": 3.36628, "loss": 3.36628, "time": 2.50753} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.01973, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41469, "top5_acc": 0.66406, "loss_cls": 3.34971, "loss": 3.34971, "time": 0.85526} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.0197, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39344, "top5_acc": 0.66438, "loss_cls": 3.3944, "loss": 3.3944, "time": 0.84991} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.01968, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.39859, "top5_acc": 0.6575, "loss_cls": 3.39925, "loss": 3.39925, "time": 0.85353} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.01966, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39781, "top5_acc": 0.65672, "loss_cls": 3.4161, "loss": 3.4161, "time": 0.85482} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.01964, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.38438, "top5_acc": 0.65031, "loss_cls": 3.43874, "loss": 3.43874, "time": 0.85185} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.01961, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38391, "top5_acc": 0.64453, "loss_cls": 3.47475, "loss": 3.47475, "time": 0.85178} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.01959, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39391, "top5_acc": 0.65219, "loss_cls": 3.41148, "loss": 3.41148, "time": 0.84872} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.01957, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39312, "top5_acc": 0.64828, "loss_cls": 3.42068, "loss": 3.42068, "time": 0.85384} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.01955, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39719, "top5_acc": 0.6575, "loss_cls": 3.37617, "loss": 3.37617, "time": 0.8548} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.01953, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38406, "top5_acc": 0.65, "loss_cls": 3.43069, "loss": 3.43069, "time": 0.85423} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.0195, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.39516, "top5_acc": 0.64906, "loss_cls": 3.42618, "loss": 3.42618, "time": 0.85253} +{"mode": "train", "epoch": 107, "iter": 1300, "lr": 0.01948, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39016, "top5_acc": 0.65312, "loss_cls": 3.43397, "loss": 3.43397, "time": 0.85414} +{"mode": "train", "epoch": 107, "iter": 1400, "lr": 0.01946, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40562, "top5_acc": 0.66438, "loss_cls": 3.36653, "loss": 3.36653, "time": 0.84807} +{"mode": "train", "epoch": 107, "iter": 1500, "lr": 0.01944, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.38578, "top5_acc": 0.64281, "loss_cls": 3.48551, "loss": 3.48551, "time": 0.85454} +{"mode": "train", "epoch": 107, "iter": 1600, "lr": 0.01942, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37984, "top5_acc": 0.64609, "loss_cls": 3.45088, "loss": 3.45088, "time": 0.84966} +{"mode": "train", "epoch": 107, "iter": 1700, "lr": 0.01939, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39594, "top5_acc": 0.65656, "loss_cls": 3.43425, "loss": 3.43425, "time": 0.84793} +{"mode": "train", "epoch": 107, "iter": 1800, "lr": 0.01937, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38969, "top5_acc": 0.64453, "loss_cls": 3.46477, "loss": 3.46477, "time": 0.84804} +{"mode": "train", "epoch": 107, "iter": 1900, "lr": 0.01935, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39812, "top5_acc": 0.66, "loss_cls": 3.39003, "loss": 3.39003, "time": 0.85431} +{"mode": "train", "epoch": 107, "iter": 2000, "lr": 0.01933, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38594, "top5_acc": 0.64719, "loss_cls": 3.46736, "loss": 3.46736, "time": 0.85181} +{"mode": "train", "epoch": 107, "iter": 2100, "lr": 0.0193, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.38969, "top5_acc": 0.65, "loss_cls": 3.44229, "loss": 3.44229, "time": 0.84828} +{"mode": "train", "epoch": 107, "iter": 2200, "lr": 0.01928, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.38438, "top5_acc": 0.65766, "loss_cls": 3.45064, "loss": 3.45064, "time": 0.85027} +{"mode": "train", "epoch": 107, "iter": 2300, "lr": 0.01926, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38391, "top5_acc": 0.64734, "loss_cls": 3.46067, "loss": 3.46067, "time": 0.84812} +{"mode": "train", "epoch": 107, "iter": 2400, "lr": 0.01924, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39016, "top5_acc": 0.64688, "loss_cls": 3.45805, "loss": 3.45805, "time": 0.84789} +{"mode": "train", "epoch": 107, "iter": 2500, "lr": 0.01922, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39047, "top5_acc": 0.65172, "loss_cls": 3.43281, "loss": 3.43281, "time": 0.84965} +{"mode": "train", "epoch": 107, "iter": 2600, "lr": 0.01919, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39156, "top5_acc": 0.65094, "loss_cls": 3.44299, "loss": 3.44299, "time": 0.85201} +{"mode": "train", "epoch": 107, "iter": 2700, "lr": 0.01917, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38672, "top5_acc": 0.64719, "loss_cls": 3.45836, "loss": 3.45836, "time": 0.84775} +{"mode": "train", "epoch": 107, "iter": 2800, "lr": 0.01915, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38766, "top5_acc": 0.64812, "loss_cls": 3.48713, "loss": 3.48713, "time": 0.85138} +{"mode": "train", "epoch": 107, "iter": 2900, "lr": 0.01913, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39734, "top5_acc": 0.65609, "loss_cls": 3.39534, "loss": 3.39534, "time": 0.84958} +{"mode": "train", "epoch": 107, "iter": 3000, "lr": 0.01911, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38609, "top5_acc": 0.64703, "loss_cls": 3.46039, "loss": 3.46039, "time": 0.85492} +{"mode": "train", "epoch": 107, "iter": 3100, "lr": 0.01908, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39172, "top5_acc": 0.64375, "loss_cls": 3.44661, "loss": 3.44661, "time": 0.85409} +{"mode": "train", "epoch": 107, "iter": 3200, "lr": 0.01906, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38828, "top5_acc": 0.64156, "loss_cls": 3.49079, "loss": 3.49079, "time": 0.85281} +{"mode": "train", "epoch": 107, "iter": 3300, "lr": 0.01904, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39219, "top5_acc": 0.64938, "loss_cls": 3.43908, "loss": 3.43908, "time": 0.85523} +{"mode": "train", "epoch": 107, "iter": 3400, "lr": 0.01902, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39578, "top5_acc": 0.66, "loss_cls": 3.41639, "loss": 3.41639, "time": 0.84978} +{"mode": "train", "epoch": 107, "iter": 3500, "lr": 0.019, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37891, "top5_acc": 0.63828, "loss_cls": 3.54765, "loss": 3.54765, "time": 0.84904} +{"mode": "train", "epoch": 107, "iter": 3600, "lr": 0.01897, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38844, "top5_acc": 0.64984, "loss_cls": 3.45824, "loss": 3.45824, "time": 0.8501} +{"mode": "train", "epoch": 107, "iter": 3700, "lr": 0.01895, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39672, "top5_acc": 0.65266, "loss_cls": 3.39982, "loss": 3.39982, "time": 0.85622} +{"mode": "val", "epoch": 107, "iter": 309, "lr": 0.01894, "top1_acc": 0.33252, "top5_acc": 0.58907, "mean_class_accuracy": 0.33239} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.01892, "memory": 15990, "data_time": 1.54415, "top1_acc": 0.40297, "top5_acc": 0.65938, "loss_cls": 3.35965, "loss": 3.35965, "time": 2.57656} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0189, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39844, "top5_acc": 0.66031, "loss_cls": 3.4074, "loss": 3.4074, "time": 0.85912} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.01888, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40125, "top5_acc": 0.65609, "loss_cls": 3.38104, "loss": 3.38104, "time": 0.85899} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.01886, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.40266, "top5_acc": 0.66438, "loss_cls": 3.35549, "loss": 3.35549, "time": 0.86094} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.01883, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39375, "top5_acc": 0.65734, "loss_cls": 3.40951, "loss": 3.40951, "time": 0.85117} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.01881, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.3975, "top5_acc": 0.66484, "loss_cls": 3.37562, "loss": 3.37562, "time": 0.85274} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.01879, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40703, "top5_acc": 0.66328, "loss_cls": 3.34224, "loss": 3.34224, "time": 0.85232} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.01877, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39953, "top5_acc": 0.6575, "loss_cls": 3.38178, "loss": 3.38178, "time": 0.85494} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.01875, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39875, "top5_acc": 0.64656, "loss_cls": 3.43531, "loss": 3.43531, "time": 0.85707} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.01872, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39938, "top5_acc": 0.65078, "loss_cls": 3.42162, "loss": 3.42162, "time": 0.85321} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.0187, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.39016, "top5_acc": 0.64812, "loss_cls": 3.43091, "loss": 3.43091, "time": 0.85621} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.01868, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.39594, "top5_acc": 0.65672, "loss_cls": 3.39193, "loss": 3.39193, "time": 0.85808} +{"mode": "train", "epoch": 108, "iter": 1300, "lr": 0.01866, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39844, "top5_acc": 0.65969, "loss_cls": 3.39023, "loss": 3.39023, "time": 0.85669} +{"mode": "train", "epoch": 108, "iter": 1400, "lr": 0.01864, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.4025, "top5_acc": 0.66188, "loss_cls": 3.37707, "loss": 3.37707, "time": 0.8568} +{"mode": "train", "epoch": 108, "iter": 1500, "lr": 0.01862, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38234, "top5_acc": 0.63672, "loss_cls": 3.48439, "loss": 3.48439, "time": 0.85268} +{"mode": "train", "epoch": 108, "iter": 1600, "lr": 0.01859, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.39359, "top5_acc": 0.65078, "loss_cls": 3.42367, "loss": 3.42367, "time": 0.8539} +{"mode": "train", "epoch": 108, "iter": 1700, "lr": 0.01857, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.39656, "top5_acc": 0.66062, "loss_cls": 3.38093, "loss": 3.38093, "time": 0.86289} +{"mode": "train", "epoch": 108, "iter": 1800, "lr": 0.01855, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39266, "top5_acc": 0.65766, "loss_cls": 3.40747, "loss": 3.40747, "time": 0.86339} +{"mode": "train", "epoch": 108, "iter": 1900, "lr": 0.01853, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39109, "top5_acc": 0.65219, "loss_cls": 3.41895, "loss": 3.41895, "time": 0.85181} +{"mode": "train", "epoch": 108, "iter": 2000, "lr": 0.01851, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39469, "top5_acc": 0.65016, "loss_cls": 3.43037, "loss": 3.43037, "time": 0.85419} +{"mode": "train", "epoch": 108, "iter": 2100, "lr": 0.01848, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.39953, "top5_acc": 0.66406, "loss_cls": 3.35283, "loss": 3.35283, "time": 0.85711} +{"mode": "train", "epoch": 108, "iter": 2200, "lr": 0.01846, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39172, "top5_acc": 0.65281, "loss_cls": 3.43974, "loss": 3.43974, "time": 0.86144} +{"mode": "train", "epoch": 108, "iter": 2300, "lr": 0.01844, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.395, "top5_acc": 0.64578, "loss_cls": 3.4432, "loss": 3.4432, "time": 0.85615} +{"mode": "train", "epoch": 108, "iter": 2400, "lr": 0.01842, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39016, "top5_acc": 0.65766, "loss_cls": 3.4035, "loss": 3.4035, "time": 0.85242} +{"mode": "train", "epoch": 108, "iter": 2500, "lr": 0.0184, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38625, "top5_acc": 0.65094, "loss_cls": 3.43757, "loss": 3.43757, "time": 0.85656} +{"mode": "train", "epoch": 108, "iter": 2600, "lr": 0.01838, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.39344, "top5_acc": 0.64703, "loss_cls": 3.41502, "loss": 3.41502, "time": 0.86281} +{"mode": "train", "epoch": 108, "iter": 2700, "lr": 0.01835, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.38266, "top5_acc": 0.63844, "loss_cls": 3.48982, "loss": 3.48982, "time": 0.86087} +{"mode": "train", "epoch": 108, "iter": 2800, "lr": 0.01833, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39688, "top5_acc": 0.66047, "loss_cls": 3.40299, "loss": 3.40299, "time": 0.85904} +{"mode": "train", "epoch": 108, "iter": 2900, "lr": 0.01831, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.38672, "top5_acc": 0.65094, "loss_cls": 3.43022, "loss": 3.43022, "time": 0.85754} +{"mode": "train", "epoch": 108, "iter": 3000, "lr": 0.01829, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39281, "top5_acc": 0.65406, "loss_cls": 3.43375, "loss": 3.43375, "time": 0.85797} +{"mode": "train", "epoch": 108, "iter": 3100, "lr": 0.01827, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39969, "top5_acc": 0.65609, "loss_cls": 3.38904, "loss": 3.38904, "time": 0.86039} +{"mode": "train", "epoch": 108, "iter": 3200, "lr": 0.01825, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.39312, "top5_acc": 0.64859, "loss_cls": 3.45285, "loss": 3.45285, "time": 0.86119} +{"mode": "train", "epoch": 108, "iter": 3300, "lr": 0.01823, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.39594, "top5_acc": 0.65109, "loss_cls": 3.42828, "loss": 3.42828, "time": 0.85848} +{"mode": "train", "epoch": 108, "iter": 3400, "lr": 0.0182, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.38203, "top5_acc": 0.64516, "loss_cls": 3.46559, "loss": 3.46559, "time": 0.86165} +{"mode": "train", "epoch": 108, "iter": 3500, "lr": 0.01818, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40141, "top5_acc": 0.65547, "loss_cls": 3.39285, "loss": 3.39285, "time": 0.85705} +{"mode": "train", "epoch": 108, "iter": 3600, "lr": 0.01816, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38875, "top5_acc": 0.65156, "loss_cls": 3.42728, "loss": 3.42728, "time": 0.86033} +{"mode": "train", "epoch": 108, "iter": 3700, "lr": 0.01814, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.395, "top5_acc": 0.65328, "loss_cls": 3.40129, "loss": 3.40129, "time": 0.85493} +{"mode": "val", "epoch": 108, "iter": 309, "lr": 0.01813, "top1_acc": 0.33901, "top5_acc": 0.59165, "mean_class_accuracy": 0.3387} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.01811, "memory": 15990, "data_time": 1.58504, "top1_acc": 0.40703, "top5_acc": 0.66828, "loss_cls": 3.31259, "loss": 3.31259, "time": 2.63059} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.01809, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41891, "top5_acc": 0.675, "loss_cls": 3.27972, "loss": 3.27972, "time": 0.85739} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.01806, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.40062, "top5_acc": 0.65391, "loss_cls": 3.3778, "loss": 3.3778, "time": 0.85964} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.01804, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.39672, "top5_acc": 0.66172, "loss_cls": 3.37103, "loss": 3.37103, "time": 0.85529} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.01802, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39781, "top5_acc": 0.66797, "loss_cls": 3.35663, "loss": 3.35663, "time": 0.85398} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.018, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40375, "top5_acc": 0.65688, "loss_cls": 3.38407, "loss": 3.38407, "time": 0.85231} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.01798, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.40812, "top5_acc": 0.65797, "loss_cls": 3.38646, "loss": 3.38646, "time": 0.85219} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.01796, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41094, "top5_acc": 0.67156, "loss_cls": 3.33095, "loss": 3.33095, "time": 0.85579} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.01794, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39344, "top5_acc": 0.6575, "loss_cls": 3.38616, "loss": 3.38616, "time": 0.85234} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.01791, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39, "top5_acc": 0.65312, "loss_cls": 3.39949, "loss": 3.39949, "time": 0.85711} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.01789, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.3975, "top5_acc": 0.66734, "loss_cls": 3.36522, "loss": 3.36522, "time": 0.8569} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.01787, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39531, "top5_acc": 0.65328, "loss_cls": 3.39532, "loss": 3.39532, "time": 0.85545} +{"mode": "train", "epoch": 109, "iter": 1300, "lr": 0.01785, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.40109, "top5_acc": 0.65406, "loss_cls": 3.42671, "loss": 3.42671, "time": 0.85018} +{"mode": "train", "epoch": 109, "iter": 1400, "lr": 0.01783, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40328, "top5_acc": 0.67016, "loss_cls": 3.35281, "loss": 3.35281, "time": 0.85661} +{"mode": "train", "epoch": 109, "iter": 1500, "lr": 0.01781, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40438, "top5_acc": 0.65953, "loss_cls": 3.37388, "loss": 3.37388, "time": 0.85658} +{"mode": "train", "epoch": 109, "iter": 1600, "lr": 0.01779, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39422, "top5_acc": 0.65672, "loss_cls": 3.41972, "loss": 3.41972, "time": 0.85632} +{"mode": "train", "epoch": 109, "iter": 1700, "lr": 0.01776, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38609, "top5_acc": 0.64906, "loss_cls": 3.43396, "loss": 3.43396, "time": 0.85511} +{"mode": "train", "epoch": 109, "iter": 1800, "lr": 0.01774, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39969, "top5_acc": 0.66641, "loss_cls": 3.37309, "loss": 3.37309, "time": 0.85119} +{"mode": "train", "epoch": 109, "iter": 1900, "lr": 0.01772, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.39484, "top5_acc": 0.66188, "loss_cls": 3.37219, "loss": 3.37219, "time": 0.85819} +{"mode": "train", "epoch": 109, "iter": 2000, "lr": 0.0177, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40188, "top5_acc": 0.65562, "loss_cls": 3.41131, "loss": 3.41131, "time": 0.8521} +{"mode": "train", "epoch": 109, "iter": 2100, "lr": 0.01768, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39906, "top5_acc": 0.65875, "loss_cls": 3.37429, "loss": 3.37429, "time": 0.85618} +{"mode": "train", "epoch": 109, "iter": 2200, "lr": 0.01766, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39578, "top5_acc": 0.65094, "loss_cls": 3.43035, "loss": 3.43035, "time": 0.85381} +{"mode": "train", "epoch": 109, "iter": 2300, "lr": 0.01764, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.38969, "top5_acc": 0.64484, "loss_cls": 3.45677, "loss": 3.45677, "time": 0.85143} +{"mode": "train", "epoch": 109, "iter": 2400, "lr": 0.01761, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38891, "top5_acc": 0.64984, "loss_cls": 3.43584, "loss": 3.43584, "time": 0.85832} +{"mode": "train", "epoch": 109, "iter": 2500, "lr": 0.01759, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.38719, "top5_acc": 0.64578, "loss_cls": 3.44441, "loss": 3.44441, "time": 0.85353} +{"mode": "train", "epoch": 109, "iter": 2600, "lr": 0.01757, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40016, "top5_acc": 0.65625, "loss_cls": 3.38086, "loss": 3.38086, "time": 0.85334} +{"mode": "train", "epoch": 109, "iter": 2700, "lr": 0.01755, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40219, "top5_acc": 0.66078, "loss_cls": 3.36309, "loss": 3.36309, "time": 0.85064} +{"mode": "train", "epoch": 109, "iter": 2800, "lr": 0.01753, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40812, "top5_acc": 0.66094, "loss_cls": 3.38349, "loss": 3.38349, "time": 0.85548} +{"mode": "train", "epoch": 109, "iter": 2900, "lr": 0.01751, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39438, "top5_acc": 0.64969, "loss_cls": 3.44052, "loss": 3.44052, "time": 0.84618} +{"mode": "train", "epoch": 109, "iter": 3000, "lr": 0.01749, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40125, "top5_acc": 0.65453, "loss_cls": 3.39045, "loss": 3.39045, "time": 0.85207} +{"mode": "train", "epoch": 109, "iter": 3100, "lr": 0.01747, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39203, "top5_acc": 0.65359, "loss_cls": 3.4129, "loss": 3.4129, "time": 0.8492} +{"mode": "train", "epoch": 109, "iter": 3200, "lr": 0.01744, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39531, "top5_acc": 0.64891, "loss_cls": 3.44203, "loss": 3.44203, "time": 0.85634} +{"mode": "train", "epoch": 109, "iter": 3300, "lr": 0.01742, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.38906, "top5_acc": 0.64062, "loss_cls": 3.45951, "loss": 3.45951, "time": 0.85011} +{"mode": "train", "epoch": 109, "iter": 3400, "lr": 0.0174, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40609, "top5_acc": 0.65359, "loss_cls": 3.39443, "loss": 3.39443, "time": 0.84801} +{"mode": "train", "epoch": 109, "iter": 3500, "lr": 0.01738, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39422, "top5_acc": 0.65109, "loss_cls": 3.43708, "loss": 3.43708, "time": 0.84303} +{"mode": "train", "epoch": 109, "iter": 3600, "lr": 0.01736, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39719, "top5_acc": 0.65719, "loss_cls": 3.40996, "loss": 3.40996, "time": 0.85341} +{"mode": "train", "epoch": 109, "iter": 3700, "lr": 0.01734, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.40906, "top5_acc": 0.66562, "loss_cls": 3.37254, "loss": 3.37254, "time": 0.85047} +{"mode": "val", "epoch": 109, "iter": 309, "lr": 0.01733, "top1_acc": 0.34199, "top5_acc": 0.59879, "mean_class_accuracy": 0.34186} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.01731, "memory": 15990, "data_time": 1.59831, "top1_acc": 0.41703, "top5_acc": 0.67062, "loss_cls": 3.30571, "loss": 3.30571, "time": 2.65223} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.01729, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40297, "top5_acc": 0.66609, "loss_cls": 3.34349, "loss": 3.34349, "time": 0.8548} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.01727, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.40734, "top5_acc": 0.67031, "loss_cls": 3.26688, "loss": 3.26688, "time": 0.85672} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.01724, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41188, "top5_acc": 0.67188, "loss_cls": 3.30734, "loss": 3.30734, "time": 0.84834} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.01722, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40359, "top5_acc": 0.66688, "loss_cls": 3.34157, "loss": 3.34157, "time": 0.85468} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.0172, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40188, "top5_acc": 0.66188, "loss_cls": 3.34986, "loss": 3.34986, "time": 0.85089} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.01718, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39984, "top5_acc": 0.65906, "loss_cls": 3.36201, "loss": 3.36201, "time": 0.85185} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.01716, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40516, "top5_acc": 0.65953, "loss_cls": 3.37302, "loss": 3.37302, "time": 0.84825} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.01714, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40703, "top5_acc": 0.66578, "loss_cls": 3.32777, "loss": 3.32777, "time": 0.8483} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.01712, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40344, "top5_acc": 0.65547, "loss_cls": 3.3818, "loss": 3.3818, "time": 0.85572} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.0171, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39875, "top5_acc": 0.65531, "loss_cls": 3.39581, "loss": 3.39581, "time": 0.85025} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.01708, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40219, "top5_acc": 0.66516, "loss_cls": 3.36382, "loss": 3.36382, "time": 0.8515} +{"mode": "train", "epoch": 110, "iter": 1300, "lr": 0.01705, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.40484, "top5_acc": 0.66219, "loss_cls": 3.36792, "loss": 3.36792, "time": 0.85663} +{"mode": "train", "epoch": 110, "iter": 1400, "lr": 0.01703, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.40203, "top5_acc": 0.65938, "loss_cls": 3.39434, "loss": 3.39434, "time": 0.85085} +{"mode": "train", "epoch": 110, "iter": 1500, "lr": 0.01701, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.40562, "top5_acc": 0.66562, "loss_cls": 3.35051, "loss": 3.35051, "time": 0.84758} +{"mode": "train", "epoch": 110, "iter": 1600, "lr": 0.01699, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40188, "top5_acc": 0.65766, "loss_cls": 3.38968, "loss": 3.38968, "time": 0.85233} +{"mode": "train", "epoch": 110, "iter": 1700, "lr": 0.01697, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40438, "top5_acc": 0.66031, "loss_cls": 3.35144, "loss": 3.35144, "time": 0.85558} +{"mode": "train", "epoch": 110, "iter": 1800, "lr": 0.01695, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40484, "top5_acc": 0.65766, "loss_cls": 3.38005, "loss": 3.38005, "time": 0.85104} +{"mode": "train", "epoch": 110, "iter": 1900, "lr": 0.01693, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38797, "top5_acc": 0.64781, "loss_cls": 3.41764, "loss": 3.41764, "time": 0.85067} +{"mode": "train", "epoch": 110, "iter": 2000, "lr": 0.01691, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40656, "top5_acc": 0.66141, "loss_cls": 3.37585, "loss": 3.37585, "time": 0.84718} +{"mode": "train", "epoch": 110, "iter": 2100, "lr": 0.01689, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39109, "top5_acc": 0.65312, "loss_cls": 3.42232, "loss": 3.42232, "time": 0.8518} +{"mode": "train", "epoch": 110, "iter": 2200, "lr": 0.01687, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39781, "top5_acc": 0.66, "loss_cls": 3.3895, "loss": 3.3895, "time": 0.84845} +{"mode": "train", "epoch": 110, "iter": 2300, "lr": 0.01685, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40344, "top5_acc": 0.65891, "loss_cls": 3.36697, "loss": 3.36697, "time": 0.85154} +{"mode": "train", "epoch": 110, "iter": 2400, "lr": 0.01682, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39953, "top5_acc": 0.65516, "loss_cls": 3.36554, "loss": 3.36554, "time": 0.84748} +{"mode": "train", "epoch": 110, "iter": 2500, "lr": 0.0168, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39578, "top5_acc": 0.65844, "loss_cls": 3.39371, "loss": 3.39371, "time": 0.84552} +{"mode": "train", "epoch": 110, "iter": 2600, "lr": 0.01678, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38969, "top5_acc": 0.65641, "loss_cls": 3.38322, "loss": 3.38322, "time": 0.84861} +{"mode": "train", "epoch": 110, "iter": 2700, "lr": 0.01676, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39828, "top5_acc": 0.65453, "loss_cls": 3.41347, "loss": 3.41347, "time": 0.85115} +{"mode": "train", "epoch": 110, "iter": 2800, "lr": 0.01674, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.39172, "top5_acc": 0.65312, "loss_cls": 3.44141, "loss": 3.44141, "time": 0.84896} +{"mode": "train", "epoch": 110, "iter": 2900, "lr": 0.01672, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.39484, "top5_acc": 0.64734, "loss_cls": 3.40723, "loss": 3.40723, "time": 0.85817} +{"mode": "train", "epoch": 110, "iter": 3000, "lr": 0.0167, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39797, "top5_acc": 0.64906, "loss_cls": 3.4528, "loss": 3.4528, "time": 0.85083} +{"mode": "train", "epoch": 110, "iter": 3100, "lr": 0.01668, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40734, "top5_acc": 0.66203, "loss_cls": 3.37403, "loss": 3.37403, "time": 0.85275} +{"mode": "train", "epoch": 110, "iter": 3200, "lr": 0.01666, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40922, "top5_acc": 0.67078, "loss_cls": 3.34435, "loss": 3.34435, "time": 0.84955} +{"mode": "train", "epoch": 110, "iter": 3300, "lr": 0.01664, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39672, "top5_acc": 0.66078, "loss_cls": 3.41687, "loss": 3.41687, "time": 0.84667} +{"mode": "train", "epoch": 110, "iter": 3400, "lr": 0.01662, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40297, "top5_acc": 0.66172, "loss_cls": 3.3515, "loss": 3.3515, "time": 0.85678} +{"mode": "train", "epoch": 110, "iter": 3500, "lr": 0.01659, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39766, "top5_acc": 0.65094, "loss_cls": 3.40573, "loss": 3.40573, "time": 0.85396} +{"mode": "train", "epoch": 110, "iter": 3600, "lr": 0.01657, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.39094, "top5_acc": 0.65406, "loss_cls": 3.40628, "loss": 3.40628, "time": 0.85378} +{"mode": "train", "epoch": 110, "iter": 3700, "lr": 0.01655, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.39141, "top5_acc": 0.66109, "loss_cls": 3.41183, "loss": 3.41183, "time": 0.85086} +{"mode": "val", "epoch": 110, "iter": 309, "lr": 0.01654, "top1_acc": 0.33936, "top5_acc": 0.59869, "mean_class_accuracy": 0.33923} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.01652, "memory": 15990, "data_time": 1.54611, "top1_acc": 0.40922, "top5_acc": 0.68047, "loss_cls": 3.27597, "loss": 3.27597, "time": 2.57698} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.0165, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42312, "top5_acc": 0.67672, "loss_cls": 3.29778, "loss": 3.29778, "time": 0.85373} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.01648, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.39953, "top5_acc": 0.67125, "loss_cls": 3.35895, "loss": 3.35895, "time": 0.85799} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.01646, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40375, "top5_acc": 0.66234, "loss_cls": 3.35344, "loss": 3.35344, "time": 0.85394} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.01644, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41391, "top5_acc": 0.66594, "loss_cls": 3.32984, "loss": 3.32984, "time": 0.84901} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.01642, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41109, "top5_acc": 0.67734, "loss_cls": 3.31037, "loss": 3.31037, "time": 0.85077} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.0164, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40422, "top5_acc": 0.66172, "loss_cls": 3.3407, "loss": 3.3407, "time": 0.85114} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.01638, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40891, "top5_acc": 0.66984, "loss_cls": 3.31362, "loss": 3.31362, "time": 0.84929} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.01636, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40156, "top5_acc": 0.65328, "loss_cls": 3.38188, "loss": 3.38188, "time": 0.85369} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.01634, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41156, "top5_acc": 0.66531, "loss_cls": 3.32565, "loss": 3.32565, "time": 0.85056} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.01632, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40109, "top5_acc": 0.66375, "loss_cls": 3.3693, "loss": 3.3693, "time": 0.85055} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.0163, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41281, "top5_acc": 0.66406, "loss_cls": 3.36366, "loss": 3.36366, "time": 0.85292} +{"mode": "train", "epoch": 111, "iter": 1300, "lr": 0.01627, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.39984, "top5_acc": 0.66875, "loss_cls": 3.35373, "loss": 3.35373, "time": 0.84985} +{"mode": "train", "epoch": 111, "iter": 1400, "lr": 0.01625, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.40562, "top5_acc": 0.65922, "loss_cls": 3.35133, "loss": 3.35133, "time": 0.85733} +{"mode": "train", "epoch": 111, "iter": 1500, "lr": 0.01623, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40219, "top5_acc": 0.66359, "loss_cls": 3.35498, "loss": 3.35498, "time": 0.85261} +{"mode": "train", "epoch": 111, "iter": 1600, "lr": 0.01621, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40922, "top5_acc": 0.66688, "loss_cls": 3.33615, "loss": 3.33615, "time": 0.84806} +{"mode": "train", "epoch": 111, "iter": 1700, "lr": 0.01619, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39734, "top5_acc": 0.65672, "loss_cls": 3.39798, "loss": 3.39798, "time": 0.85236} +{"mode": "train", "epoch": 111, "iter": 1800, "lr": 0.01617, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40906, "top5_acc": 0.66516, "loss_cls": 3.33392, "loss": 3.33392, "time": 0.85326} +{"mode": "train", "epoch": 111, "iter": 1900, "lr": 0.01615, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41234, "top5_acc": 0.66516, "loss_cls": 3.33455, "loss": 3.33455, "time": 0.85043} +{"mode": "train", "epoch": 111, "iter": 2000, "lr": 0.01613, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.41875, "top5_acc": 0.67484, "loss_cls": 3.29949, "loss": 3.29949, "time": 0.85109} +{"mode": "train", "epoch": 111, "iter": 2100, "lr": 0.01611, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39344, "top5_acc": 0.65531, "loss_cls": 3.41142, "loss": 3.41142, "time": 0.85535} +{"mode": "train", "epoch": 111, "iter": 2200, "lr": 0.01609, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40891, "top5_acc": 0.6725, "loss_cls": 3.30573, "loss": 3.30573, "time": 0.85233} +{"mode": "train", "epoch": 111, "iter": 2300, "lr": 0.01607, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40344, "top5_acc": 0.67016, "loss_cls": 3.34604, "loss": 3.34604, "time": 0.85274} +{"mode": "train", "epoch": 111, "iter": 2400, "lr": 0.01605, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40188, "top5_acc": 0.65875, "loss_cls": 3.3801, "loss": 3.3801, "time": 0.85096} +{"mode": "train", "epoch": 111, "iter": 2500, "lr": 0.01603, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40297, "top5_acc": 0.65672, "loss_cls": 3.38861, "loss": 3.38861, "time": 0.85275} +{"mode": "train", "epoch": 111, "iter": 2600, "lr": 0.01601, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40391, "top5_acc": 0.65766, "loss_cls": 3.34219, "loss": 3.34219, "time": 0.84966} +{"mode": "train", "epoch": 111, "iter": 2700, "lr": 0.01599, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.4, "top5_acc": 0.65406, "loss_cls": 3.41812, "loss": 3.41812, "time": 0.85335} +{"mode": "train", "epoch": 111, "iter": 2800, "lr": 0.01597, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41328, "top5_acc": 0.66703, "loss_cls": 3.31564, "loss": 3.31564, "time": 0.85455} +{"mode": "train", "epoch": 111, "iter": 2900, "lr": 0.01595, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.38547, "top5_acc": 0.64922, "loss_cls": 3.41602, "loss": 3.41602, "time": 0.85211} +{"mode": "train", "epoch": 111, "iter": 3000, "lr": 0.01593, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40109, "top5_acc": 0.64984, "loss_cls": 3.41228, "loss": 3.41228, "time": 0.85594} +{"mode": "train", "epoch": 111, "iter": 3100, "lr": 0.0159, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41312, "top5_acc": 0.66828, "loss_cls": 3.32117, "loss": 3.32117, "time": 0.85133} +{"mode": "train", "epoch": 111, "iter": 3200, "lr": 0.01588, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40688, "top5_acc": 0.66422, "loss_cls": 3.32189, "loss": 3.32189, "time": 0.85564} +{"mode": "train", "epoch": 111, "iter": 3300, "lr": 0.01586, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40891, "top5_acc": 0.65766, "loss_cls": 3.3897, "loss": 3.3897, "time": 0.85816} +{"mode": "train", "epoch": 111, "iter": 3400, "lr": 0.01584, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41125, "top5_acc": 0.67328, "loss_cls": 3.319, "loss": 3.319, "time": 0.85463} +{"mode": "train", "epoch": 111, "iter": 3500, "lr": 0.01582, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.39969, "top5_acc": 0.66656, "loss_cls": 3.37303, "loss": 3.37303, "time": 0.85177} +{"mode": "train", "epoch": 111, "iter": 3600, "lr": 0.0158, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39984, "top5_acc": 0.65344, "loss_cls": 3.40029, "loss": 3.40029, "time": 0.8488} +{"mode": "train", "epoch": 111, "iter": 3700, "lr": 0.01578, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39688, "top5_acc": 0.66047, "loss_cls": 3.39035, "loss": 3.39035, "time": 0.85074} +{"mode": "val", "epoch": 111, "iter": 309, "lr": 0.01577, "top1_acc": 0.34939, "top5_acc": 0.60589, "mean_class_accuracy": 0.34907} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.01575, "memory": 15990, "data_time": 1.56902, "top1_acc": 0.42641, "top5_acc": 0.69578, "loss_cls": 3.20447, "loss": 3.20447, "time": 2.61607} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.01573, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41297, "top5_acc": 0.67453, "loss_cls": 3.27379, "loss": 3.27379, "time": 0.85506} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.01571, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.41266, "top5_acc": 0.67703, "loss_cls": 3.27205, "loss": 3.27205, "time": 0.8547} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.01569, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42141, "top5_acc": 0.66859, "loss_cls": 3.28524, "loss": 3.28524, "time": 0.85312} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.01567, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.42406, "top5_acc": 0.67359, "loss_cls": 3.27149, "loss": 3.27149, "time": 0.8563} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.01565, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41219, "top5_acc": 0.67188, "loss_cls": 3.28613, "loss": 3.28613, "time": 0.86039} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.01563, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40516, "top5_acc": 0.66359, "loss_cls": 3.35223, "loss": 3.35223, "time": 0.86127} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.01561, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40547, "top5_acc": 0.66516, "loss_cls": 3.32504, "loss": 3.32504, "time": 0.85392} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.01559, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.40859, "top5_acc": 0.66938, "loss_cls": 3.33953, "loss": 3.33953, "time": 0.85481} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.01557, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.4125, "top5_acc": 0.66609, "loss_cls": 3.29346, "loss": 3.29346, "time": 0.84937} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.01555, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.40609, "top5_acc": 0.66812, "loss_cls": 3.31774, "loss": 3.31774, "time": 0.85161} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.01553, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41047, "top5_acc": 0.67016, "loss_cls": 3.32962, "loss": 3.32962, "time": 0.85769} +{"mode": "train", "epoch": 112, "iter": 1300, "lr": 0.01551, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.41234, "top5_acc": 0.68031, "loss_cls": 3.27749, "loss": 3.27749, "time": 0.86257} +{"mode": "train", "epoch": 112, "iter": 1400, "lr": 0.01549, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.4125, "top5_acc": 0.67469, "loss_cls": 3.31088, "loss": 3.31088, "time": 0.85887} +{"mode": "train", "epoch": 112, "iter": 1500, "lr": 0.01547, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41156, "top5_acc": 0.67031, "loss_cls": 3.31161, "loss": 3.31161, "time": 0.85977} +{"mode": "train", "epoch": 112, "iter": 1600, "lr": 0.01545, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41703, "top5_acc": 0.66922, "loss_cls": 3.31414, "loss": 3.31414, "time": 0.86208} +{"mode": "train", "epoch": 112, "iter": 1700, "lr": 0.01543, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40484, "top5_acc": 0.66203, "loss_cls": 3.37807, "loss": 3.37807, "time": 0.85359} +{"mode": "train", "epoch": 112, "iter": 1800, "lr": 0.01541, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.40906, "top5_acc": 0.66609, "loss_cls": 3.34419, "loss": 3.34419, "time": 0.85437} +{"mode": "train", "epoch": 112, "iter": 1900, "lr": 0.01539, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.40656, "top5_acc": 0.67359, "loss_cls": 3.31947, "loss": 3.31947, "time": 0.85215} +{"mode": "train", "epoch": 112, "iter": 2000, "lr": 0.01537, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40062, "top5_acc": 0.65766, "loss_cls": 3.372, "loss": 3.372, "time": 0.84889} +{"mode": "train", "epoch": 112, "iter": 2100, "lr": 0.01535, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42, "top5_acc": 0.67328, "loss_cls": 3.30624, "loss": 3.30624, "time": 0.85269} +{"mode": "train", "epoch": 112, "iter": 2200, "lr": 0.01533, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39766, "top5_acc": 0.65594, "loss_cls": 3.36496, "loss": 3.36496, "time": 0.85347} +{"mode": "train", "epoch": 112, "iter": 2300, "lr": 0.01531, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41, "top5_acc": 0.65922, "loss_cls": 3.36466, "loss": 3.36466, "time": 0.84691} +{"mode": "train", "epoch": 112, "iter": 2400, "lr": 0.01529, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.39703, "top5_acc": 0.65312, "loss_cls": 3.42044, "loss": 3.42044, "time": 0.85706} +{"mode": "train", "epoch": 112, "iter": 2500, "lr": 0.01527, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.40469, "top5_acc": 0.65812, "loss_cls": 3.35854, "loss": 3.35854, "time": 0.85479} +{"mode": "train", "epoch": 112, "iter": 2600, "lr": 0.01525, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40203, "top5_acc": 0.66406, "loss_cls": 3.35716, "loss": 3.35716, "time": 0.85668} +{"mode": "train", "epoch": 112, "iter": 2700, "lr": 0.01523, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40828, "top5_acc": 0.67297, "loss_cls": 3.33661, "loss": 3.33661, "time": 0.85586} +{"mode": "train", "epoch": 112, "iter": 2800, "lr": 0.01521, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39844, "top5_acc": 0.66438, "loss_cls": 3.33757, "loss": 3.33757, "time": 0.85085} +{"mode": "train", "epoch": 112, "iter": 2900, "lr": 0.01519, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.40984, "top5_acc": 0.66078, "loss_cls": 3.35813, "loss": 3.35813, "time": 0.85328} +{"mode": "train", "epoch": 112, "iter": 3000, "lr": 0.01517, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40656, "top5_acc": 0.6675, "loss_cls": 3.35222, "loss": 3.35222, "time": 0.85621} +{"mode": "train", "epoch": 112, "iter": 3100, "lr": 0.01515, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.39578, "top5_acc": 0.655, "loss_cls": 3.39507, "loss": 3.39507, "time": 0.85647} +{"mode": "train", "epoch": 112, "iter": 3200, "lr": 0.01513, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40297, "top5_acc": 0.66484, "loss_cls": 3.36237, "loss": 3.36237, "time": 0.85683} +{"mode": "train", "epoch": 112, "iter": 3300, "lr": 0.01511, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41547, "top5_acc": 0.6675, "loss_cls": 3.34545, "loss": 3.34545, "time": 0.85546} +{"mode": "train", "epoch": 112, "iter": 3400, "lr": 0.01509, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.40906, "top5_acc": 0.66172, "loss_cls": 3.34773, "loss": 3.34773, "time": 0.85427} +{"mode": "train", "epoch": 112, "iter": 3500, "lr": 0.01507, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41438, "top5_acc": 0.67469, "loss_cls": 3.29599, "loss": 3.29599, "time": 0.85414} +{"mode": "train", "epoch": 112, "iter": 3600, "lr": 0.01505, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40891, "top5_acc": 0.66859, "loss_cls": 3.32633, "loss": 3.32633, "time": 0.85752} +{"mode": "train", "epoch": 112, "iter": 3700, "lr": 0.01503, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41562, "top5_acc": 0.6675, "loss_cls": 3.31857, "loss": 3.31857, "time": 0.86065} +{"mode": "val", "epoch": 112, "iter": 309, "lr": 0.01502, "top1_acc": 0.34417, "top5_acc": 0.59905, "mean_class_accuracy": 0.34396} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.015, "memory": 15990, "data_time": 1.57052, "top1_acc": 0.43406, "top5_acc": 0.67734, "loss_cls": 3.23593, "loss": 3.23593, "time": 2.61188} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.01498, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41859, "top5_acc": 0.67828, "loss_cls": 3.24177, "loss": 3.24177, "time": 0.85338} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.01496, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.42672, "top5_acc": 0.68344, "loss_cls": 3.24875, "loss": 3.24875, "time": 0.85544} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.01494, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.425, "top5_acc": 0.68547, "loss_cls": 3.26118, "loss": 3.26118, "time": 0.85341} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.01492, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.41484, "top5_acc": 0.68375, "loss_cls": 3.25095, "loss": 3.25095, "time": 0.85587} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.0149, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42547, "top5_acc": 0.67812, "loss_cls": 3.26016, "loss": 3.26016, "time": 0.85347} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.01488, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42531, "top5_acc": 0.67312, "loss_cls": 3.27404, "loss": 3.27404, "time": 0.85705} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.01486, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.41141, "top5_acc": 0.67922, "loss_cls": 3.30705, "loss": 3.30705, "time": 0.85754} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.01484, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.41234, "top5_acc": 0.66547, "loss_cls": 3.30212, "loss": 3.30212, "time": 0.85862} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.01482, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41094, "top5_acc": 0.67984, "loss_cls": 3.29002, "loss": 3.29002, "time": 0.8577} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0148, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42281, "top5_acc": 0.67047, "loss_cls": 3.28652, "loss": 3.28652, "time": 0.85471} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.01478, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.41391, "top5_acc": 0.66641, "loss_cls": 3.32364, "loss": 3.32364, "time": 0.85705} +{"mode": "train", "epoch": 113, "iter": 1300, "lr": 0.01476, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.4025, "top5_acc": 0.67359, "loss_cls": 3.30711, "loss": 3.30711, "time": 0.85396} +{"mode": "train", "epoch": 113, "iter": 1400, "lr": 0.01474, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.40219, "top5_acc": 0.67453, "loss_cls": 3.2962, "loss": 3.2962, "time": 0.85765} +{"mode": "train", "epoch": 113, "iter": 1500, "lr": 0.01472, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.41109, "top5_acc": 0.66219, "loss_cls": 3.34555, "loss": 3.34555, "time": 0.86167} +{"mode": "train", "epoch": 113, "iter": 1600, "lr": 0.0147, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.40844, "top5_acc": 0.66953, "loss_cls": 3.31952, "loss": 3.31952, "time": 0.86153} +{"mode": "train", "epoch": 113, "iter": 1700, "lr": 0.01468, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.40172, "top5_acc": 0.66938, "loss_cls": 3.33446, "loss": 3.33446, "time": 0.85958} +{"mode": "train", "epoch": 113, "iter": 1800, "lr": 0.01466, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40719, "top5_acc": 0.67016, "loss_cls": 3.33217, "loss": 3.33217, "time": 0.85533} +{"mode": "train", "epoch": 113, "iter": 1900, "lr": 0.01464, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40938, "top5_acc": 0.66297, "loss_cls": 3.32617, "loss": 3.32617, "time": 0.85017} +{"mode": "train", "epoch": 113, "iter": 2000, "lr": 0.01462, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.40969, "top5_acc": 0.67062, "loss_cls": 3.32846, "loss": 3.32846, "time": 0.85142} +{"mode": "train", "epoch": 113, "iter": 2100, "lr": 0.0146, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40469, "top5_acc": 0.67359, "loss_cls": 3.32296, "loss": 3.32296, "time": 0.85468} +{"mode": "train", "epoch": 113, "iter": 2200, "lr": 0.01458, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.40688, "top5_acc": 0.67062, "loss_cls": 3.34129, "loss": 3.34129, "time": 0.85704} +{"mode": "train", "epoch": 113, "iter": 2300, "lr": 0.01456, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41828, "top5_acc": 0.67281, "loss_cls": 3.29057, "loss": 3.29057, "time": 0.85634} +{"mode": "train", "epoch": 113, "iter": 2400, "lr": 0.01454, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.40781, "top5_acc": 0.66578, "loss_cls": 3.34297, "loss": 3.34297, "time": 0.85796} +{"mode": "train", "epoch": 113, "iter": 2500, "lr": 0.01452, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.41234, "top5_acc": 0.67203, "loss_cls": 3.31882, "loss": 3.31882, "time": 0.86282} +{"mode": "train", "epoch": 113, "iter": 2600, "lr": 0.0145, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40828, "top5_acc": 0.66844, "loss_cls": 3.34899, "loss": 3.34899, "time": 0.85908} +{"mode": "train", "epoch": 113, "iter": 2700, "lr": 0.01448, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.40797, "top5_acc": 0.66547, "loss_cls": 3.33249, "loss": 3.33249, "time": 0.85856} +{"mode": "train", "epoch": 113, "iter": 2800, "lr": 0.01446, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.41312, "top5_acc": 0.675, "loss_cls": 3.25989, "loss": 3.25989, "time": 0.85662} +{"mode": "train", "epoch": 113, "iter": 2900, "lr": 0.01444, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41031, "top5_acc": 0.66109, "loss_cls": 3.35368, "loss": 3.35368, "time": 0.85696} +{"mode": "train", "epoch": 113, "iter": 3000, "lr": 0.01442, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.40891, "top5_acc": 0.67359, "loss_cls": 3.30265, "loss": 3.30265, "time": 0.86036} +{"mode": "train", "epoch": 113, "iter": 3100, "lr": 0.0144, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.40172, "top5_acc": 0.66578, "loss_cls": 3.33775, "loss": 3.33775, "time": 0.85765} +{"mode": "train", "epoch": 113, "iter": 3200, "lr": 0.01438, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.425, "top5_acc": 0.67219, "loss_cls": 3.30205, "loss": 3.30205, "time": 0.86242} +{"mode": "train", "epoch": 113, "iter": 3300, "lr": 0.01436, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41688, "top5_acc": 0.66203, "loss_cls": 3.33403, "loss": 3.33403, "time": 0.86246} +{"mode": "train", "epoch": 113, "iter": 3400, "lr": 0.01434, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.40938, "top5_acc": 0.66828, "loss_cls": 3.32408, "loss": 3.32408, "time": 0.8609} +{"mode": "train", "epoch": 113, "iter": 3500, "lr": 0.01432, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40938, "top5_acc": 0.66797, "loss_cls": 3.33483, "loss": 3.33483, "time": 0.85658} +{"mode": "train", "epoch": 113, "iter": 3600, "lr": 0.01431, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.40531, "top5_acc": 0.65906, "loss_cls": 3.35484, "loss": 3.35484, "time": 0.85725} +{"mode": "train", "epoch": 113, "iter": 3700, "lr": 0.01429, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.39859, "top5_acc": 0.66672, "loss_cls": 3.34032, "loss": 3.34032, "time": 0.86201} +{"mode": "val", "epoch": 113, "iter": 309, "lr": 0.01428, "top1_acc": 0.34377, "top5_acc": 0.60786, "mean_class_accuracy": 0.34358} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.01426, "memory": 15990, "data_time": 1.5518, "top1_acc": 0.43281, "top5_acc": 0.6875, "loss_cls": 3.20601, "loss": 3.20601, "time": 2.59852} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.01424, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41734, "top5_acc": 0.66859, "loss_cls": 3.30282, "loss": 3.30282, "time": 0.85373} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.01422, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.42688, "top5_acc": 0.685, "loss_cls": 3.23156, "loss": 3.23156, "time": 0.85358} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.0142, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.42266, "top5_acc": 0.67281, "loss_cls": 3.27245, "loss": 3.27245, "time": 0.85457} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.01418, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41453, "top5_acc": 0.67359, "loss_cls": 3.28062, "loss": 3.28062, "time": 0.84942} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.01416, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42516, "top5_acc": 0.68797, "loss_cls": 3.21795, "loss": 3.21795, "time": 0.85284} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.01414, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42188, "top5_acc": 0.68125, "loss_cls": 3.25762, "loss": 3.25762, "time": 0.8542} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.01412, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41844, "top5_acc": 0.67906, "loss_cls": 3.27192, "loss": 3.27192, "time": 0.85358} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.0141, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41281, "top5_acc": 0.67156, "loss_cls": 3.32815, "loss": 3.32815, "time": 0.85385} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.01408, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41984, "top5_acc": 0.68016, "loss_cls": 3.23367, "loss": 3.23367, "time": 0.85451} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.01406, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41469, "top5_acc": 0.67562, "loss_cls": 3.25261, "loss": 3.25261, "time": 0.85389} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.01404, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.4075, "top5_acc": 0.67094, "loss_cls": 3.2981, "loss": 3.2981, "time": 0.85271} +{"mode": "train", "epoch": 114, "iter": 1300, "lr": 0.01402, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.40984, "top5_acc": 0.67859, "loss_cls": 3.28172, "loss": 3.28172, "time": 0.85573} +{"mode": "train", "epoch": 114, "iter": 1400, "lr": 0.014, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41516, "top5_acc": 0.66938, "loss_cls": 3.25961, "loss": 3.25961, "time": 0.8509} +{"mode": "train", "epoch": 114, "iter": 1500, "lr": 0.01398, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42141, "top5_acc": 0.68047, "loss_cls": 3.24561, "loss": 3.24561, "time": 0.84964} +{"mode": "train", "epoch": 114, "iter": 1600, "lr": 0.01397, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40406, "top5_acc": 0.68203, "loss_cls": 3.30525, "loss": 3.30525, "time": 0.85318} +{"mode": "train", "epoch": 114, "iter": 1700, "lr": 0.01395, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.41, "top5_acc": 0.67375, "loss_cls": 3.32086, "loss": 3.32086, "time": 0.85719} +{"mode": "train", "epoch": 114, "iter": 1800, "lr": 0.01393, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42156, "top5_acc": 0.67781, "loss_cls": 3.28593, "loss": 3.28593, "time": 0.85472} +{"mode": "train", "epoch": 114, "iter": 1900, "lr": 0.01391, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.4125, "top5_acc": 0.65969, "loss_cls": 3.32692, "loss": 3.32692, "time": 0.85096} +{"mode": "train", "epoch": 114, "iter": 2000, "lr": 0.01389, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.42391, "top5_acc": 0.68219, "loss_cls": 3.25968, "loss": 3.25968, "time": 0.85326} +{"mode": "train", "epoch": 114, "iter": 2100, "lr": 0.01387, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42297, "top5_acc": 0.67531, "loss_cls": 3.25424, "loss": 3.25424, "time": 0.85043} +{"mode": "train", "epoch": 114, "iter": 2200, "lr": 0.01385, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41938, "top5_acc": 0.67812, "loss_cls": 3.26, "loss": 3.26, "time": 0.85783} +{"mode": "train", "epoch": 114, "iter": 2300, "lr": 0.01383, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40891, "top5_acc": 0.67078, "loss_cls": 3.33761, "loss": 3.33761, "time": 0.85409} +{"mode": "train", "epoch": 114, "iter": 2400, "lr": 0.01381, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41906, "top5_acc": 0.68266, "loss_cls": 3.25634, "loss": 3.25634, "time": 0.85363} +{"mode": "train", "epoch": 114, "iter": 2500, "lr": 0.01379, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.40609, "top5_acc": 0.66406, "loss_cls": 3.33003, "loss": 3.33003, "time": 0.85018} +{"mode": "train", "epoch": 114, "iter": 2600, "lr": 0.01377, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41906, "top5_acc": 0.68031, "loss_cls": 3.28561, "loss": 3.28561, "time": 0.85096} +{"mode": "train", "epoch": 114, "iter": 2700, "lr": 0.01375, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.40391, "top5_acc": 0.65641, "loss_cls": 3.37032, "loss": 3.37032, "time": 0.85156} +{"mode": "train", "epoch": 114, "iter": 2800, "lr": 0.01373, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41594, "top5_acc": 0.67391, "loss_cls": 3.28258, "loss": 3.28258, "time": 0.85209} +{"mode": "train", "epoch": 114, "iter": 2900, "lr": 0.01371, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41453, "top5_acc": 0.67578, "loss_cls": 3.27514, "loss": 3.27514, "time": 0.85104} +{"mode": "train", "epoch": 114, "iter": 3000, "lr": 0.01369, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41703, "top5_acc": 0.68422, "loss_cls": 3.26923, "loss": 3.26923, "time": 0.85855} +{"mode": "train", "epoch": 114, "iter": 3100, "lr": 0.01368, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41781, "top5_acc": 0.675, "loss_cls": 3.29367, "loss": 3.29367, "time": 0.85562} +{"mode": "train", "epoch": 114, "iter": 3200, "lr": 0.01366, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41031, "top5_acc": 0.67734, "loss_cls": 3.31226, "loss": 3.31226, "time": 0.85354} +{"mode": "train", "epoch": 114, "iter": 3300, "lr": 0.01364, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41, "top5_acc": 0.66453, "loss_cls": 3.32042, "loss": 3.32042, "time": 0.85484} +{"mode": "train", "epoch": 114, "iter": 3400, "lr": 0.01362, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41094, "top5_acc": 0.67672, "loss_cls": 3.28227, "loss": 3.28227, "time": 0.86076} +{"mode": "train", "epoch": 114, "iter": 3500, "lr": 0.0136, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41078, "top5_acc": 0.66406, "loss_cls": 3.34145, "loss": 3.34145, "time": 0.85887} +{"mode": "train", "epoch": 114, "iter": 3600, "lr": 0.01358, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41938, "top5_acc": 0.66734, "loss_cls": 3.29164, "loss": 3.29164, "time": 0.85479} +{"mode": "train", "epoch": 114, "iter": 3700, "lr": 0.01356, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40125, "top5_acc": 0.66641, "loss_cls": 3.36858, "loss": 3.36858, "time": 0.85638} +{"mode": "val", "epoch": 114, "iter": 309, "lr": 0.01355, "top1_acc": 0.35344, "top5_acc": 0.60953, "mean_class_accuracy": 0.35322} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.01353, "memory": 15990, "data_time": 1.51367, "top1_acc": 0.42844, "top5_acc": 0.68172, "loss_cls": 3.20446, "loss": 3.20446, "time": 2.55832} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.01351, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.41531, "top5_acc": 0.67672, "loss_cls": 3.25539, "loss": 3.25539, "time": 0.8514} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.01349, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43812, "top5_acc": 0.68797, "loss_cls": 3.18896, "loss": 3.18896, "time": 0.85562} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.01348, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42297, "top5_acc": 0.6825, "loss_cls": 3.24645, "loss": 3.24645, "time": 0.85144} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.01346, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43016, "top5_acc": 0.68016, "loss_cls": 3.22481, "loss": 3.22481, "time": 0.85442} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.01344, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42734, "top5_acc": 0.67891, "loss_cls": 3.2368, "loss": 3.2368, "time": 0.853} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.01342, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.41609, "top5_acc": 0.67438, "loss_cls": 3.28069, "loss": 3.28069, "time": 0.85223} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.0134, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.4225, "top5_acc": 0.68078, "loss_cls": 3.23501, "loss": 3.23501, "time": 0.85351} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.01338, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42516, "top5_acc": 0.67922, "loss_cls": 3.26364, "loss": 3.26364, "time": 0.84757} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.01336, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42781, "top5_acc": 0.68141, "loss_cls": 3.23167, "loss": 3.23167, "time": 0.85198} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.01334, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41703, "top5_acc": 0.67406, "loss_cls": 3.28587, "loss": 3.28587, "time": 0.8542} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.01332, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42172, "top5_acc": 0.67688, "loss_cls": 3.2439, "loss": 3.2439, "time": 0.84892} +{"mode": "train", "epoch": 115, "iter": 1300, "lr": 0.0133, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.42172, "top5_acc": 0.67312, "loss_cls": 3.27921, "loss": 3.27921, "time": 0.85716} +{"mode": "train", "epoch": 115, "iter": 1400, "lr": 0.01328, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.425, "top5_acc": 0.67531, "loss_cls": 3.25144, "loss": 3.25144, "time": 0.8503} +{"mode": "train", "epoch": 115, "iter": 1500, "lr": 0.01327, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41844, "top5_acc": 0.68219, "loss_cls": 3.24802, "loss": 3.24802, "time": 0.8551} +{"mode": "train", "epoch": 115, "iter": 1600, "lr": 0.01325, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.42203, "top5_acc": 0.68125, "loss_cls": 3.24504, "loss": 3.24504, "time": 0.85896} +{"mode": "train", "epoch": 115, "iter": 1700, "lr": 0.01323, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41141, "top5_acc": 0.67328, "loss_cls": 3.30419, "loss": 3.30419, "time": 0.85413} +{"mode": "train", "epoch": 115, "iter": 1800, "lr": 0.01321, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41797, "top5_acc": 0.67969, "loss_cls": 3.25021, "loss": 3.25021, "time": 0.84838} +{"mode": "train", "epoch": 115, "iter": 1900, "lr": 0.01319, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.40797, "top5_acc": 0.67469, "loss_cls": 3.28484, "loss": 3.28484, "time": 0.8487} +{"mode": "train", "epoch": 115, "iter": 2000, "lr": 0.01317, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42219, "top5_acc": 0.67906, "loss_cls": 3.25407, "loss": 3.25407, "time": 0.85153} +{"mode": "train", "epoch": 115, "iter": 2100, "lr": 0.01315, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.41125, "top5_acc": 0.67828, "loss_cls": 3.30906, "loss": 3.30906, "time": 0.85264} +{"mode": "train", "epoch": 115, "iter": 2200, "lr": 0.01313, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42172, "top5_acc": 0.67766, "loss_cls": 3.25792, "loss": 3.25792, "time": 0.85403} +{"mode": "train", "epoch": 115, "iter": 2300, "lr": 0.01311, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41219, "top5_acc": 0.66625, "loss_cls": 3.3114, "loss": 3.3114, "time": 0.85084} +{"mode": "train", "epoch": 115, "iter": 2400, "lr": 0.0131, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41922, "top5_acc": 0.67734, "loss_cls": 3.2645, "loss": 3.2645, "time": 0.85224} +{"mode": "train", "epoch": 115, "iter": 2500, "lr": 0.01308, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41109, "top5_acc": 0.67234, "loss_cls": 3.28122, "loss": 3.28122, "time": 0.8504} +{"mode": "train", "epoch": 115, "iter": 2600, "lr": 0.01306, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.42281, "top5_acc": 0.67172, "loss_cls": 3.26789, "loss": 3.26789, "time": 0.85091} +{"mode": "train", "epoch": 115, "iter": 2700, "lr": 0.01304, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42141, "top5_acc": 0.67047, "loss_cls": 3.27909, "loss": 3.27909, "time": 0.85546} +{"mode": "train", "epoch": 115, "iter": 2800, "lr": 0.01302, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41953, "top5_acc": 0.66922, "loss_cls": 3.29834, "loss": 3.29834, "time": 0.85369} +{"mode": "train", "epoch": 115, "iter": 2900, "lr": 0.013, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41953, "top5_acc": 0.67031, "loss_cls": 3.30437, "loss": 3.30437, "time": 0.85499} +{"mode": "train", "epoch": 115, "iter": 3000, "lr": 0.01298, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41062, "top5_acc": 0.67969, "loss_cls": 3.29903, "loss": 3.29903, "time": 0.85089} +{"mode": "train", "epoch": 115, "iter": 3100, "lr": 0.01296, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42094, "top5_acc": 0.67484, "loss_cls": 3.27484, "loss": 3.27484, "time": 0.85261} +{"mode": "train", "epoch": 115, "iter": 3200, "lr": 0.01295, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42203, "top5_acc": 0.6775, "loss_cls": 3.25935, "loss": 3.25935, "time": 0.85416} +{"mode": "train", "epoch": 115, "iter": 3300, "lr": 0.01293, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41484, "top5_acc": 0.6725, "loss_cls": 3.2821, "loss": 3.2821, "time": 0.85264} +{"mode": "train", "epoch": 115, "iter": 3400, "lr": 0.01291, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.41094, "top5_acc": 0.66734, "loss_cls": 3.30821, "loss": 3.30821, "time": 0.85016} +{"mode": "train", "epoch": 115, "iter": 3500, "lr": 0.01289, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.41312, "top5_acc": 0.67719, "loss_cls": 3.29321, "loss": 3.29321, "time": 0.85116} +{"mode": "train", "epoch": 115, "iter": 3600, "lr": 0.01287, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42531, "top5_acc": 0.67844, "loss_cls": 3.24925, "loss": 3.24925, "time": 0.84983} +{"mode": "train", "epoch": 115, "iter": 3700, "lr": 0.01285, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42156, "top5_acc": 0.67844, "loss_cls": 3.26162, "loss": 3.26162, "time": 0.85074} +{"mode": "val", "epoch": 115, "iter": 309, "lr": 0.01284, "top1_acc": 0.35334, "top5_acc": 0.60533, "mean_class_accuracy": 0.3532} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.01282, "memory": 15990, "data_time": 1.5249, "top1_acc": 0.44484, "top5_acc": 0.70062, "loss_cls": 3.10678, "loss": 3.10678, "time": 2.56155} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.01281, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42938, "top5_acc": 0.69672, "loss_cls": 3.1694, "loss": 3.1694, "time": 0.85115} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.01279, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.43562, "top5_acc": 0.69141, "loss_cls": 3.18223, "loss": 3.18223, "time": 0.85695} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.01277, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.42781, "top5_acc": 0.68688, "loss_cls": 3.21639, "loss": 3.21639, "time": 0.85848} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.01275, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42953, "top5_acc": 0.68141, "loss_cls": 3.19827, "loss": 3.19827, "time": 0.85839} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.01273, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43094, "top5_acc": 0.68609, "loss_cls": 3.21653, "loss": 3.21653, "time": 0.85531} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.01271, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42766, "top5_acc": 0.69391, "loss_cls": 3.17183, "loss": 3.17183, "time": 0.85519} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.01269, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42953, "top5_acc": 0.68781, "loss_cls": 3.21189, "loss": 3.21189, "time": 0.8524} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.01268, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.42078, "top5_acc": 0.67734, "loss_cls": 3.26659, "loss": 3.26659, "time": 0.85548} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.01266, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41484, "top5_acc": 0.68031, "loss_cls": 3.27243, "loss": 3.27243, "time": 0.85476} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.01264, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.42609, "top5_acc": 0.67688, "loss_cls": 3.23222, "loss": 3.23222, "time": 0.85176} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.01262, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42938, "top5_acc": 0.68625, "loss_cls": 3.22606, "loss": 3.22606, "time": 0.8563} +{"mode": "train", "epoch": 116, "iter": 1300, "lr": 0.0126, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.42156, "top5_acc": 0.68344, "loss_cls": 3.24653, "loss": 3.24653, "time": 0.85389} +{"mode": "train", "epoch": 116, "iter": 1400, "lr": 0.01258, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42062, "top5_acc": 0.67812, "loss_cls": 3.25816, "loss": 3.25816, "time": 0.85156} +{"mode": "train", "epoch": 116, "iter": 1500, "lr": 0.01256, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43688, "top5_acc": 0.69188, "loss_cls": 3.19866, "loss": 3.19866, "time": 0.85252} +{"mode": "train", "epoch": 116, "iter": 1600, "lr": 0.01255, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41875, "top5_acc": 0.68156, "loss_cls": 3.26265, "loss": 3.26265, "time": 0.84962} +{"mode": "train", "epoch": 116, "iter": 1700, "lr": 0.01253, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41328, "top5_acc": 0.68, "loss_cls": 3.28151, "loss": 3.28151, "time": 0.85072} +{"mode": "train", "epoch": 116, "iter": 1800, "lr": 0.01251, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42672, "top5_acc": 0.67625, "loss_cls": 3.26378, "loss": 3.26378, "time": 0.84992} +{"mode": "train", "epoch": 116, "iter": 1900, "lr": 0.01249, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42438, "top5_acc": 0.68141, "loss_cls": 3.23866, "loss": 3.23866, "time": 0.85187} +{"mode": "train", "epoch": 116, "iter": 2000, "lr": 0.01247, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42062, "top5_acc": 0.67703, "loss_cls": 3.24626, "loss": 3.24626, "time": 0.85288} +{"mode": "train", "epoch": 116, "iter": 2100, "lr": 0.01245, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42578, "top5_acc": 0.68984, "loss_cls": 3.2069, "loss": 3.2069, "time": 0.85531} +{"mode": "train", "epoch": 116, "iter": 2200, "lr": 0.01243, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41938, "top5_acc": 0.68078, "loss_cls": 3.24914, "loss": 3.24914, "time": 0.85069} +{"mode": "train", "epoch": 116, "iter": 2300, "lr": 0.01242, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.41891, "top5_acc": 0.67469, "loss_cls": 3.28084, "loss": 3.28084, "time": 0.85057} +{"mode": "train", "epoch": 116, "iter": 2400, "lr": 0.0124, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.415, "top5_acc": 0.67328, "loss_cls": 3.2815, "loss": 3.2815, "time": 0.8524} +{"mode": "train", "epoch": 116, "iter": 2500, "lr": 0.01238, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42, "top5_acc": 0.685, "loss_cls": 3.24683, "loss": 3.24683, "time": 0.85208} +{"mode": "train", "epoch": 116, "iter": 2600, "lr": 0.01236, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.42344, "top5_acc": 0.68125, "loss_cls": 3.23823, "loss": 3.23823, "time": 0.85194} +{"mode": "train", "epoch": 116, "iter": 2700, "lr": 0.01234, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41984, "top5_acc": 0.67594, "loss_cls": 3.29056, "loss": 3.29056, "time": 0.85338} +{"mode": "train", "epoch": 116, "iter": 2800, "lr": 0.01232, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42297, "top5_acc": 0.67531, "loss_cls": 3.26464, "loss": 3.26464, "time": 0.85483} +{"mode": "train", "epoch": 116, "iter": 2900, "lr": 0.01231, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42016, "top5_acc": 0.68641, "loss_cls": 3.2132, "loss": 3.2132, "time": 0.85368} +{"mode": "train", "epoch": 116, "iter": 3000, "lr": 0.01229, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.40219, "top5_acc": 0.6725, "loss_cls": 3.30749, "loss": 3.30749, "time": 0.85523} +{"mode": "train", "epoch": 116, "iter": 3100, "lr": 0.01227, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41562, "top5_acc": 0.67984, "loss_cls": 3.28422, "loss": 3.28422, "time": 0.8533} +{"mode": "train", "epoch": 116, "iter": 3200, "lr": 0.01225, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.41562, "top5_acc": 0.67094, "loss_cls": 3.26195, "loss": 3.26195, "time": 0.85702} +{"mode": "train", "epoch": 116, "iter": 3300, "lr": 0.01223, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43172, "top5_acc": 0.67766, "loss_cls": 3.23436, "loss": 3.23436, "time": 0.85861} +{"mode": "train", "epoch": 116, "iter": 3400, "lr": 0.01221, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.41297, "top5_acc": 0.66781, "loss_cls": 3.29942, "loss": 3.29942, "time": 0.85237} +{"mode": "train", "epoch": 116, "iter": 3500, "lr": 0.0122, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.43109, "top5_acc": 0.68906, "loss_cls": 3.21139, "loss": 3.21139, "time": 0.85514} +{"mode": "train", "epoch": 116, "iter": 3600, "lr": 0.01218, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42188, "top5_acc": 0.67125, "loss_cls": 3.25775, "loss": 3.25775, "time": 0.86023} +{"mode": "train", "epoch": 116, "iter": 3700, "lr": 0.01216, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.42891, "top5_acc": 0.67891, "loss_cls": 3.25253, "loss": 3.25253, "time": 0.85566} +{"mode": "val", "epoch": 116, "iter": 309, "lr": 0.01215, "top1_acc": 0.35841, "top5_acc": 0.61136, "mean_class_accuracy": 0.35806} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.01213, "memory": 15990, "data_time": 1.59833, "top1_acc": 0.43516, "top5_acc": 0.69156, "loss_cls": 3.14968, "loss": 3.14968, "time": 2.63517} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.01211, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.43188, "top5_acc": 0.68938, "loss_cls": 3.19314, "loss": 3.19314, "time": 0.85443} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.0121, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43531, "top5_acc": 0.69375, "loss_cls": 3.16202, "loss": 3.16202, "time": 0.85613} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.01208, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44016, "top5_acc": 0.69938, "loss_cls": 3.14464, "loss": 3.14464, "time": 0.85493} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.01206, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.43641, "top5_acc": 0.69125, "loss_cls": 3.1696, "loss": 3.1696, "time": 0.86086} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.01204, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42797, "top5_acc": 0.68797, "loss_cls": 3.1777, "loss": 3.1777, "time": 0.85552} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.01202, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.435, "top5_acc": 0.69312, "loss_cls": 3.18851, "loss": 3.18851, "time": 0.85156} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.012, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42578, "top5_acc": 0.68672, "loss_cls": 3.25849, "loss": 3.25849, "time": 0.8499} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.01199, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42469, "top5_acc": 0.68656, "loss_cls": 3.21222, "loss": 3.21222, "time": 0.85063} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.01197, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42328, "top5_acc": 0.68531, "loss_cls": 3.24442, "loss": 3.24442, "time": 0.86099} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.01195, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42047, "top5_acc": 0.68703, "loss_cls": 3.22517, "loss": 3.22517, "time": 0.84879} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.01193, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43984, "top5_acc": 0.69375, "loss_cls": 3.17471, "loss": 3.17471, "time": 0.85693} +{"mode": "train", "epoch": 117, "iter": 1300, "lr": 0.01191, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42906, "top5_acc": 0.69344, "loss_cls": 3.18986, "loss": 3.18986, "time": 0.85488} +{"mode": "train", "epoch": 117, "iter": 1400, "lr": 0.0119, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43281, "top5_acc": 0.68562, "loss_cls": 3.20527, "loss": 3.20527, "time": 0.84743} +{"mode": "train", "epoch": 117, "iter": 1500, "lr": 0.01188, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42969, "top5_acc": 0.69344, "loss_cls": 3.16748, "loss": 3.16748, "time": 0.85232} +{"mode": "train", "epoch": 117, "iter": 1600, "lr": 0.01186, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43469, "top5_acc": 0.69453, "loss_cls": 3.17277, "loss": 3.17277, "time": 0.85402} +{"mode": "train", "epoch": 117, "iter": 1700, "lr": 0.01184, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.42453, "top5_acc": 0.68203, "loss_cls": 3.22788, "loss": 3.22788, "time": 0.84528} +{"mode": "train", "epoch": 117, "iter": 1800, "lr": 0.01182, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.425, "top5_acc": 0.68766, "loss_cls": 3.21098, "loss": 3.21098, "time": 0.84938} +{"mode": "train", "epoch": 117, "iter": 1900, "lr": 0.01181, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43219, "top5_acc": 0.685, "loss_cls": 3.20138, "loss": 3.20138, "time": 0.85206} +{"mode": "train", "epoch": 117, "iter": 2000, "lr": 0.01179, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.42609, "top5_acc": 0.6775, "loss_cls": 3.2341, "loss": 3.2341, "time": 0.85652} +{"mode": "train", "epoch": 117, "iter": 2100, "lr": 0.01177, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42078, "top5_acc": 0.68719, "loss_cls": 3.24398, "loss": 3.24398, "time": 0.86049} +{"mode": "train", "epoch": 117, "iter": 2200, "lr": 0.01175, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.42219, "top5_acc": 0.67328, "loss_cls": 3.25894, "loss": 3.25894, "time": 0.85734} +{"mode": "train", "epoch": 117, "iter": 2300, "lr": 0.01173, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42484, "top5_acc": 0.68391, "loss_cls": 3.20881, "loss": 3.20881, "time": 0.85586} +{"mode": "train", "epoch": 117, "iter": 2400, "lr": 0.01172, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.41938, "top5_acc": 0.67328, "loss_cls": 3.26531, "loss": 3.26531, "time": 0.85313} +{"mode": "train", "epoch": 117, "iter": 2500, "lr": 0.0117, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42516, "top5_acc": 0.68109, "loss_cls": 3.24781, "loss": 3.24781, "time": 0.86184} +{"mode": "train", "epoch": 117, "iter": 2600, "lr": 0.01168, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.42594, "top5_acc": 0.68406, "loss_cls": 3.23245, "loss": 3.23245, "time": 0.86081} +{"mode": "train", "epoch": 117, "iter": 2700, "lr": 0.01166, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.43266, "top5_acc": 0.68688, "loss_cls": 3.17717, "loss": 3.17717, "time": 0.85941} +{"mode": "train", "epoch": 117, "iter": 2800, "lr": 0.01164, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.42344, "top5_acc": 0.68172, "loss_cls": 3.25544, "loss": 3.25544, "time": 0.86101} +{"mode": "train", "epoch": 117, "iter": 2900, "lr": 0.01163, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.42516, "top5_acc": 0.68141, "loss_cls": 3.21998, "loss": 3.21998, "time": 0.85582} +{"mode": "train", "epoch": 117, "iter": 3000, "lr": 0.01161, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42688, "top5_acc": 0.68, "loss_cls": 3.20921, "loss": 3.20921, "time": 0.856} +{"mode": "train", "epoch": 117, "iter": 3100, "lr": 0.01159, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42078, "top5_acc": 0.67828, "loss_cls": 3.26397, "loss": 3.26397, "time": 0.85614} +{"mode": "train", "epoch": 117, "iter": 3200, "lr": 0.01157, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.43078, "top5_acc": 0.68406, "loss_cls": 3.21377, "loss": 3.21377, "time": 0.85658} +{"mode": "train", "epoch": 117, "iter": 3300, "lr": 0.01155, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43234, "top5_acc": 0.67734, "loss_cls": 3.24384, "loss": 3.24384, "time": 0.86228} +{"mode": "train", "epoch": 117, "iter": 3400, "lr": 0.01154, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.41609, "top5_acc": 0.68156, "loss_cls": 3.23872, "loss": 3.23872, "time": 0.85143} +{"mode": "train", "epoch": 117, "iter": 3500, "lr": 0.01152, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42406, "top5_acc": 0.67719, "loss_cls": 3.24233, "loss": 3.24233, "time": 0.85405} +{"mode": "train", "epoch": 117, "iter": 3600, "lr": 0.0115, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.42984, "top5_acc": 0.68688, "loss_cls": 3.22568, "loss": 3.22568, "time": 0.85721} +{"mode": "train", "epoch": 117, "iter": 3700, "lr": 0.01148, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.42828, "top5_acc": 0.67922, "loss_cls": 3.24717, "loss": 3.24717, "time": 0.85995} +{"mode": "val", "epoch": 117, "iter": 309, "lr": 0.01147, "top1_acc": 0.36458, "top5_acc": 0.6149, "mean_class_accuracy": 0.36436} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.01146, "memory": 15990, "data_time": 1.55285, "top1_acc": 0.44312, "top5_acc": 0.70453, "loss_cls": 3.09836, "loss": 3.09836, "time": 2.59818} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.01144, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.43719, "top5_acc": 0.70203, "loss_cls": 3.15512, "loss": 3.15512, "time": 0.8559} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.01142, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.44891, "top5_acc": 0.69828, "loss_cls": 3.1243, "loss": 3.1243, "time": 0.85395} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.0114, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43094, "top5_acc": 0.69906, "loss_cls": 3.16653, "loss": 3.16653, "time": 0.85208} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.01139, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43297, "top5_acc": 0.69453, "loss_cls": 3.18635, "loss": 3.18635, "time": 0.85478} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.01137, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43594, "top5_acc": 0.69969, "loss_cls": 3.14735, "loss": 3.14735, "time": 0.85724} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.01135, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43562, "top5_acc": 0.69984, "loss_cls": 3.17037, "loss": 3.17037, "time": 0.85711} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.01133, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43812, "top5_acc": 0.69484, "loss_cls": 3.15136, "loss": 3.15136, "time": 0.85611} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.01131, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43062, "top5_acc": 0.69422, "loss_cls": 3.17353, "loss": 3.17353, "time": 0.85299} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.0113, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43469, "top5_acc": 0.68969, "loss_cls": 3.18546, "loss": 3.18546, "time": 0.85719} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.01128, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43938, "top5_acc": 0.69453, "loss_cls": 3.16999, "loss": 3.16999, "time": 0.85882} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.01126, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43344, "top5_acc": 0.69281, "loss_cls": 3.1666, "loss": 3.1666, "time": 0.8601} +{"mode": "train", "epoch": 118, "iter": 1300, "lr": 0.01124, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.43547, "top5_acc": 0.68953, "loss_cls": 3.18968, "loss": 3.18968, "time": 0.85148} +{"mode": "train", "epoch": 118, "iter": 1400, "lr": 0.01123, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43688, "top5_acc": 0.69344, "loss_cls": 3.17077, "loss": 3.17077, "time": 0.85181} +{"mode": "train", "epoch": 118, "iter": 1500, "lr": 0.01121, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44078, "top5_acc": 0.695, "loss_cls": 3.12833, "loss": 3.12833, "time": 0.85679} +{"mode": "train", "epoch": 118, "iter": 1600, "lr": 0.01119, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43578, "top5_acc": 0.69453, "loss_cls": 3.17883, "loss": 3.17883, "time": 0.85623} +{"mode": "train", "epoch": 118, "iter": 1700, "lr": 0.01117, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43172, "top5_acc": 0.69703, "loss_cls": 3.17006, "loss": 3.17006, "time": 0.85207} +{"mode": "train", "epoch": 118, "iter": 1800, "lr": 0.01116, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.42781, "top5_acc": 0.68281, "loss_cls": 3.22243, "loss": 3.22243, "time": 0.85049} +{"mode": "train", "epoch": 118, "iter": 1900, "lr": 0.01114, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42828, "top5_acc": 0.68, "loss_cls": 3.21493, "loss": 3.21493, "time": 0.85397} +{"mode": "train", "epoch": 118, "iter": 2000, "lr": 0.01112, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42609, "top5_acc": 0.69141, "loss_cls": 3.18695, "loss": 3.18695, "time": 0.8508} +{"mode": "train", "epoch": 118, "iter": 2100, "lr": 0.0111, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42953, "top5_acc": 0.68359, "loss_cls": 3.20418, "loss": 3.20418, "time": 0.85178} +{"mode": "train", "epoch": 118, "iter": 2200, "lr": 0.01109, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43812, "top5_acc": 0.69469, "loss_cls": 3.16842, "loss": 3.16842, "time": 0.85322} +{"mode": "train", "epoch": 118, "iter": 2300, "lr": 0.01107, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44703, "top5_acc": 0.69797, "loss_cls": 3.13501, "loss": 3.13501, "time": 0.85377} +{"mode": "train", "epoch": 118, "iter": 2400, "lr": 0.01105, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.4225, "top5_acc": 0.68125, "loss_cls": 3.20856, "loss": 3.20856, "time": 0.84974} +{"mode": "train", "epoch": 118, "iter": 2500, "lr": 0.01103, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42656, "top5_acc": 0.68422, "loss_cls": 3.20773, "loss": 3.20773, "time": 0.8547} +{"mode": "train", "epoch": 118, "iter": 2600, "lr": 0.01102, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42859, "top5_acc": 0.68094, "loss_cls": 3.21411, "loss": 3.21411, "time": 0.85452} +{"mode": "train", "epoch": 118, "iter": 2700, "lr": 0.011, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43703, "top5_acc": 0.68703, "loss_cls": 3.20216, "loss": 3.20216, "time": 0.85373} +{"mode": "train", "epoch": 118, "iter": 2800, "lr": 0.01098, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42781, "top5_acc": 0.68844, "loss_cls": 3.21127, "loss": 3.21127, "time": 0.85196} +{"mode": "train", "epoch": 118, "iter": 2900, "lr": 0.01096, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43266, "top5_acc": 0.68641, "loss_cls": 3.19611, "loss": 3.19611, "time": 0.85193} +{"mode": "train", "epoch": 118, "iter": 3000, "lr": 0.01095, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.43969, "top5_acc": 0.69094, "loss_cls": 3.17655, "loss": 3.17655, "time": 0.85757} +{"mode": "train", "epoch": 118, "iter": 3100, "lr": 0.01093, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.43266, "top5_acc": 0.68641, "loss_cls": 3.21425, "loss": 3.21425, "time": 0.85389} +{"mode": "train", "epoch": 118, "iter": 3200, "lr": 0.01091, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43344, "top5_acc": 0.68312, "loss_cls": 3.21358, "loss": 3.21358, "time": 0.84901} +{"mode": "train", "epoch": 118, "iter": 3300, "lr": 0.01089, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43391, "top5_acc": 0.67844, "loss_cls": 3.23889, "loss": 3.23889, "time": 0.84983} +{"mode": "train", "epoch": 118, "iter": 3400, "lr": 0.01088, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42531, "top5_acc": 0.68156, "loss_cls": 3.23023, "loss": 3.23023, "time": 0.85424} +{"mode": "train", "epoch": 118, "iter": 3500, "lr": 0.01086, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42609, "top5_acc": 0.67953, "loss_cls": 3.23103, "loss": 3.23103, "time": 0.86092} +{"mode": "train", "epoch": 118, "iter": 3600, "lr": 0.01084, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.41594, "top5_acc": 0.68172, "loss_cls": 3.25855, "loss": 3.25855, "time": 0.85644} +{"mode": "train", "epoch": 118, "iter": 3700, "lr": 0.01082, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41797, "top5_acc": 0.68, "loss_cls": 3.25655, "loss": 3.25655, "time": 0.85847} +{"mode": "val", "epoch": 118, "iter": 309, "lr": 0.01082, "top1_acc": 0.36924, "top5_acc": 0.62422, "mean_class_accuracy": 0.36892} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.0108, "memory": 15990, "data_time": 1.5317, "top1_acc": 0.44688, "top5_acc": 0.70047, "loss_cls": 3.09497, "loss": 3.09497, "time": 2.5614} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.01078, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43625, "top5_acc": 0.69531, "loss_cls": 3.13173, "loss": 3.13173, "time": 0.85205} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.01076, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43844, "top5_acc": 0.70359, "loss_cls": 3.11354, "loss": 3.11354, "time": 0.85485} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.01075, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43969, "top5_acc": 0.69844, "loss_cls": 3.15598, "loss": 3.15598, "time": 0.85563} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.01073, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.445, "top5_acc": 0.71234, "loss_cls": 3.09498, "loss": 3.09498, "time": 0.85559} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.01071, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44406, "top5_acc": 0.70125, "loss_cls": 3.10934, "loss": 3.10934, "time": 0.8559} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.01069, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43547, "top5_acc": 0.69812, "loss_cls": 3.14361, "loss": 3.14361, "time": 0.85547} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.01068, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.435, "top5_acc": 0.70219, "loss_cls": 3.14659, "loss": 3.14659, "time": 0.85729} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.01066, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44719, "top5_acc": 0.70641, "loss_cls": 3.10559, "loss": 3.10559, "time": 0.85328} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.01064, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44156, "top5_acc": 0.69531, "loss_cls": 3.16335, "loss": 3.16335, "time": 0.85343} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.01063, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43188, "top5_acc": 0.69344, "loss_cls": 3.17269, "loss": 3.17269, "time": 0.85509} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.01061, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.44172, "top5_acc": 0.69859, "loss_cls": 3.12272, "loss": 3.12272, "time": 0.85599} +{"mode": "train", "epoch": 119, "iter": 1300, "lr": 0.01059, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.43844, "top5_acc": 0.70234, "loss_cls": 3.13723, "loss": 3.13723, "time": 0.85654} +{"mode": "train", "epoch": 119, "iter": 1400, "lr": 0.01057, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42938, "top5_acc": 0.68906, "loss_cls": 3.22193, "loss": 3.22193, "time": 0.85251} +{"mode": "train", "epoch": 119, "iter": 1500, "lr": 0.01056, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43359, "top5_acc": 0.68844, "loss_cls": 3.17357, "loss": 3.17357, "time": 0.84803} +{"mode": "train", "epoch": 119, "iter": 1600, "lr": 0.01054, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42594, "top5_acc": 0.68641, "loss_cls": 3.19584, "loss": 3.19584, "time": 0.85421} +{"mode": "train", "epoch": 119, "iter": 1700, "lr": 0.01052, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42, "top5_acc": 0.68578, "loss_cls": 3.2289, "loss": 3.2289, "time": 0.85331} +{"mode": "train", "epoch": 119, "iter": 1800, "lr": 0.0105, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.43969, "top5_acc": 0.69375, "loss_cls": 3.13026, "loss": 3.13026, "time": 0.8536} +{"mode": "train", "epoch": 119, "iter": 1900, "lr": 0.01049, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43234, "top5_acc": 0.68578, "loss_cls": 3.21798, "loss": 3.21798, "time": 0.85329} +{"mode": "train", "epoch": 119, "iter": 2000, "lr": 0.01047, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.43188, "top5_acc": 0.69359, "loss_cls": 3.15786, "loss": 3.15786, "time": 0.85222} +{"mode": "train", "epoch": 119, "iter": 2100, "lr": 0.01045, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43734, "top5_acc": 0.69031, "loss_cls": 3.17293, "loss": 3.17293, "time": 0.85064} +{"mode": "train", "epoch": 119, "iter": 2200, "lr": 0.01044, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.44797, "top5_acc": 0.69828, "loss_cls": 3.12766, "loss": 3.12766, "time": 0.85136} +{"mode": "train", "epoch": 119, "iter": 2300, "lr": 0.01042, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44703, "top5_acc": 0.70266, "loss_cls": 3.13547, "loss": 3.13547, "time": 0.85247} +{"mode": "train", "epoch": 119, "iter": 2400, "lr": 0.0104, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43719, "top5_acc": 0.69891, "loss_cls": 3.15107, "loss": 3.15107, "time": 0.84979} +{"mode": "train", "epoch": 119, "iter": 2500, "lr": 0.01039, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.425, "top5_acc": 0.68156, "loss_cls": 3.23779, "loss": 3.23779, "time": 0.85419} +{"mode": "train", "epoch": 119, "iter": 2600, "lr": 0.01037, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43656, "top5_acc": 0.695, "loss_cls": 3.16822, "loss": 3.16822, "time": 0.85736} +{"mode": "train", "epoch": 119, "iter": 2700, "lr": 0.01035, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.4325, "top5_acc": 0.69547, "loss_cls": 3.17428, "loss": 3.17428, "time": 0.85414} +{"mode": "train", "epoch": 119, "iter": 2800, "lr": 0.01033, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44141, "top5_acc": 0.69062, "loss_cls": 3.18687, "loss": 3.18687, "time": 0.85265} +{"mode": "train", "epoch": 119, "iter": 2900, "lr": 0.01032, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42734, "top5_acc": 0.69141, "loss_cls": 3.24382, "loss": 3.24382, "time": 0.85112} +{"mode": "train", "epoch": 119, "iter": 3000, "lr": 0.0103, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.4425, "top5_acc": 0.68828, "loss_cls": 3.18131, "loss": 3.18131, "time": 0.85081} +{"mode": "train", "epoch": 119, "iter": 3100, "lr": 0.01028, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44359, "top5_acc": 0.69672, "loss_cls": 3.12946, "loss": 3.12946, "time": 0.85651} +{"mode": "train", "epoch": 119, "iter": 3200, "lr": 0.01027, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42938, "top5_acc": 0.68969, "loss_cls": 3.21211, "loss": 3.21211, "time": 0.85389} +{"mode": "train", "epoch": 119, "iter": 3300, "lr": 0.01025, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.43672, "top5_acc": 0.68188, "loss_cls": 3.21225, "loss": 3.21225, "time": 0.85067} +{"mode": "train", "epoch": 119, "iter": 3400, "lr": 0.01023, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43984, "top5_acc": 0.69422, "loss_cls": 3.12164, "loss": 3.12164, "time": 0.85215} +{"mode": "train", "epoch": 119, "iter": 3500, "lr": 0.01022, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42016, "top5_acc": 0.67719, "loss_cls": 3.26196, "loss": 3.26196, "time": 0.85053} +{"mode": "train", "epoch": 119, "iter": 3600, "lr": 0.0102, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43375, "top5_acc": 0.68531, "loss_cls": 3.2271, "loss": 3.2271, "time": 0.85642} +{"mode": "train", "epoch": 119, "iter": 3700, "lr": 0.01018, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44531, "top5_acc": 0.69719, "loss_cls": 3.13776, "loss": 3.13776, "time": 0.85436} +{"mode": "val", "epoch": 119, "iter": 309, "lr": 0.01017, "top1_acc": 0.36965, "top5_acc": 0.62361, "mean_class_accuracy": 0.36939} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.01016, "memory": 15990, "data_time": 1.53292, "top1_acc": 0.43984, "top5_acc": 0.70594, "loss_cls": 3.11527, "loss": 3.11527, "time": 2.56312} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.01014, "memory": 15990, "data_time": 0.00068, "top1_acc": 0.46203, "top5_acc": 0.71094, "loss_cls": 3.06598, "loss": 3.06598, "time": 0.85792} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.01012, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.44891, "top5_acc": 0.70781, "loss_cls": 3.08572, "loss": 3.08572, "time": 0.85633} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.01011, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44953, "top5_acc": 0.70469, "loss_cls": 3.10125, "loss": 3.10125, "time": 0.85448} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.01009, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45219, "top5_acc": 0.70891, "loss_cls": 3.0779, "loss": 3.0779, "time": 0.85507} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.01007, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45828, "top5_acc": 0.70219, "loss_cls": 3.08294, "loss": 3.08294, "time": 0.85117} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.01006, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.44859, "top5_acc": 0.69875, "loss_cls": 3.10308, "loss": 3.10308, "time": 0.85176} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.01004, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.44391, "top5_acc": 0.70328, "loss_cls": 3.12244, "loss": 3.12244, "time": 0.86074} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.01002, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.43188, "top5_acc": 0.68984, "loss_cls": 3.15209, "loss": 3.15209, "time": 0.85967} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.01001, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.44406, "top5_acc": 0.70047, "loss_cls": 3.11514, "loss": 3.11514, "time": 0.85817} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00999, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44172, "top5_acc": 0.70406, "loss_cls": 3.13696, "loss": 3.13696, "time": 0.85794} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.00997, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43641, "top5_acc": 0.69578, "loss_cls": 3.17087, "loss": 3.17087, "time": 0.85759} +{"mode": "train", "epoch": 120, "iter": 1300, "lr": 0.00996, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43531, "top5_acc": 0.70078, "loss_cls": 3.13956, "loss": 3.13956, "time": 0.85636} +{"mode": "train", "epoch": 120, "iter": 1400, "lr": 0.00994, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44453, "top5_acc": 0.71266, "loss_cls": 3.09247, "loss": 3.09247, "time": 0.85816} +{"mode": "train", "epoch": 120, "iter": 1500, "lr": 0.00992, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.44125, "top5_acc": 0.69328, "loss_cls": 3.15717, "loss": 3.15717, "time": 0.85338} +{"mode": "train", "epoch": 120, "iter": 1600, "lr": 0.0099, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.43094, "top5_acc": 0.70031, "loss_cls": 3.14054, "loss": 3.14054, "time": 0.86182} +{"mode": "train", "epoch": 120, "iter": 1700, "lr": 0.00989, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43734, "top5_acc": 0.70531, "loss_cls": 3.115, "loss": 3.115, "time": 0.85322} +{"mode": "train", "epoch": 120, "iter": 1800, "lr": 0.00987, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44297, "top5_acc": 0.70297, "loss_cls": 3.12586, "loss": 3.12586, "time": 0.85534} +{"mode": "train", "epoch": 120, "iter": 1900, "lr": 0.00985, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43453, "top5_acc": 0.69359, "loss_cls": 3.17036, "loss": 3.17036, "time": 0.85284} +{"mode": "train", "epoch": 120, "iter": 2000, "lr": 0.00984, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.43859, "top5_acc": 0.68844, "loss_cls": 3.18292, "loss": 3.18292, "time": 0.85494} +{"mode": "train", "epoch": 120, "iter": 2100, "lr": 0.00982, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44344, "top5_acc": 0.7075, "loss_cls": 3.10802, "loss": 3.10802, "time": 0.85601} +{"mode": "train", "epoch": 120, "iter": 2200, "lr": 0.0098, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.42797, "top5_acc": 0.68484, "loss_cls": 3.22451, "loss": 3.22451, "time": 0.85277} +{"mode": "train", "epoch": 120, "iter": 2300, "lr": 0.00979, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.43547, "top5_acc": 0.69406, "loss_cls": 3.18904, "loss": 3.18904, "time": 0.85661} +{"mode": "train", "epoch": 120, "iter": 2400, "lr": 0.00977, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.44609, "top5_acc": 0.70094, "loss_cls": 3.10283, "loss": 3.10283, "time": 0.85459} +{"mode": "train", "epoch": 120, "iter": 2500, "lr": 0.00976, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.43125, "top5_acc": 0.69688, "loss_cls": 3.134, "loss": 3.134, "time": 0.85542} +{"mode": "train", "epoch": 120, "iter": 2600, "lr": 0.00974, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.43984, "top5_acc": 0.67766, "loss_cls": 3.21244, "loss": 3.21244, "time": 0.86029} +{"mode": "train", "epoch": 120, "iter": 2700, "lr": 0.00972, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45031, "top5_acc": 0.70547, "loss_cls": 3.09297, "loss": 3.09297, "time": 0.86059} +{"mode": "train", "epoch": 120, "iter": 2800, "lr": 0.00971, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44562, "top5_acc": 0.695, "loss_cls": 3.14361, "loss": 3.14361, "time": 0.86034} +{"mode": "train", "epoch": 120, "iter": 2900, "lr": 0.00969, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.44719, "top5_acc": 0.69453, "loss_cls": 3.13031, "loss": 3.13031, "time": 0.86164} +{"mode": "train", "epoch": 120, "iter": 3000, "lr": 0.00967, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.42562, "top5_acc": 0.69484, "loss_cls": 3.16079, "loss": 3.16079, "time": 0.86422} +{"mode": "train", "epoch": 120, "iter": 3100, "lr": 0.00966, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.43656, "top5_acc": 0.69484, "loss_cls": 3.13171, "loss": 3.13171, "time": 0.86192} +{"mode": "train", "epoch": 120, "iter": 3200, "lr": 0.00964, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.44594, "top5_acc": 0.69938, "loss_cls": 3.13511, "loss": 3.13511, "time": 0.85472} +{"mode": "train", "epoch": 120, "iter": 3300, "lr": 0.00962, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.43516, "top5_acc": 0.68781, "loss_cls": 3.18749, "loss": 3.18749, "time": 0.85553} +{"mode": "train", "epoch": 120, "iter": 3400, "lr": 0.00961, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.43047, "top5_acc": 0.68453, "loss_cls": 3.20404, "loss": 3.20404, "time": 0.85876} +{"mode": "train", "epoch": 120, "iter": 3500, "lr": 0.00959, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.43031, "top5_acc": 0.68703, "loss_cls": 3.19567, "loss": 3.19567, "time": 0.85942} +{"mode": "train", "epoch": 120, "iter": 3600, "lr": 0.00957, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.43641, "top5_acc": 0.69516, "loss_cls": 3.14496, "loss": 3.14496, "time": 0.86225} +{"mode": "train", "epoch": 120, "iter": 3700, "lr": 0.00956, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.44125, "top5_acc": 0.70016, "loss_cls": 3.10248, "loss": 3.10248, "time": 0.85861} +{"mode": "val", "epoch": 120, "iter": 309, "lr": 0.00955, "top1_acc": 0.37634, "top5_acc": 0.63025, "mean_class_accuracy": 0.3762} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00953, "memory": 15990, "data_time": 1.57543, "top1_acc": 0.45938, "top5_acc": 0.71219, "loss_cls": 3.01798, "loss": 3.01798, "time": 2.60175} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00952, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.45344, "top5_acc": 0.70203, "loss_cls": 3.06228, "loss": 3.06228, "time": 0.84952} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.0095, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.45641, "top5_acc": 0.70875, "loss_cls": 3.06201, "loss": 3.06201, "time": 0.85523} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00948, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44, "top5_acc": 0.705, "loss_cls": 3.12648, "loss": 3.12648, "time": 0.85895} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00947, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45391, "top5_acc": 0.71078, "loss_cls": 3.07964, "loss": 3.07964, "time": 0.85902} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00945, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46219, "top5_acc": 0.70766, "loss_cls": 3.04722, "loss": 3.04722, "time": 0.85314} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.00943, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.44719, "top5_acc": 0.70188, "loss_cls": 3.1152, "loss": 3.1152, "time": 0.86088} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00942, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.44984, "top5_acc": 0.70969, "loss_cls": 3.06912, "loss": 3.06912, "time": 0.85563} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.0094, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.43828, "top5_acc": 0.70125, "loss_cls": 3.12036, "loss": 3.12036, "time": 0.85817} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00939, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.45812, "top5_acc": 0.70875, "loss_cls": 3.07547, "loss": 3.07547, "time": 0.85592} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00937, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.44844, "top5_acc": 0.70094, "loss_cls": 3.11117, "loss": 3.11117, "time": 0.85614} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00935, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.43953, "top5_acc": 0.69672, "loss_cls": 3.15219, "loss": 3.15219, "time": 0.85701} +{"mode": "train", "epoch": 121, "iter": 1300, "lr": 0.00934, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.44578, "top5_acc": 0.69625, "loss_cls": 3.11169, "loss": 3.11169, "time": 0.86076} +{"mode": "train", "epoch": 121, "iter": 1400, "lr": 0.00932, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45531, "top5_acc": 0.71359, "loss_cls": 3.04072, "loss": 3.04072, "time": 0.84985} +{"mode": "train", "epoch": 121, "iter": 1500, "lr": 0.0093, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44297, "top5_acc": 0.70234, "loss_cls": 3.10313, "loss": 3.10313, "time": 0.84911} +{"mode": "train", "epoch": 121, "iter": 1600, "lr": 0.00929, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44328, "top5_acc": 0.69969, "loss_cls": 3.13313, "loss": 3.13313, "time": 0.85341} +{"mode": "train", "epoch": 121, "iter": 1700, "lr": 0.00927, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.44344, "top5_acc": 0.70375, "loss_cls": 3.12189, "loss": 3.12189, "time": 0.8529} +{"mode": "train", "epoch": 121, "iter": 1800, "lr": 0.00926, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44344, "top5_acc": 0.69562, "loss_cls": 3.11048, "loss": 3.11048, "time": 0.85137} +{"mode": "train", "epoch": 121, "iter": 1900, "lr": 0.00924, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43078, "top5_acc": 0.7, "loss_cls": 3.15432, "loss": 3.15432, "time": 0.85082} +{"mode": "train", "epoch": 121, "iter": 2000, "lr": 0.00922, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44516, "top5_acc": 0.69547, "loss_cls": 3.13289, "loss": 3.13289, "time": 0.84587} +{"mode": "train", "epoch": 121, "iter": 2100, "lr": 0.00921, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43719, "top5_acc": 0.70828, "loss_cls": 3.10341, "loss": 3.10341, "time": 0.8462} +{"mode": "train", "epoch": 121, "iter": 2200, "lr": 0.00919, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43953, "top5_acc": 0.69094, "loss_cls": 3.1455, "loss": 3.1455, "time": 0.85299} +{"mode": "train", "epoch": 121, "iter": 2300, "lr": 0.00917, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45109, "top5_acc": 0.71203, "loss_cls": 3.0502, "loss": 3.0502, "time": 0.85691} +{"mode": "train", "epoch": 121, "iter": 2400, "lr": 0.00916, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44312, "top5_acc": 0.70047, "loss_cls": 3.13062, "loss": 3.13062, "time": 0.85151} +{"mode": "train", "epoch": 121, "iter": 2500, "lr": 0.00914, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44203, "top5_acc": 0.69484, "loss_cls": 3.13427, "loss": 3.13427, "time": 0.84623} +{"mode": "train", "epoch": 121, "iter": 2600, "lr": 0.00913, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.43672, "top5_acc": 0.70016, "loss_cls": 3.13058, "loss": 3.13058, "time": 0.85} +{"mode": "train", "epoch": 121, "iter": 2700, "lr": 0.00911, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44656, "top5_acc": 0.70266, "loss_cls": 3.1202, "loss": 3.1202, "time": 0.85139} +{"mode": "train", "epoch": 121, "iter": 2800, "lr": 0.00909, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43922, "top5_acc": 0.70125, "loss_cls": 3.16203, "loss": 3.16203, "time": 0.85343} +{"mode": "train", "epoch": 121, "iter": 2900, "lr": 0.00908, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.42766, "top5_acc": 0.68641, "loss_cls": 3.18296, "loss": 3.18296, "time": 0.85758} +{"mode": "train", "epoch": 121, "iter": 3000, "lr": 0.00906, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44219, "top5_acc": 0.70203, "loss_cls": 3.11129, "loss": 3.11129, "time": 0.84695} +{"mode": "train", "epoch": 121, "iter": 3100, "lr": 0.00905, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44328, "top5_acc": 0.69953, "loss_cls": 3.1233, "loss": 3.1233, "time": 0.8507} +{"mode": "train", "epoch": 121, "iter": 3200, "lr": 0.00903, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43984, "top5_acc": 0.69812, "loss_cls": 3.14629, "loss": 3.14629, "time": 0.85908} +{"mode": "train", "epoch": 121, "iter": 3300, "lr": 0.00901, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.44219, "top5_acc": 0.70875, "loss_cls": 3.09876, "loss": 3.09876, "time": 0.85333} +{"mode": "train", "epoch": 121, "iter": 3400, "lr": 0.009, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.43438, "top5_acc": 0.69203, "loss_cls": 3.16056, "loss": 3.16056, "time": 0.85019} +{"mode": "train", "epoch": 121, "iter": 3500, "lr": 0.00898, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.43625, "top5_acc": 0.69984, "loss_cls": 3.13704, "loss": 3.13704, "time": 0.85074} +{"mode": "train", "epoch": 121, "iter": 3600, "lr": 0.00897, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.44625, "top5_acc": 0.69328, "loss_cls": 3.11671, "loss": 3.11671, "time": 0.85386} +{"mode": "train", "epoch": 121, "iter": 3700, "lr": 0.00895, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.43359, "top5_acc": 0.69281, "loss_cls": 3.17151, "loss": 3.17151, "time": 0.85577} +{"mode": "val", "epoch": 121, "iter": 309, "lr": 0.00894, "top1_acc": 0.37856, "top5_acc": 0.63466, "mean_class_accuracy": 0.37839} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00893, "memory": 15990, "data_time": 1.58689, "top1_acc": 0.46141, "top5_acc": 0.72, "loss_cls": 3.00323, "loss": 3.00323, "time": 2.61626} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00891, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.46359, "top5_acc": 0.71984, "loss_cls": 3.00815, "loss": 3.00815, "time": 0.8486} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.00889, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45203, "top5_acc": 0.70812, "loss_cls": 3.08323, "loss": 3.08323, "time": 0.85251} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00888, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45328, "top5_acc": 0.70719, "loss_cls": 3.04089, "loss": 3.04089, "time": 0.85527} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00886, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.4475, "top5_acc": 0.71234, "loss_cls": 3.05724, "loss": 3.05724, "time": 0.85498} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00885, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45375, "top5_acc": 0.71266, "loss_cls": 3.02982, "loss": 3.02982, "time": 0.85259} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00883, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45812, "top5_acc": 0.70656, "loss_cls": 3.04505, "loss": 3.04505, "time": 0.85687} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00882, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45547, "top5_acc": 0.71422, "loss_cls": 3.03988, "loss": 3.03988, "time": 0.8569} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.0088, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44984, "top5_acc": 0.70516, "loss_cls": 3.11613, "loss": 3.11613, "time": 0.85345} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00878, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.45109, "top5_acc": 0.71141, "loss_cls": 3.04022, "loss": 3.04022, "time": 0.8567} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00877, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44844, "top5_acc": 0.70719, "loss_cls": 3.0696, "loss": 3.0696, "time": 0.85262} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.00875, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46094, "top5_acc": 0.70656, "loss_cls": 3.05795, "loss": 3.05795, "time": 0.85003} +{"mode": "train", "epoch": 122, "iter": 1300, "lr": 0.00874, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.44172, "top5_acc": 0.70484, "loss_cls": 3.08792, "loss": 3.08792, "time": 0.85069} +{"mode": "train", "epoch": 122, "iter": 1400, "lr": 0.00872, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.4525, "top5_acc": 0.70734, "loss_cls": 3.07547, "loss": 3.07547, "time": 0.85149} +{"mode": "train", "epoch": 122, "iter": 1500, "lr": 0.0087, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.45734, "top5_acc": 0.71062, "loss_cls": 3.05708, "loss": 3.05708, "time": 0.85433} +{"mode": "train", "epoch": 122, "iter": 1600, "lr": 0.00869, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44984, "top5_acc": 0.70578, "loss_cls": 3.08324, "loss": 3.08324, "time": 0.85937} +{"mode": "train", "epoch": 122, "iter": 1700, "lr": 0.00867, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.43562, "top5_acc": 0.69547, "loss_cls": 3.13146, "loss": 3.13146, "time": 0.85694} +{"mode": "train", "epoch": 122, "iter": 1800, "lr": 0.00866, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45, "top5_acc": 0.70469, "loss_cls": 3.0801, "loss": 3.0801, "time": 0.84987} +{"mode": "train", "epoch": 122, "iter": 1900, "lr": 0.00864, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44984, "top5_acc": 0.70312, "loss_cls": 3.08935, "loss": 3.08935, "time": 0.8592} +{"mode": "train", "epoch": 122, "iter": 2000, "lr": 0.00863, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44062, "top5_acc": 0.70062, "loss_cls": 3.13361, "loss": 3.13361, "time": 0.84717} +{"mode": "train", "epoch": 122, "iter": 2100, "lr": 0.00861, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45016, "top5_acc": 0.69734, "loss_cls": 3.12582, "loss": 3.12582, "time": 0.85109} +{"mode": "train", "epoch": 122, "iter": 2200, "lr": 0.00859, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45891, "top5_acc": 0.70922, "loss_cls": 3.06746, "loss": 3.06746, "time": 0.85267} +{"mode": "train", "epoch": 122, "iter": 2300, "lr": 0.00858, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44469, "top5_acc": 0.70469, "loss_cls": 3.113, "loss": 3.113, "time": 0.85147} +{"mode": "train", "epoch": 122, "iter": 2400, "lr": 0.00856, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45062, "top5_acc": 0.70172, "loss_cls": 3.10256, "loss": 3.10256, "time": 0.85289} +{"mode": "train", "epoch": 122, "iter": 2500, "lr": 0.00855, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.43953, "top5_acc": 0.68969, "loss_cls": 3.16857, "loss": 3.16857, "time": 0.85554} +{"mode": "train", "epoch": 122, "iter": 2600, "lr": 0.00853, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44984, "top5_acc": 0.705, "loss_cls": 3.10805, "loss": 3.10805, "time": 0.85123} +{"mode": "train", "epoch": 122, "iter": 2700, "lr": 0.00852, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44828, "top5_acc": 0.70031, "loss_cls": 3.12926, "loss": 3.12926, "time": 0.85233} +{"mode": "train", "epoch": 122, "iter": 2800, "lr": 0.0085, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.445, "top5_acc": 0.69328, "loss_cls": 3.13893, "loss": 3.13893, "time": 0.85141} +{"mode": "train", "epoch": 122, "iter": 2900, "lr": 0.00849, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.44844, "top5_acc": 0.71188, "loss_cls": 3.08944, "loss": 3.08944, "time": 0.85008} +{"mode": "train", "epoch": 122, "iter": 3000, "lr": 0.00847, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45469, "top5_acc": 0.70578, "loss_cls": 3.06481, "loss": 3.06481, "time": 0.85277} +{"mode": "train", "epoch": 122, "iter": 3100, "lr": 0.00845, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45344, "top5_acc": 0.70141, "loss_cls": 3.08749, "loss": 3.08749, "time": 0.85045} +{"mode": "train", "epoch": 122, "iter": 3200, "lr": 0.00844, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.44516, "top5_acc": 0.69969, "loss_cls": 3.10298, "loss": 3.10298, "time": 0.85268} +{"mode": "train", "epoch": 122, "iter": 3300, "lr": 0.00842, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44875, "top5_acc": 0.71219, "loss_cls": 3.07061, "loss": 3.07061, "time": 0.84907} +{"mode": "train", "epoch": 122, "iter": 3400, "lr": 0.00841, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45172, "top5_acc": 0.70016, "loss_cls": 3.11485, "loss": 3.11485, "time": 0.85165} +{"mode": "train", "epoch": 122, "iter": 3500, "lr": 0.00839, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.44734, "top5_acc": 0.70422, "loss_cls": 3.09075, "loss": 3.09075, "time": 0.85389} +{"mode": "train", "epoch": 122, "iter": 3600, "lr": 0.00838, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.44828, "top5_acc": 0.70125, "loss_cls": 3.09544, "loss": 3.09544, "time": 0.85461} +{"mode": "train", "epoch": 122, "iter": 3700, "lr": 0.00836, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44141, "top5_acc": 0.68969, "loss_cls": 3.17094, "loss": 3.17094, "time": 0.85237} +{"mode": "val", "epoch": 122, "iter": 309, "lr": 0.00835, "top1_acc": 0.38165, "top5_acc": 0.63521, "mean_class_accuracy": 0.38134} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00834, "memory": 15990, "data_time": 1.50553, "top1_acc": 0.46625, "top5_acc": 0.73359, "loss_cls": 2.93943, "loss": 2.93943, "time": 2.53465} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00832, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45422, "top5_acc": 0.71422, "loss_cls": 3.05702, "loss": 3.05702, "time": 0.86064} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00831, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.46109, "top5_acc": 0.715, "loss_cls": 3.0039, "loss": 3.0039, "time": 0.86054} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00829, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45484, "top5_acc": 0.71047, "loss_cls": 3.06378, "loss": 3.06378, "time": 0.85673} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00828, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.47172, "top5_acc": 0.72062, "loss_cls": 3.00006, "loss": 3.00006, "time": 0.85905} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00826, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46938, "top5_acc": 0.72281, "loss_cls": 2.96914, "loss": 2.96914, "time": 0.86275} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00825, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.46, "top5_acc": 0.71625, "loss_cls": 3.00846, "loss": 3.00846, "time": 0.85986} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.00823, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.44969, "top5_acc": 0.70859, "loss_cls": 3.07997, "loss": 3.07997, "time": 0.86281} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00822, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.45547, "top5_acc": 0.71438, "loss_cls": 3.07947, "loss": 3.07947, "time": 0.86171} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.0082, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.45375, "top5_acc": 0.71484, "loss_cls": 3.03854, "loss": 3.03854, "time": 0.86033} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00818, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.45031, "top5_acc": 0.70469, "loss_cls": 3.08592, "loss": 3.08592, "time": 0.86406} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00817, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.45109, "top5_acc": 0.70484, "loss_cls": 3.0713, "loss": 3.0713, "time": 0.85741} +{"mode": "train", "epoch": 123, "iter": 1300, "lr": 0.00815, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44391, "top5_acc": 0.69969, "loss_cls": 3.11433, "loss": 3.11433, "time": 0.8512} +{"mode": "train", "epoch": 123, "iter": 1400, "lr": 0.00814, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.45594, "top5_acc": 0.71141, "loss_cls": 3.04282, "loss": 3.04282, "time": 0.85053} +{"mode": "train", "epoch": 123, "iter": 1500, "lr": 0.00812, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45266, "top5_acc": 0.70422, "loss_cls": 3.076, "loss": 3.076, "time": 0.84992} +{"mode": "train", "epoch": 123, "iter": 1600, "lr": 0.00811, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45859, "top5_acc": 0.71484, "loss_cls": 3.03029, "loss": 3.03029, "time": 0.85453} +{"mode": "train", "epoch": 123, "iter": 1700, "lr": 0.00809, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44984, "top5_acc": 0.70922, "loss_cls": 3.06935, "loss": 3.06935, "time": 0.84798} +{"mode": "train", "epoch": 123, "iter": 1800, "lr": 0.00808, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.44797, "top5_acc": 0.70094, "loss_cls": 3.10933, "loss": 3.10933, "time": 0.85369} +{"mode": "train", "epoch": 123, "iter": 1900, "lr": 0.00806, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46016, "top5_acc": 0.71047, "loss_cls": 3.02632, "loss": 3.02632, "time": 0.84724} +{"mode": "train", "epoch": 123, "iter": 2000, "lr": 0.00805, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45469, "top5_acc": 0.70562, "loss_cls": 3.05232, "loss": 3.05232, "time": 0.85057} +{"mode": "train", "epoch": 123, "iter": 2100, "lr": 0.00803, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45422, "top5_acc": 0.71797, "loss_cls": 3.03288, "loss": 3.03288, "time": 0.85337} +{"mode": "train", "epoch": 123, "iter": 2200, "lr": 0.00802, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45688, "top5_acc": 0.70891, "loss_cls": 3.03708, "loss": 3.03708, "time": 0.8511} +{"mode": "train", "epoch": 123, "iter": 2300, "lr": 0.008, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45859, "top5_acc": 0.71859, "loss_cls": 3.01493, "loss": 3.01493, "time": 0.85225} +{"mode": "train", "epoch": 123, "iter": 2400, "lr": 0.00799, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44906, "top5_acc": 0.70859, "loss_cls": 3.08331, "loss": 3.08331, "time": 0.84914} +{"mode": "train", "epoch": 123, "iter": 2500, "lr": 0.00797, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45922, "top5_acc": 0.70766, "loss_cls": 3.08566, "loss": 3.08566, "time": 0.85297} +{"mode": "train", "epoch": 123, "iter": 2600, "lr": 0.00796, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45203, "top5_acc": 0.70094, "loss_cls": 3.07862, "loss": 3.07862, "time": 0.85114} +{"mode": "train", "epoch": 123, "iter": 2700, "lr": 0.00794, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46078, "top5_acc": 0.70453, "loss_cls": 3.07073, "loss": 3.07073, "time": 0.85472} +{"mode": "train", "epoch": 123, "iter": 2800, "lr": 0.00793, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45219, "top5_acc": 0.71219, "loss_cls": 3.06421, "loss": 3.06421, "time": 0.85341} +{"mode": "train", "epoch": 123, "iter": 2900, "lr": 0.00791, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.44953, "top5_acc": 0.70453, "loss_cls": 3.08384, "loss": 3.08384, "time": 0.84892} +{"mode": "train", "epoch": 123, "iter": 3000, "lr": 0.0079, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.44766, "top5_acc": 0.70734, "loss_cls": 3.06467, "loss": 3.06467, "time": 0.84963} +{"mode": "train", "epoch": 123, "iter": 3100, "lr": 0.00788, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45266, "top5_acc": 0.70484, "loss_cls": 3.07346, "loss": 3.07346, "time": 0.85125} +{"mode": "train", "epoch": 123, "iter": 3200, "lr": 0.00787, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45422, "top5_acc": 0.71672, "loss_cls": 3.01343, "loss": 3.01343, "time": 0.85063} +{"mode": "train", "epoch": 123, "iter": 3300, "lr": 0.00785, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44234, "top5_acc": 0.69531, "loss_cls": 3.14527, "loss": 3.14527, "time": 0.84854} +{"mode": "train", "epoch": 123, "iter": 3400, "lr": 0.00784, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45344, "top5_acc": 0.70266, "loss_cls": 3.06635, "loss": 3.06635, "time": 0.85113} +{"mode": "train", "epoch": 123, "iter": 3500, "lr": 0.00782, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45766, "top5_acc": 0.71422, "loss_cls": 3.04845, "loss": 3.04845, "time": 0.84483} +{"mode": "train", "epoch": 123, "iter": 3600, "lr": 0.00781, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45438, "top5_acc": 0.69969, "loss_cls": 3.09153, "loss": 3.09153, "time": 0.85099} +{"mode": "train", "epoch": 123, "iter": 3700, "lr": 0.00779, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44453, "top5_acc": 0.70109, "loss_cls": 3.11147, "loss": 3.11147, "time": 0.84937} +{"mode": "val", "epoch": 123, "iter": 309, "lr": 0.00778, "top1_acc": 0.38616, "top5_acc": 0.63785, "mean_class_accuracy": 0.38591} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00777, "memory": 15990, "data_time": 1.5089, "top1_acc": 0.47594, "top5_acc": 0.72656, "loss_cls": 2.9741, "loss": 2.9741, "time": 2.53449} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00775, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46406, "top5_acc": 0.71953, "loss_cls": 2.96066, "loss": 2.96066, "time": 0.84729} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00774, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46875, "top5_acc": 0.72859, "loss_cls": 2.96425, "loss": 2.96425, "time": 0.84961} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.00772, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47078, "top5_acc": 0.71969, "loss_cls": 3.00158, "loss": 3.00158, "time": 0.84806} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00771, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46062, "top5_acc": 0.71578, "loss_cls": 3.0229, "loss": 3.0229, "time": 0.84994} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00769, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46078, "top5_acc": 0.71344, "loss_cls": 3.01471, "loss": 3.01471, "time": 0.84812} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00768, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46, "top5_acc": 0.71969, "loss_cls": 2.98802, "loss": 2.98802, "time": 0.85225} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00766, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46438, "top5_acc": 0.72547, "loss_cls": 3.00506, "loss": 3.00506, "time": 0.84883} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00765, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45703, "top5_acc": 0.72078, "loss_cls": 3.01763, "loss": 3.01763, "time": 0.85329} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00763, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45375, "top5_acc": 0.71, "loss_cls": 3.0433, "loss": 3.0433, "time": 0.85387} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00762, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.46781, "top5_acc": 0.70875, "loss_cls": 3.03317, "loss": 3.03317, "time": 0.85071} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.0076, "memory": 15990, "data_time": 0.00074, "top1_acc": 0.45859, "top5_acc": 0.71578, "loss_cls": 3.02376, "loss": 3.02376, "time": 0.84955} +{"mode": "train", "epoch": 124, "iter": 1300, "lr": 0.00759, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46531, "top5_acc": 0.72297, "loss_cls": 2.97404, "loss": 2.97404, "time": 0.84861} +{"mode": "train", "epoch": 124, "iter": 1400, "lr": 0.00758, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46281, "top5_acc": 0.71469, "loss_cls": 3.02598, "loss": 3.02598, "time": 0.84814} +{"mode": "train", "epoch": 124, "iter": 1500, "lr": 0.00756, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45766, "top5_acc": 0.70766, "loss_cls": 3.0396, "loss": 3.0396, "time": 0.85143} +{"mode": "train", "epoch": 124, "iter": 1600, "lr": 0.00755, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44875, "top5_acc": 0.70156, "loss_cls": 3.09092, "loss": 3.09092, "time": 0.85247} +{"mode": "train", "epoch": 124, "iter": 1700, "lr": 0.00753, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45188, "top5_acc": 0.70891, "loss_cls": 3.05236, "loss": 3.05236, "time": 0.8525} +{"mode": "train", "epoch": 124, "iter": 1800, "lr": 0.00752, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45031, "top5_acc": 0.71016, "loss_cls": 3.06412, "loss": 3.06412, "time": 0.85314} +{"mode": "train", "epoch": 124, "iter": 1900, "lr": 0.0075, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45859, "top5_acc": 0.70547, "loss_cls": 3.06918, "loss": 3.06918, "time": 0.85724} +{"mode": "train", "epoch": 124, "iter": 2000, "lr": 0.00749, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.4575, "top5_acc": 0.71812, "loss_cls": 3.0339, "loss": 3.0339, "time": 0.85406} +{"mode": "train", "epoch": 124, "iter": 2100, "lr": 0.00747, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46141, "top5_acc": 0.71406, "loss_cls": 3.0341, "loss": 3.0341, "time": 0.84739} +{"mode": "train", "epoch": 124, "iter": 2200, "lr": 0.00746, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45812, "top5_acc": 0.71781, "loss_cls": 3.02936, "loss": 3.02936, "time": 0.84958} +{"mode": "train", "epoch": 124, "iter": 2300, "lr": 0.00744, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45297, "top5_acc": 0.70734, "loss_cls": 3.04788, "loss": 3.04788, "time": 0.85182} +{"mode": "train", "epoch": 124, "iter": 2400, "lr": 0.00743, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45703, "top5_acc": 0.70828, "loss_cls": 3.06737, "loss": 3.06737, "time": 0.85309} +{"mode": "train", "epoch": 124, "iter": 2500, "lr": 0.00741, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.46219, "top5_acc": 0.71766, "loss_cls": 3.01333, "loss": 3.01333, "time": 0.851} +{"mode": "train", "epoch": 124, "iter": 2600, "lr": 0.0074, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46359, "top5_acc": 0.71891, "loss_cls": 2.96948, "loss": 2.96948, "time": 0.84966} +{"mode": "train", "epoch": 124, "iter": 2700, "lr": 0.00738, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47031, "top5_acc": 0.72156, "loss_cls": 2.99305, "loss": 2.99305, "time": 0.84796} +{"mode": "train", "epoch": 124, "iter": 2800, "lr": 0.00737, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45281, "top5_acc": 0.70906, "loss_cls": 3.06686, "loss": 3.06686, "time": 0.84879} +{"mode": "train", "epoch": 124, "iter": 2900, "lr": 0.00735, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45938, "top5_acc": 0.71156, "loss_cls": 3.04354, "loss": 3.04354, "time": 0.8523} +{"mode": "train", "epoch": 124, "iter": 3000, "lr": 0.00734, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45078, "top5_acc": 0.70828, "loss_cls": 3.07885, "loss": 3.07885, "time": 0.8473} +{"mode": "train", "epoch": 124, "iter": 3100, "lr": 0.00733, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45766, "top5_acc": 0.70828, "loss_cls": 3.05256, "loss": 3.05256, "time": 0.84585} +{"mode": "train", "epoch": 124, "iter": 3200, "lr": 0.00731, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46453, "top5_acc": 0.71734, "loss_cls": 3.00561, "loss": 3.00561, "time": 0.8499} +{"mode": "train", "epoch": 124, "iter": 3300, "lr": 0.0073, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45281, "top5_acc": 0.70875, "loss_cls": 3.07213, "loss": 3.07213, "time": 0.85077} +{"mode": "train", "epoch": 124, "iter": 3400, "lr": 0.00728, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.44984, "top5_acc": 0.71062, "loss_cls": 3.04258, "loss": 3.04258, "time": 0.8501} +{"mode": "train", "epoch": 124, "iter": 3500, "lr": 0.00727, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.45812, "top5_acc": 0.70734, "loss_cls": 3.06015, "loss": 3.06015, "time": 0.85191} +{"mode": "train", "epoch": 124, "iter": 3600, "lr": 0.00725, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45047, "top5_acc": 0.70312, "loss_cls": 3.07462, "loss": 3.07462, "time": 0.8481} +{"mode": "train", "epoch": 124, "iter": 3700, "lr": 0.00724, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46672, "top5_acc": 0.71531, "loss_cls": 2.99713, "loss": 2.99713, "time": 0.84868} +{"mode": "val", "epoch": 124, "iter": 309, "lr": 0.00723, "top1_acc": 0.38956, "top5_acc": 0.64549, "mean_class_accuracy": 0.38933} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.00722, "memory": 15990, "data_time": 1.46169, "top1_acc": 0.47438, "top5_acc": 0.73281, "loss_cls": 2.9278, "loss": 2.9278, "time": 2.48359} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.0072, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47297, "top5_acc": 0.72781, "loss_cls": 2.94468, "loss": 2.94468, "time": 0.85167} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00719, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46641, "top5_acc": 0.72219, "loss_cls": 2.99629, "loss": 2.99629, "time": 0.85081} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00717, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47531, "top5_acc": 0.72953, "loss_cls": 2.92342, "loss": 2.92342, "time": 0.8497} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00716, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47234, "top5_acc": 0.72328, "loss_cls": 2.98226, "loss": 2.98226, "time": 0.84856} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00715, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.46969, "top5_acc": 0.72828, "loss_cls": 2.94689, "loss": 2.94689, "time": 0.8582} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00713, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.47297, "top5_acc": 0.72344, "loss_cls": 2.9555, "loss": 2.9555, "time": 0.8576} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00712, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.46547, "top5_acc": 0.72203, "loss_cls": 2.99109, "loss": 2.99109, "time": 0.85381} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.0071, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47109, "top5_acc": 0.72125, "loss_cls": 2.98386, "loss": 2.98386, "time": 0.85206} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.00709, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47344, "top5_acc": 0.72438, "loss_cls": 2.9604, "loss": 2.9604, "time": 0.85229} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00707, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47062, "top5_acc": 0.72312, "loss_cls": 2.96435, "loss": 2.96435, "time": 0.85215} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00706, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.47453, "top5_acc": 0.72109, "loss_cls": 2.97242, "loss": 2.97242, "time": 0.8584} +{"mode": "train", "epoch": 125, "iter": 1300, "lr": 0.00704, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.46109, "top5_acc": 0.71219, "loss_cls": 3.03525, "loss": 3.03525, "time": 0.84797} +{"mode": "train", "epoch": 125, "iter": 1400, "lr": 0.00703, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.46984, "top5_acc": 0.72125, "loss_cls": 2.97522, "loss": 2.97522, "time": 0.84902} +{"mode": "train", "epoch": 125, "iter": 1500, "lr": 0.00702, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44688, "top5_acc": 0.71266, "loss_cls": 3.06384, "loss": 3.06384, "time": 0.84872} +{"mode": "train", "epoch": 125, "iter": 1600, "lr": 0.007, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46422, "top5_acc": 0.71875, "loss_cls": 2.97955, "loss": 2.97955, "time": 0.85119} +{"mode": "train", "epoch": 125, "iter": 1700, "lr": 0.00699, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.45719, "top5_acc": 0.71781, "loss_cls": 3.02328, "loss": 3.02328, "time": 0.85056} +{"mode": "train", "epoch": 125, "iter": 1800, "lr": 0.00697, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.46516, "top5_acc": 0.71672, "loss_cls": 2.98938, "loss": 2.98938, "time": 0.85016} +{"mode": "train", "epoch": 125, "iter": 1900, "lr": 0.00696, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47328, "top5_acc": 0.72141, "loss_cls": 2.9897, "loss": 2.9897, "time": 0.84696} +{"mode": "train", "epoch": 125, "iter": 2000, "lr": 0.00694, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46078, "top5_acc": 0.71391, "loss_cls": 3.02965, "loss": 3.02965, "time": 0.84903} +{"mode": "train", "epoch": 125, "iter": 2100, "lr": 0.00693, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46219, "top5_acc": 0.71453, "loss_cls": 3.02038, "loss": 3.02038, "time": 0.84782} +{"mode": "train", "epoch": 125, "iter": 2200, "lr": 0.00692, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45984, "top5_acc": 0.71859, "loss_cls": 3.01012, "loss": 3.01012, "time": 0.85097} +{"mode": "train", "epoch": 125, "iter": 2300, "lr": 0.0069, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46141, "top5_acc": 0.71281, "loss_cls": 3.03505, "loss": 3.03505, "time": 0.84807} +{"mode": "train", "epoch": 125, "iter": 2400, "lr": 0.00689, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45578, "top5_acc": 0.71375, "loss_cls": 3.02045, "loss": 3.02045, "time": 0.84597} +{"mode": "train", "epoch": 125, "iter": 2500, "lr": 0.00687, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46938, "top5_acc": 0.71875, "loss_cls": 2.98651, "loss": 2.98651, "time": 0.84772} +{"mode": "train", "epoch": 125, "iter": 2600, "lr": 0.00686, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45688, "top5_acc": 0.71312, "loss_cls": 3.02453, "loss": 3.02453, "time": 0.8548} +{"mode": "train", "epoch": 125, "iter": 2700, "lr": 0.00685, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.46984, "top5_acc": 0.72688, "loss_cls": 2.97276, "loss": 2.97276, "time": 0.84975} +{"mode": "train", "epoch": 125, "iter": 2800, "lr": 0.00683, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45891, "top5_acc": 0.71422, "loss_cls": 3.01879, "loss": 3.01879, "time": 0.84894} +{"mode": "train", "epoch": 125, "iter": 2900, "lr": 0.00682, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45797, "top5_acc": 0.72094, "loss_cls": 2.99522, "loss": 2.99522, "time": 0.84833} +{"mode": "train", "epoch": 125, "iter": 3000, "lr": 0.0068, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45828, "top5_acc": 0.72453, "loss_cls": 3.00483, "loss": 3.00483, "time": 0.84987} +{"mode": "train", "epoch": 125, "iter": 3100, "lr": 0.00679, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46562, "top5_acc": 0.71984, "loss_cls": 3.00295, "loss": 3.00295, "time": 0.8463} +{"mode": "train", "epoch": 125, "iter": 3200, "lr": 0.00678, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.45828, "top5_acc": 0.71438, "loss_cls": 3.0468, "loss": 3.0468, "time": 0.8483} +{"mode": "train", "epoch": 125, "iter": 3300, "lr": 0.00676, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.4625, "top5_acc": 0.72109, "loss_cls": 3.01016, "loss": 3.01016, "time": 0.84857} +{"mode": "train", "epoch": 125, "iter": 3400, "lr": 0.00675, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45984, "top5_acc": 0.71125, "loss_cls": 3.02617, "loss": 3.02617, "time": 0.84913} +{"mode": "train", "epoch": 125, "iter": 3500, "lr": 0.00673, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46859, "top5_acc": 0.71, "loss_cls": 3.01132, "loss": 3.01132, "time": 0.852} +{"mode": "train", "epoch": 125, "iter": 3600, "lr": 0.00672, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.45766, "top5_acc": 0.71406, "loss_cls": 3.05529, "loss": 3.05529, "time": 0.85208} +{"mode": "train", "epoch": 125, "iter": 3700, "lr": 0.00671, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46641, "top5_acc": 0.71938, "loss_cls": 3.01005, "loss": 3.01005, "time": 0.85032} +{"mode": "val", "epoch": 125, "iter": 309, "lr": 0.0067, "top1_acc": 0.39102, "top5_acc": 0.64742, "mean_class_accuracy": 0.39072} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00668, "memory": 15990, "data_time": 1.44119, "top1_acc": 0.48031, "top5_acc": 0.73344, "loss_cls": 2.91853, "loss": 2.91853, "time": 2.4634} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00667, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.49, "top5_acc": 0.73812, "loss_cls": 2.87375, "loss": 2.87375, "time": 0.84824} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00666, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.4825, "top5_acc": 0.73078, "loss_cls": 2.89437, "loss": 2.89437, "time": 0.85315} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00664, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47766, "top5_acc": 0.73516, "loss_cls": 2.90219, "loss": 2.90219, "time": 0.85097} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00663, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.46922, "top5_acc": 0.72594, "loss_cls": 2.93475, "loss": 2.93475, "time": 0.84804} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00662, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.475, "top5_acc": 0.72469, "loss_cls": 2.96095, "loss": 2.96095, "time": 0.85511} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0066, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46359, "top5_acc": 0.72516, "loss_cls": 2.95672, "loss": 2.95672, "time": 0.85942} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00659, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46703, "top5_acc": 0.71969, "loss_cls": 2.97295, "loss": 2.97295, "time": 0.85434} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00657, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47938, "top5_acc": 0.73625, "loss_cls": 2.92118, "loss": 2.92118, "time": 0.85438} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00656, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46859, "top5_acc": 0.72281, "loss_cls": 2.95876, "loss": 2.95876, "time": 0.85608} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00655, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47531, "top5_acc": 0.73203, "loss_cls": 2.9377, "loss": 2.9377, "time": 0.85326} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00653, "memory": 15990, "data_time": 0.00068, "top1_acc": 0.47188, "top5_acc": 0.72266, "loss_cls": 2.94217, "loss": 2.94217, "time": 0.85365} +{"mode": "train", "epoch": 126, "iter": 1300, "lr": 0.00652, "memory": 15990, "data_time": 0.00076, "top1_acc": 0.46109, "top5_acc": 0.71172, "loss_cls": 3.00779, "loss": 3.00779, "time": 0.84453} +{"mode": "train", "epoch": 126, "iter": 1400, "lr": 0.0065, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47125, "top5_acc": 0.72141, "loss_cls": 2.9815, "loss": 2.9815, "time": 0.84986} +{"mode": "train", "epoch": 126, "iter": 1500, "lr": 0.00649, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46672, "top5_acc": 0.72812, "loss_cls": 2.9454, "loss": 2.9454, "time": 0.85121} +{"mode": "train", "epoch": 126, "iter": 1600, "lr": 0.00648, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.47812, "top5_acc": 0.72859, "loss_cls": 2.95679, "loss": 2.95679, "time": 0.84851} +{"mode": "train", "epoch": 126, "iter": 1700, "lr": 0.00646, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.47469, "top5_acc": 0.73062, "loss_cls": 2.94074, "loss": 2.94074, "time": 0.85159} +{"mode": "train", "epoch": 126, "iter": 1800, "lr": 0.00645, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.46484, "top5_acc": 0.71656, "loss_cls": 3.00091, "loss": 3.00091, "time": 0.85071} +{"mode": "train", "epoch": 126, "iter": 1900, "lr": 0.00644, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.48688, "top5_acc": 0.72484, "loss_cls": 2.9524, "loss": 2.9524, "time": 0.85037} +{"mode": "train", "epoch": 126, "iter": 2000, "lr": 0.00642, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47, "top5_acc": 0.72922, "loss_cls": 2.9624, "loss": 2.9624, "time": 0.85578} +{"mode": "train", "epoch": 126, "iter": 2100, "lr": 0.00641, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46625, "top5_acc": 0.7225, "loss_cls": 2.97823, "loss": 2.97823, "time": 0.85125} +{"mode": "train", "epoch": 126, "iter": 2200, "lr": 0.00639, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47109, "top5_acc": 0.72141, "loss_cls": 2.949, "loss": 2.949, "time": 0.85142} +{"mode": "train", "epoch": 126, "iter": 2300, "lr": 0.00638, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46688, "top5_acc": 0.72094, "loss_cls": 2.98742, "loss": 2.98742, "time": 0.85709} +{"mode": "train", "epoch": 126, "iter": 2400, "lr": 0.00637, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46297, "top5_acc": 0.72031, "loss_cls": 3.00228, "loss": 3.00228, "time": 0.84833} +{"mode": "train", "epoch": 126, "iter": 2500, "lr": 0.00635, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47562, "top5_acc": 0.72, "loss_cls": 2.97516, "loss": 2.97516, "time": 0.84421} +{"mode": "train", "epoch": 126, "iter": 2600, "lr": 0.00634, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.45953, "top5_acc": 0.71109, "loss_cls": 3.01171, "loss": 3.01171, "time": 0.848} +{"mode": "train", "epoch": 126, "iter": 2700, "lr": 0.00633, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46047, "top5_acc": 0.71094, "loss_cls": 3.02161, "loss": 3.02161, "time": 0.85241} +{"mode": "train", "epoch": 126, "iter": 2800, "lr": 0.00631, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47406, "top5_acc": 0.72188, "loss_cls": 2.96888, "loss": 2.96888, "time": 0.85305} +{"mode": "train", "epoch": 126, "iter": 2900, "lr": 0.0063, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47062, "top5_acc": 0.72484, "loss_cls": 2.97959, "loss": 2.97959, "time": 0.85287} +{"mode": "train", "epoch": 126, "iter": 3000, "lr": 0.00629, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47109, "top5_acc": 0.72578, "loss_cls": 2.96104, "loss": 2.96104, "time": 0.85253} +{"mode": "train", "epoch": 126, "iter": 3100, "lr": 0.00627, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.45203, "top5_acc": 0.71656, "loss_cls": 3.03915, "loss": 3.03915, "time": 0.85012} +{"mode": "train", "epoch": 126, "iter": 3200, "lr": 0.00626, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47094, "top5_acc": 0.73094, "loss_cls": 2.95447, "loss": 2.95447, "time": 0.85204} +{"mode": "train", "epoch": 126, "iter": 3300, "lr": 0.00625, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46844, "top5_acc": 0.71312, "loss_cls": 3.01736, "loss": 3.01736, "time": 0.84947} +{"mode": "train", "epoch": 126, "iter": 3400, "lr": 0.00623, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.46703, "top5_acc": 0.72141, "loss_cls": 2.98835, "loss": 2.98835, "time": 0.85081} +{"mode": "train", "epoch": 126, "iter": 3500, "lr": 0.00622, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.4725, "top5_acc": 0.72484, "loss_cls": 2.96641, "loss": 2.96641, "time": 0.84883} +{"mode": "train", "epoch": 126, "iter": 3600, "lr": 0.0062, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46016, "top5_acc": 0.71328, "loss_cls": 3.04013, "loss": 3.04013, "time": 0.85086} +{"mode": "train", "epoch": 126, "iter": 3700, "lr": 0.00619, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46312, "top5_acc": 0.72109, "loss_cls": 3.01655, "loss": 3.01655, "time": 0.84852} +{"mode": "val", "epoch": 126, "iter": 309, "lr": 0.00618, "top1_acc": 0.39325, "top5_acc": 0.64924, "mean_class_accuracy": 0.39295} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00617, "memory": 15990, "data_time": 1.46899, "top1_acc": 0.50141, "top5_acc": 0.74484, "loss_cls": 2.81563, "loss": 2.81563, "time": 2.494} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00616, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.48422, "top5_acc": 0.7475, "loss_cls": 2.84625, "loss": 2.84625, "time": 0.85295} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00614, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48438, "top5_acc": 0.73891, "loss_cls": 2.88348, "loss": 2.88348, "time": 0.84849} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00613, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49062, "top5_acc": 0.74156, "loss_cls": 2.85407, "loss": 2.85407, "time": 0.85053} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.00612, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47781, "top5_acc": 0.73266, "loss_cls": 2.91785, "loss": 2.91785, "time": 0.84478} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.0061, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47328, "top5_acc": 0.72969, "loss_cls": 2.94177, "loss": 2.94177, "time": 0.84496} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00609, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48312, "top5_acc": 0.73281, "loss_cls": 2.9115, "loss": 2.9115, "time": 0.85182} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00608, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47781, "top5_acc": 0.73406, "loss_cls": 2.91646, "loss": 2.91646, "time": 0.84883} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00606, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46641, "top5_acc": 0.72188, "loss_cls": 2.96782, "loss": 2.96782, "time": 0.84807} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00605, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47781, "top5_acc": 0.71984, "loss_cls": 2.94237, "loss": 2.94237, "time": 0.85134} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00604, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.46719, "top5_acc": 0.725, "loss_cls": 2.95532, "loss": 2.95532, "time": 0.84932} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00602, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.47734, "top5_acc": 0.73141, "loss_cls": 2.92757, "loss": 2.92757, "time": 0.85534} +{"mode": "train", "epoch": 127, "iter": 1300, "lr": 0.00601, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.47609, "top5_acc": 0.73312, "loss_cls": 2.89276, "loss": 2.89276, "time": 0.84993} +{"mode": "train", "epoch": 127, "iter": 1400, "lr": 0.006, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46906, "top5_acc": 0.72828, "loss_cls": 2.96151, "loss": 2.96151, "time": 0.85228} +{"mode": "train", "epoch": 127, "iter": 1500, "lr": 0.00598, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47266, "top5_acc": 0.72375, "loss_cls": 2.95084, "loss": 2.95084, "time": 0.84736} +{"mode": "train", "epoch": 127, "iter": 1600, "lr": 0.00597, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46953, "top5_acc": 0.72688, "loss_cls": 2.92765, "loss": 2.92765, "time": 0.85473} +{"mode": "train", "epoch": 127, "iter": 1700, "lr": 0.00596, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.47469, "top5_acc": 0.7325, "loss_cls": 2.90849, "loss": 2.90849, "time": 0.85225} +{"mode": "train", "epoch": 127, "iter": 1800, "lr": 0.00594, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48531, "top5_acc": 0.73703, "loss_cls": 2.90159, "loss": 2.90159, "time": 0.84947} +{"mode": "train", "epoch": 127, "iter": 1900, "lr": 0.00593, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46422, "top5_acc": 0.71469, "loss_cls": 2.99953, "loss": 2.99953, "time": 0.84903} +{"mode": "train", "epoch": 127, "iter": 2000, "lr": 0.00592, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48328, "top5_acc": 0.71859, "loss_cls": 2.95533, "loss": 2.95533, "time": 0.84924} +{"mode": "train", "epoch": 127, "iter": 2100, "lr": 0.00591, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.47547, "top5_acc": 0.72797, "loss_cls": 2.93716, "loss": 2.93716, "time": 0.84871} +{"mode": "train", "epoch": 127, "iter": 2200, "lr": 0.00589, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47125, "top5_acc": 0.72062, "loss_cls": 2.97809, "loss": 2.97809, "time": 0.85364} +{"mode": "train", "epoch": 127, "iter": 2300, "lr": 0.00588, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.47547, "top5_acc": 0.72875, "loss_cls": 2.92655, "loss": 2.92655, "time": 0.85145} +{"mode": "train", "epoch": 127, "iter": 2400, "lr": 0.00587, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47797, "top5_acc": 0.72562, "loss_cls": 2.94995, "loss": 2.94995, "time": 0.84792} +{"mode": "train", "epoch": 127, "iter": 2500, "lr": 0.00585, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46234, "top5_acc": 0.71594, "loss_cls": 2.99388, "loss": 2.99388, "time": 0.84702} +{"mode": "train", "epoch": 127, "iter": 2600, "lr": 0.00584, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47172, "top5_acc": 0.72859, "loss_cls": 2.97309, "loss": 2.97309, "time": 0.85534} +{"mode": "train", "epoch": 127, "iter": 2700, "lr": 0.00583, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47, "top5_acc": 0.72547, "loss_cls": 2.98093, "loss": 2.98093, "time": 0.84426} +{"mode": "train", "epoch": 127, "iter": 2800, "lr": 0.00581, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.48047, "top5_acc": 0.72812, "loss_cls": 2.92431, "loss": 2.92431, "time": 0.84895} +{"mode": "train", "epoch": 127, "iter": 2900, "lr": 0.0058, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48688, "top5_acc": 0.73312, "loss_cls": 2.89255, "loss": 2.89255, "time": 0.8523} +{"mode": "train", "epoch": 127, "iter": 3000, "lr": 0.00579, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46391, "top5_acc": 0.71516, "loss_cls": 3.01852, "loss": 3.01852, "time": 0.8499} +{"mode": "train", "epoch": 127, "iter": 3100, "lr": 0.00577, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46516, "top5_acc": 0.72328, "loss_cls": 2.97119, "loss": 2.97119, "time": 0.85011} +{"mode": "train", "epoch": 127, "iter": 3200, "lr": 0.00576, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.46875, "top5_acc": 0.72516, "loss_cls": 2.94756, "loss": 2.94756, "time": 0.84447} +{"mode": "train", "epoch": 127, "iter": 3300, "lr": 0.00575, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48328, "top5_acc": 0.72656, "loss_cls": 2.89675, "loss": 2.89675, "time": 0.84767} +{"mode": "train", "epoch": 127, "iter": 3400, "lr": 0.00573, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.46328, "top5_acc": 0.72031, "loss_cls": 2.98637, "loss": 2.98637, "time": 0.85047} +{"mode": "train", "epoch": 127, "iter": 3500, "lr": 0.00572, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47547, "top5_acc": 0.72547, "loss_cls": 2.91141, "loss": 2.91141, "time": 0.84654} +{"mode": "train", "epoch": 127, "iter": 3600, "lr": 0.00571, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47766, "top5_acc": 0.72266, "loss_cls": 2.923, "loss": 2.923, "time": 0.84787} +{"mode": "train", "epoch": 127, "iter": 3700, "lr": 0.0057, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.47047, "top5_acc": 0.72656, "loss_cls": 2.93228, "loss": 2.93228, "time": 0.85748} +{"mode": "val", "epoch": 127, "iter": 309, "lr": 0.00569, "top1_acc": 0.40176, "top5_acc": 0.65451, "mean_class_accuracy": 0.40143} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00568, "memory": 15990, "data_time": 1.46373, "top1_acc": 0.49562, "top5_acc": 0.74781, "loss_cls": 2.81537, "loss": 2.81537, "time": 2.48167} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.00566, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48562, "top5_acc": 0.73422, "loss_cls": 2.89873, "loss": 2.89873, "time": 0.85203} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00565, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.50156, "top5_acc": 0.74203, "loss_cls": 2.82022, "loss": 2.82022, "time": 0.85303} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00564, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48562, "top5_acc": 0.74234, "loss_cls": 2.86299, "loss": 2.86299, "time": 0.84896} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00563, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.49703, "top5_acc": 0.74031, "loss_cls": 2.85052, "loss": 2.85052, "time": 0.85394} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00561, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.48906, "top5_acc": 0.74578, "loss_cls": 2.8313, "loss": 2.8313, "time": 0.85249} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.0056, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.48609, "top5_acc": 0.73469, "loss_cls": 2.87115, "loss": 2.87115, "time": 0.85025} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00559, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48172, "top5_acc": 0.73312, "loss_cls": 2.91018, "loss": 2.91018, "time": 0.84985} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00557, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.48344, "top5_acc": 0.74125, "loss_cls": 2.88366, "loss": 2.88366, "time": 0.85098} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00556, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49234, "top5_acc": 0.73953, "loss_cls": 2.84656, "loss": 2.84656, "time": 0.85379} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00555, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49266, "top5_acc": 0.73562, "loss_cls": 2.87157, "loss": 2.87157, "time": 0.84844} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00554, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.48312, "top5_acc": 0.72984, "loss_cls": 2.93116, "loss": 2.93116, "time": 0.85343} +{"mode": "train", "epoch": 128, "iter": 1300, "lr": 0.00552, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47438, "top5_acc": 0.72781, "loss_cls": 2.9407, "loss": 2.9407, "time": 0.85129} +{"mode": "train", "epoch": 128, "iter": 1400, "lr": 0.00551, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.48438, "top5_acc": 0.73781, "loss_cls": 2.8744, "loss": 2.8744, "time": 0.85463} +{"mode": "train", "epoch": 128, "iter": 1500, "lr": 0.0055, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47625, "top5_acc": 0.72766, "loss_cls": 2.93101, "loss": 2.93101, "time": 0.84815} +{"mode": "train", "epoch": 128, "iter": 1600, "lr": 0.00548, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49422, "top5_acc": 0.73641, "loss_cls": 2.83732, "loss": 2.83732, "time": 0.85009} +{"mode": "train", "epoch": 128, "iter": 1700, "lr": 0.00547, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49219, "top5_acc": 0.73953, "loss_cls": 2.86506, "loss": 2.86506, "time": 0.84801} +{"mode": "train", "epoch": 128, "iter": 1800, "lr": 0.00546, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.48109, "top5_acc": 0.73688, "loss_cls": 2.91053, "loss": 2.91053, "time": 0.85397} +{"mode": "train", "epoch": 128, "iter": 1900, "lr": 0.00545, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48062, "top5_acc": 0.725, "loss_cls": 2.92217, "loss": 2.92217, "time": 0.84855} +{"mode": "train", "epoch": 128, "iter": 2000, "lr": 0.00543, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.4725, "top5_acc": 0.72766, "loss_cls": 2.96055, "loss": 2.96055, "time": 0.84622} +{"mode": "train", "epoch": 128, "iter": 2100, "lr": 0.00542, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47547, "top5_acc": 0.72859, "loss_cls": 2.93343, "loss": 2.93343, "time": 0.84987} +{"mode": "train", "epoch": 128, "iter": 2200, "lr": 0.00541, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47875, "top5_acc": 0.73203, "loss_cls": 2.90814, "loss": 2.90814, "time": 0.84922} +{"mode": "train", "epoch": 128, "iter": 2300, "lr": 0.0054, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48125, "top5_acc": 0.72438, "loss_cls": 2.93187, "loss": 2.93187, "time": 0.84704} +{"mode": "train", "epoch": 128, "iter": 2400, "lr": 0.00538, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47594, "top5_acc": 0.72969, "loss_cls": 2.92578, "loss": 2.92578, "time": 0.85114} +{"mode": "train", "epoch": 128, "iter": 2500, "lr": 0.00537, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47469, "top5_acc": 0.73156, "loss_cls": 2.91031, "loss": 2.91031, "time": 0.84703} +{"mode": "train", "epoch": 128, "iter": 2600, "lr": 0.00536, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.4825, "top5_acc": 0.74125, "loss_cls": 2.8828, "loss": 2.8828, "time": 0.8525} +{"mode": "train", "epoch": 128, "iter": 2700, "lr": 0.00535, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48, "top5_acc": 0.73719, "loss_cls": 2.89638, "loss": 2.89638, "time": 0.85475} +{"mode": "train", "epoch": 128, "iter": 2800, "lr": 0.00533, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47531, "top5_acc": 0.72469, "loss_cls": 2.93957, "loss": 2.93957, "time": 0.84895} +{"mode": "train", "epoch": 128, "iter": 2900, "lr": 0.00532, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47859, "top5_acc": 0.73281, "loss_cls": 2.91227, "loss": 2.91227, "time": 0.85395} +{"mode": "train", "epoch": 128, "iter": 3000, "lr": 0.00531, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.47578, "top5_acc": 0.725, "loss_cls": 2.94081, "loss": 2.94081, "time": 0.85674} +{"mode": "train", "epoch": 128, "iter": 3100, "lr": 0.0053, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48344, "top5_acc": 0.72641, "loss_cls": 2.92184, "loss": 2.92184, "time": 0.8607} +{"mode": "train", "epoch": 128, "iter": 3200, "lr": 0.00528, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.48, "top5_acc": 0.72344, "loss_cls": 2.9312, "loss": 2.9312, "time": 0.85937} +{"mode": "train", "epoch": 128, "iter": 3300, "lr": 0.00527, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47672, "top5_acc": 0.73422, "loss_cls": 2.89843, "loss": 2.89843, "time": 0.85465} +{"mode": "train", "epoch": 128, "iter": 3400, "lr": 0.00526, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.47125, "top5_acc": 0.72688, "loss_cls": 2.98354, "loss": 2.98354, "time": 0.85638} +{"mode": "train", "epoch": 128, "iter": 3500, "lr": 0.00525, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.4825, "top5_acc": 0.72375, "loss_cls": 2.92827, "loss": 2.92827, "time": 0.8599} +{"mode": "train", "epoch": 128, "iter": 3600, "lr": 0.00523, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47984, "top5_acc": 0.73156, "loss_cls": 2.92431, "loss": 2.92431, "time": 0.857} +{"mode": "train", "epoch": 128, "iter": 3700, "lr": 0.00522, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.46719, "top5_acc": 0.72375, "loss_cls": 2.97218, "loss": 2.97218, "time": 0.85394} +{"mode": "val", "epoch": 128, "iter": 309, "lr": 0.00521, "top1_acc": 0.40267, "top5_acc": 0.64985, "mean_class_accuracy": 0.4025} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.0052, "memory": 15990, "data_time": 1.50715, "top1_acc": 0.49844, "top5_acc": 0.75266, "loss_cls": 2.80332, "loss": 2.80332, "time": 2.54134} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00519, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.5, "top5_acc": 0.75594, "loss_cls": 2.79406, "loss": 2.79406, "time": 0.86351} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00518, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49672, "top5_acc": 0.74188, "loss_cls": 2.83811, "loss": 2.83811, "time": 0.86222} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00516, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.49203, "top5_acc": 0.74438, "loss_cls": 2.82215, "loss": 2.82215, "time": 0.86687} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00515, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.48547, "top5_acc": 0.74297, "loss_cls": 2.82274, "loss": 2.82274, "time": 0.8618} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00514, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.48062, "top5_acc": 0.74125, "loss_cls": 2.8744, "loss": 2.8744, "time": 0.85572} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00513, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.48922, "top5_acc": 0.74562, "loss_cls": 2.82897, "loss": 2.82897, "time": 0.86218} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00512, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48562, "top5_acc": 0.73359, "loss_cls": 2.88767, "loss": 2.88767, "time": 0.86113} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.0051, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48531, "top5_acc": 0.74156, "loss_cls": 2.86379, "loss": 2.86379, "time": 0.85776} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00509, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.50266, "top5_acc": 0.745, "loss_cls": 2.84209, "loss": 2.84209, "time": 0.86072} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00508, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49688, "top5_acc": 0.74297, "loss_cls": 2.83837, "loss": 2.83837, "time": 0.86736} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.00507, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.49672, "top5_acc": 0.75078, "loss_cls": 2.80754, "loss": 2.80754, "time": 0.86324} +{"mode": "train", "epoch": 129, "iter": 1300, "lr": 0.00505, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.49234, "top5_acc": 0.74422, "loss_cls": 2.84534, "loss": 2.84534, "time": 0.86123} +{"mode": "train", "epoch": 129, "iter": 1400, "lr": 0.00504, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.49562, "top5_acc": 0.74391, "loss_cls": 2.81641, "loss": 2.81641, "time": 0.85465} +{"mode": "train", "epoch": 129, "iter": 1500, "lr": 0.00503, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48375, "top5_acc": 0.73609, "loss_cls": 2.88587, "loss": 2.88587, "time": 0.86052} +{"mode": "train", "epoch": 129, "iter": 1600, "lr": 0.00502, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.47656, "top5_acc": 0.73938, "loss_cls": 2.8836, "loss": 2.8836, "time": 0.8597} +{"mode": "train", "epoch": 129, "iter": 1700, "lr": 0.00501, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.48484, "top5_acc": 0.7425, "loss_cls": 2.8305, "loss": 2.8305, "time": 0.85835} +{"mode": "train", "epoch": 129, "iter": 1800, "lr": 0.00499, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48422, "top5_acc": 0.73812, "loss_cls": 2.86403, "loss": 2.86403, "time": 0.8635} +{"mode": "train", "epoch": 129, "iter": 1900, "lr": 0.00498, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.48891, "top5_acc": 0.73656, "loss_cls": 2.88758, "loss": 2.88758, "time": 0.85686} +{"mode": "train", "epoch": 129, "iter": 2000, "lr": 0.00497, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.4875, "top5_acc": 0.74047, "loss_cls": 2.84509, "loss": 2.84509, "time": 0.86147} +{"mode": "train", "epoch": 129, "iter": 2100, "lr": 0.00496, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49, "top5_acc": 0.74672, "loss_cls": 2.83389, "loss": 2.83389, "time": 0.86691} +{"mode": "train", "epoch": 129, "iter": 2200, "lr": 0.00494, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.48641, "top5_acc": 0.74156, "loss_cls": 2.87339, "loss": 2.87339, "time": 0.86522} +{"mode": "train", "epoch": 129, "iter": 2300, "lr": 0.00493, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47594, "top5_acc": 0.73391, "loss_cls": 2.90214, "loss": 2.90214, "time": 0.85947} +{"mode": "train", "epoch": 129, "iter": 2400, "lr": 0.00492, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.48594, "top5_acc": 0.74016, "loss_cls": 2.86283, "loss": 2.86283, "time": 0.86071} +{"mode": "train", "epoch": 129, "iter": 2500, "lr": 0.00491, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.48953, "top5_acc": 0.73859, "loss_cls": 2.87633, "loss": 2.87633, "time": 0.86707} +{"mode": "train", "epoch": 129, "iter": 2600, "lr": 0.0049, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.48453, "top5_acc": 0.73547, "loss_cls": 2.90523, "loss": 2.90523, "time": 0.86221} +{"mode": "train", "epoch": 129, "iter": 2700, "lr": 0.00488, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.48609, "top5_acc": 0.74219, "loss_cls": 2.86689, "loss": 2.86689, "time": 0.85874} +{"mode": "train", "epoch": 129, "iter": 2800, "lr": 0.00487, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.47891, "top5_acc": 0.72531, "loss_cls": 2.90564, "loss": 2.90564, "time": 0.85638} +{"mode": "train", "epoch": 129, "iter": 2900, "lr": 0.00486, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49109, "top5_acc": 0.73688, "loss_cls": 2.86947, "loss": 2.86947, "time": 0.86689} +{"mode": "train", "epoch": 129, "iter": 3000, "lr": 0.00485, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.48656, "top5_acc": 0.73516, "loss_cls": 2.86852, "loss": 2.86852, "time": 0.86846} +{"mode": "train", "epoch": 129, "iter": 3100, "lr": 0.00484, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.48234, "top5_acc": 0.73766, "loss_cls": 2.88463, "loss": 2.88463, "time": 0.86621} +{"mode": "train", "epoch": 129, "iter": 3200, "lr": 0.00482, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.48062, "top5_acc": 0.72219, "loss_cls": 2.93361, "loss": 2.93361, "time": 0.87054} +{"mode": "train", "epoch": 129, "iter": 3300, "lr": 0.00481, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.47484, "top5_acc": 0.72688, "loss_cls": 2.93066, "loss": 2.93066, "time": 0.86882} +{"mode": "train", "epoch": 129, "iter": 3400, "lr": 0.0048, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.48031, "top5_acc": 0.72969, "loss_cls": 2.91072, "loss": 2.91072, "time": 0.87411} +{"mode": "train", "epoch": 129, "iter": 3500, "lr": 0.00479, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.49109, "top5_acc": 0.73672, "loss_cls": 2.86919, "loss": 2.86919, "time": 0.87199} +{"mode": "train", "epoch": 129, "iter": 3600, "lr": 0.00478, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.48562, "top5_acc": 0.73328, "loss_cls": 2.89619, "loss": 2.89619, "time": 0.86835} +{"mode": "train", "epoch": 129, "iter": 3700, "lr": 0.00476, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.4875, "top5_acc": 0.73297, "loss_cls": 2.89664, "loss": 2.89664, "time": 0.87341} +{"mode": "val", "epoch": 129, "iter": 309, "lr": 0.00476, "top1_acc": 0.41149, "top5_acc": 0.66256, "mean_class_accuracy": 0.41111} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00475, "memory": 15990, "data_time": 1.5174, "top1_acc": 0.50875, "top5_acc": 0.76078, "loss_cls": 2.73147, "loss": 2.73147, "time": 2.57649} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00473, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.51016, "top5_acc": 0.75438, "loss_cls": 2.7377, "loss": 2.7377, "time": 0.86647} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00472, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50328, "top5_acc": 0.76328, "loss_cls": 2.74371, "loss": 2.74371, "time": 0.86975} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00471, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50797, "top5_acc": 0.74359, "loss_cls": 2.81588, "loss": 2.81588, "time": 0.86839} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.0047, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49547, "top5_acc": 0.75078, "loss_cls": 2.82799, "loss": 2.82799, "time": 0.86885} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00469, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.49047, "top5_acc": 0.74359, "loss_cls": 2.82734, "loss": 2.82734, "time": 0.87382} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00468, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.50547, "top5_acc": 0.75594, "loss_cls": 2.7766, "loss": 2.7766, "time": 0.87535} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00466, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50578, "top5_acc": 0.75125, "loss_cls": 2.78359, "loss": 2.78359, "time": 0.8691} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00465, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.49484, "top5_acc": 0.74156, "loss_cls": 2.8407, "loss": 2.8407, "time": 0.87426} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.00464, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.48875, "top5_acc": 0.73562, "loss_cls": 2.87522, "loss": 2.87522, "time": 0.87557} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.00463, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.50016, "top5_acc": 0.75078, "loss_cls": 2.81573, "loss": 2.81573, "time": 0.86986} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00462, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.49953, "top5_acc": 0.74375, "loss_cls": 2.81294, "loss": 2.81294, "time": 0.86195} +{"mode": "train", "epoch": 130, "iter": 1300, "lr": 0.00461, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.49625, "top5_acc": 0.74422, "loss_cls": 2.82039, "loss": 2.82039, "time": 0.85429} +{"mode": "train", "epoch": 130, "iter": 1400, "lr": 0.00459, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.495, "top5_acc": 0.74734, "loss_cls": 2.803, "loss": 2.803, "time": 0.86024} +{"mode": "train", "epoch": 130, "iter": 1500, "lr": 0.00458, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.49547, "top5_acc": 0.74422, "loss_cls": 2.83536, "loss": 2.83536, "time": 0.85415} +{"mode": "train", "epoch": 130, "iter": 1600, "lr": 0.00457, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48766, "top5_acc": 0.74047, "loss_cls": 2.83911, "loss": 2.83911, "time": 0.85736} +{"mode": "train", "epoch": 130, "iter": 1700, "lr": 0.00456, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49109, "top5_acc": 0.74484, "loss_cls": 2.82756, "loss": 2.82756, "time": 0.8567} +{"mode": "train", "epoch": 130, "iter": 1800, "lr": 0.00455, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.48875, "top5_acc": 0.74172, "loss_cls": 2.86363, "loss": 2.86363, "time": 0.85796} +{"mode": "train", "epoch": 130, "iter": 1900, "lr": 0.00454, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.50922, "top5_acc": 0.75797, "loss_cls": 2.77254, "loss": 2.77254, "time": 0.86222} +{"mode": "train", "epoch": 130, "iter": 2000, "lr": 0.00452, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.48219, "top5_acc": 0.74344, "loss_cls": 2.86739, "loss": 2.86739, "time": 0.85614} +{"mode": "train", "epoch": 130, "iter": 2100, "lr": 0.00451, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.495, "top5_acc": 0.73938, "loss_cls": 2.84586, "loss": 2.84586, "time": 0.85795} +{"mode": "train", "epoch": 130, "iter": 2200, "lr": 0.0045, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48766, "top5_acc": 0.73484, "loss_cls": 2.85168, "loss": 2.85168, "time": 0.86773} +{"mode": "train", "epoch": 130, "iter": 2300, "lr": 0.00449, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.49281, "top5_acc": 0.74531, "loss_cls": 2.82488, "loss": 2.82488, "time": 0.86704} +{"mode": "train", "epoch": 130, "iter": 2400, "lr": 0.00448, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49266, "top5_acc": 0.7425, "loss_cls": 2.86328, "loss": 2.86328, "time": 0.86393} +{"mode": "train", "epoch": 130, "iter": 2500, "lr": 0.00447, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48734, "top5_acc": 0.74734, "loss_cls": 2.84913, "loss": 2.84913, "time": 0.86981} +{"mode": "train", "epoch": 130, "iter": 2600, "lr": 0.00445, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49125, "top5_acc": 0.74, "loss_cls": 2.84251, "loss": 2.84251, "time": 0.86366} +{"mode": "train", "epoch": 130, "iter": 2700, "lr": 0.00444, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49203, "top5_acc": 0.74203, "loss_cls": 2.85106, "loss": 2.85106, "time": 0.87175} +{"mode": "train", "epoch": 130, "iter": 2800, "lr": 0.00443, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48859, "top5_acc": 0.73875, "loss_cls": 2.86706, "loss": 2.86706, "time": 0.87006} +{"mode": "train", "epoch": 130, "iter": 2900, "lr": 0.00442, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.47906, "top5_acc": 0.73625, "loss_cls": 2.8898, "loss": 2.8898, "time": 0.87768} +{"mode": "train", "epoch": 130, "iter": 3000, "lr": 0.00441, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.49094, "top5_acc": 0.72891, "loss_cls": 2.87448, "loss": 2.87448, "time": 0.87596} +{"mode": "train", "epoch": 130, "iter": 3100, "lr": 0.0044, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.4875, "top5_acc": 0.73797, "loss_cls": 2.84738, "loss": 2.84738, "time": 0.8804} +{"mode": "train", "epoch": 130, "iter": 3200, "lr": 0.00439, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.49062, "top5_acc": 0.7375, "loss_cls": 2.82564, "loss": 2.82564, "time": 0.87815} +{"mode": "train", "epoch": 130, "iter": 3300, "lr": 0.00437, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.51031, "top5_acc": 0.745, "loss_cls": 2.80904, "loss": 2.80904, "time": 0.87901} +{"mode": "train", "epoch": 130, "iter": 3400, "lr": 0.00436, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.49344, "top5_acc": 0.74688, "loss_cls": 2.8213, "loss": 2.8213, "time": 0.87323} +{"mode": "train", "epoch": 130, "iter": 3500, "lr": 0.00435, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.4875, "top5_acc": 0.745, "loss_cls": 2.8786, "loss": 2.8786, "time": 0.87555} +{"mode": "train", "epoch": 130, "iter": 3600, "lr": 0.00434, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.49109, "top5_acc": 0.74047, "loss_cls": 2.83794, "loss": 2.83794, "time": 0.86931} +{"mode": "train", "epoch": 130, "iter": 3700, "lr": 0.00433, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.48562, "top5_acc": 0.74016, "loss_cls": 2.87102, "loss": 2.87102, "time": 0.88099} +{"mode": "val", "epoch": 130, "iter": 309, "lr": 0.00432, "top1_acc": 0.4084, "top5_acc": 0.66246, "mean_class_accuracy": 0.4082} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00431, "memory": 15990, "data_time": 1.52208, "top1_acc": 0.51391, "top5_acc": 0.76172, "loss_cls": 2.73143, "loss": 2.73143, "time": 2.58073} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.0043, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.51078, "top5_acc": 0.75594, "loss_cls": 2.74135, "loss": 2.74135, "time": 0.8743} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00429, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.51609, "top5_acc": 0.76156, "loss_cls": 2.70007, "loss": 2.70007, "time": 0.8707} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00428, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.50672, "top5_acc": 0.74781, "loss_cls": 2.76461, "loss": 2.76461, "time": 0.87125} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00427, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.51547, "top5_acc": 0.76641, "loss_cls": 2.71944, "loss": 2.71944, "time": 0.87298} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00425, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.49719, "top5_acc": 0.75562, "loss_cls": 2.79062, "loss": 2.79062, "time": 0.871} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00424, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.50875, "top5_acc": 0.75078, "loss_cls": 2.77668, "loss": 2.77668, "time": 0.86963} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00423, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.49812, "top5_acc": 0.74891, "loss_cls": 2.80422, "loss": 2.80422, "time": 0.87442} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00422, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.49688, "top5_acc": 0.74266, "loss_cls": 2.79293, "loss": 2.79293, "time": 0.8788} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.00421, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.50766, "top5_acc": 0.75406, "loss_cls": 2.76422, "loss": 2.76422, "time": 0.88043} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.0042, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.50969, "top5_acc": 0.75375, "loss_cls": 2.75119, "loss": 2.75119, "time": 0.87086} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00419, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.50797, "top5_acc": 0.75531, "loss_cls": 2.77066, "loss": 2.77066, "time": 0.86296} +{"mode": "train", "epoch": 131, "iter": 1300, "lr": 0.00418, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.50578, "top5_acc": 0.74828, "loss_cls": 2.78764, "loss": 2.78764, "time": 0.85462} +{"mode": "train", "epoch": 131, "iter": 1400, "lr": 0.00417, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.50328, "top5_acc": 0.75719, "loss_cls": 2.74729, "loss": 2.74729, "time": 0.86635} +{"mode": "train", "epoch": 131, "iter": 1500, "lr": 0.00415, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.50359, "top5_acc": 0.75578, "loss_cls": 2.76945, "loss": 2.76945, "time": 0.87236} +{"mode": "train", "epoch": 131, "iter": 1600, "lr": 0.00414, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.49703, "top5_acc": 0.74406, "loss_cls": 2.82648, "loss": 2.82648, "time": 0.86759} +{"mode": "train", "epoch": 131, "iter": 1700, "lr": 0.00413, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.5025, "top5_acc": 0.75938, "loss_cls": 2.79318, "loss": 2.79318, "time": 0.86857} +{"mode": "train", "epoch": 131, "iter": 1800, "lr": 0.00412, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.49641, "top5_acc": 0.74484, "loss_cls": 2.81778, "loss": 2.81778, "time": 0.86531} +{"mode": "train", "epoch": 131, "iter": 1900, "lr": 0.00411, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.49594, "top5_acc": 0.74906, "loss_cls": 2.82439, "loss": 2.82439, "time": 0.85776} +{"mode": "train", "epoch": 131, "iter": 2000, "lr": 0.0041, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.50391, "top5_acc": 0.75422, "loss_cls": 2.75956, "loss": 2.75956, "time": 0.85579} +{"mode": "train", "epoch": 131, "iter": 2100, "lr": 0.00409, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.495, "top5_acc": 0.74344, "loss_cls": 2.81306, "loss": 2.81306, "time": 0.85763} +{"mode": "train", "epoch": 131, "iter": 2200, "lr": 0.00408, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.49891, "top5_acc": 0.75297, "loss_cls": 2.79243, "loss": 2.79243, "time": 0.86336} +{"mode": "train", "epoch": 131, "iter": 2300, "lr": 0.00407, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.48719, "top5_acc": 0.73844, "loss_cls": 2.86208, "loss": 2.86208, "time": 0.87301} +{"mode": "train", "epoch": 131, "iter": 2400, "lr": 0.00405, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.49641, "top5_acc": 0.74141, "loss_cls": 2.80128, "loss": 2.80128, "time": 0.86757} +{"mode": "train", "epoch": 131, "iter": 2500, "lr": 0.00404, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.49547, "top5_acc": 0.75172, "loss_cls": 2.8267, "loss": 2.8267, "time": 0.8746} +{"mode": "train", "epoch": 131, "iter": 2600, "lr": 0.00403, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.49891, "top5_acc": 0.75172, "loss_cls": 2.79053, "loss": 2.79053, "time": 0.86989} +{"mode": "train", "epoch": 131, "iter": 2700, "lr": 0.00402, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.49219, "top5_acc": 0.74781, "loss_cls": 2.82769, "loss": 2.82769, "time": 0.86905} +{"mode": "train", "epoch": 131, "iter": 2800, "lr": 0.00401, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.49969, "top5_acc": 0.7525, "loss_cls": 2.78238, "loss": 2.78238, "time": 0.86616} +{"mode": "train", "epoch": 131, "iter": 2900, "lr": 0.004, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.50281, "top5_acc": 0.74891, "loss_cls": 2.80312, "loss": 2.80312, "time": 0.86797} +{"mode": "train", "epoch": 131, "iter": 3000, "lr": 0.00399, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.48297, "top5_acc": 0.74328, "loss_cls": 2.85746, "loss": 2.85746, "time": 0.86833} +{"mode": "train", "epoch": 131, "iter": 3100, "lr": 0.00398, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.48547, "top5_acc": 0.74312, "loss_cls": 2.84662, "loss": 2.84662, "time": 0.87075} +{"mode": "train", "epoch": 131, "iter": 3200, "lr": 0.00397, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.48484, "top5_acc": 0.74219, "loss_cls": 2.84459, "loss": 2.84459, "time": 0.86766} +{"mode": "train", "epoch": 131, "iter": 3300, "lr": 0.00396, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.49984, "top5_acc": 0.74484, "loss_cls": 2.81424, "loss": 2.81424, "time": 0.86895} +{"mode": "train", "epoch": 131, "iter": 3400, "lr": 0.00394, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.49719, "top5_acc": 0.74656, "loss_cls": 2.80366, "loss": 2.80366, "time": 0.86825} +{"mode": "train", "epoch": 131, "iter": 3500, "lr": 0.00393, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.49844, "top5_acc": 0.74188, "loss_cls": 2.83789, "loss": 2.83789, "time": 0.87318} +{"mode": "train", "epoch": 131, "iter": 3600, "lr": 0.00392, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49078, "top5_acc": 0.74312, "loss_cls": 2.85435, "loss": 2.85435, "time": 0.86965} +{"mode": "train", "epoch": 131, "iter": 3700, "lr": 0.00391, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.49922, "top5_acc": 0.75141, "loss_cls": 2.78183, "loss": 2.78183, "time": 0.87145} +{"mode": "val", "epoch": 131, "iter": 309, "lr": 0.00391, "top1_acc": 0.41204, "top5_acc": 0.66191, "mean_class_accuracy": 0.41195} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.0039, "memory": 15990, "data_time": 1.53007, "top1_acc": 0.53109, "top5_acc": 0.76109, "loss_cls": 2.67449, "loss": 2.67449, "time": 2.60758} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00389, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.51188, "top5_acc": 0.76078, "loss_cls": 2.72866, "loss": 2.72866, "time": 0.8797} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00387, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.51984, "top5_acc": 0.76094, "loss_cls": 2.72029, "loss": 2.72029, "time": 0.87837} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00386, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.51297, "top5_acc": 0.76203, "loss_cls": 2.72104, "loss": 2.72104, "time": 0.88095} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00385, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.50734, "top5_acc": 0.75469, "loss_cls": 2.74785, "loss": 2.74785, "time": 0.88167} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00384, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.52547, "top5_acc": 0.76531, "loss_cls": 2.66967, "loss": 2.66967, "time": 0.87363} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00383, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.52297, "top5_acc": 0.76734, "loss_cls": 2.69749, "loss": 2.69749, "time": 0.87855} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00382, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.51453, "top5_acc": 0.75672, "loss_cls": 2.73877, "loss": 2.73877, "time": 0.87733} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00381, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.51188, "top5_acc": 0.7575, "loss_cls": 2.72023, "loss": 2.72023, "time": 0.87394} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0038, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.49516, "top5_acc": 0.74219, "loss_cls": 2.82338, "loss": 2.82338, "time": 0.87751} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00379, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.51094, "top5_acc": 0.75109, "loss_cls": 2.76157, "loss": 2.76157, "time": 0.87219} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00378, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.52156, "top5_acc": 0.75891, "loss_cls": 2.69382, "loss": 2.69382, "time": 0.85836} +{"mode": "train", "epoch": 132, "iter": 1300, "lr": 0.00377, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.50484, "top5_acc": 0.76031, "loss_cls": 2.72174, "loss": 2.72174, "time": 0.86285} +{"mode": "train", "epoch": 132, "iter": 1400, "lr": 0.00376, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.50859, "top5_acc": 0.75672, "loss_cls": 2.73417, "loss": 2.73417, "time": 0.87345} +{"mode": "train", "epoch": 132, "iter": 1500, "lr": 0.00375, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.50625, "top5_acc": 0.75578, "loss_cls": 2.74727, "loss": 2.74727, "time": 0.87322} +{"mode": "train", "epoch": 132, "iter": 1600, "lr": 0.00374, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.5125, "top5_acc": 0.75484, "loss_cls": 2.73188, "loss": 2.73188, "time": 0.86729} +{"mode": "train", "epoch": 132, "iter": 1700, "lr": 0.00372, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50562, "top5_acc": 0.75547, "loss_cls": 2.75129, "loss": 2.75129, "time": 0.87317} +{"mode": "train", "epoch": 132, "iter": 1800, "lr": 0.00371, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51047, "top5_acc": 0.75438, "loss_cls": 2.73308, "loss": 2.73308, "time": 0.86769} +{"mode": "train", "epoch": 132, "iter": 1900, "lr": 0.0037, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.49938, "top5_acc": 0.75312, "loss_cls": 2.7787, "loss": 2.7787, "time": 0.86156} +{"mode": "train", "epoch": 132, "iter": 2000, "lr": 0.00369, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.51656, "top5_acc": 0.75781, "loss_cls": 2.70694, "loss": 2.70694, "time": 0.85566} +{"mode": "train", "epoch": 132, "iter": 2100, "lr": 0.00368, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.49859, "top5_acc": 0.75359, "loss_cls": 2.77864, "loss": 2.77864, "time": 0.8583} +{"mode": "train", "epoch": 132, "iter": 2200, "lr": 0.00367, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.50734, "top5_acc": 0.76062, "loss_cls": 2.74825, "loss": 2.74825, "time": 0.86492} +{"mode": "train", "epoch": 132, "iter": 2300, "lr": 0.00366, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.50484, "top5_acc": 0.75547, "loss_cls": 2.77755, "loss": 2.77755, "time": 0.86862} +{"mode": "train", "epoch": 132, "iter": 2400, "lr": 0.00365, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.50344, "top5_acc": 0.75, "loss_cls": 2.77647, "loss": 2.77647, "time": 0.86967} +{"mode": "train", "epoch": 132, "iter": 2500, "lr": 0.00364, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.49297, "top5_acc": 0.74766, "loss_cls": 2.78534, "loss": 2.78534, "time": 0.87155} +{"mode": "train", "epoch": 132, "iter": 2600, "lr": 0.00363, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51062, "top5_acc": 0.74734, "loss_cls": 2.78015, "loss": 2.78015, "time": 0.8693} +{"mode": "train", "epoch": 132, "iter": 2700, "lr": 0.00362, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.49484, "top5_acc": 0.75047, "loss_cls": 2.79627, "loss": 2.79627, "time": 0.87148} +{"mode": "train", "epoch": 132, "iter": 2800, "lr": 0.00361, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.49359, "top5_acc": 0.74516, "loss_cls": 2.80695, "loss": 2.80695, "time": 0.8712} +{"mode": "train", "epoch": 132, "iter": 2900, "lr": 0.0036, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.50656, "top5_acc": 0.75125, "loss_cls": 2.76302, "loss": 2.76302, "time": 0.87764} +{"mode": "train", "epoch": 132, "iter": 3000, "lr": 0.00359, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50313, "top5_acc": 0.75391, "loss_cls": 2.75411, "loss": 2.75411, "time": 0.87159} +{"mode": "train", "epoch": 132, "iter": 3100, "lr": 0.00358, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.50688, "top5_acc": 0.75422, "loss_cls": 2.75618, "loss": 2.75618, "time": 0.87366} +{"mode": "train", "epoch": 132, "iter": 3200, "lr": 0.00357, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.50453, "top5_acc": 0.75562, "loss_cls": 2.76086, "loss": 2.76086, "time": 0.87313} +{"mode": "train", "epoch": 132, "iter": 3300, "lr": 0.00356, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.50313, "top5_acc": 0.74469, "loss_cls": 2.82243, "loss": 2.82243, "time": 0.87884} +{"mode": "train", "epoch": 132, "iter": 3400, "lr": 0.00355, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.49984, "top5_acc": 0.75, "loss_cls": 2.77429, "loss": 2.77429, "time": 0.86986} +{"mode": "train", "epoch": 132, "iter": 3500, "lr": 0.00354, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.49297, "top5_acc": 0.745, "loss_cls": 2.82257, "loss": 2.82257, "time": 0.87515} +{"mode": "train", "epoch": 132, "iter": 3600, "lr": 0.00353, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.50391, "top5_acc": 0.75672, "loss_cls": 2.77855, "loss": 2.77855, "time": 0.87248} +{"mode": "train", "epoch": 132, "iter": 3700, "lr": 0.00352, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.49266, "top5_acc": 0.74109, "loss_cls": 2.84252, "loss": 2.84252, "time": 0.87444} +{"mode": "val", "epoch": 132, "iter": 309, "lr": 0.00351, "top1_acc": 0.41458, "top5_acc": 0.6615, "mean_class_accuracy": 0.41437} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.0035, "memory": 15990, "data_time": 1.52723, "top1_acc": 0.51297, "top5_acc": 0.76406, "loss_cls": 2.72241, "loss": 2.72241, "time": 2.5851} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00349, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.52797, "top5_acc": 0.77438, "loss_cls": 2.63586, "loss": 2.63586, "time": 0.87392} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00348, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.51719, "top5_acc": 0.76688, "loss_cls": 2.67029, "loss": 2.67029, "time": 0.87798} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00347, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.52422, "top5_acc": 0.77625, "loss_cls": 2.64105, "loss": 2.64105, "time": 0.88464} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00346, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.51812, "top5_acc": 0.75938, "loss_cls": 2.70912, "loss": 2.70912, "time": 0.87915} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00345, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.51172, "top5_acc": 0.76203, "loss_cls": 2.72076, "loss": 2.72076, "time": 0.88068} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00344, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.50813, "top5_acc": 0.75922, "loss_cls": 2.70653, "loss": 2.70653, "time": 0.87917} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00343, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.51031, "top5_acc": 0.75328, "loss_cls": 2.74923, "loss": 2.74923, "time": 0.87924} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00342, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.51734, "top5_acc": 0.76375, "loss_cls": 2.71202, "loss": 2.71202, "time": 0.87594} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.00341, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.52141, "top5_acc": 0.76, "loss_cls": 2.70761, "loss": 2.70761, "time": 0.87551} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0034, "memory": 15990, "data_time": 0.0007, "top1_acc": 0.52594, "top5_acc": 0.76453, "loss_cls": 2.67453, "loss": 2.67453, "time": 0.86754} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00339, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51672, "top5_acc": 0.77188, "loss_cls": 2.68536, "loss": 2.68536, "time": 0.85999} +{"mode": "train", "epoch": 133, "iter": 1300, "lr": 0.00338, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.51453, "top5_acc": 0.7675, "loss_cls": 2.69323, "loss": 2.69323, "time": 0.86169} +{"mode": "train", "epoch": 133, "iter": 1400, "lr": 0.00337, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.5125, "top5_acc": 0.75391, "loss_cls": 2.74529, "loss": 2.74529, "time": 0.87353} +{"mode": "train", "epoch": 133, "iter": 1500, "lr": 0.00336, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.51781, "top5_acc": 0.75547, "loss_cls": 2.69201, "loss": 2.69201, "time": 0.86762} +{"mode": "train", "epoch": 133, "iter": 1600, "lr": 0.00335, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.52094, "top5_acc": 0.76109, "loss_cls": 2.69092, "loss": 2.69092, "time": 0.87034} +{"mode": "train", "epoch": 133, "iter": 1700, "lr": 0.00334, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.51875, "top5_acc": 0.75812, "loss_cls": 2.7006, "loss": 2.7006, "time": 0.87227} +{"mode": "train", "epoch": 133, "iter": 1800, "lr": 0.00333, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.50969, "top5_acc": 0.75422, "loss_cls": 2.73919, "loss": 2.73919, "time": 0.868} +{"mode": "train", "epoch": 133, "iter": 1900, "lr": 0.00332, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.51594, "top5_acc": 0.75953, "loss_cls": 2.71197, "loss": 2.71197, "time": 0.8661} +{"mode": "train", "epoch": 133, "iter": 2000, "lr": 0.00331, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.51938, "top5_acc": 0.76812, "loss_cls": 2.70973, "loss": 2.70973, "time": 0.86453} +{"mode": "train", "epoch": 133, "iter": 2100, "lr": 0.0033, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.51328, "top5_acc": 0.75797, "loss_cls": 2.72627, "loss": 2.72627, "time": 0.85999} +{"mode": "train", "epoch": 133, "iter": 2200, "lr": 0.00329, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.52562, "top5_acc": 0.76609, "loss_cls": 2.67918, "loss": 2.67918, "time": 0.86146} +{"mode": "train", "epoch": 133, "iter": 2300, "lr": 0.00328, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.50766, "top5_acc": 0.75062, "loss_cls": 2.74527, "loss": 2.74527, "time": 0.86532} +{"mode": "train", "epoch": 133, "iter": 2400, "lr": 0.00327, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.5125, "top5_acc": 0.75922, "loss_cls": 2.73151, "loss": 2.73151, "time": 0.86969} +{"mode": "train", "epoch": 133, "iter": 2500, "lr": 0.00326, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.51188, "top5_acc": 0.75734, "loss_cls": 2.72575, "loss": 2.72575, "time": 0.86851} +{"mode": "train", "epoch": 133, "iter": 2600, "lr": 0.00325, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.51203, "top5_acc": 0.75859, "loss_cls": 2.72214, "loss": 2.72214, "time": 0.87105} +{"mode": "train", "epoch": 133, "iter": 2700, "lr": 0.00324, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51656, "top5_acc": 0.75656, "loss_cls": 2.70848, "loss": 2.70848, "time": 0.87191} +{"mode": "train", "epoch": 133, "iter": 2800, "lr": 0.00323, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.515, "top5_acc": 0.75891, "loss_cls": 2.73295, "loss": 2.73295, "time": 0.86506} +{"mode": "train", "epoch": 133, "iter": 2900, "lr": 0.00322, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.50781, "top5_acc": 0.7525, "loss_cls": 2.73603, "loss": 2.73603, "time": 0.86768} +{"mode": "train", "epoch": 133, "iter": 3000, "lr": 0.00321, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50969, "top5_acc": 0.75688, "loss_cls": 2.7544, "loss": 2.7544, "time": 0.87249} +{"mode": "train", "epoch": 133, "iter": 3100, "lr": 0.0032, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.51344, "top5_acc": 0.76, "loss_cls": 2.69475, "loss": 2.69475, "time": 0.8711} +{"mode": "train", "epoch": 133, "iter": 3200, "lr": 0.00319, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.52172, "top5_acc": 0.76609, "loss_cls": 2.71206, "loss": 2.71206, "time": 0.87038} +{"mode": "train", "epoch": 133, "iter": 3300, "lr": 0.00318, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.50781, "top5_acc": 0.75562, "loss_cls": 2.76856, "loss": 2.76856, "time": 0.8708} +{"mode": "train", "epoch": 133, "iter": 3400, "lr": 0.00317, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.50719, "top5_acc": 0.75453, "loss_cls": 2.75166, "loss": 2.75166, "time": 0.86899} +{"mode": "train", "epoch": 133, "iter": 3500, "lr": 0.00316, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.51172, "top5_acc": 0.76047, "loss_cls": 2.73189, "loss": 2.73189, "time": 0.86804} +{"mode": "train", "epoch": 133, "iter": 3600, "lr": 0.00315, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.52109, "top5_acc": 0.76297, "loss_cls": 2.70848, "loss": 2.70848, "time": 0.87202} +{"mode": "train", "epoch": 133, "iter": 3700, "lr": 0.00314, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.49656, "top5_acc": 0.75516, "loss_cls": 2.77958, "loss": 2.77958, "time": 0.87191} +{"mode": "val", "epoch": 133, "iter": 309, "lr": 0.00314, "top1_acc": 0.41878, "top5_acc": 0.66935, "mean_class_accuracy": 0.41858} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00313, "memory": 15990, "data_time": 1.5696, "top1_acc": 0.52375, "top5_acc": 0.77844, "loss_cls": 2.63726, "loss": 2.63726, "time": 2.6336} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00312, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.52516, "top5_acc": 0.77141, "loss_cls": 2.66737, "loss": 2.66737, "time": 0.87939} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00311, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.52938, "top5_acc": 0.77703, "loss_cls": 2.63209, "loss": 2.63209, "time": 0.8752} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.0031, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.53422, "top5_acc": 0.76875, "loss_cls": 2.62669, "loss": 2.62669, "time": 0.87701} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00309, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.525, "top5_acc": 0.77078, "loss_cls": 2.63514, "loss": 2.63514, "time": 0.88018} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00308, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.51375, "top5_acc": 0.77328, "loss_cls": 2.65898, "loss": 2.65898, "time": 0.87566} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00307, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.53312, "top5_acc": 0.77219, "loss_cls": 2.62291, "loss": 2.62291, "time": 0.8781} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00306, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.52969, "top5_acc": 0.77, "loss_cls": 2.65943, "loss": 2.65943, "time": 0.87639} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00305, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.51641, "top5_acc": 0.76594, "loss_cls": 2.67889, "loss": 2.67889, "time": 0.87438} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00304, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.52609, "top5_acc": 0.77344, "loss_cls": 2.62419, "loss": 2.62419, "time": 0.87969} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00303, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.52109, "top5_acc": 0.76422, "loss_cls": 2.67066, "loss": 2.67066, "time": 0.86329} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.00302, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.52438, "top5_acc": 0.7675, "loss_cls": 2.65907, "loss": 2.65907, "time": 0.85961} +{"mode": "train", "epoch": 134, "iter": 1300, "lr": 0.00301, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.52781, "top5_acc": 0.76578, "loss_cls": 2.64879, "loss": 2.64879, "time": 0.85413} +{"mode": "train", "epoch": 134, "iter": 1400, "lr": 0.003, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.5175, "top5_acc": 0.77078, "loss_cls": 2.67035, "loss": 2.67035, "time": 0.86281} +{"mode": "train", "epoch": 134, "iter": 1500, "lr": 0.00299, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.51234, "top5_acc": 0.75562, "loss_cls": 2.72716, "loss": 2.72716, "time": 0.86585} +{"mode": "train", "epoch": 134, "iter": 1600, "lr": 0.00298, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.53234, "top5_acc": 0.76938, "loss_cls": 2.63545, "loss": 2.63545, "time": 0.86963} +{"mode": "train", "epoch": 134, "iter": 1700, "lr": 0.00297, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.52406, "top5_acc": 0.77578, "loss_cls": 2.62946, "loss": 2.62946, "time": 0.86619} +{"mode": "train", "epoch": 134, "iter": 1800, "lr": 0.00296, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.51375, "top5_acc": 0.76141, "loss_cls": 2.69489, "loss": 2.69489, "time": 0.87156} +{"mode": "train", "epoch": 134, "iter": 1900, "lr": 0.00295, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.52812, "top5_acc": 0.76812, "loss_cls": 2.65539, "loss": 2.65539, "time": 0.87448} +{"mode": "train", "epoch": 134, "iter": 2000, "lr": 0.00294, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.52375, "top5_acc": 0.76562, "loss_cls": 2.67397, "loss": 2.67397, "time": 0.86397} +{"mode": "train", "epoch": 134, "iter": 2100, "lr": 0.00293, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.51844, "top5_acc": 0.76781, "loss_cls": 2.67649, "loss": 2.67649, "time": 0.85802} +{"mode": "train", "epoch": 134, "iter": 2200, "lr": 0.00293, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.52953, "top5_acc": 0.77156, "loss_cls": 2.64436, "loss": 2.64436, "time": 0.85556} +{"mode": "train", "epoch": 134, "iter": 2300, "lr": 0.00292, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.51656, "top5_acc": 0.76672, "loss_cls": 2.68963, "loss": 2.68963, "time": 0.86052} +{"mode": "train", "epoch": 134, "iter": 2400, "lr": 0.00291, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.52078, "top5_acc": 0.76078, "loss_cls": 2.69354, "loss": 2.69354, "time": 0.86581} +{"mode": "train", "epoch": 134, "iter": 2500, "lr": 0.0029, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.52562, "top5_acc": 0.76594, "loss_cls": 2.6806, "loss": 2.6806, "time": 0.86867} +{"mode": "train", "epoch": 134, "iter": 2600, "lr": 0.00289, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.51688, "top5_acc": 0.76141, "loss_cls": 2.67675, "loss": 2.67675, "time": 0.87005} +{"mode": "train", "epoch": 134, "iter": 2700, "lr": 0.00288, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.52062, "top5_acc": 0.76844, "loss_cls": 2.67494, "loss": 2.67494, "time": 0.87122} +{"mode": "train", "epoch": 134, "iter": 2800, "lr": 0.00287, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.52266, "top5_acc": 0.76453, "loss_cls": 2.70332, "loss": 2.70332, "time": 0.87188} +{"mode": "train", "epoch": 134, "iter": 2900, "lr": 0.00286, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.52094, "top5_acc": 0.77609, "loss_cls": 2.65794, "loss": 2.65794, "time": 0.87005} +{"mode": "train", "epoch": 134, "iter": 3000, "lr": 0.00285, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.5125, "top5_acc": 0.76094, "loss_cls": 2.7187, "loss": 2.7187, "time": 0.8706} +{"mode": "train", "epoch": 134, "iter": 3100, "lr": 0.00284, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.52484, "top5_acc": 0.76812, "loss_cls": 2.66392, "loss": 2.66392, "time": 0.86651} +{"mode": "train", "epoch": 134, "iter": 3200, "lr": 0.00283, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.52641, "top5_acc": 0.775, "loss_cls": 2.63413, "loss": 2.63413, "time": 0.86182} +{"mode": "train", "epoch": 134, "iter": 3300, "lr": 0.00282, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51422, "top5_acc": 0.76484, "loss_cls": 2.70231, "loss": 2.70231, "time": 0.86705} +{"mode": "train", "epoch": 134, "iter": 3400, "lr": 0.00281, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.51812, "top5_acc": 0.76453, "loss_cls": 2.72937, "loss": 2.72937, "time": 0.86828} +{"mode": "train", "epoch": 134, "iter": 3500, "lr": 0.0028, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.51484, "top5_acc": 0.75906, "loss_cls": 2.72433, "loss": 2.72433, "time": 0.87074} +{"mode": "train", "epoch": 134, "iter": 3600, "lr": 0.00279, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.51859, "top5_acc": 0.76125, "loss_cls": 2.70287, "loss": 2.70287, "time": 0.86748} +{"mode": "train", "epoch": 134, "iter": 3700, "lr": 0.00279, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.51641, "top5_acc": 0.76422, "loss_cls": 2.7115, "loss": 2.7115, "time": 0.86131} +{"mode": "val", "epoch": 134, "iter": 309, "lr": 0.00278, "top1_acc": 0.41888, "top5_acc": 0.67214, "mean_class_accuracy": 0.41863} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00277, "memory": 15990, "data_time": 1.60561, "top1_acc": 0.54656, "top5_acc": 0.78891, "loss_cls": 2.54242, "loss": 2.54242, "time": 2.67323} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00276, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.53844, "top5_acc": 0.77891, "loss_cls": 2.5802, "loss": 2.5802, "time": 0.88205} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00275, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.54469, "top5_acc": 0.785, "loss_cls": 2.5387, "loss": 2.5387, "time": 0.88455} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00274, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.53375, "top5_acc": 0.78391, "loss_cls": 2.59831, "loss": 2.59831, "time": 0.88517} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00274, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.53844, "top5_acc": 0.77266, "loss_cls": 2.59406, "loss": 2.59406, "time": 0.88887} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00273, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.53906, "top5_acc": 0.77203, "loss_cls": 2.61997, "loss": 2.61997, "time": 0.88533} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00272, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.53625, "top5_acc": 0.78312, "loss_cls": 2.59818, "loss": 2.59818, "time": 0.88672} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00271, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.5325, "top5_acc": 0.76844, "loss_cls": 2.65583, "loss": 2.65583, "time": 0.88816} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.0027, "memory": 15990, "data_time": 0.00065, "top1_acc": 0.52641, "top5_acc": 0.77562, "loss_cls": 2.6138, "loss": 2.6138, "time": 0.89351} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00269, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.53125, "top5_acc": 0.775, "loss_cls": 2.62056, "loss": 2.62056, "time": 0.87539} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00268, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.53844, "top5_acc": 0.78797, "loss_cls": 2.5717, "loss": 2.5717, "time": 0.86179} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00267, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.53703, "top5_acc": 0.77219, "loss_cls": 2.61188, "loss": 2.61188, "time": 0.85822} +{"mode": "train", "epoch": 135, "iter": 1300, "lr": 0.00266, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.53547, "top5_acc": 0.78094, "loss_cls": 2.60471, "loss": 2.60471, "time": 0.86889} +{"mode": "train", "epoch": 135, "iter": 1400, "lr": 0.00265, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.52609, "top5_acc": 0.77391, "loss_cls": 2.64409, "loss": 2.64409, "time": 0.88045} +{"mode": "train", "epoch": 135, "iter": 1500, "lr": 0.00265, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.5325, "top5_acc": 0.77, "loss_cls": 2.64322, "loss": 2.64322, "time": 0.88422} +{"mode": "train", "epoch": 135, "iter": 1600, "lr": 0.00264, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.51891, "top5_acc": 0.77016, "loss_cls": 2.63369, "loss": 2.63369, "time": 0.8798} +{"mode": "train", "epoch": 135, "iter": 1700, "lr": 0.00263, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.53, "top5_acc": 0.77203, "loss_cls": 2.61996, "loss": 2.61996, "time": 0.88096} +{"mode": "train", "epoch": 135, "iter": 1800, "lr": 0.00262, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.52844, "top5_acc": 0.77312, "loss_cls": 2.63477, "loss": 2.63477, "time": 0.88073} +{"mode": "train", "epoch": 135, "iter": 1900, "lr": 0.00261, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.52797, "top5_acc": 0.76562, "loss_cls": 2.66077, "loss": 2.66077, "time": 0.8829} +{"mode": "train", "epoch": 135, "iter": 2000, "lr": 0.0026, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.53047, "top5_acc": 0.7675, "loss_cls": 2.61762, "loss": 2.61762, "time": 0.87096} +{"mode": "train", "epoch": 135, "iter": 2100, "lr": 0.00259, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.53188, "top5_acc": 0.76766, "loss_cls": 2.62808, "loss": 2.62808, "time": 0.86337} +{"mode": "train", "epoch": 135, "iter": 2200, "lr": 0.00258, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.53, "top5_acc": 0.77141, "loss_cls": 2.63485, "loss": 2.63485, "time": 0.86591} +{"mode": "train", "epoch": 135, "iter": 2300, "lr": 0.00257, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.53094, "top5_acc": 0.77938, "loss_cls": 2.6141, "loss": 2.6141, "time": 0.87249} +{"mode": "train", "epoch": 135, "iter": 2400, "lr": 0.00256, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.52406, "top5_acc": 0.76594, "loss_cls": 2.66834, "loss": 2.66834, "time": 0.8794} +{"mode": "train", "epoch": 135, "iter": 2500, "lr": 0.00256, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.52609, "top5_acc": 0.77594, "loss_cls": 2.63964, "loss": 2.63964, "time": 0.88393} +{"mode": "train", "epoch": 135, "iter": 2600, "lr": 0.00255, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.52422, "top5_acc": 0.76625, "loss_cls": 2.68053, "loss": 2.68053, "time": 0.89129} +{"mode": "train", "epoch": 135, "iter": 2700, "lr": 0.00254, "memory": 15990, "data_time": 0.00067, "top1_acc": 0.53203, "top5_acc": 0.77125, "loss_cls": 2.63748, "loss": 2.63748, "time": 0.88665} +{"mode": "train", "epoch": 135, "iter": 2800, "lr": 0.00253, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.52281, "top5_acc": 0.75828, "loss_cls": 2.67481, "loss": 2.67481, "time": 0.88172} +{"mode": "train", "epoch": 135, "iter": 2900, "lr": 0.00252, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.51609, "top5_acc": 0.76984, "loss_cls": 2.64269, "loss": 2.64269, "time": 0.88618} +{"mode": "train", "epoch": 135, "iter": 3000, "lr": 0.00251, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.51906, "top5_acc": 0.76969, "loss_cls": 2.64565, "loss": 2.64565, "time": 0.88593} +{"mode": "train", "epoch": 135, "iter": 3100, "lr": 0.0025, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.51844, "top5_acc": 0.76844, "loss_cls": 2.67137, "loss": 2.67137, "time": 0.88811} +{"mode": "train", "epoch": 135, "iter": 3200, "lr": 0.00249, "memory": 15990, "data_time": 0.00061, "top1_acc": 0.53047, "top5_acc": 0.77188, "loss_cls": 2.62375, "loss": 2.62375, "time": 0.88107} +{"mode": "train", "epoch": 135, "iter": 3300, "lr": 0.00249, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.52531, "top5_acc": 0.76594, "loss_cls": 2.66021, "loss": 2.66021, "time": 0.88491} +{"mode": "train", "epoch": 135, "iter": 3400, "lr": 0.00248, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.525, "top5_acc": 0.76453, "loss_cls": 2.67298, "loss": 2.67298, "time": 0.87939} +{"mode": "train", "epoch": 135, "iter": 3500, "lr": 0.00247, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.52, "top5_acc": 0.76391, "loss_cls": 2.68296, "loss": 2.68296, "time": 0.89199} +{"mode": "train", "epoch": 135, "iter": 3600, "lr": 0.00246, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.51891, "top5_acc": 0.76953, "loss_cls": 2.66636, "loss": 2.66636, "time": 0.85517} +{"mode": "train", "epoch": 135, "iter": 3700, "lr": 0.00245, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.52891, "top5_acc": 0.7675, "loss_cls": 2.67306, "loss": 2.67306, "time": 0.8521} +{"mode": "val", "epoch": 135, "iter": 309, "lr": 0.00245, "top1_acc": 0.42319, "top5_acc": 0.67771, "mean_class_accuracy": 0.42294} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00244, "memory": 15990, "data_time": 1.67953, "top1_acc": 0.54078, "top5_acc": 0.78141, "loss_cls": 2.55664, "loss": 2.55664, "time": 2.74116} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.00243, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.54703, "top5_acc": 0.79156, "loss_cls": 2.52242, "loss": 2.52242, "time": 0.86294} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00242, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.55156, "top5_acc": 0.79375, "loss_cls": 2.53066, "loss": 2.53066, "time": 0.86859} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00241, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.54016, "top5_acc": 0.77938, "loss_cls": 2.56383, "loss": 2.56383, "time": 0.8728} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.0024, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.54422, "top5_acc": 0.79078, "loss_cls": 2.51955, "loss": 2.51955, "time": 0.86552} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.0024, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.54719, "top5_acc": 0.78797, "loss_cls": 2.55319, "loss": 2.55319, "time": 0.86814} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00239, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.53984, "top5_acc": 0.77734, "loss_cls": 2.58289, "loss": 2.58289, "time": 0.8707} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00238, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.54359, "top5_acc": 0.78547, "loss_cls": 2.5434, "loss": 2.5434, "time": 0.86403} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00237, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.54141, "top5_acc": 0.77906, "loss_cls": 2.56992, "loss": 2.56992, "time": 0.8806} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00236, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.53844, "top5_acc": 0.78656, "loss_cls": 2.57128, "loss": 2.57128, "time": 0.87325} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00235, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.53969, "top5_acc": 0.77891, "loss_cls": 2.59219, "loss": 2.59219, "time": 0.86652} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00234, "memory": 15990, "data_time": 0.00077, "top1_acc": 0.53422, "top5_acc": 0.77969, "loss_cls": 2.57236, "loss": 2.57236, "time": 0.86527} +{"mode": "train", "epoch": 136, "iter": 1300, "lr": 0.00234, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.53328, "top5_acc": 0.78469, "loss_cls": 2.57553, "loss": 2.57553, "time": 0.87019} +{"mode": "train", "epoch": 136, "iter": 1400, "lr": 0.00233, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.54, "top5_acc": 0.77984, "loss_cls": 2.59105, "loss": 2.59105, "time": 0.86526} +{"mode": "train", "epoch": 136, "iter": 1500, "lr": 0.00232, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.54141, "top5_acc": 0.77359, "loss_cls": 2.59358, "loss": 2.59358, "time": 0.86763} +{"mode": "train", "epoch": 136, "iter": 1600, "lr": 0.00231, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53641, "top5_acc": 0.78406, "loss_cls": 2.58329, "loss": 2.58329, "time": 0.85879} +{"mode": "train", "epoch": 136, "iter": 1700, "lr": 0.0023, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.53688, "top5_acc": 0.77391, "loss_cls": 2.58281, "loss": 2.58281, "time": 0.86651} +{"mode": "train", "epoch": 136, "iter": 1800, "lr": 0.00229, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.55, "top5_acc": 0.78969, "loss_cls": 2.5104, "loss": 2.5104, "time": 0.86164} +{"mode": "train", "epoch": 136, "iter": 1900, "lr": 0.00229, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.52359, "top5_acc": 0.76844, "loss_cls": 2.66188, "loss": 2.66188, "time": 0.86109} +{"mode": "train", "epoch": 136, "iter": 2000, "lr": 0.00228, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.54703, "top5_acc": 0.78266, "loss_cls": 2.56453, "loss": 2.56453, "time": 0.86708} +{"mode": "train", "epoch": 136, "iter": 2100, "lr": 0.00227, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.52953, "top5_acc": 0.77609, "loss_cls": 2.62147, "loss": 2.62147, "time": 0.86041} +{"mode": "train", "epoch": 136, "iter": 2200, "lr": 0.00226, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.53406, "top5_acc": 0.77156, "loss_cls": 2.62518, "loss": 2.62518, "time": 0.85431} +{"mode": "train", "epoch": 136, "iter": 2300, "lr": 0.00225, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.54172, "top5_acc": 0.78047, "loss_cls": 2.57821, "loss": 2.57821, "time": 0.86306} +{"mode": "train", "epoch": 136, "iter": 2400, "lr": 0.00224, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.53891, "top5_acc": 0.77984, "loss_cls": 2.59512, "loss": 2.59512, "time": 0.87186} +{"mode": "train", "epoch": 136, "iter": 2500, "lr": 0.00224, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55156, "top5_acc": 0.78094, "loss_cls": 2.55107, "loss": 2.55107, "time": 0.86733} +{"mode": "train", "epoch": 136, "iter": 2600, "lr": 0.00223, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.54141, "top5_acc": 0.77297, "loss_cls": 2.59963, "loss": 2.59963, "time": 0.86832} +{"mode": "train", "epoch": 136, "iter": 2700, "lr": 0.00222, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53938, "top5_acc": 0.77891, "loss_cls": 2.5871, "loss": 2.5871, "time": 0.8669} +{"mode": "train", "epoch": 136, "iter": 2800, "lr": 0.00221, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.525, "top5_acc": 0.77266, "loss_cls": 2.63781, "loss": 2.63781, "time": 0.8635} +{"mode": "train", "epoch": 136, "iter": 2900, "lr": 0.0022, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55172, "top5_acc": 0.79125, "loss_cls": 2.52349, "loss": 2.52349, "time": 0.86581} +{"mode": "train", "epoch": 136, "iter": 3000, "lr": 0.00219, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.53328, "top5_acc": 0.77219, "loss_cls": 2.61072, "loss": 2.61072, "time": 0.86502} +{"mode": "train", "epoch": 136, "iter": 3100, "lr": 0.00219, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.53578, "top5_acc": 0.77672, "loss_cls": 2.59749, "loss": 2.59749, "time": 0.86192} +{"mode": "train", "epoch": 136, "iter": 3200, "lr": 0.00218, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.53344, "top5_acc": 0.77906, "loss_cls": 2.60746, "loss": 2.60746, "time": 0.86452} +{"mode": "train", "epoch": 136, "iter": 3300, "lr": 0.00217, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.52969, "top5_acc": 0.77734, "loss_cls": 2.61181, "loss": 2.61181, "time": 0.86449} +{"mode": "train", "epoch": 136, "iter": 3400, "lr": 0.00216, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.53047, "top5_acc": 0.77344, "loss_cls": 2.62069, "loss": 2.62069, "time": 0.87131} +{"mode": "train", "epoch": 136, "iter": 3500, "lr": 0.00215, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.53047, "top5_acc": 0.77938, "loss_cls": 2.57777, "loss": 2.57777, "time": 0.86093} +{"mode": "train", "epoch": 136, "iter": 3600, "lr": 0.00215, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.52844, "top5_acc": 0.76703, "loss_cls": 2.62506, "loss": 2.62506, "time": 0.85466} +{"mode": "train", "epoch": 136, "iter": 3700, "lr": 0.00214, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.52484, "top5_acc": 0.77391, "loss_cls": 2.62664, "loss": 2.62664, "time": 0.85454} +{"mode": "val", "epoch": 136, "iter": 309, "lr": 0.00213, "top1_acc": 0.42952, "top5_acc": 0.67644, "mean_class_accuracy": 0.4293} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00213, "memory": 15990, "data_time": 1.6514, "top1_acc": 0.56359, "top5_acc": 0.80297, "loss_cls": 2.4437, "loss": 2.4437, "time": 2.71586} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00212, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.54406, "top5_acc": 0.79625, "loss_cls": 2.50578, "loss": 2.50578, "time": 0.88822} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00211, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.55672, "top5_acc": 0.80094, "loss_cls": 2.45797, "loss": 2.45797, "time": 0.88535} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.0021, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.54656, "top5_acc": 0.78734, "loss_cls": 2.51913, "loss": 2.51913, "time": 0.88815} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.00209, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.555, "top5_acc": 0.79906, "loss_cls": 2.49484, "loss": 2.49484, "time": 0.88567} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.00209, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.5475, "top5_acc": 0.79062, "loss_cls": 2.50685, "loss": 2.50685, "time": 0.88098} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00208, "memory": 15990, "data_time": 0.00056, "top1_acc": 0.54656, "top5_acc": 0.78469, "loss_cls": 2.53612, "loss": 2.53612, "time": 0.8878} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00207, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.54734, "top5_acc": 0.78719, "loss_cls": 2.52759, "loss": 2.52759, "time": 0.88715} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00206, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.55703, "top5_acc": 0.79281, "loss_cls": 2.49612, "loss": 2.49612, "time": 0.88587} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00205, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.55906, "top5_acc": 0.79203, "loss_cls": 2.48169, "loss": 2.48169, "time": 0.86953} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00205, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.55188, "top5_acc": 0.79125, "loss_cls": 2.51547, "loss": 2.51547, "time": 0.86518} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00204, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.54203, "top5_acc": 0.78031, "loss_cls": 2.55857, "loss": 2.55857, "time": 0.86732} +{"mode": "train", "epoch": 137, "iter": 1300, "lr": 0.00203, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.53969, "top5_acc": 0.78078, "loss_cls": 2.5724, "loss": 2.5724, "time": 0.87067} +{"mode": "train", "epoch": 137, "iter": 1400, "lr": 0.00202, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.55, "top5_acc": 0.78391, "loss_cls": 2.52464, "loss": 2.52464, "time": 0.85968} +{"mode": "train", "epoch": 137, "iter": 1500, "lr": 0.00201, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.53984, "top5_acc": 0.78703, "loss_cls": 2.54823, "loss": 2.54823, "time": 0.85535} +{"mode": "train", "epoch": 137, "iter": 1600, "lr": 0.00201, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.54797, "top5_acc": 0.78969, "loss_cls": 2.50778, "loss": 2.50778, "time": 0.85344} +{"mode": "train", "epoch": 137, "iter": 1700, "lr": 0.002, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.55062, "top5_acc": 0.78047, "loss_cls": 2.53652, "loss": 2.53652, "time": 0.85439} +{"mode": "train", "epoch": 137, "iter": 1800, "lr": 0.00199, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.545, "top5_acc": 0.78266, "loss_cls": 2.51185, "loss": 2.51185, "time": 0.85266} +{"mode": "train", "epoch": 137, "iter": 1900, "lr": 0.00198, "memory": 15990, "data_time": 0.00063, "top1_acc": 0.55, "top5_acc": 0.78625, "loss_cls": 2.52129, "loss": 2.52129, "time": 0.85083} +{"mode": "train", "epoch": 137, "iter": 2000, "lr": 0.00198, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.54891, "top5_acc": 0.78656, "loss_cls": 2.52718, "loss": 2.52718, "time": 0.85359} +{"mode": "train", "epoch": 137, "iter": 2100, "lr": 0.00197, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.54688, "top5_acc": 0.79078, "loss_cls": 2.51785, "loss": 2.51785, "time": 0.85612} +{"mode": "train", "epoch": 137, "iter": 2200, "lr": 0.00196, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.54109, "top5_acc": 0.78875, "loss_cls": 2.53992, "loss": 2.53992, "time": 0.85099} +{"mode": "train", "epoch": 137, "iter": 2300, "lr": 0.00195, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.53469, "top5_acc": 0.77453, "loss_cls": 2.57381, "loss": 2.57381, "time": 0.85362} +{"mode": "train", "epoch": 137, "iter": 2400, "lr": 0.00194, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.53797, "top5_acc": 0.78297, "loss_cls": 2.5532, "loss": 2.5532, "time": 0.85201} +{"mode": "train", "epoch": 137, "iter": 2500, "lr": 0.00194, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.54156, "top5_acc": 0.77719, "loss_cls": 2.57913, "loss": 2.57913, "time": 0.85105} +{"mode": "train", "epoch": 137, "iter": 2600, "lr": 0.00193, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.54891, "top5_acc": 0.78438, "loss_cls": 2.55041, "loss": 2.55041, "time": 0.851} +{"mode": "train", "epoch": 137, "iter": 2700, "lr": 0.00192, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.55141, "top5_acc": 0.78906, "loss_cls": 2.51187, "loss": 2.51187, "time": 0.8541} +{"mode": "train", "epoch": 137, "iter": 2800, "lr": 0.00191, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.54391, "top5_acc": 0.78562, "loss_cls": 2.52939, "loss": 2.52939, "time": 0.85189} +{"mode": "train", "epoch": 137, "iter": 2900, "lr": 0.00191, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.53234, "top5_acc": 0.77438, "loss_cls": 2.61469, "loss": 2.61469, "time": 0.8589} +{"mode": "train", "epoch": 137, "iter": 3000, "lr": 0.0019, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.55266, "top5_acc": 0.7975, "loss_cls": 2.51604, "loss": 2.51604, "time": 0.85603} +{"mode": "train", "epoch": 137, "iter": 3100, "lr": 0.00189, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.52875, "top5_acc": 0.7725, "loss_cls": 2.60269, "loss": 2.60269, "time": 0.85311} +{"mode": "train", "epoch": 137, "iter": 3200, "lr": 0.00188, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.55359, "top5_acc": 0.78594, "loss_cls": 2.52517, "loss": 2.52517, "time": 0.85284} +{"mode": "train", "epoch": 137, "iter": 3300, "lr": 0.00188, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.53719, "top5_acc": 0.77453, "loss_cls": 2.60088, "loss": 2.60088, "time": 0.85469} +{"mode": "train", "epoch": 137, "iter": 3400, "lr": 0.00187, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.54219, "top5_acc": 0.77406, "loss_cls": 2.58034, "loss": 2.58034, "time": 0.86009} +{"mode": "train", "epoch": 137, "iter": 3500, "lr": 0.00186, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.53062, "top5_acc": 0.77484, "loss_cls": 2.60365, "loss": 2.60365, "time": 0.85684} +{"mode": "train", "epoch": 137, "iter": 3600, "lr": 0.00185, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.54594, "top5_acc": 0.79062, "loss_cls": 2.5328, "loss": 2.5328, "time": 0.85297} +{"mode": "train", "epoch": 137, "iter": 3700, "lr": 0.00185, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.53609, "top5_acc": 0.77859, "loss_cls": 2.56643, "loss": 2.56643, "time": 0.84945} +{"mode": "val", "epoch": 137, "iter": 309, "lr": 0.00184, "top1_acc": 0.43464, "top5_acc": 0.67852, "mean_class_accuracy": 0.43442} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00183, "memory": 15990, "data_time": 1.57098, "top1_acc": 0.55719, "top5_acc": 0.79891, "loss_cls": 2.46728, "loss": 2.46728, "time": 2.6067} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00183, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.57766, "top5_acc": 0.80578, "loss_cls": 2.39779, "loss": 2.39779, "time": 0.85889} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00182, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.56469, "top5_acc": 0.80469, "loss_cls": 2.42778, "loss": 2.42778, "time": 0.85868} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00181, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.56844, "top5_acc": 0.80578, "loss_cls": 2.39159, "loss": 2.39159, "time": 0.85976} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.0018, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.55422, "top5_acc": 0.79734, "loss_cls": 2.4726, "loss": 2.4726, "time": 0.86358} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.0018, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.55406, "top5_acc": 0.79516, "loss_cls": 2.47404, "loss": 2.47404, "time": 0.85873} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00179, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.56047, "top5_acc": 0.79266, "loss_cls": 2.45354, "loss": 2.45354, "time": 0.86596} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00178, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.57266, "top5_acc": 0.81016, "loss_cls": 2.40632, "loss": 2.40632, "time": 0.86016} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00177, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55547, "top5_acc": 0.79125, "loss_cls": 2.48925, "loss": 2.48925, "time": 0.85665} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00177, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.5475, "top5_acc": 0.78703, "loss_cls": 2.52366, "loss": 2.52366, "time": 0.86079} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.00176, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.55125, "top5_acc": 0.79531, "loss_cls": 2.47788, "loss": 2.47788, "time": 0.85335} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.00175, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.56547, "top5_acc": 0.79562, "loss_cls": 2.46012, "loss": 2.46012, "time": 0.85493} +{"mode": "train", "epoch": 138, "iter": 1300, "lr": 0.00175, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.55984, "top5_acc": 0.7925, "loss_cls": 2.47609, "loss": 2.47609, "time": 0.85361} +{"mode": "train", "epoch": 138, "iter": 1400, "lr": 0.00174, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55719, "top5_acc": 0.79828, "loss_cls": 2.47508, "loss": 2.47508, "time": 0.85589} +{"mode": "train", "epoch": 138, "iter": 1500, "lr": 0.00173, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.56141, "top5_acc": 0.79859, "loss_cls": 2.45753, "loss": 2.45753, "time": 0.85307} +{"mode": "train", "epoch": 138, "iter": 1600, "lr": 0.00172, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.54766, "top5_acc": 0.78734, "loss_cls": 2.52961, "loss": 2.52961, "time": 0.85461} +{"mode": "train", "epoch": 138, "iter": 1700, "lr": 0.00172, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55547, "top5_acc": 0.78859, "loss_cls": 2.50217, "loss": 2.50217, "time": 0.85756} +{"mode": "train", "epoch": 138, "iter": 1800, "lr": 0.00171, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55375, "top5_acc": 0.79828, "loss_cls": 2.48049, "loss": 2.48049, "time": 0.85327} +{"mode": "train", "epoch": 138, "iter": 1900, "lr": 0.0017, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.55797, "top5_acc": 0.79531, "loss_cls": 2.48644, "loss": 2.48644, "time": 0.85341} +{"mode": "train", "epoch": 138, "iter": 2000, "lr": 0.00169, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.56359, "top5_acc": 0.79312, "loss_cls": 2.47133, "loss": 2.47133, "time": 0.84938} +{"mode": "train", "epoch": 138, "iter": 2100, "lr": 0.00169, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.55266, "top5_acc": 0.79734, "loss_cls": 2.49324, "loss": 2.49324, "time": 0.84946} +{"mode": "train", "epoch": 138, "iter": 2200, "lr": 0.00168, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.54937, "top5_acc": 0.7975, "loss_cls": 2.49082, "loss": 2.49082, "time": 0.85119} +{"mode": "train", "epoch": 138, "iter": 2300, "lr": 0.00167, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.55625, "top5_acc": 0.78625, "loss_cls": 2.49018, "loss": 2.49018, "time": 0.84966} +{"mode": "train", "epoch": 138, "iter": 2400, "lr": 0.00167, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55047, "top5_acc": 0.78547, "loss_cls": 2.50803, "loss": 2.50803, "time": 0.85403} +{"mode": "train", "epoch": 138, "iter": 2500, "lr": 0.00166, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56219, "top5_acc": 0.79203, "loss_cls": 2.48042, "loss": 2.48042, "time": 0.85582} +{"mode": "train", "epoch": 138, "iter": 2600, "lr": 0.00165, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55297, "top5_acc": 0.79156, "loss_cls": 2.48973, "loss": 2.48973, "time": 0.85282} +{"mode": "train", "epoch": 138, "iter": 2700, "lr": 0.00164, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55594, "top5_acc": 0.79344, "loss_cls": 2.47589, "loss": 2.47589, "time": 0.8473} +{"mode": "train", "epoch": 138, "iter": 2800, "lr": 0.00164, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.55453, "top5_acc": 0.79078, "loss_cls": 2.48411, "loss": 2.48411, "time": 0.85447} +{"mode": "train", "epoch": 138, "iter": 2900, "lr": 0.00163, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55594, "top5_acc": 0.79109, "loss_cls": 2.49328, "loss": 2.49328, "time": 0.84785} +{"mode": "train", "epoch": 138, "iter": 3000, "lr": 0.00162, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.55172, "top5_acc": 0.78672, "loss_cls": 2.51281, "loss": 2.51281, "time": 0.85015} +{"mode": "train", "epoch": 138, "iter": 3100, "lr": 0.00162, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.55391, "top5_acc": 0.7925, "loss_cls": 2.4988, "loss": 2.4988, "time": 0.85231} +{"mode": "train", "epoch": 138, "iter": 3200, "lr": 0.00161, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.55234, "top5_acc": 0.79328, "loss_cls": 2.50126, "loss": 2.50126, "time": 0.84855} +{"mode": "train", "epoch": 138, "iter": 3300, "lr": 0.0016, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.54406, "top5_acc": 0.78078, "loss_cls": 2.54679, "loss": 2.54679, "time": 0.8495} +{"mode": "train", "epoch": 138, "iter": 3400, "lr": 0.0016, "memory": 15990, "data_time": 0.00073, "top1_acc": 0.55562, "top5_acc": 0.79047, "loss_cls": 2.49087, "loss": 2.49087, "time": 0.84879} +{"mode": "train", "epoch": 138, "iter": 3500, "lr": 0.00159, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.5425, "top5_acc": 0.79266, "loss_cls": 2.50966, "loss": 2.50966, "time": 0.84733} +{"mode": "train", "epoch": 138, "iter": 3600, "lr": 0.00158, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55062, "top5_acc": 0.78438, "loss_cls": 2.5479, "loss": 2.5479, "time": 0.84968} +{"mode": "train", "epoch": 138, "iter": 3700, "lr": 0.00157, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.54375, "top5_acc": 0.78078, "loss_cls": 2.55145, "loss": 2.55145, "time": 0.85539} +{"mode": "val", "epoch": 138, "iter": 309, "lr": 0.00157, "top1_acc": 0.42987, "top5_acc": 0.68014, "mean_class_accuracy": 0.42958} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00156, "memory": 15990, "data_time": 1.53459, "top1_acc": 0.58234, "top5_acc": 0.80594, "loss_cls": 2.35912, "loss": 2.35912, "time": 2.57875} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00156, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57047, "top5_acc": 0.80297, "loss_cls": 2.4078, "loss": 2.4078, "time": 0.86508} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00155, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.57281, "top5_acc": 0.79969, "loss_cls": 2.42647, "loss": 2.42647, "time": 0.86063} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00154, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57109, "top5_acc": 0.80203, "loss_cls": 2.42302, "loss": 2.42302, "time": 0.85856} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00154, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56844, "top5_acc": 0.79797, "loss_cls": 2.42692, "loss": 2.42692, "time": 0.85788} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00153, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.56984, "top5_acc": 0.81109, "loss_cls": 2.40817, "loss": 2.40817, "time": 0.85849} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00152, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.57953, "top5_acc": 0.80578, "loss_cls": 2.38407, "loss": 2.38407, "time": 0.86021} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00152, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56484, "top5_acc": 0.81109, "loss_cls": 2.3915, "loss": 2.3915, "time": 0.85921} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00151, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57094, "top5_acc": 0.79938, "loss_cls": 2.40538, "loss": 2.40538, "time": 0.85561} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.0015, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.56, "top5_acc": 0.79594, "loss_cls": 2.44639, "loss": 2.44639, "time": 0.85217} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.0015, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.56953, "top5_acc": 0.80016, "loss_cls": 2.43168, "loss": 2.43168, "time": 0.85026} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00149, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.56172, "top5_acc": 0.80516, "loss_cls": 2.43689, "loss": 2.43689, "time": 0.85302} +{"mode": "train", "epoch": 139, "iter": 1300, "lr": 0.00148, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.57453, "top5_acc": 0.80797, "loss_cls": 2.38628, "loss": 2.38628, "time": 0.849} +{"mode": "train", "epoch": 139, "iter": 1400, "lr": 0.00148, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.56719, "top5_acc": 0.81062, "loss_cls": 2.40899, "loss": 2.40899, "time": 0.85632} +{"mode": "train", "epoch": 139, "iter": 1500, "lr": 0.00147, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.57688, "top5_acc": 0.81031, "loss_cls": 2.36707, "loss": 2.36707, "time": 0.85133} +{"mode": "train", "epoch": 139, "iter": 1600, "lr": 0.00146, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.56625, "top5_acc": 0.80281, "loss_cls": 2.41765, "loss": 2.41765, "time": 0.85292} +{"mode": "train", "epoch": 139, "iter": 1700, "lr": 0.00145, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56641, "top5_acc": 0.79156, "loss_cls": 2.43271, "loss": 2.43271, "time": 0.85335} +{"mode": "train", "epoch": 139, "iter": 1800, "lr": 0.00145, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55047, "top5_acc": 0.80266, "loss_cls": 2.44886, "loss": 2.44886, "time": 0.85799} +{"mode": "train", "epoch": 139, "iter": 1900, "lr": 0.00144, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.55844, "top5_acc": 0.80031, "loss_cls": 2.45552, "loss": 2.45552, "time": 0.85859} +{"mode": "train", "epoch": 139, "iter": 2000, "lr": 0.00143, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.54875, "top5_acc": 0.79109, "loss_cls": 2.47568, "loss": 2.47568, "time": 0.85436} +{"mode": "train", "epoch": 139, "iter": 2100, "lr": 0.00143, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55812, "top5_acc": 0.78906, "loss_cls": 2.47189, "loss": 2.47189, "time": 0.85522} +{"mode": "train", "epoch": 139, "iter": 2200, "lr": 0.00142, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.56297, "top5_acc": 0.79578, "loss_cls": 2.44582, "loss": 2.44582, "time": 0.85555} +{"mode": "train", "epoch": 139, "iter": 2300, "lr": 0.00142, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.56172, "top5_acc": 0.79391, "loss_cls": 2.45454, "loss": 2.45454, "time": 0.85319} +{"mode": "train", "epoch": 139, "iter": 2400, "lr": 0.00141, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55766, "top5_acc": 0.79891, "loss_cls": 2.45254, "loss": 2.45254, "time": 0.85765} +{"mode": "train", "epoch": 139, "iter": 2500, "lr": 0.0014, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55922, "top5_acc": 0.79828, "loss_cls": 2.46432, "loss": 2.46432, "time": 0.85121} +{"mode": "train", "epoch": 139, "iter": 2600, "lr": 0.0014, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.56172, "top5_acc": 0.79984, "loss_cls": 2.45288, "loss": 2.45288, "time": 0.85363} +{"mode": "train", "epoch": 139, "iter": 2700, "lr": 0.00139, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57297, "top5_acc": 0.80172, "loss_cls": 2.42376, "loss": 2.42376, "time": 0.85534} +{"mode": "train", "epoch": 139, "iter": 2800, "lr": 0.00138, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.56922, "top5_acc": 0.79734, "loss_cls": 2.4318, "loss": 2.4318, "time": 0.85304} +{"mode": "train", "epoch": 139, "iter": 2900, "lr": 0.00138, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.56312, "top5_acc": 0.80062, "loss_cls": 2.41652, "loss": 2.41652, "time": 0.85181} +{"mode": "train", "epoch": 139, "iter": 3000, "lr": 0.00137, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.55297, "top5_acc": 0.79703, "loss_cls": 2.46459, "loss": 2.46459, "time": 0.85157} +{"mode": "train", "epoch": 139, "iter": 3100, "lr": 0.00136, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55766, "top5_acc": 0.79641, "loss_cls": 2.44338, "loss": 2.44338, "time": 0.85261} +{"mode": "train", "epoch": 139, "iter": 3200, "lr": 0.00136, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.55891, "top5_acc": 0.78781, "loss_cls": 2.48667, "loss": 2.48667, "time": 0.85255} +{"mode": "train", "epoch": 139, "iter": 3300, "lr": 0.00135, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.56141, "top5_acc": 0.79641, "loss_cls": 2.46706, "loss": 2.46706, "time": 0.84928} +{"mode": "train", "epoch": 139, "iter": 3400, "lr": 0.00134, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.5625, "top5_acc": 0.80078, "loss_cls": 2.43001, "loss": 2.43001, "time": 0.8531} +{"mode": "train", "epoch": 139, "iter": 3500, "lr": 0.00134, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.56594, "top5_acc": 0.80453, "loss_cls": 2.40297, "loss": 2.40297, "time": 0.84851} +{"mode": "train", "epoch": 139, "iter": 3600, "lr": 0.00133, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.55094, "top5_acc": 0.79328, "loss_cls": 2.4834, "loss": 2.4834, "time": 0.84572} +{"mode": "train", "epoch": 139, "iter": 3700, "lr": 0.00132, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.54922, "top5_acc": 0.79, "loss_cls": 2.49032, "loss": 2.49032, "time": 0.85353} +{"mode": "val", "epoch": 139, "iter": 309, "lr": 0.00132, "top1_acc": 0.43788, "top5_acc": 0.68546, "mean_class_accuracy": 0.43754} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00131, "memory": 15990, "data_time": 1.47423, "top1_acc": 0.58328, "top5_acc": 0.81781, "loss_cls": 2.32204, "loss": 2.32204, "time": 2.51555} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00131, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.56812, "top5_acc": 0.81266, "loss_cls": 2.36115, "loss": 2.36115, "time": 0.85518} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.0013, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57016, "top5_acc": 0.8125, "loss_cls": 2.38715, "loss": 2.38715, "time": 0.85495} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.0013, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57406, "top5_acc": 0.81734, "loss_cls": 2.3421, "loss": 2.3421, "time": 0.85213} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00129, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.58094, "top5_acc": 0.80016, "loss_cls": 2.37169, "loss": 2.37169, "time": 0.8539} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.00128, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58547, "top5_acc": 0.81391, "loss_cls": 2.33121, "loss": 2.33121, "time": 0.85639} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.00128, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.57469, "top5_acc": 0.80078, "loss_cls": 2.3901, "loss": 2.3901, "time": 0.85532} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00127, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.57281, "top5_acc": 0.80438, "loss_cls": 2.39855, "loss": 2.39855, "time": 0.85144} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00126, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.59219, "top5_acc": 0.81734, "loss_cls": 2.31891, "loss": 2.31891, "time": 0.85164} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00126, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.57297, "top5_acc": 0.80609, "loss_cls": 2.39206, "loss": 2.39206, "time": 0.85513} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00125, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.59062, "top5_acc": 0.81422, "loss_cls": 2.34124, "loss": 2.34124, "time": 0.85075} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00125, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.57516, "top5_acc": 0.80906, "loss_cls": 2.39978, "loss": 2.39978, "time": 0.85264} +{"mode": "train", "epoch": 140, "iter": 1300, "lr": 0.00124, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.57312, "top5_acc": 0.80781, "loss_cls": 2.40404, "loss": 2.40404, "time": 0.86101} +{"mode": "train", "epoch": 140, "iter": 1400, "lr": 0.00123, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.57156, "top5_acc": 0.805, "loss_cls": 2.39559, "loss": 2.39559, "time": 0.85727} +{"mode": "train", "epoch": 140, "iter": 1500, "lr": 0.00123, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.57828, "top5_acc": 0.81188, "loss_cls": 2.35357, "loss": 2.35357, "time": 0.85743} +{"mode": "train", "epoch": 140, "iter": 1600, "lr": 0.00122, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.57719, "top5_acc": 0.81281, "loss_cls": 2.36088, "loss": 2.36088, "time": 0.85689} +{"mode": "train", "epoch": 140, "iter": 1700, "lr": 0.00121, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57266, "top5_acc": 0.80391, "loss_cls": 2.38462, "loss": 2.38462, "time": 0.85537} +{"mode": "train", "epoch": 140, "iter": 1800, "lr": 0.00121, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.57078, "top5_acc": 0.80922, "loss_cls": 2.40909, "loss": 2.40909, "time": 0.85647} +{"mode": "train", "epoch": 140, "iter": 1900, "lr": 0.0012, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57438, "top5_acc": 0.80703, "loss_cls": 2.39811, "loss": 2.39811, "time": 0.84957} +{"mode": "train", "epoch": 140, "iter": 2000, "lr": 0.0012, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.56422, "top5_acc": 0.80516, "loss_cls": 2.4119, "loss": 2.4119, "time": 0.84703} +{"mode": "train", "epoch": 140, "iter": 2100, "lr": 0.00119, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.57578, "top5_acc": 0.80547, "loss_cls": 2.38293, "loss": 2.38293, "time": 0.8475} +{"mode": "train", "epoch": 140, "iter": 2200, "lr": 0.00118, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.5725, "top5_acc": 0.80672, "loss_cls": 2.40346, "loss": 2.40346, "time": 0.84759} +{"mode": "train", "epoch": 140, "iter": 2300, "lr": 0.00118, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.57828, "top5_acc": 0.80953, "loss_cls": 2.36367, "loss": 2.36367, "time": 0.85085} +{"mode": "train", "epoch": 140, "iter": 2400, "lr": 0.00117, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.57531, "top5_acc": 0.80875, "loss_cls": 2.38426, "loss": 2.38426, "time": 0.85037} +{"mode": "train", "epoch": 140, "iter": 2500, "lr": 0.00117, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58578, "top5_acc": 0.81469, "loss_cls": 2.34453, "loss": 2.34453, "time": 0.85145} +{"mode": "train", "epoch": 140, "iter": 2600, "lr": 0.00116, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.57516, "top5_acc": 0.80734, "loss_cls": 2.37482, "loss": 2.37482, "time": 0.85466} +{"mode": "train", "epoch": 140, "iter": 2700, "lr": 0.00115, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58188, "top5_acc": 0.81469, "loss_cls": 2.35015, "loss": 2.35015, "time": 0.85488} +{"mode": "train", "epoch": 140, "iter": 2800, "lr": 0.00115, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.55937, "top5_acc": 0.80031, "loss_cls": 2.4349, "loss": 2.4349, "time": 0.85666} +{"mode": "train", "epoch": 140, "iter": 2900, "lr": 0.00114, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.56625, "top5_acc": 0.80406, "loss_cls": 2.40752, "loss": 2.40752, "time": 0.85333} +{"mode": "train", "epoch": 140, "iter": 3000, "lr": 0.00114, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.56906, "top5_acc": 0.80359, "loss_cls": 2.40765, "loss": 2.40765, "time": 0.85943} +{"mode": "train", "epoch": 140, "iter": 3100, "lr": 0.00113, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.56625, "top5_acc": 0.79953, "loss_cls": 2.43593, "loss": 2.43593, "time": 0.85309} +{"mode": "train", "epoch": 140, "iter": 3200, "lr": 0.00112, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.57094, "top5_acc": 0.81344, "loss_cls": 2.37213, "loss": 2.37213, "time": 0.85368} +{"mode": "train", "epoch": 140, "iter": 3300, "lr": 0.00112, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.575, "top5_acc": 0.80984, "loss_cls": 2.36898, "loss": 2.36898, "time": 0.85475} +{"mode": "train", "epoch": 140, "iter": 3400, "lr": 0.00111, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.56641, "top5_acc": 0.80531, "loss_cls": 2.3867, "loss": 2.3867, "time": 0.85012} +{"mode": "train", "epoch": 140, "iter": 3500, "lr": 0.00111, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.56812, "top5_acc": 0.79891, "loss_cls": 2.43029, "loss": 2.43029, "time": 0.85282} +{"mode": "train", "epoch": 140, "iter": 3600, "lr": 0.0011, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.57078, "top5_acc": 0.80484, "loss_cls": 2.40101, "loss": 2.40101, "time": 0.8534} +{"mode": "train", "epoch": 140, "iter": 3700, "lr": 0.0011, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.56812, "top5_acc": 0.80484, "loss_cls": 2.40474, "loss": 2.40474, "time": 0.84955} +{"mode": "val", "epoch": 140, "iter": 309, "lr": 0.00109, "top1_acc": 0.43859, "top5_acc": 0.68227, "mean_class_accuracy": 0.43837} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00109, "memory": 15990, "data_time": 1.56549, "top1_acc": 0.59375, "top5_acc": 0.82109, "loss_cls": 2.28951, "loss": 2.28951, "time": 2.60819} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00108, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.60641, "top5_acc": 0.81859, "loss_cls": 2.27246, "loss": 2.27246, "time": 0.85985} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00108, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.59469, "top5_acc": 0.81641, "loss_cls": 2.29232, "loss": 2.29232, "time": 0.85876} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00107, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.59391, "top5_acc": 0.82125, "loss_cls": 2.27156, "loss": 2.27156, "time": 0.86186} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00106, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.58016, "top5_acc": 0.81312, "loss_cls": 2.35531, "loss": 2.35531, "time": 0.86337} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00106, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.57766, "top5_acc": 0.81781, "loss_cls": 2.33937, "loss": 2.33937, "time": 0.86012} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00105, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.58094, "top5_acc": 0.82078, "loss_cls": 2.32966, "loss": 2.32966, "time": 0.85319} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00105, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.57516, "top5_acc": 0.81344, "loss_cls": 2.36891, "loss": 2.36891, "time": 0.85831} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00104, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59422, "top5_acc": 0.82625, "loss_cls": 2.25558, "loss": 2.25558, "time": 0.85789} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00104, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.59141, "top5_acc": 0.81703, "loss_cls": 2.2947, "loss": 2.2947, "time": 0.86237} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00103, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.58703, "top5_acc": 0.82078, "loss_cls": 2.30413, "loss": 2.30413, "time": 0.85316} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00102, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.58531, "top5_acc": 0.82312, "loss_cls": 2.29107, "loss": 2.29107, "time": 0.86427} +{"mode": "train", "epoch": 141, "iter": 1300, "lr": 0.00102, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59078, "top5_acc": 0.81609, "loss_cls": 2.31644, "loss": 2.31644, "time": 0.85971} +{"mode": "train", "epoch": 141, "iter": 1400, "lr": 0.00101, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.59016, "top5_acc": 0.82344, "loss_cls": 2.29594, "loss": 2.29594, "time": 0.85518} +{"mode": "train", "epoch": 141, "iter": 1500, "lr": 0.00101, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.58609, "top5_acc": 0.81859, "loss_cls": 2.32394, "loss": 2.32394, "time": 0.861} +{"mode": "train", "epoch": 141, "iter": 1600, "lr": 0.001, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.5875, "top5_acc": 0.81828, "loss_cls": 2.31427, "loss": 2.31427, "time": 0.86011} +{"mode": "train", "epoch": 141, "iter": 1700, "lr": 0.001, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.57125, "top5_acc": 0.81344, "loss_cls": 2.38593, "loss": 2.38593, "time": 0.86267} +{"mode": "train", "epoch": 141, "iter": 1800, "lr": 0.00099, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.5875, "top5_acc": 0.81328, "loss_cls": 2.31458, "loss": 2.31458, "time": 0.85696} +{"mode": "train", "epoch": 141, "iter": 1900, "lr": 0.00099, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.58078, "top5_acc": 0.80906, "loss_cls": 2.34152, "loss": 2.34152, "time": 0.85997} +{"mode": "train", "epoch": 141, "iter": 2000, "lr": 0.00098, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.58047, "top5_acc": 0.81266, "loss_cls": 2.34414, "loss": 2.34414, "time": 0.85897} +{"mode": "train", "epoch": 141, "iter": 2100, "lr": 0.00097, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59109, "top5_acc": 0.81797, "loss_cls": 2.29548, "loss": 2.29548, "time": 0.85618} +{"mode": "train", "epoch": 141, "iter": 2200, "lr": 0.00097, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.57594, "top5_acc": 0.81234, "loss_cls": 2.36376, "loss": 2.36376, "time": 0.84993} +{"mode": "train", "epoch": 141, "iter": 2300, "lr": 0.00096, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58297, "top5_acc": 0.80859, "loss_cls": 2.36034, "loss": 2.36034, "time": 0.84934} +{"mode": "train", "epoch": 141, "iter": 2400, "lr": 0.00096, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58938, "top5_acc": 0.81547, "loss_cls": 2.32549, "loss": 2.32549, "time": 0.85011} +{"mode": "train", "epoch": 141, "iter": 2500, "lr": 0.00095, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.59125, "top5_acc": 0.82203, "loss_cls": 2.2876, "loss": 2.2876, "time": 0.84416} +{"mode": "train", "epoch": 141, "iter": 2600, "lr": 0.00095, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57641, "top5_acc": 0.81328, "loss_cls": 2.34985, "loss": 2.34985, "time": 0.84971} +{"mode": "train", "epoch": 141, "iter": 2700, "lr": 0.00094, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58078, "top5_acc": 0.80656, "loss_cls": 2.38265, "loss": 2.38265, "time": 0.85345} +{"mode": "train", "epoch": 141, "iter": 2800, "lr": 0.00094, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.57812, "top5_acc": 0.81531, "loss_cls": 2.34066, "loss": 2.34066, "time": 0.85457} +{"mode": "train", "epoch": 141, "iter": 2900, "lr": 0.00093, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.58672, "top5_acc": 0.81625, "loss_cls": 2.32531, "loss": 2.32531, "time": 0.85088} +{"mode": "train", "epoch": 141, "iter": 3000, "lr": 0.00093, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.57031, "top5_acc": 0.80812, "loss_cls": 2.3847, "loss": 2.3847, "time": 0.85228} +{"mode": "train", "epoch": 141, "iter": 3100, "lr": 0.00092, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.57938, "top5_acc": 0.81234, "loss_cls": 2.35964, "loss": 2.35964, "time": 0.85369} +{"mode": "train", "epoch": 141, "iter": 3200, "lr": 0.00091, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.57656, "top5_acc": 0.80906, "loss_cls": 2.37388, "loss": 2.37388, "time": 0.856} +{"mode": "train", "epoch": 141, "iter": 3300, "lr": 0.00091, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.57734, "top5_acc": 0.81031, "loss_cls": 2.35763, "loss": 2.35763, "time": 0.85277} +{"mode": "train", "epoch": 141, "iter": 3400, "lr": 0.0009, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.58406, "top5_acc": 0.8175, "loss_cls": 2.34527, "loss": 2.34527, "time": 0.85179} +{"mode": "train", "epoch": 141, "iter": 3500, "lr": 0.0009, "memory": 15990, "data_time": 0.00071, "top1_acc": 0.58406, "top5_acc": 0.81609, "loss_cls": 2.32703, "loss": 2.32703, "time": 0.84894} +{"mode": "train", "epoch": 141, "iter": 3600, "lr": 0.00089, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.59234, "top5_acc": 0.82016, "loss_cls": 2.29209, "loss": 2.29209, "time": 0.85098} +{"mode": "train", "epoch": 141, "iter": 3700, "lr": 0.00089, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.58516, "top5_acc": 0.81406, "loss_cls": 2.32052, "loss": 2.32052, "time": 0.8519} +{"mode": "val", "epoch": 141, "iter": 309, "lr": 0.00089, "top1_acc": 0.43859, "top5_acc": 0.68764, "mean_class_accuracy": 0.43846} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00088, "memory": 15990, "data_time": 1.6165, "top1_acc": 0.60203, "top5_acc": 0.82359, "loss_cls": 2.25202, "loss": 2.25202, "time": 2.64917} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00088, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59828, "top5_acc": 0.82781, "loss_cls": 2.25091, "loss": 2.25091, "time": 0.85929} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00087, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59516, "top5_acc": 0.82547, "loss_cls": 2.25722, "loss": 2.25722, "time": 0.86143} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00086, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.60031, "top5_acc": 0.82812, "loss_cls": 2.25909, "loss": 2.25909, "time": 0.85918} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.00086, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.59156, "top5_acc": 0.82609, "loss_cls": 2.28976, "loss": 2.28976, "time": 0.85825} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.00085, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.59406, "top5_acc": 0.82469, "loss_cls": 2.27423, "loss": 2.27423, "time": 0.86016} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.00085, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.59953, "top5_acc": 0.82625, "loss_cls": 2.24806, "loss": 2.24806, "time": 0.85737} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00084, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.60297, "top5_acc": 0.82609, "loss_cls": 2.23896, "loss": 2.23896, "time": 0.86335} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00084, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.59812, "top5_acc": 0.82453, "loss_cls": 2.2692, "loss": 2.2692, "time": 0.85766} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00083, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.59828, "top5_acc": 0.82906, "loss_cls": 2.26605, "loss": 2.26605, "time": 0.86141} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00083, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.59125, "top5_acc": 0.81562, "loss_cls": 2.30777, "loss": 2.30777, "time": 0.85799} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00082, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.60125, "top5_acc": 0.82453, "loss_cls": 2.26645, "loss": 2.26645, "time": 0.85659} +{"mode": "train", "epoch": 142, "iter": 1300, "lr": 0.00082, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.58844, "top5_acc": 0.82172, "loss_cls": 2.28296, "loss": 2.28296, "time": 0.85512} +{"mode": "train", "epoch": 142, "iter": 1400, "lr": 0.00081, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.60828, "top5_acc": 0.82719, "loss_cls": 2.24263, "loss": 2.24263, "time": 0.85324} +{"mode": "train", "epoch": 142, "iter": 1500, "lr": 0.00081, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.59938, "top5_acc": 0.82266, "loss_cls": 2.25122, "loss": 2.25122, "time": 0.858} +{"mode": "train", "epoch": 142, "iter": 1600, "lr": 0.0008, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.58562, "top5_acc": 0.81875, "loss_cls": 2.30366, "loss": 2.30366, "time": 0.85253} +{"mode": "train", "epoch": 142, "iter": 1700, "lr": 0.0008, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.60328, "top5_acc": 0.81656, "loss_cls": 2.26787, "loss": 2.26787, "time": 0.85712} +{"mode": "train", "epoch": 142, "iter": 1800, "lr": 0.00079, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.58922, "top5_acc": 0.81953, "loss_cls": 2.28526, "loss": 2.28526, "time": 0.85597} +{"mode": "train", "epoch": 142, "iter": 1900, "lr": 0.00079, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.60453, "top5_acc": 0.82203, "loss_cls": 2.25569, "loss": 2.25569, "time": 0.86086} +{"mode": "train", "epoch": 142, "iter": 2000, "lr": 0.00078, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58484, "top5_acc": 0.82234, "loss_cls": 2.28495, "loss": 2.28495, "time": 0.85233} +{"mode": "train", "epoch": 142, "iter": 2100, "lr": 0.00078, "memory": 15990, "data_time": 0.00057, "top1_acc": 0.59203, "top5_acc": 0.83, "loss_cls": 2.24788, "loss": 2.24788, "time": 0.85388} +{"mode": "train", "epoch": 142, "iter": 2200, "lr": 0.00077, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.59625, "top5_acc": 0.83281, "loss_cls": 2.24941, "loss": 2.24941, "time": 0.85341} +{"mode": "train", "epoch": 142, "iter": 2300, "lr": 0.00077, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59312, "top5_acc": 0.81281, "loss_cls": 2.28677, "loss": 2.28677, "time": 0.85647} +{"mode": "train", "epoch": 142, "iter": 2400, "lr": 0.00076, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.60281, "top5_acc": 0.82234, "loss_cls": 2.27866, "loss": 2.27866, "time": 0.85983} +{"mode": "train", "epoch": 142, "iter": 2500, "lr": 0.00076, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.595, "top5_acc": 0.82422, "loss_cls": 2.27913, "loss": 2.27913, "time": 0.85412} +{"mode": "train", "epoch": 142, "iter": 2600, "lr": 0.00075, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.59453, "top5_acc": 0.825, "loss_cls": 2.27521, "loss": 2.27521, "time": 0.85744} +{"mode": "train", "epoch": 142, "iter": 2700, "lr": 0.00075, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.58609, "top5_acc": 0.81266, "loss_cls": 2.31796, "loss": 2.31796, "time": 0.86196} +{"mode": "train", "epoch": 142, "iter": 2800, "lr": 0.00075, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.60703, "top5_acc": 0.82672, "loss_cls": 2.24403, "loss": 2.24403, "time": 0.86031} +{"mode": "train", "epoch": 142, "iter": 2900, "lr": 0.00074, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.59203, "top5_acc": 0.82781, "loss_cls": 2.27782, "loss": 2.27782, "time": 0.85396} +{"mode": "train", "epoch": 142, "iter": 3000, "lr": 0.00074, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.59219, "top5_acc": 0.82281, "loss_cls": 2.28839, "loss": 2.28839, "time": 0.85711} +{"mode": "train", "epoch": 142, "iter": 3100, "lr": 0.00073, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.59344, "top5_acc": 0.82641, "loss_cls": 2.27278, "loss": 2.27278, "time": 0.85245} +{"mode": "train", "epoch": 142, "iter": 3200, "lr": 0.00073, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.58734, "top5_acc": 0.81797, "loss_cls": 2.30338, "loss": 2.30338, "time": 0.85286} +{"mode": "train", "epoch": 142, "iter": 3300, "lr": 0.00072, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.59469, "top5_acc": 0.82984, "loss_cls": 2.25594, "loss": 2.25594, "time": 0.85759} +{"mode": "train", "epoch": 142, "iter": 3400, "lr": 0.00072, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.59422, "top5_acc": 0.81344, "loss_cls": 2.29851, "loss": 2.29851, "time": 0.85472} +{"mode": "train", "epoch": 142, "iter": 3500, "lr": 0.00071, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.59188, "top5_acc": 0.82094, "loss_cls": 2.3103, "loss": 2.3103, "time": 0.85401} +{"mode": "train", "epoch": 142, "iter": 3600, "lr": 0.00071, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.58047, "top5_acc": 0.81781, "loss_cls": 2.33274, "loss": 2.33274, "time": 0.85306} +{"mode": "train", "epoch": 142, "iter": 3700, "lr": 0.0007, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59891, "top5_acc": 0.8225, "loss_cls": 2.26119, "loss": 2.26119, "time": 0.85508} +{"mode": "val", "epoch": 142, "iter": 309, "lr": 0.0007, "top1_acc": 0.44385, "top5_acc": 0.68992, "mean_class_accuracy": 0.44358} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.0007, "memory": 15990, "data_time": 1.48188, "top1_acc": 0.615, "top5_acc": 0.84188, "loss_cls": 2.17392, "loss": 2.17392, "time": 2.51482} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00069, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.61625, "top5_acc": 0.82797, "loss_cls": 2.19992, "loss": 2.19992, "time": 0.86092} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00069, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.61266, "top5_acc": 0.83781, "loss_cls": 2.19257, "loss": 2.19257, "time": 0.86114} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00068, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.62313, "top5_acc": 0.84281, "loss_cls": 2.1554, "loss": 2.1554, "time": 0.86018} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00068, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.61047, "top5_acc": 0.82844, "loss_cls": 2.23457, "loss": 2.23457, "time": 0.86764} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00067, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.60625, "top5_acc": 0.82484, "loss_cls": 2.23902, "loss": 2.23902, "time": 0.85483} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00067, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.60906, "top5_acc": 0.83078, "loss_cls": 2.21659, "loss": 2.21659, "time": 0.85801} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00066, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.60688, "top5_acc": 0.8325, "loss_cls": 2.22234, "loss": 2.22234, "time": 0.85625} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00066, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.61703, "top5_acc": 0.84109, "loss_cls": 2.12729, "loss": 2.12729, "time": 0.85424} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00065, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.60531, "top5_acc": 0.82719, "loss_cls": 2.21765, "loss": 2.21765, "time": 0.85339} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00065, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.61375, "top5_acc": 0.83547, "loss_cls": 2.18263, "loss": 2.18263, "time": 0.85141} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00065, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.6125, "top5_acc": 0.83578, "loss_cls": 2.20171, "loss": 2.20171, "time": 0.85739} +{"mode": "train", "epoch": 143, "iter": 1300, "lr": 0.00064, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.60828, "top5_acc": 0.83203, "loss_cls": 2.21187, "loss": 2.21187, "time": 0.86004} +{"mode": "train", "epoch": 143, "iter": 1400, "lr": 0.00064, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.60797, "top5_acc": 0.82797, "loss_cls": 2.23158, "loss": 2.23158, "time": 0.85684} +{"mode": "train", "epoch": 143, "iter": 1500, "lr": 0.00063, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.6125, "top5_acc": 0.82531, "loss_cls": 2.22097, "loss": 2.22097, "time": 0.86048} +{"mode": "train", "epoch": 143, "iter": 1600, "lr": 0.00063, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.61016, "top5_acc": 0.82594, "loss_cls": 2.2347, "loss": 2.2347, "time": 0.85674} +{"mode": "train", "epoch": 143, "iter": 1700, "lr": 0.00062, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59312, "top5_acc": 0.82688, "loss_cls": 2.25502, "loss": 2.25502, "time": 0.86288} +{"mode": "train", "epoch": 143, "iter": 1800, "lr": 0.00062, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.61344, "top5_acc": 0.82969, "loss_cls": 2.21782, "loss": 2.21782, "time": 0.85681} +{"mode": "train", "epoch": 143, "iter": 1900, "lr": 0.00061, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.59875, "top5_acc": 0.82359, "loss_cls": 2.26495, "loss": 2.26495, "time": 0.85621} +{"mode": "train", "epoch": 143, "iter": 2000, "lr": 0.00061, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.60984, "top5_acc": 0.83391, "loss_cls": 2.2101, "loss": 2.2101, "time": 0.85112} +{"mode": "train", "epoch": 143, "iter": 2100, "lr": 0.00061, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.60391, "top5_acc": 0.82438, "loss_cls": 2.23306, "loss": 2.23306, "time": 0.8561} +{"mode": "train", "epoch": 143, "iter": 2200, "lr": 0.0006, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.61828, "top5_acc": 0.84203, "loss_cls": 2.17432, "loss": 2.17432, "time": 0.85657} +{"mode": "train", "epoch": 143, "iter": 2300, "lr": 0.0006, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59203, "top5_acc": 0.82406, "loss_cls": 2.28728, "loss": 2.28728, "time": 0.85977} +{"mode": "train", "epoch": 143, "iter": 2400, "lr": 0.00059, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.60641, "top5_acc": 0.83375, "loss_cls": 2.21368, "loss": 2.21368, "time": 0.86156} +{"mode": "train", "epoch": 143, "iter": 2500, "lr": 0.00059, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.60391, "top5_acc": 0.83078, "loss_cls": 2.21893, "loss": 2.21893, "time": 0.86061} +{"mode": "train", "epoch": 143, "iter": 2600, "lr": 0.00058, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.60047, "top5_acc": 0.82609, "loss_cls": 2.23875, "loss": 2.23875, "time": 0.86073} +{"mode": "train", "epoch": 143, "iter": 2700, "lr": 0.00058, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.58875, "top5_acc": 0.82297, "loss_cls": 2.28408, "loss": 2.28408, "time": 0.86016} +{"mode": "train", "epoch": 143, "iter": 2800, "lr": 0.00058, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.59938, "top5_acc": 0.83125, "loss_cls": 2.23315, "loss": 2.23315, "time": 0.85504} +{"mode": "train", "epoch": 143, "iter": 2900, "lr": 0.00057, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.60234, "top5_acc": 0.83312, "loss_cls": 2.22984, "loss": 2.22984, "time": 0.85771} +{"mode": "train", "epoch": 143, "iter": 3000, "lr": 0.00057, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.60375, "top5_acc": 0.83312, "loss_cls": 2.21973, "loss": 2.21973, "time": 0.86236} +{"mode": "train", "epoch": 143, "iter": 3100, "lr": 0.00056, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.60359, "top5_acc": 0.83391, "loss_cls": 2.21642, "loss": 2.21642, "time": 0.85951} +{"mode": "train", "epoch": 143, "iter": 3200, "lr": 0.00056, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.61172, "top5_acc": 0.83359, "loss_cls": 2.19962, "loss": 2.19962, "time": 0.85777} +{"mode": "train", "epoch": 143, "iter": 3300, "lr": 0.00055, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.6025, "top5_acc": 0.82891, "loss_cls": 2.21362, "loss": 2.21362, "time": 0.85899} +{"mode": "train", "epoch": 143, "iter": 3400, "lr": 0.00055, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.60438, "top5_acc": 0.83344, "loss_cls": 2.20847, "loss": 2.20847, "time": 0.85875} +{"mode": "train", "epoch": 143, "iter": 3500, "lr": 0.00055, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.59297, "top5_acc": 0.81703, "loss_cls": 2.28014, "loss": 2.28014, "time": 0.84849} +{"mode": "train", "epoch": 143, "iter": 3600, "lr": 0.00054, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.60219, "top5_acc": 0.82703, "loss_cls": 2.24894, "loss": 2.24894, "time": 0.8488} +{"mode": "train", "epoch": 143, "iter": 3700, "lr": 0.00054, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.59109, "top5_acc": 0.82688, "loss_cls": 2.26984, "loss": 2.26984, "time": 0.85368} +{"mode": "val", "epoch": 143, "iter": 309, "lr": 0.00054, "top1_acc": 0.44183, "top5_acc": 0.69108, "mean_class_accuracy": 0.44163} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00053, "memory": 15990, "data_time": 1.59461, "top1_acc": 0.6275, "top5_acc": 0.84984, "loss_cls": 2.12672, "loss": 2.12672, "time": 2.6386} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00053, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.62578, "top5_acc": 0.84562, "loss_cls": 2.13324, "loss": 2.13324, "time": 0.8564} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00052, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.61719, "top5_acc": 0.84328, "loss_cls": 2.16271, "loss": 2.16271, "time": 0.856} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00052, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.61656, "top5_acc": 0.835, "loss_cls": 2.17719, "loss": 2.17719, "time": 0.84883} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00052, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.61656, "top5_acc": 0.84047, "loss_cls": 2.17706, "loss": 2.17706, "time": 0.85318} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00051, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.62, "top5_acc": 0.83281, "loss_cls": 2.15661, "loss": 2.15661, "time": 0.85026} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00051, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.61703, "top5_acc": 0.84688, "loss_cls": 2.13283, "loss": 2.13283, "time": 0.85097} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.0005, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.62094, "top5_acc": 0.83531, "loss_cls": 2.1622, "loss": 2.1622, "time": 0.85001} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.0005, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.61094, "top5_acc": 0.84094, "loss_cls": 2.16807, "loss": 2.16807, "time": 0.85594} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.0005, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.61281, "top5_acc": 0.83578, "loss_cls": 2.16793, "loss": 2.16793, "time": 0.85458} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.00049, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.61453, "top5_acc": 0.83938, "loss_cls": 2.15538, "loss": 2.15538, "time": 0.85356} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.00049, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.60828, "top5_acc": 0.83688, "loss_cls": 2.20009, "loss": 2.20009, "time": 0.84949} +{"mode": "train", "epoch": 144, "iter": 1300, "lr": 0.00048, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.61266, "top5_acc": 0.83359, "loss_cls": 2.18703, "loss": 2.18703, "time": 0.85044} +{"mode": "train", "epoch": 144, "iter": 1400, "lr": 0.00048, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.62031, "top5_acc": 0.83734, "loss_cls": 2.12483, "loss": 2.12483, "time": 0.84997} +{"mode": "train", "epoch": 144, "iter": 1500, "lr": 0.00048, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.62125, "top5_acc": 0.84266, "loss_cls": 2.14444, "loss": 2.14444, "time": 0.85165} +{"mode": "train", "epoch": 144, "iter": 1600, "lr": 0.00047, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.61422, "top5_acc": 0.83781, "loss_cls": 2.16767, "loss": 2.16767, "time": 0.84724} +{"mode": "train", "epoch": 144, "iter": 1700, "lr": 0.00047, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.60406, "top5_acc": 0.83672, "loss_cls": 2.19783, "loss": 2.19783, "time": 0.85378} +{"mode": "train", "epoch": 144, "iter": 1800, "lr": 0.00047, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.61547, "top5_acc": 0.83875, "loss_cls": 2.2137, "loss": 2.2137, "time": 0.85405} +{"mode": "train", "epoch": 144, "iter": 1900, "lr": 0.00046, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.60938, "top5_acc": 0.83359, "loss_cls": 2.17714, "loss": 2.17714, "time": 0.85558} +{"mode": "train", "epoch": 144, "iter": 2000, "lr": 0.00046, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.61562, "top5_acc": 0.84125, "loss_cls": 2.17947, "loss": 2.17947, "time": 0.85285} +{"mode": "train", "epoch": 144, "iter": 2100, "lr": 0.00045, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.62031, "top5_acc": 0.83578, "loss_cls": 2.1724, "loss": 2.1724, "time": 0.85577} +{"mode": "train", "epoch": 144, "iter": 2200, "lr": 0.00045, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.61656, "top5_acc": 0.83422, "loss_cls": 2.16632, "loss": 2.16632, "time": 0.85499} +{"mode": "train", "epoch": 144, "iter": 2300, "lr": 0.00045, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.615, "top5_acc": 0.83484, "loss_cls": 2.1986, "loss": 2.1986, "time": 0.85497} +{"mode": "train", "epoch": 144, "iter": 2400, "lr": 0.00044, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.62062, "top5_acc": 0.83734, "loss_cls": 2.16643, "loss": 2.16643, "time": 0.85539} +{"mode": "train", "epoch": 144, "iter": 2500, "lr": 0.00044, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.61219, "top5_acc": 0.83203, "loss_cls": 2.19499, "loss": 2.19499, "time": 0.85164} +{"mode": "train", "epoch": 144, "iter": 2600, "lr": 0.00044, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.60922, "top5_acc": 0.83031, "loss_cls": 2.2036, "loss": 2.2036, "time": 0.8563} +{"mode": "train", "epoch": 144, "iter": 2700, "lr": 0.00043, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.61047, "top5_acc": 0.83562, "loss_cls": 2.18436, "loss": 2.18436, "time": 0.85462} +{"mode": "train", "epoch": 144, "iter": 2800, "lr": 0.00043, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.61781, "top5_acc": 0.83703, "loss_cls": 2.15276, "loss": 2.15276, "time": 0.85372} +{"mode": "train", "epoch": 144, "iter": 2900, "lr": 0.00042, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.60438, "top5_acc": 0.8325, "loss_cls": 2.20902, "loss": 2.20902, "time": 0.85817} +{"mode": "train", "epoch": 144, "iter": 3000, "lr": 0.00042, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.61734, "top5_acc": 0.83297, "loss_cls": 2.19786, "loss": 2.19786, "time": 0.85796} +{"mode": "train", "epoch": 144, "iter": 3100, "lr": 0.00042, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.61312, "top5_acc": 0.83156, "loss_cls": 2.19228, "loss": 2.19228, "time": 0.85276} +{"mode": "train", "epoch": 144, "iter": 3200, "lr": 0.00041, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.62359, "top5_acc": 0.83828, "loss_cls": 2.14657, "loss": 2.14657, "time": 0.85131} +{"mode": "train", "epoch": 144, "iter": 3300, "lr": 0.00041, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.60844, "top5_acc": 0.84109, "loss_cls": 2.19402, "loss": 2.19402, "time": 0.85139} +{"mode": "train", "epoch": 144, "iter": 3400, "lr": 0.00041, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.61641, "top5_acc": 0.84203, "loss_cls": 2.15289, "loss": 2.15289, "time": 0.85519} +{"mode": "train", "epoch": 144, "iter": 3500, "lr": 0.0004, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.61234, "top5_acc": 0.83047, "loss_cls": 2.20349, "loss": 2.20349, "time": 0.84693} +{"mode": "train", "epoch": 144, "iter": 3600, "lr": 0.0004, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.61844, "top5_acc": 0.84469, "loss_cls": 2.14744, "loss": 2.14744, "time": 0.85751} +{"mode": "train", "epoch": 144, "iter": 3700, "lr": 0.0004, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.62, "top5_acc": 0.83906, "loss_cls": 2.15646, "loss": 2.15646, "time": 0.85834} +{"mode": "val", "epoch": 144, "iter": 309, "lr": 0.00039, "top1_acc": 0.44603, "top5_acc": 0.69088, "mean_class_accuracy": 0.44578} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.00039, "memory": 15990, "data_time": 1.55609, "top1_acc": 0.63422, "top5_acc": 0.85547, "loss_cls": 2.08594, "loss": 2.08594, "time": 2.59047} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 0.00039, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.62062, "top5_acc": 0.84453, "loss_cls": 2.13726, "loss": 2.13726, "time": 0.85496} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 0.00038, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.62375, "top5_acc": 0.83859, "loss_cls": 2.15164, "loss": 2.15164, "time": 0.85519} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 0.00038, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.62828, "top5_acc": 0.85266, "loss_cls": 2.11325, "loss": 2.11325, "time": 0.85104} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 0.00038, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.62875, "top5_acc": 0.84797, "loss_cls": 2.08633, "loss": 2.08633, "time": 0.85352} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 0.00037, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.62891, "top5_acc": 0.84484, "loss_cls": 2.10774, "loss": 2.10774, "time": 0.85942} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 0.00037, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.63719, "top5_acc": 0.85031, "loss_cls": 2.07756, "loss": 2.07756, "time": 0.85963} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 0.00037, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.62203, "top5_acc": 0.84625, "loss_cls": 2.13103, "loss": 2.13103, "time": 0.85601} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 0.00036, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.63359, "top5_acc": 0.85141, "loss_cls": 2.0666, "loss": 2.0666, "time": 0.85115} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 0.00036, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.63016, "top5_acc": 0.84344, "loss_cls": 2.12904, "loss": 2.12904, "time": 0.86087} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 0.00036, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.6225, "top5_acc": 0.84094, "loss_cls": 2.1431, "loss": 2.1431, "time": 0.85286} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 0.00035, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.61203, "top5_acc": 0.84016, "loss_cls": 2.17276, "loss": 2.17276, "time": 0.8547} +{"mode": "train", "epoch": 145, "iter": 1300, "lr": 0.00035, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.62406, "top5_acc": 0.84328, "loss_cls": 2.13407, "loss": 2.13407, "time": 0.85659} +{"mode": "train", "epoch": 145, "iter": 1400, "lr": 0.00035, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.63766, "top5_acc": 0.84781, "loss_cls": 2.0899, "loss": 2.0899, "time": 0.85212} +{"mode": "train", "epoch": 145, "iter": 1500, "lr": 0.00034, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.62172, "top5_acc": 0.84078, "loss_cls": 2.12422, "loss": 2.12422, "time": 0.85524} +{"mode": "train", "epoch": 145, "iter": 1600, "lr": 0.00034, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.62219, "top5_acc": 0.84016, "loss_cls": 2.14311, "loss": 2.14311, "time": 0.85901} +{"mode": "train", "epoch": 145, "iter": 1700, "lr": 0.00034, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.6325, "top5_acc": 0.84891, "loss_cls": 2.07524, "loss": 2.07524, "time": 0.84986} +{"mode": "train", "epoch": 145, "iter": 1800, "lr": 0.00033, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.63, "top5_acc": 0.84266, "loss_cls": 2.11213, "loss": 2.11213, "time": 0.85358} +{"mode": "train", "epoch": 145, "iter": 1900, "lr": 0.00033, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.63156, "top5_acc": 0.84484, "loss_cls": 2.1119, "loss": 2.1119, "time": 0.85056} +{"mode": "train", "epoch": 145, "iter": 2000, "lr": 0.00033, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.61484, "top5_acc": 0.83547, "loss_cls": 2.1683, "loss": 2.1683, "time": 0.84973} +{"mode": "train", "epoch": 145, "iter": 2100, "lr": 0.00032, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.62531, "top5_acc": 0.85266, "loss_cls": 2.1075, "loss": 2.1075, "time": 0.85113} +{"mode": "train", "epoch": 145, "iter": 2200, "lr": 0.00032, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.63094, "top5_acc": 0.84766, "loss_cls": 2.08232, "loss": 2.08232, "time": 0.85073} +{"mode": "train", "epoch": 145, "iter": 2300, "lr": 0.00032, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.62891, "top5_acc": 0.84938, "loss_cls": 2.07526, "loss": 2.07526, "time": 0.85086} +{"mode": "train", "epoch": 145, "iter": 2400, "lr": 0.00031, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.62375, "top5_acc": 0.84688, "loss_cls": 2.1285, "loss": 2.1285, "time": 0.85234} +{"mode": "train", "epoch": 145, "iter": 2500, "lr": 0.00031, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.63094, "top5_acc": 0.84656, "loss_cls": 2.11719, "loss": 2.11719, "time": 0.84961} +{"mode": "train", "epoch": 145, "iter": 2600, "lr": 0.00031, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.62516, "top5_acc": 0.83469, "loss_cls": 2.15271, "loss": 2.15271, "time": 0.85009} +{"mode": "train", "epoch": 145, "iter": 2700, "lr": 0.00031, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.61672, "top5_acc": 0.83734, "loss_cls": 2.17104, "loss": 2.17104, "time": 0.8502} +{"mode": "train", "epoch": 145, "iter": 2800, "lr": 0.0003, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.63578, "top5_acc": 0.85016, "loss_cls": 2.09223, "loss": 2.09223, "time": 0.85244} +{"mode": "train", "epoch": 145, "iter": 2900, "lr": 0.0003, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.61703, "top5_acc": 0.83391, "loss_cls": 2.16769, "loss": 2.16769, "time": 0.85061} +{"mode": "train", "epoch": 145, "iter": 3000, "lr": 0.0003, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.62391, "top5_acc": 0.84141, "loss_cls": 2.15916, "loss": 2.15916, "time": 0.85912} +{"mode": "train", "epoch": 145, "iter": 3100, "lr": 0.00029, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.61531, "top5_acc": 0.83922, "loss_cls": 2.15727, "loss": 2.15727, "time": 0.85417} +{"mode": "train", "epoch": 145, "iter": 3200, "lr": 0.00029, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.63016, "top5_acc": 0.84219, "loss_cls": 2.10927, "loss": 2.10927, "time": 0.85513} +{"mode": "train", "epoch": 145, "iter": 3300, "lr": 0.00029, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.61891, "top5_acc": 0.83641, "loss_cls": 2.16037, "loss": 2.16037, "time": 0.85168} +{"mode": "train", "epoch": 145, "iter": 3400, "lr": 0.00028, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.62562, "top5_acc": 0.84312, "loss_cls": 2.10834, "loss": 2.10834, "time": 0.8455} +{"mode": "train", "epoch": 145, "iter": 3500, "lr": 0.00028, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.62578, "top5_acc": 0.83922, "loss_cls": 2.14452, "loss": 2.14452, "time": 0.84975} +{"mode": "train", "epoch": 145, "iter": 3600, "lr": 0.00028, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.62187, "top5_acc": 0.84625, "loss_cls": 2.1157, "loss": 2.1157, "time": 0.85117} +{"mode": "train", "epoch": 145, "iter": 3700, "lr": 0.00028, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.61609, "top5_acc": 0.83906, "loss_cls": 2.16318, "loss": 2.16318, "time": 0.85215} +{"mode": "val", "epoch": 145, "iter": 309, "lr": 0.00027, "top1_acc": 0.44887, "top5_acc": 0.69387, "mean_class_accuracy": 0.44861} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 0.00027, "memory": 15990, "data_time": 1.57858, "top1_acc": 0.63516, "top5_acc": 0.84828, "loss_cls": 2.07924, "loss": 2.07924, "time": 2.61304} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 0.00027, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.63359, "top5_acc": 0.85203, "loss_cls": 2.09924, "loss": 2.09924, "time": 0.85863} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 0.00027, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.63484, "top5_acc": 0.84938, "loss_cls": 2.10596, "loss": 2.10596, "time": 0.85967} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 0.00026, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.63141, "top5_acc": 0.8425, "loss_cls": 2.10543, "loss": 2.10543, "time": 0.85704} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 0.00026, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.63734, "top5_acc": 0.84516, "loss_cls": 2.09614, "loss": 2.09614, "time": 0.85858} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 0.00026, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.63125, "top5_acc": 0.85, "loss_cls": 2.07986, "loss": 2.07986, "time": 0.85791} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 0.00025, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.635, "top5_acc": 0.85094, "loss_cls": 2.10464, "loss": 2.10464, "time": 0.85646} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 0.00025, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.63703, "top5_acc": 0.85344, "loss_cls": 2.04738, "loss": 2.04738, "time": 0.85501} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 0.00025, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.6275, "top5_acc": 0.84516, "loss_cls": 2.09298, "loss": 2.09298, "time": 0.85372} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 0.00025, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.6425, "top5_acc": 0.85359, "loss_cls": 2.03501, "loss": 2.03501, "time": 0.85637} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 0.00024, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.64047, "top5_acc": 0.84938, "loss_cls": 2.04086, "loss": 2.04086, "time": 0.85234} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 0.00024, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.63578, "top5_acc": 0.84938, "loss_cls": 2.07651, "loss": 2.07651, "time": 0.85101} +{"mode": "train", "epoch": 146, "iter": 1300, "lr": 0.00024, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.63078, "top5_acc": 0.84562, "loss_cls": 2.08703, "loss": 2.08703, "time": 0.85287} +{"mode": "train", "epoch": 146, "iter": 1400, "lr": 0.00023, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.63813, "top5_acc": 0.85781, "loss_cls": 2.04512, "loss": 2.04512, "time": 0.84908} +{"mode": "train", "epoch": 146, "iter": 1500, "lr": 0.00023, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.65016, "top5_acc": 0.85703, "loss_cls": 2.02251, "loss": 2.02251, "time": 0.85125} +{"mode": "train", "epoch": 146, "iter": 1600, "lr": 0.00023, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.63281, "top5_acc": 0.84391, "loss_cls": 2.08579, "loss": 2.08579, "time": 0.85496} +{"mode": "train", "epoch": 146, "iter": 1700, "lr": 0.00023, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.63313, "top5_acc": 0.85359, "loss_cls": 2.07862, "loss": 2.07862, "time": 0.85374} +{"mode": "train", "epoch": 146, "iter": 1800, "lr": 0.00022, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.63953, "top5_acc": 0.85375, "loss_cls": 2.05163, "loss": 2.05163, "time": 0.85544} +{"mode": "train", "epoch": 146, "iter": 1900, "lr": 0.00022, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.63203, "top5_acc": 0.84797, "loss_cls": 2.09027, "loss": 2.09027, "time": 0.84728} +{"mode": "train", "epoch": 146, "iter": 2000, "lr": 0.00022, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.63031, "top5_acc": 0.83953, "loss_cls": 2.09606, "loss": 2.09606, "time": 0.85274} +{"mode": "train", "epoch": 146, "iter": 2100, "lr": 0.00022, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.63391, "top5_acc": 0.85328, "loss_cls": 2.07831, "loss": 2.07831, "time": 0.85424} +{"mode": "train", "epoch": 146, "iter": 2200, "lr": 0.00021, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.63422, "top5_acc": 0.84859, "loss_cls": 2.08473, "loss": 2.08473, "time": 0.85959} +{"mode": "train", "epoch": 146, "iter": 2300, "lr": 0.00021, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.63406, "top5_acc": 0.85078, "loss_cls": 2.0772, "loss": 2.0772, "time": 0.85523} +{"mode": "train", "epoch": 146, "iter": 2400, "lr": 0.00021, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.63578, "top5_acc": 0.84844, "loss_cls": 2.07044, "loss": 2.07044, "time": 0.86071} +{"mode": "train", "epoch": 146, "iter": 2500, "lr": 0.00021, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.63047, "top5_acc": 0.85281, "loss_cls": 2.05651, "loss": 2.05651, "time": 0.85916} +{"mode": "train", "epoch": 146, "iter": 2600, "lr": 0.0002, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.63172, "top5_acc": 0.85, "loss_cls": 2.09331, "loss": 2.09331, "time": 0.85558} +{"mode": "train", "epoch": 146, "iter": 2700, "lr": 0.0002, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.62609, "top5_acc": 0.84219, "loss_cls": 2.11024, "loss": 2.11024, "time": 0.85456} +{"mode": "train", "epoch": 146, "iter": 2800, "lr": 0.0002, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.64016, "top5_acc": 0.845, "loss_cls": 2.07485, "loss": 2.07485, "time": 0.85823} +{"mode": "train", "epoch": 146, "iter": 2900, "lr": 0.0002, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.63391, "top5_acc": 0.85453, "loss_cls": 2.07639, "loss": 2.07639, "time": 0.85648} +{"mode": "train", "epoch": 146, "iter": 3000, "lr": 0.00019, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.63641, "top5_acc": 0.85031, "loss_cls": 2.06045, "loss": 2.06045, "time": 0.85788} +{"mode": "train", "epoch": 146, "iter": 3100, "lr": 0.00019, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.63313, "top5_acc": 0.85078, "loss_cls": 2.09413, "loss": 2.09413, "time": 0.85703} +{"mode": "train", "epoch": 146, "iter": 3200, "lr": 0.00019, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.62656, "top5_acc": 0.84234, "loss_cls": 2.10601, "loss": 2.10601, "time": 0.85463} +{"mode": "train", "epoch": 146, "iter": 3300, "lr": 0.00019, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.63562, "top5_acc": 0.85281, "loss_cls": 2.06365, "loss": 2.06365, "time": 0.85127} +{"mode": "train", "epoch": 146, "iter": 3400, "lr": 0.00018, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.63922, "top5_acc": 0.85016, "loss_cls": 2.06905, "loss": 2.06905, "time": 0.85771} +{"mode": "train", "epoch": 146, "iter": 3500, "lr": 0.00018, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.64188, "top5_acc": 0.85906, "loss_cls": 2.06601, "loss": 2.06601, "time": 0.85298} +{"mode": "train", "epoch": 146, "iter": 3600, "lr": 0.00018, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.63813, "top5_acc": 0.85156, "loss_cls": 2.06873, "loss": 2.06873, "time": 0.85209} +{"mode": "train", "epoch": 146, "iter": 3700, "lr": 0.00018, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.63172, "top5_acc": 0.85125, "loss_cls": 2.09601, "loss": 2.09601, "time": 0.8543} +{"mode": "val", "epoch": 146, "iter": 309, "lr": 0.00018, "top1_acc": 0.44826, "top5_acc": 0.69407, "mean_class_accuracy": 0.44804} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 0.00017, "memory": 15990, "data_time": 1.56813, "top1_acc": 0.64891, "top5_acc": 0.85453, "loss_cls": 2.02233, "loss": 2.02233, "time": 2.61449} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 0.00017, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.64938, "top5_acc": 0.85688, "loss_cls": 2.0094, "loss": 2.0094, "time": 0.85671} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 0.00017, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.65188, "top5_acc": 0.86125, "loss_cls": 2.00609, "loss": 2.00609, "time": 0.85843} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 0.00017, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.64797, "top5_acc": 0.85672, "loss_cls": 2.01136, "loss": 2.01136, "time": 0.85548} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 0.00016, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.64453, "top5_acc": 0.85344, "loss_cls": 2.04303, "loss": 2.04303, "time": 0.85467} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 0.00016, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.64859, "top5_acc": 0.86, "loss_cls": 2.01503, "loss": 2.01503, "time": 0.85825} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 0.00016, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.64984, "top5_acc": 0.85656, "loss_cls": 2.03081, "loss": 2.03081, "time": 0.85697} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 0.00016, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.64469, "top5_acc": 0.85797, "loss_cls": 2.03288, "loss": 2.03288, "time": 0.85541} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 0.00015, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.63875, "top5_acc": 0.85594, "loss_cls": 2.03839, "loss": 2.03839, "time": 0.85484} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 0.00015, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.64703, "top5_acc": 0.85656, "loss_cls": 2.02606, "loss": 2.02606, "time": 0.85691} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 0.00015, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.64125, "top5_acc": 0.85656, "loss_cls": 2.05792, "loss": 2.05792, "time": 0.85347} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 0.00015, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.63359, "top5_acc": 0.85391, "loss_cls": 2.06793, "loss": 2.06793, "time": 0.8516} +{"mode": "train", "epoch": 147, "iter": 1300, "lr": 0.00015, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.64797, "top5_acc": 0.84969, "loss_cls": 2.02433, "loss": 2.02433, "time": 0.85476} +{"mode": "train", "epoch": 147, "iter": 1400, "lr": 0.00014, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.63734, "top5_acc": 0.85344, "loss_cls": 2.04694, "loss": 2.04694, "time": 0.85463} +{"mode": "train", "epoch": 147, "iter": 1500, "lr": 0.00014, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.65266, "top5_acc": 0.86188, "loss_cls": 1.98933, "loss": 1.98933, "time": 0.84967} +{"mode": "train", "epoch": 147, "iter": 1600, "lr": 0.00014, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.63797, "top5_acc": 0.85719, "loss_cls": 2.04346, "loss": 2.04346, "time": 0.85022} +{"mode": "train", "epoch": 147, "iter": 1700, "lr": 0.00014, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.63, "top5_acc": 0.85375, "loss_cls": 2.07483, "loss": 2.07483, "time": 0.85157} +{"mode": "train", "epoch": 147, "iter": 1800, "lr": 0.00014, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.63266, "top5_acc": 0.84812, "loss_cls": 2.08699, "loss": 2.08699, "time": 0.85645} +{"mode": "train", "epoch": 147, "iter": 1900, "lr": 0.00013, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.63734, "top5_acc": 0.84797, "loss_cls": 2.06979, "loss": 2.06979, "time": 0.85577} +{"mode": "train", "epoch": 147, "iter": 2000, "lr": 0.00013, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.64422, "top5_acc": 0.86016, "loss_cls": 2.02075, "loss": 2.02075, "time": 0.85556} +{"mode": "train", "epoch": 147, "iter": 2100, "lr": 0.00013, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.62391, "top5_acc": 0.84453, "loss_cls": 2.11964, "loss": 2.11964, "time": 0.85799} +{"mode": "train", "epoch": 147, "iter": 2200, "lr": 0.00013, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.64578, "top5_acc": 0.85516, "loss_cls": 2.04073, "loss": 2.04073, "time": 0.86168} +{"mode": "train", "epoch": 147, "iter": 2300, "lr": 0.00013, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.64422, "top5_acc": 0.85953, "loss_cls": 2.03826, "loss": 2.03826, "time": 0.86126} +{"mode": "train", "epoch": 147, "iter": 2400, "lr": 0.00012, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.65016, "top5_acc": 0.86172, "loss_cls": 2.01647, "loss": 2.01647, "time": 0.8616} +{"mode": "train", "epoch": 147, "iter": 2500, "lr": 0.00012, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.64609, "top5_acc": 0.85375, "loss_cls": 2.03147, "loss": 2.03147, "time": 0.85989} +{"mode": "train", "epoch": 147, "iter": 2600, "lr": 0.00012, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.64547, "top5_acc": 0.85484, "loss_cls": 2.02845, "loss": 2.02845, "time": 0.86286} +{"mode": "train", "epoch": 147, "iter": 2700, "lr": 0.00012, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.63578, "top5_acc": 0.85125, "loss_cls": 2.04636, "loss": 2.04636, "time": 0.85984} +{"mode": "train", "epoch": 147, "iter": 2800, "lr": 0.00012, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.63656, "top5_acc": 0.85609, "loss_cls": 2.04212, "loss": 2.04212, "time": 0.86736} +{"mode": "train", "epoch": 147, "iter": 2900, "lr": 0.00011, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.63266, "top5_acc": 0.8475, "loss_cls": 2.10275, "loss": 2.10275, "time": 0.86286} +{"mode": "train", "epoch": 147, "iter": 3000, "lr": 0.00011, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.64172, "top5_acc": 0.85969, "loss_cls": 2.04584, "loss": 2.04584, "time": 0.86016} +{"mode": "train", "epoch": 147, "iter": 3100, "lr": 0.00011, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.63641, "top5_acc": 0.855, "loss_cls": 2.03062, "loss": 2.03062, "time": 0.86465} +{"mode": "train", "epoch": 147, "iter": 3200, "lr": 0.00011, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.62984, "top5_acc": 0.84438, "loss_cls": 2.10287, "loss": 2.10287, "time": 0.84989} +{"mode": "train", "epoch": 147, "iter": 3300, "lr": 0.00011, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.63547, "top5_acc": 0.85141, "loss_cls": 2.09025, "loss": 2.09025, "time": 0.84654} +{"mode": "train", "epoch": 147, "iter": 3400, "lr": 0.0001, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.64766, "top5_acc": 0.85984, "loss_cls": 2.01057, "loss": 2.01057, "time": 0.84898} +{"mode": "train", "epoch": 147, "iter": 3500, "lr": 0.0001, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.64281, "top5_acc": 0.85969, "loss_cls": 2.04097, "loss": 2.04097, "time": 0.85748} +{"mode": "train", "epoch": 147, "iter": 3600, "lr": 0.0001, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.64656, "top5_acc": 0.85234, "loss_cls": 2.03577, "loss": 2.03577, "time": 0.85322} +{"mode": "train", "epoch": 147, "iter": 3700, "lr": 0.0001, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.64453, "top5_acc": 0.85359, "loss_cls": 2.04451, "loss": 2.04451, "time": 0.85598} +{"mode": "val", "epoch": 147, "iter": 309, "lr": 0.0001, "top1_acc": 0.44882, "top5_acc": 0.69316, "mean_class_accuracy": 0.44862} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 0.0001, "memory": 15990, "data_time": 1.58208, "top1_acc": 0.65766, "top5_acc": 0.86406, "loss_cls": 1.98997, "loss": 1.98997, "time": 2.61305} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 0.0001, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.65344, "top5_acc": 0.86297, "loss_cls": 1.99323, "loss": 1.99323, "time": 0.85724} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 9e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.65578, "top5_acc": 0.86672, "loss_cls": 1.97022, "loss": 1.97022, "time": 0.85661} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 9e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.64438, "top5_acc": 0.85562, "loss_cls": 2.04306, "loss": 2.04306, "time": 0.85842} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 9e-05, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.66109, "top5_acc": 0.86109, "loss_cls": 1.98247, "loss": 1.98247, "time": 0.85676} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 9e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.64781, "top5_acc": 0.86344, "loss_cls": 1.99625, "loss": 1.99625, "time": 0.85786} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 9e-05, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.64938, "top5_acc": 0.85, "loss_cls": 2.0193, "loss": 2.0193, "time": 0.85418} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 9e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.65266, "top5_acc": 0.85812, "loss_cls": 2.00291, "loss": 2.00291, "time": 0.85603} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 8e-05, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.64609, "top5_acc": 0.86125, "loss_cls": 2.00814, "loss": 2.00814, "time": 0.85537} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 8e-05, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.65094, "top5_acc": 0.85797, "loss_cls": 2.02124, "loss": 2.02124, "time": 0.85583} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 8e-05, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.64609, "top5_acc": 0.86078, "loss_cls": 2.01045, "loss": 2.01045, "time": 0.85781} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 8e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.64453, "top5_acc": 0.85875, "loss_cls": 2.03366, "loss": 2.03366, "time": 0.8568} +{"mode": "train", "epoch": 148, "iter": 1300, "lr": 8e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.645, "top5_acc": 0.85391, "loss_cls": 2.028, "loss": 2.028, "time": 0.85246} +{"mode": "train", "epoch": 148, "iter": 1400, "lr": 8e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.64672, "top5_acc": 0.86391, "loss_cls": 2.00941, "loss": 2.00941, "time": 0.85407} +{"mode": "train", "epoch": 148, "iter": 1500, "lr": 7e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.64078, "top5_acc": 0.85359, "loss_cls": 2.02248, "loss": 2.02248, "time": 0.85385} +{"mode": "train", "epoch": 148, "iter": 1600, "lr": 7e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.63281, "top5_acc": 0.84719, "loss_cls": 2.08965, "loss": 2.08965, "time": 0.85023} +{"mode": "train", "epoch": 148, "iter": 1700, "lr": 7e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.6475, "top5_acc": 0.8575, "loss_cls": 2.02842, "loss": 2.02842, "time": 0.849} +{"mode": "train", "epoch": 148, "iter": 1800, "lr": 7e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.63672, "top5_acc": 0.85625, "loss_cls": 2.03566, "loss": 2.03566, "time": 0.84936} +{"mode": "train", "epoch": 148, "iter": 1900, "lr": 7e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.63813, "top5_acc": 0.85328, "loss_cls": 2.04556, "loss": 2.04556, "time": 0.85396} +{"mode": "train", "epoch": 148, "iter": 2000, "lr": 7e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.64125, "top5_acc": 0.85844, "loss_cls": 2.02918, "loss": 2.02918, "time": 0.84702} +{"mode": "train", "epoch": 148, "iter": 2100, "lr": 7e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.63531, "top5_acc": 0.85266, "loss_cls": 2.0596, "loss": 2.0596, "time": 0.85654} +{"mode": "train", "epoch": 148, "iter": 2200, "lr": 6e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65141, "top5_acc": 0.86172, "loss_cls": 2.00661, "loss": 2.00661, "time": 0.85096} +{"mode": "train", "epoch": 148, "iter": 2300, "lr": 6e-05, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65125, "top5_acc": 0.85938, "loss_cls": 2.02683, "loss": 2.02683, "time": 0.85551} +{"mode": "train", "epoch": 148, "iter": 2400, "lr": 6e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.64938, "top5_acc": 0.85781, "loss_cls": 2.01192, "loss": 2.01192, "time": 0.85344} +{"mode": "train", "epoch": 148, "iter": 2500, "lr": 6e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.65984, "top5_acc": 0.86484, "loss_cls": 1.96248, "loss": 1.96248, "time": 0.84974} +{"mode": "train", "epoch": 148, "iter": 2600, "lr": 6e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.65344, "top5_acc": 0.86359, "loss_cls": 1.96433, "loss": 1.96433, "time": 0.84965} +{"mode": "train", "epoch": 148, "iter": 2700, "lr": 6e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.64812, "top5_acc": 0.85938, "loss_cls": 2.01012, "loss": 2.01012, "time": 0.85207} +{"mode": "train", "epoch": 148, "iter": 2800, "lr": 6e-05, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.64766, "top5_acc": 0.85484, "loss_cls": 2.04178, "loss": 2.04178, "time": 0.84808} +{"mode": "train", "epoch": 148, "iter": 2900, "lr": 5e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.64297, "top5_acc": 0.85641, "loss_cls": 2.03173, "loss": 2.03173, "time": 0.85457} +{"mode": "train", "epoch": 148, "iter": 3000, "lr": 5e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.64391, "top5_acc": 0.85734, "loss_cls": 2.03546, "loss": 2.03546, "time": 0.85068} +{"mode": "train", "epoch": 148, "iter": 3100, "lr": 5e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.65047, "top5_acc": 0.85594, "loss_cls": 2.01266, "loss": 2.01266, "time": 0.84991} +{"mode": "train", "epoch": 148, "iter": 3200, "lr": 5e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.64562, "top5_acc": 0.85688, "loss_cls": 2.03433, "loss": 2.03433, "time": 0.84902} +{"mode": "train", "epoch": 148, "iter": 3300, "lr": 5e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.64609, "top5_acc": 0.85312, "loss_cls": 2.02125, "loss": 2.02125, "time": 0.85462} +{"mode": "train", "epoch": 148, "iter": 3400, "lr": 5e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.64625, "top5_acc": 0.85844, "loss_cls": 2.01503, "loss": 2.01503, "time": 0.85575} +{"mode": "train", "epoch": 148, "iter": 3500, "lr": 5e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.64688, "top5_acc": 0.86547, "loss_cls": 1.99933, "loss": 1.99933, "time": 0.85358} +{"mode": "train", "epoch": 148, "iter": 3600, "lr": 5e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.65516, "top5_acc": 0.86219, "loss_cls": 2.00442, "loss": 2.00442, "time": 0.84759} +{"mode": "train", "epoch": 148, "iter": 3700, "lr": 4e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.64203, "top5_acc": 0.85391, "loss_cls": 2.0366, "loss": 2.0366, "time": 0.85134} +{"mode": "val", "epoch": 148, "iter": 309, "lr": 4e-05, "top1_acc": 0.44907, "top5_acc": 0.69549, "mean_class_accuracy": 0.44885} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 4e-05, "memory": 15990, "data_time": 1.61833, "top1_acc": 0.66141, "top5_acc": 0.86078, "loss_cls": 1.97184, "loss": 1.97184, "time": 2.65238} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 4e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.64469, "top5_acc": 0.85531, "loss_cls": 2.03951, "loss": 2.03951, "time": 0.85381} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 4e-05, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.64562, "top5_acc": 0.85953, "loss_cls": 2.01005, "loss": 2.01005, "time": 0.8577} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 4e-05, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.65375, "top5_acc": 0.86094, "loss_cls": 1.98652, "loss": 1.98652, "time": 0.85389} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 4e-05, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.64594, "top5_acc": 0.86516, "loss_cls": 1.98001, "loss": 1.98001, "time": 0.85081} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 4e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.655, "top5_acc": 0.86188, "loss_cls": 1.98456, "loss": 1.98456, "time": 0.85373} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 4e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.65328, "top5_acc": 0.86484, "loss_cls": 1.95791, "loss": 1.95791, "time": 0.85136} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 4e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.64797, "top5_acc": 0.85375, "loss_cls": 2.0384, "loss": 2.0384, "time": 0.84954} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 3e-05, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.6425, "top5_acc": 0.85844, "loss_cls": 2.0377, "loss": 2.0377, "time": 0.85557} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 3e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.64141, "top5_acc": 0.85844, "loss_cls": 2.03032, "loss": 2.03032, "time": 0.85126} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 3e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.65062, "top5_acc": 0.85688, "loss_cls": 2.01089, "loss": 2.01089, "time": 0.84914} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 3e-05, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.65969, "top5_acc": 0.86812, "loss_cls": 1.94636, "loss": 1.94636, "time": 0.84744} +{"mode": "train", "epoch": 149, "iter": 1300, "lr": 3e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65578, "top5_acc": 0.86969, "loss_cls": 1.96607, "loss": 1.96607, "time": 0.85209} +{"mode": "train", "epoch": 149, "iter": 1400, "lr": 3e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.64609, "top5_acc": 0.85609, "loss_cls": 2.0149, "loss": 2.0149, "time": 0.85518} +{"mode": "train", "epoch": 149, "iter": 1500, "lr": 3e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.65891, "top5_acc": 0.87, "loss_cls": 1.97883, "loss": 1.97883, "time": 0.85582} +{"mode": "train", "epoch": 149, "iter": 1600, "lr": 3e-05, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.65516, "top5_acc": 0.86016, "loss_cls": 1.9972, "loss": 1.9972, "time": 0.85578} +{"mode": "train", "epoch": 149, "iter": 1700, "lr": 3e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.65844, "top5_acc": 0.86594, "loss_cls": 1.95951, "loss": 1.95951, "time": 0.85314} +{"mode": "train", "epoch": 149, "iter": 1800, "lr": 3e-05, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.655, "top5_acc": 0.86078, "loss_cls": 2.00508, "loss": 2.00508, "time": 0.8528} +{"mode": "train", "epoch": 149, "iter": 1900, "lr": 2e-05, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.64828, "top5_acc": 0.85219, "loss_cls": 2.0313, "loss": 2.0313, "time": 0.8505} +{"mode": "train", "epoch": 149, "iter": 2000, "lr": 2e-05, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.65328, "top5_acc": 0.85922, "loss_cls": 1.98769, "loss": 1.98769, "time": 0.85663} +{"mode": "train", "epoch": 149, "iter": 2100, "lr": 2e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.65109, "top5_acc": 0.85953, "loss_cls": 2.01933, "loss": 2.01933, "time": 0.854} +{"mode": "train", "epoch": 149, "iter": 2200, "lr": 2e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.64469, "top5_acc": 0.85266, "loss_cls": 2.0583, "loss": 2.0583, "time": 0.85479} +{"mode": "train", "epoch": 149, "iter": 2300, "lr": 2e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.66, "top5_acc": 0.86594, "loss_cls": 1.95949, "loss": 1.95949, "time": 0.85582} +{"mode": "train", "epoch": 149, "iter": 2400, "lr": 2e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.65172, "top5_acc": 0.86625, "loss_cls": 1.98692, "loss": 1.98692, "time": 0.85231} +{"mode": "train", "epoch": 149, "iter": 2500, "lr": 2e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.66125, "top5_acc": 0.86312, "loss_cls": 1.97541, "loss": 1.97541, "time": 0.85253} +{"mode": "train", "epoch": 149, "iter": 2600, "lr": 2e-05, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.63922, "top5_acc": 0.855, "loss_cls": 2.03463, "loss": 2.03463, "time": 0.85497} +{"mode": "train", "epoch": 149, "iter": 2700, "lr": 2e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.65094, "top5_acc": 0.85797, "loss_cls": 2.00966, "loss": 2.00966, "time": 0.85693} +{"mode": "train", "epoch": 149, "iter": 2800, "lr": 2e-05, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.66422, "top5_acc": 0.86344, "loss_cls": 1.97626, "loss": 1.97626, "time": 0.85534} +{"mode": "train", "epoch": 149, "iter": 2900, "lr": 2e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.6625, "top5_acc": 0.86141, "loss_cls": 1.98382, "loss": 1.98382, "time": 0.86211} +{"mode": "train", "epoch": 149, "iter": 3000, "lr": 2e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.65812, "top5_acc": 0.86203, "loss_cls": 1.99052, "loss": 1.99052, "time": 0.85472} +{"mode": "train", "epoch": 149, "iter": 3100, "lr": 2e-05, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.64734, "top5_acc": 0.85547, "loss_cls": 1.99199, "loss": 1.99199, "time": 0.84929} +{"mode": "train", "epoch": 149, "iter": 3200, "lr": 1e-05, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.65234, "top5_acc": 0.85438, "loss_cls": 1.99912, "loss": 1.99912, "time": 0.85305} +{"mode": "train", "epoch": 149, "iter": 3300, "lr": 1e-05, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.65219, "top5_acc": 0.86312, "loss_cls": 2.00761, "loss": 2.00761, "time": 0.84959} +{"mode": "train", "epoch": 149, "iter": 3400, "lr": 1e-05, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.65078, "top5_acc": 0.86109, "loss_cls": 2.01013, "loss": 2.01013, "time": 0.85106} +{"mode": "train", "epoch": 149, "iter": 3500, "lr": 1e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.65859, "top5_acc": 0.85719, "loss_cls": 2.00989, "loss": 2.00989, "time": 0.85506} +{"mode": "train", "epoch": 149, "iter": 3600, "lr": 1e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.65062, "top5_acc": 0.85922, "loss_cls": 1.99726, "loss": 1.99726, "time": 0.8531} +{"mode": "train", "epoch": 149, "iter": 3700, "lr": 1e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.65578, "top5_acc": 0.86984, "loss_cls": 1.96373, "loss": 1.96373, "time": 0.85261} +{"mode": "val", "epoch": 149, "iter": 309, "lr": 1e-05, "top1_acc": 0.44735, "top5_acc": 0.69255, "mean_class_accuracy": 0.44711} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 1e-05, "memory": 15990, "data_time": 1.59432, "top1_acc": 0.64953, "top5_acc": 0.86641, "loss_cls": 1.96747, "loss": 1.96747, "time": 2.62624} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 1e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.64812, "top5_acc": 0.85766, "loss_cls": 2.01503, "loss": 2.01503, "time": 0.85311} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 1e-05, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.65812, "top5_acc": 0.86016, "loss_cls": 1.98947, "loss": 1.98947, "time": 0.85516} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 1e-05, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.66156, "top5_acc": 0.86844, "loss_cls": 1.94908, "loss": 1.94908, "time": 0.84878} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 1e-05, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.64453, "top5_acc": 0.85812, "loss_cls": 2.03056, "loss": 2.03056, "time": 0.84947} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 1e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.655, "top5_acc": 0.86328, "loss_cls": 1.98182, "loss": 1.98182, "time": 0.84699} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 1e-05, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.65781, "top5_acc": 0.86312, "loss_cls": 1.98942, "loss": 1.98942, "time": 0.85225} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 1e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65734, "top5_acc": 0.85406, "loss_cls": 2.00848, "loss": 2.00848, "time": 0.84715} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 1e-05, "memory": 15990, "data_time": 0.0006, "top1_acc": 0.65641, "top5_acc": 0.85922, "loss_cls": 1.99481, "loss": 1.99481, "time": 0.85104} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 1e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.66219, "top5_acc": 0.8675, "loss_cls": 1.95208, "loss": 1.95208, "time": 0.84234} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 1e-05, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65047, "top5_acc": 0.85516, "loss_cls": 2.00494, "loss": 2.00494, "time": 0.84389} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 1e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.65578, "top5_acc": 0.86453, "loss_cls": 1.97292, "loss": 1.97292, "time": 0.84031} +{"mode": "train", "epoch": 150, "iter": 1300, "lr": 0.0, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.65234, "top5_acc": 0.86516, "loss_cls": 1.99014, "loss": 1.99014, "time": 0.84014} +{"mode": "train", "epoch": 150, "iter": 1400, "lr": 0.0, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65094, "top5_acc": 0.86688, "loss_cls": 1.97787, "loss": 1.97787, "time": 0.84509} +{"mode": "train", "epoch": 150, "iter": 1500, "lr": 0.0, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.65328, "top5_acc": 0.86016, "loss_cls": 1.98796, "loss": 1.98796, "time": 0.84874} +{"mode": "train", "epoch": 150, "iter": 1600, "lr": 0.0, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65703, "top5_acc": 0.86031, "loss_cls": 1.9968, "loss": 1.9968, "time": 0.83936} +{"mode": "train", "epoch": 150, "iter": 1700, "lr": 0.0, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.65688, "top5_acc": 0.85609, "loss_cls": 1.9908, "loss": 1.9908, "time": 0.84459} +{"mode": "train", "epoch": 150, "iter": 1800, "lr": 0.0, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.65109, "top5_acc": 0.86016, "loss_cls": 2.01344, "loss": 2.01344, "time": 0.84931} +{"mode": "train", "epoch": 150, "iter": 1900, "lr": 0.0, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.66219, "top5_acc": 0.86609, "loss_cls": 1.95123, "loss": 1.95123, "time": 0.84447} +{"mode": "train", "epoch": 150, "iter": 2000, "lr": 0.0, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65609, "top5_acc": 0.865, "loss_cls": 1.96405, "loss": 1.96405, "time": 0.84316} +{"mode": "train", "epoch": 150, "iter": 2100, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66219, "top5_acc": 0.86234, "loss_cls": 1.9832, "loss": 1.9832, "time": 0.84002} +{"mode": "train", "epoch": 150, "iter": 2200, "lr": 0.0, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65234, "top5_acc": 0.86547, "loss_cls": 1.98088, "loss": 1.98088, "time": 0.8443} +{"mode": "train", "epoch": 150, "iter": 2300, "lr": 0.0, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.65109, "top5_acc": 0.86375, "loss_cls": 1.98595, "loss": 1.98595, "time": 0.83682} +{"mode": "train", "epoch": 150, "iter": 2400, "lr": 0.0, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.6525, "top5_acc": 0.86781, "loss_cls": 1.98898, "loss": 1.98898, "time": 0.84187} +{"mode": "train", "epoch": 150, "iter": 2500, "lr": 0.0, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.6475, "top5_acc": 0.85984, "loss_cls": 1.99844, "loss": 1.99844, "time": 0.84418} +{"mode": "train", "epoch": 150, "iter": 2600, "lr": 0.0, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.65219, "top5_acc": 0.86219, "loss_cls": 1.99432, "loss": 1.99432, "time": 0.83636} +{"mode": "train", "epoch": 150, "iter": 2700, "lr": 0.0, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.64906, "top5_acc": 0.85547, "loss_cls": 2.01015, "loss": 2.01015, "time": 0.84096} +{"mode": "train", "epoch": 150, "iter": 2800, "lr": 0.0, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.64703, "top5_acc": 0.85438, "loss_cls": 2.03531, "loss": 2.03531, "time": 0.84} +{"mode": "train", "epoch": 150, "iter": 2900, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64453, "top5_acc": 0.85344, "loss_cls": 2.03021, "loss": 2.03021, "time": 0.83296} +{"mode": "train", "epoch": 150, "iter": 3000, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.64906, "top5_acc": 0.85891, "loss_cls": 2.03062, "loss": 2.03062, "time": 0.83288} +{"mode": "train", "epoch": 150, "iter": 3100, "lr": 0.0, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.65359, "top5_acc": 0.85891, "loss_cls": 2.00463, "loss": 2.00463, "time": 0.83425} +{"mode": "train", "epoch": 150, "iter": 3200, "lr": 0.0, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.65672, "top5_acc": 0.86281, "loss_cls": 1.97709, "loss": 1.97709, "time": 0.83771} +{"mode": "train", "epoch": 150, "iter": 3300, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.66844, "top5_acc": 0.87234, "loss_cls": 1.9209, "loss": 1.9209, "time": 0.83683} +{"mode": "train", "epoch": 150, "iter": 3400, "lr": 0.0, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.65734, "top5_acc": 0.85781, "loss_cls": 1.98538, "loss": 1.98538, "time": 0.84223} +{"mode": "train", "epoch": 150, "iter": 3500, "lr": 0.0, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.65125, "top5_acc": 0.85672, "loss_cls": 1.9887, "loss": 1.9887, "time": 0.833} +{"mode": "train", "epoch": 150, "iter": 3600, "lr": 0.0, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.66891, "top5_acc": 0.8725, "loss_cls": 1.92464, "loss": 1.92464, "time": 0.8397} +{"mode": "train", "epoch": 150, "iter": 3700, "lr": 0.0, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.64781, "top5_acc": 0.85719, "loss_cls": 2.036, "loss": 2.036, "time": 0.83932} +{"mode": "val", "epoch": 150, "iter": 309, "lr": 0.0, "top1_acc": 0.44882, "top5_acc": 0.69245, "mean_class_accuracy": 0.4486} diff --git a/k400/k_3/best_pred.pkl b/k400/k_3/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..e620aa39db46792afffad8a42dace4d91be653a4 --- /dev/null +++ b/k400/k_3/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ced3278441a274859d6ed22c7d092cbf20ebbb55ad3cf4960e8e0b5e99e3ca76 +size 44887271 diff --git a/k400/k_3/best_top1_acc_epoch_150.pth b/k400/k_3/best_top1_acc_epoch_150.pth new file mode 100644 index 0000000000000000000000000000000000000000..be20301ded11a36a7bd94671d8f47bc453861a1e --- /dev/null +++ b/k400/k_3/best_top1_acc_epoch_150.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e49435fba9fc44a615007a22b91b50e25308b0bbf65236cc14f4ca8f9804c8ef +size 32931889 diff --git a/k400/k_3/k_3.py b/k400/k_3/k_3.py new file mode 100644 index 0000000000000000000000000000000000000000..de0f4e1071d7080d7a7a9e3104fdeaeb15e6d831 --- /dev/null +++ b/k400/k_3/k_3.py @@ -0,0 +1,133 @@ +modality = 'k' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/k_3' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['k']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) diff --git a/k400/km/20240716_064942.log b/k400/km/20240716_064942.log new file mode 100644 index 0000000000000000000000000000000000000000..fce24e074420119cd3ccf53fc04518a7f0c1422f --- /dev/null +++ b/k400/km/20240716_064942.log @@ -0,0 +1,7328 @@ +2024-07-16 06:49:42,642 - pyskl - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] +CUDA available: True +GPU 0: Tesla V100-PCIE-32GB +CUDA_HOME: /usr/local/cuda-11.7 +NVCC: Cuda compilation tools, release 11.7, V11.7.64 +GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 +PyTorch: 1.11.0 +PyTorch compiling details: PyTorch built with: + - GCC 7.3 + - C++ Version: 201402 + - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 + - CuDNN 8.2 + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.12.0 +OpenCV: 4.8.0 +MMCV: 1.5.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.3 +pyskl: 0.1.0+ +------------------------------------------------------------ + +2024-07-16 06:49:42,989 - pyskl - INFO - Config: modality = 'km' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/km' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1) + +2024-07-16 06:49:42,989 - pyskl - INFO - Set random seed to 1546636195, deterministic: False +2024-07-16 06:49:53,566 - pyskl - INFO - 239737 videos remain after valid thresholding +2024-07-16 06:50:07,600 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-07-16 06:50:07,601 - pyskl - INFO - Start running, host: lhd@zkyd, work_dir: /home/lhd/pyskl/work_dirs/test_aclnet/k400/km +2024-07-16 06:50:07,606 - pyskl - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) CosineAnnealingLrUpdaterHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) CheckpointHook +(NORMAL ) DistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2024-07-16 06:50:07,625 - pyskl - INFO - workflow: [('train', 1)], max: 150 epochs +2024-07-16 06:50:07,630 - pyskl - INFO - Checkpoints will be saved to /home/lhd/pyskl/work_dirs/test_aclnet/k400/km by HardDiskBackend. +2024-07-16 06:53:32,673 - pyskl - INFO - Epoch [1][100/3746] lr: 1.000e-01, eta: 13 days, 7:57:52, time: 2.050, data_time: 1.339, memory: 15990, top1_acc: 0.0063, top5_acc: 0.0289, loss_cls: 6.3758, loss: 6.3758 +2024-07-16 06:54:42,863 - pyskl - INFO - Epoch [1][200/3746] lr: 1.000e-01, eta: 8 days, 22:42:37, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0141, top5_acc: 0.0563, loss_cls: 6.3455, loss: 6.3455 +2024-07-16 06:55:53,215 - pyskl - INFO - Epoch [1][300/3746] lr: 1.000e-01, eta: 7 days, 11:41:50, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0131, top5_acc: 0.0623, loss_cls: 6.2268, loss: 6.2268 +2024-07-16 06:57:03,621 - pyskl - INFO - Epoch [1][400/3746] lr: 1.000e-01, eta: 6 days, 18:12:07, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0208, top5_acc: 0.0819, loss_cls: 6.1103, loss: 6.1103 +2024-07-16 06:58:13,924 - pyskl - INFO - Epoch [1][500/3746] lr: 1.000e-01, eta: 6 days, 7:39:54, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0244, top5_acc: 0.0922, loss_cls: 6.0292, loss: 6.0292 +2024-07-16 06:59:24,625 - pyskl - INFO - Epoch [1][600/3746] lr: 1.000e-01, eta: 6 days, 0:44:13, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.0216, top5_acc: 0.0892, loss_cls: 6.0043, loss: 6.0043 +2024-07-16 07:00:34,936 - pyskl - INFO - Epoch [1][700/3746] lr: 1.000e-01, eta: 5 days, 19:41:46, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0231, top5_acc: 0.0981, loss_cls: 5.9676, loss: 5.9676 +2024-07-16 07:01:45,227 - pyskl - INFO - Epoch [1][800/3746] lr: 1.000e-01, eta: 5 days, 15:54:24, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0255, top5_acc: 0.1044, loss_cls: 5.9393, loss: 5.9393 +2024-07-16 07:02:55,663 - pyskl - INFO - Epoch [1][900/3746] lr: 1.000e-01, eta: 5 days, 12:58:48, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0291, top5_acc: 0.1103, loss_cls: 5.8781, loss: 5.8781 +2024-07-16 07:04:05,863 - pyskl - INFO - Epoch [1][1000/3746] lr: 1.000e-01, eta: 5 days, 10:35:53, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0319, top5_acc: 0.1209, loss_cls: 5.8822, loss: 5.8822 +2024-07-16 07:05:16,131 - pyskl - INFO - Epoch [1][1100/3746] lr: 1.000e-01, eta: 5 days, 8:39:19, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0289, top5_acc: 0.1186, loss_cls: 5.8414, loss: 5.8414 +2024-07-16 07:06:26,383 - pyskl - INFO - Epoch [1][1200/3746] lr: 1.000e-01, eta: 5 days, 7:01:52, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0284, top5_acc: 0.1117, loss_cls: 5.8609, loss: 5.8609 +2024-07-16 07:07:36,728 - pyskl - INFO - Epoch [1][1300/3746] lr: 1.000e-01, eta: 5 days, 5:39:53, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0330, top5_acc: 0.1308, loss_cls: 5.8150, loss: 5.8150 +2024-07-16 07:08:46,935 - pyskl - INFO - Epoch [1][1400/3746] lr: 1.000e-01, eta: 5 days, 4:28:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0378, top5_acc: 0.1320, loss_cls: 5.8018, loss: 5.8018 +2024-07-16 07:09:57,083 - pyskl - INFO - Epoch [1][1500/3746] lr: 1.000e-01, eta: 5 days, 3:26:10, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0341, top5_acc: 0.1308, loss_cls: 5.7879, loss: 5.7879 +2024-07-16 07:11:07,307 - pyskl - INFO - Epoch [1][1600/3746] lr: 1.000e-01, eta: 5 days, 2:31:54, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0408, top5_acc: 0.1367, loss_cls: 5.7379, loss: 5.7379 +2024-07-16 07:12:17,605 - pyskl - INFO - Epoch [1][1700/3746] lr: 1.000e-01, eta: 5 days, 1:44:17, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0442, top5_acc: 0.1480, loss_cls: 5.7175, loss: 5.7175 +2024-07-16 07:13:28,140 - pyskl - INFO - Epoch [1][1800/3746] lr: 1.000e-01, eta: 5 days, 1:03:03, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0425, top5_acc: 0.1403, loss_cls: 5.7075, loss: 5.7075 +2024-07-16 07:14:38,495 - pyskl - INFO - Epoch [1][1900/3746] lr: 1.000e-01, eta: 5 days, 0:25:10, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0405, top5_acc: 0.1550, loss_cls: 5.6975, loss: 5.6975 +2024-07-16 07:15:48,995 - pyskl - INFO - Epoch [1][2000/3746] lr: 1.000e-01, eta: 4 days, 23:51:37, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0469, top5_acc: 0.1427, loss_cls: 5.7202, loss: 5.7202 +2024-07-16 07:16:59,450 - pyskl - INFO - Epoch [1][2100/3746] lr: 1.000e-01, eta: 4 days, 23:20:57, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0420, top5_acc: 0.1484, loss_cls: 5.6912, loss: 5.6912 +2024-07-16 07:18:09,805 - pyskl - INFO - Epoch [1][2200/3746] lr: 1.000e-01, eta: 4 days, 22:52:33, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0448, top5_acc: 0.1533, loss_cls: 5.6480, loss: 5.6480 +2024-07-16 07:19:19,931 - pyskl - INFO - Epoch [1][2300/3746] lr: 1.000e-01, eta: 4 days, 22:25:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0466, top5_acc: 0.1548, loss_cls: 5.6586, loss: 5.6586 +2024-07-16 07:20:30,191 - pyskl - INFO - Epoch [1][2400/3746] lr: 1.000e-01, eta: 4 days, 22:01:17, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0472, top5_acc: 0.1556, loss_cls: 5.6655, loss: 5.6655 +2024-07-16 07:21:40,382 - pyskl - INFO - Epoch [1][2500/3746] lr: 1.000e-01, eta: 4 days, 21:38:34, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0561, top5_acc: 0.1702, loss_cls: 5.6105, loss: 5.6105 +2024-07-16 07:22:50,789 - pyskl - INFO - Epoch [1][2600/3746] lr: 9.999e-02, eta: 4 days, 21:18:18, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0470, top5_acc: 0.1614, loss_cls: 5.6386, loss: 5.6386 +2024-07-16 07:24:01,070 - pyskl - INFO - Epoch [1][2700/3746] lr: 9.999e-02, eta: 4 days, 20:59:01, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0520, top5_acc: 0.1717, loss_cls: 5.6158, loss: 5.6158 +2024-07-16 07:25:11,116 - pyskl - INFO - Epoch [1][2800/3746] lr: 9.999e-02, eta: 4 days, 20:40:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0498, top5_acc: 0.1655, loss_cls: 5.6124, loss: 5.6124 +2024-07-16 07:26:21,163 - pyskl - INFO - Epoch [1][2900/3746] lr: 9.999e-02, eta: 4 days, 20:22:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0517, top5_acc: 0.1720, loss_cls: 5.5962, loss: 5.5962 +2024-07-16 07:27:31,499 - pyskl - INFO - Epoch [1][3000/3746] lr: 9.999e-02, eta: 4 days, 20:07:05, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0563, top5_acc: 0.1803, loss_cls: 5.5457, loss: 5.5457 +2024-07-16 07:28:41,607 - pyskl - INFO - Epoch [1][3100/3746] lr: 9.999e-02, eta: 4 days, 19:51:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0645, top5_acc: 0.1956, loss_cls: 5.5125, loss: 5.5125 +2024-07-16 07:29:51,768 - pyskl - INFO - Epoch [1][3200/3746] lr: 9.999e-02, eta: 4 days, 19:37:28, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0617, top5_acc: 0.1952, loss_cls: 5.4877, loss: 5.4877 +2024-07-16 07:31:01,882 - pyskl - INFO - Epoch [1][3300/3746] lr: 9.999e-02, eta: 4 days, 19:23:51, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0566, top5_acc: 0.1834, loss_cls: 5.5197, loss: 5.5197 +2024-07-16 07:32:12,047 - pyskl - INFO - Epoch [1][3400/3746] lr: 9.999e-02, eta: 4 days, 19:11:06, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0589, top5_acc: 0.1984, loss_cls: 5.4914, loss: 5.4914 +2024-07-16 07:33:22,553 - pyskl - INFO - Epoch [1][3500/3746] lr: 9.999e-02, eta: 4 days, 18:59:54, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0578, top5_acc: 0.1939, loss_cls: 5.4752, loss: 5.4752 +2024-07-16 07:34:33,523 - pyskl - INFO - Epoch [1][3600/3746] lr: 9.999e-02, eta: 4 days, 18:50:29, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.0677, top5_acc: 0.1998, loss_cls: 5.4801, loss: 5.4801 +2024-07-16 07:35:44,658 - pyskl - INFO - Epoch [1][3700/3746] lr: 9.999e-02, eta: 4 days, 18:41:54, time: 0.711, data_time: 0.001, memory: 15990, top1_acc: 0.0639, top5_acc: 0.2016, loss_cls: 5.4615, loss: 5.4615 +2024-07-16 07:36:19,573 - pyskl - INFO - Saving checkpoint at 1 epochs +2024-07-16 07:38:10,329 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 07:38:11,029 - pyskl - INFO - +top1_acc 0.0416 +top5_acc 0.1346 +2024-07-16 07:38:11,030 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 07:38:11,071 - pyskl - INFO - +mean_acc 0.0416 +2024-07-16 07:38:11,319 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_1.pth. +2024-07-16 07:38:11,320 - pyskl - INFO - Best top1_acc is 0.0416 at 1 epoch. +2024-07-16 07:38:11,329 - pyskl - INFO - Epoch(val) [1][309] top1_acc: 0.0416, top5_acc: 0.1346, mean_class_accuracy: 0.0416 +2024-07-16 07:41:21,771 - pyskl - INFO - Epoch [2][100/3746] lr: 9.999e-02, eta: 4 days, 21:59:27, time: 1.904, data_time: 1.202, memory: 15990, top1_acc: 0.0680, top5_acc: 0.2098, loss_cls: 5.4362, loss: 5.4362 +2024-07-16 07:42:32,085 - pyskl - INFO - Epoch [2][200/3746] lr: 9.999e-02, eta: 4 days, 21:44:31, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0669, top5_acc: 0.2078, loss_cls: 5.4264, loss: 5.4264 +2024-07-16 07:43:42,443 - pyskl - INFO - Epoch [2][300/3746] lr: 9.999e-02, eta: 4 days, 21:30:21, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0750, top5_acc: 0.2173, loss_cls: 5.3827, loss: 5.3827 +2024-07-16 07:44:52,748 - pyskl - INFO - Epoch [2][400/3746] lr: 9.999e-02, eta: 4 days, 21:16:42, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0750, top5_acc: 0.2144, loss_cls: 5.3821, loss: 5.3821 +2024-07-16 07:46:03,049 - pyskl - INFO - Epoch [2][500/3746] lr: 9.999e-02, eta: 4 days, 21:03:37, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0705, top5_acc: 0.2183, loss_cls: 5.3816, loss: 5.3816 +2024-07-16 07:47:13,189 - pyskl - INFO - Epoch [2][600/3746] lr: 9.999e-02, eta: 4 days, 20:50:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0813, top5_acc: 0.2236, loss_cls: 5.3747, loss: 5.3747 +2024-07-16 07:48:23,549 - pyskl - INFO - Epoch [2][700/3746] lr: 9.998e-02, eta: 4 days, 20:38:52, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0784, top5_acc: 0.2336, loss_cls: 5.3625, loss: 5.3625 +2024-07-16 07:49:33,939 - pyskl - INFO - Epoch [2][800/3746] lr: 9.998e-02, eta: 4 days, 20:27:31, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.0778, top5_acc: 0.2361, loss_cls: 5.3240, loss: 5.3240 +2024-07-16 07:50:44,098 - pyskl - INFO - Epoch [2][900/3746] lr: 9.998e-02, eta: 4 days, 20:16:08, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0858, top5_acc: 0.2387, loss_cls: 5.3046, loss: 5.3046 +2024-07-16 07:51:54,303 - pyskl - INFO - Epoch [2][1000/3746] lr: 9.998e-02, eta: 4 days, 20:05:17, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0833, top5_acc: 0.2372, loss_cls: 5.2831, loss: 5.2831 +2024-07-16 07:53:04,537 - pyskl - INFO - Epoch [2][1100/3746] lr: 9.998e-02, eta: 4 days, 19:54:53, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0883, top5_acc: 0.2434, loss_cls: 5.2783, loss: 5.2783 +2024-07-16 07:54:14,861 - pyskl - INFO - Epoch [2][1200/3746] lr: 9.998e-02, eta: 4 days, 19:45:01, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0861, top5_acc: 0.2464, loss_cls: 5.2786, loss: 5.2786 +2024-07-16 07:55:24,918 - pyskl - INFO - Epoch [2][1300/3746] lr: 9.998e-02, eta: 4 days, 19:35:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0869, top5_acc: 0.2517, loss_cls: 5.2460, loss: 5.2460 +2024-07-16 07:56:34,964 - pyskl - INFO - Epoch [2][1400/3746] lr: 9.998e-02, eta: 4 days, 19:25:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.0884, top5_acc: 0.2539, loss_cls: 5.2121, loss: 5.2121 +2024-07-16 07:57:45,115 - pyskl - INFO - Epoch [2][1500/3746] lr: 9.998e-02, eta: 4 days, 19:16:10, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.0869, top5_acc: 0.2552, loss_cls: 5.2239, loss: 5.2239 +2024-07-16 07:58:55,449 - pyskl - INFO - Epoch [2][1600/3746] lr: 9.998e-02, eta: 4 days, 19:07:37, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0897, top5_acc: 0.2564, loss_cls: 5.2207, loss: 5.2207 +2024-07-16 08:00:05,775 - pyskl - INFO - Epoch [2][1700/3746] lr: 9.998e-02, eta: 4 days, 18:59:19, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0966, top5_acc: 0.2605, loss_cls: 5.1840, loss: 5.1840 +2024-07-16 08:01:16,478 - pyskl - INFO - Epoch [2][1800/3746] lr: 9.998e-02, eta: 4 days, 18:51:54, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.0906, top5_acc: 0.2603, loss_cls: 5.2269, loss: 5.2269 +2024-07-16 08:02:26,741 - pyskl - INFO - Epoch [2][1900/3746] lr: 9.998e-02, eta: 4 days, 18:44:00, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0973, top5_acc: 0.2684, loss_cls: 5.1828, loss: 5.1828 +2024-07-16 08:03:37,032 - pyskl - INFO - Epoch [2][2000/3746] lr: 9.997e-02, eta: 4 days, 18:36:22, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.0973, top5_acc: 0.2681, loss_cls: 5.1868, loss: 5.1868 +2024-07-16 08:04:47,536 - pyskl - INFO - Epoch [2][2100/3746] lr: 9.997e-02, eta: 4 days, 18:29:17, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.0977, top5_acc: 0.2709, loss_cls: 5.1544, loss: 5.1544 +2024-07-16 08:05:57,641 - pyskl - INFO - Epoch [2][2200/3746] lr: 9.997e-02, eta: 4 days, 18:21:48, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.0958, top5_acc: 0.2684, loss_cls: 5.1499, loss: 5.1499 +2024-07-16 08:07:07,972 - pyskl - INFO - Epoch [2][2300/3746] lr: 9.997e-02, eta: 4 days, 18:14:51, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1037, top5_acc: 0.2753, loss_cls: 5.1426, loss: 5.1426 +2024-07-16 08:08:18,125 - pyskl - INFO - Epoch [2][2400/3746] lr: 9.997e-02, eta: 4 days, 18:07:50, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1070, top5_acc: 0.2748, loss_cls: 5.1440, loss: 5.1440 +2024-07-16 08:09:28,373 - pyskl - INFO - Epoch [2][2500/3746] lr: 9.997e-02, eta: 4 days, 18:01:08, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1022, top5_acc: 0.2806, loss_cls: 5.1242, loss: 5.1242 +2024-07-16 08:10:38,637 - pyskl - INFO - Epoch [2][2600/3746] lr: 9.997e-02, eta: 4 days, 17:54:39, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1030, top5_acc: 0.2853, loss_cls: 5.1220, loss: 5.1220 +2024-07-16 08:11:48,980 - pyskl - INFO - Epoch [2][2700/3746] lr: 9.997e-02, eta: 4 days, 17:48:26, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1020, top5_acc: 0.2739, loss_cls: 5.1418, loss: 5.1418 +2024-07-16 08:12:59,022 - pyskl - INFO - Epoch [2][2800/3746] lr: 9.997e-02, eta: 4 days, 17:41:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1061, top5_acc: 0.2787, loss_cls: 5.1195, loss: 5.1195 +2024-07-16 08:14:09,173 - pyskl - INFO - Epoch [2][2900/3746] lr: 9.997e-02, eta: 4 days, 17:35:46, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1070, top5_acc: 0.2798, loss_cls: 5.1191, loss: 5.1191 +2024-07-16 08:15:19,410 - pyskl - INFO - Epoch [2][3000/3746] lr: 9.996e-02, eta: 4 days, 17:29:51, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1069, top5_acc: 0.2844, loss_cls: 5.0960, loss: 5.0960 +2024-07-16 08:16:29,769 - pyskl - INFO - Epoch [2][3100/3746] lr: 9.996e-02, eta: 4 days, 17:24:15, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1141, top5_acc: 0.2977, loss_cls: 5.0458, loss: 5.0458 +2024-07-16 08:17:40,070 - pyskl - INFO - Epoch [2][3200/3746] lr: 9.996e-02, eta: 4 days, 17:18:41, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1142, top5_acc: 0.2975, loss_cls: 5.0274, loss: 5.0274 +2024-07-16 08:18:50,261 - pyskl - INFO - Epoch [2][3300/3746] lr: 9.996e-02, eta: 4 days, 17:13:07, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1131, top5_acc: 0.2948, loss_cls: 5.0404, loss: 5.0404 +2024-07-16 08:20:00,657 - pyskl - INFO - Epoch [2][3400/3746] lr: 9.996e-02, eta: 4 days, 17:07:55, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1161, top5_acc: 0.3094, loss_cls: 5.0209, loss: 5.0209 +2024-07-16 08:21:11,271 - pyskl - INFO - Epoch [2][3500/3746] lr: 9.996e-02, eta: 4 days, 17:03:07, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1184, top5_acc: 0.3056, loss_cls: 5.0507, loss: 5.0507 +2024-07-16 08:22:22,010 - pyskl - INFO - Epoch [2][3600/3746] lr: 9.996e-02, eta: 4 days, 16:58:35, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1222, top5_acc: 0.3063, loss_cls: 5.0144, loss: 5.0144 +2024-07-16 08:23:32,806 - pyskl - INFO - Epoch [2][3700/3746] lr: 9.996e-02, eta: 4 days, 16:54:12, time: 0.708, data_time: 0.001, memory: 15990, top1_acc: 0.1191, top5_acc: 0.3048, loss_cls: 4.9944, loss: 4.9944 +2024-07-16 08:24:07,124 - pyskl - INFO - Saving checkpoint at 2 epochs +2024-07-16 08:25:58,529 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 08:25:59,193 - pyskl - INFO - +top1_acc 0.0594 +top5_acc 0.1920 +2024-07-16 08:25:59,193 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 08:25:59,232 - pyskl - INFO - +mean_acc 0.0593 +2024-07-16 08:25:59,236 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_1.pth was removed +2024-07-16 08:25:59,479 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_2.pth. +2024-07-16 08:25:59,480 - pyskl - INFO - Best top1_acc is 0.0594 at 2 epoch. +2024-07-16 08:25:59,489 - pyskl - INFO - Epoch(val) [2][309] top1_acc: 0.0594, top5_acc: 0.1920, mean_class_accuracy: 0.0593 +2024-07-16 08:29:10,285 - pyskl - INFO - Epoch [3][100/3746] lr: 9.995e-02, eta: 4 days, 18:34:21, time: 1.908, data_time: 1.204, memory: 15990, top1_acc: 0.1208, top5_acc: 0.3100, loss_cls: 4.9945, loss: 4.9945 +2024-07-16 08:30:20,422 - pyskl - INFO - Epoch [3][200/3746] lr: 9.995e-02, eta: 4 days, 18:27:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1291, top5_acc: 0.3186, loss_cls: 4.9759, loss: 4.9759 +2024-07-16 08:31:30,644 - pyskl - INFO - Epoch [3][300/3746] lr: 9.995e-02, eta: 4 days, 18:21:50, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1250, top5_acc: 0.3148, loss_cls: 4.9546, loss: 4.9546 +2024-07-16 08:32:40,727 - pyskl - INFO - Epoch [3][400/3746] lr: 9.995e-02, eta: 4 days, 18:15:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1205, top5_acc: 0.3161, loss_cls: 4.9861, loss: 4.9861 +2024-07-16 08:33:50,925 - pyskl - INFO - Epoch [3][500/3746] lr: 9.995e-02, eta: 4 days, 18:09:44, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1216, top5_acc: 0.3177, loss_cls: 4.9658, loss: 4.9658 +2024-07-16 08:35:01,134 - pyskl - INFO - Epoch [3][600/3746] lr: 9.995e-02, eta: 4 days, 18:03:57, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1281, top5_acc: 0.3145, loss_cls: 4.9596, loss: 4.9596 +2024-07-16 08:36:11,254 - pyskl - INFO - Epoch [3][700/3746] lr: 9.995e-02, eta: 4 days, 17:58:11, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1336, top5_acc: 0.3206, loss_cls: 4.9450, loss: 4.9450 +2024-07-16 08:37:21,350 - pyskl - INFO - Epoch [3][800/3746] lr: 9.995e-02, eta: 4 days, 17:52:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1258, top5_acc: 0.3241, loss_cls: 4.9465, loss: 4.9465 +2024-07-16 08:38:31,633 - pyskl - INFO - Epoch [3][900/3746] lr: 9.994e-02, eta: 4 days, 17:47:07, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1334, top5_acc: 0.3269, loss_cls: 4.9256, loss: 4.9256 +2024-07-16 08:39:41,987 - pyskl - INFO - Epoch [3][1000/3746] lr: 9.994e-02, eta: 4 days, 17:41:55, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1264, top5_acc: 0.3211, loss_cls: 4.9703, loss: 4.9703 +2024-07-16 08:40:52,430 - pyskl - INFO - Epoch [3][1100/3746] lr: 9.994e-02, eta: 4 days, 17:36:54, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1237, top5_acc: 0.3187, loss_cls: 4.9682, loss: 4.9682 +2024-07-16 08:42:02,660 - pyskl - INFO - Epoch [3][1200/3746] lr: 9.994e-02, eta: 4 days, 17:31:45, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1283, top5_acc: 0.3319, loss_cls: 4.9042, loss: 4.9042 +2024-07-16 08:43:12,683 - pyskl - INFO - Epoch [3][1300/3746] lr: 9.994e-02, eta: 4 days, 17:26:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1303, top5_acc: 0.3203, loss_cls: 4.9422, loss: 4.9422 +2024-07-16 08:44:23,354 - pyskl - INFO - Epoch [3][1400/3746] lr: 9.994e-02, eta: 4 days, 17:21:58, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1314, top5_acc: 0.3294, loss_cls: 4.9172, loss: 4.9172 +2024-07-16 08:45:33,684 - pyskl - INFO - Epoch [3][1500/3746] lr: 9.994e-02, eta: 4 days, 17:17:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1377, top5_acc: 0.3364, loss_cls: 4.8845, loss: 4.8845 +2024-07-16 08:46:43,916 - pyskl - INFO - Epoch [3][1600/3746] lr: 9.994e-02, eta: 4 days, 17:12:22, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1392, top5_acc: 0.3369, loss_cls: 4.9229, loss: 4.9229 +2024-07-16 08:47:54,229 - pyskl - INFO - Epoch [3][1700/3746] lr: 9.993e-02, eta: 4 days, 17:07:44, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1341, top5_acc: 0.3389, loss_cls: 4.8737, loss: 4.8737 +2024-07-16 08:49:04,739 - pyskl - INFO - Epoch [3][1800/3746] lr: 9.993e-02, eta: 4 days, 17:03:21, time: 0.705, data_time: 0.001, memory: 15990, top1_acc: 0.1241, top5_acc: 0.3186, loss_cls: 4.9476, loss: 4.9476 +2024-07-16 08:50:15,098 - pyskl - INFO - Epoch [3][1900/3746] lr: 9.993e-02, eta: 4 days, 16:58:54, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1298, top5_acc: 0.3209, loss_cls: 4.9470, loss: 4.9470 +2024-07-16 08:51:25,438 - pyskl - INFO - Epoch [3][2000/3746] lr: 9.993e-02, eta: 4 days, 16:54:29, time: 0.703, data_time: 0.001, memory: 15990, top1_acc: 0.1313, top5_acc: 0.3309, loss_cls: 4.9139, loss: 4.9139 +2024-07-16 08:52:35,818 - pyskl - INFO - Epoch [3][2100/3746] lr: 9.993e-02, eta: 4 days, 16:50:11, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1386, top5_acc: 0.3392, loss_cls: 4.8869, loss: 4.8869 +2024-07-16 08:53:46,119 - pyskl - INFO - Epoch [3][2200/3746] lr: 9.993e-02, eta: 4 days, 16:45:53, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1291, top5_acc: 0.3327, loss_cls: 4.9090, loss: 4.9090 +2024-07-16 08:54:56,258 - pyskl - INFO - Epoch [3][2300/3746] lr: 9.993e-02, eta: 4 days, 16:41:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1292, top5_acc: 0.3356, loss_cls: 4.8927, loss: 4.8927 +2024-07-16 08:56:06,446 - pyskl - INFO - Epoch [3][2400/3746] lr: 9.992e-02, eta: 4 days, 16:37:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1367, top5_acc: 0.3406, loss_cls: 4.8613, loss: 4.8613 +2024-07-16 08:57:16,539 - pyskl - INFO - Epoch [3][2500/3746] lr: 9.992e-02, eta: 4 days, 16:32:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1437, top5_acc: 0.3498, loss_cls: 4.8401, loss: 4.8401 +2024-07-16 08:58:26,688 - pyskl - INFO - Epoch [3][2600/3746] lr: 9.992e-02, eta: 4 days, 16:28:41, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1397, top5_acc: 0.3408, loss_cls: 4.8837, loss: 4.8837 +2024-07-16 08:59:36,790 - pyskl - INFO - Epoch [3][2700/3746] lr: 9.992e-02, eta: 4 days, 16:24:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1381, top5_acc: 0.3358, loss_cls: 4.9049, loss: 4.9049 +2024-07-16 09:00:46,950 - pyskl - INFO - Epoch [3][2800/3746] lr: 9.992e-02, eta: 4 days, 16:20:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1408, top5_acc: 0.3481, loss_cls: 4.8293, loss: 4.8293 +2024-07-16 09:01:57,011 - pyskl - INFO - Epoch [3][2900/3746] lr: 9.992e-02, eta: 4 days, 16:16:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1381, top5_acc: 0.3425, loss_cls: 4.8520, loss: 4.8520 +2024-07-16 09:03:07,259 - pyskl - INFO - Epoch [3][3000/3746] lr: 9.991e-02, eta: 4 days, 16:12:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1409, top5_acc: 0.3305, loss_cls: 4.8923, loss: 4.8923 +2024-07-16 09:04:17,476 - pyskl - INFO - Epoch [3][3100/3746] lr: 9.991e-02, eta: 4 days, 16:08:35, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1388, top5_acc: 0.3359, loss_cls: 4.8530, loss: 4.8530 +2024-07-16 09:05:27,551 - pyskl - INFO - Epoch [3][3200/3746] lr: 9.991e-02, eta: 4 days, 16:04:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1447, top5_acc: 0.3514, loss_cls: 4.8192, loss: 4.8192 +2024-07-16 09:06:37,802 - pyskl - INFO - Epoch [3][3300/3746] lr: 9.991e-02, eta: 4 days, 16:00:55, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1464, top5_acc: 0.3483, loss_cls: 4.8413, loss: 4.8413 +2024-07-16 09:07:48,087 - pyskl - INFO - Epoch [3][3400/3746] lr: 9.991e-02, eta: 4 days, 15:57:16, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1489, top5_acc: 0.3645, loss_cls: 4.7930, loss: 4.7930 +2024-07-16 09:08:58,367 - pyskl - INFO - Epoch [3][3500/3746] lr: 9.991e-02, eta: 4 days, 15:53:39, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1397, top5_acc: 0.3378, loss_cls: 4.8623, loss: 4.8623 +2024-07-16 09:10:09,717 - pyskl - INFO - Epoch [3][3600/3746] lr: 9.990e-02, eta: 4 days, 15:50:58, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1472, top5_acc: 0.3434, loss_cls: 4.8356, loss: 4.8356 +2024-07-16 09:11:20,693 - pyskl - INFO - Epoch [3][3700/3746] lr: 9.990e-02, eta: 4 days, 15:48:00, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1541, top5_acc: 0.3536, loss_cls: 4.7868, loss: 4.7868 +2024-07-16 09:11:54,913 - pyskl - INFO - Saving checkpoint at 3 epochs +2024-07-16 09:13:45,435 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 09:13:46,092 - pyskl - INFO - +top1_acc 0.0728 +top5_acc 0.2119 +2024-07-16 09:13:46,092 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 09:13:46,130 - pyskl - INFO - +mean_acc 0.0725 +2024-07-16 09:13:46,134 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_2.pth was removed +2024-07-16 09:13:46,372 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_3.pth. +2024-07-16 09:13:46,373 - pyskl - INFO - Best top1_acc is 0.0728 at 3 epoch. +2024-07-16 09:13:46,382 - pyskl - INFO - Epoch(val) [3][309] top1_acc: 0.0728, top5_acc: 0.2119, mean_class_accuracy: 0.0725 +2024-07-16 09:16:57,200 - pyskl - INFO - Epoch [4][100/3746] lr: 9.990e-02, eta: 4 days, 16:54:18, time: 1.908, data_time: 1.204, memory: 15990, top1_acc: 0.1539, top5_acc: 0.3608, loss_cls: 4.7511, loss: 4.7511 +2024-07-16 09:18:07,293 - pyskl - INFO - Epoch [4][200/3746] lr: 9.990e-02, eta: 4 days, 16:50:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1472, top5_acc: 0.3489, loss_cls: 4.8171, loss: 4.8171 +2024-07-16 09:19:17,517 - pyskl - INFO - Epoch [4][300/3746] lr: 9.990e-02, eta: 4 days, 16:46:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1514, top5_acc: 0.3569, loss_cls: 4.7629, loss: 4.7629 +2024-07-16 09:20:27,628 - pyskl - INFO - Epoch [4][400/3746] lr: 9.989e-02, eta: 4 days, 16:41:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1517, top5_acc: 0.3602, loss_cls: 4.7710, loss: 4.7710 +2024-07-16 09:21:37,675 - pyskl - INFO - Epoch [4][500/3746] lr: 9.989e-02, eta: 4 days, 16:37:47, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1481, top5_acc: 0.3566, loss_cls: 4.8005, loss: 4.8005 +2024-07-16 09:22:47,799 - pyskl - INFO - Epoch [4][600/3746] lr: 9.989e-02, eta: 4 days, 16:33:48, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1416, top5_acc: 0.3494, loss_cls: 4.8001, loss: 4.8001 +2024-07-16 09:23:57,918 - pyskl - INFO - Epoch [4][700/3746] lr: 9.989e-02, eta: 4 days, 16:29:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1517, top5_acc: 0.3616, loss_cls: 4.7959, loss: 4.7959 +2024-07-16 09:25:08,052 - pyskl - INFO - Epoch [4][800/3746] lr: 9.989e-02, eta: 4 days, 16:25:56, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1566, top5_acc: 0.3592, loss_cls: 4.7627, loss: 4.7627 +2024-07-16 09:26:18,096 - pyskl - INFO - Epoch [4][900/3746] lr: 9.988e-02, eta: 4 days, 16:22:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1584, top5_acc: 0.3667, loss_cls: 4.7240, loss: 4.7240 +2024-07-16 09:27:28,170 - pyskl - INFO - Epoch [4][1000/3746] lr: 9.988e-02, eta: 4 days, 16:18:10, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1456, top5_acc: 0.3670, loss_cls: 4.7713, loss: 4.7713 +2024-07-16 09:28:37,907 - pyskl - INFO - Epoch [4][1100/3746] lr: 9.988e-02, eta: 4 days, 16:14:07, time: 0.697, data_time: 0.000, memory: 15990, top1_acc: 0.1536, top5_acc: 0.3636, loss_cls: 4.7958, loss: 4.7958 +2024-07-16 09:29:48,150 - pyskl - INFO - Epoch [4][1200/3746] lr: 9.988e-02, eta: 4 days, 16:10:28, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1509, top5_acc: 0.3575, loss_cls: 4.7992, loss: 4.7992 +2024-07-16 09:30:58,194 - pyskl - INFO - Epoch [4][1300/3746] lr: 9.988e-02, eta: 4 days, 16:06:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1537, top5_acc: 0.3675, loss_cls: 4.7503, loss: 4.7503 +2024-07-16 09:32:08,308 - pyskl - INFO - Epoch [4][1400/3746] lr: 9.988e-02, eta: 4 days, 16:03:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1537, top5_acc: 0.3647, loss_cls: 4.7549, loss: 4.7549 +2024-07-16 09:33:18,330 - pyskl - INFO - Epoch [4][1500/3746] lr: 9.987e-02, eta: 4 days, 15:59:23, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1562, top5_acc: 0.3733, loss_cls: 4.7503, loss: 4.7503 +2024-07-16 09:34:28,336 - pyskl - INFO - Epoch [4][1600/3746] lr: 9.987e-02, eta: 4 days, 15:55:44, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1508, top5_acc: 0.3577, loss_cls: 4.7639, loss: 4.7639 +2024-07-16 09:35:38,363 - pyskl - INFO - Epoch [4][1700/3746] lr: 9.987e-02, eta: 4 days, 15:52:08, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1541, top5_acc: 0.3622, loss_cls: 4.7519, loss: 4.7519 +2024-07-16 09:36:48,884 - pyskl - INFO - Epoch [4][1800/3746] lr: 9.987e-02, eta: 4 days, 15:48:55, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1606, top5_acc: 0.3625, loss_cls: 4.7554, loss: 4.7554 +2024-07-16 09:37:59,179 - pyskl - INFO - Epoch [4][1900/3746] lr: 9.987e-02, eta: 4 days, 15:45:35, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1558, top5_acc: 0.3689, loss_cls: 4.7618, loss: 4.7618 +2024-07-16 09:39:09,460 - pyskl - INFO - Epoch [4][2000/3746] lr: 9.986e-02, eta: 4 days, 15:42:15, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1528, top5_acc: 0.3581, loss_cls: 4.7559, loss: 4.7559 +2024-07-16 09:40:19,858 - pyskl - INFO - Epoch [4][2100/3746] lr: 9.986e-02, eta: 4 days, 15:39:03, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1514, top5_acc: 0.3633, loss_cls: 4.7796, loss: 4.7796 +2024-07-16 09:41:30,812 - pyskl - INFO - Epoch [4][2200/3746] lr: 9.986e-02, eta: 4 days, 15:36:15, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1466, top5_acc: 0.3661, loss_cls: 4.7441, loss: 4.7441 +2024-07-16 09:42:41,014 - pyskl - INFO - Epoch [4][2300/3746] lr: 9.986e-02, eta: 4 days, 15:32:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1544, top5_acc: 0.3570, loss_cls: 4.7892, loss: 4.7892 +2024-07-16 09:43:51,117 - pyskl - INFO - Epoch [4][2400/3746] lr: 9.985e-02, eta: 4 days, 15:29:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1583, top5_acc: 0.3658, loss_cls: 4.7372, loss: 4.7372 +2024-07-16 09:45:01,303 - pyskl - INFO - Epoch [4][2500/3746] lr: 9.985e-02, eta: 4 days, 15:26:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1588, top5_acc: 0.3644, loss_cls: 4.7520, loss: 4.7520 +2024-07-16 09:46:11,447 - pyskl - INFO - Epoch [4][2600/3746] lr: 9.985e-02, eta: 4 days, 15:23:11, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1594, top5_acc: 0.3755, loss_cls: 4.7403, loss: 4.7403 +2024-07-16 09:47:21,753 - pyskl - INFO - Epoch [4][2700/3746] lr: 9.985e-02, eta: 4 days, 15:20:06, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1653, top5_acc: 0.3720, loss_cls: 4.7409, loss: 4.7409 +2024-07-16 09:48:32,029 - pyskl - INFO - Epoch [4][2800/3746] lr: 9.985e-02, eta: 4 days, 15:17:01, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1520, top5_acc: 0.3639, loss_cls: 4.7568, loss: 4.7568 +2024-07-16 09:49:42,239 - pyskl - INFO - Epoch [4][2900/3746] lr: 9.984e-02, eta: 4 days, 15:13:55, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1562, top5_acc: 0.3720, loss_cls: 4.7088, loss: 4.7088 +2024-07-16 09:50:52,422 - pyskl - INFO - Epoch [4][3000/3746] lr: 9.984e-02, eta: 4 days, 15:10:49, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1631, top5_acc: 0.3742, loss_cls: 4.7387, loss: 4.7387 +2024-07-16 09:52:02,640 - pyskl - INFO - Epoch [4][3100/3746] lr: 9.984e-02, eta: 4 days, 15:07:46, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1553, top5_acc: 0.3678, loss_cls: 4.7707, loss: 4.7707 +2024-07-16 09:53:12,920 - pyskl - INFO - Epoch [4][3200/3746] lr: 9.984e-02, eta: 4 days, 15:04:48, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1595, top5_acc: 0.3684, loss_cls: 4.7395, loss: 4.7395 +2024-07-16 09:54:23,082 - pyskl - INFO - Epoch [4][3300/3746] lr: 9.983e-02, eta: 4 days, 15:01:46, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1536, top5_acc: 0.3677, loss_cls: 4.7381, loss: 4.7381 +2024-07-16 09:55:33,131 - pyskl - INFO - Epoch [4][3400/3746] lr: 9.983e-02, eta: 4 days, 14:58:42, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1630, top5_acc: 0.3755, loss_cls: 4.7259, loss: 4.7259 +2024-07-16 09:56:43,627 - pyskl - INFO - Epoch [4][3500/3746] lr: 9.983e-02, eta: 4 days, 14:55:56, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1566, top5_acc: 0.3758, loss_cls: 4.7077, loss: 4.7077 +2024-07-16 09:57:55,169 - pyskl - INFO - Epoch [4][3600/3746] lr: 9.983e-02, eta: 4 days, 14:53:50, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1644, top5_acc: 0.3755, loss_cls: 4.6992, loss: 4.6992 +2024-07-16 09:59:06,357 - pyskl - INFO - Epoch [4][3700/3746] lr: 9.983e-02, eta: 4 days, 14:51:31, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1675, top5_acc: 0.3709, loss_cls: 4.7186, loss: 4.7186 +2024-07-16 09:59:40,521 - pyskl - INFO - Saving checkpoint at 4 epochs +2024-07-16 10:01:31,275 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 10:01:31,969 - pyskl - INFO - +top1_acc 0.1101 +top5_acc 0.2893 +2024-07-16 10:01:31,969 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 10:01:32,010 - pyskl - INFO - +mean_acc 0.1100 +2024-07-16 10:01:32,014 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_3.pth was removed +2024-07-16 10:01:32,416 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_4.pth. +2024-07-16 10:01:32,417 - pyskl - INFO - Best top1_acc is 0.1101 at 4 epoch. +2024-07-16 10:01:32,433 - pyskl - INFO - Epoch(val) [4][309] top1_acc: 0.1101, top5_acc: 0.2893, mean_class_accuracy: 0.1100 +2024-07-16 10:04:44,810 - pyskl - INFO - Epoch [5][100/3746] lr: 9.982e-02, eta: 4 days, 15:41:36, time: 1.924, data_time: 1.220, memory: 15990, top1_acc: 0.1661, top5_acc: 0.3842, loss_cls: 4.6636, loss: 4.6636 +2024-07-16 10:05:55,008 - pyskl - INFO - Epoch [5][200/3746] lr: 9.982e-02, eta: 4 days, 15:38:23, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1691, top5_acc: 0.3802, loss_cls: 4.6999, loss: 4.6999 +2024-07-16 10:07:05,429 - pyskl - INFO - Epoch [5][300/3746] lr: 9.982e-02, eta: 4 days, 15:35:19, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1645, top5_acc: 0.3786, loss_cls: 4.6876, loss: 4.6876 +2024-07-16 10:08:15,695 - pyskl - INFO - Epoch [5][400/3746] lr: 9.982e-02, eta: 4 days, 15:32:10, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1652, top5_acc: 0.3836, loss_cls: 4.7011, loss: 4.7011 +2024-07-16 10:09:25,907 - pyskl - INFO - Epoch [5][500/3746] lr: 9.981e-02, eta: 4 days, 15:29:02, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1616, top5_acc: 0.3786, loss_cls: 4.6980, loss: 4.6980 +2024-07-16 10:10:36,016 - pyskl - INFO - Epoch [5][600/3746] lr: 9.981e-02, eta: 4 days, 15:25:51, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1536, top5_acc: 0.3527, loss_cls: 4.7533, loss: 4.7533 +2024-07-16 10:11:46,265 - pyskl - INFO - Epoch [5][700/3746] lr: 9.981e-02, eta: 4 days, 15:22:47, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1633, top5_acc: 0.3828, loss_cls: 4.6788, loss: 4.6788 +2024-07-16 10:12:56,451 - pyskl - INFO - Epoch [5][800/3746] lr: 9.981e-02, eta: 4 days, 15:19:42, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1609, top5_acc: 0.3663, loss_cls: 4.7096, loss: 4.7096 +2024-07-16 10:14:06,644 - pyskl - INFO - Epoch [5][900/3746] lr: 9.980e-02, eta: 4 days, 15:16:39, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1577, top5_acc: 0.3755, loss_cls: 4.6928, loss: 4.6928 +2024-07-16 10:15:16,865 - pyskl - INFO - Epoch [5][1000/3746] lr: 9.980e-02, eta: 4 days, 15:13:38, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1658, top5_acc: 0.3784, loss_cls: 4.6952, loss: 4.6952 +2024-07-16 10:16:27,321 - pyskl - INFO - Epoch [5][1100/3746] lr: 9.980e-02, eta: 4 days, 15:10:47, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1675, top5_acc: 0.3837, loss_cls: 4.6704, loss: 4.6704 +2024-07-16 10:17:37,596 - pyskl - INFO - Epoch [5][1200/3746] lr: 9.980e-02, eta: 4 days, 15:07:50, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1634, top5_acc: 0.3777, loss_cls: 4.6764, loss: 4.6764 +2024-07-16 10:18:47,681 - pyskl - INFO - Epoch [5][1300/3746] lr: 9.979e-02, eta: 4 days, 15:04:49, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1656, top5_acc: 0.3805, loss_cls: 4.7000, loss: 4.7000 +2024-07-16 10:19:57,781 - pyskl - INFO - Epoch [5][1400/3746] lr: 9.979e-02, eta: 4 days, 15:01:49, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1650, top5_acc: 0.3781, loss_cls: 4.6898, loss: 4.6898 +2024-07-16 10:21:07,978 - pyskl - INFO - Epoch [5][1500/3746] lr: 9.979e-02, eta: 4 days, 14:58:54, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1703, top5_acc: 0.3831, loss_cls: 4.6570, loss: 4.6570 +2024-07-16 10:22:18,414 - pyskl - INFO - Epoch [5][1600/3746] lr: 9.979e-02, eta: 4 days, 14:56:08, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1598, top5_acc: 0.3741, loss_cls: 4.7017, loss: 4.7017 +2024-07-16 10:23:28,435 - pyskl - INFO - Epoch [5][1700/3746] lr: 9.978e-02, eta: 4 days, 14:53:10, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1536, top5_acc: 0.3778, loss_cls: 4.7186, loss: 4.7186 +2024-07-16 10:24:39,204 - pyskl - INFO - Epoch [5][1800/3746] lr: 9.978e-02, eta: 4 days, 14:50:37, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1630, top5_acc: 0.3852, loss_cls: 4.6762, loss: 4.6762 +2024-07-16 10:25:49,365 - pyskl - INFO - Epoch [5][1900/3746] lr: 9.978e-02, eta: 4 days, 14:47:46, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1681, top5_acc: 0.3742, loss_cls: 4.7193, loss: 4.7193 +2024-07-16 10:26:59,745 - pyskl - INFO - Epoch [5][2000/3746] lr: 9.977e-02, eta: 4 days, 14:45:03, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1616, top5_acc: 0.3814, loss_cls: 4.7174, loss: 4.7174 +2024-07-16 10:28:10,073 - pyskl - INFO - Epoch [5][2100/3746] lr: 9.977e-02, eta: 4 days, 14:42:19, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1638, top5_acc: 0.3748, loss_cls: 4.6933, loss: 4.6933 +2024-07-16 10:29:20,535 - pyskl - INFO - Epoch [5][2200/3746] lr: 9.977e-02, eta: 4 days, 14:39:40, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1688, top5_acc: 0.3798, loss_cls: 4.6913, loss: 4.6913 +2024-07-16 10:30:30,747 - pyskl - INFO - Epoch [5][2300/3746] lr: 9.977e-02, eta: 4 days, 14:36:55, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1772, top5_acc: 0.3872, loss_cls: 4.6508, loss: 4.6508 +2024-07-16 10:31:41,010 - pyskl - INFO - Epoch [5][2400/3746] lr: 9.976e-02, eta: 4 days, 14:34:13, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1723, top5_acc: 0.3825, loss_cls: 4.6834, loss: 4.6834 +2024-07-16 10:32:51,250 - pyskl - INFO - Epoch [5][2500/3746] lr: 9.976e-02, eta: 4 days, 14:31:30, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1633, top5_acc: 0.3695, loss_cls: 4.7176, loss: 4.7176 +2024-07-16 10:34:01,409 - pyskl - INFO - Epoch [5][2600/3746] lr: 9.976e-02, eta: 4 days, 14:28:47, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1667, top5_acc: 0.3862, loss_cls: 4.6702, loss: 4.6702 +2024-07-16 10:35:11,553 - pyskl - INFO - Epoch [5][2700/3746] lr: 9.976e-02, eta: 4 days, 14:26:03, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1656, top5_acc: 0.3837, loss_cls: 4.6698, loss: 4.6698 +2024-07-16 10:36:22,000 - pyskl - INFO - Epoch [5][2800/3746] lr: 9.975e-02, eta: 4 days, 14:23:31, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1684, top5_acc: 0.3828, loss_cls: 4.6538, loss: 4.6538 +2024-07-16 10:37:32,072 - pyskl - INFO - Epoch [5][2900/3746] lr: 9.975e-02, eta: 4 days, 14:20:47, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1642, top5_acc: 0.3862, loss_cls: 4.6814, loss: 4.6814 +2024-07-16 10:38:42,183 - pyskl - INFO - Epoch [5][3000/3746] lr: 9.975e-02, eta: 4 days, 14:18:06, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1631, top5_acc: 0.3798, loss_cls: 4.6866, loss: 4.6866 +2024-07-16 10:39:52,115 - pyskl - INFO - Epoch [5][3100/3746] lr: 9.974e-02, eta: 4 days, 14:15:21, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1616, top5_acc: 0.3770, loss_cls: 4.6814, loss: 4.6814 +2024-07-16 10:41:02,433 - pyskl - INFO - Epoch [5][3200/3746] lr: 9.974e-02, eta: 4 days, 14:12:48, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1703, top5_acc: 0.3872, loss_cls: 4.6567, loss: 4.6567 +2024-07-16 10:42:12,615 - pyskl - INFO - Epoch [5][3300/3746] lr: 9.974e-02, eta: 4 days, 14:10:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1717, top5_acc: 0.3834, loss_cls: 4.6572, loss: 4.6572 +2024-07-16 10:43:22,689 - pyskl - INFO - Epoch [5][3400/3746] lr: 9.974e-02, eta: 4 days, 14:07:34, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1753, top5_acc: 0.3911, loss_cls: 4.6664, loss: 4.6664 +2024-07-16 10:44:32,843 - pyskl - INFO - Epoch [5][3500/3746] lr: 9.973e-02, eta: 4 days, 14:04:59, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1655, top5_acc: 0.3880, loss_cls: 4.6596, loss: 4.6596 +2024-07-16 10:45:44,181 - pyskl - INFO - Epoch [5][3600/3746] lr: 9.973e-02, eta: 4 days, 14:02:59, time: 0.713, data_time: 0.001, memory: 15990, top1_acc: 0.1733, top5_acc: 0.3831, loss_cls: 4.6821, loss: 4.6821 +2024-07-16 10:46:55,039 - pyskl - INFO - Epoch [5][3700/3746] lr: 9.973e-02, eta: 4 days, 14:00:47, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1644, top5_acc: 0.3786, loss_cls: 4.7071, loss: 4.7071 +2024-07-16 10:47:29,737 - pyskl - INFO - Saving checkpoint at 5 epochs +2024-07-16 10:49:20,871 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 10:49:21,528 - pyskl - INFO - +top1_acc 0.0969 +top5_acc 0.2650 +2024-07-16 10:49:21,528 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 10:49:21,565 - pyskl - INFO - +mean_acc 0.0968 +2024-07-16 10:49:21,575 - pyskl - INFO - Epoch(val) [5][309] top1_acc: 0.0969, top5_acc: 0.2650, mean_class_accuracy: 0.0968 +2024-07-16 10:52:32,787 - pyskl - INFO - Epoch [6][100/3746] lr: 9.972e-02, eta: 4 days, 14:39:45, time: 1.912, data_time: 1.208, memory: 15990, top1_acc: 0.1680, top5_acc: 0.3866, loss_cls: 4.6429, loss: 4.6429 +2024-07-16 10:53:43,114 - pyskl - INFO - Epoch [6][200/3746] lr: 9.972e-02, eta: 4 days, 14:37:04, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1702, top5_acc: 0.3837, loss_cls: 4.6612, loss: 4.6612 +2024-07-16 10:54:53,355 - pyskl - INFO - Epoch [6][300/3746] lr: 9.972e-02, eta: 4 days, 14:34:22, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1767, top5_acc: 0.3950, loss_cls: 4.6421, loss: 4.6421 +2024-07-16 10:56:03,549 - pyskl - INFO - Epoch [6][400/3746] lr: 9.971e-02, eta: 4 days, 14:31:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1689, top5_acc: 0.3934, loss_cls: 4.6056, loss: 4.6056 +2024-07-16 10:57:13,485 - pyskl - INFO - Epoch [6][500/3746] lr: 9.971e-02, eta: 4 days, 14:28:52, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1700, top5_acc: 0.3908, loss_cls: 4.6186, loss: 4.6186 +2024-07-16 10:58:23,533 - pyskl - INFO - Epoch [6][600/3746] lr: 9.971e-02, eta: 4 days, 14:26:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1675, top5_acc: 0.3764, loss_cls: 4.6757, loss: 4.6757 +2024-07-16 10:59:33,710 - pyskl - INFO - Epoch [6][700/3746] lr: 9.971e-02, eta: 4 days, 14:23:28, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1731, top5_acc: 0.3841, loss_cls: 4.6581, loss: 4.6581 +2024-07-16 11:00:43,831 - pyskl - INFO - Epoch [6][800/3746] lr: 9.970e-02, eta: 4 days, 14:20:47, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1748, top5_acc: 0.3953, loss_cls: 4.6294, loss: 4.6294 +2024-07-16 11:01:53,864 - pyskl - INFO - Epoch [6][900/3746] lr: 9.970e-02, eta: 4 days, 14:18:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1850, top5_acc: 0.4031, loss_cls: 4.5920, loss: 4.5920 +2024-07-16 11:03:03,944 - pyskl - INFO - Epoch [6][1000/3746] lr: 9.970e-02, eta: 4 days, 14:15:25, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1737, top5_acc: 0.3862, loss_cls: 4.6195, loss: 4.6195 +2024-07-16 11:04:14,007 - pyskl - INFO - Epoch [6][1100/3746] lr: 9.969e-02, eta: 4 days, 14:12:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1609, top5_acc: 0.3889, loss_cls: 4.6778, loss: 4.6778 +2024-07-16 11:05:24,253 - pyskl - INFO - Epoch [6][1200/3746] lr: 9.969e-02, eta: 4 days, 14:10:13, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1752, top5_acc: 0.3930, loss_cls: 4.6294, loss: 4.6294 +2024-07-16 11:06:34,204 - pyskl - INFO - Epoch [6][1300/3746] lr: 9.969e-02, eta: 4 days, 14:07:32, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1680, top5_acc: 0.3875, loss_cls: 4.6639, loss: 4.6639 +2024-07-16 11:07:44,277 - pyskl - INFO - Epoch [6][1400/3746] lr: 9.968e-02, eta: 4 days, 14:04:56, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1711, top5_acc: 0.3820, loss_cls: 4.6552, loss: 4.6552 +2024-07-16 11:08:54,403 - pyskl - INFO - Epoch [6][1500/3746] lr: 9.968e-02, eta: 4 days, 14:02:22, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1764, top5_acc: 0.3992, loss_cls: 4.5885, loss: 4.5885 +2024-07-16 11:10:04,411 - pyskl - INFO - Epoch [6][1600/3746] lr: 9.968e-02, eta: 4 days, 13:59:45, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1773, top5_acc: 0.3955, loss_cls: 4.6124, loss: 4.6124 +2024-07-16 11:11:14,420 - pyskl - INFO - Epoch [6][1700/3746] lr: 9.967e-02, eta: 4 days, 13:57:10, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1737, top5_acc: 0.3845, loss_cls: 4.6287, loss: 4.6287 +2024-07-16 11:12:25,234 - pyskl - INFO - Epoch [6][1800/3746] lr: 9.967e-02, eta: 4 days, 13:54:56, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1691, top5_acc: 0.3830, loss_cls: 4.6853, loss: 4.6853 +2024-07-16 11:13:35,439 - pyskl - INFO - Epoch [6][1900/3746] lr: 9.967e-02, eta: 4 days, 13:52:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1716, top5_acc: 0.3845, loss_cls: 4.6584, loss: 4.6584 +2024-07-16 11:14:45,597 - pyskl - INFO - Epoch [6][2000/3746] lr: 9.966e-02, eta: 4 days, 13:49:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1739, top5_acc: 0.3942, loss_cls: 4.6169, loss: 4.6169 +2024-07-16 11:15:55,964 - pyskl - INFO - Epoch [6][2100/3746] lr: 9.966e-02, eta: 4 days, 13:47:35, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1748, top5_acc: 0.3973, loss_cls: 4.6344, loss: 4.6344 +2024-07-16 11:17:06,230 - pyskl - INFO - Epoch [6][2200/3746] lr: 9.966e-02, eta: 4 days, 13:45:10, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1741, top5_acc: 0.3866, loss_cls: 4.6324, loss: 4.6324 +2024-07-16 11:18:16,347 - pyskl - INFO - Epoch [6][2300/3746] lr: 9.965e-02, eta: 4 days, 13:42:42, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1702, top5_acc: 0.3805, loss_cls: 4.6612, loss: 4.6612 +2024-07-16 11:19:26,438 - pyskl - INFO - Epoch [6][2400/3746] lr: 9.965e-02, eta: 4 days, 13:40:14, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1767, top5_acc: 0.3862, loss_cls: 4.6444, loss: 4.6444 +2024-07-16 11:20:36,737 - pyskl - INFO - Epoch [6][2500/3746] lr: 9.965e-02, eta: 4 days, 13:37:52, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1745, top5_acc: 0.4002, loss_cls: 4.5999, loss: 4.5999 +2024-07-16 11:21:46,917 - pyskl - INFO - Epoch [6][2600/3746] lr: 9.964e-02, eta: 4 days, 13:35:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1800, top5_acc: 0.3914, loss_cls: 4.6038, loss: 4.6038 +2024-07-16 11:22:57,172 - pyskl - INFO - Epoch [6][2700/3746] lr: 9.964e-02, eta: 4 days, 13:33:05, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1695, top5_acc: 0.3853, loss_cls: 4.6531, loss: 4.6531 +2024-07-16 11:24:07,329 - pyskl - INFO - Epoch [6][2800/3746] lr: 9.964e-02, eta: 4 days, 13:30:42, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1705, top5_acc: 0.3833, loss_cls: 4.6682, loss: 4.6682 +2024-07-16 11:25:17,580 - pyskl - INFO - Epoch [6][2900/3746] lr: 9.963e-02, eta: 4 days, 13:28:21, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1720, top5_acc: 0.3842, loss_cls: 4.6645, loss: 4.6645 +2024-07-16 11:26:27,615 - pyskl - INFO - Epoch [6][3000/3746] lr: 9.963e-02, eta: 4 days, 13:25:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1781, top5_acc: 0.3939, loss_cls: 4.6340, loss: 4.6340 +2024-07-16 11:27:38,097 - pyskl - INFO - Epoch [6][3100/3746] lr: 9.963e-02, eta: 4 days, 13:23:42, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1725, top5_acc: 0.3897, loss_cls: 4.6254, loss: 4.6254 +2024-07-16 11:28:48,642 - pyskl - INFO - Epoch [6][3200/3746] lr: 9.962e-02, eta: 4 days, 13:21:31, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1731, top5_acc: 0.3861, loss_cls: 4.6243, loss: 4.6243 +2024-07-16 11:29:58,699 - pyskl - INFO - Epoch [6][3300/3746] lr: 9.962e-02, eta: 4 days, 13:19:08, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1702, top5_acc: 0.3822, loss_cls: 4.6716, loss: 4.6716 +2024-07-16 11:31:08,896 - pyskl - INFO - Epoch [6][3400/3746] lr: 9.962e-02, eta: 4 days, 13:16:49, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1764, top5_acc: 0.3950, loss_cls: 4.6271, loss: 4.6271 +2024-07-16 11:32:19,131 - pyskl - INFO - Epoch [6][3500/3746] lr: 9.961e-02, eta: 4 days, 13:14:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1725, top5_acc: 0.3927, loss_cls: 4.6312, loss: 4.6312 +2024-07-16 11:33:29,928 - pyskl - INFO - Epoch [6][3600/3746] lr: 9.961e-02, eta: 4 days, 13:12:29, time: 0.708, data_time: 0.001, memory: 15990, top1_acc: 0.1688, top5_acc: 0.3964, loss_cls: 4.6361, loss: 4.6361 +2024-07-16 11:34:40,800 - pyskl - INFO - Epoch [6][3700/3746] lr: 9.961e-02, eta: 4 days, 13:10:28, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1766, top5_acc: 0.3944, loss_cls: 4.6378, loss: 4.6378 +2024-07-16 11:35:14,987 - pyskl - INFO - Saving checkpoint at 6 epochs +2024-07-16 11:37:05,695 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 11:37:06,411 - pyskl - INFO - +top1_acc 0.1164 +top5_acc 0.3035 +2024-07-16 11:37:06,411 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 11:37:06,450 - pyskl - INFO - +mean_acc 0.1161 +2024-07-16 11:37:06,454 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_4.pth was removed +2024-07-16 11:37:06,693 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_6.pth. +2024-07-16 11:37:06,694 - pyskl - INFO - Best top1_acc is 0.1164 at 6 epoch. +2024-07-16 11:37:06,703 - pyskl - INFO - Epoch(val) [6][309] top1_acc: 0.1164, top5_acc: 0.3035, mean_class_accuracy: 0.1161 +2024-07-16 11:40:17,697 - pyskl - INFO - Epoch [7][100/3746] lr: 9.960e-02, eta: 4 days, 13:42:23, time: 1.910, data_time: 1.207, memory: 15990, top1_acc: 0.1817, top5_acc: 0.3967, loss_cls: 4.6145, loss: 4.6145 +2024-07-16 11:41:28,049 - pyskl - INFO - Epoch [7][200/3746] lr: 9.960e-02, eta: 4 days, 13:40:02, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1789, top5_acc: 0.3923, loss_cls: 4.6247, loss: 4.6247 +2024-07-16 11:42:37,916 - pyskl - INFO - Epoch [7][300/3746] lr: 9.960e-02, eta: 4 days, 13:37:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1694, top5_acc: 0.4016, loss_cls: 4.5710, loss: 4.5710 +2024-07-16 11:43:47,869 - pyskl - INFO - Epoch [7][400/3746] lr: 9.959e-02, eta: 4 days, 13:34:59, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1730, top5_acc: 0.3889, loss_cls: 4.6534, loss: 4.6534 +2024-07-16 11:44:57,916 - pyskl - INFO - Epoch [7][500/3746] lr: 9.959e-02, eta: 4 days, 13:32:33, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1819, top5_acc: 0.4070, loss_cls: 4.6082, loss: 4.6082 +2024-07-16 11:46:08,134 - pyskl - INFO - Epoch [7][600/3746] lr: 9.958e-02, eta: 4 days, 13:30:10, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1777, top5_acc: 0.4023, loss_cls: 4.6275, loss: 4.6275 +2024-07-16 11:47:18,204 - pyskl - INFO - Epoch [7][700/3746] lr: 9.958e-02, eta: 4 days, 13:27:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1753, top5_acc: 0.4025, loss_cls: 4.6032, loss: 4.6032 +2024-07-16 11:48:28,315 - pyskl - INFO - Epoch [7][800/3746] lr: 9.958e-02, eta: 4 days, 13:25:22, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1689, top5_acc: 0.3925, loss_cls: 4.6378, loss: 4.6378 +2024-07-16 11:49:38,593 - pyskl - INFO - Epoch [7][900/3746] lr: 9.957e-02, eta: 4 days, 13:23:03, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1783, top5_acc: 0.3989, loss_cls: 4.5958, loss: 4.5958 +2024-07-16 11:50:48,725 - pyskl - INFO - Epoch [7][1000/3746] lr: 9.957e-02, eta: 4 days, 13:20:41, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1705, top5_acc: 0.3872, loss_cls: 4.6561, loss: 4.6561 +2024-07-16 11:51:58,646 - pyskl - INFO - Epoch [7][1100/3746] lr: 9.957e-02, eta: 4 days, 13:18:15, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1797, top5_acc: 0.3952, loss_cls: 4.6038, loss: 4.6038 +2024-07-16 11:53:08,719 - pyskl - INFO - Epoch [7][1200/3746] lr: 9.956e-02, eta: 4 days, 13:15:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1755, top5_acc: 0.4014, loss_cls: 4.6072, loss: 4.6072 +2024-07-16 11:54:18,635 - pyskl - INFO - Epoch [7][1300/3746] lr: 9.956e-02, eta: 4 days, 13:13:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1764, top5_acc: 0.3927, loss_cls: 4.6243, loss: 4.6243 +2024-07-16 11:55:28,612 - pyskl - INFO - Epoch [7][1400/3746] lr: 9.956e-02, eta: 4 days, 13:11:06, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1756, top5_acc: 0.3869, loss_cls: 4.6423, loss: 4.6423 +2024-07-16 11:56:38,831 - pyskl - INFO - Epoch [7][1500/3746] lr: 9.955e-02, eta: 4 days, 13:08:49, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1663, top5_acc: 0.3842, loss_cls: 4.6513, loss: 4.6513 +2024-07-16 11:57:48,808 - pyskl - INFO - Epoch [7][1600/3746] lr: 9.955e-02, eta: 4 days, 13:06:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1727, top5_acc: 0.3850, loss_cls: 4.6208, loss: 4.6208 +2024-07-16 11:58:58,959 - pyskl - INFO - Epoch [7][1700/3746] lr: 9.954e-02, eta: 4 days, 13:04:10, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1748, top5_acc: 0.3914, loss_cls: 4.6311, loss: 4.6311 +2024-07-16 12:00:09,243 - pyskl - INFO - Epoch [7][1800/3746] lr: 9.954e-02, eta: 4 days, 13:01:57, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1802, top5_acc: 0.3947, loss_cls: 4.6339, loss: 4.6339 +2024-07-16 12:01:19,354 - pyskl - INFO - Epoch [7][1900/3746] lr: 9.954e-02, eta: 4 days, 12:59:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1845, top5_acc: 0.4070, loss_cls: 4.5883, loss: 4.5883 +2024-07-16 12:02:29,579 - pyskl - INFO - Epoch [7][2000/3746] lr: 9.953e-02, eta: 4 days, 12:57:26, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1811, top5_acc: 0.3902, loss_cls: 4.6134, loss: 4.6134 +2024-07-16 12:03:39,767 - pyskl - INFO - Epoch [7][2100/3746] lr: 9.953e-02, eta: 4 days, 12:55:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1733, top5_acc: 0.3942, loss_cls: 4.6119, loss: 4.6119 +2024-07-16 12:04:49,650 - pyskl - INFO - Epoch [7][2200/3746] lr: 9.952e-02, eta: 4 days, 12:52:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1836, top5_acc: 0.4058, loss_cls: 4.5731, loss: 4.5731 +2024-07-16 12:05:59,521 - pyskl - INFO - Epoch [7][2300/3746] lr: 9.952e-02, eta: 4 days, 12:50:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1811, top5_acc: 0.4033, loss_cls: 4.6010, loss: 4.6010 +2024-07-16 12:07:09,452 - pyskl - INFO - Epoch [7][2400/3746] lr: 9.952e-02, eta: 4 days, 12:48:13, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1775, top5_acc: 0.3914, loss_cls: 4.5955, loss: 4.5955 +2024-07-16 12:08:19,417 - pyskl - INFO - Epoch [7][2500/3746] lr: 9.951e-02, eta: 4 days, 12:45:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1820, top5_acc: 0.4105, loss_cls: 4.5933, loss: 4.5933 +2024-07-16 12:09:29,501 - pyskl - INFO - Epoch [7][2600/3746] lr: 9.951e-02, eta: 4 days, 12:43:42, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1825, top5_acc: 0.4064, loss_cls: 4.5772, loss: 4.5772 +2024-07-16 12:10:39,586 - pyskl - INFO - Epoch [7][2700/3746] lr: 9.951e-02, eta: 4 days, 12:41:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1864, top5_acc: 0.4106, loss_cls: 4.5411, loss: 4.5411 +2024-07-16 12:11:49,630 - pyskl - INFO - Epoch [7][2800/3746] lr: 9.950e-02, eta: 4 days, 12:39:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1770, top5_acc: 0.3995, loss_cls: 4.6030, loss: 4.6030 +2024-07-16 12:12:59,628 - pyskl - INFO - Epoch [7][2900/3746] lr: 9.950e-02, eta: 4 days, 12:37:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1766, top5_acc: 0.3934, loss_cls: 4.6072, loss: 4.6072 +2024-07-16 12:14:09,679 - pyskl - INFO - Epoch [7][3000/3746] lr: 9.949e-02, eta: 4 days, 12:34:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1808, top5_acc: 0.3994, loss_cls: 4.5905, loss: 4.5905 +2024-07-16 12:15:19,848 - pyskl - INFO - Epoch [7][3100/3746] lr: 9.949e-02, eta: 4 days, 12:32:39, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1759, top5_acc: 0.3978, loss_cls: 4.5993, loss: 4.5993 +2024-07-16 12:16:29,868 - pyskl - INFO - Epoch [7][3200/3746] lr: 9.949e-02, eta: 4 days, 12:30:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1773, top5_acc: 0.3919, loss_cls: 4.6045, loss: 4.6045 +2024-07-16 12:17:39,797 - pyskl - INFO - Epoch [7][3300/3746] lr: 9.948e-02, eta: 4 days, 12:28:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1714, top5_acc: 0.3900, loss_cls: 4.6354, loss: 4.6354 +2024-07-16 12:18:49,902 - pyskl - INFO - Epoch [7][3400/3746] lr: 9.948e-02, eta: 4 days, 12:26:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1845, top5_acc: 0.4041, loss_cls: 4.6160, loss: 4.6160 +2024-07-16 12:19:59,984 - pyskl - INFO - Epoch [7][3500/3746] lr: 9.947e-02, eta: 4 days, 12:23:55, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1773, top5_acc: 0.3959, loss_cls: 4.6032, loss: 4.6032 +2024-07-16 12:21:11,023 - pyskl - INFO - Epoch [7][3600/3746] lr: 9.947e-02, eta: 4 days, 12:22:06, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1872, top5_acc: 0.4044, loss_cls: 4.5583, loss: 4.5583 +2024-07-16 12:22:21,533 - pyskl - INFO - Epoch [7][3700/3746] lr: 9.947e-02, eta: 4 days, 12:20:06, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1825, top5_acc: 0.3980, loss_cls: 4.6189, loss: 4.6189 +2024-07-16 12:22:55,708 - pyskl - INFO - Saving checkpoint at 7 epochs +2024-07-16 12:24:46,318 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 12:24:47,045 - pyskl - INFO - +top1_acc 0.1174 +top5_acc 0.3003 +2024-07-16 12:24:47,045 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 12:24:47,092 - pyskl - INFO - +mean_acc 0.1172 +2024-07-16 12:24:47,096 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_6.pth was removed +2024-07-16 12:24:47,341 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_7.pth. +2024-07-16 12:24:47,342 - pyskl - INFO - Best top1_acc is 0.1174 at 7 epoch. +2024-07-16 12:24:47,358 - pyskl - INFO - Epoch(val) [7][309] top1_acc: 0.1174, top5_acc: 0.3003, mean_class_accuracy: 0.1172 +2024-07-16 12:27:58,321 - pyskl - INFO - Epoch [8][100/3746] lr: 9.946e-02, eta: 4 days, 12:47:02, time: 1.910, data_time: 1.202, memory: 15990, top1_acc: 0.1914, top5_acc: 0.4075, loss_cls: 4.5753, loss: 4.5753 +2024-07-16 12:29:08,592 - pyskl - INFO - Epoch [8][200/3746] lr: 9.946e-02, eta: 4 days, 12:44:51, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1755, top5_acc: 0.3984, loss_cls: 4.6009, loss: 4.6009 +2024-07-16 12:30:18,880 - pyskl - INFO - Epoch [8][300/3746] lr: 9.945e-02, eta: 4 days, 12:42:41, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1781, top5_acc: 0.4027, loss_cls: 4.5771, loss: 4.5771 +2024-07-16 12:31:28,909 - pyskl - INFO - Epoch [8][400/3746] lr: 9.945e-02, eta: 4 days, 12:40:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1827, top5_acc: 0.4028, loss_cls: 4.5745, loss: 4.5745 +2024-07-16 12:32:38,948 - pyskl - INFO - Epoch [8][500/3746] lr: 9.944e-02, eta: 4 days, 12:38:12, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1825, top5_acc: 0.4059, loss_cls: 4.5917, loss: 4.5917 +2024-07-16 12:33:49,022 - pyskl - INFO - Epoch [8][600/3746] lr: 9.944e-02, eta: 4 days, 12:35:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1805, top5_acc: 0.4059, loss_cls: 4.5596, loss: 4.5596 +2024-07-16 12:34:59,277 - pyskl - INFO - Epoch [8][700/3746] lr: 9.943e-02, eta: 4 days, 12:33:50, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1880, top5_acc: 0.4055, loss_cls: 4.5563, loss: 4.5563 +2024-07-16 12:36:09,409 - pyskl - INFO - Epoch [8][800/3746] lr: 9.943e-02, eta: 4 days, 12:31:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1870, top5_acc: 0.4159, loss_cls: 4.5609, loss: 4.5609 +2024-07-16 12:37:19,439 - pyskl - INFO - Epoch [8][900/3746] lr: 9.943e-02, eta: 4 days, 12:29:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1756, top5_acc: 0.3991, loss_cls: 4.5911, loss: 4.5911 +2024-07-16 12:38:29,546 - pyskl - INFO - Epoch [8][1000/3746] lr: 9.942e-02, eta: 4 days, 12:27:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1784, top5_acc: 0.4008, loss_cls: 4.5746, loss: 4.5746 +2024-07-16 12:39:39,581 - pyskl - INFO - Epoch [8][1100/3746] lr: 9.942e-02, eta: 4 days, 12:25:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1764, top5_acc: 0.3992, loss_cls: 4.6568, loss: 4.6568 +2024-07-16 12:40:49,925 - pyskl - INFO - Epoch [8][1200/3746] lr: 9.941e-02, eta: 4 days, 12:23:00, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1903, top5_acc: 0.4055, loss_cls: 4.5400, loss: 4.5400 +2024-07-16 12:41:59,874 - pyskl - INFO - Epoch [8][1300/3746] lr: 9.941e-02, eta: 4 days, 12:20:47, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1811, top5_acc: 0.4075, loss_cls: 4.5783, loss: 4.5783 +2024-07-16 12:43:09,896 - pyskl - INFO - Epoch [8][1400/3746] lr: 9.940e-02, eta: 4 days, 12:18:37, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1844, top5_acc: 0.4102, loss_cls: 4.5753, loss: 4.5753 +2024-07-16 12:44:19,834 - pyskl - INFO - Epoch [8][1500/3746] lr: 9.940e-02, eta: 4 days, 12:16:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1808, top5_acc: 0.4081, loss_cls: 4.5607, loss: 4.5607 +2024-07-16 12:45:30,010 - pyskl - INFO - Epoch [8][1600/3746] lr: 9.940e-02, eta: 4 days, 12:14:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1789, top5_acc: 0.3905, loss_cls: 4.6059, loss: 4.6059 +2024-07-16 12:46:40,041 - pyskl - INFO - Epoch [8][1700/3746] lr: 9.939e-02, eta: 4 days, 12:12:10, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1736, top5_acc: 0.3950, loss_cls: 4.6073, loss: 4.6073 +2024-07-16 12:47:50,470 - pyskl - INFO - Epoch [8][1800/3746] lr: 9.939e-02, eta: 4 days, 12:10:09, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1842, top5_acc: 0.4023, loss_cls: 4.5944, loss: 4.5944 +2024-07-16 12:49:00,625 - pyskl - INFO - Epoch [8][1900/3746] lr: 9.938e-02, eta: 4 days, 12:08:03, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1878, top5_acc: 0.4109, loss_cls: 4.5749, loss: 4.5749 +2024-07-16 12:50:10,849 - pyskl - INFO - Epoch [8][2000/3746] lr: 9.938e-02, eta: 4 days, 12:05:59, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1873, top5_acc: 0.4139, loss_cls: 4.5424, loss: 4.5424 +2024-07-16 12:51:20,884 - pyskl - INFO - Epoch [8][2100/3746] lr: 9.937e-02, eta: 4 days, 12:03:51, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1797, top5_acc: 0.3998, loss_cls: 4.6138, loss: 4.6138 +2024-07-16 12:52:30,926 - pyskl - INFO - Epoch [8][2200/3746] lr: 9.937e-02, eta: 4 days, 12:01:45, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1827, top5_acc: 0.4092, loss_cls: 4.5613, loss: 4.5613 +2024-07-16 12:53:40,922 - pyskl - INFO - Epoch [8][2300/3746] lr: 9.937e-02, eta: 4 days, 11:59:37, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1708, top5_acc: 0.4041, loss_cls: 4.6067, loss: 4.6067 +2024-07-16 12:54:50,717 - pyskl - INFO - Epoch [8][2400/3746] lr: 9.936e-02, eta: 4 days, 11:57:27, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1786, top5_acc: 0.3984, loss_cls: 4.5939, loss: 4.5939 +2024-07-16 12:56:00,772 - pyskl - INFO - Epoch [8][2500/3746] lr: 9.936e-02, eta: 4 days, 11:55:22, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1883, top5_acc: 0.4156, loss_cls: 4.5274, loss: 4.5274 +2024-07-16 12:57:10,737 - pyskl - INFO - Epoch [8][2600/3746] lr: 9.935e-02, eta: 4 days, 11:53:15, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1794, top5_acc: 0.3994, loss_cls: 4.5983, loss: 4.5983 +2024-07-16 12:58:20,795 - pyskl - INFO - Epoch [8][2700/3746] lr: 9.935e-02, eta: 4 days, 11:51:11, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1812, top5_acc: 0.4022, loss_cls: 4.5708, loss: 4.5708 +2024-07-16 12:59:30,872 - pyskl - INFO - Epoch [8][2800/3746] lr: 9.934e-02, eta: 4 days, 11:49:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1769, top5_acc: 0.4023, loss_cls: 4.5906, loss: 4.5906 +2024-07-16 13:00:40,811 - pyskl - INFO - Epoch [8][2900/3746] lr: 9.934e-02, eta: 4 days, 11:47:01, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1742, top5_acc: 0.3981, loss_cls: 4.5820, loss: 4.5820 +2024-07-16 13:01:50,936 - pyskl - INFO - Epoch [8][3000/3746] lr: 9.933e-02, eta: 4 days, 11:44:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1869, top5_acc: 0.3983, loss_cls: 4.5903, loss: 4.5903 +2024-07-16 13:03:00,834 - pyskl - INFO - Epoch [8][3100/3746] lr: 9.933e-02, eta: 4 days, 11:42:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1833, top5_acc: 0.4003, loss_cls: 4.6009, loss: 4.6009 +2024-07-16 13:04:10,912 - pyskl - INFO - Epoch [8][3200/3746] lr: 9.933e-02, eta: 4 days, 11:40:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1806, top5_acc: 0.4030, loss_cls: 4.5854, loss: 4.5854 +2024-07-16 13:05:21,051 - pyskl - INFO - Epoch [8][3300/3746] lr: 9.932e-02, eta: 4 days, 11:38:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1750, top5_acc: 0.3903, loss_cls: 4.6331, loss: 4.6331 +2024-07-16 13:06:31,023 - pyskl - INFO - Epoch [8][3400/3746] lr: 9.932e-02, eta: 4 days, 11:36:46, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1837, top5_acc: 0.4016, loss_cls: 4.6025, loss: 4.6025 +2024-07-16 13:07:40,888 - pyskl - INFO - Epoch [8][3500/3746] lr: 9.931e-02, eta: 4 days, 11:34:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1777, top5_acc: 0.4102, loss_cls: 4.5744, loss: 4.5744 +2024-07-16 13:08:52,075 - pyskl - INFO - Epoch [8][3600/3746] lr: 9.931e-02, eta: 4 days, 11:33:00, time: 0.712, data_time: 0.001, memory: 15990, top1_acc: 0.1828, top5_acc: 0.4113, loss_cls: 4.5617, loss: 4.5617 +2024-07-16 13:10:02,909 - pyskl - INFO - Epoch [8][3700/3746] lr: 9.930e-02, eta: 4 days, 11:31:13, time: 0.708, data_time: 0.001, memory: 15990, top1_acc: 0.1894, top5_acc: 0.4086, loss_cls: 4.5269, loss: 4.5269 +2024-07-16 13:10:37,040 - pyskl - INFO - Saving checkpoint at 8 epochs +2024-07-16 13:12:26,411 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 13:12:27,068 - pyskl - INFO - +top1_acc 0.1145 +top5_acc 0.3012 +2024-07-16 13:12:27,068 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 13:12:27,105 - pyskl - INFO - +mean_acc 0.1144 +2024-07-16 13:12:27,115 - pyskl - INFO - Epoch(val) [8][309] top1_acc: 0.1145, top5_acc: 0.3012, mean_class_accuracy: 0.1144 +2024-07-16 13:15:41,182 - pyskl - INFO - Epoch [9][100/3746] lr: 9.930e-02, eta: 4 days, 11:55:20, time: 1.941, data_time: 1.232, memory: 15990, top1_acc: 0.1908, top5_acc: 0.4152, loss_cls: 4.5431, loss: 4.5431 +2024-07-16 13:16:51,718 - pyskl - INFO - Epoch [9][200/3746] lr: 9.929e-02, eta: 4 days, 11:53:23, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1961, top5_acc: 0.4239, loss_cls: 4.4790, loss: 4.4790 +2024-07-16 13:18:01,975 - pyskl - INFO - Epoch [9][300/3746] lr: 9.929e-02, eta: 4 days, 11:51:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1859, top5_acc: 0.4059, loss_cls: 4.5626, loss: 4.5626 +2024-07-16 13:19:12,037 - pyskl - INFO - Epoch [9][400/3746] lr: 9.928e-02, eta: 4 days, 11:49:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1870, top5_acc: 0.4103, loss_cls: 4.5448, loss: 4.5448 +2024-07-16 13:20:22,251 - pyskl - INFO - Epoch [9][500/3746] lr: 9.928e-02, eta: 4 days, 11:47:13, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1820, top5_acc: 0.3989, loss_cls: 4.5776, loss: 4.5776 +2024-07-16 13:21:32,582 - pyskl - INFO - Epoch [9][600/3746] lr: 9.927e-02, eta: 4 days, 11:45:14, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1839, top5_acc: 0.4069, loss_cls: 4.5780, loss: 4.5780 +2024-07-16 13:22:43,013 - pyskl - INFO - Epoch [9][700/3746] lr: 9.927e-02, eta: 4 days, 11:43:16, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1839, top5_acc: 0.4056, loss_cls: 4.5776, loss: 4.5776 +2024-07-16 13:23:53,495 - pyskl - INFO - Epoch [9][800/3746] lr: 9.926e-02, eta: 4 days, 11:41:20, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1795, top5_acc: 0.3948, loss_cls: 4.6054, loss: 4.6054 +2024-07-16 13:25:03,884 - pyskl - INFO - Epoch [9][900/3746] lr: 9.926e-02, eta: 4 days, 11:39:22, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1944, top5_acc: 0.4164, loss_cls: 4.5398, loss: 4.5398 +2024-07-16 13:26:14,523 - pyskl - INFO - Epoch [9][1000/3746] lr: 9.925e-02, eta: 4 days, 11:37:29, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1866, top5_acc: 0.4091, loss_cls: 4.5718, loss: 4.5718 +2024-07-16 13:27:24,866 - pyskl - INFO - Epoch [9][1100/3746] lr: 9.925e-02, eta: 4 days, 11:35:31, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1834, top5_acc: 0.4075, loss_cls: 4.5513, loss: 4.5513 +2024-07-16 13:28:35,351 - pyskl - INFO - Epoch [9][1200/3746] lr: 9.924e-02, eta: 4 days, 11:33:35, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1888, top5_acc: 0.4089, loss_cls: 4.5698, loss: 4.5698 +2024-07-16 13:29:45,496 - pyskl - INFO - Epoch [9][1300/3746] lr: 9.924e-02, eta: 4 days, 11:31:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1772, top5_acc: 0.3952, loss_cls: 4.5956, loss: 4.5956 +2024-07-16 13:30:55,794 - pyskl - INFO - Epoch [9][1400/3746] lr: 9.923e-02, eta: 4 days, 11:29:37, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1873, top5_acc: 0.4109, loss_cls: 4.5497, loss: 4.5497 +2024-07-16 13:32:05,803 - pyskl - INFO - Epoch [9][1500/3746] lr: 9.923e-02, eta: 4 days, 11:27:35, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1783, top5_acc: 0.3964, loss_cls: 4.6065, loss: 4.6065 +2024-07-16 13:33:16,035 - pyskl - INFO - Epoch [9][1600/3746] lr: 9.922e-02, eta: 4 days, 11:25:36, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1792, top5_acc: 0.4072, loss_cls: 4.5735, loss: 4.5735 +2024-07-16 13:34:26,347 - pyskl - INFO - Epoch [9][1700/3746] lr: 9.922e-02, eta: 4 days, 11:23:40, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1925, top5_acc: 0.4119, loss_cls: 4.5350, loss: 4.5350 +2024-07-16 13:35:37,217 - pyskl - INFO - Epoch [9][1800/3746] lr: 9.921e-02, eta: 4 days, 11:21:53, time: 0.709, data_time: 0.001, memory: 15990, top1_acc: 0.1877, top5_acc: 0.4139, loss_cls: 4.5477, loss: 4.5477 +2024-07-16 13:36:47,417 - pyskl - INFO - Epoch [9][1900/3746] lr: 9.921e-02, eta: 4 days, 11:19:55, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1848, top5_acc: 0.4044, loss_cls: 4.5663, loss: 4.5663 +2024-07-16 13:37:57,555 - pyskl - INFO - Epoch [9][2000/3746] lr: 9.920e-02, eta: 4 days, 11:17:56, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1894, top5_acc: 0.3991, loss_cls: 4.5804, loss: 4.5804 +2024-07-16 13:39:07,819 - pyskl - INFO - Epoch [9][2100/3746] lr: 9.920e-02, eta: 4 days, 11:16:00, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1891, top5_acc: 0.4105, loss_cls: 4.5497, loss: 4.5497 +2024-07-16 13:40:18,024 - pyskl - INFO - Epoch [9][2200/3746] lr: 9.919e-02, eta: 4 days, 11:14:03, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1847, top5_acc: 0.3983, loss_cls: 4.5744, loss: 4.5744 +2024-07-16 13:41:28,256 - pyskl - INFO - Epoch [9][2300/3746] lr: 9.919e-02, eta: 4 days, 11:12:06, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1805, top5_acc: 0.4056, loss_cls: 4.5652, loss: 4.5652 +2024-07-16 13:42:38,684 - pyskl - INFO - Epoch [9][2400/3746] lr: 9.918e-02, eta: 4 days, 11:10:13, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1845, top5_acc: 0.4036, loss_cls: 4.5916, loss: 4.5916 +2024-07-16 13:43:48,737 - pyskl - INFO - Epoch [9][2500/3746] lr: 9.918e-02, eta: 4 days, 11:08:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1794, top5_acc: 0.4088, loss_cls: 4.5745, loss: 4.5745 +2024-07-16 13:44:58,490 - pyskl - INFO - Epoch [9][2600/3746] lr: 9.917e-02, eta: 4 days, 11:06:12, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1787, top5_acc: 0.3977, loss_cls: 4.5965, loss: 4.5965 +2024-07-16 13:46:08,628 - pyskl - INFO - Epoch [9][2700/3746] lr: 9.917e-02, eta: 4 days, 11:04:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1787, top5_acc: 0.4036, loss_cls: 4.5606, loss: 4.5606 +2024-07-16 13:47:18,841 - pyskl - INFO - Epoch [9][2800/3746] lr: 9.916e-02, eta: 4 days, 11:02:20, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1770, top5_acc: 0.4084, loss_cls: 4.5305, loss: 4.5305 +2024-07-16 13:48:29,058 - pyskl - INFO - Epoch [9][2900/3746] lr: 9.916e-02, eta: 4 days, 11:00:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1864, top5_acc: 0.4102, loss_cls: 4.5432, loss: 4.5432 +2024-07-16 13:49:39,405 - pyskl - INFO - Epoch [9][3000/3746] lr: 9.915e-02, eta: 4 days, 10:58:33, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1884, top5_acc: 0.4167, loss_cls: 4.5424, loss: 4.5424 +2024-07-16 13:50:49,509 - pyskl - INFO - Epoch [9][3100/3746] lr: 9.915e-02, eta: 4 days, 10:56:36, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1897, top5_acc: 0.4088, loss_cls: 4.5693, loss: 4.5693 +2024-07-16 13:52:00,081 - pyskl - INFO - Epoch [9][3200/3746] lr: 9.914e-02, eta: 4 days, 10:54:48, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1848, top5_acc: 0.4086, loss_cls: 4.5727, loss: 4.5727 +2024-07-16 13:53:10,229 - pyskl - INFO - Epoch [9][3300/3746] lr: 9.914e-02, eta: 4 days, 10:52:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1844, top5_acc: 0.4136, loss_cls: 4.5420, loss: 4.5420 +2024-07-16 13:54:20,319 - pyskl - INFO - Epoch [9][3400/3746] lr: 9.913e-02, eta: 4 days, 10:50:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1797, top5_acc: 0.4094, loss_cls: 4.5520, loss: 4.5520 +2024-07-16 13:55:30,434 - pyskl - INFO - Epoch [9][3500/3746] lr: 9.913e-02, eta: 4 days, 10:49:03, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1823, top5_acc: 0.4039, loss_cls: 4.5926, loss: 4.5926 +2024-07-16 13:56:41,147 - pyskl - INFO - Epoch [9][3600/3746] lr: 9.912e-02, eta: 4 days, 10:47:18, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1850, top5_acc: 0.4094, loss_cls: 4.5660, loss: 4.5660 +2024-07-16 13:57:51,675 - pyskl - INFO - Epoch [9][3700/3746] lr: 9.912e-02, eta: 4 days, 10:45:30, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1783, top5_acc: 0.3978, loss_cls: 4.5691, loss: 4.5691 +2024-07-16 13:58:26,073 - pyskl - INFO - Saving checkpoint at 9 epochs +2024-07-16 14:00:17,680 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 14:00:18,373 - pyskl - INFO - +top1_acc 0.0935 +top5_acc 0.2570 +2024-07-16 14:00:18,373 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 14:00:18,417 - pyskl - INFO - +mean_acc 0.0937 +2024-07-16 14:00:18,430 - pyskl - INFO - Epoch(val) [9][309] top1_acc: 0.0935, top5_acc: 0.2570, mean_class_accuracy: 0.0937 +2024-07-16 14:03:31,787 - pyskl - INFO - Epoch [10][100/3746] lr: 9.911e-02, eta: 4 days, 11:06:24, time: 1.933, data_time: 1.229, memory: 15990, top1_acc: 0.1783, top5_acc: 0.4008, loss_cls: 4.5984, loss: 4.5984 +2024-07-16 14:04:42,042 - pyskl - INFO - Epoch [10][200/3746] lr: 9.910e-02, eta: 4 days, 11:04:28, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1925, top5_acc: 0.4088, loss_cls: 4.5313, loss: 4.5313 +2024-07-16 14:05:52,104 - pyskl - INFO - Epoch [10][300/3746] lr: 9.910e-02, eta: 4 days, 11:02:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1852, top5_acc: 0.4122, loss_cls: 4.5444, loss: 4.5444 +2024-07-16 14:07:02,297 - pyskl - INFO - Epoch [10][400/3746] lr: 9.909e-02, eta: 4 days, 11:00:33, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1864, top5_acc: 0.4095, loss_cls: 4.5328, loss: 4.5328 +2024-07-16 14:08:12,700 - pyskl - INFO - Epoch [10][500/3746] lr: 9.909e-02, eta: 4 days, 10:58:40, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1972, top5_acc: 0.4266, loss_cls: 4.4801, loss: 4.4801 +2024-07-16 14:09:23,013 - pyskl - INFO - Epoch [10][600/3746] lr: 9.908e-02, eta: 4 days, 10:56:46, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1787, top5_acc: 0.4056, loss_cls: 4.5706, loss: 4.5706 +2024-07-16 14:10:33,155 - pyskl - INFO - Epoch [10][700/3746] lr: 9.908e-02, eta: 4 days, 10:54:50, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1931, top5_acc: 0.4194, loss_cls: 4.5001, loss: 4.5001 +2024-07-16 14:11:43,305 - pyskl - INFO - Epoch [10][800/3746] lr: 9.907e-02, eta: 4 days, 10:52:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1886, top5_acc: 0.4139, loss_cls: 4.5359, loss: 4.5359 +2024-07-16 14:12:53,257 - pyskl - INFO - Epoch [10][900/3746] lr: 9.907e-02, eta: 4 days, 10:50:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1825, top5_acc: 0.4089, loss_cls: 4.5566, loss: 4.5566 +2024-07-16 14:14:03,234 - pyskl - INFO - Epoch [10][1000/3746] lr: 9.906e-02, eta: 4 days, 10:48:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1922, top5_acc: 0.4103, loss_cls: 4.5349, loss: 4.5349 +2024-07-16 14:15:13,486 - pyskl - INFO - Epoch [10][1100/3746] lr: 9.906e-02, eta: 4 days, 10:47:03, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1922, top5_acc: 0.4144, loss_cls: 4.5394, loss: 4.5394 +2024-07-16 14:16:23,604 - pyskl - INFO - Epoch [10][1200/3746] lr: 9.905e-02, eta: 4 days, 10:45:08, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1900, top5_acc: 0.4105, loss_cls: 4.5158, loss: 4.5158 +2024-07-16 14:17:33,700 - pyskl - INFO - Epoch [10][1300/3746] lr: 9.905e-02, eta: 4 days, 10:43:12, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1878, top5_acc: 0.4138, loss_cls: 4.5213, loss: 4.5213 +2024-07-16 14:18:43,715 - pyskl - INFO - Epoch [10][1400/3746] lr: 9.904e-02, eta: 4 days, 10:41:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1761, top5_acc: 0.4061, loss_cls: 4.5797, loss: 4.5797 +2024-07-16 14:19:53,772 - pyskl - INFO - Epoch [10][1500/3746] lr: 9.903e-02, eta: 4 days, 10:39:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1908, top5_acc: 0.4100, loss_cls: 4.5412, loss: 4.5412 +2024-07-16 14:21:03,715 - pyskl - INFO - Epoch [10][1600/3746] lr: 9.903e-02, eta: 4 days, 10:37:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1841, top5_acc: 0.4030, loss_cls: 4.5639, loss: 4.5639 +2024-07-16 14:22:13,852 - pyskl - INFO - Epoch [10][1700/3746] lr: 9.902e-02, eta: 4 days, 10:35:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1800, top5_acc: 0.4003, loss_cls: 4.5879, loss: 4.5879 +2024-07-16 14:23:24,896 - pyskl - INFO - Epoch [10][1800/3746] lr: 9.902e-02, eta: 4 days, 10:33:49, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1908, top5_acc: 0.4113, loss_cls: 4.5582, loss: 4.5582 +2024-07-16 14:24:35,123 - pyskl - INFO - Epoch [10][1900/3746] lr: 9.901e-02, eta: 4 days, 10:31:57, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1833, top5_acc: 0.4122, loss_cls: 4.5474, loss: 4.5474 +2024-07-16 14:25:45,358 - pyskl - INFO - Epoch [10][2000/3746] lr: 9.901e-02, eta: 4 days, 10:30:05, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1903, top5_acc: 0.4114, loss_cls: 4.5462, loss: 4.5462 +2024-07-16 14:26:55,829 - pyskl - INFO - Epoch [10][2100/3746] lr: 9.900e-02, eta: 4 days, 10:28:17, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1809, top5_acc: 0.4086, loss_cls: 4.5676, loss: 4.5676 +2024-07-16 14:28:05,828 - pyskl - INFO - Epoch [10][2200/3746] lr: 9.900e-02, eta: 4 days, 10:26:22, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1883, top5_acc: 0.4150, loss_cls: 4.5223, loss: 4.5223 +2024-07-16 14:29:15,912 - pyskl - INFO - Epoch [10][2300/3746] lr: 9.899e-02, eta: 4 days, 10:24:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1913, top5_acc: 0.4077, loss_cls: 4.5622, loss: 4.5622 +2024-07-16 14:30:26,076 - pyskl - INFO - Epoch [10][2400/3746] lr: 9.898e-02, eta: 4 days, 10:22:37, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1848, top5_acc: 0.4100, loss_cls: 4.5137, loss: 4.5137 +2024-07-16 14:31:36,144 - pyskl - INFO - Epoch [10][2500/3746] lr: 9.898e-02, eta: 4 days, 10:20:44, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1894, top5_acc: 0.4123, loss_cls: 4.5610, loss: 4.5610 +2024-07-16 14:32:46,185 - pyskl - INFO - Epoch [10][2600/3746] lr: 9.897e-02, eta: 4 days, 10:18:51, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1888, top5_acc: 0.4169, loss_cls: 4.5246, loss: 4.5246 +2024-07-16 14:33:56,160 - pyskl - INFO - Epoch [10][2700/3746] lr: 9.897e-02, eta: 4 days, 10:16:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1833, top5_acc: 0.4142, loss_cls: 4.5525, loss: 4.5525 +2024-07-16 14:35:06,242 - pyskl - INFO - Epoch [10][2800/3746] lr: 9.896e-02, eta: 4 days, 10:15:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1931, top5_acc: 0.4161, loss_cls: 4.5258, loss: 4.5258 +2024-07-16 14:36:16,444 - pyskl - INFO - Epoch [10][2900/3746] lr: 9.896e-02, eta: 4 days, 10:13:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1864, top5_acc: 0.4169, loss_cls: 4.5340, loss: 4.5340 +2024-07-16 14:37:26,583 - pyskl - INFO - Epoch [10][3000/3746] lr: 9.895e-02, eta: 4 days, 10:11:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1914, top5_acc: 0.4133, loss_cls: 4.5188, loss: 4.5188 +2024-07-16 14:38:36,618 - pyskl - INFO - Epoch [10][3100/3746] lr: 9.894e-02, eta: 4 days, 10:09:32, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1900, top5_acc: 0.4184, loss_cls: 4.5430, loss: 4.5430 +2024-07-16 14:39:46,678 - pyskl - INFO - Epoch [10][3200/3746] lr: 9.894e-02, eta: 4 days, 10:07:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1825, top5_acc: 0.4014, loss_cls: 4.5702, loss: 4.5702 +2024-07-16 14:40:56,660 - pyskl - INFO - Epoch [10][3300/3746] lr: 9.893e-02, eta: 4 days, 10:05:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1894, top5_acc: 0.4144, loss_cls: 4.5587, loss: 4.5587 +2024-07-16 14:42:06,559 - pyskl - INFO - Epoch [10][3400/3746] lr: 9.893e-02, eta: 4 days, 10:03:55, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1908, top5_acc: 0.4144, loss_cls: 4.5168, loss: 4.5168 +2024-07-16 14:43:16,682 - pyskl - INFO - Epoch [10][3500/3746] lr: 9.892e-02, eta: 4 days, 10:02:05, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4127, loss_cls: 4.5152, loss: 4.5152 +2024-07-16 14:44:27,430 - pyskl - INFO - Epoch [10][3600/3746] lr: 9.892e-02, eta: 4 days, 10:00:24, time: 0.707, data_time: 0.001, memory: 15990, top1_acc: 0.1852, top5_acc: 0.4069, loss_cls: 4.5695, loss: 4.5695 +2024-07-16 14:45:38,207 - pyskl - INFO - Epoch [10][3700/3746] lr: 9.891e-02, eta: 4 days, 9:58:43, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1942, top5_acc: 0.4158, loss_cls: 4.5158, loss: 4.5158 +2024-07-16 14:46:12,765 - pyskl - INFO - Saving checkpoint at 10 epochs +2024-07-16 14:48:04,200 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 14:48:04,864 - pyskl - INFO - +top1_acc 0.1299 +top5_acc 0.3258 +2024-07-16 14:48:04,865 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 14:48:04,903 - pyskl - INFO - +mean_acc 0.1299 +2024-07-16 14:48:04,907 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_7.pth was removed +2024-07-16 14:48:05,148 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_10.pth. +2024-07-16 14:48:05,149 - pyskl - INFO - Best top1_acc is 0.1299 at 10 epoch. +2024-07-16 14:48:05,159 - pyskl - INFO - Epoch(val) [10][309] top1_acc: 0.1299, top5_acc: 0.3258, mean_class_accuracy: 0.1299 +2024-07-16 14:51:18,733 - pyskl - INFO - Epoch [11][100/3746] lr: 9.890e-02, eta: 4 days, 10:17:17, time: 1.936, data_time: 1.230, memory: 15990, top1_acc: 0.1934, top5_acc: 0.4258, loss_cls: 4.5014, loss: 4.5014 +2024-07-16 14:52:29,377 - pyskl - INFO - Epoch [11][200/3746] lr: 9.890e-02, eta: 4 days, 10:15:31, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1903, top5_acc: 0.4097, loss_cls: 4.5276, loss: 4.5276 +2024-07-16 14:53:39,797 - pyskl - INFO - Epoch [11][300/3746] lr: 9.889e-02, eta: 4 days, 10:13:43, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1934, top5_acc: 0.4147, loss_cls: 4.5033, loss: 4.5033 +2024-07-16 14:54:50,004 - pyskl - INFO - Epoch [11][400/3746] lr: 9.888e-02, eta: 4 days, 10:11:52, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1872, top5_acc: 0.4066, loss_cls: 4.5410, loss: 4.5410 +2024-07-16 14:56:00,294 - pyskl - INFO - Epoch [11][500/3746] lr: 9.888e-02, eta: 4 days, 10:10:02, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1886, top5_acc: 0.4183, loss_cls: 4.5078, loss: 4.5078 +2024-07-16 14:57:10,586 - pyskl - INFO - Epoch [11][600/3746] lr: 9.887e-02, eta: 4 days, 10:08:13, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1895, top5_acc: 0.4181, loss_cls: 4.5234, loss: 4.5234 +2024-07-16 14:58:20,892 - pyskl - INFO - Epoch [11][700/3746] lr: 9.887e-02, eta: 4 days, 10:06:24, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1783, top5_acc: 0.4002, loss_cls: 4.5596, loss: 4.5596 +2024-07-16 14:59:31,023 - pyskl - INFO - Epoch [11][800/3746] lr: 9.886e-02, eta: 4 days, 10:04:32, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1814, top5_acc: 0.4061, loss_cls: 4.5643, loss: 4.5643 +2024-07-16 15:00:41,157 - pyskl - INFO - Epoch [11][900/3746] lr: 9.885e-02, eta: 4 days, 10:02:41, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1916, top5_acc: 0.4114, loss_cls: 4.5193, loss: 4.5193 +2024-07-16 15:01:51,653 - pyskl - INFO - Epoch [11][1000/3746] lr: 9.885e-02, eta: 4 days, 10:00:55, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1834, top5_acc: 0.4072, loss_cls: 4.5335, loss: 4.5335 +2024-07-16 15:03:01,737 - pyskl - INFO - Epoch [11][1100/3746] lr: 9.884e-02, eta: 4 days, 9:59:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1870, top5_acc: 0.4125, loss_cls: 4.5431, loss: 4.5431 +2024-07-16 15:04:11,949 - pyskl - INFO - Epoch [11][1200/3746] lr: 9.884e-02, eta: 4 days, 9:57:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1778, top5_acc: 0.4012, loss_cls: 4.5795, loss: 4.5795 +2024-07-16 15:05:22,323 - pyskl - INFO - Epoch [11][1300/3746] lr: 9.883e-02, eta: 4 days, 9:55:28, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1933, top5_acc: 0.4114, loss_cls: 4.5143, loss: 4.5143 +2024-07-16 15:06:32,608 - pyskl - INFO - Epoch [11][1400/3746] lr: 9.882e-02, eta: 4 days, 9:53:40, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1891, top5_acc: 0.4120, loss_cls: 4.5187, loss: 4.5187 +2024-07-16 15:07:42,684 - pyskl - INFO - Epoch [11][1500/3746] lr: 9.882e-02, eta: 4 days, 9:51:49, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1934, top5_acc: 0.4219, loss_cls: 4.4900, loss: 4.4900 +2024-07-16 15:08:52,749 - pyskl - INFO - Epoch [11][1600/3746] lr: 9.881e-02, eta: 4 days, 9:49:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1891, top5_acc: 0.4169, loss_cls: 4.5231, loss: 4.5231 +2024-07-16 15:10:02,578 - pyskl - INFO - Epoch [11][1700/3746] lr: 9.881e-02, eta: 4 days, 9:48:05, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4169, loss_cls: 4.5009, loss: 4.5009 +2024-07-16 15:11:13,002 - pyskl - INFO - Epoch [11][1800/3746] lr: 9.880e-02, eta: 4 days, 9:46:20, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1858, top5_acc: 0.4181, loss_cls: 4.5388, loss: 4.5388 +2024-07-16 15:12:23,295 - pyskl - INFO - Epoch [11][1900/3746] lr: 9.879e-02, eta: 4 days, 9:44:33, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1934, top5_acc: 0.4239, loss_cls: 4.5121, loss: 4.5121 +2024-07-16 15:13:33,654 - pyskl - INFO - Epoch [11][2000/3746] lr: 9.879e-02, eta: 4 days, 9:42:47, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1842, top5_acc: 0.4047, loss_cls: 4.5635, loss: 4.5635 +2024-07-16 15:14:43,685 - pyskl - INFO - Epoch [11][2100/3746] lr: 9.878e-02, eta: 4 days, 9:40:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1855, top5_acc: 0.4125, loss_cls: 4.5424, loss: 4.5424 +2024-07-16 15:15:53,621 - pyskl - INFO - Epoch [11][2200/3746] lr: 9.878e-02, eta: 4 days, 9:39:06, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1933, top5_acc: 0.4217, loss_cls: 4.4984, loss: 4.4984 +2024-07-16 15:17:03,580 - pyskl - INFO - Epoch [11][2300/3746] lr: 9.877e-02, eta: 4 days, 9:37:15, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1897, top5_acc: 0.4106, loss_cls: 4.5152, loss: 4.5152 +2024-07-16 15:18:13,451 - pyskl - INFO - Epoch [11][2400/3746] lr: 9.876e-02, eta: 4 days, 9:35:24, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1927, top5_acc: 0.4095, loss_cls: 4.5387, loss: 4.5387 +2024-07-16 15:19:23,302 - pyskl - INFO - Epoch [11][2500/3746] lr: 9.876e-02, eta: 4 days, 9:33:32, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1925, top5_acc: 0.4241, loss_cls: 4.4792, loss: 4.4792 +2024-07-16 15:20:33,228 - pyskl - INFO - Epoch [11][2600/3746] lr: 9.875e-02, eta: 4 days, 9:31:42, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4158, loss_cls: 4.5196, loss: 4.5196 +2024-07-16 15:21:43,310 - pyskl - INFO - Epoch [11][2700/3746] lr: 9.874e-02, eta: 4 days, 9:29:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1905, top5_acc: 0.4156, loss_cls: 4.5586, loss: 4.5586 +2024-07-16 15:22:53,300 - pyskl - INFO - Epoch [11][2800/3746] lr: 9.874e-02, eta: 4 days, 9:28:05, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1934, top5_acc: 0.4205, loss_cls: 4.5146, loss: 4.5146 +2024-07-16 15:24:03,168 - pyskl - INFO - Epoch [11][2900/3746] lr: 9.873e-02, eta: 4 days, 9:26:14, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1938, top5_acc: 0.4217, loss_cls: 4.5010, loss: 4.5010 +2024-07-16 15:25:13,267 - pyskl - INFO - Epoch [11][3000/3746] lr: 9.873e-02, eta: 4 days, 9:24:27, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1767, top5_acc: 0.4064, loss_cls: 4.5537, loss: 4.5537 +2024-07-16 15:26:23,204 - pyskl - INFO - Epoch [11][3100/3746] lr: 9.872e-02, eta: 4 days, 9:22:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1895, top5_acc: 0.4103, loss_cls: 4.5241, loss: 4.5241 +2024-07-16 15:27:33,205 - pyskl - INFO - Epoch [11][3200/3746] lr: 9.871e-02, eta: 4 days, 9:20:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1947, top5_acc: 0.4216, loss_cls: 4.5062, loss: 4.5062 +2024-07-16 15:28:43,113 - pyskl - INFO - Epoch [11][3300/3746] lr: 9.871e-02, eta: 4 days, 9:19:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1881, top5_acc: 0.4134, loss_cls: 4.5349, loss: 4.5349 +2024-07-16 15:29:53,094 - pyskl - INFO - Epoch [11][3400/3746] lr: 9.870e-02, eta: 4 days, 9:17:12, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1891, top5_acc: 0.4177, loss_cls: 4.5303, loss: 4.5303 +2024-07-16 15:31:03,067 - pyskl - INFO - Epoch [11][3500/3746] lr: 9.869e-02, eta: 4 days, 9:15:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1861, top5_acc: 0.4175, loss_cls: 4.5413, loss: 4.5413 +2024-07-16 15:32:13,698 - pyskl - INFO - Epoch [11][3600/3746] lr: 9.869e-02, eta: 4 days, 9:13:45, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1881, top5_acc: 0.4189, loss_cls: 4.5213, loss: 4.5213 +2024-07-16 15:33:24,265 - pyskl - INFO - Epoch [11][3700/3746] lr: 9.868e-02, eta: 4 days, 9:12:04, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1958, top5_acc: 0.4172, loss_cls: 4.5260, loss: 4.5260 +2024-07-16 15:33:58,492 - pyskl - INFO - Saving checkpoint at 11 epochs +2024-07-16 15:35:49,936 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 15:35:50,606 - pyskl - INFO - +top1_acc 0.1191 +top5_acc 0.3139 +2024-07-16 15:35:50,606 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 15:35:50,644 - pyskl - INFO - +mean_acc 0.1191 +2024-07-16 15:35:50,653 - pyskl - INFO - Epoch(val) [11][309] top1_acc: 0.1191, top5_acc: 0.3139, mean_class_accuracy: 0.1191 +2024-07-16 15:39:02,184 - pyskl - INFO - Epoch [12][100/3746] lr: 9.867e-02, eta: 4 days, 9:28:14, time: 1.915, data_time: 1.209, memory: 15990, top1_acc: 0.1928, top5_acc: 0.4248, loss_cls: 4.4784, loss: 4.4784 +2024-07-16 15:40:12,425 - pyskl - INFO - Epoch [12][200/3746] lr: 9.867e-02, eta: 4 days, 9:26:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1941, top5_acc: 0.4250, loss_cls: 4.4782, loss: 4.4782 +2024-07-16 15:41:22,863 - pyskl - INFO - Epoch [12][300/3746] lr: 9.866e-02, eta: 4 days, 9:24:43, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1938, top5_acc: 0.4227, loss_cls: 4.4989, loss: 4.4989 +2024-07-16 15:42:33,187 - pyskl - INFO - Epoch [12][400/3746] lr: 9.865e-02, eta: 4 days, 9:22:57, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4273, loss_cls: 4.4695, loss: 4.4695 +2024-07-16 15:43:43,523 - pyskl - INFO - Epoch [12][500/3746] lr: 9.865e-02, eta: 4 days, 9:21:12, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1945, top5_acc: 0.4228, loss_cls: 4.5057, loss: 4.5057 +2024-07-16 15:44:53,931 - pyskl - INFO - Epoch [12][600/3746] lr: 9.864e-02, eta: 4 days, 9:19:28, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1933, top5_acc: 0.4191, loss_cls: 4.5112, loss: 4.5112 +2024-07-16 15:46:04,254 - pyskl - INFO - Epoch [12][700/3746] lr: 9.863e-02, eta: 4 days, 9:17:43, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1948, top5_acc: 0.4172, loss_cls: 4.5227, loss: 4.5227 +2024-07-16 15:47:14,253 - pyskl - INFO - Epoch [12][800/3746] lr: 9.863e-02, eta: 4 days, 9:15:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1834, top5_acc: 0.4145, loss_cls: 4.5214, loss: 4.5214 +2024-07-16 15:48:24,473 - pyskl - INFO - Epoch [12][900/3746] lr: 9.862e-02, eta: 4 days, 9:14:09, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2008, top5_acc: 0.4228, loss_cls: 4.4959, loss: 4.4959 +2024-07-16 15:49:34,678 - pyskl - INFO - Epoch [12][1000/3746] lr: 9.861e-02, eta: 4 days, 9:12:23, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1862, top5_acc: 0.4114, loss_cls: 4.5448, loss: 4.5448 +2024-07-16 15:50:44,730 - pyskl - INFO - Epoch [12][1100/3746] lr: 9.861e-02, eta: 4 days, 9:10:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2008, top5_acc: 0.4286, loss_cls: 4.5151, loss: 4.5151 +2024-07-16 15:51:55,148 - pyskl - INFO - Epoch [12][1200/3746] lr: 9.860e-02, eta: 4 days, 9:08:52, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1930, top5_acc: 0.4144, loss_cls: 4.5298, loss: 4.5298 +2024-07-16 15:53:05,300 - pyskl - INFO - Epoch [12][1300/3746] lr: 9.859e-02, eta: 4 days, 9:07:06, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1927, top5_acc: 0.4089, loss_cls: 4.5604, loss: 4.5604 +2024-07-16 15:54:15,478 - pyskl - INFO - Epoch [12][1400/3746] lr: 9.859e-02, eta: 4 days, 9:05:21, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1934, top5_acc: 0.4216, loss_cls: 4.5137, loss: 4.5137 +2024-07-16 15:55:25,696 - pyskl - INFO - Epoch [12][1500/3746] lr: 9.858e-02, eta: 4 days, 9:03:36, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1903, top5_acc: 0.4025, loss_cls: 4.5449, loss: 4.5449 +2024-07-16 15:56:35,754 - pyskl - INFO - Epoch [12][1600/3746] lr: 9.857e-02, eta: 4 days, 9:01:49, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1958, top5_acc: 0.4223, loss_cls: 4.5047, loss: 4.5047 +2024-07-16 15:57:45,811 - pyskl - INFO - Epoch [12][1700/3746] lr: 9.857e-02, eta: 4 days, 9:00:02, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1961, top5_acc: 0.4314, loss_cls: 4.4818, loss: 4.4818 +2024-07-16 15:58:56,702 - pyskl - INFO - Epoch [12][1800/3746] lr: 9.856e-02, eta: 4 days, 8:58:26, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2008, top5_acc: 0.4302, loss_cls: 4.4662, loss: 4.4662 +2024-07-16 16:00:06,992 - pyskl - INFO - Epoch [12][1900/3746] lr: 9.855e-02, eta: 4 days, 8:56:43, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1983, top5_acc: 0.4203, loss_cls: 4.5019, loss: 4.5019 +2024-07-16 16:01:17,113 - pyskl - INFO - Epoch [12][2000/3746] lr: 9.855e-02, eta: 4 days, 8:54:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1847, top5_acc: 0.4138, loss_cls: 4.5387, loss: 4.5387 +2024-07-16 16:02:27,154 - pyskl - INFO - Epoch [12][2100/3746] lr: 9.854e-02, eta: 4 days, 8:53:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1850, top5_acc: 0.4117, loss_cls: 4.5449, loss: 4.5449 +2024-07-16 16:03:37,285 - pyskl - INFO - Epoch [12][2200/3746] lr: 9.853e-02, eta: 4 days, 8:51:27, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1889, top5_acc: 0.4175, loss_cls: 4.5173, loss: 4.5173 +2024-07-16 16:04:47,267 - pyskl - INFO - Epoch [12][2300/3746] lr: 9.853e-02, eta: 4 days, 8:49:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1892, top5_acc: 0.4125, loss_cls: 4.5313, loss: 4.5313 +2024-07-16 16:05:57,533 - pyskl - INFO - Epoch [12][2400/3746] lr: 9.852e-02, eta: 4 days, 8:47:57, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1856, top5_acc: 0.4128, loss_cls: 4.5320, loss: 4.5320 +2024-07-16 16:07:07,568 - pyskl - INFO - Epoch [12][2500/3746] lr: 9.851e-02, eta: 4 days, 8:46:12, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1942, top5_acc: 0.4183, loss_cls: 4.5145, loss: 4.5145 +2024-07-16 16:08:17,494 - pyskl - INFO - Epoch [12][2600/3746] lr: 9.851e-02, eta: 4 days, 8:44:25, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4231, loss_cls: 4.4848, loss: 4.4848 +2024-07-16 16:09:27,603 - pyskl - INFO - Epoch [12][2700/3746] lr: 9.850e-02, eta: 4 days, 8:42:41, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1933, top5_acc: 0.4072, loss_cls: 4.5364, loss: 4.5364 +2024-07-16 16:10:37,643 - pyskl - INFO - Epoch [12][2800/3746] lr: 9.849e-02, eta: 4 days, 8:40:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4250, loss_cls: 4.4895, loss: 4.4895 +2024-07-16 16:11:47,706 - pyskl - INFO - Epoch [12][2900/3746] lr: 9.849e-02, eta: 4 days, 8:39:11, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1869, top5_acc: 0.4086, loss_cls: 4.5563, loss: 4.5563 +2024-07-16 16:12:57,683 - pyskl - INFO - Epoch [12][3000/3746] lr: 9.848e-02, eta: 4 days, 8:37:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1958, top5_acc: 0.4144, loss_cls: 4.5071, loss: 4.5071 +2024-07-16 16:14:07,630 - pyskl - INFO - Epoch [12][3100/3746] lr: 9.847e-02, eta: 4 days, 8:35:40, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1992, top5_acc: 0.4255, loss_cls: 4.4938, loss: 4.4938 +2024-07-16 16:15:17,700 - pyskl - INFO - Epoch [12][3200/3746] lr: 9.847e-02, eta: 4 days, 8:33:57, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1923, top5_acc: 0.4053, loss_cls: 4.5246, loss: 4.5246 +2024-07-16 16:16:27,650 - pyskl - INFO - Epoch [12][3300/3746] lr: 9.846e-02, eta: 4 days, 8:32:11, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1884, top5_acc: 0.4109, loss_cls: 4.5178, loss: 4.5178 +2024-07-16 16:17:37,624 - pyskl - INFO - Epoch [12][3400/3746] lr: 9.845e-02, eta: 4 days, 8:30:27, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1959, top5_acc: 0.4258, loss_cls: 4.5004, loss: 4.5004 +2024-07-16 16:18:47,851 - pyskl - INFO - Epoch [12][3500/3746] lr: 9.845e-02, eta: 4 days, 8:28:45, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1884, top5_acc: 0.4100, loss_cls: 4.5207, loss: 4.5207 +2024-07-16 16:19:58,710 - pyskl - INFO - Epoch [12][3600/3746] lr: 9.844e-02, eta: 4 days, 8:27:11, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1909, top5_acc: 0.4123, loss_cls: 4.5369, loss: 4.5369 +2024-07-16 16:21:09,131 - pyskl - INFO - Epoch [12][3700/3746] lr: 9.843e-02, eta: 4 days, 8:25:31, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1856, top5_acc: 0.4161, loss_cls: 4.5173, loss: 4.5173 +2024-07-16 16:21:43,151 - pyskl - INFO - Saving checkpoint at 12 epochs +2024-07-16 16:23:32,779 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 16:23:33,445 - pyskl - INFO - +top1_acc 0.1209 +top5_acc 0.3046 +2024-07-16 16:23:33,446 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 16:23:33,484 - pyskl - INFO - +mean_acc 0.1206 +2024-07-16 16:23:33,494 - pyskl - INFO - Epoch(val) [12][309] top1_acc: 0.1209, top5_acc: 0.3046, mean_class_accuracy: 0.1206 +2024-07-16 16:26:45,901 - pyskl - INFO - Epoch [13][100/3746] lr: 9.842e-02, eta: 4 days, 8:40:15, time: 1.924, data_time: 1.213, memory: 15990, top1_acc: 0.1909, top5_acc: 0.4311, loss_cls: 4.4856, loss: 4.4856 +2024-07-16 16:27:56,815 - pyskl - INFO - Epoch [13][200/3746] lr: 9.842e-02, eta: 4 days, 8:38:39, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4281, loss_cls: 4.4718, loss: 4.4718 +2024-07-16 16:29:07,722 - pyskl - INFO - Epoch [13][300/3746] lr: 9.841e-02, eta: 4 days, 8:37:03, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4241, loss_cls: 4.4779, loss: 4.4779 +2024-07-16 16:30:18,224 - pyskl - INFO - Epoch [13][400/3746] lr: 9.840e-02, eta: 4 days, 8:35:23, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1945, top5_acc: 0.4178, loss_cls: 4.4816, loss: 4.4816 +2024-07-16 16:31:28,881 - pyskl - INFO - Epoch [13][500/3746] lr: 9.839e-02, eta: 4 days, 8:33:45, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4239, loss_cls: 4.4631, loss: 4.4631 +2024-07-16 16:32:39,377 - pyskl - INFO - Epoch [13][600/3746] lr: 9.839e-02, eta: 4 days, 8:32:05, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1938, top5_acc: 0.4156, loss_cls: 4.5061, loss: 4.5061 +2024-07-16 16:33:49,749 - pyskl - INFO - Epoch [13][700/3746] lr: 9.838e-02, eta: 4 days, 8:30:23, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1914, top5_acc: 0.4191, loss_cls: 4.4852, loss: 4.4852 +2024-07-16 16:35:00,212 - pyskl - INFO - Epoch [13][800/3746] lr: 9.837e-02, eta: 4 days, 8:28:43, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1925, top5_acc: 0.4186, loss_cls: 4.5056, loss: 4.5056 +2024-07-16 16:36:10,660 - pyskl - INFO - Epoch [13][900/3746] lr: 9.837e-02, eta: 4 days, 8:27:03, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1903, top5_acc: 0.4222, loss_cls: 4.5189, loss: 4.5189 +2024-07-16 16:37:21,050 - pyskl - INFO - Epoch [13][1000/3746] lr: 9.836e-02, eta: 4 days, 8:25:22, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1950, top5_acc: 0.4219, loss_cls: 4.4804, loss: 4.4804 +2024-07-16 16:38:31,205 - pyskl - INFO - Epoch [13][1100/3746] lr: 9.835e-02, eta: 4 days, 8:23:39, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1913, top5_acc: 0.4159, loss_cls: 4.5232, loss: 4.5232 +2024-07-16 16:39:41,340 - pyskl - INFO - Epoch [13][1200/3746] lr: 9.834e-02, eta: 4 days, 8:21:56, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1908, top5_acc: 0.4144, loss_cls: 4.5016, loss: 4.5016 +2024-07-16 16:40:51,686 - pyskl - INFO - Epoch [13][1300/3746] lr: 9.834e-02, eta: 4 days, 8:20:15, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1934, top5_acc: 0.4217, loss_cls: 4.4963, loss: 4.4963 +2024-07-16 16:42:02,062 - pyskl - INFO - Epoch [13][1400/3746] lr: 9.833e-02, eta: 4 days, 8:18:35, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1925, top5_acc: 0.4172, loss_cls: 4.5301, loss: 4.5301 +2024-07-16 16:43:12,583 - pyskl - INFO - Epoch [13][1500/3746] lr: 9.832e-02, eta: 4 days, 8:16:56, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4220, loss_cls: 4.4658, loss: 4.4658 +2024-07-16 16:44:22,921 - pyskl - INFO - Epoch [13][1600/3746] lr: 9.832e-02, eta: 4 days, 8:15:16, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1809, top5_acc: 0.4097, loss_cls: 4.5574, loss: 4.5574 +2024-07-16 16:45:33,164 - pyskl - INFO - Epoch [13][1700/3746] lr: 9.831e-02, eta: 4 days, 8:13:34, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4213, loss_cls: 4.4827, loss: 4.4827 +2024-07-16 16:46:44,511 - pyskl - INFO - Epoch [13][1800/3746] lr: 9.830e-02, eta: 4 days, 8:12:05, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4258, loss_cls: 4.4804, loss: 4.4804 +2024-07-16 16:47:54,885 - pyskl - INFO - Epoch [13][1900/3746] lr: 9.829e-02, eta: 4 days, 8:10:26, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4227, loss_cls: 4.4884, loss: 4.4884 +2024-07-16 16:49:05,138 - pyskl - INFO - Epoch [13][2000/3746] lr: 9.829e-02, eta: 4 days, 8:08:45, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1872, top5_acc: 0.4144, loss_cls: 4.5443, loss: 4.5443 +2024-07-16 16:50:15,848 - pyskl - INFO - Epoch [13][2100/3746] lr: 9.828e-02, eta: 4 days, 8:07:09, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2008, top5_acc: 0.4338, loss_cls: 4.4575, loss: 4.4575 +2024-07-16 16:51:26,527 - pyskl - INFO - Epoch [13][2200/3746] lr: 9.827e-02, eta: 4 days, 8:05:33, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1911, top5_acc: 0.4216, loss_cls: 4.5452, loss: 4.5452 +2024-07-16 16:52:36,793 - pyskl - INFO - Epoch [13][2300/3746] lr: 9.827e-02, eta: 4 days, 8:03:53, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1923, top5_acc: 0.4166, loss_cls: 4.5226, loss: 4.5226 +2024-07-16 16:53:46,996 - pyskl - INFO - Epoch [13][2400/3746] lr: 9.826e-02, eta: 4 days, 8:02:12, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1923, top5_acc: 0.4042, loss_cls: 4.5497, loss: 4.5497 +2024-07-16 16:54:57,241 - pyskl - INFO - Epoch [13][2500/3746] lr: 9.825e-02, eta: 4 days, 8:00:31, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1891, top5_acc: 0.4280, loss_cls: 4.4706, loss: 4.4706 +2024-07-16 16:56:07,426 - pyskl - INFO - Epoch [13][2600/3746] lr: 9.824e-02, eta: 4 days, 7:58:50, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1964, top5_acc: 0.4270, loss_cls: 4.4793, loss: 4.4793 +2024-07-16 16:57:17,667 - pyskl - INFO - Epoch [13][2700/3746] lr: 9.824e-02, eta: 4 days, 7:57:10, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4284, loss_cls: 4.4866, loss: 4.4866 +2024-07-16 16:58:28,028 - pyskl - INFO - Epoch [13][2800/3746] lr: 9.823e-02, eta: 4 days, 7:55:31, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1984, top5_acc: 0.4173, loss_cls: 4.5035, loss: 4.5035 +2024-07-16 16:59:38,436 - pyskl - INFO - Epoch [13][2900/3746] lr: 9.822e-02, eta: 4 days, 7:53:53, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1831, top5_acc: 0.4062, loss_cls: 4.5651, loss: 4.5651 +2024-07-16 17:00:48,635 - pyskl - INFO - Epoch [13][3000/3746] lr: 9.821e-02, eta: 4 days, 7:52:13, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1925, top5_acc: 0.4144, loss_cls: 4.5244, loss: 4.5244 +2024-07-16 17:01:59,059 - pyskl - INFO - Epoch [13][3100/3746] lr: 9.821e-02, eta: 4 days, 7:50:35, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1941, top5_acc: 0.4200, loss_cls: 4.4774, loss: 4.4774 +2024-07-16 17:03:09,180 - pyskl - INFO - Epoch [13][3200/3746] lr: 9.820e-02, eta: 4 days, 7:48:55, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1900, top5_acc: 0.4200, loss_cls: 4.5001, loss: 4.5001 +2024-07-16 17:04:19,304 - pyskl - INFO - Epoch [13][3300/3746] lr: 9.819e-02, eta: 4 days, 7:47:14, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1955, top5_acc: 0.4236, loss_cls: 4.4937, loss: 4.4937 +2024-07-16 17:05:29,533 - pyskl - INFO - Epoch [13][3400/3746] lr: 9.818e-02, eta: 4 days, 7:45:34, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1906, top5_acc: 0.4211, loss_cls: 4.5240, loss: 4.5240 +2024-07-16 17:06:40,044 - pyskl - INFO - Epoch [13][3500/3746] lr: 9.818e-02, eta: 4 days, 7:43:58, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1817, top5_acc: 0.4078, loss_cls: 4.5606, loss: 4.5606 +2024-07-16 17:07:51,501 - pyskl - INFO - Epoch [13][3600/3746] lr: 9.817e-02, eta: 4 days, 7:42:32, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.1909, top5_acc: 0.4109, loss_cls: 4.5429, loss: 4.5429 +2024-07-16 17:09:02,743 - pyskl - INFO - Epoch [13][3700/3746] lr: 9.816e-02, eta: 4 days, 7:41:04, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1975, top5_acc: 0.4213, loss_cls: 4.5002, loss: 4.5002 +2024-07-16 17:09:36,986 - pyskl - INFO - Saving checkpoint at 13 epochs +2024-07-16 17:11:27,926 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 17:11:28,592 - pyskl - INFO - +top1_acc 0.1318 +top5_acc 0.3244 +2024-07-16 17:11:28,592 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 17:11:28,631 - pyskl - INFO - +mean_acc 0.1317 +2024-07-16 17:11:28,635 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_10.pth was removed +2024-07-16 17:11:28,867 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_13.pth. +2024-07-16 17:11:28,868 - pyskl - INFO - Best top1_acc is 0.1318 at 13 epoch. +2024-07-16 17:11:28,878 - pyskl - INFO - Epoch(val) [13][309] top1_acc: 0.1318, top5_acc: 0.3244, mean_class_accuracy: 0.1317 +2024-07-16 17:14:40,904 - pyskl - INFO - Epoch [14][100/3746] lr: 9.815e-02, eta: 4 days, 7:54:20, time: 1.920, data_time: 1.209, memory: 15990, top1_acc: 0.1917, top5_acc: 0.4289, loss_cls: 4.4714, loss: 4.4714 +2024-07-16 17:15:51,940 - pyskl - INFO - Epoch [14][200/3746] lr: 9.814e-02, eta: 4 days, 7:52:47, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2002, top5_acc: 0.4341, loss_cls: 4.4403, loss: 4.4403 +2024-07-16 17:17:02,982 - pyskl - INFO - Epoch [14][300/3746] lr: 9.814e-02, eta: 4 days, 7:51:15, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1845, top5_acc: 0.4044, loss_cls: 4.5513, loss: 4.5513 +2024-07-16 17:18:13,718 - pyskl - INFO - Epoch [14][400/3746] lr: 9.813e-02, eta: 4 days, 7:49:40, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1944, top5_acc: 0.4289, loss_cls: 4.4919, loss: 4.4919 +2024-07-16 17:19:24,059 - pyskl - INFO - Epoch [14][500/3746] lr: 9.812e-02, eta: 4 days, 7:48:00, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1931, top5_acc: 0.4092, loss_cls: 4.5422, loss: 4.5422 +2024-07-16 17:20:34,615 - pyskl - INFO - Epoch [14][600/3746] lr: 9.811e-02, eta: 4 days, 7:46:23, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1952, top5_acc: 0.4169, loss_cls: 4.5081, loss: 4.5081 +2024-07-16 17:21:45,190 - pyskl - INFO - Epoch [14][700/3746] lr: 9.811e-02, eta: 4 days, 7:44:46, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4263, loss_cls: 4.4802, loss: 4.4802 +2024-07-16 17:22:55,407 - pyskl - INFO - Epoch [14][800/3746] lr: 9.810e-02, eta: 4 days, 7:43:06, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1966, top5_acc: 0.4269, loss_cls: 4.4600, loss: 4.4600 +2024-07-16 17:24:05,932 - pyskl - INFO - Epoch [14][900/3746] lr: 9.809e-02, eta: 4 days, 7:41:29, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4277, loss_cls: 4.4487, loss: 4.4487 +2024-07-16 17:25:16,502 - pyskl - INFO - Epoch [14][1000/3746] lr: 9.808e-02, eta: 4 days, 7:39:52, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2011, top5_acc: 0.4255, loss_cls: 4.4872, loss: 4.4872 +2024-07-16 17:26:26,790 - pyskl - INFO - Epoch [14][1100/3746] lr: 9.807e-02, eta: 4 days, 7:38:13, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1958, top5_acc: 0.4191, loss_cls: 4.5016, loss: 4.5016 +2024-07-16 17:27:37,234 - pyskl - INFO - Epoch [14][1200/3746] lr: 9.807e-02, eta: 4 days, 7:36:35, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1952, top5_acc: 0.4181, loss_cls: 4.4903, loss: 4.4903 +2024-07-16 17:28:47,574 - pyskl - INFO - Epoch [14][1300/3746] lr: 9.806e-02, eta: 4 days, 7:34:57, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1902, top5_acc: 0.4203, loss_cls: 4.5111, loss: 4.5111 +2024-07-16 17:29:57,693 - pyskl - INFO - Epoch [14][1400/3746] lr: 9.805e-02, eta: 4 days, 7:33:16, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1975, top5_acc: 0.4161, loss_cls: 4.4983, loss: 4.4983 +2024-07-16 17:31:08,049 - pyskl - INFO - Epoch [14][1500/3746] lr: 9.804e-02, eta: 4 days, 7:31:38, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1945, top5_acc: 0.4164, loss_cls: 4.5001, loss: 4.5001 +2024-07-16 17:32:18,368 - pyskl - INFO - Epoch [14][1600/3746] lr: 9.804e-02, eta: 4 days, 7:29:59, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4223, loss_cls: 4.4792, loss: 4.4792 +2024-07-16 17:33:28,619 - pyskl - INFO - Epoch [14][1700/3746] lr: 9.803e-02, eta: 4 days, 7:28:20, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1883, top5_acc: 0.4181, loss_cls: 4.5051, loss: 4.5051 +2024-07-16 17:34:39,519 - pyskl - INFO - Epoch [14][1800/3746] lr: 9.802e-02, eta: 4 days, 7:26:48, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1933, top5_acc: 0.4206, loss_cls: 4.4921, loss: 4.4921 +2024-07-16 17:35:50,113 - pyskl - INFO - Epoch [14][1900/3746] lr: 9.801e-02, eta: 4 days, 7:25:13, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1966, top5_acc: 0.4206, loss_cls: 4.4907, loss: 4.4907 +2024-07-16 17:37:00,776 - pyskl - INFO - Epoch [14][2000/3746] lr: 9.800e-02, eta: 4 days, 7:23:38, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1873, top5_acc: 0.4116, loss_cls: 4.5430, loss: 4.5430 +2024-07-16 17:38:11,495 - pyskl - INFO - Epoch [14][2100/3746] lr: 9.800e-02, eta: 4 days, 7:22:04, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1989, top5_acc: 0.4211, loss_cls: 4.5120, loss: 4.5120 +2024-07-16 17:39:21,884 - pyskl - INFO - Epoch [14][2200/3746] lr: 9.799e-02, eta: 4 days, 7:20:27, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1889, top5_acc: 0.4100, loss_cls: 4.5226, loss: 4.5226 +2024-07-16 17:40:32,072 - pyskl - INFO - Epoch [14][2300/3746] lr: 9.798e-02, eta: 4 days, 7:18:48, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1967, top5_acc: 0.4250, loss_cls: 4.5029, loss: 4.5029 +2024-07-16 17:41:42,191 - pyskl - INFO - Epoch [14][2400/3746] lr: 9.797e-02, eta: 4 days, 7:17:09, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2037, top5_acc: 0.4238, loss_cls: 4.4643, loss: 4.4643 +2024-07-16 17:42:52,359 - pyskl - INFO - Epoch [14][2500/3746] lr: 9.797e-02, eta: 4 days, 7:15:30, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4200, loss_cls: 4.4932, loss: 4.4932 +2024-07-16 17:44:02,819 - pyskl - INFO - Epoch [14][2600/3746] lr: 9.796e-02, eta: 4 days, 7:13:54, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1933, top5_acc: 0.4180, loss_cls: 4.4988, loss: 4.4988 +2024-07-16 17:45:13,071 - pyskl - INFO - Epoch [14][2700/3746] lr: 9.795e-02, eta: 4 days, 7:12:16, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1953, top5_acc: 0.4228, loss_cls: 4.5064, loss: 4.5064 +2024-07-16 17:46:23,541 - pyskl - INFO - Epoch [14][2800/3746] lr: 9.794e-02, eta: 4 days, 7:10:40, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4194, loss_cls: 4.4970, loss: 4.4970 +2024-07-16 17:47:33,899 - pyskl - INFO - Epoch [14][2900/3746] lr: 9.793e-02, eta: 4 days, 7:09:03, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1959, top5_acc: 0.4216, loss_cls: 4.4820, loss: 4.4820 +2024-07-16 17:48:44,077 - pyskl - INFO - Epoch [14][3000/3746] lr: 9.793e-02, eta: 4 days, 7:07:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2025, top5_acc: 0.4286, loss_cls: 4.4749, loss: 4.4749 +2024-07-16 17:49:54,516 - pyskl - INFO - Epoch [14][3100/3746] lr: 9.792e-02, eta: 4 days, 7:05:49, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2019, top5_acc: 0.4242, loss_cls: 4.4560, loss: 4.4560 +2024-07-16 17:51:04,895 - pyskl - INFO - Epoch [14][3200/3746] lr: 9.791e-02, eta: 4 days, 7:04:13, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1913, top5_acc: 0.4158, loss_cls: 4.5344, loss: 4.5344 +2024-07-16 17:52:15,553 - pyskl - INFO - Epoch [14][3300/3746] lr: 9.790e-02, eta: 4 days, 7:02:40, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4164, loss_cls: 4.4758, loss: 4.4758 +2024-07-16 17:53:25,717 - pyskl - INFO - Epoch [14][3400/3746] lr: 9.789e-02, eta: 4 days, 7:01:02, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4264, loss_cls: 4.4918, loss: 4.4918 +2024-07-16 17:54:36,291 - pyskl - INFO - Epoch [14][3500/3746] lr: 9.789e-02, eta: 4 days, 6:59:28, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1936, top5_acc: 0.4155, loss_cls: 4.5311, loss: 4.5311 +2024-07-16 17:55:47,555 - pyskl - INFO - Epoch [14][3600/3746] lr: 9.788e-02, eta: 4 days, 6:58:01, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4286, loss_cls: 4.4825, loss: 4.4825 +2024-07-16 17:56:58,650 - pyskl - INFO - Epoch [14][3700/3746] lr: 9.787e-02, eta: 4 days, 6:56:32, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1967, top5_acc: 0.4134, loss_cls: 4.5150, loss: 4.5150 +2024-07-16 17:57:33,164 - pyskl - INFO - Saving checkpoint at 14 epochs +2024-07-16 17:59:23,618 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 17:59:24,278 - pyskl - INFO - +top1_acc 0.1325 +top5_acc 0.3236 +2024-07-16 17:59:24,278 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 17:59:24,317 - pyskl - INFO - +mean_acc 0.1323 +2024-07-16 17:59:24,322 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_13.pth was removed +2024-07-16 17:59:24,544 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_14.pth. +2024-07-16 17:59:24,544 - pyskl - INFO - Best top1_acc is 0.1325 at 14 epoch. +2024-07-16 17:59:24,554 - pyskl - INFO - Epoch(val) [14][309] top1_acc: 0.1325, top5_acc: 0.3236, mean_class_accuracy: 0.1323 +2024-07-16 18:02:36,027 - pyskl - INFO - Epoch [15][100/3746] lr: 9.786e-02, eta: 4 days, 7:08:32, time: 1.915, data_time: 1.202, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4239, loss_cls: 4.4748, loss: 4.4748 +2024-07-16 18:03:47,259 - pyskl - INFO - Epoch [15][200/3746] lr: 9.785e-02, eta: 4 days, 7:07:03, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.1989, top5_acc: 0.4341, loss_cls: 4.4735, loss: 4.4735 +2024-07-16 18:04:58,172 - pyskl - INFO - Epoch [15][300/3746] lr: 9.784e-02, eta: 4 days, 7:05:31, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2009, top5_acc: 0.4341, loss_cls: 4.4201, loss: 4.4201 +2024-07-16 18:06:09,082 - pyskl - INFO - Epoch [15][400/3746] lr: 9.783e-02, eta: 4 days, 7:03:59, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2019, top5_acc: 0.4313, loss_cls: 4.4562, loss: 4.4562 +2024-07-16 18:07:19,710 - pyskl - INFO - Epoch [15][500/3746] lr: 9.783e-02, eta: 4 days, 7:02:25, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4314, loss_cls: 4.4566, loss: 4.4566 +2024-07-16 18:08:30,751 - pyskl - INFO - Epoch [15][600/3746] lr: 9.782e-02, eta: 4 days, 7:00:54, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1886, top5_acc: 0.4253, loss_cls: 4.4919, loss: 4.4919 +2024-07-16 18:09:40,975 - pyskl - INFO - Epoch [15][700/3746] lr: 9.781e-02, eta: 4 days, 6:59:16, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1916, top5_acc: 0.4209, loss_cls: 4.5110, loss: 4.5110 +2024-07-16 18:10:51,359 - pyskl - INFO - Epoch [15][800/3746] lr: 9.780e-02, eta: 4 days, 6:57:39, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1948, top5_acc: 0.4266, loss_cls: 4.4782, loss: 4.4782 +2024-07-16 18:12:01,748 - pyskl - INFO - Epoch [15][900/3746] lr: 9.779e-02, eta: 4 days, 6:56:03, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4373, loss_cls: 4.4302, loss: 4.4302 +2024-07-16 18:13:12,353 - pyskl - INFO - Epoch [15][1000/3746] lr: 9.778e-02, eta: 4 days, 6:54:28, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4277, loss_cls: 4.4830, loss: 4.4830 +2024-07-16 18:14:23,082 - pyskl - INFO - Epoch [15][1100/3746] lr: 9.778e-02, eta: 4 days, 6:52:55, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1967, top5_acc: 0.4289, loss_cls: 4.4667, loss: 4.4667 +2024-07-16 18:15:33,501 - pyskl - INFO - Epoch [15][1200/3746] lr: 9.777e-02, eta: 4 days, 6:51:19, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1936, top5_acc: 0.4230, loss_cls: 4.4959, loss: 4.4959 +2024-07-16 18:16:43,867 - pyskl - INFO - Epoch [15][1300/3746] lr: 9.776e-02, eta: 4 days, 6:49:43, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1942, top5_acc: 0.4288, loss_cls: 4.4767, loss: 4.4767 +2024-07-16 18:17:54,322 - pyskl - INFO - Epoch [15][1400/3746] lr: 9.775e-02, eta: 4 days, 6:48:07, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1922, top5_acc: 0.4131, loss_cls: 4.5155, loss: 4.5155 +2024-07-16 18:19:04,657 - pyskl - INFO - Epoch [15][1500/3746] lr: 9.774e-02, eta: 4 days, 6:46:31, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1964, top5_acc: 0.4244, loss_cls: 4.4895, loss: 4.4895 +2024-07-16 18:20:15,213 - pyskl - INFO - Epoch [15][1600/3746] lr: 9.773e-02, eta: 4 days, 6:44:57, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1859, top5_acc: 0.4052, loss_cls: 4.5433, loss: 4.5433 +2024-07-16 18:21:25,816 - pyskl - INFO - Epoch [15][1700/3746] lr: 9.773e-02, eta: 4 days, 6:43:23, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1908, top5_acc: 0.4188, loss_cls: 4.4884, loss: 4.4884 +2024-07-16 18:22:36,820 - pyskl - INFO - Epoch [15][1800/3746] lr: 9.772e-02, eta: 4 days, 6:41:53, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1950, top5_acc: 0.4222, loss_cls: 4.4995, loss: 4.4995 +2024-07-16 18:23:48,120 - pyskl - INFO - Epoch [15][1900/3746] lr: 9.771e-02, eta: 4 days, 6:40:26, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1922, top5_acc: 0.4186, loss_cls: 4.5203, loss: 4.5203 +2024-07-16 18:24:59,049 - pyskl - INFO - Epoch [15][2000/3746] lr: 9.770e-02, eta: 4 days, 6:38:55, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1913, top5_acc: 0.4198, loss_cls: 4.5063, loss: 4.5063 +2024-07-16 18:26:10,160 - pyskl - INFO - Epoch [15][2100/3746] lr: 9.769e-02, eta: 4 days, 6:37:26, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2025, top5_acc: 0.4255, loss_cls: 4.4757, loss: 4.4757 +2024-07-16 18:27:21,253 - pyskl - INFO - Epoch [15][2200/3746] lr: 9.768e-02, eta: 4 days, 6:35:58, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1942, top5_acc: 0.4144, loss_cls: 4.5246, loss: 4.5246 +2024-07-16 18:28:32,106 - pyskl - INFO - Epoch [15][2300/3746] lr: 9.768e-02, eta: 4 days, 6:34:27, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1945, top5_acc: 0.4228, loss_cls: 4.4915, loss: 4.4915 +2024-07-16 18:29:42,556 - pyskl - INFO - Epoch [15][2400/3746] lr: 9.767e-02, eta: 4 days, 6:32:52, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2028, top5_acc: 0.4289, loss_cls: 4.4321, loss: 4.4321 +2024-07-16 18:30:53,085 - pyskl - INFO - Epoch [15][2500/3746] lr: 9.766e-02, eta: 4 days, 6:31:18, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1989, top5_acc: 0.4248, loss_cls: 4.4753, loss: 4.4753 +2024-07-16 18:32:03,863 - pyskl - INFO - Epoch [15][2600/3746] lr: 9.765e-02, eta: 4 days, 6:29:47, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4270, loss_cls: 4.4562, loss: 4.4562 +2024-07-16 18:33:14,273 - pyskl - INFO - Epoch [15][2700/3746] lr: 9.764e-02, eta: 4 days, 6:28:12, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2059, top5_acc: 0.4303, loss_cls: 4.4385, loss: 4.4385 +2024-07-16 18:34:25,203 - pyskl - INFO - Epoch [15][2800/3746] lr: 9.763e-02, eta: 4 days, 6:26:42, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1942, top5_acc: 0.4175, loss_cls: 4.5023, loss: 4.5023 +2024-07-16 18:35:36,057 - pyskl - INFO - Epoch [15][2900/3746] lr: 9.763e-02, eta: 4 days, 6:25:11, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1941, top5_acc: 0.4188, loss_cls: 4.5231, loss: 4.5231 +2024-07-16 18:36:46,488 - pyskl - INFO - Epoch [15][3000/3746] lr: 9.762e-02, eta: 4 days, 6:23:37, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1967, top5_acc: 0.4175, loss_cls: 4.4978, loss: 4.4978 +2024-07-16 18:37:57,300 - pyskl - INFO - Epoch [15][3100/3746] lr: 9.761e-02, eta: 4 days, 6:22:06, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1997, top5_acc: 0.4272, loss_cls: 4.5009, loss: 4.5009 +2024-07-16 18:39:07,802 - pyskl - INFO - Epoch [15][3200/3746] lr: 9.760e-02, eta: 4 days, 6:20:33, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1925, top5_acc: 0.4184, loss_cls: 4.4949, loss: 4.4949 +2024-07-16 18:40:18,356 - pyskl - INFO - Epoch [15][3300/3746] lr: 9.759e-02, eta: 4 days, 6:19:00, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1916, top5_acc: 0.4164, loss_cls: 4.4903, loss: 4.4903 +2024-07-16 18:41:28,804 - pyskl - INFO - Epoch [15][3400/3746] lr: 9.758e-02, eta: 4 days, 6:17:26, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1972, top5_acc: 0.4238, loss_cls: 4.4796, loss: 4.4796 +2024-07-16 18:42:39,708 - pyskl - INFO - Epoch [15][3500/3746] lr: 9.757e-02, eta: 4 days, 6:15:56, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1956, top5_acc: 0.4200, loss_cls: 4.5051, loss: 4.5051 +2024-07-16 18:43:50,848 - pyskl - INFO - Epoch [15][3600/3746] lr: 9.757e-02, eta: 4 days, 6:14:29, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.1944, top5_acc: 0.4188, loss_cls: 4.5080, loss: 4.5080 +2024-07-16 18:45:01,791 - pyskl - INFO - Epoch [15][3700/3746] lr: 9.756e-02, eta: 4 days, 6:13:00, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1975, top5_acc: 0.4355, loss_cls: 4.4649, loss: 4.4649 +2024-07-16 18:45:36,280 - pyskl - INFO - Saving checkpoint at 15 epochs +2024-07-16 18:47:27,174 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 18:47:27,849 - pyskl - INFO - +top1_acc 0.1361 +top5_acc 0.3297 +2024-07-16 18:47:27,849 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 18:47:27,889 - pyskl - INFO - +mean_acc 0.1361 +2024-07-16 18:47:27,893 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_14.pth was removed +2024-07-16 18:47:28,142 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_15.pth. +2024-07-16 18:47:28,143 - pyskl - INFO - Best top1_acc is 0.1361 at 15 epoch. +2024-07-16 18:47:28,154 - pyskl - INFO - Epoch(val) [15][309] top1_acc: 0.1361, top5_acc: 0.3297, mean_class_accuracy: 0.1361 +2024-07-16 18:50:41,559 - pyskl - INFO - Epoch [16][100/3746] lr: 9.754e-02, eta: 4 days, 6:24:17, time: 1.934, data_time: 1.222, memory: 15990, top1_acc: 0.1995, top5_acc: 0.4216, loss_cls: 4.4857, loss: 4.4857 +2024-07-16 18:51:52,595 - pyskl - INFO - Epoch [16][200/3746] lr: 9.754e-02, eta: 4 days, 6:22:47, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1958, top5_acc: 0.4303, loss_cls: 4.4554, loss: 4.4554 +2024-07-16 18:53:03,626 - pyskl - INFO - Epoch [16][300/3746] lr: 9.753e-02, eta: 4 days, 6:21:17, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1972, top5_acc: 0.4186, loss_cls: 4.4654, loss: 4.4654 +2024-07-16 18:54:14,605 - pyskl - INFO - Epoch [16][400/3746] lr: 9.752e-02, eta: 4 days, 6:19:47, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4303, loss_cls: 4.4409, loss: 4.4409 +2024-07-16 18:55:24,872 - pyskl - INFO - Epoch [16][500/3746] lr: 9.751e-02, eta: 4 days, 6:18:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4263, loss_cls: 4.4655, loss: 4.4655 +2024-07-16 18:56:35,554 - pyskl - INFO - Epoch [16][600/3746] lr: 9.750e-02, eta: 4 days, 6:16:38, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4308, loss_cls: 4.4549, loss: 4.4549 +2024-07-16 18:57:46,318 - pyskl - INFO - Epoch [16][700/3746] lr: 9.749e-02, eta: 4 days, 6:15:06, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1953, top5_acc: 0.4220, loss_cls: 4.4829, loss: 4.4829 +2024-07-16 18:58:56,950 - pyskl - INFO - Epoch [16][800/3746] lr: 9.748e-02, eta: 4 days, 6:13:33, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4363, loss_cls: 4.4076, loss: 4.4076 +2024-07-16 19:00:07,352 - pyskl - INFO - Epoch [16][900/3746] lr: 9.747e-02, eta: 4 days, 6:11:58, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1934, top5_acc: 0.4283, loss_cls: 4.4606, loss: 4.4606 +2024-07-16 19:01:17,916 - pyskl - INFO - Epoch [16][1000/3746] lr: 9.747e-02, eta: 4 days, 6:10:25, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1919, top5_acc: 0.4230, loss_cls: 4.4739, loss: 4.4739 +2024-07-16 19:02:28,442 - pyskl - INFO - Epoch [16][1100/3746] lr: 9.746e-02, eta: 4 days, 6:08:51, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2011, top5_acc: 0.4175, loss_cls: 4.4830, loss: 4.4830 +2024-07-16 19:03:39,063 - pyskl - INFO - Epoch [16][1200/3746] lr: 9.745e-02, eta: 4 days, 6:07:19, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1967, top5_acc: 0.4155, loss_cls: 4.4835, loss: 4.4835 +2024-07-16 19:04:49,875 - pyskl - INFO - Epoch [16][1300/3746] lr: 9.744e-02, eta: 4 days, 6:05:48, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1970, top5_acc: 0.4239, loss_cls: 4.4979, loss: 4.4979 +2024-07-16 19:06:00,524 - pyskl - INFO - Epoch [16][1400/3746] lr: 9.743e-02, eta: 4 days, 6:04:15, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1922, top5_acc: 0.4192, loss_cls: 4.5107, loss: 4.5107 +2024-07-16 19:07:11,450 - pyskl - INFO - Epoch [16][1500/3746] lr: 9.742e-02, eta: 4 days, 6:02:46, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1934, top5_acc: 0.4238, loss_cls: 4.4766, loss: 4.4766 +2024-07-16 19:08:21,984 - pyskl - INFO - Epoch [16][1600/3746] lr: 9.741e-02, eta: 4 days, 6:01:13, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4297, loss_cls: 4.4542, loss: 4.4542 +2024-07-16 19:09:32,883 - pyskl - INFO - Epoch [16][1700/3746] lr: 9.740e-02, eta: 4 days, 5:59:43, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1948, top5_acc: 0.4322, loss_cls: 4.4776, loss: 4.4776 +2024-07-16 19:10:43,893 - pyskl - INFO - Epoch [16][1800/3746] lr: 9.740e-02, eta: 4 days, 5:58:14, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4292, loss_cls: 4.4491, loss: 4.4491 +2024-07-16 19:11:54,828 - pyskl - INFO - Epoch [16][1900/3746] lr: 9.739e-02, eta: 4 days, 5:56:44, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4092, loss_cls: 4.5231, loss: 4.5231 +2024-07-16 19:13:05,543 - pyskl - INFO - Epoch [16][2000/3746] lr: 9.738e-02, eta: 4 days, 5:55:13, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1945, top5_acc: 0.4206, loss_cls: 4.4709, loss: 4.4709 +2024-07-16 19:14:16,532 - pyskl - INFO - Epoch [16][2100/3746] lr: 9.737e-02, eta: 4 days, 5:53:44, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1945, top5_acc: 0.4278, loss_cls: 4.4515, loss: 4.4515 +2024-07-16 19:15:27,218 - pyskl - INFO - Epoch [16][2200/3746] lr: 9.736e-02, eta: 4 days, 5:52:13, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1872, top5_acc: 0.4156, loss_cls: 4.5265, loss: 4.5265 +2024-07-16 19:16:37,614 - pyskl - INFO - Epoch [16][2300/3746] lr: 9.735e-02, eta: 4 days, 5:50:39, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1963, top5_acc: 0.4211, loss_cls: 4.4778, loss: 4.4778 +2024-07-16 19:17:48,155 - pyskl - INFO - Epoch [16][2400/3746] lr: 9.734e-02, eta: 4 days, 5:49:06, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4214, loss_cls: 4.4808, loss: 4.4808 +2024-07-16 19:18:59,056 - pyskl - INFO - Epoch [16][2500/3746] lr: 9.733e-02, eta: 4 days, 5:47:37, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1919, top5_acc: 0.4316, loss_cls: 4.4717, loss: 4.4717 +2024-07-16 19:20:09,631 - pyskl - INFO - Epoch [16][2600/3746] lr: 9.732e-02, eta: 4 days, 5:46:05, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1945, top5_acc: 0.4144, loss_cls: 4.5071, loss: 4.5071 +2024-07-16 19:21:20,001 - pyskl - INFO - Epoch [16][2700/3746] lr: 9.731e-02, eta: 4 days, 5:44:31, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4303, loss_cls: 4.4400, loss: 4.4400 +2024-07-16 19:22:30,100 - pyskl - INFO - Epoch [16][2800/3746] lr: 9.731e-02, eta: 4 days, 5:42:55, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4353, loss_cls: 4.4542, loss: 4.4542 +2024-07-16 19:23:40,876 - pyskl - INFO - Epoch [16][2900/3746] lr: 9.730e-02, eta: 4 days, 5:41:25, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1970, top5_acc: 0.4238, loss_cls: 4.5100, loss: 4.5100 +2024-07-16 19:24:51,374 - pyskl - INFO - Epoch [16][3000/3746] lr: 9.729e-02, eta: 4 days, 5:39:53, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1884, top5_acc: 0.4159, loss_cls: 4.5164, loss: 4.5164 +2024-07-16 19:26:01,986 - pyskl - INFO - Epoch [16][3100/3746] lr: 9.728e-02, eta: 4 days, 5:38:21, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4253, loss_cls: 4.4782, loss: 4.4782 +2024-07-16 19:27:12,858 - pyskl - INFO - Epoch [16][3200/3746] lr: 9.727e-02, eta: 4 days, 5:36:52, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2011, top5_acc: 0.4261, loss_cls: 4.4696, loss: 4.4696 +2024-07-16 19:28:23,457 - pyskl - INFO - Epoch [16][3300/3746] lr: 9.726e-02, eta: 4 days, 5:35:21, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1941, top5_acc: 0.4248, loss_cls: 4.4863, loss: 4.4863 +2024-07-16 19:29:33,645 - pyskl - INFO - Epoch [16][3400/3746] lr: 9.725e-02, eta: 4 days, 5:33:46, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1955, top5_acc: 0.4255, loss_cls: 4.4777, loss: 4.4777 +2024-07-16 19:30:45,391 - pyskl - INFO - Epoch [16][3500/3746] lr: 9.724e-02, eta: 4 days, 5:32:24, time: 0.717, data_time: 0.000, memory: 15990, top1_acc: 0.1944, top5_acc: 0.4247, loss_cls: 4.4830, loss: 4.4830 +2024-07-16 19:31:56,708 - pyskl - INFO - Epoch [16][3600/3746] lr: 9.723e-02, eta: 4 days, 5:30:59, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1923, top5_acc: 0.4178, loss_cls: 4.5253, loss: 4.5253 +2024-07-16 19:33:07,705 - pyskl - INFO - Epoch [16][3700/3746] lr: 9.722e-02, eta: 4 days, 5:29:32, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4294, loss_cls: 4.4256, loss: 4.4256 +2024-07-16 19:33:42,450 - pyskl - INFO - Saving checkpoint at 16 epochs +2024-07-16 19:35:33,833 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 19:35:34,499 - pyskl - INFO - +top1_acc 0.1275 +top5_acc 0.3138 +2024-07-16 19:35:34,499 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 19:35:34,538 - pyskl - INFO - +mean_acc 0.1274 +2024-07-16 19:35:34,548 - pyskl - INFO - Epoch(val) [16][309] top1_acc: 0.1275, top5_acc: 0.3138, mean_class_accuracy: 0.1274 +2024-07-16 19:38:48,314 - pyskl - INFO - Epoch [17][100/3746] lr: 9.721e-02, eta: 4 days, 5:39:57, time: 1.938, data_time: 1.223, memory: 15990, top1_acc: 0.1977, top5_acc: 0.4228, loss_cls: 4.4644, loss: 4.4644 +2024-07-16 19:39:59,361 - pyskl - INFO - Epoch [17][200/3746] lr: 9.720e-02, eta: 4 days, 5:38:28, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1941, top5_acc: 0.4406, loss_cls: 4.4585, loss: 4.4585 +2024-07-16 19:41:10,637 - pyskl - INFO - Epoch [17][300/3746] lr: 9.719e-02, eta: 4 days, 5:37:01, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1997, top5_acc: 0.4358, loss_cls: 4.4534, loss: 4.4534 +2024-07-16 19:42:21,553 - pyskl - INFO - Epoch [17][400/3746] lr: 9.718e-02, eta: 4 days, 5:35:32, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4248, loss_cls: 4.4441, loss: 4.4441 +2024-07-16 19:43:32,465 - pyskl - INFO - Epoch [17][500/3746] lr: 9.717e-02, eta: 4 days, 5:34:02, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4273, loss_cls: 4.4708, loss: 4.4708 +2024-07-16 19:44:43,111 - pyskl - INFO - Epoch [17][600/3746] lr: 9.716e-02, eta: 4 days, 5:32:30, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2011, top5_acc: 0.4358, loss_cls: 4.4279, loss: 4.4279 +2024-07-16 19:45:53,972 - pyskl - INFO - Epoch [17][700/3746] lr: 9.715e-02, eta: 4 days, 5:31:01, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2000, top5_acc: 0.4263, loss_cls: 4.4863, loss: 4.4863 +2024-07-16 19:47:04,250 - pyskl - INFO - Epoch [17][800/3746] lr: 9.714e-02, eta: 4 days, 5:29:26, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1920, top5_acc: 0.4200, loss_cls: 4.4739, loss: 4.4739 +2024-07-16 19:48:15,067 - pyskl - INFO - Epoch [17][900/3746] lr: 9.714e-02, eta: 4 days, 5:27:56, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1984, top5_acc: 0.4209, loss_cls: 4.4833, loss: 4.4833 +2024-07-16 19:49:25,466 - pyskl - INFO - Epoch [17][1000/3746] lr: 9.713e-02, eta: 4 days, 5:26:22, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4247, loss_cls: 4.4784, loss: 4.4784 +2024-07-16 19:50:35,430 - pyskl - INFO - Epoch [17][1100/3746] lr: 9.712e-02, eta: 4 days, 5:24:45, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2005, top5_acc: 0.4278, loss_cls: 4.4720, loss: 4.4720 +2024-07-16 19:51:45,309 - pyskl - INFO - Epoch [17][1200/3746] lr: 9.711e-02, eta: 4 days, 5:23:08, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2000, top5_acc: 0.4281, loss_cls: 4.4569, loss: 4.4569 +2024-07-16 19:52:55,450 - pyskl - INFO - Epoch [17][1300/3746] lr: 9.710e-02, eta: 4 days, 5:21:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1927, top5_acc: 0.4170, loss_cls: 4.5110, loss: 4.5110 +2024-07-16 19:54:05,520 - pyskl - INFO - Epoch [17][1400/3746] lr: 9.709e-02, eta: 4 days, 5:19:57, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4277, loss_cls: 4.4499, loss: 4.4499 +2024-07-16 19:55:15,426 - pyskl - INFO - Epoch [17][1500/3746] lr: 9.708e-02, eta: 4 days, 5:18:20, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4213, loss_cls: 4.4890, loss: 4.4890 +2024-07-16 19:56:25,565 - pyskl - INFO - Epoch [17][1600/3746] lr: 9.707e-02, eta: 4 days, 5:16:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2011, top5_acc: 0.4200, loss_cls: 4.4772, loss: 4.4772 +2024-07-16 19:57:36,113 - pyskl - INFO - Epoch [17][1700/3746] lr: 9.706e-02, eta: 4 days, 5:15:13, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2047, top5_acc: 0.4278, loss_cls: 4.4450, loss: 4.4450 +2024-07-16 19:58:46,439 - pyskl - INFO - Epoch [17][1800/3746] lr: 9.705e-02, eta: 4 days, 5:13:39, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2014, top5_acc: 0.4259, loss_cls: 4.4613, loss: 4.4613 +2024-07-16 19:59:56,992 - pyskl - INFO - Epoch [17][1900/3746] lr: 9.704e-02, eta: 4 days, 5:12:08, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2009, top5_acc: 0.4331, loss_cls: 4.4359, loss: 4.4359 +2024-07-16 20:01:07,169 - pyskl - INFO - Epoch [17][2000/3746] lr: 9.703e-02, eta: 4 days, 5:10:33, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4323, loss_cls: 4.4512, loss: 4.4512 +2024-07-16 20:02:17,283 - pyskl - INFO - Epoch [17][2100/3746] lr: 9.702e-02, eta: 4 days, 5:08:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4356, loss_cls: 4.4375, loss: 4.4375 +2024-07-16 20:03:27,534 - pyskl - INFO - Epoch [17][2200/3746] lr: 9.701e-02, eta: 4 days, 5:07:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2011, top5_acc: 0.4294, loss_cls: 4.4634, loss: 4.4634 +2024-07-16 20:04:37,504 - pyskl - INFO - Epoch [17][2300/3746] lr: 9.700e-02, eta: 4 days, 5:05:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4270, loss_cls: 4.4642, loss: 4.4642 +2024-07-16 20:05:47,594 - pyskl - INFO - Epoch [17][2400/3746] lr: 9.699e-02, eta: 4 days, 5:04:14, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1930, top5_acc: 0.4209, loss_cls: 4.4878, loss: 4.4878 +2024-07-16 20:06:57,730 - pyskl - INFO - Epoch [17][2500/3746] lr: 9.698e-02, eta: 4 days, 5:02:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1972, top5_acc: 0.4206, loss_cls: 4.5028, loss: 4.5028 +2024-07-16 20:08:07,715 - pyskl - INFO - Epoch [17][2600/3746] lr: 9.697e-02, eta: 4 days, 5:01:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1977, top5_acc: 0.4216, loss_cls: 4.4999, loss: 4.4999 +2024-07-16 20:09:17,935 - pyskl - INFO - Epoch [17][2700/3746] lr: 9.697e-02, eta: 4 days, 4:59:30, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1977, top5_acc: 0.4220, loss_cls: 4.5054, loss: 4.5054 +2024-07-16 20:10:28,057 - pyskl - INFO - Epoch [17][2800/3746] lr: 9.696e-02, eta: 4 days, 4:57:56, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1972, top5_acc: 0.4294, loss_cls: 4.4610, loss: 4.4610 +2024-07-16 20:11:38,053 - pyskl - INFO - Epoch [17][2900/3746] lr: 9.695e-02, eta: 4 days, 4:56:21, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2005, top5_acc: 0.4225, loss_cls: 4.4951, loss: 4.4951 +2024-07-16 20:12:47,859 - pyskl - INFO - Epoch [17][3000/3746] lr: 9.694e-02, eta: 4 days, 4:54:44, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1927, top5_acc: 0.4159, loss_cls: 4.4998, loss: 4.4998 +2024-07-16 20:13:58,123 - pyskl - INFO - Epoch [17][3100/3746] lr: 9.693e-02, eta: 4 days, 4:53:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1944, top5_acc: 0.4203, loss_cls: 4.4790, loss: 4.4790 +2024-07-16 20:15:08,105 - pyskl - INFO - Epoch [17][3200/3746] lr: 9.692e-02, eta: 4 days, 4:51:36, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4464, loss_cls: 4.4277, loss: 4.4277 +2024-07-16 20:16:17,991 - pyskl - INFO - Epoch [17][3300/3746] lr: 9.691e-02, eta: 4 days, 4:50:00, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4311, loss_cls: 4.4406, loss: 4.4406 +2024-07-16 20:17:28,169 - pyskl - INFO - Epoch [17][3400/3746] lr: 9.690e-02, eta: 4 days, 4:48:27, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4297, loss_cls: 4.4660, loss: 4.4660 +2024-07-16 20:18:39,043 - pyskl - INFO - Epoch [17][3500/3746] lr: 9.689e-02, eta: 4 days, 4:46:59, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1950, top5_acc: 0.4163, loss_cls: 4.5127, loss: 4.5127 +2024-07-16 20:19:49,913 - pyskl - INFO - Epoch [17][3600/3746] lr: 9.688e-02, eta: 4 days, 4:45:31, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2009, top5_acc: 0.4297, loss_cls: 4.4739, loss: 4.4739 +2024-07-16 20:21:00,351 - pyskl - INFO - Epoch [17][3700/3746] lr: 9.687e-02, eta: 4 days, 4:44:00, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2006, top5_acc: 0.4217, loss_cls: 4.4890, loss: 4.4890 +2024-07-16 20:21:34,675 - pyskl - INFO - Saving checkpoint at 17 epochs +2024-07-16 20:23:25,871 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 20:23:26,536 - pyskl - INFO - +top1_acc 0.1418 +top5_acc 0.3412 +2024-07-16 20:23:26,537 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 20:23:26,575 - pyskl - INFO - +mean_acc 0.1415 +2024-07-16 20:23:26,580 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_15.pth was removed +2024-07-16 20:23:26,987 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_17.pth. +2024-07-16 20:23:26,987 - pyskl - INFO - Best top1_acc is 0.1418 at 17 epoch. +2024-07-16 20:23:26,997 - pyskl - INFO - Epoch(val) [17][309] top1_acc: 0.1418, top5_acc: 0.3412, mean_class_accuracy: 0.1415 +2024-07-16 20:26:43,125 - pyskl - INFO - Epoch [18][100/3746] lr: 9.685e-02, eta: 4 days, 4:53:56, time: 1.961, data_time: 1.246, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4253, loss_cls: 4.4365, loss: 4.4365 +2024-07-16 20:27:54,305 - pyskl - INFO - Epoch [18][200/3746] lr: 9.684e-02, eta: 4 days, 4:52:29, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4267, loss_cls: 4.4428, loss: 4.4428 +2024-07-16 20:29:04,955 - pyskl - INFO - Epoch [18][300/3746] lr: 9.683e-02, eta: 4 days, 4:50:58, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4178, loss_cls: 4.5179, loss: 4.5179 +2024-07-16 20:30:15,594 - pyskl - INFO - Epoch [18][400/3746] lr: 9.683e-02, eta: 4 days, 4:49:28, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2059, top5_acc: 0.4341, loss_cls: 4.4326, loss: 4.4326 +2024-07-16 20:31:26,218 - pyskl - INFO - Epoch [18][500/3746] lr: 9.682e-02, eta: 4 days, 4:47:57, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1947, top5_acc: 0.4284, loss_cls: 4.4478, loss: 4.4478 +2024-07-16 20:32:37,151 - pyskl - INFO - Epoch [18][600/3746] lr: 9.681e-02, eta: 4 days, 4:46:29, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1972, top5_acc: 0.4377, loss_cls: 4.4397, loss: 4.4397 +2024-07-16 20:33:47,621 - pyskl - INFO - Epoch [18][700/3746] lr: 9.680e-02, eta: 4 days, 4:44:57, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2002, top5_acc: 0.4308, loss_cls: 4.4658, loss: 4.4658 +2024-07-16 20:34:58,377 - pyskl - INFO - Epoch [18][800/3746] lr: 9.679e-02, eta: 4 days, 4:43:28, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4244, loss_cls: 4.4699, loss: 4.4699 +2024-07-16 20:36:09,419 - pyskl - INFO - Epoch [18][900/3746] lr: 9.678e-02, eta: 4 days, 4:42:01, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1956, top5_acc: 0.4239, loss_cls: 4.4859, loss: 4.4859 +2024-07-16 20:37:20,110 - pyskl - INFO - Epoch [18][1000/3746] lr: 9.677e-02, eta: 4 days, 4:40:31, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4270, loss_cls: 4.4712, loss: 4.4712 +2024-07-16 20:38:30,517 - pyskl - INFO - Epoch [18][1100/3746] lr: 9.676e-02, eta: 4 days, 4:38:59, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1955, top5_acc: 0.4317, loss_cls: 4.4572, loss: 4.4572 +2024-07-16 20:39:40,624 - pyskl - INFO - Epoch [18][1200/3746] lr: 9.675e-02, eta: 4 days, 4:37:25, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2011, top5_acc: 0.4264, loss_cls: 4.4692, loss: 4.4692 +2024-07-16 20:40:50,479 - pyskl - INFO - Epoch [18][1300/3746] lr: 9.674e-02, eta: 4 days, 4:35:49, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2019, top5_acc: 0.4331, loss_cls: 4.4195, loss: 4.4195 +2024-07-16 20:42:00,426 - pyskl - INFO - Epoch [18][1400/3746] lr: 9.673e-02, eta: 4 days, 4:34:13, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2037, top5_acc: 0.4400, loss_cls: 4.4218, loss: 4.4218 +2024-07-16 20:43:10,455 - pyskl - INFO - Epoch [18][1500/3746] lr: 9.672e-02, eta: 4 days, 4:32:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4323, loss_cls: 4.4693, loss: 4.4693 +2024-07-16 20:44:20,619 - pyskl - INFO - Epoch [18][1600/3746] lr: 9.671e-02, eta: 4 days, 4:31:05, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4298, loss_cls: 4.4588, loss: 4.4588 +2024-07-16 20:45:31,318 - pyskl - INFO - Epoch [18][1700/3746] lr: 9.670e-02, eta: 4 days, 4:29:36, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1945, top5_acc: 0.4184, loss_cls: 4.5043, loss: 4.5043 +2024-07-16 20:46:41,791 - pyskl - INFO - Epoch [18][1800/3746] lr: 9.669e-02, eta: 4 days, 4:28:05, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1995, top5_acc: 0.4342, loss_cls: 4.4307, loss: 4.4307 +2024-07-16 20:47:52,479 - pyskl - INFO - Epoch [18][1900/3746] lr: 9.668e-02, eta: 4 days, 4:26:36, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4253, loss_cls: 4.4713, loss: 4.4713 +2024-07-16 20:49:02,664 - pyskl - INFO - Epoch [18][2000/3746] lr: 9.667e-02, eta: 4 days, 4:25:03, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4323, loss_cls: 4.4444, loss: 4.4444 +2024-07-16 20:50:12,990 - pyskl - INFO - Epoch [18][2100/3746] lr: 9.666e-02, eta: 4 days, 4:23:31, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4255, loss_cls: 4.4421, loss: 4.4421 +2024-07-16 20:51:23,046 - pyskl - INFO - Epoch [18][2200/3746] lr: 9.665e-02, eta: 4 days, 4:21:57, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2008, top5_acc: 0.4275, loss_cls: 4.4519, loss: 4.4519 +2024-07-16 20:52:33,143 - pyskl - INFO - Epoch [18][2300/3746] lr: 9.664e-02, eta: 4 days, 4:20:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1966, top5_acc: 0.4198, loss_cls: 4.4829, loss: 4.4829 +2024-07-16 20:53:43,327 - pyskl - INFO - Epoch [18][2400/3746] lr: 9.663e-02, eta: 4 days, 4:18:51, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4241, loss_cls: 4.4751, loss: 4.4751 +2024-07-16 20:54:53,186 - pyskl - INFO - Epoch [18][2500/3746] lr: 9.662e-02, eta: 4 days, 4:17:15, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2000, top5_acc: 0.4286, loss_cls: 4.4642, loss: 4.4642 +2024-07-16 20:56:03,190 - pyskl - INFO - Epoch [18][2600/3746] lr: 9.661e-02, eta: 4 days, 4:15:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4461, loss_cls: 4.3822, loss: 4.3822 +2024-07-16 20:57:13,256 - pyskl - INFO - Epoch [18][2700/3746] lr: 9.660e-02, eta: 4 days, 4:14:08, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1931, top5_acc: 0.4231, loss_cls: 4.4903, loss: 4.4903 +2024-07-16 20:58:23,462 - pyskl - INFO - Epoch [18][2800/3746] lr: 9.659e-02, eta: 4 days, 4:12:36, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4302, loss_cls: 4.4761, loss: 4.4761 +2024-07-16 20:59:33,759 - pyskl - INFO - Epoch [18][2900/3746] lr: 9.658e-02, eta: 4 days, 4:11:04, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1939, top5_acc: 0.4222, loss_cls: 4.4914, loss: 4.4914 +2024-07-16 21:00:43,993 - pyskl - INFO - Epoch [18][3000/3746] lr: 9.657e-02, eta: 4 days, 4:09:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1908, top5_acc: 0.4078, loss_cls: 4.5353, loss: 4.5353 +2024-07-16 21:01:54,102 - pyskl - INFO - Epoch [18][3100/3746] lr: 9.656e-02, eta: 4 days, 4:07:59, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4331, loss_cls: 4.4563, loss: 4.4563 +2024-07-16 21:03:04,358 - pyskl - INFO - Epoch [18][3200/3746] lr: 9.654e-02, eta: 4 days, 4:06:28, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4338, loss_cls: 4.4347, loss: 4.4347 +2024-07-16 21:04:14,505 - pyskl - INFO - Epoch [18][3300/3746] lr: 9.653e-02, eta: 4 days, 4:04:55, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1998, top5_acc: 0.4253, loss_cls: 4.4815, loss: 4.4815 +2024-07-16 21:05:24,954 - pyskl - INFO - Epoch [18][3400/3746] lr: 9.652e-02, eta: 4 days, 4:03:25, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4350, loss_cls: 4.4396, loss: 4.4396 +2024-07-16 21:06:36,282 - pyskl - INFO - Epoch [18][3500/3746] lr: 9.651e-02, eta: 4 days, 4:02:01, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2027, top5_acc: 0.4213, loss_cls: 4.4824, loss: 4.4824 +2024-07-16 21:07:46,999 - pyskl - INFO - Epoch [18][3600/3746] lr: 9.650e-02, eta: 4 days, 4:00:33, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1984, top5_acc: 0.4255, loss_cls: 4.4785, loss: 4.4785 +2024-07-16 21:08:57,952 - pyskl - INFO - Epoch [18][3700/3746] lr: 9.649e-02, eta: 4 days, 3:59:07, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2000, top5_acc: 0.4286, loss_cls: 4.4828, loss: 4.4828 +2024-07-16 21:09:32,473 - pyskl - INFO - Saving checkpoint at 18 epochs +2024-07-16 21:11:25,194 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 21:11:25,914 - pyskl - INFO - +top1_acc 0.1109 +top5_acc 0.2904 +2024-07-16 21:11:25,915 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 21:11:25,960 - pyskl - INFO - +mean_acc 0.1111 +2024-07-16 21:11:25,973 - pyskl - INFO - Epoch(val) [18][309] top1_acc: 0.1109, top5_acc: 0.2904, mean_class_accuracy: 0.1111 +2024-07-16 21:14:44,572 - pyskl - INFO - Epoch [19][100/3746] lr: 9.648e-02, eta: 4 days, 4:08:37, time: 1.986, data_time: 1.271, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4325, loss_cls: 4.4578, loss: 4.4578 +2024-07-16 21:15:55,711 - pyskl - INFO - Epoch [19][200/3746] lr: 9.647e-02, eta: 4 days, 4:07:11, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4305, loss_cls: 4.4522, loss: 4.4522 +2024-07-16 21:17:07,027 - pyskl - INFO - Epoch [19][300/3746] lr: 9.646e-02, eta: 4 days, 4:05:46, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4219, loss_cls: 4.4683, loss: 4.4683 +2024-07-16 21:18:17,806 - pyskl - INFO - Epoch [19][400/3746] lr: 9.645e-02, eta: 4 days, 4:04:17, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4263, loss_cls: 4.4414, loss: 4.4414 +2024-07-16 21:19:28,381 - pyskl - INFO - Epoch [19][500/3746] lr: 9.644e-02, eta: 4 days, 4:02:48, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4387, loss_cls: 4.4062, loss: 4.4062 +2024-07-16 21:20:39,092 - pyskl - INFO - Epoch [19][600/3746] lr: 9.643e-02, eta: 4 days, 4:01:19, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1997, top5_acc: 0.4258, loss_cls: 4.4384, loss: 4.4384 +2024-07-16 21:21:50,037 - pyskl - INFO - Epoch [19][700/3746] lr: 9.642e-02, eta: 4 days, 3:59:51, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4228, loss_cls: 4.4382, loss: 4.4382 +2024-07-16 21:23:00,582 - pyskl - INFO - Epoch [19][800/3746] lr: 9.641e-02, eta: 4 days, 3:58:21, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1958, top5_acc: 0.4241, loss_cls: 4.4714, loss: 4.4714 +2024-07-16 21:24:11,194 - pyskl - INFO - Epoch [19][900/3746] lr: 9.640e-02, eta: 4 days, 3:56:52, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4330, loss_cls: 4.4096, loss: 4.4096 +2024-07-16 21:25:21,670 - pyskl - INFO - Epoch [19][1000/3746] lr: 9.639e-02, eta: 4 days, 3:55:22, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4367, loss_cls: 4.4350, loss: 4.4350 +2024-07-16 21:26:32,203 - pyskl - INFO - Epoch [19][1100/3746] lr: 9.637e-02, eta: 4 days, 3:53:52, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1997, top5_acc: 0.4255, loss_cls: 4.4777, loss: 4.4777 +2024-07-16 21:27:42,755 - pyskl - INFO - Epoch [19][1200/3746] lr: 9.636e-02, eta: 4 days, 3:52:22, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4244, loss_cls: 4.4609, loss: 4.4609 +2024-07-16 21:28:53,359 - pyskl - INFO - Epoch [19][1300/3746] lr: 9.635e-02, eta: 4 days, 3:50:53, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1927, top5_acc: 0.4288, loss_cls: 4.4689, loss: 4.4689 +2024-07-16 21:30:04,077 - pyskl - INFO - Epoch [19][1400/3746] lr: 9.634e-02, eta: 4 days, 3:49:24, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1958, top5_acc: 0.4267, loss_cls: 4.4775, loss: 4.4775 +2024-07-16 21:31:14,904 - pyskl - INFO - Epoch [19][1500/3746] lr: 9.633e-02, eta: 4 days, 3:47:57, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2028, top5_acc: 0.4344, loss_cls: 4.4450, loss: 4.4450 +2024-07-16 21:32:25,535 - pyskl - INFO - Epoch [19][1600/3746] lr: 9.632e-02, eta: 4 days, 3:46:28, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4328, loss_cls: 4.4384, loss: 4.4384 +2024-07-16 21:33:36,068 - pyskl - INFO - Epoch [19][1700/3746] lr: 9.631e-02, eta: 4 days, 3:44:58, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2030, top5_acc: 0.4306, loss_cls: 4.4527, loss: 4.4527 +2024-07-16 21:34:46,206 - pyskl - INFO - Epoch [19][1800/3746] lr: 9.630e-02, eta: 4 days, 3:43:26, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2028, top5_acc: 0.4348, loss_cls: 4.4468, loss: 4.4468 +2024-07-16 21:35:56,505 - pyskl - INFO - Epoch [19][1900/3746] lr: 9.629e-02, eta: 4 days, 3:41:55, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1928, top5_acc: 0.4284, loss_cls: 4.4917, loss: 4.4917 +2024-07-16 21:37:06,742 - pyskl - INFO - Epoch [19][2000/3746] lr: 9.628e-02, eta: 4 days, 3:40:23, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1997, top5_acc: 0.4288, loss_cls: 4.4448, loss: 4.4448 +2024-07-16 21:38:16,992 - pyskl - INFO - Epoch [19][2100/3746] lr: 9.627e-02, eta: 4 days, 3:38:52, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1950, top5_acc: 0.4247, loss_cls: 4.4673, loss: 4.4673 +2024-07-16 21:39:27,418 - pyskl - INFO - Epoch [19][2200/3746] lr: 9.626e-02, eta: 4 days, 3:37:22, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2059, top5_acc: 0.4394, loss_cls: 4.4333, loss: 4.4333 +2024-07-16 21:40:37,685 - pyskl - INFO - Epoch [19][2300/3746] lr: 9.625e-02, eta: 4 days, 3:35:51, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1967, top5_acc: 0.4203, loss_cls: 4.4906, loss: 4.4906 +2024-07-16 21:41:47,686 - pyskl - INFO - Epoch [19][2400/3746] lr: 9.624e-02, eta: 4 days, 3:34:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4319, loss_cls: 4.4455, loss: 4.4455 +2024-07-16 21:42:57,727 - pyskl - INFO - Epoch [19][2500/3746] lr: 9.623e-02, eta: 4 days, 3:32:45, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4242, loss_cls: 4.4746, loss: 4.4746 +2024-07-16 21:44:08,153 - pyskl - INFO - Epoch [19][2600/3746] lr: 9.622e-02, eta: 4 days, 3:31:15, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1989, top5_acc: 0.4316, loss_cls: 4.4361, loss: 4.4361 +2024-07-16 21:45:18,541 - pyskl - INFO - Epoch [19][2700/3746] lr: 9.621e-02, eta: 4 days, 3:29:45, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2019, top5_acc: 0.4203, loss_cls: 4.4678, loss: 4.4678 +2024-07-16 21:46:28,683 - pyskl - INFO - Epoch [19][2800/3746] lr: 9.620e-02, eta: 4 days, 3:28:14, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2019, top5_acc: 0.4234, loss_cls: 4.4748, loss: 4.4748 +2024-07-16 21:47:39,031 - pyskl - INFO - Epoch [19][2900/3746] lr: 9.618e-02, eta: 4 days, 3:26:43, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1953, top5_acc: 0.4305, loss_cls: 4.4616, loss: 4.4616 +2024-07-16 21:48:49,295 - pyskl - INFO - Epoch [19][3000/3746] lr: 9.617e-02, eta: 4 days, 3:25:13, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2036, top5_acc: 0.4266, loss_cls: 4.4485, loss: 4.4485 +2024-07-16 21:49:59,767 - pyskl - INFO - Epoch [19][3100/3746] lr: 9.616e-02, eta: 4 days, 3:23:43, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4369, loss_cls: 4.4206, loss: 4.4206 +2024-07-16 21:51:09,759 - pyskl - INFO - Epoch [19][3200/3746] lr: 9.615e-02, eta: 4 days, 3:22:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1933, top5_acc: 0.4256, loss_cls: 4.4878, loss: 4.4878 +2024-07-16 21:52:19,735 - pyskl - INFO - Epoch [19][3300/3746] lr: 9.614e-02, eta: 4 days, 3:20:38, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1995, top5_acc: 0.4336, loss_cls: 4.4469, loss: 4.4469 +2024-07-16 21:53:30,670 - pyskl - INFO - Epoch [19][3400/3746] lr: 9.613e-02, eta: 4 days, 3:19:12, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1959, top5_acc: 0.4256, loss_cls: 4.4769, loss: 4.4769 +2024-07-16 21:54:41,622 - pyskl - INFO - Epoch [19][3500/3746] lr: 9.612e-02, eta: 4 days, 3:17:47, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4250, loss_cls: 4.4809, loss: 4.4809 +2024-07-16 21:55:52,249 - pyskl - INFO - Epoch [19][3600/3746] lr: 9.611e-02, eta: 4 days, 3:16:19, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4208, loss_cls: 4.4946, loss: 4.4946 +2024-07-16 21:57:03,287 - pyskl - INFO - Epoch [19][3700/3746] lr: 9.610e-02, eta: 4 days, 3:14:54, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4288, loss_cls: 4.4870, loss: 4.4870 +2024-07-16 21:57:37,733 - pyskl - INFO - Saving checkpoint at 19 epochs +2024-07-16 21:59:29,906 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 21:59:30,576 - pyskl - INFO - +top1_acc 0.1584 +top5_acc 0.3647 +2024-07-16 21:59:30,576 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 21:59:30,619 - pyskl - INFO - +mean_acc 0.1583 +2024-07-16 21:59:30,624 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_17.pth was removed +2024-07-16 21:59:30,901 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_19.pth. +2024-07-16 21:59:30,902 - pyskl - INFO - Best top1_acc is 0.1584 at 19 epoch. +2024-07-16 21:59:30,916 - pyskl - INFO - Epoch(val) [19][309] top1_acc: 0.1584, top5_acc: 0.3647, mean_class_accuracy: 0.1583 +2024-07-16 22:02:49,833 - pyskl - INFO - Epoch [20][100/3746] lr: 9.608e-02, eta: 4 days, 3:23:45, time: 1.989, data_time: 1.273, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4297, loss_cls: 4.4444, loss: 4.4444 +2024-07-16 22:04:00,873 - pyskl - INFO - Epoch [20][200/3746] lr: 9.607e-02, eta: 4 days, 3:22:19, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4458, loss_cls: 4.3891, loss: 4.3891 +2024-07-16 22:05:11,979 - pyskl - INFO - Epoch [20][300/3746] lr: 9.606e-02, eta: 4 days, 3:20:54, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2059, top5_acc: 0.4353, loss_cls: 4.4401, loss: 4.4401 +2024-07-16 22:06:22,647 - pyskl - INFO - Epoch [20][400/3746] lr: 9.605e-02, eta: 4 days, 3:19:25, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4450, loss_cls: 4.3870, loss: 4.3870 +2024-07-16 22:07:33,187 - pyskl - INFO - Epoch [20][500/3746] lr: 9.604e-02, eta: 4 days, 3:17:56, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4387, loss_cls: 4.4347, loss: 4.4347 +2024-07-16 22:08:43,836 - pyskl - INFO - Epoch [20][600/3746] lr: 9.603e-02, eta: 4 days, 3:16:28, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4420, loss_cls: 4.3890, loss: 4.3890 +2024-07-16 22:09:54,355 - pyskl - INFO - Epoch [20][700/3746] lr: 9.602e-02, eta: 4 days, 3:14:58, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1973, top5_acc: 0.4228, loss_cls: 4.4581, loss: 4.4581 +2024-07-16 22:11:05,064 - pyskl - INFO - Epoch [20][800/3746] lr: 9.601e-02, eta: 4 days, 3:13:30, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4328, loss_cls: 4.4170, loss: 4.4170 +2024-07-16 22:12:15,667 - pyskl - INFO - Epoch [20][900/3746] lr: 9.600e-02, eta: 4 days, 3:12:02, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1961, top5_acc: 0.4238, loss_cls: 4.5053, loss: 4.5053 +2024-07-16 22:13:26,219 - pyskl - INFO - Epoch [20][1000/3746] lr: 9.598e-02, eta: 4 days, 3:10:33, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4289, loss_cls: 4.4795, loss: 4.4795 +2024-07-16 22:14:37,074 - pyskl - INFO - Epoch [20][1100/3746] lr: 9.597e-02, eta: 4 days, 3:09:06, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2069, top5_acc: 0.4353, loss_cls: 4.4521, loss: 4.4521 +2024-07-16 22:15:47,718 - pyskl - INFO - Epoch [20][1200/3746] lr: 9.596e-02, eta: 4 days, 3:07:38, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2027, top5_acc: 0.4373, loss_cls: 4.4440, loss: 4.4440 +2024-07-16 22:16:58,148 - pyskl - INFO - Epoch [20][1300/3746] lr: 9.595e-02, eta: 4 days, 3:06:08, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2030, top5_acc: 0.4292, loss_cls: 4.4430, loss: 4.4430 +2024-07-16 22:18:09,056 - pyskl - INFO - Epoch [20][1400/3746] lr: 9.594e-02, eta: 4 days, 3:04:42, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4408, loss_cls: 4.4139, loss: 4.4139 +2024-07-16 22:19:19,744 - pyskl - INFO - Epoch [20][1500/3746] lr: 9.593e-02, eta: 4 days, 3:03:14, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4291, loss_cls: 4.4749, loss: 4.4749 +2024-07-16 22:20:30,230 - pyskl - INFO - Epoch [20][1600/3746] lr: 9.592e-02, eta: 4 days, 3:01:45, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4328, loss_cls: 4.4237, loss: 4.4237 +2024-07-16 22:21:40,716 - pyskl - INFO - Epoch [20][1700/3746] lr: 9.591e-02, eta: 4 days, 3:00:16, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4255, loss_cls: 4.4829, loss: 4.4829 +2024-07-16 22:22:50,838 - pyskl - INFO - Epoch [20][1800/3746] lr: 9.590e-02, eta: 4 days, 2:58:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4214, loss_cls: 4.4652, loss: 4.4652 +2024-07-16 22:24:01,213 - pyskl - INFO - Epoch [20][1900/3746] lr: 9.588e-02, eta: 4 days, 2:57:15, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4402, loss_cls: 4.4506, loss: 4.4506 +2024-07-16 22:25:11,609 - pyskl - INFO - Epoch [20][2000/3746] lr: 9.587e-02, eta: 4 days, 2:55:46, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2019, top5_acc: 0.4292, loss_cls: 4.4595, loss: 4.4595 +2024-07-16 22:26:21,742 - pyskl - INFO - Epoch [20][2100/3746] lr: 9.586e-02, eta: 4 days, 2:54:15, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4402, loss_cls: 4.4272, loss: 4.4272 +2024-07-16 22:27:31,810 - pyskl - INFO - Epoch [20][2200/3746] lr: 9.585e-02, eta: 4 days, 2:52:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2006, top5_acc: 0.4264, loss_cls: 4.4526, loss: 4.4526 +2024-07-16 22:28:41,918 - pyskl - INFO - Epoch [20][2300/3746] lr: 9.584e-02, eta: 4 days, 2:51:12, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2069, top5_acc: 0.4344, loss_cls: 4.4513, loss: 4.4513 +2024-07-16 22:29:51,988 - pyskl - INFO - Epoch [20][2400/3746] lr: 9.583e-02, eta: 4 days, 2:49:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4288, loss_cls: 4.4727, loss: 4.4727 +2024-07-16 22:31:01,983 - pyskl - INFO - Epoch [20][2500/3746] lr: 9.582e-02, eta: 4 days, 2:48:09, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2009, top5_acc: 0.4267, loss_cls: 4.4578, loss: 4.4578 +2024-07-16 22:32:12,086 - pyskl - INFO - Epoch [20][2600/3746] lr: 9.581e-02, eta: 4 days, 2:46:38, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1916, top5_acc: 0.4195, loss_cls: 4.4777, loss: 4.4777 +2024-07-16 22:33:22,006 - pyskl - INFO - Epoch [20][2700/3746] lr: 9.580e-02, eta: 4 days, 2:45:05, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2009, top5_acc: 0.4264, loss_cls: 4.4786, loss: 4.4786 +2024-07-16 22:34:32,131 - pyskl - INFO - Epoch [20][2800/3746] lr: 9.578e-02, eta: 4 days, 2:43:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1994, top5_acc: 0.4336, loss_cls: 4.4012, loss: 4.4012 +2024-07-16 22:35:42,239 - pyskl - INFO - Epoch [20][2900/3746] lr: 9.577e-02, eta: 4 days, 2:42:04, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4425, loss_cls: 4.4084, loss: 4.4084 +2024-07-16 22:36:52,210 - pyskl - INFO - Epoch [20][3000/3746] lr: 9.576e-02, eta: 4 days, 2:40:32, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1920, top5_acc: 0.4219, loss_cls: 4.4881, loss: 4.4881 +2024-07-16 22:38:02,316 - pyskl - INFO - Epoch [20][3100/3746] lr: 9.575e-02, eta: 4 days, 2:39:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1959, top5_acc: 0.4255, loss_cls: 4.4555, loss: 4.4555 +2024-07-16 22:39:12,652 - pyskl - INFO - Epoch [20][3200/3746] lr: 9.574e-02, eta: 4 days, 2:37:32, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4320, loss_cls: 4.4454, loss: 4.4454 +2024-07-16 22:40:22,766 - pyskl - INFO - Epoch [20][3300/3746] lr: 9.573e-02, eta: 4 days, 2:36:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4372, loss_cls: 4.4322, loss: 4.4322 +2024-07-16 22:41:33,565 - pyskl - INFO - Epoch [20][3400/3746] lr: 9.572e-02, eta: 4 days, 2:34:35, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4392, loss_cls: 4.4159, loss: 4.4159 +2024-07-16 22:42:44,179 - pyskl - INFO - Epoch [20][3500/3746] lr: 9.571e-02, eta: 4 days, 2:33:08, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2005, top5_acc: 0.4347, loss_cls: 4.4198, loss: 4.4198 +2024-07-16 22:43:54,794 - pyskl - INFO - Epoch [20][3600/3746] lr: 9.569e-02, eta: 4 days, 2:31:41, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4398, loss_cls: 4.4393, loss: 4.4393 +2024-07-16 22:45:05,731 - pyskl - INFO - Epoch [20][3700/3746] lr: 9.568e-02, eta: 4 days, 2:30:16, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1952, top5_acc: 0.4256, loss_cls: 4.4797, loss: 4.4797 +2024-07-16 22:45:40,141 - pyskl - INFO - Saving checkpoint at 20 epochs +2024-07-16 22:47:32,171 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 22:47:32,850 - pyskl - INFO - +top1_acc 0.1336 +top5_acc 0.3215 +2024-07-16 22:47:32,850 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 22:47:32,894 - pyskl - INFO - +mean_acc 0.1335 +2024-07-16 22:47:32,907 - pyskl - INFO - Epoch(val) [20][309] top1_acc: 0.1336, top5_acc: 0.3215, mean_class_accuracy: 0.1335 +2024-07-16 22:50:58,071 - pyskl - INFO - Epoch [21][100/3746] lr: 9.567e-02, eta: 4 days, 2:39:11, time: 2.052, data_time: 1.338, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4406, loss_cls: 4.3873, loss: 4.3873 +2024-07-16 22:52:09,373 - pyskl - INFO - Epoch [21][200/3746] lr: 9.565e-02, eta: 4 days, 2:37:48, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4400, loss_cls: 4.4181, loss: 4.4181 +2024-07-16 22:53:20,144 - pyskl - INFO - Epoch [21][300/3746] lr: 9.564e-02, eta: 4 days, 2:36:21, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2006, top5_acc: 0.4317, loss_cls: 4.4317, loss: 4.4317 +2024-07-16 22:54:31,080 - pyskl - INFO - Epoch [21][400/3746] lr: 9.563e-02, eta: 4 days, 2:34:55, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1956, top5_acc: 0.4327, loss_cls: 4.4655, loss: 4.4655 +2024-07-16 22:55:41,605 - pyskl - INFO - Epoch [21][500/3746] lr: 9.562e-02, eta: 4 days, 2:33:26, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4403, loss_cls: 4.4108, loss: 4.4108 +2024-07-16 22:56:52,479 - pyskl - INFO - Epoch [21][600/3746] lr: 9.561e-02, eta: 4 days, 2:32:00, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4288, loss_cls: 4.4588, loss: 4.4588 +2024-07-16 22:58:03,054 - pyskl - INFO - Epoch [21][700/3746] lr: 9.560e-02, eta: 4 days, 2:30:32, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4256, loss_cls: 4.4643, loss: 4.4643 +2024-07-16 22:59:13,815 - pyskl - INFO - Epoch [21][800/3746] lr: 9.559e-02, eta: 4 days, 2:29:05, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4298, loss_cls: 4.4504, loss: 4.4504 +2024-07-16 23:00:24,423 - pyskl - INFO - Epoch [21][900/3746] lr: 9.557e-02, eta: 4 days, 2:27:37, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4389, loss_cls: 4.3995, loss: 4.3995 +2024-07-16 23:01:35,098 - pyskl - INFO - Epoch [21][1000/3746] lr: 9.556e-02, eta: 4 days, 2:26:10, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2009, top5_acc: 0.4400, loss_cls: 4.4120, loss: 4.4120 +2024-07-16 23:02:45,865 - pyskl - INFO - Epoch [21][1100/3746] lr: 9.555e-02, eta: 4 days, 2:24:43, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4338, loss_cls: 4.4273, loss: 4.4273 +2024-07-16 23:03:56,436 - pyskl - INFO - Epoch [21][1200/3746] lr: 9.554e-02, eta: 4 days, 2:23:15, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4323, loss_cls: 4.4455, loss: 4.4455 +2024-07-16 23:05:07,148 - pyskl - INFO - Epoch [21][1300/3746] lr: 9.553e-02, eta: 4 days, 2:21:48, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4447, loss_cls: 4.4008, loss: 4.4008 +2024-07-16 23:06:18,219 - pyskl - INFO - Epoch [21][1400/3746] lr: 9.552e-02, eta: 4 days, 2:20:24, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4375, loss_cls: 4.4455, loss: 4.4455 +2024-07-16 23:07:29,008 - pyskl - INFO - Epoch [21][1500/3746] lr: 9.551e-02, eta: 4 days, 2:18:57, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4288, loss_cls: 4.4668, loss: 4.4668 +2024-07-16 23:08:40,559 - pyskl - INFO - Epoch [21][1600/3746] lr: 9.549e-02, eta: 4 days, 2:17:36, time: 0.715, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4305, loss_cls: 4.4275, loss: 4.4275 +2024-07-16 23:09:51,069 - pyskl - INFO - Epoch [21][1700/3746] lr: 9.548e-02, eta: 4 days, 2:16:08, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4398, loss_cls: 4.3962, loss: 4.3962 +2024-07-16 23:11:01,573 - pyskl - INFO - Epoch [21][1800/3746] lr: 9.547e-02, eta: 4 days, 2:14:40, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4383, loss_cls: 4.4472, loss: 4.4472 +2024-07-16 23:12:12,606 - pyskl - INFO - Epoch [21][1900/3746] lr: 9.546e-02, eta: 4 days, 2:13:15, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4383, loss_cls: 4.4380, loss: 4.4380 +2024-07-16 23:13:23,687 - pyskl - INFO - Epoch [21][2000/3746] lr: 9.545e-02, eta: 4 days, 2:11:51, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2008, top5_acc: 0.4319, loss_cls: 4.4472, loss: 4.4472 +2024-07-16 23:14:34,585 - pyskl - INFO - Epoch [21][2100/3746] lr: 9.544e-02, eta: 4 days, 2:10:25, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.1970, top5_acc: 0.4280, loss_cls: 4.4619, loss: 4.4619 +2024-07-16 23:15:45,035 - pyskl - INFO - Epoch [21][2200/3746] lr: 9.542e-02, eta: 4 days, 2:08:57, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4286, loss_cls: 4.4540, loss: 4.4540 +2024-07-16 23:16:55,470 - pyskl - INFO - Epoch [21][2300/3746] lr: 9.541e-02, eta: 4 days, 2:07:29, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2036, top5_acc: 0.4281, loss_cls: 4.4595, loss: 4.4595 +2024-07-16 23:18:06,068 - pyskl - INFO - Epoch [21][2400/3746] lr: 9.540e-02, eta: 4 days, 2:06:02, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4306, loss_cls: 4.4510, loss: 4.4510 +2024-07-16 23:19:16,621 - pyskl - INFO - Epoch [21][2500/3746] lr: 9.539e-02, eta: 4 days, 2:04:34, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2014, top5_acc: 0.4247, loss_cls: 4.4982, loss: 4.4982 +2024-07-16 23:20:27,170 - pyskl - INFO - Epoch [21][2600/3746] lr: 9.538e-02, eta: 4 days, 2:03:07, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2114, top5_acc: 0.4387, loss_cls: 4.4281, loss: 4.4281 +2024-07-16 23:21:37,572 - pyskl - INFO - Epoch [21][2700/3746] lr: 9.537e-02, eta: 4 days, 2:01:39, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4320, loss_cls: 4.4633, loss: 4.4633 +2024-07-16 23:22:47,905 - pyskl - INFO - Epoch [21][2800/3746] lr: 9.535e-02, eta: 4 days, 2:00:10, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1959, top5_acc: 0.4286, loss_cls: 4.4470, loss: 4.4470 +2024-07-16 23:23:58,059 - pyskl - INFO - Epoch [21][2900/3746] lr: 9.534e-02, eta: 4 days, 1:58:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4325, loss_cls: 4.4240, loss: 4.4240 +2024-07-16 23:25:08,174 - pyskl - INFO - Epoch [21][3000/3746] lr: 9.533e-02, eta: 4 days, 1:57:10, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4348, loss_cls: 4.4321, loss: 4.4321 +2024-07-16 23:26:18,276 - pyskl - INFO - Epoch [21][3100/3746] lr: 9.532e-02, eta: 4 days, 1:55:40, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4297, loss_cls: 4.4264, loss: 4.4264 +2024-07-16 23:27:28,551 - pyskl - INFO - Epoch [21][3200/3746] lr: 9.531e-02, eta: 4 days, 1:54:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4355, loss_cls: 4.4528, loss: 4.4528 +2024-07-16 23:28:39,023 - pyskl - INFO - Epoch [21][3300/3746] lr: 9.529e-02, eta: 4 days, 1:52:44, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4437, loss_cls: 4.3961, loss: 4.3961 +2024-07-16 23:29:50,118 - pyskl - INFO - Epoch [21][3400/3746] lr: 9.528e-02, eta: 4 days, 1:51:20, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2025, top5_acc: 0.4356, loss_cls: 4.4397, loss: 4.4397 +2024-07-16 23:31:00,920 - pyskl - INFO - Epoch [21][3500/3746] lr: 9.527e-02, eta: 4 days, 1:49:55, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4288, loss_cls: 4.4728, loss: 4.4728 +2024-07-16 23:32:11,928 - pyskl - INFO - Epoch [21][3600/3746] lr: 9.526e-02, eta: 4 days, 1:48:30, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4316, loss_cls: 4.4555, loss: 4.4555 +2024-07-16 23:33:22,788 - pyskl - INFO - Epoch [21][3700/3746] lr: 9.525e-02, eta: 4 days, 1:47:05, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4336, loss_cls: 4.4381, loss: 4.4381 +2024-07-16 23:33:57,194 - pyskl - INFO - Saving checkpoint at 21 epochs +2024-07-16 23:35:49,376 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-16 23:35:50,054 - pyskl - INFO - +top1_acc 0.1446 +top5_acc 0.3436 +2024-07-16 23:35:50,054 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-16 23:35:50,096 - pyskl - INFO - +mean_acc 0.1444 +2024-07-16 23:35:50,108 - pyskl - INFO - Epoch(val) [21][309] top1_acc: 0.1446, top5_acc: 0.3436, mean_class_accuracy: 0.1444 +2024-07-16 23:39:10,526 - pyskl - INFO - Epoch [22][100/3746] lr: 9.523e-02, eta: 4 days, 1:54:56, time: 2.004, data_time: 1.301, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4366, loss_cls: 4.4095, loss: 4.4095 +2024-07-16 23:40:20,783 - pyskl - INFO - Epoch [22][200/3746] lr: 9.522e-02, eta: 4 days, 1:53:26, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2114, top5_acc: 0.4430, loss_cls: 4.3711, loss: 4.3711 +2024-07-16 23:41:30,740 - pyskl - INFO - Epoch [22][300/3746] lr: 9.521e-02, eta: 4 days, 1:51:55, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1983, top5_acc: 0.4434, loss_cls: 4.3948, loss: 4.3948 +2024-07-16 23:42:40,842 - pyskl - INFO - Epoch [22][400/3746] lr: 9.519e-02, eta: 4 days, 1:50:25, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4445, loss_cls: 4.3872, loss: 4.3872 +2024-07-16 23:43:50,739 - pyskl - INFO - Epoch [22][500/3746] lr: 9.518e-02, eta: 4 days, 1:48:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1975, top5_acc: 0.4136, loss_cls: 4.5070, loss: 4.5070 +2024-07-16 23:45:00,931 - pyskl - INFO - Epoch [22][600/3746] lr: 9.517e-02, eta: 4 days, 1:47:23, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2006, top5_acc: 0.4320, loss_cls: 4.4309, loss: 4.4309 +2024-07-16 23:46:10,957 - pyskl - INFO - Epoch [22][700/3746] lr: 9.516e-02, eta: 4 days, 1:45:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1972, top5_acc: 0.4273, loss_cls: 4.4555, loss: 4.4555 +2024-07-16 23:47:21,203 - pyskl - INFO - Epoch [22][800/3746] lr: 9.515e-02, eta: 4 days, 1:44:23, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4331, loss_cls: 4.4352, loss: 4.4352 +2024-07-16 23:48:31,306 - pyskl - INFO - Epoch [22][900/3746] lr: 9.513e-02, eta: 4 days, 1:42:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4253, loss_cls: 4.4492, loss: 4.4492 +2024-07-16 23:49:41,249 - pyskl - INFO - Epoch [22][1000/3746] lr: 9.512e-02, eta: 4 days, 1:41:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4428, loss_cls: 4.4252, loss: 4.4252 +2024-07-16 23:50:51,633 - pyskl - INFO - Epoch [22][1100/3746] lr: 9.511e-02, eta: 4 days, 1:39:54, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1997, top5_acc: 0.4405, loss_cls: 4.4220, loss: 4.4220 +2024-07-16 23:52:01,725 - pyskl - INFO - Epoch [22][1200/3746] lr: 9.510e-02, eta: 4 days, 1:38:24, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4377, loss_cls: 4.4203, loss: 4.4203 +2024-07-16 23:53:11,588 - pyskl - INFO - Epoch [22][1300/3746] lr: 9.509e-02, eta: 4 days, 1:36:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1944, top5_acc: 0.4150, loss_cls: 4.4818, loss: 4.4818 +2024-07-16 23:54:21,559 - pyskl - INFO - Epoch [22][1400/3746] lr: 9.507e-02, eta: 4 days, 1:35:22, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4369, loss_cls: 4.4391, loss: 4.4391 +2024-07-16 23:55:32,187 - pyskl - INFO - Epoch [22][1500/3746] lr: 9.506e-02, eta: 4 days, 1:33:55, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4344, loss_cls: 4.4175, loss: 4.4175 +2024-07-16 23:56:42,219 - pyskl - INFO - Epoch [22][1600/3746] lr: 9.505e-02, eta: 4 days, 1:32:25, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4338, loss_cls: 4.4412, loss: 4.4412 +2024-07-16 23:57:52,563 - pyskl - INFO - Epoch [22][1700/3746] lr: 9.504e-02, eta: 4 days, 1:30:57, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2008, top5_acc: 0.4213, loss_cls: 4.4921, loss: 4.4921 +2024-07-16 23:59:02,977 - pyskl - INFO - Epoch [22][1800/3746] lr: 9.502e-02, eta: 4 days, 1:29:29, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2013, top5_acc: 0.4322, loss_cls: 4.4429, loss: 4.4429 +2024-07-17 00:00:13,192 - pyskl - INFO - Epoch [22][1900/3746] lr: 9.501e-02, eta: 4 days, 1:28:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4455, loss_cls: 4.4113, loss: 4.4113 +2024-07-17 00:01:23,491 - pyskl - INFO - Epoch [22][2000/3746] lr: 9.500e-02, eta: 4 days, 1:26:32, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4306, loss_cls: 4.4274, loss: 4.4274 +2024-07-17 00:02:33,480 - pyskl - INFO - Epoch [22][2100/3746] lr: 9.499e-02, eta: 4 days, 1:25:02, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4353, loss_cls: 4.4354, loss: 4.4354 +2024-07-17 00:03:43,455 - pyskl - INFO - Epoch [22][2200/3746] lr: 9.498e-02, eta: 4 days, 1:23:31, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4328, loss_cls: 4.4160, loss: 4.4160 +2024-07-17 00:04:53,227 - pyskl - INFO - Epoch [22][2300/3746] lr: 9.496e-02, eta: 4 days, 1:22:00, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4383, loss_cls: 4.4421, loss: 4.4421 +2024-07-17 00:06:03,314 - pyskl - INFO - Epoch [22][2400/3746] lr: 9.495e-02, eta: 4 days, 1:20:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4375, loss_cls: 4.4333, loss: 4.4333 +2024-07-17 00:07:13,337 - pyskl - INFO - Epoch [22][2500/3746] lr: 9.494e-02, eta: 4 days, 1:19:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4389, loss_cls: 4.4244, loss: 4.4244 +2024-07-17 00:08:23,271 - pyskl - INFO - Epoch [22][2600/3746] lr: 9.493e-02, eta: 4 days, 1:17:30, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1972, top5_acc: 0.4334, loss_cls: 4.4553, loss: 4.4553 +2024-07-17 00:09:33,085 - pyskl - INFO - Epoch [22][2700/3746] lr: 9.491e-02, eta: 4 days, 1:15:59, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4334, loss_cls: 4.4274, loss: 4.4274 +2024-07-17 00:10:42,859 - pyskl - INFO - Epoch [22][2800/3746] lr: 9.490e-02, eta: 4 days, 1:14:28, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4381, loss_cls: 4.4338, loss: 4.4338 +2024-07-17 00:11:52,759 - pyskl - INFO - Epoch [22][2900/3746] lr: 9.489e-02, eta: 4 days, 1:12:58, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4211, loss_cls: 4.4972, loss: 4.4972 +2024-07-17 00:13:03,096 - pyskl - INFO - Epoch [22][3000/3746] lr: 9.488e-02, eta: 4 days, 1:11:30, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.1978, top5_acc: 0.4239, loss_cls: 4.4740, loss: 4.4740 +2024-07-17 00:14:13,243 - pyskl - INFO - Epoch [22][3100/3746] lr: 9.487e-02, eta: 4 days, 1:10:01, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2047, top5_acc: 0.4325, loss_cls: 4.4285, loss: 4.4285 +2024-07-17 00:15:23,129 - pyskl - INFO - Epoch [22][3200/3746] lr: 9.485e-02, eta: 4 days, 1:08:31, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4377, loss_cls: 4.3883, loss: 4.3883 +2024-07-17 00:16:33,658 - pyskl - INFO - Epoch [22][3300/3746] lr: 9.484e-02, eta: 4 days, 1:07:05, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1967, top5_acc: 0.4267, loss_cls: 4.4527, loss: 4.4527 +2024-07-17 00:17:44,111 - pyskl - INFO - Epoch [22][3400/3746] lr: 9.483e-02, eta: 4 days, 1:05:38, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2028, top5_acc: 0.4245, loss_cls: 4.4613, loss: 4.4613 +2024-07-17 00:18:54,543 - pyskl - INFO - Epoch [22][3500/3746] lr: 9.482e-02, eta: 4 days, 1:04:11, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4369, loss_cls: 4.4228, loss: 4.4228 +2024-07-17 00:20:05,842 - pyskl - INFO - Epoch [22][3600/3746] lr: 9.480e-02, eta: 4 days, 1:02:49, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4353, loss_cls: 4.4342, loss: 4.4342 +2024-07-17 00:21:16,653 - pyskl - INFO - Epoch [22][3700/3746] lr: 9.479e-02, eta: 4 days, 1:01:24, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4422, loss_cls: 4.4000, loss: 4.4000 +2024-07-17 00:21:51,074 - pyskl - INFO - Saving checkpoint at 22 epochs +2024-07-17 00:23:43,125 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 00:23:43,822 - pyskl - INFO - +top1_acc 0.1333 +top5_acc 0.3157 +2024-07-17 00:23:43,822 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 00:23:43,866 - pyskl - INFO - +mean_acc 0.1331 +2024-07-17 00:23:43,881 - pyskl - INFO - Epoch(val) [22][309] top1_acc: 0.1333, top5_acc: 0.3157, mean_class_accuracy: 0.1331 +2024-07-17 00:27:05,225 - pyskl - INFO - Epoch [23][100/3746] lr: 9.477e-02, eta: 4 days, 1:08:50, time: 2.013, data_time: 1.306, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4436, loss_cls: 4.3853, loss: 4.3853 +2024-07-17 00:28:15,296 - pyskl - INFO - Epoch [23][200/3746] lr: 9.476e-02, eta: 4 days, 1:07:20, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4330, loss_cls: 4.4384, loss: 4.4384 +2024-07-17 00:29:25,585 - pyskl - INFO - Epoch [23][300/3746] lr: 9.475e-02, eta: 4 days, 1:05:52, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4380, loss_cls: 4.3997, loss: 4.3997 +2024-07-17 00:30:35,797 - pyskl - INFO - Epoch [23][400/3746] lr: 9.474e-02, eta: 4 days, 1:04:23, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2036, top5_acc: 0.4325, loss_cls: 4.4383, loss: 4.4383 +2024-07-17 00:31:45,786 - pyskl - INFO - Epoch [23][500/3746] lr: 9.472e-02, eta: 4 days, 1:02:53, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4350, loss_cls: 4.3951, loss: 4.3951 +2024-07-17 00:32:55,541 - pyskl - INFO - Epoch [23][600/3746] lr: 9.471e-02, eta: 4 days, 1:01:22, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4370, loss_cls: 4.3921, loss: 4.3921 +2024-07-17 00:34:05,395 - pyskl - INFO - Epoch [23][700/3746] lr: 9.470e-02, eta: 4 days, 0:59:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4308, loss_cls: 4.4455, loss: 4.4455 +2024-07-17 00:35:15,369 - pyskl - INFO - Epoch [23][800/3746] lr: 9.469e-02, eta: 4 days, 0:58:21, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4364, loss_cls: 4.4394, loss: 4.4394 +2024-07-17 00:36:25,517 - pyskl - INFO - Epoch [23][900/3746] lr: 9.467e-02, eta: 4 days, 0:56:52, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1953, top5_acc: 0.4228, loss_cls: 4.4742, loss: 4.4742 +2024-07-17 00:37:35,639 - pyskl - INFO - Epoch [23][1000/3746] lr: 9.466e-02, eta: 4 days, 0:55:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1939, top5_acc: 0.4217, loss_cls: 4.4833, loss: 4.4833 +2024-07-17 00:38:45,491 - pyskl - INFO - Epoch [23][1100/3746] lr: 9.465e-02, eta: 4 days, 0:53:53, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4288, loss_cls: 4.4380, loss: 4.4380 +2024-07-17 00:39:55,916 - pyskl - INFO - Epoch [23][1200/3746] lr: 9.464e-02, eta: 4 days, 0:52:25, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4422, loss_cls: 4.4268, loss: 4.4268 +2024-07-17 00:41:06,031 - pyskl - INFO - Epoch [23][1300/3746] lr: 9.462e-02, eta: 4 days, 0:50:57, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4363, loss_cls: 4.4156, loss: 4.4156 +2024-07-17 00:42:16,085 - pyskl - INFO - Epoch [23][1400/3746] lr: 9.461e-02, eta: 4 days, 0:49:27, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4405, loss_cls: 4.3845, loss: 4.3845 +2024-07-17 00:43:26,467 - pyskl - INFO - Epoch [23][1500/3746] lr: 9.460e-02, eta: 4 days, 0:48:00, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1988, top5_acc: 0.4242, loss_cls: 4.4531, loss: 4.4531 +2024-07-17 00:44:36,845 - pyskl - INFO - Epoch [23][1600/3746] lr: 9.459e-02, eta: 4 days, 0:46:33, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4305, loss_cls: 4.4395, loss: 4.4395 +2024-07-17 00:45:47,441 - pyskl - INFO - Epoch [23][1700/3746] lr: 9.457e-02, eta: 4 days, 0:45:07, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4387, loss_cls: 4.4217, loss: 4.4217 +2024-07-17 00:46:57,597 - pyskl - INFO - Epoch [23][1800/3746] lr: 9.456e-02, eta: 4 days, 0:43:38, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4402, loss_cls: 4.4164, loss: 4.4164 +2024-07-17 00:48:07,666 - pyskl - INFO - Epoch [23][1900/3746] lr: 9.455e-02, eta: 4 days, 0:42:09, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2037, top5_acc: 0.4352, loss_cls: 4.4349, loss: 4.4349 +2024-07-17 00:49:17,649 - pyskl - INFO - Epoch [23][2000/3746] lr: 9.453e-02, eta: 4 days, 0:40:40, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4372, loss_cls: 4.4234, loss: 4.4234 +2024-07-17 00:50:27,637 - pyskl - INFO - Epoch [23][2100/3746] lr: 9.452e-02, eta: 4 days, 0:39:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2022, top5_acc: 0.4339, loss_cls: 4.4513, loss: 4.4513 +2024-07-17 00:51:37,488 - pyskl - INFO - Epoch [23][2200/3746] lr: 9.451e-02, eta: 4 days, 0:37:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4439, loss_cls: 4.4003, loss: 4.4003 +2024-07-17 00:52:47,488 - pyskl - INFO - Epoch [23][2300/3746] lr: 9.450e-02, eta: 4 days, 0:36:12, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4342, loss_cls: 4.4060, loss: 4.4060 +2024-07-17 00:53:57,529 - pyskl - INFO - Epoch [23][2400/3746] lr: 9.448e-02, eta: 4 days, 0:34:43, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4387, loss_cls: 4.4090, loss: 4.4090 +2024-07-17 00:55:07,711 - pyskl - INFO - Epoch [23][2500/3746] lr: 9.447e-02, eta: 4 days, 0:33:15, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4336, loss_cls: 4.4459, loss: 4.4459 +2024-07-17 00:56:17,966 - pyskl - INFO - Epoch [23][2600/3746] lr: 9.446e-02, eta: 4 days, 0:31:47, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2037, top5_acc: 0.4384, loss_cls: 4.4441, loss: 4.4441 +2024-07-17 00:57:28,069 - pyskl - INFO - Epoch [23][2700/3746] lr: 9.445e-02, eta: 4 days, 0:30:19, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4286, loss_cls: 4.4613, loss: 4.4613 +2024-07-17 00:58:38,059 - pyskl - INFO - Epoch [23][2800/3746] lr: 9.443e-02, eta: 4 days, 0:28:50, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4236, loss_cls: 4.4509, loss: 4.4509 +2024-07-17 00:59:48,037 - pyskl - INFO - Epoch [23][2900/3746] lr: 9.442e-02, eta: 4 days, 0:27:21, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4333, loss_cls: 4.4150, loss: 4.4150 +2024-07-17 01:00:57,949 - pyskl - INFO - Epoch [23][3000/3746] lr: 9.441e-02, eta: 4 days, 0:25:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1977, top5_acc: 0.4309, loss_cls: 4.4591, loss: 4.4591 +2024-07-17 01:02:08,058 - pyskl - INFO - Epoch [23][3100/3746] lr: 9.439e-02, eta: 4 days, 0:24:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2056, top5_acc: 0.4319, loss_cls: 4.4373, loss: 4.4373 +2024-07-17 01:03:18,000 - pyskl - INFO - Epoch [23][3200/3746] lr: 9.438e-02, eta: 4 days, 0:22:54, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1973, top5_acc: 0.4311, loss_cls: 4.4464, loss: 4.4464 +2024-07-17 01:04:28,630 - pyskl - INFO - Epoch [23][3300/3746] lr: 9.437e-02, eta: 4 days, 0:21:29, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4414, loss_cls: 4.3859, loss: 4.3859 +2024-07-17 01:05:39,708 - pyskl - INFO - Epoch [23][3400/3746] lr: 9.436e-02, eta: 4 days, 0:20:06, time: 0.711, data_time: 0.000, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4311, loss_cls: 4.4545, loss: 4.4545 +2024-07-17 01:06:50,542 - pyskl - INFO - Epoch [23][3500/3746] lr: 9.434e-02, eta: 4 days, 0:18:42, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2000, top5_acc: 0.4231, loss_cls: 4.4717, loss: 4.4717 +2024-07-17 01:08:01,340 - pyskl - INFO - Epoch [23][3600/3746] lr: 9.433e-02, eta: 4 days, 0:17:18, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4359, loss_cls: 4.4313, loss: 4.4313 +2024-07-17 01:09:11,548 - pyskl - INFO - Epoch [23][3700/3746] lr: 9.432e-02, eta: 4 days, 0:15:51, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4367, loss_cls: 4.4402, loss: 4.4402 +2024-07-17 01:09:46,176 - pyskl - INFO - Saving checkpoint at 23 epochs +2024-07-17 01:11:38,394 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 01:11:39,045 - pyskl - INFO - +top1_acc 0.1353 +top5_acc 0.3269 +2024-07-17 01:11:39,046 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 01:11:39,086 - pyskl - INFO - +mean_acc 0.1354 +2024-07-17 01:11:39,097 - pyskl - INFO - Epoch(val) [23][309] top1_acc: 0.1353, top5_acc: 0.3269, mean_class_accuracy: 0.1354 +2024-07-17 01:15:03,113 - pyskl - INFO - Epoch [24][100/3746] lr: 9.430e-02, eta: 4 days, 0:23:03, time: 2.040, data_time: 1.332, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4409, loss_cls: 4.3946, loss: 4.3946 +2024-07-17 01:16:13,519 - pyskl - INFO - Epoch [24][200/3746] lr: 9.428e-02, eta: 4 days, 0:21:36, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4484, loss_cls: 4.3527, loss: 4.3527 +2024-07-17 01:17:23,595 - pyskl - INFO - Epoch [24][300/3746] lr: 9.427e-02, eta: 4 days, 0:20:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2036, top5_acc: 0.4241, loss_cls: 4.4405, loss: 4.4405 +2024-07-17 01:18:33,674 - pyskl - INFO - Epoch [24][400/3746] lr: 9.426e-02, eta: 4 days, 0:18:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2030, top5_acc: 0.4320, loss_cls: 4.4512, loss: 4.4512 +2024-07-17 01:19:43,612 - pyskl - INFO - Epoch [24][500/3746] lr: 9.425e-02, eta: 4 days, 0:17:09, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4472, loss_cls: 4.3988, loss: 4.3988 +2024-07-17 01:20:53,552 - pyskl - INFO - Epoch [24][600/3746] lr: 9.423e-02, eta: 4 days, 0:15:40, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4428, loss_cls: 4.3969, loss: 4.3969 +2024-07-17 01:22:03,531 - pyskl - INFO - Epoch [24][700/3746] lr: 9.422e-02, eta: 4 days, 0:14:11, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1956, top5_acc: 0.4347, loss_cls: 4.4373, loss: 4.4373 +2024-07-17 01:23:13,658 - pyskl - INFO - Epoch [24][800/3746] lr: 9.421e-02, eta: 4 days, 0:12:43, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4264, loss_cls: 4.4358, loss: 4.4358 +2024-07-17 01:24:23,707 - pyskl - INFO - Epoch [24][900/3746] lr: 9.419e-02, eta: 4 days, 0:11:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1980, top5_acc: 0.4419, loss_cls: 4.4404, loss: 4.4404 +2024-07-17 01:25:33,761 - pyskl - INFO - Epoch [24][1000/3746] lr: 9.418e-02, eta: 4 days, 0:09:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4359, loss_cls: 4.4207, loss: 4.4207 +2024-07-17 01:26:43,848 - pyskl - INFO - Epoch [24][1100/3746] lr: 9.417e-02, eta: 4 days, 0:08:17, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4430, loss_cls: 4.4061, loss: 4.4061 +2024-07-17 01:27:53,879 - pyskl - INFO - Epoch [24][1200/3746] lr: 9.415e-02, eta: 4 days, 0:06:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4495, loss_cls: 4.3628, loss: 4.3628 +2024-07-17 01:29:03,741 - pyskl - INFO - Epoch [24][1300/3746] lr: 9.414e-02, eta: 4 days, 0:05:19, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1997, top5_acc: 0.4269, loss_cls: 4.4645, loss: 4.4645 +2024-07-17 01:30:13,868 - pyskl - INFO - Epoch [24][1400/3746] lr: 9.413e-02, eta: 4 days, 0:03:51, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4412, loss_cls: 4.3882, loss: 4.3882 +2024-07-17 01:31:24,432 - pyskl - INFO - Epoch [24][1500/3746] lr: 9.411e-02, eta: 4 days, 0:02:25, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4377, loss_cls: 4.4376, loss: 4.4376 +2024-07-17 01:32:34,434 - pyskl - INFO - Epoch [24][1600/3746] lr: 9.410e-02, eta: 4 days, 0:00:57, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1989, top5_acc: 0.4311, loss_cls: 4.4560, loss: 4.4560 +2024-07-17 01:33:44,738 - pyskl - INFO - Epoch [24][1700/3746] lr: 9.409e-02, eta: 3 days, 23:59:30, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4344, loss_cls: 4.4430, loss: 4.4430 +2024-07-17 01:34:55,249 - pyskl - INFO - Epoch [24][1800/3746] lr: 9.407e-02, eta: 3 days, 23:58:04, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4397, loss_cls: 4.4457, loss: 4.4457 +2024-07-17 01:36:05,335 - pyskl - INFO - Epoch [24][1900/3746] lr: 9.406e-02, eta: 3 days, 23:56:36, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1973, top5_acc: 0.4303, loss_cls: 4.4736, loss: 4.4736 +2024-07-17 01:37:15,392 - pyskl - INFO - Epoch [24][2000/3746] lr: 9.405e-02, eta: 3 days, 23:55:08, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1978, top5_acc: 0.4206, loss_cls: 4.4712, loss: 4.4712 +2024-07-17 01:38:25,209 - pyskl - INFO - Epoch [24][2100/3746] lr: 9.404e-02, eta: 3 days, 23:53:39, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2056, top5_acc: 0.4350, loss_cls: 4.4374, loss: 4.4374 +2024-07-17 01:39:35,339 - pyskl - INFO - Epoch [24][2200/3746] lr: 9.402e-02, eta: 3 days, 23:52:11, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2009, top5_acc: 0.4383, loss_cls: 4.4137, loss: 4.4137 +2024-07-17 01:40:45,208 - pyskl - INFO - Epoch [24][2300/3746] lr: 9.401e-02, eta: 3 days, 23:50:42, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2002, top5_acc: 0.4302, loss_cls: 4.4564, loss: 4.4564 +2024-07-17 01:41:55,544 - pyskl - INFO - Epoch [24][2400/3746] lr: 9.400e-02, eta: 3 days, 23:49:15, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4373, loss_cls: 4.4093, loss: 4.4093 +2024-07-17 01:43:05,724 - pyskl - INFO - Epoch [24][2500/3746] lr: 9.398e-02, eta: 3 days, 23:47:48, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4370, loss_cls: 4.4111, loss: 4.4111 +2024-07-17 01:44:15,760 - pyskl - INFO - Epoch [24][2600/3746] lr: 9.397e-02, eta: 3 days, 23:46:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4294, loss_cls: 4.4527, loss: 4.4527 +2024-07-17 01:45:25,643 - pyskl - INFO - Epoch [24][2700/3746] lr: 9.396e-02, eta: 3 days, 23:44:51, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4331, loss_cls: 4.4369, loss: 4.4369 +2024-07-17 01:46:35,503 - pyskl - INFO - Epoch [24][2800/3746] lr: 9.394e-02, eta: 3 days, 23:43:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2000, top5_acc: 0.4331, loss_cls: 4.4310, loss: 4.4310 +2024-07-17 01:47:45,348 - pyskl - INFO - Epoch [24][2900/3746] lr: 9.393e-02, eta: 3 days, 23:41:54, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.1927, top5_acc: 0.4295, loss_cls: 4.4285, loss: 4.4285 +2024-07-17 01:48:55,619 - pyskl - INFO - Epoch [24][3000/3746] lr: 9.392e-02, eta: 3 days, 23:40:27, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4419, loss_cls: 4.4031, loss: 4.4031 +2024-07-17 01:50:05,719 - pyskl - INFO - Epoch [24][3100/3746] lr: 9.390e-02, eta: 3 days, 23:39:00, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4494, loss_cls: 4.3884, loss: 4.3884 +2024-07-17 01:51:16,365 - pyskl - INFO - Epoch [24][3200/3746] lr: 9.389e-02, eta: 3 days, 23:37:35, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4333, loss_cls: 4.4116, loss: 4.4116 +2024-07-17 01:52:27,146 - pyskl - INFO - Epoch [24][3300/3746] lr: 9.388e-02, eta: 3 days, 23:36:11, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2069, top5_acc: 0.4247, loss_cls: 4.4448, loss: 4.4448 +2024-07-17 01:53:37,944 - pyskl - INFO - Epoch [24][3400/3746] lr: 9.386e-02, eta: 3 days, 23:34:48, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2028, top5_acc: 0.4284, loss_cls: 4.4496, loss: 4.4496 +2024-07-17 01:54:48,628 - pyskl - INFO - Epoch [24][3500/3746] lr: 9.385e-02, eta: 3 days, 23:33:23, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1958, top5_acc: 0.4313, loss_cls: 4.4514, loss: 4.4514 +2024-07-17 01:55:59,356 - pyskl - INFO - Epoch [24][3600/3746] lr: 9.384e-02, eta: 3 days, 23:31:59, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4341, loss_cls: 4.4310, loss: 4.4310 +2024-07-17 01:57:09,511 - pyskl - INFO - Epoch [24][3700/3746] lr: 9.382e-02, eta: 3 days, 23:30:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4378, loss_cls: 4.4421, loss: 4.4421 +2024-07-17 01:57:43,948 - pyskl - INFO - Saving checkpoint at 24 epochs +2024-07-17 01:59:34,798 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 01:59:35,467 - pyskl - INFO - +top1_acc 0.1347 +top5_acc 0.3226 +2024-07-17 01:59:35,467 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 01:59:35,509 - pyskl - INFO - +mean_acc 0.1344 +2024-07-17 01:59:35,521 - pyskl - INFO - Epoch(val) [24][309] top1_acc: 0.1347, top5_acc: 0.3226, mean_class_accuracy: 0.1344 +2024-07-17 02:02:55,608 - pyskl - INFO - Epoch [25][100/3746] lr: 9.380e-02, eta: 3 days, 23:36:57, time: 2.001, data_time: 1.296, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4395, loss_cls: 4.3976, loss: 4.3976 +2024-07-17 02:04:05,758 - pyskl - INFO - Epoch [25][200/3746] lr: 9.379e-02, eta: 3 days, 23:35:30, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4338, loss_cls: 4.4282, loss: 4.4282 +2024-07-17 02:05:16,183 - pyskl - INFO - Epoch [25][300/3746] lr: 9.378e-02, eta: 3 days, 23:34:04, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4369, loss_cls: 4.4134, loss: 4.4134 +2024-07-17 02:06:26,074 - pyskl - INFO - Epoch [25][400/3746] lr: 9.376e-02, eta: 3 days, 23:32:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4439, loss_cls: 4.4079, loss: 4.4079 +2024-07-17 02:07:36,106 - pyskl - INFO - Epoch [25][500/3746] lr: 9.375e-02, eta: 3 days, 23:31:07, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4434, loss_cls: 4.3646, loss: 4.3646 +2024-07-17 02:08:46,044 - pyskl - INFO - Epoch [25][600/3746] lr: 9.373e-02, eta: 3 days, 23:29:38, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2045, top5_acc: 0.4320, loss_cls: 4.4575, loss: 4.4575 +2024-07-17 02:09:56,236 - pyskl - INFO - Epoch [25][700/3746] lr: 9.372e-02, eta: 3 days, 23:28:11, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4358, loss_cls: 4.4050, loss: 4.4050 +2024-07-17 02:11:06,789 - pyskl - INFO - Epoch [25][800/3746] lr: 9.371e-02, eta: 3 days, 23:26:46, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4322, loss_cls: 4.4609, loss: 4.4609 +2024-07-17 02:12:16,961 - pyskl - INFO - Epoch [25][900/3746] lr: 9.369e-02, eta: 3 days, 23:25:19, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4278, loss_cls: 4.4430, loss: 4.4430 +2024-07-17 02:13:27,231 - pyskl - INFO - Epoch [25][1000/3746] lr: 9.368e-02, eta: 3 days, 23:23:52, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4236, loss_cls: 4.4367, loss: 4.4367 +2024-07-17 02:14:37,475 - pyskl - INFO - Epoch [25][1100/3746] lr: 9.367e-02, eta: 3 days, 23:22:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4398, loss_cls: 4.4286, loss: 4.4286 +2024-07-17 02:15:47,618 - pyskl - INFO - Epoch [25][1200/3746] lr: 9.365e-02, eta: 3 days, 23:20:58, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4412, loss_cls: 4.4064, loss: 4.4064 +2024-07-17 02:16:57,739 - pyskl - INFO - Epoch [25][1300/3746] lr: 9.364e-02, eta: 3 days, 23:19:31, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4367, loss_cls: 4.4088, loss: 4.4088 +2024-07-17 02:18:07,898 - pyskl - INFO - Epoch [25][1400/3746] lr: 9.363e-02, eta: 3 days, 23:18:04, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4347, loss_cls: 4.4285, loss: 4.4285 +2024-07-17 02:19:19,163 - pyskl - INFO - Epoch [25][1500/3746] lr: 9.361e-02, eta: 3 days, 23:16:42, time: 0.713, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4352, loss_cls: 4.4296, loss: 4.4296 +2024-07-17 02:20:29,640 - pyskl - INFO - Epoch [25][1600/3746] lr: 9.360e-02, eta: 3 days, 23:15:17, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2014, top5_acc: 0.4366, loss_cls: 4.4366, loss: 4.4366 +2024-07-17 02:21:40,112 - pyskl - INFO - Epoch [25][1700/3746] lr: 9.358e-02, eta: 3 days, 23:13:52, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4336, loss_cls: 4.4356, loss: 4.4356 +2024-07-17 02:22:50,562 - pyskl - INFO - Epoch [25][1800/3746] lr: 9.357e-02, eta: 3 days, 23:12:26, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4281, loss_cls: 4.4120, loss: 4.4120 +2024-07-17 02:24:00,879 - pyskl - INFO - Epoch [25][1900/3746] lr: 9.356e-02, eta: 3 days, 23:11:00, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4275, loss_cls: 4.4396, loss: 4.4396 +2024-07-17 02:25:11,018 - pyskl - INFO - Epoch [25][2000/3746] lr: 9.354e-02, eta: 3 days, 23:09:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4216, loss_cls: 4.4646, loss: 4.4646 +2024-07-17 02:26:21,094 - pyskl - INFO - Epoch [25][2100/3746] lr: 9.353e-02, eta: 3 days, 23:08:06, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4414, loss_cls: 4.3876, loss: 4.3876 +2024-07-17 02:27:31,576 - pyskl - INFO - Epoch [25][2200/3746] lr: 9.352e-02, eta: 3 days, 23:06:41, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.1942, top5_acc: 0.4314, loss_cls: 4.4532, loss: 4.4532 +2024-07-17 02:28:41,604 - pyskl - INFO - Epoch [25][2300/3746] lr: 9.350e-02, eta: 3 days, 23:05:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4352, loss_cls: 4.4118, loss: 4.4118 +2024-07-17 02:29:51,609 - pyskl - INFO - Epoch [25][2400/3746] lr: 9.349e-02, eta: 3 days, 23:03:46, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4433, loss_cls: 4.3742, loss: 4.3742 +2024-07-17 02:31:01,640 - pyskl - INFO - Epoch [25][2500/3746] lr: 9.347e-02, eta: 3 days, 23:02:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2009, top5_acc: 0.4375, loss_cls: 4.4257, loss: 4.4257 +2024-07-17 02:32:11,746 - pyskl - INFO - Epoch [25][2600/3746] lr: 9.346e-02, eta: 3 days, 23:00:51, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4386, loss_cls: 4.4170, loss: 4.4170 +2024-07-17 02:33:21,946 - pyskl - INFO - Epoch [25][2700/3746] lr: 9.345e-02, eta: 3 days, 22:59:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4397, loss_cls: 4.4126, loss: 4.4126 +2024-07-17 02:34:32,335 - pyskl - INFO - Epoch [25][2800/3746] lr: 9.343e-02, eta: 3 days, 22:58:00, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2069, top5_acc: 0.4344, loss_cls: 4.4366, loss: 4.4366 +2024-07-17 02:35:42,479 - pyskl - INFO - Epoch [25][2900/3746] lr: 9.342e-02, eta: 3 days, 22:56:33, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4366, loss_cls: 4.4062, loss: 4.4062 +2024-07-17 02:36:52,838 - pyskl - INFO - Epoch [25][3000/3746] lr: 9.341e-02, eta: 3 days, 22:55:07, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2003, top5_acc: 0.4370, loss_cls: 4.4379, loss: 4.4379 +2024-07-17 02:38:02,935 - pyskl - INFO - Epoch [25][3100/3746] lr: 9.339e-02, eta: 3 days, 22:53:41, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2069, top5_acc: 0.4341, loss_cls: 4.4246, loss: 4.4246 +2024-07-17 02:39:13,584 - pyskl - INFO - Epoch [25][3200/3746] lr: 9.338e-02, eta: 3 days, 22:52:17, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4361, loss_cls: 4.4095, loss: 4.4095 +2024-07-17 02:40:24,237 - pyskl - INFO - Epoch [25][3300/3746] lr: 9.336e-02, eta: 3 days, 22:50:53, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2008, top5_acc: 0.4331, loss_cls: 4.4439, loss: 4.4439 +2024-07-17 02:41:34,942 - pyskl - INFO - Epoch [25][3400/3746] lr: 9.335e-02, eta: 3 days, 22:49:29, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4411, loss_cls: 4.4041, loss: 4.4041 +2024-07-17 02:42:45,591 - pyskl - INFO - Epoch [25][3500/3746] lr: 9.334e-02, eta: 3 days, 22:48:05, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4403, loss_cls: 4.4142, loss: 4.4142 +2024-07-17 02:43:56,577 - pyskl - INFO - Epoch [25][3600/3746] lr: 9.332e-02, eta: 3 days, 22:46:43, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4400, loss_cls: 4.4194, loss: 4.4194 +2024-07-17 02:45:07,232 - pyskl - INFO - Epoch [25][3700/3746] lr: 9.331e-02, eta: 3 days, 22:45:19, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4363, loss_cls: 4.4331, loss: 4.4331 +2024-07-17 02:45:42,023 - pyskl - INFO - Saving checkpoint at 25 epochs +2024-07-17 02:47:34,683 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 02:47:35,352 - pyskl - INFO - +top1_acc 0.1279 +top5_acc 0.3047 +2024-07-17 02:47:35,352 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 02:47:35,393 - pyskl - INFO - +mean_acc 0.1277 +2024-07-17 02:47:35,406 - pyskl - INFO - Epoch(val) [25][309] top1_acc: 0.1279, top5_acc: 0.3047, mean_class_accuracy: 0.1277 +2024-07-17 02:50:59,268 - pyskl - INFO - Epoch [26][100/3746] lr: 9.329e-02, eta: 3 days, 22:51:40, time: 2.039, data_time: 1.332, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4459, loss_cls: 4.3736, loss: 4.3736 +2024-07-17 02:52:09,493 - pyskl - INFO - Epoch [26][200/3746] lr: 9.327e-02, eta: 3 days, 22:50:13, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4400, loss_cls: 4.3930, loss: 4.3930 +2024-07-17 02:53:19,638 - pyskl - INFO - Epoch [26][300/3746] lr: 9.326e-02, eta: 3 days, 22:48:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4375, loss_cls: 4.4272, loss: 4.4272 +2024-07-17 02:54:29,886 - pyskl - INFO - Epoch [26][400/3746] lr: 9.325e-02, eta: 3 days, 22:47:20, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4395, loss_cls: 4.3730, loss: 4.3730 +2024-07-17 02:55:39,968 - pyskl - INFO - Epoch [26][500/3746] lr: 9.323e-02, eta: 3 days, 22:45:53, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2044, top5_acc: 0.4331, loss_cls: 4.4130, loss: 4.4130 +2024-07-17 02:56:50,237 - pyskl - INFO - Epoch [26][600/3746] lr: 9.322e-02, eta: 3 days, 22:44:27, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4305, loss_cls: 4.4338, loss: 4.4338 +2024-07-17 02:58:00,589 - pyskl - INFO - Epoch [26][700/3746] lr: 9.320e-02, eta: 3 days, 22:43:01, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4464, loss_cls: 4.3945, loss: 4.3945 +2024-07-17 02:59:10,918 - pyskl - INFO - Epoch [26][800/3746] lr: 9.319e-02, eta: 3 days, 22:41:35, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4339, loss_cls: 4.4016, loss: 4.4016 +2024-07-17 03:00:21,085 - pyskl - INFO - Epoch [26][900/3746] lr: 9.318e-02, eta: 3 days, 22:40:09, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4353, loss_cls: 4.4038, loss: 4.4038 +2024-07-17 03:01:31,234 - pyskl - INFO - Epoch [26][1000/3746] lr: 9.316e-02, eta: 3 days, 22:38:42, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4277, loss_cls: 4.4324, loss: 4.4324 +2024-07-17 03:02:41,453 - pyskl - INFO - Epoch [26][1100/3746] lr: 9.315e-02, eta: 3 days, 22:37:16, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4341, loss_cls: 4.4362, loss: 4.4362 +2024-07-17 03:03:51,855 - pyskl - INFO - Epoch [26][1200/3746] lr: 9.313e-02, eta: 3 days, 22:35:50, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4423, loss_cls: 4.3777, loss: 4.3777 +2024-07-17 03:05:02,060 - pyskl - INFO - Epoch [26][1300/3746] lr: 9.312e-02, eta: 3 days, 22:34:24, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4433, loss_cls: 4.3962, loss: 4.3962 +2024-07-17 03:06:12,310 - pyskl - INFO - Epoch [26][1400/3746] lr: 9.310e-02, eta: 3 days, 22:32:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4419, loss_cls: 4.3904, loss: 4.3904 +2024-07-17 03:07:22,930 - pyskl - INFO - Epoch [26][1500/3746] lr: 9.309e-02, eta: 3 days, 22:31:34, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.1995, top5_acc: 0.4381, loss_cls: 4.4327, loss: 4.4327 +2024-07-17 03:08:33,671 - pyskl - INFO - Epoch [26][1600/3746] lr: 9.308e-02, eta: 3 days, 22:30:11, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4422, loss_cls: 4.4039, loss: 4.4039 +2024-07-17 03:09:44,356 - pyskl - INFO - Epoch [26][1700/3746] lr: 9.306e-02, eta: 3 days, 22:28:47, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4272, loss_cls: 4.4352, loss: 4.4352 +2024-07-17 03:10:54,796 - pyskl - INFO - Epoch [26][1800/3746] lr: 9.305e-02, eta: 3 days, 22:27:22, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2014, top5_acc: 0.4406, loss_cls: 4.4269, loss: 4.4269 +2024-07-17 03:12:04,987 - pyskl - INFO - Epoch [26][1900/3746] lr: 9.303e-02, eta: 3 days, 22:25:56, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4409, loss_cls: 4.3871, loss: 4.3871 +2024-07-17 03:13:14,983 - pyskl - INFO - Epoch [26][2000/3746] lr: 9.302e-02, eta: 3 days, 22:24:29, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4512, loss_cls: 4.3366, loss: 4.3366 +2024-07-17 03:14:25,048 - pyskl - INFO - Epoch [26][2100/3746] lr: 9.300e-02, eta: 3 days, 22:23:02, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4305, loss_cls: 4.4614, loss: 4.4614 +2024-07-17 03:15:35,233 - pyskl - INFO - Epoch [26][2200/3746] lr: 9.299e-02, eta: 3 days, 22:21:36, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4431, loss_cls: 4.3898, loss: 4.3898 +2024-07-17 03:16:45,559 - pyskl - INFO - Epoch [26][2300/3746] lr: 9.298e-02, eta: 3 days, 22:20:11, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4261, loss_cls: 4.4517, loss: 4.4517 +2024-07-17 03:17:55,655 - pyskl - INFO - Epoch [26][2400/3746] lr: 9.296e-02, eta: 3 days, 22:18:44, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2056, top5_acc: 0.4366, loss_cls: 4.4342, loss: 4.4342 +2024-07-17 03:19:06,455 - pyskl - INFO - Epoch [26][2500/3746] lr: 9.295e-02, eta: 3 days, 22:17:21, time: 0.708, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4338, loss_cls: 4.4273, loss: 4.4273 +2024-07-17 03:20:16,712 - pyskl - INFO - Epoch [26][2600/3746] lr: 9.293e-02, eta: 3 days, 22:15:56, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4331, loss_cls: 4.4379, loss: 4.4379 +2024-07-17 03:21:26,826 - pyskl - INFO - Epoch [26][2700/3746] lr: 9.292e-02, eta: 3 days, 22:14:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2033, top5_acc: 0.4297, loss_cls: 4.4487, loss: 4.4487 +2024-07-17 03:22:37,402 - pyskl - INFO - Epoch [26][2800/3746] lr: 9.290e-02, eta: 3 days, 22:13:05, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4428, loss_cls: 4.4023, loss: 4.4023 +2024-07-17 03:23:47,578 - pyskl - INFO - Epoch [26][2900/3746] lr: 9.289e-02, eta: 3 days, 22:11:40, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2069, top5_acc: 0.4386, loss_cls: 4.4223, loss: 4.4223 +2024-07-17 03:24:57,621 - pyskl - INFO - Epoch [26][3000/3746] lr: 9.288e-02, eta: 3 days, 22:10:13, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2047, top5_acc: 0.4294, loss_cls: 4.4315, loss: 4.4315 +2024-07-17 03:26:07,799 - pyskl - INFO - Epoch [26][3100/3746] lr: 9.286e-02, eta: 3 days, 22:08:47, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4383, loss_cls: 4.3921, loss: 4.3921 +2024-07-17 03:27:18,469 - pyskl - INFO - Epoch [26][3200/3746] lr: 9.285e-02, eta: 3 days, 22:07:24, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2002, top5_acc: 0.4242, loss_cls: 4.4412, loss: 4.4412 +2024-07-17 03:28:29,080 - pyskl - INFO - Epoch [26][3300/3746] lr: 9.283e-02, eta: 3 days, 22:06:00, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2011, top5_acc: 0.4358, loss_cls: 4.4225, loss: 4.4225 +2024-07-17 03:29:39,999 - pyskl - INFO - Epoch [26][3400/3746] lr: 9.282e-02, eta: 3 days, 22:04:38, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4316, loss_cls: 4.4260, loss: 4.4260 +2024-07-17 03:30:50,725 - pyskl - INFO - Epoch [26][3500/3746] lr: 9.280e-02, eta: 3 days, 22:03:15, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4283, loss_cls: 4.4738, loss: 4.4738 +2024-07-17 03:32:01,923 - pyskl - INFO - Epoch [26][3600/3746] lr: 9.279e-02, eta: 3 days, 22:01:54, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4333, loss_cls: 4.4331, loss: 4.4331 +2024-07-17 03:33:11,908 - pyskl - INFO - Epoch [26][3700/3746] lr: 9.278e-02, eta: 3 days, 22:00:28, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4416, loss_cls: 4.3956, loss: 4.3956 +2024-07-17 03:33:46,437 - pyskl - INFO - Saving checkpoint at 26 epochs +2024-07-17 03:35:38,574 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 03:35:39,266 - pyskl - INFO - +top1_acc 0.1394 +top5_acc 0.3205 +2024-07-17 03:35:39,266 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 03:35:39,309 - pyskl - INFO - +mean_acc 0.1392 +2024-07-17 03:35:39,322 - pyskl - INFO - Epoch(val) [26][309] top1_acc: 0.1394, top5_acc: 0.3205, mean_class_accuracy: 0.1392 +2024-07-17 03:39:02,713 - pyskl - INFO - Epoch [27][100/3746] lr: 9.275e-02, eta: 3 days, 22:06:23, time: 2.034, data_time: 1.327, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4475, loss_cls: 4.3547, loss: 4.3547 +2024-07-17 03:40:13,102 - pyskl - INFO - Epoch [27][200/3746] lr: 9.274e-02, eta: 3 days, 22:04:58, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4587, loss_cls: 4.3502, loss: 4.3502 +2024-07-17 03:41:23,715 - pyskl - INFO - Epoch [27][300/3746] lr: 9.272e-02, eta: 3 days, 22:03:34, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4486, loss_cls: 4.3859, loss: 4.3859 +2024-07-17 03:42:33,929 - pyskl - INFO - Epoch [27][400/3746] lr: 9.271e-02, eta: 3 days, 22:02:08, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2023, top5_acc: 0.4483, loss_cls: 4.4163, loss: 4.4163 +2024-07-17 03:43:43,905 - pyskl - INFO - Epoch [27][500/3746] lr: 9.270e-02, eta: 3 days, 22:00:41, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4344, loss_cls: 4.4132, loss: 4.4132 +2024-07-17 03:44:54,215 - pyskl - INFO - Epoch [27][600/3746] lr: 9.268e-02, eta: 3 days, 21:59:16, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4412, loss_cls: 4.3827, loss: 4.3827 +2024-07-17 03:46:04,448 - pyskl - INFO - Epoch [27][700/3746] lr: 9.267e-02, eta: 3 days, 21:57:50, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4428, loss_cls: 4.4145, loss: 4.4145 +2024-07-17 03:47:14,498 - pyskl - INFO - Epoch [27][800/3746] lr: 9.265e-02, eta: 3 days, 21:56:24, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4458, loss_cls: 4.3542, loss: 4.3542 +2024-07-17 03:48:24,722 - pyskl - INFO - Epoch [27][900/3746] lr: 9.264e-02, eta: 3 days, 21:54:58, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1978, top5_acc: 0.4448, loss_cls: 4.4419, loss: 4.4419 +2024-07-17 03:49:34,910 - pyskl - INFO - Epoch [27][1000/3746] lr: 9.262e-02, eta: 3 days, 21:53:32, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4364, loss_cls: 4.4275, loss: 4.4275 +2024-07-17 03:50:45,196 - pyskl - INFO - Epoch [27][1100/3746] lr: 9.261e-02, eta: 3 days, 21:52:07, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4358, loss_cls: 4.4146, loss: 4.4146 +2024-07-17 03:51:55,367 - pyskl - INFO - Epoch [27][1200/3746] lr: 9.259e-02, eta: 3 days, 21:50:41, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2027, top5_acc: 0.4248, loss_cls: 4.4254, loss: 4.4254 +2024-07-17 03:53:05,414 - pyskl - INFO - Epoch [27][1300/3746] lr: 9.258e-02, eta: 3 days, 21:49:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4389, loss_cls: 4.4243, loss: 4.4243 +2024-07-17 03:54:15,662 - pyskl - INFO - Epoch [27][1400/3746] lr: 9.256e-02, eta: 3 days, 21:47:49, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2016, top5_acc: 0.4219, loss_cls: 4.4321, loss: 4.4321 +2024-07-17 03:55:26,557 - pyskl - INFO - Epoch [27][1500/3746] lr: 9.255e-02, eta: 3 days, 21:46:27, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4378, loss_cls: 4.4117, loss: 4.4117 +2024-07-17 03:56:36,623 - pyskl - INFO - Epoch [27][1600/3746] lr: 9.253e-02, eta: 3 days, 21:45:00, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4161, loss_cls: 4.4435, loss: 4.4435 +2024-07-17 03:57:46,971 - pyskl - INFO - Epoch [27][1700/3746] lr: 9.252e-02, eta: 3 days, 21:43:36, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2028, top5_acc: 0.4398, loss_cls: 4.3962, loss: 4.3962 +2024-07-17 03:58:57,356 - pyskl - INFO - Epoch [27][1800/3746] lr: 9.251e-02, eta: 3 days, 21:42:11, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4320, loss_cls: 4.4125, loss: 4.4125 +2024-07-17 04:00:07,554 - pyskl - INFO - Epoch [27][1900/3746] lr: 9.249e-02, eta: 3 days, 21:40:45, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4366, loss_cls: 4.4366, loss: 4.4366 +2024-07-17 04:01:17,890 - pyskl - INFO - Epoch [27][2000/3746] lr: 9.248e-02, eta: 3 days, 21:39:21, time: 0.703, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4470, loss_cls: 4.3623, loss: 4.3623 +2024-07-17 04:02:27,889 - pyskl - INFO - Epoch [27][2100/3746] lr: 9.246e-02, eta: 3 days, 21:37:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2058, top5_acc: 0.4442, loss_cls: 4.3916, loss: 4.3916 +2024-07-17 04:03:37,817 - pyskl - INFO - Epoch [27][2200/3746] lr: 9.245e-02, eta: 3 days, 21:36:27, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4402, loss_cls: 4.4099, loss: 4.4099 +2024-07-17 04:04:47,767 - pyskl - INFO - Epoch [27][2300/3746] lr: 9.243e-02, eta: 3 days, 21:35:01, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4416, loss_cls: 4.4000, loss: 4.4000 +2024-07-17 04:05:57,732 - pyskl - INFO - Epoch [27][2400/3746] lr: 9.242e-02, eta: 3 days, 21:33:34, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4436, loss_cls: 4.3930, loss: 4.3930 +2024-07-17 04:07:07,658 - pyskl - INFO - Epoch [27][2500/3746] lr: 9.240e-02, eta: 3 days, 21:32:08, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4303, loss_cls: 4.4118, loss: 4.4118 +2024-07-17 04:08:17,688 - pyskl - INFO - Epoch [27][2600/3746] lr: 9.239e-02, eta: 3 days, 21:30:42, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4367, loss_cls: 4.4314, loss: 4.4314 +2024-07-17 04:09:27,447 - pyskl - INFO - Epoch [27][2700/3746] lr: 9.237e-02, eta: 3 days, 21:29:15, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4394, loss_cls: 4.4122, loss: 4.4122 +2024-07-17 04:10:37,439 - pyskl - INFO - Epoch [27][2800/3746] lr: 9.236e-02, eta: 3 days, 21:27:48, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1992, top5_acc: 0.4395, loss_cls: 4.4303, loss: 4.4303 +2024-07-17 04:11:47,370 - pyskl - INFO - Epoch [27][2900/3746] lr: 9.234e-02, eta: 3 days, 21:26:22, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4391, loss_cls: 4.4321, loss: 4.4321 +2024-07-17 04:12:57,136 - pyskl - INFO - Epoch [27][3000/3746] lr: 9.233e-02, eta: 3 days, 21:24:55, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4444, loss_cls: 4.3847, loss: 4.3847 +2024-07-17 04:14:07,195 - pyskl - INFO - Epoch [27][3100/3746] lr: 9.231e-02, eta: 3 days, 21:23:29, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4392, loss_cls: 4.3994, loss: 4.3994 +2024-07-17 04:15:17,756 - pyskl - INFO - Epoch [27][3200/3746] lr: 9.230e-02, eta: 3 days, 21:22:06, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4405, loss_cls: 4.4104, loss: 4.4104 +2024-07-17 04:16:28,926 - pyskl - INFO - Epoch [27][3300/3746] lr: 9.228e-02, eta: 3 days, 21:20:45, time: 0.712, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4450, loss_cls: 4.3848, loss: 4.3848 +2024-07-17 04:17:39,541 - pyskl - INFO - Epoch [27][3400/3746] lr: 9.227e-02, eta: 3 days, 21:19:22, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2031, top5_acc: 0.4266, loss_cls: 4.4389, loss: 4.4389 +2024-07-17 04:18:50,055 - pyskl - INFO - Epoch [27][3500/3746] lr: 9.225e-02, eta: 3 days, 21:17:58, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4592, loss_cls: 4.3398, loss: 4.3398 +2024-07-17 04:20:00,718 - pyskl - INFO - Epoch [27][3600/3746] lr: 9.224e-02, eta: 3 days, 21:16:35, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.1961, top5_acc: 0.4252, loss_cls: 4.4788, loss: 4.4788 +2024-07-17 04:21:10,948 - pyskl - INFO - Epoch [27][3700/3746] lr: 9.222e-02, eta: 3 days, 21:15:10, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4430, loss_cls: 4.4012, loss: 4.4012 +2024-07-17 04:21:45,324 - pyskl - INFO - Saving checkpoint at 27 epochs +2024-07-17 04:23:36,059 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 04:23:36,746 - pyskl - INFO - +top1_acc 0.1489 +top5_acc 0.3433 +2024-07-17 04:23:36,747 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 04:23:36,789 - pyskl - INFO - +mean_acc 0.1489 +2024-07-17 04:23:36,801 - pyskl - INFO - Epoch(val) [27][309] top1_acc: 0.1489, top5_acc: 0.3433, mean_class_accuracy: 0.1489 +2024-07-17 04:26:58,663 - pyskl - INFO - Epoch [28][100/3746] lr: 9.220e-02, eta: 3 days, 21:20:39, time: 2.019, data_time: 1.313, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4558, loss_cls: 4.2998, loss: 4.2998 +2024-07-17 04:28:09,037 - pyskl - INFO - Epoch [28][200/3746] lr: 9.219e-02, eta: 3 days, 21:19:14, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4434, loss_cls: 4.3819, loss: 4.3819 +2024-07-17 04:29:19,229 - pyskl - INFO - Epoch [28][300/3746] lr: 9.217e-02, eta: 3 days, 21:17:49, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.1991, top5_acc: 0.4422, loss_cls: 4.3886, loss: 4.3886 +2024-07-17 04:30:29,276 - pyskl - INFO - Epoch [28][400/3746] lr: 9.216e-02, eta: 3 days, 21:16:22, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4405, loss_cls: 4.4166, loss: 4.4166 +2024-07-17 04:31:39,263 - pyskl - INFO - Epoch [28][500/3746] lr: 9.214e-02, eta: 3 days, 21:14:56, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4403, loss_cls: 4.3866, loss: 4.3866 +2024-07-17 04:32:49,270 - pyskl - INFO - Epoch [28][600/3746] lr: 9.213e-02, eta: 3 days, 21:13:30, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4392, loss_cls: 4.4127, loss: 4.4127 +2024-07-17 04:33:59,251 - pyskl - INFO - Epoch [28][700/3746] lr: 9.211e-02, eta: 3 days, 21:12:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4320, loss_cls: 4.4067, loss: 4.4067 +2024-07-17 04:35:09,215 - pyskl - INFO - Epoch [28][800/3746] lr: 9.210e-02, eta: 3 days, 21:10:37, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4428, loss_cls: 4.3786, loss: 4.3786 +2024-07-17 04:36:19,346 - pyskl - INFO - Epoch [28][900/3746] lr: 9.208e-02, eta: 3 days, 21:09:12, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4469, loss_cls: 4.3916, loss: 4.3916 +2024-07-17 04:37:29,435 - pyskl - INFO - Epoch [28][1000/3746] lr: 9.207e-02, eta: 3 days, 21:07:46, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2078, top5_acc: 0.4364, loss_cls: 4.4417, loss: 4.4417 +2024-07-17 04:38:39,394 - pyskl - INFO - Epoch [28][1100/3746] lr: 9.205e-02, eta: 3 days, 21:06:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4473, loss_cls: 4.3936, loss: 4.3936 +2024-07-17 04:39:49,469 - pyskl - INFO - Epoch [28][1200/3746] lr: 9.204e-02, eta: 3 days, 21:04:54, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2050, top5_acc: 0.4319, loss_cls: 4.4245, loss: 4.4245 +2024-07-17 04:40:59,391 - pyskl - INFO - Epoch [28][1300/3746] lr: 9.202e-02, eta: 3 days, 21:03:28, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1936, top5_acc: 0.4339, loss_cls: 4.4195, loss: 4.4195 +2024-07-17 04:42:09,248 - pyskl - INFO - Epoch [28][1400/3746] lr: 9.201e-02, eta: 3 days, 21:02:01, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4416, loss_cls: 4.3599, loss: 4.3599 +2024-07-17 04:43:19,746 - pyskl - INFO - Epoch [28][1500/3746] lr: 9.199e-02, eta: 3 days, 21:00:37, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4439, loss_cls: 4.3933, loss: 4.3933 +2024-07-17 04:44:30,332 - pyskl - INFO - Epoch [28][1600/3746] lr: 9.198e-02, eta: 3 days, 20:59:14, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4498, loss_cls: 4.3860, loss: 4.3860 +2024-07-17 04:45:40,840 - pyskl - INFO - Epoch [28][1700/3746] lr: 9.196e-02, eta: 3 days, 20:57:51, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4325, loss_cls: 4.4451, loss: 4.4451 +2024-07-17 04:46:51,254 - pyskl - INFO - Epoch [28][1800/3746] lr: 9.194e-02, eta: 3 days, 20:56:27, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4327, loss_cls: 4.3981, loss: 4.3981 +2024-07-17 04:48:01,278 - pyskl - INFO - Epoch [28][1900/3746] lr: 9.193e-02, eta: 3 days, 20:55:01, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4461, loss_cls: 4.3739, loss: 4.3739 +2024-07-17 04:49:11,359 - pyskl - INFO - Epoch [28][2000/3746] lr: 9.191e-02, eta: 3 days, 20:53:35, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4387, loss_cls: 4.4018, loss: 4.4018 +2024-07-17 04:50:21,340 - pyskl - INFO - Epoch [28][2100/3746] lr: 9.190e-02, eta: 3 days, 20:52:10, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4372, loss_cls: 4.4190, loss: 4.4190 +2024-07-17 04:51:31,282 - pyskl - INFO - Epoch [28][2200/3746] lr: 9.188e-02, eta: 3 days, 20:50:44, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4392, loss_cls: 4.4082, loss: 4.4082 +2024-07-17 04:52:41,239 - pyskl - INFO - Epoch [28][2300/3746] lr: 9.187e-02, eta: 3 days, 20:49:18, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4389, loss_cls: 4.4046, loss: 4.4046 +2024-07-17 04:53:51,223 - pyskl - INFO - Epoch [28][2400/3746] lr: 9.185e-02, eta: 3 days, 20:47:52, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4414, loss_cls: 4.3811, loss: 4.3811 +2024-07-17 04:55:01,263 - pyskl - INFO - Epoch [28][2500/3746] lr: 9.184e-02, eta: 3 days, 20:46:26, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4391, loss_cls: 4.4141, loss: 4.4141 +2024-07-17 04:56:11,409 - pyskl - INFO - Epoch [28][2600/3746] lr: 9.182e-02, eta: 3 days, 20:45:02, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4372, loss_cls: 4.3998, loss: 4.3998 +2024-07-17 04:57:21,294 - pyskl - INFO - Epoch [28][2700/3746] lr: 9.181e-02, eta: 3 days, 20:43:35, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2034, top5_acc: 0.4367, loss_cls: 4.4056, loss: 4.4056 +2024-07-17 04:58:31,437 - pyskl - INFO - Epoch [28][2800/3746] lr: 9.179e-02, eta: 3 days, 20:42:10, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4394, loss_cls: 4.4052, loss: 4.4052 +2024-07-17 04:59:41,541 - pyskl - INFO - Epoch [28][2900/3746] lr: 9.178e-02, eta: 3 days, 20:40:45, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4370, loss_cls: 4.4099, loss: 4.4099 +2024-07-17 05:00:51,555 - pyskl - INFO - Epoch [28][3000/3746] lr: 9.176e-02, eta: 3 days, 20:39:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2005, top5_acc: 0.4361, loss_cls: 4.4522, loss: 4.4522 +2024-07-17 05:02:01,377 - pyskl - INFO - Epoch [28][3100/3746] lr: 9.175e-02, eta: 3 days, 20:37:54, time: 0.698, data_time: 0.000, memory: 15990, top1_acc: 0.2108, top5_acc: 0.4481, loss_cls: 4.4196, loss: 4.4196 +2024-07-17 05:03:12,116 - pyskl - INFO - Epoch [28][3200/3746] lr: 9.173e-02, eta: 3 days, 20:36:32, time: 0.707, data_time: 0.001, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4486, loss_cls: 4.3692, loss: 4.3692 +2024-07-17 05:04:23,118 - pyskl - INFO - Epoch [28][3300/3746] lr: 9.172e-02, eta: 3 days, 20:35:11, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4358, loss_cls: 4.4430, loss: 4.4430 +2024-07-17 05:05:33,756 - pyskl - INFO - Epoch [28][3400/3746] lr: 9.170e-02, eta: 3 days, 20:33:48, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2041, top5_acc: 0.4355, loss_cls: 4.4299, loss: 4.4299 +2024-07-17 05:06:44,277 - pyskl - INFO - Epoch [28][3500/3746] lr: 9.168e-02, eta: 3 days, 20:32:25, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4391, loss_cls: 4.4252, loss: 4.4252 +2024-07-17 05:07:55,237 - pyskl - INFO - Epoch [28][3600/3746] lr: 9.167e-02, eta: 3 days, 20:31:04, time: 0.710, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4338, loss_cls: 4.4230, loss: 4.4230 +2024-07-17 05:09:05,261 - pyskl - INFO - Epoch [28][3700/3746] lr: 9.165e-02, eta: 3 days, 20:29:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1969, top5_acc: 0.4286, loss_cls: 4.4332, loss: 4.4332 +2024-07-17 05:09:39,915 - pyskl - INFO - Saving checkpoint at 28 epochs +2024-07-17 05:11:30,607 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 05:11:31,343 - pyskl - INFO - +top1_acc 0.1359 +top5_acc 0.3308 +2024-07-17 05:11:31,343 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 05:11:31,383 - pyskl - INFO - +mean_acc 0.1357 +2024-07-17 05:11:31,396 - pyskl - INFO - Epoch(val) [28][309] top1_acc: 0.1359, top5_acc: 0.3308, mean_class_accuracy: 0.1357 +2024-07-17 05:14:49,187 - pyskl - INFO - Epoch [29][100/3746] lr: 9.163e-02, eta: 3 days, 20:34:30, time: 1.978, data_time: 1.276, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4511, loss_cls: 4.3663, loss: 4.3663 +2024-07-17 05:15:59,154 - pyskl - INFO - Epoch [29][200/3746] lr: 9.162e-02, eta: 3 days, 20:33:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2069, top5_acc: 0.4434, loss_cls: 4.3996, loss: 4.3996 +2024-07-17 05:17:09,247 - pyskl - INFO - Epoch [29][300/3746] lr: 9.160e-02, eta: 3 days, 20:31:39, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4469, loss_cls: 4.3848, loss: 4.3848 +2024-07-17 05:18:19,468 - pyskl - INFO - Epoch [29][400/3746] lr: 9.158e-02, eta: 3 days, 20:30:14, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4494, loss_cls: 4.3841, loss: 4.3841 +2024-07-17 05:19:29,403 - pyskl - INFO - Epoch [29][500/3746] lr: 9.157e-02, eta: 3 days, 20:28:48, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4433, loss_cls: 4.3680, loss: 4.3680 +2024-07-17 05:20:39,501 - pyskl - INFO - Epoch [29][600/3746] lr: 9.155e-02, eta: 3 days, 20:27:23, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4458, loss_cls: 4.3821, loss: 4.3821 +2024-07-17 05:21:49,534 - pyskl - INFO - Epoch [29][700/3746] lr: 9.154e-02, eta: 3 days, 20:25:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4353, loss_cls: 4.4001, loss: 4.4001 +2024-07-17 05:22:59,570 - pyskl - INFO - Epoch [29][800/3746] lr: 9.152e-02, eta: 3 days, 20:24:32, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4356, loss_cls: 4.4102, loss: 4.4102 +2024-07-17 05:24:09,650 - pyskl - INFO - Epoch [29][900/3746] lr: 9.151e-02, eta: 3 days, 20:23:07, time: 0.701, data_time: 0.000, memory: 15990, top1_acc: 0.2011, top5_acc: 0.4372, loss_cls: 4.4258, loss: 4.4258 +2024-07-17 05:25:19,553 - pyskl - INFO - Epoch [29][1000/3746] lr: 9.149e-02, eta: 3 days, 20:21:41, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4411, loss_cls: 4.3810, loss: 4.3810 +2024-07-17 05:26:29,561 - pyskl - INFO - Epoch [29][1100/3746] lr: 9.148e-02, eta: 3 days, 20:20:16, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.1986, top5_acc: 0.4330, loss_cls: 4.4579, loss: 4.4579 +2024-07-17 05:27:39,421 - pyskl - INFO - Epoch [29][1200/3746] lr: 9.146e-02, eta: 3 days, 20:18:50, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4422, loss_cls: 4.4183, loss: 4.4183 +2024-07-17 05:28:49,623 - pyskl - INFO - Epoch [29][1300/3746] lr: 9.144e-02, eta: 3 days, 20:17:25, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4542, loss_cls: 4.3226, loss: 4.3226 +2024-07-17 05:30:00,059 - pyskl - INFO - Epoch [29][1400/3746] lr: 9.143e-02, eta: 3 days, 20:16:02, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4492, loss_cls: 4.3609, loss: 4.3609 +2024-07-17 05:31:10,534 - pyskl - INFO - Epoch [29][1500/3746] lr: 9.141e-02, eta: 3 days, 20:14:38, time: 0.705, data_time: 0.000, memory: 15990, top1_acc: 0.2070, top5_acc: 0.4439, loss_cls: 4.4013, loss: 4.4013 +2024-07-17 05:32:20,783 - pyskl - INFO - Epoch [29][1600/3746] lr: 9.140e-02, eta: 3 days, 20:13:14, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2194, top5_acc: 0.4533, loss_cls: 4.3592, loss: 4.3592 +2024-07-17 05:33:30,741 - pyskl - INFO - Epoch [29][1700/3746] lr: 9.138e-02, eta: 3 days, 20:11:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4384, loss_cls: 4.4130, loss: 4.4130 +2024-07-17 05:34:40,976 - pyskl - INFO - Epoch [29][1800/3746] lr: 9.137e-02, eta: 3 days, 20:10:24, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4409, loss_cls: 4.3947, loss: 4.3947 +2024-07-17 05:35:51,173 - pyskl - INFO - Epoch [29][1900/3746] lr: 9.135e-02, eta: 3 days, 20:09:00, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4377, loss_cls: 4.3911, loss: 4.3911 +2024-07-17 05:37:01,131 - pyskl - INFO - Epoch [29][2000/3746] lr: 9.133e-02, eta: 3 days, 20:07:35, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4334, loss_cls: 4.3978, loss: 4.3978 +2024-07-17 05:38:11,305 - pyskl - INFO - Epoch [29][2100/3746] lr: 9.132e-02, eta: 3 days, 20:06:10, time: 0.702, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4391, loss_cls: 4.3962, loss: 4.3962 +2024-07-17 05:39:21,205 - pyskl - INFO - Epoch [29][2200/3746] lr: 9.130e-02, eta: 3 days, 20:04:45, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4366, loss_cls: 4.4142, loss: 4.4142 +2024-07-17 05:40:31,222 - pyskl - INFO - Epoch [29][2300/3746] lr: 9.129e-02, eta: 3 days, 20:03:20, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4298, loss_cls: 4.4202, loss: 4.4202 +2024-07-17 05:41:41,196 - pyskl - INFO - Epoch [29][2400/3746] lr: 9.127e-02, eta: 3 days, 20:01:54, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2089, top5_acc: 0.4341, loss_cls: 4.4248, loss: 4.4248 +2024-07-17 05:42:51,076 - pyskl - INFO - Epoch [29][2500/3746] lr: 9.126e-02, eta: 3 days, 20:00:29, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.1955, top5_acc: 0.4231, loss_cls: 4.4780, loss: 4.4780 +2024-07-17 05:44:01,059 - pyskl - INFO - Epoch [29][2600/3746] lr: 9.124e-02, eta: 3 days, 19:59:04, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4364, loss_cls: 4.4071, loss: 4.4071 +2024-07-17 05:45:11,053 - pyskl - INFO - Epoch [29][2700/3746] lr: 9.122e-02, eta: 3 days, 19:57:39, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4358, loss_cls: 4.4090, loss: 4.4090 +2024-07-17 05:46:21,077 - pyskl - INFO - Epoch [29][2800/3746] lr: 9.121e-02, eta: 3 days, 19:56:14, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4458, loss_cls: 4.3633, loss: 4.3633 +2024-07-17 05:47:31,032 - pyskl - INFO - Epoch [29][2900/3746] lr: 9.119e-02, eta: 3 days, 19:54:49, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4452, loss_cls: 4.3855, loss: 4.3855 +2024-07-17 05:48:40,890 - pyskl - INFO - Epoch [29][3000/3746] lr: 9.118e-02, eta: 3 days, 19:53:23, time: 0.699, data_time: 0.000, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4403, loss_cls: 4.4139, loss: 4.4139 +2024-07-17 05:49:50,892 - pyskl - INFO - Epoch [29][3100/3746] lr: 9.116e-02, eta: 3 days, 19:51:58, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4392, loss_cls: 4.3963, loss: 4.3963 +2024-07-17 05:51:01,491 - pyskl - INFO - Epoch [29][3200/3746] lr: 9.114e-02, eta: 3 days, 19:50:36, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4402, loss_cls: 4.3981, loss: 4.3981 +2024-07-17 05:52:12,378 - pyskl - INFO - Epoch [29][3300/3746] lr: 9.113e-02, eta: 3 days, 19:49:15, time: 0.709, data_time: 0.000, memory: 15990, top1_acc: 0.2059, top5_acc: 0.4283, loss_cls: 4.4292, loss: 4.4292 +2024-07-17 05:53:22,952 - pyskl - INFO - Epoch [29][3400/3746] lr: 9.111e-02, eta: 3 days, 19:47:52, time: 0.706, data_time: 0.000, memory: 15990, top1_acc: 0.2048, top5_acc: 0.4230, loss_cls: 4.4311, loss: 4.4311 +2024-07-17 05:54:33,334 - pyskl - INFO - Epoch [29][3500/3746] lr: 9.110e-02, eta: 3 days, 19:46:29, time: 0.704, data_time: 0.000, memory: 15990, top1_acc: 0.2072, top5_acc: 0.4392, loss_cls: 4.4295, loss: 4.4295 +2024-07-17 05:55:43,987 - pyskl - INFO - Epoch [29][3600/3746] lr: 9.108e-02, eta: 3 days, 19:45:07, time: 0.707, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4405, loss_cls: 4.4139, loss: 4.4139 +2024-07-17 05:56:53,942 - pyskl - INFO - Epoch [29][3700/3746] lr: 9.106e-02, eta: 3 days, 19:43:42, time: 0.700, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4403, loss_cls: 4.4014, loss: 4.4014 +2024-07-17 05:57:28,682 - pyskl - INFO - Saving checkpoint at 29 epochs +2024-07-17 05:59:19,973 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 05:59:20,639 - pyskl - INFO - +top1_acc 0.1344 +top5_acc 0.3219 +2024-07-17 05:59:20,639 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 05:59:20,681 - pyskl - INFO - +mean_acc 0.1344 +2024-07-17 05:59:20,692 - pyskl - INFO - Epoch(val) [29][309] top1_acc: 0.1344, top5_acc: 0.3219, mean_class_accuracy: 0.1344 +2024-07-17 06:02:58,695 - pyskl - INFO - Epoch [30][100/3746] lr: 9.104e-02, eta: 3 days, 19:49:41, time: 2.180, data_time: 1.357, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4523, loss_cls: 4.3645, loss: 4.3645 +2024-07-17 06:04:20,315 - pyskl - INFO - Epoch [30][200/3746] lr: 9.103e-02, eta: 3 days, 19:49:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4530, loss_cls: 4.3229, loss: 4.3229 +2024-07-17 06:05:41,584 - pyskl - INFO - Epoch [30][300/3746] lr: 9.101e-02, eta: 3 days, 19:48:26, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4378, loss_cls: 4.4151, loss: 4.4151 +2024-07-17 06:07:03,047 - pyskl - INFO - Epoch [30][400/3746] lr: 9.099e-02, eta: 3 days, 19:47:48, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4431, loss_cls: 4.4045, loss: 4.4045 +2024-07-17 06:08:24,416 - pyskl - INFO - Epoch [30][500/3746] lr: 9.098e-02, eta: 3 days, 19:47:10, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4372, loss_cls: 4.4127, loss: 4.4127 +2024-07-17 06:09:45,625 - pyskl - INFO - Epoch [30][600/3746] lr: 9.096e-02, eta: 3 days, 19:46:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4539, loss_cls: 4.3480, loss: 4.3480 +2024-07-17 06:11:07,437 - pyskl - INFO - Epoch [30][700/3746] lr: 9.095e-02, eta: 3 days, 19:45:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4402, loss_cls: 4.3658, loss: 4.3658 +2024-07-17 06:12:28,919 - pyskl - INFO - Epoch [30][800/3746] lr: 9.093e-02, eta: 3 days, 19:45:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4273, loss_cls: 4.4314, loss: 4.4314 +2024-07-17 06:13:50,930 - pyskl - INFO - Epoch [30][900/3746] lr: 9.091e-02, eta: 3 days, 19:44:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4558, loss_cls: 4.3600, loss: 4.3600 +2024-07-17 06:15:13,091 - pyskl - INFO - Epoch [30][1000/3746] lr: 9.090e-02, eta: 3 days, 19:44:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.1992, top5_acc: 0.4305, loss_cls: 4.4473, loss: 4.4473 +2024-07-17 06:16:35,261 - pyskl - INFO - Epoch [30][1100/3746] lr: 9.088e-02, eta: 3 days, 19:43:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4469, loss_cls: 4.3701, loss: 4.3701 +2024-07-17 06:17:56,564 - pyskl - INFO - Epoch [30][1200/3746] lr: 9.087e-02, eta: 3 days, 19:42:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4323, loss_cls: 4.4294, loss: 4.4294 +2024-07-17 06:19:17,553 - pyskl - INFO - Epoch [30][1300/3746] lr: 9.085e-02, eta: 3 days, 19:42:11, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4389, loss_cls: 4.4187, loss: 4.4187 +2024-07-17 06:20:38,659 - pyskl - INFO - Epoch [30][1400/3746] lr: 9.083e-02, eta: 3 days, 19:41:31, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2039, top5_acc: 0.4358, loss_cls: 4.4349, loss: 4.4349 +2024-07-17 06:21:59,826 - pyskl - INFO - Epoch [30][1500/3746] lr: 9.082e-02, eta: 3 days, 19:40:51, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4562, loss_cls: 4.3212, loss: 4.3212 +2024-07-17 06:23:21,171 - pyskl - INFO - Epoch [30][1600/3746] lr: 9.080e-02, eta: 3 days, 19:40:12, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4442, loss_cls: 4.3439, loss: 4.3439 +2024-07-17 06:24:42,277 - pyskl - INFO - Epoch [30][1700/3746] lr: 9.078e-02, eta: 3 days, 19:39:32, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4542, loss_cls: 4.3595, loss: 4.3595 +2024-07-17 06:26:04,017 - pyskl - INFO - Epoch [30][1800/3746] lr: 9.077e-02, eta: 3 days, 19:38:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4306, loss_cls: 4.4245, loss: 4.4245 +2024-07-17 06:27:25,484 - pyskl - INFO - Epoch [30][1900/3746] lr: 9.075e-02, eta: 3 days, 19:38:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4416, loss_cls: 4.3761, loss: 4.3761 +2024-07-17 06:28:47,580 - pyskl - INFO - Epoch [30][2000/3746] lr: 9.074e-02, eta: 3 days, 19:37:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2020, top5_acc: 0.4361, loss_cls: 4.4206, loss: 4.4206 +2024-07-17 06:30:10,120 - pyskl - INFO - Epoch [30][2100/3746] lr: 9.072e-02, eta: 3 days, 19:37:04, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2078, top5_acc: 0.4405, loss_cls: 4.4071, loss: 4.4071 +2024-07-17 06:31:31,191 - pyskl - INFO - Epoch [30][2200/3746] lr: 9.070e-02, eta: 3 days, 19:36:24, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4405, loss_cls: 4.3783, loss: 4.3783 +2024-07-17 06:32:52,952 - pyskl - INFO - Epoch [30][2300/3746] lr: 9.069e-02, eta: 3 days, 19:35:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4516, loss_cls: 4.3659, loss: 4.3659 +2024-07-17 06:34:14,114 - pyskl - INFO - Epoch [30][2400/3746] lr: 9.067e-02, eta: 3 days, 19:35:05, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4348, loss_cls: 4.4210, loss: 4.4210 +2024-07-17 06:35:35,363 - pyskl - INFO - Epoch [30][2500/3746] lr: 9.065e-02, eta: 3 days, 19:34:25, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4511, loss_cls: 4.3887, loss: 4.3887 +2024-07-17 06:36:57,082 - pyskl - INFO - Epoch [30][2600/3746] lr: 9.064e-02, eta: 3 days, 19:33:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2014, top5_acc: 0.4341, loss_cls: 4.4327, loss: 4.4327 +2024-07-17 06:38:18,517 - pyskl - INFO - Epoch [30][2700/3746] lr: 9.062e-02, eta: 3 days, 19:33:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4489, loss_cls: 4.3822, loss: 4.3822 +2024-07-17 06:39:39,201 - pyskl - INFO - Epoch [30][2800/3746] lr: 9.061e-02, eta: 3 days, 19:32:25, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4436, loss_cls: 4.3977, loss: 4.3977 +2024-07-17 06:40:59,285 - pyskl - INFO - Epoch [30][2900/3746] lr: 9.059e-02, eta: 3 days, 19:31:39, time: 0.801, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4411, loss_cls: 4.3909, loss: 4.3909 +2024-07-17 06:42:19,953 - pyskl - INFO - Epoch [30][3000/3746] lr: 9.057e-02, eta: 3 days, 19:30:56, time: 0.807, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4480, loss_cls: 4.3610, loss: 4.3610 +2024-07-17 06:43:40,446 - pyskl - INFO - Epoch [30][3100/3746] lr: 9.056e-02, eta: 3 days, 19:30:13, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4398, loss_cls: 4.3904, loss: 4.3904 +2024-07-17 06:45:01,940 - pyskl - INFO - Epoch [30][3200/3746] lr: 9.054e-02, eta: 3 days, 19:29:33, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4372, loss_cls: 4.4126, loss: 4.4126 +2024-07-17 06:46:22,736 - pyskl - INFO - Epoch [30][3300/3746] lr: 9.052e-02, eta: 3 days, 19:28:51, time: 0.808, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4423, loss_cls: 4.4043, loss: 4.4043 +2024-07-17 06:47:43,077 - pyskl - INFO - Epoch [30][3400/3746] lr: 9.051e-02, eta: 3 days, 19:28:06, time: 0.803, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4506, loss_cls: 4.3809, loss: 4.3809 +2024-07-17 06:49:04,180 - pyskl - INFO - Epoch [30][3500/3746] lr: 9.049e-02, eta: 3 days, 19:27:25, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4448, loss_cls: 4.4021, loss: 4.4021 +2024-07-17 06:50:24,673 - pyskl - INFO - Epoch [30][3600/3746] lr: 9.047e-02, eta: 3 days, 19:26:41, time: 0.805, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4375, loss_cls: 4.3836, loss: 4.3836 +2024-07-17 06:51:45,298 - pyskl - INFO - Epoch [30][3700/3746] lr: 9.046e-02, eta: 3 days, 19:25:57, time: 0.806, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4347, loss_cls: 4.4073, loss: 4.4073 +2024-07-17 06:52:24,528 - pyskl - INFO - Saving checkpoint at 30 epochs +2024-07-17 06:54:14,908 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 06:54:15,573 - pyskl - INFO - +top1_acc 0.1556 +top5_acc 0.3592 +2024-07-17 06:54:15,573 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 06:54:15,617 - pyskl - INFO - +mean_acc 0.1555 +2024-07-17 06:54:15,631 - pyskl - INFO - Epoch(val) [30][309] top1_acc: 0.1556, top5_acc: 0.3592, mean_class_accuracy: 0.1555 +2024-07-17 06:58:05,472 - pyskl - INFO - Epoch [31][100/3746] lr: 9.043e-02, eta: 3 days, 19:32:21, time: 2.298, data_time: 1.317, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4570, loss_cls: 4.5893, loss: 4.5893 +2024-07-17 06:59:27,631 - pyskl - INFO - Epoch [31][200/3746] lr: 9.042e-02, eta: 3 days, 19:31:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4603, loss_cls: 4.5856, loss: 4.5856 +2024-07-17 07:00:49,115 - pyskl - INFO - Epoch [31][300/3746] lr: 9.040e-02, eta: 3 days, 19:31:02, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4434, loss_cls: 4.6393, loss: 4.6393 +2024-07-17 07:02:11,046 - pyskl - INFO - Epoch [31][400/3746] lr: 9.039e-02, eta: 3 days, 19:30:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4498, loss_cls: 4.6012, loss: 4.6012 +2024-07-17 07:03:32,893 - pyskl - INFO - Epoch [31][500/3746] lr: 9.037e-02, eta: 3 days, 19:29:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4433, loss_cls: 4.6311, loss: 4.6311 +2024-07-17 07:04:54,467 - pyskl - INFO - Epoch [31][600/3746] lr: 9.035e-02, eta: 3 days, 19:29:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4380, loss_cls: 4.6268, loss: 4.6268 +2024-07-17 07:06:16,663 - pyskl - INFO - Epoch [31][700/3746] lr: 9.034e-02, eta: 3 days, 19:28:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2098, top5_acc: 0.4503, loss_cls: 4.6192, loss: 4.6192 +2024-07-17 07:07:38,662 - pyskl - INFO - Epoch [31][800/3746] lr: 9.032e-02, eta: 3 days, 19:27:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4437, loss_cls: 4.6132, loss: 4.6132 +2024-07-17 07:09:00,242 - pyskl - INFO - Epoch [31][900/3746] lr: 9.030e-02, eta: 3 days, 19:27:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4487, loss_cls: 4.6045, loss: 4.6045 +2024-07-17 07:10:21,748 - pyskl - INFO - Epoch [31][1000/3746] lr: 9.029e-02, eta: 3 days, 19:26:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4437, loss_cls: 4.6341, loss: 4.6341 +2024-07-17 07:11:43,671 - pyskl - INFO - Epoch [31][1100/3746] lr: 9.027e-02, eta: 3 days, 19:25:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4448, loss_cls: 4.6475, loss: 4.6475 +2024-07-17 07:13:05,280 - pyskl - INFO - Epoch [31][1200/3746] lr: 9.025e-02, eta: 3 days, 19:25:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4480, loss_cls: 4.6089, loss: 4.6089 +2024-07-17 07:14:27,034 - pyskl - INFO - Epoch [31][1300/3746] lr: 9.024e-02, eta: 3 days, 19:24:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4492, loss_cls: 4.6362, loss: 4.6362 +2024-07-17 07:15:48,676 - pyskl - INFO - Epoch [31][1400/3746] lr: 9.022e-02, eta: 3 days, 19:23:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4313, loss_cls: 4.6458, loss: 4.6458 +2024-07-17 07:17:10,832 - pyskl - INFO - Epoch [31][1500/3746] lr: 9.020e-02, eta: 3 days, 19:23:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4494, loss_cls: 4.5983, loss: 4.5983 +2024-07-17 07:18:32,883 - pyskl - INFO - Epoch [31][1600/3746] lr: 9.019e-02, eta: 3 days, 19:22:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.1978, top5_acc: 0.4263, loss_cls: 4.7001, loss: 4.7001 +2024-07-17 07:19:55,313 - pyskl - INFO - Epoch [31][1700/3746] lr: 9.017e-02, eta: 3 days, 19:21:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4406, loss_cls: 4.6030, loss: 4.6030 +2024-07-17 07:21:17,533 - pyskl - INFO - Epoch [31][1800/3746] lr: 9.015e-02, eta: 3 days, 19:21:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4341, loss_cls: 4.6778, loss: 4.6778 +2024-07-17 07:22:39,869 - pyskl - INFO - Epoch [31][1900/3746] lr: 9.014e-02, eta: 3 days, 19:20:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2002, top5_acc: 0.4402, loss_cls: 4.6642, loss: 4.6642 +2024-07-17 07:24:02,201 - pyskl - INFO - Epoch [31][2000/3746] lr: 9.012e-02, eta: 3 days, 19:19:48, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4475, loss_cls: 4.5881, loss: 4.5881 +2024-07-17 07:25:25,096 - pyskl - INFO - Epoch [31][2100/3746] lr: 9.010e-02, eta: 3 days, 19:19:11, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4484, loss_cls: 4.5908, loss: 4.5908 +2024-07-17 07:26:47,443 - pyskl - INFO - Epoch [31][2200/3746] lr: 9.009e-02, eta: 3 days, 19:18:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4441, loss_cls: 4.6150, loss: 4.6150 +2024-07-17 07:28:09,249 - pyskl - INFO - Epoch [31][2300/3746] lr: 9.007e-02, eta: 3 days, 19:17:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4416, loss_cls: 4.6432, loss: 4.6432 +2024-07-17 07:29:31,271 - pyskl - INFO - Epoch [31][2400/3746] lr: 9.005e-02, eta: 3 days, 19:17:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4508, loss_cls: 4.5865, loss: 4.5865 +2024-07-17 07:30:53,011 - pyskl - INFO - Epoch [31][2500/3746] lr: 9.004e-02, eta: 3 days, 19:16:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2062, top5_acc: 0.4419, loss_cls: 4.6625, loss: 4.6625 +2024-07-17 07:32:14,809 - pyskl - INFO - Epoch [31][2600/3746] lr: 9.002e-02, eta: 3 days, 19:15:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4458, loss_cls: 4.6234, loss: 4.6234 +2024-07-17 07:33:36,785 - pyskl - INFO - Epoch [31][2700/3746] lr: 9.000e-02, eta: 3 days, 19:15:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2081, top5_acc: 0.4411, loss_cls: 4.6298, loss: 4.6298 +2024-07-17 07:34:58,916 - pyskl - INFO - Epoch [31][2800/3746] lr: 8.999e-02, eta: 3 days, 19:14:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4338, loss_cls: 4.6566, loss: 4.6566 +2024-07-17 07:36:20,800 - pyskl - INFO - Epoch [31][2900/3746] lr: 8.997e-02, eta: 3 days, 19:13:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4455, loss_cls: 4.6267, loss: 4.6267 +2024-07-17 07:37:42,563 - pyskl - INFO - Epoch [31][3000/3746] lr: 8.995e-02, eta: 3 days, 19:13:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.1981, top5_acc: 0.4425, loss_cls: 4.6704, loss: 4.6704 +2024-07-17 07:39:04,729 - pyskl - INFO - Epoch [31][3100/3746] lr: 8.994e-02, eta: 3 days, 19:12:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4461, loss_cls: 4.6063, loss: 4.6063 +2024-07-17 07:40:27,449 - pyskl - INFO - Epoch [31][3200/3746] lr: 8.992e-02, eta: 3 days, 19:11:46, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4387, loss_cls: 4.6316, loss: 4.6316 +2024-07-17 07:41:49,758 - pyskl - INFO - Epoch [31][3300/3746] lr: 8.990e-02, eta: 3 days, 19:11:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4366, loss_cls: 4.6300, loss: 4.6300 +2024-07-17 07:43:12,388 - pyskl - INFO - Epoch [31][3400/3746] lr: 8.989e-02, eta: 3 days, 19:10:28, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4450, loss_cls: 4.6399, loss: 4.6399 +2024-07-17 07:44:34,739 - pyskl - INFO - Epoch [31][3500/3746] lr: 8.987e-02, eta: 3 days, 19:09:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4469, loss_cls: 4.6245, loss: 4.6245 +2024-07-17 07:45:56,919 - pyskl - INFO - Epoch [31][3600/3746] lr: 8.985e-02, eta: 3 days, 19:09:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2047, top5_acc: 0.4422, loss_cls: 4.6414, loss: 4.6414 +2024-07-17 07:47:18,913 - pyskl - INFO - Epoch [31][3700/3746] lr: 8.983e-02, eta: 3 days, 19:08:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4512, loss_cls: 4.6130, loss: 4.6130 +2024-07-17 07:47:58,984 - pyskl - INFO - Saving checkpoint at 31 epochs +2024-07-17 07:49:49,513 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 07:49:50,173 - pyskl - INFO - +top1_acc 0.1585 +top5_acc 0.3676 +2024-07-17 07:49:50,174 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 07:49:50,217 - pyskl - INFO - +mean_acc 0.1585 +2024-07-17 07:49:50,221 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_19.pth was removed +2024-07-17 07:49:50,474 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_31.pth. +2024-07-17 07:49:50,475 - pyskl - INFO - Best top1_acc is 0.1585 at 31 epoch. +2024-07-17 07:49:50,488 - pyskl - INFO - Epoch(val) [31][309] top1_acc: 0.1585, top5_acc: 0.3676, mean_class_accuracy: 0.1585 +2024-07-17 07:53:36,041 - pyskl - INFO - Epoch [32][100/3746] lr: 8.981e-02, eta: 3 days, 19:14:12, time: 2.255, data_time: 1.274, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4566, loss_cls: 4.5820, loss: 4.5820 +2024-07-17 07:54:58,099 - pyskl - INFO - Epoch [32][200/3746] lr: 8.979e-02, eta: 3 days, 19:13:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4506, loss_cls: 4.5958, loss: 4.5958 +2024-07-17 07:56:20,034 - pyskl - INFO - Epoch [32][300/3746] lr: 8.978e-02, eta: 3 days, 19:12:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4445, loss_cls: 4.6158, loss: 4.6158 +2024-07-17 07:57:41,860 - pyskl - INFO - Epoch [32][400/3746] lr: 8.976e-02, eta: 3 days, 19:12:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4586, loss_cls: 4.5602, loss: 4.5602 +2024-07-17 07:59:03,359 - pyskl - INFO - Epoch [32][500/3746] lr: 8.974e-02, eta: 3 days, 19:11:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4419, loss_cls: 4.6163, loss: 4.6163 +2024-07-17 08:00:25,115 - pyskl - INFO - Epoch [32][600/3746] lr: 8.973e-02, eta: 3 days, 19:10:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4339, loss_cls: 4.6668, loss: 4.6668 +2024-07-17 08:01:46,709 - pyskl - INFO - Epoch [32][700/3746] lr: 8.971e-02, eta: 3 days, 19:09:54, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4491, loss_cls: 4.6073, loss: 4.6073 +2024-07-17 08:03:08,478 - pyskl - INFO - Epoch [32][800/3746] lr: 8.969e-02, eta: 3 days, 19:09:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4419, loss_cls: 4.6188, loss: 4.6188 +2024-07-17 08:04:30,192 - pyskl - INFO - Epoch [32][900/3746] lr: 8.967e-02, eta: 3 days, 19:08:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2127, top5_acc: 0.4467, loss_cls: 4.6124, loss: 4.6124 +2024-07-17 08:05:52,448 - pyskl - INFO - Epoch [32][1000/3746] lr: 8.966e-02, eta: 3 days, 19:07:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4464, loss_cls: 4.6155, loss: 4.6155 +2024-07-17 08:07:14,522 - pyskl - INFO - Epoch [32][1100/3746] lr: 8.964e-02, eta: 3 days, 19:07:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2047, top5_acc: 0.4372, loss_cls: 4.6231, loss: 4.6231 +2024-07-17 08:08:36,816 - pyskl - INFO - Epoch [32][1200/3746] lr: 8.962e-02, eta: 3 days, 19:06:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2092, top5_acc: 0.4472, loss_cls: 4.6316, loss: 4.6316 +2024-07-17 08:09:59,145 - pyskl - INFO - Epoch [32][1300/3746] lr: 8.961e-02, eta: 3 days, 19:05:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4473, loss_cls: 4.6206, loss: 4.6206 +2024-07-17 08:11:21,239 - pyskl - INFO - Epoch [32][1400/3746] lr: 8.959e-02, eta: 3 days, 19:04:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2092, top5_acc: 0.4423, loss_cls: 4.6273, loss: 4.6273 +2024-07-17 08:12:43,379 - pyskl - INFO - Epoch [32][1500/3746] lr: 8.957e-02, eta: 3 days, 19:04:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4548, loss_cls: 4.5876, loss: 4.5876 +2024-07-17 08:14:05,538 - pyskl - INFO - Epoch [32][1600/3746] lr: 8.955e-02, eta: 3 days, 19:03:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4439, loss_cls: 4.6155, loss: 4.6155 +2024-07-17 08:15:28,174 - pyskl - INFO - Epoch [32][1700/3746] lr: 8.954e-02, eta: 3 days, 19:02:54, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2027, top5_acc: 0.4434, loss_cls: 4.6305, loss: 4.6305 +2024-07-17 08:16:49,815 - pyskl - INFO - Epoch [32][1800/3746] lr: 8.952e-02, eta: 3 days, 19:02:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4533, loss_cls: 4.5646, loss: 4.5646 +2024-07-17 08:18:12,172 - pyskl - INFO - Epoch [32][1900/3746] lr: 8.950e-02, eta: 3 days, 19:01:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2155, top5_acc: 0.4519, loss_cls: 4.5940, loss: 4.5940 +2024-07-17 08:19:34,081 - pyskl - INFO - Epoch [32][2000/3746] lr: 8.949e-02, eta: 3 days, 19:00:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4422, loss_cls: 4.6397, loss: 4.6397 +2024-07-17 08:20:55,827 - pyskl - INFO - Epoch [32][2100/3746] lr: 8.947e-02, eta: 3 days, 19:00:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4439, loss_cls: 4.6134, loss: 4.6134 +2024-07-17 08:22:17,986 - pyskl - INFO - Epoch [32][2200/3746] lr: 8.945e-02, eta: 3 days, 18:59:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2108, top5_acc: 0.4417, loss_cls: 4.6632, loss: 4.6632 +2024-07-17 08:23:40,124 - pyskl - INFO - Epoch [32][2300/3746] lr: 8.943e-02, eta: 3 days, 18:58:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2114, top5_acc: 0.4436, loss_cls: 4.6473, loss: 4.6473 +2024-07-17 08:25:02,062 - pyskl - INFO - Epoch [32][2400/3746] lr: 8.942e-02, eta: 3 days, 18:57:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4495, loss_cls: 4.6232, loss: 4.6232 +2024-07-17 08:26:24,227 - pyskl - INFO - Epoch [32][2500/3746] lr: 8.940e-02, eta: 3 days, 18:57:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4370, loss_cls: 4.6254, loss: 4.6254 +2024-07-17 08:27:46,706 - pyskl - INFO - Epoch [32][2600/3746] lr: 8.938e-02, eta: 3 days, 18:56:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2052, top5_acc: 0.4314, loss_cls: 4.6665, loss: 4.6665 +2024-07-17 08:29:08,987 - pyskl - INFO - Epoch [32][2700/3746] lr: 8.937e-02, eta: 3 days, 18:55:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2073, top5_acc: 0.4417, loss_cls: 4.6335, loss: 4.6335 +2024-07-17 08:30:31,076 - pyskl - INFO - Epoch [32][2800/3746] lr: 8.935e-02, eta: 3 days, 18:55:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4539, loss_cls: 4.5911, loss: 4.5911 +2024-07-17 08:31:52,492 - pyskl - INFO - Epoch [32][2900/3746] lr: 8.933e-02, eta: 3 days, 18:54:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4462, loss_cls: 4.6482, loss: 4.6482 +2024-07-17 08:33:14,251 - pyskl - INFO - Epoch [32][3000/3746] lr: 8.931e-02, eta: 3 days, 18:53:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2108, top5_acc: 0.4559, loss_cls: 4.6050, loss: 4.6050 +2024-07-17 08:34:36,602 - pyskl - INFO - Epoch [32][3100/3746] lr: 8.930e-02, eta: 3 days, 18:52:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4470, loss_cls: 4.6147, loss: 4.6147 +2024-07-17 08:35:58,817 - pyskl - INFO - Epoch [32][3200/3746] lr: 8.928e-02, eta: 3 days, 18:52:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4350, loss_cls: 4.6346, loss: 4.6346 +2024-07-17 08:37:21,842 - pyskl - INFO - Epoch [32][3300/3746] lr: 8.926e-02, eta: 3 days, 18:51:24, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4397, loss_cls: 4.6245, loss: 4.6245 +2024-07-17 08:38:44,050 - pyskl - INFO - Epoch [32][3400/3746] lr: 8.924e-02, eta: 3 days, 18:50:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4480, loss_cls: 4.6116, loss: 4.6116 +2024-07-17 08:40:06,077 - pyskl - INFO - Epoch [32][3500/3746] lr: 8.923e-02, eta: 3 days, 18:49:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4473, loss_cls: 4.5825, loss: 4.5825 +2024-07-17 08:41:29,156 - pyskl - INFO - Epoch [32][3600/3746] lr: 8.921e-02, eta: 3 days, 18:49:17, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4333, loss_cls: 4.6536, loss: 4.6536 +2024-07-17 08:42:50,719 - pyskl - INFO - Epoch [32][3700/3746] lr: 8.919e-02, eta: 3 days, 18:48:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4472, loss_cls: 4.6181, loss: 4.6181 +2024-07-17 08:43:30,474 - pyskl - INFO - Saving checkpoint at 32 epochs +2024-07-17 08:45:20,766 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 08:45:21,422 - pyskl - INFO - +top1_acc 0.1325 +top5_acc 0.3260 +2024-07-17 08:45:21,423 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 08:45:21,464 - pyskl - INFO - +mean_acc 0.1323 +2024-07-17 08:45:21,476 - pyskl - INFO - Epoch(val) [32][309] top1_acc: 0.1325, top5_acc: 0.3260, mean_class_accuracy: 0.1323 +2024-07-17 08:49:06,280 - pyskl - INFO - Epoch [33][100/3746] lr: 8.917e-02, eta: 3 days, 18:53:53, time: 2.248, data_time: 1.264, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4481, loss_cls: 4.5677, loss: 4.5677 +2024-07-17 08:50:28,348 - pyskl - INFO - Epoch [33][200/3746] lr: 8.915e-02, eta: 3 days, 18:53:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4430, loss_cls: 4.6027, loss: 4.6027 +2024-07-17 08:51:50,086 - pyskl - INFO - Epoch [33][300/3746] lr: 8.913e-02, eta: 3 days, 18:52:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4430, loss_cls: 4.6033, loss: 4.6033 +2024-07-17 08:53:11,692 - pyskl - INFO - Epoch [33][400/3746] lr: 8.912e-02, eta: 3 days, 18:51:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4516, loss_cls: 4.6122, loss: 4.6122 +2024-07-17 08:54:33,194 - pyskl - INFO - Epoch [33][500/3746] lr: 8.910e-02, eta: 3 days, 18:50:50, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2042, top5_acc: 0.4441, loss_cls: 4.6124, loss: 4.6124 +2024-07-17 08:55:55,221 - pyskl - INFO - Epoch [33][600/3746] lr: 8.908e-02, eta: 3 days, 18:50:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4398, loss_cls: 4.6276, loss: 4.6276 +2024-07-17 08:57:17,200 - pyskl - INFO - Epoch [33][700/3746] lr: 8.906e-02, eta: 3 days, 18:49:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4491, loss_cls: 4.5830, loss: 4.5830 +2024-07-17 08:58:38,881 - pyskl - INFO - Epoch [33][800/3746] lr: 8.905e-02, eta: 3 days, 18:48:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2155, top5_acc: 0.4439, loss_cls: 4.6184, loss: 4.6184 +2024-07-17 09:00:00,644 - pyskl - INFO - Epoch [33][900/3746] lr: 8.903e-02, eta: 3 days, 18:47:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4456, loss_cls: 4.5977, loss: 4.5977 +2024-07-17 09:01:22,183 - pyskl - INFO - Epoch [33][1000/3746] lr: 8.901e-02, eta: 3 days, 18:47:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4386, loss_cls: 4.6343, loss: 4.6343 +2024-07-17 09:02:43,730 - pyskl - INFO - Epoch [33][1100/3746] lr: 8.899e-02, eta: 3 days, 18:46:13, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4419, loss_cls: 4.6446, loss: 4.6446 +2024-07-17 09:04:05,920 - pyskl - INFO - Epoch [33][1200/3746] lr: 8.898e-02, eta: 3 days, 18:45:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4466, loss_cls: 4.6016, loss: 4.6016 +2024-07-17 09:05:27,416 - pyskl - INFO - Epoch [33][1300/3746] lr: 8.896e-02, eta: 3 days, 18:44:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4453, loss_cls: 4.6123, loss: 4.6123 +2024-07-17 09:06:49,506 - pyskl - INFO - Epoch [33][1400/3746] lr: 8.894e-02, eta: 3 days, 18:43:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4520, loss_cls: 4.5848, loss: 4.5848 +2024-07-17 09:08:11,841 - pyskl - INFO - Epoch [33][1500/3746] lr: 8.892e-02, eta: 3 days, 18:43:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4395, loss_cls: 4.6226, loss: 4.6226 +2024-07-17 09:09:33,352 - pyskl - INFO - Epoch [33][1600/3746] lr: 8.891e-02, eta: 3 days, 18:42:25, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4483, loss_cls: 4.6049, loss: 4.6049 +2024-07-17 09:10:55,853 - pyskl - INFO - Epoch [33][1700/3746] lr: 8.889e-02, eta: 3 days, 18:41:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4452, loss_cls: 4.6365, loss: 4.6365 +2024-07-17 09:12:17,909 - pyskl - INFO - Epoch [33][1800/3746] lr: 8.887e-02, eta: 3 days, 18:40:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4461, loss_cls: 4.5806, loss: 4.5806 +2024-07-17 09:13:40,538 - pyskl - INFO - Epoch [33][1900/3746] lr: 8.885e-02, eta: 3 days, 18:40:12, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4478, loss_cls: 4.6000, loss: 4.6000 +2024-07-17 09:15:02,532 - pyskl - INFO - Epoch [33][2000/3746] lr: 8.884e-02, eta: 3 days, 18:39:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4548, loss_cls: 4.5901, loss: 4.5901 +2024-07-17 09:16:24,503 - pyskl - INFO - Epoch [33][2100/3746] lr: 8.882e-02, eta: 3 days, 18:38:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2106, top5_acc: 0.4427, loss_cls: 4.6253, loss: 4.6253 +2024-07-17 09:17:46,565 - pyskl - INFO - Epoch [33][2200/3746] lr: 8.880e-02, eta: 3 days, 18:37:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4484, loss_cls: 4.6272, loss: 4.6272 +2024-07-17 09:19:08,557 - pyskl - INFO - Epoch [33][2300/3746] lr: 8.878e-02, eta: 3 days, 18:37:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4427, loss_cls: 4.6168, loss: 4.6168 +2024-07-17 09:20:30,357 - pyskl - INFO - Epoch [33][2400/3746] lr: 8.876e-02, eta: 3 days, 18:36:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2066, top5_acc: 0.4408, loss_cls: 4.6760, loss: 4.6760 +2024-07-17 09:21:52,019 - pyskl - INFO - Epoch [33][2500/3746] lr: 8.875e-02, eta: 3 days, 18:35:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2053, top5_acc: 0.4372, loss_cls: 4.6358, loss: 4.6358 +2024-07-17 09:23:13,974 - pyskl - INFO - Epoch [33][2600/3746] lr: 8.873e-02, eta: 3 days, 18:34:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4523, loss_cls: 4.5621, loss: 4.5621 +2024-07-17 09:24:35,834 - pyskl - INFO - Epoch [33][2700/3746] lr: 8.871e-02, eta: 3 days, 18:34:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2017, top5_acc: 0.4364, loss_cls: 4.6464, loss: 4.6464 +2024-07-17 09:25:58,234 - pyskl - INFO - Epoch [33][2800/3746] lr: 8.869e-02, eta: 3 days, 18:33:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4441, loss_cls: 4.6244, loss: 4.6244 +2024-07-17 09:27:20,530 - pyskl - INFO - Epoch [33][2900/3746] lr: 8.868e-02, eta: 3 days, 18:32:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2103, top5_acc: 0.4439, loss_cls: 4.6122, loss: 4.6122 +2024-07-17 09:28:42,872 - pyskl - INFO - Epoch [33][3000/3746] lr: 8.866e-02, eta: 3 days, 18:31:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4573, loss_cls: 4.5676, loss: 4.5676 +2024-07-17 09:30:05,765 - pyskl - INFO - Epoch [33][3100/3746] lr: 8.864e-02, eta: 3 days, 18:31:02, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4392, loss_cls: 4.6406, loss: 4.6406 +2024-07-17 09:31:27,832 - pyskl - INFO - Epoch [33][3200/3746] lr: 8.862e-02, eta: 3 days, 18:30:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2000, top5_acc: 0.4258, loss_cls: 4.6868, loss: 4.6868 +2024-07-17 09:32:51,122 - pyskl - INFO - Epoch [33][3300/3746] lr: 8.861e-02, eta: 3 days, 18:29:33, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4497, loss_cls: 4.5706, loss: 4.5706 +2024-07-17 09:34:13,803 - pyskl - INFO - Epoch [33][3400/3746] lr: 8.859e-02, eta: 3 days, 18:28:49, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4611, loss_cls: 4.5800, loss: 4.5800 +2024-07-17 09:35:36,204 - pyskl - INFO - Epoch [33][3500/3746] lr: 8.857e-02, eta: 3 days, 18:28:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4425, loss_cls: 4.6325, loss: 4.6325 +2024-07-17 09:36:58,908 - pyskl - INFO - Epoch [33][3600/3746] lr: 8.855e-02, eta: 3 days, 18:27:19, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4569, loss_cls: 4.5500, loss: 4.5500 +2024-07-17 09:38:20,857 - pyskl - INFO - Epoch [33][3700/3746] lr: 8.853e-02, eta: 3 days, 18:26:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2056, top5_acc: 0.4267, loss_cls: 4.6784, loss: 4.6784 +2024-07-17 09:39:00,759 - pyskl - INFO - Saving checkpoint at 33 epochs +2024-07-17 09:40:52,162 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 09:40:52,918 - pyskl - INFO - +top1_acc 0.1393 +top5_acc 0.3388 +2024-07-17 09:40:52,918 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 09:40:52,965 - pyskl - INFO - +mean_acc 0.1391 +2024-07-17 09:40:52,979 - pyskl - INFO - Epoch(val) [33][309] top1_acc: 0.1393, top5_acc: 0.3388, mean_class_accuracy: 0.1391 +2024-07-17 09:44:45,260 - pyskl - INFO - Epoch [34][100/3746] lr: 8.851e-02, eta: 3 days, 18:32:02, time: 2.323, data_time: 1.322, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4498, loss_cls: 4.5795, loss: 4.5795 +2024-07-17 09:46:08,699 - pyskl - INFO - Epoch [34][200/3746] lr: 8.849e-02, eta: 3 days, 18:31:20, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4455, loss_cls: 4.6044, loss: 4.6044 +2024-07-17 09:47:31,649 - pyskl - INFO - Epoch [34][300/3746] lr: 8.847e-02, eta: 3 days, 18:30:36, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4548, loss_cls: 4.5961, loss: 4.5961 +2024-07-17 09:48:53,663 - pyskl - INFO - Epoch [34][400/3746] lr: 8.845e-02, eta: 3 days, 18:29:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4414, loss_cls: 4.5992, loss: 4.5992 +2024-07-17 09:50:16,716 - pyskl - INFO - Epoch [34][500/3746] lr: 8.844e-02, eta: 3 days, 18:29:05, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2108, top5_acc: 0.4430, loss_cls: 4.6174, loss: 4.6174 +2024-07-17 09:51:39,789 - pyskl - INFO - Epoch [34][600/3746] lr: 8.842e-02, eta: 3 days, 18:28:21, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4544, loss_cls: 4.5626, loss: 4.5626 +2024-07-17 09:53:03,218 - pyskl - INFO - Epoch [34][700/3746] lr: 8.840e-02, eta: 3 days, 18:27:38, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4430, loss_cls: 4.6407, loss: 4.6407 +2024-07-17 09:54:26,499 - pyskl - INFO - Epoch [34][800/3746] lr: 8.838e-02, eta: 3 days, 18:26:55, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4541, loss_cls: 4.5632, loss: 4.5632 +2024-07-17 09:55:50,005 - pyskl - INFO - Epoch [34][900/3746] lr: 8.836e-02, eta: 3 days, 18:26:12, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4619, loss_cls: 4.5970, loss: 4.5970 +2024-07-17 09:57:13,087 - pyskl - INFO - Epoch [34][1000/3746] lr: 8.835e-02, eta: 3 days, 18:25:28, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2131, top5_acc: 0.4503, loss_cls: 4.6044, loss: 4.6044 +2024-07-17 09:58:35,994 - pyskl - INFO - Epoch [34][1100/3746] lr: 8.833e-02, eta: 3 days, 18:24:43, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4528, loss_cls: 4.6022, loss: 4.6022 +2024-07-17 09:59:58,811 - pyskl - INFO - Epoch [34][1200/3746] lr: 8.831e-02, eta: 3 days, 18:23:58, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4433, loss_cls: 4.6291, loss: 4.6291 +2024-07-17 10:01:22,004 - pyskl - INFO - Epoch [34][1300/3746] lr: 8.829e-02, eta: 3 days, 18:23:14, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4444, loss_cls: 4.6060, loss: 4.6060 +2024-07-17 10:02:45,288 - pyskl - INFO - Epoch [34][1400/3746] lr: 8.828e-02, eta: 3 days, 18:22:31, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4473, loss_cls: 4.5817, loss: 4.5817 +2024-07-17 10:04:08,310 - pyskl - INFO - Epoch [34][1500/3746] lr: 8.826e-02, eta: 3 days, 18:21:46, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4469, loss_cls: 4.5929, loss: 4.5929 +2024-07-17 10:05:31,208 - pyskl - INFO - Epoch [34][1600/3746] lr: 8.824e-02, eta: 3 days, 18:21:01, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4400, loss_cls: 4.6455, loss: 4.6455 +2024-07-17 10:06:53,819 - pyskl - INFO - Epoch [34][1700/3746] lr: 8.822e-02, eta: 3 days, 18:20:15, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4537, loss_cls: 4.5877, loss: 4.5877 +2024-07-17 10:08:16,414 - pyskl - INFO - Epoch [34][1800/3746] lr: 8.820e-02, eta: 3 days, 18:19:28, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4431, loss_cls: 4.6289, loss: 4.6289 +2024-07-17 10:09:38,803 - pyskl - INFO - Epoch [34][1900/3746] lr: 8.819e-02, eta: 3 days, 18:18:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2112, top5_acc: 0.4517, loss_cls: 4.6106, loss: 4.6106 +2024-07-17 10:11:01,681 - pyskl - INFO - Epoch [34][2000/3746] lr: 8.817e-02, eta: 3 days, 18:17:56, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4564, loss_cls: 4.5753, loss: 4.5753 +2024-07-17 10:12:24,375 - pyskl - INFO - Epoch [34][2100/3746] lr: 8.815e-02, eta: 3 days, 18:17:10, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2009, top5_acc: 0.4325, loss_cls: 4.6739, loss: 4.6739 +2024-07-17 10:13:46,461 - pyskl - INFO - Epoch [34][2200/3746] lr: 8.813e-02, eta: 3 days, 18:16:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4448, loss_cls: 4.5982, loss: 4.5982 +2024-07-17 10:15:08,414 - pyskl - INFO - Epoch [34][2300/3746] lr: 8.811e-02, eta: 3 days, 18:15:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4469, loss_cls: 4.6138, loss: 4.6138 +2024-07-17 10:16:30,744 - pyskl - INFO - Epoch [34][2400/3746] lr: 8.809e-02, eta: 3 days, 18:14:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2095, top5_acc: 0.4453, loss_cls: 4.6217, loss: 4.6217 +2024-07-17 10:17:52,185 - pyskl - INFO - Epoch [34][2500/3746] lr: 8.808e-02, eta: 3 days, 18:13:54, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2102, top5_acc: 0.4350, loss_cls: 4.6314, loss: 4.6314 +2024-07-17 10:19:14,340 - pyskl - INFO - Epoch [34][2600/3746] lr: 8.806e-02, eta: 3 days, 18:13:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4500, loss_cls: 4.5998, loss: 4.5998 +2024-07-17 10:20:36,531 - pyskl - INFO - Epoch [34][2700/3746] lr: 8.804e-02, eta: 3 days, 18:12:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4506, loss_cls: 4.6166, loss: 4.6166 +2024-07-17 10:21:58,599 - pyskl - INFO - Epoch [34][2800/3746] lr: 8.802e-02, eta: 3 days, 18:11:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4411, loss_cls: 4.6243, loss: 4.6243 +2024-07-17 10:23:20,641 - pyskl - INFO - Epoch [34][2900/3746] lr: 8.800e-02, eta: 3 days, 18:10:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4647, loss_cls: 4.5544, loss: 4.5544 +2024-07-17 10:24:43,332 - pyskl - INFO - Epoch [34][3000/3746] lr: 8.799e-02, eta: 3 days, 18:09:54, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2108, top5_acc: 0.4253, loss_cls: 4.6602, loss: 4.6602 +2024-07-17 10:26:05,517 - pyskl - INFO - Epoch [34][3100/3746] lr: 8.797e-02, eta: 3 days, 18:09:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4369, loss_cls: 4.6527, loss: 4.6527 +2024-07-17 10:27:28,288 - pyskl - INFO - Epoch [34][3200/3746] lr: 8.795e-02, eta: 3 days, 18:08:19, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4503, loss_cls: 4.6079, loss: 4.6079 +2024-07-17 10:28:50,696 - pyskl - INFO - Epoch [34][3300/3746] lr: 8.793e-02, eta: 3 days, 18:07:31, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4469, loss_cls: 4.6161, loss: 4.6161 +2024-07-17 10:30:13,497 - pyskl - INFO - Epoch [34][3400/3746] lr: 8.791e-02, eta: 3 days, 18:06:45, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4456, loss_cls: 4.6225, loss: 4.6225 +2024-07-17 10:31:36,012 - pyskl - INFO - Epoch [34][3500/3746] lr: 8.789e-02, eta: 3 days, 18:05:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4569, loss_cls: 4.5625, loss: 4.5625 +2024-07-17 10:32:57,773 - pyskl - INFO - Epoch [34][3600/3746] lr: 8.788e-02, eta: 3 days, 18:05:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4431, loss_cls: 4.6209, loss: 4.6209 +2024-07-17 10:34:20,900 - pyskl - INFO - Epoch [34][3700/3746] lr: 8.786e-02, eta: 3 days, 18:04:22, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4428, loss_cls: 4.6185, loss: 4.6185 +2024-07-17 10:35:00,585 - pyskl - INFO - Saving checkpoint at 34 epochs +2024-07-17 10:36:52,650 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 10:36:53,321 - pyskl - INFO - +top1_acc 0.1567 +top5_acc 0.3611 +2024-07-17 10:36:53,321 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 10:36:53,365 - pyskl - INFO - +mean_acc 0.1565 +2024-07-17 10:36:53,379 - pyskl - INFO - Epoch(val) [34][309] top1_acc: 0.1567, top5_acc: 0.3611, mean_class_accuracy: 0.1565 +2024-07-17 10:40:42,371 - pyskl - INFO - Epoch [35][100/3746] lr: 8.783e-02, eta: 3 days, 18:09:22, time: 2.290, data_time: 1.294, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4437, loss_cls: 4.5834, loss: 4.5834 +2024-07-17 10:42:05,655 - pyskl - INFO - Epoch [35][200/3746] lr: 8.781e-02, eta: 3 days, 18:08:36, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4459, loss_cls: 4.5704, loss: 4.5704 +2024-07-17 10:43:28,966 - pyskl - INFO - Epoch [35][300/3746] lr: 8.780e-02, eta: 3 days, 18:07:51, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4523, loss_cls: 4.6038, loss: 4.6038 +2024-07-17 10:44:52,192 - pyskl - INFO - Epoch [35][400/3746] lr: 8.778e-02, eta: 3 days, 18:07:05, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4444, loss_cls: 4.6219, loss: 4.6219 +2024-07-17 10:46:15,098 - pyskl - INFO - Epoch [35][500/3746] lr: 8.776e-02, eta: 3 days, 18:06:18, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4439, loss_cls: 4.6097, loss: 4.6097 +2024-07-17 10:47:38,258 - pyskl - INFO - Epoch [35][600/3746] lr: 8.774e-02, eta: 3 days, 18:05:32, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4602, loss_cls: 4.5656, loss: 4.5656 +2024-07-17 10:49:01,427 - pyskl - INFO - Epoch [35][700/3746] lr: 8.772e-02, eta: 3 days, 18:04:46, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4458, loss_cls: 4.6075, loss: 4.6075 +2024-07-17 10:50:25,050 - pyskl - INFO - Epoch [35][800/3746] lr: 8.770e-02, eta: 3 days, 18:04:01, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2094, top5_acc: 0.4492, loss_cls: 4.6155, loss: 4.6155 +2024-07-17 10:51:47,735 - pyskl - INFO - Epoch [35][900/3746] lr: 8.769e-02, eta: 3 days, 18:03:13, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4436, loss_cls: 4.6219, loss: 4.6219 +2024-07-17 10:53:10,233 - pyskl - INFO - Epoch [35][1000/3746] lr: 8.767e-02, eta: 3 days, 18:02:24, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4369, loss_cls: 4.6294, loss: 4.6294 +2024-07-17 10:54:32,120 - pyskl - INFO - Epoch [35][1100/3746] lr: 8.765e-02, eta: 3 days, 18:01:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4489, loss_cls: 4.5881, loss: 4.5881 +2024-07-17 10:55:54,516 - pyskl - INFO - Epoch [35][1200/3746] lr: 8.763e-02, eta: 3 days, 18:00:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4575, loss_cls: 4.5578, loss: 4.5578 +2024-07-17 10:57:16,907 - pyskl - INFO - Epoch [35][1300/3746] lr: 8.761e-02, eta: 3 days, 17:59:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4527, loss_cls: 4.5775, loss: 4.5775 +2024-07-17 10:58:39,422 - pyskl - INFO - Epoch [35][1400/3746] lr: 8.759e-02, eta: 3 days, 17:59:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4472, loss_cls: 4.6140, loss: 4.6140 +2024-07-17 11:00:01,633 - pyskl - INFO - Epoch [35][1500/3746] lr: 8.757e-02, eta: 3 days, 17:58:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4462, loss_cls: 4.6251, loss: 4.6251 +2024-07-17 11:01:24,712 - pyskl - INFO - Epoch [35][1600/3746] lr: 8.756e-02, eta: 3 days, 17:57:30, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4498, loss_cls: 4.5975, loss: 4.5975 +2024-07-17 11:02:46,436 - pyskl - INFO - Epoch [35][1700/3746] lr: 8.754e-02, eta: 3 days, 17:56:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4547, loss_cls: 4.5744, loss: 4.5744 +2024-07-17 11:04:08,613 - pyskl - INFO - Epoch [35][1800/3746] lr: 8.752e-02, eta: 3 days, 17:55:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4591, loss_cls: 4.5426, loss: 4.5426 +2024-07-17 11:05:30,832 - pyskl - INFO - Epoch [35][1900/3746] lr: 8.750e-02, eta: 3 days, 17:54:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4519, loss_cls: 4.6053, loss: 4.6053 +2024-07-17 11:06:52,942 - pyskl - INFO - Epoch [35][2000/3746] lr: 8.748e-02, eta: 3 days, 17:54:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2087, top5_acc: 0.4469, loss_cls: 4.5967, loss: 4.5967 +2024-07-17 11:08:15,471 - pyskl - INFO - Epoch [35][2100/3746] lr: 8.746e-02, eta: 3 days, 17:53:18, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4459, loss_cls: 4.6077, loss: 4.6077 +2024-07-17 11:09:37,518 - pyskl - INFO - Epoch [35][2200/3746] lr: 8.745e-02, eta: 3 days, 17:52:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4472, loss_cls: 4.5978, loss: 4.5978 +2024-07-17 11:11:00,043 - pyskl - INFO - Epoch [35][2300/3746] lr: 8.743e-02, eta: 3 days, 17:51:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4609, loss_cls: 4.5472, loss: 4.5472 +2024-07-17 11:12:22,011 - pyskl - INFO - Epoch [35][2400/3746] lr: 8.741e-02, eta: 3 days, 17:50:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4436, loss_cls: 4.6178, loss: 4.6178 +2024-07-17 11:13:43,950 - pyskl - INFO - Epoch [35][2500/3746] lr: 8.739e-02, eta: 3 days, 17:49:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2091, top5_acc: 0.4464, loss_cls: 4.6031, loss: 4.6031 +2024-07-17 11:15:05,640 - pyskl - INFO - Epoch [35][2600/3746] lr: 8.737e-02, eta: 3 days, 17:49:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4484, loss_cls: 4.6131, loss: 4.6131 +2024-07-17 11:16:27,533 - pyskl - INFO - Epoch [35][2700/3746] lr: 8.735e-02, eta: 3 days, 17:48:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4480, loss_cls: 4.6106, loss: 4.6106 +2024-07-17 11:17:49,242 - pyskl - INFO - Epoch [35][2800/3746] lr: 8.733e-02, eta: 3 days, 17:47:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4586, loss_cls: 4.5375, loss: 4.5375 +2024-07-17 11:19:11,042 - pyskl - INFO - Epoch [35][2900/3746] lr: 8.732e-02, eta: 3 days, 17:46:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4484, loss_cls: 4.6420, loss: 4.6420 +2024-07-17 11:20:34,029 - pyskl - INFO - Epoch [35][3000/3746] lr: 8.730e-02, eta: 3 days, 17:45:40, time: 0.830, data_time: 0.001, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4494, loss_cls: 4.5982, loss: 4.5982 +2024-07-17 11:21:56,171 - pyskl - INFO - Epoch [35][3100/3746] lr: 8.728e-02, eta: 3 days, 17:44:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4391, loss_cls: 4.6377, loss: 4.6377 +2024-07-17 11:23:19,615 - pyskl - INFO - Epoch [35][3200/3746] lr: 8.726e-02, eta: 3 days, 17:44:03, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2061, top5_acc: 0.4395, loss_cls: 4.6648, loss: 4.6648 +2024-07-17 11:24:42,175 - pyskl - INFO - Epoch [35][3300/3746] lr: 8.724e-02, eta: 3 days, 17:43:13, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4487, loss_cls: 4.5952, loss: 4.5952 +2024-07-17 11:26:04,485 - pyskl - INFO - Epoch [35][3400/3746] lr: 8.722e-02, eta: 3 days, 17:42:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2056, top5_acc: 0.4419, loss_cls: 4.6628, loss: 4.6628 +2024-07-17 11:27:26,748 - pyskl - INFO - Epoch [35][3500/3746] lr: 8.720e-02, eta: 3 days, 17:41:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2155, top5_acc: 0.4462, loss_cls: 4.6072, loss: 4.6072 +2024-07-17 11:28:48,765 - pyskl - INFO - Epoch [35][3600/3746] lr: 8.718e-02, eta: 3 days, 17:40:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2130, top5_acc: 0.4445, loss_cls: 4.6202, loss: 4.6202 +2024-07-17 11:30:11,646 - pyskl - INFO - Epoch [35][3700/3746] lr: 8.717e-02, eta: 3 days, 17:39:52, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4569, loss_cls: 4.5706, loss: 4.5706 +2024-07-17 11:30:51,564 - pyskl - INFO - Saving checkpoint at 35 epochs +2024-07-17 11:32:42,080 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 11:32:42,851 - pyskl - INFO - +top1_acc 0.1290 +top5_acc 0.3184 +2024-07-17 11:32:42,851 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 11:32:42,902 - pyskl - INFO - +mean_acc 0.1288 +2024-07-17 11:32:42,918 - pyskl - INFO - Epoch(val) [35][309] top1_acc: 0.1290, top5_acc: 0.3184, mean_class_accuracy: 0.1288 +2024-07-17 11:36:31,970 - pyskl - INFO - Epoch [36][100/3746] lr: 8.714e-02, eta: 3 days, 17:44:35, time: 2.290, data_time: 1.287, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4480, loss_cls: 4.5429, loss: 4.5429 +2024-07-17 11:37:54,966 - pyskl - INFO - Epoch [36][200/3746] lr: 8.712e-02, eta: 3 days, 17:43:46, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4495, loss_cls: 4.5821, loss: 4.5821 +2024-07-17 11:39:18,024 - pyskl - INFO - Epoch [36][300/3746] lr: 8.710e-02, eta: 3 days, 17:42:58, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4573, loss_cls: 4.5367, loss: 4.5367 +2024-07-17 11:40:41,256 - pyskl - INFO - Epoch [36][400/3746] lr: 8.708e-02, eta: 3 days, 17:42:09, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4464, loss_cls: 4.5829, loss: 4.5829 +2024-07-17 11:42:04,329 - pyskl - INFO - Epoch [36][500/3746] lr: 8.706e-02, eta: 3 days, 17:41:21, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4489, loss_cls: 4.5711, loss: 4.5711 +2024-07-17 11:43:26,849 - pyskl - INFO - Epoch [36][600/3746] lr: 8.704e-02, eta: 3 days, 17:40:30, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4533, loss_cls: 4.5634, loss: 4.5634 +2024-07-17 11:44:50,068 - pyskl - INFO - Epoch [36][700/3746] lr: 8.703e-02, eta: 3 days, 17:39:42, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4414, loss_cls: 4.6347, loss: 4.6347 +2024-07-17 11:46:12,794 - pyskl - INFO - Epoch [36][800/3746] lr: 8.701e-02, eta: 3 days, 17:38:52, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2097, top5_acc: 0.4480, loss_cls: 4.6009, loss: 4.6009 +2024-07-17 11:47:36,205 - pyskl - INFO - Epoch [36][900/3746] lr: 8.699e-02, eta: 3 days, 17:38:04, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4606, loss_cls: 4.5375, loss: 4.5375 +2024-07-17 11:48:59,106 - pyskl - INFO - Epoch [36][1000/3746] lr: 8.697e-02, eta: 3 days, 17:37:14, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2155, top5_acc: 0.4439, loss_cls: 4.6049, loss: 4.6049 +2024-07-17 11:50:22,288 - pyskl - INFO - Epoch [36][1100/3746] lr: 8.695e-02, eta: 3 days, 17:36:26, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4491, loss_cls: 4.6031, loss: 4.6031 +2024-07-17 11:51:44,653 - pyskl - INFO - Epoch [36][1200/3746] lr: 8.693e-02, eta: 3 days, 17:35:34, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4375, loss_cls: 4.6365, loss: 4.6365 +2024-07-17 11:53:07,672 - pyskl - INFO - Epoch [36][1300/3746] lr: 8.691e-02, eta: 3 days, 17:34:45, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4478, loss_cls: 4.5836, loss: 4.5836 +2024-07-17 11:54:31,234 - pyskl - INFO - Epoch [36][1400/3746] lr: 8.689e-02, eta: 3 days, 17:33:57, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4423, loss_cls: 4.6384, loss: 4.6384 +2024-07-17 11:55:54,443 - pyskl - INFO - Epoch [36][1500/3746] lr: 8.688e-02, eta: 3 days, 17:33:08, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4544, loss_cls: 4.5869, loss: 4.5869 +2024-07-17 11:57:16,856 - pyskl - INFO - Epoch [36][1600/3746] lr: 8.686e-02, eta: 3 days, 17:32:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4464, loss_cls: 4.6125, loss: 4.6125 +2024-07-17 11:58:39,188 - pyskl - INFO - Epoch [36][1700/3746] lr: 8.684e-02, eta: 3 days, 17:31:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4448, loss_cls: 4.6454, loss: 4.6454 +2024-07-17 12:00:02,067 - pyskl - INFO - Epoch [36][1800/3746] lr: 8.682e-02, eta: 3 days, 17:30:35, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4448, loss_cls: 4.6560, loss: 4.6560 +2024-07-17 12:01:24,556 - pyskl - INFO - Epoch [36][1900/3746] lr: 8.680e-02, eta: 3 days, 17:29:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4502, loss_cls: 4.5822, loss: 4.5822 +2024-07-17 12:02:46,419 - pyskl - INFO - Epoch [36][2000/3746] lr: 8.678e-02, eta: 3 days, 17:28:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4572, loss_cls: 4.5603, loss: 4.5603 +2024-07-17 12:04:08,348 - pyskl - INFO - Epoch [36][2100/3746] lr: 8.676e-02, eta: 3 days, 17:27:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4373, loss_cls: 4.6265, loss: 4.6265 +2024-07-17 12:05:30,385 - pyskl - INFO - Epoch [36][2200/3746] lr: 8.674e-02, eta: 3 days, 17:27:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4662, loss_cls: 4.5313, loss: 4.5313 +2024-07-17 12:06:52,515 - pyskl - INFO - Epoch [36][2300/3746] lr: 8.672e-02, eta: 3 days, 17:26:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4380, loss_cls: 4.6280, loss: 4.6280 +2024-07-17 12:08:14,672 - pyskl - INFO - Epoch [36][2400/3746] lr: 8.671e-02, eta: 3 days, 17:25:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4487, loss_cls: 4.6192, loss: 4.6192 +2024-07-17 12:09:37,121 - pyskl - INFO - Epoch [36][2500/3746] lr: 8.669e-02, eta: 3 days, 17:24:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4508, loss_cls: 4.6146, loss: 4.6146 +2024-07-17 12:10:58,423 - pyskl - INFO - Epoch [36][2600/3746] lr: 8.667e-02, eta: 3 days, 17:23:32, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2150, top5_acc: 0.4541, loss_cls: 4.6007, loss: 4.6007 +2024-07-17 12:12:19,986 - pyskl - INFO - Epoch [36][2700/3746] lr: 8.665e-02, eta: 3 days, 17:22:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4375, loss_cls: 4.6162, loss: 4.6162 +2024-07-17 12:13:41,406 - pyskl - INFO - Epoch [36][2800/3746] lr: 8.663e-02, eta: 3 days, 17:21:42, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4456, loss_cls: 4.6081, loss: 4.6081 +2024-07-17 12:15:03,799 - pyskl - INFO - Epoch [36][2900/3746] lr: 8.661e-02, eta: 3 days, 17:20:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4502, loss_cls: 4.5893, loss: 4.5893 +2024-07-17 12:16:25,812 - pyskl - INFO - Epoch [36][3000/3746] lr: 8.659e-02, eta: 3 days, 17:19:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4509, loss_cls: 4.6278, loss: 4.6278 +2024-07-17 12:17:48,368 - pyskl - INFO - Epoch [36][3100/3746] lr: 8.657e-02, eta: 3 days, 17:19:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4555, loss_cls: 4.5646, loss: 4.5646 +2024-07-17 12:19:10,587 - pyskl - INFO - Epoch [36][3200/3746] lr: 8.655e-02, eta: 3 days, 17:18:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4587, loss_cls: 4.5785, loss: 4.5785 +2024-07-17 12:20:33,191 - pyskl - INFO - Epoch [36][3300/3746] lr: 8.653e-02, eta: 3 days, 17:17:20, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4433, loss_cls: 4.6076, loss: 4.6076 +2024-07-17 12:21:55,621 - pyskl - INFO - Epoch [36][3400/3746] lr: 8.651e-02, eta: 3 days, 17:16:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4442, loss_cls: 4.6008, loss: 4.6008 +2024-07-17 12:23:17,569 - pyskl - INFO - Epoch [36][3500/3746] lr: 8.650e-02, eta: 3 days, 17:15:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2092, top5_acc: 0.4433, loss_cls: 4.6091, loss: 4.6091 +2024-07-17 12:24:40,177 - pyskl - INFO - Epoch [36][3600/3746] lr: 8.648e-02, eta: 3 days, 17:14:43, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2111, top5_acc: 0.4428, loss_cls: 4.6243, loss: 4.6243 +2024-07-17 12:26:03,312 - pyskl - INFO - Epoch [36][3700/3746] lr: 8.646e-02, eta: 3 days, 17:13:53, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4492, loss_cls: 4.5602, loss: 4.5602 +2024-07-17 12:26:42,959 - pyskl - INFO - Saving checkpoint at 36 epochs +2024-07-17 12:28:34,215 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 12:28:34,893 - pyskl - INFO - +top1_acc 0.1432 +top5_acc 0.3416 +2024-07-17 12:28:34,893 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 12:28:34,940 - pyskl - INFO - +mean_acc 0.1432 +2024-07-17 12:28:34,956 - pyskl - INFO - Epoch(val) [36][309] top1_acc: 0.1432, top5_acc: 0.3416, mean_class_accuracy: 0.1432 +2024-07-17 12:32:22,183 - pyskl - INFO - Epoch [37][100/3746] lr: 8.643e-02, eta: 3 days, 17:18:14, time: 2.272, data_time: 1.272, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4520, loss_cls: 4.5860, loss: 4.5860 +2024-07-17 12:33:45,565 - pyskl - INFO - Epoch [37][200/3746] lr: 8.641e-02, eta: 3 days, 17:17:24, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4458, loss_cls: 4.6019, loss: 4.6019 +2024-07-17 12:35:08,918 - pyskl - INFO - Epoch [37][300/3746] lr: 8.639e-02, eta: 3 days, 17:16:35, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4584, loss_cls: 4.5689, loss: 4.5689 +2024-07-17 12:36:32,195 - pyskl - INFO - Epoch [37][400/3746] lr: 8.637e-02, eta: 3 days, 17:15:44, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4617, loss_cls: 4.5700, loss: 4.5700 +2024-07-17 12:37:55,070 - pyskl - INFO - Epoch [37][500/3746] lr: 8.635e-02, eta: 3 days, 17:14:53, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4659, loss_cls: 4.5301, loss: 4.5301 +2024-07-17 12:39:16,977 - pyskl - INFO - Epoch [37][600/3746] lr: 8.633e-02, eta: 3 days, 17:13:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2086, top5_acc: 0.4475, loss_cls: 4.5776, loss: 4.5776 +2024-07-17 12:40:38,945 - pyskl - INFO - Epoch [37][700/3746] lr: 8.631e-02, eta: 3 days, 17:13:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4619, loss_cls: 4.5306, loss: 4.5306 +2024-07-17 12:42:00,911 - pyskl - INFO - Epoch [37][800/3746] lr: 8.630e-02, eta: 3 days, 17:12:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2055, top5_acc: 0.4442, loss_cls: 4.6476, loss: 4.6476 +2024-07-17 12:43:23,250 - pyskl - INFO - Epoch [37][900/3746] lr: 8.628e-02, eta: 3 days, 17:11:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2105, top5_acc: 0.4520, loss_cls: 4.5992, loss: 4.5992 +2024-07-17 12:44:45,406 - pyskl - INFO - Epoch [37][1000/3746] lr: 8.626e-02, eta: 3 days, 17:10:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4552, loss_cls: 4.5730, loss: 4.5730 +2024-07-17 12:46:07,528 - pyskl - INFO - Epoch [37][1100/3746] lr: 8.624e-02, eta: 3 days, 17:09:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4487, loss_cls: 4.5762, loss: 4.5762 +2024-07-17 12:47:29,523 - pyskl - INFO - Epoch [37][1200/3746] lr: 8.622e-02, eta: 3 days, 17:08:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2133, top5_acc: 0.4508, loss_cls: 4.5959, loss: 4.5959 +2024-07-17 12:48:51,050 - pyskl - INFO - Epoch [37][1300/3746] lr: 8.620e-02, eta: 3 days, 17:07:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4484, loss_cls: 4.5780, loss: 4.5780 +2024-07-17 12:50:13,015 - pyskl - INFO - Epoch [37][1400/3746] lr: 8.618e-02, eta: 3 days, 17:06:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4573, loss_cls: 4.5799, loss: 4.5799 +2024-07-17 12:51:35,446 - pyskl - INFO - Epoch [37][1500/3746] lr: 8.616e-02, eta: 3 days, 17:05:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4520, loss_cls: 4.5571, loss: 4.5571 +2024-07-17 12:52:57,912 - pyskl - INFO - Epoch [37][1600/3746] lr: 8.614e-02, eta: 3 days, 17:04:56, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4453, loss_cls: 4.6096, loss: 4.6096 +2024-07-17 12:54:19,954 - pyskl - INFO - Epoch [37][1700/3746] lr: 8.612e-02, eta: 3 days, 17:04:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4480, loss_cls: 4.6095, loss: 4.6095 +2024-07-17 12:55:41,978 - pyskl - INFO - Epoch [37][1800/3746] lr: 8.610e-02, eta: 3 days, 17:03:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4508, loss_cls: 4.5890, loss: 4.5890 +2024-07-17 12:57:04,357 - pyskl - INFO - Epoch [37][1900/3746] lr: 8.608e-02, eta: 3 days, 17:02:13, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4417, loss_cls: 4.6236, loss: 4.6236 +2024-07-17 12:58:26,504 - pyskl - INFO - Epoch [37][2000/3746] lr: 8.606e-02, eta: 3 days, 17:01:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2122, top5_acc: 0.4422, loss_cls: 4.5744, loss: 4.5744 +2024-07-17 12:59:48,337 - pyskl - INFO - Epoch [37][2100/3746] lr: 8.604e-02, eta: 3 days, 17:00:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4552, loss_cls: 4.5814, loss: 4.5814 +2024-07-17 13:01:10,418 - pyskl - INFO - Epoch [37][2200/3746] lr: 8.602e-02, eta: 3 days, 16:59:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4525, loss_cls: 4.5785, loss: 4.5785 +2024-07-17 13:02:32,648 - pyskl - INFO - Epoch [37][2300/3746] lr: 8.601e-02, eta: 3 days, 16:58:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2155, top5_acc: 0.4505, loss_cls: 4.6071, loss: 4.6071 +2024-07-17 13:03:55,010 - pyskl - INFO - Epoch [37][2400/3746] lr: 8.599e-02, eta: 3 days, 16:57:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2194, top5_acc: 0.4545, loss_cls: 4.5731, loss: 4.5731 +2024-07-17 13:05:16,800 - pyskl - INFO - Epoch [37][2500/3746] lr: 8.597e-02, eta: 3 days, 16:56:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4458, loss_cls: 4.6000, loss: 4.6000 +2024-07-17 13:06:38,747 - pyskl - INFO - Epoch [37][2600/3746] lr: 8.595e-02, eta: 3 days, 16:55:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4506, loss_cls: 4.6164, loss: 4.6164 +2024-07-17 13:08:00,506 - pyskl - INFO - Epoch [37][2700/3746] lr: 8.593e-02, eta: 3 days, 16:54:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4469, loss_cls: 4.5859, loss: 4.5859 +2024-07-17 13:09:22,459 - pyskl - INFO - Epoch [37][2800/3746] lr: 8.591e-02, eta: 3 days, 16:53:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4512, loss_cls: 4.5826, loss: 4.5826 +2024-07-17 13:10:44,824 - pyskl - INFO - Epoch [37][2900/3746] lr: 8.589e-02, eta: 3 days, 16:53:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4537, loss_cls: 4.5927, loss: 4.5927 +2024-07-17 13:12:06,797 - pyskl - INFO - Epoch [37][3000/3746] lr: 8.587e-02, eta: 3 days, 16:52:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2077, top5_acc: 0.4416, loss_cls: 4.6233, loss: 4.6233 +2024-07-17 13:13:29,684 - pyskl - INFO - Epoch [37][3100/3746] lr: 8.585e-02, eta: 3 days, 16:51:16, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2067, top5_acc: 0.4372, loss_cls: 4.6750, loss: 4.6750 +2024-07-17 13:14:52,238 - pyskl - INFO - Epoch [37][3200/3746] lr: 8.583e-02, eta: 3 days, 16:50:22, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2064, top5_acc: 0.4431, loss_cls: 4.6273, loss: 4.6273 +2024-07-17 13:16:14,913 - pyskl - INFO - Epoch [37][3300/3746] lr: 8.581e-02, eta: 3 days, 16:49:29, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4525, loss_cls: 4.5580, loss: 4.5580 +2024-07-17 13:17:37,407 - pyskl - INFO - Epoch [37][3400/3746] lr: 8.579e-02, eta: 3 days, 16:48:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4517, loss_cls: 4.5843, loss: 4.5843 +2024-07-17 13:18:59,273 - pyskl - INFO - Epoch [37][3500/3746] lr: 8.577e-02, eta: 3 days, 16:47:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4437, loss_cls: 4.6053, loss: 4.6053 +2024-07-17 13:20:22,108 - pyskl - INFO - Epoch [37][3600/3746] lr: 8.575e-02, eta: 3 days, 16:46:46, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4462, loss_cls: 4.6493, loss: 4.6493 +2024-07-17 13:21:43,916 - pyskl - INFO - Epoch [37][3700/3746] lr: 8.573e-02, eta: 3 days, 16:45:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4541, loss_cls: 4.5666, loss: 4.5666 +2024-07-17 13:22:23,708 - pyskl - INFO - Saving checkpoint at 37 epochs +2024-07-17 13:24:15,472 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 13:24:16,140 - pyskl - INFO - +top1_acc 0.1633 +top5_acc 0.3743 +2024-07-17 13:24:16,140 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 13:24:16,183 - pyskl - INFO - +mean_acc 0.1634 +2024-07-17 13:24:16,188 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_31.pth was removed +2024-07-17 13:24:16,429 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_37.pth. +2024-07-17 13:24:16,430 - pyskl - INFO - Best top1_acc is 0.1633 at 37 epoch. +2024-07-17 13:24:16,443 - pyskl - INFO - Epoch(val) [37][309] top1_acc: 0.1633, top5_acc: 0.3743, mean_class_accuracy: 0.1634 +2024-07-17 13:28:07,861 - pyskl - INFO - Epoch [38][100/3746] lr: 8.570e-02, eta: 3 days, 16:50:09, time: 2.314, data_time: 1.323, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4536, loss_cls: 4.5664, loss: 4.5664 +2024-07-17 13:29:30,748 - pyskl - INFO - Epoch [38][200/3746] lr: 8.568e-02, eta: 3 days, 16:49:16, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4628, loss_cls: 4.5516, loss: 4.5516 +2024-07-17 13:30:53,049 - pyskl - INFO - Epoch [38][300/3746] lr: 8.567e-02, eta: 3 days, 16:48:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4522, loss_cls: 4.5796, loss: 4.5796 +2024-07-17 13:32:15,765 - pyskl - INFO - Epoch [38][400/3746] lr: 8.565e-02, eta: 3 days, 16:47:27, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4478, loss_cls: 4.5939, loss: 4.5939 +2024-07-17 13:33:38,554 - pyskl - INFO - Epoch [38][500/3746] lr: 8.563e-02, eta: 3 days, 16:46:33, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4581, loss_cls: 4.5776, loss: 4.5776 +2024-07-17 13:35:00,304 - pyskl - INFO - Epoch [38][600/3746] lr: 8.561e-02, eta: 3 days, 16:45:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4550, loss_cls: 4.5848, loss: 4.5848 +2024-07-17 13:36:22,470 - pyskl - INFO - Epoch [38][700/3746] lr: 8.559e-02, eta: 3 days, 16:44:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4492, loss_cls: 4.6069, loss: 4.6069 +2024-07-17 13:37:44,694 - pyskl - INFO - Epoch [38][800/3746] lr: 8.557e-02, eta: 3 days, 16:43:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4464, loss_cls: 4.6128, loss: 4.6128 +2024-07-17 13:39:06,912 - pyskl - INFO - Epoch [38][900/3746] lr: 8.555e-02, eta: 3 days, 16:42:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4470, loss_cls: 4.5824, loss: 4.5824 +2024-07-17 13:40:29,640 - pyskl - INFO - Epoch [38][1000/3746] lr: 8.553e-02, eta: 3 days, 16:41:56, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4581, loss_cls: 4.5448, loss: 4.5448 +2024-07-17 13:41:51,233 - pyskl - INFO - Epoch [38][1100/3746] lr: 8.551e-02, eta: 3 days, 16:40:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4495, loss_cls: 4.5840, loss: 4.5840 +2024-07-17 13:43:13,051 - pyskl - INFO - Epoch [38][1200/3746] lr: 8.549e-02, eta: 3 days, 16:40:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4412, loss_cls: 4.6029, loss: 4.6029 +2024-07-17 13:44:35,081 - pyskl - INFO - Epoch [38][1300/3746] lr: 8.547e-02, eta: 3 days, 16:39:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4548, loss_cls: 4.5844, loss: 4.5844 +2024-07-17 13:45:56,599 - pyskl - INFO - Epoch [38][1400/3746] lr: 8.545e-02, eta: 3 days, 16:38:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4478, loss_cls: 4.5987, loss: 4.5987 +2024-07-17 13:47:19,388 - pyskl - INFO - Epoch [38][1500/3746] lr: 8.543e-02, eta: 3 days, 16:37:13, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4475, loss_cls: 4.5848, loss: 4.5848 +2024-07-17 13:48:40,889 - pyskl - INFO - Epoch [38][1600/3746] lr: 8.541e-02, eta: 3 days, 16:36:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4594, loss_cls: 4.5668, loss: 4.5668 +2024-07-17 13:50:04,147 - pyskl - INFO - Epoch [38][1700/3746] lr: 8.539e-02, eta: 3 days, 16:35:22, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4627, loss_cls: 4.5474, loss: 4.5474 +2024-07-17 13:51:25,960 - pyskl - INFO - Epoch [38][1800/3746] lr: 8.537e-02, eta: 3 days, 16:34:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4405, loss_cls: 4.6156, loss: 4.6156 +2024-07-17 13:52:47,870 - pyskl - INFO - Epoch [38][1900/3746] lr: 8.535e-02, eta: 3 days, 16:33:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4619, loss_cls: 4.5250, loss: 4.5250 +2024-07-17 13:54:09,855 - pyskl - INFO - Epoch [38][2000/3746] lr: 8.533e-02, eta: 3 days, 16:32:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4606, loss_cls: 4.5744, loss: 4.5744 +2024-07-17 13:55:31,412 - pyskl - INFO - Epoch [38][2100/3746] lr: 8.531e-02, eta: 3 days, 16:31:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2131, top5_acc: 0.4447, loss_cls: 4.6206, loss: 4.6206 +2024-07-17 13:56:53,860 - pyskl - INFO - Epoch [38][2200/3746] lr: 8.529e-02, eta: 3 days, 16:30:38, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2128, top5_acc: 0.4478, loss_cls: 4.6260, loss: 4.6260 +2024-07-17 13:58:16,011 - pyskl - INFO - Epoch [38][2300/3746] lr: 8.527e-02, eta: 3 days, 16:29:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4508, loss_cls: 4.5667, loss: 4.5667 +2024-07-17 13:59:38,179 - pyskl - INFO - Epoch [38][2400/3746] lr: 8.525e-02, eta: 3 days, 16:28:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4572, loss_cls: 4.5784, loss: 4.5784 +2024-07-17 14:01:00,760 - pyskl - INFO - Epoch [38][2500/3746] lr: 8.523e-02, eta: 3 days, 16:27:50, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4511, loss_cls: 4.5717, loss: 4.5717 +2024-07-17 14:02:22,803 - pyskl - INFO - Epoch [38][2600/3746] lr: 8.521e-02, eta: 3 days, 16:26:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4502, loss_cls: 4.5810, loss: 4.5810 +2024-07-17 14:03:45,323 - pyskl - INFO - Epoch [38][2700/3746] lr: 8.519e-02, eta: 3 days, 16:25:58, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2119, top5_acc: 0.4367, loss_cls: 4.6221, loss: 4.6221 +2024-07-17 14:05:08,018 - pyskl - INFO - Epoch [38][2800/3746] lr: 8.517e-02, eta: 3 days, 16:25:04, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2117, top5_acc: 0.4419, loss_cls: 4.6290, loss: 4.6290 +2024-07-17 14:06:30,332 - pyskl - INFO - Epoch [38][2900/3746] lr: 8.515e-02, eta: 3 days, 16:24:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2155, top5_acc: 0.4533, loss_cls: 4.5538, loss: 4.5538 +2024-07-17 14:07:52,437 - pyskl - INFO - Epoch [38][3000/3746] lr: 8.513e-02, eta: 3 days, 16:23:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4623, loss_cls: 4.5593, loss: 4.5593 +2024-07-17 14:09:15,027 - pyskl - INFO - Epoch [38][3100/3746] lr: 8.511e-02, eta: 3 days, 16:22:15, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2080, top5_acc: 0.4452, loss_cls: 4.6199, loss: 4.6199 +2024-07-17 14:10:36,984 - pyskl - INFO - Epoch [38][3200/3746] lr: 8.509e-02, eta: 3 days, 16:21:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4534, loss_cls: 4.5401, loss: 4.5401 +2024-07-17 14:12:00,416 - pyskl - INFO - Epoch [38][3300/3746] lr: 8.507e-02, eta: 3 days, 16:20:25, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4622, loss_cls: 4.5569, loss: 4.5569 +2024-07-17 14:13:22,351 - pyskl - INFO - Epoch [38][3400/3746] lr: 8.505e-02, eta: 3 days, 16:19:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4550, loss_cls: 4.5861, loss: 4.5861 +2024-07-17 14:14:44,214 - pyskl - INFO - Epoch [38][3500/3746] lr: 8.503e-02, eta: 3 days, 16:18:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4559, loss_cls: 4.5651, loss: 4.5651 +2024-07-17 14:16:06,811 - pyskl - INFO - Epoch [38][3600/3746] lr: 8.501e-02, eta: 3 days, 16:17:35, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4445, loss_cls: 4.6342, loss: 4.6342 +2024-07-17 14:17:29,373 - pyskl - INFO - Epoch [38][3700/3746] lr: 8.499e-02, eta: 3 days, 16:16:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4486, loss_cls: 4.5846, loss: 4.5846 +2024-07-17 14:18:09,080 - pyskl - INFO - Saving checkpoint at 38 epochs +2024-07-17 14:20:01,227 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 14:20:01,896 - pyskl - INFO - +top1_acc 0.1706 +top5_acc 0.3842 +2024-07-17 14:20:01,896 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 14:20:01,939 - pyskl - INFO - +mean_acc 0.1704 +2024-07-17 14:20:01,945 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_37.pth was removed +2024-07-17 14:20:02,197 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_38.pth. +2024-07-17 14:20:02,197 - pyskl - INFO - Best top1_acc is 0.1706 at 38 epoch. +2024-07-17 14:20:02,210 - pyskl - INFO - Epoch(val) [38][309] top1_acc: 0.1706, top5_acc: 0.3842, mean_class_accuracy: 0.1704 +2024-07-17 14:23:54,359 - pyskl - INFO - Epoch [39][100/3746] lr: 8.496e-02, eta: 3 days, 16:20:47, time: 2.321, data_time: 1.324, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4637, loss_cls: 4.5335, loss: 4.5335 +2024-07-17 14:25:18,086 - pyskl - INFO - Epoch [39][200/3746] lr: 8.494e-02, eta: 3 days, 16:19:54, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4673, loss_cls: 4.5135, loss: 4.5135 +2024-07-17 14:26:41,332 - pyskl - INFO - Epoch [39][300/3746] lr: 8.492e-02, eta: 3 days, 16:19:00, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4559, loss_cls: 4.5697, loss: 4.5697 +2024-07-17 14:28:04,911 - pyskl - INFO - Epoch [39][400/3746] lr: 8.490e-02, eta: 3 days, 16:18:07, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4587, loss_cls: 4.5470, loss: 4.5470 +2024-07-17 14:29:28,404 - pyskl - INFO - Epoch [39][500/3746] lr: 8.488e-02, eta: 3 days, 16:17:14, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4542, loss_cls: 4.5683, loss: 4.5683 +2024-07-17 14:30:50,788 - pyskl - INFO - Epoch [39][600/3746] lr: 8.486e-02, eta: 3 days, 16:16:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4602, loss_cls: 4.5376, loss: 4.5376 +2024-07-17 14:32:12,880 - pyskl - INFO - Epoch [39][700/3746] lr: 8.484e-02, eta: 3 days, 16:15:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4612, loss_cls: 4.5379, loss: 4.5379 +2024-07-17 14:33:34,668 - pyskl - INFO - Epoch [39][800/3746] lr: 8.482e-02, eta: 3 days, 16:14:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4600, loss_cls: 4.5450, loss: 4.5450 +2024-07-17 14:34:56,815 - pyskl - INFO - Epoch [39][900/3746] lr: 8.480e-02, eta: 3 days, 16:13:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4533, loss_cls: 4.5756, loss: 4.5756 +2024-07-17 14:36:19,346 - pyskl - INFO - Epoch [39][1000/3746] lr: 8.478e-02, eta: 3 days, 16:12:27, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4527, loss_cls: 4.5782, loss: 4.5782 +2024-07-17 14:37:41,774 - pyskl - INFO - Epoch [39][1100/3746] lr: 8.476e-02, eta: 3 days, 16:11:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4420, loss_cls: 4.6198, loss: 4.6198 +2024-07-17 14:39:03,760 - pyskl - INFO - Epoch [39][1200/3746] lr: 8.474e-02, eta: 3 days, 16:10:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4516, loss_cls: 4.5398, loss: 4.5398 +2024-07-17 14:40:25,999 - pyskl - INFO - Epoch [39][1300/3746] lr: 8.472e-02, eta: 3 days, 16:09:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4545, loss_cls: 4.5417, loss: 4.5417 +2024-07-17 14:41:48,579 - pyskl - INFO - Epoch [39][1400/3746] lr: 8.470e-02, eta: 3 days, 16:08:38, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2100, top5_acc: 0.4430, loss_cls: 4.5953, loss: 4.5953 +2024-07-17 14:43:11,262 - pyskl - INFO - Epoch [39][1500/3746] lr: 8.468e-02, eta: 3 days, 16:07:42, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4597, loss_cls: 4.5797, loss: 4.5797 +2024-07-17 14:44:33,337 - pyskl - INFO - Epoch [39][1600/3746] lr: 8.466e-02, eta: 3 days, 16:06:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4530, loss_cls: 4.5736, loss: 4.5736 +2024-07-17 14:45:55,539 - pyskl - INFO - Epoch [39][1700/3746] lr: 8.464e-02, eta: 3 days, 16:05:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4542, loss_cls: 4.5901, loss: 4.5901 +2024-07-17 14:47:17,630 - pyskl - INFO - Epoch [39][1800/3746] lr: 8.462e-02, eta: 3 days, 16:04:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4447, loss_cls: 4.6190, loss: 4.6190 +2024-07-17 14:48:39,688 - pyskl - INFO - Epoch [39][1900/3746] lr: 8.460e-02, eta: 3 days, 16:03:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4552, loss_cls: 4.5762, loss: 4.5762 +2024-07-17 14:50:01,403 - pyskl - INFO - Epoch [39][2000/3746] lr: 8.458e-02, eta: 3 days, 16:02:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4550, loss_cls: 4.5608, loss: 4.5608 +2024-07-17 14:51:23,818 - pyskl - INFO - Epoch [39][2100/3746] lr: 8.456e-02, eta: 3 days, 16:01:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2139, top5_acc: 0.4552, loss_cls: 4.5687, loss: 4.5687 +2024-07-17 14:52:46,018 - pyskl - INFO - Epoch [39][2200/3746] lr: 8.454e-02, eta: 3 days, 16:00:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2108, top5_acc: 0.4495, loss_cls: 4.5875, loss: 4.5875 +2024-07-17 14:54:08,094 - pyskl - INFO - Epoch [39][2300/3746] lr: 8.452e-02, eta: 3 days, 15:59:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4477, loss_cls: 4.6021, loss: 4.6021 +2024-07-17 14:55:30,450 - pyskl - INFO - Epoch [39][2400/3746] lr: 8.450e-02, eta: 3 days, 15:59:01, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4578, loss_cls: 4.5641, loss: 4.5641 +2024-07-17 14:56:52,544 - pyskl - INFO - Epoch [39][2500/3746] lr: 8.448e-02, eta: 3 days, 15:58:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4419, loss_cls: 4.6066, loss: 4.6066 +2024-07-17 14:58:14,796 - pyskl - INFO - Epoch [39][2600/3746] lr: 8.446e-02, eta: 3 days, 15:57:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4559, loss_cls: 4.5870, loss: 4.5870 +2024-07-17 14:59:36,385 - pyskl - INFO - Epoch [39][2700/3746] lr: 8.444e-02, eta: 3 days, 15:56:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4533, loss_cls: 4.5715, loss: 4.5715 +2024-07-17 15:00:58,719 - pyskl - INFO - Epoch [39][2800/3746] lr: 8.442e-02, eta: 3 days, 15:55:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2109, top5_acc: 0.4406, loss_cls: 4.6163, loss: 4.6163 +2024-07-17 15:02:20,670 - pyskl - INFO - Epoch [39][2900/3746] lr: 8.440e-02, eta: 3 days, 15:54:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4517, loss_cls: 4.5759, loss: 4.5759 +2024-07-17 15:03:43,904 - pyskl - INFO - Epoch [39][3000/3746] lr: 8.438e-02, eta: 3 days, 15:53:14, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2131, top5_acc: 0.4372, loss_cls: 4.6292, loss: 4.6292 +2024-07-17 15:05:05,815 - pyskl - INFO - Epoch [39][3100/3746] lr: 8.436e-02, eta: 3 days, 15:52:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4534, loss_cls: 4.5556, loss: 4.5556 +2024-07-17 15:06:28,510 - pyskl - INFO - Epoch [39][3200/3746] lr: 8.434e-02, eta: 3 days, 15:51:19, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2120, top5_acc: 0.4445, loss_cls: 4.6097, loss: 4.6097 +2024-07-17 15:07:50,806 - pyskl - INFO - Epoch [39][3300/3746] lr: 8.432e-02, eta: 3 days, 15:50:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2177, top5_acc: 0.4469, loss_cls: 4.5854, loss: 4.5854 +2024-07-17 15:09:13,109 - pyskl - INFO - Epoch [39][3400/3746] lr: 8.430e-02, eta: 3 days, 15:49:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4480, loss_cls: 4.5793, loss: 4.5793 +2024-07-17 15:10:34,997 - pyskl - INFO - Epoch [39][3500/3746] lr: 8.428e-02, eta: 3 days, 15:48:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4575, loss_cls: 4.5464, loss: 4.5464 +2024-07-17 15:11:57,491 - pyskl - INFO - Epoch [39][3600/3746] lr: 8.426e-02, eta: 3 days, 15:47:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4512, loss_cls: 4.5729, loss: 4.5729 +2024-07-17 15:13:19,427 - pyskl - INFO - Epoch [39][3700/3746] lr: 8.424e-02, eta: 3 days, 15:46:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2194, top5_acc: 0.4527, loss_cls: 4.5844, loss: 4.5844 +2024-07-17 15:13:58,835 - pyskl - INFO - Saving checkpoint at 39 epochs +2024-07-17 15:15:49,728 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 15:15:50,414 - pyskl - INFO - +top1_acc 0.1597 +top5_acc 0.3769 +2024-07-17 15:15:50,414 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 15:15:50,469 - pyskl - INFO - +mean_acc 0.1595 +2024-07-17 15:15:50,485 - pyskl - INFO - Epoch(val) [39][309] top1_acc: 0.1597, top5_acc: 0.3769, mean_class_accuracy: 0.1595 +2024-07-17 15:19:41,170 - pyskl - INFO - Epoch [40][100/3746] lr: 8.421e-02, eta: 3 days, 15:50:17, time: 2.307, data_time: 1.307, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4627, loss_cls: 4.5613, loss: 4.5613 +2024-07-17 15:21:05,273 - pyskl - INFO - Epoch [40][200/3746] lr: 8.419e-02, eta: 3 days, 15:49:24, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4548, loss_cls: 4.5475, loss: 4.5475 +2024-07-17 15:22:28,352 - pyskl - INFO - Epoch [40][300/3746] lr: 8.417e-02, eta: 3 days, 15:48:27, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2181, top5_acc: 0.4586, loss_cls: 4.5603, loss: 4.5603 +2024-07-17 15:23:51,548 - pyskl - INFO - Epoch [40][400/3746] lr: 8.415e-02, eta: 3 days, 15:47:31, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4512, loss_cls: 4.5467, loss: 4.5467 +2024-07-17 15:25:14,681 - pyskl - INFO - Epoch [40][500/3746] lr: 8.413e-02, eta: 3 days, 15:46:35, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2136, top5_acc: 0.4586, loss_cls: 4.5566, loss: 4.5566 +2024-07-17 15:26:37,843 - pyskl - INFO - Epoch [40][600/3746] lr: 8.411e-02, eta: 3 days, 15:45:39, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4562, loss_cls: 4.5462, loss: 4.5462 +2024-07-17 15:28:01,101 - pyskl - INFO - Epoch [40][700/3746] lr: 8.408e-02, eta: 3 days, 15:44:43, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4500, loss_cls: 4.5674, loss: 4.5674 +2024-07-17 15:29:24,507 - pyskl - INFO - Epoch [40][800/3746] lr: 8.406e-02, eta: 3 days, 15:43:48, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4562, loss_cls: 4.5240, loss: 4.5240 +2024-07-17 15:30:47,449 - pyskl - INFO - Epoch [40][900/3746] lr: 8.404e-02, eta: 3 days, 15:42:51, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2159, top5_acc: 0.4372, loss_cls: 4.6300, loss: 4.6300 +2024-07-17 15:32:09,699 - pyskl - INFO - Epoch [40][1000/3746] lr: 8.402e-02, eta: 3 days, 15:41:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4525, loss_cls: 4.5577, loss: 4.5577 +2024-07-17 15:33:32,055 - pyskl - INFO - Epoch [40][1100/3746] lr: 8.400e-02, eta: 3 days, 15:40:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4531, loss_cls: 4.5736, loss: 4.5736 +2024-07-17 15:34:54,324 - pyskl - INFO - Epoch [40][1200/3746] lr: 8.398e-02, eta: 3 days, 15:39:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4552, loss_cls: 4.5733, loss: 4.5733 +2024-07-17 15:36:16,562 - pyskl - INFO - Epoch [40][1300/3746] lr: 8.396e-02, eta: 3 days, 15:38:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4508, loss_cls: 4.5853, loss: 4.5853 +2024-07-17 15:37:38,424 - pyskl - INFO - Epoch [40][1400/3746] lr: 8.394e-02, eta: 3 days, 15:37:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4600, loss_cls: 4.5434, loss: 4.5434 +2024-07-17 15:39:00,351 - pyskl - INFO - Epoch [40][1500/3746] lr: 8.392e-02, eta: 3 days, 15:36:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4502, loss_cls: 4.5613, loss: 4.5613 +2024-07-17 15:40:22,615 - pyskl - INFO - Epoch [40][1600/3746] lr: 8.390e-02, eta: 3 days, 15:35:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4556, loss_cls: 4.5519, loss: 4.5519 +2024-07-17 15:41:45,520 - pyskl - INFO - Epoch [40][1700/3746] lr: 8.388e-02, eta: 3 days, 15:35:00, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4636, loss_cls: 4.5447, loss: 4.5447 +2024-07-17 15:43:07,949 - pyskl - INFO - Epoch [40][1800/3746] lr: 8.386e-02, eta: 3 days, 15:34:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4450, loss_cls: 4.6081, loss: 4.6081 +2024-07-17 15:44:30,340 - pyskl - INFO - Epoch [40][1900/3746] lr: 8.384e-02, eta: 3 days, 15:33:03, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4467, loss_cls: 4.5959, loss: 4.5959 +2024-07-17 15:45:52,134 - pyskl - INFO - Epoch [40][2000/3746] lr: 8.382e-02, eta: 3 days, 15:32:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4464, loss_cls: 4.5818, loss: 4.5818 +2024-07-17 15:47:14,398 - pyskl - INFO - Epoch [40][2100/3746] lr: 8.380e-02, eta: 3 days, 15:31:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2194, top5_acc: 0.4555, loss_cls: 4.5591, loss: 4.5591 +2024-07-17 15:48:36,473 - pyskl - INFO - Epoch [40][2200/3746] lr: 8.378e-02, eta: 3 days, 15:30:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4473, loss_cls: 4.5631, loss: 4.5631 +2024-07-17 15:49:58,180 - pyskl - INFO - Epoch [40][2300/3746] lr: 8.376e-02, eta: 3 days, 15:29:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4437, loss_cls: 4.6021, loss: 4.6021 +2024-07-17 15:51:19,949 - pyskl - INFO - Epoch [40][2400/3746] lr: 8.374e-02, eta: 3 days, 15:28:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2075, top5_acc: 0.4486, loss_cls: 4.6026, loss: 4.6026 +2024-07-17 15:52:42,267 - pyskl - INFO - Epoch [40][2500/3746] lr: 8.371e-02, eta: 3 days, 15:27:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4511, loss_cls: 4.5869, loss: 4.5869 +2024-07-17 15:54:04,312 - pyskl - INFO - Epoch [40][2600/3746] lr: 8.369e-02, eta: 3 days, 15:26:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4581, loss_cls: 4.5851, loss: 4.5851 +2024-07-17 15:55:26,497 - pyskl - INFO - Epoch [40][2700/3746] lr: 8.367e-02, eta: 3 days, 15:25:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2083, top5_acc: 0.4442, loss_cls: 4.6084, loss: 4.6084 +2024-07-17 15:56:48,586 - pyskl - INFO - Epoch [40][2800/3746] lr: 8.365e-02, eta: 3 days, 15:24:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4581, loss_cls: 4.5443, loss: 4.5443 +2024-07-17 15:58:11,378 - pyskl - INFO - Epoch [40][2900/3746] lr: 8.363e-02, eta: 3 days, 15:23:07, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4537, loss_cls: 4.5707, loss: 4.5707 +2024-07-17 15:59:34,012 - pyskl - INFO - Epoch [40][3000/3746] lr: 8.361e-02, eta: 3 days, 15:22:08, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2148, top5_acc: 0.4483, loss_cls: 4.5934, loss: 4.5934 +2024-07-17 16:00:56,148 - pyskl - INFO - Epoch [40][3100/3746] lr: 8.359e-02, eta: 3 days, 15:21:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2116, top5_acc: 0.4430, loss_cls: 4.5942, loss: 4.5942 +2024-07-17 16:02:18,680 - pyskl - INFO - Epoch [40][3200/3746] lr: 8.357e-02, eta: 3 days, 15:20:10, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4591, loss_cls: 4.5324, loss: 4.5324 +2024-07-17 16:03:40,538 - pyskl - INFO - Epoch [40][3300/3746] lr: 8.355e-02, eta: 3 days, 15:19:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4464, loss_cls: 4.6162, loss: 4.6162 +2024-07-17 16:05:03,034 - pyskl - INFO - Epoch [40][3400/3746] lr: 8.353e-02, eta: 3 days, 15:18:10, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4547, loss_cls: 4.5814, loss: 4.5814 +2024-07-17 16:06:25,018 - pyskl - INFO - Epoch [40][3500/3746] lr: 8.351e-02, eta: 3 days, 15:17:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2223, top5_acc: 0.4597, loss_cls: 4.5601, loss: 4.5601 +2024-07-17 16:07:46,825 - pyskl - INFO - Epoch [40][3600/3746] lr: 8.349e-02, eta: 3 days, 15:16:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4592, loss_cls: 4.5149, loss: 4.5149 +2024-07-17 16:09:09,269 - pyskl - INFO - Epoch [40][3700/3746] lr: 8.347e-02, eta: 3 days, 15:15:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2142, top5_acc: 0.4530, loss_cls: 4.5805, loss: 4.5805 +2024-07-17 16:09:48,865 - pyskl - INFO - Saving checkpoint at 40 epochs +2024-07-17 16:11:41,715 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 16:11:42,403 - pyskl - INFO - +top1_acc 0.1549 +top5_acc 0.3630 +2024-07-17 16:11:42,403 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 16:11:42,451 - pyskl - INFO - +mean_acc 0.1549 +2024-07-17 16:11:42,466 - pyskl - INFO - Epoch(val) [40][309] top1_acc: 0.1549, top5_acc: 0.3630, mean_class_accuracy: 0.1549 +2024-07-17 16:15:32,289 - pyskl - INFO - Epoch [41][100/3746] lr: 8.344e-02, eta: 3 days, 15:18:44, time: 2.298, data_time: 1.304, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4547, loss_cls: 4.5398, loss: 4.5398 +2024-07-17 16:16:54,825 - pyskl - INFO - Epoch [41][200/3746] lr: 8.342e-02, eta: 3 days, 15:17:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4616, loss_cls: 4.5185, loss: 4.5185 +2024-07-17 16:18:17,027 - pyskl - INFO - Epoch [41][300/3746] lr: 8.339e-02, eta: 3 days, 15:16:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4462, loss_cls: 4.5630, loss: 4.5630 +2024-07-17 16:19:38,994 - pyskl - INFO - Epoch [41][400/3746] lr: 8.337e-02, eta: 3 days, 15:15:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4592, loss_cls: 4.5501, loss: 4.5501 +2024-07-17 16:21:01,121 - pyskl - INFO - Epoch [41][500/3746] lr: 8.335e-02, eta: 3 days, 15:14:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4475, loss_cls: 4.5772, loss: 4.5772 +2024-07-17 16:22:23,102 - pyskl - INFO - Epoch [41][600/3746] lr: 8.333e-02, eta: 3 days, 15:13:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4509, loss_cls: 4.5679, loss: 4.5679 +2024-07-17 16:23:44,919 - pyskl - INFO - Epoch [41][700/3746] lr: 8.331e-02, eta: 3 days, 15:12:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2125, top5_acc: 0.4597, loss_cls: 4.5433, loss: 4.5433 +2024-07-17 16:25:07,296 - pyskl - INFO - Epoch [41][800/3746] lr: 8.329e-02, eta: 3 days, 15:11:42, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4600, loss_cls: 4.5508, loss: 4.5508 +2024-07-17 16:26:29,226 - pyskl - INFO - Epoch [41][900/3746] lr: 8.327e-02, eta: 3 days, 15:10:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4534, loss_cls: 4.5555, loss: 4.5555 +2024-07-17 16:27:50,812 - pyskl - INFO - Epoch [41][1000/3746] lr: 8.325e-02, eta: 3 days, 15:09:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2114, top5_acc: 0.4511, loss_cls: 4.6105, loss: 4.6105 +2024-07-17 16:29:12,688 - pyskl - INFO - Epoch [41][1100/3746] lr: 8.323e-02, eta: 3 days, 15:08:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4502, loss_cls: 4.5785, loss: 4.5785 +2024-07-17 16:30:34,744 - pyskl - INFO - Epoch [41][1200/3746] lr: 8.321e-02, eta: 3 days, 15:07:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4495, loss_cls: 4.5992, loss: 4.5992 +2024-07-17 16:31:57,219 - pyskl - INFO - Epoch [41][1300/3746] lr: 8.319e-02, eta: 3 days, 15:06:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4628, loss_cls: 4.5393, loss: 4.5393 +2024-07-17 16:33:19,558 - pyskl - INFO - Epoch [41][1400/3746] lr: 8.316e-02, eta: 3 days, 15:05:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4531, loss_cls: 4.5812, loss: 4.5812 +2024-07-17 16:34:41,513 - pyskl - INFO - Epoch [41][1500/3746] lr: 8.314e-02, eta: 3 days, 15:04:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4544, loss_cls: 4.5655, loss: 4.5655 +2024-07-17 16:36:04,138 - pyskl - INFO - Epoch [41][1600/3746] lr: 8.312e-02, eta: 3 days, 15:03:37, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4561, loss_cls: 4.5549, loss: 4.5549 +2024-07-17 16:37:27,123 - pyskl - INFO - Epoch [41][1700/3746] lr: 8.310e-02, eta: 3 days, 15:02:38, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2169, top5_acc: 0.4459, loss_cls: 4.5955, loss: 4.5955 +2024-07-17 16:38:49,077 - pyskl - INFO - Epoch [41][1800/3746] lr: 8.308e-02, eta: 3 days, 15:01:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4522, loss_cls: 4.5451, loss: 4.5451 +2024-07-17 16:40:10,992 - pyskl - INFO - Epoch [41][1900/3746] lr: 8.306e-02, eta: 3 days, 15:00:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2134, top5_acc: 0.4509, loss_cls: 4.6029, loss: 4.6029 +2024-07-17 16:41:33,761 - pyskl - INFO - Epoch [41][2000/3746] lr: 8.304e-02, eta: 3 days, 14:59:37, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4692, loss_cls: 4.4902, loss: 4.4902 +2024-07-17 16:42:55,548 - pyskl - INFO - Epoch [41][2100/3746] lr: 8.302e-02, eta: 3 days, 14:58:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4614, loss_cls: 4.5524, loss: 4.5524 +2024-07-17 16:44:17,618 - pyskl - INFO - Epoch [41][2200/3746] lr: 8.300e-02, eta: 3 days, 14:57:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4478, loss_cls: 4.5977, loss: 4.5977 +2024-07-17 16:45:39,450 - pyskl - INFO - Epoch [41][2300/3746] lr: 8.298e-02, eta: 3 days, 14:56:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4589, loss_cls: 4.5468, loss: 4.5468 +2024-07-17 16:47:01,276 - pyskl - INFO - Epoch [41][2400/3746] lr: 8.296e-02, eta: 3 days, 14:55:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4512, loss_cls: 4.5859, loss: 4.5859 +2024-07-17 16:48:23,329 - pyskl - INFO - Epoch [41][2500/3746] lr: 8.293e-02, eta: 3 days, 14:54:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4519, loss_cls: 4.5519, loss: 4.5519 +2024-07-17 16:49:45,319 - pyskl - INFO - Epoch [41][2600/3746] lr: 8.291e-02, eta: 3 days, 14:53:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4591, loss_cls: 4.5578, loss: 4.5578 +2024-07-17 16:51:08,189 - pyskl - INFO - Epoch [41][2700/3746] lr: 8.289e-02, eta: 3 days, 14:52:29, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4545, loss_cls: 4.5438, loss: 4.5438 +2024-07-17 16:52:30,395 - pyskl - INFO - Epoch [41][2800/3746] lr: 8.287e-02, eta: 3 days, 14:51:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4577, loss_cls: 4.5749, loss: 4.5749 +2024-07-17 16:53:53,331 - pyskl - INFO - Epoch [41][2900/3746] lr: 8.285e-02, eta: 3 days, 14:50:29, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4591, loss_cls: 4.5563, loss: 4.5563 +2024-07-17 16:55:15,744 - pyskl - INFO - Epoch [41][3000/3746] lr: 8.283e-02, eta: 3 days, 14:49:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4559, loss_cls: 4.5640, loss: 4.5640 +2024-07-17 16:56:38,961 - pyskl - INFO - Epoch [41][3100/3746] lr: 8.281e-02, eta: 3 days, 14:48:30, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4537, loss_cls: 4.5792, loss: 4.5792 +2024-07-17 16:58:01,363 - pyskl - INFO - Epoch [41][3200/3746] lr: 8.279e-02, eta: 3 days, 14:47:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4509, loss_cls: 4.5777, loss: 4.5777 +2024-07-17 16:59:23,894 - pyskl - INFO - Epoch [41][3300/3746] lr: 8.277e-02, eta: 3 days, 14:46:30, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4578, loss_cls: 4.5447, loss: 4.5447 +2024-07-17 17:00:46,593 - pyskl - INFO - Epoch [41][3400/3746] lr: 8.274e-02, eta: 3 days, 14:45:30, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2189, top5_acc: 0.4523, loss_cls: 4.5806, loss: 4.5806 +2024-07-17 17:02:08,569 - pyskl - INFO - Epoch [41][3500/3746] lr: 8.272e-02, eta: 3 days, 14:44:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4555, loss_cls: 4.5651, loss: 4.5651 +2024-07-17 17:03:31,001 - pyskl - INFO - Epoch [41][3600/3746] lr: 8.270e-02, eta: 3 days, 14:43:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2123, top5_acc: 0.4548, loss_cls: 4.5748, loss: 4.5748 +2024-07-17 17:04:53,187 - pyskl - INFO - Epoch [41][3700/3746] lr: 8.268e-02, eta: 3 days, 14:42:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2141, top5_acc: 0.4530, loss_cls: 4.5835, loss: 4.5835 +2024-07-17 17:05:33,167 - pyskl - INFO - Saving checkpoint at 41 epochs +2024-07-17 17:07:25,852 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 17:07:26,518 - pyskl - INFO - +top1_acc 0.1708 +top5_acc 0.3774 +2024-07-17 17:07:26,519 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 17:07:26,561 - pyskl - INFO - +mean_acc 0.1708 +2024-07-17 17:07:26,566 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_38.pth was removed +2024-07-17 17:07:26,822 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_41.pth. +2024-07-17 17:07:26,823 - pyskl - INFO - Best top1_acc is 0.1708 at 41 epoch. +2024-07-17 17:07:26,835 - pyskl - INFO - Epoch(val) [41][309] top1_acc: 0.1708, top5_acc: 0.3774, mean_class_accuracy: 0.1708 +2024-07-17 17:11:19,147 - pyskl - INFO - Epoch [42][100/3746] lr: 8.265e-02, eta: 3 days, 14:45:56, time: 2.323, data_time: 1.333, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4650, loss_cls: 4.5223, loss: 4.5223 +2024-07-17 17:12:41,583 - pyskl - INFO - Epoch [42][200/3746] lr: 8.263e-02, eta: 3 days, 14:44:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4559, loss_cls: 4.5579, loss: 4.5579 +2024-07-17 17:14:03,334 - pyskl - INFO - Epoch [42][300/3746] lr: 8.261e-02, eta: 3 days, 14:43:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4603, loss_cls: 4.5174, loss: 4.5174 +2024-07-17 17:15:25,505 - pyskl - INFO - Epoch [42][400/3746] lr: 8.259e-02, eta: 3 days, 14:42:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2152, top5_acc: 0.4533, loss_cls: 4.5545, loss: 4.5545 +2024-07-17 17:16:47,735 - pyskl - INFO - Epoch [42][500/3746] lr: 8.257e-02, eta: 3 days, 14:41:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4664, loss_cls: 4.5186, loss: 4.5186 +2024-07-17 17:18:09,521 - pyskl - INFO - Epoch [42][600/3746] lr: 8.254e-02, eta: 3 days, 14:40:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4655, loss_cls: 4.5332, loss: 4.5332 +2024-07-17 17:19:31,143 - pyskl - INFO - Epoch [42][700/3746] lr: 8.252e-02, eta: 3 days, 14:39:44, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4623, loss_cls: 4.5264, loss: 4.5264 +2024-07-17 17:20:52,729 - pyskl - INFO - Epoch [42][800/3746] lr: 8.250e-02, eta: 3 days, 14:38:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4595, loss_cls: 4.5551, loss: 4.5551 +2024-07-17 17:22:14,847 - pyskl - INFO - Epoch [42][900/3746] lr: 8.248e-02, eta: 3 days, 14:37:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2162, top5_acc: 0.4559, loss_cls: 4.5613, loss: 4.5613 +2024-07-17 17:23:36,578 - pyskl - INFO - Epoch [42][1000/3746] lr: 8.246e-02, eta: 3 days, 14:36:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4702, loss_cls: 4.4922, loss: 4.4922 +2024-07-17 17:24:58,777 - pyskl - INFO - Epoch [42][1100/3746] lr: 8.244e-02, eta: 3 days, 14:35:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4547, loss_cls: 4.5731, loss: 4.5731 +2024-07-17 17:26:21,165 - pyskl - INFO - Epoch [42][1200/3746] lr: 8.242e-02, eta: 3 days, 14:34:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4673, loss_cls: 4.5553, loss: 4.5553 +2024-07-17 17:27:43,302 - pyskl - INFO - Epoch [42][1300/3746] lr: 8.240e-02, eta: 3 days, 14:33:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4550, loss_cls: 4.5800, loss: 4.5800 +2024-07-17 17:29:06,043 - pyskl - INFO - Epoch [42][1400/3746] lr: 8.237e-02, eta: 3 days, 14:32:31, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4523, loss_cls: 4.5892, loss: 4.5892 +2024-07-17 17:30:27,982 - pyskl - INFO - Epoch [42][1500/3746] lr: 8.235e-02, eta: 3 days, 14:31:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4497, loss_cls: 4.5616, loss: 4.5616 +2024-07-17 17:31:51,256 - pyskl - INFO - Epoch [42][1600/3746] lr: 8.233e-02, eta: 3 days, 14:30:29, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4616, loss_cls: 4.5602, loss: 4.5602 +2024-07-17 17:33:13,264 - pyskl - INFO - Epoch [42][1700/3746] lr: 8.231e-02, eta: 3 days, 14:29:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4584, loss_cls: 4.5464, loss: 4.5464 +2024-07-17 17:34:35,464 - pyskl - INFO - Epoch [42][1800/3746] lr: 8.229e-02, eta: 3 days, 14:28:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4533, loss_cls: 4.5810, loss: 4.5810 +2024-07-17 17:35:57,332 - pyskl - INFO - Epoch [42][1900/3746] lr: 8.227e-02, eta: 3 days, 14:27:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4569, loss_cls: 4.5483, loss: 4.5483 +2024-07-17 17:37:19,256 - pyskl - INFO - Epoch [42][2000/3746] lr: 8.225e-02, eta: 3 days, 14:26:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4614, loss_cls: 4.5694, loss: 4.5694 +2024-07-17 17:38:41,126 - pyskl - INFO - Epoch [42][2100/3746] lr: 8.222e-02, eta: 3 days, 14:25:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2180, top5_acc: 0.4567, loss_cls: 4.5604, loss: 4.5604 +2024-07-17 17:40:03,162 - pyskl - INFO - Epoch [42][2200/3746] lr: 8.220e-02, eta: 3 days, 14:24:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4541, loss_cls: 4.5735, loss: 4.5735 +2024-07-17 17:41:25,133 - pyskl - INFO - Epoch [42][2300/3746] lr: 8.218e-02, eta: 3 days, 14:23:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4594, loss_cls: 4.5402, loss: 4.5402 +2024-07-17 17:42:46,896 - pyskl - INFO - Epoch [42][2400/3746] lr: 8.216e-02, eta: 3 days, 14:22:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4481, loss_cls: 4.5859, loss: 4.5859 +2024-07-17 17:44:08,787 - pyskl - INFO - Epoch [42][2500/3746] lr: 8.214e-02, eta: 3 days, 14:21:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4587, loss_cls: 4.5359, loss: 4.5359 +2024-07-17 17:45:31,408 - pyskl - INFO - Epoch [42][2600/3746] lr: 8.212e-02, eta: 3 days, 14:20:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4567, loss_cls: 4.5562, loss: 4.5562 +2024-07-17 17:46:53,313 - pyskl - INFO - Epoch [42][2700/3746] lr: 8.210e-02, eta: 3 days, 14:19:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2153, top5_acc: 0.4456, loss_cls: 4.5976, loss: 4.5976 +2024-07-17 17:48:15,852 - pyskl - INFO - Epoch [42][2800/3746] lr: 8.207e-02, eta: 3 days, 14:18:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4516, loss_cls: 4.5530, loss: 4.5530 +2024-07-17 17:49:39,030 - pyskl - INFO - Epoch [42][2900/3746] lr: 8.205e-02, eta: 3 days, 14:17:01, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4541, loss_cls: 4.5755, loss: 4.5755 +2024-07-17 17:51:01,306 - pyskl - INFO - Epoch [42][3000/3746] lr: 8.203e-02, eta: 3 days, 14:15:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4566, loss_cls: 4.5649, loss: 4.5649 +2024-07-17 17:52:24,374 - pyskl - INFO - Epoch [42][3100/3746] lr: 8.201e-02, eta: 3 days, 14:14:59, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4500, loss_cls: 4.5774, loss: 4.5774 +2024-07-17 17:53:46,294 - pyskl - INFO - Epoch [42][3200/3746] lr: 8.199e-02, eta: 3 days, 14:13:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2188, top5_acc: 0.4537, loss_cls: 4.5856, loss: 4.5856 +2024-07-17 17:55:08,072 - pyskl - INFO - Epoch [42][3300/3746] lr: 8.197e-02, eta: 3 days, 14:12:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4614, loss_cls: 4.5326, loss: 4.5326 +2024-07-17 17:56:29,965 - pyskl - INFO - Epoch [42][3400/3746] lr: 8.195e-02, eta: 3 days, 14:11:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4500, loss_cls: 4.5629, loss: 4.5629 +2024-07-17 17:57:51,968 - pyskl - INFO - Epoch [42][3500/3746] lr: 8.192e-02, eta: 3 days, 14:10:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4481, loss_cls: 4.5822, loss: 4.5822 +2024-07-17 17:59:15,104 - pyskl - INFO - Epoch [42][3600/3746] lr: 8.190e-02, eta: 3 days, 14:09:47, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4467, loss_cls: 4.5742, loss: 4.5742 +2024-07-17 18:00:37,709 - pyskl - INFO - Epoch [42][3700/3746] lr: 8.188e-02, eta: 3 days, 14:08:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4548, loss_cls: 4.5625, loss: 4.5625 +2024-07-17 18:01:17,951 - pyskl - INFO - Saving checkpoint at 42 epochs +2024-07-17 18:03:08,743 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 18:03:09,399 - pyskl - INFO - +top1_acc 0.1794 +top5_acc 0.3897 +2024-07-17 18:03:09,399 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 18:03:09,440 - pyskl - INFO - +mean_acc 0.1793 +2024-07-17 18:03:09,445 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_41.pth was removed +2024-07-17 18:03:09,685 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_42.pth. +2024-07-17 18:03:09,686 - pyskl - INFO - Best top1_acc is 0.1794 at 42 epoch. +2024-07-17 18:03:09,697 - pyskl - INFO - Epoch(val) [42][309] top1_acc: 0.1794, top5_acc: 0.3897, mean_class_accuracy: 0.1793 +2024-07-17 18:06:53,353 - pyskl - INFO - Epoch [43][100/3746] lr: 8.185e-02, eta: 3 days, 14:11:41, time: 2.236, data_time: 1.261, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4656, loss_cls: 4.5116, loss: 4.5116 +2024-07-17 18:08:15,654 - pyskl - INFO - Epoch [43][200/3746] lr: 8.183e-02, eta: 3 days, 14:10:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4659, loss_cls: 4.5085, loss: 4.5085 +2024-07-17 18:09:37,691 - pyskl - INFO - Epoch [43][300/3746] lr: 8.181e-02, eta: 3 days, 14:09:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4569, loss_cls: 4.5507, loss: 4.5507 +2024-07-17 18:10:59,687 - pyskl - INFO - Epoch [43][400/3746] lr: 8.179e-02, eta: 3 days, 14:08:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4572, loss_cls: 4.5791, loss: 4.5791 +2024-07-17 18:12:21,528 - pyskl - INFO - Epoch [43][500/3746] lr: 8.176e-02, eta: 3 days, 14:07:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4602, loss_cls: 4.5096, loss: 4.5096 +2024-07-17 18:13:43,447 - pyskl - INFO - Epoch [43][600/3746] lr: 8.174e-02, eta: 3 days, 14:06:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4595, loss_cls: 4.5638, loss: 4.5638 +2024-07-17 18:15:05,589 - pyskl - INFO - Epoch [43][700/3746] lr: 8.172e-02, eta: 3 days, 14:05:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4577, loss_cls: 4.5815, loss: 4.5815 +2024-07-17 18:16:27,780 - pyskl - INFO - Epoch [43][800/3746] lr: 8.170e-02, eta: 3 days, 14:04:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2203, top5_acc: 0.4547, loss_cls: 4.5272, loss: 4.5272 +2024-07-17 18:17:50,211 - pyskl - INFO - Epoch [43][900/3746] lr: 8.168e-02, eta: 3 days, 14:03:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2192, top5_acc: 0.4550, loss_cls: 4.5483, loss: 4.5483 +2024-07-17 18:19:12,422 - pyskl - INFO - Epoch [43][1000/3746] lr: 8.166e-02, eta: 3 days, 14:02:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4636, loss_cls: 4.5141, loss: 4.5141 +2024-07-17 18:20:33,758 - pyskl - INFO - Epoch [43][1100/3746] lr: 8.163e-02, eta: 3 days, 14:01:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4503, loss_cls: 4.6226, loss: 4.6226 +2024-07-17 18:21:55,569 - pyskl - INFO - Epoch [43][1200/3746] lr: 8.161e-02, eta: 3 days, 14:00:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4670, loss_cls: 4.5261, loss: 4.5261 +2024-07-17 18:23:17,015 - pyskl - INFO - Epoch [43][1300/3746] lr: 8.159e-02, eta: 3 days, 13:59:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4695, loss_cls: 4.5091, loss: 4.5091 +2024-07-17 18:24:39,495 - pyskl - INFO - Epoch [43][1400/3746] lr: 8.157e-02, eta: 3 days, 13:57:58, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4575, loss_cls: 4.5953, loss: 4.5953 +2024-07-17 18:26:01,929 - pyskl - INFO - Epoch [43][1500/3746] lr: 8.155e-02, eta: 3 days, 13:56:56, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4602, loss_cls: 4.5233, loss: 4.5233 +2024-07-17 18:27:24,534 - pyskl - INFO - Epoch [43][1600/3746] lr: 8.153e-02, eta: 3 days, 13:55:54, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4642, loss_cls: 4.5350, loss: 4.5350 +2024-07-17 18:28:46,801 - pyskl - INFO - Epoch [43][1700/3746] lr: 8.150e-02, eta: 3 days, 13:54:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4605, loss_cls: 4.5301, loss: 4.5301 +2024-07-17 18:30:08,950 - pyskl - INFO - Epoch [43][1800/3746] lr: 8.148e-02, eta: 3 days, 13:53:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4516, loss_cls: 4.5625, loss: 4.5625 +2024-07-17 18:31:30,498 - pyskl - INFO - Epoch [43][1900/3746] lr: 8.146e-02, eta: 3 days, 13:52:43, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4555, loss_cls: 4.5221, loss: 4.5221 +2024-07-17 18:32:52,441 - pyskl - INFO - Epoch [43][2000/3746] lr: 8.144e-02, eta: 3 days, 13:51:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4534, loss_cls: 4.5575, loss: 4.5575 +2024-07-17 18:34:14,600 - pyskl - INFO - Epoch [43][2100/3746] lr: 8.142e-02, eta: 3 days, 13:50:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4587, loss_cls: 4.5387, loss: 4.5387 +2024-07-17 18:35:36,877 - pyskl - INFO - Epoch [43][2200/3746] lr: 8.140e-02, eta: 3 days, 13:49:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4619, loss_cls: 4.5565, loss: 4.5565 +2024-07-17 18:36:58,689 - pyskl - INFO - Epoch [43][2300/3746] lr: 8.137e-02, eta: 3 days, 13:48:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4564, loss_cls: 4.5553, loss: 4.5553 +2024-07-17 18:38:20,634 - pyskl - INFO - Epoch [43][2400/3746] lr: 8.135e-02, eta: 3 days, 13:47:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2167, top5_acc: 0.4578, loss_cls: 4.5707, loss: 4.5707 +2024-07-17 18:39:43,209 - pyskl - INFO - Epoch [43][2500/3746] lr: 8.133e-02, eta: 3 days, 13:46:23, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4573, loss_cls: 4.5264, loss: 4.5264 +2024-07-17 18:41:05,145 - pyskl - INFO - Epoch [43][2600/3746] lr: 8.131e-02, eta: 3 days, 13:45:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4566, loss_cls: 4.5385, loss: 4.5385 +2024-07-17 18:42:27,430 - pyskl - INFO - Epoch [43][2700/3746] lr: 8.129e-02, eta: 3 days, 13:44:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2084, top5_acc: 0.4400, loss_cls: 4.6468, loss: 4.6468 +2024-07-17 18:43:49,982 - pyskl - INFO - Epoch [43][2800/3746] lr: 8.126e-02, eta: 3 days, 13:43:14, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4648, loss_cls: 4.5418, loss: 4.5418 +2024-07-17 18:45:13,002 - pyskl - INFO - Epoch [43][2900/3746] lr: 8.124e-02, eta: 3 days, 13:42:13, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4587, loss_cls: 4.5478, loss: 4.5478 +2024-07-17 18:46:35,225 - pyskl - INFO - Epoch [43][3000/3746] lr: 8.122e-02, eta: 3 days, 13:41:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4537, loss_cls: 4.5497, loss: 4.5497 +2024-07-17 18:47:57,685 - pyskl - INFO - Epoch [43][3100/3746] lr: 8.120e-02, eta: 3 days, 13:40:07, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2156, top5_acc: 0.4537, loss_cls: 4.5649, loss: 4.5649 +2024-07-17 18:49:19,820 - pyskl - INFO - Epoch [43][3200/3746] lr: 8.118e-02, eta: 3 days, 13:39:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4578, loss_cls: 4.5381, loss: 4.5381 +2024-07-17 18:50:41,842 - pyskl - INFO - Epoch [43][3300/3746] lr: 8.116e-02, eta: 3 days, 13:37:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4583, loss_cls: 4.5582, loss: 4.5582 +2024-07-17 18:52:03,698 - pyskl - INFO - Epoch [43][3400/3746] lr: 8.113e-02, eta: 3 days, 13:36:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4619, loss_cls: 4.5195, loss: 4.5195 +2024-07-17 18:53:26,161 - pyskl - INFO - Epoch [43][3500/3746] lr: 8.111e-02, eta: 3 days, 13:35:52, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4589, loss_cls: 4.5416, loss: 4.5416 +2024-07-17 18:54:48,117 - pyskl - INFO - Epoch [43][3600/3746] lr: 8.109e-02, eta: 3 days, 13:34:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4570, loss_cls: 4.5433, loss: 4.5433 +2024-07-17 18:56:10,005 - pyskl - INFO - Epoch [43][3700/3746] lr: 8.107e-02, eta: 3 days, 13:33:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4567, loss_cls: 4.5781, loss: 4.5781 +2024-07-17 18:56:49,629 - pyskl - INFO - Saving checkpoint at 43 epochs +2024-07-17 18:58:40,480 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 18:58:41,146 - pyskl - INFO - +top1_acc 0.1682 +top5_acc 0.3803 +2024-07-17 18:58:41,146 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 18:58:41,187 - pyskl - INFO - +mean_acc 0.1680 +2024-07-17 18:58:41,198 - pyskl - INFO - Epoch(val) [43][309] top1_acc: 0.1682, top5_acc: 0.3803, mean_class_accuracy: 0.1680 +2024-07-17 19:02:26,611 - pyskl - INFO - Epoch [44][100/3746] lr: 8.104e-02, eta: 3 days, 13:36:33, time: 2.254, data_time: 1.270, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4670, loss_cls: 4.5201, loss: 4.5201 +2024-07-17 19:03:48,795 - pyskl - INFO - Epoch [44][200/3746] lr: 8.101e-02, eta: 3 days, 13:35:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2186, top5_acc: 0.4633, loss_cls: 4.5365, loss: 4.5365 +2024-07-17 19:05:11,062 - pyskl - INFO - Epoch [44][300/3746] lr: 8.099e-02, eta: 3 days, 13:34:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4586, loss_cls: 4.5401, loss: 4.5401 +2024-07-17 19:06:33,164 - pyskl - INFO - Epoch [44][400/3746] lr: 8.097e-02, eta: 3 days, 13:33:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4623, loss_cls: 4.5258, loss: 4.5258 +2024-07-17 19:07:55,075 - pyskl - INFO - Epoch [44][500/3746] lr: 8.095e-02, eta: 3 days, 13:32:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4689, loss_cls: 4.5335, loss: 4.5335 +2024-07-17 19:09:17,006 - pyskl - INFO - Epoch [44][600/3746] lr: 8.093e-02, eta: 3 days, 13:31:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4673, loss_cls: 4.4998, loss: 4.4998 +2024-07-17 19:10:38,948 - pyskl - INFO - Epoch [44][700/3746] lr: 8.090e-02, eta: 3 days, 13:30:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4609, loss_cls: 4.5473, loss: 4.5473 +2024-07-17 19:12:01,700 - pyskl - INFO - Epoch [44][800/3746] lr: 8.088e-02, eta: 3 days, 13:29:06, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4486, loss_cls: 4.5928, loss: 4.5928 +2024-07-17 19:13:23,683 - pyskl - INFO - Epoch [44][900/3746] lr: 8.086e-02, eta: 3 days, 13:28:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4655, loss_cls: 4.4962, loss: 4.4962 +2024-07-17 19:14:45,384 - pyskl - INFO - Epoch [44][1000/3746] lr: 8.084e-02, eta: 3 days, 13:26:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4512, loss_cls: 4.5882, loss: 4.5882 +2024-07-17 19:16:07,088 - pyskl - INFO - Epoch [44][1100/3746] lr: 8.082e-02, eta: 3 days, 13:25:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4589, loss_cls: 4.5538, loss: 4.5538 +2024-07-17 19:17:28,952 - pyskl - INFO - Epoch [44][1200/3746] lr: 8.079e-02, eta: 3 days, 13:24:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4683, loss_cls: 4.5264, loss: 4.5264 +2024-07-17 19:18:50,846 - pyskl - INFO - Epoch [44][1300/3746] lr: 8.077e-02, eta: 3 days, 13:23:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4656, loss_cls: 4.5245, loss: 4.5245 +2024-07-17 19:20:13,214 - pyskl - INFO - Epoch [44][1400/3746] lr: 8.075e-02, eta: 3 days, 13:22:38, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2209, top5_acc: 0.4603, loss_cls: 4.5580, loss: 4.5580 +2024-07-17 19:21:36,082 - pyskl - INFO - Epoch [44][1500/3746] lr: 8.073e-02, eta: 3 days, 13:21:35, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4600, loss_cls: 4.5477, loss: 4.5477 +2024-07-17 19:22:58,286 - pyskl - INFO - Epoch [44][1600/3746] lr: 8.071e-02, eta: 3 days, 13:20:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4694, loss_cls: 4.5135, loss: 4.5135 +2024-07-17 19:24:20,986 - pyskl - INFO - Epoch [44][1700/3746] lr: 8.068e-02, eta: 3 days, 13:19:28, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2220, top5_acc: 0.4641, loss_cls: 4.4847, loss: 4.4847 +2024-07-17 19:25:42,501 - pyskl - INFO - Epoch [44][1800/3746] lr: 8.066e-02, eta: 3 days, 13:18:22, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2230, top5_acc: 0.4539, loss_cls: 4.5799, loss: 4.5799 +2024-07-17 19:27:04,235 - pyskl - INFO - Epoch [44][1900/3746] lr: 8.064e-02, eta: 3 days, 13:17:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4592, loss_cls: 4.5436, loss: 4.5436 +2024-07-17 19:28:26,397 - pyskl - INFO - Epoch [44][2000/3746] lr: 8.062e-02, eta: 3 days, 13:16:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2145, top5_acc: 0.4500, loss_cls: 4.5849, loss: 4.5849 +2024-07-17 19:29:48,033 - pyskl - INFO - Epoch [44][2100/3746] lr: 8.060e-02, eta: 3 days, 13:15:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4566, loss_cls: 4.5822, loss: 4.5822 +2024-07-17 19:31:09,524 - pyskl - INFO - Epoch [44][2200/3746] lr: 8.057e-02, eta: 3 days, 13:14:01, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4541, loss_cls: 4.5872, loss: 4.5872 +2024-07-17 19:32:31,384 - pyskl - INFO - Epoch [44][2300/3746] lr: 8.055e-02, eta: 3 days, 13:12:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4652, loss_cls: 4.4928, loss: 4.4928 +2024-07-17 19:33:53,290 - pyskl - INFO - Epoch [44][2400/3746] lr: 8.053e-02, eta: 3 days, 13:11:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2147, top5_acc: 0.4436, loss_cls: 4.5740, loss: 4.5740 +2024-07-17 19:35:15,915 - pyskl - INFO - Epoch [44][2500/3746] lr: 8.051e-02, eta: 3 days, 13:10:48, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4633, loss_cls: 4.5035, loss: 4.5035 +2024-07-17 19:36:38,177 - pyskl - INFO - Epoch [44][2600/3746] lr: 8.048e-02, eta: 3 days, 13:09:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4466, loss_cls: 4.5868, loss: 4.5868 +2024-07-17 19:38:00,563 - pyskl - INFO - Epoch [44][2700/3746] lr: 8.046e-02, eta: 3 days, 13:08:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4581, loss_cls: 4.5674, loss: 4.5674 +2024-07-17 19:39:23,053 - pyskl - INFO - Epoch [44][2800/3746] lr: 8.044e-02, eta: 3 days, 13:07:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4592, loss_cls: 4.5431, loss: 4.5431 +2024-07-17 19:40:45,720 - pyskl - INFO - Epoch [44][2900/3746] lr: 8.042e-02, eta: 3 days, 13:06:33, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4684, loss_cls: 4.5414, loss: 4.5414 +2024-07-17 19:42:07,899 - pyskl - INFO - Epoch [44][3000/3746] lr: 8.040e-02, eta: 3 days, 13:05:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4581, loss_cls: 4.5540, loss: 4.5540 +2024-07-17 19:43:30,104 - pyskl - INFO - Epoch [44][3100/3746] lr: 8.037e-02, eta: 3 days, 13:04:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4650, loss_cls: 4.5490, loss: 4.5490 +2024-07-17 19:44:52,309 - pyskl - INFO - Epoch [44][3200/3746] lr: 8.035e-02, eta: 3 days, 13:03:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4544, loss_cls: 4.5506, loss: 4.5506 +2024-07-17 19:46:14,695 - pyskl - INFO - Epoch [44][3300/3746] lr: 8.033e-02, eta: 3 days, 13:02:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4644, loss_cls: 4.5208, loss: 4.5208 +2024-07-17 19:47:36,520 - pyskl - INFO - Epoch [44][3400/3746] lr: 8.031e-02, eta: 3 days, 13:01:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4562, loss_cls: 4.5467, loss: 4.5467 +2024-07-17 19:48:57,903 - pyskl - INFO - Epoch [44][3500/3746] lr: 8.028e-02, eta: 3 days, 13:00:04, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4612, loss_cls: 4.5381, loss: 4.5381 +2024-07-17 19:50:20,344 - pyskl - INFO - Epoch [44][3600/3746] lr: 8.026e-02, eta: 3 days, 12:59:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4519, loss_cls: 4.5830, loss: 4.5830 +2024-07-17 19:51:42,645 - pyskl - INFO - Epoch [44][3700/3746] lr: 8.024e-02, eta: 3 days, 12:57:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4652, loss_cls: 4.5166, loss: 4.5166 +2024-07-17 19:52:22,160 - pyskl - INFO - Saving checkpoint at 44 epochs +2024-07-17 19:54:12,773 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 19:54:13,438 - pyskl - INFO - +top1_acc 0.1539 +top5_acc 0.3480 +2024-07-17 19:54:13,438 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 19:54:13,480 - pyskl - INFO - +mean_acc 0.1537 +2024-07-17 19:54:13,492 - pyskl - INFO - Epoch(val) [44][309] top1_acc: 0.1539, top5_acc: 0.3480, mean_class_accuracy: 0.1537 +2024-07-17 19:57:59,259 - pyskl - INFO - Epoch [45][100/3746] lr: 8.021e-02, eta: 3 days, 13:00:36, time: 2.258, data_time: 1.276, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4670, loss_cls: 4.5164, loss: 4.5164 +2024-07-17 19:59:21,400 - pyskl - INFO - Epoch [45][200/3746] lr: 8.019e-02, eta: 3 days, 12:59:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4539, loss_cls: 4.5622, loss: 4.5622 +2024-07-17 20:00:43,002 - pyskl - INFO - Epoch [45][300/3746] lr: 8.016e-02, eta: 3 days, 12:58:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4661, loss_cls: 4.5277, loss: 4.5277 +2024-07-17 20:02:04,991 - pyskl - INFO - Epoch [45][400/3746] lr: 8.014e-02, eta: 3 days, 12:57:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4548, loss_cls: 4.5391, loss: 4.5391 +2024-07-17 20:03:26,923 - pyskl - INFO - Epoch [45][500/3746] lr: 8.012e-02, eta: 3 days, 12:56:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2131, top5_acc: 0.4603, loss_cls: 4.5392, loss: 4.5392 +2024-07-17 20:04:48,342 - pyskl - INFO - Epoch [45][600/3746] lr: 8.010e-02, eta: 3 days, 12:55:07, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4744, loss_cls: 4.4878, loss: 4.4878 +2024-07-17 20:06:10,143 - pyskl - INFO - Epoch [45][700/3746] lr: 8.007e-02, eta: 3 days, 12:54:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2178, top5_acc: 0.4503, loss_cls: 4.5531, loss: 4.5531 +2024-07-17 20:07:31,734 - pyskl - INFO - Epoch [45][800/3746] lr: 8.005e-02, eta: 3 days, 12:52:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2236, top5_acc: 0.4652, loss_cls: 4.5066, loss: 4.5066 +2024-07-17 20:08:54,116 - pyskl - INFO - Epoch [45][900/3746] lr: 8.003e-02, eta: 3 days, 12:51:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4653, loss_cls: 4.5355, loss: 4.5355 +2024-07-17 20:10:16,451 - pyskl - INFO - Epoch [45][1000/3746] lr: 8.001e-02, eta: 3 days, 12:50:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4617, loss_cls: 4.5291, loss: 4.5291 +2024-07-17 20:11:38,856 - pyskl - INFO - Epoch [45][1100/3746] lr: 7.998e-02, eta: 3 days, 12:49:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4564, loss_cls: 4.5411, loss: 4.5411 +2024-07-17 20:13:00,650 - pyskl - INFO - Epoch [45][1200/3746] lr: 7.996e-02, eta: 3 days, 12:48:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2247, top5_acc: 0.4639, loss_cls: 4.5263, loss: 4.5263 +2024-07-17 20:14:23,333 - pyskl - INFO - Epoch [45][1300/3746] lr: 7.994e-02, eta: 3 days, 12:47:31, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4652, loss_cls: 4.5237, loss: 4.5237 +2024-07-17 20:15:46,097 - pyskl - INFO - Epoch [45][1400/3746] lr: 7.992e-02, eta: 3 days, 12:46:28, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2217, top5_acc: 0.4655, loss_cls: 4.5075, loss: 4.5075 +2024-07-17 20:17:08,772 - pyskl - INFO - Epoch [45][1500/3746] lr: 7.990e-02, eta: 3 days, 12:45:24, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4653, loss_cls: 4.5290, loss: 4.5290 +2024-07-17 20:18:30,795 - pyskl - INFO - Epoch [45][1600/3746] lr: 7.987e-02, eta: 3 days, 12:44:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4633, loss_cls: 4.5309, loss: 4.5309 +2024-07-17 20:19:52,951 - pyskl - INFO - Epoch [45][1700/3746] lr: 7.985e-02, eta: 3 days, 12:43:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4658, loss_cls: 4.5118, loss: 4.5118 +2024-07-17 20:21:14,390 - pyskl - INFO - Epoch [45][1800/3746] lr: 7.983e-02, eta: 3 days, 12:42:06, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2164, top5_acc: 0.4575, loss_cls: 4.5895, loss: 4.5895 +2024-07-17 20:22:36,703 - pyskl - INFO - Epoch [45][1900/3746] lr: 7.981e-02, eta: 3 days, 12:41:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4541, loss_cls: 4.5489, loss: 4.5489 +2024-07-17 20:23:58,475 - pyskl - INFO - Epoch [45][2000/3746] lr: 7.978e-02, eta: 3 days, 12:39:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4744, loss_cls: 4.4946, loss: 4.4946 +2024-07-17 20:25:20,196 - pyskl - INFO - Epoch [45][2100/3746] lr: 7.976e-02, eta: 3 days, 12:38:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2161, top5_acc: 0.4498, loss_cls: 4.5926, loss: 4.5926 +2024-07-17 20:26:41,979 - pyskl - INFO - Epoch [45][2200/3746] lr: 7.974e-02, eta: 3 days, 12:37:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4677, loss_cls: 4.5203, loss: 4.5203 +2024-07-17 20:28:03,521 - pyskl - INFO - Epoch [45][2300/3746] lr: 7.972e-02, eta: 3 days, 12:36:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2194, top5_acc: 0.4556, loss_cls: 4.5364, loss: 4.5364 +2024-07-17 20:29:25,871 - pyskl - INFO - Epoch [45][2400/3746] lr: 7.969e-02, eta: 3 days, 12:35:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4634, loss_cls: 4.4997, loss: 4.4997 +2024-07-17 20:30:47,410 - pyskl - INFO - Epoch [45][2500/3746] lr: 7.967e-02, eta: 3 days, 12:34:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2114, top5_acc: 0.4473, loss_cls: 4.6055, loss: 4.6055 +2024-07-17 20:32:10,079 - pyskl - INFO - Epoch [45][2600/3746] lr: 7.965e-02, eta: 3 days, 12:33:20, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2175, top5_acc: 0.4536, loss_cls: 4.5751, loss: 4.5751 +2024-07-17 20:33:32,063 - pyskl - INFO - Epoch [45][2700/3746] lr: 7.963e-02, eta: 3 days, 12:32:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4592, loss_cls: 4.5276, loss: 4.5276 +2024-07-17 20:34:54,759 - pyskl - INFO - Epoch [45][2800/3746] lr: 7.960e-02, eta: 3 days, 12:31:10, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4522, loss_cls: 4.5577, loss: 4.5577 +2024-07-17 20:36:17,429 - pyskl - INFO - Epoch [45][2900/3746] lr: 7.958e-02, eta: 3 days, 12:30:06, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4602, loss_cls: 4.5622, loss: 4.5622 +2024-07-17 20:37:40,095 - pyskl - INFO - Epoch [45][3000/3746] lr: 7.956e-02, eta: 3 days, 12:29:01, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4609, loss_cls: 4.5256, loss: 4.5256 +2024-07-17 20:39:02,274 - pyskl - INFO - Epoch [45][3100/3746] lr: 7.954e-02, eta: 3 days, 12:27:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2173, top5_acc: 0.4562, loss_cls: 4.5588, loss: 4.5588 +2024-07-17 20:40:24,185 - pyskl - INFO - Epoch [45][3200/3746] lr: 7.951e-02, eta: 3 days, 12:26:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4686, loss_cls: 4.5052, loss: 4.5052 +2024-07-17 20:41:46,264 - pyskl - INFO - Epoch [45][3300/3746] lr: 7.949e-02, eta: 3 days, 12:25:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2172, top5_acc: 0.4573, loss_cls: 4.5611, loss: 4.5611 +2024-07-17 20:43:08,648 - pyskl - INFO - Epoch [45][3400/3746] lr: 7.947e-02, eta: 3 days, 12:24:39, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4581, loss_cls: 4.5334, loss: 4.5334 +2024-07-17 20:44:30,037 - pyskl - INFO - Epoch [45][3500/3746] lr: 7.945e-02, eta: 3 days, 12:23:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4552, loss_cls: 4.5944, loss: 4.5944 +2024-07-17 20:45:52,656 - pyskl - INFO - Epoch [45][3600/3746] lr: 7.942e-02, eta: 3 days, 12:22:27, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4672, loss_cls: 4.5084, loss: 4.5084 +2024-07-17 20:47:14,582 - pyskl - INFO - Epoch [45][3700/3746] lr: 7.940e-02, eta: 3 days, 12:21:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4586, loss_cls: 4.5327, loss: 4.5327 +2024-07-17 20:47:54,267 - pyskl - INFO - Saving checkpoint at 45 epochs +2024-07-17 20:49:45,316 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 20:49:46,017 - pyskl - INFO - +top1_acc 0.1371 +top5_acc 0.3399 +2024-07-17 20:49:46,017 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 20:49:46,068 - pyskl - INFO - +mean_acc 0.1371 +2024-07-17 20:49:46,082 - pyskl - INFO - Epoch(val) [45][309] top1_acc: 0.1371, top5_acc: 0.3399, mean_class_accuracy: 0.1371 +2024-07-17 20:53:41,537 - pyskl - INFO - Epoch [46][100/3746] lr: 7.937e-02, eta: 3 days, 12:24:14, time: 2.354, data_time: 1.346, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4727, loss_cls: 4.4757, loss: 4.4757 +2024-07-17 20:55:04,600 - pyskl - INFO - Epoch [46][200/3746] lr: 7.934e-02, eta: 3 days, 12:23:11, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4639, loss_cls: 4.5215, loss: 4.5215 +2024-07-17 20:56:27,465 - pyskl - INFO - Epoch [46][300/3746] lr: 7.932e-02, eta: 3 days, 12:22:06, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4658, loss_cls: 4.4793, loss: 4.4793 +2024-07-17 20:57:49,903 - pyskl - INFO - Epoch [46][400/3746] lr: 7.930e-02, eta: 3 days, 12:21:01, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4684, loss_cls: 4.5059, loss: 4.5059 +2024-07-17 20:59:12,340 - pyskl - INFO - Epoch [46][500/3746] lr: 7.928e-02, eta: 3 days, 12:19:56, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4516, loss_cls: 4.5610, loss: 4.5610 +2024-07-17 21:00:34,149 - pyskl - INFO - Epoch [46][600/3746] lr: 7.925e-02, eta: 3 days, 12:18:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4631, loss_cls: 4.5275, loss: 4.5275 +2024-07-17 21:01:55,925 - pyskl - INFO - Epoch [46][700/3746] lr: 7.923e-02, eta: 3 days, 12:17:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2233, top5_acc: 0.4580, loss_cls: 4.5865, loss: 4.5865 +2024-07-17 21:03:17,879 - pyskl - INFO - Epoch [46][800/3746] lr: 7.921e-02, eta: 3 days, 12:16:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4611, loss_cls: 4.5399, loss: 4.5399 +2024-07-17 21:04:40,178 - pyskl - INFO - Epoch [46][900/3746] lr: 7.919e-02, eta: 3 days, 12:15:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4589, loss_cls: 4.5100, loss: 4.5100 +2024-07-17 21:06:02,568 - pyskl - INFO - Epoch [46][1000/3746] lr: 7.916e-02, eta: 3 days, 12:14:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4583, loss_cls: 4.5121, loss: 4.5121 +2024-07-17 21:07:24,138 - pyskl - INFO - Epoch [46][1100/3746] lr: 7.914e-02, eta: 3 days, 12:13:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4509, loss_cls: 4.5765, loss: 4.5765 +2024-07-17 21:08:46,128 - pyskl - INFO - Epoch [46][1200/3746] lr: 7.912e-02, eta: 3 days, 12:12:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4480, loss_cls: 4.5777, loss: 4.5777 +2024-07-17 21:10:08,555 - pyskl - INFO - Epoch [46][1300/3746] lr: 7.909e-02, eta: 3 days, 12:11:05, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4595, loss_cls: 4.5537, loss: 4.5537 +2024-07-17 21:11:31,196 - pyskl - INFO - Epoch [46][1400/3746] lr: 7.907e-02, eta: 3 days, 12:10:00, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4695, loss_cls: 4.4808, loss: 4.4808 +2024-07-17 21:12:54,139 - pyskl - INFO - Epoch [46][1500/3746] lr: 7.905e-02, eta: 3 days, 12:08:56, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4597, loss_cls: 4.5310, loss: 4.5310 +2024-07-17 21:14:16,771 - pyskl - INFO - Epoch [46][1600/3746] lr: 7.903e-02, eta: 3 days, 12:07:51, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4662, loss_cls: 4.4938, loss: 4.4938 +2024-07-17 21:15:38,370 - pyskl - INFO - Epoch [46][1700/3746] lr: 7.900e-02, eta: 3 days, 12:06:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4689, loss_cls: 4.5136, loss: 4.5136 +2024-07-17 21:17:00,127 - pyskl - INFO - Epoch [46][1800/3746] lr: 7.898e-02, eta: 3 days, 12:05:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4677, loss_cls: 4.4918, loss: 4.4918 +2024-07-17 21:18:22,384 - pyskl - INFO - Epoch [46][1900/3746] lr: 7.896e-02, eta: 3 days, 12:04:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4577, loss_cls: 4.5224, loss: 4.5224 +2024-07-17 21:19:44,485 - pyskl - INFO - Epoch [46][2000/3746] lr: 7.894e-02, eta: 3 days, 12:03:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4628, loss_cls: 4.5337, loss: 4.5337 +2024-07-17 21:21:06,115 - pyskl - INFO - Epoch [46][2100/3746] lr: 7.891e-02, eta: 3 days, 12:02:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4637, loss_cls: 4.4874, loss: 4.4874 +2024-07-17 21:22:28,596 - pyskl - INFO - Epoch [46][2200/3746] lr: 7.889e-02, eta: 3 days, 12:01:11, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4641, loss_cls: 4.5410, loss: 4.5410 +2024-07-17 21:23:50,551 - pyskl - INFO - Epoch [46][2300/3746] lr: 7.887e-02, eta: 3 days, 12:00:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4645, loss_cls: 4.5450, loss: 4.5450 +2024-07-17 21:25:12,577 - pyskl - INFO - Epoch [46][2400/3746] lr: 7.884e-02, eta: 3 days, 11:58:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4642, loss_cls: 4.5096, loss: 4.5096 +2024-07-17 21:26:34,620 - pyskl - INFO - Epoch [46][2500/3746] lr: 7.882e-02, eta: 3 days, 11:57:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4666, loss_cls: 4.5334, loss: 4.5334 +2024-07-17 21:27:56,698 - pyskl - INFO - Epoch [46][2600/3746] lr: 7.880e-02, eta: 3 days, 11:56:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2137, top5_acc: 0.4514, loss_cls: 4.5899, loss: 4.5899 +2024-07-17 21:29:18,614 - pyskl - INFO - Epoch [46][2700/3746] lr: 7.878e-02, eta: 3 days, 11:55:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4466, loss_cls: 4.5684, loss: 4.5684 +2024-07-17 21:30:41,362 - pyskl - INFO - Epoch [46][2800/3746] lr: 7.875e-02, eta: 3 days, 11:54:32, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4583, loss_cls: 4.5762, loss: 4.5762 +2024-07-17 21:32:04,156 - pyskl - INFO - Epoch [46][2900/3746] lr: 7.873e-02, eta: 3 days, 11:53:27, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4633, loss_cls: 4.5195, loss: 4.5195 +2024-07-17 21:33:26,550 - pyskl - INFO - Epoch [46][3000/3746] lr: 7.871e-02, eta: 3 days, 11:52:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4675, loss_cls: 4.5192, loss: 4.5192 +2024-07-17 21:34:49,207 - pyskl - INFO - Epoch [46][3100/3746] lr: 7.868e-02, eta: 3 days, 11:51:16, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2191, top5_acc: 0.4609, loss_cls: 4.5351, loss: 4.5351 +2024-07-17 21:36:11,485 - pyskl - INFO - Epoch [46][3200/3746] lr: 7.866e-02, eta: 3 days, 11:50:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4505, loss_cls: 4.5821, loss: 4.5821 +2024-07-17 21:37:34,075 - pyskl - INFO - Epoch [46][3300/3746] lr: 7.864e-02, eta: 3 days, 11:49:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4727, loss_cls: 4.4964, loss: 4.4964 +2024-07-17 21:38:56,213 - pyskl - INFO - Epoch [46][3400/3746] lr: 7.862e-02, eta: 3 days, 11:47:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2227, top5_acc: 0.4495, loss_cls: 4.5687, loss: 4.5687 +2024-07-17 21:40:18,029 - pyskl - INFO - Epoch [46][3500/3746] lr: 7.859e-02, eta: 3 days, 11:46:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4684, loss_cls: 4.5204, loss: 4.5204 +2024-07-17 21:41:40,698 - pyskl - INFO - Epoch [46][3600/3746] lr: 7.857e-02, eta: 3 days, 11:45:45, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4603, loss_cls: 4.5451, loss: 4.5451 +2024-07-17 21:43:03,643 - pyskl - INFO - Epoch [46][3700/3746] lr: 7.855e-02, eta: 3 days, 11:44:40, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4602, loss_cls: 4.5499, loss: 4.5499 +2024-07-17 21:43:43,095 - pyskl - INFO - Saving checkpoint at 46 epochs +2024-07-17 21:45:34,452 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 21:45:35,132 - pyskl - INFO - +top1_acc 0.1763 +top5_acc 0.3928 +2024-07-17 21:45:35,132 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 21:45:35,178 - pyskl - INFO - +mean_acc 0.1761 +2024-07-17 21:45:35,192 - pyskl - INFO - Epoch(val) [46][309] top1_acc: 0.1763, top5_acc: 0.3928, mean_class_accuracy: 0.1761 +2024-07-17 21:49:29,687 - pyskl - INFO - Epoch [47][100/3746] lr: 7.851e-02, eta: 3 days, 11:47:22, time: 2.345, data_time: 1.345, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4738, loss_cls: 4.4870, loss: 4.4870 +2024-07-17 21:50:53,130 - pyskl - INFO - Epoch [47][200/3746] lr: 7.849e-02, eta: 3 days, 11:46:18, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2166, top5_acc: 0.4509, loss_cls: 4.5331, loss: 4.5331 +2024-07-17 21:52:16,736 - pyskl - INFO - Epoch [47][300/3746] lr: 7.847e-02, eta: 3 days, 11:45:14, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4758, loss_cls: 4.4661, loss: 4.4661 +2024-07-17 21:53:39,804 - pyskl - INFO - Epoch [47][400/3746] lr: 7.844e-02, eta: 3 days, 11:44:10, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4647, loss_cls: 4.4949, loss: 4.4949 +2024-07-17 21:55:02,642 - pyskl - INFO - Epoch [47][500/3746] lr: 7.842e-02, eta: 3 days, 11:43:04, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4561, loss_cls: 4.5325, loss: 4.5325 +2024-07-17 21:56:24,600 - pyskl - INFO - Epoch [47][600/3746] lr: 7.840e-02, eta: 3 days, 11:41:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4572, loss_cls: 4.5199, loss: 4.5199 +2024-07-17 21:57:46,395 - pyskl - INFO - Epoch [47][700/3746] lr: 7.838e-02, eta: 3 days, 11:40:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4547, loss_cls: 4.5617, loss: 4.5617 +2024-07-17 21:59:08,538 - pyskl - INFO - Epoch [47][800/3746] lr: 7.835e-02, eta: 3 days, 11:39:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4620, loss_cls: 4.5356, loss: 4.5356 +2024-07-17 22:00:30,788 - pyskl - INFO - Epoch [47][900/3746] lr: 7.833e-02, eta: 3 days, 11:38:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4652, loss_cls: 4.5020, loss: 4.5020 +2024-07-17 22:01:52,736 - pyskl - INFO - Epoch [47][1000/3746] lr: 7.831e-02, eta: 3 days, 11:37:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4636, loss_cls: 4.5343, loss: 4.5343 +2024-07-17 22:03:14,987 - pyskl - INFO - Epoch [47][1100/3746] lr: 7.828e-02, eta: 3 days, 11:36:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4623, loss_cls: 4.5278, loss: 4.5278 +2024-07-17 22:04:36,908 - pyskl - INFO - Epoch [47][1200/3746] lr: 7.826e-02, eta: 3 days, 11:35:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4630, loss_cls: 4.5361, loss: 4.5361 +2024-07-17 22:05:59,844 - pyskl - INFO - Epoch [47][1300/3746] lr: 7.824e-02, eta: 3 days, 11:34:08, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4622, loss_cls: 4.5265, loss: 4.5265 +2024-07-17 22:07:21,705 - pyskl - INFO - Epoch [47][1400/3746] lr: 7.821e-02, eta: 3 days, 11:33:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4603, loss_cls: 4.5367, loss: 4.5367 +2024-07-17 22:08:43,822 - pyskl - INFO - Epoch [47][1500/3746] lr: 7.819e-02, eta: 3 days, 11:31:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4688, loss_cls: 4.4907, loss: 4.4907 +2024-07-17 22:10:05,793 - pyskl - INFO - Epoch [47][1600/3746] lr: 7.817e-02, eta: 3 days, 11:30:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4673, loss_cls: 4.5218, loss: 4.5218 +2024-07-17 22:11:27,918 - pyskl - INFO - Epoch [47][1700/3746] lr: 7.814e-02, eta: 3 days, 11:29:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4664, loss_cls: 4.5287, loss: 4.5287 +2024-07-17 22:12:49,872 - pyskl - INFO - Epoch [47][1800/3746] lr: 7.812e-02, eta: 3 days, 11:28:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4689, loss_cls: 4.4649, loss: 4.4649 +2024-07-17 22:14:12,308 - pyskl - INFO - Epoch [47][1900/3746] lr: 7.810e-02, eta: 3 days, 11:27:25, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4659, loss_cls: 4.4975, loss: 4.4975 +2024-07-17 22:15:33,897 - pyskl - INFO - Epoch [47][2000/3746] lr: 7.808e-02, eta: 3 days, 11:26:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4723, loss_cls: 4.5011, loss: 4.5011 +2024-07-17 22:16:55,865 - pyskl - INFO - Epoch [47][2100/3746] lr: 7.805e-02, eta: 3 days, 11:25:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2206, top5_acc: 0.4592, loss_cls: 4.5547, loss: 4.5547 +2024-07-17 22:18:17,738 - pyskl - INFO - Epoch [47][2200/3746] lr: 7.803e-02, eta: 3 days, 11:24:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4592, loss_cls: 4.5494, loss: 4.5494 +2024-07-17 22:19:39,583 - pyskl - INFO - Epoch [47][2300/3746] lr: 7.801e-02, eta: 3 days, 11:22:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4655, loss_cls: 4.5057, loss: 4.5057 +2024-07-17 22:21:02,170 - pyskl - INFO - Epoch [47][2400/3746] lr: 7.798e-02, eta: 3 days, 11:21:47, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4637, loss_cls: 4.5270, loss: 4.5270 +2024-07-17 22:22:24,637 - pyskl - INFO - Epoch [47][2500/3746] lr: 7.796e-02, eta: 3 days, 11:20:40, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4561, loss_cls: 4.5190, loss: 4.5190 +2024-07-17 22:23:46,974 - pyskl - INFO - Epoch [47][2600/3746] lr: 7.794e-02, eta: 3 days, 11:19:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2158, top5_acc: 0.4559, loss_cls: 4.5693, loss: 4.5693 +2024-07-17 22:25:09,169 - pyskl - INFO - Epoch [47][2700/3746] lr: 7.791e-02, eta: 3 days, 11:18:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4648, loss_cls: 4.5089, loss: 4.5089 +2024-07-17 22:26:31,736 - pyskl - INFO - Epoch [47][2800/3746] lr: 7.789e-02, eta: 3 days, 11:17:20, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4731, loss_cls: 4.4787, loss: 4.4787 +2024-07-17 22:27:54,420 - pyskl - INFO - Epoch [47][2900/3746] lr: 7.787e-02, eta: 3 days, 11:16:14, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4562, loss_cls: 4.5469, loss: 4.5469 +2024-07-17 22:29:16,278 - pyskl - INFO - Epoch [47][3000/3746] lr: 7.784e-02, eta: 3 days, 11:15:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2234, top5_acc: 0.4642, loss_cls: 4.5187, loss: 4.5187 +2024-07-17 22:30:38,525 - pyskl - INFO - Epoch [47][3100/3746] lr: 7.782e-02, eta: 3 days, 11:13:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4609, loss_cls: 4.5348, loss: 4.5348 +2024-07-17 22:32:00,126 - pyskl - INFO - Epoch [47][3200/3746] lr: 7.780e-02, eta: 3 days, 11:12:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4633, loss_cls: 4.4932, loss: 4.4932 +2024-07-17 22:33:22,310 - pyskl - INFO - Epoch [47][3300/3746] lr: 7.777e-02, eta: 3 days, 11:11:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2214, top5_acc: 0.4523, loss_cls: 4.5752, loss: 4.5752 +2024-07-17 22:34:44,342 - pyskl - INFO - Epoch [47][3400/3746] lr: 7.775e-02, eta: 3 days, 11:10:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4570, loss_cls: 4.5490, loss: 4.5490 +2024-07-17 22:36:06,623 - pyskl - INFO - Epoch [47][3500/3746] lr: 7.773e-02, eta: 3 days, 11:09:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2170, top5_acc: 0.4605, loss_cls: 4.5600, loss: 4.5600 +2024-07-17 22:37:29,728 - pyskl - INFO - Epoch [47][3600/3746] lr: 7.770e-02, eta: 3 days, 11:08:23, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4608, loss_cls: 4.5329, loss: 4.5329 +2024-07-17 22:38:52,583 - pyskl - INFO - Epoch [47][3700/3746] lr: 7.768e-02, eta: 3 days, 11:07:17, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4633, loss_cls: 4.5044, loss: 4.5044 +2024-07-17 22:39:32,009 - pyskl - INFO - Saving checkpoint at 47 epochs +2024-07-17 22:41:23,403 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 22:41:24,199 - pyskl - INFO - +top1_acc 0.1465 +top5_acc 0.3477 +2024-07-17 22:41:24,199 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 22:41:24,252 - pyskl - INFO - +mean_acc 0.1464 +2024-07-17 22:41:24,273 - pyskl - INFO - Epoch(val) [47][309] top1_acc: 0.1465, top5_acc: 0.3477, mean_class_accuracy: 0.1464 +2024-07-17 22:45:19,106 - pyskl - INFO - Epoch [48][100/3746] lr: 7.765e-02, eta: 3 days, 11:09:50, time: 2.348, data_time: 1.352, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4736, loss_cls: 4.4374, loss: 4.4374 +2024-07-17 22:46:41,513 - pyskl - INFO - Epoch [48][200/3746] lr: 7.762e-02, eta: 3 days, 11:08:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4609, loss_cls: 4.5516, loss: 4.5516 +2024-07-17 22:48:03,814 - pyskl - INFO - Epoch [48][300/3746] lr: 7.760e-02, eta: 3 days, 11:07:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4719, loss_cls: 4.5098, loss: 4.5098 +2024-07-17 22:49:25,911 - pyskl - INFO - Epoch [48][400/3746] lr: 7.758e-02, eta: 3 days, 11:06:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2283, top5_acc: 0.4611, loss_cls: 4.5065, loss: 4.5065 +2024-07-17 22:50:47,721 - pyskl - INFO - Epoch [48][500/3746] lr: 7.755e-02, eta: 3 days, 11:05:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4681, loss_cls: 4.5213, loss: 4.5213 +2024-07-17 22:52:10,464 - pyskl - INFO - Epoch [48][600/3746] lr: 7.753e-02, eta: 3 days, 11:04:13, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4675, loss_cls: 4.5237, loss: 4.5237 +2024-07-17 22:53:33,258 - pyskl - INFO - Epoch [48][700/3746] lr: 7.751e-02, eta: 3 days, 11:03:06, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4647, loss_cls: 4.5397, loss: 4.5397 +2024-07-17 22:54:55,138 - pyskl - INFO - Epoch [48][800/3746] lr: 7.748e-02, eta: 3 days, 11:01:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4634, loss_cls: 4.5179, loss: 4.5179 +2024-07-17 22:56:16,768 - pyskl - INFO - Epoch [48][900/3746] lr: 7.746e-02, eta: 3 days, 11:00:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4673, loss_cls: 4.5003, loss: 4.5003 +2024-07-17 22:57:39,171 - pyskl - INFO - Epoch [48][1000/3746] lr: 7.744e-02, eta: 3 days, 10:59:42, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4647, loss_cls: 4.5405, loss: 4.5405 +2024-07-17 22:59:00,842 - pyskl - INFO - Epoch [48][1100/3746] lr: 7.741e-02, eta: 3 days, 10:58:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4689, loss_cls: 4.4964, loss: 4.4964 +2024-07-17 23:00:23,064 - pyskl - INFO - Epoch [48][1200/3746] lr: 7.739e-02, eta: 3 days, 10:57:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4648, loss_cls: 4.5134, loss: 4.5134 +2024-07-17 23:01:45,466 - pyskl - INFO - Epoch [48][1300/3746] lr: 7.737e-02, eta: 3 days, 10:56:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2216, top5_acc: 0.4612, loss_cls: 4.5284, loss: 4.5284 +2024-07-17 23:03:07,826 - pyskl - INFO - Epoch [48][1400/3746] lr: 7.734e-02, eta: 3 days, 10:55:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4750, loss_cls: 4.4507, loss: 4.4507 +2024-07-17 23:04:30,118 - pyskl - INFO - Epoch [48][1500/3746] lr: 7.732e-02, eta: 3 days, 10:54:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4634, loss_cls: 4.5187, loss: 4.5187 +2024-07-17 23:05:51,804 - pyskl - INFO - Epoch [48][1600/3746] lr: 7.730e-02, eta: 3 days, 10:52:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2202, top5_acc: 0.4606, loss_cls: 4.5575, loss: 4.5575 +2024-07-17 23:07:13,741 - pyskl - INFO - Epoch [48][1700/3746] lr: 7.727e-02, eta: 3 days, 10:51:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4750, loss_cls: 4.4470, loss: 4.4470 +2024-07-17 23:08:35,615 - pyskl - INFO - Epoch [48][1800/3746] lr: 7.725e-02, eta: 3 days, 10:50:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4622, loss_cls: 4.5515, loss: 4.5515 +2024-07-17 23:09:57,381 - pyskl - INFO - Epoch [48][1900/3746] lr: 7.723e-02, eta: 3 days, 10:49:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2198, top5_acc: 0.4555, loss_cls: 4.5477, loss: 4.5477 +2024-07-17 23:11:19,270 - pyskl - INFO - Epoch [48][2000/3746] lr: 7.720e-02, eta: 3 days, 10:48:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4714, loss_cls: 4.4957, loss: 4.4957 +2024-07-17 23:12:41,567 - pyskl - INFO - Epoch [48][2100/3746] lr: 7.718e-02, eta: 3 days, 10:47:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4616, loss_cls: 4.5560, loss: 4.5560 +2024-07-17 23:14:03,350 - pyskl - INFO - Epoch [48][2200/3746] lr: 7.716e-02, eta: 3 days, 10:46:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4694, loss_cls: 4.5238, loss: 4.5238 +2024-07-17 23:15:25,283 - pyskl - INFO - Epoch [48][2300/3746] lr: 7.713e-02, eta: 3 days, 10:44:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4666, loss_cls: 4.4832, loss: 4.4832 +2024-07-17 23:16:47,115 - pyskl - INFO - Epoch [48][2400/3746] lr: 7.711e-02, eta: 3 days, 10:43:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4770, loss_cls: 4.4824, loss: 4.4824 +2024-07-17 23:18:10,017 - pyskl - INFO - Epoch [48][2500/3746] lr: 7.709e-02, eta: 3 days, 10:42:39, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4611, loss_cls: 4.5321, loss: 4.5321 +2024-07-17 23:19:32,028 - pyskl - INFO - Epoch [48][2600/3746] lr: 7.706e-02, eta: 3 days, 10:41:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4605, loss_cls: 4.5198, loss: 4.5198 +2024-07-17 23:20:54,587 - pyskl - INFO - Epoch [48][2700/3746] lr: 7.704e-02, eta: 3 days, 10:40:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4703, loss_cls: 4.4952, loss: 4.4952 +2024-07-17 23:22:16,964 - pyskl - INFO - Epoch [48][2800/3746] lr: 7.701e-02, eta: 3 days, 10:39:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4630, loss_cls: 4.5360, loss: 4.5360 +2024-07-17 23:23:39,495 - pyskl - INFO - Epoch [48][2900/3746] lr: 7.699e-02, eta: 3 days, 10:38:09, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2183, top5_acc: 0.4603, loss_cls: 4.5266, loss: 4.5266 +2024-07-17 23:25:02,179 - pyskl - INFO - Epoch [48][3000/3746] lr: 7.697e-02, eta: 3 days, 10:37:02, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4642, loss_cls: 4.5032, loss: 4.5032 +2024-07-17 23:26:23,953 - pyskl - INFO - Epoch [48][3100/3746] lr: 7.694e-02, eta: 3 days, 10:35:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2144, top5_acc: 0.4592, loss_cls: 4.5720, loss: 4.5720 +2024-07-17 23:27:46,147 - pyskl - INFO - Epoch [48][3200/3746] lr: 7.692e-02, eta: 3 days, 10:34:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4688, loss_cls: 4.4721, loss: 4.4721 +2024-07-17 23:29:07,839 - pyskl - INFO - Epoch [48][3300/3746] lr: 7.690e-02, eta: 3 days, 10:33:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4645, loss_cls: 4.5135, loss: 4.5135 +2024-07-17 23:30:29,898 - pyskl - INFO - Epoch [48][3400/3746] lr: 7.687e-02, eta: 3 days, 10:32:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4619, loss_cls: 4.5120, loss: 4.5120 +2024-07-17 23:31:52,066 - pyskl - INFO - Epoch [48][3500/3746] lr: 7.685e-02, eta: 3 days, 10:31:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4622, loss_cls: 4.5253, loss: 4.5253 +2024-07-17 23:33:13,923 - pyskl - INFO - Epoch [48][3600/3746] lr: 7.683e-02, eta: 3 days, 10:30:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4689, loss_cls: 4.5242, loss: 4.5242 +2024-07-17 23:34:35,842 - pyskl - INFO - Epoch [48][3700/3746] lr: 7.680e-02, eta: 3 days, 10:29:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2195, top5_acc: 0.4625, loss_cls: 4.5305, loss: 4.5305 +2024-07-17 23:35:15,362 - pyskl - INFO - Saving checkpoint at 48 epochs +2024-07-17 23:37:07,140 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-17 23:37:07,825 - pyskl - INFO - +top1_acc 0.1602 +top5_acc 0.3703 +2024-07-17 23:37:07,826 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-17 23:37:07,868 - pyskl - INFO - +mean_acc 0.1601 +2024-07-17 23:37:07,881 - pyskl - INFO - Epoch(val) [48][309] top1_acc: 0.1602, top5_acc: 0.3703, mean_class_accuracy: 0.1601 +2024-07-17 23:41:00,222 - pyskl - INFO - Epoch [49][100/3746] lr: 7.677e-02, eta: 3 days, 10:31:20, time: 2.323, data_time: 1.335, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4698, loss_cls: 4.4619, loss: 4.4619 +2024-07-17 23:42:22,485 - pyskl - INFO - Epoch [49][200/3746] lr: 7.674e-02, eta: 3 days, 10:30:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4705, loss_cls: 4.4633, loss: 4.4633 +2024-07-17 23:43:44,139 - pyskl - INFO - Epoch [49][300/3746] lr: 7.672e-02, eta: 3 days, 10:29:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4673, loss_cls: 4.4726, loss: 4.4726 +2024-07-17 23:45:06,507 - pyskl - INFO - Epoch [49][400/3746] lr: 7.670e-02, eta: 3 days, 10:27:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4734, loss_cls: 4.4419, loss: 4.4419 +2024-07-17 23:46:28,521 - pyskl - INFO - Epoch [49][500/3746] lr: 7.667e-02, eta: 3 days, 10:26:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4603, loss_cls: 4.4990, loss: 4.4990 +2024-07-17 23:47:50,028 - pyskl - INFO - Epoch [49][600/3746] lr: 7.665e-02, eta: 3 days, 10:25:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4658, loss_cls: 4.4998, loss: 4.4998 +2024-07-17 23:49:11,548 - pyskl - INFO - Epoch [49][700/3746] lr: 7.663e-02, eta: 3 days, 10:24:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4678, loss_cls: 4.4887, loss: 4.4887 +2024-07-17 23:50:33,940 - pyskl - INFO - Epoch [49][800/3746] lr: 7.660e-02, eta: 3 days, 10:23:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2205, top5_acc: 0.4748, loss_cls: 4.5275, loss: 4.5275 +2024-07-17 23:51:56,291 - pyskl - INFO - Epoch [49][900/3746] lr: 7.658e-02, eta: 3 days, 10:22:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4528, loss_cls: 4.5616, loss: 4.5616 +2024-07-17 23:53:18,084 - pyskl - INFO - Epoch [49][1000/3746] lr: 7.656e-02, eta: 3 days, 10:21:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4661, loss_cls: 4.5123, loss: 4.5123 +2024-07-17 23:54:39,915 - pyskl - INFO - Epoch [49][1100/3746] lr: 7.653e-02, eta: 3 days, 10:19:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4658, loss_cls: 4.4992, loss: 4.4992 +2024-07-17 23:56:01,884 - pyskl - INFO - Epoch [49][1200/3746] lr: 7.651e-02, eta: 3 days, 10:18:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4609, loss_cls: 4.4945, loss: 4.4945 +2024-07-17 23:57:23,921 - pyskl - INFO - Epoch [49][1300/3746] lr: 7.648e-02, eta: 3 days, 10:17:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2253, top5_acc: 0.4628, loss_cls: 4.4998, loss: 4.4998 +2024-07-17 23:58:46,664 - pyskl - INFO - Epoch [49][1400/3746] lr: 7.646e-02, eta: 3 days, 10:16:26, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4669, loss_cls: 4.4918, loss: 4.4918 +2024-07-18 00:00:08,935 - pyskl - INFO - Epoch [49][1500/3746] lr: 7.644e-02, eta: 3 days, 10:15:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4672, loss_cls: 4.5057, loss: 4.5057 +2024-07-18 00:01:30,819 - pyskl - INFO - Epoch [49][1600/3746] lr: 7.641e-02, eta: 3 days, 10:14:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4666, loss_cls: 4.4755, loss: 4.4755 +2024-07-18 00:02:52,923 - pyskl - INFO - Epoch [49][1700/3746] lr: 7.639e-02, eta: 3 days, 10:12:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4664, loss_cls: 4.4764, loss: 4.4764 +2024-07-18 00:04:14,809 - pyskl - INFO - Epoch [49][1800/3746] lr: 7.637e-02, eta: 3 days, 10:11:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4681, loss_cls: 4.5134, loss: 4.5134 +2024-07-18 00:05:36,105 - pyskl - INFO - Epoch [49][1900/3746] lr: 7.634e-02, eta: 3 days, 10:10:40, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2278, top5_acc: 0.4523, loss_cls: 4.5214, loss: 4.5214 +2024-07-18 00:06:57,994 - pyskl - INFO - Epoch [49][2000/3746] lr: 7.632e-02, eta: 3 days, 10:09:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4675, loss_cls: 4.5073, loss: 4.5073 +2024-07-18 00:08:19,738 - pyskl - INFO - Epoch [49][2100/3746] lr: 7.629e-02, eta: 3 days, 10:08:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4691, loss_cls: 4.5013, loss: 4.5013 +2024-07-18 00:09:41,597 - pyskl - INFO - Epoch [49][2200/3746] lr: 7.627e-02, eta: 3 days, 10:07:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4627, loss_cls: 4.5248, loss: 4.5248 +2024-07-18 00:11:03,630 - pyskl - INFO - Epoch [49][2300/3746] lr: 7.625e-02, eta: 3 days, 10:06:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2259, top5_acc: 0.4728, loss_cls: 4.4976, loss: 4.4976 +2024-07-18 00:12:25,952 - pyskl - INFO - Epoch [49][2400/3746] lr: 7.622e-02, eta: 3 days, 10:04:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4614, loss_cls: 4.5166, loss: 4.5166 +2024-07-18 00:13:47,920 - pyskl - INFO - Epoch [49][2500/3746] lr: 7.620e-02, eta: 3 days, 10:03:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2219, top5_acc: 0.4653, loss_cls: 4.5468, loss: 4.5468 +2024-07-18 00:15:10,103 - pyskl - INFO - Epoch [49][2600/3746] lr: 7.618e-02, eta: 3 days, 10:02:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4578, loss_cls: 4.5397, loss: 4.5397 +2024-07-18 00:16:32,433 - pyskl - INFO - Epoch [49][2700/3746] lr: 7.615e-02, eta: 3 days, 10:01:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4577, loss_cls: 4.5464, loss: 4.5464 +2024-07-18 00:17:54,689 - pyskl - INFO - Epoch [49][2800/3746] lr: 7.613e-02, eta: 3 days, 10:00:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4587, loss_cls: 4.5389, loss: 4.5389 +2024-07-18 00:19:16,721 - pyskl - INFO - Epoch [49][2900/3746] lr: 7.610e-02, eta: 3 days, 9:59:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2222, top5_acc: 0.4592, loss_cls: 4.5329, loss: 4.5329 +2024-07-18 00:20:39,154 - pyskl - INFO - Epoch [49][3000/3746] lr: 7.608e-02, eta: 3 days, 9:58:01, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4691, loss_cls: 4.4832, loss: 4.4832 +2024-07-18 00:22:01,288 - pyskl - INFO - Epoch [49][3100/3746] lr: 7.606e-02, eta: 3 days, 9:56:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4745, loss_cls: 4.4707, loss: 4.4707 +2024-07-18 00:23:23,373 - pyskl - INFO - Epoch [49][3200/3746] lr: 7.603e-02, eta: 3 days, 9:55:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4691, loss_cls: 4.4807, loss: 4.4807 +2024-07-18 00:24:45,251 - pyskl - INFO - Epoch [49][3300/3746] lr: 7.601e-02, eta: 3 days, 9:54:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4658, loss_cls: 4.4974, loss: 4.4974 +2024-07-18 00:26:07,205 - pyskl - INFO - Epoch [49][3400/3746] lr: 7.598e-02, eta: 3 days, 9:53:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4566, loss_cls: 4.5433, loss: 4.5433 +2024-07-18 00:27:29,378 - pyskl - INFO - Epoch [49][3500/3746] lr: 7.596e-02, eta: 3 days, 9:52:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4667, loss_cls: 4.4846, loss: 4.4846 +2024-07-18 00:28:51,773 - pyskl - INFO - Epoch [49][3600/3746] lr: 7.594e-02, eta: 3 days, 9:51:07, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4694, loss_cls: 4.5199, loss: 4.5199 +2024-07-18 00:30:14,151 - pyskl - INFO - Epoch [49][3700/3746] lr: 7.591e-02, eta: 3 days, 9:49:58, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2261, top5_acc: 0.4627, loss_cls: 4.5291, loss: 4.5291 +2024-07-18 00:30:54,090 - pyskl - INFO - Saving checkpoint at 49 epochs +2024-07-18 00:32:45,893 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 00:32:46,587 - pyskl - INFO - +top1_acc 0.1233 +top5_acc 0.3054 +2024-07-18 00:32:46,587 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 00:32:46,632 - pyskl - INFO - +mean_acc 0.1231 +2024-07-18 00:32:46,646 - pyskl - INFO - Epoch(val) [49][309] top1_acc: 0.1233, top5_acc: 0.3054, mean_class_accuracy: 0.1231 +2024-07-18 00:36:37,270 - pyskl - INFO - Epoch [50][100/3746] lr: 7.588e-02, eta: 3 days, 9:52:06, time: 2.306, data_time: 1.320, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4692, loss_cls: 4.4429, loss: 4.4429 +2024-07-18 00:38:00,283 - pyskl - INFO - Epoch [50][200/3746] lr: 7.585e-02, eta: 3 days, 9:50:58, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4672, loss_cls: 4.4924, loss: 4.4924 +2024-07-18 00:39:22,317 - pyskl - INFO - Epoch [50][300/3746] lr: 7.583e-02, eta: 3 days, 9:49:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4723, loss_cls: 4.4698, loss: 4.4698 +2024-07-18 00:40:44,553 - pyskl - INFO - Epoch [50][400/3746] lr: 7.581e-02, eta: 3 days, 9:48:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4708, loss_cls: 4.4800, loss: 4.4800 +2024-07-18 00:42:06,363 - pyskl - INFO - Epoch [50][500/3746] lr: 7.578e-02, eta: 3 days, 9:47:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4748, loss_cls: 4.4428, loss: 4.4428 +2024-07-18 00:43:28,234 - pyskl - INFO - Epoch [50][600/3746] lr: 7.576e-02, eta: 3 days, 9:46:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4789, loss_cls: 4.4446, loss: 4.4446 +2024-07-18 00:44:50,106 - pyskl - INFO - Epoch [50][700/3746] lr: 7.573e-02, eta: 3 days, 9:45:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4547, loss_cls: 4.5233, loss: 4.5233 +2024-07-18 00:46:12,514 - pyskl - INFO - Epoch [50][800/3746] lr: 7.571e-02, eta: 3 days, 9:44:01, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4655, loss_cls: 4.4981, loss: 4.4981 +2024-07-18 00:47:35,202 - pyskl - INFO - Epoch [50][900/3746] lr: 7.569e-02, eta: 3 days, 9:42:53, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4703, loss_cls: 4.5141, loss: 4.5141 +2024-07-18 00:48:56,862 - pyskl - INFO - Epoch [50][1000/3746] lr: 7.566e-02, eta: 3 days, 9:41:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4784, loss_cls: 4.4652, loss: 4.4652 +2024-07-18 00:50:19,163 - pyskl - INFO - Epoch [50][1100/3746] lr: 7.564e-02, eta: 3 days, 9:40:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4795, loss_cls: 4.4768, loss: 4.4768 +2024-07-18 00:51:41,459 - pyskl - INFO - Epoch [50][1200/3746] lr: 7.561e-02, eta: 3 days, 9:39:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4689, loss_cls: 4.5337, loss: 4.5337 +2024-07-18 00:53:03,594 - pyskl - INFO - Epoch [50][1300/3746] lr: 7.559e-02, eta: 3 days, 9:38:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4717, loss_cls: 4.4824, loss: 4.4824 +2024-07-18 00:54:26,049 - pyskl - INFO - Epoch [50][1400/3746] lr: 7.557e-02, eta: 3 days, 9:37:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2248, top5_acc: 0.4648, loss_cls: 4.5284, loss: 4.5284 +2024-07-18 00:55:48,229 - pyskl - INFO - Epoch [50][1500/3746] lr: 7.554e-02, eta: 3 days, 9:35:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4669, loss_cls: 4.4970, loss: 4.4970 +2024-07-18 00:57:09,967 - pyskl - INFO - Epoch [50][1600/3746] lr: 7.552e-02, eta: 3 days, 9:34:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2231, top5_acc: 0.4580, loss_cls: 4.5444, loss: 4.5444 +2024-07-18 00:58:31,670 - pyskl - INFO - Epoch [50][1700/3746] lr: 7.549e-02, eta: 3 days, 9:33:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4713, loss_cls: 4.4595, loss: 4.4595 +2024-07-18 00:59:53,707 - pyskl - INFO - Epoch [50][1800/3746] lr: 7.547e-02, eta: 3 days, 9:32:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4678, loss_cls: 4.5055, loss: 4.5055 +2024-07-18 01:01:15,868 - pyskl - INFO - Epoch [50][1900/3746] lr: 7.545e-02, eta: 3 days, 9:31:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4672, loss_cls: 4.4935, loss: 4.4935 +2024-07-18 01:02:38,035 - pyskl - INFO - Epoch [50][2000/3746] lr: 7.542e-02, eta: 3 days, 9:30:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4647, loss_cls: 4.5147, loss: 4.5147 +2024-07-18 01:04:00,024 - pyskl - INFO - Epoch [50][2100/3746] lr: 7.540e-02, eta: 3 days, 9:28:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4848, loss_cls: 4.4389, loss: 4.4389 +2024-07-18 01:05:21,638 - pyskl - INFO - Epoch [50][2200/3746] lr: 7.537e-02, eta: 3 days, 9:27:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4656, loss_cls: 4.4872, loss: 4.4872 +2024-07-18 01:06:43,498 - pyskl - INFO - Epoch [50][2300/3746] lr: 7.535e-02, eta: 3 days, 9:26:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2225, top5_acc: 0.4681, loss_cls: 4.5303, loss: 4.5303 +2024-07-18 01:08:06,332 - pyskl - INFO - Epoch [50][2400/3746] lr: 7.533e-02, eta: 3 days, 9:25:29, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4684, loss_cls: 4.4993, loss: 4.4993 +2024-07-18 01:09:27,996 - pyskl - INFO - Epoch [50][2500/3746] lr: 7.530e-02, eta: 3 days, 9:24:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4623, loss_cls: 4.4878, loss: 4.4878 +2024-07-18 01:10:49,967 - pyskl - INFO - Epoch [50][2600/3746] lr: 7.528e-02, eta: 3 days, 9:23:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2281, top5_acc: 0.4677, loss_cls: 4.4875, loss: 4.4875 +2024-07-18 01:12:12,315 - pyskl - INFO - Epoch [50][2700/3746] lr: 7.525e-02, eta: 3 days, 9:22:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4595, loss_cls: 4.5266, loss: 4.5266 +2024-07-18 01:13:34,113 - pyskl - INFO - Epoch [50][2800/3746] lr: 7.523e-02, eta: 3 days, 9:20:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4634, loss_cls: 4.5091, loss: 4.5091 +2024-07-18 01:14:56,534 - pyskl - INFO - Epoch [50][2900/3746] lr: 7.520e-02, eta: 3 days, 9:19:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4722, loss_cls: 4.4767, loss: 4.4767 +2024-07-18 01:16:18,765 - pyskl - INFO - Epoch [50][3000/3746] lr: 7.518e-02, eta: 3 days, 9:18:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4731, loss_cls: 4.4888, loss: 4.4888 +2024-07-18 01:17:41,065 - pyskl - INFO - Epoch [50][3100/3746] lr: 7.516e-02, eta: 3 days, 9:17:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2197, top5_acc: 0.4477, loss_cls: 4.5646, loss: 4.5646 +2024-07-18 01:19:02,665 - pyskl - INFO - Epoch [50][3200/3746] lr: 7.513e-02, eta: 3 days, 9:16:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4609, loss_cls: 4.5320, loss: 4.5320 +2024-07-18 01:20:24,877 - pyskl - INFO - Epoch [50][3300/3746] lr: 7.511e-02, eta: 3 days, 9:15:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2211, top5_acc: 0.4684, loss_cls: 4.5059, loss: 4.5059 +2024-07-18 01:21:47,538 - pyskl - INFO - Epoch [50][3400/3746] lr: 7.508e-02, eta: 3 days, 9:13:53, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2228, top5_acc: 0.4589, loss_cls: 4.5342, loss: 4.5342 +2024-07-18 01:23:10,067 - pyskl - INFO - Epoch [50][3500/3746] lr: 7.506e-02, eta: 3 days, 9:12:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4675, loss_cls: 4.5111, loss: 4.5111 +2024-07-18 01:24:31,917 - pyskl - INFO - Epoch [50][3600/3746] lr: 7.504e-02, eta: 3 days, 9:11:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4717, loss_cls: 4.4822, loss: 4.4822 +2024-07-18 01:25:54,150 - pyskl - INFO - Epoch [50][3700/3746] lr: 7.501e-02, eta: 3 days, 9:10:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4705, loss_cls: 4.4922, loss: 4.4922 +2024-07-18 01:26:34,166 - pyskl - INFO - Saving checkpoint at 50 epochs +2024-07-18 01:28:25,823 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 01:28:26,492 - pyskl - INFO - +top1_acc 0.1678 +top5_acc 0.3750 +2024-07-18 01:28:26,492 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 01:28:26,535 - pyskl - INFO - +mean_acc 0.1676 +2024-07-18 01:28:26,548 - pyskl - INFO - Epoch(val) [50][309] top1_acc: 0.1678, top5_acc: 0.3750, mean_class_accuracy: 0.1676 +2024-07-18 01:32:15,951 - pyskl - INFO - Epoch [51][100/3746] lr: 7.498e-02, eta: 3 days, 9:12:21, time: 2.294, data_time: 1.304, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4733, loss_cls: 4.4680, loss: 4.4680 +2024-07-18 01:33:38,410 - pyskl - INFO - Epoch [51][200/3746] lr: 7.495e-02, eta: 3 days, 9:11:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4788, loss_cls: 4.4532, loss: 4.4532 +2024-07-18 01:35:00,523 - pyskl - INFO - Epoch [51][300/3746] lr: 7.493e-02, eta: 3 days, 9:10:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4733, loss_cls: 4.4839, loss: 4.4839 +2024-07-18 01:36:23,195 - pyskl - INFO - Epoch [51][400/3746] lr: 7.490e-02, eta: 3 days, 9:08:53, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2273, top5_acc: 0.4703, loss_cls: 4.5015, loss: 4.5015 +2024-07-18 01:37:44,821 - pyskl - INFO - Epoch [51][500/3746] lr: 7.488e-02, eta: 3 days, 9:07:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2213, top5_acc: 0.4672, loss_cls: 4.5008, loss: 4.5008 +2024-07-18 01:39:06,945 - pyskl - INFO - Epoch [51][600/3746] lr: 7.485e-02, eta: 3 days, 9:06:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4723, loss_cls: 4.5003, loss: 4.5003 +2024-07-18 01:40:29,633 - pyskl - INFO - Epoch [51][700/3746] lr: 7.483e-02, eta: 3 days, 9:05:23, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4730, loss_cls: 4.5015, loss: 4.5015 +2024-07-18 01:41:51,449 - pyskl - INFO - Epoch [51][800/3746] lr: 7.481e-02, eta: 3 days, 9:04:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4748, loss_cls: 4.4516, loss: 4.4516 +2024-07-18 01:43:13,242 - pyskl - INFO - Epoch [51][900/3746] lr: 7.478e-02, eta: 3 days, 9:03:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4664, loss_cls: 4.5005, loss: 4.5005 +2024-07-18 01:44:35,461 - pyskl - INFO - Epoch [51][1000/3746] lr: 7.476e-02, eta: 3 days, 9:01:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2241, top5_acc: 0.4655, loss_cls: 4.5100, loss: 4.5100 +2024-07-18 01:45:57,373 - pyskl - INFO - Epoch [51][1100/3746] lr: 7.473e-02, eta: 3 days, 9:00:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4728, loss_cls: 4.4836, loss: 4.4836 +2024-07-18 01:47:19,853 - pyskl - INFO - Epoch [51][1200/3746] lr: 7.471e-02, eta: 3 days, 8:59:32, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4567, loss_cls: 4.5353, loss: 4.5353 +2024-07-18 01:48:41,748 - pyskl - INFO - Epoch [51][1300/3746] lr: 7.468e-02, eta: 3 days, 8:58:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4764, loss_cls: 4.4673, loss: 4.4673 +2024-07-18 01:50:03,812 - pyskl - INFO - Epoch [51][1400/3746] lr: 7.466e-02, eta: 3 days, 8:57:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2242, top5_acc: 0.4661, loss_cls: 4.5260, loss: 4.5260 +2024-07-18 01:51:25,930 - pyskl - INFO - Epoch [51][1500/3746] lr: 7.464e-02, eta: 3 days, 8:56:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4734, loss_cls: 4.4580, loss: 4.4580 +2024-07-18 01:52:48,034 - pyskl - INFO - Epoch [51][1600/3746] lr: 7.461e-02, eta: 3 days, 8:54:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4611, loss_cls: 4.4864, loss: 4.4864 +2024-07-18 01:54:09,880 - pyskl - INFO - Epoch [51][1700/3746] lr: 7.459e-02, eta: 3 days, 8:53:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4653, loss_cls: 4.5140, loss: 4.5140 +2024-07-18 01:55:31,193 - pyskl - INFO - Epoch [51][1800/3746] lr: 7.456e-02, eta: 3 days, 8:52:29, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2239, top5_acc: 0.4681, loss_cls: 4.5042, loss: 4.5042 +2024-07-18 01:56:53,142 - pyskl - INFO - Epoch [51][1900/3746] lr: 7.454e-02, eta: 3 days, 8:51:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4731, loss_cls: 4.4526, loss: 4.4526 +2024-07-18 01:58:15,321 - pyskl - INFO - Epoch [51][2000/3746] lr: 7.451e-02, eta: 3 days, 8:50:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4827, loss_cls: 4.4294, loss: 4.4294 +2024-07-18 01:59:36,613 - pyskl - INFO - Epoch [51][2100/3746] lr: 7.449e-02, eta: 3 days, 8:48:56, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4639, loss_cls: 4.4988, loss: 4.4988 +2024-07-18 02:00:58,174 - pyskl - INFO - Epoch [51][2200/3746] lr: 7.447e-02, eta: 3 days, 8:47:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4802, loss_cls: 4.4413, loss: 4.4413 +2024-07-18 02:02:20,201 - pyskl - INFO - Epoch [51][2300/3746] lr: 7.444e-02, eta: 3 days, 8:46:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4731, loss_cls: 4.5061, loss: 4.5061 +2024-07-18 02:03:43,026 - pyskl - INFO - Epoch [51][2400/3746] lr: 7.442e-02, eta: 3 days, 8:45:26, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4630, loss_cls: 4.5138, loss: 4.5138 +2024-07-18 02:05:04,730 - pyskl - INFO - Epoch [51][2500/3746] lr: 7.439e-02, eta: 3 days, 8:44:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4695, loss_cls: 4.5127, loss: 4.5127 +2024-07-18 02:06:28,397 - pyskl - INFO - Epoch [51][2600/3746] lr: 7.437e-02, eta: 3 days, 8:43:07, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.2184, top5_acc: 0.4600, loss_cls: 4.5431, loss: 4.5431 +2024-07-18 02:07:50,967 - pyskl - INFO - Epoch [51][2700/3746] lr: 7.434e-02, eta: 3 days, 8:41:58, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4597, loss_cls: 4.5339, loss: 4.5339 +2024-07-18 02:09:13,727 - pyskl - INFO - Epoch [51][2800/3746] lr: 7.432e-02, eta: 3 days, 8:40:49, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4666, loss_cls: 4.5008, loss: 4.5008 +2024-07-18 02:10:36,031 - pyskl - INFO - Epoch [51][2900/3746] lr: 7.429e-02, eta: 3 days, 8:39:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4800, loss_cls: 4.4662, loss: 4.4662 +2024-07-18 02:11:58,093 - pyskl - INFO - Epoch [51][3000/3746] lr: 7.427e-02, eta: 3 days, 8:38:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4669, loss_cls: 4.4884, loss: 4.4884 +2024-07-18 02:13:20,394 - pyskl - INFO - Epoch [51][3100/3746] lr: 7.425e-02, eta: 3 days, 8:37:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4655, loss_cls: 4.4910, loss: 4.4910 +2024-07-18 02:14:42,492 - pyskl - INFO - Epoch [51][3200/3746] lr: 7.422e-02, eta: 3 days, 8:36:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4713, loss_cls: 4.4798, loss: 4.4798 +2024-07-18 02:16:04,229 - pyskl - INFO - Epoch [51][3300/3746] lr: 7.420e-02, eta: 3 days, 8:34:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4772, loss_cls: 4.4894, loss: 4.4894 +2024-07-18 02:17:26,221 - pyskl - INFO - Epoch [51][3400/3746] lr: 7.417e-02, eta: 3 days, 8:33:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4722, loss_cls: 4.4785, loss: 4.4785 +2024-07-18 02:18:48,309 - pyskl - INFO - Epoch [51][3500/3746] lr: 7.415e-02, eta: 3 days, 8:32:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4684, loss_cls: 4.4987, loss: 4.4987 +2024-07-18 02:20:10,802 - pyskl - INFO - Epoch [51][3600/3746] lr: 7.412e-02, eta: 3 days, 8:31:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4675, loss_cls: 4.4943, loss: 4.4943 +2024-07-18 02:21:33,241 - pyskl - INFO - Epoch [51][3700/3746] lr: 7.410e-02, eta: 3 days, 8:30:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4633, loss_cls: 4.5244, loss: 4.5244 +2024-07-18 02:22:13,151 - pyskl - INFO - Saving checkpoint at 51 epochs +2024-07-18 02:24:04,523 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 02:24:05,208 - pyskl - INFO - +top1_acc 0.1492 +top5_acc 0.3477 +2024-07-18 02:24:05,209 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 02:24:05,256 - pyskl - INFO - +mean_acc 0.1491 +2024-07-18 02:24:05,269 - pyskl - INFO - Epoch(val) [51][309] top1_acc: 0.1492, top5_acc: 0.3477, mean_class_accuracy: 0.1491 +2024-07-18 02:27:54,845 - pyskl - INFO - Epoch [52][100/3746] lr: 7.406e-02, eta: 3 days, 8:32:06, time: 2.296, data_time: 1.308, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4695, loss_cls: 4.4457, loss: 4.4457 +2024-07-18 02:29:16,984 - pyskl - INFO - Epoch [52][200/3746] lr: 7.404e-02, eta: 3 days, 8:30:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4753, loss_cls: 4.4834, loss: 4.4834 +2024-07-18 02:30:38,744 - pyskl - INFO - Epoch [52][300/3746] lr: 7.401e-02, eta: 3 days, 8:29:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4766, loss_cls: 4.4401, loss: 4.4401 +2024-07-18 02:32:00,910 - pyskl - INFO - Epoch [52][400/3746] lr: 7.399e-02, eta: 3 days, 8:28:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4650, loss_cls: 4.5174, loss: 4.5174 +2024-07-18 02:33:22,692 - pyskl - INFO - Epoch [52][500/3746] lr: 7.397e-02, eta: 3 days, 8:27:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4817, loss_cls: 4.4124, loss: 4.4124 +2024-07-18 02:34:44,282 - pyskl - INFO - Epoch [52][600/3746] lr: 7.394e-02, eta: 3 days, 8:26:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4739, loss_cls: 4.4859, loss: 4.4859 +2024-07-18 02:36:06,312 - pyskl - INFO - Epoch [52][700/3746] lr: 7.392e-02, eta: 3 days, 8:25:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4734, loss_cls: 4.4685, loss: 4.4685 +2024-07-18 02:37:28,376 - pyskl - INFO - Epoch [52][800/3746] lr: 7.389e-02, eta: 3 days, 8:23:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4694, loss_cls: 4.4977, loss: 4.4977 +2024-07-18 02:38:50,161 - pyskl - INFO - Epoch [52][900/3746] lr: 7.387e-02, eta: 3 days, 8:22:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2306, top5_acc: 0.4683, loss_cls: 4.4753, loss: 4.4753 +2024-07-18 02:40:12,003 - pyskl - INFO - Epoch [52][1000/3746] lr: 7.384e-02, eta: 3 days, 8:21:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4673, loss_cls: 4.5199, loss: 4.5199 +2024-07-18 02:41:34,006 - pyskl - INFO - Epoch [52][1100/3746] lr: 7.382e-02, eta: 3 days, 8:20:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4688, loss_cls: 4.4637, loss: 4.4637 +2024-07-18 02:42:56,423 - pyskl - INFO - Epoch [52][1200/3746] lr: 7.379e-02, eta: 3 days, 8:19:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4591, loss_cls: 4.5070, loss: 4.5070 +2024-07-18 02:44:19,330 - pyskl - INFO - Epoch [52][1300/3746] lr: 7.377e-02, eta: 3 days, 8:17:57, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4772, loss_cls: 4.4844, loss: 4.4844 +2024-07-18 02:45:41,973 - pyskl - INFO - Epoch [52][1400/3746] lr: 7.374e-02, eta: 3 days, 8:16:47, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4711, loss_cls: 4.4685, loss: 4.4685 +2024-07-18 02:47:05,060 - pyskl - INFO - Epoch [52][1500/3746] lr: 7.372e-02, eta: 3 days, 8:15:38, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4728, loss_cls: 4.4913, loss: 4.4913 +2024-07-18 02:48:27,107 - pyskl - INFO - Epoch [52][1600/3746] lr: 7.370e-02, eta: 3 days, 8:14:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2256, top5_acc: 0.4570, loss_cls: 4.5371, loss: 4.5371 +2024-07-18 02:49:49,028 - pyskl - INFO - Epoch [52][1700/3746] lr: 7.367e-02, eta: 3 days, 8:13:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4770, loss_cls: 4.4390, loss: 4.4390 +2024-07-18 02:51:10,855 - pyskl - INFO - Epoch [52][1800/3746] lr: 7.365e-02, eta: 3 days, 8:12:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4706, loss_cls: 4.5043, loss: 4.5043 +2024-07-18 02:52:32,740 - pyskl - INFO - Epoch [52][1900/3746] lr: 7.362e-02, eta: 3 days, 8:10:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4661, loss_cls: 4.5175, loss: 4.5175 +2024-07-18 02:53:54,628 - pyskl - INFO - Epoch [52][2000/3746] lr: 7.360e-02, eta: 3 days, 8:09:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4741, loss_cls: 4.4717, loss: 4.4717 +2024-07-18 02:55:16,856 - pyskl - INFO - Epoch [52][2100/3746] lr: 7.357e-02, eta: 3 days, 8:08:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4653, loss_cls: 4.5192, loss: 4.5192 +2024-07-18 02:56:38,970 - pyskl - INFO - Epoch [52][2200/3746] lr: 7.355e-02, eta: 3 days, 8:07:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4627, loss_cls: 4.5350, loss: 4.5350 +2024-07-18 02:58:00,890 - pyskl - INFO - Epoch [52][2300/3746] lr: 7.352e-02, eta: 3 days, 8:06:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4766, loss_cls: 4.4667, loss: 4.4667 +2024-07-18 02:59:23,083 - pyskl - INFO - Epoch [52][2400/3746] lr: 7.350e-02, eta: 3 days, 8:04:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4636, loss_cls: 4.5268, loss: 4.5268 +2024-07-18 03:00:45,165 - pyskl - INFO - Epoch [52][2500/3746] lr: 7.347e-02, eta: 3 days, 8:03:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2333, top5_acc: 0.4722, loss_cls: 4.4742, loss: 4.4742 +2024-07-18 03:02:08,238 - pyskl - INFO - Epoch [52][2600/3746] lr: 7.345e-02, eta: 3 days, 8:02:38, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4683, loss_cls: 4.4961, loss: 4.4961 +2024-07-18 03:03:30,601 - pyskl - INFO - Epoch [52][2700/3746] lr: 7.342e-02, eta: 3 days, 8:01:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4650, loss_cls: 4.5014, loss: 4.5014 +2024-07-18 03:04:52,553 - pyskl - INFO - Epoch [52][2800/3746] lr: 7.340e-02, eta: 3 days, 8:00:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4639, loss_cls: 4.5061, loss: 4.5061 +2024-07-18 03:06:15,121 - pyskl - INFO - Epoch [52][2900/3746] lr: 7.337e-02, eta: 3 days, 7:59:07, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4752, loss_cls: 4.4787, loss: 4.4787 +2024-07-18 03:07:36,756 - pyskl - INFO - Epoch [52][3000/3746] lr: 7.335e-02, eta: 3 days, 7:57:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4694, loss_cls: 4.4924, loss: 4.4924 +2024-07-18 03:08:58,582 - pyskl - INFO - Epoch [52][3100/3746] lr: 7.332e-02, eta: 3 days, 7:56:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4697, loss_cls: 4.4826, loss: 4.4826 +2024-07-18 03:10:20,947 - pyskl - INFO - Epoch [52][3200/3746] lr: 7.330e-02, eta: 3 days, 7:55:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4633, loss_cls: 4.5331, loss: 4.5331 +2024-07-18 03:11:42,825 - pyskl - INFO - Epoch [52][3300/3746] lr: 7.328e-02, eta: 3 days, 7:54:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4806, loss_cls: 4.4495, loss: 4.4495 +2024-07-18 03:13:04,862 - pyskl - INFO - Epoch [52][3400/3746] lr: 7.325e-02, eta: 3 days, 7:53:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4739, loss_cls: 4.4833, loss: 4.4833 +2024-07-18 03:14:27,487 - pyskl - INFO - Epoch [52][3500/3746] lr: 7.323e-02, eta: 3 days, 7:52:00, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2347, top5_acc: 0.4736, loss_cls: 4.4599, loss: 4.4599 +2024-07-18 03:15:49,316 - pyskl - INFO - Epoch [52][3600/3746] lr: 7.320e-02, eta: 3 days, 7:50:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4727, loss_cls: 4.4911, loss: 4.4911 +2024-07-18 03:17:11,553 - pyskl - INFO - Epoch [52][3700/3746] lr: 7.318e-02, eta: 3 days, 7:49:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4808, loss_cls: 4.4341, loss: 4.4341 +2024-07-18 03:17:51,127 - pyskl - INFO - Saving checkpoint at 52 epochs +2024-07-18 03:19:42,080 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 03:19:42,749 - pyskl - INFO - +top1_acc 0.1572 +top5_acc 0.3710 +2024-07-18 03:19:42,750 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 03:19:42,793 - pyskl - INFO - +mean_acc 0.1568 +2024-07-18 03:19:42,806 - pyskl - INFO - Epoch(val) [52][309] top1_acc: 0.1572, top5_acc: 0.3710, mean_class_accuracy: 0.1568 +2024-07-18 03:23:32,752 - pyskl - INFO - Epoch [53][100/3746] lr: 7.314e-02, eta: 3 days, 7:51:21, time: 2.299, data_time: 1.313, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4841, loss_cls: 4.4453, loss: 4.4453 +2024-07-18 03:24:55,544 - pyskl - INFO - Epoch [53][200/3746] lr: 7.312e-02, eta: 3 days, 7:50:12, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2275, top5_acc: 0.4750, loss_cls: 4.4875, loss: 4.4875 +2024-07-18 03:26:17,459 - pyskl - INFO - Epoch [53][300/3746] lr: 7.309e-02, eta: 3 days, 7:49:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4763, loss_cls: 4.4356, loss: 4.4356 +2024-07-18 03:27:39,225 - pyskl - INFO - Epoch [53][400/3746] lr: 7.307e-02, eta: 3 days, 7:47:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4759, loss_cls: 4.4602, loss: 4.4602 +2024-07-18 03:29:01,164 - pyskl - INFO - Epoch [53][500/3746] lr: 7.304e-02, eta: 3 days, 7:46:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4747, loss_cls: 4.4561, loss: 4.4561 +2024-07-18 03:30:23,083 - pyskl - INFO - Epoch [53][600/3746] lr: 7.302e-02, eta: 3 days, 7:45:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2284, top5_acc: 0.4714, loss_cls: 4.4823, loss: 4.4823 +2024-07-18 03:31:45,117 - pyskl - INFO - Epoch [53][700/3746] lr: 7.299e-02, eta: 3 days, 7:44:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2208, top5_acc: 0.4612, loss_cls: 4.5407, loss: 4.5407 +2024-07-18 03:33:07,029 - pyskl - INFO - Epoch [53][800/3746] lr: 7.297e-02, eta: 3 days, 7:43:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4605, loss_cls: 4.4927, loss: 4.4927 +2024-07-18 03:34:28,896 - pyskl - INFO - Epoch [53][900/3746] lr: 7.294e-02, eta: 3 days, 7:41:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2266, top5_acc: 0.4675, loss_cls: 4.5165, loss: 4.5165 +2024-07-18 03:35:51,081 - pyskl - INFO - Epoch [53][1000/3746] lr: 7.292e-02, eta: 3 days, 7:40:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4778, loss_cls: 4.4508, loss: 4.4508 +2024-07-18 03:37:13,176 - pyskl - INFO - Epoch [53][1100/3746] lr: 7.289e-02, eta: 3 days, 7:39:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4798, loss_cls: 4.4700, loss: 4.4700 +2024-07-18 03:38:35,036 - pyskl - INFO - Epoch [53][1200/3746] lr: 7.287e-02, eta: 3 days, 7:38:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2244, top5_acc: 0.4577, loss_cls: 4.5244, loss: 4.5244 +2024-07-18 03:39:56,832 - pyskl - INFO - Epoch [53][1300/3746] lr: 7.284e-02, eta: 3 days, 7:37:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4761, loss_cls: 4.4534, loss: 4.4534 +2024-07-18 03:41:19,289 - pyskl - INFO - Epoch [53][1400/3746] lr: 7.282e-02, eta: 3 days, 7:35:53, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4758, loss_cls: 4.4588, loss: 4.4588 +2024-07-18 03:42:41,566 - pyskl - INFO - Epoch [53][1500/3746] lr: 7.279e-02, eta: 3 days, 7:34:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4725, loss_cls: 4.4833, loss: 4.4833 +2024-07-18 03:44:03,465 - pyskl - INFO - Epoch [53][1600/3746] lr: 7.277e-02, eta: 3 days, 7:33:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4623, loss_cls: 4.5120, loss: 4.5120 +2024-07-18 03:45:24,763 - pyskl - INFO - Epoch [53][1700/3746] lr: 7.274e-02, eta: 3 days, 7:32:17, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4742, loss_cls: 4.4642, loss: 4.4642 +2024-07-18 03:46:47,026 - pyskl - INFO - Epoch [53][1800/3746] lr: 7.272e-02, eta: 3 days, 7:31:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4744, loss_cls: 4.4649, loss: 4.4649 +2024-07-18 03:48:09,022 - pyskl - INFO - Epoch [53][1900/3746] lr: 7.269e-02, eta: 3 days, 7:29:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4727, loss_cls: 4.4917, loss: 4.4917 +2024-07-18 03:49:31,083 - pyskl - INFO - Epoch [53][2000/3746] lr: 7.267e-02, eta: 3 days, 7:28:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4694, loss_cls: 4.5088, loss: 4.5088 +2024-07-18 03:50:53,082 - pyskl - INFO - Epoch [53][2100/3746] lr: 7.264e-02, eta: 3 days, 7:27:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2270, top5_acc: 0.4686, loss_cls: 4.4629, loss: 4.4629 +2024-07-18 03:52:15,183 - pyskl - INFO - Epoch [53][2200/3746] lr: 7.262e-02, eta: 3 days, 7:26:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4692, loss_cls: 4.4662, loss: 4.4662 +2024-07-18 03:53:37,588 - pyskl - INFO - Epoch [53][2300/3746] lr: 7.259e-02, eta: 3 days, 7:25:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4808, loss_cls: 4.4335, loss: 4.4335 +2024-07-18 03:54:59,224 - pyskl - INFO - Epoch [53][2400/3746] lr: 7.257e-02, eta: 3 days, 7:23:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4711, loss_cls: 4.4754, loss: 4.4754 +2024-07-18 03:56:21,906 - pyskl - INFO - Epoch [53][2500/3746] lr: 7.254e-02, eta: 3 days, 7:22:46, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2280, top5_acc: 0.4595, loss_cls: 4.5139, loss: 4.5139 +2024-07-18 03:57:44,323 - pyskl - INFO - Epoch [53][2600/3746] lr: 7.252e-02, eta: 3 days, 7:21:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4755, loss_cls: 4.4641, loss: 4.4641 +2024-07-18 03:59:06,647 - pyskl - INFO - Epoch [53][2700/3746] lr: 7.249e-02, eta: 3 days, 7:20:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4702, loss_cls: 4.4897, loss: 4.4897 +2024-07-18 04:00:29,530 - pyskl - INFO - Epoch [53][2800/3746] lr: 7.247e-02, eta: 3 days, 7:19:14, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4656, loss_cls: 4.4863, loss: 4.4863 +2024-07-18 04:01:52,027 - pyskl - INFO - Epoch [53][2900/3746] lr: 7.244e-02, eta: 3 days, 7:18:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4788, loss_cls: 4.4362, loss: 4.4362 +2024-07-18 04:03:14,129 - pyskl - INFO - Epoch [53][3000/3746] lr: 7.242e-02, eta: 3 days, 7:16:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4738, loss_cls: 4.4622, loss: 4.4622 +2024-07-18 04:04:35,960 - pyskl - INFO - Epoch [53][3100/3746] lr: 7.239e-02, eta: 3 days, 7:15:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4728, loss_cls: 4.4685, loss: 4.4685 +2024-07-18 04:05:57,968 - pyskl - INFO - Epoch [53][3200/3746] lr: 7.237e-02, eta: 3 days, 7:14:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4714, loss_cls: 4.4621, loss: 4.4621 +2024-07-18 04:07:19,407 - pyskl - INFO - Epoch [53][3300/3746] lr: 7.234e-02, eta: 3 days, 7:13:15, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4714, loss_cls: 4.4750, loss: 4.4750 +2024-07-18 04:08:40,918 - pyskl - INFO - Epoch [53][3400/3746] lr: 7.232e-02, eta: 3 days, 7:12:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4673, loss_cls: 4.4956, loss: 4.4956 +2024-07-18 04:10:02,624 - pyskl - INFO - Epoch [53][3500/3746] lr: 7.229e-02, eta: 3 days, 7:10:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4756, loss_cls: 4.4472, loss: 4.4472 +2024-07-18 04:11:24,734 - pyskl - INFO - Epoch [53][3600/3746] lr: 7.227e-02, eta: 3 days, 7:09:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2258, top5_acc: 0.4591, loss_cls: 4.5117, loss: 4.5117 +2024-07-18 04:12:47,090 - pyskl - INFO - Epoch [53][3700/3746] lr: 7.224e-02, eta: 3 days, 7:08:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4683, loss_cls: 4.4967, loss: 4.4967 +2024-07-18 04:13:27,046 - pyskl - INFO - Saving checkpoint at 53 epochs +2024-07-18 04:15:18,512 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 04:15:19,173 - pyskl - INFO - +top1_acc 0.1704 +top5_acc 0.3811 +2024-07-18 04:15:19,174 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 04:15:19,217 - pyskl - INFO - +mean_acc 0.1702 +2024-07-18 04:15:19,230 - pyskl - INFO - Epoch(val) [53][309] top1_acc: 0.1704, top5_acc: 0.3811, mean_class_accuracy: 0.1702 +2024-07-18 04:19:10,585 - pyskl - INFO - Epoch [54][100/3746] lr: 7.221e-02, eta: 3 days, 7:10:07, time: 2.313, data_time: 1.329, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4764, loss_cls: 4.4377, loss: 4.4377 +2024-07-18 04:20:32,680 - pyskl - INFO - Epoch [54][200/3746] lr: 7.218e-02, eta: 3 days, 7:08:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4761, loss_cls: 4.4430, loss: 4.4430 +2024-07-18 04:21:54,870 - pyskl - INFO - Epoch [54][300/3746] lr: 7.216e-02, eta: 3 days, 7:07:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2305, top5_acc: 0.4713, loss_cls: 4.4941, loss: 4.4941 +2024-07-18 04:23:16,713 - pyskl - INFO - Epoch [54][400/3746] lr: 7.213e-02, eta: 3 days, 7:06:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4752, loss_cls: 4.4433, loss: 4.4433 +2024-07-18 04:24:39,301 - pyskl - INFO - Epoch [54][500/3746] lr: 7.211e-02, eta: 3 days, 7:05:20, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4739, loss_cls: 4.4562, loss: 4.4562 +2024-07-18 04:26:01,394 - pyskl - INFO - Epoch [54][600/3746] lr: 7.208e-02, eta: 3 days, 7:04:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4858, loss_cls: 4.4034, loss: 4.4034 +2024-07-18 04:27:23,602 - pyskl - INFO - Epoch [54][700/3746] lr: 7.206e-02, eta: 3 days, 7:02:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2291, top5_acc: 0.4772, loss_cls: 4.4836, loss: 4.4836 +2024-07-18 04:28:45,615 - pyskl - INFO - Epoch [54][800/3746] lr: 7.203e-02, eta: 3 days, 7:01:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4783, loss_cls: 4.4730, loss: 4.4730 +2024-07-18 04:30:07,722 - pyskl - INFO - Epoch [54][900/3746] lr: 7.201e-02, eta: 3 days, 7:00:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4845, loss_cls: 4.4351, loss: 4.4351 +2024-07-18 04:31:29,498 - pyskl - INFO - Epoch [54][1000/3746] lr: 7.198e-02, eta: 3 days, 6:59:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4791, loss_cls: 4.4341, loss: 4.4341 +2024-07-18 04:32:51,569 - pyskl - INFO - Epoch [54][1100/3746] lr: 7.196e-02, eta: 3 days, 6:58:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4625, loss_cls: 4.4899, loss: 4.4899 +2024-07-18 04:34:14,034 - pyskl - INFO - Epoch [54][1200/3746] lr: 7.193e-02, eta: 3 days, 6:56:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4684, loss_cls: 4.4790, loss: 4.4790 +2024-07-18 04:35:36,524 - pyskl - INFO - Epoch [54][1300/3746] lr: 7.191e-02, eta: 3 days, 6:55:46, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2339, top5_acc: 0.4756, loss_cls: 4.4625, loss: 4.4625 +2024-07-18 04:36:59,161 - pyskl - INFO - Epoch [54][1400/3746] lr: 7.188e-02, eta: 3 days, 6:54:35, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2309, top5_acc: 0.4606, loss_cls: 4.5041, loss: 4.5041 +2024-07-18 04:38:21,387 - pyskl - INFO - Epoch [54][1500/3746] lr: 7.186e-02, eta: 3 days, 6:53:23, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4628, loss_cls: 4.5047, loss: 4.5047 +2024-07-18 04:39:43,429 - pyskl - INFO - Epoch [54][1600/3746] lr: 7.183e-02, eta: 3 days, 6:52:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4641, loss_cls: 4.4918, loss: 4.4918 +2024-07-18 04:41:05,449 - pyskl - INFO - Epoch [54][1700/3746] lr: 7.181e-02, eta: 3 days, 6:50:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4678, loss_cls: 4.4718, loss: 4.4718 +2024-07-18 04:42:28,245 - pyskl - INFO - Epoch [54][1800/3746] lr: 7.178e-02, eta: 3 days, 6:49:48, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4633, loss_cls: 4.5233, loss: 4.5233 +2024-07-18 04:43:50,235 - pyskl - INFO - Epoch [54][1900/3746] lr: 7.176e-02, eta: 3 days, 6:48:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4689, loss_cls: 4.4884, loss: 4.4884 +2024-07-18 04:45:12,096 - pyskl - INFO - Epoch [54][2000/3746] lr: 7.173e-02, eta: 3 days, 6:47:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4786, loss_cls: 4.4444, loss: 4.4444 +2024-07-18 04:46:34,867 - pyskl - INFO - Epoch [54][2100/3746] lr: 7.170e-02, eta: 3 days, 6:46:13, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4578, loss_cls: 4.5262, loss: 4.5262 +2024-07-18 04:47:57,155 - pyskl - INFO - Epoch [54][2200/3746] lr: 7.168e-02, eta: 3 days, 6:45:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4864, loss_cls: 4.4371, loss: 4.4371 +2024-07-18 04:49:19,934 - pyskl - INFO - Epoch [54][2300/3746] lr: 7.165e-02, eta: 3 days, 6:43:50, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4636, loss_cls: 4.4909, loss: 4.4909 +2024-07-18 04:50:42,272 - pyskl - INFO - Epoch [54][2400/3746] lr: 7.163e-02, eta: 3 days, 6:42:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4756, loss_cls: 4.4594, loss: 4.4594 +2024-07-18 04:52:05,389 - pyskl - INFO - Epoch [54][2500/3746] lr: 7.160e-02, eta: 3 days, 6:41:29, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4811, loss_cls: 4.4591, loss: 4.4591 +2024-07-18 04:53:28,166 - pyskl - INFO - Epoch [54][2600/3746] lr: 7.158e-02, eta: 3 days, 6:40:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4641, loss_cls: 4.4863, loss: 4.4863 +2024-07-18 04:54:50,722 - pyskl - INFO - Epoch [54][2700/3746] lr: 7.155e-02, eta: 3 days, 6:39:07, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4711, loss_cls: 4.4470, loss: 4.4470 +2024-07-18 04:56:13,605 - pyskl - INFO - Epoch [54][2800/3746] lr: 7.153e-02, eta: 3 days, 6:37:56, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4684, loss_cls: 4.4979, loss: 4.4979 +2024-07-18 04:57:36,153 - pyskl - INFO - Epoch [54][2900/3746] lr: 7.150e-02, eta: 3 days, 6:36:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4697, loss_cls: 4.4964, loss: 4.4964 +2024-07-18 04:58:57,612 - pyskl - INFO - Epoch [54][3000/3746] lr: 7.148e-02, eta: 3 days, 6:35:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4775, loss_cls: 4.4673, loss: 4.4673 +2024-07-18 05:00:19,804 - pyskl - INFO - Epoch [54][3100/3746] lr: 7.145e-02, eta: 3 days, 6:34:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4750, loss_cls: 4.4255, loss: 4.4255 +2024-07-18 05:01:41,762 - pyskl - INFO - Epoch [54][3200/3746] lr: 7.143e-02, eta: 3 days, 6:33:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4753, loss_cls: 4.4784, loss: 4.4784 +2024-07-18 05:03:03,799 - pyskl - INFO - Epoch [54][3300/3746] lr: 7.140e-02, eta: 3 days, 6:31:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4755, loss_cls: 4.4649, loss: 4.4649 +2024-07-18 05:04:26,229 - pyskl - INFO - Epoch [54][3400/3746] lr: 7.138e-02, eta: 3 days, 6:30:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2295, top5_acc: 0.4636, loss_cls: 4.5163, loss: 4.5163 +2024-07-18 05:05:48,138 - pyskl - INFO - Epoch [54][3500/3746] lr: 7.135e-02, eta: 3 days, 6:29:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2238, top5_acc: 0.4645, loss_cls: 4.5250, loss: 4.5250 +2024-07-18 05:07:10,337 - pyskl - INFO - Epoch [54][3600/3746] lr: 7.133e-02, eta: 3 days, 6:28:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2250, top5_acc: 0.4627, loss_cls: 4.5327, loss: 4.5327 +2024-07-18 05:08:32,508 - pyskl - INFO - Epoch [54][3700/3746] lr: 7.130e-02, eta: 3 days, 6:27:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4681, loss_cls: 4.4688, loss: 4.4688 +2024-07-18 05:09:12,616 - pyskl - INFO - Saving checkpoint at 54 epochs +2024-07-18 05:11:04,228 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 05:11:04,897 - pyskl - INFO - +top1_acc 0.1686 +top5_acc 0.3935 +2024-07-18 05:11:04,897 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 05:11:04,940 - pyskl - INFO - +mean_acc 0.1684 +2024-07-18 05:11:04,952 - pyskl - INFO - Epoch(val) [54][309] top1_acc: 0.1686, top5_acc: 0.3935, mean_class_accuracy: 0.1684 +2024-07-18 05:14:55,337 - pyskl - INFO - Epoch [55][100/3746] lr: 7.126e-02, eta: 3 days, 6:28:38, time: 2.304, data_time: 1.315, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4992, loss_cls: 4.3855, loss: 4.3855 +2024-07-18 05:16:17,180 - pyskl - INFO - Epoch [55][200/3746] lr: 7.124e-02, eta: 3 days, 6:27:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4827, loss_cls: 4.4082, loss: 4.4082 +2024-07-18 05:17:39,255 - pyskl - INFO - Epoch [55][300/3746] lr: 7.121e-02, eta: 3 days, 6:26:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4788, loss_cls: 4.4476, loss: 4.4476 +2024-07-18 05:19:01,145 - pyskl - INFO - Epoch [55][400/3746] lr: 7.119e-02, eta: 3 days, 6:25:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4797, loss_cls: 4.4296, loss: 4.4296 +2024-07-18 05:20:23,204 - pyskl - INFO - Epoch [55][500/3746] lr: 7.116e-02, eta: 3 days, 6:23:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4742, loss_cls: 4.4494, loss: 4.4494 +2024-07-18 05:21:45,694 - pyskl - INFO - Epoch [55][600/3746] lr: 7.114e-02, eta: 3 days, 6:22:36, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4758, loss_cls: 4.4698, loss: 4.4698 +2024-07-18 05:23:07,499 - pyskl - INFO - Epoch [55][700/3746] lr: 7.111e-02, eta: 3 days, 6:21:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4872, loss_cls: 4.4252, loss: 4.4252 +2024-07-18 05:24:29,826 - pyskl - INFO - Epoch [55][800/3746] lr: 7.109e-02, eta: 3 days, 6:20:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2311, top5_acc: 0.4736, loss_cls: 4.4741, loss: 4.4741 +2024-07-18 05:25:51,374 - pyskl - INFO - Epoch [55][900/3746] lr: 7.106e-02, eta: 3 days, 6:18:57, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4769, loss_cls: 4.4547, loss: 4.4547 +2024-07-18 05:27:13,290 - pyskl - INFO - Epoch [55][1000/3746] lr: 7.104e-02, eta: 3 days, 6:17:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4780, loss_cls: 4.4683, loss: 4.4683 +2024-07-18 05:28:35,594 - pyskl - INFO - Epoch [55][1100/3746] lr: 7.101e-02, eta: 3 days, 6:16:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2300, top5_acc: 0.4747, loss_cls: 4.4749, loss: 4.4749 +2024-07-18 05:29:58,230 - pyskl - INFO - Epoch [55][1200/3746] lr: 7.099e-02, eta: 3 days, 6:15:21, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4766, loss_cls: 4.4533, loss: 4.4533 +2024-07-18 05:31:21,038 - pyskl - INFO - Epoch [55][1300/3746] lr: 7.096e-02, eta: 3 days, 6:14:10, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4695, loss_cls: 4.4993, loss: 4.4993 +2024-07-18 05:32:43,641 - pyskl - INFO - Epoch [55][1400/3746] lr: 7.093e-02, eta: 3 days, 6:12:58, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4756, loss_cls: 4.4667, loss: 4.4667 +2024-07-18 05:34:05,823 - pyskl - INFO - Epoch [55][1500/3746] lr: 7.091e-02, eta: 3 days, 6:11:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4642, loss_cls: 4.5005, loss: 4.5005 +2024-07-18 05:35:28,009 - pyskl - INFO - Epoch [55][1600/3746] lr: 7.088e-02, eta: 3 days, 6:10:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4877, loss_cls: 4.4151, loss: 4.4151 +2024-07-18 05:36:50,131 - pyskl - INFO - Epoch [55][1700/3746] lr: 7.086e-02, eta: 3 days, 6:09:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4681, loss_cls: 4.4947, loss: 4.4947 +2024-07-18 05:38:11,957 - pyskl - INFO - Epoch [55][1800/3746] lr: 7.083e-02, eta: 3 days, 6:08:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4680, loss_cls: 4.4720, loss: 4.4720 +2024-07-18 05:39:33,704 - pyskl - INFO - Epoch [55][1900/3746] lr: 7.081e-02, eta: 3 days, 6:06:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4688, loss_cls: 4.5161, loss: 4.5161 +2024-07-18 05:40:55,489 - pyskl - INFO - Epoch [55][2000/3746] lr: 7.078e-02, eta: 3 days, 6:05:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4908, loss_cls: 4.4147, loss: 4.4147 +2024-07-18 05:42:17,325 - pyskl - INFO - Epoch [55][2100/3746] lr: 7.076e-02, eta: 3 days, 6:04:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4816, loss_cls: 4.4421, loss: 4.4421 +2024-07-18 05:43:39,222 - pyskl - INFO - Epoch [55][2200/3746] lr: 7.073e-02, eta: 3 days, 6:03:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4741, loss_cls: 4.4797, loss: 4.4797 +2024-07-18 05:45:01,387 - pyskl - INFO - Epoch [55][2300/3746] lr: 7.071e-02, eta: 3 days, 6:02:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4808, loss_cls: 4.4124, loss: 4.4124 +2024-07-18 05:46:23,493 - pyskl - INFO - Epoch [55][2400/3746] lr: 7.068e-02, eta: 3 days, 6:00:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4731, loss_cls: 4.4754, loss: 4.4754 +2024-07-18 05:47:46,496 - pyskl - INFO - Epoch [55][2500/3746] lr: 7.065e-02, eta: 3 days, 5:59:40, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4686, loss_cls: 4.4915, loss: 4.4915 +2024-07-18 05:49:08,765 - pyskl - INFO - Epoch [55][2600/3746] lr: 7.063e-02, eta: 3 days, 5:58:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4727, loss_cls: 4.4635, loss: 4.4635 +2024-07-18 05:50:31,370 - pyskl - INFO - Epoch [55][2700/3746] lr: 7.060e-02, eta: 3 days, 5:57:16, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2245, top5_acc: 0.4666, loss_cls: 4.5125, loss: 4.5125 +2024-07-18 05:51:53,957 - pyskl - INFO - Epoch [55][2800/3746] lr: 7.058e-02, eta: 3 days, 5:56:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4644, loss_cls: 4.5192, loss: 4.5192 +2024-07-18 05:53:16,133 - pyskl - INFO - Epoch [55][2900/3746] lr: 7.055e-02, eta: 3 days, 5:54:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2252, top5_acc: 0.4566, loss_cls: 4.5405, loss: 4.5405 +2024-07-18 05:54:37,787 - pyskl - INFO - Epoch [55][3000/3746] lr: 7.053e-02, eta: 3 days, 5:53:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2292, top5_acc: 0.4688, loss_cls: 4.4659, loss: 4.4659 +2024-07-18 05:55:59,596 - pyskl - INFO - Epoch [55][3100/3746] lr: 7.050e-02, eta: 3 days, 5:52:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4677, loss_cls: 4.4802, loss: 4.4802 +2024-07-18 05:57:21,983 - pyskl - INFO - Epoch [55][3200/3746] lr: 7.048e-02, eta: 3 days, 5:51:13, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4606, loss_cls: 4.5113, loss: 4.5113 +2024-07-18 05:58:43,607 - pyskl - INFO - Epoch [55][3300/3746] lr: 7.045e-02, eta: 3 days, 5:50:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2303, top5_acc: 0.4750, loss_cls: 4.4717, loss: 4.4717 +2024-07-18 06:00:05,628 - pyskl - INFO - Epoch [55][3400/3746] lr: 7.043e-02, eta: 3 days, 5:48:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4659, loss_cls: 4.4872, loss: 4.4872 +2024-07-18 06:01:28,059 - pyskl - INFO - Epoch [55][3500/3746] lr: 7.040e-02, eta: 3 days, 5:47:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4763, loss_cls: 4.4402, loss: 4.4402 +2024-07-18 06:02:49,839 - pyskl - INFO - Epoch [55][3600/3746] lr: 7.037e-02, eta: 3 days, 5:46:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4770, loss_cls: 4.4653, loss: 4.4653 +2024-07-18 06:04:11,587 - pyskl - INFO - Epoch [55][3700/3746] lr: 7.035e-02, eta: 3 days, 5:45:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4809, loss_cls: 4.4587, loss: 4.4587 +2024-07-18 06:04:51,576 - pyskl - INFO - Saving checkpoint at 55 epochs +2024-07-18 06:06:43,451 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 06:06:44,140 - pyskl - INFO - +top1_acc 0.1763 +top5_acc 0.3936 +2024-07-18 06:06:44,140 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 06:06:44,187 - pyskl - INFO - +mean_acc 0.1760 +2024-07-18 06:06:44,201 - pyskl - INFO - Epoch(val) [55][309] top1_acc: 0.1763, top5_acc: 0.3936, mean_class_accuracy: 0.1760 +2024-07-18 06:10:35,159 - pyskl - INFO - Epoch [56][100/3746] lr: 7.031e-02, eta: 3 days, 5:46:34, time: 2.309, data_time: 1.321, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4886, loss_cls: 4.4137, loss: 4.4137 +2024-07-18 06:11:57,794 - pyskl - INFO - Epoch [56][200/3746] lr: 7.029e-02, eta: 3 days, 5:45:22, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4811, loss_cls: 4.4454, loss: 4.4454 +2024-07-18 06:13:19,842 - pyskl - INFO - Epoch [56][300/3746] lr: 7.026e-02, eta: 3 days, 5:44:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4806, loss_cls: 4.3993, loss: 4.3993 +2024-07-18 06:14:41,806 - pyskl - INFO - Epoch [56][400/3746] lr: 7.023e-02, eta: 3 days, 5:42:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2263, top5_acc: 0.4691, loss_cls: 4.5065, loss: 4.5065 +2024-07-18 06:16:03,348 - pyskl - INFO - Epoch [56][500/3746] lr: 7.021e-02, eta: 3 days, 5:41:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4734, loss_cls: 4.4420, loss: 4.4420 +2024-07-18 06:17:25,293 - pyskl - INFO - Epoch [56][600/3746] lr: 7.018e-02, eta: 3 days, 5:40:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4794, loss_cls: 4.4659, loss: 4.4659 +2024-07-18 06:18:47,223 - pyskl - INFO - Epoch [56][700/3746] lr: 7.016e-02, eta: 3 days, 5:39:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4700, loss_cls: 4.4782, loss: 4.4782 +2024-07-18 06:20:09,614 - pyskl - INFO - Epoch [56][800/3746] lr: 7.013e-02, eta: 3 days, 5:38:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4870, loss_cls: 4.4172, loss: 4.4172 +2024-07-18 06:21:31,425 - pyskl - INFO - Epoch [56][900/3746] lr: 7.011e-02, eta: 3 days, 5:36:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4700, loss_cls: 4.4828, loss: 4.4828 +2024-07-18 06:22:53,685 - pyskl - INFO - Epoch [56][1000/3746] lr: 7.008e-02, eta: 3 days, 5:35:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2264, top5_acc: 0.4605, loss_cls: 4.5281, loss: 4.5281 +2024-07-18 06:24:15,743 - pyskl - INFO - Epoch [56][1100/3746] lr: 7.006e-02, eta: 3 days, 5:34:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2319, top5_acc: 0.4655, loss_cls: 4.4871, loss: 4.4871 +2024-07-18 06:25:37,720 - pyskl - INFO - Epoch [56][1200/3746] lr: 7.003e-02, eta: 3 days, 5:33:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4700, loss_cls: 4.4544, loss: 4.4544 +2024-07-18 06:26:59,844 - pyskl - INFO - Epoch [56][1300/3746] lr: 7.000e-02, eta: 3 days, 5:31:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4761, loss_cls: 4.4466, loss: 4.4466 +2024-07-18 06:28:21,706 - pyskl - INFO - Epoch [56][1400/3746] lr: 6.998e-02, eta: 3 days, 5:30:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4656, loss_cls: 4.4908, loss: 4.4908 +2024-07-18 06:29:44,512 - pyskl - INFO - Epoch [56][1500/3746] lr: 6.995e-02, eta: 3 days, 5:29:34, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2200, top5_acc: 0.4694, loss_cls: 4.4997, loss: 4.4997 +2024-07-18 06:31:06,764 - pyskl - INFO - Epoch [56][1600/3746] lr: 6.993e-02, eta: 3 days, 5:28:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4847, loss_cls: 4.4116, loss: 4.4116 +2024-07-18 06:32:28,469 - pyskl - INFO - Epoch [56][1700/3746] lr: 6.990e-02, eta: 3 days, 5:27:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2412, top5_acc: 0.4806, loss_cls: 4.4303, loss: 4.4303 +2024-07-18 06:33:50,238 - pyskl - INFO - Epoch [56][1800/3746] lr: 6.988e-02, eta: 3 days, 5:25:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2314, top5_acc: 0.4667, loss_cls: 4.4860, loss: 4.4860 +2024-07-18 06:35:11,942 - pyskl - INFO - Epoch [56][1900/3746] lr: 6.985e-02, eta: 3 days, 5:24:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4744, loss_cls: 4.4399, loss: 4.4399 +2024-07-18 06:36:33,545 - pyskl - INFO - Epoch [56][2000/3746] lr: 6.983e-02, eta: 3 days, 5:23:26, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2298, top5_acc: 0.4763, loss_cls: 4.4674, loss: 4.4674 +2024-07-18 06:37:55,109 - pyskl - INFO - Epoch [56][2100/3746] lr: 6.980e-02, eta: 3 days, 5:22:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4806, loss_cls: 4.4394, loss: 4.4394 +2024-07-18 06:39:17,527 - pyskl - INFO - Epoch [56][2200/3746] lr: 6.977e-02, eta: 3 days, 5:21:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4756, loss_cls: 4.4681, loss: 4.4681 +2024-07-18 06:40:39,169 - pyskl - INFO - Epoch [56][2300/3746] lr: 6.975e-02, eta: 3 days, 5:19:46, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2269, top5_acc: 0.4586, loss_cls: 4.5163, loss: 4.5163 +2024-07-18 06:42:01,591 - pyskl - INFO - Epoch [56][2400/3746] lr: 6.972e-02, eta: 3 days, 5:18:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4714, loss_cls: 4.4744, loss: 4.4744 +2024-07-18 06:43:24,314 - pyskl - INFO - Epoch [56][2500/3746] lr: 6.970e-02, eta: 3 days, 5:17:21, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4769, loss_cls: 4.4625, loss: 4.4625 +2024-07-18 06:44:46,461 - pyskl - INFO - Epoch [56][2600/3746] lr: 6.967e-02, eta: 3 days, 5:16:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4750, loss_cls: 4.4562, loss: 4.4562 +2024-07-18 06:46:08,560 - pyskl - INFO - Epoch [56][2700/3746] lr: 6.965e-02, eta: 3 days, 5:14:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2267, top5_acc: 0.4637, loss_cls: 4.5192, loss: 4.5192 +2024-07-18 06:47:30,872 - pyskl - INFO - Epoch [56][2800/3746] lr: 6.962e-02, eta: 3 days, 5:13:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4823, loss_cls: 4.4214, loss: 4.4214 +2024-07-18 06:48:52,796 - pyskl - INFO - Epoch [56][2900/3746] lr: 6.959e-02, eta: 3 days, 5:12:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4872, loss_cls: 4.4117, loss: 4.4117 +2024-07-18 06:50:15,256 - pyskl - INFO - Epoch [56][3000/3746] lr: 6.957e-02, eta: 3 days, 5:11:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4656, loss_cls: 4.5039, loss: 4.5039 +2024-07-18 06:51:37,000 - pyskl - INFO - Epoch [56][3100/3746] lr: 6.954e-02, eta: 3 days, 5:10:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4783, loss_cls: 4.4330, loss: 4.4330 +2024-07-18 06:52:58,834 - pyskl - INFO - Epoch [56][3200/3746] lr: 6.952e-02, eta: 3 days, 5:08:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4730, loss_cls: 4.4522, loss: 4.4522 +2024-07-18 06:54:21,179 - pyskl - INFO - Epoch [56][3300/3746] lr: 6.949e-02, eta: 3 days, 5:07:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4752, loss_cls: 4.4553, loss: 4.4553 +2024-07-18 06:55:43,176 - pyskl - INFO - Epoch [56][3400/3746] lr: 6.947e-02, eta: 3 days, 5:06:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4667, loss_cls: 4.4948, loss: 4.4948 +2024-07-18 06:57:05,070 - pyskl - INFO - Epoch [56][3500/3746] lr: 6.944e-02, eta: 3 days, 5:05:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4742, loss_cls: 4.4484, loss: 4.4484 +2024-07-18 06:58:27,131 - pyskl - INFO - Epoch [56][3600/3746] lr: 6.941e-02, eta: 3 days, 5:03:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4720, loss_cls: 4.4447, loss: 4.4447 +2024-07-18 06:59:49,771 - pyskl - INFO - Epoch [56][3700/3746] lr: 6.939e-02, eta: 3 days, 5:02:45, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4719, loss_cls: 4.5050, loss: 4.5050 +2024-07-18 07:00:29,354 - pyskl - INFO - Saving checkpoint at 56 epochs +2024-07-18 07:02:21,574 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 07:02:22,240 - pyskl - INFO - +top1_acc 0.1764 +top5_acc 0.3958 +2024-07-18 07:02:22,240 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 07:02:22,283 - pyskl - INFO - +mean_acc 0.1763 +2024-07-18 07:02:22,298 - pyskl - INFO - Epoch(val) [56][309] top1_acc: 0.1764, top5_acc: 0.3958, mean_class_accuracy: 0.1763 +2024-07-18 07:06:08,336 - pyskl - INFO - Epoch [57][100/3746] lr: 6.935e-02, eta: 3 days, 5:03:56, time: 2.260, data_time: 1.273, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4811, loss_cls: 4.4417, loss: 4.4417 +2024-07-18 07:07:30,560 - pyskl - INFO - Epoch [57][200/3746] lr: 6.932e-02, eta: 3 days, 5:02:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4819, loss_cls: 4.4249, loss: 4.4249 +2024-07-18 07:08:53,035 - pyskl - INFO - Epoch [57][300/3746] lr: 6.930e-02, eta: 3 days, 5:01:30, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2405, top5_acc: 0.4886, loss_cls: 4.4102, loss: 4.4102 +2024-07-18 07:10:14,756 - pyskl - INFO - Epoch [57][400/3746] lr: 6.927e-02, eta: 3 days, 5:00:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4822, loss_cls: 4.4624, loss: 4.4624 +2024-07-18 07:11:36,646 - pyskl - INFO - Epoch [57][500/3746] lr: 6.925e-02, eta: 3 days, 4:59:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4823, loss_cls: 4.3832, loss: 4.3832 +2024-07-18 07:12:58,500 - pyskl - INFO - Epoch [57][600/3746] lr: 6.922e-02, eta: 3 days, 4:57:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4786, loss_cls: 4.4567, loss: 4.4567 +2024-07-18 07:14:20,268 - pyskl - INFO - Epoch [57][700/3746] lr: 6.920e-02, eta: 3 days, 4:56:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4820, loss_cls: 4.4299, loss: 4.4299 +2024-07-18 07:15:42,535 - pyskl - INFO - Epoch [57][800/3746] lr: 6.917e-02, eta: 3 days, 4:55:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4808, loss_cls: 4.4444, loss: 4.4444 +2024-07-18 07:17:04,620 - pyskl - INFO - Epoch [57][900/3746] lr: 6.914e-02, eta: 3 days, 4:54:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4666, loss_cls: 4.4993, loss: 4.4993 +2024-07-18 07:18:26,610 - pyskl - INFO - Epoch [57][1000/3746] lr: 6.912e-02, eta: 3 days, 4:52:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4698, loss_cls: 4.4645, loss: 4.4645 +2024-07-18 07:19:48,951 - pyskl - INFO - Epoch [57][1100/3746] lr: 6.909e-02, eta: 3 days, 4:51:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4773, loss_cls: 4.4257, loss: 4.4257 +2024-07-18 07:21:11,053 - pyskl - INFO - Epoch [57][1200/3746] lr: 6.907e-02, eta: 3 days, 4:50:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4861, loss_cls: 4.4282, loss: 4.4282 +2024-07-18 07:22:33,196 - pyskl - INFO - Epoch [57][1300/3746] lr: 6.904e-02, eta: 3 days, 4:49:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4836, loss_cls: 4.4069, loss: 4.4069 +2024-07-18 07:23:55,738 - pyskl - INFO - Epoch [57][1400/3746] lr: 6.901e-02, eta: 3 days, 4:48:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4722, loss_cls: 4.4469, loss: 4.4469 +2024-07-18 07:25:17,792 - pyskl - INFO - Epoch [57][1500/3746] lr: 6.899e-02, eta: 3 days, 4:46:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4670, loss_cls: 4.4877, loss: 4.4877 +2024-07-18 07:26:39,638 - pyskl - INFO - Epoch [57][1600/3746] lr: 6.896e-02, eta: 3 days, 4:45:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4725, loss_cls: 4.4640, loss: 4.4640 +2024-07-18 07:28:01,636 - pyskl - INFO - Epoch [57][1700/3746] lr: 6.894e-02, eta: 3 days, 4:44:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4719, loss_cls: 4.4716, loss: 4.4716 +2024-07-18 07:29:24,140 - pyskl - INFO - Epoch [57][1800/3746] lr: 6.891e-02, eta: 3 days, 4:43:09, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4817, loss_cls: 4.4272, loss: 4.4272 +2024-07-18 07:30:46,363 - pyskl - INFO - Epoch [57][1900/3746] lr: 6.889e-02, eta: 3 days, 4:41:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4723, loss_cls: 4.4491, loss: 4.4491 +2024-07-18 07:32:08,470 - pyskl - INFO - Epoch [57][2000/3746] lr: 6.886e-02, eta: 3 days, 4:40:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4808, loss_cls: 4.4584, loss: 4.4584 +2024-07-18 07:33:30,706 - pyskl - INFO - Epoch [57][2100/3746] lr: 6.883e-02, eta: 3 days, 4:39:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4813, loss_cls: 4.4182, loss: 4.4182 +2024-07-18 07:34:52,888 - pyskl - INFO - Epoch [57][2200/3746] lr: 6.881e-02, eta: 3 days, 4:38:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4842, loss_cls: 4.4000, loss: 4.4000 +2024-07-18 07:36:15,013 - pyskl - INFO - Epoch [57][2300/3746] lr: 6.878e-02, eta: 3 days, 4:37:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4755, loss_cls: 4.4639, loss: 4.4639 +2024-07-18 07:37:37,478 - pyskl - INFO - Epoch [57][2400/3746] lr: 6.876e-02, eta: 3 days, 4:35:49, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4795, loss_cls: 4.4509, loss: 4.4509 +2024-07-18 07:39:00,092 - pyskl - INFO - Epoch [57][2500/3746] lr: 6.873e-02, eta: 3 days, 4:34:37, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4877, loss_cls: 4.4075, loss: 4.4075 +2024-07-18 07:40:22,239 - pyskl - INFO - Epoch [57][2600/3746] lr: 6.870e-02, eta: 3 days, 4:33:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4691, loss_cls: 4.5033, loss: 4.5033 +2024-07-18 07:41:44,875 - pyskl - INFO - Epoch [57][2700/3746] lr: 6.868e-02, eta: 3 days, 4:32:11, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4795, loss_cls: 4.4312, loss: 4.4312 +2024-07-18 07:43:07,234 - pyskl - INFO - Epoch [57][2800/3746] lr: 6.865e-02, eta: 3 days, 4:30:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2286, top5_acc: 0.4678, loss_cls: 4.4731, loss: 4.4731 +2024-07-18 07:44:29,071 - pyskl - INFO - Epoch [57][2900/3746] lr: 6.863e-02, eta: 3 days, 4:29:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4648, loss_cls: 4.4790, loss: 4.4790 +2024-07-18 07:45:50,889 - pyskl - INFO - Epoch [57][3000/3746] lr: 6.860e-02, eta: 3 days, 4:28:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4675, loss_cls: 4.4640, loss: 4.4640 +2024-07-18 07:47:12,810 - pyskl - INFO - Epoch [57][3100/3746] lr: 6.857e-02, eta: 3 days, 4:27:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2323, top5_acc: 0.4741, loss_cls: 4.4605, loss: 4.4605 +2024-07-18 07:48:34,665 - pyskl - INFO - Epoch [57][3200/3746] lr: 6.855e-02, eta: 3 days, 4:26:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4642, loss_cls: 4.4948, loss: 4.4948 +2024-07-18 07:49:56,687 - pyskl - INFO - Epoch [57][3300/3746] lr: 6.852e-02, eta: 3 days, 4:24:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4711, loss_cls: 4.4641, loss: 4.4641 +2024-07-18 07:51:18,524 - pyskl - INFO - Epoch [57][3400/3746] lr: 6.850e-02, eta: 3 days, 4:23:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4823, loss_cls: 4.4341, loss: 4.4341 +2024-07-18 07:52:40,622 - pyskl - INFO - Epoch [57][3500/3746] lr: 6.847e-02, eta: 3 days, 4:22:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4742, loss_cls: 4.4933, loss: 4.4933 +2024-07-18 07:54:03,091 - pyskl - INFO - Epoch [57][3600/3746] lr: 6.844e-02, eta: 3 days, 4:21:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4702, loss_cls: 4.4795, loss: 4.4795 +2024-07-18 07:55:25,856 - pyskl - INFO - Epoch [57][3700/3746] lr: 6.842e-02, eta: 3 days, 4:19:55, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4767, loss_cls: 4.4594, loss: 4.4594 +2024-07-18 07:56:05,875 - pyskl - INFO - Saving checkpoint at 57 epochs +2024-07-18 07:57:57,787 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 07:57:58,491 - pyskl - INFO - +top1_acc 0.1890 +top5_acc 0.4135 +2024-07-18 07:57:58,491 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 07:57:58,535 - pyskl - INFO - +mean_acc 0.1889 +2024-07-18 07:57:58,540 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_42.pth was removed +2024-07-18 07:57:58,787 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_57.pth. +2024-07-18 07:57:58,788 - pyskl - INFO - Best top1_acc is 0.1890 at 57 epoch. +2024-07-18 07:57:58,801 - pyskl - INFO - Epoch(val) [57][309] top1_acc: 0.1890, top5_acc: 0.4135, mean_class_accuracy: 0.1889 +2024-07-18 08:01:54,668 - pyskl - INFO - Epoch [58][100/3746] lr: 6.838e-02, eta: 3 days, 4:21:17, time: 2.359, data_time: 1.355, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4702, loss_cls: 4.4746, loss: 4.4746 +2024-07-18 08:03:17,827 - pyskl - INFO - Epoch [58][200/3746] lr: 6.835e-02, eta: 3 days, 4:20:05, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4825, loss_cls: 4.4221, loss: 4.4221 +2024-07-18 08:04:40,318 - pyskl - INFO - Epoch [58][300/3746] lr: 6.833e-02, eta: 3 days, 4:18:52, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4850, loss_cls: 4.4000, loss: 4.4000 +2024-07-18 08:06:02,740 - pyskl - INFO - Epoch [58][400/3746] lr: 6.830e-02, eta: 3 days, 4:17:38, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4803, loss_cls: 4.4204, loss: 4.4204 +2024-07-18 08:07:25,107 - pyskl - INFO - Epoch [58][500/3746] lr: 6.828e-02, eta: 3 days, 4:16:25, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.5002, loss_cls: 4.3568, loss: 4.3568 +2024-07-18 08:08:46,696 - pyskl - INFO - Epoch [58][600/3746] lr: 6.825e-02, eta: 3 days, 4:15:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4798, loss_cls: 4.4094, loss: 4.4094 +2024-07-18 08:10:08,700 - pyskl - INFO - Epoch [58][700/3746] lr: 6.822e-02, eta: 3 days, 4:13:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4766, loss_cls: 4.4346, loss: 4.4346 +2024-07-18 08:11:30,569 - pyskl - INFO - Epoch [58][800/3746] lr: 6.820e-02, eta: 3 days, 4:12:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4755, loss_cls: 4.4554, loss: 4.4554 +2024-07-18 08:12:52,551 - pyskl - INFO - Epoch [58][900/3746] lr: 6.817e-02, eta: 3 days, 4:11:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4897, loss_cls: 4.3974, loss: 4.3974 +2024-07-18 08:14:14,655 - pyskl - INFO - Epoch [58][1000/3746] lr: 6.815e-02, eta: 3 days, 4:10:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4917, loss_cls: 4.3805, loss: 4.3805 +2024-07-18 08:15:37,507 - pyskl - INFO - Epoch [58][1100/3746] lr: 6.812e-02, eta: 3 days, 4:09:02, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4900, loss_cls: 4.3961, loss: 4.3961 +2024-07-18 08:16:59,895 - pyskl - INFO - Epoch [58][1200/3746] lr: 6.809e-02, eta: 3 days, 4:07:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4820, loss_cls: 4.4314, loss: 4.4314 +2024-07-18 08:18:22,202 - pyskl - INFO - Epoch [58][1300/3746] lr: 6.807e-02, eta: 3 days, 4:06:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4822, loss_cls: 4.4433, loss: 4.4433 +2024-07-18 08:19:44,479 - pyskl - INFO - Epoch [58][1400/3746] lr: 6.804e-02, eta: 3 days, 4:05:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4764, loss_cls: 4.4232, loss: 4.4232 +2024-07-18 08:21:06,829 - pyskl - INFO - Epoch [58][1500/3746] lr: 6.802e-02, eta: 3 days, 4:04:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2344, top5_acc: 0.4766, loss_cls: 4.4666, loss: 4.4666 +2024-07-18 08:22:28,448 - pyskl - INFO - Epoch [58][1600/3746] lr: 6.799e-02, eta: 3 days, 4:02:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2255, top5_acc: 0.4670, loss_cls: 4.5143, loss: 4.5143 +2024-07-18 08:23:50,325 - pyskl - INFO - Epoch [58][1700/3746] lr: 6.796e-02, eta: 3 days, 4:01:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4767, loss_cls: 4.4255, loss: 4.4255 +2024-07-18 08:25:12,006 - pyskl - INFO - Epoch [58][1800/3746] lr: 6.794e-02, eta: 3 days, 4:00:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2345, top5_acc: 0.4731, loss_cls: 4.4398, loss: 4.4398 +2024-07-18 08:26:34,361 - pyskl - INFO - Epoch [58][1900/3746] lr: 6.791e-02, eta: 3 days, 3:59:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4838, loss_cls: 4.4239, loss: 4.4239 +2024-07-18 08:27:56,437 - pyskl - INFO - Epoch [58][2000/3746] lr: 6.789e-02, eta: 3 days, 3:57:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4858, loss_cls: 4.4285, loss: 4.4285 +2024-07-18 08:29:19,053 - pyskl - INFO - Epoch [58][2100/3746] lr: 6.786e-02, eta: 3 days, 3:56:44, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4769, loss_cls: 4.4551, loss: 4.4551 +2024-07-18 08:30:41,549 - pyskl - INFO - Epoch [58][2200/3746] lr: 6.783e-02, eta: 3 days, 3:55:31, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4780, loss_cls: 4.4360, loss: 4.4360 +2024-07-18 08:32:04,016 - pyskl - INFO - Epoch [58][2300/3746] lr: 6.781e-02, eta: 3 days, 3:54:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4728, loss_cls: 4.4594, loss: 4.4594 +2024-07-18 08:33:27,388 - pyskl - INFO - Epoch [58][2400/3746] lr: 6.778e-02, eta: 3 days, 3:53:05, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2277, top5_acc: 0.4716, loss_cls: 4.4823, loss: 4.4823 +2024-07-18 08:34:49,366 - pyskl - INFO - Epoch [58][2500/3746] lr: 6.775e-02, eta: 3 days, 3:51:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4716, loss_cls: 4.4854, loss: 4.4854 +2024-07-18 08:36:12,050 - pyskl - INFO - Epoch [58][2600/3746] lr: 6.773e-02, eta: 3 days, 3:50:38, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2294, top5_acc: 0.4742, loss_cls: 4.4685, loss: 4.4685 +2024-07-18 08:37:34,301 - pyskl - INFO - Epoch [58][2700/3746] lr: 6.770e-02, eta: 3 days, 3:49:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4659, loss_cls: 4.5025, loss: 4.5025 +2024-07-18 08:38:56,307 - pyskl - INFO - Epoch [58][2800/3746] lr: 6.768e-02, eta: 3 days, 3:48:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4788, loss_cls: 4.4516, loss: 4.4516 +2024-07-18 08:40:18,712 - pyskl - INFO - Epoch [58][2900/3746] lr: 6.765e-02, eta: 3 days, 3:46:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4828, loss_cls: 4.4246, loss: 4.4246 +2024-07-18 08:41:40,772 - pyskl - INFO - Epoch [58][3000/3746] lr: 6.762e-02, eta: 3 days, 3:45:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4795, loss_cls: 4.4249, loss: 4.4249 +2024-07-18 08:43:02,637 - pyskl - INFO - Epoch [58][3100/3746] lr: 6.760e-02, eta: 3 days, 3:44:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4566, loss_cls: 4.4808, loss: 4.4808 +2024-07-18 08:44:24,643 - pyskl - INFO - Epoch [58][3200/3746] lr: 6.757e-02, eta: 3 days, 3:43:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2289, top5_acc: 0.4800, loss_cls: 4.4631, loss: 4.4631 +2024-07-18 08:45:47,368 - pyskl - INFO - Epoch [58][3300/3746] lr: 6.755e-02, eta: 3 days, 3:42:02, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4736, loss_cls: 4.4754, loss: 4.4754 +2024-07-18 08:47:09,195 - pyskl - INFO - Epoch [58][3400/3746] lr: 6.752e-02, eta: 3 days, 3:40:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4795, loss_cls: 4.4328, loss: 4.4328 +2024-07-18 08:48:31,798 - pyskl - INFO - Epoch [58][3500/3746] lr: 6.749e-02, eta: 3 days, 3:39:34, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4738, loss_cls: 4.4595, loss: 4.4595 +2024-07-18 08:49:53,960 - pyskl - INFO - Epoch [58][3600/3746] lr: 6.747e-02, eta: 3 days, 3:38:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4803, loss_cls: 4.4476, loss: 4.4476 +2024-07-18 08:51:17,009 - pyskl - INFO - Epoch [58][3700/3746] lr: 6.744e-02, eta: 3 days, 3:37:07, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2364, top5_acc: 0.4814, loss_cls: 4.4680, loss: 4.4680 +2024-07-18 08:51:56,937 - pyskl - INFO - Saving checkpoint at 58 epochs +2024-07-18 08:53:47,841 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 08:53:48,520 - pyskl - INFO - +top1_acc 0.1618 +top5_acc 0.3629 +2024-07-18 08:53:48,520 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 08:53:48,565 - pyskl - INFO - +mean_acc 0.1618 +2024-07-18 08:53:48,579 - pyskl - INFO - Epoch(val) [58][309] top1_acc: 0.1618, top5_acc: 0.3629, mean_class_accuracy: 0.1618 +2024-07-18 08:57:43,378 - pyskl - INFO - Epoch [59][100/3746] lr: 6.740e-02, eta: 3 days, 3:38:21, time: 2.348, data_time: 1.361, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4823, loss_cls: 4.4248, loss: 4.4248 +2024-07-18 08:59:05,908 - pyskl - INFO - Epoch [59][200/3746] lr: 6.738e-02, eta: 3 days, 3:37:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4841, loss_cls: 4.3808, loss: 4.3808 +2024-07-18 09:00:28,253 - pyskl - INFO - Epoch [59][300/3746] lr: 6.735e-02, eta: 3 days, 3:35:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4773, loss_cls: 4.4432, loss: 4.4432 +2024-07-18 09:01:50,410 - pyskl - INFO - Epoch [59][400/3746] lr: 6.732e-02, eta: 3 days, 3:34:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4952, loss_cls: 4.3570, loss: 4.3570 +2024-07-18 09:03:12,078 - pyskl - INFO - Epoch [59][500/3746] lr: 6.730e-02, eta: 3 days, 3:33:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4798, loss_cls: 4.4613, loss: 4.4613 +2024-07-18 09:04:34,354 - pyskl - INFO - Epoch [59][600/3746] lr: 6.727e-02, eta: 3 days, 3:32:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4880, loss_cls: 4.4078, loss: 4.4078 +2024-07-18 09:05:56,172 - pyskl - INFO - Epoch [59][700/3746] lr: 6.725e-02, eta: 3 days, 3:30:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4834, loss_cls: 4.3773, loss: 4.3773 +2024-07-18 09:07:17,689 - pyskl - INFO - Epoch [59][800/3746] lr: 6.722e-02, eta: 3 days, 3:29:41, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2313, top5_acc: 0.4669, loss_cls: 4.4710, loss: 4.4710 +2024-07-18 09:08:40,486 - pyskl - INFO - Epoch [59][900/3746] lr: 6.719e-02, eta: 3 days, 3:28:28, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4938, loss_cls: 4.3717, loss: 4.3717 +2024-07-18 09:10:02,709 - pyskl - INFO - Epoch [59][1000/3746] lr: 6.717e-02, eta: 3 days, 3:27:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4763, loss_cls: 4.4262, loss: 4.4262 +2024-07-18 09:11:25,039 - pyskl - INFO - Epoch [59][1100/3746] lr: 6.714e-02, eta: 3 days, 3:26:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4669, loss_cls: 4.4736, loss: 4.4736 +2024-07-18 09:12:47,046 - pyskl - INFO - Epoch [59][1200/3746] lr: 6.711e-02, eta: 3 days, 3:24:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2380, top5_acc: 0.4734, loss_cls: 4.4769, loss: 4.4769 +2024-07-18 09:14:09,185 - pyskl - INFO - Epoch [59][1300/3746] lr: 6.709e-02, eta: 3 days, 3:23:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4834, loss_cls: 4.4007, loss: 4.4007 +2024-07-18 09:15:31,483 - pyskl - INFO - Epoch [59][1400/3746] lr: 6.706e-02, eta: 3 days, 3:22:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4788, loss_cls: 4.4789, loss: 4.4789 +2024-07-18 09:16:53,150 - pyskl - INFO - Epoch [59][1500/3746] lr: 6.704e-02, eta: 3 days, 3:21:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4784, loss_cls: 4.4573, loss: 4.4573 +2024-07-18 09:18:15,295 - pyskl - INFO - Epoch [59][1600/3746] lr: 6.701e-02, eta: 3 days, 3:19:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4709, loss_cls: 4.4607, loss: 4.4607 +2024-07-18 09:19:37,331 - pyskl - INFO - Epoch [59][1700/3746] lr: 6.698e-02, eta: 3 days, 3:18:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4841, loss_cls: 4.4323, loss: 4.4323 +2024-07-18 09:20:58,807 - pyskl - INFO - Epoch [59][1800/3746] lr: 6.696e-02, eta: 3 days, 3:17:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2328, top5_acc: 0.4731, loss_cls: 4.4642, loss: 4.4642 +2024-07-18 09:22:21,261 - pyskl - INFO - Epoch [59][1900/3746] lr: 6.693e-02, eta: 3 days, 3:16:05, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2331, top5_acc: 0.4816, loss_cls: 4.4401, loss: 4.4401 +2024-07-18 09:23:43,082 - pyskl - INFO - Epoch [59][2000/3746] lr: 6.690e-02, eta: 3 days, 3:14:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4789, loss_cls: 4.4313, loss: 4.4313 +2024-07-18 09:25:05,408 - pyskl - INFO - Epoch [59][2100/3746] lr: 6.688e-02, eta: 3 days, 3:13:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2348, top5_acc: 0.4811, loss_cls: 4.4545, loss: 4.4545 +2024-07-18 09:26:27,126 - pyskl - INFO - Epoch [59][2200/3746] lr: 6.685e-02, eta: 3 days, 3:12:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4855, loss_cls: 4.3901, loss: 4.3901 +2024-07-18 09:27:49,571 - pyskl - INFO - Epoch [59][2300/3746] lr: 6.682e-02, eta: 3 days, 3:11:08, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4773, loss_cls: 4.4552, loss: 4.4552 +2024-07-18 09:29:12,895 - pyskl - INFO - Epoch [59][2400/3746] lr: 6.680e-02, eta: 3 days, 3:09:55, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4805, loss_cls: 4.4234, loss: 4.4234 +2024-07-18 09:30:35,294 - pyskl - INFO - Epoch [59][2500/3746] lr: 6.677e-02, eta: 3 days, 3:08:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4728, loss_cls: 4.4458, loss: 4.4458 +2024-07-18 09:31:57,480 - pyskl - INFO - Epoch [59][2600/3746] lr: 6.675e-02, eta: 3 days, 3:07:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4806, loss_cls: 4.4326, loss: 4.4326 +2024-07-18 09:33:19,347 - pyskl - INFO - Epoch [59][2700/3746] lr: 6.672e-02, eta: 3 days, 3:06:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4800, loss_cls: 4.4042, loss: 4.4042 +2024-07-18 09:34:41,617 - pyskl - INFO - Epoch [59][2800/3746] lr: 6.669e-02, eta: 3 days, 3:04:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4817, loss_cls: 4.4387, loss: 4.4387 +2024-07-18 09:36:03,258 - pyskl - INFO - Epoch [59][2900/3746] lr: 6.667e-02, eta: 3 days, 3:03:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4795, loss_cls: 4.4405, loss: 4.4405 +2024-07-18 09:37:25,326 - pyskl - INFO - Epoch [59][3000/3746] lr: 6.664e-02, eta: 3 days, 3:02:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4752, loss_cls: 4.4346, loss: 4.4346 +2024-07-18 09:38:46,861 - pyskl - INFO - Epoch [59][3100/3746] lr: 6.661e-02, eta: 3 days, 3:01:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4770, loss_cls: 4.4422, loss: 4.4422 +2024-07-18 09:40:08,521 - pyskl - INFO - Epoch [59][3200/3746] lr: 6.659e-02, eta: 3 days, 2:59:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4738, loss_cls: 4.4548, loss: 4.4548 +2024-07-18 09:41:30,299 - pyskl - INFO - Epoch [59][3300/3746] lr: 6.656e-02, eta: 3 days, 2:58:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2327, top5_acc: 0.4727, loss_cls: 4.4642, loss: 4.4642 +2024-07-18 09:42:52,824 - pyskl - INFO - Epoch [59][3400/3746] lr: 6.653e-02, eta: 3 days, 2:57:30, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4725, loss_cls: 4.4533, loss: 4.4533 +2024-07-18 09:44:15,154 - pyskl - INFO - Epoch [59][3500/3746] lr: 6.651e-02, eta: 3 days, 2:56:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4842, loss_cls: 4.3964, loss: 4.3964 +2024-07-18 09:45:37,665 - pyskl - INFO - Epoch [59][3600/3746] lr: 6.648e-02, eta: 3 days, 2:55:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4802, loss_cls: 4.4390, loss: 4.4390 +2024-07-18 09:46:59,494 - pyskl - INFO - Epoch [59][3700/3746] lr: 6.646e-02, eta: 3 days, 2:53:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4816, loss_cls: 4.4282, loss: 4.4282 +2024-07-18 09:47:39,537 - pyskl - INFO - Saving checkpoint at 59 epochs +2024-07-18 09:49:31,040 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 09:49:31,726 - pyskl - INFO - +top1_acc 0.1881 +top5_acc 0.4032 +2024-07-18 09:49:31,726 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 09:49:31,769 - pyskl - INFO - +mean_acc 0.1880 +2024-07-18 09:49:31,781 - pyskl - INFO - Epoch(val) [59][309] top1_acc: 0.1881, top5_acc: 0.4032, mean_class_accuracy: 0.1880 +2024-07-18 09:53:22,862 - pyskl - INFO - Epoch [60][100/3746] lr: 6.642e-02, eta: 3 days, 2:54:50, time: 2.311, data_time: 1.321, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4881, loss_cls: 4.3604, loss: 4.3604 +2024-07-18 09:54:44,930 - pyskl - INFO - Epoch [60][200/3746] lr: 6.639e-02, eta: 3 days, 2:53:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4883, loss_cls: 4.3720, loss: 4.3720 +2024-07-18 09:56:06,978 - pyskl - INFO - Epoch [60][300/3746] lr: 6.636e-02, eta: 3 days, 2:52:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4898, loss_cls: 4.4038, loss: 4.4038 +2024-07-18 09:57:29,401 - pyskl - INFO - Epoch [60][400/3746] lr: 6.634e-02, eta: 3 days, 2:51:07, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4705, loss_cls: 4.4551, loss: 4.4551 +2024-07-18 09:58:51,270 - pyskl - INFO - Epoch [60][500/3746] lr: 6.631e-02, eta: 3 days, 2:49:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4920, loss_cls: 4.3610, loss: 4.3610 +2024-07-18 10:00:13,804 - pyskl - INFO - Epoch [60][600/3746] lr: 6.629e-02, eta: 3 days, 2:48:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4803, loss_cls: 4.4118, loss: 4.4118 +2024-07-18 10:01:35,764 - pyskl - INFO - Epoch [60][700/3746] lr: 6.626e-02, eta: 3 days, 2:47:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4895, loss_cls: 4.3906, loss: 4.3906 +2024-07-18 10:02:57,430 - pyskl - INFO - Epoch [60][800/3746] lr: 6.623e-02, eta: 3 days, 2:46:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4795, loss_cls: 4.4288, loss: 4.4288 +2024-07-18 10:04:19,659 - pyskl - INFO - Epoch [60][900/3746] lr: 6.621e-02, eta: 3 days, 2:44:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2322, top5_acc: 0.4770, loss_cls: 4.4183, loss: 4.4183 +2024-07-18 10:05:41,871 - pyskl - INFO - Epoch [60][1000/3746] lr: 6.618e-02, eta: 3 days, 2:43:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4819, loss_cls: 4.4273, loss: 4.4273 +2024-07-18 10:07:04,579 - pyskl - INFO - Epoch [60][1100/3746] lr: 6.615e-02, eta: 3 days, 2:42:25, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4878, loss_cls: 4.4081, loss: 4.4081 +2024-07-18 10:08:26,771 - pyskl - INFO - Epoch [60][1200/3746] lr: 6.613e-02, eta: 3 days, 2:41:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4916, loss_cls: 4.3738, loss: 4.3738 +2024-07-18 10:09:48,896 - pyskl - INFO - Epoch [60][1300/3746] lr: 6.610e-02, eta: 3 days, 2:39:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2338, top5_acc: 0.4742, loss_cls: 4.4727, loss: 4.4727 +2024-07-18 10:11:10,820 - pyskl - INFO - Epoch [60][1400/3746] lr: 6.607e-02, eta: 3 days, 2:38:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4798, loss_cls: 4.4225, loss: 4.4225 +2024-07-18 10:12:32,702 - pyskl - INFO - Epoch [60][1500/3746] lr: 6.605e-02, eta: 3 days, 2:37:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4817, loss_cls: 4.4244, loss: 4.4244 +2024-07-18 10:13:55,064 - pyskl - INFO - Epoch [60][1600/3746] lr: 6.602e-02, eta: 3 days, 2:36:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2352, top5_acc: 0.4833, loss_cls: 4.4589, loss: 4.4589 +2024-07-18 10:15:16,671 - pyskl - INFO - Epoch [60][1700/3746] lr: 6.599e-02, eta: 3 days, 2:34:56, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2356, top5_acc: 0.4688, loss_cls: 4.4704, loss: 4.4704 +2024-07-18 10:16:38,882 - pyskl - INFO - Epoch [60][1800/3746] lr: 6.597e-02, eta: 3 days, 2:33:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4809, loss_cls: 4.4094, loss: 4.4094 +2024-07-18 10:18:00,588 - pyskl - INFO - Epoch [60][1900/3746] lr: 6.594e-02, eta: 3 days, 2:32:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4863, loss_cls: 4.3943, loss: 4.3943 +2024-07-18 10:19:22,578 - pyskl - INFO - Epoch [60][2000/3746] lr: 6.591e-02, eta: 3 days, 2:31:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4722, loss_cls: 4.4782, loss: 4.4782 +2024-07-18 10:20:45,178 - pyskl - INFO - Epoch [60][2100/3746] lr: 6.589e-02, eta: 3 days, 2:29:58, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2334, top5_acc: 0.4794, loss_cls: 4.4156, loss: 4.4156 +2024-07-18 10:22:07,387 - pyskl - INFO - Epoch [60][2200/3746] lr: 6.586e-02, eta: 3 days, 2:28:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4836, loss_cls: 4.4413, loss: 4.4413 +2024-07-18 10:23:30,109 - pyskl - INFO - Epoch [60][2300/3746] lr: 6.584e-02, eta: 3 days, 2:27:29, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2372, top5_acc: 0.4713, loss_cls: 4.4517, loss: 4.4517 +2024-07-18 10:24:52,721 - pyskl - INFO - Epoch [60][2400/3746] lr: 6.581e-02, eta: 3 days, 2:26:15, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4834, loss_cls: 4.4338, loss: 4.4338 +2024-07-18 10:26:14,867 - pyskl - INFO - Epoch [60][2500/3746] lr: 6.578e-02, eta: 3 days, 2:25:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4727, loss_cls: 4.4660, loss: 4.4660 +2024-07-18 10:27:37,026 - pyskl - INFO - Epoch [60][2600/3746] lr: 6.576e-02, eta: 3 days, 2:23:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4853, loss_cls: 4.4138, loss: 4.4138 +2024-07-18 10:28:58,829 - pyskl - INFO - Epoch [60][2700/3746] lr: 6.573e-02, eta: 3 days, 2:22:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4773, loss_cls: 4.4793, loss: 4.4793 +2024-07-18 10:30:21,175 - pyskl - INFO - Epoch [60][2800/3746] lr: 6.570e-02, eta: 3 days, 2:21:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4766, loss_cls: 4.4720, loss: 4.4720 +2024-07-18 10:31:43,329 - pyskl - INFO - Epoch [60][2900/3746] lr: 6.568e-02, eta: 3 days, 2:20:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4853, loss_cls: 4.3878, loss: 4.3878 +2024-07-18 10:33:05,826 - pyskl - INFO - Epoch [60][3000/3746] lr: 6.565e-02, eta: 3 days, 2:18:48, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2342, top5_acc: 0.4708, loss_cls: 4.4590, loss: 4.4590 +2024-07-18 10:34:28,169 - pyskl - INFO - Epoch [60][3100/3746] lr: 6.562e-02, eta: 3 days, 2:17:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4828, loss_cls: 4.3960, loss: 4.3960 +2024-07-18 10:35:50,057 - pyskl - INFO - Epoch [60][3200/3746] lr: 6.560e-02, eta: 3 days, 2:16:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4775, loss_cls: 4.4180, loss: 4.4180 +2024-07-18 10:37:12,337 - pyskl - INFO - Epoch [60][3300/3746] lr: 6.557e-02, eta: 3 days, 2:15:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4822, loss_cls: 4.4385, loss: 4.4385 +2024-07-18 10:38:35,001 - pyskl - INFO - Epoch [60][3400/3746] lr: 6.554e-02, eta: 3 days, 2:13:50, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2288, top5_acc: 0.4684, loss_cls: 4.4778, loss: 4.4778 +2024-07-18 10:39:57,971 - pyskl - INFO - Epoch [60][3500/3746] lr: 6.552e-02, eta: 3 days, 2:12:36, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4730, loss_cls: 4.4345, loss: 4.4345 +2024-07-18 10:41:20,265 - pyskl - INFO - Epoch [60][3600/3746] lr: 6.549e-02, eta: 3 days, 2:11:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4819, loss_cls: 4.4443, loss: 4.4443 +2024-07-18 10:42:43,043 - pyskl - INFO - Epoch [60][3700/3746] lr: 6.546e-02, eta: 3 days, 2:10:08, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4895, loss_cls: 4.3901, loss: 4.3901 +2024-07-18 10:43:23,325 - pyskl - INFO - Saving checkpoint at 60 epochs +2024-07-18 10:45:14,998 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 10:45:15,728 - pyskl - INFO - +top1_acc 0.1919 +top5_acc 0.4056 +2024-07-18 10:45:15,728 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 10:45:15,776 - pyskl - INFO - +mean_acc 0.1918 +2024-07-18 10:45:15,781 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_57.pth was removed +2024-07-18 10:45:16,041 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_60.pth. +2024-07-18 10:45:16,042 - pyskl - INFO - Best top1_acc is 0.1919 at 60 epoch. +2024-07-18 10:45:16,053 - pyskl - INFO - Epoch(val) [60][309] top1_acc: 0.1919, top5_acc: 0.4056, mean_class_accuracy: 0.1918 +2024-07-18 10:49:01,859 - pyskl - INFO - Epoch [61][100/3746] lr: 6.542e-02, eta: 3 days, 2:10:57, time: 2.258, data_time: 1.275, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4925, loss_cls: 4.3930, loss: 4.3930 +2024-07-18 10:50:24,345 - pyskl - INFO - Epoch [61][200/3746] lr: 6.540e-02, eta: 3 days, 2:09:43, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4942, loss_cls: 4.3514, loss: 4.3514 +2024-07-18 10:51:47,155 - pyskl - INFO - Epoch [61][300/3746] lr: 6.537e-02, eta: 3 days, 2:08:29, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4975, loss_cls: 4.3506, loss: 4.3506 +2024-07-18 10:53:08,929 - pyskl - INFO - Epoch [61][400/3746] lr: 6.534e-02, eta: 3 days, 2:07:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2316, top5_acc: 0.4716, loss_cls: 4.4705, loss: 4.4705 +2024-07-18 10:54:31,067 - pyskl - INFO - Epoch [61][500/3746] lr: 6.532e-02, eta: 3 days, 2:05:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4836, loss_cls: 4.3846, loss: 4.3846 +2024-07-18 10:55:52,981 - pyskl - INFO - Epoch [61][600/3746] lr: 6.529e-02, eta: 3 days, 2:04:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4855, loss_cls: 4.4239, loss: 4.4239 +2024-07-18 10:57:14,444 - pyskl - INFO - Epoch [61][700/3746] lr: 6.526e-02, eta: 3 days, 2:03:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2308, top5_acc: 0.4723, loss_cls: 4.4564, loss: 4.4564 +2024-07-18 10:58:36,108 - pyskl - INFO - Epoch [61][800/3746] lr: 6.524e-02, eta: 3 days, 2:02:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4922, loss_cls: 4.3782, loss: 4.3782 +2024-07-18 10:59:58,455 - pyskl - INFO - Epoch [61][900/3746] lr: 6.521e-02, eta: 3 days, 2:00:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4902, loss_cls: 4.3981, loss: 4.3981 +2024-07-18 11:01:20,340 - pyskl - INFO - Epoch [61][1000/3746] lr: 6.519e-02, eta: 3 days, 1:59:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4758, loss_cls: 4.4375, loss: 4.4375 +2024-07-18 11:02:42,277 - pyskl - INFO - Epoch [61][1100/3746] lr: 6.516e-02, eta: 3 days, 1:58:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4770, loss_cls: 4.4230, loss: 4.4230 +2024-07-18 11:04:04,432 - pyskl - INFO - Epoch [61][1200/3746] lr: 6.513e-02, eta: 3 days, 1:57:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4828, loss_cls: 4.4328, loss: 4.4328 +2024-07-18 11:05:26,486 - pyskl - INFO - Epoch [61][1300/3746] lr: 6.511e-02, eta: 3 days, 1:55:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4880, loss_cls: 4.3937, loss: 4.3937 +2024-07-18 11:06:48,712 - pyskl - INFO - Epoch [61][1400/3746] lr: 6.508e-02, eta: 3 days, 1:54:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2320, top5_acc: 0.4714, loss_cls: 4.4524, loss: 4.4524 +2024-07-18 11:08:10,505 - pyskl - INFO - Epoch [61][1500/3746] lr: 6.505e-02, eta: 3 days, 1:53:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4877, loss_cls: 4.3775, loss: 4.3775 +2024-07-18 11:09:32,522 - pyskl - INFO - Epoch [61][1600/3746] lr: 6.503e-02, eta: 3 days, 1:52:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4786, loss_cls: 4.4428, loss: 4.4428 +2024-07-18 11:10:54,245 - pyskl - INFO - Epoch [61][1700/3746] lr: 6.500e-02, eta: 3 days, 1:50:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2330, top5_acc: 0.4720, loss_cls: 4.4506, loss: 4.4506 +2024-07-18 11:12:15,825 - pyskl - INFO - Epoch [61][1800/3746] lr: 6.497e-02, eta: 3 days, 1:49:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4852, loss_cls: 4.3941, loss: 4.3941 +2024-07-18 11:13:38,061 - pyskl - INFO - Epoch [61][1900/3746] lr: 6.495e-02, eta: 3 days, 1:48:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4883, loss_cls: 4.3809, loss: 4.3809 +2024-07-18 11:14:59,729 - pyskl - INFO - Epoch [61][2000/3746] lr: 6.492e-02, eta: 3 days, 1:47:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2461, top5_acc: 0.4880, loss_cls: 4.3786, loss: 4.3786 +2024-07-18 11:16:22,025 - pyskl - INFO - Epoch [61][2100/3746] lr: 6.489e-02, eta: 3 days, 1:45:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4814, loss_cls: 4.4315, loss: 4.4315 +2024-07-18 11:17:44,278 - pyskl - INFO - Epoch [61][2200/3746] lr: 6.487e-02, eta: 3 days, 1:44:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4816, loss_cls: 4.4290, loss: 4.4290 +2024-07-18 11:19:06,622 - pyskl - INFO - Epoch [61][2300/3746] lr: 6.484e-02, eta: 3 days, 1:43:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4806, loss_cls: 4.4101, loss: 4.4101 +2024-07-18 11:20:28,926 - pyskl - INFO - Epoch [61][2400/3746] lr: 6.481e-02, eta: 3 days, 1:42:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4778, loss_cls: 4.4474, loss: 4.4474 +2024-07-18 11:21:51,378 - pyskl - INFO - Epoch [61][2500/3746] lr: 6.478e-02, eta: 3 days, 1:40:56, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4784, loss_cls: 4.4362, loss: 4.4362 +2024-07-18 11:23:14,038 - pyskl - INFO - Epoch [61][2600/3746] lr: 6.476e-02, eta: 3 days, 1:39:41, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4870, loss_cls: 4.4101, loss: 4.4101 +2024-07-18 11:24:36,122 - pyskl - INFO - Epoch [61][2700/3746] lr: 6.473e-02, eta: 3 days, 1:38:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4923, loss_cls: 4.3948, loss: 4.3948 +2024-07-18 11:25:57,789 - pyskl - INFO - Epoch [61][2800/3746] lr: 6.470e-02, eta: 3 days, 1:37:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2394, top5_acc: 0.4805, loss_cls: 4.4300, loss: 4.4300 +2024-07-18 11:27:19,851 - pyskl - INFO - Epoch [61][2900/3746] lr: 6.468e-02, eta: 3 days, 1:35:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4823, loss_cls: 4.4215, loss: 4.4215 +2024-07-18 11:28:41,454 - pyskl - INFO - Epoch [61][3000/3746] lr: 6.465e-02, eta: 3 days, 1:34:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4775, loss_cls: 4.4421, loss: 4.4421 +2024-07-18 11:30:03,810 - pyskl - INFO - Epoch [61][3100/3746] lr: 6.462e-02, eta: 3 days, 1:33:25, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4845, loss_cls: 4.4272, loss: 4.4272 +2024-07-18 11:31:25,763 - pyskl - INFO - Epoch [61][3200/3746] lr: 6.460e-02, eta: 3 days, 1:32:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4798, loss_cls: 4.4182, loss: 4.4182 +2024-07-18 11:32:47,521 - pyskl - INFO - Epoch [61][3300/3746] lr: 6.457e-02, eta: 3 days, 1:30:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4752, loss_cls: 4.4565, loss: 4.4565 +2024-07-18 11:34:09,305 - pyskl - INFO - Epoch [61][3400/3746] lr: 6.454e-02, eta: 3 days, 1:29:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2350, top5_acc: 0.4811, loss_cls: 4.4236, loss: 4.4236 +2024-07-18 11:35:31,732 - pyskl - INFO - Epoch [61][3500/3746] lr: 6.452e-02, eta: 3 days, 1:28:23, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4773, loss_cls: 4.4350, loss: 4.4350 +2024-07-18 11:36:54,051 - pyskl - INFO - Epoch [61][3600/3746] lr: 6.449e-02, eta: 3 days, 1:27:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2366, top5_acc: 0.4775, loss_cls: 4.4310, loss: 4.4310 +2024-07-18 11:38:16,397 - pyskl - INFO - Epoch [61][3700/3746] lr: 6.446e-02, eta: 3 days, 1:25:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2375, top5_acc: 0.4777, loss_cls: 4.4594, loss: 4.4594 +2024-07-18 11:38:56,511 - pyskl - INFO - Saving checkpoint at 61 epochs +2024-07-18 11:40:47,676 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 11:40:48,342 - pyskl - INFO - +top1_acc 0.1765 +top5_acc 0.3899 +2024-07-18 11:40:48,343 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 11:40:48,384 - pyskl - INFO - +mean_acc 0.1762 +2024-07-18 11:40:48,396 - pyskl - INFO - Epoch(val) [61][309] top1_acc: 0.1765, top5_acc: 0.3899, mean_class_accuracy: 0.1762 +2024-07-18 11:44:35,162 - pyskl - INFO - Epoch [62][100/3746] lr: 6.443e-02, eta: 3 days, 1:26:40, time: 2.268, data_time: 1.277, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4938, loss_cls: 4.3395, loss: 4.3395 +2024-07-18 11:45:57,663 - pyskl - INFO - Epoch [62][200/3746] lr: 6.440e-02, eta: 3 days, 1:25:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4961, loss_cls: 4.3643, loss: 4.3643 +2024-07-18 11:47:19,539 - pyskl - INFO - Epoch [62][300/3746] lr: 6.437e-02, eta: 3 days, 1:24:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4867, loss_cls: 4.3882, loss: 4.3882 +2024-07-18 11:48:41,765 - pyskl - INFO - Epoch [62][400/3746] lr: 6.434e-02, eta: 3 days, 1:22:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2433, top5_acc: 0.4773, loss_cls: 4.4250, loss: 4.4250 +2024-07-18 11:50:03,646 - pyskl - INFO - Epoch [62][500/3746] lr: 6.432e-02, eta: 3 days, 1:21:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2402, top5_acc: 0.4831, loss_cls: 4.4418, loss: 4.4418 +2024-07-18 11:51:25,267 - pyskl - INFO - Epoch [62][600/3746] lr: 6.429e-02, eta: 3 days, 1:20:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4853, loss_cls: 4.3777, loss: 4.3777 +2024-07-18 11:52:47,143 - pyskl - INFO - Epoch [62][700/3746] lr: 6.426e-02, eta: 3 days, 1:19:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4894, loss_cls: 4.3609, loss: 4.3609 +2024-07-18 11:54:09,396 - pyskl - INFO - Epoch [62][800/3746] lr: 6.424e-02, eta: 3 days, 1:17:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4830, loss_cls: 4.4171, loss: 4.4171 +2024-07-18 11:55:31,293 - pyskl - INFO - Epoch [62][900/3746] lr: 6.421e-02, eta: 3 days, 1:16:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2377, top5_acc: 0.4889, loss_cls: 4.4109, loss: 4.4109 +2024-07-18 11:56:53,640 - pyskl - INFO - Epoch [62][1000/3746] lr: 6.418e-02, eta: 3 days, 1:15:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4873, loss_cls: 4.4045, loss: 4.4045 +2024-07-18 11:58:15,794 - pyskl - INFO - Epoch [62][1100/3746] lr: 6.416e-02, eta: 3 days, 1:14:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2406, top5_acc: 0.4806, loss_cls: 4.4165, loss: 4.4165 +2024-07-18 11:59:38,516 - pyskl - INFO - Epoch [62][1200/3746] lr: 6.413e-02, eta: 3 days, 1:12:52, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2341, top5_acc: 0.4809, loss_cls: 4.4374, loss: 4.4374 +2024-07-18 12:01:00,487 - pyskl - INFO - Epoch [62][1300/3746] lr: 6.410e-02, eta: 3 days, 1:11:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4764, loss_cls: 4.4369, loss: 4.4369 +2024-07-18 12:02:22,385 - pyskl - INFO - Epoch [62][1400/3746] lr: 6.408e-02, eta: 3 days, 1:10:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4914, loss_cls: 4.3680, loss: 4.3680 +2024-07-18 12:03:44,804 - pyskl - INFO - Epoch [62][1500/3746] lr: 6.405e-02, eta: 3 days, 1:09:05, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4891, loss_cls: 4.3912, loss: 4.3912 +2024-07-18 12:05:06,762 - pyskl - INFO - Epoch [62][1600/3746] lr: 6.402e-02, eta: 3 days, 1:07:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4848, loss_cls: 4.3985, loss: 4.3985 +2024-07-18 12:06:28,866 - pyskl - INFO - Epoch [62][1700/3746] lr: 6.400e-02, eta: 3 days, 1:06:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2358, top5_acc: 0.4859, loss_cls: 4.3963, loss: 4.3963 +2024-07-18 12:07:50,583 - pyskl - INFO - Epoch [62][1800/3746] lr: 6.397e-02, eta: 3 days, 1:05:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4802, loss_cls: 4.4275, loss: 4.4275 +2024-07-18 12:09:12,799 - pyskl - INFO - Epoch [62][1900/3746] lr: 6.394e-02, eta: 3 days, 1:04:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4842, loss_cls: 4.4138, loss: 4.4138 +2024-07-18 12:10:34,688 - pyskl - INFO - Epoch [62][2000/3746] lr: 6.392e-02, eta: 3 days, 1:02:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4906, loss_cls: 4.4136, loss: 4.4136 +2024-07-18 12:11:56,935 - pyskl - INFO - Epoch [62][2100/3746] lr: 6.389e-02, eta: 3 days, 1:01:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4811, loss_cls: 4.4301, loss: 4.4301 +2024-07-18 12:13:19,154 - pyskl - INFO - Epoch [62][2200/3746] lr: 6.386e-02, eta: 3 days, 1:00:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4927, loss_cls: 4.3708, loss: 4.3708 +2024-07-18 12:14:41,954 - pyskl - INFO - Epoch [62][2300/3746] lr: 6.384e-02, eta: 3 days, 0:59:03, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4800, loss_cls: 4.4170, loss: 4.4170 +2024-07-18 12:16:03,952 - pyskl - INFO - Epoch [62][2400/3746] lr: 6.381e-02, eta: 3 days, 0:57:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.4834, loss_cls: 4.3876, loss: 4.3876 +2024-07-18 12:17:26,252 - pyskl - INFO - Epoch [62][2500/3746] lr: 6.378e-02, eta: 3 days, 0:56:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4867, loss_cls: 4.4071, loss: 4.4071 +2024-07-18 12:18:47,871 - pyskl - INFO - Epoch [62][2600/3746] lr: 6.375e-02, eta: 3 days, 0:55:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2398, top5_acc: 0.4836, loss_cls: 4.4205, loss: 4.4205 +2024-07-18 12:20:09,622 - pyskl - INFO - Epoch [62][2700/3746] lr: 6.373e-02, eta: 3 days, 0:54:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4863, loss_cls: 4.4029, loss: 4.4029 +2024-07-18 12:21:32,075 - pyskl - INFO - Epoch [62][2800/3746] lr: 6.370e-02, eta: 3 days, 0:52:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4895, loss_cls: 4.4035, loss: 4.4035 +2024-07-18 12:22:54,151 - pyskl - INFO - Epoch [62][2900/3746] lr: 6.367e-02, eta: 3 days, 0:51:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2369, top5_acc: 0.4725, loss_cls: 4.4631, loss: 4.4631 +2024-07-18 12:24:15,818 - pyskl - INFO - Epoch [62][3000/3746] lr: 6.365e-02, eta: 3 days, 0:50:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4838, loss_cls: 4.4221, loss: 4.4221 +2024-07-18 12:25:37,242 - pyskl - INFO - Epoch [62][3100/3746] lr: 6.362e-02, eta: 3 days, 0:48:57, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4838, loss_cls: 4.4093, loss: 4.4093 +2024-07-18 12:26:59,333 - pyskl - INFO - Epoch [62][3200/3746] lr: 6.359e-02, eta: 3 days, 0:47:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4816, loss_cls: 4.4268, loss: 4.4268 +2024-07-18 12:28:21,492 - pyskl - INFO - Epoch [62][3300/3746] lr: 6.357e-02, eta: 3 days, 0:46:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2336, top5_acc: 0.4755, loss_cls: 4.4532, loss: 4.4532 +2024-07-18 12:29:43,419 - pyskl - INFO - Epoch [62][3400/3746] lr: 6.354e-02, eta: 3 days, 0:45:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2383, top5_acc: 0.4747, loss_cls: 4.4631, loss: 4.4631 +2024-07-18 12:31:05,823 - pyskl - INFO - Epoch [62][3500/3746] lr: 6.351e-02, eta: 3 days, 0:43:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2353, top5_acc: 0.4764, loss_cls: 4.4209, loss: 4.4209 +2024-07-18 12:32:27,719 - pyskl - INFO - Epoch [62][3600/3746] lr: 6.349e-02, eta: 3 days, 0:42:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4714, loss_cls: 4.4908, loss: 4.4908 +2024-07-18 12:33:50,084 - pyskl - INFO - Epoch [62][3700/3746] lr: 6.346e-02, eta: 3 days, 0:41:25, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4795, loss_cls: 4.4195, loss: 4.4195 +2024-07-18 12:34:29,753 - pyskl - INFO - Saving checkpoint at 62 epochs +2024-07-18 12:36:21,016 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 12:36:21,727 - pyskl - INFO - +top1_acc 0.1864 +top5_acc 0.4022 +2024-07-18 12:36:21,727 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 12:36:21,769 - pyskl - INFO - +mean_acc 0.1865 +2024-07-18 12:36:21,780 - pyskl - INFO - Epoch(val) [62][309] top1_acc: 0.1864, top5_acc: 0.4022, mean_class_accuracy: 0.1865 +2024-07-18 12:40:10,981 - pyskl - INFO - Epoch [63][100/3746] lr: 6.342e-02, eta: 3 days, 0:42:09, time: 2.292, data_time: 1.308, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4894, loss_cls: 4.3846, loss: 4.3846 +2024-07-18 12:41:33,389 - pyskl - INFO - Epoch [63][200/3746] lr: 6.339e-02, eta: 3 days, 0:40:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2392, top5_acc: 0.4872, loss_cls: 4.4124, loss: 4.4124 +2024-07-18 12:42:55,215 - pyskl - INFO - Epoch [63][300/3746] lr: 6.337e-02, eta: 3 days, 0:39:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4992, loss_cls: 4.3686, loss: 4.3686 +2024-07-18 12:44:17,853 - pyskl - INFO - Epoch [63][400/3746] lr: 6.334e-02, eta: 3 days, 0:38:23, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2272, top5_acc: 0.4755, loss_cls: 4.4544, loss: 4.4544 +2024-07-18 12:45:39,975 - pyskl - INFO - Epoch [63][500/3746] lr: 6.331e-02, eta: 3 days, 0:37:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2362, top5_acc: 0.4863, loss_cls: 4.3949, loss: 4.3949 +2024-07-18 12:47:01,691 - pyskl - INFO - Epoch [63][600/3746] lr: 6.328e-02, eta: 3 days, 0:35:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4931, loss_cls: 4.4017, loss: 4.4017 +2024-07-18 12:48:23,434 - pyskl - INFO - Epoch [63][700/3746] lr: 6.326e-02, eta: 3 days, 0:34:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2456, top5_acc: 0.4972, loss_cls: 4.3639, loss: 4.3639 +2024-07-18 12:49:45,013 - pyskl - INFO - Epoch [63][800/3746] lr: 6.323e-02, eta: 3 days, 0:33:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2395, top5_acc: 0.4834, loss_cls: 4.3952, loss: 4.3952 +2024-07-18 12:51:06,862 - pyskl - INFO - Epoch [63][900/3746] lr: 6.320e-02, eta: 3 days, 0:32:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2389, top5_acc: 0.4852, loss_cls: 4.4028, loss: 4.4028 +2024-07-18 12:52:30,126 - pyskl - INFO - Epoch [63][1000/3746] lr: 6.318e-02, eta: 3 days, 0:30:49, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4875, loss_cls: 4.4237, loss: 4.4237 +2024-07-18 12:53:52,519 - pyskl - INFO - Epoch [63][1100/3746] lr: 6.315e-02, eta: 3 days, 0:29:34, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4902, loss_cls: 4.3855, loss: 4.3855 +2024-07-18 12:55:14,823 - pyskl - INFO - Epoch [63][1200/3746] lr: 6.312e-02, eta: 3 days, 0:28:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4909, loss_cls: 4.3922, loss: 4.3922 +2024-07-18 12:56:37,061 - pyskl - INFO - Epoch [63][1300/3746] lr: 6.310e-02, eta: 3 days, 0:27:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4797, loss_cls: 4.4314, loss: 4.4314 +2024-07-18 12:57:59,340 - pyskl - INFO - Epoch [63][1400/3746] lr: 6.307e-02, eta: 3 days, 0:25:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4855, loss_cls: 4.4125, loss: 4.4125 +2024-07-18 12:59:20,814 - pyskl - INFO - Epoch [63][1500/3746] lr: 6.304e-02, eta: 3 days, 0:24:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4861, loss_cls: 4.3924, loss: 4.3924 +2024-07-18 13:00:42,251 - pyskl - INFO - Epoch [63][1600/3746] lr: 6.301e-02, eta: 3 days, 0:23:14, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2317, top5_acc: 0.4717, loss_cls: 4.4374, loss: 4.4374 +2024-07-18 13:02:04,871 - pyskl - INFO - Epoch [63][1700/3746] lr: 6.299e-02, eta: 3 days, 0:21:59, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4828, loss_cls: 4.4303, loss: 4.4303 +2024-07-18 13:03:26,853 - pyskl - INFO - Epoch [63][1800/3746] lr: 6.296e-02, eta: 3 days, 0:20:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.4909, loss_cls: 4.3718, loss: 4.3718 +2024-07-18 13:04:48,560 - pyskl - INFO - Epoch [63][1900/3746] lr: 6.293e-02, eta: 3 days, 0:19:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4833, loss_cls: 4.4114, loss: 4.4114 +2024-07-18 13:06:10,486 - pyskl - INFO - Epoch [63][2000/3746] lr: 6.291e-02, eta: 3 days, 0:18:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4956, loss_cls: 4.3607, loss: 4.3607 +2024-07-18 13:07:33,022 - pyskl - INFO - Epoch [63][2100/3746] lr: 6.288e-02, eta: 3 days, 0:16:56, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4817, loss_cls: 4.3936, loss: 4.3936 +2024-07-18 13:08:55,079 - pyskl - INFO - Epoch [63][2200/3746] lr: 6.285e-02, eta: 3 days, 0:15:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4927, loss_cls: 4.3579, loss: 4.3579 +2024-07-18 13:10:18,335 - pyskl - INFO - Epoch [63][2300/3746] lr: 6.283e-02, eta: 3 days, 0:14:26, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4702, loss_cls: 4.4454, loss: 4.4454 +2024-07-18 13:11:40,089 - pyskl - INFO - Epoch [63][2400/3746] lr: 6.280e-02, eta: 3 days, 0:13:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4842, loss_cls: 4.4122, loss: 4.4122 +2024-07-18 13:13:02,391 - pyskl - INFO - Epoch [63][2500/3746] lr: 6.277e-02, eta: 3 days, 0:11:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4813, loss_cls: 4.4045, loss: 4.4045 +2024-07-18 13:14:24,208 - pyskl - INFO - Epoch [63][2600/3746] lr: 6.274e-02, eta: 3 days, 0:10:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4766, loss_cls: 4.4471, loss: 4.4471 +2024-07-18 13:15:46,365 - pyskl - INFO - Epoch [63][2700/3746] lr: 6.272e-02, eta: 3 days, 0:09:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2361, top5_acc: 0.4803, loss_cls: 4.4541, loss: 4.4541 +2024-07-18 13:17:08,263 - pyskl - INFO - Epoch [63][2800/3746] lr: 6.269e-02, eta: 3 days, 0:08:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4905, loss_cls: 4.3931, loss: 4.3931 +2024-07-18 13:18:30,629 - pyskl - INFO - Epoch [63][2900/3746] lr: 6.266e-02, eta: 3 days, 0:06:51, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2302, top5_acc: 0.4833, loss_cls: 4.4194, loss: 4.4194 +2024-07-18 13:19:52,336 - pyskl - INFO - Epoch [63][3000/3746] lr: 6.264e-02, eta: 3 days, 0:05:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.5009, loss_cls: 4.3361, loss: 4.3361 +2024-07-18 13:21:14,579 - pyskl - INFO - Epoch [63][3100/3746] lr: 6.261e-02, eta: 3 days, 0:04:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4902, loss_cls: 4.3692, loss: 4.3692 +2024-07-18 13:22:36,533 - pyskl - INFO - Epoch [63][3200/3746] lr: 6.258e-02, eta: 3 days, 0:03:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4798, loss_cls: 4.4271, loss: 4.4271 +2024-07-18 13:23:58,569 - pyskl - INFO - Epoch [63][3300/3746] lr: 6.256e-02, eta: 3 days, 0:01:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4800, loss_cls: 4.4176, loss: 4.4176 +2024-07-18 13:25:20,686 - pyskl - INFO - Epoch [63][3400/3746] lr: 6.253e-02, eta: 3 days, 0:00:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4903, loss_cls: 4.3636, loss: 4.3636 +2024-07-18 13:26:42,858 - pyskl - INFO - Epoch [63][3500/3746] lr: 6.250e-02, eta: 2 days, 23:59:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4766, loss_cls: 4.4338, loss: 4.4338 +2024-07-18 13:28:05,226 - pyskl - INFO - Epoch [63][3600/3746] lr: 6.247e-02, eta: 2 days, 23:58:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2423, top5_acc: 0.4814, loss_cls: 4.4213, loss: 4.4213 +2024-07-18 13:29:28,124 - pyskl - INFO - Epoch [63][3700/3746] lr: 6.245e-02, eta: 2 days, 23:56:46, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4877, loss_cls: 4.3803, loss: 4.3803 +2024-07-18 13:30:08,189 - pyskl - INFO - Saving checkpoint at 63 epochs +2024-07-18 13:31:59,680 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 13:32:00,448 - pyskl - INFO - +top1_acc 0.1651 +top5_acc 0.3765 +2024-07-18 13:32:00,448 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 13:32:00,496 - pyskl - INFO - +mean_acc 0.1650 +2024-07-18 13:32:00,514 - pyskl - INFO - Epoch(val) [63][309] top1_acc: 0.1651, top5_acc: 0.3765, mean_class_accuracy: 0.1650 +2024-07-18 13:35:58,293 - pyskl - INFO - Epoch [64][100/3746] lr: 6.241e-02, eta: 2 days, 23:57:37, time: 2.378, data_time: 1.384, memory: 15990, top1_acc: 0.2447, top5_acc: 0.4933, loss_cls: 4.3733, loss: 4.3733 +2024-07-18 13:37:21,613 - pyskl - INFO - Epoch [64][200/3746] lr: 6.238e-02, eta: 2 days, 23:56:23, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5022, loss_cls: 4.3291, loss: 4.3291 +2024-07-18 13:38:43,506 - pyskl - INFO - Epoch [64][300/3746] lr: 6.235e-02, eta: 2 days, 23:55:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2450, top5_acc: 0.4930, loss_cls: 4.3480, loss: 4.3480 +2024-07-18 13:40:05,748 - pyskl - INFO - Epoch [64][400/3746] lr: 6.233e-02, eta: 2 days, 23:53:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4878, loss_cls: 4.3954, loss: 4.3954 +2024-07-18 13:41:28,156 - pyskl - INFO - Epoch [64][500/3746] lr: 6.230e-02, eta: 2 days, 23:52:36, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4942, loss_cls: 4.3857, loss: 4.3857 +2024-07-18 13:42:49,821 - pyskl - INFO - Epoch [64][600/3746] lr: 6.227e-02, eta: 2 days, 23:51:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4852, loss_cls: 4.3845, loss: 4.3845 +2024-07-18 13:44:11,535 - pyskl - INFO - Epoch [64][700/3746] lr: 6.225e-02, eta: 2 days, 23:50:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4781, loss_cls: 4.4247, loss: 4.4247 +2024-07-18 13:45:34,067 - pyskl - INFO - Epoch [64][800/3746] lr: 6.222e-02, eta: 2 days, 23:48:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2483, top5_acc: 0.4848, loss_cls: 4.3901, loss: 4.3901 +2024-07-18 13:46:56,194 - pyskl - INFO - Epoch [64][900/3746] lr: 6.219e-02, eta: 2 days, 23:47:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4855, loss_cls: 4.3711, loss: 4.3711 +2024-07-18 13:48:18,636 - pyskl - INFO - Epoch [64][1000/3746] lr: 6.216e-02, eta: 2 days, 23:46:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2397, top5_acc: 0.4822, loss_cls: 4.4155, loss: 4.4155 +2024-07-18 13:49:40,722 - pyskl - INFO - Epoch [64][1100/3746] lr: 6.214e-02, eta: 2 days, 23:45:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4780, loss_cls: 4.4089, loss: 4.4089 +2024-07-18 13:51:03,110 - pyskl - INFO - Epoch [64][1200/3746] lr: 6.211e-02, eta: 2 days, 23:43:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2431, top5_acc: 0.4813, loss_cls: 4.4192, loss: 4.4192 +2024-07-18 13:52:25,255 - pyskl - INFO - Epoch [64][1300/3746] lr: 6.208e-02, eta: 2 days, 23:42:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2381, top5_acc: 0.4731, loss_cls: 4.4193, loss: 4.4193 +2024-07-18 13:53:47,264 - pyskl - INFO - Epoch [64][1400/3746] lr: 6.206e-02, eta: 2 days, 23:41:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2434, top5_acc: 0.4863, loss_cls: 4.4216, loss: 4.4216 +2024-07-18 13:55:09,591 - pyskl - INFO - Epoch [64][1500/3746] lr: 6.203e-02, eta: 2 days, 23:39:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4880, loss_cls: 4.3991, loss: 4.3991 +2024-07-18 13:56:31,127 - pyskl - INFO - Epoch [64][1600/3746] lr: 6.200e-02, eta: 2 days, 23:38:40, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.4930, loss_cls: 4.3843, loss: 4.3843 +2024-07-18 13:57:53,389 - pyskl - INFO - Epoch [64][1700/3746] lr: 6.197e-02, eta: 2 days, 23:37:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4939, loss_cls: 4.3646, loss: 4.3646 +2024-07-18 13:59:15,300 - pyskl - INFO - Epoch [64][1800/3746] lr: 6.195e-02, eta: 2 days, 23:36:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4925, loss_cls: 4.3771, loss: 4.3771 +2024-07-18 14:00:37,507 - pyskl - INFO - Epoch [64][1900/3746] lr: 6.192e-02, eta: 2 days, 23:34:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4794, loss_cls: 4.4257, loss: 4.4257 +2024-07-18 14:01:59,703 - pyskl - INFO - Epoch [64][2000/3746] lr: 6.189e-02, eta: 2 days, 23:33:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2325, top5_acc: 0.4723, loss_cls: 4.4609, loss: 4.4609 +2024-07-18 14:03:21,605 - pyskl - INFO - Epoch [64][2100/3746] lr: 6.187e-02, eta: 2 days, 23:32:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4859, loss_cls: 4.3984, loss: 4.3984 +2024-07-18 14:04:43,991 - pyskl - INFO - Epoch [64][2200/3746] lr: 6.184e-02, eta: 2 days, 23:31:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4825, loss_cls: 4.4259, loss: 4.4259 +2024-07-18 14:06:07,145 - pyskl - INFO - Epoch [64][2300/3746] lr: 6.181e-02, eta: 2 days, 23:29:49, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4831, loss_cls: 4.3927, loss: 4.3927 +2024-07-18 14:07:28,768 - pyskl - INFO - Epoch [64][2400/3746] lr: 6.178e-02, eta: 2 days, 23:28:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4903, loss_cls: 4.4058, loss: 4.4058 +2024-07-18 14:08:50,775 - pyskl - INFO - Epoch [64][2500/3746] lr: 6.176e-02, eta: 2 days, 23:27:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4852, loss_cls: 4.3874, loss: 4.3874 +2024-07-18 14:10:13,951 - pyskl - INFO - Epoch [64][2600/3746] lr: 6.173e-02, eta: 2 days, 23:26:01, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4897, loss_cls: 4.3925, loss: 4.3925 +2024-07-18 14:11:35,848 - pyskl - INFO - Epoch [64][2700/3746] lr: 6.170e-02, eta: 2 days, 23:24:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2475, top5_acc: 0.4856, loss_cls: 4.3705, loss: 4.3705 +2024-07-18 14:12:57,852 - pyskl - INFO - Epoch [64][2800/3746] lr: 6.168e-02, eta: 2 days, 23:23:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2386, top5_acc: 0.4842, loss_cls: 4.3841, loss: 4.3841 +2024-07-18 14:14:19,323 - pyskl - INFO - Epoch [64][2900/3746] lr: 6.165e-02, eta: 2 days, 23:22:12, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4878, loss_cls: 4.4091, loss: 4.4091 +2024-07-18 14:15:41,318 - pyskl - INFO - Epoch [64][3000/3746] lr: 6.162e-02, eta: 2 days, 23:20:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4909, loss_cls: 4.3868, loss: 4.3868 +2024-07-18 14:17:03,771 - pyskl - INFO - Epoch [64][3100/3746] lr: 6.159e-02, eta: 2 days, 23:19:40, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4847, loss_cls: 4.4122, loss: 4.4122 +2024-07-18 14:18:26,005 - pyskl - INFO - Epoch [64][3200/3746] lr: 6.157e-02, eta: 2 days, 23:18:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4828, loss_cls: 4.3816, loss: 4.3816 +2024-07-18 14:19:47,785 - pyskl - INFO - Epoch [64][3300/3746] lr: 6.154e-02, eta: 2 days, 23:17:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4877, loss_cls: 4.3842, loss: 4.3842 +2024-07-18 14:21:09,414 - pyskl - INFO - Epoch [64][3400/3746] lr: 6.151e-02, eta: 2 days, 23:15:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4828, loss_cls: 4.4162, loss: 4.4162 +2024-07-18 14:22:30,767 - pyskl - INFO - Epoch [64][3500/3746] lr: 6.148e-02, eta: 2 days, 23:14:34, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4861, loss_cls: 4.4166, loss: 4.4166 +2024-07-18 14:23:53,243 - pyskl - INFO - Epoch [64][3600/3746] lr: 6.146e-02, eta: 2 days, 23:13:18, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5028, loss_cls: 4.3300, loss: 4.3300 +2024-07-18 14:25:14,678 - pyskl - INFO - Epoch [64][3700/3746] lr: 6.143e-02, eta: 2 days, 23:12:01, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.4888, loss_cls: 4.3763, loss: 4.3763 +2024-07-18 14:25:54,417 - pyskl - INFO - Saving checkpoint at 64 epochs +2024-07-18 14:27:45,037 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 14:27:45,708 - pyskl - INFO - +top1_acc 0.1692 +top5_acc 0.3850 +2024-07-18 14:27:45,708 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 14:27:45,752 - pyskl - INFO - +mean_acc 0.1691 +2024-07-18 14:27:45,765 - pyskl - INFO - Epoch(val) [64][309] top1_acc: 0.1692, top5_acc: 0.3850, mean_class_accuracy: 0.1691 +2024-07-18 14:31:34,666 - pyskl - INFO - Epoch [65][100/3746] lr: 6.139e-02, eta: 2 days, 23:12:36, time: 2.289, data_time: 1.284, memory: 15990, top1_acc: 0.2378, top5_acc: 0.4856, loss_cls: 4.3917, loss: 4.3917 +2024-07-18 14:32:57,560 - pyskl - INFO - Epoch [65][200/3746] lr: 6.136e-02, eta: 2 days, 23:11:21, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4956, loss_cls: 4.3387, loss: 4.3387 +2024-07-18 14:34:20,420 - pyskl - INFO - Epoch [65][300/3746] lr: 6.134e-02, eta: 2 days, 23:10:06, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5078, loss_cls: 4.3068, loss: 4.3068 +2024-07-18 14:35:42,350 - pyskl - INFO - Epoch [65][400/3746] lr: 6.131e-02, eta: 2 days, 23:08:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4894, loss_cls: 4.3687, loss: 4.3687 +2024-07-18 14:37:04,311 - pyskl - INFO - Epoch [65][500/3746] lr: 6.128e-02, eta: 2 days, 23:07:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4981, loss_cls: 4.3668, loss: 4.3668 +2024-07-18 14:38:26,032 - pyskl - INFO - Epoch [65][600/3746] lr: 6.125e-02, eta: 2 days, 23:06:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4958, loss_cls: 4.3259, loss: 4.3259 +2024-07-18 14:39:47,673 - pyskl - INFO - Epoch [65][700/3746] lr: 6.123e-02, eta: 2 days, 23:04:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4969, loss_cls: 4.3421, loss: 4.3421 +2024-07-18 14:41:10,116 - pyskl - INFO - Epoch [65][800/3746] lr: 6.120e-02, eta: 2 days, 23:03:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2384, top5_acc: 0.4834, loss_cls: 4.4170, loss: 4.4170 +2024-07-18 14:42:32,254 - pyskl - INFO - Epoch [65][900/3746] lr: 6.117e-02, eta: 2 days, 23:02:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2355, top5_acc: 0.4839, loss_cls: 4.4146, loss: 4.4146 +2024-07-18 14:43:54,729 - pyskl - INFO - Epoch [65][1000/3746] lr: 6.115e-02, eta: 2 days, 23:01:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2367, top5_acc: 0.4853, loss_cls: 4.3803, loss: 4.3803 +2024-07-18 14:45:17,009 - pyskl - INFO - Epoch [65][1100/3746] lr: 6.112e-02, eta: 2 days, 22:59:55, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4994, loss_cls: 4.3502, loss: 4.3502 +2024-07-18 14:46:39,291 - pyskl - INFO - Epoch [65][1200/3746] lr: 6.109e-02, eta: 2 days, 22:58:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4841, loss_cls: 4.3962, loss: 4.3962 +2024-07-18 14:48:01,870 - pyskl - INFO - Epoch [65][1300/3746] lr: 6.106e-02, eta: 2 days, 22:57:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4950, loss_cls: 4.3443, loss: 4.3443 +2024-07-18 14:49:23,941 - pyskl - INFO - Epoch [65][1400/3746] lr: 6.104e-02, eta: 2 days, 22:56:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4863, loss_cls: 4.3683, loss: 4.3683 +2024-07-18 14:50:46,325 - pyskl - INFO - Epoch [65][1500/3746] lr: 6.101e-02, eta: 2 days, 22:54:51, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2467, top5_acc: 0.4894, loss_cls: 4.3886, loss: 4.3886 +2024-07-18 14:52:07,814 - pyskl - INFO - Epoch [65][1600/3746] lr: 6.098e-02, eta: 2 days, 22:53:34, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4800, loss_cls: 4.4122, loss: 4.4122 +2024-07-18 14:53:29,425 - pyskl - INFO - Epoch [65][1700/3746] lr: 6.095e-02, eta: 2 days, 22:52:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4864, loss_cls: 4.4082, loss: 4.4082 +2024-07-18 14:54:51,145 - pyskl - INFO - Epoch [65][1800/3746] lr: 6.093e-02, eta: 2 days, 22:51:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4847, loss_cls: 4.4033, loss: 4.4033 +2024-07-18 14:56:12,898 - pyskl - INFO - Epoch [65][1900/3746] lr: 6.090e-02, eta: 2 days, 22:49:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4814, loss_cls: 4.4032, loss: 4.4032 +2024-07-18 14:57:34,979 - pyskl - INFO - Epoch [65][2000/3746] lr: 6.087e-02, eta: 2 days, 22:48:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4747, loss_cls: 4.4151, loss: 4.4151 +2024-07-18 14:58:57,069 - pyskl - INFO - Epoch [65][2100/3746] lr: 6.085e-02, eta: 2 days, 22:47:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4927, loss_cls: 4.3803, loss: 4.3803 +2024-07-18 15:00:19,453 - pyskl - INFO - Epoch [65][2200/3746] lr: 6.082e-02, eta: 2 days, 22:45:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2400, top5_acc: 0.4744, loss_cls: 4.4179, loss: 4.4179 +2024-07-18 15:01:42,034 - pyskl - INFO - Epoch [65][2300/3746] lr: 6.079e-02, eta: 2 days, 22:44:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4925, loss_cls: 4.3864, loss: 4.3864 +2024-07-18 15:03:03,718 - pyskl - INFO - Epoch [65][2400/3746] lr: 6.076e-02, eta: 2 days, 22:43:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2391, top5_acc: 0.4866, loss_cls: 4.4051, loss: 4.4051 +2024-07-18 15:04:26,347 - pyskl - INFO - Epoch [65][2500/3746] lr: 6.074e-02, eta: 2 days, 22:42:07, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4892, loss_cls: 4.4077, loss: 4.4077 +2024-07-18 15:05:48,010 - pyskl - INFO - Epoch [65][2600/3746] lr: 6.071e-02, eta: 2 days, 22:40:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4784, loss_cls: 4.4079, loss: 4.4079 +2024-07-18 15:07:10,090 - pyskl - INFO - Epoch [65][2700/3746] lr: 6.068e-02, eta: 2 days, 22:39:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2419, top5_acc: 0.4861, loss_cls: 4.4264, loss: 4.4264 +2024-07-18 15:08:32,451 - pyskl - INFO - Epoch [65][2800/3746] lr: 6.065e-02, eta: 2 days, 22:38:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2425, top5_acc: 0.4800, loss_cls: 4.4495, loss: 4.4495 +2024-07-18 15:09:54,424 - pyskl - INFO - Epoch [65][2900/3746] lr: 6.063e-02, eta: 2 days, 22:37:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2373, top5_acc: 0.4828, loss_cls: 4.4103, loss: 4.4103 +2024-07-18 15:11:16,543 - pyskl - INFO - Epoch [65][3000/3746] lr: 6.060e-02, eta: 2 days, 22:35:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4953, loss_cls: 4.3610, loss: 4.3610 +2024-07-18 15:12:38,436 - pyskl - INFO - Epoch [65][3100/3746] lr: 6.057e-02, eta: 2 days, 22:34:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4884, loss_cls: 4.3856, loss: 4.3856 +2024-07-18 15:14:00,005 - pyskl - INFO - Epoch [65][3200/3746] lr: 6.055e-02, eta: 2 days, 22:33:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.4986, loss_cls: 4.3380, loss: 4.3380 +2024-07-18 15:15:21,736 - pyskl - INFO - Epoch [65][3300/3746] lr: 6.052e-02, eta: 2 days, 22:31:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4838, loss_cls: 4.3878, loss: 4.3878 +2024-07-18 15:16:43,359 - pyskl - INFO - Epoch [65][3400/3746] lr: 6.049e-02, eta: 2 days, 22:30:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4750, loss_cls: 4.4521, loss: 4.4521 +2024-07-18 15:18:05,512 - pyskl - INFO - Epoch [65][3500/3746] lr: 6.046e-02, eta: 2 days, 22:29:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4931, loss_cls: 4.3559, loss: 4.3559 +2024-07-18 15:19:27,298 - pyskl - INFO - Epoch [65][3600/3746] lr: 6.044e-02, eta: 2 days, 22:28:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4839, loss_cls: 4.4007, loss: 4.4007 +2024-07-18 15:20:50,400 - pyskl - INFO - Epoch [65][3700/3746] lr: 6.041e-02, eta: 2 days, 22:26:49, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4869, loss_cls: 4.3884, loss: 4.3884 +2024-07-18 15:21:30,521 - pyskl - INFO - Saving checkpoint at 65 epochs +2024-07-18 15:23:22,128 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 15:23:22,799 - pyskl - INFO - +top1_acc 0.1751 +top5_acc 0.3918 +2024-07-18 15:23:22,799 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 15:23:22,843 - pyskl - INFO - +mean_acc 0.1747 +2024-07-18 15:23:22,856 - pyskl - INFO - Epoch(val) [65][309] top1_acc: 0.1751, top5_acc: 0.3918, mean_class_accuracy: 0.1747 +2024-07-18 15:27:12,667 - pyskl - INFO - Epoch [66][100/3746] lr: 6.037e-02, eta: 2 days, 22:27:21, time: 2.298, data_time: 1.309, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5020, loss_cls: 4.2938, loss: 4.2938 +2024-07-18 15:28:35,305 - pyskl - INFO - Epoch [66][200/3746] lr: 6.034e-02, eta: 2 days, 22:26:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2387, top5_acc: 0.4850, loss_cls: 4.3860, loss: 4.3860 +2024-07-18 15:29:57,452 - pyskl - INFO - Epoch [66][300/3746] lr: 6.031e-02, eta: 2 days, 22:24:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4859, loss_cls: 4.3484, loss: 4.3484 +2024-07-18 15:31:19,953 - pyskl - INFO - Epoch [66][400/3746] lr: 6.029e-02, eta: 2 days, 22:23:32, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.4870, loss_cls: 4.3893, loss: 4.3893 +2024-07-18 15:32:41,858 - pyskl - INFO - Epoch [66][500/3746] lr: 6.026e-02, eta: 2 days, 22:22:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4909, loss_cls: 4.3764, loss: 4.3764 +2024-07-18 15:34:03,855 - pyskl - INFO - Epoch [66][600/3746] lr: 6.023e-02, eta: 2 days, 22:20:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.5077, loss_cls: 4.3098, loss: 4.3098 +2024-07-18 15:35:25,562 - pyskl - INFO - Epoch [66][700/3746] lr: 6.020e-02, eta: 2 days, 22:19:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4863, loss_cls: 4.3866, loss: 4.3866 +2024-07-18 15:36:46,921 - pyskl - INFO - Epoch [66][800/3746] lr: 6.018e-02, eta: 2 days, 22:18:24, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4942, loss_cls: 4.3834, loss: 4.3834 +2024-07-18 15:38:09,018 - pyskl - INFO - Epoch [66][900/3746] lr: 6.015e-02, eta: 2 days, 22:17:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4909, loss_cls: 4.3567, loss: 4.3567 +2024-07-18 15:39:31,479 - pyskl - INFO - Epoch [66][1000/3746] lr: 6.012e-02, eta: 2 days, 22:15:52, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4975, loss_cls: 4.3577, loss: 4.3577 +2024-07-18 15:40:54,437 - pyskl - INFO - Epoch [66][1100/3746] lr: 6.009e-02, eta: 2 days, 22:14:36, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4841, loss_cls: 4.3769, loss: 4.3769 +2024-07-18 15:42:16,792 - pyskl - INFO - Epoch [66][1200/3746] lr: 6.007e-02, eta: 2 days, 22:13:20, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4906, loss_cls: 4.3900, loss: 4.3900 +2024-07-18 15:43:38,837 - pyskl - INFO - Epoch [66][1300/3746] lr: 6.004e-02, eta: 2 days, 22:12:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4977, loss_cls: 4.3641, loss: 4.3641 +2024-07-18 15:45:00,785 - pyskl - INFO - Epoch [66][1400/3746] lr: 6.001e-02, eta: 2 days, 22:10:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4908, loss_cls: 4.3930, loss: 4.3930 +2024-07-18 15:46:22,689 - pyskl - INFO - Epoch [66][1500/3746] lr: 5.999e-02, eta: 2 days, 22:09:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.4872, loss_cls: 4.4056, loss: 4.4056 +2024-07-18 15:47:44,263 - pyskl - INFO - Epoch [66][1600/3746] lr: 5.996e-02, eta: 2 days, 22:08:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4908, loss_cls: 4.3732, loss: 4.3732 +2024-07-18 15:49:06,340 - pyskl - INFO - Epoch [66][1700/3746] lr: 5.993e-02, eta: 2 days, 22:06:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4847, loss_cls: 4.3990, loss: 4.3990 +2024-07-18 15:50:28,910 - pyskl - INFO - Epoch [66][1800/3746] lr: 5.990e-02, eta: 2 days, 22:05:40, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4873, loss_cls: 4.3763, loss: 4.3763 +2024-07-18 15:51:50,608 - pyskl - INFO - Epoch [66][1900/3746] lr: 5.988e-02, eta: 2 days, 22:04:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4870, loss_cls: 4.4012, loss: 4.4012 +2024-07-18 15:53:12,790 - pyskl - INFO - Epoch [66][2000/3746] lr: 5.985e-02, eta: 2 days, 22:03:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2437, top5_acc: 0.4992, loss_cls: 4.3565, loss: 4.3565 +2024-07-18 15:54:34,647 - pyskl - INFO - Epoch [66][2100/3746] lr: 5.982e-02, eta: 2 days, 22:01:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2416, top5_acc: 0.4898, loss_cls: 4.4040, loss: 4.4040 +2024-07-18 15:55:57,482 - pyskl - INFO - Epoch [66][2200/3746] lr: 5.979e-02, eta: 2 days, 22:00:34, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2494, top5_acc: 0.4898, loss_cls: 4.3971, loss: 4.3971 +2024-07-18 15:57:20,643 - pyskl - INFO - Epoch [66][2300/3746] lr: 5.977e-02, eta: 2 days, 21:59:18, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.4916, loss_cls: 4.3229, loss: 4.3229 +2024-07-18 15:58:43,089 - pyskl - INFO - Epoch [66][2400/3746] lr: 5.974e-02, eta: 2 days, 21:58:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4848, loss_cls: 4.3612, loss: 4.3612 +2024-07-18 16:00:04,945 - pyskl - INFO - Epoch [66][2500/3746] lr: 5.971e-02, eta: 2 days, 21:56:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4897, loss_cls: 4.3984, loss: 4.3984 +2024-07-18 16:01:27,363 - pyskl - INFO - Epoch [66][2600/3746] lr: 5.968e-02, eta: 2 days, 21:55:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4892, loss_cls: 4.3732, loss: 4.3732 +2024-07-18 16:02:49,012 - pyskl - INFO - Epoch [66][2700/3746] lr: 5.966e-02, eta: 2 days, 21:54:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2448, top5_acc: 0.4813, loss_cls: 4.4019, loss: 4.4019 +2024-07-18 16:04:11,091 - pyskl - INFO - Epoch [66][2800/3746] lr: 5.963e-02, eta: 2 days, 21:52:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4884, loss_cls: 4.3917, loss: 4.3917 +2024-07-18 16:05:33,280 - pyskl - INFO - Epoch [66][2900/3746] lr: 5.960e-02, eta: 2 days, 21:51:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4773, loss_cls: 4.4481, loss: 4.4481 +2024-07-18 16:06:55,573 - pyskl - INFO - Epoch [66][3000/3746] lr: 5.957e-02, eta: 2 days, 21:50:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4884, loss_cls: 4.3833, loss: 4.3833 +2024-07-18 16:08:17,114 - pyskl - INFO - Epoch [66][3100/3746] lr: 5.955e-02, eta: 2 days, 21:49:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4995, loss_cls: 4.3562, loss: 4.3562 +2024-07-18 16:09:38,781 - pyskl - INFO - Epoch [66][3200/3746] lr: 5.952e-02, eta: 2 days, 21:47:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4958, loss_cls: 4.3486, loss: 4.3486 +2024-07-18 16:11:00,568 - pyskl - INFO - Epoch [66][3300/3746] lr: 5.949e-02, eta: 2 days, 21:46:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4916, loss_cls: 4.3894, loss: 4.3894 +2024-07-18 16:12:22,436 - pyskl - INFO - Epoch [66][3400/3746] lr: 5.946e-02, eta: 2 days, 21:45:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4986, loss_cls: 4.3423, loss: 4.3423 +2024-07-18 16:13:44,361 - pyskl - INFO - Epoch [66][3500/3746] lr: 5.944e-02, eta: 2 days, 21:43:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.4906, loss_cls: 4.3632, loss: 4.3632 +2024-07-18 16:15:06,188 - pyskl - INFO - Epoch [66][3600/3746] lr: 5.941e-02, eta: 2 days, 21:42:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2439, top5_acc: 0.4875, loss_cls: 4.4060, loss: 4.4060 +2024-07-18 16:16:28,258 - pyskl - INFO - Epoch [66][3700/3746] lr: 5.938e-02, eta: 2 days, 21:41:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4864, loss_cls: 4.3558, loss: 4.3558 +2024-07-18 16:17:07,834 - pyskl - INFO - Saving checkpoint at 66 epochs +2024-07-18 16:18:58,863 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 16:18:59,550 - pyskl - INFO - +top1_acc 0.1663 +top5_acc 0.3718 +2024-07-18 16:18:59,550 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 16:18:59,601 - pyskl - INFO - +mean_acc 0.1658 +2024-07-18 16:18:59,616 - pyskl - INFO - Epoch(val) [66][309] top1_acc: 0.1663, top5_acc: 0.3718, mean_class_accuracy: 0.1658 +2024-07-18 16:22:49,648 - pyskl - INFO - Epoch [67][100/3746] lr: 5.934e-02, eta: 2 days, 21:41:51, time: 2.300, data_time: 1.311, memory: 15990, top1_acc: 0.2484, top5_acc: 0.5031, loss_cls: 4.3005, loss: 4.3005 +2024-07-18 16:24:11,937 - pyskl - INFO - Epoch [67][200/3746] lr: 5.931e-02, eta: 2 days, 21:40:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5038, loss_cls: 4.2869, loss: 4.2869 +2024-07-18 16:25:34,422 - pyskl - INFO - Epoch [67][300/3746] lr: 5.929e-02, eta: 2 days, 21:39:18, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4841, loss_cls: 4.3920, loss: 4.3920 +2024-07-18 16:26:56,667 - pyskl - INFO - Epoch [67][400/3746] lr: 5.926e-02, eta: 2 days, 21:38:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5036, loss_cls: 4.3317, loss: 4.3317 +2024-07-18 16:28:18,536 - pyskl - INFO - Epoch [67][500/3746] lr: 5.923e-02, eta: 2 days, 21:36:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5028, loss_cls: 4.2871, loss: 4.2871 +2024-07-18 16:29:40,436 - pyskl - INFO - Epoch [67][600/3746] lr: 5.920e-02, eta: 2 days, 21:35:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.4916, loss_cls: 4.3723, loss: 4.3723 +2024-07-18 16:31:02,354 - pyskl - INFO - Epoch [67][700/3746] lr: 5.918e-02, eta: 2 days, 21:34:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.5025, loss_cls: 4.3410, loss: 4.3410 +2024-07-18 16:32:24,434 - pyskl - INFO - Epoch [67][800/3746] lr: 5.915e-02, eta: 2 days, 21:32:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4838, loss_cls: 4.3715, loss: 4.3715 +2024-07-18 16:33:46,524 - pyskl - INFO - Epoch [67][900/3746] lr: 5.912e-02, eta: 2 days, 21:31:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4992, loss_cls: 4.3513, loss: 4.3513 +2024-07-18 16:35:08,494 - pyskl - INFO - Epoch [67][1000/3746] lr: 5.909e-02, eta: 2 days, 21:30:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2555, top5_acc: 0.4891, loss_cls: 4.3714, loss: 4.3714 +2024-07-18 16:36:30,983 - pyskl - INFO - Epoch [67][1100/3746] lr: 5.907e-02, eta: 2 days, 21:29:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2484, top5_acc: 0.4867, loss_cls: 4.3756, loss: 4.3756 +2024-07-18 16:37:53,101 - pyskl - INFO - Epoch [67][1200/3746] lr: 5.904e-02, eta: 2 days, 21:27:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4863, loss_cls: 4.4128, loss: 4.4128 +2024-07-18 16:39:15,390 - pyskl - INFO - Epoch [67][1300/3746] lr: 5.901e-02, eta: 2 days, 21:26:30, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.4925, loss_cls: 4.3228, loss: 4.3228 +2024-07-18 16:40:37,638 - pyskl - INFO - Epoch [67][1400/3746] lr: 5.898e-02, eta: 2 days, 21:25:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.4948, loss_cls: 4.3416, loss: 4.3416 +2024-07-18 16:41:59,890 - pyskl - INFO - Epoch [67][1500/3746] lr: 5.896e-02, eta: 2 days, 21:23:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4989, loss_cls: 4.3528, loss: 4.3528 +2024-07-18 16:43:22,229 - pyskl - INFO - Epoch [67][1600/3746] lr: 5.893e-02, eta: 2 days, 21:22:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4934, loss_cls: 4.3678, loss: 4.3678 +2024-07-18 16:44:43,591 - pyskl - INFO - Epoch [67][1700/3746] lr: 5.890e-02, eta: 2 days, 21:21:22, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4934, loss_cls: 4.3906, loss: 4.3906 +2024-07-18 16:46:05,114 - pyskl - INFO - Epoch [67][1800/3746] lr: 5.887e-02, eta: 2 days, 21:20:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4945, loss_cls: 4.3606, loss: 4.3606 +2024-07-18 16:47:27,185 - pyskl - INFO - Epoch [67][1900/3746] lr: 5.885e-02, eta: 2 days, 21:18:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4978, loss_cls: 4.3685, loss: 4.3685 +2024-07-18 16:48:49,421 - pyskl - INFO - Epoch [67][2000/3746] lr: 5.882e-02, eta: 2 days, 21:17:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4933, loss_cls: 4.3599, loss: 4.3599 +2024-07-18 16:50:11,883 - pyskl - INFO - Epoch [67][2100/3746] lr: 5.879e-02, eta: 2 days, 21:16:14, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4948, loss_cls: 4.3466, loss: 4.3466 +2024-07-18 16:51:34,551 - pyskl - INFO - Epoch [67][2200/3746] lr: 5.876e-02, eta: 2 days, 21:14:58, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4848, loss_cls: 4.3746, loss: 4.3746 +2024-07-18 16:52:57,405 - pyskl - INFO - Epoch [67][2300/3746] lr: 5.874e-02, eta: 2 days, 21:13:42, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4969, loss_cls: 4.3315, loss: 4.3315 +2024-07-18 16:54:19,948 - pyskl - INFO - Epoch [67][2400/3746] lr: 5.871e-02, eta: 2 days, 21:12:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2409, top5_acc: 0.4878, loss_cls: 4.3806, loss: 4.3806 +2024-07-18 16:55:42,167 - pyskl - INFO - Epoch [67][2500/3746] lr: 5.868e-02, eta: 2 days, 21:11:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4922, loss_cls: 4.3688, loss: 4.3688 +2024-07-18 16:57:04,027 - pyskl - INFO - Epoch [67][2600/3746] lr: 5.865e-02, eta: 2 days, 21:09:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2473, top5_acc: 0.4830, loss_cls: 4.4397, loss: 4.4397 +2024-07-18 16:58:25,949 - pyskl - INFO - Epoch [67][2700/3746] lr: 5.863e-02, eta: 2 days, 21:08:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4934, loss_cls: 4.3454, loss: 4.3454 +2024-07-18 16:59:47,543 - pyskl - INFO - Epoch [67][2800/3746] lr: 5.860e-02, eta: 2 days, 21:07:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4898, loss_cls: 4.3955, loss: 4.3955 +2024-07-18 17:01:09,404 - pyskl - INFO - Epoch [67][2900/3746] lr: 5.857e-02, eta: 2 days, 21:06:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4920, loss_cls: 4.3593, loss: 4.3593 +2024-07-18 17:02:31,160 - pyskl - INFO - Epoch [67][3000/3746] lr: 5.854e-02, eta: 2 days, 21:04:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2422, top5_acc: 0.4816, loss_cls: 4.4144, loss: 4.4144 +2024-07-18 17:03:53,200 - pyskl - INFO - Epoch [67][3100/3746] lr: 5.852e-02, eta: 2 days, 21:03:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2427, top5_acc: 0.4897, loss_cls: 4.3759, loss: 4.3759 +2024-07-18 17:05:15,305 - pyskl - INFO - Epoch [67][3200/3746] lr: 5.849e-02, eta: 2 days, 21:02:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.4883, loss_cls: 4.3384, loss: 4.3384 +2024-07-18 17:06:37,645 - pyskl - INFO - Epoch [67][3300/3746] lr: 5.846e-02, eta: 2 days, 21:00:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4917, loss_cls: 4.3726, loss: 4.3726 +2024-07-18 17:07:59,293 - pyskl - INFO - Epoch [67][3400/3746] lr: 5.843e-02, eta: 2 days, 20:59:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2359, top5_acc: 0.4877, loss_cls: 4.3946, loss: 4.3946 +2024-07-18 17:09:21,261 - pyskl - INFO - Epoch [67][3500/3746] lr: 5.841e-02, eta: 2 days, 20:58:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2441, top5_acc: 0.4863, loss_cls: 4.3624, loss: 4.3624 +2024-07-18 17:10:42,815 - pyskl - INFO - Epoch [67][3600/3746] lr: 5.838e-02, eta: 2 days, 20:57:00, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4836, loss_cls: 4.4130, loss: 4.4130 +2024-07-18 17:12:05,072 - pyskl - INFO - Epoch [67][3700/3746] lr: 5.835e-02, eta: 2 days, 20:55:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4917, loss_cls: 4.3525, loss: 4.3525 +2024-07-18 17:12:44,764 - pyskl - INFO - Saving checkpoint at 67 epochs +2024-07-18 17:14:36,304 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 17:14:36,975 - pyskl - INFO - +top1_acc 0.1788 +top5_acc 0.3991 +2024-07-18 17:14:36,975 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 17:14:37,018 - pyskl - INFO - +mean_acc 0.1786 +2024-07-18 17:14:37,031 - pyskl - INFO - Epoch(val) [67][309] top1_acc: 0.1788, top5_acc: 0.3991, mean_class_accuracy: 0.1786 +2024-07-18 17:18:29,707 - pyskl - INFO - Epoch [68][100/3746] lr: 5.831e-02, eta: 2 days, 20:56:11, time: 2.327, data_time: 1.338, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5067, loss_cls: 4.3116, loss: 4.3116 +2024-07-18 17:19:51,593 - pyskl - INFO - Epoch [68][200/3746] lr: 5.828e-02, eta: 2 days, 20:54:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5008, loss_cls: 4.3236, loss: 4.3236 +2024-07-18 17:21:13,354 - pyskl - INFO - Epoch [68][300/3746] lr: 5.826e-02, eta: 2 days, 20:53:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5023, loss_cls: 4.3093, loss: 4.3093 +2024-07-18 17:22:35,626 - pyskl - INFO - Epoch [68][400/3746] lr: 5.823e-02, eta: 2 days, 20:52:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2464, top5_acc: 0.4970, loss_cls: 4.3390, loss: 4.3390 +2024-07-18 17:23:57,733 - pyskl - INFO - Epoch [68][500/3746] lr: 5.820e-02, eta: 2 days, 20:51:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4936, loss_cls: 4.3488, loss: 4.3488 +2024-07-18 17:25:19,955 - pyskl - INFO - Epoch [68][600/3746] lr: 5.817e-02, eta: 2 days, 20:49:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2523, top5_acc: 0.4938, loss_cls: 4.3543, loss: 4.3543 +2024-07-18 17:26:41,785 - pyskl - INFO - Epoch [68][700/3746] lr: 5.815e-02, eta: 2 days, 20:48:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4922, loss_cls: 4.3865, loss: 4.3865 +2024-07-18 17:28:03,395 - pyskl - INFO - Epoch [68][800/3746] lr: 5.812e-02, eta: 2 days, 20:47:10, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.4966, loss_cls: 4.3174, loss: 4.3174 +2024-07-18 17:29:25,640 - pyskl - INFO - Epoch [68][900/3746] lr: 5.809e-02, eta: 2 days, 20:45:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4959, loss_cls: 4.3511, loss: 4.3511 +2024-07-18 17:30:47,869 - pyskl - INFO - Epoch [68][1000/3746] lr: 5.806e-02, eta: 2 days, 20:44:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5041, loss_cls: 4.3079, loss: 4.3079 +2024-07-18 17:32:10,529 - pyskl - INFO - Epoch [68][1100/3746] lr: 5.804e-02, eta: 2 days, 20:43:20, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2411, top5_acc: 0.4863, loss_cls: 4.4012, loss: 4.4012 +2024-07-18 17:33:32,248 - pyskl - INFO - Epoch [68][1200/3746] lr: 5.801e-02, eta: 2 days, 20:42:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4888, loss_cls: 4.3706, loss: 4.3706 +2024-07-18 17:34:54,294 - pyskl - INFO - Epoch [68][1300/3746] lr: 5.798e-02, eta: 2 days, 20:40:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.4986, loss_cls: 4.3419, loss: 4.3419 +2024-07-18 17:36:15,944 - pyskl - INFO - Epoch [68][1400/3746] lr: 5.795e-02, eta: 2 days, 20:39:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4955, loss_cls: 4.3885, loss: 4.3885 +2024-07-18 17:37:37,606 - pyskl - INFO - Epoch [68][1500/3746] lr: 5.792e-02, eta: 2 days, 20:38:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.4998, loss_cls: 4.2942, loss: 4.2942 +2024-07-18 17:38:59,580 - pyskl - INFO - Epoch [68][1600/3746] lr: 5.790e-02, eta: 2 days, 20:36:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4903, loss_cls: 4.3678, loss: 4.3678 +2024-07-18 17:40:21,231 - pyskl - INFO - Epoch [68][1700/3746] lr: 5.787e-02, eta: 2 days, 20:35:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.4983, loss_cls: 4.3334, loss: 4.3334 +2024-07-18 17:41:43,498 - pyskl - INFO - Epoch [68][1800/3746] lr: 5.784e-02, eta: 2 days, 20:34:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4828, loss_cls: 4.4164, loss: 4.4164 +2024-07-18 17:43:05,568 - pyskl - INFO - Epoch [68][1900/3746] lr: 5.781e-02, eta: 2 days, 20:33:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4927, loss_cls: 4.3975, loss: 4.3975 +2024-07-18 17:44:27,938 - pyskl - INFO - Epoch [68][2000/3746] lr: 5.779e-02, eta: 2 days, 20:31:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4923, loss_cls: 4.3776, loss: 4.3776 +2024-07-18 17:45:50,373 - pyskl - INFO - Epoch [68][2100/3746] lr: 5.776e-02, eta: 2 days, 20:30:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2480, top5_acc: 0.4984, loss_cls: 4.3251, loss: 4.3251 +2024-07-18 17:47:12,714 - pyskl - INFO - Epoch [68][2200/3746] lr: 5.773e-02, eta: 2 days, 20:29:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4922, loss_cls: 4.3359, loss: 4.3359 +2024-07-18 17:48:35,108 - pyskl - INFO - Epoch [68][2300/3746] lr: 5.770e-02, eta: 2 days, 20:27:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.4859, loss_cls: 4.3734, loss: 4.3734 +2024-07-18 17:49:57,477 - pyskl - INFO - Epoch [68][2400/3746] lr: 5.768e-02, eta: 2 days, 20:26:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4891, loss_cls: 4.3796, loss: 4.3796 +2024-07-18 17:51:19,452 - pyskl - INFO - Epoch [68][2500/3746] lr: 5.765e-02, eta: 2 days, 20:25:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2370, top5_acc: 0.4889, loss_cls: 4.3978, loss: 4.3978 +2024-07-18 17:52:41,126 - pyskl - INFO - Epoch [68][2600/3746] lr: 5.762e-02, eta: 2 days, 20:24:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5003, loss_cls: 4.3371, loss: 4.3371 +2024-07-18 17:54:02,826 - pyskl - INFO - Epoch [68][2700/3746] lr: 5.759e-02, eta: 2 days, 20:22:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.4914, loss_cls: 4.3557, loss: 4.3557 +2024-07-18 17:55:25,081 - pyskl - INFO - Epoch [68][2800/3746] lr: 5.757e-02, eta: 2 days, 20:21:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5006, loss_cls: 4.3182, loss: 4.3182 +2024-07-18 17:56:46,859 - pyskl - INFO - Epoch [68][2900/3746] lr: 5.754e-02, eta: 2 days, 20:20:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.5042, loss_cls: 4.3208, loss: 4.3208 +2024-07-18 17:58:08,659 - pyskl - INFO - Epoch [68][3000/3746] lr: 5.751e-02, eta: 2 days, 20:18:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2414, top5_acc: 0.4866, loss_cls: 4.3854, loss: 4.3854 +2024-07-18 17:59:31,174 - pyskl - INFO - Epoch [68][3100/3746] lr: 5.748e-02, eta: 2 days, 20:17:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4969, loss_cls: 4.3231, loss: 4.3231 +2024-07-18 18:00:53,183 - pyskl - INFO - Epoch [68][3200/3746] lr: 5.746e-02, eta: 2 days, 20:16:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2466, top5_acc: 0.4934, loss_cls: 4.3526, loss: 4.3526 +2024-07-18 18:02:15,895 - pyskl - INFO - Epoch [68][3300/3746] lr: 5.743e-02, eta: 2 days, 20:15:02, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4813, loss_cls: 4.3895, loss: 4.3895 +2024-07-18 18:03:38,189 - pyskl - INFO - Epoch [68][3400/3746] lr: 5.740e-02, eta: 2 days, 20:13:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2508, top5_acc: 0.4927, loss_cls: 4.3681, loss: 4.3681 +2024-07-18 18:04:59,965 - pyskl - INFO - Epoch [68][3500/3746] lr: 5.737e-02, eta: 2 days, 20:12:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4917, loss_cls: 4.3354, loss: 4.3354 +2024-07-18 18:06:22,302 - pyskl - INFO - Epoch [68][3600/3746] lr: 5.734e-02, eta: 2 days, 20:11:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5012, loss_cls: 4.3437, loss: 4.3437 +2024-07-18 18:07:45,102 - pyskl - INFO - Epoch [68][3700/3746] lr: 5.732e-02, eta: 2 days, 20:09:54, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4858, loss_cls: 4.3771, loss: 4.3771 +2024-07-18 18:08:24,697 - pyskl - INFO - Saving checkpoint at 68 epochs +2024-07-18 18:10:16,116 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 18:10:16,791 - pyskl - INFO - +top1_acc 0.1958 +top5_acc 0.4185 +2024-07-18 18:10:16,792 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 18:10:16,836 - pyskl - INFO - +mean_acc 0.1956 +2024-07-18 18:10:16,841 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_60.pth was removed +2024-07-18 18:10:17,102 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_68.pth. +2024-07-18 18:10:17,102 - pyskl - INFO - Best top1_acc is 0.1958 at 68 epoch. +2024-07-18 18:10:17,117 - pyskl - INFO - Epoch(val) [68][309] top1_acc: 0.1958, top5_acc: 0.4185, mean_class_accuracy: 0.1956 +2024-07-18 18:14:07,229 - pyskl - INFO - Epoch [69][100/3746] lr: 5.728e-02, eta: 2 days, 20:10:14, time: 2.301, data_time: 1.310, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5142, loss_cls: 4.3122, loss: 4.3122 +2024-07-18 18:15:29,629 - pyskl - INFO - Epoch [69][200/3746] lr: 5.725e-02, eta: 2 days, 20:08:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5047, loss_cls: 4.2818, loss: 4.2818 +2024-07-18 18:16:52,067 - pyskl - INFO - Epoch [69][300/3746] lr: 5.722e-02, eta: 2 days, 20:07:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4969, loss_cls: 4.3215, loss: 4.3215 +2024-07-18 18:18:13,736 - pyskl - INFO - Epoch [69][400/3746] lr: 5.719e-02, eta: 2 days, 20:06:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.4961, loss_cls: 4.3381, loss: 4.3381 +2024-07-18 18:19:35,805 - pyskl - INFO - Epoch [69][500/3746] lr: 5.717e-02, eta: 2 days, 20:05:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4992, loss_cls: 4.3426, loss: 4.3426 +2024-07-18 18:20:57,829 - pyskl - INFO - Epoch [69][600/3746] lr: 5.714e-02, eta: 2 days, 20:03:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5100, loss_cls: 4.2954, loss: 4.2954 +2024-07-18 18:22:19,553 - pyskl - INFO - Epoch [69][700/3746] lr: 5.711e-02, eta: 2 days, 20:02:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2611, top5_acc: 0.5095, loss_cls: 4.2992, loss: 4.2992 +2024-07-18 18:23:41,804 - pyskl - INFO - Epoch [69][800/3746] lr: 5.708e-02, eta: 2 days, 20:01:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5014, loss_cls: 4.3500, loss: 4.3500 +2024-07-18 18:25:04,551 - pyskl - INFO - Epoch [69][900/3746] lr: 5.706e-02, eta: 2 days, 19:59:56, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4900, loss_cls: 4.3441, loss: 4.3441 +2024-07-18 18:26:26,751 - pyskl - INFO - Epoch [69][1000/3746] lr: 5.703e-02, eta: 2 days, 19:58:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.4984, loss_cls: 4.3334, loss: 4.3334 +2024-07-18 18:27:49,065 - pyskl - INFO - Epoch [69][1100/3746] lr: 5.700e-02, eta: 2 days, 19:57:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2553, top5_acc: 0.4944, loss_cls: 4.3350, loss: 4.3350 +2024-07-18 18:29:11,291 - pyskl - INFO - Epoch [69][1200/3746] lr: 5.697e-02, eta: 2 days, 19:56:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4919, loss_cls: 4.3621, loss: 4.3621 +2024-07-18 18:30:33,363 - pyskl - INFO - Epoch [69][1300/3746] lr: 5.694e-02, eta: 2 days, 19:54:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2444, top5_acc: 0.4956, loss_cls: 4.3380, loss: 4.3380 +2024-07-18 18:31:55,182 - pyskl - INFO - Epoch [69][1400/3746] lr: 5.692e-02, eta: 2 days, 19:53:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4969, loss_cls: 4.3438, loss: 4.3438 +2024-07-18 18:33:17,239 - pyskl - INFO - Epoch [69][1500/3746] lr: 5.689e-02, eta: 2 days, 19:52:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2548, top5_acc: 0.4897, loss_cls: 4.3554, loss: 4.3554 +2024-07-18 18:34:39,069 - pyskl - INFO - Epoch [69][1600/3746] lr: 5.686e-02, eta: 2 days, 19:50:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4981, loss_cls: 4.3659, loss: 4.3659 +2024-07-18 18:36:00,876 - pyskl - INFO - Epoch [69][1700/3746] lr: 5.683e-02, eta: 2 days, 19:49:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4989, loss_cls: 4.3498, loss: 4.3498 +2024-07-18 18:37:23,512 - pyskl - INFO - Epoch [69][1800/3746] lr: 5.681e-02, eta: 2 days, 19:48:20, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.4922, loss_cls: 4.3396, loss: 4.3396 +2024-07-18 18:38:46,025 - pyskl - INFO - Epoch [69][1900/3746] lr: 5.678e-02, eta: 2 days, 19:47:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4961, loss_cls: 4.3161, loss: 4.3161 +2024-07-18 18:40:08,868 - pyskl - INFO - Epoch [69][2000/3746] lr: 5.675e-02, eta: 2 days, 19:45:47, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5055, loss_cls: 4.2818, loss: 4.2818 +2024-07-18 18:41:31,277 - pyskl - INFO - Epoch [69][2100/3746] lr: 5.672e-02, eta: 2 days, 19:44:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.4948, loss_cls: 4.3485, loss: 4.3485 +2024-07-18 18:42:53,233 - pyskl - INFO - Epoch [69][2200/3746] lr: 5.670e-02, eta: 2 days, 19:43:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2436, top5_acc: 0.4883, loss_cls: 4.3725, loss: 4.3725 +2024-07-18 18:44:15,598 - pyskl - INFO - Epoch [69][2300/3746] lr: 5.667e-02, eta: 2 days, 19:41:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.4994, loss_cls: 4.3275, loss: 4.3275 +2024-07-18 18:45:38,207 - pyskl - INFO - Epoch [69][2400/3746] lr: 5.664e-02, eta: 2 days, 19:40:38, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2452, top5_acc: 0.4922, loss_cls: 4.3884, loss: 4.3884 +2024-07-18 18:47:00,440 - pyskl - INFO - Epoch [69][2500/3746] lr: 5.661e-02, eta: 2 days, 19:39:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4878, loss_cls: 4.3807, loss: 4.3807 +2024-07-18 18:48:22,427 - pyskl - INFO - Epoch [69][2600/3746] lr: 5.658e-02, eta: 2 days, 19:38:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2470, top5_acc: 0.4927, loss_cls: 4.3385, loss: 4.3385 +2024-07-18 18:49:44,541 - pyskl - INFO - Epoch [69][2700/3746] lr: 5.656e-02, eta: 2 days, 19:36:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2428, top5_acc: 0.4906, loss_cls: 4.3844, loss: 4.3844 +2024-07-18 18:51:06,461 - pyskl - INFO - Epoch [69][2800/3746] lr: 5.653e-02, eta: 2 days, 19:35:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.5000, loss_cls: 4.3742, loss: 4.3742 +2024-07-18 18:52:28,259 - pyskl - INFO - Epoch [69][2900/3746] lr: 5.650e-02, eta: 2 days, 19:34:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2458, top5_acc: 0.4952, loss_cls: 4.3620, loss: 4.3620 +2024-07-18 18:53:50,540 - pyskl - INFO - Epoch [69][3000/3746] lr: 5.647e-02, eta: 2 days, 19:32:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.4922, loss_cls: 4.3620, loss: 4.3620 +2024-07-18 18:55:12,451 - pyskl - INFO - Epoch [69][3100/3746] lr: 5.645e-02, eta: 2 days, 19:31:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4833, loss_cls: 4.3753, loss: 4.3753 +2024-07-18 18:56:34,316 - pyskl - INFO - Epoch [69][3200/3746] lr: 5.642e-02, eta: 2 days, 19:30:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.4905, loss_cls: 4.3406, loss: 4.3406 +2024-07-18 18:57:56,564 - pyskl - INFO - Epoch [69][3300/3746] lr: 5.639e-02, eta: 2 days, 19:29:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2417, top5_acc: 0.4966, loss_cls: 4.3560, loss: 4.3560 +2024-07-18 18:59:18,506 - pyskl - INFO - Epoch [69][3400/3746] lr: 5.636e-02, eta: 2 days, 19:27:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4970, loss_cls: 4.3670, loss: 4.3670 +2024-07-18 19:00:41,389 - pyskl - INFO - Epoch [69][3500/3746] lr: 5.634e-02, eta: 2 days, 19:26:27, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5027, loss_cls: 4.3218, loss: 4.3218 +2024-07-18 19:02:03,470 - pyskl - INFO - Epoch [69][3600/3746] lr: 5.631e-02, eta: 2 days, 19:25:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.4955, loss_cls: 4.3561, loss: 4.3561 +2024-07-18 19:03:25,306 - pyskl - INFO - Epoch [69][3700/3746] lr: 5.628e-02, eta: 2 days, 19:23:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2486, top5_acc: 0.4881, loss_cls: 4.3526, loss: 4.3526 +2024-07-18 19:04:05,511 - pyskl - INFO - Saving checkpoint at 69 epochs +2024-07-18 19:05:55,636 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 19:05:56,298 - pyskl - INFO - +top1_acc 0.1939 +top5_acc 0.4205 +2024-07-18 19:05:56,299 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 19:05:56,344 - pyskl - INFO - +mean_acc 0.1938 +2024-07-18 19:05:56,357 - pyskl - INFO - Epoch(val) [69][309] top1_acc: 0.1939, top5_acc: 0.4205, mean_class_accuracy: 0.1938 +2024-07-18 19:09:42,647 - pyskl - INFO - Epoch [70][100/3746] lr: 5.624e-02, eta: 2 days, 19:24:04, time: 2.263, data_time: 1.274, memory: 15990, top1_acc: 0.2622, top5_acc: 0.4988, loss_cls: 4.3164, loss: 4.3164 +2024-07-18 19:11:04,615 - pyskl - INFO - Epoch [70][200/3746] lr: 5.621e-02, eta: 2 days, 19:22:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5088, loss_cls: 4.2524, loss: 4.2524 +2024-07-18 19:12:26,443 - pyskl - INFO - Epoch [70][300/3746] lr: 5.618e-02, eta: 2 days, 19:21:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5008, loss_cls: 4.3045, loss: 4.3045 +2024-07-18 19:13:48,219 - pyskl - INFO - Epoch [70][400/3746] lr: 5.616e-02, eta: 2 days, 19:20:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2492, top5_acc: 0.4953, loss_cls: 4.3371, loss: 4.3371 +2024-07-18 19:15:09,878 - pyskl - INFO - Epoch [70][500/3746] lr: 5.613e-02, eta: 2 days, 19:18:52, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.4977, loss_cls: 4.3160, loss: 4.3160 +2024-07-18 19:16:31,785 - pyskl - INFO - Epoch [70][600/3746] lr: 5.610e-02, eta: 2 days, 19:17:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5044, loss_cls: 4.3325, loss: 4.3325 +2024-07-18 19:17:53,612 - pyskl - INFO - Epoch [70][700/3746] lr: 5.607e-02, eta: 2 days, 19:16:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.4950, loss_cls: 4.3377, loss: 4.3377 +2024-07-18 19:19:16,169 - pyskl - INFO - Epoch [70][800/3746] lr: 5.605e-02, eta: 2 days, 19:14:59, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.5034, loss_cls: 4.3187, loss: 4.3187 +2024-07-18 19:20:38,722 - pyskl - INFO - Epoch [70][900/3746] lr: 5.602e-02, eta: 2 days, 19:13:42, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2408, top5_acc: 0.4997, loss_cls: 4.3674, loss: 4.3674 +2024-07-18 19:22:00,342 - pyskl - INFO - Epoch [70][1000/3746] lr: 5.599e-02, eta: 2 days, 19:12:24, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5042, loss_cls: 4.3108, loss: 4.3108 +2024-07-18 19:23:22,681 - pyskl - INFO - Epoch [70][1100/3746] lr: 5.596e-02, eta: 2 days, 19:11:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4948, loss_cls: 4.3374, loss: 4.3374 +2024-07-18 19:24:44,724 - pyskl - INFO - Epoch [70][1200/3746] lr: 5.593e-02, eta: 2 days, 19:09:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5088, loss_cls: 4.2795, loss: 4.2795 +2024-07-18 19:26:06,925 - pyskl - INFO - Epoch [70][1300/3746] lr: 5.591e-02, eta: 2 days, 19:08:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.4920, loss_cls: 4.3423, loss: 4.3423 +2024-07-18 19:27:28,808 - pyskl - INFO - Epoch [70][1400/3746] lr: 5.588e-02, eta: 2 days, 19:07:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5000, loss_cls: 4.3261, loss: 4.3261 +2024-07-18 19:28:50,625 - pyskl - INFO - Epoch [70][1500/3746] lr: 5.585e-02, eta: 2 days, 19:05:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2430, top5_acc: 0.4845, loss_cls: 4.3824, loss: 4.3824 +2024-07-18 19:30:12,738 - pyskl - INFO - Epoch [70][1600/3746] lr: 5.582e-02, eta: 2 days, 19:04:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.4934, loss_cls: 4.3344, loss: 4.3344 +2024-07-18 19:31:35,146 - pyskl - INFO - Epoch [70][1700/3746] lr: 5.580e-02, eta: 2 days, 19:03:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.4995, loss_cls: 4.3215, loss: 4.3215 +2024-07-18 19:32:57,285 - pyskl - INFO - Epoch [70][1800/3746] lr: 5.577e-02, eta: 2 days, 19:02:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.4980, loss_cls: 4.3137, loss: 4.3137 +2024-07-18 19:34:19,917 - pyskl - INFO - Epoch [70][1900/3746] lr: 5.574e-02, eta: 2 days, 19:00:47, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5086, loss_cls: 4.2871, loss: 4.2871 +2024-07-18 19:35:42,738 - pyskl - INFO - Epoch [70][2000/3746] lr: 5.571e-02, eta: 2 days, 18:59:30, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4886, loss_cls: 4.3853, loss: 4.3853 +2024-07-18 19:37:05,854 - pyskl - INFO - Epoch [70][2100/3746] lr: 5.568e-02, eta: 2 days, 18:58:14, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.4994, loss_cls: 4.3133, loss: 4.3133 +2024-07-18 19:38:27,916 - pyskl - INFO - Epoch [70][2200/3746] lr: 5.566e-02, eta: 2 days, 18:56:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2487, top5_acc: 0.4897, loss_cls: 4.3852, loss: 4.3852 +2024-07-18 19:39:50,544 - pyskl - INFO - Epoch [70][2300/3746] lr: 5.563e-02, eta: 2 days, 18:55:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4995, loss_cls: 4.3420, loss: 4.3420 +2024-07-18 19:41:12,390 - pyskl - INFO - Epoch [70][2400/3746] lr: 5.560e-02, eta: 2 days, 18:54:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5014, loss_cls: 4.2973, loss: 4.2973 +2024-07-18 19:42:35,024 - pyskl - INFO - Epoch [70][2500/3746] lr: 5.557e-02, eta: 2 days, 18:53:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.4938, loss_cls: 4.3094, loss: 4.3094 +2024-07-18 19:43:57,227 - pyskl - INFO - Epoch [70][2600/3746] lr: 5.555e-02, eta: 2 days, 18:51:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4903, loss_cls: 4.3526, loss: 4.3526 +2024-07-18 19:45:18,853 - pyskl - INFO - Epoch [70][2700/3746] lr: 5.552e-02, eta: 2 days, 18:50:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2498, top5_acc: 0.4897, loss_cls: 4.3499, loss: 4.3499 +2024-07-18 19:46:40,939 - pyskl - INFO - Epoch [70][2800/3746] lr: 5.549e-02, eta: 2 days, 18:49:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5012, loss_cls: 4.3293, loss: 4.3293 +2024-07-18 19:48:02,687 - pyskl - INFO - Epoch [70][2900/3746] lr: 5.546e-02, eta: 2 days, 18:47:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2442, top5_acc: 0.4950, loss_cls: 4.3647, loss: 4.3647 +2024-07-18 19:49:24,244 - pyskl - INFO - Epoch [70][3000/3746] lr: 5.543e-02, eta: 2 days, 18:46:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2497, top5_acc: 0.4930, loss_cls: 4.3408, loss: 4.3408 +2024-07-18 19:50:46,559 - pyskl - INFO - Epoch [70][3100/3746] lr: 5.541e-02, eta: 2 days, 18:45:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.4955, loss_cls: 4.3505, loss: 4.3505 +2024-07-18 19:52:08,643 - pyskl - INFO - Epoch [70][3200/3746] lr: 5.538e-02, eta: 2 days, 18:44:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4992, loss_cls: 4.3513, loss: 4.3513 +2024-07-18 19:53:30,677 - pyskl - INFO - Epoch [70][3300/3746] lr: 5.535e-02, eta: 2 days, 18:42:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2462, top5_acc: 0.4972, loss_cls: 4.3526, loss: 4.3526 +2024-07-18 19:54:52,750 - pyskl - INFO - Epoch [70][3400/3746] lr: 5.532e-02, eta: 2 days, 18:41:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.4995, loss_cls: 4.3328, loss: 4.3328 +2024-07-18 19:56:14,970 - pyskl - INFO - Epoch [70][3500/3746] lr: 5.530e-02, eta: 2 days, 18:40:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.4880, loss_cls: 4.3537, loss: 4.3537 +2024-07-18 19:57:36,686 - pyskl - INFO - Epoch [70][3600/3746] lr: 5.527e-02, eta: 2 days, 18:38:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2478, top5_acc: 0.4950, loss_cls: 4.3457, loss: 4.3457 +2024-07-18 19:58:58,959 - pyskl - INFO - Epoch [70][3700/3746] lr: 5.524e-02, eta: 2 days, 18:37:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5003, loss_cls: 4.3234, loss: 4.3234 +2024-07-18 19:59:39,482 - pyskl - INFO - Saving checkpoint at 70 epochs +2024-07-18 20:01:29,918 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 20:01:30,582 - pyskl - INFO - +top1_acc 0.1862 +top5_acc 0.3987 +2024-07-18 20:01:30,582 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 20:01:30,623 - pyskl - INFO - +mean_acc 0.1861 +2024-07-18 20:01:30,636 - pyskl - INFO - Epoch(val) [70][309] top1_acc: 0.1862, top5_acc: 0.3987, mean_class_accuracy: 0.1861 +2024-07-18 20:05:20,429 - pyskl - INFO - Epoch [71][100/3746] lr: 5.520e-02, eta: 2 days, 18:37:43, time: 2.298, data_time: 1.304, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5017, loss_cls: 4.3061, loss: 4.3061 +2024-07-18 20:06:42,889 - pyskl - INFO - Epoch [71][200/3746] lr: 5.517e-02, eta: 2 days, 18:36:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2469, top5_acc: 0.4931, loss_cls: 4.3197, loss: 4.3197 +2024-07-18 20:08:04,466 - pyskl - INFO - Epoch [71][300/3746] lr: 5.514e-02, eta: 2 days, 18:35:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5145, loss_cls: 4.2570, loss: 4.2570 +2024-07-18 20:09:26,203 - pyskl - INFO - Epoch [71][400/3746] lr: 5.512e-02, eta: 2 days, 18:33:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2491, top5_acc: 0.4964, loss_cls: 4.3301, loss: 4.3301 +2024-07-18 20:10:48,173 - pyskl - INFO - Epoch [71][500/3746] lr: 5.509e-02, eta: 2 days, 18:32:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5025, loss_cls: 4.3026, loss: 4.3026 +2024-07-18 20:12:10,435 - pyskl - INFO - Epoch [71][600/3746] lr: 5.506e-02, eta: 2 days, 18:31:14, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5067, loss_cls: 4.2904, loss: 4.2904 +2024-07-18 20:13:32,215 - pyskl - INFO - Epoch [71][700/3746] lr: 5.503e-02, eta: 2 days, 18:29:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.4930, loss_cls: 4.3258, loss: 4.3258 +2024-07-18 20:14:53,990 - pyskl - INFO - Epoch [71][800/3746] lr: 5.500e-02, eta: 2 days, 18:28:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5055, loss_cls: 4.3095, loss: 4.3095 +2024-07-18 20:16:16,114 - pyskl - INFO - Epoch [71][900/3746] lr: 5.498e-02, eta: 2 days, 18:27:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.4938, loss_cls: 4.3294, loss: 4.3294 +2024-07-18 20:17:38,001 - pyskl - INFO - Epoch [71][1000/3746] lr: 5.495e-02, eta: 2 days, 18:26:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.4941, loss_cls: 4.3591, loss: 4.3591 +2024-07-18 20:19:00,296 - pyskl - INFO - Epoch [71][1100/3746] lr: 5.492e-02, eta: 2 days, 18:24:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5025, loss_cls: 4.3265, loss: 4.3265 +2024-07-18 20:20:21,999 - pyskl - INFO - Epoch [71][1200/3746] lr: 5.489e-02, eta: 2 days, 18:23:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2502, top5_acc: 0.5033, loss_cls: 4.3235, loss: 4.3235 +2024-07-18 20:21:43,641 - pyskl - INFO - Epoch [71][1300/3746] lr: 5.487e-02, eta: 2 days, 18:22:08, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2453, top5_acc: 0.4880, loss_cls: 4.4089, loss: 4.4089 +2024-07-18 20:23:05,437 - pyskl - INFO - Epoch [71][1400/3746] lr: 5.484e-02, eta: 2 days, 18:20:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.4908, loss_cls: 4.3263, loss: 4.3263 +2024-07-18 20:24:27,648 - pyskl - INFO - Epoch [71][1500/3746] lr: 5.481e-02, eta: 2 days, 18:19:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5005, loss_cls: 4.2974, loss: 4.2974 +2024-07-18 20:25:49,538 - pyskl - INFO - Epoch [71][1600/3746] lr: 5.478e-02, eta: 2 days, 18:18:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.4988, loss_cls: 4.3116, loss: 4.3116 +2024-07-18 20:27:11,505 - pyskl - INFO - Epoch [71][1700/3746] lr: 5.475e-02, eta: 2 days, 18:16:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4892, loss_cls: 4.3599, loss: 4.3599 +2024-07-18 20:28:33,798 - pyskl - INFO - Epoch [71][1800/3746] lr: 5.473e-02, eta: 2 days, 18:15:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.4989, loss_cls: 4.3307, loss: 4.3307 +2024-07-18 20:29:55,811 - pyskl - INFO - Epoch [71][1900/3746] lr: 5.470e-02, eta: 2 days, 18:14:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5008, loss_cls: 4.3090, loss: 4.3090 +2024-07-18 20:31:17,910 - pyskl - INFO - Epoch [71][2000/3746] lr: 5.467e-02, eta: 2 days, 18:13:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2517, top5_acc: 0.4998, loss_cls: 4.3162, loss: 4.3162 +2024-07-18 20:32:39,896 - pyskl - INFO - Epoch [71][2100/3746] lr: 5.464e-02, eta: 2 days, 18:11:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.4928, loss_cls: 4.3503, loss: 4.3503 +2024-07-18 20:34:01,947 - pyskl - INFO - Epoch [71][2200/3746] lr: 5.461e-02, eta: 2 days, 18:10:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.4972, loss_cls: 4.3239, loss: 4.3239 +2024-07-18 20:35:24,078 - pyskl - INFO - Epoch [71][2300/3746] lr: 5.459e-02, eta: 2 days, 18:09:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4888, loss_cls: 4.3496, loss: 4.3496 +2024-07-18 20:36:46,526 - pyskl - INFO - Epoch [71][2400/3746] lr: 5.456e-02, eta: 2 days, 18:07:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.4994, loss_cls: 4.3242, loss: 4.3242 +2024-07-18 20:38:09,087 - pyskl - INFO - Epoch [71][2500/3746] lr: 5.453e-02, eta: 2 days, 18:06:35, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5025, loss_cls: 4.3083, loss: 4.3083 +2024-07-18 20:39:30,902 - pyskl - INFO - Epoch [71][2600/3746] lr: 5.450e-02, eta: 2 days, 18:05:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5000, loss_cls: 4.3137, loss: 4.3137 +2024-07-18 20:40:53,435 - pyskl - INFO - Epoch [71][2700/3746] lr: 5.448e-02, eta: 2 days, 18:03:59, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.5006, loss_cls: 4.3230, loss: 4.3230 +2024-07-18 20:42:15,180 - pyskl - INFO - Epoch [71][2800/3746] lr: 5.445e-02, eta: 2 days, 18:02:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4975, loss_cls: 4.3126, loss: 4.3126 +2024-07-18 20:43:37,121 - pyskl - INFO - Epoch [71][2900/3746] lr: 5.442e-02, eta: 2 days, 18:01:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4861, loss_cls: 4.3684, loss: 4.3684 +2024-07-18 20:44:58,805 - pyskl - INFO - Epoch [71][3000/3746] lr: 5.439e-02, eta: 2 days, 18:00:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.4977, loss_cls: 4.3370, loss: 4.3370 +2024-07-18 20:46:21,095 - pyskl - INFO - Epoch [71][3100/3746] lr: 5.436e-02, eta: 2 days, 17:58:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5009, loss_cls: 4.3184, loss: 4.3184 +2024-07-18 20:47:42,716 - pyskl - INFO - Epoch [71][3200/3746] lr: 5.434e-02, eta: 2 days, 17:57:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.4977, loss_cls: 4.2933, loss: 4.2933 +2024-07-18 20:49:04,990 - pyskl - INFO - Epoch [71][3300/3746] lr: 5.431e-02, eta: 2 days, 17:56:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5045, loss_cls: 4.2859, loss: 4.2859 +2024-07-18 20:50:26,728 - pyskl - INFO - Epoch [71][3400/3746] lr: 5.428e-02, eta: 2 days, 17:54:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2559, top5_acc: 0.5084, loss_cls: 4.3074, loss: 4.3074 +2024-07-18 20:51:49,157 - pyskl - INFO - Epoch [71][3500/3746] lr: 5.425e-02, eta: 2 days, 17:53:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4941, loss_cls: 4.3725, loss: 4.3725 +2024-07-18 20:53:10,686 - pyskl - INFO - Epoch [71][3600/3746] lr: 5.422e-02, eta: 2 days, 17:52:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5002, loss_cls: 4.3316, loss: 4.3316 +2024-07-18 20:54:32,880 - pyskl - INFO - Epoch [71][3700/3746] lr: 5.420e-02, eta: 2 days, 17:50:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2481, top5_acc: 0.4911, loss_cls: 4.3507, loss: 4.3507 +2024-07-18 20:55:12,172 - pyskl - INFO - Saving checkpoint at 71 epochs +2024-07-18 20:57:02,902 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 20:57:03,577 - pyskl - INFO - +top1_acc 0.2064 +top5_acc 0.4344 +2024-07-18 20:57:03,577 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 20:57:03,619 - pyskl - INFO - +mean_acc 0.2062 +2024-07-18 20:57:03,624 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_68.pth was removed +2024-07-18 20:57:03,865 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_71.pth. +2024-07-18 20:57:03,866 - pyskl - INFO - Best top1_acc is 0.2064 at 71 epoch. +2024-07-18 20:57:03,879 - pyskl - INFO - Epoch(val) [71][309] top1_acc: 0.2064, top5_acc: 0.4344, mean_class_accuracy: 0.2062 +2024-07-18 21:00:53,522 - pyskl - INFO - Epoch [72][100/3746] lr: 5.416e-02, eta: 2 days, 17:51:07, time: 2.296, data_time: 1.293, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5017, loss_cls: 4.3056, loss: 4.3056 +2024-07-18 21:02:15,364 - pyskl - INFO - Epoch [72][200/3746] lr: 5.413e-02, eta: 2 days, 17:49:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5186, loss_cls: 4.2400, loss: 4.2400 +2024-07-18 21:03:37,362 - pyskl - INFO - Epoch [72][300/3746] lr: 5.410e-02, eta: 2 days, 17:48:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2598, top5_acc: 0.5031, loss_cls: 4.2650, loss: 4.2650 +2024-07-18 21:04:59,866 - pyskl - INFO - Epoch [72][400/3746] lr: 5.407e-02, eta: 2 days, 17:47:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5089, loss_cls: 4.2861, loss: 4.2861 +2024-07-18 21:06:22,135 - pyskl - INFO - Epoch [72][500/3746] lr: 5.404e-02, eta: 2 days, 17:45:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5062, loss_cls: 4.2914, loss: 4.2914 +2024-07-18 21:07:43,986 - pyskl - INFO - Epoch [72][600/3746] lr: 5.402e-02, eta: 2 days, 17:44:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.5002, loss_cls: 4.3225, loss: 4.3225 +2024-07-18 21:09:06,033 - pyskl - INFO - Epoch [72][700/3746] lr: 5.399e-02, eta: 2 days, 17:43:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.4977, loss_cls: 4.3284, loss: 4.3284 +2024-07-18 21:10:28,066 - pyskl - INFO - Epoch [72][800/3746] lr: 5.396e-02, eta: 2 days, 17:42:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2587, top5_acc: 0.4947, loss_cls: 4.3170, loss: 4.3170 +2024-07-18 21:11:50,661 - pyskl - INFO - Epoch [72][900/3746] lr: 5.393e-02, eta: 2 days, 17:40:44, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4963, loss_cls: 4.3165, loss: 4.3165 +2024-07-18 21:13:12,996 - pyskl - INFO - Epoch [72][1000/3746] lr: 5.391e-02, eta: 2 days, 17:39:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2477, top5_acc: 0.4994, loss_cls: 4.3372, loss: 4.3372 +2024-07-18 21:14:35,104 - pyskl - INFO - Epoch [72][1100/3746] lr: 5.388e-02, eta: 2 days, 17:38:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4992, loss_cls: 4.3282, loss: 4.3282 +2024-07-18 21:15:57,000 - pyskl - INFO - Epoch [72][1200/3746] lr: 5.385e-02, eta: 2 days, 17:36:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5034, loss_cls: 4.3121, loss: 4.3121 +2024-07-18 21:17:19,036 - pyskl - INFO - Epoch [72][1300/3746] lr: 5.382e-02, eta: 2 days, 17:35:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2564, top5_acc: 0.5070, loss_cls: 4.2761, loss: 4.2761 +2024-07-18 21:18:40,982 - pyskl - INFO - Epoch [72][1400/3746] lr: 5.379e-02, eta: 2 days, 17:34:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.5045, loss_cls: 4.3166, loss: 4.3166 +2024-07-18 21:20:03,300 - pyskl - INFO - Epoch [72][1500/3746] lr: 5.377e-02, eta: 2 days, 17:32:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2455, top5_acc: 0.4920, loss_cls: 4.3837, loss: 4.3837 +2024-07-18 21:21:25,385 - pyskl - INFO - Epoch [72][1600/3746] lr: 5.374e-02, eta: 2 days, 17:31:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2472, top5_acc: 0.5048, loss_cls: 4.3458, loss: 4.3458 +2024-07-18 21:22:47,117 - pyskl - INFO - Epoch [72][1700/3746] lr: 5.371e-02, eta: 2 days, 17:30:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4994, loss_cls: 4.3475, loss: 4.3475 +2024-07-18 21:24:09,804 - pyskl - INFO - Epoch [72][1800/3746] lr: 5.368e-02, eta: 2 days, 17:29:02, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2558, top5_acc: 0.4928, loss_cls: 4.3321, loss: 4.3321 +2024-07-18 21:25:31,822 - pyskl - INFO - Epoch [72][1900/3746] lr: 5.365e-02, eta: 2 days, 17:27:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5048, loss_cls: 4.2842, loss: 4.2842 +2024-07-18 21:26:53,973 - pyskl - INFO - Epoch [72][2000/3746] lr: 5.363e-02, eta: 2 days, 17:26:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4898, loss_cls: 4.3548, loss: 4.3548 +2024-07-18 21:28:15,710 - pyskl - INFO - Epoch [72][2100/3746] lr: 5.360e-02, eta: 2 days, 17:25:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.4997, loss_cls: 4.3093, loss: 4.3093 +2024-07-18 21:29:38,558 - pyskl - INFO - Epoch [72][2200/3746] lr: 5.357e-02, eta: 2 days, 17:23:51, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.4986, loss_cls: 4.2983, loss: 4.2983 +2024-07-18 21:31:00,606 - pyskl - INFO - Epoch [72][2300/3746] lr: 5.354e-02, eta: 2 days, 17:22:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4922, loss_cls: 4.3208, loss: 4.3208 +2024-07-18 21:32:22,736 - pyskl - INFO - Epoch [72][2400/3746] lr: 5.352e-02, eta: 2 days, 17:21:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5078, loss_cls: 4.2971, loss: 4.2971 +2024-07-18 21:33:45,154 - pyskl - INFO - Epoch [72][2500/3746] lr: 5.349e-02, eta: 2 days, 17:19:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.4967, loss_cls: 4.2939, loss: 4.2939 +2024-07-18 21:35:06,781 - pyskl - INFO - Epoch [72][2600/3746] lr: 5.346e-02, eta: 2 days, 17:18:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5123, loss_cls: 4.2547, loss: 4.2547 +2024-07-18 21:36:28,727 - pyskl - INFO - Epoch [72][2700/3746] lr: 5.343e-02, eta: 2 days, 17:17:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4947, loss_cls: 4.3619, loss: 4.3619 +2024-07-18 21:37:50,402 - pyskl - INFO - Epoch [72][2800/3746] lr: 5.340e-02, eta: 2 days, 17:16:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5083, loss_cls: 4.2825, loss: 4.2825 +2024-07-18 21:39:12,427 - pyskl - INFO - Epoch [72][2900/3746] lr: 5.338e-02, eta: 2 days, 17:14:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2459, top5_acc: 0.4953, loss_cls: 4.3503, loss: 4.3503 +2024-07-18 21:40:34,775 - pyskl - INFO - Epoch [72][3000/3746] lr: 5.335e-02, eta: 2 days, 17:13:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5008, loss_cls: 4.3337, loss: 4.3337 +2024-07-18 21:41:56,520 - pyskl - INFO - Epoch [72][3100/3746] lr: 5.332e-02, eta: 2 days, 17:12:07, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5094, loss_cls: 4.2696, loss: 4.2696 +2024-07-18 21:43:18,599 - pyskl - INFO - Epoch [72][3200/3746] lr: 5.329e-02, eta: 2 days, 17:10:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2531, top5_acc: 0.5066, loss_cls: 4.2823, loss: 4.2823 +2024-07-18 21:44:40,455 - pyskl - INFO - Epoch [72][3300/3746] lr: 5.326e-02, eta: 2 days, 17:09:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2403, top5_acc: 0.4883, loss_cls: 4.3703, loss: 4.3703 +2024-07-18 21:46:02,516 - pyskl - INFO - Epoch [72][3400/3746] lr: 5.324e-02, eta: 2 days, 17:08:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2509, top5_acc: 0.4964, loss_cls: 4.3509, loss: 4.3509 +2024-07-18 21:47:24,923 - pyskl - INFO - Epoch [72][3500/3746] lr: 5.321e-02, eta: 2 days, 17:06:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5039, loss_cls: 4.2789, loss: 4.2789 +2024-07-18 21:48:46,947 - pyskl - INFO - Epoch [72][3600/3746] lr: 5.318e-02, eta: 2 days, 17:05:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5022, loss_cls: 4.3042, loss: 4.3042 +2024-07-18 21:50:08,818 - pyskl - INFO - Epoch [72][3700/3746] lr: 5.315e-02, eta: 2 days, 17:04:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2519, top5_acc: 0.5027, loss_cls: 4.3101, loss: 4.3101 +2024-07-18 21:50:48,585 - pyskl - INFO - Saving checkpoint at 72 epochs +2024-07-18 21:52:39,458 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 21:52:40,127 - pyskl - INFO - +top1_acc 0.2004 +top5_acc 0.4258 +2024-07-18 21:52:40,127 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 21:52:40,170 - pyskl - INFO - +mean_acc 0.2002 +2024-07-18 21:52:40,183 - pyskl - INFO - Epoch(val) [72][309] top1_acc: 0.2004, top5_acc: 0.4258, mean_class_accuracy: 0.2002 +2024-07-18 21:56:32,364 - pyskl - INFO - Epoch [73][100/3746] lr: 5.311e-02, eta: 2 days, 17:04:26, time: 2.322, data_time: 1.330, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5170, loss_cls: 4.2614, loss: 4.2614 +2024-07-18 21:57:54,685 - pyskl - INFO - Epoch [73][200/3746] lr: 5.308e-02, eta: 2 days, 17:03:08, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5089, loss_cls: 4.2763, loss: 4.2763 +2024-07-18 21:59:16,867 - pyskl - INFO - Epoch [73][300/3746] lr: 5.306e-02, eta: 2 days, 17:01:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5092, loss_cls: 4.2946, loss: 4.2946 +2024-07-18 22:00:38,895 - pyskl - INFO - Epoch [73][400/3746] lr: 5.303e-02, eta: 2 days, 17:00:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5052, loss_cls: 4.2905, loss: 4.2905 +2024-07-18 22:02:00,847 - pyskl - INFO - Epoch [73][500/3746] lr: 5.300e-02, eta: 2 days, 16:59:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5177, loss_cls: 4.2370, loss: 4.2370 +2024-07-18 22:03:22,559 - pyskl - INFO - Epoch [73][600/3746] lr: 5.297e-02, eta: 2 days, 16:57:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.4988, loss_cls: 4.3100, loss: 4.3100 +2024-07-18 22:04:44,308 - pyskl - INFO - Epoch [73][700/3746] lr: 5.294e-02, eta: 2 days, 16:56:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5169, loss_cls: 4.2609, loss: 4.2609 +2024-07-18 22:06:06,596 - pyskl - INFO - Epoch [73][800/3746] lr: 5.292e-02, eta: 2 days, 16:55:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2511, top5_acc: 0.5041, loss_cls: 4.3218, loss: 4.3218 +2024-07-18 22:07:29,213 - pyskl - INFO - Epoch [73][900/3746] lr: 5.289e-02, eta: 2 days, 16:54:01, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5089, loss_cls: 4.2523, loss: 4.2523 +2024-07-18 22:08:51,263 - pyskl - INFO - Epoch [73][1000/3746] lr: 5.286e-02, eta: 2 days, 16:52:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.5086, loss_cls: 4.2907, loss: 4.2907 +2024-07-18 22:10:14,079 - pyskl - INFO - Epoch [73][1100/3746] lr: 5.283e-02, eta: 2 days, 16:51:25, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2445, top5_acc: 0.4905, loss_cls: 4.3542, loss: 4.3542 +2024-07-18 22:11:36,102 - pyskl - INFO - Epoch [73][1200/3746] lr: 5.280e-02, eta: 2 days, 16:50:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.5005, loss_cls: 4.3246, loss: 4.3246 +2024-07-18 22:12:57,898 - pyskl - INFO - Epoch [73][1300/3746] lr: 5.278e-02, eta: 2 days, 16:48:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.4986, loss_cls: 4.3162, loss: 4.3162 +2024-07-18 22:14:19,577 - pyskl - INFO - Epoch [73][1400/3746] lr: 5.275e-02, eta: 2 days, 16:47:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2550, top5_acc: 0.4984, loss_cls: 4.3038, loss: 4.3038 +2024-07-18 22:15:41,790 - pyskl - INFO - Epoch [73][1500/3746] lr: 5.272e-02, eta: 2 days, 16:46:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5048, loss_cls: 4.3082, loss: 4.3082 +2024-07-18 22:17:04,230 - pyskl - INFO - Epoch [73][1600/3746] lr: 5.269e-02, eta: 2 days, 16:44:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5023, loss_cls: 4.2835, loss: 4.2835 +2024-07-18 22:18:26,057 - pyskl - INFO - Epoch [73][1700/3746] lr: 5.267e-02, eta: 2 days, 16:43:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2534, top5_acc: 0.4891, loss_cls: 4.3646, loss: 4.3646 +2024-07-18 22:19:48,729 - pyskl - INFO - Epoch [73][1800/3746] lr: 5.264e-02, eta: 2 days, 16:42:18, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5052, loss_cls: 4.2792, loss: 4.2792 +2024-07-18 22:21:11,144 - pyskl - INFO - Epoch [73][1900/3746] lr: 5.261e-02, eta: 2 days, 16:41:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.4894, loss_cls: 4.3321, loss: 4.3321 +2024-07-18 22:22:33,853 - pyskl - INFO - Epoch [73][2000/3746] lr: 5.258e-02, eta: 2 days, 16:39:43, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5141, loss_cls: 4.2919, loss: 4.2919 +2024-07-18 22:23:55,908 - pyskl - INFO - Epoch [73][2100/3746] lr: 5.255e-02, eta: 2 days, 16:38:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5027, loss_cls: 4.2802, loss: 4.2802 +2024-07-18 22:25:19,021 - pyskl - INFO - Epoch [73][2200/3746] lr: 5.253e-02, eta: 2 days, 16:37:07, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5091, loss_cls: 4.2975, loss: 4.2975 +2024-07-18 22:26:41,245 - pyskl - INFO - Epoch [73][2300/3746] lr: 5.250e-02, eta: 2 days, 16:35:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2522, top5_acc: 0.5047, loss_cls: 4.3353, loss: 4.3353 +2024-07-18 22:28:03,731 - pyskl - INFO - Epoch [73][2400/3746] lr: 5.247e-02, eta: 2 days, 16:34:31, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2541, top5_acc: 0.4923, loss_cls: 4.3385, loss: 4.3385 +2024-07-18 22:29:26,554 - pyskl - INFO - Epoch [73][2500/3746] lr: 5.244e-02, eta: 2 days, 16:33:14, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.4989, loss_cls: 4.3014, loss: 4.3014 +2024-07-18 22:30:48,804 - pyskl - INFO - Epoch [73][2600/3746] lr: 5.241e-02, eta: 2 days, 16:31:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2505, top5_acc: 0.5020, loss_cls: 4.3408, loss: 4.3408 +2024-07-18 22:32:11,192 - pyskl - INFO - Epoch [73][2700/3746] lr: 5.239e-02, eta: 2 days, 16:30:38, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2567, top5_acc: 0.5092, loss_cls: 4.2997, loss: 4.2997 +2024-07-18 22:33:33,135 - pyskl - INFO - Epoch [73][2800/3746] lr: 5.236e-02, eta: 2 days, 16:29:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5064, loss_cls: 4.3012, loss: 4.3012 +2024-07-18 22:34:55,257 - pyskl - INFO - Epoch [73][2900/3746] lr: 5.233e-02, eta: 2 days, 16:28:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2525, top5_acc: 0.5075, loss_cls: 4.3285, loss: 4.3285 +2024-07-18 22:36:17,161 - pyskl - INFO - Epoch [73][3000/3746] lr: 5.230e-02, eta: 2 days, 16:26:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5039, loss_cls: 4.2934, loss: 4.2934 +2024-07-18 22:37:38,690 - pyskl - INFO - Epoch [73][3100/3746] lr: 5.227e-02, eta: 2 days, 16:25:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2520, top5_acc: 0.4986, loss_cls: 4.3260, loss: 4.3260 +2024-07-18 22:39:00,707 - pyskl - INFO - Epoch [73][3200/3746] lr: 5.225e-02, eta: 2 days, 16:24:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5036, loss_cls: 4.2926, loss: 4.2926 +2024-07-18 22:40:22,666 - pyskl - INFO - Epoch [73][3300/3746] lr: 5.222e-02, eta: 2 days, 16:22:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5044, loss_cls: 4.2733, loss: 4.2733 +2024-07-18 22:41:44,671 - pyskl - INFO - Epoch [73][3400/3746] lr: 5.219e-02, eta: 2 days, 16:21:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.4942, loss_cls: 4.3331, loss: 4.3331 +2024-07-18 22:43:06,864 - pyskl - INFO - Epoch [73][3500/3746] lr: 5.216e-02, eta: 2 days, 16:20:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2528, top5_acc: 0.5103, loss_cls: 4.2671, loss: 4.2671 +2024-07-18 22:44:28,842 - pyskl - INFO - Epoch [73][3600/3746] lr: 5.213e-02, eta: 2 days, 16:18:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2420, top5_acc: 0.4859, loss_cls: 4.3778, loss: 4.3778 +2024-07-18 22:45:50,194 - pyskl - INFO - Epoch [73][3700/3746] lr: 5.211e-02, eta: 2 days, 16:17:33, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5000, loss_cls: 4.3001, loss: 4.3001 +2024-07-18 22:46:29,593 - pyskl - INFO - Saving checkpoint at 73 epochs +2024-07-18 22:48:19,493 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 22:48:20,155 - pyskl - INFO - +top1_acc 0.2001 +top5_acc 0.4285 +2024-07-18 22:48:20,155 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 22:48:20,198 - pyskl - INFO - +mean_acc 0.1998 +2024-07-18 22:48:20,210 - pyskl - INFO - Epoch(val) [73][309] top1_acc: 0.2001, top5_acc: 0.4285, mean_class_accuracy: 0.1998 +2024-07-18 22:52:05,050 - pyskl - INFO - Epoch [74][100/3746] lr: 5.207e-02, eta: 2 days, 16:17:30, time: 2.248, data_time: 1.263, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5153, loss_cls: 4.2522, loss: 4.2522 +2024-07-18 22:53:27,566 - pyskl - INFO - Epoch [74][200/3746] lr: 5.204e-02, eta: 2 days, 16:16:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5100, loss_cls: 4.2491, loss: 4.2491 +2024-07-18 22:54:49,472 - pyskl - INFO - Epoch [74][300/3746] lr: 5.201e-02, eta: 2 days, 16:14:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5069, loss_cls: 4.2847, loss: 4.2847 +2024-07-18 22:56:11,876 - pyskl - INFO - Epoch [74][400/3746] lr: 5.198e-02, eta: 2 days, 16:13:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5122, loss_cls: 4.2767, loss: 4.2767 +2024-07-18 22:57:34,249 - pyskl - INFO - Epoch [74][500/3746] lr: 5.195e-02, eta: 2 days, 16:12:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5028, loss_cls: 4.2770, loss: 4.2770 +2024-07-18 22:58:55,837 - pyskl - INFO - Epoch [74][600/3746] lr: 5.193e-02, eta: 2 days, 16:10:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5138, loss_cls: 4.2728, loss: 4.2728 +2024-07-18 23:00:17,831 - pyskl - INFO - Epoch [74][700/3746] lr: 5.190e-02, eta: 2 days, 16:09:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.4975, loss_cls: 4.3198, loss: 4.3198 +2024-07-18 23:01:40,307 - pyskl - INFO - Epoch [74][800/3746] lr: 5.187e-02, eta: 2 days, 16:08:22, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5178, loss_cls: 4.2209, loss: 4.2209 +2024-07-18 23:03:03,137 - pyskl - INFO - Epoch [74][900/3746] lr: 5.184e-02, eta: 2 days, 16:07:04, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5025, loss_cls: 4.2831, loss: 4.2831 +2024-07-18 23:04:25,681 - pyskl - INFO - Epoch [74][1000/3746] lr: 5.181e-02, eta: 2 days, 16:05:46, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2506, top5_acc: 0.5006, loss_cls: 4.3366, loss: 4.3366 +2024-07-18 23:05:48,217 - pyskl - INFO - Epoch [74][1100/3746] lr: 5.179e-02, eta: 2 days, 16:04:28, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.4969, loss_cls: 4.3170, loss: 4.3170 +2024-07-18 23:07:10,841 - pyskl - INFO - Epoch [74][1200/3746] lr: 5.176e-02, eta: 2 days, 16:03:11, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.4942, loss_cls: 4.3132, loss: 4.3132 +2024-07-18 23:08:32,751 - pyskl - INFO - Epoch [74][1300/3746] lr: 5.173e-02, eta: 2 days, 16:01:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5081, loss_cls: 4.2737, loss: 4.2737 +2024-07-18 23:09:54,942 - pyskl - INFO - Epoch [74][1400/3746] lr: 5.170e-02, eta: 2 days, 16:00:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5084, loss_cls: 4.2718, loss: 4.2718 +2024-07-18 23:11:17,401 - pyskl - INFO - Epoch [74][1500/3746] lr: 5.168e-02, eta: 2 days, 15:59:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5164, loss_cls: 4.2498, loss: 4.2498 +2024-07-18 23:12:39,302 - pyskl - INFO - Epoch [74][1600/3746] lr: 5.165e-02, eta: 2 days, 15:57:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2489, top5_acc: 0.4866, loss_cls: 4.3640, loss: 4.3640 +2024-07-18 23:14:01,149 - pyskl - INFO - Epoch [74][1700/3746] lr: 5.162e-02, eta: 2 days, 15:56:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5089, loss_cls: 4.2639, loss: 4.2639 +2024-07-18 23:15:23,059 - pyskl - INFO - Epoch [74][1800/3746] lr: 5.159e-02, eta: 2 days, 15:55:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.4964, loss_cls: 4.3025, loss: 4.3025 +2024-07-18 23:16:45,302 - pyskl - INFO - Epoch [74][1900/3746] lr: 5.156e-02, eta: 2 days, 15:54:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4952, loss_cls: 4.3335, loss: 4.3335 +2024-07-18 23:18:07,978 - pyskl - INFO - Epoch [74][2000/3746] lr: 5.154e-02, eta: 2 days, 15:52:44, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2570, top5_acc: 0.5083, loss_cls: 4.2936, loss: 4.2936 +2024-07-18 23:19:30,041 - pyskl - INFO - Epoch [74][2100/3746] lr: 5.151e-02, eta: 2 days, 15:51:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2530, top5_acc: 0.4930, loss_cls: 4.3261, loss: 4.3261 +2024-07-18 23:20:52,706 - pyskl - INFO - Epoch [74][2200/3746] lr: 5.148e-02, eta: 2 days, 15:50:08, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5127, loss_cls: 4.2518, loss: 4.2518 +2024-07-18 23:22:15,158 - pyskl - INFO - Epoch [74][2300/3746] lr: 5.145e-02, eta: 2 days, 15:48:50, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5000, loss_cls: 4.3036, loss: 4.3036 +2024-07-18 23:23:37,575 - pyskl - INFO - Epoch [74][2400/3746] lr: 5.142e-02, eta: 2 days, 15:47:32, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5062, loss_cls: 4.2751, loss: 4.2751 +2024-07-18 23:24:59,993 - pyskl - INFO - Epoch [74][2500/3746] lr: 5.140e-02, eta: 2 days, 15:46:13, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2566, top5_acc: 0.5009, loss_cls: 4.2934, loss: 4.2934 +2024-07-18 23:26:22,110 - pyskl - INFO - Epoch [74][2600/3746] lr: 5.137e-02, eta: 2 days, 15:44:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5064, loss_cls: 4.2468, loss: 4.2468 +2024-07-18 23:27:44,342 - pyskl - INFO - Epoch [74][2700/3746] lr: 5.134e-02, eta: 2 days, 15:43:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2539, top5_acc: 0.4988, loss_cls: 4.3187, loss: 4.3187 +2024-07-18 23:29:06,215 - pyskl - INFO - Epoch [74][2800/3746] lr: 5.131e-02, eta: 2 days, 15:42:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2516, top5_acc: 0.4991, loss_cls: 4.3195, loss: 4.3195 +2024-07-18 23:30:28,432 - pyskl - INFO - Epoch [74][2900/3746] lr: 5.128e-02, eta: 2 days, 15:41:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5028, loss_cls: 4.2664, loss: 4.2664 +2024-07-18 23:31:50,473 - pyskl - INFO - Epoch [74][3000/3746] lr: 5.126e-02, eta: 2 days, 15:39:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5070, loss_cls: 4.2756, loss: 4.2756 +2024-07-18 23:33:12,142 - pyskl - INFO - Epoch [74][3100/3746] lr: 5.123e-02, eta: 2 days, 15:38:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2514, top5_acc: 0.4952, loss_cls: 4.3351, loss: 4.3351 +2024-07-18 23:34:34,301 - pyskl - INFO - Epoch [74][3200/3746] lr: 5.120e-02, eta: 2 days, 15:37:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2500, top5_acc: 0.4952, loss_cls: 4.3274, loss: 4.3274 +2024-07-18 23:35:55,907 - pyskl - INFO - Epoch [74][3300/3746] lr: 5.117e-02, eta: 2 days, 15:35:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.5008, loss_cls: 4.3019, loss: 4.3019 +2024-07-18 23:37:17,743 - pyskl - INFO - Epoch [74][3400/3746] lr: 5.114e-02, eta: 2 days, 15:34:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2577, top5_acc: 0.5047, loss_cls: 4.2783, loss: 4.2783 +2024-07-18 23:38:39,914 - pyskl - INFO - Epoch [74][3500/3746] lr: 5.112e-02, eta: 2 days, 15:33:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5036, loss_cls: 4.2831, loss: 4.2831 +2024-07-18 23:40:01,625 - pyskl - INFO - Epoch [74][3600/3746] lr: 5.109e-02, eta: 2 days, 15:31:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5089, loss_cls: 4.2915, loss: 4.2915 +2024-07-18 23:41:23,271 - pyskl - INFO - Epoch [74][3700/3746] lr: 5.106e-02, eta: 2 days, 15:30:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5006, loss_cls: 4.3013, loss: 4.3013 +2024-07-18 23:42:02,731 - pyskl - INFO - Saving checkpoint at 74 epochs +2024-07-18 23:43:54,451 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-18 23:43:55,130 - pyskl - INFO - +top1_acc 0.1866 +top5_acc 0.3944 +2024-07-18 23:43:55,130 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-18 23:43:55,172 - pyskl - INFO - +mean_acc 0.1863 +2024-07-18 23:43:55,183 - pyskl - INFO - Epoch(val) [74][309] top1_acc: 0.1866, top5_acc: 0.3944, mean_class_accuracy: 0.1863 +2024-07-18 23:47:41,991 - pyskl - INFO - Epoch [75][100/3746] lr: 5.102e-02, eta: 2 days, 15:30:26, time: 2.268, data_time: 1.280, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5217, loss_cls: 4.2239, loss: 4.2239 +2024-07-18 23:49:04,138 - pyskl - INFO - Epoch [75][200/3746] lr: 5.099e-02, eta: 2 days, 15:29:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5048, loss_cls: 4.2500, loss: 4.2500 +2024-07-18 23:50:25,868 - pyskl - INFO - Epoch [75][300/3746] lr: 5.096e-02, eta: 2 days, 15:27:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5030, loss_cls: 4.2648, loss: 4.2648 +2024-07-18 23:51:47,899 - pyskl - INFO - Epoch [75][400/3746] lr: 5.094e-02, eta: 2 days, 15:26:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5175, loss_cls: 4.2223, loss: 4.2223 +2024-07-18 23:53:09,620 - pyskl - INFO - Epoch [75][500/3746] lr: 5.091e-02, eta: 2 days, 15:25:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.4959, loss_cls: 4.3364, loss: 4.3364 +2024-07-18 23:54:31,552 - pyskl - INFO - Epoch [75][600/3746] lr: 5.088e-02, eta: 2 days, 15:23:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5150, loss_cls: 4.2378, loss: 4.2378 +2024-07-18 23:55:53,489 - pyskl - INFO - Epoch [75][700/3746] lr: 5.085e-02, eta: 2 days, 15:22:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5125, loss_cls: 4.2508, loss: 4.2508 +2024-07-18 23:57:15,591 - pyskl - INFO - Epoch [75][800/3746] lr: 5.082e-02, eta: 2 days, 15:21:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5106, loss_cls: 4.2730, loss: 4.2730 +2024-07-18 23:58:37,771 - pyskl - INFO - Epoch [75][900/3746] lr: 5.080e-02, eta: 2 days, 15:19:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5175, loss_cls: 4.2688, loss: 4.2688 +2024-07-19 00:00:00,359 - pyskl - INFO - Epoch [75][1000/3746] lr: 5.077e-02, eta: 2 days, 15:18:38, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2584, top5_acc: 0.5127, loss_cls: 4.2912, loss: 4.2912 +2024-07-19 00:01:22,534 - pyskl - INFO - Epoch [75][1100/3746] lr: 5.074e-02, eta: 2 days, 15:17:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2561, top5_acc: 0.4978, loss_cls: 4.3159, loss: 4.3159 +2024-07-19 00:02:44,533 - pyskl - INFO - Epoch [75][1200/3746] lr: 5.071e-02, eta: 2 days, 15:16:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2544, top5_acc: 0.5025, loss_cls: 4.2740, loss: 4.2740 +2024-07-19 00:04:06,940 - pyskl - INFO - Epoch [75][1300/3746] lr: 5.068e-02, eta: 2 days, 15:14:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.5019, loss_cls: 4.2996, loss: 4.2996 +2024-07-19 00:05:29,243 - pyskl - INFO - Epoch [75][1400/3746] lr: 5.066e-02, eta: 2 days, 15:13:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.4955, loss_cls: 4.2993, loss: 4.2993 +2024-07-19 00:06:51,232 - pyskl - INFO - Epoch [75][1500/3746] lr: 5.063e-02, eta: 2 days, 15:12:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.4967, loss_cls: 4.3192, loss: 4.3192 +2024-07-19 00:08:13,216 - pyskl - INFO - Epoch [75][1600/3746] lr: 5.060e-02, eta: 2 days, 15:10:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5138, loss_cls: 4.2420, loss: 4.2420 +2024-07-19 00:09:36,167 - pyskl - INFO - Epoch [75][1700/3746] lr: 5.057e-02, eta: 2 days, 15:09:29, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5094, loss_cls: 4.2626, loss: 4.2626 +2024-07-19 00:10:58,564 - pyskl - INFO - Epoch [75][1800/3746] lr: 5.054e-02, eta: 2 days, 15:08:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2536, top5_acc: 0.5030, loss_cls: 4.3166, loss: 4.3166 +2024-07-19 00:12:21,258 - pyskl - INFO - Epoch [75][1900/3746] lr: 5.052e-02, eta: 2 days, 15:06:53, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5102, loss_cls: 4.2924, loss: 4.2924 +2024-07-19 00:13:43,326 - pyskl - INFO - Epoch [75][2000/3746] lr: 5.049e-02, eta: 2 days, 15:05:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2594, top5_acc: 0.5042, loss_cls: 4.2839, loss: 4.2839 +2024-07-19 00:15:04,884 - pyskl - INFO - Epoch [75][2100/3746] lr: 5.046e-02, eta: 2 days, 15:04:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5067, loss_cls: 4.3027, loss: 4.3027 +2024-07-19 00:16:27,423 - pyskl - INFO - Epoch [75][2200/3746] lr: 5.043e-02, eta: 2 days, 15:02:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5089, loss_cls: 4.2796, loss: 4.2796 +2024-07-19 00:17:49,919 - pyskl - INFO - Epoch [75][2300/3746] lr: 5.040e-02, eta: 2 days, 15:01:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5144, loss_cls: 4.2991, loss: 4.2991 +2024-07-19 00:19:11,969 - pyskl - INFO - Epoch [75][2400/3746] lr: 5.038e-02, eta: 2 days, 15:00:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5020, loss_cls: 4.3114, loss: 4.3114 +2024-07-19 00:20:34,135 - pyskl - INFO - Epoch [75][2500/3746] lr: 5.035e-02, eta: 2 days, 14:59:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5127, loss_cls: 4.2690, loss: 4.2690 +2024-07-19 00:21:56,067 - pyskl - INFO - Epoch [75][2600/3746] lr: 5.032e-02, eta: 2 days, 14:57:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5083, loss_cls: 4.2682, loss: 4.2682 +2024-07-19 00:23:17,903 - pyskl - INFO - Epoch [75][2700/3746] lr: 5.029e-02, eta: 2 days, 14:56:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5153, loss_cls: 4.2447, loss: 4.2447 +2024-07-19 00:24:39,524 - pyskl - INFO - Epoch [75][2800/3746] lr: 5.026e-02, eta: 2 days, 14:55:05, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2578, top5_acc: 0.5070, loss_cls: 4.2766, loss: 4.2766 +2024-07-19 00:26:01,542 - pyskl - INFO - Epoch [75][2900/3746] lr: 5.024e-02, eta: 2 days, 14:53:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5077, loss_cls: 4.2736, loss: 4.2736 +2024-07-19 00:27:23,266 - pyskl - INFO - Epoch [75][3000/3746] lr: 5.021e-02, eta: 2 days, 14:52:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5123, loss_cls: 4.2595, loss: 4.2595 +2024-07-19 00:28:45,180 - pyskl - INFO - Epoch [75][3100/3746] lr: 5.018e-02, eta: 2 days, 14:51:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5056, loss_cls: 4.3060, loss: 4.3060 +2024-07-19 00:30:06,900 - pyskl - INFO - Epoch [75][3200/3746] lr: 5.015e-02, eta: 2 days, 14:49:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5148, loss_cls: 4.2521, loss: 4.2521 +2024-07-19 00:31:28,482 - pyskl - INFO - Epoch [75][3300/3746] lr: 5.012e-02, eta: 2 days, 14:48:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5081, loss_cls: 4.2982, loss: 4.2982 +2024-07-19 00:32:51,007 - pyskl - INFO - Epoch [75][3400/3746] lr: 5.010e-02, eta: 2 days, 14:47:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5066, loss_cls: 4.2829, loss: 4.2829 +2024-07-19 00:34:12,994 - pyskl - INFO - Epoch [75][3500/3746] lr: 5.007e-02, eta: 2 days, 14:45:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5136, loss_cls: 4.2691, loss: 4.2691 +2024-07-19 00:35:35,725 - pyskl - INFO - Epoch [75][3600/3746] lr: 5.004e-02, eta: 2 days, 14:44:36, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2580, top5_acc: 0.5044, loss_cls: 4.3079, loss: 4.3079 +2024-07-19 00:36:58,318 - pyskl - INFO - Epoch [75][3700/3746] lr: 5.001e-02, eta: 2 days, 14:43:18, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5045, loss_cls: 4.2666, loss: 4.2666 +2024-07-19 00:37:38,099 - pyskl - INFO - Saving checkpoint at 75 epochs +2024-07-19 00:39:30,781 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 00:39:31,452 - pyskl - INFO - +top1_acc 0.1976 +top5_acc 0.4171 +2024-07-19 00:39:31,452 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 00:39:31,498 - pyskl - INFO - +mean_acc 0.1974 +2024-07-19 00:39:31,511 - pyskl - INFO - Epoch(val) [75][309] top1_acc: 0.1976, top5_acc: 0.4171, mean_class_accuracy: 0.1974 +2024-07-19 00:43:21,232 - pyskl - INFO - Epoch [76][100/3746] lr: 4.997e-02, eta: 2 days, 14:43:13, time: 2.297, data_time: 1.275, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5108, loss_cls: 4.2362, loss: 4.2362 +2024-07-19 00:44:43,895 - pyskl - INFO - Epoch [76][200/3746] lr: 4.994e-02, eta: 2 days, 14:41:55, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5203, loss_cls: 4.1975, loss: 4.1975 +2024-07-19 00:46:05,541 - pyskl - INFO - Epoch [76][300/3746] lr: 4.992e-02, eta: 2 days, 14:40:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5108, loss_cls: 4.2517, loss: 4.2517 +2024-07-19 00:47:27,091 - pyskl - INFO - Epoch [76][400/3746] lr: 4.989e-02, eta: 2 days, 14:39:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5120, loss_cls: 4.2424, loss: 4.2424 +2024-07-19 00:48:48,788 - pyskl - INFO - Epoch [76][500/3746] lr: 4.986e-02, eta: 2 days, 14:37:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5109, loss_cls: 4.2655, loss: 4.2655 +2024-07-19 00:50:10,343 - pyskl - INFO - Epoch [76][600/3746] lr: 4.983e-02, eta: 2 days, 14:36:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2581, top5_acc: 0.5044, loss_cls: 4.2860, loss: 4.2860 +2024-07-19 00:51:32,201 - pyskl - INFO - Epoch [76][700/3746] lr: 4.980e-02, eta: 2 days, 14:35:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5041, loss_cls: 4.2551, loss: 4.2551 +2024-07-19 00:52:54,398 - pyskl - INFO - Epoch [76][800/3746] lr: 4.978e-02, eta: 2 days, 14:34:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5062, loss_cls: 4.2910, loss: 4.2910 +2024-07-19 00:54:16,469 - pyskl - INFO - Epoch [76][900/3746] lr: 4.975e-02, eta: 2 days, 14:32:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2656, top5_acc: 0.5167, loss_cls: 4.2472, loss: 4.2472 +2024-07-19 00:55:38,900 - pyskl - INFO - Epoch [76][1000/3746] lr: 4.972e-02, eta: 2 days, 14:31:23, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2527, top5_acc: 0.5125, loss_cls: 4.2836, loss: 4.2836 +2024-07-19 00:57:00,924 - pyskl - INFO - Epoch [76][1100/3746] lr: 4.969e-02, eta: 2 days, 14:30:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2562, top5_acc: 0.4956, loss_cls: 4.3082, loss: 4.3082 +2024-07-19 00:58:22,982 - pyskl - INFO - Epoch [76][1200/3746] lr: 4.966e-02, eta: 2 days, 14:28:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5119, loss_cls: 4.2521, loss: 4.2521 +2024-07-19 00:59:44,584 - pyskl - INFO - Epoch [76][1300/3746] lr: 4.964e-02, eta: 2 days, 14:27:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2537, top5_acc: 0.5000, loss_cls: 4.3099, loss: 4.3099 +2024-07-19 01:01:06,602 - pyskl - INFO - Epoch [76][1400/3746] lr: 4.961e-02, eta: 2 days, 14:26:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5147, loss_cls: 4.2433, loss: 4.2433 +2024-07-19 01:02:28,321 - pyskl - INFO - Epoch [76][1500/3746] lr: 4.958e-02, eta: 2 days, 14:24:49, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5047, loss_cls: 4.2699, loss: 4.2699 +2024-07-19 01:03:50,098 - pyskl - INFO - Epoch [76][1600/3746] lr: 4.955e-02, eta: 2 days, 14:23:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5109, loss_cls: 4.2435, loss: 4.2435 +2024-07-19 01:05:13,115 - pyskl - INFO - Epoch [76][1700/3746] lr: 4.953e-02, eta: 2 days, 14:22:12, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5120, loss_cls: 4.2613, loss: 4.2613 +2024-07-19 01:06:35,106 - pyskl - INFO - Epoch [76][1800/3746] lr: 4.950e-02, eta: 2 days, 14:20:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2512, top5_acc: 0.4981, loss_cls: 4.3286, loss: 4.3286 +2024-07-19 01:07:57,629 - pyskl - INFO - Epoch [76][1900/3746] lr: 4.947e-02, eta: 2 days, 14:19:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2495, top5_acc: 0.5006, loss_cls: 4.3200, loss: 4.3200 +2024-07-19 01:09:19,807 - pyskl - INFO - Epoch [76][2000/3746] lr: 4.944e-02, eta: 2 days, 14:18:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5066, loss_cls: 4.2698, loss: 4.2698 +2024-07-19 01:10:42,049 - pyskl - INFO - Epoch [76][2100/3746] lr: 4.941e-02, eta: 2 days, 14:16:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2717, top5_acc: 0.5130, loss_cls: 4.2345, loss: 4.2345 +2024-07-19 01:12:04,097 - pyskl - INFO - Epoch [76][2200/3746] lr: 4.939e-02, eta: 2 days, 14:15:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2573, top5_acc: 0.5098, loss_cls: 4.2616, loss: 4.2616 +2024-07-19 01:13:26,992 - pyskl - INFO - Epoch [76][2300/3746] lr: 4.936e-02, eta: 2 days, 14:14:21, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5064, loss_cls: 4.2724, loss: 4.2724 +2024-07-19 01:14:49,487 - pyskl - INFO - Epoch [76][2400/3746] lr: 4.933e-02, eta: 2 days, 14:13:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.4983, loss_cls: 4.3110, loss: 4.3110 +2024-07-19 01:16:11,577 - pyskl - INFO - Epoch [76][2500/3746] lr: 4.930e-02, eta: 2 days, 14:11:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5130, loss_cls: 4.2651, loss: 4.2651 +2024-07-19 01:17:33,604 - pyskl - INFO - Epoch [76][2600/3746] lr: 4.927e-02, eta: 2 days, 14:10:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5167, loss_cls: 4.2419, loss: 4.2419 +2024-07-19 01:18:55,705 - pyskl - INFO - Epoch [76][2700/3746] lr: 4.925e-02, eta: 2 days, 14:09:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2597, top5_acc: 0.5109, loss_cls: 4.2676, loss: 4.2676 +2024-07-19 01:20:18,163 - pyskl - INFO - Epoch [76][2800/3746] lr: 4.922e-02, eta: 2 days, 14:07:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5095, loss_cls: 4.2817, loss: 4.2817 +2024-07-19 01:21:40,005 - pyskl - INFO - Epoch [76][2900/3746] lr: 4.919e-02, eta: 2 days, 14:06:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5058, loss_cls: 4.2705, loss: 4.2705 +2024-07-19 01:23:01,874 - pyskl - INFO - Epoch [76][3000/3746] lr: 4.916e-02, eta: 2 days, 14:05:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5089, loss_cls: 4.2675, loss: 4.2675 +2024-07-19 01:24:23,694 - pyskl - INFO - Epoch [76][3100/3746] lr: 4.913e-02, eta: 2 days, 14:03:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2572, top5_acc: 0.4930, loss_cls: 4.3214, loss: 4.3214 +2024-07-19 01:25:45,615 - pyskl - INFO - Epoch [76][3200/3746] lr: 4.911e-02, eta: 2 days, 14:02:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5152, loss_cls: 4.2445, loss: 4.2445 +2024-07-19 01:27:07,193 - pyskl - INFO - Epoch [76][3300/3746] lr: 4.908e-02, eta: 2 days, 14:01:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2556, top5_acc: 0.5041, loss_cls: 4.2925, loss: 4.2925 +2024-07-19 01:28:29,575 - pyskl - INFO - Epoch [76][3400/3746] lr: 4.905e-02, eta: 2 days, 13:59:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5216, loss_cls: 4.2183, loss: 4.2183 +2024-07-19 01:29:52,062 - pyskl - INFO - Epoch [76][3500/3746] lr: 4.902e-02, eta: 2 days, 13:58:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5005, loss_cls: 4.3172, loss: 4.3172 +2024-07-19 01:31:14,169 - pyskl - INFO - Epoch [76][3600/3746] lr: 4.899e-02, eta: 2 days, 13:57:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2586, top5_acc: 0.5027, loss_cls: 4.2830, loss: 4.2830 +2024-07-19 01:32:36,077 - pyskl - INFO - Epoch [76][3700/3746] lr: 4.897e-02, eta: 2 days, 13:55:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5133, loss_cls: 4.2662, loss: 4.2662 +2024-07-19 01:33:15,867 - pyskl - INFO - Saving checkpoint at 76 epochs +2024-07-19 01:35:07,112 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 01:35:07,774 - pyskl - INFO - +top1_acc 0.2072 +top5_acc 0.4373 +2024-07-19 01:35:07,774 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 01:35:07,816 - pyskl - INFO - +mean_acc 0.2069 +2024-07-19 01:35:07,821 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_71.pth was removed +2024-07-19 01:35:08,070 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_76.pth. +2024-07-19 01:35:08,071 - pyskl - INFO - Best top1_acc is 0.2072 at 76 epoch. +2024-07-19 01:35:08,083 - pyskl - INFO - Epoch(val) [76][309] top1_acc: 0.2072, top5_acc: 0.4373, mean_class_accuracy: 0.2069 +2024-07-19 01:38:55,851 - pyskl - INFO - Epoch [77][100/3746] lr: 4.893e-02, eta: 2 days, 13:55:48, time: 2.278, data_time: 1.296, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5239, loss_cls: 4.2030, loss: 4.2030 +2024-07-19 01:40:17,864 - pyskl - INFO - Epoch [77][200/3746] lr: 4.890e-02, eta: 2 days, 13:54:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5102, loss_cls: 4.2356, loss: 4.2356 +2024-07-19 01:41:39,698 - pyskl - INFO - Epoch [77][300/3746] lr: 4.887e-02, eta: 2 days, 13:53:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5261, loss_cls: 4.1910, loss: 4.1910 +2024-07-19 01:43:01,540 - pyskl - INFO - Epoch [77][400/3746] lr: 4.884e-02, eta: 2 days, 13:51:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5100, loss_cls: 4.2505, loss: 4.2505 +2024-07-19 01:44:23,651 - pyskl - INFO - Epoch [77][500/3746] lr: 4.881e-02, eta: 2 days, 13:50:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5025, loss_cls: 4.2812, loss: 4.2812 +2024-07-19 01:45:45,952 - pyskl - INFO - Epoch [77][600/3746] lr: 4.879e-02, eta: 2 days, 13:49:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5206, loss_cls: 4.1890, loss: 4.1890 +2024-07-19 01:47:07,506 - pyskl - INFO - Epoch [77][700/3746] lr: 4.876e-02, eta: 2 days, 13:47:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5081, loss_cls: 4.2520, loss: 4.2520 +2024-07-19 01:48:30,556 - pyskl - INFO - Epoch [77][800/3746] lr: 4.873e-02, eta: 2 days, 13:46:35, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2620, top5_acc: 0.5086, loss_cls: 4.2676, loss: 4.2676 +2024-07-19 01:49:52,375 - pyskl - INFO - Epoch [77][900/3746] lr: 4.870e-02, eta: 2 days, 13:45:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5138, loss_cls: 4.2466, loss: 4.2466 +2024-07-19 01:51:14,778 - pyskl - INFO - Epoch [77][1000/3746] lr: 4.867e-02, eta: 2 days, 13:43:58, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2655, top5_acc: 0.5233, loss_cls: 4.2012, loss: 4.2012 +2024-07-19 01:52:36,870 - pyskl - INFO - Epoch [77][1100/3746] lr: 4.865e-02, eta: 2 days, 13:42:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5114, loss_cls: 4.2761, loss: 4.2761 +2024-07-19 01:53:58,440 - pyskl - INFO - Epoch [77][1200/3746] lr: 4.862e-02, eta: 2 days, 13:41:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2592, top5_acc: 0.5111, loss_cls: 4.2654, loss: 4.2654 +2024-07-19 01:55:20,118 - pyskl - INFO - Epoch [77][1300/3746] lr: 4.859e-02, eta: 2 days, 13:40:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5208, loss_cls: 4.2129, loss: 4.2129 +2024-07-19 01:56:42,368 - pyskl - INFO - Epoch [77][1400/3746] lr: 4.856e-02, eta: 2 days, 13:38:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5142, loss_cls: 4.2508, loss: 4.2508 +2024-07-19 01:58:04,205 - pyskl - INFO - Epoch [77][1500/3746] lr: 4.853e-02, eta: 2 days, 13:37:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5066, loss_cls: 4.2758, loss: 4.2758 +2024-07-19 01:59:27,332 - pyskl - INFO - Epoch [77][1600/3746] lr: 4.851e-02, eta: 2 days, 13:36:04, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5138, loss_cls: 4.2353, loss: 4.2353 +2024-07-19 02:00:49,352 - pyskl - INFO - Epoch [77][1700/3746] lr: 4.848e-02, eta: 2 days, 13:34:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5253, loss_cls: 4.2251, loss: 4.2251 +2024-07-19 02:02:11,255 - pyskl - INFO - Epoch [77][1800/3746] lr: 4.845e-02, eta: 2 days, 13:33:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2503, top5_acc: 0.5061, loss_cls: 4.2796, loss: 4.2796 +2024-07-19 02:03:34,186 - pyskl - INFO - Epoch [77][1900/3746] lr: 4.842e-02, eta: 2 days, 13:32:08, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2664, top5_acc: 0.5159, loss_cls: 4.2583, loss: 4.2583 +2024-07-19 02:04:56,441 - pyskl - INFO - Epoch [77][2000/3746] lr: 4.839e-02, eta: 2 days, 13:30:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2633, top5_acc: 0.5097, loss_cls: 4.2662, loss: 4.2662 +2024-07-19 02:06:18,541 - pyskl - INFO - Epoch [77][2100/3746] lr: 4.837e-02, eta: 2 days, 13:29:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2653, top5_acc: 0.5105, loss_cls: 4.2463, loss: 4.2463 +2024-07-19 02:07:40,975 - pyskl - INFO - Epoch [77][2200/3746] lr: 4.834e-02, eta: 2 days, 13:28:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2542, top5_acc: 0.5030, loss_cls: 4.3166, loss: 4.3166 +2024-07-19 02:09:03,336 - pyskl - INFO - Epoch [77][2300/3746] lr: 4.831e-02, eta: 2 days, 13:26:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5039, loss_cls: 4.2796, loss: 4.2796 +2024-07-19 02:10:25,883 - pyskl - INFO - Epoch [77][2400/3746] lr: 4.828e-02, eta: 2 days, 13:25:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2658, top5_acc: 0.5145, loss_cls: 4.2619, loss: 4.2619 +2024-07-19 02:11:47,970 - pyskl - INFO - Epoch [77][2500/3746] lr: 4.825e-02, eta: 2 days, 13:24:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2627, top5_acc: 0.5181, loss_cls: 4.2853, loss: 4.2853 +2024-07-19 02:13:10,026 - pyskl - INFO - Epoch [77][2600/3746] lr: 4.823e-02, eta: 2 days, 13:22:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5245, loss_cls: 4.2136, loss: 4.2136 +2024-07-19 02:14:31,493 - pyskl - INFO - Epoch [77][2700/3746] lr: 4.820e-02, eta: 2 days, 13:21:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5092, loss_cls: 4.2583, loss: 4.2583 +2024-07-19 02:15:53,439 - pyskl - INFO - Epoch [77][2800/3746] lr: 4.817e-02, eta: 2 days, 13:20:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.5050, loss_cls: 4.2831, loss: 4.2831 +2024-07-19 02:17:14,981 - pyskl - INFO - Epoch [77][2900/3746] lr: 4.814e-02, eta: 2 days, 13:18:59, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2639, top5_acc: 0.5089, loss_cls: 4.2537, loss: 4.2537 +2024-07-19 02:18:37,064 - pyskl - INFO - Epoch [77][3000/3746] lr: 4.811e-02, eta: 2 days, 13:17:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5109, loss_cls: 4.2166, loss: 4.2166 +2024-07-19 02:19:59,194 - pyskl - INFO - Epoch [77][3100/3746] lr: 4.809e-02, eta: 2 days, 13:16:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2617, top5_acc: 0.5075, loss_cls: 4.2741, loss: 4.2741 +2024-07-19 02:21:21,268 - pyskl - INFO - Epoch [77][3200/3746] lr: 4.806e-02, eta: 2 days, 13:15:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2608, top5_acc: 0.5039, loss_cls: 4.3032, loss: 4.3032 +2024-07-19 02:22:43,093 - pyskl - INFO - Epoch [77][3300/3746] lr: 4.803e-02, eta: 2 days, 13:13:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5136, loss_cls: 4.2602, loss: 4.2602 +2024-07-19 02:24:05,056 - pyskl - INFO - Epoch [77][3400/3746] lr: 4.800e-02, eta: 2 days, 13:12:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5197, loss_cls: 4.2420, loss: 4.2420 +2024-07-19 02:25:26,850 - pyskl - INFO - Epoch [77][3500/3746] lr: 4.798e-02, eta: 2 days, 13:11:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5023, loss_cls: 4.2746, loss: 4.2746 +2024-07-19 02:26:48,984 - pyskl - INFO - Epoch [77][3600/3746] lr: 4.795e-02, eta: 2 days, 13:09:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2569, top5_acc: 0.5122, loss_cls: 4.2734, loss: 4.2734 +2024-07-19 02:28:10,853 - pyskl - INFO - Epoch [77][3700/3746] lr: 4.792e-02, eta: 2 days, 13:08:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5052, loss_cls: 4.2939, loss: 4.2939 +2024-07-19 02:28:50,856 - pyskl - INFO - Saving checkpoint at 77 epochs +2024-07-19 02:30:41,036 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 02:30:41,772 - pyskl - INFO - +top1_acc 0.1911 +top5_acc 0.4197 +2024-07-19 02:30:41,772 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 02:30:41,818 - pyskl - INFO - +mean_acc 0.1909 +2024-07-19 02:30:41,830 - pyskl - INFO - Epoch(val) [77][309] top1_acc: 0.1911, top5_acc: 0.4197, mean_class_accuracy: 0.1909 +2024-07-19 02:34:31,985 - pyskl - INFO - Epoch [78][100/3746] lr: 4.788e-02, eta: 2 days, 13:08:16, time: 2.301, data_time: 1.317, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5306, loss_cls: 4.1722, loss: 4.1722 +2024-07-19 02:35:54,406 - pyskl - INFO - Epoch [78][200/3746] lr: 4.785e-02, eta: 2 days, 13:06:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2659, top5_acc: 0.5102, loss_cls: 4.2491, loss: 4.2491 +2024-07-19 02:37:16,616 - pyskl - INFO - Epoch [78][300/3746] lr: 4.782e-02, eta: 2 days, 13:05:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5067, loss_cls: 4.2579, loss: 4.2579 +2024-07-19 02:38:38,581 - pyskl - INFO - Epoch [78][400/3746] lr: 4.779e-02, eta: 2 days, 13:04:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2644, top5_acc: 0.5116, loss_cls: 4.2482, loss: 4.2482 +2024-07-19 02:40:00,256 - pyskl - INFO - Epoch [78][500/3746] lr: 4.777e-02, eta: 2 days, 13:03:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5158, loss_cls: 4.2288, loss: 4.2288 +2024-07-19 02:41:22,282 - pyskl - INFO - Epoch [78][600/3746] lr: 4.774e-02, eta: 2 days, 13:01:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5222, loss_cls: 4.2003, loss: 4.2003 +2024-07-19 02:42:44,439 - pyskl - INFO - Epoch [78][700/3746] lr: 4.771e-02, eta: 2 days, 13:00:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5202, loss_cls: 4.2190, loss: 4.2190 +2024-07-19 02:44:06,695 - pyskl - INFO - Epoch [78][800/3746] lr: 4.768e-02, eta: 2 days, 12:59:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2616, top5_acc: 0.5144, loss_cls: 4.2213, loss: 4.2213 +2024-07-19 02:45:28,981 - pyskl - INFO - Epoch [78][900/3746] lr: 4.766e-02, eta: 2 days, 12:57:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5112, loss_cls: 4.2513, loss: 4.2513 +2024-07-19 02:46:51,348 - pyskl - INFO - Epoch [78][1000/3746] lr: 4.763e-02, eta: 2 days, 12:56:25, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5155, loss_cls: 4.2456, loss: 4.2456 +2024-07-19 02:48:13,891 - pyskl - INFO - Epoch [78][1100/3746] lr: 4.760e-02, eta: 2 days, 12:55:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5145, loss_cls: 4.2582, loss: 4.2582 +2024-07-19 02:49:36,373 - pyskl - INFO - Epoch [78][1200/3746] lr: 4.757e-02, eta: 2 days, 12:53:48, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5272, loss_cls: 4.1754, loss: 4.1754 +2024-07-19 02:50:58,281 - pyskl - INFO - Epoch [78][1300/3746] lr: 4.754e-02, eta: 2 days, 12:52:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5095, loss_cls: 4.2342, loss: 4.2342 +2024-07-19 02:52:20,235 - pyskl - INFO - Epoch [78][1400/3746] lr: 4.752e-02, eta: 2 days, 12:51:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2583, top5_acc: 0.5061, loss_cls: 4.2707, loss: 4.2707 +2024-07-19 02:53:42,511 - pyskl - INFO - Epoch [78][1500/3746] lr: 4.749e-02, eta: 2 days, 12:49:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5070, loss_cls: 4.2777, loss: 4.2777 +2024-07-19 02:55:05,043 - pyskl - INFO - Epoch [78][1600/3746] lr: 4.746e-02, eta: 2 days, 12:48:32, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5188, loss_cls: 4.2425, loss: 4.2425 +2024-07-19 02:56:27,014 - pyskl - INFO - Epoch [78][1700/3746] lr: 4.743e-02, eta: 2 days, 12:47:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2605, top5_acc: 0.5105, loss_cls: 4.2822, loss: 4.2822 +2024-07-19 02:57:50,003 - pyskl - INFO - Epoch [78][1800/3746] lr: 4.740e-02, eta: 2 days, 12:45:54, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5052, loss_cls: 4.2978, loss: 4.2978 +2024-07-19 02:59:12,011 - pyskl - INFO - Epoch [78][1900/3746] lr: 4.738e-02, eta: 2 days, 12:44:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5155, loss_cls: 4.2245, loss: 4.2245 +2024-07-19 03:00:34,152 - pyskl - INFO - Epoch [78][2000/3746] lr: 4.735e-02, eta: 2 days, 12:43:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2545, top5_acc: 0.5028, loss_cls: 4.2806, loss: 4.2806 +2024-07-19 03:01:56,317 - pyskl - INFO - Epoch [78][2100/3746] lr: 4.732e-02, eta: 2 days, 12:41:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5212, loss_cls: 4.2114, loss: 4.2114 +2024-07-19 03:03:18,661 - pyskl - INFO - Epoch [78][2200/3746] lr: 4.729e-02, eta: 2 days, 12:40:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2622, top5_acc: 0.5044, loss_cls: 4.2317, loss: 4.2317 +2024-07-19 03:04:40,785 - pyskl - INFO - Epoch [78][2300/3746] lr: 4.726e-02, eta: 2 days, 12:39:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5170, loss_cls: 4.2597, loss: 4.2597 +2024-07-19 03:06:03,619 - pyskl - INFO - Epoch [78][2400/3746] lr: 4.724e-02, eta: 2 days, 12:38:01, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2609, top5_acc: 0.5094, loss_cls: 4.2522, loss: 4.2522 +2024-07-19 03:07:26,125 - pyskl - INFO - Epoch [78][2500/3746] lr: 4.721e-02, eta: 2 days, 12:36:42, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2552, top5_acc: 0.5184, loss_cls: 4.2652, loss: 4.2652 +2024-07-19 03:08:48,159 - pyskl - INFO - Epoch [78][2600/3746] lr: 4.718e-02, eta: 2 days, 12:35:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5145, loss_cls: 4.2629, loss: 4.2629 +2024-07-19 03:10:10,009 - pyskl - INFO - Epoch [78][2700/3746] lr: 4.715e-02, eta: 2 days, 12:34:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2591, top5_acc: 0.4992, loss_cls: 4.2577, loss: 4.2577 +2024-07-19 03:11:31,904 - pyskl - INFO - Epoch [78][2800/3746] lr: 4.712e-02, eta: 2 days, 12:32:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5217, loss_cls: 4.2171, loss: 4.2171 +2024-07-19 03:12:53,605 - pyskl - INFO - Epoch [78][2900/3746] lr: 4.710e-02, eta: 2 days, 12:31:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.4970, loss_cls: 4.3188, loss: 4.3188 +2024-07-19 03:14:15,126 - pyskl - INFO - Epoch [78][3000/3746] lr: 4.707e-02, eta: 2 days, 12:30:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5106, loss_cls: 4.2379, loss: 4.2379 +2024-07-19 03:15:37,805 - pyskl - INFO - Epoch [78][3100/3746] lr: 4.704e-02, eta: 2 days, 12:28:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2603, top5_acc: 0.5112, loss_cls: 4.2683, loss: 4.2683 +2024-07-19 03:17:00,002 - pyskl - INFO - Epoch [78][3200/3746] lr: 4.701e-02, eta: 2 days, 12:27:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5230, loss_cls: 4.1979, loss: 4.1979 +2024-07-19 03:18:22,760 - pyskl - INFO - Epoch [78][3300/3746] lr: 4.699e-02, eta: 2 days, 12:26:10, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5214, loss_cls: 4.2228, loss: 4.2228 +2024-07-19 03:19:44,318 - pyskl - INFO - Epoch [78][3400/3746] lr: 4.696e-02, eta: 2 days, 12:24:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2589, top5_acc: 0.5161, loss_cls: 4.2696, loss: 4.2696 +2024-07-19 03:21:06,267 - pyskl - INFO - Epoch [78][3500/3746] lr: 4.693e-02, eta: 2 days, 12:23:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5156, loss_cls: 4.2369, loss: 4.2369 +2024-07-19 03:22:29,232 - pyskl - INFO - Epoch [78][3600/3746] lr: 4.690e-02, eta: 2 days, 12:22:12, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2669, top5_acc: 0.5162, loss_cls: 4.2584, loss: 4.2584 +2024-07-19 03:23:51,199 - pyskl - INFO - Epoch [78][3700/3746] lr: 4.687e-02, eta: 2 days, 12:20:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5089, loss_cls: 4.2868, loss: 4.2868 +2024-07-19 03:24:31,215 - pyskl - INFO - Saving checkpoint at 78 epochs +2024-07-19 03:26:22,703 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 03:26:23,437 - pyskl - INFO - +top1_acc 0.2063 +top5_acc 0.4275 +2024-07-19 03:26:23,438 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 03:26:23,481 - pyskl - INFO - +mean_acc 0.2062 +2024-07-19 03:26:23,494 - pyskl - INFO - Epoch(val) [78][309] top1_acc: 0.2063, top5_acc: 0.4275, mean_class_accuracy: 0.2062 +2024-07-19 03:30:16,379 - pyskl - INFO - Epoch [79][100/3746] lr: 4.683e-02, eta: 2 days, 12:20:42, time: 2.329, data_time: 1.314, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5262, loss_cls: 4.1655, loss: 4.1655 +2024-07-19 03:31:39,385 - pyskl - INFO - Epoch [79][200/3746] lr: 4.680e-02, eta: 2 days, 12:19:24, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2630, top5_acc: 0.5081, loss_cls: 4.2388, loss: 4.2388 +2024-07-19 03:33:01,387 - pyskl - INFO - Epoch [79][300/3746] lr: 4.678e-02, eta: 2 days, 12:18:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5133, loss_cls: 4.2313, loss: 4.2313 +2024-07-19 03:34:23,691 - pyskl - INFO - Epoch [79][400/3746] lr: 4.675e-02, eta: 2 days, 12:16:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5248, loss_cls: 4.1741, loss: 4.1741 +2024-07-19 03:35:45,851 - pyskl - INFO - Epoch [79][500/3746] lr: 4.672e-02, eta: 2 days, 12:15:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5203, loss_cls: 4.2166, loss: 4.2166 +2024-07-19 03:37:07,827 - pyskl - INFO - Epoch [79][600/3746] lr: 4.669e-02, eta: 2 days, 12:14:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2652, top5_acc: 0.5070, loss_cls: 4.2253, loss: 4.2253 +2024-07-19 03:38:30,231 - pyskl - INFO - Epoch [79][700/3746] lr: 4.667e-02, eta: 2 days, 12:12:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5133, loss_cls: 4.2301, loss: 4.2301 +2024-07-19 03:39:53,083 - pyskl - INFO - Epoch [79][800/3746] lr: 4.664e-02, eta: 2 days, 12:11:30, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2619, top5_acc: 0.5155, loss_cls: 4.2370, loss: 4.2370 +2024-07-19 03:41:15,389 - pyskl - INFO - Epoch [79][900/3746] lr: 4.661e-02, eta: 2 days, 12:10:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5142, loss_cls: 4.2098, loss: 4.2098 +2024-07-19 03:42:37,562 - pyskl - INFO - Epoch [79][1000/3746] lr: 4.658e-02, eta: 2 days, 12:08:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5125, loss_cls: 4.2102, loss: 4.2102 +2024-07-19 03:43:59,866 - pyskl - INFO - Epoch [79][1100/3746] lr: 4.655e-02, eta: 2 days, 12:07:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5072, loss_cls: 4.2743, loss: 4.2743 +2024-07-19 03:45:21,866 - pyskl - INFO - Epoch [79][1200/3746] lr: 4.653e-02, eta: 2 days, 12:06:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2677, top5_acc: 0.5128, loss_cls: 4.2414, loss: 4.2414 +2024-07-19 03:46:43,669 - pyskl - INFO - Epoch [79][1300/3746] lr: 4.650e-02, eta: 2 days, 12:04:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5069, loss_cls: 4.2833, loss: 4.2833 +2024-07-19 03:48:05,579 - pyskl - INFO - Epoch [79][1400/3746] lr: 4.647e-02, eta: 2 days, 12:03:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5252, loss_cls: 4.1987, loss: 4.1987 +2024-07-19 03:49:27,734 - pyskl - INFO - Epoch [79][1500/3746] lr: 4.644e-02, eta: 2 days, 12:02:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2681, top5_acc: 0.5189, loss_cls: 4.2435, loss: 4.2435 +2024-07-19 03:50:49,843 - pyskl - INFO - Epoch [79][1600/3746] lr: 4.641e-02, eta: 2 days, 12:00:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2547, top5_acc: 0.5055, loss_cls: 4.3053, loss: 4.3053 +2024-07-19 03:52:12,138 - pyskl - INFO - Epoch [79][1700/3746] lr: 4.639e-02, eta: 2 days, 11:59:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5177, loss_cls: 4.2406, loss: 4.2406 +2024-07-19 03:53:34,478 - pyskl - INFO - Epoch [79][1800/3746] lr: 4.636e-02, eta: 2 days, 11:58:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5220, loss_cls: 4.2163, loss: 4.2163 +2024-07-19 03:54:56,622 - pyskl - INFO - Epoch [79][1900/3746] lr: 4.633e-02, eta: 2 days, 11:56:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5172, loss_cls: 4.2469, loss: 4.2469 +2024-07-19 03:56:18,651 - pyskl - INFO - Epoch [79][2000/3746] lr: 4.630e-02, eta: 2 days, 11:55:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5180, loss_cls: 4.2458, loss: 4.2458 +2024-07-19 03:57:41,020 - pyskl - INFO - Epoch [79][2100/3746] lr: 4.628e-02, eta: 2 days, 11:54:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2637, top5_acc: 0.5002, loss_cls: 4.2887, loss: 4.2887 +2024-07-19 03:59:03,485 - pyskl - INFO - Epoch [79][2200/3746] lr: 4.625e-02, eta: 2 days, 11:53:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5139, loss_cls: 4.2416, loss: 4.2416 +2024-07-19 04:00:25,556 - pyskl - INFO - Epoch [79][2300/3746] lr: 4.622e-02, eta: 2 days, 11:51:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5147, loss_cls: 4.2362, loss: 4.2362 +2024-07-19 04:01:48,305 - pyskl - INFO - Epoch [79][2400/3746] lr: 4.619e-02, eta: 2 days, 11:50:24, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2602, top5_acc: 0.5128, loss_cls: 4.2523, loss: 4.2523 +2024-07-19 04:03:10,141 - pyskl - INFO - Epoch [79][2500/3746] lr: 4.616e-02, eta: 2 days, 11:49:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5225, loss_cls: 4.2228, loss: 4.2228 +2024-07-19 04:04:31,898 - pyskl - INFO - Epoch [79][2600/3746] lr: 4.614e-02, eta: 2 days, 11:47:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5181, loss_cls: 4.2297, loss: 4.2297 +2024-07-19 04:05:54,281 - pyskl - INFO - Epoch [79][2700/3746] lr: 4.611e-02, eta: 2 days, 11:46:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2714, top5_acc: 0.5103, loss_cls: 4.2226, loss: 4.2226 +2024-07-19 04:07:15,960 - pyskl - INFO - Epoch [79][2800/3746] lr: 4.608e-02, eta: 2 days, 11:45:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2623, top5_acc: 0.5086, loss_cls: 4.2634, loss: 4.2634 +2024-07-19 04:08:37,589 - pyskl - INFO - Epoch [79][2900/3746] lr: 4.605e-02, eta: 2 days, 11:43:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5233, loss_cls: 4.1916, loss: 4.1916 +2024-07-19 04:09:59,408 - pyskl - INFO - Epoch [79][3000/3746] lr: 4.602e-02, eta: 2 days, 11:42:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5169, loss_cls: 4.2252, loss: 4.2252 +2024-07-19 04:11:20,927 - pyskl - INFO - Epoch [79][3100/3746] lr: 4.600e-02, eta: 2 days, 11:41:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2625, top5_acc: 0.5061, loss_cls: 4.2589, loss: 4.2589 +2024-07-19 04:12:42,855 - pyskl - INFO - Epoch [79][3200/3746] lr: 4.597e-02, eta: 2 days, 11:39:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5184, loss_cls: 4.2381, loss: 4.2381 +2024-07-19 04:14:04,725 - pyskl - INFO - Epoch [79][3300/3746] lr: 4.594e-02, eta: 2 days, 11:38:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2694, top5_acc: 0.5155, loss_cls: 4.2468, loss: 4.2468 +2024-07-19 04:15:26,495 - pyskl - INFO - Epoch [79][3400/3746] lr: 4.591e-02, eta: 2 days, 11:37:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5220, loss_cls: 4.2055, loss: 4.2055 +2024-07-19 04:16:48,422 - pyskl - INFO - Epoch [79][3500/3746] lr: 4.588e-02, eta: 2 days, 11:35:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5186, loss_cls: 4.2106, loss: 4.2106 +2024-07-19 04:18:10,354 - pyskl - INFO - Epoch [79][3600/3746] lr: 4.586e-02, eta: 2 days, 11:34:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5078, loss_cls: 4.2769, loss: 4.2769 +2024-07-19 04:19:32,598 - pyskl - INFO - Epoch [79][3700/3746] lr: 4.583e-02, eta: 2 days, 11:33:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5208, loss_cls: 4.2153, loss: 4.2153 +2024-07-19 04:20:12,281 - pyskl - INFO - Saving checkpoint at 79 epochs +2024-07-19 04:22:03,415 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 04:22:04,087 - pyskl - INFO - +top1_acc 0.2173 +top5_acc 0.4597 +2024-07-19 04:22:04,087 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 04:22:04,130 - pyskl - INFO - +mean_acc 0.2172 +2024-07-19 04:22:04,135 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_76.pth was removed +2024-07-19 04:22:04,405 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_79.pth. +2024-07-19 04:22:04,405 - pyskl - INFO - Best top1_acc is 0.2173 at 79 epoch. +2024-07-19 04:22:04,418 - pyskl - INFO - Epoch(val) [79][309] top1_acc: 0.2173, top5_acc: 0.4597, mean_class_accuracy: 0.2172 +2024-07-19 04:25:54,839 - pyskl - INFO - Epoch [80][100/3746] lr: 4.579e-02, eta: 2 days, 11:32:55, time: 2.304, data_time: 1.312, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5253, loss_cls: 4.1816, loss: 4.1816 +2024-07-19 04:27:17,783 - pyskl - INFO - Epoch [80][200/3746] lr: 4.576e-02, eta: 2 days, 11:31:36, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5181, loss_cls: 4.1862, loss: 4.1862 +2024-07-19 04:28:39,798 - pyskl - INFO - Epoch [80][300/3746] lr: 4.573e-02, eta: 2 days, 11:30:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5147, loss_cls: 4.2340, loss: 4.2340 +2024-07-19 04:30:01,935 - pyskl - INFO - Epoch [80][400/3746] lr: 4.570e-02, eta: 2 days, 11:28:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2533, top5_acc: 0.5122, loss_cls: 4.2508, loss: 4.2508 +2024-07-19 04:31:24,005 - pyskl - INFO - Epoch [80][500/3746] lr: 4.568e-02, eta: 2 days, 11:27:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5189, loss_cls: 4.2483, loss: 4.2483 +2024-07-19 04:32:46,033 - pyskl - INFO - Epoch [80][600/3746] lr: 4.565e-02, eta: 2 days, 11:26:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5159, loss_cls: 4.2084, loss: 4.2084 +2024-07-19 04:34:08,326 - pyskl - INFO - Epoch [80][700/3746] lr: 4.562e-02, eta: 2 days, 11:25:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5264, loss_cls: 4.1843, loss: 4.1843 +2024-07-19 04:35:31,220 - pyskl - INFO - Epoch [80][800/3746] lr: 4.559e-02, eta: 2 days, 11:23:41, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5291, loss_cls: 4.1664, loss: 4.1664 +2024-07-19 04:36:53,556 - pyskl - INFO - Epoch [80][900/3746] lr: 4.557e-02, eta: 2 days, 11:22:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5194, loss_cls: 4.2123, loss: 4.2123 +2024-07-19 04:38:15,910 - pyskl - INFO - Epoch [80][1000/3746] lr: 4.554e-02, eta: 2 days, 11:21:03, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5169, loss_cls: 4.1825, loss: 4.1825 +2024-07-19 04:39:37,914 - pyskl - INFO - Epoch [80][1100/3746] lr: 4.551e-02, eta: 2 days, 11:19:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2661, top5_acc: 0.5059, loss_cls: 4.2660, loss: 4.2660 +2024-07-19 04:40:59,940 - pyskl - INFO - Epoch [80][1200/3746] lr: 4.548e-02, eta: 2 days, 11:18:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2614, top5_acc: 0.5153, loss_cls: 4.2507, loss: 4.2507 +2024-07-19 04:42:21,620 - pyskl - INFO - Epoch [80][1300/3746] lr: 4.545e-02, eta: 2 days, 11:17:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5211, loss_cls: 4.2109, loss: 4.2109 +2024-07-19 04:43:43,778 - pyskl - INFO - Epoch [80][1400/3746] lr: 4.543e-02, eta: 2 days, 11:15:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5202, loss_cls: 4.2168, loss: 4.2168 +2024-07-19 04:45:05,899 - pyskl - INFO - Epoch [80][1500/3746] lr: 4.540e-02, eta: 2 days, 11:14:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2736, top5_acc: 0.5214, loss_cls: 4.2069, loss: 4.2069 +2024-07-19 04:46:28,309 - pyskl - INFO - Epoch [80][1600/3746] lr: 4.537e-02, eta: 2 days, 11:13:07, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5178, loss_cls: 4.2217, loss: 4.2217 +2024-07-19 04:47:51,032 - pyskl - INFO - Epoch [80][1700/3746] lr: 4.534e-02, eta: 2 days, 11:11:48, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5133, loss_cls: 4.2483, loss: 4.2483 +2024-07-19 04:49:13,022 - pyskl - INFO - Epoch [80][1800/3746] lr: 4.532e-02, eta: 2 days, 11:10:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2650, top5_acc: 0.5136, loss_cls: 4.2430, loss: 4.2430 +2024-07-19 04:50:34,799 - pyskl - INFO - Epoch [80][1900/3746] lr: 4.529e-02, eta: 2 days, 11:09:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5220, loss_cls: 4.1943, loss: 4.1943 +2024-07-19 04:51:56,675 - pyskl - INFO - Epoch [80][2000/3746] lr: 4.526e-02, eta: 2 days, 11:07:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5261, loss_cls: 4.1980, loss: 4.1980 +2024-07-19 04:53:18,783 - pyskl - INFO - Epoch [80][2100/3746] lr: 4.523e-02, eta: 2 days, 11:06:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2575, top5_acc: 0.4992, loss_cls: 4.3067, loss: 4.3067 +2024-07-19 04:54:41,232 - pyskl - INFO - Epoch [80][2200/3746] lr: 4.520e-02, eta: 2 days, 11:05:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2689, top5_acc: 0.5212, loss_cls: 4.2433, loss: 4.2433 +2024-07-19 04:56:03,300 - pyskl - INFO - Epoch [80][2300/3746] lr: 4.518e-02, eta: 2 days, 11:03:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5188, loss_cls: 4.2351, loss: 4.2351 +2024-07-19 04:57:26,279 - pyskl - INFO - Epoch [80][2400/3746] lr: 4.515e-02, eta: 2 days, 11:02:33, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2648, top5_acc: 0.5162, loss_cls: 4.2411, loss: 4.2411 +2024-07-19 04:58:48,418 - pyskl - INFO - Epoch [80][2500/3746] lr: 4.512e-02, eta: 2 days, 11:01:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2702, top5_acc: 0.5122, loss_cls: 4.2370, loss: 4.2370 +2024-07-19 05:00:11,060 - pyskl - INFO - Epoch [80][2600/3746] lr: 4.509e-02, eta: 2 days, 10:59:55, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5312, loss_cls: 4.1712, loss: 4.1712 +2024-07-19 05:01:32,950 - pyskl - INFO - Epoch [80][2700/3746] lr: 4.506e-02, eta: 2 days, 10:58:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5181, loss_cls: 4.2044, loss: 4.2044 +2024-07-19 05:02:55,072 - pyskl - INFO - Epoch [80][2800/3746] lr: 4.504e-02, eta: 2 days, 10:57:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5189, loss_cls: 4.2226, loss: 4.2226 +2024-07-19 05:04:17,065 - pyskl - INFO - Epoch [80][2900/3746] lr: 4.501e-02, eta: 2 days, 10:55:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5184, loss_cls: 4.2196, loss: 4.2196 +2024-07-19 05:05:38,852 - pyskl - INFO - Epoch [80][3000/3746] lr: 4.498e-02, eta: 2 days, 10:54:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5162, loss_cls: 4.2275, loss: 4.2275 +2024-07-19 05:07:01,368 - pyskl - INFO - Epoch [80][3100/3746] lr: 4.495e-02, eta: 2 days, 10:53:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2691, top5_acc: 0.5236, loss_cls: 4.1958, loss: 4.1958 +2024-07-19 05:08:23,210 - pyskl - INFO - Epoch [80][3200/3746] lr: 4.493e-02, eta: 2 days, 10:51:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5308, loss_cls: 4.1774, loss: 4.1774 +2024-07-19 05:09:45,063 - pyskl - INFO - Epoch [80][3300/3746] lr: 4.490e-02, eta: 2 days, 10:50:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5303, loss_cls: 4.1698, loss: 4.1698 +2024-07-19 05:11:07,005 - pyskl - INFO - Epoch [80][3400/3746] lr: 4.487e-02, eta: 2 days, 10:49:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5150, loss_cls: 4.2447, loss: 4.2447 +2024-07-19 05:12:29,498 - pyskl - INFO - Epoch [80][3500/3746] lr: 4.484e-02, eta: 2 days, 10:48:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5212, loss_cls: 4.2055, loss: 4.2055 +2024-07-19 05:13:51,941 - pyskl - INFO - Epoch [80][3600/3746] lr: 4.481e-02, eta: 2 days, 10:46:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2675, top5_acc: 0.5088, loss_cls: 4.2358, loss: 4.2358 +2024-07-19 05:15:13,750 - pyskl - INFO - Epoch [80][3700/3746] lr: 4.479e-02, eta: 2 days, 10:45:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2595, top5_acc: 0.5111, loss_cls: 4.2590, loss: 4.2590 +2024-07-19 05:15:53,220 - pyskl - INFO - Saving checkpoint at 80 epochs +2024-07-19 05:17:44,017 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 05:17:44,691 - pyskl - INFO - +top1_acc 0.2084 +top5_acc 0.4369 +2024-07-19 05:17:44,691 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 05:17:44,734 - pyskl - INFO - +mean_acc 0.2082 +2024-07-19 05:17:44,747 - pyskl - INFO - Epoch(val) [80][309] top1_acc: 0.2084, top5_acc: 0.4369, mean_class_accuracy: 0.2082 +2024-07-19 05:21:36,362 - pyskl - INFO - Epoch [81][100/3746] lr: 4.475e-02, eta: 2 days, 10:45:03, time: 2.316, data_time: 1.328, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5198, loss_cls: 4.1962, loss: 4.1962 +2024-07-19 05:22:59,286 - pyskl - INFO - Epoch [81][200/3746] lr: 4.472e-02, eta: 2 days, 10:43:44, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5147, loss_cls: 4.2061, loss: 4.2061 +2024-07-19 05:24:21,400 - pyskl - INFO - Epoch [81][300/3746] lr: 4.469e-02, eta: 2 days, 10:42:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5311, loss_cls: 4.1692, loss: 4.1692 +2024-07-19 05:25:43,454 - pyskl - INFO - Epoch [81][400/3746] lr: 4.466e-02, eta: 2 days, 10:41:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5211, loss_cls: 4.1736, loss: 4.1736 +2024-07-19 05:27:06,056 - pyskl - INFO - Epoch [81][500/3746] lr: 4.463e-02, eta: 2 days, 10:39:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5330, loss_cls: 4.1269, loss: 4.1269 +2024-07-19 05:28:28,014 - pyskl - INFO - Epoch [81][600/3746] lr: 4.461e-02, eta: 2 days, 10:38:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5273, loss_cls: 4.1904, loss: 4.1904 +2024-07-19 05:29:50,080 - pyskl - INFO - Epoch [81][700/3746] lr: 4.458e-02, eta: 2 days, 10:37:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5266, loss_cls: 4.1905, loss: 4.1905 +2024-07-19 05:31:11,833 - pyskl - INFO - Epoch [81][800/3746] lr: 4.455e-02, eta: 2 days, 10:35:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5294, loss_cls: 4.1791, loss: 4.1791 +2024-07-19 05:32:33,797 - pyskl - INFO - Epoch [81][900/3746] lr: 4.452e-02, eta: 2 days, 10:34:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5292, loss_cls: 4.1463, loss: 4.1463 +2024-07-19 05:33:56,582 - pyskl - INFO - Epoch [81][1000/3746] lr: 4.450e-02, eta: 2 days, 10:33:09, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2706, top5_acc: 0.5206, loss_cls: 4.2064, loss: 4.2064 +2024-07-19 05:35:18,744 - pyskl - INFO - Epoch [81][1100/3746] lr: 4.447e-02, eta: 2 days, 10:31:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5248, loss_cls: 4.1867, loss: 4.1867 +2024-07-19 05:36:40,712 - pyskl - INFO - Epoch [81][1200/3746] lr: 4.444e-02, eta: 2 days, 10:30:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5238, loss_cls: 4.1625, loss: 4.1625 +2024-07-19 05:38:02,821 - pyskl - INFO - Epoch [81][1300/3746] lr: 4.441e-02, eta: 2 days, 10:29:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5166, loss_cls: 4.1818, loss: 4.1818 +2024-07-19 05:39:24,673 - pyskl - INFO - Epoch [81][1400/3746] lr: 4.438e-02, eta: 2 days, 10:27:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5198, loss_cls: 4.1871, loss: 4.1871 +2024-07-19 05:40:47,251 - pyskl - INFO - Epoch [81][1500/3746] lr: 4.436e-02, eta: 2 days, 10:26:32, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5273, loss_cls: 4.1965, loss: 4.1965 +2024-07-19 05:42:09,199 - pyskl - INFO - Epoch [81][1600/3746] lr: 4.433e-02, eta: 2 days, 10:25:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5138, loss_cls: 4.2696, loss: 4.2696 +2024-07-19 05:43:31,621 - pyskl - INFO - Epoch [81][1700/3746] lr: 4.430e-02, eta: 2 days, 10:23:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2708, top5_acc: 0.5119, loss_cls: 4.2399, loss: 4.2399 +2024-07-19 05:44:53,945 - pyskl - INFO - Epoch [81][1800/3746] lr: 4.427e-02, eta: 2 days, 10:22:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5198, loss_cls: 4.2029, loss: 4.2029 +2024-07-19 05:46:15,749 - pyskl - INFO - Epoch [81][1900/3746] lr: 4.425e-02, eta: 2 days, 10:21:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5127, loss_cls: 4.2162, loss: 4.2162 +2024-07-19 05:47:38,026 - pyskl - INFO - Epoch [81][2000/3746] lr: 4.422e-02, eta: 2 days, 10:19:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2662, top5_acc: 0.5083, loss_cls: 4.2624, loss: 4.2624 +2024-07-19 05:49:00,203 - pyskl - INFO - Epoch [81][2100/3746] lr: 4.419e-02, eta: 2 days, 10:18:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5130, loss_cls: 4.2552, loss: 4.2552 +2024-07-19 05:50:22,819 - pyskl - INFO - Epoch [81][2200/3746] lr: 4.416e-02, eta: 2 days, 10:17:16, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5177, loss_cls: 4.2179, loss: 4.2179 +2024-07-19 05:51:44,881 - pyskl - INFO - Epoch [81][2300/3746] lr: 4.413e-02, eta: 2 days, 10:15:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5284, loss_cls: 4.1468, loss: 4.1468 +2024-07-19 05:53:07,048 - pyskl - INFO - Epoch [81][2400/3746] lr: 4.411e-02, eta: 2 days, 10:14:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5234, loss_cls: 4.2127, loss: 4.2127 +2024-07-19 05:54:29,433 - pyskl - INFO - Epoch [81][2500/3746] lr: 4.408e-02, eta: 2 days, 10:13:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2687, top5_acc: 0.5178, loss_cls: 4.2122, loss: 4.2122 +2024-07-19 05:55:52,493 - pyskl - INFO - Epoch [81][2600/3746] lr: 4.405e-02, eta: 2 days, 10:11:59, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5253, loss_cls: 4.1791, loss: 4.1791 +2024-07-19 05:57:14,988 - pyskl - INFO - Epoch [81][2700/3746] lr: 4.402e-02, eta: 2 days, 10:10:40, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2645, top5_acc: 0.5155, loss_cls: 4.2228, loss: 4.2228 +2024-07-19 05:58:36,898 - pyskl - INFO - Epoch [81][2800/3746] lr: 4.400e-02, eta: 2 days, 10:09:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2606, top5_acc: 0.5088, loss_cls: 4.2689, loss: 4.2689 +2024-07-19 05:59:58,904 - pyskl - INFO - Epoch [81][2900/3746] lr: 4.397e-02, eta: 2 days, 10:08:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2612, top5_acc: 0.5131, loss_cls: 4.2499, loss: 4.2499 +2024-07-19 06:01:21,943 - pyskl - INFO - Epoch [81][3000/3746] lr: 4.394e-02, eta: 2 days, 10:06:42, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5127, loss_cls: 4.2321, loss: 4.2321 +2024-07-19 06:02:43,698 - pyskl - INFO - Epoch [81][3100/3746] lr: 4.391e-02, eta: 2 days, 10:05:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2734, top5_acc: 0.5264, loss_cls: 4.1784, loss: 4.1784 +2024-07-19 06:04:05,740 - pyskl - INFO - Epoch [81][3200/3746] lr: 4.389e-02, eta: 2 days, 10:04:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5209, loss_cls: 4.2135, loss: 4.2135 +2024-07-19 06:05:27,830 - pyskl - INFO - Epoch [81][3300/3746] lr: 4.386e-02, eta: 2 days, 10:02:43, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2719, top5_acc: 0.5164, loss_cls: 4.2320, loss: 4.2320 +2024-07-19 06:06:49,790 - pyskl - INFO - Epoch [81][3400/3746] lr: 4.383e-02, eta: 2 days, 10:01:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2628, top5_acc: 0.5144, loss_cls: 4.2432, loss: 4.2432 +2024-07-19 06:08:11,642 - pyskl - INFO - Epoch [81][3500/3746] lr: 4.380e-02, eta: 2 days, 10:00:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5138, loss_cls: 4.2206, loss: 4.2206 +2024-07-19 06:09:33,518 - pyskl - INFO - Epoch [81][3600/3746] lr: 4.377e-02, eta: 2 days, 9:58:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5188, loss_cls: 4.2488, loss: 4.2488 +2024-07-19 06:10:55,177 - pyskl - INFO - Epoch [81][3700/3746] lr: 4.375e-02, eta: 2 days, 9:57:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2672, top5_acc: 0.5197, loss_cls: 4.2143, loss: 4.2143 +2024-07-19 06:11:35,028 - pyskl - INFO - Saving checkpoint at 81 epochs +2024-07-19 06:13:25,740 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 06:13:26,424 - pyskl - INFO - +top1_acc 0.1981 +top5_acc 0.4199 +2024-07-19 06:13:26,424 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 06:13:26,467 - pyskl - INFO - +mean_acc 0.1980 +2024-07-19 06:13:26,481 - pyskl - INFO - Epoch(val) [81][309] top1_acc: 0.1981, top5_acc: 0.4199, mean_class_accuracy: 0.1980 +2024-07-19 06:17:17,122 - pyskl - INFO - Epoch [82][100/3746] lr: 4.371e-02, eta: 2 days, 9:57:02, time: 2.306, data_time: 1.321, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5327, loss_cls: 4.1402, loss: 4.1402 +2024-07-19 06:18:39,963 - pyskl - INFO - Epoch [82][200/3746] lr: 4.368e-02, eta: 2 days, 9:55:43, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5280, loss_cls: 4.1458, loss: 4.1458 +2024-07-19 06:20:02,078 - pyskl - INFO - Epoch [82][300/3746] lr: 4.365e-02, eta: 2 days, 9:54:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2739, top5_acc: 0.5255, loss_cls: 4.1692, loss: 4.1692 +2024-07-19 06:21:24,580 - pyskl - INFO - Epoch [82][400/3746] lr: 4.362e-02, eta: 2 days, 9:53:04, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5173, loss_cls: 4.2106, loss: 4.2106 +2024-07-19 06:22:46,482 - pyskl - INFO - Epoch [82][500/3746] lr: 4.359e-02, eta: 2 days, 9:51:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5156, loss_cls: 4.2175, loss: 4.2175 +2024-07-19 06:24:08,531 - pyskl - INFO - Epoch [82][600/3746] lr: 4.357e-02, eta: 2 days, 9:50:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2698, top5_acc: 0.5105, loss_cls: 4.2283, loss: 4.2283 +2024-07-19 06:25:31,032 - pyskl - INFO - Epoch [82][700/3746] lr: 4.354e-02, eta: 2 days, 9:49:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5281, loss_cls: 4.2023, loss: 4.2023 +2024-07-19 06:26:53,561 - pyskl - INFO - Epoch [82][800/3746] lr: 4.351e-02, eta: 2 days, 9:47:46, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2636, top5_acc: 0.5212, loss_cls: 4.2234, loss: 4.2234 +2024-07-19 06:28:15,843 - pyskl - INFO - Epoch [82][900/3746] lr: 4.348e-02, eta: 2 days, 9:46:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5194, loss_cls: 4.1985, loss: 4.1985 +2024-07-19 06:29:37,941 - pyskl - INFO - Epoch [82][1000/3746] lr: 4.346e-02, eta: 2 days, 9:45:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5273, loss_cls: 4.1736, loss: 4.1736 +2024-07-19 06:31:00,490 - pyskl - INFO - Epoch [82][1100/3746] lr: 4.343e-02, eta: 2 days, 9:43:48, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5292, loss_cls: 4.1618, loss: 4.1618 +2024-07-19 06:32:22,654 - pyskl - INFO - Epoch [82][1200/3746] lr: 4.340e-02, eta: 2 days, 9:42:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5250, loss_cls: 4.1958, loss: 4.1958 +2024-07-19 06:33:44,942 - pyskl - INFO - Epoch [82][1300/3746] lr: 4.337e-02, eta: 2 days, 9:41:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5288, loss_cls: 4.1680, loss: 4.1680 +2024-07-19 06:35:07,059 - pyskl - INFO - Epoch [82][1400/3746] lr: 4.335e-02, eta: 2 days, 9:39:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2641, top5_acc: 0.5120, loss_cls: 4.2642, loss: 4.2642 +2024-07-19 06:36:30,076 - pyskl - INFO - Epoch [82][1500/3746] lr: 4.332e-02, eta: 2 days, 9:38:31, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2705, top5_acc: 0.5197, loss_cls: 4.2244, loss: 4.2244 +2024-07-19 06:37:52,429 - pyskl - INFO - Epoch [82][1600/3746] lr: 4.329e-02, eta: 2 days, 9:37:11, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2631, top5_acc: 0.5183, loss_cls: 4.2168, loss: 4.2168 +2024-07-19 06:39:15,238 - pyskl - INFO - Epoch [82][1700/3746] lr: 4.326e-02, eta: 2 days, 9:35:52, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5261, loss_cls: 4.1697, loss: 4.1697 +2024-07-19 06:40:37,010 - pyskl - INFO - Epoch [82][1800/3746] lr: 4.323e-02, eta: 2 days, 9:34:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2670, top5_acc: 0.5245, loss_cls: 4.2299, loss: 4.2299 +2024-07-19 06:41:58,632 - pyskl - INFO - Epoch [82][1900/3746] lr: 4.321e-02, eta: 2 days, 9:33:12, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5289, loss_cls: 4.1511, loss: 4.1511 +2024-07-19 06:43:20,520 - pyskl - INFO - Epoch [82][2000/3746] lr: 4.318e-02, eta: 2 days, 9:31:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5270, loss_cls: 4.1990, loss: 4.1990 +2024-07-19 06:44:42,430 - pyskl - INFO - Epoch [82][2100/3746] lr: 4.315e-02, eta: 2 days, 9:30:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5317, loss_cls: 4.1627, loss: 4.1627 +2024-07-19 06:46:05,197 - pyskl - INFO - Epoch [82][2200/3746] lr: 4.312e-02, eta: 2 days, 9:29:14, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2600, top5_acc: 0.5167, loss_cls: 4.2554, loss: 4.2554 +2024-07-19 06:47:27,330 - pyskl - INFO - Epoch [82][2300/3746] lr: 4.310e-02, eta: 2 days, 9:27:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5241, loss_cls: 4.1878, loss: 4.1878 +2024-07-19 06:48:49,640 - pyskl - INFO - Epoch [82][2400/3746] lr: 4.307e-02, eta: 2 days, 9:26:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5245, loss_cls: 4.1897, loss: 4.1897 +2024-07-19 06:50:11,845 - pyskl - INFO - Epoch [82][2500/3746] lr: 4.304e-02, eta: 2 days, 9:25:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2673, top5_acc: 0.5252, loss_cls: 4.2328, loss: 4.2328 +2024-07-19 06:51:33,876 - pyskl - INFO - Epoch [82][2600/3746] lr: 4.301e-02, eta: 2 days, 9:23:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5192, loss_cls: 4.2063, loss: 4.2063 +2024-07-19 06:52:55,688 - pyskl - INFO - Epoch [82][2700/3746] lr: 4.299e-02, eta: 2 days, 9:22:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5122, loss_cls: 4.2199, loss: 4.2199 +2024-07-19 06:54:17,767 - pyskl - INFO - Epoch [82][2800/3746] lr: 4.296e-02, eta: 2 days, 9:21:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5267, loss_cls: 4.1603, loss: 4.1603 +2024-07-19 06:55:39,482 - pyskl - INFO - Epoch [82][2900/3746] lr: 4.293e-02, eta: 2 days, 9:19:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2808, top5_acc: 0.5258, loss_cls: 4.1761, loss: 4.1761 +2024-07-19 06:57:01,192 - pyskl - INFO - Epoch [82][3000/3746] lr: 4.290e-02, eta: 2 days, 9:18:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5161, loss_cls: 4.2242, loss: 4.2242 +2024-07-19 06:58:22,824 - pyskl - INFO - Epoch [82][3100/3746] lr: 4.287e-02, eta: 2 days, 9:17:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5186, loss_cls: 4.1794, loss: 4.1794 +2024-07-19 06:59:44,724 - pyskl - INFO - Epoch [82][3200/3746] lr: 4.285e-02, eta: 2 days, 9:15:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5275, loss_cls: 4.1670, loss: 4.1670 +2024-07-19 07:01:06,630 - pyskl - INFO - Epoch [82][3300/3746] lr: 4.282e-02, eta: 2 days, 9:14:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2700, top5_acc: 0.5277, loss_cls: 4.1946, loss: 4.1946 +2024-07-19 07:02:28,909 - pyskl - INFO - Epoch [82][3400/3746] lr: 4.279e-02, eta: 2 days, 9:13:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2647, top5_acc: 0.5134, loss_cls: 4.2231, loss: 4.2231 +2024-07-19 07:03:50,873 - pyskl - INFO - Epoch [82][3500/3746] lr: 4.276e-02, eta: 2 days, 9:11:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5173, loss_cls: 4.1907, loss: 4.1907 +2024-07-19 07:05:12,809 - pyskl - INFO - Epoch [82][3600/3746] lr: 4.274e-02, eta: 2 days, 9:10:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5172, loss_cls: 4.2121, loss: 4.2121 +2024-07-19 07:06:34,737 - pyskl - INFO - Epoch [82][3700/3746] lr: 4.271e-02, eta: 2 days, 9:09:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5192, loss_cls: 4.2014, loss: 4.2014 +2024-07-19 07:07:14,600 - pyskl - INFO - Saving checkpoint at 82 epochs +2024-07-19 07:09:05,787 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 07:09:06,455 - pyskl - INFO - +top1_acc 0.2158 +top5_acc 0.4452 +2024-07-19 07:09:06,455 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 07:09:06,498 - pyskl - INFO - +mean_acc 0.2158 +2024-07-19 07:09:06,511 - pyskl - INFO - Epoch(val) [82][309] top1_acc: 0.2158, top5_acc: 0.4452, mean_class_accuracy: 0.2158 +2024-07-19 07:12:59,761 - pyskl - INFO - Epoch [83][100/3746] lr: 4.267e-02, eta: 2 days, 9:08:55, time: 2.332, data_time: 1.344, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5392, loss_cls: 4.0954, loss: 4.0954 +2024-07-19 07:14:22,379 - pyskl - INFO - Epoch [83][200/3746] lr: 4.264e-02, eta: 2 days, 9:07:36, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5358, loss_cls: 4.0994, loss: 4.0994 +2024-07-19 07:15:44,939 - pyskl - INFO - Epoch [83][300/3746] lr: 4.261e-02, eta: 2 days, 9:06:17, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5258, loss_cls: 4.1707, loss: 4.1707 +2024-07-19 07:17:06,790 - pyskl - INFO - Epoch [83][400/3746] lr: 4.259e-02, eta: 2 days, 9:04:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5300, loss_cls: 4.1431, loss: 4.1431 +2024-07-19 07:18:28,854 - pyskl - INFO - Epoch [83][500/3746] lr: 4.256e-02, eta: 2 days, 9:03:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5302, loss_cls: 4.1574, loss: 4.1574 +2024-07-19 07:19:50,685 - pyskl - INFO - Epoch [83][600/3746] lr: 4.253e-02, eta: 2 days, 9:02:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2820, top5_acc: 0.5189, loss_cls: 4.1734, loss: 4.1734 +2024-07-19 07:21:13,737 - pyskl - INFO - Epoch [83][700/3746] lr: 4.250e-02, eta: 2 days, 9:00:58, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5273, loss_cls: 4.1699, loss: 4.1699 +2024-07-19 07:22:35,391 - pyskl - INFO - Epoch [83][800/3746] lr: 4.247e-02, eta: 2 days, 8:59:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5295, loss_cls: 4.1759, loss: 4.1759 +2024-07-19 07:23:58,121 - pyskl - INFO - Epoch [83][900/3746] lr: 4.245e-02, eta: 2 days, 8:58:19, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5267, loss_cls: 4.1824, loss: 4.1824 +2024-07-19 07:25:20,441 - pyskl - INFO - Epoch [83][1000/3746] lr: 4.242e-02, eta: 2 days, 8:56:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5223, loss_cls: 4.1742, loss: 4.1742 +2024-07-19 07:26:42,036 - pyskl - INFO - Epoch [83][1100/3746] lr: 4.239e-02, eta: 2 days, 8:55:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5242, loss_cls: 4.1839, loss: 4.1839 +2024-07-19 07:28:03,712 - pyskl - INFO - Epoch [83][1200/3746] lr: 4.236e-02, eta: 2 days, 8:54:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2716, top5_acc: 0.5180, loss_cls: 4.2261, loss: 4.2261 +2024-07-19 07:29:26,111 - pyskl - INFO - Epoch [83][1300/3746] lr: 4.234e-02, eta: 2 days, 8:52:59, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5238, loss_cls: 4.1836, loss: 4.1836 +2024-07-19 07:30:48,053 - pyskl - INFO - Epoch [83][1400/3746] lr: 4.231e-02, eta: 2 days, 8:51:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5247, loss_cls: 4.1387, loss: 4.1387 +2024-07-19 07:32:10,269 - pyskl - INFO - Epoch [83][1500/3746] lr: 4.228e-02, eta: 2 days, 8:50:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5336, loss_cls: 4.1559, loss: 4.1559 +2024-07-19 07:33:32,559 - pyskl - INFO - Epoch [83][1600/3746] lr: 4.225e-02, eta: 2 days, 8:49:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5212, loss_cls: 4.1875, loss: 4.1875 +2024-07-19 07:34:55,235 - pyskl - INFO - Epoch [83][1700/3746] lr: 4.223e-02, eta: 2 days, 8:47:41, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5283, loss_cls: 4.1586, loss: 4.1586 +2024-07-19 07:36:17,346 - pyskl - INFO - Epoch [83][1800/3746] lr: 4.220e-02, eta: 2 days, 8:46:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5286, loss_cls: 4.1551, loss: 4.1551 +2024-07-19 07:37:39,510 - pyskl - INFO - Epoch [83][1900/3746] lr: 4.217e-02, eta: 2 days, 8:45:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5188, loss_cls: 4.2480, loss: 4.2480 +2024-07-19 07:39:01,430 - pyskl - INFO - Epoch [83][2000/3746] lr: 4.214e-02, eta: 2 days, 8:43:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5273, loss_cls: 4.1779, loss: 4.1779 +2024-07-19 07:40:23,134 - pyskl - INFO - Epoch [83][2100/3746] lr: 4.212e-02, eta: 2 days, 8:42:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2725, top5_acc: 0.5230, loss_cls: 4.2042, loss: 4.2042 +2024-07-19 07:41:46,518 - pyskl - INFO - Epoch [83][2200/3746] lr: 4.209e-02, eta: 2 days, 8:41:03, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5180, loss_cls: 4.1879, loss: 4.1879 +2024-07-19 07:43:08,775 - pyskl - INFO - Epoch [83][2300/3746] lr: 4.206e-02, eta: 2 days, 8:39:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2755, top5_acc: 0.5206, loss_cls: 4.2010, loss: 4.2010 +2024-07-19 07:44:30,915 - pyskl - INFO - Epoch [83][2400/3746] lr: 4.203e-02, eta: 2 days, 8:38:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2697, top5_acc: 0.5166, loss_cls: 4.2353, loss: 4.2353 +2024-07-19 07:45:53,315 - pyskl - INFO - Epoch [83][2500/3746] lr: 4.201e-02, eta: 2 days, 8:37:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5197, loss_cls: 4.1774, loss: 4.1774 +2024-07-19 07:47:14,808 - pyskl - INFO - Epoch [83][2600/3746] lr: 4.198e-02, eta: 2 days, 8:35:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5302, loss_cls: 4.1707, loss: 4.1707 +2024-07-19 07:48:36,711 - pyskl - INFO - Epoch [83][2700/3746] lr: 4.195e-02, eta: 2 days, 8:34:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2712, top5_acc: 0.5272, loss_cls: 4.1799, loss: 4.1799 +2024-07-19 07:49:58,354 - pyskl - INFO - Epoch [83][2800/3746] lr: 4.192e-02, eta: 2 days, 8:33:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5289, loss_cls: 4.1597, loss: 4.1597 +2024-07-19 07:51:19,677 - pyskl - INFO - Epoch [83][2900/3746] lr: 4.190e-02, eta: 2 days, 8:31:43, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5219, loss_cls: 4.2004, loss: 4.2004 +2024-07-19 07:52:41,491 - pyskl - INFO - Epoch [83][3000/3746] lr: 4.187e-02, eta: 2 days, 8:30:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5164, loss_cls: 4.2146, loss: 4.2146 +2024-07-19 07:54:03,961 - pyskl - INFO - Epoch [83][3100/3746] lr: 4.184e-02, eta: 2 days, 8:29:04, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5233, loss_cls: 4.2011, loss: 4.2011 +2024-07-19 07:55:26,265 - pyskl - INFO - Epoch [83][3200/3746] lr: 4.181e-02, eta: 2 days, 8:27:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5186, loss_cls: 4.1881, loss: 4.1881 +2024-07-19 07:56:48,024 - pyskl - INFO - Epoch [83][3300/3746] lr: 4.178e-02, eta: 2 days, 8:26:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5186, loss_cls: 4.2192, loss: 4.2192 +2024-07-19 07:58:10,195 - pyskl - INFO - Epoch [83][3400/3746] lr: 4.176e-02, eta: 2 days, 8:25:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2642, top5_acc: 0.5167, loss_cls: 4.2123, loss: 4.2123 +2024-07-19 07:59:31,710 - pyskl - INFO - Epoch [83][3500/3746] lr: 4.173e-02, eta: 2 days, 8:23:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5236, loss_cls: 4.1688, loss: 4.1688 +2024-07-19 08:00:54,072 - pyskl - INFO - Epoch [83][3600/3746] lr: 4.170e-02, eta: 2 days, 8:22:25, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2728, top5_acc: 0.5122, loss_cls: 4.2017, loss: 4.2017 +2024-07-19 08:02:16,084 - pyskl - INFO - Epoch [83][3700/3746] lr: 4.167e-02, eta: 2 days, 8:21:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2686, top5_acc: 0.5223, loss_cls: 4.2004, loss: 4.2004 +2024-07-19 08:02:55,812 - pyskl - INFO - Saving checkpoint at 83 epochs +2024-07-19 08:04:46,020 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 08:04:46,773 - pyskl - INFO - +top1_acc 0.2072 +top5_acc 0.4344 +2024-07-19 08:04:46,773 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 08:04:46,826 - pyskl - INFO - +mean_acc 0.2069 +2024-07-19 08:04:46,839 - pyskl - INFO - Epoch(val) [83][309] top1_acc: 0.2072, top5_acc: 0.4344, mean_class_accuracy: 0.2069 +2024-07-19 08:08:39,794 - pyskl - INFO - Epoch [84][100/3746] lr: 4.163e-02, eta: 2 days, 8:20:40, time: 2.329, data_time: 1.342, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5298, loss_cls: 4.1223, loss: 4.1223 +2024-07-19 08:10:02,496 - pyskl - INFO - Epoch [84][200/3746] lr: 4.161e-02, eta: 2 days, 8:19:20, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5342, loss_cls: 4.1212, loss: 4.1212 +2024-07-19 08:11:25,671 - pyskl - INFO - Epoch [84][300/3746] lr: 4.158e-02, eta: 2 days, 8:18:01, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5380, loss_cls: 4.1275, loss: 4.1275 +2024-07-19 08:12:47,815 - pyskl - INFO - Epoch [84][400/3746] lr: 4.155e-02, eta: 2 days, 8:16:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5277, loss_cls: 4.1331, loss: 4.1331 +2024-07-19 08:14:10,213 - pyskl - INFO - Epoch [84][500/3746] lr: 4.152e-02, eta: 2 days, 8:15:22, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2723, top5_acc: 0.5283, loss_cls: 4.1774, loss: 4.1774 +2024-07-19 08:15:32,543 - pyskl - INFO - Epoch [84][600/3746] lr: 4.150e-02, eta: 2 days, 8:14:02, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2680, top5_acc: 0.5236, loss_cls: 4.2051, loss: 4.2051 +2024-07-19 08:16:55,307 - pyskl - INFO - Epoch [84][700/3746] lr: 4.147e-02, eta: 2 days, 8:12:43, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5316, loss_cls: 4.1465, loss: 4.1465 +2024-07-19 08:18:17,304 - pyskl - INFO - Epoch [84][800/3746] lr: 4.144e-02, eta: 2 days, 8:11:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5189, loss_cls: 4.2146, loss: 4.2146 +2024-07-19 08:19:39,887 - pyskl - INFO - Epoch [84][900/3746] lr: 4.141e-02, eta: 2 days, 8:10:03, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5241, loss_cls: 4.1570, loss: 4.1570 +2024-07-19 08:21:01,905 - pyskl - INFO - Epoch [84][1000/3746] lr: 4.139e-02, eta: 2 days, 8:08:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5383, loss_cls: 4.1098, loss: 4.1098 +2024-07-19 08:22:24,108 - pyskl - INFO - Epoch [84][1100/3746] lr: 4.136e-02, eta: 2 days, 8:07:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5363, loss_cls: 4.1538, loss: 4.1538 +2024-07-19 08:23:46,226 - pyskl - INFO - Epoch [84][1200/3746] lr: 4.133e-02, eta: 2 days, 8:06:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5308, loss_cls: 4.1389, loss: 4.1389 +2024-07-19 08:25:07,943 - pyskl - INFO - Epoch [84][1300/3746] lr: 4.130e-02, eta: 2 days, 8:04:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2752, top5_acc: 0.5247, loss_cls: 4.1650, loss: 4.1650 +2024-07-19 08:26:30,253 - pyskl - INFO - Epoch [84][1400/3746] lr: 4.128e-02, eta: 2 days, 8:03:24, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2731, top5_acc: 0.5325, loss_cls: 4.1668, loss: 4.1668 +2024-07-19 08:27:52,942 - pyskl - INFO - Epoch [84][1500/3746] lr: 4.125e-02, eta: 2 days, 8:02:05, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2753, top5_acc: 0.5247, loss_cls: 4.1915, loss: 4.1915 +2024-07-19 08:29:15,045 - pyskl - INFO - Epoch [84][1600/3746] lr: 4.122e-02, eta: 2 days, 8:00:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2667, top5_acc: 0.5231, loss_cls: 4.1878, loss: 4.1878 +2024-07-19 08:30:37,148 - pyskl - INFO - Epoch [84][1700/3746] lr: 4.119e-02, eta: 2 days, 7:59:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2666, top5_acc: 0.5175, loss_cls: 4.2333, loss: 4.2333 +2024-07-19 08:31:59,515 - pyskl - INFO - Epoch [84][1800/3746] lr: 4.117e-02, eta: 2 days, 7:58:05, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5205, loss_cls: 4.1881, loss: 4.1881 +2024-07-19 08:33:21,672 - pyskl - INFO - Epoch [84][1900/3746] lr: 4.114e-02, eta: 2 days, 7:56:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5156, loss_cls: 4.2194, loss: 4.2194 +2024-07-19 08:34:43,916 - pyskl - INFO - Epoch [84][2000/3746] lr: 4.111e-02, eta: 2 days, 7:55:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5311, loss_cls: 4.1446, loss: 4.1446 +2024-07-19 08:36:05,689 - pyskl - INFO - Epoch [84][2100/3746] lr: 4.108e-02, eta: 2 days, 7:54:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2769, top5_acc: 0.5272, loss_cls: 4.1685, loss: 4.1685 +2024-07-19 08:37:28,570 - pyskl - INFO - Epoch [84][2200/3746] lr: 4.106e-02, eta: 2 days, 7:52:46, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2709, top5_acc: 0.5295, loss_cls: 4.1612, loss: 4.1612 +2024-07-19 08:38:50,945 - pyskl - INFO - Epoch [84][2300/3746] lr: 4.103e-02, eta: 2 days, 7:51:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5189, loss_cls: 4.2119, loss: 4.2119 +2024-07-19 08:40:13,637 - pyskl - INFO - Epoch [84][2400/3746] lr: 4.100e-02, eta: 2 days, 7:50:07, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5380, loss_cls: 4.1190, loss: 4.1190 +2024-07-19 08:41:35,823 - pyskl - INFO - Epoch [84][2500/3746] lr: 4.097e-02, eta: 2 days, 7:48:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2786, top5_acc: 0.5278, loss_cls: 4.1705, loss: 4.1705 +2024-07-19 08:42:57,834 - pyskl - INFO - Epoch [84][2600/3746] lr: 4.095e-02, eta: 2 days, 7:47:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5344, loss_cls: 4.1602, loss: 4.1602 +2024-07-19 08:44:19,652 - pyskl - INFO - Epoch [84][2700/3746] lr: 4.092e-02, eta: 2 days, 7:46:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5314, loss_cls: 4.1699, loss: 4.1699 +2024-07-19 08:45:41,489 - pyskl - INFO - Epoch [84][2800/3746] lr: 4.089e-02, eta: 2 days, 7:44:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2741, top5_acc: 0.5227, loss_cls: 4.1960, loss: 4.1960 +2024-07-19 08:47:03,360 - pyskl - INFO - Epoch [84][2900/3746] lr: 4.086e-02, eta: 2 days, 7:43:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5292, loss_cls: 4.1465, loss: 4.1465 +2024-07-19 08:48:25,445 - pyskl - INFO - Epoch [84][3000/3746] lr: 4.084e-02, eta: 2 days, 7:42:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5259, loss_cls: 4.1637, loss: 4.1637 +2024-07-19 08:49:47,362 - pyskl - INFO - Epoch [84][3100/3746] lr: 4.081e-02, eta: 2 days, 7:40:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5275, loss_cls: 4.1669, loss: 4.1669 +2024-07-19 08:51:08,957 - pyskl - INFO - Epoch [84][3200/3746] lr: 4.078e-02, eta: 2 days, 7:39:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2742, top5_acc: 0.5178, loss_cls: 4.1883, loss: 4.1883 +2024-07-19 08:52:30,452 - pyskl - INFO - Epoch [84][3300/3746] lr: 4.075e-02, eta: 2 days, 7:38:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2816, top5_acc: 0.5217, loss_cls: 4.1866, loss: 4.1866 +2024-07-19 08:53:52,282 - pyskl - INFO - Epoch [84][3400/3746] lr: 4.073e-02, eta: 2 days, 7:36:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2733, top5_acc: 0.5200, loss_cls: 4.2086, loss: 4.2086 +2024-07-19 08:55:14,311 - pyskl - INFO - Epoch [84][3500/3746] lr: 4.070e-02, eta: 2 days, 7:35:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2711, top5_acc: 0.5244, loss_cls: 4.1744, loss: 4.1744 +2024-07-19 08:56:36,582 - pyskl - INFO - Epoch [84][3600/3746] lr: 4.067e-02, eta: 2 days, 7:34:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5305, loss_cls: 4.1703, loss: 4.1703 +2024-07-19 08:57:59,624 - pyskl - INFO - Epoch [84][3700/3746] lr: 4.064e-02, eta: 2 days, 7:32:48, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5230, loss_cls: 4.1833, loss: 4.1833 +2024-07-19 08:58:39,202 - pyskl - INFO - Saving checkpoint at 84 epochs +2024-07-19 09:00:30,251 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 09:00:30,992 - pyskl - INFO - +top1_acc 0.2204 +top5_acc 0.4535 +2024-07-19 09:00:30,993 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 09:00:31,037 - pyskl - INFO - +mean_acc 0.2201 +2024-07-19 09:00:31,041 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_79.pth was removed +2024-07-19 09:00:31,294 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_84.pth. +2024-07-19 09:00:31,295 - pyskl - INFO - Best top1_acc is 0.2204 at 84 epoch. +2024-07-19 09:00:31,307 - pyskl - INFO - Epoch(val) [84][309] top1_acc: 0.2204, top5_acc: 0.4535, mean_class_accuracy: 0.2201 +2024-07-19 09:04:32,039 - pyskl - INFO - Epoch [85][100/3746] lr: 4.060e-02, eta: 2 days, 7:32:26, time: 2.407, data_time: 1.406, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5392, loss_cls: 4.1373, loss: 4.1373 +2024-07-19 09:05:54,400 - pyskl - INFO - Epoch [85][200/3746] lr: 4.058e-02, eta: 2 days, 7:31:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2850, top5_acc: 0.5366, loss_cls: 4.1206, loss: 4.1206 +2024-07-19 09:07:17,147 - pyskl - INFO - Epoch [85][300/3746] lr: 4.055e-02, eta: 2 days, 7:29:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2773, top5_acc: 0.5364, loss_cls: 4.1088, loss: 4.1088 +2024-07-19 09:08:39,360 - pyskl - INFO - Epoch [85][400/3746] lr: 4.052e-02, eta: 2 days, 7:28:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5342, loss_cls: 4.1344, loss: 4.1344 +2024-07-19 09:10:01,036 - pyskl - INFO - Epoch [85][500/3746] lr: 4.049e-02, eta: 2 days, 7:27:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5375, loss_cls: 4.1461, loss: 4.1461 +2024-07-19 09:11:23,099 - pyskl - INFO - Epoch [85][600/3746] lr: 4.047e-02, eta: 2 days, 7:25:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5289, loss_cls: 4.1417, loss: 4.1417 +2024-07-19 09:12:45,256 - pyskl - INFO - Epoch [85][700/3746] lr: 4.044e-02, eta: 2 days, 7:24:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2678, top5_acc: 0.5220, loss_cls: 4.1856, loss: 4.1856 +2024-07-19 09:14:07,480 - pyskl - INFO - Epoch [85][800/3746] lr: 4.041e-02, eta: 2 days, 7:23:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5238, loss_cls: 4.1824, loss: 4.1824 +2024-07-19 09:15:29,898 - pyskl - INFO - Epoch [85][900/3746] lr: 4.038e-02, eta: 2 days, 7:21:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5347, loss_cls: 4.1433, loss: 4.1433 +2024-07-19 09:16:51,868 - pyskl - INFO - Epoch [85][1000/3746] lr: 4.036e-02, eta: 2 days, 7:20:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5317, loss_cls: 4.1304, loss: 4.1304 +2024-07-19 09:18:13,525 - pyskl - INFO - Epoch [85][1100/3746] lr: 4.033e-02, eta: 2 days, 7:19:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2781, top5_acc: 0.5339, loss_cls: 4.1296, loss: 4.1296 +2024-07-19 09:19:35,416 - pyskl - INFO - Epoch [85][1200/3746] lr: 4.030e-02, eta: 2 days, 7:17:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5291, loss_cls: 4.1515, loss: 4.1515 +2024-07-19 09:20:57,834 - pyskl - INFO - Epoch [85][1300/3746] lr: 4.027e-02, eta: 2 days, 7:16:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2814, top5_acc: 0.5300, loss_cls: 4.1606, loss: 4.1606 +2024-07-19 09:22:20,770 - pyskl - INFO - Epoch [85][1400/3746] lr: 4.025e-02, eta: 2 days, 7:15:07, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2806, top5_acc: 0.5245, loss_cls: 4.1590, loss: 4.1590 +2024-07-19 09:23:43,358 - pyskl - INFO - Epoch [85][1500/3746] lr: 4.022e-02, eta: 2 days, 7:13:48, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5384, loss_cls: 4.1436, loss: 4.1436 +2024-07-19 09:25:05,433 - pyskl - INFO - Epoch [85][1600/3746] lr: 4.019e-02, eta: 2 days, 7:12:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5284, loss_cls: 4.1443, loss: 4.1443 +2024-07-19 09:26:27,545 - pyskl - INFO - Epoch [85][1700/3746] lr: 4.016e-02, eta: 2 days, 7:11:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5295, loss_cls: 4.1507, loss: 4.1507 +2024-07-19 09:27:49,288 - pyskl - INFO - Epoch [85][1800/3746] lr: 4.014e-02, eta: 2 days, 7:09:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5355, loss_cls: 4.1583, loss: 4.1583 +2024-07-19 09:29:11,604 - pyskl - INFO - Epoch [85][1900/3746] lr: 4.011e-02, eta: 2 days, 7:08:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2748, top5_acc: 0.5227, loss_cls: 4.1892, loss: 4.1892 +2024-07-19 09:30:33,202 - pyskl - INFO - Epoch [85][2000/3746] lr: 4.008e-02, eta: 2 days, 7:07:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5316, loss_cls: 4.1437, loss: 4.1437 +2024-07-19 09:31:55,219 - pyskl - INFO - Epoch [85][2100/3746] lr: 4.006e-02, eta: 2 days, 7:05:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2747, top5_acc: 0.5352, loss_cls: 4.1448, loss: 4.1448 +2024-07-19 09:33:17,451 - pyskl - INFO - Epoch [85][2200/3746] lr: 4.003e-02, eta: 2 days, 7:04:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2777, top5_acc: 0.5317, loss_cls: 4.1540, loss: 4.1540 +2024-07-19 09:34:39,592 - pyskl - INFO - Epoch [85][2300/3746] lr: 4.000e-02, eta: 2 days, 7:03:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2897, top5_acc: 0.5397, loss_cls: 4.1192, loss: 4.1192 +2024-07-19 09:36:02,210 - pyskl - INFO - Epoch [85][2400/3746] lr: 3.997e-02, eta: 2 days, 7:01:48, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5188, loss_cls: 4.1745, loss: 4.1745 +2024-07-19 09:37:23,551 - pyskl - INFO - Epoch [85][2500/3746] lr: 3.995e-02, eta: 2 days, 7:00:27, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2770, top5_acc: 0.5300, loss_cls: 4.1551, loss: 4.1551 +2024-07-19 09:38:45,614 - pyskl - INFO - Epoch [85][2600/3746] lr: 3.992e-02, eta: 2 days, 6:59:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5250, loss_cls: 4.1725, loss: 4.1725 +2024-07-19 09:40:07,952 - pyskl - INFO - Epoch [85][2700/3746] lr: 3.989e-02, eta: 2 days, 6:57:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2692, top5_acc: 0.5250, loss_cls: 4.1832, loss: 4.1832 +2024-07-19 09:41:30,432 - pyskl - INFO - Epoch [85][2800/3746] lr: 3.986e-02, eta: 2 days, 6:56:27, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2737, top5_acc: 0.5203, loss_cls: 4.1870, loss: 4.1870 +2024-07-19 09:42:52,924 - pyskl - INFO - Epoch [85][2900/3746] lr: 3.984e-02, eta: 2 days, 6:55:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5292, loss_cls: 4.1179, loss: 4.1179 +2024-07-19 09:44:15,159 - pyskl - INFO - Epoch [85][3000/3746] lr: 3.981e-02, eta: 2 days, 6:53:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2684, top5_acc: 0.5158, loss_cls: 4.2239, loss: 4.2239 +2024-07-19 09:45:37,280 - pyskl - INFO - Epoch [85][3100/3746] lr: 3.978e-02, eta: 2 days, 6:52:28, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2745, top5_acc: 0.5200, loss_cls: 4.1800, loss: 4.1800 +2024-07-19 09:46:59,013 - pyskl - INFO - Epoch [85][3200/3746] lr: 3.975e-02, eta: 2 days, 6:51:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2783, top5_acc: 0.5230, loss_cls: 4.1714, loss: 4.1714 +2024-07-19 09:48:21,156 - pyskl - INFO - Epoch [85][3300/3746] lr: 3.973e-02, eta: 2 days, 6:49:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5292, loss_cls: 4.1573, loss: 4.1573 +2024-07-19 09:49:42,990 - pyskl - INFO - Epoch [85][3400/3746] lr: 3.970e-02, eta: 2 days, 6:48:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5280, loss_cls: 4.1509, loss: 4.1509 +2024-07-19 09:51:04,920 - pyskl - INFO - Epoch [85][3500/3746] lr: 3.967e-02, eta: 2 days, 6:47:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5245, loss_cls: 4.1903, loss: 4.1903 +2024-07-19 09:52:26,861 - pyskl - INFO - Epoch [85][3600/3746] lr: 3.964e-02, eta: 2 days, 6:45:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2758, top5_acc: 0.5200, loss_cls: 4.1839, loss: 4.1839 +2024-07-19 09:53:48,701 - pyskl - INFO - Epoch [85][3700/3746] lr: 3.962e-02, eta: 2 days, 6:44:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5255, loss_cls: 4.1712, loss: 4.1712 +2024-07-19 09:54:28,400 - pyskl - INFO - Saving checkpoint at 85 epochs +2024-07-19 09:56:19,824 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 09:56:20,491 - pyskl - INFO - +top1_acc 0.2261 +top5_acc 0.4663 +2024-07-19 09:56:20,491 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 09:56:20,534 - pyskl - INFO - +mean_acc 0.2259 +2024-07-19 09:56:20,538 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_84.pth was removed +2024-07-19 09:56:20,790 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_85.pth. +2024-07-19 09:56:20,790 - pyskl - INFO - Best top1_acc is 0.2261 at 85 epoch. +2024-07-19 09:56:20,802 - pyskl - INFO - Epoch(val) [85][309] top1_acc: 0.2261, top5_acc: 0.4663, mean_class_accuracy: 0.2259 +2024-07-19 10:00:23,450 - pyskl - INFO - Epoch [86][100/3746] lr: 3.958e-02, eta: 2 days, 6:44:04, time: 2.426, data_time: 1.400, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5467, loss_cls: 4.0864, loss: 4.0864 +2024-07-19 10:01:46,453 - pyskl - INFO - Epoch [86][200/3746] lr: 3.955e-02, eta: 2 days, 6:42:44, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5330, loss_cls: 4.1184, loss: 4.1184 +2024-07-19 10:03:09,896 - pyskl - INFO - Epoch [86][300/3746] lr: 3.952e-02, eta: 2 days, 6:41:25, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5400, loss_cls: 4.1070, loss: 4.1070 +2024-07-19 10:04:32,850 - pyskl - INFO - Epoch [86][400/3746] lr: 3.950e-02, eta: 2 days, 6:40:06, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5294, loss_cls: 4.1509, loss: 4.1509 +2024-07-19 10:05:55,546 - pyskl - INFO - Epoch [86][500/3746] lr: 3.947e-02, eta: 2 days, 6:38:46, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5355, loss_cls: 4.1026, loss: 4.1026 +2024-07-19 10:07:18,716 - pyskl - INFO - Epoch [86][600/3746] lr: 3.944e-02, eta: 2 days, 6:37:27, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5387, loss_cls: 4.1024, loss: 4.1024 +2024-07-19 10:08:40,488 - pyskl - INFO - Epoch [86][700/3746] lr: 3.941e-02, eta: 2 days, 6:36:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5344, loss_cls: 4.1419, loss: 4.1419 +2024-07-19 10:10:03,060 - pyskl - INFO - Epoch [86][800/3746] lr: 3.939e-02, eta: 2 days, 6:34:47, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5414, loss_cls: 4.0965, loss: 4.0965 +2024-07-19 10:11:24,852 - pyskl - INFO - Epoch [86][900/3746] lr: 3.936e-02, eta: 2 days, 6:33:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5245, loss_cls: 4.1668, loss: 4.1668 +2024-07-19 10:12:46,566 - pyskl - INFO - Epoch [86][1000/3746] lr: 3.933e-02, eta: 2 days, 6:32:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5266, loss_cls: 4.1487, loss: 4.1487 +2024-07-19 10:14:08,825 - pyskl - INFO - Epoch [86][1100/3746] lr: 3.930e-02, eta: 2 days, 6:30:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5328, loss_cls: 4.1217, loss: 4.1217 +2024-07-19 10:15:31,125 - pyskl - INFO - Epoch [86][1200/3746] lr: 3.928e-02, eta: 2 days, 6:29:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5283, loss_cls: 4.1427, loss: 4.1427 +2024-07-19 10:16:53,671 - pyskl - INFO - Epoch [86][1300/3746] lr: 3.925e-02, eta: 2 days, 6:28:07, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5227, loss_cls: 4.1603, loss: 4.1603 +2024-07-19 10:18:15,619 - pyskl - INFO - Epoch [86][1400/3746] lr: 3.922e-02, eta: 2 days, 6:26:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5481, loss_cls: 4.0849, loss: 4.0849 +2024-07-19 10:19:38,200 - pyskl - INFO - Epoch [86][1500/3746] lr: 3.919e-02, eta: 2 days, 6:25:27, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5295, loss_cls: 4.1717, loss: 4.1717 +2024-07-19 10:21:00,290 - pyskl - INFO - Epoch [86][1600/3746] lr: 3.917e-02, eta: 2 days, 6:24:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2727, top5_acc: 0.5281, loss_cls: 4.1546, loss: 4.1546 +2024-07-19 10:22:21,996 - pyskl - INFO - Epoch [86][1700/3746] lr: 3.914e-02, eta: 2 days, 6:22:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5309, loss_cls: 4.1386, loss: 4.1386 +2024-07-19 10:23:44,176 - pyskl - INFO - Epoch [86][1800/3746] lr: 3.911e-02, eta: 2 days, 6:21:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5331, loss_cls: 4.1432, loss: 4.1432 +2024-07-19 10:25:06,861 - pyskl - INFO - Epoch [86][1900/3746] lr: 3.909e-02, eta: 2 days, 6:20:06, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5323, loss_cls: 4.1446, loss: 4.1446 +2024-07-19 10:26:29,322 - pyskl - INFO - Epoch [86][2000/3746] lr: 3.906e-02, eta: 2 days, 6:18:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5347, loss_cls: 4.1292, loss: 4.1292 +2024-07-19 10:27:51,815 - pyskl - INFO - Epoch [86][2100/3746] lr: 3.903e-02, eta: 2 days, 6:17:27, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5317, loss_cls: 4.1508, loss: 4.1508 +2024-07-19 10:29:13,410 - pyskl - INFO - Epoch [86][2200/3746] lr: 3.900e-02, eta: 2 days, 6:16:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5262, loss_cls: 4.1676, loss: 4.1676 +2024-07-19 10:30:36,329 - pyskl - INFO - Epoch [86][2300/3746] lr: 3.898e-02, eta: 2 days, 6:14:47, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2750, top5_acc: 0.5269, loss_cls: 4.2011, loss: 4.2011 +2024-07-19 10:31:58,297 - pyskl - INFO - Epoch [86][2400/3746] lr: 3.895e-02, eta: 2 days, 6:13:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5220, loss_cls: 4.1711, loss: 4.1711 +2024-07-19 10:33:20,639 - pyskl - INFO - Epoch [86][2500/3746] lr: 3.892e-02, eta: 2 days, 6:12:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2798, top5_acc: 0.5373, loss_cls: 4.1488, loss: 4.1488 +2024-07-19 10:34:42,758 - pyskl - INFO - Epoch [86][2600/3746] lr: 3.889e-02, eta: 2 days, 6:10:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5322, loss_cls: 4.1431, loss: 4.1431 +2024-07-19 10:36:05,077 - pyskl - INFO - Epoch [86][2700/3746] lr: 3.887e-02, eta: 2 days, 6:09:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5369, loss_cls: 4.0748, loss: 4.0748 +2024-07-19 10:37:26,835 - pyskl - INFO - Epoch [86][2800/3746] lr: 3.884e-02, eta: 2 days, 6:08:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2905, top5_acc: 0.5380, loss_cls: 4.0974, loss: 4.0974 +2024-07-19 10:38:49,008 - pyskl - INFO - Epoch [86][2900/3746] lr: 3.881e-02, eta: 2 days, 6:06:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2744, top5_acc: 0.5258, loss_cls: 4.1975, loss: 4.1975 +2024-07-19 10:40:11,617 - pyskl - INFO - Epoch [86][3000/3746] lr: 3.879e-02, eta: 2 days, 6:05:26, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2761, top5_acc: 0.5216, loss_cls: 4.1654, loss: 4.1654 +2024-07-19 10:41:33,205 - pyskl - INFO - Epoch [86][3100/3746] lr: 3.876e-02, eta: 2 days, 6:04:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2828, top5_acc: 0.5248, loss_cls: 4.1514, loss: 4.1514 +2024-07-19 10:42:54,591 - pyskl - INFO - Epoch [86][3200/3746] lr: 3.873e-02, eta: 2 days, 6:02:45, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2703, top5_acc: 0.5169, loss_cls: 4.2022, loss: 4.2022 +2024-07-19 10:44:16,479 - pyskl - INFO - Epoch [86][3300/3746] lr: 3.870e-02, eta: 2 days, 6:01:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2766, top5_acc: 0.5162, loss_cls: 4.1887, loss: 4.1887 +2024-07-19 10:45:38,118 - pyskl - INFO - Epoch [86][3400/3746] lr: 3.868e-02, eta: 2 days, 6:00:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2775, top5_acc: 0.5295, loss_cls: 4.1775, loss: 4.1775 +2024-07-19 10:46:59,661 - pyskl - INFO - Epoch [86][3500/3746] lr: 3.865e-02, eta: 2 days, 5:58:44, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5220, loss_cls: 4.1879, loss: 4.1879 +2024-07-19 10:48:21,264 - pyskl - INFO - Epoch [86][3600/3746] lr: 3.862e-02, eta: 2 days, 5:57:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5294, loss_cls: 4.1125, loss: 4.1125 +2024-07-19 10:49:42,919 - pyskl - INFO - Epoch [86][3700/3746] lr: 3.860e-02, eta: 2 days, 5:56:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5289, loss_cls: 4.1506, loss: 4.1506 +2024-07-19 10:50:22,041 - pyskl - INFO - Saving checkpoint at 86 epochs +2024-07-19 10:52:14,073 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 10:52:14,740 - pyskl - INFO - +top1_acc 0.2296 +top5_acc 0.4643 +2024-07-19 10:52:14,740 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 10:52:14,788 - pyskl - INFO - +mean_acc 0.2295 +2024-07-19 10:52:14,793 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_85.pth was removed +2024-07-19 10:52:15,040 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_86.pth. +2024-07-19 10:52:15,041 - pyskl - INFO - Best top1_acc is 0.2296 at 86 epoch. +2024-07-19 10:52:15,053 - pyskl - INFO - Epoch(val) [86][309] top1_acc: 0.2296, top5_acc: 0.4643, mean_class_accuracy: 0.2295 +2024-07-19 10:56:05,212 - pyskl - INFO - Epoch [87][100/3746] lr: 3.856e-02, eta: 2 days, 5:55:28, time: 2.301, data_time: 1.311, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5598, loss_cls: 4.0284, loss: 4.0284 +2024-07-19 10:57:27,787 - pyskl - INFO - Epoch [87][200/3746] lr: 3.853e-02, eta: 2 days, 5:54:08, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5336, loss_cls: 4.1280, loss: 4.1280 +2024-07-19 10:58:49,889 - pyskl - INFO - Epoch [87][300/3746] lr: 3.850e-02, eta: 2 days, 5:52:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5302, loss_cls: 4.1286, loss: 4.1286 +2024-07-19 11:00:11,566 - pyskl - INFO - Epoch [87][400/3746] lr: 3.847e-02, eta: 2 days, 5:51:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5342, loss_cls: 4.1031, loss: 4.1031 +2024-07-19 11:01:33,776 - pyskl - INFO - Epoch [87][500/3746] lr: 3.845e-02, eta: 2 days, 5:50:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5475, loss_cls: 4.0373, loss: 4.0373 +2024-07-19 11:02:56,435 - pyskl - INFO - Epoch [87][600/3746] lr: 3.842e-02, eta: 2 days, 5:48:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5377, loss_cls: 4.1215, loss: 4.1215 +2024-07-19 11:04:18,414 - pyskl - INFO - Epoch [87][700/3746] lr: 3.839e-02, eta: 2 days, 5:47:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5436, loss_cls: 4.0876, loss: 4.0876 +2024-07-19 11:05:40,870 - pyskl - INFO - Epoch [87][800/3746] lr: 3.837e-02, eta: 2 days, 5:46:07, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5375, loss_cls: 4.1232, loss: 4.1232 +2024-07-19 11:07:03,248 - pyskl - INFO - Epoch [87][900/3746] lr: 3.834e-02, eta: 2 days, 5:44:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2902, top5_acc: 0.5281, loss_cls: 4.1286, loss: 4.1286 +2024-07-19 11:08:25,012 - pyskl - INFO - Epoch [87][1000/3746] lr: 3.831e-02, eta: 2 days, 5:43:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5398, loss_cls: 4.0625, loss: 4.0625 +2024-07-19 11:09:47,004 - pyskl - INFO - Epoch [87][1100/3746] lr: 3.828e-02, eta: 2 days, 5:42:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5294, loss_cls: 4.1008, loss: 4.1008 +2024-07-19 11:11:09,534 - pyskl - INFO - Epoch [87][1200/3746] lr: 3.826e-02, eta: 2 days, 5:40:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5337, loss_cls: 4.1012, loss: 4.1012 +2024-07-19 11:12:31,712 - pyskl - INFO - Epoch [87][1300/3746] lr: 3.823e-02, eta: 2 days, 5:39:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2791, top5_acc: 0.5284, loss_cls: 4.1396, loss: 4.1396 +2024-07-19 11:13:54,203 - pyskl - INFO - Epoch [87][1400/3746] lr: 3.820e-02, eta: 2 days, 5:38:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5383, loss_cls: 4.1209, loss: 4.1209 +2024-07-19 11:15:16,566 - pyskl - INFO - Epoch [87][1500/3746] lr: 3.817e-02, eta: 2 days, 5:36:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2759, top5_acc: 0.5320, loss_cls: 4.1618, loss: 4.1618 +2024-07-19 11:16:38,669 - pyskl - INFO - Epoch [87][1600/3746] lr: 3.815e-02, eta: 2 days, 5:35:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5380, loss_cls: 4.1282, loss: 4.1282 +2024-07-19 11:18:00,710 - pyskl - INFO - Epoch [87][1700/3746] lr: 3.812e-02, eta: 2 days, 5:34:06, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2825, top5_acc: 0.5278, loss_cls: 4.1688, loss: 4.1688 +2024-07-19 11:19:22,162 - pyskl - INFO - Epoch [87][1800/3746] lr: 3.809e-02, eta: 2 days, 5:32:45, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2764, top5_acc: 0.5217, loss_cls: 4.1740, loss: 4.1740 +2024-07-19 11:20:43,958 - pyskl - INFO - Epoch [87][1900/3746] lr: 3.807e-02, eta: 2 days, 5:31:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5339, loss_cls: 4.1347, loss: 4.1347 +2024-07-19 11:22:05,958 - pyskl - INFO - Epoch [87][2000/3746] lr: 3.804e-02, eta: 2 days, 5:30:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5344, loss_cls: 4.1593, loss: 4.1593 +2024-07-19 11:23:28,053 - pyskl - INFO - Epoch [87][2100/3746] lr: 3.801e-02, eta: 2 days, 5:28:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5413, loss_cls: 4.1327, loss: 4.1327 +2024-07-19 11:24:50,055 - pyskl - INFO - Epoch [87][2200/3746] lr: 3.798e-02, eta: 2 days, 5:27:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2800, top5_acc: 0.5294, loss_cls: 4.1328, loss: 4.1328 +2024-07-19 11:26:12,652 - pyskl - INFO - Epoch [87][2300/3746] lr: 3.796e-02, eta: 2 days, 5:26:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5353, loss_cls: 4.1104, loss: 4.1104 +2024-07-19 11:27:35,188 - pyskl - INFO - Epoch [87][2400/3746] lr: 3.793e-02, eta: 2 days, 5:24:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5306, loss_cls: 4.1468, loss: 4.1468 +2024-07-19 11:28:57,572 - pyskl - INFO - Epoch [87][2500/3746] lr: 3.790e-02, eta: 2 days, 5:23:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5325, loss_cls: 4.1281, loss: 4.1281 +2024-07-19 11:30:19,885 - pyskl - INFO - Epoch [87][2600/3746] lr: 3.788e-02, eta: 2 days, 5:22:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2767, top5_acc: 0.5323, loss_cls: 4.1622, loss: 4.1622 +2024-07-19 11:31:41,972 - pyskl - INFO - Epoch [87][2700/3746] lr: 3.785e-02, eta: 2 days, 5:20:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5330, loss_cls: 4.1406, loss: 4.1406 +2024-07-19 11:33:03,585 - pyskl - INFO - Epoch [87][2800/3746] lr: 3.782e-02, eta: 2 days, 5:19:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2805, top5_acc: 0.5302, loss_cls: 4.1461, loss: 4.1461 +2024-07-19 11:34:25,232 - pyskl - INFO - Epoch [87][2900/3746] lr: 3.779e-02, eta: 2 days, 5:18:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2822, top5_acc: 0.5300, loss_cls: 4.1531, loss: 4.1531 +2024-07-19 11:35:46,818 - pyskl - INFO - Epoch [87][3000/3746] lr: 3.777e-02, eta: 2 days, 5:16:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5350, loss_cls: 4.1395, loss: 4.1395 +2024-07-19 11:37:08,939 - pyskl - INFO - Epoch [87][3100/3746] lr: 3.774e-02, eta: 2 days, 5:15:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5238, loss_cls: 4.1654, loss: 4.1654 +2024-07-19 11:38:30,670 - pyskl - INFO - Epoch [87][3200/3746] lr: 3.771e-02, eta: 2 days, 5:14:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2756, top5_acc: 0.5309, loss_cls: 4.1320, loss: 4.1320 +2024-07-19 11:39:52,225 - pyskl - INFO - Epoch [87][3300/3746] lr: 3.769e-02, eta: 2 days, 5:12:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2794, top5_acc: 0.5383, loss_cls: 4.1240, loss: 4.1240 +2024-07-19 11:41:14,456 - pyskl - INFO - Epoch [87][3400/3746] lr: 3.766e-02, eta: 2 days, 5:11:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5275, loss_cls: 4.1529, loss: 4.1529 +2024-07-19 11:42:36,337 - pyskl - INFO - Epoch [87][3500/3746] lr: 3.763e-02, eta: 2 days, 5:10:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5295, loss_cls: 4.1581, loss: 4.1581 +2024-07-19 11:43:58,675 - pyskl - INFO - Epoch [87][3600/3746] lr: 3.761e-02, eta: 2 days, 5:08:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2811, top5_acc: 0.5277, loss_cls: 4.1511, loss: 4.1511 +2024-07-19 11:45:20,818 - pyskl - INFO - Epoch [87][3700/3746] lr: 3.758e-02, eta: 2 days, 5:07:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2847, top5_acc: 0.5345, loss_cls: 4.1440, loss: 4.1440 +2024-07-19 11:46:00,577 - pyskl - INFO - Saving checkpoint at 87 epochs +2024-07-19 11:47:52,005 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 11:47:52,674 - pyskl - INFO - +top1_acc 0.2279 +top5_acc 0.4658 +2024-07-19 11:47:52,675 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 11:47:52,718 - pyskl - INFO - +mean_acc 0.2275 +2024-07-19 11:47:52,730 - pyskl - INFO - Epoch(val) [87][309] top1_acc: 0.2279, top5_acc: 0.4658, mean_class_accuracy: 0.2275 +2024-07-19 11:51:41,327 - pyskl - INFO - Epoch [88][100/3746] lr: 3.754e-02, eta: 2 days, 5:06:42, time: 2.286, data_time: 1.297, memory: 15990, top1_acc: 0.3014, top5_acc: 0.5530, loss_cls: 4.0177, loss: 4.0177 +2024-07-19 11:53:03,780 - pyskl - INFO - Epoch [88][200/3746] lr: 3.751e-02, eta: 2 days, 5:05:22, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5378, loss_cls: 4.0937, loss: 4.0937 +2024-07-19 11:54:25,968 - pyskl - INFO - Epoch [88][300/3746] lr: 3.748e-02, eta: 2 days, 5:04:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2866, top5_acc: 0.5372, loss_cls: 4.1227, loss: 4.1227 +2024-07-19 11:55:47,913 - pyskl - INFO - Epoch [88][400/3746] lr: 3.746e-02, eta: 2 days, 5:02:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5473, loss_cls: 4.0834, loss: 4.0834 +2024-07-19 11:57:10,263 - pyskl - INFO - Epoch [88][500/3746] lr: 3.743e-02, eta: 2 days, 5:01:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5380, loss_cls: 4.0700, loss: 4.0700 +2024-07-19 11:58:32,953 - pyskl - INFO - Epoch [88][600/3746] lr: 3.740e-02, eta: 2 days, 5:00:01, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2819, top5_acc: 0.5353, loss_cls: 4.1241, loss: 4.1241 +2024-07-19 11:59:54,640 - pyskl - INFO - Epoch [88][700/3746] lr: 3.738e-02, eta: 2 days, 4:58:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5405, loss_cls: 4.0761, loss: 4.0761 +2024-07-19 12:01:16,783 - pyskl - INFO - Epoch [88][800/3746] lr: 3.735e-02, eta: 2 days, 4:57:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2894, top5_acc: 0.5286, loss_cls: 4.1148, loss: 4.1148 +2024-07-19 12:02:38,740 - pyskl - INFO - Epoch [88][900/3746] lr: 3.732e-02, eta: 2 days, 4:56:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2762, top5_acc: 0.5341, loss_cls: 4.1283, loss: 4.1283 +2024-07-19 12:04:00,607 - pyskl - INFO - Epoch [88][1000/3746] lr: 3.730e-02, eta: 2 days, 4:54:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5361, loss_cls: 4.1081, loss: 4.1081 +2024-07-19 12:05:22,647 - pyskl - INFO - Epoch [88][1100/3746] lr: 3.727e-02, eta: 2 days, 4:53:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5380, loss_cls: 4.1006, loss: 4.1006 +2024-07-19 12:06:45,434 - pyskl - INFO - Epoch [88][1200/3746] lr: 3.724e-02, eta: 2 days, 4:51:59, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2797, top5_acc: 0.5281, loss_cls: 4.1465, loss: 4.1465 +2024-07-19 12:08:07,485 - pyskl - INFO - Epoch [88][1300/3746] lr: 3.721e-02, eta: 2 days, 4:50:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5337, loss_cls: 4.1046, loss: 4.1046 +2024-07-19 12:09:30,487 - pyskl - INFO - Epoch [88][1400/3746] lr: 3.719e-02, eta: 2 days, 4:49:19, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2830, top5_acc: 0.5358, loss_cls: 4.1204, loss: 4.1204 +2024-07-19 12:10:52,907 - pyskl - INFO - Epoch [88][1500/3746] lr: 3.716e-02, eta: 2 days, 4:47:59, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5403, loss_cls: 4.1019, loss: 4.1019 +2024-07-19 12:12:14,796 - pyskl - INFO - Epoch [88][1600/3746] lr: 3.713e-02, eta: 2 days, 4:46:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5327, loss_cls: 4.1079, loss: 4.1079 +2024-07-19 12:13:37,080 - pyskl - INFO - Epoch [88][1700/3746] lr: 3.711e-02, eta: 2 days, 4:45:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2827, top5_acc: 0.5372, loss_cls: 4.1233, loss: 4.1233 +2024-07-19 12:14:58,897 - pyskl - INFO - Epoch [88][1800/3746] lr: 3.708e-02, eta: 2 days, 4:43:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5453, loss_cls: 4.0639, loss: 4.0639 +2024-07-19 12:16:20,608 - pyskl - INFO - Epoch [88][1900/3746] lr: 3.705e-02, eta: 2 days, 4:42:37, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5431, loss_cls: 4.0571, loss: 4.0571 +2024-07-19 12:17:42,636 - pyskl - INFO - Epoch [88][2000/3746] lr: 3.703e-02, eta: 2 days, 4:41:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5336, loss_cls: 4.1311, loss: 4.1311 +2024-07-19 12:19:05,157 - pyskl - INFO - Epoch [88][2100/3746] lr: 3.700e-02, eta: 2 days, 4:39:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2784, top5_acc: 0.5331, loss_cls: 4.1288, loss: 4.1288 +2024-07-19 12:20:27,276 - pyskl - INFO - Epoch [88][2200/3746] lr: 3.697e-02, eta: 2 days, 4:38:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5344, loss_cls: 4.1087, loss: 4.1087 +2024-07-19 12:21:49,116 - pyskl - INFO - Epoch [88][2300/3746] lr: 3.694e-02, eta: 2 days, 4:37:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5297, loss_cls: 4.1510, loss: 4.1510 +2024-07-19 12:23:11,114 - pyskl - INFO - Epoch [88][2400/3746] lr: 3.692e-02, eta: 2 days, 4:35:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2845, top5_acc: 0.5402, loss_cls: 4.1018, loss: 4.1018 +2024-07-19 12:24:33,371 - pyskl - INFO - Epoch [88][2500/3746] lr: 3.689e-02, eta: 2 days, 4:34:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5395, loss_cls: 4.1288, loss: 4.1288 +2024-07-19 12:25:55,510 - pyskl - INFO - Epoch [88][2600/3746] lr: 3.686e-02, eta: 2 days, 4:33:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5339, loss_cls: 4.1285, loss: 4.1285 +2024-07-19 12:27:17,580 - pyskl - INFO - Epoch [88][2700/3746] lr: 3.684e-02, eta: 2 days, 4:31:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2848, top5_acc: 0.5288, loss_cls: 4.1371, loss: 4.1371 +2024-07-19 12:28:39,353 - pyskl - INFO - Epoch [88][2800/3746] lr: 3.681e-02, eta: 2 days, 4:30:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2855, top5_acc: 0.5292, loss_cls: 4.1437, loss: 4.1437 +2024-07-19 12:30:01,231 - pyskl - INFO - Epoch [88][2900/3746] lr: 3.678e-02, eta: 2 days, 4:29:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2877, top5_acc: 0.5228, loss_cls: 4.1317, loss: 4.1317 +2024-07-19 12:31:23,553 - pyskl - INFO - Epoch [88][3000/3746] lr: 3.676e-02, eta: 2 days, 4:27:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5427, loss_cls: 4.0835, loss: 4.0835 +2024-07-19 12:32:45,680 - pyskl - INFO - Epoch [88][3100/3746] lr: 3.673e-02, eta: 2 days, 4:26:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5241, loss_cls: 4.1461, loss: 4.1461 +2024-07-19 12:34:08,048 - pyskl - INFO - Epoch [88][3200/3746] lr: 3.670e-02, eta: 2 days, 4:25:13, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2695, top5_acc: 0.5247, loss_cls: 4.1867, loss: 4.1867 +2024-07-19 12:35:30,047 - pyskl - INFO - Epoch [88][3300/3746] lr: 3.667e-02, eta: 2 days, 4:23:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2839, top5_acc: 0.5377, loss_cls: 4.1070, loss: 4.1070 +2024-07-19 12:36:52,353 - pyskl - INFO - Epoch [88][3400/3746] lr: 3.665e-02, eta: 2 days, 4:22:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2730, top5_acc: 0.5275, loss_cls: 4.1645, loss: 4.1645 +2024-07-19 12:38:14,338 - pyskl - INFO - Epoch [88][3500/3746] lr: 3.662e-02, eta: 2 days, 4:21:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5363, loss_cls: 4.0900, loss: 4.0900 +2024-07-19 12:39:36,544 - pyskl - INFO - Epoch [88][3600/3746] lr: 3.659e-02, eta: 2 days, 4:19:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5261, loss_cls: 4.1260, loss: 4.1260 +2024-07-19 12:40:57,887 - pyskl - INFO - Epoch [88][3700/3746] lr: 3.657e-02, eta: 2 days, 4:18:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5280, loss_cls: 4.1386, loss: 4.1386 +2024-07-19 12:41:38,220 - pyskl - INFO - Saving checkpoint at 88 epochs +2024-07-19 12:43:29,605 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 12:43:30,404 - pyskl - INFO - +top1_acc 0.2224 +top5_acc 0.4504 +2024-07-19 12:43:30,404 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 12:43:30,455 - pyskl - INFO - +mean_acc 0.2222 +2024-07-19 12:43:30,468 - pyskl - INFO - Epoch(val) [88][309] top1_acc: 0.2224, top5_acc: 0.4504, mean_class_accuracy: 0.2222 +2024-07-19 12:47:20,979 - pyskl - INFO - Epoch [89][100/3746] lr: 3.653e-02, eta: 2 days, 4:17:52, time: 2.305, data_time: 1.312, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5456, loss_cls: 4.0712, loss: 4.0712 +2024-07-19 12:48:44,257 - pyskl - INFO - Epoch [89][200/3746] lr: 3.650e-02, eta: 2 days, 4:16:32, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5386, loss_cls: 4.0768, loss: 4.0768 +2024-07-19 12:50:06,875 - pyskl - INFO - Epoch [89][300/3746] lr: 3.647e-02, eta: 2 days, 4:15:12, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5487, loss_cls: 4.0592, loss: 4.0592 +2024-07-19 12:51:28,328 - pyskl - INFO - Epoch [89][400/3746] lr: 3.645e-02, eta: 2 days, 4:13:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5447, loss_cls: 4.0719, loss: 4.0719 +2024-07-19 12:52:50,215 - pyskl - INFO - Epoch [89][500/3746] lr: 3.642e-02, eta: 2 days, 4:12:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5523, loss_cls: 4.0686, loss: 4.0686 +2024-07-19 12:54:13,098 - pyskl - INFO - Epoch [89][600/3746] lr: 3.639e-02, eta: 2 days, 4:11:11, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5309, loss_cls: 4.1133, loss: 4.1133 +2024-07-19 12:55:35,222 - pyskl - INFO - Epoch [89][700/3746] lr: 3.637e-02, eta: 2 days, 4:09:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5434, loss_cls: 4.0842, loss: 4.0842 +2024-07-19 12:56:57,814 - pyskl - INFO - Epoch [89][800/3746] lr: 3.634e-02, eta: 2 days, 4:08:30, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2841, top5_acc: 0.5439, loss_cls: 4.0717, loss: 4.0717 +2024-07-19 12:58:20,176 - pyskl - INFO - Epoch [89][900/3746] lr: 3.631e-02, eta: 2 days, 4:07:10, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5409, loss_cls: 4.0666, loss: 4.0666 +2024-07-19 12:59:42,225 - pyskl - INFO - Epoch [89][1000/3746] lr: 3.629e-02, eta: 2 days, 4:05:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2933, top5_acc: 0.5406, loss_cls: 4.0685, loss: 4.0685 +2024-07-19 13:01:03,715 - pyskl - INFO - Epoch [89][1100/3746] lr: 3.626e-02, eta: 2 days, 4:04:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5461, loss_cls: 4.0801, loss: 4.0801 +2024-07-19 13:02:26,547 - pyskl - INFO - Epoch [89][1200/3746] lr: 3.623e-02, eta: 2 days, 4:03:09, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2858, top5_acc: 0.5383, loss_cls: 4.0946, loss: 4.0946 +2024-07-19 13:03:48,976 - pyskl - INFO - Epoch [89][1300/3746] lr: 3.620e-02, eta: 2 days, 4:01:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2789, top5_acc: 0.5389, loss_cls: 4.1428, loss: 4.1428 +2024-07-19 13:05:11,466 - pyskl - INFO - Epoch [89][1400/3746] lr: 3.618e-02, eta: 2 days, 4:00:29, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2908, top5_acc: 0.5452, loss_cls: 4.0637, loss: 4.0637 +2024-07-19 13:06:34,037 - pyskl - INFO - Epoch [89][1500/3746] lr: 3.615e-02, eta: 2 days, 3:59:08, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5419, loss_cls: 4.0640, loss: 4.0640 +2024-07-19 13:07:56,097 - pyskl - INFO - Epoch [89][1600/3746] lr: 3.612e-02, eta: 2 days, 3:57:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5291, loss_cls: 4.1288, loss: 4.1288 +2024-07-19 13:09:17,880 - pyskl - INFO - Epoch [89][1700/3746] lr: 3.610e-02, eta: 2 days, 3:56:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2772, top5_acc: 0.5409, loss_cls: 4.1055, loss: 4.1055 +2024-07-19 13:10:40,091 - pyskl - INFO - Epoch [89][1800/3746] lr: 3.607e-02, eta: 2 days, 3:55:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2795, top5_acc: 0.5334, loss_cls: 4.1245, loss: 4.1245 +2024-07-19 13:12:01,873 - pyskl - INFO - Epoch [89][1900/3746] lr: 3.604e-02, eta: 2 days, 3:53:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5377, loss_cls: 4.1267, loss: 4.1267 +2024-07-19 13:13:23,920 - pyskl - INFO - Epoch [89][2000/3746] lr: 3.602e-02, eta: 2 days, 3:52:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5430, loss_cls: 4.0868, loss: 4.0868 +2024-07-19 13:14:46,955 - pyskl - INFO - Epoch [89][2100/3746] lr: 3.599e-02, eta: 2 days, 3:51:06, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2842, top5_acc: 0.5342, loss_cls: 4.1176, loss: 4.1176 +2024-07-19 13:16:08,952 - pyskl - INFO - Epoch [89][2200/3746] lr: 3.596e-02, eta: 2 days, 3:49:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2778, top5_acc: 0.5325, loss_cls: 4.1509, loss: 4.1509 +2024-07-19 13:17:30,940 - pyskl - INFO - Epoch [89][2300/3746] lr: 3.594e-02, eta: 2 days, 3:48:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5367, loss_cls: 4.1213, loss: 4.1213 +2024-07-19 13:18:52,843 - pyskl - INFO - Epoch [89][2400/3746] lr: 3.591e-02, eta: 2 days, 3:47:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2891, top5_acc: 0.5520, loss_cls: 4.0809, loss: 4.0809 +2024-07-19 13:20:15,013 - pyskl - INFO - Epoch [89][2500/3746] lr: 3.588e-02, eta: 2 days, 3:45:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5384, loss_cls: 4.0959, loss: 4.0959 +2024-07-19 13:21:36,917 - pyskl - INFO - Epoch [89][2600/3746] lr: 3.586e-02, eta: 2 days, 3:44:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5314, loss_cls: 4.0929, loss: 4.0929 +2024-07-19 13:22:58,706 - pyskl - INFO - Epoch [89][2700/3746] lr: 3.583e-02, eta: 2 days, 3:43:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5370, loss_cls: 4.0812, loss: 4.0812 +2024-07-19 13:24:20,993 - pyskl - INFO - Epoch [89][2800/3746] lr: 3.580e-02, eta: 2 days, 3:41:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2780, top5_acc: 0.5373, loss_cls: 4.1412, loss: 4.1412 +2024-07-19 13:25:42,801 - pyskl - INFO - Epoch [89][2900/3746] lr: 3.578e-02, eta: 2 days, 3:40:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5345, loss_cls: 4.1167, loss: 4.1167 +2024-07-19 13:27:04,438 - pyskl - INFO - Epoch [89][3000/3746] lr: 3.575e-02, eta: 2 days, 3:39:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5367, loss_cls: 4.1042, loss: 4.1042 +2024-07-19 13:28:26,404 - pyskl - INFO - Epoch [89][3100/3746] lr: 3.572e-02, eta: 2 days, 3:37:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5352, loss_cls: 4.1058, loss: 4.1058 +2024-07-19 13:29:48,435 - pyskl - INFO - Epoch [89][3200/3746] lr: 3.569e-02, eta: 2 days, 3:36:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5330, loss_cls: 4.1163, loss: 4.1163 +2024-07-19 13:31:10,930 - pyskl - INFO - Epoch [89][3300/3746] lr: 3.567e-02, eta: 2 days, 3:35:00, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5303, loss_cls: 4.1234, loss: 4.1234 +2024-07-19 13:32:33,014 - pyskl - INFO - Epoch [89][3400/3746] lr: 3.564e-02, eta: 2 days, 3:33:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5272, loss_cls: 4.1333, loss: 4.1333 +2024-07-19 13:33:54,903 - pyskl - INFO - Epoch [89][3500/3746] lr: 3.561e-02, eta: 2 days, 3:32:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5248, loss_cls: 4.1450, loss: 4.1450 +2024-07-19 13:35:16,865 - pyskl - INFO - Epoch [89][3600/3746] lr: 3.559e-02, eta: 2 days, 3:30:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2836, top5_acc: 0.5361, loss_cls: 4.1345, loss: 4.1345 +2024-07-19 13:36:38,983 - pyskl - INFO - Epoch [89][3700/3746] lr: 3.556e-02, eta: 2 days, 3:29:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5355, loss_cls: 4.1298, loss: 4.1298 +2024-07-19 13:37:18,712 - pyskl - INFO - Saving checkpoint at 89 epochs +2024-07-19 13:39:10,279 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 13:39:10,951 - pyskl - INFO - +top1_acc 0.2096 +top5_acc 0.4458 +2024-07-19 13:39:10,951 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 13:39:10,996 - pyskl - INFO - +mean_acc 0.2094 +2024-07-19 13:39:11,008 - pyskl - INFO - Epoch(val) [89][309] top1_acc: 0.2096, top5_acc: 0.4458, mean_class_accuracy: 0.2094 +2024-07-19 13:42:59,994 - pyskl - INFO - Epoch [90][100/3746] lr: 3.552e-02, eta: 2 days, 3:28:56, time: 2.290, data_time: 1.302, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5527, loss_cls: 4.0279, loss: 4.0279 +2024-07-19 13:44:22,073 - pyskl - INFO - Epoch [90][200/3746] lr: 3.550e-02, eta: 2 days, 3:27:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5522, loss_cls: 4.0394, loss: 4.0394 +2024-07-19 13:45:44,560 - pyskl - INFO - Epoch [90][300/3746] lr: 3.547e-02, eta: 2 days, 3:26:15, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5473, loss_cls: 4.0340, loss: 4.0340 +2024-07-19 13:47:06,359 - pyskl - INFO - Epoch [90][400/3746] lr: 3.544e-02, eta: 2 days, 3:24:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5500, loss_cls: 4.0223, loss: 4.0223 +2024-07-19 13:48:28,583 - pyskl - INFO - Epoch [90][500/3746] lr: 3.541e-02, eta: 2 days, 3:23:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5439, loss_cls: 4.0494, loss: 4.0494 +2024-07-19 13:49:51,195 - pyskl - INFO - Epoch [90][600/3746] lr: 3.539e-02, eta: 2 days, 3:22:14, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5522, loss_cls: 4.0309, loss: 4.0309 +2024-07-19 13:51:13,330 - pyskl - INFO - Epoch [90][700/3746] lr: 3.536e-02, eta: 2 days, 3:20:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5370, loss_cls: 4.1159, loss: 4.1159 +2024-07-19 13:52:36,114 - pyskl - INFO - Epoch [90][800/3746] lr: 3.533e-02, eta: 2 days, 3:19:33, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2867, top5_acc: 0.5433, loss_cls: 4.0832, loss: 4.0832 +2024-07-19 13:53:58,785 - pyskl - INFO - Epoch [90][900/3746] lr: 3.531e-02, eta: 2 days, 3:18:13, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5408, loss_cls: 4.1165, loss: 4.1165 +2024-07-19 13:55:20,267 - pyskl - INFO - Epoch [90][1000/3746] lr: 3.528e-02, eta: 2 days, 3:16:52, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2945, top5_acc: 0.5405, loss_cls: 4.0766, loss: 4.0766 +2024-07-19 13:56:42,277 - pyskl - INFO - Epoch [90][1100/3746] lr: 3.525e-02, eta: 2 days, 3:15:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5423, loss_cls: 4.1022, loss: 4.1022 +2024-07-19 13:58:04,769 - pyskl - INFO - Epoch [90][1200/3746] lr: 3.523e-02, eta: 2 days, 3:14:11, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2792, top5_acc: 0.5342, loss_cls: 4.1083, loss: 4.1083 +2024-07-19 13:59:27,007 - pyskl - INFO - Epoch [90][1300/3746] lr: 3.520e-02, eta: 2 days, 3:12:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5469, loss_cls: 4.0474, loss: 4.0474 +2024-07-19 14:00:49,130 - pyskl - INFO - Epoch [90][1400/3746] lr: 3.517e-02, eta: 2 days, 3:11:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5330, loss_cls: 4.1173, loss: 4.1173 +2024-07-19 14:02:10,948 - pyskl - INFO - Epoch [90][1500/3746] lr: 3.515e-02, eta: 2 days, 3:10:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2833, top5_acc: 0.5317, loss_cls: 4.1324, loss: 4.1324 +2024-07-19 14:03:32,926 - pyskl - INFO - Epoch [90][1600/3746] lr: 3.512e-02, eta: 2 days, 3:08:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2917, top5_acc: 0.5373, loss_cls: 4.0929, loss: 4.0929 +2024-07-19 14:04:55,190 - pyskl - INFO - Epoch [90][1700/3746] lr: 3.509e-02, eta: 2 days, 3:07:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5316, loss_cls: 4.1423, loss: 4.1423 +2024-07-19 14:06:17,369 - pyskl - INFO - Epoch [90][1800/3746] lr: 3.507e-02, eta: 2 days, 3:06:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5436, loss_cls: 4.0759, loss: 4.0759 +2024-07-19 14:07:39,577 - pyskl - INFO - Epoch [90][1900/3746] lr: 3.504e-02, eta: 2 days, 3:04:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2873, top5_acc: 0.5445, loss_cls: 4.0908, loss: 4.0908 +2024-07-19 14:09:01,503 - pyskl - INFO - Epoch [90][2000/3746] lr: 3.501e-02, eta: 2 days, 3:03:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5366, loss_cls: 4.1106, loss: 4.1106 +2024-07-19 14:10:24,363 - pyskl - INFO - Epoch [90][2100/3746] lr: 3.499e-02, eta: 2 days, 3:02:07, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5475, loss_cls: 4.0834, loss: 4.0834 +2024-07-19 14:11:46,505 - pyskl - INFO - Epoch [90][2200/3746] lr: 3.496e-02, eta: 2 days, 3:00:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5386, loss_cls: 4.0956, loss: 4.0956 +2024-07-19 14:13:09,086 - pyskl - INFO - Epoch [90][2300/3746] lr: 3.493e-02, eta: 2 days, 2:59:27, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5341, loss_cls: 4.1123, loss: 4.1123 +2024-07-19 14:14:31,421 - pyskl - INFO - Epoch [90][2400/3746] lr: 3.491e-02, eta: 2 days, 2:58:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5517, loss_cls: 4.0260, loss: 4.0260 +2024-07-19 14:15:53,301 - pyskl - INFO - Epoch [90][2500/3746] lr: 3.488e-02, eta: 2 days, 2:56:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2823, top5_acc: 0.5391, loss_cls: 4.1014, loss: 4.1014 +2024-07-19 14:17:15,724 - pyskl - INFO - Epoch [90][2600/3746] lr: 3.485e-02, eta: 2 days, 2:55:25, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2886, top5_acc: 0.5386, loss_cls: 4.0849, loss: 4.0849 +2024-07-19 14:18:37,601 - pyskl - INFO - Epoch [90][2700/3746] lr: 3.483e-02, eta: 2 days, 2:54:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2852, top5_acc: 0.5352, loss_cls: 4.1008, loss: 4.1008 +2024-07-19 14:19:59,580 - pyskl - INFO - Epoch [90][2800/3746] lr: 3.480e-02, eta: 2 days, 2:52:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5323, loss_cls: 4.1074, loss: 4.1074 +2024-07-19 14:21:21,336 - pyskl - INFO - Epoch [90][2900/3746] lr: 3.477e-02, eta: 2 days, 2:51:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5491, loss_cls: 4.0632, loss: 4.0632 +2024-07-19 14:22:43,515 - pyskl - INFO - Epoch [90][3000/3746] lr: 3.475e-02, eta: 2 days, 2:50:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5427, loss_cls: 4.0961, loss: 4.0961 +2024-07-19 14:24:05,164 - pyskl - INFO - Epoch [90][3100/3746] lr: 3.472e-02, eta: 2 days, 2:48:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5320, loss_cls: 4.1236, loss: 4.1236 +2024-07-19 14:25:27,515 - pyskl - INFO - Epoch [90][3200/3746] lr: 3.469e-02, eta: 2 days, 2:47:22, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5342, loss_cls: 4.0915, loss: 4.0915 +2024-07-19 14:26:49,712 - pyskl - INFO - Epoch [90][3300/3746] lr: 3.467e-02, eta: 2 days, 2:46:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2817, top5_acc: 0.5345, loss_cls: 4.1209, loss: 4.1209 +2024-07-19 14:28:11,719 - pyskl - INFO - Epoch [90][3400/3746] lr: 3.464e-02, eta: 2 days, 2:44:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2856, top5_acc: 0.5319, loss_cls: 4.1339, loss: 4.1339 +2024-07-19 14:29:33,248 - pyskl - INFO - Epoch [90][3500/3746] lr: 3.461e-02, eta: 2 days, 2:43:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.2914, top5_acc: 0.5367, loss_cls: 4.1094, loss: 4.1094 +2024-07-19 14:30:55,576 - pyskl - INFO - Epoch [90][3600/3746] lr: 3.459e-02, eta: 2 days, 2:41:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2838, top5_acc: 0.5434, loss_cls: 4.0998, loss: 4.0998 +2024-07-19 14:32:17,950 - pyskl - INFO - Epoch [90][3700/3746] lr: 3.456e-02, eta: 2 days, 2:40:39, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5408, loss_cls: 4.0854, loss: 4.0854 +2024-07-19 14:32:57,696 - pyskl - INFO - Saving checkpoint at 90 epochs +2024-07-19 14:34:50,180 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 14:34:50,857 - pyskl - INFO - +top1_acc 0.2389 +top5_acc 0.4692 +2024-07-19 14:34:50,857 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 14:34:50,899 - pyskl - INFO - +mean_acc 0.2388 +2024-07-19 14:34:50,903 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_86.pth was removed +2024-07-19 14:34:51,152 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_90.pth. +2024-07-19 14:34:51,153 - pyskl - INFO - Best top1_acc is 0.2389 at 90 epoch. +2024-07-19 14:34:51,165 - pyskl - INFO - Epoch(val) [90][309] top1_acc: 0.2389, top5_acc: 0.4692, mean_class_accuracy: 0.2388 +2024-07-19 14:38:37,430 - pyskl - INFO - Epoch [91][100/3746] lr: 3.452e-02, eta: 2 days, 2:39:52, time: 2.263, data_time: 1.284, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5563, loss_cls: 4.0293, loss: 4.0293 +2024-07-19 14:39:59,702 - pyskl - INFO - Epoch [91][200/3746] lr: 3.450e-02, eta: 2 days, 2:38:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5511, loss_cls: 4.0136, loss: 4.0136 +2024-07-19 14:41:21,609 - pyskl - INFO - Epoch [91][300/3746] lr: 3.447e-02, eta: 2 days, 2:37:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5439, loss_cls: 4.0686, loss: 4.0686 +2024-07-19 14:42:43,622 - pyskl - INFO - Epoch [91][400/3746] lr: 3.444e-02, eta: 2 days, 2:35:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2947, top5_acc: 0.5508, loss_cls: 4.0455, loss: 4.0455 +2024-07-19 14:44:05,749 - pyskl - INFO - Epoch [91][500/3746] lr: 3.442e-02, eta: 2 days, 2:34:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2869, top5_acc: 0.5378, loss_cls: 4.1005, loss: 4.1005 +2024-07-19 14:45:28,765 - pyskl - INFO - Epoch [91][600/3746] lr: 3.439e-02, eta: 2 days, 2:33:10, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5458, loss_cls: 4.0487, loss: 4.0487 +2024-07-19 14:46:51,303 - pyskl - INFO - Epoch [91][700/3746] lr: 3.436e-02, eta: 2 days, 2:31:50, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2898, top5_acc: 0.5416, loss_cls: 4.0926, loss: 4.0926 +2024-07-19 14:48:14,312 - pyskl - INFO - Epoch [91][800/3746] lr: 3.434e-02, eta: 2 days, 2:30:30, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5414, loss_cls: 4.0592, loss: 4.0592 +2024-07-19 14:49:36,448 - pyskl - INFO - Epoch [91][900/3746] lr: 3.431e-02, eta: 2 days, 2:29:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5509, loss_cls: 4.0475, loss: 4.0475 +2024-07-19 14:50:58,961 - pyskl - INFO - Epoch [91][1000/3746] lr: 3.428e-02, eta: 2 days, 2:27:49, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2861, top5_acc: 0.5347, loss_cls: 4.0958, loss: 4.0958 +2024-07-19 14:52:21,362 - pyskl - INFO - Epoch [91][1100/3746] lr: 3.426e-02, eta: 2 days, 2:26:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5272, loss_cls: 4.1308, loss: 4.1308 +2024-07-19 14:53:44,008 - pyskl - INFO - Epoch [91][1200/3746] lr: 3.423e-02, eta: 2 days, 2:25:08, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2964, top5_acc: 0.5428, loss_cls: 4.0690, loss: 4.0690 +2024-07-19 14:55:06,557 - pyskl - INFO - Epoch [91][1300/3746] lr: 3.420e-02, eta: 2 days, 2:23:48, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5461, loss_cls: 4.0730, loss: 4.0730 +2024-07-19 14:56:28,952 - pyskl - INFO - Epoch [91][1400/3746] lr: 3.418e-02, eta: 2 days, 2:22:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5364, loss_cls: 4.0798, loss: 4.0798 +2024-07-19 14:57:50,814 - pyskl - INFO - Epoch [91][1500/3746] lr: 3.415e-02, eta: 2 days, 2:21:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3038, top5_acc: 0.5477, loss_cls: 4.0249, loss: 4.0249 +2024-07-19 14:59:12,715 - pyskl - INFO - Epoch [91][1600/3746] lr: 3.412e-02, eta: 2 days, 2:19:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2809, top5_acc: 0.5380, loss_cls: 4.1295, loss: 4.1295 +2024-07-19 15:00:34,405 - pyskl - INFO - Epoch [91][1700/3746] lr: 3.410e-02, eta: 2 days, 2:18:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2888, top5_acc: 0.5228, loss_cls: 4.1126, loss: 4.1126 +2024-07-19 15:01:56,097 - pyskl - INFO - Epoch [91][1800/3746] lr: 3.407e-02, eta: 2 days, 2:17:04, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2863, top5_acc: 0.5408, loss_cls: 4.0869, loss: 4.0869 +2024-07-19 15:03:17,840 - pyskl - INFO - Epoch [91][1900/3746] lr: 3.405e-02, eta: 2 days, 2:15:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2834, top5_acc: 0.5366, loss_cls: 4.1309, loss: 4.1309 +2024-07-19 15:04:39,830 - pyskl - INFO - Epoch [91][2000/3746] lr: 3.402e-02, eta: 2 days, 2:14:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5486, loss_cls: 4.0583, loss: 4.0583 +2024-07-19 15:06:02,114 - pyskl - INFO - Epoch [91][2100/3746] lr: 3.399e-02, eta: 2 days, 2:13:02, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2872, top5_acc: 0.5420, loss_cls: 4.1001, loss: 4.1001 +2024-07-19 15:07:24,880 - pyskl - INFO - Epoch [91][2200/3746] lr: 3.397e-02, eta: 2 days, 2:11:42, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5452, loss_cls: 4.0389, loss: 4.0389 +2024-07-19 15:08:47,228 - pyskl - INFO - Epoch [91][2300/3746] lr: 3.394e-02, eta: 2 days, 2:10:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2928, top5_acc: 0.5422, loss_cls: 4.0864, loss: 4.0864 +2024-07-19 15:10:08,903 - pyskl - INFO - Epoch [91][2400/3746] lr: 3.391e-02, eta: 2 days, 2:09:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5481, loss_cls: 4.0595, loss: 4.0595 +2024-07-19 15:11:31,086 - pyskl - INFO - Epoch [91][2500/3746] lr: 3.389e-02, eta: 2 days, 2:07:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2923, top5_acc: 0.5403, loss_cls: 4.0704, loss: 4.0704 +2024-07-19 15:12:53,251 - pyskl - INFO - Epoch [91][2600/3746] lr: 3.386e-02, eta: 2 days, 2:06:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5498, loss_cls: 4.0628, loss: 4.0628 +2024-07-19 15:14:15,282 - pyskl - INFO - Epoch [91][2700/3746] lr: 3.383e-02, eta: 2 days, 2:04:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2875, top5_acc: 0.5364, loss_cls: 4.0966, loss: 4.0966 +2024-07-19 15:15:37,500 - pyskl - INFO - Epoch [91][2800/3746] lr: 3.381e-02, eta: 2 days, 2:03:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5458, loss_cls: 4.0751, loss: 4.0751 +2024-07-19 15:16:59,603 - pyskl - INFO - Epoch [91][2900/3746] lr: 3.378e-02, eta: 2 days, 2:02:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5491, loss_cls: 4.0399, loss: 4.0399 +2024-07-19 15:18:21,942 - pyskl - INFO - Epoch [91][3000/3746] lr: 3.375e-02, eta: 2 days, 2:00:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5442, loss_cls: 4.0510, loss: 4.0510 +2024-07-19 15:19:43,790 - pyskl - INFO - Epoch [91][3100/3746] lr: 3.373e-02, eta: 2 days, 1:59:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2922, top5_acc: 0.5492, loss_cls: 4.0323, loss: 4.0323 +2024-07-19 15:21:06,233 - pyskl - INFO - Epoch [91][3200/3746] lr: 3.370e-02, eta: 2 days, 1:58:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5356, loss_cls: 4.1207, loss: 4.1207 +2024-07-19 15:22:27,940 - pyskl - INFO - Epoch [91][3300/3746] lr: 3.367e-02, eta: 2 days, 1:56:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2883, top5_acc: 0.5355, loss_cls: 4.0997, loss: 4.0997 +2024-07-19 15:23:49,922 - pyskl - INFO - Epoch [91][3400/3746] lr: 3.365e-02, eta: 2 days, 1:55:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2803, top5_acc: 0.5306, loss_cls: 4.1138, loss: 4.1138 +2024-07-19 15:25:11,865 - pyskl - INFO - Epoch [91][3500/3746] lr: 3.362e-02, eta: 2 days, 1:54:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2859, top5_acc: 0.5447, loss_cls: 4.0777, loss: 4.0777 +2024-07-19 15:26:33,958 - pyskl - INFO - Epoch [91][3600/3746] lr: 3.360e-02, eta: 2 days, 1:52:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2864, top5_acc: 0.5452, loss_cls: 4.0885, loss: 4.0885 +2024-07-19 15:27:55,600 - pyskl - INFO - Epoch [91][3700/3746] lr: 3.357e-02, eta: 2 days, 1:51:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5436, loss_cls: 4.0599, loss: 4.0599 +2024-07-19 15:28:35,167 - pyskl - INFO - Saving checkpoint at 91 epochs +2024-07-19 15:30:26,957 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 15:30:27,632 - pyskl - INFO - +top1_acc 0.2244 +top5_acc 0.4571 +2024-07-19 15:30:27,633 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 15:30:27,676 - pyskl - INFO - +mean_acc 0.2243 +2024-07-19 15:30:27,688 - pyskl - INFO - Epoch(val) [91][309] top1_acc: 0.2244, top5_acc: 0.4571, mean_class_accuracy: 0.2243 +2024-07-19 15:34:16,082 - pyskl - INFO - Epoch [92][100/3746] lr: 3.353e-02, eta: 2 days, 1:50:45, time: 2.284, data_time: 1.295, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5467, loss_cls: 4.0207, loss: 4.0207 +2024-07-19 15:35:38,553 - pyskl - INFO - Epoch [92][200/3746] lr: 3.350e-02, eta: 2 days, 1:49:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5628, loss_cls: 3.9877, loss: 3.9877 +2024-07-19 15:37:01,502 - pyskl - INFO - Epoch [92][300/3746] lr: 3.348e-02, eta: 2 days, 1:48:05, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5623, loss_cls: 4.0087, loss: 4.0087 +2024-07-19 15:38:23,865 - pyskl - INFO - Epoch [92][400/3746] lr: 3.345e-02, eta: 2 days, 1:46:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5447, loss_cls: 4.0390, loss: 4.0390 +2024-07-19 15:39:46,451 - pyskl - INFO - Epoch [92][500/3746] lr: 3.342e-02, eta: 2 days, 1:45:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5527, loss_cls: 3.9988, loss: 3.9988 +2024-07-19 15:41:09,774 - pyskl - INFO - Epoch [92][600/3746] lr: 3.340e-02, eta: 2 days, 1:44:04, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5552, loss_cls: 4.0221, loss: 4.0221 +2024-07-19 15:42:32,149 - pyskl - INFO - Epoch [92][700/3746] lr: 3.337e-02, eta: 2 days, 1:42:43, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5434, loss_cls: 4.0445, loss: 4.0445 +2024-07-19 15:43:54,235 - pyskl - INFO - Epoch [92][800/3746] lr: 3.335e-02, eta: 2 days, 1:41:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5478, loss_cls: 4.0473, loss: 4.0473 +2024-07-19 15:45:15,869 - pyskl - INFO - Epoch [92][900/3746] lr: 3.332e-02, eta: 2 days, 1:40:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5484, loss_cls: 4.0152, loss: 4.0152 +2024-07-19 15:46:38,250 - pyskl - INFO - Epoch [92][1000/3746] lr: 3.329e-02, eta: 2 days, 1:38:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5475, loss_cls: 4.0099, loss: 4.0099 +2024-07-19 15:48:00,691 - pyskl - INFO - Epoch [92][1100/3746] lr: 3.327e-02, eta: 2 days, 1:37:21, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5486, loss_cls: 4.0414, loss: 4.0414 +2024-07-19 15:49:22,844 - pyskl - INFO - Epoch [92][1200/3746] lr: 3.324e-02, eta: 2 days, 1:36:00, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5502, loss_cls: 4.0112, loss: 4.0112 +2024-07-19 15:50:45,624 - pyskl - INFO - Epoch [92][1300/3746] lr: 3.321e-02, eta: 2 days, 1:34:40, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2900, top5_acc: 0.5427, loss_cls: 4.0762, loss: 4.0762 +2024-07-19 15:52:07,743 - pyskl - INFO - Epoch [92][1400/3746] lr: 3.319e-02, eta: 2 days, 1:33:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5383, loss_cls: 4.1001, loss: 4.1001 +2024-07-19 15:53:29,646 - pyskl - INFO - Epoch [92][1500/3746] lr: 3.316e-02, eta: 2 days, 1:31:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5439, loss_cls: 4.0592, loss: 4.0592 +2024-07-19 15:54:51,598 - pyskl - INFO - Epoch [92][1600/3746] lr: 3.314e-02, eta: 2 days, 1:30:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5422, loss_cls: 4.0565, loss: 4.0565 +2024-07-19 15:56:13,422 - pyskl - INFO - Epoch [92][1700/3746] lr: 3.311e-02, eta: 2 days, 1:29:17, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5553, loss_cls: 4.0257, loss: 4.0257 +2024-07-19 15:57:35,150 - pyskl - INFO - Epoch [92][1800/3746] lr: 3.308e-02, eta: 2 days, 1:27:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5541, loss_cls: 4.0319, loss: 4.0319 +2024-07-19 15:58:57,607 - pyskl - INFO - Epoch [92][1900/3746] lr: 3.306e-02, eta: 2 days, 1:26:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2919, top5_acc: 0.5445, loss_cls: 4.0937, loss: 4.0937 +2024-07-19 16:00:19,762 - pyskl - INFO - Epoch [92][2000/3746] lr: 3.303e-02, eta: 2 days, 1:25:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5502, loss_cls: 4.0266, loss: 4.0266 +2024-07-19 16:01:42,002 - pyskl - INFO - Epoch [92][2100/3746] lr: 3.300e-02, eta: 2 days, 1:23:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2880, top5_acc: 0.5400, loss_cls: 4.0748, loss: 4.0748 +2024-07-19 16:03:05,135 - pyskl - INFO - Epoch [92][2200/3746] lr: 3.298e-02, eta: 2 days, 1:22:34, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.2870, top5_acc: 0.5400, loss_cls: 4.1060, loss: 4.1060 +2024-07-19 16:04:26,844 - pyskl - INFO - Epoch [92][2300/3746] lr: 3.295e-02, eta: 2 days, 1:21:13, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2948, top5_acc: 0.5478, loss_cls: 4.0615, loss: 4.0615 +2024-07-19 16:05:49,310 - pyskl - INFO - Epoch [92][2400/3746] lr: 3.292e-02, eta: 2 days, 1:19:53, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5413, loss_cls: 4.0393, loss: 4.0393 +2024-07-19 16:07:11,514 - pyskl - INFO - Epoch [92][2500/3746] lr: 3.290e-02, eta: 2 days, 1:18:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5484, loss_cls: 4.0462, loss: 4.0462 +2024-07-19 16:08:33,711 - pyskl - INFO - Epoch [92][2600/3746] lr: 3.287e-02, eta: 2 days, 1:17:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2895, top5_acc: 0.5353, loss_cls: 4.1126, loss: 4.1126 +2024-07-19 16:09:55,621 - pyskl - INFO - Epoch [92][2700/3746] lr: 3.285e-02, eta: 2 days, 1:15:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5505, loss_cls: 4.0466, loss: 4.0466 +2024-07-19 16:11:17,494 - pyskl - INFO - Epoch [92][2800/3746] lr: 3.282e-02, eta: 2 days, 1:14:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5339, loss_cls: 4.1061, loss: 4.1061 +2024-07-19 16:12:39,272 - pyskl - INFO - Epoch [92][2900/3746] lr: 3.279e-02, eta: 2 days, 1:13:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2802, top5_acc: 0.5302, loss_cls: 4.1615, loss: 4.1615 +2024-07-19 16:14:00,875 - pyskl - INFO - Epoch [92][3000/3746] lr: 3.277e-02, eta: 2 days, 1:11:48, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5531, loss_cls: 4.0368, loss: 4.0368 +2024-07-19 16:15:22,520 - pyskl - INFO - Epoch [92][3100/3746] lr: 3.274e-02, eta: 2 days, 1:10:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2844, top5_acc: 0.5364, loss_cls: 4.0986, loss: 4.0986 +2024-07-19 16:16:44,243 - pyskl - INFO - Epoch [92][3200/3746] lr: 3.271e-02, eta: 2 days, 1:09:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5430, loss_cls: 4.0488, loss: 4.0488 +2024-07-19 16:18:06,491 - pyskl - INFO - Epoch [92][3300/3746] lr: 3.269e-02, eta: 2 days, 1:07:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5420, loss_cls: 4.0706, loss: 4.0706 +2024-07-19 16:19:27,796 - pyskl - INFO - Epoch [92][3400/3746] lr: 3.266e-02, eta: 2 days, 1:06:24, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5480, loss_cls: 4.0493, loss: 4.0493 +2024-07-19 16:20:49,787 - pyskl - INFO - Epoch [92][3500/3746] lr: 3.264e-02, eta: 2 days, 1:05:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2989, top5_acc: 0.5434, loss_cls: 4.0455, loss: 4.0455 +2024-07-19 16:22:11,737 - pyskl - INFO - Epoch [92][3600/3746] lr: 3.261e-02, eta: 2 days, 1:03:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5416, loss_cls: 4.0822, loss: 4.0822 +2024-07-19 16:23:33,694 - pyskl - INFO - Epoch [92][3700/3746] lr: 3.258e-02, eta: 2 days, 1:02:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2988, top5_acc: 0.5444, loss_cls: 4.0448, loss: 4.0448 +2024-07-19 16:24:13,545 - pyskl - INFO - Saving checkpoint at 92 epochs +2024-07-19 16:26:02,766 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 16:26:03,436 - pyskl - INFO - +top1_acc 0.2441 +top5_acc 0.4821 +2024-07-19 16:26:03,436 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 16:26:03,479 - pyskl - INFO - +mean_acc 0.2441 +2024-07-19 16:26:03,483 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_90.pth was removed +2024-07-19 16:26:03,736 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_92.pth. +2024-07-19 16:26:03,737 - pyskl - INFO - Best top1_acc is 0.2441 at 92 epoch. +2024-07-19 16:26:03,749 - pyskl - INFO - Epoch(val) [92][309] top1_acc: 0.2441, top5_acc: 0.4821, mean_class_accuracy: 0.2441 +2024-07-19 16:29:53,898 - pyskl - INFO - Epoch [93][100/3746] lr: 3.255e-02, eta: 2 days, 1:01:34, time: 2.301, data_time: 1.311, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5572, loss_cls: 4.0037, loss: 4.0037 +2024-07-19 16:31:16,392 - pyskl - INFO - Epoch [93][200/3746] lr: 3.252e-02, eta: 2 days, 1:00:13, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5663, loss_cls: 3.9354, loss: 3.9354 +2024-07-19 16:32:38,570 - pyskl - INFO - Epoch [93][300/3746] lr: 3.249e-02, eta: 2 days, 0:58:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5495, loss_cls: 4.0314, loss: 4.0314 +2024-07-19 16:34:01,412 - pyskl - INFO - Epoch [93][400/3746] lr: 3.247e-02, eta: 2 days, 0:57:32, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5480, loss_cls: 4.0474, loss: 4.0474 +2024-07-19 16:35:24,227 - pyskl - INFO - Epoch [93][500/3746] lr: 3.244e-02, eta: 2 days, 0:56:12, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2906, top5_acc: 0.5466, loss_cls: 4.0415, loss: 4.0415 +2024-07-19 16:36:46,505 - pyskl - INFO - Epoch [93][600/3746] lr: 3.241e-02, eta: 2 days, 0:54:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5394, loss_cls: 4.0806, loss: 4.0806 +2024-07-19 16:38:09,390 - pyskl - INFO - Epoch [93][700/3746] lr: 3.239e-02, eta: 2 days, 0:53:31, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.2909, top5_acc: 0.5383, loss_cls: 4.0942, loss: 4.0942 +2024-07-19 16:39:31,650 - pyskl - INFO - Epoch [93][800/3746] lr: 3.236e-02, eta: 2 days, 0:52:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5508, loss_cls: 4.0250, loss: 4.0250 +2024-07-19 16:40:53,869 - pyskl - INFO - Epoch [93][900/3746] lr: 3.234e-02, eta: 2 days, 0:50:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5514, loss_cls: 4.0321, loss: 4.0321 +2024-07-19 16:42:16,398 - pyskl - INFO - Epoch [93][1000/3746] lr: 3.231e-02, eta: 2 days, 0:49:29, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5472, loss_cls: 4.0305, loss: 4.0305 +2024-07-19 16:43:38,885 - pyskl - INFO - Epoch [93][1100/3746] lr: 3.228e-02, eta: 2 days, 0:48:09, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2944, top5_acc: 0.5420, loss_cls: 4.0748, loss: 4.0748 +2024-07-19 16:45:01,730 - pyskl - INFO - Epoch [93][1200/3746] lr: 3.226e-02, eta: 2 days, 0:46:48, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5537, loss_cls: 4.0389, loss: 4.0389 +2024-07-19 16:46:23,886 - pyskl - INFO - Epoch [93][1300/3746] lr: 3.223e-02, eta: 2 days, 0:45:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5539, loss_cls: 4.0079, loss: 4.0079 +2024-07-19 16:47:45,894 - pyskl - INFO - Epoch [93][1400/3746] lr: 3.221e-02, eta: 2 days, 0:44:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2925, top5_acc: 0.5353, loss_cls: 4.0814, loss: 4.0814 +2024-07-19 16:49:08,516 - pyskl - INFO - Epoch [93][1500/3746] lr: 3.218e-02, eta: 2 days, 0:42:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2936, top5_acc: 0.5444, loss_cls: 4.0857, loss: 4.0857 +2024-07-19 16:50:30,343 - pyskl - INFO - Epoch [93][1600/3746] lr: 3.215e-02, eta: 2 days, 0:41:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5466, loss_cls: 4.0375, loss: 4.0375 +2024-07-19 16:51:52,390 - pyskl - INFO - Epoch [93][1700/3746] lr: 3.213e-02, eta: 2 days, 0:40:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3044, top5_acc: 0.5537, loss_cls: 3.9804, loss: 3.9804 +2024-07-19 16:53:14,198 - pyskl - INFO - Epoch [93][1800/3746] lr: 3.210e-02, eta: 2 days, 0:38:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2903, top5_acc: 0.5430, loss_cls: 4.0788, loss: 4.0788 +2024-07-19 16:54:36,228 - pyskl - INFO - Epoch [93][1900/3746] lr: 3.207e-02, eta: 2 days, 0:37:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2966, top5_acc: 0.5522, loss_cls: 4.0405, loss: 4.0405 +2024-07-19 16:55:58,083 - pyskl - INFO - Epoch [93][2000/3746] lr: 3.205e-02, eta: 2 days, 0:36:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2970, top5_acc: 0.5430, loss_cls: 4.0464, loss: 4.0464 +2024-07-19 16:57:20,135 - pyskl - INFO - Epoch [93][2100/3746] lr: 3.202e-02, eta: 2 days, 0:34:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5548, loss_cls: 4.0088, loss: 4.0088 +2024-07-19 16:58:42,953 - pyskl - INFO - Epoch [93][2200/3746] lr: 3.200e-02, eta: 2 days, 0:33:21, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5419, loss_cls: 4.0621, loss: 4.0621 +2024-07-19 17:00:05,552 - pyskl - INFO - Epoch [93][2300/3746] lr: 3.197e-02, eta: 2 days, 0:32:00, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3094, top5_acc: 0.5613, loss_cls: 3.9863, loss: 3.9863 +2024-07-19 17:01:28,336 - pyskl - INFO - Epoch [93][2400/3746] lr: 3.194e-02, eta: 2 days, 0:30:40, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5522, loss_cls: 4.0558, loss: 4.0558 +2024-07-19 17:02:49,894 - pyskl - INFO - Epoch [93][2500/3746] lr: 3.192e-02, eta: 2 days, 0:29:19, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5472, loss_cls: 4.0489, loss: 4.0489 +2024-07-19 17:04:12,066 - pyskl - INFO - Epoch [93][2600/3746] lr: 3.189e-02, eta: 2 days, 0:27:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5530, loss_cls: 4.0219, loss: 4.0219 +2024-07-19 17:05:34,073 - pyskl - INFO - Epoch [93][2700/3746] lr: 3.187e-02, eta: 2 days, 0:26:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2969, top5_acc: 0.5550, loss_cls: 4.0338, loss: 4.0338 +2024-07-19 17:06:55,645 - pyskl - INFO - Epoch [93][2800/3746] lr: 3.184e-02, eta: 2 days, 0:25:16, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5525, loss_cls: 4.0340, loss: 4.0340 +2024-07-19 17:08:17,563 - pyskl - INFO - Epoch [93][2900/3746] lr: 3.181e-02, eta: 2 days, 0:23:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5491, loss_cls: 4.0456, loss: 4.0456 +2024-07-19 17:09:39,673 - pyskl - INFO - Epoch [93][3000/3746] lr: 3.179e-02, eta: 2 days, 0:22:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2881, top5_acc: 0.5450, loss_cls: 4.0843, loss: 4.0843 +2024-07-19 17:11:01,368 - pyskl - INFO - Epoch [93][3100/3746] lr: 3.176e-02, eta: 2 days, 0:21:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5605, loss_cls: 3.9989, loss: 3.9989 +2024-07-19 17:12:23,473 - pyskl - INFO - Epoch [93][3200/3746] lr: 3.174e-02, eta: 2 days, 0:19:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2978, top5_acc: 0.5363, loss_cls: 4.0848, loss: 4.0848 +2024-07-19 17:13:45,308 - pyskl - INFO - Epoch [93][3300/3746] lr: 3.171e-02, eta: 2 days, 0:18:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5430, loss_cls: 4.0624, loss: 4.0624 +2024-07-19 17:15:08,030 - pyskl - INFO - Epoch [93][3400/3746] lr: 3.168e-02, eta: 2 days, 0:17:11, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2812, top5_acc: 0.5494, loss_cls: 4.0712, loss: 4.0712 +2024-07-19 17:16:29,817 - pyskl - INFO - Epoch [93][3500/3746] lr: 3.166e-02, eta: 2 days, 0:15:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5534, loss_cls: 4.0351, loss: 4.0351 +2024-07-19 17:17:51,893 - pyskl - INFO - Epoch [93][3600/3746] lr: 3.163e-02, eta: 2 days, 0:14:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5502, loss_cls: 4.0548, loss: 4.0548 +2024-07-19 17:19:13,329 - pyskl - INFO - Epoch [93][3700/3746] lr: 3.161e-02, eta: 2 days, 0:13:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2952, top5_acc: 0.5406, loss_cls: 4.0654, loss: 4.0654 +2024-07-19 17:19:53,136 - pyskl - INFO - Saving checkpoint at 93 epochs +2024-07-19 17:21:44,229 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 17:21:44,896 - pyskl - INFO - +top1_acc 0.2334 +top5_acc 0.4691 +2024-07-19 17:21:44,897 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 17:21:44,944 - pyskl - INFO - +mean_acc 0.2335 +2024-07-19 17:21:44,957 - pyskl - INFO - Epoch(val) [93][309] top1_acc: 0.2334, top5_acc: 0.4691, mean_class_accuracy: 0.2335 +2024-07-19 17:25:32,043 - pyskl - INFO - Epoch [94][100/3746] lr: 3.157e-02, eta: 2 days, 0:12:16, time: 2.271, data_time: 1.286, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5672, loss_cls: 3.9650, loss: 3.9650 +2024-07-19 17:26:54,105 - pyskl - INFO - Epoch [94][200/3746] lr: 3.154e-02, eta: 2 days, 0:10:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3005, top5_acc: 0.5463, loss_cls: 4.0466, loss: 4.0466 +2024-07-19 17:28:16,196 - pyskl - INFO - Epoch [94][300/3746] lr: 3.152e-02, eta: 2 days, 0:09:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5625, loss_cls: 3.9422, loss: 3.9422 +2024-07-19 17:29:38,115 - pyskl - INFO - Epoch [94][400/3746] lr: 3.149e-02, eta: 2 days, 0:08:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5553, loss_cls: 3.9876, loss: 3.9876 +2024-07-19 17:30:59,838 - pyskl - INFO - Epoch [94][500/3746] lr: 3.146e-02, eta: 2 days, 0:06:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5567, loss_cls: 4.0166, loss: 4.0166 +2024-07-19 17:32:22,198 - pyskl - INFO - Epoch [94][600/3746] lr: 3.144e-02, eta: 2 days, 0:05:32, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5505, loss_cls: 4.0325, loss: 4.0325 +2024-07-19 17:33:44,777 - pyskl - INFO - Epoch [94][700/3746] lr: 3.141e-02, eta: 2 days, 0:04:11, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5581, loss_cls: 4.0146, loss: 4.0146 +2024-07-19 17:35:06,711 - pyskl - INFO - Epoch [94][800/3746] lr: 3.139e-02, eta: 2 days, 0:02:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5489, loss_cls: 4.0550, loss: 4.0550 +2024-07-19 17:36:28,421 - pyskl - INFO - Epoch [94][900/3746] lr: 3.136e-02, eta: 2 days, 0:01:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2941, top5_acc: 0.5509, loss_cls: 4.0359, loss: 4.0359 +2024-07-19 17:37:50,649 - pyskl - INFO - Epoch [94][1000/3746] lr: 3.133e-02, eta: 2 days, 0:00:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5492, loss_cls: 3.9936, loss: 3.9936 +2024-07-19 17:39:13,023 - pyskl - INFO - Epoch [94][1100/3746] lr: 3.131e-02, eta: 1 day, 23:58:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5567, loss_cls: 4.0137, loss: 4.0137 +2024-07-19 17:40:35,344 - pyskl - INFO - Epoch [94][1200/3746] lr: 3.128e-02, eta: 1 day, 23:57:27, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5608, loss_cls: 3.9944, loss: 3.9944 +2024-07-19 17:41:57,157 - pyskl - INFO - Epoch [94][1300/3746] lr: 3.126e-02, eta: 1 day, 23:56:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5450, loss_cls: 4.0573, loss: 4.0573 +2024-07-19 17:43:19,545 - pyskl - INFO - Epoch [94][1400/3746] lr: 3.123e-02, eta: 1 day, 23:54:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5520, loss_cls: 4.0524, loss: 4.0524 +2024-07-19 17:44:41,268 - pyskl - INFO - Epoch [94][1500/3746] lr: 3.120e-02, eta: 1 day, 23:53:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3050, top5_acc: 0.5552, loss_cls: 4.0149, loss: 4.0149 +2024-07-19 17:46:03,686 - pyskl - INFO - Epoch [94][1600/3746] lr: 3.118e-02, eta: 1 day, 23:52:04, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2913, top5_acc: 0.5366, loss_cls: 4.0820, loss: 4.0820 +2024-07-19 17:47:25,652 - pyskl - INFO - Epoch [94][1700/3746] lr: 3.115e-02, eta: 1 day, 23:50:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5537, loss_cls: 4.0166, loss: 4.0166 +2024-07-19 17:48:47,665 - pyskl - INFO - Epoch [94][1800/3746] lr: 3.113e-02, eta: 1 day, 23:49:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5625, loss_cls: 3.9963, loss: 3.9963 +2024-07-19 17:50:09,256 - pyskl - INFO - Epoch [94][1900/3746] lr: 3.110e-02, eta: 1 day, 23:48:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2950, top5_acc: 0.5514, loss_cls: 4.0086, loss: 4.0086 +2024-07-19 17:51:31,013 - pyskl - INFO - Epoch [94][2000/3746] lr: 3.108e-02, eta: 1 day, 23:46:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2984, top5_acc: 0.5481, loss_cls: 4.0349, loss: 4.0349 +2024-07-19 17:52:53,309 - pyskl - INFO - Epoch [94][2100/3746] lr: 3.105e-02, eta: 1 day, 23:45:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5569, loss_cls: 4.0235, loss: 4.0235 +2024-07-19 17:54:15,647 - pyskl - INFO - Epoch [94][2200/3746] lr: 3.102e-02, eta: 1 day, 23:43:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2920, top5_acc: 0.5431, loss_cls: 4.0819, loss: 4.0819 +2024-07-19 17:55:38,112 - pyskl - INFO - Epoch [94][2300/3746] lr: 3.100e-02, eta: 1 day, 23:42:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2831, top5_acc: 0.5472, loss_cls: 4.0782, loss: 4.0782 +2024-07-19 17:57:00,214 - pyskl - INFO - Epoch [94][2400/3746] lr: 3.097e-02, eta: 1 day, 23:41:17, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5445, loss_cls: 4.0517, loss: 4.0517 +2024-07-19 17:58:23,228 - pyskl - INFO - Epoch [94][2500/3746] lr: 3.095e-02, eta: 1 day, 23:39:57, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5592, loss_cls: 3.9921, loss: 3.9921 +2024-07-19 17:59:45,449 - pyskl - INFO - Epoch [94][2600/3746] lr: 3.092e-02, eta: 1 day, 23:38:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5466, loss_cls: 4.0730, loss: 4.0730 +2024-07-19 18:01:07,461 - pyskl - INFO - Epoch [94][2700/3746] lr: 3.089e-02, eta: 1 day, 23:37:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2958, top5_acc: 0.5436, loss_cls: 4.0518, loss: 4.0518 +2024-07-19 18:02:29,363 - pyskl - INFO - Epoch [94][2800/3746] lr: 3.087e-02, eta: 1 day, 23:35:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2961, top5_acc: 0.5516, loss_cls: 4.0173, loss: 4.0173 +2024-07-19 18:03:51,311 - pyskl - INFO - Epoch [94][2900/3746] lr: 3.084e-02, eta: 1 day, 23:34:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5547, loss_cls: 4.0003, loss: 4.0003 +2024-07-19 18:05:13,080 - pyskl - INFO - Epoch [94][3000/3746] lr: 3.082e-02, eta: 1 day, 23:33:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3027, top5_acc: 0.5528, loss_cls: 4.0328, loss: 4.0328 +2024-07-19 18:06:34,941 - pyskl - INFO - Epoch [94][3100/3746] lr: 3.079e-02, eta: 1 day, 23:31:51, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2955, top5_acc: 0.5492, loss_cls: 4.0413, loss: 4.0413 +2024-07-19 18:07:56,691 - pyskl - INFO - Epoch [94][3200/3746] lr: 3.077e-02, eta: 1 day, 23:30:30, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5484, loss_cls: 4.0124, loss: 4.0124 +2024-07-19 18:09:18,764 - pyskl - INFO - Epoch [94][3300/3746] lr: 3.074e-02, eta: 1 day, 23:29:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5437, loss_cls: 4.0481, loss: 4.0481 +2024-07-19 18:10:40,659 - pyskl - INFO - Epoch [94][3400/3746] lr: 3.071e-02, eta: 1 day, 23:27:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5420, loss_cls: 4.0774, loss: 4.0774 +2024-07-19 18:12:02,681 - pyskl - INFO - Epoch [94][3500/3746] lr: 3.069e-02, eta: 1 day, 23:26:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5609, loss_cls: 3.9953, loss: 3.9953 +2024-07-19 18:13:24,914 - pyskl - INFO - Epoch [94][3600/3746] lr: 3.066e-02, eta: 1 day, 23:25:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5502, loss_cls: 4.0088, loss: 4.0088 +2024-07-19 18:14:47,436 - pyskl - INFO - Epoch [94][3700/3746] lr: 3.064e-02, eta: 1 day, 23:23:46, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.2938, top5_acc: 0.5456, loss_cls: 4.0523, loss: 4.0523 +2024-07-19 18:15:27,179 - pyskl - INFO - Saving checkpoint at 94 epochs +2024-07-19 18:17:17,851 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 18:17:18,533 - pyskl - INFO - +top1_acc 0.2365 +top5_acc 0.4684 +2024-07-19 18:17:18,533 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 18:17:18,581 - pyskl - INFO - +mean_acc 0.2362 +2024-07-19 18:17:18,595 - pyskl - INFO - Epoch(val) [94][309] top1_acc: 0.2365, top5_acc: 0.4684, mean_class_accuracy: 0.2362 +2024-07-19 18:21:06,797 - pyskl - INFO - Epoch [95][100/3746] lr: 3.060e-02, eta: 1 day, 23:22:52, time: 2.282, data_time: 1.295, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5622, loss_cls: 3.9791, loss: 3.9791 +2024-07-19 18:22:28,951 - pyskl - INFO - Epoch [95][200/3746] lr: 3.057e-02, eta: 1 day, 23:21:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5587, loss_cls: 3.9555, loss: 3.9555 +2024-07-19 18:23:51,244 - pyskl - INFO - Epoch [95][300/3746] lr: 3.055e-02, eta: 1 day, 23:20:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5527, loss_cls: 4.0044, loss: 4.0044 +2024-07-19 18:25:13,456 - pyskl - INFO - Epoch [95][400/3746] lr: 3.052e-02, eta: 1 day, 23:18:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5630, loss_cls: 3.9709, loss: 3.9709 +2024-07-19 18:26:35,278 - pyskl - INFO - Epoch [95][500/3746] lr: 3.050e-02, eta: 1 day, 23:17:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2992, top5_acc: 0.5464, loss_cls: 4.0307, loss: 4.0307 +2024-07-19 18:27:57,130 - pyskl - INFO - Epoch [95][600/3746] lr: 3.047e-02, eta: 1 day, 23:16:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5636, loss_cls: 3.9735, loss: 3.9735 +2024-07-19 18:29:20,119 - pyskl - INFO - Epoch [95][700/3746] lr: 3.044e-02, eta: 1 day, 23:14:47, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5542, loss_cls: 4.0023, loss: 4.0023 +2024-07-19 18:30:42,064 - pyskl - INFO - Epoch [95][800/3746] lr: 3.042e-02, eta: 1 day, 23:13:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5464, loss_cls: 4.0181, loss: 4.0181 +2024-07-19 18:32:03,842 - pyskl - INFO - Epoch [95][900/3746] lr: 3.039e-02, eta: 1 day, 23:12:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5587, loss_cls: 4.0110, loss: 4.0110 +2024-07-19 18:33:26,466 - pyskl - INFO - Epoch [95][1000/3746] lr: 3.037e-02, eta: 1 day, 23:10:45, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2973, top5_acc: 0.5498, loss_cls: 4.0329, loss: 4.0329 +2024-07-19 18:34:48,130 - pyskl - INFO - Epoch [95][1100/3746] lr: 3.034e-02, eta: 1 day, 23:09:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5541, loss_cls: 4.0382, loss: 4.0382 +2024-07-19 18:36:11,141 - pyskl - INFO - Epoch [95][1200/3746] lr: 3.032e-02, eta: 1 day, 23:08:03, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5503, loss_cls: 4.0001, loss: 4.0001 +2024-07-19 18:37:33,206 - pyskl - INFO - Epoch [95][1300/3746] lr: 3.029e-02, eta: 1 day, 23:06:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5475, loss_cls: 4.0611, loss: 4.0611 +2024-07-19 18:38:54,979 - pyskl - INFO - Epoch [95][1400/3746] lr: 3.026e-02, eta: 1 day, 23:05:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5519, loss_cls: 4.0454, loss: 4.0454 +2024-07-19 18:40:17,058 - pyskl - INFO - Epoch [95][1500/3746] lr: 3.024e-02, eta: 1 day, 23:04:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5502, loss_cls: 4.0180, loss: 4.0180 +2024-07-19 18:41:39,086 - pyskl - INFO - Epoch [95][1600/3746] lr: 3.021e-02, eta: 1 day, 23:02:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5466, loss_cls: 4.0375, loss: 4.0375 +2024-07-19 18:43:01,330 - pyskl - INFO - Epoch [95][1700/3746] lr: 3.019e-02, eta: 1 day, 23:01:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5545, loss_cls: 3.9873, loss: 3.9873 +2024-07-19 18:44:22,918 - pyskl - INFO - Epoch [95][1800/3746] lr: 3.016e-02, eta: 1 day, 22:59:57, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3055, top5_acc: 0.5480, loss_cls: 4.0139, loss: 4.0139 +2024-07-19 18:45:44,628 - pyskl - INFO - Epoch [95][1900/3746] lr: 3.014e-02, eta: 1 day, 22:58:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5537, loss_cls: 3.9670, loss: 3.9670 +2024-07-19 18:47:06,517 - pyskl - INFO - Epoch [95][2000/3746] lr: 3.011e-02, eta: 1 day, 22:57:15, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2983, top5_acc: 0.5495, loss_cls: 4.0303, loss: 4.0303 +2024-07-19 18:48:28,941 - pyskl - INFO - Epoch [95][2100/3746] lr: 3.008e-02, eta: 1 day, 22:55:54, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5655, loss_cls: 3.9802, loss: 3.9802 +2024-07-19 18:49:51,088 - pyskl - INFO - Epoch [95][2200/3746] lr: 3.006e-02, eta: 1 day, 22:54:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3186, top5_acc: 0.5687, loss_cls: 3.9617, loss: 3.9617 +2024-07-19 18:51:14,484 - pyskl - INFO - Epoch [95][2300/3746] lr: 3.003e-02, eta: 1 day, 22:53:13, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.2931, top5_acc: 0.5447, loss_cls: 4.0556, loss: 4.0556 +2024-07-19 18:52:36,580 - pyskl - INFO - Epoch [95][2400/3746] lr: 3.001e-02, eta: 1 day, 22:51:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5498, loss_cls: 4.0370, loss: 4.0370 +2024-07-19 18:53:58,774 - pyskl - INFO - Epoch [95][2500/3746] lr: 2.998e-02, eta: 1 day, 22:50:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2998, top5_acc: 0.5664, loss_cls: 4.0089, loss: 4.0089 +2024-07-19 18:55:20,562 - pyskl - INFO - Epoch [95][2600/3746] lr: 2.996e-02, eta: 1 day, 22:49:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5620, loss_cls: 3.9721, loss: 3.9721 +2024-07-19 18:56:42,173 - pyskl - INFO - Epoch [95][2700/3746] lr: 2.993e-02, eta: 1 day, 22:47:49, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3002, top5_acc: 0.5469, loss_cls: 4.0257, loss: 4.0257 +2024-07-19 18:58:04,381 - pyskl - INFO - Epoch [95][2800/3746] lr: 2.991e-02, eta: 1 day, 22:46:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5563, loss_cls: 4.0286, loss: 4.0286 +2024-07-19 18:59:26,448 - pyskl - INFO - Epoch [95][2900/3746] lr: 2.988e-02, eta: 1 day, 22:45:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3077, top5_acc: 0.5547, loss_cls: 4.0047, loss: 4.0047 +2024-07-19 19:00:48,733 - pyskl - INFO - Epoch [95][3000/3746] lr: 2.985e-02, eta: 1 day, 22:43:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3030, top5_acc: 0.5577, loss_cls: 3.9841, loss: 3.9841 +2024-07-19 19:02:10,492 - pyskl - INFO - Epoch [95][3100/3746] lr: 2.983e-02, eta: 1 day, 22:42:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2997, top5_acc: 0.5686, loss_cls: 3.9625, loss: 3.9625 +2024-07-19 19:03:32,045 - pyskl - INFO - Epoch [95][3200/3746] lr: 2.980e-02, eta: 1 day, 22:41:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.2991, top5_acc: 0.5547, loss_cls: 4.0200, loss: 4.0200 +2024-07-19 19:04:53,833 - pyskl - INFO - Epoch [95][3300/3746] lr: 2.978e-02, eta: 1 day, 22:39:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2972, top5_acc: 0.5605, loss_cls: 4.0120, loss: 4.0120 +2024-07-19 19:06:15,667 - pyskl - INFO - Epoch [95][3400/3746] lr: 2.975e-02, eta: 1 day, 22:38:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2980, top5_acc: 0.5523, loss_cls: 4.0700, loss: 4.0700 +2024-07-19 19:07:37,588 - pyskl - INFO - Epoch [95][3500/3746] lr: 2.973e-02, eta: 1 day, 22:37:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3039, top5_acc: 0.5530, loss_cls: 4.0189, loss: 4.0189 +2024-07-19 19:08:59,618 - pyskl - INFO - Epoch [95][3600/3746] lr: 2.970e-02, eta: 1 day, 22:35:40, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5527, loss_cls: 4.0159, loss: 4.0159 +2024-07-19 19:10:21,364 - pyskl - INFO - Epoch [95][3700/3746] lr: 2.968e-02, eta: 1 day, 22:34:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5539, loss_cls: 4.0163, loss: 4.0163 +2024-07-19 19:11:00,630 - pyskl - INFO - Saving checkpoint at 95 epochs +2024-07-19 19:12:51,896 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 19:12:52,557 - pyskl - INFO - +top1_acc 0.2322 +top5_acc 0.4701 +2024-07-19 19:12:52,557 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 19:12:52,599 - pyskl - INFO - +mean_acc 0.2321 +2024-07-19 19:12:52,610 - pyskl - INFO - Epoch(val) [95][309] top1_acc: 0.2322, top5_acc: 0.4701, mean_class_accuracy: 0.2321 +2024-07-19 19:16:40,434 - pyskl - INFO - Epoch [96][100/3746] lr: 2.964e-02, eta: 1 day, 22:33:23, time: 2.278, data_time: 1.286, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5722, loss_cls: 3.9362, loss: 3.9362 +2024-07-19 19:18:02,480 - pyskl - INFO - Epoch [96][200/3746] lr: 2.961e-02, eta: 1 day, 22:32:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5525, loss_cls: 3.9644, loss: 3.9644 +2024-07-19 19:19:25,182 - pyskl - INFO - Epoch [96][300/3746] lr: 2.959e-02, eta: 1 day, 22:30:41, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5587, loss_cls: 3.9671, loss: 3.9671 +2024-07-19 19:20:47,349 - pyskl - INFO - Epoch [96][400/3746] lr: 2.956e-02, eta: 1 day, 22:29:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5505, loss_cls: 4.0308, loss: 4.0308 +2024-07-19 19:22:10,222 - pyskl - INFO - Epoch [96][500/3746] lr: 2.954e-02, eta: 1 day, 22:28:00, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5570, loss_cls: 3.9723, loss: 3.9723 +2024-07-19 19:23:32,319 - pyskl - INFO - Epoch [96][600/3746] lr: 2.951e-02, eta: 1 day, 22:26:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5578, loss_cls: 3.9892, loss: 3.9892 +2024-07-19 19:24:54,588 - pyskl - INFO - Epoch [96][700/3746] lr: 2.948e-02, eta: 1 day, 22:25:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5605, loss_cls: 3.9828, loss: 3.9828 +2024-07-19 19:26:17,179 - pyskl - INFO - Epoch [96][800/3746] lr: 2.946e-02, eta: 1 day, 22:23:57, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5694, loss_cls: 3.9300, loss: 3.9300 +2024-07-19 19:27:39,059 - pyskl - INFO - Epoch [96][900/3746] lr: 2.943e-02, eta: 1 day, 22:22:36, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2889, top5_acc: 0.5441, loss_cls: 4.0807, loss: 4.0807 +2024-07-19 19:29:01,459 - pyskl - INFO - Epoch [96][1000/3746] lr: 2.941e-02, eta: 1 day, 22:21:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5641, loss_cls: 3.9451, loss: 3.9451 +2024-07-19 19:30:23,922 - pyskl - INFO - Epoch [96][1100/3746] lr: 2.938e-02, eta: 1 day, 22:19:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5575, loss_cls: 3.9563, loss: 3.9563 +2024-07-19 19:31:46,394 - pyskl - INFO - Epoch [96][1200/3746] lr: 2.936e-02, eta: 1 day, 22:18:34, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5611, loss_cls: 3.9941, loss: 3.9941 +2024-07-19 19:33:08,394 - pyskl - INFO - Epoch [96][1300/3746] lr: 2.933e-02, eta: 1 day, 22:17:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2927, top5_acc: 0.5498, loss_cls: 4.0498, loss: 4.0498 +2024-07-19 19:34:30,531 - pyskl - INFO - Epoch [96][1400/3746] lr: 2.931e-02, eta: 1 day, 22:15:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3091, top5_acc: 0.5548, loss_cls: 3.9807, loss: 3.9807 +2024-07-19 19:35:52,455 - pyskl - INFO - Epoch [96][1500/3746] lr: 2.928e-02, eta: 1 day, 22:14:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3017, top5_acc: 0.5609, loss_cls: 4.0011, loss: 4.0011 +2024-07-19 19:37:14,470 - pyskl - INFO - Epoch [96][1600/3746] lr: 2.926e-02, eta: 1 day, 22:13:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3080, top5_acc: 0.5539, loss_cls: 3.9921, loss: 3.9921 +2024-07-19 19:38:36,940 - pyskl - INFO - Epoch [96][1700/3746] lr: 2.923e-02, eta: 1 day, 22:11:49, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3009, top5_acc: 0.5466, loss_cls: 4.0483, loss: 4.0483 +2024-07-19 19:39:58,754 - pyskl - INFO - Epoch [96][1800/3746] lr: 2.920e-02, eta: 1 day, 22:10:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5600, loss_cls: 3.9871, loss: 3.9871 +2024-07-19 19:41:20,708 - pyskl - INFO - Epoch [96][1900/3746] lr: 2.918e-02, eta: 1 day, 22:09:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5427, loss_cls: 4.0639, loss: 4.0639 +2024-07-19 19:42:42,657 - pyskl - INFO - Epoch [96][2000/3746] lr: 2.915e-02, eta: 1 day, 22:07:46, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3070, top5_acc: 0.5606, loss_cls: 3.9767, loss: 3.9767 +2024-07-19 19:44:04,786 - pyskl - INFO - Epoch [96][2100/3746] lr: 2.913e-02, eta: 1 day, 22:06:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.2930, top5_acc: 0.5583, loss_cls: 4.0036, loss: 4.0036 +2024-07-19 19:45:27,083 - pyskl - INFO - Epoch [96][2200/3746] lr: 2.910e-02, eta: 1 day, 22:05:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5561, loss_cls: 3.9788, loss: 3.9788 +2024-07-19 19:46:49,792 - pyskl - INFO - Epoch [96][2300/3746] lr: 2.908e-02, eta: 1 day, 22:03:43, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3019, top5_acc: 0.5600, loss_cls: 4.0281, loss: 4.0281 +2024-07-19 19:48:11,683 - pyskl - INFO - Epoch [96][2400/3746] lr: 2.905e-02, eta: 1 day, 22:02:22, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5531, loss_cls: 3.9831, loss: 3.9831 +2024-07-19 19:49:33,463 - pyskl - INFO - Epoch [96][2500/3746] lr: 2.903e-02, eta: 1 day, 22:01:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5572, loss_cls: 3.9898, loss: 3.9898 +2024-07-19 19:50:55,223 - pyskl - INFO - Epoch [96][2600/3746] lr: 2.900e-02, eta: 1 day, 21:59:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5516, loss_cls: 3.9878, loss: 3.9878 +2024-07-19 19:52:16,878 - pyskl - INFO - Epoch [96][2700/3746] lr: 2.898e-02, eta: 1 day, 21:58:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3008, top5_acc: 0.5478, loss_cls: 4.0141, loss: 4.0141 +2024-07-19 19:53:38,750 - pyskl - INFO - Epoch [96][2800/3746] lr: 2.895e-02, eta: 1 day, 21:56:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5480, loss_cls: 4.0223, loss: 4.0223 +2024-07-19 19:55:00,806 - pyskl - INFO - Epoch [96][2900/3746] lr: 2.893e-02, eta: 1 day, 21:55:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3072, top5_acc: 0.5567, loss_cls: 3.9751, loss: 3.9751 +2024-07-19 19:56:22,846 - pyskl - INFO - Epoch [96][3000/3746] lr: 2.890e-02, eta: 1 day, 21:54:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5627, loss_cls: 3.9893, loss: 3.9893 +2024-07-19 19:57:44,769 - pyskl - INFO - Epoch [96][3100/3746] lr: 2.887e-02, eta: 1 day, 21:52:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2977, top5_acc: 0.5458, loss_cls: 4.0500, loss: 4.0500 +2024-07-19 19:59:06,394 - pyskl - INFO - Epoch [96][3200/3746] lr: 2.885e-02, eta: 1 day, 21:51:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5644, loss_cls: 3.9779, loss: 3.9779 +2024-07-19 20:00:28,265 - pyskl - INFO - Epoch [96][3300/3746] lr: 2.882e-02, eta: 1 day, 21:50:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2934, top5_acc: 0.5531, loss_cls: 4.0096, loss: 4.0096 +2024-07-19 20:01:50,114 - pyskl - INFO - Epoch [96][3400/3746] lr: 2.880e-02, eta: 1 day, 21:48:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5470, loss_cls: 4.0129, loss: 4.0129 +2024-07-19 20:03:12,278 - pyskl - INFO - Epoch [96][3500/3746] lr: 2.877e-02, eta: 1 day, 21:47:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5652, loss_cls: 3.9452, loss: 3.9452 +2024-07-19 20:04:34,351 - pyskl - INFO - Epoch [96][3600/3746] lr: 2.875e-02, eta: 1 day, 21:46:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5625, loss_cls: 3.9498, loss: 3.9498 +2024-07-19 20:05:56,009 - pyskl - INFO - Epoch [96][3700/3746] lr: 2.872e-02, eta: 1 day, 21:44:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3058, top5_acc: 0.5477, loss_cls: 4.0228, loss: 4.0228 +2024-07-19 20:06:35,559 - pyskl - INFO - Saving checkpoint at 96 epochs +2024-07-19 20:08:27,056 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 20:08:27,717 - pyskl - INFO - +top1_acc 0.2430 +top5_acc 0.4739 +2024-07-19 20:08:27,718 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 20:08:27,761 - pyskl - INFO - +mean_acc 0.2426 +2024-07-19 20:08:27,773 - pyskl - INFO - Epoch(val) [96][309] top1_acc: 0.2430, top5_acc: 0.4739, mean_class_accuracy: 0.2426 +2024-07-19 20:12:14,529 - pyskl - INFO - Epoch [97][100/3746] lr: 2.869e-02, eta: 1 day, 21:43:49, time: 2.267, data_time: 1.280, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5730, loss_cls: 3.9011, loss: 3.9011 +2024-07-19 20:13:37,120 - pyskl - INFO - Epoch [97][200/3746] lr: 2.866e-02, eta: 1 day, 21:42:28, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5613, loss_cls: 3.9480, loss: 3.9480 +2024-07-19 20:14:59,441 - pyskl - INFO - Epoch [97][300/3746] lr: 2.864e-02, eta: 1 day, 21:41:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3081, top5_acc: 0.5711, loss_cls: 3.9223, loss: 3.9223 +2024-07-19 20:16:21,403 - pyskl - INFO - Epoch [97][400/3746] lr: 2.861e-02, eta: 1 day, 21:39:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5777, loss_cls: 3.9471, loss: 3.9471 +2024-07-19 20:17:44,117 - pyskl - INFO - Epoch [97][500/3746] lr: 2.858e-02, eta: 1 day, 21:38:26, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5523, loss_cls: 4.0158, loss: 4.0158 +2024-07-19 20:19:06,064 - pyskl - INFO - Epoch [97][600/3746] lr: 2.856e-02, eta: 1 day, 21:37:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5634, loss_cls: 3.9517, loss: 3.9517 +2024-07-19 20:20:28,609 - pyskl - INFO - Epoch [97][700/3746] lr: 2.853e-02, eta: 1 day, 21:35:44, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5725, loss_cls: 3.9370, loss: 3.9370 +2024-07-19 20:21:50,406 - pyskl - INFO - Epoch [97][800/3746] lr: 2.851e-02, eta: 1 day, 21:34:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5666, loss_cls: 3.9367, loss: 3.9367 +2024-07-19 20:23:12,835 - pyskl - INFO - Epoch [97][900/3746] lr: 2.848e-02, eta: 1 day, 21:33:02, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5483, loss_cls: 4.0229, loss: 4.0229 +2024-07-19 20:24:35,852 - pyskl - INFO - Epoch [97][1000/3746] lr: 2.846e-02, eta: 1 day, 21:31:41, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.2967, top5_acc: 0.5558, loss_cls: 4.0304, loss: 4.0304 +2024-07-19 20:25:58,406 - pyskl - INFO - Epoch [97][1100/3746] lr: 2.843e-02, eta: 1 day, 21:30:20, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.2994, top5_acc: 0.5586, loss_cls: 3.9817, loss: 3.9817 +2024-07-19 20:27:20,436 - pyskl - INFO - Epoch [97][1200/3746] lr: 2.841e-02, eta: 1 day, 21:28:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5645, loss_cls: 3.9562, loss: 3.9562 +2024-07-19 20:28:42,172 - pyskl - INFO - Epoch [97][1300/3746] lr: 2.838e-02, eta: 1 day, 21:27:38, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5653, loss_cls: 3.9631, loss: 3.9631 +2024-07-19 20:30:04,398 - pyskl - INFO - Epoch [97][1400/3746] lr: 2.836e-02, eta: 1 day, 21:26:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3084, top5_acc: 0.5605, loss_cls: 3.9773, loss: 3.9773 +2024-07-19 20:31:26,690 - pyskl - INFO - Epoch [97][1500/3746] lr: 2.833e-02, eta: 1 day, 21:24:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5544, loss_cls: 3.9883, loss: 3.9883 +2024-07-19 20:32:48,335 - pyskl - INFO - Epoch [97][1600/3746] lr: 2.831e-02, eta: 1 day, 21:23:35, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3016, top5_acc: 0.5583, loss_cls: 4.0001, loss: 4.0001 +2024-07-19 20:34:10,124 - pyskl - INFO - Epoch [97][1700/3746] lr: 2.828e-02, eta: 1 day, 21:22:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5561, loss_cls: 3.9816, loss: 3.9816 +2024-07-19 20:35:31,556 - pyskl - INFO - Epoch [97][1800/3746] lr: 2.826e-02, eta: 1 day, 21:20:52, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5675, loss_cls: 3.9338, loss: 3.9338 +2024-07-19 20:36:53,253 - pyskl - INFO - Epoch [97][1900/3746] lr: 2.823e-02, eta: 1 day, 21:19:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3000, top5_acc: 0.5519, loss_cls: 4.0084, loss: 4.0084 +2024-07-19 20:38:14,915 - pyskl - INFO - Epoch [97][2000/3746] lr: 2.821e-02, eta: 1 day, 21:18:09, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5489, loss_cls: 4.0171, loss: 4.0171 +2024-07-19 20:39:36,899 - pyskl - INFO - Epoch [97][2100/3746] lr: 2.818e-02, eta: 1 day, 21:16:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5587, loss_cls: 3.9751, loss: 3.9751 +2024-07-19 20:40:58,953 - pyskl - INFO - Epoch [97][2200/3746] lr: 2.816e-02, eta: 1 day, 21:15:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5553, loss_cls: 3.9734, loss: 3.9734 +2024-07-19 20:42:21,284 - pyskl - INFO - Epoch [97][2300/3746] lr: 2.813e-02, eta: 1 day, 21:14:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3041, top5_acc: 0.5463, loss_cls: 4.0305, loss: 4.0305 +2024-07-19 20:43:43,091 - pyskl - INFO - Epoch [97][2400/3746] lr: 2.811e-02, eta: 1 day, 21:12:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3088, top5_acc: 0.5552, loss_cls: 3.9740, loss: 3.9740 +2024-07-19 20:45:05,260 - pyskl - INFO - Epoch [97][2500/3746] lr: 2.808e-02, eta: 1 day, 21:11:24, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3047, top5_acc: 0.5586, loss_cls: 3.9983, loss: 3.9983 +2024-07-19 20:46:27,636 - pyskl - INFO - Epoch [97][2600/3746] lr: 2.806e-02, eta: 1 day, 21:10:03, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.2986, top5_acc: 0.5514, loss_cls: 4.0006, loss: 4.0006 +2024-07-19 20:47:49,604 - pyskl - INFO - Epoch [97][2700/3746] lr: 2.803e-02, eta: 1 day, 21:08:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3063, top5_acc: 0.5602, loss_cls: 3.9910, loss: 3.9910 +2024-07-19 20:49:12,088 - pyskl - INFO - Epoch [97][2800/3746] lr: 2.801e-02, eta: 1 day, 21:07:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5658, loss_cls: 3.9797, loss: 3.9797 +2024-07-19 20:50:34,443 - pyskl - INFO - Epoch [97][2900/3746] lr: 2.798e-02, eta: 1 day, 21:06:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3003, top5_acc: 0.5587, loss_cls: 3.9812, loss: 3.9812 +2024-07-19 20:51:56,597 - pyskl - INFO - Epoch [97][3000/3746] lr: 2.796e-02, eta: 1 day, 21:04:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5666, loss_cls: 3.9276, loss: 3.9276 +2024-07-19 20:53:18,146 - pyskl - INFO - Epoch [97][3100/3746] lr: 2.793e-02, eta: 1 day, 21:03:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3069, top5_acc: 0.5631, loss_cls: 3.9582, loss: 3.9582 +2024-07-19 20:54:39,938 - pyskl - INFO - Epoch [97][3200/3746] lr: 2.791e-02, eta: 1 day, 21:01:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3025, top5_acc: 0.5542, loss_cls: 4.0154, loss: 4.0154 +2024-07-19 20:56:01,936 - pyskl - INFO - Epoch [97][3300/3746] lr: 2.788e-02, eta: 1 day, 21:00:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5608, loss_cls: 3.9675, loss: 3.9675 +2024-07-19 20:57:23,658 - pyskl - INFO - Epoch [97][3400/3746] lr: 2.786e-02, eta: 1 day, 20:59:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3042, top5_acc: 0.5509, loss_cls: 4.0274, loss: 4.0274 +2024-07-19 20:58:45,172 - pyskl - INFO - Epoch [97][3500/3746] lr: 2.783e-02, eta: 1 day, 20:57:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5589, loss_cls: 3.9996, loss: 3.9996 +2024-07-19 21:00:06,649 - pyskl - INFO - Epoch [97][3600/3746] lr: 2.781e-02, eta: 1 day, 20:56:31, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5587, loss_cls: 3.9749, loss: 3.9749 +2024-07-19 21:01:28,836 - pyskl - INFO - Epoch [97][3700/3746] lr: 2.778e-02, eta: 1 day, 20:55:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5511, loss_cls: 4.0032, loss: 4.0032 +2024-07-19 21:02:08,415 - pyskl - INFO - Saving checkpoint at 97 epochs +2024-07-19 21:03:59,863 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 21:04:00,528 - pyskl - INFO - +top1_acc 0.2584 +top5_acc 0.4982 +2024-07-19 21:04:00,529 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 21:04:00,570 - pyskl - INFO - +mean_acc 0.2582 +2024-07-19 21:04:00,575 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_92.pth was removed +2024-07-19 21:04:00,828 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_97.pth. +2024-07-19 21:04:00,829 - pyskl - INFO - Best top1_acc is 0.2584 at 97 epoch. +2024-07-19 21:04:00,841 - pyskl - INFO - Epoch(val) [97][309] top1_acc: 0.2584, top5_acc: 0.4982, mean_class_accuracy: 0.2582 +2024-07-19 21:07:51,606 - pyskl - INFO - Epoch [98][100/3746] lr: 2.774e-02, eta: 1 day, 20:54:13, time: 2.308, data_time: 1.315, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5745, loss_cls: 3.8804, loss: 3.8804 +2024-07-19 21:09:13,882 - pyskl - INFO - Epoch [98][200/3746] lr: 2.772e-02, eta: 1 day, 20:52:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5611, loss_cls: 3.9617, loss: 3.9617 +2024-07-19 21:10:36,228 - pyskl - INFO - Epoch [98][300/3746] lr: 2.769e-02, eta: 1 day, 20:51:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3100, top5_acc: 0.5664, loss_cls: 3.9317, loss: 3.9317 +2024-07-19 21:11:58,558 - pyskl - INFO - Epoch [98][400/3746] lr: 2.767e-02, eta: 1 day, 20:50:10, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5672, loss_cls: 3.9211, loss: 3.9211 +2024-07-19 21:13:21,391 - pyskl - INFO - Epoch [98][500/3746] lr: 2.764e-02, eta: 1 day, 20:48:49, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5652, loss_cls: 3.9569, loss: 3.9569 +2024-07-19 21:14:43,737 - pyskl - INFO - Epoch [98][600/3746] lr: 2.762e-02, eta: 1 day, 20:47:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5708, loss_cls: 3.9168, loss: 3.9168 +2024-07-19 21:16:06,347 - pyskl - INFO - Epoch [98][700/3746] lr: 2.759e-02, eta: 1 day, 20:46:07, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5634, loss_cls: 3.9321, loss: 3.9321 +2024-07-19 21:17:28,508 - pyskl - INFO - Epoch [98][800/3746] lr: 2.757e-02, eta: 1 day, 20:44:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2953, top5_acc: 0.5517, loss_cls: 4.0049, loss: 4.0049 +2024-07-19 21:18:51,194 - pyskl - INFO - Epoch [98][900/3746] lr: 2.754e-02, eta: 1 day, 20:43:25, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5652, loss_cls: 3.9605, loss: 3.9605 +2024-07-19 21:20:13,035 - pyskl - INFO - Epoch [98][1000/3746] lr: 2.752e-02, eta: 1 day, 20:42:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5608, loss_cls: 3.9654, loss: 3.9654 +2024-07-19 21:21:34,909 - pyskl - INFO - Epoch [98][1100/3746] lr: 2.749e-02, eta: 1 day, 20:40:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3006, top5_acc: 0.5695, loss_cls: 3.9637, loss: 3.9637 +2024-07-19 21:22:57,289 - pyskl - INFO - Epoch [98][1200/3746] lr: 2.747e-02, eta: 1 day, 20:39:22, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5664, loss_cls: 3.9146, loss: 3.9146 +2024-07-19 21:24:19,426 - pyskl - INFO - Epoch [98][1300/3746] lr: 2.744e-02, eta: 1 day, 20:38:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5681, loss_cls: 3.9155, loss: 3.9155 +2024-07-19 21:25:41,346 - pyskl - INFO - Epoch [98][1400/3746] lr: 2.742e-02, eta: 1 day, 20:36:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5647, loss_cls: 3.9420, loss: 3.9420 +2024-07-19 21:27:03,254 - pyskl - INFO - Epoch [98][1500/3746] lr: 2.739e-02, eta: 1 day, 20:35:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5656, loss_cls: 3.9199, loss: 3.9199 +2024-07-19 21:28:25,283 - pyskl - INFO - Epoch [98][1600/3746] lr: 2.737e-02, eta: 1 day, 20:33:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5613, loss_cls: 3.9619, loss: 3.9619 +2024-07-19 21:29:47,282 - pyskl - INFO - Epoch [98][1700/3746] lr: 2.734e-02, eta: 1 day, 20:32:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3011, top5_acc: 0.5452, loss_cls: 4.0398, loss: 4.0398 +2024-07-19 21:31:09,329 - pyskl - INFO - Epoch [98][1800/3746] lr: 2.732e-02, eta: 1 day, 20:31:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5569, loss_cls: 3.9675, loss: 3.9675 +2024-07-19 21:32:31,797 - pyskl - INFO - Epoch [98][1900/3746] lr: 2.729e-02, eta: 1 day, 20:29:54, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5652, loss_cls: 3.9415, loss: 3.9415 +2024-07-19 21:33:53,585 - pyskl - INFO - Epoch [98][2000/3746] lr: 2.727e-02, eta: 1 day, 20:28:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.2975, top5_acc: 0.5533, loss_cls: 3.9954, loss: 3.9954 +2024-07-19 21:35:15,483 - pyskl - INFO - Epoch [98][2100/3746] lr: 2.724e-02, eta: 1 day, 20:27:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5625, loss_cls: 3.9709, loss: 3.9709 +2024-07-19 21:36:37,794 - pyskl - INFO - Epoch [98][2200/3746] lr: 2.722e-02, eta: 1 day, 20:25:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5739, loss_cls: 3.9209, loss: 3.9209 +2024-07-19 21:38:00,609 - pyskl - INFO - Epoch [98][2300/3746] lr: 2.719e-02, eta: 1 day, 20:24:29, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5598, loss_cls: 3.9632, loss: 3.9632 +2024-07-19 21:39:22,287 - pyskl - INFO - Epoch [98][2400/3746] lr: 2.717e-02, eta: 1 day, 20:23:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5647, loss_cls: 3.9560, loss: 3.9560 +2024-07-19 21:40:43,736 - pyskl - INFO - Epoch [98][2500/3746] lr: 2.714e-02, eta: 1 day, 20:21:47, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5572, loss_cls: 3.9760, loss: 3.9760 +2024-07-19 21:42:05,145 - pyskl - INFO - Epoch [98][2600/3746] lr: 2.712e-02, eta: 1 day, 20:20:25, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.2963, top5_acc: 0.5528, loss_cls: 3.9944, loss: 3.9944 +2024-07-19 21:43:27,227 - pyskl - INFO - Epoch [98][2700/3746] lr: 2.709e-02, eta: 1 day, 20:19:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5648, loss_cls: 3.9404, loss: 3.9404 +2024-07-19 21:44:49,172 - pyskl - INFO - Epoch [98][2800/3746] lr: 2.707e-02, eta: 1 day, 20:17:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5558, loss_cls: 3.9966, loss: 3.9966 +2024-07-19 21:46:10,929 - pyskl - INFO - Epoch [98][2900/3746] lr: 2.705e-02, eta: 1 day, 20:16:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5608, loss_cls: 3.9663, loss: 3.9663 +2024-07-19 21:47:33,206 - pyskl - INFO - Epoch [98][3000/3746] lr: 2.702e-02, eta: 1 day, 20:15:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.2959, top5_acc: 0.5552, loss_cls: 4.0204, loss: 4.0204 +2024-07-19 21:48:54,845 - pyskl - INFO - Epoch [98][3100/3746] lr: 2.700e-02, eta: 1 day, 20:13:39, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5678, loss_cls: 3.9369, loss: 3.9369 +2024-07-19 21:50:17,042 - pyskl - INFO - Epoch [98][3200/3746] lr: 2.697e-02, eta: 1 day, 20:12:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.2911, top5_acc: 0.5478, loss_cls: 4.0564, loss: 4.0564 +2024-07-19 21:51:39,342 - pyskl - INFO - Epoch [98][3300/3746] lr: 2.695e-02, eta: 1 day, 20:10:57, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3028, top5_acc: 0.5570, loss_cls: 4.0187, loss: 4.0187 +2024-07-19 21:53:01,016 - pyskl - INFO - Epoch [98][3400/3746] lr: 2.692e-02, eta: 1 day, 20:09:35, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3045, top5_acc: 0.5650, loss_cls: 3.9560, loss: 3.9560 +2024-07-19 21:54:23,359 - pyskl - INFO - Epoch [98][3500/3746] lr: 2.690e-02, eta: 1 day, 20:08:14, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3023, top5_acc: 0.5636, loss_cls: 3.9963, loss: 3.9963 +2024-07-19 21:55:45,049 - pyskl - INFO - Epoch [98][3600/3746] lr: 2.687e-02, eta: 1 day, 20:06:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5619, loss_cls: 3.9788, loss: 3.9788 +2024-07-19 21:57:07,065 - pyskl - INFO - Epoch [98][3700/3746] lr: 2.685e-02, eta: 1 day, 20:05:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.2942, top5_acc: 0.5475, loss_cls: 4.0577, loss: 4.0577 +2024-07-19 21:57:46,917 - pyskl - INFO - Saving checkpoint at 98 epochs +2024-07-19 21:59:38,369 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 21:59:39,081 - pyskl - INFO - +top1_acc 0.2491 +top5_acc 0.4911 +2024-07-19 21:59:39,081 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 21:59:39,128 - pyskl - INFO - +mean_acc 0.2488 +2024-07-19 21:59:39,141 - pyskl - INFO - Epoch(val) [98][309] top1_acc: 0.2491, top5_acc: 0.4911, mean_class_accuracy: 0.2488 +2024-07-19 22:03:30,821 - pyskl - INFO - Epoch [99][100/3746] lr: 2.681e-02, eta: 1 day, 20:04:33, time: 2.317, data_time: 1.327, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5714, loss_cls: 3.9008, loss: 3.9008 +2024-07-19 22:04:53,660 - pyskl - INFO - Epoch [99][200/3746] lr: 2.679e-02, eta: 1 day, 20:03:12, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3198, top5_acc: 0.5758, loss_cls: 3.8713, loss: 3.8713 +2024-07-19 22:06:16,618 - pyskl - INFO - Epoch [99][300/3746] lr: 2.676e-02, eta: 1 day, 20:01:51, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5670, loss_cls: 3.9525, loss: 3.9525 +2024-07-19 22:07:39,544 - pyskl - INFO - Epoch [99][400/3746] lr: 2.674e-02, eta: 1 day, 20:00:31, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3123, top5_acc: 0.5764, loss_cls: 3.8961, loss: 3.8961 +2024-07-19 22:09:02,198 - pyskl - INFO - Epoch [99][500/3746] lr: 2.671e-02, eta: 1 day, 19:59:10, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5859, loss_cls: 3.8903, loss: 3.8903 +2024-07-19 22:10:24,764 - pyskl - INFO - Epoch [99][600/3746] lr: 2.669e-02, eta: 1 day, 19:57:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5631, loss_cls: 3.9270, loss: 3.9270 +2024-07-19 22:11:46,927 - pyskl - INFO - Epoch [99][700/3746] lr: 2.666e-02, eta: 1 day, 19:56:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5609, loss_cls: 3.9918, loss: 3.9918 +2024-07-19 22:13:09,484 - pyskl - INFO - Epoch [99][800/3746] lr: 2.664e-02, eta: 1 day, 19:55:07, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3139, top5_acc: 0.5637, loss_cls: 3.9152, loss: 3.9152 +2024-07-19 22:14:32,261 - pyskl - INFO - Epoch [99][900/3746] lr: 2.661e-02, eta: 1 day, 19:53:46, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5673, loss_cls: 3.9556, loss: 3.9556 +2024-07-19 22:15:54,198 - pyskl - INFO - Epoch [99][1000/3746] lr: 2.659e-02, eta: 1 day, 19:52:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3075, top5_acc: 0.5608, loss_cls: 3.9665, loss: 3.9665 +2024-07-19 22:17:16,828 - pyskl - INFO - Epoch [99][1100/3746] lr: 2.656e-02, eta: 1 day, 19:51:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5577, loss_cls: 3.9135, loss: 3.9135 +2024-07-19 22:18:39,032 - pyskl - INFO - Epoch [99][1200/3746] lr: 2.654e-02, eta: 1 day, 19:49:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3114, top5_acc: 0.5641, loss_cls: 3.9532, loss: 3.9532 +2024-07-19 22:20:01,094 - pyskl - INFO - Epoch [99][1300/3746] lr: 2.651e-02, eta: 1 day, 19:48:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5616, loss_cls: 3.9347, loss: 3.9347 +2024-07-19 22:21:23,007 - pyskl - INFO - Epoch [99][1400/3746] lr: 2.649e-02, eta: 1 day, 19:47:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5670, loss_cls: 3.9000, loss: 3.9000 +2024-07-19 22:22:44,899 - pyskl - INFO - Epoch [99][1500/3746] lr: 2.646e-02, eta: 1 day, 19:45:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3222, top5_acc: 0.5666, loss_cls: 3.9197, loss: 3.9197 +2024-07-19 22:24:06,543 - pyskl - INFO - Epoch [99][1600/3746] lr: 2.644e-02, eta: 1 day, 19:44:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5723, loss_cls: 3.8943, loss: 3.8943 +2024-07-19 22:25:28,796 - pyskl - INFO - Epoch [99][1700/3746] lr: 2.642e-02, eta: 1 day, 19:42:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5677, loss_cls: 3.9142, loss: 3.9142 +2024-07-19 22:26:50,827 - pyskl - INFO - Epoch [99][1800/3746] lr: 2.639e-02, eta: 1 day, 19:41:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5613, loss_cls: 3.9538, loss: 3.9538 +2024-07-19 22:28:13,209 - pyskl - INFO - Epoch [99][1900/3746] lr: 2.637e-02, eta: 1 day, 19:40:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3133, top5_acc: 0.5600, loss_cls: 3.9627, loss: 3.9627 +2024-07-19 22:29:35,164 - pyskl - INFO - Epoch [99][2000/3746] lr: 2.634e-02, eta: 1 day, 19:38:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5731, loss_cls: 3.9066, loss: 3.9066 +2024-07-19 22:30:57,875 - pyskl - INFO - Epoch [99][2100/3746] lr: 2.632e-02, eta: 1 day, 19:37:32, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5547, loss_cls: 3.9665, loss: 3.9665 +2024-07-19 22:32:20,033 - pyskl - INFO - Epoch [99][2200/3746] lr: 2.629e-02, eta: 1 day, 19:36:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3103, top5_acc: 0.5711, loss_cls: 3.9453, loss: 3.9453 +2024-07-19 22:33:42,773 - pyskl - INFO - Epoch [99][2300/3746] lr: 2.627e-02, eta: 1 day, 19:34:50, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5536, loss_cls: 3.9792, loss: 3.9792 +2024-07-19 22:35:04,530 - pyskl - INFO - Epoch [99][2400/3746] lr: 2.624e-02, eta: 1 day, 19:33:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5672, loss_cls: 3.9470, loss: 3.9470 +2024-07-19 22:36:26,861 - pyskl - INFO - Epoch [99][2500/3746] lr: 2.622e-02, eta: 1 day, 19:32:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3092, top5_acc: 0.5697, loss_cls: 3.9505, loss: 3.9505 +2024-07-19 22:37:48,638 - pyskl - INFO - Epoch [99][2600/3746] lr: 2.619e-02, eta: 1 day, 19:30:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3053, top5_acc: 0.5594, loss_cls: 3.9723, loss: 3.9723 +2024-07-19 22:39:10,698 - pyskl - INFO - Epoch [99][2700/3746] lr: 2.617e-02, eta: 1 day, 19:29:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5673, loss_cls: 3.9595, loss: 3.9595 +2024-07-19 22:40:32,547 - pyskl - INFO - Epoch [99][2800/3746] lr: 2.614e-02, eta: 1 day, 19:28:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5630, loss_cls: 3.9536, loss: 3.9536 +2024-07-19 22:41:54,487 - pyskl - INFO - Epoch [99][2900/3746] lr: 2.612e-02, eta: 1 day, 19:26:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2981, top5_acc: 0.5550, loss_cls: 3.9937, loss: 3.9937 +2024-07-19 22:43:17,066 - pyskl - INFO - Epoch [99][3000/3746] lr: 2.610e-02, eta: 1 day, 19:25:21, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5569, loss_cls: 3.9908, loss: 3.9908 +2024-07-19 22:44:39,405 - pyskl - INFO - Epoch [99][3100/3746] lr: 2.607e-02, eta: 1 day, 19:24:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5642, loss_cls: 3.9457, loss: 3.9457 +2024-07-19 22:46:01,743 - pyskl - INFO - Epoch [99][3200/3746] lr: 2.605e-02, eta: 1 day, 19:22:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3111, top5_acc: 0.5673, loss_cls: 3.9597, loss: 3.9597 +2024-07-19 22:47:23,910 - pyskl - INFO - Epoch [99][3300/3746] lr: 2.602e-02, eta: 1 day, 19:21:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3073, top5_acc: 0.5609, loss_cls: 3.9583, loss: 3.9583 +2024-07-19 22:48:46,087 - pyskl - INFO - Epoch [99][3400/3746] lr: 2.600e-02, eta: 1 day, 19:19:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3098, top5_acc: 0.5581, loss_cls: 3.9813, loss: 3.9813 +2024-07-19 22:50:08,199 - pyskl - INFO - Epoch [99][3500/3746] lr: 2.597e-02, eta: 1 day, 19:18:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3048, top5_acc: 0.5616, loss_cls: 3.9943, loss: 3.9943 +2024-07-19 22:51:29,776 - pyskl - INFO - Epoch [99][3600/3746] lr: 2.595e-02, eta: 1 day, 19:17:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5727, loss_cls: 3.9279, loss: 3.9279 +2024-07-19 22:52:51,495 - pyskl - INFO - Epoch [99][3700/3746] lr: 2.592e-02, eta: 1 day, 19:15:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5637, loss_cls: 3.9646, loss: 3.9646 +2024-07-19 22:53:31,126 - pyskl - INFO - Saving checkpoint at 99 epochs +2024-07-19 22:55:22,385 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 22:55:23,052 - pyskl - INFO - +top1_acc 0.2584 +top5_acc 0.4979 +2024-07-19 22:55:23,052 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 22:55:23,095 - pyskl - INFO - +mean_acc 0.2581 +2024-07-19 22:55:23,107 - pyskl - INFO - Epoch(val) [99][309] top1_acc: 0.2584, top5_acc: 0.4979, mean_class_accuracy: 0.2581 +2024-07-19 22:59:13,893 - pyskl - INFO - Epoch [100][100/3746] lr: 2.589e-02, eta: 1 day, 19:14:51, time: 2.308, data_time: 1.317, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5677, loss_cls: 3.9302, loss: 3.9302 +2024-07-19 23:00:36,833 - pyskl - INFO - Epoch [100][200/3746] lr: 2.586e-02, eta: 1 day, 19:13:30, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5750, loss_cls: 3.8505, loss: 3.8505 +2024-07-19 23:01:59,423 - pyskl - INFO - Epoch [100][300/3746] lr: 2.584e-02, eta: 1 day, 19:12:09, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3158, top5_acc: 0.5658, loss_cls: 3.9314, loss: 3.9314 +2024-07-19 23:03:21,650 - pyskl - INFO - Epoch [100][400/3746] lr: 2.581e-02, eta: 1 day, 19:10:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3170, top5_acc: 0.5733, loss_cls: 3.9153, loss: 3.9153 +2024-07-19 23:04:43,513 - pyskl - INFO - Epoch [100][500/3746] lr: 2.579e-02, eta: 1 day, 19:09:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5825, loss_cls: 3.8649, loss: 3.8649 +2024-07-19 23:06:06,348 - pyskl - INFO - Epoch [100][600/3746] lr: 2.577e-02, eta: 1 day, 19:08:06, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5691, loss_cls: 3.8989, loss: 3.8989 +2024-07-19 23:07:28,457 - pyskl - INFO - Epoch [100][700/3746] lr: 2.574e-02, eta: 1 day, 19:06:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5689, loss_cls: 3.9364, loss: 3.9364 +2024-07-19 23:08:50,565 - pyskl - INFO - Epoch [100][800/3746] lr: 2.572e-02, eta: 1 day, 19:05:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3086, top5_acc: 0.5578, loss_cls: 3.9510, loss: 3.9510 +2024-07-19 23:10:12,892 - pyskl - INFO - Epoch [100][900/3746] lr: 2.569e-02, eta: 1 day, 19:04:02, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3031, top5_acc: 0.5637, loss_cls: 3.9411, loss: 3.9411 +2024-07-19 23:11:35,195 - pyskl - INFO - Epoch [100][1000/3746] lr: 2.567e-02, eta: 1 day, 19:02:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3183, top5_acc: 0.5695, loss_cls: 3.9359, loss: 3.9359 +2024-07-19 23:12:57,124 - pyskl - INFO - Epoch [100][1100/3746] lr: 2.564e-02, eta: 1 day, 19:01:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3106, top5_acc: 0.5675, loss_cls: 3.9432, loss: 3.9432 +2024-07-19 23:14:19,318 - pyskl - INFO - Epoch [100][1200/3746] lr: 2.562e-02, eta: 1 day, 18:59:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5769, loss_cls: 3.8873, loss: 3.8873 +2024-07-19 23:15:41,181 - pyskl - INFO - Epoch [100][1300/3746] lr: 2.559e-02, eta: 1 day, 18:58:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3034, top5_acc: 0.5595, loss_cls: 3.9568, loss: 3.9568 +2024-07-19 23:17:03,470 - pyskl - INFO - Epoch [100][1400/3746] lr: 2.557e-02, eta: 1 day, 18:57:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5689, loss_cls: 3.9289, loss: 3.9289 +2024-07-19 23:18:25,247 - pyskl - INFO - Epoch [100][1500/3746] lr: 2.555e-02, eta: 1 day, 18:55:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5655, loss_cls: 3.9560, loss: 3.9560 +2024-07-19 23:19:47,608 - pyskl - INFO - Epoch [100][1600/3746] lr: 2.552e-02, eta: 1 day, 18:54:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3175, top5_acc: 0.5705, loss_cls: 3.9300, loss: 3.9300 +2024-07-19 23:21:10,013 - pyskl - INFO - Epoch [100][1700/3746] lr: 2.550e-02, eta: 1 day, 18:53:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3195, top5_acc: 0.5736, loss_cls: 3.9282, loss: 3.9282 +2024-07-19 23:22:31,642 - pyskl - INFO - Epoch [100][1800/3746] lr: 2.547e-02, eta: 1 day, 18:51:51, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3136, top5_acc: 0.5736, loss_cls: 3.9150, loss: 3.9150 +2024-07-19 23:23:53,518 - pyskl - INFO - Epoch [100][1900/3746] lr: 2.545e-02, eta: 1 day, 18:50:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5791, loss_cls: 3.8840, loss: 3.8840 +2024-07-19 23:25:15,710 - pyskl - INFO - Epoch [100][2000/3746] lr: 2.542e-02, eta: 1 day, 18:49:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3097, top5_acc: 0.5652, loss_cls: 3.9384, loss: 3.9384 +2024-07-19 23:26:38,128 - pyskl - INFO - Epoch [100][2100/3746] lr: 2.540e-02, eta: 1 day, 18:47:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5755, loss_cls: 3.9134, loss: 3.9134 +2024-07-19 23:28:00,019 - pyskl - INFO - Epoch [100][2200/3746] lr: 2.538e-02, eta: 1 day, 18:46:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5592, loss_cls: 3.9552, loss: 3.9552 +2024-07-19 23:29:22,713 - pyskl - INFO - Epoch [100][2300/3746] lr: 2.535e-02, eta: 1 day, 18:45:05, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5722, loss_cls: 3.9272, loss: 3.9272 +2024-07-19 23:30:45,500 - pyskl - INFO - Epoch [100][2400/3746] lr: 2.533e-02, eta: 1 day, 18:43:44, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3022, top5_acc: 0.5673, loss_cls: 3.9611, loss: 3.9611 +2024-07-19 23:32:08,054 - pyskl - INFO - Epoch [100][2500/3746] lr: 2.530e-02, eta: 1 day, 18:42:23, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3142, top5_acc: 0.5713, loss_cls: 3.9029, loss: 3.9029 +2024-07-19 23:33:30,155 - pyskl - INFO - Epoch [100][2600/3746] lr: 2.528e-02, eta: 1 day, 18:41:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3078, top5_acc: 0.5592, loss_cls: 3.9968, loss: 3.9968 +2024-07-19 23:34:51,813 - pyskl - INFO - Epoch [100][2700/3746] lr: 2.525e-02, eta: 1 day, 18:39:40, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3061, top5_acc: 0.5595, loss_cls: 3.9751, loss: 3.9751 +2024-07-19 23:36:13,547 - pyskl - INFO - Epoch [100][2800/3746] lr: 2.523e-02, eta: 1 day, 18:38:19, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3122, top5_acc: 0.5673, loss_cls: 3.9500, loss: 3.9500 +2024-07-19 23:37:35,401 - pyskl - INFO - Epoch [100][2900/3746] lr: 2.521e-02, eta: 1 day, 18:36:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5734, loss_cls: 3.9088, loss: 3.9088 +2024-07-19 23:38:57,108 - pyskl - INFO - Epoch [100][3000/3746] lr: 2.518e-02, eta: 1 day, 18:35:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5780, loss_cls: 3.8919, loss: 3.8919 +2024-07-19 23:40:18,560 - pyskl - INFO - Epoch [100][3100/3746] lr: 2.516e-02, eta: 1 day, 18:34:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5708, loss_cls: 3.9336, loss: 3.9336 +2024-07-19 23:41:40,363 - pyskl - INFO - Epoch [100][3200/3746] lr: 2.513e-02, eta: 1 day, 18:32:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5728, loss_cls: 3.9094, loss: 3.9094 +2024-07-19 23:43:02,165 - pyskl - INFO - Epoch [100][3300/3746] lr: 2.511e-02, eta: 1 day, 18:31:31, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5759, loss_cls: 3.9205, loss: 3.9205 +2024-07-19 23:44:24,028 - pyskl - INFO - Epoch [100][3400/3746] lr: 2.508e-02, eta: 1 day, 18:30:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3095, top5_acc: 0.5659, loss_cls: 3.9341, loss: 3.9341 +2024-07-19 23:45:46,202 - pyskl - INFO - Epoch [100][3500/3746] lr: 2.506e-02, eta: 1 day, 18:28:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3059, top5_acc: 0.5620, loss_cls: 3.9516, loss: 3.9516 +2024-07-19 23:47:08,378 - pyskl - INFO - Epoch [100][3600/3746] lr: 2.504e-02, eta: 1 day, 18:27:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3052, top5_acc: 0.5564, loss_cls: 3.9730, loss: 3.9730 +2024-07-19 23:48:29,904 - pyskl - INFO - Epoch [100][3700/3746] lr: 2.501e-02, eta: 1 day, 18:26:06, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5739, loss_cls: 3.9070, loss: 3.9070 +2024-07-19 23:49:09,731 - pyskl - INFO - Saving checkpoint at 100 epochs +2024-07-19 23:50:59,908 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-19 23:51:00,647 - pyskl - INFO - +top1_acc 0.2569 +top5_acc 0.4954 +2024-07-19 23:51:00,647 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-19 23:51:00,696 - pyskl - INFO - +mean_acc 0.2567 +2024-07-19 23:51:00,714 - pyskl - INFO - Epoch(val) [100][309] top1_acc: 0.2569, top5_acc: 0.4954, mean_class_accuracy: 0.2567 +2024-07-19 23:54:50,552 - pyskl - INFO - Epoch [101][100/3746] lr: 2.498e-02, eta: 1 day, 18:25:02, time: 2.298, data_time: 1.310, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5759, loss_cls: 3.8549, loss: 3.8549 +2024-07-19 23:56:13,038 - pyskl - INFO - Epoch [101][200/3746] lr: 2.495e-02, eta: 1 day, 18:23:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5791, loss_cls: 3.8640, loss: 3.8640 +2024-07-19 23:57:35,202 - pyskl - INFO - Epoch [101][300/3746] lr: 2.493e-02, eta: 1 day, 18:22:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5750, loss_cls: 3.8787, loss: 3.8787 +2024-07-19 23:58:57,909 - pyskl - INFO - Epoch [101][400/3746] lr: 2.490e-02, eta: 1 day, 18:20:59, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5720, loss_cls: 3.8827, loss: 3.8827 +2024-07-20 00:00:20,380 - pyskl - INFO - Epoch [101][500/3746] lr: 2.488e-02, eta: 1 day, 18:19:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5809, loss_cls: 3.8551, loss: 3.8551 +2024-07-20 00:01:42,713 - pyskl - INFO - Epoch [101][600/3746] lr: 2.486e-02, eta: 1 day, 18:18:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5694, loss_cls: 3.8853, loss: 3.8853 +2024-07-20 00:03:04,702 - pyskl - INFO - Epoch [101][700/3746] lr: 2.483e-02, eta: 1 day, 18:16:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5739, loss_cls: 3.9018, loss: 3.9018 +2024-07-20 00:04:26,813 - pyskl - INFO - Epoch [101][800/3746] lr: 2.481e-02, eta: 1 day, 18:15:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5687, loss_cls: 3.8807, loss: 3.8807 +2024-07-20 00:05:49,814 - pyskl - INFO - Epoch [101][900/3746] lr: 2.478e-02, eta: 1 day, 18:14:13, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5697, loss_cls: 3.9001, loss: 3.9001 +2024-07-20 00:07:12,197 - pyskl - INFO - Epoch [101][1000/3746] lr: 2.476e-02, eta: 1 day, 18:12:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5730, loss_cls: 3.8947, loss: 3.8947 +2024-07-20 00:08:34,502 - pyskl - INFO - Epoch [101][1100/3746] lr: 2.473e-02, eta: 1 day, 18:11:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5637, loss_cls: 3.9194, loss: 3.9194 +2024-07-20 00:09:56,579 - pyskl - INFO - Epoch [101][1200/3746] lr: 2.471e-02, eta: 1 day, 18:10:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3162, top5_acc: 0.5775, loss_cls: 3.9043, loss: 3.9043 +2024-07-20 00:11:18,343 - pyskl - INFO - Epoch [101][1300/3746] lr: 2.469e-02, eta: 1 day, 18:08:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5805, loss_cls: 3.8692, loss: 3.8692 +2024-07-20 00:12:39,772 - pyskl - INFO - Epoch [101][1400/3746] lr: 2.466e-02, eta: 1 day, 18:07:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3102, top5_acc: 0.5680, loss_cls: 3.9163, loss: 3.9163 +2024-07-20 00:14:02,061 - pyskl - INFO - Epoch [101][1500/3746] lr: 2.464e-02, eta: 1 day, 18:06:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3137, top5_acc: 0.5734, loss_cls: 3.9116, loss: 3.9116 +2024-07-20 00:15:24,360 - pyskl - INFO - Epoch [101][1600/3746] lr: 2.461e-02, eta: 1 day, 18:04:44, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5708, loss_cls: 3.9072, loss: 3.9072 +2024-07-20 00:16:46,110 - pyskl - INFO - Epoch [101][1700/3746] lr: 2.459e-02, eta: 1 day, 18:03:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5677, loss_cls: 3.9271, loss: 3.9271 +2024-07-20 00:18:07,758 - pyskl - INFO - Epoch [101][1800/3746] lr: 2.457e-02, eta: 1 day, 18:02:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5709, loss_cls: 3.9305, loss: 3.9305 +2024-07-20 00:19:29,302 - pyskl - INFO - Epoch [101][1900/3746] lr: 2.454e-02, eta: 1 day, 18:00:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5817, loss_cls: 3.8606, loss: 3.8606 +2024-07-20 00:20:51,342 - pyskl - INFO - Epoch [101][2000/3746] lr: 2.452e-02, eta: 1 day, 17:59:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3152, top5_acc: 0.5695, loss_cls: 3.9139, loss: 3.9139 +2024-07-20 00:22:13,720 - pyskl - INFO - Epoch [101][2100/3746] lr: 2.449e-02, eta: 1 day, 17:57:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3064, top5_acc: 0.5625, loss_cls: 3.9630, loss: 3.9630 +2024-07-20 00:23:36,080 - pyskl - INFO - Epoch [101][2200/3746] lr: 2.447e-02, eta: 1 day, 17:56:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5603, loss_cls: 3.9647, loss: 3.9647 +2024-07-20 00:24:58,273 - pyskl - INFO - Epoch [101][2300/3746] lr: 2.445e-02, eta: 1 day, 17:55:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3178, top5_acc: 0.5748, loss_cls: 3.8905, loss: 3.8905 +2024-07-20 00:26:20,629 - pyskl - INFO - Epoch [101][2400/3746] lr: 2.442e-02, eta: 1 day, 17:53:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5698, loss_cls: 3.9400, loss: 3.9400 +2024-07-20 00:27:42,741 - pyskl - INFO - Epoch [101][2500/3746] lr: 2.440e-02, eta: 1 day, 17:52:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3125, top5_acc: 0.5691, loss_cls: 3.9180, loss: 3.9180 +2024-07-20 00:29:04,593 - pyskl - INFO - Epoch [101][2600/3746] lr: 2.437e-02, eta: 1 day, 17:51:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.2956, top5_acc: 0.5691, loss_cls: 3.9479, loss: 3.9479 +2024-07-20 00:30:26,490 - pyskl - INFO - Epoch [101][2700/3746] lr: 2.435e-02, eta: 1 day, 17:49:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5652, loss_cls: 3.9299, loss: 3.9299 +2024-07-20 00:31:48,137 - pyskl - INFO - Epoch [101][2800/3746] lr: 2.433e-02, eta: 1 day, 17:48:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5633, loss_cls: 3.9504, loss: 3.9504 +2024-07-20 00:33:10,286 - pyskl - INFO - Epoch [101][2900/3746] lr: 2.430e-02, eta: 1 day, 17:47:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3108, top5_acc: 0.5647, loss_cls: 3.9659, loss: 3.9659 +2024-07-20 00:34:32,206 - pyskl - INFO - Epoch [101][3000/3746] lr: 2.428e-02, eta: 1 day, 17:45:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5858, loss_cls: 3.8605, loss: 3.8605 +2024-07-20 00:35:54,058 - pyskl - INFO - Epoch [101][3100/3746] lr: 2.425e-02, eta: 1 day, 17:44:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5706, loss_cls: 3.9088, loss: 3.9088 +2024-07-20 00:37:16,173 - pyskl - INFO - Epoch [101][3200/3746] lr: 2.423e-02, eta: 1 day, 17:43:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5656, loss_cls: 3.9503, loss: 3.9503 +2024-07-20 00:38:37,971 - pyskl - INFO - Epoch [101][3300/3746] lr: 2.421e-02, eta: 1 day, 17:41:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5713, loss_cls: 3.8857, loss: 3.8857 +2024-07-20 00:39:59,767 - pyskl - INFO - Epoch [101][3400/3746] lr: 2.418e-02, eta: 1 day, 17:40:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5736, loss_cls: 3.9086, loss: 3.9086 +2024-07-20 00:41:21,523 - pyskl - INFO - Epoch [101][3500/3746] lr: 2.416e-02, eta: 1 day, 17:38:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3109, top5_acc: 0.5594, loss_cls: 3.9590, loss: 3.9590 +2024-07-20 00:42:43,556 - pyskl - INFO - Epoch [101][3600/3746] lr: 2.413e-02, eta: 1 day, 17:37:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3208, top5_acc: 0.5723, loss_cls: 3.9046, loss: 3.9046 +2024-07-20 00:44:05,519 - pyskl - INFO - Epoch [101][3700/3746] lr: 2.411e-02, eta: 1 day, 17:36:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5644, loss_cls: 3.9543, loss: 3.9543 +2024-07-20 00:44:45,103 - pyskl - INFO - Saving checkpoint at 101 epochs +2024-07-20 00:46:36,311 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 00:46:36,973 - pyskl - INFO - +top1_acc 0.2591 +top5_acc 0.4956 +2024-07-20 00:46:36,973 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 00:46:37,016 - pyskl - INFO - +mean_acc 0.2589 +2024-07-20 00:46:37,020 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_97.pth was removed +2024-07-20 00:46:37,272 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_101.pth. +2024-07-20 00:46:37,273 - pyskl - INFO - Best top1_acc is 0.2591 at 101 epoch. +2024-07-20 00:46:37,287 - pyskl - INFO - Epoch(val) [101][309] top1_acc: 0.2591, top5_acc: 0.4956, mean_class_accuracy: 0.2589 +2024-07-20 00:50:26,245 - pyskl - INFO - Epoch [102][100/3746] lr: 2.407e-02, eta: 1 day, 17:35:09, time: 2.289, data_time: 1.298, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5728, loss_cls: 3.8959, loss: 3.8959 +2024-07-20 00:51:48,882 - pyskl - INFO - Epoch [102][200/3746] lr: 2.405e-02, eta: 1 day, 17:33:48, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5770, loss_cls: 3.8474, loss: 3.8474 +2024-07-20 00:53:11,246 - pyskl - INFO - Epoch [102][300/3746] lr: 2.403e-02, eta: 1 day, 17:32:26, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5742, loss_cls: 3.8752, loss: 3.8752 +2024-07-20 00:54:34,008 - pyskl - INFO - Epoch [102][400/3746] lr: 2.400e-02, eta: 1 day, 17:31:05, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5742, loss_cls: 3.8996, loss: 3.8996 +2024-07-20 00:55:55,741 - pyskl - INFO - Epoch [102][500/3746] lr: 2.398e-02, eta: 1 day, 17:29:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3248, top5_acc: 0.5863, loss_cls: 3.8284, loss: 3.8284 +2024-07-20 00:57:18,302 - pyskl - INFO - Epoch [102][600/3746] lr: 2.396e-02, eta: 1 day, 17:28:23, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5808, loss_cls: 3.8602, loss: 3.8602 +2024-07-20 00:58:40,980 - pyskl - INFO - Epoch [102][700/3746] lr: 2.393e-02, eta: 1 day, 17:27:02, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5687, loss_cls: 3.8939, loss: 3.8939 +2024-07-20 01:00:03,748 - pyskl - INFO - Epoch [102][800/3746] lr: 2.391e-02, eta: 1 day, 17:25:41, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5864, loss_cls: 3.8429, loss: 3.8429 +2024-07-20 01:01:26,198 - pyskl - INFO - Epoch [102][900/3746] lr: 2.388e-02, eta: 1 day, 17:24:19, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5736, loss_cls: 3.8579, loss: 3.8579 +2024-07-20 01:02:48,518 - pyskl - INFO - Epoch [102][1000/3746] lr: 2.386e-02, eta: 1 day, 17:22:58, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5734, loss_cls: 3.8738, loss: 3.8738 +2024-07-20 01:04:10,867 - pyskl - INFO - Epoch [102][1100/3746] lr: 2.384e-02, eta: 1 day, 17:21:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3148, top5_acc: 0.5661, loss_cls: 3.9182, loss: 3.9182 +2024-07-20 01:05:32,382 - pyskl - INFO - Epoch [102][1200/3746] lr: 2.381e-02, eta: 1 day, 17:20:15, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3202, top5_acc: 0.5742, loss_cls: 3.9062, loss: 3.9062 +2024-07-20 01:06:54,089 - pyskl - INFO - Epoch [102][1300/3746] lr: 2.379e-02, eta: 1 day, 17:18:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3177, top5_acc: 0.5805, loss_cls: 3.9094, loss: 3.9094 +2024-07-20 01:08:16,074 - pyskl - INFO - Epoch [102][1400/3746] lr: 2.376e-02, eta: 1 day, 17:17:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3113, top5_acc: 0.5820, loss_cls: 3.8949, loss: 3.8949 +2024-07-20 01:09:37,908 - pyskl - INFO - Epoch [102][1500/3746] lr: 2.374e-02, eta: 1 day, 17:16:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5750, loss_cls: 3.8677, loss: 3.8677 +2024-07-20 01:10:59,872 - pyskl - INFO - Epoch [102][1600/3746] lr: 2.372e-02, eta: 1 day, 17:14:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3119, top5_acc: 0.5703, loss_cls: 3.9237, loss: 3.9237 +2024-07-20 01:12:21,917 - pyskl - INFO - Epoch [102][1700/3746] lr: 2.369e-02, eta: 1 day, 17:13:28, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5708, loss_cls: 3.9097, loss: 3.9097 +2024-07-20 01:13:43,691 - pyskl - INFO - Epoch [102][1800/3746] lr: 2.367e-02, eta: 1 day, 17:12:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3144, top5_acc: 0.5681, loss_cls: 3.9180, loss: 3.9180 +2024-07-20 01:15:05,830 - pyskl - INFO - Epoch [102][1900/3746] lr: 2.365e-02, eta: 1 day, 17:10:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3150, top5_acc: 0.5689, loss_cls: 3.9087, loss: 3.9087 +2024-07-20 01:16:27,892 - pyskl - INFO - Epoch [102][2000/3746] lr: 2.362e-02, eta: 1 day, 17:09:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3117, top5_acc: 0.5677, loss_cls: 3.9487, loss: 3.9487 +2024-07-20 01:17:50,131 - pyskl - INFO - Epoch [102][2100/3746] lr: 2.360e-02, eta: 1 day, 17:08:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5692, loss_cls: 3.8788, loss: 3.8788 +2024-07-20 01:19:11,698 - pyskl - INFO - Epoch [102][2200/3746] lr: 2.357e-02, eta: 1 day, 17:06:41, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5717, loss_cls: 3.8974, loss: 3.8974 +2024-07-20 01:20:34,891 - pyskl - INFO - Epoch [102][2300/3746] lr: 2.355e-02, eta: 1 day, 17:05:20, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3130, top5_acc: 0.5645, loss_cls: 3.9513, loss: 3.9513 +2024-07-20 01:21:57,148 - pyskl - INFO - Epoch [102][2400/3746] lr: 2.353e-02, eta: 1 day, 17:03:59, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5744, loss_cls: 3.8775, loss: 3.8775 +2024-07-20 01:23:19,714 - pyskl - INFO - Epoch [102][2500/3746] lr: 2.350e-02, eta: 1 day, 17:02:37, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5689, loss_cls: 3.9121, loss: 3.9121 +2024-07-20 01:24:42,534 - pyskl - INFO - Epoch [102][2600/3746] lr: 2.348e-02, eta: 1 day, 17:01:16, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5759, loss_cls: 3.8748, loss: 3.8748 +2024-07-20 01:26:04,727 - pyskl - INFO - Epoch [102][2700/3746] lr: 2.346e-02, eta: 1 day, 16:59:55, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3189, top5_acc: 0.5761, loss_cls: 3.8841, loss: 3.8841 +2024-07-20 01:27:26,691 - pyskl - INFO - Epoch [102][2800/3746] lr: 2.343e-02, eta: 1 day, 16:58:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5700, loss_cls: 3.8868, loss: 3.8868 +2024-07-20 01:28:48,540 - pyskl - INFO - Epoch [102][2900/3746] lr: 2.341e-02, eta: 1 day, 16:57:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5745, loss_cls: 3.8894, loss: 3.8894 +2024-07-20 01:30:10,798 - pyskl - INFO - Epoch [102][3000/3746] lr: 2.339e-02, eta: 1 day, 16:55:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5800, loss_cls: 3.8738, loss: 3.8738 +2024-07-20 01:31:32,620 - pyskl - INFO - Epoch [102][3100/3746] lr: 2.336e-02, eta: 1 day, 16:54:29, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3120, top5_acc: 0.5656, loss_cls: 3.9313, loss: 3.9313 +2024-07-20 01:32:54,661 - pyskl - INFO - Epoch [102][3200/3746] lr: 2.334e-02, eta: 1 day, 16:53:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5767, loss_cls: 3.8881, loss: 3.8881 +2024-07-20 01:34:16,635 - pyskl - INFO - Epoch [102][3300/3746] lr: 2.331e-02, eta: 1 day, 16:51:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5631, loss_cls: 3.9281, loss: 3.9281 +2024-07-20 01:35:38,871 - pyskl - INFO - Epoch [102][3400/3746] lr: 2.329e-02, eta: 1 day, 16:50:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3131, top5_acc: 0.5727, loss_cls: 3.9261, loss: 3.9261 +2024-07-20 01:37:00,468 - pyskl - INFO - Epoch [102][3500/3746] lr: 2.327e-02, eta: 1 day, 16:49:03, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3155, top5_acc: 0.5648, loss_cls: 3.9266, loss: 3.9266 +2024-07-20 01:38:21,999 - pyskl - INFO - Epoch [102][3600/3746] lr: 2.324e-02, eta: 1 day, 16:47:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5764, loss_cls: 3.8812, loss: 3.8812 +2024-07-20 01:39:43,584 - pyskl - INFO - Epoch [102][3700/3746] lr: 2.322e-02, eta: 1 day, 16:46:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3181, top5_acc: 0.5739, loss_cls: 3.9256, loss: 3.9256 +2024-07-20 01:40:23,325 - pyskl - INFO - Saving checkpoint at 102 epochs +2024-07-20 01:42:14,651 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 01:42:15,336 - pyskl - INFO - +top1_acc 0.2573 +top5_acc 0.5016 +2024-07-20 01:42:15,336 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 01:42:15,379 - pyskl - INFO - +mean_acc 0.2570 +2024-07-20 01:42:15,392 - pyskl - INFO - Epoch(val) [102][309] top1_acc: 0.2573, top5_acc: 0.5016, mean_class_accuracy: 0.2570 +2024-07-20 01:46:07,958 - pyskl - INFO - Epoch [103][100/3746] lr: 2.319e-02, eta: 1 day, 16:45:14, time: 2.326, data_time: 1.336, memory: 15990, top1_acc: 0.3358, top5_acc: 0.5914, loss_cls: 3.7780, loss: 3.7780 +2024-07-20 01:47:30,803 - pyskl - INFO - Epoch [103][200/3746] lr: 2.316e-02, eta: 1 day, 16:43:53, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3147, top5_acc: 0.5772, loss_cls: 3.8761, loss: 3.8761 +2024-07-20 01:48:52,859 - pyskl - INFO - Epoch [103][300/3746] lr: 2.314e-02, eta: 1 day, 16:42:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3267, top5_acc: 0.5748, loss_cls: 3.8678, loss: 3.8678 +2024-07-20 01:50:15,495 - pyskl - INFO - Epoch [103][400/3746] lr: 2.311e-02, eta: 1 day, 16:41:11, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5833, loss_cls: 3.8011, loss: 3.8011 +2024-07-20 01:51:38,080 - pyskl - INFO - Epoch [103][500/3746] lr: 2.309e-02, eta: 1 day, 16:39:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5894, loss_cls: 3.8251, loss: 3.8251 +2024-07-20 01:53:00,370 - pyskl - INFO - Epoch [103][600/3746] lr: 2.307e-02, eta: 1 day, 16:38:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5847, loss_cls: 3.8248, loss: 3.8248 +2024-07-20 01:54:22,465 - pyskl - INFO - Epoch [103][700/3746] lr: 2.304e-02, eta: 1 day, 16:37:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3261, top5_acc: 0.5828, loss_cls: 3.8387, loss: 3.8387 +2024-07-20 01:55:44,796 - pyskl - INFO - Epoch [103][800/3746] lr: 2.302e-02, eta: 1 day, 16:35:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5814, loss_cls: 3.8586, loss: 3.8586 +2024-07-20 01:57:07,514 - pyskl - INFO - Epoch [103][900/3746] lr: 2.300e-02, eta: 1 day, 16:34:24, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5789, loss_cls: 3.8554, loss: 3.8554 +2024-07-20 01:58:30,323 - pyskl - INFO - Epoch [103][1000/3746] lr: 2.297e-02, eta: 1 day, 16:33:03, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3247, top5_acc: 0.5813, loss_cls: 3.8796, loss: 3.8796 +2024-07-20 01:59:52,109 - pyskl - INFO - Epoch [103][1100/3746] lr: 2.295e-02, eta: 1 day, 16:31:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5770, loss_cls: 3.8591, loss: 3.8591 +2024-07-20 02:01:13,947 - pyskl - INFO - Epoch [103][1200/3746] lr: 2.293e-02, eta: 1 day, 16:30:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3273, top5_acc: 0.5839, loss_cls: 3.8486, loss: 3.8486 +2024-07-20 02:02:36,693 - pyskl - INFO - Epoch [103][1300/3746] lr: 2.290e-02, eta: 1 day, 16:28:59, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5739, loss_cls: 3.8742, loss: 3.8742 +2024-07-20 02:03:58,456 - pyskl - INFO - Epoch [103][1400/3746] lr: 2.288e-02, eta: 1 day, 16:27:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3105, top5_acc: 0.5622, loss_cls: 3.9469, loss: 3.9469 +2024-07-20 02:05:20,282 - pyskl - INFO - Epoch [103][1500/3746] lr: 2.286e-02, eta: 1 day, 16:26:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3291, top5_acc: 0.5820, loss_cls: 3.8678, loss: 3.8678 +2024-07-20 02:06:42,204 - pyskl - INFO - Epoch [103][1600/3746] lr: 2.283e-02, eta: 1 day, 16:24:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5795, loss_cls: 3.8879, loss: 3.8879 +2024-07-20 02:08:03,871 - pyskl - INFO - Epoch [103][1700/3746] lr: 2.281e-02, eta: 1 day, 16:23:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3194, top5_acc: 0.5667, loss_cls: 3.9153, loss: 3.9153 +2024-07-20 02:09:25,636 - pyskl - INFO - Epoch [103][1800/3746] lr: 2.279e-02, eta: 1 day, 16:22:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5798, loss_cls: 3.8453, loss: 3.8453 +2024-07-20 02:10:47,502 - pyskl - INFO - Epoch [103][1900/3746] lr: 2.276e-02, eta: 1 day, 16:20:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3220, top5_acc: 0.5814, loss_cls: 3.8665, loss: 3.8665 +2024-07-20 02:12:09,329 - pyskl - INFO - Epoch [103][2000/3746] lr: 2.274e-02, eta: 1 day, 16:19:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3173, top5_acc: 0.5758, loss_cls: 3.9063, loss: 3.9063 +2024-07-20 02:13:32,033 - pyskl - INFO - Epoch [103][2100/3746] lr: 2.272e-02, eta: 1 day, 16:18:07, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3241, top5_acc: 0.5814, loss_cls: 3.8603, loss: 3.8603 +2024-07-20 02:14:54,644 - pyskl - INFO - Epoch [103][2200/3746] lr: 2.269e-02, eta: 1 day, 16:16:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3172, top5_acc: 0.5691, loss_cls: 3.9203, loss: 3.9203 +2024-07-20 02:16:17,010 - pyskl - INFO - Epoch [103][2300/3746] lr: 2.267e-02, eta: 1 day, 16:15:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5852, loss_cls: 3.8388, loss: 3.8388 +2024-07-20 02:17:39,353 - pyskl - INFO - Epoch [103][2400/3746] lr: 2.264e-02, eta: 1 day, 16:14:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5717, loss_cls: 3.8702, loss: 3.8702 +2024-07-20 02:19:01,716 - pyskl - INFO - Epoch [103][2500/3746] lr: 2.262e-02, eta: 1 day, 16:12:42, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3127, top5_acc: 0.5775, loss_cls: 3.8963, loss: 3.8963 +2024-07-20 02:20:23,569 - pyskl - INFO - Epoch [103][2600/3746] lr: 2.260e-02, eta: 1 day, 16:11:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3209, top5_acc: 0.5780, loss_cls: 3.8720, loss: 3.8720 +2024-07-20 02:21:45,717 - pyskl - INFO - Epoch [103][2700/3746] lr: 2.257e-02, eta: 1 day, 16:09:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5648, loss_cls: 3.9161, loss: 3.9161 +2024-07-20 02:23:08,053 - pyskl - INFO - Epoch [103][2800/3746] lr: 2.255e-02, eta: 1 day, 16:08:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5792, loss_cls: 3.8779, loss: 3.8779 +2024-07-20 02:24:30,309 - pyskl - INFO - Epoch [103][2900/3746] lr: 2.253e-02, eta: 1 day, 16:07:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3206, top5_acc: 0.5750, loss_cls: 3.8817, loss: 3.8817 +2024-07-20 02:25:52,045 - pyskl - INFO - Epoch [103][3000/3746] lr: 2.250e-02, eta: 1 day, 16:05:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5764, loss_cls: 3.8407, loss: 3.8407 +2024-07-20 02:27:14,637 - pyskl - INFO - Epoch [103][3100/3746] lr: 2.248e-02, eta: 1 day, 16:04:33, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5728, loss_cls: 3.8997, loss: 3.8997 +2024-07-20 02:28:36,178 - pyskl - INFO - Epoch [103][3200/3746] lr: 2.246e-02, eta: 1 day, 16:03:11, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3184, top5_acc: 0.5709, loss_cls: 3.9041, loss: 3.9041 +2024-07-20 02:29:58,302 - pyskl - INFO - Epoch [103][3300/3746] lr: 2.243e-02, eta: 1 day, 16:01:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3164, top5_acc: 0.5673, loss_cls: 3.9259, loss: 3.9259 +2024-07-20 02:31:20,162 - pyskl - INFO - Epoch [103][3400/3746] lr: 2.241e-02, eta: 1 day, 16:00:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5830, loss_cls: 3.8539, loss: 3.8539 +2024-07-20 02:32:41,745 - pyskl - INFO - Epoch [103][3500/3746] lr: 2.239e-02, eta: 1 day, 15:59:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5736, loss_cls: 3.8989, loss: 3.8989 +2024-07-20 02:34:03,992 - pyskl - INFO - Epoch [103][3600/3746] lr: 2.236e-02, eta: 1 day, 15:57:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3134, top5_acc: 0.5634, loss_cls: 3.9219, loss: 3.9219 +2024-07-20 02:35:25,485 - pyskl - INFO - Epoch [103][3700/3746] lr: 2.234e-02, eta: 1 day, 15:56:24, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3166, top5_acc: 0.5747, loss_cls: 3.8791, loss: 3.8791 +2024-07-20 02:36:05,089 - pyskl - INFO - Saving checkpoint at 103 epochs +2024-07-20 02:37:56,723 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 02:37:57,409 - pyskl - INFO - +top1_acc 0.2625 +top5_acc 0.5105 +2024-07-20 02:37:57,409 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 02:37:57,458 - pyskl - INFO - +mean_acc 0.2623 +2024-07-20 02:37:57,463 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_101.pth was removed +2024-07-20 02:37:57,727 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_103.pth. +2024-07-20 02:37:57,728 - pyskl - INFO - Best top1_acc is 0.2625 at 103 epoch. +2024-07-20 02:37:57,748 - pyskl - INFO - Epoch(val) [103][309] top1_acc: 0.2625, top5_acc: 0.5105, mean_class_accuracy: 0.2623 +2024-07-20 02:41:48,875 - pyskl - INFO - Epoch [104][100/3746] lr: 2.231e-02, eta: 1 day, 15:55:16, time: 2.311, data_time: 1.324, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5877, loss_cls: 3.8335, loss: 3.8335 +2024-07-20 02:43:11,480 - pyskl - INFO - Epoch [104][200/3746] lr: 2.228e-02, eta: 1 day, 15:53:54, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5798, loss_cls: 3.8578, loss: 3.8578 +2024-07-20 02:44:33,894 - pyskl - INFO - Epoch [104][300/3746] lr: 2.226e-02, eta: 1 day, 15:52:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3256, top5_acc: 0.5825, loss_cls: 3.8415, loss: 3.8415 +2024-07-20 02:45:57,025 - pyskl - INFO - Epoch [104][400/3746] lr: 2.224e-02, eta: 1 day, 15:51:12, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5775, loss_cls: 3.8741, loss: 3.8741 +2024-07-20 02:47:20,006 - pyskl - INFO - Epoch [104][500/3746] lr: 2.221e-02, eta: 1 day, 15:49:51, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3200, top5_acc: 0.5756, loss_cls: 3.8566, loss: 3.8566 +2024-07-20 02:48:42,944 - pyskl - INFO - Epoch [104][600/3746] lr: 2.219e-02, eta: 1 day, 15:48:30, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5709, loss_cls: 3.8563, loss: 3.8563 +2024-07-20 02:50:05,933 - pyskl - INFO - Epoch [104][700/3746] lr: 2.217e-02, eta: 1 day, 15:47:09, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5853, loss_cls: 3.7987, loss: 3.7987 +2024-07-20 02:51:28,431 - pyskl - INFO - Epoch [104][800/3746] lr: 2.214e-02, eta: 1 day, 15:45:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5842, loss_cls: 3.8030, loss: 3.8030 +2024-07-20 02:52:50,992 - pyskl - INFO - Epoch [104][900/3746] lr: 2.212e-02, eta: 1 day, 15:44:26, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5867, loss_cls: 3.8123, loss: 3.8123 +2024-07-20 02:54:13,113 - pyskl - INFO - Epoch [104][1000/3746] lr: 2.210e-02, eta: 1 day, 15:43:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3269, top5_acc: 0.5777, loss_cls: 3.8817, loss: 3.8817 +2024-07-20 02:55:35,043 - pyskl - INFO - Epoch [104][1100/3746] lr: 2.208e-02, eta: 1 day, 15:41:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5877, loss_cls: 3.8432, loss: 3.8432 +2024-07-20 02:56:57,271 - pyskl - INFO - Epoch [104][1200/3746] lr: 2.205e-02, eta: 1 day, 15:40:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3253, top5_acc: 0.5720, loss_cls: 3.8658, loss: 3.8658 +2024-07-20 02:58:19,008 - pyskl - INFO - Epoch [104][1300/3746] lr: 2.203e-02, eta: 1 day, 15:39:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5808, loss_cls: 3.8537, loss: 3.8537 +2024-07-20 02:59:41,138 - pyskl - INFO - Epoch [104][1400/3746] lr: 2.201e-02, eta: 1 day, 15:37:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3191, top5_acc: 0.5723, loss_cls: 3.8909, loss: 3.8909 +2024-07-20 03:01:02,857 - pyskl - INFO - Epoch [104][1500/3746] lr: 2.198e-02, eta: 1 day, 15:36:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5908, loss_cls: 3.8249, loss: 3.8249 +2024-07-20 03:02:24,749 - pyskl - INFO - Epoch [104][1600/3746] lr: 2.196e-02, eta: 1 day, 15:34:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3216, top5_acc: 0.5756, loss_cls: 3.8805, loss: 3.8805 +2024-07-20 03:03:46,591 - pyskl - INFO - Epoch [104][1700/3746] lr: 2.194e-02, eta: 1 day, 15:33:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5880, loss_cls: 3.8237, loss: 3.8237 +2024-07-20 03:05:08,608 - pyskl - INFO - Epoch [104][1800/3746] lr: 2.191e-02, eta: 1 day, 15:32:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5778, loss_cls: 3.8396, loss: 3.8396 +2024-07-20 03:06:30,775 - pyskl - INFO - Epoch [104][1900/3746] lr: 2.189e-02, eta: 1 day, 15:30:51, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5837, loss_cls: 3.8644, loss: 3.8644 +2024-07-20 03:07:52,300 - pyskl - INFO - Epoch [104][2000/3746] lr: 2.187e-02, eta: 1 day, 15:29:29, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5831, loss_cls: 3.8287, loss: 3.8287 +2024-07-20 03:09:14,895 - pyskl - INFO - Epoch [104][2100/3746] lr: 2.184e-02, eta: 1 day, 15:28:08, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5855, loss_cls: 3.8418, loss: 3.8418 +2024-07-20 03:10:36,591 - pyskl - INFO - Epoch [104][2200/3746] lr: 2.182e-02, eta: 1 day, 15:26:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3264, top5_acc: 0.5809, loss_cls: 3.8610, loss: 3.8610 +2024-07-20 03:11:58,876 - pyskl - INFO - Epoch [104][2300/3746] lr: 2.180e-02, eta: 1 day, 15:25:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5783, loss_cls: 3.8773, loss: 3.8773 +2024-07-20 03:13:20,941 - pyskl - INFO - Epoch [104][2400/3746] lr: 2.177e-02, eta: 1 day, 15:24:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5866, loss_cls: 3.8195, loss: 3.8195 +2024-07-20 03:14:43,337 - pyskl - INFO - Epoch [104][2500/3746] lr: 2.175e-02, eta: 1 day, 15:22:42, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5691, loss_cls: 3.8897, loss: 3.8897 +2024-07-20 03:16:05,817 - pyskl - INFO - Epoch [104][2600/3746] lr: 2.173e-02, eta: 1 day, 15:21:20, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3239, top5_acc: 0.5770, loss_cls: 3.8857, loss: 3.8857 +2024-07-20 03:17:27,805 - pyskl - INFO - Epoch [104][2700/3746] lr: 2.171e-02, eta: 1 day, 15:19:59, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5858, loss_cls: 3.8364, loss: 3.8364 +2024-07-20 03:18:50,130 - pyskl - INFO - Epoch [104][2800/3746] lr: 2.168e-02, eta: 1 day, 15:18:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5734, loss_cls: 3.9079, loss: 3.9079 +2024-07-20 03:20:12,798 - pyskl - INFO - Epoch [104][2900/3746] lr: 2.166e-02, eta: 1 day, 15:17:16, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5739, loss_cls: 3.8952, loss: 3.8952 +2024-07-20 03:21:34,777 - pyskl - INFO - Epoch [104][3000/3746] lr: 2.164e-02, eta: 1 day, 15:15:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3219, top5_acc: 0.5755, loss_cls: 3.8969, loss: 3.8969 +2024-07-20 03:22:56,716 - pyskl - INFO - Epoch [104][3100/3746] lr: 2.161e-02, eta: 1 day, 15:14:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5741, loss_cls: 3.8467, loss: 3.8467 +2024-07-20 03:24:18,777 - pyskl - INFO - Epoch [104][3200/3746] lr: 2.159e-02, eta: 1 day, 15:13:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3245, top5_acc: 0.5800, loss_cls: 3.8543, loss: 3.8543 +2024-07-20 03:25:40,457 - pyskl - INFO - Epoch [104][3300/3746] lr: 2.157e-02, eta: 1 day, 15:11:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3225, top5_acc: 0.5806, loss_cls: 3.8694, loss: 3.8694 +2024-07-20 03:27:02,274 - pyskl - INFO - Epoch [104][3400/3746] lr: 2.154e-02, eta: 1 day, 15:10:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5966, loss_cls: 3.7897, loss: 3.7897 +2024-07-20 03:28:24,400 - pyskl - INFO - Epoch [104][3500/3746] lr: 2.152e-02, eta: 1 day, 15:09:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5922, loss_cls: 3.8335, loss: 3.8335 +2024-07-20 03:29:46,319 - pyskl - INFO - Epoch [104][3600/3746] lr: 2.150e-02, eta: 1 day, 15:07:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3167, top5_acc: 0.5725, loss_cls: 3.9004, loss: 3.9004 +2024-07-20 03:31:08,888 - pyskl - INFO - Epoch [104][3700/3746] lr: 2.148e-02, eta: 1 day, 15:06:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5742, loss_cls: 3.8699, loss: 3.8699 +2024-07-20 03:31:48,660 - pyskl - INFO - Saving checkpoint at 104 epochs +2024-07-20 03:33:39,706 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 03:33:40,522 - pyskl - INFO - +top1_acc 0.2689 +top5_acc 0.5122 +2024-07-20 03:33:40,522 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 03:33:40,573 - pyskl - INFO - +mean_acc 0.2687 +2024-07-20 03:33:40,578 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_103.pth was removed +2024-07-20 03:33:40,853 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_104.pth. +2024-07-20 03:33:40,854 - pyskl - INFO - Best top1_acc is 0.2689 at 104 epoch. +2024-07-20 03:33:40,867 - pyskl - INFO - Epoch(val) [104][309] top1_acc: 0.2689, top5_acc: 0.5122, mean_class_accuracy: 0.2687 +2024-07-20 03:37:31,070 - pyskl - INFO - Epoch [105][100/3746] lr: 2.144e-02, eta: 1 day, 15:05:14, time: 2.302, data_time: 1.310, memory: 15990, top1_acc: 0.3378, top5_acc: 0.5938, loss_cls: 3.8021, loss: 3.8021 +2024-07-20 03:38:53,480 - pyskl - INFO - Epoch [105][200/3746] lr: 2.142e-02, eta: 1 day, 15:03:52, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5866, loss_cls: 3.8377, loss: 3.8377 +2024-07-20 03:40:16,035 - pyskl - INFO - Epoch [105][300/3746] lr: 2.140e-02, eta: 1 day, 15:02:31, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5938, loss_cls: 3.7816, loss: 3.7816 +2024-07-20 03:41:38,544 - pyskl - INFO - Epoch [105][400/3746] lr: 2.137e-02, eta: 1 day, 15:01:10, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5916, loss_cls: 3.7677, loss: 3.7677 +2024-07-20 03:43:00,452 - pyskl - INFO - Epoch [105][500/3746] lr: 2.135e-02, eta: 1 day, 14:59:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5902, loss_cls: 3.7749, loss: 3.7749 +2024-07-20 03:44:22,672 - pyskl - INFO - Epoch [105][600/3746] lr: 2.133e-02, eta: 1 day, 14:58:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3295, top5_acc: 0.5834, loss_cls: 3.8436, loss: 3.8436 +2024-07-20 03:45:45,982 - pyskl - INFO - Epoch [105][700/3746] lr: 2.130e-02, eta: 1 day, 14:57:06, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5809, loss_cls: 3.8318, loss: 3.8318 +2024-07-20 03:47:08,558 - pyskl - INFO - Epoch [105][800/3746] lr: 2.128e-02, eta: 1 day, 14:55:44, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5794, loss_cls: 3.8081, loss: 3.8081 +2024-07-20 03:48:31,070 - pyskl - INFO - Epoch [105][900/3746] lr: 2.126e-02, eta: 1 day, 14:54:23, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3228, top5_acc: 0.5837, loss_cls: 3.8556, loss: 3.8556 +2024-07-20 03:49:52,879 - pyskl - INFO - Epoch [105][1000/3746] lr: 2.124e-02, eta: 1 day, 14:53:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5837, loss_cls: 3.8183, loss: 3.8183 +2024-07-20 03:51:14,798 - pyskl - INFO - Epoch [105][1100/3746] lr: 2.121e-02, eta: 1 day, 14:51:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5863, loss_cls: 3.8044, loss: 3.8044 +2024-07-20 03:52:36,499 - pyskl - INFO - Epoch [105][1200/3746] lr: 2.119e-02, eta: 1 day, 14:50:18, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3161, top5_acc: 0.5709, loss_cls: 3.9043, loss: 3.9043 +2024-07-20 03:53:58,374 - pyskl - INFO - Epoch [105][1300/3746] lr: 2.117e-02, eta: 1 day, 14:48:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5795, loss_cls: 3.8524, loss: 3.8524 +2024-07-20 03:55:20,092 - pyskl - INFO - Epoch [105][1400/3746] lr: 2.114e-02, eta: 1 day, 14:47:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.5984, loss_cls: 3.7625, loss: 3.7625 +2024-07-20 03:56:42,652 - pyskl - INFO - Epoch [105][1500/3746] lr: 2.112e-02, eta: 1 day, 14:46:13, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.5930, loss_cls: 3.7611, loss: 3.7611 +2024-07-20 03:58:04,468 - pyskl - INFO - Epoch [105][1600/3746] lr: 2.110e-02, eta: 1 day, 14:44:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3187, top5_acc: 0.5772, loss_cls: 3.8599, loss: 3.8599 +2024-07-20 03:59:26,479 - pyskl - INFO - Epoch [105][1700/3746] lr: 2.108e-02, eta: 1 day, 14:43:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5798, loss_cls: 3.8722, loss: 3.8722 +2024-07-20 04:00:48,250 - pyskl - INFO - Epoch [105][1800/3746] lr: 2.105e-02, eta: 1 day, 14:42:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5750, loss_cls: 3.8716, loss: 3.8716 +2024-07-20 04:02:10,067 - pyskl - INFO - Epoch [105][1900/3746] lr: 2.103e-02, eta: 1 day, 14:40:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5637, loss_cls: 3.8842, loss: 3.8842 +2024-07-20 04:03:31,839 - pyskl - INFO - Epoch [105][2000/3746] lr: 2.101e-02, eta: 1 day, 14:39:25, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5783, loss_cls: 3.8644, loss: 3.8644 +2024-07-20 04:04:54,061 - pyskl - INFO - Epoch [105][2100/3746] lr: 2.098e-02, eta: 1 day, 14:38:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3217, top5_acc: 0.5853, loss_cls: 3.8455, loss: 3.8455 +2024-07-20 04:06:15,695 - pyskl - INFO - Epoch [105][2200/3746] lr: 2.096e-02, eta: 1 day, 14:36:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3386, top5_acc: 0.5933, loss_cls: 3.7985, loss: 3.7985 +2024-07-20 04:07:37,996 - pyskl - INFO - Epoch [105][2300/3746] lr: 2.094e-02, eta: 1 day, 14:35:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3212, top5_acc: 0.5786, loss_cls: 3.8868, loss: 3.8868 +2024-07-20 04:09:00,903 - pyskl - INFO - Epoch [105][2400/3746] lr: 2.092e-02, eta: 1 day, 14:33:59, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5881, loss_cls: 3.8187, loss: 3.8187 +2024-07-20 04:10:22,682 - pyskl - INFO - Epoch [105][2500/3746] lr: 2.089e-02, eta: 1 day, 14:32:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3197, top5_acc: 0.5731, loss_cls: 3.8917, loss: 3.8917 +2024-07-20 04:11:45,226 - pyskl - INFO - Epoch [105][2600/3746] lr: 2.087e-02, eta: 1 day, 14:31:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5892, loss_cls: 3.8186, loss: 3.8186 +2024-07-20 04:13:07,232 - pyskl - INFO - Epoch [105][2700/3746] lr: 2.085e-02, eta: 1 day, 14:29:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3278, top5_acc: 0.5837, loss_cls: 3.8323, loss: 3.8323 +2024-07-20 04:14:29,396 - pyskl - INFO - Epoch [105][2800/3746] lr: 2.083e-02, eta: 1 day, 14:28:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5806, loss_cls: 3.8364, loss: 3.8364 +2024-07-20 04:15:51,218 - pyskl - INFO - Epoch [105][2900/3746] lr: 2.080e-02, eta: 1 day, 14:27:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5806, loss_cls: 3.8188, loss: 3.8188 +2024-07-20 04:17:12,684 - pyskl - INFO - Epoch [105][3000/3746] lr: 2.078e-02, eta: 1 day, 14:25:49, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5875, loss_cls: 3.8305, loss: 3.8305 +2024-07-20 04:18:34,428 - pyskl - INFO - Epoch [105][3100/3746] lr: 2.076e-02, eta: 1 day, 14:24:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3231, top5_acc: 0.5783, loss_cls: 3.8727, loss: 3.8727 +2024-07-20 04:19:56,335 - pyskl - INFO - Epoch [105][3200/3746] lr: 2.073e-02, eta: 1 day, 14:23:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3242, top5_acc: 0.5870, loss_cls: 3.8516, loss: 3.8516 +2024-07-20 04:21:18,353 - pyskl - INFO - Epoch [105][3300/3746] lr: 2.071e-02, eta: 1 day, 14:21:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3227, top5_acc: 0.5803, loss_cls: 3.8577, loss: 3.8577 +2024-07-20 04:22:40,266 - pyskl - INFO - Epoch [105][3400/3746] lr: 2.069e-02, eta: 1 day, 14:20:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5872, loss_cls: 3.8222, loss: 3.8222 +2024-07-20 04:24:02,213 - pyskl - INFO - Epoch [105][3500/3746] lr: 2.067e-02, eta: 1 day, 14:19:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5852, loss_cls: 3.8280, loss: 3.8280 +2024-07-20 04:25:23,760 - pyskl - INFO - Epoch [105][3600/3746] lr: 2.064e-02, eta: 1 day, 14:17:39, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3328, top5_acc: 0.5845, loss_cls: 3.8414, loss: 3.8414 +2024-07-20 04:26:45,754 - pyskl - INFO - Epoch [105][3700/3746] lr: 2.062e-02, eta: 1 day, 14:16:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5766, loss_cls: 3.8585, loss: 3.8585 +2024-07-20 04:27:25,290 - pyskl - INFO - Saving checkpoint at 105 epochs +2024-07-20 04:29:16,546 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 04:29:17,217 - pyskl - INFO - +top1_acc 0.2688 +top5_acc 0.5112 +2024-07-20 04:29:17,217 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 04:29:17,261 - pyskl - INFO - +mean_acc 0.2686 +2024-07-20 04:29:17,274 - pyskl - INFO - Epoch(val) [105][309] top1_acc: 0.2688, top5_acc: 0.5112, mean_class_accuracy: 0.2686 +2024-07-20 04:33:09,049 - pyskl - INFO - Epoch [106][100/3746] lr: 2.059e-02, eta: 1 day, 14:15:07, time: 2.318, data_time: 1.327, memory: 15990, top1_acc: 0.3441, top5_acc: 0.5928, loss_cls: 3.7309, loss: 3.7309 +2024-07-20 04:34:31,456 - pyskl - INFO - Epoch [106][200/3746] lr: 2.057e-02, eta: 1 day, 14:13:45, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5938, loss_cls: 3.8001, loss: 3.8001 +2024-07-20 04:35:54,075 - pyskl - INFO - Epoch [106][300/3746] lr: 2.054e-02, eta: 1 day, 14:12:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5941, loss_cls: 3.7531, loss: 3.7531 +2024-07-20 04:37:16,216 - pyskl - INFO - Epoch [106][400/3746] lr: 2.052e-02, eta: 1 day, 14:11:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3352, top5_acc: 0.5925, loss_cls: 3.7691, loss: 3.7691 +2024-07-20 04:38:38,029 - pyskl - INFO - Epoch [106][500/3746] lr: 2.050e-02, eta: 1 day, 14:09:41, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5880, loss_cls: 3.8120, loss: 3.8120 +2024-07-20 04:40:00,256 - pyskl - INFO - Epoch [106][600/3746] lr: 2.048e-02, eta: 1 day, 14:08:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3234, top5_acc: 0.5856, loss_cls: 3.8411, loss: 3.8411 +2024-07-20 04:41:23,122 - pyskl - INFO - Epoch [106][700/3746] lr: 2.045e-02, eta: 1 day, 14:06:58, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5894, loss_cls: 3.8055, loss: 3.8055 +2024-07-20 04:42:45,437 - pyskl - INFO - Epoch [106][800/3746] lr: 2.043e-02, eta: 1 day, 14:05:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5970, loss_cls: 3.7720, loss: 3.7720 +2024-07-20 04:44:08,510 - pyskl - INFO - Epoch [106][900/3746] lr: 2.041e-02, eta: 1 day, 14:04:15, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5842, loss_cls: 3.8528, loss: 3.8528 +2024-07-20 04:45:30,639 - pyskl - INFO - Epoch [106][1000/3746] lr: 2.039e-02, eta: 1 day, 14:02:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5933, loss_cls: 3.8022, loss: 3.8022 +2024-07-20 04:46:52,512 - pyskl - INFO - Epoch [106][1100/3746] lr: 2.036e-02, eta: 1 day, 14:01:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5944, loss_cls: 3.7901, loss: 3.7901 +2024-07-20 04:48:14,737 - pyskl - INFO - Epoch [106][1200/3746] lr: 2.034e-02, eta: 1 day, 14:00:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.5964, loss_cls: 3.7666, loss: 3.7666 +2024-07-20 04:49:36,582 - pyskl - INFO - Epoch [106][1300/3746] lr: 2.032e-02, eta: 1 day, 13:58:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3262, top5_acc: 0.5902, loss_cls: 3.8056, loss: 3.8056 +2024-07-20 04:50:58,324 - pyskl - INFO - Epoch [106][1400/3746] lr: 2.030e-02, eta: 1 day, 13:57:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3203, top5_acc: 0.5802, loss_cls: 3.8723, loss: 3.8723 +2024-07-20 04:52:20,535 - pyskl - INFO - Epoch [106][1500/3746] lr: 2.027e-02, eta: 1 day, 13:56:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5956, loss_cls: 3.7968, loss: 3.7968 +2024-07-20 04:53:42,937 - pyskl - INFO - Epoch [106][1600/3746] lr: 2.025e-02, eta: 1 day, 13:54:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3275, top5_acc: 0.5927, loss_cls: 3.7946, loss: 3.7946 +2024-07-20 04:55:04,944 - pyskl - INFO - Epoch [106][1700/3746] lr: 2.023e-02, eta: 1 day, 13:53:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5966, loss_cls: 3.7822, loss: 3.7822 +2024-07-20 04:56:27,179 - pyskl - INFO - Epoch [106][1800/3746] lr: 2.021e-02, eta: 1 day, 13:52:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3322, top5_acc: 0.5859, loss_cls: 3.8369, loss: 3.8369 +2024-07-20 04:57:49,875 - pyskl - INFO - Epoch [106][1900/3746] lr: 2.018e-02, eta: 1 day, 13:50:39, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3259, top5_acc: 0.5767, loss_cls: 3.8436, loss: 3.8436 +2024-07-20 04:59:12,129 - pyskl - INFO - Epoch [106][2000/3746] lr: 2.016e-02, eta: 1 day, 13:49:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5908, loss_cls: 3.8036, loss: 3.8036 +2024-07-20 05:00:34,946 - pyskl - INFO - Epoch [106][2100/3746] lr: 2.014e-02, eta: 1 day, 13:47:57, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3223, top5_acc: 0.5825, loss_cls: 3.8480, loss: 3.8480 +2024-07-20 05:01:56,854 - pyskl - INFO - Epoch [106][2200/3746] lr: 2.012e-02, eta: 1 day, 13:46:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3153, top5_acc: 0.5652, loss_cls: 3.9043, loss: 3.9043 +2024-07-20 05:03:19,048 - pyskl - INFO - Epoch [106][2300/3746] lr: 2.009e-02, eta: 1 day, 13:45:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5920, loss_cls: 3.8022, loss: 3.8022 +2024-07-20 05:04:41,251 - pyskl - INFO - Epoch [106][2400/3746] lr: 2.007e-02, eta: 1 day, 13:43:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5950, loss_cls: 3.7820, loss: 3.7820 +2024-07-20 05:06:03,102 - pyskl - INFO - Epoch [106][2500/3746] lr: 2.005e-02, eta: 1 day, 13:42:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5942, loss_cls: 3.7976, loss: 3.7976 +2024-07-20 05:07:25,504 - pyskl - INFO - Epoch [106][2600/3746] lr: 2.003e-02, eta: 1 day, 13:41:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3287, top5_acc: 0.5863, loss_cls: 3.8414, loss: 3.8414 +2024-07-20 05:08:47,733 - pyskl - INFO - Epoch [106][2700/3746] lr: 2.000e-02, eta: 1 day, 13:39:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3233, top5_acc: 0.5714, loss_cls: 3.8863, loss: 3.8863 +2024-07-20 05:10:09,686 - pyskl - INFO - Epoch [106][2800/3746] lr: 1.998e-02, eta: 1 day, 13:38:25, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3272, top5_acc: 0.5806, loss_cls: 3.8406, loss: 3.8406 +2024-07-20 05:11:31,247 - pyskl - INFO - Epoch [106][2900/3746] lr: 1.996e-02, eta: 1 day, 13:37:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5745, loss_cls: 3.8193, loss: 3.8193 +2024-07-20 05:12:53,168 - pyskl - INFO - Epoch [106][3000/3746] lr: 1.994e-02, eta: 1 day, 13:35:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3180, top5_acc: 0.5723, loss_cls: 3.9255, loss: 3.9255 +2024-07-20 05:14:14,961 - pyskl - INFO - Epoch [106][3100/3746] lr: 1.991e-02, eta: 1 day, 13:34:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5864, loss_cls: 3.8624, loss: 3.8624 +2024-07-20 05:15:36,439 - pyskl - INFO - Epoch [106][3200/3746] lr: 1.989e-02, eta: 1 day, 13:32:58, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5886, loss_cls: 3.7927, loss: 3.7927 +2024-07-20 05:16:57,863 - pyskl - INFO - Epoch [106][3300/3746] lr: 1.987e-02, eta: 1 day, 13:31:37, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5816, loss_cls: 3.8182, loss: 3.8182 +2024-07-20 05:18:20,314 - pyskl - INFO - Epoch [106][3400/3746] lr: 1.985e-02, eta: 1 day, 13:30:15, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3214, top5_acc: 0.5652, loss_cls: 3.9027, loss: 3.9027 +2024-07-20 05:19:41,907 - pyskl - INFO - Epoch [106][3500/3746] lr: 1.983e-02, eta: 1 day, 13:28:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5814, loss_cls: 3.8512, loss: 3.8512 +2024-07-20 05:21:04,553 - pyskl - INFO - Epoch [106][3600/3746] lr: 1.980e-02, eta: 1 day, 13:27:32, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3294, top5_acc: 0.5834, loss_cls: 3.8044, loss: 3.8044 +2024-07-20 05:22:26,660 - pyskl - INFO - Epoch [106][3700/3746] lr: 1.978e-02, eta: 1 day, 13:26:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3344, top5_acc: 0.5803, loss_cls: 3.8333, loss: 3.8333 +2024-07-20 05:23:06,471 - pyskl - INFO - Saving checkpoint at 106 epochs +2024-07-20 05:24:58,282 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 05:24:59,012 - pyskl - INFO - +top1_acc 0.2715 +top5_acc 0.5184 +2024-07-20 05:24:59,012 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 05:24:59,061 - pyskl - INFO - +mean_acc 0.2713 +2024-07-20 05:24:59,066 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_104.pth was removed +2024-07-20 05:24:59,344 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_106.pth. +2024-07-20 05:24:59,345 - pyskl - INFO - Best top1_acc is 0.2715 at 106 epoch. +2024-07-20 05:24:59,358 - pyskl - INFO - Epoch(val) [106][309] top1_acc: 0.2715, top5_acc: 0.5184, mean_class_accuracy: 0.2713 +2024-07-20 05:28:52,017 - pyskl - INFO - Epoch [107][100/3746] lr: 1.975e-02, eta: 1 day, 13:24:58, time: 2.326, data_time: 1.335, memory: 15990, top1_acc: 0.3447, top5_acc: 0.6003, loss_cls: 3.7064, loss: 3.7064 +2024-07-20 05:30:14,633 - pyskl - INFO - Epoch [107][200/3746] lr: 1.973e-02, eta: 1 day, 13:23:37, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3355, top5_acc: 0.5950, loss_cls: 3.7565, loss: 3.7565 +2024-07-20 05:31:37,024 - pyskl - INFO - Epoch [107][300/3746] lr: 1.970e-02, eta: 1 day, 13:22:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3375, top5_acc: 0.6016, loss_cls: 3.7446, loss: 3.7446 +2024-07-20 05:32:59,377 - pyskl - INFO - Epoch [107][400/3746] lr: 1.968e-02, eta: 1 day, 13:20:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5933, loss_cls: 3.7650, loss: 3.7650 +2024-07-20 05:34:21,624 - pyskl - INFO - Epoch [107][500/3746] lr: 1.966e-02, eta: 1 day, 13:19:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.5967, loss_cls: 3.7497, loss: 3.7497 +2024-07-20 05:35:43,805 - pyskl - INFO - Epoch [107][600/3746] lr: 1.964e-02, eta: 1 day, 13:18:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3298, top5_acc: 0.5880, loss_cls: 3.8238, loss: 3.8238 +2024-07-20 05:37:05,856 - pyskl - INFO - Epoch [107][700/3746] lr: 1.961e-02, eta: 1 day, 13:16:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.5933, loss_cls: 3.7655, loss: 3.7655 +2024-07-20 05:38:28,552 - pyskl - INFO - Epoch [107][800/3746] lr: 1.959e-02, eta: 1 day, 13:15:27, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3412, top5_acc: 0.5900, loss_cls: 3.7840, loss: 3.7840 +2024-07-20 05:39:50,608 - pyskl - INFO - Epoch [107][900/3746] lr: 1.957e-02, eta: 1 day, 13:14:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5920, loss_cls: 3.7746, loss: 3.7746 +2024-07-20 05:41:12,772 - pyskl - INFO - Epoch [107][1000/3746] lr: 1.955e-02, eta: 1 day, 13:12:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5955, loss_cls: 3.7881, loss: 3.7881 +2024-07-20 05:42:35,004 - pyskl - INFO - Epoch [107][1100/3746] lr: 1.953e-02, eta: 1 day, 13:11:22, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3347, top5_acc: 0.5950, loss_cls: 3.7974, loss: 3.7974 +2024-07-20 05:43:57,457 - pyskl - INFO - Epoch [107][1200/3746] lr: 1.950e-02, eta: 1 day, 13:10:01, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3237, top5_acc: 0.5870, loss_cls: 3.8443, loss: 3.8443 +2024-07-20 05:45:19,815 - pyskl - INFO - Epoch [107][1300/3746] lr: 1.948e-02, eta: 1 day, 13:08:39, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3450, top5_acc: 0.5972, loss_cls: 3.7580, loss: 3.7580 +2024-07-20 05:46:42,180 - pyskl - INFO - Epoch [107][1400/3746] lr: 1.946e-02, eta: 1 day, 13:07:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3319, top5_acc: 0.5869, loss_cls: 3.8127, loss: 3.8127 +2024-07-20 05:48:04,131 - pyskl - INFO - Epoch [107][1500/3746] lr: 1.944e-02, eta: 1 day, 13:05:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5902, loss_cls: 3.8156, loss: 3.8156 +2024-07-20 05:49:26,527 - pyskl - INFO - Epoch [107][1600/3746] lr: 1.942e-02, eta: 1 day, 13:04:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3336, top5_acc: 0.5806, loss_cls: 3.8211, loss: 3.8211 +2024-07-20 05:50:48,775 - pyskl - INFO - Epoch [107][1700/3746] lr: 1.939e-02, eta: 1 day, 13:03:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.6052, loss_cls: 3.7423, loss: 3.7423 +2024-07-20 05:52:11,446 - pyskl - INFO - Epoch [107][1800/3746] lr: 1.937e-02, eta: 1 day, 13:01:52, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3334, top5_acc: 0.5933, loss_cls: 3.7871, loss: 3.7871 +2024-07-20 05:53:33,034 - pyskl - INFO - Epoch [107][1900/3746] lr: 1.935e-02, eta: 1 day, 13:00:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3258, top5_acc: 0.5803, loss_cls: 3.8450, loss: 3.8450 +2024-07-20 05:54:55,127 - pyskl - INFO - Epoch [107][2000/3746] lr: 1.933e-02, eta: 1 day, 12:59:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.6047, loss_cls: 3.7625, loss: 3.7625 +2024-07-20 05:56:17,856 - pyskl - INFO - Epoch [107][2100/3746] lr: 1.930e-02, eta: 1 day, 12:57:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5817, loss_cls: 3.8365, loss: 3.8365 +2024-07-20 05:57:40,354 - pyskl - INFO - Epoch [107][2200/3746] lr: 1.928e-02, eta: 1 day, 12:56:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.5864, loss_cls: 3.8240, loss: 3.8240 +2024-07-20 05:59:02,934 - pyskl - INFO - Epoch [107][2300/3746] lr: 1.926e-02, eta: 1 day, 12:55:04, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3286, top5_acc: 0.5752, loss_cls: 3.8142, loss: 3.8142 +2024-07-20 06:00:25,095 - pyskl - INFO - Epoch [107][2400/3746] lr: 1.924e-02, eta: 1 day, 12:53:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3211, top5_acc: 0.5794, loss_cls: 3.8530, loss: 3.8530 +2024-07-20 06:01:47,563 - pyskl - INFO - Epoch [107][2500/3746] lr: 1.922e-02, eta: 1 day, 12:52:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3364, top5_acc: 0.5834, loss_cls: 3.8050, loss: 3.8050 +2024-07-20 06:03:09,468 - pyskl - INFO - Epoch [107][2600/3746] lr: 1.919e-02, eta: 1 day, 12:50:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3314, top5_acc: 0.5859, loss_cls: 3.8141, loss: 3.8141 +2024-07-20 06:04:31,282 - pyskl - INFO - Epoch [107][2700/3746] lr: 1.917e-02, eta: 1 day, 12:49:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3380, top5_acc: 0.5991, loss_cls: 3.7685, loss: 3.7685 +2024-07-20 06:05:53,635 - pyskl - INFO - Epoch [107][2800/3746] lr: 1.915e-02, eta: 1 day, 12:48:16, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3300, top5_acc: 0.5863, loss_cls: 3.8136, loss: 3.8136 +2024-07-20 06:07:15,442 - pyskl - INFO - Epoch [107][2900/3746] lr: 1.913e-02, eta: 1 day, 12:46:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5914, loss_cls: 3.8096, loss: 3.8096 +2024-07-20 06:08:38,119 - pyskl - INFO - Epoch [107][3000/3746] lr: 1.911e-02, eta: 1 day, 12:45:33, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3277, top5_acc: 0.5845, loss_cls: 3.8109, loss: 3.8109 +2024-07-20 06:10:00,196 - pyskl - INFO - Epoch [107][3100/3746] lr: 1.908e-02, eta: 1 day, 12:44:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3283, top5_acc: 0.5798, loss_cls: 3.8489, loss: 3.8489 +2024-07-20 06:11:22,180 - pyskl - INFO - Epoch [107][3200/3746] lr: 1.906e-02, eta: 1 day, 12:42:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.5906, loss_cls: 3.8125, loss: 3.8125 +2024-07-20 06:12:44,597 - pyskl - INFO - Epoch [107][3300/3746] lr: 1.904e-02, eta: 1 day, 12:41:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5883, loss_cls: 3.8106, loss: 3.8106 +2024-07-20 06:14:06,524 - pyskl - INFO - Epoch [107][3400/3746] lr: 1.902e-02, eta: 1 day, 12:40:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3255, top5_acc: 0.5802, loss_cls: 3.8136, loss: 3.8136 +2024-07-20 06:15:28,379 - pyskl - INFO - Epoch [107][3500/3746] lr: 1.900e-02, eta: 1 day, 12:38:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3297, top5_acc: 0.5861, loss_cls: 3.8103, loss: 3.8103 +2024-07-20 06:16:50,097 - pyskl - INFO - Epoch [107][3600/3746] lr: 1.897e-02, eta: 1 day, 12:37:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3281, top5_acc: 0.5861, loss_cls: 3.8219, loss: 3.8219 +2024-07-20 06:18:11,742 - pyskl - INFO - Epoch [107][3700/3746] lr: 1.895e-02, eta: 1 day, 12:36:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3266, top5_acc: 0.5805, loss_cls: 3.8446, loss: 3.8446 +2024-07-20 06:18:51,484 - pyskl - INFO - Saving checkpoint at 107 epochs +2024-07-20 06:20:43,071 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 06:20:43,741 - pyskl - INFO - +top1_acc 0.2711 +top5_acc 0.5181 +2024-07-20 06:20:43,741 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 06:20:43,786 - pyskl - INFO - +mean_acc 0.2709 +2024-07-20 06:20:43,800 - pyskl - INFO - Epoch(val) [107][309] top1_acc: 0.2711, top5_acc: 0.5181, mean_class_accuracy: 0.2709 +2024-07-20 06:24:34,859 - pyskl - INFO - Epoch [108][100/3746] lr: 1.892e-02, eta: 1 day, 12:34:46, time: 2.310, data_time: 1.319, memory: 15990, top1_acc: 0.3459, top5_acc: 0.5983, loss_cls: 3.7581, loss: 3.7581 +2024-07-20 06:25:57,414 - pyskl - INFO - Epoch [108][200/3746] lr: 1.890e-02, eta: 1 day, 12:33:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.6048, loss_cls: 3.7457, loss: 3.7457 +2024-07-20 06:27:20,529 - pyskl - INFO - Epoch [108][300/3746] lr: 1.888e-02, eta: 1 day, 12:32:03, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.5936, loss_cls: 3.7619, loss: 3.7619 +2024-07-20 06:28:43,190 - pyskl - INFO - Epoch [108][400/3746] lr: 1.886e-02, eta: 1 day, 12:30:42, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6006, loss_cls: 3.7600, loss: 3.7600 +2024-07-20 06:30:05,456 - pyskl - INFO - Epoch [108][500/3746] lr: 1.883e-02, eta: 1 day, 12:29:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6077, loss_cls: 3.7001, loss: 3.7001 +2024-07-20 06:31:28,292 - pyskl - INFO - Epoch [108][600/3746] lr: 1.881e-02, eta: 1 day, 12:27:59, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5942, loss_cls: 3.7857, loss: 3.7857 +2024-07-20 06:32:50,433 - pyskl - INFO - Epoch [108][700/3746] lr: 1.879e-02, eta: 1 day, 12:26:37, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3438, top5_acc: 0.5881, loss_cls: 3.7677, loss: 3.7677 +2024-07-20 06:34:12,656 - pyskl - INFO - Epoch [108][800/3746] lr: 1.877e-02, eta: 1 day, 12:25:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.5911, loss_cls: 3.7492, loss: 3.7492 +2024-07-20 06:35:34,462 - pyskl - INFO - Epoch [108][900/3746] lr: 1.875e-02, eta: 1 day, 12:23:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3394, top5_acc: 0.5959, loss_cls: 3.7543, loss: 3.7543 +2024-07-20 06:36:56,757 - pyskl - INFO - Epoch [108][1000/3746] lr: 1.872e-02, eta: 1 day, 12:22:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5950, loss_cls: 3.7765, loss: 3.7765 +2024-07-20 06:38:18,529 - pyskl - INFO - Epoch [108][1100/3746] lr: 1.870e-02, eta: 1 day, 12:21:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6011, loss_cls: 3.7302, loss: 3.7302 +2024-07-20 06:39:40,629 - pyskl - INFO - Epoch [108][1200/3746] lr: 1.868e-02, eta: 1 day, 12:19:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.5925, loss_cls: 3.7540, loss: 3.7540 +2024-07-20 06:41:02,646 - pyskl - INFO - Epoch [108][1300/3746] lr: 1.866e-02, eta: 1 day, 12:18:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.5920, loss_cls: 3.7677, loss: 3.7677 +2024-07-20 06:42:24,433 - pyskl - INFO - Epoch [108][1400/3746] lr: 1.864e-02, eta: 1 day, 12:17:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.5986, loss_cls: 3.7767, loss: 3.7767 +2024-07-20 06:43:46,220 - pyskl - INFO - Epoch [108][1500/3746] lr: 1.862e-02, eta: 1 day, 12:15:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3362, top5_acc: 0.5897, loss_cls: 3.7973, loss: 3.7973 +2024-07-20 06:45:07,839 - pyskl - INFO - Epoch [108][1600/3746] lr: 1.859e-02, eta: 1 day, 12:14:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3383, top5_acc: 0.5936, loss_cls: 3.7731, loss: 3.7731 +2024-07-20 06:46:29,827 - pyskl - INFO - Epoch [108][1700/3746] lr: 1.857e-02, eta: 1 day, 12:13:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3292, top5_acc: 0.5877, loss_cls: 3.8233, loss: 3.8233 +2024-07-20 06:47:52,114 - pyskl - INFO - Epoch [108][1800/3746] lr: 1.855e-02, eta: 1 day, 12:11:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5927, loss_cls: 3.7986, loss: 3.7986 +2024-07-20 06:49:14,010 - pyskl - INFO - Epoch [108][1900/3746] lr: 1.853e-02, eta: 1 day, 12:10:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5950, loss_cls: 3.7680, loss: 3.7680 +2024-07-20 06:50:35,682 - pyskl - INFO - Epoch [108][2000/3746] lr: 1.851e-02, eta: 1 day, 12:08:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.5925, loss_cls: 3.7953, loss: 3.7953 +2024-07-20 06:51:57,902 - pyskl - INFO - Epoch [108][2100/3746] lr: 1.848e-02, eta: 1 day, 12:07:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5900, loss_cls: 3.7735, loss: 3.7735 +2024-07-20 06:53:19,854 - pyskl - INFO - Epoch [108][2200/3746] lr: 1.846e-02, eta: 1 day, 12:06:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5916, loss_cls: 3.7821, loss: 3.7821 +2024-07-20 06:54:42,710 - pyskl - INFO - Epoch [108][2300/3746] lr: 1.844e-02, eta: 1 day, 12:04:50, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3312, top5_acc: 0.5914, loss_cls: 3.8136, loss: 3.8136 +2024-07-20 06:56:05,254 - pyskl - INFO - Epoch [108][2400/3746] lr: 1.842e-02, eta: 1 day, 12:03:28, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5891, loss_cls: 3.8365, loss: 3.8365 +2024-07-20 06:57:27,005 - pyskl - INFO - Epoch [108][2500/3746] lr: 1.840e-02, eta: 1 day, 12:02:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3252, top5_acc: 0.5878, loss_cls: 3.8186, loss: 3.8186 +2024-07-20 06:58:48,855 - pyskl - INFO - Epoch [108][2600/3746] lr: 1.838e-02, eta: 1 day, 12:00:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3306, top5_acc: 0.5923, loss_cls: 3.7930, loss: 3.7930 +2024-07-20 07:00:10,928 - pyskl - INFO - Epoch [108][2700/3746] lr: 1.835e-02, eta: 1 day, 11:59:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5958, loss_cls: 3.7819, loss: 3.7819 +2024-07-20 07:01:33,111 - pyskl - INFO - Epoch [108][2800/3746] lr: 1.833e-02, eta: 1 day, 11:58:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3395, top5_acc: 0.5964, loss_cls: 3.7672, loss: 3.7672 +2024-07-20 07:02:54,791 - pyskl - INFO - Epoch [108][2900/3746] lr: 1.831e-02, eta: 1 day, 11:56:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.5967, loss_cls: 3.7791, loss: 3.7791 +2024-07-20 07:04:16,952 - pyskl - INFO - Epoch [108][3000/3746] lr: 1.829e-02, eta: 1 day, 11:55:17, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.5938, loss_cls: 3.7738, loss: 3.7738 +2024-07-20 07:05:38,730 - pyskl - INFO - Epoch [108][3100/3746] lr: 1.827e-02, eta: 1 day, 11:53:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.5887, loss_cls: 3.7887, loss: 3.7887 +2024-07-20 07:07:00,651 - pyskl - INFO - Epoch [108][3200/3746] lr: 1.825e-02, eta: 1 day, 11:52:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.5913, loss_cls: 3.7645, loss: 3.7645 +2024-07-20 07:08:22,673 - pyskl - INFO - Epoch [108][3300/3746] lr: 1.823e-02, eta: 1 day, 11:51:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3284, top5_acc: 0.5920, loss_cls: 3.8063, loss: 3.8063 +2024-07-20 07:09:44,257 - pyskl - INFO - Epoch [108][3400/3746] lr: 1.820e-02, eta: 1 day, 11:49:50, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3341, top5_acc: 0.5908, loss_cls: 3.7953, loss: 3.7953 +2024-07-20 07:11:06,014 - pyskl - INFO - Epoch [108][3500/3746] lr: 1.818e-02, eta: 1 day, 11:48:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3330, top5_acc: 0.5855, loss_cls: 3.8111, loss: 3.8111 +2024-07-20 07:12:27,849 - pyskl - INFO - Epoch [108][3600/3746] lr: 1.816e-02, eta: 1 day, 11:47:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5891, loss_cls: 3.8018, loss: 3.8018 +2024-07-20 07:13:49,645 - pyskl - INFO - Epoch [108][3700/3746] lr: 1.814e-02, eta: 1 day, 11:45:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3311, top5_acc: 0.5931, loss_cls: 3.8060, loss: 3.8060 +2024-07-20 07:14:29,404 - pyskl - INFO - Saving checkpoint at 108 epochs +2024-07-20 07:16:21,551 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 07:16:22,221 - pyskl - INFO - +top1_acc 0.2643 +top5_acc 0.4993 +2024-07-20 07:16:22,221 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 07:16:22,265 - pyskl - INFO - +mean_acc 0.2641 +2024-07-20 07:16:22,278 - pyskl - INFO - Epoch(val) [108][309] top1_acc: 0.2643, top5_acc: 0.4993, mean_class_accuracy: 0.2641 +2024-07-20 07:20:13,323 - pyskl - INFO - Epoch [109][100/3746] lr: 1.811e-02, eta: 1 day, 11:44:29, time: 2.310, data_time: 1.322, memory: 15990, top1_acc: 0.3387, top5_acc: 0.5959, loss_cls: 3.7810, loss: 3.7810 +2024-07-20 07:21:35,850 - pyskl - INFO - Epoch [109][200/3746] lr: 1.809e-02, eta: 1 day, 11:43:07, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3491, top5_acc: 0.6119, loss_cls: 3.6967, loss: 3.6967 +2024-07-20 07:22:58,071 - pyskl - INFO - Epoch [109][300/3746] lr: 1.806e-02, eta: 1 day, 11:41:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6081, loss_cls: 3.7296, loss: 3.7296 +2024-07-20 07:24:21,192 - pyskl - INFO - Epoch [109][400/3746] lr: 1.804e-02, eta: 1 day, 11:40:24, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3436, top5_acc: 0.6084, loss_cls: 3.7189, loss: 3.7189 +2024-07-20 07:25:43,336 - pyskl - INFO - Epoch [109][500/3746] lr: 1.802e-02, eta: 1 day, 11:39:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3484, top5_acc: 0.6014, loss_cls: 3.7207, loss: 3.7207 +2024-07-20 07:27:06,207 - pyskl - INFO - Epoch [109][600/3746] lr: 1.800e-02, eta: 1 day, 11:37:41, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3508, top5_acc: 0.6111, loss_cls: 3.6893, loss: 3.6893 +2024-07-20 07:28:28,760 - pyskl - INFO - Epoch [109][700/3746] lr: 1.798e-02, eta: 1 day, 11:36:19, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6050, loss_cls: 3.7360, loss: 3.7360 +2024-07-20 07:29:51,895 - pyskl - INFO - Epoch [109][800/3746] lr: 1.796e-02, eta: 1 day, 11:34:58, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.5966, loss_cls: 3.7615, loss: 3.7615 +2024-07-20 07:31:14,384 - pyskl - INFO - Epoch [109][900/3746] lr: 1.794e-02, eta: 1 day, 11:33:37, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5908, loss_cls: 3.8012, loss: 3.8012 +2024-07-20 07:32:36,417 - pyskl - INFO - Epoch [109][1000/3746] lr: 1.791e-02, eta: 1 day, 11:32:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6017, loss_cls: 3.7325, loss: 3.7325 +2024-07-20 07:33:58,256 - pyskl - INFO - Epoch [109][1100/3746] lr: 1.789e-02, eta: 1 day, 11:30:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6017, loss_cls: 3.7373, loss: 3.7373 +2024-07-20 07:35:19,870 - pyskl - INFO - Epoch [109][1200/3746] lr: 1.787e-02, eta: 1 day, 11:29:31, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3403, top5_acc: 0.5958, loss_cls: 3.7358, loss: 3.7358 +2024-07-20 07:36:41,691 - pyskl - INFO - Epoch [109][1300/3746] lr: 1.785e-02, eta: 1 day, 11:28:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3353, top5_acc: 0.5831, loss_cls: 3.7899, loss: 3.7899 +2024-07-20 07:38:03,436 - pyskl - INFO - Epoch [109][1400/3746] lr: 1.783e-02, eta: 1 day, 11:26:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3378, top5_acc: 0.5852, loss_cls: 3.8066, loss: 3.8066 +2024-07-20 07:39:25,637 - pyskl - INFO - Epoch [109][1500/3746] lr: 1.781e-02, eta: 1 day, 11:25:26, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.6022, loss_cls: 3.7207, loss: 3.7207 +2024-07-20 07:40:48,297 - pyskl - INFO - Epoch [109][1600/3746] lr: 1.779e-02, eta: 1 day, 11:24:04, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3373, top5_acc: 0.5881, loss_cls: 3.8067, loss: 3.8067 +2024-07-20 07:42:10,073 - pyskl - INFO - Epoch [109][1700/3746] lr: 1.776e-02, eta: 1 day, 11:22:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6005, loss_cls: 3.7385, loss: 3.7385 +2024-07-20 07:43:32,076 - pyskl - INFO - Epoch [109][1800/3746] lr: 1.774e-02, eta: 1 day, 11:21:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3289, top5_acc: 0.5800, loss_cls: 3.8478, loss: 3.8478 +2024-07-20 07:44:53,807 - pyskl - INFO - Epoch [109][1900/3746] lr: 1.772e-02, eta: 1 day, 11:19:59, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3337, top5_acc: 0.5945, loss_cls: 3.7909, loss: 3.7909 +2024-07-20 07:46:16,225 - pyskl - INFO - Epoch [109][2000/3746] lr: 1.770e-02, eta: 1 day, 11:18:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3303, top5_acc: 0.5845, loss_cls: 3.7974, loss: 3.7974 +2024-07-20 07:47:38,437 - pyskl - INFO - Epoch [109][2100/3746] lr: 1.768e-02, eta: 1 day, 11:17:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.5927, loss_cls: 3.7455, loss: 3.7455 +2024-07-20 07:49:00,502 - pyskl - INFO - Epoch [109][2200/3746] lr: 1.766e-02, eta: 1 day, 11:15:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.5967, loss_cls: 3.7526, loss: 3.7526 +2024-07-20 07:50:23,512 - pyskl - INFO - Epoch [109][2300/3746] lr: 1.764e-02, eta: 1 day, 11:14:32, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.6008, loss_cls: 3.7452, loss: 3.7452 +2024-07-20 07:51:45,678 - pyskl - INFO - Epoch [109][2400/3746] lr: 1.761e-02, eta: 1 day, 11:13:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3367, top5_acc: 0.5898, loss_cls: 3.8217, loss: 3.8217 +2024-07-20 07:53:07,656 - pyskl - INFO - Epoch [109][2500/3746] lr: 1.759e-02, eta: 1 day, 11:11:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.6050, loss_cls: 3.7477, loss: 3.7477 +2024-07-20 07:54:29,191 - pyskl - INFO - Epoch [109][2600/3746] lr: 1.757e-02, eta: 1 day, 11:10:27, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3309, top5_acc: 0.5856, loss_cls: 3.8112, loss: 3.8112 +2024-07-20 07:55:51,183 - pyskl - INFO - Epoch [109][2700/3746] lr: 1.755e-02, eta: 1 day, 11:09:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.5945, loss_cls: 3.7495, loss: 3.7495 +2024-07-20 07:57:13,110 - pyskl - INFO - Epoch [109][2800/3746] lr: 1.753e-02, eta: 1 day, 11:07:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3350, top5_acc: 0.5972, loss_cls: 3.7819, loss: 3.7819 +2024-07-20 07:58:35,337 - pyskl - INFO - Epoch [109][2900/3746] lr: 1.751e-02, eta: 1 day, 11:06:21, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6045, loss_cls: 3.7227, loss: 3.7227 +2024-07-20 07:59:57,001 - pyskl - INFO - Epoch [109][3000/3746] lr: 1.749e-02, eta: 1 day, 11:05:00, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6016, loss_cls: 3.7313, loss: 3.7313 +2024-07-20 08:01:18,821 - pyskl - INFO - Epoch [109][3100/3746] lr: 1.747e-02, eta: 1 day, 11:03:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3325, top5_acc: 0.5875, loss_cls: 3.8113, loss: 3.8113 +2024-07-20 08:02:40,705 - pyskl - INFO - Epoch [109][3200/3746] lr: 1.744e-02, eta: 1 day, 11:02:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.5933, loss_cls: 3.7856, loss: 3.7856 +2024-07-20 08:04:02,171 - pyskl - INFO - Epoch [109][3300/3746] lr: 1.742e-02, eta: 1 day, 11:00:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3452, top5_acc: 0.6017, loss_cls: 3.7345, loss: 3.7345 +2024-07-20 08:05:24,210 - pyskl - INFO - Epoch [109][3400/3746] lr: 1.740e-02, eta: 1 day, 10:59:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3430, top5_acc: 0.5967, loss_cls: 3.7532, loss: 3.7532 +2024-07-20 08:06:46,050 - pyskl - INFO - Epoch [109][3500/3746] lr: 1.738e-02, eta: 1 day, 10:58:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.5939, loss_cls: 3.7595, loss: 3.7595 +2024-07-20 08:08:07,854 - pyskl - INFO - Epoch [109][3600/3746] lr: 1.736e-02, eta: 1 day, 10:56:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3398, top5_acc: 0.5994, loss_cls: 3.7705, loss: 3.7705 +2024-07-20 08:09:29,604 - pyskl - INFO - Epoch [109][3700/3746] lr: 1.734e-02, eta: 1 day, 10:55:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.6006, loss_cls: 3.7412, loss: 3.7412 +2024-07-20 08:10:09,302 - pyskl - INFO - Saving checkpoint at 109 epochs +2024-07-20 08:12:01,101 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 08:12:01,770 - pyskl - INFO - +top1_acc 0.2770 +top5_acc 0.5275 +2024-07-20 08:12:01,770 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 08:12:01,812 - pyskl - INFO - +mean_acc 0.2767 +2024-07-20 08:12:01,817 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_106.pth was removed +2024-07-20 08:12:02,071 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_109.pth. +2024-07-20 08:12:02,072 - pyskl - INFO - Best top1_acc is 0.2770 at 109 epoch. +2024-07-20 08:12:02,084 - pyskl - INFO - Epoch(val) [109][309] top1_acc: 0.2770, top5_acc: 0.5275, mean_class_accuracy: 0.2767 +2024-07-20 08:15:50,221 - pyskl - INFO - Epoch [110][100/3746] lr: 1.731e-02, eta: 1 day, 10:54:08, time: 2.281, data_time: 1.296, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6075, loss_cls: 3.6904, loss: 3.6904 +2024-07-20 08:17:12,544 - pyskl - INFO - Epoch [110][200/3746] lr: 1.729e-02, eta: 1 day, 10:52:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6109, loss_cls: 3.6857, loss: 3.6857 +2024-07-20 08:18:35,057 - pyskl - INFO - Epoch [110][300/3746] lr: 1.727e-02, eta: 1 day, 10:51:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3495, top5_acc: 0.5992, loss_cls: 3.7454, loss: 3.7454 +2024-07-20 08:19:57,705 - pyskl - INFO - Epoch [110][400/3746] lr: 1.724e-02, eta: 1 day, 10:50:03, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.6053, loss_cls: 3.7187, loss: 3.7187 +2024-07-20 08:21:20,541 - pyskl - INFO - Epoch [110][500/3746] lr: 1.722e-02, eta: 1 day, 10:48:42, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6019, loss_cls: 3.7047, loss: 3.7047 +2024-07-20 08:22:42,875 - pyskl - INFO - Epoch [110][600/3746] lr: 1.720e-02, eta: 1 day, 10:47:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6102, loss_cls: 3.7193, loss: 3.7193 +2024-07-20 08:24:05,104 - pyskl - INFO - Epoch [110][700/3746] lr: 1.718e-02, eta: 1 day, 10:45:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3473, top5_acc: 0.5956, loss_cls: 3.7745, loss: 3.7745 +2024-07-20 08:25:27,196 - pyskl - INFO - Epoch [110][800/3746] lr: 1.716e-02, eta: 1 day, 10:44:36, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6077, loss_cls: 3.6863, loss: 3.6863 +2024-07-20 08:26:49,456 - pyskl - INFO - Epoch [110][900/3746] lr: 1.714e-02, eta: 1 day, 10:43:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6055, loss_cls: 3.7070, loss: 3.7070 +2024-07-20 08:28:11,444 - pyskl - INFO - Epoch [110][1000/3746] lr: 1.712e-02, eta: 1 day, 10:41:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3381, top5_acc: 0.6028, loss_cls: 3.7708, loss: 3.7708 +2024-07-20 08:29:33,338 - pyskl - INFO - Epoch [110][1100/3746] lr: 1.710e-02, eta: 1 day, 10:40:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6047, loss_cls: 3.7029, loss: 3.7029 +2024-07-20 08:30:54,791 - pyskl - INFO - Epoch [110][1200/3746] lr: 1.708e-02, eta: 1 day, 10:39:09, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3422, top5_acc: 0.5978, loss_cls: 3.7529, loss: 3.7529 +2024-07-20 08:32:16,458 - pyskl - INFO - Epoch [110][1300/3746] lr: 1.705e-02, eta: 1 day, 10:37:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6181, loss_cls: 3.6539, loss: 3.6539 +2024-07-20 08:33:38,377 - pyskl - INFO - Epoch [110][1400/3746] lr: 1.703e-02, eta: 1 day, 10:36:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3411, top5_acc: 0.6005, loss_cls: 3.7563, loss: 3.7563 +2024-07-20 08:35:00,166 - pyskl - INFO - Epoch [110][1500/3746] lr: 1.701e-02, eta: 1 day, 10:35:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.5995, loss_cls: 3.7324, loss: 3.7324 +2024-07-20 08:36:22,250 - pyskl - INFO - Epoch [110][1600/3746] lr: 1.699e-02, eta: 1 day, 10:33:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.5947, loss_cls: 3.7465, loss: 3.7465 +2024-07-20 08:37:43,895 - pyskl - INFO - Epoch [110][1700/3746] lr: 1.697e-02, eta: 1 day, 10:32:20, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6036, loss_cls: 3.7313, loss: 3.7313 +2024-07-20 08:39:05,832 - pyskl - INFO - Epoch [110][1800/3746] lr: 1.695e-02, eta: 1 day, 10:30:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3433, top5_acc: 0.6002, loss_cls: 3.7266, loss: 3.7266 +2024-07-20 08:40:27,987 - pyskl - INFO - Epoch [110][1900/3746] lr: 1.693e-02, eta: 1 day, 10:29:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3428, top5_acc: 0.6011, loss_cls: 3.6971, loss: 3.6971 +2024-07-20 08:41:49,581 - pyskl - INFO - Epoch [110][2000/3746] lr: 1.691e-02, eta: 1 day, 10:28:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6044, loss_cls: 3.7284, loss: 3.7284 +2024-07-20 08:43:12,851 - pyskl - INFO - Epoch [110][2100/3746] lr: 1.689e-02, eta: 1 day, 10:26:53, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.5938, loss_cls: 3.7949, loss: 3.7949 +2024-07-20 08:44:34,530 - pyskl - INFO - Epoch [110][2200/3746] lr: 1.687e-02, eta: 1 day, 10:25:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3420, top5_acc: 0.5931, loss_cls: 3.7627, loss: 3.7627 +2024-07-20 08:45:56,883 - pyskl - INFO - Epoch [110][2300/3746] lr: 1.685e-02, eta: 1 day, 10:24:09, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3372, top5_acc: 0.5959, loss_cls: 3.7706, loss: 3.7706 +2024-07-20 08:47:19,067 - pyskl - INFO - Epoch [110][2400/3746] lr: 1.682e-02, eta: 1 day, 10:22:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3416, top5_acc: 0.6014, loss_cls: 3.7582, loss: 3.7582 +2024-07-20 08:48:41,526 - pyskl - INFO - Epoch [110][2500/3746] lr: 1.680e-02, eta: 1 day, 10:21:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6089, loss_cls: 3.7148, loss: 3.7148 +2024-07-20 08:50:03,596 - pyskl - INFO - Epoch [110][2600/3746] lr: 1.678e-02, eta: 1 day, 10:20:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.6033, loss_cls: 3.7288, loss: 3.7288 +2024-07-20 08:51:25,299 - pyskl - INFO - Epoch [110][2700/3746] lr: 1.676e-02, eta: 1 day, 10:18:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.5923, loss_cls: 3.7496, loss: 3.7496 +2024-07-20 08:52:46,959 - pyskl - INFO - Epoch [110][2800/3746] lr: 1.674e-02, eta: 1 day, 10:17:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6019, loss_cls: 3.7607, loss: 3.7607 +2024-07-20 08:54:08,531 - pyskl - INFO - Epoch [110][2900/3746] lr: 1.672e-02, eta: 1 day, 10:15:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3366, top5_acc: 0.6014, loss_cls: 3.7693, loss: 3.7693 +2024-07-20 08:55:30,097 - pyskl - INFO - Epoch [110][3000/3746] lr: 1.670e-02, eta: 1 day, 10:14:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3361, top5_acc: 0.5905, loss_cls: 3.7780, loss: 3.7780 +2024-07-20 08:56:51,617 - pyskl - INFO - Epoch [110][3100/3746] lr: 1.668e-02, eta: 1 day, 10:13:14, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6000, loss_cls: 3.7272, loss: 3.7272 +2024-07-20 08:58:13,815 - pyskl - INFO - Epoch [110][3200/3746] lr: 1.666e-02, eta: 1 day, 10:11:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.5913, loss_cls: 3.7347, loss: 3.7347 +2024-07-20 08:59:35,457 - pyskl - INFO - Epoch [110][3300/3746] lr: 1.664e-02, eta: 1 day, 10:10:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3369, top5_acc: 0.6008, loss_cls: 3.7422, loss: 3.7422 +2024-07-20 09:00:57,228 - pyskl - INFO - Epoch [110][3400/3746] lr: 1.662e-02, eta: 1 day, 10:09:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3342, top5_acc: 0.5923, loss_cls: 3.7921, loss: 3.7921 +2024-07-20 09:02:18,821 - pyskl - INFO - Epoch [110][3500/3746] lr: 1.659e-02, eta: 1 day, 10:07:47, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3439, top5_acc: 0.6005, loss_cls: 3.7290, loss: 3.7290 +2024-07-20 09:03:40,520 - pyskl - INFO - Epoch [110][3600/3746] lr: 1.657e-02, eta: 1 day, 10:06:25, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3370, top5_acc: 0.5970, loss_cls: 3.7691, loss: 3.7691 +2024-07-20 09:05:02,466 - pyskl - INFO - Epoch [110][3700/3746] lr: 1.655e-02, eta: 1 day, 10:05:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3469, top5_acc: 0.6066, loss_cls: 3.7352, loss: 3.7352 +2024-07-20 09:05:42,131 - pyskl - INFO - Saving checkpoint at 110 epochs +2024-07-20 09:07:32,864 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 09:07:33,545 - pyskl - INFO - +top1_acc 0.2806 +top5_acc 0.5263 +2024-07-20 09:07:33,546 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 09:07:33,595 - pyskl - INFO - +mean_acc 0.2802 +2024-07-20 09:07:33,599 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_109.pth was removed +2024-07-20 09:07:33,863 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_110.pth. +2024-07-20 09:07:33,864 - pyskl - INFO - Best top1_acc is 0.2806 at 110 epoch. +2024-07-20 09:07:33,880 - pyskl - INFO - Epoch(val) [110][309] top1_acc: 0.2806, top5_acc: 0.5263, mean_class_accuracy: 0.2802 +2024-07-20 09:11:26,192 - pyskl - INFO - Epoch [111][100/3746] lr: 1.652e-02, eta: 1 day, 10:03:44, time: 2.323, data_time: 1.326, memory: 15990, top1_acc: 0.3527, top5_acc: 0.6122, loss_cls: 3.6943, loss: 3.6943 +2024-07-20 09:12:49,462 - pyskl - INFO - Epoch [111][200/3746] lr: 1.650e-02, eta: 1 day, 10:02:23, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.3519, top5_acc: 0.6109, loss_cls: 3.6942, loss: 3.6942 +2024-07-20 09:14:11,612 - pyskl - INFO - Epoch [111][300/3746] lr: 1.648e-02, eta: 1 day, 10:01:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.6091, loss_cls: 3.6796, loss: 3.6796 +2024-07-20 09:15:33,678 - pyskl - INFO - Epoch [111][400/3746] lr: 1.646e-02, eta: 1 day, 9:59:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3444, top5_acc: 0.6070, loss_cls: 3.7003, loss: 3.7003 +2024-07-20 09:16:56,780 - pyskl - INFO - Epoch [111][500/3746] lr: 1.644e-02, eta: 1 day, 9:58:18, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.5998, loss_cls: 3.7098, loss: 3.7098 +2024-07-20 09:18:19,341 - pyskl - INFO - Epoch [111][600/3746] lr: 1.642e-02, eta: 1 day, 9:56:56, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6111, loss_cls: 3.6617, loss: 3.6617 +2024-07-20 09:19:41,983 - pyskl - INFO - Epoch [111][700/3746] lr: 1.640e-02, eta: 1 day, 9:55:34, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6094, loss_cls: 3.6931, loss: 3.6931 +2024-07-20 09:21:03,971 - pyskl - INFO - Epoch [111][800/3746] lr: 1.638e-02, eta: 1 day, 9:54:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3463, top5_acc: 0.6003, loss_cls: 3.7209, loss: 3.7209 +2024-07-20 09:22:25,736 - pyskl - INFO - Epoch [111][900/3746] lr: 1.636e-02, eta: 1 day, 9:52:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3558, top5_acc: 0.6091, loss_cls: 3.6736, loss: 3.6736 +2024-07-20 09:23:47,843 - pyskl - INFO - Epoch [111][1000/3746] lr: 1.634e-02, eta: 1 day, 9:51:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6100, loss_cls: 3.6728, loss: 3.6728 +2024-07-20 09:25:09,323 - pyskl - INFO - Epoch [111][1100/3746] lr: 1.632e-02, eta: 1 day, 9:50:07, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6064, loss_cls: 3.6840, loss: 3.6840 +2024-07-20 09:26:31,216 - pyskl - INFO - Epoch [111][1200/3746] lr: 1.630e-02, eta: 1 day, 9:48:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3414, top5_acc: 0.5928, loss_cls: 3.7505, loss: 3.7505 +2024-07-20 09:27:53,116 - pyskl - INFO - Epoch [111][1300/3746] lr: 1.627e-02, eta: 1 day, 9:47:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6094, loss_cls: 3.6820, loss: 3.6820 +2024-07-20 09:29:15,120 - pyskl - INFO - Epoch [111][1400/3746] lr: 1.625e-02, eta: 1 day, 9:46:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3461, top5_acc: 0.6069, loss_cls: 3.7093, loss: 3.7093 +2024-07-20 09:30:37,287 - pyskl - INFO - Epoch [111][1500/3746] lr: 1.623e-02, eta: 1 day, 9:44:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3392, top5_acc: 0.6027, loss_cls: 3.7166, loss: 3.7166 +2024-07-20 09:31:59,144 - pyskl - INFO - Epoch [111][1600/3746] lr: 1.621e-02, eta: 1 day, 9:43:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3486, top5_acc: 0.6067, loss_cls: 3.6876, loss: 3.6876 +2024-07-20 09:33:21,173 - pyskl - INFO - Epoch [111][1700/3746] lr: 1.619e-02, eta: 1 day, 9:41:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6003, loss_cls: 3.7371, loss: 3.7371 +2024-07-20 09:34:42,781 - pyskl - INFO - Epoch [111][1800/3746] lr: 1.617e-02, eta: 1 day, 9:40:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6075, loss_cls: 3.6810, loss: 3.6810 +2024-07-20 09:36:04,538 - pyskl - INFO - Epoch [111][1900/3746] lr: 1.615e-02, eta: 1 day, 9:39:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6030, loss_cls: 3.7144, loss: 3.7144 +2024-07-20 09:37:26,650 - pyskl - INFO - Epoch [111][2000/3746] lr: 1.613e-02, eta: 1 day, 9:37:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3498, top5_acc: 0.6127, loss_cls: 3.6839, loss: 3.6839 +2024-07-20 09:38:49,412 - pyskl - INFO - Epoch [111][2100/3746] lr: 1.611e-02, eta: 1 day, 9:36:28, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3359, top5_acc: 0.5919, loss_cls: 3.7654, loss: 3.7654 +2024-07-20 09:40:11,590 - pyskl - INFO - Epoch [111][2200/3746] lr: 1.609e-02, eta: 1 day, 9:35:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3402, top5_acc: 0.6028, loss_cls: 3.7295, loss: 3.7295 +2024-07-20 09:41:34,302 - pyskl - INFO - Epoch [111][2300/3746] lr: 1.607e-02, eta: 1 day, 9:33:45, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3516, top5_acc: 0.6012, loss_cls: 3.7358, loss: 3.7358 +2024-07-20 09:42:56,275 - pyskl - INFO - Epoch [111][2400/3746] lr: 1.605e-02, eta: 1 day, 9:32:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3397, top5_acc: 0.5961, loss_cls: 3.7477, loss: 3.7477 +2024-07-20 09:44:18,200 - pyskl - INFO - Epoch [111][2500/3746] lr: 1.603e-02, eta: 1 day, 9:31:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3545, top5_acc: 0.6002, loss_cls: 3.6998, loss: 3.6998 +2024-07-20 09:45:40,452 - pyskl - INFO - Epoch [111][2600/3746] lr: 1.601e-02, eta: 1 day, 9:29:39, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3455, top5_acc: 0.5986, loss_cls: 3.7501, loss: 3.7501 +2024-07-20 09:47:02,419 - pyskl - INFO - Epoch [111][2700/3746] lr: 1.599e-02, eta: 1 day, 9:28:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6012, loss_cls: 3.7467, loss: 3.7467 +2024-07-20 09:48:24,342 - pyskl - INFO - Epoch [111][2800/3746] lr: 1.597e-02, eta: 1 day, 9:26:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3500, top5_acc: 0.6052, loss_cls: 3.7116, loss: 3.7116 +2024-07-20 09:49:46,569 - pyskl - INFO - Epoch [111][2900/3746] lr: 1.595e-02, eta: 1 day, 9:25:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3453, top5_acc: 0.5995, loss_cls: 3.7234, loss: 3.7234 +2024-07-20 09:51:08,792 - pyskl - INFO - Epoch [111][3000/3746] lr: 1.593e-02, eta: 1 day, 9:24:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6088, loss_cls: 3.6840, loss: 3.6840 +2024-07-20 09:52:30,605 - pyskl - INFO - Epoch [111][3100/3746] lr: 1.590e-02, eta: 1 day, 9:22:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.5989, loss_cls: 3.7067, loss: 3.7067 +2024-07-20 09:53:52,425 - pyskl - INFO - Epoch [111][3200/3746] lr: 1.588e-02, eta: 1 day, 9:21:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3384, top5_acc: 0.5889, loss_cls: 3.7854, loss: 3.7854 +2024-07-20 09:55:14,942 - pyskl - INFO - Epoch [111][3300/3746] lr: 1.586e-02, eta: 1 day, 9:20:07, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3442, top5_acc: 0.6028, loss_cls: 3.7521, loss: 3.7521 +2024-07-20 09:56:37,168 - pyskl - INFO - Epoch [111][3400/3746] lr: 1.584e-02, eta: 1 day, 9:18:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3339, top5_acc: 0.5984, loss_cls: 3.7974, loss: 3.7974 +2024-07-20 09:57:59,188 - pyskl - INFO - Epoch [111][3500/3746] lr: 1.582e-02, eta: 1 day, 9:17:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.5973, loss_cls: 3.7537, loss: 3.7537 +2024-07-20 09:59:21,368 - pyskl - INFO - Epoch [111][3600/3746] lr: 1.580e-02, eta: 1 day, 9:16:01, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3481, top5_acc: 0.6117, loss_cls: 3.6828, loss: 3.6828 +2024-07-20 10:00:43,164 - pyskl - INFO - Epoch [111][3700/3746] lr: 1.578e-02, eta: 1 day, 9:14:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3409, top5_acc: 0.5964, loss_cls: 3.7770, loss: 3.7770 +2024-07-20 10:01:22,843 - pyskl - INFO - Saving checkpoint at 111 epochs +2024-07-20 10:03:14,986 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 10:03:15,650 - pyskl - INFO - +top1_acc 0.2906 +top5_acc 0.5406 +2024-07-20 10:03:15,650 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 10:03:15,693 - pyskl - INFO - +mean_acc 0.2905 +2024-07-20 10:03:15,697 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_110.pth was removed +2024-07-20 10:03:15,948 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_111.pth. +2024-07-20 10:03:15,949 - pyskl - INFO - Best top1_acc is 0.2906 at 111 epoch. +2024-07-20 10:03:15,962 - pyskl - INFO - Epoch(val) [111][309] top1_acc: 0.2906, top5_acc: 0.5406, mean_class_accuracy: 0.2905 +2024-07-20 10:07:01,842 - pyskl - INFO - Epoch [112][100/3746] lr: 1.575e-02, eta: 1 day, 9:13:17, time: 2.259, data_time: 1.271, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6083, loss_cls: 3.6511, loss: 3.6511 +2024-07-20 10:08:24,557 - pyskl - INFO - Epoch [112][200/3746] lr: 1.573e-02, eta: 1 day, 9:11:55, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3595, top5_acc: 0.6081, loss_cls: 3.6416, loss: 3.6416 +2024-07-20 10:09:47,229 - pyskl - INFO - Epoch [112][300/3746] lr: 1.571e-02, eta: 1 day, 9:10:34, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3617, top5_acc: 0.6238, loss_cls: 3.5955, loss: 3.5955 +2024-07-20 10:11:09,558 - pyskl - INFO - Epoch [112][400/3746] lr: 1.569e-02, eta: 1 day, 9:09:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6131, loss_cls: 3.6647, loss: 3.6647 +2024-07-20 10:12:32,588 - pyskl - INFO - Epoch [112][500/3746] lr: 1.567e-02, eta: 1 day, 9:07:50, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6155, loss_cls: 3.6257, loss: 3.6257 +2024-07-20 10:13:55,114 - pyskl - INFO - Epoch [112][600/3746] lr: 1.565e-02, eta: 1 day, 9:06:29, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3539, top5_acc: 0.6083, loss_cls: 3.6728, loss: 3.6728 +2024-07-20 10:15:17,891 - pyskl - INFO - Epoch [112][700/3746] lr: 1.563e-02, eta: 1 day, 9:05:07, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6219, loss_cls: 3.6481, loss: 3.6481 +2024-07-20 10:16:39,908 - pyskl - INFO - Epoch [112][800/3746] lr: 1.561e-02, eta: 1 day, 9:03:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6023, loss_cls: 3.6914, loss: 3.6914 +2024-07-20 10:18:02,178 - pyskl - INFO - Epoch [112][900/3746] lr: 1.559e-02, eta: 1 day, 9:02:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6128, loss_cls: 3.6955, loss: 3.6955 +2024-07-20 10:19:24,066 - pyskl - INFO - Epoch [112][1000/3746] lr: 1.557e-02, eta: 1 day, 9:01:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3447, top5_acc: 0.5961, loss_cls: 3.7298, loss: 3.7298 +2024-07-20 10:20:46,014 - pyskl - INFO - Epoch [112][1100/3746] lr: 1.555e-02, eta: 1 day, 8:59:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3480, top5_acc: 0.6070, loss_cls: 3.7088, loss: 3.7088 +2024-07-20 10:22:07,898 - pyskl - INFO - Epoch [112][1200/3746] lr: 1.553e-02, eta: 1 day, 8:58:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3452, top5_acc: 0.6030, loss_cls: 3.7194, loss: 3.7194 +2024-07-20 10:23:30,051 - pyskl - INFO - Epoch [112][1300/3746] lr: 1.551e-02, eta: 1 day, 8:56:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6148, loss_cls: 3.6622, loss: 3.6622 +2024-07-20 10:24:52,568 - pyskl - INFO - Epoch [112][1400/3746] lr: 1.549e-02, eta: 1 day, 8:55:34, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3575, top5_acc: 0.6034, loss_cls: 3.6952, loss: 3.6952 +2024-07-20 10:26:14,706 - pyskl - INFO - Epoch [112][1500/3746] lr: 1.547e-02, eta: 1 day, 8:54:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6027, loss_cls: 3.7030, loss: 3.7030 +2024-07-20 10:27:37,149 - pyskl - INFO - Epoch [112][1600/3746] lr: 1.545e-02, eta: 1 day, 8:52:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3477, top5_acc: 0.6120, loss_cls: 3.6903, loss: 3.6903 +2024-07-20 10:28:59,017 - pyskl - INFO - Epoch [112][1700/3746] lr: 1.543e-02, eta: 1 day, 8:51:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3419, top5_acc: 0.6023, loss_cls: 3.7260, loss: 3.7260 +2024-07-20 10:30:21,181 - pyskl - INFO - Epoch [112][1800/3746] lr: 1.541e-02, eta: 1 day, 8:50:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3467, top5_acc: 0.6041, loss_cls: 3.7080, loss: 3.7080 +2024-07-20 10:31:43,007 - pyskl - INFO - Epoch [112][1900/3746] lr: 1.539e-02, eta: 1 day, 8:48:45, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6117, loss_cls: 3.6704, loss: 3.6704 +2024-07-20 10:33:05,068 - pyskl - INFO - Epoch [112][2000/3746] lr: 1.537e-02, eta: 1 day, 8:47:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6167, loss_cls: 3.6847, loss: 3.6847 +2024-07-20 10:34:27,331 - pyskl - INFO - Epoch [112][2100/3746] lr: 1.535e-02, eta: 1 day, 8:46:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6066, loss_cls: 3.6923, loss: 3.6923 +2024-07-20 10:35:49,148 - pyskl - INFO - Epoch [112][2200/3746] lr: 1.533e-02, eta: 1 day, 8:44:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3441, top5_acc: 0.6066, loss_cls: 3.7073, loss: 3.7073 +2024-07-20 10:37:11,907 - pyskl - INFO - Epoch [112][2300/3746] lr: 1.531e-02, eta: 1 day, 8:43:17, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6062, loss_cls: 3.7091, loss: 3.7091 +2024-07-20 10:38:34,496 - pyskl - INFO - Epoch [112][2400/3746] lr: 1.529e-02, eta: 1 day, 8:41:56, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3577, top5_acc: 0.6169, loss_cls: 3.6744, loss: 3.6744 +2024-07-20 10:39:56,437 - pyskl - INFO - Epoch [112][2500/3746] lr: 1.527e-02, eta: 1 day, 8:40:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6100, loss_cls: 3.6757, loss: 3.6757 +2024-07-20 10:41:18,393 - pyskl - INFO - Epoch [112][2600/3746] lr: 1.525e-02, eta: 1 day, 8:39:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3431, top5_acc: 0.6008, loss_cls: 3.7139, loss: 3.7139 +2024-07-20 10:42:40,538 - pyskl - INFO - Epoch [112][2700/3746] lr: 1.523e-02, eta: 1 day, 8:37:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3589, top5_acc: 0.6116, loss_cls: 3.6791, loss: 3.6791 +2024-07-20 10:44:02,331 - pyskl - INFO - Epoch [112][2800/3746] lr: 1.521e-02, eta: 1 day, 8:36:28, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3470, top5_acc: 0.5995, loss_cls: 3.7520, loss: 3.7520 +2024-07-20 10:45:24,798 - pyskl - INFO - Epoch [112][2900/3746] lr: 1.519e-02, eta: 1 day, 8:35:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.5966, loss_cls: 3.7365, loss: 3.7365 +2024-07-20 10:46:46,868 - pyskl - INFO - Epoch [112][3000/3746] lr: 1.517e-02, eta: 1 day, 8:33:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6059, loss_cls: 3.7075, loss: 3.7075 +2024-07-20 10:48:08,615 - pyskl - INFO - Epoch [112][3100/3746] lr: 1.515e-02, eta: 1 day, 8:32:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6031, loss_cls: 3.7138, loss: 3.7138 +2024-07-20 10:49:30,695 - pyskl - INFO - Epoch [112][3200/3746] lr: 1.513e-02, eta: 1 day, 8:31:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3566, top5_acc: 0.6114, loss_cls: 3.6792, loss: 3.6792 +2024-07-20 10:50:52,578 - pyskl - INFO - Epoch [112][3300/3746] lr: 1.511e-02, eta: 1 day, 8:29:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3522, top5_acc: 0.5980, loss_cls: 3.7259, loss: 3.7259 +2024-07-20 10:52:15,287 - pyskl - INFO - Epoch [112][3400/3746] lr: 1.509e-02, eta: 1 day, 8:28:17, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3423, top5_acc: 0.5975, loss_cls: 3.7512, loss: 3.7512 +2024-07-20 10:53:37,400 - pyskl - INFO - Epoch [112][3500/3746] lr: 1.507e-02, eta: 1 day, 8:26:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3464, top5_acc: 0.6095, loss_cls: 3.6804, loss: 3.6804 +2024-07-20 10:54:59,217 - pyskl - INFO - Epoch [112][3600/3746] lr: 1.505e-02, eta: 1 day, 8:25:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3505, top5_acc: 0.6012, loss_cls: 3.7186, loss: 3.7186 +2024-07-20 10:56:21,100 - pyskl - INFO - Epoch [112][3700/3746] lr: 1.503e-02, eta: 1 day, 8:24:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3445, top5_acc: 0.5988, loss_cls: 3.7200, loss: 3.7200 +2024-07-20 10:57:01,303 - pyskl - INFO - Saving checkpoint at 112 epochs +2024-07-20 10:58:53,502 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 10:58:54,161 - pyskl - INFO - +top1_acc 0.2858 +top5_acc 0.5369 +2024-07-20 10:58:54,161 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 10:58:54,203 - pyskl - INFO - +mean_acc 0.2857 +2024-07-20 10:58:54,215 - pyskl - INFO - Epoch(val) [112][309] top1_acc: 0.2858, top5_acc: 0.5369, mean_class_accuracy: 0.2857 +2024-07-20 11:02:39,839 - pyskl - INFO - Epoch [113][100/3746] lr: 1.500e-02, eta: 1 day, 8:22:48, time: 2.256, data_time: 1.266, memory: 15990, top1_acc: 0.3636, top5_acc: 0.6238, loss_cls: 3.6079, loss: 3.6079 +2024-07-20 11:04:02,510 - pyskl - INFO - Epoch [113][200/3746] lr: 1.498e-02, eta: 1 day, 8:21:26, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3555, top5_acc: 0.6175, loss_cls: 3.6491, loss: 3.6491 +2024-07-20 11:05:25,558 - pyskl - INFO - Epoch [113][300/3746] lr: 1.496e-02, eta: 1 day, 8:20:04, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3664, top5_acc: 0.6244, loss_cls: 3.5985, loss: 3.5985 +2024-07-20 11:06:48,160 - pyskl - INFO - Epoch [113][400/3746] lr: 1.494e-02, eta: 1 day, 8:18:43, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3506, top5_acc: 0.6078, loss_cls: 3.6806, loss: 3.6806 +2024-07-20 11:08:10,937 - pyskl - INFO - Epoch [113][500/3746] lr: 1.492e-02, eta: 1 day, 8:17:21, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6203, loss_cls: 3.6387, loss: 3.6387 +2024-07-20 11:09:33,937 - pyskl - INFO - Epoch [113][600/3746] lr: 1.490e-02, eta: 1 day, 8:15:59, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3547, top5_acc: 0.6089, loss_cls: 3.6335, loss: 3.6335 +2024-07-20 11:10:56,194 - pyskl - INFO - Epoch [113][700/3746] lr: 1.488e-02, eta: 1 day, 8:14:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6288, loss_cls: 3.5978, loss: 3.5978 +2024-07-20 11:12:18,488 - pyskl - INFO - Epoch [113][800/3746] lr: 1.486e-02, eta: 1 day, 8:13:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6220, loss_cls: 3.6466, loss: 3.6466 +2024-07-20 11:13:40,406 - pyskl - INFO - Epoch [113][900/3746] lr: 1.484e-02, eta: 1 day, 8:11:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3489, top5_acc: 0.6066, loss_cls: 3.6808, loss: 3.6808 +2024-07-20 11:15:01,808 - pyskl - INFO - Epoch [113][1000/3746] lr: 1.482e-02, eta: 1 day, 8:10:32, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6103, loss_cls: 3.6764, loss: 3.6764 +2024-07-20 11:16:23,915 - pyskl - INFO - Epoch [113][1100/3746] lr: 1.480e-02, eta: 1 day, 8:09:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3530, top5_acc: 0.6114, loss_cls: 3.6784, loss: 3.6784 +2024-07-20 11:17:46,606 - pyskl - INFO - Epoch [113][1200/3746] lr: 1.478e-02, eta: 1 day, 8:07:48, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6159, loss_cls: 3.6625, loss: 3.6625 +2024-07-20 11:19:08,444 - pyskl - INFO - Epoch [113][1300/3746] lr: 1.476e-02, eta: 1 day, 8:06:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6062, loss_cls: 3.6869, loss: 3.6869 +2024-07-20 11:20:30,535 - pyskl - INFO - Epoch [113][1400/3746] lr: 1.474e-02, eta: 1 day, 8:05:04, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3400, top5_acc: 0.6030, loss_cls: 3.7092, loss: 3.7092 +2024-07-20 11:21:52,231 - pyskl - INFO - Epoch [113][1500/3746] lr: 1.472e-02, eta: 1 day, 8:03:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6112, loss_cls: 3.6668, loss: 3.6668 +2024-07-20 11:23:13,762 - pyskl - INFO - Epoch [113][1600/3746] lr: 1.470e-02, eta: 1 day, 8:02:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3538, top5_acc: 0.6083, loss_cls: 3.7069, loss: 3.7069 +2024-07-20 11:24:35,678 - pyskl - INFO - Epoch [113][1700/3746] lr: 1.468e-02, eta: 1 day, 8:00:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3548, top5_acc: 0.6120, loss_cls: 3.6607, loss: 3.6607 +2024-07-20 11:25:57,312 - pyskl - INFO - Epoch [113][1800/3746] lr: 1.466e-02, eta: 1 day, 7:59:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6145, loss_cls: 3.6253, loss: 3.6253 +2024-07-20 11:27:19,546 - pyskl - INFO - Epoch [113][1900/3746] lr: 1.464e-02, eta: 1 day, 7:58:14, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6058, loss_cls: 3.7051, loss: 3.7051 +2024-07-20 11:28:41,709 - pyskl - INFO - Epoch [113][2000/3746] lr: 1.462e-02, eta: 1 day, 7:56:52, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6048, loss_cls: 3.7172, loss: 3.7172 +2024-07-20 11:30:03,574 - pyskl - INFO - Epoch [113][2100/3746] lr: 1.460e-02, eta: 1 day, 7:55:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.5969, loss_cls: 3.7274, loss: 3.7274 +2024-07-20 11:31:26,937 - pyskl - INFO - Epoch [113][2200/3746] lr: 1.458e-02, eta: 1 day, 7:54:09, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6050, loss_cls: 3.6915, loss: 3.6915 +2024-07-20 11:32:49,358 - pyskl - INFO - Epoch [113][2300/3746] lr: 1.456e-02, eta: 1 day, 7:52:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3578, top5_acc: 0.6169, loss_cls: 3.6666, loss: 3.6666 +2024-07-20 11:34:11,835 - pyskl - INFO - Epoch [113][2400/3746] lr: 1.454e-02, eta: 1 day, 7:51:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6155, loss_cls: 3.6652, loss: 3.6652 +2024-07-20 11:35:33,675 - pyskl - INFO - Epoch [113][2500/3746] lr: 1.452e-02, eta: 1 day, 7:50:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3586, top5_acc: 0.6116, loss_cls: 3.6524, loss: 3.6524 +2024-07-20 11:36:55,879 - pyskl - INFO - Epoch [113][2600/3746] lr: 1.450e-02, eta: 1 day, 7:48:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3472, top5_acc: 0.6055, loss_cls: 3.7056, loss: 3.7056 +2024-07-20 11:38:18,109 - pyskl - INFO - Epoch [113][2700/3746] lr: 1.448e-02, eta: 1 day, 7:47:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6236, loss_cls: 3.6470, loss: 3.6470 +2024-07-20 11:39:40,074 - pyskl - INFO - Epoch [113][2800/3746] lr: 1.446e-02, eta: 1 day, 7:45:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6184, loss_cls: 3.6408, loss: 3.6408 +2024-07-20 11:41:01,659 - pyskl - INFO - Epoch [113][2900/3746] lr: 1.444e-02, eta: 1 day, 7:44:36, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3483, top5_acc: 0.6008, loss_cls: 3.7116, loss: 3.7116 +2024-07-20 11:42:23,804 - pyskl - INFO - Epoch [113][3000/3746] lr: 1.442e-02, eta: 1 day, 7:43:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3458, top5_acc: 0.5956, loss_cls: 3.7237, loss: 3.7237 +2024-07-20 11:43:45,743 - pyskl - INFO - Epoch [113][3100/3746] lr: 1.440e-02, eta: 1 day, 7:41:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3497, top5_acc: 0.6156, loss_cls: 3.6934, loss: 3.6934 +2024-07-20 11:45:08,942 - pyskl - INFO - Epoch [113][3200/3746] lr: 1.438e-02, eta: 1 day, 7:40:30, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3459, top5_acc: 0.6050, loss_cls: 3.7056, loss: 3.7056 +2024-07-20 11:46:30,700 - pyskl - INFO - Epoch [113][3300/3746] lr: 1.436e-02, eta: 1 day, 7:39:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3502, top5_acc: 0.6039, loss_cls: 3.7323, loss: 3.7323 +2024-07-20 11:47:52,379 - pyskl - INFO - Epoch [113][3400/3746] lr: 1.434e-02, eta: 1 day, 7:37:46, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3466, top5_acc: 0.6058, loss_cls: 3.6757, loss: 3.6757 +2024-07-20 11:49:14,262 - pyskl - INFO - Epoch [113][3500/3746] lr: 1.432e-02, eta: 1 day, 7:36:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3581, top5_acc: 0.6059, loss_cls: 3.6657, loss: 3.6657 +2024-07-20 11:50:36,046 - pyskl - INFO - Epoch [113][3600/3746] lr: 1.431e-02, eta: 1 day, 7:35:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3503, top5_acc: 0.6077, loss_cls: 3.6834, loss: 3.6834 +2024-07-20 11:51:58,205 - pyskl - INFO - Epoch [113][3700/3746] lr: 1.429e-02, eta: 1 day, 7:33:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6075, loss_cls: 3.7004, loss: 3.7004 +2024-07-20 11:52:38,044 - pyskl - INFO - Saving checkpoint at 113 epochs +2024-07-20 11:54:31,011 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 11:54:31,672 - pyskl - INFO - +top1_acc 0.3001 +top5_acc 0.5433 +2024-07-20 11:54:31,673 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 11:54:31,714 - pyskl - INFO - +mean_acc 0.2998 +2024-07-20 11:54:31,718 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_111.pth was removed +2024-07-20 11:54:31,970 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_113.pth. +2024-07-20 11:54:31,971 - pyskl - INFO - Best top1_acc is 0.3001 at 113 epoch. +2024-07-20 11:54:31,983 - pyskl - INFO - Epoch(val) [113][309] top1_acc: 0.3001, top5_acc: 0.5433, mean_class_accuracy: 0.2998 +2024-07-20 11:58:18,485 - pyskl - INFO - Epoch [114][100/3746] lr: 1.426e-02, eta: 1 day, 7:32:16, time: 2.265, data_time: 1.267, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6272, loss_cls: 3.5566, loss: 3.5566 +2024-07-20 11:59:41,712 - pyskl - INFO - Epoch [114][200/3746] lr: 1.424e-02, eta: 1 day, 7:30:54, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6189, loss_cls: 3.6211, loss: 3.6211 +2024-07-20 12:01:03,980 - pyskl - INFO - Epoch [114][300/3746] lr: 1.422e-02, eta: 1 day, 7:29:32, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6212, loss_cls: 3.5780, loss: 3.5780 +2024-07-20 12:02:26,767 - pyskl - INFO - Epoch [114][400/3746] lr: 1.420e-02, eta: 1 day, 7:28:10, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6128, loss_cls: 3.6594, loss: 3.6594 +2024-07-20 12:03:48,782 - pyskl - INFO - Epoch [114][500/3746] lr: 1.418e-02, eta: 1 day, 7:26:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6255, loss_cls: 3.6012, loss: 3.6012 +2024-07-20 12:05:11,564 - pyskl - INFO - Epoch [114][600/3746] lr: 1.416e-02, eta: 1 day, 7:25:27, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6255, loss_cls: 3.6130, loss: 3.6130 +2024-07-20 12:06:34,080 - pyskl - INFO - Epoch [114][700/3746] lr: 1.414e-02, eta: 1 day, 7:24:05, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3606, top5_acc: 0.6170, loss_cls: 3.6479, loss: 3.6479 +2024-07-20 12:07:55,875 - pyskl - INFO - Epoch [114][800/3746] lr: 1.412e-02, eta: 1 day, 7:22:43, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3602, top5_acc: 0.6122, loss_cls: 3.6449, loss: 3.6449 +2024-07-20 12:09:18,225 - pyskl - INFO - Epoch [114][900/3746] lr: 1.410e-02, eta: 1 day, 7:21:21, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3475, top5_acc: 0.6122, loss_cls: 3.6964, loss: 3.6964 +2024-07-20 12:10:40,016 - pyskl - INFO - Epoch [114][1000/3746] lr: 1.408e-02, eta: 1 day, 7:19:59, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6197, loss_cls: 3.6122, loss: 3.6122 +2024-07-20 12:12:01,489 - pyskl - INFO - Epoch [114][1100/3746] lr: 1.406e-02, eta: 1 day, 7:18:37, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3608, top5_acc: 0.6216, loss_cls: 3.6177, loss: 3.6177 +2024-07-20 12:13:23,235 - pyskl - INFO - Epoch [114][1200/3746] lr: 1.404e-02, eta: 1 day, 7:17:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6161, loss_cls: 3.6595, loss: 3.6595 +2024-07-20 12:14:44,921 - pyskl - INFO - Epoch [114][1300/3746] lr: 1.402e-02, eta: 1 day, 7:15:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3569, top5_acc: 0.6070, loss_cls: 3.6667, loss: 3.6667 +2024-07-20 12:16:07,269 - pyskl - INFO - Epoch [114][1400/3746] lr: 1.400e-02, eta: 1 day, 7:14:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6069, loss_cls: 3.6773, loss: 3.6773 +2024-07-20 12:17:29,283 - pyskl - INFO - Epoch [114][1500/3746] lr: 1.398e-02, eta: 1 day, 7:13:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3683, top5_acc: 0.6155, loss_cls: 3.6513, loss: 3.6513 +2024-07-20 12:18:51,746 - pyskl - INFO - Epoch [114][1600/3746] lr: 1.397e-02, eta: 1 day, 7:11:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3520, top5_acc: 0.6103, loss_cls: 3.6656, loss: 3.6656 +2024-07-20 12:20:13,371 - pyskl - INFO - Epoch [114][1700/3746] lr: 1.395e-02, eta: 1 day, 7:10:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6097, loss_cls: 3.6817, loss: 3.6817 +2024-07-20 12:21:34,557 - pyskl - INFO - Epoch [114][1800/3746] lr: 1.393e-02, eta: 1 day, 7:09:03, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3536, top5_acc: 0.6106, loss_cls: 3.6450, loss: 3.6450 +2024-07-20 12:22:56,672 - pyskl - INFO - Epoch [114][1900/3746] lr: 1.391e-02, eta: 1 day, 7:07:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6109, loss_cls: 3.6428, loss: 3.6428 +2024-07-20 12:24:18,750 - pyskl - INFO - Epoch [114][2000/3746] lr: 1.389e-02, eta: 1 day, 7:06:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6178, loss_cls: 3.6378, loss: 3.6378 +2024-07-20 12:25:40,544 - pyskl - INFO - Epoch [114][2100/3746] lr: 1.387e-02, eta: 1 day, 7:04:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6150, loss_cls: 3.6364, loss: 3.6364 +2024-07-20 12:27:03,745 - pyskl - INFO - Epoch [114][2200/3746] lr: 1.385e-02, eta: 1 day, 7:03:35, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6122, loss_cls: 3.6637, loss: 3.6637 +2024-07-20 12:28:26,091 - pyskl - INFO - Epoch [114][2300/3746] lr: 1.383e-02, eta: 1 day, 7:02:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6122, loss_cls: 3.6624, loss: 3.6624 +2024-07-20 12:29:48,567 - pyskl - INFO - Epoch [114][2400/3746] lr: 1.381e-02, eta: 1 day, 7:00:52, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3531, top5_acc: 0.6141, loss_cls: 3.6676, loss: 3.6676 +2024-07-20 12:31:10,562 - pyskl - INFO - Epoch [114][2500/3746] lr: 1.379e-02, eta: 1 day, 6:59:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3525, top5_acc: 0.6103, loss_cls: 3.6780, loss: 3.6780 +2024-07-20 12:32:32,786 - pyskl - INFO - Epoch [114][2600/3746] lr: 1.377e-02, eta: 1 day, 6:58:08, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3605, top5_acc: 0.6167, loss_cls: 3.6500, loss: 3.6500 +2024-07-20 12:33:55,069 - pyskl - INFO - Epoch [114][2700/3746] lr: 1.375e-02, eta: 1 day, 6:56:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3552, top5_acc: 0.6138, loss_cls: 3.6580, loss: 3.6580 +2024-07-20 12:35:17,188 - pyskl - INFO - Epoch [114][2800/3746] lr: 1.373e-02, eta: 1 day, 6:55:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3550, top5_acc: 0.6047, loss_cls: 3.6728, loss: 3.6728 +2024-07-20 12:36:39,335 - pyskl - INFO - Epoch [114][2900/3746] lr: 1.371e-02, eta: 1 day, 6:54:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6138, loss_cls: 3.6706, loss: 3.6706 +2024-07-20 12:38:01,422 - pyskl - INFO - Epoch [114][3000/3746] lr: 1.369e-02, eta: 1 day, 6:52:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6194, loss_cls: 3.5967, loss: 3.5967 +2024-07-20 12:39:23,493 - pyskl - INFO - Epoch [114][3100/3746] lr: 1.368e-02, eta: 1 day, 6:51:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3561, top5_acc: 0.6086, loss_cls: 3.6733, loss: 3.6733 +2024-07-20 12:40:45,329 - pyskl - INFO - Epoch [114][3200/3746] lr: 1.366e-02, eta: 1 day, 6:49:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3614, top5_acc: 0.6122, loss_cls: 3.6316, loss: 3.6316 +2024-07-20 12:42:07,221 - pyskl - INFO - Epoch [114][3300/3746] lr: 1.364e-02, eta: 1 day, 6:48:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6102, loss_cls: 3.7027, loss: 3.7027 +2024-07-20 12:43:29,393 - pyskl - INFO - Epoch [114][3400/3746] lr: 1.362e-02, eta: 1 day, 6:47:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6127, loss_cls: 3.6551, loss: 3.6551 +2024-07-20 12:44:52,501 - pyskl - INFO - Epoch [114][3500/3746] lr: 1.360e-02, eta: 1 day, 6:45:51, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6141, loss_cls: 3.6172, loss: 3.6172 +2024-07-20 12:46:14,107 - pyskl - INFO - Epoch [114][3600/3746] lr: 1.358e-02, eta: 1 day, 6:44:28, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6162, loss_cls: 3.6281, loss: 3.6281 +2024-07-20 12:47:35,940 - pyskl - INFO - Epoch [114][3700/3746] lr: 1.356e-02, eta: 1 day, 6:43:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6083, loss_cls: 3.6922, loss: 3.6922 +2024-07-20 12:48:15,919 - pyskl - INFO - Saving checkpoint at 114 epochs +2024-07-20 12:50:07,046 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 12:50:07,718 - pyskl - INFO - +top1_acc 0.2996 +top5_acc 0.5487 +2024-07-20 12:50:07,718 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 12:50:07,764 - pyskl - INFO - +mean_acc 0.2994 +2024-07-20 12:50:07,777 - pyskl - INFO - Epoch(val) [114][309] top1_acc: 0.2996, top5_acc: 0.5487, mean_class_accuracy: 0.2994 +2024-07-20 12:53:53,787 - pyskl - INFO - Epoch [115][100/3746] lr: 1.353e-02, eta: 1 day, 6:41:40, time: 2.260, data_time: 1.271, memory: 15990, top1_acc: 0.3720, top5_acc: 0.6252, loss_cls: 3.5800, loss: 3.5800 +2024-07-20 12:55:16,171 - pyskl - INFO - Epoch [115][200/3746] lr: 1.351e-02, eta: 1 day, 6:40:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6317, loss_cls: 3.5371, loss: 3.5371 +2024-07-20 12:56:38,264 - pyskl - INFO - Epoch [115][300/3746] lr: 1.349e-02, eta: 1 day, 6:38:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6231, loss_cls: 3.6110, loss: 3.6110 +2024-07-20 12:58:01,090 - pyskl - INFO - Epoch [115][400/3746] lr: 1.348e-02, eta: 1 day, 6:37:35, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3570, top5_acc: 0.6217, loss_cls: 3.5988, loss: 3.5988 +2024-07-20 12:59:23,270 - pyskl - INFO - Epoch [115][500/3746] lr: 1.346e-02, eta: 1 day, 6:36:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3666, top5_acc: 0.6202, loss_cls: 3.5955, loss: 3.5955 +2024-07-20 13:00:45,720 - pyskl - INFO - Epoch [115][600/3746] lr: 1.344e-02, eta: 1 day, 6:34:51, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6211, loss_cls: 3.6181, loss: 3.6181 +2024-07-20 13:02:08,873 - pyskl - INFO - Epoch [115][700/3746] lr: 1.342e-02, eta: 1 day, 6:33:29, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.3542, top5_acc: 0.6059, loss_cls: 3.6422, loss: 3.6422 +2024-07-20 13:03:31,201 - pyskl - INFO - Epoch [115][800/3746] lr: 1.340e-02, eta: 1 day, 6:32:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6250, loss_cls: 3.5979, loss: 3.5979 +2024-07-20 13:04:53,210 - pyskl - INFO - Epoch [115][900/3746] lr: 1.338e-02, eta: 1 day, 6:30:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3448, top5_acc: 0.6069, loss_cls: 3.6796, loss: 3.6796 +2024-07-20 13:06:15,519 - pyskl - INFO - Epoch [115][1000/3746] lr: 1.336e-02, eta: 1 day, 6:29:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3591, top5_acc: 0.6208, loss_cls: 3.6206, loss: 3.6206 +2024-07-20 13:07:37,508 - pyskl - INFO - Epoch [115][1100/3746] lr: 1.334e-02, eta: 1 day, 6:28:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3613, top5_acc: 0.6172, loss_cls: 3.6314, loss: 3.6314 +2024-07-20 13:08:59,648 - pyskl - INFO - Epoch [115][1200/3746] lr: 1.332e-02, eta: 1 day, 6:26:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3511, top5_acc: 0.6144, loss_cls: 3.6668, loss: 3.6668 +2024-07-20 13:10:21,766 - pyskl - INFO - Epoch [115][1300/3746] lr: 1.330e-02, eta: 1 day, 6:25:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6264, loss_cls: 3.5711, loss: 3.5711 +2024-07-20 13:11:43,480 - pyskl - INFO - Epoch [115][1400/3746] lr: 1.328e-02, eta: 1 day, 6:23:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3528, top5_acc: 0.6120, loss_cls: 3.6810, loss: 3.6810 +2024-07-20 13:13:05,541 - pyskl - INFO - Epoch [115][1500/3746] lr: 1.327e-02, eta: 1 day, 6:22:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6233, loss_cls: 3.6495, loss: 3.6495 +2024-07-20 13:14:27,432 - pyskl - INFO - Epoch [115][1600/3746] lr: 1.325e-02, eta: 1 day, 6:21:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3534, top5_acc: 0.6123, loss_cls: 3.6655, loss: 3.6655 +2024-07-20 13:15:49,334 - pyskl - INFO - Epoch [115][1700/3746] lr: 1.323e-02, eta: 1 day, 6:19:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3647, top5_acc: 0.6139, loss_cls: 3.6472, loss: 3.6472 +2024-07-20 13:17:11,022 - pyskl - INFO - Epoch [115][1800/3746] lr: 1.321e-02, eta: 1 day, 6:18:27, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6280, loss_cls: 3.6112, loss: 3.6112 +2024-07-20 13:18:33,432 - pyskl - INFO - Epoch [115][1900/3746] lr: 1.319e-02, eta: 1 day, 6:17:05, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3622, top5_acc: 0.6211, loss_cls: 3.6140, loss: 3.6140 +2024-07-20 13:19:54,999 - pyskl - INFO - Epoch [115][2000/3746] lr: 1.317e-02, eta: 1 day, 6:15:43, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3583, top5_acc: 0.6094, loss_cls: 3.6681, loss: 3.6681 +2024-07-20 13:21:17,533 - pyskl - INFO - Epoch [115][2100/3746] lr: 1.315e-02, eta: 1 day, 6:14:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6230, loss_cls: 3.6074, loss: 3.6074 +2024-07-20 13:22:40,348 - pyskl - INFO - Epoch [115][2200/3746] lr: 1.313e-02, eta: 1 day, 6:13:00, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6248, loss_cls: 3.6209, loss: 3.6209 +2024-07-20 13:24:02,544 - pyskl - INFO - Epoch [115][2300/3746] lr: 1.311e-02, eta: 1 day, 6:11:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3523, top5_acc: 0.6178, loss_cls: 3.6406, loss: 3.6406 +2024-07-20 13:25:24,774 - pyskl - INFO - Epoch [115][2400/3746] lr: 1.310e-02, eta: 1 day, 6:10:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3533, top5_acc: 0.6127, loss_cls: 3.6853, loss: 3.6853 +2024-07-20 13:26:47,027 - pyskl - INFO - Epoch [115][2500/3746] lr: 1.308e-02, eta: 1 day, 6:08:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6234, loss_cls: 3.6302, loss: 3.6302 +2024-07-20 13:28:08,844 - pyskl - INFO - Epoch [115][2600/3746] lr: 1.306e-02, eta: 1 day, 6:07:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6244, loss_cls: 3.5975, loss: 3.5975 +2024-07-20 13:29:30,597 - pyskl - INFO - Epoch [115][2700/3746] lr: 1.304e-02, eta: 1 day, 6:06:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6291, loss_cls: 3.5484, loss: 3.5484 +2024-07-20 13:30:52,442 - pyskl - INFO - Epoch [115][2800/3746] lr: 1.302e-02, eta: 1 day, 6:04:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3564, top5_acc: 0.6234, loss_cls: 3.6182, loss: 3.6182 +2024-07-20 13:32:14,206 - pyskl - INFO - Epoch [115][2900/3746] lr: 1.300e-02, eta: 1 day, 6:03:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6255, loss_cls: 3.6212, loss: 3.6212 +2024-07-20 13:33:35,656 - pyskl - INFO - Epoch [115][3000/3746] lr: 1.298e-02, eta: 1 day, 6:02:03, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3584, top5_acc: 0.6181, loss_cls: 3.6271, loss: 3.6271 +2024-07-20 13:34:57,627 - pyskl - INFO - Epoch [115][3100/3746] lr: 1.296e-02, eta: 1 day, 6:00:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6161, loss_cls: 3.6290, loss: 3.6290 +2024-07-20 13:36:19,399 - pyskl - INFO - Epoch [115][3200/3746] lr: 1.295e-02, eta: 1 day, 5:59:19, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3580, top5_acc: 0.6177, loss_cls: 3.6269, loss: 3.6269 +2024-07-20 13:37:41,150 - pyskl - INFO - Epoch [115][3300/3746] lr: 1.293e-02, eta: 1 day, 5:57:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6130, loss_cls: 3.6483, loss: 3.6483 +2024-07-20 13:39:02,904 - pyskl - INFO - Epoch [115][3400/3746] lr: 1.291e-02, eta: 1 day, 5:56:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6169, loss_cls: 3.6372, loss: 3.6372 +2024-07-20 13:40:24,902 - pyskl - INFO - Epoch [115][3500/3746] lr: 1.289e-02, eta: 1 day, 5:55:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3663, top5_acc: 0.6209, loss_cls: 3.6342, loss: 3.6342 +2024-07-20 13:41:46,893 - pyskl - INFO - Epoch [115][3600/3746] lr: 1.287e-02, eta: 1 day, 5:53:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3509, top5_acc: 0.6134, loss_cls: 3.6807, loss: 3.6807 +2024-07-20 13:43:08,803 - pyskl - INFO - Epoch [115][3700/3746] lr: 1.285e-02, eta: 1 day, 5:52:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6189, loss_cls: 3.6325, loss: 3.6325 +2024-07-20 13:43:48,847 - pyskl - INFO - Saving checkpoint at 115 epochs +2024-07-20 13:45:39,713 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 13:45:40,377 - pyskl - INFO - +top1_acc 0.2959 +top5_acc 0.5443 +2024-07-20 13:45:40,378 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 13:45:40,420 - pyskl - INFO - +mean_acc 0.2956 +2024-07-20 13:45:40,432 - pyskl - INFO - Epoch(val) [115][309] top1_acc: 0.2959, top5_acc: 0.5443, mean_class_accuracy: 0.2956 +2024-07-20 13:49:29,725 - pyskl - INFO - Epoch [116][100/3746] lr: 1.282e-02, eta: 1 day, 5:51:03, time: 2.293, data_time: 1.304, memory: 15990, top1_acc: 0.3695, top5_acc: 0.6266, loss_cls: 3.5720, loss: 3.5720 +2024-07-20 13:50:53,102 - pyskl - INFO - Epoch [116][200/3746] lr: 1.281e-02, eta: 1 day, 5:49:41, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3728, top5_acc: 0.6277, loss_cls: 3.5510, loss: 3.5510 +2024-07-20 13:52:15,719 - pyskl - INFO - Epoch [116][300/3746] lr: 1.279e-02, eta: 1 day, 5:48:19, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3656, top5_acc: 0.6358, loss_cls: 3.5612, loss: 3.5612 +2024-07-20 13:53:38,397 - pyskl - INFO - Epoch [116][400/3746] lr: 1.277e-02, eta: 1 day, 5:46:57, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6175, loss_cls: 3.6160, loss: 3.6160 +2024-07-20 13:55:00,377 - pyskl - INFO - Epoch [116][500/3746] lr: 1.275e-02, eta: 1 day, 5:45:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3731, top5_acc: 0.6297, loss_cls: 3.5512, loss: 3.5512 +2024-07-20 13:56:22,361 - pyskl - INFO - Epoch [116][600/3746] lr: 1.273e-02, eta: 1 day, 5:44:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6275, loss_cls: 3.5406, loss: 3.5406 +2024-07-20 13:57:44,765 - pyskl - INFO - Epoch [116][700/3746] lr: 1.271e-02, eta: 1 day, 5:42:51, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3641, top5_acc: 0.6252, loss_cls: 3.5809, loss: 3.5809 +2024-07-20 13:59:06,407 - pyskl - INFO - Epoch [116][800/3746] lr: 1.269e-02, eta: 1 day, 5:41:29, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6147, loss_cls: 3.6099, loss: 3.6099 +2024-07-20 14:00:28,621 - pyskl - INFO - Epoch [116][900/3746] lr: 1.268e-02, eta: 1 day, 5:40:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6248, loss_cls: 3.5846, loss: 3.5846 +2024-07-20 14:01:50,496 - pyskl - INFO - Epoch [116][1000/3746] lr: 1.266e-02, eta: 1 day, 5:38:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6281, loss_cls: 3.5633, loss: 3.5633 +2024-07-20 14:03:12,354 - pyskl - INFO - Epoch [116][1100/3746] lr: 1.264e-02, eta: 1 day, 5:37:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6345, loss_cls: 3.5641, loss: 3.5641 +2024-07-20 14:04:34,645 - pyskl - INFO - Epoch [116][1200/3746] lr: 1.262e-02, eta: 1 day, 5:36:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6252, loss_cls: 3.5999, loss: 3.5999 +2024-07-20 14:05:56,448 - pyskl - INFO - Epoch [116][1300/3746] lr: 1.260e-02, eta: 1 day, 5:34:39, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6241, loss_cls: 3.6041, loss: 3.6041 +2024-07-20 14:07:18,818 - pyskl - INFO - Epoch [116][1400/3746] lr: 1.258e-02, eta: 1 day, 5:33:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3623, top5_acc: 0.6117, loss_cls: 3.6443, loss: 3.6443 +2024-07-20 14:08:40,887 - pyskl - INFO - Epoch [116][1500/3746] lr: 1.256e-02, eta: 1 day, 5:31:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6258, loss_cls: 3.5669, loss: 3.5669 +2024-07-20 14:10:02,716 - pyskl - INFO - Epoch [116][1600/3746] lr: 1.255e-02, eta: 1 day, 5:30:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6294, loss_cls: 3.5922, loss: 3.5922 +2024-07-20 14:11:24,439 - pyskl - INFO - Epoch [116][1700/3746] lr: 1.253e-02, eta: 1 day, 5:29:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6184, loss_cls: 3.6333, loss: 3.6333 +2024-07-20 14:12:46,315 - pyskl - INFO - Epoch [116][1800/3746] lr: 1.251e-02, eta: 1 day, 5:27:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6312, loss_cls: 3.5719, loss: 3.5719 +2024-07-20 14:14:09,004 - pyskl - INFO - Epoch [116][1900/3746] lr: 1.249e-02, eta: 1 day, 5:26:27, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3639, top5_acc: 0.6234, loss_cls: 3.6040, loss: 3.6040 +2024-07-20 14:15:31,317 - pyskl - INFO - Epoch [116][2000/3746] lr: 1.247e-02, eta: 1 day, 5:25:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3553, top5_acc: 0.6147, loss_cls: 3.6597, loss: 3.6597 +2024-07-20 14:16:54,129 - pyskl - INFO - Epoch [116][2100/3746] lr: 1.245e-02, eta: 1 day, 5:23:43, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3513, top5_acc: 0.6162, loss_cls: 3.6434, loss: 3.6434 +2024-07-20 14:18:16,176 - pyskl - INFO - Epoch [116][2200/3746] lr: 1.243e-02, eta: 1 day, 5:22:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6214, loss_cls: 3.6425, loss: 3.6425 +2024-07-20 14:19:38,325 - pyskl - INFO - Epoch [116][2300/3746] lr: 1.242e-02, eta: 1 day, 5:20:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3600, top5_acc: 0.6194, loss_cls: 3.6265, loss: 3.6265 +2024-07-20 14:21:01,452 - pyskl - INFO - Epoch [116][2400/3746] lr: 1.240e-02, eta: 1 day, 5:19:38, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3678, top5_acc: 0.6258, loss_cls: 3.5887, loss: 3.5887 +2024-07-20 14:22:23,427 - pyskl - INFO - Epoch [116][2500/3746] lr: 1.238e-02, eta: 1 day, 5:18:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3711, top5_acc: 0.6262, loss_cls: 3.5966, loss: 3.5966 +2024-07-20 14:23:45,543 - pyskl - INFO - Epoch [116][2600/3746] lr: 1.236e-02, eta: 1 day, 5:16:54, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6281, loss_cls: 3.5763, loss: 3.5763 +2024-07-20 14:25:07,561 - pyskl - INFO - Epoch [116][2700/3746] lr: 1.234e-02, eta: 1 day, 5:15:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6203, loss_cls: 3.6045, loss: 3.6045 +2024-07-20 14:26:29,604 - pyskl - INFO - Epoch [116][2800/3746] lr: 1.232e-02, eta: 1 day, 5:14:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6258, loss_cls: 3.5823, loss: 3.5823 +2024-07-20 14:27:51,634 - pyskl - INFO - Epoch [116][2900/3746] lr: 1.231e-02, eta: 1 day, 5:12:48, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3588, top5_acc: 0.6177, loss_cls: 3.6472, loss: 3.6472 +2024-07-20 14:29:13,644 - pyskl - INFO - Epoch [116][3000/3746] lr: 1.229e-02, eta: 1 day, 5:11:26, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3544, top5_acc: 0.6142, loss_cls: 3.6357, loss: 3.6357 +2024-07-20 14:30:35,300 - pyskl - INFO - Epoch [116][3100/3746] lr: 1.227e-02, eta: 1 day, 5:10:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6127, loss_cls: 3.6463, loss: 3.6463 +2024-07-20 14:31:57,282 - pyskl - INFO - Epoch [116][3200/3746] lr: 1.225e-02, eta: 1 day, 5:08:41, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6292, loss_cls: 3.5778, loss: 3.5778 +2024-07-20 14:33:19,398 - pyskl - INFO - Epoch [116][3300/3746] lr: 1.223e-02, eta: 1 day, 5:07:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6212, loss_cls: 3.5989, loss: 3.5989 +2024-07-20 14:34:41,333 - pyskl - INFO - Epoch [116][3400/3746] lr: 1.221e-02, eta: 1 day, 5:05:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6123, loss_cls: 3.6612, loss: 3.6612 +2024-07-20 14:36:03,629 - pyskl - INFO - Epoch [116][3500/3746] lr: 1.220e-02, eta: 1 day, 5:04:35, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3603, top5_acc: 0.6192, loss_cls: 3.6155, loss: 3.6155 +2024-07-20 14:37:25,466 - pyskl - INFO - Epoch [116][3600/3746] lr: 1.218e-02, eta: 1 day, 5:03:13, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6256, loss_cls: 3.5899, loss: 3.5899 +2024-07-20 14:38:46,766 - pyskl - INFO - Epoch [116][3700/3746] lr: 1.216e-02, eta: 1 day, 5:01:51, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3619, top5_acc: 0.6138, loss_cls: 3.6354, loss: 3.6354 +2024-07-20 14:39:26,480 - pyskl - INFO - Saving checkpoint at 116 epochs +2024-07-20 14:41:17,119 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 14:41:17,787 - pyskl - INFO - +top1_acc 0.3009 +top5_acc 0.5555 +2024-07-20 14:41:17,787 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 14:41:17,829 - pyskl - INFO - +mean_acc 0.3006 +2024-07-20 14:41:17,835 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_113.pth was removed +2024-07-20 14:41:18,077 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_116.pth. +2024-07-20 14:41:18,078 - pyskl - INFO - Best top1_acc is 0.3009 at 116 epoch. +2024-07-20 14:41:18,090 - pyskl - INFO - Epoch(val) [116][309] top1_acc: 0.3009, top5_acc: 0.5555, mean_class_accuracy: 0.3006 +2024-07-20 14:45:07,880 - pyskl - INFO - Epoch [117][100/3746] lr: 1.213e-02, eta: 1 day, 5:00:23, time: 2.298, data_time: 1.314, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6280, loss_cls: 3.5304, loss: 3.5304 +2024-07-20 14:46:30,931 - pyskl - INFO - Epoch [117][200/3746] lr: 1.211e-02, eta: 1 day, 4:59:02, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6308, loss_cls: 3.5306, loss: 3.5306 +2024-07-20 14:47:53,324 - pyskl - INFO - Epoch [117][300/3746] lr: 1.210e-02, eta: 1 day, 4:57:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3811, top5_acc: 0.6339, loss_cls: 3.5298, loss: 3.5298 +2024-07-20 14:49:15,745 - pyskl - INFO - Epoch [117][400/3746] lr: 1.208e-02, eta: 1 day, 4:56:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6380, loss_cls: 3.5306, loss: 3.5306 +2024-07-20 14:50:37,784 - pyskl - INFO - Epoch [117][500/3746] lr: 1.206e-02, eta: 1 day, 4:54:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3627, top5_acc: 0.6205, loss_cls: 3.6102, loss: 3.6102 +2024-07-20 14:51:59,907 - pyskl - INFO - Epoch [117][600/3746] lr: 1.204e-02, eta: 1 day, 4:53:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6392, loss_cls: 3.5352, loss: 3.5352 +2024-07-20 14:53:22,215 - pyskl - INFO - Epoch [117][700/3746] lr: 1.202e-02, eta: 1 day, 4:52:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3738, top5_acc: 0.6286, loss_cls: 3.5779, loss: 3.5779 +2024-07-20 14:54:44,672 - pyskl - INFO - Epoch [117][800/3746] lr: 1.200e-02, eta: 1 day, 4:50:50, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6317, loss_cls: 3.5570, loss: 3.5570 +2024-07-20 14:56:06,365 - pyskl - INFO - Epoch [117][900/3746] lr: 1.199e-02, eta: 1 day, 4:49:28, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3764, top5_acc: 0.6356, loss_cls: 3.5281, loss: 3.5281 +2024-07-20 14:57:28,100 - pyskl - INFO - Epoch [117][1000/3746] lr: 1.197e-02, eta: 1 day, 4:48:05, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6275, loss_cls: 3.5732, loss: 3.5732 +2024-07-20 14:58:50,117 - pyskl - INFO - Epoch [117][1100/3746] lr: 1.195e-02, eta: 1 day, 4:46:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3634, top5_acc: 0.6298, loss_cls: 3.5676, loss: 3.5676 +2024-07-20 15:00:11,927 - pyskl - INFO - Epoch [117][1200/3746] lr: 1.193e-02, eta: 1 day, 4:45:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6353, loss_cls: 3.5234, loss: 3.5234 +2024-07-20 15:01:33,523 - pyskl - INFO - Epoch [117][1300/3746] lr: 1.191e-02, eta: 1 day, 4:43:59, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6323, loss_cls: 3.5538, loss: 3.5538 +2024-07-20 15:02:55,779 - pyskl - INFO - Epoch [117][1400/3746] lr: 1.190e-02, eta: 1 day, 4:42:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3638, top5_acc: 0.6322, loss_cls: 3.5726, loss: 3.5726 +2024-07-20 15:04:17,935 - pyskl - INFO - Epoch [117][1500/3746] lr: 1.188e-02, eta: 1 day, 4:41:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6270, loss_cls: 3.5576, loss: 3.5576 +2024-07-20 15:05:39,443 - pyskl - INFO - Epoch [117][1600/3746] lr: 1.186e-02, eta: 1 day, 4:39:53, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3642, top5_acc: 0.6231, loss_cls: 3.5953, loss: 3.5953 +2024-07-20 15:07:01,505 - pyskl - INFO - Epoch [117][1700/3746] lr: 1.184e-02, eta: 1 day, 4:38:31, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3611, top5_acc: 0.6172, loss_cls: 3.6091, loss: 3.6091 +2024-07-20 15:08:24,093 - pyskl - INFO - Epoch [117][1800/3746] lr: 1.182e-02, eta: 1 day, 4:37:09, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3727, top5_acc: 0.6247, loss_cls: 3.5769, loss: 3.5769 +2024-07-20 15:09:46,089 - pyskl - INFO - Epoch [117][1900/3746] lr: 1.181e-02, eta: 1 day, 4:35:47, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6259, loss_cls: 3.5575, loss: 3.5575 +2024-07-20 15:11:07,669 - pyskl - INFO - Epoch [117][2000/3746] lr: 1.179e-02, eta: 1 day, 4:34:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3597, top5_acc: 0.6230, loss_cls: 3.6279, loss: 3.6279 +2024-07-20 15:12:29,836 - pyskl - INFO - Epoch [117][2100/3746] lr: 1.177e-02, eta: 1 day, 4:33:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3625, top5_acc: 0.6183, loss_cls: 3.6145, loss: 3.6145 +2024-07-20 15:13:52,391 - pyskl - INFO - Epoch [117][2200/3746] lr: 1.175e-02, eta: 1 day, 4:31:41, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6223, loss_cls: 3.6105, loss: 3.6105 +2024-07-20 15:15:14,786 - pyskl - INFO - Epoch [117][2300/3746] lr: 1.173e-02, eta: 1 day, 4:30:19, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6350, loss_cls: 3.5852, loss: 3.5852 +2024-07-20 15:16:37,160 - pyskl - INFO - Epoch [117][2400/3746] lr: 1.172e-02, eta: 1 day, 4:28:57, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3616, top5_acc: 0.6248, loss_cls: 3.6017, loss: 3.6017 +2024-07-20 15:17:59,410 - pyskl - INFO - Epoch [117][2500/3746] lr: 1.170e-02, eta: 1 day, 4:27:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3669, top5_acc: 0.6273, loss_cls: 3.5713, loss: 3.5713 +2024-07-20 15:19:21,688 - pyskl - INFO - Epoch [117][2600/3746] lr: 1.168e-02, eta: 1 day, 4:26:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3698, top5_acc: 0.6250, loss_cls: 3.5857, loss: 3.5857 +2024-07-20 15:20:44,524 - pyskl - INFO - Epoch [117][2700/3746] lr: 1.166e-02, eta: 1 day, 4:24:51, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6314, loss_cls: 3.5781, loss: 3.5781 +2024-07-20 15:22:06,225 - pyskl - INFO - Epoch [117][2800/3746] lr: 1.164e-02, eta: 1 day, 4:23:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3684, top5_acc: 0.6311, loss_cls: 3.5636, loss: 3.5636 +2024-07-20 15:23:28,374 - pyskl - INFO - Epoch [117][2900/3746] lr: 1.163e-02, eta: 1 day, 4:22:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3622, top5_acc: 0.6248, loss_cls: 3.5997, loss: 3.5997 +2024-07-20 15:24:50,320 - pyskl - INFO - Epoch [117][3000/3746] lr: 1.161e-02, eta: 1 day, 4:20:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6264, loss_cls: 3.5641, loss: 3.5641 +2024-07-20 15:26:12,413 - pyskl - INFO - Epoch [117][3100/3746] lr: 1.159e-02, eta: 1 day, 4:19:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3628, top5_acc: 0.6273, loss_cls: 3.5914, loss: 3.5914 +2024-07-20 15:27:34,313 - pyskl - INFO - Epoch [117][3200/3746] lr: 1.157e-02, eta: 1 day, 4:18:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6277, loss_cls: 3.5380, loss: 3.5380 +2024-07-20 15:28:55,768 - pyskl - INFO - Epoch [117][3300/3746] lr: 1.155e-02, eta: 1 day, 4:16:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6122, loss_cls: 3.6121, loss: 3.6121 +2024-07-20 15:30:17,427 - pyskl - INFO - Epoch [117][3400/3746] lr: 1.154e-02, eta: 1 day, 4:15:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3658, top5_acc: 0.6267, loss_cls: 3.5979, loss: 3.5979 +2024-07-20 15:31:38,909 - pyskl - INFO - Epoch [117][3500/3746] lr: 1.152e-02, eta: 1 day, 4:13:54, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6245, loss_cls: 3.5888, loss: 3.5888 +2024-07-20 15:33:01,319 - pyskl - INFO - Epoch [117][3600/3746] lr: 1.150e-02, eta: 1 day, 4:12:32, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3800, top5_acc: 0.6233, loss_cls: 3.5672, loss: 3.5672 +2024-07-20 15:34:23,199 - pyskl - INFO - Epoch [117][3700/3746] lr: 1.148e-02, eta: 1 day, 4:11:10, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3650, top5_acc: 0.6234, loss_cls: 3.6351, loss: 3.6351 +2024-07-20 15:35:02,638 - pyskl - INFO - Saving checkpoint at 117 epochs +2024-07-20 15:36:53,446 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 15:36:54,114 - pyskl - INFO - +top1_acc 0.3082 +top5_acc 0.5653 +2024-07-20 15:36:54,114 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 15:36:54,157 - pyskl - INFO - +mean_acc 0.3081 +2024-07-20 15:36:54,161 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_116.pth was removed +2024-07-20 15:36:54,419 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_117.pth. +2024-07-20 15:36:54,420 - pyskl - INFO - Best top1_acc is 0.3082 at 117 epoch. +2024-07-20 15:36:54,432 - pyskl - INFO - Epoch(val) [117][309] top1_acc: 0.3082, top5_acc: 0.5653, mean_class_accuracy: 0.3081 +2024-07-20 15:40:41,861 - pyskl - INFO - Epoch [118][100/3746] lr: 1.146e-02, eta: 1 day, 4:09:40, time: 2.274, data_time: 1.284, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6477, loss_cls: 3.4866, loss: 3.4866 +2024-07-20 15:42:04,548 - pyskl - INFO - Epoch [118][200/3746] lr: 1.144e-02, eta: 1 day, 4:08:19, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6373, loss_cls: 3.5203, loss: 3.5203 +2024-07-20 15:43:27,222 - pyskl - INFO - Epoch [118][300/3746] lr: 1.142e-02, eta: 1 day, 4:06:57, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6325, loss_cls: 3.5461, loss: 3.5461 +2024-07-20 15:44:50,298 - pyskl - INFO - Epoch [118][400/3746] lr: 1.140e-02, eta: 1 day, 4:05:35, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6422, loss_cls: 3.4872, loss: 3.4872 +2024-07-20 15:46:12,415 - pyskl - INFO - Epoch [118][500/3746] lr: 1.139e-02, eta: 1 day, 4:04:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6447, loss_cls: 3.4748, loss: 3.4748 +2024-07-20 15:47:34,426 - pyskl - INFO - Epoch [118][600/3746] lr: 1.137e-02, eta: 1 day, 4:02:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6352, loss_cls: 3.5321, loss: 3.5321 +2024-07-20 15:48:56,655 - pyskl - INFO - Epoch [118][700/3746] lr: 1.135e-02, eta: 1 day, 4:01:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6352, loss_cls: 3.4980, loss: 3.4980 +2024-07-20 15:50:19,098 - pyskl - INFO - Epoch [118][800/3746] lr: 1.133e-02, eta: 1 day, 4:00:07, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3734, top5_acc: 0.6333, loss_cls: 3.5531, loss: 3.5531 +2024-07-20 15:51:41,837 - pyskl - INFO - Epoch [118][900/3746] lr: 1.131e-02, eta: 1 day, 3:58:45, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6294, loss_cls: 3.5440, loss: 3.5440 +2024-07-20 15:53:03,672 - pyskl - INFO - Epoch [118][1000/3746] lr: 1.130e-02, eta: 1 day, 3:57:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6323, loss_cls: 3.5441, loss: 3.5441 +2024-07-20 15:54:25,789 - pyskl - INFO - Epoch [118][1100/3746] lr: 1.128e-02, eta: 1 day, 3:56:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3670, top5_acc: 0.6275, loss_cls: 3.5784, loss: 3.5784 +2024-07-20 15:55:48,404 - pyskl - INFO - Epoch [118][1200/3746] lr: 1.126e-02, eta: 1 day, 3:54:39, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6288, loss_cls: 3.5637, loss: 3.5637 +2024-07-20 15:57:10,303 - pyskl - INFO - Epoch [118][1300/3746] lr: 1.124e-02, eta: 1 day, 3:53:17, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6266, loss_cls: 3.5658, loss: 3.5658 +2024-07-20 15:58:32,287 - pyskl - INFO - Epoch [118][1400/3746] lr: 1.123e-02, eta: 1 day, 3:51:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6522, loss_cls: 3.4862, loss: 3.4862 +2024-07-20 15:59:54,180 - pyskl - INFO - Epoch [118][1500/3746] lr: 1.121e-02, eta: 1 day, 3:50:32, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6359, loss_cls: 3.5154, loss: 3.5154 +2024-07-20 16:01:15,911 - pyskl - INFO - Epoch [118][1600/3746] lr: 1.119e-02, eta: 1 day, 3:49:10, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3853, top5_acc: 0.6383, loss_cls: 3.5341, loss: 3.5341 +2024-07-20 16:02:37,650 - pyskl - INFO - Epoch [118][1700/3746] lr: 1.117e-02, eta: 1 day, 3:47:48, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3673, top5_acc: 0.6247, loss_cls: 3.5665, loss: 3.5665 +2024-07-20 16:03:59,381 - pyskl - INFO - Epoch [118][1800/3746] lr: 1.116e-02, eta: 1 day, 3:46:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3694, top5_acc: 0.6256, loss_cls: 3.5661, loss: 3.5661 +2024-07-20 16:05:20,901 - pyskl - INFO - Epoch [118][1900/3746] lr: 1.114e-02, eta: 1 day, 3:45:04, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3697, top5_acc: 0.6281, loss_cls: 3.5720, loss: 3.5720 +2024-07-20 16:06:43,001 - pyskl - INFO - Epoch [118][2000/3746] lr: 1.112e-02, eta: 1 day, 3:43:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3592, top5_acc: 0.6192, loss_cls: 3.6103, loss: 3.6103 +2024-07-20 16:08:05,097 - pyskl - INFO - Epoch [118][2100/3746] lr: 1.110e-02, eta: 1 day, 3:42:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6278, loss_cls: 3.5444, loss: 3.5444 +2024-07-20 16:09:27,267 - pyskl - INFO - Epoch [118][2200/3746] lr: 1.109e-02, eta: 1 day, 3:40:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3880, top5_acc: 0.6334, loss_cls: 3.5111, loss: 3.5111 +2024-07-20 16:10:49,432 - pyskl - INFO - Epoch [118][2300/3746] lr: 1.107e-02, eta: 1 day, 3:39:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6262, loss_cls: 3.5773, loss: 3.5773 +2024-07-20 16:12:12,367 - pyskl - INFO - Epoch [118][2400/3746] lr: 1.105e-02, eta: 1 day, 3:38:13, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3630, top5_acc: 0.6281, loss_cls: 3.5800, loss: 3.5800 +2024-07-20 16:13:34,079 - pyskl - INFO - Epoch [118][2500/3746] lr: 1.103e-02, eta: 1 day, 3:36:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3758, top5_acc: 0.6289, loss_cls: 3.5594, loss: 3.5594 +2024-07-20 16:14:56,263 - pyskl - INFO - Epoch [118][2600/3746] lr: 1.102e-02, eta: 1 day, 3:35:29, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3677, top5_acc: 0.6267, loss_cls: 3.6093, loss: 3.6093 +2024-07-20 16:16:17,882 - pyskl - INFO - Epoch [118][2700/3746] lr: 1.100e-02, eta: 1 day, 3:34:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6252, loss_cls: 3.5681, loss: 3.5681 +2024-07-20 16:17:39,783 - pyskl - INFO - Epoch [118][2800/3746] lr: 1.098e-02, eta: 1 day, 3:32:45, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6255, loss_cls: 3.5710, loss: 3.5710 +2024-07-20 16:19:02,094 - pyskl - INFO - Epoch [118][2900/3746] lr: 1.096e-02, eta: 1 day, 3:31:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3633, top5_acc: 0.6277, loss_cls: 3.5585, loss: 3.5585 +2024-07-20 16:20:23,823 - pyskl - INFO - Epoch [118][3000/3746] lr: 1.095e-02, eta: 1 day, 3:30:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6327, loss_cls: 3.5669, loss: 3.5669 +2024-07-20 16:21:45,362 - pyskl - INFO - Epoch [118][3100/3746] lr: 1.093e-02, eta: 1 day, 3:28:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6247, loss_cls: 3.5747, loss: 3.5747 +2024-07-20 16:23:07,046 - pyskl - INFO - Epoch [118][3200/3746] lr: 1.091e-02, eta: 1 day, 3:27:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6281, loss_cls: 3.5364, loss: 3.5364 +2024-07-20 16:24:28,956 - pyskl - INFO - Epoch [118][3300/3746] lr: 1.089e-02, eta: 1 day, 3:25:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3688, top5_acc: 0.6227, loss_cls: 3.5801, loss: 3.5801 +2024-07-20 16:25:51,101 - pyskl - INFO - Epoch [118][3400/3746] lr: 1.088e-02, eta: 1 day, 3:24:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3700, top5_acc: 0.6353, loss_cls: 3.5522, loss: 3.5522 +2024-07-20 16:27:13,090 - pyskl - INFO - Epoch [118][3500/3746] lr: 1.086e-02, eta: 1 day, 3:23:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6228, loss_cls: 3.5461, loss: 3.5461 +2024-07-20 16:28:34,871 - pyskl - INFO - Epoch [118][3600/3746] lr: 1.084e-02, eta: 1 day, 3:21:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3745, top5_acc: 0.6297, loss_cls: 3.5767, loss: 3.5767 +2024-07-20 16:29:56,669 - pyskl - INFO - Epoch [118][3700/3746] lr: 1.082e-02, eta: 1 day, 3:20:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3652, top5_acc: 0.6247, loss_cls: 3.5974, loss: 3.5974 +2024-07-20 16:30:36,578 - pyskl - INFO - Saving checkpoint at 118 epochs +2024-07-20 16:32:28,202 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 16:32:28,867 - pyskl - INFO - +top1_acc 0.3112 +top5_acc 0.5664 +2024-07-20 16:32:28,867 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 16:32:28,909 - pyskl - INFO - +mean_acc 0.3111 +2024-07-20 16:32:28,913 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_117.pth was removed +2024-07-20 16:32:29,164 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_118.pth. +2024-07-20 16:32:29,165 - pyskl - INFO - Best top1_acc is 0.3112 at 118 epoch. +2024-07-20 16:32:29,177 - pyskl - INFO - Epoch(val) [118][309] top1_acc: 0.3112, top5_acc: 0.5664, mean_class_accuracy: 0.3111 +2024-07-20 16:36:19,315 - pyskl - INFO - Epoch [119][100/3746] lr: 1.080e-02, eta: 1 day, 3:18:56, time: 2.301, data_time: 1.298, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6430, loss_cls: 3.4589, loss: 3.4589 +2024-07-20 16:37:42,686 - pyskl - INFO - Epoch [119][200/3746] lr: 1.078e-02, eta: 1 day, 3:17:34, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6400, loss_cls: 3.4564, loss: 3.4564 +2024-07-20 16:39:04,909 - pyskl - INFO - Epoch [119][300/3746] lr: 1.076e-02, eta: 1 day, 3:16:12, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6503, loss_cls: 3.4539, loss: 3.4539 +2024-07-20 16:40:27,519 - pyskl - INFO - Epoch [119][400/3746] lr: 1.075e-02, eta: 1 day, 3:14:50, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3844, top5_acc: 0.6450, loss_cls: 3.4440, loss: 3.4440 +2024-07-20 16:41:50,150 - pyskl - INFO - Epoch [119][500/3746] lr: 1.073e-02, eta: 1 day, 3:13:28, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3702, top5_acc: 0.6391, loss_cls: 3.5247, loss: 3.5247 +2024-07-20 16:43:12,482 - pyskl - INFO - Epoch [119][600/3746] lr: 1.071e-02, eta: 1 day, 3:12:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6458, loss_cls: 3.4547, loss: 3.4547 +2024-07-20 16:44:35,069 - pyskl - INFO - Epoch [119][700/3746] lr: 1.069e-02, eta: 1 day, 3:10:44, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6412, loss_cls: 3.5016, loss: 3.5016 +2024-07-20 16:45:57,556 - pyskl - INFO - Epoch [119][800/3746] lr: 1.068e-02, eta: 1 day, 3:09:22, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3748, top5_acc: 0.6320, loss_cls: 3.5424, loss: 3.5424 +2024-07-20 16:47:19,933 - pyskl - INFO - Epoch [119][900/3746] lr: 1.066e-02, eta: 1 day, 3:08:00, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6422, loss_cls: 3.4804, loss: 3.4804 +2024-07-20 16:48:41,780 - pyskl - INFO - Epoch [119][1000/3746] lr: 1.064e-02, eta: 1 day, 3:06:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3692, top5_acc: 0.6300, loss_cls: 3.5557, loss: 3.5557 +2024-07-20 16:50:03,710 - pyskl - INFO - Epoch [119][1100/3746] lr: 1.063e-02, eta: 1 day, 3:05:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3789, top5_acc: 0.6373, loss_cls: 3.4846, loss: 3.4846 +2024-07-20 16:51:25,678 - pyskl - INFO - Epoch [119][1200/3746] lr: 1.061e-02, eta: 1 day, 3:03:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3833, top5_acc: 0.6444, loss_cls: 3.4788, loss: 3.4788 +2024-07-20 16:52:47,627 - pyskl - INFO - Epoch [119][1300/3746] lr: 1.059e-02, eta: 1 day, 3:02:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6358, loss_cls: 3.5189, loss: 3.5189 +2024-07-20 16:54:09,433 - pyskl - INFO - Epoch [119][1400/3746] lr: 1.057e-02, eta: 1 day, 3:01:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3716, top5_acc: 0.6295, loss_cls: 3.5978, loss: 3.5978 +2024-07-20 16:55:30,677 - pyskl - INFO - Epoch [119][1500/3746] lr: 1.056e-02, eta: 1 day, 2:59:47, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6327, loss_cls: 3.5547, loss: 3.5547 +2024-07-20 16:56:52,529 - pyskl - INFO - Epoch [119][1600/3746] lr: 1.054e-02, eta: 1 day, 2:58:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3867, top5_acc: 0.6494, loss_cls: 3.4467, loss: 3.4467 +2024-07-20 16:58:15,513 - pyskl - INFO - Epoch [119][1700/3746] lr: 1.052e-02, eta: 1 day, 2:57:03, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6400, loss_cls: 3.4875, loss: 3.4875 +2024-07-20 16:59:37,166 - pyskl - INFO - Epoch [119][1800/3746] lr: 1.050e-02, eta: 1 day, 2:55:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3747, top5_acc: 0.6377, loss_cls: 3.5266, loss: 3.5266 +2024-07-20 17:00:59,066 - pyskl - INFO - Epoch [119][1900/3746] lr: 1.049e-02, eta: 1 day, 2:54:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3609, top5_acc: 0.6211, loss_cls: 3.6104, loss: 3.6104 +2024-07-20 17:02:21,893 - pyskl - INFO - Epoch [119][2000/3746] lr: 1.047e-02, eta: 1 day, 2:52:56, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3875, top5_acc: 0.6464, loss_cls: 3.4758, loss: 3.4758 +2024-07-20 17:03:43,505 - pyskl - INFO - Epoch [119][2100/3746] lr: 1.045e-02, eta: 1 day, 2:51:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3620, top5_acc: 0.6286, loss_cls: 3.5602, loss: 3.5602 +2024-07-20 17:05:06,545 - pyskl - INFO - Epoch [119][2200/3746] lr: 1.044e-02, eta: 1 day, 2:50:12, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.3736, top5_acc: 0.6359, loss_cls: 3.5310, loss: 3.5310 +2024-07-20 17:06:28,676 - pyskl - INFO - Epoch [119][2300/3746] lr: 1.042e-02, eta: 1 day, 2:48:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3686, top5_acc: 0.6294, loss_cls: 3.5607, loss: 3.5607 +2024-07-20 17:07:50,964 - pyskl - INFO - Epoch [119][2400/3746] lr: 1.040e-02, eta: 1 day, 2:47:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3778, top5_acc: 0.6352, loss_cls: 3.5200, loss: 3.5200 +2024-07-20 17:09:12,893 - pyskl - INFO - Epoch [119][2500/3746] lr: 1.039e-02, eta: 1 day, 2:46:06, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3794, top5_acc: 0.6248, loss_cls: 3.5609, loss: 3.5609 +2024-07-20 17:10:34,714 - pyskl - INFO - Epoch [119][2600/3746] lr: 1.037e-02, eta: 1 day, 2:44:44, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3773, top5_acc: 0.6309, loss_cls: 3.5300, loss: 3.5300 +2024-07-20 17:11:57,042 - pyskl - INFO - Epoch [119][2700/3746] lr: 1.035e-02, eta: 1 day, 2:43:22, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6378, loss_cls: 3.4958, loss: 3.4958 +2024-07-20 17:13:19,303 - pyskl - INFO - Epoch [119][2800/3746] lr: 1.033e-02, eta: 1 day, 2:42:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3681, top5_acc: 0.6208, loss_cls: 3.5706, loss: 3.5706 +2024-07-20 17:14:41,054 - pyskl - INFO - Epoch [119][2900/3746] lr: 1.032e-02, eta: 1 day, 2:40:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3644, top5_acc: 0.6298, loss_cls: 3.5589, loss: 3.5589 +2024-07-20 17:16:02,791 - pyskl - INFO - Epoch [119][3000/3746] lr: 1.030e-02, eta: 1 day, 2:39:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3661, top5_acc: 0.6286, loss_cls: 3.5891, loss: 3.5891 +2024-07-20 17:17:24,365 - pyskl - INFO - Epoch [119][3100/3746] lr: 1.028e-02, eta: 1 day, 2:37:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3653, top5_acc: 0.6309, loss_cls: 3.5784, loss: 3.5784 +2024-07-20 17:18:46,384 - pyskl - INFO - Epoch [119][3200/3746] lr: 1.027e-02, eta: 1 day, 2:36:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6323, loss_cls: 3.5519, loss: 3.5519 +2024-07-20 17:20:07,789 - pyskl - INFO - Epoch [119][3300/3746] lr: 1.025e-02, eta: 1 day, 2:35:09, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3777, top5_acc: 0.6317, loss_cls: 3.5561, loss: 3.5561 +2024-07-20 17:21:29,907 - pyskl - INFO - Epoch [119][3400/3746] lr: 1.023e-02, eta: 1 day, 2:33:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3792, top5_acc: 0.6377, loss_cls: 3.5297, loss: 3.5297 +2024-07-20 17:22:51,748 - pyskl - INFO - Epoch [119][3500/3746] lr: 1.022e-02, eta: 1 day, 2:32:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6331, loss_cls: 3.5379, loss: 3.5379 +2024-07-20 17:24:13,397 - pyskl - INFO - Epoch [119][3600/3746] lr: 1.020e-02, eta: 1 day, 2:31:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6370, loss_cls: 3.5158, loss: 3.5158 +2024-07-20 17:25:35,450 - pyskl - INFO - Epoch [119][3700/3746] lr: 1.018e-02, eta: 1 day, 2:29:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3645, top5_acc: 0.6258, loss_cls: 3.6046, loss: 3.6046 +2024-07-20 17:26:15,382 - pyskl - INFO - Saving checkpoint at 119 epochs +2024-07-20 17:28:05,954 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 17:28:06,621 - pyskl - INFO - +top1_acc 0.3125 +top5_acc 0.5663 +2024-07-20 17:28:06,621 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 17:28:06,663 - pyskl - INFO - +mean_acc 0.3124 +2024-07-20 17:28:06,668 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_118.pth was removed +2024-07-20 17:28:06,919 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_119.pth. +2024-07-20 17:28:06,920 - pyskl - INFO - Best top1_acc is 0.3125 at 119 epoch. +2024-07-20 17:28:06,932 - pyskl - INFO - Epoch(val) [119][309] top1_acc: 0.3125, top5_acc: 0.5663, mean_class_accuracy: 0.3124 +2024-07-20 17:31:55,169 - pyskl - INFO - Epoch [120][100/3746] lr: 1.016e-02, eta: 1 day, 2:28:08, time: 2.282, data_time: 1.294, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6661, loss_cls: 3.3616, loss: 3.3616 +2024-07-20 17:33:18,554 - pyskl - INFO - Epoch [120][200/3746] lr: 1.014e-02, eta: 1 day, 2:26:47, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3864, top5_acc: 0.6453, loss_cls: 3.4853, loss: 3.4853 +2024-07-20 17:34:41,131 - pyskl - INFO - Epoch [120][300/3746] lr: 1.012e-02, eta: 1 day, 2:25:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6503, loss_cls: 3.4412, loss: 3.4412 +2024-07-20 17:36:03,315 - pyskl - INFO - Epoch [120][400/3746] lr: 1.011e-02, eta: 1 day, 2:24:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6500, loss_cls: 3.4593, loss: 3.4593 +2024-07-20 17:37:25,851 - pyskl - INFO - Epoch [120][500/3746] lr: 1.009e-02, eta: 1 day, 2:22:40, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6497, loss_cls: 3.4277, loss: 3.4277 +2024-07-20 17:38:48,424 - pyskl - INFO - Epoch [120][600/3746] lr: 1.007e-02, eta: 1 day, 2:21:18, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6391, loss_cls: 3.5005, loss: 3.5005 +2024-07-20 17:40:10,539 - pyskl - INFO - Epoch [120][700/3746] lr: 1.006e-02, eta: 1 day, 2:19:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6600, loss_cls: 3.4240, loss: 3.4240 +2024-07-20 17:41:32,441 - pyskl - INFO - Epoch [120][800/3746] lr: 1.004e-02, eta: 1 day, 2:18:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3742, top5_acc: 0.6322, loss_cls: 3.5453, loss: 3.5453 +2024-07-20 17:42:54,115 - pyskl - INFO - Epoch [120][900/3746] lr: 1.002e-02, eta: 1 day, 2:17:12, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6358, loss_cls: 3.5378, loss: 3.5378 +2024-07-20 17:44:16,363 - pyskl - INFO - Epoch [120][1000/3746] lr: 1.001e-02, eta: 1 day, 2:15:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3837, top5_acc: 0.6475, loss_cls: 3.4658, loss: 3.4658 +2024-07-20 17:45:38,298 - pyskl - INFO - Epoch [120][1100/3746] lr: 9.989e-03, eta: 1 day, 2:14:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3803, top5_acc: 0.6342, loss_cls: 3.5203, loss: 3.5203 +2024-07-20 17:47:00,215 - pyskl - INFO - Epoch [120][1200/3746] lr: 9.972e-03, eta: 1 day, 2:13:05, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3844, top5_acc: 0.6411, loss_cls: 3.4889, loss: 3.4889 +2024-07-20 17:48:22,205 - pyskl - INFO - Epoch [120][1300/3746] lr: 9.955e-03, eta: 1 day, 2:11:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3741, top5_acc: 0.6344, loss_cls: 3.5329, loss: 3.5329 +2024-07-20 17:49:44,050 - pyskl - INFO - Epoch [120][1400/3746] lr: 9.938e-03, eta: 1 day, 2:10:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3895, top5_acc: 0.6486, loss_cls: 3.4564, loss: 3.4564 +2024-07-20 17:51:06,260 - pyskl - INFO - Epoch [120][1500/3746] lr: 9.922e-03, eta: 1 day, 2:08:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6444, loss_cls: 3.4547, loss: 3.4547 +2024-07-20 17:52:27,848 - pyskl - INFO - Epoch [120][1600/3746] lr: 9.905e-03, eta: 1 day, 2:07:37, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6455, loss_cls: 3.4929, loss: 3.4929 +2024-07-20 17:53:49,823 - pyskl - INFO - Epoch [120][1700/3746] lr: 9.888e-03, eta: 1 day, 2:06:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3812, top5_acc: 0.6438, loss_cls: 3.4938, loss: 3.4938 +2024-07-20 17:55:11,971 - pyskl - INFO - Epoch [120][1800/3746] lr: 9.871e-03, eta: 1 day, 2:04:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6427, loss_cls: 3.4928, loss: 3.4928 +2024-07-20 17:56:34,113 - pyskl - INFO - Epoch [120][1900/3746] lr: 9.855e-03, eta: 1 day, 2:03:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3822, top5_acc: 0.6395, loss_cls: 3.5067, loss: 3.5067 +2024-07-20 17:57:56,693 - pyskl - INFO - Epoch [120][2000/3746] lr: 9.838e-03, eta: 1 day, 2:02:08, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3848, top5_acc: 0.6283, loss_cls: 3.5329, loss: 3.5329 +2024-07-20 17:59:18,212 - pyskl - INFO - Epoch [120][2100/3746] lr: 9.821e-03, eta: 1 day, 2:00:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3791, top5_acc: 0.6302, loss_cls: 3.5272, loss: 3.5272 +2024-07-20 18:00:40,809 - pyskl - INFO - Epoch [120][2200/3746] lr: 9.805e-03, eta: 1 day, 1:59:24, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3714, top5_acc: 0.6419, loss_cls: 3.5176, loss: 3.5176 +2024-07-20 18:02:02,547 - pyskl - INFO - Epoch [120][2300/3746] lr: 9.788e-03, eta: 1 day, 1:58:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6361, loss_cls: 3.4834, loss: 3.4834 +2024-07-20 18:03:25,214 - pyskl - INFO - Epoch [120][2400/3746] lr: 9.772e-03, eta: 1 day, 1:56:40, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3675, top5_acc: 0.6331, loss_cls: 3.5496, loss: 3.5496 +2024-07-20 18:04:47,234 - pyskl - INFO - Epoch [120][2500/3746] lr: 9.755e-03, eta: 1 day, 1:55:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3761, top5_acc: 0.6369, loss_cls: 3.5216, loss: 3.5216 +2024-07-20 18:06:08,879 - pyskl - INFO - Epoch [120][2600/3746] lr: 9.738e-03, eta: 1 day, 1:53:55, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3783, top5_acc: 0.6342, loss_cls: 3.5419, loss: 3.5419 +2024-07-20 18:07:30,471 - pyskl - INFO - Epoch [120][2700/3746] lr: 9.722e-03, eta: 1 day, 1:52:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3772, top5_acc: 0.6428, loss_cls: 3.4937, loss: 3.4937 +2024-07-20 18:08:52,121 - pyskl - INFO - Epoch [120][2800/3746] lr: 9.705e-03, eta: 1 day, 1:51:11, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3775, top5_acc: 0.6430, loss_cls: 3.5139, loss: 3.5139 +2024-07-20 18:10:13,498 - pyskl - INFO - Epoch [120][2900/3746] lr: 9.689e-03, eta: 1 day, 1:49:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3841, top5_acc: 0.6366, loss_cls: 3.5085, loss: 3.5085 +2024-07-20 18:11:34,948 - pyskl - INFO - Epoch [120][3000/3746] lr: 9.672e-03, eta: 1 day, 1:48:26, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3814, top5_acc: 0.6353, loss_cls: 3.4978, loss: 3.4978 +2024-07-20 18:12:56,735 - pyskl - INFO - Epoch [120][3100/3746] lr: 9.656e-03, eta: 1 day, 1:47:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3766, top5_acc: 0.6331, loss_cls: 3.5231, loss: 3.5231 +2024-07-20 18:14:18,575 - pyskl - INFO - Epoch [120][3200/3746] lr: 9.639e-03, eta: 1 day, 1:45:42, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3755, top5_acc: 0.6348, loss_cls: 3.5361, loss: 3.5361 +2024-07-20 18:15:40,283 - pyskl - INFO - Epoch [120][3300/3746] lr: 9.623e-03, eta: 1 day, 1:44:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6352, loss_cls: 3.5192, loss: 3.5192 +2024-07-20 18:17:02,225 - pyskl - INFO - Epoch [120][3400/3746] lr: 9.606e-03, eta: 1 day, 1:42:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3689, top5_acc: 0.6330, loss_cls: 3.5503, loss: 3.5503 +2024-07-20 18:18:24,419 - pyskl - INFO - Epoch [120][3500/3746] lr: 9.590e-03, eta: 1 day, 1:41:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3759, top5_acc: 0.6295, loss_cls: 3.5450, loss: 3.5450 +2024-07-20 18:19:47,267 - pyskl - INFO - Epoch [120][3600/3746] lr: 9.573e-03, eta: 1 day, 1:40:13, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6400, loss_cls: 3.4994, loss: 3.4994 +2024-07-20 18:21:08,934 - pyskl - INFO - Epoch [120][3700/3746] lr: 9.557e-03, eta: 1 day, 1:38:51, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3797, top5_acc: 0.6372, loss_cls: 3.5213, loss: 3.5213 +2024-07-20 18:21:48,783 - pyskl - INFO - Saving checkpoint at 120 epochs +2024-07-20 18:23:39,183 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 18:23:39,941 - pyskl - INFO - +top1_acc 0.3142 +top5_acc 0.5725 +2024-07-20 18:23:39,941 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 18:23:39,990 - pyskl - INFO - +mean_acc 0.3140 +2024-07-20 18:23:39,995 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_119.pth was removed +2024-07-20 18:23:40,271 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_120.pth. +2024-07-20 18:23:40,272 - pyskl - INFO - Best top1_acc is 0.3142 at 120 epoch. +2024-07-20 18:23:40,295 - pyskl - INFO - Epoch(val) [120][309] top1_acc: 0.3142, top5_acc: 0.5725, mean_class_accuracy: 0.3140 +2024-07-20 18:27:30,401 - pyskl - INFO - Epoch [121][100/3746] lr: 9.533e-03, eta: 1 day, 1:37:19, time: 2.301, data_time: 1.306, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6561, loss_cls: 3.4049, loss: 3.4049 +2024-07-20 18:28:53,752 - pyskl - INFO - Epoch [121][200/3746] lr: 9.516e-03, eta: 1 day, 1:35:57, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.3983, top5_acc: 0.6486, loss_cls: 3.4206, loss: 3.4206 +2024-07-20 18:30:16,124 - pyskl - INFO - Epoch [121][300/3746] lr: 9.500e-03, eta: 1 day, 1:34:35, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6512, loss_cls: 3.4004, loss: 3.4004 +2024-07-20 18:31:39,227 - pyskl - INFO - Epoch [121][400/3746] lr: 9.484e-03, eta: 1 day, 1:33:13, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6434, loss_cls: 3.4735, loss: 3.4735 +2024-07-20 18:33:01,568 - pyskl - INFO - Epoch [121][500/3746] lr: 9.467e-03, eta: 1 day, 1:31:51, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3753, top5_acc: 0.6409, loss_cls: 3.4785, loss: 3.4785 +2024-07-20 18:34:23,988 - pyskl - INFO - Epoch [121][600/3746] lr: 9.451e-03, eta: 1 day, 1:30:29, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3894, top5_acc: 0.6466, loss_cls: 3.4667, loss: 3.4667 +2024-07-20 18:35:46,255 - pyskl - INFO - Epoch [121][700/3746] lr: 9.435e-03, eta: 1 day, 1:29:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6391, loss_cls: 3.4907, loss: 3.4907 +2024-07-20 18:37:08,265 - pyskl - INFO - Epoch [121][800/3746] lr: 9.418e-03, eta: 1 day, 1:27:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3847, top5_acc: 0.6475, loss_cls: 3.4611, loss: 3.4611 +2024-07-20 18:38:29,951 - pyskl - INFO - Epoch [121][900/3746] lr: 9.402e-03, eta: 1 day, 1:26:22, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6386, loss_cls: 3.4840, loss: 3.4840 +2024-07-20 18:39:52,015 - pyskl - INFO - Epoch [121][1000/3746] lr: 9.386e-03, eta: 1 day, 1:25:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6517, loss_cls: 3.4243, loss: 3.4243 +2024-07-20 18:41:13,945 - pyskl - INFO - Epoch [121][1100/3746] lr: 9.369e-03, eta: 1 day, 1:23:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3839, top5_acc: 0.6395, loss_cls: 3.4853, loss: 3.4853 +2024-07-20 18:42:36,069 - pyskl - INFO - Epoch [121][1200/3746] lr: 9.353e-03, eta: 1 day, 1:22:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3972, top5_acc: 0.6500, loss_cls: 3.4589, loss: 3.4589 +2024-07-20 18:43:57,849 - pyskl - INFO - Epoch [121][1300/3746] lr: 9.337e-03, eta: 1 day, 1:20:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6442, loss_cls: 3.4466, loss: 3.4466 +2024-07-20 18:45:19,562 - pyskl - INFO - Epoch [121][1400/3746] lr: 9.321e-03, eta: 1 day, 1:19:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6433, loss_cls: 3.4665, loss: 3.4665 +2024-07-20 18:46:41,504 - pyskl - INFO - Epoch [121][1500/3746] lr: 9.304e-03, eta: 1 day, 1:18:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6478, loss_cls: 3.4635, loss: 3.4635 +2024-07-20 18:48:03,319 - pyskl - INFO - Epoch [121][1600/3746] lr: 9.288e-03, eta: 1 day, 1:16:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3836, top5_acc: 0.6473, loss_cls: 3.4755, loss: 3.4755 +2024-07-20 18:49:25,039 - pyskl - INFO - Epoch [121][1700/3746] lr: 9.272e-03, eta: 1 day, 1:15:24, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3850, top5_acc: 0.6425, loss_cls: 3.4951, loss: 3.4951 +2024-07-20 18:50:46,924 - pyskl - INFO - Epoch [121][1800/3746] lr: 9.256e-03, eta: 1 day, 1:14:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6345, loss_cls: 3.5186, loss: 3.5186 +2024-07-20 18:52:08,874 - pyskl - INFO - Epoch [121][1900/3746] lr: 9.239e-03, eta: 1 day, 1:12:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3892, top5_acc: 0.6367, loss_cls: 3.4754, loss: 3.4754 +2024-07-20 18:53:31,637 - pyskl - INFO - Epoch [121][2000/3746] lr: 9.223e-03, eta: 1 day, 1:11:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6352, loss_cls: 3.4868, loss: 3.4868 +2024-07-20 18:54:54,507 - pyskl - INFO - Epoch [121][2100/3746] lr: 9.207e-03, eta: 1 day, 1:09:56, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3966, top5_acc: 0.6458, loss_cls: 3.4365, loss: 3.4365 +2024-07-20 18:56:16,482 - pyskl - INFO - Epoch [121][2200/3746] lr: 9.191e-03, eta: 1 day, 1:08:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6389, loss_cls: 3.4758, loss: 3.4758 +2024-07-20 18:57:38,367 - pyskl - INFO - Epoch [121][2300/3746] lr: 9.175e-03, eta: 1 day, 1:07:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3756, top5_acc: 0.6366, loss_cls: 3.5267, loss: 3.5267 +2024-07-20 18:59:00,606 - pyskl - INFO - Epoch [121][2400/3746] lr: 9.159e-03, eta: 1 day, 1:05:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6409, loss_cls: 3.4796, loss: 3.4796 +2024-07-20 19:00:22,022 - pyskl - INFO - Epoch [121][2500/3746] lr: 9.142e-03, eta: 1 day, 1:04:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3873, top5_acc: 0.6369, loss_cls: 3.5150, loss: 3.5150 +2024-07-20 19:01:43,836 - pyskl - INFO - Epoch [121][2600/3746] lr: 9.126e-03, eta: 1 day, 1:03:05, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3866, top5_acc: 0.6388, loss_cls: 3.4737, loss: 3.4737 +2024-07-20 19:03:05,587 - pyskl - INFO - Epoch [121][2700/3746] lr: 9.110e-03, eta: 1 day, 1:01:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3802, top5_acc: 0.6403, loss_cls: 3.4951, loss: 3.4951 +2024-07-20 19:04:27,274 - pyskl - INFO - Epoch [121][2800/3746] lr: 9.094e-03, eta: 1 day, 1:00:20, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3950, top5_acc: 0.6416, loss_cls: 3.4497, loss: 3.4497 +2024-07-20 19:05:49,686 - pyskl - INFO - Epoch [121][2900/3746] lr: 9.078e-03, eta: 1 day, 0:58:58, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3870, top5_acc: 0.6456, loss_cls: 3.4551, loss: 3.4551 +2024-07-20 19:07:11,212 - pyskl - INFO - Epoch [121][3000/3746] lr: 9.062e-03, eta: 1 day, 0:57:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3719, top5_acc: 0.6366, loss_cls: 3.5111, loss: 3.5111 +2024-07-20 19:08:32,904 - pyskl - INFO - Epoch [121][3100/3746] lr: 9.046e-03, eta: 1 day, 0:56:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3787, top5_acc: 0.6298, loss_cls: 3.5593, loss: 3.5593 +2024-07-20 19:09:55,195 - pyskl - INFO - Epoch [121][3200/3746] lr: 9.030e-03, eta: 1 day, 0:54:52, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6509, loss_cls: 3.4438, loss: 3.4438 +2024-07-20 19:11:17,534 - pyskl - INFO - Epoch [121][3300/3746] lr: 9.014e-03, eta: 1 day, 0:53:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6506, loss_cls: 3.4671, loss: 3.4671 +2024-07-20 19:12:39,363 - pyskl - INFO - Epoch [121][3400/3746] lr: 8.998e-03, eta: 1 day, 0:52:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6506, loss_cls: 3.4161, loss: 3.4161 +2024-07-20 19:14:01,372 - pyskl - INFO - Epoch [121][3500/3746] lr: 8.982e-03, eta: 1 day, 0:50:45, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3744, top5_acc: 0.6316, loss_cls: 3.5318, loss: 3.5318 +2024-07-20 19:15:22,893 - pyskl - INFO - Epoch [121][3600/3746] lr: 8.966e-03, eta: 1 day, 0:49:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3931, top5_acc: 0.6492, loss_cls: 3.4546, loss: 3.4546 +2024-07-20 19:16:44,652 - pyskl - INFO - Epoch [121][3700/3746] lr: 8.950e-03, eta: 1 day, 0:48:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3798, top5_acc: 0.6391, loss_cls: 3.5217, loss: 3.5217 +2024-07-20 19:17:24,482 - pyskl - INFO - Saving checkpoint at 121 epochs +2024-07-20 19:19:14,285 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 19:19:14,953 - pyskl - INFO - +top1_acc 0.3092 +top5_acc 0.5577 +2024-07-20 19:19:14,953 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 19:19:14,998 - pyskl - INFO - +mean_acc 0.3090 +2024-07-20 19:19:15,011 - pyskl - INFO - Epoch(val) [121][309] top1_acc: 0.3092, top5_acc: 0.5577, mean_class_accuracy: 0.3090 +2024-07-20 19:23:04,143 - pyskl - INFO - Epoch [122][100/3746] lr: 8.927e-03, eta: 1 day, 0:46:27, time: 2.291, data_time: 1.303, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6594, loss_cls: 3.3802, loss: 3.3802 +2024-07-20 19:24:27,037 - pyskl - INFO - Epoch [122][200/3746] lr: 8.911e-03, eta: 1 day, 0:45:05, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6547, loss_cls: 3.4245, loss: 3.4245 +2024-07-20 19:25:49,526 - pyskl - INFO - Epoch [122][300/3746] lr: 8.895e-03, eta: 1 day, 0:43:43, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4044, top5_acc: 0.6559, loss_cls: 3.3928, loss: 3.3928 +2024-07-20 19:27:12,055 - pyskl - INFO - Epoch [122][400/3746] lr: 8.879e-03, eta: 1 day, 0:42:21, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6491, loss_cls: 3.4102, loss: 3.4102 +2024-07-20 19:28:34,269 - pyskl - INFO - Epoch [122][500/3746] lr: 8.863e-03, eta: 1 day, 0:40:58, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6530, loss_cls: 3.4330, loss: 3.4330 +2024-07-20 19:29:56,507 - pyskl - INFO - Epoch [122][600/3746] lr: 8.847e-03, eta: 1 day, 0:39:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6536, loss_cls: 3.4043, loss: 3.4043 +2024-07-20 19:31:18,947 - pyskl - INFO - Epoch [122][700/3746] lr: 8.831e-03, eta: 1 day, 0:38:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3897, top5_acc: 0.6500, loss_cls: 3.4251, loss: 3.4251 +2024-07-20 19:32:41,097 - pyskl - INFO - Epoch [122][800/3746] lr: 8.815e-03, eta: 1 day, 0:36:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3966, top5_acc: 0.6561, loss_cls: 3.3944, loss: 3.3944 +2024-07-20 19:34:03,029 - pyskl - INFO - Epoch [122][900/3746] lr: 8.800e-03, eta: 1 day, 0:35:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6608, loss_cls: 3.3556, loss: 3.3556 +2024-07-20 19:35:24,637 - pyskl - INFO - Epoch [122][1000/3746] lr: 8.784e-03, eta: 1 day, 0:34:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3872, top5_acc: 0.6431, loss_cls: 3.4661, loss: 3.4661 +2024-07-20 19:36:46,875 - pyskl - INFO - Epoch [122][1100/3746] lr: 8.768e-03, eta: 1 day, 0:32:45, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3977, top5_acc: 0.6527, loss_cls: 3.4213, loss: 3.4213 +2024-07-20 19:38:08,741 - pyskl - INFO - Epoch [122][1200/3746] lr: 8.752e-03, eta: 1 day, 0:31:23, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6491, loss_cls: 3.4496, loss: 3.4496 +2024-07-20 19:39:31,051 - pyskl - INFO - Epoch [122][1300/3746] lr: 8.736e-03, eta: 1 day, 0:30:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6517, loss_cls: 3.4210, loss: 3.4210 +2024-07-20 19:40:53,013 - pyskl - INFO - Epoch [122][1400/3746] lr: 8.721e-03, eta: 1 day, 0:28:39, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3922, top5_acc: 0.6481, loss_cls: 3.4311, loss: 3.4311 +2024-07-20 19:42:14,878 - pyskl - INFO - Epoch [122][1500/3746] lr: 8.705e-03, eta: 1 day, 0:27:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6527, loss_cls: 3.4229, loss: 3.4229 +2024-07-20 19:43:37,105 - pyskl - INFO - Epoch [122][1600/3746] lr: 8.689e-03, eta: 1 day, 0:25:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3945, top5_acc: 0.6486, loss_cls: 3.4529, loss: 3.4529 +2024-07-20 19:44:59,502 - pyskl - INFO - Epoch [122][1700/3746] lr: 8.673e-03, eta: 1 day, 0:24:32, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6558, loss_cls: 3.4288, loss: 3.4288 +2024-07-20 19:46:21,496 - pyskl - INFO - Epoch [122][1800/3746] lr: 8.658e-03, eta: 1 day, 0:23:10, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6512, loss_cls: 3.4614, loss: 3.4614 +2024-07-20 19:47:43,737 - pyskl - INFO - Epoch [122][1900/3746] lr: 8.642e-03, eta: 1 day, 0:21:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3770, top5_acc: 0.6317, loss_cls: 3.5420, loss: 3.5420 +2024-07-20 19:49:05,859 - pyskl - INFO - Epoch [122][2000/3746] lr: 8.626e-03, eta: 1 day, 0:20:26, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3795, top5_acc: 0.6394, loss_cls: 3.4849, loss: 3.4849 +2024-07-20 19:50:28,574 - pyskl - INFO - Epoch [122][2100/3746] lr: 8.610e-03, eta: 1 day, 0:19:03, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3906, top5_acc: 0.6458, loss_cls: 3.4748, loss: 3.4748 +2024-07-20 19:51:50,791 - pyskl - INFO - Epoch [122][2200/3746] lr: 8.595e-03, eta: 1 day, 0:17:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6491, loss_cls: 3.4344, loss: 3.4344 +2024-07-20 19:53:12,817 - pyskl - INFO - Epoch [122][2300/3746] lr: 8.579e-03, eta: 1 day, 0:16:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6470, loss_cls: 3.4522, loss: 3.4522 +2024-07-20 19:54:34,979 - pyskl - INFO - Epoch [122][2400/3746] lr: 8.563e-03, eta: 1 day, 0:14:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3905, top5_acc: 0.6405, loss_cls: 3.4706, loss: 3.4706 +2024-07-20 19:55:56,788 - pyskl - INFO - Epoch [122][2500/3746] lr: 8.548e-03, eta: 1 day, 0:13:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4025, top5_acc: 0.6538, loss_cls: 3.3937, loss: 3.3937 +2024-07-20 19:57:18,825 - pyskl - INFO - Epoch [122][2600/3746] lr: 8.532e-03, eta: 1 day, 0:12:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3781, top5_acc: 0.6383, loss_cls: 3.5152, loss: 3.5152 +2024-07-20 19:58:40,619 - pyskl - INFO - Epoch [122][2700/3746] lr: 8.517e-03, eta: 1 day, 0:10:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6420, loss_cls: 3.4653, loss: 3.4653 +2024-07-20 20:00:02,133 - pyskl - INFO - Epoch [122][2800/3746] lr: 8.501e-03, eta: 1 day, 0:09:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3848, top5_acc: 0.6458, loss_cls: 3.4754, loss: 3.4754 +2024-07-20 20:01:24,340 - pyskl - INFO - Epoch [122][2900/3746] lr: 8.485e-03, eta: 1 day, 0:08:06, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6469, loss_cls: 3.4420, loss: 3.4420 +2024-07-20 20:02:46,198 - pyskl - INFO - Epoch [122][3000/3746] lr: 8.470e-03, eta: 1 day, 0:06:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6520, loss_cls: 3.4264, loss: 3.4264 +2024-07-20 20:04:07,725 - pyskl - INFO - Epoch [122][3100/3746] lr: 8.454e-03, eta: 1 day, 0:05:21, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3823, top5_acc: 0.6403, loss_cls: 3.4882, loss: 3.4882 +2024-07-20 20:05:29,640 - pyskl - INFO - Epoch [122][3200/3746] lr: 8.439e-03, eta: 1 day, 0:03:59, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3978, top5_acc: 0.6481, loss_cls: 3.4404, loss: 3.4404 +2024-07-20 20:06:51,578 - pyskl - INFO - Epoch [122][3300/3746] lr: 8.423e-03, eta: 1 day, 0:02:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3887, top5_acc: 0.6566, loss_cls: 3.4332, loss: 3.4332 +2024-07-20 20:08:13,573 - pyskl - INFO - Epoch [122][3400/3746] lr: 8.408e-03, eta: 1 day, 0:01:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3872, top5_acc: 0.6486, loss_cls: 3.4456, loss: 3.4456 +2024-07-20 20:09:36,224 - pyskl - INFO - Epoch [122][3500/3746] lr: 8.392e-03, eta: 23:59:52, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3834, top5_acc: 0.6419, loss_cls: 3.4912, loss: 3.4912 +2024-07-20 20:10:58,049 - pyskl - INFO - Epoch [122][3600/3746] lr: 8.377e-03, eta: 23:58:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3842, top5_acc: 0.6439, loss_cls: 3.4779, loss: 3.4779 +2024-07-20 20:12:19,883 - pyskl - INFO - Epoch [122][3700/3746] lr: 8.361e-03, eta: 23:57:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6417, loss_cls: 3.4495, loss: 3.4495 +2024-07-20 20:12:59,980 - pyskl - INFO - Saving checkpoint at 122 epochs +2024-07-20 20:14:50,420 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 20:14:51,088 - pyskl - INFO - +top1_acc 0.3209 +top5_acc 0.5685 +2024-07-20 20:14:51,088 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 20:14:51,130 - pyskl - INFO - +mean_acc 0.3208 +2024-07-20 20:14:51,135 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_120.pth was removed +2024-07-20 20:14:51,385 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_122.pth. +2024-07-20 20:14:51,386 - pyskl - INFO - Best top1_acc is 0.3209 at 122 epoch. +2024-07-20 20:14:51,400 - pyskl - INFO - Epoch(val) [122][309] top1_acc: 0.3209, top5_acc: 0.5685, mean_class_accuracy: 0.3208 +2024-07-20 20:18:40,417 - pyskl - INFO - Epoch [123][100/3746] lr: 8.339e-03, eta: 23:55:33, time: 2.290, data_time: 1.302, memory: 15990, top1_acc: 0.4058, top5_acc: 0.6664, loss_cls: 3.3582, loss: 3.3582 +2024-07-20 20:20:03,084 - pyskl - INFO - Epoch [123][200/3746] lr: 8.323e-03, eta: 23:54:11, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4036, top5_acc: 0.6580, loss_cls: 3.3415, loss: 3.3415 +2024-07-20 20:21:25,576 - pyskl - INFO - Epoch [123][300/3746] lr: 8.308e-03, eta: 23:52:49, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6559, loss_cls: 3.3791, loss: 3.3791 +2024-07-20 20:22:48,001 - pyskl - INFO - Epoch [123][400/3746] lr: 8.292e-03, eta: 23:51:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6536, loss_cls: 3.3932, loss: 3.3932 +2024-07-20 20:24:10,335 - pyskl - INFO - Epoch [123][500/3746] lr: 8.277e-03, eta: 23:50:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3923, top5_acc: 0.6553, loss_cls: 3.4492, loss: 3.4492 +2024-07-20 20:25:33,014 - pyskl - INFO - Epoch [123][600/3746] lr: 8.262e-03, eta: 23:48:42, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4070, top5_acc: 0.6545, loss_cls: 3.3908, loss: 3.3908 +2024-07-20 20:26:55,042 - pyskl - INFO - Epoch [123][700/3746] lr: 8.246e-03, eta: 23:47:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3934, top5_acc: 0.6559, loss_cls: 3.4070, loss: 3.4070 +2024-07-20 20:28:17,000 - pyskl - INFO - Epoch [123][800/3746] lr: 8.231e-03, eta: 23:45:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4069, top5_acc: 0.6555, loss_cls: 3.3728, loss: 3.3728 +2024-07-20 20:29:38,782 - pyskl - INFO - Epoch [123][900/3746] lr: 8.215e-03, eta: 23:44:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6472, loss_cls: 3.4290, loss: 3.4290 +2024-07-20 20:31:00,780 - pyskl - INFO - Epoch [123][1000/3746] lr: 8.200e-03, eta: 23:43:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6613, loss_cls: 3.3680, loss: 3.3680 +2024-07-20 20:32:22,913 - pyskl - INFO - Epoch [123][1100/3746] lr: 8.185e-03, eta: 23:41:51, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3884, top5_acc: 0.6425, loss_cls: 3.4667, loss: 3.4667 +2024-07-20 20:33:44,997 - pyskl - INFO - Epoch [123][1200/3746] lr: 8.169e-03, eta: 23:40:29, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6505, loss_cls: 3.4289, loss: 3.4289 +2024-07-20 20:35:06,651 - pyskl - INFO - Epoch [123][1300/3746] lr: 8.154e-03, eta: 23:39:06, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6530, loss_cls: 3.4208, loss: 3.4208 +2024-07-20 20:36:29,045 - pyskl - INFO - Epoch [123][1400/3746] lr: 8.139e-03, eta: 23:37:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3947, top5_acc: 0.6514, loss_cls: 3.4471, loss: 3.4471 +2024-07-20 20:37:50,797 - pyskl - INFO - Epoch [123][1500/3746] lr: 8.124e-03, eta: 23:36:22, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3942, top5_acc: 0.6534, loss_cls: 3.4059, loss: 3.4059 +2024-07-20 20:39:13,090 - pyskl - INFO - Epoch [123][1600/3746] lr: 8.108e-03, eta: 23:35:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6586, loss_cls: 3.3869, loss: 3.3869 +2024-07-20 20:40:35,198 - pyskl - INFO - Epoch [123][1700/3746] lr: 8.093e-03, eta: 23:33:38, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3930, top5_acc: 0.6531, loss_cls: 3.4201, loss: 3.4201 +2024-07-20 20:41:57,267 - pyskl - INFO - Epoch [123][1800/3746] lr: 8.078e-03, eta: 23:32:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3956, top5_acc: 0.6420, loss_cls: 3.4415, loss: 3.4415 +2024-07-20 20:43:19,635 - pyskl - INFO - Epoch [123][1900/3746] lr: 8.063e-03, eta: 23:30:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3972, top5_acc: 0.6583, loss_cls: 3.4217, loss: 3.4217 +2024-07-20 20:44:42,176 - pyskl - INFO - Epoch [123][2000/3746] lr: 8.047e-03, eta: 23:29:31, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6502, loss_cls: 3.4534, loss: 3.4534 +2024-07-20 20:46:04,838 - pyskl - INFO - Epoch [123][2100/3746] lr: 8.032e-03, eta: 23:28:09, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3967, top5_acc: 0.6523, loss_cls: 3.4161, loss: 3.4161 +2024-07-20 20:47:27,403 - pyskl - INFO - Epoch [123][2200/3746] lr: 8.017e-03, eta: 23:26:47, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3917, top5_acc: 0.6478, loss_cls: 3.4500, loss: 3.4500 +2024-07-20 20:48:49,641 - pyskl - INFO - Epoch [123][2300/3746] lr: 8.002e-03, eta: 23:25:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3928, top5_acc: 0.6481, loss_cls: 3.4472, loss: 3.4472 +2024-07-20 20:50:12,312 - pyskl - INFO - Epoch [123][2400/3746] lr: 7.987e-03, eta: 23:24:03, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6566, loss_cls: 3.4170, loss: 3.4170 +2024-07-20 20:51:34,558 - pyskl - INFO - Epoch [123][2500/3746] lr: 7.971e-03, eta: 23:22:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6475, loss_cls: 3.4502, loss: 3.4502 +2024-07-20 20:52:56,076 - pyskl - INFO - Epoch [123][2600/3746] lr: 7.956e-03, eta: 23:21:18, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4047, top5_acc: 0.6598, loss_cls: 3.3804, loss: 3.3804 +2024-07-20 20:54:18,135 - pyskl - INFO - Epoch [123][2700/3746] lr: 7.941e-03, eta: 23:19:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3861, top5_acc: 0.6456, loss_cls: 3.4479, loss: 3.4479 +2024-07-20 20:55:40,129 - pyskl - INFO - Epoch [123][2800/3746] lr: 7.926e-03, eta: 23:18:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3883, top5_acc: 0.6448, loss_cls: 3.4479, loss: 3.4479 +2024-07-20 20:57:02,777 - pyskl - INFO - Epoch [123][2900/3746] lr: 7.911e-03, eta: 23:17:12, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3973, top5_acc: 0.6548, loss_cls: 3.4216, loss: 3.4216 +2024-07-20 20:58:24,816 - pyskl - INFO - Epoch [123][3000/3746] lr: 7.896e-03, eta: 23:15:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3911, top5_acc: 0.6439, loss_cls: 3.4225, loss: 3.4225 +2024-07-20 20:59:46,825 - pyskl - INFO - Epoch [123][3100/3746] lr: 7.881e-03, eta: 23:14:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3912, top5_acc: 0.6580, loss_cls: 3.3847, loss: 3.3847 +2024-07-20 21:01:08,346 - pyskl - INFO - Epoch [123][3200/3746] lr: 7.866e-03, eta: 23:13:05, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3908, top5_acc: 0.6505, loss_cls: 3.4211, loss: 3.4211 +2024-07-20 21:02:29,966 - pyskl - INFO - Epoch [123][3300/3746] lr: 7.851e-03, eta: 23:11:42, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.3980, top5_acc: 0.6470, loss_cls: 3.4089, loss: 3.4089 +2024-07-20 21:03:51,441 - pyskl - INFO - Epoch [123][3400/3746] lr: 7.836e-03, eta: 23:10:20, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.3914, top5_acc: 0.6466, loss_cls: 3.4471, loss: 3.4471 +2024-07-20 21:05:14,260 - pyskl - INFO - Epoch [123][3500/3746] lr: 7.821e-03, eta: 23:08:58, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.3881, top5_acc: 0.6444, loss_cls: 3.4554, loss: 3.4554 +2024-07-20 21:06:36,517 - pyskl - INFO - Epoch [123][3600/3746] lr: 7.806e-03, eta: 23:07:36, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3886, top5_acc: 0.6453, loss_cls: 3.4639, loss: 3.4639 +2024-07-20 21:07:58,502 - pyskl - INFO - Epoch [123][3700/3746] lr: 7.791e-03, eta: 23:06:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3955, top5_acc: 0.6561, loss_cls: 3.4061, loss: 3.4061 +2024-07-20 21:08:37,955 - pyskl - INFO - Saving checkpoint at 123 epochs +2024-07-20 21:10:28,598 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 21:10:29,268 - pyskl - INFO - +top1_acc 0.3301 +top5_acc 0.5821 +2024-07-20 21:10:29,268 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 21:10:29,311 - pyskl - INFO - +mean_acc 0.3299 +2024-07-20 21:10:29,316 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_122.pth was removed +2024-07-20 21:10:29,566 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_123.pth. +2024-07-20 21:10:29,566 - pyskl - INFO - Best top1_acc is 0.3301 at 123 epoch. +2024-07-20 21:10:29,583 - pyskl - INFO - Epoch(val) [123][309] top1_acc: 0.3301, top5_acc: 0.5821, mean_class_accuracy: 0.3299 +2024-07-20 21:14:13,745 - pyskl - INFO - Epoch [124][100/3746] lr: 7.769e-03, eta: 23:04:36, time: 2.242, data_time: 1.261, memory: 15990, top1_acc: 0.4161, top5_acc: 0.6734, loss_cls: 3.2951, loss: 3.2951 +2024-07-20 21:15:36,471 - pyskl - INFO - Epoch [124][200/3746] lr: 7.754e-03, eta: 23:03:14, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4125, top5_acc: 0.6711, loss_cls: 3.3188, loss: 3.3188 +2024-07-20 21:17:00,225 - pyskl - INFO - Epoch [124][300/3746] lr: 7.739e-03, eta: 23:01:52, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.4153, top5_acc: 0.6747, loss_cls: 3.3022, loss: 3.3022 +2024-07-20 21:18:22,646 - pyskl - INFO - Epoch [124][400/3746] lr: 7.724e-03, eta: 23:00:30, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4008, top5_acc: 0.6595, loss_cls: 3.3757, loss: 3.3757 +2024-07-20 21:19:45,304 - pyskl - INFO - Epoch [124][500/3746] lr: 7.709e-03, eta: 22:59:08, time: 0.827, data_time: 0.001, memory: 15990, top1_acc: 0.4109, top5_acc: 0.6634, loss_cls: 3.3640, loss: 3.3640 +2024-07-20 21:21:08,238 - pyskl - INFO - Epoch [124][600/3746] lr: 7.694e-03, eta: 22:57:46, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6595, loss_cls: 3.3857, loss: 3.3857 +2024-07-20 21:22:30,255 - pyskl - INFO - Epoch [124][700/3746] lr: 7.679e-03, eta: 22:56:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4113, top5_acc: 0.6659, loss_cls: 3.3426, loss: 3.3426 +2024-07-20 21:23:52,441 - pyskl - INFO - Epoch [124][800/3746] lr: 7.664e-03, eta: 22:55:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6608, loss_cls: 3.3334, loss: 3.3334 +2024-07-20 21:25:14,580 - pyskl - INFO - Epoch [124][900/3746] lr: 7.649e-03, eta: 22:53:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4008, top5_acc: 0.6611, loss_cls: 3.3876, loss: 3.3876 +2024-07-20 21:26:36,006 - pyskl - INFO - Epoch [124][1000/3746] lr: 7.635e-03, eta: 22:52:17, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.3995, top5_acc: 0.6566, loss_cls: 3.3786, loss: 3.3786 +2024-07-20 21:27:57,849 - pyskl - INFO - Epoch [124][1100/3746] lr: 7.620e-03, eta: 22:50:55, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6645, loss_cls: 3.3868, loss: 3.3868 +2024-07-20 21:29:19,918 - pyskl - INFO - Epoch [124][1200/3746] lr: 7.605e-03, eta: 22:49:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3953, top5_acc: 0.6556, loss_cls: 3.3977, loss: 3.3977 +2024-07-20 21:30:41,738 - pyskl - INFO - Epoch [124][1300/3746] lr: 7.590e-03, eta: 22:48:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3923, top5_acc: 0.6673, loss_cls: 3.3506, loss: 3.3506 +2024-07-20 21:32:03,887 - pyskl - INFO - Epoch [124][1400/3746] lr: 7.575e-03, eta: 22:46:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3958, top5_acc: 0.6477, loss_cls: 3.4210, loss: 3.4210 +2024-07-20 21:33:25,788 - pyskl - INFO - Epoch [124][1500/3746] lr: 7.561e-03, eta: 22:45:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6552, loss_cls: 3.3933, loss: 3.3933 +2024-07-20 21:34:47,724 - pyskl - INFO - Epoch [124][1600/3746] lr: 7.546e-03, eta: 22:44:03, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4005, top5_acc: 0.6631, loss_cls: 3.3545, loss: 3.3545 +2024-07-20 21:36:09,396 - pyskl - INFO - Epoch [124][1700/3746] lr: 7.531e-03, eta: 22:42:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3986, top5_acc: 0.6580, loss_cls: 3.3628, loss: 3.3628 +2024-07-20 21:37:31,550 - pyskl - INFO - Epoch [124][1800/3746] lr: 7.516e-03, eta: 22:41:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4023, top5_acc: 0.6561, loss_cls: 3.3628, loss: 3.3628 +2024-07-20 21:38:53,278 - pyskl - INFO - Epoch [124][1900/3746] lr: 7.502e-03, eta: 22:39:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3962, top5_acc: 0.6503, loss_cls: 3.4196, loss: 3.4196 +2024-07-20 21:40:15,133 - pyskl - INFO - Epoch [124][2000/3746] lr: 7.487e-03, eta: 22:38:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6663, loss_cls: 3.3302, loss: 3.3302 +2024-07-20 21:41:37,104 - pyskl - INFO - Epoch [124][2100/3746] lr: 7.472e-03, eta: 22:37:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6570, loss_cls: 3.3794, loss: 3.3794 +2024-07-20 21:42:59,643 - pyskl - INFO - Epoch [124][2200/3746] lr: 7.457e-03, eta: 22:35:50, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.3977, top5_acc: 0.6484, loss_cls: 3.4077, loss: 3.4077 +2024-07-20 21:44:21,590 - pyskl - INFO - Epoch [124][2300/3746] lr: 7.443e-03, eta: 22:34:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3937, top5_acc: 0.6528, loss_cls: 3.4229, loss: 3.4229 +2024-07-20 21:45:43,644 - pyskl - INFO - Epoch [124][2400/3746] lr: 7.428e-03, eta: 22:33:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4019, top5_acc: 0.6606, loss_cls: 3.3832, loss: 3.3832 +2024-07-20 21:47:05,305 - pyskl - INFO - Epoch [124][2500/3746] lr: 7.413e-03, eta: 22:31:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4039, top5_acc: 0.6587, loss_cls: 3.3802, loss: 3.3802 +2024-07-20 21:48:26,611 - pyskl - INFO - Epoch [124][2600/3746] lr: 7.399e-03, eta: 22:30:20, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.3845, top5_acc: 0.6388, loss_cls: 3.4842, loss: 3.4842 +2024-07-20 21:49:48,496 - pyskl - INFO - Epoch [124][2700/3746] lr: 7.384e-03, eta: 22:28:58, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3991, top5_acc: 0.6614, loss_cls: 3.3892, loss: 3.3892 +2024-07-20 21:51:10,234 - pyskl - INFO - Epoch [124][2800/3746] lr: 7.370e-03, eta: 22:27:36, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3939, top5_acc: 0.6572, loss_cls: 3.3865, loss: 3.3865 +2024-07-20 21:52:32,295 - pyskl - INFO - Epoch [124][2900/3746] lr: 7.355e-03, eta: 22:26:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6586, loss_cls: 3.4265, loss: 3.4265 +2024-07-20 21:53:54,086 - pyskl - INFO - Epoch [124][3000/3746] lr: 7.340e-03, eta: 22:24:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.3903, top5_acc: 0.6512, loss_cls: 3.4132, loss: 3.4132 +2024-07-20 21:55:15,993 - pyskl - INFO - Epoch [124][3100/3746] lr: 7.326e-03, eta: 22:23:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6488, loss_cls: 3.4072, loss: 3.4072 +2024-07-20 21:56:38,331 - pyskl - INFO - Epoch [124][3200/3746] lr: 7.311e-03, eta: 22:22:07, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.3902, top5_acc: 0.6500, loss_cls: 3.4588, loss: 3.4588 +2024-07-20 21:58:00,070 - pyskl - INFO - Epoch [124][3300/3746] lr: 7.297e-03, eta: 22:20:44, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6473, loss_cls: 3.4220, loss: 3.4220 +2024-07-20 21:59:22,120 - pyskl - INFO - Epoch [124][3400/3746] lr: 7.282e-03, eta: 22:19:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3925, top5_acc: 0.6541, loss_cls: 3.4077, loss: 3.4077 +2024-07-20 22:00:43,993 - pyskl - INFO - Epoch [124][3500/3746] lr: 7.268e-03, eta: 22:18:00, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.3927, top5_acc: 0.6581, loss_cls: 3.3979, loss: 3.3979 +2024-07-20 22:02:05,949 - pyskl - INFO - Epoch [124][3600/3746] lr: 7.253e-03, eta: 22:16:37, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3961, top5_acc: 0.6436, loss_cls: 3.4383, loss: 3.4383 +2024-07-20 22:03:28,061 - pyskl - INFO - Epoch [124][3700/3746] lr: 7.239e-03, eta: 22:15:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4042, top5_acc: 0.6577, loss_cls: 3.3766, loss: 3.3766 +2024-07-20 22:04:08,131 - pyskl - INFO - Saving checkpoint at 124 epochs +2024-07-20 22:05:59,380 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 22:06:00,066 - pyskl - INFO - +top1_acc 0.3349 +top5_acc 0.5823 +2024-07-20 22:06:00,066 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 22:06:00,109 - pyskl - INFO - +mean_acc 0.3346 +2024-07-20 22:06:00,113 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_123.pth was removed +2024-07-20 22:06:00,368 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_124.pth. +2024-07-20 22:06:00,368 - pyskl - INFO - Best top1_acc is 0.3349 at 124 epoch. +2024-07-20 22:06:00,382 - pyskl - INFO - Epoch(val) [124][309] top1_acc: 0.3349, top5_acc: 0.5823, mean_class_accuracy: 0.3346 +2024-07-20 22:09:46,861 - pyskl - INFO - Epoch [125][100/3746] lr: 7.217e-03, eta: 22:13:38, time: 2.265, data_time: 1.276, memory: 15990, top1_acc: 0.4142, top5_acc: 0.6711, loss_cls: 3.3022, loss: 3.3022 +2024-07-20 22:11:09,462 - pyskl - INFO - Epoch [125][200/3746] lr: 7.203e-03, eta: 22:12:15, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6636, loss_cls: 3.3400, loss: 3.3400 +2024-07-20 22:12:31,454 - pyskl - INFO - Epoch [125][300/3746] lr: 7.189e-03, eta: 22:10:53, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4098, top5_acc: 0.6713, loss_cls: 3.3154, loss: 3.3154 +2024-07-20 22:13:54,597 - pyskl - INFO - Epoch [125][400/3746] lr: 7.174e-03, eta: 22:09:31, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4025, top5_acc: 0.6658, loss_cls: 3.3374, loss: 3.3374 +2024-07-20 22:15:16,762 - pyskl - INFO - Epoch [125][500/3746] lr: 7.160e-03, eta: 22:08:09, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4177, top5_acc: 0.6692, loss_cls: 3.3002, loss: 3.3002 +2024-07-20 22:16:39,192 - pyskl - INFO - Epoch [125][600/3746] lr: 7.145e-03, eta: 22:06:47, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4122, top5_acc: 0.6670, loss_cls: 3.3031, loss: 3.3031 +2024-07-20 22:18:01,680 - pyskl - INFO - Epoch [125][700/3746] lr: 7.131e-03, eta: 22:05:24, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4088, top5_acc: 0.6645, loss_cls: 3.3259, loss: 3.3259 +2024-07-20 22:19:23,685 - pyskl - INFO - Epoch [125][800/3746] lr: 7.117e-03, eta: 22:04:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6617, loss_cls: 3.3492, loss: 3.3492 +2024-07-20 22:20:45,616 - pyskl - INFO - Epoch [125][900/3746] lr: 7.102e-03, eta: 22:02:40, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4095, top5_acc: 0.6694, loss_cls: 3.3033, loss: 3.3033 +2024-07-20 22:22:07,354 - pyskl - INFO - Epoch [125][1000/3746] lr: 7.088e-03, eta: 22:01:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4184, top5_acc: 0.6753, loss_cls: 3.2663, loss: 3.2663 +2024-07-20 22:23:29,374 - pyskl - INFO - Epoch [125][1100/3746] lr: 7.073e-03, eta: 21:59:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6592, loss_cls: 3.3738, loss: 3.3738 +2024-07-20 22:24:51,584 - pyskl - INFO - Epoch [125][1200/3746] lr: 7.059e-03, eta: 21:58:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3944, top5_acc: 0.6475, loss_cls: 3.4278, loss: 3.4278 +2024-07-20 22:26:13,688 - pyskl - INFO - Epoch [125][1300/3746] lr: 7.045e-03, eta: 21:57:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3984, top5_acc: 0.6547, loss_cls: 3.3859, loss: 3.3859 +2024-07-20 22:27:36,054 - pyskl - INFO - Epoch [125][1400/3746] lr: 7.031e-03, eta: 21:55:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6489, loss_cls: 3.4012, loss: 3.4012 +2024-07-20 22:28:57,911 - pyskl - INFO - Epoch [125][1500/3746] lr: 7.016e-03, eta: 21:54:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4033, top5_acc: 0.6639, loss_cls: 3.3633, loss: 3.3633 +2024-07-20 22:30:19,923 - pyskl - INFO - Epoch [125][1600/3746] lr: 7.002e-03, eta: 21:53:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.3998, top5_acc: 0.6650, loss_cls: 3.3892, loss: 3.3892 +2024-07-20 22:31:41,804 - pyskl - INFO - Epoch [125][1700/3746] lr: 6.988e-03, eta: 21:51:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6627, loss_cls: 3.3703, loss: 3.3703 +2024-07-20 22:33:03,865 - pyskl - INFO - Epoch [125][1800/3746] lr: 6.973e-03, eta: 21:50:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3959, top5_acc: 0.6519, loss_cls: 3.4088, loss: 3.4088 +2024-07-20 22:34:26,069 - pyskl - INFO - Epoch [125][1900/3746] lr: 6.959e-03, eta: 21:48:57, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4072, top5_acc: 0.6577, loss_cls: 3.3957, loss: 3.3957 +2024-07-20 22:35:48,177 - pyskl - INFO - Epoch [125][2000/3746] lr: 6.945e-03, eta: 21:47:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.3877, top5_acc: 0.6661, loss_cls: 3.3764, loss: 3.3764 +2024-07-20 22:37:10,027 - pyskl - INFO - Epoch [125][2100/3746] lr: 6.931e-03, eta: 21:46:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4109, top5_acc: 0.6603, loss_cls: 3.3457, loss: 3.3457 +2024-07-20 22:38:32,690 - pyskl - INFO - Epoch [125][2200/3746] lr: 6.917e-03, eta: 21:44:50, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4030, top5_acc: 0.6602, loss_cls: 3.3518, loss: 3.3518 +2024-07-20 22:39:55,367 - pyskl - INFO - Epoch [125][2300/3746] lr: 6.902e-03, eta: 21:43:28, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4006, top5_acc: 0.6673, loss_cls: 3.3639, loss: 3.3639 +2024-07-20 22:41:17,845 - pyskl - INFO - Epoch [125][2400/3746] lr: 6.888e-03, eta: 21:42:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4041, top5_acc: 0.6666, loss_cls: 3.3439, loss: 3.3439 +2024-07-20 22:42:39,983 - pyskl - INFO - Epoch [125][2500/3746] lr: 6.874e-03, eta: 21:40:44, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4139, top5_acc: 0.6667, loss_cls: 3.3492, loss: 3.3492 +2024-07-20 22:44:01,661 - pyskl - INFO - Epoch [125][2600/3746] lr: 6.860e-03, eta: 21:39:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.3975, top5_acc: 0.6491, loss_cls: 3.4236, loss: 3.4236 +2024-07-20 22:45:23,771 - pyskl - INFO - Epoch [125][2700/3746] lr: 6.846e-03, eta: 21:37:59, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4062, top5_acc: 0.6587, loss_cls: 3.3539, loss: 3.3539 +2024-07-20 22:46:46,079 - pyskl - INFO - Epoch [125][2800/3746] lr: 6.832e-03, eta: 21:36:37, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6539, loss_cls: 3.3596, loss: 3.3596 +2024-07-20 22:48:07,701 - pyskl - INFO - Epoch [125][2900/3746] lr: 6.818e-03, eta: 21:35:14, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4044, top5_acc: 0.6578, loss_cls: 3.3875, loss: 3.3875 +2024-07-20 22:49:29,767 - pyskl - INFO - Epoch [125][3000/3746] lr: 6.804e-03, eta: 21:33:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6573, loss_cls: 3.3645, loss: 3.3645 +2024-07-20 22:50:51,519 - pyskl - INFO - Epoch [125][3100/3746] lr: 6.789e-03, eta: 21:32:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6613, loss_cls: 3.3432, loss: 3.3432 +2024-07-20 22:52:13,414 - pyskl - INFO - Epoch [125][3200/3746] lr: 6.775e-03, eta: 21:31:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6748, loss_cls: 3.2914, loss: 3.2914 +2024-07-20 22:53:35,524 - pyskl - INFO - Epoch [125][3300/3746] lr: 6.761e-03, eta: 21:29:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6641, loss_cls: 3.3509, loss: 3.3509 +2024-07-20 22:54:57,531 - pyskl - INFO - Epoch [125][3400/3746] lr: 6.747e-03, eta: 21:28:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6614, loss_cls: 3.3285, loss: 3.3285 +2024-07-20 22:56:19,033 - pyskl - INFO - Epoch [125][3500/3746] lr: 6.733e-03, eta: 21:27:00, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4000, top5_acc: 0.6572, loss_cls: 3.4060, loss: 3.4060 +2024-07-20 22:57:40,679 - pyskl - INFO - Epoch [125][3600/3746] lr: 6.719e-03, eta: 21:25:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4022, top5_acc: 0.6577, loss_cls: 3.3784, loss: 3.3784 +2024-07-20 22:59:02,170 - pyskl - INFO - Epoch [125][3700/3746] lr: 6.705e-03, eta: 21:24:16, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6561, loss_cls: 3.3836, loss: 3.3836 +2024-07-20 22:59:41,723 - pyskl - INFO - Saving checkpoint at 125 epochs +2024-07-20 23:01:33,078 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 23:01:33,746 - pyskl - INFO - +top1_acc 0.3326 +top5_acc 0.5877 +2024-07-20 23:01:33,746 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 23:01:33,789 - pyskl - INFO - +mean_acc 0.3324 +2024-07-20 23:01:33,802 - pyskl - INFO - Epoch(val) [125][309] top1_acc: 0.3326, top5_acc: 0.5877, mean_class_accuracy: 0.3324 +2024-07-20 23:05:25,182 - pyskl - INFO - Epoch [126][100/3746] lr: 6.685e-03, eta: 21:22:38, time: 2.314, data_time: 1.329, memory: 15990, top1_acc: 0.4300, top5_acc: 0.6894, loss_cls: 3.2374, loss: 3.2374 +2024-07-20 23:06:48,573 - pyskl - INFO - Epoch [126][200/3746] lr: 6.671e-03, eta: 21:21:16, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.4272, top5_acc: 0.6833, loss_cls: 3.2462, loss: 3.2462 +2024-07-20 23:08:10,890 - pyskl - INFO - Epoch [126][300/3746] lr: 6.657e-03, eta: 21:19:54, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4200, top5_acc: 0.6828, loss_cls: 3.2659, loss: 3.2659 +2024-07-20 23:09:34,066 - pyskl - INFO - Epoch [126][400/3746] lr: 6.643e-03, eta: 21:18:31, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6628, loss_cls: 3.3665, loss: 3.3665 +2024-07-20 23:10:56,950 - pyskl - INFO - Epoch [126][500/3746] lr: 6.629e-03, eta: 21:17:09, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4263, top5_acc: 0.6742, loss_cls: 3.2549, loss: 3.2549 +2024-07-20 23:12:19,197 - pyskl - INFO - Epoch [126][600/3746] lr: 6.615e-03, eta: 21:15:47, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4145, top5_acc: 0.6758, loss_cls: 3.2773, loss: 3.2773 +2024-07-20 23:13:41,826 - pyskl - INFO - Epoch [126][700/3746] lr: 6.601e-03, eta: 21:14:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4145, top5_acc: 0.6745, loss_cls: 3.2470, loss: 3.2470 +2024-07-20 23:15:03,967 - pyskl - INFO - Epoch [126][800/3746] lr: 6.587e-03, eta: 21:13:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6772, loss_cls: 3.2618, loss: 3.2618 +2024-07-20 23:16:26,470 - pyskl - INFO - Epoch [126][900/3746] lr: 6.574e-03, eta: 21:11:40, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4105, top5_acc: 0.6641, loss_cls: 3.3352, loss: 3.3352 +2024-07-20 23:17:48,387 - pyskl - INFO - Epoch [126][1000/3746] lr: 6.560e-03, eta: 21:10:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6706, loss_cls: 3.2982, loss: 3.2982 +2024-07-20 23:19:10,691 - pyskl - INFO - Epoch [126][1100/3746] lr: 6.546e-03, eta: 21:08:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4173, top5_acc: 0.6763, loss_cls: 3.3083, loss: 3.3083 +2024-07-20 23:20:32,298 - pyskl - INFO - Epoch [126][1200/3746] lr: 6.532e-03, eta: 21:07:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4077, top5_acc: 0.6747, loss_cls: 3.3126, loss: 3.3126 +2024-07-20 23:21:54,078 - pyskl - INFO - Epoch [126][1300/3746] lr: 6.518e-03, eta: 21:06:11, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4225, top5_acc: 0.6781, loss_cls: 3.2452, loss: 3.2452 +2024-07-20 23:23:15,982 - pyskl - INFO - Epoch [126][1400/3746] lr: 6.505e-03, eta: 21:04:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4025, top5_acc: 0.6700, loss_cls: 3.3324, loss: 3.3324 +2024-07-20 23:24:38,480 - pyskl - INFO - Epoch [126][1500/3746] lr: 6.491e-03, eta: 21:03:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4064, top5_acc: 0.6603, loss_cls: 3.3560, loss: 3.3560 +2024-07-20 23:26:00,510 - pyskl - INFO - Epoch [126][1600/3746] lr: 6.477e-03, eta: 21:02:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4120, top5_acc: 0.6692, loss_cls: 3.3195, loss: 3.3195 +2024-07-20 23:27:23,085 - pyskl - INFO - Epoch [126][1700/3746] lr: 6.463e-03, eta: 21:00:42, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4056, top5_acc: 0.6644, loss_cls: 3.3532, loss: 3.3532 +2024-07-20 23:28:44,924 - pyskl - INFO - Epoch [126][1800/3746] lr: 6.449e-03, eta: 20:59:20, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4088, top5_acc: 0.6633, loss_cls: 3.3516, loss: 3.3516 +2024-07-20 23:30:06,787 - pyskl - INFO - Epoch [126][1900/3746] lr: 6.436e-03, eta: 20:57:57, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6730, loss_cls: 3.2602, loss: 3.2602 +2024-07-20 23:31:28,828 - pyskl - INFO - Epoch [126][2000/3746] lr: 6.422e-03, eta: 20:56:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4259, top5_acc: 0.6694, loss_cls: 3.3031, loss: 3.3031 +2024-07-20 23:32:51,084 - pyskl - INFO - Epoch [126][2100/3746] lr: 6.408e-03, eta: 20:55:13, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4116, top5_acc: 0.6559, loss_cls: 3.3628, loss: 3.3628 +2024-07-20 23:34:13,736 - pyskl - INFO - Epoch [126][2200/3746] lr: 6.395e-03, eta: 20:53:51, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4055, top5_acc: 0.6619, loss_cls: 3.3186, loss: 3.3186 +2024-07-20 23:35:36,437 - pyskl - INFO - Epoch [126][2300/3746] lr: 6.381e-03, eta: 20:52:28, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4070, top5_acc: 0.6609, loss_cls: 3.3434, loss: 3.3434 +2024-07-20 23:36:58,773 - pyskl - INFO - Epoch [126][2400/3746] lr: 6.367e-03, eta: 20:51:06, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4027, top5_acc: 0.6641, loss_cls: 3.3407, loss: 3.3407 +2024-07-20 23:38:20,159 - pyskl - INFO - Epoch [126][2500/3746] lr: 6.354e-03, eta: 20:49:44, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6617, loss_cls: 3.3617, loss: 3.3617 +2024-07-20 23:39:42,044 - pyskl - INFO - Epoch [126][2600/3746] lr: 6.340e-03, eta: 20:48:21, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4163, top5_acc: 0.6656, loss_cls: 3.3389, loss: 3.3389 +2024-07-20 23:41:03,432 - pyskl - INFO - Epoch [126][2700/3746] lr: 6.326e-03, eta: 20:46:59, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4012, top5_acc: 0.6586, loss_cls: 3.3673, loss: 3.3673 +2024-07-20 23:42:25,328 - pyskl - INFO - Epoch [126][2800/3746] lr: 6.313e-03, eta: 20:45:37, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4037, top5_acc: 0.6591, loss_cls: 3.3507, loss: 3.3507 +2024-07-20 23:43:47,288 - pyskl - INFO - Epoch [126][2900/3746] lr: 6.299e-03, eta: 20:44:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4011, top5_acc: 0.6628, loss_cls: 3.3607, loss: 3.3607 +2024-07-20 23:45:09,233 - pyskl - INFO - Epoch [126][3000/3746] lr: 6.286e-03, eta: 20:42:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4028, top5_acc: 0.6650, loss_cls: 3.3491, loss: 3.3491 +2024-07-20 23:46:31,400 - pyskl - INFO - Epoch [126][3100/3746] lr: 6.272e-03, eta: 20:41:30, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.3969, top5_acc: 0.6650, loss_cls: 3.3381, loss: 3.3381 +2024-07-20 23:47:53,437 - pyskl - INFO - Epoch [126][3200/3746] lr: 6.259e-03, eta: 20:40:07, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4067, top5_acc: 0.6617, loss_cls: 3.3629, loss: 3.3629 +2024-07-20 23:49:16,083 - pyskl - INFO - Epoch [126][3300/3746] lr: 6.245e-03, eta: 20:38:45, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.3919, top5_acc: 0.6527, loss_cls: 3.4084, loss: 3.4084 +2024-07-20 23:50:37,929 - pyskl - INFO - Epoch [126][3400/3746] lr: 6.231e-03, eta: 20:37:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4005, top5_acc: 0.6606, loss_cls: 3.3776, loss: 3.3776 +2024-07-20 23:52:00,003 - pyskl - INFO - Epoch [126][3500/3746] lr: 6.218e-03, eta: 20:36:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4188, top5_acc: 0.6689, loss_cls: 3.3011, loss: 3.3011 +2024-07-20 23:53:21,949 - pyskl - INFO - Epoch [126][3600/3746] lr: 6.204e-03, eta: 20:34:38, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4102, top5_acc: 0.6648, loss_cls: 3.3423, loss: 3.3423 +2024-07-20 23:54:43,674 - pyskl - INFO - Epoch [126][3700/3746] lr: 6.191e-03, eta: 20:33:16, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4144, top5_acc: 0.6609, loss_cls: 3.3377, loss: 3.3377 +2024-07-20 23:55:23,602 - pyskl - INFO - Saving checkpoint at 126 epochs +2024-07-20 23:57:13,890 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-20 23:57:14,556 - pyskl - INFO - +top1_acc 0.3353 +top5_acc 0.5818 +2024-07-20 23:57:14,556 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-20 23:57:14,599 - pyskl - INFO - +mean_acc 0.3350 +2024-07-20 23:57:14,603 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_124.pth was removed +2024-07-20 23:57:14,870 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_126.pth. +2024-07-20 23:57:14,871 - pyskl - INFO - Best top1_acc is 0.3353 at 126 epoch. +2024-07-20 23:57:14,885 - pyskl - INFO - Epoch(val) [126][309] top1_acc: 0.3353, top5_acc: 0.5818, mean_class_accuracy: 0.3350 +2024-07-21 00:01:06,588 - pyskl - INFO - Epoch [127][100/3746] lr: 6.171e-03, eta: 20:31:37, time: 2.317, data_time: 1.327, memory: 15990, top1_acc: 0.4048, top5_acc: 0.6747, loss_cls: 3.2770, loss: 3.2770 +2024-07-21 00:02:29,514 - pyskl - INFO - Epoch [127][200/3746] lr: 6.158e-03, eta: 20:30:15, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4291, top5_acc: 0.6823, loss_cls: 3.2222, loss: 3.2222 +2024-07-21 00:03:52,107 - pyskl - INFO - Epoch [127][300/3746] lr: 6.144e-03, eta: 20:28:52, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4250, top5_acc: 0.6869, loss_cls: 3.2212, loss: 3.2212 +2024-07-21 00:05:15,117 - pyskl - INFO - Epoch [127][400/3746] lr: 6.131e-03, eta: 20:27:30, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6719, loss_cls: 3.2760, loss: 3.2760 +2024-07-21 00:06:38,307 - pyskl - INFO - Epoch [127][500/3746] lr: 6.118e-03, eta: 20:26:08, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4134, top5_acc: 0.6770, loss_cls: 3.2537, loss: 3.2537 +2024-07-21 00:08:00,516 - pyskl - INFO - Epoch [127][600/3746] lr: 6.104e-03, eta: 20:24:46, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4169, top5_acc: 0.6809, loss_cls: 3.2726, loss: 3.2726 +2024-07-21 00:09:22,987 - pyskl - INFO - Epoch [127][700/3746] lr: 6.091e-03, eta: 20:23:24, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6861, loss_cls: 3.2216, loss: 3.2216 +2024-07-21 00:10:45,136 - pyskl - INFO - Epoch [127][800/3746] lr: 6.077e-03, eta: 20:22:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4134, top5_acc: 0.6661, loss_cls: 3.2964, loss: 3.2964 +2024-07-21 00:12:07,357 - pyskl - INFO - Epoch [127][900/3746] lr: 6.064e-03, eta: 20:20:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6773, loss_cls: 3.2753, loss: 3.2753 +2024-07-21 00:13:29,340 - pyskl - INFO - Epoch [127][1000/3746] lr: 6.051e-03, eta: 20:19:17, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6802, loss_cls: 3.2651, loss: 3.2651 +2024-07-21 00:14:51,361 - pyskl - INFO - Epoch [127][1100/3746] lr: 6.037e-03, eta: 20:17:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4216, top5_acc: 0.6792, loss_cls: 3.2652, loss: 3.2652 +2024-07-21 00:16:13,095 - pyskl - INFO - Epoch [127][1200/3746] lr: 6.024e-03, eta: 20:16:32, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4117, top5_acc: 0.6728, loss_cls: 3.2749, loss: 3.2749 +2024-07-21 00:17:35,240 - pyskl - INFO - Epoch [127][1300/3746] lr: 6.011e-03, eta: 20:15:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4116, top5_acc: 0.6691, loss_cls: 3.3203, loss: 3.3203 +2024-07-21 00:18:56,979 - pyskl - INFO - Epoch [127][1400/3746] lr: 5.998e-03, eta: 20:13:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4139, top5_acc: 0.6711, loss_cls: 3.2848, loss: 3.2848 +2024-07-21 00:20:18,884 - pyskl - INFO - Epoch [127][1500/3746] lr: 5.984e-03, eta: 20:12:25, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4136, top5_acc: 0.6656, loss_cls: 3.3302, loss: 3.3302 +2024-07-21 00:21:41,048 - pyskl - INFO - Epoch [127][1600/3746] lr: 5.971e-03, eta: 20:11:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4161, top5_acc: 0.6794, loss_cls: 3.2823, loss: 3.2823 +2024-07-21 00:23:03,488 - pyskl - INFO - Epoch [127][1700/3746] lr: 5.958e-03, eta: 20:09:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.3952, top5_acc: 0.6556, loss_cls: 3.3785, loss: 3.3785 +2024-07-21 00:24:25,726 - pyskl - INFO - Epoch [127][1800/3746] lr: 5.945e-03, eta: 20:08:18, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4273, top5_acc: 0.6772, loss_cls: 3.2535, loss: 3.2535 +2024-07-21 00:25:48,187 - pyskl - INFO - Epoch [127][1900/3746] lr: 5.931e-03, eta: 20:06:56, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6781, loss_cls: 3.2465, loss: 3.2465 +2024-07-21 00:27:10,187 - pyskl - INFO - Epoch [127][2000/3746] lr: 5.918e-03, eta: 20:05:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6614, loss_cls: 3.3302, loss: 3.3302 +2024-07-21 00:28:32,303 - pyskl - INFO - Epoch [127][2100/3746] lr: 5.905e-03, eta: 20:04:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6727, loss_cls: 3.2796, loss: 3.2796 +2024-07-21 00:29:54,591 - pyskl - INFO - Epoch [127][2200/3746] lr: 5.892e-03, eta: 20:02:49, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4139, top5_acc: 0.6711, loss_cls: 3.3244, loss: 3.3244 +2024-07-21 00:31:16,671 - pyskl - INFO - Epoch [127][2300/3746] lr: 5.879e-03, eta: 20:01:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4142, top5_acc: 0.6658, loss_cls: 3.2958, loss: 3.2958 +2024-07-21 00:32:38,942 - pyskl - INFO - Epoch [127][2400/3746] lr: 5.866e-03, eta: 20:00:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4097, top5_acc: 0.6698, loss_cls: 3.2803, loss: 3.2803 +2024-07-21 00:34:01,099 - pyskl - INFO - Epoch [127][2500/3746] lr: 5.852e-03, eta: 19:58:42, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4130, top5_acc: 0.6744, loss_cls: 3.2911, loss: 3.2911 +2024-07-21 00:35:23,156 - pyskl - INFO - Epoch [127][2600/3746] lr: 5.839e-03, eta: 19:57:20, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4161, top5_acc: 0.6594, loss_cls: 3.3515, loss: 3.3515 +2024-07-21 00:36:45,246 - pyskl - INFO - Epoch [127][2700/3746] lr: 5.826e-03, eta: 19:55:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6752, loss_cls: 3.3060, loss: 3.3060 +2024-07-21 00:38:07,099 - pyskl - INFO - Epoch [127][2800/3746] lr: 5.813e-03, eta: 19:54:35, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4091, top5_acc: 0.6628, loss_cls: 3.3286, loss: 3.3286 +2024-07-21 00:39:29,329 - pyskl - INFO - Epoch [127][2900/3746] lr: 5.800e-03, eta: 19:53:13, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4128, top5_acc: 0.6686, loss_cls: 3.3412, loss: 3.3412 +2024-07-21 00:40:51,335 - pyskl - INFO - Epoch [127][3000/3746] lr: 5.787e-03, eta: 19:51:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6755, loss_cls: 3.2891, loss: 3.2891 +2024-07-21 00:42:12,873 - pyskl - INFO - Epoch [127][3100/3746] lr: 5.774e-03, eta: 19:50:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6803, loss_cls: 3.2620, loss: 3.2620 +2024-07-21 00:43:35,325 - pyskl - INFO - Epoch [127][3200/3746] lr: 5.761e-03, eta: 19:49:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4172, top5_acc: 0.6737, loss_cls: 3.3057, loss: 3.3057 +2024-07-21 00:44:57,179 - pyskl - INFO - Epoch [127][3300/3746] lr: 5.748e-03, eta: 19:47:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6583, loss_cls: 3.3411, loss: 3.3411 +2024-07-21 00:46:18,908 - pyskl - INFO - Epoch [127][3400/3746] lr: 5.735e-03, eta: 19:46:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4045, top5_acc: 0.6663, loss_cls: 3.3216, loss: 3.3216 +2024-07-21 00:47:40,522 - pyskl - INFO - Epoch [127][3500/3746] lr: 5.722e-03, eta: 19:44:58, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4108, top5_acc: 0.6619, loss_cls: 3.3477, loss: 3.3477 +2024-07-21 00:49:01,980 - pyskl - INFO - Epoch [127][3600/3746] lr: 5.709e-03, eta: 19:43:36, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4141, top5_acc: 0.6719, loss_cls: 3.3017, loss: 3.3017 +2024-07-21 00:50:23,860 - pyskl - INFO - Epoch [127][3700/3746] lr: 5.696e-03, eta: 19:42:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4111, top5_acc: 0.6663, loss_cls: 3.3206, loss: 3.3206 +2024-07-21 00:51:03,431 - pyskl - INFO - Saving checkpoint at 127 epochs +2024-07-21 00:52:54,767 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 00:52:55,438 - pyskl - INFO - +top1_acc 0.3418 +top5_acc 0.5945 +2024-07-21 00:52:55,438 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 00:52:55,480 - pyskl - INFO - +mean_acc 0.3416 +2024-07-21 00:52:55,485 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_126.pth was removed +2024-07-21 00:52:55,755 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_127.pth. +2024-07-21 00:52:55,756 - pyskl - INFO - Best top1_acc is 0.3418 at 127 epoch. +2024-07-21 00:52:55,769 - pyskl - INFO - Epoch(val) [127][309] top1_acc: 0.3418, top5_acc: 0.5945, mean_class_accuracy: 0.3416 +2024-07-21 00:56:44,510 - pyskl - INFO - Epoch [128][100/3746] lr: 5.677e-03, eta: 19:40:33, time: 2.287, data_time: 1.296, memory: 15990, top1_acc: 0.4378, top5_acc: 0.6970, loss_cls: 3.1813, loss: 3.1813 +2024-07-21 00:58:06,723 - pyskl - INFO - Epoch [128][200/3746] lr: 5.664e-03, eta: 19:39:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6820, loss_cls: 3.2592, loss: 3.2592 +2024-07-21 00:59:29,417 - pyskl - INFO - Epoch [128][300/3746] lr: 5.651e-03, eta: 19:37:49, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4348, top5_acc: 0.6866, loss_cls: 3.2137, loss: 3.2137 +2024-07-21 01:00:52,279 - pyskl - INFO - Epoch [128][400/3746] lr: 5.638e-03, eta: 19:36:26, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4294, top5_acc: 0.6839, loss_cls: 3.1932, loss: 3.1932 +2024-07-21 01:02:14,993 - pyskl - INFO - Epoch [128][500/3746] lr: 5.625e-03, eta: 19:35:04, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4189, top5_acc: 0.6781, loss_cls: 3.2349, loss: 3.2349 +2024-07-21 01:03:37,100 - pyskl - INFO - Epoch [128][600/3746] lr: 5.612e-03, eta: 19:33:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4211, top5_acc: 0.6825, loss_cls: 3.2294, loss: 3.2294 +2024-07-21 01:04:59,824 - pyskl - INFO - Epoch [128][700/3746] lr: 5.600e-03, eta: 19:32:20, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4180, top5_acc: 0.6745, loss_cls: 3.2625, loss: 3.2625 +2024-07-21 01:06:22,364 - pyskl - INFO - Epoch [128][800/3746] lr: 5.587e-03, eta: 19:30:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4181, top5_acc: 0.6897, loss_cls: 3.2088, loss: 3.2088 +2024-07-21 01:07:44,209 - pyskl - INFO - Epoch [128][900/3746] lr: 5.574e-03, eta: 19:29:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4256, top5_acc: 0.6767, loss_cls: 3.2488, loss: 3.2488 +2024-07-21 01:09:06,316 - pyskl - INFO - Epoch [128][1000/3746] lr: 5.561e-03, eta: 19:28:13, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4127, top5_acc: 0.6692, loss_cls: 3.3072, loss: 3.3072 +2024-07-21 01:10:28,013 - pyskl - INFO - Epoch [128][1100/3746] lr: 5.548e-03, eta: 19:26:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6798, loss_cls: 3.2469, loss: 3.2469 +2024-07-21 01:11:50,194 - pyskl - INFO - Epoch [128][1200/3746] lr: 5.536e-03, eta: 19:25:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6713, loss_cls: 3.2556, loss: 3.2556 +2024-07-21 01:13:12,492 - pyskl - INFO - Epoch [128][1300/3746] lr: 5.523e-03, eta: 19:24:05, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4227, top5_acc: 0.6703, loss_cls: 3.2554, loss: 3.2554 +2024-07-21 01:14:34,383 - pyskl - INFO - Epoch [128][1400/3746] lr: 5.510e-03, eta: 19:22:43, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4094, top5_acc: 0.6777, loss_cls: 3.2760, loss: 3.2760 +2024-07-21 01:15:56,203 - pyskl - INFO - Epoch [128][1500/3746] lr: 5.497e-03, eta: 19:21:21, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4269, top5_acc: 0.6816, loss_cls: 3.2440, loss: 3.2440 +2024-07-21 01:17:18,171 - pyskl - INFO - Epoch [128][1600/3746] lr: 5.485e-03, eta: 19:19:58, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6811, loss_cls: 3.2122, loss: 3.2122 +2024-07-21 01:18:40,333 - pyskl - INFO - Epoch [128][1700/3746] lr: 5.472e-03, eta: 19:18:36, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4191, top5_acc: 0.6758, loss_cls: 3.2593, loss: 3.2593 +2024-07-21 01:20:02,443 - pyskl - INFO - Epoch [128][1800/3746] lr: 5.459e-03, eta: 19:17:14, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4189, top5_acc: 0.6808, loss_cls: 3.2592, loss: 3.2592 +2024-07-21 01:21:24,801 - pyskl - INFO - Epoch [128][1900/3746] lr: 5.446e-03, eta: 19:15:51, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4156, top5_acc: 0.6763, loss_cls: 3.2828, loss: 3.2828 +2024-07-21 01:22:46,792 - pyskl - INFO - Epoch [128][2000/3746] lr: 5.434e-03, eta: 19:14:29, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4292, top5_acc: 0.6836, loss_cls: 3.2279, loss: 3.2279 +2024-07-21 01:24:08,435 - pyskl - INFO - Epoch [128][2100/3746] lr: 5.421e-03, eta: 19:13:07, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4211, top5_acc: 0.6798, loss_cls: 3.2471, loss: 3.2471 +2024-07-21 01:25:31,514 - pyskl - INFO - Epoch [128][2200/3746] lr: 5.408e-03, eta: 19:11:44, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4236, top5_acc: 0.6741, loss_cls: 3.2797, loss: 3.2797 +2024-07-21 01:26:53,969 - pyskl - INFO - Epoch [128][2300/3746] lr: 5.396e-03, eta: 19:10:22, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4245, top5_acc: 0.6791, loss_cls: 3.2626, loss: 3.2626 +2024-07-21 01:28:16,098 - pyskl - INFO - Epoch [128][2400/3746] lr: 5.383e-03, eta: 19:09:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6689, loss_cls: 3.2743, loss: 3.2743 +2024-07-21 01:29:38,548 - pyskl - INFO - Epoch [128][2500/3746] lr: 5.370e-03, eta: 19:07:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4303, top5_acc: 0.6775, loss_cls: 3.2587, loss: 3.2587 +2024-07-21 01:31:00,619 - pyskl - INFO - Epoch [128][2600/3746] lr: 5.358e-03, eta: 19:06:15, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4183, top5_acc: 0.6714, loss_cls: 3.3108, loss: 3.3108 +2024-07-21 01:32:22,400 - pyskl - INFO - Epoch [128][2700/3746] lr: 5.345e-03, eta: 19:04:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6687, loss_cls: 3.2977, loss: 3.2977 +2024-07-21 01:33:44,287 - pyskl - INFO - Epoch [128][2800/3746] lr: 5.333e-03, eta: 19:03:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4194, top5_acc: 0.6702, loss_cls: 3.2863, loss: 3.2863 +2024-07-21 01:35:06,433 - pyskl - INFO - Epoch [128][2900/3746] lr: 5.320e-03, eta: 19:02:08, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4233, top5_acc: 0.6722, loss_cls: 3.2758, loss: 3.2758 +2024-07-21 01:36:28,275 - pyskl - INFO - Epoch [128][3000/3746] lr: 5.308e-03, eta: 19:00:46, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4217, top5_acc: 0.6856, loss_cls: 3.2390, loss: 3.2390 +2024-07-21 01:37:50,011 - pyskl - INFO - Epoch [128][3100/3746] lr: 5.295e-03, eta: 18:59:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4150, top5_acc: 0.6681, loss_cls: 3.2885, loss: 3.2885 +2024-07-21 01:39:12,012 - pyskl - INFO - Epoch [128][3200/3746] lr: 5.283e-03, eta: 18:58:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4178, top5_acc: 0.6691, loss_cls: 3.2887, loss: 3.2887 +2024-07-21 01:40:33,616 - pyskl - INFO - Epoch [128][3300/3746] lr: 5.270e-03, eta: 18:56:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4131, top5_acc: 0.6652, loss_cls: 3.2940, loss: 3.2940 +2024-07-21 01:41:55,582 - pyskl - INFO - Epoch [128][3400/3746] lr: 5.258e-03, eta: 18:55:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4080, top5_acc: 0.6698, loss_cls: 3.3016, loss: 3.3016 +2024-07-21 01:43:17,615 - pyskl - INFO - Epoch [128][3500/3746] lr: 5.245e-03, eta: 18:53:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4072, top5_acc: 0.6677, loss_cls: 3.3149, loss: 3.3149 +2024-07-21 01:44:39,767 - pyskl - INFO - Epoch [128][3600/3746] lr: 5.233e-03, eta: 18:52:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4166, top5_acc: 0.6694, loss_cls: 3.2948, loss: 3.2948 +2024-07-21 01:46:01,719 - pyskl - INFO - Epoch [128][3700/3746] lr: 5.220e-03, eta: 18:51:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4181, top5_acc: 0.6716, loss_cls: 3.2684, loss: 3.2684 +2024-07-21 01:46:41,940 - pyskl - INFO - Saving checkpoint at 128 epochs +2024-07-21 01:48:33,041 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 01:48:33,709 - pyskl - INFO - +top1_acc 0.3464 +top5_acc 0.5993 +2024-07-21 01:48:33,709 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 01:48:33,752 - pyskl - INFO - +mean_acc 0.3461 +2024-07-21 01:48:33,758 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_127.pth was removed +2024-07-21 01:48:34,010 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_128.pth. +2024-07-21 01:48:34,010 - pyskl - INFO - Best top1_acc is 0.3464 at 128 epoch. +2024-07-21 01:48:34,024 - pyskl - INFO - Epoch(val) [128][309] top1_acc: 0.3464, top5_acc: 0.5993, mean_class_accuracy: 0.3461 +2024-07-21 01:52:21,824 - pyskl - INFO - Epoch [129][100/3746] lr: 5.202e-03, eta: 18:49:27, time: 2.278, data_time: 1.290, memory: 15990, top1_acc: 0.4408, top5_acc: 0.6997, loss_cls: 3.1509, loss: 3.1509 +2024-07-21 01:53:44,280 - pyskl - INFO - Epoch [129][200/3746] lr: 5.190e-03, eta: 18:48:05, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4317, top5_acc: 0.6942, loss_cls: 3.1934, loss: 3.1934 +2024-07-21 01:55:07,059 - pyskl - INFO - Epoch [129][300/3746] lr: 5.177e-03, eta: 18:46:43, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.7013, loss_cls: 3.1374, loss: 3.1374 +2024-07-21 01:56:29,405 - pyskl - INFO - Epoch [129][400/3746] lr: 5.165e-03, eta: 18:45:20, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4311, top5_acc: 0.6855, loss_cls: 3.1857, loss: 3.1857 +2024-07-21 01:57:51,833 - pyskl - INFO - Epoch [129][500/3746] lr: 5.153e-03, eta: 18:43:58, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6864, loss_cls: 3.1849, loss: 3.1849 +2024-07-21 01:59:13,816 - pyskl - INFO - Epoch [129][600/3746] lr: 5.140e-03, eta: 18:42:36, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4320, top5_acc: 0.6945, loss_cls: 3.1780, loss: 3.1780 +2024-07-21 02:00:36,419 - pyskl - INFO - Epoch [129][700/3746] lr: 5.128e-03, eta: 18:41:13, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6836, loss_cls: 3.1985, loss: 3.1985 +2024-07-21 02:01:57,929 - pyskl - INFO - Epoch [129][800/3746] lr: 5.116e-03, eta: 18:39:51, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.6808, loss_cls: 3.2062, loss: 3.2062 +2024-07-21 02:03:20,250 - pyskl - INFO - Epoch [129][900/3746] lr: 5.103e-03, eta: 18:38:29, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4348, top5_acc: 0.6853, loss_cls: 3.2028, loss: 3.2028 +2024-07-21 02:04:42,392 - pyskl - INFO - Epoch [129][1000/3746] lr: 5.091e-03, eta: 18:37:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.6958, loss_cls: 3.1701, loss: 3.1701 +2024-07-21 02:06:04,612 - pyskl - INFO - Epoch [129][1100/3746] lr: 5.079e-03, eta: 18:35:44, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4352, top5_acc: 0.6828, loss_cls: 3.2285, loss: 3.2285 +2024-07-21 02:07:26,230 - pyskl - INFO - Epoch [129][1200/3746] lr: 5.066e-03, eta: 18:34:21, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4277, top5_acc: 0.6828, loss_cls: 3.2292, loss: 3.2292 +2024-07-21 02:08:48,443 - pyskl - INFO - Epoch [129][1300/3746] lr: 5.054e-03, eta: 18:32:59, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4439, top5_acc: 0.6955, loss_cls: 3.1453, loss: 3.1453 +2024-07-21 02:10:11,065 - pyskl - INFO - Epoch [129][1400/3746] lr: 5.042e-03, eta: 18:31:37, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4370, top5_acc: 0.6944, loss_cls: 3.1624, loss: 3.1624 +2024-07-21 02:11:33,350 - pyskl - INFO - Epoch [129][1500/3746] lr: 5.030e-03, eta: 18:30:14, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6753, loss_cls: 3.2541, loss: 3.2541 +2024-07-21 02:12:55,146 - pyskl - INFO - Epoch [129][1600/3746] lr: 5.017e-03, eta: 18:28:52, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4255, top5_acc: 0.6764, loss_cls: 3.2220, loss: 3.2220 +2024-07-21 02:14:16,759 - pyskl - INFO - Epoch [129][1700/3746] lr: 5.005e-03, eta: 18:27:30, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4239, top5_acc: 0.6834, loss_cls: 3.2506, loss: 3.2506 +2024-07-21 02:15:38,637 - pyskl - INFO - Epoch [129][1800/3746] lr: 4.993e-03, eta: 18:26:07, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4359, top5_acc: 0.6884, loss_cls: 3.1844, loss: 3.1844 +2024-07-21 02:17:00,706 - pyskl - INFO - Epoch [129][1900/3746] lr: 4.981e-03, eta: 18:24:45, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4277, top5_acc: 0.6836, loss_cls: 3.2119, loss: 3.2119 +2024-07-21 02:18:23,144 - pyskl - INFO - Epoch [129][2000/3746] lr: 4.969e-03, eta: 18:23:22, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6891, loss_cls: 3.1961, loss: 3.1961 +2024-07-21 02:19:45,241 - pyskl - INFO - Epoch [129][2100/3746] lr: 4.957e-03, eta: 18:22:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4334, top5_acc: 0.6859, loss_cls: 3.1907, loss: 3.1907 +2024-07-21 02:21:07,899 - pyskl - INFO - Epoch [129][2200/3746] lr: 4.944e-03, eta: 18:20:38, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4264, top5_acc: 0.6777, loss_cls: 3.2359, loss: 3.2359 +2024-07-21 02:22:29,616 - pyskl - INFO - Epoch [129][2300/3746] lr: 4.932e-03, eta: 18:19:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4186, top5_acc: 0.6842, loss_cls: 3.2355, loss: 3.2355 +2024-07-21 02:23:52,025 - pyskl - INFO - Epoch [129][2400/3746] lr: 4.920e-03, eta: 18:17:53, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4272, top5_acc: 0.6766, loss_cls: 3.2371, loss: 3.2371 +2024-07-21 02:25:13,736 - pyskl - INFO - Epoch [129][2500/3746] lr: 4.908e-03, eta: 18:16:31, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4208, top5_acc: 0.6777, loss_cls: 3.2535, loss: 3.2535 +2024-07-21 02:26:36,093 - pyskl - INFO - Epoch [129][2600/3746] lr: 4.896e-03, eta: 18:15:08, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6800, loss_cls: 3.2442, loss: 3.2442 +2024-07-21 02:27:58,155 - pyskl - INFO - Epoch [129][2700/3746] lr: 4.884e-03, eta: 18:13:46, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4334, top5_acc: 0.6761, loss_cls: 3.2448, loss: 3.2448 +2024-07-21 02:29:20,230 - pyskl - INFO - Epoch [129][2800/3746] lr: 4.872e-03, eta: 18:12:24, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4136, top5_acc: 0.6739, loss_cls: 3.3176, loss: 3.3176 +2024-07-21 02:30:41,820 - pyskl - INFO - Epoch [129][2900/3746] lr: 4.860e-03, eta: 18:11:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4100, top5_acc: 0.6698, loss_cls: 3.3003, loss: 3.3003 +2024-07-21 02:32:03,745 - pyskl - INFO - Epoch [129][3000/3746] lr: 4.848e-03, eta: 18:09:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4206, top5_acc: 0.6756, loss_cls: 3.2786, loss: 3.2786 +2024-07-21 02:33:25,732 - pyskl - INFO - Epoch [129][3100/3746] lr: 4.836e-03, eta: 18:08:16, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4244, top5_acc: 0.6784, loss_cls: 3.2620, loss: 3.2620 +2024-07-21 02:34:47,738 - pyskl - INFO - Epoch [129][3200/3746] lr: 4.824e-03, eta: 18:06:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4264, top5_acc: 0.6750, loss_cls: 3.2475, loss: 3.2475 +2024-07-21 02:36:09,517 - pyskl - INFO - Epoch [129][3300/3746] lr: 4.812e-03, eta: 18:05:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4231, top5_acc: 0.6872, loss_cls: 3.2143, loss: 3.2143 +2024-07-21 02:37:31,305 - pyskl - INFO - Epoch [129][3400/3746] lr: 4.800e-03, eta: 18:04:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4159, top5_acc: 0.6719, loss_cls: 3.2857, loss: 3.2857 +2024-07-21 02:38:53,082 - pyskl - INFO - Epoch [129][3500/3746] lr: 4.788e-03, eta: 18:02:47, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4161, top5_acc: 0.6755, loss_cls: 3.2770, loss: 3.2770 +2024-07-21 02:40:15,003 - pyskl - INFO - Epoch [129][3600/3746] lr: 4.776e-03, eta: 18:01:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6820, loss_cls: 3.2560, loss: 3.2560 +2024-07-21 02:41:37,122 - pyskl - INFO - Epoch [129][3700/3746] lr: 4.764e-03, eta: 18:00:02, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4170, top5_acc: 0.6706, loss_cls: 3.3172, loss: 3.3172 +2024-07-21 02:42:16,758 - pyskl - INFO - Saving checkpoint at 129 epochs +2024-07-21 02:44:07,486 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 02:44:08,165 - pyskl - INFO - +top1_acc 0.3508 +top5_acc 0.6038 +2024-07-21 02:44:08,165 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 02:44:08,211 - pyskl - INFO - +mean_acc 0.3506 +2024-07-21 02:44:08,216 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_128.pth was removed +2024-07-21 02:44:08,477 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_129.pth. +2024-07-21 02:44:08,478 - pyskl - INFO - Best top1_acc is 0.3508 at 129 epoch. +2024-07-21 02:44:08,495 - pyskl - INFO - Epoch(val) [129][309] top1_acc: 0.3508, top5_acc: 0.6038, mean_class_accuracy: 0.3506 +2024-07-21 02:47:57,402 - pyskl - INFO - Epoch [130][100/3746] lr: 4.747e-03, eta: 17:58:19, time: 2.289, data_time: 1.297, memory: 15990, top1_acc: 0.4375, top5_acc: 0.6903, loss_cls: 3.1788, loss: 3.1788 +2024-07-21 02:49:20,299 - pyskl - INFO - Epoch [130][200/3746] lr: 4.735e-03, eta: 17:56:57, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4416, top5_acc: 0.6981, loss_cls: 3.1507, loss: 3.1507 +2024-07-21 02:50:43,074 - pyskl - INFO - Epoch [130][300/3746] lr: 4.723e-03, eta: 17:55:35, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4416, top5_acc: 0.6948, loss_cls: 3.1482, loss: 3.1482 +2024-07-21 02:52:04,988 - pyskl - INFO - Epoch [130][400/3746] lr: 4.711e-03, eta: 17:54:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4356, top5_acc: 0.6969, loss_cls: 3.1631, loss: 3.1631 +2024-07-21 02:53:28,239 - pyskl - INFO - Epoch [130][500/3746] lr: 4.699e-03, eta: 17:52:50, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4384, top5_acc: 0.7017, loss_cls: 3.1497, loss: 3.1497 +2024-07-21 02:54:50,625 - pyskl - INFO - Epoch [130][600/3746] lr: 4.688e-03, eta: 17:51:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4347, top5_acc: 0.6855, loss_cls: 3.1742, loss: 3.1742 +2024-07-21 02:56:12,711 - pyskl - INFO - Epoch [130][700/3746] lr: 4.676e-03, eta: 17:50:06, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4375, top5_acc: 0.6933, loss_cls: 3.1574, loss: 3.1574 +2024-07-21 02:57:35,413 - pyskl - INFO - Epoch [130][800/3746] lr: 4.664e-03, eta: 17:48:43, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4380, top5_acc: 0.6920, loss_cls: 3.1344, loss: 3.1344 +2024-07-21 02:58:57,559 - pyskl - INFO - Epoch [130][900/3746] lr: 4.652e-03, eta: 17:47:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4394, top5_acc: 0.6900, loss_cls: 3.1502, loss: 3.1502 +2024-07-21 03:00:19,397 - pyskl - INFO - Epoch [130][1000/3746] lr: 4.640e-03, eta: 17:45:58, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.6884, loss_cls: 3.1597, loss: 3.1597 +2024-07-21 03:01:41,234 - pyskl - INFO - Epoch [130][1100/3746] lr: 4.629e-03, eta: 17:44:36, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4355, top5_acc: 0.6986, loss_cls: 3.1512, loss: 3.1512 +2024-07-21 03:03:02,886 - pyskl - INFO - Epoch [130][1200/3746] lr: 4.617e-03, eta: 17:43:14, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4355, top5_acc: 0.6919, loss_cls: 3.2051, loss: 3.2051 +2024-07-21 03:04:24,663 - pyskl - INFO - Epoch [130][1300/3746] lr: 4.605e-03, eta: 17:41:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4328, top5_acc: 0.6883, loss_cls: 3.1833, loss: 3.1833 +2024-07-21 03:05:46,105 - pyskl - INFO - Epoch [130][1400/3746] lr: 4.594e-03, eta: 17:40:29, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6998, loss_cls: 3.1598, loss: 3.1598 +2024-07-21 03:07:07,681 - pyskl - INFO - Epoch [130][1500/3746] lr: 4.582e-03, eta: 17:39:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4197, top5_acc: 0.6819, loss_cls: 3.2144, loss: 3.2144 +2024-07-21 03:08:30,126 - pyskl - INFO - Epoch [130][1600/3746] lr: 4.570e-03, eta: 17:37:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4292, top5_acc: 0.6852, loss_cls: 3.2368, loss: 3.2368 +2024-07-21 03:09:52,082 - pyskl - INFO - Epoch [130][1700/3746] lr: 4.558e-03, eta: 17:36:21, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4280, top5_acc: 0.6795, loss_cls: 3.2216, loss: 3.2216 +2024-07-21 03:11:14,698 - pyskl - INFO - Epoch [130][1800/3746] lr: 4.547e-03, eta: 17:34:59, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4348, top5_acc: 0.6956, loss_cls: 3.1790, loss: 3.1790 +2024-07-21 03:12:36,884 - pyskl - INFO - Epoch [130][1900/3746] lr: 4.535e-03, eta: 17:33:37, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4288, top5_acc: 0.6831, loss_cls: 3.2088, loss: 3.2088 +2024-07-21 03:13:58,884 - pyskl - INFO - Epoch [130][2000/3746] lr: 4.524e-03, eta: 17:32:14, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4284, top5_acc: 0.6848, loss_cls: 3.1931, loss: 3.1931 +2024-07-21 03:15:20,798 - pyskl - INFO - Epoch [130][2100/3746] lr: 4.512e-03, eta: 17:30:52, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4325, top5_acc: 0.6827, loss_cls: 3.1740, loss: 3.1740 +2024-07-21 03:16:43,491 - pyskl - INFO - Epoch [130][2200/3746] lr: 4.500e-03, eta: 17:29:30, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6853, loss_cls: 3.2192, loss: 3.2192 +2024-07-21 03:18:05,701 - pyskl - INFO - Epoch [130][2300/3746] lr: 4.489e-03, eta: 17:28:07, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4406, top5_acc: 0.6889, loss_cls: 3.1730, loss: 3.1730 +2024-07-21 03:19:28,169 - pyskl - INFO - Epoch [130][2400/3746] lr: 4.477e-03, eta: 17:26:45, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4331, top5_acc: 0.6902, loss_cls: 3.1802, loss: 3.1802 +2024-07-21 03:20:50,184 - pyskl - INFO - Epoch [130][2500/3746] lr: 4.466e-03, eta: 17:25:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4384, top5_acc: 0.6963, loss_cls: 3.1548, loss: 3.1548 +2024-07-21 03:22:12,529 - pyskl - INFO - Epoch [130][2600/3746] lr: 4.454e-03, eta: 17:24:00, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6813, loss_cls: 3.1884, loss: 3.1884 +2024-07-21 03:23:34,940 - pyskl - INFO - Epoch [130][2700/3746] lr: 4.443e-03, eta: 17:22:38, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4234, top5_acc: 0.6767, loss_cls: 3.2482, loss: 3.2482 +2024-07-21 03:24:57,381 - pyskl - INFO - Epoch [130][2800/3746] lr: 4.431e-03, eta: 17:21:15, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.6930, loss_cls: 3.1776, loss: 3.1776 +2024-07-21 03:26:19,639 - pyskl - INFO - Epoch [130][2900/3746] lr: 4.420e-03, eta: 17:19:53, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4223, top5_acc: 0.6873, loss_cls: 3.1989, loss: 3.1989 +2024-07-21 03:27:41,589 - pyskl - INFO - Epoch [130][3000/3746] lr: 4.408e-03, eta: 17:18:31, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4359, top5_acc: 0.6911, loss_cls: 3.1744, loss: 3.1744 +2024-07-21 03:29:03,242 - pyskl - INFO - Epoch [130][3100/3746] lr: 4.397e-03, eta: 17:17:08, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4192, top5_acc: 0.6839, loss_cls: 3.2614, loss: 3.2614 +2024-07-21 03:30:25,628 - pyskl - INFO - Epoch [130][3200/3746] lr: 4.385e-03, eta: 17:15:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4350, top5_acc: 0.6911, loss_cls: 3.1789, loss: 3.1789 +2024-07-21 03:31:47,696 - pyskl - INFO - Epoch [130][3300/3746] lr: 4.374e-03, eta: 17:14:23, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4270, top5_acc: 0.6828, loss_cls: 3.1940, loss: 3.1940 +2024-07-21 03:33:09,537 - pyskl - INFO - Epoch [130][3400/3746] lr: 4.362e-03, eta: 17:13:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6863, loss_cls: 3.2070, loss: 3.2070 +2024-07-21 03:34:31,210 - pyskl - INFO - Epoch [130][3500/3746] lr: 4.351e-03, eta: 17:11:39, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4280, top5_acc: 0.6831, loss_cls: 3.2331, loss: 3.2331 +2024-07-21 03:35:53,138 - pyskl - INFO - Epoch [130][3600/3746] lr: 4.339e-03, eta: 17:10:16, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4242, top5_acc: 0.6783, loss_cls: 3.2390, loss: 3.2390 +2024-07-21 03:37:14,946 - pyskl - INFO - Epoch [130][3700/3746] lr: 4.328e-03, eta: 17:08:54, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4339, top5_acc: 0.6877, loss_cls: 3.2111, loss: 3.2111 +2024-07-21 03:37:54,971 - pyskl - INFO - Saving checkpoint at 130 epochs +2024-07-21 03:39:46,306 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 03:39:46,972 - pyskl - INFO - +top1_acc 0.3539 +top5_acc 0.6069 +2024-07-21 03:39:46,972 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 03:39:47,014 - pyskl - INFO - +mean_acc 0.3537 +2024-07-21 03:39:47,018 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_129.pth was removed +2024-07-21 03:39:47,272 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_130.pth. +2024-07-21 03:39:47,273 - pyskl - INFO - Best top1_acc is 0.3539 at 130 epoch. +2024-07-21 03:39:47,285 - pyskl - INFO - Epoch(val) [130][309] top1_acc: 0.3539, top5_acc: 0.6069, mean_class_accuracy: 0.3537 +2024-07-21 03:43:38,824 - pyskl - INFO - Epoch [131][100/3746] lr: 4.311e-03, eta: 17:07:11, time: 2.315, data_time: 1.317, memory: 15990, top1_acc: 0.4511, top5_acc: 0.7036, loss_cls: 3.0802, loss: 3.0802 +2024-07-21 03:45:01,721 - pyskl - INFO - Epoch [131][200/3746] lr: 4.300e-03, eta: 17:05:48, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4523, top5_acc: 0.7144, loss_cls: 3.0713, loss: 3.0713 +2024-07-21 03:46:24,031 - pyskl - INFO - Epoch [131][300/3746] lr: 4.289e-03, eta: 17:04:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4428, top5_acc: 0.7023, loss_cls: 3.0856, loss: 3.0856 +2024-07-21 03:47:46,325 - pyskl - INFO - Epoch [131][400/3746] lr: 4.277e-03, eta: 17:03:04, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.6978, loss_cls: 3.1042, loss: 3.1042 +2024-07-21 03:49:08,856 - pyskl - INFO - Epoch [131][500/3746] lr: 4.266e-03, eta: 17:01:41, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4570, top5_acc: 0.7102, loss_cls: 3.0694, loss: 3.0694 +2024-07-21 03:50:31,164 - pyskl - INFO - Epoch [131][600/3746] lr: 4.255e-03, eta: 17:00:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4477, top5_acc: 0.7002, loss_cls: 3.0944, loss: 3.0944 +2024-07-21 03:51:53,525 - pyskl - INFO - Epoch [131][700/3746] lr: 4.244e-03, eta: 16:58:56, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4439, top5_acc: 0.6980, loss_cls: 3.1306, loss: 3.1306 +2024-07-21 03:53:15,593 - pyskl - INFO - Epoch [131][800/3746] lr: 4.232e-03, eta: 16:57:34, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4384, top5_acc: 0.6948, loss_cls: 3.1240, loss: 3.1240 +2024-07-21 03:54:37,509 - pyskl - INFO - Epoch [131][900/3746] lr: 4.221e-03, eta: 16:56:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.6959, loss_cls: 3.1579, loss: 3.1579 +2024-07-21 03:55:59,403 - pyskl - INFO - Epoch [131][1000/3746] lr: 4.210e-03, eta: 16:54:49, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4556, top5_acc: 0.7016, loss_cls: 3.0976, loss: 3.0976 +2024-07-21 03:57:21,318 - pyskl - INFO - Epoch [131][1100/3746] lr: 4.199e-03, eta: 16:53:27, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4489, top5_acc: 0.6914, loss_cls: 3.1145, loss: 3.1145 +2024-07-21 03:58:42,871 - pyskl - INFO - Epoch [131][1200/3746] lr: 4.187e-03, eta: 16:52:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4416, top5_acc: 0.6969, loss_cls: 3.1080, loss: 3.1080 +2024-07-21 04:00:04,363 - pyskl - INFO - Epoch [131][1300/3746] lr: 4.176e-03, eta: 16:50:42, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4400, top5_acc: 0.6975, loss_cls: 3.1384, loss: 3.1384 +2024-07-21 04:01:25,909 - pyskl - INFO - Epoch [131][1400/3746] lr: 4.165e-03, eta: 16:49:19, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4522, top5_acc: 0.6973, loss_cls: 3.1126, loss: 3.1126 +2024-07-21 04:02:48,458 - pyskl - INFO - Epoch [131][1500/3746] lr: 4.154e-03, eta: 16:47:57, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.6977, loss_cls: 3.1094, loss: 3.1094 +2024-07-21 04:04:10,334 - pyskl - INFO - Epoch [131][1600/3746] lr: 4.143e-03, eta: 16:46:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4364, top5_acc: 0.6995, loss_cls: 3.1543, loss: 3.1543 +2024-07-21 04:05:32,479 - pyskl - INFO - Epoch [131][1700/3746] lr: 4.132e-03, eta: 16:45:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4311, top5_acc: 0.6895, loss_cls: 3.1774, loss: 3.1774 +2024-07-21 04:06:54,723 - pyskl - INFO - Epoch [131][1800/3746] lr: 4.120e-03, eta: 16:43:50, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4397, top5_acc: 0.6900, loss_cls: 3.1611, loss: 3.1611 +2024-07-21 04:08:17,356 - pyskl - INFO - Epoch [131][1900/3746] lr: 4.109e-03, eta: 16:42:27, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4339, top5_acc: 0.6905, loss_cls: 3.1703, loss: 3.1703 +2024-07-21 04:09:39,843 - pyskl - INFO - Epoch [131][2000/3746] lr: 4.098e-03, eta: 16:41:05, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4333, top5_acc: 0.6931, loss_cls: 3.1774, loss: 3.1774 +2024-07-21 04:11:01,709 - pyskl - INFO - Epoch [131][2100/3746] lr: 4.087e-03, eta: 16:39:42, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4253, top5_acc: 0.6858, loss_cls: 3.1930, loss: 3.1930 +2024-07-21 04:12:24,210 - pyskl - INFO - Epoch [131][2200/3746] lr: 4.076e-03, eta: 16:38:20, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4264, top5_acc: 0.6925, loss_cls: 3.1963, loss: 3.1963 +2024-07-21 04:13:46,318 - pyskl - INFO - Epoch [131][2300/3746] lr: 4.065e-03, eta: 16:36:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4350, top5_acc: 0.6869, loss_cls: 3.1931, loss: 3.1931 +2024-07-21 04:15:08,885 - pyskl - INFO - Epoch [131][2400/3746] lr: 4.054e-03, eta: 16:35:35, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4392, top5_acc: 0.6969, loss_cls: 3.1557, loss: 3.1557 +2024-07-21 04:16:30,907 - pyskl - INFO - Epoch [131][2500/3746] lr: 4.043e-03, eta: 16:34:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4363, top5_acc: 0.6914, loss_cls: 3.1454, loss: 3.1454 +2024-07-21 04:17:52,905 - pyskl - INFO - Epoch [131][2600/3746] lr: 4.032e-03, eta: 16:32:50, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4300, top5_acc: 0.6777, loss_cls: 3.2056, loss: 3.2056 +2024-07-21 04:19:15,309 - pyskl - INFO - Epoch [131][2700/3746] lr: 4.021e-03, eta: 16:31:28, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4295, top5_acc: 0.6836, loss_cls: 3.2057, loss: 3.2057 +2024-07-21 04:20:36,924 - pyskl - INFO - Epoch [131][2800/3746] lr: 4.010e-03, eta: 16:30:06, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4377, top5_acc: 0.6891, loss_cls: 3.1826, loss: 3.1826 +2024-07-21 04:21:58,912 - pyskl - INFO - Epoch [131][2900/3746] lr: 3.999e-03, eta: 16:28:43, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4267, top5_acc: 0.6798, loss_cls: 3.2231, loss: 3.2231 +2024-07-21 04:23:20,622 - pyskl - INFO - Epoch [131][3000/3746] lr: 3.988e-03, eta: 16:27:21, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4430, top5_acc: 0.6922, loss_cls: 3.1528, loss: 3.1528 +2024-07-21 04:24:42,282 - pyskl - INFO - Epoch [131][3100/3746] lr: 3.977e-03, eta: 16:25:58, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4353, top5_acc: 0.6894, loss_cls: 3.1586, loss: 3.1586 +2024-07-21 04:26:05,281 - pyskl - INFO - Epoch [131][3200/3746] lr: 3.966e-03, eta: 16:24:36, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4366, top5_acc: 0.6952, loss_cls: 3.1658, loss: 3.1658 +2024-07-21 04:27:27,179 - pyskl - INFO - Epoch [131][3300/3746] lr: 3.955e-03, eta: 16:23:14, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4328, top5_acc: 0.6853, loss_cls: 3.1993, loss: 3.1993 +2024-07-21 04:28:48,999 - pyskl - INFO - Epoch [131][3400/3746] lr: 3.945e-03, eta: 16:21:51, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4261, top5_acc: 0.6834, loss_cls: 3.2022, loss: 3.2022 +2024-07-21 04:30:10,917 - pyskl - INFO - Epoch [131][3500/3746] lr: 3.934e-03, eta: 16:20:29, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4391, top5_acc: 0.6964, loss_cls: 3.1472, loss: 3.1472 +2024-07-21 04:31:32,745 - pyskl - INFO - Epoch [131][3600/3746] lr: 3.923e-03, eta: 16:19:06, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.6913, loss_cls: 3.1557, loss: 3.1557 +2024-07-21 04:32:54,709 - pyskl - INFO - Epoch [131][3700/3746] lr: 3.912e-03, eta: 16:17:44, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.6898, loss_cls: 3.1653, loss: 3.1653 +2024-07-21 04:33:34,379 - pyskl - INFO - Saving checkpoint at 131 epochs +2024-07-21 04:35:26,021 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 04:35:26,689 - pyskl - INFO - +top1_acc 0.3604 +top5_acc 0.6106 +2024-07-21 04:35:26,690 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 04:35:26,733 - pyskl - INFO - +mean_acc 0.3602 +2024-07-21 04:35:26,738 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_130.pth was removed +2024-07-21 04:35:26,994 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_131.pth. +2024-07-21 04:35:26,995 - pyskl - INFO - Best top1_acc is 0.3604 at 131 epoch. +2024-07-21 04:35:27,008 - pyskl - INFO - Epoch(val) [131][309] top1_acc: 0.3604, top5_acc: 0.6106, mean_class_accuracy: 0.3602 +2024-07-21 04:39:21,456 - pyskl - INFO - Epoch [132][100/3746] lr: 3.896e-03, eta: 16:16:00, time: 2.344, data_time: 1.356, memory: 15990, top1_acc: 0.4550, top5_acc: 0.7052, loss_cls: 3.0638, loss: 3.0638 +2024-07-21 04:40:44,151 - pyskl - INFO - Epoch [132][200/3746] lr: 3.885e-03, eta: 16:14:38, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4647, top5_acc: 0.7183, loss_cls: 3.0143, loss: 3.0143 +2024-07-21 04:42:06,939 - pyskl - INFO - Epoch [132][300/3746] lr: 3.875e-03, eta: 16:13:15, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4528, top5_acc: 0.7058, loss_cls: 3.0753, loss: 3.0753 +2024-07-21 04:43:28,717 - pyskl - INFO - Epoch [132][400/3746] lr: 3.864e-03, eta: 16:11:53, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4502, top5_acc: 0.6980, loss_cls: 3.0819, loss: 3.0819 +2024-07-21 04:44:51,835 - pyskl - INFO - Epoch [132][500/3746] lr: 3.853e-03, eta: 16:10:31, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4447, top5_acc: 0.7045, loss_cls: 3.1028, loss: 3.1028 +2024-07-21 04:46:14,323 - pyskl - INFO - Epoch [132][600/3746] lr: 3.842e-03, eta: 16:09:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4509, top5_acc: 0.7123, loss_cls: 3.0727, loss: 3.0727 +2024-07-21 04:47:36,619 - pyskl - INFO - Epoch [132][700/3746] lr: 3.831e-03, eta: 16:07:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4544, top5_acc: 0.6952, loss_cls: 3.1209, loss: 3.1209 +2024-07-21 04:48:58,917 - pyskl - INFO - Epoch [132][800/3746] lr: 3.821e-03, eta: 16:06:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4502, top5_acc: 0.7048, loss_cls: 3.0686, loss: 3.0686 +2024-07-21 04:50:20,582 - pyskl - INFO - Epoch [132][900/3746] lr: 3.810e-03, eta: 16:05:01, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4372, top5_acc: 0.6934, loss_cls: 3.1360, loss: 3.1360 +2024-07-21 04:51:42,830 - pyskl - INFO - Epoch [132][1000/3746] lr: 3.799e-03, eta: 16:03:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4405, top5_acc: 0.6922, loss_cls: 3.1544, loss: 3.1544 +2024-07-21 04:53:05,114 - pyskl - INFO - Epoch [132][1100/3746] lr: 3.789e-03, eta: 16:02:16, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4481, top5_acc: 0.6997, loss_cls: 3.0969, loss: 3.0969 +2024-07-21 04:54:27,161 - pyskl - INFO - Epoch [132][1200/3746] lr: 3.778e-03, eta: 16:00:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4417, top5_acc: 0.6997, loss_cls: 3.1104, loss: 3.1104 +2024-07-21 04:55:49,209 - pyskl - INFO - Epoch [132][1300/3746] lr: 3.767e-03, eta: 15:59:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4527, top5_acc: 0.6997, loss_cls: 3.1272, loss: 3.1272 +2024-07-21 04:57:11,134 - pyskl - INFO - Epoch [132][1400/3746] lr: 3.757e-03, eta: 15:58:09, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4498, top5_acc: 0.6963, loss_cls: 3.1147, loss: 3.1147 +2024-07-21 04:58:33,569 - pyskl - INFO - Epoch [132][1500/3746] lr: 3.746e-03, eta: 15:56:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4573, top5_acc: 0.7089, loss_cls: 3.0867, loss: 3.0867 +2024-07-21 04:59:55,325 - pyskl - INFO - Epoch [132][1600/3746] lr: 3.735e-03, eta: 15:55:24, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4517, top5_acc: 0.7027, loss_cls: 3.0908, loss: 3.0908 +2024-07-21 05:01:17,766 - pyskl - INFO - Epoch [132][1700/3746] lr: 3.725e-03, eta: 15:54:01, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4444, top5_acc: 0.7075, loss_cls: 3.0808, loss: 3.0808 +2024-07-21 05:02:40,230 - pyskl - INFO - Epoch [132][1800/3746] lr: 3.714e-03, eta: 15:52:39, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4464, top5_acc: 0.7002, loss_cls: 3.1082, loss: 3.1082 +2024-07-21 05:04:02,492 - pyskl - INFO - Epoch [132][1900/3746] lr: 3.704e-03, eta: 15:51:17, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4370, top5_acc: 0.6972, loss_cls: 3.1379, loss: 3.1379 +2024-07-21 05:05:24,456 - pyskl - INFO - Epoch [132][2000/3746] lr: 3.693e-03, eta: 15:49:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4411, top5_acc: 0.6969, loss_cls: 3.1142, loss: 3.1142 +2024-07-21 05:06:46,595 - pyskl - INFO - Epoch [132][2100/3746] lr: 3.683e-03, eta: 15:48:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4367, top5_acc: 0.6963, loss_cls: 3.1344, loss: 3.1344 +2024-07-21 05:08:08,674 - pyskl - INFO - Epoch [132][2200/3746] lr: 3.672e-03, eta: 15:47:09, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4370, top5_acc: 0.6905, loss_cls: 3.1683, loss: 3.1683 +2024-07-21 05:09:30,329 - pyskl - INFO - Epoch [132][2300/3746] lr: 3.662e-03, eta: 15:45:47, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.7039, loss_cls: 3.0961, loss: 3.0961 +2024-07-21 05:10:52,283 - pyskl - INFO - Epoch [132][2400/3746] lr: 3.651e-03, eta: 15:44:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4402, top5_acc: 0.7056, loss_cls: 3.1077, loss: 3.1077 +2024-07-21 05:12:14,068 - pyskl - INFO - Epoch [132][2500/3746] lr: 3.641e-03, eta: 15:43:02, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4394, top5_acc: 0.6977, loss_cls: 3.1479, loss: 3.1479 +2024-07-21 05:13:36,565 - pyskl - INFO - Epoch [132][2600/3746] lr: 3.630e-03, eta: 15:41:40, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.7080, loss_cls: 3.0767, loss: 3.0767 +2024-07-21 05:14:59,073 - pyskl - INFO - Epoch [132][2700/3746] lr: 3.620e-03, eta: 15:40:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4469, top5_acc: 0.6967, loss_cls: 3.1152, loss: 3.1152 +2024-07-21 05:16:21,091 - pyskl - INFO - Epoch [132][2800/3746] lr: 3.609e-03, eta: 15:38:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4406, top5_acc: 0.6937, loss_cls: 3.1354, loss: 3.1354 +2024-07-21 05:17:42,662 - pyskl - INFO - Epoch [132][2900/3746] lr: 3.599e-03, eta: 15:37:32, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4389, top5_acc: 0.6945, loss_cls: 3.1475, loss: 3.1475 +2024-07-21 05:19:04,437 - pyskl - INFO - Epoch [132][3000/3746] lr: 3.588e-03, eta: 15:36:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4464, top5_acc: 0.6964, loss_cls: 3.1308, loss: 3.1308 +2024-07-21 05:20:26,307 - pyskl - INFO - Epoch [132][3100/3746] lr: 3.578e-03, eta: 15:34:47, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4341, top5_acc: 0.6963, loss_cls: 3.1472, loss: 3.1472 +2024-07-21 05:21:48,835 - pyskl - INFO - Epoch [132][3200/3746] lr: 3.568e-03, eta: 15:33:25, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4467, top5_acc: 0.6934, loss_cls: 3.1294, loss: 3.1294 +2024-07-21 05:23:10,853 - pyskl - INFO - Epoch [132][3300/3746] lr: 3.557e-03, eta: 15:32:02, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4366, top5_acc: 0.6898, loss_cls: 3.1738, loss: 3.1738 +2024-07-21 05:24:32,463 - pyskl - INFO - Epoch [132][3400/3746] lr: 3.547e-03, eta: 15:30:40, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4378, top5_acc: 0.6933, loss_cls: 3.1527, loss: 3.1527 +2024-07-21 05:25:54,171 - pyskl - INFO - Epoch [132][3500/3746] lr: 3.537e-03, eta: 15:29:17, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4467, top5_acc: 0.6966, loss_cls: 3.1411, loss: 3.1411 +2024-07-21 05:27:16,076 - pyskl - INFO - Epoch [132][3600/3746] lr: 3.526e-03, eta: 15:27:55, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4428, top5_acc: 0.6984, loss_cls: 3.1302, loss: 3.1302 +2024-07-21 05:28:38,541 - pyskl - INFO - Epoch [132][3700/3746] lr: 3.516e-03, eta: 15:26:33, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4436, top5_acc: 0.6942, loss_cls: 3.1500, loss: 3.1500 +2024-07-21 05:29:18,812 - pyskl - INFO - Saving checkpoint at 132 epochs +2024-07-21 05:31:10,619 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 05:31:11,294 - pyskl - INFO - +top1_acc 0.3622 +top5_acc 0.6166 +2024-07-21 05:31:11,294 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 05:31:11,339 - pyskl - INFO - +mean_acc 0.3620 +2024-07-21 05:31:11,344 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_131.pth was removed +2024-07-21 05:31:11,604 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_132.pth. +2024-07-21 05:31:11,605 - pyskl - INFO - Best top1_acc is 0.3622 at 132 epoch. +2024-07-21 05:31:11,622 - pyskl - INFO - Epoch(val) [132][309] top1_acc: 0.3622, top5_acc: 0.6166, mean_class_accuracy: 0.3620 +2024-07-21 05:35:05,168 - pyskl - INFO - Epoch [133][100/3746] lr: 3.501e-03, eta: 15:24:48, time: 2.335, data_time: 1.324, memory: 15990, top1_acc: 0.4594, top5_acc: 0.7139, loss_cls: 3.0367, loss: 3.0367 +2024-07-21 05:36:27,424 - pyskl - INFO - Epoch [133][200/3746] lr: 3.491e-03, eta: 15:23:25, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4467, top5_acc: 0.7130, loss_cls: 3.0731, loss: 3.0731 +2024-07-21 05:37:49,881 - pyskl - INFO - Epoch [133][300/3746] lr: 3.480e-03, eta: 15:22:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4633, top5_acc: 0.7180, loss_cls: 3.0049, loss: 3.0049 +2024-07-21 05:39:12,372 - pyskl - INFO - Epoch [133][400/3746] lr: 3.470e-03, eta: 15:20:40, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4644, top5_acc: 0.7128, loss_cls: 2.9973, loss: 2.9973 +2024-07-21 05:40:34,761 - pyskl - INFO - Epoch [133][500/3746] lr: 3.460e-03, eta: 15:19:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4425, top5_acc: 0.7086, loss_cls: 3.0502, loss: 3.0502 +2024-07-21 05:41:56,973 - pyskl - INFO - Epoch [133][600/3746] lr: 3.450e-03, eta: 15:17:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4506, top5_acc: 0.7098, loss_cls: 3.0694, loss: 3.0694 +2024-07-21 05:43:19,485 - pyskl - INFO - Epoch [133][700/3746] lr: 3.440e-03, eta: 15:16:33, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4567, top5_acc: 0.7073, loss_cls: 3.0732, loss: 3.0732 +2024-07-21 05:44:41,458 - pyskl - INFO - Epoch [133][800/3746] lr: 3.429e-03, eta: 15:15:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4497, top5_acc: 0.7084, loss_cls: 3.0653, loss: 3.0653 +2024-07-21 05:46:03,210 - pyskl - INFO - Epoch [133][900/3746] lr: 3.419e-03, eta: 15:13:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4441, top5_acc: 0.7042, loss_cls: 3.0975, loss: 3.0975 +2024-07-21 05:47:25,044 - pyskl - INFO - Epoch [133][1000/3746] lr: 3.409e-03, eta: 15:12:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4550, top5_acc: 0.7087, loss_cls: 3.0526, loss: 3.0526 +2024-07-21 05:48:47,260 - pyskl - INFO - Epoch [133][1100/3746] lr: 3.399e-03, eta: 15:11:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4486, top5_acc: 0.7030, loss_cls: 3.0722, loss: 3.0722 +2024-07-21 05:50:08,937 - pyskl - INFO - Epoch [133][1200/3746] lr: 3.389e-03, eta: 15:09:41, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4539, top5_acc: 0.7055, loss_cls: 3.0532, loss: 3.0532 +2024-07-21 05:51:31,305 - pyskl - INFO - Epoch [133][1300/3746] lr: 3.379e-03, eta: 15:08:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4472, top5_acc: 0.7095, loss_cls: 3.0870, loss: 3.0870 +2024-07-21 05:52:53,271 - pyskl - INFO - Epoch [133][1400/3746] lr: 3.369e-03, eta: 15:06:56, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4539, top5_acc: 0.7122, loss_cls: 3.0458, loss: 3.0458 +2024-07-21 05:54:15,828 - pyskl - INFO - Epoch [133][1500/3746] lr: 3.359e-03, eta: 15:05:34, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4467, top5_acc: 0.6987, loss_cls: 3.0976, loss: 3.0976 +2024-07-21 05:55:38,429 - pyskl - INFO - Epoch [133][1600/3746] lr: 3.348e-03, eta: 15:04:11, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4623, top5_acc: 0.7064, loss_cls: 3.0643, loss: 3.0643 +2024-07-21 05:57:00,644 - pyskl - INFO - Epoch [133][1700/3746] lr: 3.338e-03, eta: 15:02:49, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4502, top5_acc: 0.7097, loss_cls: 3.0526, loss: 3.0526 +2024-07-21 05:58:22,456 - pyskl - INFO - Epoch [133][1800/3746] lr: 3.328e-03, eta: 15:01:26, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4416, top5_acc: 0.7011, loss_cls: 3.1139, loss: 3.1139 +2024-07-21 05:59:44,263 - pyskl - INFO - Epoch [133][1900/3746] lr: 3.318e-03, eta: 15:00:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4569, top5_acc: 0.7020, loss_cls: 3.0613, loss: 3.0613 +2024-07-21 06:01:06,525 - pyskl - INFO - Epoch [133][2000/3746] lr: 3.308e-03, eta: 14:58:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4567, top5_acc: 0.7109, loss_cls: 3.0494, loss: 3.0494 +2024-07-21 06:02:28,691 - pyskl - INFO - Epoch [133][2100/3746] lr: 3.298e-03, eta: 14:57:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4445, top5_acc: 0.7039, loss_cls: 3.1265, loss: 3.1265 +2024-07-21 06:03:51,269 - pyskl - INFO - Epoch [133][2200/3746] lr: 3.288e-03, eta: 14:55:56, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4420, top5_acc: 0.7072, loss_cls: 3.0951, loss: 3.0951 +2024-07-21 06:05:13,560 - pyskl - INFO - Epoch [133][2300/3746] lr: 3.278e-03, eta: 14:54:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4392, top5_acc: 0.6931, loss_cls: 3.1198, loss: 3.1198 +2024-07-21 06:06:36,063 - pyskl - INFO - Epoch [133][2400/3746] lr: 3.268e-03, eta: 14:53:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4577, top5_acc: 0.7163, loss_cls: 3.0355, loss: 3.0355 +2024-07-21 06:07:58,484 - pyskl - INFO - Epoch [133][2500/3746] lr: 3.259e-03, eta: 14:51:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4531, top5_acc: 0.7105, loss_cls: 3.0643, loss: 3.0643 +2024-07-21 06:09:20,509 - pyskl - INFO - Epoch [133][2600/3746] lr: 3.249e-03, eta: 14:50:27, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4514, top5_acc: 0.7081, loss_cls: 3.0770, loss: 3.0770 +2024-07-21 06:10:42,519 - pyskl - INFO - Epoch [133][2700/3746] lr: 3.239e-03, eta: 14:49:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4505, top5_acc: 0.7083, loss_cls: 3.0868, loss: 3.0868 +2024-07-21 06:12:04,529 - pyskl - INFO - Epoch [133][2800/3746] lr: 3.229e-03, eta: 14:47:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4447, top5_acc: 0.6956, loss_cls: 3.1308, loss: 3.1308 +2024-07-21 06:13:26,408 - pyskl - INFO - Epoch [133][2900/3746] lr: 3.219e-03, eta: 14:46:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4614, top5_acc: 0.7061, loss_cls: 3.0466, loss: 3.0466 +2024-07-21 06:14:48,470 - pyskl - INFO - Epoch [133][3000/3746] lr: 3.209e-03, eta: 14:44:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4477, top5_acc: 0.7019, loss_cls: 3.0708, loss: 3.0708 +2024-07-21 06:16:10,286 - pyskl - INFO - Epoch [133][3100/3746] lr: 3.199e-03, eta: 14:43:34, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4456, top5_acc: 0.7092, loss_cls: 3.0732, loss: 3.0732 +2024-07-21 06:17:32,205 - pyskl - INFO - Epoch [133][3200/3746] lr: 3.189e-03, eta: 14:42:12, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4423, top5_acc: 0.6970, loss_cls: 3.1136, loss: 3.1136 +2024-07-21 06:18:54,798 - pyskl - INFO - Epoch [133][3300/3746] lr: 3.180e-03, eta: 14:40:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4514, top5_acc: 0.7013, loss_cls: 3.0948, loss: 3.0948 +2024-07-21 06:20:16,389 - pyskl - INFO - Epoch [133][3400/3746] lr: 3.170e-03, eta: 14:39:27, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4542, top5_acc: 0.6950, loss_cls: 3.1082, loss: 3.1082 +2024-07-21 06:21:38,580 - pyskl - INFO - Epoch [133][3500/3746] lr: 3.160e-03, eta: 14:38:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4336, top5_acc: 0.6944, loss_cls: 3.1402, loss: 3.1402 +2024-07-21 06:23:01,187 - pyskl - INFO - Epoch [133][3600/3746] lr: 3.150e-03, eta: 14:36:42, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4462, top5_acc: 0.7078, loss_cls: 3.1018, loss: 3.1018 +2024-07-21 06:24:23,371 - pyskl - INFO - Epoch [133][3700/3746] lr: 3.140e-03, eta: 14:35:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4530, top5_acc: 0.7034, loss_cls: 3.0788, loss: 3.0788 +2024-07-21 06:25:03,172 - pyskl - INFO - Saving checkpoint at 133 epochs +2024-07-21 06:26:54,556 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 06:26:55,270 - pyskl - INFO - +top1_acc 0.3686 +top5_acc 0.6145 +2024-07-21 06:26:55,270 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 06:26:55,314 - pyskl - INFO - +mean_acc 0.3683 +2024-07-21 06:26:55,319 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_132.pth was removed +2024-07-21 06:26:55,591 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_133.pth. +2024-07-21 06:26:55,591 - pyskl - INFO - Best top1_acc is 0.3686 at 133 epoch. +2024-07-21 06:26:55,605 - pyskl - INFO - Epoch(val) [133][309] top1_acc: 0.3686, top5_acc: 0.6145, mean_class_accuracy: 0.3683 +2024-07-21 06:30:48,222 - pyskl - INFO - Epoch [134][100/3746] lr: 3.126e-03, eta: 14:33:34, time: 2.326, data_time: 1.333, memory: 15990, top1_acc: 0.4625, top5_acc: 0.7250, loss_cls: 2.9667, loss: 2.9667 +2024-07-21 06:32:11,383 - pyskl - INFO - Epoch [134][200/3746] lr: 3.117e-03, eta: 14:32:11, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4702, top5_acc: 0.7163, loss_cls: 2.9898, loss: 2.9898 +2024-07-21 06:33:34,240 - pyskl - INFO - Epoch [134][300/3746] lr: 3.107e-03, eta: 14:30:49, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4589, top5_acc: 0.7125, loss_cls: 3.0367, loss: 3.0367 +2024-07-21 06:34:56,181 - pyskl - INFO - Epoch [134][400/3746] lr: 3.097e-03, eta: 14:29:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4636, top5_acc: 0.7273, loss_cls: 2.9503, loss: 2.9503 +2024-07-21 06:36:18,907 - pyskl - INFO - Epoch [134][500/3746] lr: 3.087e-03, eta: 14:28:04, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4611, top5_acc: 0.7113, loss_cls: 3.0066, loss: 3.0066 +2024-07-21 06:37:41,222 - pyskl - INFO - Epoch [134][600/3746] lr: 3.078e-03, eta: 14:26:42, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4755, top5_acc: 0.7252, loss_cls: 2.9682, loss: 2.9682 +2024-07-21 06:39:04,165 - pyskl - INFO - Epoch [134][700/3746] lr: 3.068e-03, eta: 14:25:19, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4603, top5_acc: 0.7206, loss_cls: 3.0217, loss: 3.0217 +2024-07-21 06:40:26,178 - pyskl - INFO - Epoch [134][800/3746] lr: 3.059e-03, eta: 14:23:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4553, top5_acc: 0.7064, loss_cls: 3.0711, loss: 3.0711 +2024-07-21 06:41:48,349 - pyskl - INFO - Epoch [134][900/3746] lr: 3.049e-03, eta: 14:22:34, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4548, top5_acc: 0.7095, loss_cls: 3.0193, loss: 3.0193 +2024-07-21 06:43:10,153 - pyskl - INFO - Epoch [134][1000/3746] lr: 3.039e-03, eta: 14:21:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4609, top5_acc: 0.7152, loss_cls: 3.0167, loss: 3.0167 +2024-07-21 06:44:32,508 - pyskl - INFO - Epoch [134][1100/3746] lr: 3.030e-03, eta: 14:19:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4512, top5_acc: 0.7103, loss_cls: 3.0521, loss: 3.0521 +2024-07-21 06:45:54,902 - pyskl - INFO - Epoch [134][1200/3746] lr: 3.020e-03, eta: 14:18:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4567, top5_acc: 0.7105, loss_cls: 3.0597, loss: 3.0597 +2024-07-21 06:47:16,859 - pyskl - INFO - Epoch [134][1300/3746] lr: 3.011e-03, eta: 14:17:04, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4659, top5_acc: 0.7234, loss_cls: 2.9623, loss: 2.9623 +2024-07-21 06:48:38,884 - pyskl - INFO - Epoch [134][1400/3746] lr: 3.001e-03, eta: 14:15:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4769, top5_acc: 0.7275, loss_cls: 2.9473, loss: 2.9473 +2024-07-21 06:50:00,850 - pyskl - INFO - Epoch [134][1500/3746] lr: 2.991e-03, eta: 14:14:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7037, loss_cls: 3.0635, loss: 3.0635 +2024-07-21 06:51:23,607 - pyskl - INFO - Epoch [134][1600/3746] lr: 2.982e-03, eta: 14:12:57, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4641, top5_acc: 0.7153, loss_cls: 3.0493, loss: 3.0493 +2024-07-21 06:52:46,162 - pyskl - INFO - Epoch [134][1700/3746] lr: 2.972e-03, eta: 14:11:35, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4573, top5_acc: 0.7111, loss_cls: 3.0304, loss: 3.0304 +2024-07-21 06:54:08,479 - pyskl - INFO - Epoch [134][1800/3746] lr: 2.963e-03, eta: 14:10:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4592, top5_acc: 0.7127, loss_cls: 3.0203, loss: 3.0203 +2024-07-21 06:55:30,219 - pyskl - INFO - Epoch [134][1900/3746] lr: 2.953e-03, eta: 14:08:50, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4586, top5_acc: 0.7091, loss_cls: 3.0379, loss: 3.0379 +2024-07-21 06:56:52,365 - pyskl - INFO - Epoch [134][2000/3746] lr: 2.944e-03, eta: 14:07:27, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.7127, loss_cls: 3.0523, loss: 3.0523 +2024-07-21 06:58:15,197 - pyskl - INFO - Epoch [134][2100/3746] lr: 2.935e-03, eta: 14:06:05, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4684, top5_acc: 0.7133, loss_cls: 3.0075, loss: 3.0075 +2024-07-21 06:59:37,885 - pyskl - INFO - Epoch [134][2200/3746] lr: 2.925e-03, eta: 14:04:42, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4645, top5_acc: 0.7133, loss_cls: 3.0210, loss: 3.0210 +2024-07-21 07:01:00,276 - pyskl - INFO - Epoch [134][2300/3746] lr: 2.916e-03, eta: 14:03:20, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4383, top5_acc: 0.6958, loss_cls: 3.1370, loss: 3.1370 +2024-07-21 07:02:22,239 - pyskl - INFO - Epoch [134][2400/3746] lr: 2.906e-03, eta: 14:01:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4620, top5_acc: 0.7130, loss_cls: 3.0441, loss: 3.0441 +2024-07-21 07:03:44,262 - pyskl - INFO - Epoch [134][2500/3746] lr: 2.897e-03, eta: 14:00:35, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4516, top5_acc: 0.7045, loss_cls: 3.0904, loss: 3.0904 +2024-07-21 07:05:06,360 - pyskl - INFO - Epoch [134][2600/3746] lr: 2.888e-03, eta: 13:59:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4639, top5_acc: 0.7131, loss_cls: 3.0479, loss: 3.0479 +2024-07-21 07:06:28,719 - pyskl - INFO - Epoch [134][2700/3746] lr: 2.878e-03, eta: 13:57:50, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4473, top5_acc: 0.7097, loss_cls: 3.0823, loss: 3.0823 +2024-07-21 07:07:50,647 - pyskl - INFO - Epoch [134][2800/3746] lr: 2.869e-03, eta: 13:56:28, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4525, top5_acc: 0.7098, loss_cls: 3.0280, loss: 3.0280 +2024-07-21 07:09:14,263 - pyskl - INFO - Epoch [134][2900/3746] lr: 2.860e-03, eta: 13:55:05, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4425, top5_acc: 0.6992, loss_cls: 3.0930, loss: 3.0930 +2024-07-21 07:10:35,936 - pyskl - INFO - Epoch [134][3000/3746] lr: 2.850e-03, eta: 13:53:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4403, top5_acc: 0.6923, loss_cls: 3.1611, loss: 3.1611 +2024-07-21 07:11:58,738 - pyskl - INFO - Epoch [134][3100/3746] lr: 2.841e-03, eta: 13:52:20, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4487, top5_acc: 0.7084, loss_cls: 3.0501, loss: 3.0501 +2024-07-21 07:13:21,644 - pyskl - INFO - Epoch [134][3200/3746] lr: 2.832e-03, eta: 13:50:58, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4597, top5_acc: 0.7142, loss_cls: 3.0164, loss: 3.0164 +2024-07-21 07:14:43,874 - pyskl - INFO - Epoch [134][3300/3746] lr: 2.822e-03, eta: 13:49:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4478, top5_acc: 0.7073, loss_cls: 3.0876, loss: 3.0876 +2024-07-21 07:16:05,511 - pyskl - INFO - Epoch [134][3400/3746] lr: 2.813e-03, eta: 13:48:13, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4533, top5_acc: 0.6984, loss_cls: 3.0772, loss: 3.0772 +2024-07-21 07:17:27,452 - pyskl - INFO - Epoch [134][3500/3746] lr: 2.804e-03, eta: 13:46:50, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4564, top5_acc: 0.7086, loss_cls: 3.0584, loss: 3.0584 +2024-07-21 07:18:48,997 - pyskl - INFO - Epoch [134][3600/3746] lr: 2.795e-03, eta: 13:45:28, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4606, top5_acc: 0.7159, loss_cls: 3.0057, loss: 3.0057 +2024-07-21 07:20:11,002 - pyskl - INFO - Epoch [134][3700/3746] lr: 2.786e-03, eta: 13:44:05, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4583, top5_acc: 0.7114, loss_cls: 3.0194, loss: 3.0194 +2024-07-21 07:20:50,850 - pyskl - INFO - Saving checkpoint at 134 epochs +2024-07-21 07:22:42,175 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 07:22:42,871 - pyskl - INFO - +top1_acc 0.3683 +top5_acc 0.6220 +2024-07-21 07:22:42,871 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 07:22:42,914 - pyskl - INFO - +mean_acc 0.3681 +2024-07-21 07:22:42,927 - pyskl - INFO - Epoch(val) [134][309] top1_acc: 0.3683, top5_acc: 0.6220, mean_class_accuracy: 0.3681 +2024-07-21 07:26:32,606 - pyskl - INFO - Epoch [135][100/3746] lr: 2.772e-03, eta: 13:42:18, time: 2.297, data_time: 1.298, memory: 15990, top1_acc: 0.4797, top5_acc: 0.7419, loss_cls: 2.8913, loss: 2.8913 +2024-07-21 07:27:54,460 - pyskl - INFO - Epoch [135][200/3746] lr: 2.763e-03, eta: 13:40:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4705, top5_acc: 0.7256, loss_cls: 2.9489, loss: 2.9489 +2024-07-21 07:29:16,165 - pyskl - INFO - Epoch [135][300/3746] lr: 2.754e-03, eta: 13:39:33, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4702, top5_acc: 0.7233, loss_cls: 2.9785, loss: 2.9785 +2024-07-21 07:30:38,737 - pyskl - INFO - Epoch [135][400/3746] lr: 2.745e-03, eta: 13:38:11, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4612, top5_acc: 0.7205, loss_cls: 2.9831, loss: 2.9831 +2024-07-21 07:32:01,397 - pyskl - INFO - Epoch [135][500/3746] lr: 2.735e-03, eta: 13:36:48, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4666, top5_acc: 0.7108, loss_cls: 3.0085, loss: 3.0085 +2024-07-21 07:33:24,343 - pyskl - INFO - Epoch [135][600/3746] lr: 2.726e-03, eta: 13:35:26, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4736, top5_acc: 0.7233, loss_cls: 2.9357, loss: 2.9357 +2024-07-21 07:34:46,842 - pyskl - INFO - Epoch [135][700/3746] lr: 2.717e-03, eta: 13:34:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4714, top5_acc: 0.7328, loss_cls: 2.9312, loss: 2.9312 +2024-07-21 07:36:09,020 - pyskl - INFO - Epoch [135][800/3746] lr: 2.708e-03, eta: 13:32:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4567, top5_acc: 0.7169, loss_cls: 3.0285, loss: 3.0285 +2024-07-21 07:37:31,354 - pyskl - INFO - Epoch [135][900/3746] lr: 2.699e-03, eta: 13:31:18, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4655, top5_acc: 0.7170, loss_cls: 2.9876, loss: 2.9876 +2024-07-21 07:38:53,420 - pyskl - INFO - Epoch [135][1000/3746] lr: 2.690e-03, eta: 13:29:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4620, top5_acc: 0.7238, loss_cls: 2.9728, loss: 2.9728 +2024-07-21 07:40:15,874 - pyskl - INFO - Epoch [135][1100/3746] lr: 2.681e-03, eta: 13:28:33, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4706, top5_acc: 0.7234, loss_cls: 2.9492, loss: 2.9492 +2024-07-21 07:41:37,948 - pyskl - INFO - Epoch [135][1200/3746] lr: 2.672e-03, eta: 13:27:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4650, top5_acc: 0.7183, loss_cls: 3.0026, loss: 3.0026 +2024-07-21 07:42:59,895 - pyskl - INFO - Epoch [135][1300/3746] lr: 2.663e-03, eta: 13:25:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4672, top5_acc: 0.7175, loss_cls: 3.0022, loss: 3.0022 +2024-07-21 07:44:21,833 - pyskl - INFO - Epoch [135][1400/3746] lr: 2.654e-03, eta: 13:24:26, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4559, top5_acc: 0.7144, loss_cls: 3.0246, loss: 3.0246 +2024-07-21 07:45:43,867 - pyskl - INFO - Epoch [135][1500/3746] lr: 2.645e-03, eta: 13:23:03, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4689, top5_acc: 0.7192, loss_cls: 2.9755, loss: 2.9755 +2024-07-21 07:47:05,999 - pyskl - INFO - Epoch [135][1600/3746] lr: 2.636e-03, eta: 13:21:41, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4620, top5_acc: 0.7145, loss_cls: 3.0091, loss: 3.0091 +2024-07-21 07:48:27,865 - pyskl - INFO - Epoch [135][1700/3746] lr: 2.627e-03, eta: 13:20:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4662, top5_acc: 0.7175, loss_cls: 2.9895, loss: 2.9895 +2024-07-21 07:49:50,209 - pyskl - INFO - Epoch [135][1800/3746] lr: 2.618e-03, eta: 13:18:56, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4633, top5_acc: 0.7183, loss_cls: 3.0198, loss: 3.0198 +2024-07-21 07:51:12,621 - pyskl - INFO - Epoch [135][1900/3746] lr: 2.609e-03, eta: 13:17:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4662, top5_acc: 0.7202, loss_cls: 2.9827, loss: 2.9827 +2024-07-21 07:52:34,481 - pyskl - INFO - Epoch [135][2000/3746] lr: 2.600e-03, eta: 13:16:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4786, top5_acc: 0.7247, loss_cls: 2.9602, loss: 2.9602 +2024-07-21 07:53:56,697 - pyskl - INFO - Epoch [135][2100/3746] lr: 2.591e-03, eta: 13:14:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4594, top5_acc: 0.7081, loss_cls: 3.0550, loss: 3.0550 +2024-07-21 07:55:19,482 - pyskl - INFO - Epoch [135][2200/3746] lr: 2.583e-03, eta: 13:13:26, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4623, top5_acc: 0.7217, loss_cls: 2.9951, loss: 2.9951 +2024-07-21 07:56:41,695 - pyskl - INFO - Epoch [135][2300/3746] lr: 2.574e-03, eta: 13:12:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4631, top5_acc: 0.7147, loss_cls: 2.9990, loss: 2.9990 +2024-07-21 07:58:04,402 - pyskl - INFO - Epoch [135][2400/3746] lr: 2.565e-03, eta: 13:10:41, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4678, top5_acc: 0.7200, loss_cls: 2.9777, loss: 2.9777 +2024-07-21 07:59:26,685 - pyskl - INFO - Epoch [135][2500/3746] lr: 2.556e-03, eta: 13:09:19, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4661, top5_acc: 0.7211, loss_cls: 3.0017, loss: 3.0017 +2024-07-21 08:00:49,437 - pyskl - INFO - Epoch [135][2600/3746] lr: 2.547e-03, eta: 13:07:56, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4589, top5_acc: 0.7134, loss_cls: 3.0348, loss: 3.0348 +2024-07-21 08:02:11,374 - pyskl - INFO - Epoch [135][2700/3746] lr: 2.538e-03, eta: 13:06:34, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4686, top5_acc: 0.7159, loss_cls: 2.9878, loss: 2.9878 +2024-07-21 08:03:33,696 - pyskl - INFO - Epoch [135][2800/3746] lr: 2.530e-03, eta: 13:05:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4750, top5_acc: 0.7228, loss_cls: 2.9595, loss: 2.9595 +2024-07-21 08:04:56,228 - pyskl - INFO - Epoch [135][2900/3746] lr: 2.521e-03, eta: 13:03:49, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4705, top5_acc: 0.7163, loss_cls: 3.0020, loss: 3.0020 +2024-07-21 08:06:18,523 - pyskl - INFO - Epoch [135][3000/3746] lr: 2.512e-03, eta: 13:02:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4650, top5_acc: 0.7144, loss_cls: 3.0045, loss: 3.0045 +2024-07-21 08:07:40,722 - pyskl - INFO - Epoch [135][3100/3746] lr: 2.503e-03, eta: 13:01:04, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4636, top5_acc: 0.7156, loss_cls: 3.0152, loss: 3.0152 +2024-07-21 08:09:02,668 - pyskl - INFO - Epoch [135][3200/3746] lr: 2.495e-03, eta: 12:59:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4673, top5_acc: 0.7189, loss_cls: 2.9911, loss: 2.9911 +2024-07-21 08:10:24,704 - pyskl - INFO - Epoch [135][3300/3746] lr: 2.486e-03, eta: 12:58:19, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4559, top5_acc: 0.7075, loss_cls: 3.0295, loss: 3.0295 +2024-07-21 08:11:46,952 - pyskl - INFO - Epoch [135][3400/3746] lr: 2.477e-03, eta: 12:56:56, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4634, top5_acc: 0.7155, loss_cls: 2.9996, loss: 2.9996 +2024-07-21 08:13:08,622 - pyskl - INFO - Epoch [135][3500/3746] lr: 2.469e-03, eta: 12:55:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4566, top5_acc: 0.7184, loss_cls: 3.0178, loss: 3.0178 +2024-07-21 08:14:30,929 - pyskl - INFO - Epoch [135][3600/3746] lr: 2.460e-03, eta: 12:54:11, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4650, top5_acc: 0.7164, loss_cls: 2.9947, loss: 2.9947 +2024-07-21 08:15:52,752 - pyskl - INFO - Epoch [135][3700/3746] lr: 2.451e-03, eta: 12:52:49, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4592, top5_acc: 0.7083, loss_cls: 3.0157, loss: 3.0157 +2024-07-21 08:16:32,477 - pyskl - INFO - Saving checkpoint at 135 epochs +2024-07-21 08:18:24,375 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 08:18:25,028 - pyskl - INFO - +top1_acc 0.3721 +top5_acc 0.6176 +2024-07-21 08:18:25,028 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 08:18:25,070 - pyskl - INFO - +mean_acc 0.3718 +2024-07-21 08:18:25,075 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_133.pth was removed +2024-07-21 08:18:25,334 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_135.pth. +2024-07-21 08:18:25,335 - pyskl - INFO - Best top1_acc is 0.3721 at 135 epoch. +2024-07-21 08:18:25,347 - pyskl - INFO - Epoch(val) [135][309] top1_acc: 0.3721, top5_acc: 0.6176, mean_class_accuracy: 0.3718 +2024-07-21 08:22:16,644 - pyskl - INFO - Epoch [136][100/3746] lr: 2.439e-03, eta: 12:51:01, time: 2.313, data_time: 1.309, memory: 15990, top1_acc: 0.4823, top5_acc: 0.7403, loss_cls: 2.8695, loss: 2.8695 +2024-07-21 08:23:39,125 - pyskl - INFO - Epoch [136][200/3746] lr: 2.430e-03, eta: 12:49:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4833, top5_acc: 0.7316, loss_cls: 2.8757, loss: 2.8757 +2024-07-21 08:25:01,228 - pyskl - INFO - Epoch [136][300/3746] lr: 2.421e-03, eta: 12:48:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4886, top5_acc: 0.7330, loss_cls: 2.8740, loss: 2.8740 +2024-07-21 08:26:24,271 - pyskl - INFO - Epoch [136][400/3746] lr: 2.413e-03, eta: 12:46:53, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.4736, top5_acc: 0.7239, loss_cls: 2.9577, loss: 2.9577 +2024-07-21 08:27:46,270 - pyskl - INFO - Epoch [136][500/3746] lr: 2.404e-03, eta: 12:45:31, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4850, top5_acc: 0.7297, loss_cls: 2.8948, loss: 2.8948 +2024-07-21 08:29:09,009 - pyskl - INFO - Epoch [136][600/3746] lr: 2.396e-03, eta: 12:44:08, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4809, top5_acc: 0.7309, loss_cls: 2.9239, loss: 2.9239 +2024-07-21 08:30:31,573 - pyskl - INFO - Epoch [136][700/3746] lr: 2.387e-03, eta: 12:42:46, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4725, top5_acc: 0.7309, loss_cls: 2.9448, loss: 2.9448 +2024-07-21 08:31:53,958 - pyskl - INFO - Epoch [136][800/3746] lr: 2.379e-03, eta: 12:41:23, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4759, top5_acc: 0.7281, loss_cls: 2.9180, loss: 2.9180 +2024-07-21 08:33:15,599 - pyskl - INFO - Epoch [136][900/3746] lr: 2.370e-03, eta: 12:40:01, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4723, top5_acc: 0.7302, loss_cls: 2.9357, loss: 2.9357 +2024-07-21 08:34:37,396 - pyskl - INFO - Epoch [136][1000/3746] lr: 2.362e-03, eta: 12:38:38, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4731, top5_acc: 0.7250, loss_cls: 2.9227, loss: 2.9227 +2024-07-21 08:35:59,575 - pyskl - INFO - Epoch [136][1100/3746] lr: 2.353e-03, eta: 12:37:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4741, top5_acc: 0.7281, loss_cls: 2.9266, loss: 2.9266 +2024-07-21 08:37:21,138 - pyskl - INFO - Epoch [136][1200/3746] lr: 2.345e-03, eta: 12:35:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4713, top5_acc: 0.7245, loss_cls: 2.9703, loss: 2.9703 +2024-07-21 08:38:43,572 - pyskl - INFO - Epoch [136][1300/3746] lr: 2.336e-03, eta: 12:34:31, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4772, top5_acc: 0.7327, loss_cls: 2.9151, loss: 2.9151 +2024-07-21 08:40:05,520 - pyskl - INFO - Epoch [136][1400/3746] lr: 2.328e-03, eta: 12:33:08, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4733, top5_acc: 0.7298, loss_cls: 2.9498, loss: 2.9498 +2024-07-21 08:41:27,838 - pyskl - INFO - Epoch [136][1500/3746] lr: 2.319e-03, eta: 12:31:46, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4789, top5_acc: 0.7262, loss_cls: 2.9333, loss: 2.9333 +2024-07-21 08:42:50,347 - pyskl - INFO - Epoch [136][1600/3746] lr: 2.311e-03, eta: 12:30:23, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4555, top5_acc: 0.7084, loss_cls: 3.0253, loss: 3.0253 +2024-07-21 08:44:12,637 - pyskl - INFO - Epoch [136][1700/3746] lr: 2.303e-03, eta: 12:29:01, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4766, top5_acc: 0.7266, loss_cls: 2.9607, loss: 2.9607 +2024-07-21 08:45:34,841 - pyskl - INFO - Epoch [136][1800/3746] lr: 2.294e-03, eta: 12:27:38, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4713, top5_acc: 0.7211, loss_cls: 2.9844, loss: 2.9844 +2024-07-21 08:46:57,302 - pyskl - INFO - Epoch [136][1900/3746] lr: 2.286e-03, eta: 12:26:16, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4647, top5_acc: 0.7180, loss_cls: 2.9903, loss: 2.9903 +2024-07-21 08:48:19,398 - pyskl - INFO - Epoch [136][2000/3746] lr: 2.277e-03, eta: 12:24:53, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4686, top5_acc: 0.7202, loss_cls: 2.9557, loss: 2.9557 +2024-07-21 08:49:41,617 - pyskl - INFO - Epoch [136][2100/3746] lr: 2.269e-03, eta: 12:23:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4783, top5_acc: 0.7308, loss_cls: 2.9232, loss: 2.9232 +2024-07-21 08:51:04,312 - pyskl - INFO - Epoch [136][2200/3746] lr: 2.261e-03, eta: 12:22:08, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4748, top5_acc: 0.7244, loss_cls: 2.9387, loss: 2.9387 +2024-07-21 08:52:26,974 - pyskl - INFO - Epoch [136][2300/3746] lr: 2.253e-03, eta: 12:20:46, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4700, top5_acc: 0.7217, loss_cls: 2.9549, loss: 2.9549 +2024-07-21 08:53:49,005 - pyskl - INFO - Epoch [136][2400/3746] lr: 2.244e-03, eta: 12:19:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4713, top5_acc: 0.7244, loss_cls: 2.9489, loss: 2.9489 +2024-07-21 08:55:10,846 - pyskl - INFO - Epoch [136][2500/3746] lr: 2.236e-03, eta: 12:18:01, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4730, top5_acc: 0.7227, loss_cls: 2.9857, loss: 2.9857 +2024-07-21 08:56:32,475 - pyskl - INFO - Epoch [136][2600/3746] lr: 2.228e-03, eta: 12:16:38, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4725, top5_acc: 0.7242, loss_cls: 2.9390, loss: 2.9390 +2024-07-21 08:57:54,248 - pyskl - INFO - Epoch [136][2700/3746] lr: 2.219e-03, eta: 12:15:16, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4722, top5_acc: 0.7281, loss_cls: 2.9507, loss: 2.9507 +2024-07-21 08:59:15,953 - pyskl - INFO - Epoch [136][2800/3746] lr: 2.211e-03, eta: 12:13:53, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4686, top5_acc: 0.7188, loss_cls: 2.9983, loss: 2.9983 +2024-07-21 09:00:38,104 - pyskl - INFO - Epoch [136][2900/3746] lr: 2.203e-03, eta: 12:12:30, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4622, top5_acc: 0.7188, loss_cls: 2.9830, loss: 2.9830 +2024-07-21 09:02:00,140 - pyskl - INFO - Epoch [136][3000/3746] lr: 2.195e-03, eta: 12:11:08, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4770, top5_acc: 0.7303, loss_cls: 2.9147, loss: 2.9147 +2024-07-21 09:03:22,717 - pyskl - INFO - Epoch [136][3100/3746] lr: 2.187e-03, eta: 12:09:45, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4763, top5_acc: 0.7262, loss_cls: 2.9478, loss: 2.9478 +2024-07-21 09:04:44,309 - pyskl - INFO - Epoch [136][3200/3746] lr: 2.178e-03, eta: 12:08:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4795, top5_acc: 0.7291, loss_cls: 2.9459, loss: 2.9459 +2024-07-21 09:06:06,078 - pyskl - INFO - Epoch [136][3300/3746] lr: 2.170e-03, eta: 12:07:00, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4741, top5_acc: 0.7208, loss_cls: 2.9589, loss: 2.9589 +2024-07-21 09:07:28,331 - pyskl - INFO - Epoch [136][3400/3746] lr: 2.162e-03, eta: 12:05:38, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4644, top5_acc: 0.7183, loss_cls: 2.9884, loss: 2.9884 +2024-07-21 09:08:51,149 - pyskl - INFO - Epoch [136][3500/3746] lr: 2.154e-03, eta: 12:04:15, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4706, top5_acc: 0.7148, loss_cls: 3.0118, loss: 3.0118 +2024-07-21 09:10:13,069 - pyskl - INFO - Epoch [136][3600/3746] lr: 2.146e-03, eta: 12:02:53, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4694, top5_acc: 0.7202, loss_cls: 2.9636, loss: 2.9636 +2024-07-21 09:11:34,884 - pyskl - INFO - Epoch [136][3700/3746] lr: 2.138e-03, eta: 12:01:30, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4619, top5_acc: 0.7202, loss_cls: 2.9842, loss: 2.9842 +2024-07-21 09:12:14,609 - pyskl - INFO - Saving checkpoint at 136 epochs +2024-07-21 09:14:07,360 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 09:14:08,017 - pyskl - INFO - +top1_acc 0.3781 +top5_acc 0.6296 +2024-07-21 09:14:08,017 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 09:14:08,059 - pyskl - INFO - +mean_acc 0.3778 +2024-07-21 09:14:08,064 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_135.pth was removed +2024-07-21 09:14:08,313 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_136.pth. +2024-07-21 09:14:08,314 - pyskl - INFO - Best top1_acc is 0.3781 at 136 epoch. +2024-07-21 09:14:08,326 - pyskl - INFO - Epoch(val) [136][309] top1_acc: 0.3781, top5_acc: 0.6296, mean_class_accuracy: 0.3778 +2024-07-21 09:17:55,120 - pyskl - INFO - Epoch [137][100/3746] lr: 2.126e-03, eta: 11:59:41, time: 2.268, data_time: 1.285, memory: 15990, top1_acc: 0.4944, top5_acc: 0.7461, loss_cls: 2.8155, loss: 2.8155 +2024-07-21 09:19:17,108 - pyskl - INFO - Epoch [137][200/3746] lr: 2.118e-03, eta: 11:58:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4963, top5_acc: 0.7392, loss_cls: 2.8546, loss: 2.8546 +2024-07-21 09:20:39,547 - pyskl - INFO - Epoch [137][300/3746] lr: 2.110e-03, eta: 11:56:56, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4897, top5_acc: 0.7445, loss_cls: 2.8428, loss: 2.8428 +2024-07-21 09:22:01,378 - pyskl - INFO - Epoch [137][400/3746] lr: 2.102e-03, eta: 11:55:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4877, top5_acc: 0.7400, loss_cls: 2.8700, loss: 2.8700 +2024-07-21 09:23:23,533 - pyskl - INFO - Epoch [137][500/3746] lr: 2.094e-03, eta: 11:54:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4867, top5_acc: 0.7417, loss_cls: 2.8433, loss: 2.8433 +2024-07-21 09:24:45,768 - pyskl - INFO - Epoch [137][600/3746] lr: 2.086e-03, eta: 11:52:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4903, top5_acc: 0.7427, loss_cls: 2.8763, loss: 2.8763 +2024-07-21 09:26:08,983 - pyskl - INFO - Epoch [137][700/3746] lr: 2.078e-03, eta: 11:51:26, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4895, top5_acc: 0.7427, loss_cls: 2.8540, loss: 2.8540 +2024-07-21 09:27:31,210 - pyskl - INFO - Epoch [137][800/3746] lr: 2.070e-03, eta: 11:50:03, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7425, loss_cls: 2.8589, loss: 2.8589 +2024-07-21 09:28:53,096 - pyskl - INFO - Epoch [137][900/3746] lr: 2.062e-03, eta: 11:48:41, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4784, top5_acc: 0.7317, loss_cls: 2.9154, loss: 2.9154 +2024-07-21 09:30:15,068 - pyskl - INFO - Epoch [137][1000/3746] lr: 2.054e-03, eta: 11:47:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4856, top5_acc: 0.7322, loss_cls: 2.9004, loss: 2.9004 +2024-07-21 09:31:36,866 - pyskl - INFO - Epoch [137][1100/3746] lr: 2.046e-03, eta: 11:45:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4830, top5_acc: 0.7261, loss_cls: 2.9180, loss: 2.9180 +2024-07-21 09:32:58,687 - pyskl - INFO - Epoch [137][1200/3746] lr: 2.038e-03, eta: 11:44:33, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4863, top5_acc: 0.7328, loss_cls: 2.8867, loss: 2.8867 +2024-07-21 09:34:20,891 - pyskl - INFO - Epoch [137][1300/3746] lr: 2.030e-03, eta: 11:43:11, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4758, top5_acc: 0.7284, loss_cls: 2.9284, loss: 2.9284 +2024-07-21 09:35:43,299 - pyskl - INFO - Epoch [137][1400/3746] lr: 2.022e-03, eta: 11:41:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4817, top5_acc: 0.7353, loss_cls: 2.8938, loss: 2.8938 +2024-07-21 09:37:05,789 - pyskl - INFO - Epoch [137][1500/3746] lr: 2.015e-03, eta: 11:40:26, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4861, top5_acc: 0.7320, loss_cls: 2.8980, loss: 2.8980 +2024-07-21 09:38:28,122 - pyskl - INFO - Epoch [137][1600/3746] lr: 2.007e-03, eta: 11:39:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4738, top5_acc: 0.7223, loss_cls: 2.9403, loss: 2.9403 +2024-07-21 09:39:50,195 - pyskl - INFO - Epoch [137][1700/3746] lr: 1.999e-03, eta: 11:37:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4831, top5_acc: 0.7267, loss_cls: 2.9271, loss: 2.9271 +2024-07-21 09:41:12,224 - pyskl - INFO - Epoch [137][1800/3746] lr: 1.991e-03, eta: 11:36:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4870, top5_acc: 0.7355, loss_cls: 2.8707, loss: 2.8707 +2024-07-21 09:42:33,967 - pyskl - INFO - Epoch [137][1900/3746] lr: 1.983e-03, eta: 11:34:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4798, top5_acc: 0.7331, loss_cls: 2.9157, loss: 2.9157 +2024-07-21 09:43:55,580 - pyskl - INFO - Epoch [137][2000/3746] lr: 1.976e-03, eta: 11:33:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4713, top5_acc: 0.7275, loss_cls: 2.9299, loss: 2.9299 +2024-07-21 09:45:18,149 - pyskl - INFO - Epoch [137][2100/3746] lr: 1.968e-03, eta: 11:32:10, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4741, top5_acc: 0.7297, loss_cls: 2.9121, loss: 2.9121 +2024-07-21 09:46:40,624 - pyskl - INFO - Epoch [137][2200/3746] lr: 1.960e-03, eta: 11:30:48, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4903, top5_acc: 0.7311, loss_cls: 2.8901, loss: 2.8901 +2024-07-21 09:48:02,843 - pyskl - INFO - Epoch [137][2300/3746] lr: 1.952e-03, eta: 11:29:25, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4820, top5_acc: 0.7311, loss_cls: 2.9113, loss: 2.9113 +2024-07-21 09:49:25,165 - pyskl - INFO - Epoch [137][2400/3746] lr: 1.944e-03, eta: 11:28:03, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4767, top5_acc: 0.7284, loss_cls: 2.9049, loss: 2.9049 +2024-07-21 09:50:47,849 - pyskl - INFO - Epoch [137][2500/3746] lr: 1.937e-03, eta: 11:26:40, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4813, top5_acc: 0.7342, loss_cls: 2.9094, loss: 2.9094 +2024-07-21 09:52:09,901 - pyskl - INFO - Epoch [137][2600/3746] lr: 1.929e-03, eta: 11:25:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4719, top5_acc: 0.7266, loss_cls: 2.9235, loss: 2.9235 +2024-07-21 09:53:31,600 - pyskl - INFO - Epoch [137][2700/3746] lr: 1.921e-03, eta: 11:23:55, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4686, top5_acc: 0.7280, loss_cls: 2.9305, loss: 2.9305 +2024-07-21 09:54:54,137 - pyskl - INFO - Epoch [137][2800/3746] lr: 1.914e-03, eta: 11:22:33, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4764, top5_acc: 0.7273, loss_cls: 2.9129, loss: 2.9129 +2024-07-21 09:56:15,915 - pyskl - INFO - Epoch [137][2900/3746] lr: 1.906e-03, eta: 11:21:10, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4731, top5_acc: 0.7239, loss_cls: 2.9576, loss: 2.9576 +2024-07-21 09:57:38,165 - pyskl - INFO - Epoch [137][3000/3746] lr: 1.898e-03, eta: 11:19:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4806, top5_acc: 0.7302, loss_cls: 2.9247, loss: 2.9247 +2024-07-21 09:59:00,801 - pyskl - INFO - Epoch [137][3100/3746] lr: 1.891e-03, eta: 11:18:25, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4850, top5_acc: 0.7314, loss_cls: 2.9041, loss: 2.9041 +2024-07-21 10:00:22,619 - pyskl - INFO - Epoch [137][3200/3746] lr: 1.883e-03, eta: 11:17:03, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4828, top5_acc: 0.7355, loss_cls: 2.8735, loss: 2.8735 +2024-07-21 10:01:44,430 - pyskl - INFO - Epoch [137][3300/3746] lr: 1.876e-03, eta: 11:15:40, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4903, top5_acc: 0.7280, loss_cls: 2.8876, loss: 2.8876 +2024-07-21 10:03:06,928 - pyskl - INFO - Epoch [137][3400/3746] lr: 1.868e-03, eta: 11:14:18, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4664, top5_acc: 0.7308, loss_cls: 2.9359, loss: 2.9359 +2024-07-21 10:04:28,999 - pyskl - INFO - Epoch [137][3500/3746] lr: 1.860e-03, eta: 11:12:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4723, top5_acc: 0.7327, loss_cls: 2.9201, loss: 2.9201 +2024-07-21 10:05:50,847 - pyskl - INFO - Epoch [137][3600/3746] lr: 1.853e-03, eta: 11:11:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4833, top5_acc: 0.7372, loss_cls: 2.8867, loss: 2.8867 +2024-07-21 10:07:13,017 - pyskl - INFO - Epoch [137][3700/3746] lr: 1.845e-03, eta: 11:10:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4742, top5_acc: 0.7245, loss_cls: 2.9445, loss: 2.9445 +2024-07-21 10:07:52,813 - pyskl - INFO - Saving checkpoint at 137 epochs +2024-07-21 10:09:45,174 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 10:09:45,836 - pyskl - INFO - +top1_acc 0.3777 +top5_acc 0.6246 +2024-07-21 10:09:45,837 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 10:09:45,879 - pyskl - INFO - +mean_acc 0.3775 +2024-07-21 10:09:45,891 - pyskl - INFO - Epoch(val) [137][309] top1_acc: 0.3777, top5_acc: 0.6246, mean_class_accuracy: 0.3775 +2024-07-21 10:13:35,230 - pyskl - INFO - Epoch [138][100/3746] lr: 1.834e-03, eta: 11:08:20, time: 2.293, data_time: 1.304, memory: 15990, top1_acc: 0.5036, top5_acc: 0.7522, loss_cls: 2.7992, loss: 2.7992 +2024-07-21 10:14:57,234 - pyskl - INFO - Epoch [138][200/3746] lr: 1.827e-03, eta: 11:06:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4977, top5_acc: 0.7428, loss_cls: 2.8052, loss: 2.8052 +2024-07-21 10:16:19,779 - pyskl - INFO - Epoch [138][300/3746] lr: 1.819e-03, eta: 11:05:35, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5033, top5_acc: 0.7484, loss_cls: 2.7948, loss: 2.7948 +2024-07-21 10:17:41,621 - pyskl - INFO - Epoch [138][400/3746] lr: 1.812e-03, eta: 11:04:12, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7441, loss_cls: 2.8501, loss: 2.8501 +2024-07-21 10:19:04,245 - pyskl - INFO - Epoch [138][500/3746] lr: 1.805e-03, eta: 11:02:50, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5014, top5_acc: 0.7517, loss_cls: 2.7945, loss: 2.7945 +2024-07-21 10:20:27,091 - pyskl - INFO - Epoch [138][600/3746] lr: 1.797e-03, eta: 11:01:27, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.4992, top5_acc: 0.7534, loss_cls: 2.7892, loss: 2.7892 +2024-07-21 10:21:49,709 - pyskl - INFO - Epoch [138][700/3746] lr: 1.790e-03, eta: 11:00:05, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4994, top5_acc: 0.7488, loss_cls: 2.7991, loss: 2.7991 +2024-07-21 10:23:12,459 - pyskl - INFO - Epoch [138][800/3746] lr: 1.782e-03, eta: 10:58:42, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4806, top5_acc: 0.7356, loss_cls: 2.8588, loss: 2.8588 +2024-07-21 10:24:34,317 - pyskl - INFO - Epoch [138][900/3746] lr: 1.775e-03, eta: 10:57:20, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5016, top5_acc: 0.7478, loss_cls: 2.8084, loss: 2.8084 +2024-07-21 10:25:56,163 - pyskl - INFO - Epoch [138][1000/3746] lr: 1.768e-03, eta: 10:55:57, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4805, top5_acc: 0.7334, loss_cls: 2.8825, loss: 2.8825 +2024-07-21 10:27:18,140 - pyskl - INFO - Epoch [138][1100/3746] lr: 1.760e-03, eta: 10:54:34, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4942, top5_acc: 0.7445, loss_cls: 2.8177, loss: 2.8177 +2024-07-21 10:28:40,503 - pyskl - INFO - Epoch [138][1200/3746] lr: 1.753e-03, eta: 10:53:12, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4934, top5_acc: 0.7438, loss_cls: 2.8286, loss: 2.8286 +2024-07-21 10:30:03,041 - pyskl - INFO - Epoch [138][1300/3746] lr: 1.745e-03, eta: 10:51:49, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4933, top5_acc: 0.7391, loss_cls: 2.8533, loss: 2.8533 +2024-07-21 10:31:25,948 - pyskl - INFO - Epoch [138][1400/3746] lr: 1.738e-03, eta: 10:50:27, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.4861, top5_acc: 0.7377, loss_cls: 2.8657, loss: 2.8657 +2024-07-21 10:32:47,587 - pyskl - INFO - Epoch [138][1500/3746] lr: 1.731e-03, eta: 10:49:04, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4919, top5_acc: 0.7355, loss_cls: 2.8507, loss: 2.8507 +2024-07-21 10:34:10,201 - pyskl - INFO - Epoch [138][1600/3746] lr: 1.724e-03, eta: 10:47:42, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4886, top5_acc: 0.7336, loss_cls: 2.8739, loss: 2.8739 +2024-07-21 10:35:32,150 - pyskl - INFO - Epoch [138][1700/3746] lr: 1.716e-03, eta: 10:46:19, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4933, top5_acc: 0.7423, loss_cls: 2.8309, loss: 2.8309 +2024-07-21 10:36:54,179 - pyskl - INFO - Epoch [138][1800/3746] lr: 1.709e-03, eta: 10:44:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4931, top5_acc: 0.7420, loss_cls: 2.8371, loss: 2.8371 +2024-07-21 10:38:15,796 - pyskl - INFO - Epoch [138][1900/3746] lr: 1.702e-03, eta: 10:43:34, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4947, top5_acc: 0.7489, loss_cls: 2.8221, loss: 2.8221 +2024-07-21 10:39:38,109 - pyskl - INFO - Epoch [138][2000/3746] lr: 1.695e-03, eta: 10:42:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4917, top5_acc: 0.7369, loss_cls: 2.8583, loss: 2.8583 +2024-07-21 10:41:00,496 - pyskl - INFO - Epoch [138][2100/3746] lr: 1.687e-03, eta: 10:40:49, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4852, top5_acc: 0.7317, loss_cls: 2.8934, loss: 2.8934 +2024-07-21 10:42:22,920 - pyskl - INFO - Epoch [138][2200/3746] lr: 1.680e-03, eta: 10:39:27, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4925, top5_acc: 0.7364, loss_cls: 2.8675, loss: 2.8675 +2024-07-21 10:43:45,416 - pyskl - INFO - Epoch [138][2300/3746] lr: 1.673e-03, eta: 10:38:04, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4905, top5_acc: 0.7398, loss_cls: 2.8442, loss: 2.8442 +2024-07-21 10:45:07,570 - pyskl - INFO - Epoch [138][2400/3746] lr: 1.666e-03, eta: 10:36:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.4858, top5_acc: 0.7377, loss_cls: 2.8727, loss: 2.8727 +2024-07-21 10:46:29,645 - pyskl - INFO - Epoch [138][2500/3746] lr: 1.659e-03, eta: 10:35:19, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5036, top5_acc: 0.7503, loss_cls: 2.7833, loss: 2.7833 +2024-07-21 10:47:51,774 - pyskl - INFO - Epoch [138][2600/3746] lr: 1.652e-03, eta: 10:33:56, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4880, top5_acc: 0.7347, loss_cls: 2.8756, loss: 2.8756 +2024-07-21 10:49:14,231 - pyskl - INFO - Epoch [138][2700/3746] lr: 1.644e-03, eta: 10:32:34, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4934, top5_acc: 0.7412, loss_cls: 2.8502, loss: 2.8502 +2024-07-21 10:50:36,336 - pyskl - INFO - Epoch [138][2800/3746] lr: 1.637e-03, eta: 10:31:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4897, top5_acc: 0.7375, loss_cls: 2.8598, loss: 2.8598 +2024-07-21 10:51:58,392 - pyskl - INFO - Epoch [138][2900/3746] lr: 1.630e-03, eta: 10:29:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4844, top5_acc: 0.7291, loss_cls: 2.8942, loss: 2.8942 +2024-07-21 10:53:19,918 - pyskl - INFO - Epoch [138][3000/3746] lr: 1.623e-03, eta: 10:28:26, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.4994, top5_acc: 0.7433, loss_cls: 2.8483, loss: 2.8483 +2024-07-21 10:54:41,705 - pyskl - INFO - Epoch [138][3100/3746] lr: 1.616e-03, eta: 10:27:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4839, top5_acc: 0.7394, loss_cls: 2.8742, loss: 2.8742 +2024-07-21 10:56:04,096 - pyskl - INFO - Epoch [138][3200/3746] lr: 1.609e-03, eta: 10:25:41, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4753, top5_acc: 0.7258, loss_cls: 2.9283, loss: 2.9283 +2024-07-21 10:57:26,210 - pyskl - INFO - Epoch [138][3300/3746] lr: 1.602e-03, eta: 10:24:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4909, top5_acc: 0.7394, loss_cls: 2.8370, loss: 2.8370 +2024-07-21 10:58:47,898 - pyskl - INFO - Epoch [138][3400/3746] lr: 1.595e-03, eta: 10:22:56, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4825, top5_acc: 0.7319, loss_cls: 2.8727, loss: 2.8727 +2024-07-21 11:00:09,931 - pyskl - INFO - Epoch [138][3500/3746] lr: 1.588e-03, eta: 10:21:33, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4853, top5_acc: 0.7438, loss_cls: 2.8687, loss: 2.8687 +2024-07-21 11:01:31,965 - pyskl - INFO - Epoch [138][3600/3746] lr: 1.581e-03, eta: 10:20:11, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4894, top5_acc: 0.7355, loss_cls: 2.8673, loss: 2.8673 +2024-07-21 11:02:53,751 - pyskl - INFO - Epoch [138][3700/3746] lr: 1.574e-03, eta: 10:18:48, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.4763, top5_acc: 0.7303, loss_cls: 2.9155, loss: 2.9155 +2024-07-21 11:03:33,764 - pyskl - INFO - Saving checkpoint at 138 epochs +2024-07-21 11:05:26,274 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 11:05:26,935 - pyskl - INFO - +top1_acc 0.3803 +top5_acc 0.6301 +2024-07-21 11:05:26,935 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 11:05:26,977 - pyskl - INFO - +mean_acc 0.3801 +2024-07-21 11:05:26,983 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_136.pth was removed +2024-07-21 11:05:27,238 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_138.pth. +2024-07-21 11:05:27,239 - pyskl - INFO - Best top1_acc is 0.3803 at 138 epoch. +2024-07-21 11:05:27,252 - pyskl - INFO - Epoch(val) [138][309] top1_acc: 0.3803, top5_acc: 0.6301, mean_class_accuracy: 0.3801 +2024-07-21 11:09:12,867 - pyskl - INFO - Epoch [139][100/3746] lr: 1.564e-03, eta: 10:16:57, time: 2.256, data_time: 1.255, memory: 15990, top1_acc: 0.4997, top5_acc: 0.7548, loss_cls: 2.7616, loss: 2.7616 +2024-07-21 11:10:35,132 - pyskl - INFO - Epoch [139][200/3746] lr: 1.557e-03, eta: 10:15:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5050, top5_acc: 0.7488, loss_cls: 2.7914, loss: 2.7914 +2024-07-21 11:11:57,416 - pyskl - INFO - Epoch [139][300/3746] lr: 1.550e-03, eta: 10:14:12, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5059, top5_acc: 0.7559, loss_cls: 2.7839, loss: 2.7839 +2024-07-21 11:13:19,368 - pyskl - INFO - Epoch [139][400/3746] lr: 1.543e-03, eta: 10:12:49, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5023, top5_acc: 0.7498, loss_cls: 2.7829, loss: 2.7829 +2024-07-21 11:14:41,986 - pyskl - INFO - Epoch [139][500/3746] lr: 1.536e-03, eta: 10:11:27, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4994, top5_acc: 0.7541, loss_cls: 2.7788, loss: 2.7788 +2024-07-21 11:16:03,783 - pyskl - INFO - Epoch [139][600/3746] lr: 1.529e-03, eta: 10:10:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5128, top5_acc: 0.7494, loss_cls: 2.7477, loss: 2.7477 +2024-07-21 11:17:26,267 - pyskl - INFO - Epoch [139][700/3746] lr: 1.523e-03, eta: 10:08:42, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5050, top5_acc: 0.7475, loss_cls: 2.7833, loss: 2.7833 +2024-07-21 11:18:48,749 - pyskl - INFO - Epoch [139][800/3746] lr: 1.516e-03, eta: 10:07:19, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4991, top5_acc: 0.7445, loss_cls: 2.8196, loss: 2.8196 +2024-07-21 11:20:11,213 - pyskl - INFO - Epoch [139][900/3746] lr: 1.509e-03, eta: 10:05:56, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5131, top5_acc: 0.7589, loss_cls: 2.7113, loss: 2.7113 +2024-07-21 11:21:33,885 - pyskl - INFO - Epoch [139][1000/3746] lr: 1.502e-03, eta: 10:04:34, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5034, top5_acc: 0.7567, loss_cls: 2.7669, loss: 2.7669 +2024-07-21 11:22:56,374 - pyskl - INFO - Epoch [139][1100/3746] lr: 1.495e-03, eta: 10:03:11, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4978, top5_acc: 0.7419, loss_cls: 2.8169, loss: 2.8169 +2024-07-21 11:24:19,513 - pyskl - INFO - Epoch [139][1200/3746] lr: 1.489e-03, eta: 10:01:49, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5005, top5_acc: 0.7445, loss_cls: 2.8054, loss: 2.8054 +2024-07-21 11:25:43,081 - pyskl - INFO - Epoch [139][1300/3746] lr: 1.482e-03, eta: 10:00:26, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.4955, top5_acc: 0.7511, loss_cls: 2.8066, loss: 2.8066 +2024-07-21 11:27:06,247 - pyskl - INFO - Epoch [139][1400/3746] lr: 1.475e-03, eta: 9:59:04, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.4919, top5_acc: 0.7439, loss_cls: 2.8133, loss: 2.8133 +2024-07-21 11:28:28,542 - pyskl - INFO - Epoch [139][1500/3746] lr: 1.468e-03, eta: 9:57:41, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5111, top5_acc: 0.7503, loss_cls: 2.7758, loss: 2.7758 +2024-07-21 11:29:51,373 - pyskl - INFO - Epoch [139][1600/3746] lr: 1.462e-03, eta: 9:56:19, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5067, top5_acc: 0.7522, loss_cls: 2.7617, loss: 2.7617 +2024-07-21 11:31:13,245 - pyskl - INFO - Epoch [139][1700/3746] lr: 1.455e-03, eta: 9:54:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4920, top5_acc: 0.7388, loss_cls: 2.8420, loss: 2.8420 +2024-07-21 11:32:34,896 - pyskl - INFO - Epoch [139][1800/3746] lr: 1.448e-03, eta: 9:53:34, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5128, top5_acc: 0.7555, loss_cls: 2.7577, loss: 2.7577 +2024-07-21 11:33:56,841 - pyskl - INFO - Epoch [139][1900/3746] lr: 1.442e-03, eta: 9:52:11, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4911, top5_acc: 0.7422, loss_cls: 2.8155, loss: 2.8155 +2024-07-21 11:35:18,959 - pyskl - INFO - Epoch [139][2000/3746] lr: 1.435e-03, eta: 9:50:49, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4869, top5_acc: 0.7542, loss_cls: 2.8121, loss: 2.8121 +2024-07-21 11:36:41,546 - pyskl - INFO - Epoch [139][2100/3746] lr: 1.428e-03, eta: 9:49:26, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.4923, top5_acc: 0.7419, loss_cls: 2.8485, loss: 2.8485 +2024-07-21 11:38:03,629 - pyskl - INFO - Epoch [139][2200/3746] lr: 1.422e-03, eta: 9:48:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4975, top5_acc: 0.7473, loss_cls: 2.8008, loss: 2.8008 +2024-07-21 11:39:26,743 - pyskl - INFO - Epoch [139][2300/3746] lr: 1.415e-03, eta: 9:46:41, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.4958, top5_acc: 0.7492, loss_cls: 2.7940, loss: 2.7940 +2024-07-21 11:40:48,603 - pyskl - INFO - Epoch [139][2400/3746] lr: 1.408e-03, eta: 9:45:18, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5067, top5_acc: 0.7488, loss_cls: 2.7735, loss: 2.7735 +2024-07-21 11:42:10,485 - pyskl - INFO - Epoch [139][2500/3746] lr: 1.402e-03, eta: 9:43:56, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5012, top5_acc: 0.7434, loss_cls: 2.8135, loss: 2.8135 +2024-07-21 11:43:33,159 - pyskl - INFO - Epoch [139][2600/3746] lr: 1.395e-03, eta: 9:42:33, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4972, top5_acc: 0.7367, loss_cls: 2.8438, loss: 2.8438 +2024-07-21 11:44:54,881 - pyskl - INFO - Epoch [139][2700/3746] lr: 1.389e-03, eta: 9:41:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.4913, top5_acc: 0.7447, loss_cls: 2.8249, loss: 2.8249 +2024-07-21 11:46:16,935 - pyskl - INFO - Epoch [139][2800/3746] lr: 1.382e-03, eta: 9:39:48, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4966, top5_acc: 0.7522, loss_cls: 2.7934, loss: 2.7934 +2024-07-21 11:47:38,601 - pyskl - INFO - Epoch [139][2900/3746] lr: 1.376e-03, eta: 9:38:26, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5094, top5_acc: 0.7473, loss_cls: 2.7897, loss: 2.7897 +2024-07-21 11:49:00,717 - pyskl - INFO - Epoch [139][3000/3746] lr: 1.369e-03, eta: 9:37:03, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4902, top5_acc: 0.7400, loss_cls: 2.8350, loss: 2.8350 +2024-07-21 11:50:23,032 - pyskl - INFO - Epoch [139][3100/3746] lr: 1.363e-03, eta: 9:35:40, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4959, top5_acc: 0.7400, loss_cls: 2.8381, loss: 2.8381 +2024-07-21 11:51:45,038 - pyskl - INFO - Epoch [139][3200/3746] lr: 1.356e-03, eta: 9:34:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4936, top5_acc: 0.7406, loss_cls: 2.8481, loss: 2.8481 +2024-07-21 11:53:07,013 - pyskl - INFO - Epoch [139][3300/3746] lr: 1.350e-03, eta: 9:32:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4978, top5_acc: 0.7458, loss_cls: 2.7864, loss: 2.7864 +2024-07-21 11:54:28,930 - pyskl - INFO - Epoch [139][3400/3746] lr: 1.343e-03, eta: 9:31:33, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4959, top5_acc: 0.7475, loss_cls: 2.7961, loss: 2.7961 +2024-07-21 11:55:51,636 - pyskl - INFO - Epoch [139][3500/3746] lr: 1.337e-03, eta: 9:30:10, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4922, top5_acc: 0.7480, loss_cls: 2.8293, loss: 2.8293 +2024-07-21 11:57:13,942 - pyskl - INFO - Epoch [139][3600/3746] lr: 1.330e-03, eta: 9:28:48, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.4959, top5_acc: 0.7491, loss_cls: 2.7888, loss: 2.7888 +2024-07-21 11:58:35,558 - pyskl - INFO - Epoch [139][3700/3746] lr: 1.324e-03, eta: 9:27:25, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.4922, top5_acc: 0.7447, loss_cls: 2.8360, loss: 2.8360 +2024-07-21 11:59:15,405 - pyskl - INFO - Saving checkpoint at 139 epochs +2024-07-21 12:01:06,932 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 12:01:07,602 - pyskl - INFO - +top1_acc 0.3853 +top5_acc 0.6315 +2024-07-21 12:01:07,602 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 12:01:07,648 - pyskl - INFO - +mean_acc 0.3849 +2024-07-21 12:01:07,653 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_138.pth was removed +2024-07-21 12:01:07,908 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_139.pth. +2024-07-21 12:01:07,909 - pyskl - INFO - Best top1_acc is 0.3853 at 139 epoch. +2024-07-21 12:01:07,925 - pyskl - INFO - Epoch(val) [139][309] top1_acc: 0.3853, top5_acc: 0.6315, mean_class_accuracy: 0.3849 +2024-07-21 12:04:59,197 - pyskl - INFO - Epoch [140][100/3746] lr: 1.315e-03, eta: 9:25:33, time: 2.313, data_time: 1.316, memory: 15990, top1_acc: 0.5156, top5_acc: 0.7656, loss_cls: 2.6738, loss: 2.6738 +2024-07-21 12:06:22,294 - pyskl - INFO - Epoch [140][200/3746] lr: 1.308e-03, eta: 9:24:11, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5288, top5_acc: 0.7692, loss_cls: 2.6478, loss: 2.6478 +2024-07-21 12:07:44,660 - pyskl - INFO - Epoch [140][300/3746] lr: 1.302e-03, eta: 9:22:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5122, top5_acc: 0.7570, loss_cls: 2.7410, loss: 2.7410 +2024-07-21 12:09:06,986 - pyskl - INFO - Epoch [140][400/3746] lr: 1.296e-03, eta: 9:21:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5183, top5_acc: 0.7588, loss_cls: 2.7000, loss: 2.7000 +2024-07-21 12:10:29,472 - pyskl - INFO - Epoch [140][500/3746] lr: 1.289e-03, eta: 9:20:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5175, top5_acc: 0.7666, loss_cls: 2.6907, loss: 2.6907 +2024-07-21 12:11:52,162 - pyskl - INFO - Epoch [140][600/3746] lr: 1.283e-03, eta: 9:18:41, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5181, top5_acc: 0.7597, loss_cls: 2.7100, loss: 2.7100 +2024-07-21 12:13:14,113 - pyskl - INFO - Epoch [140][700/3746] lr: 1.277e-03, eta: 9:17:18, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5030, top5_acc: 0.7578, loss_cls: 2.7333, loss: 2.7333 +2024-07-21 12:14:36,566 - pyskl - INFO - Epoch [140][800/3746] lr: 1.271e-03, eta: 9:15:55, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5041, top5_acc: 0.7550, loss_cls: 2.7522, loss: 2.7522 +2024-07-21 12:15:58,732 - pyskl - INFO - Epoch [140][900/3746] lr: 1.264e-03, eta: 9:14:33, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5183, top5_acc: 0.7670, loss_cls: 2.7129, loss: 2.7129 +2024-07-21 12:17:20,908 - pyskl - INFO - Epoch [140][1000/3746] lr: 1.258e-03, eta: 9:13:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5031, top5_acc: 0.7558, loss_cls: 2.7641, loss: 2.7641 +2024-07-21 12:18:42,834 - pyskl - INFO - Epoch [140][1100/3746] lr: 1.252e-03, eta: 9:11:48, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5147, top5_acc: 0.7547, loss_cls: 2.7340, loss: 2.7340 +2024-07-21 12:20:05,704 - pyskl - INFO - Epoch [140][1200/3746] lr: 1.246e-03, eta: 9:10:25, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5098, top5_acc: 0.7622, loss_cls: 2.7389, loss: 2.7389 +2024-07-21 12:21:27,645 - pyskl - INFO - Epoch [140][1300/3746] lr: 1.239e-03, eta: 9:09:02, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5219, top5_acc: 0.7606, loss_cls: 2.7140, loss: 2.7140 +2024-07-21 12:22:49,797 - pyskl - INFO - Epoch [140][1400/3746] lr: 1.233e-03, eta: 9:07:40, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5148, top5_acc: 0.7617, loss_cls: 2.7205, loss: 2.7205 +2024-07-21 12:24:12,238 - pyskl - INFO - Epoch [140][1500/3746] lr: 1.227e-03, eta: 9:06:17, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5023, top5_acc: 0.7516, loss_cls: 2.7539, loss: 2.7539 +2024-07-21 12:25:34,352 - pyskl - INFO - Epoch [140][1600/3746] lr: 1.221e-03, eta: 9:04:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.4991, top5_acc: 0.7492, loss_cls: 2.7758, loss: 2.7758 +2024-07-21 12:26:56,484 - pyskl - INFO - Epoch [140][1700/3746] lr: 1.215e-03, eta: 9:03:32, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5055, top5_acc: 0.7570, loss_cls: 2.7493, loss: 2.7493 +2024-07-21 12:28:18,545 - pyskl - INFO - Epoch [140][1800/3746] lr: 1.209e-03, eta: 9:02:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5102, top5_acc: 0.7558, loss_cls: 2.7314, loss: 2.7314 +2024-07-21 12:29:41,289 - pyskl - INFO - Epoch [140][1900/3746] lr: 1.203e-03, eta: 9:00:47, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.4983, top5_acc: 0.7564, loss_cls: 2.7706, loss: 2.7706 +2024-07-21 12:31:03,220 - pyskl - INFO - Epoch [140][2000/3746] lr: 1.196e-03, eta: 8:59:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4941, top5_acc: 0.7472, loss_cls: 2.8369, loss: 2.8369 +2024-07-21 12:32:26,740 - pyskl - INFO - Epoch [140][2100/3746] lr: 1.190e-03, eta: 8:58:02, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5225, top5_acc: 0.7633, loss_cls: 2.6865, loss: 2.6865 +2024-07-21 12:33:48,803 - pyskl - INFO - Epoch [140][2200/3746] lr: 1.184e-03, eta: 8:56:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5089, top5_acc: 0.7391, loss_cls: 2.7490, loss: 2.7490 +2024-07-21 12:35:11,559 - pyskl - INFO - Epoch [140][2300/3746] lr: 1.178e-03, eta: 8:55:17, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5022, top5_acc: 0.7527, loss_cls: 2.7695, loss: 2.7695 +2024-07-21 12:36:33,466 - pyskl - INFO - Epoch [140][2400/3746] lr: 1.172e-03, eta: 8:53:54, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5042, top5_acc: 0.7544, loss_cls: 2.7826, loss: 2.7826 +2024-07-21 12:37:55,462 - pyskl - INFO - Epoch [140][2500/3746] lr: 1.166e-03, eta: 8:52:32, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.4991, top5_acc: 0.7434, loss_cls: 2.7967, loss: 2.7967 +2024-07-21 12:39:17,267 - pyskl - INFO - Epoch [140][2600/3746] lr: 1.160e-03, eta: 8:51:09, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5039, top5_acc: 0.7509, loss_cls: 2.7533, loss: 2.7533 +2024-07-21 12:40:39,285 - pyskl - INFO - Epoch [140][2700/3746] lr: 1.154e-03, eta: 8:49:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5061, top5_acc: 0.7498, loss_cls: 2.7642, loss: 2.7642 +2024-07-21 12:42:01,140 - pyskl - INFO - Epoch [140][2800/3746] lr: 1.148e-03, eta: 8:48:24, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5069, top5_acc: 0.7581, loss_cls: 2.7353, loss: 2.7353 +2024-07-21 12:43:23,053 - pyskl - INFO - Epoch [140][2900/3746] lr: 1.142e-03, eta: 8:47:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5108, top5_acc: 0.7545, loss_cls: 2.7541, loss: 2.7541 +2024-07-21 12:44:44,970 - pyskl - INFO - Epoch [140][3000/3746] lr: 1.136e-03, eta: 8:45:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5031, top5_acc: 0.7552, loss_cls: 2.7593, loss: 2.7593 +2024-07-21 12:46:07,155 - pyskl - INFO - Epoch [140][3100/3746] lr: 1.131e-03, eta: 8:44:16, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5102, top5_acc: 0.7614, loss_cls: 2.7305, loss: 2.7305 +2024-07-21 12:47:29,132 - pyskl - INFO - Epoch [140][3200/3746] lr: 1.125e-03, eta: 8:42:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5005, top5_acc: 0.7505, loss_cls: 2.7791, loss: 2.7791 +2024-07-21 12:48:51,400 - pyskl - INFO - Epoch [140][3300/3746] lr: 1.119e-03, eta: 8:41:31, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5023, top5_acc: 0.7562, loss_cls: 2.7644, loss: 2.7644 +2024-07-21 12:50:13,891 - pyskl - INFO - Epoch [140][3400/3746] lr: 1.113e-03, eta: 8:40:08, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.4989, top5_acc: 0.7481, loss_cls: 2.8073, loss: 2.8073 +2024-07-21 12:51:36,308 - pyskl - INFO - Epoch [140][3500/3746] lr: 1.107e-03, eta: 8:38:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.4964, top5_acc: 0.7455, loss_cls: 2.8114, loss: 2.8114 +2024-07-21 12:52:58,019 - pyskl - INFO - Epoch [140][3600/3746] lr: 1.101e-03, eta: 8:37:23, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5070, top5_acc: 0.7561, loss_cls: 2.7552, loss: 2.7552 +2024-07-21 12:54:20,032 - pyskl - INFO - Epoch [140][3700/3746] lr: 1.095e-03, eta: 8:36:01, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5025, top5_acc: 0.7544, loss_cls: 2.7707, loss: 2.7707 +2024-07-21 12:55:00,138 - pyskl - INFO - Saving checkpoint at 140 epochs +2024-07-21 12:56:52,073 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 12:56:52,741 - pyskl - INFO - +top1_acc 0.3879 +top5_acc 0.6374 +2024-07-21 12:56:52,741 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 12:56:52,782 - pyskl - INFO - +mean_acc 0.3877 +2024-07-21 12:56:52,786 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_139.pth was removed +2024-07-21 12:56:53,037 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_140.pth. +2024-07-21 12:56:53,038 - pyskl - INFO - Best top1_acc is 0.3879 at 140 epoch. +2024-07-21 12:56:53,050 - pyskl - INFO - Epoch(val) [140][309] top1_acc: 0.3879, top5_acc: 0.6374, mean_class_accuracy: 0.3877 +2024-07-21 13:00:46,848 - pyskl - INFO - Epoch [141][100/3746] lr: 1.087e-03, eta: 8:34:08, time: 2.338, data_time: 1.332, memory: 15990, top1_acc: 0.5245, top5_acc: 0.7730, loss_cls: 2.6437, loss: 2.6437 +2024-07-21 13:02:09,286 - pyskl - INFO - Epoch [141][200/3746] lr: 1.081e-03, eta: 8:32:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5308, top5_acc: 0.7733, loss_cls: 2.6365, loss: 2.6365 +2024-07-21 13:03:31,581 - pyskl - INFO - Epoch [141][300/3746] lr: 1.075e-03, eta: 8:31:23, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5283, top5_acc: 0.7638, loss_cls: 2.6764, loss: 2.6764 +2024-07-21 13:04:53,589 - pyskl - INFO - Epoch [141][400/3746] lr: 1.070e-03, eta: 8:30:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5269, top5_acc: 0.7609, loss_cls: 2.6502, loss: 2.6502 +2024-07-21 13:06:16,451 - pyskl - INFO - Epoch [141][500/3746] lr: 1.064e-03, eta: 8:28:38, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5177, top5_acc: 0.7670, loss_cls: 2.7182, loss: 2.7182 +2024-07-21 13:07:38,937 - pyskl - INFO - Epoch [141][600/3746] lr: 1.058e-03, eta: 8:27:15, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5186, top5_acc: 0.7716, loss_cls: 2.6795, loss: 2.6795 +2024-07-21 13:09:01,519 - pyskl - INFO - Epoch [141][700/3746] lr: 1.052e-03, eta: 8:25:53, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5114, top5_acc: 0.7661, loss_cls: 2.7080, loss: 2.7080 +2024-07-21 13:10:23,559 - pyskl - INFO - Epoch [141][800/3746] lr: 1.047e-03, eta: 8:24:30, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5175, top5_acc: 0.7592, loss_cls: 2.7147, loss: 2.7147 +2024-07-21 13:11:46,261 - pyskl - INFO - Epoch [141][900/3746] lr: 1.041e-03, eta: 8:23:08, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5227, top5_acc: 0.7730, loss_cls: 2.6756, loss: 2.6756 +2024-07-21 13:13:08,529 - pyskl - INFO - Epoch [141][1000/3746] lr: 1.035e-03, eta: 8:21:45, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5183, top5_acc: 0.7642, loss_cls: 2.6925, loss: 2.6925 +2024-07-21 13:14:31,358 - pyskl - INFO - Epoch [141][1100/3746] lr: 1.030e-03, eta: 8:20:22, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5175, top5_acc: 0.7589, loss_cls: 2.6982, loss: 2.6982 +2024-07-21 13:15:53,507 - pyskl - INFO - Epoch [141][1200/3746] lr: 1.024e-03, eta: 8:19:00, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5184, top5_acc: 0.7650, loss_cls: 2.6794, loss: 2.6794 +2024-07-21 13:17:15,912 - pyskl - INFO - Epoch [141][1300/3746] lr: 1.018e-03, eta: 8:17:37, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5200, top5_acc: 0.7709, loss_cls: 2.6657, loss: 2.6657 +2024-07-21 13:18:38,233 - pyskl - INFO - Epoch [141][1400/3746] lr: 1.013e-03, eta: 8:16:15, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5202, top5_acc: 0.7630, loss_cls: 2.6797, loss: 2.6797 +2024-07-21 13:20:00,312 - pyskl - INFO - Epoch [141][1500/3746] lr: 1.007e-03, eta: 8:14:52, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5208, top5_acc: 0.7653, loss_cls: 2.6763, loss: 2.6763 +2024-07-21 13:21:22,787 - pyskl - INFO - Epoch [141][1600/3746] lr: 1.002e-03, eta: 8:13:29, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5214, top5_acc: 0.7730, loss_cls: 2.6615, loss: 2.6615 +2024-07-21 13:22:44,557 - pyskl - INFO - Epoch [141][1700/3746] lr: 9.961e-04, eta: 8:12:07, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5159, top5_acc: 0.7573, loss_cls: 2.7055, loss: 2.7055 +2024-07-21 13:24:06,435 - pyskl - INFO - Epoch [141][1800/3746] lr: 9.905e-04, eta: 8:10:44, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.4991, top5_acc: 0.7547, loss_cls: 2.7633, loss: 2.7633 +2024-07-21 13:25:28,489 - pyskl - INFO - Epoch [141][1900/3746] lr: 9.850e-04, eta: 8:09:22, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5161, top5_acc: 0.7573, loss_cls: 2.7244, loss: 2.7244 +2024-07-21 13:26:50,877 - pyskl - INFO - Epoch [141][2000/3746] lr: 9.795e-04, eta: 8:07:59, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5139, top5_acc: 0.7609, loss_cls: 2.7123, loss: 2.7123 +2024-07-21 13:28:13,453 - pyskl - INFO - Epoch [141][2100/3746] lr: 9.740e-04, eta: 8:06:36, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5206, top5_acc: 0.7658, loss_cls: 2.6783, loss: 2.6783 +2024-07-21 13:29:35,301 - pyskl - INFO - Epoch [141][2200/3746] lr: 9.685e-04, eta: 8:05:14, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5095, top5_acc: 0.7544, loss_cls: 2.7576, loss: 2.7576 +2024-07-21 13:30:57,332 - pyskl - INFO - Epoch [141][2300/3746] lr: 9.630e-04, eta: 8:03:51, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5144, top5_acc: 0.7625, loss_cls: 2.6924, loss: 2.6924 +2024-07-21 13:32:19,075 - pyskl - INFO - Epoch [141][2400/3746] lr: 9.576e-04, eta: 8:02:29, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5164, top5_acc: 0.7678, loss_cls: 2.6899, loss: 2.6899 +2024-07-21 13:33:41,461 - pyskl - INFO - Epoch [141][2500/3746] lr: 9.522e-04, eta: 8:01:06, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5170, top5_acc: 0.7680, loss_cls: 2.6857, loss: 2.6857 +2024-07-21 13:35:03,163 - pyskl - INFO - Epoch [141][2600/3746] lr: 9.467e-04, eta: 7:59:43, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5103, top5_acc: 0.7606, loss_cls: 2.6966, loss: 2.6966 +2024-07-21 13:36:25,220 - pyskl - INFO - Epoch [141][2700/3746] lr: 9.413e-04, eta: 7:58:21, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5161, top5_acc: 0.7631, loss_cls: 2.6998, loss: 2.6998 +2024-07-21 13:37:46,526 - pyskl - INFO - Epoch [141][2800/3746] lr: 9.359e-04, eta: 7:56:58, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5122, top5_acc: 0.7570, loss_cls: 2.7138, loss: 2.7138 +2024-07-21 13:39:08,329 - pyskl - INFO - Epoch [141][2900/3746] lr: 9.306e-04, eta: 7:55:35, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5239, top5_acc: 0.7589, loss_cls: 2.6995, loss: 2.6995 +2024-07-21 13:40:30,365 - pyskl - INFO - Epoch [141][3000/3746] lr: 9.252e-04, eta: 7:54:13, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5189, top5_acc: 0.7597, loss_cls: 2.7023, loss: 2.7023 +2024-07-21 13:41:52,205 - pyskl - INFO - Epoch [141][3100/3746] lr: 9.199e-04, eta: 7:52:50, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5148, top5_acc: 0.7625, loss_cls: 2.7001, loss: 2.7001 +2024-07-21 13:43:14,395 - pyskl - INFO - Epoch [141][3200/3746] lr: 9.145e-04, eta: 7:51:28, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5178, top5_acc: 0.7603, loss_cls: 2.6950, loss: 2.6950 +2024-07-21 13:44:36,575 - pyskl - INFO - Epoch [141][3300/3746] lr: 9.092e-04, eta: 7:50:05, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5119, top5_acc: 0.7617, loss_cls: 2.6994, loss: 2.6994 +2024-07-21 13:45:58,598 - pyskl - INFO - Epoch [141][3400/3746] lr: 9.039e-04, eta: 7:48:42, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5189, top5_acc: 0.7598, loss_cls: 2.6896, loss: 2.6896 +2024-07-21 13:47:21,629 - pyskl - INFO - Epoch [141][3500/3746] lr: 8.986e-04, eta: 7:47:20, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5105, top5_acc: 0.7617, loss_cls: 2.7090, loss: 2.7090 +2024-07-21 13:48:43,307 - pyskl - INFO - Epoch [141][3600/3746] lr: 8.934e-04, eta: 7:45:57, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5144, top5_acc: 0.7664, loss_cls: 2.6803, loss: 2.6803 +2024-07-21 13:50:04,843 - pyskl - INFO - Epoch [141][3700/3746] lr: 8.881e-04, eta: 7:44:35, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5091, top5_acc: 0.7508, loss_cls: 2.7581, loss: 2.7581 +2024-07-21 13:50:44,391 - pyskl - INFO - Saving checkpoint at 141 epochs +2024-07-21 13:52:35,081 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 13:52:35,748 - pyskl - INFO - +top1_acc 0.3911 +top5_acc 0.6408 +2024-07-21 13:52:35,748 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 13:52:35,790 - pyskl - INFO - +mean_acc 0.3908 +2024-07-21 13:52:35,795 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_140.pth was removed +2024-07-21 13:52:36,068 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_141.pth. +2024-07-21 13:52:36,068 - pyskl - INFO - Best top1_acc is 0.3911 at 141 epoch. +2024-07-21 13:52:36,081 - pyskl - INFO - Epoch(val) [141][309] top1_acc: 0.3911, top5_acc: 0.6408, mean_class_accuracy: 0.3908 +2024-07-21 13:56:26,740 - pyskl - INFO - Epoch [142][100/3746] lr: 8.805e-04, eta: 7:42:41, time: 2.306, data_time: 1.316, memory: 15990, top1_acc: 0.5309, top5_acc: 0.7719, loss_cls: 2.6225, loss: 2.6225 +2024-07-21 13:57:49,424 - pyskl - INFO - Epoch [142][200/3746] lr: 8.752e-04, eta: 7:41:19, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5258, top5_acc: 0.7672, loss_cls: 2.6611, loss: 2.6611 +2024-07-21 13:59:11,975 - pyskl - INFO - Epoch [142][300/3746] lr: 8.700e-04, eta: 7:39:56, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5328, top5_acc: 0.7789, loss_cls: 2.6031, loss: 2.6031 +2024-07-21 14:00:34,254 - pyskl - INFO - Epoch [142][400/3746] lr: 8.649e-04, eta: 7:38:33, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5395, top5_acc: 0.7767, loss_cls: 2.6011, loss: 2.6011 +2024-07-21 14:01:56,337 - pyskl - INFO - Epoch [142][500/3746] lr: 8.597e-04, eta: 7:37:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5277, top5_acc: 0.7816, loss_cls: 2.6186, loss: 2.6186 +2024-07-21 14:03:18,700 - pyskl - INFO - Epoch [142][600/3746] lr: 8.545e-04, eta: 7:35:48, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5238, top5_acc: 0.7725, loss_cls: 2.6362, loss: 2.6362 +2024-07-21 14:04:40,835 - pyskl - INFO - Epoch [142][700/3746] lr: 8.494e-04, eta: 7:34:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5416, top5_acc: 0.7788, loss_cls: 2.5625, loss: 2.5625 +2024-07-21 14:06:03,605 - pyskl - INFO - Epoch [142][800/3746] lr: 8.443e-04, eta: 7:33:03, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5264, top5_acc: 0.7731, loss_cls: 2.6349, loss: 2.6349 +2024-07-21 14:07:26,013 - pyskl - INFO - Epoch [142][900/3746] lr: 8.392e-04, eta: 7:31:40, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5247, top5_acc: 0.7605, loss_cls: 2.6880, loss: 2.6880 +2024-07-21 14:08:48,438 - pyskl - INFO - Epoch [142][1000/3746] lr: 8.341e-04, eta: 7:30:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5291, top5_acc: 0.7773, loss_cls: 2.6042, loss: 2.6042 +2024-07-21 14:10:11,190 - pyskl - INFO - Epoch [142][1100/3746] lr: 8.290e-04, eta: 7:28:55, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5216, top5_acc: 0.7748, loss_cls: 2.6520, loss: 2.6520 +2024-07-21 14:11:33,291 - pyskl - INFO - Epoch [142][1200/3746] lr: 8.239e-04, eta: 7:27:33, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5302, top5_acc: 0.7817, loss_cls: 2.6020, loss: 2.6020 +2024-07-21 14:12:55,443 - pyskl - INFO - Epoch [142][1300/3746] lr: 8.189e-04, eta: 7:26:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5183, top5_acc: 0.7620, loss_cls: 2.7036, loss: 2.7036 +2024-07-21 14:14:17,750 - pyskl - INFO - Epoch [142][1400/3746] lr: 8.139e-04, eta: 7:24:47, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5267, top5_acc: 0.7716, loss_cls: 2.6264, loss: 2.6264 +2024-07-21 14:15:39,817 - pyskl - INFO - Epoch [142][1500/3746] lr: 8.088e-04, eta: 7:23:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5186, top5_acc: 0.7680, loss_cls: 2.6874, loss: 2.6874 +2024-07-21 14:17:01,373 - pyskl - INFO - Epoch [142][1600/3746] lr: 8.038e-04, eta: 7:22:02, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5358, top5_acc: 0.7775, loss_cls: 2.6065, loss: 2.6065 +2024-07-21 14:18:23,519 - pyskl - INFO - Epoch [142][1700/3746] lr: 7.989e-04, eta: 7:20:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5272, top5_acc: 0.7675, loss_cls: 2.6431, loss: 2.6431 +2024-07-21 14:19:45,093 - pyskl - INFO - Epoch [142][1800/3746] lr: 7.939e-04, eta: 7:19:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5238, top5_acc: 0.7739, loss_cls: 2.6482, loss: 2.6482 +2024-07-21 14:21:07,250 - pyskl - INFO - Epoch [142][1900/3746] lr: 7.889e-04, eta: 7:17:54, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5283, top5_acc: 0.7667, loss_cls: 2.6559, loss: 2.6559 +2024-07-21 14:22:29,078 - pyskl - INFO - Epoch [142][2000/3746] lr: 7.840e-04, eta: 7:16:32, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5363, top5_acc: 0.7766, loss_cls: 2.6245, loss: 2.6245 +2024-07-21 14:23:51,384 - pyskl - INFO - Epoch [142][2100/3746] lr: 7.791e-04, eta: 7:15:09, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5147, top5_acc: 0.7623, loss_cls: 2.7065, loss: 2.7065 +2024-07-21 14:25:14,074 - pyskl - INFO - Epoch [142][2200/3746] lr: 7.742e-04, eta: 7:13:46, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5261, top5_acc: 0.7734, loss_cls: 2.6171, loss: 2.6171 +2024-07-21 14:26:37,168 - pyskl - INFO - Epoch [142][2300/3746] lr: 7.693e-04, eta: 7:12:24, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5227, top5_acc: 0.7655, loss_cls: 2.6743, loss: 2.6743 +2024-07-21 14:27:59,220 - pyskl - INFO - Epoch [142][2400/3746] lr: 7.644e-04, eta: 7:11:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5180, top5_acc: 0.7630, loss_cls: 2.7004, loss: 2.7004 +2024-07-21 14:29:21,435 - pyskl - INFO - Epoch [142][2500/3746] lr: 7.595e-04, eta: 7:09:39, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5286, top5_acc: 0.7645, loss_cls: 2.6549, loss: 2.6549 +2024-07-21 14:30:44,136 - pyskl - INFO - Epoch [142][2600/3746] lr: 7.547e-04, eta: 7:08:16, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5305, top5_acc: 0.7688, loss_cls: 2.6300, loss: 2.6300 +2024-07-21 14:32:05,711 - pyskl - INFO - Epoch [142][2700/3746] lr: 7.499e-04, eta: 7:06:53, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5325, top5_acc: 0.7720, loss_cls: 2.6471, loss: 2.6471 +2024-07-21 14:33:26,966 - pyskl - INFO - Epoch [142][2800/3746] lr: 7.450e-04, eta: 7:05:31, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5259, top5_acc: 0.7689, loss_cls: 2.6399, loss: 2.6399 +2024-07-21 14:34:48,718 - pyskl - INFO - Epoch [142][2900/3746] lr: 7.402e-04, eta: 7:04:08, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5272, top5_acc: 0.7659, loss_cls: 2.6702, loss: 2.6702 +2024-07-21 14:36:10,358 - pyskl - INFO - Epoch [142][3000/3746] lr: 7.355e-04, eta: 7:02:45, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5270, top5_acc: 0.7659, loss_cls: 2.6712, loss: 2.6712 +2024-07-21 14:37:31,879 - pyskl - INFO - Epoch [142][3100/3746] lr: 7.307e-04, eta: 7:01:23, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5325, top5_acc: 0.7736, loss_cls: 2.6234, loss: 2.6234 +2024-07-21 14:38:53,917 - pyskl - INFO - Epoch [142][3200/3746] lr: 7.259e-04, eta: 7:00:00, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5256, top5_acc: 0.7653, loss_cls: 2.6799, loss: 2.6799 +2024-07-21 14:40:16,429 - pyskl - INFO - Epoch [142][3300/3746] lr: 7.212e-04, eta: 6:58:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5341, top5_acc: 0.7728, loss_cls: 2.6227, loss: 2.6227 +2024-07-21 14:41:38,382 - pyskl - INFO - Epoch [142][3400/3746] lr: 7.165e-04, eta: 6:57:15, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5288, top5_acc: 0.7808, loss_cls: 2.5931, loss: 2.5931 +2024-07-21 14:43:00,839 - pyskl - INFO - Epoch [142][3500/3746] lr: 7.118e-04, eta: 6:55:52, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5247, top5_acc: 0.7702, loss_cls: 2.6492, loss: 2.6492 +2024-07-21 14:44:22,781 - pyskl - INFO - Epoch [142][3600/3746] lr: 7.071e-04, eta: 6:54:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5153, top5_acc: 0.7622, loss_cls: 2.6654, loss: 2.6654 +2024-07-21 14:45:45,587 - pyskl - INFO - Epoch [142][3700/3746] lr: 7.024e-04, eta: 6:53:07, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5230, top5_acc: 0.7716, loss_cls: 2.6594, loss: 2.6594 +2024-07-21 14:46:25,644 - pyskl - INFO - Saving checkpoint at 142 epochs +2024-07-21 14:48:16,440 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 14:48:17,109 - pyskl - INFO - +top1_acc 0.3929 +top5_acc 0.6417 +2024-07-21 14:48:17,109 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 14:48:17,151 - pyskl - INFO - +mean_acc 0.3927 +2024-07-21 14:48:17,156 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_141.pth was removed +2024-07-21 14:48:17,400 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_142.pth. +2024-07-21 14:48:17,400 - pyskl - INFO - Best top1_acc is 0.3929 at 142 epoch. +2024-07-21 14:48:17,413 - pyskl - INFO - Epoch(val) [142][309] top1_acc: 0.3929, top5_acc: 0.6417, mean_class_accuracy: 0.3927 +2024-07-21 14:52:05,303 - pyskl - INFO - Epoch [143][100/3746] lr: 6.956e-04, eta: 6:51:13, time: 2.279, data_time: 1.287, memory: 15990, top1_acc: 0.5352, top5_acc: 0.7781, loss_cls: 2.6000, loss: 2.6000 +2024-07-21 14:53:27,636 - pyskl - INFO - Epoch [143][200/3746] lr: 6.910e-04, eta: 6:49:50, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5425, top5_acc: 0.7834, loss_cls: 2.5657, loss: 2.5657 +2024-07-21 14:54:49,477 - pyskl - INFO - Epoch [143][300/3746] lr: 6.863e-04, eta: 6:48:27, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5405, top5_acc: 0.7869, loss_cls: 2.5499, loss: 2.5499 +2024-07-21 14:56:11,590 - pyskl - INFO - Epoch [143][400/3746] lr: 6.817e-04, eta: 6:47:05, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5487, top5_acc: 0.7837, loss_cls: 2.5162, loss: 2.5162 +2024-07-21 14:57:34,056 - pyskl - INFO - Epoch [143][500/3746] lr: 6.771e-04, eta: 6:45:42, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5477, top5_acc: 0.7791, loss_cls: 2.5799, loss: 2.5799 +2024-07-21 14:58:56,292 - pyskl - INFO - Epoch [143][600/3746] lr: 6.725e-04, eta: 6:44:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5381, top5_acc: 0.7831, loss_cls: 2.5853, loss: 2.5853 +2024-07-21 15:00:18,323 - pyskl - INFO - Epoch [143][700/3746] lr: 6.680e-04, eta: 6:42:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5363, top5_acc: 0.7834, loss_cls: 2.5778, loss: 2.5778 +2024-07-21 15:01:40,584 - pyskl - INFO - Epoch [143][800/3746] lr: 6.634e-04, eta: 6:41:34, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5320, top5_acc: 0.7773, loss_cls: 2.6122, loss: 2.6122 +2024-07-21 15:03:03,122 - pyskl - INFO - Epoch [143][900/3746] lr: 6.589e-04, eta: 6:40:12, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5455, top5_acc: 0.7875, loss_cls: 2.5591, loss: 2.5591 +2024-07-21 15:04:25,693 - pyskl - INFO - Epoch [143][1000/3746] lr: 6.544e-04, eta: 6:38:49, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5353, top5_acc: 0.7778, loss_cls: 2.6015, loss: 2.6015 +2024-07-21 15:05:48,355 - pyskl - INFO - Epoch [143][1100/3746] lr: 6.499e-04, eta: 6:37:26, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5319, top5_acc: 0.7831, loss_cls: 2.5706, loss: 2.5706 +2024-07-21 15:07:10,109 - pyskl - INFO - Epoch [143][1200/3746] lr: 6.454e-04, eta: 6:36:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5453, top5_acc: 0.7834, loss_cls: 2.5663, loss: 2.5663 +2024-07-21 15:08:32,270 - pyskl - INFO - Epoch [143][1300/3746] lr: 6.409e-04, eta: 6:34:41, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5361, top5_acc: 0.7808, loss_cls: 2.6035, loss: 2.6035 +2024-07-21 15:09:54,334 - pyskl - INFO - Epoch [143][1400/3746] lr: 6.365e-04, eta: 6:33:18, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5414, top5_acc: 0.7742, loss_cls: 2.6027, loss: 2.6027 +2024-07-21 15:11:16,949 - pyskl - INFO - Epoch [143][1500/3746] lr: 6.320e-04, eta: 6:31:56, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5397, top5_acc: 0.7820, loss_cls: 2.5806, loss: 2.5806 +2024-07-21 15:12:39,311 - pyskl - INFO - Epoch [143][1600/3746] lr: 6.276e-04, eta: 6:30:33, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5413, top5_acc: 0.7830, loss_cls: 2.5776, loss: 2.5776 +2024-07-21 15:14:01,431 - pyskl - INFO - Epoch [143][1700/3746] lr: 6.232e-04, eta: 6:29:11, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5378, top5_acc: 0.7705, loss_cls: 2.6216, loss: 2.6216 +2024-07-21 15:15:23,768 - pyskl - INFO - Epoch [143][1800/3746] lr: 6.188e-04, eta: 6:27:48, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5367, top5_acc: 0.7744, loss_cls: 2.5873, loss: 2.5873 +2024-07-21 15:16:46,455 - pyskl - INFO - Epoch [143][1900/3746] lr: 6.144e-04, eta: 6:26:25, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5378, top5_acc: 0.7816, loss_cls: 2.6046, loss: 2.6046 +2024-07-21 15:18:08,953 - pyskl - INFO - Epoch [143][2000/3746] lr: 6.101e-04, eta: 6:25:03, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5409, top5_acc: 0.7700, loss_cls: 2.5818, loss: 2.5818 +2024-07-21 15:19:31,756 - pyskl - INFO - Epoch [143][2100/3746] lr: 6.057e-04, eta: 6:23:40, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5444, top5_acc: 0.7814, loss_cls: 2.5610, loss: 2.5610 +2024-07-21 15:20:54,226 - pyskl - INFO - Epoch [143][2200/3746] lr: 6.014e-04, eta: 6:22:17, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5336, top5_acc: 0.7769, loss_cls: 2.5987, loss: 2.5987 +2024-07-21 15:22:16,307 - pyskl - INFO - Epoch [143][2300/3746] lr: 5.971e-04, eta: 6:20:55, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5389, top5_acc: 0.7792, loss_cls: 2.5900, loss: 2.5900 +2024-07-21 15:23:38,524 - pyskl - INFO - Epoch [143][2400/3746] lr: 5.928e-04, eta: 6:19:32, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5364, top5_acc: 0.7759, loss_cls: 2.5829, loss: 2.5829 +2024-07-21 15:25:00,624 - pyskl - INFO - Epoch [143][2500/3746] lr: 5.885e-04, eta: 6:18:10, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5366, top5_acc: 0.7767, loss_cls: 2.5862, loss: 2.5862 +2024-07-21 15:26:23,265 - pyskl - INFO - Epoch [143][2600/3746] lr: 5.842e-04, eta: 6:16:47, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5306, top5_acc: 0.7722, loss_cls: 2.6329, loss: 2.6329 +2024-07-21 15:27:45,272 - pyskl - INFO - Epoch [143][2700/3746] lr: 5.800e-04, eta: 6:15:24, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5342, top5_acc: 0.7808, loss_cls: 2.5684, loss: 2.5684 +2024-07-21 15:29:07,488 - pyskl - INFO - Epoch [143][2800/3746] lr: 5.757e-04, eta: 6:14:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5288, top5_acc: 0.7670, loss_cls: 2.6631, loss: 2.6631 +2024-07-21 15:30:29,564 - pyskl - INFO - Epoch [143][2900/3746] lr: 5.715e-04, eta: 6:12:39, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5236, top5_acc: 0.7705, loss_cls: 2.6401, loss: 2.6401 +2024-07-21 15:31:51,680 - pyskl - INFO - Epoch [143][3000/3746] lr: 5.673e-04, eta: 6:11:16, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5467, top5_acc: 0.7798, loss_cls: 2.5486, loss: 2.5486 +2024-07-21 15:33:13,632 - pyskl - INFO - Epoch [143][3100/3746] lr: 5.631e-04, eta: 6:09:54, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5302, top5_acc: 0.7794, loss_cls: 2.6096, loss: 2.6096 +2024-07-21 15:34:35,839 - pyskl - INFO - Epoch [143][3200/3746] lr: 5.590e-04, eta: 6:08:31, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5348, top5_acc: 0.7803, loss_cls: 2.5892, loss: 2.5892 +2024-07-21 15:35:57,852 - pyskl - INFO - Epoch [143][3300/3746] lr: 5.548e-04, eta: 6:07:09, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5327, top5_acc: 0.7786, loss_cls: 2.6070, loss: 2.6070 +2024-07-21 15:37:20,277 - pyskl - INFO - Epoch [143][3400/3746] lr: 5.506e-04, eta: 6:05:46, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5316, top5_acc: 0.7720, loss_cls: 2.6149, loss: 2.6149 +2024-07-21 15:38:42,029 - pyskl - INFO - Epoch [143][3500/3746] lr: 5.465e-04, eta: 6:04:23, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5452, top5_acc: 0.7786, loss_cls: 2.5710, loss: 2.5710 +2024-07-21 15:40:04,411 - pyskl - INFO - Epoch [143][3600/3746] lr: 5.424e-04, eta: 6:03:01, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5355, top5_acc: 0.7766, loss_cls: 2.6112, loss: 2.6112 +2024-07-21 15:41:26,445 - pyskl - INFO - Epoch [143][3700/3746] lr: 5.383e-04, eta: 6:01:38, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5380, top5_acc: 0.7733, loss_cls: 2.5940, loss: 2.5940 +2024-07-21 15:42:06,011 - pyskl - INFO - Saving checkpoint at 143 epochs +2024-07-21 15:43:58,046 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 15:43:58,712 - pyskl - INFO - +top1_acc 0.3955 +top5_acc 0.6416 +2024-07-21 15:43:58,712 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 15:43:58,753 - pyskl - INFO - +mean_acc 0.3952 +2024-07-21 15:43:58,758 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_142.pth was removed +2024-07-21 15:43:58,995 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_143.pth. +2024-07-21 15:43:58,996 - pyskl - INFO - Best top1_acc is 0.3955 at 143 epoch. +2024-07-21 15:43:59,008 - pyskl - INFO - Epoch(val) [143][309] top1_acc: 0.3955, top5_acc: 0.6416, mean_class_accuracy: 0.3952 +2024-07-21 15:47:48,837 - pyskl - INFO - Epoch [144][100/3746] lr: 5.323e-04, eta: 5:59:43, time: 2.298, data_time: 1.297, memory: 15990, top1_acc: 0.5556, top5_acc: 0.7947, loss_cls: 2.4901, loss: 2.4901 +2024-07-21 15:49:10,886 - pyskl - INFO - Epoch [144][200/3746] lr: 5.283e-04, eta: 5:58:20, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5527, top5_acc: 0.7877, loss_cls: 2.5252, loss: 2.5252 +2024-07-21 15:50:32,994 - pyskl - INFO - Epoch [144][300/3746] lr: 5.242e-04, eta: 5:56:57, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5498, top5_acc: 0.7908, loss_cls: 2.5132, loss: 2.5132 +2024-07-21 15:51:55,187 - pyskl - INFO - Epoch [144][400/3746] lr: 5.202e-04, eta: 5:55:35, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5466, top5_acc: 0.7812, loss_cls: 2.5376, loss: 2.5376 +2024-07-21 15:53:17,265 - pyskl - INFO - Epoch [144][500/3746] lr: 5.162e-04, eta: 5:54:12, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5556, top5_acc: 0.8008, loss_cls: 2.4942, loss: 2.4942 +2024-07-21 15:54:39,387 - pyskl - INFO - Epoch [144][600/3746] lr: 5.122e-04, eta: 5:52:50, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5470, top5_acc: 0.7852, loss_cls: 2.5468, loss: 2.5468 +2024-07-21 15:56:01,905 - pyskl - INFO - Epoch [144][700/3746] lr: 5.082e-04, eta: 5:51:27, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5591, top5_acc: 0.7917, loss_cls: 2.5014, loss: 2.5014 +2024-07-21 15:57:23,834 - pyskl - INFO - Epoch [144][800/3746] lr: 5.042e-04, eta: 5:50:04, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5573, top5_acc: 0.7897, loss_cls: 2.4991, loss: 2.4991 +2024-07-21 15:58:45,974 - pyskl - INFO - Epoch [144][900/3746] lr: 5.003e-04, eta: 5:48:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5623, top5_acc: 0.8005, loss_cls: 2.4469, loss: 2.4469 +2024-07-21 16:00:08,205 - pyskl - INFO - Epoch [144][1000/3746] lr: 4.964e-04, eta: 5:47:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5417, top5_acc: 0.7878, loss_cls: 2.5554, loss: 2.5554 +2024-07-21 16:01:30,953 - pyskl - INFO - Epoch [144][1100/3746] lr: 4.924e-04, eta: 5:45:56, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5478, top5_acc: 0.7878, loss_cls: 2.5253, loss: 2.5253 +2024-07-21 16:02:53,542 - pyskl - INFO - Epoch [144][1200/3746] lr: 4.885e-04, eta: 5:44:34, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5473, top5_acc: 0.7928, loss_cls: 2.5250, loss: 2.5250 +2024-07-21 16:04:16,031 - pyskl - INFO - Epoch [144][1300/3746] lr: 4.846e-04, eta: 5:43:11, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5420, top5_acc: 0.7828, loss_cls: 2.5896, loss: 2.5896 +2024-07-21 16:05:38,249 - pyskl - INFO - Epoch [144][1400/3746] lr: 4.808e-04, eta: 5:41:48, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5459, top5_acc: 0.7825, loss_cls: 2.5606, loss: 2.5606 +2024-07-21 16:07:00,541 - pyskl - INFO - Epoch [144][1500/3746] lr: 4.769e-04, eta: 5:40:26, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5420, top5_acc: 0.7803, loss_cls: 2.5597, loss: 2.5597 +2024-07-21 16:08:22,205 - pyskl - INFO - Epoch [144][1600/3746] lr: 4.731e-04, eta: 5:39:03, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5422, top5_acc: 0.7841, loss_cls: 2.5454, loss: 2.5454 +2024-07-21 16:09:44,279 - pyskl - INFO - Epoch [144][1700/3746] lr: 4.692e-04, eta: 5:37:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5620, top5_acc: 0.7967, loss_cls: 2.4847, loss: 2.4847 +2024-07-21 16:11:06,705 - pyskl - INFO - Epoch [144][1800/3746] lr: 4.654e-04, eta: 5:36:18, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5455, top5_acc: 0.7877, loss_cls: 2.5530, loss: 2.5530 +2024-07-21 16:12:28,680 - pyskl - INFO - Epoch [144][1900/3746] lr: 4.616e-04, eta: 5:34:55, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5398, top5_acc: 0.7788, loss_cls: 2.5772, loss: 2.5772 +2024-07-21 16:13:51,498 - pyskl - INFO - Epoch [144][2000/3746] lr: 4.578e-04, eta: 5:33:33, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5411, top5_acc: 0.7859, loss_cls: 2.5358, loss: 2.5358 +2024-07-21 16:15:13,688 - pyskl - INFO - Epoch [144][2100/3746] lr: 4.541e-04, eta: 5:32:10, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5386, top5_acc: 0.7842, loss_cls: 2.5517, loss: 2.5517 +2024-07-21 16:16:35,747 - pyskl - INFO - Epoch [144][2200/3746] lr: 4.503e-04, eta: 5:30:47, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5556, top5_acc: 0.7917, loss_cls: 2.5152, loss: 2.5152 +2024-07-21 16:17:57,852 - pyskl - INFO - Epoch [144][2300/3746] lr: 4.466e-04, eta: 5:29:25, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5434, top5_acc: 0.7770, loss_cls: 2.5926, loss: 2.5926 +2024-07-21 16:19:19,515 - pyskl - INFO - Epoch [144][2400/3746] lr: 4.429e-04, eta: 5:28:02, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5519, top5_acc: 0.7925, loss_cls: 2.5165, loss: 2.5165 +2024-07-21 16:20:41,463 - pyskl - INFO - Epoch [144][2500/3746] lr: 4.392e-04, eta: 5:26:39, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5522, top5_acc: 0.7900, loss_cls: 2.5274, loss: 2.5274 +2024-07-21 16:22:03,062 - pyskl - INFO - Epoch [144][2600/3746] lr: 4.355e-04, eta: 5:25:17, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5472, top5_acc: 0.7863, loss_cls: 2.5181, loss: 2.5181 +2024-07-21 16:23:24,772 - pyskl - INFO - Epoch [144][2700/3746] lr: 4.318e-04, eta: 5:23:54, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5503, top5_acc: 0.7819, loss_cls: 2.5428, loss: 2.5428 +2024-07-21 16:24:46,178 - pyskl - INFO - Epoch [144][2800/3746] lr: 4.281e-04, eta: 5:22:31, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5411, top5_acc: 0.7798, loss_cls: 2.5648, loss: 2.5648 +2024-07-21 16:26:07,784 - pyskl - INFO - Epoch [144][2900/3746] lr: 4.245e-04, eta: 5:21:09, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5391, top5_acc: 0.7845, loss_cls: 2.5614, loss: 2.5614 +2024-07-21 16:27:29,817 - pyskl - INFO - Epoch [144][3000/3746] lr: 4.209e-04, eta: 5:19:46, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5427, top5_acc: 0.7858, loss_cls: 2.5398, loss: 2.5398 +2024-07-21 16:28:51,867 - pyskl - INFO - Epoch [144][3100/3746] lr: 4.173e-04, eta: 5:18:23, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5487, top5_acc: 0.7880, loss_cls: 2.5348, loss: 2.5348 +2024-07-21 16:30:13,721 - pyskl - INFO - Epoch [144][3200/3746] lr: 4.137e-04, eta: 5:17:01, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5463, top5_acc: 0.7856, loss_cls: 2.5424, loss: 2.5424 +2024-07-21 16:31:35,251 - pyskl - INFO - Epoch [144][3300/3746] lr: 4.101e-04, eta: 5:15:38, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5402, top5_acc: 0.7762, loss_cls: 2.5819, loss: 2.5819 +2024-07-21 16:32:56,991 - pyskl - INFO - Epoch [144][3400/3746] lr: 4.065e-04, eta: 5:14:15, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5345, top5_acc: 0.7744, loss_cls: 2.6011, loss: 2.6011 +2024-07-21 16:34:19,166 - pyskl - INFO - Epoch [144][3500/3746] lr: 4.030e-04, eta: 5:12:53, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5400, top5_acc: 0.7886, loss_cls: 2.5298, loss: 2.5298 +2024-07-21 16:35:41,082 - pyskl - INFO - Epoch [144][3600/3746] lr: 3.994e-04, eta: 5:11:30, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5453, top5_acc: 0.7875, loss_cls: 2.5530, loss: 2.5530 +2024-07-21 16:37:03,221 - pyskl - INFO - Epoch [144][3700/3746] lr: 3.959e-04, eta: 5:10:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5498, top5_acc: 0.7855, loss_cls: 2.5422, loss: 2.5422 +2024-07-21 16:37:42,739 - pyskl - INFO - Saving checkpoint at 144 epochs +2024-07-21 16:39:32,767 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 16:39:33,501 - pyskl - INFO - +top1_acc 0.3978 +top5_acc 0.6453 +2024-07-21 16:39:33,501 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 16:39:33,547 - pyskl - INFO - +mean_acc 0.3976 +2024-07-21 16:39:33,552 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_143.pth was removed +2024-07-21 16:39:33,807 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_144.pth. +2024-07-21 16:39:33,807 - pyskl - INFO - Best top1_acc is 0.3978 at 144 epoch. +2024-07-21 16:39:33,821 - pyskl - INFO - Epoch(val) [144][309] top1_acc: 0.3978, top5_acc: 0.6453, mean_class_accuracy: 0.3976 +2024-07-21 16:43:25,106 - pyskl - INFO - Epoch [145][100/3746] lr: 3.908e-04, eta: 5:08:11, time: 2.313, data_time: 1.316, memory: 15990, top1_acc: 0.5677, top5_acc: 0.7964, loss_cls: 2.4554, loss: 2.4554 +2024-07-21 16:44:49,350 - pyskl - INFO - Epoch [145][200/3746] lr: 3.873e-04, eta: 5:06:49, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5556, top5_acc: 0.7875, loss_cls: 2.4849, loss: 2.4849 +2024-07-21 16:46:12,494 - pyskl - INFO - Epoch [145][300/3746] lr: 3.839e-04, eta: 5:05:26, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5483, top5_acc: 0.7870, loss_cls: 2.5564, loss: 2.5564 +2024-07-21 16:47:36,370 - pyskl - INFO - Epoch [145][400/3746] lr: 3.804e-04, eta: 5:04:04, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5633, top5_acc: 0.8066, loss_cls: 2.4414, loss: 2.4414 +2024-07-21 16:49:00,489 - pyskl - INFO - Epoch [145][500/3746] lr: 3.770e-04, eta: 5:02:41, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.5658, top5_acc: 0.7923, loss_cls: 2.4599, loss: 2.4599 +2024-07-21 16:50:23,277 - pyskl - INFO - Epoch [145][600/3746] lr: 3.736e-04, eta: 5:01:18, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5634, top5_acc: 0.7986, loss_cls: 2.4206, loss: 2.4206 +2024-07-21 16:51:46,640 - pyskl - INFO - Epoch [145][700/3746] lr: 3.702e-04, eta: 4:59:56, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5500, top5_acc: 0.7891, loss_cls: 2.5274, loss: 2.5274 +2024-07-21 16:53:09,868 - pyskl - INFO - Epoch [145][800/3746] lr: 3.668e-04, eta: 4:58:33, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5519, top5_acc: 0.7922, loss_cls: 2.5108, loss: 2.5108 +2024-07-21 16:54:33,758 - pyskl - INFO - Epoch [145][900/3746] lr: 3.634e-04, eta: 4:57:11, time: 0.839, data_time: 0.001, memory: 15990, top1_acc: 0.5470, top5_acc: 0.7914, loss_cls: 2.5129, loss: 2.5129 +2024-07-21 16:55:57,371 - pyskl - INFO - Epoch [145][1000/3746] lr: 3.600e-04, eta: 4:55:48, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5616, top5_acc: 0.7952, loss_cls: 2.4668, loss: 2.4668 +2024-07-21 16:57:20,117 - pyskl - INFO - Epoch [145][1100/3746] lr: 3.567e-04, eta: 4:54:25, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5700, top5_acc: 0.8003, loss_cls: 2.4325, loss: 2.4325 +2024-07-21 16:58:43,212 - pyskl - INFO - Epoch [145][1200/3746] lr: 3.534e-04, eta: 4:53:03, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5580, top5_acc: 0.7898, loss_cls: 2.4791, loss: 2.4791 +2024-07-21 17:00:07,281 - pyskl - INFO - Epoch [145][1300/3746] lr: 3.501e-04, eta: 4:51:40, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.5608, top5_acc: 0.7963, loss_cls: 2.4618, loss: 2.4618 +2024-07-21 17:01:30,797 - pyskl - INFO - Epoch [145][1400/3746] lr: 3.468e-04, eta: 4:50:18, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5642, top5_acc: 0.7983, loss_cls: 2.4594, loss: 2.4594 +2024-07-21 17:02:53,228 - pyskl - INFO - Epoch [145][1500/3746] lr: 3.435e-04, eta: 4:48:55, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5684, top5_acc: 0.7994, loss_cls: 2.4458, loss: 2.4458 +2024-07-21 17:04:16,235 - pyskl - INFO - Epoch [145][1600/3746] lr: 3.402e-04, eta: 4:47:32, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5627, top5_acc: 0.8014, loss_cls: 2.4635, loss: 2.4635 +2024-07-21 17:05:39,296 - pyskl - INFO - Epoch [145][1700/3746] lr: 3.370e-04, eta: 4:46:10, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5663, top5_acc: 0.7922, loss_cls: 2.4722, loss: 2.4722 +2024-07-21 17:07:01,752 - pyskl - INFO - Epoch [145][1800/3746] lr: 3.337e-04, eta: 4:44:47, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5563, top5_acc: 0.7942, loss_cls: 2.5018, loss: 2.5018 +2024-07-21 17:08:24,190 - pyskl - INFO - Epoch [145][1900/3746] lr: 3.305e-04, eta: 4:43:24, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5508, top5_acc: 0.7958, loss_cls: 2.5163, loss: 2.5163 +2024-07-21 17:09:46,651 - pyskl - INFO - Epoch [145][2000/3746] lr: 3.273e-04, eta: 4:42:02, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5550, top5_acc: 0.7941, loss_cls: 2.4925, loss: 2.4925 +2024-07-21 17:11:09,791 - pyskl - INFO - Epoch [145][2100/3746] lr: 3.241e-04, eta: 4:40:39, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5489, top5_acc: 0.7845, loss_cls: 2.5225, loss: 2.5225 +2024-07-21 17:12:32,506 - pyskl - INFO - Epoch [145][2200/3746] lr: 3.210e-04, eta: 4:39:16, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5536, top5_acc: 0.7950, loss_cls: 2.5053, loss: 2.5053 +2024-07-21 17:13:55,689 - pyskl - INFO - Epoch [145][2300/3746] lr: 3.178e-04, eta: 4:37:54, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5675, top5_acc: 0.7997, loss_cls: 2.4395, loss: 2.4395 +2024-07-21 17:15:19,561 - pyskl - INFO - Epoch [145][2400/3746] lr: 3.147e-04, eta: 4:36:31, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5463, top5_acc: 0.7788, loss_cls: 2.5508, loss: 2.5508 +2024-07-21 17:16:43,074 - pyskl - INFO - Epoch [145][2500/3746] lr: 3.116e-04, eta: 4:35:09, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5448, top5_acc: 0.7922, loss_cls: 2.5035, loss: 2.5035 +2024-07-21 17:18:06,237 - pyskl - INFO - Epoch [145][2600/3746] lr: 3.084e-04, eta: 4:33:46, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5519, top5_acc: 0.7884, loss_cls: 2.5126, loss: 2.5126 +2024-07-21 17:19:28,764 - pyskl - INFO - Epoch [145][2700/3746] lr: 3.054e-04, eta: 4:32:23, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5578, top5_acc: 0.7989, loss_cls: 2.4915, loss: 2.4915 +2024-07-21 17:20:50,825 - pyskl - INFO - Epoch [145][2800/3746] lr: 3.023e-04, eta: 4:31:01, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5534, top5_acc: 0.7875, loss_cls: 2.5054, loss: 2.5054 +2024-07-21 17:22:13,842 - pyskl - INFO - Epoch [145][2900/3746] lr: 2.992e-04, eta: 4:29:38, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5528, top5_acc: 0.7930, loss_cls: 2.5029, loss: 2.5029 +2024-07-21 17:23:36,314 - pyskl - INFO - Epoch [145][3000/3746] lr: 2.962e-04, eta: 4:28:15, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5514, top5_acc: 0.7837, loss_cls: 2.5280, loss: 2.5280 +2024-07-21 17:24:58,992 - pyskl - INFO - Epoch [145][3100/3746] lr: 2.931e-04, eta: 4:26:53, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5583, top5_acc: 0.7987, loss_cls: 2.4643, loss: 2.4643 +2024-07-21 17:26:21,677 - pyskl - INFO - Epoch [145][3200/3746] lr: 2.901e-04, eta: 4:25:30, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5614, top5_acc: 0.7873, loss_cls: 2.4936, loss: 2.4936 +2024-07-21 17:27:43,789 - pyskl - INFO - Epoch [145][3300/3746] lr: 2.871e-04, eta: 4:24:07, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5545, top5_acc: 0.7847, loss_cls: 2.5192, loss: 2.5192 +2024-07-21 17:29:06,381 - pyskl - INFO - Epoch [145][3400/3746] lr: 2.841e-04, eta: 4:22:45, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5620, top5_acc: 0.7952, loss_cls: 2.4854, loss: 2.4854 +2024-07-21 17:30:28,423 - pyskl - INFO - Epoch [145][3500/3746] lr: 2.812e-04, eta: 4:21:22, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5461, top5_acc: 0.7858, loss_cls: 2.5370, loss: 2.5370 +2024-07-21 17:31:50,836 - pyskl - INFO - Epoch [145][3600/3746] lr: 2.782e-04, eta: 4:19:59, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5581, top5_acc: 0.7817, loss_cls: 2.5458, loss: 2.5458 +2024-07-21 17:33:12,639 - pyskl - INFO - Epoch [145][3700/3746] lr: 2.753e-04, eta: 4:18:37, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5527, top5_acc: 0.7909, loss_cls: 2.5345, loss: 2.5345 +2024-07-21 17:33:51,871 - pyskl - INFO - Saving checkpoint at 145 epochs +2024-07-21 17:35:42,237 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 17:35:42,985 - pyskl - INFO - +top1_acc 0.3955 +top5_acc 0.6446 +2024-07-21 17:35:42,986 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 17:35:43,031 - pyskl - INFO - +mean_acc 0.3953 +2024-07-21 17:35:43,041 - pyskl - INFO - Epoch(val) [145][309] top1_acc: 0.3955, top5_acc: 0.6446, mean_class_accuracy: 0.3953 +2024-07-21 17:39:30,977 - pyskl - INFO - Epoch [146][100/3746] lr: 2.710e-04, eta: 4:16:40, time: 2.279, data_time: 1.283, memory: 15990, top1_acc: 0.5670, top5_acc: 0.8111, loss_cls: 2.3920, loss: 2.3920 +2024-07-21 17:40:53,580 - pyskl - INFO - Epoch [146][200/3746] lr: 2.681e-04, eta: 4:15:17, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5625, top5_acc: 0.7963, loss_cls: 2.4700, loss: 2.4700 +2024-07-21 17:42:16,280 - pyskl - INFO - Epoch [146][300/3746] lr: 2.652e-04, eta: 4:13:54, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5675, top5_acc: 0.8052, loss_cls: 2.4399, loss: 2.4399 +2024-07-21 17:43:40,326 - pyskl - INFO - Epoch [146][400/3746] lr: 2.624e-04, eta: 4:12:32, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5694, top5_acc: 0.7973, loss_cls: 2.4359, loss: 2.4359 +2024-07-21 17:45:03,914 - pyskl - INFO - Epoch [146][500/3746] lr: 2.595e-04, eta: 4:11:09, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5620, top5_acc: 0.8027, loss_cls: 2.4288, loss: 2.4288 +2024-07-21 17:46:26,482 - pyskl - INFO - Epoch [146][600/3746] lr: 2.567e-04, eta: 4:09:47, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5663, top5_acc: 0.7992, loss_cls: 2.4358, loss: 2.4358 +2024-07-21 17:47:50,438 - pyskl - INFO - Epoch [146][700/3746] lr: 2.539e-04, eta: 4:08:24, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5672, top5_acc: 0.8069, loss_cls: 2.4342, loss: 2.4342 +2024-07-21 17:49:13,927 - pyskl - INFO - Epoch [146][800/3746] lr: 2.511e-04, eta: 4:07:01, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5711, top5_acc: 0.7967, loss_cls: 2.4400, loss: 2.4400 +2024-07-21 17:50:38,187 - pyskl - INFO - Epoch [146][900/3746] lr: 2.483e-04, eta: 4:05:39, time: 0.843, data_time: 0.000, memory: 15990, top1_acc: 0.5645, top5_acc: 0.7975, loss_cls: 2.4486, loss: 2.4486 +2024-07-21 17:52:01,057 - pyskl - INFO - Epoch [146][1000/3746] lr: 2.455e-04, eta: 4:04:16, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5637, top5_acc: 0.7941, loss_cls: 2.4678, loss: 2.4678 +2024-07-21 17:53:24,177 - pyskl - INFO - Epoch [146][1100/3746] lr: 2.427e-04, eta: 4:02:53, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5573, top5_acc: 0.8003, loss_cls: 2.4357, loss: 2.4357 +2024-07-21 17:54:47,966 - pyskl - INFO - Epoch [146][1200/3746] lr: 2.400e-04, eta: 4:01:31, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5570, top5_acc: 0.7930, loss_cls: 2.4833, loss: 2.4833 +2024-07-21 17:56:11,897 - pyskl - INFO - Epoch [146][1300/3746] lr: 2.373e-04, eta: 4:00:08, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5700, top5_acc: 0.8017, loss_cls: 2.4202, loss: 2.4202 +2024-07-21 17:57:35,721 - pyskl - INFO - Epoch [146][1400/3746] lr: 2.345e-04, eta: 3:58:45, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5567, top5_acc: 0.8008, loss_cls: 2.4655, loss: 2.4655 +2024-07-21 17:58:58,595 - pyskl - INFO - Epoch [146][1500/3746] lr: 2.318e-04, eta: 3:57:23, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5656, top5_acc: 0.8031, loss_cls: 2.4426, loss: 2.4426 +2024-07-21 18:00:21,740 - pyskl - INFO - Epoch [146][1600/3746] lr: 2.292e-04, eta: 3:56:00, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5728, top5_acc: 0.7956, loss_cls: 2.4487, loss: 2.4487 +2024-07-21 18:01:44,407 - pyskl - INFO - Epoch [146][1700/3746] lr: 2.265e-04, eta: 3:54:37, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5642, top5_acc: 0.8031, loss_cls: 2.4314, loss: 2.4314 +2024-07-21 18:03:06,579 - pyskl - INFO - Epoch [146][1800/3746] lr: 2.239e-04, eta: 3:53:15, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5644, top5_acc: 0.7991, loss_cls: 2.4560, loss: 2.4560 +2024-07-21 18:04:28,592 - pyskl - INFO - Epoch [146][1900/3746] lr: 2.212e-04, eta: 3:51:52, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5681, top5_acc: 0.8031, loss_cls: 2.4333, loss: 2.4333 +2024-07-21 18:05:51,956 - pyskl - INFO - Epoch [146][2000/3746] lr: 2.186e-04, eta: 3:50:29, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5731, top5_acc: 0.7958, loss_cls: 2.4284, loss: 2.4284 +2024-07-21 18:07:15,118 - pyskl - INFO - Epoch [146][2100/3746] lr: 2.160e-04, eta: 3:49:07, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5563, top5_acc: 0.7923, loss_cls: 2.4669, loss: 2.4669 +2024-07-21 18:08:37,869 - pyskl - INFO - Epoch [146][2200/3746] lr: 2.134e-04, eta: 3:47:44, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5683, top5_acc: 0.8023, loss_cls: 2.4183, loss: 2.4183 +2024-07-21 18:10:00,991 - pyskl - INFO - Epoch [146][2300/3746] lr: 2.108e-04, eta: 3:46:21, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5619, top5_acc: 0.7998, loss_cls: 2.4507, loss: 2.4507 +2024-07-21 18:11:24,734 - pyskl - INFO - Epoch [146][2400/3746] lr: 2.083e-04, eta: 3:44:59, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5680, top5_acc: 0.7998, loss_cls: 2.4314, loss: 2.4314 +2024-07-21 18:12:47,895 - pyskl - INFO - Epoch [146][2500/3746] lr: 2.057e-04, eta: 3:43:36, time: 0.832, data_time: 0.000, memory: 15990, top1_acc: 0.5722, top5_acc: 0.8103, loss_cls: 2.4025, loss: 2.4025 +2024-07-21 18:14:10,293 - pyskl - INFO - Epoch [146][2600/3746] lr: 2.032e-04, eta: 3:42:14, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5664, top5_acc: 0.8011, loss_cls: 2.4425, loss: 2.4425 +2024-07-21 18:15:32,751 - pyskl - INFO - Epoch [146][2700/3746] lr: 2.007e-04, eta: 3:40:51, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5653, top5_acc: 0.7997, loss_cls: 2.4660, loss: 2.4660 +2024-07-21 18:16:55,034 - pyskl - INFO - Epoch [146][2800/3746] lr: 1.982e-04, eta: 3:39:28, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5675, top5_acc: 0.7981, loss_cls: 2.4687, loss: 2.4687 +2024-07-21 18:18:17,570 - pyskl - INFO - Epoch [146][2900/3746] lr: 1.957e-04, eta: 3:38:06, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5592, top5_acc: 0.7931, loss_cls: 2.4817, loss: 2.4817 +2024-07-21 18:19:39,827 - pyskl - INFO - Epoch [146][3000/3746] lr: 1.933e-04, eta: 3:36:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5670, top5_acc: 0.7980, loss_cls: 2.4555, loss: 2.4555 +2024-07-21 18:21:02,033 - pyskl - INFO - Epoch [146][3100/3746] lr: 1.908e-04, eta: 3:35:20, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5617, top5_acc: 0.8013, loss_cls: 2.4509, loss: 2.4509 +2024-07-21 18:22:24,027 - pyskl - INFO - Epoch [146][3200/3746] lr: 1.884e-04, eta: 3:33:57, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5672, top5_acc: 0.7914, loss_cls: 2.4597, loss: 2.4597 +2024-07-21 18:23:46,113 - pyskl - INFO - Epoch [146][3300/3746] lr: 1.860e-04, eta: 3:32:35, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5725, top5_acc: 0.8025, loss_cls: 2.4290, loss: 2.4290 +2024-07-21 18:25:08,067 - pyskl - INFO - Epoch [146][3400/3746] lr: 1.836e-04, eta: 3:31:12, time: 0.820, data_time: 0.000, memory: 15990, top1_acc: 0.5614, top5_acc: 0.7961, loss_cls: 2.4593, loss: 2.4593 +2024-07-21 18:26:29,432 - pyskl - INFO - Epoch [146][3500/3746] lr: 1.812e-04, eta: 3:29:49, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5645, top5_acc: 0.7992, loss_cls: 2.4255, loss: 2.4255 +2024-07-21 18:27:50,838 - pyskl - INFO - Epoch [146][3600/3746] lr: 1.788e-04, eta: 3:28:27, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5663, top5_acc: 0.7964, loss_cls: 2.4564, loss: 2.4564 +2024-07-21 18:29:12,617 - pyskl - INFO - Epoch [146][3700/3746] lr: 1.765e-04, eta: 3:27:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5628, top5_acc: 0.7963, loss_cls: 2.4761, loss: 2.4761 +2024-07-21 18:29:52,082 - pyskl - INFO - Saving checkpoint at 146 epochs +2024-07-21 18:31:42,113 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 18:31:42,827 - pyskl - INFO - +top1_acc 0.3993 +top5_acc 0.6437 +2024-07-21 18:31:42,828 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 18:31:42,872 - pyskl - INFO - +mean_acc 0.3991 +2024-07-21 18:31:42,877 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_144.pth was removed +2024-07-21 18:31:43,121 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_146.pth. +2024-07-21 18:31:43,121 - pyskl - INFO - Best top1_acc is 0.3993 at 146 epoch. +2024-07-21 18:31:43,131 - pyskl - INFO - Epoch(val) [146][309] top1_acc: 0.3993, top5_acc: 0.6437, mean_class_accuracy: 0.3991 +2024-07-21 18:35:26,859 - pyskl - INFO - Epoch [147][100/3746] lr: 1.730e-04, eta: 3:25:06, time: 2.237, data_time: 1.242, memory: 15990, top1_acc: 0.5739, top5_acc: 0.8137, loss_cls: 2.3732, loss: 2.3732 +2024-07-21 18:36:49,109 - pyskl - INFO - Epoch [147][200/3746] lr: 1.707e-04, eta: 3:23:43, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5778, top5_acc: 0.8102, loss_cls: 2.3805, loss: 2.3805 +2024-07-21 18:38:11,875 - pyskl - INFO - Epoch [147][300/3746] lr: 1.684e-04, eta: 3:22:21, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5683, top5_acc: 0.8039, loss_cls: 2.4152, loss: 2.4152 +2024-07-21 18:39:35,132 - pyskl - INFO - Epoch [147][400/3746] lr: 1.661e-04, eta: 3:20:58, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5777, top5_acc: 0.8069, loss_cls: 2.3946, loss: 2.3946 +2024-07-21 18:40:57,990 - pyskl - INFO - Epoch [147][500/3746] lr: 1.639e-04, eta: 3:19:35, time: 0.829, data_time: 0.000, memory: 15990, top1_acc: 0.5770, top5_acc: 0.8086, loss_cls: 2.3841, loss: 2.3841 +2024-07-21 18:42:20,976 - pyskl - INFO - Epoch [147][600/3746] lr: 1.616e-04, eta: 3:18:13, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5636, top5_acc: 0.7991, loss_cls: 2.4596, loss: 2.4596 +2024-07-21 18:43:44,065 - pyskl - INFO - Epoch [147][700/3746] lr: 1.594e-04, eta: 3:16:50, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5703, top5_acc: 0.8008, loss_cls: 2.4350, loss: 2.4350 +2024-07-21 18:45:07,075 - pyskl - INFO - Epoch [147][800/3746] lr: 1.572e-04, eta: 3:15:27, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5781, top5_acc: 0.8128, loss_cls: 2.3584, loss: 2.3584 +2024-07-21 18:46:30,368 - pyskl - INFO - Epoch [147][900/3746] lr: 1.550e-04, eta: 3:14:05, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5673, top5_acc: 0.8036, loss_cls: 2.4289, loss: 2.4289 +2024-07-21 18:47:52,515 - pyskl - INFO - Epoch [147][1000/3746] lr: 1.528e-04, eta: 3:12:42, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5716, top5_acc: 0.8091, loss_cls: 2.4028, loss: 2.4028 +2024-07-21 18:49:15,161 - pyskl - INFO - Epoch [147][1100/3746] lr: 1.506e-04, eta: 3:11:19, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5694, top5_acc: 0.8047, loss_cls: 2.3857, loss: 2.3857 +2024-07-21 18:50:39,220 - pyskl - INFO - Epoch [147][1200/3746] lr: 1.484e-04, eta: 3:09:57, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.5736, top5_acc: 0.7970, loss_cls: 2.4195, loss: 2.4195 +2024-07-21 18:52:03,019 - pyskl - INFO - Epoch [147][1300/3746] lr: 1.463e-04, eta: 3:08:34, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5800, top5_acc: 0.8067, loss_cls: 2.3620, loss: 2.3620 +2024-07-21 18:53:26,683 - pyskl - INFO - Epoch [147][1400/3746] lr: 1.442e-04, eta: 3:07:11, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5705, top5_acc: 0.8069, loss_cls: 2.4134, loss: 2.4134 +2024-07-21 18:54:50,291 - pyskl - INFO - Epoch [147][1500/3746] lr: 1.420e-04, eta: 3:05:49, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5744, top5_acc: 0.8003, loss_cls: 2.4171, loss: 2.4171 +2024-07-21 18:56:13,748 - pyskl - INFO - Epoch [147][1600/3746] lr: 1.399e-04, eta: 3:04:26, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5594, top5_acc: 0.8042, loss_cls: 2.4396, loss: 2.4396 +2024-07-21 18:57:37,554 - pyskl - INFO - Epoch [147][1700/3746] lr: 1.379e-04, eta: 3:03:03, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5778, top5_acc: 0.8041, loss_cls: 2.4079, loss: 2.4079 +2024-07-21 18:59:01,458 - pyskl - INFO - Epoch [147][1800/3746] lr: 1.358e-04, eta: 3:01:41, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5644, top5_acc: 0.8063, loss_cls: 2.4253, loss: 2.4253 +2024-07-21 19:00:25,225 - pyskl - INFO - Epoch [147][1900/3746] lr: 1.337e-04, eta: 3:00:18, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5556, top5_acc: 0.7945, loss_cls: 2.4755, loss: 2.4755 +2024-07-21 19:01:49,540 - pyskl - INFO - Epoch [147][2000/3746] lr: 1.317e-04, eta: 2:58:55, time: 0.843, data_time: 0.001, memory: 15990, top1_acc: 0.5734, top5_acc: 0.8072, loss_cls: 2.3995, loss: 2.3995 +2024-07-21 19:03:12,235 - pyskl - INFO - Epoch [147][2100/3746] lr: 1.297e-04, eta: 2:57:33, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5605, top5_acc: 0.7995, loss_cls: 2.4457, loss: 2.4457 +2024-07-21 19:04:35,343 - pyskl - INFO - Epoch [147][2200/3746] lr: 1.277e-04, eta: 2:56:10, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5756, top5_acc: 0.8037, loss_cls: 2.4027, loss: 2.4027 +2024-07-21 19:05:59,049 - pyskl - INFO - Epoch [147][2300/3746] lr: 1.257e-04, eta: 2:54:47, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5730, top5_acc: 0.7972, loss_cls: 2.3991, loss: 2.3991 +2024-07-21 19:07:22,995 - pyskl - INFO - Epoch [147][2400/3746] lr: 1.237e-04, eta: 2:53:25, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5761, top5_acc: 0.8042, loss_cls: 2.4021, loss: 2.4021 +2024-07-21 19:08:46,901 - pyskl - INFO - Epoch [147][2500/3746] lr: 1.218e-04, eta: 2:52:02, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5717, top5_acc: 0.8017, loss_cls: 2.4249, loss: 2.4249 +2024-07-21 19:10:10,721 - pyskl - INFO - Epoch [147][2600/3746] lr: 1.198e-04, eta: 2:50:39, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5708, top5_acc: 0.8059, loss_cls: 2.4228, loss: 2.4228 +2024-07-21 19:11:34,075 - pyskl - INFO - Epoch [147][2700/3746] lr: 1.179e-04, eta: 2:49:17, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5589, top5_acc: 0.8044, loss_cls: 2.4569, loss: 2.4569 +2024-07-21 19:12:57,337 - pyskl - INFO - Epoch [147][2800/3746] lr: 1.160e-04, eta: 2:47:54, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5803, top5_acc: 0.8105, loss_cls: 2.3742, loss: 2.3742 +2024-07-21 19:14:21,328 - pyskl - INFO - Epoch [147][2900/3746] lr: 1.141e-04, eta: 2:46:31, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5745, top5_acc: 0.8041, loss_cls: 2.4029, loss: 2.4029 +2024-07-21 19:15:44,922 - pyskl - INFO - Epoch [147][3000/3746] lr: 1.122e-04, eta: 2:45:09, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5791, top5_acc: 0.8087, loss_cls: 2.3874, loss: 2.3874 +2024-07-21 19:17:08,645 - pyskl - INFO - Epoch [147][3100/3746] lr: 1.103e-04, eta: 2:43:46, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5809, top5_acc: 0.8061, loss_cls: 2.3801, loss: 2.3801 +2024-07-21 19:18:32,112 - pyskl - INFO - Epoch [147][3200/3746] lr: 1.085e-04, eta: 2:42:23, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5734, top5_acc: 0.8086, loss_cls: 2.4047, loss: 2.4047 +2024-07-21 19:19:56,098 - pyskl - INFO - Epoch [147][3300/3746] lr: 1.067e-04, eta: 2:41:01, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5739, top5_acc: 0.8067, loss_cls: 2.3982, loss: 2.3982 +2024-07-21 19:21:20,070 - pyskl - INFO - Epoch [147][3400/3746] lr: 1.048e-04, eta: 2:39:38, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5700, top5_acc: 0.8077, loss_cls: 2.3981, loss: 2.3981 +2024-07-21 19:22:44,102 - pyskl - INFO - Epoch [147][3500/3746] lr: 1.030e-04, eta: 2:38:15, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5756, top5_acc: 0.8048, loss_cls: 2.3907, loss: 2.3907 +2024-07-21 19:24:08,107 - pyskl - INFO - Epoch [147][3600/3746] lr: 1.013e-04, eta: 2:36:53, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5681, top5_acc: 0.8073, loss_cls: 2.4445, loss: 2.4445 +2024-07-21 19:25:32,140 - pyskl - INFO - Epoch [147][3700/3746] lr: 9.949e-05, eta: 2:35:30, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5711, top5_acc: 0.8025, loss_cls: 2.4166, loss: 2.4166 +2024-07-21 19:26:12,090 - pyskl - INFO - Saving checkpoint at 147 epochs +2024-07-21 19:28:01,929 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 19:28:02,662 - pyskl - INFO - +top1_acc 0.3995 +top5_acc 0.6454 +2024-07-21 19:28:02,662 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 19:28:02,706 - pyskl - INFO - +mean_acc 0.3993 +2024-07-21 19:28:02,711 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_146.pth was removed +2024-07-21 19:28:02,961 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_147.pth. +2024-07-21 19:28:02,962 - pyskl - INFO - Best top1_acc is 0.3995 at 147 epoch. +2024-07-21 19:28:02,972 - pyskl - INFO - Epoch(val) [147][309] top1_acc: 0.3995, top5_acc: 0.6454, mean_class_accuracy: 0.3993 +2024-07-21 19:31:51,000 - pyskl - INFO - Epoch [148][100/3746] lr: 9.693e-05, eta: 2:33:32, time: 2.280, data_time: 1.275, memory: 15990, top1_acc: 0.5867, top5_acc: 0.8137, loss_cls: 2.3379, loss: 2.3379 +2024-07-21 19:33:14,798 - pyskl - INFO - Epoch [148][200/3746] lr: 9.520e-05, eta: 2:32:09, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5809, top5_acc: 0.8106, loss_cls: 2.3687, loss: 2.3687 +2024-07-21 19:34:39,252 - pyskl - INFO - Epoch [148][300/3746] lr: 9.348e-05, eta: 2:30:46, time: 0.845, data_time: 0.000, memory: 15990, top1_acc: 0.5683, top5_acc: 0.8030, loss_cls: 2.4150, loss: 2.4150 +2024-07-21 19:36:03,037 - pyskl - INFO - Epoch [148][400/3746] lr: 9.178e-05, eta: 2:29:24, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5769, top5_acc: 0.8094, loss_cls: 2.3792, loss: 2.3792 +2024-07-21 19:37:25,713 - pyskl - INFO - Epoch [148][500/3746] lr: 9.010e-05, eta: 2:28:01, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5795, top5_acc: 0.8156, loss_cls: 2.3657, loss: 2.3657 +2024-07-21 19:38:49,312 - pyskl - INFO - Epoch [148][600/3746] lr: 8.843e-05, eta: 2:26:38, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5727, top5_acc: 0.8037, loss_cls: 2.4113, loss: 2.4113 +2024-07-21 19:40:13,234 - pyskl - INFO - Epoch [148][700/3746] lr: 8.678e-05, eta: 2:25:15, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5809, top5_acc: 0.8134, loss_cls: 2.3752, loss: 2.3752 +2024-07-21 19:41:36,602 - pyskl - INFO - Epoch [148][800/3746] lr: 8.514e-05, eta: 2:23:53, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5853, top5_acc: 0.8092, loss_cls: 2.3648, loss: 2.3648 +2024-07-21 19:42:59,112 - pyskl - INFO - Epoch [148][900/3746] lr: 8.351e-05, eta: 2:22:30, time: 0.825, data_time: 0.001, memory: 15990, top1_acc: 0.5806, top5_acc: 0.8102, loss_cls: 2.3428, loss: 2.3428 +2024-07-21 19:44:22,802 - pyskl - INFO - Epoch [148][1000/3746] lr: 8.191e-05, eta: 2:21:07, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5770, top5_acc: 0.8053, loss_cls: 2.3952, loss: 2.3952 +2024-07-21 19:45:46,093 - pyskl - INFO - Epoch [148][1100/3746] lr: 8.031e-05, eta: 2:19:45, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5730, top5_acc: 0.8077, loss_cls: 2.4008, loss: 2.4008 +2024-07-21 19:47:09,642 - pyskl - INFO - Epoch [148][1200/3746] lr: 7.874e-05, eta: 2:18:22, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5714, top5_acc: 0.8089, loss_cls: 2.4010, loss: 2.4010 +2024-07-21 19:48:33,490 - pyskl - INFO - Epoch [148][1300/3746] lr: 7.718e-05, eta: 2:16:59, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5763, top5_acc: 0.8033, loss_cls: 2.4156, loss: 2.4156 +2024-07-21 19:49:57,542 - pyskl - INFO - Epoch [148][1400/3746] lr: 7.563e-05, eta: 2:15:37, time: 0.841, data_time: 0.000, memory: 15990, top1_acc: 0.5731, top5_acc: 0.8041, loss_cls: 2.4088, loss: 2.4088 +2024-07-21 19:51:21,381 - pyskl - INFO - Epoch [148][1500/3746] lr: 7.410e-05, eta: 2:14:14, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5791, top5_acc: 0.8111, loss_cls: 2.3782, loss: 2.3782 +2024-07-21 19:52:44,947 - pyskl - INFO - Epoch [148][1600/3746] lr: 7.259e-05, eta: 2:12:51, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5756, top5_acc: 0.8022, loss_cls: 2.3944, loss: 2.3944 +2024-07-21 19:54:08,947 - pyskl - INFO - Epoch [148][1700/3746] lr: 7.109e-05, eta: 2:11:28, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5809, top5_acc: 0.8108, loss_cls: 2.3800, loss: 2.3800 +2024-07-21 19:55:32,906 - pyskl - INFO - Epoch [148][1800/3746] lr: 6.961e-05, eta: 2:10:06, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5653, top5_acc: 0.8036, loss_cls: 2.4360, loss: 2.4360 +2024-07-21 19:56:56,630 - pyskl - INFO - Epoch [148][1900/3746] lr: 6.814e-05, eta: 2:08:43, time: 0.837, data_time: 0.001, memory: 15990, top1_acc: 0.5833, top5_acc: 0.8127, loss_cls: 2.3501, loss: 2.3501 +2024-07-21 19:58:20,340 - pyskl - INFO - Epoch [148][2000/3746] lr: 6.669e-05, eta: 2:07:20, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5775, top5_acc: 0.8053, loss_cls: 2.4070, loss: 2.4070 +2024-07-21 19:59:42,412 - pyskl - INFO - Epoch [148][2100/3746] lr: 6.526e-05, eta: 2:05:58, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5769, top5_acc: 0.8075, loss_cls: 2.3823, loss: 2.3823 +2024-07-21 20:01:05,856 - pyskl - INFO - Epoch [148][2200/3746] lr: 6.384e-05, eta: 2:04:35, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5783, top5_acc: 0.8009, loss_cls: 2.3779, loss: 2.3779 +2024-07-21 20:02:29,165 - pyskl - INFO - Epoch [148][2300/3746] lr: 6.243e-05, eta: 2:03:12, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5833, top5_acc: 0.8086, loss_cls: 2.3925, loss: 2.3925 +2024-07-21 20:03:52,720 - pyskl - INFO - Epoch [148][2400/3746] lr: 6.104e-05, eta: 2:01:50, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5884, top5_acc: 0.8203, loss_cls: 2.3134, loss: 2.3134 +2024-07-21 20:05:16,714 - pyskl - INFO - Epoch [148][2500/3746] lr: 5.967e-05, eta: 2:00:27, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5861, top5_acc: 0.8158, loss_cls: 2.3547, loss: 2.3547 +2024-07-21 20:06:40,169 - pyskl - INFO - Epoch [148][2600/3746] lr: 5.831e-05, eta: 1:59:04, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5734, top5_acc: 0.8023, loss_cls: 2.4285, loss: 2.4285 +2024-07-21 20:08:03,863 - pyskl - INFO - Epoch [148][2700/3746] lr: 5.697e-05, eta: 1:57:41, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5777, top5_acc: 0.8114, loss_cls: 2.3497, loss: 2.3497 +2024-07-21 20:09:27,335 - pyskl - INFO - Epoch [148][2800/3746] lr: 5.564e-05, eta: 1:56:19, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5781, top5_acc: 0.8069, loss_cls: 2.3802, loss: 2.3802 +2024-07-21 20:10:50,856 - pyskl - INFO - Epoch [148][2900/3746] lr: 5.433e-05, eta: 1:54:56, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5713, top5_acc: 0.8091, loss_cls: 2.3926, loss: 2.3926 +2024-07-21 20:12:14,557 - pyskl - INFO - Epoch [148][3000/3746] lr: 5.304e-05, eta: 1:53:33, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5831, top5_acc: 0.8063, loss_cls: 2.3728, loss: 2.3728 +2024-07-21 20:13:38,501 - pyskl - INFO - Epoch [148][3100/3746] lr: 5.176e-05, eta: 1:52:11, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5752, top5_acc: 0.8009, loss_cls: 2.4046, loss: 2.4046 +2024-07-21 20:15:02,050 - pyskl - INFO - Epoch [148][3200/3746] lr: 5.050e-05, eta: 1:50:48, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5656, top5_acc: 0.8084, loss_cls: 2.4245, loss: 2.4245 +2024-07-21 20:16:25,521 - pyskl - INFO - Epoch [148][3300/3746] lr: 4.925e-05, eta: 1:49:25, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5698, top5_acc: 0.8013, loss_cls: 2.4159, loss: 2.4159 +2024-07-21 20:17:49,151 - pyskl - INFO - Epoch [148][3400/3746] lr: 4.801e-05, eta: 1:48:03, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5766, top5_acc: 0.8127, loss_cls: 2.3668, loss: 2.3668 +2024-07-21 20:19:13,021 - pyskl - INFO - Epoch [148][3500/3746] lr: 4.680e-05, eta: 1:46:40, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5814, top5_acc: 0.8019, loss_cls: 2.3976, loss: 2.3976 +2024-07-21 20:20:36,990 - pyskl - INFO - Epoch [148][3600/3746] lr: 4.560e-05, eta: 1:45:17, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5705, top5_acc: 0.8033, loss_cls: 2.3977, loss: 2.3977 +2024-07-21 20:22:00,726 - pyskl - INFO - Epoch [148][3700/3746] lr: 4.441e-05, eta: 1:43:54, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5837, top5_acc: 0.8106, loss_cls: 2.3513, loss: 2.3513 +2024-07-21 20:22:40,914 - pyskl - INFO - Saving checkpoint at 148 epochs +2024-07-21 20:24:31,780 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 20:24:32,452 - pyskl - INFO - +top1_acc 0.4002 +top5_acc 0.6456 +2024-07-21 20:24:32,452 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 20:24:32,496 - pyskl - INFO - +mean_acc 0.4000 +2024-07-21 20:24:32,501 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_147.pth was removed +2024-07-21 20:24:32,756 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_148.pth. +2024-07-21 20:24:32,756 - pyskl - INFO - Best top1_acc is 0.4002 at 148 epoch. +2024-07-21 20:24:32,771 - pyskl - INFO - Epoch(val) [148][309] top1_acc: 0.4002, top5_acc: 0.6456, mean_class_accuracy: 0.4000 +2024-07-21 20:28:25,857 - pyskl - INFO - Epoch [149][100/3746] lr: 4.271e-05, eta: 1:41:55, time: 2.331, data_time: 1.334, memory: 15990, top1_acc: 0.5853, top5_acc: 0.8125, loss_cls: 2.3555, loss: 2.3555 +2024-07-21 20:29:50,076 - pyskl - INFO - Epoch [149][200/3746] lr: 4.156e-05, eta: 1:40:33, time: 0.842, data_time: 0.000, memory: 15990, top1_acc: 0.5823, top5_acc: 0.8091, loss_cls: 2.3952, loss: 2.3952 +2024-07-21 20:31:13,860 - pyskl - INFO - Epoch [149][300/3746] lr: 4.043e-05, eta: 1:39:10, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5856, top5_acc: 0.8147, loss_cls: 2.3217, loss: 2.3217 +2024-07-21 20:32:36,954 - pyskl - INFO - Epoch [149][400/3746] lr: 3.931e-05, eta: 1:37:47, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5802, top5_acc: 0.8184, loss_cls: 2.3416, loss: 2.3416 +2024-07-21 20:34:00,423 - pyskl - INFO - Epoch [149][500/3746] lr: 3.821e-05, eta: 1:36:24, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5750, top5_acc: 0.8017, loss_cls: 2.4066, loss: 2.4066 +2024-07-21 20:35:24,092 - pyskl - INFO - Epoch [149][600/3746] lr: 3.713e-05, eta: 1:35:02, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5806, top5_acc: 0.8147, loss_cls: 2.3552, loss: 2.3552 +2024-07-21 20:36:47,693 - pyskl - INFO - Epoch [149][700/3746] lr: 3.606e-05, eta: 1:33:39, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5753, top5_acc: 0.8073, loss_cls: 2.3835, loss: 2.3835 +2024-07-21 20:38:11,174 - pyskl - INFO - Epoch [149][800/3746] lr: 3.500e-05, eta: 1:32:16, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5787, top5_acc: 0.8158, loss_cls: 2.3413, loss: 2.3413 +2024-07-21 20:39:35,106 - pyskl - INFO - Epoch [149][900/3746] lr: 3.397e-05, eta: 1:30:53, time: 0.839, data_time: 0.001, memory: 15990, top1_acc: 0.5734, top5_acc: 0.8034, loss_cls: 2.4051, loss: 2.4051 +2024-07-21 20:40:59,042 - pyskl - INFO - Epoch [149][1000/3746] lr: 3.294e-05, eta: 1:29:31, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5858, top5_acc: 0.8163, loss_cls: 2.3595, loss: 2.3595 +2024-07-21 20:42:22,358 - pyskl - INFO - Epoch [149][1100/3746] lr: 3.194e-05, eta: 1:28:08, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5725, top5_acc: 0.8039, loss_cls: 2.4089, loss: 2.4089 +2024-07-21 20:43:45,934 - pyskl - INFO - Epoch [149][1200/3746] lr: 3.095e-05, eta: 1:26:45, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5914, top5_acc: 0.8086, loss_cls: 2.3446, loss: 2.3446 +2024-07-21 20:45:09,597 - pyskl - INFO - Epoch [149][1300/3746] lr: 2.997e-05, eta: 1:25:23, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5855, top5_acc: 0.8161, loss_cls: 2.3461, loss: 2.3461 +2024-07-21 20:46:33,081 - pyskl - INFO - Epoch [149][1400/3746] lr: 2.901e-05, eta: 1:24:00, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5930, top5_acc: 0.8181, loss_cls: 2.3221, loss: 2.3221 +2024-07-21 20:47:56,752 - pyskl - INFO - Epoch [149][1500/3746] lr: 2.807e-05, eta: 1:22:37, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5967, top5_acc: 0.8120, loss_cls: 2.3092, loss: 2.3092 +2024-07-21 20:49:19,600 - pyskl - INFO - Epoch [149][1600/3746] lr: 2.714e-05, eta: 1:21:14, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5875, top5_acc: 0.8084, loss_cls: 2.3669, loss: 2.3669 +2024-07-21 20:50:43,058 - pyskl - INFO - Epoch [149][1700/3746] lr: 2.622e-05, eta: 1:19:52, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5891, top5_acc: 0.8172, loss_cls: 2.3288, loss: 2.3288 +2024-07-21 20:52:06,671 - pyskl - INFO - Epoch [149][1800/3746] lr: 2.533e-05, eta: 1:18:29, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5820, top5_acc: 0.8092, loss_cls: 2.3702, loss: 2.3702 +2024-07-21 20:53:30,552 - pyskl - INFO - Epoch [149][1900/3746] lr: 2.444e-05, eta: 1:17:06, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5745, top5_acc: 0.8031, loss_cls: 2.3917, loss: 2.3917 +2024-07-21 20:54:52,896 - pyskl - INFO - Epoch [149][2000/3746] lr: 2.358e-05, eta: 1:15:43, time: 0.823, data_time: 0.000, memory: 15990, top1_acc: 0.5720, top5_acc: 0.8020, loss_cls: 2.4171, loss: 2.4171 +2024-07-21 20:56:15,890 - pyskl - INFO - Epoch [149][2100/3746] lr: 2.273e-05, eta: 1:14:21, time: 0.830, data_time: 0.000, memory: 15990, top1_acc: 0.5781, top5_acc: 0.8133, loss_cls: 2.3717, loss: 2.3717 +2024-07-21 20:57:39,555 - pyskl - INFO - Epoch [149][2200/3746] lr: 2.189e-05, eta: 1:12:58, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5777, top5_acc: 0.8127, loss_cls: 2.3777, loss: 2.3777 +2024-07-21 20:59:03,515 - pyskl - INFO - Epoch [149][2300/3746] lr: 2.107e-05, eta: 1:11:35, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5756, top5_acc: 0.8055, loss_cls: 2.3762, loss: 2.3762 +2024-07-21 21:00:27,487 - pyskl - INFO - Epoch [149][2400/3746] lr: 2.027e-05, eta: 1:10:13, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5827, top5_acc: 0.8130, loss_cls: 2.3611, loss: 2.3611 +2024-07-21 21:01:50,811 - pyskl - INFO - Epoch [149][2500/3746] lr: 1.948e-05, eta: 1:08:50, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5903, top5_acc: 0.8194, loss_cls: 2.3201, loss: 2.3201 +2024-07-21 21:03:14,577 - pyskl - INFO - Epoch [149][2600/3746] lr: 1.871e-05, eta: 1:07:27, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5719, top5_acc: 0.8061, loss_cls: 2.4043, loss: 2.4043 +2024-07-21 21:04:38,052 - pyskl - INFO - Epoch [149][2700/3746] lr: 1.795e-05, eta: 1:06:04, time: 0.835, data_time: 0.000, memory: 15990, top1_acc: 0.5650, top5_acc: 0.8025, loss_cls: 2.4343, loss: 2.4343 +2024-07-21 21:06:01,774 - pyskl - INFO - Epoch [149][2800/3746] lr: 1.721e-05, eta: 1:04:42, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5822, top5_acc: 0.8077, loss_cls: 2.3499, loss: 2.3499 +2024-07-21 21:07:25,659 - pyskl - INFO - Epoch [149][2900/3746] lr: 1.649e-05, eta: 1:03:19, time: 0.839, data_time: 0.000, memory: 15990, top1_acc: 0.5687, top5_acc: 0.8009, loss_cls: 2.4165, loss: 2.4165 +2024-07-21 21:08:49,302 - pyskl - INFO - Epoch [149][3000/3746] lr: 1.578e-05, eta: 1:01:56, time: 0.836, data_time: 0.000, memory: 15990, top1_acc: 0.5816, top5_acc: 0.8116, loss_cls: 2.3571, loss: 2.3571 +2024-07-21 21:10:12,995 - pyskl - INFO - Epoch [149][3100/3746] lr: 1.508e-05, eta: 1:00:33, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5805, top5_acc: 0.8155, loss_cls: 2.3534, loss: 2.3534 +2024-07-21 21:11:36,386 - pyskl - INFO - Epoch [149][3200/3746] lr: 1.440e-05, eta: 0:59:11, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5831, top5_acc: 0.8047, loss_cls: 2.3795, loss: 2.3795 +2024-07-21 21:13:00,171 - pyskl - INFO - Epoch [149][3300/3746] lr: 1.374e-05, eta: 0:57:48, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5866, top5_acc: 0.8122, loss_cls: 2.3337, loss: 2.3337 +2024-07-21 21:14:23,619 - pyskl - INFO - Epoch [149][3400/3746] lr: 1.309e-05, eta: 0:56:25, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5853, top5_acc: 0.8081, loss_cls: 2.3495, loss: 2.3495 +2024-07-21 21:15:46,990 - pyskl - INFO - Epoch [149][3500/3746] lr: 1.246e-05, eta: 0:55:02, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5897, top5_acc: 0.8144, loss_cls: 2.3457, loss: 2.3457 +2024-07-21 21:17:10,386 - pyskl - INFO - Epoch [149][3600/3746] lr: 1.184e-05, eta: 0:53:40, time: 0.834, data_time: 0.000, memory: 15990, top1_acc: 0.5823, top5_acc: 0.8117, loss_cls: 2.3527, loss: 2.3527 +2024-07-21 21:18:34,154 - pyskl - INFO - Epoch [149][3700/3746] lr: 1.124e-05, eta: 0:52:17, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5892, top5_acc: 0.8187, loss_cls: 2.3421, loss: 2.3421 +2024-07-21 21:19:14,422 - pyskl - INFO - Saving checkpoint at 149 epochs +2024-07-21 21:21:06,491 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 21:21:07,188 - pyskl - INFO - +top1_acc 0.4017 +top5_acc 0.6463 +2024-07-21 21:21:07,188 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 21:21:07,236 - pyskl - INFO - +mean_acc 0.4014 +2024-07-21 21:21:07,240 - pyskl - INFO - The previous best checkpoint /home/lhd/pyskl/work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_148.pth was removed +2024-07-21 21:21:07,497 - pyskl - INFO - Now best checkpoint is saved as best_top1_acc_epoch_149.pth. +2024-07-21 21:21:07,498 - pyskl - INFO - Best top1_acc is 0.4017 at 149 epoch. +2024-07-21 21:21:07,516 - pyskl - INFO - Epoch(val) [149][309] top1_acc: 0.4017, top5_acc: 0.6463, mean_class_accuracy: 0.4014 +2024-07-21 21:24:57,311 - pyskl - INFO - Epoch [150][100/3746] lr: 1.039e-05, eta: 0:50:17, time: 2.298, data_time: 1.293, memory: 15990, top1_acc: 0.5875, top5_acc: 0.8184, loss_cls: 2.3325, loss: 2.3325 +2024-07-21 21:26:21,266 - pyskl - INFO - Epoch [150][200/3746] lr: 9.832e-06, eta: 0:48:54, time: 0.840, data_time: 0.000, memory: 15990, top1_acc: 0.5734, top5_acc: 0.8045, loss_cls: 2.4002, loss: 2.4002 +2024-07-21 21:27:44,403 - pyskl - INFO - Epoch [150][300/3746] lr: 9.285e-06, eta: 0:47:31, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5863, top5_acc: 0.8159, loss_cls: 2.3337, loss: 2.3337 +2024-07-21 21:29:07,173 - pyskl - INFO - Epoch [150][400/3746] lr: 8.754e-06, eta: 0:46:09, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5745, top5_acc: 0.8145, loss_cls: 2.3785, loss: 2.3785 +2024-07-21 21:30:30,856 - pyskl - INFO - Epoch [150][500/3746] lr: 8.239e-06, eta: 0:44:46, time: 0.837, data_time: 0.000, memory: 15990, top1_acc: 0.5884, top5_acc: 0.8141, loss_cls: 2.3363, loss: 2.3363 +2024-07-21 21:31:53,551 - pyskl - INFO - Epoch [150][600/3746] lr: 7.739e-06, eta: 0:43:23, time: 0.827, data_time: 0.000, memory: 15990, top1_acc: 0.5961, top5_acc: 0.8191, loss_cls: 2.3129, loss: 2.3129 +2024-07-21 21:33:16,854 - pyskl - INFO - Epoch [150][700/3746] lr: 7.255e-06, eta: 0:42:00, time: 0.833, data_time: 0.000, memory: 15990, top1_acc: 0.5727, top5_acc: 0.8116, loss_cls: 2.3774, loss: 2.3774 +2024-07-21 21:34:39,354 - pyskl - INFO - Epoch [150][800/3746] lr: 6.787e-06, eta: 0:40:38, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5908, top5_acc: 0.8181, loss_cls: 2.3252, loss: 2.3252 +2024-07-21 21:36:01,400 - pyskl - INFO - Epoch [150][900/3746] lr: 6.334e-06, eta: 0:39:15, time: 0.820, data_time: 0.001, memory: 15990, top1_acc: 0.5952, top5_acc: 0.8219, loss_cls: 2.2969, loss: 2.2969 +2024-07-21 21:37:23,902 - pyskl - INFO - Epoch [150][1000/3746] lr: 5.897e-06, eta: 0:37:52, time: 0.825, data_time: 0.000, memory: 15990, top1_acc: 0.5697, top5_acc: 0.8078, loss_cls: 2.3926, loss: 2.3926 +2024-07-21 21:38:46,530 - pyskl - INFO - Epoch [150][1100/3746] lr: 5.475e-06, eta: 0:36:29, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5848, top5_acc: 0.8050, loss_cls: 2.3795, loss: 2.3795 +2024-07-21 21:40:09,298 - pyskl - INFO - Epoch [150][1200/3746] lr: 5.070e-06, eta: 0:35:07, time: 0.828, data_time: 0.000, memory: 15990, top1_acc: 0.5848, top5_acc: 0.8109, loss_cls: 2.3699, loss: 2.3699 +2024-07-21 21:41:31,677 - pyskl - INFO - Epoch [150][1300/3746] lr: 4.679e-06, eta: 0:33:44, time: 0.824, data_time: 0.000, memory: 15990, top1_acc: 0.5725, top5_acc: 0.8086, loss_cls: 2.4026, loss: 2.4026 +2024-07-21 21:42:54,248 - pyskl - INFO - Epoch [150][1400/3746] lr: 4.305e-06, eta: 0:32:21, time: 0.826, data_time: 0.000, memory: 15990, top1_acc: 0.5819, top5_acc: 0.8133, loss_cls: 2.3433, loss: 2.3433 +2024-07-21 21:44:17,001 - pyskl - INFO - Epoch [150][1500/3746] lr: 3.946e-06, eta: 0:30:58, time: 0.828, data_time: 0.001, memory: 15990, top1_acc: 0.5859, top5_acc: 0.8125, loss_cls: 2.3645, loss: 2.3645 +2024-07-21 21:45:40,067 - pyskl - INFO - Epoch [150][1600/3746] lr: 3.602e-06, eta: 0:29:36, time: 0.831, data_time: 0.000, memory: 15990, top1_acc: 0.5697, top5_acc: 0.8117, loss_cls: 2.3770, loss: 2.3770 +2024-07-21 21:47:01,933 - pyskl - INFO - Epoch [150][1700/3746] lr: 3.275e-06, eta: 0:28:13, time: 0.819, data_time: 0.000, memory: 15990, top1_acc: 0.5764, top5_acc: 0.8059, loss_cls: 2.3779, loss: 2.3779 +2024-07-21 21:48:25,703 - pyskl - INFO - Epoch [150][1800/3746] lr: 2.962e-06, eta: 0:26:50, time: 0.838, data_time: 0.000, memory: 15990, top1_acc: 0.5866, top5_acc: 0.8220, loss_cls: 2.3196, loss: 2.3196 +2024-07-21 21:49:47,868 - pyskl - INFO - Epoch [150][1900/3746] lr: 2.666e-06, eta: 0:25:27, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5839, top5_acc: 0.8147, loss_cls: 2.3379, loss: 2.3379 +2024-07-21 21:51:09,659 - pyskl - INFO - Epoch [150][2000/3746] lr: 2.385e-06, eta: 0:24:04, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5881, top5_acc: 0.8123, loss_cls: 2.3476, loss: 2.3476 +2024-07-21 21:52:31,405 - pyskl - INFO - Epoch [150][2100/3746] lr: 2.120e-06, eta: 0:22:42, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5772, top5_acc: 0.8044, loss_cls: 2.3820, loss: 2.3820 +2024-07-21 21:53:53,567 - pyskl - INFO - Epoch [150][2200/3746] lr: 1.870e-06, eta: 0:21:19, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5770, top5_acc: 0.8097, loss_cls: 2.3625, loss: 2.3625 +2024-07-21 21:55:15,319 - pyskl - INFO - Epoch [150][2300/3746] lr: 1.636e-06, eta: 0:19:56, time: 0.818, data_time: 0.000, memory: 15990, top1_acc: 0.5741, top5_acc: 0.8070, loss_cls: 2.3661, loss: 2.3661 +2024-07-21 21:56:36,892 - pyskl - INFO - Epoch [150][2400/3746] lr: 1.418e-06, eta: 0:18:33, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5737, top5_acc: 0.8044, loss_cls: 2.3863, loss: 2.3863 +2024-07-21 21:57:58,616 - pyskl - INFO - Epoch [150][2500/3746] lr: 1.215e-06, eta: 0:17:11, time: 0.817, data_time: 0.000, memory: 15990, top1_acc: 0.5808, top5_acc: 0.8036, loss_cls: 2.3901, loss: 2.3901 +2024-07-21 21:59:19,990 - pyskl - INFO - Epoch [150][2600/3746] lr: 1.028e-06, eta: 0:15:48, time: 0.814, data_time: 0.000, memory: 15990, top1_acc: 0.5833, top5_acc: 0.8172, loss_cls: 2.3187, loss: 2.3187 +2024-07-21 22:00:41,100 - pyskl - INFO - Epoch [150][2700/3746] lr: 8.567e-07, eta: 0:14:25, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5828, top5_acc: 0.8080, loss_cls: 2.3679, loss: 2.3679 +2024-07-21 22:02:03,322 - pyskl - INFO - Epoch [150][2800/3746] lr: 7.008e-07, eta: 0:13:02, time: 0.822, data_time: 0.000, memory: 15990, top1_acc: 0.5792, top5_acc: 0.8169, loss_cls: 2.3338, loss: 2.3338 +2024-07-21 22:03:25,422 - pyskl - INFO - Epoch [150][2900/3746] lr: 5.606e-07, eta: 0:11:40, time: 0.821, data_time: 0.000, memory: 15990, top1_acc: 0.5905, top5_acc: 0.8100, loss_cls: 2.3496, loss: 2.3496 +2024-07-21 22:04:46,909 - pyskl - INFO - Epoch [150][3000/3746] lr: 4.361e-07, eta: 0:10:17, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5922, top5_acc: 0.8206, loss_cls: 2.3277, loss: 2.3277 +2024-07-21 22:06:08,022 - pyskl - INFO - Epoch [150][3100/3746] lr: 3.271e-07, eta: 0:08:54, time: 0.811, data_time: 0.000, memory: 15990, top1_acc: 0.5755, top5_acc: 0.8092, loss_cls: 2.3722, loss: 2.3722 +2024-07-21 22:07:29,240 - pyskl - INFO - Epoch [150][3200/3746] lr: 2.338e-07, eta: 0:07:31, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5863, top5_acc: 0.8195, loss_cls: 2.3224, loss: 2.3224 +2024-07-21 22:08:50,523 - pyskl - INFO - Epoch [150][3300/3746] lr: 1.561e-07, eta: 0:06:09, time: 0.813, data_time: 0.000, memory: 15990, top1_acc: 0.5809, top5_acc: 0.8136, loss_cls: 2.3741, loss: 2.3741 +2024-07-21 22:10:12,071 - pyskl - INFO - Epoch [150][3400/3746] lr: 9.410e-08, eta: 0:04:46, time: 0.815, data_time: 0.000, memory: 15990, top1_acc: 0.5787, top5_acc: 0.8080, loss_cls: 2.3775, loss: 2.3775 +2024-07-21 22:11:33,659 - pyskl - INFO - Epoch [150][3500/3746] lr: 4.768e-08, eta: 0:03:23, time: 0.816, data_time: 0.000, memory: 15990, top1_acc: 0.5866, top5_acc: 0.8113, loss_cls: 2.3632, loss: 2.3632 +2024-07-21 22:12:54,627 - pyskl - INFO - Epoch [150][3600/3746] lr: 1.689e-08, eta: 0:02:00, time: 0.810, data_time: 0.000, memory: 15990, top1_acc: 0.5877, top5_acc: 0.8153, loss_cls: 2.3647, loss: 2.3647 +2024-07-21 22:14:15,801 - pyskl - INFO - Epoch [150][3700/3746] lr: 1.726e-09, eta: 0:00:38, time: 0.812, data_time: 0.000, memory: 15990, top1_acc: 0.5913, top5_acc: 0.8156, loss_cls: 2.3319, loss: 2.3319 +2024-07-21 22:14:54,420 - pyskl - INFO - Saving checkpoint at 150 epochs +2024-07-21 22:16:41,436 - pyskl - INFO - Evaluating top_k_accuracy ... +2024-07-21 22:16:42,096 - pyskl - INFO - +top1_acc 0.3990 +top5_acc 0.6459 +2024-07-21 22:16:42,096 - pyskl - INFO - Evaluating mean_class_accuracy ... +2024-07-21 22:16:42,142 - pyskl - INFO - +mean_acc 0.3987 +2024-07-21 22:16:42,160 - pyskl - INFO - Epoch(val) [150][309] top1_acc: 0.3990, top5_acc: 0.6459, mean_class_accuracy: 0.3987 +2024-07-21 22:16:56,065 - pyskl - INFO - 19743 videos remain after valid thresholding +2024-07-21 22:28:52,127 - pyskl - INFO - Testing results of the last checkpoint +2024-07-21 22:28:52,127 - pyskl - INFO - top1_acc: 0.4173 +2024-07-21 22:28:52,127 - pyskl - INFO - top5_acc: 0.6654 +2024-07-21 22:28:52,127 - pyskl - INFO - mean_class_accuracy: 0.4171 +2024-07-21 22:28:52,128 - pyskl - INFO - load checkpoint from local path: ./work_dirs/test_aclnet/k400/km/best_top1_acc_epoch_149.pth +2024-07-21 22:40:49,535 - pyskl - INFO - Testing results of the best checkpoint +2024-07-21 22:40:49,536 - pyskl - INFO - top1_acc: 0.4199 +2024-07-21 22:40:49,536 - pyskl - INFO - top5_acc: 0.6674 +2024-07-21 22:40:49,536 - pyskl - INFO - mean_class_accuracy: 0.4197 diff --git a/k400/km/20240716_064942.log.json b/k400/km/20240716_064942.log.json new file mode 100644 index 0000000000000000000000000000000000000000..c5d7ab7817f403b31e509682bfa5eed58549ab56 --- /dev/null +++ b/k400/km/20240716_064942.log.json @@ -0,0 +1,5701 @@ +{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.64\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0\nOpenCV: 4.8.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\npyskl: 0.1.0+", "seed": 1546636195, "config_name": "km.py", "work_dir": "km", "hook_msgs": {}} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.1, "memory": 15990, "data_time": 1.33923, "top1_acc": 0.00625, "top5_acc": 0.02891, "loss_cls": 6.37576, "loss": 6.37576, "time": 2.05032} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.01406, "top5_acc": 0.05625, "loss_cls": 6.34546, "loss": 6.34546, "time": 0.70188} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.01312, "top5_acc": 0.06234, "loss_cls": 6.22677, "loss": 6.22677, "time": 0.70351} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.1, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.02078, "top5_acc": 0.08188, "loss_cls": 6.11026, "loss": 6.11026, "time": 0.70405} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.02438, "top5_acc": 0.09219, "loss_cls": 6.02917, "loss": 6.02917, "time": 0.70302} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.1, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.02156, "top5_acc": 0.08922, "loss_cls": 6.00429, "loss": 6.00429, "time": 0.707} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.02312, "top5_acc": 0.09812, "loss_cls": 5.96756, "loss": 5.96756, "time": 0.7031} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.1, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.02547, "top5_acc": 0.10438, "loss_cls": 5.93935, "loss": 5.93935, "time": 0.7029} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.02906, "top5_acc": 0.11031, "loss_cls": 5.87812, "loss": 5.87812, "time": 0.70436} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.03188, "top5_acc": 0.12094, "loss_cls": 5.88217, "loss": 5.88217, "time": 0.70199} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.02891, "top5_acc": 0.11859, "loss_cls": 5.84137, "loss": 5.84137, "time": 0.70267} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.02844, "top5_acc": 0.11172, "loss_cls": 5.8609, "loss": 5.8609, "time": 0.70251} +{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.03297, "top5_acc": 0.13078, "loss_cls": 5.815, "loss": 5.815, "time": 0.70344} +{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.03781, "top5_acc": 0.13203, "loss_cls": 5.80176, "loss": 5.80176, "time": 0.70207} +{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.1, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.03406, "top5_acc": 0.13078, "loss_cls": 5.78794, "loss": 5.78794, "time": 0.70147} +{"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.04078, "top5_acc": 0.13672, "loss_cls": 5.73789, "loss": 5.73789, "time": 0.70223} +{"mode": "train", "epoch": 1, "iter": 1700, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.04422, "top5_acc": 0.14797, "loss_cls": 5.71752, "loss": 5.71752, "time": 0.70297} +{"mode": "train", "epoch": 1, "iter": 1800, "lr": 0.1, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.0425, "top5_acc": 0.14031, "loss_cls": 5.70753, "loss": 5.70753, "time": 0.70534} +{"mode": "train", "epoch": 1, "iter": 1900, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.04047, "top5_acc": 0.155, "loss_cls": 5.6975, "loss": 5.6975, "time": 0.70355} +{"mode": "train", "epoch": 1, "iter": 2000, "lr": 0.1, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.04688, "top5_acc": 0.14266, "loss_cls": 5.72022, "loss": 5.72022, "time": 0.70499} +{"mode": "train", "epoch": 1, "iter": 2100, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.04203, "top5_acc": 0.14844, "loss_cls": 5.69118, "loss": 5.69118, "time": 0.70455} +{"mode": "train", "epoch": 1, "iter": 2200, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.04484, "top5_acc": 0.15328, "loss_cls": 5.64799, "loss": 5.64799, "time": 0.70354} +{"mode": "train", "epoch": 1, "iter": 2300, "lr": 0.1, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.04656, "top5_acc": 0.15484, "loss_cls": 5.65862, "loss": 5.65862, "time": 0.70125} +{"mode": "train", "epoch": 1, "iter": 2400, "lr": 0.1, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.04719, "top5_acc": 0.15563, "loss_cls": 5.66547, "loss": 5.66547, "time": 0.70258} +{"mode": "train", "epoch": 1, "iter": 2500, "lr": 0.1, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.05609, "top5_acc": 0.17016, "loss_cls": 5.61053, "loss": 5.61053, "time": 0.70191} +{"mode": "train", "epoch": 1, "iter": 2600, "lr": 0.09999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.04703, "top5_acc": 0.16141, "loss_cls": 5.63864, "loss": 5.63864, "time": 0.70406} +{"mode": "train", "epoch": 1, "iter": 2700, "lr": 0.09999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.05203, "top5_acc": 0.17172, "loss_cls": 5.61576, "loss": 5.61576, "time": 0.70281} +{"mode": "train", "epoch": 1, "iter": 2800, "lr": 0.09999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.04984, "top5_acc": 0.16547, "loss_cls": 5.6124, "loss": 5.6124, "time": 0.70045} +{"mode": "train", "epoch": 1, "iter": 2900, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.05172, "top5_acc": 0.17203, "loss_cls": 5.59618, "loss": 5.59618, "time": 0.70046} +{"mode": "train", "epoch": 1, "iter": 3000, "lr": 0.09999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.05625, "top5_acc": 0.18031, "loss_cls": 5.54572, "loss": 5.54572, "time": 0.70335} +{"mode": "train", "epoch": 1, "iter": 3100, "lr": 0.09999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.06453, "top5_acc": 0.19562, "loss_cls": 5.51255, "loss": 5.51255, "time": 0.70107} +{"mode": "train", "epoch": 1, "iter": 3200, "lr": 0.09999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.06172, "top5_acc": 0.19516, "loss_cls": 5.48773, "loss": 5.48773, "time": 0.7016} +{"mode": "train", "epoch": 1, "iter": 3300, "lr": 0.09999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.05656, "top5_acc": 0.18344, "loss_cls": 5.51967, "loss": 5.51967, "time": 0.70114} +{"mode": "train", "epoch": 1, "iter": 3400, "lr": 0.09999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.05891, "top5_acc": 0.19844, "loss_cls": 5.49139, "loss": 5.49139, "time": 0.70164} +{"mode": "train", "epoch": 1, "iter": 3500, "lr": 0.09999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.05781, "top5_acc": 0.19391, "loss_cls": 5.47515, "loss": 5.47515, "time": 0.70505} +{"mode": "train", "epoch": 1, "iter": 3600, "lr": 0.09999, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.06766, "top5_acc": 0.19984, "loss_cls": 5.4801, "loss": 5.4801, "time": 0.7097} +{"mode": "train", "epoch": 1, "iter": 3700, "lr": 0.09999, "memory": 15990, "data_time": 0.00059, "top1_acc": 0.06391, "top5_acc": 0.20156, "loss_cls": 5.46152, "loss": 5.46152, "time": 0.71133} +{"mode": "val", "epoch": 1, "iter": 309, "lr": 0.09999, "top1_acc": 0.04158, "top5_acc": 0.13463, "mean_class_accuracy": 0.0416} +{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.09999, "memory": 15990, "data_time": 1.20236, "top1_acc": 0.06797, "top5_acc": 0.20984, "loss_cls": 5.43617, "loss": 5.43617, "time": 1.90433} +{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.06688, "top5_acc": 0.20781, "loss_cls": 5.42636, "loss": 5.42636, "time": 0.70314} +{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.075, "top5_acc": 0.21734, "loss_cls": 5.38268, "loss": 5.38268, "time": 0.70357} +{"mode": "train", "epoch": 2, "iter": 400, "lr": 0.09999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.075, "top5_acc": 0.21438, "loss_cls": 5.3821, "loss": 5.3821, "time": 0.70304} +{"mode": "train", "epoch": 2, "iter": 500, "lr": 0.09999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.07047, "top5_acc": 0.21828, "loss_cls": 5.38162, "loss": 5.38162, "time": 0.703} +{"mode": "train", "epoch": 2, "iter": 600, "lr": 0.09999, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.08125, "top5_acc": 0.22359, "loss_cls": 5.37471, "loss": 5.37471, "time": 0.70139} +{"mode": "train", "epoch": 2, "iter": 700, "lr": 0.09998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.07844, "top5_acc": 0.23359, "loss_cls": 5.36246, "loss": 5.36246, "time": 0.7036} +{"mode": "train", "epoch": 2, "iter": 800, "lr": 0.09998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.07781, "top5_acc": 0.23609, "loss_cls": 5.32404, "loss": 5.32404, "time": 0.70388} +{"mode": "train", "epoch": 2, "iter": 900, "lr": 0.09998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.08578, "top5_acc": 0.23875, "loss_cls": 5.30456, "loss": 5.30456, "time": 0.70158} +{"mode": "train", "epoch": 2, "iter": 1000, "lr": 0.09998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.08328, "top5_acc": 0.23719, "loss_cls": 5.28306, "loss": 5.28306, "time": 0.70204} +{"mode": "train", "epoch": 2, "iter": 1100, "lr": 0.09998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.08828, "top5_acc": 0.24344, "loss_cls": 5.27829, "loss": 5.27829, "time": 0.70234} +{"mode": "train", "epoch": 2, "iter": 1200, "lr": 0.09998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.08609, "top5_acc": 0.24641, "loss_cls": 5.27859, "loss": 5.27859, "time": 0.70323} +{"mode": "train", "epoch": 2, "iter": 1300, "lr": 0.09998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.08688, "top5_acc": 0.25172, "loss_cls": 5.24604, "loss": 5.24604, "time": 0.70056} +{"mode": "train", "epoch": 2, "iter": 1400, "lr": 0.09998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.08844, "top5_acc": 0.25391, "loss_cls": 5.2121, "loss": 5.2121, "time": 0.70045} +{"mode": "train", "epoch": 2, "iter": 1500, "lr": 0.09998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.08688, "top5_acc": 0.25516, "loss_cls": 5.22386, "loss": 5.22386, "time": 0.7015} +{"mode": "train", "epoch": 2, "iter": 1600, "lr": 0.09998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.08969, "top5_acc": 0.25641, "loss_cls": 5.22075, "loss": 5.22075, "time": 0.70333} +{"mode": "train", "epoch": 2, "iter": 1700, "lr": 0.09998, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.09656, "top5_acc": 0.26047, "loss_cls": 5.18401, "loss": 5.18401, "time": 0.70324} +{"mode": "train", "epoch": 2, "iter": 1800, "lr": 0.09998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.09062, "top5_acc": 0.26031, "loss_cls": 5.22688, "loss": 5.22688, "time": 0.70703} +{"mode": "train", "epoch": 2, "iter": 1900, "lr": 0.09998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.09734, "top5_acc": 0.26844, "loss_cls": 5.18277, "loss": 5.18277, "time": 0.70262} +{"mode": "train", "epoch": 2, "iter": 2000, "lr": 0.09997, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.09734, "top5_acc": 0.26812, "loss_cls": 5.18678, "loss": 5.18678, "time": 0.70291} +{"mode": "train", "epoch": 2, "iter": 2100, "lr": 0.09997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.09766, "top5_acc": 0.27094, "loss_cls": 5.15443, "loss": 5.15443, "time": 0.70502} +{"mode": "train", "epoch": 2, "iter": 2200, "lr": 0.09997, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.09578, "top5_acc": 0.26844, "loss_cls": 5.14987, "loss": 5.14987, "time": 0.70105} +{"mode": "train", "epoch": 2, "iter": 2300, "lr": 0.09997, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.10375, "top5_acc": 0.27531, "loss_cls": 5.14262, "loss": 5.14262, "time": 0.70331} +{"mode": "train", "epoch": 2, "iter": 2400, "lr": 0.09997, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.10703, "top5_acc": 0.27484, "loss_cls": 5.144, "loss": 5.144, "time": 0.70152} +{"mode": "train", "epoch": 2, "iter": 2500, "lr": 0.09997, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.10219, "top5_acc": 0.28062, "loss_cls": 5.12424, "loss": 5.12424, "time": 0.70248} +{"mode": "train", "epoch": 2, "iter": 2600, "lr": 0.09997, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.10297, "top5_acc": 0.28531, "loss_cls": 5.122, "loss": 5.122, "time": 0.70263} +{"mode": "train", "epoch": 2, "iter": 2700, "lr": 0.09997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.10203, "top5_acc": 0.27391, "loss_cls": 5.14184, "loss": 5.14184, "time": 0.70342} +{"mode": "train", "epoch": 2, "iter": 2800, "lr": 0.09997, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.10609, "top5_acc": 0.27875, "loss_cls": 5.11949, "loss": 5.11949, "time": 0.70041} +{"mode": "train", "epoch": 2, "iter": 2900, "lr": 0.09997, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.10703, "top5_acc": 0.27984, "loss_cls": 5.11909, "loss": 5.11909, "time": 0.7015} +{"mode": "train", "epoch": 2, "iter": 3000, "lr": 0.09996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.10688, "top5_acc": 0.28438, "loss_cls": 5.09601, "loss": 5.09601, "time": 0.70236} +{"mode": "train", "epoch": 2, "iter": 3100, "lr": 0.09996, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.11406, "top5_acc": 0.29766, "loss_cls": 5.04576, "loss": 5.04576, "time": 0.70359} +{"mode": "train", "epoch": 2, "iter": 3200, "lr": 0.09996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.11422, "top5_acc": 0.2975, "loss_cls": 5.02745, "loss": 5.02745, "time": 0.703} +{"mode": "train", "epoch": 2, "iter": 3300, "lr": 0.09996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.11312, "top5_acc": 0.29484, "loss_cls": 5.04042, "loss": 5.04042, "time": 0.7019} +{"mode": "train", "epoch": 2, "iter": 3400, "lr": 0.09996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.11609, "top5_acc": 0.30938, "loss_cls": 5.02093, "loss": 5.02093, "time": 0.70396} +{"mode": "train", "epoch": 2, "iter": 3500, "lr": 0.09996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.11844, "top5_acc": 0.30562, "loss_cls": 5.05072, "loss": 5.05072, "time": 0.70613} +{"mode": "train", "epoch": 2, "iter": 3600, "lr": 0.09996, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.12219, "top5_acc": 0.30625, "loss_cls": 5.01435, "loss": 5.01435, "time": 0.70739} +{"mode": "train", "epoch": 2, "iter": 3700, "lr": 0.09996, "memory": 15990, "data_time": 0.00069, "top1_acc": 0.11906, "top5_acc": 0.30484, "loss_cls": 4.99441, "loss": 4.99441, "time": 0.70796} +{"mode": "val", "epoch": 2, "iter": 309, "lr": 0.09996, "top1_acc": 0.05936, "top5_acc": 0.19197, "mean_class_accuracy": 0.05928} +{"mode": "train", "epoch": 3, "iter": 100, "lr": 0.09995, "memory": 15990, "data_time": 1.2036, "top1_acc": 0.12078, "top5_acc": 0.31, "loss_cls": 4.99452, "loss": 4.99452, "time": 1.90788} +{"mode": "train", "epoch": 3, "iter": 200, "lr": 0.09995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.12906, "top5_acc": 0.31859, "loss_cls": 4.97594, "loss": 4.97594, "time": 0.70136} +{"mode": "train", "epoch": 3, "iter": 300, "lr": 0.09995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.125, "top5_acc": 0.31484, "loss_cls": 4.95458, "loss": 4.95458, "time": 0.70221} +{"mode": "train", "epoch": 3, "iter": 400, "lr": 0.09995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.12047, "top5_acc": 0.31609, "loss_cls": 4.98614, "loss": 4.98614, "time": 0.70081} +{"mode": "train", "epoch": 3, "iter": 500, "lr": 0.09995, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.12156, "top5_acc": 0.31766, "loss_cls": 4.96579, "loss": 4.96579, "time": 0.70197} +{"mode": "train", "epoch": 3, "iter": 600, "lr": 0.09995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.12812, "top5_acc": 0.31453, "loss_cls": 4.9596, "loss": 4.9596, "time": 0.70209} +{"mode": "train", "epoch": 3, "iter": 700, "lr": 0.09995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.13359, "top5_acc": 0.32062, "loss_cls": 4.945, "loss": 4.945, "time": 0.70119} +{"mode": "train", "epoch": 3, "iter": 800, "lr": 0.09995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.12578, "top5_acc": 0.32406, "loss_cls": 4.94654, "loss": 4.94654, "time": 0.70095} +{"mode": "train", "epoch": 3, "iter": 900, "lr": 0.09994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.13344, "top5_acc": 0.32688, "loss_cls": 4.92558, "loss": 4.92558, "time": 0.70282} +{"mode": "train", "epoch": 3, "iter": 1000, "lr": 0.09994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.12641, "top5_acc": 0.32109, "loss_cls": 4.97034, "loss": 4.97034, "time": 0.70354} +{"mode": "train", "epoch": 3, "iter": 1100, "lr": 0.09994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.12375, "top5_acc": 0.31875, "loss_cls": 4.96822, "loss": 4.96822, "time": 0.70441} +{"mode": "train", "epoch": 3, "iter": 1200, "lr": 0.09994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.12828, "top5_acc": 0.33188, "loss_cls": 4.90423, "loss": 4.90423, "time": 0.7023} +{"mode": "train", "epoch": 3, "iter": 1300, "lr": 0.09994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.13031, "top5_acc": 0.32031, "loss_cls": 4.94224, "loss": 4.94224, "time": 0.70022} +{"mode": "train", "epoch": 3, "iter": 1400, "lr": 0.09994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.13141, "top5_acc": 0.32938, "loss_cls": 4.91724, "loss": 4.91724, "time": 0.7067} +{"mode": "train", "epoch": 3, "iter": 1500, "lr": 0.09994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.13766, "top5_acc": 0.33641, "loss_cls": 4.88451, "loss": 4.88451, "time": 0.70329} +{"mode": "train", "epoch": 3, "iter": 1600, "lr": 0.09994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.13922, "top5_acc": 0.33688, "loss_cls": 4.92291, "loss": 4.92291, "time": 0.70231} +{"mode": "train", "epoch": 3, "iter": 1700, "lr": 0.09993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.13406, "top5_acc": 0.33891, "loss_cls": 4.87368, "loss": 4.87368, "time": 0.70313} +{"mode": "train", "epoch": 3, "iter": 1800, "lr": 0.09993, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.12406, "top5_acc": 0.31859, "loss_cls": 4.94765, "loss": 4.94765, "time": 0.70509} +{"mode": "train", "epoch": 3, "iter": 1900, "lr": 0.09993, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.12984, "top5_acc": 0.32094, "loss_cls": 4.947, "loss": 4.947, "time": 0.70358} +{"mode": "train", "epoch": 3, "iter": 2000, "lr": 0.09993, "memory": 15990, "data_time": 0.00085, "top1_acc": 0.13125, "top5_acc": 0.33094, "loss_cls": 4.91389, "loss": 4.91389, "time": 0.7034} +{"mode": "train", "epoch": 3, "iter": 2100, "lr": 0.09993, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.13859, "top5_acc": 0.33922, "loss_cls": 4.8869, "loss": 4.8869, "time": 0.70379} +{"mode": "train", "epoch": 3, "iter": 2200, "lr": 0.09993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.12906, "top5_acc": 0.33266, "loss_cls": 4.90899, "loss": 4.90899, "time": 0.703} +{"mode": "train", "epoch": 3, "iter": 2300, "lr": 0.09993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.12922, "top5_acc": 0.33562, "loss_cls": 4.89272, "loss": 4.89272, "time": 0.70138} +{"mode": "train", "epoch": 3, "iter": 2400, "lr": 0.09992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.13672, "top5_acc": 0.34062, "loss_cls": 4.8613, "loss": 4.8613, "time": 0.70187} +{"mode": "train", "epoch": 3, "iter": 2500, "lr": 0.09992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.14375, "top5_acc": 0.34984, "loss_cls": 4.84008, "loss": 4.84008, "time": 0.70093} +{"mode": "train", "epoch": 3, "iter": 2600, "lr": 0.09992, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.13969, "top5_acc": 0.34078, "loss_cls": 4.8837, "loss": 4.8837, "time": 0.70148} +{"mode": "train", "epoch": 3, "iter": 2700, "lr": 0.09992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.13812, "top5_acc": 0.33578, "loss_cls": 4.90493, "loss": 4.90493, "time": 0.70102} +{"mode": "train", "epoch": 3, "iter": 2800, "lr": 0.09992, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.14078, "top5_acc": 0.34812, "loss_cls": 4.82932, "loss": 4.82932, "time": 0.70159} +{"mode": "train", "epoch": 3, "iter": 2900, "lr": 0.09992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.13812, "top5_acc": 0.3425, "loss_cls": 4.85204, "loss": 4.85204, "time": 0.7006} +{"mode": "train", "epoch": 3, "iter": 3000, "lr": 0.09991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.14094, "top5_acc": 0.33047, "loss_cls": 4.89229, "loss": 4.89229, "time": 0.70247} +{"mode": "train", "epoch": 3, "iter": 3100, "lr": 0.09991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.13875, "top5_acc": 0.33594, "loss_cls": 4.85296, "loss": 4.85296, "time": 0.70216} +{"mode": "train", "epoch": 3, "iter": 3200, "lr": 0.09991, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.14469, "top5_acc": 0.35141, "loss_cls": 4.81922, "loss": 4.81922, "time": 0.70074} +{"mode": "train", "epoch": 3, "iter": 3300, "lr": 0.09991, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.14641, "top5_acc": 0.34828, "loss_cls": 4.84131, "loss": 4.84131, "time": 0.70251} +{"mode": "train", "epoch": 3, "iter": 3400, "lr": 0.09991, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.14891, "top5_acc": 0.36453, "loss_cls": 4.79296, "loss": 4.79296, "time": 0.70283} +{"mode": "train", "epoch": 3, "iter": 3500, "lr": 0.09991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.13969, "top5_acc": 0.33781, "loss_cls": 4.86233, "loss": 4.86233, "time": 0.70279} +{"mode": "train", "epoch": 3, "iter": 3600, "lr": 0.0999, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.14719, "top5_acc": 0.34344, "loss_cls": 4.83564, "loss": 4.83564, "time": 0.7135} +{"mode": "train", "epoch": 3, "iter": 3700, "lr": 0.0999, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.15406, "top5_acc": 0.35359, "loss_cls": 4.78677, "loss": 4.78677, "time": 0.70975} +{"mode": "val", "epoch": 3, "iter": 309, "lr": 0.0999, "top1_acc": 0.07284, "top5_acc": 0.21187, "mean_class_accuracy": 0.0725} +{"mode": "train", "epoch": 4, "iter": 100, "lr": 0.0999, "memory": 15990, "data_time": 1.20445, "top1_acc": 0.15391, "top5_acc": 0.36078, "loss_cls": 4.75114, "loss": 4.75114, "time": 1.9081} +{"mode": "train", "epoch": 4, "iter": 200, "lr": 0.0999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.14719, "top5_acc": 0.34891, "loss_cls": 4.81715, "loss": 4.81715, "time": 0.70093} +{"mode": "train", "epoch": 4, "iter": 300, "lr": 0.0999, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.15141, "top5_acc": 0.35688, "loss_cls": 4.76285, "loss": 4.76285, "time": 0.70223} +{"mode": "train", "epoch": 4, "iter": 400, "lr": 0.09989, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15172, "top5_acc": 0.36016, "loss_cls": 4.77099, "loss": 4.77099, "time": 0.7011} +{"mode": "train", "epoch": 4, "iter": 500, "lr": 0.09989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.14812, "top5_acc": 0.35656, "loss_cls": 4.80052, "loss": 4.80052, "time": 0.70046} +{"mode": "train", "epoch": 4, "iter": 600, "lr": 0.09989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.14156, "top5_acc": 0.34938, "loss_cls": 4.80008, "loss": 4.80008, "time": 0.70123} +{"mode": "train", "epoch": 4, "iter": 700, "lr": 0.09989, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15172, "top5_acc": 0.36156, "loss_cls": 4.79592, "loss": 4.79592, "time": 0.70119} +{"mode": "train", "epoch": 4, "iter": 800, "lr": 0.09989, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15656, "top5_acc": 0.35922, "loss_cls": 4.76271, "loss": 4.76271, "time": 0.70133} +{"mode": "train", "epoch": 4, "iter": 900, "lr": 0.09988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15844, "top5_acc": 0.36672, "loss_cls": 4.72405, "loss": 4.72405, "time": 0.70043} +{"mode": "train", "epoch": 4, "iter": 1000, "lr": 0.09988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.14562, "top5_acc": 0.36703, "loss_cls": 4.77135, "loss": 4.77135, "time": 0.70073} +{"mode": "train", "epoch": 4, "iter": 1100, "lr": 0.09988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15359, "top5_acc": 0.36359, "loss_cls": 4.79579, "loss": 4.79579, "time": 0.69736} +{"mode": "train", "epoch": 4, "iter": 1200, "lr": 0.09988, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15094, "top5_acc": 0.3575, "loss_cls": 4.79916, "loss": 4.79916, "time": 0.70242} +{"mode": "train", "epoch": 4, "iter": 1300, "lr": 0.09988, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15375, "top5_acc": 0.3675, "loss_cls": 4.75031, "loss": 4.75031, "time": 0.70043} +{"mode": "train", "epoch": 4, "iter": 1400, "lr": 0.09988, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.15375, "top5_acc": 0.36469, "loss_cls": 4.75489, "loss": 4.75489, "time": 0.70114} +{"mode": "train", "epoch": 4, "iter": 1500, "lr": 0.09987, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.15625, "top5_acc": 0.37328, "loss_cls": 4.75028, "loss": 4.75028, "time": 0.70021} +{"mode": "train", "epoch": 4, "iter": 1600, "lr": 0.09987, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15078, "top5_acc": 0.35766, "loss_cls": 4.76392, "loss": 4.76392, "time": 0.70006} +{"mode": "train", "epoch": 4, "iter": 1700, "lr": 0.09987, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15406, "top5_acc": 0.36219, "loss_cls": 4.75191, "loss": 4.75191, "time": 0.70026} +{"mode": "train", "epoch": 4, "iter": 1800, "lr": 0.09987, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.16062, "top5_acc": 0.3625, "loss_cls": 4.75541, "loss": 4.75541, "time": 0.7052} +{"mode": "train", "epoch": 4, "iter": 1900, "lr": 0.09987, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15578, "top5_acc": 0.36891, "loss_cls": 4.76179, "loss": 4.76179, "time": 0.70295} +{"mode": "train", "epoch": 4, "iter": 2000, "lr": 0.09986, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15281, "top5_acc": 0.35812, "loss_cls": 4.75594, "loss": 4.75594, "time": 0.7028} +{"mode": "train", "epoch": 4, "iter": 2100, "lr": 0.09986, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15141, "top5_acc": 0.36328, "loss_cls": 4.7796, "loss": 4.7796, "time": 0.70397} +{"mode": "train", "epoch": 4, "iter": 2200, "lr": 0.09986, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.14656, "top5_acc": 0.36609, "loss_cls": 4.74414, "loss": 4.74414, "time": 0.70953} +{"mode": "train", "epoch": 4, "iter": 2300, "lr": 0.09986, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.15438, "top5_acc": 0.35703, "loss_cls": 4.78924, "loss": 4.78924, "time": 0.70201} +{"mode": "train", "epoch": 4, "iter": 2400, "lr": 0.09985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15828, "top5_acc": 0.36578, "loss_cls": 4.73717, "loss": 4.73717, "time": 0.70103} +{"mode": "train", "epoch": 4, "iter": 2500, "lr": 0.09985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15875, "top5_acc": 0.36438, "loss_cls": 4.75201, "loss": 4.75201, "time": 0.70185} +{"mode": "train", "epoch": 4, "iter": 2600, "lr": 0.09985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15937, "top5_acc": 0.37547, "loss_cls": 4.74034, "loss": 4.74034, "time": 0.70143} +{"mode": "train", "epoch": 4, "iter": 2700, "lr": 0.09985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16531, "top5_acc": 0.37203, "loss_cls": 4.74085, "loss": 4.74085, "time": 0.70304} +{"mode": "train", "epoch": 4, "iter": 2800, "lr": 0.09985, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.15203, "top5_acc": 0.36391, "loss_cls": 4.75681, "loss": 4.75681, "time": 0.70276} +{"mode": "train", "epoch": 4, "iter": 2900, "lr": 0.09984, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.15625, "top5_acc": 0.37203, "loss_cls": 4.70877, "loss": 4.70877, "time": 0.70209} +{"mode": "train", "epoch": 4, "iter": 3000, "lr": 0.09984, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16312, "top5_acc": 0.37422, "loss_cls": 4.73872, "loss": 4.73872, "time": 0.70182} +{"mode": "train", "epoch": 4, "iter": 3100, "lr": 0.09984, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.15531, "top5_acc": 0.36781, "loss_cls": 4.77074, "loss": 4.77074, "time": 0.70218} +{"mode": "train", "epoch": 4, "iter": 3200, "lr": 0.09984, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15953, "top5_acc": 0.36844, "loss_cls": 4.73952, "loss": 4.73952, "time": 0.70279} +{"mode": "train", "epoch": 4, "iter": 3300, "lr": 0.09983, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.15359, "top5_acc": 0.36766, "loss_cls": 4.7381, "loss": 4.7381, "time": 0.70161} +{"mode": "train", "epoch": 4, "iter": 3400, "lr": 0.09983, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.16297, "top5_acc": 0.37547, "loss_cls": 4.72586, "loss": 4.72586, "time": 0.70048} +{"mode": "train", "epoch": 4, "iter": 3500, "lr": 0.09983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15656, "top5_acc": 0.37578, "loss_cls": 4.70767, "loss": 4.70767, "time": 0.70496} +{"mode": "train", "epoch": 4, "iter": 3600, "lr": 0.09983, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.16438, "top5_acc": 0.37547, "loss_cls": 4.69917, "loss": 4.69917, "time": 0.71541} +{"mode": "train", "epoch": 4, "iter": 3700, "lr": 0.09983, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.1675, "top5_acc": 0.37094, "loss_cls": 4.71861, "loss": 4.71861, "time": 0.71187} +{"mode": "val", "epoch": 4, "iter": 309, "lr": 0.09982, "top1_acc": 0.11011, "top5_acc": 0.28927, "mean_class_accuracy": 0.10996} +{"mode": "train", "epoch": 5, "iter": 100, "lr": 0.09982, "memory": 15990, "data_time": 1.22013, "top1_acc": 0.16609, "top5_acc": 0.38422, "loss_cls": 4.66357, "loss": 4.66357, "time": 1.92368} +{"mode": "train", "epoch": 5, "iter": 200, "lr": 0.09982, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16906, "top5_acc": 0.38016, "loss_cls": 4.69992, "loss": 4.69992, "time": 0.70198} +{"mode": "train", "epoch": 5, "iter": 300, "lr": 0.09982, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.16453, "top5_acc": 0.37859, "loss_cls": 4.6876, "loss": 4.6876, "time": 0.7042} +{"mode": "train", "epoch": 5, "iter": 400, "lr": 0.09982, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.16516, "top5_acc": 0.38359, "loss_cls": 4.70114, "loss": 4.70114, "time": 0.70265} +{"mode": "train", "epoch": 5, "iter": 500, "lr": 0.09981, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16156, "top5_acc": 0.37859, "loss_cls": 4.69797, "loss": 4.69797, "time": 0.70212} +{"mode": "train", "epoch": 5, "iter": 600, "lr": 0.09981, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.15359, "top5_acc": 0.35266, "loss_cls": 4.75332, "loss": 4.75332, "time": 0.70107} +{"mode": "train", "epoch": 5, "iter": 700, "lr": 0.09981, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16328, "top5_acc": 0.38281, "loss_cls": 4.6788, "loss": 4.6788, "time": 0.70249} +{"mode": "train", "epoch": 5, "iter": 800, "lr": 0.09981, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16094, "top5_acc": 0.36625, "loss_cls": 4.70961, "loss": 4.70961, "time": 0.70184} +{"mode": "train", "epoch": 5, "iter": 900, "lr": 0.0998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15766, "top5_acc": 0.37547, "loss_cls": 4.69279, "loss": 4.69279, "time": 0.70193} +{"mode": "train", "epoch": 5, "iter": 1000, "lr": 0.0998, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16578, "top5_acc": 0.37844, "loss_cls": 4.69524, "loss": 4.69524, "time": 0.7022} +{"mode": "train", "epoch": 5, "iter": 1100, "lr": 0.0998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.1675, "top5_acc": 0.38375, "loss_cls": 4.67042, "loss": 4.67042, "time": 0.70455} +{"mode": "train", "epoch": 5, "iter": 1200, "lr": 0.0998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16344, "top5_acc": 0.37766, "loss_cls": 4.67641, "loss": 4.67641, "time": 0.70274} +{"mode": "train", "epoch": 5, "iter": 1300, "lr": 0.09979, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16562, "top5_acc": 0.38047, "loss_cls": 4.69998, "loss": 4.69998, "time": 0.70085} +{"mode": "train", "epoch": 5, "iter": 1400, "lr": 0.09979, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.165, "top5_acc": 0.37812, "loss_cls": 4.68983, "loss": 4.68983, "time": 0.70099} +{"mode": "train", "epoch": 5, "iter": 1500, "lr": 0.09979, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17031, "top5_acc": 0.38312, "loss_cls": 4.65696, "loss": 4.65696, "time": 0.70196} +{"mode": "train", "epoch": 5, "iter": 1600, "lr": 0.09979, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.15984, "top5_acc": 0.37406, "loss_cls": 4.70165, "loss": 4.70165, "time": 0.70436} +{"mode": "train", "epoch": 5, "iter": 1700, "lr": 0.09978, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.15359, "top5_acc": 0.37781, "loss_cls": 4.71861, "loss": 4.71861, "time": 0.70019} +{"mode": "train", "epoch": 5, "iter": 1800, "lr": 0.09978, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.16297, "top5_acc": 0.38516, "loss_cls": 4.67622, "loss": 4.67622, "time": 0.70768} +{"mode": "train", "epoch": 5, "iter": 1900, "lr": 0.09978, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.16812, "top5_acc": 0.37422, "loss_cls": 4.71932, "loss": 4.71932, "time": 0.70161} +{"mode": "train", "epoch": 5, "iter": 2000, "lr": 0.09977, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.16156, "top5_acc": 0.38141, "loss_cls": 4.71741, "loss": 4.71741, "time": 0.70379} +{"mode": "train", "epoch": 5, "iter": 2100, "lr": 0.09977, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.16375, "top5_acc": 0.37484, "loss_cls": 4.69328, "loss": 4.69328, "time": 0.70327} +{"mode": "train", "epoch": 5, "iter": 2200, "lr": 0.09977, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16875, "top5_acc": 0.37984, "loss_cls": 4.69131, "loss": 4.69131, "time": 0.70462} +{"mode": "train", "epoch": 5, "iter": 2300, "lr": 0.09977, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17719, "top5_acc": 0.38719, "loss_cls": 4.6508, "loss": 4.6508, "time": 0.70212} +{"mode": "train", "epoch": 5, "iter": 2400, "lr": 0.09976, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17234, "top5_acc": 0.3825, "loss_cls": 4.68344, "loss": 4.68344, "time": 0.70262} +{"mode": "train", "epoch": 5, "iter": 2500, "lr": 0.09976, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.16328, "top5_acc": 0.36953, "loss_cls": 4.71759, "loss": 4.71759, "time": 0.70239} +{"mode": "train", "epoch": 5, "iter": 2600, "lr": 0.09976, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.16672, "top5_acc": 0.38625, "loss_cls": 4.67021, "loss": 4.67021, "time": 0.70159} +{"mode": "train", "epoch": 5, "iter": 2700, "lr": 0.09976, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.16562, "top5_acc": 0.38375, "loss_cls": 4.6698, "loss": 4.6698, "time": 0.70143} +{"mode": "train", "epoch": 5, "iter": 2800, "lr": 0.09975, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16844, "top5_acc": 0.38281, "loss_cls": 4.65383, "loss": 4.65383, "time": 0.70447} +{"mode": "train", "epoch": 5, "iter": 2900, "lr": 0.09975, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16422, "top5_acc": 0.38625, "loss_cls": 4.6814, "loss": 4.6814, "time": 0.70071} +{"mode": "train", "epoch": 5, "iter": 3000, "lr": 0.09975, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16312, "top5_acc": 0.37984, "loss_cls": 4.68659, "loss": 4.68659, "time": 0.7011} +{"mode": "train", "epoch": 5, "iter": 3100, "lr": 0.09974, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16156, "top5_acc": 0.37703, "loss_cls": 4.68142, "loss": 4.68142, "time": 0.69931} +{"mode": "train", "epoch": 5, "iter": 3200, "lr": 0.09974, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.17031, "top5_acc": 0.38719, "loss_cls": 4.65671, "loss": 4.65671, "time": 0.70318} +{"mode": "train", "epoch": 5, "iter": 3300, "lr": 0.09974, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17172, "top5_acc": 0.38344, "loss_cls": 4.65723, "loss": 4.65723, "time": 0.70181} +{"mode": "train", "epoch": 5, "iter": 3400, "lr": 0.09974, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17531, "top5_acc": 0.39109, "loss_cls": 4.66642, "loss": 4.66642, "time": 0.70074} +{"mode": "train", "epoch": 5, "iter": 3500, "lr": 0.09973, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16547, "top5_acc": 0.38797, "loss_cls": 4.65958, "loss": 4.65958, "time": 0.70153} +{"mode": "train", "epoch": 5, "iter": 3600, "lr": 0.09973, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.17328, "top5_acc": 0.38312, "loss_cls": 4.68211, "loss": 4.68211, "time": 0.71337} +{"mode": "train", "epoch": 5, "iter": 3700, "lr": 0.09973, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.16438, "top5_acc": 0.37859, "loss_cls": 4.70714, "loss": 4.70714, "time": 0.70857} +{"mode": "val", "epoch": 5, "iter": 309, "lr": 0.09973, "top1_acc": 0.09695, "top5_acc": 0.26501, "mean_class_accuracy": 0.09685} +{"mode": "train", "epoch": 6, "iter": 100, "lr": 0.09972, "memory": 15990, "data_time": 1.20846, "top1_acc": 0.16797, "top5_acc": 0.38656, "loss_cls": 4.64286, "loss": 4.64286, "time": 1.91205} +{"mode": "train", "epoch": 6, "iter": 200, "lr": 0.09972, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17016, "top5_acc": 0.38375, "loss_cls": 4.66115, "loss": 4.66115, "time": 0.70326} +{"mode": "train", "epoch": 6, "iter": 300, "lr": 0.09972, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.17672, "top5_acc": 0.395, "loss_cls": 4.64206, "loss": 4.64206, "time": 0.7024} +{"mode": "train", "epoch": 6, "iter": 400, "lr": 0.09971, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16891, "top5_acc": 0.39344, "loss_cls": 4.60563, "loss": 4.60563, "time": 0.70193} +{"mode": "train", "epoch": 6, "iter": 500, "lr": 0.09971, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17, "top5_acc": 0.39078, "loss_cls": 4.6186, "loss": 4.6186, "time": 0.69935} +{"mode": "train", "epoch": 6, "iter": 600, "lr": 0.09971, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.1675, "top5_acc": 0.37641, "loss_cls": 4.67569, "loss": 4.67569, "time": 0.70047} +{"mode": "train", "epoch": 6, "iter": 700, "lr": 0.09971, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17312, "top5_acc": 0.38406, "loss_cls": 4.65807, "loss": 4.65807, "time": 0.70176} +{"mode": "train", "epoch": 6, "iter": 800, "lr": 0.0997, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17484, "top5_acc": 0.39531, "loss_cls": 4.62945, "loss": 4.62945, "time": 0.7012} +{"mode": "train", "epoch": 6, "iter": 900, "lr": 0.0997, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.185, "top5_acc": 0.40312, "loss_cls": 4.59196, "loss": 4.59196, "time": 0.70032} +{"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.0997, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17375, "top5_acc": 0.38625, "loss_cls": 4.61952, "loss": 4.61952, "time": 0.70079} +{"mode": "train", "epoch": 6, "iter": 1100, "lr": 0.09969, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16094, "top5_acc": 0.38891, "loss_cls": 4.67784, "loss": 4.67784, "time": 0.70062} +{"mode": "train", "epoch": 6, "iter": 1200, "lr": 0.09969, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17516, "top5_acc": 0.39297, "loss_cls": 4.6294, "loss": 4.6294, "time": 0.70245} +{"mode": "train", "epoch": 6, "iter": 1300, "lr": 0.09969, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16797, "top5_acc": 0.3875, "loss_cls": 4.6639, "loss": 4.6639, "time": 0.69951} +{"mode": "train", "epoch": 6, "iter": 1400, "lr": 0.09968, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17109, "top5_acc": 0.38203, "loss_cls": 4.65524, "loss": 4.65524, "time": 0.70072} +{"mode": "train", "epoch": 6, "iter": 1500, "lr": 0.09968, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17641, "top5_acc": 0.39922, "loss_cls": 4.58851, "loss": 4.58851, "time": 0.70126} +{"mode": "train", "epoch": 6, "iter": 1600, "lr": 0.09968, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17734, "top5_acc": 0.39547, "loss_cls": 4.61239, "loss": 4.61239, "time": 0.70007} +{"mode": "train", "epoch": 6, "iter": 1700, "lr": 0.09967, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17375, "top5_acc": 0.38453, "loss_cls": 4.62873, "loss": 4.62873, "time": 0.70008} +{"mode": "train", "epoch": 6, "iter": 1800, "lr": 0.09967, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16906, "top5_acc": 0.38297, "loss_cls": 4.68526, "loss": 4.68526, "time": 0.70814} +{"mode": "train", "epoch": 6, "iter": 1900, "lr": 0.09967, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.17156, "top5_acc": 0.38453, "loss_cls": 4.65842, "loss": 4.65842, "time": 0.70204} +{"mode": "train", "epoch": 6, "iter": 2000, "lr": 0.09966, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17391, "top5_acc": 0.39422, "loss_cls": 4.61686, "loss": 4.61686, "time": 0.70157} +{"mode": "train", "epoch": 6, "iter": 2100, "lr": 0.09966, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17484, "top5_acc": 0.39734, "loss_cls": 4.6344, "loss": 4.6344, "time": 0.70366} +{"mode": "train", "epoch": 6, "iter": 2200, "lr": 0.09966, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17406, "top5_acc": 0.38656, "loss_cls": 4.63238, "loss": 4.63238, "time": 0.70265} +{"mode": "train", "epoch": 6, "iter": 2300, "lr": 0.09965, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17016, "top5_acc": 0.38047, "loss_cls": 4.66118, "loss": 4.66118, "time": 0.70117} +{"mode": "train", "epoch": 6, "iter": 2400, "lr": 0.09965, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.17672, "top5_acc": 0.38625, "loss_cls": 4.64437, "loss": 4.64437, "time": 0.7009} +{"mode": "train", "epoch": 6, "iter": 2500, "lr": 0.09965, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17453, "top5_acc": 0.40016, "loss_cls": 4.59987, "loss": 4.59987, "time": 0.70299} +{"mode": "train", "epoch": 6, "iter": 2600, "lr": 0.09964, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18, "top5_acc": 0.39141, "loss_cls": 4.60385, "loss": 4.60385, "time": 0.70179} +{"mode": "train", "epoch": 6, "iter": 2700, "lr": 0.09964, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.16953, "top5_acc": 0.38531, "loss_cls": 4.65313, "loss": 4.65313, "time": 0.70254} +{"mode": "train", "epoch": 6, "iter": 2800, "lr": 0.09964, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17047, "top5_acc": 0.38328, "loss_cls": 4.66822, "loss": 4.66822, "time": 0.70157} +{"mode": "train", "epoch": 6, "iter": 2900, "lr": 0.09963, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.17203, "top5_acc": 0.38422, "loss_cls": 4.66448, "loss": 4.66448, "time": 0.7025} +{"mode": "train", "epoch": 6, "iter": 3000, "lr": 0.09963, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17812, "top5_acc": 0.39391, "loss_cls": 4.63397, "loss": 4.63397, "time": 0.70034} +{"mode": "train", "epoch": 6, "iter": 3100, "lr": 0.09963, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.1725, "top5_acc": 0.38969, "loss_cls": 4.62538, "loss": 4.62538, "time": 0.70482} +{"mode": "train", "epoch": 6, "iter": 3200, "lr": 0.09962, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17312, "top5_acc": 0.38609, "loss_cls": 4.62428, "loss": 4.62428, "time": 0.70544} +{"mode": "train", "epoch": 6, "iter": 3300, "lr": 0.09962, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17016, "top5_acc": 0.38219, "loss_cls": 4.6716, "loss": 4.6716, "time": 0.70057} +{"mode": "train", "epoch": 6, "iter": 3400, "lr": 0.09962, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17641, "top5_acc": 0.395, "loss_cls": 4.62711, "loss": 4.62711, "time": 0.70196} +{"mode": "train", "epoch": 6, "iter": 3500, "lr": 0.09961, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.1725, "top5_acc": 0.39266, "loss_cls": 4.63122, "loss": 4.63122, "time": 0.70234} +{"mode": "train", "epoch": 6, "iter": 3600, "lr": 0.09961, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.16875, "top5_acc": 0.39641, "loss_cls": 4.63614, "loss": 4.63614, "time": 0.70795} +{"mode": "train", "epoch": 6, "iter": 3700, "lr": 0.09961, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.17656, "top5_acc": 0.39438, "loss_cls": 4.63777, "loss": 4.63777, "time": 0.70871} +{"mode": "val", "epoch": 6, "iter": 309, "lr": 0.09961, "top1_acc": 0.1164, "top5_acc": 0.3035, "mean_class_accuracy": 0.11612} +{"mode": "train", "epoch": 7, "iter": 100, "lr": 0.0996, "memory": 15990, "data_time": 1.20746, "top1_acc": 0.18172, "top5_acc": 0.39672, "loss_cls": 4.6145, "loss": 4.6145, "time": 1.90986} +{"mode": "train", "epoch": 7, "iter": 200, "lr": 0.0996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17891, "top5_acc": 0.39234, "loss_cls": 4.62466, "loss": 4.62466, "time": 0.70351} +{"mode": "train", "epoch": 7, "iter": 300, "lr": 0.0996, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16938, "top5_acc": 0.40156, "loss_cls": 4.57096, "loss": 4.57096, "time": 0.69866} +{"mode": "train", "epoch": 7, "iter": 400, "lr": 0.09959, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17297, "top5_acc": 0.38891, "loss_cls": 4.65343, "loss": 4.65343, "time": 0.69953} +{"mode": "train", "epoch": 7, "iter": 500, "lr": 0.09959, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18188, "top5_acc": 0.40703, "loss_cls": 4.60816, "loss": 4.60816, "time": 0.70046} +{"mode": "train", "epoch": 7, "iter": 600, "lr": 0.09958, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17766, "top5_acc": 0.40234, "loss_cls": 4.62749, "loss": 4.62749, "time": 0.70217} +{"mode": "train", "epoch": 7, "iter": 700, "lr": 0.09958, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17531, "top5_acc": 0.4025, "loss_cls": 4.60319, "loss": 4.60319, "time": 0.70069} +{"mode": "train", "epoch": 7, "iter": 800, "lr": 0.09958, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.16891, "top5_acc": 0.3925, "loss_cls": 4.63782, "loss": 4.63782, "time": 0.70111} +{"mode": "train", "epoch": 7, "iter": 900, "lr": 0.09957, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.17828, "top5_acc": 0.39891, "loss_cls": 4.59581, "loss": 4.59581, "time": 0.70277} +{"mode": "train", "epoch": 7, "iter": 1000, "lr": 0.09957, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17047, "top5_acc": 0.38719, "loss_cls": 4.65606, "loss": 4.65606, "time": 0.70131} +{"mode": "train", "epoch": 7, "iter": 1100, "lr": 0.09957, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17969, "top5_acc": 0.39516, "loss_cls": 4.60379, "loss": 4.60379, "time": 0.6992} +{"mode": "train", "epoch": 7, "iter": 1200, "lr": 0.09956, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17547, "top5_acc": 0.40141, "loss_cls": 4.60715, "loss": 4.60715, "time": 0.70072} +{"mode": "train", "epoch": 7, "iter": 1300, "lr": 0.09956, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17641, "top5_acc": 0.39266, "loss_cls": 4.62433, "loss": 4.62433, "time": 0.69916} +{"mode": "train", "epoch": 7, "iter": 1400, "lr": 0.09956, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17562, "top5_acc": 0.38688, "loss_cls": 4.64233, "loss": 4.64233, "time": 0.69976} +{"mode": "train", "epoch": 7, "iter": 1500, "lr": 0.09955, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.16625, "top5_acc": 0.38422, "loss_cls": 4.65133, "loss": 4.65133, "time": 0.70218} +{"mode": "train", "epoch": 7, "iter": 1600, "lr": 0.09955, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17266, "top5_acc": 0.385, "loss_cls": 4.62084, "loss": 4.62084, "time": 0.69975} +{"mode": "train", "epoch": 7, "iter": 1700, "lr": 0.09954, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17484, "top5_acc": 0.39141, "loss_cls": 4.63112, "loss": 4.63112, "time": 0.70151} +{"mode": "train", "epoch": 7, "iter": 1800, "lr": 0.09954, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18016, "top5_acc": 0.39469, "loss_cls": 4.63389, "loss": 4.63389, "time": 0.70283} +{"mode": "train", "epoch": 7, "iter": 1900, "lr": 0.09954, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18453, "top5_acc": 0.40703, "loss_cls": 4.58832, "loss": 4.58832, "time": 0.7011} +{"mode": "train", "epoch": 7, "iter": 2000, "lr": 0.09953, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18109, "top5_acc": 0.39016, "loss_cls": 4.61344, "loss": 4.61344, "time": 0.70224} +{"mode": "train", "epoch": 7, "iter": 2100, "lr": 0.09953, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.17328, "top5_acc": 0.39422, "loss_cls": 4.61189, "loss": 4.61189, "time": 0.70187} +{"mode": "train", "epoch": 7, "iter": 2200, "lr": 0.09952, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18359, "top5_acc": 0.40578, "loss_cls": 4.57311, "loss": 4.57311, "time": 0.69882} +{"mode": "train", "epoch": 7, "iter": 2300, "lr": 0.09952, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18109, "top5_acc": 0.40328, "loss_cls": 4.60102, "loss": 4.60102, "time": 0.6987} +{"mode": "train", "epoch": 7, "iter": 2400, "lr": 0.09952, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.1775, "top5_acc": 0.39141, "loss_cls": 4.59551, "loss": 4.59551, "time": 0.6993} +{"mode": "train", "epoch": 7, "iter": 2500, "lr": 0.09951, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18203, "top5_acc": 0.41047, "loss_cls": 4.59327, "loss": 4.59327, "time": 0.69964} +{"mode": "train", "epoch": 7, "iter": 2600, "lr": 0.09951, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.1825, "top5_acc": 0.40641, "loss_cls": 4.57722, "loss": 4.57722, "time": 0.70084} +{"mode": "train", "epoch": 7, "iter": 2700, "lr": 0.09951, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18641, "top5_acc": 0.41062, "loss_cls": 4.54115, "loss": 4.54115, "time": 0.70084} +{"mode": "train", "epoch": 7, "iter": 2800, "lr": 0.0995, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17703, "top5_acc": 0.39953, "loss_cls": 4.60296, "loss": 4.60296, "time": 0.70043} +{"mode": "train", "epoch": 7, "iter": 2900, "lr": 0.0995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17656, "top5_acc": 0.39344, "loss_cls": 4.60723, "loss": 4.60723, "time": 0.69998} +{"mode": "train", "epoch": 7, "iter": 3000, "lr": 0.09949, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18078, "top5_acc": 0.39938, "loss_cls": 4.59051, "loss": 4.59051, "time": 0.7005} +{"mode": "train", "epoch": 7, "iter": 3100, "lr": 0.09949, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17594, "top5_acc": 0.39781, "loss_cls": 4.5993, "loss": 4.5993, "time": 0.70168} +{"mode": "train", "epoch": 7, "iter": 3200, "lr": 0.09949, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17734, "top5_acc": 0.39188, "loss_cls": 4.60446, "loss": 4.60446, "time": 0.7002} +{"mode": "train", "epoch": 7, "iter": 3300, "lr": 0.09948, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17141, "top5_acc": 0.39, "loss_cls": 4.6354, "loss": 4.6354, "time": 0.69929} +{"mode": "train", "epoch": 7, "iter": 3400, "lr": 0.09948, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18453, "top5_acc": 0.40406, "loss_cls": 4.61605, "loss": 4.61605, "time": 0.70103} +{"mode": "train", "epoch": 7, "iter": 3500, "lr": 0.09947, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17734, "top5_acc": 0.39594, "loss_cls": 4.60325, "loss": 4.60325, "time": 0.70082} +{"mode": "train", "epoch": 7, "iter": 3600, "lr": 0.09947, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18719, "top5_acc": 0.40438, "loss_cls": 4.55826, "loss": 4.55826, "time": 0.71038} +{"mode": "train", "epoch": 7, "iter": 3700, "lr": 0.09947, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.1825, "top5_acc": 0.39797, "loss_cls": 4.6189, "loss": 4.6189, "time": 0.70509} +{"mode": "val", "epoch": 7, "iter": 309, "lr": 0.09946, "top1_acc": 0.11741, "top5_acc": 0.30026, "mean_class_accuracy": 0.1172} +{"mode": "train", "epoch": 8, "iter": 100, "lr": 0.09946, "memory": 15990, "data_time": 1.20176, "top1_acc": 0.19141, "top5_acc": 0.4075, "loss_cls": 4.57528, "loss": 4.57528, "time": 1.90953} +{"mode": "train", "epoch": 8, "iter": 200, "lr": 0.09946, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17547, "top5_acc": 0.39844, "loss_cls": 4.60089, "loss": 4.60089, "time": 0.7027} +{"mode": "train", "epoch": 8, "iter": 300, "lr": 0.09945, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17812, "top5_acc": 0.40266, "loss_cls": 4.57706, "loss": 4.57706, "time": 0.70287} +{"mode": "train", "epoch": 8, "iter": 400, "lr": 0.09945, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18266, "top5_acc": 0.40281, "loss_cls": 4.57446, "loss": 4.57446, "time": 0.70028} +{"mode": "train", "epoch": 8, "iter": 500, "lr": 0.09944, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.1825, "top5_acc": 0.40594, "loss_cls": 4.59172, "loss": 4.59172, "time": 0.70038} +{"mode": "train", "epoch": 8, "iter": 600, "lr": 0.09944, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.18047, "top5_acc": 0.40594, "loss_cls": 4.55963, "loss": 4.55963, "time": 0.70073} +{"mode": "train", "epoch": 8, "iter": 700, "lr": 0.09943, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.18797, "top5_acc": 0.40547, "loss_cls": 4.55627, "loss": 4.55627, "time": 0.70254} +{"mode": "train", "epoch": 8, "iter": 800, "lr": 0.09943, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18703, "top5_acc": 0.41594, "loss_cls": 4.56094, "loss": 4.56094, "time": 0.70132} +{"mode": "train", "epoch": 8, "iter": 900, "lr": 0.09943, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17562, "top5_acc": 0.39906, "loss_cls": 4.59107, "loss": 4.59107, "time": 0.70029} +{"mode": "train", "epoch": 8, "iter": 1000, "lr": 0.09942, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17844, "top5_acc": 0.40078, "loss_cls": 4.57462, "loss": 4.57462, "time": 0.70106} +{"mode": "train", "epoch": 8, "iter": 1100, "lr": 0.09942, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17641, "top5_acc": 0.39922, "loss_cls": 4.65677, "loss": 4.65677, "time": 0.70034} +{"mode": "train", "epoch": 8, "iter": 1200, "lr": 0.09941, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19031, "top5_acc": 0.40547, "loss_cls": 4.54004, "loss": 4.54004, "time": 0.70344} +{"mode": "train", "epoch": 8, "iter": 1300, "lr": 0.09941, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18109, "top5_acc": 0.4075, "loss_cls": 4.57826, "loss": 4.57826, "time": 0.69948} +{"mode": "train", "epoch": 8, "iter": 1400, "lr": 0.0994, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18438, "top5_acc": 0.41016, "loss_cls": 4.57532, "loss": 4.57532, "time": 0.70021} +{"mode": "train", "epoch": 8, "iter": 1500, "lr": 0.0994, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18078, "top5_acc": 0.40812, "loss_cls": 4.56065, "loss": 4.56065, "time": 0.69937} +{"mode": "train", "epoch": 8, "iter": 1600, "lr": 0.0994, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17891, "top5_acc": 0.39047, "loss_cls": 4.60593, "loss": 4.60593, "time": 0.70175} +{"mode": "train", "epoch": 8, "iter": 1700, "lr": 0.09939, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17359, "top5_acc": 0.395, "loss_cls": 4.6073, "loss": 4.6073, "time": 0.7003} +{"mode": "train", "epoch": 8, "iter": 1800, "lr": 0.09939, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18422, "top5_acc": 0.40234, "loss_cls": 4.59442, "loss": 4.59442, "time": 0.70428} +{"mode": "train", "epoch": 8, "iter": 1900, "lr": 0.09938, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.18781, "top5_acc": 0.41094, "loss_cls": 4.57487, "loss": 4.57487, "time": 0.70154} +{"mode": "train", "epoch": 8, "iter": 2000, "lr": 0.09938, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18734, "top5_acc": 0.41391, "loss_cls": 4.54245, "loss": 4.54245, "time": 0.70223} +{"mode": "train", "epoch": 8, "iter": 2100, "lr": 0.09937, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17969, "top5_acc": 0.39984, "loss_cls": 4.61378, "loss": 4.61378, "time": 0.70034} +{"mode": "train", "epoch": 8, "iter": 2200, "lr": 0.09937, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18266, "top5_acc": 0.40922, "loss_cls": 4.56131, "loss": 4.56131, "time": 0.70042} +{"mode": "train", "epoch": 8, "iter": 2300, "lr": 0.09937, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17078, "top5_acc": 0.40406, "loss_cls": 4.60666, "loss": 4.60666, "time": 0.69995} +{"mode": "train", "epoch": 8, "iter": 2400, "lr": 0.09936, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.17859, "top5_acc": 0.39844, "loss_cls": 4.59389, "loss": 4.59389, "time": 0.69794} +{"mode": "train", "epoch": 8, "iter": 2500, "lr": 0.09936, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18828, "top5_acc": 0.41562, "loss_cls": 4.52736, "loss": 4.52736, "time": 0.70054} +{"mode": "train", "epoch": 8, "iter": 2600, "lr": 0.09935, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17938, "top5_acc": 0.39938, "loss_cls": 4.59832, "loss": 4.59832, "time": 0.69964} +{"mode": "train", "epoch": 8, "iter": 2700, "lr": 0.09935, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18125, "top5_acc": 0.40219, "loss_cls": 4.5708, "loss": 4.5708, "time": 0.70058} +{"mode": "train", "epoch": 8, "iter": 2800, "lr": 0.09934, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17688, "top5_acc": 0.40234, "loss_cls": 4.59057, "loss": 4.59057, "time": 0.70076} +{"mode": "train", "epoch": 8, "iter": 2900, "lr": 0.09934, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17422, "top5_acc": 0.39812, "loss_cls": 4.58199, "loss": 4.58199, "time": 0.69938} +{"mode": "train", "epoch": 8, "iter": 3000, "lr": 0.09933, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18688, "top5_acc": 0.39828, "loss_cls": 4.59029, "loss": 4.59029, "time": 0.70124} +{"mode": "train", "epoch": 8, "iter": 3100, "lr": 0.09933, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18328, "top5_acc": 0.40031, "loss_cls": 4.60092, "loss": 4.60092, "time": 0.69897} +{"mode": "train", "epoch": 8, "iter": 3200, "lr": 0.09933, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18062, "top5_acc": 0.40297, "loss_cls": 4.5854, "loss": 4.5854, "time": 0.70078} +{"mode": "train", "epoch": 8, "iter": 3300, "lr": 0.09932, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.175, "top5_acc": 0.39031, "loss_cls": 4.63313, "loss": 4.63313, "time": 0.70138} +{"mode": "train", "epoch": 8, "iter": 3400, "lr": 0.09932, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18375, "top5_acc": 0.40156, "loss_cls": 4.60251, "loss": 4.60251, "time": 0.69971} +{"mode": "train", "epoch": 8, "iter": 3500, "lr": 0.09931, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.17766, "top5_acc": 0.41016, "loss_cls": 4.57437, "loss": 4.57437, "time": 0.69865} +{"mode": "train", "epoch": 8, "iter": 3600, "lr": 0.09931, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.18281, "top5_acc": 0.41125, "loss_cls": 4.56171, "loss": 4.56171, "time": 0.71186} +{"mode": "train", "epoch": 8, "iter": 3700, "lr": 0.0993, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.18938, "top5_acc": 0.40859, "loss_cls": 4.52695, "loss": 4.52695, "time": 0.70833} +{"mode": "val", "epoch": 8, "iter": 309, "lr": 0.0993, "top1_acc": 0.11452, "top5_acc": 0.30122, "mean_class_accuracy": 0.11441} +{"mode": "train", "epoch": 9, "iter": 100, "lr": 0.0993, "memory": 15990, "data_time": 1.2323, "top1_acc": 0.19078, "top5_acc": 0.41516, "loss_cls": 4.54314, "loss": 4.54314, "time": 1.9406} +{"mode": "train", "epoch": 9, "iter": 200, "lr": 0.09929, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19609, "top5_acc": 0.42391, "loss_cls": 4.47903, "loss": 4.47903, "time": 0.70534} +{"mode": "train", "epoch": 9, "iter": 300, "lr": 0.09929, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.18594, "top5_acc": 0.40594, "loss_cls": 4.56265, "loss": 4.56265, "time": 0.70256} +{"mode": "train", "epoch": 9, "iter": 400, "lr": 0.09928, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18703, "top5_acc": 0.41031, "loss_cls": 4.5448, "loss": 4.5448, "time": 0.70061} +{"mode": "train", "epoch": 9, "iter": 500, "lr": 0.09928, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.18203, "top5_acc": 0.39891, "loss_cls": 4.57761, "loss": 4.57761, "time": 0.70214} +{"mode": "train", "epoch": 9, "iter": 600, "lr": 0.09927, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18391, "top5_acc": 0.40688, "loss_cls": 4.57803, "loss": 4.57803, "time": 0.7033} +{"mode": "train", "epoch": 9, "iter": 700, "lr": 0.09927, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.18391, "top5_acc": 0.40562, "loss_cls": 4.5776, "loss": 4.5776, "time": 0.7043} +{"mode": "train", "epoch": 9, "iter": 800, "lr": 0.09926, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17953, "top5_acc": 0.39484, "loss_cls": 4.60536, "loss": 4.60536, "time": 0.70482} +{"mode": "train", "epoch": 9, "iter": 900, "lr": 0.09926, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.19438, "top5_acc": 0.41641, "loss_cls": 4.5398, "loss": 4.5398, "time": 0.70387} +{"mode": "train", "epoch": 9, "iter": 1000, "lr": 0.09925, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.18656, "top5_acc": 0.40906, "loss_cls": 4.57181, "loss": 4.57181, "time": 0.70638} +{"mode": "train", "epoch": 9, "iter": 1100, "lr": 0.09925, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18344, "top5_acc": 0.4075, "loss_cls": 4.55134, "loss": 4.55134, "time": 0.70343} +{"mode": "train", "epoch": 9, "iter": 1200, "lr": 0.09924, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18875, "top5_acc": 0.40891, "loss_cls": 4.56985, "loss": 4.56985, "time": 0.70484} +{"mode": "train", "epoch": 9, "iter": 1300, "lr": 0.09924, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17719, "top5_acc": 0.39516, "loss_cls": 4.59564, "loss": 4.59564, "time": 0.70144} +{"mode": "train", "epoch": 9, "iter": 1400, "lr": 0.09923, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18734, "top5_acc": 0.41094, "loss_cls": 4.54968, "loss": 4.54968, "time": 0.70297} +{"mode": "train", "epoch": 9, "iter": 1500, "lr": 0.09923, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17828, "top5_acc": 0.39641, "loss_cls": 4.60653, "loss": 4.60653, "time": 0.70008} +{"mode": "train", "epoch": 9, "iter": 1600, "lr": 0.09922, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17922, "top5_acc": 0.40719, "loss_cls": 4.57346, "loss": 4.57346, "time": 0.70231} +{"mode": "train", "epoch": 9, "iter": 1700, "lr": 0.09922, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.1925, "top5_acc": 0.41188, "loss_cls": 4.53498, "loss": 4.53498, "time": 0.70312} +{"mode": "train", "epoch": 9, "iter": 1800, "lr": 0.09921, "memory": 15990, "data_time": 0.00052, "top1_acc": 0.18766, "top5_acc": 0.41391, "loss_cls": 4.54769, "loss": 4.54769, "time": 0.70869} +{"mode": "train", "epoch": 9, "iter": 1900, "lr": 0.09921, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18484, "top5_acc": 0.40438, "loss_cls": 4.56631, "loss": 4.56631, "time": 0.70199} +{"mode": "train", "epoch": 9, "iter": 2000, "lr": 0.0992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18938, "top5_acc": 0.39906, "loss_cls": 4.58045, "loss": 4.58045, "time": 0.70138} +{"mode": "train", "epoch": 9, "iter": 2100, "lr": 0.0992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18906, "top5_acc": 0.41047, "loss_cls": 4.5497, "loss": 4.5497, "time": 0.70263} +{"mode": "train", "epoch": 9, "iter": 2200, "lr": 0.09919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18469, "top5_acc": 0.39828, "loss_cls": 4.57439, "loss": 4.57439, "time": 0.70204} +{"mode": "train", "epoch": 9, "iter": 2300, "lr": 0.09919, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18047, "top5_acc": 0.40562, "loss_cls": 4.56519, "loss": 4.56519, "time": 0.70231} +{"mode": "train", "epoch": 9, "iter": 2400, "lr": 0.09918, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18453, "top5_acc": 0.40359, "loss_cls": 4.59163, "loss": 4.59163, "time": 0.70427} +{"mode": "train", "epoch": 9, "iter": 2500, "lr": 0.09918, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17938, "top5_acc": 0.40875, "loss_cls": 4.57451, "loss": 4.57451, "time": 0.70053} +{"mode": "train", "epoch": 9, "iter": 2600, "lr": 0.09917, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17875, "top5_acc": 0.39766, "loss_cls": 4.59647, "loss": 4.59647, "time": 0.69752} +{"mode": "train", "epoch": 9, "iter": 2700, "lr": 0.09917, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17875, "top5_acc": 0.40359, "loss_cls": 4.56057, "loss": 4.56057, "time": 0.70138} +{"mode": "train", "epoch": 9, "iter": 2800, "lr": 0.09916, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17703, "top5_acc": 0.40844, "loss_cls": 4.53052, "loss": 4.53052, "time": 0.70212} +{"mode": "train", "epoch": 9, "iter": 2900, "lr": 0.09916, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18641, "top5_acc": 0.41016, "loss_cls": 4.5432, "loss": 4.5432, "time": 0.70216} +{"mode": "train", "epoch": 9, "iter": 3000, "lr": 0.09915, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18844, "top5_acc": 0.41672, "loss_cls": 4.54238, "loss": 4.54238, "time": 0.70346} +{"mode": "train", "epoch": 9, "iter": 3100, "lr": 0.09915, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18969, "top5_acc": 0.40875, "loss_cls": 4.56928, "loss": 4.56928, "time": 0.70103} +{"mode": "train", "epoch": 9, "iter": 3200, "lr": 0.09914, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18484, "top5_acc": 0.40859, "loss_cls": 4.57269, "loss": 4.57269, "time": 0.70571} +{"mode": "train", "epoch": 9, "iter": 3300, "lr": 0.09914, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18438, "top5_acc": 0.41359, "loss_cls": 4.54198, "loss": 4.54198, "time": 0.70147} +{"mode": "train", "epoch": 9, "iter": 3400, "lr": 0.09913, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17969, "top5_acc": 0.40938, "loss_cls": 4.55199, "loss": 4.55199, "time": 0.70089} +{"mode": "train", "epoch": 9, "iter": 3500, "lr": 0.09913, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18234, "top5_acc": 0.40391, "loss_cls": 4.59265, "loss": 4.59265, "time": 0.70114} +{"mode": "train", "epoch": 9, "iter": 3600, "lr": 0.09912, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.185, "top5_acc": 0.40938, "loss_cls": 4.56604, "loss": 4.56604, "time": 0.70713} +{"mode": "train", "epoch": 9, "iter": 3700, "lr": 0.09912, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17828, "top5_acc": 0.39781, "loss_cls": 4.56906, "loss": 4.56906, "time": 0.70527} +{"mode": "val", "epoch": 9, "iter": 309, "lr": 0.09911, "top1_acc": 0.0935, "top5_acc": 0.257, "mean_class_accuracy": 0.09366} +{"mode": "train", "epoch": 10, "iter": 100, "lr": 0.09911, "memory": 15990, "data_time": 1.22871, "top1_acc": 0.17828, "top5_acc": 0.40078, "loss_cls": 4.59844, "loss": 4.59844, "time": 1.93349} +{"mode": "train", "epoch": 10, "iter": 200, "lr": 0.0991, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.1925, "top5_acc": 0.40875, "loss_cls": 4.53131, "loss": 4.53131, "time": 0.70254} +{"mode": "train", "epoch": 10, "iter": 300, "lr": 0.0991, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.18516, "top5_acc": 0.41219, "loss_cls": 4.54442, "loss": 4.54442, "time": 0.7006} +{"mode": "train", "epoch": 10, "iter": 400, "lr": 0.09909, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18641, "top5_acc": 0.40953, "loss_cls": 4.53277, "loss": 4.53277, "time": 0.70192} +{"mode": "train", "epoch": 10, "iter": 500, "lr": 0.09909, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19719, "top5_acc": 0.42656, "loss_cls": 4.48009, "loss": 4.48009, "time": 0.70403} +{"mode": "train", "epoch": 10, "iter": 600, "lr": 0.09908, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.17875, "top5_acc": 0.40562, "loss_cls": 4.57062, "loss": 4.57062, "time": 0.70312} +{"mode": "train", "epoch": 10, "iter": 700, "lr": 0.09908, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19312, "top5_acc": 0.41938, "loss_cls": 4.5001, "loss": 4.5001, "time": 0.7014} +{"mode": "train", "epoch": 10, "iter": 800, "lr": 0.09907, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18859, "top5_acc": 0.41391, "loss_cls": 4.53586, "loss": 4.53586, "time": 0.7015} +{"mode": "train", "epoch": 10, "iter": 900, "lr": 0.09907, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.1825, "top5_acc": 0.40891, "loss_cls": 4.55657, "loss": 4.55657, "time": 0.69951} +{"mode": "train", "epoch": 10, "iter": 1000, "lr": 0.09906, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19219, "top5_acc": 0.41031, "loss_cls": 4.53492, "loss": 4.53492, "time": 0.69976} +{"mode": "train", "epoch": 10, "iter": 1100, "lr": 0.09906, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19219, "top5_acc": 0.41438, "loss_cls": 4.53937, "loss": 4.53937, "time": 0.70251} +{"mode": "train", "epoch": 10, "iter": 1200, "lr": 0.09905, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19, "top5_acc": 0.41047, "loss_cls": 4.51585, "loss": 4.51585, "time": 0.70117} +{"mode": "train", "epoch": 10, "iter": 1300, "lr": 0.09905, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18781, "top5_acc": 0.41375, "loss_cls": 4.52125, "loss": 4.52125, "time": 0.70095} +{"mode": "train", "epoch": 10, "iter": 1400, "lr": 0.09904, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17609, "top5_acc": 0.40609, "loss_cls": 4.57968, "loss": 4.57968, "time": 0.70015} +{"mode": "train", "epoch": 10, "iter": 1500, "lr": 0.09903, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19078, "top5_acc": 0.41, "loss_cls": 4.54121, "loss": 4.54121, "time": 0.70056} +{"mode": "train", "epoch": 10, "iter": 1600, "lr": 0.09903, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18406, "top5_acc": 0.40297, "loss_cls": 4.56393, "loss": 4.56393, "time": 0.69943} +{"mode": "train", "epoch": 10, "iter": 1700, "lr": 0.09902, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18, "top5_acc": 0.40031, "loss_cls": 4.58789, "loss": 4.58789, "time": 0.70136} +{"mode": "train", "epoch": 10, "iter": 1800, "lr": 0.09902, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19078, "top5_acc": 0.41125, "loss_cls": 4.55817, "loss": 4.55817, "time": 0.71043} +{"mode": "train", "epoch": 10, "iter": 1900, "lr": 0.09901, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18328, "top5_acc": 0.41219, "loss_cls": 4.54741, "loss": 4.54741, "time": 0.70226} +{"mode": "train", "epoch": 10, "iter": 2000, "lr": 0.09901, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19031, "top5_acc": 0.41141, "loss_cls": 4.54621, "loss": 4.54621, "time": 0.70234} +{"mode": "train", "epoch": 10, "iter": 2100, "lr": 0.099, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.18094, "top5_acc": 0.40859, "loss_cls": 4.56762, "loss": 4.56762, "time": 0.7047} +{"mode": "train", "epoch": 10, "iter": 2200, "lr": 0.099, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18828, "top5_acc": 0.415, "loss_cls": 4.52232, "loss": 4.52232, "time": 0.69999} +{"mode": "train", "epoch": 10, "iter": 2300, "lr": 0.09899, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19125, "top5_acc": 0.40766, "loss_cls": 4.56221, "loss": 4.56221, "time": 0.70083} +{"mode": "train", "epoch": 10, "iter": 2400, "lr": 0.09898, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18484, "top5_acc": 0.41, "loss_cls": 4.51374, "loss": 4.51374, "time": 0.70164} +{"mode": "train", "epoch": 10, "iter": 2500, "lr": 0.09898, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18938, "top5_acc": 0.41234, "loss_cls": 4.561, "loss": 4.561, "time": 0.70067} +{"mode": "train", "epoch": 10, "iter": 2600, "lr": 0.09897, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18875, "top5_acc": 0.41688, "loss_cls": 4.52462, "loss": 4.52462, "time": 0.70041} +{"mode": "train", "epoch": 10, "iter": 2700, "lr": 0.09897, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18328, "top5_acc": 0.41422, "loss_cls": 4.55247, "loss": 4.55247, "time": 0.69974} +{"mode": "train", "epoch": 10, "iter": 2800, "lr": 0.09896, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19312, "top5_acc": 0.41609, "loss_cls": 4.52576, "loss": 4.52576, "time": 0.70081} +{"mode": "train", "epoch": 10, "iter": 2900, "lr": 0.09896, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18641, "top5_acc": 0.41688, "loss_cls": 4.53403, "loss": 4.53403, "time": 0.70201} +{"mode": "train", "epoch": 10, "iter": 3000, "lr": 0.09895, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19141, "top5_acc": 0.41328, "loss_cls": 4.51881, "loss": 4.51881, "time": 0.70138} +{"mode": "train", "epoch": 10, "iter": 3100, "lr": 0.09894, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19, "top5_acc": 0.41844, "loss_cls": 4.54299, "loss": 4.54299, "time": 0.70034} +{"mode": "train", "epoch": 10, "iter": 3200, "lr": 0.09894, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.1825, "top5_acc": 0.40141, "loss_cls": 4.57019, "loss": 4.57019, "time": 0.7006} +{"mode": "train", "epoch": 10, "iter": 3300, "lr": 0.09893, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18938, "top5_acc": 0.41438, "loss_cls": 4.55873, "loss": 4.55873, "time": 0.69981} +{"mode": "train", "epoch": 10, "iter": 3400, "lr": 0.09893, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19078, "top5_acc": 0.41438, "loss_cls": 4.51676, "loss": 4.51676, "time": 0.69899} +{"mode": "train", "epoch": 10, "iter": 3500, "lr": 0.09892, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19797, "top5_acc": 0.41266, "loss_cls": 4.51521, "loss": 4.51521, "time": 0.70122} +{"mode": "train", "epoch": 10, "iter": 3600, "lr": 0.09892, "memory": 15990, "data_time": 0.00062, "top1_acc": 0.18516, "top5_acc": 0.40688, "loss_cls": 4.56947, "loss": 4.56947, "time": 0.70747} +{"mode": "train", "epoch": 10, "iter": 3700, "lr": 0.09891, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19422, "top5_acc": 0.41578, "loss_cls": 4.51581, "loss": 4.51581, "time": 0.70776} +{"mode": "val", "epoch": 10, "iter": 309, "lr": 0.09891, "top1_acc": 0.12992, "top5_acc": 0.32579, "mean_class_accuracy": 0.1299} +{"mode": "train", "epoch": 11, "iter": 100, "lr": 0.0989, "memory": 15990, "data_time": 1.22971, "top1_acc": 0.19344, "top5_acc": 0.42578, "loss_cls": 4.50136, "loss": 4.50136, "time": 1.93566} +{"mode": "train", "epoch": 11, "iter": 200, "lr": 0.0989, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19031, "top5_acc": 0.40969, "loss_cls": 4.52757, "loss": 4.52757, "time": 0.70642} +{"mode": "train", "epoch": 11, "iter": 300, "lr": 0.09889, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19344, "top5_acc": 0.41469, "loss_cls": 4.50334, "loss": 4.50334, "time": 0.70419} +{"mode": "train", "epoch": 11, "iter": 400, "lr": 0.09888, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18719, "top5_acc": 0.40656, "loss_cls": 4.54101, "loss": 4.54101, "time": 0.70206} +{"mode": "train", "epoch": 11, "iter": 500, "lr": 0.09888, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18859, "top5_acc": 0.41828, "loss_cls": 4.50781, "loss": 4.50781, "time": 0.7029} +{"mode": "train", "epoch": 11, "iter": 600, "lr": 0.09887, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18953, "top5_acc": 0.41812, "loss_cls": 4.52336, "loss": 4.52336, "time": 0.70291} +{"mode": "train", "epoch": 11, "iter": 700, "lr": 0.09887, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.17828, "top5_acc": 0.40016, "loss_cls": 4.55961, "loss": 4.55961, "time": 0.70305} +{"mode": "train", "epoch": 11, "iter": 800, "lr": 0.09886, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18141, "top5_acc": 0.40609, "loss_cls": 4.56429, "loss": 4.56429, "time": 0.7013} +{"mode": "train", "epoch": 11, "iter": 900, "lr": 0.09885, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19156, "top5_acc": 0.41141, "loss_cls": 4.51927, "loss": 4.51927, "time": 0.70134} +{"mode": "train", "epoch": 11, "iter": 1000, "lr": 0.09885, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18344, "top5_acc": 0.40719, "loss_cls": 4.53346, "loss": 4.53346, "time": 0.70495} +{"mode": "train", "epoch": 11, "iter": 1100, "lr": 0.09884, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18703, "top5_acc": 0.4125, "loss_cls": 4.54312, "loss": 4.54312, "time": 0.70083} +{"mode": "train", "epoch": 11, "iter": 1200, "lr": 0.09884, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17781, "top5_acc": 0.40125, "loss_cls": 4.57947, "loss": 4.57947, "time": 0.70211} +{"mode": "train", "epoch": 11, "iter": 1300, "lr": 0.09883, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19328, "top5_acc": 0.41141, "loss_cls": 4.51433, "loss": 4.51433, "time": 0.70372} +{"mode": "train", "epoch": 11, "iter": 1400, "lr": 0.09882, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18906, "top5_acc": 0.41203, "loss_cls": 4.51873, "loss": 4.51873, "time": 0.70285} +{"mode": "train", "epoch": 11, "iter": 1500, "lr": 0.09882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19344, "top5_acc": 0.42188, "loss_cls": 4.49002, "loss": 4.49002, "time": 0.70075} +{"mode": "train", "epoch": 11, "iter": 1600, "lr": 0.09881, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18906, "top5_acc": 0.41688, "loss_cls": 4.52313, "loss": 4.52313, "time": 0.70065} +{"mode": "train", "epoch": 11, "iter": 1700, "lr": 0.09881, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19812, "top5_acc": 0.41688, "loss_cls": 4.50087, "loss": 4.50087, "time": 0.69828} +{"mode": "train", "epoch": 11, "iter": 1800, "lr": 0.0988, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.18578, "top5_acc": 0.41812, "loss_cls": 4.53878, "loss": 4.53878, "time": 0.70423} +{"mode": "train", "epoch": 11, "iter": 1900, "lr": 0.09879, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19344, "top5_acc": 0.42391, "loss_cls": 4.51213, "loss": 4.51213, "time": 0.70293} +{"mode": "train", "epoch": 11, "iter": 2000, "lr": 0.09879, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18422, "top5_acc": 0.40469, "loss_cls": 4.56354, "loss": 4.56354, "time": 0.70358} +{"mode": "train", "epoch": 11, "iter": 2100, "lr": 0.09878, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18547, "top5_acc": 0.4125, "loss_cls": 4.54236, "loss": 4.54236, "time": 0.7003} +{"mode": "train", "epoch": 11, "iter": 2200, "lr": 0.09878, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19328, "top5_acc": 0.42172, "loss_cls": 4.4984, "loss": 4.4984, "time": 0.69935} +{"mode": "train", "epoch": 11, "iter": 2300, "lr": 0.09877, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18969, "top5_acc": 0.41062, "loss_cls": 4.51519, "loss": 4.51519, "time": 0.69959} +{"mode": "train", "epoch": 11, "iter": 2400, "lr": 0.09876, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19266, "top5_acc": 0.40953, "loss_cls": 4.53869, "loss": 4.53869, "time": 0.6987} +{"mode": "train", "epoch": 11, "iter": 2500, "lr": 0.09876, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.1925, "top5_acc": 0.42406, "loss_cls": 4.47922, "loss": 4.47922, "time": 0.69851} +{"mode": "train", "epoch": 11, "iter": 2600, "lr": 0.09875, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19859, "top5_acc": 0.41578, "loss_cls": 4.51959, "loss": 4.51959, "time": 0.69926} +{"mode": "train", "epoch": 11, "iter": 2700, "lr": 0.09874, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19047, "top5_acc": 0.41562, "loss_cls": 4.55861, "loss": 4.55861, "time": 0.70081} +{"mode": "train", "epoch": 11, "iter": 2800, "lr": 0.09874, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19344, "top5_acc": 0.42047, "loss_cls": 4.5146, "loss": 4.5146, "time": 0.69989} +{"mode": "train", "epoch": 11, "iter": 2900, "lr": 0.09873, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19375, "top5_acc": 0.42172, "loss_cls": 4.50102, "loss": 4.50102, "time": 0.69867} +{"mode": "train", "epoch": 11, "iter": 3000, "lr": 0.09873, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.17672, "top5_acc": 0.40641, "loss_cls": 4.55368, "loss": 4.55368, "time": 0.70098} +{"mode": "train", "epoch": 11, "iter": 3100, "lr": 0.09872, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18953, "top5_acc": 0.41031, "loss_cls": 4.52407, "loss": 4.52407, "time": 0.69937} +{"mode": "train", "epoch": 11, "iter": 3200, "lr": 0.09871, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19469, "top5_acc": 0.42156, "loss_cls": 4.50623, "loss": 4.50623, "time": 0.7} +{"mode": "train", "epoch": 11, "iter": 3300, "lr": 0.09871, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18812, "top5_acc": 0.41344, "loss_cls": 4.53494, "loss": 4.53494, "time": 0.69907} +{"mode": "train", "epoch": 11, "iter": 3400, "lr": 0.0987, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18906, "top5_acc": 0.41766, "loss_cls": 4.53029, "loss": 4.53029, "time": 0.6998} +{"mode": "train", "epoch": 11, "iter": 3500, "lr": 0.09869, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18609, "top5_acc": 0.4175, "loss_cls": 4.54133, "loss": 4.54133, "time": 0.69973} +{"mode": "train", "epoch": 11, "iter": 3600, "lr": 0.09869, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.18812, "top5_acc": 0.41891, "loss_cls": 4.52125, "loss": 4.52125, "time": 0.7063} +{"mode": "train", "epoch": 11, "iter": 3700, "lr": 0.09868, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19578, "top5_acc": 0.41719, "loss_cls": 4.52603, "loss": 4.52603, "time": 0.70566} +{"mode": "val", "epoch": 11, "iter": 309, "lr": 0.09868, "top1_acc": 0.11913, "top5_acc": 0.31393, "mean_class_accuracy": 0.11914} +{"mode": "train", "epoch": 12, "iter": 100, "lr": 0.09867, "memory": 15990, "data_time": 1.20924, "top1_acc": 0.19281, "top5_acc": 0.42484, "loss_cls": 4.47844, "loss": 4.47844, "time": 1.91522} +{"mode": "train", "epoch": 12, "iter": 200, "lr": 0.09867, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19406, "top5_acc": 0.425, "loss_cls": 4.47821, "loss": 4.47821, "time": 0.7024} +{"mode": "train", "epoch": 12, "iter": 300, "lr": 0.09866, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19375, "top5_acc": 0.42266, "loss_cls": 4.49895, "loss": 4.49895, "time": 0.70438} +{"mode": "train", "epoch": 12, "iter": 400, "lr": 0.09865, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19906, "top5_acc": 0.42734, "loss_cls": 4.46948, "loss": 4.46948, "time": 0.70322} +{"mode": "train", "epoch": 12, "iter": 500, "lr": 0.09865, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19453, "top5_acc": 0.42281, "loss_cls": 4.50572, "loss": 4.50572, "time": 0.70335} +{"mode": "train", "epoch": 12, "iter": 600, "lr": 0.09864, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19328, "top5_acc": 0.41906, "loss_cls": 4.51117, "loss": 4.51117, "time": 0.70407} +{"mode": "train", "epoch": 12, "iter": 700, "lr": 0.09863, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19484, "top5_acc": 0.41719, "loss_cls": 4.52271, "loss": 4.52271, "time": 0.70322} +{"mode": "train", "epoch": 12, "iter": 800, "lr": 0.09863, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18344, "top5_acc": 0.41453, "loss_cls": 4.52136, "loss": 4.52136, "time": 0.69998} +{"mode": "train", "epoch": 12, "iter": 900, "lr": 0.09862, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20078, "top5_acc": 0.42281, "loss_cls": 4.49588, "loss": 4.49588, "time": 0.70218} +{"mode": "train", "epoch": 12, "iter": 1000, "lr": 0.09861, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18625, "top5_acc": 0.41141, "loss_cls": 4.54482, "loss": 4.54482, "time": 0.70205} +{"mode": "train", "epoch": 12, "iter": 1100, "lr": 0.09861, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20078, "top5_acc": 0.42859, "loss_cls": 4.51505, "loss": 4.51505, "time": 0.70051} +{"mode": "train", "epoch": 12, "iter": 1200, "lr": 0.0986, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19297, "top5_acc": 0.41438, "loss_cls": 4.5298, "loss": 4.5298, "time": 0.70418} +{"mode": "train", "epoch": 12, "iter": 1300, "lr": 0.09859, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19266, "top5_acc": 0.40891, "loss_cls": 4.56039, "loss": 4.56039, "time": 0.70151} +{"mode": "train", "epoch": 12, "iter": 1400, "lr": 0.09859, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19344, "top5_acc": 0.42156, "loss_cls": 4.5137, "loss": 4.5137, "time": 0.70177} +{"mode": "train", "epoch": 12, "iter": 1500, "lr": 0.09858, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19031, "top5_acc": 0.4025, "loss_cls": 4.54486, "loss": 4.54486, "time": 0.70217} +{"mode": "train", "epoch": 12, "iter": 1600, "lr": 0.09857, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19578, "top5_acc": 0.42234, "loss_cls": 4.50474, "loss": 4.50474, "time": 0.70058} +{"mode": "train", "epoch": 12, "iter": 1700, "lr": 0.09857, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19609, "top5_acc": 0.43141, "loss_cls": 4.48181, "loss": 4.48181, "time": 0.70056} +{"mode": "train", "epoch": 12, "iter": 1800, "lr": 0.09856, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20078, "top5_acc": 0.43016, "loss_cls": 4.4662, "loss": 4.4662, "time": 0.7089} +{"mode": "train", "epoch": 12, "iter": 1900, "lr": 0.09855, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19828, "top5_acc": 0.42031, "loss_cls": 4.50192, "loss": 4.50192, "time": 0.70289} +{"mode": "train", "epoch": 12, "iter": 2000, "lr": 0.09855, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18469, "top5_acc": 0.41375, "loss_cls": 4.53869, "loss": 4.53869, "time": 0.70121} +{"mode": "train", "epoch": 12, "iter": 2100, "lr": 0.09854, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.185, "top5_acc": 0.41172, "loss_cls": 4.5449, "loss": 4.5449, "time": 0.70039} +{"mode": "train", "epoch": 12, "iter": 2200, "lr": 0.09853, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18891, "top5_acc": 0.4175, "loss_cls": 4.51728, "loss": 4.51728, "time": 0.7013} +{"mode": "train", "epoch": 12, "iter": 2300, "lr": 0.09853, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18922, "top5_acc": 0.4125, "loss_cls": 4.53126, "loss": 4.53126, "time": 0.69982} +{"mode": "train", "epoch": 12, "iter": 2400, "lr": 0.09852, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18562, "top5_acc": 0.41281, "loss_cls": 4.53205, "loss": 4.53205, "time": 0.70265} +{"mode": "train", "epoch": 12, "iter": 2500, "lr": 0.09851, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19422, "top5_acc": 0.41828, "loss_cls": 4.51445, "loss": 4.51445, "time": 0.70034} +{"mode": "train", "epoch": 12, "iter": 2600, "lr": 0.09851, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19688, "top5_acc": 0.42312, "loss_cls": 4.48484, "loss": 4.48484, "time": 0.69925} +{"mode": "train", "epoch": 12, "iter": 2700, "lr": 0.0985, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19328, "top5_acc": 0.40719, "loss_cls": 4.53641, "loss": 4.53641, "time": 0.70108} +{"mode": "train", "epoch": 12, "iter": 2800, "lr": 0.09849, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19859, "top5_acc": 0.425, "loss_cls": 4.48947, "loss": 4.48947, "time": 0.7004} +{"mode": "train", "epoch": 12, "iter": 2900, "lr": 0.09849, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18688, "top5_acc": 0.40859, "loss_cls": 4.55632, "loss": 4.55632, "time": 0.70062} +{"mode": "train", "epoch": 12, "iter": 3000, "lr": 0.09848, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19578, "top5_acc": 0.41438, "loss_cls": 4.50715, "loss": 4.50715, "time": 0.69977} +{"mode": "train", "epoch": 12, "iter": 3100, "lr": 0.09847, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19922, "top5_acc": 0.42547, "loss_cls": 4.49385, "loss": 4.49385, "time": 0.69946} +{"mode": "train", "epoch": 12, "iter": 3200, "lr": 0.09847, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19234, "top5_acc": 0.40531, "loss_cls": 4.52458, "loss": 4.52458, "time": 0.7007} +{"mode": "train", "epoch": 12, "iter": 3300, "lr": 0.09846, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18844, "top5_acc": 0.41094, "loss_cls": 4.51777, "loss": 4.51777, "time": 0.69949} +{"mode": "train", "epoch": 12, "iter": 3400, "lr": 0.09845, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19594, "top5_acc": 0.42578, "loss_cls": 4.50036, "loss": 4.50036, "time": 0.69973} +{"mode": "train", "epoch": 12, "iter": 3500, "lr": 0.09845, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.18844, "top5_acc": 0.41, "loss_cls": 4.52065, "loss": 4.52065, "time": 0.70226} +{"mode": "train", "epoch": 12, "iter": 3600, "lr": 0.09844, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19094, "top5_acc": 0.41234, "loss_cls": 4.53689, "loss": 4.53689, "time": 0.70858} +{"mode": "train", "epoch": 12, "iter": 3700, "lr": 0.09843, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.18562, "top5_acc": 0.41609, "loss_cls": 4.51732, "loss": 4.51732, "time": 0.7042} +{"mode": "val", "epoch": 12, "iter": 309, "lr": 0.09843, "top1_acc": 0.12085, "top5_acc": 0.30461, "mean_class_accuracy": 0.12058} +{"mode": "train", "epoch": 13, "iter": 100, "lr": 0.09842, "memory": 15990, "data_time": 1.21316, "top1_acc": 0.19094, "top5_acc": 0.43109, "loss_cls": 4.48556, "loss": 4.48556, "time": 1.92398} +{"mode": "train", "epoch": 13, "iter": 200, "lr": 0.09842, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19688, "top5_acc": 0.42812, "loss_cls": 4.47177, "loss": 4.47177, "time": 0.70913} +{"mode": "train", "epoch": 13, "iter": 300, "lr": 0.09841, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19797, "top5_acc": 0.42406, "loss_cls": 4.47789, "loss": 4.47789, "time": 0.70906} +{"mode": "train", "epoch": 13, "iter": 400, "lr": 0.0984, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19453, "top5_acc": 0.41781, "loss_cls": 4.48163, "loss": 4.48163, "time": 0.70501} +{"mode": "train", "epoch": 13, "iter": 500, "lr": 0.09839, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20391, "top5_acc": 0.42391, "loss_cls": 4.46309, "loss": 4.46309, "time": 0.70656} +{"mode": "train", "epoch": 13, "iter": 600, "lr": 0.09839, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19375, "top5_acc": 0.41562, "loss_cls": 4.50611, "loss": 4.50611, "time": 0.70495} +{"mode": "train", "epoch": 13, "iter": 700, "lr": 0.09838, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19141, "top5_acc": 0.41906, "loss_cls": 4.48521, "loss": 4.48521, "time": 0.70372} +{"mode": "train", "epoch": 13, "iter": 800, "lr": 0.09837, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.1925, "top5_acc": 0.41859, "loss_cls": 4.50558, "loss": 4.50558, "time": 0.70462} +{"mode": "train", "epoch": 13, "iter": 900, "lr": 0.09837, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19031, "top5_acc": 0.42219, "loss_cls": 4.51887, "loss": 4.51887, "time": 0.70447} +{"mode": "train", "epoch": 13, "iter": 1000, "lr": 0.09836, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.195, "top5_acc": 0.42188, "loss_cls": 4.48035, "loss": 4.48035, "time": 0.70389} +{"mode": "train", "epoch": 13, "iter": 1100, "lr": 0.09835, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19125, "top5_acc": 0.41594, "loss_cls": 4.52319, "loss": 4.52319, "time": 0.70154} +{"mode": "train", "epoch": 13, "iter": 1200, "lr": 0.09834, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19078, "top5_acc": 0.41438, "loss_cls": 4.50156, "loss": 4.50156, "time": 0.70135} +{"mode": "train", "epoch": 13, "iter": 1300, "lr": 0.09834, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19344, "top5_acc": 0.42172, "loss_cls": 4.49627, "loss": 4.49627, "time": 0.70345} +{"mode": "train", "epoch": 13, "iter": 1400, "lr": 0.09833, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.1925, "top5_acc": 0.41719, "loss_cls": 4.53012, "loss": 4.53012, "time": 0.70375} +{"mode": "train", "epoch": 13, "iter": 1500, "lr": 0.09832, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20156, "top5_acc": 0.42203, "loss_cls": 4.46579, "loss": 4.46579, "time": 0.7052} +{"mode": "train", "epoch": 13, "iter": 1600, "lr": 0.09832, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18094, "top5_acc": 0.40969, "loss_cls": 4.55743, "loss": 4.55743, "time": 0.70337} +{"mode": "train", "epoch": 13, "iter": 1700, "lr": 0.09831, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19688, "top5_acc": 0.42125, "loss_cls": 4.48266, "loss": 4.48266, "time": 0.70243} +{"mode": "train", "epoch": 13, "iter": 1800, "lr": 0.0983, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.20344, "top5_acc": 0.42578, "loss_cls": 4.48036, "loss": 4.48036, "time": 0.71346} +{"mode": "train", "epoch": 13, "iter": 1900, "lr": 0.09829, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20125, "top5_acc": 0.42266, "loss_cls": 4.48839, "loss": 4.48839, "time": 0.70374} +{"mode": "train", "epoch": 13, "iter": 2000, "lr": 0.09829, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.18719, "top5_acc": 0.41438, "loss_cls": 4.54425, "loss": 4.54425, "time": 0.70253} +{"mode": "train", "epoch": 13, "iter": 2100, "lr": 0.09828, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20078, "top5_acc": 0.43375, "loss_cls": 4.45751, "loss": 4.45751, "time": 0.70708} +{"mode": "train", "epoch": 13, "iter": 2200, "lr": 0.09827, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19109, "top5_acc": 0.42156, "loss_cls": 4.54517, "loss": 4.54517, "time": 0.70678} +{"mode": "train", "epoch": 13, "iter": 2300, "lr": 0.09827, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19234, "top5_acc": 0.41656, "loss_cls": 4.5226, "loss": 4.5226, "time": 0.70266} +{"mode": "train", "epoch": 13, "iter": 2400, "lr": 0.09826, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19234, "top5_acc": 0.40422, "loss_cls": 4.54969, "loss": 4.54969, "time": 0.70202} +{"mode": "train", "epoch": 13, "iter": 2500, "lr": 0.09825, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18906, "top5_acc": 0.42797, "loss_cls": 4.47061, "loss": 4.47061, "time": 0.70245} +{"mode": "train", "epoch": 13, "iter": 2600, "lr": 0.09824, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19641, "top5_acc": 0.42703, "loss_cls": 4.4793, "loss": 4.4793, "time": 0.70185} +{"mode": "train", "epoch": 13, "iter": 2700, "lr": 0.09824, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20234, "top5_acc": 0.42844, "loss_cls": 4.48656, "loss": 4.48656, "time": 0.70239} +{"mode": "train", "epoch": 13, "iter": 2800, "lr": 0.09823, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19844, "top5_acc": 0.41734, "loss_cls": 4.50346, "loss": 4.50346, "time": 0.7036} +{"mode": "train", "epoch": 13, "iter": 2900, "lr": 0.09822, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18312, "top5_acc": 0.40625, "loss_cls": 4.56508, "loss": 4.56508, "time": 0.70407} +{"mode": "train", "epoch": 13, "iter": 3000, "lr": 0.09821, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.1925, "top5_acc": 0.41438, "loss_cls": 4.52438, "loss": 4.52438, "time": 0.70198} +{"mode": "train", "epoch": 13, "iter": 3100, "lr": 0.09821, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19406, "top5_acc": 0.42, "loss_cls": 4.47737, "loss": 4.47737, "time": 0.70424} +{"mode": "train", "epoch": 13, "iter": 3200, "lr": 0.0982, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19, "top5_acc": 0.42, "loss_cls": 4.50006, "loss": 4.50006, "time": 0.7012} +{"mode": "train", "epoch": 13, "iter": 3300, "lr": 0.09819, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19547, "top5_acc": 0.42359, "loss_cls": 4.49374, "loss": 4.49374, "time": 0.70123} +{"mode": "train", "epoch": 13, "iter": 3400, "lr": 0.09818, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19062, "top5_acc": 0.42109, "loss_cls": 4.524, "loss": 4.524, "time": 0.70228} +{"mode": "train", "epoch": 13, "iter": 3500, "lr": 0.09818, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18172, "top5_acc": 0.40781, "loss_cls": 4.56056, "loss": 4.56056, "time": 0.70509} +{"mode": "train", "epoch": 13, "iter": 3600, "lr": 0.09817, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19094, "top5_acc": 0.41094, "loss_cls": 4.5429, "loss": 4.5429, "time": 0.71457} +{"mode": "train", "epoch": 13, "iter": 3700, "lr": 0.09816, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.1975, "top5_acc": 0.42125, "loss_cls": 4.50022, "loss": 4.50022, "time": 0.71241} +{"mode": "val", "epoch": 13, "iter": 309, "lr": 0.09816, "top1_acc": 0.13184, "top5_acc": 0.32442, "mean_class_accuracy": 0.13166} +{"mode": "train", "epoch": 14, "iter": 100, "lr": 0.09815, "memory": 15990, "data_time": 1.20942, "top1_acc": 0.19172, "top5_acc": 0.42891, "loss_cls": 4.47137, "loss": 4.47137, "time": 1.92018} +{"mode": "train", "epoch": 14, "iter": 200, "lr": 0.09814, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.20016, "top5_acc": 0.43406, "loss_cls": 4.44027, "loss": 4.44027, "time": 0.71035} +{"mode": "train", "epoch": 14, "iter": 300, "lr": 0.09814, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.18453, "top5_acc": 0.40438, "loss_cls": 4.55131, "loss": 4.55131, "time": 0.71041} +{"mode": "train", "epoch": 14, "iter": 400, "lr": 0.09813, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19438, "top5_acc": 0.42891, "loss_cls": 4.49188, "loss": 4.49188, "time": 0.70735} +{"mode": "train", "epoch": 14, "iter": 500, "lr": 0.09812, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19312, "top5_acc": 0.40922, "loss_cls": 4.54223, "loss": 4.54223, "time": 0.7034} +{"mode": "train", "epoch": 14, "iter": 600, "lr": 0.09811, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19516, "top5_acc": 0.41688, "loss_cls": 4.50812, "loss": 4.50812, "time": 0.70555} +{"mode": "train", "epoch": 14, "iter": 700, "lr": 0.09811, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20234, "top5_acc": 0.42625, "loss_cls": 4.48021, "loss": 4.48021, "time": 0.70575} +{"mode": "train", "epoch": 14, "iter": 800, "lr": 0.0981, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19656, "top5_acc": 0.42688, "loss_cls": 4.46, "loss": 4.46, "time": 0.70215} +{"mode": "train", "epoch": 14, "iter": 900, "lr": 0.09809, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20641, "top5_acc": 0.42766, "loss_cls": 4.44869, "loss": 4.44869, "time": 0.70525} +{"mode": "train", "epoch": 14, "iter": 1000, "lr": 0.09808, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20109, "top5_acc": 0.42547, "loss_cls": 4.48724, "loss": 4.48724, "time": 0.70568} +{"mode": "train", "epoch": 14, "iter": 1100, "lr": 0.09807, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19578, "top5_acc": 0.41906, "loss_cls": 4.50155, "loss": 4.50155, "time": 0.70287} +{"mode": "train", "epoch": 14, "iter": 1200, "lr": 0.09807, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19516, "top5_acc": 0.41812, "loss_cls": 4.49034, "loss": 4.49034, "time": 0.70443} +{"mode": "train", "epoch": 14, "iter": 1300, "lr": 0.09806, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19016, "top5_acc": 0.42031, "loss_cls": 4.51114, "loss": 4.51114, "time": 0.70339} +{"mode": "train", "epoch": 14, "iter": 1400, "lr": 0.09805, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.1975, "top5_acc": 0.41609, "loss_cls": 4.49833, "loss": 4.49833, "time": 0.70119} +{"mode": "train", "epoch": 14, "iter": 1500, "lr": 0.09804, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19453, "top5_acc": 0.41641, "loss_cls": 4.5001, "loss": 4.5001, "time": 0.70355} +{"mode": "train", "epoch": 14, "iter": 1600, "lr": 0.09804, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20203, "top5_acc": 0.42234, "loss_cls": 4.47919, "loss": 4.47919, "time": 0.70318} +{"mode": "train", "epoch": 14, "iter": 1700, "lr": 0.09803, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18828, "top5_acc": 0.41812, "loss_cls": 4.50507, "loss": 4.50507, "time": 0.70251} +{"mode": "train", "epoch": 14, "iter": 1800, "lr": 0.09802, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.19328, "top5_acc": 0.42062, "loss_cls": 4.49208, "loss": 4.49208, "time": 0.70899} +{"mode": "train", "epoch": 14, "iter": 1900, "lr": 0.09801, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19656, "top5_acc": 0.42062, "loss_cls": 4.49073, "loss": 4.49073, "time": 0.70593} +{"mode": "train", "epoch": 14, "iter": 2000, "lr": 0.098, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18734, "top5_acc": 0.41156, "loss_cls": 4.54301, "loss": 4.54301, "time": 0.70661} +{"mode": "train", "epoch": 14, "iter": 2100, "lr": 0.098, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19891, "top5_acc": 0.42109, "loss_cls": 4.51204, "loss": 4.51204, "time": 0.70718} +{"mode": "train", "epoch": 14, "iter": 2200, "lr": 0.09799, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.18891, "top5_acc": 0.41, "loss_cls": 4.52263, "loss": 4.52263, "time": 0.70389} +{"mode": "train", "epoch": 14, "iter": 2300, "lr": 0.09798, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19672, "top5_acc": 0.425, "loss_cls": 4.50287, "loss": 4.50287, "time": 0.70187} +{"mode": "train", "epoch": 14, "iter": 2400, "lr": 0.09797, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20375, "top5_acc": 0.42375, "loss_cls": 4.46433, "loss": 4.46433, "time": 0.70119} +{"mode": "train", "epoch": 14, "iter": 2500, "lr": 0.09797, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20156, "top5_acc": 0.42, "loss_cls": 4.49325, "loss": 4.49325, "time": 0.70167} +{"mode": "train", "epoch": 14, "iter": 2600, "lr": 0.09796, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19328, "top5_acc": 0.41797, "loss_cls": 4.49881, "loss": 4.49881, "time": 0.70459} +{"mode": "train", "epoch": 14, "iter": 2700, "lr": 0.09795, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19531, "top5_acc": 0.42281, "loss_cls": 4.50639, "loss": 4.50639, "time": 0.70252} +{"mode": "train", "epoch": 14, "iter": 2800, "lr": 0.09794, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20203, "top5_acc": 0.41938, "loss_cls": 4.497, "loss": 4.497, "time": 0.70469} +{"mode": "train", "epoch": 14, "iter": 2900, "lr": 0.09793, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19594, "top5_acc": 0.42156, "loss_cls": 4.482, "loss": 4.482, "time": 0.70358} +{"mode": "train", "epoch": 14, "iter": 3000, "lr": 0.09793, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2025, "top5_acc": 0.42859, "loss_cls": 4.47494, "loss": 4.47494, "time": 0.70176} +{"mode": "train", "epoch": 14, "iter": 3100, "lr": 0.09792, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20188, "top5_acc": 0.42422, "loss_cls": 4.45604, "loss": 4.45604, "time": 0.7044} +{"mode": "train", "epoch": 14, "iter": 3200, "lr": 0.09791, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19125, "top5_acc": 0.41578, "loss_cls": 4.53439, "loss": 4.53439, "time": 0.70378} +{"mode": "train", "epoch": 14, "iter": 3300, "lr": 0.0979, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19875, "top5_acc": 0.41641, "loss_cls": 4.4758, "loss": 4.4758, "time": 0.70658} +{"mode": "train", "epoch": 14, "iter": 3400, "lr": 0.09789, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19797, "top5_acc": 0.42641, "loss_cls": 4.49177, "loss": 4.49177, "time": 0.70163} +{"mode": "train", "epoch": 14, "iter": 3500, "lr": 0.09789, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.19359, "top5_acc": 0.41547, "loss_cls": 4.53111, "loss": 4.53111, "time": 0.70573} +{"mode": "train", "epoch": 14, "iter": 3600, "lr": 0.09788, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19688, "top5_acc": 0.42859, "loss_cls": 4.48246, "loss": 4.48246, "time": 0.71263} +{"mode": "train", "epoch": 14, "iter": 3700, "lr": 0.09787, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19672, "top5_acc": 0.41344, "loss_cls": 4.51503, "loss": 4.51503, "time": 0.71094} +{"mode": "val", "epoch": 14, "iter": 309, "lr": 0.09787, "top1_acc": 0.13245, "top5_acc": 0.32361, "mean_class_accuracy": 0.13228} +{"mode": "train", "epoch": 15, "iter": 100, "lr": 0.09786, "memory": 15990, "data_time": 1.20196, "top1_acc": 0.20156, "top5_acc": 0.42391, "loss_cls": 4.4748, "loss": 4.4748, "time": 1.91466} +{"mode": "train", "epoch": 15, "iter": 200, "lr": 0.09785, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19891, "top5_acc": 0.43406, "loss_cls": 4.47346, "loss": 4.47346, "time": 0.71231} +{"mode": "train", "epoch": 15, "iter": 300, "lr": 0.09784, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20094, "top5_acc": 0.43406, "loss_cls": 4.42013, "loss": 4.42013, "time": 0.70913} +{"mode": "train", "epoch": 15, "iter": 400, "lr": 0.09783, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20188, "top5_acc": 0.43125, "loss_cls": 4.45618, "loss": 4.45618, "time": 0.70909} +{"mode": "train", "epoch": 15, "iter": 500, "lr": 0.09783, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20172, "top5_acc": 0.43141, "loss_cls": 4.45663, "loss": 4.45663, "time": 0.70627} +{"mode": "train", "epoch": 15, "iter": 600, "lr": 0.09782, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.18859, "top5_acc": 0.42531, "loss_cls": 4.49193, "loss": 4.49193, "time": 0.7104} +{"mode": "train", "epoch": 15, "iter": 700, "lr": 0.09781, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19156, "top5_acc": 0.42094, "loss_cls": 4.511, "loss": 4.511, "time": 0.70223} +{"mode": "train", "epoch": 15, "iter": 800, "lr": 0.0978, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19484, "top5_acc": 0.42656, "loss_cls": 4.47817, "loss": 4.47817, "time": 0.70383} +{"mode": "train", "epoch": 15, "iter": 900, "lr": 0.09779, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21281, "top5_acc": 0.43734, "loss_cls": 4.43025, "loss": 4.43025, "time": 0.70389} +{"mode": "train", "epoch": 15, "iter": 1000, "lr": 0.09778, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19797, "top5_acc": 0.42766, "loss_cls": 4.48295, "loss": 4.48295, "time": 0.70604} +{"mode": "train", "epoch": 15, "iter": 1100, "lr": 0.09778, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19672, "top5_acc": 0.42891, "loss_cls": 4.4667, "loss": 4.4667, "time": 0.70729} +{"mode": "train", "epoch": 15, "iter": 1200, "lr": 0.09777, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19359, "top5_acc": 0.42297, "loss_cls": 4.49595, "loss": 4.49595, "time": 0.70418} +{"mode": "train", "epoch": 15, "iter": 1300, "lr": 0.09776, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19422, "top5_acc": 0.42875, "loss_cls": 4.47667, "loss": 4.47667, "time": 0.70365} +{"mode": "train", "epoch": 15, "iter": 1400, "lr": 0.09775, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19219, "top5_acc": 0.41312, "loss_cls": 4.51553, "loss": 4.51553, "time": 0.70454} +{"mode": "train", "epoch": 15, "iter": 1500, "lr": 0.09774, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19641, "top5_acc": 0.42438, "loss_cls": 4.48946, "loss": 4.48946, "time": 0.70334} +{"mode": "train", "epoch": 15, "iter": 1600, "lr": 0.09773, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.18594, "top5_acc": 0.40516, "loss_cls": 4.54326, "loss": 4.54326, "time": 0.70555} +{"mode": "train", "epoch": 15, "iter": 1700, "lr": 0.09773, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19078, "top5_acc": 0.41875, "loss_cls": 4.48839, "loss": 4.48839, "time": 0.70602} +{"mode": "train", "epoch": 15, "iter": 1800, "lr": 0.09772, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.195, "top5_acc": 0.42219, "loss_cls": 4.49947, "loss": 4.49947, "time": 0.71004} +{"mode": "train", "epoch": 15, "iter": 1900, "lr": 0.09771, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19219, "top5_acc": 0.41859, "loss_cls": 4.5203, "loss": 4.5203, "time": 0.71298} +{"mode": "train", "epoch": 15, "iter": 2000, "lr": 0.0977, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19125, "top5_acc": 0.41984, "loss_cls": 4.50626, "loss": 4.50626, "time": 0.70929} +{"mode": "train", "epoch": 15, "iter": 2100, "lr": 0.09769, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2025, "top5_acc": 0.42547, "loss_cls": 4.47574, "loss": 4.47574, "time": 0.7111} +{"mode": "train", "epoch": 15, "iter": 2200, "lr": 0.09768, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19422, "top5_acc": 0.41438, "loss_cls": 4.52459, "loss": 4.52459, "time": 0.71092} +{"mode": "train", "epoch": 15, "iter": 2300, "lr": 0.09768, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19453, "top5_acc": 0.42281, "loss_cls": 4.49155, "loss": 4.49155, "time": 0.70852} +{"mode": "train", "epoch": 15, "iter": 2400, "lr": 0.09767, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20281, "top5_acc": 0.42891, "loss_cls": 4.43208, "loss": 4.43208, "time": 0.70449} +{"mode": "train", "epoch": 15, "iter": 2500, "lr": 0.09766, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19891, "top5_acc": 0.42484, "loss_cls": 4.47534, "loss": 4.47534, "time": 0.70528} +{"mode": "train", "epoch": 15, "iter": 2600, "lr": 0.09765, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20484, "top5_acc": 0.42703, "loss_cls": 4.45617, "loss": 4.45617, "time": 0.70777} +{"mode": "train", "epoch": 15, "iter": 2700, "lr": 0.09764, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20594, "top5_acc": 0.43031, "loss_cls": 4.43852, "loss": 4.43852, "time": 0.70409} +{"mode": "train", "epoch": 15, "iter": 2800, "lr": 0.09763, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19422, "top5_acc": 0.4175, "loss_cls": 4.5023, "loss": 4.5023, "time": 0.70929} +{"mode": "train", "epoch": 15, "iter": 2900, "lr": 0.09763, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19406, "top5_acc": 0.41875, "loss_cls": 4.52306, "loss": 4.52306, "time": 0.70853} +{"mode": "train", "epoch": 15, "iter": 3000, "lr": 0.09762, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19672, "top5_acc": 0.4175, "loss_cls": 4.49778, "loss": 4.49778, "time": 0.70431} +{"mode": "train", "epoch": 15, "iter": 3100, "lr": 0.09761, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19969, "top5_acc": 0.42719, "loss_cls": 4.50091, "loss": 4.50091, "time": 0.70811} +{"mode": "train", "epoch": 15, "iter": 3200, "lr": 0.0976, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.1925, "top5_acc": 0.41844, "loss_cls": 4.4949, "loss": 4.4949, "time": 0.70501} +{"mode": "train", "epoch": 15, "iter": 3300, "lr": 0.09759, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19156, "top5_acc": 0.41641, "loss_cls": 4.49028, "loss": 4.49028, "time": 0.70554} +{"mode": "train", "epoch": 15, "iter": 3400, "lr": 0.09758, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19719, "top5_acc": 0.42375, "loss_cls": 4.47959, "loss": 4.47959, "time": 0.70446} +{"mode": "train", "epoch": 15, "iter": 3500, "lr": 0.09757, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19562, "top5_acc": 0.42, "loss_cls": 4.5051, "loss": 4.5051, "time": 0.70904} +{"mode": "train", "epoch": 15, "iter": 3600, "lr": 0.09757, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19438, "top5_acc": 0.41875, "loss_cls": 4.508, "loss": 4.508, "time": 0.71139} +{"mode": "train", "epoch": 15, "iter": 3700, "lr": 0.09756, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.1975, "top5_acc": 0.43547, "loss_cls": 4.46491, "loss": 4.46491, "time": 0.70943} +{"mode": "val", "epoch": 15, "iter": 309, "lr": 0.09755, "top1_acc": 0.1361, "top5_acc": 0.32969, "mean_class_accuracy": 0.13611} +{"mode": "train", "epoch": 16, "iter": 100, "lr": 0.09754, "memory": 15990, "data_time": 1.22219, "top1_acc": 0.19953, "top5_acc": 0.42156, "loss_cls": 4.48569, "loss": 4.48569, "time": 1.93397} +{"mode": "train", "epoch": 16, "iter": 200, "lr": 0.09754, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19578, "top5_acc": 0.43031, "loss_cls": 4.45541, "loss": 4.45541, "time": 0.71035} +{"mode": "train", "epoch": 16, "iter": 300, "lr": 0.09753, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19719, "top5_acc": 0.41859, "loss_cls": 4.46542, "loss": 4.46542, "time": 0.7103} +{"mode": "train", "epoch": 16, "iter": 400, "lr": 0.09752, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20172, "top5_acc": 0.43031, "loss_cls": 4.44094, "loss": 4.44094, "time": 0.70978} +{"mode": "train", "epoch": 16, "iter": 500, "lr": 0.09751, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19906, "top5_acc": 0.42625, "loss_cls": 4.46555, "loss": 4.46555, "time": 0.70266} +{"mode": "train", "epoch": 16, "iter": 600, "lr": 0.0975, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20203, "top5_acc": 0.43078, "loss_cls": 4.45493, "loss": 4.45493, "time": 0.7068} +{"mode": "train", "epoch": 16, "iter": 700, "lr": 0.09749, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19531, "top5_acc": 0.42203, "loss_cls": 4.48294, "loss": 4.48294, "time": 0.70763} +{"mode": "train", "epoch": 16, "iter": 800, "lr": 0.09748, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20938, "top5_acc": 0.43625, "loss_cls": 4.40757, "loss": 4.40757, "time": 0.70631} +{"mode": "train", "epoch": 16, "iter": 900, "lr": 0.09747, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19344, "top5_acc": 0.42828, "loss_cls": 4.46065, "loss": 4.46065, "time": 0.70401} +{"mode": "train", "epoch": 16, "iter": 1000, "lr": 0.09747, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19188, "top5_acc": 0.42297, "loss_cls": 4.47389, "loss": 4.47389, "time": 0.70563} +{"mode": "train", "epoch": 16, "iter": 1100, "lr": 0.09746, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20109, "top5_acc": 0.4175, "loss_cls": 4.48296, "loss": 4.48296, "time": 0.70525} +{"mode": "train", "epoch": 16, "iter": 1200, "lr": 0.09745, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19672, "top5_acc": 0.41547, "loss_cls": 4.48348, "loss": 4.48348, "time": 0.7062} +{"mode": "train", "epoch": 16, "iter": 1300, "lr": 0.09744, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19703, "top5_acc": 0.42391, "loss_cls": 4.49787, "loss": 4.49787, "time": 0.70812} +{"mode": "train", "epoch": 16, "iter": 1400, "lr": 0.09743, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19219, "top5_acc": 0.41922, "loss_cls": 4.51072, "loss": 4.51072, "time": 0.70648} +{"mode": "train", "epoch": 16, "iter": 1500, "lr": 0.09742, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.19344, "top5_acc": 0.42375, "loss_cls": 4.47655, "loss": 4.47655, "time": 0.70926} +{"mode": "train", "epoch": 16, "iter": 1600, "lr": 0.09741, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20172, "top5_acc": 0.42969, "loss_cls": 4.45422, "loss": 4.45422, "time": 0.70533} +{"mode": "train", "epoch": 16, "iter": 1700, "lr": 0.0974, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.19484, "top5_acc": 0.43219, "loss_cls": 4.47763, "loss": 4.47763, "time": 0.70898} +{"mode": "train", "epoch": 16, "iter": 1800, "lr": 0.0974, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20844, "top5_acc": 0.42922, "loss_cls": 4.44908, "loss": 4.44908, "time": 0.71009} +{"mode": "train", "epoch": 16, "iter": 1900, "lr": 0.09739, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.19938, "top5_acc": 0.40922, "loss_cls": 4.52313, "loss": 4.52313, "time": 0.70935} +{"mode": "train", "epoch": 16, "iter": 2000, "lr": 0.09738, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19453, "top5_acc": 0.42062, "loss_cls": 4.47092, "loss": 4.47092, "time": 0.70714} +{"mode": "train", "epoch": 16, "iter": 2100, "lr": 0.09737, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19453, "top5_acc": 0.42781, "loss_cls": 4.45146, "loss": 4.45146, "time": 0.70989} +{"mode": "train", "epoch": 16, "iter": 2200, "lr": 0.09736, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.18719, "top5_acc": 0.41562, "loss_cls": 4.52649, "loss": 4.52649, "time": 0.70685} +{"mode": "train", "epoch": 16, "iter": 2300, "lr": 0.09735, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19625, "top5_acc": 0.42109, "loss_cls": 4.47782, "loss": 4.47782, "time": 0.70395} +{"mode": "train", "epoch": 16, "iter": 2400, "lr": 0.09734, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19688, "top5_acc": 0.42141, "loss_cls": 4.48076, "loss": 4.48076, "time": 0.7054} +{"mode": "train", "epoch": 16, "iter": 2500, "lr": 0.09733, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19188, "top5_acc": 0.43156, "loss_cls": 4.47165, "loss": 4.47165, "time": 0.709} +{"mode": "train", "epoch": 16, "iter": 2600, "lr": 0.09732, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19453, "top5_acc": 0.41438, "loss_cls": 4.50708, "loss": 4.50708, "time": 0.70575} +{"mode": "train", "epoch": 16, "iter": 2700, "lr": 0.09731, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20547, "top5_acc": 0.43031, "loss_cls": 4.44, "loss": 4.44, "time": 0.70368} +{"mode": "train", "epoch": 16, "iter": 2800, "lr": 0.09731, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20516, "top5_acc": 0.43531, "loss_cls": 4.45418, "loss": 4.45418, "time": 0.70099} +{"mode": "train", "epoch": 16, "iter": 2900, "lr": 0.0973, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19703, "top5_acc": 0.42375, "loss_cls": 4.51002, "loss": 4.51002, "time": 0.70775} +{"mode": "train", "epoch": 16, "iter": 3000, "lr": 0.09729, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.18844, "top5_acc": 0.41594, "loss_cls": 4.51645, "loss": 4.51645, "time": 0.70497} +{"mode": "train", "epoch": 16, "iter": 3100, "lr": 0.09728, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20172, "top5_acc": 0.42531, "loss_cls": 4.47825, "loss": 4.47825, "time": 0.70612} +{"mode": "train", "epoch": 16, "iter": 3200, "lr": 0.09727, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20109, "top5_acc": 0.42609, "loss_cls": 4.46958, "loss": 4.46958, "time": 0.70872} +{"mode": "train", "epoch": 16, "iter": 3300, "lr": 0.09726, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19406, "top5_acc": 0.42484, "loss_cls": 4.48628, "loss": 4.48628, "time": 0.70598} +{"mode": "train", "epoch": 16, "iter": 3400, "lr": 0.09725, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19547, "top5_acc": 0.42547, "loss_cls": 4.47766, "loss": 4.47766, "time": 0.70187} +{"mode": "train", "epoch": 16, "iter": 3500, "lr": 0.09724, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19438, "top5_acc": 0.42469, "loss_cls": 4.48304, "loss": 4.48304, "time": 0.71745} +{"mode": "train", "epoch": 16, "iter": 3600, "lr": 0.09723, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.19234, "top5_acc": 0.41781, "loss_cls": 4.52535, "loss": 4.52535, "time": 0.71316} +{"mode": "train", "epoch": 16, "iter": 3700, "lr": 0.09722, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.205, "top5_acc": 0.42938, "loss_cls": 4.42559, "loss": 4.42559, "time": 0.70996} +{"mode": "val", "epoch": 16, "iter": 309, "lr": 0.09722, "top1_acc": 0.12754, "top5_acc": 0.31383, "mean_class_accuracy": 0.12735} +{"mode": "train", "epoch": 17, "iter": 100, "lr": 0.09721, "memory": 15990, "data_time": 1.22319, "top1_acc": 0.19766, "top5_acc": 0.42281, "loss_cls": 4.4644, "loss": 4.4644, "time": 1.93758} +{"mode": "train", "epoch": 17, "iter": 200, "lr": 0.0972, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19406, "top5_acc": 0.44062, "loss_cls": 4.45852, "loss": 4.45852, "time": 0.71046} +{"mode": "train", "epoch": 17, "iter": 300, "lr": 0.09719, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19969, "top5_acc": 0.43578, "loss_cls": 4.45341, "loss": 4.45341, "time": 0.71275} +{"mode": "train", "epoch": 17, "iter": 400, "lr": 0.09718, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19812, "top5_acc": 0.42484, "loss_cls": 4.44414, "loss": 4.44414, "time": 0.70914} +{"mode": "train", "epoch": 17, "iter": 500, "lr": 0.09717, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20125, "top5_acc": 0.42734, "loss_cls": 4.47079, "loss": 4.47079, "time": 0.70911} +{"mode": "train", "epoch": 17, "iter": 600, "lr": 0.09716, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20109, "top5_acc": 0.43578, "loss_cls": 4.42789, "loss": 4.42789, "time": 0.70645} +{"mode": "train", "epoch": 17, "iter": 700, "lr": 0.09715, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2, "top5_acc": 0.42625, "loss_cls": 4.48628, "loss": 4.48628, "time": 0.70859} +{"mode": "train", "epoch": 17, "iter": 800, "lr": 0.09714, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19203, "top5_acc": 0.42, "loss_cls": 4.47394, "loss": 4.47394, "time": 0.70278} +{"mode": "train", "epoch": 17, "iter": 900, "lr": 0.09714, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19844, "top5_acc": 0.42094, "loss_cls": 4.48334, "loss": 4.48334, "time": 0.70816} +{"mode": "train", "epoch": 17, "iter": 1000, "lr": 0.09713, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20203, "top5_acc": 0.42469, "loss_cls": 4.47838, "loss": 4.47838, "time": 0.70398} +{"mode": "train", "epoch": 17, "iter": 1100, "lr": 0.09712, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20047, "top5_acc": 0.42781, "loss_cls": 4.47201, "loss": 4.47201, "time": 0.69963} +{"mode": "train", "epoch": 17, "iter": 1200, "lr": 0.09711, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2, "top5_acc": 0.42812, "loss_cls": 4.4569, "loss": 4.4569, "time": 0.69878} +{"mode": "train", "epoch": 17, "iter": 1300, "lr": 0.0971, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19266, "top5_acc": 0.41703, "loss_cls": 4.51095, "loss": 4.51095, "time": 0.7014} +{"mode": "train", "epoch": 17, "iter": 1400, "lr": 0.09709, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19906, "top5_acc": 0.42766, "loss_cls": 4.44994, "loss": 4.44994, "time": 0.7007} +{"mode": "train", "epoch": 17, "iter": 1500, "lr": 0.09708, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20031, "top5_acc": 0.42125, "loss_cls": 4.48902, "loss": 4.48902, "time": 0.69905} +{"mode": "train", "epoch": 17, "iter": 1600, "lr": 0.09707, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20109, "top5_acc": 0.42, "loss_cls": 4.4772, "loss": 4.4772, "time": 0.70138} +{"mode": "train", "epoch": 17, "iter": 1700, "lr": 0.09706, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20469, "top5_acc": 0.42781, "loss_cls": 4.44502, "loss": 4.44502, "time": 0.70546} +{"mode": "train", "epoch": 17, "iter": 1800, "lr": 0.09705, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20141, "top5_acc": 0.42594, "loss_cls": 4.46133, "loss": 4.46133, "time": 0.70326} +{"mode": "train", "epoch": 17, "iter": 1900, "lr": 0.09704, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20094, "top5_acc": 0.43312, "loss_cls": 4.43589, "loss": 4.43589, "time": 0.70552} +{"mode": "train", "epoch": 17, "iter": 2000, "lr": 0.09703, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20641, "top5_acc": 0.43234, "loss_cls": 4.45121, "loss": 4.45121, "time": 0.70177} +{"mode": "train", "epoch": 17, "iter": 2100, "lr": 0.09702, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20344, "top5_acc": 0.43562, "loss_cls": 4.43755, "loss": 4.43755, "time": 0.70113} +{"mode": "train", "epoch": 17, "iter": 2200, "lr": 0.09701, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20109, "top5_acc": 0.42938, "loss_cls": 4.46342, "loss": 4.46342, "time": 0.7025} +{"mode": "train", "epoch": 17, "iter": 2300, "lr": 0.097, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20625, "top5_acc": 0.42703, "loss_cls": 4.46423, "loss": 4.46423, "time": 0.6997} +{"mode": "train", "epoch": 17, "iter": 2400, "lr": 0.09699, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19297, "top5_acc": 0.42094, "loss_cls": 4.48778, "loss": 4.48778, "time": 0.70088} +{"mode": "train", "epoch": 17, "iter": 2500, "lr": 0.09698, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19719, "top5_acc": 0.42062, "loss_cls": 4.5028, "loss": 4.5028, "time": 0.70135} +{"mode": "train", "epoch": 17, "iter": 2600, "lr": 0.09697, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19766, "top5_acc": 0.42156, "loss_cls": 4.49987, "loss": 4.49987, "time": 0.69984} +{"mode": "train", "epoch": 17, "iter": 2700, "lr": 0.09697, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.19766, "top5_acc": 0.42203, "loss_cls": 4.50544, "loss": 4.50544, "time": 0.70219} +{"mode": "train", "epoch": 17, "iter": 2800, "lr": 0.09696, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.19719, "top5_acc": 0.42938, "loss_cls": 4.46095, "loss": 4.46095, "time": 0.70121} +{"mode": "train", "epoch": 17, "iter": 2900, "lr": 0.09695, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20047, "top5_acc": 0.4225, "loss_cls": 4.49506, "loss": 4.49506, "time": 0.69995} +{"mode": "train", "epoch": 17, "iter": 3000, "lr": 0.09694, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19266, "top5_acc": 0.41594, "loss_cls": 4.49981, "loss": 4.49981, "time": 0.69806} +{"mode": "train", "epoch": 17, "iter": 3100, "lr": 0.09693, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19438, "top5_acc": 0.42031, "loss_cls": 4.47902, "loss": 4.47902, "time": 0.70263} +{"mode": "train", "epoch": 17, "iter": 3200, "lr": 0.09692, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19875, "top5_acc": 0.44641, "loss_cls": 4.42773, "loss": 4.42773, "time": 0.69982} +{"mode": "train", "epoch": 17, "iter": 3300, "lr": 0.09691, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20125, "top5_acc": 0.43109, "loss_cls": 4.44065, "loss": 4.44065, "time": 0.69885} +{"mode": "train", "epoch": 17, "iter": 3400, "lr": 0.0969, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20031, "top5_acc": 0.42969, "loss_cls": 4.46603, "loss": 4.46603, "time": 0.70177} +{"mode": "train", "epoch": 17, "iter": 3500, "lr": 0.09689, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.195, "top5_acc": 0.41625, "loss_cls": 4.51267, "loss": 4.51267, "time": 0.70874} +{"mode": "train", "epoch": 17, "iter": 3600, "lr": 0.09688, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20094, "top5_acc": 0.42969, "loss_cls": 4.47394, "loss": 4.47394, "time": 0.70869} +{"mode": "train", "epoch": 17, "iter": 3700, "lr": 0.09687, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20062, "top5_acc": 0.42172, "loss_cls": 4.48904, "loss": 4.48904, "time": 0.70437} +{"mode": "val", "epoch": 17, "iter": 309, "lr": 0.09686, "top1_acc": 0.14177, "top5_acc": 0.34118, "mean_class_accuracy": 0.14146} +{"mode": "train", "epoch": 18, "iter": 100, "lr": 0.09685, "memory": 15990, "data_time": 1.2463, "top1_acc": 0.20953, "top5_acc": 0.42531, "loss_cls": 4.43648, "loss": 4.43648, "time": 1.96119} +{"mode": "train", "epoch": 18, "iter": 200, "lr": 0.09684, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20172, "top5_acc": 0.42672, "loss_cls": 4.44276, "loss": 4.44276, "time": 0.71179} +{"mode": "train", "epoch": 18, "iter": 300, "lr": 0.09683, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20203, "top5_acc": 0.41781, "loss_cls": 4.51787, "loss": 4.51787, "time": 0.70649} +{"mode": "train", "epoch": 18, "iter": 400, "lr": 0.09683, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20594, "top5_acc": 0.43406, "loss_cls": 4.43258, "loss": 4.43258, "time": 0.70638} +{"mode": "train", "epoch": 18, "iter": 500, "lr": 0.09682, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19469, "top5_acc": 0.42844, "loss_cls": 4.44778, "loss": 4.44778, "time": 0.70623} +{"mode": "train", "epoch": 18, "iter": 600, "lr": 0.09681, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.19719, "top5_acc": 0.43766, "loss_cls": 4.4397, "loss": 4.4397, "time": 0.70932} +{"mode": "train", "epoch": 18, "iter": 700, "lr": 0.0968, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20016, "top5_acc": 0.43078, "loss_cls": 4.46585, "loss": 4.46585, "time": 0.70469} +{"mode": "train", "epoch": 18, "iter": 800, "lr": 0.09679, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19812, "top5_acc": 0.42438, "loss_cls": 4.46988, "loss": 4.46988, "time": 0.70756} +{"mode": "train", "epoch": 18, "iter": 900, "lr": 0.09678, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19562, "top5_acc": 0.42391, "loss_cls": 4.48586, "loss": 4.48586, "time": 0.71041} +{"mode": "train", "epoch": 18, "iter": 1000, "lr": 0.09677, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19906, "top5_acc": 0.42703, "loss_cls": 4.47124, "loss": 4.47124, "time": 0.7069} +{"mode": "train", "epoch": 18, "iter": 1100, "lr": 0.09676, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19547, "top5_acc": 0.43172, "loss_cls": 4.45718, "loss": 4.45718, "time": 0.70406} +{"mode": "train", "epoch": 18, "iter": 1200, "lr": 0.09675, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20109, "top5_acc": 0.42641, "loss_cls": 4.46922, "loss": 4.46922, "time": 0.70106} +{"mode": "train", "epoch": 18, "iter": 1300, "lr": 0.09674, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20188, "top5_acc": 0.43312, "loss_cls": 4.41945, "loss": 4.41945, "time": 0.69855} +{"mode": "train", "epoch": 18, "iter": 1400, "lr": 0.09673, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20375, "top5_acc": 0.44, "loss_cls": 4.42176, "loss": 4.42176, "time": 0.69946} +{"mode": "train", "epoch": 18, "iter": 1500, "lr": 0.09672, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19688, "top5_acc": 0.43234, "loss_cls": 4.46931, "loss": 4.46931, "time": 0.70028} +{"mode": "train", "epoch": 18, "iter": 1600, "lr": 0.09671, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20406, "top5_acc": 0.42984, "loss_cls": 4.45876, "loss": 4.45876, "time": 0.70163} +{"mode": "train", "epoch": 18, "iter": 1700, "lr": 0.0967, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19453, "top5_acc": 0.41844, "loss_cls": 4.50431, "loss": 4.50431, "time": 0.70698} +{"mode": "train", "epoch": 18, "iter": 1800, "lr": 0.09669, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19953, "top5_acc": 0.43422, "loss_cls": 4.43067, "loss": 4.43067, "time": 0.70472} +{"mode": "train", "epoch": 18, "iter": 1900, "lr": 0.09668, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.20812, "top5_acc": 0.42531, "loss_cls": 4.47128, "loss": 4.47128, "time": 0.70687} +{"mode": "train", "epoch": 18, "iter": 2000, "lr": 0.09667, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20734, "top5_acc": 0.43234, "loss_cls": 4.44445, "loss": 4.44445, "time": 0.70184} +{"mode": "train", "epoch": 18, "iter": 2100, "lr": 0.09666, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19812, "top5_acc": 0.42547, "loss_cls": 4.44215, "loss": 4.44215, "time": 0.70325} +{"mode": "train", "epoch": 18, "iter": 2200, "lr": 0.09665, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20078, "top5_acc": 0.4275, "loss_cls": 4.45192, "loss": 4.45192, "time": 0.70054} +{"mode": "train", "epoch": 18, "iter": 2300, "lr": 0.09664, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19656, "top5_acc": 0.41984, "loss_cls": 4.48291, "loss": 4.48291, "time": 0.70097} +{"mode": "train", "epoch": 18, "iter": 2400, "lr": 0.09663, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20234, "top5_acc": 0.42406, "loss_cls": 4.4751, "loss": 4.4751, "time": 0.70183} +{"mode": "train", "epoch": 18, "iter": 2500, "lr": 0.09662, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2, "top5_acc": 0.42859, "loss_cls": 4.46423, "loss": 4.46423, "time": 0.69858} +{"mode": "train", "epoch": 18, "iter": 2600, "lr": 0.09661, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21062, "top5_acc": 0.44609, "loss_cls": 4.38217, "loss": 4.38217, "time": 0.70004} +{"mode": "train", "epoch": 18, "iter": 2700, "lr": 0.0966, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19312, "top5_acc": 0.42312, "loss_cls": 4.49033, "loss": 4.49033, "time": 0.70064} +{"mode": "train", "epoch": 18, "iter": 2800, "lr": 0.09659, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20656, "top5_acc": 0.43016, "loss_cls": 4.47613, "loss": 4.47613, "time": 0.70206} +{"mode": "train", "epoch": 18, "iter": 2900, "lr": 0.09658, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19391, "top5_acc": 0.42219, "loss_cls": 4.49137, "loss": 4.49137, "time": 0.70296} +{"mode": "train", "epoch": 18, "iter": 3000, "lr": 0.09657, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19078, "top5_acc": 0.40781, "loss_cls": 4.53532, "loss": 4.53532, "time": 0.70232} +{"mode": "train", "epoch": 18, "iter": 3100, "lr": 0.09656, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20234, "top5_acc": 0.43312, "loss_cls": 4.45628, "loss": 4.45628, "time": 0.70109} +{"mode": "train", "epoch": 18, "iter": 3200, "lr": 0.09654, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2125, "top5_acc": 0.43375, "loss_cls": 4.43471, "loss": 4.43471, "time": 0.70254} +{"mode": "train", "epoch": 18, "iter": 3300, "lr": 0.09653, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19984, "top5_acc": 0.42531, "loss_cls": 4.48153, "loss": 4.48153, "time": 0.70146} +{"mode": "train", "epoch": 18, "iter": 3400, "lr": 0.09652, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20828, "top5_acc": 0.435, "loss_cls": 4.43956, "loss": 4.43956, "time": 0.70448} +{"mode": "train", "epoch": 18, "iter": 3500, "lr": 0.09651, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20266, "top5_acc": 0.42125, "loss_cls": 4.48237, "loss": 4.48237, "time": 0.71327} +{"mode": "train", "epoch": 18, "iter": 3600, "lr": 0.0965, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.19844, "top5_acc": 0.42547, "loss_cls": 4.47849, "loss": 4.47849, "time": 0.70716} +{"mode": "train", "epoch": 18, "iter": 3700, "lr": 0.09649, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.2, "top5_acc": 0.42859, "loss_cls": 4.48283, "loss": 4.48283, "time": 0.70953} +{"mode": "val", "epoch": 18, "iter": 309, "lr": 0.09649, "top1_acc": 0.11093, "top5_acc": 0.29038, "mean_class_accuracy": 0.11105} +{"mode": "train", "epoch": 19, "iter": 100, "lr": 0.09648, "memory": 15990, "data_time": 1.27063, "top1_acc": 0.19688, "top5_acc": 0.4325, "loss_cls": 4.45784, "loss": 4.45784, "time": 1.98589} +{"mode": "train", "epoch": 19, "iter": 200, "lr": 0.09647, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20125, "top5_acc": 0.43047, "loss_cls": 4.45216, "loss": 4.45216, "time": 0.71139} +{"mode": "train", "epoch": 19, "iter": 300, "lr": 0.09646, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19688, "top5_acc": 0.42188, "loss_cls": 4.46835, "loss": 4.46835, "time": 0.71314} +{"mode": "train", "epoch": 19, "iter": 400, "lr": 0.09645, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20438, "top5_acc": 0.42625, "loss_cls": 4.44141, "loss": 4.44141, "time": 0.70778} +{"mode": "train", "epoch": 19, "iter": 500, "lr": 0.09644, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20891, "top5_acc": 0.43875, "loss_cls": 4.40615, "loss": 4.40615, "time": 0.70575} +{"mode": "train", "epoch": 19, "iter": 600, "lr": 0.09643, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19969, "top5_acc": 0.42578, "loss_cls": 4.4384, "loss": 4.4384, "time": 0.7071} +{"mode": "train", "epoch": 19, "iter": 700, "lr": 0.09642, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19859, "top5_acc": 0.42281, "loss_cls": 4.43825, "loss": 4.43825, "time": 0.70945} +{"mode": "train", "epoch": 19, "iter": 800, "lr": 0.09641, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19578, "top5_acc": 0.42406, "loss_cls": 4.47139, "loss": 4.47139, "time": 0.70544} +{"mode": "train", "epoch": 19, "iter": 900, "lr": 0.0964, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20812, "top5_acc": 0.43297, "loss_cls": 4.40962, "loss": 4.40962, "time": 0.70611} +{"mode": "train", "epoch": 19, "iter": 1000, "lr": 0.09639, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20656, "top5_acc": 0.43672, "loss_cls": 4.43502, "loss": 4.43502, "time": 0.70475} +{"mode": "train", "epoch": 19, "iter": 1100, "lr": 0.09637, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19969, "top5_acc": 0.42547, "loss_cls": 4.4777, "loss": 4.4777, "time": 0.70532} +{"mode": "train", "epoch": 19, "iter": 1200, "lr": 0.09636, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20438, "top5_acc": 0.42438, "loss_cls": 4.46094, "loss": 4.46094, "time": 0.70551} +{"mode": "train", "epoch": 19, "iter": 1300, "lr": 0.09635, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19266, "top5_acc": 0.42875, "loss_cls": 4.4689, "loss": 4.4689, "time": 0.70603} +{"mode": "train", "epoch": 19, "iter": 1400, "lr": 0.09634, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19578, "top5_acc": 0.42672, "loss_cls": 4.47752, "loss": 4.47752, "time": 0.70717} +{"mode": "train", "epoch": 19, "iter": 1500, "lr": 0.09633, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20281, "top5_acc": 0.43438, "loss_cls": 4.44498, "loss": 4.44498, "time": 0.70827} +{"mode": "train", "epoch": 19, "iter": 1600, "lr": 0.09632, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19797, "top5_acc": 0.43281, "loss_cls": 4.43844, "loss": 4.43844, "time": 0.7063} +{"mode": "train", "epoch": 19, "iter": 1700, "lr": 0.09631, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20297, "top5_acc": 0.43062, "loss_cls": 4.45271, "loss": 4.45271, "time": 0.70531} +{"mode": "train", "epoch": 19, "iter": 1800, "lr": 0.0963, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20281, "top5_acc": 0.43484, "loss_cls": 4.44679, "loss": 4.44679, "time": 0.70138} +{"mode": "train", "epoch": 19, "iter": 1900, "lr": 0.09629, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19281, "top5_acc": 0.42844, "loss_cls": 4.49173, "loss": 4.49173, "time": 0.70298} +{"mode": "train", "epoch": 19, "iter": 2000, "lr": 0.09628, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19969, "top5_acc": 0.42875, "loss_cls": 4.44485, "loss": 4.44485, "time": 0.70236} +{"mode": "train", "epoch": 19, "iter": 2100, "lr": 0.09627, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.195, "top5_acc": 0.42469, "loss_cls": 4.46732, "loss": 4.46732, "time": 0.7025} +{"mode": "train", "epoch": 19, "iter": 2200, "lr": 0.09626, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20594, "top5_acc": 0.43938, "loss_cls": 4.43332, "loss": 4.43332, "time": 0.70425} +{"mode": "train", "epoch": 19, "iter": 2300, "lr": 0.09625, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19672, "top5_acc": 0.42031, "loss_cls": 4.49057, "loss": 4.49057, "time": 0.70266} +{"mode": "train", "epoch": 19, "iter": 2400, "lr": 0.09624, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19906, "top5_acc": 0.43188, "loss_cls": 4.44552, "loss": 4.44552, "time": 0.7} +{"mode": "train", "epoch": 19, "iter": 2500, "lr": 0.09623, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.19812, "top5_acc": 0.42422, "loss_cls": 4.47463, "loss": 4.47463, "time": 0.7004} +{"mode": "train", "epoch": 19, "iter": 2600, "lr": 0.09622, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19891, "top5_acc": 0.43156, "loss_cls": 4.43608, "loss": 4.43608, "time": 0.70426} +{"mode": "train", "epoch": 19, "iter": 2700, "lr": 0.09621, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20188, "top5_acc": 0.42031, "loss_cls": 4.46778, "loss": 4.46778, "time": 0.70386} +{"mode": "train", "epoch": 19, "iter": 2800, "lr": 0.0962, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20188, "top5_acc": 0.42344, "loss_cls": 4.47483, "loss": 4.47483, "time": 0.70142} +{"mode": "train", "epoch": 19, "iter": 2900, "lr": 0.09618, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19531, "top5_acc": 0.43047, "loss_cls": 4.46161, "loss": 4.46161, "time": 0.70348} +{"mode": "train", "epoch": 19, "iter": 3000, "lr": 0.09617, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20359, "top5_acc": 0.42656, "loss_cls": 4.44849, "loss": 4.44849, "time": 0.70262} +{"mode": "train", "epoch": 19, "iter": 3100, "lr": 0.09616, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20938, "top5_acc": 0.43688, "loss_cls": 4.42063, "loss": 4.42063, "time": 0.70472} +{"mode": "train", "epoch": 19, "iter": 3200, "lr": 0.09615, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.19328, "top5_acc": 0.42562, "loss_cls": 4.48779, "loss": 4.48779, "time": 0.69991} +{"mode": "train", "epoch": 19, "iter": 3300, "lr": 0.09614, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19953, "top5_acc": 0.43359, "loss_cls": 4.44694, "loss": 4.44694, "time": 0.69975} +{"mode": "train", "epoch": 19, "iter": 3400, "lr": 0.09613, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19594, "top5_acc": 0.42562, "loss_cls": 4.47692, "loss": 4.47692, "time": 0.70935} +{"mode": "train", "epoch": 19, "iter": 3500, "lr": 0.09612, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19906, "top5_acc": 0.425, "loss_cls": 4.48088, "loss": 4.48088, "time": 0.70951} +{"mode": "train", "epoch": 19, "iter": 3600, "lr": 0.09611, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19859, "top5_acc": 0.42078, "loss_cls": 4.49465, "loss": 4.49465, "time": 0.70626} +{"mode": "train", "epoch": 19, "iter": 3700, "lr": 0.0961, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.20422, "top5_acc": 0.42875, "loss_cls": 4.48696, "loss": 4.48696, "time": 0.71024} +{"mode": "val", "epoch": 19, "iter": 309, "lr": 0.09609, "top1_acc": 0.15844, "top5_acc": 0.36474, "mean_class_accuracy": 0.15826} +{"mode": "train", "epoch": 20, "iter": 100, "lr": 0.09608, "memory": 15990, "data_time": 1.27274, "top1_acc": 0.19797, "top5_acc": 0.42969, "loss_cls": 4.44439, "loss": 4.44439, "time": 1.98907} +{"mode": "train", "epoch": 20, "iter": 200, "lr": 0.09607, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20547, "top5_acc": 0.44578, "loss_cls": 4.38911, "loss": 4.38911, "time": 0.71038} +{"mode": "train", "epoch": 20, "iter": 300, "lr": 0.09606, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20594, "top5_acc": 0.43531, "loss_cls": 4.44006, "loss": 4.44006, "time": 0.71105} +{"mode": "train", "epoch": 20, "iter": 400, "lr": 0.09605, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21203, "top5_acc": 0.445, "loss_cls": 4.38701, "loss": 4.38701, "time": 0.70667} +{"mode": "train", "epoch": 20, "iter": 500, "lr": 0.09604, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21266, "top5_acc": 0.43875, "loss_cls": 4.43468, "loss": 4.43468, "time": 0.70539} +{"mode": "train", "epoch": 20, "iter": 600, "lr": 0.09603, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20703, "top5_acc": 0.44203, "loss_cls": 4.389, "loss": 4.389, "time": 0.70648} +{"mode": "train", "epoch": 20, "iter": 700, "lr": 0.09602, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19734, "top5_acc": 0.42281, "loss_cls": 4.4581, "loss": 4.4581, "time": 0.70518} +{"mode": "train", "epoch": 20, "iter": 800, "lr": 0.09601, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.205, "top5_acc": 0.43281, "loss_cls": 4.41704, "loss": 4.41704, "time": 0.70708} +{"mode": "train", "epoch": 20, "iter": 900, "lr": 0.096, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19609, "top5_acc": 0.42375, "loss_cls": 4.50533, "loss": 4.50533, "time": 0.70601} +{"mode": "train", "epoch": 20, "iter": 1000, "lr": 0.09598, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19797, "top5_acc": 0.42891, "loss_cls": 4.4795, "loss": 4.4795, "time": 0.70552} +{"mode": "train", "epoch": 20, "iter": 1100, "lr": 0.09597, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20688, "top5_acc": 0.43531, "loss_cls": 4.45206, "loss": 4.45206, "time": 0.70854} +{"mode": "train", "epoch": 20, "iter": 1200, "lr": 0.09596, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20266, "top5_acc": 0.43734, "loss_cls": 4.44403, "loss": 4.44403, "time": 0.70643} +{"mode": "train", "epoch": 20, "iter": 1300, "lr": 0.09595, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20297, "top5_acc": 0.42922, "loss_cls": 4.443, "loss": 4.443, "time": 0.70429} +{"mode": "train", "epoch": 20, "iter": 1400, "lr": 0.09594, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21328, "top5_acc": 0.44078, "loss_cls": 4.41388, "loss": 4.41388, "time": 0.70908} +{"mode": "train", "epoch": 20, "iter": 1500, "lr": 0.09593, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19875, "top5_acc": 0.42906, "loss_cls": 4.47494, "loss": 4.47494, "time": 0.70686} +{"mode": "train", "epoch": 20, "iter": 1600, "lr": 0.09592, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20797, "top5_acc": 0.43281, "loss_cls": 4.42365, "loss": 4.42365, "time": 0.70485} +{"mode": "train", "epoch": 20, "iter": 1700, "lr": 0.09591, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19797, "top5_acc": 0.42547, "loss_cls": 4.48289, "loss": 4.48289, "time": 0.70484} +{"mode": "train", "epoch": 20, "iter": 1800, "lr": 0.0959, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20625, "top5_acc": 0.42141, "loss_cls": 4.46525, "loss": 4.46525, "time": 0.70122} +{"mode": "train", "epoch": 20, "iter": 1900, "lr": 0.09588, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20656, "top5_acc": 0.44016, "loss_cls": 4.45059, "loss": 4.45059, "time": 0.70374} +{"mode": "train", "epoch": 20, "iter": 2000, "lr": 0.09587, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20188, "top5_acc": 0.42922, "loss_cls": 4.45946, "loss": 4.45946, "time": 0.70395} +{"mode": "train", "epoch": 20, "iter": 2100, "lr": 0.09586, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20672, "top5_acc": 0.44016, "loss_cls": 4.42722, "loss": 4.42722, "time": 0.70132} +{"mode": "train", "epoch": 20, "iter": 2200, "lr": 0.09585, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20062, "top5_acc": 0.42641, "loss_cls": 4.45255, "loss": 4.45255, "time": 0.70067} +{"mode": "train", "epoch": 20, "iter": 2300, "lr": 0.09584, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20688, "top5_acc": 0.43438, "loss_cls": 4.45131, "loss": 4.45131, "time": 0.70107} +{"mode": "train", "epoch": 20, "iter": 2400, "lr": 0.09583, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20391, "top5_acc": 0.42875, "loss_cls": 4.4727, "loss": 4.4727, "time": 0.70069} +{"mode": "train", "epoch": 20, "iter": 2500, "lr": 0.09582, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20094, "top5_acc": 0.42672, "loss_cls": 4.45784, "loss": 4.45784, "time": 0.69994} +{"mode": "train", "epoch": 20, "iter": 2600, "lr": 0.09581, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19156, "top5_acc": 0.41953, "loss_cls": 4.47767, "loss": 4.47767, "time": 0.70102} +{"mode": "train", "epoch": 20, "iter": 2700, "lr": 0.0958, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20094, "top5_acc": 0.42641, "loss_cls": 4.4786, "loss": 4.4786, "time": 0.69919} +{"mode": "train", "epoch": 20, "iter": 2800, "lr": 0.09578, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19938, "top5_acc": 0.43359, "loss_cls": 4.40121, "loss": 4.40121, "time": 0.70124} +{"mode": "train", "epoch": 20, "iter": 2900, "lr": 0.09577, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20703, "top5_acc": 0.4425, "loss_cls": 4.40841, "loss": 4.40841, "time": 0.70107} +{"mode": "train", "epoch": 20, "iter": 3000, "lr": 0.09576, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.19203, "top5_acc": 0.42188, "loss_cls": 4.4881, "loss": 4.4881, "time": 0.6997} +{"mode": "train", "epoch": 20, "iter": 3100, "lr": 0.09575, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19594, "top5_acc": 0.42547, "loss_cls": 4.4555, "loss": 4.4555, "time": 0.70105} +{"mode": "train", "epoch": 20, "iter": 3200, "lr": 0.09574, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20125, "top5_acc": 0.43203, "loss_cls": 4.44545, "loss": 4.44545, "time": 0.70335} +{"mode": "train", "epoch": 20, "iter": 3300, "lr": 0.09573, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20125, "top5_acc": 0.43719, "loss_cls": 4.43216, "loss": 4.43216, "time": 0.70113} +{"mode": "train", "epoch": 20, "iter": 3400, "lr": 0.09572, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20719, "top5_acc": 0.43922, "loss_cls": 4.41586, "loss": 4.41586, "time": 0.70798} +{"mode": "train", "epoch": 20, "iter": 3500, "lr": 0.09571, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20047, "top5_acc": 0.43469, "loss_cls": 4.41981, "loss": 4.41981, "time": 0.70613} +{"mode": "train", "epoch": 20, "iter": 3600, "lr": 0.09569, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20031, "top5_acc": 0.43984, "loss_cls": 4.43934, "loss": 4.43934, "time": 0.70614} +{"mode": "train", "epoch": 20, "iter": 3700, "lr": 0.09568, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19516, "top5_acc": 0.42562, "loss_cls": 4.4797, "loss": 4.4797, "time": 0.70936} +{"mode": "val", "epoch": 20, "iter": 309, "lr": 0.09568, "top1_acc": 0.13362, "top5_acc": 0.32153, "mean_class_accuracy": 0.13346} +{"mode": "train", "epoch": 21, "iter": 100, "lr": 0.09567, "memory": 15990, "data_time": 1.33788, "top1_acc": 0.21438, "top5_acc": 0.44062, "loss_cls": 4.38725, "loss": 4.38725, "time": 2.05154} +{"mode": "train", "epoch": 21, "iter": 200, "lr": 0.09565, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20797, "top5_acc": 0.44, "loss_cls": 4.41811, "loss": 4.41811, "time": 0.71302} +{"mode": "train", "epoch": 21, "iter": 300, "lr": 0.09564, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20062, "top5_acc": 0.43172, "loss_cls": 4.43169, "loss": 4.43169, "time": 0.70769} +{"mode": "train", "epoch": 21, "iter": 400, "lr": 0.09563, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19562, "top5_acc": 0.43266, "loss_cls": 4.46552, "loss": 4.46552, "time": 0.70935} +{"mode": "train", "epoch": 21, "iter": 500, "lr": 0.09562, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20234, "top5_acc": 0.44031, "loss_cls": 4.41082, "loss": 4.41082, "time": 0.70524} +{"mode": "train", "epoch": 21, "iter": 600, "lr": 0.09561, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20484, "top5_acc": 0.42875, "loss_cls": 4.45878, "loss": 4.45878, "time": 0.70873} +{"mode": "train", "epoch": 21, "iter": 700, "lr": 0.0956, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19859, "top5_acc": 0.42562, "loss_cls": 4.46434, "loss": 4.46434, "time": 0.70575} +{"mode": "train", "epoch": 21, "iter": 800, "lr": 0.09559, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.205, "top5_acc": 0.42984, "loss_cls": 4.45037, "loss": 4.45037, "time": 0.70759} +{"mode": "train", "epoch": 21, "iter": 900, "lr": 0.09557, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21531, "top5_acc": 0.43891, "loss_cls": 4.39951, "loss": 4.39951, "time": 0.70607} +{"mode": "train", "epoch": 21, "iter": 1000, "lr": 0.09556, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20094, "top5_acc": 0.44, "loss_cls": 4.41203, "loss": 4.41203, "time": 0.70674} +{"mode": "train", "epoch": 21, "iter": 1100, "lr": 0.09555, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20625, "top5_acc": 0.43375, "loss_cls": 4.42728, "loss": 4.42728, "time": 0.70767} +{"mode": "train", "epoch": 21, "iter": 1200, "lr": 0.09554, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20547, "top5_acc": 0.43234, "loss_cls": 4.44546, "loss": 4.44546, "time": 0.7057} +{"mode": "train", "epoch": 21, "iter": 1300, "lr": 0.09553, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20391, "top5_acc": 0.44469, "loss_cls": 4.40083, "loss": 4.40083, "time": 0.7071} +{"mode": "train", "epoch": 21, "iter": 1400, "lr": 0.09552, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20422, "top5_acc": 0.4375, "loss_cls": 4.44547, "loss": 4.44547, "time": 0.71071} +{"mode": "train", "epoch": 21, "iter": 1500, "lr": 0.09551, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20125, "top5_acc": 0.42875, "loss_cls": 4.46682, "loss": 4.46682, "time": 0.70788} +{"mode": "train", "epoch": 21, "iter": 1600, "lr": 0.09549, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20859, "top5_acc": 0.43047, "loss_cls": 4.42754, "loss": 4.42754, "time": 0.71549} +{"mode": "train", "epoch": 21, "iter": 1700, "lr": 0.09548, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21109, "top5_acc": 0.43984, "loss_cls": 4.39622, "loss": 4.39622, "time": 0.7051} +{"mode": "train", "epoch": 21, "iter": 1800, "lr": 0.09547, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20438, "top5_acc": 0.43828, "loss_cls": 4.4472, "loss": 4.4472, "time": 0.70503} +{"mode": "train", "epoch": 21, "iter": 1900, "lr": 0.09546, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20625, "top5_acc": 0.43828, "loss_cls": 4.43796, "loss": 4.43796, "time": 0.71032} +{"mode": "train", "epoch": 21, "iter": 2000, "lr": 0.09545, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20078, "top5_acc": 0.43188, "loss_cls": 4.44722, "loss": 4.44722, "time": 0.7108} +{"mode": "train", "epoch": 21, "iter": 2100, "lr": 0.09544, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19703, "top5_acc": 0.42797, "loss_cls": 4.46194, "loss": 4.46194, "time": 0.70897} +{"mode": "train", "epoch": 21, "iter": 2200, "lr": 0.09542, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20203, "top5_acc": 0.42859, "loss_cls": 4.45396, "loss": 4.45396, "time": 0.70449} +{"mode": "train", "epoch": 21, "iter": 2300, "lr": 0.09541, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20359, "top5_acc": 0.42812, "loss_cls": 4.45947, "loss": 4.45947, "time": 0.70434} +{"mode": "train", "epoch": 21, "iter": 2400, "lr": 0.0954, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19906, "top5_acc": 0.43062, "loss_cls": 4.45099, "loss": 4.45099, "time": 0.70598} +{"mode": "train", "epoch": 21, "iter": 2500, "lr": 0.09539, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20141, "top5_acc": 0.42469, "loss_cls": 4.49817, "loss": 4.49817, "time": 0.70552} +{"mode": "train", "epoch": 21, "iter": 2600, "lr": 0.09538, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21141, "top5_acc": 0.43875, "loss_cls": 4.42812, "loss": 4.42812, "time": 0.70549} +{"mode": "train", "epoch": 21, "iter": 2700, "lr": 0.09537, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19875, "top5_acc": 0.43203, "loss_cls": 4.46334, "loss": 4.46334, "time": 0.70401} +{"mode": "train", "epoch": 21, "iter": 2800, "lr": 0.09535, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19594, "top5_acc": 0.42859, "loss_cls": 4.44699, "loss": 4.44699, "time": 0.70332} +{"mode": "train", "epoch": 21, "iter": 2900, "lr": 0.09534, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20406, "top5_acc": 0.4325, "loss_cls": 4.42398, "loss": 4.42398, "time": 0.70153} +{"mode": "train", "epoch": 21, "iter": 3000, "lr": 0.09533, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.205, "top5_acc": 0.43484, "loss_cls": 4.43214, "loss": 4.43214, "time": 0.70115} +{"mode": "train", "epoch": 21, "iter": 3100, "lr": 0.09532, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2075, "top5_acc": 0.42969, "loss_cls": 4.42645, "loss": 4.42645, "time": 0.70101} +{"mode": "train", "epoch": 21, "iter": 3200, "lr": 0.09531, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2075, "top5_acc": 0.43547, "loss_cls": 4.45282, "loss": 4.45282, "time": 0.70272} +{"mode": "train", "epoch": 21, "iter": 3300, "lr": 0.09529, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21047, "top5_acc": 0.44375, "loss_cls": 4.3961, "loss": 4.3961, "time": 0.70462} +{"mode": "train", "epoch": 21, "iter": 3400, "lr": 0.09528, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.2025, "top5_acc": 0.43562, "loss_cls": 4.43974, "loss": 4.43974, "time": 0.71103} +{"mode": "train", "epoch": 21, "iter": 3500, "lr": 0.09527, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20125, "top5_acc": 0.42875, "loss_cls": 4.47278, "loss": 4.47278, "time": 0.70801} +{"mode": "train", "epoch": 21, "iter": 3600, "lr": 0.09526, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20172, "top5_acc": 0.43156, "loss_cls": 4.45546, "loss": 4.45546, "time": 0.71008} +{"mode": "train", "epoch": 21, "iter": 3700, "lr": 0.09525, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20312, "top5_acc": 0.43359, "loss_cls": 4.43813, "loss": 4.43813, "time": 0.70859} +{"mode": "val", "epoch": 21, "iter": 309, "lr": 0.09524, "top1_acc": 0.14461, "top5_acc": 0.34362, "mean_class_accuracy": 0.14442} +{"mode": "train", "epoch": 22, "iter": 100, "lr": 0.09523, "memory": 15990, "data_time": 1.30072, "top1_acc": 0.2125, "top5_acc": 0.43656, "loss_cls": 4.40946, "loss": 4.40946, "time": 2.00409} +{"mode": "train", "epoch": 22, "iter": 200, "lr": 0.09522, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21141, "top5_acc": 0.44297, "loss_cls": 4.37107, "loss": 4.37107, "time": 0.70255} +{"mode": "train", "epoch": 22, "iter": 300, "lr": 0.09521, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19828, "top5_acc": 0.44344, "loss_cls": 4.39484, "loss": 4.39484, "time": 0.69956} +{"mode": "train", "epoch": 22, "iter": 400, "lr": 0.09519, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22281, "top5_acc": 0.44453, "loss_cls": 4.3872, "loss": 4.3872, "time": 0.70101} +{"mode": "train", "epoch": 22, "iter": 500, "lr": 0.09518, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.1975, "top5_acc": 0.41359, "loss_cls": 4.50699, "loss": 4.50699, "time": 0.69896} +{"mode": "train", "epoch": 22, "iter": 600, "lr": 0.09517, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20062, "top5_acc": 0.43203, "loss_cls": 4.43086, "loss": 4.43086, "time": 0.70189} +{"mode": "train", "epoch": 22, "iter": 700, "lr": 0.09516, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19719, "top5_acc": 0.42734, "loss_cls": 4.45546, "loss": 4.45546, "time": 0.70025} +{"mode": "train", "epoch": 22, "iter": 800, "lr": 0.09515, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20312, "top5_acc": 0.43312, "loss_cls": 4.43521, "loss": 4.43521, "time": 0.70246} +{"mode": "train", "epoch": 22, "iter": 900, "lr": 0.09513, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20125, "top5_acc": 0.42531, "loss_cls": 4.44925, "loss": 4.44925, "time": 0.70102} +{"mode": "train", "epoch": 22, "iter": 1000, "lr": 0.09512, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21094, "top5_acc": 0.44281, "loss_cls": 4.42523, "loss": 4.42523, "time": 0.69942} +{"mode": "train", "epoch": 22, "iter": 1100, "lr": 0.09511, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19969, "top5_acc": 0.44047, "loss_cls": 4.42198, "loss": 4.42198, "time": 0.70384} +{"mode": "train", "epoch": 22, "iter": 1200, "lr": 0.0951, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21359, "top5_acc": 0.43766, "loss_cls": 4.42029, "loss": 4.42029, "time": 0.70091} +{"mode": "train", "epoch": 22, "iter": 1300, "lr": 0.09509, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19438, "top5_acc": 0.415, "loss_cls": 4.48185, "loss": 4.48185, "time": 0.69862} +{"mode": "train", "epoch": 22, "iter": 1400, "lr": 0.09507, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20703, "top5_acc": 0.43688, "loss_cls": 4.43908, "loss": 4.43908, "time": 0.69971} +{"mode": "train", "epoch": 22, "iter": 1500, "lr": 0.09506, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20906, "top5_acc": 0.43438, "loss_cls": 4.41754, "loss": 4.41754, "time": 0.70627} +{"mode": "train", "epoch": 22, "iter": 1600, "lr": 0.09505, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20547, "top5_acc": 0.43375, "loss_cls": 4.44119, "loss": 4.44119, "time": 0.70031} +{"mode": "train", "epoch": 22, "iter": 1700, "lr": 0.09504, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20078, "top5_acc": 0.42125, "loss_cls": 4.49213, "loss": 4.49213, "time": 0.70343} +{"mode": "train", "epoch": 22, "iter": 1800, "lr": 0.09502, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20125, "top5_acc": 0.43219, "loss_cls": 4.44286, "loss": 4.44286, "time": 0.70413} +{"mode": "train", "epoch": 22, "iter": 1900, "lr": 0.09501, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20703, "top5_acc": 0.44547, "loss_cls": 4.41131, "loss": 4.41131, "time": 0.70214} +{"mode": "train", "epoch": 22, "iter": 2000, "lr": 0.095, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20812, "top5_acc": 0.43062, "loss_cls": 4.42737, "loss": 4.42737, "time": 0.70298} +{"mode": "train", "epoch": 22, "iter": 2100, "lr": 0.09499, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20625, "top5_acc": 0.43531, "loss_cls": 4.43537, "loss": 4.43537, "time": 0.69988} +{"mode": "train", "epoch": 22, "iter": 2200, "lr": 0.09498, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.20625, "top5_acc": 0.43281, "loss_cls": 4.41596, "loss": 4.41596, "time": 0.69974} +{"mode": "train", "epoch": 22, "iter": 2300, "lr": 0.09496, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20203, "top5_acc": 0.43828, "loss_cls": 4.44206, "loss": 4.44206, "time": 0.69772} +{"mode": "train", "epoch": 22, "iter": 2400, "lr": 0.09495, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20625, "top5_acc": 0.4375, "loss_cls": 4.43329, "loss": 4.43329, "time": 0.70086} +{"mode": "train", "epoch": 22, "iter": 2500, "lr": 0.09494, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20875, "top5_acc": 0.43891, "loss_cls": 4.42439, "loss": 4.42439, "time": 0.70022} +{"mode": "train", "epoch": 22, "iter": 2600, "lr": 0.09493, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19719, "top5_acc": 0.43344, "loss_cls": 4.45526, "loss": 4.45526, "time": 0.69933} +{"mode": "train", "epoch": 22, "iter": 2700, "lr": 0.09491, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20406, "top5_acc": 0.43344, "loss_cls": 4.42745, "loss": 4.42745, "time": 0.69814} +{"mode": "train", "epoch": 22, "iter": 2800, "lr": 0.0949, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.20812, "top5_acc": 0.43812, "loss_cls": 4.43378, "loss": 4.43378, "time": 0.69773} +{"mode": "train", "epoch": 22, "iter": 2900, "lr": 0.09489, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19797, "top5_acc": 0.42109, "loss_cls": 4.49724, "loss": 4.49724, "time": 0.69899} +{"mode": "train", "epoch": 22, "iter": 3000, "lr": 0.09488, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19781, "top5_acc": 0.42391, "loss_cls": 4.47401, "loss": 4.47401, "time": 0.70337} +{"mode": "train", "epoch": 22, "iter": 3100, "lr": 0.09487, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.20469, "top5_acc": 0.4325, "loss_cls": 4.42847, "loss": 4.42847, "time": 0.70146} +{"mode": "train", "epoch": 22, "iter": 3200, "lr": 0.09485, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21734, "top5_acc": 0.43766, "loss_cls": 4.38835, "loss": 4.38835, "time": 0.69885} +{"mode": "train", "epoch": 22, "iter": 3300, "lr": 0.09484, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.19672, "top5_acc": 0.42672, "loss_cls": 4.45267, "loss": 4.45267, "time": 0.70529} +{"mode": "train", "epoch": 22, "iter": 3400, "lr": 0.09483, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20281, "top5_acc": 0.42453, "loss_cls": 4.46125, "loss": 4.46125, "time": 0.70452} +{"mode": "train", "epoch": 22, "iter": 3500, "lr": 0.09482, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20172, "top5_acc": 0.43688, "loss_cls": 4.42282, "loss": 4.42282, "time": 0.70431} +{"mode": "train", "epoch": 22, "iter": 3600, "lr": 0.0948, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20641, "top5_acc": 0.43531, "loss_cls": 4.43422, "loss": 4.43422, "time": 0.71298} +{"mode": "train", "epoch": 22, "iter": 3700, "lr": 0.09479, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21281, "top5_acc": 0.44219, "loss_cls": 4.40005, "loss": 4.40005, "time": 0.7081} +{"mode": "val", "epoch": 22, "iter": 309, "lr": 0.09479, "top1_acc": 0.13331, "top5_acc": 0.31571, "mean_class_accuracy": 0.13315} +{"mode": "train", "epoch": 23, "iter": 100, "lr": 0.09477, "memory": 15990, "data_time": 1.30569, "top1_acc": 0.21266, "top5_acc": 0.44359, "loss_cls": 4.38528, "loss": 4.38528, "time": 2.01334} +{"mode": "train", "epoch": 23, "iter": 200, "lr": 0.09476, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20516, "top5_acc": 0.43297, "loss_cls": 4.43836, "loss": 4.43836, "time": 0.7007} +{"mode": "train", "epoch": 23, "iter": 300, "lr": 0.09475, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20641, "top5_acc": 0.43797, "loss_cls": 4.39974, "loss": 4.39974, "time": 0.70288} +{"mode": "train", "epoch": 23, "iter": 400, "lr": 0.09474, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20359, "top5_acc": 0.4325, "loss_cls": 4.43827, "loss": 4.43827, "time": 0.70211} +{"mode": "train", "epoch": 23, "iter": 500, "lr": 0.09472, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21172, "top5_acc": 0.435, "loss_cls": 4.39507, "loss": 4.39507, "time": 0.69988} +{"mode": "train", "epoch": 23, "iter": 600, "lr": 0.09471, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20984, "top5_acc": 0.43703, "loss_cls": 4.39213, "loss": 4.39213, "time": 0.69754} +{"mode": "train", "epoch": 23, "iter": 700, "lr": 0.0947, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20234, "top5_acc": 0.43078, "loss_cls": 4.44546, "loss": 4.44546, "time": 0.69853} +{"mode": "train", "epoch": 23, "iter": 800, "lr": 0.09469, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20797, "top5_acc": 0.43641, "loss_cls": 4.43938, "loss": 4.43938, "time": 0.69973} +{"mode": "train", "epoch": 23, "iter": 900, "lr": 0.09467, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19531, "top5_acc": 0.42281, "loss_cls": 4.47422, "loss": 4.47422, "time": 0.70148} +{"mode": "train", "epoch": 23, "iter": 1000, "lr": 0.09466, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19391, "top5_acc": 0.42172, "loss_cls": 4.48331, "loss": 4.48331, "time": 0.70121} +{"mode": "train", "epoch": 23, "iter": 1100, "lr": 0.09465, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20703, "top5_acc": 0.42875, "loss_cls": 4.43799, "loss": 4.43799, "time": 0.69852} +{"mode": "train", "epoch": 23, "iter": 1200, "lr": 0.09464, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2075, "top5_acc": 0.44219, "loss_cls": 4.42677, "loss": 4.42677, "time": 0.70424} +{"mode": "train", "epoch": 23, "iter": 1300, "lr": 0.09462, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21062, "top5_acc": 0.43625, "loss_cls": 4.41558, "loss": 4.41558, "time": 0.70115} +{"mode": "train", "epoch": 23, "iter": 1400, "lr": 0.09461, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21266, "top5_acc": 0.44047, "loss_cls": 4.38452, "loss": 4.38452, "time": 0.70053} +{"mode": "train", "epoch": 23, "iter": 1500, "lr": 0.0946, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.19875, "top5_acc": 0.42422, "loss_cls": 4.4531, "loss": 4.4531, "time": 0.70381} +{"mode": "train", "epoch": 23, "iter": 1600, "lr": 0.09459, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20672, "top5_acc": 0.43047, "loss_cls": 4.43954, "loss": 4.43954, "time": 0.70377} +{"mode": "train", "epoch": 23, "iter": 1700, "lr": 0.09457, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.20844, "top5_acc": 0.43875, "loss_cls": 4.42168, "loss": 4.42168, "time": 0.70596} +{"mode": "train", "epoch": 23, "iter": 1800, "lr": 0.09456, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21125, "top5_acc": 0.44016, "loss_cls": 4.41644, "loss": 4.41644, "time": 0.70155} +{"mode": "train", "epoch": 23, "iter": 1900, "lr": 0.09455, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20375, "top5_acc": 0.43516, "loss_cls": 4.43491, "loss": 4.43491, "time": 0.70068} +{"mode": "train", "epoch": 23, "iter": 2000, "lr": 0.09453, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20906, "top5_acc": 0.43719, "loss_cls": 4.42339, "loss": 4.42339, "time": 0.69983} +{"mode": "train", "epoch": 23, "iter": 2100, "lr": 0.09452, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20219, "top5_acc": 0.43391, "loss_cls": 4.45131, "loss": 4.45131, "time": 0.69986} +{"mode": "train", "epoch": 23, "iter": 2200, "lr": 0.09451, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21266, "top5_acc": 0.44391, "loss_cls": 4.40026, "loss": 4.40026, "time": 0.69851} +{"mode": "train", "epoch": 23, "iter": 2300, "lr": 0.0945, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21125, "top5_acc": 0.43422, "loss_cls": 4.40605, "loss": 4.40605, "time": 0.69999} +{"mode": "train", "epoch": 23, "iter": 2400, "lr": 0.09448, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21375, "top5_acc": 0.43875, "loss_cls": 4.409, "loss": 4.409, "time": 0.7004} +{"mode": "train", "epoch": 23, "iter": 2500, "lr": 0.09447, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20625, "top5_acc": 0.43359, "loss_cls": 4.44592, "loss": 4.44592, "time": 0.70181} +{"mode": "train", "epoch": 23, "iter": 2600, "lr": 0.09446, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20375, "top5_acc": 0.43844, "loss_cls": 4.44413, "loss": 4.44413, "time": 0.70254} +{"mode": "train", "epoch": 23, "iter": 2700, "lr": 0.09445, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20312, "top5_acc": 0.42859, "loss_cls": 4.46135, "loss": 4.46135, "time": 0.70103} +{"mode": "train", "epoch": 23, "iter": 2800, "lr": 0.09443, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20625, "top5_acc": 0.42359, "loss_cls": 4.45087, "loss": 4.45087, "time": 0.69989} +{"mode": "train", "epoch": 23, "iter": 2900, "lr": 0.09442, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2075, "top5_acc": 0.43328, "loss_cls": 4.41502, "loss": 4.41502, "time": 0.69977} +{"mode": "train", "epoch": 23, "iter": 3000, "lr": 0.09441, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19766, "top5_acc": 0.43094, "loss_cls": 4.45915, "loss": 4.45915, "time": 0.69911} +{"mode": "train", "epoch": 23, "iter": 3100, "lr": 0.09439, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20562, "top5_acc": 0.43188, "loss_cls": 4.43728, "loss": 4.43728, "time": 0.70108} +{"mode": "train", "epoch": 23, "iter": 3200, "lr": 0.09438, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19734, "top5_acc": 0.43109, "loss_cls": 4.44637, "loss": 4.44637, "time": 0.69942} +{"mode": "train", "epoch": 23, "iter": 3300, "lr": 0.09437, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21031, "top5_acc": 0.44141, "loss_cls": 4.38585, "loss": 4.38585, "time": 0.70629} +{"mode": "train", "epoch": 23, "iter": 3400, "lr": 0.09436, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20344, "top5_acc": 0.43109, "loss_cls": 4.4545, "loss": 4.4545, "time": 0.71078} +{"mode": "train", "epoch": 23, "iter": 3500, "lr": 0.09434, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2, "top5_acc": 0.42312, "loss_cls": 4.4717, "loss": 4.4717, "time": 0.70833} +{"mode": "train", "epoch": 23, "iter": 3600, "lr": 0.09433, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20203, "top5_acc": 0.43594, "loss_cls": 4.43127, "loss": 4.43127, "time": 0.70797} +{"mode": "train", "epoch": 23, "iter": 3700, "lr": 0.09432, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21172, "top5_acc": 0.43672, "loss_cls": 4.44019, "loss": 4.44019, "time": 0.70207} +{"mode": "val", "epoch": 23, "iter": 309, "lr": 0.09431, "top1_acc": 0.13534, "top5_acc": 0.32685, "mean_class_accuracy": 0.13539} +{"mode": "train", "epoch": 24, "iter": 100, "lr": 0.0943, "memory": 15990, "data_time": 1.33156, "top1_acc": 0.20672, "top5_acc": 0.44094, "loss_cls": 4.39458, "loss": 4.39458, "time": 2.04007} +{"mode": "train", "epoch": 24, "iter": 200, "lr": 0.09428, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22, "top5_acc": 0.44844, "loss_cls": 4.35269, "loss": 4.35269, "time": 0.70405} +{"mode": "train", "epoch": 24, "iter": 300, "lr": 0.09427, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20359, "top5_acc": 0.42406, "loss_cls": 4.44046, "loss": 4.44046, "time": 0.70075} +{"mode": "train", "epoch": 24, "iter": 400, "lr": 0.09426, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20297, "top5_acc": 0.43203, "loss_cls": 4.45123, "loss": 4.45123, "time": 0.70077} +{"mode": "train", "epoch": 24, "iter": 500, "lr": 0.09425, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21516, "top5_acc": 0.44719, "loss_cls": 4.39885, "loss": 4.39885, "time": 0.69938} +{"mode": "train", "epoch": 24, "iter": 600, "lr": 0.09423, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21062, "top5_acc": 0.44281, "loss_cls": 4.39689, "loss": 4.39689, "time": 0.69938} +{"mode": "train", "epoch": 24, "iter": 700, "lr": 0.09422, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19562, "top5_acc": 0.43469, "loss_cls": 4.43728, "loss": 4.43728, "time": 0.69978} +{"mode": "train", "epoch": 24, "iter": 800, "lr": 0.09421, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20484, "top5_acc": 0.42641, "loss_cls": 4.43582, "loss": 4.43582, "time": 0.70126} +{"mode": "train", "epoch": 24, "iter": 900, "lr": 0.09419, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19797, "top5_acc": 0.44188, "loss_cls": 4.44044, "loss": 4.44044, "time": 0.70049} +{"mode": "train", "epoch": 24, "iter": 1000, "lr": 0.09418, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20625, "top5_acc": 0.43594, "loss_cls": 4.4207, "loss": 4.4207, "time": 0.70053} +{"mode": "train", "epoch": 24, "iter": 1100, "lr": 0.09417, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20734, "top5_acc": 0.44297, "loss_cls": 4.40606, "loss": 4.40606, "time": 0.70086} +{"mode": "train", "epoch": 24, "iter": 1200, "lr": 0.09415, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21734, "top5_acc": 0.44953, "loss_cls": 4.36276, "loss": 4.36276, "time": 0.7003} +{"mode": "train", "epoch": 24, "iter": 1300, "lr": 0.09414, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19969, "top5_acc": 0.42688, "loss_cls": 4.46454, "loss": 4.46454, "time": 0.69861} +{"mode": "train", "epoch": 24, "iter": 1400, "lr": 0.09413, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21344, "top5_acc": 0.44125, "loss_cls": 4.38821, "loss": 4.38821, "time": 0.70126} +{"mode": "train", "epoch": 24, "iter": 1500, "lr": 0.09411, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20516, "top5_acc": 0.43766, "loss_cls": 4.43759, "loss": 4.43759, "time": 0.70564} +{"mode": "train", "epoch": 24, "iter": 1600, "lr": 0.0941, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19891, "top5_acc": 0.43109, "loss_cls": 4.45604, "loss": 4.45604, "time": 0.70001} +{"mode": "train", "epoch": 24, "iter": 1700, "lr": 0.09409, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20312, "top5_acc": 0.43438, "loss_cls": 4.44299, "loss": 4.44299, "time": 0.70303} +{"mode": "train", "epoch": 24, "iter": 1800, "lr": 0.09407, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21125, "top5_acc": 0.43969, "loss_cls": 4.44567, "loss": 4.44567, "time": 0.7051} +{"mode": "train", "epoch": 24, "iter": 1900, "lr": 0.09406, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19734, "top5_acc": 0.43031, "loss_cls": 4.47358, "loss": 4.47358, "time": 0.70086} +{"mode": "train", "epoch": 24, "iter": 2000, "lr": 0.09405, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19781, "top5_acc": 0.42062, "loss_cls": 4.47118, "loss": 4.47118, "time": 0.70056} +{"mode": "train", "epoch": 24, "iter": 2100, "lr": 0.09404, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20562, "top5_acc": 0.435, "loss_cls": 4.43743, "loss": 4.43743, "time": 0.69817} +{"mode": "train", "epoch": 24, "iter": 2200, "lr": 0.09402, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.20094, "top5_acc": 0.43828, "loss_cls": 4.41375, "loss": 4.41375, "time": 0.70129} +{"mode": "train", "epoch": 24, "iter": 2300, "lr": 0.09401, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20016, "top5_acc": 0.43016, "loss_cls": 4.45643, "loss": 4.45643, "time": 0.69869} +{"mode": "train", "epoch": 24, "iter": 2400, "lr": 0.094, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20859, "top5_acc": 0.43734, "loss_cls": 4.40927, "loss": 4.40927, "time": 0.70335} +{"mode": "train", "epoch": 24, "iter": 2500, "lr": 0.09398, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20984, "top5_acc": 0.43703, "loss_cls": 4.41108, "loss": 4.41108, "time": 0.70179} +{"mode": "train", "epoch": 24, "iter": 2600, "lr": 0.09397, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20875, "top5_acc": 0.42938, "loss_cls": 4.45272, "loss": 4.45272, "time": 0.70036} +{"mode": "train", "epoch": 24, "iter": 2700, "lr": 0.09396, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20203, "top5_acc": 0.43312, "loss_cls": 4.43693, "loss": 4.43693, "time": 0.69882} +{"mode": "train", "epoch": 24, "iter": 2800, "lr": 0.09394, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2, "top5_acc": 0.43312, "loss_cls": 4.43099, "loss": 4.43099, "time": 0.69859} +{"mode": "train", "epoch": 24, "iter": 2900, "lr": 0.09393, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19266, "top5_acc": 0.42953, "loss_cls": 4.42847, "loss": 4.42847, "time": 0.69844} +{"mode": "train", "epoch": 24, "iter": 3000, "lr": 0.09392, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21047, "top5_acc": 0.44188, "loss_cls": 4.40314, "loss": 4.40314, "time": 0.7027} +{"mode": "train", "epoch": 24, "iter": 3100, "lr": 0.0939, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21391, "top5_acc": 0.44938, "loss_cls": 4.38841, "loss": 4.38841, "time": 0.70099} +{"mode": "train", "epoch": 24, "iter": 3200, "lr": 0.09389, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20797, "top5_acc": 0.43328, "loss_cls": 4.4116, "loss": 4.4116, "time": 0.70645} +{"mode": "train", "epoch": 24, "iter": 3300, "lr": 0.09388, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.20688, "top5_acc": 0.42469, "loss_cls": 4.44478, "loss": 4.44478, "time": 0.70781} +{"mode": "train", "epoch": 24, "iter": 3400, "lr": 0.09386, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.20281, "top5_acc": 0.42844, "loss_cls": 4.44958, "loss": 4.44958, "time": 0.70797} +{"mode": "train", "epoch": 24, "iter": 3500, "lr": 0.09385, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.19578, "top5_acc": 0.43125, "loss_cls": 4.45144, "loss": 4.45144, "time": 0.70683} +{"mode": "train", "epoch": 24, "iter": 3600, "lr": 0.09384, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20641, "top5_acc": 0.43406, "loss_cls": 4.43101, "loss": 4.43101, "time": 0.70728} +{"mode": "train", "epoch": 24, "iter": 3700, "lr": 0.09382, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20734, "top5_acc": 0.43781, "loss_cls": 4.44214, "loss": 4.44214, "time": 0.70154} +{"mode": "val", "epoch": 24, "iter": 309, "lr": 0.09382, "top1_acc": 0.13468, "top5_acc": 0.32265, "mean_class_accuracy": 0.1344} +{"mode": "train", "epoch": 25, "iter": 100, "lr": 0.0938, "memory": 15990, "data_time": 1.29556, "top1_acc": 0.2125, "top5_acc": 0.43953, "loss_cls": 4.39763, "loss": 4.39763, "time": 2.00078} +{"mode": "train", "epoch": 25, "iter": 200, "lr": 0.09379, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.205, "top5_acc": 0.43375, "loss_cls": 4.42818, "loss": 4.42818, "time": 0.70149} +{"mode": "train", "epoch": 25, "iter": 300, "lr": 0.09378, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20844, "top5_acc": 0.43688, "loss_cls": 4.41341, "loss": 4.41341, "time": 0.70424} +{"mode": "train", "epoch": 25, "iter": 400, "lr": 0.09376, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20656, "top5_acc": 0.44391, "loss_cls": 4.40789, "loss": 4.40789, "time": 0.6989} +{"mode": "train", "epoch": 25, "iter": 500, "lr": 0.09375, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20641, "top5_acc": 0.44344, "loss_cls": 4.36456, "loss": 4.36456, "time": 0.70031} +{"mode": "train", "epoch": 25, "iter": 600, "lr": 0.09373, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20453, "top5_acc": 0.43203, "loss_cls": 4.45752, "loss": 4.45752, "time": 0.69937} +{"mode": "train", "epoch": 25, "iter": 700, "lr": 0.09372, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21281, "top5_acc": 0.43578, "loss_cls": 4.40501, "loss": 4.40501, "time": 0.70191} +{"mode": "train", "epoch": 25, "iter": 800, "lr": 0.09371, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20406, "top5_acc": 0.43219, "loss_cls": 4.46088, "loss": 4.46088, "time": 0.70552} +{"mode": "train", "epoch": 25, "iter": 900, "lr": 0.09369, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20406, "top5_acc": 0.42781, "loss_cls": 4.443, "loss": 4.443, "time": 0.70171} +{"mode": "train", "epoch": 25, "iter": 1000, "lr": 0.09368, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20438, "top5_acc": 0.42359, "loss_cls": 4.43668, "loss": 4.43668, "time": 0.70269} +{"mode": "train", "epoch": 25, "iter": 1100, "lr": 0.09367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20516, "top5_acc": 0.43984, "loss_cls": 4.4286, "loss": 4.4286, "time": 0.70243} +{"mode": "train", "epoch": 25, "iter": 1200, "lr": 0.09365, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20797, "top5_acc": 0.44125, "loss_cls": 4.40645, "loss": 4.40645, "time": 0.70142} +{"mode": "train", "epoch": 25, "iter": 1300, "lr": 0.09364, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20734, "top5_acc": 0.43672, "loss_cls": 4.40878, "loss": 4.40878, "time": 0.7012} +{"mode": "train", "epoch": 25, "iter": 1400, "lr": 0.09363, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21297, "top5_acc": 0.43469, "loss_cls": 4.4285, "loss": 4.4285, "time": 0.70158} +{"mode": "train", "epoch": 25, "iter": 1500, "lr": 0.09361, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20812, "top5_acc": 0.43516, "loss_cls": 4.42964, "loss": 4.42964, "time": 0.71263} +{"mode": "train", "epoch": 25, "iter": 1600, "lr": 0.0936, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20141, "top5_acc": 0.43656, "loss_cls": 4.43664, "loss": 4.43664, "time": 0.70477} +{"mode": "train", "epoch": 25, "iter": 1700, "lr": 0.09358, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20172, "top5_acc": 0.43359, "loss_cls": 4.43556, "loss": 4.43556, "time": 0.7047} +{"mode": "train", "epoch": 25, "iter": 1800, "lr": 0.09357, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.19859, "top5_acc": 0.42812, "loss_cls": 4.41196, "loss": 4.41196, "time": 0.70449} +{"mode": "train", "epoch": 25, "iter": 1900, "lr": 0.09356, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20156, "top5_acc": 0.4275, "loss_cls": 4.43963, "loss": 4.43963, "time": 0.70316} +{"mode": "train", "epoch": 25, "iter": 2000, "lr": 0.09354, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19859, "top5_acc": 0.42156, "loss_cls": 4.46459, "loss": 4.46459, "time": 0.70138} +{"mode": "train", "epoch": 25, "iter": 2100, "lr": 0.09353, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21422, "top5_acc": 0.44141, "loss_cls": 4.3876, "loss": 4.3876, "time": 0.70075} +{"mode": "train", "epoch": 25, "iter": 2200, "lr": 0.09352, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19422, "top5_acc": 0.43141, "loss_cls": 4.45321, "loss": 4.45321, "time": 0.70482} +{"mode": "train", "epoch": 25, "iter": 2300, "lr": 0.0935, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20656, "top5_acc": 0.43516, "loss_cls": 4.41176, "loss": 4.41176, "time": 0.70027} +{"mode": "train", "epoch": 25, "iter": 2400, "lr": 0.09349, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21594, "top5_acc": 0.44328, "loss_cls": 4.37416, "loss": 4.37416, "time": 0.70004} +{"mode": "train", "epoch": 25, "iter": 2500, "lr": 0.09347, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20094, "top5_acc": 0.4375, "loss_cls": 4.4257, "loss": 4.4257, "time": 0.7003} +{"mode": "train", "epoch": 25, "iter": 2600, "lr": 0.09346, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20875, "top5_acc": 0.43859, "loss_cls": 4.41695, "loss": 4.41695, "time": 0.70106} +{"mode": "train", "epoch": 25, "iter": 2700, "lr": 0.09345, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2125, "top5_acc": 0.43969, "loss_cls": 4.41261, "loss": 4.41261, "time": 0.70198} +{"mode": "train", "epoch": 25, "iter": 2800, "lr": 0.09343, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20688, "top5_acc": 0.43438, "loss_cls": 4.43659, "loss": 4.43659, "time": 0.70388} +{"mode": "train", "epoch": 25, "iter": 2900, "lr": 0.09342, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20734, "top5_acc": 0.43656, "loss_cls": 4.40616, "loss": 4.40616, "time": 0.70144} +{"mode": "train", "epoch": 25, "iter": 3000, "lr": 0.09341, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20031, "top5_acc": 0.43703, "loss_cls": 4.43793, "loss": 4.43793, "time": 0.70358} +{"mode": "train", "epoch": 25, "iter": 3100, "lr": 0.09339, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20688, "top5_acc": 0.43406, "loss_cls": 4.42457, "loss": 4.42457, "time": 0.70096} +{"mode": "train", "epoch": 25, "iter": 3200, "lr": 0.09338, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20625, "top5_acc": 0.43609, "loss_cls": 4.4095, "loss": 4.4095, "time": 0.70648} +{"mode": "train", "epoch": 25, "iter": 3300, "lr": 0.09336, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.20078, "top5_acc": 0.43312, "loss_cls": 4.44395, "loss": 4.44395, "time": 0.70652} +{"mode": "train", "epoch": 25, "iter": 3400, "lr": 0.09335, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21016, "top5_acc": 0.44109, "loss_cls": 4.40414, "loss": 4.40414, "time": 0.70703} +{"mode": "train", "epoch": 25, "iter": 3500, "lr": 0.09334, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.20516, "top5_acc": 0.44031, "loss_cls": 4.41419, "loss": 4.41419, "time": 0.70648} +{"mode": "train", "epoch": 25, "iter": 3600, "lr": 0.09332, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20766, "top5_acc": 0.44, "loss_cls": 4.41943, "loss": 4.41943, "time": 0.70987} +{"mode": "train", "epoch": 25, "iter": 3700, "lr": 0.09331, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20547, "top5_acc": 0.43625, "loss_cls": 4.43312, "loss": 4.43312, "time": 0.70654} +{"mode": "val", "epoch": 25, "iter": 309, "lr": 0.0933, "top1_acc": 0.12789, "top5_acc": 0.30472, "mean_class_accuracy": 0.12769} +{"mode": "train", "epoch": 26, "iter": 100, "lr": 0.09329, "memory": 15990, "data_time": 1.33185, "top1_acc": 0.21219, "top5_acc": 0.44594, "loss_cls": 4.37357, "loss": 4.37357, "time": 2.03852} +{"mode": "train", "epoch": 26, "iter": 200, "lr": 0.09327, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20891, "top5_acc": 0.44, "loss_cls": 4.39302, "loss": 4.39302, "time": 0.70223} +{"mode": "train", "epoch": 26, "iter": 300, "lr": 0.09326, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21016, "top5_acc": 0.4375, "loss_cls": 4.42725, "loss": 4.42725, "time": 0.70145} +{"mode": "train", "epoch": 26, "iter": 400, "lr": 0.09325, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21422, "top5_acc": 0.43953, "loss_cls": 4.37304, "loss": 4.37304, "time": 0.70246} +{"mode": "train", "epoch": 26, "iter": 500, "lr": 0.09323, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20438, "top5_acc": 0.43312, "loss_cls": 4.41303, "loss": 4.41303, "time": 0.70081} +{"mode": "train", "epoch": 26, "iter": 600, "lr": 0.09322, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20484, "top5_acc": 0.43047, "loss_cls": 4.43385, "loss": 4.43385, "time": 0.70269} +{"mode": "train", "epoch": 26, "iter": 700, "lr": 0.0932, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2125, "top5_acc": 0.44641, "loss_cls": 4.39451, "loss": 4.39451, "time": 0.7035} +{"mode": "train", "epoch": 26, "iter": 800, "lr": 0.09319, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20875, "top5_acc": 0.43391, "loss_cls": 4.40163, "loss": 4.40163, "time": 0.70329} +{"mode": "train", "epoch": 26, "iter": 900, "lr": 0.09318, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20578, "top5_acc": 0.43531, "loss_cls": 4.40378, "loss": 4.40378, "time": 0.70165} +{"mode": "train", "epoch": 26, "iter": 1000, "lr": 0.09316, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20656, "top5_acc": 0.42766, "loss_cls": 4.43236, "loss": 4.43236, "time": 0.70148} +{"mode": "train", "epoch": 26, "iter": 1100, "lr": 0.09315, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20391, "top5_acc": 0.43406, "loss_cls": 4.43619, "loss": 4.43619, "time": 0.70219} +{"mode": "train", "epoch": 26, "iter": 1200, "lr": 0.09313, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20703, "top5_acc": 0.44234, "loss_cls": 4.37768, "loss": 4.37768, "time": 0.704} +{"mode": "train", "epoch": 26, "iter": 1300, "lr": 0.09312, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20641, "top5_acc": 0.44328, "loss_cls": 4.39618, "loss": 4.39618, "time": 0.70204} +{"mode": "train", "epoch": 26, "iter": 1400, "lr": 0.0931, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20812, "top5_acc": 0.44188, "loss_cls": 4.39043, "loss": 4.39043, "time": 0.7025} +{"mode": "train", "epoch": 26, "iter": 1500, "lr": 0.09309, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19953, "top5_acc": 0.43812, "loss_cls": 4.43266, "loss": 4.43266, "time": 0.70618} +{"mode": "train", "epoch": 26, "iter": 1600, "lr": 0.09308, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20984, "top5_acc": 0.44219, "loss_cls": 4.40387, "loss": 4.40387, "time": 0.70741} +{"mode": "train", "epoch": 26, "iter": 1700, "lr": 0.09306, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20156, "top5_acc": 0.42719, "loss_cls": 4.43517, "loss": 4.43517, "time": 0.70684} +{"mode": "train", "epoch": 26, "iter": 1800, "lr": 0.09305, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20141, "top5_acc": 0.44062, "loss_cls": 4.42687, "loss": 4.42687, "time": 0.7044} +{"mode": "train", "epoch": 26, "iter": 1900, "lr": 0.09303, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20531, "top5_acc": 0.44094, "loss_cls": 4.38712, "loss": 4.38712, "time": 0.70189} +{"mode": "train", "epoch": 26, "iter": 2000, "lr": 0.09302, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21812, "top5_acc": 0.45125, "loss_cls": 4.33658, "loss": 4.33658, "time": 0.69996} +{"mode": "train", "epoch": 26, "iter": 2100, "lr": 0.093, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20406, "top5_acc": 0.43047, "loss_cls": 4.46142, "loss": 4.46142, "time": 0.70063} +{"mode": "train", "epoch": 26, "iter": 2200, "lr": 0.09299, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21156, "top5_acc": 0.44312, "loss_cls": 4.38984, "loss": 4.38984, "time": 0.70185} +{"mode": "train", "epoch": 26, "iter": 2300, "lr": 0.09298, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20344, "top5_acc": 0.42609, "loss_cls": 4.45173, "loss": 4.45173, "time": 0.70326} +{"mode": "train", "epoch": 26, "iter": 2400, "lr": 0.09296, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20562, "top5_acc": 0.43656, "loss_cls": 4.43419, "loss": 4.43419, "time": 0.70095} +{"mode": "train", "epoch": 26, "iter": 2500, "lr": 0.09295, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21016, "top5_acc": 0.43375, "loss_cls": 4.42726, "loss": 4.42726, "time": 0.70799} +{"mode": "train", "epoch": 26, "iter": 2600, "lr": 0.09293, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20797, "top5_acc": 0.43312, "loss_cls": 4.43794, "loss": 4.43794, "time": 0.70257} +{"mode": "train", "epoch": 26, "iter": 2700, "lr": 0.09292, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20328, "top5_acc": 0.42969, "loss_cls": 4.44874, "loss": 4.44874, "time": 0.70113} +{"mode": "train", "epoch": 26, "iter": 2800, "lr": 0.0929, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21203, "top5_acc": 0.44281, "loss_cls": 4.40233, "loss": 4.40233, "time": 0.70576} +{"mode": "train", "epoch": 26, "iter": 2900, "lr": 0.09289, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20688, "top5_acc": 0.43859, "loss_cls": 4.42234, "loss": 4.42234, "time": 0.70174} +{"mode": "train", "epoch": 26, "iter": 3000, "lr": 0.09288, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20469, "top5_acc": 0.42938, "loss_cls": 4.43149, "loss": 4.43149, "time": 0.70042} +{"mode": "train", "epoch": 26, "iter": 3100, "lr": 0.09286, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21094, "top5_acc": 0.43828, "loss_cls": 4.39212, "loss": 4.39212, "time": 0.70178} +{"mode": "train", "epoch": 26, "iter": 3200, "lr": 0.09285, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20016, "top5_acc": 0.42422, "loss_cls": 4.4412, "loss": 4.4412, "time": 0.70669} +{"mode": "train", "epoch": 26, "iter": 3300, "lr": 0.09283, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20109, "top5_acc": 0.43578, "loss_cls": 4.42248, "loss": 4.42248, "time": 0.7061} +{"mode": "train", "epoch": 26, "iter": 3400, "lr": 0.09282, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20312, "top5_acc": 0.43156, "loss_cls": 4.426, "loss": 4.426, "time": 0.70917} +{"mode": "train", "epoch": 26, "iter": 3500, "lr": 0.0928, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.20984, "top5_acc": 0.42828, "loss_cls": 4.47375, "loss": 4.47375, "time": 0.70725} +{"mode": "train", "epoch": 26, "iter": 3600, "lr": 0.09279, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20828, "top5_acc": 0.43328, "loss_cls": 4.43306, "loss": 4.43306, "time": 0.71198} +{"mode": "train", "epoch": 26, "iter": 3700, "lr": 0.09278, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20828, "top5_acc": 0.44156, "loss_cls": 4.39558, "loss": 4.39558, "time": 0.69984} +{"mode": "val", "epoch": 26, "iter": 309, "lr": 0.09277, "top1_acc": 0.13944, "top5_acc": 0.32047, "mean_class_accuracy": 0.13919} +{"mode": "train", "epoch": 27, "iter": 100, "lr": 0.09275, "memory": 15990, "data_time": 1.32695, "top1_acc": 0.21484, "top5_acc": 0.4475, "loss_cls": 4.35468, "loss": 4.35468, "time": 2.03381} +{"mode": "train", "epoch": 27, "iter": 200, "lr": 0.09274, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22297, "top5_acc": 0.45875, "loss_cls": 4.35018, "loss": 4.35018, "time": 0.70386} +{"mode": "train", "epoch": 27, "iter": 300, "lr": 0.09272, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21531, "top5_acc": 0.44859, "loss_cls": 4.38593, "loss": 4.38593, "time": 0.70612} +{"mode": "train", "epoch": 27, "iter": 400, "lr": 0.09271, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20234, "top5_acc": 0.44828, "loss_cls": 4.41633, "loss": 4.41633, "time": 0.70213} +{"mode": "train", "epoch": 27, "iter": 500, "lr": 0.0927, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20859, "top5_acc": 0.43438, "loss_cls": 4.4132, "loss": 4.4132, "time": 0.69975} +{"mode": "train", "epoch": 27, "iter": 600, "lr": 0.09268, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21344, "top5_acc": 0.44125, "loss_cls": 4.38269, "loss": 4.38269, "time": 0.70309} +{"mode": "train", "epoch": 27, "iter": 700, "lr": 0.09267, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19859, "top5_acc": 0.44281, "loss_cls": 4.41449, "loss": 4.41449, "time": 0.70232} +{"mode": "train", "epoch": 27, "iter": 800, "lr": 0.09265, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21469, "top5_acc": 0.44578, "loss_cls": 4.35422, "loss": 4.35422, "time": 0.70049} +{"mode": "train", "epoch": 27, "iter": 900, "lr": 0.09264, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.19781, "top5_acc": 0.44484, "loss_cls": 4.44192, "loss": 4.44192, "time": 0.70223} +{"mode": "train", "epoch": 27, "iter": 1000, "lr": 0.09262, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20391, "top5_acc": 0.43641, "loss_cls": 4.42746, "loss": 4.42746, "time": 0.70187} +{"mode": "train", "epoch": 27, "iter": 1100, "lr": 0.09261, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20609, "top5_acc": 0.43578, "loss_cls": 4.41463, "loss": 4.41463, "time": 0.70286} +{"mode": "train", "epoch": 27, "iter": 1200, "lr": 0.09259, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20266, "top5_acc": 0.42484, "loss_cls": 4.42541, "loss": 4.42541, "time": 0.7017} +{"mode": "train", "epoch": 27, "iter": 1300, "lr": 0.09258, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2075, "top5_acc": 0.43891, "loss_cls": 4.42434, "loss": 4.42434, "time": 0.70045} +{"mode": "train", "epoch": 27, "iter": 1400, "lr": 0.09256, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20156, "top5_acc": 0.42188, "loss_cls": 4.43212, "loss": 4.43212, "time": 0.70248} +{"mode": "train", "epoch": 27, "iter": 1500, "lr": 0.09255, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21438, "top5_acc": 0.43781, "loss_cls": 4.41168, "loss": 4.41168, "time": 0.70895} +{"mode": "train", "epoch": 27, "iter": 1600, "lr": 0.09253, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20625, "top5_acc": 0.41609, "loss_cls": 4.44347, "loss": 4.44347, "time": 0.70065} +{"mode": "train", "epoch": 27, "iter": 1700, "lr": 0.09252, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20281, "top5_acc": 0.43984, "loss_cls": 4.39615, "loss": 4.39615, "time": 0.70347} +{"mode": "train", "epoch": 27, "iter": 1800, "lr": 0.09251, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21266, "top5_acc": 0.43203, "loss_cls": 4.41252, "loss": 4.41252, "time": 0.70385} +{"mode": "train", "epoch": 27, "iter": 1900, "lr": 0.09249, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20609, "top5_acc": 0.43656, "loss_cls": 4.43661, "loss": 4.43661, "time": 0.70196} +{"mode": "train", "epoch": 27, "iter": 2000, "lr": 0.09248, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22, "top5_acc": 0.44703, "loss_cls": 4.36228, "loss": 4.36228, "time": 0.70335} +{"mode": "train", "epoch": 27, "iter": 2100, "lr": 0.09246, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20578, "top5_acc": 0.44422, "loss_cls": 4.39162, "loss": 4.39162, "time": 0.69999} +{"mode": "train", "epoch": 27, "iter": 2200, "lr": 0.09245, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.20953, "top5_acc": 0.44016, "loss_cls": 4.40993, "loss": 4.40993, "time": 0.69927} +{"mode": "train", "epoch": 27, "iter": 2300, "lr": 0.09243, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21172, "top5_acc": 0.44156, "loss_cls": 4.40005, "loss": 4.40005, "time": 0.69949} +{"mode": "train", "epoch": 27, "iter": 2400, "lr": 0.09242, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21062, "top5_acc": 0.44359, "loss_cls": 4.39301, "loss": 4.39301, "time": 0.69964} +{"mode": "train", "epoch": 27, "iter": 2500, "lr": 0.0924, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21406, "top5_acc": 0.43031, "loss_cls": 4.41183, "loss": 4.41183, "time": 0.69925} +{"mode": "train", "epoch": 27, "iter": 2600, "lr": 0.09239, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20766, "top5_acc": 0.43672, "loss_cls": 4.43136, "loss": 4.43136, "time": 0.7003} +{"mode": "train", "epoch": 27, "iter": 2700, "lr": 0.09237, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21188, "top5_acc": 0.43938, "loss_cls": 4.41221, "loss": 4.41221, "time": 0.69758} +{"mode": "train", "epoch": 27, "iter": 2800, "lr": 0.09236, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19922, "top5_acc": 0.43953, "loss_cls": 4.43034, "loss": 4.43034, "time": 0.69991} +{"mode": "train", "epoch": 27, "iter": 2900, "lr": 0.09234, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20797, "top5_acc": 0.43906, "loss_cls": 4.43209, "loss": 4.43209, "time": 0.6993} +{"mode": "train", "epoch": 27, "iter": 3000, "lr": 0.09233, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21688, "top5_acc": 0.44438, "loss_cls": 4.38473, "loss": 4.38473, "time": 0.69766} +{"mode": "train", "epoch": 27, "iter": 3100, "lr": 0.09231, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21156, "top5_acc": 0.43922, "loss_cls": 4.39943, "loss": 4.39943, "time": 0.70058} +{"mode": "train", "epoch": 27, "iter": 3200, "lr": 0.0923, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20422, "top5_acc": 0.44047, "loss_cls": 4.41035, "loss": 4.41035, "time": 0.7056} +{"mode": "train", "epoch": 27, "iter": 3300, "lr": 0.09228, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20719, "top5_acc": 0.445, "loss_cls": 4.38485, "loss": 4.38485, "time": 0.7117} +{"mode": "train", "epoch": 27, "iter": 3400, "lr": 0.09227, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20312, "top5_acc": 0.42656, "loss_cls": 4.43891, "loss": 4.43891, "time": 0.70614} +{"mode": "train", "epoch": 27, "iter": 3500, "lr": 0.09225, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22016, "top5_acc": 0.45922, "loss_cls": 4.33981, "loss": 4.33981, "time": 0.70514} +{"mode": "train", "epoch": 27, "iter": 3600, "lr": 0.09224, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.19609, "top5_acc": 0.42516, "loss_cls": 4.47881, "loss": 4.47881, "time": 0.70662} +{"mode": "train", "epoch": 27, "iter": 3700, "lr": 0.09222, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20969, "top5_acc": 0.44297, "loss_cls": 4.4012, "loss": 4.4012, "time": 0.70229} +{"mode": "val", "epoch": 27, "iter": 309, "lr": 0.09222, "top1_acc": 0.14891, "top5_acc": 0.34331, "mean_class_accuracy": 0.14885} +{"mode": "train", "epoch": 28, "iter": 100, "lr": 0.0922, "memory": 15990, "data_time": 1.31327, "top1_acc": 0.22016, "top5_acc": 0.45578, "loss_cls": 4.29975, "loss": 4.29975, "time": 2.01854} +{"mode": "train", "epoch": 28, "iter": 200, "lr": 0.09219, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21344, "top5_acc": 0.44344, "loss_cls": 4.38186, "loss": 4.38186, "time": 0.70373} +{"mode": "train", "epoch": 28, "iter": 300, "lr": 0.09217, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19906, "top5_acc": 0.44219, "loss_cls": 4.38857, "loss": 4.38857, "time": 0.70192} +{"mode": "train", "epoch": 28, "iter": 400, "lr": 0.09216, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20609, "top5_acc": 0.44047, "loss_cls": 4.41662, "loss": 4.41662, "time": 0.70046} +{"mode": "train", "epoch": 28, "iter": 500, "lr": 0.09214, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21219, "top5_acc": 0.44031, "loss_cls": 4.38657, "loss": 4.38657, "time": 0.69986} +{"mode": "train", "epoch": 28, "iter": 600, "lr": 0.09213, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.20844, "top5_acc": 0.43922, "loss_cls": 4.41274, "loss": 4.41274, "time": 0.70006} +{"mode": "train", "epoch": 28, "iter": 700, "lr": 0.09211, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20859, "top5_acc": 0.43203, "loss_cls": 4.40667, "loss": 4.40667, "time": 0.69981} +{"mode": "train", "epoch": 28, "iter": 800, "lr": 0.0921, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21484, "top5_acc": 0.44281, "loss_cls": 4.37859, "loss": 4.37859, "time": 0.69962} +{"mode": "train", "epoch": 28, "iter": 900, "lr": 0.09208, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21234, "top5_acc": 0.44688, "loss_cls": 4.39163, "loss": 4.39163, "time": 0.7013} +{"mode": "train", "epoch": 28, "iter": 1000, "lr": 0.09207, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20781, "top5_acc": 0.43641, "loss_cls": 4.44172, "loss": 4.44172, "time": 0.70088} +{"mode": "train", "epoch": 28, "iter": 1100, "lr": 0.09205, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20422, "top5_acc": 0.44734, "loss_cls": 4.39357, "loss": 4.39357, "time": 0.69958} +{"mode": "train", "epoch": 28, "iter": 1200, "lr": 0.09204, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.205, "top5_acc": 0.43188, "loss_cls": 4.42451, "loss": 4.42451, "time": 0.70075} +{"mode": "train", "epoch": 28, "iter": 1300, "lr": 0.09202, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.19359, "top5_acc": 0.43391, "loss_cls": 4.41947, "loss": 4.41947, "time": 0.69921} +{"mode": "train", "epoch": 28, "iter": 1400, "lr": 0.09201, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20859, "top5_acc": 0.44156, "loss_cls": 4.35993, "loss": 4.35993, "time": 0.69857} +{"mode": "train", "epoch": 28, "iter": 1500, "lr": 0.09199, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2075, "top5_acc": 0.44391, "loss_cls": 4.39329, "loss": 4.39329, "time": 0.70497} +{"mode": "train", "epoch": 28, "iter": 1600, "lr": 0.09198, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20797, "top5_acc": 0.44984, "loss_cls": 4.386, "loss": 4.386, "time": 0.70585} +{"mode": "train", "epoch": 28, "iter": 1700, "lr": 0.09196, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20516, "top5_acc": 0.4325, "loss_cls": 4.44513, "loss": 4.44513, "time": 0.70508} +{"mode": "train", "epoch": 28, "iter": 1800, "lr": 0.09194, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20969, "top5_acc": 0.43266, "loss_cls": 4.39814, "loss": 4.39814, "time": 0.70413} +{"mode": "train", "epoch": 28, "iter": 1900, "lr": 0.09193, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21391, "top5_acc": 0.44609, "loss_cls": 4.37386, "loss": 4.37386, "time": 0.70022} +{"mode": "train", "epoch": 28, "iter": 2000, "lr": 0.09191, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20844, "top5_acc": 0.43875, "loss_cls": 4.40176, "loss": 4.40176, "time": 0.70081} +{"mode": "train", "epoch": 28, "iter": 2100, "lr": 0.0919, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20953, "top5_acc": 0.43719, "loss_cls": 4.41897, "loss": 4.41897, "time": 0.6998} +{"mode": "train", "epoch": 28, "iter": 2200, "lr": 0.09188, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21453, "top5_acc": 0.43922, "loss_cls": 4.40816, "loss": 4.40816, "time": 0.69941} +{"mode": "train", "epoch": 28, "iter": 2300, "lr": 0.09187, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21031, "top5_acc": 0.43891, "loss_cls": 4.4046, "loss": 4.4046, "time": 0.69956} +{"mode": "train", "epoch": 28, "iter": 2400, "lr": 0.09185, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21922, "top5_acc": 0.44141, "loss_cls": 4.38106, "loss": 4.38106, "time": 0.69983} +{"mode": "train", "epoch": 28, "iter": 2500, "lr": 0.09184, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21281, "top5_acc": 0.43906, "loss_cls": 4.41414, "loss": 4.41414, "time": 0.70039} +{"mode": "train", "epoch": 28, "iter": 2600, "lr": 0.09182, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20422, "top5_acc": 0.43719, "loss_cls": 4.39982, "loss": 4.39982, "time": 0.70145} +{"mode": "train", "epoch": 28, "iter": 2700, "lr": 0.09181, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20344, "top5_acc": 0.43672, "loss_cls": 4.40555, "loss": 4.40555, "time": 0.69883} +{"mode": "train", "epoch": 28, "iter": 2800, "lr": 0.09179, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21109, "top5_acc": 0.43938, "loss_cls": 4.4052, "loss": 4.4052, "time": 0.70143} +{"mode": "train", "epoch": 28, "iter": 2900, "lr": 0.09178, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20625, "top5_acc": 0.43703, "loss_cls": 4.40993, "loss": 4.40993, "time": 0.70104} +{"mode": "train", "epoch": 28, "iter": 3000, "lr": 0.09176, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20047, "top5_acc": 0.43609, "loss_cls": 4.45223, "loss": 4.45223, "time": 0.70013} +{"mode": "train", "epoch": 28, "iter": 3100, "lr": 0.09175, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21078, "top5_acc": 0.44812, "loss_cls": 4.41955, "loss": 4.41955, "time": 0.69822} +{"mode": "train", "epoch": 28, "iter": 3200, "lr": 0.09173, "memory": 15990, "data_time": 0.00053, "top1_acc": 0.21969, "top5_acc": 0.44859, "loss_cls": 4.36921, "loss": 4.36921, "time": 0.70738} +{"mode": "train", "epoch": 28, "iter": 3300, "lr": 0.09172, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20734, "top5_acc": 0.43578, "loss_cls": 4.44296, "loss": 4.44296, "time": 0.71001} +{"mode": "train", "epoch": 28, "iter": 3400, "lr": 0.0917, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20406, "top5_acc": 0.43547, "loss_cls": 4.42991, "loss": 4.42991, "time": 0.70638} +{"mode": "train", "epoch": 28, "iter": 3500, "lr": 0.09168, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20969, "top5_acc": 0.43906, "loss_cls": 4.42521, "loss": 4.42521, "time": 0.7052} +{"mode": "train", "epoch": 28, "iter": 3600, "lr": 0.09167, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21, "top5_acc": 0.43375, "loss_cls": 4.42299, "loss": 4.42299, "time": 0.7096} +{"mode": "train", "epoch": 28, "iter": 3700, "lr": 0.09165, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19688, "top5_acc": 0.42859, "loss_cls": 4.43317, "loss": 4.43317, "time": 0.70023} +{"mode": "val", "epoch": 28, "iter": 309, "lr": 0.09165, "top1_acc": 0.1359, "top5_acc": 0.33075, "mean_class_accuracy": 0.13571} +{"mode": "train", "epoch": 29, "iter": 100, "lr": 0.09163, "memory": 15990, "data_time": 1.27552, "top1_acc": 0.21641, "top5_acc": 0.45109, "loss_cls": 4.36633, "loss": 4.36633, "time": 1.97781} +{"mode": "train", "epoch": 29, "iter": 200, "lr": 0.09162, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20688, "top5_acc": 0.44344, "loss_cls": 4.39962, "loss": 4.39962, "time": 0.69967} +{"mode": "train", "epoch": 29, "iter": 300, "lr": 0.0916, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20719, "top5_acc": 0.44688, "loss_cls": 4.38484, "loss": 4.38484, "time": 0.70092} +{"mode": "train", "epoch": 29, "iter": 400, "lr": 0.09158, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21188, "top5_acc": 0.44938, "loss_cls": 4.38413, "loss": 4.38413, "time": 0.7022} +{"mode": "train", "epoch": 29, "iter": 500, "lr": 0.09157, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21031, "top5_acc": 0.44328, "loss_cls": 4.36796, "loss": 4.36796, "time": 0.69934} +{"mode": "train", "epoch": 29, "iter": 600, "lr": 0.09155, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.2125, "top5_acc": 0.44578, "loss_cls": 4.3821, "loss": 4.3821, "time": 0.70097} +{"mode": "train", "epoch": 29, "iter": 700, "lr": 0.09154, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21188, "top5_acc": 0.43531, "loss_cls": 4.40012, "loss": 4.40012, "time": 0.70033} +{"mode": "train", "epoch": 29, "iter": 800, "lr": 0.09152, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.21266, "top5_acc": 0.43562, "loss_cls": 4.41024, "loss": 4.41024, "time": 0.70034} +{"mode": "train", "epoch": 29, "iter": 900, "lr": 0.09151, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20109, "top5_acc": 0.43719, "loss_cls": 4.42581, "loss": 4.42581, "time": 0.70079} +{"mode": "train", "epoch": 29, "iter": 1000, "lr": 0.09149, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.20703, "top5_acc": 0.44109, "loss_cls": 4.38101, "loss": 4.38101, "time": 0.69902} +{"mode": "train", "epoch": 29, "iter": 1100, "lr": 0.09148, "memory": 15990, "data_time": 0.00021, "top1_acc": 0.19859, "top5_acc": 0.43297, "loss_cls": 4.45795, "loss": 4.45795, "time": 0.70007} +{"mode": "train", "epoch": 29, "iter": 1200, "lr": 0.09146, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21047, "top5_acc": 0.44219, "loss_cls": 4.41828, "loss": 4.41828, "time": 0.69859} +{"mode": "train", "epoch": 29, "iter": 1300, "lr": 0.09144, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21766, "top5_acc": 0.45422, "loss_cls": 4.32258, "loss": 4.32258, "time": 0.70201} +{"mode": "train", "epoch": 29, "iter": 1400, "lr": 0.09143, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21859, "top5_acc": 0.44922, "loss_cls": 4.36089, "loss": 4.36089, "time": 0.70435} +{"mode": "train", "epoch": 29, "iter": 1500, "lr": 0.09141, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20703, "top5_acc": 0.44391, "loss_cls": 4.40133, "loss": 4.40133, "time": 0.70474} +{"mode": "train", "epoch": 29, "iter": 1600, "lr": 0.0914, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21938, "top5_acc": 0.45328, "loss_cls": 4.35921, "loss": 4.35921, "time": 0.70249} +{"mode": "train", "epoch": 29, "iter": 1700, "lr": 0.09138, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.20953, "top5_acc": 0.43844, "loss_cls": 4.41299, "loss": 4.41299, "time": 0.69957} +{"mode": "train", "epoch": 29, "iter": 1800, "lr": 0.09137, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20891, "top5_acc": 0.44094, "loss_cls": 4.39469, "loss": 4.39469, "time": 0.70235} +{"mode": "train", "epoch": 29, "iter": 1900, "lr": 0.09135, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20828, "top5_acc": 0.43766, "loss_cls": 4.39109, "loss": 4.39109, "time": 0.70196} +{"mode": "train", "epoch": 29, "iter": 2000, "lr": 0.09133, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21781, "top5_acc": 0.43344, "loss_cls": 4.39781, "loss": 4.39781, "time": 0.69958} +{"mode": "train", "epoch": 29, "iter": 2100, "lr": 0.09132, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21469, "top5_acc": 0.43906, "loss_cls": 4.39621, "loss": 4.39621, "time": 0.70172} +{"mode": "train", "epoch": 29, "iter": 2200, "lr": 0.0913, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20516, "top5_acc": 0.43656, "loss_cls": 4.41423, "loss": 4.41423, "time": 0.699} +{"mode": "train", "epoch": 29, "iter": 2300, "lr": 0.09129, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21, "top5_acc": 0.42984, "loss_cls": 4.42019, "loss": 4.42019, "time": 0.70016} +{"mode": "train", "epoch": 29, "iter": 2400, "lr": 0.09127, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20891, "top5_acc": 0.43406, "loss_cls": 4.42479, "loss": 4.42479, "time": 0.69974} +{"mode": "train", "epoch": 29, "iter": 2500, "lr": 0.09126, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.19547, "top5_acc": 0.42312, "loss_cls": 4.47799, "loss": 4.47799, "time": 0.69879} +{"mode": "train", "epoch": 29, "iter": 2600, "lr": 0.09124, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21, "top5_acc": 0.43641, "loss_cls": 4.40706, "loss": 4.40706, "time": 0.69983} +{"mode": "train", "epoch": 29, "iter": 2700, "lr": 0.09122, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20812, "top5_acc": 0.43578, "loss_cls": 4.40905, "loss": 4.40905, "time": 0.69992} +{"mode": "train", "epoch": 29, "iter": 2800, "lr": 0.09121, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21344, "top5_acc": 0.44578, "loss_cls": 4.36334, "loss": 4.36334, "time": 0.70024} +{"mode": "train", "epoch": 29, "iter": 2900, "lr": 0.09119, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21156, "top5_acc": 0.44516, "loss_cls": 4.38552, "loss": 4.38552, "time": 0.69954} +{"mode": "train", "epoch": 29, "iter": 3000, "lr": 0.09118, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21062, "top5_acc": 0.44031, "loss_cls": 4.41386, "loss": 4.41386, "time": 0.69857} +{"mode": "train", "epoch": 29, "iter": 3100, "lr": 0.09116, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20672, "top5_acc": 0.43922, "loss_cls": 4.39625, "loss": 4.39625, "time": 0.7} +{"mode": "train", "epoch": 29, "iter": 3200, "lr": 0.09114, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20984, "top5_acc": 0.44016, "loss_cls": 4.39815, "loss": 4.39815, "time": 0.70598} +{"mode": "train", "epoch": 29, "iter": 3300, "lr": 0.09113, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20594, "top5_acc": 0.42828, "loss_cls": 4.42916, "loss": 4.42916, "time": 0.70886} +{"mode": "train", "epoch": 29, "iter": 3400, "lr": 0.09111, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20484, "top5_acc": 0.42297, "loss_cls": 4.4311, "loss": 4.4311, "time": 0.70573} +{"mode": "train", "epoch": 29, "iter": 3500, "lr": 0.0911, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20719, "top5_acc": 0.43922, "loss_cls": 4.42954, "loss": 4.42954, "time": 0.70382} +{"mode": "train", "epoch": 29, "iter": 3600, "lr": 0.09108, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20984, "top5_acc": 0.44047, "loss_cls": 4.41388, "loss": 4.41388, "time": 0.70652} +{"mode": "train", "epoch": 29, "iter": 3700, "lr": 0.09106, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.20641, "top5_acc": 0.44031, "loss_cls": 4.40141, "loss": 4.40141, "time": 0.69955} +{"mode": "val", "epoch": 29, "iter": 309, "lr": 0.09106, "top1_acc": 0.13443, "top5_acc": 0.32189, "mean_class_accuracy": 0.13437} +{"mode": "train", "epoch": 30, "iter": 100, "lr": 0.09104, "memory": 15990, "data_time": 1.35672, "top1_acc": 0.21344, "top5_acc": 0.45234, "loss_cls": 4.36448, "loss": 4.36448, "time": 2.17994} +{"mode": "train", "epoch": 30, "iter": 200, "lr": 0.09103, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22125, "top5_acc": 0.45297, "loss_cls": 4.32293, "loss": 4.32293, "time": 0.81618} +{"mode": "train", "epoch": 30, "iter": 300, "lr": 0.09101, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20859, "top5_acc": 0.43781, "loss_cls": 4.41512, "loss": 4.41512, "time": 0.81269} +{"mode": "train", "epoch": 30, "iter": 400, "lr": 0.09099, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20766, "top5_acc": 0.44312, "loss_cls": 4.40451, "loss": 4.40451, "time": 0.81462} +{"mode": "train", "epoch": 30, "iter": 500, "lr": 0.09098, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20422, "top5_acc": 0.43719, "loss_cls": 4.41274, "loss": 4.41274, "time": 0.81368} +{"mode": "train", "epoch": 30, "iter": 600, "lr": 0.09096, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21375, "top5_acc": 0.45391, "loss_cls": 4.34798, "loss": 4.34798, "time": 0.81207} +{"mode": "train", "epoch": 30, "iter": 700, "lr": 0.09095, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21188, "top5_acc": 0.44016, "loss_cls": 4.36577, "loss": 4.36577, "time": 0.81811} +{"mode": "train", "epoch": 30, "iter": 800, "lr": 0.09093, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20953, "top5_acc": 0.42734, "loss_cls": 4.43143, "loss": 4.43143, "time": 0.81481} +{"mode": "train", "epoch": 30, "iter": 900, "lr": 0.09091, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21, "top5_acc": 0.45578, "loss_cls": 4.36, "loss": 4.36, "time": 0.82011} +{"mode": "train", "epoch": 30, "iter": 1000, "lr": 0.0909, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.19922, "top5_acc": 0.43047, "loss_cls": 4.44728, "loss": 4.44728, "time": 0.8216} +{"mode": "train", "epoch": 30, "iter": 1100, "lr": 0.09088, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21375, "top5_acc": 0.44688, "loss_cls": 4.37013, "loss": 4.37013, "time": 0.82169} +{"mode": "train", "epoch": 30, "iter": 1200, "lr": 0.09087, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20516, "top5_acc": 0.43234, "loss_cls": 4.42939, "loss": 4.42939, "time": 0.81301} +{"mode": "train", "epoch": 30, "iter": 1300, "lr": 0.09085, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21688, "top5_acc": 0.43891, "loss_cls": 4.41869, "loss": 4.41869, "time": 0.80988} +{"mode": "train", "epoch": 30, "iter": 1400, "lr": 0.09083, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20391, "top5_acc": 0.43578, "loss_cls": 4.43487, "loss": 4.43487, "time": 0.81105} +{"mode": "train", "epoch": 30, "iter": 1500, "lr": 0.09082, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22109, "top5_acc": 0.45625, "loss_cls": 4.32122, "loss": 4.32122, "time": 0.81166} +{"mode": "train", "epoch": 30, "iter": 1600, "lr": 0.0908, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22672, "top5_acc": 0.44422, "loss_cls": 4.34386, "loss": 4.34386, "time": 0.81344} +{"mode": "train", "epoch": 30, "iter": 1700, "lr": 0.09078, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21453, "top5_acc": 0.45422, "loss_cls": 4.35949, "loss": 4.35949, "time": 0.81104} +{"mode": "train", "epoch": 30, "iter": 1800, "lr": 0.09077, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20531, "top5_acc": 0.43062, "loss_cls": 4.42447, "loss": 4.42447, "time": 0.81739} +{"mode": "train", "epoch": 30, "iter": 1900, "lr": 0.09075, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21328, "top5_acc": 0.44156, "loss_cls": 4.37607, "loss": 4.37607, "time": 0.81465} +{"mode": "train", "epoch": 30, "iter": 2000, "lr": 0.09074, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20203, "top5_acc": 0.43609, "loss_cls": 4.42063, "loss": 4.42063, "time": 0.82096} +{"mode": "train", "epoch": 30, "iter": 2100, "lr": 0.09072, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20781, "top5_acc": 0.44047, "loss_cls": 4.40711, "loss": 4.40711, "time": 0.82539} +{"mode": "train", "epoch": 30, "iter": 2200, "lr": 0.0907, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21844, "top5_acc": 0.44047, "loss_cls": 4.37828, "loss": 4.37828, "time": 0.8107} +{"mode": "train", "epoch": 30, "iter": 2300, "lr": 0.09069, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21188, "top5_acc": 0.45156, "loss_cls": 4.36588, "loss": 4.36588, "time": 0.8176} +{"mode": "train", "epoch": 30, "iter": 2400, "lr": 0.09067, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21203, "top5_acc": 0.43484, "loss_cls": 4.42104, "loss": 4.42104, "time": 0.8116} +{"mode": "train", "epoch": 30, "iter": 2500, "lr": 0.09065, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21391, "top5_acc": 0.45109, "loss_cls": 4.38874, "loss": 4.38874, "time": 0.81249} +{"mode": "train", "epoch": 30, "iter": 2600, "lr": 0.09064, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20141, "top5_acc": 0.43406, "loss_cls": 4.43266, "loss": 4.43266, "time": 0.81719} +{"mode": "train", "epoch": 30, "iter": 2700, "lr": 0.09062, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21734, "top5_acc": 0.44891, "loss_cls": 4.38222, "loss": 4.38222, "time": 0.81433} +{"mode": "train", "epoch": 30, "iter": 2800, "lr": 0.09061, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20766, "top5_acc": 0.44359, "loss_cls": 4.39766, "loss": 4.39766, "time": 0.80684} +{"mode": "train", "epoch": 30, "iter": 2900, "lr": 0.09059, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21172, "top5_acc": 0.44109, "loss_cls": 4.39088, "loss": 4.39088, "time": 0.80083} +{"mode": "train", "epoch": 30, "iter": 3000, "lr": 0.09057, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21328, "top5_acc": 0.44797, "loss_cls": 4.36102, "loss": 4.36102, "time": 0.80667} +{"mode": "train", "epoch": 30, "iter": 3100, "lr": 0.09056, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20859, "top5_acc": 0.43984, "loss_cls": 4.39042, "loss": 4.39042, "time": 0.80492} +{"mode": "train", "epoch": 30, "iter": 3200, "lr": 0.09054, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20875, "top5_acc": 0.43719, "loss_cls": 4.41258, "loss": 4.41258, "time": 0.81493} +{"mode": "train", "epoch": 30, "iter": 3300, "lr": 0.09052, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21203, "top5_acc": 0.44234, "loss_cls": 4.40431, "loss": 4.40431, "time": 0.80796} +{"mode": "train", "epoch": 30, "iter": 3400, "lr": 0.09051, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22, "top5_acc": 0.45062, "loss_cls": 4.38089, "loss": 4.38089, "time": 0.8034} +{"mode": "train", "epoch": 30, "iter": 3500, "lr": 0.09049, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21234, "top5_acc": 0.44484, "loss_cls": 4.40213, "loss": 4.40213, "time": 0.81103} +{"mode": "train", "epoch": 30, "iter": 3600, "lr": 0.09047, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21406, "top5_acc": 0.4375, "loss_cls": 4.38358, "loss": 4.38358, "time": 0.80492} +{"mode": "train", "epoch": 30, "iter": 3700, "lr": 0.09046, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21203, "top5_acc": 0.43469, "loss_cls": 4.40728, "loss": 4.40728, "time": 0.80624} +{"mode": "val", "epoch": 30, "iter": 309, "lr": 0.09045, "top1_acc": 0.1556, "top5_acc": 0.35917, "mean_class_accuracy": 0.1555} +{"mode": "train", "epoch": 31, "iter": 100, "lr": 0.09043, "memory": 15990, "data_time": 1.31727, "top1_acc": 0.22156, "top5_acc": 0.45703, "loss_cls": 4.58929, "loss": 4.58929, "time": 2.29832} +{"mode": "train", "epoch": 31, "iter": 200, "lr": 0.09042, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21438, "top5_acc": 0.46031, "loss_cls": 4.58561, "loss": 4.58561, "time": 0.82158} +{"mode": "train", "epoch": 31, "iter": 300, "lr": 0.0904, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21391, "top5_acc": 0.44344, "loss_cls": 4.63929, "loss": 4.63929, "time": 0.81483} +{"mode": "train", "epoch": 31, "iter": 400, "lr": 0.09039, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21656, "top5_acc": 0.44984, "loss_cls": 4.60121, "loss": 4.60121, "time": 0.8193} +{"mode": "train", "epoch": 31, "iter": 500, "lr": 0.09037, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21578, "top5_acc": 0.44328, "loss_cls": 4.63114, "loss": 4.63114, "time": 0.81846} +{"mode": "train", "epoch": 31, "iter": 600, "lr": 0.09035, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20906, "top5_acc": 0.43797, "loss_cls": 4.62678, "loss": 4.62678, "time": 0.81574} +{"mode": "train", "epoch": 31, "iter": 700, "lr": 0.09034, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20984, "top5_acc": 0.45031, "loss_cls": 4.61921, "loss": 4.61921, "time": 0.82194} +{"mode": "train", "epoch": 31, "iter": 800, "lr": 0.09032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21344, "top5_acc": 0.44375, "loss_cls": 4.61323, "loss": 4.61323, "time": 0.81998} +{"mode": "train", "epoch": 31, "iter": 900, "lr": 0.0903, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21672, "top5_acc": 0.44875, "loss_cls": 4.60447, "loss": 4.60447, "time": 0.81579} +{"mode": "train", "epoch": 31, "iter": 1000, "lr": 0.09029, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21281, "top5_acc": 0.44375, "loss_cls": 4.63407, "loss": 4.63407, "time": 0.81506} +{"mode": "train", "epoch": 31, "iter": 1100, "lr": 0.09027, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21094, "top5_acc": 0.44484, "loss_cls": 4.64748, "loss": 4.64748, "time": 0.81921} +{"mode": "train", "epoch": 31, "iter": 1200, "lr": 0.09025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21469, "top5_acc": 0.44797, "loss_cls": 4.60894, "loss": 4.60894, "time": 0.81609} +{"mode": "train", "epoch": 31, "iter": 1300, "lr": 0.09024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21094, "top5_acc": 0.44922, "loss_cls": 4.63618, "loss": 4.63618, "time": 0.81753} +{"mode": "train", "epoch": 31, "iter": 1400, "lr": 0.09022, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21328, "top5_acc": 0.43125, "loss_cls": 4.64579, "loss": 4.64579, "time": 0.81641} +{"mode": "train", "epoch": 31, "iter": 1500, "lr": 0.0902, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.215, "top5_acc": 0.44938, "loss_cls": 4.59827, "loss": 4.59827, "time": 0.82156} +{"mode": "train", "epoch": 31, "iter": 1600, "lr": 0.09019, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19781, "top5_acc": 0.42625, "loss_cls": 4.70008, "loss": 4.70008, "time": 0.8205} +{"mode": "train", "epoch": 31, "iter": 1700, "lr": 0.09017, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21391, "top5_acc": 0.44062, "loss_cls": 4.60298, "loss": 4.60298, "time": 0.82429} +{"mode": "train", "epoch": 31, "iter": 1800, "lr": 0.09015, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20844, "top5_acc": 0.43406, "loss_cls": 4.67779, "loss": 4.67779, "time": 0.82219} +{"mode": "train", "epoch": 31, "iter": 1900, "lr": 0.09014, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.20016, "top5_acc": 0.44016, "loss_cls": 4.66419, "loss": 4.66419, "time": 0.82335} +{"mode": "train", "epoch": 31, "iter": 2000, "lr": 0.09012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21641, "top5_acc": 0.4475, "loss_cls": 4.58809, "loss": 4.58809, "time": 0.82331} +{"mode": "train", "epoch": 31, "iter": 2100, "lr": 0.0901, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21859, "top5_acc": 0.44844, "loss_cls": 4.59081, "loss": 4.59081, "time": 0.82894} +{"mode": "train", "epoch": 31, "iter": 2200, "lr": 0.09009, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21297, "top5_acc": 0.44406, "loss_cls": 4.61496, "loss": 4.61496, "time": 0.82346} +{"mode": "train", "epoch": 31, "iter": 2300, "lr": 0.09007, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20906, "top5_acc": 0.44156, "loss_cls": 4.64315, "loss": 4.64315, "time": 0.81805} +{"mode": "train", "epoch": 31, "iter": 2400, "lr": 0.09005, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21906, "top5_acc": 0.45078, "loss_cls": 4.58648, "loss": 4.58648, "time": 0.82021} +{"mode": "train", "epoch": 31, "iter": 2500, "lr": 0.09004, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20625, "top5_acc": 0.44188, "loss_cls": 4.66251, "loss": 4.66251, "time": 0.81739} +{"mode": "train", "epoch": 31, "iter": 2600, "lr": 0.09002, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2075, "top5_acc": 0.44578, "loss_cls": 4.62338, "loss": 4.62338, "time": 0.81797} +{"mode": "train", "epoch": 31, "iter": 2700, "lr": 0.09, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20812, "top5_acc": 0.44109, "loss_cls": 4.62977, "loss": 4.62977, "time": 0.81975} +{"mode": "train", "epoch": 31, "iter": 2800, "lr": 0.08999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21047, "top5_acc": 0.43375, "loss_cls": 4.65663, "loss": 4.65663, "time": 0.82131} +{"mode": "train", "epoch": 31, "iter": 2900, "lr": 0.08997, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21031, "top5_acc": 0.44547, "loss_cls": 4.62668, "loss": 4.62668, "time": 0.81883} +{"mode": "train", "epoch": 31, "iter": 3000, "lr": 0.08995, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.19812, "top5_acc": 0.4425, "loss_cls": 4.67038, "loss": 4.67038, "time": 0.81761} +{"mode": "train", "epoch": 31, "iter": 3100, "lr": 0.08994, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21625, "top5_acc": 0.44609, "loss_cls": 4.60628, "loss": 4.60628, "time": 0.82165} +{"mode": "train", "epoch": 31, "iter": 3200, "lr": 0.08992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21016, "top5_acc": 0.43875, "loss_cls": 4.63156, "loss": 4.63156, "time": 0.8272} +{"mode": "train", "epoch": 31, "iter": 3300, "lr": 0.0899, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.215, "top5_acc": 0.43656, "loss_cls": 4.63, "loss": 4.63, "time": 0.82308} +{"mode": "train", "epoch": 31, "iter": 3400, "lr": 0.08989, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21031, "top5_acc": 0.445, "loss_cls": 4.63991, "loss": 4.63991, "time": 0.8263} +{"mode": "train", "epoch": 31, "iter": 3500, "lr": 0.08987, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21656, "top5_acc": 0.44688, "loss_cls": 4.62452, "loss": 4.62452, "time": 0.8235} +{"mode": "train", "epoch": 31, "iter": 3600, "lr": 0.08985, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20469, "top5_acc": 0.44219, "loss_cls": 4.64141, "loss": 4.64141, "time": 0.82179} +{"mode": "train", "epoch": 31, "iter": 3700, "lr": 0.08983, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20828, "top5_acc": 0.45125, "loss_cls": 4.61299, "loss": 4.61299, "time": 0.81993} +{"mode": "val", "epoch": 31, "iter": 309, "lr": 0.08983, "top1_acc": 0.15854, "top5_acc": 0.36762, "mean_class_accuracy": 0.15848} +{"mode": "train", "epoch": 32, "iter": 100, "lr": 0.08981, "memory": 15990, "data_time": 1.27431, "top1_acc": 0.22234, "top5_acc": 0.45656, "loss_cls": 4.58197, "loss": 4.58197, "time": 2.25542} +{"mode": "train", "epoch": 32, "iter": 200, "lr": 0.08979, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21344, "top5_acc": 0.45062, "loss_cls": 4.59579, "loss": 4.59579, "time": 0.82056} +{"mode": "train", "epoch": 32, "iter": 300, "lr": 0.08978, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21844, "top5_acc": 0.44453, "loss_cls": 4.6158, "loss": 4.6158, "time": 0.81934} +{"mode": "train", "epoch": 32, "iter": 400, "lr": 0.08976, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22234, "top5_acc": 0.45859, "loss_cls": 4.56022, "loss": 4.56022, "time": 0.81825} +{"mode": "train", "epoch": 32, "iter": 500, "lr": 0.08974, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20547, "top5_acc": 0.44188, "loss_cls": 4.61629, "loss": 4.61629, "time": 0.81498} +{"mode": "train", "epoch": 32, "iter": 600, "lr": 0.08973, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20875, "top5_acc": 0.43391, "loss_cls": 4.66678, "loss": 4.66678, "time": 0.81755} +{"mode": "train", "epoch": 32, "iter": 700, "lr": 0.08971, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21281, "top5_acc": 0.44906, "loss_cls": 4.60728, "loss": 4.60728, "time": 0.81593} +{"mode": "train", "epoch": 32, "iter": 800, "lr": 0.08969, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21844, "top5_acc": 0.44188, "loss_cls": 4.61884, "loss": 4.61884, "time": 0.81768} +{"mode": "train", "epoch": 32, "iter": 900, "lr": 0.08967, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21266, "top5_acc": 0.44672, "loss_cls": 4.61237, "loss": 4.61237, "time": 0.81713} +{"mode": "train", "epoch": 32, "iter": 1000, "lr": 0.08966, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21203, "top5_acc": 0.44641, "loss_cls": 4.61546, "loss": 4.61546, "time": 0.82255} +{"mode": "train", "epoch": 32, "iter": 1100, "lr": 0.08964, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20469, "top5_acc": 0.43719, "loss_cls": 4.62305, "loss": 4.62305, "time": 0.82073} +{"mode": "train", "epoch": 32, "iter": 1200, "lr": 0.08962, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20922, "top5_acc": 0.44719, "loss_cls": 4.63157, "loss": 4.63157, "time": 0.82294} +{"mode": "train", "epoch": 32, "iter": 1300, "lr": 0.08961, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21359, "top5_acc": 0.44734, "loss_cls": 4.62056, "loss": 4.62056, "time": 0.82328} +{"mode": "train", "epoch": 32, "iter": 1400, "lr": 0.08959, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20922, "top5_acc": 0.44234, "loss_cls": 4.62733, "loss": 4.62733, "time": 0.82093} +{"mode": "train", "epoch": 32, "iter": 1500, "lr": 0.08957, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21859, "top5_acc": 0.45484, "loss_cls": 4.58757, "loss": 4.58757, "time": 0.82139} +{"mode": "train", "epoch": 32, "iter": 1600, "lr": 0.08955, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20938, "top5_acc": 0.44391, "loss_cls": 4.6155, "loss": 4.6155, "time": 0.82158} +{"mode": "train", "epoch": 32, "iter": 1700, "lr": 0.08954, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.20266, "top5_acc": 0.44344, "loss_cls": 4.63048, "loss": 4.63048, "time": 0.82635} +{"mode": "train", "epoch": 32, "iter": 1800, "lr": 0.08952, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22609, "top5_acc": 0.45328, "loss_cls": 4.56461, "loss": 4.56461, "time": 0.8164} +{"mode": "train", "epoch": 32, "iter": 1900, "lr": 0.0895, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21547, "top5_acc": 0.45188, "loss_cls": 4.59398, "loss": 4.59398, "time": 0.82356} +{"mode": "train", "epoch": 32, "iter": 2000, "lr": 0.08949, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21484, "top5_acc": 0.44219, "loss_cls": 4.63971, "loss": 4.63971, "time": 0.81908} +{"mode": "train", "epoch": 32, "iter": 2100, "lr": 0.08947, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21578, "top5_acc": 0.44391, "loss_cls": 4.61336, "loss": 4.61336, "time": 0.81745} +{"mode": "train", "epoch": 32, "iter": 2200, "lr": 0.08945, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21078, "top5_acc": 0.44172, "loss_cls": 4.66323, "loss": 4.66323, "time": 0.82158} +{"mode": "train", "epoch": 32, "iter": 2300, "lr": 0.08943, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21141, "top5_acc": 0.44359, "loss_cls": 4.6473, "loss": 4.6473, "time": 0.82137} +{"mode": "train", "epoch": 32, "iter": 2400, "lr": 0.08942, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20906, "top5_acc": 0.44953, "loss_cls": 4.62315, "loss": 4.62315, "time": 0.81938} +{"mode": "train", "epoch": 32, "iter": 2500, "lr": 0.0894, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20656, "top5_acc": 0.43703, "loss_cls": 4.62544, "loss": 4.62544, "time": 0.82164} +{"mode": "train", "epoch": 32, "iter": 2600, "lr": 0.08938, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20516, "top5_acc": 0.43141, "loss_cls": 4.6665, "loss": 4.6665, "time": 0.82478} +{"mode": "train", "epoch": 32, "iter": 2700, "lr": 0.08937, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20734, "top5_acc": 0.44172, "loss_cls": 4.63355, "loss": 4.63355, "time": 0.82281} +{"mode": "train", "epoch": 32, "iter": 2800, "lr": 0.08935, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21766, "top5_acc": 0.45391, "loss_cls": 4.59114, "loss": 4.59114, "time": 0.82088} +{"mode": "train", "epoch": 32, "iter": 2900, "lr": 0.08933, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2075, "top5_acc": 0.44625, "loss_cls": 4.64822, "loss": 4.64822, "time": 0.81415} +{"mode": "train", "epoch": 32, "iter": 3000, "lr": 0.08931, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21078, "top5_acc": 0.45594, "loss_cls": 4.60496, "loss": 4.60496, "time": 0.81759} +{"mode": "train", "epoch": 32, "iter": 3100, "lr": 0.0893, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20875, "top5_acc": 0.44703, "loss_cls": 4.61466, "loss": 4.61466, "time": 0.8235} +{"mode": "train", "epoch": 32, "iter": 3200, "lr": 0.08928, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21203, "top5_acc": 0.435, "loss_cls": 4.6346, "loss": 4.6346, "time": 0.82215} +{"mode": "train", "epoch": 32, "iter": 3300, "lr": 0.08926, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20969, "top5_acc": 0.43969, "loss_cls": 4.62447, "loss": 4.62447, "time": 0.83024} +{"mode": "train", "epoch": 32, "iter": 3400, "lr": 0.08924, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21906, "top5_acc": 0.44797, "loss_cls": 4.61162, "loss": 4.61162, "time": 0.82206} +{"mode": "train", "epoch": 32, "iter": 3500, "lr": 0.08923, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21656, "top5_acc": 0.44734, "loss_cls": 4.5825, "loss": 4.5825, "time": 0.82027} +{"mode": "train", "epoch": 32, "iter": 3600, "lr": 0.08921, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.21219, "top5_acc": 0.43328, "loss_cls": 4.6536, "loss": 4.6536, "time": 0.83078} +{"mode": "train", "epoch": 32, "iter": 3700, "lr": 0.08919, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21188, "top5_acc": 0.44719, "loss_cls": 4.61807, "loss": 4.61807, "time": 0.81562} +{"mode": "val", "epoch": 32, "iter": 309, "lr": 0.08918, "top1_acc": 0.1325, "top5_acc": 0.32604, "mean_class_accuracy": 0.13229} +{"mode": "train", "epoch": 33, "iter": 100, "lr": 0.08917, "memory": 15990, "data_time": 1.26432, "top1_acc": 0.22062, "top5_acc": 0.44812, "loss_cls": 4.5677, "loss": 4.5677, "time": 2.24794} +{"mode": "train", "epoch": 33, "iter": 200, "lr": 0.08915, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21062, "top5_acc": 0.44297, "loss_cls": 4.60273, "loss": 4.60273, "time": 0.82067} +{"mode": "train", "epoch": 33, "iter": 300, "lr": 0.08913, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21859, "top5_acc": 0.44297, "loss_cls": 4.60332, "loss": 4.60332, "time": 0.81738} +{"mode": "train", "epoch": 33, "iter": 400, "lr": 0.08912, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21375, "top5_acc": 0.45156, "loss_cls": 4.61216, "loss": 4.61216, "time": 0.81605} +{"mode": "train", "epoch": 33, "iter": 500, "lr": 0.0891, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20422, "top5_acc": 0.44406, "loss_cls": 4.61243, "loss": 4.61243, "time": 0.81501} +{"mode": "train", "epoch": 33, "iter": 600, "lr": 0.08908, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.21672, "top5_acc": 0.43984, "loss_cls": 4.62759, "loss": 4.62759, "time": 0.82026} +{"mode": "train", "epoch": 33, "iter": 700, "lr": 0.08906, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21906, "top5_acc": 0.44906, "loss_cls": 4.58305, "loss": 4.58305, "time": 0.81978} +{"mode": "train", "epoch": 33, "iter": 800, "lr": 0.08905, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21547, "top5_acc": 0.44391, "loss_cls": 4.61842, "loss": 4.61842, "time": 0.81679} +{"mode": "train", "epoch": 33, "iter": 900, "lr": 0.08903, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21656, "top5_acc": 0.44562, "loss_cls": 4.59771, "loss": 4.59771, "time": 0.81763} +{"mode": "train", "epoch": 33, "iter": 1000, "lr": 0.08901, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21438, "top5_acc": 0.43859, "loss_cls": 4.63429, "loss": 4.63429, "time": 0.81538} +{"mode": "train", "epoch": 33, "iter": 1100, "lr": 0.08899, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21703, "top5_acc": 0.44188, "loss_cls": 4.64462, "loss": 4.64462, "time": 0.81547} +{"mode": "train", "epoch": 33, "iter": 1200, "lr": 0.08898, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21703, "top5_acc": 0.44656, "loss_cls": 4.60157, "loss": 4.60157, "time": 0.82189} +{"mode": "train", "epoch": 33, "iter": 1300, "lr": 0.08896, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20531, "top5_acc": 0.44531, "loss_cls": 4.61232, "loss": 4.61232, "time": 0.81495} +{"mode": "train", "epoch": 33, "iter": 1400, "lr": 0.08894, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21875, "top5_acc": 0.45203, "loss_cls": 4.58479, "loss": 4.58479, "time": 0.82089} +{"mode": "train", "epoch": 33, "iter": 1500, "lr": 0.08892, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.20953, "top5_acc": 0.43953, "loss_cls": 4.62258, "loss": 4.62258, "time": 0.82334} +{"mode": "train", "epoch": 33, "iter": 1600, "lr": 0.08891, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22203, "top5_acc": 0.44828, "loss_cls": 4.60495, "loss": 4.60495, "time": 0.81511} +{"mode": "train", "epoch": 33, "iter": 1700, "lr": 0.08889, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21484, "top5_acc": 0.44516, "loss_cls": 4.63647, "loss": 4.63647, "time": 0.825} +{"mode": "train", "epoch": 33, "iter": 1800, "lr": 0.08887, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21781, "top5_acc": 0.44609, "loss_cls": 4.58063, "loss": 4.58063, "time": 0.82056} +{"mode": "train", "epoch": 33, "iter": 1900, "lr": 0.08885, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21906, "top5_acc": 0.44781, "loss_cls": 4.60001, "loss": 4.60001, "time": 0.82628} +{"mode": "train", "epoch": 33, "iter": 2000, "lr": 0.08884, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21641, "top5_acc": 0.45484, "loss_cls": 4.59006, "loss": 4.59006, "time": 0.81993} +{"mode": "train", "epoch": 33, "iter": 2100, "lr": 0.08882, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21062, "top5_acc": 0.44266, "loss_cls": 4.62531, "loss": 4.62531, "time": 0.8197} +{"mode": "train", "epoch": 33, "iter": 2200, "lr": 0.0888, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21359, "top5_acc": 0.44844, "loss_cls": 4.62716, "loss": 4.62716, "time": 0.82062} +{"mode": "train", "epoch": 33, "iter": 2300, "lr": 0.08878, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21391, "top5_acc": 0.44266, "loss_cls": 4.61682, "loss": 4.61682, "time": 0.8199} +{"mode": "train", "epoch": 33, "iter": 2400, "lr": 0.08876, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.20656, "top5_acc": 0.44078, "loss_cls": 4.67603, "loss": 4.67603, "time": 0.818} +{"mode": "train", "epoch": 33, "iter": 2500, "lr": 0.08875, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.20531, "top5_acc": 0.43719, "loss_cls": 4.63584, "loss": 4.63584, "time": 0.81661} +{"mode": "train", "epoch": 33, "iter": 2600, "lr": 0.08873, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22203, "top5_acc": 0.45234, "loss_cls": 4.56207, "loss": 4.56207, "time": 0.81954} +{"mode": "train", "epoch": 33, "iter": 2700, "lr": 0.08871, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20172, "top5_acc": 0.43641, "loss_cls": 4.64636, "loss": 4.64636, "time": 0.81859} +{"mode": "train", "epoch": 33, "iter": 2800, "lr": 0.08869, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.215, "top5_acc": 0.44406, "loss_cls": 4.62441, "loss": 4.62441, "time": 0.82399} +{"mode": "train", "epoch": 33, "iter": 2900, "lr": 0.08868, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21031, "top5_acc": 0.44391, "loss_cls": 4.61216, "loss": 4.61216, "time": 0.82296} +{"mode": "train", "epoch": 33, "iter": 3000, "lr": 0.08866, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22359, "top5_acc": 0.45734, "loss_cls": 4.56761, "loss": 4.56761, "time": 0.82341} +{"mode": "train", "epoch": 33, "iter": 3100, "lr": 0.08864, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.20953, "top5_acc": 0.43922, "loss_cls": 4.64063, "loss": 4.64063, "time": 0.82892} +{"mode": "train", "epoch": 33, "iter": 3200, "lr": 0.08862, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2, "top5_acc": 0.42578, "loss_cls": 4.68676, "loss": 4.68676, "time": 0.82066} +{"mode": "train", "epoch": 33, "iter": 3300, "lr": 0.08861, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21234, "top5_acc": 0.44969, "loss_cls": 4.57064, "loss": 4.57064, "time": 0.83289} +{"mode": "train", "epoch": 33, "iter": 3400, "lr": 0.08859, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22125, "top5_acc": 0.46109, "loss_cls": 4.58, "loss": 4.58, "time": 0.8268} +{"mode": "train", "epoch": 33, "iter": 3500, "lr": 0.08857, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21109, "top5_acc": 0.4425, "loss_cls": 4.63254, "loss": 4.63254, "time": 0.82401} +{"mode": "train", "epoch": 33, "iter": 3600, "lr": 0.08855, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.22234, "top5_acc": 0.45688, "loss_cls": 4.54997, "loss": 4.54997, "time": 0.82703} +{"mode": "train", "epoch": 33, "iter": 3700, "lr": 0.08853, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20562, "top5_acc": 0.42672, "loss_cls": 4.67837, "loss": 4.67837, "time": 0.81948} +{"mode": "val", "epoch": 33, "iter": 309, "lr": 0.08853, "top1_acc": 0.13934, "top5_acc": 0.3388, "mean_class_accuracy": 0.13909} +{"mode": "train", "epoch": 34, "iter": 100, "lr": 0.08851, "memory": 15990, "data_time": 1.32243, "top1_acc": 0.21625, "top5_acc": 0.44984, "loss_cls": 4.57946, "loss": 4.57946, "time": 2.32272} +{"mode": "train", "epoch": 34, "iter": 200, "lr": 0.08849, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21359, "top5_acc": 0.44547, "loss_cls": 4.60436, "loss": 4.60436, "time": 0.83438} +{"mode": "train", "epoch": 34, "iter": 300, "lr": 0.08847, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.215, "top5_acc": 0.45484, "loss_cls": 4.59614, "loss": 4.59614, "time": 0.82948} +{"mode": "train", "epoch": 34, "iter": 400, "lr": 0.08845, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21453, "top5_acc": 0.44141, "loss_cls": 4.59921, "loss": 4.59921, "time": 0.82013} +{"mode": "train", "epoch": 34, "iter": 500, "lr": 0.08844, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21078, "top5_acc": 0.44297, "loss_cls": 4.61738, "loss": 4.61738, "time": 0.83052} +{"mode": "train", "epoch": 34, "iter": 600, "lr": 0.08842, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2175, "top5_acc": 0.45438, "loss_cls": 4.56258, "loss": 4.56258, "time": 0.83072} +{"mode": "train", "epoch": 34, "iter": 700, "lr": 0.0884, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21219, "top5_acc": 0.44297, "loss_cls": 4.64068, "loss": 4.64068, "time": 0.83428} +{"mode": "train", "epoch": 34, "iter": 800, "lr": 0.08838, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22266, "top5_acc": 0.45406, "loss_cls": 4.56322, "loss": 4.56322, "time": 0.83279} +{"mode": "train", "epoch": 34, "iter": 900, "lr": 0.08836, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21578, "top5_acc": 0.46188, "loss_cls": 4.59703, "loss": 4.59703, "time": 0.83505} +{"mode": "train", "epoch": 34, "iter": 1000, "lr": 0.08835, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21312, "top5_acc": 0.45031, "loss_cls": 4.60443, "loss": 4.60443, "time": 0.83081} +{"mode": "train", "epoch": 34, "iter": 1100, "lr": 0.08833, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21344, "top5_acc": 0.45281, "loss_cls": 4.60223, "loss": 4.60223, "time": 0.82906} +{"mode": "train", "epoch": 34, "iter": 1200, "lr": 0.08831, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21703, "top5_acc": 0.44328, "loss_cls": 4.62908, "loss": 4.62908, "time": 0.82816} +{"mode": "train", "epoch": 34, "iter": 1300, "lr": 0.08829, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21781, "top5_acc": 0.44438, "loss_cls": 4.60603, "loss": 4.60603, "time": 0.83192} +{"mode": "train", "epoch": 34, "iter": 1400, "lr": 0.08828, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22297, "top5_acc": 0.44734, "loss_cls": 4.58173, "loss": 4.58173, "time": 0.83283} +{"mode": "train", "epoch": 34, "iter": 1500, "lr": 0.08826, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.215, "top5_acc": 0.44688, "loss_cls": 4.59291, "loss": 4.59291, "time": 0.83021} +{"mode": "train", "epoch": 34, "iter": 1600, "lr": 0.08824, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21609, "top5_acc": 0.44, "loss_cls": 4.64548, "loss": 4.64548, "time": 0.82897} +{"mode": "train", "epoch": 34, "iter": 1700, "lr": 0.08822, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21391, "top5_acc": 0.45375, "loss_cls": 4.58766, "loss": 4.58766, "time": 0.82611} +{"mode": "train", "epoch": 34, "iter": 1800, "lr": 0.0882, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21328, "top5_acc": 0.44312, "loss_cls": 4.62895, "loss": 4.62895, "time": 0.82593} +{"mode": "train", "epoch": 34, "iter": 1900, "lr": 0.08819, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21125, "top5_acc": 0.45172, "loss_cls": 4.61063, "loss": 4.61063, "time": 0.82389} +{"mode": "train", "epoch": 34, "iter": 2000, "lr": 0.08817, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22219, "top5_acc": 0.45641, "loss_cls": 4.57526, "loss": 4.57526, "time": 0.82877} +{"mode": "train", "epoch": 34, "iter": 2100, "lr": 0.08815, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20094, "top5_acc": 0.4325, "loss_cls": 4.67393, "loss": 4.67393, "time": 0.82693} +{"mode": "train", "epoch": 34, "iter": 2200, "lr": 0.08813, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21375, "top5_acc": 0.44484, "loss_cls": 4.5982, "loss": 4.5982, "time": 0.82085} +{"mode": "train", "epoch": 34, "iter": 2300, "lr": 0.08811, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21484, "top5_acc": 0.44688, "loss_cls": 4.61379, "loss": 4.61379, "time": 0.81952} +{"mode": "train", "epoch": 34, "iter": 2400, "lr": 0.08809, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20953, "top5_acc": 0.44531, "loss_cls": 4.62166, "loss": 4.62166, "time": 0.82329} +{"mode": "train", "epoch": 34, "iter": 2500, "lr": 0.08808, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21016, "top5_acc": 0.435, "loss_cls": 4.63137, "loss": 4.63137, "time": 0.81441} +{"mode": "train", "epoch": 34, "iter": 2600, "lr": 0.08806, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2175, "top5_acc": 0.45, "loss_cls": 4.59983, "loss": 4.59983, "time": 0.82154} +{"mode": "train", "epoch": 34, "iter": 2700, "lr": 0.08804, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21859, "top5_acc": 0.45062, "loss_cls": 4.61662, "loss": 4.61662, "time": 0.8219} +{"mode": "train", "epoch": 34, "iter": 2800, "lr": 0.08802, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21641, "top5_acc": 0.44109, "loss_cls": 4.62433, "loss": 4.62433, "time": 0.82068} +{"mode": "train", "epoch": 34, "iter": 2900, "lr": 0.088, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22688, "top5_acc": 0.46469, "loss_cls": 4.55437, "loss": 4.55437, "time": 0.82041} +{"mode": "train", "epoch": 34, "iter": 3000, "lr": 0.08799, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21078, "top5_acc": 0.42531, "loss_cls": 4.66016, "loss": 4.66016, "time": 0.8269} +{"mode": "train", "epoch": 34, "iter": 3100, "lr": 0.08797, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21203, "top5_acc": 0.43688, "loss_cls": 4.65269, "loss": 4.65269, "time": 0.82183} +{"mode": "train", "epoch": 34, "iter": 3200, "lr": 0.08795, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21422, "top5_acc": 0.45031, "loss_cls": 4.60793, "loss": 4.60793, "time": 0.82771} +{"mode": "train", "epoch": 34, "iter": 3300, "lr": 0.08793, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21375, "top5_acc": 0.44688, "loss_cls": 4.61605, "loss": 4.61605, "time": 0.82407} +{"mode": "train", "epoch": 34, "iter": 3400, "lr": 0.08791, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.21422, "top5_acc": 0.44562, "loss_cls": 4.62252, "loss": 4.62252, "time": 0.828} +{"mode": "train", "epoch": 34, "iter": 3500, "lr": 0.08789, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.22344, "top5_acc": 0.45688, "loss_cls": 4.56251, "loss": 4.56251, "time": 0.82514} +{"mode": "train", "epoch": 34, "iter": 3600, "lr": 0.08788, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21188, "top5_acc": 0.44312, "loss_cls": 4.6209, "loss": 4.6209, "time": 0.81761} +{"mode": "train", "epoch": 34, "iter": 3700, "lr": 0.08786, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21875, "top5_acc": 0.44281, "loss_cls": 4.61852, "loss": 4.61852, "time": 0.83126} +{"mode": "val", "epoch": 34, "iter": 309, "lr": 0.08785, "top1_acc": 0.15666, "top5_acc": 0.36109, "mean_class_accuracy": 0.15654} +{"mode": "train", "epoch": 35, "iter": 100, "lr": 0.08783, "memory": 15990, "data_time": 1.29398, "top1_acc": 0.21188, "top5_acc": 0.44375, "loss_cls": 4.58337, "loss": 4.58337, "time": 2.28982} +{"mode": "train", "epoch": 35, "iter": 200, "lr": 0.08781, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22688, "top5_acc": 0.44594, "loss_cls": 4.57036, "loss": 4.57036, "time": 0.83283} +{"mode": "train", "epoch": 35, "iter": 300, "lr": 0.0878, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22141, "top5_acc": 0.45234, "loss_cls": 4.60375, "loss": 4.60375, "time": 0.8331} +{"mode": "train", "epoch": 35, "iter": 400, "lr": 0.08778, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20875, "top5_acc": 0.44438, "loss_cls": 4.62193, "loss": 4.62193, "time": 0.83226} +{"mode": "train", "epoch": 35, "iter": 500, "lr": 0.08776, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22078, "top5_acc": 0.44391, "loss_cls": 4.60969, "loss": 4.60969, "time": 0.82904} +{"mode": "train", "epoch": 35, "iter": 600, "lr": 0.08774, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22141, "top5_acc": 0.46016, "loss_cls": 4.56562, "loss": 4.56562, "time": 0.83159} +{"mode": "train", "epoch": 35, "iter": 700, "lr": 0.08772, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21406, "top5_acc": 0.44578, "loss_cls": 4.60745, "loss": 4.60745, "time": 0.83168} +{"mode": "train", "epoch": 35, "iter": 800, "lr": 0.0877, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20938, "top5_acc": 0.44922, "loss_cls": 4.61545, "loss": 4.61545, "time": 0.83622} +{"mode": "train", "epoch": 35, "iter": 900, "lr": 0.08769, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2075, "top5_acc": 0.44359, "loss_cls": 4.62191, "loss": 4.62191, "time": 0.82684} +{"mode": "train", "epoch": 35, "iter": 1000, "lr": 0.08767, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21359, "top5_acc": 0.43688, "loss_cls": 4.62943, "loss": 4.62943, "time": 0.82497} +{"mode": "train", "epoch": 35, "iter": 1100, "lr": 0.08765, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2225, "top5_acc": 0.44891, "loss_cls": 4.58813, "loss": 4.58813, "time": 0.81886} +{"mode": "train", "epoch": 35, "iter": 1200, "lr": 0.08763, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22594, "top5_acc": 0.4575, "loss_cls": 4.55783, "loss": 4.55783, "time": 0.82394} +{"mode": "train", "epoch": 35, "iter": 1300, "lr": 0.08761, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22359, "top5_acc": 0.45266, "loss_cls": 4.57755, "loss": 4.57755, "time": 0.82391} +{"mode": "train", "epoch": 35, "iter": 1400, "lr": 0.08759, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21609, "top5_acc": 0.44719, "loss_cls": 4.61396, "loss": 4.61396, "time": 0.82514} +{"mode": "train", "epoch": 35, "iter": 1500, "lr": 0.08757, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20828, "top5_acc": 0.44625, "loss_cls": 4.62513, "loss": 4.62513, "time": 0.8221} +{"mode": "train", "epoch": 35, "iter": 1600, "lr": 0.08756, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21422, "top5_acc": 0.44984, "loss_cls": 4.59748, "loss": 4.59748, "time": 0.83077} +{"mode": "train", "epoch": 35, "iter": 1700, "lr": 0.08754, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21609, "top5_acc": 0.45469, "loss_cls": 4.57441, "loss": 4.57441, "time": 0.81723} +{"mode": "train", "epoch": 35, "iter": 1800, "lr": 0.08752, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22188, "top5_acc": 0.45906, "loss_cls": 4.54263, "loss": 4.54263, "time": 0.82176} +{"mode": "train", "epoch": 35, "iter": 1900, "lr": 0.0875, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21406, "top5_acc": 0.45188, "loss_cls": 4.60531, "loss": 4.60531, "time": 0.82218} +{"mode": "train", "epoch": 35, "iter": 2000, "lr": 0.08748, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20875, "top5_acc": 0.44688, "loss_cls": 4.59674, "loss": 4.59674, "time": 0.8211} +{"mode": "train", "epoch": 35, "iter": 2100, "lr": 0.08746, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22281, "top5_acc": 0.44594, "loss_cls": 4.60767, "loss": 4.60767, "time": 0.82527} +{"mode": "train", "epoch": 35, "iter": 2200, "lr": 0.08745, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21828, "top5_acc": 0.44719, "loss_cls": 4.59782, "loss": 4.59782, "time": 0.82047} +{"mode": "train", "epoch": 35, "iter": 2300, "lr": 0.08743, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.225, "top5_acc": 0.46094, "loss_cls": 4.54717, "loss": 4.54717, "time": 0.82524} +{"mode": "train", "epoch": 35, "iter": 2400, "lr": 0.08741, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21156, "top5_acc": 0.44359, "loss_cls": 4.61781, "loss": 4.61781, "time": 0.81967} +{"mode": "train", "epoch": 35, "iter": 2500, "lr": 0.08739, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20906, "top5_acc": 0.44641, "loss_cls": 4.60311, "loss": 4.60311, "time": 0.81938} +{"mode": "train", "epoch": 35, "iter": 2600, "lr": 0.08737, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21156, "top5_acc": 0.44844, "loss_cls": 4.61307, "loss": 4.61307, "time": 0.81689} +{"mode": "train", "epoch": 35, "iter": 2700, "lr": 0.08735, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21, "top5_acc": 0.44797, "loss_cls": 4.61058, "loss": 4.61058, "time": 0.81892} +{"mode": "train", "epoch": 35, "iter": 2800, "lr": 0.08733, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21766, "top5_acc": 0.45859, "loss_cls": 4.53754, "loss": 4.53754, "time": 0.81709} +{"mode": "train", "epoch": 35, "iter": 2900, "lr": 0.08732, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21562, "top5_acc": 0.44844, "loss_cls": 4.64197, "loss": 4.64197, "time": 0.81799} +{"mode": "train", "epoch": 35, "iter": 3000, "lr": 0.0873, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.2125, "top5_acc": 0.44938, "loss_cls": 4.59824, "loss": 4.59824, "time": 0.82986} +{"mode": "train", "epoch": 35, "iter": 3100, "lr": 0.08728, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21672, "top5_acc": 0.43906, "loss_cls": 4.63771, "loss": 4.63771, "time": 0.82141} +{"mode": "train", "epoch": 35, "iter": 3200, "lr": 0.08726, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.20609, "top5_acc": 0.43953, "loss_cls": 4.66482, "loss": 4.66482, "time": 0.83443} +{"mode": "train", "epoch": 35, "iter": 3300, "lr": 0.08724, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21672, "top5_acc": 0.44875, "loss_cls": 4.59518, "loss": 4.59518, "time": 0.8256} +{"mode": "train", "epoch": 35, "iter": 3400, "lr": 0.08722, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.20562, "top5_acc": 0.44188, "loss_cls": 4.66278, "loss": 4.66278, "time": 0.82309} +{"mode": "train", "epoch": 35, "iter": 3500, "lr": 0.0872, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21547, "top5_acc": 0.44625, "loss_cls": 4.6072, "loss": 4.6072, "time": 0.82262} +{"mode": "train", "epoch": 35, "iter": 3600, "lr": 0.08718, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21297, "top5_acc": 0.44453, "loss_cls": 4.62015, "loss": 4.62015, "time": 0.82016} +{"mode": "train", "epoch": 35, "iter": 3700, "lr": 0.08717, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21375, "top5_acc": 0.45688, "loss_cls": 4.57058, "loss": 4.57058, "time": 0.82881} +{"mode": "val", "epoch": 35, "iter": 309, "lr": 0.08716, "top1_acc": 0.12896, "top5_acc": 0.31839, "mean_class_accuracy": 0.12882} +{"mode": "train", "epoch": 36, "iter": 100, "lr": 0.08714, "memory": 15990, "data_time": 1.28742, "top1_acc": 0.22016, "top5_acc": 0.44797, "loss_cls": 4.54286, "loss": 4.54286, "time": 2.29041} +{"mode": "train", "epoch": 36, "iter": 200, "lr": 0.08712, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21734, "top5_acc": 0.44953, "loss_cls": 4.58211, "loss": 4.58211, "time": 0.82994} +{"mode": "train", "epoch": 36, "iter": 300, "lr": 0.0871, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22188, "top5_acc": 0.45734, "loss_cls": 4.53674, "loss": 4.53674, "time": 0.83057} +{"mode": "train", "epoch": 36, "iter": 400, "lr": 0.08708, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.215, "top5_acc": 0.44641, "loss_cls": 4.5829, "loss": 4.5829, "time": 0.83231} +{"mode": "train", "epoch": 36, "iter": 500, "lr": 0.08706, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22031, "top5_acc": 0.44891, "loss_cls": 4.57111, "loss": 4.57111, "time": 0.83072} +{"mode": "train", "epoch": 36, "iter": 600, "lr": 0.08704, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22422, "top5_acc": 0.45328, "loss_cls": 4.56342, "loss": 4.56342, "time": 0.82519} +{"mode": "train", "epoch": 36, "iter": 700, "lr": 0.08703, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21375, "top5_acc": 0.44141, "loss_cls": 4.63471, "loss": 4.63471, "time": 0.83218} +{"mode": "train", "epoch": 36, "iter": 800, "lr": 0.08701, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20969, "top5_acc": 0.44797, "loss_cls": 4.60094, "loss": 4.60094, "time": 0.82725} +{"mode": "train", "epoch": 36, "iter": 900, "lr": 0.08699, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21703, "top5_acc": 0.46062, "loss_cls": 4.5375, "loss": 4.5375, "time": 0.8341} +{"mode": "train", "epoch": 36, "iter": 1000, "lr": 0.08697, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21547, "top5_acc": 0.44391, "loss_cls": 4.60494, "loss": 4.60494, "time": 0.829} +{"mode": "train", "epoch": 36, "iter": 1100, "lr": 0.08695, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21375, "top5_acc": 0.44906, "loss_cls": 4.60308, "loss": 4.60308, "time": 0.83181} +{"mode": "train", "epoch": 36, "iter": 1200, "lr": 0.08693, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21047, "top5_acc": 0.4375, "loss_cls": 4.63655, "loss": 4.63655, "time": 0.82363} +{"mode": "train", "epoch": 36, "iter": 1300, "lr": 0.08691, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21578, "top5_acc": 0.44781, "loss_cls": 4.5836, "loss": 4.5836, "time": 0.83018} +{"mode": "train", "epoch": 36, "iter": 1400, "lr": 0.08689, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21406, "top5_acc": 0.44234, "loss_cls": 4.63838, "loss": 4.63838, "time": 0.83562} +{"mode": "train", "epoch": 36, "iter": 1500, "lr": 0.08688, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21781, "top5_acc": 0.45438, "loss_cls": 4.5869, "loss": 4.5869, "time": 0.83207} +{"mode": "train", "epoch": 36, "iter": 1600, "lr": 0.08686, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21094, "top5_acc": 0.44641, "loss_cls": 4.61245, "loss": 4.61245, "time": 0.82413} +{"mode": "train", "epoch": 36, "iter": 1700, "lr": 0.08684, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20797, "top5_acc": 0.44484, "loss_cls": 4.64543, "loss": 4.64543, "time": 0.82331} +{"mode": "train", "epoch": 36, "iter": 1800, "lr": 0.08682, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21688, "top5_acc": 0.44484, "loss_cls": 4.65602, "loss": 4.65602, "time": 0.82879} +{"mode": "train", "epoch": 36, "iter": 1900, "lr": 0.0868, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21922, "top5_acc": 0.45016, "loss_cls": 4.58218, "loss": 4.58218, "time": 0.82487} +{"mode": "train", "epoch": 36, "iter": 2000, "lr": 0.08678, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22328, "top5_acc": 0.45719, "loss_cls": 4.56034, "loss": 4.56034, "time": 0.81862} +{"mode": "train", "epoch": 36, "iter": 2100, "lr": 0.08676, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21219, "top5_acc": 0.43734, "loss_cls": 4.62655, "loss": 4.62655, "time": 0.81928} +{"mode": "train", "epoch": 36, "iter": 2200, "lr": 0.08674, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22109, "top5_acc": 0.46625, "loss_cls": 4.53134, "loss": 4.53134, "time": 0.82036} +{"mode": "train", "epoch": 36, "iter": 2300, "lr": 0.08672, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22203, "top5_acc": 0.43797, "loss_cls": 4.62798, "loss": 4.62798, "time": 0.8213} +{"mode": "train", "epoch": 36, "iter": 2400, "lr": 0.08671, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21328, "top5_acc": 0.44875, "loss_cls": 4.61921, "loss": 4.61921, "time": 0.82155} +{"mode": "train", "epoch": 36, "iter": 2500, "lr": 0.08669, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21922, "top5_acc": 0.45078, "loss_cls": 4.61458, "loss": 4.61458, "time": 0.82448} +{"mode": "train", "epoch": 36, "iter": 2600, "lr": 0.08667, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.215, "top5_acc": 0.45406, "loss_cls": 4.6007, "loss": 4.6007, "time": 0.81301} +{"mode": "train", "epoch": 36, "iter": 2700, "lr": 0.08665, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21578, "top5_acc": 0.4375, "loss_cls": 4.61623, "loss": 4.61623, "time": 0.81562} +{"mode": "train", "epoch": 36, "iter": 2800, "lr": 0.08663, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21812, "top5_acc": 0.44562, "loss_cls": 4.60813, "loss": 4.60813, "time": 0.81419} +{"mode": "train", "epoch": 36, "iter": 2900, "lr": 0.08661, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22484, "top5_acc": 0.45016, "loss_cls": 4.58933, "loss": 4.58933, "time": 0.82392} +{"mode": "train", "epoch": 36, "iter": 3000, "lr": 0.08659, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21906, "top5_acc": 0.45094, "loss_cls": 4.62781, "loss": 4.62781, "time": 0.82012} +{"mode": "train", "epoch": 36, "iter": 3100, "lr": 0.08657, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21469, "top5_acc": 0.45547, "loss_cls": 4.56455, "loss": 4.56455, "time": 0.82556} +{"mode": "train", "epoch": 36, "iter": 3200, "lr": 0.08655, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21672, "top5_acc": 0.45875, "loss_cls": 4.57854, "loss": 4.57854, "time": 0.82218} +{"mode": "train", "epoch": 36, "iter": 3300, "lr": 0.08653, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21562, "top5_acc": 0.44328, "loss_cls": 4.60762, "loss": 4.60762, "time": 0.82603} +{"mode": "train", "epoch": 36, "iter": 3400, "lr": 0.08651, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.20766, "top5_acc": 0.44422, "loss_cls": 4.60079, "loss": 4.60079, "time": 0.82429} +{"mode": "train", "epoch": 36, "iter": 3500, "lr": 0.0865, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20922, "top5_acc": 0.44328, "loss_cls": 4.60909, "loss": 4.60909, "time": 0.81946} +{"mode": "train", "epoch": 36, "iter": 3600, "lr": 0.08648, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21109, "top5_acc": 0.44281, "loss_cls": 4.6243, "loss": 4.6243, "time": 0.82608} +{"mode": "train", "epoch": 36, "iter": 3700, "lr": 0.08646, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22344, "top5_acc": 0.44922, "loss_cls": 4.56024, "loss": 4.56024, "time": 0.83134} +{"mode": "val", "epoch": 36, "iter": 309, "lr": 0.08645, "top1_acc": 0.14319, "top5_acc": 0.34164, "mean_class_accuracy": 0.14322} +{"mode": "train", "epoch": 37, "iter": 100, "lr": 0.08643, "memory": 15990, "data_time": 1.27196, "top1_acc": 0.21719, "top5_acc": 0.45203, "loss_cls": 4.58596, "loss": 4.58596, "time": 2.27218} +{"mode": "train", "epoch": 37, "iter": 200, "lr": 0.08641, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21453, "top5_acc": 0.44578, "loss_cls": 4.60195, "loss": 4.60195, "time": 0.8338} +{"mode": "train", "epoch": 37, "iter": 300, "lr": 0.08639, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22016, "top5_acc": 0.45844, "loss_cls": 4.56892, "loss": 4.56892, "time": 0.83352} +{"mode": "train", "epoch": 37, "iter": 400, "lr": 0.08637, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21734, "top5_acc": 0.46172, "loss_cls": 4.56998, "loss": 4.56998, "time": 0.83276} +{"mode": "train", "epoch": 37, "iter": 500, "lr": 0.08635, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22609, "top5_acc": 0.46594, "loss_cls": 4.53005, "loss": 4.53005, "time": 0.82874} +{"mode": "train", "epoch": 37, "iter": 600, "lr": 0.08633, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20859, "top5_acc": 0.4475, "loss_cls": 4.57757, "loss": 4.57757, "time": 0.81906} +{"mode": "train", "epoch": 37, "iter": 700, "lr": 0.08631, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22078, "top5_acc": 0.46188, "loss_cls": 4.53062, "loss": 4.53062, "time": 0.81966} +{"mode": "train", "epoch": 37, "iter": 800, "lr": 0.0863, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20547, "top5_acc": 0.44422, "loss_cls": 4.64757, "loss": 4.64757, "time": 0.81965} +{"mode": "train", "epoch": 37, "iter": 900, "lr": 0.08628, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21047, "top5_acc": 0.45203, "loss_cls": 4.59924, "loss": 4.59924, "time": 0.82338} +{"mode": "train", "epoch": 37, "iter": 1000, "lr": 0.08626, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21891, "top5_acc": 0.45516, "loss_cls": 4.57297, "loss": 4.57297, "time": 0.82155} +{"mode": "train", "epoch": 37, "iter": 1100, "lr": 0.08624, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22344, "top5_acc": 0.44875, "loss_cls": 4.57622, "loss": 4.57622, "time": 0.82122} +{"mode": "train", "epoch": 37, "iter": 1200, "lr": 0.08622, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21328, "top5_acc": 0.45078, "loss_cls": 4.59591, "loss": 4.59591, "time": 0.81994} +{"mode": "train", "epoch": 37, "iter": 1300, "lr": 0.0862, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22016, "top5_acc": 0.44844, "loss_cls": 4.57797, "loss": 4.57797, "time": 0.81526} +{"mode": "train", "epoch": 37, "iter": 1400, "lr": 0.08618, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21672, "top5_acc": 0.45734, "loss_cls": 4.57986, "loss": 4.57986, "time": 0.81964} +{"mode": "train", "epoch": 37, "iter": 1500, "lr": 0.08616, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21625, "top5_acc": 0.45203, "loss_cls": 4.55705, "loss": 4.55705, "time": 0.8243} +{"mode": "train", "epoch": 37, "iter": 1600, "lr": 0.08614, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2125, "top5_acc": 0.44531, "loss_cls": 4.6096, "loss": 4.6096, "time": 0.82465} +{"mode": "train", "epoch": 37, "iter": 1700, "lr": 0.08612, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21875, "top5_acc": 0.44797, "loss_cls": 4.60951, "loss": 4.60951, "time": 0.82041} +{"mode": "train", "epoch": 37, "iter": 1800, "lr": 0.0861, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21344, "top5_acc": 0.45078, "loss_cls": 4.58899, "loss": 4.58899, "time": 0.82023} +{"mode": "train", "epoch": 37, "iter": 1900, "lr": 0.08608, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21281, "top5_acc": 0.44172, "loss_cls": 4.62361, "loss": 4.62361, "time": 0.82378} +{"mode": "train", "epoch": 37, "iter": 2000, "lr": 0.08606, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21219, "top5_acc": 0.44219, "loss_cls": 4.5744, "loss": 4.5744, "time": 0.82146} +{"mode": "train", "epoch": 37, "iter": 2100, "lr": 0.08604, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22188, "top5_acc": 0.45516, "loss_cls": 4.5814, "loss": 4.5814, "time": 0.81832} +{"mode": "train", "epoch": 37, "iter": 2200, "lr": 0.08602, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21969, "top5_acc": 0.4525, "loss_cls": 4.57851, "loss": 4.57851, "time": 0.8208} +{"mode": "train", "epoch": 37, "iter": 2300, "lr": 0.08601, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21547, "top5_acc": 0.45047, "loss_cls": 4.60706, "loss": 4.60706, "time": 0.82229} +{"mode": "train", "epoch": 37, "iter": 2400, "lr": 0.08599, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21938, "top5_acc": 0.45453, "loss_cls": 4.57315, "loss": 4.57315, "time": 0.82361} +{"mode": "train", "epoch": 37, "iter": 2500, "lr": 0.08597, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21641, "top5_acc": 0.44578, "loss_cls": 4.59998, "loss": 4.59998, "time": 0.81789} +{"mode": "train", "epoch": 37, "iter": 2600, "lr": 0.08595, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2175, "top5_acc": 0.45062, "loss_cls": 4.61644, "loss": 4.61644, "time": 0.81946} +{"mode": "train", "epoch": 37, "iter": 2700, "lr": 0.08593, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21781, "top5_acc": 0.44688, "loss_cls": 4.58586, "loss": 4.58586, "time": 0.81758} +{"mode": "train", "epoch": 37, "iter": 2800, "lr": 0.08591, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21453, "top5_acc": 0.45125, "loss_cls": 4.58261, "loss": 4.58261, "time": 0.81952} +{"mode": "train", "epoch": 37, "iter": 2900, "lr": 0.08589, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21625, "top5_acc": 0.45375, "loss_cls": 4.59267, "loss": 4.59267, "time": 0.82364} +{"mode": "train", "epoch": 37, "iter": 3000, "lr": 0.08587, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20766, "top5_acc": 0.44156, "loss_cls": 4.62333, "loss": 4.62333, "time": 0.81972} +{"mode": "train", "epoch": 37, "iter": 3100, "lr": 0.08585, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.20672, "top5_acc": 0.43719, "loss_cls": 4.67499, "loss": 4.67499, "time": 0.82886} +{"mode": "train", "epoch": 37, "iter": 3200, "lr": 0.08583, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.20641, "top5_acc": 0.44312, "loss_cls": 4.62734, "loss": 4.62734, "time": 0.82554} +{"mode": "train", "epoch": 37, "iter": 3300, "lr": 0.08581, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22734, "top5_acc": 0.4525, "loss_cls": 4.558, "loss": 4.558, "time": 0.82674} +{"mode": "train", "epoch": 37, "iter": 3400, "lr": 0.08579, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21969, "top5_acc": 0.45172, "loss_cls": 4.58432, "loss": 4.58432, "time": 0.82493} +{"mode": "train", "epoch": 37, "iter": 3500, "lr": 0.08577, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22062, "top5_acc": 0.44375, "loss_cls": 4.6053, "loss": 4.6053, "time": 0.81865} +{"mode": "train", "epoch": 37, "iter": 3600, "lr": 0.08575, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2075, "top5_acc": 0.44625, "loss_cls": 4.64926, "loss": 4.64926, "time": 0.82824} +{"mode": "train", "epoch": 37, "iter": 3700, "lr": 0.08573, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.22812, "top5_acc": 0.45406, "loss_cls": 4.5666, "loss": 4.5666, "time": 0.81817} +{"mode": "val", "epoch": 37, "iter": 309, "lr": 0.08572, "top1_acc": 0.16335, "top5_acc": 0.37426, "mean_class_accuracy": 0.16339} +{"mode": "train", "epoch": 38, "iter": 100, "lr": 0.0857, "memory": 15990, "data_time": 1.3227, "top1_acc": 0.22391, "top5_acc": 0.45359, "loss_cls": 4.56639, "loss": 4.56639, "time": 2.31409} +{"mode": "train", "epoch": 38, "iter": 200, "lr": 0.08568, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22391, "top5_acc": 0.46281, "loss_cls": 4.55161, "loss": 4.55161, "time": 0.82886} +{"mode": "train", "epoch": 38, "iter": 300, "lr": 0.08567, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22266, "top5_acc": 0.45219, "loss_cls": 4.57962, "loss": 4.57962, "time": 0.823} +{"mode": "train", "epoch": 38, "iter": 400, "lr": 0.08565, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21422, "top5_acc": 0.44781, "loss_cls": 4.59386, "loss": 4.59386, "time": 0.82715} +{"mode": "train", "epoch": 38, "iter": 500, "lr": 0.08563, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21781, "top5_acc": 0.45812, "loss_cls": 4.57759, "loss": 4.57759, "time": 0.82787} +{"mode": "train", "epoch": 38, "iter": 600, "lr": 0.08561, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21422, "top5_acc": 0.455, "loss_cls": 4.58483, "loss": 4.58483, "time": 0.8175} +{"mode": "train", "epoch": 38, "iter": 700, "lr": 0.08559, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21391, "top5_acc": 0.44922, "loss_cls": 4.60692, "loss": 4.60692, "time": 0.82164} +{"mode": "train", "epoch": 38, "iter": 800, "lr": 0.08557, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.20844, "top5_acc": 0.44641, "loss_cls": 4.61278, "loss": 4.61278, "time": 0.82224} +{"mode": "train", "epoch": 38, "iter": 900, "lr": 0.08555, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21922, "top5_acc": 0.44703, "loss_cls": 4.5824, "loss": 4.5824, "time": 0.82216} +{"mode": "train", "epoch": 38, "iter": 1000, "lr": 0.08553, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22453, "top5_acc": 0.45812, "loss_cls": 4.54483, "loss": 4.54483, "time": 0.82727} +{"mode": "train", "epoch": 38, "iter": 1100, "lr": 0.08551, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21609, "top5_acc": 0.44953, "loss_cls": 4.58402, "loss": 4.58402, "time": 0.81593} +{"mode": "train", "epoch": 38, "iter": 1200, "lr": 0.08549, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21688, "top5_acc": 0.44125, "loss_cls": 4.60294, "loss": 4.60294, "time": 0.81816} +{"mode": "train", "epoch": 38, "iter": 1300, "lr": 0.08547, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21359, "top5_acc": 0.45484, "loss_cls": 4.58436, "loss": 4.58436, "time": 0.8203} +{"mode": "train", "epoch": 38, "iter": 1400, "lr": 0.08545, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21578, "top5_acc": 0.44781, "loss_cls": 4.59872, "loss": 4.59872, "time": 0.81517} +{"mode": "train", "epoch": 38, "iter": 1500, "lr": 0.08543, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21906, "top5_acc": 0.4475, "loss_cls": 4.58483, "loss": 4.58483, "time": 0.82788} +{"mode": "train", "epoch": 38, "iter": 1600, "lr": 0.08541, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21703, "top5_acc": 0.45938, "loss_cls": 4.56684, "loss": 4.56684, "time": 0.815} +{"mode": "train", "epoch": 38, "iter": 1700, "lr": 0.08539, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22297, "top5_acc": 0.46266, "loss_cls": 4.54745, "loss": 4.54745, "time": 0.83257} +{"mode": "train", "epoch": 38, "iter": 1800, "lr": 0.08537, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2175, "top5_acc": 0.44047, "loss_cls": 4.61561, "loss": 4.61561, "time": 0.81813} +{"mode": "train", "epoch": 38, "iter": 1900, "lr": 0.08535, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.21953, "top5_acc": 0.46188, "loss_cls": 4.52505, "loss": 4.52505, "time": 0.81909} +{"mode": "train", "epoch": 38, "iter": 2000, "lr": 0.08533, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2225, "top5_acc": 0.46062, "loss_cls": 4.57441, "loss": 4.57441, "time": 0.81984} +{"mode": "train", "epoch": 38, "iter": 2100, "lr": 0.08531, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21312, "top5_acc": 0.44469, "loss_cls": 4.62055, "loss": 4.62055, "time": 0.81556} +{"mode": "train", "epoch": 38, "iter": 2200, "lr": 0.08529, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21281, "top5_acc": 0.44781, "loss_cls": 4.62595, "loss": 4.62595, "time": 0.82448} +{"mode": "train", "epoch": 38, "iter": 2300, "lr": 0.08527, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22016, "top5_acc": 0.45078, "loss_cls": 4.56671, "loss": 4.56671, "time": 0.8215} +{"mode": "train", "epoch": 38, "iter": 2400, "lr": 0.08525, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22016, "top5_acc": 0.45719, "loss_cls": 4.57842, "loss": 4.57842, "time": 0.82167} +{"mode": "train", "epoch": 38, "iter": 2500, "lr": 0.08523, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21469, "top5_acc": 0.45109, "loss_cls": 4.5717, "loss": 4.5717, "time": 0.82581} +{"mode": "train", "epoch": 38, "iter": 2600, "lr": 0.08521, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22062, "top5_acc": 0.45016, "loss_cls": 4.58104, "loss": 4.58104, "time": 0.82042} +{"mode": "train", "epoch": 38, "iter": 2700, "lr": 0.08519, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21188, "top5_acc": 0.43672, "loss_cls": 4.62212, "loss": 4.62212, "time": 0.8252} +{"mode": "train", "epoch": 38, "iter": 2800, "lr": 0.08517, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21172, "top5_acc": 0.44188, "loss_cls": 4.62897, "loss": 4.62897, "time": 0.82694} +{"mode": "train", "epoch": 38, "iter": 2900, "lr": 0.08515, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21547, "top5_acc": 0.45328, "loss_cls": 4.55377, "loss": 4.55377, "time": 0.82313} +{"mode": "train", "epoch": 38, "iter": 3000, "lr": 0.08513, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22703, "top5_acc": 0.46234, "loss_cls": 4.55933, "loss": 4.55933, "time": 0.82104} +{"mode": "train", "epoch": 38, "iter": 3100, "lr": 0.08511, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.20797, "top5_acc": 0.44516, "loss_cls": 4.61995, "loss": 4.61995, "time": 0.82589} +{"mode": "train", "epoch": 38, "iter": 3200, "lr": 0.08509, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22625, "top5_acc": 0.45344, "loss_cls": 4.54011, "loss": 4.54011, "time": 0.81957} +{"mode": "train", "epoch": 38, "iter": 3300, "lr": 0.08507, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.21625, "top5_acc": 0.46219, "loss_cls": 4.55693, "loss": 4.55693, "time": 0.83432} +{"mode": "train", "epoch": 38, "iter": 3400, "lr": 0.08505, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21906, "top5_acc": 0.455, "loss_cls": 4.58607, "loss": 4.58607, "time": 0.81934} +{"mode": "train", "epoch": 38, "iter": 3500, "lr": 0.08503, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.22031, "top5_acc": 0.45594, "loss_cls": 4.56508, "loss": 4.56508, "time": 0.81861} +{"mode": "train", "epoch": 38, "iter": 3600, "lr": 0.08501, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.21875, "top5_acc": 0.44453, "loss_cls": 4.63419, "loss": 4.63419, "time": 0.82596} +{"mode": "train", "epoch": 38, "iter": 3700, "lr": 0.08499, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21969, "top5_acc": 0.44859, "loss_cls": 4.58458, "loss": 4.58458, "time": 0.82561} +{"mode": "val", "epoch": 38, "iter": 309, "lr": 0.08498, "top1_acc": 0.17059, "top5_acc": 0.38424, "mean_class_accuracy": 0.17043} +{"mode": "train", "epoch": 39, "iter": 100, "lr": 0.08496, "memory": 15990, "data_time": 1.32378, "top1_acc": 0.23422, "top5_acc": 0.46375, "loss_cls": 4.53346, "loss": 4.53346, "time": 2.32139} +{"mode": "train", "epoch": 39, "iter": 200, "lr": 0.08494, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22406, "top5_acc": 0.46734, "loss_cls": 4.51345, "loss": 4.51345, "time": 0.83726} +{"mode": "train", "epoch": 39, "iter": 300, "lr": 0.08492, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21969, "top5_acc": 0.45594, "loss_cls": 4.56969, "loss": 4.56969, "time": 0.83245} +{"mode": "train", "epoch": 39, "iter": 400, "lr": 0.0849, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21516, "top5_acc": 0.45875, "loss_cls": 4.54695, "loss": 4.54695, "time": 0.83579} +{"mode": "train", "epoch": 39, "iter": 500, "lr": 0.08488, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21453, "top5_acc": 0.45422, "loss_cls": 4.56825, "loss": 4.56825, "time": 0.83492} +{"mode": "train", "epoch": 39, "iter": 600, "lr": 0.08486, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22875, "top5_acc": 0.46016, "loss_cls": 4.53759, "loss": 4.53759, "time": 0.82382} +{"mode": "train", "epoch": 39, "iter": 700, "lr": 0.08484, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21641, "top5_acc": 0.46125, "loss_cls": 4.53787, "loss": 4.53787, "time": 0.82091} +{"mode": "train", "epoch": 39, "iter": 800, "lr": 0.08482, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22703, "top5_acc": 0.46, "loss_cls": 4.54502, "loss": 4.54502, "time": 0.81787} +{"mode": "train", "epoch": 39, "iter": 900, "lr": 0.0848, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22562, "top5_acc": 0.45328, "loss_cls": 4.57563, "loss": 4.57563, "time": 0.82146} +{"mode": "train", "epoch": 39, "iter": 1000, "lr": 0.08478, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21516, "top5_acc": 0.45266, "loss_cls": 4.57823, "loss": 4.57823, "time": 0.82529} +{"mode": "train", "epoch": 39, "iter": 1100, "lr": 0.08476, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21375, "top5_acc": 0.44203, "loss_cls": 4.61977, "loss": 4.61977, "time": 0.82428} +{"mode": "train", "epoch": 39, "iter": 1200, "lr": 0.08474, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22484, "top5_acc": 0.45156, "loss_cls": 4.53978, "loss": 4.53978, "time": 0.81985} +{"mode": "train", "epoch": 39, "iter": 1300, "lr": 0.08472, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22344, "top5_acc": 0.45453, "loss_cls": 4.54174, "loss": 4.54174, "time": 0.82238} +{"mode": "train", "epoch": 39, "iter": 1400, "lr": 0.0847, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21, "top5_acc": 0.44297, "loss_cls": 4.59533, "loss": 4.59533, "time": 0.82579} +{"mode": "train", "epoch": 39, "iter": 1500, "lr": 0.08468, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22156, "top5_acc": 0.45969, "loss_cls": 4.57972, "loss": 4.57972, "time": 0.82681} +{"mode": "train", "epoch": 39, "iter": 1600, "lr": 0.08466, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22141, "top5_acc": 0.45297, "loss_cls": 4.57364, "loss": 4.57364, "time": 0.82075} +{"mode": "train", "epoch": 39, "iter": 1700, "lr": 0.08464, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21156, "top5_acc": 0.45422, "loss_cls": 4.59015, "loss": 4.59015, "time": 0.82201} +{"mode": "train", "epoch": 39, "iter": 1800, "lr": 0.08462, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21391, "top5_acc": 0.44469, "loss_cls": 4.61903, "loss": 4.61903, "time": 0.82089} +{"mode": "train", "epoch": 39, "iter": 1900, "lr": 0.0846, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22172, "top5_acc": 0.45516, "loss_cls": 4.57624, "loss": 4.57624, "time": 0.82057} +{"mode": "train", "epoch": 39, "iter": 2000, "lr": 0.08458, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22297, "top5_acc": 0.455, "loss_cls": 4.56081, "loss": 4.56081, "time": 0.81714} +{"mode": "train", "epoch": 39, "iter": 2100, "lr": 0.08456, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21391, "top5_acc": 0.45516, "loss_cls": 4.56867, "loss": 4.56867, "time": 0.82414} +{"mode": "train", "epoch": 39, "iter": 2200, "lr": 0.08454, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21078, "top5_acc": 0.44953, "loss_cls": 4.58749, "loss": 4.58749, "time": 0.82199} +{"mode": "train", "epoch": 39, "iter": 2300, "lr": 0.08452, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22016, "top5_acc": 0.44766, "loss_cls": 4.60207, "loss": 4.60207, "time": 0.82076} +{"mode": "train", "epoch": 39, "iter": 2400, "lr": 0.0845, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21984, "top5_acc": 0.45781, "loss_cls": 4.56413, "loss": 4.56413, "time": 0.82355} +{"mode": "train", "epoch": 39, "iter": 2500, "lr": 0.08448, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21625, "top5_acc": 0.44188, "loss_cls": 4.60655, "loss": 4.60655, "time": 0.82093} +{"mode": "train", "epoch": 39, "iter": 2600, "lr": 0.08446, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21797, "top5_acc": 0.45594, "loss_cls": 4.58696, "loss": 4.58696, "time": 0.82251} +{"mode": "train", "epoch": 39, "iter": 2700, "lr": 0.08444, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22203, "top5_acc": 0.45328, "loss_cls": 4.57151, "loss": 4.57151, "time": 0.81588} +{"mode": "train", "epoch": 39, "iter": 2800, "lr": 0.08442, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.21094, "top5_acc": 0.44062, "loss_cls": 4.61628, "loss": 4.61628, "time": 0.82333} +{"mode": "train", "epoch": 39, "iter": 2900, "lr": 0.0844, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21719, "top5_acc": 0.45172, "loss_cls": 4.57591, "loss": 4.57591, "time": 0.8195} +{"mode": "train", "epoch": 39, "iter": 3000, "lr": 0.08438, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21312, "top5_acc": 0.43719, "loss_cls": 4.6292, "loss": 4.6292, "time": 0.83233} +{"mode": "train", "epoch": 39, "iter": 3100, "lr": 0.08436, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2225, "top5_acc": 0.45344, "loss_cls": 4.55563, "loss": 4.55563, "time": 0.8191} +{"mode": "train", "epoch": 39, "iter": 3200, "lr": 0.08434, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21203, "top5_acc": 0.44453, "loss_cls": 4.60969, "loss": 4.60969, "time": 0.82695} +{"mode": "train", "epoch": 39, "iter": 3300, "lr": 0.08432, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21766, "top5_acc": 0.44688, "loss_cls": 4.58541, "loss": 4.58541, "time": 0.82295} +{"mode": "train", "epoch": 39, "iter": 3400, "lr": 0.0843, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22141, "top5_acc": 0.44797, "loss_cls": 4.57935, "loss": 4.57935, "time": 0.82301} +{"mode": "train", "epoch": 39, "iter": 3500, "lr": 0.08428, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21469, "top5_acc": 0.4575, "loss_cls": 4.54645, "loss": 4.54645, "time": 0.81888} +{"mode": "train", "epoch": 39, "iter": 3600, "lr": 0.08426, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21672, "top5_acc": 0.45125, "loss_cls": 4.57291, "loss": 4.57291, "time": 0.82493} +{"mode": "train", "epoch": 39, "iter": 3700, "lr": 0.08424, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21938, "top5_acc": 0.45266, "loss_cls": 4.58439, "loss": 4.58439, "time": 0.81935} +{"mode": "val", "epoch": 39, "iter": 309, "lr": 0.08423, "top1_acc": 0.15965, "top5_acc": 0.37689, "mean_class_accuracy": 0.15946} +{"mode": "train", "epoch": 40, "iter": 100, "lr": 0.08421, "memory": 15990, "data_time": 1.30651, "top1_acc": 0.22844, "top5_acc": 0.46266, "loss_cls": 4.56132, "loss": 4.56132, "time": 2.30674} +{"mode": "train", "epoch": 40, "iter": 200, "lr": 0.08419, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21984, "top5_acc": 0.45484, "loss_cls": 4.54754, "loss": 4.54754, "time": 0.84102} +{"mode": "train", "epoch": 40, "iter": 300, "lr": 0.08417, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21812, "top5_acc": 0.45859, "loss_cls": 4.56033, "loss": 4.56033, "time": 0.83078} +{"mode": "train", "epoch": 40, "iter": 400, "lr": 0.08415, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22047, "top5_acc": 0.45125, "loss_cls": 4.54673, "loss": 4.54673, "time": 0.83195} +{"mode": "train", "epoch": 40, "iter": 500, "lr": 0.08413, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21359, "top5_acc": 0.45859, "loss_cls": 4.55658, "loss": 4.55658, "time": 0.83131} +{"mode": "train", "epoch": 40, "iter": 600, "lr": 0.08411, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21875, "top5_acc": 0.45625, "loss_cls": 4.54616, "loss": 4.54616, "time": 0.83162} +{"mode": "train", "epoch": 40, "iter": 700, "lr": 0.08408, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21859, "top5_acc": 0.45, "loss_cls": 4.56735, "loss": 4.56735, "time": 0.83257} +{"mode": "train", "epoch": 40, "iter": 800, "lr": 0.08406, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22625, "top5_acc": 0.45625, "loss_cls": 4.52396, "loss": 4.52396, "time": 0.83405} +{"mode": "train", "epoch": 40, "iter": 900, "lr": 0.08404, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21594, "top5_acc": 0.43719, "loss_cls": 4.62997, "loss": 4.62997, "time": 0.8294} +{"mode": "train", "epoch": 40, "iter": 1000, "lr": 0.08402, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22172, "top5_acc": 0.4525, "loss_cls": 4.5577, "loss": 4.5577, "time": 0.82249} +{"mode": "train", "epoch": 40, "iter": 1100, "lr": 0.084, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22203, "top5_acc": 0.45312, "loss_cls": 4.57361, "loss": 4.57361, "time": 0.82355} +{"mode": "train", "epoch": 40, "iter": 1200, "lr": 0.08398, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22406, "top5_acc": 0.45516, "loss_cls": 4.57328, "loss": 4.57328, "time": 0.82268} +{"mode": "train", "epoch": 40, "iter": 1300, "lr": 0.08396, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22828, "top5_acc": 0.45078, "loss_cls": 4.58526, "loss": 4.58526, "time": 0.82238} +{"mode": "train", "epoch": 40, "iter": 1400, "lr": 0.08394, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22359, "top5_acc": 0.46, "loss_cls": 4.54339, "loss": 4.54339, "time": 0.81861} +{"mode": "train", "epoch": 40, "iter": 1500, "lr": 0.08392, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21422, "top5_acc": 0.45016, "loss_cls": 4.5613, "loss": 4.5613, "time": 0.81926} +{"mode": "train", "epoch": 40, "iter": 1600, "lr": 0.0839, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23094, "top5_acc": 0.45562, "loss_cls": 4.55187, "loss": 4.55187, "time": 0.82263} +{"mode": "train", "epoch": 40, "iter": 1700, "lr": 0.08388, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22469, "top5_acc": 0.46359, "loss_cls": 4.54465, "loss": 4.54465, "time": 0.82904} +{"mode": "train", "epoch": 40, "iter": 1800, "lr": 0.08386, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.21875, "top5_acc": 0.445, "loss_cls": 4.60806, "loss": 4.60806, "time": 0.82429} +{"mode": "train", "epoch": 40, "iter": 1900, "lr": 0.08384, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21156, "top5_acc": 0.44672, "loss_cls": 4.59593, "loss": 4.59593, "time": 0.8239} +{"mode": "train", "epoch": 40, "iter": 2000, "lr": 0.08382, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21734, "top5_acc": 0.44641, "loss_cls": 4.58183, "loss": 4.58183, "time": 0.81794} +{"mode": "train", "epoch": 40, "iter": 2100, "lr": 0.0838, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21938, "top5_acc": 0.45547, "loss_cls": 4.55915, "loss": 4.55915, "time": 0.82263} +{"mode": "train", "epoch": 40, "iter": 2200, "lr": 0.08378, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21875, "top5_acc": 0.44734, "loss_cls": 4.56311, "loss": 4.56311, "time": 0.82074} +{"mode": "train", "epoch": 40, "iter": 2300, "lr": 0.08376, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21609, "top5_acc": 0.44375, "loss_cls": 4.60213, "loss": 4.60213, "time": 0.81707} +{"mode": "train", "epoch": 40, "iter": 2400, "lr": 0.08374, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2075, "top5_acc": 0.44859, "loss_cls": 4.60262, "loss": 4.60262, "time": 0.81768} +{"mode": "train", "epoch": 40, "iter": 2500, "lr": 0.08371, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21891, "top5_acc": 0.45109, "loss_cls": 4.5869, "loss": 4.5869, "time": 0.82317} +{"mode": "train", "epoch": 40, "iter": 2600, "lr": 0.08369, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22469, "top5_acc": 0.45812, "loss_cls": 4.5851, "loss": 4.5851, "time": 0.82044} +{"mode": "train", "epoch": 40, "iter": 2700, "lr": 0.08367, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20828, "top5_acc": 0.44422, "loss_cls": 4.60839, "loss": 4.60839, "time": 0.82184} +{"mode": "train", "epoch": 40, "iter": 2800, "lr": 0.08365, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22781, "top5_acc": 0.45812, "loss_cls": 4.54434, "loss": 4.54434, "time": 0.82088} +{"mode": "train", "epoch": 40, "iter": 2900, "lr": 0.08363, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22234, "top5_acc": 0.45375, "loss_cls": 4.57071, "loss": 4.57071, "time": 0.82791} +{"mode": "train", "epoch": 40, "iter": 3000, "lr": 0.08361, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.21484, "top5_acc": 0.44828, "loss_cls": 4.59336, "loss": 4.59336, "time": 0.82633} +{"mode": "train", "epoch": 40, "iter": 3100, "lr": 0.08359, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21156, "top5_acc": 0.44297, "loss_cls": 4.5942, "loss": 4.5942, "time": 0.82134} +{"mode": "train", "epoch": 40, "iter": 3200, "lr": 0.08357, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22562, "top5_acc": 0.45906, "loss_cls": 4.53242, "loss": 4.53242, "time": 0.82532} +{"mode": "train", "epoch": 40, "iter": 3300, "lr": 0.08355, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21453, "top5_acc": 0.44641, "loss_cls": 4.61618, "loss": 4.61618, "time": 0.81857} +{"mode": "train", "epoch": 40, "iter": 3400, "lr": 0.08353, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.21797, "top5_acc": 0.45469, "loss_cls": 4.58139, "loss": 4.58139, "time": 0.82495} +{"mode": "train", "epoch": 40, "iter": 3500, "lr": 0.08351, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22234, "top5_acc": 0.45969, "loss_cls": 4.56007, "loss": 4.56007, "time": 0.81984} +{"mode": "train", "epoch": 40, "iter": 3600, "lr": 0.08349, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22484, "top5_acc": 0.45922, "loss_cls": 4.5149, "loss": 4.5149, "time": 0.81806} +{"mode": "train", "epoch": 40, "iter": 3700, "lr": 0.08347, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21422, "top5_acc": 0.45297, "loss_cls": 4.58052, "loss": 4.58052, "time": 0.82443} +{"mode": "val", "epoch": 40, "iter": 309, "lr": 0.08346, "top1_acc": 0.15489, "top5_acc": 0.36301, "mean_class_accuracy": 0.1549} +{"mode": "train", "epoch": 41, "iter": 100, "lr": 0.08344, "memory": 15990, "data_time": 1.30398, "top1_acc": 0.23406, "top5_acc": 0.45469, "loss_cls": 4.53977, "loss": 4.53977, "time": 2.29813} +{"mode": "train", "epoch": 41, "iter": 200, "lr": 0.08342, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2225, "top5_acc": 0.46156, "loss_cls": 4.51851, "loss": 4.51851, "time": 0.82535} +{"mode": "train", "epoch": 41, "iter": 300, "lr": 0.08339, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2175, "top5_acc": 0.44625, "loss_cls": 4.56298, "loss": 4.56298, "time": 0.82201} +{"mode": "train", "epoch": 41, "iter": 400, "lr": 0.08337, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.225, "top5_acc": 0.45922, "loss_cls": 4.55014, "loss": 4.55014, "time": 0.81966} +{"mode": "train", "epoch": 41, "iter": 500, "lr": 0.08335, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21438, "top5_acc": 0.4475, "loss_cls": 4.57722, "loss": 4.57722, "time": 0.82126} +{"mode": "train", "epoch": 41, "iter": 600, "lr": 0.08333, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21781, "top5_acc": 0.45094, "loss_cls": 4.56786, "loss": 4.56786, "time": 0.81979} +{"mode": "train", "epoch": 41, "iter": 700, "lr": 0.08331, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2125, "top5_acc": 0.45969, "loss_cls": 4.54333, "loss": 4.54333, "time": 0.81816} +{"mode": "train", "epoch": 41, "iter": 800, "lr": 0.08329, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21875, "top5_acc": 0.46, "loss_cls": 4.55083, "loss": 4.55083, "time": 0.82376} +{"mode": "train", "epoch": 41, "iter": 900, "lr": 0.08327, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21984, "top5_acc": 0.45344, "loss_cls": 4.55545, "loss": 4.55545, "time": 0.81929} +{"mode": "train", "epoch": 41, "iter": 1000, "lr": 0.08325, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21141, "top5_acc": 0.45109, "loss_cls": 4.61049, "loss": 4.61049, "time": 0.81585} +{"mode": "train", "epoch": 41, "iter": 1100, "lr": 0.08323, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22125, "top5_acc": 0.45016, "loss_cls": 4.57849, "loss": 4.57849, "time": 0.81875} +{"mode": "train", "epoch": 41, "iter": 1200, "lr": 0.08321, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21656, "top5_acc": 0.44953, "loss_cls": 4.59922, "loss": 4.59922, "time": 0.82056} +{"mode": "train", "epoch": 41, "iter": 1300, "lr": 0.08319, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22891, "top5_acc": 0.46281, "loss_cls": 4.53932, "loss": 4.53932, "time": 0.82474} +{"mode": "train", "epoch": 41, "iter": 1400, "lr": 0.08316, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21906, "top5_acc": 0.45312, "loss_cls": 4.58116, "loss": 4.58116, "time": 0.82338} +{"mode": "train", "epoch": 41, "iter": 1500, "lr": 0.08314, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22328, "top5_acc": 0.45438, "loss_cls": 4.56549, "loss": 4.56549, "time": 0.81954} +{"mode": "train", "epoch": 41, "iter": 1600, "lr": 0.08312, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21922, "top5_acc": 0.45609, "loss_cls": 4.55491, "loss": 4.55491, "time": 0.82625} +{"mode": "train", "epoch": 41, "iter": 1700, "lr": 0.0831, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21688, "top5_acc": 0.44594, "loss_cls": 4.59547, "loss": 4.59547, "time": 0.82984} +{"mode": "train", "epoch": 41, "iter": 1800, "lr": 0.08308, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22312, "top5_acc": 0.45219, "loss_cls": 4.54509, "loss": 4.54509, "time": 0.81954} +{"mode": "train", "epoch": 41, "iter": 1900, "lr": 0.08306, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21344, "top5_acc": 0.45094, "loss_cls": 4.60287, "loss": 4.60287, "time": 0.81914} +{"mode": "train", "epoch": 41, "iter": 2000, "lr": 0.08304, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23141, "top5_acc": 0.46922, "loss_cls": 4.49024, "loss": 4.49024, "time": 0.82769} +{"mode": "train", "epoch": 41, "iter": 2100, "lr": 0.08302, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22344, "top5_acc": 0.46141, "loss_cls": 4.55237, "loss": 4.55237, "time": 0.81786} +{"mode": "train", "epoch": 41, "iter": 2200, "lr": 0.083, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21656, "top5_acc": 0.44781, "loss_cls": 4.59769, "loss": 4.59769, "time": 0.82069} +{"mode": "train", "epoch": 41, "iter": 2300, "lr": 0.08298, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22609, "top5_acc": 0.45891, "loss_cls": 4.54683, "loss": 4.54683, "time": 0.81831} +{"mode": "train", "epoch": 41, "iter": 2400, "lr": 0.08296, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22156, "top5_acc": 0.45125, "loss_cls": 4.5859, "loss": 4.5859, "time": 0.81825} +{"mode": "train", "epoch": 41, "iter": 2500, "lr": 0.08293, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.225, "top5_acc": 0.45188, "loss_cls": 4.55191, "loss": 4.55191, "time": 0.82052} +{"mode": "train", "epoch": 41, "iter": 2600, "lr": 0.08291, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22297, "top5_acc": 0.45906, "loss_cls": 4.55777, "loss": 4.55777, "time": 0.81989} +{"mode": "train", "epoch": 41, "iter": 2700, "lr": 0.08289, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22125, "top5_acc": 0.45453, "loss_cls": 4.54381, "loss": 4.54381, "time": 0.82869} +{"mode": "train", "epoch": 41, "iter": 2800, "lr": 0.08287, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21469, "top5_acc": 0.45766, "loss_cls": 4.57492, "loss": 4.57492, "time": 0.82206} +{"mode": "train", "epoch": 41, "iter": 2900, "lr": 0.08285, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21859, "top5_acc": 0.45906, "loss_cls": 4.55633, "loss": 4.55633, "time": 0.82935} +{"mode": "train", "epoch": 41, "iter": 3000, "lr": 0.08283, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22109, "top5_acc": 0.45594, "loss_cls": 4.56401, "loss": 4.56401, "time": 0.82412} +{"mode": "train", "epoch": 41, "iter": 3100, "lr": 0.08281, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22016, "top5_acc": 0.45375, "loss_cls": 4.57924, "loss": 4.57924, "time": 0.83216} +{"mode": "train", "epoch": 41, "iter": 3200, "lr": 0.08279, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2225, "top5_acc": 0.45094, "loss_cls": 4.57769, "loss": 4.57769, "time": 0.82401} +{"mode": "train", "epoch": 41, "iter": 3300, "lr": 0.08277, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21922, "top5_acc": 0.45781, "loss_cls": 4.54469, "loss": 4.54469, "time": 0.8253} +{"mode": "train", "epoch": 41, "iter": 3400, "lr": 0.08274, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21891, "top5_acc": 0.45234, "loss_cls": 4.58064, "loss": 4.58064, "time": 0.82698} +{"mode": "train", "epoch": 41, "iter": 3500, "lr": 0.08272, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22078, "top5_acc": 0.45547, "loss_cls": 4.56515, "loss": 4.56515, "time": 0.81976} +{"mode": "train", "epoch": 41, "iter": 3600, "lr": 0.0827, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21234, "top5_acc": 0.45484, "loss_cls": 4.57482, "loss": 4.57482, "time": 0.82431} +{"mode": "train", "epoch": 41, "iter": 3700, "lr": 0.08268, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21406, "top5_acc": 0.45297, "loss_cls": 4.58349, "loss": 4.58349, "time": 0.82185} +{"mode": "val", "epoch": 41, "iter": 309, "lr": 0.08267, "top1_acc": 0.17085, "top5_acc": 0.3774, "mean_class_accuracy": 0.1708} +{"mode": "train", "epoch": 42, "iter": 100, "lr": 0.08265, "memory": 15990, "data_time": 1.3329, "top1_acc": 0.22625, "top5_acc": 0.465, "loss_cls": 4.52228, "loss": 4.52228, "time": 2.32303} +{"mode": "train", "epoch": 42, "iter": 200, "lr": 0.08263, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2225, "top5_acc": 0.45594, "loss_cls": 4.55789, "loss": 4.55789, "time": 0.82434} +{"mode": "train", "epoch": 42, "iter": 300, "lr": 0.08261, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22266, "top5_acc": 0.46031, "loss_cls": 4.51736, "loss": 4.51736, "time": 0.8175} +{"mode": "train", "epoch": 42, "iter": 400, "lr": 0.08259, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21516, "top5_acc": 0.45328, "loss_cls": 4.55445, "loss": 4.55445, "time": 0.8217} +{"mode": "train", "epoch": 42, "iter": 500, "lr": 0.08257, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22453, "top5_acc": 0.46641, "loss_cls": 4.51856, "loss": 4.51856, "time": 0.82229} +{"mode": "train", "epoch": 42, "iter": 600, "lr": 0.08254, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22328, "top5_acc": 0.46547, "loss_cls": 4.53316, "loss": 4.53316, "time": 0.81785} +{"mode": "train", "epoch": 42, "iter": 700, "lr": 0.08252, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22188, "top5_acc": 0.46234, "loss_cls": 4.52645, "loss": 4.52645, "time": 0.81622} +{"mode": "train", "epoch": 42, "iter": 800, "lr": 0.0825, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22141, "top5_acc": 0.45953, "loss_cls": 4.55509, "loss": 4.55509, "time": 0.81584} +{"mode": "train", "epoch": 42, "iter": 900, "lr": 0.08248, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21625, "top5_acc": 0.45594, "loss_cls": 4.56125, "loss": 4.56125, "time": 0.82117} +{"mode": "train", "epoch": 42, "iter": 1000, "lr": 0.08246, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22484, "top5_acc": 0.47016, "loss_cls": 4.49223, "loss": 4.49223, "time": 0.8173} +{"mode": "train", "epoch": 42, "iter": 1100, "lr": 0.08244, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21609, "top5_acc": 0.45469, "loss_cls": 4.57311, "loss": 4.57311, "time": 0.82198} +{"mode": "train", "epoch": 42, "iter": 1200, "lr": 0.08242, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22422, "top5_acc": 0.46734, "loss_cls": 4.55529, "loss": 4.55529, "time": 0.82387} +{"mode": "train", "epoch": 42, "iter": 1300, "lr": 0.0824, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21844, "top5_acc": 0.455, "loss_cls": 4.58003, "loss": 4.58003, "time": 0.82136} +{"mode": "train", "epoch": 42, "iter": 1400, "lr": 0.08237, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21844, "top5_acc": 0.45234, "loss_cls": 4.58919, "loss": 4.58919, "time": 0.8274} +{"mode": "train", "epoch": 42, "iter": 1500, "lr": 0.08235, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21844, "top5_acc": 0.44969, "loss_cls": 4.56156, "loss": 4.56156, "time": 0.81938} +{"mode": "train", "epoch": 42, "iter": 1600, "lr": 0.08233, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22797, "top5_acc": 0.46156, "loss_cls": 4.56021, "loss": 4.56021, "time": 0.83273} +{"mode": "train", "epoch": 42, "iter": 1700, "lr": 0.08231, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22094, "top5_acc": 0.45844, "loss_cls": 4.54637, "loss": 4.54637, "time": 0.82008} +{"mode": "train", "epoch": 42, "iter": 1800, "lr": 0.08229, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22125, "top5_acc": 0.45328, "loss_cls": 4.58101, "loss": 4.58101, "time": 0.82199} +{"mode": "train", "epoch": 42, "iter": 1900, "lr": 0.08227, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21844, "top5_acc": 0.45688, "loss_cls": 4.54835, "loss": 4.54835, "time": 0.81867} +{"mode": "train", "epoch": 42, "iter": 2000, "lr": 0.08225, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22266, "top5_acc": 0.46141, "loss_cls": 4.56943, "loss": 4.56943, "time": 0.81922} +{"mode": "train", "epoch": 42, "iter": 2100, "lr": 0.08222, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21797, "top5_acc": 0.45672, "loss_cls": 4.56035, "loss": 4.56035, "time": 0.81869} +{"mode": "train", "epoch": 42, "iter": 2200, "lr": 0.0822, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22219, "top5_acc": 0.45406, "loss_cls": 4.57351, "loss": 4.57351, "time": 0.82035} +{"mode": "train", "epoch": 42, "iter": 2300, "lr": 0.08218, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22812, "top5_acc": 0.45938, "loss_cls": 4.54022, "loss": 4.54022, "time": 0.8197} +{"mode": "train", "epoch": 42, "iter": 2400, "lr": 0.08216, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22078, "top5_acc": 0.44812, "loss_cls": 4.58587, "loss": 4.58587, "time": 0.81763} +{"mode": "train", "epoch": 42, "iter": 2500, "lr": 0.08214, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22562, "top5_acc": 0.45875, "loss_cls": 4.53589, "loss": 4.53589, "time": 0.81889} +{"mode": "train", "epoch": 42, "iter": 2600, "lr": 0.08212, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.21531, "top5_acc": 0.45672, "loss_cls": 4.5562, "loss": 4.5562, "time": 0.82621} +{"mode": "train", "epoch": 42, "iter": 2700, "lr": 0.0821, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21531, "top5_acc": 0.44562, "loss_cls": 4.59761, "loss": 4.59761, "time": 0.81904} +{"mode": "train", "epoch": 42, "iter": 2800, "lr": 0.08207, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21375, "top5_acc": 0.45156, "loss_cls": 4.55303, "loss": 4.55303, "time": 0.82538} +{"mode": "train", "epoch": 42, "iter": 2900, "lr": 0.08205, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22516, "top5_acc": 0.45406, "loss_cls": 4.57552, "loss": 4.57552, "time": 0.83177} +{"mode": "train", "epoch": 42, "iter": 3000, "lr": 0.08203, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22047, "top5_acc": 0.45656, "loss_cls": 4.56495, "loss": 4.56495, "time": 0.82275} +{"mode": "train", "epoch": 42, "iter": 3100, "lr": 0.08201, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21562, "top5_acc": 0.45, "loss_cls": 4.57743, "loss": 4.57743, "time": 0.83068} +{"mode": "train", "epoch": 42, "iter": 3200, "lr": 0.08199, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21875, "top5_acc": 0.45375, "loss_cls": 4.5856, "loss": 4.5856, "time": 0.81919} +{"mode": "train", "epoch": 42, "iter": 3300, "lr": 0.08197, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23125, "top5_acc": 0.46141, "loss_cls": 4.53259, "loss": 4.53259, "time": 0.81777} +{"mode": "train", "epoch": 42, "iter": 3400, "lr": 0.08195, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2225, "top5_acc": 0.45, "loss_cls": 4.56286, "loss": 4.56286, "time": 0.81893} +{"mode": "train", "epoch": 42, "iter": 3500, "lr": 0.08192, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21922, "top5_acc": 0.44812, "loss_cls": 4.58224, "loss": 4.58224, "time": 0.82002} +{"mode": "train", "epoch": 42, "iter": 3600, "lr": 0.0819, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22734, "top5_acc": 0.44672, "loss_cls": 4.57416, "loss": 4.57416, "time": 0.83135} +{"mode": "train", "epoch": 42, "iter": 3700, "lr": 0.08188, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22312, "top5_acc": 0.45484, "loss_cls": 4.56247, "loss": 4.56247, "time": 0.82605} +{"mode": "val", "epoch": 42, "iter": 309, "lr": 0.08187, "top1_acc": 0.17935, "top5_acc": 0.38971, "mean_class_accuracy": 0.17931} +{"mode": "train", "epoch": 43, "iter": 100, "lr": 0.08185, "memory": 15990, "data_time": 1.26087, "top1_acc": 0.2275, "top5_acc": 0.46562, "loss_cls": 4.51163, "loss": 4.51163, "time": 2.23648} +{"mode": "train", "epoch": 43, "iter": 200, "lr": 0.08183, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22141, "top5_acc": 0.46594, "loss_cls": 4.50855, "loss": 4.50855, "time": 0.82299} +{"mode": "train", "epoch": 43, "iter": 300, "lr": 0.08181, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21828, "top5_acc": 0.45688, "loss_cls": 4.55071, "loss": 4.55071, "time": 0.82036} +{"mode": "train", "epoch": 43, "iter": 400, "lr": 0.08179, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21641, "top5_acc": 0.45719, "loss_cls": 4.57912, "loss": 4.57912, "time": 0.81995} +{"mode": "train", "epoch": 43, "iter": 500, "lr": 0.08176, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22734, "top5_acc": 0.46016, "loss_cls": 4.50962, "loss": 4.50962, "time": 0.81839} +{"mode": "train", "epoch": 43, "iter": 600, "lr": 0.08174, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22547, "top5_acc": 0.45953, "loss_cls": 4.56378, "loss": 4.56378, "time": 0.81918} +{"mode": "train", "epoch": 43, "iter": 700, "lr": 0.08172, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22, "top5_acc": 0.45766, "loss_cls": 4.58151, "loss": 4.58151, "time": 0.82141} +{"mode": "train", "epoch": 43, "iter": 800, "lr": 0.0817, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22031, "top5_acc": 0.45469, "loss_cls": 4.52715, "loss": 4.52715, "time": 0.8219} +{"mode": "train", "epoch": 43, "iter": 900, "lr": 0.08168, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21922, "top5_acc": 0.455, "loss_cls": 4.54834, "loss": 4.54834, "time": 0.8243} +{"mode": "train", "epoch": 43, "iter": 1000, "lr": 0.08166, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23094, "top5_acc": 0.46359, "loss_cls": 4.5141, "loss": 4.5141, "time": 0.8221} +{"mode": "train", "epoch": 43, "iter": 1100, "lr": 0.08163, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21672, "top5_acc": 0.45031, "loss_cls": 4.62257, "loss": 4.62257, "time": 0.81336} +{"mode": "train", "epoch": 43, "iter": 1200, "lr": 0.08161, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22875, "top5_acc": 0.46703, "loss_cls": 4.52609, "loss": 4.52609, "time": 0.8181} +{"mode": "train", "epoch": 43, "iter": 1300, "lr": 0.08159, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22953, "top5_acc": 0.46953, "loss_cls": 4.50909, "loss": 4.50909, "time": 0.81444} +{"mode": "train", "epoch": 43, "iter": 1400, "lr": 0.08157, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22359, "top5_acc": 0.4575, "loss_cls": 4.59531, "loss": 4.59531, "time": 0.8248} +{"mode": "train", "epoch": 43, "iter": 1500, "lr": 0.08155, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22828, "top5_acc": 0.46016, "loss_cls": 4.5233, "loss": 4.5233, "time": 0.82433} +{"mode": "train", "epoch": 43, "iter": 1600, "lr": 0.08153, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22672, "top5_acc": 0.46422, "loss_cls": 4.53495, "loss": 4.53495, "time": 0.82604} +{"mode": "train", "epoch": 43, "iter": 1700, "lr": 0.0815, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22281, "top5_acc": 0.46047, "loss_cls": 4.53014, "loss": 4.53014, "time": 0.82267} +{"mode": "train", "epoch": 43, "iter": 1800, "lr": 0.08148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22109, "top5_acc": 0.45156, "loss_cls": 4.5625, "loss": 4.5625, "time": 0.82148} +{"mode": "train", "epoch": 43, "iter": 1900, "lr": 0.08146, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.22641, "top5_acc": 0.45547, "loss_cls": 4.52208, "loss": 4.52208, "time": 0.81547} +{"mode": "train", "epoch": 43, "iter": 2000, "lr": 0.08144, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22828, "top5_acc": 0.45344, "loss_cls": 4.55749, "loss": 4.55749, "time": 0.81943} +{"mode": "train", "epoch": 43, "iter": 2100, "lr": 0.08142, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21906, "top5_acc": 0.45875, "loss_cls": 4.53866, "loss": 4.53866, "time": 0.82157} +{"mode": "train", "epoch": 43, "iter": 2200, "lr": 0.0814, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21969, "top5_acc": 0.46188, "loss_cls": 4.55648, "loss": 4.55648, "time": 0.82277} +{"mode": "train", "epoch": 43, "iter": 2300, "lr": 0.08137, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22422, "top5_acc": 0.45641, "loss_cls": 4.55533, "loss": 4.55533, "time": 0.81811} +{"mode": "train", "epoch": 43, "iter": 2400, "lr": 0.08135, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21672, "top5_acc": 0.45781, "loss_cls": 4.57074, "loss": 4.57074, "time": 0.81944} +{"mode": "train", "epoch": 43, "iter": 2500, "lr": 0.08133, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22562, "top5_acc": 0.45734, "loss_cls": 4.5264, "loss": 4.5264, "time": 0.82574} +{"mode": "train", "epoch": 43, "iter": 2600, "lr": 0.08131, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22047, "top5_acc": 0.45656, "loss_cls": 4.53851, "loss": 4.53851, "time": 0.81935} +{"mode": "train", "epoch": 43, "iter": 2700, "lr": 0.08129, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.20844, "top5_acc": 0.44, "loss_cls": 4.64678, "loss": 4.64678, "time": 0.82284} +{"mode": "train", "epoch": 43, "iter": 2800, "lr": 0.08126, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22469, "top5_acc": 0.46484, "loss_cls": 4.54182, "loss": 4.54182, "time": 0.8255} +{"mode": "train", "epoch": 43, "iter": 2900, "lr": 0.08124, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.225, "top5_acc": 0.45875, "loss_cls": 4.54784, "loss": 4.54784, "time": 0.8302} +{"mode": "train", "epoch": 43, "iter": 3000, "lr": 0.08122, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22125, "top5_acc": 0.45375, "loss_cls": 4.54972, "loss": 4.54972, "time": 0.82222} +{"mode": "train", "epoch": 43, "iter": 3100, "lr": 0.0812, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.21562, "top5_acc": 0.45375, "loss_cls": 4.56489, "loss": 4.56489, "time": 0.8246} +{"mode": "train", "epoch": 43, "iter": 3200, "lr": 0.08118, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22172, "top5_acc": 0.45781, "loss_cls": 4.53806, "loss": 4.53806, "time": 0.82133} +{"mode": "train", "epoch": 43, "iter": 3300, "lr": 0.08116, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21703, "top5_acc": 0.45828, "loss_cls": 4.55818, "loss": 4.55818, "time": 0.82021} +{"mode": "train", "epoch": 43, "iter": 3400, "lr": 0.08113, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22484, "top5_acc": 0.46188, "loss_cls": 4.51946, "loss": 4.51946, "time": 0.81856} +{"mode": "train", "epoch": 43, "iter": 3500, "lr": 0.08111, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22312, "top5_acc": 0.45891, "loss_cls": 4.54163, "loss": 4.54163, "time": 0.82462} +{"mode": "train", "epoch": 43, "iter": 3600, "lr": 0.08109, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.22531, "top5_acc": 0.45703, "loss_cls": 4.5433, "loss": 4.5433, "time": 0.81955} +{"mode": "train", "epoch": 43, "iter": 3700, "lr": 0.08107, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22125, "top5_acc": 0.45672, "loss_cls": 4.5781, "loss": 4.5781, "time": 0.81886} +{"mode": "val", "epoch": 43, "iter": 309, "lr": 0.08106, "top1_acc": 0.16821, "top5_acc": 0.38029, "mean_class_accuracy": 0.16802} +{"mode": "train", "epoch": 44, "iter": 100, "lr": 0.08104, "memory": 15990, "data_time": 1.26963, "top1_acc": 0.23109, "top5_acc": 0.46703, "loss_cls": 4.52014, "loss": 4.52014, "time": 2.25404} +{"mode": "train", "epoch": 44, "iter": 200, "lr": 0.08101, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21859, "top5_acc": 0.46328, "loss_cls": 4.53647, "loss": 4.53647, "time": 0.82184} +{"mode": "train", "epoch": 44, "iter": 300, "lr": 0.08099, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22078, "top5_acc": 0.45859, "loss_cls": 4.54011, "loss": 4.54011, "time": 0.82265} +{"mode": "train", "epoch": 44, "iter": 400, "lr": 0.08097, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22109, "top5_acc": 0.46234, "loss_cls": 4.52584, "loss": 4.52584, "time": 0.82101} +{"mode": "train", "epoch": 44, "iter": 500, "lr": 0.08095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22781, "top5_acc": 0.46891, "loss_cls": 4.53351, "loss": 4.53351, "time": 0.81911} +{"mode": "train", "epoch": 44, "iter": 600, "lr": 0.08093, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23203, "top5_acc": 0.46734, "loss_cls": 4.49976, "loss": 4.49976, "time": 0.81929} +{"mode": "train", "epoch": 44, "iter": 700, "lr": 0.0809, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22859, "top5_acc": 0.46094, "loss_cls": 4.54734, "loss": 4.54734, "time": 0.81941} +{"mode": "train", "epoch": 44, "iter": 800, "lr": 0.08088, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21969, "top5_acc": 0.44859, "loss_cls": 4.59284, "loss": 4.59284, "time": 0.82751} +{"mode": "train", "epoch": 44, "iter": 900, "lr": 0.08086, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22719, "top5_acc": 0.46547, "loss_cls": 4.4962, "loss": 4.4962, "time": 0.81982} +{"mode": "train", "epoch": 44, "iter": 1000, "lr": 0.08084, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21906, "top5_acc": 0.45125, "loss_cls": 4.58818, "loss": 4.58818, "time": 0.81699} +{"mode": "train", "epoch": 44, "iter": 1100, "lr": 0.08082, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22594, "top5_acc": 0.45891, "loss_cls": 4.55384, "loss": 4.55384, "time": 0.81704} +{"mode": "train", "epoch": 44, "iter": 1200, "lr": 0.08079, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22562, "top5_acc": 0.46828, "loss_cls": 4.52636, "loss": 4.52636, "time": 0.81863} +{"mode": "train", "epoch": 44, "iter": 1300, "lr": 0.08077, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22906, "top5_acc": 0.46562, "loss_cls": 4.52446, "loss": 4.52446, "time": 0.81893} +{"mode": "train", "epoch": 44, "iter": 1400, "lr": 0.08075, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22094, "top5_acc": 0.46031, "loss_cls": 4.55802, "loss": 4.55802, "time": 0.82367} +{"mode": "train", "epoch": 44, "iter": 1500, "lr": 0.08073, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22203, "top5_acc": 0.46, "loss_cls": 4.54774, "loss": 4.54774, "time": 0.82867} +{"mode": "train", "epoch": 44, "iter": 1600, "lr": 0.08071, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22391, "top5_acc": 0.46938, "loss_cls": 4.51353, "loss": 4.51353, "time": 0.82203} +{"mode": "train", "epoch": 44, "iter": 1700, "lr": 0.08068, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22203, "top5_acc": 0.46406, "loss_cls": 4.48469, "loss": 4.48469, "time": 0.82699} +{"mode": "train", "epoch": 44, "iter": 1800, "lr": 0.08066, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22297, "top5_acc": 0.45391, "loss_cls": 4.57995, "loss": 4.57995, "time": 0.81515} +{"mode": "train", "epoch": 44, "iter": 1900, "lr": 0.08064, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22812, "top5_acc": 0.45922, "loss_cls": 4.54363, "loss": 4.54363, "time": 0.81733} +{"mode": "train", "epoch": 44, "iter": 2000, "lr": 0.08062, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21453, "top5_acc": 0.45, "loss_cls": 4.58488, "loss": 4.58488, "time": 0.82161} +{"mode": "train", "epoch": 44, "iter": 2100, "lr": 0.0806, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22016, "top5_acc": 0.45656, "loss_cls": 4.58215, "loss": 4.58215, "time": 0.81635} +{"mode": "train", "epoch": 44, "iter": 2200, "lr": 0.08057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22109, "top5_acc": 0.45406, "loss_cls": 4.58716, "loss": 4.58716, "time": 0.81491} +{"mode": "train", "epoch": 44, "iter": 2300, "lr": 0.08055, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2375, "top5_acc": 0.46516, "loss_cls": 4.4928, "loss": 4.4928, "time": 0.8186} +{"mode": "train", "epoch": 44, "iter": 2400, "lr": 0.08053, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21469, "top5_acc": 0.44359, "loss_cls": 4.57397, "loss": 4.57397, "time": 0.81904} +{"mode": "train", "epoch": 44, "iter": 2500, "lr": 0.08051, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22641, "top5_acc": 0.46328, "loss_cls": 4.50348, "loss": 4.50348, "time": 0.82625} +{"mode": "train", "epoch": 44, "iter": 2600, "lr": 0.08048, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21734, "top5_acc": 0.44656, "loss_cls": 4.58681, "loss": 4.58681, "time": 0.82261} +{"mode": "train", "epoch": 44, "iter": 2700, "lr": 0.08046, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22422, "top5_acc": 0.45812, "loss_cls": 4.56742, "loss": 4.56742, "time": 0.82385} +{"mode": "train", "epoch": 44, "iter": 2800, "lr": 0.08044, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22469, "top5_acc": 0.45922, "loss_cls": 4.54308, "loss": 4.54308, "time": 0.82489} +{"mode": "train", "epoch": 44, "iter": 2900, "lr": 0.08042, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22453, "top5_acc": 0.46844, "loss_cls": 4.54144, "loss": 4.54144, "time": 0.82666} +{"mode": "train", "epoch": 44, "iter": 3000, "lr": 0.0804, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22281, "top5_acc": 0.45812, "loss_cls": 4.55398, "loss": 4.55398, "time": 0.82178} +{"mode": "train", "epoch": 44, "iter": 3100, "lr": 0.08037, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21984, "top5_acc": 0.465, "loss_cls": 4.54903, "loss": 4.54903, "time": 0.82205} +{"mode": "train", "epoch": 44, "iter": 3200, "lr": 0.08035, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21953, "top5_acc": 0.45438, "loss_cls": 4.55063, "loss": 4.55063, "time": 0.82204} +{"mode": "train", "epoch": 44, "iter": 3300, "lr": 0.08033, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23172, "top5_acc": 0.46438, "loss_cls": 4.52083, "loss": 4.52083, "time": 0.82385} +{"mode": "train", "epoch": 44, "iter": 3400, "lr": 0.08031, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22641, "top5_acc": 0.45625, "loss_cls": 4.54671, "loss": 4.54671, "time": 0.81824} +{"mode": "train", "epoch": 44, "iter": 3500, "lr": 0.08028, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22016, "top5_acc": 0.46125, "loss_cls": 4.53813, "loss": 4.53813, "time": 0.81382} +{"mode": "train", "epoch": 44, "iter": 3600, "lr": 0.08026, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21828, "top5_acc": 0.45188, "loss_cls": 4.58301, "loss": 4.58301, "time": 0.8244} +{"mode": "train", "epoch": 44, "iter": 3700, "lr": 0.08024, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23016, "top5_acc": 0.46516, "loss_cls": 4.51663, "loss": 4.51663, "time": 0.823} +{"mode": "val", "epoch": 44, "iter": 309, "lr": 0.08023, "top1_acc": 0.15393, "top5_acc": 0.34797, "mean_class_accuracy": 0.15374} +{"mode": "train", "epoch": 45, "iter": 100, "lr": 0.08021, "memory": 15990, "data_time": 1.2757, "top1_acc": 0.22859, "top5_acc": 0.46703, "loss_cls": 4.51638, "loss": 4.51638, "time": 2.25757} +{"mode": "train", "epoch": 45, "iter": 200, "lr": 0.08019, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21656, "top5_acc": 0.45391, "loss_cls": 4.56219, "loss": 4.56219, "time": 0.8214} +{"mode": "train", "epoch": 45, "iter": 300, "lr": 0.08016, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22266, "top5_acc": 0.46609, "loss_cls": 4.52765, "loss": 4.52765, "time": 0.81601} +{"mode": "train", "epoch": 45, "iter": 400, "lr": 0.08014, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22719, "top5_acc": 0.45484, "loss_cls": 4.53914, "loss": 4.53914, "time": 0.81988} +{"mode": "train", "epoch": 45, "iter": 500, "lr": 0.08012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21312, "top5_acc": 0.46031, "loss_cls": 4.53917, "loss": 4.53917, "time": 0.8193} +{"mode": "train", "epoch": 45, "iter": 600, "lr": 0.0801, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22922, "top5_acc": 0.47438, "loss_cls": 4.48781, "loss": 4.48781, "time": 0.81419} +{"mode": "train", "epoch": 45, "iter": 700, "lr": 0.08007, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21781, "top5_acc": 0.45031, "loss_cls": 4.55312, "loss": 4.55312, "time": 0.81801} +{"mode": "train", "epoch": 45, "iter": 800, "lr": 0.08005, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22359, "top5_acc": 0.46516, "loss_cls": 4.50662, "loss": 4.50662, "time": 0.8159} +{"mode": "train", "epoch": 45, "iter": 900, "lr": 0.08003, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22516, "top5_acc": 0.46531, "loss_cls": 4.53551, "loss": 4.53551, "time": 0.82381} +{"mode": "train", "epoch": 45, "iter": 1000, "lr": 0.08001, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22438, "top5_acc": 0.46172, "loss_cls": 4.52915, "loss": 4.52915, "time": 0.82335} +{"mode": "train", "epoch": 45, "iter": 1100, "lr": 0.07998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23219, "top5_acc": 0.45641, "loss_cls": 4.5411, "loss": 4.5411, "time": 0.82405} +{"mode": "train", "epoch": 45, "iter": 1200, "lr": 0.07996, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22469, "top5_acc": 0.46391, "loss_cls": 4.52629, "loss": 4.52629, "time": 0.81793} +{"mode": "train", "epoch": 45, "iter": 1300, "lr": 0.07994, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21969, "top5_acc": 0.46516, "loss_cls": 4.52372, "loss": 4.52372, "time": 0.82681} +{"mode": "train", "epoch": 45, "iter": 1400, "lr": 0.07992, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22172, "top5_acc": 0.46547, "loss_cls": 4.50751, "loss": 4.50751, "time": 0.82763} +{"mode": "train", "epoch": 45, "iter": 1500, "lr": 0.0799, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2275, "top5_acc": 0.46531, "loss_cls": 4.52903, "loss": 4.52903, "time": 0.82674} +{"mode": "train", "epoch": 45, "iter": 1600, "lr": 0.07987, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22688, "top5_acc": 0.46328, "loss_cls": 4.53092, "loss": 4.53092, "time": 0.82022} +{"mode": "train", "epoch": 45, "iter": 1700, "lr": 0.07985, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23656, "top5_acc": 0.46578, "loss_cls": 4.51178, "loss": 4.51178, "time": 0.82155} +{"mode": "train", "epoch": 45, "iter": 1800, "lr": 0.07983, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21641, "top5_acc": 0.4575, "loss_cls": 4.58951, "loss": 4.58951, "time": 0.81439} +{"mode": "train", "epoch": 45, "iter": 1900, "lr": 0.07981, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22125, "top5_acc": 0.45406, "loss_cls": 4.54885, "loss": 4.54885, "time": 0.82312} +{"mode": "train", "epoch": 45, "iter": 2000, "lr": 0.07978, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22391, "top5_acc": 0.47438, "loss_cls": 4.4946, "loss": 4.4946, "time": 0.81772} +{"mode": "train", "epoch": 45, "iter": 2100, "lr": 0.07976, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21609, "top5_acc": 0.44984, "loss_cls": 4.59258, "loss": 4.59258, "time": 0.81719} +{"mode": "train", "epoch": 45, "iter": 2200, "lr": 0.07974, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23156, "top5_acc": 0.46766, "loss_cls": 4.52031, "loss": 4.52031, "time": 0.81783} +{"mode": "train", "epoch": 45, "iter": 2300, "lr": 0.07972, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.21938, "top5_acc": 0.45562, "loss_cls": 4.53637, "loss": 4.53637, "time": 0.81541} +{"mode": "train", "epoch": 45, "iter": 2400, "lr": 0.07969, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23, "top5_acc": 0.46344, "loss_cls": 4.49972, "loss": 4.49972, "time": 0.82349} +{"mode": "train", "epoch": 45, "iter": 2500, "lr": 0.07967, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.21141, "top5_acc": 0.44734, "loss_cls": 4.60554, "loss": 4.60554, "time": 0.81538} +{"mode": "train", "epoch": 45, "iter": 2600, "lr": 0.07965, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2175, "top5_acc": 0.45359, "loss_cls": 4.57514, "loss": 4.57514, "time": 0.82668} +{"mode": "train", "epoch": 45, "iter": 2700, "lr": 0.07963, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2225, "top5_acc": 0.45922, "loss_cls": 4.52763, "loss": 4.52763, "time": 0.81983} +{"mode": "train", "epoch": 45, "iter": 2800, "lr": 0.0796, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22078, "top5_acc": 0.45219, "loss_cls": 4.55773, "loss": 4.55773, "time": 0.82695} +{"mode": "train", "epoch": 45, "iter": 2900, "lr": 0.07958, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22016, "top5_acc": 0.46016, "loss_cls": 4.56218, "loss": 4.56218, "time": 0.82669} +{"mode": "train", "epoch": 45, "iter": 3000, "lr": 0.07956, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22531, "top5_acc": 0.46094, "loss_cls": 4.52561, "loss": 4.52561, "time": 0.82665} +{"mode": "train", "epoch": 45, "iter": 3100, "lr": 0.07954, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21734, "top5_acc": 0.45625, "loss_cls": 4.55877, "loss": 4.55877, "time": 0.82178} +{"mode": "train", "epoch": 45, "iter": 3200, "lr": 0.07951, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22562, "top5_acc": 0.46859, "loss_cls": 4.50523, "loss": 4.50523, "time": 0.81911} +{"mode": "train", "epoch": 45, "iter": 3300, "lr": 0.07949, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21719, "top5_acc": 0.45734, "loss_cls": 4.56112, "loss": 4.56112, "time": 0.82076} +{"mode": "train", "epoch": 45, "iter": 3400, "lr": 0.07947, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22609, "top5_acc": 0.45812, "loss_cls": 4.53342, "loss": 4.53342, "time": 0.82385} +{"mode": "train", "epoch": 45, "iter": 3500, "lr": 0.07945, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21828, "top5_acc": 0.45516, "loss_cls": 4.59442, "loss": 4.59442, "time": 0.81388} +{"mode": "train", "epoch": 45, "iter": 3600, "lr": 0.07942, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.225, "top5_acc": 0.46719, "loss_cls": 4.50838, "loss": 4.50838, "time": 0.82618} +{"mode": "train", "epoch": 45, "iter": 3700, "lr": 0.0794, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22641, "top5_acc": 0.45859, "loss_cls": 4.53271, "loss": 4.53271, "time": 0.81925} +{"mode": "val", "epoch": 45, "iter": 309, "lr": 0.07939, "top1_acc": 0.13711, "top5_acc": 0.33992, "mean_class_accuracy": 0.13707} +{"mode": "train", "epoch": 46, "iter": 100, "lr": 0.07937, "memory": 15990, "data_time": 1.34623, "top1_acc": 0.22922, "top5_acc": 0.47266, "loss_cls": 4.47569, "loss": 4.47569, "time": 2.35443} +{"mode": "train", "epoch": 46, "iter": 200, "lr": 0.07934, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2275, "top5_acc": 0.46391, "loss_cls": 4.52149, "loss": 4.52149, "time": 0.83062} +{"mode": "train", "epoch": 46, "iter": 300, "lr": 0.07932, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23, "top5_acc": 0.46578, "loss_cls": 4.47928, "loss": 4.47928, "time": 0.82864} +{"mode": "train", "epoch": 46, "iter": 400, "lr": 0.0793, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23422, "top5_acc": 0.46844, "loss_cls": 4.50585, "loss": 4.50585, "time": 0.82437} +{"mode": "train", "epoch": 46, "iter": 500, "lr": 0.07928, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22016, "top5_acc": 0.45156, "loss_cls": 4.56101, "loss": 4.56101, "time": 0.82436} +{"mode": "train", "epoch": 46, "iter": 600, "lr": 0.07925, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.225, "top5_acc": 0.46312, "loss_cls": 4.52746, "loss": 4.52746, "time": 0.81808} +{"mode": "train", "epoch": 46, "iter": 700, "lr": 0.07923, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22328, "top5_acc": 0.45797, "loss_cls": 4.58652, "loss": 4.58652, "time": 0.81775} +{"mode": "train", "epoch": 46, "iter": 800, "lr": 0.07921, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22438, "top5_acc": 0.46109, "loss_cls": 4.5399, "loss": 4.5399, "time": 0.81953} +{"mode": "train", "epoch": 46, "iter": 900, "lr": 0.07919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22516, "top5_acc": 0.45891, "loss_cls": 4.51002, "loss": 4.51002, "time": 0.82298} +{"mode": "train", "epoch": 46, "iter": 1000, "lr": 0.07916, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22672, "top5_acc": 0.45828, "loss_cls": 4.51205, "loss": 4.51205, "time": 0.82388} +{"mode": "train", "epoch": 46, "iter": 1100, "lr": 0.07914, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22406, "top5_acc": 0.45094, "loss_cls": 4.57653, "loss": 4.57653, "time": 0.8157} +{"mode": "train", "epoch": 46, "iter": 1200, "lr": 0.07912, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22219, "top5_acc": 0.44797, "loss_cls": 4.57774, "loss": 4.57774, "time": 0.81989} +{"mode": "train", "epoch": 46, "iter": 1300, "lr": 0.07909, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22562, "top5_acc": 0.45953, "loss_cls": 4.5537, "loss": 4.5537, "time": 0.82426} +{"mode": "train", "epoch": 46, "iter": 1400, "lr": 0.07907, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23172, "top5_acc": 0.46953, "loss_cls": 4.48083, "loss": 4.48083, "time": 0.8264} +{"mode": "train", "epoch": 46, "iter": 1500, "lr": 0.07905, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22406, "top5_acc": 0.45969, "loss_cls": 4.53097, "loss": 4.53097, "time": 0.82942} +{"mode": "train", "epoch": 46, "iter": 1600, "lr": 0.07903, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23016, "top5_acc": 0.46625, "loss_cls": 4.49379, "loss": 4.49379, "time": 0.82631} +{"mode": "train", "epoch": 46, "iter": 1700, "lr": 0.079, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22453, "top5_acc": 0.46891, "loss_cls": 4.5136, "loss": 4.5136, "time": 0.81597} +{"mode": "train", "epoch": 46, "iter": 1800, "lr": 0.07898, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23812, "top5_acc": 0.46766, "loss_cls": 4.49184, "loss": 4.49184, "time": 0.81757} +{"mode": "train", "epoch": 46, "iter": 1900, "lr": 0.07896, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22812, "top5_acc": 0.45766, "loss_cls": 4.52242, "loss": 4.52242, "time": 0.82256} +{"mode": "train", "epoch": 46, "iter": 2000, "lr": 0.07894, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22547, "top5_acc": 0.46281, "loss_cls": 4.53373, "loss": 4.53373, "time": 0.82101} +{"mode": "train", "epoch": 46, "iter": 2100, "lr": 0.07891, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22938, "top5_acc": 0.46375, "loss_cls": 4.48741, "loss": 4.48741, "time": 0.81629} +{"mode": "train", "epoch": 46, "iter": 2200, "lr": 0.07889, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22078, "top5_acc": 0.46406, "loss_cls": 4.54103, "loss": 4.54103, "time": 0.8248} +{"mode": "train", "epoch": 46, "iter": 2300, "lr": 0.07887, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22828, "top5_acc": 0.46453, "loss_cls": 4.54503, "loss": 4.54503, "time": 0.81954} +{"mode": "train", "epoch": 46, "iter": 2400, "lr": 0.07884, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2325, "top5_acc": 0.46422, "loss_cls": 4.50959, "loss": 4.50959, "time": 0.82025} +{"mode": "train", "epoch": 46, "iter": 2500, "lr": 0.07882, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22, "top5_acc": 0.46656, "loss_cls": 4.53337, "loss": 4.53337, "time": 0.82043} +{"mode": "train", "epoch": 46, "iter": 2600, "lr": 0.0788, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21375, "top5_acc": 0.45141, "loss_cls": 4.58993, "loss": 4.58993, "time": 0.82077} +{"mode": "train", "epoch": 46, "iter": 2700, "lr": 0.07878, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21906, "top5_acc": 0.44656, "loss_cls": 4.5684, "loss": 4.5684, "time": 0.81915} +{"mode": "train", "epoch": 46, "iter": 2800, "lr": 0.07875, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22578, "top5_acc": 0.45828, "loss_cls": 4.57622, "loss": 4.57622, "time": 0.82747} +{"mode": "train", "epoch": 46, "iter": 2900, "lr": 0.07873, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23375, "top5_acc": 0.46328, "loss_cls": 4.51955, "loss": 4.51955, "time": 0.82793} +{"mode": "train", "epoch": 46, "iter": 3000, "lr": 0.07871, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22859, "top5_acc": 0.4675, "loss_cls": 4.51916, "loss": 4.51916, "time": 0.82392} +{"mode": "train", "epoch": 46, "iter": 3100, "lr": 0.07868, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.21906, "top5_acc": 0.46094, "loss_cls": 4.53509, "loss": 4.53509, "time": 0.82656} +{"mode": "train", "epoch": 46, "iter": 3200, "lr": 0.07866, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22109, "top5_acc": 0.45047, "loss_cls": 4.5821, "loss": 4.5821, "time": 0.82277} +{"mode": "train", "epoch": 46, "iter": 3300, "lr": 0.07864, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23375, "top5_acc": 0.47266, "loss_cls": 4.4964, "loss": 4.4964, "time": 0.8259} +{"mode": "train", "epoch": 46, "iter": 3400, "lr": 0.07862, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22266, "top5_acc": 0.44953, "loss_cls": 4.56873, "loss": 4.56873, "time": 0.82136} +{"mode": "train", "epoch": 46, "iter": 3500, "lr": 0.07859, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22844, "top5_acc": 0.46844, "loss_cls": 4.5204, "loss": 4.5204, "time": 0.81816} +{"mode": "train", "epoch": 46, "iter": 3600, "lr": 0.07857, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22375, "top5_acc": 0.46031, "loss_cls": 4.54512, "loss": 4.54512, "time": 0.82668} +{"mode": "train", "epoch": 46, "iter": 3700, "lr": 0.07855, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22562, "top5_acc": 0.46016, "loss_cls": 4.54994, "loss": 4.54994, "time": 0.82944} +{"mode": "val", "epoch": 46, "iter": 309, "lr": 0.07854, "top1_acc": 0.17632, "top5_acc": 0.3928, "mean_class_accuracy": 0.17614} +{"mode": "train", "epoch": 47, "iter": 100, "lr": 0.07851, "memory": 15990, "data_time": 1.34464, "top1_acc": 0.22891, "top5_acc": 0.47375, "loss_cls": 4.48699, "loss": 4.48699, "time": 2.34485} +{"mode": "train", "epoch": 47, "iter": 200, "lr": 0.07849, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21656, "top5_acc": 0.45094, "loss_cls": 4.53311, "loss": 4.53311, "time": 0.83442} +{"mode": "train", "epoch": 47, "iter": 300, "lr": 0.07847, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23125, "top5_acc": 0.47578, "loss_cls": 4.46615, "loss": 4.46615, "time": 0.83604} +{"mode": "train", "epoch": 47, "iter": 400, "lr": 0.07844, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23062, "top5_acc": 0.46469, "loss_cls": 4.4949, "loss": 4.4949, "time": 0.83067} +{"mode": "train", "epoch": 47, "iter": 500, "lr": 0.07842, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21828, "top5_acc": 0.45609, "loss_cls": 4.53252, "loss": 4.53252, "time": 0.82838} +{"mode": "train", "epoch": 47, "iter": 600, "lr": 0.0784, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22922, "top5_acc": 0.45719, "loss_cls": 4.51992, "loss": 4.51992, "time": 0.81956} +{"mode": "train", "epoch": 47, "iter": 700, "lr": 0.07838, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22391, "top5_acc": 0.45469, "loss_cls": 4.56172, "loss": 4.56172, "time": 0.81795} +{"mode": "train", "epoch": 47, "iter": 800, "lr": 0.07835, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22438, "top5_acc": 0.46203, "loss_cls": 4.53558, "loss": 4.53558, "time": 0.82141} +{"mode": "train", "epoch": 47, "iter": 900, "lr": 0.07833, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23359, "top5_acc": 0.46516, "loss_cls": 4.50195, "loss": 4.50195, "time": 0.82249} +{"mode": "train", "epoch": 47, "iter": 1000, "lr": 0.07831, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22, "top5_acc": 0.46359, "loss_cls": 4.53433, "loss": 4.53433, "time": 0.81947} +{"mode": "train", "epoch": 47, "iter": 1100, "lr": 0.07828, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23172, "top5_acc": 0.46234, "loss_cls": 4.52782, "loss": 4.52782, "time": 0.8225} +{"mode": "train", "epoch": 47, "iter": 1200, "lr": 0.07826, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22859, "top5_acc": 0.46297, "loss_cls": 4.53608, "loss": 4.53608, "time": 0.8192} +{"mode": "train", "epoch": 47, "iter": 1300, "lr": 0.07824, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22703, "top5_acc": 0.46219, "loss_cls": 4.52647, "loss": 4.52647, "time": 0.82936} +{"mode": "train", "epoch": 47, "iter": 1400, "lr": 0.07821, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22484, "top5_acc": 0.46031, "loss_cls": 4.5367, "loss": 4.5367, "time": 0.81859} +{"mode": "train", "epoch": 47, "iter": 1500, "lr": 0.07819, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23625, "top5_acc": 0.46875, "loss_cls": 4.4907, "loss": 4.4907, "time": 0.82116} +{"mode": "train", "epoch": 47, "iter": 1600, "lr": 0.07817, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23328, "top5_acc": 0.46734, "loss_cls": 4.52177, "loss": 4.52177, "time": 0.81971} +{"mode": "train", "epoch": 47, "iter": 1700, "lr": 0.07814, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23, "top5_acc": 0.46641, "loss_cls": 4.52866, "loss": 4.52866, "time": 0.82124} +{"mode": "train", "epoch": 47, "iter": 1800, "lr": 0.07812, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23016, "top5_acc": 0.46891, "loss_cls": 4.46492, "loss": 4.46492, "time": 0.81953} +{"mode": "train", "epoch": 47, "iter": 1900, "lr": 0.0781, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22875, "top5_acc": 0.46594, "loss_cls": 4.49752, "loss": 4.49752, "time": 0.82435} +{"mode": "train", "epoch": 47, "iter": 2000, "lr": 0.07808, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23484, "top5_acc": 0.47234, "loss_cls": 4.50111, "loss": 4.50111, "time": 0.81588} +{"mode": "train", "epoch": 47, "iter": 2100, "lr": 0.07805, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22062, "top5_acc": 0.45922, "loss_cls": 4.55466, "loss": 4.55466, "time": 0.81967} +{"mode": "train", "epoch": 47, "iter": 2200, "lr": 0.07803, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21844, "top5_acc": 0.45922, "loss_cls": 4.54945, "loss": 4.54945, "time": 0.81871} +{"mode": "train", "epoch": 47, "iter": 2300, "lr": 0.07801, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22656, "top5_acc": 0.46547, "loss_cls": 4.50574, "loss": 4.50574, "time": 0.81846} +{"mode": "train", "epoch": 47, "iter": 2400, "lr": 0.07798, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22562, "top5_acc": 0.46375, "loss_cls": 4.52695, "loss": 4.52695, "time": 0.82585} +{"mode": "train", "epoch": 47, "iter": 2500, "lr": 0.07796, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22562, "top5_acc": 0.45609, "loss_cls": 4.51901, "loss": 4.51901, "time": 0.82466} +{"mode": "train", "epoch": 47, "iter": 2600, "lr": 0.07794, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21578, "top5_acc": 0.45594, "loss_cls": 4.56934, "loss": 4.56934, "time": 0.82336} +{"mode": "train", "epoch": 47, "iter": 2700, "lr": 0.07791, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23141, "top5_acc": 0.46484, "loss_cls": 4.5089, "loss": 4.5089, "time": 0.82194} +{"mode": "train", "epoch": 47, "iter": 2800, "lr": 0.07789, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23219, "top5_acc": 0.47312, "loss_cls": 4.47866, "loss": 4.47866, "time": 0.82567} +{"mode": "train", "epoch": 47, "iter": 2900, "lr": 0.07787, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22641, "top5_acc": 0.45625, "loss_cls": 4.54694, "loss": 4.54694, "time": 0.82683} +{"mode": "train", "epoch": 47, "iter": 3000, "lr": 0.07784, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22344, "top5_acc": 0.46422, "loss_cls": 4.51873, "loss": 4.51873, "time": 0.81857} +{"mode": "train", "epoch": 47, "iter": 3100, "lr": 0.07782, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22453, "top5_acc": 0.46094, "loss_cls": 4.53485, "loss": 4.53485, "time": 0.82247} +{"mode": "train", "epoch": 47, "iter": 3200, "lr": 0.0778, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23438, "top5_acc": 0.46328, "loss_cls": 4.4932, "loss": 4.4932, "time": 0.816} +{"mode": "train", "epoch": 47, "iter": 3300, "lr": 0.07777, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22141, "top5_acc": 0.45234, "loss_cls": 4.57519, "loss": 4.57519, "time": 0.82182} +{"mode": "train", "epoch": 47, "iter": 3400, "lr": 0.07775, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22188, "top5_acc": 0.45703, "loss_cls": 4.54896, "loss": 4.54896, "time": 0.82032} +{"mode": "train", "epoch": 47, "iter": 3500, "lr": 0.07773, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21703, "top5_acc": 0.46047, "loss_cls": 4.56002, "loss": 4.56002, "time": 0.8228} +{"mode": "train", "epoch": 47, "iter": 3600, "lr": 0.0777, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.22703, "top5_acc": 0.46078, "loss_cls": 4.53287, "loss": 4.53287, "time": 0.83104} +{"mode": "train", "epoch": 47, "iter": 3700, "lr": 0.07768, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22703, "top5_acc": 0.46328, "loss_cls": 4.50439, "loss": 4.50439, "time": 0.8285} +{"mode": "val", "epoch": 47, "iter": 309, "lr": 0.07767, "top1_acc": 0.14648, "top5_acc": 0.34772, "mean_class_accuracy": 0.14644} +{"mode": "train", "epoch": 48, "iter": 100, "lr": 0.07765, "memory": 15990, "data_time": 1.35196, "top1_acc": 0.2425, "top5_acc": 0.47359, "loss_cls": 4.43743, "loss": 4.43743, "time": 2.34821} +{"mode": "train", "epoch": 48, "iter": 200, "lr": 0.07762, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22125, "top5_acc": 0.46094, "loss_cls": 4.55163, "loss": 4.55163, "time": 0.82405} +{"mode": "train", "epoch": 48, "iter": 300, "lr": 0.0776, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22891, "top5_acc": 0.47188, "loss_cls": 4.50984, "loss": 4.50984, "time": 0.823} +{"mode": "train", "epoch": 48, "iter": 400, "lr": 0.07758, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22828, "top5_acc": 0.46109, "loss_cls": 4.5065, "loss": 4.5065, "time": 0.82096} +{"mode": "train", "epoch": 48, "iter": 500, "lr": 0.07755, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22391, "top5_acc": 0.46812, "loss_cls": 4.52135, "loss": 4.52135, "time": 0.81809} +{"mode": "train", "epoch": 48, "iter": 600, "lr": 0.07753, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22312, "top5_acc": 0.4675, "loss_cls": 4.52368, "loss": 4.52368, "time": 0.82742} +{"mode": "train", "epoch": 48, "iter": 700, "lr": 0.07751, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22641, "top5_acc": 0.46469, "loss_cls": 4.53965, "loss": 4.53965, "time": 0.82793} +{"mode": "train", "epoch": 48, "iter": 800, "lr": 0.07748, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22047, "top5_acc": 0.46344, "loss_cls": 4.5179, "loss": 4.5179, "time": 0.81879} +{"mode": "train", "epoch": 48, "iter": 900, "lr": 0.07746, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22953, "top5_acc": 0.46734, "loss_cls": 4.50026, "loss": 4.50026, "time": 0.81629} +{"mode": "train", "epoch": 48, "iter": 1000, "lr": 0.07744, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22375, "top5_acc": 0.46469, "loss_cls": 4.54053, "loss": 4.54053, "time": 0.82403} +{"mode": "train", "epoch": 48, "iter": 1100, "lr": 0.07741, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22656, "top5_acc": 0.46891, "loss_cls": 4.49641, "loss": 4.49641, "time": 0.8167} +{"mode": "train", "epoch": 48, "iter": 1200, "lr": 0.07739, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23031, "top5_acc": 0.46484, "loss_cls": 4.51342, "loss": 4.51342, "time": 0.82221} +{"mode": "train", "epoch": 48, "iter": 1300, "lr": 0.07737, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22156, "top5_acc": 0.46125, "loss_cls": 4.52839, "loss": 4.52839, "time": 0.82401} +{"mode": "train", "epoch": 48, "iter": 1400, "lr": 0.07734, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23484, "top5_acc": 0.475, "loss_cls": 4.45072, "loss": 4.45072, "time": 0.82359} +{"mode": "train", "epoch": 48, "iter": 1500, "lr": 0.07732, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22078, "top5_acc": 0.46344, "loss_cls": 4.51869, "loss": 4.51869, "time": 0.82292} +{"mode": "train", "epoch": 48, "iter": 1600, "lr": 0.0773, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22016, "top5_acc": 0.46062, "loss_cls": 4.5575, "loss": 4.5575, "time": 0.81685} +{"mode": "train", "epoch": 48, "iter": 1700, "lr": 0.07727, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23953, "top5_acc": 0.475, "loss_cls": 4.44704, "loss": 4.44704, "time": 0.81936} +{"mode": "train", "epoch": 48, "iter": 1800, "lr": 0.07725, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22891, "top5_acc": 0.46219, "loss_cls": 4.55149, "loss": 4.55149, "time": 0.81873} +{"mode": "train", "epoch": 48, "iter": 1900, "lr": 0.07723, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21984, "top5_acc": 0.45547, "loss_cls": 4.5477, "loss": 4.5477, "time": 0.81766} +{"mode": "train", "epoch": 48, "iter": 2000, "lr": 0.0772, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23375, "top5_acc": 0.47141, "loss_cls": 4.49568, "loss": 4.49568, "time": 0.81888} +{"mode": "train", "epoch": 48, "iter": 2100, "lr": 0.07718, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.21844, "top5_acc": 0.46156, "loss_cls": 4.55603, "loss": 4.55603, "time": 0.82295} +{"mode": "train", "epoch": 48, "iter": 2200, "lr": 0.07716, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22938, "top5_acc": 0.46938, "loss_cls": 4.52379, "loss": 4.52379, "time": 0.81783} +{"mode": "train", "epoch": 48, "iter": 2300, "lr": 0.07713, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23, "top5_acc": 0.46656, "loss_cls": 4.48323, "loss": 4.48323, "time": 0.81932} +{"mode": "train", "epoch": 48, "iter": 2400, "lr": 0.07711, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23344, "top5_acc": 0.47703, "loss_cls": 4.48245, "loss": 4.48245, "time": 0.81831} +{"mode": "train", "epoch": 48, "iter": 2500, "lr": 0.07709, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22625, "top5_acc": 0.46109, "loss_cls": 4.53212, "loss": 4.53212, "time": 0.82901} +{"mode": "train", "epoch": 48, "iter": 2600, "lr": 0.07706, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21953, "top5_acc": 0.46047, "loss_cls": 4.51981, "loss": 4.51981, "time": 0.82009} +{"mode": "train", "epoch": 48, "iter": 2700, "lr": 0.07704, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23, "top5_acc": 0.47031, "loss_cls": 4.49525, "loss": 4.49525, "time": 0.82558} +{"mode": "train", "epoch": 48, "iter": 2800, "lr": 0.07701, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.22703, "top5_acc": 0.46297, "loss_cls": 4.53595, "loss": 4.53595, "time": 0.82376} +{"mode": "train", "epoch": 48, "iter": 2900, "lr": 0.07699, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21828, "top5_acc": 0.46031, "loss_cls": 4.52662, "loss": 4.52662, "time": 0.8253} +{"mode": "train", "epoch": 48, "iter": 3000, "lr": 0.07697, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22547, "top5_acc": 0.46422, "loss_cls": 4.50324, "loss": 4.50324, "time": 0.82683} +{"mode": "train", "epoch": 48, "iter": 3100, "lr": 0.07694, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21438, "top5_acc": 0.45922, "loss_cls": 4.57203, "loss": 4.57203, "time": 0.81774} +{"mode": "train", "epoch": 48, "iter": 3200, "lr": 0.07692, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23188, "top5_acc": 0.46875, "loss_cls": 4.47207, "loss": 4.47207, "time": 0.82193} +{"mode": "train", "epoch": 48, "iter": 3300, "lr": 0.0769, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.235, "top5_acc": 0.46453, "loss_cls": 4.51354, "loss": 4.51354, "time": 0.81692} +{"mode": "train", "epoch": 48, "iter": 3400, "lr": 0.07687, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22516, "top5_acc": 0.46188, "loss_cls": 4.51195, "loss": 4.51195, "time": 0.82057} +{"mode": "train", "epoch": 48, "iter": 3500, "lr": 0.07685, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22984, "top5_acc": 0.46219, "loss_cls": 4.52532, "loss": 4.52532, "time": 0.82168} +{"mode": "train", "epoch": 48, "iter": 3600, "lr": 0.07683, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22734, "top5_acc": 0.46891, "loss_cls": 4.52419, "loss": 4.52419, "time": 0.81856} +{"mode": "train", "epoch": 48, "iter": 3700, "lr": 0.0768, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.21953, "top5_acc": 0.4625, "loss_cls": 4.5305, "loss": 4.5305, "time": 0.81918} +{"mode": "val", "epoch": 48, "iter": 309, "lr": 0.07679, "top1_acc": 0.16021, "top5_acc": 0.37026, "mean_class_accuracy": 0.16013} +{"mode": "train", "epoch": 49, "iter": 100, "lr": 0.07677, "memory": 15990, "data_time": 1.33474, "top1_acc": 0.22953, "top5_acc": 0.46984, "loss_cls": 4.46192, "loss": 4.46192, "time": 2.32331} +{"mode": "train", "epoch": 49, "iter": 200, "lr": 0.07674, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23875, "top5_acc": 0.47047, "loss_cls": 4.4633, "loss": 4.4633, "time": 0.82262} +{"mode": "train", "epoch": 49, "iter": 300, "lr": 0.07672, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23156, "top5_acc": 0.46734, "loss_cls": 4.47259, "loss": 4.47259, "time": 0.81654} +{"mode": "train", "epoch": 49, "iter": 400, "lr": 0.0767, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23453, "top5_acc": 0.47344, "loss_cls": 4.44187, "loss": 4.44187, "time": 0.82367} +{"mode": "train", "epoch": 49, "iter": 500, "lr": 0.07667, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23203, "top5_acc": 0.46031, "loss_cls": 4.49895, "loss": 4.49895, "time": 0.82013} +{"mode": "train", "epoch": 49, "iter": 600, "lr": 0.07665, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2275, "top5_acc": 0.46578, "loss_cls": 4.49978, "loss": 4.49978, "time": 0.81505} +{"mode": "train", "epoch": 49, "iter": 700, "lr": 0.07663, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22734, "top5_acc": 0.46781, "loss_cls": 4.4887, "loss": 4.4887, "time": 0.8152} +{"mode": "train", "epoch": 49, "iter": 800, "lr": 0.0766, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22047, "top5_acc": 0.47484, "loss_cls": 4.52751, "loss": 4.52751, "time": 0.82391} +{"mode": "train", "epoch": 49, "iter": 900, "lr": 0.07658, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22281, "top5_acc": 0.45281, "loss_cls": 4.56162, "loss": 4.56162, "time": 0.8235} +{"mode": "train", "epoch": 49, "iter": 1000, "lr": 0.07656, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22625, "top5_acc": 0.46609, "loss_cls": 4.51232, "loss": 4.51232, "time": 0.81792} +{"mode": "train", "epoch": 49, "iter": 1100, "lr": 0.07653, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22516, "top5_acc": 0.46578, "loss_cls": 4.49919, "loss": 4.49919, "time": 0.8183} +{"mode": "train", "epoch": 49, "iter": 1200, "lr": 0.07651, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2325, "top5_acc": 0.46094, "loss_cls": 4.49455, "loss": 4.49455, "time": 0.81968} +{"mode": "train", "epoch": 49, "iter": 1300, "lr": 0.07648, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22531, "top5_acc": 0.46281, "loss_cls": 4.49976, "loss": 4.49976, "time": 0.82036} +{"mode": "train", "epoch": 49, "iter": 1400, "lr": 0.07646, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22922, "top5_acc": 0.46688, "loss_cls": 4.49184, "loss": 4.49184, "time": 0.82742} +{"mode": "train", "epoch": 49, "iter": 1500, "lr": 0.07644, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22781, "top5_acc": 0.46719, "loss_cls": 4.50572, "loss": 4.50572, "time": 0.8227} +{"mode": "train", "epoch": 49, "iter": 1600, "lr": 0.07641, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23641, "top5_acc": 0.46656, "loss_cls": 4.47547, "loss": 4.47547, "time": 0.81884} +{"mode": "train", "epoch": 49, "iter": 1700, "lr": 0.07639, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22859, "top5_acc": 0.46641, "loss_cls": 4.47636, "loss": 4.47636, "time": 0.82102} +{"mode": "train", "epoch": 49, "iter": 1800, "lr": 0.07637, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23156, "top5_acc": 0.46812, "loss_cls": 4.51342, "loss": 4.51342, "time": 0.81885} +{"mode": "train", "epoch": 49, "iter": 1900, "lr": 0.07634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22781, "top5_acc": 0.45234, "loss_cls": 4.5214, "loss": 4.5214, "time": 0.81296} +{"mode": "train", "epoch": 49, "iter": 2000, "lr": 0.07632, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22797, "top5_acc": 0.4675, "loss_cls": 4.50733, "loss": 4.50733, "time": 0.81888} +{"mode": "train", "epoch": 49, "iter": 2100, "lr": 0.07629, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23359, "top5_acc": 0.46906, "loss_cls": 4.50134, "loss": 4.50134, "time": 0.81743} +{"mode": "train", "epoch": 49, "iter": 2200, "lr": 0.07627, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22625, "top5_acc": 0.46266, "loss_cls": 4.52479, "loss": 4.52479, "time": 0.81858} +{"mode": "train", "epoch": 49, "iter": 2300, "lr": 0.07625, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22594, "top5_acc": 0.47281, "loss_cls": 4.49759, "loss": 4.49759, "time": 0.82033} +{"mode": "train", "epoch": 49, "iter": 2400, "lr": 0.07622, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.225, "top5_acc": 0.46141, "loss_cls": 4.51661, "loss": 4.51661, "time": 0.8232} +{"mode": "train", "epoch": 49, "iter": 2500, "lr": 0.0762, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22188, "top5_acc": 0.46531, "loss_cls": 4.54678, "loss": 4.54678, "time": 0.81968} +{"mode": "train", "epoch": 49, "iter": 2600, "lr": 0.07618, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22562, "top5_acc": 0.45781, "loss_cls": 4.53975, "loss": 4.53975, "time": 0.82182} +{"mode": "train", "epoch": 49, "iter": 2700, "lr": 0.07615, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22109, "top5_acc": 0.45766, "loss_cls": 4.54636, "loss": 4.54636, "time": 0.82329} +{"mode": "train", "epoch": 49, "iter": 2800, "lr": 0.07613, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22281, "top5_acc": 0.45875, "loss_cls": 4.53886, "loss": 4.53886, "time": 0.82255} +{"mode": "train", "epoch": 49, "iter": 2900, "lr": 0.0761, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22219, "top5_acc": 0.45922, "loss_cls": 4.53289, "loss": 4.53289, "time": 0.82031} +{"mode": "train", "epoch": 49, "iter": 3000, "lr": 0.07608, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23531, "top5_acc": 0.46906, "loss_cls": 4.4832, "loss": 4.4832, "time": 0.82432} +{"mode": "train", "epoch": 49, "iter": 3100, "lr": 0.07606, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23672, "top5_acc": 0.47453, "loss_cls": 4.47066, "loss": 4.47066, "time": 0.82133} +{"mode": "train", "epoch": 49, "iter": 3200, "lr": 0.07603, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22125, "top5_acc": 0.46906, "loss_cls": 4.48066, "loss": 4.48066, "time": 0.82085} +{"mode": "train", "epoch": 49, "iter": 3300, "lr": 0.07601, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22984, "top5_acc": 0.46578, "loss_cls": 4.49737, "loss": 4.49737, "time": 0.81877} +{"mode": "train", "epoch": 49, "iter": 3400, "lr": 0.07598, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22453, "top5_acc": 0.45656, "loss_cls": 4.54331, "loss": 4.54331, "time": 0.81953} +{"mode": "train", "epoch": 49, "iter": 3500, "lr": 0.07596, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23156, "top5_acc": 0.46672, "loss_cls": 4.48465, "loss": 4.48465, "time": 0.82173} +{"mode": "train", "epoch": 49, "iter": 3600, "lr": 0.07594, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23016, "top5_acc": 0.46938, "loss_cls": 4.51989, "loss": 4.51989, "time": 0.82393} +{"mode": "train", "epoch": 49, "iter": 3700, "lr": 0.07591, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22609, "top5_acc": 0.46266, "loss_cls": 4.52915, "loss": 4.52915, "time": 0.82378} +{"mode": "val", "epoch": 49, "iter": 309, "lr": 0.0759, "top1_acc": 0.12328, "top5_acc": 0.30542, "mean_class_accuracy": 0.12312} +{"mode": "train", "epoch": 50, "iter": 100, "lr": 0.07588, "memory": 15990, "data_time": 1.32008, "top1_acc": 0.24, "top5_acc": 0.46922, "loss_cls": 4.44288, "loss": 4.44288, "time": 2.30615} +{"mode": "train", "epoch": 50, "iter": 200, "lr": 0.07585, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23141, "top5_acc": 0.46719, "loss_cls": 4.49236, "loss": 4.49236, "time": 0.83012} +{"mode": "train", "epoch": 50, "iter": 300, "lr": 0.07583, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23484, "top5_acc": 0.47234, "loss_cls": 4.46982, "loss": 4.46982, "time": 0.82033} +{"mode": "train", "epoch": 50, "iter": 400, "lr": 0.07581, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23234, "top5_acc": 0.47078, "loss_cls": 4.48002, "loss": 4.48002, "time": 0.82236} +{"mode": "train", "epoch": 50, "iter": 500, "lr": 0.07578, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23188, "top5_acc": 0.47484, "loss_cls": 4.4428, "loss": 4.4428, "time": 0.81809} +{"mode": "train", "epoch": 50, "iter": 600, "lr": 0.07576, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23922, "top5_acc": 0.47891, "loss_cls": 4.44458, "loss": 4.44458, "time": 0.81869} +{"mode": "train", "epoch": 50, "iter": 700, "lr": 0.07573, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22984, "top5_acc": 0.45469, "loss_cls": 4.52334, "loss": 4.52334, "time": 0.81872} +{"mode": "train", "epoch": 50, "iter": 800, "lr": 0.07571, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23109, "top5_acc": 0.46547, "loss_cls": 4.4981, "loss": 4.4981, "time": 0.82407} +{"mode": "train", "epoch": 50, "iter": 900, "lr": 0.07569, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23406, "top5_acc": 0.47031, "loss_cls": 4.51405, "loss": 4.51405, "time": 0.82687} +{"mode": "train", "epoch": 50, "iter": 1000, "lr": 0.07566, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23266, "top5_acc": 0.47844, "loss_cls": 4.46521, "loss": 4.46521, "time": 0.81659} +{"mode": "train", "epoch": 50, "iter": 1100, "lr": 0.07564, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23406, "top5_acc": 0.47953, "loss_cls": 4.4768, "loss": 4.4768, "time": 0.82301} +{"mode": "train", "epoch": 50, "iter": 1200, "lr": 0.07561, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23109, "top5_acc": 0.46891, "loss_cls": 4.53368, "loss": 4.53368, "time": 0.82295} +{"mode": "train", "epoch": 50, "iter": 1300, "lr": 0.07559, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23359, "top5_acc": 0.47172, "loss_cls": 4.48239, "loss": 4.48239, "time": 0.82134} +{"mode": "train", "epoch": 50, "iter": 1400, "lr": 0.07557, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22484, "top5_acc": 0.46484, "loss_cls": 4.52836, "loss": 4.52836, "time": 0.82454} +{"mode": "train", "epoch": 50, "iter": 1500, "lr": 0.07554, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22797, "top5_acc": 0.46688, "loss_cls": 4.49697, "loss": 4.49697, "time": 0.82179} +{"mode": "train", "epoch": 50, "iter": 1600, "lr": 0.07552, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22312, "top5_acc": 0.45797, "loss_cls": 4.54444, "loss": 4.54444, "time": 0.81738} +{"mode": "train", "epoch": 50, "iter": 1700, "lr": 0.07549, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23625, "top5_acc": 0.47125, "loss_cls": 4.45948, "loss": 4.45948, "time": 0.81703} +{"mode": "train", "epoch": 50, "iter": 1800, "lr": 0.07547, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23094, "top5_acc": 0.46781, "loss_cls": 4.50548, "loss": 4.50548, "time": 0.82036} +{"mode": "train", "epoch": 50, "iter": 1900, "lr": 0.07545, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23469, "top5_acc": 0.46719, "loss_cls": 4.49354, "loss": 4.49354, "time": 0.8216} +{"mode": "train", "epoch": 50, "iter": 2000, "lr": 0.07542, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23156, "top5_acc": 0.46469, "loss_cls": 4.51468, "loss": 4.51468, "time": 0.82167} +{"mode": "train", "epoch": 50, "iter": 2100, "lr": 0.0754, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23656, "top5_acc": 0.48484, "loss_cls": 4.43887, "loss": 4.43887, "time": 0.81988} +{"mode": "train", "epoch": 50, "iter": 2200, "lr": 0.07537, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23453, "top5_acc": 0.46562, "loss_cls": 4.48718, "loss": 4.48718, "time": 0.81613} +{"mode": "train", "epoch": 50, "iter": 2300, "lr": 0.07535, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2225, "top5_acc": 0.46812, "loss_cls": 4.53032, "loss": 4.53032, "time": 0.81859} +{"mode": "train", "epoch": 50, "iter": 2400, "lr": 0.07533, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23297, "top5_acc": 0.46844, "loss_cls": 4.49929, "loss": 4.49929, "time": 0.82833} +{"mode": "train", "epoch": 50, "iter": 2500, "lr": 0.0753, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23672, "top5_acc": 0.46234, "loss_cls": 4.48782, "loss": 4.48782, "time": 0.81663} +{"mode": "train", "epoch": 50, "iter": 2600, "lr": 0.07528, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22812, "top5_acc": 0.46766, "loss_cls": 4.48751, "loss": 4.48751, "time": 0.81971} +{"mode": "train", "epoch": 50, "iter": 2700, "lr": 0.07525, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22766, "top5_acc": 0.45953, "loss_cls": 4.52664, "loss": 4.52664, "time": 0.82347} +{"mode": "train", "epoch": 50, "iter": 2800, "lr": 0.07523, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22672, "top5_acc": 0.46344, "loss_cls": 4.50913, "loss": 4.50913, "time": 0.81797} +{"mode": "train", "epoch": 50, "iter": 2900, "lr": 0.0752, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2375, "top5_acc": 0.47219, "loss_cls": 4.47673, "loss": 4.47673, "time": 0.8242} +{"mode": "train", "epoch": 50, "iter": 3000, "lr": 0.07518, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23047, "top5_acc": 0.47312, "loss_cls": 4.48881, "loss": 4.48881, "time": 0.8223} +{"mode": "train", "epoch": 50, "iter": 3100, "lr": 0.07516, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.21969, "top5_acc": 0.44766, "loss_cls": 4.56459, "loss": 4.56459, "time": 0.82299} +{"mode": "train", "epoch": 50, "iter": 3200, "lr": 0.07513, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22125, "top5_acc": 0.46094, "loss_cls": 4.53202, "loss": 4.53202, "time": 0.816} +{"mode": "train", "epoch": 50, "iter": 3300, "lr": 0.07511, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22109, "top5_acc": 0.46844, "loss_cls": 4.50586, "loss": 4.50586, "time": 0.82211} +{"mode": "train", "epoch": 50, "iter": 3400, "lr": 0.07508, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22281, "top5_acc": 0.45891, "loss_cls": 4.53422, "loss": 4.53422, "time": 0.8266} +{"mode": "train", "epoch": 50, "iter": 3500, "lr": 0.07506, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22516, "top5_acc": 0.4675, "loss_cls": 4.51113, "loss": 4.51113, "time": 0.82528} +{"mode": "train", "epoch": 50, "iter": 3600, "lr": 0.07504, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23766, "top5_acc": 0.47172, "loss_cls": 4.48216, "loss": 4.48216, "time": 0.8185} +{"mode": "train", "epoch": 50, "iter": 3700, "lr": 0.07501, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23359, "top5_acc": 0.47047, "loss_cls": 4.49218, "loss": 4.49218, "time": 0.82232} +{"mode": "val", "epoch": 50, "iter": 309, "lr": 0.075, "top1_acc": 0.16781, "top5_acc": 0.37502, "mean_class_accuracy": 0.16765} +{"mode": "train", "epoch": 51, "iter": 100, "lr": 0.07498, "memory": 15990, "data_time": 1.30363, "top1_acc": 0.23031, "top5_acc": 0.47328, "loss_cls": 4.46799, "loss": 4.46799, "time": 2.29394} +{"mode": "train", "epoch": 51, "iter": 200, "lr": 0.07495, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23531, "top5_acc": 0.47875, "loss_cls": 4.45319, "loss": 4.45319, "time": 0.82457} +{"mode": "train", "epoch": 51, "iter": 300, "lr": 0.07493, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23781, "top5_acc": 0.47328, "loss_cls": 4.48389, "loss": 4.48389, "time": 0.82112} +{"mode": "train", "epoch": 51, "iter": 400, "lr": 0.0749, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22734, "top5_acc": 0.47031, "loss_cls": 4.50151, "loss": 4.50151, "time": 0.82671} +{"mode": "train", "epoch": 51, "iter": 500, "lr": 0.07488, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22125, "top5_acc": 0.46719, "loss_cls": 4.50084, "loss": 4.50084, "time": 0.81626} +{"mode": "train", "epoch": 51, "iter": 600, "lr": 0.07485, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23312, "top5_acc": 0.47234, "loss_cls": 4.50029, "loss": 4.50029, "time": 0.82123} +{"mode": "train", "epoch": 51, "iter": 700, "lr": 0.07483, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23156, "top5_acc": 0.47297, "loss_cls": 4.50147, "loss": 4.50147, "time": 0.82687} +{"mode": "train", "epoch": 51, "iter": 800, "lr": 0.07481, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23641, "top5_acc": 0.47484, "loss_cls": 4.45163, "loss": 4.45163, "time": 0.81815} +{"mode": "train", "epoch": 51, "iter": 900, "lr": 0.07478, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22719, "top5_acc": 0.46641, "loss_cls": 4.50054, "loss": 4.50054, "time": 0.81792} +{"mode": "train", "epoch": 51, "iter": 1000, "lr": 0.07476, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22406, "top5_acc": 0.46547, "loss_cls": 4.51001, "loss": 4.51001, "time": 0.82218} +{"mode": "train", "epoch": 51, "iter": 1100, "lr": 0.07473, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23719, "top5_acc": 0.47281, "loss_cls": 4.48364, "loss": 4.48364, "time": 0.81911} +{"mode": "train", "epoch": 51, "iter": 1200, "lr": 0.07471, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22078, "top5_acc": 0.45672, "loss_cls": 4.53534, "loss": 4.53534, "time": 0.82479} +{"mode": "train", "epoch": 51, "iter": 1300, "lr": 0.07468, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23016, "top5_acc": 0.47641, "loss_cls": 4.4673, "loss": 4.4673, "time": 0.81894} +{"mode": "train", "epoch": 51, "iter": 1400, "lr": 0.07466, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22422, "top5_acc": 0.46609, "loss_cls": 4.52603, "loss": 4.52603, "time": 0.82063} +{"mode": "train", "epoch": 51, "iter": 1500, "lr": 0.07464, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24031, "top5_acc": 0.47344, "loss_cls": 4.45803, "loss": 4.45803, "time": 0.82117} +{"mode": "train", "epoch": 51, "iter": 1600, "lr": 0.07461, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23016, "top5_acc": 0.46109, "loss_cls": 4.4864, "loss": 4.4864, "time": 0.82103} +{"mode": "train", "epoch": 51, "iter": 1700, "lr": 0.07459, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22922, "top5_acc": 0.46531, "loss_cls": 4.514, "loss": 4.514, "time": 0.81845} +{"mode": "train", "epoch": 51, "iter": 1800, "lr": 0.07456, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22391, "top5_acc": 0.46812, "loss_cls": 4.50425, "loss": 4.50425, "time": 0.81312} +{"mode": "train", "epoch": 51, "iter": 1900, "lr": 0.07454, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23719, "top5_acc": 0.47312, "loss_cls": 4.45264, "loss": 4.45264, "time": 0.81948} +{"mode": "train", "epoch": 51, "iter": 2000, "lr": 0.07451, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23875, "top5_acc": 0.48266, "loss_cls": 4.42941, "loss": 4.42941, "time": 0.82179} +{"mode": "train", "epoch": 51, "iter": 2100, "lr": 0.07449, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23609, "top5_acc": 0.46391, "loss_cls": 4.49879, "loss": 4.49879, "time": 0.81292} +{"mode": "train", "epoch": 51, "iter": 2200, "lr": 0.07447, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23453, "top5_acc": 0.48016, "loss_cls": 4.44126, "loss": 4.44126, "time": 0.8156} +{"mode": "train", "epoch": 51, "iter": 2300, "lr": 0.07444, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22688, "top5_acc": 0.47312, "loss_cls": 4.50607, "loss": 4.50607, "time": 0.82026} +{"mode": "train", "epoch": 51, "iter": 2400, "lr": 0.07442, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22656, "top5_acc": 0.46297, "loss_cls": 4.51376, "loss": 4.51376, "time": 0.82824} +{"mode": "train", "epoch": 51, "iter": 2500, "lr": 0.07439, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22922, "top5_acc": 0.46953, "loss_cls": 4.51268, "loss": 4.51268, "time": 0.81704} +{"mode": "train", "epoch": 51, "iter": 2600, "lr": 0.07437, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.21844, "top5_acc": 0.46, "loss_cls": 4.54313, "loss": 4.54313, "time": 0.83666} +{"mode": "train", "epoch": 51, "iter": 2700, "lr": 0.07434, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22906, "top5_acc": 0.45969, "loss_cls": 4.53386, "loss": 4.53386, "time": 0.82569} +{"mode": "train", "epoch": 51, "iter": 2800, "lr": 0.07432, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24, "top5_acc": 0.46656, "loss_cls": 4.50082, "loss": 4.50082, "time": 0.82759} +{"mode": "train", "epoch": 51, "iter": 2900, "lr": 0.07429, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23484, "top5_acc": 0.48, "loss_cls": 4.46617, "loss": 4.46617, "time": 0.82303} +{"mode": "train", "epoch": 51, "iter": 3000, "lr": 0.07427, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23328, "top5_acc": 0.46688, "loss_cls": 4.4884, "loss": 4.4884, "time": 0.82061} +{"mode": "train", "epoch": 51, "iter": 3100, "lr": 0.07425, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23109, "top5_acc": 0.46547, "loss_cls": 4.49096, "loss": 4.49096, "time": 0.82301} +{"mode": "train", "epoch": 51, "iter": 3200, "lr": 0.07422, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23734, "top5_acc": 0.47125, "loss_cls": 4.47981, "loss": 4.47981, "time": 0.82097} +{"mode": "train", "epoch": 51, "iter": 3300, "lr": 0.0742, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22906, "top5_acc": 0.47719, "loss_cls": 4.4894, "loss": 4.4894, "time": 0.81736} +{"mode": "train", "epoch": 51, "iter": 3400, "lr": 0.07417, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23375, "top5_acc": 0.47219, "loss_cls": 4.47852, "loss": 4.47852, "time": 0.81991} +{"mode": "train", "epoch": 51, "iter": 3500, "lr": 0.07415, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23016, "top5_acc": 0.46844, "loss_cls": 4.49866, "loss": 4.49866, "time": 0.82088} +{"mode": "train", "epoch": 51, "iter": 3600, "lr": 0.07412, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23984, "top5_acc": 0.4675, "loss_cls": 4.49427, "loss": 4.49427, "time": 0.82492} +{"mode": "train", "epoch": 51, "iter": 3700, "lr": 0.0741, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22984, "top5_acc": 0.46328, "loss_cls": 4.52439, "loss": 4.52439, "time": 0.82439} +{"mode": "val", "epoch": 51, "iter": 309, "lr": 0.07409, "top1_acc": 0.14922, "top5_acc": 0.34767, "mean_class_accuracy": 0.14912} +{"mode": "train", "epoch": 52, "iter": 100, "lr": 0.07406, "memory": 15990, "data_time": 1.30765, "top1_acc": 0.23328, "top5_acc": 0.46953, "loss_cls": 4.44569, "loss": 4.44569, "time": 2.29566} +{"mode": "train", "epoch": 52, "iter": 200, "lr": 0.07404, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23281, "top5_acc": 0.47531, "loss_cls": 4.48335, "loss": 4.48335, "time": 0.82139} +{"mode": "train", "epoch": 52, "iter": 300, "lr": 0.07401, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23891, "top5_acc": 0.47656, "loss_cls": 4.44011, "loss": 4.44011, "time": 0.81758} +{"mode": "train", "epoch": 52, "iter": 400, "lr": 0.07399, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22891, "top5_acc": 0.465, "loss_cls": 4.5174, "loss": 4.5174, "time": 0.82165} +{"mode": "train", "epoch": 52, "iter": 500, "lr": 0.07397, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24297, "top5_acc": 0.48172, "loss_cls": 4.41241, "loss": 4.41241, "time": 0.81781} +{"mode": "train", "epoch": 52, "iter": 600, "lr": 0.07394, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23438, "top5_acc": 0.47391, "loss_cls": 4.48594, "loss": 4.48594, "time": 0.81589} +{"mode": "train", "epoch": 52, "iter": 700, "lr": 0.07392, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23203, "top5_acc": 0.47344, "loss_cls": 4.46854, "loss": 4.46854, "time": 0.82029} +{"mode": "train", "epoch": 52, "iter": 800, "lr": 0.07389, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23078, "top5_acc": 0.46938, "loss_cls": 4.49767, "loss": 4.49767, "time": 0.82063} +{"mode": "train", "epoch": 52, "iter": 900, "lr": 0.07387, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23062, "top5_acc": 0.46828, "loss_cls": 4.47527, "loss": 4.47527, "time": 0.81784} +{"mode": "train", "epoch": 52, "iter": 1000, "lr": 0.07384, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22953, "top5_acc": 0.46734, "loss_cls": 4.51986, "loss": 4.51986, "time": 0.81841} +{"mode": "train", "epoch": 52, "iter": 1100, "lr": 0.07382, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23516, "top5_acc": 0.46875, "loss_cls": 4.46373, "loss": 4.46373, "time": 0.82002} +{"mode": "train", "epoch": 52, "iter": 1200, "lr": 0.07379, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23016, "top5_acc": 0.45906, "loss_cls": 4.50696, "loss": 4.50696, "time": 0.82415} +{"mode": "train", "epoch": 52, "iter": 1300, "lr": 0.07377, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22875, "top5_acc": 0.47719, "loss_cls": 4.48436, "loss": 4.48436, "time": 0.82907} +{"mode": "train", "epoch": 52, "iter": 1400, "lr": 0.07374, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23406, "top5_acc": 0.47109, "loss_cls": 4.46848, "loss": 4.46848, "time": 0.82642} +{"mode": "train", "epoch": 52, "iter": 1500, "lr": 0.07372, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22797, "top5_acc": 0.47281, "loss_cls": 4.49129, "loss": 4.49129, "time": 0.83086} +{"mode": "train", "epoch": 52, "iter": 1600, "lr": 0.0737, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22562, "top5_acc": 0.45703, "loss_cls": 4.5371, "loss": 4.5371, "time": 0.82046} +{"mode": "train", "epoch": 52, "iter": 1700, "lr": 0.07367, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24219, "top5_acc": 0.47703, "loss_cls": 4.43897, "loss": 4.43897, "time": 0.81921} +{"mode": "train", "epoch": 52, "iter": 1800, "lr": 0.07365, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22891, "top5_acc": 0.47062, "loss_cls": 4.50429, "loss": 4.50429, "time": 0.81827} +{"mode": "train", "epoch": 52, "iter": 1900, "lr": 0.07362, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22453, "top5_acc": 0.46609, "loss_cls": 4.5175, "loss": 4.5175, "time": 0.81883} +{"mode": "train", "epoch": 52, "iter": 2000, "lr": 0.0736, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23281, "top5_acc": 0.47406, "loss_cls": 4.47173, "loss": 4.47173, "time": 0.81887} +{"mode": "train", "epoch": 52, "iter": 2100, "lr": 0.07357, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22844, "top5_acc": 0.46531, "loss_cls": 4.51918, "loss": 4.51918, "time": 0.82227} +{"mode": "train", "epoch": 52, "iter": 2200, "lr": 0.07355, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22375, "top5_acc": 0.46266, "loss_cls": 4.53503, "loss": 4.53503, "time": 0.82114} +{"mode": "train", "epoch": 52, "iter": 2300, "lr": 0.07352, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22938, "top5_acc": 0.47656, "loss_cls": 4.46667, "loss": 4.46667, "time": 0.81919} +{"mode": "train", "epoch": 52, "iter": 2400, "lr": 0.0735, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23141, "top5_acc": 0.46359, "loss_cls": 4.5268, "loss": 4.5268, "time": 0.82192} +{"mode": "train", "epoch": 52, "iter": 2500, "lr": 0.07347, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23328, "top5_acc": 0.47219, "loss_cls": 4.47424, "loss": 4.47424, "time": 0.82081} +{"mode": "train", "epoch": 52, "iter": 2600, "lr": 0.07345, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.22766, "top5_acc": 0.46828, "loss_cls": 4.49611, "loss": 4.49611, "time": 0.83072} +{"mode": "train", "epoch": 52, "iter": 2700, "lr": 0.07342, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22891, "top5_acc": 0.465, "loss_cls": 4.50144, "loss": 4.50144, "time": 0.82362} +{"mode": "train", "epoch": 52, "iter": 2800, "lr": 0.0734, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22859, "top5_acc": 0.46391, "loss_cls": 4.50612, "loss": 4.50612, "time": 0.81952} +{"mode": "train", "epoch": 52, "iter": 2900, "lr": 0.07337, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.22516, "top5_acc": 0.47516, "loss_cls": 4.47873, "loss": 4.47873, "time": 0.82567} +{"mode": "train", "epoch": 52, "iter": 3000, "lr": 0.07335, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23266, "top5_acc": 0.46938, "loss_cls": 4.49245, "loss": 4.49245, "time": 0.81634} +{"mode": "train", "epoch": 52, "iter": 3100, "lr": 0.07332, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23156, "top5_acc": 0.46969, "loss_cls": 4.48259, "loss": 4.48259, "time": 0.81825} +{"mode": "train", "epoch": 52, "iter": 3200, "lr": 0.0733, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22875, "top5_acc": 0.46328, "loss_cls": 4.5331, "loss": 4.5331, "time": 0.82364} +{"mode": "train", "epoch": 52, "iter": 3300, "lr": 0.07328, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23359, "top5_acc": 0.48062, "loss_cls": 4.44954, "loss": 4.44954, "time": 0.81877} +{"mode": "train", "epoch": 52, "iter": 3400, "lr": 0.07325, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23734, "top5_acc": 0.47391, "loss_cls": 4.48332, "loss": 4.48332, "time": 0.82036} +{"mode": "train", "epoch": 52, "iter": 3500, "lr": 0.07323, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23469, "top5_acc": 0.47359, "loss_cls": 4.45992, "loss": 4.45992, "time": 0.82624} +{"mode": "train", "epoch": 52, "iter": 3600, "lr": 0.0732, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23094, "top5_acc": 0.47266, "loss_cls": 4.49111, "loss": 4.49111, "time": 0.81828} +{"mode": "train", "epoch": 52, "iter": 3700, "lr": 0.07318, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24344, "top5_acc": 0.48078, "loss_cls": 4.43413, "loss": 4.43413, "time": 0.82236} +{"mode": "val", "epoch": 52, "iter": 309, "lr": 0.07317, "top1_acc": 0.15717, "top5_acc": 0.37102, "mean_class_accuracy": 0.15683} +{"mode": "train", "epoch": 53, "iter": 100, "lr": 0.07314, "memory": 15990, "data_time": 1.31343, "top1_acc": 0.23562, "top5_acc": 0.48406, "loss_cls": 4.44529, "loss": 4.44529, "time": 2.29936} +{"mode": "train", "epoch": 53, "iter": 200, "lr": 0.07312, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2275, "top5_acc": 0.475, "loss_cls": 4.48747, "loss": 4.48747, "time": 0.82791} +{"mode": "train", "epoch": 53, "iter": 300, "lr": 0.07309, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23281, "top5_acc": 0.47625, "loss_cls": 4.4356, "loss": 4.4356, "time": 0.81915} +{"mode": "train", "epoch": 53, "iter": 400, "lr": 0.07307, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23594, "top5_acc": 0.47594, "loss_cls": 4.46025, "loss": 4.46025, "time": 0.81764} +{"mode": "train", "epoch": 53, "iter": 500, "lr": 0.07304, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24266, "top5_acc": 0.47469, "loss_cls": 4.45612, "loss": 4.45612, "time": 0.81938} +{"mode": "train", "epoch": 53, "iter": 600, "lr": 0.07302, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22844, "top5_acc": 0.47141, "loss_cls": 4.48234, "loss": 4.48234, "time": 0.81918} +{"mode": "train", "epoch": 53, "iter": 700, "lr": 0.07299, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22078, "top5_acc": 0.46125, "loss_cls": 4.54071, "loss": 4.54071, "time": 0.82034} +{"mode": "train", "epoch": 53, "iter": 800, "lr": 0.07297, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23531, "top5_acc": 0.46047, "loss_cls": 4.49269, "loss": 4.49269, "time": 0.81911} +{"mode": "train", "epoch": 53, "iter": 900, "lr": 0.07294, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22656, "top5_acc": 0.4675, "loss_cls": 4.51648, "loss": 4.51648, "time": 0.81866} +{"mode": "train", "epoch": 53, "iter": 1000, "lr": 0.07292, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23828, "top5_acc": 0.47781, "loss_cls": 4.4508, "loss": 4.4508, "time": 0.82184} +{"mode": "train", "epoch": 53, "iter": 1100, "lr": 0.07289, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23188, "top5_acc": 0.47984, "loss_cls": 4.46997, "loss": 4.46997, "time": 0.82095} +{"mode": "train", "epoch": 53, "iter": 1200, "lr": 0.07287, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22438, "top5_acc": 0.45766, "loss_cls": 4.52443, "loss": 4.52443, "time": 0.81859} +{"mode": "train", "epoch": 53, "iter": 1300, "lr": 0.07284, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23031, "top5_acc": 0.47609, "loss_cls": 4.45343, "loss": 4.45343, "time": 0.81795} +{"mode": "train", "epoch": 53, "iter": 1400, "lr": 0.07282, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23281, "top5_acc": 0.47578, "loss_cls": 4.45882, "loss": 4.45882, "time": 0.82456} +{"mode": "train", "epoch": 53, "iter": 1500, "lr": 0.07279, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23312, "top5_acc": 0.4725, "loss_cls": 4.48327, "loss": 4.48327, "time": 0.82277} +{"mode": "train", "epoch": 53, "iter": 1600, "lr": 0.07277, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22547, "top5_acc": 0.46234, "loss_cls": 4.51204, "loss": 4.51204, "time": 0.81898} +{"mode": "train", "epoch": 53, "iter": 1700, "lr": 0.07274, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23078, "top5_acc": 0.47422, "loss_cls": 4.46419, "loss": 4.46419, "time": 0.81297} +{"mode": "train", "epoch": 53, "iter": 1800, "lr": 0.07272, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23531, "top5_acc": 0.47438, "loss_cls": 4.4649, "loss": 4.4649, "time": 0.82262} +{"mode": "train", "epoch": 53, "iter": 1900, "lr": 0.07269, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23828, "top5_acc": 0.47266, "loss_cls": 4.49167, "loss": 4.49167, "time": 0.81996} +{"mode": "train", "epoch": 53, "iter": 2000, "lr": 0.07267, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22922, "top5_acc": 0.46938, "loss_cls": 4.50885, "loss": 4.50885, "time": 0.8206} +{"mode": "train", "epoch": 53, "iter": 2100, "lr": 0.07264, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22703, "top5_acc": 0.46859, "loss_cls": 4.46287, "loss": 4.46287, "time": 0.81998} +{"mode": "train", "epoch": 53, "iter": 2200, "lr": 0.07262, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24203, "top5_acc": 0.46922, "loss_cls": 4.46615, "loss": 4.46615, "time": 0.82101} +{"mode": "train", "epoch": 53, "iter": 2300, "lr": 0.07259, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23375, "top5_acc": 0.48078, "loss_cls": 4.43349, "loss": 4.43349, "time": 0.82403} +{"mode": "train", "epoch": 53, "iter": 2400, "lr": 0.07257, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23234, "top5_acc": 0.47109, "loss_cls": 4.47544, "loss": 4.47544, "time": 0.81635} +{"mode": "train", "epoch": 53, "iter": 2500, "lr": 0.07254, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22797, "top5_acc": 0.45953, "loss_cls": 4.51394, "loss": 4.51394, "time": 0.82682} +{"mode": "train", "epoch": 53, "iter": 2600, "lr": 0.07252, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23438, "top5_acc": 0.47547, "loss_cls": 4.46412, "loss": 4.46412, "time": 0.82417} +{"mode": "train", "epoch": 53, "iter": 2700, "lr": 0.07249, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22719, "top5_acc": 0.47016, "loss_cls": 4.48965, "loss": 4.48965, "time": 0.82323} +{"mode": "train", "epoch": 53, "iter": 2800, "lr": 0.07247, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23312, "top5_acc": 0.46562, "loss_cls": 4.4863, "loss": 4.4863, "time": 0.82882} +{"mode": "train", "epoch": 53, "iter": 2900, "lr": 0.07244, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24078, "top5_acc": 0.47875, "loss_cls": 4.43621, "loss": 4.43621, "time": 0.82496} +{"mode": "train", "epoch": 53, "iter": 3000, "lr": 0.07242, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24172, "top5_acc": 0.47375, "loss_cls": 4.46219, "loss": 4.46219, "time": 0.82101} +{"mode": "train", "epoch": 53, "iter": 3100, "lr": 0.07239, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23828, "top5_acc": 0.47281, "loss_cls": 4.46851, "loss": 4.46851, "time": 0.8183} +{"mode": "train", "epoch": 53, "iter": 3200, "lr": 0.07237, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24156, "top5_acc": 0.47141, "loss_cls": 4.46212, "loss": 4.46212, "time": 0.82007} +{"mode": "train", "epoch": 53, "iter": 3300, "lr": 0.07234, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23078, "top5_acc": 0.47141, "loss_cls": 4.47505, "loss": 4.47505, "time": 0.81439} +{"mode": "train", "epoch": 53, "iter": 3400, "lr": 0.07232, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23141, "top5_acc": 0.46734, "loss_cls": 4.49559, "loss": 4.49559, "time": 0.8151} +{"mode": "train", "epoch": 53, "iter": 3500, "lr": 0.07229, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23438, "top5_acc": 0.47562, "loss_cls": 4.44718, "loss": 4.44718, "time": 0.81705} +{"mode": "train", "epoch": 53, "iter": 3600, "lr": 0.07227, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22578, "top5_acc": 0.45906, "loss_cls": 4.51168, "loss": 4.51168, "time": 0.82109} +{"mode": "train", "epoch": 53, "iter": 3700, "lr": 0.07224, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23516, "top5_acc": 0.46828, "loss_cls": 4.49668, "loss": 4.49668, "time": 0.82355} +{"mode": "val", "epoch": 53, "iter": 309, "lr": 0.07223, "top1_acc": 0.17039, "top5_acc": 0.38115, "mean_class_accuracy": 0.17021} +{"mode": "train", "epoch": 54, "iter": 100, "lr": 0.07221, "memory": 15990, "data_time": 1.32856, "top1_acc": 0.24391, "top5_acc": 0.47641, "loss_cls": 4.43775, "loss": 4.43775, "time": 2.31346} +{"mode": "train", "epoch": 54, "iter": 200, "lr": 0.07218, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23875, "top5_acc": 0.47609, "loss_cls": 4.44304, "loss": 4.44304, "time": 0.82094} +{"mode": "train", "epoch": 54, "iter": 300, "lr": 0.07216, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23047, "top5_acc": 0.47125, "loss_cls": 4.49408, "loss": 4.49408, "time": 0.82189} +{"mode": "train", "epoch": 54, "iter": 400, "lr": 0.07213, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23312, "top5_acc": 0.47516, "loss_cls": 4.44329, "loss": 4.44329, "time": 0.81842} +{"mode": "train", "epoch": 54, "iter": 500, "lr": 0.07211, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24, "top5_acc": 0.47391, "loss_cls": 4.45616, "loss": 4.45616, "time": 0.82587} +{"mode": "train", "epoch": 54, "iter": 600, "lr": 0.07208, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24281, "top5_acc": 0.48578, "loss_cls": 4.40342, "loss": 4.40342, "time": 0.82092} +{"mode": "train", "epoch": 54, "iter": 700, "lr": 0.07206, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22906, "top5_acc": 0.47719, "loss_cls": 4.48357, "loss": 4.48357, "time": 0.82207} +{"mode": "train", "epoch": 54, "iter": 800, "lr": 0.07203, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2375, "top5_acc": 0.47828, "loss_cls": 4.47299, "loss": 4.47299, "time": 0.82012} +{"mode": "train", "epoch": 54, "iter": 900, "lr": 0.07201, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23844, "top5_acc": 0.48453, "loss_cls": 4.4351, "loss": 4.4351, "time": 0.82106} +{"mode": "train", "epoch": 54, "iter": 1000, "lr": 0.07198, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23766, "top5_acc": 0.47906, "loss_cls": 4.43413, "loss": 4.43413, "time": 0.81775} +{"mode": "train", "epoch": 54, "iter": 1100, "lr": 0.07196, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22953, "top5_acc": 0.4625, "loss_cls": 4.48987, "loss": 4.48987, "time": 0.8207} +{"mode": "train", "epoch": 54, "iter": 1200, "lr": 0.07193, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22547, "top5_acc": 0.46844, "loss_cls": 4.47902, "loss": 4.47902, "time": 0.82465} +{"mode": "train", "epoch": 54, "iter": 1300, "lr": 0.07191, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.23391, "top5_acc": 0.47562, "loss_cls": 4.46247, "loss": 4.46247, "time": 0.82488} +{"mode": "train", "epoch": 54, "iter": 1400, "lr": 0.07188, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23094, "top5_acc": 0.46062, "loss_cls": 4.50415, "loss": 4.50415, "time": 0.82636} +{"mode": "train", "epoch": 54, "iter": 1500, "lr": 0.07186, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23016, "top5_acc": 0.46281, "loss_cls": 4.50468, "loss": 4.50468, "time": 0.82225} +{"mode": "train", "epoch": 54, "iter": 1600, "lr": 0.07183, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.225, "top5_acc": 0.46406, "loss_cls": 4.49177, "loss": 4.49177, "time": 0.82042} +{"mode": "train", "epoch": 54, "iter": 1700, "lr": 0.07181, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23516, "top5_acc": 0.46781, "loss_cls": 4.47179, "loss": 4.47179, "time": 0.82018} +{"mode": "train", "epoch": 54, "iter": 1800, "lr": 0.07178, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22719, "top5_acc": 0.46328, "loss_cls": 4.52331, "loss": 4.52331, "time": 0.82796} +{"mode": "train", "epoch": 54, "iter": 1900, "lr": 0.07176, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2325, "top5_acc": 0.46891, "loss_cls": 4.48837, "loss": 4.48837, "time": 0.81989} +{"mode": "train", "epoch": 54, "iter": 2000, "lr": 0.07173, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23406, "top5_acc": 0.47859, "loss_cls": 4.44437, "loss": 4.44437, "time": 0.8186} +{"mode": "train", "epoch": 54, "iter": 2100, "lr": 0.0717, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.225, "top5_acc": 0.45781, "loss_cls": 4.52617, "loss": 4.52617, "time": 0.8277} +{"mode": "train", "epoch": 54, "iter": 2200, "lr": 0.07168, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24234, "top5_acc": 0.48641, "loss_cls": 4.43709, "loss": 4.43709, "time": 0.82287} +{"mode": "train", "epoch": 54, "iter": 2300, "lr": 0.07165, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.22625, "top5_acc": 0.46359, "loss_cls": 4.49086, "loss": 4.49086, "time": 0.82779} +{"mode": "train", "epoch": 54, "iter": 2400, "lr": 0.07163, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23828, "top5_acc": 0.47562, "loss_cls": 4.45944, "loss": 4.45944, "time": 0.82337} +{"mode": "train", "epoch": 54, "iter": 2500, "lr": 0.0716, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24641, "top5_acc": 0.48109, "loss_cls": 4.45911, "loss": 4.45911, "time": 0.83116} +{"mode": "train", "epoch": 54, "iter": 2600, "lr": 0.07158, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23719, "top5_acc": 0.46406, "loss_cls": 4.48634, "loss": 4.48634, "time": 0.82776} +{"mode": "train", "epoch": 54, "iter": 2700, "lr": 0.07155, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24062, "top5_acc": 0.47109, "loss_cls": 4.44697, "loss": 4.44697, "time": 0.82555} +{"mode": "train", "epoch": 54, "iter": 2800, "lr": 0.07153, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22922, "top5_acc": 0.46844, "loss_cls": 4.49791, "loss": 4.49791, "time": 0.82882} +{"mode": "train", "epoch": 54, "iter": 2900, "lr": 0.0715, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23031, "top5_acc": 0.46969, "loss_cls": 4.49644, "loss": 4.49644, "time": 0.82548} +{"mode": "train", "epoch": 54, "iter": 3000, "lr": 0.07148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23531, "top5_acc": 0.4775, "loss_cls": 4.46735, "loss": 4.46735, "time": 0.81458} +{"mode": "train", "epoch": 54, "iter": 3100, "lr": 0.07145, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23562, "top5_acc": 0.475, "loss_cls": 4.42553, "loss": 4.42553, "time": 0.82191} +{"mode": "train", "epoch": 54, "iter": 3200, "lr": 0.07143, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23031, "top5_acc": 0.47531, "loss_cls": 4.47835, "loss": 4.47835, "time": 0.81957} +{"mode": "train", "epoch": 54, "iter": 3300, "lr": 0.0714, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23016, "top5_acc": 0.47547, "loss_cls": 4.46493, "loss": 4.46493, "time": 0.82036} +{"mode": "train", "epoch": 54, "iter": 3400, "lr": 0.07138, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.22953, "top5_acc": 0.46359, "loss_cls": 4.51633, "loss": 4.51633, "time": 0.82429} +{"mode": "train", "epoch": 54, "iter": 3500, "lr": 0.07135, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22375, "top5_acc": 0.46453, "loss_cls": 4.52502, "loss": 4.52502, "time": 0.81909} +{"mode": "train", "epoch": 54, "iter": 3600, "lr": 0.07133, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.225, "top5_acc": 0.46266, "loss_cls": 4.53265, "loss": 4.53265, "time": 0.82198} +{"mode": "train", "epoch": 54, "iter": 3700, "lr": 0.0713, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23734, "top5_acc": 0.46812, "loss_cls": 4.46882, "loss": 4.46882, "time": 0.82171} +{"mode": "val", "epoch": 54, "iter": 309, "lr": 0.07129, "top1_acc": 0.16862, "top5_acc": 0.39346, "mean_class_accuracy": 0.16842} +{"mode": "train", "epoch": 55, "iter": 100, "lr": 0.07126, "memory": 15990, "data_time": 1.31501, "top1_acc": 0.24422, "top5_acc": 0.49922, "loss_cls": 4.38552, "loss": 4.38552, "time": 2.30376} +{"mode": "train", "epoch": 55, "iter": 200, "lr": 0.07124, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24562, "top5_acc": 0.48266, "loss_cls": 4.40819, "loss": 4.40819, "time": 0.81842} +{"mode": "train", "epoch": 55, "iter": 300, "lr": 0.07121, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2325, "top5_acc": 0.47875, "loss_cls": 4.44761, "loss": 4.44761, "time": 0.82074} +{"mode": "train", "epoch": 55, "iter": 400, "lr": 0.07119, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24, "top5_acc": 0.47969, "loss_cls": 4.42964, "loss": 4.42964, "time": 0.81889} +{"mode": "train", "epoch": 55, "iter": 500, "lr": 0.07116, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22938, "top5_acc": 0.47422, "loss_cls": 4.44936, "loss": 4.44936, "time": 0.82059} +{"mode": "train", "epoch": 55, "iter": 600, "lr": 0.07114, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23578, "top5_acc": 0.47578, "loss_cls": 4.46985, "loss": 4.46985, "time": 0.82489} +{"mode": "train", "epoch": 55, "iter": 700, "lr": 0.07111, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24016, "top5_acc": 0.48719, "loss_cls": 4.42516, "loss": 4.42516, "time": 0.81804} +{"mode": "train", "epoch": 55, "iter": 800, "lr": 0.07109, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23109, "top5_acc": 0.47359, "loss_cls": 4.47408, "loss": 4.47408, "time": 0.82327} +{"mode": "train", "epoch": 55, "iter": 900, "lr": 0.07106, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24047, "top5_acc": 0.47688, "loss_cls": 4.45475, "loss": 4.45475, "time": 0.81547} +{"mode": "train", "epoch": 55, "iter": 1000, "lr": 0.07104, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23359, "top5_acc": 0.47797, "loss_cls": 4.46831, "loss": 4.46831, "time": 0.81915} +{"mode": "train", "epoch": 55, "iter": 1100, "lr": 0.07101, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.23, "top5_acc": 0.47469, "loss_cls": 4.47488, "loss": 4.47488, "time": 0.82303} +{"mode": "train", "epoch": 55, "iter": 1200, "lr": 0.07099, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24062, "top5_acc": 0.47656, "loss_cls": 4.45332, "loss": 4.45332, "time": 0.82635} +{"mode": "train", "epoch": 55, "iter": 1300, "lr": 0.07096, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23312, "top5_acc": 0.46953, "loss_cls": 4.49925, "loss": 4.49925, "time": 0.82807} +{"mode": "train", "epoch": 55, "iter": 1400, "lr": 0.07093, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24188, "top5_acc": 0.47562, "loss_cls": 4.46674, "loss": 4.46674, "time": 0.82602} +{"mode": "train", "epoch": 55, "iter": 1500, "lr": 0.07091, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22672, "top5_acc": 0.46422, "loss_cls": 4.50048, "loss": 4.50048, "time": 0.82182} +{"mode": "train", "epoch": 55, "iter": 1600, "lr": 0.07088, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24312, "top5_acc": 0.48766, "loss_cls": 4.4151, "loss": 4.4151, "time": 0.82185} +{"mode": "train", "epoch": 55, "iter": 1700, "lr": 0.07086, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23281, "top5_acc": 0.46812, "loss_cls": 4.49466, "loss": 4.49466, "time": 0.82121} +{"mode": "train", "epoch": 55, "iter": 1800, "lr": 0.07083, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23578, "top5_acc": 0.46797, "loss_cls": 4.47198, "loss": 4.47198, "time": 0.81825} +{"mode": "train", "epoch": 55, "iter": 1900, "lr": 0.07081, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.22984, "top5_acc": 0.46875, "loss_cls": 4.51613, "loss": 4.51613, "time": 0.81747} +{"mode": "train", "epoch": 55, "iter": 2000, "lr": 0.07078, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24672, "top5_acc": 0.49078, "loss_cls": 4.41471, "loss": 4.41471, "time": 0.81784} +{"mode": "train", "epoch": 55, "iter": 2100, "lr": 0.07076, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24031, "top5_acc": 0.48156, "loss_cls": 4.44211, "loss": 4.44211, "time": 0.81834} +{"mode": "train", "epoch": 55, "iter": 2200, "lr": 0.07073, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22922, "top5_acc": 0.47406, "loss_cls": 4.47972, "loss": 4.47972, "time": 0.81897} +{"mode": "train", "epoch": 55, "iter": 2300, "lr": 0.07071, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24578, "top5_acc": 0.48078, "loss_cls": 4.41244, "loss": 4.41244, "time": 0.82164} +{"mode": "train", "epoch": 55, "iter": 2400, "lr": 0.07068, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23984, "top5_acc": 0.47312, "loss_cls": 4.4754, "loss": 4.4754, "time": 0.82106} +{"mode": "train", "epoch": 55, "iter": 2500, "lr": 0.07065, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23141, "top5_acc": 0.46859, "loss_cls": 4.49149, "loss": 4.49149, "time": 0.83002} +{"mode": "train", "epoch": 55, "iter": 2600, "lr": 0.07063, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23906, "top5_acc": 0.47266, "loss_cls": 4.46355, "loss": 4.46355, "time": 0.82267} +{"mode": "train", "epoch": 55, "iter": 2700, "lr": 0.0706, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.22453, "top5_acc": 0.46656, "loss_cls": 4.51252, "loss": 4.51252, "time": 0.82605} +{"mode": "train", "epoch": 55, "iter": 2800, "lr": 0.07058, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.22984, "top5_acc": 0.46438, "loss_cls": 4.51919, "loss": 4.51919, "time": 0.82586} +{"mode": "train", "epoch": 55, "iter": 2900, "lr": 0.07055, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22516, "top5_acc": 0.45656, "loss_cls": 4.54047, "loss": 4.54047, "time": 0.82175} +{"mode": "train", "epoch": 55, "iter": 3000, "lr": 0.07053, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22922, "top5_acc": 0.46875, "loss_cls": 4.46593, "loss": 4.46593, "time": 0.81652} +{"mode": "train", "epoch": 55, "iter": 3100, "lr": 0.0705, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23188, "top5_acc": 0.46766, "loss_cls": 4.48022, "loss": 4.48022, "time": 0.81809} +{"mode": "train", "epoch": 55, "iter": 3200, "lr": 0.07048, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23266, "top5_acc": 0.46062, "loss_cls": 4.51134, "loss": 4.51134, "time": 0.82386} +{"mode": "train", "epoch": 55, "iter": 3300, "lr": 0.07045, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23031, "top5_acc": 0.475, "loss_cls": 4.47171, "loss": 4.47171, "time": 0.81623} +{"mode": "train", "epoch": 55, "iter": 3400, "lr": 0.07043, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23828, "top5_acc": 0.46594, "loss_cls": 4.48725, "loss": 4.48725, "time": 0.8202} +{"mode": "train", "epoch": 55, "iter": 3500, "lr": 0.0704, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23672, "top5_acc": 0.47625, "loss_cls": 4.44023, "loss": 4.44023, "time": 0.82431} +{"mode": "train", "epoch": 55, "iter": 3600, "lr": 0.07037, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.2325, "top5_acc": 0.47703, "loss_cls": 4.46528, "loss": 4.46528, "time": 0.81779} +{"mode": "train", "epoch": 55, "iter": 3700, "lr": 0.07035, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24016, "top5_acc": 0.48094, "loss_cls": 4.45872, "loss": 4.45872, "time": 0.81747} +{"mode": "val", "epoch": 55, "iter": 309, "lr": 0.07034, "top1_acc": 0.17627, "top5_acc": 0.39356, "mean_class_accuracy": 0.176} +{"mode": "train", "epoch": 56, "iter": 100, "lr": 0.07031, "memory": 15990, "data_time": 1.3209, "top1_acc": 0.23562, "top5_acc": 0.48859, "loss_cls": 4.41369, "loss": 4.41369, "time": 2.30947} +{"mode": "train", "epoch": 56, "iter": 200, "lr": 0.07029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24031, "top5_acc": 0.48109, "loss_cls": 4.44535, "loss": 4.44535, "time": 0.82634} +{"mode": "train", "epoch": 56, "iter": 300, "lr": 0.07026, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24062, "top5_acc": 0.48062, "loss_cls": 4.39932, "loss": 4.39932, "time": 0.82048} +{"mode": "train", "epoch": 56, "iter": 400, "lr": 0.07023, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.22625, "top5_acc": 0.46906, "loss_cls": 4.50654, "loss": 4.50654, "time": 0.81962} +{"mode": "train", "epoch": 56, "iter": 500, "lr": 0.07021, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23828, "top5_acc": 0.47344, "loss_cls": 4.44203, "loss": 4.44203, "time": 0.81541} +{"mode": "train", "epoch": 56, "iter": 600, "lr": 0.07018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23891, "top5_acc": 0.47938, "loss_cls": 4.46586, "loss": 4.46586, "time": 0.81944} +{"mode": "train", "epoch": 56, "iter": 700, "lr": 0.07016, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22938, "top5_acc": 0.47, "loss_cls": 4.47817, "loss": 4.47817, "time": 0.81929} +{"mode": "train", "epoch": 56, "iter": 800, "lr": 0.07013, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24203, "top5_acc": 0.48703, "loss_cls": 4.41715, "loss": 4.41715, "time": 0.8239} +{"mode": "train", "epoch": 56, "iter": 900, "lr": 0.07011, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23344, "top5_acc": 0.47, "loss_cls": 4.48282, "loss": 4.48282, "time": 0.8181} +{"mode": "train", "epoch": 56, "iter": 1000, "lr": 0.07008, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22641, "top5_acc": 0.46047, "loss_cls": 4.52806, "loss": 4.52806, "time": 0.82259} +{"mode": "train", "epoch": 56, "iter": 1100, "lr": 0.07006, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23188, "top5_acc": 0.46547, "loss_cls": 4.48715, "loss": 4.48715, "time": 0.82057} +{"mode": "train", "epoch": 56, "iter": 1200, "lr": 0.07003, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23359, "top5_acc": 0.47, "loss_cls": 4.45442, "loss": 4.45442, "time": 0.81977} +{"mode": "train", "epoch": 56, "iter": 1300, "lr": 0.07, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23859, "top5_acc": 0.47609, "loss_cls": 4.44663, "loss": 4.44663, "time": 0.82123} +{"mode": "train", "epoch": 56, "iter": 1400, "lr": 0.06998, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22938, "top5_acc": 0.46562, "loss_cls": 4.49082, "loss": 4.49082, "time": 0.81861} +{"mode": "train", "epoch": 56, "iter": 1500, "lr": 0.06995, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22, "top5_acc": 0.46938, "loss_cls": 4.49974, "loss": 4.49974, "time": 0.82805} +{"mode": "train", "epoch": 56, "iter": 1600, "lr": 0.06993, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24391, "top5_acc": 0.48469, "loss_cls": 4.4116, "loss": 4.4116, "time": 0.82252} +{"mode": "train", "epoch": 56, "iter": 1700, "lr": 0.0699, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24125, "top5_acc": 0.48062, "loss_cls": 4.43031, "loss": 4.43031, "time": 0.81704} +{"mode": "train", "epoch": 56, "iter": 1800, "lr": 0.06988, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23141, "top5_acc": 0.46672, "loss_cls": 4.48598, "loss": 4.48598, "time": 0.81768} +{"mode": "train", "epoch": 56, "iter": 1900, "lr": 0.06985, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23422, "top5_acc": 0.47438, "loss_cls": 4.43989, "loss": 4.43989, "time": 0.81703} +{"mode": "train", "epoch": 56, "iter": 2000, "lr": 0.06983, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22984, "top5_acc": 0.47625, "loss_cls": 4.46736, "loss": 4.46736, "time": 0.81602} +{"mode": "train", "epoch": 56, "iter": 2100, "lr": 0.0698, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24094, "top5_acc": 0.48062, "loss_cls": 4.43944, "loss": 4.43944, "time": 0.81563} +{"mode": "train", "epoch": 56, "iter": 2200, "lr": 0.06977, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23375, "top5_acc": 0.47562, "loss_cls": 4.46811, "loss": 4.46811, "time": 0.82417} +{"mode": "train", "epoch": 56, "iter": 2300, "lr": 0.06975, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22688, "top5_acc": 0.45859, "loss_cls": 4.51633, "loss": 4.51633, "time": 0.81642} +{"mode": "train", "epoch": 56, "iter": 2400, "lr": 0.06972, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2325, "top5_acc": 0.47141, "loss_cls": 4.47445, "loss": 4.47445, "time": 0.8242} +{"mode": "train", "epoch": 56, "iter": 2500, "lr": 0.0697, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2375, "top5_acc": 0.47688, "loss_cls": 4.46247, "loss": 4.46247, "time": 0.82722} +{"mode": "train", "epoch": 56, "iter": 2600, "lr": 0.06967, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24141, "top5_acc": 0.475, "loss_cls": 4.45615, "loss": 4.45615, "time": 0.82146} +{"mode": "train", "epoch": 56, "iter": 2700, "lr": 0.06965, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22672, "top5_acc": 0.46375, "loss_cls": 4.51924, "loss": 4.51924, "time": 0.82098} +{"mode": "train", "epoch": 56, "iter": 2800, "lr": 0.06962, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24266, "top5_acc": 0.48234, "loss_cls": 4.42142, "loss": 4.42142, "time": 0.82311} +{"mode": "train", "epoch": 56, "iter": 2900, "lr": 0.06959, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24719, "top5_acc": 0.48719, "loss_cls": 4.41174, "loss": 4.41174, "time": 0.81923} +{"mode": "train", "epoch": 56, "iter": 3000, "lr": 0.06957, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23938, "top5_acc": 0.46562, "loss_cls": 4.50386, "loss": 4.50386, "time": 0.82459} +{"mode": "train", "epoch": 56, "iter": 3100, "lr": 0.06954, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24031, "top5_acc": 0.47828, "loss_cls": 4.43299, "loss": 4.43299, "time": 0.81743} +{"mode": "train", "epoch": 56, "iter": 3200, "lr": 0.06952, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24312, "top5_acc": 0.47297, "loss_cls": 4.45222, "loss": 4.45222, "time": 0.81833} +{"mode": "train", "epoch": 56, "iter": 3300, "lr": 0.06949, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23984, "top5_acc": 0.47516, "loss_cls": 4.45528, "loss": 4.45528, "time": 0.82344} +{"mode": "train", "epoch": 56, "iter": 3400, "lr": 0.06947, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23297, "top5_acc": 0.46672, "loss_cls": 4.49485, "loss": 4.49485, "time": 0.81997} +{"mode": "train", "epoch": 56, "iter": 3500, "lr": 0.06944, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.23594, "top5_acc": 0.47422, "loss_cls": 4.44841, "loss": 4.44841, "time": 0.81893} +{"mode": "train", "epoch": 56, "iter": 3600, "lr": 0.06941, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24141, "top5_acc": 0.47203, "loss_cls": 4.44469, "loss": 4.44469, "time": 0.82061} +{"mode": "train", "epoch": 56, "iter": 3700, "lr": 0.06939, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23359, "top5_acc": 0.47188, "loss_cls": 4.50499, "loss": 4.50499, "time": 0.82639} +{"mode": "val", "epoch": 56, "iter": 309, "lr": 0.06938, "top1_acc": 0.17642, "top5_acc": 0.39584, "mean_class_accuracy": 0.17627} +{"mode": "train", "epoch": 57, "iter": 100, "lr": 0.06935, "memory": 15990, "data_time": 1.27303, "top1_acc": 0.24031, "top5_acc": 0.48109, "loss_cls": 4.44167, "loss": 4.44167, "time": 2.26027} +{"mode": "train", "epoch": 57, "iter": 200, "lr": 0.06932, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24344, "top5_acc": 0.48188, "loss_cls": 4.42488, "loss": 4.42488, "time": 0.82224} +{"mode": "train", "epoch": 57, "iter": 300, "lr": 0.0693, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24047, "top5_acc": 0.48859, "loss_cls": 4.41016, "loss": 4.41016, "time": 0.82474} +{"mode": "train", "epoch": 57, "iter": 400, "lr": 0.06927, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23766, "top5_acc": 0.48219, "loss_cls": 4.46243, "loss": 4.46243, "time": 0.8172} +{"mode": "train", "epoch": 57, "iter": 500, "lr": 0.06925, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23984, "top5_acc": 0.48234, "loss_cls": 4.38321, "loss": 4.38321, "time": 0.8189} +{"mode": "train", "epoch": 57, "iter": 600, "lr": 0.06922, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23703, "top5_acc": 0.47859, "loss_cls": 4.45666, "loss": 4.45666, "time": 0.81852} +{"mode": "train", "epoch": 57, "iter": 700, "lr": 0.0692, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24312, "top5_acc": 0.48203, "loss_cls": 4.42994, "loss": 4.42994, "time": 0.81767} +{"mode": "train", "epoch": 57, "iter": 800, "lr": 0.06917, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2425, "top5_acc": 0.48078, "loss_cls": 4.4444, "loss": 4.4444, "time": 0.82266} +{"mode": "train", "epoch": 57, "iter": 900, "lr": 0.06914, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23562, "top5_acc": 0.46656, "loss_cls": 4.49934, "loss": 4.49934, "time": 0.82084} +{"mode": "train", "epoch": 57, "iter": 1000, "lr": 0.06912, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23281, "top5_acc": 0.46984, "loss_cls": 4.4645, "loss": 4.4645, "time": 0.81989} +{"mode": "train", "epoch": 57, "iter": 1100, "lr": 0.06909, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24359, "top5_acc": 0.47734, "loss_cls": 4.42572, "loss": 4.42572, "time": 0.82341} +{"mode": "train", "epoch": 57, "iter": 1200, "lr": 0.06907, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.245, "top5_acc": 0.48609, "loss_cls": 4.42824, "loss": 4.42824, "time": 0.82101} +{"mode": "train", "epoch": 57, "iter": 1300, "lr": 0.06904, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24188, "top5_acc": 0.48359, "loss_cls": 4.40687, "loss": 4.40687, "time": 0.82142} +{"mode": "train", "epoch": 57, "iter": 1400, "lr": 0.06901, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23906, "top5_acc": 0.47219, "loss_cls": 4.44687, "loss": 4.44687, "time": 0.82541} +{"mode": "train", "epoch": 57, "iter": 1500, "lr": 0.06899, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23359, "top5_acc": 0.46703, "loss_cls": 4.48771, "loss": 4.48771, "time": 0.82053} +{"mode": "train", "epoch": 57, "iter": 1600, "lr": 0.06896, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23406, "top5_acc": 0.4725, "loss_cls": 4.46399, "loss": 4.46399, "time": 0.81846} +{"mode": "train", "epoch": 57, "iter": 1700, "lr": 0.06894, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23297, "top5_acc": 0.47188, "loss_cls": 4.47165, "loss": 4.47165, "time": 0.81997} +{"mode": "train", "epoch": 57, "iter": 1800, "lr": 0.06891, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24266, "top5_acc": 0.48172, "loss_cls": 4.42717, "loss": 4.42717, "time": 0.82503} +{"mode": "train", "epoch": 57, "iter": 1900, "lr": 0.06889, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.235, "top5_acc": 0.47234, "loss_cls": 4.44909, "loss": 4.44909, "time": 0.82222} +{"mode": "train", "epoch": 57, "iter": 2000, "lr": 0.06886, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23297, "top5_acc": 0.48078, "loss_cls": 4.45842, "loss": 4.45842, "time": 0.82107} +{"mode": "train", "epoch": 57, "iter": 2100, "lr": 0.06883, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24469, "top5_acc": 0.48125, "loss_cls": 4.41819, "loss": 4.41819, "time": 0.82235} +{"mode": "train", "epoch": 57, "iter": 2200, "lr": 0.06881, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24344, "top5_acc": 0.48422, "loss_cls": 4.40004, "loss": 4.40004, "time": 0.82181} +{"mode": "train", "epoch": 57, "iter": 2300, "lr": 0.06878, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24, "top5_acc": 0.47547, "loss_cls": 4.46395, "loss": 4.46395, "time": 0.82124} +{"mode": "train", "epoch": 57, "iter": 2400, "lr": 0.06876, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23672, "top5_acc": 0.47953, "loss_cls": 4.45093, "loss": 4.45093, "time": 0.82464} +{"mode": "train", "epoch": 57, "iter": 2500, "lr": 0.06873, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24844, "top5_acc": 0.48766, "loss_cls": 4.40746, "loss": 4.40746, "time": 0.82613} +{"mode": "train", "epoch": 57, "iter": 2600, "lr": 0.0687, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23281, "top5_acc": 0.46906, "loss_cls": 4.50328, "loss": 4.50328, "time": 0.82146} +{"mode": "train", "epoch": 57, "iter": 2700, "lr": 0.06868, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23688, "top5_acc": 0.47953, "loss_cls": 4.43122, "loss": 4.43122, "time": 0.82635} +{"mode": "train", "epoch": 57, "iter": 2800, "lr": 0.06865, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22859, "top5_acc": 0.46781, "loss_cls": 4.47307, "loss": 4.47307, "time": 0.82358} +{"mode": "train", "epoch": 57, "iter": 2900, "lr": 0.06863, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23578, "top5_acc": 0.46484, "loss_cls": 4.47895, "loss": 4.47895, "time": 0.81837} +{"mode": "train", "epoch": 57, "iter": 3000, "lr": 0.0686, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.235, "top5_acc": 0.4675, "loss_cls": 4.46397, "loss": 4.46397, "time": 0.81816} +{"mode": "train", "epoch": 57, "iter": 3100, "lr": 0.06857, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23234, "top5_acc": 0.47406, "loss_cls": 4.46053, "loss": 4.46053, "time": 0.8192} +{"mode": "train", "epoch": 57, "iter": 3200, "lr": 0.06855, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23531, "top5_acc": 0.46422, "loss_cls": 4.49478, "loss": 4.49478, "time": 0.81854} +{"mode": "train", "epoch": 57, "iter": 3300, "lr": 0.06852, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23344, "top5_acc": 0.47109, "loss_cls": 4.46407, "loss": 4.46407, "time": 0.82022} +{"mode": "train", "epoch": 57, "iter": 3400, "lr": 0.0685, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24016, "top5_acc": 0.48234, "loss_cls": 4.43412, "loss": 4.43412, "time": 0.81836} +{"mode": "train", "epoch": 57, "iter": 3500, "lr": 0.06847, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.22891, "top5_acc": 0.47422, "loss_cls": 4.49331, "loss": 4.49331, "time": 0.82096} +{"mode": "train", "epoch": 57, "iter": 3600, "lr": 0.06844, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23625, "top5_acc": 0.47016, "loss_cls": 4.47954, "loss": 4.47954, "time": 0.82469} +{"mode": "train", "epoch": 57, "iter": 3700, "lr": 0.06842, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23266, "top5_acc": 0.47672, "loss_cls": 4.45935, "loss": 4.45935, "time": 0.82764} +{"mode": "val", "epoch": 57, "iter": 309, "lr": 0.06841, "top1_acc": 0.18898, "top5_acc": 0.41346, "mean_class_accuracy": 0.18889} +{"mode": "train", "epoch": 58, "iter": 100, "lr": 0.06838, "memory": 15990, "data_time": 1.35534, "top1_acc": 0.23266, "top5_acc": 0.47016, "loss_cls": 4.47462, "loss": 4.47462, "time": 2.35857} +{"mode": "train", "epoch": 58, "iter": 200, "lr": 0.06835, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2375, "top5_acc": 0.4825, "loss_cls": 4.42212, "loss": 4.42212, "time": 0.83158} +{"mode": "train", "epoch": 58, "iter": 300, "lr": 0.06833, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24453, "top5_acc": 0.485, "loss_cls": 4.39997, "loss": 4.39997, "time": 0.8249} +{"mode": "train", "epoch": 58, "iter": 400, "lr": 0.0683, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23844, "top5_acc": 0.48031, "loss_cls": 4.42038, "loss": 4.42038, "time": 0.82421} +{"mode": "train", "epoch": 58, "iter": 500, "lr": 0.06828, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24766, "top5_acc": 0.50016, "loss_cls": 4.35677, "loss": 4.35677, "time": 0.82366} +{"mode": "train", "epoch": 58, "iter": 600, "lr": 0.06825, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23781, "top5_acc": 0.47984, "loss_cls": 4.40939, "loss": 4.40939, "time": 0.81588} +{"mode": "train", "epoch": 58, "iter": 700, "lr": 0.06822, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23734, "top5_acc": 0.47656, "loss_cls": 4.4346, "loss": 4.4346, "time": 0.82003} +{"mode": "train", "epoch": 58, "iter": 800, "lr": 0.0682, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24156, "top5_acc": 0.47547, "loss_cls": 4.45541, "loss": 4.45541, "time": 0.81867} +{"mode": "train", "epoch": 58, "iter": 900, "lr": 0.06817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24438, "top5_acc": 0.48969, "loss_cls": 4.39738, "loss": 4.39738, "time": 0.81981} +{"mode": "train", "epoch": 58, "iter": 1000, "lr": 0.06815, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24219, "top5_acc": 0.49172, "loss_cls": 4.38048, "loss": 4.38048, "time": 0.82104} +{"mode": "train", "epoch": 58, "iter": 1100, "lr": 0.06812, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24203, "top5_acc": 0.49, "loss_cls": 4.39608, "loss": 4.39608, "time": 0.82851} +{"mode": "train", "epoch": 58, "iter": 1200, "lr": 0.06809, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.23766, "top5_acc": 0.48203, "loss_cls": 4.43138, "loss": 4.43138, "time": 0.82387} +{"mode": "train", "epoch": 58, "iter": 1300, "lr": 0.06807, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.23688, "top5_acc": 0.48219, "loss_cls": 4.4433, "loss": 4.4433, "time": 0.82306} +{"mode": "train", "epoch": 58, "iter": 1400, "lr": 0.06804, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24156, "top5_acc": 0.47641, "loss_cls": 4.42322, "loss": 4.42322, "time": 0.82277} +{"mode": "train", "epoch": 58, "iter": 1500, "lr": 0.06802, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23438, "top5_acc": 0.47656, "loss_cls": 4.46656, "loss": 4.46656, "time": 0.82349} +{"mode": "train", "epoch": 58, "iter": 1600, "lr": 0.06799, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22547, "top5_acc": 0.46703, "loss_cls": 4.5143, "loss": 4.5143, "time": 0.81618} +{"mode": "train", "epoch": 58, "iter": 1700, "lr": 0.06796, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23594, "top5_acc": 0.47672, "loss_cls": 4.4255, "loss": 4.4255, "time": 0.81876} +{"mode": "train", "epoch": 58, "iter": 1800, "lr": 0.06794, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23453, "top5_acc": 0.47312, "loss_cls": 4.43983, "loss": 4.43983, "time": 0.8168} +{"mode": "train", "epoch": 58, "iter": 1900, "lr": 0.06791, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24188, "top5_acc": 0.48375, "loss_cls": 4.42386, "loss": 4.42386, "time": 0.82353} +{"mode": "train", "epoch": 58, "iter": 2000, "lr": 0.06789, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23172, "top5_acc": 0.48578, "loss_cls": 4.4285, "loss": 4.4285, "time": 0.82075} +{"mode": "train", "epoch": 58, "iter": 2100, "lr": 0.06786, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23656, "top5_acc": 0.47688, "loss_cls": 4.45514, "loss": 4.45514, "time": 0.82615} +{"mode": "train", "epoch": 58, "iter": 2200, "lr": 0.06783, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.23875, "top5_acc": 0.47797, "loss_cls": 4.436, "loss": 4.436, "time": 0.82495} +{"mode": "train", "epoch": 58, "iter": 2300, "lr": 0.06781, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23266, "top5_acc": 0.47281, "loss_cls": 4.45937, "loss": 4.45937, "time": 0.82467} +{"mode": "train", "epoch": 58, "iter": 2400, "lr": 0.06778, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.22766, "top5_acc": 0.47156, "loss_cls": 4.48234, "loss": 4.48234, "time": 0.8337} +{"mode": "train", "epoch": 58, "iter": 2500, "lr": 0.06775, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23484, "top5_acc": 0.47156, "loss_cls": 4.48535, "loss": 4.48535, "time": 0.81978} +{"mode": "train", "epoch": 58, "iter": 2600, "lr": 0.06773, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.22938, "top5_acc": 0.47422, "loss_cls": 4.46854, "loss": 4.46854, "time": 0.82683} +{"mode": "train", "epoch": 58, "iter": 2700, "lr": 0.0677, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23016, "top5_acc": 0.46594, "loss_cls": 4.50246, "loss": 4.50246, "time": 0.8225} +{"mode": "train", "epoch": 58, "iter": 2800, "lr": 0.06768, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23516, "top5_acc": 0.47875, "loss_cls": 4.45158, "loss": 4.45158, "time": 0.82005} +{"mode": "train", "epoch": 58, "iter": 2900, "lr": 0.06765, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2425, "top5_acc": 0.48281, "loss_cls": 4.42455, "loss": 4.42455, "time": 0.82404} +{"mode": "train", "epoch": 58, "iter": 3000, "lr": 0.06762, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23719, "top5_acc": 0.47953, "loss_cls": 4.42487, "loss": 4.42487, "time": 0.82059} +{"mode": "train", "epoch": 58, "iter": 3100, "lr": 0.0676, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23562, "top5_acc": 0.45656, "loss_cls": 4.48084, "loss": 4.48084, "time": 0.81864} +{"mode": "train", "epoch": 58, "iter": 3200, "lr": 0.06757, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.22891, "top5_acc": 0.48, "loss_cls": 4.46312, "loss": 4.46312, "time": 0.82005} +{"mode": "train", "epoch": 58, "iter": 3300, "lr": 0.06755, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23281, "top5_acc": 0.47359, "loss_cls": 4.47537, "loss": 4.47537, "time": 0.82725} +{"mode": "train", "epoch": 58, "iter": 3400, "lr": 0.06752, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23969, "top5_acc": 0.47953, "loss_cls": 4.43284, "loss": 4.43284, "time": 0.81826} +{"mode": "train", "epoch": 58, "iter": 3500, "lr": 0.06749, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23812, "top5_acc": 0.47375, "loss_cls": 4.45952, "loss": 4.45952, "time": 0.82603} +{"mode": "train", "epoch": 58, "iter": 3600, "lr": 0.06747, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23516, "top5_acc": 0.48031, "loss_cls": 4.44756, "loss": 4.44756, "time": 0.82161} +{"mode": "train", "epoch": 58, "iter": 3700, "lr": 0.06744, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23641, "top5_acc": 0.48141, "loss_cls": 4.46802, "loss": 4.46802, "time": 0.83048} +{"mode": "val", "epoch": 58, "iter": 309, "lr": 0.06743, "top1_acc": 0.16183, "top5_acc": 0.36286, "mean_class_accuracy": 0.16178} +{"mode": "train", "epoch": 59, "iter": 100, "lr": 0.0674, "memory": 15990, "data_time": 1.36076, "top1_acc": 0.24422, "top5_acc": 0.48234, "loss_cls": 4.42483, "loss": 4.42483, "time": 2.34789} +{"mode": "train", "epoch": 59, "iter": 200, "lr": 0.06738, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24375, "top5_acc": 0.48406, "loss_cls": 4.38081, "loss": 4.38081, "time": 0.82529} +{"mode": "train", "epoch": 59, "iter": 300, "lr": 0.06735, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2325, "top5_acc": 0.47734, "loss_cls": 4.4432, "loss": 4.4432, "time": 0.82344} +{"mode": "train", "epoch": 59, "iter": 400, "lr": 0.06732, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24375, "top5_acc": 0.49516, "loss_cls": 4.35699, "loss": 4.35699, "time": 0.82156} +{"mode": "train", "epoch": 59, "iter": 500, "lr": 0.0673, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23172, "top5_acc": 0.47984, "loss_cls": 4.46135, "loss": 4.46135, "time": 0.81667} +{"mode": "train", "epoch": 59, "iter": 600, "lr": 0.06727, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24406, "top5_acc": 0.48797, "loss_cls": 4.40778, "loss": 4.40778, "time": 0.82275} +{"mode": "train", "epoch": 59, "iter": 700, "lr": 0.06725, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24781, "top5_acc": 0.48344, "loss_cls": 4.37727, "loss": 4.37727, "time": 0.81817} +{"mode": "train", "epoch": 59, "iter": 800, "lr": 0.06722, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23125, "top5_acc": 0.46688, "loss_cls": 4.47098, "loss": 4.47098, "time": 0.81516} +{"mode": "train", "epoch": 59, "iter": 900, "lr": 0.06719, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24781, "top5_acc": 0.49375, "loss_cls": 4.37171, "loss": 4.37171, "time": 0.82796} +{"mode": "train", "epoch": 59, "iter": 1000, "lr": 0.06717, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23312, "top5_acc": 0.47625, "loss_cls": 4.4262, "loss": 4.4262, "time": 0.82223} +{"mode": "train", "epoch": 59, "iter": 1100, "lr": 0.06714, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23656, "top5_acc": 0.46688, "loss_cls": 4.47361, "loss": 4.47361, "time": 0.82329} +{"mode": "train", "epoch": 59, "iter": 1200, "lr": 0.06711, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23797, "top5_acc": 0.47344, "loss_cls": 4.47687, "loss": 4.47687, "time": 0.82006} +{"mode": "train", "epoch": 59, "iter": 1300, "lr": 0.06709, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24453, "top5_acc": 0.48344, "loss_cls": 4.40065, "loss": 4.40065, "time": 0.82138} +{"mode": "train", "epoch": 59, "iter": 1400, "lr": 0.06706, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23531, "top5_acc": 0.47875, "loss_cls": 4.47887, "loss": 4.47887, "time": 0.82297} +{"mode": "train", "epoch": 59, "iter": 1500, "lr": 0.06704, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23422, "top5_acc": 0.47844, "loss_cls": 4.45731, "loss": 4.45731, "time": 0.81667} +{"mode": "train", "epoch": 59, "iter": 1600, "lr": 0.06701, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23375, "top5_acc": 0.47094, "loss_cls": 4.46071, "loss": 4.46071, "time": 0.82144} +{"mode": "train", "epoch": 59, "iter": 1700, "lr": 0.06698, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23547, "top5_acc": 0.48406, "loss_cls": 4.43228, "loss": 4.43228, "time": 0.82035} +{"mode": "train", "epoch": 59, "iter": 1800, "lr": 0.06696, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23281, "top5_acc": 0.47312, "loss_cls": 4.46422, "loss": 4.46422, "time": 0.81475} +{"mode": "train", "epoch": 59, "iter": 1900, "lr": 0.06693, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23312, "top5_acc": 0.48156, "loss_cls": 4.44009, "loss": 4.44009, "time": 0.82453} +{"mode": "train", "epoch": 59, "iter": 2000, "lr": 0.0669, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24391, "top5_acc": 0.47891, "loss_cls": 4.43135, "loss": 4.43135, "time": 0.8182} +{"mode": "train", "epoch": 59, "iter": 2100, "lr": 0.06688, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23484, "top5_acc": 0.48109, "loss_cls": 4.45446, "loss": 4.45446, "time": 0.82325} +{"mode": "train", "epoch": 59, "iter": 2200, "lr": 0.06685, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24562, "top5_acc": 0.48547, "loss_cls": 4.39005, "loss": 4.39005, "time": 0.81717} +{"mode": "train", "epoch": 59, "iter": 2300, "lr": 0.06682, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24062, "top5_acc": 0.47734, "loss_cls": 4.45517, "loss": 4.45517, "time": 0.82445} +{"mode": "train", "epoch": 59, "iter": 2400, "lr": 0.0668, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24344, "top5_acc": 0.48047, "loss_cls": 4.42338, "loss": 4.42338, "time": 0.83323} +{"mode": "train", "epoch": 59, "iter": 2500, "lr": 0.06677, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24188, "top5_acc": 0.47281, "loss_cls": 4.44577, "loss": 4.44577, "time": 0.82398} +{"mode": "train", "epoch": 59, "iter": 2600, "lr": 0.06675, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23375, "top5_acc": 0.48062, "loss_cls": 4.43256, "loss": 4.43256, "time": 0.82184} +{"mode": "train", "epoch": 59, "iter": 2700, "lr": 0.06672, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23938, "top5_acc": 0.48, "loss_cls": 4.40422, "loss": 4.40422, "time": 0.81867} +{"mode": "train", "epoch": 59, "iter": 2800, "lr": 0.06669, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23422, "top5_acc": 0.48172, "loss_cls": 4.43872, "loss": 4.43872, "time": 0.82269} +{"mode": "train", "epoch": 59, "iter": 2900, "lr": 0.06667, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23562, "top5_acc": 0.47953, "loss_cls": 4.44049, "loss": 4.44049, "time": 0.81641} +{"mode": "train", "epoch": 59, "iter": 3000, "lr": 0.06664, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24516, "top5_acc": 0.47516, "loss_cls": 4.43459, "loss": 4.43459, "time": 0.82067} +{"mode": "train", "epoch": 59, "iter": 3100, "lr": 0.06661, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2375, "top5_acc": 0.47703, "loss_cls": 4.44217, "loss": 4.44217, "time": 0.81534} +{"mode": "train", "epoch": 59, "iter": 3200, "lr": 0.06659, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23375, "top5_acc": 0.47375, "loss_cls": 4.45477, "loss": 4.45477, "time": 0.81659} +{"mode": "train", "epoch": 59, "iter": 3300, "lr": 0.06656, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23266, "top5_acc": 0.47266, "loss_cls": 4.46418, "loss": 4.46418, "time": 0.81777} +{"mode": "train", "epoch": 59, "iter": 3400, "lr": 0.06653, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23547, "top5_acc": 0.4725, "loss_cls": 4.45331, "loss": 4.45331, "time": 0.82524} +{"mode": "train", "epoch": 59, "iter": 3500, "lr": 0.06651, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.24109, "top5_acc": 0.48422, "loss_cls": 4.39639, "loss": 4.39639, "time": 0.8233} +{"mode": "train", "epoch": 59, "iter": 3600, "lr": 0.06648, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23734, "top5_acc": 0.48016, "loss_cls": 4.43896, "loss": 4.43896, "time": 0.8251} +{"mode": "train", "epoch": 59, "iter": 3700, "lr": 0.06646, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24547, "top5_acc": 0.48156, "loss_cls": 4.42817, "loss": 4.42817, "time": 0.81829} +{"mode": "val", "epoch": 59, "iter": 309, "lr": 0.06644, "top1_acc": 0.18807, "top5_acc": 0.40323, "mean_class_accuracy": 0.18803} +{"mode": "train", "epoch": 60, "iter": 100, "lr": 0.06642, "memory": 15990, "data_time": 1.32094, "top1_acc": 0.24734, "top5_acc": 0.48812, "loss_cls": 4.36041, "loss": 4.36041, "time": 2.31072} +{"mode": "train", "epoch": 60, "iter": 200, "lr": 0.06639, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24312, "top5_acc": 0.48828, "loss_cls": 4.37199, "loss": 4.37199, "time": 0.82067} +{"mode": "train", "epoch": 60, "iter": 300, "lr": 0.06636, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24188, "top5_acc": 0.48984, "loss_cls": 4.40382, "loss": 4.40382, "time": 0.82046} +{"mode": "train", "epoch": 60, "iter": 400, "lr": 0.06634, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23297, "top5_acc": 0.47047, "loss_cls": 4.45506, "loss": 4.45506, "time": 0.82423} +{"mode": "train", "epoch": 60, "iter": 500, "lr": 0.06631, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25078, "top5_acc": 0.49203, "loss_cls": 4.36097, "loss": 4.36097, "time": 0.81867} +{"mode": "train", "epoch": 60, "iter": 600, "lr": 0.06629, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24172, "top5_acc": 0.48031, "loss_cls": 4.41181, "loss": 4.41181, "time": 0.82533} +{"mode": "train", "epoch": 60, "iter": 700, "lr": 0.06626, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24844, "top5_acc": 0.48953, "loss_cls": 4.39056, "loss": 4.39056, "time": 0.81959} +{"mode": "train", "epoch": 60, "iter": 800, "lr": 0.06623, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24266, "top5_acc": 0.47953, "loss_cls": 4.42879, "loss": 4.42879, "time": 0.81665} +{"mode": "train", "epoch": 60, "iter": 900, "lr": 0.06621, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23219, "top5_acc": 0.47703, "loss_cls": 4.41827, "loss": 4.41827, "time": 0.82227} +{"mode": "train", "epoch": 60, "iter": 1000, "lr": 0.06618, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24844, "top5_acc": 0.48188, "loss_cls": 4.42728, "loss": 4.42728, "time": 0.82211} +{"mode": "train", "epoch": 60, "iter": 1100, "lr": 0.06615, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24953, "top5_acc": 0.48781, "loss_cls": 4.40806, "loss": 4.40806, "time": 0.82708} +{"mode": "train", "epoch": 60, "iter": 1200, "lr": 0.06613, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24328, "top5_acc": 0.49156, "loss_cls": 4.37381, "loss": 4.37381, "time": 0.8219} +{"mode": "train", "epoch": 60, "iter": 1300, "lr": 0.0661, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23375, "top5_acc": 0.47422, "loss_cls": 4.47275, "loss": 4.47275, "time": 0.82124} +{"mode": "train", "epoch": 60, "iter": 1400, "lr": 0.06607, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24266, "top5_acc": 0.47984, "loss_cls": 4.42245, "loss": 4.42245, "time": 0.81924} +{"mode": "train", "epoch": 60, "iter": 1500, "lr": 0.06605, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24172, "top5_acc": 0.48172, "loss_cls": 4.42437, "loss": 4.42437, "time": 0.81882} +{"mode": "train", "epoch": 60, "iter": 1600, "lr": 0.06602, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23516, "top5_acc": 0.48328, "loss_cls": 4.45887, "loss": 4.45887, "time": 0.8236} +{"mode": "train", "epoch": 60, "iter": 1700, "lr": 0.06599, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23562, "top5_acc": 0.46875, "loss_cls": 4.47035, "loss": 4.47035, "time": 0.81606} +{"mode": "train", "epoch": 60, "iter": 1800, "lr": 0.06597, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24109, "top5_acc": 0.48094, "loss_cls": 4.40943, "loss": 4.40943, "time": 0.82211} +{"mode": "train", "epoch": 60, "iter": 1900, "lr": 0.06594, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24516, "top5_acc": 0.48625, "loss_cls": 4.3943, "loss": 4.3943, "time": 0.81705} +{"mode": "train", "epoch": 60, "iter": 2000, "lr": 0.06591, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23781, "top5_acc": 0.47219, "loss_cls": 4.47815, "loss": 4.47815, "time": 0.8199} +{"mode": "train", "epoch": 60, "iter": 2100, "lr": 0.06589, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23344, "top5_acc": 0.47938, "loss_cls": 4.41564, "loss": 4.41564, "time": 0.82599} +{"mode": "train", "epoch": 60, "iter": 2200, "lr": 0.06586, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24391, "top5_acc": 0.48359, "loss_cls": 4.44132, "loss": 4.44132, "time": 0.82208} +{"mode": "train", "epoch": 60, "iter": 2300, "lr": 0.06584, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23719, "top5_acc": 0.47125, "loss_cls": 4.45172, "loss": 4.45172, "time": 0.8272} +{"mode": "train", "epoch": 60, "iter": 2400, "lr": 0.06581, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23938, "top5_acc": 0.48344, "loss_cls": 4.43384, "loss": 4.43384, "time": 0.82611} +{"mode": "train", "epoch": 60, "iter": 2500, "lr": 0.06578, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24172, "top5_acc": 0.47266, "loss_cls": 4.46603, "loss": 4.46603, "time": 0.82146} +{"mode": "train", "epoch": 60, "iter": 2600, "lr": 0.06576, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23828, "top5_acc": 0.48531, "loss_cls": 4.4138, "loss": 4.4138, "time": 0.82158} +{"mode": "train", "epoch": 60, "iter": 2700, "lr": 0.06573, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24219, "top5_acc": 0.47734, "loss_cls": 4.47929, "loss": 4.47929, "time": 0.81802} +{"mode": "train", "epoch": 60, "iter": 2800, "lr": 0.0657, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24422, "top5_acc": 0.47656, "loss_cls": 4.47196, "loss": 4.47196, "time": 0.82346} +{"mode": "train", "epoch": 60, "iter": 2900, "lr": 0.06568, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24219, "top5_acc": 0.48531, "loss_cls": 4.3878, "loss": 4.3878, "time": 0.82152} +{"mode": "train", "epoch": 60, "iter": 3000, "lr": 0.06565, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23422, "top5_acc": 0.47078, "loss_cls": 4.45898, "loss": 4.45898, "time": 0.82496} +{"mode": "train", "epoch": 60, "iter": 3100, "lr": 0.06562, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24375, "top5_acc": 0.48281, "loss_cls": 4.39597, "loss": 4.39597, "time": 0.82342} +{"mode": "train", "epoch": 60, "iter": 3200, "lr": 0.0656, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2425, "top5_acc": 0.4775, "loss_cls": 4.41796, "loss": 4.41796, "time": 0.81887} +{"mode": "train", "epoch": 60, "iter": 3300, "lr": 0.06557, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24031, "top5_acc": 0.48219, "loss_cls": 4.43852, "loss": 4.43852, "time": 0.8228} +{"mode": "train", "epoch": 60, "iter": 3400, "lr": 0.06554, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22875, "top5_acc": 0.46844, "loss_cls": 4.47779, "loss": 4.47779, "time": 0.82663} +{"mode": "train", "epoch": 60, "iter": 3500, "lr": 0.06552, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.24344, "top5_acc": 0.47297, "loss_cls": 4.43449, "loss": 4.43449, "time": 0.82969} +{"mode": "train", "epoch": 60, "iter": 3600, "lr": 0.06549, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23594, "top5_acc": 0.48188, "loss_cls": 4.44431, "loss": 4.44431, "time": 0.82293} +{"mode": "train", "epoch": 60, "iter": 3700, "lr": 0.06546, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.23781, "top5_acc": 0.48953, "loss_cls": 4.39007, "loss": 4.39007, "time": 0.82777} +{"mode": "val", "epoch": 60, "iter": 309, "lr": 0.06545, "top1_acc": 0.19192, "top5_acc": 0.40556, "mean_class_accuracy": 0.19184} +{"mode": "train", "epoch": 61, "iter": 100, "lr": 0.06542, "memory": 15990, "data_time": 1.27532, "top1_acc": 0.24453, "top5_acc": 0.4925, "loss_cls": 4.39304, "loss": 4.39304, "time": 2.25796} +{"mode": "train", "epoch": 61, "iter": 200, "lr": 0.0654, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24781, "top5_acc": 0.49422, "loss_cls": 4.35142, "loss": 4.35142, "time": 0.82485} +{"mode": "train", "epoch": 61, "iter": 300, "lr": 0.06537, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25188, "top5_acc": 0.4975, "loss_cls": 4.35057, "loss": 4.35057, "time": 0.82809} +{"mode": "train", "epoch": 61, "iter": 400, "lr": 0.06534, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23156, "top5_acc": 0.47156, "loss_cls": 4.47049, "loss": 4.47049, "time": 0.81772} +{"mode": "train", "epoch": 61, "iter": 500, "lr": 0.06532, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.245, "top5_acc": 0.48359, "loss_cls": 4.38457, "loss": 4.38457, "time": 0.82138} +{"mode": "train", "epoch": 61, "iter": 600, "lr": 0.06529, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23969, "top5_acc": 0.48547, "loss_cls": 4.42387, "loss": 4.42387, "time": 0.81913} +{"mode": "train", "epoch": 61, "iter": 700, "lr": 0.06526, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23078, "top5_acc": 0.47234, "loss_cls": 4.45635, "loss": 4.45635, "time": 0.81462} +{"mode": "train", "epoch": 61, "iter": 800, "lr": 0.06524, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25047, "top5_acc": 0.49219, "loss_cls": 4.37821, "loss": 4.37821, "time": 0.81663} +{"mode": "train", "epoch": 61, "iter": 900, "lr": 0.06521, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24609, "top5_acc": 0.49016, "loss_cls": 4.39814, "loss": 4.39814, "time": 0.82346} +{"mode": "train", "epoch": 61, "iter": 1000, "lr": 0.06519, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.24234, "top5_acc": 0.47578, "loss_cls": 4.43747, "loss": 4.43747, "time": 0.81883} +{"mode": "train", "epoch": 61, "iter": 1100, "lr": 0.06516, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24969, "top5_acc": 0.47703, "loss_cls": 4.42302, "loss": 4.42302, "time": 0.81937} +{"mode": "train", "epoch": 61, "iter": 1200, "lr": 0.06513, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24344, "top5_acc": 0.48281, "loss_cls": 4.43284, "loss": 4.43284, "time": 0.82154} +{"mode": "train", "epoch": 61, "iter": 1300, "lr": 0.06511, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24719, "top5_acc": 0.48797, "loss_cls": 4.39368, "loss": 4.39368, "time": 0.82053} +{"mode": "train", "epoch": 61, "iter": 1400, "lr": 0.06508, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23203, "top5_acc": 0.47141, "loss_cls": 4.45244, "loss": 4.45244, "time": 0.82225} +{"mode": "train", "epoch": 61, "iter": 1500, "lr": 0.06505, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24797, "top5_acc": 0.48766, "loss_cls": 4.37749, "loss": 4.37749, "time": 0.81792} +{"mode": "train", "epoch": 61, "iter": 1600, "lr": 0.06503, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23922, "top5_acc": 0.47859, "loss_cls": 4.44277, "loss": 4.44277, "time": 0.82017} +{"mode": "train", "epoch": 61, "iter": 1700, "lr": 0.065, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23297, "top5_acc": 0.47203, "loss_cls": 4.45058, "loss": 4.45058, "time": 0.81722} +{"mode": "train", "epoch": 61, "iter": 1800, "lr": 0.06497, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24219, "top5_acc": 0.48516, "loss_cls": 4.39414, "loss": 4.39414, "time": 0.81578} +{"mode": "train", "epoch": 61, "iter": 1900, "lr": 0.06495, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24734, "top5_acc": 0.48828, "loss_cls": 4.38087, "loss": 4.38087, "time": 0.82234} +{"mode": "train", "epoch": 61, "iter": 2000, "lr": 0.06492, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24609, "top5_acc": 0.48797, "loss_cls": 4.37862, "loss": 4.37862, "time": 0.81668} +{"mode": "train", "epoch": 61, "iter": 2100, "lr": 0.06489, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23609, "top5_acc": 0.48141, "loss_cls": 4.43152, "loss": 4.43152, "time": 0.82295} +{"mode": "train", "epoch": 61, "iter": 2200, "lr": 0.06487, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24391, "top5_acc": 0.48156, "loss_cls": 4.42897, "loss": 4.42897, "time": 0.82252} +{"mode": "train", "epoch": 61, "iter": 2300, "lr": 0.06484, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24266, "top5_acc": 0.48062, "loss_cls": 4.41005, "loss": 4.41005, "time": 0.82343} +{"mode": "train", "epoch": 61, "iter": 2400, "lr": 0.06481, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24219, "top5_acc": 0.47781, "loss_cls": 4.44742, "loss": 4.44742, "time": 0.82303} +{"mode": "train", "epoch": 61, "iter": 2500, "lr": 0.06478, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24094, "top5_acc": 0.47844, "loss_cls": 4.43618, "loss": 4.43618, "time": 0.82452} +{"mode": "train", "epoch": 61, "iter": 2600, "lr": 0.06476, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23891, "top5_acc": 0.48703, "loss_cls": 4.41011, "loss": 4.41011, "time": 0.82659} +{"mode": "train", "epoch": 61, "iter": 2700, "lr": 0.06473, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24844, "top5_acc": 0.49234, "loss_cls": 4.39478, "loss": 4.39478, "time": 0.82083} +{"mode": "train", "epoch": 61, "iter": 2800, "lr": 0.0647, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23938, "top5_acc": 0.48047, "loss_cls": 4.42998, "loss": 4.42998, "time": 0.81666} +{"mode": "train", "epoch": 61, "iter": 2900, "lr": 0.06468, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23984, "top5_acc": 0.48234, "loss_cls": 4.42152, "loss": 4.42152, "time": 0.82062} +{"mode": "train", "epoch": 61, "iter": 3000, "lr": 0.06465, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24172, "top5_acc": 0.4775, "loss_cls": 4.44207, "loss": 4.44207, "time": 0.81602} +{"mode": "train", "epoch": 61, "iter": 3100, "lr": 0.06462, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24438, "top5_acc": 0.48453, "loss_cls": 4.42717, "loss": 4.42717, "time": 0.82355} +{"mode": "train", "epoch": 61, "iter": 3200, "lr": 0.0646, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23969, "top5_acc": 0.47984, "loss_cls": 4.41817, "loss": 4.41817, "time": 0.81953} +{"mode": "train", "epoch": 61, "iter": 3300, "lr": 0.06457, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23734, "top5_acc": 0.47516, "loss_cls": 4.45646, "loss": 4.45646, "time": 0.81758} +{"mode": "train", "epoch": 61, "iter": 3400, "lr": 0.06454, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.235, "top5_acc": 0.48109, "loss_cls": 4.4236, "loss": 4.4236, "time": 0.81783} +{"mode": "train", "epoch": 61, "iter": 3500, "lr": 0.06452, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.2425, "top5_acc": 0.47734, "loss_cls": 4.43498, "loss": 4.43498, "time": 0.82427} +{"mode": "train", "epoch": 61, "iter": 3600, "lr": 0.06449, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23656, "top5_acc": 0.4775, "loss_cls": 4.431, "loss": 4.431, "time": 0.82318} +{"mode": "train", "epoch": 61, "iter": 3700, "lr": 0.06446, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2375, "top5_acc": 0.47766, "loss_cls": 4.45937, "loss": 4.45937, "time": 0.82345} +{"mode": "val", "epoch": 61, "iter": 309, "lr": 0.06445, "top1_acc": 0.17652, "top5_acc": 0.38986, "mean_class_accuracy": 0.17622} +{"mode": "train", "epoch": 62, "iter": 100, "lr": 0.06443, "memory": 15990, "data_time": 1.27704, "top1_acc": 0.24719, "top5_acc": 0.49375, "loss_cls": 4.33945, "loss": 4.33945, "time": 2.26757} +{"mode": "train", "epoch": 62, "iter": 200, "lr": 0.0644, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24703, "top5_acc": 0.49609, "loss_cls": 4.36431, "loss": 4.36431, "time": 0.825} +{"mode": "train", "epoch": 62, "iter": 300, "lr": 0.06437, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24797, "top5_acc": 0.48672, "loss_cls": 4.38821, "loss": 4.38821, "time": 0.81875} +{"mode": "train", "epoch": 62, "iter": 400, "lr": 0.06434, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24328, "top5_acc": 0.47734, "loss_cls": 4.42496, "loss": 4.42496, "time": 0.82225} +{"mode": "train", "epoch": 62, "iter": 500, "lr": 0.06432, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24016, "top5_acc": 0.48312, "loss_cls": 4.4418, "loss": 4.4418, "time": 0.81879} +{"mode": "train", "epoch": 62, "iter": 600, "lr": 0.06429, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24203, "top5_acc": 0.48531, "loss_cls": 4.37771, "loss": 4.37771, "time": 0.81621} +{"mode": "train", "epoch": 62, "iter": 700, "lr": 0.06426, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25062, "top5_acc": 0.48938, "loss_cls": 4.36095, "loss": 4.36095, "time": 0.81875} +{"mode": "train", "epoch": 62, "iter": 800, "lr": 0.06424, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24344, "top5_acc": 0.48297, "loss_cls": 4.41715, "loss": 4.41715, "time": 0.82252} +{"mode": "train", "epoch": 62, "iter": 900, "lr": 0.06421, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23766, "top5_acc": 0.48891, "loss_cls": 4.41086, "loss": 4.41086, "time": 0.81895} +{"mode": "train", "epoch": 62, "iter": 1000, "lr": 0.06418, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24562, "top5_acc": 0.48734, "loss_cls": 4.40451, "loss": 4.40451, "time": 0.82346} +{"mode": "train", "epoch": 62, "iter": 1100, "lr": 0.06416, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24062, "top5_acc": 0.48062, "loss_cls": 4.41651, "loss": 4.41651, "time": 0.82154} +{"mode": "train", "epoch": 62, "iter": 1200, "lr": 0.06413, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23406, "top5_acc": 0.48094, "loss_cls": 4.43738, "loss": 4.43738, "time": 0.82721} +{"mode": "train", "epoch": 62, "iter": 1300, "lr": 0.0641, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23828, "top5_acc": 0.47641, "loss_cls": 4.43694, "loss": 4.43694, "time": 0.8197} +{"mode": "train", "epoch": 62, "iter": 1400, "lr": 0.06408, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24516, "top5_acc": 0.49141, "loss_cls": 4.368, "loss": 4.368, "time": 0.81898} +{"mode": "train", "epoch": 62, "iter": 1500, "lr": 0.06405, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.245, "top5_acc": 0.48906, "loss_cls": 4.39117, "loss": 4.39117, "time": 0.82418} +{"mode": "train", "epoch": 62, "iter": 1600, "lr": 0.06402, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24594, "top5_acc": 0.48484, "loss_cls": 4.39855, "loss": 4.39855, "time": 0.81957} +{"mode": "train", "epoch": 62, "iter": 1700, "lr": 0.064, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23578, "top5_acc": 0.48594, "loss_cls": 4.39629, "loss": 4.39629, "time": 0.82103} +{"mode": "train", "epoch": 62, "iter": 1800, "lr": 0.06397, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24516, "top5_acc": 0.48016, "loss_cls": 4.42746, "loss": 4.42746, "time": 0.81716} +{"mode": "train", "epoch": 62, "iter": 1900, "lr": 0.06394, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24469, "top5_acc": 0.48422, "loss_cls": 4.41378, "loss": 4.41378, "time": 0.82215} +{"mode": "train", "epoch": 62, "iter": 2000, "lr": 0.06392, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24594, "top5_acc": 0.49062, "loss_cls": 4.41359, "loss": 4.41359, "time": 0.81889} +{"mode": "train", "epoch": 62, "iter": 2100, "lr": 0.06389, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24297, "top5_acc": 0.48109, "loss_cls": 4.4301, "loss": 4.4301, "time": 0.82245} +{"mode": "train", "epoch": 62, "iter": 2200, "lr": 0.06386, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24828, "top5_acc": 0.49266, "loss_cls": 4.37085, "loss": 4.37085, "time": 0.82219} +{"mode": "train", "epoch": 62, "iter": 2300, "lr": 0.06384, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24109, "top5_acc": 0.48, "loss_cls": 4.41696, "loss": 4.41696, "time": 0.82799} +{"mode": "train", "epoch": 62, "iter": 2400, "lr": 0.06381, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25359, "top5_acc": 0.48344, "loss_cls": 4.38759, "loss": 4.38759, "time": 0.81997} +{"mode": "train", "epoch": 62, "iter": 2500, "lr": 0.06378, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24781, "top5_acc": 0.48672, "loss_cls": 4.40707, "loss": 4.40707, "time": 0.82298} +{"mode": "train", "epoch": 62, "iter": 2600, "lr": 0.06375, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23984, "top5_acc": 0.48359, "loss_cls": 4.4205, "loss": 4.4205, "time": 0.81619} +{"mode": "train", "epoch": 62, "iter": 2700, "lr": 0.06373, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25203, "top5_acc": 0.48625, "loss_cls": 4.40288, "loss": 4.40288, "time": 0.8175} +{"mode": "train", "epoch": 62, "iter": 2800, "lr": 0.0637, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24344, "top5_acc": 0.48953, "loss_cls": 4.4035, "loss": 4.4035, "time": 0.82452} +{"mode": "train", "epoch": 62, "iter": 2900, "lr": 0.06367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23688, "top5_acc": 0.4725, "loss_cls": 4.46305, "loss": 4.46305, "time": 0.82076} +{"mode": "train", "epoch": 62, "iter": 3000, "lr": 0.06365, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24094, "top5_acc": 0.48375, "loss_cls": 4.42212, "loss": 4.42212, "time": 0.81665} +{"mode": "train", "epoch": 62, "iter": 3100, "lr": 0.06362, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24469, "top5_acc": 0.48375, "loss_cls": 4.40931, "loss": 4.40931, "time": 0.81424} +{"mode": "train", "epoch": 62, "iter": 3200, "lr": 0.06359, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24781, "top5_acc": 0.48156, "loss_cls": 4.42678, "loss": 4.42678, "time": 0.8209} +{"mode": "train", "epoch": 62, "iter": 3300, "lr": 0.06357, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23359, "top5_acc": 0.47547, "loss_cls": 4.4532, "loss": 4.4532, "time": 0.82158} +{"mode": "train", "epoch": 62, "iter": 3400, "lr": 0.06354, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23828, "top5_acc": 0.47469, "loss_cls": 4.46314, "loss": 4.46314, "time": 0.81926} +{"mode": "train", "epoch": 62, "iter": 3500, "lr": 0.06351, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23531, "top5_acc": 0.47641, "loss_cls": 4.42091, "loss": 4.42091, "time": 0.82403} +{"mode": "train", "epoch": 62, "iter": 3600, "lr": 0.06349, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2325, "top5_acc": 0.47141, "loss_cls": 4.4908, "loss": 4.4908, "time": 0.81895} +{"mode": "train", "epoch": 62, "iter": 3700, "lr": 0.06346, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.245, "top5_acc": 0.47953, "loss_cls": 4.41951, "loss": 4.41951, "time": 0.82364} +{"mode": "val", "epoch": 62, "iter": 309, "lr": 0.06345, "top1_acc": 0.1864, "top5_acc": 0.40222, "mean_class_accuracy": 0.18646} +{"mode": "train", "epoch": 63, "iter": 100, "lr": 0.06342, "memory": 15990, "data_time": 1.30765, "top1_acc": 0.24359, "top5_acc": 0.48938, "loss_cls": 4.38459, "loss": 4.38459, "time": 2.29192} +{"mode": "train", "epoch": 63, "iter": 200, "lr": 0.06339, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23922, "top5_acc": 0.48719, "loss_cls": 4.4124, "loss": 4.4124, "time": 0.82407} +{"mode": "train", "epoch": 63, "iter": 300, "lr": 0.06337, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25, "top5_acc": 0.49922, "loss_cls": 4.36856, "loss": 4.36856, "time": 0.81825} +{"mode": "train", "epoch": 63, "iter": 400, "lr": 0.06334, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.22719, "top5_acc": 0.47547, "loss_cls": 4.45441, "loss": 4.45441, "time": 0.82637} +{"mode": "train", "epoch": 63, "iter": 500, "lr": 0.06331, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23625, "top5_acc": 0.48625, "loss_cls": 4.39489, "loss": 4.39489, "time": 0.82122} +{"mode": "train", "epoch": 63, "iter": 600, "lr": 0.06328, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25219, "top5_acc": 0.49312, "loss_cls": 4.40165, "loss": 4.40165, "time": 0.81714} +{"mode": "train", "epoch": 63, "iter": 700, "lr": 0.06326, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24562, "top5_acc": 0.49719, "loss_cls": 4.36395, "loss": 4.36395, "time": 0.81743} +{"mode": "train", "epoch": 63, "iter": 800, "lr": 0.06323, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23953, "top5_acc": 0.48344, "loss_cls": 4.39524, "loss": 4.39524, "time": 0.81578} +{"mode": "train", "epoch": 63, "iter": 900, "lr": 0.0632, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23891, "top5_acc": 0.48516, "loss_cls": 4.40276, "loss": 4.40276, "time": 0.81848} +{"mode": "train", "epoch": 63, "iter": 1000, "lr": 0.06318, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25016, "top5_acc": 0.4875, "loss_cls": 4.42369, "loss": 4.42369, "time": 0.83263} +{"mode": "train", "epoch": 63, "iter": 1100, "lr": 0.06315, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24859, "top5_acc": 0.49016, "loss_cls": 4.38551, "loss": 4.38551, "time": 0.82392} +{"mode": "train", "epoch": 63, "iter": 1200, "lr": 0.06312, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2475, "top5_acc": 0.49094, "loss_cls": 4.39217, "loss": 4.39217, "time": 0.82302} +{"mode": "train", "epoch": 63, "iter": 1300, "lr": 0.0631, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24078, "top5_acc": 0.47969, "loss_cls": 4.43138, "loss": 4.43138, "time": 0.82237} +{"mode": "train", "epoch": 63, "iter": 1400, "lr": 0.06307, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24344, "top5_acc": 0.48547, "loss_cls": 4.4125, "loss": 4.4125, "time": 0.82278} +{"mode": "train", "epoch": 63, "iter": 1500, "lr": 0.06304, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2425, "top5_acc": 0.48609, "loss_cls": 4.39243, "loss": 4.39243, "time": 0.81474} +{"mode": "train", "epoch": 63, "iter": 1600, "lr": 0.06301, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23172, "top5_acc": 0.47172, "loss_cls": 4.43738, "loss": 4.43738, "time": 0.81437} +{"mode": "train", "epoch": 63, "iter": 1700, "lr": 0.06299, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24094, "top5_acc": 0.48281, "loss_cls": 4.43034, "loss": 4.43034, "time": 0.82618} +{"mode": "train", "epoch": 63, "iter": 1800, "lr": 0.06296, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25672, "top5_acc": 0.49094, "loss_cls": 4.37177, "loss": 4.37177, "time": 0.81982} +{"mode": "train", "epoch": 63, "iter": 1900, "lr": 0.06293, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24188, "top5_acc": 0.48328, "loss_cls": 4.4114, "loss": 4.4114, "time": 0.81706} +{"mode": "train", "epoch": 63, "iter": 2000, "lr": 0.06291, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25516, "top5_acc": 0.49562, "loss_cls": 4.36074, "loss": 4.36074, "time": 0.81925} +{"mode": "train", "epoch": 63, "iter": 2100, "lr": 0.06288, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25172, "top5_acc": 0.48172, "loss_cls": 4.39362, "loss": 4.39362, "time": 0.82535} +{"mode": "train", "epoch": 63, "iter": 2200, "lr": 0.06285, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24703, "top5_acc": 0.49266, "loss_cls": 4.35788, "loss": 4.35788, "time": 0.82056} +{"mode": "train", "epoch": 63, "iter": 2300, "lr": 0.06283, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.23703, "top5_acc": 0.47016, "loss_cls": 4.44545, "loss": 4.44545, "time": 0.83255} +{"mode": "train", "epoch": 63, "iter": 2400, "lr": 0.0628, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24781, "top5_acc": 0.48422, "loss_cls": 4.41221, "loss": 4.41221, "time": 0.81753} +{"mode": "train", "epoch": 63, "iter": 2500, "lr": 0.06277, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24906, "top5_acc": 0.48125, "loss_cls": 4.40448, "loss": 4.40448, "time": 0.82301} +{"mode": "train", "epoch": 63, "iter": 2600, "lr": 0.06274, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23547, "top5_acc": 0.47656, "loss_cls": 4.44714, "loss": 4.44714, "time": 0.81817} +{"mode": "train", "epoch": 63, "iter": 2700, "lr": 0.06272, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23609, "top5_acc": 0.48031, "loss_cls": 4.45411, "loss": 4.45411, "time": 0.82155} +{"mode": "train", "epoch": 63, "iter": 2800, "lr": 0.06269, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24297, "top5_acc": 0.49047, "loss_cls": 4.39314, "loss": 4.39314, "time": 0.81898} +{"mode": "train", "epoch": 63, "iter": 2900, "lr": 0.06266, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23016, "top5_acc": 0.48328, "loss_cls": 4.41944, "loss": 4.41944, "time": 0.82365} +{"mode": "train", "epoch": 63, "iter": 3000, "lr": 0.06264, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24734, "top5_acc": 0.50094, "loss_cls": 4.33611, "loss": 4.33611, "time": 0.81706} +{"mode": "train", "epoch": 63, "iter": 3100, "lr": 0.06261, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24969, "top5_acc": 0.49016, "loss_cls": 4.36915, "loss": 4.36915, "time": 0.82243} +{"mode": "train", "epoch": 63, "iter": 3200, "lr": 0.06258, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24078, "top5_acc": 0.47984, "loss_cls": 4.42705, "loss": 4.42705, "time": 0.81953} +{"mode": "train", "epoch": 63, "iter": 3300, "lr": 0.06256, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23844, "top5_acc": 0.48, "loss_cls": 4.41757, "loss": 4.41757, "time": 0.82035} +{"mode": "train", "epoch": 63, "iter": 3400, "lr": 0.06253, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24734, "top5_acc": 0.49031, "loss_cls": 4.36357, "loss": 4.36357, "time": 0.82115} +{"mode": "train", "epoch": 63, "iter": 3500, "lr": 0.0625, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24156, "top5_acc": 0.47656, "loss_cls": 4.4338, "loss": 4.4338, "time": 0.82171} +{"mode": "train", "epoch": 63, "iter": 3600, "lr": 0.06247, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24234, "top5_acc": 0.48141, "loss_cls": 4.42125, "loss": 4.42125, "time": 0.82367} +{"mode": "train", "epoch": 63, "iter": 3700, "lr": 0.06245, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24594, "top5_acc": 0.48766, "loss_cls": 4.38032, "loss": 4.38032, "time": 0.82898} +{"mode": "val", "epoch": 63, "iter": 309, "lr": 0.06243, "top1_acc": 0.16507, "top5_acc": 0.37654, "mean_class_accuracy": 0.16504} +{"mode": "train", "epoch": 64, "iter": 100, "lr": 0.06241, "memory": 15990, "data_time": 1.38386, "top1_acc": 0.24469, "top5_acc": 0.49328, "loss_cls": 4.37325, "loss": 4.37325, "time": 2.37762} +{"mode": "train", "epoch": 64, "iter": 200, "lr": 0.06238, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2525, "top5_acc": 0.50219, "loss_cls": 4.32907, "loss": 4.32907, "time": 0.83319} +{"mode": "train", "epoch": 64, "iter": 300, "lr": 0.06235, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.245, "top5_acc": 0.49297, "loss_cls": 4.34795, "loss": 4.34795, "time": 0.81892} +{"mode": "train", "epoch": 64, "iter": 400, "lr": 0.06233, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24031, "top5_acc": 0.48781, "loss_cls": 4.3954, "loss": 4.3954, "time": 0.82241} +{"mode": "train", "epoch": 64, "iter": 500, "lr": 0.0623, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25094, "top5_acc": 0.49422, "loss_cls": 4.38573, "loss": 4.38573, "time": 0.82407} +{"mode": "train", "epoch": 64, "iter": 600, "lr": 0.06227, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24406, "top5_acc": 0.48516, "loss_cls": 4.38453, "loss": 4.38453, "time": 0.81664} +{"mode": "train", "epoch": 64, "iter": 700, "lr": 0.06225, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.23734, "top5_acc": 0.47812, "loss_cls": 4.42474, "loss": 4.42474, "time": 0.81714} +{"mode": "train", "epoch": 64, "iter": 800, "lr": 0.06222, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24828, "top5_acc": 0.48484, "loss_cls": 4.39008, "loss": 4.39008, "time": 0.82531} +{"mode": "train", "epoch": 64, "iter": 900, "lr": 0.06219, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25062, "top5_acc": 0.48547, "loss_cls": 4.37108, "loss": 4.37108, "time": 0.82126} +{"mode": "train", "epoch": 64, "iter": 1000, "lr": 0.06216, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23969, "top5_acc": 0.48219, "loss_cls": 4.41548, "loss": 4.41548, "time": 0.82441} +{"mode": "train", "epoch": 64, "iter": 1100, "lr": 0.06214, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24094, "top5_acc": 0.47797, "loss_cls": 4.40894, "loss": 4.40894, "time": 0.82085} +{"mode": "train", "epoch": 64, "iter": 1200, "lr": 0.06211, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24312, "top5_acc": 0.48125, "loss_cls": 4.41918, "loss": 4.41918, "time": 0.82388} +{"mode": "train", "epoch": 64, "iter": 1300, "lr": 0.06208, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.23812, "top5_acc": 0.47312, "loss_cls": 4.41929, "loss": 4.41929, "time": 0.82144} +{"mode": "train", "epoch": 64, "iter": 1400, "lr": 0.06206, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24344, "top5_acc": 0.48625, "loss_cls": 4.42156, "loss": 4.42156, "time": 0.82009} +{"mode": "train", "epoch": 64, "iter": 1500, "lr": 0.06203, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24297, "top5_acc": 0.48797, "loss_cls": 4.39909, "loss": 4.39909, "time": 0.82326} +{"mode": "train", "epoch": 64, "iter": 1600, "lr": 0.062, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25875, "top5_acc": 0.49297, "loss_cls": 4.38425, "loss": 4.38425, "time": 0.81535} +{"mode": "train", "epoch": 64, "iter": 1700, "lr": 0.06197, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25391, "top5_acc": 0.49391, "loss_cls": 4.3646, "loss": 4.3646, "time": 0.82261} +{"mode": "train", "epoch": 64, "iter": 1800, "lr": 0.06195, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24625, "top5_acc": 0.4925, "loss_cls": 4.37714, "loss": 4.37714, "time": 0.8191} +{"mode": "train", "epoch": 64, "iter": 1900, "lr": 0.06192, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24578, "top5_acc": 0.47938, "loss_cls": 4.42565, "loss": 4.42565, "time": 0.82206} +{"mode": "train", "epoch": 64, "iter": 2000, "lr": 0.06189, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2325, "top5_acc": 0.47234, "loss_cls": 4.46085, "loss": 4.46085, "time": 0.82195} +{"mode": "train", "epoch": 64, "iter": 2100, "lr": 0.06187, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24625, "top5_acc": 0.48594, "loss_cls": 4.39842, "loss": 4.39842, "time": 0.81901} +{"mode": "train", "epoch": 64, "iter": 2200, "lr": 0.06184, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24, "top5_acc": 0.4825, "loss_cls": 4.42593, "loss": 4.42593, "time": 0.82385} +{"mode": "train", "epoch": 64, "iter": 2300, "lr": 0.06181, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24938, "top5_acc": 0.48312, "loss_cls": 4.39273, "loss": 4.39273, "time": 0.83154} +{"mode": "train", "epoch": 64, "iter": 2400, "lr": 0.06178, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24156, "top5_acc": 0.49031, "loss_cls": 4.40576, "loss": 4.40576, "time": 0.81621} +{"mode": "train", "epoch": 64, "iter": 2500, "lr": 0.06176, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24422, "top5_acc": 0.48516, "loss_cls": 4.38741, "loss": 4.38741, "time": 0.82007} +{"mode": "train", "epoch": 64, "iter": 2600, "lr": 0.06173, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24266, "top5_acc": 0.48969, "loss_cls": 4.39246, "loss": 4.39246, "time": 0.83175} +{"mode": "train", "epoch": 64, "iter": 2700, "lr": 0.0617, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2475, "top5_acc": 0.48562, "loss_cls": 4.37051, "loss": 4.37051, "time": 0.81896} +{"mode": "train", "epoch": 64, "iter": 2800, "lr": 0.06168, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23859, "top5_acc": 0.48422, "loss_cls": 4.38408, "loss": 4.38408, "time": 0.82003} +{"mode": "train", "epoch": 64, "iter": 2900, "lr": 0.06165, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24375, "top5_acc": 0.48781, "loss_cls": 4.40908, "loss": 4.40908, "time": 0.81471} +{"mode": "train", "epoch": 64, "iter": 3000, "lr": 0.06162, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24281, "top5_acc": 0.49094, "loss_cls": 4.38676, "loss": 4.38676, "time": 0.81993} +{"mode": "train", "epoch": 64, "iter": 3100, "lr": 0.06159, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24984, "top5_acc": 0.48469, "loss_cls": 4.41216, "loss": 4.41216, "time": 0.82453} +{"mode": "train", "epoch": 64, "iter": 3200, "lr": 0.06157, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24906, "top5_acc": 0.48281, "loss_cls": 4.38156, "loss": 4.38156, "time": 0.82233} +{"mode": "train", "epoch": 64, "iter": 3300, "lr": 0.06154, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24953, "top5_acc": 0.48766, "loss_cls": 4.3842, "loss": 4.3842, "time": 0.8178} +{"mode": "train", "epoch": 64, "iter": 3400, "lr": 0.06151, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24141, "top5_acc": 0.48281, "loss_cls": 4.41624, "loss": 4.41624, "time": 0.81628} +{"mode": "train", "epoch": 64, "iter": 3500, "lr": 0.06148, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24484, "top5_acc": 0.48609, "loss_cls": 4.41656, "loss": 4.41656, "time": 0.81352} +{"mode": "train", "epoch": 64, "iter": 3600, "lr": 0.06146, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25203, "top5_acc": 0.50281, "loss_cls": 4.33001, "loss": 4.33001, "time": 0.82476} +{"mode": "train", "epoch": 64, "iter": 3700, "lr": 0.06143, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25438, "top5_acc": 0.48875, "loss_cls": 4.37626, "loss": 4.37626, "time": 0.81435} +{"mode": "val", "epoch": 64, "iter": 309, "lr": 0.06142, "top1_acc": 0.16922, "top5_acc": 0.38505, "mean_class_accuracy": 0.16908} +{"mode": "train", "epoch": 65, "iter": 100, "lr": 0.06139, "memory": 15990, "data_time": 1.28395, "top1_acc": 0.23781, "top5_acc": 0.48562, "loss_cls": 4.39171, "loss": 4.39171, "time": 2.28891} +{"mode": "train", "epoch": 65, "iter": 200, "lr": 0.06136, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25344, "top5_acc": 0.49562, "loss_cls": 4.33875, "loss": 4.33875, "time": 0.82893} +{"mode": "train", "epoch": 65, "iter": 300, "lr": 0.06134, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25266, "top5_acc": 0.50781, "loss_cls": 4.30678, "loss": 4.30678, "time": 0.82859} +{"mode": "train", "epoch": 65, "iter": 400, "lr": 0.06131, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24625, "top5_acc": 0.48938, "loss_cls": 4.36868, "loss": 4.36868, "time": 0.81929} +{"mode": "train", "epoch": 65, "iter": 500, "lr": 0.06128, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25, "top5_acc": 0.49812, "loss_cls": 4.36678, "loss": 4.36678, "time": 0.8196} +{"mode": "train", "epoch": 65, "iter": 600, "lr": 0.06125, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25406, "top5_acc": 0.49578, "loss_cls": 4.32593, "loss": 4.32593, "time": 0.81721} +{"mode": "train", "epoch": 65, "iter": 700, "lr": 0.06123, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24844, "top5_acc": 0.49688, "loss_cls": 4.34212, "loss": 4.34212, "time": 0.81639} +{"mode": "train", "epoch": 65, "iter": 800, "lr": 0.0612, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23844, "top5_acc": 0.48344, "loss_cls": 4.41704, "loss": 4.41704, "time": 0.82442} +{"mode": "train", "epoch": 65, "iter": 900, "lr": 0.06117, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.23547, "top5_acc": 0.48391, "loss_cls": 4.41455, "loss": 4.41455, "time": 0.82137} +{"mode": "train", "epoch": 65, "iter": 1000, "lr": 0.06115, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23672, "top5_acc": 0.48531, "loss_cls": 4.38025, "loss": 4.38025, "time": 0.82473} +{"mode": "train", "epoch": 65, "iter": 1100, "lr": 0.06112, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25391, "top5_acc": 0.49938, "loss_cls": 4.35015, "loss": 4.35015, "time": 0.8228} +{"mode": "train", "epoch": 65, "iter": 1200, "lr": 0.06109, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.24984, "top5_acc": 0.48406, "loss_cls": 4.39624, "loss": 4.39624, "time": 0.82281} +{"mode": "train", "epoch": 65, "iter": 1300, "lr": 0.06106, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25578, "top5_acc": 0.495, "loss_cls": 4.34427, "loss": 4.34427, "time": 0.82578} +{"mode": "train", "epoch": 65, "iter": 1400, "lr": 0.06104, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24656, "top5_acc": 0.48625, "loss_cls": 4.36834, "loss": 4.36834, "time": 0.82071} +{"mode": "train", "epoch": 65, "iter": 1500, "lr": 0.06101, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24672, "top5_acc": 0.48938, "loss_cls": 4.38856, "loss": 4.38856, "time": 0.82383} +{"mode": "train", "epoch": 65, "iter": 1600, "lr": 0.06098, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24438, "top5_acc": 0.48, "loss_cls": 4.41217, "loss": 4.41217, "time": 0.81488} +{"mode": "train", "epoch": 65, "iter": 1700, "lr": 0.06095, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24031, "top5_acc": 0.48641, "loss_cls": 4.40822, "loss": 4.40822, "time": 0.8161} +{"mode": "train", "epoch": 65, "iter": 1800, "lr": 0.06093, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24, "top5_acc": 0.48469, "loss_cls": 4.40333, "loss": 4.40333, "time": 0.8172} +{"mode": "train", "epoch": 65, "iter": 1900, "lr": 0.0609, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25078, "top5_acc": 0.48141, "loss_cls": 4.40319, "loss": 4.40319, "time": 0.81752} +{"mode": "train", "epoch": 65, "iter": 2000, "lr": 0.06087, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24547, "top5_acc": 0.47469, "loss_cls": 4.41513, "loss": 4.41513, "time": 0.8208} +{"mode": "train", "epoch": 65, "iter": 2100, "lr": 0.06085, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24625, "top5_acc": 0.49266, "loss_cls": 4.3803, "loss": 4.3803, "time": 0.82089} +{"mode": "train", "epoch": 65, "iter": 2200, "lr": 0.06082, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24, "top5_acc": 0.47438, "loss_cls": 4.41791, "loss": 4.41791, "time": 0.82383} +{"mode": "train", "epoch": 65, "iter": 2300, "lr": 0.06079, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24266, "top5_acc": 0.4925, "loss_cls": 4.38642, "loss": 4.38642, "time": 0.8258} +{"mode": "train", "epoch": 65, "iter": 2400, "lr": 0.06076, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.23906, "top5_acc": 0.48656, "loss_cls": 4.4051, "loss": 4.4051, "time": 0.81683} +{"mode": "train", "epoch": 65, "iter": 2500, "lr": 0.06074, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24375, "top5_acc": 0.48922, "loss_cls": 4.40769, "loss": 4.40769, "time": 0.82628} +{"mode": "train", "epoch": 65, "iter": 2600, "lr": 0.06071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24031, "top5_acc": 0.47844, "loss_cls": 4.40791, "loss": 4.40791, "time": 0.81663} +{"mode": "train", "epoch": 65, "iter": 2700, "lr": 0.06068, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24188, "top5_acc": 0.48609, "loss_cls": 4.42641, "loss": 4.42641, "time": 0.82079} +{"mode": "train", "epoch": 65, "iter": 2800, "lr": 0.06065, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2425, "top5_acc": 0.48, "loss_cls": 4.44954, "loss": 4.44954, "time": 0.8236} +{"mode": "train", "epoch": 65, "iter": 2900, "lr": 0.06063, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23734, "top5_acc": 0.48281, "loss_cls": 4.41033, "loss": 4.41033, "time": 0.81972} +{"mode": "train", "epoch": 65, "iter": 3000, "lr": 0.0606, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25172, "top5_acc": 0.49531, "loss_cls": 4.36102, "loss": 4.36102, "time": 0.82117} +{"mode": "train", "epoch": 65, "iter": 3100, "lr": 0.06057, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25047, "top5_acc": 0.48844, "loss_cls": 4.3856, "loss": 4.3856, "time": 0.81893} +{"mode": "train", "epoch": 65, "iter": 3200, "lr": 0.06055, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25594, "top5_acc": 0.49859, "loss_cls": 4.33799, "loss": 4.33799, "time": 0.81568} +{"mode": "train", "epoch": 65, "iter": 3300, "lr": 0.06052, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24531, "top5_acc": 0.48375, "loss_cls": 4.38777, "loss": 4.38777, "time": 0.8173} +{"mode": "train", "epoch": 65, "iter": 3400, "lr": 0.06049, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24266, "top5_acc": 0.475, "loss_cls": 4.45207, "loss": 4.45207, "time": 0.81622} +{"mode": "train", "epoch": 65, "iter": 3500, "lr": 0.06046, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25422, "top5_acc": 0.49312, "loss_cls": 4.35589, "loss": 4.35589, "time": 0.82152} +{"mode": "train", "epoch": 65, "iter": 3600, "lr": 0.06044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24375, "top5_acc": 0.48391, "loss_cls": 4.4007, "loss": 4.4007, "time": 0.81786} +{"mode": "train", "epoch": 65, "iter": 3700, "lr": 0.06041, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24359, "top5_acc": 0.48688, "loss_cls": 4.38841, "loss": 4.38841, "time": 0.83101} +{"mode": "val", "epoch": 65, "iter": 309, "lr": 0.0604, "top1_acc": 0.1751, "top5_acc": 0.39178, "mean_class_accuracy": 0.17474} +{"mode": "train", "epoch": 66, "iter": 100, "lr": 0.06037, "memory": 15990, "data_time": 1.3095, "top1_acc": 0.26312, "top5_acc": 0.50203, "loss_cls": 4.29376, "loss": 4.29376, "time": 2.298} +{"mode": "train", "epoch": 66, "iter": 200, "lr": 0.06034, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.23875, "top5_acc": 0.485, "loss_cls": 4.38603, "loss": 4.38603, "time": 0.82637} +{"mode": "train", "epoch": 66, "iter": 300, "lr": 0.06031, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25609, "top5_acc": 0.48594, "loss_cls": 4.34839, "loss": 4.34839, "time": 0.82146} +{"mode": "train", "epoch": 66, "iter": 400, "lr": 0.06029, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25234, "top5_acc": 0.48703, "loss_cls": 4.38932, "loss": 4.38932, "time": 0.82501} +{"mode": "train", "epoch": 66, "iter": 500, "lr": 0.06026, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24484, "top5_acc": 0.49094, "loss_cls": 4.37638, "loss": 4.37638, "time": 0.81904} +{"mode": "train", "epoch": 66, "iter": 600, "lr": 0.06023, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25234, "top5_acc": 0.50766, "loss_cls": 4.30976, "loss": 4.30976, "time": 0.81996} +{"mode": "train", "epoch": 66, "iter": 700, "lr": 0.0602, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24812, "top5_acc": 0.48625, "loss_cls": 4.38663, "loss": 4.38663, "time": 0.81707} +{"mode": "train", "epoch": 66, "iter": 800, "lr": 0.06018, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2525, "top5_acc": 0.49422, "loss_cls": 4.3834, "loss": 4.3834, "time": 0.81358} +{"mode": "train", "epoch": 66, "iter": 900, "lr": 0.06015, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25516, "top5_acc": 0.49094, "loss_cls": 4.35668, "loss": 4.35668, "time": 0.82096} +{"mode": "train", "epoch": 66, "iter": 1000, "lr": 0.06012, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24594, "top5_acc": 0.4975, "loss_cls": 4.35773, "loss": 4.35773, "time": 0.8246} +{"mode": "train", "epoch": 66, "iter": 1100, "lr": 0.06009, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24109, "top5_acc": 0.48406, "loss_cls": 4.37689, "loss": 4.37689, "time": 0.82957} +{"mode": "train", "epoch": 66, "iter": 1200, "lr": 0.06007, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24219, "top5_acc": 0.49062, "loss_cls": 4.38998, "loss": 4.38998, "time": 0.82354} +{"mode": "train", "epoch": 66, "iter": 1300, "lr": 0.06004, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24891, "top5_acc": 0.49766, "loss_cls": 4.36408, "loss": 4.36408, "time": 0.82044} +{"mode": "train", "epoch": 66, "iter": 1400, "lr": 0.06001, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24391, "top5_acc": 0.49078, "loss_cls": 4.39305, "loss": 4.39305, "time": 0.81947} +{"mode": "train", "epoch": 66, "iter": 1500, "lr": 0.05999, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24719, "top5_acc": 0.48719, "loss_cls": 4.40558, "loss": 4.40558, "time": 0.81903} +{"mode": "train", "epoch": 66, "iter": 1600, "lr": 0.05996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25094, "top5_acc": 0.49078, "loss_cls": 4.37317, "loss": 4.37317, "time": 0.81573} +{"mode": "train", "epoch": 66, "iter": 1700, "lr": 0.05993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24453, "top5_acc": 0.48469, "loss_cls": 4.39899, "loss": 4.39899, "time": 0.82076} +{"mode": "train", "epoch": 66, "iter": 1800, "lr": 0.0599, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24266, "top5_acc": 0.48734, "loss_cls": 4.37629, "loss": 4.37629, "time": 0.8257} +{"mode": "train", "epoch": 66, "iter": 1900, "lr": 0.05988, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24891, "top5_acc": 0.48703, "loss_cls": 4.40125, "loss": 4.40125, "time": 0.81698} +{"mode": "train", "epoch": 66, "iter": 2000, "lr": 0.05985, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24375, "top5_acc": 0.49922, "loss_cls": 4.35647, "loss": 4.35647, "time": 0.82181} +{"mode": "train", "epoch": 66, "iter": 2100, "lr": 0.05982, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24156, "top5_acc": 0.48984, "loss_cls": 4.40396, "loss": 4.40396, "time": 0.81855} +{"mode": "train", "epoch": 66, "iter": 2200, "lr": 0.05979, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24938, "top5_acc": 0.48984, "loss_cls": 4.39712, "loss": 4.39712, "time": 0.82834} +{"mode": "train", "epoch": 66, "iter": 2300, "lr": 0.05977, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25484, "top5_acc": 0.49156, "loss_cls": 4.32291, "loss": 4.32291, "time": 0.8316} +{"mode": "train", "epoch": 66, "iter": 2400, "lr": 0.05974, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24781, "top5_acc": 0.48484, "loss_cls": 4.36121, "loss": 4.36121, "time": 0.82446} +{"mode": "train", "epoch": 66, "iter": 2500, "lr": 0.05971, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24656, "top5_acc": 0.48969, "loss_cls": 4.39839, "loss": 4.39839, "time": 0.81854} +{"mode": "train", "epoch": 66, "iter": 2600, "lr": 0.05968, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24688, "top5_acc": 0.48922, "loss_cls": 4.37318, "loss": 4.37318, "time": 0.82418} +{"mode": "train", "epoch": 66, "iter": 2700, "lr": 0.05966, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24484, "top5_acc": 0.48125, "loss_cls": 4.40188, "loss": 4.40188, "time": 0.81648} +{"mode": "train", "epoch": 66, "iter": 2800, "lr": 0.05963, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24812, "top5_acc": 0.48844, "loss_cls": 4.39171, "loss": 4.39171, "time": 0.82079} +{"mode": "train", "epoch": 66, "iter": 2900, "lr": 0.0596, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24297, "top5_acc": 0.47734, "loss_cls": 4.44815, "loss": 4.44815, "time": 0.82187} +{"mode": "train", "epoch": 66, "iter": 3000, "lr": 0.05957, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24438, "top5_acc": 0.48844, "loss_cls": 4.38328, "loss": 4.38328, "time": 0.82293} +{"mode": "train", "epoch": 66, "iter": 3100, "lr": 0.05955, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.255, "top5_acc": 0.49953, "loss_cls": 4.35624, "loss": 4.35624, "time": 0.8154} +{"mode": "train", "epoch": 66, "iter": 3200, "lr": 0.05952, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24766, "top5_acc": 0.49578, "loss_cls": 4.34857, "loss": 4.34857, "time": 0.81666} +{"mode": "train", "epoch": 66, "iter": 3300, "lr": 0.05949, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24406, "top5_acc": 0.49156, "loss_cls": 4.38936, "loss": 4.38936, "time": 0.81786} +{"mode": "train", "epoch": 66, "iter": 3400, "lr": 0.05946, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25609, "top5_acc": 0.49859, "loss_cls": 4.34228, "loss": 4.34228, "time": 0.81867} +{"mode": "train", "epoch": 66, "iter": 3500, "lr": 0.05944, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25594, "top5_acc": 0.49062, "loss_cls": 4.36319, "loss": 4.36319, "time": 0.81924} +{"mode": "train", "epoch": 66, "iter": 3600, "lr": 0.05941, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24391, "top5_acc": 0.4875, "loss_cls": 4.40599, "loss": 4.40599, "time": 0.81826} +{"mode": "train", "epoch": 66, "iter": 3700, "lr": 0.05938, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2525, "top5_acc": 0.48641, "loss_cls": 4.35577, "loss": 4.35577, "time": 0.8207} +{"mode": "val", "epoch": 66, "iter": 309, "lr": 0.05937, "top1_acc": 0.16629, "top5_acc": 0.37183, "mean_class_accuracy": 0.16584} +{"mode": "train", "epoch": 67, "iter": 100, "lr": 0.05934, "memory": 15990, "data_time": 1.31114, "top1_acc": 0.24844, "top5_acc": 0.50313, "loss_cls": 4.30055, "loss": 4.30055, "time": 2.30021} +{"mode": "train", "epoch": 67, "iter": 200, "lr": 0.05931, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25422, "top5_acc": 0.50375, "loss_cls": 4.28688, "loss": 4.28688, "time": 0.82288} +{"mode": "train", "epoch": 67, "iter": 300, "lr": 0.05929, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24453, "top5_acc": 0.48406, "loss_cls": 4.39196, "loss": 4.39196, "time": 0.82484} +{"mode": "train", "epoch": 67, "iter": 400, "lr": 0.05926, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25, "top5_acc": 0.50359, "loss_cls": 4.33171, "loss": 4.33171, "time": 0.82244} +{"mode": "train", "epoch": 67, "iter": 500, "lr": 0.05923, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25719, "top5_acc": 0.50281, "loss_cls": 4.28713, "loss": 4.28713, "time": 0.81868} +{"mode": "train", "epoch": 67, "iter": 600, "lr": 0.0592, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25641, "top5_acc": 0.49156, "loss_cls": 4.37229, "loss": 4.37229, "time": 0.81899} +{"mode": "train", "epoch": 67, "iter": 700, "lr": 0.05918, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25125, "top5_acc": 0.5025, "loss_cls": 4.34098, "loss": 4.34098, "time": 0.81917} +{"mode": "train", "epoch": 67, "iter": 800, "lr": 0.05915, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24922, "top5_acc": 0.48375, "loss_cls": 4.37145, "loss": 4.37145, "time": 0.82078} +{"mode": "train", "epoch": 67, "iter": 900, "lr": 0.05912, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25328, "top5_acc": 0.49922, "loss_cls": 4.35131, "loss": 4.35131, "time": 0.82089} +{"mode": "train", "epoch": 67, "iter": 1000, "lr": 0.05909, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25547, "top5_acc": 0.48906, "loss_cls": 4.3714, "loss": 4.3714, "time": 0.81968} +{"mode": "train", "epoch": 67, "iter": 1100, "lr": 0.05907, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24844, "top5_acc": 0.48672, "loss_cls": 4.37557, "loss": 4.37557, "time": 0.82488} +{"mode": "train", "epoch": 67, "iter": 1200, "lr": 0.05904, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24422, "top5_acc": 0.48625, "loss_cls": 4.41275, "loss": 4.41275, "time": 0.82117} +{"mode": "train", "epoch": 67, "iter": 1300, "lr": 0.05901, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25531, "top5_acc": 0.4925, "loss_cls": 4.32276, "loss": 4.32276, "time": 0.82288} +{"mode": "train", "epoch": 67, "iter": 1400, "lr": 0.05898, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25656, "top5_acc": 0.49484, "loss_cls": 4.34161, "loss": 4.34161, "time": 0.82247} +{"mode": "train", "epoch": 67, "iter": 1500, "lr": 0.05896, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25516, "top5_acc": 0.49891, "loss_cls": 4.3528, "loss": 4.3528, "time": 0.82251} +{"mode": "train", "epoch": 67, "iter": 1600, "lr": 0.05893, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25375, "top5_acc": 0.49344, "loss_cls": 4.36776, "loss": 4.36776, "time": 0.82339} +{"mode": "train", "epoch": 67, "iter": 1700, "lr": 0.0589, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25297, "top5_acc": 0.49344, "loss_cls": 4.39057, "loss": 4.39057, "time": 0.8136} +{"mode": "train", "epoch": 67, "iter": 1800, "lr": 0.05887, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25047, "top5_acc": 0.49453, "loss_cls": 4.36064, "loss": 4.36064, "time": 0.81522} +{"mode": "train", "epoch": 67, "iter": 1900, "lr": 0.05885, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25141, "top5_acc": 0.49781, "loss_cls": 4.36851, "loss": 4.36851, "time": 0.82071} +{"mode": "train", "epoch": 67, "iter": 2000, "lr": 0.05882, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.24953, "top5_acc": 0.49328, "loss_cls": 4.35989, "loss": 4.35989, "time": 0.82235} +{"mode": "train", "epoch": 67, "iter": 2100, "lr": 0.05879, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25172, "top5_acc": 0.49484, "loss_cls": 4.34664, "loss": 4.34664, "time": 0.82461} +{"mode": "train", "epoch": 67, "iter": 2200, "lr": 0.05876, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24734, "top5_acc": 0.48484, "loss_cls": 4.37463, "loss": 4.37463, "time": 0.82668} +{"mode": "train", "epoch": 67, "iter": 2300, "lr": 0.05874, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.24969, "top5_acc": 0.49688, "loss_cls": 4.3315, "loss": 4.3315, "time": 0.82853} +{"mode": "train", "epoch": 67, "iter": 2400, "lr": 0.05871, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24094, "top5_acc": 0.48781, "loss_cls": 4.38056, "loss": 4.38056, "time": 0.82542} +{"mode": "train", "epoch": 67, "iter": 2500, "lr": 0.05868, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25, "top5_acc": 0.49219, "loss_cls": 4.36884, "loss": 4.36884, "time": 0.82219} +{"mode": "train", "epoch": 67, "iter": 2600, "lr": 0.05865, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24734, "top5_acc": 0.48297, "loss_cls": 4.43973, "loss": 4.43973, "time": 0.81859} +{"mode": "train", "epoch": 67, "iter": 2700, "lr": 0.05863, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24875, "top5_acc": 0.49344, "loss_cls": 4.34537, "loss": 4.34537, "time": 0.81922} +{"mode": "train", "epoch": 67, "iter": 2800, "lr": 0.0586, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24031, "top5_acc": 0.48984, "loss_cls": 4.39548, "loss": 4.39548, "time": 0.81593} +{"mode": "train", "epoch": 67, "iter": 2900, "lr": 0.05857, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24141, "top5_acc": 0.49203, "loss_cls": 4.3593, "loss": 4.3593, "time": 0.8186} +{"mode": "train", "epoch": 67, "iter": 3000, "lr": 0.05854, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24219, "top5_acc": 0.48156, "loss_cls": 4.41436, "loss": 4.41436, "time": 0.81755} +{"mode": "train", "epoch": 67, "iter": 3100, "lr": 0.05852, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24266, "top5_acc": 0.48969, "loss_cls": 4.37594, "loss": 4.37594, "time": 0.82039} +{"mode": "train", "epoch": 67, "iter": 3200, "lr": 0.05849, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25719, "top5_acc": 0.48828, "loss_cls": 4.33838, "loss": 4.33838, "time": 0.82104} +{"mode": "train", "epoch": 67, "iter": 3300, "lr": 0.05846, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25375, "top5_acc": 0.49172, "loss_cls": 4.3726, "loss": 4.3726, "time": 0.82339} +{"mode": "train", "epoch": 67, "iter": 3400, "lr": 0.05843, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.23594, "top5_acc": 0.48766, "loss_cls": 4.3946, "loss": 4.3946, "time": 0.81647} +{"mode": "train", "epoch": 67, "iter": 3500, "lr": 0.05841, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24406, "top5_acc": 0.48625, "loss_cls": 4.36235, "loss": 4.36235, "time": 0.81967} +{"mode": "train", "epoch": 67, "iter": 3600, "lr": 0.05838, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24641, "top5_acc": 0.48359, "loss_cls": 4.41301, "loss": 4.41301, "time": 0.81553} +{"mode": "train", "epoch": 67, "iter": 3700, "lr": 0.05835, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24703, "top5_acc": 0.49172, "loss_cls": 4.35251, "loss": 4.35251, "time": 0.82255} +{"mode": "val", "epoch": 67, "iter": 309, "lr": 0.05834, "top1_acc": 0.17885, "top5_acc": 0.39908, "mean_class_accuracy": 0.17856} +{"mode": "train", "epoch": 68, "iter": 100, "lr": 0.05831, "memory": 15990, "data_time": 1.33808, "top1_acc": 0.25656, "top5_acc": 0.50672, "loss_cls": 4.3116, "loss": 4.3116, "time": 2.32667} +{"mode": "train", "epoch": 68, "iter": 200, "lr": 0.05828, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25469, "top5_acc": 0.50078, "loss_cls": 4.32362, "loss": 4.32362, "time": 0.81885} +{"mode": "train", "epoch": 68, "iter": 300, "lr": 0.05826, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25766, "top5_acc": 0.50234, "loss_cls": 4.30933, "loss": 4.30933, "time": 0.81759} +{"mode": "train", "epoch": 68, "iter": 400, "lr": 0.05823, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24641, "top5_acc": 0.49703, "loss_cls": 4.33899, "loss": 4.33899, "time": 0.82271} +{"mode": "train", "epoch": 68, "iter": 500, "lr": 0.0582, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25219, "top5_acc": 0.49359, "loss_cls": 4.34883, "loss": 4.34883, "time": 0.82106} +{"mode": "train", "epoch": 68, "iter": 600, "lr": 0.05817, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25234, "top5_acc": 0.49375, "loss_cls": 4.3543, "loss": 4.3543, "time": 0.8222} +{"mode": "train", "epoch": 68, "iter": 700, "lr": 0.05815, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24906, "top5_acc": 0.49219, "loss_cls": 4.38652, "loss": 4.38652, "time": 0.8183} +{"mode": "train", "epoch": 68, "iter": 800, "lr": 0.05812, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25453, "top5_acc": 0.49656, "loss_cls": 4.31741, "loss": 4.31741, "time": 0.81608} +{"mode": "train", "epoch": 68, "iter": 900, "lr": 0.05809, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25156, "top5_acc": 0.49594, "loss_cls": 4.35109, "loss": 4.35109, "time": 0.82244} +{"mode": "train", "epoch": 68, "iter": 1000, "lr": 0.05806, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25688, "top5_acc": 0.50406, "loss_cls": 4.30791, "loss": 4.30791, "time": 0.82228} +{"mode": "train", "epoch": 68, "iter": 1100, "lr": 0.05804, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24109, "top5_acc": 0.48625, "loss_cls": 4.40119, "loss": 4.40119, "time": 0.82659} +{"mode": "train", "epoch": 68, "iter": 1200, "lr": 0.05801, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25391, "top5_acc": 0.48875, "loss_cls": 4.37063, "loss": 4.37063, "time": 0.81718} +{"mode": "train", "epoch": 68, "iter": 1300, "lr": 0.05798, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25719, "top5_acc": 0.49859, "loss_cls": 4.34188, "loss": 4.34188, "time": 0.82045} +{"mode": "train", "epoch": 68, "iter": 1400, "lr": 0.05795, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25047, "top5_acc": 0.49547, "loss_cls": 4.38846, "loss": 4.38846, "time": 0.8165} +{"mode": "train", "epoch": 68, "iter": 1500, "lr": 0.05792, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25828, "top5_acc": 0.49984, "loss_cls": 4.29417, "loss": 4.29417, "time": 0.81661} +{"mode": "train", "epoch": 68, "iter": 1600, "lr": 0.0579, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24797, "top5_acc": 0.49031, "loss_cls": 4.36777, "loss": 4.36777, "time": 0.81973} +{"mode": "train", "epoch": 68, "iter": 1700, "lr": 0.05787, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25969, "top5_acc": 0.49828, "loss_cls": 4.33337, "loss": 4.33337, "time": 0.8165} +{"mode": "train", "epoch": 68, "iter": 1800, "lr": 0.05784, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24172, "top5_acc": 0.48281, "loss_cls": 4.41642, "loss": 4.41642, "time": 0.82266} +{"mode": "train", "epoch": 68, "iter": 1900, "lr": 0.05781, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25266, "top5_acc": 0.49266, "loss_cls": 4.39752, "loss": 4.39752, "time": 0.82069} +{"mode": "train", "epoch": 68, "iter": 2000, "lr": 0.05779, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24578, "top5_acc": 0.49234, "loss_cls": 4.37762, "loss": 4.37762, "time": 0.82369} +{"mode": "train", "epoch": 68, "iter": 2100, "lr": 0.05776, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.24797, "top5_acc": 0.49844, "loss_cls": 4.32507, "loss": 4.32507, "time": 0.82434} +{"mode": "train", "epoch": 68, "iter": 2200, "lr": 0.05773, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.255, "top5_acc": 0.49219, "loss_cls": 4.33589, "loss": 4.33589, "time": 0.8234} +{"mode": "train", "epoch": 68, "iter": 2300, "lr": 0.0577, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2575, "top5_acc": 0.48594, "loss_cls": 4.37338, "loss": 4.37338, "time": 0.82393} +{"mode": "train", "epoch": 68, "iter": 2400, "lr": 0.05768, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24969, "top5_acc": 0.48906, "loss_cls": 4.3796, "loss": 4.3796, "time": 0.82367} +{"mode": "train", "epoch": 68, "iter": 2500, "lr": 0.05765, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.23703, "top5_acc": 0.48891, "loss_cls": 4.39784, "loss": 4.39784, "time": 0.81975} +{"mode": "train", "epoch": 68, "iter": 2600, "lr": 0.05762, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25453, "top5_acc": 0.50031, "loss_cls": 4.33707, "loss": 4.33707, "time": 0.81674} +{"mode": "train", "epoch": 68, "iter": 2700, "lr": 0.05759, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25281, "top5_acc": 0.49141, "loss_cls": 4.35569, "loss": 4.35569, "time": 0.81698} +{"mode": "train", "epoch": 68, "iter": 2800, "lr": 0.05757, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25578, "top5_acc": 0.50062, "loss_cls": 4.31825, "loss": 4.31825, "time": 0.82255} +{"mode": "train", "epoch": 68, "iter": 2900, "lr": 0.05754, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25344, "top5_acc": 0.50422, "loss_cls": 4.32077, "loss": 4.32077, "time": 0.81777} +{"mode": "train", "epoch": 68, "iter": 3000, "lr": 0.05751, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24141, "top5_acc": 0.48656, "loss_cls": 4.38545, "loss": 4.38545, "time": 0.81799} +{"mode": "train", "epoch": 68, "iter": 3100, "lr": 0.05748, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25141, "top5_acc": 0.49688, "loss_cls": 4.32313, "loss": 4.32313, "time": 0.82515} +{"mode": "train", "epoch": 68, "iter": 3200, "lr": 0.05746, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24656, "top5_acc": 0.49344, "loss_cls": 4.35256, "loss": 4.35256, "time": 0.82008} +{"mode": "train", "epoch": 68, "iter": 3300, "lr": 0.05743, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25062, "top5_acc": 0.48125, "loss_cls": 4.38951, "loss": 4.38951, "time": 0.82711} +{"mode": "train", "epoch": 68, "iter": 3400, "lr": 0.0574, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25078, "top5_acc": 0.49266, "loss_cls": 4.36805, "loss": 4.36805, "time": 0.82294} +{"mode": "train", "epoch": 68, "iter": 3500, "lr": 0.05737, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24766, "top5_acc": 0.49172, "loss_cls": 4.3354, "loss": 4.3354, "time": 0.81775} +{"mode": "train", "epoch": 68, "iter": 3600, "lr": 0.05734, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25219, "top5_acc": 0.50125, "loss_cls": 4.34374, "loss": 4.34374, "time": 0.82336} +{"mode": "train", "epoch": 68, "iter": 3700, "lr": 0.05732, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.24453, "top5_acc": 0.48578, "loss_cls": 4.37709, "loss": 4.37709, "time": 0.82799} +{"mode": "val", "epoch": 68, "iter": 309, "lr": 0.0573, "top1_acc": 0.19577, "top5_acc": 0.41853, "mean_class_accuracy": 0.19563} +{"mode": "train", "epoch": 69, "iter": 100, "lr": 0.05728, "memory": 15990, "data_time": 1.30958, "top1_acc": 0.25984, "top5_acc": 0.51422, "loss_cls": 4.31216, "loss": 4.31216, "time": 2.30101} +{"mode": "train", "epoch": 69, "iter": 200, "lr": 0.05725, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25328, "top5_acc": 0.50469, "loss_cls": 4.28184, "loss": 4.28184, "time": 0.82399} +{"mode": "train", "epoch": 69, "iter": 300, "lr": 0.05722, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25422, "top5_acc": 0.49688, "loss_cls": 4.32153, "loss": 4.32153, "time": 0.82437} +{"mode": "train", "epoch": 69, "iter": 400, "lr": 0.05719, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25219, "top5_acc": 0.49609, "loss_cls": 4.33811, "loss": 4.33811, "time": 0.81668} +{"mode": "train", "epoch": 69, "iter": 500, "lr": 0.05717, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25141, "top5_acc": 0.49922, "loss_cls": 4.34263, "loss": 4.34263, "time": 0.82067} +{"mode": "train", "epoch": 69, "iter": 600, "lr": 0.05714, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25859, "top5_acc": 0.51, "loss_cls": 4.29543, "loss": 4.29543, "time": 0.82024} +{"mode": "train", "epoch": 69, "iter": 700, "lr": 0.05711, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26109, "top5_acc": 0.50953, "loss_cls": 4.2992, "loss": 4.2992, "time": 0.81722} +{"mode": "train", "epoch": 69, "iter": 800, "lr": 0.05708, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2525, "top5_acc": 0.50141, "loss_cls": 4.35003, "loss": 4.35003, "time": 0.82251} +{"mode": "train", "epoch": 69, "iter": 900, "lr": 0.05706, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25141, "top5_acc": 0.49, "loss_cls": 4.34406, "loss": 4.34406, "time": 0.82746} +{"mode": "train", "epoch": 69, "iter": 1000, "lr": 0.05703, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25984, "top5_acc": 0.49844, "loss_cls": 4.33339, "loss": 4.33339, "time": 0.82199} +{"mode": "train", "epoch": 69, "iter": 1100, "lr": 0.057, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25531, "top5_acc": 0.49438, "loss_cls": 4.33496, "loss": 4.33496, "time": 0.82313} +{"mode": "train", "epoch": 69, "iter": 1200, "lr": 0.05697, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24984, "top5_acc": 0.49188, "loss_cls": 4.36213, "loss": 4.36213, "time": 0.82225} +{"mode": "train", "epoch": 69, "iter": 1300, "lr": 0.05694, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24438, "top5_acc": 0.49562, "loss_cls": 4.33796, "loss": 4.33796, "time": 0.8207} +{"mode": "train", "epoch": 69, "iter": 1400, "lr": 0.05692, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25609, "top5_acc": 0.49688, "loss_cls": 4.34377, "loss": 4.34377, "time": 0.81819} +{"mode": "train", "epoch": 69, "iter": 1500, "lr": 0.05689, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25484, "top5_acc": 0.48969, "loss_cls": 4.35536, "loss": 4.35536, "time": 0.82057} +{"mode": "train", "epoch": 69, "iter": 1600, "lr": 0.05686, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24578, "top5_acc": 0.49812, "loss_cls": 4.36594, "loss": 4.36594, "time": 0.81829} +{"mode": "train", "epoch": 69, "iter": 1700, "lr": 0.05683, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.25328, "top5_acc": 0.49891, "loss_cls": 4.34983, "loss": 4.34983, "time": 0.81806} +{"mode": "train", "epoch": 69, "iter": 1800, "lr": 0.05681, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25781, "top5_acc": 0.49219, "loss_cls": 4.3396, "loss": 4.3396, "time": 0.82635} +{"mode": "train", "epoch": 69, "iter": 1900, "lr": 0.05678, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25031, "top5_acc": 0.49609, "loss_cls": 4.31611, "loss": 4.31611, "time": 0.82512} +{"mode": "train", "epoch": 69, "iter": 2000, "lr": 0.05675, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26297, "top5_acc": 0.50547, "loss_cls": 4.28179, "loss": 4.28179, "time": 0.82842} +{"mode": "train", "epoch": 69, "iter": 2100, "lr": 0.05672, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.25672, "top5_acc": 0.49484, "loss_cls": 4.3485, "loss": 4.3485, "time": 0.82408} +{"mode": "train", "epoch": 69, "iter": 2200, "lr": 0.0567, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24359, "top5_acc": 0.48828, "loss_cls": 4.37251, "loss": 4.37251, "time": 0.81955} +{"mode": "train", "epoch": 69, "iter": 2300, "lr": 0.05667, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26312, "top5_acc": 0.49938, "loss_cls": 4.32752, "loss": 4.32752, "time": 0.82364} +{"mode": "train", "epoch": 69, "iter": 2400, "lr": 0.05664, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.24516, "top5_acc": 0.49219, "loss_cls": 4.38835, "loss": 4.38835, "time": 0.82608} +{"mode": "train", "epoch": 69, "iter": 2500, "lr": 0.05661, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24625, "top5_acc": 0.48781, "loss_cls": 4.38071, "loss": 4.38071, "time": 0.82232} +{"mode": "train", "epoch": 69, "iter": 2600, "lr": 0.05658, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.24703, "top5_acc": 0.49266, "loss_cls": 4.33855, "loss": 4.33855, "time": 0.81987} +{"mode": "train", "epoch": 69, "iter": 2700, "lr": 0.05656, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.24281, "top5_acc": 0.49062, "loss_cls": 4.38442, "loss": 4.38442, "time": 0.82113} +{"mode": "train", "epoch": 69, "iter": 2800, "lr": 0.05653, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25203, "top5_acc": 0.5, "loss_cls": 4.37416, "loss": 4.37416, "time": 0.81919} +{"mode": "train", "epoch": 69, "iter": 2900, "lr": 0.0565, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24578, "top5_acc": 0.49516, "loss_cls": 4.36198, "loss": 4.36198, "time": 0.81797} +{"mode": "train", "epoch": 69, "iter": 3000, "lr": 0.05647, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25375, "top5_acc": 0.49219, "loss_cls": 4.36199, "loss": 4.36199, "time": 0.8228} +{"mode": "train", "epoch": 69, "iter": 3100, "lr": 0.05645, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24078, "top5_acc": 0.48328, "loss_cls": 4.37526, "loss": 4.37526, "time": 0.8191} +{"mode": "train", "epoch": 69, "iter": 3200, "lr": 0.05642, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25328, "top5_acc": 0.49047, "loss_cls": 4.34065, "loss": 4.34065, "time": 0.81865} +{"mode": "train", "epoch": 69, "iter": 3300, "lr": 0.05639, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24172, "top5_acc": 0.49656, "loss_cls": 4.35596, "loss": 4.35596, "time": 0.82248} +{"mode": "train", "epoch": 69, "iter": 3400, "lr": 0.05636, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24891, "top5_acc": 0.49703, "loss_cls": 4.36697, "loss": 4.36697, "time": 0.81941} +{"mode": "train", "epoch": 69, "iter": 3500, "lr": 0.05634, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25578, "top5_acc": 0.50266, "loss_cls": 4.32183, "loss": 4.32183, "time": 0.82882} +{"mode": "train", "epoch": 69, "iter": 3600, "lr": 0.05631, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25469, "top5_acc": 0.49547, "loss_cls": 4.35614, "loss": 4.35614, "time": 0.8208} +{"mode": "train", "epoch": 69, "iter": 3700, "lr": 0.05628, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24859, "top5_acc": 0.48812, "loss_cls": 4.35257, "loss": 4.35257, "time": 0.81834} +{"mode": "val", "epoch": 69, "iter": 309, "lr": 0.05627, "top1_acc": 0.19389, "top5_acc": 0.4205, "mean_class_accuracy": 0.19378} +{"mode": "train", "epoch": 70, "iter": 100, "lr": 0.05624, "memory": 15990, "data_time": 1.27379, "top1_acc": 0.26219, "top5_acc": 0.49875, "loss_cls": 4.31635, "loss": 4.31635, "time": 2.26281} +{"mode": "train", "epoch": 70, "iter": 200, "lr": 0.05621, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26766, "top5_acc": 0.50875, "loss_cls": 4.25239, "loss": 4.25239, "time": 0.81967} +{"mode": "train", "epoch": 70, "iter": 300, "lr": 0.05618, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25359, "top5_acc": 0.50078, "loss_cls": 4.30452, "loss": 4.30452, "time": 0.81827} +{"mode": "train", "epoch": 70, "iter": 400, "lr": 0.05616, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.24922, "top5_acc": 0.49531, "loss_cls": 4.33711, "loss": 4.33711, "time": 0.81776} +{"mode": "train", "epoch": 70, "iter": 500, "lr": 0.05613, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25359, "top5_acc": 0.49766, "loss_cls": 4.31597, "loss": 4.31597, "time": 0.81658} +{"mode": "train", "epoch": 70, "iter": 600, "lr": 0.0561, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26016, "top5_acc": 0.50438, "loss_cls": 4.33255, "loss": 4.33255, "time": 0.81906} +{"mode": "train", "epoch": 70, "iter": 700, "lr": 0.05607, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25312, "top5_acc": 0.495, "loss_cls": 4.33766, "loss": 4.33766, "time": 0.81827} +{"mode": "train", "epoch": 70, "iter": 800, "lr": 0.05605, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25578, "top5_acc": 0.50344, "loss_cls": 4.31874, "loss": 4.31874, "time": 0.82556} +{"mode": "train", "epoch": 70, "iter": 900, "lr": 0.05602, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24078, "top5_acc": 0.49969, "loss_cls": 4.36738, "loss": 4.36738, "time": 0.82552} +{"mode": "train", "epoch": 70, "iter": 1000, "lr": 0.05599, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25812, "top5_acc": 0.50422, "loss_cls": 4.31079, "loss": 4.31079, "time": 0.81618} +{"mode": "train", "epoch": 70, "iter": 1100, "lr": 0.05596, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25, "top5_acc": 0.49484, "loss_cls": 4.33744, "loss": 4.33744, "time": 0.82338} +{"mode": "train", "epoch": 70, "iter": 1200, "lr": 0.05593, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25797, "top5_acc": 0.50875, "loss_cls": 4.27951, "loss": 4.27951, "time": 0.82042} +{"mode": "train", "epoch": 70, "iter": 1300, "lr": 0.05591, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25781, "top5_acc": 0.49203, "loss_cls": 4.34235, "loss": 4.34235, "time": 0.822} +{"mode": "train", "epoch": 70, "iter": 1400, "lr": 0.05588, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.2575, "top5_acc": 0.5, "loss_cls": 4.3261, "loss": 4.3261, "time": 0.81883} +{"mode": "train", "epoch": 70, "iter": 1500, "lr": 0.05585, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24297, "top5_acc": 0.48453, "loss_cls": 4.38235, "loss": 4.38235, "time": 0.81816} +{"mode": "train", "epoch": 70, "iter": 1600, "lr": 0.05582, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25594, "top5_acc": 0.49344, "loss_cls": 4.3344, "loss": 4.3344, "time": 0.82112} +{"mode": "train", "epoch": 70, "iter": 1700, "lr": 0.0558, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25625, "top5_acc": 0.49953, "loss_cls": 4.32146, "loss": 4.32146, "time": 0.82406} +{"mode": "train", "epoch": 70, "iter": 1800, "lr": 0.05577, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25781, "top5_acc": 0.49797, "loss_cls": 4.31373, "loss": 4.31373, "time": 0.82139} +{"mode": "train", "epoch": 70, "iter": 1900, "lr": 0.05574, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26094, "top5_acc": 0.50859, "loss_cls": 4.28714, "loss": 4.28714, "time": 0.82631} +{"mode": "train", "epoch": 70, "iter": 2000, "lr": 0.05571, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24875, "top5_acc": 0.48859, "loss_cls": 4.38532, "loss": 4.38532, "time": 0.8282} +{"mode": "train", "epoch": 70, "iter": 2100, "lr": 0.05568, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.26078, "top5_acc": 0.49938, "loss_cls": 4.31325, "loss": 4.31325, "time": 0.83115} +{"mode": "train", "epoch": 70, "iter": 2200, "lr": 0.05566, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24875, "top5_acc": 0.48969, "loss_cls": 4.38523, "loss": 4.38523, "time": 0.82061} +{"mode": "train", "epoch": 70, "iter": 2300, "lr": 0.05563, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.255, "top5_acc": 0.49953, "loss_cls": 4.34205, "loss": 4.34205, "time": 0.82628} +{"mode": "train", "epoch": 70, "iter": 2400, "lr": 0.0556, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25828, "top5_acc": 0.50141, "loss_cls": 4.29733, "loss": 4.29733, "time": 0.81844} +{"mode": "train", "epoch": 70, "iter": 2500, "lr": 0.05557, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25688, "top5_acc": 0.49375, "loss_cls": 4.30936, "loss": 4.30936, "time": 0.82633} +{"mode": "train", "epoch": 70, "iter": 2600, "lr": 0.05555, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.255, "top5_acc": 0.49031, "loss_cls": 4.35258, "loss": 4.35258, "time": 0.82202} +{"mode": "train", "epoch": 70, "iter": 2700, "lr": 0.05552, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24984, "top5_acc": 0.48969, "loss_cls": 4.34987, "loss": 4.34987, "time": 0.81626} +{"mode": "train", "epoch": 70, "iter": 2800, "lr": 0.05549, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26031, "top5_acc": 0.50125, "loss_cls": 4.32931, "loss": 4.32931, "time": 0.82085} +{"mode": "train", "epoch": 70, "iter": 2900, "lr": 0.05546, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24422, "top5_acc": 0.495, "loss_cls": 4.36466, "loss": 4.36466, "time": 0.81747} +{"mode": "train", "epoch": 70, "iter": 3000, "lr": 0.05543, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24969, "top5_acc": 0.49297, "loss_cls": 4.34084, "loss": 4.34084, "time": 0.81556} +{"mode": "train", "epoch": 70, "iter": 3100, "lr": 0.05541, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25016, "top5_acc": 0.49547, "loss_cls": 4.3505, "loss": 4.3505, "time": 0.82314} +{"mode": "train", "epoch": 70, "iter": 3200, "lr": 0.05538, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25172, "top5_acc": 0.49922, "loss_cls": 4.3513, "loss": 4.3513, "time": 0.82084} +{"mode": "train", "epoch": 70, "iter": 3300, "lr": 0.05535, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24625, "top5_acc": 0.49719, "loss_cls": 4.35259, "loss": 4.35259, "time": 0.82033} +{"mode": "train", "epoch": 70, "iter": 3400, "lr": 0.05532, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25422, "top5_acc": 0.49953, "loss_cls": 4.33278, "loss": 4.33278, "time": 0.82073} +{"mode": "train", "epoch": 70, "iter": 3500, "lr": 0.0553, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2575, "top5_acc": 0.48797, "loss_cls": 4.35369, "loss": 4.35369, "time": 0.82219} +{"mode": "train", "epoch": 70, "iter": 3600, "lr": 0.05527, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24781, "top5_acc": 0.495, "loss_cls": 4.34567, "loss": 4.34567, "time": 0.81714} +{"mode": "train", "epoch": 70, "iter": 3700, "lr": 0.05524, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26094, "top5_acc": 0.50031, "loss_cls": 4.3234, "loss": 4.3234, "time": 0.82273} +{"mode": "val", "epoch": 70, "iter": 309, "lr": 0.05523, "top1_acc": 0.18619, "top5_acc": 0.39872, "mean_class_accuracy": 0.18609} +{"mode": "train", "epoch": 71, "iter": 100, "lr": 0.0552, "memory": 15990, "data_time": 1.30363, "top1_acc": 0.25906, "top5_acc": 0.50172, "loss_cls": 4.30611, "loss": 4.30611, "time": 2.29783} +{"mode": "train", "epoch": 71, "iter": 200, "lr": 0.05517, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24688, "top5_acc": 0.49312, "loss_cls": 4.31975, "loss": 4.31975, "time": 0.82458} +{"mode": "train", "epoch": 71, "iter": 300, "lr": 0.05514, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26297, "top5_acc": 0.51453, "loss_cls": 4.257, "loss": 4.257, "time": 0.81576} +{"mode": "train", "epoch": 71, "iter": 400, "lr": 0.05512, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24906, "top5_acc": 0.49641, "loss_cls": 4.33008, "loss": 4.33008, "time": 0.81736} +{"mode": "train", "epoch": 71, "iter": 500, "lr": 0.05509, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25828, "top5_acc": 0.5025, "loss_cls": 4.30262, "loss": 4.30262, "time": 0.81969} +{"mode": "train", "epoch": 71, "iter": 600, "lr": 0.05506, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25625, "top5_acc": 0.50672, "loss_cls": 4.29041, "loss": 4.29041, "time": 0.82261} +{"mode": "train", "epoch": 71, "iter": 700, "lr": 0.05503, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25672, "top5_acc": 0.49297, "loss_cls": 4.32582, "loss": 4.32582, "time": 0.81779} +{"mode": "train", "epoch": 71, "iter": 800, "lr": 0.055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26281, "top5_acc": 0.50547, "loss_cls": 4.30953, "loss": 4.30953, "time": 0.81775} +{"mode": "train", "epoch": 71, "iter": 900, "lr": 0.05498, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25188, "top5_acc": 0.49375, "loss_cls": 4.32936, "loss": 4.32936, "time": 0.82122} +{"mode": "train", "epoch": 71, "iter": 1000, "lr": 0.05495, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25359, "top5_acc": 0.49406, "loss_cls": 4.35913, "loss": 4.35913, "time": 0.81887} +{"mode": "train", "epoch": 71, "iter": 1100, "lr": 0.05492, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25812, "top5_acc": 0.5025, "loss_cls": 4.32645, "loss": 4.32645, "time": 0.82294} +{"mode": "train", "epoch": 71, "iter": 1200, "lr": 0.05489, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25016, "top5_acc": 0.50328, "loss_cls": 4.32352, "loss": 4.32352, "time": 0.81702} +{"mode": "train", "epoch": 71, "iter": 1300, "lr": 0.05487, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24531, "top5_acc": 0.48797, "loss_cls": 4.40888, "loss": 4.40888, "time": 0.81641} +{"mode": "train", "epoch": 71, "iter": 1400, "lr": 0.05484, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25906, "top5_acc": 0.49078, "loss_cls": 4.32635, "loss": 4.32635, "time": 0.81795} +{"mode": "train", "epoch": 71, "iter": 1500, "lr": 0.05481, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26312, "top5_acc": 0.50047, "loss_cls": 4.29735, "loss": 4.29735, "time": 0.82209} +{"mode": "train", "epoch": 71, "iter": 1600, "lr": 0.05478, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25906, "top5_acc": 0.49875, "loss_cls": 4.31161, "loss": 4.31161, "time": 0.8189} +{"mode": "train", "epoch": 71, "iter": 1700, "lr": 0.05475, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25156, "top5_acc": 0.48922, "loss_cls": 4.35995, "loss": 4.35995, "time": 0.81966} +{"mode": "train", "epoch": 71, "iter": 1800, "lr": 0.05473, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26203, "top5_acc": 0.49891, "loss_cls": 4.33067, "loss": 4.33067, "time": 0.82292} +{"mode": "train", "epoch": 71, "iter": 1900, "lr": 0.0547, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25797, "top5_acc": 0.50078, "loss_cls": 4.30903, "loss": 4.30903, "time": 0.82011} +{"mode": "train", "epoch": 71, "iter": 2000, "lr": 0.05467, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25172, "top5_acc": 0.49984, "loss_cls": 4.31624, "loss": 4.31624, "time": 0.82099} +{"mode": "train", "epoch": 71, "iter": 2100, "lr": 0.05464, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24953, "top5_acc": 0.49281, "loss_cls": 4.35027, "loss": 4.35027, "time": 0.81985} +{"mode": "train", "epoch": 71, "iter": 2200, "lr": 0.05461, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25062, "top5_acc": 0.49719, "loss_cls": 4.32391, "loss": 4.32391, "time": 0.8205} +{"mode": "train", "epoch": 71, "iter": 2300, "lr": 0.05459, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25125, "top5_acc": 0.48875, "loss_cls": 4.34959, "loss": 4.34959, "time": 0.82131} +{"mode": "train", "epoch": 71, "iter": 2400, "lr": 0.05456, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25109, "top5_acc": 0.49938, "loss_cls": 4.32422, "loss": 4.32422, "time": 0.82447} +{"mode": "train", "epoch": 71, "iter": 2500, "lr": 0.05453, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25641, "top5_acc": 0.5025, "loss_cls": 4.30831, "loss": 4.30831, "time": 0.8256} +{"mode": "train", "epoch": 71, "iter": 2600, "lr": 0.0545, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2625, "top5_acc": 0.5, "loss_cls": 4.31369, "loss": 4.31369, "time": 0.81815} +{"mode": "train", "epoch": 71, "iter": 2700, "lr": 0.05448, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25625, "top5_acc": 0.50062, "loss_cls": 4.32304, "loss": 4.32304, "time": 0.82532} +{"mode": "train", "epoch": 71, "iter": 2800, "lr": 0.05445, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25578, "top5_acc": 0.4975, "loss_cls": 4.31262, "loss": 4.31262, "time": 0.81744} +{"mode": "train", "epoch": 71, "iter": 2900, "lr": 0.05442, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25047, "top5_acc": 0.48609, "loss_cls": 4.36844, "loss": 4.36844, "time": 0.81941} +{"mode": "train", "epoch": 71, "iter": 3000, "lr": 0.05439, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25453, "top5_acc": 0.49766, "loss_cls": 4.33699, "loss": 4.33699, "time": 0.81682} +{"mode": "train", "epoch": 71, "iter": 3100, "lr": 0.05436, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25109, "top5_acc": 0.50094, "loss_cls": 4.31842, "loss": 4.31842, "time": 0.8229} +{"mode": "train", "epoch": 71, "iter": 3200, "lr": 0.05434, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25875, "top5_acc": 0.49766, "loss_cls": 4.29328, "loss": 4.29328, "time": 0.8162} +{"mode": "train", "epoch": 71, "iter": 3300, "lr": 0.05431, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25953, "top5_acc": 0.50453, "loss_cls": 4.28589, "loss": 4.28589, "time": 0.82274} +{"mode": "train", "epoch": 71, "iter": 3400, "lr": 0.05428, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25594, "top5_acc": 0.50844, "loss_cls": 4.30744, "loss": 4.30744, "time": 0.81737} +{"mode": "train", "epoch": 71, "iter": 3500, "lr": 0.05425, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25047, "top5_acc": 0.49406, "loss_cls": 4.37249, "loss": 4.37249, "time": 0.82429} +{"mode": "train", "epoch": 71, "iter": 3600, "lr": 0.05422, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25672, "top5_acc": 0.50016, "loss_cls": 4.33157, "loss": 4.33157, "time": 0.81528} +{"mode": "train", "epoch": 71, "iter": 3700, "lr": 0.0542, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24812, "top5_acc": 0.49109, "loss_cls": 4.35067, "loss": 4.35067, "time": 0.82193} +{"mode": "val", "epoch": 71, "iter": 309, "lr": 0.05418, "top1_acc": 0.20635, "top5_acc": 0.43438, "mean_class_accuracy": 0.20619} +{"mode": "train", "epoch": 72, "iter": 100, "lr": 0.05416, "memory": 15990, "data_time": 1.29253, "top1_acc": 0.25766, "top5_acc": 0.50172, "loss_cls": 4.30562, "loss": 4.30562, "time": 2.29633} +{"mode": "train", "epoch": 72, "iter": 200, "lr": 0.05413, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27266, "top5_acc": 0.51859, "loss_cls": 4.23998, "loss": 4.23998, "time": 0.81841} +{"mode": "train", "epoch": 72, "iter": 300, "lr": 0.0541, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25984, "top5_acc": 0.50313, "loss_cls": 4.26496, "loss": 4.26496, "time": 0.81997} +{"mode": "train", "epoch": 72, "iter": 400, "lr": 0.05407, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26469, "top5_acc": 0.50891, "loss_cls": 4.2861, "loss": 4.2861, "time": 0.82503} +{"mode": "train", "epoch": 72, "iter": 500, "lr": 0.05404, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25766, "top5_acc": 0.50625, "loss_cls": 4.29142, "loss": 4.29142, "time": 0.82268} +{"mode": "train", "epoch": 72, "iter": 600, "lr": 0.05402, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24594, "top5_acc": 0.50016, "loss_cls": 4.32248, "loss": 4.32248, "time": 0.8185} +{"mode": "train", "epoch": 72, "iter": 700, "lr": 0.05399, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25047, "top5_acc": 0.49766, "loss_cls": 4.32841, "loss": 4.32841, "time": 0.82047} +{"mode": "train", "epoch": 72, "iter": 800, "lr": 0.05396, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25875, "top5_acc": 0.49469, "loss_cls": 4.317, "loss": 4.317, "time": 0.82031} +{"mode": "train", "epoch": 72, "iter": 900, "lr": 0.05393, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25578, "top5_acc": 0.49625, "loss_cls": 4.31648, "loss": 4.31648, "time": 0.82594} +{"mode": "train", "epoch": 72, "iter": 1000, "lr": 0.05391, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24766, "top5_acc": 0.49938, "loss_cls": 4.3372, "loss": 4.3372, "time": 0.82334} +{"mode": "train", "epoch": 72, "iter": 1100, "lr": 0.05388, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25094, "top5_acc": 0.49922, "loss_cls": 4.32817, "loss": 4.32817, "time": 0.82107} +{"mode": "train", "epoch": 72, "iter": 1200, "lr": 0.05385, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25641, "top5_acc": 0.50344, "loss_cls": 4.31205, "loss": 4.31205, "time": 0.81895} +{"mode": "train", "epoch": 72, "iter": 1300, "lr": 0.05382, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25641, "top5_acc": 0.50703, "loss_cls": 4.2761, "loss": 4.2761, "time": 0.82036} +{"mode": "train", "epoch": 72, "iter": 1400, "lr": 0.05379, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25, "top5_acc": 0.50453, "loss_cls": 4.3166, "loss": 4.3166, "time": 0.81945} +{"mode": "train", "epoch": 72, "iter": 1500, "lr": 0.05377, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24547, "top5_acc": 0.49203, "loss_cls": 4.38366, "loss": 4.38366, "time": 0.82317} +{"mode": "train", "epoch": 72, "iter": 1600, "lr": 0.05374, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.24719, "top5_acc": 0.50484, "loss_cls": 4.34584, "loss": 4.34584, "time": 0.82084} +{"mode": "train", "epoch": 72, "iter": 1700, "lr": 0.05371, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24891, "top5_acc": 0.49938, "loss_cls": 4.34753, "loss": 4.34753, "time": 0.81732} +{"mode": "train", "epoch": 72, "iter": 1800, "lr": 0.05368, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25578, "top5_acc": 0.49281, "loss_cls": 4.33206, "loss": 4.33206, "time": 0.82686} +{"mode": "train", "epoch": 72, "iter": 1900, "lr": 0.05365, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26922, "top5_acc": 0.50484, "loss_cls": 4.28422, "loss": 4.28422, "time": 0.82016} +{"mode": "train", "epoch": 72, "iter": 2000, "lr": 0.05363, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25406, "top5_acc": 0.48984, "loss_cls": 4.35479, "loss": 4.35479, "time": 0.8215} +{"mode": "train", "epoch": 72, "iter": 2100, "lr": 0.0536, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26719, "top5_acc": 0.49969, "loss_cls": 4.30932, "loss": 4.30932, "time": 0.81737} +{"mode": "train", "epoch": 72, "iter": 2200, "lr": 0.05357, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26203, "top5_acc": 0.49859, "loss_cls": 4.29831, "loss": 4.29831, "time": 0.82846} +{"mode": "train", "epoch": 72, "iter": 2300, "lr": 0.05354, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.25516, "top5_acc": 0.49219, "loss_cls": 4.32077, "loss": 4.32077, "time": 0.82048} +{"mode": "train", "epoch": 72, "iter": 2400, "lr": 0.05352, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26375, "top5_acc": 0.50781, "loss_cls": 4.29712, "loss": 4.29712, "time": 0.82129} +{"mode": "train", "epoch": 72, "iter": 2500, "lr": 0.05349, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25266, "top5_acc": 0.49672, "loss_cls": 4.29386, "loss": 4.29386, "time": 0.82418} +{"mode": "train", "epoch": 72, "iter": 2600, "lr": 0.05346, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27187, "top5_acc": 0.51234, "loss_cls": 4.25468, "loss": 4.25468, "time": 0.81626} +{"mode": "train", "epoch": 72, "iter": 2700, "lr": 0.05343, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25344, "top5_acc": 0.49469, "loss_cls": 4.36191, "loss": 4.36191, "time": 0.81945} +{"mode": "train", "epoch": 72, "iter": 2800, "lr": 0.0534, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26562, "top5_acc": 0.50828, "loss_cls": 4.28248, "loss": 4.28248, "time": 0.81674} +{"mode": "train", "epoch": 72, "iter": 2900, "lr": 0.05338, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.24594, "top5_acc": 0.49531, "loss_cls": 4.35029, "loss": 4.35029, "time": 0.82024} +{"mode": "train", "epoch": 72, "iter": 3000, "lr": 0.05335, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25656, "top5_acc": 0.50078, "loss_cls": 4.33373, "loss": 4.33373, "time": 0.82347} +{"mode": "train", "epoch": 72, "iter": 3100, "lr": 0.05332, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26844, "top5_acc": 0.50938, "loss_cls": 4.26958, "loss": 4.26958, "time": 0.81744} +{"mode": "train", "epoch": 72, "iter": 3200, "lr": 0.05329, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25312, "top5_acc": 0.50656, "loss_cls": 4.2823, "loss": 4.2823, "time": 0.82078} +{"mode": "train", "epoch": 72, "iter": 3300, "lr": 0.05326, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24031, "top5_acc": 0.48828, "loss_cls": 4.37028, "loss": 4.37028, "time": 0.81856} +{"mode": "train", "epoch": 72, "iter": 3400, "lr": 0.05324, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25094, "top5_acc": 0.49641, "loss_cls": 4.35086, "loss": 4.35086, "time": 0.8206} +{"mode": "train", "epoch": 72, "iter": 3500, "lr": 0.05321, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25734, "top5_acc": 0.50391, "loss_cls": 4.27894, "loss": 4.27894, "time": 0.82406} +{"mode": "train", "epoch": 72, "iter": 3600, "lr": 0.05318, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26141, "top5_acc": 0.50219, "loss_cls": 4.30415, "loss": 4.30415, "time": 0.82023} +{"mode": "train", "epoch": 72, "iter": 3700, "lr": 0.05315, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25188, "top5_acc": 0.50266, "loss_cls": 4.31008, "loss": 4.31008, "time": 0.81871} +{"mode": "val", "epoch": 72, "iter": 309, "lr": 0.05314, "top1_acc": 0.20037, "top5_acc": 0.42577, "mean_class_accuracy": 0.20019} +{"mode": "train", "epoch": 73, "iter": 100, "lr": 0.05311, "memory": 15990, "data_time": 1.33038, "top1_acc": 0.26406, "top5_acc": 0.51703, "loss_cls": 4.26138, "loss": 4.26138, "time": 2.32171} +{"mode": "train", "epoch": 73, "iter": 200, "lr": 0.05308, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26859, "top5_acc": 0.50891, "loss_cls": 4.27626, "loss": 4.27626, "time": 0.8232} +{"mode": "train", "epoch": 73, "iter": 300, "lr": 0.05306, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25938, "top5_acc": 0.50922, "loss_cls": 4.29459, "loss": 4.29459, "time": 0.82181} +{"mode": "train", "epoch": 73, "iter": 400, "lr": 0.05303, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26562, "top5_acc": 0.50516, "loss_cls": 4.29053, "loss": 4.29053, "time": 0.82027} +{"mode": "train", "epoch": 73, "iter": 500, "lr": 0.053, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26453, "top5_acc": 0.51766, "loss_cls": 4.23699, "loss": 4.23699, "time": 0.81951} +{"mode": "train", "epoch": 73, "iter": 600, "lr": 0.05297, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25469, "top5_acc": 0.49875, "loss_cls": 4.30997, "loss": 4.30997, "time": 0.81711} +{"mode": "train", "epoch": 73, "iter": 700, "lr": 0.05294, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26281, "top5_acc": 0.51688, "loss_cls": 4.26091, "loss": 4.26091, "time": 0.81748} +{"mode": "train", "epoch": 73, "iter": 800, "lr": 0.05292, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25109, "top5_acc": 0.50406, "loss_cls": 4.32175, "loss": 4.32175, "time": 0.82287} +{"mode": "train", "epoch": 73, "iter": 900, "lr": 0.05289, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26438, "top5_acc": 0.50891, "loss_cls": 4.25225, "loss": 4.25225, "time": 0.82616} +{"mode": "train", "epoch": 73, "iter": 1000, "lr": 0.05286, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25609, "top5_acc": 0.50859, "loss_cls": 4.29072, "loss": 4.29072, "time": 0.82049} +{"mode": "train", "epoch": 73, "iter": 1100, "lr": 0.05283, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.24453, "top5_acc": 0.49047, "loss_cls": 4.35423, "loss": 4.35423, "time": 0.82815} +{"mode": "train", "epoch": 73, "iter": 1200, "lr": 0.0528, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24891, "top5_acc": 0.50047, "loss_cls": 4.32461, "loss": 4.32461, "time": 0.82022} +{"mode": "train", "epoch": 73, "iter": 1300, "lr": 0.05278, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2525, "top5_acc": 0.49859, "loss_cls": 4.31621, "loss": 4.31621, "time": 0.81795} +{"mode": "train", "epoch": 73, "iter": 1400, "lr": 0.05275, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.255, "top5_acc": 0.49844, "loss_cls": 4.30382, "loss": 4.30382, "time": 0.81678} +{"mode": "train", "epoch": 73, "iter": 1500, "lr": 0.05272, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25844, "top5_acc": 0.50484, "loss_cls": 4.30818, "loss": 4.30818, "time": 0.82212} +{"mode": "train", "epoch": 73, "iter": 1600, "lr": 0.05269, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26328, "top5_acc": 0.50234, "loss_cls": 4.28353, "loss": 4.28353, "time": 0.8244} +{"mode": "train", "epoch": 73, "iter": 1700, "lr": 0.05267, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25344, "top5_acc": 0.48906, "loss_cls": 4.36456, "loss": 4.36456, "time": 0.81826} +{"mode": "train", "epoch": 73, "iter": 1800, "lr": 0.05264, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26531, "top5_acc": 0.50516, "loss_cls": 4.27918, "loss": 4.27918, "time": 0.82671} +{"mode": "train", "epoch": 73, "iter": 1900, "lr": 0.05261, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26219, "top5_acc": 0.48938, "loss_cls": 4.33208, "loss": 4.33208, "time": 0.82414} +{"mode": "train", "epoch": 73, "iter": 2000, "lr": 0.05258, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26156, "top5_acc": 0.51406, "loss_cls": 4.29193, "loss": 4.29193, "time": 0.82708} +{"mode": "train", "epoch": 73, "iter": 2100, "lr": 0.05255, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26031, "top5_acc": 0.50266, "loss_cls": 4.28017, "loss": 4.28017, "time": 0.82054} +{"mode": "train", "epoch": 73, "iter": 2200, "lr": 0.05253, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26328, "top5_acc": 0.50906, "loss_cls": 4.29747, "loss": 4.29747, "time": 0.83112} +{"mode": "train", "epoch": 73, "iter": 2300, "lr": 0.0525, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.25219, "top5_acc": 0.50469, "loss_cls": 4.33528, "loss": 4.33528, "time": 0.82223} +{"mode": "train", "epoch": 73, "iter": 2400, "lr": 0.05247, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.25406, "top5_acc": 0.49234, "loss_cls": 4.33848, "loss": 4.33848, "time": 0.82485} +{"mode": "train", "epoch": 73, "iter": 2500, "lr": 0.05244, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25953, "top5_acc": 0.49891, "loss_cls": 4.30144, "loss": 4.30144, "time": 0.82822} +{"mode": "train", "epoch": 73, "iter": 2600, "lr": 0.05241, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25047, "top5_acc": 0.50203, "loss_cls": 4.3408, "loss": 4.3408, "time": 0.8225} +{"mode": "train", "epoch": 73, "iter": 2700, "lr": 0.05239, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25672, "top5_acc": 0.50922, "loss_cls": 4.2997, "loss": 4.2997, "time": 0.82386} +{"mode": "train", "epoch": 73, "iter": 2800, "lr": 0.05236, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25719, "top5_acc": 0.50641, "loss_cls": 4.30125, "loss": 4.30125, "time": 0.81943} +{"mode": "train", "epoch": 73, "iter": 2900, "lr": 0.05233, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2525, "top5_acc": 0.5075, "loss_cls": 4.32854, "loss": 4.32854, "time": 0.82121} +{"mode": "train", "epoch": 73, "iter": 3000, "lr": 0.0523, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26531, "top5_acc": 0.50391, "loss_cls": 4.29338, "loss": 4.29338, "time": 0.81903} +{"mode": "train", "epoch": 73, "iter": 3100, "lr": 0.05227, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25203, "top5_acc": 0.49859, "loss_cls": 4.32595, "loss": 4.32595, "time": 0.81529} +{"mode": "train", "epoch": 73, "iter": 3200, "lr": 0.05225, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25797, "top5_acc": 0.50359, "loss_cls": 4.29261, "loss": 4.29261, "time": 0.82016} +{"mode": "train", "epoch": 73, "iter": 3300, "lr": 0.05222, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26625, "top5_acc": 0.50438, "loss_cls": 4.27328, "loss": 4.27328, "time": 0.81958} +{"mode": "train", "epoch": 73, "iter": 3400, "lr": 0.05219, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26234, "top5_acc": 0.49422, "loss_cls": 4.33314, "loss": 4.33314, "time": 0.82005} +{"mode": "train", "epoch": 73, "iter": 3500, "lr": 0.05216, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25281, "top5_acc": 0.51031, "loss_cls": 4.26714, "loss": 4.26714, "time": 0.82192} +{"mode": "train", "epoch": 73, "iter": 3600, "lr": 0.05213, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.24203, "top5_acc": 0.48594, "loss_cls": 4.37779, "loss": 4.37779, "time": 0.81977} +{"mode": "train", "epoch": 73, "iter": 3700, "lr": 0.05211, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25859, "top5_acc": 0.5, "loss_cls": 4.30007, "loss": 4.30007, "time": 0.81352} +{"mode": "val", "epoch": 73, "iter": 309, "lr": 0.05209, "top1_acc": 0.20007, "top5_acc": 0.42846, "mean_class_accuracy": 0.19982} +{"mode": "train", "epoch": 74, "iter": 100, "lr": 0.05207, "memory": 15990, "data_time": 1.26299, "top1_acc": 0.26937, "top5_acc": 0.51531, "loss_cls": 4.25224, "loss": 4.25224, "time": 2.24831} +{"mode": "train", "epoch": 74, "iter": 200, "lr": 0.05204, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26453, "top5_acc": 0.51, "loss_cls": 4.24912, "loss": 4.24912, "time": 0.82515} +{"mode": "train", "epoch": 74, "iter": 300, "lr": 0.05201, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25469, "top5_acc": 0.50688, "loss_cls": 4.28474, "loss": 4.28474, "time": 0.81905} +{"mode": "train", "epoch": 74, "iter": 400, "lr": 0.05198, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26672, "top5_acc": 0.51219, "loss_cls": 4.2767, "loss": 4.2767, "time": 0.82403} +{"mode": "train", "epoch": 74, "iter": 500, "lr": 0.05195, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26, "top5_acc": 0.50281, "loss_cls": 4.27704, "loss": 4.27704, "time": 0.82372} +{"mode": "train", "epoch": 74, "iter": 600, "lr": 0.05193, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26922, "top5_acc": 0.51375, "loss_cls": 4.27282, "loss": 4.27282, "time": 0.81586} +{"mode": "train", "epoch": 74, "iter": 700, "lr": 0.0519, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25031, "top5_acc": 0.4975, "loss_cls": 4.31976, "loss": 4.31976, "time": 0.81994} +{"mode": "train", "epoch": 74, "iter": 800, "lr": 0.05187, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26609, "top5_acc": 0.51781, "loss_cls": 4.2209, "loss": 4.2209, "time": 0.82475} +{"mode": "train", "epoch": 74, "iter": 900, "lr": 0.05184, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26062, "top5_acc": 0.5025, "loss_cls": 4.28312, "loss": 4.28312, "time": 0.82829} +{"mode": "train", "epoch": 74, "iter": 1000, "lr": 0.05181, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25062, "top5_acc": 0.50062, "loss_cls": 4.33658, "loss": 4.33658, "time": 0.82543} +{"mode": "train", "epoch": 74, "iter": 1100, "lr": 0.05179, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25516, "top5_acc": 0.49688, "loss_cls": 4.317, "loss": 4.317, "time": 0.82535} +{"mode": "train", "epoch": 74, "iter": 1200, "lr": 0.05176, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25625, "top5_acc": 0.49422, "loss_cls": 4.31316, "loss": 4.31316, "time": 0.82624} +{"mode": "train", "epoch": 74, "iter": 1300, "lr": 0.05173, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26562, "top5_acc": 0.50813, "loss_cls": 4.27375, "loss": 4.27375, "time": 0.81909} +{"mode": "train", "epoch": 74, "iter": 1400, "lr": 0.0517, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26875, "top5_acc": 0.50844, "loss_cls": 4.27176, "loss": 4.27176, "time": 0.8219} +{"mode": "train", "epoch": 74, "iter": 1500, "lr": 0.05168, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26625, "top5_acc": 0.51641, "loss_cls": 4.24979, "loss": 4.24979, "time": 0.82459} +{"mode": "train", "epoch": 74, "iter": 1600, "lr": 0.05165, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.24891, "top5_acc": 0.48656, "loss_cls": 4.36402, "loss": 4.36402, "time": 0.819} +{"mode": "train", "epoch": 74, "iter": 1700, "lr": 0.05162, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26094, "top5_acc": 0.50891, "loss_cls": 4.26394, "loss": 4.26394, "time": 0.81846} +{"mode": "train", "epoch": 74, "iter": 1800, "lr": 0.05159, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26625, "top5_acc": 0.49641, "loss_cls": 4.30246, "loss": 4.30246, "time": 0.81909} +{"mode": "train", "epoch": 74, "iter": 1900, "lr": 0.05156, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.25297, "top5_acc": 0.49516, "loss_cls": 4.33349, "loss": 4.33349, "time": 0.82242} +{"mode": "train", "epoch": 74, "iter": 2000, "lr": 0.05154, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25703, "top5_acc": 0.50828, "loss_cls": 4.29362, "loss": 4.29362, "time": 0.82676} +{"mode": "train", "epoch": 74, "iter": 2100, "lr": 0.05151, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25297, "top5_acc": 0.49297, "loss_cls": 4.32613, "loss": 4.32613, "time": 0.82061} +{"mode": "train", "epoch": 74, "iter": 2200, "lr": 0.05148, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.27469, "top5_acc": 0.51266, "loss_cls": 4.25176, "loss": 4.25176, "time": 0.82665} +{"mode": "train", "epoch": 74, "iter": 2300, "lr": 0.05145, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2675, "top5_acc": 0.5, "loss_cls": 4.3036, "loss": 4.3036, "time": 0.8245} +{"mode": "train", "epoch": 74, "iter": 2400, "lr": 0.05142, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.265, "top5_acc": 0.50625, "loss_cls": 4.27511, "loss": 4.27511, "time": 0.82417} +{"mode": "train", "epoch": 74, "iter": 2500, "lr": 0.0514, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25656, "top5_acc": 0.50094, "loss_cls": 4.2934, "loss": 4.2934, "time": 0.82417} +{"mode": "train", "epoch": 74, "iter": 2600, "lr": 0.05137, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26453, "top5_acc": 0.50641, "loss_cls": 4.24683, "loss": 4.24683, "time": 0.82116} +{"mode": "train", "epoch": 74, "iter": 2700, "lr": 0.05134, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25391, "top5_acc": 0.49875, "loss_cls": 4.31871, "loss": 4.31871, "time": 0.82231} +{"mode": "train", "epoch": 74, "iter": 2800, "lr": 0.05131, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25156, "top5_acc": 0.49906, "loss_cls": 4.31951, "loss": 4.31951, "time": 0.81872} +{"mode": "train", "epoch": 74, "iter": 2900, "lr": 0.05128, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26359, "top5_acc": 0.50281, "loss_cls": 4.2664, "loss": 4.2664, "time": 0.82216} +{"mode": "train", "epoch": 74, "iter": 3000, "lr": 0.05126, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26, "top5_acc": 0.50703, "loss_cls": 4.27557, "loss": 4.27557, "time": 0.8204} +{"mode": "train", "epoch": 74, "iter": 3100, "lr": 0.05123, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25141, "top5_acc": 0.49516, "loss_cls": 4.33513, "loss": 4.33513, "time": 0.81668} +{"mode": "train", "epoch": 74, "iter": 3200, "lr": 0.0512, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25, "top5_acc": 0.49516, "loss_cls": 4.32744, "loss": 4.32744, "time": 0.82158} +{"mode": "train", "epoch": 74, "iter": 3300, "lr": 0.05117, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2575, "top5_acc": 0.50078, "loss_cls": 4.30192, "loss": 4.30192, "time": 0.81606} +{"mode": "train", "epoch": 74, "iter": 3400, "lr": 0.05114, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25766, "top5_acc": 0.50469, "loss_cls": 4.27831, "loss": 4.27831, "time": 0.81834} +{"mode": "train", "epoch": 74, "iter": 3500, "lr": 0.05112, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26156, "top5_acc": 0.50359, "loss_cls": 4.28311, "loss": 4.28311, "time": 0.82171} +{"mode": "train", "epoch": 74, "iter": 3600, "lr": 0.05109, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26203, "top5_acc": 0.50891, "loss_cls": 4.29148, "loss": 4.29148, "time": 0.8171} +{"mode": "train", "epoch": 74, "iter": 3700, "lr": 0.05106, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26188, "top5_acc": 0.50062, "loss_cls": 4.30126, "loss": 4.30126, "time": 0.81645} +{"mode": "val", "epoch": 74, "iter": 309, "lr": 0.05105, "top1_acc": 0.1866, "top5_acc": 0.39437, "mean_class_accuracy": 0.18627} +{"mode": "train", "epoch": 75, "iter": 100, "lr": 0.05102, "memory": 15990, "data_time": 1.27981, "top1_acc": 0.26656, "top5_acc": 0.52172, "loss_cls": 4.2239, "loss": 4.2239, "time": 2.26798} +{"mode": "train", "epoch": 75, "iter": 200, "lr": 0.05099, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26719, "top5_acc": 0.50484, "loss_cls": 4.25001, "loss": 4.25001, "time": 0.82146} +{"mode": "train", "epoch": 75, "iter": 300, "lr": 0.05096, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26047, "top5_acc": 0.50297, "loss_cls": 4.26483, "loss": 4.26483, "time": 0.81729} +{"mode": "train", "epoch": 75, "iter": 400, "lr": 0.05094, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27047, "top5_acc": 0.5175, "loss_cls": 4.22227, "loss": 4.22227, "time": 0.8203} +{"mode": "train", "epoch": 75, "iter": 500, "lr": 0.05091, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25969, "top5_acc": 0.49594, "loss_cls": 4.33639, "loss": 4.33639, "time": 0.81721} +{"mode": "train", "epoch": 75, "iter": 600, "lr": 0.05088, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26891, "top5_acc": 0.515, "loss_cls": 4.23784, "loss": 4.23784, "time": 0.81931} +{"mode": "train", "epoch": 75, "iter": 700, "lr": 0.05085, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26078, "top5_acc": 0.5125, "loss_cls": 4.25082, "loss": 4.25082, "time": 0.81936} +{"mode": "train", "epoch": 75, "iter": 800, "lr": 0.05082, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26359, "top5_acc": 0.51062, "loss_cls": 4.27298, "loss": 4.27298, "time": 0.82101} +{"mode": "train", "epoch": 75, "iter": 900, "lr": 0.0508, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25781, "top5_acc": 0.5175, "loss_cls": 4.26882, "loss": 4.26882, "time": 0.8218} +{"mode": "train", "epoch": 75, "iter": 1000, "lr": 0.05077, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25844, "top5_acc": 0.51266, "loss_cls": 4.2912, "loss": 4.2912, "time": 0.82587} +{"mode": "train", "epoch": 75, "iter": 1100, "lr": 0.05074, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.25609, "top5_acc": 0.49781, "loss_cls": 4.31586, "loss": 4.31586, "time": 0.82174} +{"mode": "train", "epoch": 75, "iter": 1200, "lr": 0.05071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25438, "top5_acc": 0.5025, "loss_cls": 4.27396, "loss": 4.27396, "time": 0.81998} +{"mode": "train", "epoch": 75, "iter": 1300, "lr": 0.05068, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25719, "top5_acc": 0.50187, "loss_cls": 4.29955, "loss": 4.29955, "time": 0.82405} +{"mode": "train", "epoch": 75, "iter": 1400, "lr": 0.05066, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26312, "top5_acc": 0.49547, "loss_cls": 4.29932, "loss": 4.29932, "time": 0.82302} +{"mode": "train", "epoch": 75, "iter": 1500, "lr": 0.05063, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25562, "top5_acc": 0.49672, "loss_cls": 4.31921, "loss": 4.31921, "time": 0.81989} +{"mode": "train", "epoch": 75, "iter": 1600, "lr": 0.0506, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26516, "top5_acc": 0.51375, "loss_cls": 4.24204, "loss": 4.24204, "time": 0.81983} +{"mode": "train", "epoch": 75, "iter": 1700, "lr": 0.05057, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26516, "top5_acc": 0.50938, "loss_cls": 4.26256, "loss": 4.26256, "time": 0.8295} +{"mode": "train", "epoch": 75, "iter": 1800, "lr": 0.05054, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.25359, "top5_acc": 0.50297, "loss_cls": 4.31663, "loss": 4.31663, "time": 0.82396} +{"mode": "train", "epoch": 75, "iter": 1900, "lr": 0.05052, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.265, "top5_acc": 0.51016, "loss_cls": 4.29237, "loss": 4.29237, "time": 0.82693} +{"mode": "train", "epoch": 75, "iter": 2000, "lr": 0.05049, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25938, "top5_acc": 0.50422, "loss_cls": 4.2839, "loss": 4.2839, "time": 0.82067} +{"mode": "train", "epoch": 75, "iter": 2100, "lr": 0.05046, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26609, "top5_acc": 0.50672, "loss_cls": 4.30268, "loss": 4.30268, "time": 0.81558} +{"mode": "train", "epoch": 75, "iter": 2200, "lr": 0.05043, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26375, "top5_acc": 0.50891, "loss_cls": 4.27962, "loss": 4.27962, "time": 0.82538} +{"mode": "train", "epoch": 75, "iter": 2300, "lr": 0.0504, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.26359, "top5_acc": 0.51438, "loss_cls": 4.29909, "loss": 4.29909, "time": 0.82495} +{"mode": "train", "epoch": 75, "iter": 2400, "lr": 0.05038, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25516, "top5_acc": 0.50203, "loss_cls": 4.31143, "loss": 4.31143, "time": 0.82048} +{"mode": "train", "epoch": 75, "iter": 2500, "lr": 0.05035, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26125, "top5_acc": 0.51266, "loss_cls": 4.26903, "loss": 4.26903, "time": 0.82166} +{"mode": "train", "epoch": 75, "iter": 2600, "lr": 0.05032, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26391, "top5_acc": 0.50828, "loss_cls": 4.26819, "loss": 4.26819, "time": 0.81932} +{"mode": "train", "epoch": 75, "iter": 2700, "lr": 0.05029, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26062, "top5_acc": 0.51531, "loss_cls": 4.2447, "loss": 4.2447, "time": 0.81834} +{"mode": "train", "epoch": 75, "iter": 2800, "lr": 0.05026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25781, "top5_acc": 0.50703, "loss_cls": 4.27657, "loss": 4.27657, "time": 0.8162} +{"mode": "train", "epoch": 75, "iter": 2900, "lr": 0.05024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26203, "top5_acc": 0.50766, "loss_cls": 4.27358, "loss": 4.27358, "time": 0.82017} +{"mode": "train", "epoch": 75, "iter": 3000, "lr": 0.05021, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27, "top5_acc": 0.51234, "loss_cls": 4.25949, "loss": 4.25949, "time": 0.81723} +{"mode": "train", "epoch": 75, "iter": 3100, "lr": 0.05018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2625, "top5_acc": 0.50562, "loss_cls": 4.306, "loss": 4.306, "time": 0.81914} +{"mode": "train", "epoch": 75, "iter": 3200, "lr": 0.05015, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26812, "top5_acc": 0.51484, "loss_cls": 4.25206, "loss": 4.25206, "time": 0.81719} +{"mode": "train", "epoch": 75, "iter": 3300, "lr": 0.05012, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.25953, "top5_acc": 0.50813, "loss_cls": 4.29815, "loss": 4.29815, "time": 0.81581} +{"mode": "train", "epoch": 75, "iter": 3400, "lr": 0.0501, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25922, "top5_acc": 0.50656, "loss_cls": 4.28287, "loss": 4.28287, "time": 0.82524} +{"mode": "train", "epoch": 75, "iter": 3500, "lr": 0.05007, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27047, "top5_acc": 0.51359, "loss_cls": 4.26915, "loss": 4.26915, "time": 0.81986} +{"mode": "train", "epoch": 75, "iter": 3600, "lr": 0.05004, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25797, "top5_acc": 0.50438, "loss_cls": 4.30789, "loss": 4.30789, "time": 0.82731} +{"mode": "train", "epoch": 75, "iter": 3700, "lr": 0.05001, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26094, "top5_acc": 0.50453, "loss_cls": 4.26662, "loss": 4.26662, "time": 0.82592} +{"mode": "val", "epoch": 75, "iter": 309, "lr": 0.05, "top1_acc": 0.19764, "top5_acc": 0.41711, "mean_class_accuracy": 0.19741} +{"mode": "train", "epoch": 76, "iter": 100, "lr": 0.04997, "memory": 15990, "data_time": 1.27456, "top1_acc": 0.26797, "top5_acc": 0.51078, "loss_cls": 4.23616, "loss": 4.23616, "time": 2.29711} +{"mode": "train", "epoch": 76, "iter": 200, "lr": 0.04994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27266, "top5_acc": 0.52031, "loss_cls": 4.19748, "loss": 4.19748, "time": 0.82663} +{"mode": "train", "epoch": 76, "iter": 300, "lr": 0.04992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26969, "top5_acc": 0.51078, "loss_cls": 4.25169, "loss": 4.25169, "time": 0.81645} +{"mode": "train", "epoch": 76, "iter": 400, "lr": 0.04989, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27359, "top5_acc": 0.51203, "loss_cls": 4.24236, "loss": 4.24236, "time": 0.81549} +{"mode": "train", "epoch": 76, "iter": 500, "lr": 0.04986, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.25859, "top5_acc": 0.51094, "loss_cls": 4.26553, "loss": 4.26553, "time": 0.81696} +{"mode": "train", "epoch": 76, "iter": 600, "lr": 0.04983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25812, "top5_acc": 0.50438, "loss_cls": 4.28604, "loss": 4.28604, "time": 0.81553} +{"mode": "train", "epoch": 76, "iter": 700, "lr": 0.0498, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26219, "top5_acc": 0.50406, "loss_cls": 4.25506, "loss": 4.25506, "time": 0.81858} +{"mode": "train", "epoch": 76, "iter": 800, "lr": 0.04978, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26984, "top5_acc": 0.50625, "loss_cls": 4.29097, "loss": 4.29097, "time": 0.82195} +{"mode": "train", "epoch": 76, "iter": 900, "lr": 0.04975, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26562, "top5_acc": 0.51672, "loss_cls": 4.2472, "loss": 4.2472, "time": 0.8207} +{"mode": "train", "epoch": 76, "iter": 1000, "lr": 0.04972, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25266, "top5_acc": 0.5125, "loss_cls": 4.28359, "loss": 4.28359, "time": 0.8243} +{"mode": "train", "epoch": 76, "iter": 1100, "lr": 0.04969, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25625, "top5_acc": 0.49562, "loss_cls": 4.30815, "loss": 4.30815, "time": 0.82024} +{"mode": "train", "epoch": 76, "iter": 1200, "lr": 0.04966, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27, "top5_acc": 0.51188, "loss_cls": 4.25206, "loss": 4.25206, "time": 0.82057} +{"mode": "train", "epoch": 76, "iter": 1300, "lr": 0.04964, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25375, "top5_acc": 0.5, "loss_cls": 4.30986, "loss": 4.30986, "time": 0.81601} +{"mode": "train", "epoch": 76, "iter": 1400, "lr": 0.04961, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25969, "top5_acc": 0.51469, "loss_cls": 4.24333, "loss": 4.24333, "time": 0.82018} +{"mode": "train", "epoch": 76, "iter": 1500, "lr": 0.04958, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27141, "top5_acc": 0.50469, "loss_cls": 4.26992, "loss": 4.26992, "time": 0.81718} +{"mode": "train", "epoch": 76, "iter": 1600, "lr": 0.04955, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26203, "top5_acc": 0.51094, "loss_cls": 4.24349, "loss": 4.24349, "time": 0.81776} +{"mode": "train", "epoch": 76, "iter": 1700, "lr": 0.04953, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26094, "top5_acc": 0.51203, "loss_cls": 4.26134, "loss": 4.26134, "time": 0.83016} +{"mode": "train", "epoch": 76, "iter": 1800, "lr": 0.0495, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25125, "top5_acc": 0.49812, "loss_cls": 4.32863, "loss": 4.32863, "time": 0.8199} +{"mode": "train", "epoch": 76, "iter": 1900, "lr": 0.04947, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.24953, "top5_acc": 0.50062, "loss_cls": 4.32001, "loss": 4.32001, "time": 0.82522} +{"mode": "train", "epoch": 76, "iter": 2000, "lr": 0.04944, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26078, "top5_acc": 0.50656, "loss_cls": 4.26977, "loss": 4.26977, "time": 0.82176} +{"mode": "train", "epoch": 76, "iter": 2100, "lr": 0.04941, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27172, "top5_acc": 0.51297, "loss_cls": 4.23449, "loss": 4.23449, "time": 0.82242} +{"mode": "train", "epoch": 76, "iter": 2200, "lr": 0.04939, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.25734, "top5_acc": 0.50984, "loss_cls": 4.26158, "loss": 4.26158, "time": 0.82047} +{"mode": "train", "epoch": 76, "iter": 2300, "lr": 0.04936, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2625, "top5_acc": 0.50641, "loss_cls": 4.27237, "loss": 4.27237, "time": 0.82893} +{"mode": "train", "epoch": 76, "iter": 2400, "lr": 0.04933, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26156, "top5_acc": 0.49828, "loss_cls": 4.31103, "loss": 4.31103, "time": 0.82494} +{"mode": "train", "epoch": 76, "iter": 2500, "lr": 0.0493, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26516, "top5_acc": 0.51297, "loss_cls": 4.26511, "loss": 4.26511, "time": 0.82089} +{"mode": "train", "epoch": 76, "iter": 2600, "lr": 0.04927, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27078, "top5_acc": 0.51672, "loss_cls": 4.24187, "loss": 4.24187, "time": 0.82026} +{"mode": "train", "epoch": 76, "iter": 2700, "lr": 0.04925, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25969, "top5_acc": 0.51094, "loss_cls": 4.26757, "loss": 4.26757, "time": 0.82099} +{"mode": "train", "epoch": 76, "iter": 2800, "lr": 0.04922, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26609, "top5_acc": 0.50953, "loss_cls": 4.2817, "loss": 4.2817, "time": 0.82458} +{"mode": "train", "epoch": 76, "iter": 2900, "lr": 0.04919, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26453, "top5_acc": 0.50578, "loss_cls": 4.27049, "loss": 4.27049, "time": 0.81842} +{"mode": "train", "epoch": 76, "iter": 3000, "lr": 0.04916, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25516, "top5_acc": 0.50891, "loss_cls": 4.26751, "loss": 4.26751, "time": 0.81868} +{"mode": "train", "epoch": 76, "iter": 3100, "lr": 0.04913, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25719, "top5_acc": 0.49297, "loss_cls": 4.32135, "loss": 4.32135, "time": 0.81819} +{"mode": "train", "epoch": 76, "iter": 3200, "lr": 0.04911, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26656, "top5_acc": 0.51516, "loss_cls": 4.24446, "loss": 4.24446, "time": 0.81919} +{"mode": "train", "epoch": 76, "iter": 3300, "lr": 0.04908, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25562, "top5_acc": 0.50406, "loss_cls": 4.29249, "loss": 4.29249, "time": 0.81578} +{"mode": "train", "epoch": 76, "iter": 3400, "lr": 0.04905, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27328, "top5_acc": 0.52156, "loss_cls": 4.21833, "loss": 4.21833, "time": 0.82381} +{"mode": "train", "epoch": 76, "iter": 3500, "lr": 0.04902, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25906, "top5_acc": 0.50047, "loss_cls": 4.31721, "loss": 4.31721, "time": 0.82486} +{"mode": "train", "epoch": 76, "iter": 3600, "lr": 0.04899, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25859, "top5_acc": 0.50266, "loss_cls": 4.28302, "loss": 4.28302, "time": 0.82106} +{"mode": "train", "epoch": 76, "iter": 3700, "lr": 0.04897, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26359, "top5_acc": 0.51328, "loss_cls": 4.26619, "loss": 4.26619, "time": 0.81907} +{"mode": "val", "epoch": 76, "iter": 309, "lr": 0.04895, "top1_acc": 0.20716, "top5_acc": 0.43727, "mean_class_accuracy": 0.20691} +{"mode": "train", "epoch": 77, "iter": 100, "lr": 0.04893, "memory": 15990, "data_time": 1.29624, "top1_acc": 0.27078, "top5_acc": 0.52391, "loss_cls": 4.20299, "loss": 4.20299, "time": 2.27759} +{"mode": "train", "epoch": 77, "iter": 200, "lr": 0.0489, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27094, "top5_acc": 0.51016, "loss_cls": 4.23556, "loss": 4.23556, "time": 0.82012} +{"mode": "train", "epoch": 77, "iter": 300, "lr": 0.04887, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27094, "top5_acc": 0.52609, "loss_cls": 4.19098, "loss": 4.19098, "time": 0.81832} +{"mode": "train", "epoch": 77, "iter": 400, "lr": 0.04884, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26859, "top5_acc": 0.51, "loss_cls": 4.25049, "loss": 4.25049, "time": 0.81842} +{"mode": "train", "epoch": 77, "iter": 500, "lr": 0.04881, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26, "top5_acc": 0.5025, "loss_cls": 4.28119, "loss": 4.28119, "time": 0.8211} +{"mode": "train", "epoch": 77, "iter": 600, "lr": 0.04879, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27266, "top5_acc": 0.52062, "loss_cls": 4.18895, "loss": 4.18895, "time": 0.82299} +{"mode": "train", "epoch": 77, "iter": 700, "lr": 0.04876, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26859, "top5_acc": 0.50813, "loss_cls": 4.25198, "loss": 4.25198, "time": 0.81553} +{"mode": "train", "epoch": 77, "iter": 800, "lr": 0.04873, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26203, "top5_acc": 0.50859, "loss_cls": 4.26755, "loss": 4.26755, "time": 0.83049} +{"mode": "train", "epoch": 77, "iter": 900, "lr": 0.0487, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26937, "top5_acc": 0.51375, "loss_cls": 4.24661, "loss": 4.24661, "time": 0.81818} +{"mode": "train", "epoch": 77, "iter": 1000, "lr": 0.04867, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26547, "top5_acc": 0.52328, "loss_cls": 4.20116, "loss": 4.20116, "time": 0.82402} +{"mode": "train", "epoch": 77, "iter": 1100, "lr": 0.04865, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26078, "top5_acc": 0.51141, "loss_cls": 4.27613, "loss": 4.27613, "time": 0.82091} +{"mode": "train", "epoch": 77, "iter": 1200, "lr": 0.04862, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25922, "top5_acc": 0.51109, "loss_cls": 4.26538, "loss": 4.26538, "time": 0.8157} +{"mode": "train", "epoch": 77, "iter": 1300, "lr": 0.04859, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26781, "top5_acc": 0.52078, "loss_cls": 4.21291, "loss": 4.21291, "time": 0.81677} +{"mode": "train", "epoch": 77, "iter": 1400, "lr": 0.04856, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26422, "top5_acc": 0.51422, "loss_cls": 4.25084, "loss": 4.25084, "time": 0.8225} +{"mode": "train", "epoch": 77, "iter": 1500, "lr": 0.04853, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26297, "top5_acc": 0.50656, "loss_cls": 4.27576, "loss": 4.27576, "time": 0.81835} +{"mode": "train", "epoch": 77, "iter": 1600, "lr": 0.04851, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27047, "top5_acc": 0.51375, "loss_cls": 4.2353, "loss": 4.2353, "time": 0.83126} +{"mode": "train", "epoch": 77, "iter": 1700, "lr": 0.04848, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27797, "top5_acc": 0.52531, "loss_cls": 4.22511, "loss": 4.22511, "time": 0.82019} +{"mode": "train", "epoch": 77, "iter": 1800, "lr": 0.04845, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25031, "top5_acc": 0.50609, "loss_cls": 4.27955, "loss": 4.27955, "time": 0.81902} +{"mode": "train", "epoch": 77, "iter": 1900, "lr": 0.04842, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26641, "top5_acc": 0.51594, "loss_cls": 4.25835, "loss": 4.25835, "time": 0.8293} +{"mode": "train", "epoch": 77, "iter": 2000, "lr": 0.04839, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26328, "top5_acc": 0.50969, "loss_cls": 4.26618, "loss": 4.26618, "time": 0.82254} +{"mode": "train", "epoch": 77, "iter": 2100, "lr": 0.04837, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26531, "top5_acc": 0.51047, "loss_cls": 4.24635, "loss": 4.24635, "time": 0.821} +{"mode": "train", "epoch": 77, "iter": 2200, "lr": 0.04834, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25422, "top5_acc": 0.50297, "loss_cls": 4.31663, "loss": 4.31663, "time": 0.82432} +{"mode": "train", "epoch": 77, "iter": 2300, "lr": 0.04831, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2625, "top5_acc": 0.50391, "loss_cls": 4.27959, "loss": 4.27959, "time": 0.82361} +{"mode": "train", "epoch": 77, "iter": 2400, "lr": 0.04828, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26578, "top5_acc": 0.51453, "loss_cls": 4.26186, "loss": 4.26186, "time": 0.82546} +{"mode": "train", "epoch": 77, "iter": 2500, "lr": 0.04825, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26266, "top5_acc": 0.51812, "loss_cls": 4.28527, "loss": 4.28527, "time": 0.82086} +{"mode": "train", "epoch": 77, "iter": 2600, "lr": 0.04823, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27281, "top5_acc": 0.52453, "loss_cls": 4.21363, "loss": 4.21363, "time": 0.82055} +{"mode": "train", "epoch": 77, "iter": 2700, "lr": 0.0482, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26172, "top5_acc": 0.50922, "loss_cls": 4.25834, "loss": 4.25834, "time": 0.81466} +{"mode": "train", "epoch": 77, "iter": 2800, "lr": 0.04817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25906, "top5_acc": 0.505, "loss_cls": 4.28308, "loss": 4.28308, "time": 0.81945} +{"mode": "train", "epoch": 77, "iter": 2900, "lr": 0.04814, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26391, "top5_acc": 0.50891, "loss_cls": 4.25368, "loss": 4.25368, "time": 0.81541} +{"mode": "train", "epoch": 77, "iter": 3000, "lr": 0.04811, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26594, "top5_acc": 0.51094, "loss_cls": 4.21665, "loss": 4.21665, "time": 0.82082} +{"mode": "train", "epoch": 77, "iter": 3100, "lr": 0.04809, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26172, "top5_acc": 0.5075, "loss_cls": 4.27413, "loss": 4.27413, "time": 0.8213} +{"mode": "train", "epoch": 77, "iter": 3200, "lr": 0.04806, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26078, "top5_acc": 0.50391, "loss_cls": 4.30322, "loss": 4.30322, "time": 0.82073} +{"mode": "train", "epoch": 77, "iter": 3300, "lr": 0.04803, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26156, "top5_acc": 0.51359, "loss_cls": 4.26016, "loss": 4.26016, "time": 0.81825} +{"mode": "train", "epoch": 77, "iter": 3400, "lr": 0.048, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26656, "top5_acc": 0.51969, "loss_cls": 4.24198, "loss": 4.24198, "time": 0.81962} +{"mode": "train", "epoch": 77, "iter": 3500, "lr": 0.04798, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26219, "top5_acc": 0.50234, "loss_cls": 4.27463, "loss": 4.27463, "time": 0.81794} +{"mode": "train", "epoch": 77, "iter": 3600, "lr": 0.04795, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25688, "top5_acc": 0.51219, "loss_cls": 4.27336, "loss": 4.27336, "time": 0.82133} +{"mode": "train", "epoch": 77, "iter": 3700, "lr": 0.04792, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26094, "top5_acc": 0.50516, "loss_cls": 4.29385, "loss": 4.29385, "time": 0.81869} +{"mode": "val", "epoch": 77, "iter": 309, "lr": 0.04791, "top1_acc": 0.19106, "top5_acc": 0.41974, "mean_class_accuracy": 0.19086} +{"mode": "train", "epoch": 78, "iter": 100, "lr": 0.04788, "memory": 15990, "data_time": 1.31708, "top1_acc": 0.27328, "top5_acc": 0.53062, "loss_cls": 4.17216, "loss": 4.17216, "time": 2.30145} +{"mode": "train", "epoch": 78, "iter": 200, "lr": 0.04785, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26594, "top5_acc": 0.51016, "loss_cls": 4.24908, "loss": 4.24908, "time": 0.8242} +{"mode": "train", "epoch": 78, "iter": 300, "lr": 0.04782, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2625, "top5_acc": 0.50672, "loss_cls": 4.25786, "loss": 4.25786, "time": 0.82208} +{"mode": "train", "epoch": 78, "iter": 400, "lr": 0.04779, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26438, "top5_acc": 0.51156, "loss_cls": 4.24818, "loss": 4.24818, "time": 0.81964} +{"mode": "train", "epoch": 78, "iter": 500, "lr": 0.04777, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27344, "top5_acc": 0.51578, "loss_cls": 4.22884, "loss": 4.22884, "time": 0.81674} +{"mode": "train", "epoch": 78, "iter": 600, "lr": 0.04774, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27281, "top5_acc": 0.52219, "loss_cls": 4.20032, "loss": 4.20032, "time": 0.82025} +{"mode": "train", "epoch": 78, "iter": 700, "lr": 0.04771, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26875, "top5_acc": 0.52016, "loss_cls": 4.21897, "loss": 4.21897, "time": 0.82156} +{"mode": "train", "epoch": 78, "iter": 800, "lr": 0.04768, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26156, "top5_acc": 0.51438, "loss_cls": 4.22132, "loss": 4.22132, "time": 0.82255} +{"mode": "train", "epoch": 78, "iter": 900, "lr": 0.04766, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2675, "top5_acc": 0.51125, "loss_cls": 4.25133, "loss": 4.25133, "time": 0.82285} +{"mode": "train", "epoch": 78, "iter": 1000, "lr": 0.04763, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26703, "top5_acc": 0.51547, "loss_cls": 4.24561, "loss": 4.24561, "time": 0.82366} +{"mode": "train", "epoch": 78, "iter": 1100, "lr": 0.0476, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26188, "top5_acc": 0.51453, "loss_cls": 4.25816, "loss": 4.25816, "time": 0.82542} +{"mode": "train", "epoch": 78, "iter": 1200, "lr": 0.04757, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.52719, "loss_cls": 4.17542, "loss": 4.17542, "time": 0.82481} +{"mode": "train", "epoch": 78, "iter": 1300, "lr": 0.04754, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2675, "top5_acc": 0.50953, "loss_cls": 4.23417, "loss": 4.23417, "time": 0.81907} +{"mode": "train", "epoch": 78, "iter": 1400, "lr": 0.04752, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25828, "top5_acc": 0.50609, "loss_cls": 4.27069, "loss": 4.27069, "time": 0.81952} +{"mode": "train", "epoch": 78, "iter": 1500, "lr": 0.04749, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26656, "top5_acc": 0.50703, "loss_cls": 4.27771, "loss": 4.27771, "time": 0.82275} +{"mode": "train", "epoch": 78, "iter": 1600, "lr": 0.04746, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27, "top5_acc": 0.51875, "loss_cls": 4.24252, "loss": 4.24252, "time": 0.82532} +{"mode": "train", "epoch": 78, "iter": 1700, "lr": 0.04743, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26047, "top5_acc": 0.51047, "loss_cls": 4.28224, "loss": 4.28224, "time": 0.8197} +{"mode": "train", "epoch": 78, "iter": 1800, "lr": 0.0474, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25453, "top5_acc": 0.50516, "loss_cls": 4.29784, "loss": 4.29784, "time": 0.82988} +{"mode": "train", "epoch": 78, "iter": 1900, "lr": 0.04738, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.51547, "loss_cls": 4.22452, "loss": 4.22452, "time": 0.82007} +{"mode": "train", "epoch": 78, "iter": 2000, "lr": 0.04735, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25453, "top5_acc": 0.50281, "loss_cls": 4.28064, "loss": 4.28064, "time": 0.8214} +{"mode": "train", "epoch": 78, "iter": 2100, "lr": 0.04732, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26984, "top5_acc": 0.52125, "loss_cls": 4.21139, "loss": 4.21139, "time": 0.82163} +{"mode": "train", "epoch": 78, "iter": 2200, "lr": 0.04729, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26219, "top5_acc": 0.50438, "loss_cls": 4.23172, "loss": 4.23172, "time": 0.82343} +{"mode": "train", "epoch": 78, "iter": 2300, "lr": 0.04726, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.2725, "top5_acc": 0.51703, "loss_cls": 4.25966, "loss": 4.25966, "time": 0.82123} +{"mode": "train", "epoch": 78, "iter": 2400, "lr": 0.04724, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26094, "top5_acc": 0.50938, "loss_cls": 4.25223, "loss": 4.25223, "time": 0.82832} +{"mode": "train", "epoch": 78, "iter": 2500, "lr": 0.04721, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.25516, "top5_acc": 0.51844, "loss_cls": 4.26516, "loss": 4.26516, "time": 0.82506} +{"mode": "train", "epoch": 78, "iter": 2600, "lr": 0.04718, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2625, "top5_acc": 0.51453, "loss_cls": 4.26293, "loss": 4.26293, "time": 0.82033} +{"mode": "train", "epoch": 78, "iter": 2700, "lr": 0.04715, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25906, "top5_acc": 0.49922, "loss_cls": 4.2577, "loss": 4.2577, "time": 0.81849} +{"mode": "train", "epoch": 78, "iter": 2800, "lr": 0.04712, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27141, "top5_acc": 0.52172, "loss_cls": 4.21712, "loss": 4.21712, "time": 0.81894} +{"mode": "train", "epoch": 78, "iter": 2900, "lr": 0.0471, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.25891, "top5_acc": 0.49703, "loss_cls": 4.31885, "loss": 4.31885, "time": 0.817} +{"mode": "train", "epoch": 78, "iter": 3000, "lr": 0.04707, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26656, "top5_acc": 0.51062, "loss_cls": 4.23786, "loss": 4.23786, "time": 0.8152} +{"mode": "train", "epoch": 78, "iter": 3100, "lr": 0.04704, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26031, "top5_acc": 0.51125, "loss_cls": 4.2683, "loss": 4.2683, "time": 0.82679} +{"mode": "train", "epoch": 78, "iter": 3200, "lr": 0.04701, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2675, "top5_acc": 0.52297, "loss_cls": 4.19794, "loss": 4.19794, "time": 0.82196} +{"mode": "train", "epoch": 78, "iter": 3300, "lr": 0.04699, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26484, "top5_acc": 0.52141, "loss_cls": 4.22277, "loss": 4.22277, "time": 0.82756} +{"mode": "train", "epoch": 78, "iter": 3400, "lr": 0.04696, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25891, "top5_acc": 0.51609, "loss_cls": 4.26963, "loss": 4.26963, "time": 0.81558} +{"mode": "train", "epoch": 78, "iter": 3500, "lr": 0.04693, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26766, "top5_acc": 0.51562, "loss_cls": 4.23686, "loss": 4.23686, "time": 0.81948} +{"mode": "train", "epoch": 78, "iter": 3600, "lr": 0.0469, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26687, "top5_acc": 0.51625, "loss_cls": 4.25844, "loss": 4.25844, "time": 0.82964} +{"mode": "train", "epoch": 78, "iter": 3700, "lr": 0.04687, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26922, "top5_acc": 0.50891, "loss_cls": 4.28683, "loss": 4.28683, "time": 0.81967} +{"mode": "val", "epoch": 78, "iter": 309, "lr": 0.04686, "top1_acc": 0.20625, "top5_acc": 0.42754, "mean_class_accuracy": 0.20623} +{"mode": "train", "epoch": 79, "iter": 100, "lr": 0.04683, "memory": 15990, "data_time": 1.31435, "top1_acc": 0.27625, "top5_acc": 0.52625, "loss_cls": 4.16546, "loss": 4.16546, "time": 2.32875} +{"mode": "train", "epoch": 79, "iter": 200, "lr": 0.0468, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.26297, "top5_acc": 0.50813, "loss_cls": 4.23884, "loss": 4.23884, "time": 0.83004} +{"mode": "train", "epoch": 79, "iter": 300, "lr": 0.04678, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26875, "top5_acc": 0.51328, "loss_cls": 4.23128, "loss": 4.23128, "time": 0.82001} +{"mode": "train", "epoch": 79, "iter": 400, "lr": 0.04675, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27859, "top5_acc": 0.52484, "loss_cls": 4.17414, "loss": 4.17414, "time": 0.82303} +{"mode": "train", "epoch": 79, "iter": 500, "lr": 0.04672, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28016, "top5_acc": 0.52031, "loss_cls": 4.21657, "loss": 4.21657, "time": 0.8216} +{"mode": "train", "epoch": 79, "iter": 600, "lr": 0.04669, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26516, "top5_acc": 0.50703, "loss_cls": 4.22526, "loss": 4.22526, "time": 0.81975} +{"mode": "train", "epoch": 79, "iter": 700, "lr": 0.04667, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26672, "top5_acc": 0.51328, "loss_cls": 4.23011, "loss": 4.23011, "time": 0.82403} +{"mode": "train", "epoch": 79, "iter": 800, "lr": 0.04664, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26188, "top5_acc": 0.51547, "loss_cls": 4.23702, "loss": 4.23702, "time": 0.82851} +{"mode": "train", "epoch": 79, "iter": 900, "lr": 0.04661, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26469, "top5_acc": 0.51422, "loss_cls": 4.20981, "loss": 4.20981, "time": 0.82305} +{"mode": "train", "epoch": 79, "iter": 1000, "lr": 0.04658, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27516, "top5_acc": 0.5125, "loss_cls": 4.2102, "loss": 4.2102, "time": 0.82172} +{"mode": "train", "epoch": 79, "iter": 1100, "lr": 0.04655, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26484, "top5_acc": 0.50719, "loss_cls": 4.2743, "loss": 4.2743, "time": 0.82303} +{"mode": "train", "epoch": 79, "iter": 1200, "lr": 0.04653, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26766, "top5_acc": 0.51281, "loss_cls": 4.24136, "loss": 4.24136, "time": 0.82} +{"mode": "train", "epoch": 79, "iter": 1300, "lr": 0.0465, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26422, "top5_acc": 0.50688, "loss_cls": 4.2833, "loss": 4.2833, "time": 0.81802} +{"mode": "train", "epoch": 79, "iter": 1400, "lr": 0.04647, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26906, "top5_acc": 0.52516, "loss_cls": 4.19871, "loss": 4.19871, "time": 0.8191} +{"mode": "train", "epoch": 79, "iter": 1500, "lr": 0.04644, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26812, "top5_acc": 0.51891, "loss_cls": 4.24348, "loss": 4.24348, "time": 0.82154} +{"mode": "train", "epoch": 79, "iter": 1600, "lr": 0.04641, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.25469, "top5_acc": 0.50547, "loss_cls": 4.30532, "loss": 4.30532, "time": 0.82108} +{"mode": "train", "epoch": 79, "iter": 1700, "lr": 0.04639, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27156, "top5_acc": 0.51766, "loss_cls": 4.2406, "loss": 4.2406, "time": 0.82294} +{"mode": "train", "epoch": 79, "iter": 1800, "lr": 0.04636, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26875, "top5_acc": 0.52203, "loss_cls": 4.21628, "loss": 4.21628, "time": 0.82339} +{"mode": "train", "epoch": 79, "iter": 1900, "lr": 0.04633, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26906, "top5_acc": 0.51719, "loss_cls": 4.2469, "loss": 4.2469, "time": 0.82143} +{"mode": "train", "epoch": 79, "iter": 2000, "lr": 0.0463, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26984, "top5_acc": 0.51797, "loss_cls": 4.24583, "loss": 4.24583, "time": 0.82028} +{"mode": "train", "epoch": 79, "iter": 2100, "lr": 0.04628, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.26375, "top5_acc": 0.50016, "loss_cls": 4.28875, "loss": 4.28875, "time": 0.82368} +{"mode": "train", "epoch": 79, "iter": 2200, "lr": 0.04625, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27109, "top5_acc": 0.51391, "loss_cls": 4.24165, "loss": 4.24165, "time": 0.82465} +{"mode": "train", "epoch": 79, "iter": 2300, "lr": 0.04622, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26922, "top5_acc": 0.51469, "loss_cls": 4.23619, "loss": 4.23619, "time": 0.8207} +{"mode": "train", "epoch": 79, "iter": 2400, "lr": 0.04619, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26016, "top5_acc": 0.51281, "loss_cls": 4.25234, "loss": 4.25234, "time": 0.82748} +{"mode": "train", "epoch": 79, "iter": 2500, "lr": 0.04616, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2675, "top5_acc": 0.5225, "loss_cls": 4.22283, "loss": 4.22283, "time": 0.81835} +{"mode": "train", "epoch": 79, "iter": 2600, "lr": 0.04614, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27516, "top5_acc": 0.51812, "loss_cls": 4.22967, "loss": 4.22967, "time": 0.81757} +{"mode": "train", "epoch": 79, "iter": 2700, "lr": 0.04611, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27141, "top5_acc": 0.51031, "loss_cls": 4.22261, "loss": 4.22261, "time": 0.82382} +{"mode": "train", "epoch": 79, "iter": 2800, "lr": 0.04608, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26234, "top5_acc": 0.50859, "loss_cls": 4.26338, "loss": 4.26338, "time": 0.81679} +{"mode": "train", "epoch": 79, "iter": 2900, "lr": 0.04605, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28062, "top5_acc": 0.52328, "loss_cls": 4.19157, "loss": 4.19157, "time": 0.81629} +{"mode": "train", "epoch": 79, "iter": 3000, "lr": 0.04602, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27344, "top5_acc": 0.51688, "loss_cls": 4.22516, "loss": 4.22516, "time": 0.81817} +{"mode": "train", "epoch": 79, "iter": 3100, "lr": 0.046, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2625, "top5_acc": 0.50609, "loss_cls": 4.25893, "loss": 4.25893, "time": 0.81519} +{"mode": "train", "epoch": 79, "iter": 3200, "lr": 0.04597, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26656, "top5_acc": 0.51844, "loss_cls": 4.23814, "loss": 4.23814, "time": 0.81926} +{"mode": "train", "epoch": 79, "iter": 3300, "lr": 0.04594, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26937, "top5_acc": 0.51547, "loss_cls": 4.24678, "loss": 4.24678, "time": 0.8187} +{"mode": "train", "epoch": 79, "iter": 3400, "lr": 0.04591, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26969, "top5_acc": 0.52203, "loss_cls": 4.20552, "loss": 4.20552, "time": 0.81768} +{"mode": "train", "epoch": 79, "iter": 3500, "lr": 0.04588, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26734, "top5_acc": 0.51859, "loss_cls": 4.21065, "loss": 4.21065, "time": 0.81927} +{"mode": "train", "epoch": 79, "iter": 3600, "lr": 0.04586, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26875, "top5_acc": 0.50781, "loss_cls": 4.27695, "loss": 4.27695, "time": 0.8193} +{"mode": "train", "epoch": 79, "iter": 3700, "lr": 0.04583, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26969, "top5_acc": 0.52078, "loss_cls": 4.21526, "loss": 4.21526, "time": 0.82243} +{"mode": "val", "epoch": 79, "iter": 309, "lr": 0.04582, "top1_acc": 0.21729, "top5_acc": 0.45966, "mean_class_accuracy": 0.21718} +{"mode": "train", "epoch": 80, "iter": 100, "lr": 0.04579, "memory": 15990, "data_time": 1.3122, "top1_acc": 0.27391, "top5_acc": 0.52531, "loss_cls": 4.18164, "loss": 4.18164, "time": 2.30412} +{"mode": "train", "epoch": 80, "iter": 200, "lr": 0.04576, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27562, "top5_acc": 0.51812, "loss_cls": 4.18621, "loss": 4.18621, "time": 0.82942} +{"mode": "train", "epoch": 80, "iter": 300, "lr": 0.04573, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27062, "top5_acc": 0.51469, "loss_cls": 4.23403, "loss": 4.23403, "time": 0.82014} +{"mode": "train", "epoch": 80, "iter": 400, "lr": 0.0457, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.25328, "top5_acc": 0.51219, "loss_cls": 4.2508, "loss": 4.2508, "time": 0.82136} +{"mode": "train", "epoch": 80, "iter": 500, "lr": 0.04568, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26672, "top5_acc": 0.51891, "loss_cls": 4.24834, "loss": 4.24834, "time": 0.82069} +{"mode": "train", "epoch": 80, "iter": 600, "lr": 0.04565, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27328, "top5_acc": 0.51594, "loss_cls": 4.20836, "loss": 4.20836, "time": 0.82027} +{"mode": "train", "epoch": 80, "iter": 700, "lr": 0.04562, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.275, "top5_acc": 0.52641, "loss_cls": 4.18432, "loss": 4.18432, "time": 0.82293} +{"mode": "train", "epoch": 80, "iter": 800, "lr": 0.04559, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27375, "top5_acc": 0.52906, "loss_cls": 4.16638, "loss": 4.16638, "time": 0.82892} +{"mode": "train", "epoch": 80, "iter": 900, "lr": 0.04557, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27938, "top5_acc": 0.51938, "loss_cls": 4.21231, "loss": 4.21231, "time": 0.82335} +{"mode": "train", "epoch": 80, "iter": 1000, "lr": 0.04554, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26953, "top5_acc": 0.51688, "loss_cls": 4.18247, "loss": 4.18247, "time": 0.82354} +{"mode": "train", "epoch": 80, "iter": 1100, "lr": 0.04551, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26609, "top5_acc": 0.50594, "loss_cls": 4.266, "loss": 4.266, "time": 0.82003} +{"mode": "train", "epoch": 80, "iter": 1200, "lr": 0.04548, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26141, "top5_acc": 0.51531, "loss_cls": 4.25073, "loss": 4.25073, "time": 0.82026} +{"mode": "train", "epoch": 80, "iter": 1300, "lr": 0.04545, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27109, "top5_acc": 0.52109, "loss_cls": 4.21086, "loss": 4.21086, "time": 0.81678} +{"mode": "train", "epoch": 80, "iter": 1400, "lr": 0.04543, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27656, "top5_acc": 0.52016, "loss_cls": 4.21678, "loss": 4.21678, "time": 0.82157} +{"mode": "train", "epoch": 80, "iter": 1500, "lr": 0.0454, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27359, "top5_acc": 0.52141, "loss_cls": 4.20694, "loss": 4.20694, "time": 0.8212} +{"mode": "train", "epoch": 80, "iter": 1600, "lr": 0.04537, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26781, "top5_acc": 0.51781, "loss_cls": 4.22166, "loss": 4.22166, "time": 0.8241} +{"mode": "train", "epoch": 80, "iter": 1700, "lr": 0.04534, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27, "top5_acc": 0.51328, "loss_cls": 4.24831, "loss": 4.24831, "time": 0.82722} +{"mode": "train", "epoch": 80, "iter": 1800, "lr": 0.04532, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.265, "top5_acc": 0.51359, "loss_cls": 4.24302, "loss": 4.24302, "time": 0.8199} +{"mode": "train", "epoch": 80, "iter": 1900, "lr": 0.04529, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26906, "top5_acc": 0.52203, "loss_cls": 4.19425, "loss": 4.19425, "time": 0.81776} +{"mode": "train", "epoch": 80, "iter": 2000, "lr": 0.04526, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27453, "top5_acc": 0.52609, "loss_cls": 4.19803, "loss": 4.19803, "time": 0.81875} +{"mode": "train", "epoch": 80, "iter": 2100, "lr": 0.04523, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2575, "top5_acc": 0.49922, "loss_cls": 4.30666, "loss": 4.30666, "time": 0.82107} +{"mode": "train", "epoch": 80, "iter": 2200, "lr": 0.0452, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26891, "top5_acc": 0.52125, "loss_cls": 4.2433, "loss": 4.2433, "time": 0.82448} +{"mode": "train", "epoch": 80, "iter": 2300, "lr": 0.04518, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27062, "top5_acc": 0.51875, "loss_cls": 4.23514, "loss": 4.23514, "time": 0.82067} +{"mode": "train", "epoch": 80, "iter": 2400, "lr": 0.04515, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26484, "top5_acc": 0.51625, "loss_cls": 4.24112, "loss": 4.24112, "time": 0.82979} +{"mode": "train", "epoch": 80, "iter": 2500, "lr": 0.04512, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27016, "top5_acc": 0.51219, "loss_cls": 4.23703, "loss": 4.23703, "time": 0.82138} +{"mode": "train", "epoch": 80, "iter": 2600, "lr": 0.04509, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27547, "top5_acc": 0.53125, "loss_cls": 4.17125, "loss": 4.17125, "time": 0.82642} +{"mode": "train", "epoch": 80, "iter": 2700, "lr": 0.04506, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27469, "top5_acc": 0.51812, "loss_cls": 4.20444, "loss": 4.20444, "time": 0.81889} +{"mode": "train", "epoch": 80, "iter": 2800, "lr": 0.04504, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26875, "top5_acc": 0.51891, "loss_cls": 4.22257, "loss": 4.22257, "time": 0.82121} +{"mode": "train", "epoch": 80, "iter": 2900, "lr": 0.04501, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27406, "top5_acc": 0.51844, "loss_cls": 4.21957, "loss": 4.21957, "time": 0.81993} +{"mode": "train", "epoch": 80, "iter": 3000, "lr": 0.04498, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26625, "top5_acc": 0.51625, "loss_cls": 4.22751, "loss": 4.22751, "time": 0.81785} +{"mode": "train", "epoch": 80, "iter": 3100, "lr": 0.04495, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26906, "top5_acc": 0.52359, "loss_cls": 4.19581, "loss": 4.19581, "time": 0.82515} +{"mode": "train", "epoch": 80, "iter": 3200, "lr": 0.04493, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27844, "top5_acc": 0.53078, "loss_cls": 4.17743, "loss": 4.17743, "time": 0.81841} +{"mode": "train", "epoch": 80, "iter": 3300, "lr": 0.0449, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27516, "top5_acc": 0.53031, "loss_cls": 4.1698, "loss": 4.1698, "time": 0.81852} +{"mode": "train", "epoch": 80, "iter": 3400, "lr": 0.04487, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27297, "top5_acc": 0.515, "loss_cls": 4.24471, "loss": 4.24471, "time": 0.81941} +{"mode": "train", "epoch": 80, "iter": 3500, "lr": 0.04484, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26953, "top5_acc": 0.52125, "loss_cls": 4.20545, "loss": 4.20545, "time": 0.82492} +{"mode": "train", "epoch": 80, "iter": 3600, "lr": 0.04481, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2675, "top5_acc": 0.50875, "loss_cls": 4.2358, "loss": 4.2358, "time": 0.82443} +{"mode": "train", "epoch": 80, "iter": 3700, "lr": 0.04479, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.25953, "top5_acc": 0.51109, "loss_cls": 4.25896, "loss": 4.25896, "time": 0.81808} +{"mode": "val", "epoch": 80, "iter": 309, "lr": 0.04477, "top1_acc": 0.20843, "top5_acc": 0.43686, "mean_class_accuracy": 0.20819} +{"mode": "train", "epoch": 81, "iter": 100, "lr": 0.04475, "memory": 15990, "data_time": 1.32808, "top1_acc": 0.27484, "top5_acc": 0.51984, "loss_cls": 4.19617, "loss": 4.19617, "time": 2.31605} +{"mode": "train", "epoch": 81, "iter": 200, "lr": 0.04472, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27312, "top5_acc": 0.51469, "loss_cls": 4.20609, "loss": 4.20609, "time": 0.82923} +{"mode": "train", "epoch": 81, "iter": 300, "lr": 0.04469, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27375, "top5_acc": 0.53109, "loss_cls": 4.16918, "loss": 4.16918, "time": 0.82113} +{"mode": "train", "epoch": 81, "iter": 400, "lr": 0.04466, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27344, "top5_acc": 0.52109, "loss_cls": 4.17362, "loss": 4.17362, "time": 0.82052} +{"mode": "train", "epoch": 81, "iter": 500, "lr": 0.04463, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27609, "top5_acc": 0.53297, "loss_cls": 4.12687, "loss": 4.12687, "time": 0.82602} +{"mode": "train", "epoch": 81, "iter": 600, "lr": 0.04461, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2775, "top5_acc": 0.52734, "loss_cls": 4.1904, "loss": 4.1904, "time": 0.81957} +{"mode": "train", "epoch": 81, "iter": 700, "lr": 0.04458, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27562, "top5_acc": 0.52656, "loss_cls": 4.19047, "loss": 4.19047, "time": 0.82065} +{"mode": "train", "epoch": 81, "iter": 800, "lr": 0.04455, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26922, "top5_acc": 0.52938, "loss_cls": 4.17909, "loss": 4.17909, "time": 0.81752} +{"mode": "train", "epoch": 81, "iter": 900, "lr": 0.04452, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28391, "top5_acc": 0.52922, "loss_cls": 4.14627, "loss": 4.14627, "time": 0.81963} +{"mode": "train", "epoch": 81, "iter": 1000, "lr": 0.0445, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27062, "top5_acc": 0.52062, "loss_cls": 4.20638, "loss": 4.20638, "time": 0.82784} +{"mode": "train", "epoch": 81, "iter": 1100, "lr": 0.04447, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27547, "top5_acc": 0.52484, "loss_cls": 4.18671, "loss": 4.18671, "time": 0.82161} +{"mode": "train", "epoch": 81, "iter": 1200, "lr": 0.04444, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28141, "top5_acc": 0.52375, "loss_cls": 4.16246, "loss": 4.16246, "time": 0.81967} +{"mode": "train", "epoch": 81, "iter": 1300, "lr": 0.04441, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28281, "top5_acc": 0.51656, "loss_cls": 4.18178, "loss": 4.18178, "time": 0.82109} +{"mode": "train", "epoch": 81, "iter": 1400, "lr": 0.04438, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28, "top5_acc": 0.51984, "loss_cls": 4.1871, "loss": 4.1871, "time": 0.81851} +{"mode": "train", "epoch": 81, "iter": 1500, "lr": 0.04436, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27047, "top5_acc": 0.52734, "loss_cls": 4.19651, "loss": 4.19651, "time": 0.82577} +{"mode": "train", "epoch": 81, "iter": 1600, "lr": 0.04433, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27094, "top5_acc": 0.51375, "loss_cls": 4.26962, "loss": 4.26962, "time": 0.81947} +{"mode": "train", "epoch": 81, "iter": 1700, "lr": 0.0443, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27078, "top5_acc": 0.51188, "loss_cls": 4.23989, "loss": 4.23989, "time": 0.82421} +{"mode": "train", "epoch": 81, "iter": 1800, "lr": 0.04427, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27031, "top5_acc": 0.51984, "loss_cls": 4.20292, "loss": 4.20292, "time": 0.82323} +{"mode": "train", "epoch": 81, "iter": 1900, "lr": 0.04425, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26969, "top5_acc": 0.51266, "loss_cls": 4.21617, "loss": 4.21617, "time": 0.81803} +{"mode": "train", "epoch": 81, "iter": 2000, "lr": 0.04422, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26625, "top5_acc": 0.50828, "loss_cls": 4.2624, "loss": 4.2624, "time": 0.82276} +{"mode": "train", "epoch": 81, "iter": 2100, "lr": 0.04419, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26844, "top5_acc": 0.51297, "loss_cls": 4.25518, "loss": 4.25518, "time": 0.82176} +{"mode": "train", "epoch": 81, "iter": 2200, "lr": 0.04416, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26469, "top5_acc": 0.51766, "loss_cls": 4.21792, "loss": 4.21792, "time": 0.82615} +{"mode": "train", "epoch": 81, "iter": 2300, "lr": 0.04413, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.2825, "top5_acc": 0.52844, "loss_cls": 4.14681, "loss": 4.14681, "time": 0.82062} +{"mode": "train", "epoch": 81, "iter": 2400, "lr": 0.04411, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.27094, "top5_acc": 0.52344, "loss_cls": 4.21274, "loss": 4.21274, "time": 0.82166} +{"mode": "train", "epoch": 81, "iter": 2500, "lr": 0.04408, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26875, "top5_acc": 0.51781, "loss_cls": 4.2122, "loss": 4.2122, "time": 0.82384} +{"mode": "train", "epoch": 81, "iter": 2600, "lr": 0.04405, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27828, "top5_acc": 0.52531, "loss_cls": 4.1791, "loss": 4.1791, "time": 0.8306} +{"mode": "train", "epoch": 81, "iter": 2700, "lr": 0.04402, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.26453, "top5_acc": 0.51547, "loss_cls": 4.2228, "loss": 4.2228, "time": 0.82494} +{"mode": "train", "epoch": 81, "iter": 2800, "lr": 0.044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26062, "top5_acc": 0.50875, "loss_cls": 4.26892, "loss": 4.26892, "time": 0.8191} +{"mode": "train", "epoch": 81, "iter": 2900, "lr": 0.04397, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26125, "top5_acc": 0.51313, "loss_cls": 4.2499, "loss": 4.2499, "time": 0.82005} +{"mode": "train", "epoch": 81, "iter": 3000, "lr": 0.04394, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26656, "top5_acc": 0.51266, "loss_cls": 4.23213, "loss": 4.23213, "time": 0.83038} +{"mode": "train", "epoch": 81, "iter": 3100, "lr": 0.04391, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27344, "top5_acc": 0.52641, "loss_cls": 4.17839, "loss": 4.17839, "time": 0.81754} +{"mode": "train", "epoch": 81, "iter": 3200, "lr": 0.04389, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26781, "top5_acc": 0.52094, "loss_cls": 4.21348, "loss": 4.21348, "time": 0.82041} +{"mode": "train", "epoch": 81, "iter": 3300, "lr": 0.04386, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27187, "top5_acc": 0.51641, "loss_cls": 4.23202, "loss": 4.23202, "time": 0.82089} +{"mode": "train", "epoch": 81, "iter": 3400, "lr": 0.04383, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26281, "top5_acc": 0.51438, "loss_cls": 4.24319, "loss": 4.24319, "time": 0.81959} +{"mode": "train", "epoch": 81, "iter": 3500, "lr": 0.0438, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27625, "top5_acc": 0.51375, "loss_cls": 4.2206, "loss": 4.2206, "time": 0.81851} +{"mode": "train", "epoch": 81, "iter": 3600, "lr": 0.04377, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26859, "top5_acc": 0.51875, "loss_cls": 4.24879, "loss": 4.24879, "time": 0.81876} +{"mode": "train", "epoch": 81, "iter": 3700, "lr": 0.04375, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26719, "top5_acc": 0.51969, "loss_cls": 4.21434, "loss": 4.21434, "time": 0.81658} +{"mode": "val", "epoch": 81, "iter": 309, "lr": 0.04373, "top1_acc": 0.19815, "top5_acc": 0.41995, "mean_class_accuracy": 0.19795} +{"mode": "train", "epoch": 82, "iter": 100, "lr": 0.04371, "memory": 15990, "data_time": 1.32064, "top1_acc": 0.27688, "top5_acc": 0.53266, "loss_cls": 4.14024, "loss": 4.14024, "time": 2.30631} +{"mode": "train", "epoch": 82, "iter": 200, "lr": 0.04368, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27312, "top5_acc": 0.52797, "loss_cls": 4.14584, "loss": 4.14584, "time": 0.82839} +{"mode": "train", "epoch": 82, "iter": 300, "lr": 0.04365, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27391, "top5_acc": 0.52547, "loss_cls": 4.1692, "loss": 4.1692, "time": 0.82115} +{"mode": "train", "epoch": 82, "iter": 400, "lr": 0.04362, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26922, "top5_acc": 0.51734, "loss_cls": 4.21064, "loss": 4.21064, "time": 0.82501} +{"mode": "train", "epoch": 82, "iter": 500, "lr": 0.04359, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27812, "top5_acc": 0.51562, "loss_cls": 4.21747, "loss": 4.21747, "time": 0.81901} +{"mode": "train", "epoch": 82, "iter": 600, "lr": 0.04357, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.26984, "top5_acc": 0.51047, "loss_cls": 4.22831, "loss": 4.22831, "time": 0.82048} +{"mode": "train", "epoch": 82, "iter": 700, "lr": 0.04354, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27984, "top5_acc": 0.52812, "loss_cls": 4.20232, "loss": 4.20232, "time": 0.825} +{"mode": "train", "epoch": 82, "iter": 800, "lr": 0.04351, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26359, "top5_acc": 0.52125, "loss_cls": 4.22341, "loss": 4.22341, "time": 0.82528} +{"mode": "train", "epoch": 82, "iter": 900, "lr": 0.04348, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28406, "top5_acc": 0.51938, "loss_cls": 4.19848, "loss": 4.19848, "time": 0.82281} +{"mode": "train", "epoch": 82, "iter": 1000, "lr": 0.04346, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27609, "top5_acc": 0.52734, "loss_cls": 4.17361, "loss": 4.17361, "time": 0.82097} +{"mode": "train", "epoch": 82, "iter": 1100, "lr": 0.04343, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27609, "top5_acc": 0.52922, "loss_cls": 4.16182, "loss": 4.16182, "time": 0.82548} +{"mode": "train", "epoch": 82, "iter": 1200, "lr": 0.0434, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27281, "top5_acc": 0.525, "loss_cls": 4.19581, "loss": 4.19581, "time": 0.82163} +{"mode": "train", "epoch": 82, "iter": 1300, "lr": 0.04337, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27562, "top5_acc": 0.52875, "loss_cls": 4.16801, "loss": 4.16801, "time": 0.82288} +{"mode": "train", "epoch": 82, "iter": 1400, "lr": 0.04335, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.26406, "top5_acc": 0.51203, "loss_cls": 4.26416, "loss": 4.26416, "time": 0.82116} +{"mode": "train", "epoch": 82, "iter": 1500, "lr": 0.04332, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27047, "top5_acc": 0.51969, "loss_cls": 4.22443, "loss": 4.22443, "time": 0.83016} +{"mode": "train", "epoch": 82, "iter": 1600, "lr": 0.04329, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26312, "top5_acc": 0.51828, "loss_cls": 4.21679, "loss": 4.21679, "time": 0.82351} +{"mode": "train", "epoch": 82, "iter": 1700, "lr": 0.04326, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28141, "top5_acc": 0.52609, "loss_cls": 4.16975, "loss": 4.16975, "time": 0.82809} +{"mode": "train", "epoch": 82, "iter": 1800, "lr": 0.04323, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26703, "top5_acc": 0.52453, "loss_cls": 4.22991, "loss": 4.22991, "time": 0.81771} +{"mode": "train", "epoch": 82, "iter": 1900, "lr": 0.04321, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.2825, "top5_acc": 0.52891, "loss_cls": 4.1511, "loss": 4.1511, "time": 0.81621} +{"mode": "train", "epoch": 82, "iter": 2000, "lr": 0.04318, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27484, "top5_acc": 0.52703, "loss_cls": 4.19904, "loss": 4.19904, "time": 0.81886} +{"mode": "train", "epoch": 82, "iter": 2100, "lr": 0.04315, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28453, "top5_acc": 0.53172, "loss_cls": 4.16266, "loss": 4.16266, "time": 0.8191} +{"mode": "train", "epoch": 82, "iter": 2200, "lr": 0.04312, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.26, "top5_acc": 0.51672, "loss_cls": 4.25539, "loss": 4.25539, "time": 0.82765} +{"mode": "train", "epoch": 82, "iter": 2300, "lr": 0.0431, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26844, "top5_acc": 0.52406, "loss_cls": 4.1878, "loss": 4.1878, "time": 0.82132} +{"mode": "train", "epoch": 82, "iter": 2400, "lr": 0.04307, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27688, "top5_acc": 0.52453, "loss_cls": 4.18966, "loss": 4.18966, "time": 0.82309} +{"mode": "train", "epoch": 82, "iter": 2500, "lr": 0.04304, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26734, "top5_acc": 0.52516, "loss_cls": 4.23278, "loss": 4.23278, "time": 0.82204} +{"mode": "train", "epoch": 82, "iter": 2600, "lr": 0.04301, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2725, "top5_acc": 0.51922, "loss_cls": 4.20633, "loss": 4.20633, "time": 0.8203} +{"mode": "train", "epoch": 82, "iter": 2700, "lr": 0.04299, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27125, "top5_acc": 0.51219, "loss_cls": 4.21988, "loss": 4.21988, "time": 0.81812} +{"mode": "train", "epoch": 82, "iter": 2800, "lr": 0.04296, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28, "top5_acc": 0.52672, "loss_cls": 4.16027, "loss": 4.16027, "time": 0.82077} +{"mode": "train", "epoch": 82, "iter": 2900, "lr": 0.04293, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28078, "top5_acc": 0.52578, "loss_cls": 4.1761, "loss": 4.1761, "time": 0.81715} +{"mode": "train", "epoch": 82, "iter": 3000, "lr": 0.0429, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27469, "top5_acc": 0.51609, "loss_cls": 4.22423, "loss": 4.22423, "time": 0.81709} +{"mode": "train", "epoch": 82, "iter": 3100, "lr": 0.04287, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27859, "top5_acc": 0.51859, "loss_cls": 4.17944, "loss": 4.17944, "time": 0.81632} +{"mode": "train", "epoch": 82, "iter": 3200, "lr": 0.04285, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27328, "top5_acc": 0.5275, "loss_cls": 4.16702, "loss": 4.16702, "time": 0.81899} +{"mode": "train", "epoch": 82, "iter": 3300, "lr": 0.04282, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27, "top5_acc": 0.52766, "loss_cls": 4.19459, "loss": 4.19459, "time": 0.81906} +{"mode": "train", "epoch": 82, "iter": 3400, "lr": 0.04279, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26469, "top5_acc": 0.51344, "loss_cls": 4.22308, "loss": 4.22308, "time": 0.82278} +{"mode": "train", "epoch": 82, "iter": 3500, "lr": 0.04276, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27297, "top5_acc": 0.51734, "loss_cls": 4.19075, "loss": 4.19075, "time": 0.81963} +{"mode": "train", "epoch": 82, "iter": 3600, "lr": 0.04274, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27125, "top5_acc": 0.51719, "loss_cls": 4.21209, "loss": 4.21209, "time": 0.81935} +{"mode": "train", "epoch": 82, "iter": 3700, "lr": 0.04271, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27844, "top5_acc": 0.51922, "loss_cls": 4.20139, "loss": 4.20139, "time": 0.81927} +{"mode": "val", "epoch": 82, "iter": 309, "lr": 0.0427, "top1_acc": 0.21582, "top5_acc": 0.44517, "mean_class_accuracy": 0.21576} +{"mode": "train", "epoch": 83, "iter": 100, "lr": 0.04267, "memory": 15990, "data_time": 1.34395, "top1_acc": 0.29375, "top5_acc": 0.53922, "loss_cls": 4.09539, "loss": 4.09539, "time": 2.3324} +{"mode": "train", "epoch": 83, "iter": 200, "lr": 0.04264, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28234, "top5_acc": 0.53578, "loss_cls": 4.09943, "loss": 4.09943, "time": 0.82616} +{"mode": "train", "epoch": 83, "iter": 300, "lr": 0.04261, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2825, "top5_acc": 0.52578, "loss_cls": 4.17073, "loss": 4.17073, "time": 0.8256} +{"mode": "train", "epoch": 83, "iter": 400, "lr": 0.04259, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27609, "top5_acc": 0.53, "loss_cls": 4.14311, "loss": 4.14311, "time": 0.8185} +{"mode": "train", "epoch": 83, "iter": 500, "lr": 0.04256, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27656, "top5_acc": 0.53016, "loss_cls": 4.15741, "loss": 4.15741, "time": 0.82063} +{"mode": "train", "epoch": 83, "iter": 600, "lr": 0.04253, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28203, "top5_acc": 0.51891, "loss_cls": 4.17338, "loss": 4.17338, "time": 0.8183} +{"mode": "train", "epoch": 83, "iter": 700, "lr": 0.0425, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27953, "top5_acc": 0.52734, "loss_cls": 4.16992, "loss": 4.16992, "time": 0.83051} +{"mode": "train", "epoch": 83, "iter": 800, "lr": 0.04247, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27281, "top5_acc": 0.52953, "loss_cls": 4.17594, "loss": 4.17594, "time": 0.81653} +{"mode": "train", "epoch": 83, "iter": 900, "lr": 0.04245, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27547, "top5_acc": 0.52672, "loss_cls": 4.18245, "loss": 4.18245, "time": 0.82729} +{"mode": "train", "epoch": 83, "iter": 1000, "lr": 0.04242, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27797, "top5_acc": 0.52234, "loss_cls": 4.17418, "loss": 4.17418, "time": 0.82319} +{"mode": "train", "epoch": 83, "iter": 1100, "lr": 0.04239, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28375, "top5_acc": 0.52422, "loss_cls": 4.18387, "loss": 4.18387, "time": 0.81595} +{"mode": "train", "epoch": 83, "iter": 1200, "lr": 0.04236, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27156, "top5_acc": 0.51797, "loss_cls": 4.22607, "loss": 4.22607, "time": 0.81675} +{"mode": "train", "epoch": 83, "iter": 1300, "lr": 0.04234, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27422, "top5_acc": 0.52375, "loss_cls": 4.18363, "loss": 4.18363, "time": 0.82398} +{"mode": "train", "epoch": 83, "iter": 1400, "lr": 0.04231, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27656, "top5_acc": 0.52469, "loss_cls": 4.13868, "loss": 4.13868, "time": 0.81941} +{"mode": "train", "epoch": 83, "iter": 1500, "lr": 0.04228, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28562, "top5_acc": 0.53359, "loss_cls": 4.15593, "loss": 4.15593, "time": 0.82215} +{"mode": "train", "epoch": 83, "iter": 1600, "lr": 0.04225, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27281, "top5_acc": 0.52125, "loss_cls": 4.18745, "loss": 4.18745, "time": 0.82289} +{"mode": "train", "epoch": 83, "iter": 1700, "lr": 0.04223, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27688, "top5_acc": 0.52828, "loss_cls": 4.15855, "loss": 4.15855, "time": 0.82675} +{"mode": "train", "epoch": 83, "iter": 1800, "lr": 0.0422, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27938, "top5_acc": 0.52859, "loss_cls": 4.15509, "loss": 4.15509, "time": 0.82111} +{"mode": "train", "epoch": 83, "iter": 1900, "lr": 0.04217, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27312, "top5_acc": 0.51875, "loss_cls": 4.24802, "loss": 4.24802, "time": 0.82163} +{"mode": "train", "epoch": 83, "iter": 2000, "lr": 0.04214, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27844, "top5_acc": 0.52734, "loss_cls": 4.17786, "loss": 4.17786, "time": 0.81919} +{"mode": "train", "epoch": 83, "iter": 2100, "lr": 0.04212, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2725, "top5_acc": 0.52297, "loss_cls": 4.20423, "loss": 4.20423, "time": 0.81703} +{"mode": "train", "epoch": 83, "iter": 2200, "lr": 0.04209, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27281, "top5_acc": 0.51797, "loss_cls": 4.1879, "loss": 4.1879, "time": 0.83383} +{"mode": "train", "epoch": 83, "iter": 2300, "lr": 0.04206, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.27547, "top5_acc": 0.52062, "loss_cls": 4.20099, "loss": 4.20099, "time": 0.82256} +{"mode": "train", "epoch": 83, "iter": 2400, "lr": 0.04203, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.26969, "top5_acc": 0.51656, "loss_cls": 4.23527, "loss": 4.23527, "time": 0.82139} +{"mode": "train", "epoch": 83, "iter": 2500, "lr": 0.04201, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28, "top5_acc": 0.51969, "loss_cls": 4.17743, "loss": 4.17743, "time": 0.82399} +{"mode": "train", "epoch": 83, "iter": 2600, "lr": 0.04198, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27625, "top5_acc": 0.53016, "loss_cls": 4.17067, "loss": 4.17067, "time": 0.81493} +{"mode": "train", "epoch": 83, "iter": 2700, "lr": 0.04195, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27125, "top5_acc": 0.52719, "loss_cls": 4.17994, "loss": 4.17994, "time": 0.81902} +{"mode": "train", "epoch": 83, "iter": 2800, "lr": 0.04192, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27594, "top5_acc": 0.52891, "loss_cls": 4.15969, "loss": 4.15969, "time": 0.81643} +{"mode": "train", "epoch": 83, "iter": 2900, "lr": 0.0419, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.275, "top5_acc": 0.52188, "loss_cls": 4.20043, "loss": 4.20043, "time": 0.81322} +{"mode": "train", "epoch": 83, "iter": 3000, "lr": 0.04187, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27094, "top5_acc": 0.51641, "loss_cls": 4.21455, "loss": 4.21455, "time": 0.81813} +{"mode": "train", "epoch": 83, "iter": 3100, "lr": 0.04184, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2775, "top5_acc": 0.52328, "loss_cls": 4.20111, "loss": 4.20111, "time": 0.82469} +{"mode": "train", "epoch": 83, "iter": 3200, "lr": 0.04181, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27484, "top5_acc": 0.51859, "loss_cls": 4.18814, "loss": 4.18814, "time": 0.82304} +{"mode": "train", "epoch": 83, "iter": 3300, "lr": 0.04178, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26922, "top5_acc": 0.51859, "loss_cls": 4.21917, "loss": 4.21917, "time": 0.81759} +{"mode": "train", "epoch": 83, "iter": 3400, "lr": 0.04176, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.26422, "top5_acc": 0.51672, "loss_cls": 4.21233, "loss": 4.21233, "time": 0.8217} +{"mode": "train", "epoch": 83, "iter": 3500, "lr": 0.04173, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28469, "top5_acc": 0.52359, "loss_cls": 4.16883, "loss": 4.16883, "time": 0.81514} +{"mode": "train", "epoch": 83, "iter": 3600, "lr": 0.0417, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27281, "top5_acc": 0.51219, "loss_cls": 4.20173, "loss": 4.20173, "time": 0.82362} +{"mode": "train", "epoch": 83, "iter": 3700, "lr": 0.04167, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26859, "top5_acc": 0.52234, "loss_cls": 4.20037, "loss": 4.20037, "time": 0.82011} +{"mode": "val", "epoch": 83, "iter": 309, "lr": 0.04166, "top1_acc": 0.20716, "top5_acc": 0.43443, "mean_class_accuracy": 0.20688} +{"mode": "train", "epoch": 84, "iter": 100, "lr": 0.04163, "memory": 15990, "data_time": 1.34187, "top1_acc": 0.28391, "top5_acc": 0.52984, "loss_cls": 4.12232, "loss": 4.12232, "time": 2.32944} +{"mode": "train", "epoch": 84, "iter": 200, "lr": 0.04161, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28125, "top5_acc": 0.53422, "loss_cls": 4.12121, "loss": 4.12121, "time": 0.82701} +{"mode": "train", "epoch": 84, "iter": 300, "lr": 0.04158, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28547, "top5_acc": 0.53797, "loss_cls": 4.12746, "loss": 4.12746, "time": 0.83174} +{"mode": "train", "epoch": 84, "iter": 400, "lr": 0.04155, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28609, "top5_acc": 0.52766, "loss_cls": 4.13309, "loss": 4.13309, "time": 0.82142} +{"mode": "train", "epoch": 84, "iter": 500, "lr": 0.04152, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27234, "top5_acc": 0.52828, "loss_cls": 4.17739, "loss": 4.17739, "time": 0.82398} +{"mode": "train", "epoch": 84, "iter": 600, "lr": 0.0415, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26797, "top5_acc": 0.52359, "loss_cls": 4.20511, "loss": 4.20511, "time": 0.82329} +{"mode": "train", "epoch": 84, "iter": 700, "lr": 0.04147, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28422, "top5_acc": 0.53156, "loss_cls": 4.14654, "loss": 4.14654, "time": 0.82763} +{"mode": "train", "epoch": 84, "iter": 800, "lr": 0.04144, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27297, "top5_acc": 0.51891, "loss_cls": 4.21464, "loss": 4.21464, "time": 0.81996} +{"mode": "train", "epoch": 84, "iter": 900, "lr": 0.04141, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27938, "top5_acc": 0.52406, "loss_cls": 4.15705, "loss": 4.15705, "time": 0.82582} +{"mode": "train", "epoch": 84, "iter": 1000, "lr": 0.04139, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28969, "top5_acc": 0.53828, "loss_cls": 4.10977, "loss": 4.10977, "time": 0.82018} +{"mode": "train", "epoch": 84, "iter": 1100, "lr": 0.04136, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28188, "top5_acc": 0.53625, "loss_cls": 4.15379, "loss": 4.15379, "time": 0.82202} +{"mode": "train", "epoch": 84, "iter": 1200, "lr": 0.04133, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28141, "top5_acc": 0.53078, "loss_cls": 4.13886, "loss": 4.13886, "time": 0.82117} +{"mode": "train", "epoch": 84, "iter": 1300, "lr": 0.0413, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27516, "top5_acc": 0.52469, "loss_cls": 4.165, "loss": 4.165, "time": 0.81717} +{"mode": "train", "epoch": 84, "iter": 1400, "lr": 0.04128, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27312, "top5_acc": 0.5325, "loss_cls": 4.16683, "loss": 4.16683, "time": 0.82309} +{"mode": "train", "epoch": 84, "iter": 1500, "lr": 0.04125, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27531, "top5_acc": 0.52469, "loss_cls": 4.19149, "loss": 4.19149, "time": 0.82687} +{"mode": "train", "epoch": 84, "iter": 1600, "lr": 0.04122, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.26672, "top5_acc": 0.52312, "loss_cls": 4.18785, "loss": 4.18785, "time": 0.82103} +{"mode": "train", "epoch": 84, "iter": 1700, "lr": 0.04119, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26656, "top5_acc": 0.5175, "loss_cls": 4.2333, "loss": 4.2333, "time": 0.82102} +{"mode": "train", "epoch": 84, "iter": 1800, "lr": 0.04117, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26953, "top5_acc": 0.52047, "loss_cls": 4.18814, "loss": 4.18814, "time": 0.82366} +{"mode": "train", "epoch": 84, "iter": 1900, "lr": 0.04114, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27828, "top5_acc": 0.51562, "loss_cls": 4.21941, "loss": 4.21941, "time": 0.82157} +{"mode": "train", "epoch": 84, "iter": 2000, "lr": 0.04111, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27688, "top5_acc": 0.53109, "loss_cls": 4.14462, "loss": 4.14462, "time": 0.82242} +{"mode": "train", "epoch": 84, "iter": 2100, "lr": 0.04108, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27688, "top5_acc": 0.52719, "loss_cls": 4.16852, "loss": 4.16852, "time": 0.81773} +{"mode": "train", "epoch": 84, "iter": 2200, "lr": 0.04106, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27094, "top5_acc": 0.52953, "loss_cls": 4.16117, "loss": 4.16117, "time": 0.8288} +{"mode": "train", "epoch": 84, "iter": 2300, "lr": 0.04103, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27594, "top5_acc": 0.51891, "loss_cls": 4.2119, "loss": 4.2119, "time": 0.82374} +{"mode": "train", "epoch": 84, "iter": 2400, "lr": 0.041, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.2875, "top5_acc": 0.53797, "loss_cls": 4.11902, "loss": 4.11902, "time": 0.82691} +{"mode": "train", "epoch": 84, "iter": 2500, "lr": 0.04097, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27859, "top5_acc": 0.52781, "loss_cls": 4.17048, "loss": 4.17048, "time": 0.82185} +{"mode": "train", "epoch": 84, "iter": 2600, "lr": 0.04095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27578, "top5_acc": 0.53438, "loss_cls": 4.16018, "loss": 4.16018, "time": 0.82011} +{"mode": "train", "epoch": 84, "iter": 2700, "lr": 0.04092, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27609, "top5_acc": 0.53141, "loss_cls": 4.16992, "loss": 4.16992, "time": 0.81817} +{"mode": "train", "epoch": 84, "iter": 2800, "lr": 0.04089, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27406, "top5_acc": 0.52266, "loss_cls": 4.19604, "loss": 4.19604, "time": 0.81837} +{"mode": "train", "epoch": 84, "iter": 2900, "lr": 0.04086, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2825, "top5_acc": 0.52922, "loss_cls": 4.14654, "loss": 4.14654, "time": 0.8187} +{"mode": "train", "epoch": 84, "iter": 3000, "lr": 0.04084, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27484, "top5_acc": 0.52594, "loss_cls": 4.16368, "loss": 4.16368, "time": 0.82084} +{"mode": "train", "epoch": 84, "iter": 3100, "lr": 0.04081, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27656, "top5_acc": 0.5275, "loss_cls": 4.16687, "loss": 4.16687, "time": 0.81916} +{"mode": "train", "epoch": 84, "iter": 3200, "lr": 0.04078, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27422, "top5_acc": 0.51781, "loss_cls": 4.18831, "loss": 4.18831, "time": 0.81595} +{"mode": "train", "epoch": 84, "iter": 3300, "lr": 0.04075, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28156, "top5_acc": 0.52172, "loss_cls": 4.18663, "loss": 4.18663, "time": 0.81494} +{"mode": "train", "epoch": 84, "iter": 3400, "lr": 0.04073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27328, "top5_acc": 0.52, "loss_cls": 4.20858, "loss": 4.20858, "time": 0.81828} +{"mode": "train", "epoch": 84, "iter": 3500, "lr": 0.0407, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27109, "top5_acc": 0.52438, "loss_cls": 4.17444, "loss": 4.17444, "time": 0.82029} +{"mode": "train", "epoch": 84, "iter": 3600, "lr": 0.04067, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27797, "top5_acc": 0.53047, "loss_cls": 4.17027, "loss": 4.17027, "time": 0.8227} +{"mode": "train", "epoch": 84, "iter": 3700, "lr": 0.04064, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27812, "top5_acc": 0.52297, "loss_cls": 4.18327, "loss": 4.18327, "time": 0.83042} +{"mode": "val", "epoch": 84, "iter": 309, "lr": 0.04063, "top1_acc": 0.22038, "top5_acc": 0.45353, "mean_class_accuracy": 0.22007} +{"mode": "train", "epoch": 85, "iter": 100, "lr": 0.0406, "memory": 15990, "data_time": 1.40636, "top1_acc": 0.28375, "top5_acc": 0.53922, "loss_cls": 4.13726, "loss": 4.13726, "time": 2.40722} +{"mode": "train", "epoch": 85, "iter": 200, "lr": 0.04058, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.285, "top5_acc": 0.53656, "loss_cls": 4.12057, "loss": 4.12057, "time": 0.82359} +{"mode": "train", "epoch": 85, "iter": 300, "lr": 0.04055, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.27734, "top5_acc": 0.53641, "loss_cls": 4.10877, "loss": 4.10877, "time": 0.82746} +{"mode": "train", "epoch": 85, "iter": 400, "lr": 0.04052, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28703, "top5_acc": 0.53422, "loss_cls": 4.1344, "loss": 4.1344, "time": 0.82213} +{"mode": "train", "epoch": 85, "iter": 500, "lr": 0.04049, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28062, "top5_acc": 0.5375, "loss_cls": 4.14613, "loss": 4.14613, "time": 0.81674} +{"mode": "train", "epoch": 85, "iter": 600, "lr": 0.04047, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28297, "top5_acc": 0.52891, "loss_cls": 4.14166, "loss": 4.14166, "time": 0.82062} +{"mode": "train", "epoch": 85, "iter": 700, "lr": 0.04044, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.26781, "top5_acc": 0.52203, "loss_cls": 4.18561, "loss": 4.18561, "time": 0.82157} +{"mode": "train", "epoch": 85, "iter": 800, "lr": 0.04041, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27578, "top5_acc": 0.52375, "loss_cls": 4.18243, "loss": 4.18243, "time": 0.82222} +{"mode": "train", "epoch": 85, "iter": 900, "lr": 0.04038, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28703, "top5_acc": 0.53469, "loss_cls": 4.14329, "loss": 4.14329, "time": 0.82418} +{"mode": "train", "epoch": 85, "iter": 1000, "lr": 0.04036, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28359, "top5_acc": 0.53172, "loss_cls": 4.1304, "loss": 4.1304, "time": 0.81969} +{"mode": "train", "epoch": 85, "iter": 1100, "lr": 0.04033, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27812, "top5_acc": 0.53391, "loss_cls": 4.12964, "loss": 4.12964, "time": 0.81656} +{"mode": "train", "epoch": 85, "iter": 1200, "lr": 0.0403, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28281, "top5_acc": 0.52906, "loss_cls": 4.15149, "loss": 4.15149, "time": 0.8189} +{"mode": "train", "epoch": 85, "iter": 1300, "lr": 0.04027, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28141, "top5_acc": 0.53, "loss_cls": 4.16062, "loss": 4.16062, "time": 0.82417} +{"mode": "train", "epoch": 85, "iter": 1400, "lr": 0.04025, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28062, "top5_acc": 0.52453, "loss_cls": 4.15899, "loss": 4.15899, "time": 0.82935} +{"mode": "train", "epoch": 85, "iter": 1500, "lr": 0.04022, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28016, "top5_acc": 0.53844, "loss_cls": 4.14358, "loss": 4.14358, "time": 0.82586} +{"mode": "train", "epoch": 85, "iter": 1600, "lr": 0.04019, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29047, "top5_acc": 0.52844, "loss_cls": 4.14428, "loss": 4.14428, "time": 0.82075} +{"mode": "train", "epoch": 85, "iter": 1700, "lr": 0.04016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27969, "top5_acc": 0.52953, "loss_cls": 4.15071, "loss": 4.15071, "time": 0.82111} +{"mode": "train", "epoch": 85, "iter": 1800, "lr": 0.04014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27984, "top5_acc": 0.53547, "loss_cls": 4.15829, "loss": 4.15829, "time": 0.81742} +{"mode": "train", "epoch": 85, "iter": 1900, "lr": 0.04011, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27484, "top5_acc": 0.52266, "loss_cls": 4.18918, "loss": 4.18918, "time": 0.82315} +{"mode": "train", "epoch": 85, "iter": 2000, "lr": 0.04008, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27922, "top5_acc": 0.53156, "loss_cls": 4.14368, "loss": 4.14368, "time": 0.81597} +{"mode": "train", "epoch": 85, "iter": 2100, "lr": 0.04006, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27469, "top5_acc": 0.53516, "loss_cls": 4.14475, "loss": 4.14475, "time": 0.82016} +{"mode": "train", "epoch": 85, "iter": 2200, "lr": 0.04003, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27766, "top5_acc": 0.53172, "loss_cls": 4.15396, "loss": 4.15396, "time": 0.82231} +{"mode": "train", "epoch": 85, "iter": 2300, "lr": 0.04, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28969, "top5_acc": 0.53969, "loss_cls": 4.11917, "loss": 4.11917, "time": 0.8214} +{"mode": "train", "epoch": 85, "iter": 2400, "lr": 0.03997, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28016, "top5_acc": 0.51875, "loss_cls": 4.17445, "loss": 4.17445, "time": 0.82617} +{"mode": "train", "epoch": 85, "iter": 2500, "lr": 0.03995, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27703, "top5_acc": 0.53, "loss_cls": 4.15514, "loss": 4.15514, "time": 0.8134} +{"mode": "train", "epoch": 85, "iter": 2600, "lr": 0.03992, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27594, "top5_acc": 0.525, "loss_cls": 4.17252, "loss": 4.17252, "time": 0.82063} +{"mode": "train", "epoch": 85, "iter": 2700, "lr": 0.03989, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.26922, "top5_acc": 0.525, "loss_cls": 4.18324, "loss": 4.18324, "time": 0.82337} +{"mode": "train", "epoch": 85, "iter": 2800, "lr": 0.03986, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.27375, "top5_acc": 0.52031, "loss_cls": 4.187, "loss": 4.187, "time": 0.82479} +{"mode": "train", "epoch": 85, "iter": 2900, "lr": 0.03984, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28734, "top5_acc": 0.52922, "loss_cls": 4.11794, "loss": 4.11794, "time": 0.82491} +{"mode": "train", "epoch": 85, "iter": 3000, "lr": 0.03981, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26844, "top5_acc": 0.51578, "loss_cls": 4.22395, "loss": 4.22395, "time": 0.82234} +{"mode": "train", "epoch": 85, "iter": 3100, "lr": 0.03978, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27453, "top5_acc": 0.52, "loss_cls": 4.18005, "loss": 4.18005, "time": 0.8212} +{"mode": "train", "epoch": 85, "iter": 3200, "lr": 0.03975, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27828, "top5_acc": 0.52297, "loss_cls": 4.17138, "loss": 4.17138, "time": 0.81732} +{"mode": "train", "epoch": 85, "iter": 3300, "lr": 0.03973, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27844, "top5_acc": 0.52922, "loss_cls": 4.15733, "loss": 4.15733, "time": 0.82141} +{"mode": "train", "epoch": 85, "iter": 3400, "lr": 0.0397, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2775, "top5_acc": 0.52797, "loss_cls": 4.15088, "loss": 4.15088, "time": 0.81834} +{"mode": "train", "epoch": 85, "iter": 3500, "lr": 0.03967, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28359, "top5_acc": 0.52453, "loss_cls": 4.19031, "loss": 4.19031, "time": 0.81929} +{"mode": "train", "epoch": 85, "iter": 3600, "lr": 0.03964, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27578, "top5_acc": 0.52, "loss_cls": 4.18389, "loss": 4.18389, "time": 0.81941} +{"mode": "train", "epoch": 85, "iter": 3700, "lr": 0.03962, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28422, "top5_acc": 0.52547, "loss_cls": 4.1712, "loss": 4.1712, "time": 0.81839} +{"mode": "val", "epoch": 85, "iter": 309, "lr": 0.0396, "top1_acc": 0.22611, "top5_acc": 0.46634, "mean_class_accuracy": 0.22591} +{"mode": "train", "epoch": 86, "iter": 100, "lr": 0.03958, "memory": 15990, "data_time": 1.39972, "top1_acc": 0.28422, "top5_acc": 0.54672, "loss_cls": 4.08637, "loss": 4.08637, "time": 2.42638} +{"mode": "train", "epoch": 86, "iter": 200, "lr": 0.03955, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28312, "top5_acc": 0.53297, "loss_cls": 4.11838, "loss": 4.11838, "time": 0.83001} +{"mode": "train", "epoch": 86, "iter": 300, "lr": 0.03952, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28438, "top5_acc": 0.54, "loss_cls": 4.10702, "loss": 4.10702, "time": 0.83442} +{"mode": "train", "epoch": 86, "iter": 400, "lr": 0.0395, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28172, "top5_acc": 0.52938, "loss_cls": 4.1509, "loss": 4.1509, "time": 0.82953} +{"mode": "train", "epoch": 86, "iter": 500, "lr": 0.03947, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27781, "top5_acc": 0.53547, "loss_cls": 4.10259, "loss": 4.10259, "time": 0.82694} +{"mode": "train", "epoch": 86, "iter": 600, "lr": 0.03944, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28859, "top5_acc": 0.53875, "loss_cls": 4.10245, "loss": 4.10245, "time": 0.83169} +{"mode": "train", "epoch": 86, "iter": 700, "lr": 0.03941, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28406, "top5_acc": 0.53438, "loss_cls": 4.14195, "loss": 4.14195, "time": 0.81772} +{"mode": "train", "epoch": 86, "iter": 800, "lr": 0.03939, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28578, "top5_acc": 0.54141, "loss_cls": 4.09645, "loss": 4.09645, "time": 0.8257} +{"mode": "train", "epoch": 86, "iter": 900, "lr": 0.03936, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27938, "top5_acc": 0.52453, "loss_cls": 4.16678, "loss": 4.16678, "time": 0.81791} +{"mode": "train", "epoch": 86, "iter": 1000, "lr": 0.03933, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28516, "top5_acc": 0.52656, "loss_cls": 4.14872, "loss": 4.14872, "time": 0.81713} +{"mode": "train", "epoch": 86, "iter": 1100, "lr": 0.0393, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28125, "top5_acc": 0.53281, "loss_cls": 4.12172, "loss": 4.12172, "time": 0.82258} +{"mode": "train", "epoch": 86, "iter": 1200, "lr": 0.03928, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2825, "top5_acc": 0.52828, "loss_cls": 4.14274, "loss": 4.14274, "time": 0.82299} +{"mode": "train", "epoch": 86, "iter": 1300, "lr": 0.03925, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27922, "top5_acc": 0.52266, "loss_cls": 4.16029, "loss": 4.16029, "time": 0.82545} +{"mode": "train", "epoch": 86, "iter": 1400, "lr": 0.03922, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29547, "top5_acc": 0.54812, "loss_cls": 4.08493, "loss": 4.08493, "time": 0.81948} +{"mode": "train", "epoch": 86, "iter": 1500, "lr": 0.03919, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.27844, "top5_acc": 0.52953, "loss_cls": 4.1717, "loss": 4.1717, "time": 0.8258} +{"mode": "train", "epoch": 86, "iter": 1600, "lr": 0.03917, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27266, "top5_acc": 0.52812, "loss_cls": 4.15461, "loss": 4.15461, "time": 0.82089} +{"mode": "train", "epoch": 86, "iter": 1700, "lr": 0.03914, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28438, "top5_acc": 0.53094, "loss_cls": 4.13863, "loss": 4.13863, "time": 0.81705} +{"mode": "train", "epoch": 86, "iter": 1800, "lr": 0.03911, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27656, "top5_acc": 0.53312, "loss_cls": 4.14323, "loss": 4.14323, "time": 0.82179} +{"mode": "train", "epoch": 86, "iter": 1900, "lr": 0.03909, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28516, "top5_acc": 0.53234, "loss_cls": 4.14455, "loss": 4.14455, "time": 0.82685} +{"mode": "train", "epoch": 86, "iter": 2000, "lr": 0.03906, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28297, "top5_acc": 0.53469, "loss_cls": 4.12925, "loss": 4.12925, "time": 0.8246} +{"mode": "train", "epoch": 86, "iter": 2100, "lr": 0.03903, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27609, "top5_acc": 0.53172, "loss_cls": 4.15079, "loss": 4.15079, "time": 0.82491} +{"mode": "train", "epoch": 86, "iter": 2200, "lr": 0.039, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27797, "top5_acc": 0.52625, "loss_cls": 4.16758, "loss": 4.16758, "time": 0.81594} +{"mode": "train", "epoch": 86, "iter": 2300, "lr": 0.03898, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.275, "top5_acc": 0.52688, "loss_cls": 4.20113, "loss": 4.20113, "time": 0.82918} +{"mode": "train", "epoch": 86, "iter": 2400, "lr": 0.03895, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28219, "top5_acc": 0.52203, "loss_cls": 4.17107, "loss": 4.17107, "time": 0.81967} +{"mode": "train", "epoch": 86, "iter": 2500, "lr": 0.03892, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.27984, "top5_acc": 0.53734, "loss_cls": 4.14885, "loss": 4.14885, "time": 0.82341} +{"mode": "train", "epoch": 86, "iter": 2600, "lr": 0.03889, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28813, "top5_acc": 0.53219, "loss_cls": 4.14309, "loss": 4.14309, "time": 0.82118} +{"mode": "train", "epoch": 86, "iter": 2700, "lr": 0.03887, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29359, "top5_acc": 0.53688, "loss_cls": 4.07481, "loss": 4.07481, "time": 0.82318} +{"mode": "train", "epoch": 86, "iter": 2800, "lr": 0.03884, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29047, "top5_acc": 0.53797, "loss_cls": 4.09745, "loss": 4.09745, "time": 0.81758} +{"mode": "train", "epoch": 86, "iter": 2900, "lr": 0.03881, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27437, "top5_acc": 0.52578, "loss_cls": 4.19749, "loss": 4.19749, "time": 0.82171} +{"mode": "train", "epoch": 86, "iter": 3000, "lr": 0.03879, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27609, "top5_acc": 0.52156, "loss_cls": 4.16537, "loss": 4.16537, "time": 0.82608} +{"mode": "train", "epoch": 86, "iter": 3100, "lr": 0.03876, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28281, "top5_acc": 0.52484, "loss_cls": 4.15142, "loss": 4.15142, "time": 0.81588} +{"mode": "train", "epoch": 86, "iter": 3200, "lr": 0.03873, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27031, "top5_acc": 0.51688, "loss_cls": 4.20219, "loss": 4.20219, "time": 0.81385} +{"mode": "train", "epoch": 86, "iter": 3300, "lr": 0.0387, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27656, "top5_acc": 0.51625, "loss_cls": 4.18872, "loss": 4.18872, "time": 0.81887} +{"mode": "train", "epoch": 86, "iter": 3400, "lr": 0.03868, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2775, "top5_acc": 0.52953, "loss_cls": 4.17754, "loss": 4.17754, "time": 0.81637} +{"mode": "train", "epoch": 86, "iter": 3500, "lr": 0.03865, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28266, "top5_acc": 0.52203, "loss_cls": 4.18788, "loss": 4.18788, "time": 0.81543} +{"mode": "train", "epoch": 86, "iter": 3600, "lr": 0.03862, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28, "top5_acc": 0.52938, "loss_cls": 4.11252, "loss": 4.11252, "time": 0.81602} +{"mode": "train", "epoch": 86, "iter": 3700, "lr": 0.0386, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28031, "top5_acc": 0.52891, "loss_cls": 4.15061, "loss": 4.15061, "time": 0.81654} +{"mode": "val", "epoch": 86, "iter": 309, "lr": 0.03858, "top1_acc": 0.2296, "top5_acc": 0.46432, "mean_class_accuracy": 0.22951} +{"mode": "train", "epoch": 87, "iter": 100, "lr": 0.03856, "memory": 15990, "data_time": 1.3112, "top1_acc": 0.30141, "top5_acc": 0.55984, "loss_cls": 4.0284, "loss": 4.0284, "time": 2.3015} +{"mode": "train", "epoch": 87, "iter": 200, "lr": 0.03853, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28328, "top5_acc": 0.53359, "loss_cls": 4.12805, "loss": 4.12805, "time": 0.82574} +{"mode": "train", "epoch": 87, "iter": 300, "lr": 0.0385, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28312, "top5_acc": 0.53016, "loss_cls": 4.12857, "loss": 4.12857, "time": 0.82101} +{"mode": "train", "epoch": 87, "iter": 400, "lr": 0.03847, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28859, "top5_acc": 0.53422, "loss_cls": 4.10306, "loss": 4.10306, "time": 0.81676} +{"mode": "train", "epoch": 87, "iter": 500, "lr": 0.03845, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29016, "top5_acc": 0.5475, "loss_cls": 4.03731, "loss": 4.03731, "time": 0.82209} +{"mode": "train", "epoch": 87, "iter": 600, "lr": 0.03842, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28172, "top5_acc": 0.53766, "loss_cls": 4.12149, "loss": 4.12149, "time": 0.82657} +{"mode": "train", "epoch": 87, "iter": 700, "lr": 0.03839, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29344, "top5_acc": 0.54359, "loss_cls": 4.08761, "loss": 4.08761, "time": 0.81979} +{"mode": "train", "epoch": 87, "iter": 800, "lr": 0.03837, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28625, "top5_acc": 0.5375, "loss_cls": 4.1232, "loss": 4.1232, "time": 0.82455} +{"mode": "train", "epoch": 87, "iter": 900, "lr": 0.03834, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29016, "top5_acc": 0.52812, "loss_cls": 4.12856, "loss": 4.12856, "time": 0.82378} +{"mode": "train", "epoch": 87, "iter": 1000, "lr": 0.03831, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29672, "top5_acc": 0.53984, "loss_cls": 4.06249, "loss": 4.06249, "time": 0.81762} +{"mode": "train", "epoch": 87, "iter": 1100, "lr": 0.03828, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29188, "top5_acc": 0.52938, "loss_cls": 4.10075, "loss": 4.10075, "time": 0.81992} +{"mode": "train", "epoch": 87, "iter": 1200, "lr": 0.03826, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28562, "top5_acc": 0.53375, "loss_cls": 4.10118, "loss": 4.10118, "time": 0.82529} +{"mode": "train", "epoch": 87, "iter": 1300, "lr": 0.03823, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27906, "top5_acc": 0.52844, "loss_cls": 4.13958, "loss": 4.13958, "time": 0.82177} +{"mode": "train", "epoch": 87, "iter": 1400, "lr": 0.0382, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29078, "top5_acc": 0.53828, "loss_cls": 4.12087, "loss": 4.12087, "time": 0.8249} +{"mode": "train", "epoch": 87, "iter": 1500, "lr": 0.03817, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.27594, "top5_acc": 0.53203, "loss_cls": 4.16177, "loss": 4.16177, "time": 0.82363} +{"mode": "train", "epoch": 87, "iter": 1600, "lr": 0.03815, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28766, "top5_acc": 0.53797, "loss_cls": 4.12816, "loss": 4.12816, "time": 0.82103} +{"mode": "train", "epoch": 87, "iter": 1700, "lr": 0.03812, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2825, "top5_acc": 0.52781, "loss_cls": 4.16885, "loss": 4.16885, "time": 0.8204} +{"mode": "train", "epoch": 87, "iter": 1800, "lr": 0.03809, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27641, "top5_acc": 0.52172, "loss_cls": 4.17397, "loss": 4.17397, "time": 0.81452} +{"mode": "train", "epoch": 87, "iter": 1900, "lr": 0.03807, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28719, "top5_acc": 0.53391, "loss_cls": 4.13469, "loss": 4.13469, "time": 0.81796} +{"mode": "train", "epoch": 87, "iter": 2000, "lr": 0.03804, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28422, "top5_acc": 0.53438, "loss_cls": 4.15931, "loss": 4.15931, "time": 0.81999} +{"mode": "train", "epoch": 87, "iter": 2100, "lr": 0.03801, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28516, "top5_acc": 0.54125, "loss_cls": 4.13269, "loss": 4.13269, "time": 0.82094} +{"mode": "train", "epoch": 87, "iter": 2200, "lr": 0.03798, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28, "top5_acc": 0.52938, "loss_cls": 4.13282, "loss": 4.13282, "time": 0.82002} +{"mode": "train", "epoch": 87, "iter": 2300, "lr": 0.03796, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28172, "top5_acc": 0.53531, "loss_cls": 4.11037, "loss": 4.11037, "time": 0.82596} +{"mode": "train", "epoch": 87, "iter": 2400, "lr": 0.03793, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.28328, "top5_acc": 0.53062, "loss_cls": 4.14678, "loss": 4.14678, "time": 0.82535} +{"mode": "train", "epoch": 87, "iter": 2500, "lr": 0.0379, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28172, "top5_acc": 0.5325, "loss_cls": 4.12812, "loss": 4.12812, "time": 0.82383} +{"mode": "train", "epoch": 87, "iter": 2600, "lr": 0.03788, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.27672, "top5_acc": 0.53234, "loss_cls": 4.16222, "loss": 4.16222, "time": 0.82312} +{"mode": "train", "epoch": 87, "iter": 2700, "lr": 0.03785, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28031, "top5_acc": 0.53297, "loss_cls": 4.14062, "loss": 4.14062, "time": 0.82086} +{"mode": "train", "epoch": 87, "iter": 2800, "lr": 0.03782, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28047, "top5_acc": 0.53016, "loss_cls": 4.14606, "loss": 4.14606, "time": 0.81612} +{"mode": "train", "epoch": 87, "iter": 2900, "lr": 0.03779, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28219, "top5_acc": 0.53, "loss_cls": 4.15307, "loss": 4.15307, "time": 0.81646} +{"mode": "train", "epoch": 87, "iter": 3000, "lr": 0.03777, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29109, "top5_acc": 0.535, "loss_cls": 4.13949, "loss": 4.13949, "time": 0.81585} +{"mode": "train", "epoch": 87, "iter": 3100, "lr": 0.03774, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27781, "top5_acc": 0.52375, "loss_cls": 4.16537, "loss": 4.16537, "time": 0.82119} +{"mode": "train", "epoch": 87, "iter": 3200, "lr": 0.03771, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27562, "top5_acc": 0.53094, "loss_cls": 4.13204, "loss": 4.13204, "time": 0.81731} +{"mode": "train", "epoch": 87, "iter": 3300, "lr": 0.03769, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.27938, "top5_acc": 0.53828, "loss_cls": 4.12401, "loss": 4.12401, "time": 0.81554} +{"mode": "train", "epoch": 87, "iter": 3400, "lr": 0.03766, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28016, "top5_acc": 0.5275, "loss_cls": 4.15286, "loss": 4.15286, "time": 0.8223} +{"mode": "train", "epoch": 87, "iter": 3500, "lr": 0.03763, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.27625, "top5_acc": 0.52953, "loss_cls": 4.15806, "loss": 4.15806, "time": 0.8188} +{"mode": "train", "epoch": 87, "iter": 3600, "lr": 0.03761, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28109, "top5_acc": 0.52766, "loss_cls": 4.15106, "loss": 4.15106, "time": 0.82337} +{"mode": "train", "epoch": 87, "iter": 3700, "lr": 0.03758, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28469, "top5_acc": 0.53453, "loss_cls": 4.14404, "loss": 4.14404, "time": 0.82142} +{"mode": "val", "epoch": 87, "iter": 309, "lr": 0.03757, "top1_acc": 0.22788, "top5_acc": 0.46579, "mean_class_accuracy": 0.22751} +{"mode": "train", "epoch": 88, "iter": 100, "lr": 0.03754, "memory": 15990, "data_time": 1.29653, "top1_acc": 0.30141, "top5_acc": 0.55297, "loss_cls": 4.01774, "loss": 4.01774, "time": 2.28588} +{"mode": "train", "epoch": 88, "iter": 200, "lr": 0.03751, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.29297, "top5_acc": 0.53781, "loss_cls": 4.09374, "loss": 4.09374, "time": 0.82452} +{"mode": "train", "epoch": 88, "iter": 300, "lr": 0.03748, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28656, "top5_acc": 0.53719, "loss_cls": 4.1227, "loss": 4.1227, "time": 0.82187} +{"mode": "train", "epoch": 88, "iter": 400, "lr": 0.03746, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28984, "top5_acc": 0.54734, "loss_cls": 4.08335, "loss": 4.08335, "time": 0.81944} +{"mode": "train", "epoch": 88, "iter": 500, "lr": 0.03743, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29844, "top5_acc": 0.53797, "loss_cls": 4.07005, "loss": 4.07005, "time": 0.82349} +{"mode": "train", "epoch": 88, "iter": 600, "lr": 0.0374, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28188, "top5_acc": 0.53531, "loss_cls": 4.12415, "loss": 4.12415, "time": 0.82689} +{"mode": "train", "epoch": 88, "iter": 700, "lr": 0.03738, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28875, "top5_acc": 0.54047, "loss_cls": 4.07614, "loss": 4.07614, "time": 0.81685} +{"mode": "train", "epoch": 88, "iter": 800, "lr": 0.03735, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28938, "top5_acc": 0.52859, "loss_cls": 4.11482, "loss": 4.11482, "time": 0.82143} +{"mode": "train", "epoch": 88, "iter": 900, "lr": 0.03732, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27625, "top5_acc": 0.53406, "loss_cls": 4.12825, "loss": 4.12825, "time": 0.81957} +{"mode": "train", "epoch": 88, "iter": 1000, "lr": 0.0373, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28953, "top5_acc": 0.53609, "loss_cls": 4.10806, "loss": 4.10806, "time": 0.81866} +{"mode": "train", "epoch": 88, "iter": 1100, "lr": 0.03727, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28406, "top5_acc": 0.53797, "loss_cls": 4.10062, "loss": 4.10062, "time": 0.8204} +{"mode": "train", "epoch": 88, "iter": 1200, "lr": 0.03724, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27969, "top5_acc": 0.52812, "loss_cls": 4.14652, "loss": 4.14652, "time": 0.82785} +{"mode": "train", "epoch": 88, "iter": 1300, "lr": 0.03721, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28391, "top5_acc": 0.53375, "loss_cls": 4.10459, "loss": 4.10459, "time": 0.82051} +{"mode": "train", "epoch": 88, "iter": 1400, "lr": 0.03719, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28297, "top5_acc": 0.53578, "loss_cls": 4.12043, "loss": 4.12043, "time": 0.83002} +{"mode": "train", "epoch": 88, "iter": 1500, "lr": 0.03716, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29078, "top5_acc": 0.54031, "loss_cls": 4.10192, "loss": 4.10192, "time": 0.82418} +{"mode": "train", "epoch": 88, "iter": 1600, "lr": 0.03713, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28609, "top5_acc": 0.53266, "loss_cls": 4.10787, "loss": 4.10787, "time": 0.81889} +{"mode": "train", "epoch": 88, "iter": 1700, "lr": 0.03711, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28266, "top5_acc": 0.53719, "loss_cls": 4.12326, "loss": 4.12326, "time": 0.82282} +{"mode": "train", "epoch": 88, "iter": 1800, "lr": 0.03708, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29453, "top5_acc": 0.54531, "loss_cls": 4.0639, "loss": 4.0639, "time": 0.81817} +{"mode": "train", "epoch": 88, "iter": 1900, "lr": 0.03705, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29266, "top5_acc": 0.54312, "loss_cls": 4.05709, "loss": 4.05709, "time": 0.8171} +{"mode": "train", "epoch": 88, "iter": 2000, "lr": 0.03703, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28594, "top5_acc": 0.53359, "loss_cls": 4.13113, "loss": 4.13113, "time": 0.82027} +{"mode": "train", "epoch": 88, "iter": 2100, "lr": 0.037, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27844, "top5_acc": 0.53312, "loss_cls": 4.12877, "loss": 4.12877, "time": 0.82521} +{"mode": "train", "epoch": 88, "iter": 2200, "lr": 0.03697, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29297, "top5_acc": 0.53438, "loss_cls": 4.10866, "loss": 4.10866, "time": 0.82113} +{"mode": "train", "epoch": 88, "iter": 2300, "lr": 0.03694, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.28094, "top5_acc": 0.52969, "loss_cls": 4.15104, "loss": 4.15104, "time": 0.81844} +{"mode": "train", "epoch": 88, "iter": 2400, "lr": 0.03692, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.28453, "top5_acc": 0.54016, "loss_cls": 4.10175, "loss": 4.10175, "time": 0.81997} +{"mode": "train", "epoch": 88, "iter": 2500, "lr": 0.03689, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28484, "top5_acc": 0.53953, "loss_cls": 4.1288, "loss": 4.1288, "time": 0.82256} +{"mode": "train", "epoch": 88, "iter": 2600, "lr": 0.03686, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28172, "top5_acc": 0.53391, "loss_cls": 4.12851, "loss": 4.12851, "time": 0.82139} +{"mode": "train", "epoch": 88, "iter": 2700, "lr": 0.03684, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28484, "top5_acc": 0.52875, "loss_cls": 4.13706, "loss": 4.13706, "time": 0.82069} +{"mode": "train", "epoch": 88, "iter": 2800, "lr": 0.03681, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28547, "top5_acc": 0.52922, "loss_cls": 4.14373, "loss": 4.14373, "time": 0.81773} +{"mode": "train", "epoch": 88, "iter": 2900, "lr": 0.03678, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28766, "top5_acc": 0.52281, "loss_cls": 4.1317, "loss": 4.1317, "time": 0.81877} +{"mode": "train", "epoch": 88, "iter": 3000, "lr": 0.03676, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29281, "top5_acc": 0.54266, "loss_cls": 4.08354, "loss": 4.08354, "time": 0.82321} +{"mode": "train", "epoch": 88, "iter": 3100, "lr": 0.03673, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28406, "top5_acc": 0.52406, "loss_cls": 4.14607, "loss": 4.14607, "time": 0.82126} +{"mode": "train", "epoch": 88, "iter": 3200, "lr": 0.0367, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.26953, "top5_acc": 0.52469, "loss_cls": 4.18675, "loss": 4.18675, "time": 0.82368} +{"mode": "train", "epoch": 88, "iter": 3300, "lr": 0.03667, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28391, "top5_acc": 0.53766, "loss_cls": 4.10705, "loss": 4.10705, "time": 0.81998} +{"mode": "train", "epoch": 88, "iter": 3400, "lr": 0.03665, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27297, "top5_acc": 0.5275, "loss_cls": 4.16453, "loss": 4.16453, "time": 0.82305} +{"mode": "train", "epoch": 88, "iter": 3500, "lr": 0.03662, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28797, "top5_acc": 0.53625, "loss_cls": 4.08999, "loss": 4.08999, "time": 0.81984} +{"mode": "train", "epoch": 88, "iter": 3600, "lr": 0.03659, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28438, "top5_acc": 0.52609, "loss_cls": 4.12603, "loss": 4.12603, "time": 0.82206} +{"mode": "train", "epoch": 88, "iter": 3700, "lr": 0.03657, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28688, "top5_acc": 0.52797, "loss_cls": 4.13855, "loss": 4.13855, "time": 0.81341} +{"mode": "val", "epoch": 88, "iter": 309, "lr": 0.03655, "top1_acc": 0.22241, "top5_acc": 0.45044, "mean_class_accuracy": 0.22219} +{"mode": "train", "epoch": 89, "iter": 100, "lr": 0.03653, "memory": 15990, "data_time": 1.31194, "top1_acc": 0.28688, "top5_acc": 0.54562, "loss_cls": 4.0712, "loss": 4.0712, "time": 2.30502} +{"mode": "train", "epoch": 89, "iter": 200, "lr": 0.0365, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29172, "top5_acc": 0.53859, "loss_cls": 4.07677, "loss": 4.07677, "time": 0.83277} +{"mode": "train", "epoch": 89, "iter": 300, "lr": 0.03647, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30312, "top5_acc": 0.54875, "loss_cls": 4.05919, "loss": 4.05919, "time": 0.82616} +{"mode": "train", "epoch": 89, "iter": 400, "lr": 0.03645, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29859, "top5_acc": 0.54469, "loss_cls": 4.07189, "loss": 4.07189, "time": 0.81452} +{"mode": "train", "epoch": 89, "iter": 500, "lr": 0.03642, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29828, "top5_acc": 0.55234, "loss_cls": 4.06864, "loss": 4.06864, "time": 0.81886} +{"mode": "train", "epoch": 89, "iter": 600, "lr": 0.03639, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28609, "top5_acc": 0.53094, "loss_cls": 4.11326, "loss": 4.11326, "time": 0.82882} +{"mode": "train", "epoch": 89, "iter": 700, "lr": 0.03637, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28641, "top5_acc": 0.54344, "loss_cls": 4.08425, "loss": 4.08425, "time": 0.82123} +{"mode": "train", "epoch": 89, "iter": 800, "lr": 0.03634, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.28406, "top5_acc": 0.54391, "loss_cls": 4.07171, "loss": 4.07171, "time": 0.82591} +{"mode": "train", "epoch": 89, "iter": 900, "lr": 0.03631, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28813, "top5_acc": 0.54094, "loss_cls": 4.06664, "loss": 4.06664, "time": 0.82361} +{"mode": "train", "epoch": 89, "iter": 1000, "lr": 0.03629, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29328, "top5_acc": 0.54062, "loss_cls": 4.06852, "loss": 4.06852, "time": 0.82049} +{"mode": "train", "epoch": 89, "iter": 1100, "lr": 0.03626, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28578, "top5_acc": 0.54609, "loss_cls": 4.08007, "loss": 4.08007, "time": 0.81489} +{"mode": "train", "epoch": 89, "iter": 1200, "lr": 0.03623, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.28578, "top5_acc": 0.53828, "loss_cls": 4.09461, "loss": 4.09461, "time": 0.82831} +{"mode": "train", "epoch": 89, "iter": 1300, "lr": 0.0362, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27891, "top5_acc": 0.53891, "loss_cls": 4.14281, "loss": 4.14281, "time": 0.82428} +{"mode": "train", "epoch": 89, "iter": 1400, "lr": 0.03618, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29078, "top5_acc": 0.54516, "loss_cls": 4.06365, "loss": 4.06365, "time": 0.82489} +{"mode": "train", "epoch": 89, "iter": 1500, "lr": 0.03615, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.295, "top5_acc": 0.54188, "loss_cls": 4.06403, "loss": 4.06403, "time": 0.8257} +{"mode": "train", "epoch": 89, "iter": 1600, "lr": 0.03612, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28875, "top5_acc": 0.52906, "loss_cls": 4.12879, "loss": 4.12879, "time": 0.8206} +{"mode": "train", "epoch": 89, "iter": 1700, "lr": 0.0361, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27719, "top5_acc": 0.54094, "loss_cls": 4.10546, "loss": 4.10546, "time": 0.81782} +{"mode": "train", "epoch": 89, "iter": 1800, "lr": 0.03607, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27953, "top5_acc": 0.53344, "loss_cls": 4.12446, "loss": 4.12446, "time": 0.82211} +{"mode": "train", "epoch": 89, "iter": 1900, "lr": 0.03604, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29, "top5_acc": 0.53766, "loss_cls": 4.12668, "loss": 4.12668, "time": 0.8178} +{"mode": "train", "epoch": 89, "iter": 2000, "lr": 0.03602, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29063, "top5_acc": 0.54297, "loss_cls": 4.08683, "loss": 4.08683, "time": 0.82046} +{"mode": "train", "epoch": 89, "iter": 2100, "lr": 0.03599, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28422, "top5_acc": 0.53422, "loss_cls": 4.11764, "loss": 4.11764, "time": 0.83034} +{"mode": "train", "epoch": 89, "iter": 2200, "lr": 0.03596, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.27781, "top5_acc": 0.5325, "loss_cls": 4.15088, "loss": 4.15088, "time": 0.81997} +{"mode": "train", "epoch": 89, "iter": 2300, "lr": 0.03594, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28359, "top5_acc": 0.53672, "loss_cls": 4.12134, "loss": 4.12134, "time": 0.81987} +{"mode": "train", "epoch": 89, "iter": 2400, "lr": 0.03591, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28906, "top5_acc": 0.55203, "loss_cls": 4.08085, "loss": 4.08085, "time": 0.81902} +{"mode": "train", "epoch": 89, "iter": 2500, "lr": 0.03588, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28797, "top5_acc": 0.53844, "loss_cls": 4.09588, "loss": 4.09588, "time": 0.82169} +{"mode": "train", "epoch": 89, "iter": 2600, "lr": 0.03586, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29109, "top5_acc": 0.53141, "loss_cls": 4.09286, "loss": 4.09286, "time": 0.81903} +{"mode": "train", "epoch": 89, "iter": 2700, "lr": 0.03583, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29141, "top5_acc": 0.53703, "loss_cls": 4.08116, "loss": 4.08116, "time": 0.81788} +{"mode": "train", "epoch": 89, "iter": 2800, "lr": 0.0358, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.27797, "top5_acc": 0.53734, "loss_cls": 4.14118, "loss": 4.14118, "time": 0.82286} +{"mode": "train", "epoch": 89, "iter": 2900, "lr": 0.03578, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28641, "top5_acc": 0.53453, "loss_cls": 4.11666, "loss": 4.11666, "time": 0.81808} +{"mode": "train", "epoch": 89, "iter": 3000, "lr": 0.03575, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29109, "top5_acc": 0.53672, "loss_cls": 4.10421, "loss": 4.10421, "time": 0.81635} +{"mode": "train", "epoch": 89, "iter": 3100, "lr": 0.03572, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28797, "top5_acc": 0.53516, "loss_cls": 4.10579, "loss": 4.10579, "time": 0.81965} +{"mode": "train", "epoch": 89, "iter": 3200, "lr": 0.03569, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28719, "top5_acc": 0.53297, "loss_cls": 4.11634, "loss": 4.11634, "time": 0.8203} +{"mode": "train", "epoch": 89, "iter": 3300, "lr": 0.03567, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2875, "top5_acc": 0.53031, "loss_cls": 4.1234, "loss": 4.1234, "time": 0.82495} +{"mode": "train", "epoch": 89, "iter": 3400, "lr": 0.03564, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28516, "top5_acc": 0.52719, "loss_cls": 4.1333, "loss": 4.1333, "time": 0.82083} +{"mode": "train", "epoch": 89, "iter": 3500, "lr": 0.03561, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28562, "top5_acc": 0.52484, "loss_cls": 4.14495, "loss": 4.14495, "time": 0.81888} +{"mode": "train", "epoch": 89, "iter": 3600, "lr": 0.03559, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28359, "top5_acc": 0.53609, "loss_cls": 4.13446, "loss": 4.13446, "time": 0.81961} +{"mode": "train", "epoch": 89, "iter": 3700, "lr": 0.03556, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28234, "top5_acc": 0.53547, "loss_cls": 4.1298, "loss": 4.1298, "time": 0.82117} +{"mode": "val", "epoch": 89, "iter": 309, "lr": 0.03555, "top1_acc": 0.20959, "top5_acc": 0.44578, "mean_class_accuracy": 0.20943} +{"mode": "train", "epoch": 90, "iter": 100, "lr": 0.03552, "memory": 15990, "data_time": 1.30205, "top1_acc": 0.29484, "top5_acc": 0.55266, "loss_cls": 4.02792, "loss": 4.02792, "time": 2.28975} +{"mode": "train", "epoch": 90, "iter": 200, "lr": 0.0355, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29938, "top5_acc": 0.55219, "loss_cls": 4.03938, "loss": 4.03938, "time": 0.82078} +{"mode": "train", "epoch": 90, "iter": 300, "lr": 0.03547, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29922, "top5_acc": 0.54734, "loss_cls": 4.03395, "loss": 4.03395, "time": 0.82486} +{"mode": "train", "epoch": 90, "iter": 400, "lr": 0.03544, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30031, "top5_acc": 0.55, "loss_cls": 4.0223, "loss": 4.0223, "time": 0.81799} +{"mode": "train", "epoch": 90, "iter": 500, "lr": 0.03541, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29141, "top5_acc": 0.54391, "loss_cls": 4.04938, "loss": 4.04938, "time": 0.82222} +{"mode": "train", "epoch": 90, "iter": 600, "lr": 0.03539, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30094, "top5_acc": 0.55219, "loss_cls": 4.03088, "loss": 4.03088, "time": 0.82612} +{"mode": "train", "epoch": 90, "iter": 700, "lr": 0.03536, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28328, "top5_acc": 0.53703, "loss_cls": 4.11594, "loss": 4.11594, "time": 0.82134} +{"mode": "train", "epoch": 90, "iter": 800, "lr": 0.03533, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28672, "top5_acc": 0.54328, "loss_cls": 4.08318, "loss": 4.08318, "time": 0.82782} +{"mode": "train", "epoch": 90, "iter": 900, "lr": 0.03531, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28797, "top5_acc": 0.54078, "loss_cls": 4.11647, "loss": 4.11647, "time": 0.8267} +{"mode": "train", "epoch": 90, "iter": 1000, "lr": 0.03528, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29453, "top5_acc": 0.54047, "loss_cls": 4.07657, "loss": 4.07657, "time": 0.81481} +{"mode": "train", "epoch": 90, "iter": 1100, "lr": 0.03525, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28875, "top5_acc": 0.54234, "loss_cls": 4.10221, "loss": 4.10221, "time": 0.82009} +{"mode": "train", "epoch": 90, "iter": 1200, "lr": 0.03523, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.27922, "top5_acc": 0.53422, "loss_cls": 4.10827, "loss": 4.10827, "time": 0.82492} +{"mode": "train", "epoch": 90, "iter": 1300, "lr": 0.0352, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29422, "top5_acc": 0.54688, "loss_cls": 4.04737, "loss": 4.04737, "time": 0.82237} +{"mode": "train", "epoch": 90, "iter": 1400, "lr": 0.03517, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28234, "top5_acc": 0.53297, "loss_cls": 4.11732, "loss": 4.11732, "time": 0.82122} +{"mode": "train", "epoch": 90, "iter": 1500, "lr": 0.03515, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28328, "top5_acc": 0.53172, "loss_cls": 4.13243, "loss": 4.13243, "time": 0.81817} +{"mode": "train", "epoch": 90, "iter": 1600, "lr": 0.03512, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29172, "top5_acc": 0.53734, "loss_cls": 4.09289, "loss": 4.09289, "time": 0.81977} +{"mode": "train", "epoch": 90, "iter": 1700, "lr": 0.03509, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28734, "top5_acc": 0.53156, "loss_cls": 4.14231, "loss": 4.14231, "time": 0.82263} +{"mode": "train", "epoch": 90, "iter": 1800, "lr": 0.03507, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29, "top5_acc": 0.54359, "loss_cls": 4.07594, "loss": 4.07594, "time": 0.82178} +{"mode": "train", "epoch": 90, "iter": 1900, "lr": 0.03504, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28734, "top5_acc": 0.54453, "loss_cls": 4.09082, "loss": 4.09082, "time": 0.82207} +{"mode": "train", "epoch": 90, "iter": 2000, "lr": 0.03501, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28234, "top5_acc": 0.53656, "loss_cls": 4.11064, "loss": 4.11064, "time": 0.81925} +{"mode": "train", "epoch": 90, "iter": 2100, "lr": 0.03499, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29781, "top5_acc": 0.5475, "loss_cls": 4.08336, "loss": 4.08336, "time": 0.82859} +{"mode": "train", "epoch": 90, "iter": 2200, "lr": 0.03496, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28859, "top5_acc": 0.53859, "loss_cls": 4.09559, "loss": 4.09559, "time": 0.82142} +{"mode": "train", "epoch": 90, "iter": 2300, "lr": 0.03493, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29031, "top5_acc": 0.53406, "loss_cls": 4.11232, "loss": 4.11232, "time": 0.82579} +{"mode": "train", "epoch": 90, "iter": 2400, "lr": 0.03491, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30062, "top5_acc": 0.55172, "loss_cls": 4.02596, "loss": 4.02596, "time": 0.82335} +{"mode": "train", "epoch": 90, "iter": 2500, "lr": 0.03488, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28234, "top5_acc": 0.53906, "loss_cls": 4.10144, "loss": 4.10144, "time": 0.81879} +{"mode": "train", "epoch": 90, "iter": 2600, "lr": 0.03485, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28859, "top5_acc": 0.53859, "loss_cls": 4.08488, "loss": 4.08488, "time": 0.82422} +{"mode": "train", "epoch": 90, "iter": 2700, "lr": 0.03483, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28516, "top5_acc": 0.53516, "loss_cls": 4.1008, "loss": 4.1008, "time": 0.81876} +{"mode": "train", "epoch": 90, "iter": 2800, "lr": 0.0348, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29094, "top5_acc": 0.53234, "loss_cls": 4.10741, "loss": 4.10741, "time": 0.81979} +{"mode": "train", "epoch": 90, "iter": 2900, "lr": 0.03477, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29141, "top5_acc": 0.54906, "loss_cls": 4.06316, "loss": 4.06316, "time": 0.81755} +{"mode": "train", "epoch": 90, "iter": 3000, "lr": 0.03475, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29344, "top5_acc": 0.54266, "loss_cls": 4.09613, "loss": 4.09613, "time": 0.82178} +{"mode": "train", "epoch": 90, "iter": 3100, "lr": 0.03472, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28016, "top5_acc": 0.53203, "loss_cls": 4.12361, "loss": 4.12361, "time": 0.81648} +{"mode": "train", "epoch": 90, "iter": 3200, "lr": 0.03469, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28703, "top5_acc": 0.53422, "loss_cls": 4.09148, "loss": 4.09148, "time": 0.82351} +{"mode": "train", "epoch": 90, "iter": 3300, "lr": 0.03467, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28172, "top5_acc": 0.53453, "loss_cls": 4.12089, "loss": 4.12089, "time": 0.82196} +{"mode": "train", "epoch": 90, "iter": 3400, "lr": 0.03464, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28562, "top5_acc": 0.53188, "loss_cls": 4.13388, "loss": 4.13388, "time": 0.82006} +{"mode": "train", "epoch": 90, "iter": 3500, "lr": 0.03461, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29141, "top5_acc": 0.53672, "loss_cls": 4.10936, "loss": 4.10936, "time": 0.81529} +{"mode": "train", "epoch": 90, "iter": 3600, "lr": 0.03459, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28375, "top5_acc": 0.54344, "loss_cls": 4.09978, "loss": 4.09978, "time": 0.82327} +{"mode": "train", "epoch": 90, "iter": 3700, "lr": 0.03456, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29031, "top5_acc": 0.54078, "loss_cls": 4.08538, "loss": 4.08538, "time": 0.82372} +{"mode": "val", "epoch": 90, "iter": 309, "lr": 0.03455, "top1_acc": 0.23892, "top5_acc": 0.46918, "mean_class_accuracy": 0.23878} +{"mode": "train", "epoch": 91, "iter": 100, "lr": 0.03452, "memory": 15990, "data_time": 1.28416, "top1_acc": 0.30438, "top5_acc": 0.55625, "loss_cls": 4.02932, "loss": 4.02932, "time": 2.26256} +{"mode": "train", "epoch": 91, "iter": 200, "lr": 0.0345, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29859, "top5_acc": 0.55109, "loss_cls": 4.01365, "loss": 4.01365, "time": 0.82271} +{"mode": "train", "epoch": 91, "iter": 300, "lr": 0.03447, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30031, "top5_acc": 0.54391, "loss_cls": 4.06863, "loss": 4.06863, "time": 0.81905} +{"mode": "train", "epoch": 91, "iter": 400, "lr": 0.03444, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29469, "top5_acc": 0.55078, "loss_cls": 4.04547, "loss": 4.04547, "time": 0.82012} +{"mode": "train", "epoch": 91, "iter": 500, "lr": 0.03442, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28688, "top5_acc": 0.53781, "loss_cls": 4.10051, "loss": 4.10051, "time": 0.82127} +{"mode": "train", "epoch": 91, "iter": 600, "lr": 0.03439, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29281, "top5_acc": 0.54578, "loss_cls": 4.04874, "loss": 4.04874, "time": 0.83015} +{"mode": "train", "epoch": 91, "iter": 700, "lr": 0.03436, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.28984, "top5_acc": 0.54156, "loss_cls": 4.09256, "loss": 4.09256, "time": 0.82537} +{"mode": "train", "epoch": 91, "iter": 800, "lr": 0.03434, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.29281, "top5_acc": 0.54141, "loss_cls": 4.05924, "loss": 4.05924, "time": 0.83008} +{"mode": "train", "epoch": 91, "iter": 900, "lr": 0.03431, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30016, "top5_acc": 0.55094, "loss_cls": 4.04749, "loss": 4.04749, "time": 0.82136} +{"mode": "train", "epoch": 91, "iter": 1000, "lr": 0.03428, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28609, "top5_acc": 0.53469, "loss_cls": 4.09579, "loss": 4.09579, "time": 0.82512} +{"mode": "train", "epoch": 91, "iter": 1100, "lr": 0.03426, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29125, "top5_acc": 0.52719, "loss_cls": 4.1308, "loss": 4.1308, "time": 0.82401} +{"mode": "train", "epoch": 91, "iter": 1200, "lr": 0.03423, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29641, "top5_acc": 0.54281, "loss_cls": 4.06901, "loss": 4.06901, "time": 0.82644} +{"mode": "train", "epoch": 91, "iter": 1300, "lr": 0.0342, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29688, "top5_acc": 0.54609, "loss_cls": 4.07295, "loss": 4.07295, "time": 0.82548} +{"mode": "train", "epoch": 91, "iter": 1400, "lr": 0.03418, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29297, "top5_acc": 0.53641, "loss_cls": 4.07984, "loss": 4.07984, "time": 0.82395} +{"mode": "train", "epoch": 91, "iter": 1500, "lr": 0.03415, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30375, "top5_acc": 0.54766, "loss_cls": 4.0249, "loss": 4.0249, "time": 0.8186} +{"mode": "train", "epoch": 91, "iter": 1600, "lr": 0.03412, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28094, "top5_acc": 0.53797, "loss_cls": 4.12953, "loss": 4.12953, "time": 0.81901} +{"mode": "train", "epoch": 91, "iter": 1700, "lr": 0.0341, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28875, "top5_acc": 0.52281, "loss_cls": 4.11259, "loss": 4.11259, "time": 0.81689} +{"mode": "train", "epoch": 91, "iter": 1800, "lr": 0.03407, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28625, "top5_acc": 0.54078, "loss_cls": 4.08694, "loss": 4.08694, "time": 0.8169} +{"mode": "train", "epoch": 91, "iter": 1900, "lr": 0.03405, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.28344, "top5_acc": 0.53656, "loss_cls": 4.13089, "loss": 4.13089, "time": 0.81743} +{"mode": "train", "epoch": 91, "iter": 2000, "lr": 0.03402, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29938, "top5_acc": 0.54859, "loss_cls": 4.05827, "loss": 4.05827, "time": 0.8199} +{"mode": "train", "epoch": 91, "iter": 2100, "lr": 0.03399, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28719, "top5_acc": 0.54203, "loss_cls": 4.10007, "loss": 4.10007, "time": 0.82283} +{"mode": "train", "epoch": 91, "iter": 2200, "lr": 0.03397, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29656, "top5_acc": 0.54516, "loss_cls": 4.03892, "loss": 4.03892, "time": 0.82765} +{"mode": "train", "epoch": 91, "iter": 2300, "lr": 0.03394, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29281, "top5_acc": 0.54219, "loss_cls": 4.08637, "loss": 4.08637, "time": 0.82347} +{"mode": "train", "epoch": 91, "iter": 2400, "lr": 0.03391, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29547, "top5_acc": 0.54812, "loss_cls": 4.05952, "loss": 4.05952, "time": 0.81674} +{"mode": "train", "epoch": 91, "iter": 2500, "lr": 0.03389, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29234, "top5_acc": 0.54031, "loss_cls": 4.0704, "loss": 4.0704, "time": 0.82182} +{"mode": "train", "epoch": 91, "iter": 2600, "lr": 0.03386, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29734, "top5_acc": 0.54984, "loss_cls": 4.06282, "loss": 4.06282, "time": 0.82165} +{"mode": "train", "epoch": 91, "iter": 2700, "lr": 0.03383, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2875, "top5_acc": 0.53641, "loss_cls": 4.09664, "loss": 4.09664, "time": 0.8203} +{"mode": "train", "epoch": 91, "iter": 2800, "lr": 0.03381, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29125, "top5_acc": 0.54578, "loss_cls": 4.07509, "loss": 4.07509, "time": 0.82217} +{"mode": "train", "epoch": 91, "iter": 2900, "lr": 0.03378, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30219, "top5_acc": 0.54906, "loss_cls": 4.03993, "loss": 4.03993, "time": 0.82102} +{"mode": "train", "epoch": 91, "iter": 3000, "lr": 0.03375, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29766, "top5_acc": 0.54422, "loss_cls": 4.05097, "loss": 4.05097, "time": 0.82338} +{"mode": "train", "epoch": 91, "iter": 3100, "lr": 0.03373, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29219, "top5_acc": 0.54922, "loss_cls": 4.03227, "loss": 4.03227, "time": 0.81847} +{"mode": "train", "epoch": 91, "iter": 3200, "lr": 0.0337, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29031, "top5_acc": 0.53562, "loss_cls": 4.12068, "loss": 4.12068, "time": 0.82443} +{"mode": "train", "epoch": 91, "iter": 3300, "lr": 0.03367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28828, "top5_acc": 0.53547, "loss_cls": 4.09966, "loss": 4.09966, "time": 0.81706} +{"mode": "train", "epoch": 91, "iter": 3400, "lr": 0.03365, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28031, "top5_acc": 0.53062, "loss_cls": 4.1138, "loss": 4.1138, "time": 0.81981} +{"mode": "train", "epoch": 91, "iter": 3500, "lr": 0.03362, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28594, "top5_acc": 0.54469, "loss_cls": 4.07768, "loss": 4.07768, "time": 0.81942} +{"mode": "train", "epoch": 91, "iter": 3600, "lr": 0.0336, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28641, "top5_acc": 0.54516, "loss_cls": 4.08855, "loss": 4.08855, "time": 0.82093} +{"mode": "train", "epoch": 91, "iter": 3700, "lr": 0.03357, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28891, "top5_acc": 0.54359, "loss_cls": 4.05991, "loss": 4.05991, "time": 0.81641} +{"mode": "val", "epoch": 91, "iter": 309, "lr": 0.03356, "top1_acc": 0.22438, "top5_acc": 0.45712, "mean_class_accuracy": 0.22427} +{"mode": "train", "epoch": 92, "iter": 100, "lr": 0.03353, "memory": 15990, "data_time": 1.29541, "top1_acc": 0.30078, "top5_acc": 0.54672, "loss_cls": 4.02069, "loss": 4.02069, "time": 2.28384} +{"mode": "train", "epoch": 92, "iter": 200, "lr": 0.0335, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30547, "top5_acc": 0.56281, "loss_cls": 3.9877, "loss": 3.9877, "time": 0.8247} +{"mode": "train", "epoch": 92, "iter": 300, "lr": 0.03348, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.30859, "top5_acc": 0.56234, "loss_cls": 4.00866, "loss": 4.00866, "time": 0.82947} +{"mode": "train", "epoch": 92, "iter": 400, "lr": 0.03345, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.305, "top5_acc": 0.54469, "loss_cls": 4.03905, "loss": 4.03905, "time": 0.82362} +{"mode": "train", "epoch": 92, "iter": 500, "lr": 0.03342, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30922, "top5_acc": 0.55266, "loss_cls": 3.99875, "loss": 3.99875, "time": 0.82585} +{"mode": "train", "epoch": 92, "iter": 600, "lr": 0.0334, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30047, "top5_acc": 0.55516, "loss_cls": 4.02209, "loss": 4.02209, "time": 0.83323} +{"mode": "train", "epoch": 92, "iter": 700, "lr": 0.03337, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29563, "top5_acc": 0.54344, "loss_cls": 4.04448, "loss": 4.04448, "time": 0.82373} +{"mode": "train", "epoch": 92, "iter": 800, "lr": 0.03335, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29922, "top5_acc": 0.54781, "loss_cls": 4.04733, "loss": 4.04733, "time": 0.82085} +{"mode": "train", "epoch": 92, "iter": 900, "lr": 0.03332, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29734, "top5_acc": 0.54844, "loss_cls": 4.01516, "loss": 4.01516, "time": 0.81634} +{"mode": "train", "epoch": 92, "iter": 1000, "lr": 0.03329, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3, "top5_acc": 0.5475, "loss_cls": 4.00992, "loss": 4.00992, "time": 0.8238} +{"mode": "train", "epoch": 92, "iter": 1100, "lr": 0.03327, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29969, "top5_acc": 0.54859, "loss_cls": 4.04137, "loss": 4.04137, "time": 0.8244} +{"mode": "train", "epoch": 92, "iter": 1200, "lr": 0.03324, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30047, "top5_acc": 0.55016, "loss_cls": 4.01115, "loss": 4.01115, "time": 0.82152} +{"mode": "train", "epoch": 92, "iter": 1300, "lr": 0.03321, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29, "top5_acc": 0.54266, "loss_cls": 4.07621, "loss": 4.07621, "time": 0.82779} +{"mode": "train", "epoch": 92, "iter": 1400, "lr": 0.03319, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29203, "top5_acc": 0.53828, "loss_cls": 4.10007, "loss": 4.10007, "time": 0.82119} +{"mode": "train", "epoch": 92, "iter": 1500, "lr": 0.03316, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28797, "top5_acc": 0.54391, "loss_cls": 4.0592, "loss": 4.0592, "time": 0.81902} +{"mode": "train", "epoch": 92, "iter": 1600, "lr": 0.03314, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29203, "top5_acc": 0.54219, "loss_cls": 4.05653, "loss": 4.05653, "time": 0.81952} +{"mode": "train", "epoch": 92, "iter": 1700, "lr": 0.03311, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29438, "top5_acc": 0.55531, "loss_cls": 4.02573, "loss": 4.02573, "time": 0.81823} +{"mode": "train", "epoch": 92, "iter": 1800, "lr": 0.03308, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30047, "top5_acc": 0.55406, "loss_cls": 4.03191, "loss": 4.03191, "time": 0.81727} +{"mode": "train", "epoch": 92, "iter": 1900, "lr": 0.03306, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29188, "top5_acc": 0.54453, "loss_cls": 4.09368, "loss": 4.09368, "time": 0.82456} +{"mode": "train", "epoch": 92, "iter": 2000, "lr": 0.03303, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29875, "top5_acc": 0.55016, "loss_cls": 4.0266, "loss": 4.0266, "time": 0.82154} +{"mode": "train", "epoch": 92, "iter": 2100, "lr": 0.033, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.28797, "top5_acc": 0.54, "loss_cls": 4.07479, "loss": 4.07479, "time": 0.82239} +{"mode": "train", "epoch": 92, "iter": 2200, "lr": 0.03298, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.28703, "top5_acc": 0.54, "loss_cls": 4.10602, "loss": 4.10602, "time": 0.83132} +{"mode": "train", "epoch": 92, "iter": 2300, "lr": 0.03295, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29484, "top5_acc": 0.54781, "loss_cls": 4.06147, "loss": 4.06147, "time": 0.81709} +{"mode": "train", "epoch": 92, "iter": 2400, "lr": 0.03292, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.29672, "top5_acc": 0.54125, "loss_cls": 4.03933, "loss": 4.03933, "time": 0.82465} +{"mode": "train", "epoch": 92, "iter": 2500, "lr": 0.0329, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29719, "top5_acc": 0.54844, "loss_cls": 4.04616, "loss": 4.04616, "time": 0.82203} +{"mode": "train", "epoch": 92, "iter": 2600, "lr": 0.03287, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28953, "top5_acc": 0.53531, "loss_cls": 4.11261, "loss": 4.11261, "time": 0.82197} +{"mode": "train", "epoch": 92, "iter": 2700, "lr": 0.03285, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29031, "top5_acc": 0.55047, "loss_cls": 4.04657, "loss": 4.04657, "time": 0.81909} +{"mode": "train", "epoch": 92, "iter": 2800, "lr": 0.03282, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29125, "top5_acc": 0.53391, "loss_cls": 4.10609, "loss": 4.10609, "time": 0.81872} +{"mode": "train", "epoch": 92, "iter": 2900, "lr": 0.03279, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28016, "top5_acc": 0.53016, "loss_cls": 4.16145, "loss": 4.16145, "time": 0.81777} +{"mode": "train", "epoch": 92, "iter": 3000, "lr": 0.03277, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29734, "top5_acc": 0.55312, "loss_cls": 4.03681, "loss": 4.03681, "time": 0.81602} +{"mode": "train", "epoch": 92, "iter": 3100, "lr": 0.03274, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.28438, "top5_acc": 0.53641, "loss_cls": 4.09863, "loss": 4.09863, "time": 0.81644} +{"mode": "train", "epoch": 92, "iter": 3200, "lr": 0.03271, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29375, "top5_acc": 0.54297, "loss_cls": 4.0488, "loss": 4.0488, "time": 0.81723} +{"mode": "train", "epoch": 92, "iter": 3300, "lr": 0.03269, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29875, "top5_acc": 0.54203, "loss_cls": 4.07064, "loss": 4.07064, "time": 0.82247} +{"mode": "train", "epoch": 92, "iter": 3400, "lr": 0.03266, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2975, "top5_acc": 0.54797, "loss_cls": 4.0493, "loss": 4.0493, "time": 0.81304} +{"mode": "train", "epoch": 92, "iter": 3500, "lr": 0.03264, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29891, "top5_acc": 0.54344, "loss_cls": 4.04546, "loss": 4.04546, "time": 0.8199} +{"mode": "train", "epoch": 92, "iter": 3600, "lr": 0.03261, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.2925, "top5_acc": 0.54156, "loss_cls": 4.08221, "loss": 4.08221, "time": 0.8195} +{"mode": "train", "epoch": 92, "iter": 3700, "lr": 0.03258, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29875, "top5_acc": 0.54438, "loss_cls": 4.04481, "loss": 4.04481, "time": 0.81955} +{"mode": "val", "epoch": 92, "iter": 309, "lr": 0.03257, "top1_acc": 0.24414, "top5_acc": 0.48209, "mean_class_accuracy": 0.24408} +{"mode": "train", "epoch": 93, "iter": 100, "lr": 0.03255, "memory": 15990, "data_time": 1.3114, "top1_acc": 0.30531, "top5_acc": 0.55719, "loss_cls": 4.0037, "loss": 4.0037, "time": 2.30138} +{"mode": "train", "epoch": 93, "iter": 200, "lr": 0.03252, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31766, "top5_acc": 0.56625, "loss_cls": 3.93542, "loss": 3.93542, "time": 0.82494} +{"mode": "train", "epoch": 93, "iter": 300, "lr": 0.03249, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30094, "top5_acc": 0.54953, "loss_cls": 4.03142, "loss": 4.03142, "time": 0.82177} +{"mode": "train", "epoch": 93, "iter": 400, "lr": 0.03247, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29578, "top5_acc": 0.54797, "loss_cls": 4.04741, "loss": 4.04741, "time": 0.82842} +{"mode": "train", "epoch": 93, "iter": 500, "lr": 0.03244, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29063, "top5_acc": 0.54656, "loss_cls": 4.04153, "loss": 4.04153, "time": 0.82814} +{"mode": "train", "epoch": 93, "iter": 600, "lr": 0.03241, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29313, "top5_acc": 0.53938, "loss_cls": 4.08061, "loss": 4.08061, "time": 0.82277} +{"mode": "train", "epoch": 93, "iter": 700, "lr": 0.03239, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29094, "top5_acc": 0.53828, "loss_cls": 4.09416, "loss": 4.09416, "time": 0.82883} +{"mode": "train", "epoch": 93, "iter": 800, "lr": 0.03236, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29656, "top5_acc": 0.55078, "loss_cls": 4.025, "loss": 4.025, "time": 0.8226} +{"mode": "train", "epoch": 93, "iter": 900, "lr": 0.03234, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30172, "top5_acc": 0.55141, "loss_cls": 4.03208, "loss": 4.03208, "time": 0.82219} +{"mode": "train", "epoch": 93, "iter": 1000, "lr": 0.03231, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29594, "top5_acc": 0.54719, "loss_cls": 4.03047, "loss": 4.03047, "time": 0.82528} +{"mode": "train", "epoch": 93, "iter": 1100, "lr": 0.03228, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29438, "top5_acc": 0.54203, "loss_cls": 4.0748, "loss": 4.0748, "time": 0.82485} +{"mode": "train", "epoch": 93, "iter": 1200, "lr": 0.03226, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30188, "top5_acc": 0.55375, "loss_cls": 4.03886, "loss": 4.03886, "time": 0.82844} +{"mode": "train", "epoch": 93, "iter": 1300, "lr": 0.03223, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31078, "top5_acc": 0.55391, "loss_cls": 4.00787, "loss": 4.00787, "time": 0.82156} +{"mode": "train", "epoch": 93, "iter": 1400, "lr": 0.03221, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.2925, "top5_acc": 0.53531, "loss_cls": 4.08141, "loss": 4.08141, "time": 0.82007} +{"mode": "train", "epoch": 93, "iter": 1500, "lr": 0.03218, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29359, "top5_acc": 0.54438, "loss_cls": 4.08566, "loss": 4.08566, "time": 0.82621} +{"mode": "train", "epoch": 93, "iter": 1600, "lr": 0.03215, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29625, "top5_acc": 0.54656, "loss_cls": 4.03748, "loss": 4.03748, "time": 0.81827} +{"mode": "train", "epoch": 93, "iter": 1700, "lr": 0.03213, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30438, "top5_acc": 0.55375, "loss_cls": 3.9804, "loss": 3.9804, "time": 0.82045} +{"mode": "train", "epoch": 93, "iter": 1800, "lr": 0.0321, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29031, "top5_acc": 0.54297, "loss_cls": 4.0788, "loss": 4.0788, "time": 0.81808} +{"mode": "train", "epoch": 93, "iter": 1900, "lr": 0.03207, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29656, "top5_acc": 0.55219, "loss_cls": 4.04049, "loss": 4.04049, "time": 0.82029} +{"mode": "train", "epoch": 93, "iter": 2000, "lr": 0.03205, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29703, "top5_acc": 0.54297, "loss_cls": 4.04645, "loss": 4.04645, "time": 0.81854} +{"mode": "train", "epoch": 93, "iter": 2100, "lr": 0.03202, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29547, "top5_acc": 0.55484, "loss_cls": 4.00877, "loss": 4.00877, "time": 0.8205} +{"mode": "train", "epoch": 93, "iter": 2200, "lr": 0.032, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.29203, "top5_acc": 0.54188, "loss_cls": 4.06208, "loss": 4.06208, "time": 0.82818} +{"mode": "train", "epoch": 93, "iter": 2300, "lr": 0.03197, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30938, "top5_acc": 0.56125, "loss_cls": 3.98632, "loss": 3.98632, "time": 0.82598} +{"mode": "train", "epoch": 93, "iter": 2400, "lr": 0.03194, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29906, "top5_acc": 0.55219, "loss_cls": 4.05584, "loss": 4.05584, "time": 0.82783} +{"mode": "train", "epoch": 93, "iter": 2500, "lr": 0.03192, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29734, "top5_acc": 0.54719, "loss_cls": 4.04886, "loss": 4.04886, "time": 0.81557} +{"mode": "train", "epoch": 93, "iter": 2600, "lr": 0.03189, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29906, "top5_acc": 0.55297, "loss_cls": 4.02192, "loss": 4.02192, "time": 0.82171} +{"mode": "train", "epoch": 93, "iter": 2700, "lr": 0.03187, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29688, "top5_acc": 0.555, "loss_cls": 4.03376, "loss": 4.03376, "time": 0.82006} +{"mode": "train", "epoch": 93, "iter": 2800, "lr": 0.03184, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30188, "top5_acc": 0.5525, "loss_cls": 4.03403, "loss": 4.03403, "time": 0.81572} +{"mode": "train", "epoch": 93, "iter": 2900, "lr": 0.03181, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29719, "top5_acc": 0.54906, "loss_cls": 4.04565, "loss": 4.04565, "time": 0.81916} +{"mode": "train", "epoch": 93, "iter": 3000, "lr": 0.03179, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28813, "top5_acc": 0.545, "loss_cls": 4.08425, "loss": 4.08425, "time": 0.8211} +{"mode": "train", "epoch": 93, "iter": 3100, "lr": 0.03176, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31203, "top5_acc": 0.56047, "loss_cls": 3.99894, "loss": 3.99894, "time": 0.81694} +{"mode": "train", "epoch": 93, "iter": 3200, "lr": 0.03174, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29781, "top5_acc": 0.53625, "loss_cls": 4.08482, "loss": 4.08482, "time": 0.82104} +{"mode": "train", "epoch": 93, "iter": 3300, "lr": 0.03171, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.295, "top5_acc": 0.54297, "loss_cls": 4.06237, "loss": 4.06237, "time": 0.81835} +{"mode": "train", "epoch": 93, "iter": 3400, "lr": 0.03168, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.28125, "top5_acc": 0.54937, "loss_cls": 4.07119, "loss": 4.07119, "time": 0.82721} +{"mode": "train", "epoch": 93, "iter": 3500, "lr": 0.03166, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29906, "top5_acc": 0.55344, "loss_cls": 4.03506, "loss": 4.03506, "time": 0.81786} +{"mode": "train", "epoch": 93, "iter": 3600, "lr": 0.03163, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29625, "top5_acc": 0.55016, "loss_cls": 4.05482, "loss": 4.05482, "time": 0.82075} +{"mode": "train", "epoch": 93, "iter": 3700, "lr": 0.03161, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29516, "top5_acc": 0.54062, "loss_cls": 4.06536, "loss": 4.06536, "time": 0.81436} +{"mode": "val", "epoch": 93, "iter": 309, "lr": 0.03159, "top1_acc": 0.2334, "top5_acc": 0.46913, "mean_class_accuracy": 0.23347} +{"mode": "train", "epoch": 94, "iter": 100, "lr": 0.03157, "memory": 15990, "data_time": 1.28645, "top1_acc": 0.31297, "top5_acc": 0.56719, "loss_cls": 3.96499, "loss": 3.96499, "time": 2.27077} +{"mode": "train", "epoch": 94, "iter": 200, "lr": 0.03154, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30047, "top5_acc": 0.54625, "loss_cls": 4.04656, "loss": 4.04656, "time": 0.82061} +{"mode": "train", "epoch": 94, "iter": 300, "lr": 0.03152, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30953, "top5_acc": 0.5625, "loss_cls": 3.94219, "loss": 3.94219, "time": 0.82089} +{"mode": "train", "epoch": 94, "iter": 400, "lr": 0.03149, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30406, "top5_acc": 0.55531, "loss_cls": 3.98759, "loss": 3.98759, "time": 0.81918} +{"mode": "train", "epoch": 94, "iter": 500, "lr": 0.03146, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.29344, "top5_acc": 0.55672, "loss_cls": 4.01662, "loss": 4.01662, "time": 0.81722} +{"mode": "train", "epoch": 94, "iter": 600, "lr": 0.03144, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.295, "top5_acc": 0.55047, "loss_cls": 4.0325, "loss": 4.0325, "time": 0.82358} +{"mode": "train", "epoch": 94, "iter": 700, "lr": 0.03141, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30531, "top5_acc": 0.55812, "loss_cls": 4.01464, "loss": 4.01464, "time": 0.82578} +{"mode": "train", "epoch": 94, "iter": 800, "lr": 0.03139, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29719, "top5_acc": 0.54891, "loss_cls": 4.05496, "loss": 4.05496, "time": 0.81934} +{"mode": "train", "epoch": 94, "iter": 900, "lr": 0.03136, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29406, "top5_acc": 0.55094, "loss_cls": 4.03595, "loss": 4.03595, "time": 0.81709} +{"mode": "train", "epoch": 94, "iter": 1000, "lr": 0.03133, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30781, "top5_acc": 0.54922, "loss_cls": 3.99355, "loss": 3.99355, "time": 0.82227} +{"mode": "train", "epoch": 94, "iter": 1100, "lr": 0.03131, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29859, "top5_acc": 0.55672, "loss_cls": 4.01371, "loss": 4.01371, "time": 0.82373} +{"mode": "train", "epoch": 94, "iter": 1200, "lr": 0.03128, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31375, "top5_acc": 0.56078, "loss_cls": 3.99438, "loss": 3.99438, "time": 0.8232} +{"mode": "train", "epoch": 94, "iter": 1300, "lr": 0.03126, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30062, "top5_acc": 0.545, "loss_cls": 4.05725, "loss": 4.05725, "time": 0.81811} +{"mode": "train", "epoch": 94, "iter": 1400, "lr": 0.03123, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29547, "top5_acc": 0.55203, "loss_cls": 4.05236, "loss": 4.05236, "time": 0.82387} +{"mode": "train", "epoch": 94, "iter": 1500, "lr": 0.0312, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.305, "top5_acc": 0.55516, "loss_cls": 4.01491, "loss": 4.01491, "time": 0.81722} +{"mode": "train", "epoch": 94, "iter": 1600, "lr": 0.03118, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29125, "top5_acc": 0.53656, "loss_cls": 4.08202, "loss": 4.08202, "time": 0.82417} +{"mode": "train", "epoch": 94, "iter": 1700, "lr": 0.03115, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29266, "top5_acc": 0.55375, "loss_cls": 4.01658, "loss": 4.01658, "time": 0.81965} +{"mode": "train", "epoch": 94, "iter": 1800, "lr": 0.03113, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3125, "top5_acc": 0.5625, "loss_cls": 3.99625, "loss": 3.99625, "time": 0.82013} +{"mode": "train", "epoch": 94, "iter": 1900, "lr": 0.0311, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.295, "top5_acc": 0.55141, "loss_cls": 4.00856, "loss": 4.00856, "time": 0.81591} +{"mode": "train", "epoch": 94, "iter": 2000, "lr": 0.03108, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29844, "top5_acc": 0.54812, "loss_cls": 4.03491, "loss": 4.03491, "time": 0.81756} +{"mode": "train", "epoch": 94, "iter": 2100, "lr": 0.03105, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30312, "top5_acc": 0.55688, "loss_cls": 4.02351, "loss": 4.02351, "time": 0.82294} +{"mode": "train", "epoch": 94, "iter": 2200, "lr": 0.03102, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.29203, "top5_acc": 0.54312, "loss_cls": 4.08194, "loss": 4.08194, "time": 0.82338} +{"mode": "train", "epoch": 94, "iter": 2300, "lr": 0.031, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.28312, "top5_acc": 0.54719, "loss_cls": 4.07819, "loss": 4.07819, "time": 0.82464} +{"mode": "train", "epoch": 94, "iter": 2400, "lr": 0.03097, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30016, "top5_acc": 0.54453, "loss_cls": 4.05169, "loss": 4.05169, "time": 0.82101} +{"mode": "train", "epoch": 94, "iter": 2500, "lr": 0.03095, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30188, "top5_acc": 0.55922, "loss_cls": 3.99209, "loss": 3.99209, "time": 0.83014} +{"mode": "train", "epoch": 94, "iter": 2600, "lr": 0.03092, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29797, "top5_acc": 0.54656, "loss_cls": 4.07304, "loss": 4.07304, "time": 0.8222} +{"mode": "train", "epoch": 94, "iter": 2700, "lr": 0.03089, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29578, "top5_acc": 0.54359, "loss_cls": 4.05184, "loss": 4.05184, "time": 0.82011} +{"mode": "train", "epoch": 94, "iter": 2800, "lr": 0.03087, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29609, "top5_acc": 0.55156, "loss_cls": 4.0173, "loss": 4.0173, "time": 0.81901} +{"mode": "train", "epoch": 94, "iter": 2900, "lr": 0.03084, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30266, "top5_acc": 0.55469, "loss_cls": 4.00027, "loss": 4.00027, "time": 0.81946} +{"mode": "train", "epoch": 94, "iter": 3000, "lr": 0.03082, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30266, "top5_acc": 0.55281, "loss_cls": 4.03283, "loss": 4.03283, "time": 0.81768} +{"mode": "train", "epoch": 94, "iter": 3100, "lr": 0.03079, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29547, "top5_acc": 0.54922, "loss_cls": 4.04133, "loss": 4.04133, "time": 0.8186} +{"mode": "train", "epoch": 94, "iter": 3200, "lr": 0.03077, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29922, "top5_acc": 0.54844, "loss_cls": 4.01244, "loss": 4.01244, "time": 0.81749} +{"mode": "train", "epoch": 94, "iter": 3300, "lr": 0.03074, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30094, "top5_acc": 0.54375, "loss_cls": 4.0481, "loss": 4.0481, "time": 0.82071} +{"mode": "train", "epoch": 94, "iter": 3400, "lr": 0.03071, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29906, "top5_acc": 0.54203, "loss_cls": 4.07739, "loss": 4.07739, "time": 0.81894} +{"mode": "train", "epoch": 94, "iter": 3500, "lr": 0.03069, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30109, "top5_acc": 0.56094, "loss_cls": 3.99532, "loss": 3.99532, "time": 0.82021} +{"mode": "train", "epoch": 94, "iter": 3600, "lr": 0.03066, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30109, "top5_acc": 0.55016, "loss_cls": 4.00877, "loss": 4.00877, "time": 0.82233} +{"mode": "train", "epoch": 94, "iter": 3700, "lr": 0.03064, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29375, "top5_acc": 0.54562, "loss_cls": 4.05229, "loss": 4.05229, "time": 0.82521} +{"mode": "val", "epoch": 94, "iter": 309, "lr": 0.03062, "top1_acc": 0.23649, "top5_acc": 0.46837, "mean_class_accuracy": 0.23616} +{"mode": "train", "epoch": 95, "iter": 100, "lr": 0.0306, "memory": 15990, "data_time": 1.29545, "top1_acc": 0.30703, "top5_acc": 0.56219, "loss_cls": 3.97913, "loss": 3.97913, "time": 2.28192} +{"mode": "train", "epoch": 95, "iter": 200, "lr": 0.03057, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31328, "top5_acc": 0.55875, "loss_cls": 3.95547, "loss": 3.95547, "time": 0.82153} +{"mode": "train", "epoch": 95, "iter": 300, "lr": 0.03055, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30469, "top5_acc": 0.55266, "loss_cls": 4.00442, "loss": 4.00442, "time": 0.82292} +{"mode": "train", "epoch": 95, "iter": 400, "lr": 0.03052, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30031, "top5_acc": 0.56297, "loss_cls": 3.97093, "loss": 3.97093, "time": 0.82211} +{"mode": "train", "epoch": 95, "iter": 500, "lr": 0.0305, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29922, "top5_acc": 0.54641, "loss_cls": 4.0307, "loss": 4.0307, "time": 0.81821} +{"mode": "train", "epoch": 95, "iter": 600, "lr": 0.03047, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31094, "top5_acc": 0.56359, "loss_cls": 3.97348, "loss": 3.97348, "time": 0.81851} +{"mode": "train", "epoch": 95, "iter": 700, "lr": 0.03044, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.30406, "top5_acc": 0.55422, "loss_cls": 4.00233, "loss": 4.00233, "time": 0.82987} +{"mode": "train", "epoch": 95, "iter": 800, "lr": 0.03042, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30422, "top5_acc": 0.54641, "loss_cls": 4.01812, "loss": 4.01812, "time": 0.81945} +{"mode": "train", "epoch": 95, "iter": 900, "lr": 0.03039, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29625, "top5_acc": 0.55875, "loss_cls": 4.01098, "loss": 4.01098, "time": 0.81777} +{"mode": "train", "epoch": 95, "iter": 1000, "lr": 0.03037, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29734, "top5_acc": 0.54984, "loss_cls": 4.03286, "loss": 4.03286, "time": 0.82623} +{"mode": "train", "epoch": 95, "iter": 1100, "lr": 0.03034, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29625, "top5_acc": 0.55406, "loss_cls": 4.03825, "loss": 4.03825, "time": 0.81663} +{"mode": "train", "epoch": 95, "iter": 1200, "lr": 0.03032, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30609, "top5_acc": 0.55031, "loss_cls": 4.00012, "loss": 4.00012, "time": 0.8301} +{"mode": "train", "epoch": 95, "iter": 1300, "lr": 0.03029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29313, "top5_acc": 0.5475, "loss_cls": 4.06106, "loss": 4.06106, "time": 0.82064} +{"mode": "train", "epoch": 95, "iter": 1400, "lr": 0.03026, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29422, "top5_acc": 0.55188, "loss_cls": 4.04536, "loss": 4.04536, "time": 0.81772} +{"mode": "train", "epoch": 95, "iter": 1500, "lr": 0.03024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30188, "top5_acc": 0.55016, "loss_cls": 4.01795, "loss": 4.01795, "time": 0.82078} +{"mode": "train", "epoch": 95, "iter": 1600, "lr": 0.03021, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3, "top5_acc": 0.54656, "loss_cls": 4.03754, "loss": 4.03754, "time": 0.82028} +{"mode": "train", "epoch": 95, "iter": 1700, "lr": 0.03019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30688, "top5_acc": 0.55453, "loss_cls": 3.9873, "loss": 3.9873, "time": 0.82243} +{"mode": "train", "epoch": 95, "iter": 1800, "lr": 0.03016, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30547, "top5_acc": 0.54797, "loss_cls": 4.01394, "loss": 4.01394, "time": 0.81587} +{"mode": "train", "epoch": 95, "iter": 1900, "lr": 0.03014, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30797, "top5_acc": 0.55375, "loss_cls": 3.96703, "loss": 3.96703, "time": 0.81709} +{"mode": "train", "epoch": 95, "iter": 2000, "lr": 0.03011, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29828, "top5_acc": 0.54953, "loss_cls": 4.03029, "loss": 4.03029, "time": 0.81888} +{"mode": "train", "epoch": 95, "iter": 2100, "lr": 0.03008, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30969, "top5_acc": 0.56547, "loss_cls": 3.98022, "loss": 3.98022, "time": 0.82423} +{"mode": "train", "epoch": 95, "iter": 2200, "lr": 0.03006, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31859, "top5_acc": 0.56875, "loss_cls": 3.96174, "loss": 3.96174, "time": 0.82147} +{"mode": "train", "epoch": 95, "iter": 2300, "lr": 0.03003, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29313, "top5_acc": 0.54469, "loss_cls": 4.05565, "loss": 4.05565, "time": 0.83395} +{"mode": "train", "epoch": 95, "iter": 2400, "lr": 0.03001, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30016, "top5_acc": 0.54984, "loss_cls": 4.03702, "loss": 4.03702, "time": 0.82095} +{"mode": "train", "epoch": 95, "iter": 2500, "lr": 0.02998, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29984, "top5_acc": 0.56641, "loss_cls": 4.00888, "loss": 4.00888, "time": 0.82193} +{"mode": "train", "epoch": 95, "iter": 2600, "lr": 0.02996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30609, "top5_acc": 0.56203, "loss_cls": 3.97212, "loss": 3.97212, "time": 0.81787} +{"mode": "train", "epoch": 95, "iter": 2700, "lr": 0.02993, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30016, "top5_acc": 0.54688, "loss_cls": 4.02565, "loss": 4.02565, "time": 0.8161} +{"mode": "train", "epoch": 95, "iter": 2800, "lr": 0.02991, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30531, "top5_acc": 0.55625, "loss_cls": 4.02855, "loss": 4.02855, "time": 0.82208} +{"mode": "train", "epoch": 95, "iter": 2900, "lr": 0.02988, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30766, "top5_acc": 0.55469, "loss_cls": 4.00468, "loss": 4.00468, "time": 0.82066} +{"mode": "train", "epoch": 95, "iter": 3000, "lr": 0.02985, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30297, "top5_acc": 0.55766, "loss_cls": 3.9841, "loss": 3.9841, "time": 0.82284} +{"mode": "train", "epoch": 95, "iter": 3100, "lr": 0.02983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29969, "top5_acc": 0.56859, "loss_cls": 3.96247, "loss": 3.96247, "time": 0.81759} +{"mode": "train", "epoch": 95, "iter": 3200, "lr": 0.0298, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29906, "top5_acc": 0.55469, "loss_cls": 4.01995, "loss": 4.01995, "time": 0.81552} +{"mode": "train", "epoch": 95, "iter": 3300, "lr": 0.02978, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29719, "top5_acc": 0.56047, "loss_cls": 4.01203, "loss": 4.01203, "time": 0.81787} +{"mode": "train", "epoch": 95, "iter": 3400, "lr": 0.02975, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29797, "top5_acc": 0.55234, "loss_cls": 4.06995, "loss": 4.06995, "time": 0.81833} +{"mode": "train", "epoch": 95, "iter": 3500, "lr": 0.02973, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30391, "top5_acc": 0.55297, "loss_cls": 4.01888, "loss": 4.01888, "time": 0.8192} +{"mode": "train", "epoch": 95, "iter": 3600, "lr": 0.0297, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30078, "top5_acc": 0.55266, "loss_cls": 4.01587, "loss": 4.01587, "time": 0.8203} +{"mode": "train", "epoch": 95, "iter": 3700, "lr": 0.02968, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30406, "top5_acc": 0.55391, "loss_cls": 4.0163, "loss": 4.0163, "time": 0.81745} +{"mode": "val", "epoch": 95, "iter": 309, "lr": 0.02966, "top1_acc": 0.23218, "top5_acc": 0.47014, "mean_class_accuracy": 0.2321} +{"mode": "train", "epoch": 96, "iter": 100, "lr": 0.02964, "memory": 15990, "data_time": 1.28642, "top1_acc": 0.31266, "top5_acc": 0.57219, "loss_cls": 3.93616, "loss": 3.93616, "time": 2.27813} +{"mode": "train", "epoch": 96, "iter": 200, "lr": 0.02961, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30953, "top5_acc": 0.5525, "loss_cls": 3.96441, "loss": 3.96441, "time": 0.82046} +{"mode": "train", "epoch": 96, "iter": 300, "lr": 0.02959, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31203, "top5_acc": 0.55875, "loss_cls": 3.96714, "loss": 3.96714, "time": 0.82701} +{"mode": "train", "epoch": 96, "iter": 400, "lr": 0.02956, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30281, "top5_acc": 0.55047, "loss_cls": 4.03077, "loss": 4.03077, "time": 0.82166} +{"mode": "train", "epoch": 96, "iter": 500, "lr": 0.02954, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30531, "top5_acc": 0.55703, "loss_cls": 3.97227, "loss": 3.97227, "time": 0.82872} +{"mode": "train", "epoch": 96, "iter": 600, "lr": 0.02951, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30641, "top5_acc": 0.55781, "loss_cls": 3.98918, "loss": 3.98918, "time": 0.82095} +{"mode": "train", "epoch": 96, "iter": 700, "lr": 0.02948, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30703, "top5_acc": 0.56047, "loss_cls": 3.98281, "loss": 3.98281, "time": 0.82269} +{"mode": "train", "epoch": 96, "iter": 800, "lr": 0.02946, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31531, "top5_acc": 0.56938, "loss_cls": 3.93005, "loss": 3.93005, "time": 0.8259} +{"mode": "train", "epoch": 96, "iter": 900, "lr": 0.02943, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.28891, "top5_acc": 0.54406, "loss_cls": 4.08074, "loss": 4.08074, "time": 0.81878} +{"mode": "train", "epoch": 96, "iter": 1000, "lr": 0.02941, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31953, "top5_acc": 0.56406, "loss_cls": 3.94515, "loss": 3.94515, "time": 0.82399} +{"mode": "train", "epoch": 96, "iter": 1100, "lr": 0.02938, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30719, "top5_acc": 0.5575, "loss_cls": 3.95625, "loss": 3.95625, "time": 0.82463} +{"mode": "train", "epoch": 96, "iter": 1200, "lr": 0.02936, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30422, "top5_acc": 0.56109, "loss_cls": 3.99409, "loss": 3.99409, "time": 0.82471} +{"mode": "train", "epoch": 96, "iter": 1300, "lr": 0.02933, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29266, "top5_acc": 0.54984, "loss_cls": 4.04979, "loss": 4.04979, "time": 0.81999} +{"mode": "train", "epoch": 96, "iter": 1400, "lr": 0.02931, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30906, "top5_acc": 0.55484, "loss_cls": 3.98074, "loss": 3.98074, "time": 0.82136} +{"mode": "train", "epoch": 96, "iter": 1500, "lr": 0.02928, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30172, "top5_acc": 0.56094, "loss_cls": 4.00109, "loss": 4.00109, "time": 0.81923} +{"mode": "train", "epoch": 96, "iter": 1600, "lr": 0.02926, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30797, "top5_acc": 0.55391, "loss_cls": 3.99207, "loss": 3.99207, "time": 0.82015} +{"mode": "train", "epoch": 96, "iter": 1700, "lr": 0.02923, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30094, "top5_acc": 0.54656, "loss_cls": 4.04829, "loss": 4.04829, "time": 0.82468} +{"mode": "train", "epoch": 96, "iter": 1800, "lr": 0.0292, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30922, "top5_acc": 0.56, "loss_cls": 3.98705, "loss": 3.98705, "time": 0.81813} +{"mode": "train", "epoch": 96, "iter": 1900, "lr": 0.02918, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29531, "top5_acc": 0.54266, "loss_cls": 4.06387, "loss": 4.06387, "time": 0.81953} +{"mode": "train", "epoch": 96, "iter": 2000, "lr": 0.02915, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30703, "top5_acc": 0.56063, "loss_cls": 3.97671, "loss": 3.97671, "time": 0.81949} +{"mode": "train", "epoch": 96, "iter": 2100, "lr": 0.02913, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.29297, "top5_acc": 0.55828, "loss_cls": 4.0036, "loss": 4.0036, "time": 0.82128} +{"mode": "train", "epoch": 96, "iter": 2200, "lr": 0.0291, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30484, "top5_acc": 0.55609, "loss_cls": 3.97884, "loss": 3.97884, "time": 0.82296} +{"mode": "train", "epoch": 96, "iter": 2300, "lr": 0.02908, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30188, "top5_acc": 0.56, "loss_cls": 4.0281, "loss": 4.0281, "time": 0.82708} +{"mode": "train", "epoch": 96, "iter": 2400, "lr": 0.02905, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.30641, "top5_acc": 0.55312, "loss_cls": 3.98309, "loss": 3.98309, "time": 0.81889} +{"mode": "train", "epoch": 96, "iter": 2500, "lr": 0.02903, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31031, "top5_acc": 0.55719, "loss_cls": 3.9898, "loss": 3.9898, "time": 0.81779} +{"mode": "train", "epoch": 96, "iter": 2600, "lr": 0.029, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30953, "top5_acc": 0.55156, "loss_cls": 3.98777, "loss": 3.98777, "time": 0.81759} +{"mode": "train", "epoch": 96, "iter": 2700, "lr": 0.02898, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30078, "top5_acc": 0.54781, "loss_cls": 4.01407, "loss": 4.01407, "time": 0.81654} +{"mode": "train", "epoch": 96, "iter": 2800, "lr": 0.02895, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29766, "top5_acc": 0.54797, "loss_cls": 4.02233, "loss": 4.02233, "time": 0.81871} +{"mode": "train", "epoch": 96, "iter": 2900, "lr": 0.02893, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30719, "top5_acc": 0.55672, "loss_cls": 3.97509, "loss": 3.97509, "time": 0.82055} +{"mode": "train", "epoch": 96, "iter": 3000, "lr": 0.0289, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30594, "top5_acc": 0.56266, "loss_cls": 3.98933, "loss": 3.98933, "time": 0.82039} +{"mode": "train", "epoch": 96, "iter": 3100, "lr": 0.02887, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29766, "top5_acc": 0.54578, "loss_cls": 4.05001, "loss": 4.05001, "time": 0.81922} +{"mode": "train", "epoch": 96, "iter": 3200, "lr": 0.02885, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30594, "top5_acc": 0.56437, "loss_cls": 3.97789, "loss": 3.97789, "time": 0.81625} +{"mode": "train", "epoch": 96, "iter": 3300, "lr": 0.02882, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29344, "top5_acc": 0.55312, "loss_cls": 4.00958, "loss": 4.00958, "time": 0.8187} +{"mode": "train", "epoch": 96, "iter": 3400, "lr": 0.0288, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30625, "top5_acc": 0.54703, "loss_cls": 4.01295, "loss": 4.01295, "time": 0.81848} +{"mode": "train", "epoch": 96, "iter": 3500, "lr": 0.02877, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31172, "top5_acc": 0.56516, "loss_cls": 3.94517, "loss": 3.94517, "time": 0.82163} +{"mode": "train", "epoch": 96, "iter": 3600, "lr": 0.02875, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31344, "top5_acc": 0.5625, "loss_cls": 3.94977, "loss": 3.94977, "time": 0.82072} +{"mode": "train", "epoch": 96, "iter": 3700, "lr": 0.02872, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30578, "top5_acc": 0.54766, "loss_cls": 4.0228, "loss": 4.0228, "time": 0.81657} +{"mode": "val", "epoch": 96, "iter": 309, "lr": 0.02871, "top1_acc": 0.24297, "top5_acc": 0.47389, "mean_class_accuracy": 0.24264} +{"mode": "train", "epoch": 97, "iter": 100, "lr": 0.02869, "memory": 15990, "data_time": 1.27984, "top1_acc": 0.31469, "top5_acc": 0.57297, "loss_cls": 3.9011, "loss": 3.9011, "time": 2.26746} +{"mode": "train", "epoch": 97, "iter": 200, "lr": 0.02866, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32125, "top5_acc": 0.56125, "loss_cls": 3.948, "loss": 3.948, "time": 0.82591} +{"mode": "train", "epoch": 97, "iter": 300, "lr": 0.02864, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.30812, "top5_acc": 0.57109, "loss_cls": 3.92228, "loss": 3.92228, "time": 0.82319} +{"mode": "train", "epoch": 97, "iter": 400, "lr": 0.02861, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31766, "top5_acc": 0.57766, "loss_cls": 3.94711, "loss": 3.94711, "time": 0.81961} +{"mode": "train", "epoch": 97, "iter": 500, "lr": 0.02858, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.29625, "top5_acc": 0.55234, "loss_cls": 4.01579, "loss": 4.01579, "time": 0.82713} +{"mode": "train", "epoch": 97, "iter": 600, "lr": 0.02856, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30859, "top5_acc": 0.56344, "loss_cls": 3.9517, "loss": 3.9517, "time": 0.81946} +{"mode": "train", "epoch": 97, "iter": 700, "lr": 0.02853, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31297, "top5_acc": 0.5725, "loss_cls": 3.937, "loss": 3.937, "time": 0.82544} +{"mode": "train", "epoch": 97, "iter": 800, "lr": 0.02851, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31469, "top5_acc": 0.56656, "loss_cls": 3.93674, "loss": 3.93674, "time": 0.81796} +{"mode": "train", "epoch": 97, "iter": 900, "lr": 0.02848, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3025, "top5_acc": 0.54828, "loss_cls": 4.02292, "loss": 4.02292, "time": 0.82428} +{"mode": "train", "epoch": 97, "iter": 1000, "lr": 0.02846, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29672, "top5_acc": 0.55578, "loss_cls": 4.03044, "loss": 4.03044, "time": 0.83017} +{"mode": "train", "epoch": 97, "iter": 1100, "lr": 0.02843, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29938, "top5_acc": 0.55859, "loss_cls": 3.98166, "loss": 3.98166, "time": 0.82553} +{"mode": "train", "epoch": 97, "iter": 1200, "lr": 0.02841, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31109, "top5_acc": 0.56453, "loss_cls": 3.95617, "loss": 3.95617, "time": 0.82029} +{"mode": "train", "epoch": 97, "iter": 1300, "lr": 0.02838, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30109, "top5_acc": 0.56531, "loss_cls": 3.96314, "loss": 3.96314, "time": 0.81735} +{"mode": "train", "epoch": 97, "iter": 1400, "lr": 0.02836, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30844, "top5_acc": 0.56047, "loss_cls": 3.97729, "loss": 3.97729, "time": 0.82225} +{"mode": "train", "epoch": 97, "iter": 1500, "lr": 0.02833, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30641, "top5_acc": 0.55437, "loss_cls": 3.98831, "loss": 3.98831, "time": 0.82291} +{"mode": "train", "epoch": 97, "iter": 1600, "lr": 0.02831, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30156, "top5_acc": 0.55828, "loss_cls": 4.00012, "loss": 4.00012, "time": 0.81644} +{"mode": "train", "epoch": 97, "iter": 1700, "lr": 0.02828, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30734, "top5_acc": 0.55609, "loss_cls": 3.98156, "loss": 3.98156, "time": 0.81789} +{"mode": "train", "epoch": 97, "iter": 1800, "lr": 0.02826, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31672, "top5_acc": 0.5675, "loss_cls": 3.93379, "loss": 3.93379, "time": 0.81431} +{"mode": "train", "epoch": 97, "iter": 1900, "lr": 0.02823, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3, "top5_acc": 0.55188, "loss_cls": 4.00842, "loss": 4.00842, "time": 0.81696} +{"mode": "train", "epoch": 97, "iter": 2000, "lr": 0.02821, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31141, "top5_acc": 0.54891, "loss_cls": 4.0171, "loss": 4.0171, "time": 0.81661} +{"mode": "train", "epoch": 97, "iter": 2100, "lr": 0.02818, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.30781, "top5_acc": 0.55875, "loss_cls": 3.97507, "loss": 3.97507, "time": 0.81983} +{"mode": "train", "epoch": 97, "iter": 2200, "lr": 0.02816, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.3075, "top5_acc": 0.55531, "loss_cls": 3.97339, "loss": 3.97339, "time": 0.82053} +{"mode": "train", "epoch": 97, "iter": 2300, "lr": 0.02813, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30406, "top5_acc": 0.54625, "loss_cls": 4.03053, "loss": 4.03053, "time": 0.8233} +{"mode": "train", "epoch": 97, "iter": 2400, "lr": 0.02811, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30875, "top5_acc": 0.55516, "loss_cls": 3.97401, "loss": 3.97401, "time": 0.81806} +{"mode": "train", "epoch": 97, "iter": 2500, "lr": 0.02808, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30469, "top5_acc": 0.55859, "loss_cls": 3.99828, "loss": 3.99828, "time": 0.82168} +{"mode": "train", "epoch": 97, "iter": 2600, "lr": 0.02806, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29859, "top5_acc": 0.55141, "loss_cls": 4.00057, "loss": 4.00057, "time": 0.82375} +{"mode": "train", "epoch": 97, "iter": 2700, "lr": 0.02803, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30625, "top5_acc": 0.56016, "loss_cls": 3.99099, "loss": 3.99099, "time": 0.81967} +{"mode": "train", "epoch": 97, "iter": 2800, "lr": 0.02801, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30922, "top5_acc": 0.56578, "loss_cls": 3.97972, "loss": 3.97972, "time": 0.82484} +{"mode": "train", "epoch": 97, "iter": 2900, "lr": 0.02798, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30031, "top5_acc": 0.55875, "loss_cls": 3.9812, "loss": 3.9812, "time": 0.82354} +{"mode": "train", "epoch": 97, "iter": 3000, "lr": 0.02796, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31609, "top5_acc": 0.56656, "loss_cls": 3.92763, "loss": 3.92763, "time": 0.82153} +{"mode": "train", "epoch": 97, "iter": 3100, "lr": 0.02793, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30688, "top5_acc": 0.56312, "loss_cls": 3.95821, "loss": 3.95821, "time": 0.81549} +{"mode": "train", "epoch": 97, "iter": 3200, "lr": 0.02791, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3025, "top5_acc": 0.55422, "loss_cls": 4.01536, "loss": 4.01536, "time": 0.81791} +{"mode": "train", "epoch": 97, "iter": 3300, "lr": 0.02788, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31188, "top5_acc": 0.56078, "loss_cls": 3.96747, "loss": 3.96747, "time": 0.81997} +{"mode": "train", "epoch": 97, "iter": 3400, "lr": 0.02786, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30422, "top5_acc": 0.55094, "loss_cls": 4.02739, "loss": 4.02739, "time": 0.81722} +{"mode": "train", "epoch": 97, "iter": 3500, "lr": 0.02783, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30641, "top5_acc": 0.55891, "loss_cls": 3.99964, "loss": 3.99964, "time": 0.81513} +{"mode": "train", "epoch": 97, "iter": 3600, "lr": 0.02781, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3075, "top5_acc": 0.55875, "loss_cls": 3.97485, "loss": 3.97485, "time": 0.81476} +{"mode": "train", "epoch": 97, "iter": 3700, "lr": 0.02778, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30609, "top5_acc": 0.55109, "loss_cls": 4.00323, "loss": 4.00323, "time": 0.82187} +{"mode": "val", "epoch": 97, "iter": 309, "lr": 0.02777, "top1_acc": 0.25837, "top5_acc": 0.49815, "mean_class_accuracy": 0.25823} +{"mode": "train", "epoch": 98, "iter": 100, "lr": 0.02774, "memory": 15990, "data_time": 1.31543, "top1_acc": 0.31719, "top5_acc": 0.57453, "loss_cls": 3.88036, "loss": 3.88036, "time": 2.30755} +{"mode": "train", "epoch": 98, "iter": 200, "lr": 0.02772, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31312, "top5_acc": 0.56109, "loss_cls": 3.96174, "loss": 3.96174, "time": 0.82275} +{"mode": "train", "epoch": 98, "iter": 300, "lr": 0.02769, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31, "top5_acc": 0.56641, "loss_cls": 3.93168, "loss": 3.93168, "time": 0.82345} +{"mode": "train", "epoch": 98, "iter": 400, "lr": 0.02767, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31906, "top5_acc": 0.56719, "loss_cls": 3.92105, "loss": 3.92105, "time": 0.82329} +{"mode": "train", "epoch": 98, "iter": 500, "lr": 0.02764, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.31266, "top5_acc": 0.56516, "loss_cls": 3.95686, "loss": 3.95686, "time": 0.82832} +{"mode": "train", "epoch": 98, "iter": 600, "lr": 0.02762, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31844, "top5_acc": 0.57078, "loss_cls": 3.91684, "loss": 3.91684, "time": 0.82345} +{"mode": "train", "epoch": 98, "iter": 700, "lr": 0.02759, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31844, "top5_acc": 0.56344, "loss_cls": 3.93207, "loss": 3.93207, "time": 0.82609} +{"mode": "train", "epoch": 98, "iter": 800, "lr": 0.02757, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29531, "top5_acc": 0.55172, "loss_cls": 4.00487, "loss": 4.00487, "time": 0.8216} +{"mode": "train", "epoch": 98, "iter": 900, "lr": 0.02754, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30859, "top5_acc": 0.56516, "loss_cls": 3.96049, "loss": 3.96049, "time": 0.82685} +{"mode": "train", "epoch": 98, "iter": 1000, "lr": 0.02752, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30344, "top5_acc": 0.56078, "loss_cls": 3.96543, "loss": 3.96543, "time": 0.8184} +{"mode": "train", "epoch": 98, "iter": 1100, "lr": 0.02749, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30062, "top5_acc": 0.56953, "loss_cls": 3.96374, "loss": 3.96374, "time": 0.81874} +{"mode": "train", "epoch": 98, "iter": 1200, "lr": 0.02747, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31516, "top5_acc": 0.56641, "loss_cls": 3.91455, "loss": 3.91455, "time": 0.82379} +{"mode": "train", "epoch": 98, "iter": 1300, "lr": 0.02744, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31766, "top5_acc": 0.56812, "loss_cls": 3.91549, "loss": 3.91549, "time": 0.82136} +{"mode": "train", "epoch": 98, "iter": 1400, "lr": 0.02742, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31031, "top5_acc": 0.56469, "loss_cls": 3.94198, "loss": 3.94198, "time": 0.81919} +{"mode": "train", "epoch": 98, "iter": 1500, "lr": 0.02739, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32141, "top5_acc": 0.56563, "loss_cls": 3.9199, "loss": 3.9199, "time": 0.81908} +{"mode": "train", "epoch": 98, "iter": 1600, "lr": 0.02737, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.30984, "top5_acc": 0.56125, "loss_cls": 3.9619, "loss": 3.9619, "time": 0.82028} +{"mode": "train", "epoch": 98, "iter": 1700, "lr": 0.02734, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30109, "top5_acc": 0.54516, "loss_cls": 4.03976, "loss": 4.03976, "time": 0.81998} +{"mode": "train", "epoch": 98, "iter": 1800, "lr": 0.02732, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31312, "top5_acc": 0.55688, "loss_cls": 3.96754, "loss": 3.96754, "time": 0.82047} +{"mode": "train", "epoch": 98, "iter": 1900, "lr": 0.02729, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3175, "top5_acc": 0.56516, "loss_cls": 3.94145, "loss": 3.94145, "time": 0.82467} +{"mode": "train", "epoch": 98, "iter": 2000, "lr": 0.02727, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.2975, "top5_acc": 0.55328, "loss_cls": 3.99537, "loss": 3.99537, "time": 0.81786} +{"mode": "train", "epoch": 98, "iter": 2100, "lr": 0.02724, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31047, "top5_acc": 0.5625, "loss_cls": 3.97087, "loss": 3.97087, "time": 0.81898} +{"mode": "train", "epoch": 98, "iter": 2200, "lr": 0.02722, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31719, "top5_acc": 0.57391, "loss_cls": 3.92088, "loss": 3.92088, "time": 0.8231} +{"mode": "train", "epoch": 98, "iter": 2300, "lr": 0.02719, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.31812, "top5_acc": 0.55984, "loss_cls": 3.96323, "loss": 3.96323, "time": 0.82814} +{"mode": "train", "epoch": 98, "iter": 2400, "lr": 0.02717, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31016, "top5_acc": 0.56469, "loss_cls": 3.95598, "loss": 3.95598, "time": 0.81677} +{"mode": "train", "epoch": 98, "iter": 2500, "lr": 0.02714, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31234, "top5_acc": 0.55719, "loss_cls": 3.97599, "loss": 3.97599, "time": 0.81448} +{"mode": "train", "epoch": 98, "iter": 2600, "lr": 0.02712, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29625, "top5_acc": 0.55281, "loss_cls": 3.99439, "loss": 3.99439, "time": 0.81409} +{"mode": "train", "epoch": 98, "iter": 2700, "lr": 0.02709, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.31219, "top5_acc": 0.56484, "loss_cls": 3.94045, "loss": 3.94045, "time": 0.82081} +{"mode": "train", "epoch": 98, "iter": 2800, "lr": 0.02707, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30531, "top5_acc": 0.55578, "loss_cls": 3.99655, "loss": 3.99655, "time": 0.81944} +{"mode": "train", "epoch": 98, "iter": 2900, "lr": 0.02705, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31203, "top5_acc": 0.56078, "loss_cls": 3.96626, "loss": 3.96626, "time": 0.81756} +{"mode": "train", "epoch": 98, "iter": 3000, "lr": 0.02702, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.29594, "top5_acc": 0.55516, "loss_cls": 4.02036, "loss": 4.02036, "time": 0.82275} +{"mode": "train", "epoch": 98, "iter": 3100, "lr": 0.027, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31625, "top5_acc": 0.56781, "loss_cls": 3.93691, "loss": 3.93691, "time": 0.8164} +{"mode": "train", "epoch": 98, "iter": 3200, "lr": 0.02697, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.29109, "top5_acc": 0.54781, "loss_cls": 4.05638, "loss": 4.05638, "time": 0.82196} +{"mode": "train", "epoch": 98, "iter": 3300, "lr": 0.02695, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30281, "top5_acc": 0.55703, "loss_cls": 4.01865, "loss": 4.01865, "time": 0.823} +{"mode": "train", "epoch": 98, "iter": 3400, "lr": 0.02692, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30453, "top5_acc": 0.565, "loss_cls": 3.956, "loss": 3.956, "time": 0.81673} +{"mode": "train", "epoch": 98, "iter": 3500, "lr": 0.0269, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30234, "top5_acc": 0.56359, "loss_cls": 3.99632, "loss": 3.99632, "time": 0.82342} +{"mode": "train", "epoch": 98, "iter": 3600, "lr": 0.02687, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31016, "top5_acc": 0.56188, "loss_cls": 3.9788, "loss": 3.9788, "time": 0.8169} +{"mode": "train", "epoch": 98, "iter": 3700, "lr": 0.02685, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.29422, "top5_acc": 0.5475, "loss_cls": 4.05768, "loss": 4.05768, "time": 0.82015} +{"mode": "val", "epoch": 98, "iter": 309, "lr": 0.02684, "top1_acc": 0.24905, "top5_acc": 0.49111, "mean_class_accuracy": 0.2488} +{"mode": "train", "epoch": 99, "iter": 100, "lr": 0.02681, "memory": 15990, "data_time": 1.3272, "top1_acc": 0.31953, "top5_acc": 0.57141, "loss_cls": 3.90085, "loss": 3.90085, "time": 2.3167} +{"mode": "train", "epoch": 99, "iter": 200, "lr": 0.02679, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31984, "top5_acc": 0.57578, "loss_cls": 3.87133, "loss": 3.87133, "time": 0.82838} +{"mode": "train", "epoch": 99, "iter": 300, "lr": 0.02676, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30953, "top5_acc": 0.56703, "loss_cls": 3.95246, "loss": 3.95246, "time": 0.82956} +{"mode": "train", "epoch": 99, "iter": 400, "lr": 0.02674, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.31234, "top5_acc": 0.57641, "loss_cls": 3.89611, "loss": 3.89611, "time": 0.82926} +{"mode": "train", "epoch": 99, "iter": 500, "lr": 0.02671, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32125, "top5_acc": 0.58594, "loss_cls": 3.89027, "loss": 3.89027, "time": 0.82652} +{"mode": "train", "epoch": 99, "iter": 600, "lr": 0.02669, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30859, "top5_acc": 0.56312, "loss_cls": 3.927, "loss": 3.927, "time": 0.82565} +{"mode": "train", "epoch": 99, "iter": 700, "lr": 0.02666, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30344, "top5_acc": 0.56094, "loss_cls": 3.99184, "loss": 3.99184, "time": 0.82162} +{"mode": "train", "epoch": 99, "iter": 800, "lr": 0.02664, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31391, "top5_acc": 0.56375, "loss_cls": 3.91518, "loss": 3.91518, "time": 0.82556} +{"mode": "train", "epoch": 99, "iter": 900, "lr": 0.02661, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.31297, "top5_acc": 0.56734, "loss_cls": 3.95562, "loss": 3.95562, "time": 0.82776} +{"mode": "train", "epoch": 99, "iter": 1000, "lr": 0.02659, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3075, "top5_acc": 0.56078, "loss_cls": 3.96648, "loss": 3.96648, "time": 0.81936} +{"mode": "train", "epoch": 99, "iter": 1100, "lr": 0.02656, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31047, "top5_acc": 0.55766, "loss_cls": 3.91353, "loss": 3.91353, "time": 0.82629} +{"mode": "train", "epoch": 99, "iter": 1200, "lr": 0.02654, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31141, "top5_acc": 0.56406, "loss_cls": 3.95318, "loss": 3.95318, "time": 0.82203} +{"mode": "train", "epoch": 99, "iter": 1300, "lr": 0.02651, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31656, "top5_acc": 0.56156, "loss_cls": 3.93468, "loss": 3.93468, "time": 0.82061} +{"mode": "train", "epoch": 99, "iter": 1400, "lr": 0.02649, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31516, "top5_acc": 0.56703, "loss_cls": 3.90004, "loss": 3.90004, "time": 0.81912} +{"mode": "train", "epoch": 99, "iter": 1500, "lr": 0.02646, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32219, "top5_acc": 0.56656, "loss_cls": 3.91972, "loss": 3.91972, "time": 0.81891} +{"mode": "train", "epoch": 99, "iter": 1600, "lr": 0.02644, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32281, "top5_acc": 0.57234, "loss_cls": 3.89431, "loss": 3.89431, "time": 0.81643} +{"mode": "train", "epoch": 99, "iter": 1700, "lr": 0.02642, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32344, "top5_acc": 0.56766, "loss_cls": 3.91415, "loss": 3.91415, "time": 0.82252} +{"mode": "train", "epoch": 99, "iter": 1800, "lr": 0.02639, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30984, "top5_acc": 0.56125, "loss_cls": 3.95382, "loss": 3.95382, "time": 0.82031} +{"mode": "train", "epoch": 99, "iter": 1900, "lr": 0.02637, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31328, "top5_acc": 0.56, "loss_cls": 3.96272, "loss": 3.96272, "time": 0.8238} +{"mode": "train", "epoch": 99, "iter": 2000, "lr": 0.02634, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31188, "top5_acc": 0.57312, "loss_cls": 3.90659, "loss": 3.90659, "time": 0.81955} +{"mode": "train", "epoch": 99, "iter": 2100, "lr": 0.02632, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31672, "top5_acc": 0.55469, "loss_cls": 3.96651, "loss": 3.96651, "time": 0.8271} +{"mode": "train", "epoch": 99, "iter": 2200, "lr": 0.02629, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31031, "top5_acc": 0.57109, "loss_cls": 3.94526, "loss": 3.94526, "time": 0.82157} +{"mode": "train", "epoch": 99, "iter": 2300, "lr": 0.02627, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.30344, "top5_acc": 0.55359, "loss_cls": 3.97925, "loss": 3.97925, "time": 0.82739} +{"mode": "train", "epoch": 99, "iter": 2400, "lr": 0.02624, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31188, "top5_acc": 0.56719, "loss_cls": 3.94703, "loss": 3.94703, "time": 0.81755} +{"mode": "train", "epoch": 99, "iter": 2500, "lr": 0.02622, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30922, "top5_acc": 0.56969, "loss_cls": 3.95055, "loss": 3.95055, "time": 0.8233} +{"mode": "train", "epoch": 99, "iter": 2600, "lr": 0.02619, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30531, "top5_acc": 0.55937, "loss_cls": 3.97234, "loss": 3.97234, "time": 0.81777} +{"mode": "train", "epoch": 99, "iter": 2700, "lr": 0.02617, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31438, "top5_acc": 0.56734, "loss_cls": 3.95949, "loss": 3.95949, "time": 0.82059} +{"mode": "train", "epoch": 99, "iter": 2800, "lr": 0.02614, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32328, "top5_acc": 0.56297, "loss_cls": 3.95356, "loss": 3.95356, "time": 0.81848} +{"mode": "train", "epoch": 99, "iter": 2900, "lr": 0.02612, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.29812, "top5_acc": 0.555, "loss_cls": 3.99373, "loss": 3.99373, "time": 0.81939} +{"mode": "train", "epoch": 99, "iter": 3000, "lr": 0.0261, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31656, "top5_acc": 0.55688, "loss_cls": 3.99084, "loss": 3.99084, "time": 0.82578} +{"mode": "train", "epoch": 99, "iter": 3100, "lr": 0.02607, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31516, "top5_acc": 0.56422, "loss_cls": 3.94569, "loss": 3.94569, "time": 0.82338} +{"mode": "train", "epoch": 99, "iter": 3200, "lr": 0.02605, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31109, "top5_acc": 0.56734, "loss_cls": 3.95969, "loss": 3.95969, "time": 0.82337} +{"mode": "train", "epoch": 99, "iter": 3300, "lr": 0.02602, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30734, "top5_acc": 0.56094, "loss_cls": 3.95833, "loss": 3.95833, "time": 0.82167} +{"mode": "train", "epoch": 99, "iter": 3400, "lr": 0.026, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30984, "top5_acc": 0.55812, "loss_cls": 3.98135, "loss": 3.98135, "time": 0.82176} +{"mode": "train", "epoch": 99, "iter": 3500, "lr": 0.02597, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30484, "top5_acc": 0.56156, "loss_cls": 3.99429, "loss": 3.99429, "time": 0.82111} +{"mode": "train", "epoch": 99, "iter": 3600, "lr": 0.02595, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31797, "top5_acc": 0.57266, "loss_cls": 3.92791, "loss": 3.92791, "time": 0.81575} +{"mode": "train", "epoch": 99, "iter": 3700, "lr": 0.02592, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31484, "top5_acc": 0.56375, "loss_cls": 3.96465, "loss": 3.96465, "time": 0.81719} +{"mode": "val", "epoch": 99, "iter": 309, "lr": 0.02591, "top1_acc": 0.25837, "top5_acc": 0.49795, "mean_class_accuracy": 0.25807} +{"mode": "train", "epoch": 100, "iter": 100, "lr": 0.02589, "memory": 15990, "data_time": 1.31711, "top1_acc": 0.31672, "top5_acc": 0.56766, "loss_cls": 3.93015, "loss": 3.93015, "time": 2.30775} +{"mode": "train", "epoch": 100, "iter": 200, "lr": 0.02586, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31703, "top5_acc": 0.575, "loss_cls": 3.85049, "loss": 3.85049, "time": 0.82939} +{"mode": "train", "epoch": 100, "iter": 300, "lr": 0.02584, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.31578, "top5_acc": 0.56578, "loss_cls": 3.93138, "loss": 3.93138, "time": 0.82588} +{"mode": "train", "epoch": 100, "iter": 400, "lr": 0.02581, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31703, "top5_acc": 0.57328, "loss_cls": 3.91527, "loss": 3.91527, "time": 0.82226} +{"mode": "train", "epoch": 100, "iter": 500, "lr": 0.02579, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32734, "top5_acc": 0.5825, "loss_cls": 3.86492, "loss": 3.86492, "time": 0.81862} +{"mode": "train", "epoch": 100, "iter": 600, "lr": 0.02577, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.32312, "top5_acc": 0.56906, "loss_cls": 3.89888, "loss": 3.89888, "time": 0.82834} +{"mode": "train", "epoch": 100, "iter": 700, "lr": 0.02574, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31516, "top5_acc": 0.56891, "loss_cls": 3.93642, "loss": 3.93642, "time": 0.82108} +{"mode": "train", "epoch": 100, "iter": 800, "lr": 0.02572, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30859, "top5_acc": 0.55781, "loss_cls": 3.95102, "loss": 3.95102, "time": 0.82107} +{"mode": "train", "epoch": 100, "iter": 900, "lr": 0.02569, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30312, "top5_acc": 0.56375, "loss_cls": 3.94105, "loss": 3.94105, "time": 0.82327} +{"mode": "train", "epoch": 100, "iter": 1000, "lr": 0.02567, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31828, "top5_acc": 0.56953, "loss_cls": 3.93592, "loss": 3.93592, "time": 0.82302} +{"mode": "train", "epoch": 100, "iter": 1100, "lr": 0.02564, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31062, "top5_acc": 0.5675, "loss_cls": 3.94324, "loss": 3.94324, "time": 0.81928} +{"mode": "train", "epoch": 100, "iter": 1200, "lr": 0.02562, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32188, "top5_acc": 0.57688, "loss_cls": 3.88733, "loss": 3.88733, "time": 0.82193} +{"mode": "train", "epoch": 100, "iter": 1300, "lr": 0.02559, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30344, "top5_acc": 0.55953, "loss_cls": 3.95683, "loss": 3.95683, "time": 0.81862} +{"mode": "train", "epoch": 100, "iter": 1400, "lr": 0.02557, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31656, "top5_acc": 0.56891, "loss_cls": 3.92892, "loss": 3.92892, "time": 0.82288} +{"mode": "train", "epoch": 100, "iter": 1500, "lr": 0.02555, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31438, "top5_acc": 0.56547, "loss_cls": 3.95603, "loss": 3.95603, "time": 0.81776} +{"mode": "train", "epoch": 100, "iter": 1600, "lr": 0.02552, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3175, "top5_acc": 0.57047, "loss_cls": 3.92996, "loss": 3.92996, "time": 0.8236} +{"mode": "train", "epoch": 100, "iter": 1700, "lr": 0.0255, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31953, "top5_acc": 0.57359, "loss_cls": 3.92825, "loss": 3.92825, "time": 0.82405} +{"mode": "train", "epoch": 100, "iter": 1800, "lr": 0.02547, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31359, "top5_acc": 0.57359, "loss_cls": 3.91504, "loss": 3.91504, "time": 0.81627} +{"mode": "train", "epoch": 100, "iter": 1900, "lr": 0.02545, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32328, "top5_acc": 0.57906, "loss_cls": 3.884, "loss": 3.884, "time": 0.81876} +{"mode": "train", "epoch": 100, "iter": 2000, "lr": 0.02542, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30969, "top5_acc": 0.56516, "loss_cls": 3.9384, "loss": 3.9384, "time": 0.82191} +{"mode": "train", "epoch": 100, "iter": 2100, "lr": 0.0254, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32078, "top5_acc": 0.57547, "loss_cls": 3.91338, "loss": 3.91338, "time": 0.82417} +{"mode": "train", "epoch": 100, "iter": 2200, "lr": 0.02538, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31516, "top5_acc": 0.55922, "loss_cls": 3.95521, "loss": 3.95521, "time": 0.8189} +{"mode": "train", "epoch": 100, "iter": 2300, "lr": 0.02535, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31297, "top5_acc": 0.57219, "loss_cls": 3.92718, "loss": 3.92718, "time": 0.82693} +{"mode": "train", "epoch": 100, "iter": 2400, "lr": 0.02533, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.30219, "top5_acc": 0.56734, "loss_cls": 3.96111, "loss": 3.96111, "time": 0.82787} +{"mode": "train", "epoch": 100, "iter": 2500, "lr": 0.0253, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31422, "top5_acc": 0.57125, "loss_cls": 3.90294, "loss": 3.90294, "time": 0.82553} +{"mode": "train", "epoch": 100, "iter": 2600, "lr": 0.02528, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.30781, "top5_acc": 0.55922, "loss_cls": 3.99677, "loss": 3.99677, "time": 0.821} +{"mode": "train", "epoch": 100, "iter": 2700, "lr": 0.02525, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.30609, "top5_acc": 0.55953, "loss_cls": 3.97514, "loss": 3.97514, "time": 0.81657} +{"mode": "train", "epoch": 100, "iter": 2800, "lr": 0.02523, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31219, "top5_acc": 0.56734, "loss_cls": 3.95002, "loss": 3.95002, "time": 0.81733} +{"mode": "train", "epoch": 100, "iter": 2900, "lr": 0.02521, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31812, "top5_acc": 0.57344, "loss_cls": 3.90879, "loss": 3.90879, "time": 0.81853} +{"mode": "train", "epoch": 100, "iter": 3000, "lr": 0.02518, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31609, "top5_acc": 0.57797, "loss_cls": 3.89193, "loss": 3.89193, "time": 0.81706} +{"mode": "train", "epoch": 100, "iter": 3100, "lr": 0.02516, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32172, "top5_acc": 0.57078, "loss_cls": 3.93363, "loss": 3.93363, "time": 0.81452} +{"mode": "train", "epoch": 100, "iter": 3200, "lr": 0.02513, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31609, "top5_acc": 0.57281, "loss_cls": 3.90935, "loss": 3.90935, "time": 0.81802} +{"mode": "train", "epoch": 100, "iter": 3300, "lr": 0.02511, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31094, "top5_acc": 0.57594, "loss_cls": 3.92049, "loss": 3.92049, "time": 0.81801} +{"mode": "train", "epoch": 100, "iter": 3400, "lr": 0.02508, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.30953, "top5_acc": 0.56594, "loss_cls": 3.93406, "loss": 3.93406, "time": 0.81863} +{"mode": "train", "epoch": 100, "iter": 3500, "lr": 0.02506, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.30594, "top5_acc": 0.56203, "loss_cls": 3.95162, "loss": 3.95162, "time": 0.82173} +{"mode": "train", "epoch": 100, "iter": 3600, "lr": 0.02504, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.30516, "top5_acc": 0.55641, "loss_cls": 3.97303, "loss": 3.97303, "time": 0.82175} +{"mode": "train", "epoch": 100, "iter": 3700, "lr": 0.02501, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31906, "top5_acc": 0.57391, "loss_cls": 3.90697, "loss": 3.90697, "time": 0.81525} +{"mode": "val", "epoch": 100, "iter": 309, "lr": 0.025, "top1_acc": 0.2569, "top5_acc": 0.49537, "mean_class_accuracy": 0.25671} +{"mode": "train", "epoch": 101, "iter": 100, "lr": 0.02498, "memory": 15990, "data_time": 1.3098, "top1_acc": 0.31906, "top5_acc": 0.57594, "loss_cls": 3.85489, "loss": 3.85489, "time": 2.29829} +{"mode": "train", "epoch": 101, "iter": 200, "lr": 0.02495, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.32609, "top5_acc": 0.57906, "loss_cls": 3.86399, "loss": 3.86399, "time": 0.82484} +{"mode": "train", "epoch": 101, "iter": 300, "lr": 0.02493, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31547, "top5_acc": 0.575, "loss_cls": 3.8787, "loss": 3.8787, "time": 0.82163} +{"mode": "train", "epoch": 101, "iter": 400, "lr": 0.0249, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32609, "top5_acc": 0.57203, "loss_cls": 3.8827, "loss": 3.8827, "time": 0.82706} +{"mode": "train", "epoch": 101, "iter": 500, "lr": 0.02488, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32812, "top5_acc": 0.58094, "loss_cls": 3.85506, "loss": 3.85506, "time": 0.8247} +{"mode": "train", "epoch": 101, "iter": 600, "lr": 0.02486, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33031, "top5_acc": 0.56938, "loss_cls": 3.88533, "loss": 3.88533, "time": 0.82331} +{"mode": "train", "epoch": 101, "iter": 700, "lr": 0.02483, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.31766, "top5_acc": 0.57391, "loss_cls": 3.9018, "loss": 3.9018, "time": 0.81988} +{"mode": "train", "epoch": 101, "iter": 800, "lr": 0.02481, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32172, "top5_acc": 0.56875, "loss_cls": 3.88066, "loss": 3.88066, "time": 0.8211} +{"mode": "train", "epoch": 101, "iter": 900, "lr": 0.02478, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31516, "top5_acc": 0.56969, "loss_cls": 3.90008, "loss": 3.90008, "time": 0.83} +{"mode": "train", "epoch": 101, "iter": 1000, "lr": 0.02476, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31531, "top5_acc": 0.57297, "loss_cls": 3.89467, "loss": 3.89467, "time": 0.82382} +{"mode": "train", "epoch": 101, "iter": 1100, "lr": 0.02473, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.31766, "top5_acc": 0.56375, "loss_cls": 3.91936, "loss": 3.91936, "time": 0.82304} +{"mode": "train", "epoch": 101, "iter": 1200, "lr": 0.02471, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31625, "top5_acc": 0.5775, "loss_cls": 3.90432, "loss": 3.90432, "time": 0.82075} +{"mode": "train", "epoch": 101, "iter": 1300, "lr": 0.02469, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32281, "top5_acc": 0.58047, "loss_cls": 3.86916, "loss": 3.86916, "time": 0.81764} +{"mode": "train", "epoch": 101, "iter": 1400, "lr": 0.02466, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31016, "top5_acc": 0.56797, "loss_cls": 3.91625, "loss": 3.91625, "time": 0.81428} +{"mode": "train", "epoch": 101, "iter": 1500, "lr": 0.02464, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31375, "top5_acc": 0.57344, "loss_cls": 3.91162, "loss": 3.91162, "time": 0.82288} +{"mode": "train", "epoch": 101, "iter": 1600, "lr": 0.02461, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31125, "top5_acc": 0.57078, "loss_cls": 3.90721, "loss": 3.90721, "time": 0.82298} +{"mode": "train", "epoch": 101, "iter": 1700, "lr": 0.02459, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31516, "top5_acc": 0.56766, "loss_cls": 3.92705, "loss": 3.92705, "time": 0.8175} +{"mode": "train", "epoch": 101, "iter": 1800, "lr": 0.02457, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31469, "top5_acc": 0.57094, "loss_cls": 3.93055, "loss": 3.93055, "time": 0.81647} +{"mode": "train", "epoch": 101, "iter": 1900, "lr": 0.02454, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32234, "top5_acc": 0.58172, "loss_cls": 3.86059, "loss": 3.86059, "time": 0.81544} +{"mode": "train", "epoch": 101, "iter": 2000, "lr": 0.02452, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31516, "top5_acc": 0.56953, "loss_cls": 3.91388, "loss": 3.91388, "time": 0.82039} +{"mode": "train", "epoch": 101, "iter": 2100, "lr": 0.02449, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.30641, "top5_acc": 0.5625, "loss_cls": 3.96296, "loss": 3.96296, "time": 0.82377} +{"mode": "train", "epoch": 101, "iter": 2200, "lr": 0.02447, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31188, "top5_acc": 0.56031, "loss_cls": 3.96473, "loss": 3.96473, "time": 0.8236} +{"mode": "train", "epoch": 101, "iter": 2300, "lr": 0.02445, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.31781, "top5_acc": 0.57484, "loss_cls": 3.89053, "loss": 3.89053, "time": 0.82192} +{"mode": "train", "epoch": 101, "iter": 2400, "lr": 0.02442, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31297, "top5_acc": 0.56984, "loss_cls": 3.94001, "loss": 3.94001, "time": 0.82355} +{"mode": "train", "epoch": 101, "iter": 2500, "lr": 0.0244, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3125, "top5_acc": 0.56906, "loss_cls": 3.91802, "loss": 3.91802, "time": 0.82111} +{"mode": "train", "epoch": 101, "iter": 2600, "lr": 0.02437, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.29563, "top5_acc": 0.56906, "loss_cls": 3.94789, "loss": 3.94789, "time": 0.81851} +{"mode": "train", "epoch": 101, "iter": 2700, "lr": 0.02435, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31641, "top5_acc": 0.56516, "loss_cls": 3.92986, "loss": 3.92986, "time": 0.81896} +{"mode": "train", "epoch": 101, "iter": 2800, "lr": 0.02433, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31047, "top5_acc": 0.56328, "loss_cls": 3.95042, "loss": 3.95042, "time": 0.81647} +{"mode": "train", "epoch": 101, "iter": 2900, "lr": 0.0243, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31078, "top5_acc": 0.56469, "loss_cls": 3.96594, "loss": 3.96594, "time": 0.82148} +{"mode": "train", "epoch": 101, "iter": 3000, "lr": 0.02428, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32078, "top5_acc": 0.58578, "loss_cls": 3.86049, "loss": 3.86049, "time": 0.81919} +{"mode": "train", "epoch": 101, "iter": 3100, "lr": 0.02425, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32016, "top5_acc": 0.57063, "loss_cls": 3.90882, "loss": 3.90882, "time": 0.81852} +{"mode": "train", "epoch": 101, "iter": 3200, "lr": 0.02423, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31484, "top5_acc": 0.56563, "loss_cls": 3.95028, "loss": 3.95028, "time": 0.82114} +{"mode": "train", "epoch": 101, "iter": 3300, "lr": 0.02421, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32484, "top5_acc": 0.57125, "loss_cls": 3.88572, "loss": 3.88572, "time": 0.81798} +{"mode": "train", "epoch": 101, "iter": 3400, "lr": 0.02418, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32188, "top5_acc": 0.57359, "loss_cls": 3.90864, "loss": 3.90864, "time": 0.81795} +{"mode": "train", "epoch": 101, "iter": 3500, "lr": 0.02416, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31094, "top5_acc": 0.55937, "loss_cls": 3.95897, "loss": 3.95897, "time": 0.81754} +{"mode": "train", "epoch": 101, "iter": 3600, "lr": 0.02413, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32078, "top5_acc": 0.57234, "loss_cls": 3.90456, "loss": 3.90456, "time": 0.82033} +{"mode": "train", "epoch": 101, "iter": 3700, "lr": 0.02411, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31891, "top5_acc": 0.56437, "loss_cls": 3.95431, "loss": 3.95431, "time": 0.81962} +{"mode": "val", "epoch": 101, "iter": 309, "lr": 0.0241, "top1_acc": 0.25908, "top5_acc": 0.49557, "mean_class_accuracy": 0.25885} +{"mode": "train", "epoch": 102, "iter": 100, "lr": 0.02407, "memory": 15990, "data_time": 1.29814, "top1_acc": 0.32125, "top5_acc": 0.57281, "loss_cls": 3.89587, "loss": 3.89587, "time": 2.28948} +{"mode": "train", "epoch": 102, "iter": 200, "lr": 0.02405, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32016, "top5_acc": 0.57703, "loss_cls": 3.84736, "loss": 3.84736, "time": 0.82636} +{"mode": "train", "epoch": 102, "iter": 300, "lr": 0.02403, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32453, "top5_acc": 0.57422, "loss_cls": 3.87518, "loss": 3.87518, "time": 0.82363} +{"mode": "train", "epoch": 102, "iter": 400, "lr": 0.024, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32266, "top5_acc": 0.57422, "loss_cls": 3.89958, "loss": 3.89958, "time": 0.8276} +{"mode": "train", "epoch": 102, "iter": 500, "lr": 0.02398, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32484, "top5_acc": 0.58625, "loss_cls": 3.82839, "loss": 3.82839, "time": 0.81733} +{"mode": "train", "epoch": 102, "iter": 600, "lr": 0.02396, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32312, "top5_acc": 0.58078, "loss_cls": 3.86017, "loss": 3.86017, "time": 0.82559} +{"mode": "train", "epoch": 102, "iter": 700, "lr": 0.02393, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32062, "top5_acc": 0.56875, "loss_cls": 3.89393, "loss": 3.89393, "time": 0.82677} +{"mode": "train", "epoch": 102, "iter": 800, "lr": 0.02391, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32516, "top5_acc": 0.58641, "loss_cls": 3.84294, "loss": 3.84294, "time": 0.82767} +{"mode": "train", "epoch": 102, "iter": 900, "lr": 0.02388, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33141, "top5_acc": 0.57359, "loss_cls": 3.85789, "loss": 3.85789, "time": 0.82449} +{"mode": "train", "epoch": 102, "iter": 1000, "lr": 0.02386, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31734, "top5_acc": 0.57344, "loss_cls": 3.8738, "loss": 3.8738, "time": 0.82319} +{"mode": "train", "epoch": 102, "iter": 1100, "lr": 0.02384, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31484, "top5_acc": 0.56609, "loss_cls": 3.91824, "loss": 3.91824, "time": 0.82348} +{"mode": "train", "epoch": 102, "iter": 1200, "lr": 0.02381, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32016, "top5_acc": 0.57422, "loss_cls": 3.90623, "loss": 3.90623, "time": 0.81514} +{"mode": "train", "epoch": 102, "iter": 1300, "lr": 0.02379, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31766, "top5_acc": 0.58047, "loss_cls": 3.90941, "loss": 3.90941, "time": 0.81706} +{"mode": "train", "epoch": 102, "iter": 1400, "lr": 0.02376, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31125, "top5_acc": 0.58203, "loss_cls": 3.89487, "loss": 3.89487, "time": 0.81984} +{"mode": "train", "epoch": 102, "iter": 1500, "lr": 0.02374, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32812, "top5_acc": 0.575, "loss_cls": 3.86767, "loss": 3.86767, "time": 0.81834} +{"mode": "train", "epoch": 102, "iter": 1600, "lr": 0.02372, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31188, "top5_acc": 0.57031, "loss_cls": 3.92365, "loss": 3.92365, "time": 0.81963} +{"mode": "train", "epoch": 102, "iter": 1700, "lr": 0.02369, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31656, "top5_acc": 0.57078, "loss_cls": 3.90974, "loss": 3.90974, "time": 0.82044} +{"mode": "train", "epoch": 102, "iter": 1800, "lr": 0.02367, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31438, "top5_acc": 0.56812, "loss_cls": 3.918, "loss": 3.918, "time": 0.81773} +{"mode": "train", "epoch": 102, "iter": 1900, "lr": 0.02365, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.315, "top5_acc": 0.56891, "loss_cls": 3.90873, "loss": 3.90873, "time": 0.82138} +{"mode": "train", "epoch": 102, "iter": 2000, "lr": 0.02362, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31172, "top5_acc": 0.56766, "loss_cls": 3.94867, "loss": 3.94867, "time": 0.82061} +{"mode": "train", "epoch": 102, "iter": 2100, "lr": 0.0236, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32406, "top5_acc": 0.56922, "loss_cls": 3.87879, "loss": 3.87879, "time": 0.82239} +{"mode": "train", "epoch": 102, "iter": 2200, "lr": 0.02357, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32031, "top5_acc": 0.57172, "loss_cls": 3.89738, "loss": 3.89738, "time": 0.81566} +{"mode": "train", "epoch": 102, "iter": 2300, "lr": 0.02355, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31297, "top5_acc": 0.56453, "loss_cls": 3.95134, "loss": 3.95134, "time": 0.83193} +{"mode": "train", "epoch": 102, "iter": 2400, "lr": 0.02353, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31938, "top5_acc": 0.57438, "loss_cls": 3.87747, "loss": 3.87747, "time": 0.82255} +{"mode": "train", "epoch": 102, "iter": 2500, "lr": 0.0235, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32281, "top5_acc": 0.56891, "loss_cls": 3.91206, "loss": 3.91206, "time": 0.82565} +{"mode": "train", "epoch": 102, "iter": 2600, "lr": 0.02348, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32203, "top5_acc": 0.57594, "loss_cls": 3.87482, "loss": 3.87482, "time": 0.82819} +{"mode": "train", "epoch": 102, "iter": 2700, "lr": 0.02346, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31891, "top5_acc": 0.57609, "loss_cls": 3.88413, "loss": 3.88413, "time": 0.82193} +{"mode": "train", "epoch": 102, "iter": 2800, "lr": 0.02343, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32203, "top5_acc": 0.57, "loss_cls": 3.88678, "loss": 3.88678, "time": 0.81963} +{"mode": "train", "epoch": 102, "iter": 2900, "lr": 0.02341, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31734, "top5_acc": 0.57453, "loss_cls": 3.88942, "loss": 3.88942, "time": 0.81848} +{"mode": "train", "epoch": 102, "iter": 3000, "lr": 0.02339, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32391, "top5_acc": 0.58, "loss_cls": 3.8738, "loss": 3.8738, "time": 0.82258} +{"mode": "train", "epoch": 102, "iter": 3100, "lr": 0.02336, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31203, "top5_acc": 0.56563, "loss_cls": 3.93127, "loss": 3.93127, "time": 0.81821} +{"mode": "train", "epoch": 102, "iter": 3200, "lr": 0.02334, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32281, "top5_acc": 0.57672, "loss_cls": 3.88808, "loss": 3.88808, "time": 0.8204} +{"mode": "train", "epoch": 102, "iter": 3300, "lr": 0.02331, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31906, "top5_acc": 0.56312, "loss_cls": 3.92806, "loss": 3.92806, "time": 0.81973} +{"mode": "train", "epoch": 102, "iter": 3400, "lr": 0.02329, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31312, "top5_acc": 0.57266, "loss_cls": 3.92612, "loss": 3.92612, "time": 0.82234} +{"mode": "train", "epoch": 102, "iter": 3500, "lr": 0.02327, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31547, "top5_acc": 0.56484, "loss_cls": 3.92657, "loss": 3.92657, "time": 0.81597} +{"mode": "train", "epoch": 102, "iter": 3600, "lr": 0.02324, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32516, "top5_acc": 0.57641, "loss_cls": 3.88115, "loss": 3.88115, "time": 0.81531} +{"mode": "train", "epoch": 102, "iter": 3700, "lr": 0.02322, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31812, "top5_acc": 0.57391, "loss_cls": 3.9256, "loss": 3.9256, "time": 0.81584} +{"mode": "val", "epoch": 102, "iter": 309, "lr": 0.02321, "top1_acc": 0.25726, "top5_acc": 0.5016, "mean_class_accuracy": 0.257} +{"mode": "train", "epoch": 103, "iter": 100, "lr": 0.02319, "memory": 15990, "data_time": 1.3361, "top1_acc": 0.33578, "top5_acc": 0.59141, "loss_cls": 3.77802, "loss": 3.77802, "time": 2.32557} +{"mode": "train", "epoch": 103, "iter": 200, "lr": 0.02316, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.31469, "top5_acc": 0.57719, "loss_cls": 3.87614, "loss": 3.87614, "time": 0.82842} +{"mode": "train", "epoch": 103, "iter": 300, "lr": 0.02314, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32672, "top5_acc": 0.57484, "loss_cls": 3.86783, "loss": 3.86783, "time": 0.82056} +{"mode": "train", "epoch": 103, "iter": 400, "lr": 0.02311, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33672, "top5_acc": 0.58328, "loss_cls": 3.80113, "loss": 3.80113, "time": 0.82635} +{"mode": "train", "epoch": 103, "iter": 500, "lr": 0.02309, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.32531, "top5_acc": 0.58938, "loss_cls": 3.82514, "loss": 3.82514, "time": 0.82584} +{"mode": "train", "epoch": 103, "iter": 600, "lr": 0.02307, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.32328, "top5_acc": 0.58469, "loss_cls": 3.8248, "loss": 3.8248, "time": 0.82289} +{"mode": "train", "epoch": 103, "iter": 700, "lr": 0.02304, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32609, "top5_acc": 0.58281, "loss_cls": 3.83869, "loss": 3.83869, "time": 0.82095} +{"mode": "train", "epoch": 103, "iter": 800, "lr": 0.02302, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32781, "top5_acc": 0.58141, "loss_cls": 3.85861, "loss": 3.85861, "time": 0.8233} +{"mode": "train", "epoch": 103, "iter": 900, "lr": 0.023, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.32734, "top5_acc": 0.57891, "loss_cls": 3.85544, "loss": 3.85544, "time": 0.82716} +{"mode": "train", "epoch": 103, "iter": 1000, "lr": 0.02297, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32469, "top5_acc": 0.58125, "loss_cls": 3.87962, "loss": 3.87962, "time": 0.82808} +{"mode": "train", "epoch": 103, "iter": 1100, "lr": 0.02295, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32375, "top5_acc": 0.57703, "loss_cls": 3.8591, "loss": 3.8591, "time": 0.81785} +{"mode": "train", "epoch": 103, "iter": 1200, "lr": 0.02293, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32734, "top5_acc": 0.58391, "loss_cls": 3.84864, "loss": 3.84864, "time": 0.81836} +{"mode": "train", "epoch": 103, "iter": 1300, "lr": 0.0229, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32125, "top5_acc": 0.57391, "loss_cls": 3.87424, "loss": 3.87424, "time": 0.82745} +{"mode": "train", "epoch": 103, "iter": 1400, "lr": 0.02288, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31047, "top5_acc": 0.56219, "loss_cls": 3.94695, "loss": 3.94695, "time": 0.81763} +{"mode": "train", "epoch": 103, "iter": 1500, "lr": 0.02286, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32906, "top5_acc": 0.58203, "loss_cls": 3.8678, "loss": 3.8678, "time": 0.81825} +{"mode": "train", "epoch": 103, "iter": 1600, "lr": 0.02283, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31344, "top5_acc": 0.57953, "loss_cls": 3.88792, "loss": 3.88792, "time": 0.81921} +{"mode": "train", "epoch": 103, "iter": 1700, "lr": 0.02281, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31938, "top5_acc": 0.56672, "loss_cls": 3.91526, "loss": 3.91526, "time": 0.81666} +{"mode": "train", "epoch": 103, "iter": 1800, "lr": 0.02279, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32406, "top5_acc": 0.57984, "loss_cls": 3.8453, "loss": 3.8453, "time": 0.81765} +{"mode": "train", "epoch": 103, "iter": 1900, "lr": 0.02276, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32203, "top5_acc": 0.58141, "loss_cls": 3.8665, "loss": 3.8665, "time": 0.81865} +{"mode": "train", "epoch": 103, "iter": 2000, "lr": 0.02274, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31734, "top5_acc": 0.57578, "loss_cls": 3.90627, "loss": 3.90627, "time": 0.81827} +{"mode": "train", "epoch": 103, "iter": 2100, "lr": 0.02272, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32406, "top5_acc": 0.58141, "loss_cls": 3.86033, "loss": 3.86033, "time": 0.82703} +{"mode": "train", "epoch": 103, "iter": 2200, "lr": 0.02269, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.31719, "top5_acc": 0.56906, "loss_cls": 3.9203, "loss": 3.9203, "time": 0.8261} +{"mode": "train", "epoch": 103, "iter": 2300, "lr": 0.02267, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.32828, "top5_acc": 0.58516, "loss_cls": 3.83879, "loss": 3.83879, "time": 0.82365} +{"mode": "train", "epoch": 103, "iter": 2400, "lr": 0.02264, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32125, "top5_acc": 0.57172, "loss_cls": 3.87022, "loss": 3.87022, "time": 0.82343} +{"mode": "train", "epoch": 103, "iter": 2500, "lr": 0.02262, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31266, "top5_acc": 0.5775, "loss_cls": 3.89629, "loss": 3.89629, "time": 0.82362} +{"mode": "train", "epoch": 103, "iter": 2600, "lr": 0.0226, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32094, "top5_acc": 0.57797, "loss_cls": 3.87199, "loss": 3.87199, "time": 0.81852} +{"mode": "train", "epoch": 103, "iter": 2700, "lr": 0.02257, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31656, "top5_acc": 0.56484, "loss_cls": 3.91608, "loss": 3.91608, "time": 0.82146} +{"mode": "train", "epoch": 103, "iter": 2800, "lr": 0.02255, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32281, "top5_acc": 0.57922, "loss_cls": 3.87792, "loss": 3.87792, "time": 0.82336} +{"mode": "train", "epoch": 103, "iter": 2900, "lr": 0.02253, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32062, "top5_acc": 0.575, "loss_cls": 3.88175, "loss": 3.88175, "time": 0.82256} +{"mode": "train", "epoch": 103, "iter": 3000, "lr": 0.0225, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32859, "top5_acc": 0.57641, "loss_cls": 3.84069, "loss": 3.84069, "time": 0.81735} +{"mode": "train", "epoch": 103, "iter": 3100, "lr": 0.02248, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31672, "top5_acc": 0.57281, "loss_cls": 3.89965, "loss": 3.89965, "time": 0.8259} +{"mode": "train", "epoch": 103, "iter": 3200, "lr": 0.02246, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31844, "top5_acc": 0.57094, "loss_cls": 3.90405, "loss": 3.90405, "time": 0.81541} +{"mode": "train", "epoch": 103, "iter": 3300, "lr": 0.02243, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31641, "top5_acc": 0.56734, "loss_cls": 3.92592, "loss": 3.92592, "time": 0.82123} +{"mode": "train", "epoch": 103, "iter": 3400, "lr": 0.02241, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32688, "top5_acc": 0.58297, "loss_cls": 3.85387, "loss": 3.85387, "time": 0.81858} +{"mode": "train", "epoch": 103, "iter": 3500, "lr": 0.02239, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32141, "top5_acc": 0.57359, "loss_cls": 3.89891, "loss": 3.89891, "time": 0.81583} +{"mode": "train", "epoch": 103, "iter": 3600, "lr": 0.02236, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31344, "top5_acc": 0.56344, "loss_cls": 3.92189, "loss": 3.92189, "time": 0.82246} +{"mode": "train", "epoch": 103, "iter": 3700, "lr": 0.02234, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31656, "top5_acc": 0.57469, "loss_cls": 3.87911, "loss": 3.87911, "time": 0.81492} +{"mode": "val", "epoch": 103, "iter": 309, "lr": 0.02233, "top1_acc": 0.26247, "top5_acc": 0.51046, "mean_class_accuracy": 0.26235} +{"mode": "train", "epoch": 104, "iter": 100, "lr": 0.02231, "memory": 15990, "data_time": 1.32352, "top1_acc": 0.32766, "top5_acc": 0.58766, "loss_cls": 3.83352, "loss": 3.83352, "time": 2.31116} +{"mode": "train", "epoch": 104, "iter": 200, "lr": 0.02228, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32172, "top5_acc": 0.57984, "loss_cls": 3.85777, "loss": 3.85777, "time": 0.82603} +{"mode": "train", "epoch": 104, "iter": 300, "lr": 0.02226, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32562, "top5_acc": 0.5825, "loss_cls": 3.84152, "loss": 3.84152, "time": 0.82413} +{"mode": "train", "epoch": 104, "iter": 400, "lr": 0.02224, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32891, "top5_acc": 0.5775, "loss_cls": 3.87405, "loss": 3.87405, "time": 0.8313} +{"mode": "train", "epoch": 104, "iter": 500, "lr": 0.02221, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32, "top5_acc": 0.57563, "loss_cls": 3.85657, "loss": 3.85657, "time": 0.8298} +{"mode": "train", "epoch": 104, "iter": 600, "lr": 0.02219, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32656, "top5_acc": 0.57094, "loss_cls": 3.85633, "loss": 3.85633, "time": 0.82936} +{"mode": "train", "epoch": 104, "iter": 700, "lr": 0.02217, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.33391, "top5_acc": 0.58531, "loss_cls": 3.79867, "loss": 3.79867, "time": 0.82989} +{"mode": "train", "epoch": 104, "iter": 800, "lr": 0.02214, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32688, "top5_acc": 0.58422, "loss_cls": 3.80299, "loss": 3.80299, "time": 0.82496} +{"mode": "train", "epoch": 104, "iter": 900, "lr": 0.02212, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32984, "top5_acc": 0.58672, "loss_cls": 3.81234, "loss": 3.81234, "time": 0.8256} +{"mode": "train", "epoch": 104, "iter": 1000, "lr": 0.0221, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32688, "top5_acc": 0.57766, "loss_cls": 3.8817, "loss": 3.8817, "time": 0.82121} +{"mode": "train", "epoch": 104, "iter": 1100, "lr": 0.02208, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32312, "top5_acc": 0.58766, "loss_cls": 3.8432, "loss": 3.8432, "time": 0.81929} +{"mode": "train", "epoch": 104, "iter": 1200, "lr": 0.02205, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32531, "top5_acc": 0.57203, "loss_cls": 3.86576, "loss": 3.86576, "time": 0.82227} +{"mode": "train", "epoch": 104, "iter": 1300, "lr": 0.02203, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32875, "top5_acc": 0.58078, "loss_cls": 3.85367, "loss": 3.85367, "time": 0.81736} +{"mode": "train", "epoch": 104, "iter": 1400, "lr": 0.02201, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.31906, "top5_acc": 0.57234, "loss_cls": 3.89094, "loss": 3.89094, "time": 0.8213} +{"mode": "train", "epoch": 104, "iter": 1500, "lr": 0.02198, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33031, "top5_acc": 0.59078, "loss_cls": 3.82488, "loss": 3.82488, "time": 0.81718} +{"mode": "train", "epoch": 104, "iter": 1600, "lr": 0.02196, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32156, "top5_acc": 0.57563, "loss_cls": 3.88048, "loss": 3.88048, "time": 0.81891} +{"mode": "train", "epoch": 104, "iter": 1700, "lr": 0.02194, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33438, "top5_acc": 0.58797, "loss_cls": 3.82368, "loss": 3.82368, "time": 0.81841} +{"mode": "train", "epoch": 104, "iter": 1800, "lr": 0.02191, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33297, "top5_acc": 0.57781, "loss_cls": 3.83959, "loss": 3.83959, "time": 0.82017} +{"mode": "train", "epoch": 104, "iter": 1900, "lr": 0.02189, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32125, "top5_acc": 0.58375, "loss_cls": 3.86439, "loss": 3.86439, "time": 0.82166} +{"mode": "train", "epoch": 104, "iter": 2000, "lr": 0.02187, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32594, "top5_acc": 0.58312, "loss_cls": 3.82872, "loss": 3.82872, "time": 0.81524} +{"mode": "train", "epoch": 104, "iter": 2100, "lr": 0.02184, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32859, "top5_acc": 0.58547, "loss_cls": 3.84176, "loss": 3.84176, "time": 0.82594} +{"mode": "train", "epoch": 104, "iter": 2200, "lr": 0.02182, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32641, "top5_acc": 0.58094, "loss_cls": 3.861, "loss": 3.861, "time": 0.81696} +{"mode": "train", "epoch": 104, "iter": 2300, "lr": 0.0218, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32141, "top5_acc": 0.57828, "loss_cls": 3.87732, "loss": 3.87732, "time": 0.82284} +{"mode": "train", "epoch": 104, "iter": 2400, "lr": 0.02177, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32875, "top5_acc": 0.58656, "loss_cls": 3.81947, "loss": 3.81947, "time": 0.82064} +{"mode": "train", "epoch": 104, "iter": 2500, "lr": 0.02175, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3225, "top5_acc": 0.56906, "loss_cls": 3.88967, "loss": 3.88967, "time": 0.82395} +{"mode": "train", "epoch": 104, "iter": 2600, "lr": 0.02173, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32391, "top5_acc": 0.57703, "loss_cls": 3.8857, "loss": 3.8857, "time": 0.82479} +{"mode": "train", "epoch": 104, "iter": 2700, "lr": 0.02171, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33516, "top5_acc": 0.58578, "loss_cls": 3.83639, "loss": 3.83639, "time": 0.81987} +{"mode": "train", "epoch": 104, "iter": 2800, "lr": 0.02168, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32125, "top5_acc": 0.57344, "loss_cls": 3.90788, "loss": 3.90788, "time": 0.82324} +{"mode": "train", "epoch": 104, "iter": 2900, "lr": 0.02166, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32516, "top5_acc": 0.57391, "loss_cls": 3.89519, "loss": 3.89519, "time": 0.82667} +{"mode": "train", "epoch": 104, "iter": 3000, "lr": 0.02164, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32188, "top5_acc": 0.57547, "loss_cls": 3.89692, "loss": 3.89692, "time": 0.81979} +{"mode": "train", "epoch": 104, "iter": 3100, "lr": 0.02161, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32547, "top5_acc": 0.57406, "loss_cls": 3.84671, "loss": 3.84671, "time": 0.81938} +{"mode": "train", "epoch": 104, "iter": 3200, "lr": 0.02159, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32453, "top5_acc": 0.58, "loss_cls": 3.85426, "loss": 3.85426, "time": 0.8206} +{"mode": "train", "epoch": 104, "iter": 3300, "lr": 0.02157, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3225, "top5_acc": 0.58062, "loss_cls": 3.86943, "loss": 3.86943, "time": 0.81679} +{"mode": "train", "epoch": 104, "iter": 3400, "lr": 0.02154, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33625, "top5_acc": 0.59656, "loss_cls": 3.78973, "loss": 3.78973, "time": 0.81817} +{"mode": "train", "epoch": 104, "iter": 3500, "lr": 0.02152, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33094, "top5_acc": 0.59219, "loss_cls": 3.8335, "loss": 3.8335, "time": 0.82125} +{"mode": "train", "epoch": 104, "iter": 3600, "lr": 0.0215, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31672, "top5_acc": 0.5725, "loss_cls": 3.90044, "loss": 3.90044, "time": 0.81919} +{"mode": "train", "epoch": 104, "iter": 3700, "lr": 0.02148, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32812, "top5_acc": 0.57422, "loss_cls": 3.86995, "loss": 3.86995, "time": 0.82568} +{"mode": "val", "epoch": 104, "iter": 309, "lr": 0.02146, "top1_acc": 0.26885, "top5_acc": 0.51218, "mean_class_accuracy": 0.2687} +{"mode": "train", "epoch": 105, "iter": 100, "lr": 0.02144, "memory": 15990, "data_time": 1.31022, "top1_acc": 0.33781, "top5_acc": 0.59375, "loss_cls": 3.80213, "loss": 3.80213, "time": 2.30193} +{"mode": "train", "epoch": 105, "iter": 200, "lr": 0.02142, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33141, "top5_acc": 0.58656, "loss_cls": 3.83767, "loss": 3.83767, "time": 0.8241} +{"mode": "train", "epoch": 105, "iter": 300, "lr": 0.0214, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33594, "top5_acc": 0.59375, "loss_cls": 3.7816, "loss": 3.7816, "time": 0.82554} +{"mode": "train", "epoch": 105, "iter": 400, "lr": 0.02137, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.33219, "top5_acc": 0.59156, "loss_cls": 3.76769, "loss": 3.76769, "time": 0.82507} +{"mode": "train", "epoch": 105, "iter": 500, "lr": 0.02135, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33344, "top5_acc": 0.59016, "loss_cls": 3.77488, "loss": 3.77488, "time": 0.81908} +{"mode": "train", "epoch": 105, "iter": 600, "lr": 0.02133, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.32953, "top5_acc": 0.58344, "loss_cls": 3.84362, "loss": 3.84362, "time": 0.82219} +{"mode": "train", "epoch": 105, "iter": 700, "lr": 0.0213, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32875, "top5_acc": 0.58094, "loss_cls": 3.83181, "loss": 3.83181, "time": 0.83309} +{"mode": "train", "epoch": 105, "iter": 800, "lr": 0.02128, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32922, "top5_acc": 0.57938, "loss_cls": 3.80805, "loss": 3.80805, "time": 0.82575} +{"mode": "train", "epoch": 105, "iter": 900, "lr": 0.02126, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32281, "top5_acc": 0.58375, "loss_cls": 3.85564, "loss": 3.85564, "time": 0.82511} +{"mode": "train", "epoch": 105, "iter": 1000, "lr": 0.02124, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.335, "top5_acc": 0.58375, "loss_cls": 3.81834, "loss": 3.81834, "time": 0.81808} +{"mode": "train", "epoch": 105, "iter": 1100, "lr": 0.02121, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33344, "top5_acc": 0.58625, "loss_cls": 3.80436, "loss": 3.80436, "time": 0.81919} +{"mode": "train", "epoch": 105, "iter": 1200, "lr": 0.02119, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.31609, "top5_acc": 0.57094, "loss_cls": 3.90429, "loss": 3.90429, "time": 0.817} +{"mode": "train", "epoch": 105, "iter": 1300, "lr": 0.02117, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33109, "top5_acc": 0.57953, "loss_cls": 3.85236, "loss": 3.85236, "time": 0.81874} +{"mode": "train", "epoch": 105, "iter": 1400, "lr": 0.02114, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33844, "top5_acc": 0.59844, "loss_cls": 3.76254, "loss": 3.76254, "time": 0.81718} +{"mode": "train", "epoch": 105, "iter": 1500, "lr": 0.02112, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34156, "top5_acc": 0.59297, "loss_cls": 3.76114, "loss": 3.76114, "time": 0.82559} +{"mode": "train", "epoch": 105, "iter": 1600, "lr": 0.0211, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31875, "top5_acc": 0.57719, "loss_cls": 3.85995, "loss": 3.85995, "time": 0.81816} +{"mode": "train", "epoch": 105, "iter": 1700, "lr": 0.02108, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.32375, "top5_acc": 0.57984, "loss_cls": 3.87221, "loss": 3.87221, "time": 0.82009} +{"mode": "train", "epoch": 105, "iter": 1800, "lr": 0.02105, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32266, "top5_acc": 0.575, "loss_cls": 3.87163, "loss": 3.87163, "time": 0.81771} +{"mode": "train", "epoch": 105, "iter": 1900, "lr": 0.02103, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32719, "top5_acc": 0.56375, "loss_cls": 3.88425, "loss": 3.88425, "time": 0.81816} +{"mode": "train", "epoch": 105, "iter": 2000, "lr": 0.02101, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32312, "top5_acc": 0.57828, "loss_cls": 3.8644, "loss": 3.8644, "time": 0.81771} +{"mode": "train", "epoch": 105, "iter": 2100, "lr": 0.02098, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32172, "top5_acc": 0.58531, "loss_cls": 3.84545, "loss": 3.84545, "time": 0.82221} +{"mode": "train", "epoch": 105, "iter": 2200, "lr": 0.02096, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33859, "top5_acc": 0.59328, "loss_cls": 3.79848, "loss": 3.79848, "time": 0.81632} +{"mode": "train", "epoch": 105, "iter": 2300, "lr": 0.02094, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.32125, "top5_acc": 0.57859, "loss_cls": 3.88683, "loss": 3.88683, "time": 0.823} +{"mode": "train", "epoch": 105, "iter": 2400, "lr": 0.02092, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33094, "top5_acc": 0.58812, "loss_cls": 3.81871, "loss": 3.81871, "time": 0.82907} +{"mode": "train", "epoch": 105, "iter": 2500, "lr": 0.02089, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31969, "top5_acc": 0.57312, "loss_cls": 3.89168, "loss": 3.89168, "time": 0.81778} +{"mode": "train", "epoch": 105, "iter": 2600, "lr": 0.02087, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33297, "top5_acc": 0.58922, "loss_cls": 3.81863, "loss": 3.81863, "time": 0.82543} +{"mode": "train", "epoch": 105, "iter": 2700, "lr": 0.02085, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32781, "top5_acc": 0.58375, "loss_cls": 3.83235, "loss": 3.83235, "time": 0.82006} +{"mode": "train", "epoch": 105, "iter": 2800, "lr": 0.02083, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33125, "top5_acc": 0.58062, "loss_cls": 3.83638, "loss": 3.83638, "time": 0.82163} +{"mode": "train", "epoch": 105, "iter": 2900, "lr": 0.0208, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32766, "top5_acc": 0.58062, "loss_cls": 3.81884, "loss": 3.81884, "time": 0.81821} +{"mode": "train", "epoch": 105, "iter": 3000, "lr": 0.02078, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32234, "top5_acc": 0.5875, "loss_cls": 3.83047, "loss": 3.83047, "time": 0.81465} +{"mode": "train", "epoch": 105, "iter": 3100, "lr": 0.02076, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32312, "top5_acc": 0.57828, "loss_cls": 3.87272, "loss": 3.87272, "time": 0.81743} +{"mode": "train", "epoch": 105, "iter": 3200, "lr": 0.02073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32422, "top5_acc": 0.58703, "loss_cls": 3.85165, "loss": 3.85165, "time": 0.81907} +{"mode": "train", "epoch": 105, "iter": 3300, "lr": 0.02071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32266, "top5_acc": 0.58031, "loss_cls": 3.85767, "loss": 3.85767, "time": 0.82017} +{"mode": "train", "epoch": 105, "iter": 3400, "lr": 0.02069, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.335, "top5_acc": 0.58719, "loss_cls": 3.8222, "loss": 3.8222, "time": 0.81911} +{"mode": "train", "epoch": 105, "iter": 3500, "lr": 0.02067, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32766, "top5_acc": 0.58516, "loss_cls": 3.82803, "loss": 3.82803, "time": 0.81947} +{"mode": "train", "epoch": 105, "iter": 3600, "lr": 0.02064, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33281, "top5_acc": 0.58453, "loss_cls": 3.84141, "loss": 3.84141, "time": 0.81546} +{"mode": "train", "epoch": 105, "iter": 3700, "lr": 0.02062, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32141, "top5_acc": 0.57656, "loss_cls": 3.85845, "loss": 3.85845, "time": 0.81993} +{"mode": "val", "epoch": 105, "iter": 309, "lr": 0.02061, "top1_acc": 0.2688, "top5_acc": 0.51117, "mean_class_accuracy": 0.26858} +{"mode": "train", "epoch": 106, "iter": 100, "lr": 0.02059, "memory": 15990, "data_time": 1.32679, "top1_acc": 0.34406, "top5_acc": 0.59281, "loss_cls": 3.73092, "loss": 3.73092, "time": 2.31765} +{"mode": "train", "epoch": 106, "iter": 200, "lr": 0.02057, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.33875, "top5_acc": 0.59375, "loss_cls": 3.80005, "loss": 3.80005, "time": 0.82407} +{"mode": "train", "epoch": 106, "iter": 300, "lr": 0.02054, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33422, "top5_acc": 0.59406, "loss_cls": 3.75311, "loss": 3.75311, "time": 0.82617} +{"mode": "train", "epoch": 106, "iter": 400, "lr": 0.02052, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33516, "top5_acc": 0.5925, "loss_cls": 3.76907, "loss": 3.76907, "time": 0.8214} +{"mode": "train", "epoch": 106, "iter": 500, "lr": 0.0205, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33359, "top5_acc": 0.58797, "loss_cls": 3.81197, "loss": 3.81197, "time": 0.81812} +{"mode": "train", "epoch": 106, "iter": 600, "lr": 0.02048, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32344, "top5_acc": 0.58562, "loss_cls": 3.84112, "loss": 3.84112, "time": 0.82225} +{"mode": "train", "epoch": 106, "iter": 700, "lr": 0.02045, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33391, "top5_acc": 0.58938, "loss_cls": 3.80547, "loss": 3.80547, "time": 0.82866} +{"mode": "train", "epoch": 106, "iter": 800, "lr": 0.02043, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3275, "top5_acc": 0.59703, "loss_cls": 3.77202, "loss": 3.77202, "time": 0.82314} +{"mode": "train", "epoch": 106, "iter": 900, "lr": 0.02041, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32766, "top5_acc": 0.58422, "loss_cls": 3.85282, "loss": 3.85282, "time": 0.83073} +{"mode": "train", "epoch": 106, "iter": 1000, "lr": 0.02039, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33422, "top5_acc": 0.59328, "loss_cls": 3.80217, "loss": 3.80217, "time": 0.82128} +{"mode": "train", "epoch": 106, "iter": 1100, "lr": 0.02036, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33406, "top5_acc": 0.59438, "loss_cls": 3.7901, "loss": 3.7901, "time": 0.81872} +{"mode": "train", "epoch": 106, "iter": 1200, "lr": 0.02034, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34234, "top5_acc": 0.59641, "loss_cls": 3.76664, "loss": 3.76664, "time": 0.82224} +{"mode": "train", "epoch": 106, "iter": 1300, "lr": 0.02032, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32625, "top5_acc": 0.59016, "loss_cls": 3.80563, "loss": 3.80563, "time": 0.81844} +{"mode": "train", "epoch": 106, "iter": 1400, "lr": 0.0203, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32031, "top5_acc": 0.58016, "loss_cls": 3.87235, "loss": 3.87235, "time": 0.81742} +{"mode": "train", "epoch": 106, "iter": 1500, "lr": 0.02027, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33797, "top5_acc": 0.59562, "loss_cls": 3.79676, "loss": 3.79676, "time": 0.82211} +{"mode": "train", "epoch": 106, "iter": 1600, "lr": 0.02025, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3275, "top5_acc": 0.59266, "loss_cls": 3.79459, "loss": 3.79459, "time": 0.82401} +{"mode": "train", "epoch": 106, "iter": 1700, "lr": 0.02023, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.33734, "top5_acc": 0.59656, "loss_cls": 3.7822, "loss": 3.7822, "time": 0.82006} +{"mode": "train", "epoch": 106, "iter": 1800, "lr": 0.02021, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33219, "top5_acc": 0.58594, "loss_cls": 3.83694, "loss": 3.83694, "time": 0.82234} +{"mode": "train", "epoch": 106, "iter": 1900, "lr": 0.02018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32594, "top5_acc": 0.57672, "loss_cls": 3.84358, "loss": 3.84358, "time": 0.82695} +{"mode": "train", "epoch": 106, "iter": 2000, "lr": 0.02016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33344, "top5_acc": 0.59078, "loss_cls": 3.8036, "loss": 3.8036, "time": 0.82254} +{"mode": "train", "epoch": 106, "iter": 2100, "lr": 0.02014, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32234, "top5_acc": 0.5825, "loss_cls": 3.84797, "loss": 3.84797, "time": 0.82816} +{"mode": "train", "epoch": 106, "iter": 2200, "lr": 0.02012, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.31531, "top5_acc": 0.56516, "loss_cls": 3.90428, "loss": 3.90428, "time": 0.81907} +{"mode": "train", "epoch": 106, "iter": 2300, "lr": 0.02009, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33109, "top5_acc": 0.59203, "loss_cls": 3.80222, "loss": 3.80222, "time": 0.82193} +{"mode": "train", "epoch": 106, "iter": 2400, "lr": 0.02007, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33188, "top5_acc": 0.595, "loss_cls": 3.78197, "loss": 3.78197, "time": 0.82201} +{"mode": "train", "epoch": 106, "iter": 2500, "lr": 0.02005, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.33422, "top5_acc": 0.59422, "loss_cls": 3.79756, "loss": 3.79756, "time": 0.81851} +{"mode": "train", "epoch": 106, "iter": 2600, "lr": 0.02003, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32875, "top5_acc": 0.58625, "loss_cls": 3.84142, "loss": 3.84142, "time": 0.82401} +{"mode": "train", "epoch": 106, "iter": 2700, "lr": 0.02, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32328, "top5_acc": 0.57141, "loss_cls": 3.88626, "loss": 3.88626, "time": 0.82229} +{"mode": "train", "epoch": 106, "iter": 2800, "lr": 0.01998, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32719, "top5_acc": 0.58062, "loss_cls": 3.84064, "loss": 3.84064, "time": 0.81952} +{"mode": "train", "epoch": 106, "iter": 2900, "lr": 0.01996, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33188, "top5_acc": 0.57453, "loss_cls": 3.81926, "loss": 3.81926, "time": 0.8156} +{"mode": "train", "epoch": 106, "iter": 3000, "lr": 0.01994, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.31797, "top5_acc": 0.57234, "loss_cls": 3.92552, "loss": 3.92552, "time": 0.8192} +{"mode": "train", "epoch": 106, "iter": 3100, "lr": 0.01991, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33031, "top5_acc": 0.58641, "loss_cls": 3.86236, "loss": 3.86236, "time": 0.81793} +{"mode": "train", "epoch": 106, "iter": 3200, "lr": 0.01989, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33641, "top5_acc": 0.58859, "loss_cls": 3.79274, "loss": 3.79274, "time": 0.81477} +{"mode": "train", "epoch": 106, "iter": 3300, "lr": 0.01987, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33141, "top5_acc": 0.58156, "loss_cls": 3.81822, "loss": 3.81822, "time": 0.81423} +{"mode": "train", "epoch": 106, "iter": 3400, "lr": 0.01985, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32141, "top5_acc": 0.56516, "loss_cls": 3.90267, "loss": 3.90267, "time": 0.82451} +{"mode": "train", "epoch": 106, "iter": 3500, "lr": 0.01983, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33406, "top5_acc": 0.58141, "loss_cls": 3.85118, "loss": 3.85118, "time": 0.81592} +{"mode": "train", "epoch": 106, "iter": 3600, "lr": 0.0198, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32938, "top5_acc": 0.58344, "loss_cls": 3.80435, "loss": 3.80435, "time": 0.82645} +{"mode": "train", "epoch": 106, "iter": 3700, "lr": 0.01978, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33438, "top5_acc": 0.58031, "loss_cls": 3.83334, "loss": 3.83334, "time": 0.82107} +{"mode": "val", "epoch": 106, "iter": 309, "lr": 0.01977, "top1_acc": 0.27149, "top5_acc": 0.51841, "mean_class_accuracy": 0.27133} +{"mode": "train", "epoch": 107, "iter": 100, "lr": 0.01975, "memory": 15990, "data_time": 1.33464, "top1_acc": 0.34469, "top5_acc": 0.60031, "loss_cls": 3.70641, "loss": 3.70641, "time": 2.32649} +{"mode": "train", "epoch": 107, "iter": 200, "lr": 0.01973, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.33547, "top5_acc": 0.595, "loss_cls": 3.75653, "loss": 3.75653, "time": 0.82614} +{"mode": "train", "epoch": 107, "iter": 300, "lr": 0.0197, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3375, "top5_acc": 0.60156, "loss_cls": 3.74464, "loss": 3.74464, "time": 0.82391} +{"mode": "train", "epoch": 107, "iter": 400, "lr": 0.01968, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.33719, "top5_acc": 0.59328, "loss_cls": 3.76502, "loss": 3.76502, "time": 0.82352} +{"mode": "train", "epoch": 107, "iter": 500, "lr": 0.01966, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34609, "top5_acc": 0.59672, "loss_cls": 3.74967, "loss": 3.74967, "time": 0.82246} +{"mode": "train", "epoch": 107, "iter": 600, "lr": 0.01964, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32984, "top5_acc": 0.58797, "loss_cls": 3.82379, "loss": 3.82379, "time": 0.82179} +{"mode": "train", "epoch": 107, "iter": 700, "lr": 0.01961, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.34125, "top5_acc": 0.59328, "loss_cls": 3.76546, "loss": 3.76546, "time": 0.8205} +{"mode": "train", "epoch": 107, "iter": 800, "lr": 0.01959, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34125, "top5_acc": 0.59, "loss_cls": 3.78403, "loss": 3.78403, "time": 0.82696} +{"mode": "train", "epoch": 107, "iter": 900, "lr": 0.01957, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33594, "top5_acc": 0.59203, "loss_cls": 3.77462, "loss": 3.77462, "time": 0.82055} +{"mode": "train", "epoch": 107, "iter": 1000, "lr": 0.01955, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33797, "top5_acc": 0.59547, "loss_cls": 3.78815, "loss": 3.78815, "time": 0.82163} +{"mode": "train", "epoch": 107, "iter": 1100, "lr": 0.01953, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33469, "top5_acc": 0.595, "loss_cls": 3.79744, "loss": 3.79744, "time": 0.8223} +{"mode": "train", "epoch": 107, "iter": 1200, "lr": 0.0195, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32375, "top5_acc": 0.58703, "loss_cls": 3.84426, "loss": 3.84426, "time": 0.82453} +{"mode": "train", "epoch": 107, "iter": 1300, "lr": 0.01948, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.345, "top5_acc": 0.59719, "loss_cls": 3.75798, "loss": 3.75798, "time": 0.82357} +{"mode": "train", "epoch": 107, "iter": 1400, "lr": 0.01946, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33188, "top5_acc": 0.58688, "loss_cls": 3.8127, "loss": 3.8127, "time": 0.82364} +{"mode": "train", "epoch": 107, "iter": 1500, "lr": 0.01944, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33719, "top5_acc": 0.59016, "loss_cls": 3.81564, "loss": 3.81564, "time": 0.8195} +{"mode": "train", "epoch": 107, "iter": 1600, "lr": 0.01942, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33359, "top5_acc": 0.58062, "loss_cls": 3.82111, "loss": 3.82111, "time": 0.82396} +{"mode": "train", "epoch": 107, "iter": 1700, "lr": 0.01939, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34375, "top5_acc": 0.60516, "loss_cls": 3.74229, "loss": 3.74229, "time": 0.82247} +{"mode": "train", "epoch": 107, "iter": 1800, "lr": 0.01937, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33344, "top5_acc": 0.59328, "loss_cls": 3.78707, "loss": 3.78707, "time": 0.8267} +{"mode": "train", "epoch": 107, "iter": 1900, "lr": 0.01935, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.32578, "top5_acc": 0.58031, "loss_cls": 3.84502, "loss": 3.84502, "time": 0.81587} +{"mode": "train", "epoch": 107, "iter": 2000, "lr": 0.01933, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34453, "top5_acc": 0.60469, "loss_cls": 3.76247, "loss": 3.76247, "time": 0.82092} +{"mode": "train", "epoch": 107, "iter": 2100, "lr": 0.0193, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32844, "top5_acc": 0.58172, "loss_cls": 3.83649, "loss": 3.83649, "time": 0.82728} +{"mode": "train", "epoch": 107, "iter": 2200, "lr": 0.01928, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33781, "top5_acc": 0.58641, "loss_cls": 3.82396, "loss": 3.82396, "time": 0.82497} +{"mode": "train", "epoch": 107, "iter": 2300, "lr": 0.01926, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.32859, "top5_acc": 0.57516, "loss_cls": 3.81422, "loss": 3.81422, "time": 0.82579} +{"mode": "train", "epoch": 107, "iter": 2400, "lr": 0.01924, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32109, "top5_acc": 0.57938, "loss_cls": 3.85296, "loss": 3.85296, "time": 0.82161} +{"mode": "train", "epoch": 107, "iter": 2500, "lr": 0.01922, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33641, "top5_acc": 0.58344, "loss_cls": 3.80496, "loss": 3.80496, "time": 0.82468} +{"mode": "train", "epoch": 107, "iter": 2600, "lr": 0.01919, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33141, "top5_acc": 0.58594, "loss_cls": 3.81405, "loss": 3.81405, "time": 0.81904} +{"mode": "train", "epoch": 107, "iter": 2700, "lr": 0.01917, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33797, "top5_acc": 0.59906, "loss_cls": 3.76846, "loss": 3.76846, "time": 0.81812} +{"mode": "train", "epoch": 107, "iter": 2800, "lr": 0.01915, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33, "top5_acc": 0.58625, "loss_cls": 3.81359, "loss": 3.81359, "time": 0.82352} +{"mode": "train", "epoch": 107, "iter": 2900, "lr": 0.01913, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33422, "top5_acc": 0.59141, "loss_cls": 3.80959, "loss": 3.80959, "time": 0.81807} +{"mode": "train", "epoch": 107, "iter": 3000, "lr": 0.01911, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32766, "top5_acc": 0.58453, "loss_cls": 3.81089, "loss": 3.81089, "time": 0.82676} +{"mode": "train", "epoch": 107, "iter": 3100, "lr": 0.01908, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32828, "top5_acc": 0.57984, "loss_cls": 3.84893, "loss": 3.84893, "time": 0.82076} +{"mode": "train", "epoch": 107, "iter": 3200, "lr": 0.01906, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33969, "top5_acc": 0.59062, "loss_cls": 3.81249, "loss": 3.81249, "time": 0.81984} +{"mode": "train", "epoch": 107, "iter": 3300, "lr": 0.01904, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33672, "top5_acc": 0.58828, "loss_cls": 3.81057, "loss": 3.81057, "time": 0.82416} +{"mode": "train", "epoch": 107, "iter": 3400, "lr": 0.01902, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32547, "top5_acc": 0.58016, "loss_cls": 3.81361, "loss": 3.81361, "time": 0.81926} +{"mode": "train", "epoch": 107, "iter": 3500, "lr": 0.019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32969, "top5_acc": 0.58609, "loss_cls": 3.81028, "loss": 3.81028, "time": 0.81855} +{"mode": "train", "epoch": 107, "iter": 3600, "lr": 0.01897, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32812, "top5_acc": 0.58609, "loss_cls": 3.82193, "loss": 3.82193, "time": 0.81717} +{"mode": "train", "epoch": 107, "iter": 3700, "lr": 0.01895, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32656, "top5_acc": 0.58047, "loss_cls": 3.84458, "loss": 3.84458, "time": 0.81645} +{"mode": "val", "epoch": 107, "iter": 309, "lr": 0.01894, "top1_acc": 0.27113, "top5_acc": 0.51811, "mean_class_accuracy": 0.27093} +{"mode": "train", "epoch": 108, "iter": 100, "lr": 0.01892, "memory": 15990, "data_time": 1.31906, "top1_acc": 0.34594, "top5_acc": 0.59828, "loss_cls": 3.75812, "loss": 3.75812, "time": 2.31049} +{"mode": "train", "epoch": 108, "iter": 200, "lr": 0.0189, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34531, "top5_acc": 0.60484, "loss_cls": 3.74573, "loss": 3.74573, "time": 0.82555} +{"mode": "train", "epoch": 108, "iter": 300, "lr": 0.01888, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34, "top5_acc": 0.59359, "loss_cls": 3.76193, "loss": 3.76193, "time": 0.83113} +{"mode": "train", "epoch": 108, "iter": 400, "lr": 0.01886, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34766, "top5_acc": 0.60062, "loss_cls": 3.76002, "loss": 3.76002, "time": 0.8266} +{"mode": "train", "epoch": 108, "iter": 500, "lr": 0.01883, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34891, "top5_acc": 0.60766, "loss_cls": 3.70009, "loss": 3.70009, "time": 0.82265} +{"mode": "train", "epoch": 108, "iter": 600, "lr": 0.01881, "memory": 15990, "data_time": 0.00046, "top1_acc": 0.34031, "top5_acc": 0.59422, "loss_cls": 3.78571, "loss": 3.78571, "time": 0.82836} +{"mode": "train", "epoch": 108, "iter": 700, "lr": 0.01879, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34375, "top5_acc": 0.58812, "loss_cls": 3.76769, "loss": 3.76769, "time": 0.8214} +{"mode": "train", "epoch": 108, "iter": 800, "lr": 0.01877, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34422, "top5_acc": 0.59109, "loss_cls": 3.74922, "loss": 3.74922, "time": 0.82222} +{"mode": "train", "epoch": 108, "iter": 900, "lr": 0.01875, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33938, "top5_acc": 0.59594, "loss_cls": 3.75434, "loss": 3.75434, "time": 0.81805} +{"mode": "train", "epoch": 108, "iter": 1000, "lr": 0.01872, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33109, "top5_acc": 0.595, "loss_cls": 3.77646, "loss": 3.77646, "time": 0.82294} +{"mode": "train", "epoch": 108, "iter": 1100, "lr": 0.0187, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34625, "top5_acc": 0.60109, "loss_cls": 3.73023, "loss": 3.73023, "time": 0.81772} +{"mode": "train", "epoch": 108, "iter": 1200, "lr": 0.01868, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34531, "top5_acc": 0.5925, "loss_cls": 3.75404, "loss": 3.75404, "time": 0.82099} +{"mode": "train", "epoch": 108, "iter": 1300, "lr": 0.01866, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34484, "top5_acc": 0.59203, "loss_cls": 3.76767, "loss": 3.76767, "time": 0.82016} +{"mode": "train", "epoch": 108, "iter": 1400, "lr": 0.01864, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33781, "top5_acc": 0.59859, "loss_cls": 3.77668, "loss": 3.77668, "time": 0.81786} +{"mode": "train", "epoch": 108, "iter": 1500, "lr": 0.01862, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33625, "top5_acc": 0.58969, "loss_cls": 3.79728, "loss": 3.79728, "time": 0.81785} +{"mode": "train", "epoch": 108, "iter": 1600, "lr": 0.01859, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33828, "top5_acc": 0.59359, "loss_cls": 3.77312, "loss": 3.77312, "time": 0.81619} +{"mode": "train", "epoch": 108, "iter": 1700, "lr": 0.01857, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.32922, "top5_acc": 0.58766, "loss_cls": 3.82327, "loss": 3.82327, "time": 0.81987} +{"mode": "train", "epoch": 108, "iter": 1800, "lr": 0.01855, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33109, "top5_acc": 0.59266, "loss_cls": 3.79859, "loss": 3.79859, "time": 0.82286} +{"mode": "train", "epoch": 108, "iter": 1900, "lr": 0.01853, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33672, "top5_acc": 0.595, "loss_cls": 3.76805, "loss": 3.76805, "time": 0.81895} +{"mode": "train", "epoch": 108, "iter": 2000, "lr": 0.01851, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34, "top5_acc": 0.5925, "loss_cls": 3.79534, "loss": 3.79534, "time": 0.81671} +{"mode": "train", "epoch": 108, "iter": 2100, "lr": 0.01848, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33531, "top5_acc": 0.59, "loss_cls": 3.77348, "loss": 3.77348, "time": 0.82219} +{"mode": "train", "epoch": 108, "iter": 2200, "lr": 0.01846, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33375, "top5_acc": 0.59156, "loss_cls": 3.78214, "loss": 3.78214, "time": 0.81951} +{"mode": "train", "epoch": 108, "iter": 2300, "lr": 0.01844, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33125, "top5_acc": 0.59141, "loss_cls": 3.81362, "loss": 3.81362, "time": 0.82856} +{"mode": "train", "epoch": 108, "iter": 2400, "lr": 0.01842, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33109, "top5_acc": 0.58906, "loss_cls": 3.83651, "loss": 3.83651, "time": 0.82543} +{"mode": "train", "epoch": 108, "iter": 2500, "lr": 0.0184, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.32516, "top5_acc": 0.58781, "loss_cls": 3.81861, "loss": 3.81861, "time": 0.8175} +{"mode": "train", "epoch": 108, "iter": 2600, "lr": 0.01838, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.33062, "top5_acc": 0.59234, "loss_cls": 3.79296, "loss": 3.79296, "time": 0.81849} +{"mode": "train", "epoch": 108, "iter": 2700, "lr": 0.01835, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33422, "top5_acc": 0.59578, "loss_cls": 3.78194, "loss": 3.78194, "time": 0.82073} +{"mode": "train", "epoch": 108, "iter": 2800, "lr": 0.01833, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33953, "top5_acc": 0.59641, "loss_cls": 3.76718, "loss": 3.76718, "time": 0.82181} +{"mode": "train", "epoch": 108, "iter": 2900, "lr": 0.01831, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34203, "top5_acc": 0.59672, "loss_cls": 3.77906, "loss": 3.77906, "time": 0.8168} +{"mode": "train", "epoch": 108, "iter": 3000, "lr": 0.01829, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33656, "top5_acc": 0.59375, "loss_cls": 3.77378, "loss": 3.77378, "time": 0.82159} +{"mode": "train", "epoch": 108, "iter": 3100, "lr": 0.01827, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34812, "top5_acc": 0.58875, "loss_cls": 3.78872, "loss": 3.78872, "time": 0.81778} +{"mode": "train", "epoch": 108, "iter": 3200, "lr": 0.01825, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33688, "top5_acc": 0.59125, "loss_cls": 3.76453, "loss": 3.76453, "time": 0.8192} +{"mode": "train", "epoch": 108, "iter": 3300, "lr": 0.01823, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.32844, "top5_acc": 0.59203, "loss_cls": 3.80635, "loss": 3.80635, "time": 0.82021} +{"mode": "train", "epoch": 108, "iter": 3400, "lr": 0.0182, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33406, "top5_acc": 0.59078, "loss_cls": 3.79533, "loss": 3.79533, "time": 0.81584} +{"mode": "train", "epoch": 108, "iter": 3500, "lr": 0.01818, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.33297, "top5_acc": 0.58547, "loss_cls": 3.81112, "loss": 3.81112, "time": 0.81755} +{"mode": "train", "epoch": 108, "iter": 3600, "lr": 0.01816, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3325, "top5_acc": 0.58906, "loss_cls": 3.80183, "loss": 3.80183, "time": 0.81835} +{"mode": "train", "epoch": 108, "iter": 3700, "lr": 0.01814, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33109, "top5_acc": 0.59312, "loss_cls": 3.80599, "loss": 3.80599, "time": 0.81794} +{"mode": "val", "epoch": 108, "iter": 309, "lr": 0.01813, "top1_acc": 0.2643, "top5_acc": 0.49932, "mean_class_accuracy": 0.26414} +{"mode": "train", "epoch": 109, "iter": 100, "lr": 0.01811, "memory": 15990, "data_time": 1.32248, "top1_acc": 0.33875, "top5_acc": 0.59594, "loss_cls": 3.78101, "loss": 3.78101, "time": 2.31035} +{"mode": "train", "epoch": 109, "iter": 200, "lr": 0.01809, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34906, "top5_acc": 0.61187, "loss_cls": 3.69668, "loss": 3.69668, "time": 0.82526} +{"mode": "train", "epoch": 109, "iter": 300, "lr": 0.01806, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34422, "top5_acc": 0.60812, "loss_cls": 3.72963, "loss": 3.72963, "time": 0.8222} +{"mode": "train", "epoch": 109, "iter": 400, "lr": 0.01804, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34359, "top5_acc": 0.60844, "loss_cls": 3.71894, "loss": 3.71894, "time": 0.83121} +{"mode": "train", "epoch": 109, "iter": 500, "lr": 0.01802, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34844, "top5_acc": 0.60141, "loss_cls": 3.72069, "loss": 3.72069, "time": 0.82143} +{"mode": "train", "epoch": 109, "iter": 600, "lr": 0.018, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35078, "top5_acc": 0.61109, "loss_cls": 3.68934, "loss": 3.68934, "time": 0.8287} +{"mode": "train", "epoch": 109, "iter": 700, "lr": 0.01798, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34406, "top5_acc": 0.605, "loss_cls": 3.73595, "loss": 3.73595, "time": 0.82551} +{"mode": "train", "epoch": 109, "iter": 800, "lr": 0.01796, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34, "top5_acc": 0.59656, "loss_cls": 3.7615, "loss": 3.7615, "time": 0.83135} +{"mode": "train", "epoch": 109, "iter": 900, "lr": 0.01794, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33672, "top5_acc": 0.59078, "loss_cls": 3.80125, "loss": 3.80125, "time": 0.82488} +{"mode": "train", "epoch": 109, "iter": 1000, "lr": 0.01791, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34391, "top5_acc": 0.60172, "loss_cls": 3.73255, "loss": 3.73255, "time": 0.82032} +{"mode": "train", "epoch": 109, "iter": 1100, "lr": 0.01789, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34672, "top5_acc": 0.60172, "loss_cls": 3.73728, "loss": 3.73728, "time": 0.81838} +{"mode": "train", "epoch": 109, "iter": 1200, "lr": 0.01787, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34031, "top5_acc": 0.59578, "loss_cls": 3.73582, "loss": 3.73582, "time": 0.81612} +{"mode": "train", "epoch": 109, "iter": 1300, "lr": 0.01785, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33531, "top5_acc": 0.58312, "loss_cls": 3.7899, "loss": 3.7899, "time": 0.81821} +{"mode": "train", "epoch": 109, "iter": 1400, "lr": 0.01783, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33781, "top5_acc": 0.58516, "loss_cls": 3.80659, "loss": 3.80659, "time": 0.81744} +{"mode": "train", "epoch": 109, "iter": 1500, "lr": 0.01781, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34953, "top5_acc": 0.60219, "loss_cls": 3.72075, "loss": 3.72075, "time": 0.822} +{"mode": "train", "epoch": 109, "iter": 1600, "lr": 0.01779, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33734, "top5_acc": 0.58812, "loss_cls": 3.80665, "loss": 3.80665, "time": 0.8266} +{"mode": "train", "epoch": 109, "iter": 1700, "lr": 0.01776, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34609, "top5_acc": 0.60047, "loss_cls": 3.73849, "loss": 3.73849, "time": 0.81775} +{"mode": "train", "epoch": 109, "iter": 1800, "lr": 0.01774, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.32891, "top5_acc": 0.58, "loss_cls": 3.84784, "loss": 3.84784, "time": 0.82002} +{"mode": "train", "epoch": 109, "iter": 1900, "lr": 0.01772, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33375, "top5_acc": 0.59453, "loss_cls": 3.79092, "loss": 3.79092, "time": 0.8173} +{"mode": "train", "epoch": 109, "iter": 2000, "lr": 0.0177, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.33031, "top5_acc": 0.58453, "loss_cls": 3.79737, "loss": 3.79737, "time": 0.82417} +{"mode": "train", "epoch": 109, "iter": 2100, "lr": 0.01768, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.34406, "top5_acc": 0.59266, "loss_cls": 3.74554, "loss": 3.74554, "time": 0.82212} +{"mode": "train", "epoch": 109, "iter": 2200, "lr": 0.01766, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34719, "top5_acc": 0.59672, "loss_cls": 3.75264, "loss": 3.75264, "time": 0.82064} +{"mode": "train", "epoch": 109, "iter": 2300, "lr": 0.01764, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34094, "top5_acc": 0.60078, "loss_cls": 3.7452, "loss": 3.7452, "time": 0.83009} +{"mode": "train", "epoch": 109, "iter": 2400, "lr": 0.01761, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33672, "top5_acc": 0.58984, "loss_cls": 3.82172, "loss": 3.82172, "time": 0.82165} +{"mode": "train", "epoch": 109, "iter": 2500, "lr": 0.01759, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34297, "top5_acc": 0.605, "loss_cls": 3.74768, "loss": 3.74768, "time": 0.81977} +{"mode": "train", "epoch": 109, "iter": 2600, "lr": 0.01757, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.33094, "top5_acc": 0.58562, "loss_cls": 3.81123, "loss": 3.81123, "time": 0.81535} +{"mode": "train", "epoch": 109, "iter": 2700, "lr": 0.01755, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34109, "top5_acc": 0.59453, "loss_cls": 3.74947, "loss": 3.74947, "time": 0.81991} +{"mode": "train", "epoch": 109, "iter": 2800, "lr": 0.01753, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.335, "top5_acc": 0.59719, "loss_cls": 3.78186, "loss": 3.78186, "time": 0.81927} +{"mode": "train", "epoch": 109, "iter": 2900, "lr": 0.01751, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34438, "top5_acc": 0.60453, "loss_cls": 3.72274, "loss": 3.72274, "time": 0.82225} +{"mode": "train", "epoch": 109, "iter": 3000, "lr": 0.01749, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34594, "top5_acc": 0.60156, "loss_cls": 3.73132, "loss": 3.73132, "time": 0.81663} +{"mode": "train", "epoch": 109, "iter": 3100, "lr": 0.01747, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3325, "top5_acc": 0.5875, "loss_cls": 3.81126, "loss": 3.81126, "time": 0.81819} +{"mode": "train", "epoch": 109, "iter": 3200, "lr": 0.01744, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34328, "top5_acc": 0.59328, "loss_cls": 3.78556, "loss": 3.78556, "time": 0.81884} +{"mode": "train", "epoch": 109, "iter": 3300, "lr": 0.01742, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34516, "top5_acc": 0.60172, "loss_cls": 3.73447, "loss": 3.73447, "time": 0.81465} +{"mode": "train", "epoch": 109, "iter": 3400, "lr": 0.0174, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34297, "top5_acc": 0.59672, "loss_cls": 3.75322, "loss": 3.75322, "time": 0.82039} +{"mode": "train", "epoch": 109, "iter": 3500, "lr": 0.01738, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34531, "top5_acc": 0.59391, "loss_cls": 3.75949, "loss": 3.75949, "time": 0.81839} +{"mode": "train", "epoch": 109, "iter": 3600, "lr": 0.01736, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33984, "top5_acc": 0.59938, "loss_cls": 3.77051, "loss": 3.77051, "time": 0.81803} +{"mode": "train", "epoch": 109, "iter": 3700, "lr": 0.01734, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34734, "top5_acc": 0.60062, "loss_cls": 3.74122, "loss": 3.74122, "time": 0.81748} +{"mode": "val", "epoch": 109, "iter": 309, "lr": 0.01733, "top1_acc": 0.27701, "top5_acc": 0.52748, "mean_class_accuracy": 0.27666} +{"mode": "train", "epoch": 110, "iter": 100, "lr": 0.01731, "memory": 15990, "data_time": 1.29648, "top1_acc": 0.35016, "top5_acc": 0.6075, "loss_cls": 3.69036, "loss": 3.69036, "time": 2.28127} +{"mode": "train", "epoch": 110, "iter": 200, "lr": 0.01729, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.35281, "top5_acc": 0.61094, "loss_cls": 3.68566, "loss": 3.68566, "time": 0.82322} +{"mode": "train", "epoch": 110, "iter": 300, "lr": 0.01727, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34953, "top5_acc": 0.59922, "loss_cls": 3.74542, "loss": 3.74542, "time": 0.82512} +{"mode": "train", "epoch": 110, "iter": 400, "lr": 0.01724, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34219, "top5_acc": 0.60531, "loss_cls": 3.71874, "loss": 3.71874, "time": 0.82647} +{"mode": "train", "epoch": 110, "iter": 500, "lr": 0.01722, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35266, "top5_acc": 0.60188, "loss_cls": 3.70469, "loss": 3.70469, "time": 0.82835} +{"mode": "train", "epoch": 110, "iter": 600, "lr": 0.0172, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34, "top5_acc": 0.61016, "loss_cls": 3.71928, "loss": 3.71928, "time": 0.82333} +{"mode": "train", "epoch": 110, "iter": 700, "lr": 0.01718, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34734, "top5_acc": 0.59562, "loss_cls": 3.77445, "loss": 3.77445, "time": 0.82227} +{"mode": "train", "epoch": 110, "iter": 800, "lr": 0.01716, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35234, "top5_acc": 0.60766, "loss_cls": 3.68632, "loss": 3.68632, "time": 0.82093} +{"mode": "train", "epoch": 110, "iter": 900, "lr": 0.01714, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35188, "top5_acc": 0.60547, "loss_cls": 3.70705, "loss": 3.70705, "time": 0.82258} +{"mode": "train", "epoch": 110, "iter": 1000, "lr": 0.01712, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33812, "top5_acc": 0.60281, "loss_cls": 3.77076, "loss": 3.77076, "time": 0.81988} +{"mode": "train", "epoch": 110, "iter": 1100, "lr": 0.0171, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35281, "top5_acc": 0.60469, "loss_cls": 3.70287, "loss": 3.70287, "time": 0.81893} +{"mode": "train", "epoch": 110, "iter": 1200, "lr": 0.01708, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34219, "top5_acc": 0.59781, "loss_cls": 3.75289, "loss": 3.75289, "time": 0.81451} +{"mode": "train", "epoch": 110, "iter": 1300, "lr": 0.01705, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35359, "top5_acc": 0.61812, "loss_cls": 3.65387, "loss": 3.65387, "time": 0.81666} +{"mode": "train", "epoch": 110, "iter": 1400, "lr": 0.01703, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34109, "top5_acc": 0.60047, "loss_cls": 3.75625, "loss": 3.75625, "time": 0.81919} +{"mode": "train", "epoch": 110, "iter": 1500, "lr": 0.01701, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34328, "top5_acc": 0.59953, "loss_cls": 3.73237, "loss": 3.73237, "time": 0.81788} +{"mode": "train", "epoch": 110, "iter": 1600, "lr": 0.01699, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34312, "top5_acc": 0.59469, "loss_cls": 3.74652, "loss": 3.74652, "time": 0.82083} +{"mode": "train", "epoch": 110, "iter": 1700, "lr": 0.01697, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34719, "top5_acc": 0.60359, "loss_cls": 3.73131, "loss": 3.73131, "time": 0.81644} +{"mode": "train", "epoch": 110, "iter": 1800, "lr": 0.01695, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34328, "top5_acc": 0.60016, "loss_cls": 3.72655, "loss": 3.72655, "time": 0.81937} +{"mode": "train", "epoch": 110, "iter": 1900, "lr": 0.01693, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34281, "top5_acc": 0.60109, "loss_cls": 3.69714, "loss": 3.69714, "time": 0.82154} +{"mode": "train", "epoch": 110, "iter": 2000, "lr": 0.01691, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34438, "top5_acc": 0.60438, "loss_cls": 3.72839, "loss": 3.72839, "time": 0.81594} +{"mode": "train", "epoch": 110, "iter": 2100, "lr": 0.01689, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.33844, "top5_acc": 0.59375, "loss_cls": 3.79494, "loss": 3.79494, "time": 0.83268} +{"mode": "train", "epoch": 110, "iter": 2200, "lr": 0.01687, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.34203, "top5_acc": 0.59312, "loss_cls": 3.76271, "loss": 3.76271, "time": 0.81678} +{"mode": "train", "epoch": 110, "iter": 2300, "lr": 0.01685, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.33719, "top5_acc": 0.59594, "loss_cls": 3.77056, "loss": 3.77056, "time": 0.82353} +{"mode": "train", "epoch": 110, "iter": 2400, "lr": 0.01682, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34156, "top5_acc": 0.60141, "loss_cls": 3.75818, "loss": 3.75818, "time": 0.82183} +{"mode": "train", "epoch": 110, "iter": 2500, "lr": 0.0168, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.34672, "top5_acc": 0.60891, "loss_cls": 3.71476, "loss": 3.71476, "time": 0.82458} +{"mode": "train", "epoch": 110, "iter": 2600, "lr": 0.01678, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34234, "top5_acc": 0.60328, "loss_cls": 3.72884, "loss": 3.72884, "time": 0.82069} +{"mode": "train", "epoch": 110, "iter": 2700, "lr": 0.01676, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33969, "top5_acc": 0.59234, "loss_cls": 3.74961, "loss": 3.74961, "time": 0.81702} +{"mode": "train", "epoch": 110, "iter": 2800, "lr": 0.01674, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34, "top5_acc": 0.60188, "loss_cls": 3.76066, "loss": 3.76066, "time": 0.81659} +{"mode": "train", "epoch": 110, "iter": 2900, "lr": 0.01672, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33656, "top5_acc": 0.60141, "loss_cls": 3.76926, "loss": 3.76926, "time": 0.81571} +{"mode": "train", "epoch": 110, "iter": 3000, "lr": 0.0167, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33609, "top5_acc": 0.59047, "loss_cls": 3.77801, "loss": 3.77801, "time": 0.81565} +{"mode": "train", "epoch": 110, "iter": 3100, "lr": 0.01668, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34984, "top5_acc": 0.6, "loss_cls": 3.72716, "loss": 3.72716, "time": 0.81519} +{"mode": "train", "epoch": 110, "iter": 3200, "lr": 0.01666, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34406, "top5_acc": 0.59125, "loss_cls": 3.73474, "loss": 3.73474, "time": 0.82197} +{"mode": "train", "epoch": 110, "iter": 3300, "lr": 0.01664, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33688, "top5_acc": 0.60078, "loss_cls": 3.74222, "loss": 3.74222, "time": 0.81641} +{"mode": "train", "epoch": 110, "iter": 3400, "lr": 0.01662, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33422, "top5_acc": 0.59234, "loss_cls": 3.79214, "loss": 3.79214, "time": 0.8177} +{"mode": "train", "epoch": 110, "iter": 3500, "lr": 0.01659, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34391, "top5_acc": 0.60047, "loss_cls": 3.72905, "loss": 3.72905, "time": 0.81592} +{"mode": "train", "epoch": 110, "iter": 3600, "lr": 0.01657, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33703, "top5_acc": 0.59703, "loss_cls": 3.76913, "loss": 3.76913, "time": 0.81699} +{"mode": "train", "epoch": 110, "iter": 3700, "lr": 0.01655, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34688, "top5_acc": 0.60656, "loss_cls": 3.73517, "loss": 3.73517, "time": 0.81945} +{"mode": "val", "epoch": 110, "iter": 309, "lr": 0.01654, "top1_acc": 0.28056, "top5_acc": 0.52626, "mean_class_accuracy": 0.2802} +{"mode": "train", "epoch": 111, "iter": 100, "lr": 0.01652, "memory": 15990, "data_time": 1.32591, "top1_acc": 0.35266, "top5_acc": 0.61219, "loss_cls": 3.69434, "loss": 3.69434, "time": 2.32303} +{"mode": "train", "epoch": 111, "iter": 200, "lr": 0.0165, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35188, "top5_acc": 0.61094, "loss_cls": 3.69416, "loss": 3.69416, "time": 0.83269} +{"mode": "train", "epoch": 111, "iter": 300, "lr": 0.01648, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35219, "top5_acc": 0.60906, "loss_cls": 3.67956, "loss": 3.67956, "time": 0.82149} +{"mode": "train", "epoch": 111, "iter": 400, "lr": 0.01646, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.34438, "top5_acc": 0.60703, "loss_cls": 3.70029, "loss": 3.70029, "time": 0.82065} +{"mode": "train", "epoch": 111, "iter": 500, "lr": 0.01644, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35234, "top5_acc": 0.59984, "loss_cls": 3.70978, "loss": 3.70978, "time": 0.83101} +{"mode": "train", "epoch": 111, "iter": 600, "lr": 0.01642, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.355, "top5_acc": 0.61109, "loss_cls": 3.66169, "loss": 3.66169, "time": 0.8256} +{"mode": "train", "epoch": 111, "iter": 700, "lr": 0.0164, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.35484, "top5_acc": 0.60938, "loss_cls": 3.69309, "loss": 3.69309, "time": 0.82641} +{"mode": "train", "epoch": 111, "iter": 800, "lr": 0.01638, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34625, "top5_acc": 0.60031, "loss_cls": 3.7209, "loss": 3.7209, "time": 0.81988} +{"mode": "train", "epoch": 111, "iter": 900, "lr": 0.01636, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35578, "top5_acc": 0.60906, "loss_cls": 3.67362, "loss": 3.67362, "time": 0.81764} +{"mode": "train", "epoch": 111, "iter": 1000, "lr": 0.01634, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35391, "top5_acc": 0.61, "loss_cls": 3.67281, "loss": 3.67281, "time": 0.82106} +{"mode": "train", "epoch": 111, "iter": 1100, "lr": 0.01632, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34594, "top5_acc": 0.60641, "loss_cls": 3.68404, "loss": 3.68404, "time": 0.81479} +{"mode": "train", "epoch": 111, "iter": 1200, "lr": 0.0163, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34141, "top5_acc": 0.59281, "loss_cls": 3.75048, "loss": 3.75048, "time": 0.81892} +{"mode": "train", "epoch": 111, "iter": 1300, "lr": 0.01627, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35, "top5_acc": 0.60938, "loss_cls": 3.68203, "loss": 3.68203, "time": 0.81898} +{"mode": "train", "epoch": 111, "iter": 1400, "lr": 0.01625, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34609, "top5_acc": 0.60688, "loss_cls": 3.70933, "loss": 3.70933, "time": 0.82004} +{"mode": "train", "epoch": 111, "iter": 1500, "lr": 0.01623, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33922, "top5_acc": 0.60266, "loss_cls": 3.71663, "loss": 3.71663, "time": 0.82165} +{"mode": "train", "epoch": 111, "iter": 1600, "lr": 0.01621, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.34859, "top5_acc": 0.60672, "loss_cls": 3.68762, "loss": 3.68762, "time": 0.81856} +{"mode": "train", "epoch": 111, "iter": 1700, "lr": 0.01619, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.34812, "top5_acc": 0.60031, "loss_cls": 3.73711, "loss": 3.73711, "time": 0.82028} +{"mode": "train", "epoch": 111, "iter": 1800, "lr": 0.01617, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35781, "top5_acc": 0.6075, "loss_cls": 3.68098, "loss": 3.68098, "time": 0.81607} +{"mode": "train", "epoch": 111, "iter": 1900, "lr": 0.01615, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34766, "top5_acc": 0.60297, "loss_cls": 3.7144, "loss": 3.7144, "time": 0.81757} +{"mode": "train", "epoch": 111, "iter": 2000, "lr": 0.01613, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34984, "top5_acc": 0.61266, "loss_cls": 3.6839, "loss": 3.6839, "time": 0.82111} +{"mode": "train", "epoch": 111, "iter": 2100, "lr": 0.01611, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.33594, "top5_acc": 0.59188, "loss_cls": 3.76536, "loss": 3.76536, "time": 0.82761} +{"mode": "train", "epoch": 111, "iter": 2200, "lr": 0.01609, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.34016, "top5_acc": 0.60281, "loss_cls": 3.72949, "loss": 3.72949, "time": 0.82177} +{"mode": "train", "epoch": 111, "iter": 2300, "lr": 0.01607, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35156, "top5_acc": 0.60125, "loss_cls": 3.73575, "loss": 3.73575, "time": 0.82711} +{"mode": "train", "epoch": 111, "iter": 2400, "lr": 0.01605, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33969, "top5_acc": 0.59609, "loss_cls": 3.74775, "loss": 3.74775, "time": 0.81973} +{"mode": "train", "epoch": 111, "iter": 2500, "lr": 0.01603, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.35453, "top5_acc": 0.60016, "loss_cls": 3.69983, "loss": 3.69983, "time": 0.81924} +{"mode": "train", "epoch": 111, "iter": 2600, "lr": 0.01601, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34547, "top5_acc": 0.59859, "loss_cls": 3.75007, "loss": 3.75007, "time": 0.82251} +{"mode": "train", "epoch": 111, "iter": 2700, "lr": 0.01599, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34, "top5_acc": 0.60125, "loss_cls": 3.7467, "loss": 3.7467, "time": 0.81966} +{"mode": "train", "epoch": 111, "iter": 2800, "lr": 0.01597, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35, "top5_acc": 0.60516, "loss_cls": 3.71156, "loss": 3.71156, "time": 0.81922} +{"mode": "train", "epoch": 111, "iter": 2900, "lr": 0.01595, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34531, "top5_acc": 0.59953, "loss_cls": 3.72344, "loss": 3.72344, "time": 0.82226} +{"mode": "train", "epoch": 111, "iter": 3000, "lr": 0.01593, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34719, "top5_acc": 0.60875, "loss_cls": 3.68399, "loss": 3.68399, "time": 0.82222} +{"mode": "train", "epoch": 111, "iter": 3100, "lr": 0.0159, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34672, "top5_acc": 0.59891, "loss_cls": 3.70673, "loss": 3.70673, "time": 0.81812} +{"mode": "train", "epoch": 111, "iter": 3200, "lr": 0.01588, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33844, "top5_acc": 0.58891, "loss_cls": 3.78535, "loss": 3.78535, "time": 0.81819} +{"mode": "train", "epoch": 111, "iter": 3300, "lr": 0.01586, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34422, "top5_acc": 0.60281, "loss_cls": 3.75215, "loss": 3.75215, "time": 0.82516} +{"mode": "train", "epoch": 111, "iter": 3400, "lr": 0.01584, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.33391, "top5_acc": 0.59844, "loss_cls": 3.79745, "loss": 3.79745, "time": 0.82225} +{"mode": "train", "epoch": 111, "iter": 3500, "lr": 0.01582, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34188, "top5_acc": 0.59734, "loss_cls": 3.75373, "loss": 3.75373, "time": 0.82019} +{"mode": "train", "epoch": 111, "iter": 3600, "lr": 0.0158, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34812, "top5_acc": 0.61172, "loss_cls": 3.68284, "loss": 3.68284, "time": 0.8218} +{"mode": "train", "epoch": 111, "iter": 3700, "lr": 0.01578, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34094, "top5_acc": 0.59641, "loss_cls": 3.77705, "loss": 3.77705, "time": 0.81795} +{"mode": "val", "epoch": 111, "iter": 309, "lr": 0.01577, "top1_acc": 0.29058, "top5_acc": 0.54065, "mean_class_accuracy": 0.29054} +{"mode": "train", "epoch": 112, "iter": 100, "lr": 0.01575, "memory": 15990, "data_time": 1.27076, "top1_acc": 0.3625, "top5_acc": 0.60828, "loss_cls": 3.65109, "loss": 3.65109, "time": 2.25871} +{"mode": "train", "epoch": 112, "iter": 200, "lr": 0.01573, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35953, "top5_acc": 0.60812, "loss_cls": 3.64165, "loss": 3.64165, "time": 0.82714} +{"mode": "train", "epoch": 112, "iter": 300, "lr": 0.01571, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36172, "top5_acc": 0.62375, "loss_cls": 3.59554, "loss": 3.59554, "time": 0.82672} +{"mode": "train", "epoch": 112, "iter": 400, "lr": 0.01569, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35609, "top5_acc": 0.61312, "loss_cls": 3.66469, "loss": 3.66469, "time": 0.82328} +{"mode": "train", "epoch": 112, "iter": 500, "lr": 0.01567, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36453, "top5_acc": 0.61547, "loss_cls": 3.62566, "loss": 3.62566, "time": 0.83028} +{"mode": "train", "epoch": 112, "iter": 600, "lr": 0.01565, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35391, "top5_acc": 0.60828, "loss_cls": 3.6728, "loss": 3.6728, "time": 0.82526} +{"mode": "train", "epoch": 112, "iter": 700, "lr": 0.01563, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35328, "top5_acc": 0.62187, "loss_cls": 3.64812, "loss": 3.64812, "time": 0.82776} +{"mode": "train", "epoch": 112, "iter": 800, "lr": 0.01561, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35094, "top5_acc": 0.60234, "loss_cls": 3.69137, "loss": 3.69137, "time": 0.82016} +{"mode": "train", "epoch": 112, "iter": 900, "lr": 0.01559, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35359, "top5_acc": 0.61281, "loss_cls": 3.69553, "loss": 3.69553, "time": 0.82269} +{"mode": "train", "epoch": 112, "iter": 1000, "lr": 0.01557, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34469, "top5_acc": 0.59609, "loss_cls": 3.72975, "loss": 3.72975, "time": 0.81887} +{"mode": "train", "epoch": 112, "iter": 1100, "lr": 0.01555, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34797, "top5_acc": 0.60703, "loss_cls": 3.70879, "loss": 3.70879, "time": 0.81947} +{"mode": "train", "epoch": 112, "iter": 1200, "lr": 0.01553, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34516, "top5_acc": 0.60297, "loss_cls": 3.7194, "loss": 3.7194, "time": 0.81883} +{"mode": "train", "epoch": 112, "iter": 1300, "lr": 0.01551, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36344, "top5_acc": 0.61484, "loss_cls": 3.6622, "loss": 3.6622, "time": 0.82152} +{"mode": "train", "epoch": 112, "iter": 1400, "lr": 0.01549, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3575, "top5_acc": 0.60344, "loss_cls": 3.69522, "loss": 3.69522, "time": 0.82516} +{"mode": "train", "epoch": 112, "iter": 1500, "lr": 0.01547, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35031, "top5_acc": 0.60266, "loss_cls": 3.70304, "loss": 3.70304, "time": 0.82137} +{"mode": "train", "epoch": 112, "iter": 1600, "lr": 0.01545, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34766, "top5_acc": 0.61203, "loss_cls": 3.6903, "loss": 3.6903, "time": 0.82442} +{"mode": "train", "epoch": 112, "iter": 1700, "lr": 0.01543, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34188, "top5_acc": 0.60234, "loss_cls": 3.72595, "loss": 3.72595, "time": 0.81868} +{"mode": "train", "epoch": 112, "iter": 1800, "lr": 0.01541, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34672, "top5_acc": 0.60406, "loss_cls": 3.70796, "loss": 3.70796, "time": 0.82163} +{"mode": "train", "epoch": 112, "iter": 1900, "lr": 0.01539, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35469, "top5_acc": 0.61172, "loss_cls": 3.67043, "loss": 3.67043, "time": 0.81825} +{"mode": "train", "epoch": 112, "iter": 2000, "lr": 0.01537, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35031, "top5_acc": 0.61672, "loss_cls": 3.68469, "loss": 3.68469, "time": 0.8206} +{"mode": "train", "epoch": 112, "iter": 2100, "lr": 0.01535, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35312, "top5_acc": 0.60656, "loss_cls": 3.69234, "loss": 3.69234, "time": 0.82262} +{"mode": "train", "epoch": 112, "iter": 2200, "lr": 0.01533, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34406, "top5_acc": 0.60656, "loss_cls": 3.70731, "loss": 3.70731, "time": 0.81817} +{"mode": "train", "epoch": 112, "iter": 2300, "lr": 0.01531, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35359, "top5_acc": 0.60625, "loss_cls": 3.7091, "loss": 3.7091, "time": 0.82758} +{"mode": "train", "epoch": 112, "iter": 2400, "lr": 0.01529, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35766, "top5_acc": 0.61687, "loss_cls": 3.67438, "loss": 3.67438, "time": 0.82588} +{"mode": "train", "epoch": 112, "iter": 2500, "lr": 0.01527, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35375, "top5_acc": 0.61, "loss_cls": 3.67571, "loss": 3.67571, "time": 0.81941} +{"mode": "train", "epoch": 112, "iter": 2600, "lr": 0.01525, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34312, "top5_acc": 0.60078, "loss_cls": 3.71386, "loss": 3.71386, "time": 0.81955} +{"mode": "train", "epoch": 112, "iter": 2700, "lr": 0.01523, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35891, "top5_acc": 0.61156, "loss_cls": 3.67908, "loss": 3.67908, "time": 0.82144} +{"mode": "train", "epoch": 112, "iter": 2800, "lr": 0.01521, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34703, "top5_acc": 0.59953, "loss_cls": 3.75204, "loss": 3.75204, "time": 0.81793} +{"mode": "train", "epoch": 112, "iter": 2900, "lr": 0.01519, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35016, "top5_acc": 0.59656, "loss_cls": 3.73651, "loss": 3.73651, "time": 0.82465} +{"mode": "train", "epoch": 112, "iter": 3000, "lr": 0.01517, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35094, "top5_acc": 0.60594, "loss_cls": 3.70748, "loss": 3.70748, "time": 0.8207} +{"mode": "train", "epoch": 112, "iter": 3100, "lr": 0.01515, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35234, "top5_acc": 0.60312, "loss_cls": 3.71375, "loss": 3.71375, "time": 0.81746} +{"mode": "train", "epoch": 112, "iter": 3200, "lr": 0.01513, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35656, "top5_acc": 0.61141, "loss_cls": 3.67923, "loss": 3.67923, "time": 0.8208} +{"mode": "train", "epoch": 112, "iter": 3300, "lr": 0.01511, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35219, "top5_acc": 0.59797, "loss_cls": 3.72593, "loss": 3.72593, "time": 0.81882} +{"mode": "train", "epoch": 112, "iter": 3400, "lr": 0.01509, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34234, "top5_acc": 0.5975, "loss_cls": 3.75124, "loss": 3.75124, "time": 0.82709} +{"mode": "train", "epoch": 112, "iter": 3500, "lr": 0.01507, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34641, "top5_acc": 0.60953, "loss_cls": 3.68038, "loss": 3.68038, "time": 0.82112} +{"mode": "train", "epoch": 112, "iter": 3600, "lr": 0.01505, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35047, "top5_acc": 0.60125, "loss_cls": 3.71859, "loss": 3.71859, "time": 0.81817} +{"mode": "train", "epoch": 112, "iter": 3700, "lr": 0.01503, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.34453, "top5_acc": 0.59875, "loss_cls": 3.72001, "loss": 3.72001, "time": 0.81883} +{"mode": "val", "epoch": 112, "iter": 309, "lr": 0.01502, "top1_acc": 0.28582, "top5_acc": 0.53695, "mean_class_accuracy": 0.28569} +{"mode": "train", "epoch": 113, "iter": 100, "lr": 0.015, "memory": 15990, "data_time": 1.2664, "top1_acc": 0.36359, "top5_acc": 0.62375, "loss_cls": 3.60787, "loss": 3.60787, "time": 2.25614} +{"mode": "train", "epoch": 113, "iter": 200, "lr": 0.01498, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35547, "top5_acc": 0.6175, "loss_cls": 3.64913, "loss": 3.64913, "time": 0.8267} +{"mode": "train", "epoch": 113, "iter": 300, "lr": 0.01496, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36641, "top5_acc": 0.62438, "loss_cls": 3.59847, "loss": 3.59847, "time": 0.83046} +{"mode": "train", "epoch": 113, "iter": 400, "lr": 0.01494, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.35062, "top5_acc": 0.60781, "loss_cls": 3.68063, "loss": 3.68063, "time": 0.82601} +{"mode": "train", "epoch": 113, "iter": 500, "lr": 0.01492, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36234, "top5_acc": 0.62031, "loss_cls": 3.63866, "loss": 3.63866, "time": 0.82776} +{"mode": "train", "epoch": 113, "iter": 600, "lr": 0.0149, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.35469, "top5_acc": 0.60891, "loss_cls": 3.63352, "loss": 3.63352, "time": 0.82999} +{"mode": "train", "epoch": 113, "iter": 700, "lr": 0.01488, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36375, "top5_acc": 0.62875, "loss_cls": 3.59778, "loss": 3.59778, "time": 0.82256} +{"mode": "train", "epoch": 113, "iter": 800, "lr": 0.01486, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36266, "top5_acc": 0.62203, "loss_cls": 3.64664, "loss": 3.64664, "time": 0.82294} +{"mode": "train", "epoch": 113, "iter": 900, "lr": 0.01484, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34891, "top5_acc": 0.60656, "loss_cls": 3.68077, "loss": 3.68077, "time": 0.81917} +{"mode": "train", "epoch": 113, "iter": 1000, "lr": 0.01482, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35031, "top5_acc": 0.61031, "loss_cls": 3.67639, "loss": 3.67639, "time": 0.81401} +{"mode": "train", "epoch": 113, "iter": 1100, "lr": 0.0148, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35297, "top5_acc": 0.61141, "loss_cls": 3.67842, "loss": 3.67842, "time": 0.82106} +{"mode": "train", "epoch": 113, "iter": 1200, "lr": 0.01478, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35828, "top5_acc": 0.61594, "loss_cls": 3.6625, "loss": 3.6625, "time": 0.8269} +{"mode": "train", "epoch": 113, "iter": 1300, "lr": 0.01476, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35609, "top5_acc": 0.60625, "loss_cls": 3.68687, "loss": 3.68687, "time": 0.81837} +{"mode": "train", "epoch": 113, "iter": 1400, "lr": 0.01474, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34, "top5_acc": 0.60297, "loss_cls": 3.70922, "loss": 3.70922, "time": 0.8209} +{"mode": "train", "epoch": 113, "iter": 1500, "lr": 0.01472, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36094, "top5_acc": 0.61125, "loss_cls": 3.66678, "loss": 3.66678, "time": 0.81695} +{"mode": "train", "epoch": 113, "iter": 1600, "lr": 0.0147, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35375, "top5_acc": 0.60828, "loss_cls": 3.70692, "loss": 3.70692, "time": 0.8153} +{"mode": "train", "epoch": 113, "iter": 1700, "lr": 0.01468, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35484, "top5_acc": 0.61203, "loss_cls": 3.66071, "loss": 3.66071, "time": 0.81915} +{"mode": "train", "epoch": 113, "iter": 1800, "lr": 0.01466, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35859, "top5_acc": 0.61453, "loss_cls": 3.62528, "loss": 3.62528, "time": 0.81633} +{"mode": "train", "epoch": 113, "iter": 1900, "lr": 0.01464, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34969, "top5_acc": 0.60578, "loss_cls": 3.70507, "loss": 3.70507, "time": 0.82233} +{"mode": "train", "epoch": 113, "iter": 2000, "lr": 0.01462, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.355, "top5_acc": 0.60484, "loss_cls": 3.71719, "loss": 3.71719, "time": 0.82162} +{"mode": "train", "epoch": 113, "iter": 2100, "lr": 0.0146, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3475, "top5_acc": 0.59688, "loss_cls": 3.72736, "loss": 3.72736, "time": 0.81864} +{"mode": "train", "epoch": 113, "iter": 2200, "lr": 0.01458, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35031, "top5_acc": 0.605, "loss_cls": 3.69151, "loss": 3.69151, "time": 0.83362} +{"mode": "train", "epoch": 113, "iter": 2300, "lr": 0.01456, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35781, "top5_acc": 0.61687, "loss_cls": 3.66664, "loss": 3.66664, "time": 0.82421} +{"mode": "train", "epoch": 113, "iter": 2400, "lr": 0.01454, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35125, "top5_acc": 0.61547, "loss_cls": 3.66523, "loss": 3.66523, "time": 0.82477} +{"mode": "train", "epoch": 113, "iter": 2500, "lr": 0.01452, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35859, "top5_acc": 0.61156, "loss_cls": 3.65238, "loss": 3.65238, "time": 0.81839} +{"mode": "train", "epoch": 113, "iter": 2600, "lr": 0.0145, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34719, "top5_acc": 0.60547, "loss_cls": 3.70559, "loss": 3.70559, "time": 0.82203} +{"mode": "train", "epoch": 113, "iter": 2700, "lr": 0.01448, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36297, "top5_acc": 0.62359, "loss_cls": 3.64697, "loss": 3.64697, "time": 0.82229} +{"mode": "train", "epoch": 113, "iter": 2800, "lr": 0.01446, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35281, "top5_acc": 0.61844, "loss_cls": 3.64084, "loss": 3.64084, "time": 0.81965} +{"mode": "train", "epoch": 113, "iter": 2900, "lr": 0.01444, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34828, "top5_acc": 0.60078, "loss_cls": 3.7116, "loss": 3.7116, "time": 0.81584} +{"mode": "train", "epoch": 113, "iter": 3000, "lr": 0.01442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34578, "top5_acc": 0.59562, "loss_cls": 3.72368, "loss": 3.72368, "time": 0.82144} +{"mode": "train", "epoch": 113, "iter": 3100, "lr": 0.0144, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.34969, "top5_acc": 0.61562, "loss_cls": 3.6934, "loss": 3.6934, "time": 0.81939} +{"mode": "train", "epoch": 113, "iter": 3200, "lr": 0.01438, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34594, "top5_acc": 0.605, "loss_cls": 3.70555, "loss": 3.70555, "time": 0.83199} +{"mode": "train", "epoch": 113, "iter": 3300, "lr": 0.01436, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35016, "top5_acc": 0.60391, "loss_cls": 3.73228, "loss": 3.73228, "time": 0.81756} +{"mode": "train", "epoch": 113, "iter": 3400, "lr": 0.01434, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34656, "top5_acc": 0.60578, "loss_cls": 3.67572, "loss": 3.67572, "time": 0.81679} +{"mode": "train", "epoch": 113, "iter": 3500, "lr": 0.01432, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35812, "top5_acc": 0.60594, "loss_cls": 3.66575, "loss": 3.66575, "time": 0.81882} +{"mode": "train", "epoch": 113, "iter": 3600, "lr": 0.01431, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35031, "top5_acc": 0.60766, "loss_cls": 3.68339, "loss": 3.68339, "time": 0.81783} +{"mode": "train", "epoch": 113, "iter": 3700, "lr": 0.01429, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35438, "top5_acc": 0.6075, "loss_cls": 3.70037, "loss": 3.70037, "time": 0.82159} +{"mode": "val", "epoch": 113, "iter": 309, "lr": 0.01428, "top1_acc": 0.30011, "top5_acc": 0.54328, "mean_class_accuracy": 0.29981} +{"mode": "train", "epoch": 114, "iter": 100, "lr": 0.01426, "memory": 15990, "data_time": 1.26702, "top1_acc": 0.37375, "top5_acc": 0.62719, "loss_cls": 3.55665, "loss": 3.55665, "time": 2.26492} +{"mode": "train", "epoch": 114, "iter": 200, "lr": 0.01424, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35438, "top5_acc": 0.61891, "loss_cls": 3.62113, "loss": 3.62113, "time": 0.83226} +{"mode": "train", "epoch": 114, "iter": 300, "lr": 0.01422, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37, "top5_acc": 0.62125, "loss_cls": 3.57797, "loss": 3.57797, "time": 0.82267} +{"mode": "train", "epoch": 114, "iter": 400, "lr": 0.0142, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.36062, "top5_acc": 0.61281, "loss_cls": 3.65939, "loss": 3.65939, "time": 0.82785} +{"mode": "train", "epoch": 114, "iter": 500, "lr": 0.01418, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36734, "top5_acc": 0.62547, "loss_cls": 3.60123, "loss": 3.60123, "time": 0.82014} +{"mode": "train", "epoch": 114, "iter": 600, "lr": 0.01416, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36, "top5_acc": 0.62547, "loss_cls": 3.61301, "loss": 3.61301, "time": 0.82782} +{"mode": "train", "epoch": 114, "iter": 700, "lr": 0.01414, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36062, "top5_acc": 0.61703, "loss_cls": 3.64791, "loss": 3.64791, "time": 0.82514} +{"mode": "train", "epoch": 114, "iter": 800, "lr": 0.01412, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36016, "top5_acc": 0.61219, "loss_cls": 3.64492, "loss": 3.64492, "time": 0.81795} +{"mode": "train", "epoch": 114, "iter": 900, "lr": 0.0141, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3475, "top5_acc": 0.61219, "loss_cls": 3.69639, "loss": 3.69639, "time": 0.82349} +{"mode": "train", "epoch": 114, "iter": 1000, "lr": 0.01408, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36578, "top5_acc": 0.61969, "loss_cls": 3.61223, "loss": 3.61223, "time": 0.8179} +{"mode": "train", "epoch": 114, "iter": 1100, "lr": 0.01406, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36078, "top5_acc": 0.62156, "loss_cls": 3.61774, "loss": 3.61774, "time": 0.81472} +{"mode": "train", "epoch": 114, "iter": 1200, "lr": 0.01404, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35203, "top5_acc": 0.61609, "loss_cls": 3.65955, "loss": 3.65955, "time": 0.81745} +{"mode": "train", "epoch": 114, "iter": 1300, "lr": 0.01402, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35688, "top5_acc": 0.60703, "loss_cls": 3.66669, "loss": 3.66669, "time": 0.81685} +{"mode": "train", "epoch": 114, "iter": 1400, "lr": 0.014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36047, "top5_acc": 0.60688, "loss_cls": 3.67732, "loss": 3.67732, "time": 0.82347} +{"mode": "train", "epoch": 114, "iter": 1500, "lr": 0.01398, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36828, "top5_acc": 0.61547, "loss_cls": 3.65131, "loss": 3.65131, "time": 0.82013} +{"mode": "train", "epoch": 114, "iter": 1600, "lr": 0.01397, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35203, "top5_acc": 0.61031, "loss_cls": 3.66564, "loss": 3.66564, "time": 0.82462} +{"mode": "train", "epoch": 114, "iter": 1700, "lr": 0.01395, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35828, "top5_acc": 0.60969, "loss_cls": 3.68173, "loss": 3.68173, "time": 0.81624} +{"mode": "train", "epoch": 114, "iter": 1800, "lr": 0.01393, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35359, "top5_acc": 0.61062, "loss_cls": 3.64501, "loss": 3.64501, "time": 0.81185} +{"mode": "train", "epoch": 114, "iter": 1900, "lr": 0.01391, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.35641, "top5_acc": 0.61094, "loss_cls": 3.64284, "loss": 3.64284, "time": 0.82115} +{"mode": "train", "epoch": 114, "iter": 2000, "lr": 0.01389, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35969, "top5_acc": 0.61781, "loss_cls": 3.6378, "loss": 3.6378, "time": 0.82076} +{"mode": "train", "epoch": 114, "iter": 2100, "lr": 0.01387, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36391, "top5_acc": 0.615, "loss_cls": 3.6364, "loss": 3.6364, "time": 0.81794} +{"mode": "train", "epoch": 114, "iter": 2200, "lr": 0.01385, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36047, "top5_acc": 0.61219, "loss_cls": 3.66374, "loss": 3.66374, "time": 0.832} +{"mode": "train", "epoch": 114, "iter": 2300, "lr": 0.01383, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35094, "top5_acc": 0.61219, "loss_cls": 3.66239, "loss": 3.66239, "time": 0.82345} +{"mode": "train", "epoch": 114, "iter": 2400, "lr": 0.01381, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.35312, "top5_acc": 0.61406, "loss_cls": 3.66762, "loss": 3.66762, "time": 0.82476} +{"mode": "train", "epoch": 114, "iter": 2500, "lr": 0.01379, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3525, "top5_acc": 0.61031, "loss_cls": 3.678, "loss": 3.678, "time": 0.81994} +{"mode": "train", "epoch": 114, "iter": 2600, "lr": 0.01377, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36047, "top5_acc": 0.61672, "loss_cls": 3.64999, "loss": 3.64999, "time": 0.82223} +{"mode": "train", "epoch": 114, "iter": 2700, "lr": 0.01375, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35516, "top5_acc": 0.61375, "loss_cls": 3.65796, "loss": 3.65796, "time": 0.82282} +{"mode": "train", "epoch": 114, "iter": 2800, "lr": 0.01373, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.355, "top5_acc": 0.60469, "loss_cls": 3.67283, "loss": 3.67283, "time": 0.82119} +{"mode": "train", "epoch": 114, "iter": 2900, "lr": 0.01371, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35703, "top5_acc": 0.61375, "loss_cls": 3.67061, "loss": 3.67061, "time": 0.82145} +{"mode": "train", "epoch": 114, "iter": 3000, "lr": 0.01369, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36656, "top5_acc": 0.61938, "loss_cls": 3.59671, "loss": 3.59671, "time": 0.82086} +{"mode": "train", "epoch": 114, "iter": 3100, "lr": 0.01368, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35609, "top5_acc": 0.60859, "loss_cls": 3.6733, "loss": 3.6733, "time": 0.82071} +{"mode": "train", "epoch": 114, "iter": 3200, "lr": 0.01366, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36141, "top5_acc": 0.61219, "loss_cls": 3.63163, "loss": 3.63163, "time": 0.81835} +{"mode": "train", "epoch": 114, "iter": 3300, "lr": 0.01364, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35234, "top5_acc": 0.61016, "loss_cls": 3.70271, "loss": 3.70271, "time": 0.81891} +{"mode": "train", "epoch": 114, "iter": 3400, "lr": 0.01362, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36266, "top5_acc": 0.61266, "loss_cls": 3.65508, "loss": 3.65508, "time": 0.82171} +{"mode": "train", "epoch": 114, "iter": 3500, "lr": 0.0136, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36625, "top5_acc": 0.61406, "loss_cls": 3.61723, "loss": 3.61723, "time": 0.83107} +{"mode": "train", "epoch": 114, "iter": 3600, "lr": 0.01358, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36516, "top5_acc": 0.61625, "loss_cls": 3.62813, "loss": 3.62813, "time": 0.81605} +{"mode": "train", "epoch": 114, "iter": 3700, "lr": 0.01356, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35109, "top5_acc": 0.60828, "loss_cls": 3.69218, "loss": 3.69218, "time": 0.81832} +{"mode": "val", "epoch": 114, "iter": 309, "lr": 0.01355, "top1_acc": 0.2996, "top5_acc": 0.5487, "mean_class_accuracy": 0.29944} +{"mode": "train", "epoch": 115, "iter": 100, "lr": 0.01353, "memory": 15990, "data_time": 1.27123, "top1_acc": 0.37203, "top5_acc": 0.62516, "loss_cls": 3.57998, "loss": 3.57998, "time": 2.25999} +{"mode": "train", "epoch": 115, "iter": 200, "lr": 0.01351, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.37531, "top5_acc": 0.63172, "loss_cls": 3.53706, "loss": 3.53706, "time": 0.82384} +{"mode": "train", "epoch": 115, "iter": 300, "lr": 0.01349, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36344, "top5_acc": 0.62313, "loss_cls": 3.61096, "loss": 3.61096, "time": 0.82092} +{"mode": "train", "epoch": 115, "iter": 400, "lr": 0.01348, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.35703, "top5_acc": 0.62172, "loss_cls": 3.59885, "loss": 3.59885, "time": 0.82824} +{"mode": "train", "epoch": 115, "iter": 500, "lr": 0.01346, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36656, "top5_acc": 0.62016, "loss_cls": 3.59554, "loss": 3.59554, "time": 0.82179} +{"mode": "train", "epoch": 115, "iter": 600, "lr": 0.01344, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36188, "top5_acc": 0.62109, "loss_cls": 3.61809, "loss": 3.61809, "time": 0.82449} +{"mode": "train", "epoch": 115, "iter": 700, "lr": 0.01342, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35422, "top5_acc": 0.60594, "loss_cls": 3.64225, "loss": 3.64225, "time": 0.83152} +{"mode": "train", "epoch": 115, "iter": 800, "lr": 0.0134, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36625, "top5_acc": 0.625, "loss_cls": 3.5979, "loss": 3.5979, "time": 0.82327} +{"mode": "train", "epoch": 115, "iter": 900, "lr": 0.01338, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.34484, "top5_acc": 0.60688, "loss_cls": 3.67959, "loss": 3.67959, "time": 0.82009} +{"mode": "train", "epoch": 115, "iter": 1000, "lr": 0.01336, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35906, "top5_acc": 0.62078, "loss_cls": 3.62059, "loss": 3.62059, "time": 0.82307} +{"mode": "train", "epoch": 115, "iter": 1100, "lr": 0.01334, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36125, "top5_acc": 0.61719, "loss_cls": 3.63139, "loss": 3.63139, "time": 0.81989} +{"mode": "train", "epoch": 115, "iter": 1200, "lr": 0.01332, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35109, "top5_acc": 0.61438, "loss_cls": 3.66684, "loss": 3.66684, "time": 0.82139} +{"mode": "train", "epoch": 115, "iter": 1300, "lr": 0.0133, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37, "top5_acc": 0.62641, "loss_cls": 3.57108, "loss": 3.57108, "time": 0.82118} +{"mode": "train", "epoch": 115, "iter": 1400, "lr": 0.01328, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35281, "top5_acc": 0.61203, "loss_cls": 3.68101, "loss": 3.68101, "time": 0.81713} +{"mode": "train", "epoch": 115, "iter": 1500, "lr": 0.01327, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35234, "top5_acc": 0.62328, "loss_cls": 3.64949, "loss": 3.64949, "time": 0.8206} +{"mode": "train", "epoch": 115, "iter": 1600, "lr": 0.01325, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35344, "top5_acc": 0.61234, "loss_cls": 3.66548, "loss": 3.66548, "time": 0.8189} +{"mode": "train", "epoch": 115, "iter": 1700, "lr": 0.01323, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36469, "top5_acc": 0.61391, "loss_cls": 3.64716, "loss": 3.64716, "time": 0.81902} +{"mode": "train", "epoch": 115, "iter": 1800, "lr": 0.01321, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.36328, "top5_acc": 0.62797, "loss_cls": 3.61119, "loss": 3.61119, "time": 0.81687} +{"mode": "train", "epoch": 115, "iter": 1900, "lr": 0.01319, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36219, "top5_acc": 0.62109, "loss_cls": 3.61403, "loss": 3.61403, "time": 0.82409} +{"mode": "train", "epoch": 115, "iter": 2000, "lr": 0.01317, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35828, "top5_acc": 0.60938, "loss_cls": 3.66814, "loss": 3.66814, "time": 0.81566} +{"mode": "train", "epoch": 115, "iter": 2100, "lr": 0.01315, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36188, "top5_acc": 0.62297, "loss_cls": 3.60737, "loss": 3.60737, "time": 0.82533} +{"mode": "train", "epoch": 115, "iter": 2200, "lr": 0.01313, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36094, "top5_acc": 0.62484, "loss_cls": 3.62089, "loss": 3.62089, "time": 0.82815} +{"mode": "train", "epoch": 115, "iter": 2300, "lr": 0.01311, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.35234, "top5_acc": 0.61781, "loss_cls": 3.64063, "loss": 3.64063, "time": 0.82195} +{"mode": "train", "epoch": 115, "iter": 2400, "lr": 0.0131, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35328, "top5_acc": 0.61266, "loss_cls": 3.68533, "loss": 3.68533, "time": 0.8223} +{"mode": "train", "epoch": 115, "iter": 2500, "lr": 0.01308, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36328, "top5_acc": 0.62344, "loss_cls": 3.63019, "loss": 3.63019, "time": 0.82252} +{"mode": "train", "epoch": 115, "iter": 2600, "lr": 0.01306, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36438, "top5_acc": 0.62438, "loss_cls": 3.59749, "loss": 3.59749, "time": 0.81816} +{"mode": "train", "epoch": 115, "iter": 2700, "lr": 0.01304, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36844, "top5_acc": 0.62906, "loss_cls": 3.54844, "loss": 3.54844, "time": 0.81752} +{"mode": "train", "epoch": 115, "iter": 2800, "lr": 0.01302, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35641, "top5_acc": 0.62344, "loss_cls": 3.61824, "loss": 3.61824, "time": 0.81843} +{"mode": "train", "epoch": 115, "iter": 2900, "lr": 0.013, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36297, "top5_acc": 0.62547, "loss_cls": 3.62121, "loss": 3.62121, "time": 0.81764} +{"mode": "train", "epoch": 115, "iter": 3000, "lr": 0.01298, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35844, "top5_acc": 0.61812, "loss_cls": 3.62711, "loss": 3.62711, "time": 0.8145} +{"mode": "train", "epoch": 115, "iter": 3100, "lr": 0.01296, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.365, "top5_acc": 0.61609, "loss_cls": 3.62899, "loss": 3.62899, "time": 0.8197} +{"mode": "train", "epoch": 115, "iter": 3200, "lr": 0.01295, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35797, "top5_acc": 0.61766, "loss_cls": 3.62693, "loss": 3.62693, "time": 0.81771} +{"mode": "train", "epoch": 115, "iter": 3300, "lr": 0.01293, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36609, "top5_acc": 0.61297, "loss_cls": 3.64826, "loss": 3.64826, "time": 0.8175} +{"mode": "train", "epoch": 115, "iter": 3400, "lr": 0.01291, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36734, "top5_acc": 0.61687, "loss_cls": 3.63721, "loss": 3.63721, "time": 0.81753} +{"mode": "train", "epoch": 115, "iter": 3500, "lr": 0.01289, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36625, "top5_acc": 0.62094, "loss_cls": 3.63421, "loss": 3.63421, "time": 0.81998} +{"mode": "train", "epoch": 115, "iter": 3600, "lr": 0.01287, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35094, "top5_acc": 0.61344, "loss_cls": 3.68069, "loss": 3.68069, "time": 0.8199} +{"mode": "train", "epoch": 115, "iter": 3700, "lr": 0.01285, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36234, "top5_acc": 0.61891, "loss_cls": 3.63248, "loss": 3.63248, "time": 0.81909} +{"mode": "val", "epoch": 115, "iter": 309, "lr": 0.01284, "top1_acc": 0.29585, "top5_acc": 0.54434, "mean_class_accuracy": 0.29564} +{"mode": "train", "epoch": 116, "iter": 100, "lr": 0.01282, "memory": 15990, "data_time": 1.30417, "top1_acc": 0.36953, "top5_acc": 0.62656, "loss_cls": 3.57195, "loss": 3.57195, "time": 2.29283} +{"mode": "train", "epoch": 116, "iter": 200, "lr": 0.01281, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.37281, "top5_acc": 0.62766, "loss_cls": 3.55097, "loss": 3.55097, "time": 0.83375} +{"mode": "train", "epoch": 116, "iter": 300, "lr": 0.01279, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.36562, "top5_acc": 0.63578, "loss_cls": 3.56118, "loss": 3.56118, "time": 0.82616} +{"mode": "train", "epoch": 116, "iter": 400, "lr": 0.01277, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.36203, "top5_acc": 0.6175, "loss_cls": 3.61603, "loss": 3.61603, "time": 0.82677} +{"mode": "train", "epoch": 116, "iter": 500, "lr": 0.01275, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37312, "top5_acc": 0.62969, "loss_cls": 3.55123, "loss": 3.55123, "time": 0.8198} +{"mode": "train", "epoch": 116, "iter": 600, "lr": 0.01273, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.3775, "top5_acc": 0.6275, "loss_cls": 3.54062, "loss": 3.54062, "time": 0.81982} +{"mode": "train", "epoch": 116, "iter": 700, "lr": 0.01271, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36406, "top5_acc": 0.62516, "loss_cls": 3.58089, "loss": 3.58089, "time": 0.82404} +{"mode": "train", "epoch": 116, "iter": 800, "lr": 0.01269, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36188, "top5_acc": 0.61469, "loss_cls": 3.60991, "loss": 3.60991, "time": 0.8164} +{"mode": "train", "epoch": 116, "iter": 900, "lr": 0.01268, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37109, "top5_acc": 0.62484, "loss_cls": 3.58465, "loss": 3.58465, "time": 0.82213} +{"mode": "train", "epoch": 116, "iter": 1000, "lr": 0.01266, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37188, "top5_acc": 0.62813, "loss_cls": 3.56329, "loss": 3.56329, "time": 0.81874} +{"mode": "train", "epoch": 116, "iter": 1100, "lr": 0.01264, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37531, "top5_acc": 0.63453, "loss_cls": 3.56407, "loss": 3.56407, "time": 0.81858} +{"mode": "train", "epoch": 116, "iter": 1200, "lr": 0.01262, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36422, "top5_acc": 0.62516, "loss_cls": 3.59995, "loss": 3.59995, "time": 0.82289} +{"mode": "train", "epoch": 116, "iter": 1300, "lr": 0.0126, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36094, "top5_acc": 0.62406, "loss_cls": 3.60406, "loss": 3.60406, "time": 0.81803} +{"mode": "train", "epoch": 116, "iter": 1400, "lr": 0.01258, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36234, "top5_acc": 0.61172, "loss_cls": 3.6443, "loss": 3.6443, "time": 0.82369} +{"mode": "train", "epoch": 116, "iter": 1500, "lr": 0.01256, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36734, "top5_acc": 0.62578, "loss_cls": 3.56689, "loss": 3.56689, "time": 0.82068} +{"mode": "train", "epoch": 116, "iter": 1600, "lr": 0.01255, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36688, "top5_acc": 0.62938, "loss_cls": 3.59225, "loss": 3.59225, "time": 0.81828} +{"mode": "train", "epoch": 116, "iter": 1700, "lr": 0.01253, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36703, "top5_acc": 0.61844, "loss_cls": 3.63326, "loss": 3.63326, "time": 0.81722} +{"mode": "train", "epoch": 116, "iter": 1800, "lr": 0.01251, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3675, "top5_acc": 0.63125, "loss_cls": 3.57194, "loss": 3.57194, "time": 0.81875} +{"mode": "train", "epoch": 116, "iter": 1900, "lr": 0.01249, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36391, "top5_acc": 0.62344, "loss_cls": 3.60403, "loss": 3.60403, "time": 0.82688} +{"mode": "train", "epoch": 116, "iter": 2000, "lr": 0.01247, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35531, "top5_acc": 0.61469, "loss_cls": 3.65966, "loss": 3.65966, "time": 0.82312} +{"mode": "train", "epoch": 116, "iter": 2100, "lr": 0.01245, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.35125, "top5_acc": 0.61625, "loss_cls": 3.64341, "loss": 3.64341, "time": 0.82811} +{"mode": "train", "epoch": 116, "iter": 2200, "lr": 0.01243, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36344, "top5_acc": 0.62141, "loss_cls": 3.6425, "loss": 3.6425, "time": 0.82047} +{"mode": "train", "epoch": 116, "iter": 2300, "lr": 0.01242, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.36, "top5_acc": 0.61938, "loss_cls": 3.6265, "loss": 3.6265, "time": 0.82148} +{"mode": "train", "epoch": 116, "iter": 2400, "lr": 0.0124, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36781, "top5_acc": 0.62578, "loss_cls": 3.58874, "loss": 3.58874, "time": 0.83125} +{"mode": "train", "epoch": 116, "iter": 2500, "lr": 0.01238, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37109, "top5_acc": 0.62625, "loss_cls": 3.59664, "loss": 3.59664, "time": 0.81975} +{"mode": "train", "epoch": 116, "iter": 2600, "lr": 0.01236, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3675, "top5_acc": 0.62813, "loss_cls": 3.57627, "loss": 3.57627, "time": 0.82115} +{"mode": "train", "epoch": 116, "iter": 2700, "lr": 0.01234, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3675, "top5_acc": 0.62031, "loss_cls": 3.60446, "loss": 3.60446, "time": 0.82017} +{"mode": "train", "epoch": 116, "iter": 2800, "lr": 0.01232, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37578, "top5_acc": 0.62578, "loss_cls": 3.58227, "loss": 3.58227, "time": 0.82042} +{"mode": "train", "epoch": 116, "iter": 2900, "lr": 0.01231, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35875, "top5_acc": 0.61766, "loss_cls": 3.64718, "loss": 3.64718, "time": 0.82029} +{"mode": "train", "epoch": 116, "iter": 3000, "lr": 0.01229, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35438, "top5_acc": 0.61422, "loss_cls": 3.63573, "loss": 3.63573, "time": 0.82009} +{"mode": "train", "epoch": 116, "iter": 3100, "lr": 0.01227, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36438, "top5_acc": 0.61266, "loss_cls": 3.64633, "loss": 3.64633, "time": 0.81656} +{"mode": "train", "epoch": 116, "iter": 3200, "lr": 0.01225, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37562, "top5_acc": 0.62922, "loss_cls": 3.57779, "loss": 3.57779, "time": 0.81981} +{"mode": "train", "epoch": 116, "iter": 3300, "lr": 0.01223, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36156, "top5_acc": 0.62125, "loss_cls": 3.59893, "loss": 3.59893, "time": 0.82115} +{"mode": "train", "epoch": 116, "iter": 3400, "lr": 0.01221, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35969, "top5_acc": 0.61234, "loss_cls": 3.6612, "loss": 3.6612, "time": 0.81934} +{"mode": "train", "epoch": 116, "iter": 3500, "lr": 0.0122, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36031, "top5_acc": 0.61922, "loss_cls": 3.61546, "loss": 3.61546, "time": 0.82296} +{"mode": "train", "epoch": 116, "iter": 3600, "lr": 0.01218, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36938, "top5_acc": 0.62562, "loss_cls": 3.58994, "loss": 3.58994, "time": 0.81836} +{"mode": "train", "epoch": 116, "iter": 3700, "lr": 0.01216, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36188, "top5_acc": 0.61375, "loss_cls": 3.63539, "loss": 3.63539, "time": 0.81299} +{"mode": "val", "epoch": 116, "iter": 309, "lr": 0.01215, "top1_acc": 0.30092, "top5_acc": 0.55549, "mean_class_accuracy": 0.30064} +{"mode": "train", "epoch": 117, "iter": 100, "lr": 0.01213, "memory": 15990, "data_time": 1.31353, "top1_acc": 0.37453, "top5_acc": 0.62797, "loss_cls": 3.53036, "loss": 3.53036, "time": 2.29779} +{"mode": "train", "epoch": 117, "iter": 200, "lr": 0.01211, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.37984, "top5_acc": 0.63078, "loss_cls": 3.53059, "loss": 3.53059, "time": 0.8305} +{"mode": "train", "epoch": 117, "iter": 300, "lr": 0.0121, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.38109, "top5_acc": 0.63391, "loss_cls": 3.52979, "loss": 3.52979, "time": 0.82391} +{"mode": "train", "epoch": 117, "iter": 400, "lr": 0.01208, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.38, "top5_acc": 0.63797, "loss_cls": 3.53059, "loss": 3.53059, "time": 0.8242} +{"mode": "train", "epoch": 117, "iter": 500, "lr": 0.01206, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36266, "top5_acc": 0.62047, "loss_cls": 3.61019, "loss": 3.61019, "time": 0.82038} +{"mode": "train", "epoch": 117, "iter": 600, "lr": 0.01204, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37969, "top5_acc": 0.63922, "loss_cls": 3.53521, "loss": 3.53521, "time": 0.82122} +{"mode": "train", "epoch": 117, "iter": 700, "lr": 0.01202, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37375, "top5_acc": 0.62859, "loss_cls": 3.57791, "loss": 3.57791, "time": 0.82307} +{"mode": "train", "epoch": 117, "iter": 800, "lr": 0.012, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37141, "top5_acc": 0.63172, "loss_cls": 3.55701, "loss": 3.55701, "time": 0.82456} +{"mode": "train", "epoch": 117, "iter": 900, "lr": 0.01199, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37641, "top5_acc": 0.63562, "loss_cls": 3.52815, "loss": 3.52815, "time": 0.81692} +{"mode": "train", "epoch": 117, "iter": 1000, "lr": 0.01197, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36438, "top5_acc": 0.6275, "loss_cls": 3.5732, "loss": 3.5732, "time": 0.81734} +{"mode": "train", "epoch": 117, "iter": 1100, "lr": 0.01195, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36344, "top5_acc": 0.62984, "loss_cls": 3.56761, "loss": 3.56761, "time": 0.82016} +{"mode": "train", "epoch": 117, "iter": 1200, "lr": 0.01193, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38016, "top5_acc": 0.63531, "loss_cls": 3.5234, "loss": 3.5234, "time": 0.81808} +{"mode": "train", "epoch": 117, "iter": 1300, "lr": 0.01191, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37359, "top5_acc": 0.63234, "loss_cls": 3.55381, "loss": 3.55381, "time": 0.81596} +{"mode": "train", "epoch": 117, "iter": 1400, "lr": 0.0119, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36375, "top5_acc": 0.63219, "loss_cls": 3.57258, "loss": 3.57258, "time": 0.82255} +{"mode": "train", "epoch": 117, "iter": 1500, "lr": 0.01188, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37141, "top5_acc": 0.62703, "loss_cls": 3.55763, "loss": 3.55763, "time": 0.82155} +{"mode": "train", "epoch": 117, "iter": 1600, "lr": 0.01186, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36422, "top5_acc": 0.62313, "loss_cls": 3.5953, "loss": 3.5953, "time": 0.81508} +{"mode": "train", "epoch": 117, "iter": 1700, "lr": 0.01184, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36109, "top5_acc": 0.61719, "loss_cls": 3.60914, "loss": 3.60914, "time": 0.82061} +{"mode": "train", "epoch": 117, "iter": 1800, "lr": 0.01182, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37266, "top5_acc": 0.62469, "loss_cls": 3.5769, "loss": 3.5769, "time": 0.82587} +{"mode": "train", "epoch": 117, "iter": 1900, "lr": 0.01181, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37453, "top5_acc": 0.62594, "loss_cls": 3.55751, "loss": 3.55751, "time": 0.81996} +{"mode": "train", "epoch": 117, "iter": 2000, "lr": 0.01179, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.35969, "top5_acc": 0.62297, "loss_cls": 3.62795, "loss": 3.62795, "time": 0.81579} +{"mode": "train", "epoch": 117, "iter": 2100, "lr": 0.01177, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.3625, "top5_acc": 0.61828, "loss_cls": 3.6145, "loss": 3.6145, "time": 0.82167} +{"mode": "train", "epoch": 117, "iter": 2200, "lr": 0.01175, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.36516, "top5_acc": 0.62234, "loss_cls": 3.61046, "loss": 3.61046, "time": 0.82554} +{"mode": "train", "epoch": 117, "iter": 2300, "lr": 0.01173, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36844, "top5_acc": 0.635, "loss_cls": 3.58525, "loss": 3.58525, "time": 0.82394} +{"mode": "train", "epoch": 117, "iter": 2400, "lr": 0.01172, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36156, "top5_acc": 0.62484, "loss_cls": 3.60166, "loss": 3.60166, "time": 0.82373} +{"mode": "train", "epoch": 117, "iter": 2500, "lr": 0.0117, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36688, "top5_acc": 0.62734, "loss_cls": 3.57125, "loss": 3.57125, "time": 0.82249} +{"mode": "train", "epoch": 117, "iter": 2600, "lr": 0.01168, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36984, "top5_acc": 0.625, "loss_cls": 3.58575, "loss": 3.58575, "time": 0.82277} +{"mode": "train", "epoch": 117, "iter": 2700, "lr": 0.01166, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.36297, "top5_acc": 0.63141, "loss_cls": 3.57806, "loss": 3.57806, "time": 0.82836} +{"mode": "train", "epoch": 117, "iter": 2800, "lr": 0.01164, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36844, "top5_acc": 0.63109, "loss_cls": 3.56361, "loss": 3.56361, "time": 0.817} +{"mode": "train", "epoch": 117, "iter": 2900, "lr": 0.01163, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36219, "top5_acc": 0.62484, "loss_cls": 3.59966, "loss": 3.59966, "time": 0.82148} +{"mode": "train", "epoch": 117, "iter": 3000, "lr": 0.01161, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37734, "top5_acc": 0.62641, "loss_cls": 3.5641, "loss": 3.5641, "time": 0.81945} +{"mode": "train", "epoch": 117, "iter": 3100, "lr": 0.01159, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36281, "top5_acc": 0.62734, "loss_cls": 3.59138, "loss": 3.59138, "time": 0.82092} +{"mode": "train", "epoch": 117, "iter": 3200, "lr": 0.01157, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37469, "top5_acc": 0.62766, "loss_cls": 3.53795, "loss": 3.53795, "time": 0.819} +{"mode": "train", "epoch": 117, "iter": 3300, "lr": 0.01155, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36875, "top5_acc": 0.61219, "loss_cls": 3.61213, "loss": 3.61213, "time": 0.81453} +{"mode": "train", "epoch": 117, "iter": 3400, "lr": 0.01154, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36578, "top5_acc": 0.62672, "loss_cls": 3.59794, "loss": 3.59794, "time": 0.81659} +{"mode": "train", "epoch": 117, "iter": 3500, "lr": 0.01152, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37812, "top5_acc": 0.62453, "loss_cls": 3.58881, "loss": 3.58881, "time": 0.81481} +{"mode": "train", "epoch": 117, "iter": 3600, "lr": 0.0115, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38, "top5_acc": 0.62328, "loss_cls": 3.56725, "loss": 3.56725, "time": 0.82409} +{"mode": "train", "epoch": 117, "iter": 3700, "lr": 0.01148, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.365, "top5_acc": 0.62344, "loss_cls": 3.63515, "loss": 3.63515, "time": 0.8188} +{"mode": "val", "epoch": 117, "iter": 309, "lr": 0.01147, "top1_acc": 0.30821, "top5_acc": 0.56526, "mean_class_accuracy": 0.30811} +{"mode": "train", "epoch": 118, "iter": 100, "lr": 0.01146, "memory": 15990, "data_time": 1.28377, "top1_acc": 0.37891, "top5_acc": 0.64766, "loss_cls": 3.4866, "loss": 3.4866, "time": 2.27419} +{"mode": "train", "epoch": 118, "iter": 200, "lr": 0.01144, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38031, "top5_acc": 0.63734, "loss_cls": 3.52028, "loss": 3.52028, "time": 0.82686} +{"mode": "train", "epoch": 118, "iter": 300, "lr": 0.01142, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.37422, "top5_acc": 0.6325, "loss_cls": 3.54606, "loss": 3.54606, "time": 0.82672} +{"mode": "train", "epoch": 118, "iter": 400, "lr": 0.0114, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38797, "top5_acc": 0.64219, "loss_cls": 3.48718, "loss": 3.48718, "time": 0.83074} +{"mode": "train", "epoch": 118, "iter": 500, "lr": 0.01139, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37953, "top5_acc": 0.64469, "loss_cls": 3.4748, "loss": 3.4748, "time": 0.82117} +{"mode": "train", "epoch": 118, "iter": 600, "lr": 0.01137, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37766, "top5_acc": 0.63516, "loss_cls": 3.53205, "loss": 3.53205, "time": 0.8201} +{"mode": "train", "epoch": 118, "iter": 700, "lr": 0.01135, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38469, "top5_acc": 0.63516, "loss_cls": 3.49796, "loss": 3.49796, "time": 0.82228} +{"mode": "train", "epoch": 118, "iter": 800, "lr": 0.01133, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37344, "top5_acc": 0.63328, "loss_cls": 3.55306, "loss": 3.55306, "time": 0.82442} +{"mode": "train", "epoch": 118, "iter": 900, "lr": 0.01131, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36938, "top5_acc": 0.62938, "loss_cls": 3.54402, "loss": 3.54402, "time": 0.82738} +{"mode": "train", "epoch": 118, "iter": 1000, "lr": 0.0113, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37547, "top5_acc": 0.63234, "loss_cls": 3.54412, "loss": 3.54412, "time": 0.81834} +{"mode": "train", "epoch": 118, "iter": 1100, "lr": 0.01128, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36703, "top5_acc": 0.6275, "loss_cls": 3.57843, "loss": 3.57843, "time": 0.82116} +{"mode": "train", "epoch": 118, "iter": 1200, "lr": 0.01126, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37188, "top5_acc": 0.62875, "loss_cls": 3.5637, "loss": 3.5637, "time": 0.82615} +{"mode": "train", "epoch": 118, "iter": 1300, "lr": 0.01124, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37156, "top5_acc": 0.62656, "loss_cls": 3.56576, "loss": 3.56576, "time": 0.81898} +{"mode": "train", "epoch": 118, "iter": 1400, "lr": 0.01123, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.38328, "top5_acc": 0.65219, "loss_cls": 3.48624, "loss": 3.48624, "time": 0.81984} +{"mode": "train", "epoch": 118, "iter": 1500, "lr": 0.01121, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38031, "top5_acc": 0.63594, "loss_cls": 3.51543, "loss": 3.51543, "time": 0.81892} +{"mode": "train", "epoch": 118, "iter": 1600, "lr": 0.01119, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38531, "top5_acc": 0.63828, "loss_cls": 3.53406, "loss": 3.53406, "time": 0.81731} +{"mode": "train", "epoch": 118, "iter": 1700, "lr": 0.01117, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36734, "top5_acc": 0.62469, "loss_cls": 3.56646, "loss": 3.56646, "time": 0.81738} +{"mode": "train", "epoch": 118, "iter": 1800, "lr": 0.01116, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36938, "top5_acc": 0.62562, "loss_cls": 3.56607, "loss": 3.56607, "time": 0.8173} +{"mode": "train", "epoch": 118, "iter": 1900, "lr": 0.01114, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36969, "top5_acc": 0.62813, "loss_cls": 3.57203, "loss": 3.57203, "time": 0.81519} +{"mode": "train", "epoch": 118, "iter": 2000, "lr": 0.01112, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.35922, "top5_acc": 0.61922, "loss_cls": 3.61029, "loss": 3.61029, "time": 0.82099} +{"mode": "train", "epoch": 118, "iter": 2100, "lr": 0.0111, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37562, "top5_acc": 0.62781, "loss_cls": 3.5444, "loss": 3.5444, "time": 0.82095} +{"mode": "train", "epoch": 118, "iter": 2200, "lr": 0.01109, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38797, "top5_acc": 0.63344, "loss_cls": 3.51114, "loss": 3.51114, "time": 0.82169} +{"mode": "train", "epoch": 118, "iter": 2300, "lr": 0.01107, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37594, "top5_acc": 0.62625, "loss_cls": 3.57726, "loss": 3.57726, "time": 0.82164} +{"mode": "train", "epoch": 118, "iter": 2400, "lr": 0.01105, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36297, "top5_acc": 0.62813, "loss_cls": 3.57997, "loss": 3.57997, "time": 0.82935} +{"mode": "train", "epoch": 118, "iter": 2500, "lr": 0.01103, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37578, "top5_acc": 0.62891, "loss_cls": 3.55937, "loss": 3.55937, "time": 0.81711} +{"mode": "train", "epoch": 118, "iter": 2600, "lr": 0.01102, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36766, "top5_acc": 0.62672, "loss_cls": 3.6093, "loss": 3.6093, "time": 0.82183} +{"mode": "train", "epoch": 118, "iter": 2700, "lr": 0.011, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37, "top5_acc": 0.62516, "loss_cls": 3.5681, "loss": 3.5681, "time": 0.81618} +{"mode": "train", "epoch": 118, "iter": 2800, "lr": 0.01098, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37469, "top5_acc": 0.62547, "loss_cls": 3.57104, "loss": 3.57104, "time": 0.819} +{"mode": "train", "epoch": 118, "iter": 2900, "lr": 0.01096, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.36328, "top5_acc": 0.62766, "loss_cls": 3.55852, "loss": 3.55852, "time": 0.82311} +{"mode": "train", "epoch": 118, "iter": 3000, "lr": 0.01095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37422, "top5_acc": 0.63266, "loss_cls": 3.56689, "loss": 3.56689, "time": 0.81728} +{"mode": "train", "epoch": 118, "iter": 3100, "lr": 0.01093, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37734, "top5_acc": 0.62469, "loss_cls": 3.57473, "loss": 3.57473, "time": 0.81538} +{"mode": "train", "epoch": 118, "iter": 3200, "lr": 0.01091, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38734, "top5_acc": 0.62813, "loss_cls": 3.53642, "loss": 3.53642, "time": 0.81683} +{"mode": "train", "epoch": 118, "iter": 3300, "lr": 0.01089, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36875, "top5_acc": 0.62266, "loss_cls": 3.58013, "loss": 3.58013, "time": 0.81909} +{"mode": "train", "epoch": 118, "iter": 3400, "lr": 0.01088, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37, "top5_acc": 0.63531, "loss_cls": 3.55217, "loss": 3.55217, "time": 0.82145} +{"mode": "train", "epoch": 118, "iter": 3500, "lr": 0.01086, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37938, "top5_acc": 0.62281, "loss_cls": 3.54605, "loss": 3.54605, "time": 0.81987} +{"mode": "train", "epoch": 118, "iter": 3600, "lr": 0.01084, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37453, "top5_acc": 0.62969, "loss_cls": 3.57671, "loss": 3.57671, "time": 0.81781} +{"mode": "train", "epoch": 118, "iter": 3700, "lr": 0.01082, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36516, "top5_acc": 0.62469, "loss_cls": 3.59743, "loss": 3.59743, "time": 0.81798} +{"mode": "val", "epoch": 118, "iter": 309, "lr": 0.01082, "top1_acc": 0.31125, "top5_acc": 0.56643, "mean_class_accuracy": 0.31108} +{"mode": "train", "epoch": 119, "iter": 100, "lr": 0.0108, "memory": 15990, "data_time": 1.29779, "top1_acc": 0.39188, "top5_acc": 0.64297, "loss_cls": 3.45889, "loss": 3.45889, "time": 2.30128} +{"mode": "train", "epoch": 119, "iter": 200, "lr": 0.01078, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.38844, "top5_acc": 0.64, "loss_cls": 3.45636, "loss": 3.45636, "time": 0.83369} +{"mode": "train", "epoch": 119, "iter": 300, "lr": 0.01076, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38875, "top5_acc": 0.65031, "loss_cls": 3.45385, "loss": 3.45385, "time": 0.82222} +{"mode": "train", "epoch": 119, "iter": 400, "lr": 0.01075, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38438, "top5_acc": 0.645, "loss_cls": 3.44396, "loss": 3.44396, "time": 0.82608} +{"mode": "train", "epoch": 119, "iter": 500, "lr": 0.01073, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37016, "top5_acc": 0.63906, "loss_cls": 3.5247, "loss": 3.5247, "time": 0.82631} +{"mode": "train", "epoch": 119, "iter": 600, "lr": 0.01071, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39109, "top5_acc": 0.64578, "loss_cls": 3.45471, "loss": 3.45471, "time": 0.82331} +{"mode": "train", "epoch": 119, "iter": 700, "lr": 0.01069, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37766, "top5_acc": 0.64125, "loss_cls": 3.50162, "loss": 3.50162, "time": 0.82586} +{"mode": "train", "epoch": 119, "iter": 800, "lr": 0.01068, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37484, "top5_acc": 0.63203, "loss_cls": 3.54242, "loss": 3.54242, "time": 0.82486} +{"mode": "train", "epoch": 119, "iter": 900, "lr": 0.01066, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37766, "top5_acc": 0.64219, "loss_cls": 3.4804, "loss": 3.4804, "time": 0.82377} +{"mode": "train", "epoch": 119, "iter": 1000, "lr": 0.01064, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36922, "top5_acc": 0.63, "loss_cls": 3.55566, "loss": 3.55566, "time": 0.81846} +{"mode": "train", "epoch": 119, "iter": 1100, "lr": 0.01063, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37891, "top5_acc": 0.63734, "loss_cls": 3.48459, "loss": 3.48459, "time": 0.81929} +{"mode": "train", "epoch": 119, "iter": 1200, "lr": 0.01061, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38328, "top5_acc": 0.64438, "loss_cls": 3.47877, "loss": 3.47877, "time": 0.81966} +{"mode": "train", "epoch": 119, "iter": 1300, "lr": 0.01059, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37656, "top5_acc": 0.63578, "loss_cls": 3.51885, "loss": 3.51885, "time": 0.81948} +{"mode": "train", "epoch": 119, "iter": 1400, "lr": 0.01057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37156, "top5_acc": 0.62953, "loss_cls": 3.59782, "loss": 3.59782, "time": 0.81806} +{"mode": "train", "epoch": 119, "iter": 1500, "lr": 0.01056, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37594, "top5_acc": 0.63266, "loss_cls": 3.55471, "loss": 3.55471, "time": 0.81243} +{"mode": "train", "epoch": 119, "iter": 1600, "lr": 0.01054, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38672, "top5_acc": 0.64938, "loss_cls": 3.44673, "loss": 3.44673, "time": 0.81851} +{"mode": "train", "epoch": 119, "iter": 1700, "lr": 0.01052, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38453, "top5_acc": 0.64, "loss_cls": 3.48749, "loss": 3.48749, "time": 0.82982} +{"mode": "train", "epoch": 119, "iter": 1800, "lr": 0.0105, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37469, "top5_acc": 0.63766, "loss_cls": 3.52664, "loss": 3.52664, "time": 0.81652} +{"mode": "train", "epoch": 119, "iter": 1900, "lr": 0.01049, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.36094, "top5_acc": 0.62109, "loss_cls": 3.61038, "loss": 3.61038, "time": 0.819} +{"mode": "train", "epoch": 119, "iter": 2000, "lr": 0.01047, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.3875, "top5_acc": 0.64641, "loss_cls": 3.47582, "loss": 3.47582, "time": 0.82826} +{"mode": "train", "epoch": 119, "iter": 2100, "lr": 0.01045, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.36203, "top5_acc": 0.62859, "loss_cls": 3.56024, "loss": 3.56024, "time": 0.81611} +{"mode": "train", "epoch": 119, "iter": 2200, "lr": 0.01044, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.37359, "top5_acc": 0.63594, "loss_cls": 3.53105, "loss": 3.53105, "time": 0.83039} +{"mode": "train", "epoch": 119, "iter": 2300, "lr": 0.01042, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36859, "top5_acc": 0.62938, "loss_cls": 3.56068, "loss": 3.56068, "time": 0.8213} +{"mode": "train", "epoch": 119, "iter": 2400, "lr": 0.0104, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37781, "top5_acc": 0.63516, "loss_cls": 3.51996, "loss": 3.51996, "time": 0.82288} +{"mode": "train", "epoch": 119, "iter": 2500, "lr": 0.01039, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37938, "top5_acc": 0.62484, "loss_cls": 3.56093, "loss": 3.56093, "time": 0.81927} +{"mode": "train", "epoch": 119, "iter": 2600, "lr": 0.01037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37734, "top5_acc": 0.63094, "loss_cls": 3.53004, "loss": 3.53004, "time": 0.81821} +{"mode": "train", "epoch": 119, "iter": 2700, "lr": 0.01035, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38828, "top5_acc": 0.63781, "loss_cls": 3.49582, "loss": 3.49582, "time": 0.82327} +{"mode": "train", "epoch": 119, "iter": 2800, "lr": 0.01033, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36812, "top5_acc": 0.62078, "loss_cls": 3.57057, "loss": 3.57057, "time": 0.8226} +{"mode": "train", "epoch": 119, "iter": 2900, "lr": 0.01032, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36438, "top5_acc": 0.62984, "loss_cls": 3.55891, "loss": 3.55891, "time": 0.81751} +{"mode": "train", "epoch": 119, "iter": 3000, "lr": 0.0103, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36609, "top5_acc": 0.62859, "loss_cls": 3.58907, "loss": 3.58907, "time": 0.81736} +{"mode": "train", "epoch": 119, "iter": 3100, "lr": 0.01028, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.36531, "top5_acc": 0.63094, "loss_cls": 3.57837, "loss": 3.57837, "time": 0.81572} +{"mode": "train", "epoch": 119, "iter": 3200, "lr": 0.01027, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.37422, "top5_acc": 0.63234, "loss_cls": 3.55191, "loss": 3.55191, "time": 0.82019} +{"mode": "train", "epoch": 119, "iter": 3300, "lr": 0.01025, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37766, "top5_acc": 0.63172, "loss_cls": 3.55611, "loss": 3.55611, "time": 0.81404} +{"mode": "train", "epoch": 119, "iter": 3400, "lr": 0.01023, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37922, "top5_acc": 0.63766, "loss_cls": 3.52968, "loss": 3.52968, "time": 0.82117} +{"mode": "train", "epoch": 119, "iter": 3500, "lr": 0.01022, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38344, "top5_acc": 0.63313, "loss_cls": 3.5379, "loss": 3.5379, "time": 0.81841} +{"mode": "train", "epoch": 119, "iter": 3600, "lr": 0.0102, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37719, "top5_acc": 0.63703, "loss_cls": 3.51577, "loss": 3.51577, "time": 0.81648} +{"mode": "train", "epoch": 119, "iter": 3700, "lr": 0.01018, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.36453, "top5_acc": 0.62578, "loss_cls": 3.60456, "loss": 3.60456, "time": 0.82052} +{"mode": "val", "epoch": 119, "iter": 309, "lr": 0.01017, "top1_acc": 0.31247, "top5_acc": 0.56633, "mean_class_accuracy": 0.31239} +{"mode": "train", "epoch": 120, "iter": 100, "lr": 0.01016, "memory": 15990, "data_time": 1.29436, "top1_acc": 0.40375, "top5_acc": 0.66609, "loss_cls": 3.36165, "loss": 3.36165, "time": 2.28227} +{"mode": "train", "epoch": 120, "iter": 200, "lr": 0.01014, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.38641, "top5_acc": 0.64531, "loss_cls": 3.48534, "loss": 3.48534, "time": 0.83385} +{"mode": "train", "epoch": 120, "iter": 300, "lr": 0.01012, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39688, "top5_acc": 0.65031, "loss_cls": 3.4412, "loss": 3.4412, "time": 0.82576} +{"mode": "train", "epoch": 120, "iter": 400, "lr": 0.01011, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.38734, "top5_acc": 0.65, "loss_cls": 3.45929, "loss": 3.45929, "time": 0.82183} +{"mode": "train", "epoch": 120, "iter": 500, "lr": 0.01009, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39438, "top5_acc": 0.64969, "loss_cls": 3.42767, "loss": 3.42767, "time": 0.82535} +{"mode": "train", "epoch": 120, "iter": 600, "lr": 0.01007, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37547, "top5_acc": 0.63906, "loss_cls": 3.50054, "loss": 3.50054, "time": 0.82572} +{"mode": "train", "epoch": 120, "iter": 700, "lr": 0.01006, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.39422, "top5_acc": 0.66, "loss_cls": 3.42404, "loss": 3.42404, "time": 0.82113} +{"mode": "train", "epoch": 120, "iter": 800, "lr": 0.01004, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.37422, "top5_acc": 0.63219, "loss_cls": 3.54534, "loss": 3.54534, "time": 0.81902} +{"mode": "train", "epoch": 120, "iter": 900, "lr": 0.01002, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37547, "top5_acc": 0.63578, "loss_cls": 3.53782, "loss": 3.53782, "time": 0.81673} +{"mode": "train", "epoch": 120, "iter": 1000, "lr": 0.01001, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38375, "top5_acc": 0.6475, "loss_cls": 3.46579, "loss": 3.46579, "time": 0.82247} +{"mode": "train", "epoch": 120, "iter": 1100, "lr": 0.00999, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38031, "top5_acc": 0.63422, "loss_cls": 3.52033, "loss": 3.52033, "time": 0.81934} +{"mode": "train", "epoch": 120, "iter": 1200, "lr": 0.00997, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38438, "top5_acc": 0.64109, "loss_cls": 3.48886, "loss": 3.48886, "time": 0.81916} +{"mode": "train", "epoch": 120, "iter": 1300, "lr": 0.00996, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37406, "top5_acc": 0.63438, "loss_cls": 3.53288, "loss": 3.53288, "time": 0.81989} +{"mode": "train", "epoch": 120, "iter": 1400, "lr": 0.00994, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38953, "top5_acc": 0.64859, "loss_cls": 3.45637, "loss": 3.45637, "time": 0.81844} +{"mode": "train", "epoch": 120, "iter": 1500, "lr": 0.00992, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39125, "top5_acc": 0.64438, "loss_cls": 3.45467, "loss": 3.45467, "time": 0.8221} +{"mode": "train", "epoch": 120, "iter": 1600, "lr": 0.0099, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38234, "top5_acc": 0.64547, "loss_cls": 3.49285, "loss": 3.49285, "time": 0.81587} +{"mode": "train", "epoch": 120, "iter": 1700, "lr": 0.00989, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.38125, "top5_acc": 0.64375, "loss_cls": 3.49377, "loss": 3.49377, "time": 0.81974} +{"mode": "train", "epoch": 120, "iter": 1800, "lr": 0.00987, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38766, "top5_acc": 0.64266, "loss_cls": 3.4928, "loss": 3.4928, "time": 0.82147} +{"mode": "train", "epoch": 120, "iter": 1900, "lr": 0.00985, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38219, "top5_acc": 0.63953, "loss_cls": 3.50668, "loss": 3.50668, "time": 0.82142} +{"mode": "train", "epoch": 120, "iter": 2000, "lr": 0.00984, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38484, "top5_acc": 0.62828, "loss_cls": 3.53294, "loss": 3.53294, "time": 0.82579} +{"mode": "train", "epoch": 120, "iter": 2100, "lr": 0.00982, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37906, "top5_acc": 0.63016, "loss_cls": 3.52716, "loss": 3.52716, "time": 0.81517} +{"mode": "train", "epoch": 120, "iter": 2200, "lr": 0.0098, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.37141, "top5_acc": 0.64188, "loss_cls": 3.51756, "loss": 3.51756, "time": 0.82597} +{"mode": "train", "epoch": 120, "iter": 2300, "lr": 0.00979, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39125, "top5_acc": 0.63609, "loss_cls": 3.48339, "loss": 3.48339, "time": 0.81737} +{"mode": "train", "epoch": 120, "iter": 2400, "lr": 0.00977, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.3675, "top5_acc": 0.63313, "loss_cls": 3.54958, "loss": 3.54958, "time": 0.82666} +{"mode": "train", "epoch": 120, "iter": 2500, "lr": 0.00976, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37609, "top5_acc": 0.63688, "loss_cls": 3.52155, "loss": 3.52155, "time": 0.82018} +{"mode": "train", "epoch": 120, "iter": 2600, "lr": 0.00974, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37828, "top5_acc": 0.63422, "loss_cls": 3.54189, "loss": 3.54189, "time": 0.81645} +{"mode": "train", "epoch": 120, "iter": 2700, "lr": 0.00972, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37719, "top5_acc": 0.64281, "loss_cls": 3.49369, "loss": 3.49369, "time": 0.81591} +{"mode": "train", "epoch": 120, "iter": 2800, "lr": 0.00971, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3775, "top5_acc": 0.64297, "loss_cls": 3.51386, "loss": 3.51386, "time": 0.8165} +{"mode": "train", "epoch": 120, "iter": 2900, "lr": 0.00969, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38406, "top5_acc": 0.63656, "loss_cls": 3.50855, "loss": 3.50855, "time": 0.81376} +{"mode": "train", "epoch": 120, "iter": 3000, "lr": 0.00967, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38141, "top5_acc": 0.63531, "loss_cls": 3.49778, "loss": 3.49778, "time": 0.81449} +{"mode": "train", "epoch": 120, "iter": 3100, "lr": 0.00966, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37656, "top5_acc": 0.63313, "loss_cls": 3.52308, "loss": 3.52308, "time": 0.81786} +{"mode": "train", "epoch": 120, "iter": 3200, "lr": 0.00964, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37547, "top5_acc": 0.63484, "loss_cls": 3.53606, "loss": 3.53606, "time": 0.81839} +{"mode": "train", "epoch": 120, "iter": 3300, "lr": 0.00962, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37984, "top5_acc": 0.63516, "loss_cls": 3.51918, "loss": 3.51918, "time": 0.81707} +{"mode": "train", "epoch": 120, "iter": 3400, "lr": 0.00961, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.36891, "top5_acc": 0.63297, "loss_cls": 3.55034, "loss": 3.55034, "time": 0.81941} +{"mode": "train", "epoch": 120, "iter": 3500, "lr": 0.00959, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37594, "top5_acc": 0.62953, "loss_cls": 3.54498, "loss": 3.54498, "time": 0.82193} +{"mode": "train", "epoch": 120, "iter": 3600, "lr": 0.00957, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37438, "top5_acc": 0.64, "loss_cls": 3.49938, "loss": 3.49938, "time": 0.82847} +{"mode": "train", "epoch": 120, "iter": 3700, "lr": 0.00956, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37969, "top5_acc": 0.63719, "loss_cls": 3.52128, "loss": 3.52128, "time": 0.81667} +{"mode": "val", "epoch": 120, "iter": 309, "lr": 0.00955, "top1_acc": 0.31419, "top5_acc": 0.57251, "mean_class_accuracy": 0.314} +{"mode": "train", "epoch": 121, "iter": 100, "lr": 0.00953, "memory": 15990, "data_time": 1.30557, "top1_acc": 0.39594, "top5_acc": 0.65609, "loss_cls": 3.40485, "loss": 3.40485, "time": 2.30092} +{"mode": "train", "epoch": 121, "iter": 200, "lr": 0.00952, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.39828, "top5_acc": 0.64859, "loss_cls": 3.42057, "loss": 3.42057, "time": 0.8335} +{"mode": "train", "epoch": 121, "iter": 300, "lr": 0.0095, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39844, "top5_acc": 0.65125, "loss_cls": 3.40038, "loss": 3.40038, "time": 0.82371} +{"mode": "train", "epoch": 121, "iter": 400, "lr": 0.00948, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38859, "top5_acc": 0.64344, "loss_cls": 3.4735, "loss": 3.4735, "time": 0.83102} +{"mode": "train", "epoch": 121, "iter": 500, "lr": 0.00947, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37531, "top5_acc": 0.64094, "loss_cls": 3.47845, "loss": 3.47845, "time": 0.8234} +{"mode": "train", "epoch": 121, "iter": 600, "lr": 0.00945, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38938, "top5_acc": 0.64656, "loss_cls": 3.46667, "loss": 3.46667, "time": 0.8242} +{"mode": "train", "epoch": 121, "iter": 700, "lr": 0.00943, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38453, "top5_acc": 0.63906, "loss_cls": 3.49068, "loss": 3.49068, "time": 0.82265} +{"mode": "train", "epoch": 121, "iter": 800, "lr": 0.00942, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.38469, "top5_acc": 0.6475, "loss_cls": 3.46114, "loss": 3.46114, "time": 0.82009} +{"mode": "train", "epoch": 121, "iter": 900, "lr": 0.0094, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38766, "top5_acc": 0.63859, "loss_cls": 3.48404, "loss": 3.48404, "time": 0.81685} +{"mode": "train", "epoch": 121, "iter": 1000, "lr": 0.00939, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39047, "top5_acc": 0.65172, "loss_cls": 3.42433, "loss": 3.42433, "time": 0.82063} +{"mode": "train", "epoch": 121, "iter": 1100, "lr": 0.00937, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38391, "top5_acc": 0.63953, "loss_cls": 3.48528, "loss": 3.48528, "time": 0.81929} +{"mode": "train", "epoch": 121, "iter": 1200, "lr": 0.00935, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39719, "top5_acc": 0.65, "loss_cls": 3.45888, "loss": 3.45888, "time": 0.82124} +{"mode": "train", "epoch": 121, "iter": 1300, "lr": 0.00934, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39062, "top5_acc": 0.64422, "loss_cls": 3.44656, "loss": 3.44656, "time": 0.81778} +{"mode": "train", "epoch": 121, "iter": 1400, "lr": 0.00932, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38656, "top5_acc": 0.64328, "loss_cls": 3.46648, "loss": 3.46648, "time": 0.81713} +{"mode": "train", "epoch": 121, "iter": 1500, "lr": 0.0093, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39109, "top5_acc": 0.64781, "loss_cls": 3.46346, "loss": 3.46346, "time": 0.81941} +{"mode": "train", "epoch": 121, "iter": 1600, "lr": 0.00929, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38359, "top5_acc": 0.64734, "loss_cls": 3.47551, "loss": 3.47551, "time": 0.81815} +{"mode": "train", "epoch": 121, "iter": 1700, "lr": 0.00927, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.385, "top5_acc": 0.6425, "loss_cls": 3.49512, "loss": 3.49512, "time": 0.81719} +{"mode": "train", "epoch": 121, "iter": 1800, "lr": 0.00926, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38016, "top5_acc": 0.63453, "loss_cls": 3.51863, "loss": 3.51863, "time": 0.81884} +{"mode": "train", "epoch": 121, "iter": 1900, "lr": 0.00924, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.38922, "top5_acc": 0.63672, "loss_cls": 3.47538, "loss": 3.47538, "time": 0.81948} +{"mode": "train", "epoch": 121, "iter": 2000, "lr": 0.00922, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38859, "top5_acc": 0.63516, "loss_cls": 3.48682, "loss": 3.48682, "time": 0.82763} +{"mode": "train", "epoch": 121, "iter": 2100, "lr": 0.00921, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39656, "top5_acc": 0.64578, "loss_cls": 3.43654, "loss": 3.43654, "time": 0.82869} +{"mode": "train", "epoch": 121, "iter": 2200, "lr": 0.00919, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38844, "top5_acc": 0.63891, "loss_cls": 3.47578, "loss": 3.47578, "time": 0.81974} +{"mode": "train", "epoch": 121, "iter": 2300, "lr": 0.00917, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37562, "top5_acc": 0.63656, "loss_cls": 3.52669, "loss": 3.52669, "time": 0.81884} +{"mode": "train", "epoch": 121, "iter": 2400, "lr": 0.00916, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37953, "top5_acc": 0.64094, "loss_cls": 3.47963, "loss": 3.47963, "time": 0.82237} +{"mode": "train", "epoch": 121, "iter": 2500, "lr": 0.00914, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38734, "top5_acc": 0.63688, "loss_cls": 3.51505, "loss": 3.51505, "time": 0.81416} +{"mode": "train", "epoch": 121, "iter": 2600, "lr": 0.00913, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38656, "top5_acc": 0.63875, "loss_cls": 3.47368, "loss": 3.47368, "time": 0.81813} +{"mode": "train", "epoch": 121, "iter": 2700, "lr": 0.00911, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38016, "top5_acc": 0.64031, "loss_cls": 3.49506, "loss": 3.49506, "time": 0.8175} +{"mode": "train", "epoch": 121, "iter": 2800, "lr": 0.00909, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.395, "top5_acc": 0.64156, "loss_cls": 3.44966, "loss": 3.44966, "time": 0.81687} +{"mode": "train", "epoch": 121, "iter": 2900, "lr": 0.00908, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38703, "top5_acc": 0.64562, "loss_cls": 3.45509, "loss": 3.45509, "time": 0.82411} +{"mode": "train", "epoch": 121, "iter": 3000, "lr": 0.00906, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37188, "top5_acc": 0.63656, "loss_cls": 3.51106, "loss": 3.51106, "time": 0.81525} +{"mode": "train", "epoch": 121, "iter": 3100, "lr": 0.00905, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37875, "top5_acc": 0.62984, "loss_cls": 3.55934, "loss": 3.55934, "time": 0.81692} +{"mode": "train", "epoch": 121, "iter": 3200, "lr": 0.00903, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39297, "top5_acc": 0.65094, "loss_cls": 3.44385, "loss": 3.44385, "time": 0.82289} +{"mode": "train", "epoch": 121, "iter": 3300, "lr": 0.00901, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39578, "top5_acc": 0.65062, "loss_cls": 3.46713, "loss": 3.46713, "time": 0.82339} +{"mode": "train", "epoch": 121, "iter": 3400, "lr": 0.009, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39688, "top5_acc": 0.65062, "loss_cls": 3.41615, "loss": 3.41615, "time": 0.81828} +{"mode": "train", "epoch": 121, "iter": 3500, "lr": 0.00898, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37438, "top5_acc": 0.63156, "loss_cls": 3.53183, "loss": 3.53183, "time": 0.82009} +{"mode": "train", "epoch": 121, "iter": 3600, "lr": 0.00897, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39312, "top5_acc": 0.64922, "loss_cls": 3.45461, "loss": 3.45461, "time": 0.8152} +{"mode": "train", "epoch": 121, "iter": 3700, "lr": 0.00895, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.37984, "top5_acc": 0.63906, "loss_cls": 3.52173, "loss": 3.52173, "time": 0.81758} +{"mode": "val", "epoch": 121, "iter": 309, "lr": 0.00894, "top1_acc": 0.30917, "top5_acc": 0.55772, "mean_class_accuracy": 0.30899} +{"mode": "train", "epoch": 122, "iter": 100, "lr": 0.00893, "memory": 15990, "data_time": 1.30333, "top1_acc": 0.40109, "top5_acc": 0.65938, "loss_cls": 3.3802, "loss": 3.3802, "time": 2.29123} +{"mode": "train", "epoch": 122, "iter": 200, "lr": 0.00891, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39422, "top5_acc": 0.65469, "loss_cls": 3.42453, "loss": 3.42453, "time": 0.82893} +{"mode": "train", "epoch": 122, "iter": 300, "lr": 0.00889, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40438, "top5_acc": 0.65594, "loss_cls": 3.39276, "loss": 3.39276, "time": 0.82488} +{"mode": "train", "epoch": 122, "iter": 400, "lr": 0.00888, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.39516, "top5_acc": 0.64906, "loss_cls": 3.41015, "loss": 3.41015, "time": 0.82528} +{"mode": "train", "epoch": 122, "iter": 500, "lr": 0.00886, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39438, "top5_acc": 0.65297, "loss_cls": 3.43302, "loss": 3.43302, "time": 0.82213} +{"mode": "train", "epoch": 122, "iter": 600, "lr": 0.00885, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.39281, "top5_acc": 0.65359, "loss_cls": 3.40429, "loss": 3.40429, "time": 0.82236} +{"mode": "train", "epoch": 122, "iter": 700, "lr": 0.00883, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38969, "top5_acc": 0.65, "loss_cls": 3.42506, "loss": 3.42506, "time": 0.82439} +{"mode": "train", "epoch": 122, "iter": 800, "lr": 0.00882, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39656, "top5_acc": 0.65609, "loss_cls": 3.39444, "loss": 3.39444, "time": 0.82149} +{"mode": "train", "epoch": 122, "iter": 900, "lr": 0.0088, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40688, "top5_acc": 0.66078, "loss_cls": 3.35562, "loss": 3.35562, "time": 0.81931} +{"mode": "train", "epoch": 122, "iter": 1000, "lr": 0.00878, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38719, "top5_acc": 0.64312, "loss_cls": 3.46611, "loss": 3.46611, "time": 0.81606} +{"mode": "train", "epoch": 122, "iter": 1100, "lr": 0.00877, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39766, "top5_acc": 0.65266, "loss_cls": 3.42133, "loss": 3.42133, "time": 0.82238} +{"mode": "train", "epoch": 122, "iter": 1200, "lr": 0.00875, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39047, "top5_acc": 0.64906, "loss_cls": 3.44964, "loss": 3.44964, "time": 0.81865} +{"mode": "train", "epoch": 122, "iter": 1300, "lr": 0.00874, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39734, "top5_acc": 0.65172, "loss_cls": 3.42102, "loss": 3.42102, "time": 0.82309} +{"mode": "train", "epoch": 122, "iter": 1400, "lr": 0.00872, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39219, "top5_acc": 0.64812, "loss_cls": 3.43106, "loss": 3.43106, "time": 0.81962} +{"mode": "train", "epoch": 122, "iter": 1500, "lr": 0.0087, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39297, "top5_acc": 0.65266, "loss_cls": 3.42288, "loss": 3.42288, "time": 0.81863} +{"mode": "train", "epoch": 122, "iter": 1600, "lr": 0.00869, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39453, "top5_acc": 0.64859, "loss_cls": 3.45292, "loss": 3.45292, "time": 0.82226} +{"mode": "train", "epoch": 122, "iter": 1700, "lr": 0.00867, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39344, "top5_acc": 0.65578, "loss_cls": 3.4288, "loss": 3.4288, "time": 0.82396} +{"mode": "train", "epoch": 122, "iter": 1800, "lr": 0.00866, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38844, "top5_acc": 0.65125, "loss_cls": 3.46144, "loss": 3.46144, "time": 0.81993} +{"mode": "train", "epoch": 122, "iter": 1900, "lr": 0.00864, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37703, "top5_acc": 0.63172, "loss_cls": 3.54201, "loss": 3.54201, "time": 0.8224} +{"mode": "train", "epoch": 122, "iter": 2000, "lr": 0.00863, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.37953, "top5_acc": 0.63938, "loss_cls": 3.48487, "loss": 3.48487, "time": 0.82121} +{"mode": "train", "epoch": 122, "iter": 2100, "lr": 0.00861, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39062, "top5_acc": 0.64578, "loss_cls": 3.47479, "loss": 3.47479, "time": 0.82714} +{"mode": "train", "epoch": 122, "iter": 2200, "lr": 0.00859, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39688, "top5_acc": 0.64906, "loss_cls": 3.43439, "loss": 3.43439, "time": 0.82216} +{"mode": "train", "epoch": 122, "iter": 2300, "lr": 0.00858, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39438, "top5_acc": 0.64703, "loss_cls": 3.45216, "loss": 3.45216, "time": 0.82026} +{"mode": "train", "epoch": 122, "iter": 2400, "lr": 0.00856, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39047, "top5_acc": 0.64047, "loss_cls": 3.47057, "loss": 3.47057, "time": 0.82161} +{"mode": "train", "epoch": 122, "iter": 2500, "lr": 0.00855, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4025, "top5_acc": 0.65375, "loss_cls": 3.39373, "loss": 3.39373, "time": 0.81808} +{"mode": "train", "epoch": 122, "iter": 2600, "lr": 0.00853, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.37812, "top5_acc": 0.63828, "loss_cls": 3.51521, "loss": 3.51521, "time": 0.82036} +{"mode": "train", "epoch": 122, "iter": 2700, "lr": 0.00852, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39109, "top5_acc": 0.64203, "loss_cls": 3.46532, "loss": 3.46532, "time": 0.81793} +{"mode": "train", "epoch": 122, "iter": 2800, "lr": 0.0085, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38484, "top5_acc": 0.64578, "loss_cls": 3.47535, "loss": 3.47535, "time": 0.81512} +{"mode": "train", "epoch": 122, "iter": 2900, "lr": 0.00849, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3925, "top5_acc": 0.64688, "loss_cls": 3.442, "loss": 3.442, "time": 0.82207} +{"mode": "train", "epoch": 122, "iter": 3000, "lr": 0.00847, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39438, "top5_acc": 0.65203, "loss_cls": 3.42643, "loss": 3.42643, "time": 0.81857} +{"mode": "train", "epoch": 122, "iter": 3100, "lr": 0.00845, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38234, "top5_acc": 0.64031, "loss_cls": 3.48815, "loss": 3.48815, "time": 0.81527} +{"mode": "train", "epoch": 122, "iter": 3200, "lr": 0.00844, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39781, "top5_acc": 0.64812, "loss_cls": 3.44039, "loss": 3.44039, "time": 0.81914} +{"mode": "train", "epoch": 122, "iter": 3300, "lr": 0.00842, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38875, "top5_acc": 0.65656, "loss_cls": 3.43324, "loss": 3.43324, "time": 0.81938} +{"mode": "train", "epoch": 122, "iter": 3400, "lr": 0.00841, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38719, "top5_acc": 0.64859, "loss_cls": 3.44561, "loss": 3.44561, "time": 0.81994} +{"mode": "train", "epoch": 122, "iter": 3500, "lr": 0.00839, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38344, "top5_acc": 0.64188, "loss_cls": 3.49118, "loss": 3.49118, "time": 0.8265} +{"mode": "train", "epoch": 122, "iter": 3600, "lr": 0.00838, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38422, "top5_acc": 0.64391, "loss_cls": 3.47793, "loss": 3.47793, "time": 0.81824} +{"mode": "train", "epoch": 122, "iter": 3700, "lr": 0.00836, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39547, "top5_acc": 0.64172, "loss_cls": 3.44951, "loss": 3.44951, "time": 0.81833} +{"mode": "val", "epoch": 122, "iter": 309, "lr": 0.00835, "top1_acc": 0.32092, "top5_acc": 0.56851, "mean_class_accuracy": 0.32076} +{"mode": "train", "epoch": 123, "iter": 100, "lr": 0.00834, "memory": 15990, "data_time": 1.30214, "top1_acc": 0.40578, "top5_acc": 0.66641, "loss_cls": 3.35818, "loss": 3.35818, "time": 2.29007} +{"mode": "train", "epoch": 123, "iter": 200, "lr": 0.00832, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40359, "top5_acc": 0.65797, "loss_cls": 3.34148, "loss": 3.34148, "time": 0.82666} +{"mode": "train", "epoch": 123, "iter": 300, "lr": 0.00831, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.4, "top5_acc": 0.65594, "loss_cls": 3.37906, "loss": 3.37906, "time": 0.82491} +{"mode": "train", "epoch": 123, "iter": 400, "lr": 0.00829, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39844, "top5_acc": 0.65359, "loss_cls": 3.39324, "loss": 3.39324, "time": 0.82424} +{"mode": "train", "epoch": 123, "iter": 500, "lr": 0.00828, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39234, "top5_acc": 0.65531, "loss_cls": 3.44916, "loss": 3.44916, "time": 0.82333} +{"mode": "train", "epoch": 123, "iter": 600, "lr": 0.00826, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40703, "top5_acc": 0.65453, "loss_cls": 3.39079, "loss": 3.39079, "time": 0.82678} +{"mode": "train", "epoch": 123, "iter": 700, "lr": 0.00825, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39344, "top5_acc": 0.65594, "loss_cls": 3.407, "loss": 3.407, "time": 0.82028} +{"mode": "train", "epoch": 123, "iter": 800, "lr": 0.00823, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40688, "top5_acc": 0.65547, "loss_cls": 3.37277, "loss": 3.37277, "time": 0.81957} +{"mode": "train", "epoch": 123, "iter": 900, "lr": 0.00822, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39688, "top5_acc": 0.64719, "loss_cls": 3.42902, "loss": 3.42902, "time": 0.81781} +{"mode": "train", "epoch": 123, "iter": 1000, "lr": 0.0082, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40297, "top5_acc": 0.66125, "loss_cls": 3.36799, "loss": 3.36799, "time": 0.81997} +{"mode": "train", "epoch": 123, "iter": 1100, "lr": 0.00818, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.38844, "top5_acc": 0.6425, "loss_cls": 3.46666, "loss": 3.46666, "time": 0.82132} +{"mode": "train", "epoch": 123, "iter": 1200, "lr": 0.00817, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39172, "top5_acc": 0.65047, "loss_cls": 3.42885, "loss": 3.42885, "time": 0.82084} +{"mode": "train", "epoch": 123, "iter": 1300, "lr": 0.00815, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39984, "top5_acc": 0.65297, "loss_cls": 3.42079, "loss": 3.42079, "time": 0.81653} +{"mode": "train", "epoch": 123, "iter": 1400, "lr": 0.00814, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39469, "top5_acc": 0.65141, "loss_cls": 3.44706, "loss": 3.44706, "time": 0.82392} +{"mode": "train", "epoch": 123, "iter": 1500, "lr": 0.00812, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39422, "top5_acc": 0.65344, "loss_cls": 3.40586, "loss": 3.40586, "time": 0.81751} +{"mode": "train", "epoch": 123, "iter": 1600, "lr": 0.00811, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40766, "top5_acc": 0.65859, "loss_cls": 3.38686, "loss": 3.38686, "time": 0.82292} +{"mode": "train", "epoch": 123, "iter": 1700, "lr": 0.00809, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.39297, "top5_acc": 0.65312, "loss_cls": 3.4201, "loss": 3.4201, "time": 0.82107} +{"mode": "train", "epoch": 123, "iter": 1800, "lr": 0.00808, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.39562, "top5_acc": 0.64203, "loss_cls": 3.44148, "loss": 3.44148, "time": 0.82069} +{"mode": "train", "epoch": 123, "iter": 1900, "lr": 0.00806, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.39719, "top5_acc": 0.65828, "loss_cls": 3.42168, "loss": 3.42168, "time": 0.82367} +{"mode": "train", "epoch": 123, "iter": 2000, "lr": 0.00805, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39391, "top5_acc": 0.65016, "loss_cls": 3.45341, "loss": 3.45341, "time": 0.82541} +{"mode": "train", "epoch": 123, "iter": 2100, "lr": 0.00803, "memory": 15990, "data_time": 0.0004, "top1_acc": 0.39672, "top5_acc": 0.65234, "loss_cls": 3.41607, "loss": 3.41607, "time": 0.8266} +{"mode": "train", "epoch": 123, "iter": 2200, "lr": 0.00802, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39172, "top5_acc": 0.64781, "loss_cls": 3.45003, "loss": 3.45003, "time": 0.82565} +{"mode": "train", "epoch": 123, "iter": 2300, "lr": 0.008, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39281, "top5_acc": 0.64812, "loss_cls": 3.44718, "loss": 3.44718, "time": 0.82237} +{"mode": "train", "epoch": 123, "iter": 2400, "lr": 0.00799, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39906, "top5_acc": 0.65656, "loss_cls": 3.41702, "loss": 3.41702, "time": 0.8267} +{"mode": "train", "epoch": 123, "iter": 2500, "lr": 0.00797, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39438, "top5_acc": 0.6475, "loss_cls": 3.45018, "loss": 3.45018, "time": 0.82246} +{"mode": "train", "epoch": 123, "iter": 2600, "lr": 0.00796, "memory": 15990, "data_time": 0.00022, "top1_acc": 0.40469, "top5_acc": 0.65984, "loss_cls": 3.38038, "loss": 3.38038, "time": 0.81516} +{"mode": "train", "epoch": 123, "iter": 2700, "lr": 0.00794, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38609, "top5_acc": 0.64562, "loss_cls": 3.44792, "loss": 3.44792, "time": 0.82059} +{"mode": "train", "epoch": 123, "iter": 2800, "lr": 0.00793, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38828, "top5_acc": 0.64484, "loss_cls": 3.44794, "loss": 3.44794, "time": 0.81993} +{"mode": "train", "epoch": 123, "iter": 2900, "lr": 0.00791, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39734, "top5_acc": 0.65484, "loss_cls": 3.42157, "loss": 3.42157, "time": 0.82647} +{"mode": "train", "epoch": 123, "iter": 3000, "lr": 0.0079, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39109, "top5_acc": 0.64391, "loss_cls": 3.42247, "loss": 3.42247, "time": 0.82038} +{"mode": "train", "epoch": 123, "iter": 3100, "lr": 0.00788, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39125, "top5_acc": 0.65797, "loss_cls": 3.3847, "loss": 3.3847, "time": 0.82008} +{"mode": "train", "epoch": 123, "iter": 3200, "lr": 0.00787, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39078, "top5_acc": 0.65047, "loss_cls": 3.42109, "loss": 3.42109, "time": 0.8152} +{"mode": "train", "epoch": 123, "iter": 3300, "lr": 0.00785, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39797, "top5_acc": 0.64703, "loss_cls": 3.40888, "loss": 3.40888, "time": 0.8162} +{"mode": "train", "epoch": 123, "iter": 3400, "lr": 0.00784, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39141, "top5_acc": 0.64656, "loss_cls": 3.44709, "loss": 3.44709, "time": 0.81474} +{"mode": "train", "epoch": 123, "iter": 3500, "lr": 0.00782, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38812, "top5_acc": 0.64438, "loss_cls": 3.45539, "loss": 3.45539, "time": 0.82818} +{"mode": "train", "epoch": 123, "iter": 3600, "lr": 0.00781, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.38859, "top5_acc": 0.64531, "loss_cls": 3.46387, "loss": 3.46387, "time": 0.82256} +{"mode": "train", "epoch": 123, "iter": 3700, "lr": 0.00779, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39547, "top5_acc": 0.65609, "loss_cls": 3.4061, "loss": 3.4061, "time": 0.81985} +{"mode": "val", "epoch": 123, "iter": 309, "lr": 0.00778, "top1_acc": 0.33014, "top5_acc": 0.58208, "mean_class_accuracy": 0.32992} +{"mode": "train", "epoch": 124, "iter": 100, "lr": 0.00777, "memory": 15990, "data_time": 1.26083, "top1_acc": 0.41609, "top5_acc": 0.67344, "loss_cls": 3.29509, "loss": 3.29509, "time": 2.24153} +{"mode": "train", "epoch": 124, "iter": 200, "lr": 0.00775, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.4125, "top5_acc": 0.67109, "loss_cls": 3.31883, "loss": 3.31883, "time": 0.82725} +{"mode": "train", "epoch": 124, "iter": 300, "lr": 0.00774, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41531, "top5_acc": 0.67469, "loss_cls": 3.30219, "loss": 3.30219, "time": 0.83753} +{"mode": "train", "epoch": 124, "iter": 400, "lr": 0.00772, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.40078, "top5_acc": 0.65953, "loss_cls": 3.37568, "loss": 3.37568, "time": 0.8242} +{"mode": "train", "epoch": 124, "iter": 500, "lr": 0.00771, "memory": 15990, "data_time": 0.00054, "top1_acc": 0.41094, "top5_acc": 0.66344, "loss_cls": 3.36402, "loss": 3.36402, "time": 0.82657} +{"mode": "train", "epoch": 124, "iter": 600, "lr": 0.00769, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39594, "top5_acc": 0.65953, "loss_cls": 3.38568, "loss": 3.38568, "time": 0.82933} +{"mode": "train", "epoch": 124, "iter": 700, "lr": 0.00768, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41125, "top5_acc": 0.66594, "loss_cls": 3.34261, "loss": 3.34261, "time": 0.82016} +{"mode": "train", "epoch": 124, "iter": 800, "lr": 0.00766, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40938, "top5_acc": 0.66078, "loss_cls": 3.33345, "loss": 3.33345, "time": 0.82186} +{"mode": "train", "epoch": 124, "iter": 900, "lr": 0.00765, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40078, "top5_acc": 0.66109, "loss_cls": 3.38755, "loss": 3.38755, "time": 0.82137} +{"mode": "train", "epoch": 124, "iter": 1000, "lr": 0.00763, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39953, "top5_acc": 0.65656, "loss_cls": 3.3786, "loss": 3.3786, "time": 0.81425} +{"mode": "train", "epoch": 124, "iter": 1100, "lr": 0.00762, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39688, "top5_acc": 0.66453, "loss_cls": 3.3868, "loss": 3.3868, "time": 0.81842} +{"mode": "train", "epoch": 124, "iter": 1200, "lr": 0.0076, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39531, "top5_acc": 0.65562, "loss_cls": 3.39774, "loss": 3.39774, "time": 0.82069} +{"mode": "train", "epoch": 124, "iter": 1300, "lr": 0.00759, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39234, "top5_acc": 0.66734, "loss_cls": 3.35059, "loss": 3.35059, "time": 0.81819} +{"mode": "train", "epoch": 124, "iter": 1400, "lr": 0.00758, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39578, "top5_acc": 0.64766, "loss_cls": 3.42098, "loss": 3.42098, "time": 0.82149} +{"mode": "train", "epoch": 124, "iter": 1500, "lr": 0.00756, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40281, "top5_acc": 0.65516, "loss_cls": 3.39332, "loss": 3.39332, "time": 0.819} +{"mode": "train", "epoch": 124, "iter": 1600, "lr": 0.00755, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40047, "top5_acc": 0.66312, "loss_cls": 3.35451, "loss": 3.35451, "time": 0.81935} +{"mode": "train", "epoch": 124, "iter": 1700, "lr": 0.00753, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39859, "top5_acc": 0.65797, "loss_cls": 3.36282, "loss": 3.36282, "time": 0.81671} +{"mode": "train", "epoch": 124, "iter": 1800, "lr": 0.00752, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40234, "top5_acc": 0.65609, "loss_cls": 3.3628, "loss": 3.3628, "time": 0.82153} +{"mode": "train", "epoch": 124, "iter": 1900, "lr": 0.0075, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39625, "top5_acc": 0.65031, "loss_cls": 3.41956, "loss": 3.41956, "time": 0.81728} +{"mode": "train", "epoch": 124, "iter": 2000, "lr": 0.00749, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41203, "top5_acc": 0.66625, "loss_cls": 3.33022, "loss": 3.33022, "time": 0.81854} +{"mode": "train", "epoch": 124, "iter": 2100, "lr": 0.00747, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40391, "top5_acc": 0.65703, "loss_cls": 3.37937, "loss": 3.37937, "time": 0.8197} +{"mode": "train", "epoch": 124, "iter": 2200, "lr": 0.00746, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39766, "top5_acc": 0.64844, "loss_cls": 3.40773, "loss": 3.40773, "time": 0.82538} +{"mode": "train", "epoch": 124, "iter": 2300, "lr": 0.00744, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39375, "top5_acc": 0.65281, "loss_cls": 3.42286, "loss": 3.42286, "time": 0.81946} +{"mode": "train", "epoch": 124, "iter": 2400, "lr": 0.00743, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40188, "top5_acc": 0.66062, "loss_cls": 3.38318, "loss": 3.38318, "time": 0.82053} +{"mode": "train", "epoch": 124, "iter": 2500, "lr": 0.00741, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40391, "top5_acc": 0.65875, "loss_cls": 3.3802, "loss": 3.3802, "time": 0.8166} +{"mode": "train", "epoch": 124, "iter": 2600, "lr": 0.0074, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.38453, "top5_acc": 0.63875, "loss_cls": 3.48424, "loss": 3.48424, "time": 0.81306} +{"mode": "train", "epoch": 124, "iter": 2700, "lr": 0.00738, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39906, "top5_acc": 0.66141, "loss_cls": 3.38922, "loss": 3.38922, "time": 0.81883} +{"mode": "train", "epoch": 124, "iter": 2800, "lr": 0.00737, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39391, "top5_acc": 0.65719, "loss_cls": 3.3865, "loss": 3.3865, "time": 0.81738} +{"mode": "train", "epoch": 124, "iter": 2900, "lr": 0.00735, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3975, "top5_acc": 0.65859, "loss_cls": 3.42649, "loss": 3.42649, "time": 0.8206} +{"mode": "train", "epoch": 124, "iter": 3000, "lr": 0.00734, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39031, "top5_acc": 0.65125, "loss_cls": 3.41322, "loss": 3.41322, "time": 0.8179} +{"mode": "train", "epoch": 124, "iter": 3100, "lr": 0.00733, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.3975, "top5_acc": 0.64875, "loss_cls": 3.40719, "loss": 3.40719, "time": 0.81907} +{"mode": "train", "epoch": 124, "iter": 3200, "lr": 0.00731, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39016, "top5_acc": 0.65, "loss_cls": 3.45881, "loss": 3.45881, "time": 0.82337} +{"mode": "train", "epoch": 124, "iter": 3300, "lr": 0.0073, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39438, "top5_acc": 0.64734, "loss_cls": 3.42198, "loss": 3.42198, "time": 0.81738} +{"mode": "train", "epoch": 124, "iter": 3400, "lr": 0.00728, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.3925, "top5_acc": 0.65406, "loss_cls": 3.40767, "loss": 3.40767, "time": 0.82049} +{"mode": "train", "epoch": 124, "iter": 3500, "lr": 0.00727, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.39266, "top5_acc": 0.65812, "loss_cls": 3.39787, "loss": 3.39787, "time": 0.81872} +{"mode": "train", "epoch": 124, "iter": 3600, "lr": 0.00725, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39609, "top5_acc": 0.64359, "loss_cls": 3.43829, "loss": 3.43829, "time": 0.81955} +{"mode": "train", "epoch": 124, "iter": 3700, "lr": 0.00724, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40422, "top5_acc": 0.65766, "loss_cls": 3.37659, "loss": 3.37659, "time": 0.82111} +{"mode": "val", "epoch": 124, "iter": 309, "lr": 0.00723, "top1_acc": 0.33485, "top5_acc": 0.58228, "mean_class_accuracy": 0.33461} +{"mode": "train", "epoch": 125, "iter": 100, "lr": 0.00722, "memory": 15990, "data_time": 1.2757, "top1_acc": 0.41422, "top5_acc": 0.67109, "loss_cls": 3.30217, "loss": 3.30217, "time": 2.26469} +{"mode": "train", "epoch": 125, "iter": 200, "lr": 0.0072, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41047, "top5_acc": 0.66359, "loss_cls": 3.33995, "loss": 3.33995, "time": 0.826} +{"mode": "train", "epoch": 125, "iter": 300, "lr": 0.00719, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40984, "top5_acc": 0.67125, "loss_cls": 3.31537, "loss": 3.31537, "time": 0.81992} +{"mode": "train", "epoch": 125, "iter": 400, "lr": 0.00717, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.4025, "top5_acc": 0.66578, "loss_cls": 3.33742, "loss": 3.33742, "time": 0.83142} +{"mode": "train", "epoch": 125, "iter": 500, "lr": 0.00716, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41766, "top5_acc": 0.66922, "loss_cls": 3.30017, "loss": 3.30017, "time": 0.82164} +{"mode": "train", "epoch": 125, "iter": 600, "lr": 0.00715, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41219, "top5_acc": 0.66703, "loss_cls": 3.30315, "loss": 3.30315, "time": 0.8243} +{"mode": "train", "epoch": 125, "iter": 700, "lr": 0.00713, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40875, "top5_acc": 0.66453, "loss_cls": 3.32587, "loss": 3.32587, "time": 0.82487} +{"mode": "train", "epoch": 125, "iter": 800, "lr": 0.00712, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40297, "top5_acc": 0.66172, "loss_cls": 3.34922, "loss": 3.34922, "time": 0.82005} +{"mode": "train", "epoch": 125, "iter": 900, "lr": 0.0071, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40953, "top5_acc": 0.66938, "loss_cls": 3.30326, "loss": 3.30326, "time": 0.8193} +{"mode": "train", "epoch": 125, "iter": 1000, "lr": 0.00709, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41844, "top5_acc": 0.67531, "loss_cls": 3.26635, "loss": 3.26635, "time": 0.81737} +{"mode": "train", "epoch": 125, "iter": 1100, "lr": 0.00707, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40297, "top5_acc": 0.65922, "loss_cls": 3.37375, "loss": 3.37375, "time": 0.82019} +{"mode": "train", "epoch": 125, "iter": 1200, "lr": 0.00706, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39438, "top5_acc": 0.6475, "loss_cls": 3.42785, "loss": 3.42785, "time": 0.82209} +{"mode": "train", "epoch": 125, "iter": 1300, "lr": 0.00704, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39844, "top5_acc": 0.65469, "loss_cls": 3.38594, "loss": 3.38594, "time": 0.82103} +{"mode": "train", "epoch": 125, "iter": 1400, "lr": 0.00703, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40547, "top5_acc": 0.64891, "loss_cls": 3.40122, "loss": 3.40122, "time": 0.82364} +{"mode": "train", "epoch": 125, "iter": 1500, "lr": 0.00702, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40328, "top5_acc": 0.66391, "loss_cls": 3.36335, "loss": 3.36335, "time": 0.81857} +{"mode": "train", "epoch": 125, "iter": 1600, "lr": 0.007, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.39984, "top5_acc": 0.665, "loss_cls": 3.38925, "loss": 3.38925, "time": 0.82011} +{"mode": "train", "epoch": 125, "iter": 1700, "lr": 0.00699, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40375, "top5_acc": 0.66266, "loss_cls": 3.37027, "loss": 3.37027, "time": 0.81881} +{"mode": "train", "epoch": 125, "iter": 1800, "lr": 0.00697, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.39594, "top5_acc": 0.65188, "loss_cls": 3.40879, "loss": 3.40879, "time": 0.8206} +{"mode": "train", "epoch": 125, "iter": 1900, "lr": 0.00696, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40719, "top5_acc": 0.65766, "loss_cls": 3.39566, "loss": 3.39566, "time": 0.82203} +{"mode": "train", "epoch": 125, "iter": 2000, "lr": 0.00694, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.38766, "top5_acc": 0.66609, "loss_cls": 3.37642, "loss": 3.37642, "time": 0.82107} +{"mode": "train", "epoch": 125, "iter": 2100, "lr": 0.00693, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41094, "top5_acc": 0.66031, "loss_cls": 3.34574, "loss": 3.34574, "time": 0.81849} +{"mode": "train", "epoch": 125, "iter": 2200, "lr": 0.00692, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40297, "top5_acc": 0.66016, "loss_cls": 3.3518, "loss": 3.3518, "time": 0.82662} +{"mode": "train", "epoch": 125, "iter": 2300, "lr": 0.0069, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40062, "top5_acc": 0.66734, "loss_cls": 3.36387, "loss": 3.36387, "time": 0.82676} +{"mode": "train", "epoch": 125, "iter": 2400, "lr": 0.00689, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.40406, "top5_acc": 0.66656, "loss_cls": 3.34393, "loss": 3.34393, "time": 0.82477} +{"mode": "train", "epoch": 125, "iter": 2500, "lr": 0.00687, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41391, "top5_acc": 0.66672, "loss_cls": 3.34916, "loss": 3.34916, "time": 0.82137} +{"mode": "train", "epoch": 125, "iter": 2600, "lr": 0.00686, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.3975, "top5_acc": 0.64906, "loss_cls": 3.42364, "loss": 3.42364, "time": 0.81678} +{"mode": "train", "epoch": 125, "iter": 2700, "lr": 0.00685, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40625, "top5_acc": 0.65875, "loss_cls": 3.35388, "loss": 3.35388, "time": 0.82109} +{"mode": "train", "epoch": 125, "iter": 2800, "lr": 0.00683, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40547, "top5_acc": 0.65391, "loss_cls": 3.35961, "loss": 3.35961, "time": 0.82308} +{"mode": "train", "epoch": 125, "iter": 2900, "lr": 0.00682, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40438, "top5_acc": 0.65781, "loss_cls": 3.38747, "loss": 3.38747, "time": 0.81621} +{"mode": "train", "epoch": 125, "iter": 3000, "lr": 0.0068, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40375, "top5_acc": 0.65734, "loss_cls": 3.36447, "loss": 3.36447, "time": 0.82065} +{"mode": "train", "epoch": 125, "iter": 3100, "lr": 0.00679, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40547, "top5_acc": 0.66125, "loss_cls": 3.3432, "loss": 3.3432, "time": 0.81751} +{"mode": "train", "epoch": 125, "iter": 3200, "lr": 0.00678, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40938, "top5_acc": 0.67484, "loss_cls": 3.29138, "loss": 3.29138, "time": 0.81894} +{"mode": "train", "epoch": 125, "iter": 3300, "lr": 0.00676, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40766, "top5_acc": 0.66406, "loss_cls": 3.35091, "loss": 3.35091, "time": 0.82109} +{"mode": "train", "epoch": 125, "iter": 3400, "lr": 0.00675, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40938, "top5_acc": 0.66141, "loss_cls": 3.32853, "loss": 3.32853, "time": 0.82006} +{"mode": "train", "epoch": 125, "iter": 3500, "lr": 0.00673, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4, "top5_acc": 0.65719, "loss_cls": 3.40597, "loss": 3.40597, "time": 0.81501} +{"mode": "train", "epoch": 125, "iter": 3600, "lr": 0.00672, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40219, "top5_acc": 0.65766, "loss_cls": 3.37843, "loss": 3.37843, "time": 0.81645} +{"mode": "train", "epoch": 125, "iter": 3700, "lr": 0.00671, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40453, "top5_acc": 0.65609, "loss_cls": 3.38362, "loss": 3.38362, "time": 0.81491} +{"mode": "val", "epoch": 125, "iter": 309, "lr": 0.0067, "top1_acc": 0.33257, "top5_acc": 0.58765, "mean_class_accuracy": 0.3324} +{"mode": "train", "epoch": 126, "iter": 100, "lr": 0.00668, "memory": 15990, "data_time": 1.32886, "top1_acc": 0.43, "top5_acc": 0.68938, "loss_cls": 3.23737, "loss": 3.23737, "time": 2.31371} +{"mode": "train", "epoch": 126, "iter": 200, "lr": 0.00667, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.42719, "top5_acc": 0.68328, "loss_cls": 3.24625, "loss": 3.24625, "time": 0.83389} +{"mode": "train", "epoch": 126, "iter": 300, "lr": 0.00666, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.42, "top5_acc": 0.68281, "loss_cls": 3.26585, "loss": 3.26585, "time": 0.82316} +{"mode": "train", "epoch": 126, "iter": 400, "lr": 0.00664, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.40547, "top5_acc": 0.66281, "loss_cls": 3.36653, "loss": 3.36653, "time": 0.83175} +{"mode": "train", "epoch": 126, "iter": 500, "lr": 0.00663, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42625, "top5_acc": 0.67422, "loss_cls": 3.25486, "loss": 3.25486, "time": 0.82883} +{"mode": "train", "epoch": 126, "iter": 600, "lr": 0.00662, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.41453, "top5_acc": 0.67578, "loss_cls": 3.27726, "loss": 3.27726, "time": 0.82246} +{"mode": "train", "epoch": 126, "iter": 700, "lr": 0.0066, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41453, "top5_acc": 0.67453, "loss_cls": 3.24703, "loss": 3.24703, "time": 0.82627} +{"mode": "train", "epoch": 126, "iter": 800, "lr": 0.00659, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42062, "top5_acc": 0.67719, "loss_cls": 3.26176, "loss": 3.26176, "time": 0.82141} +{"mode": "train", "epoch": 126, "iter": 900, "lr": 0.00657, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41047, "top5_acc": 0.66406, "loss_cls": 3.33516, "loss": 3.33516, "time": 0.82502} +{"mode": "train", "epoch": 126, "iter": 1000, "lr": 0.00656, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41312, "top5_acc": 0.67062, "loss_cls": 3.29821, "loss": 3.29821, "time": 0.81917} +{"mode": "train", "epoch": 126, "iter": 1100, "lr": 0.00655, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41734, "top5_acc": 0.67625, "loss_cls": 3.30829, "loss": 3.30829, "time": 0.82303} +{"mode": "train", "epoch": 126, "iter": 1200, "lr": 0.00653, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40766, "top5_acc": 0.67469, "loss_cls": 3.31261, "loss": 3.31261, "time": 0.81606} +{"mode": "train", "epoch": 126, "iter": 1300, "lr": 0.00652, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4225, "top5_acc": 0.67812, "loss_cls": 3.24517, "loss": 3.24517, "time": 0.81779} +{"mode": "train", "epoch": 126, "iter": 1400, "lr": 0.0065, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4025, "top5_acc": 0.67, "loss_cls": 3.33235, "loss": 3.33235, "time": 0.81904} +{"mode": "train", "epoch": 126, "iter": 1500, "lr": 0.00649, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40641, "top5_acc": 0.66031, "loss_cls": 3.356, "loss": 3.356, "time": 0.82497} +{"mode": "train", "epoch": 126, "iter": 1600, "lr": 0.00648, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41203, "top5_acc": 0.66922, "loss_cls": 3.31952, "loss": 3.31952, "time": 0.82029} +{"mode": "train", "epoch": 126, "iter": 1700, "lr": 0.00646, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40562, "top5_acc": 0.66438, "loss_cls": 3.35317, "loss": 3.35317, "time": 0.82574} +{"mode": "train", "epoch": 126, "iter": 1800, "lr": 0.00645, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40875, "top5_acc": 0.66328, "loss_cls": 3.35162, "loss": 3.35162, "time": 0.81839} +{"mode": "train", "epoch": 126, "iter": 1900, "lr": 0.00644, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41938, "top5_acc": 0.67297, "loss_cls": 3.26023, "loss": 3.26023, "time": 0.81862} +{"mode": "train", "epoch": 126, "iter": 2000, "lr": 0.00642, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42594, "top5_acc": 0.66938, "loss_cls": 3.30309, "loss": 3.30309, "time": 0.8204} +{"mode": "train", "epoch": 126, "iter": 2100, "lr": 0.00641, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41156, "top5_acc": 0.65594, "loss_cls": 3.36282, "loss": 3.36282, "time": 0.82255} +{"mode": "train", "epoch": 126, "iter": 2200, "lr": 0.00639, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.40547, "top5_acc": 0.66188, "loss_cls": 3.3186, "loss": 3.3186, "time": 0.82651} +{"mode": "train", "epoch": 126, "iter": 2300, "lr": 0.00638, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40703, "top5_acc": 0.66094, "loss_cls": 3.34342, "loss": 3.34342, "time": 0.82701} +{"mode": "train", "epoch": 126, "iter": 2400, "lr": 0.00637, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40266, "top5_acc": 0.66406, "loss_cls": 3.34065, "loss": 3.34065, "time": 0.82335} +{"mode": "train", "epoch": 126, "iter": 2500, "lr": 0.00635, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40453, "top5_acc": 0.66172, "loss_cls": 3.36173, "loss": 3.36173, "time": 0.81385} +{"mode": "train", "epoch": 126, "iter": 2600, "lr": 0.00634, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41625, "top5_acc": 0.66562, "loss_cls": 3.33892, "loss": 3.33892, "time": 0.81884} +{"mode": "train", "epoch": 126, "iter": 2700, "lr": 0.00633, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40125, "top5_acc": 0.65859, "loss_cls": 3.36728, "loss": 3.36728, "time": 0.81388} +{"mode": "train", "epoch": 126, "iter": 2800, "lr": 0.00631, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40375, "top5_acc": 0.65906, "loss_cls": 3.35068, "loss": 3.35068, "time": 0.81895} +{"mode": "train", "epoch": 126, "iter": 2900, "lr": 0.0063, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40109, "top5_acc": 0.66281, "loss_cls": 3.36067, "loss": 3.36067, "time": 0.81959} +{"mode": "train", "epoch": 126, "iter": 3000, "lr": 0.00629, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40281, "top5_acc": 0.665, "loss_cls": 3.34907, "loss": 3.34907, "time": 0.81944} +{"mode": "train", "epoch": 126, "iter": 3100, "lr": 0.00627, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.39688, "top5_acc": 0.665, "loss_cls": 3.33813, "loss": 3.33813, "time": 0.82167} +{"mode": "train", "epoch": 126, "iter": 3200, "lr": 0.00626, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40672, "top5_acc": 0.66172, "loss_cls": 3.36291, "loss": 3.36291, "time": 0.82036} +{"mode": "train", "epoch": 126, "iter": 3300, "lr": 0.00625, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.39188, "top5_acc": 0.65266, "loss_cls": 3.40836, "loss": 3.40836, "time": 0.82645} +{"mode": "train", "epoch": 126, "iter": 3400, "lr": 0.00623, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.40047, "top5_acc": 0.66062, "loss_cls": 3.37759, "loss": 3.37759, "time": 0.81846} +{"mode": "train", "epoch": 126, "iter": 3500, "lr": 0.00622, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41875, "top5_acc": 0.66891, "loss_cls": 3.30113, "loss": 3.30113, "time": 0.82073} +{"mode": "train", "epoch": 126, "iter": 3600, "lr": 0.0062, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41016, "top5_acc": 0.66484, "loss_cls": 3.34233, "loss": 3.34233, "time": 0.81946} +{"mode": "train", "epoch": 126, "iter": 3700, "lr": 0.00619, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41438, "top5_acc": 0.66094, "loss_cls": 3.33773, "loss": 3.33773, "time": 0.81724} +{"mode": "val", "epoch": 126, "iter": 309, "lr": 0.00618, "top1_acc": 0.33531, "top5_acc": 0.58183, "mean_class_accuracy": 0.33502} +{"mode": "train", "epoch": 127, "iter": 100, "lr": 0.00617, "memory": 15990, "data_time": 1.32651, "top1_acc": 0.40484, "top5_acc": 0.67469, "loss_cls": 3.27704, "loss": 3.27704, "time": 2.31692} +{"mode": "train", "epoch": 127, "iter": 200, "lr": 0.00616, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42906, "top5_acc": 0.68234, "loss_cls": 3.22216, "loss": 3.22216, "time": 0.82926} +{"mode": "train", "epoch": 127, "iter": 300, "lr": 0.00614, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.425, "top5_acc": 0.68688, "loss_cls": 3.22121, "loss": 3.22121, "time": 0.82591} +{"mode": "train", "epoch": 127, "iter": 400, "lr": 0.00613, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42344, "top5_acc": 0.67188, "loss_cls": 3.27596, "loss": 3.27596, "time": 0.8301} +{"mode": "train", "epoch": 127, "iter": 500, "lr": 0.00612, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41344, "top5_acc": 0.67703, "loss_cls": 3.25372, "loss": 3.25372, "time": 0.83189} +{"mode": "train", "epoch": 127, "iter": 600, "lr": 0.0061, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41688, "top5_acc": 0.68094, "loss_cls": 3.27258, "loss": 3.27258, "time": 0.82208} +{"mode": "train", "epoch": 127, "iter": 700, "lr": 0.00609, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42328, "top5_acc": 0.68609, "loss_cls": 3.22159, "loss": 3.22159, "time": 0.8247} +{"mode": "train", "epoch": 127, "iter": 800, "lr": 0.00608, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41344, "top5_acc": 0.66609, "loss_cls": 3.29642, "loss": 3.29642, "time": 0.82148} +{"mode": "train", "epoch": 127, "iter": 900, "lr": 0.00606, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41938, "top5_acc": 0.67734, "loss_cls": 3.27526, "loss": 3.27526, "time": 0.8222} +{"mode": "train", "epoch": 127, "iter": 1000, "lr": 0.00605, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42172, "top5_acc": 0.68016, "loss_cls": 3.26508, "loss": 3.26508, "time": 0.81982} +{"mode": "train", "epoch": 127, "iter": 1100, "lr": 0.00604, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42156, "top5_acc": 0.67922, "loss_cls": 3.26522, "loss": 3.26522, "time": 0.8202} +{"mode": "train", "epoch": 127, "iter": 1200, "lr": 0.00602, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41172, "top5_acc": 0.67281, "loss_cls": 3.27487, "loss": 3.27487, "time": 0.81733} +{"mode": "train", "epoch": 127, "iter": 1300, "lr": 0.00601, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41156, "top5_acc": 0.66906, "loss_cls": 3.32026, "loss": 3.32026, "time": 0.82144} +{"mode": "train", "epoch": 127, "iter": 1400, "lr": 0.006, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41391, "top5_acc": 0.67109, "loss_cls": 3.28479, "loss": 3.28479, "time": 0.81738} +{"mode": "train", "epoch": 127, "iter": 1500, "lr": 0.00598, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41359, "top5_acc": 0.66562, "loss_cls": 3.33024, "loss": 3.33024, "time": 0.81905} +{"mode": "train", "epoch": 127, "iter": 1600, "lr": 0.00597, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41609, "top5_acc": 0.67938, "loss_cls": 3.28227, "loss": 3.28227, "time": 0.82163} +{"mode": "train", "epoch": 127, "iter": 1700, "lr": 0.00596, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.39516, "top5_acc": 0.65562, "loss_cls": 3.37846, "loss": 3.37846, "time": 0.82439} +{"mode": "train", "epoch": 127, "iter": 1800, "lr": 0.00594, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42734, "top5_acc": 0.67719, "loss_cls": 3.25346, "loss": 3.25346, "time": 0.82237} +{"mode": "train", "epoch": 127, "iter": 1900, "lr": 0.00593, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.42609, "top5_acc": 0.67812, "loss_cls": 3.24649, "loss": 3.24649, "time": 0.8246} +{"mode": "train", "epoch": 127, "iter": 2000, "lr": 0.00592, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.415, "top5_acc": 0.66141, "loss_cls": 3.33025, "loss": 3.33025, "time": 0.81999} +{"mode": "train", "epoch": 127, "iter": 2100, "lr": 0.00591, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41719, "top5_acc": 0.67266, "loss_cls": 3.27958, "loss": 3.27958, "time": 0.82116} +{"mode": "train", "epoch": 127, "iter": 2200, "lr": 0.00589, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41391, "top5_acc": 0.67109, "loss_cls": 3.32437, "loss": 3.32437, "time": 0.82286} +{"mode": "train", "epoch": 127, "iter": 2300, "lr": 0.00588, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41422, "top5_acc": 0.66578, "loss_cls": 3.29584, "loss": 3.29584, "time": 0.82079} +{"mode": "train", "epoch": 127, "iter": 2400, "lr": 0.00587, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.40969, "top5_acc": 0.66984, "loss_cls": 3.28032, "loss": 3.28032, "time": 0.8227} +{"mode": "train", "epoch": 127, "iter": 2500, "lr": 0.00585, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41297, "top5_acc": 0.67438, "loss_cls": 3.29112, "loss": 3.29112, "time": 0.82156} +{"mode": "train", "epoch": 127, "iter": 2600, "lr": 0.00584, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41609, "top5_acc": 0.65938, "loss_cls": 3.35153, "loss": 3.35153, "time": 0.82056} +{"mode": "train", "epoch": 127, "iter": 2700, "lr": 0.00583, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41109, "top5_acc": 0.67516, "loss_cls": 3.30599, "loss": 3.30599, "time": 0.82088} +{"mode": "train", "epoch": 127, "iter": 2800, "lr": 0.00581, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40906, "top5_acc": 0.66281, "loss_cls": 3.32858, "loss": 3.32858, "time": 0.81853} +{"mode": "train", "epoch": 127, "iter": 2900, "lr": 0.0058, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41281, "top5_acc": 0.66859, "loss_cls": 3.34122, "loss": 3.34122, "time": 0.82229} +{"mode": "train", "epoch": 127, "iter": 3000, "lr": 0.00579, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42172, "top5_acc": 0.67547, "loss_cls": 3.28908, "loss": 3.28908, "time": 0.82005} +{"mode": "train", "epoch": 127, "iter": 3100, "lr": 0.00577, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42672, "top5_acc": 0.68031, "loss_cls": 3.26197, "loss": 3.26197, "time": 0.81538} +{"mode": "train", "epoch": 127, "iter": 3200, "lr": 0.00576, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41719, "top5_acc": 0.67375, "loss_cls": 3.30574, "loss": 3.30574, "time": 0.8245} +{"mode": "train", "epoch": 127, "iter": 3300, "lr": 0.00575, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41312, "top5_acc": 0.65828, "loss_cls": 3.3411, "loss": 3.3411, "time": 0.81854} +{"mode": "train", "epoch": 127, "iter": 3400, "lr": 0.00573, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.40453, "top5_acc": 0.66625, "loss_cls": 3.3216, "loss": 3.3216, "time": 0.81728} +{"mode": "train", "epoch": 127, "iter": 3500, "lr": 0.00572, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41078, "top5_acc": 0.66188, "loss_cls": 3.34767, "loss": 3.34767, "time": 0.81613} +{"mode": "train", "epoch": 127, "iter": 3600, "lr": 0.00571, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41406, "top5_acc": 0.67188, "loss_cls": 3.30171, "loss": 3.30171, "time": 0.81457} +{"mode": "train", "epoch": 127, "iter": 3700, "lr": 0.0057, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.41109, "top5_acc": 0.66625, "loss_cls": 3.32058, "loss": 3.32058, "time": 0.81879} +{"mode": "val", "epoch": 127, "iter": 309, "lr": 0.00569, "top1_acc": 0.34184, "top5_acc": 0.59449, "mean_class_accuracy": 0.34163} +{"mode": "train", "epoch": 128, "iter": 100, "lr": 0.00568, "memory": 15990, "data_time": 1.29646, "top1_acc": 0.43781, "top5_acc": 0.69703, "loss_cls": 3.18134, "loss": 3.18134, "time": 2.28732} +{"mode": "train", "epoch": 128, "iter": 200, "lr": 0.00566, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42531, "top5_acc": 0.68203, "loss_cls": 3.25917, "loss": 3.25917, "time": 0.82212} +{"mode": "train", "epoch": 128, "iter": 300, "lr": 0.00565, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43484, "top5_acc": 0.68656, "loss_cls": 3.21372, "loss": 3.21372, "time": 0.82693} +{"mode": "train", "epoch": 128, "iter": 400, "lr": 0.00564, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42938, "top5_acc": 0.68391, "loss_cls": 3.1932, "loss": 3.1932, "time": 0.82861} +{"mode": "train", "epoch": 128, "iter": 500, "lr": 0.00563, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.41891, "top5_acc": 0.67812, "loss_cls": 3.23494, "loss": 3.23494, "time": 0.82713} +{"mode": "train", "epoch": 128, "iter": 600, "lr": 0.00561, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.42109, "top5_acc": 0.6825, "loss_cls": 3.22938, "loss": 3.22938, "time": 0.82106} +{"mode": "train", "epoch": 128, "iter": 700, "lr": 0.0056, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.41797, "top5_acc": 0.67453, "loss_cls": 3.26254, "loss": 3.26254, "time": 0.82722} +{"mode": "train", "epoch": 128, "iter": 800, "lr": 0.00559, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41812, "top5_acc": 0.68969, "loss_cls": 3.20878, "loss": 3.20878, "time": 0.8254} +{"mode": "train", "epoch": 128, "iter": 900, "lr": 0.00557, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42562, "top5_acc": 0.67672, "loss_cls": 3.24878, "loss": 3.24878, "time": 0.81843} +{"mode": "train", "epoch": 128, "iter": 1000, "lr": 0.00556, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41266, "top5_acc": 0.66922, "loss_cls": 3.30718, "loss": 3.30718, "time": 0.82107} +{"mode": "train", "epoch": 128, "iter": 1100, "lr": 0.00555, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42234, "top5_acc": 0.67984, "loss_cls": 3.24685, "loss": 3.24685, "time": 0.81696} +{"mode": "train", "epoch": 128, "iter": 1200, "lr": 0.00554, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42391, "top5_acc": 0.67125, "loss_cls": 3.25558, "loss": 3.25558, "time": 0.8218} +{"mode": "train", "epoch": 128, "iter": 1300, "lr": 0.00552, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42266, "top5_acc": 0.67031, "loss_cls": 3.25543, "loss": 3.25543, "time": 0.82297} +{"mode": "train", "epoch": 128, "iter": 1400, "lr": 0.00551, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40938, "top5_acc": 0.67766, "loss_cls": 3.27599, "loss": 3.27599, "time": 0.8189} +{"mode": "train", "epoch": 128, "iter": 1500, "lr": 0.0055, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42688, "top5_acc": 0.68156, "loss_cls": 3.24402, "loss": 3.24402, "time": 0.81819} +{"mode": "train", "epoch": 128, "iter": 1600, "lr": 0.00548, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42844, "top5_acc": 0.68109, "loss_cls": 3.21221, "loss": 3.21221, "time": 0.81968} +{"mode": "train", "epoch": 128, "iter": 1700, "lr": 0.00547, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.41906, "top5_acc": 0.67578, "loss_cls": 3.25932, "loss": 3.25932, "time": 0.8216} +{"mode": "train", "epoch": 128, "iter": 1800, "lr": 0.00546, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.41891, "top5_acc": 0.68078, "loss_cls": 3.25924, "loss": 3.25924, "time": 0.8211} +{"mode": "train", "epoch": 128, "iter": 1900, "lr": 0.00545, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41562, "top5_acc": 0.67625, "loss_cls": 3.28278, "loss": 3.28278, "time": 0.82358} +{"mode": "train", "epoch": 128, "iter": 2000, "lr": 0.00543, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42922, "top5_acc": 0.68359, "loss_cls": 3.22786, "loss": 3.22786, "time": 0.8199} +{"mode": "train", "epoch": 128, "iter": 2100, "lr": 0.00542, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42109, "top5_acc": 0.67984, "loss_cls": 3.24707, "loss": 3.24707, "time": 0.81641} +{"mode": "train", "epoch": 128, "iter": 2200, "lr": 0.00541, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.42359, "top5_acc": 0.67406, "loss_cls": 3.27975, "loss": 3.27975, "time": 0.83078} +{"mode": "train", "epoch": 128, "iter": 2300, "lr": 0.0054, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42453, "top5_acc": 0.67906, "loss_cls": 3.26263, "loss": 3.26263, "time": 0.82454} +{"mode": "train", "epoch": 128, "iter": 2400, "lr": 0.00538, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.41922, "top5_acc": 0.66891, "loss_cls": 3.27431, "loss": 3.27431, "time": 0.82128} +{"mode": "train", "epoch": 128, "iter": 2500, "lr": 0.00537, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43031, "top5_acc": 0.6775, "loss_cls": 3.25872, "loss": 3.25872, "time": 0.82449} +{"mode": "train", "epoch": 128, "iter": 2600, "lr": 0.00536, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41828, "top5_acc": 0.67141, "loss_cls": 3.31078, "loss": 3.31078, "time": 0.82071} +{"mode": "train", "epoch": 128, "iter": 2700, "lr": 0.00535, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41594, "top5_acc": 0.66875, "loss_cls": 3.29765, "loss": 3.29765, "time": 0.8178} +{"mode": "train", "epoch": 128, "iter": 2800, "lr": 0.00533, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41938, "top5_acc": 0.67016, "loss_cls": 3.28632, "loss": 3.28632, "time": 0.81886} +{"mode": "train", "epoch": 128, "iter": 2900, "lr": 0.00532, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42328, "top5_acc": 0.67219, "loss_cls": 3.27581, "loss": 3.27581, "time": 0.82145} +{"mode": "train", "epoch": 128, "iter": 3000, "lr": 0.00531, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42172, "top5_acc": 0.68562, "loss_cls": 3.23895, "loss": 3.23895, "time": 0.81841} +{"mode": "train", "epoch": 128, "iter": 3100, "lr": 0.0053, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.415, "top5_acc": 0.66812, "loss_cls": 3.28847, "loss": 3.28847, "time": 0.81735} +{"mode": "train", "epoch": 128, "iter": 3200, "lr": 0.00528, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41781, "top5_acc": 0.66906, "loss_cls": 3.28866, "loss": 3.28866, "time": 0.82001} +{"mode": "train", "epoch": 128, "iter": 3300, "lr": 0.00527, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41312, "top5_acc": 0.66516, "loss_cls": 3.29395, "loss": 3.29395, "time": 0.81602} +{"mode": "train", "epoch": 128, "iter": 3400, "lr": 0.00526, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.40797, "top5_acc": 0.66984, "loss_cls": 3.30161, "loss": 3.30161, "time": 0.81965} +{"mode": "train", "epoch": 128, "iter": 3500, "lr": 0.00525, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.40719, "top5_acc": 0.66766, "loss_cls": 3.31491, "loss": 3.31491, "time": 0.82033} +{"mode": "train", "epoch": 128, "iter": 3600, "lr": 0.00523, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41656, "top5_acc": 0.66938, "loss_cls": 3.29479, "loss": 3.29479, "time": 0.82151} +{"mode": "train", "epoch": 128, "iter": 3700, "lr": 0.00522, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41812, "top5_acc": 0.67156, "loss_cls": 3.26842, "loss": 3.26842, "time": 0.81951} +{"mode": "val", "epoch": 128, "iter": 309, "lr": 0.00521, "top1_acc": 0.3464, "top5_acc": 0.59925, "mean_class_accuracy": 0.34612} +{"mode": "train", "epoch": 129, "iter": 100, "lr": 0.0052, "memory": 15990, "data_time": 1.29037, "top1_acc": 0.44078, "top5_acc": 0.69969, "loss_cls": 3.15085, "loss": 3.15085, "time": 2.27789} +{"mode": "train", "epoch": 129, "iter": 200, "lr": 0.00519, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43172, "top5_acc": 0.69422, "loss_cls": 3.19338, "loss": 3.19338, "time": 0.82455} +{"mode": "train", "epoch": 129, "iter": 300, "lr": 0.00518, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44297, "top5_acc": 0.70125, "loss_cls": 3.13736, "loss": 3.13736, "time": 0.82778} +{"mode": "train", "epoch": 129, "iter": 400, "lr": 0.00516, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43109, "top5_acc": 0.68547, "loss_cls": 3.18572, "loss": 3.18572, "time": 0.82345} +{"mode": "train", "epoch": 129, "iter": 500, "lr": 0.00515, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42875, "top5_acc": 0.68641, "loss_cls": 3.18486, "loss": 3.18486, "time": 0.82428} +{"mode": "train", "epoch": 129, "iter": 600, "lr": 0.00514, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43203, "top5_acc": 0.69453, "loss_cls": 3.17799, "loss": 3.17799, "time": 0.81981} +{"mode": "train", "epoch": 129, "iter": 700, "lr": 0.00513, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.42875, "top5_acc": 0.68359, "loss_cls": 3.19846, "loss": 3.19846, "time": 0.82602} +{"mode": "train", "epoch": 129, "iter": 800, "lr": 0.00512, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43641, "top5_acc": 0.68078, "loss_cls": 3.20624, "loss": 3.20624, "time": 0.81509} +{"mode": "train", "epoch": 129, "iter": 900, "lr": 0.0051, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43484, "top5_acc": 0.68531, "loss_cls": 3.20277, "loss": 3.20277, "time": 0.8232} +{"mode": "train", "epoch": 129, "iter": 1000, "lr": 0.00509, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43531, "top5_acc": 0.69578, "loss_cls": 3.17014, "loss": 3.17014, "time": 0.82142} +{"mode": "train", "epoch": 129, "iter": 1100, "lr": 0.00508, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43516, "top5_acc": 0.68281, "loss_cls": 3.22851, "loss": 3.22851, "time": 0.82219} +{"mode": "train", "epoch": 129, "iter": 1200, "lr": 0.00507, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42766, "top5_acc": 0.68281, "loss_cls": 3.22916, "loss": 3.22916, "time": 0.81617} +{"mode": "train", "epoch": 129, "iter": 1300, "lr": 0.00505, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44391, "top5_acc": 0.69547, "loss_cls": 3.1453, "loss": 3.1453, "time": 0.82213} +{"mode": "train", "epoch": 129, "iter": 1400, "lr": 0.00504, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43703, "top5_acc": 0.69438, "loss_cls": 3.16237, "loss": 3.16237, "time": 0.82621} +{"mode": "train", "epoch": 129, "iter": 1500, "lr": 0.00503, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41922, "top5_acc": 0.67531, "loss_cls": 3.25408, "loss": 3.25408, "time": 0.82284} +{"mode": "train", "epoch": 129, "iter": 1600, "lr": 0.00502, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42547, "top5_acc": 0.67641, "loss_cls": 3.22204, "loss": 3.22204, "time": 0.81795} +{"mode": "train", "epoch": 129, "iter": 1700, "lr": 0.00501, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42391, "top5_acc": 0.68344, "loss_cls": 3.25058, "loss": 3.25058, "time": 0.81611} +{"mode": "train", "epoch": 129, "iter": 1800, "lr": 0.00499, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43594, "top5_acc": 0.68844, "loss_cls": 3.18444, "loss": 3.18444, "time": 0.81878} +{"mode": "train", "epoch": 129, "iter": 1900, "lr": 0.00498, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42766, "top5_acc": 0.68359, "loss_cls": 3.21192, "loss": 3.21192, "time": 0.82068} +{"mode": "train", "epoch": 129, "iter": 2000, "lr": 0.00497, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42531, "top5_acc": 0.68906, "loss_cls": 3.19609, "loss": 3.19609, "time": 0.82437} +{"mode": "train", "epoch": 129, "iter": 2100, "lr": 0.00496, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.43344, "top5_acc": 0.68594, "loss_cls": 3.19067, "loss": 3.19067, "time": 0.82095} +{"mode": "train", "epoch": 129, "iter": 2200, "lr": 0.00494, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42641, "top5_acc": 0.67766, "loss_cls": 3.23591, "loss": 3.23591, "time": 0.82658} +{"mode": "train", "epoch": 129, "iter": 2300, "lr": 0.00493, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.41859, "top5_acc": 0.68422, "loss_cls": 3.23548, "loss": 3.23548, "time": 0.81715} +{"mode": "train", "epoch": 129, "iter": 2400, "lr": 0.00492, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42719, "top5_acc": 0.67656, "loss_cls": 3.23707, "loss": 3.23707, "time": 0.82408} +{"mode": "train", "epoch": 129, "iter": 2500, "lr": 0.00491, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42078, "top5_acc": 0.67766, "loss_cls": 3.25354, "loss": 3.25354, "time": 0.8171} +{"mode": "train", "epoch": 129, "iter": 2600, "lr": 0.0049, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42844, "top5_acc": 0.68, "loss_cls": 3.24425, "loss": 3.24425, "time": 0.82356} +{"mode": "train", "epoch": 129, "iter": 2700, "lr": 0.00488, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43344, "top5_acc": 0.67609, "loss_cls": 3.24482, "loss": 3.24482, "time": 0.82061} +{"mode": "train", "epoch": 129, "iter": 2800, "lr": 0.00487, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41359, "top5_acc": 0.67391, "loss_cls": 3.31765, "loss": 3.31765, "time": 0.82075} +{"mode": "train", "epoch": 129, "iter": 2900, "lr": 0.00486, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.41, "top5_acc": 0.66984, "loss_cls": 3.30032, "loss": 3.30032, "time": 0.8159} +{"mode": "train", "epoch": 129, "iter": 3000, "lr": 0.00485, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42062, "top5_acc": 0.67562, "loss_cls": 3.27857, "loss": 3.27857, "time": 0.81923} +{"mode": "train", "epoch": 129, "iter": 3100, "lr": 0.00484, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42438, "top5_acc": 0.67844, "loss_cls": 3.26201, "loss": 3.26201, "time": 0.81987} +{"mode": "train", "epoch": 129, "iter": 3200, "lr": 0.00482, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42641, "top5_acc": 0.675, "loss_cls": 3.24752, "loss": 3.24752, "time": 0.82005} +{"mode": "train", "epoch": 129, "iter": 3300, "lr": 0.00481, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42312, "top5_acc": 0.68719, "loss_cls": 3.21427, "loss": 3.21427, "time": 0.81779} +{"mode": "train", "epoch": 129, "iter": 3400, "lr": 0.0048, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41594, "top5_acc": 0.67188, "loss_cls": 3.28568, "loss": 3.28568, "time": 0.81786} +{"mode": "train", "epoch": 129, "iter": 3500, "lr": 0.00479, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41609, "top5_acc": 0.67547, "loss_cls": 3.27697, "loss": 3.27697, "time": 0.81777} +{"mode": "train", "epoch": 129, "iter": 3600, "lr": 0.00478, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42609, "top5_acc": 0.68203, "loss_cls": 3.25595, "loss": 3.25595, "time": 0.8192} +{"mode": "train", "epoch": 129, "iter": 3700, "lr": 0.00476, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41703, "top5_acc": 0.67062, "loss_cls": 3.31724, "loss": 3.31724, "time": 0.82118} +{"mode": "val", "epoch": 129, "iter": 309, "lr": 0.00476, "top1_acc": 0.35081, "top5_acc": 0.60376, "mean_class_accuracy": 0.35059} +{"mode": "train", "epoch": 130, "iter": 100, "lr": 0.00475, "memory": 15990, "data_time": 1.29659, "top1_acc": 0.4375, "top5_acc": 0.69031, "loss_cls": 3.17883, "loss": 3.17883, "time": 2.28897} +{"mode": "train", "epoch": 130, "iter": 200, "lr": 0.00473, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44156, "top5_acc": 0.69812, "loss_cls": 3.15068, "loss": 3.15068, "time": 0.82896} +{"mode": "train", "epoch": 130, "iter": 300, "lr": 0.00472, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44156, "top5_acc": 0.69484, "loss_cls": 3.14815, "loss": 3.14815, "time": 0.82774} +{"mode": "train", "epoch": 130, "iter": 400, "lr": 0.00471, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43562, "top5_acc": 0.69688, "loss_cls": 3.1631, "loss": 3.1631, "time": 0.81913} +{"mode": "train", "epoch": 130, "iter": 500, "lr": 0.0047, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.43844, "top5_acc": 0.70172, "loss_cls": 3.14972, "loss": 3.14972, "time": 0.8325} +{"mode": "train", "epoch": 130, "iter": 600, "lr": 0.00469, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43469, "top5_acc": 0.68547, "loss_cls": 3.17416, "loss": 3.17416, "time": 0.82385} +{"mode": "train", "epoch": 130, "iter": 700, "lr": 0.00468, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.4375, "top5_acc": 0.69328, "loss_cls": 3.15735, "loss": 3.15735, "time": 0.82086} +{"mode": "train", "epoch": 130, "iter": 800, "lr": 0.00466, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43797, "top5_acc": 0.69203, "loss_cls": 3.13443, "loss": 3.13443, "time": 0.82701} +{"mode": "train", "epoch": 130, "iter": 900, "lr": 0.00465, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43938, "top5_acc": 0.69, "loss_cls": 3.15018, "loss": 3.15018, "time": 0.82146} +{"mode": "train", "epoch": 130, "iter": 1000, "lr": 0.00464, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43531, "top5_acc": 0.68844, "loss_cls": 3.15974, "loss": 3.15974, "time": 0.81837} +{"mode": "train", "epoch": 130, "iter": 1100, "lr": 0.00463, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43547, "top5_acc": 0.69859, "loss_cls": 3.15117, "loss": 3.15117, "time": 0.81836} +{"mode": "train", "epoch": 130, "iter": 1200, "lr": 0.00462, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43547, "top5_acc": 0.69188, "loss_cls": 3.20507, "loss": 3.20507, "time": 0.81651} +{"mode": "train", "epoch": 130, "iter": 1300, "lr": 0.00461, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43281, "top5_acc": 0.68828, "loss_cls": 3.18331, "loss": 3.18331, "time": 0.81776} +{"mode": "train", "epoch": 130, "iter": 1400, "lr": 0.00459, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43359, "top5_acc": 0.69984, "loss_cls": 3.15983, "loss": 3.15983, "time": 0.81441} +{"mode": "train", "epoch": 130, "iter": 1500, "lr": 0.00458, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41969, "top5_acc": 0.68188, "loss_cls": 3.21441, "loss": 3.21441, "time": 0.81575} +{"mode": "train", "epoch": 130, "iter": 1600, "lr": 0.00457, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.42922, "top5_acc": 0.68516, "loss_cls": 3.23676, "loss": 3.23676, "time": 0.82444} +{"mode": "train", "epoch": 130, "iter": 1700, "lr": 0.00456, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42797, "top5_acc": 0.67953, "loss_cls": 3.22156, "loss": 3.22156, "time": 0.81955} +{"mode": "train", "epoch": 130, "iter": 1800, "lr": 0.00455, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43484, "top5_acc": 0.69562, "loss_cls": 3.179, "loss": 3.179, "time": 0.82614} +{"mode": "train", "epoch": 130, "iter": 1900, "lr": 0.00454, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42875, "top5_acc": 0.68312, "loss_cls": 3.20876, "loss": 3.20876, "time": 0.82186} +{"mode": "train", "epoch": 130, "iter": 2000, "lr": 0.00452, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42844, "top5_acc": 0.68484, "loss_cls": 3.1931, "loss": 3.1931, "time": 0.81999} +{"mode": "train", "epoch": 130, "iter": 2100, "lr": 0.00451, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4325, "top5_acc": 0.68266, "loss_cls": 3.17397, "loss": 3.17397, "time": 0.81912} +{"mode": "train", "epoch": 130, "iter": 2200, "lr": 0.0045, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42672, "top5_acc": 0.68531, "loss_cls": 3.21916, "loss": 3.21916, "time": 0.82692} +{"mode": "train", "epoch": 130, "iter": 2300, "lr": 0.00449, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44062, "top5_acc": 0.68891, "loss_cls": 3.17297, "loss": 3.17297, "time": 0.82209} +{"mode": "train", "epoch": 130, "iter": 2400, "lr": 0.00448, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43312, "top5_acc": 0.69016, "loss_cls": 3.18017, "loss": 3.18017, "time": 0.82467} +{"mode": "train", "epoch": 130, "iter": 2500, "lr": 0.00447, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43844, "top5_acc": 0.69625, "loss_cls": 3.15483, "loss": 3.15483, "time": 0.82014} +{"mode": "train", "epoch": 130, "iter": 2600, "lr": 0.00445, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43359, "top5_acc": 0.68125, "loss_cls": 3.18844, "loss": 3.18844, "time": 0.82344} +{"mode": "train", "epoch": 130, "iter": 2700, "lr": 0.00444, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42344, "top5_acc": 0.67672, "loss_cls": 3.24816, "loss": 3.24816, "time": 0.8241} +{"mode": "train", "epoch": 130, "iter": 2800, "lr": 0.00443, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44109, "top5_acc": 0.69297, "loss_cls": 3.17755, "loss": 3.17755, "time": 0.8244} +{"mode": "train", "epoch": 130, "iter": 2900, "lr": 0.00442, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42234, "top5_acc": 0.68734, "loss_cls": 3.19886, "loss": 3.19886, "time": 0.82257} +{"mode": "train", "epoch": 130, "iter": 3000, "lr": 0.00441, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43594, "top5_acc": 0.69109, "loss_cls": 3.17445, "loss": 3.17445, "time": 0.81949} +{"mode": "train", "epoch": 130, "iter": 3100, "lr": 0.0044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.41922, "top5_acc": 0.68391, "loss_cls": 3.26143, "loss": 3.26143, "time": 0.81653} +{"mode": "train", "epoch": 130, "iter": 3200, "lr": 0.00439, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.435, "top5_acc": 0.69109, "loss_cls": 3.17887, "loss": 3.17887, "time": 0.82385} +{"mode": "train", "epoch": 130, "iter": 3300, "lr": 0.00437, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42703, "top5_acc": 0.68281, "loss_cls": 3.19395, "loss": 3.19395, "time": 0.82067} +{"mode": "train", "epoch": 130, "iter": 3400, "lr": 0.00436, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.42672, "top5_acc": 0.68625, "loss_cls": 3.20703, "loss": 3.20703, "time": 0.8184} +{"mode": "train", "epoch": 130, "iter": 3500, "lr": 0.00435, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42797, "top5_acc": 0.68312, "loss_cls": 3.23308, "loss": 3.23308, "time": 0.81672} +{"mode": "train", "epoch": 130, "iter": 3600, "lr": 0.00434, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42422, "top5_acc": 0.67828, "loss_cls": 3.23903, "loss": 3.23903, "time": 0.81928} +{"mode": "train", "epoch": 130, "iter": 3700, "lr": 0.00433, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43391, "top5_acc": 0.68766, "loss_cls": 3.2111, "loss": 3.2111, "time": 0.81807} +{"mode": "val", "epoch": 130, "iter": 309, "lr": 0.00432, "top1_acc": 0.35395, "top5_acc": 0.60695, "mean_class_accuracy": 0.35367} +{"mode": "train", "epoch": 131, "iter": 100, "lr": 0.00431, "memory": 15990, "data_time": 1.31748, "top1_acc": 0.45109, "top5_acc": 0.70359, "loss_cls": 3.08024, "loss": 3.08024, "time": 2.3153} +{"mode": "train", "epoch": 131, "iter": 200, "lr": 0.0043, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45234, "top5_acc": 0.71438, "loss_cls": 3.07134, "loss": 3.07134, "time": 0.82896} +{"mode": "train", "epoch": 131, "iter": 300, "lr": 0.00429, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44281, "top5_acc": 0.70234, "loss_cls": 3.08559, "loss": 3.08559, "time": 0.82309} +{"mode": "train", "epoch": 131, "iter": 400, "lr": 0.00428, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44984, "top5_acc": 0.69781, "loss_cls": 3.10421, "loss": 3.10421, "time": 0.82293} +{"mode": "train", "epoch": 131, "iter": 500, "lr": 0.00427, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.45703, "top5_acc": 0.71016, "loss_cls": 3.06941, "loss": 3.06941, "time": 0.8253} +{"mode": "train", "epoch": 131, "iter": 600, "lr": 0.00425, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44766, "top5_acc": 0.70016, "loss_cls": 3.09438, "loss": 3.09438, "time": 0.82307} +{"mode": "train", "epoch": 131, "iter": 700, "lr": 0.00424, "memory": 15990, "data_time": 0.00039, "top1_acc": 0.44391, "top5_acc": 0.69797, "loss_cls": 3.13064, "loss": 3.13064, "time": 0.8236} +{"mode": "train", "epoch": 131, "iter": 800, "lr": 0.00423, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.43844, "top5_acc": 0.69484, "loss_cls": 3.12402, "loss": 3.12402, "time": 0.82067} +{"mode": "train", "epoch": 131, "iter": 900, "lr": 0.00422, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43969, "top5_acc": 0.69594, "loss_cls": 3.15786, "loss": 3.15786, "time": 0.81915} +{"mode": "train", "epoch": 131, "iter": 1000, "lr": 0.00421, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45562, "top5_acc": 0.70156, "loss_cls": 3.09757, "loss": 3.09757, "time": 0.81893} +{"mode": "train", "epoch": 131, "iter": 1100, "lr": 0.0042, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44891, "top5_acc": 0.69141, "loss_cls": 3.11448, "loss": 3.11448, "time": 0.81915} +{"mode": "train", "epoch": 131, "iter": 1200, "lr": 0.00419, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44156, "top5_acc": 0.69688, "loss_cls": 3.10802, "loss": 3.10802, "time": 0.81552} +{"mode": "train", "epoch": 131, "iter": 1300, "lr": 0.00418, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44, "top5_acc": 0.6975, "loss_cls": 3.13841, "loss": 3.13841, "time": 0.81491} +{"mode": "train", "epoch": 131, "iter": 1400, "lr": 0.00417, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45219, "top5_acc": 0.69734, "loss_cls": 3.11258, "loss": 3.11258, "time": 0.81546} +{"mode": "train", "epoch": 131, "iter": 1500, "lr": 0.00415, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44719, "top5_acc": 0.69766, "loss_cls": 3.10942, "loss": 3.10942, "time": 0.82548} +{"mode": "train", "epoch": 131, "iter": 1600, "lr": 0.00414, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43641, "top5_acc": 0.69953, "loss_cls": 3.15429, "loss": 3.15429, "time": 0.81874} +{"mode": "train", "epoch": 131, "iter": 1700, "lr": 0.00413, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43109, "top5_acc": 0.68953, "loss_cls": 3.17736, "loss": 3.17736, "time": 0.82144} +{"mode": "train", "epoch": 131, "iter": 1800, "lr": 0.00412, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43969, "top5_acc": 0.69, "loss_cls": 3.16112, "loss": 3.16112, "time": 0.82244} +{"mode": "train", "epoch": 131, "iter": 1900, "lr": 0.00411, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43391, "top5_acc": 0.69047, "loss_cls": 3.17028, "loss": 3.17028, "time": 0.82632} +{"mode": "train", "epoch": 131, "iter": 2000, "lr": 0.0041, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43328, "top5_acc": 0.69312, "loss_cls": 3.17739, "loss": 3.17739, "time": 0.82486} +{"mode": "train", "epoch": 131, "iter": 2100, "lr": 0.00409, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.42531, "top5_acc": 0.68578, "loss_cls": 3.19296, "loss": 3.19296, "time": 0.81865} +{"mode": "train", "epoch": 131, "iter": 2200, "lr": 0.00408, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.42641, "top5_acc": 0.6925, "loss_cls": 3.19629, "loss": 3.19629, "time": 0.825} +{"mode": "train", "epoch": 131, "iter": 2300, "lr": 0.00407, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.435, "top5_acc": 0.68688, "loss_cls": 3.1931, "loss": 3.1931, "time": 0.82107} +{"mode": "train", "epoch": 131, "iter": 2400, "lr": 0.00405, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43922, "top5_acc": 0.69688, "loss_cls": 3.15575, "loss": 3.15575, "time": 0.82566} +{"mode": "train", "epoch": 131, "iter": 2500, "lr": 0.00404, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43625, "top5_acc": 0.69141, "loss_cls": 3.14545, "loss": 3.14545, "time": 0.82021} +{"mode": "train", "epoch": 131, "iter": 2600, "lr": 0.00403, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43, "top5_acc": 0.67766, "loss_cls": 3.20557, "loss": 3.20557, "time": 0.81997} +{"mode": "train", "epoch": 131, "iter": 2700, "lr": 0.00402, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.42953, "top5_acc": 0.68359, "loss_cls": 3.20569, "loss": 3.20569, "time": 0.82404} +{"mode": "train", "epoch": 131, "iter": 2800, "lr": 0.00401, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43766, "top5_acc": 0.68906, "loss_cls": 3.18261, "loss": 3.18261, "time": 0.81614} +{"mode": "train", "epoch": 131, "iter": 2900, "lr": 0.004, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.42672, "top5_acc": 0.67984, "loss_cls": 3.22312, "loss": 3.22312, "time": 0.81986} +{"mode": "train", "epoch": 131, "iter": 3000, "lr": 0.00399, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44297, "top5_acc": 0.69219, "loss_cls": 3.15277, "loss": 3.15277, "time": 0.81709} +{"mode": "train", "epoch": 131, "iter": 3100, "lr": 0.00398, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43531, "top5_acc": 0.68938, "loss_cls": 3.15863, "loss": 3.15863, "time": 0.8166} +{"mode": "train", "epoch": 131, "iter": 3200, "lr": 0.00397, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43656, "top5_acc": 0.69516, "loss_cls": 3.16578, "loss": 3.16578, "time": 0.82998} +{"mode": "train", "epoch": 131, "iter": 3300, "lr": 0.00396, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.43281, "top5_acc": 0.68531, "loss_cls": 3.19925, "loss": 3.19925, "time": 0.81897} +{"mode": "train", "epoch": 131, "iter": 3400, "lr": 0.00394, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.42609, "top5_acc": 0.68344, "loss_cls": 3.20222, "loss": 3.20222, "time": 0.81819} +{"mode": "train", "epoch": 131, "iter": 3500, "lr": 0.00393, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43906, "top5_acc": 0.69641, "loss_cls": 3.14725, "loss": 3.14725, "time": 0.81917} +{"mode": "train", "epoch": 131, "iter": 3600, "lr": 0.00392, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.44234, "top5_acc": 0.69125, "loss_cls": 3.15575, "loss": 3.15575, "time": 0.81828} +{"mode": "train", "epoch": 131, "iter": 3700, "lr": 0.00391, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43672, "top5_acc": 0.68984, "loss_cls": 3.16533, "loss": 3.16533, "time": 0.81963} +{"mode": "val", "epoch": 131, "iter": 309, "lr": 0.00391, "top1_acc": 0.36038, "top5_acc": 0.61065, "mean_class_accuracy": 0.36022} +{"mode": "train", "epoch": 132, "iter": 100, "lr": 0.0039, "memory": 15990, "data_time": 1.35604, "top1_acc": 0.455, "top5_acc": 0.70516, "loss_cls": 3.06377, "loss": 3.06377, "time": 2.34439} +{"mode": "train", "epoch": 132, "iter": 200, "lr": 0.00389, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46469, "top5_acc": 0.71828, "loss_cls": 3.01429, "loss": 3.01429, "time": 0.82693} +{"mode": "train", "epoch": 132, "iter": 300, "lr": 0.00387, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45281, "top5_acc": 0.70578, "loss_cls": 3.07528, "loss": 3.07528, "time": 0.82788} +{"mode": "train", "epoch": 132, "iter": 400, "lr": 0.00386, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45016, "top5_acc": 0.69797, "loss_cls": 3.0819, "loss": 3.0819, "time": 0.81776} +{"mode": "train", "epoch": 132, "iter": 500, "lr": 0.00385, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44469, "top5_acc": 0.70453, "loss_cls": 3.10279, "loss": 3.10279, "time": 0.83118} +{"mode": "train", "epoch": 132, "iter": 600, "lr": 0.00384, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45094, "top5_acc": 0.71234, "loss_cls": 3.07268, "loss": 3.07268, "time": 0.82486} +{"mode": "train", "epoch": 132, "iter": 700, "lr": 0.00383, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45438, "top5_acc": 0.69516, "loss_cls": 3.12093, "loss": 3.12093, "time": 0.82295} +{"mode": "train", "epoch": 132, "iter": 800, "lr": 0.00382, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45016, "top5_acc": 0.70484, "loss_cls": 3.06856, "loss": 3.06856, "time": 0.82297} +{"mode": "train", "epoch": 132, "iter": 900, "lr": 0.00381, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43719, "top5_acc": 0.69344, "loss_cls": 3.13604, "loss": 3.13604, "time": 0.81663} +{"mode": "train", "epoch": 132, "iter": 1000, "lr": 0.0038, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.44047, "top5_acc": 0.69219, "loss_cls": 3.15442, "loss": 3.15442, "time": 0.82247} +{"mode": "train", "epoch": 132, "iter": 1100, "lr": 0.00379, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44812, "top5_acc": 0.69969, "loss_cls": 3.09686, "loss": 3.09686, "time": 0.82284} +{"mode": "train", "epoch": 132, "iter": 1200, "lr": 0.00378, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44172, "top5_acc": 0.69969, "loss_cls": 3.11038, "loss": 3.11038, "time": 0.82045} +{"mode": "train", "epoch": 132, "iter": 1300, "lr": 0.00377, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45266, "top5_acc": 0.69969, "loss_cls": 3.12722, "loss": 3.12722, "time": 0.82048} +{"mode": "train", "epoch": 132, "iter": 1400, "lr": 0.00376, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44984, "top5_acc": 0.69625, "loss_cls": 3.11474, "loss": 3.11474, "time": 0.81923} +{"mode": "train", "epoch": 132, "iter": 1500, "lr": 0.00375, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45734, "top5_acc": 0.70891, "loss_cls": 3.08666, "loss": 3.08666, "time": 0.82435} +{"mode": "train", "epoch": 132, "iter": 1600, "lr": 0.00374, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45172, "top5_acc": 0.70266, "loss_cls": 3.09077, "loss": 3.09077, "time": 0.81755} +{"mode": "train", "epoch": 132, "iter": 1700, "lr": 0.00372, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.44438, "top5_acc": 0.7075, "loss_cls": 3.08076, "loss": 3.08076, "time": 0.8244} +{"mode": "train", "epoch": 132, "iter": 1800, "lr": 0.00371, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44641, "top5_acc": 0.70016, "loss_cls": 3.10818, "loss": 3.10818, "time": 0.82464} +{"mode": "train", "epoch": 132, "iter": 1900, "lr": 0.0037, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43703, "top5_acc": 0.69719, "loss_cls": 3.13791, "loss": 3.13791, "time": 0.8226} +{"mode": "train", "epoch": 132, "iter": 2000, "lr": 0.00369, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44109, "top5_acc": 0.69688, "loss_cls": 3.11421, "loss": 3.11421, "time": 0.81964} +{"mode": "train", "epoch": 132, "iter": 2100, "lr": 0.00368, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43672, "top5_acc": 0.69625, "loss_cls": 3.13439, "loss": 3.13439, "time": 0.82138} +{"mode": "train", "epoch": 132, "iter": 2200, "lr": 0.00367, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43703, "top5_acc": 0.69047, "loss_cls": 3.16831, "loss": 3.16831, "time": 0.82079} +{"mode": "train", "epoch": 132, "iter": 2300, "lr": 0.00366, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44969, "top5_acc": 0.70391, "loss_cls": 3.09608, "loss": 3.09608, "time": 0.81654} +{"mode": "train", "epoch": 132, "iter": 2400, "lr": 0.00365, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44016, "top5_acc": 0.70562, "loss_cls": 3.10771, "loss": 3.10771, "time": 0.81952} +{"mode": "train", "epoch": 132, "iter": 2500, "lr": 0.00364, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43938, "top5_acc": 0.69766, "loss_cls": 3.14786, "loss": 3.14786, "time": 0.81785} +{"mode": "train", "epoch": 132, "iter": 2600, "lr": 0.00363, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44719, "top5_acc": 0.70797, "loss_cls": 3.07669, "loss": 3.07669, "time": 0.82495} +{"mode": "train", "epoch": 132, "iter": 2700, "lr": 0.00362, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44688, "top5_acc": 0.69672, "loss_cls": 3.1152, "loss": 3.1152, "time": 0.82508} +{"mode": "train", "epoch": 132, "iter": 2800, "lr": 0.00361, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44062, "top5_acc": 0.69375, "loss_cls": 3.1354, "loss": 3.1354, "time": 0.82017} +{"mode": "train", "epoch": 132, "iter": 2900, "lr": 0.0036, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43891, "top5_acc": 0.69453, "loss_cls": 3.14747, "loss": 3.14747, "time": 0.81571} +{"mode": "train", "epoch": 132, "iter": 3000, "lr": 0.00359, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44641, "top5_acc": 0.69641, "loss_cls": 3.13075, "loss": 3.13075, "time": 0.81774} +{"mode": "train", "epoch": 132, "iter": 3100, "lr": 0.00358, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43406, "top5_acc": 0.69625, "loss_cls": 3.14724, "loss": 3.14724, "time": 0.8187} +{"mode": "train", "epoch": 132, "iter": 3200, "lr": 0.00357, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44672, "top5_acc": 0.69344, "loss_cls": 3.12944, "loss": 3.12944, "time": 0.82527} +{"mode": "train", "epoch": 132, "iter": 3300, "lr": 0.00356, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.43656, "top5_acc": 0.68984, "loss_cls": 3.17381, "loss": 3.17381, "time": 0.82017} +{"mode": "train", "epoch": 132, "iter": 3400, "lr": 0.00355, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.43781, "top5_acc": 0.69328, "loss_cls": 3.15268, "loss": 3.15268, "time": 0.81609} +{"mode": "train", "epoch": 132, "iter": 3500, "lr": 0.00354, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44672, "top5_acc": 0.69656, "loss_cls": 3.14114, "loss": 3.14114, "time": 0.81706} +{"mode": "train", "epoch": 132, "iter": 3600, "lr": 0.00353, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44281, "top5_acc": 0.69844, "loss_cls": 3.13025, "loss": 3.13025, "time": 0.81905} +{"mode": "train", "epoch": 132, "iter": 3700, "lr": 0.00352, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44359, "top5_acc": 0.69422, "loss_cls": 3.15, "loss": 3.15, "time": 0.82464} +{"mode": "val", "epoch": 132, "iter": 309, "lr": 0.00351, "top1_acc": 0.3622, "top5_acc": 0.61662, "mean_class_accuracy": 0.36199} +{"mode": "train", "epoch": 133, "iter": 100, "lr": 0.0035, "memory": 15990, "data_time": 1.32412, "top1_acc": 0.45938, "top5_acc": 0.71391, "loss_cls": 3.03673, "loss": 3.03673, "time": 2.33537} +{"mode": "train", "epoch": 133, "iter": 200, "lr": 0.00349, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.44672, "top5_acc": 0.71297, "loss_cls": 3.07309, "loss": 3.07309, "time": 0.82255} +{"mode": "train", "epoch": 133, "iter": 300, "lr": 0.00348, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46328, "top5_acc": 0.71797, "loss_cls": 3.0049, "loss": 3.0049, "time": 0.82455} +{"mode": "train", "epoch": 133, "iter": 400, "lr": 0.00347, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46438, "top5_acc": 0.71281, "loss_cls": 2.99725, "loss": 2.99725, "time": 0.82491} +{"mode": "train", "epoch": 133, "iter": 500, "lr": 0.00346, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4425, "top5_acc": 0.70859, "loss_cls": 3.05023, "loss": 3.05023, "time": 0.82388} +{"mode": "train", "epoch": 133, "iter": 600, "lr": 0.00345, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45062, "top5_acc": 0.70984, "loss_cls": 3.06941, "loss": 3.06941, "time": 0.82211} +{"mode": "train", "epoch": 133, "iter": 700, "lr": 0.00344, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45672, "top5_acc": 0.70734, "loss_cls": 3.07325, "loss": 3.07325, "time": 0.82512} +{"mode": "train", "epoch": 133, "iter": 800, "lr": 0.00343, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44969, "top5_acc": 0.70844, "loss_cls": 3.06534, "loss": 3.06534, "time": 0.81971} +{"mode": "train", "epoch": 133, "iter": 900, "lr": 0.00342, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44406, "top5_acc": 0.70422, "loss_cls": 3.09747, "loss": 3.09747, "time": 0.81752} +{"mode": "train", "epoch": 133, "iter": 1000, "lr": 0.00341, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.455, "top5_acc": 0.70875, "loss_cls": 3.05261, "loss": 3.05261, "time": 0.81833} +{"mode": "train", "epoch": 133, "iter": 1100, "lr": 0.0034, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44859, "top5_acc": 0.70297, "loss_cls": 3.07216, "loss": 3.07216, "time": 0.82214} +{"mode": "train", "epoch": 133, "iter": 1200, "lr": 0.00339, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45391, "top5_acc": 0.70547, "loss_cls": 3.05317, "loss": 3.05317, "time": 0.81677} +{"mode": "train", "epoch": 133, "iter": 1300, "lr": 0.00338, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44719, "top5_acc": 0.70953, "loss_cls": 3.08699, "loss": 3.08699, "time": 0.82367} +{"mode": "train", "epoch": 133, "iter": 1400, "lr": 0.00337, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.45391, "top5_acc": 0.71219, "loss_cls": 3.04576, "loss": 3.04576, "time": 0.81965} +{"mode": "train", "epoch": 133, "iter": 1500, "lr": 0.00336, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44672, "top5_acc": 0.69875, "loss_cls": 3.0976, "loss": 3.0976, "time": 0.82556} +{"mode": "train", "epoch": 133, "iter": 1600, "lr": 0.00335, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46234, "top5_acc": 0.70641, "loss_cls": 3.06432, "loss": 3.06432, "time": 0.826} +{"mode": "train", "epoch": 133, "iter": 1700, "lr": 0.00334, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45016, "top5_acc": 0.70969, "loss_cls": 3.05261, "loss": 3.05261, "time": 0.82214} +{"mode": "train", "epoch": 133, "iter": 1800, "lr": 0.00333, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44156, "top5_acc": 0.70109, "loss_cls": 3.11393, "loss": 3.11393, "time": 0.81811} +{"mode": "train", "epoch": 133, "iter": 1900, "lr": 0.00332, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45688, "top5_acc": 0.70203, "loss_cls": 3.06133, "loss": 3.06133, "time": 0.81805} +{"mode": "train", "epoch": 133, "iter": 2000, "lr": 0.00331, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45672, "top5_acc": 0.71094, "loss_cls": 3.0494, "loss": 3.0494, "time": 0.82263} +{"mode": "train", "epoch": 133, "iter": 2100, "lr": 0.0033, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44453, "top5_acc": 0.70391, "loss_cls": 3.12649, "loss": 3.12649, "time": 0.82165} +{"mode": "train", "epoch": 133, "iter": 2200, "lr": 0.00329, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44203, "top5_acc": 0.70719, "loss_cls": 3.09513, "loss": 3.09513, "time": 0.82577} +{"mode": "train", "epoch": 133, "iter": 2300, "lr": 0.00328, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.43922, "top5_acc": 0.69312, "loss_cls": 3.11976, "loss": 3.11976, "time": 0.82289} +{"mode": "train", "epoch": 133, "iter": 2400, "lr": 0.00327, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45766, "top5_acc": 0.71625, "loss_cls": 3.03552, "loss": 3.03552, "time": 0.82503} +{"mode": "train", "epoch": 133, "iter": 2500, "lr": 0.00326, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45312, "top5_acc": 0.71047, "loss_cls": 3.06431, "loss": 3.06431, "time": 0.8242} +{"mode": "train", "epoch": 133, "iter": 2600, "lr": 0.00325, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45141, "top5_acc": 0.70812, "loss_cls": 3.07703, "loss": 3.07703, "time": 0.82024} +{"mode": "train", "epoch": 133, "iter": 2700, "lr": 0.00324, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45047, "top5_acc": 0.70828, "loss_cls": 3.08682, "loss": 3.08682, "time": 0.8201} +{"mode": "train", "epoch": 133, "iter": 2800, "lr": 0.00323, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44469, "top5_acc": 0.69562, "loss_cls": 3.13075, "loss": 3.13075, "time": 0.82009} +{"mode": "train", "epoch": 133, "iter": 2900, "lr": 0.00322, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46141, "top5_acc": 0.70609, "loss_cls": 3.04659, "loss": 3.04659, "time": 0.81878} +{"mode": "train", "epoch": 133, "iter": 3000, "lr": 0.00321, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44766, "top5_acc": 0.70188, "loss_cls": 3.07077, "loss": 3.07077, "time": 0.82061} +{"mode": "train", "epoch": 133, "iter": 3100, "lr": 0.0032, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.44562, "top5_acc": 0.70922, "loss_cls": 3.0732, "loss": 3.0732, "time": 0.81816} +{"mode": "train", "epoch": 133, "iter": 3200, "lr": 0.00319, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44234, "top5_acc": 0.69703, "loss_cls": 3.11362, "loss": 3.11362, "time": 0.81918} +{"mode": "train", "epoch": 133, "iter": 3300, "lr": 0.00318, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45141, "top5_acc": 0.70125, "loss_cls": 3.09476, "loss": 3.09476, "time": 0.82592} +{"mode": "train", "epoch": 133, "iter": 3400, "lr": 0.00317, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45422, "top5_acc": 0.695, "loss_cls": 3.10815, "loss": 3.10815, "time": 0.8159} +{"mode": "train", "epoch": 133, "iter": 3500, "lr": 0.00316, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.43359, "top5_acc": 0.69438, "loss_cls": 3.14021, "loss": 3.14021, "time": 0.8219} +{"mode": "train", "epoch": 133, "iter": 3600, "lr": 0.00315, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.44625, "top5_acc": 0.70781, "loss_cls": 3.1018, "loss": 3.1018, "time": 0.82606} +{"mode": "train", "epoch": 133, "iter": 3700, "lr": 0.00314, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45297, "top5_acc": 0.70344, "loss_cls": 3.07883, "loss": 3.07883, "time": 0.82183} +{"mode": "val", "epoch": 133, "iter": 309, "lr": 0.00314, "top1_acc": 0.36864, "top5_acc": 0.61455, "mean_class_accuracy": 0.3683} +{"mode": "train", "epoch": 134, "iter": 100, "lr": 0.00313, "memory": 15990, "data_time": 1.33309, "top1_acc": 0.4625, "top5_acc": 0.725, "loss_cls": 2.96673, "loss": 2.96673, "time": 2.32608} +{"mode": "train", "epoch": 134, "iter": 200, "lr": 0.00312, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47016, "top5_acc": 0.71625, "loss_cls": 2.98977, "loss": 2.98977, "time": 0.83159} +{"mode": "train", "epoch": 134, "iter": 300, "lr": 0.00311, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.45891, "top5_acc": 0.7125, "loss_cls": 3.03667, "loss": 3.03667, "time": 0.82856} +{"mode": "train", "epoch": 134, "iter": 400, "lr": 0.0031, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46359, "top5_acc": 0.72734, "loss_cls": 2.95031, "loss": 2.95031, "time": 0.8194} +{"mode": "train", "epoch": 134, "iter": 500, "lr": 0.00309, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46109, "top5_acc": 0.71125, "loss_cls": 3.00656, "loss": 3.00656, "time": 0.82725} +{"mode": "train", "epoch": 134, "iter": 600, "lr": 0.00308, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47547, "top5_acc": 0.72516, "loss_cls": 2.96822, "loss": 2.96822, "time": 0.82314} +{"mode": "train", "epoch": 134, "iter": 700, "lr": 0.00307, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46031, "top5_acc": 0.72062, "loss_cls": 3.02175, "loss": 3.02175, "time": 0.82942} +{"mode": "train", "epoch": 134, "iter": 800, "lr": 0.00306, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45531, "top5_acc": 0.70641, "loss_cls": 3.07111, "loss": 3.07111, "time": 0.82012} +{"mode": "train", "epoch": 134, "iter": 900, "lr": 0.00305, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45484, "top5_acc": 0.70953, "loss_cls": 3.01929, "loss": 3.01929, "time": 0.8217} +{"mode": "train", "epoch": 134, "iter": 1000, "lr": 0.00304, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46094, "top5_acc": 0.71516, "loss_cls": 3.01667, "loss": 3.01667, "time": 0.81803} +{"mode": "train", "epoch": 134, "iter": 1100, "lr": 0.00303, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.45125, "top5_acc": 0.71031, "loss_cls": 3.05206, "loss": 3.05206, "time": 0.82354} +{"mode": "train", "epoch": 134, "iter": 1200, "lr": 0.00302, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45672, "top5_acc": 0.71047, "loss_cls": 3.05971, "loss": 3.05971, "time": 0.82393} +{"mode": "train", "epoch": 134, "iter": 1300, "lr": 0.00301, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46594, "top5_acc": 0.72344, "loss_cls": 2.96226, "loss": 2.96226, "time": 0.81956} +{"mode": "train", "epoch": 134, "iter": 1400, "lr": 0.003, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47688, "top5_acc": 0.7275, "loss_cls": 2.94727, "loss": 2.94727, "time": 0.82024} +{"mode": "train", "epoch": 134, "iter": 1500, "lr": 0.00299, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.45156, "top5_acc": 0.70375, "loss_cls": 3.06348, "loss": 3.06348, "time": 0.81964} +{"mode": "train", "epoch": 134, "iter": 1600, "lr": 0.00298, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.46406, "top5_acc": 0.71531, "loss_cls": 3.04933, "loss": 3.04933, "time": 0.82757} +{"mode": "train", "epoch": 134, "iter": 1700, "lr": 0.00297, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45734, "top5_acc": 0.71109, "loss_cls": 3.03036, "loss": 3.03036, "time": 0.82553} +{"mode": "train", "epoch": 134, "iter": 1800, "lr": 0.00296, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45922, "top5_acc": 0.71266, "loss_cls": 3.02029, "loss": 3.02029, "time": 0.82317} +{"mode": "train", "epoch": 134, "iter": 1900, "lr": 0.00295, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45859, "top5_acc": 0.70906, "loss_cls": 3.03792, "loss": 3.03792, "time": 0.81739} +{"mode": "train", "epoch": 134, "iter": 2000, "lr": 0.00294, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44875, "top5_acc": 0.71266, "loss_cls": 3.05227, "loss": 3.05227, "time": 0.82145} +{"mode": "train", "epoch": 134, "iter": 2100, "lr": 0.00293, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46844, "top5_acc": 0.71328, "loss_cls": 3.00748, "loss": 3.00748, "time": 0.82831} +{"mode": "train", "epoch": 134, "iter": 2200, "lr": 0.00293, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46453, "top5_acc": 0.71328, "loss_cls": 3.02098, "loss": 3.02098, "time": 0.82687} +{"mode": "train", "epoch": 134, "iter": 2300, "lr": 0.00292, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.43828, "top5_acc": 0.69578, "loss_cls": 3.137, "loss": 3.137, "time": 0.8239} +{"mode": "train", "epoch": 134, "iter": 2400, "lr": 0.00291, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.46203, "top5_acc": 0.71297, "loss_cls": 3.04406, "loss": 3.04406, "time": 0.81962} +{"mode": "train", "epoch": 134, "iter": 2500, "lr": 0.0029, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45156, "top5_acc": 0.70453, "loss_cls": 3.09036, "loss": 3.09036, "time": 0.82022} +{"mode": "train", "epoch": 134, "iter": 2600, "lr": 0.00289, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46391, "top5_acc": 0.71312, "loss_cls": 3.04792, "loss": 3.04792, "time": 0.82098} +{"mode": "train", "epoch": 134, "iter": 2700, "lr": 0.00288, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.44734, "top5_acc": 0.70969, "loss_cls": 3.0823, "loss": 3.0823, "time": 0.82358} +{"mode": "train", "epoch": 134, "iter": 2800, "lr": 0.00287, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4525, "top5_acc": 0.70984, "loss_cls": 3.02801, "loss": 3.02801, "time": 0.81927} +{"mode": "train", "epoch": 134, "iter": 2900, "lr": 0.00286, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4425, "top5_acc": 0.69922, "loss_cls": 3.093, "loss": 3.093, "time": 0.83615} +{"mode": "train", "epoch": 134, "iter": 3000, "lr": 0.00285, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44031, "top5_acc": 0.69234, "loss_cls": 3.16106, "loss": 3.16106, "time": 0.81672} +{"mode": "train", "epoch": 134, "iter": 3100, "lr": 0.00284, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.44875, "top5_acc": 0.70844, "loss_cls": 3.0501, "loss": 3.0501, "time": 0.82801} +{"mode": "train", "epoch": 134, "iter": 3200, "lr": 0.00283, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.45969, "top5_acc": 0.71422, "loss_cls": 3.01636, "loss": 3.01636, "time": 0.82905} +{"mode": "train", "epoch": 134, "iter": 3300, "lr": 0.00282, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.44781, "top5_acc": 0.70734, "loss_cls": 3.08757, "loss": 3.08757, "time": 0.82229} +{"mode": "train", "epoch": 134, "iter": 3400, "lr": 0.00281, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45328, "top5_acc": 0.69844, "loss_cls": 3.07718, "loss": 3.07718, "time": 0.81636} +{"mode": "train", "epoch": 134, "iter": 3500, "lr": 0.0028, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45641, "top5_acc": 0.70859, "loss_cls": 3.05839, "loss": 3.05839, "time": 0.81941} +{"mode": "train", "epoch": 134, "iter": 3600, "lr": 0.00279, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46062, "top5_acc": 0.71594, "loss_cls": 3.00572, "loss": 3.00572, "time": 0.81543} +{"mode": "train", "epoch": 134, "iter": 3700, "lr": 0.00279, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45828, "top5_acc": 0.71141, "loss_cls": 3.01937, "loss": 3.01937, "time": 0.82005} +{"mode": "val", "epoch": 134, "iter": 309, "lr": 0.00278, "top1_acc": 0.36833, "top5_acc": 0.62204, "mean_class_accuracy": 0.36806} +{"mode": "train", "epoch": 135, "iter": 100, "lr": 0.00277, "memory": 15990, "data_time": 1.29837, "top1_acc": 0.47969, "top5_acc": 0.74188, "loss_cls": 2.89128, "loss": 2.89128, "time": 2.29669} +{"mode": "train", "epoch": 135, "iter": 200, "lr": 0.00276, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47047, "top5_acc": 0.72562, "loss_cls": 2.94886, "loss": 2.94886, "time": 0.81854} +{"mode": "train", "epoch": 135, "iter": 300, "lr": 0.00275, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47016, "top5_acc": 0.72328, "loss_cls": 2.97852, "loss": 2.97852, "time": 0.81705} +{"mode": "train", "epoch": 135, "iter": 400, "lr": 0.00274, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46125, "top5_acc": 0.72047, "loss_cls": 2.98307, "loss": 2.98307, "time": 0.82571} +{"mode": "train", "epoch": 135, "iter": 500, "lr": 0.00274, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46656, "top5_acc": 0.71078, "loss_cls": 3.00846, "loss": 3.00846, "time": 0.82659} +{"mode": "train", "epoch": 135, "iter": 600, "lr": 0.00273, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47359, "top5_acc": 0.72328, "loss_cls": 2.93565, "loss": 2.93565, "time": 0.82945} +{"mode": "train", "epoch": 135, "iter": 700, "lr": 0.00272, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47141, "top5_acc": 0.73281, "loss_cls": 2.9312, "loss": 2.9312, "time": 0.82498} +{"mode": "train", "epoch": 135, "iter": 800, "lr": 0.00271, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45672, "top5_acc": 0.71688, "loss_cls": 3.02847, "loss": 3.02847, "time": 0.82177} +{"mode": "train", "epoch": 135, "iter": 900, "lr": 0.0027, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46547, "top5_acc": 0.71703, "loss_cls": 2.98764, "loss": 2.98764, "time": 0.82333} +{"mode": "train", "epoch": 135, "iter": 1000, "lr": 0.00269, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.46203, "top5_acc": 0.72375, "loss_cls": 2.97283, "loss": 2.97283, "time": 0.82065} +{"mode": "train", "epoch": 135, "iter": 1100, "lr": 0.00268, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47062, "top5_acc": 0.72344, "loss_cls": 2.94918, "loss": 2.94918, "time": 0.82453} +{"mode": "train", "epoch": 135, "iter": 1200, "lr": 0.00267, "memory": 15990, "data_time": 0.00035, "top1_acc": 0.465, "top5_acc": 0.71828, "loss_cls": 3.00258, "loss": 3.00258, "time": 0.82073} +{"mode": "train", "epoch": 135, "iter": 1300, "lr": 0.00266, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46719, "top5_acc": 0.7175, "loss_cls": 3.0022, "loss": 3.0022, "time": 0.81946} +{"mode": "train", "epoch": 135, "iter": 1400, "lr": 0.00265, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.45594, "top5_acc": 0.71438, "loss_cls": 3.02461, "loss": 3.02461, "time": 0.81937} +{"mode": "train", "epoch": 135, "iter": 1500, "lr": 0.00265, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.46891, "top5_acc": 0.71922, "loss_cls": 2.97545, "loss": 2.97545, "time": 0.82033} +{"mode": "train", "epoch": 135, "iter": 1600, "lr": 0.00264, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46203, "top5_acc": 0.71453, "loss_cls": 3.00915, "loss": 3.00915, "time": 0.82131} +{"mode": "train", "epoch": 135, "iter": 1700, "lr": 0.00263, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46625, "top5_acc": 0.7175, "loss_cls": 2.98945, "loss": 2.98945, "time": 0.81866} +{"mode": "train", "epoch": 135, "iter": 1800, "lr": 0.00262, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46328, "top5_acc": 0.71828, "loss_cls": 3.01983, "loss": 3.01983, "time": 0.82342} +{"mode": "train", "epoch": 135, "iter": 1900, "lr": 0.00261, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46625, "top5_acc": 0.72016, "loss_cls": 2.98268, "loss": 2.98268, "time": 0.82411} +{"mode": "train", "epoch": 135, "iter": 2000, "lr": 0.0026, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47859, "top5_acc": 0.72469, "loss_cls": 2.9602, "loss": 2.9602, "time": 0.81859} +{"mode": "train", "epoch": 135, "iter": 2100, "lr": 0.00259, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45938, "top5_acc": 0.70812, "loss_cls": 3.05501, "loss": 3.05501, "time": 0.82216} +{"mode": "train", "epoch": 135, "iter": 2200, "lr": 0.00258, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46234, "top5_acc": 0.72172, "loss_cls": 2.99507, "loss": 2.99507, "time": 0.82784} +{"mode": "train", "epoch": 135, "iter": 2300, "lr": 0.00257, "memory": 15990, "data_time": 0.00041, "top1_acc": 0.46312, "top5_acc": 0.71469, "loss_cls": 2.99904, "loss": 2.99904, "time": 0.82212} +{"mode": "train", "epoch": 135, "iter": 2400, "lr": 0.00256, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46781, "top5_acc": 0.72, "loss_cls": 2.9777, "loss": 2.9777, "time": 0.82705} +{"mode": "train", "epoch": 135, "iter": 2500, "lr": 0.00256, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.46609, "top5_acc": 0.72109, "loss_cls": 3.00166, "loss": 3.00166, "time": 0.82283} +{"mode": "train", "epoch": 135, "iter": 2600, "lr": 0.00255, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45891, "top5_acc": 0.71344, "loss_cls": 3.03483, "loss": 3.03483, "time": 0.82752} +{"mode": "train", "epoch": 135, "iter": 2700, "lr": 0.00254, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46859, "top5_acc": 0.71594, "loss_cls": 2.98782, "loss": 2.98782, "time": 0.81936} +{"mode": "train", "epoch": 135, "iter": 2800, "lr": 0.00253, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.475, "top5_acc": 0.72281, "loss_cls": 2.95949, "loss": 2.95949, "time": 0.82321} +{"mode": "train", "epoch": 135, "iter": 2900, "lr": 0.00252, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47047, "top5_acc": 0.71625, "loss_cls": 3.00203, "loss": 3.00203, "time": 0.82531} +{"mode": "train", "epoch": 135, "iter": 3000, "lr": 0.00251, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.465, "top5_acc": 0.71438, "loss_cls": 3.00453, "loss": 3.00453, "time": 0.82294} +{"mode": "train", "epoch": 135, "iter": 3100, "lr": 0.0025, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46359, "top5_acc": 0.71562, "loss_cls": 3.0152, "loss": 3.0152, "time": 0.82198} +{"mode": "train", "epoch": 135, "iter": 3200, "lr": 0.00249, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46734, "top5_acc": 0.71891, "loss_cls": 2.99112, "loss": 2.99112, "time": 0.81946} +{"mode": "train", "epoch": 135, "iter": 3300, "lr": 0.00249, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.45594, "top5_acc": 0.7075, "loss_cls": 3.02952, "loss": 3.02952, "time": 0.82036} +{"mode": "train", "epoch": 135, "iter": 3400, "lr": 0.00248, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46344, "top5_acc": 0.71547, "loss_cls": 2.99958, "loss": 2.99958, "time": 0.82247} +{"mode": "train", "epoch": 135, "iter": 3500, "lr": 0.00247, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45656, "top5_acc": 0.71844, "loss_cls": 3.01782, "loss": 3.01782, "time": 0.81669} +{"mode": "train", "epoch": 135, "iter": 3600, "lr": 0.00246, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.465, "top5_acc": 0.71641, "loss_cls": 2.9947, "loss": 2.9947, "time": 0.82306} +{"mode": "train", "epoch": 135, "iter": 3700, "lr": 0.00245, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.45922, "top5_acc": 0.70828, "loss_cls": 3.01568, "loss": 3.01568, "time": 0.81823} +{"mode": "val", "epoch": 135, "iter": 309, "lr": 0.00245, "top1_acc": 0.37213, "top5_acc": 0.61759, "mean_class_accuracy": 0.37182} +{"mode": "train", "epoch": 136, "iter": 100, "lr": 0.00244, "memory": 15990, "data_time": 1.3088, "top1_acc": 0.48234, "top5_acc": 0.74031, "loss_cls": 2.86954, "loss": 2.86954, "time": 2.31288} +{"mode": "train", "epoch": 136, "iter": 200, "lr": 0.00243, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48328, "top5_acc": 0.73156, "loss_cls": 2.87569, "loss": 2.87569, "time": 0.8248} +{"mode": "train", "epoch": 136, "iter": 300, "lr": 0.00242, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48859, "top5_acc": 0.73297, "loss_cls": 2.87395, "loss": 2.87395, "time": 0.82101} +{"mode": "train", "epoch": 136, "iter": 400, "lr": 0.00241, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47359, "top5_acc": 0.72391, "loss_cls": 2.95766, "loss": 2.95766, "time": 0.83043} +{"mode": "train", "epoch": 136, "iter": 500, "lr": 0.0024, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.485, "top5_acc": 0.72969, "loss_cls": 2.8948, "loss": 2.8948, "time": 0.81997} +{"mode": "train", "epoch": 136, "iter": 600, "lr": 0.0024, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.48094, "top5_acc": 0.73094, "loss_cls": 2.92392, "loss": 2.92392, "time": 0.82739} +{"mode": "train", "epoch": 136, "iter": 700, "lr": 0.00239, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.4725, "top5_acc": 0.73094, "loss_cls": 2.94483, "loss": 2.94483, "time": 0.82563} +{"mode": "train", "epoch": 136, "iter": 800, "lr": 0.00238, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.47594, "top5_acc": 0.72812, "loss_cls": 2.91803, "loss": 2.91803, "time": 0.82384} +{"mode": "train", "epoch": 136, "iter": 900, "lr": 0.00237, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47234, "top5_acc": 0.73016, "loss_cls": 2.93566, "loss": 2.93566, "time": 0.81641} +{"mode": "train", "epoch": 136, "iter": 1000, "lr": 0.00236, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47312, "top5_acc": 0.725, "loss_cls": 2.92273, "loss": 2.92273, "time": 0.81796} +{"mode": "train", "epoch": 136, "iter": 1100, "lr": 0.00235, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47406, "top5_acc": 0.72812, "loss_cls": 2.92655, "loss": 2.92655, "time": 0.82178} +{"mode": "train", "epoch": 136, "iter": 1200, "lr": 0.00234, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47125, "top5_acc": 0.72453, "loss_cls": 2.97035, "loss": 2.97035, "time": 0.81563} +{"mode": "train", "epoch": 136, "iter": 1300, "lr": 0.00234, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47719, "top5_acc": 0.73266, "loss_cls": 2.9151, "loss": 2.9151, "time": 0.82432} +{"mode": "train", "epoch": 136, "iter": 1400, "lr": 0.00233, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.47328, "top5_acc": 0.72984, "loss_cls": 2.94977, "loss": 2.94977, "time": 0.81947} +{"mode": "train", "epoch": 136, "iter": 1500, "lr": 0.00232, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.47891, "top5_acc": 0.72625, "loss_cls": 2.93334, "loss": 2.93334, "time": 0.82318} +{"mode": "train", "epoch": 136, "iter": 1600, "lr": 0.00231, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.45547, "top5_acc": 0.70844, "loss_cls": 3.02527, "loss": 3.02527, "time": 0.82508} +{"mode": "train", "epoch": 136, "iter": 1700, "lr": 0.0023, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47656, "top5_acc": 0.72656, "loss_cls": 2.96067, "loss": 2.96067, "time": 0.82289} +{"mode": "train", "epoch": 136, "iter": 1800, "lr": 0.00229, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47125, "top5_acc": 0.72109, "loss_cls": 2.98443, "loss": 2.98443, "time": 0.82204} +{"mode": "train", "epoch": 136, "iter": 1900, "lr": 0.00229, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46469, "top5_acc": 0.71797, "loss_cls": 2.99034, "loss": 2.99034, "time": 0.82459} +{"mode": "train", "epoch": 136, "iter": 2000, "lr": 0.00228, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46859, "top5_acc": 0.72016, "loss_cls": 2.9557, "loss": 2.9557, "time": 0.82096} +{"mode": "train", "epoch": 136, "iter": 2100, "lr": 0.00227, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.47828, "top5_acc": 0.73078, "loss_cls": 2.92316, "loss": 2.92316, "time": 0.82218} +{"mode": "train", "epoch": 136, "iter": 2200, "lr": 0.00226, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47484, "top5_acc": 0.72438, "loss_cls": 2.93873, "loss": 2.93873, "time": 0.82694} +{"mode": "train", "epoch": 136, "iter": 2300, "lr": 0.00225, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.47, "top5_acc": 0.72172, "loss_cls": 2.95488, "loss": 2.95488, "time": 0.82662} +{"mode": "train", "epoch": 136, "iter": 2400, "lr": 0.00224, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47125, "top5_acc": 0.72438, "loss_cls": 2.94885, "loss": 2.94885, "time": 0.8203} +{"mode": "train", "epoch": 136, "iter": 2500, "lr": 0.00224, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47297, "top5_acc": 0.72266, "loss_cls": 2.98566, "loss": 2.98566, "time": 0.8184} +{"mode": "train", "epoch": 136, "iter": 2600, "lr": 0.00223, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.4725, "top5_acc": 0.72422, "loss_cls": 2.93899, "loss": 2.93899, "time": 0.81628} +{"mode": "train", "epoch": 136, "iter": 2700, "lr": 0.00222, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47219, "top5_acc": 0.72812, "loss_cls": 2.9507, "loss": 2.9507, "time": 0.81773} +{"mode": "train", "epoch": 136, "iter": 2800, "lr": 0.00221, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46859, "top5_acc": 0.71875, "loss_cls": 2.99826, "loss": 2.99826, "time": 0.81705} +{"mode": "train", "epoch": 136, "iter": 2900, "lr": 0.0022, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.46219, "top5_acc": 0.71875, "loss_cls": 2.98295, "loss": 2.98295, "time": 0.82149} +{"mode": "train", "epoch": 136, "iter": 3000, "lr": 0.00219, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47703, "top5_acc": 0.73031, "loss_cls": 2.91465, "loss": 2.91465, "time": 0.82036} +{"mode": "train", "epoch": 136, "iter": 3100, "lr": 0.00219, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47625, "top5_acc": 0.72625, "loss_cls": 2.94778, "loss": 2.94778, "time": 0.82576} +{"mode": "train", "epoch": 136, "iter": 3200, "lr": 0.00218, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47953, "top5_acc": 0.72906, "loss_cls": 2.94592, "loss": 2.94592, "time": 0.81592} +{"mode": "train", "epoch": 136, "iter": 3300, "lr": 0.00217, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47406, "top5_acc": 0.72078, "loss_cls": 2.95885, "loss": 2.95885, "time": 0.81768} +{"mode": "train", "epoch": 136, "iter": 3400, "lr": 0.00216, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.46438, "top5_acc": 0.71828, "loss_cls": 2.98842, "loss": 2.98842, "time": 0.82252} +{"mode": "train", "epoch": 136, "iter": 3500, "lr": 0.00215, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47062, "top5_acc": 0.71484, "loss_cls": 3.01178, "loss": 3.01178, "time": 0.82818} +{"mode": "train", "epoch": 136, "iter": 3600, "lr": 0.00215, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46938, "top5_acc": 0.72016, "loss_cls": 2.96355, "loss": 2.96355, "time": 0.81919} +{"mode": "train", "epoch": 136, "iter": 3700, "lr": 0.00214, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.46188, "top5_acc": 0.72016, "loss_cls": 2.98416, "loss": 2.98416, "time": 0.81814} +{"mode": "val", "epoch": 136, "iter": 309, "lr": 0.00213, "top1_acc": 0.37811, "top5_acc": 0.62959, "mean_class_accuracy": 0.3778} +{"mode": "train", "epoch": 137, "iter": 100, "lr": 0.00213, "memory": 15990, "data_time": 1.28546, "top1_acc": 0.49438, "top5_acc": 0.74609, "loss_cls": 2.81555, "loss": 2.81555, "time": 2.26785} +{"mode": "train", "epoch": 137, "iter": 200, "lr": 0.00212, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49625, "top5_acc": 0.73922, "loss_cls": 2.85462, "loss": 2.85462, "time": 0.81987} +{"mode": "train", "epoch": 137, "iter": 300, "lr": 0.00211, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48969, "top5_acc": 0.74453, "loss_cls": 2.84278, "loss": 2.84278, "time": 0.82437} +{"mode": "train", "epoch": 137, "iter": 400, "lr": 0.0021, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48766, "top5_acc": 0.74, "loss_cls": 2.87, "loss": 2.87, "time": 0.8183} +{"mode": "train", "epoch": 137, "iter": 500, "lr": 0.00209, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48672, "top5_acc": 0.74172, "loss_cls": 2.84329, "loss": 2.84329, "time": 0.82154} +{"mode": "train", "epoch": 137, "iter": 600, "lr": 0.00209, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49031, "top5_acc": 0.74266, "loss_cls": 2.87634, "loss": 2.87634, "time": 0.82233} +{"mode": "train", "epoch": 137, "iter": 700, "lr": 0.00208, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48953, "top5_acc": 0.74266, "loss_cls": 2.85401, "loss": 2.85401, "time": 0.83214} +{"mode": "train", "epoch": 137, "iter": 800, "lr": 0.00207, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.48562, "top5_acc": 0.7425, "loss_cls": 2.85894, "loss": 2.85894, "time": 0.82226} +{"mode": "train", "epoch": 137, "iter": 900, "lr": 0.00206, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47844, "top5_acc": 0.73172, "loss_cls": 2.91539, "loss": 2.91539, "time": 0.81885} +{"mode": "train", "epoch": 137, "iter": 1000, "lr": 0.00205, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48562, "top5_acc": 0.73219, "loss_cls": 2.90041, "loss": 2.90041, "time": 0.81971} +{"mode": "train", "epoch": 137, "iter": 1100, "lr": 0.00205, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48297, "top5_acc": 0.72609, "loss_cls": 2.918, "loss": 2.918, "time": 0.81797} +{"mode": "train", "epoch": 137, "iter": 1200, "lr": 0.00204, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48625, "top5_acc": 0.73281, "loss_cls": 2.8867, "loss": 2.8867, "time": 0.8182} +{"mode": "train", "epoch": 137, "iter": 1300, "lr": 0.00203, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.47578, "top5_acc": 0.72844, "loss_cls": 2.92841, "loss": 2.92841, "time": 0.82204} +{"mode": "train", "epoch": 137, "iter": 1400, "lr": 0.00202, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48172, "top5_acc": 0.73531, "loss_cls": 2.89381, "loss": 2.89381, "time": 0.82407} +{"mode": "train", "epoch": 137, "iter": 1500, "lr": 0.00201, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.48609, "top5_acc": 0.73203, "loss_cls": 2.89799, "loss": 2.89799, "time": 0.82489} +{"mode": "train", "epoch": 137, "iter": 1600, "lr": 0.00201, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47375, "top5_acc": 0.72234, "loss_cls": 2.94028, "loss": 2.94028, "time": 0.82332} +{"mode": "train", "epoch": 137, "iter": 1700, "lr": 0.002, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48312, "top5_acc": 0.72672, "loss_cls": 2.92705, "loss": 2.92705, "time": 0.82073} +{"mode": "train", "epoch": 137, "iter": 1800, "lr": 0.00199, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48703, "top5_acc": 0.73547, "loss_cls": 2.87073, "loss": 2.87073, "time": 0.82028} +{"mode": "train", "epoch": 137, "iter": 1900, "lr": 0.00198, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.47984, "top5_acc": 0.73312, "loss_cls": 2.91568, "loss": 2.91568, "time": 0.81742} +{"mode": "train", "epoch": 137, "iter": 2000, "lr": 0.00198, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47125, "top5_acc": 0.7275, "loss_cls": 2.92994, "loss": 2.92994, "time": 0.81612} +{"mode": "train", "epoch": 137, "iter": 2100, "lr": 0.00197, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47406, "top5_acc": 0.72969, "loss_cls": 2.91214, "loss": 2.91214, "time": 0.82568} +{"mode": "train", "epoch": 137, "iter": 2200, "lr": 0.00196, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49031, "top5_acc": 0.73109, "loss_cls": 2.89006, "loss": 2.89006, "time": 0.82474} +{"mode": "train", "epoch": 137, "iter": 2300, "lr": 0.00195, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.48203, "top5_acc": 0.73109, "loss_cls": 2.91125, "loss": 2.91125, "time": 0.82219} +{"mode": "train", "epoch": 137, "iter": 2400, "lr": 0.00194, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47672, "top5_acc": 0.72844, "loss_cls": 2.90491, "loss": 2.90491, "time": 0.82321} +{"mode": "train", "epoch": 137, "iter": 2500, "lr": 0.00194, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48125, "top5_acc": 0.73422, "loss_cls": 2.90938, "loss": 2.90938, "time": 0.82683} +{"mode": "train", "epoch": 137, "iter": 2600, "lr": 0.00193, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47188, "top5_acc": 0.72656, "loss_cls": 2.92347, "loss": 2.92347, "time": 0.82051} +{"mode": "train", "epoch": 137, "iter": 2700, "lr": 0.00192, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.46859, "top5_acc": 0.72797, "loss_cls": 2.9305, "loss": 2.9305, "time": 0.81698} +{"mode": "train", "epoch": 137, "iter": 2800, "lr": 0.00191, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.47641, "top5_acc": 0.72734, "loss_cls": 2.91287, "loss": 2.91287, "time": 0.82536} +{"mode": "train", "epoch": 137, "iter": 2900, "lr": 0.00191, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47312, "top5_acc": 0.72391, "loss_cls": 2.95763, "loss": 2.95763, "time": 0.81777} +{"mode": "train", "epoch": 137, "iter": 3000, "lr": 0.0019, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48062, "top5_acc": 0.73016, "loss_cls": 2.92467, "loss": 2.92467, "time": 0.8225} +{"mode": "train", "epoch": 137, "iter": 3100, "lr": 0.00189, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.485, "top5_acc": 0.73141, "loss_cls": 2.90409, "loss": 2.90409, "time": 0.82635} +{"mode": "train", "epoch": 137, "iter": 3200, "lr": 0.00188, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48281, "top5_acc": 0.73547, "loss_cls": 2.87354, "loss": 2.87354, "time": 0.81817} +{"mode": "train", "epoch": 137, "iter": 3300, "lr": 0.00188, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49031, "top5_acc": 0.72797, "loss_cls": 2.88757, "loss": 2.88757, "time": 0.81811} +{"mode": "train", "epoch": 137, "iter": 3400, "lr": 0.00187, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.46641, "top5_acc": 0.73078, "loss_cls": 2.93587, "loss": 2.93587, "time": 0.82497} +{"mode": "train", "epoch": 137, "iter": 3500, "lr": 0.00186, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47234, "top5_acc": 0.73266, "loss_cls": 2.92005, "loss": 2.92005, "time": 0.8207} +{"mode": "train", "epoch": 137, "iter": 3600, "lr": 0.00185, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48328, "top5_acc": 0.73719, "loss_cls": 2.88671, "loss": 2.88671, "time": 0.81848} +{"mode": "train", "epoch": 137, "iter": 3700, "lr": 0.00185, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.47422, "top5_acc": 0.72453, "loss_cls": 2.94451, "loss": 2.94451, "time": 0.8217} +{"mode": "val", "epoch": 137, "iter": 309, "lr": 0.00184, "top1_acc": 0.37765, "top5_acc": 0.62463, "mean_class_accuracy": 0.37747} +{"mode": "train", "epoch": 138, "iter": 100, "lr": 0.00183, "memory": 15990, "data_time": 1.30411, "top1_acc": 0.50359, "top5_acc": 0.75219, "loss_cls": 2.79924, "loss": 2.79924, "time": 2.29329} +{"mode": "train", "epoch": 138, "iter": 200, "lr": 0.00183, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49766, "top5_acc": 0.74281, "loss_cls": 2.80517, "loss": 2.80517, "time": 0.82004} +{"mode": "train", "epoch": 138, "iter": 300, "lr": 0.00182, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50328, "top5_acc": 0.74844, "loss_cls": 2.7948, "loss": 2.7948, "time": 0.82544} +{"mode": "train", "epoch": 138, "iter": 400, "lr": 0.00181, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49578, "top5_acc": 0.74406, "loss_cls": 2.85006, "loss": 2.85006, "time": 0.81841} +{"mode": "train", "epoch": 138, "iter": 500, "lr": 0.0018, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50141, "top5_acc": 0.75172, "loss_cls": 2.79449, "loss": 2.79449, "time": 0.82623} +{"mode": "train", "epoch": 138, "iter": 600, "lr": 0.0018, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49922, "top5_acc": 0.75344, "loss_cls": 2.78924, "loss": 2.78924, "time": 0.82846} +{"mode": "train", "epoch": 138, "iter": 700, "lr": 0.00179, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49938, "top5_acc": 0.74875, "loss_cls": 2.79907, "loss": 2.79907, "time": 0.82617} +{"mode": "train", "epoch": 138, "iter": 800, "lr": 0.00178, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48062, "top5_acc": 0.73562, "loss_cls": 2.8588, "loss": 2.8588, "time": 0.8275} +{"mode": "train", "epoch": 138, "iter": 900, "lr": 0.00177, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50156, "top5_acc": 0.74781, "loss_cls": 2.80838, "loss": 2.80838, "time": 0.81856} +{"mode": "train", "epoch": 138, "iter": 1000, "lr": 0.00177, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48047, "top5_acc": 0.73344, "loss_cls": 2.88248, "loss": 2.88248, "time": 0.81846} +{"mode": "train", "epoch": 138, "iter": 1100, "lr": 0.00176, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49422, "top5_acc": 0.74453, "loss_cls": 2.81774, "loss": 2.81774, "time": 0.81976} +{"mode": "train", "epoch": 138, "iter": 1200, "lr": 0.00175, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.49344, "top5_acc": 0.74375, "loss_cls": 2.82862, "loss": 2.82862, "time": 0.82362} +{"mode": "train", "epoch": 138, "iter": 1300, "lr": 0.00175, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49328, "top5_acc": 0.73906, "loss_cls": 2.85331, "loss": 2.85331, "time": 0.82537} +{"mode": "train", "epoch": 138, "iter": 1400, "lr": 0.00174, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48609, "top5_acc": 0.73766, "loss_cls": 2.86567, "loss": 2.86567, "time": 0.82906} +{"mode": "train", "epoch": 138, "iter": 1500, "lr": 0.00173, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49188, "top5_acc": 0.73547, "loss_cls": 2.85066, "loss": 2.85066, "time": 0.81638} +{"mode": "train", "epoch": 138, "iter": 1600, "lr": 0.00172, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48859, "top5_acc": 0.73359, "loss_cls": 2.87387, "loss": 2.87387, "time": 0.82613} +{"mode": "train", "epoch": 138, "iter": 1700, "lr": 0.00172, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49328, "top5_acc": 0.74234, "loss_cls": 2.8309, "loss": 2.8309, "time": 0.81949} +{"mode": "train", "epoch": 138, "iter": 1800, "lr": 0.00171, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49312, "top5_acc": 0.74203, "loss_cls": 2.83711, "loss": 2.83711, "time": 0.82028} +{"mode": "train", "epoch": 138, "iter": 1900, "lr": 0.0017, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49469, "top5_acc": 0.74891, "loss_cls": 2.82207, "loss": 2.82207, "time": 0.81616} +{"mode": "train", "epoch": 138, "iter": 2000, "lr": 0.00169, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49172, "top5_acc": 0.73688, "loss_cls": 2.85834, "loss": 2.85834, "time": 0.82312} +{"mode": "train", "epoch": 138, "iter": 2100, "lr": 0.00169, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.48516, "top5_acc": 0.73172, "loss_cls": 2.89336, "loss": 2.89336, "time": 0.82386} +{"mode": "train", "epoch": 138, "iter": 2200, "lr": 0.00168, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.4925, "top5_acc": 0.73641, "loss_cls": 2.86746, "loss": 2.86746, "time": 0.82423} +{"mode": "train", "epoch": 138, "iter": 2300, "lr": 0.00167, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.49047, "top5_acc": 0.73984, "loss_cls": 2.84423, "loss": 2.84423, "time": 0.82496} +{"mode": "train", "epoch": 138, "iter": 2400, "lr": 0.00167, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48578, "top5_acc": 0.73766, "loss_cls": 2.87266, "loss": 2.87266, "time": 0.82153} +{"mode": "train", "epoch": 138, "iter": 2500, "lr": 0.00166, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.50359, "top5_acc": 0.75031, "loss_cls": 2.78335, "loss": 2.78335, "time": 0.82074} +{"mode": "train", "epoch": 138, "iter": 2600, "lr": 0.00165, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48797, "top5_acc": 0.73469, "loss_cls": 2.87557, "loss": 2.87557, "time": 0.82128} +{"mode": "train", "epoch": 138, "iter": 2700, "lr": 0.00164, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49344, "top5_acc": 0.74125, "loss_cls": 2.85022, "loss": 2.85022, "time": 0.82456} +{"mode": "train", "epoch": 138, "iter": 2800, "lr": 0.00164, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.48969, "top5_acc": 0.7375, "loss_cls": 2.8598, "loss": 2.8598, "time": 0.82104} +{"mode": "train", "epoch": 138, "iter": 2900, "lr": 0.00163, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48438, "top5_acc": 0.72906, "loss_cls": 2.89425, "loss": 2.89425, "time": 0.82055} +{"mode": "train", "epoch": 138, "iter": 3000, "lr": 0.00162, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.49938, "top5_acc": 0.74328, "loss_cls": 2.8483, "loss": 2.8483, "time": 0.81526} +{"mode": "train", "epoch": 138, "iter": 3100, "lr": 0.00162, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48391, "top5_acc": 0.73938, "loss_cls": 2.87417, "loss": 2.87417, "time": 0.81786} +{"mode": "train", "epoch": 138, "iter": 3200, "lr": 0.00161, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.47531, "top5_acc": 0.72578, "loss_cls": 2.92829, "loss": 2.92829, "time": 0.82391} +{"mode": "train", "epoch": 138, "iter": 3300, "lr": 0.0016, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49094, "top5_acc": 0.73938, "loss_cls": 2.837, "loss": 2.837, "time": 0.82112} +{"mode": "train", "epoch": 138, "iter": 3400, "lr": 0.0016, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.4825, "top5_acc": 0.73188, "loss_cls": 2.8727, "loss": 2.8727, "time": 0.81688} +{"mode": "train", "epoch": 138, "iter": 3500, "lr": 0.00159, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.48531, "top5_acc": 0.74375, "loss_cls": 2.86875, "loss": 2.86875, "time": 0.82032} +{"mode": "train", "epoch": 138, "iter": 3600, "lr": 0.00158, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.48938, "top5_acc": 0.73547, "loss_cls": 2.86728, "loss": 2.86728, "time": 0.82033} +{"mode": "train", "epoch": 138, "iter": 3700, "lr": 0.00157, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.47625, "top5_acc": 0.73031, "loss_cls": 2.9155, "loss": 2.9155, "time": 0.81786} +{"mode": "val", "epoch": 138, "iter": 309, "lr": 0.00157, "top1_acc": 0.38029, "top5_acc": 0.6301, "mean_class_accuracy": 0.38009} +{"mode": "train", "epoch": 139, "iter": 100, "lr": 0.00156, "memory": 15990, "data_time": 1.25512, "top1_acc": 0.49969, "top5_acc": 0.75484, "loss_cls": 2.76157, "loss": 2.76157, "time": 2.25606} +{"mode": "train", "epoch": 139, "iter": 200, "lr": 0.00156, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.505, "top5_acc": 0.74875, "loss_cls": 2.79137, "loss": 2.79137, "time": 0.82264} +{"mode": "train", "epoch": 139, "iter": 300, "lr": 0.00155, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50594, "top5_acc": 0.75594, "loss_cls": 2.78389, "loss": 2.78389, "time": 0.82283} +{"mode": "train", "epoch": 139, "iter": 400, "lr": 0.00154, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50234, "top5_acc": 0.74984, "loss_cls": 2.78287, "loss": 2.78287, "time": 0.81951} +{"mode": "train", "epoch": 139, "iter": 500, "lr": 0.00154, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49938, "top5_acc": 0.75406, "loss_cls": 2.77883, "loss": 2.77883, "time": 0.82617} +{"mode": "train", "epoch": 139, "iter": 600, "lr": 0.00153, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51281, "top5_acc": 0.74938, "loss_cls": 2.74772, "loss": 2.74772, "time": 0.81796} +{"mode": "train", "epoch": 139, "iter": 700, "lr": 0.00152, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.505, "top5_acc": 0.7475, "loss_cls": 2.78329, "loss": 2.78329, "time": 0.82483} +{"mode": "train", "epoch": 139, "iter": 800, "lr": 0.00152, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49906, "top5_acc": 0.74453, "loss_cls": 2.81957, "loss": 2.81957, "time": 0.82481} +{"mode": "train", "epoch": 139, "iter": 900, "lr": 0.00151, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51313, "top5_acc": 0.75891, "loss_cls": 2.71127, "loss": 2.71127, "time": 0.82463} +{"mode": "train", "epoch": 139, "iter": 1000, "lr": 0.0015, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50344, "top5_acc": 0.75672, "loss_cls": 2.76687, "loss": 2.76687, "time": 0.82671} +{"mode": "train", "epoch": 139, "iter": 1100, "lr": 0.0015, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49781, "top5_acc": 0.74188, "loss_cls": 2.8169, "loss": 2.8169, "time": 0.82488} +{"mode": "train", "epoch": 139, "iter": 1200, "lr": 0.00149, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50047, "top5_acc": 0.74453, "loss_cls": 2.80539, "loss": 2.80539, "time": 0.83138} +{"mode": "train", "epoch": 139, "iter": 1300, "lr": 0.00148, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.49547, "top5_acc": 0.75109, "loss_cls": 2.80656, "loss": 2.80656, "time": 0.83566} +{"mode": "train", "epoch": 139, "iter": 1400, "lr": 0.00148, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.49188, "top5_acc": 0.74391, "loss_cls": 2.81331, "loss": 2.81331, "time": 0.83166} +{"mode": "train", "epoch": 139, "iter": 1500, "lr": 0.00147, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51109, "top5_acc": 0.75031, "loss_cls": 2.77577, "loss": 2.77577, "time": 0.82294} +{"mode": "train", "epoch": 139, "iter": 1600, "lr": 0.00146, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50672, "top5_acc": 0.75219, "loss_cls": 2.76167, "loss": 2.76167, "time": 0.8283} +{"mode": "train", "epoch": 139, "iter": 1700, "lr": 0.00145, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49203, "top5_acc": 0.73875, "loss_cls": 2.84205, "loss": 2.84205, "time": 0.81872} +{"mode": "train", "epoch": 139, "iter": 1800, "lr": 0.00145, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51281, "top5_acc": 0.75547, "loss_cls": 2.75768, "loss": 2.75768, "time": 0.81651} +{"mode": "train", "epoch": 139, "iter": 1900, "lr": 0.00144, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49109, "top5_acc": 0.74219, "loss_cls": 2.81554, "loss": 2.81554, "time": 0.81943} +{"mode": "train", "epoch": 139, "iter": 2000, "lr": 0.00143, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.48688, "top5_acc": 0.75422, "loss_cls": 2.81214, "loss": 2.81214, "time": 0.82118} +{"mode": "train", "epoch": 139, "iter": 2100, "lr": 0.00143, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49234, "top5_acc": 0.74188, "loss_cls": 2.84845, "loss": 2.84845, "time": 0.82586} +{"mode": "train", "epoch": 139, "iter": 2200, "lr": 0.00142, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.4975, "top5_acc": 0.74734, "loss_cls": 2.80076, "loss": 2.80076, "time": 0.82082} +{"mode": "train", "epoch": 139, "iter": 2300, "lr": 0.00142, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.49578, "top5_acc": 0.74922, "loss_cls": 2.79399, "loss": 2.79399, "time": 0.83113} +{"mode": "train", "epoch": 139, "iter": 2400, "lr": 0.00141, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50672, "top5_acc": 0.74875, "loss_cls": 2.7735, "loss": 2.7735, "time": 0.81859} +{"mode": "train", "epoch": 139, "iter": 2500, "lr": 0.0014, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50125, "top5_acc": 0.74344, "loss_cls": 2.81352, "loss": 2.81352, "time": 0.81882} +{"mode": "train", "epoch": 139, "iter": 2600, "lr": 0.0014, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49719, "top5_acc": 0.73672, "loss_cls": 2.84383, "loss": 2.84383, "time": 0.82674} +{"mode": "train", "epoch": 139, "iter": 2700, "lr": 0.00139, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49125, "top5_acc": 0.74469, "loss_cls": 2.82493, "loss": 2.82493, "time": 0.81721} +{"mode": "train", "epoch": 139, "iter": 2800, "lr": 0.00138, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.49656, "top5_acc": 0.75219, "loss_cls": 2.79337, "loss": 2.79337, "time": 0.82054} +{"mode": "train", "epoch": 139, "iter": 2900, "lr": 0.00138, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50938, "top5_acc": 0.74734, "loss_cls": 2.78969, "loss": 2.78969, "time": 0.81665} +{"mode": "train", "epoch": 139, "iter": 3000, "lr": 0.00137, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49016, "top5_acc": 0.74, "loss_cls": 2.83498, "loss": 2.83498, "time": 0.82115} +{"mode": "train", "epoch": 139, "iter": 3100, "lr": 0.00136, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49594, "top5_acc": 0.74, "loss_cls": 2.83812, "loss": 2.83812, "time": 0.82314} +{"mode": "train", "epoch": 139, "iter": 3200, "lr": 0.00136, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49359, "top5_acc": 0.74062, "loss_cls": 2.84814, "loss": 2.84814, "time": 0.82005} +{"mode": "train", "epoch": 139, "iter": 3300, "lr": 0.00135, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49781, "top5_acc": 0.74578, "loss_cls": 2.78642, "loss": 2.78642, "time": 0.81974} +{"mode": "train", "epoch": 139, "iter": 3400, "lr": 0.00134, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.49594, "top5_acc": 0.7475, "loss_cls": 2.79613, "loss": 2.79613, "time": 0.81916} +{"mode": "train", "epoch": 139, "iter": 3500, "lr": 0.00134, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49219, "top5_acc": 0.74797, "loss_cls": 2.82932, "loss": 2.82932, "time": 0.82705} +{"mode": "train", "epoch": 139, "iter": 3600, "lr": 0.00133, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49594, "top5_acc": 0.74906, "loss_cls": 2.78883, "loss": 2.78883, "time": 0.82306} +{"mode": "train", "epoch": 139, "iter": 3700, "lr": 0.00132, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49219, "top5_acc": 0.74469, "loss_cls": 2.83601, "loss": 2.83601, "time": 0.81615} +{"mode": "val", "epoch": 139, "iter": 309, "lr": 0.00132, "top1_acc": 0.38525, "top5_acc": 0.63151, "mean_class_accuracy": 0.38492} +{"mode": "train", "epoch": 140, "iter": 100, "lr": 0.00131, "memory": 15990, "data_time": 1.31645, "top1_acc": 0.51562, "top5_acc": 0.76562, "loss_cls": 2.67377, "loss": 2.67377, "time": 2.31263} +{"mode": "train", "epoch": 140, "iter": 200, "lr": 0.00131, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.52875, "top5_acc": 0.76922, "loss_cls": 2.64783, "loss": 2.64783, "time": 0.83095} +{"mode": "train", "epoch": 140, "iter": 300, "lr": 0.0013, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.51219, "top5_acc": 0.75703, "loss_cls": 2.74104, "loss": 2.74104, "time": 0.82365} +{"mode": "train", "epoch": 140, "iter": 400, "lr": 0.0013, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51828, "top5_acc": 0.75875, "loss_cls": 2.70001, "loss": 2.70001, "time": 0.82325} +{"mode": "train", "epoch": 140, "iter": 500, "lr": 0.00129, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.5175, "top5_acc": 0.76656, "loss_cls": 2.69071, "loss": 2.69071, "time": 0.82486} +{"mode": "train", "epoch": 140, "iter": 600, "lr": 0.00128, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51812, "top5_acc": 0.75969, "loss_cls": 2.70996, "loss": 2.70996, "time": 0.82689} +{"mode": "train", "epoch": 140, "iter": 700, "lr": 0.00128, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50297, "top5_acc": 0.75781, "loss_cls": 2.73326, "loss": 2.73326, "time": 0.8195} +{"mode": "train", "epoch": 140, "iter": 800, "lr": 0.00127, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50406, "top5_acc": 0.755, "loss_cls": 2.75221, "loss": 2.75221, "time": 0.82453} +{"mode": "train", "epoch": 140, "iter": 900, "lr": 0.00126, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51828, "top5_acc": 0.76703, "loss_cls": 2.71293, "loss": 2.71293, "time": 0.82165} +{"mode": "train", "epoch": 140, "iter": 1000, "lr": 0.00126, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50313, "top5_acc": 0.75578, "loss_cls": 2.76407, "loss": 2.76407, "time": 0.82175} +{"mode": "train", "epoch": 140, "iter": 1100, "lr": 0.00125, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51469, "top5_acc": 0.75469, "loss_cls": 2.734, "loss": 2.734, "time": 0.81925} +{"mode": "train", "epoch": 140, "iter": 1200, "lr": 0.00125, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.50984, "top5_acc": 0.76219, "loss_cls": 2.73894, "loss": 2.73894, "time": 0.82869} +{"mode": "train", "epoch": 140, "iter": 1300, "lr": 0.00124, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52188, "top5_acc": 0.76062, "loss_cls": 2.71401, "loss": 2.71401, "time": 0.81941} +{"mode": "train", "epoch": 140, "iter": 1400, "lr": 0.00123, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51484, "top5_acc": 0.76172, "loss_cls": 2.72052, "loss": 2.72052, "time": 0.8215} +{"mode": "train", "epoch": 140, "iter": 1500, "lr": 0.00123, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.50234, "top5_acc": 0.75156, "loss_cls": 2.75385, "loss": 2.75385, "time": 0.8244} +{"mode": "train", "epoch": 140, "iter": 1600, "lr": 0.00122, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49906, "top5_acc": 0.74922, "loss_cls": 2.77578, "loss": 2.77578, "time": 0.82113} +{"mode": "train", "epoch": 140, "iter": 1700, "lr": 0.00121, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50547, "top5_acc": 0.75703, "loss_cls": 2.74933, "loss": 2.74933, "time": 0.82132} +{"mode": "train", "epoch": 140, "iter": 1800, "lr": 0.00121, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51016, "top5_acc": 0.75578, "loss_cls": 2.73138, "loss": 2.73138, "time": 0.8206} +{"mode": "train", "epoch": 140, "iter": 1900, "lr": 0.0012, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49828, "top5_acc": 0.75641, "loss_cls": 2.7706, "loss": 2.7706, "time": 0.82742} +{"mode": "train", "epoch": 140, "iter": 2000, "lr": 0.0012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49406, "top5_acc": 0.74719, "loss_cls": 2.8369, "loss": 2.8369, "time": 0.81931} +{"mode": "train", "epoch": 140, "iter": 2100, "lr": 0.00119, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.5225, "top5_acc": 0.76328, "loss_cls": 2.68646, "loss": 2.68646, "time": 0.83519} +{"mode": "train", "epoch": 140, "iter": 2200, "lr": 0.00118, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50891, "top5_acc": 0.73906, "loss_cls": 2.74901, "loss": 2.74901, "time": 0.82062} +{"mode": "train", "epoch": 140, "iter": 2300, "lr": 0.00118, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.50219, "top5_acc": 0.75266, "loss_cls": 2.76954, "loss": 2.76954, "time": 0.82755} +{"mode": "train", "epoch": 140, "iter": 2400, "lr": 0.00117, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50422, "top5_acc": 0.75438, "loss_cls": 2.78257, "loss": 2.78257, "time": 0.81907} +{"mode": "train", "epoch": 140, "iter": 2500, "lr": 0.00117, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.49906, "top5_acc": 0.74344, "loss_cls": 2.79666, "loss": 2.79666, "time": 0.81995} +{"mode": "train", "epoch": 140, "iter": 2600, "lr": 0.00116, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50391, "top5_acc": 0.75094, "loss_cls": 2.75332, "loss": 2.75332, "time": 0.81804} +{"mode": "train", "epoch": 140, "iter": 2700, "lr": 0.00115, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.50609, "top5_acc": 0.74984, "loss_cls": 2.7642, "loss": 2.7642, "time": 0.82017} +{"mode": "train", "epoch": 140, "iter": 2800, "lr": 0.00115, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.50688, "top5_acc": 0.75812, "loss_cls": 2.73535, "loss": 2.73535, "time": 0.81854} +{"mode": "train", "epoch": 140, "iter": 2900, "lr": 0.00114, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.51078, "top5_acc": 0.75453, "loss_cls": 2.75413, "loss": 2.75413, "time": 0.81913} +{"mode": "train", "epoch": 140, "iter": 3000, "lr": 0.00114, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50313, "top5_acc": 0.75516, "loss_cls": 2.75931, "loss": 2.75931, "time": 0.81916} +{"mode": "train", "epoch": 140, "iter": 3100, "lr": 0.00113, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51016, "top5_acc": 0.76141, "loss_cls": 2.73052, "loss": 2.73052, "time": 0.82184} +{"mode": "train", "epoch": 140, "iter": 3200, "lr": 0.00112, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50047, "top5_acc": 0.75047, "loss_cls": 2.77912, "loss": 2.77912, "time": 0.81976} +{"mode": "train", "epoch": 140, "iter": 3300, "lr": 0.00112, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50234, "top5_acc": 0.75625, "loss_cls": 2.76443, "loss": 2.76443, "time": 0.82268} +{"mode": "train", "epoch": 140, "iter": 3400, "lr": 0.00111, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.49891, "top5_acc": 0.74812, "loss_cls": 2.80729, "loss": 2.80729, "time": 0.82491} +{"mode": "train", "epoch": 140, "iter": 3500, "lr": 0.00111, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49641, "top5_acc": 0.74547, "loss_cls": 2.8114, "loss": 2.8114, "time": 0.82415} +{"mode": "train", "epoch": 140, "iter": 3600, "lr": 0.0011, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50703, "top5_acc": 0.75609, "loss_cls": 2.75522, "loss": 2.75522, "time": 0.81711} +{"mode": "train", "epoch": 140, "iter": 3700, "lr": 0.0011, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5025, "top5_acc": 0.75438, "loss_cls": 2.77066, "loss": 2.77066, "time": 0.82013} +{"mode": "val", "epoch": 140, "iter": 309, "lr": 0.00109, "top1_acc": 0.38788, "top5_acc": 0.63739, "mean_class_accuracy": 0.38765} +{"mode": "train", "epoch": 141, "iter": 100, "lr": 0.00109, "memory": 15990, "data_time": 1.33168, "top1_acc": 0.52453, "top5_acc": 0.77297, "loss_cls": 2.64373, "loss": 2.64373, "time": 2.33788} +{"mode": "train", "epoch": 141, "iter": 200, "lr": 0.00108, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53078, "top5_acc": 0.77328, "loss_cls": 2.63649, "loss": 2.63649, "time": 0.82436} +{"mode": "train", "epoch": 141, "iter": 300, "lr": 0.00108, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52828, "top5_acc": 0.76375, "loss_cls": 2.67638, "loss": 2.67638, "time": 0.82295} +{"mode": "train", "epoch": 141, "iter": 400, "lr": 0.00107, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52688, "top5_acc": 0.76094, "loss_cls": 2.65024, "loss": 2.65024, "time": 0.82007} +{"mode": "train", "epoch": 141, "iter": 500, "lr": 0.00106, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.51766, "top5_acc": 0.76703, "loss_cls": 2.71817, "loss": 2.71817, "time": 0.82861} +{"mode": "train", "epoch": 141, "iter": 600, "lr": 0.00106, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51859, "top5_acc": 0.77156, "loss_cls": 2.67952, "loss": 2.67952, "time": 0.82484} +{"mode": "train", "epoch": 141, "iter": 700, "lr": 0.00105, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51141, "top5_acc": 0.76609, "loss_cls": 2.70798, "loss": 2.70798, "time": 0.82581} +{"mode": "train", "epoch": 141, "iter": 800, "lr": 0.00105, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5175, "top5_acc": 0.75922, "loss_cls": 2.7147, "loss": 2.7147, "time": 0.82039} +{"mode": "train", "epoch": 141, "iter": 900, "lr": 0.00104, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52266, "top5_acc": 0.77297, "loss_cls": 2.67561, "loss": 2.67561, "time": 0.82702} +{"mode": "train", "epoch": 141, "iter": 1000, "lr": 0.00104, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51828, "top5_acc": 0.76422, "loss_cls": 2.6925, "loss": 2.6925, "time": 0.82266} +{"mode": "train", "epoch": 141, "iter": 1100, "lr": 0.00103, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.5175, "top5_acc": 0.75891, "loss_cls": 2.69823, "loss": 2.69823, "time": 0.82828} +{"mode": "train", "epoch": 141, "iter": 1200, "lr": 0.00102, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51844, "top5_acc": 0.765, "loss_cls": 2.67937, "loss": 2.67937, "time": 0.82148} +{"mode": "train", "epoch": 141, "iter": 1300, "lr": 0.00102, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52, "top5_acc": 0.77094, "loss_cls": 2.6657, "loss": 2.6657, "time": 0.82405} +{"mode": "train", "epoch": 141, "iter": 1400, "lr": 0.00101, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52016, "top5_acc": 0.76297, "loss_cls": 2.67971, "loss": 2.67971, "time": 0.8232} +{"mode": "train", "epoch": 141, "iter": 1500, "lr": 0.00101, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52078, "top5_acc": 0.76531, "loss_cls": 2.67632, "loss": 2.67632, "time": 0.82078} +{"mode": "train", "epoch": 141, "iter": 1600, "lr": 0.001, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.52141, "top5_acc": 0.77297, "loss_cls": 2.66152, "loss": 2.66152, "time": 0.82475} +{"mode": "train", "epoch": 141, "iter": 1700, "lr": 0.001, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51594, "top5_acc": 0.75734, "loss_cls": 2.70552, "loss": 2.70552, "time": 0.81769} +{"mode": "train", "epoch": 141, "iter": 1800, "lr": 0.00099, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.49906, "top5_acc": 0.75469, "loss_cls": 2.76332, "loss": 2.76332, "time": 0.81876} +{"mode": "train", "epoch": 141, "iter": 1900, "lr": 0.00099, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51609, "top5_acc": 0.75734, "loss_cls": 2.72435, "loss": 2.72435, "time": 0.82053} +{"mode": "train", "epoch": 141, "iter": 2000, "lr": 0.00098, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51391, "top5_acc": 0.76094, "loss_cls": 2.7123, "loss": 2.7123, "time": 0.82388} +{"mode": "train", "epoch": 141, "iter": 2100, "lr": 0.00097, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.52062, "top5_acc": 0.76578, "loss_cls": 2.67832, "loss": 2.67832, "time": 0.82574} +{"mode": "train", "epoch": 141, "iter": 2200, "lr": 0.00097, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.50953, "top5_acc": 0.75438, "loss_cls": 2.75757, "loss": 2.75757, "time": 0.81847} +{"mode": "train", "epoch": 141, "iter": 2300, "lr": 0.00096, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51438, "top5_acc": 0.7625, "loss_cls": 2.69242, "loss": 2.69242, "time": 0.82031} +{"mode": "train", "epoch": 141, "iter": 2400, "lr": 0.00096, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51641, "top5_acc": 0.76781, "loss_cls": 2.68988, "loss": 2.68988, "time": 0.81742} +{"mode": "train", "epoch": 141, "iter": 2500, "lr": 0.00095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51703, "top5_acc": 0.76797, "loss_cls": 2.68569, "loss": 2.68569, "time": 0.82385} +{"mode": "train", "epoch": 141, "iter": 2600, "lr": 0.00095, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51031, "top5_acc": 0.76062, "loss_cls": 2.69663, "loss": 2.69663, "time": 0.81702} +{"mode": "train", "epoch": 141, "iter": 2700, "lr": 0.00094, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51609, "top5_acc": 0.76312, "loss_cls": 2.6998, "loss": 2.6998, "time": 0.82056} +{"mode": "train", "epoch": 141, "iter": 2800, "lr": 0.00094, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51219, "top5_acc": 0.75703, "loss_cls": 2.71384, "loss": 2.71384, "time": 0.81305} +{"mode": "train", "epoch": 141, "iter": 2900, "lr": 0.00093, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.52391, "top5_acc": 0.75891, "loss_cls": 2.69951, "loss": 2.69951, "time": 0.81802} +{"mode": "train", "epoch": 141, "iter": 3000, "lr": 0.00093, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51891, "top5_acc": 0.75969, "loss_cls": 2.70226, "loss": 2.70226, "time": 0.82035} +{"mode": "train", "epoch": 141, "iter": 3100, "lr": 0.00092, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.51484, "top5_acc": 0.7625, "loss_cls": 2.70015, "loss": 2.70015, "time": 0.8184} +{"mode": "train", "epoch": 141, "iter": 3200, "lr": 0.00091, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51781, "top5_acc": 0.76031, "loss_cls": 2.695, "loss": 2.695, "time": 0.82189} +{"mode": "train", "epoch": 141, "iter": 3300, "lr": 0.00091, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.51188, "top5_acc": 0.76172, "loss_cls": 2.69942, "loss": 2.69942, "time": 0.82179} +{"mode": "train", "epoch": 141, "iter": 3400, "lr": 0.0009, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51891, "top5_acc": 0.75984, "loss_cls": 2.68956, "loss": 2.68956, "time": 0.82022} +{"mode": "train", "epoch": 141, "iter": 3500, "lr": 0.0009, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51047, "top5_acc": 0.76172, "loss_cls": 2.70898, "loss": 2.70898, "time": 0.83031} +{"mode": "train", "epoch": 141, "iter": 3600, "lr": 0.00089, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51438, "top5_acc": 0.76641, "loss_cls": 2.68025, "loss": 2.68025, "time": 0.81677} +{"mode": "train", "epoch": 141, "iter": 3700, "lr": 0.00089, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.50906, "top5_acc": 0.75078, "loss_cls": 2.75811, "loss": 2.75811, "time": 0.81534} +{"mode": "val", "epoch": 141, "iter": 309, "lr": 0.00089, "top1_acc": 0.39108, "top5_acc": 0.64083, "mean_class_accuracy": 0.3908} +{"mode": "train", "epoch": 142, "iter": 100, "lr": 0.00088, "memory": 15990, "data_time": 1.3162, "top1_acc": 0.53094, "top5_acc": 0.77188, "loss_cls": 2.62249, "loss": 2.62249, "time": 2.30649} +{"mode": "train", "epoch": 142, "iter": 200, "lr": 0.00088, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.52578, "top5_acc": 0.76719, "loss_cls": 2.66107, "loss": 2.66107, "time": 0.82682} +{"mode": "train", "epoch": 142, "iter": 300, "lr": 0.00087, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.53281, "top5_acc": 0.77891, "loss_cls": 2.60309, "loss": 2.60309, "time": 0.82551} +{"mode": "train", "epoch": 142, "iter": 400, "lr": 0.00086, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53953, "top5_acc": 0.77672, "loss_cls": 2.60106, "loss": 2.60106, "time": 0.82278} +{"mode": "train", "epoch": 142, "iter": 500, "lr": 0.00086, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52766, "top5_acc": 0.78156, "loss_cls": 2.61857, "loss": 2.61857, "time": 0.82082} +{"mode": "train", "epoch": 142, "iter": 600, "lr": 0.00085, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52375, "top5_acc": 0.7725, "loss_cls": 2.63618, "loss": 2.63618, "time": 0.82361} +{"mode": "train", "epoch": 142, "iter": 700, "lr": 0.00085, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54156, "top5_acc": 0.77875, "loss_cls": 2.56253, "loss": 2.56253, "time": 0.82135} +{"mode": "train", "epoch": 142, "iter": 800, "lr": 0.00084, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52641, "top5_acc": 0.77312, "loss_cls": 2.63489, "loss": 2.63489, "time": 0.82769} +{"mode": "train", "epoch": 142, "iter": 900, "lr": 0.00084, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52469, "top5_acc": 0.76047, "loss_cls": 2.68803, "loss": 2.68803, "time": 0.82407} +{"mode": "train", "epoch": 142, "iter": 1000, "lr": 0.00083, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52906, "top5_acc": 0.77734, "loss_cls": 2.60422, "loss": 2.60422, "time": 0.82424} +{"mode": "train", "epoch": 142, "iter": 1100, "lr": 0.00083, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52156, "top5_acc": 0.77484, "loss_cls": 2.65203, "loss": 2.65203, "time": 0.82752} +{"mode": "train", "epoch": 142, "iter": 1200, "lr": 0.00082, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53016, "top5_acc": 0.78172, "loss_cls": 2.60195, "loss": 2.60195, "time": 0.821} +{"mode": "train", "epoch": 142, "iter": 1300, "lr": 0.00082, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51828, "top5_acc": 0.76203, "loss_cls": 2.7036, "loss": 2.7036, "time": 0.82151} +{"mode": "train", "epoch": 142, "iter": 1400, "lr": 0.00081, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52672, "top5_acc": 0.77156, "loss_cls": 2.62643, "loss": 2.62643, "time": 0.82306} +{"mode": "train", "epoch": 142, "iter": 1500, "lr": 0.00081, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.51859, "top5_acc": 0.76797, "loss_cls": 2.68736, "loss": 2.68736, "time": 0.82066} +{"mode": "train", "epoch": 142, "iter": 1600, "lr": 0.0008, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53578, "top5_acc": 0.7775, "loss_cls": 2.60655, "loss": 2.60655, "time": 0.81556} +{"mode": "train", "epoch": 142, "iter": 1700, "lr": 0.0008, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52719, "top5_acc": 0.7675, "loss_cls": 2.64314, "loss": 2.64314, "time": 0.82145} +{"mode": "train", "epoch": 142, "iter": 1800, "lr": 0.00079, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52375, "top5_acc": 0.77391, "loss_cls": 2.64816, "loss": 2.64816, "time": 0.81573} +{"mode": "train", "epoch": 142, "iter": 1900, "lr": 0.00079, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52828, "top5_acc": 0.76672, "loss_cls": 2.65587, "loss": 2.65587, "time": 0.82156} +{"mode": "train", "epoch": 142, "iter": 2000, "lr": 0.00078, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53625, "top5_acc": 0.77656, "loss_cls": 2.62454, "loss": 2.62454, "time": 0.81827} +{"mode": "train", "epoch": 142, "iter": 2100, "lr": 0.00078, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.51469, "top5_acc": 0.76234, "loss_cls": 2.70646, "loss": 2.70646, "time": 0.82305} +{"mode": "train", "epoch": 142, "iter": 2200, "lr": 0.00077, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.52609, "top5_acc": 0.77344, "loss_cls": 2.61707, "loss": 2.61707, "time": 0.82686} +{"mode": "train", "epoch": 142, "iter": 2300, "lr": 0.00077, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.52266, "top5_acc": 0.76547, "loss_cls": 2.67429, "loss": 2.67429, "time": 0.83097} +{"mode": "train", "epoch": 142, "iter": 2400, "lr": 0.00076, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51797, "top5_acc": 0.76297, "loss_cls": 2.70044, "loss": 2.70044, "time": 0.82051} +{"mode": "train", "epoch": 142, "iter": 2500, "lr": 0.00076, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52859, "top5_acc": 0.76453, "loss_cls": 2.65491, "loss": 2.65491, "time": 0.82214} +{"mode": "train", "epoch": 142, "iter": 2600, "lr": 0.00075, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53047, "top5_acc": 0.76875, "loss_cls": 2.63005, "loss": 2.63005, "time": 0.82701} +{"mode": "train", "epoch": 142, "iter": 2700, "lr": 0.00075, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5325, "top5_acc": 0.77203, "loss_cls": 2.64711, "loss": 2.64711, "time": 0.81573} +{"mode": "train", "epoch": 142, "iter": 2800, "lr": 0.00075, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52594, "top5_acc": 0.76891, "loss_cls": 2.63986, "loss": 2.63986, "time": 0.81255} +{"mode": "train", "epoch": 142, "iter": 2900, "lr": 0.00074, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52719, "top5_acc": 0.76594, "loss_cls": 2.67015, "loss": 2.67015, "time": 0.81751} +{"mode": "train", "epoch": 142, "iter": 3000, "lr": 0.00074, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52703, "top5_acc": 0.76594, "loss_cls": 2.67123, "loss": 2.67123, "time": 0.81639} +{"mode": "train", "epoch": 142, "iter": 3100, "lr": 0.00073, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5325, "top5_acc": 0.77359, "loss_cls": 2.62343, "loss": 2.62343, "time": 0.81521} +{"mode": "train", "epoch": 142, "iter": 3200, "lr": 0.00073, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52562, "top5_acc": 0.76531, "loss_cls": 2.67985, "loss": 2.67985, "time": 0.82037} +{"mode": "train", "epoch": 142, "iter": 3300, "lr": 0.00072, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53406, "top5_acc": 0.77281, "loss_cls": 2.62267, "loss": 2.62267, "time": 0.82511} +{"mode": "train", "epoch": 142, "iter": 3400, "lr": 0.00072, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52875, "top5_acc": 0.78078, "loss_cls": 2.59309, "loss": 2.59309, "time": 0.81952} +{"mode": "train", "epoch": 142, "iter": 3500, "lr": 0.00071, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52469, "top5_acc": 0.77016, "loss_cls": 2.64924, "loss": 2.64924, "time": 0.82456} +{"mode": "train", "epoch": 142, "iter": 3600, "lr": 0.00071, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.51531, "top5_acc": 0.76219, "loss_cls": 2.66539, "loss": 2.66539, "time": 0.81942} +{"mode": "train", "epoch": 142, "iter": 3700, "lr": 0.0007, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.52297, "top5_acc": 0.77156, "loss_cls": 2.65941, "loss": 2.65941, "time": 0.82804} +{"mode": "val", "epoch": 142, "iter": 309, "lr": 0.0007, "top1_acc": 0.39295, "top5_acc": 0.64175, "mean_class_accuracy": 0.39265} +{"mode": "train", "epoch": 143, "iter": 100, "lr": 0.0007, "memory": 15990, "data_time": 1.28713, "top1_acc": 0.53516, "top5_acc": 0.77812, "loss_cls": 2.59996, "loss": 2.59996, "time": 2.27878} +{"mode": "train", "epoch": 143, "iter": 200, "lr": 0.00069, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5425, "top5_acc": 0.78344, "loss_cls": 2.56575, "loss": 2.56575, "time": 0.82332} +{"mode": "train", "epoch": 143, "iter": 300, "lr": 0.00069, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54047, "top5_acc": 0.78688, "loss_cls": 2.54993, "loss": 2.54993, "time": 0.8184} +{"mode": "train", "epoch": 143, "iter": 400, "lr": 0.00068, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54875, "top5_acc": 0.78375, "loss_cls": 2.51625, "loss": 2.51625, "time": 0.82111} +{"mode": "train", "epoch": 143, "iter": 500, "lr": 0.00068, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.54766, "top5_acc": 0.77906, "loss_cls": 2.57988, "loss": 2.57988, "time": 0.82465} +{"mode": "train", "epoch": 143, "iter": 600, "lr": 0.00067, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53812, "top5_acc": 0.78312, "loss_cls": 2.58532, "loss": 2.58532, "time": 0.82235} +{"mode": "train", "epoch": 143, "iter": 700, "lr": 0.00067, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53625, "top5_acc": 0.78344, "loss_cls": 2.57778, "loss": 2.57778, "time": 0.8203} +{"mode": "train", "epoch": 143, "iter": 800, "lr": 0.00066, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53203, "top5_acc": 0.77734, "loss_cls": 2.61216, "loss": 2.61216, "time": 0.8226} +{"mode": "train", "epoch": 143, "iter": 900, "lr": 0.00066, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54547, "top5_acc": 0.7875, "loss_cls": 2.55914, "loss": 2.55914, "time": 0.82538} +{"mode": "train", "epoch": 143, "iter": 1000, "lr": 0.00065, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.53531, "top5_acc": 0.77781, "loss_cls": 2.60153, "loss": 2.60153, "time": 0.82569} +{"mode": "train", "epoch": 143, "iter": 1100, "lr": 0.00065, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53188, "top5_acc": 0.78312, "loss_cls": 2.57063, "loss": 2.57063, "time": 0.82661} +{"mode": "train", "epoch": 143, "iter": 1200, "lr": 0.00065, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54531, "top5_acc": 0.78344, "loss_cls": 2.56626, "loss": 2.56626, "time": 0.81752} +{"mode": "train", "epoch": 143, "iter": 1300, "lr": 0.00064, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53609, "top5_acc": 0.78078, "loss_cls": 2.60349, "loss": 2.60349, "time": 0.8216} +{"mode": "train", "epoch": 143, "iter": 1400, "lr": 0.00064, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54141, "top5_acc": 0.77422, "loss_cls": 2.60275, "loss": 2.60275, "time": 0.82063} +{"mode": "train", "epoch": 143, "iter": 1500, "lr": 0.00063, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53969, "top5_acc": 0.78203, "loss_cls": 2.58061, "loss": 2.58061, "time": 0.82613} +{"mode": "train", "epoch": 143, "iter": 1600, "lr": 0.00063, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54125, "top5_acc": 0.78297, "loss_cls": 2.57758, "loss": 2.57758, "time": 0.82362} +{"mode": "train", "epoch": 143, "iter": 1700, "lr": 0.00062, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53781, "top5_acc": 0.77047, "loss_cls": 2.62164, "loss": 2.62164, "time": 0.82119} +{"mode": "train", "epoch": 143, "iter": 1800, "lr": 0.00062, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53672, "top5_acc": 0.77438, "loss_cls": 2.58734, "loss": 2.58734, "time": 0.82336} +{"mode": "train", "epoch": 143, "iter": 1900, "lr": 0.00061, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53781, "top5_acc": 0.78156, "loss_cls": 2.60456, "loss": 2.60456, "time": 0.82686} +{"mode": "train", "epoch": 143, "iter": 2000, "lr": 0.00061, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54094, "top5_acc": 0.77, "loss_cls": 2.58175, "loss": 2.58175, "time": 0.82497} +{"mode": "train", "epoch": 143, "iter": 2100, "lr": 0.00061, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54438, "top5_acc": 0.78141, "loss_cls": 2.56098, "loss": 2.56098, "time": 0.82802} +{"mode": "train", "epoch": 143, "iter": 2200, "lr": 0.0006, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53359, "top5_acc": 0.77688, "loss_cls": 2.59874, "loss": 2.59874, "time": 0.8247} +{"mode": "train", "epoch": 143, "iter": 2300, "lr": 0.0006, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.53891, "top5_acc": 0.77922, "loss_cls": 2.59003, "loss": 2.59003, "time": 0.8208} +{"mode": "train", "epoch": 143, "iter": 2400, "lr": 0.00059, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.53641, "top5_acc": 0.77594, "loss_cls": 2.5829, "loss": 2.5829, "time": 0.82217} +{"mode": "train", "epoch": 143, "iter": 2500, "lr": 0.00059, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53656, "top5_acc": 0.77672, "loss_cls": 2.5862, "loss": 2.5862, "time": 0.82099} +{"mode": "train", "epoch": 143, "iter": 2600, "lr": 0.00058, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53062, "top5_acc": 0.77219, "loss_cls": 2.63288, "loss": 2.63288, "time": 0.8264} +{"mode": "train", "epoch": 143, "iter": 2700, "lr": 0.00058, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53422, "top5_acc": 0.78078, "loss_cls": 2.56838, "loss": 2.56838, "time": 0.82007} +{"mode": "train", "epoch": 143, "iter": 2800, "lr": 0.00058, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.52875, "top5_acc": 0.76703, "loss_cls": 2.66313, "loss": 2.66313, "time": 0.82214} +{"mode": "train", "epoch": 143, "iter": 2900, "lr": 0.00057, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.52359, "top5_acc": 0.77047, "loss_cls": 2.64015, "loss": 2.64015, "time": 0.82076} +{"mode": "train", "epoch": 143, "iter": 3000, "lr": 0.00057, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54672, "top5_acc": 0.77984, "loss_cls": 2.54857, "loss": 2.54857, "time": 0.82114} +{"mode": "train", "epoch": 143, "iter": 3100, "lr": 0.00056, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53016, "top5_acc": 0.77938, "loss_cls": 2.60961, "loss": 2.60961, "time": 0.81951} +{"mode": "train", "epoch": 143, "iter": 3200, "lr": 0.00056, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53484, "top5_acc": 0.78031, "loss_cls": 2.58916, "loss": 2.58916, "time": 0.82207} +{"mode": "train", "epoch": 143, "iter": 3300, "lr": 0.00055, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.53266, "top5_acc": 0.77859, "loss_cls": 2.60699, "loss": 2.60699, "time": 0.82012} +{"mode": "train", "epoch": 143, "iter": 3400, "lr": 0.00055, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53156, "top5_acc": 0.77203, "loss_cls": 2.61489, "loss": 2.61489, "time": 0.82424} +{"mode": "train", "epoch": 143, "iter": 3500, "lr": 0.00055, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54516, "top5_acc": 0.77859, "loss_cls": 2.57102, "loss": 2.57102, "time": 0.81752} +{"mode": "train", "epoch": 143, "iter": 3600, "lr": 0.00054, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53547, "top5_acc": 0.77656, "loss_cls": 2.61118, "loss": 2.61118, "time": 0.82381} +{"mode": "train", "epoch": 143, "iter": 3700, "lr": 0.00054, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53797, "top5_acc": 0.77328, "loss_cls": 2.59396, "loss": 2.59396, "time": 0.82034} +{"mode": "val", "epoch": 143, "iter": 309, "lr": 0.00054, "top1_acc": 0.39548, "top5_acc": 0.64165, "mean_class_accuracy": 0.39523} +{"mode": "train", "epoch": 144, "iter": 100, "lr": 0.00053, "memory": 15990, "data_time": 1.29694, "top1_acc": 0.55562, "top5_acc": 0.79469, "loss_cls": 2.49015, "loss": 2.49015, "time": 2.29819} +{"mode": "train", "epoch": 144, "iter": 200, "lr": 0.00053, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55266, "top5_acc": 0.78766, "loss_cls": 2.52519, "loss": 2.52519, "time": 0.82048} +{"mode": "train", "epoch": 144, "iter": 300, "lr": 0.00052, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54984, "top5_acc": 0.79078, "loss_cls": 2.51318, "loss": 2.51318, "time": 0.82108} +{"mode": "train", "epoch": 144, "iter": 400, "lr": 0.00052, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54656, "top5_acc": 0.78125, "loss_cls": 2.53757, "loss": 2.53757, "time": 0.82192} +{"mode": "train", "epoch": 144, "iter": 500, "lr": 0.00052, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.55562, "top5_acc": 0.80078, "loss_cls": 2.49419, "loss": 2.49419, "time": 0.82078} +{"mode": "train", "epoch": 144, "iter": 600, "lr": 0.00051, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54703, "top5_acc": 0.78516, "loss_cls": 2.5468, "loss": 2.5468, "time": 0.8212} +{"mode": "train", "epoch": 144, "iter": 700, "lr": 0.00051, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.55906, "top5_acc": 0.79172, "loss_cls": 2.50136, "loss": 2.50136, "time": 0.82517} +{"mode": "train", "epoch": 144, "iter": 800, "lr": 0.0005, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55734, "top5_acc": 0.78969, "loss_cls": 2.49911, "loss": 2.49911, "time": 0.81929} +{"mode": "train", "epoch": 144, "iter": 900, "lr": 0.0005, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.56234, "top5_acc": 0.80047, "loss_cls": 2.44687, "loss": 2.44687, "time": 0.82138} +{"mode": "train", "epoch": 144, "iter": 1000, "lr": 0.0005, "memory": 15990, "data_time": 0.00034, "top1_acc": 0.54172, "top5_acc": 0.78781, "loss_cls": 2.55537, "loss": 2.55537, "time": 0.82231} +{"mode": "train", "epoch": 144, "iter": 1100, "lr": 0.00049, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.54781, "top5_acc": 0.78781, "loss_cls": 2.52526, "loss": 2.52526, "time": 0.82747} +{"mode": "train", "epoch": 144, "iter": 1200, "lr": 0.00049, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.54734, "top5_acc": 0.79281, "loss_cls": 2.52505, "loss": 2.52505, "time": 0.82588} +{"mode": "train", "epoch": 144, "iter": 1300, "lr": 0.00048, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54203, "top5_acc": 0.78281, "loss_cls": 2.58963, "loss": 2.58963, "time": 0.82488} +{"mode": "train", "epoch": 144, "iter": 1400, "lr": 0.00048, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54594, "top5_acc": 0.7825, "loss_cls": 2.56061, "loss": 2.56061, "time": 0.82217} +{"mode": "train", "epoch": 144, "iter": 1500, "lr": 0.00048, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54203, "top5_acc": 0.78031, "loss_cls": 2.55973, "loss": 2.55973, "time": 0.82291} +{"mode": "train", "epoch": 144, "iter": 1600, "lr": 0.00047, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54219, "top5_acc": 0.78406, "loss_cls": 2.54544, "loss": 2.54544, "time": 0.81663} +{"mode": "train", "epoch": 144, "iter": 1700, "lr": 0.00047, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.56203, "top5_acc": 0.79672, "loss_cls": 2.48474, "loss": 2.48474, "time": 0.82074} +{"mode": "train", "epoch": 144, "iter": 1800, "lr": 0.00047, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54547, "top5_acc": 0.78766, "loss_cls": 2.55299, "loss": 2.55299, "time": 0.82425} +{"mode": "train", "epoch": 144, "iter": 1900, "lr": 0.00046, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53984, "top5_acc": 0.77875, "loss_cls": 2.57721, "loss": 2.57721, "time": 0.81974} +{"mode": "train", "epoch": 144, "iter": 2000, "lr": 0.00046, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.54109, "top5_acc": 0.78594, "loss_cls": 2.53576, "loss": 2.53576, "time": 0.82818} +{"mode": "train", "epoch": 144, "iter": 2100, "lr": 0.00045, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.53859, "top5_acc": 0.78422, "loss_cls": 2.55169, "loss": 2.55169, "time": 0.82189} +{"mode": "train", "epoch": 144, "iter": 2200, "lr": 0.00045, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55562, "top5_acc": 0.79172, "loss_cls": 2.51517, "loss": 2.51517, "time": 0.82058} +{"mode": "train", "epoch": 144, "iter": 2300, "lr": 0.00045, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.54344, "top5_acc": 0.77703, "loss_cls": 2.59264, "loss": 2.59264, "time": 0.82104} +{"mode": "train", "epoch": 144, "iter": 2400, "lr": 0.00044, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55188, "top5_acc": 0.7925, "loss_cls": 2.51648, "loss": 2.51648, "time": 0.81663} +{"mode": "train", "epoch": 144, "iter": 2500, "lr": 0.00044, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55219, "top5_acc": 0.79, "loss_cls": 2.52735, "loss": 2.52735, "time": 0.81947} +{"mode": "train", "epoch": 144, "iter": 2600, "lr": 0.00044, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54719, "top5_acc": 0.78625, "loss_cls": 2.51809, "loss": 2.51809, "time": 0.81599} +{"mode": "train", "epoch": 144, "iter": 2700, "lr": 0.00043, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.55031, "top5_acc": 0.78188, "loss_cls": 2.54279, "loss": 2.54279, "time": 0.81709} +{"mode": "train", "epoch": 144, "iter": 2800, "lr": 0.00043, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54109, "top5_acc": 0.77984, "loss_cls": 2.5648, "loss": 2.5648, "time": 0.81405} +{"mode": "train", "epoch": 144, "iter": 2900, "lr": 0.00042, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.53906, "top5_acc": 0.78453, "loss_cls": 2.56141, "loss": 2.56141, "time": 0.81605} +{"mode": "train", "epoch": 144, "iter": 3000, "lr": 0.00042, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54266, "top5_acc": 0.78578, "loss_cls": 2.53975, "loss": 2.53975, "time": 0.82032} +{"mode": "train", "epoch": 144, "iter": 3100, "lr": 0.00042, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54875, "top5_acc": 0.78797, "loss_cls": 2.53475, "loss": 2.53475, "time": 0.82049} +{"mode": "train", "epoch": 144, "iter": 3200, "lr": 0.00041, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54625, "top5_acc": 0.78562, "loss_cls": 2.5424, "loss": 2.5424, "time": 0.81854} +{"mode": "train", "epoch": 144, "iter": 3300, "lr": 0.00041, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.54016, "top5_acc": 0.77625, "loss_cls": 2.5819, "loss": 2.5819, "time": 0.81529} +{"mode": "train", "epoch": 144, "iter": 3400, "lr": 0.00041, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.53453, "top5_acc": 0.77438, "loss_cls": 2.60113, "loss": 2.60113, "time": 0.81738} +{"mode": "train", "epoch": 144, "iter": 3500, "lr": 0.0004, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.54, "top5_acc": 0.78859, "loss_cls": 2.52984, "loss": 2.52984, "time": 0.82175} +{"mode": "train", "epoch": 144, "iter": 3600, "lr": 0.0004, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54531, "top5_acc": 0.7875, "loss_cls": 2.55295, "loss": 2.55295, "time": 0.81915} +{"mode": "train", "epoch": 144, "iter": 3700, "lr": 0.0004, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.54984, "top5_acc": 0.78547, "loss_cls": 2.54223, "loss": 2.54223, "time": 0.82138} +{"mode": "val", "epoch": 144, "iter": 309, "lr": 0.00039, "top1_acc": 0.39781, "top5_acc": 0.64534, "mean_class_accuracy": 0.39756} +{"mode": "train", "epoch": 145, "iter": 100, "lr": 0.00039, "memory": 15990, "data_time": 1.31603, "top1_acc": 0.56766, "top5_acc": 0.79641, "loss_cls": 2.45542, "loss": 2.45542, "time": 2.31275} +{"mode": "train", "epoch": 145, "iter": 200, "lr": 0.00039, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.55562, "top5_acc": 0.7875, "loss_cls": 2.48494, "loss": 2.48494, "time": 0.84243} +{"mode": "train", "epoch": 145, "iter": 300, "lr": 0.00038, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.54828, "top5_acc": 0.78703, "loss_cls": 2.55645, "loss": 2.55645, "time": 0.83143} +{"mode": "train", "epoch": 145, "iter": 400, "lr": 0.00038, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56328, "top5_acc": 0.80656, "loss_cls": 2.44138, "loss": 2.44138, "time": 0.83875} +{"mode": "train", "epoch": 145, "iter": 500, "lr": 0.00038, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.56578, "top5_acc": 0.79234, "loss_cls": 2.45988, "loss": 2.45988, "time": 0.84118} +{"mode": "train", "epoch": 145, "iter": 600, "lr": 0.00037, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56344, "top5_acc": 0.79859, "loss_cls": 2.42063, "loss": 2.42063, "time": 0.82787} +{"mode": "train", "epoch": 145, "iter": 700, "lr": 0.00037, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55, "top5_acc": 0.78906, "loss_cls": 2.52736, "loss": 2.52736, "time": 0.83362} +{"mode": "train", "epoch": 145, "iter": 800, "lr": 0.00037, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55188, "top5_acc": 0.79219, "loss_cls": 2.51078, "loss": 2.51078, "time": 0.83227} +{"mode": "train", "epoch": 145, "iter": 900, "lr": 0.00036, "memory": 15990, "data_time": 0.0005, "top1_acc": 0.54703, "top5_acc": 0.79141, "loss_cls": 2.51292, "loss": 2.51292, "time": 0.83889} +{"mode": "train", "epoch": 145, "iter": 1000, "lr": 0.00036, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.56156, "top5_acc": 0.79516, "loss_cls": 2.46684, "loss": 2.46684, "time": 0.83612} +{"mode": "train", "epoch": 145, "iter": 1100, "lr": 0.00036, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57, "top5_acc": 0.80031, "loss_cls": 2.43247, "loss": 2.43247, "time": 0.82745} +{"mode": "train", "epoch": 145, "iter": 1200, "lr": 0.00035, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55797, "top5_acc": 0.78984, "loss_cls": 2.47906, "loss": 2.47906, "time": 0.83094} +{"mode": "train", "epoch": 145, "iter": 1300, "lr": 0.00035, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56078, "top5_acc": 0.79625, "loss_cls": 2.46183, "loss": 2.46183, "time": 0.84068} +{"mode": "train", "epoch": 145, "iter": 1400, "lr": 0.00035, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.56422, "top5_acc": 0.79828, "loss_cls": 2.45945, "loss": 2.45945, "time": 0.83515} +{"mode": "train", "epoch": 145, "iter": 1500, "lr": 0.00034, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56844, "top5_acc": 0.79938, "loss_cls": 2.44584, "loss": 2.44584, "time": 0.8243} +{"mode": "train", "epoch": 145, "iter": 1600, "lr": 0.00034, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56266, "top5_acc": 0.80141, "loss_cls": 2.4635, "loss": 2.4635, "time": 0.83007} +{"mode": "train", "epoch": 145, "iter": 1700, "lr": 0.00034, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56625, "top5_acc": 0.79219, "loss_cls": 2.47224, "loss": 2.47224, "time": 0.83059} +{"mode": "train", "epoch": 145, "iter": 1800, "lr": 0.00033, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.55625, "top5_acc": 0.79422, "loss_cls": 2.50178, "loss": 2.50178, "time": 0.82456} +{"mode": "train", "epoch": 145, "iter": 1900, "lr": 0.00033, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55078, "top5_acc": 0.79578, "loss_cls": 2.51629, "loss": 2.51629, "time": 0.82437} +{"mode": "train", "epoch": 145, "iter": 2000, "lr": 0.00033, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.555, "top5_acc": 0.79406, "loss_cls": 2.49252, "loss": 2.49252, "time": 0.8246} +{"mode": "train", "epoch": 145, "iter": 2100, "lr": 0.00032, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54891, "top5_acc": 0.78453, "loss_cls": 2.52253, "loss": 2.52253, "time": 0.8314} +{"mode": "train", "epoch": 145, "iter": 2200, "lr": 0.00032, "memory": 15990, "data_time": 0.00043, "top1_acc": 0.55359, "top5_acc": 0.795, "loss_cls": 2.50532, "loss": 2.50532, "time": 0.82714} +{"mode": "train", "epoch": 145, "iter": 2300, "lr": 0.00032, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.5675, "top5_acc": 0.79969, "loss_cls": 2.43949, "loss": 2.43949, "time": 0.83183} +{"mode": "train", "epoch": 145, "iter": 2400, "lr": 0.00031, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54625, "top5_acc": 0.77875, "loss_cls": 2.55075, "loss": 2.55075, "time": 0.83871} +{"mode": "train", "epoch": 145, "iter": 2500, "lr": 0.00031, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.54484, "top5_acc": 0.79219, "loss_cls": 2.50352, "loss": 2.50352, "time": 0.83512} +{"mode": "train", "epoch": 145, "iter": 2600, "lr": 0.00031, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55188, "top5_acc": 0.78844, "loss_cls": 2.51257, "loss": 2.51257, "time": 0.83163} +{"mode": "train", "epoch": 145, "iter": 2700, "lr": 0.00031, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.55781, "top5_acc": 0.79891, "loss_cls": 2.49149, "loss": 2.49149, "time": 0.82526} +{"mode": "train", "epoch": 145, "iter": 2800, "lr": 0.0003, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55344, "top5_acc": 0.7875, "loss_cls": 2.50543, "loss": 2.50543, "time": 0.8206} +{"mode": "train", "epoch": 145, "iter": 2900, "lr": 0.0003, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.55281, "top5_acc": 0.79297, "loss_cls": 2.5029, "loss": 2.5029, "time": 0.83017} +{"mode": "train", "epoch": 145, "iter": 3000, "lr": 0.0003, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.55141, "top5_acc": 0.78375, "loss_cls": 2.52795, "loss": 2.52795, "time": 0.82471} +{"mode": "train", "epoch": 145, "iter": 3100, "lr": 0.00029, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.55828, "top5_acc": 0.79875, "loss_cls": 2.46432, "loss": 2.46432, "time": 0.82678} +{"mode": "train", "epoch": 145, "iter": 3200, "lr": 0.00029, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.56141, "top5_acc": 0.78734, "loss_cls": 2.49358, "loss": 2.49358, "time": 0.82684} +{"mode": "train", "epoch": 145, "iter": 3300, "lr": 0.00029, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55453, "top5_acc": 0.78469, "loss_cls": 2.51917, "loss": 2.51917, "time": 0.82111} +{"mode": "train", "epoch": 145, "iter": 3400, "lr": 0.00028, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.56203, "top5_acc": 0.79516, "loss_cls": 2.48541, "loss": 2.48541, "time": 0.82592} +{"mode": "train", "epoch": 145, "iter": 3500, "lr": 0.00028, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.54609, "top5_acc": 0.78578, "loss_cls": 2.53695, "loss": 2.53695, "time": 0.82041} +{"mode": "train", "epoch": 145, "iter": 3600, "lr": 0.00028, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55812, "top5_acc": 0.78172, "loss_cls": 2.54577, "loss": 2.54577, "time": 0.82413} +{"mode": "train", "epoch": 145, "iter": 3700, "lr": 0.00028, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55266, "top5_acc": 0.79094, "loss_cls": 2.53447, "loss": 2.53447, "time": 0.81802} +{"mode": "val", "epoch": 145, "iter": 309, "lr": 0.00027, "top1_acc": 0.39548, "top5_acc": 0.64458, "mean_class_accuracy": 0.39529} +{"mode": "train", "epoch": 146, "iter": 100, "lr": 0.00027, "memory": 15990, "data_time": 1.28308, "top1_acc": 0.56703, "top5_acc": 0.81109, "loss_cls": 2.39196, "loss": 2.39196, "time": 2.27927} +{"mode": "train", "epoch": 146, "iter": 200, "lr": 0.00027, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.5625, "top5_acc": 0.79625, "loss_cls": 2.46997, "loss": 2.46997, "time": 0.82603} +{"mode": "train", "epoch": 146, "iter": 300, "lr": 0.00027, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5675, "top5_acc": 0.80516, "loss_cls": 2.4399, "loss": 2.4399, "time": 0.82699} +{"mode": "train", "epoch": 146, "iter": 400, "lr": 0.00026, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.56938, "top5_acc": 0.79734, "loss_cls": 2.43592, "loss": 2.43592, "time": 0.84044} +{"mode": "train", "epoch": 146, "iter": 500, "lr": 0.00026, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.56203, "top5_acc": 0.80266, "loss_cls": 2.42878, "loss": 2.42878, "time": 0.83587} +{"mode": "train", "epoch": 146, "iter": 600, "lr": 0.00026, "memory": 15990, "data_time": 0.00045, "top1_acc": 0.56625, "top5_acc": 0.79922, "loss_cls": 2.43579, "loss": 2.43579, "time": 0.82567} +{"mode": "train", "epoch": 146, "iter": 700, "lr": 0.00025, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56719, "top5_acc": 0.80688, "loss_cls": 2.43421, "loss": 2.43421, "time": 0.83955} +{"mode": "train", "epoch": 146, "iter": 800, "lr": 0.00025, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57109, "top5_acc": 0.79672, "loss_cls": 2.44004, "loss": 2.44004, "time": 0.83488} +{"mode": "train", "epoch": 146, "iter": 900, "lr": 0.00025, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.56453, "top5_acc": 0.7975, "loss_cls": 2.4486, "loss": 2.4486, "time": 0.84259} +{"mode": "train", "epoch": 146, "iter": 1000, "lr": 0.00025, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56375, "top5_acc": 0.79406, "loss_cls": 2.4678, "loss": 2.4678, "time": 0.82869} +{"mode": "train", "epoch": 146, "iter": 1100, "lr": 0.00024, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55734, "top5_acc": 0.80031, "loss_cls": 2.43566, "loss": 2.43566, "time": 0.83119} +{"mode": "train", "epoch": 146, "iter": 1200, "lr": 0.00024, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55703, "top5_acc": 0.79297, "loss_cls": 2.48329, "loss": 2.48329, "time": 0.83788} +{"mode": "train", "epoch": 146, "iter": 1300, "lr": 0.00024, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57, "top5_acc": 0.80172, "loss_cls": 2.42023, "loss": 2.42023, "time": 0.8393} +{"mode": "train", "epoch": 146, "iter": 1400, "lr": 0.00023, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55672, "top5_acc": 0.80078, "loss_cls": 2.46546, "loss": 2.46546, "time": 0.83823} +{"mode": "train", "epoch": 146, "iter": 1500, "lr": 0.00023, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56563, "top5_acc": 0.80312, "loss_cls": 2.4426, "loss": 2.4426, "time": 0.82873} +{"mode": "train", "epoch": 146, "iter": 1600, "lr": 0.00023, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57281, "top5_acc": 0.79562, "loss_cls": 2.44873, "loss": 2.44873, "time": 0.83144} +{"mode": "train", "epoch": 146, "iter": 1700, "lr": 0.00023, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56422, "top5_acc": 0.80312, "loss_cls": 2.43139, "loss": 2.43139, "time": 0.82666} +{"mode": "train", "epoch": 146, "iter": 1800, "lr": 0.00022, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56437, "top5_acc": 0.79906, "loss_cls": 2.45604, "loss": 2.45604, "time": 0.82171} +{"mode": "train", "epoch": 146, "iter": 1900, "lr": 0.00022, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56812, "top5_acc": 0.80312, "loss_cls": 2.43331, "loss": 2.43331, "time": 0.82013} +{"mode": "train", "epoch": 146, "iter": 2000, "lr": 0.00022, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57312, "top5_acc": 0.79578, "loss_cls": 2.42836, "loss": 2.42836, "time": 0.83362} +{"mode": "train", "epoch": 146, "iter": 2100, "lr": 0.00022, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.55625, "top5_acc": 0.79234, "loss_cls": 2.46693, "loss": 2.46693, "time": 0.83161} +{"mode": "train", "epoch": 146, "iter": 2200, "lr": 0.00021, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56828, "top5_acc": 0.80234, "loss_cls": 2.41828, "loss": 2.41828, "time": 0.82749} +{"mode": "train", "epoch": 146, "iter": 2300, "lr": 0.00021, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56188, "top5_acc": 0.79984, "loss_cls": 2.45067, "loss": 2.45067, "time": 0.83122} +{"mode": "train", "epoch": 146, "iter": 2400, "lr": 0.00021, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56797, "top5_acc": 0.79984, "loss_cls": 2.4314, "loss": 2.4314, "time": 0.83742} +{"mode": "train", "epoch": 146, "iter": 2500, "lr": 0.00021, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.57219, "top5_acc": 0.81031, "loss_cls": 2.40253, "loss": 2.40253, "time": 0.8316} +{"mode": "train", "epoch": 146, "iter": 2600, "lr": 0.0002, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56641, "top5_acc": 0.80109, "loss_cls": 2.44253, "loss": 2.44253, "time": 0.82398} +{"mode": "train", "epoch": 146, "iter": 2700, "lr": 0.0002, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56531, "top5_acc": 0.79969, "loss_cls": 2.46602, "loss": 2.46602, "time": 0.82457} +{"mode": "train", "epoch": 146, "iter": 2800, "lr": 0.0002, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5675, "top5_acc": 0.79812, "loss_cls": 2.46868, "loss": 2.46868, "time": 0.82282} +{"mode": "train", "epoch": 146, "iter": 2900, "lr": 0.0002, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55922, "top5_acc": 0.79312, "loss_cls": 2.48167, "loss": 2.48167, "time": 0.82535} +{"mode": "train", "epoch": 146, "iter": 3000, "lr": 0.00019, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56703, "top5_acc": 0.79797, "loss_cls": 2.4555, "loss": 2.4555, "time": 0.82256} +{"mode": "train", "epoch": 146, "iter": 3100, "lr": 0.00019, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56172, "top5_acc": 0.80125, "loss_cls": 2.45089, "loss": 2.45089, "time": 0.82206} +{"mode": "train", "epoch": 146, "iter": 3200, "lr": 0.00019, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56719, "top5_acc": 0.79141, "loss_cls": 2.45974, "loss": 2.45974, "time": 0.81993} +{"mode": "train", "epoch": 146, "iter": 3300, "lr": 0.00019, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5725, "top5_acc": 0.8025, "loss_cls": 2.42904, "loss": 2.42904, "time": 0.82086} +{"mode": "train", "epoch": 146, "iter": 3400, "lr": 0.00018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56141, "top5_acc": 0.79609, "loss_cls": 2.45933, "loss": 2.45933, "time": 0.81953} +{"mode": "train", "epoch": 146, "iter": 3500, "lr": 0.00018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56453, "top5_acc": 0.79922, "loss_cls": 2.42554, "loss": 2.42554, "time": 0.81364} +{"mode": "train", "epoch": 146, "iter": 3600, "lr": 0.00018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56625, "top5_acc": 0.79641, "loss_cls": 2.45636, "loss": 2.45636, "time": 0.81406} +{"mode": "train", "epoch": 146, "iter": 3700, "lr": 0.00018, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56281, "top5_acc": 0.79625, "loss_cls": 2.47606, "loss": 2.47606, "time": 0.81778} +{"mode": "val", "epoch": 146, "iter": 309, "lr": 0.00018, "top1_acc": 0.39928, "top5_acc": 0.64367, "mean_class_accuracy": 0.39907} +{"mode": "train", "epoch": 147, "iter": 100, "lr": 0.00017, "memory": 15990, "data_time": 1.24219, "top1_acc": 0.57391, "top5_acc": 0.81375, "loss_cls": 2.37324, "loss": 2.37324, "time": 2.2372} +{"mode": "train", "epoch": 147, "iter": 200, "lr": 0.00017, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57781, "top5_acc": 0.81016, "loss_cls": 2.38048, "loss": 2.38048, "time": 0.82248} +{"mode": "train", "epoch": 147, "iter": 300, "lr": 0.00017, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.56828, "top5_acc": 0.80391, "loss_cls": 2.41522, "loss": 2.41522, "time": 0.82765} +{"mode": "train", "epoch": 147, "iter": 400, "lr": 0.00017, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57766, "top5_acc": 0.80688, "loss_cls": 2.39464, "loss": 2.39464, "time": 0.83257} +{"mode": "train", "epoch": 147, "iter": 500, "lr": 0.00016, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57703, "top5_acc": 0.80859, "loss_cls": 2.38411, "loss": 2.38411, "time": 0.82856} +{"mode": "train", "epoch": 147, "iter": 600, "lr": 0.00016, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56359, "top5_acc": 0.79906, "loss_cls": 2.45958, "loss": 2.45958, "time": 0.82985} +{"mode": "train", "epoch": 147, "iter": 700, "lr": 0.00016, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57031, "top5_acc": 0.80078, "loss_cls": 2.43503, "loss": 2.43503, "time": 0.83088} +{"mode": "train", "epoch": 147, "iter": 800, "lr": 0.00016, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57812, "top5_acc": 0.81281, "loss_cls": 2.35836, "loss": 2.35836, "time": 0.83008} +{"mode": "train", "epoch": 147, "iter": 900, "lr": 0.00015, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56734, "top5_acc": 0.80359, "loss_cls": 2.42891, "loss": 2.42891, "time": 0.83293} +{"mode": "train", "epoch": 147, "iter": 1000, "lr": 0.00015, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57156, "top5_acc": 0.80906, "loss_cls": 2.40277, "loss": 2.40277, "time": 0.82146} +{"mode": "train", "epoch": 147, "iter": 1100, "lr": 0.00015, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56938, "top5_acc": 0.80469, "loss_cls": 2.38573, "loss": 2.38573, "time": 0.82645} +{"mode": "train", "epoch": 147, "iter": 1200, "lr": 0.00015, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57359, "top5_acc": 0.79703, "loss_cls": 2.41953, "loss": 2.41953, "time": 0.84059} +{"mode": "train", "epoch": 147, "iter": 1300, "lr": 0.00015, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58, "top5_acc": 0.80672, "loss_cls": 2.36196, "loss": 2.36196, "time": 0.83798} +{"mode": "train", "epoch": 147, "iter": 1400, "lr": 0.00014, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57047, "top5_acc": 0.80688, "loss_cls": 2.41341, "loss": 2.41341, "time": 0.83663} +{"mode": "train", "epoch": 147, "iter": 1500, "lr": 0.00014, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57438, "top5_acc": 0.80031, "loss_cls": 2.41713, "loss": 2.41713, "time": 0.83608} +{"mode": "train", "epoch": 147, "iter": 1600, "lr": 0.00014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55937, "top5_acc": 0.80422, "loss_cls": 2.43961, "loss": 2.43961, "time": 0.83455} +{"mode": "train", "epoch": 147, "iter": 1700, "lr": 0.00014, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57781, "top5_acc": 0.80406, "loss_cls": 2.40789, "loss": 2.40789, "time": 0.83805} +{"mode": "train", "epoch": 147, "iter": 1800, "lr": 0.00014, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.56437, "top5_acc": 0.80625, "loss_cls": 2.42533, "loss": 2.42533, "time": 0.83904} +{"mode": "train", "epoch": 147, "iter": 1900, "lr": 0.00013, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.55562, "top5_acc": 0.79453, "loss_cls": 2.47554, "loss": 2.47554, "time": 0.83766} +{"mode": "train", "epoch": 147, "iter": 2000, "lr": 0.00013, "memory": 15990, "data_time": 0.00058, "top1_acc": 0.57344, "top5_acc": 0.80719, "loss_cls": 2.39945, "loss": 2.39945, "time": 0.84314} +{"mode": "train", "epoch": 147, "iter": 2100, "lr": 0.00013, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56047, "top5_acc": 0.79953, "loss_cls": 2.44572, "loss": 2.44572, "time": 0.82694} +{"mode": "train", "epoch": 147, "iter": 2200, "lr": 0.00013, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57563, "top5_acc": 0.80375, "loss_cls": 2.40269, "loss": 2.40269, "time": 0.83107} +{"mode": "train", "epoch": 147, "iter": 2300, "lr": 0.00013, "memory": 15990, "data_time": 0.00033, "top1_acc": 0.57297, "top5_acc": 0.79719, "loss_cls": 2.39914, "loss": 2.39914, "time": 0.83705} +{"mode": "train", "epoch": 147, "iter": 2400, "lr": 0.00012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57609, "top5_acc": 0.80422, "loss_cls": 2.40215, "loss": 2.40215, "time": 0.83946} +{"mode": "train", "epoch": 147, "iter": 2500, "lr": 0.00012, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57172, "top5_acc": 0.80172, "loss_cls": 2.42492, "loss": 2.42492, "time": 0.83904} +{"mode": "train", "epoch": 147, "iter": 2600, "lr": 0.00012, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.57078, "top5_acc": 0.80594, "loss_cls": 2.42276, "loss": 2.42276, "time": 0.8382} +{"mode": "train", "epoch": 147, "iter": 2700, "lr": 0.00012, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.55891, "top5_acc": 0.80438, "loss_cls": 2.45689, "loss": 2.45689, "time": 0.83353} +{"mode": "train", "epoch": 147, "iter": 2800, "lr": 0.00012, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58031, "top5_acc": 0.81047, "loss_cls": 2.37417, "loss": 2.37417, "time": 0.83261} +{"mode": "train", "epoch": 147, "iter": 2900, "lr": 0.00011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57453, "top5_acc": 0.80406, "loss_cls": 2.40295, "loss": 2.40295, "time": 0.8399} +{"mode": "train", "epoch": 147, "iter": 3000, "lr": 0.00011, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57906, "top5_acc": 0.80875, "loss_cls": 2.38743, "loss": 2.38743, "time": 0.83593} +{"mode": "train", "epoch": 147, "iter": 3100, "lr": 0.00011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58094, "top5_acc": 0.80609, "loss_cls": 2.38013, "loss": 2.38013, "time": 0.83723} +{"mode": "train", "epoch": 147, "iter": 3200, "lr": 0.00011, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.57344, "top5_acc": 0.80859, "loss_cls": 2.40471, "loss": 2.40471, "time": 0.83467} +{"mode": "train", "epoch": 147, "iter": 3300, "lr": 0.00011, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57391, "top5_acc": 0.80672, "loss_cls": 2.39818, "loss": 2.39818, "time": 0.83985} +{"mode": "train", "epoch": 147, "iter": 3400, "lr": 0.0001, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57, "top5_acc": 0.80766, "loss_cls": 2.3981, "loss": 2.3981, "time": 0.83972} +{"mode": "train", "epoch": 147, "iter": 3500, "lr": 0.0001, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57563, "top5_acc": 0.80484, "loss_cls": 2.39069, "loss": 2.39069, "time": 0.84031} +{"mode": "train", "epoch": 147, "iter": 3600, "lr": 0.0001, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56812, "top5_acc": 0.80734, "loss_cls": 2.4445, "loss": 2.4445, "time": 0.84003} +{"mode": "train", "epoch": 147, "iter": 3700, "lr": 0.0001, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.57109, "top5_acc": 0.8025, "loss_cls": 2.41656, "loss": 2.41656, "time": 0.84033} +{"mode": "val", "epoch": 147, "iter": 309, "lr": 0.0001, "top1_acc": 0.39953, "top5_acc": 0.64544, "mean_class_accuracy": 0.39926} +{"mode": "train", "epoch": 148, "iter": 100, "lr": 0.0001, "memory": 15990, "data_time": 1.27468, "top1_acc": 0.58672, "top5_acc": 0.81375, "loss_cls": 2.33791, "loss": 2.33791, "time": 2.28018} +{"mode": "train", "epoch": 148, "iter": 200, "lr": 0.0001, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58094, "top5_acc": 0.81062, "loss_cls": 2.36869, "loss": 2.36869, "time": 0.83797} +{"mode": "train", "epoch": 148, "iter": 300, "lr": 9e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.56828, "top5_acc": 0.80297, "loss_cls": 2.41502, "loss": 2.41502, "time": 0.84453} +{"mode": "train", "epoch": 148, "iter": 400, "lr": 9e-05, "memory": 15990, "data_time": 0.00037, "top1_acc": 0.57688, "top5_acc": 0.80938, "loss_cls": 2.37924, "loss": 2.37924, "time": 0.83785} +{"mode": "train", "epoch": 148, "iter": 500, "lr": 9e-05, "memory": 15990, "data_time": 0.00038, "top1_acc": 0.57953, "top5_acc": 0.81562, "loss_cls": 2.36571, "loss": 2.36571, "time": 0.82674} +{"mode": "train", "epoch": 148, "iter": 600, "lr": 9e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57266, "top5_acc": 0.80375, "loss_cls": 2.41129, "loss": 2.41129, "time": 0.83598} +{"mode": "train", "epoch": 148, "iter": 700, "lr": 9e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58094, "top5_acc": 0.81344, "loss_cls": 2.37515, "loss": 2.37515, "time": 0.83921} +{"mode": "train", "epoch": 148, "iter": 800, "lr": 9e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.58531, "top5_acc": 0.80922, "loss_cls": 2.36481, "loss": 2.36481, "time": 0.83366} +{"mode": "train", "epoch": 148, "iter": 900, "lr": 8e-05, "memory": 15990, "data_time": 0.00066, "top1_acc": 0.58062, "top5_acc": 0.81016, "loss_cls": 2.34278, "loss": 2.34278, "time": 0.82509} +{"mode": "train", "epoch": 148, "iter": 1000, "lr": 8e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.57703, "top5_acc": 0.80531, "loss_cls": 2.3952, "loss": 2.3952, "time": 0.83689} +{"mode": "train", "epoch": 148, "iter": 1100, "lr": 8e-05, "memory": 15990, "data_time": 0.00036, "top1_acc": 0.57297, "top5_acc": 0.80766, "loss_cls": 2.40078, "loss": 2.40078, "time": 0.8329} +{"mode": "train", "epoch": 148, "iter": 1200, "lr": 8e-05, "memory": 15990, "data_time": 0.00047, "top1_acc": 0.57141, "top5_acc": 0.80891, "loss_cls": 2.40104, "loss": 2.40104, "time": 0.83548} +{"mode": "train", "epoch": 148, "iter": 1300, "lr": 8e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57625, "top5_acc": 0.80328, "loss_cls": 2.41563, "loss": 2.41563, "time": 0.83848} +{"mode": "train", "epoch": 148, "iter": 1400, "lr": 8e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.57312, "top5_acc": 0.80406, "loss_cls": 2.40876, "loss": 2.40876, "time": 0.84051} +{"mode": "train", "epoch": 148, "iter": 1500, "lr": 7e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57906, "top5_acc": 0.81109, "loss_cls": 2.37817, "loss": 2.37817, "time": 0.83838} +{"mode": "train", "epoch": 148, "iter": 1600, "lr": 7e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57563, "top5_acc": 0.80219, "loss_cls": 2.3944, "loss": 2.3944, "time": 0.83566} +{"mode": "train", "epoch": 148, "iter": 1700, "lr": 7e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58094, "top5_acc": 0.81078, "loss_cls": 2.38002, "loss": 2.38002, "time": 0.83998} +{"mode": "train", "epoch": 148, "iter": 1800, "lr": 7e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56531, "top5_acc": 0.80359, "loss_cls": 2.43602, "loss": 2.43602, "time": 0.83958} +{"mode": "train", "epoch": 148, "iter": 1900, "lr": 7e-05, "memory": 15990, "data_time": 0.00072, "top1_acc": 0.58328, "top5_acc": 0.81266, "loss_cls": 2.35009, "loss": 2.35009, "time": 0.83723} +{"mode": "train", "epoch": 148, "iter": 2000, "lr": 7e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.5775, "top5_acc": 0.80531, "loss_cls": 2.40695, "loss": 2.40695, "time": 0.83709} +{"mode": "train", "epoch": 148, "iter": 2100, "lr": 7e-05, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.57688, "top5_acc": 0.8075, "loss_cls": 2.38228, "loss": 2.38228, "time": 0.82071} +{"mode": "train", "epoch": 148, "iter": 2200, "lr": 6e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57828, "top5_acc": 0.80094, "loss_cls": 2.37789, "loss": 2.37789, "time": 0.83444} +{"mode": "train", "epoch": 148, "iter": 2300, "lr": 6e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58328, "top5_acc": 0.80859, "loss_cls": 2.39254, "loss": 2.39254, "time": 0.83308} +{"mode": "train", "epoch": 148, "iter": 2400, "lr": 6e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58844, "top5_acc": 0.82031, "loss_cls": 2.31344, "loss": 2.31344, "time": 0.83554} +{"mode": "train", "epoch": 148, "iter": 2500, "lr": 6e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.58609, "top5_acc": 0.81578, "loss_cls": 2.35469, "loss": 2.35469, "time": 0.83993} +{"mode": "train", "epoch": 148, "iter": 2600, "lr": 6e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57344, "top5_acc": 0.80234, "loss_cls": 2.42847, "loss": 2.42847, "time": 0.83454} +{"mode": "train", "epoch": 148, "iter": 2700, "lr": 6e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57766, "top5_acc": 0.81141, "loss_cls": 2.34971, "loss": 2.34971, "time": 0.83693} +{"mode": "train", "epoch": 148, "iter": 2800, "lr": 6e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57812, "top5_acc": 0.80688, "loss_cls": 2.38018, "loss": 2.38018, "time": 0.83472} +{"mode": "train", "epoch": 148, "iter": 2900, "lr": 5e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57125, "top5_acc": 0.80906, "loss_cls": 2.39257, "loss": 2.39257, "time": 0.8352} +{"mode": "train", "epoch": 148, "iter": 3000, "lr": 5e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58312, "top5_acc": 0.80625, "loss_cls": 2.37283, "loss": 2.37283, "time": 0.837} +{"mode": "train", "epoch": 148, "iter": 3100, "lr": 5e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.57516, "top5_acc": 0.80094, "loss_cls": 2.4046, "loss": 2.4046, "time": 0.83943} +{"mode": "train", "epoch": 148, "iter": 3200, "lr": 5e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.56563, "top5_acc": 0.80844, "loss_cls": 2.42452, "loss": 2.42452, "time": 0.83549} +{"mode": "train", "epoch": 148, "iter": 3300, "lr": 5e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56984, "top5_acc": 0.80125, "loss_cls": 2.41591, "loss": 2.41591, "time": 0.8347} +{"mode": "train", "epoch": 148, "iter": 3400, "lr": 5e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57656, "top5_acc": 0.81266, "loss_cls": 2.36678, "loss": 2.36678, "time": 0.83629} +{"mode": "train", "epoch": 148, "iter": 3500, "lr": 5e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58141, "top5_acc": 0.80188, "loss_cls": 2.3976, "loss": 2.3976, "time": 0.83869} +{"mode": "train", "epoch": 148, "iter": 3600, "lr": 5e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.57047, "top5_acc": 0.80328, "loss_cls": 2.39771, "loss": 2.39771, "time": 0.83969} +{"mode": "train", "epoch": 148, "iter": 3700, "lr": 4e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58375, "top5_acc": 0.81062, "loss_cls": 2.35125, "loss": 2.35125, "time": 0.83735} +{"mode": "val", "epoch": 148, "iter": 309, "lr": 4e-05, "top1_acc": 0.40024, "top5_acc": 0.6456, "mean_class_accuracy": 0.40002} +{"mode": "train", "epoch": 149, "iter": 100, "lr": 4e-05, "memory": 15990, "data_time": 1.33368, "top1_acc": 0.58531, "top5_acc": 0.8125, "loss_cls": 2.35546, "loss": 2.35546, "time": 2.33077} +{"mode": "train", "epoch": 149, "iter": 200, "lr": 4e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58234, "top5_acc": 0.80906, "loss_cls": 2.3952, "loss": 2.3952, "time": 0.84217} +{"mode": "train", "epoch": 149, "iter": 300, "lr": 4e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58562, "top5_acc": 0.81469, "loss_cls": 2.32168, "loss": 2.32168, "time": 0.83783} +{"mode": "train", "epoch": 149, "iter": 400, "lr": 4e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58016, "top5_acc": 0.81844, "loss_cls": 2.34161, "loss": 2.34161, "time": 0.83092} +{"mode": "train", "epoch": 149, "iter": 500, "lr": 4e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.575, "top5_acc": 0.80172, "loss_cls": 2.40662, "loss": 2.40662, "time": 0.83469} +{"mode": "train", "epoch": 149, "iter": 600, "lr": 4e-05, "memory": 15990, "data_time": 0.00049, "top1_acc": 0.58062, "top5_acc": 0.81469, "loss_cls": 2.35515, "loss": 2.35515, "time": 0.83668} +{"mode": "train", "epoch": 149, "iter": 700, "lr": 4e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57531, "top5_acc": 0.80734, "loss_cls": 2.38353, "loss": 2.38353, "time": 0.836} +{"mode": "train", "epoch": 149, "iter": 800, "lr": 4e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.57875, "top5_acc": 0.81578, "loss_cls": 2.34132, "loss": 2.34132, "time": 0.8348} +{"mode": "train", "epoch": 149, "iter": 900, "lr": 3e-05, "memory": 15990, "data_time": 0.00051, "top1_acc": 0.57344, "top5_acc": 0.80344, "loss_cls": 2.40507, "loss": 2.40507, "time": 0.83931} +{"mode": "train", "epoch": 149, "iter": 1000, "lr": 3e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58578, "top5_acc": 0.81625, "loss_cls": 2.35951, "loss": 2.35951, "time": 0.83935} +{"mode": "train", "epoch": 149, "iter": 1100, "lr": 3e-05, "memory": 15990, "data_time": 0.00044, "top1_acc": 0.5725, "top5_acc": 0.80391, "loss_cls": 2.40887, "loss": 2.40887, "time": 0.83315} +{"mode": "train", "epoch": 149, "iter": 1200, "lr": 3e-05, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.59141, "top5_acc": 0.80859, "loss_cls": 2.34462, "loss": 2.34462, "time": 0.83576} +{"mode": "train", "epoch": 149, "iter": 1300, "lr": 3e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58547, "top5_acc": 0.81609, "loss_cls": 2.34612, "loss": 2.34612, "time": 0.83662} +{"mode": "train", "epoch": 149, "iter": 1400, "lr": 3e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59297, "top5_acc": 0.81812, "loss_cls": 2.32207, "loss": 2.32207, "time": 0.83482} +{"mode": "train", "epoch": 149, "iter": 1500, "lr": 3e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59672, "top5_acc": 0.81203, "loss_cls": 2.30916, "loss": 2.30916, "time": 0.8367} +{"mode": "train", "epoch": 149, "iter": 1600, "lr": 3e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.5875, "top5_acc": 0.80844, "loss_cls": 2.36695, "loss": 2.36695, "time": 0.82847} +{"mode": "train", "epoch": 149, "iter": 1700, "lr": 3e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.58906, "top5_acc": 0.81719, "loss_cls": 2.3288, "loss": 2.3288, "time": 0.83457} +{"mode": "train", "epoch": 149, "iter": 1800, "lr": 3e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58203, "top5_acc": 0.80922, "loss_cls": 2.37016, "loss": 2.37016, "time": 0.83613} +{"mode": "train", "epoch": 149, "iter": 1900, "lr": 2e-05, "memory": 15990, "data_time": 0.00042, "top1_acc": 0.57453, "top5_acc": 0.80312, "loss_cls": 2.39169, "loss": 2.39169, "time": 0.83879} +{"mode": "train", "epoch": 149, "iter": 2000, "lr": 2e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57203, "top5_acc": 0.80203, "loss_cls": 2.4171, "loss": 2.4171, "time": 0.82344} +{"mode": "train", "epoch": 149, "iter": 2100, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57812, "top5_acc": 0.81328, "loss_cls": 2.3717, "loss": 2.3717, "time": 0.82993} +{"mode": "train", "epoch": 149, "iter": 2200, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57766, "top5_acc": 0.81266, "loss_cls": 2.37768, "loss": 2.37768, "time": 0.83664} +{"mode": "train", "epoch": 149, "iter": 2300, "lr": 2e-05, "memory": 15990, "data_time": 0.00031, "top1_acc": 0.57563, "top5_acc": 0.80547, "loss_cls": 2.37623, "loss": 2.37623, "time": 0.83959} +{"mode": "train", "epoch": 149, "iter": 2400, "lr": 2e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58266, "top5_acc": 0.81297, "loss_cls": 2.36111, "loss": 2.36111, "time": 0.83971} +{"mode": "train", "epoch": 149, "iter": 2500, "lr": 2e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.59031, "top5_acc": 0.81938, "loss_cls": 2.32009, "loss": 2.32009, "time": 0.83323} +{"mode": "train", "epoch": 149, "iter": 2600, "lr": 2e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57188, "top5_acc": 0.80609, "loss_cls": 2.40429, "loss": 2.40429, "time": 0.83766} +{"mode": "train", "epoch": 149, "iter": 2700, "lr": 2e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.565, "top5_acc": 0.8025, "loss_cls": 2.4343, "loss": 2.4343, "time": 0.83474} +{"mode": "train", "epoch": 149, "iter": 2800, "lr": 2e-05, "memory": 15990, "data_time": 0.00029, "top1_acc": 0.58219, "top5_acc": 0.80766, "loss_cls": 2.34994, "loss": 2.34994, "time": 0.83721} +{"mode": "train", "epoch": 149, "iter": 2900, "lr": 2e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.56875, "top5_acc": 0.80094, "loss_cls": 2.4165, "loss": 2.4165, "time": 0.83885} +{"mode": "train", "epoch": 149, "iter": 3000, "lr": 2e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58156, "top5_acc": 0.81156, "loss_cls": 2.35707, "loss": 2.35707, "time": 0.83641} +{"mode": "train", "epoch": 149, "iter": 3100, "lr": 2e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58047, "top5_acc": 0.81547, "loss_cls": 2.35343, "loss": 2.35343, "time": 0.83693} +{"mode": "train", "epoch": 149, "iter": 3200, "lr": 1e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58312, "top5_acc": 0.80469, "loss_cls": 2.37948, "loss": 2.37948, "time": 0.83389} +{"mode": "train", "epoch": 149, "iter": 3300, "lr": 1e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.58656, "top5_acc": 0.81219, "loss_cls": 2.33367, "loss": 2.33367, "time": 0.83785} +{"mode": "train", "epoch": 149, "iter": 3400, "lr": 1e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.58531, "top5_acc": 0.80812, "loss_cls": 2.34949, "loss": 2.34949, "time": 0.83447} +{"mode": "train", "epoch": 149, "iter": 3500, "lr": 1e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58969, "top5_acc": 0.81438, "loss_cls": 2.34575, "loss": 2.34575, "time": 0.83369} +{"mode": "train", "epoch": 149, "iter": 3600, "lr": 1e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58234, "top5_acc": 0.81172, "loss_cls": 2.35273, "loss": 2.35273, "time": 0.83396} +{"mode": "train", "epoch": 149, "iter": 3700, "lr": 1e-05, "memory": 15990, "data_time": 0.0003, "top1_acc": 0.58922, "top5_acc": 0.81875, "loss_cls": 2.34207, "loss": 2.34207, "time": 0.83767} +{"mode": "val", "epoch": 149, "iter": 309, "lr": 1e-05, "top1_acc": 0.40166, "top5_acc": 0.64625, "mean_class_accuracy": 0.4014} +{"mode": "train", "epoch": 150, "iter": 100, "lr": 1e-05, "memory": 15990, "data_time": 1.29275, "top1_acc": 0.5875, "top5_acc": 0.81844, "loss_cls": 2.33252, "loss": 2.33252, "time": 2.29786} +{"mode": "train", "epoch": 150, "iter": 200, "lr": 1e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.57344, "top5_acc": 0.80453, "loss_cls": 2.40018, "loss": 2.40018, "time": 0.83954} +{"mode": "train", "epoch": 150, "iter": 300, "lr": 1e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58625, "top5_acc": 0.81594, "loss_cls": 2.33368, "loss": 2.33368, "time": 0.83136} +{"mode": "train", "epoch": 150, "iter": 400, "lr": 1e-05, "memory": 15990, "data_time": 0.00028, "top1_acc": 0.57453, "top5_acc": 0.81453, "loss_cls": 2.37845, "loss": 2.37845, "time": 0.82769} +{"mode": "train", "epoch": 150, "iter": 500, "lr": 1e-05, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.58844, "top5_acc": 0.81406, "loss_cls": 2.3363, "loss": 2.3363, "time": 0.83682} +{"mode": "train", "epoch": 150, "iter": 600, "lr": 1e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.59609, "top5_acc": 0.81906, "loss_cls": 2.31294, "loss": 2.31294, "time": 0.82694} +{"mode": "train", "epoch": 150, "iter": 700, "lr": 1e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57266, "top5_acc": 0.81156, "loss_cls": 2.37745, "loss": 2.37745, "time": 0.83302} +{"mode": "train", "epoch": 150, "iter": 800, "lr": 1e-05, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59078, "top5_acc": 0.81812, "loss_cls": 2.32517, "loss": 2.32517, "time": 0.82498} +{"mode": "train", "epoch": 150, "iter": 900, "lr": 1e-05, "memory": 15990, "data_time": 0.00064, "top1_acc": 0.59516, "top5_acc": 0.82188, "loss_cls": 2.29686, "loss": 2.29686, "time": 0.82046} +{"mode": "train", "epoch": 150, "iter": 1000, "lr": 1e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56969, "top5_acc": 0.80781, "loss_cls": 2.39263, "loss": 2.39263, "time": 0.825} +{"mode": "train", "epoch": 150, "iter": 1100, "lr": 1e-05, "memory": 15990, "data_time": 0.00048, "top1_acc": 0.58484, "top5_acc": 0.805, "loss_cls": 2.3795, "loss": 2.3795, "time": 0.82627} +{"mode": "train", "epoch": 150, "iter": 1200, "lr": 1e-05, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58484, "top5_acc": 0.81094, "loss_cls": 2.36992, "loss": 2.36992, "time": 0.82767} +{"mode": "train", "epoch": 150, "iter": 1300, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.5725, "top5_acc": 0.80859, "loss_cls": 2.40259, "loss": 2.40259, "time": 0.82378} +{"mode": "train", "epoch": 150, "iter": 1400, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58188, "top5_acc": 0.81328, "loss_cls": 2.34331, "loss": 2.34331, "time": 0.82571} +{"mode": "train", "epoch": 150, "iter": 1500, "lr": 0.0, "memory": 15990, "data_time": 0.00055, "top1_acc": 0.58594, "top5_acc": 0.8125, "loss_cls": 2.36453, "loss": 2.36453, "time": 0.82752} +{"mode": "train", "epoch": 150, "iter": 1600, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.56969, "top5_acc": 0.81172, "loss_cls": 2.37695, "loss": 2.37695, "time": 0.83065} +{"mode": "train", "epoch": 150, "iter": 1700, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57641, "top5_acc": 0.80594, "loss_cls": 2.37792, "loss": 2.37792, "time": 0.81864} +{"mode": "train", "epoch": 150, "iter": 1800, "lr": 0.0, "memory": 15990, "data_time": 0.00032, "top1_acc": 0.58656, "top5_acc": 0.82203, "loss_cls": 2.31964, "loss": 2.31964, "time": 0.8377} +{"mode": "train", "epoch": 150, "iter": 1900, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58391, "top5_acc": 0.81469, "loss_cls": 2.33795, "loss": 2.33795, "time": 0.82164} +{"mode": "train", "epoch": 150, "iter": 2000, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58812, "top5_acc": 0.81234, "loss_cls": 2.34764, "loss": 2.34764, "time": 0.81791} +{"mode": "train", "epoch": 150, "iter": 2100, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57719, "top5_acc": 0.80438, "loss_cls": 2.38203, "loss": 2.38203, "time": 0.81744} +{"mode": "train", "epoch": 150, "iter": 2200, "lr": 0.0, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57703, "top5_acc": 0.80969, "loss_cls": 2.36246, "loss": 2.36246, "time": 0.82161} +{"mode": "train", "epoch": 150, "iter": 2300, "lr": 0.0, "memory": 15990, "data_time": 0.00027, "top1_acc": 0.57406, "top5_acc": 0.80703, "loss_cls": 2.36608, "loss": 2.36608, "time": 0.81751} +{"mode": "train", "epoch": 150, "iter": 2400, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.57375, "top5_acc": 0.80438, "loss_cls": 2.38635, "loss": 2.38635, "time": 0.81572} +{"mode": "train", "epoch": 150, "iter": 2500, "lr": 0.0, "memory": 15990, "data_time": 0.00026, "top1_acc": 0.58078, "top5_acc": 0.80359, "loss_cls": 2.39015, "loss": 2.39015, "time": 0.81724} +{"mode": "train", "epoch": 150, "iter": 2600, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58328, "top5_acc": 0.81719, "loss_cls": 2.31871, "loss": 2.31871, "time": 0.81373} +{"mode": "train", "epoch": 150, "iter": 2700, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58281, "top5_acc": 0.80797, "loss_cls": 2.3679, "loss": 2.3679, "time": 0.8111} +{"mode": "train", "epoch": 150, "iter": 2800, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.57922, "top5_acc": 0.81688, "loss_cls": 2.33382, "loss": 2.33382, "time": 0.8222} +{"mode": "train", "epoch": 150, "iter": 2900, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.59047, "top5_acc": 0.81, "loss_cls": 2.34964, "loss": 2.34964, "time": 0.821} +{"mode": "train", "epoch": 150, "iter": 3000, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59219, "top5_acc": 0.82062, "loss_cls": 2.32765, "loss": 2.32765, "time": 0.81485} +{"mode": "train", "epoch": 150, "iter": 3100, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57547, "top5_acc": 0.80922, "loss_cls": 2.37219, "loss": 2.37219, "time": 0.81113} +{"mode": "train", "epoch": 150, "iter": 3200, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58625, "top5_acc": 0.81953, "loss_cls": 2.32244, "loss": 2.32244, "time": 0.81217} +{"mode": "train", "epoch": 150, "iter": 3300, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.58094, "top5_acc": 0.81359, "loss_cls": 2.37408, "loss": 2.37408, "time": 0.81283} +{"mode": "train", "epoch": 150, "iter": 3400, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.57875, "top5_acc": 0.80797, "loss_cls": 2.37746, "loss": 2.37746, "time": 0.81547} +{"mode": "train", "epoch": 150, "iter": 3500, "lr": 0.0, "memory": 15990, "data_time": 0.00025, "top1_acc": 0.58656, "top5_acc": 0.81125, "loss_cls": 2.36319, "loss": 2.36319, "time": 0.81588} +{"mode": "train", "epoch": 150, "iter": 3600, "lr": 0.0, "memory": 15990, "data_time": 0.00023, "top1_acc": 0.58766, "top5_acc": 0.81531, "loss_cls": 2.36471, "loss": 2.36471, "time": 0.80968} +{"mode": "train", "epoch": 150, "iter": 3700, "lr": 0.0, "memory": 15990, "data_time": 0.00024, "top1_acc": 0.59125, "top5_acc": 0.81562, "loss_cls": 2.33191, "loss": 2.33191, "time": 0.81173} +{"mode": "val", "epoch": 150, "iter": 309, "lr": 0.0, "top1_acc": 0.39898, "top5_acc": 0.6459, "mean_class_accuracy": 0.39873} diff --git a/k400/km/best_pred.pkl b/k400/km/best_pred.pkl new file mode 100644 index 0000000000000000000000000000000000000000..810a960132c71915300000d271d64eeed8b06ff2 --- /dev/null +++ b/k400/km/best_pred.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a20e83d5c11cda94f4a9a20d1225470fbacc13003cba90c148d64838081b6d5e +size 44882350 diff --git a/k400/km/best_top1_acc_epoch_149.pth b/k400/km/best_top1_acc_epoch_149.pth new file mode 100644 index 0000000000000000000000000000000000000000..62c94f26ae2db831fdac90194cd51cca6eb118ea --- /dev/null +++ b/k400/km/best_top1_acc_epoch_149.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:69eb56641e4838ec4135e41952fab530e783acf135dae25e394c8ab88dbbc939 +size 32926705 diff --git a/k400/km/km.py b/k400/km/km.py new file mode 100644 index 0000000000000000000000000000000000000000..54b198ef6ed3493282498ad10ab46e2ed7b191dd --- /dev/null +++ b/k400/km/km.py @@ -0,0 +1,133 @@ +modality = 'km' +graph = 'coco_new' +work_dir = './work_dirs/test_aclnet/k400/km' +model = dict( + type='RecognizerGCN', + backbone=dict( + type='GCN_Module', + gcn_ratio=0.125, + gcn_ctr='T', + gcn_ada='T', + tcn_ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + graph_cfg=dict( + layout='coco_new', + mode='random', + num_filter=8, + init_off=0.04, + init_std=0.02)), + cls_head=dict(type='SimpleHead', data_cfg='k400', num_classes=400, in_channels=384)) +memcached = True +mc_cfg = ('localhost', 22077) +dataset_type = 'PoseDataset' +ann_file = '/data/lhd/pyskl_data/k400/k400_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +box_thr = 0.5 +valid_ratio = 0.0 +train_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=64, + workers_per_gpu=16, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='train', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100), + dict(type='PoseDecode'), + dict( + type='Flip', + flip_ratio=0.5, + left_kp=[1, 3, 5, 7, 9, 11, 13, 15], + right_kp=[2, 4, 6, 8, 10, 12, 14, 16]), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + valid_ratio=0.0, + memcached=True, + mc_cfg=('localhost', 22077)), + val=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=1), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077)), + test=dict( + type='PoseDataset', + ann_file='/data/lhd/pyskl_data/k400/k400_hrnet.pkl', + split='val', + pipeline=[ + dict(type='DecompressPose', squeeze=True), + dict(type='UniformSampleFrames', clip_len=100, num_clips=10), + dict(type='PoseDecode'), + dict(type='Kinetics_Transform'), + dict(type='GenSkeFeat', dataset='coco_new', feats=['km']), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) + ], + box_thr=0.5, + memcached=True, + mc_cfg=('localhost', 22077))) +optimizer = dict( + type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 150 +checkpoint_config = dict(interval=1) +evaluation = dict( + interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +gpu_ids = range(0, 1)